diff --git a/0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa/metadata.json b/0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ea10183e18992cb284cc3fe5573e426d85309453 --- /dev/null +++ b/0120cbbdf5abcce247bf35686d0d3fbc3c94f93c709d874b56ecf9271a6516aa/metadata.json @@ -0,0 +1,151 @@ +{ + "title": "Pt nanoshells with a high NIR-II photothermal conversion efficiency mediates multimodal neuromodulation against ventricular arrhythmias", + "pre_title": "Pt nanoshell with ultra-high NIR-\u2161 photothermal conversion efficiency mediates multifunctional neuromodulation for cardiac protection", + "journal": "Nature Communications", + "published": "28 July 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-50557-w/MediaObjects/41467_2024_50557_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-50557-w/MediaObjects/41467_2024_50557_MOESM2_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-50557-w/MediaObjects/41467_2024_50557_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-50557-w/MediaObjects/41467_2024_50557_MOESM4_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-50557-w#Sec38" + ], + "code": [], + "subject": [ + "Biomedical materials", + "Inhibition\u2013excitation balance", + "Nanoparticles" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3985327/v1.pdf?c=1722251221000", + "research_square_link": "https://www.researchsquare.com//article/rs-3985327/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-50557-w.pdf", + "preprint_posted": "14 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The autonomic nervous system plays a pivotal role in the pathophysiology of cardiovascular diseases. Regulating it is essential for preventing and treating acute ventricular arrhythmias (VAs). Photothermal neuromodulation is a nonimplanted technique, but the response temperature ranges of transient receptor potential vanilloid 1 (TRPV1) and TWIK-elated K+ Channel 1 (TREK1) exhibit differences while being closely aligned, and the acute nature of VAs require that it must be rapid and precise. However, the low photothermal conversion efficiency (PCE) still poses limitations on achieving rapid and precise treatment. Here, we achieved nearly perfect blackbody absorption and one of the highest PCE in the second near infrared (NIR-II) window (73.7% at 1064 nm) via a Pt nanoparticle shell (PtNP-shell). By precisely manipulating the photothermal effect, we successfully achieved rapid and precise multifunctional neuromodulation encompassing neural activation (41.0\u201342.9 oC) and inhibition (45.0\u201346.9 oC). The NIR-II photothermal modulation additionally achieved bi-directional reversible autonomic modulation and conferred protection against acute VAs associated with myocardial ischemia and reperfusion injury in interventional therapy.Physical sciences/Nanoscience and technology/Nanoscale materials/NanoparticlesBiological sciences/Biotechnology/Biomaterials", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "supplementaryinformation.docx", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Autonomic nervous system disorders play a pivotal role in the pathophysiology of cardiovascular diseases. Regulating it is essential for preventing and treating acute ventricular arrhythmias (VAs). Photothermal neuromodulation is a nonimplanted technique, but the response temperature ranges of transient receptor potential vanilloid 1 (TRPV1) and TWIK-related K+ Channel 1 (TREK1) exhibit differences while being closely aligned, and the acute nature of VAs require that it must be rapid and precise. However, the low photothermal conversion efficiency (PCE) still poses limitations in achieving rapid and precise treatment. Here, we achieve a nearly perfect blackbody absorption and a high PCE in the second near infrared (NIR-II) window (73.7% at 1064\u2009nm) via a Pt nanoparticle shell (PtNP-shell). By precisely manipulating the photothermal effect, we successfully achieve rapid and precise multimodal neuromodulation encompassing neural activation (41.0\u201342.9\u2009\u00b0C) and inhibition (45.0\u201346.9\u2009\u00b0C) in a male canine model. The NIR-II photothermal modulation additionally achieves multimodal reversible autonomic modulation and confers protection against acute VAs associated with myocardial ischemia and reperfusion injury in interventional therapy.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Cardiovascular disease has emerged as a leading cause of mortality, with acute myocardial infarction being one of the most pernicious ailments1,2. Myocardial ischemia (MI) frequently precipitates acute ventricular arrhythmias (VAs), impeding prompt and efficacious treatment for acute myocardial infarction. Furthermore, conventional interventional procedures for MI are unable to circumvent concomitant myocardial reperfusion injury and associated VAs3. The autonomic nervous system, encompassing sympathetic and parasympathetic nerves, plays a role in cardiovascular modulation; both are naturally antagonistic. Sympathetic inhibition or parasympathetic activation has been shown to stabilize cardiac electrophysiology, safeguard against MI, and reduce the incidence of VAs4. However, cardiac sympathetic denervation (CSD), stellate ganglion block (SGB), and renal denervation (RDN) are associated with certain adverse effects, including Horner\u2019s syndrome5, inadvertent bleeding, and inconsistent ablation outcomes6. Both conventional vagus nerve stimulation (VNS) and optogenetic neuromodulation necessitate the implantation of in vivo electrical stimulation7 or light source8 devices. Furthermore, optogenetic neuromodulation requires viral transfection of photosensitive proteins8, thereby limiting the clinical advancement of these therapeutic approaches.\n\nIn recent years, several studies have demonstrated that light-activated nanotransducers can induce local heating effects, leading to the activation or inhibition of nerves9,10,11. This discovery is attributed to the identification of temperature-sensitive ion channels in neurons, such as transient receptor potential vanilloid 1 (TRPV1)12 and TWIK-related K+ Channel 1 (TREK1)13. The activation of specific temperature-sensitive ion channels necessitates precise temperature ranges12,13,14. Considering the acute nature of neural responses, a therapeutic strategy with rapid and accurate modulation is required. The light in the second near-infrared window (NIR-II, 900\u20131700\u2009nm) has reduced tissue scattering and absorption and increased maximum permissible exposure (MPE) for biological tissues compared to the light in the first near-infrared (NIR-I, 650\u2013900\u2009nm) and visible window15. Consequently, this enables non-invasive and non-implantable neuromodulation using the NIR-II photothermal effect. However, its neural response rate and accuracy are currently limited by low photothermal conversion efficiency (PCE).\n\nIn this work, we report a near blackbody NIR-II Pt nanoparticle shell (PtNP-shell) for protection against MI and myocardial reperfusion injury accompanying intervention. The PtNP-shell, synthesized through a simple electrocoupling substitution reaction using liquid metal nanoparticles as templates (Fig.\u00a01a), possesses surface pores and a hollow structure. It demonstrates a nearly perfect blackbody absorption, enhanced absorption of light, and then a high PCE in the NIR-II window (73.7% at 1064\u2009nm). By leveraging the local heating effect mediated by PtNP-shell, we achieve rapid, efficient, and precise multimodal autonomic neuromodulation. Specifically, parasympathetic activation and sympathetic inhibition are accomplished by activating TRPV1 (41.0\u201342.9\u2009\u00b0C) and TREK1 (45.0\u201346.9\u2009\u00b0C) channels, respectively. Photothermal autonomic neuromodulation mediated by PtNP-shell effectively stabilizes cardiac electrophysiology and reduces VAs incidence in both myocardial ischemia-reperfusion (I/R) injury model and MI model, respectively (Fig.\u00a01b).\n\na The synthesis steps of PtNP-shell and schematic diagram of photothermal effect. b Schematic diagram of multimodal autonomic modulation mediated by photothermal effect of PtNP-shell for the treatment of myocardial ischemia-reperfusion injury and myocardial ischemia-induced ventricular arrhythmias.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50557-w/MediaObjects/41467_2024_50557_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "The PtNP-shell was synthesized through an electrocoupling substitution reaction between chloroplatinate and Ga nanoparticles (GaNPs). Ga nanoparticles were obtained by sonication of pure metal Ga. To achieve a balanced particle size and oxidation degree of GaNPs, pure gallium was sequentially sonicated in ethanol and water for 30\u2009min to obtain gallium nanoparticles with reduced oxidation (Supplementary Fig.\u00a01a). In accordance with the electrochemical redox potential of the redox couple (Ga3+/Ga: \u22120.529\u2009V; PtCl62\u2212/PtCl42\u2212: 0.726\u2009V; PtCl42\u2212/Pt: 0.758\u2009V)16,17, Pt (IV) can be in situ reduced by Ga and deposited on the surface of GaNPs to form a core-shell structure (Supplementary Fig.\u00a01b, c). The hollow PtNP-shell is synthesized after completion of the reaction (Fig.\u00a02a). Simultaneously with the reduction of Pt (IV), Ga oxide is formed, creating the skeleton of the PtNP-shell (right in Fig.\u00a02a). The surface of the PtNP-shell exhibits a rough texture (Supplementary Fig.\u00a02). The scanning transmission electron microscopy (STEM) images reveal numerous irregular and uneven pores on its surface (Supplementary Fig.\u00a03a) and PtNP-shell is composed of Pt nanoparticles (PtNPs) with 2\u20135\u2009nm (Fig.\u00a02b). High-resolution TEM (HRTEM) image is acquired to character the structure of PtNPs. As shown in Supplementary Fig.\u00a03b, PtNPs exhibit a single crystal structure with a lattice stripe spacing of 0.23\u2009nm corresponding to the (111) crystal plane. Meanwhile, the corresponding Fast Fourier Transform (FFT) pattern (inset in Supplementary Fig.\u00a03b) shows the typical diffraction patterns of face-centered cubic structure along [111] zone axis.\n\na TEM image of PtNP-shell, n\u2009=\u200910 independent replicates (inset: schematic diagram of PtNP-shell; right: element mapping). b STEM images of PtNP-shell surface, n\u2009=\u20096 independent replicates. c XRD spectrum of PtNP-shell (inset: SAED pattern). d UV\u2013vis\u2013NIR absorption spectrum of GaNPs, Ga@Pt NPs, and PtNP-shell (75\u2009\u03bcg\u2009mL\u22121). e Temperature elevation curves of PBS, GaNPs, Ga@Pt NPs and PtNP-shell (50\u2009\u03bcg\u2009mL\u22121) under NIR-II laser irradiation (1\u2009W\u2009cm\u22122). f Calculation of the PCE at 1064\u2009nm (PtNP-shell: 50\u2009\u03bcg\u2009mL\u22121). Source data are provided as a Source Data file.\n\nIn the X-ray powder diffraction (XRD) spectrogram result (Fig.\u00a02c), all peaks can be attributed to the crystal phase of Pt (JCPDS: 87-0640), consistent with the selected area electron diffraction (SAED) pattern findings (inset in Fig.\u00a02c). However, no peaks corresponding to gallium oxide were observed in the XRD spectrogram, possibly due to its low content. The XRD spectrogram (Supplementary Fig.\u00a04) of PtNP-shell prior to reacting with KOH showed that the gallium oxide contained in PtNP-shell was GaOOH (JCPDS: 06-0180). X-ray photoelectron spectroscopy (XPS) analysis reveals the presence of Ga and O in GaNPs, while Ga@Pt NPs and PtNP-shell exhibit the coexistence of Ga, O, and Pt (Supplementary Fig.\u00a05). As depicted in the Supplementary Fig.\u00a06, despite treatment with KOH, no presence of K element was detected in the PtNP-shell. The strong signals of Pt 4f5/2 and Pt 4f7/2 indicate that the Pt in Ga@Pt NPs and PtNP-shell exists in a metallic state18. In GaNPs, the peak centered at 1118.11\u2009eV is attributed to Ga3+ 2p3/2, while the peak centered at 1115.89\u2009eV corresponds to Ga 2p3/2. In Ga@Pt NPs, the peak centered at 1118.80\u2009eV is assigned to Ga3+ 2p3/2, and the peak centered at 1116.52\u2009eV corresponds to Ga 2p3/2. As for PtNP-shell, the presence of a peak around 1118.56\u2009eV indicates complete oxidation of Ga in PtNP-shell into Ga3+\u200916. The O 1s spectrum was fitted using two peak functions, which were assigned to Ga\u2013O at 530.44\u2009eV (GaNPs), 530.98\u2009eV (Ga@Pt NPs), 531.74\u2009eV (PtNP-shell) and Ga\u2013OH at 531.71\u2009eV (GaNPs), 532.08\u2009eV (Ga@Pt NPs), 532.74\u2009eV (PtNP-shell)19. Compared to GaNPs and Ga@Pt NPs, the binding energy of the Ga\u2013O and Ga\u2013OH peaks in the PtNP-shell is shifted toward higher values, indicating a lower oxidation degree of the PtNP-shell. PtNP-shell was treated with KOH (0.67\u2009M) to reduce the gallium oxide content and the surface potential was reduced from 45.8 to \u221225.7\u2009mV, and then encapsulated with methoxypoly(ethylene glycol) thiol (mPEG-SH5000) to enhance its biocompatibility and the surface potential was changed to \u221219.9\u2009mV (Supplementary Table.\u00a01). The statistically averaged hydrated nanoparticle size of PtNP-shell based on the dynamic light scattering diagram was 200.1\u2009nm with a uniform size distribution, indicating the nanoparticle was well dispersed in water (Supplementary Fig.\u00a07). After standing for different days, the statistically averaged hydrodynamic size of PtNP-shell was determined using dynamic light scattering (Supplementary Fig.\u00a08). It is worth noting that the change of the average hydrated nanoparticle size of PtNP-shell remains negligible after 14 days, indicating its excellent stability.\n\nDue to the presence of pores and a hollow structure in the PtNP-shell, light propagating in the space bounces at the rough surface of PtNP-shell until it encounters one of the pores, where it continues to bounce within the PtNP-shell. The random distribution of these pores results in completely random light reflection, akin to Brownian motion20. Consequently, the probability of light escaping from other pores is extremely low, rendering PtNP-shell behave like a blackbody and produce an efficient infrared heater21,22,23. This enhanced absorption of light by PtNP-shell exhibits nearly perfect blackbody absorption characteristics (Supplementary Fig.\u00a09a). In the range of 250\u20131300\u2009nm, the PtNP-shell exhibits an absorbance close to 1 at 75\u2009\u03bcg\u2009mL\u22121, which is significantly enhanced in the range of 400\u20131300\u2009nm compared to GaNPs and Pt-coated Ga nanoparticles (Ga@Pt NPs) (Fig.\u00a02d). According to the Lambert\u2013Beer law (A/L\u2009=\u2009\u03b5C, where \u03b5 is the extinction coefficient), a linear relationship between absorption intensity (at 1064\u2009nm) and concentration was established, with an extinction coefficient measured as 13.3\u2009Lg\u22121\u2009cm\u22121 at 1064\u2009nm (Supplementary Fig.\u00a09b). Varying concentrations of PtNP-shell resulted in different shades of gray being generated, with significantly darker grayness observed under identical conditions compared to GaNPs and Ga@Pt NPs (Supplementary Fig.\u00a010a). These distinctive features were characterized by their respective positions within an RGB cube representation, wherein on the diagonal connecting darkest and brightest points, PtNP-shell was found closer to the darkest point than both other materials (Supplementary Fig.\u00a010b).\n\nThe photothermal properties of PtNP-shell were verified by irradiating the dispersion of PtNP-shell in water with NIR-II light at 1064\u2009nm (1\u2009W\u2009cm\u22122). Even in vitro, PtNP-shell (50\u2009\u03bcg\u2009mL\u22121) exhibited rapid temperature elevation, achieving a rise from room temperature to 41.0 and 45.0\u2009\u00b0C within only 96 and 133\u2009s, respectively. However, for GaNPs (409\u2009s and over 600\u2009s) and Ga@Pt NPs (317\u2009s and over 600\u2009s), it took significantly longer time to reach the same temperatures (Fig.\u00a02e). The corresponding thermal images of the PtNP-shell with different concentrations under different irradiation times are shown in Supplementary Fig.\u00a011. The heating effect of the PtNP-shell (50\u2009\u03bcg\u2009mL\u22121) gradually increased the \u2206T from 7.72 to 52.17\u2009\u00b0C when exposed to NIR-II laser for a duration of 600\u2009s while varying the optical power density at 1064\u2009nm between 0.25 and 1.5\u2009W\u2009cm\u22122 (Supplementary Fig.\u00a012). The heating rate of the SH-PEG modified PtNP-shell is significantly higher compared to that of the unmodified PtNP-shell (Supplementary Fig.\u00a013a), potentially attributed to the agglomeration tendency of unmodified PtNP-shell at elevated temperatures, leading to a reduction in photothermal performance. Following 600\u2009s of laser irradiation at 1064\u2009nm, the statistically averaged hydrodynamic size for SH-PEG-modified PtNP-shell was measured as 206.5\u2009nm (Supplementary Fig.\u00a013b), whereas unmodified PtNP-shell exhibited a size of 1517\u2009nm (Supplementary Fig.\u00a013c). TEM analysis further confirmed the observed agglomeration behavior in unmodified PtNP-shell subsequent to laser irradiation (Supplementary Fig.\u00a013). The PCE of PtNP-shell was quantified as 73.7% when balancing the energy input from photons with heat dissipation within the system (Fig.\u00a02f), representing a high PCE at 1064\u2009nm (Supplementary Table\u00a02). These results indicate that PtNP-shell exhibits excellent photothermal performance in the NIR-II window. Additionally, no significant changes in temperature or morphology were observed even after five cycles of irradiation (Supplementary Fig.\u00a014), suggesting exceptional photothermal stability.\n\nTo investigate the photothermal effects of PtNP-shell on neuronal activity at multiple levels, we conducted calcium imaging experiments in hippocampal neuron (HT-22) cells (Fig.\u00a03a, b). The immunoblotting results revealed abundant expression of both TRPV1 and TREK1 ion channels in HT-22 cells (Fig.\u00a03c). The direct effect of PtNP-shell on the excitability of these two different ion channels was assessed under NIR-II irradiation using a calcium ion indicator (Fluo-4 AM). Upon NIR-II laser irradiation, the temperature of the PtNP-shell (+) group increased compared to that of the PtNP-shell (\u2212) group, resulting in a significantly higher percentage of responding cells (Fig.\u00a03d) (p\u2009<\u20090.001). The micrographs fluorescence intensity curve of HT-22 neurons cultured with PtNP-shell showed significant Ca2+ influx upon NIR-II laser irradiation for 35\u2009\u00b1\u20095\u2009s and after the temperature reached 42.0\u2009\u00b0C (Fig.\u00a03e). In contrast, application of NIR-II laser irradiation with phosphate-buffered saline (PBS) did not induce significant Ca2+ influx.\n\na Flowchart of calcium imaging assay performed on HT-22 cells. b calcium imaging of HT-22 cells under different experimental conditions, n\u2009=\u20096 biologically independent replicates. c Western blotting for TRPV1 and TREK1 from HT-22 and HEK-293T cells, n\u2009=\u20094 biologically independent replicates. Percentage of d TRPV1 and f TREK1 groups of HT-22 cells within the field of view of the fluorescence microscope that responded to laser stimulation, n\u2009=\u20096 biologically independent replicates. Temporal dynamics of Ca2+ signals in e TRPV1 and g TREK1 groups of cells. The solid lines indicate the mean, and the shade represents the standard error of the mean (SEM). h Cell viability of HT-22 treated with different concentrations of PtNP-shell for 24\u2009h. Effect of NIR-II laser irradiation with varying durations on the viability of HT-22 cells treated with PtNP-shell (50\u2009\u03bcg\u2009mL\u22121) (Power densities: i 0.75\u2009W\u2009cm\u22122 and j 1\u2009W\u2009cm\u22122), n\u2009=\u20096 biologically independent replicates. The error bar indicates S.E.M. Two-way ANOVA with Tukey\u2019s honestly significant difference (HSD) test was applied for statistical analysis of (d) and (f). One-way ANOVA with Dunnett\u2019s multiple comparisons test was applied for statistical analysis of (i) and (j). Source data are provided as a Source Data file.\n\nSubsequently, neuronal excitation was induced and calcium signals were increased by perfusion of 15\u2009mM KCl in the PtNP-shell (\u2212) group and PtNP-shell (+) group (50\u2009\u03bcg\u2009mL\u22121), respectively. This phenomenon can be attributed to the elevation of extracellular potassium ion concentration, which triggers neuronal depolarization and subsequently leads to a substantial increase in intracellular calcium ion concentration24. Under NIR-II laser irradiation, the proportion of HT-22 cells responding to high-concentration KCl stimulation was significantly lower in the PtNP-shell (+) group compared to that in the PtNP-shell (\u2212) group at ~46.0\u2009\u00b0C (Fig.\u00a03f). The difference may be due to the activation of the TERK1 ion channel in the PtNP-shell (+) group, which can induce neuronal hyperpolarization and make intracellular and extracellular calcium ion concentrations tend to recover25. Interestingly, the PtNP-shell influenced the fluorescence intensity of HT-22 cells not with a sustained decrease but with an initial rise followed by a subsequent decrease (Fig.\u00a03g). This observation may be associated with the activation of TRPV1 channel at around 42.0\u2009\u00b0C9. With increasing temperature, TRPV1 and TREK1 channels were sequentially activated. These findings suggest that PtNP-shell can achieve precise temperature control within a short duration through its own high PCE for both neuronal excitation and inhibition.\n\nCytotoxicity assays were then conducted to investigate the potential neurotoxicity of PtNP-shell application. As shown in Fig.\u00a03h, concentrations of PtNP-shell below 100\u2009\u03bcg\u2009mL\u22121 exhibited no significant toxic effects on HT-22 cells. Even when the concentration of PtNP-shell was increased to 200\u2009\u03bcg\u2009mL\u22121, the survival rate of neuronal cells remained approximately at 52.11%. After neurons were co-cultured with PtNP-shell (50\u2009\u03bcg\u2009mL\u22121) for 24\u2009h, cross-sectional TEM and SEM images showed that PtNP-shell was randomly distributed inside or on the surface of the cells (Supplementary Fig.\u00a015). This is attributed to the fact that the PtNP-shell exhibits a particle size of ~200\u2009nm, enabling smaller particles to traverse the cell membrane. Furthermore, the impact of PtNP-shell photothermal stimulation parameters on cell viability was assessed through analysis of HT-22 cell survival under NIR-II laser irradiation. Notably, when a concentration of 50\u2009\u03bcg\u2009mL\u22121 PtNP-shell and an NIR-II laser with a power density of 0.75\u2009W\u2009cm\u22122 was applied for a brief duration, the survival rate exceeded 91.87% for HT-22 cells (Fig.\u00a03i). Even with an increase in power density to 1\u2009W\u2009cm\u22122, the survival rate for HT-22 cells remained around 82.71% after 90\u2009s of irradiation (Fig.\u00a03j). These results indicate that PtNP-shell does not induce significant damage to neurons under controlled NIR-II laser irradiation.\n\nWestern blotting analysis of peripheral ganglia from the canine autonomic nervous system revealed the expression of TRPV1 and TREK1 heat-sensitive ion channels in both the nodose ganglion (NG) and the left stellate ganglion (LSG). Notably, TRPV1 was abundantly expressed in the NG of the parasympathetic nervous system, while TREK1 exhibited higher levels in the LSG of the sympathetic nervous system (Supplementary Fig.\u00a016). To investigate whether the photothermal effect induced by PtNP-shell under NIR-II irradiation can precisely regulate the parasympathetic nerve, 100\u2009\u03bcL PtNP-shell (50\u2009\u03bcg\u2009mL\u22121) and PBS were injected into NG of PtNP-shell group and control group (six beagle dogs in each group), respectively (Fig.\u00a04a, b). It can be observed that upon irradiation with NIR-II laser (0.8\u2009W\u2009cm\u22122), the temperature of NG injected with PtNP-shell increased to 41.0\u2009\u00b0C within a very short period of time (12\u2009\u00b1\u20093\u2009s). Subsequently, the temperature of NG could be kept in the range of 41.0\u201342.9\u2009\u00b0C for 5\u2009min by adjusting the power density to 0.45\u2009W\u2009cm\u22122 (Fig.\u00a04c, d). As a crucial node within the parasympathetic neural network, activation of NG significantly reduces heart rate (HR) (Fig.\u00a04e)26. Therefore, NG function was assessed by the maximum decrease in heart rate under direct electrical stimulation. As shown in Fig.\u00a04f\u2013h, NG function and activity were significantly elevated in the PtNP-shell group than in the control group after stimulation. The function and activity of NG recovered close to baseline within 3\u2009h after turning off the NIR-II laser, indicating that the photothermal modulation induced by PtNP-shell was reversible within NGs (Fig.\u00a04h, Supplementary Figs.\u00a017, 18). After NIR irradiation with the same parameters, the local temperature increase of the NG was <2\u2009\u00b0C, while there was no significant change in parasympathetic nerve function (Supplementary Fig.\u00a019).\n\na Location of the canine NG. b Schematic illustration of the process of photothermal modulation of NG. c Temperature curves of NG under NIR-II laser irradiation. d Typical thermal imaging diagram of photothermally modulated activation of NG. e Representative images of HR reduction induced after stimulation of NG with different voltages. Maximal HR changes of beagle treatment with PtNP-shell or control f before and g after NIR-II exposure, n\u2009=\u20096 biologically independent replicates. h Quantification of the NG neural activity recordings, n\u2009=\u20096 biologically independent replicates. i Representative immunofluorescent images of VAChT, c-Fos, and TRPV1 in the NG of beagles following different treatments. Data are shown as the mean\u2009\u00b1\u2009S.E.M. Unpaired two-tailed Student\u2019s t-test was applied for statistical analysis of (g). Two-way ANOVA with Tukey\u2019s HSD test was applied for statistical analysis of (h). NG nodose ganglion, HR heart rhythm. Source data are provided as a Source Data file.\n\nIn addition, the stability of the electrophysiology of the heart is reflected by measuring the effective refractory period (ERP) in various regions, including left ventricular apex (LVA), median left ventricular area (LVM), and left ventricular base (LVB). In the PtNP-shell group, the ERP was significantly elevated compared to the control group and remained elevated for 1\u2009h after photothermal intervention in NG (Supplementary Fig.\u00a020). Furthermore, immunofluorescence staining for vesicular acetylcholine transporter protein (VAChT), c-Fos, and TRPV1 on histopathological sections of photothermally modulated NGs served to localize parasympathetic neurons and reflect neuronal activity as well as TRPV1 protein expression, respectively (Fig.\u00a04i). Quantitative analysis (Supplementary Fig.\u00a021) revealed a substantial increase in the proportion of TRPV1+ (86.63\u2009\u00b1\u20092.65 vs. 45.45\u2009\u00b1\u20092.98) and c-Fos+ (77.81\u2009\u00b1\u20093.91 vs. 17.27\u2009\u00b1\u20093.08) neurons among VAChT+ parasympathetic neurons in the PtNP-shell group compared to the control group (all P\u2009<\u20090.001). These findings suggest that PtNP-shell can precisely regulate temperature and subsequently activate TRPV1 ion channels on NG to enhance parasympathetic activity.\n\nAnimal modeling and intervention manipulations were conducted to further elucidate the protective effects of precise modulation of NG by PtNP-shell against myocardial ischemia-reperfusion (I/R) injury and associated VAs, following the experimental protocols depicted in Fig.\u00a05a, b. PtNP-shell and PBS were microinjected into the NG of the PtNP-shell group and control group, respectively, each consisting of six beagle dogs. The left anterior descending (LAD) coronary artery was occluded for 1\u2009h to induce myocardial ischemia. Subsequently, NIR-II laser irradiation was applied to NG for 5\u2009min, followed by reperfusion treatment achieved by opening the ligated knot.\n\na Schematic diagram and b flowchart of regulating NG to protect against myocardial I/R injury and associated VAs. c Representative visual depictions of VAs, including VPB, VT, and VF. d Quantitative analysis of the ratio of sVT and VF incidence between different groups, n\u2009=\u20096 biologically independent replicates. Quantitative analysis of the number of e VPBs, f VTs, and g the duration of sVT of beagles. Effects on ventricular ERP at different sites in beagles treatment with PtNP-shell or control h before and i after myocardial I/R injury modeling. Levels of markers of myocardial injury, including j MYO and k c-TnI, after different treatments in beagles n\u2009=\u20096 biologically independent replicates. Data are shown as the mean\u2009\u00b1\u2009S.E.M. Unpaired two-tailed Student\u2019s t-test was applied for statistical analysis of (e\u2013k). NG nodose ganglion, LADO left anterior descending coronary occlusion, VPB ventricular premature beat, VT ventricular tachycardia, sVT sustained VT, VF ventricular fibrillation, ERP effective refractory period, LVA left ventricular apex, LVM median left ventricular area, LVB left ventricular base. Source data are provided as a Source Data file.\n\nFollowing I/R injury, electrocardiography (ECG) was recorded to monitor the occurrence of VAs events within 1\u2009h, including ventricular premature beats (VPBs), ventricular tachycardia (VT), and ventricular fibrillation (VF) (Fig.\u00a05c)27. Under NIR-II laser irradiation, the PtNP-shell group exhibited a lower incidence of sustained VT (sVT, duration >30\u2009s) or VF compared to the control group (50% vs. 83%) (Fig.\u00a05d). Moreover, the number of recorded VPBs (70.83\u2009\u00b1\u20095.38 vs. 116.00\u2009\u00b1\u20096.36, P\u2009<\u20090.05), VTs (3.17\u2009\u00b1\u20090.87 vs. 8.83\u2009\u00b1\u20092.15, P\u2009<\u20090.05) and duration of the VTs (7.00\u2009\u00b1\u20093.173\u2009s vs. 26.83\u2009\u00b1\u20097.89\u2009s, P\u2009<\u20090.05) in the PtNP-shell group were significantly reduced compared to that in the control group (Fig.\u00a05e\u2013g).\n\nThere were no statistically significant differences between the two groups in terms of preoperative ERP for LVB, LVM, and LVA. In the postoperative period, all three positions showed shortened ERPs in the control group. The PtNP-shell group exhibited significantly higher ERPs compared to the control group, indicating that photothermal modulation of nerves by PtNP-shell has a protective effect on cardiac electrophysiology (Fig.\u00a05h, i). Serum ELISA assay revealed significantly lower levels of markers of myocardial injury (MYO and c-TnI) at 4\u20135\u2009h post-infarction in the PtNP-shell group compared to the control group (all p\u2009<\u20090.05, Fig.\u00a05j, k), indicating an improvement in myocardial injury28. This may be attributed to the activation of \u03b1-7 nicotinic acetylcholine receptor by stimulating parasympathetic nerves, thereby alleviating inflammatory reactions and oxidative stress29,30. Postoperatively, heart rate variability analysis demonstrated lower low frequency (LF) and higher high frequency (HF) and the lower ratio of LF to HF (LF/HF) values in the PtNP-shell group compared to the control group (all p\u2009<\u20090.05, Supplementary Fig.\u00a022). These results suggest that PtNP-shell reduces VAs by activating the parasympathetic nerve.\n\nThe sympathetic nervous system was modulated by performing microinjections of PtNP-shell or PBS into the LSG, followed by irradiation with a NIR-II laser (Fig.\u00a06a, b). The temperature curve demonstrates that upon exposure to a NIR-II laser (0.8\u2009W\u2009cm\u22121) for 25\u2009\u00b1\u20095\u2009s, the temperature rapidly escalated to 45.0\u2009\u00b0C, crossing the range of 41.0\u201342.9\u2009\u00b0C within a mere duration of 6\u2009\u00b1\u20091\u2009s. Subsequently, the power density was immediately decreased to 0.6\u2009W\u2009cm\u22122, effectively maintaining LSG at a steady temperature between 45.0 and 46.9\u2009\u00b0C (Fig.\u00a06c, d). Due to the substantial increase in systolic blood pressure (SBP) induced by LSG activation (Fig.\u00a06e), the function of LSG was evaluated by quantifying the maximum SBP change corresponding to five consecutive incremental voltages of high-frequency electrical stimulation. After 5\u2009min of NIR-II laser irradiation, the activity and function of LSG in the PtNP-shell group were significantly suppressed compared to the control group (p\u2009<\u20090.05) and they returned close to baseline after 3\u2009h (Fig.\u00a06f\u2013h and Supplementary Figs.\u00a023, 24). Similarly, after NIR irradiation with the same parameters, the local temperature increase in the LSG did not exceed 2\u2009\u00b0C, while there was no significant change in sympathetic nerve function (Supplementary Fig.\u00a025).\n\na Location of the canine LSG. b Schematic illustration of the process of photothermal modulation of LSG. c Temperature curves of LSG under NIR-II laser irradiation. d Typical thermal imaging diagram of photothermally modulated activation of LSG. e Representative images of BP elevation induced after stimulation of LSG with different voltages. Maximal SBP changes of beagle treatment with PtNP-shell or control f before and g after NIR-II exposure, n\u2009=\u20096 biologically independent replicates. h Quantification of the LSG neural activity recordings, n\u2009=\u20096 biologically independent replicates. i Representative immunofluorescent images of TH, c-Fos, and TREK1 in the LSG of beagles following different treatments. Data are shown as the mean\u2009\u00b1\u2009S.E.M. Unpaired two-tailed Student\u2019s t-test was applied for statistical analysis of (g). Two-way ANOVA with Tukey\u2019s HSD test was applied for statistical analysis of (h). LSG left stellate ganglion, BP blood pressure, SBP systolic BP. Source data are provided as a Source Data file.\n\nProlonged ERP effects were observed in all left ventricles, while the protective effect exhibited a duration of only 2\u2009h (Supplementary Fig.\u00a026). Furthermore, immunofluorescence staining was conducted on LSG tissues to examine the expression of tyrosine hydroxylase (TH), c-Fos, and TREK1 (Fig.\u00a06i). These markers indicate the localization of sympathetic neurons, neuronal activity, and TREK1 protein expression, respectively. The quantitative analysis (Supplementary Fig.\u00a027) revealed a significant decrease in the proportion of c-Fos+ expression in TH+ neurons within the PtNP-shell group (8.80\u2009\u00b1\u20091.80 vs. 44.78\u2009\u00b1\u20095.55, P\u2009<\u20090.001) indicating that PtNP-shell exerted a photothermal inhibitory effect on LSG neurons under NIR-II irradiation. However, the proportion of TREK+ expression was significantly increased within TH+ neurons in the PtNP-shell group (83.51\u2009\u00b1\u20093.72 vs. 57.20\u2009\u00b1\u20095.89, P\u2009<\u20090.01). This increase could lead to hyperpolarization of the cell membrane potential, reduction in neuronal excitability and inhibition of sympathetic nerve activity.\n\nIn order to investigate the impact of PtNP-shell photothermal effect on reducing VAs occurrence induced by MI, NIR-II light was applied to make LSG reach the target temperature of about 46.0\u2009\u00b0C before ligating LAD coronary artery (Fig.\u00a07a, b). Under NIR-II laser irradiation, the PtNP-shell group exhibited a significantly reduced incidence of sVTs or VF compared to the control group (16% vs. 50%) (Fig.\u00a07c). In the PtNP-shell group, ECG recordings within infarction 1 exhibited a reduced incidence of VAs events compared to the control group, with fewer VPBs recorded in the PtNP-shell group than in the control group (51.50\u2009\u00b1\u20095.53 vs. 70.83\u2009\u00b1\u20095.375, P\u2009<\u20090.05, Fig.\u00a07d). However, there were no significant differences between the two groups in terms of VT numbers and duration (Supplementary Fig.\u00a028). Additionally, VA inducibility measurements demonstrated that after photothermal neuromodulation with PtNP-shell, there was a decrease in VA score (1.50\u2009\u00b1\u20090.76 vs. 4.83\u2009\u00b1\u20091.14, P\u2009<\u20090.05) effective heart protection (Fig.\u00a07e, f). Furthermore, PtNP-shell photothermal inhibition of LSG produced similar protective effects on ventricular electrophysiological index ERP as activation of NG (Fig.\u00a07g, h), and had a higher VF threshold than the control group (24.33\u2009\u00b1\u20094.24 vs. 12.33\u2009\u00b1\u20093.16, P\u2009<\u20090.05, Fig.\u00a07i). In addition, the light inhibition of LSG followed the same trend as heart rate variability after activation of NG (Supplementary Fig.\u00a029). These results suggest that PtNP-shell protects against cardiac damage and reduces VAs by modulating the autonomic nervous system, specifically by decreasing sympathetic activity and enhancing parasympathetic tone.\n\nModulation of LSG to protect against MI and associated VAs a schematic diagram and b flowchart. c Quantitative analysis of the ratio of sVT and VF incidence between different groups, n\u2009=\u20096 biologically independent replicates. d Quantitative analysis of the number of VPBs of beagles. e Typical images of VA induced by programmed electrical stimulation. f Quantitative analysis of VAs score in different groups. Effects on ventricular ERP at different sites in Beagles treatment with PtNP-shell or control g before and h after MI modeling. i Quantitative analysis of VF threshold in different groups, n\u2009=\u20096 biologically independent replicates. Data are shown as the mean\u2009\u00b1\u2009S.E.M. Unpaired two-tailed Student\u2019s t-test was applied for statistical analysis of (d, f\u2013i). LSG left stellate ganglion, SBP systolic blood pressure, MI myocardial infarction, VPB ventricular premature beat, VT ventricular tachycardia, VF ventricular fibrillation, VA ventricular arrhythmia, ERP effective refractory period, LVA left ventricular apex, LVM median left ventricular area, LVB left ventricular base. Source data are provided as a Source Data file.\n\nTo validate the biocompatibility of PtNP-shell photothermal modulation on the autonomic nervous system, we conducted rapid excision of LSG and NG tissues followed by hematoxylin and eosin (H&E) staining and Terminal deoxynucleotidyl transferase (TdT) dUTP Nick-End Labeling (TUNEL) assay. As shown in Supplementary Fig.\u00a030, H&E and TUNEL staining did not reveal any indications of neuronal damage in both the PtNP-shell and control groups for both NG and LSG, indicating that the neuromodulation of PtNP-shell is repeatable. Meanwhile, to further investigate the long-term biosafety of PtNP-shell, a microinjection of 200\u2009\u03bcl PtNP-shell (50\u2009\u03bcg\u2009mL\u22121) or PBS was administered into the ganglion of dogs and the tail vein of rats, respectively. After a follow-up period of 30 days, no significant damage was detected in the ganglia or major organs, including the heart, liver, spleen, lungs, and kidneys (Supplementary Figs.\u00a030, 31a, i). Furthermore, blood biochemical analyses indicated no hepatotoxicity, nephrotoxicity, and induction of inflammatory responses (Supplementary Fig.\u00a031b\u2013h, j\u2013p). These results unequivocally demonstrate that PtNP-shell exhibits exceptional biocompatibility and long-term biological safety.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50557-w/MediaObjects/41467_2024_50557_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50557-w/MediaObjects/41467_2024_50557_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50557-w/MediaObjects/41467_2024_50557_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50557-w/MediaObjects/41467_2024_50557_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50557-w/MediaObjects/41467_2024_50557_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50557-w/MediaObjects/41467_2024_50557_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The PtNP-shell reported in this study exhibits nearly perfect blackbody absorption properties, making it an efficient absorber with a high PCE in the NIR-II window (73.7% at 1064\u2009nm). Furthermore, local heating induced by PtNP-shell activation effectively triggers temperature-sensitive ion channels TRPV1 and TREK1, enabling precise and efficient modulation of autonomic nerves. This innovative approach holds great potential for non-invasive treatment of MI and associated VAs, as well as protection against reperfusion injury during interventional therapy.\n\nCardiac sympathetic denervation is a clinical procedure aimed at targeting the autonomic ganglia for refractory ventricular arrhythmias. However, ganglion removal can be traumatic for patients and may lead to complications due to the loss of original physiological function31. Currently, \u03b2-blockers are the primary pharmacological drugs employed in clinical practice for arrhythmia treatment32,33. However, their administration during acute myocardial ischemia remains unclear and is contraindicated in patients with heart failure. Additionally, previous research investigated the local ganglion blockade using botulinum toxin A to protect the heart34. Nevertheless, its prolonged blocking effect renders it unsuitable for acute myocardial ischemia management. Conversely, PtNP-shell-based photothermal neuromodulation offers reversible modulation within a short timeframe, exhibiting superior efficacy and controllability. In this study, we validated the protective efficacy of PtNP-shell photothermal neuromodulation strategy in models of acute myocardial infarction and acute reperfusion injury to mitigate ventricular arrhythmia incidence. However, further evaluation through experiments such as assessment of cardiac function and infarct area is required to determine the cardioprotective potential of this strategy in chronic myocardial injury models.\n\nMoreover, the minimal tissue damage caused by light can be disregarded within the maximum permissible exposure (MPE) range, rendering it one of the safest interventions for organisms. The interaction between light and tissue is intricate, and further research could aid in selecting more suitable wavelengths to achieve deeper penetration within the MPE range. Leveraging the nearly impeccable blackbody absorption of PtNP-shell and ultrasound-guided microinjection technology, remote and precise neuromodulation strategies can be developed, holding promise for non-invasive protection against MI and reperfusion injury-associated VAs. Simultaneously, exploiting the presence of blood vessels surrounding the ganglion presents an opportunity to minimize photon propagation within tissues. Consequently, photothermal modulation via NIR-II fibers in proximity to the ganglion through vascular routes during interventional therapy emerges as a promising avenue for direct clinical translation. The significance of this approach extends beyond VAs as it exhibits broad therapeutic prospects for chronic diseases like refractory hypertension35 and stable atherosclerosis36 due to the wide distribution of autonomic nerves and the universality of nerve regulation.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The gallium was purchased from Shanghai Minor Metals Co., Ltd. Anhydrous ethanol (\u226599.7%) and KOH (AR) were purchased from Sinopharm Chemical Reagent Co., Ltd. Na2PtCl6 (98%) was purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. mPEG-SH5000 was purchased from Shanghai Macklin Biochemical Co., Ltd. STR-identified correct HT-22 cells or human embryonic kidney 293T (HEK-293T) cells were purchased with the corresponding specialized cell culture media (Procell, Wuhan, China). Rabbit monoclonal anti-TRPV1 antibody (Cat. No. A23386, Clone No. ARC57842) used for protein blotting was purchased from ABclonal (Wuhan, China). Mouse monoclonal anti-TREK1 antibody (Cat. No. sc-398449, Clone No. F-6) used in western blot and immunofluorescence staining was purchased from Santa Cruz Biotechnology (TX, USA). Rabbit monoclonal Anti-c-Fos (Cat. No. A24620, Clone No. ARC63309), rabbit polyclonal anti-VAChT (Cat. No. A16068), rabbit polyclonal anti-TH (Cat. No. A12756) and rabbit polyclonal Anti-TRPV1 (Cat. No. A8564) antibodies used in immunofluorescence staining were purchased from ABclonal. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was purchased from Servicebio (Wuhan, China). Serum troponin I (c-TnI) and myoglobin (MYO) ELISA kits were purchased from MIbio (Shanghai, China). Tumor necrosis factor alpha (TNF-\u03b1) and Interleukin 6 (IL-6) ELISA kits were purchased from Jianglaibio (Shanghai, China). 4,6-diamidino-2-phenylindole (DAPI) was purchased from Servicebio (Wuhan, China).\n\nThe morphology of PtNP-shell was characterized by an F200 transmission electron microscope (TEM) (JEOL, Japan) operated at 200\u2009kV. STEM and HRTEM images were obtained by a JEM-ARM200CF (JEOL, Japan) at 200\u2009kV. The EDX elemental mapping was carried out using the JEOL SDD-detector with two 100\u2009mm2 X-ray sensors. X-ray diffraction (XRD) patterns were performed on a SmartLab 9\u2009kW X-ray powder diffractometer (Cu-target, 0.154\u2009nm) (Rigaku, Japan). XPS measurements were carried out with an ESCALAB 250Xi spectrometer (Thermo Fisher Scientific, USA) under vacuum. Ultraviolet\u2013visible\u2013near-infrared light (UV\u2013vis\u2013NIR) absorption spectra were collected using a UV-3600 spectrophotometer (Shimadzu, Japan). Zeta potential (Z) and dynamic light scattering (DLS) were recorded using a Zetasizer Nano ZSP (Malvern Panalytical, UK). The fluorescence microscopy images of HT-22 cells were acquired by FV3000 Microscope (Olympus, Japan), excited with a 488\u2009nm laser. TEM of HT-22 cells was characterized by HT7800 TEM (HITACHI, Japan). SEM of HT-22 cells was characterized by SU8100 SEM (HITACHI, Japan). Beagle\u2019s respiration is maintained by a WATO EX-20VET ventilator (Mindray, Shenzhen, China). ECG and blood pressure data were recorded by a Lead 7000 Computerized Laboratory System (Jinjiang, Chengdu, China). NIR-II light at 1064\u2009nm is generated by LWIRPD-1064-5F laser (Laserwave, Beijing, China). Thermal imaging was obtained by FLIR C2 thermal imager (FLIR, USA). High-frequency electrical stimulation was performed by a Grass stimulator (Astro-Med; West Warwick, RI, USA). The electrical signals of autonomic nerves are recorded by the Power Lab data acquisition system (AD Instruments, NSW, Australia) and visualized and analyzed by Labchart software (version 8.0, AD Instruments). Serum biochemical indices were determined by a fully automatic biochemical analyzer BK-1200 (BIOBASE, Jinan, China).\n\nThe GaNPs were obtained by sonication of liquid Ga. The liquid Ga (300\u2009mg) was transferred to anhydrous ethanol (8\u2009mL), and the solution was sonicated by nanoprobe sonication for 1\u2009h (3\u2009s on and 3\u2009s off) at the power of 290\u2009W. Then the ethanol was replaced with Milli-Q water to continue sonication for 1\u2009h. The solution at the end of sonication was collected and centrifuged at 102\u2009\u00d7\u2009g for 5\u2009min, and the upper liquid layer was aspirated for later use.\n\nFirst, the GaNPs and 3\u2009mL Na2PtCl6 (0.1\u2009M) were evacuated for 30\u2009min and Ar was introduced for 15\u2009min. Then, 3\u2009mL Na2PtCl6 (0.1\u2009M) was added dropwise to GaNPs and the solution was stirred for 4\u2009h (droplet volume: 0.02\u2009mL, dropwise rate: 1 droplet/s). After the reaction, the solution was collected and centrifuged at 8326\u2009\u00d7\u2009g for 10\u2009min. The solids at the bottom were washed with Milli-Q water three times and finally dispersed in 6\u2009mL Milli-Q for later use.\n\nThe PtNP-shell was first covered with a small amount of mPEG-SH to protect the structure from KOH. 30\u2009mg mPEG-SH5000 was added to 6\u2009mL PtNP-shell and the solution was stirred for 12\u2009h. After the reaction, the solution was collected and centrifuged at 8326\u2009\u00d7\u2009g for 10\u2009min. The solids at the bottom were washed with Milli-Q water three times and dispersed in 6\u2009mL Milli-Q water. The above solution was stirred with 12\u2009mL of KOH (1\u2009M) for 4\u2009h. The reaction-completed solution was collected and centrifuged at 8326\u2009\u00d7\u2009g for 10\u2009min, and the solids at the bottom were washed three times with Milli-Q water and finally dispersed in 6\u2009mL Milli-Q water. The above solution was stirred with 60\u2009mg mPEG-SH5000 for 12\u2009h. After the reaction, the solution was collected and centrifuged. The solids at the bottom were washed with Milli-Q water three times and finally dispersed in 6\u2009mL PBS.\n\nFirst, the GaNPs and 1\u2009mL Na2PtCl6 (0.04\u2009M) were evacuated for 30\u2009min and Ar was introduced for 15\u2009min. Then, 1\u2009mL Na2PtCl6 (0.04\u2009M) was added dropwise to GaNPs and the solution was stirred for 10\u2009min (droplet volume: 0.02\u2009mL, dropwise rate: 1 droplet/s). After the reaction, the solution was collected and centrifuged at 8326\u2009\u00d7\u2009g for 10\u2009min, washed three times with Milli-Q water, and dispersed in 6\u2009mL Milli-Q water. The above solution was stirred with 60\u2009mg mPEG-SH5000 for 12\u2009h. After the reaction, the solution was collected and centrifuged (8326\u2009\u00d7\u2009g, 10\u2009min). The solids at the bottom were washed with Milli-Q water three times and finally dispersed in 6\u2009mL PBS.\n\nThe relationship between temperature rise and energy transfer in the system can be described by Eq.\u00a01,\n\nwhere \\({Q}_{{{\\rm{abs}}}}\\) is the total energy absorbed by the system, \\({Q}_{{{\\rm{NPs}}}}\\) is the energy absorbed by the nanoparticles, \\({Q}_{{{\\rm{solvent}}}}\\) is the energy absorbed by the solvent, \\({Q}_{{{\\rm{ext}}}}\\) is the energy loss from the system to the environment. mi and ci are the mass and specific heat capacity of the solution, respectively. T is the solution temperature and t is the irradiation time. The conversion of the light energy into heat energy can be expressed in terms of Eq.\u00a02,\n\nwhere I is the laser power, A is the absorbance value of PtNP-shell at 1064\u2009nm, \u03b7 is the photothermal conversion efficiency. \\({Q}_{{{\\rm{solvent}}}}\\) can be calculated by the following Eq.\u00a03,\n\nwhere h is the convective heat transfer coefficient and s is the surface area of the sample cell. \\({T}_{{{\\rm{solvent}}}}\\) is the maximum temperature that the solvent can reach under laser irradiation. \\({T}_{{{\\rm{surr}}}}\\) is the ambient temperature. \\({Q}_{{{\\rm{ext}}}}\\) can also be written as,\n\nThe heat output will increase with the increase in temperature when the NIR-II laser power is determined according to Eq.\u00a04. The temperature of the system will reach the maximum when the heat input is equal to the heat output, so the following equation can be obtained,\n\nwhere \\({Q}_{{{\\rm{ext}}}-\\max }\\) is the heat transferred from the system surface through the air when the sample cell reaches equilibrium temperature, and \\({T}_{\\max }\\) is the equilibrium temperature. Combining Eqs.\u00a02, 3, and 5, \u03b7 can be expressed as,\n\nwhere A is the PtNP-shell absorption at 1064\u2009nm. To obtain hs, the dimensionless temperature \u03b8 is introduced,\n\nand a time constant of sample system, \\({\\tau }_{{{\\rm{s}}}}\\)\n\nCombining Eqs.\u00a01, 4, 7, and 8, the following equation can be obtained,\n\nAfter the laser is turned off, in the cooling stage, there is no external input energy, \\({Q}_{{{\\rm{NPs}}}}+{Q}_{{{\\rm{solvent}}}}=0\\), and Eq.\u00a09 can be written as,\n\nBy integrating Eq.\u00a010, the following equation can be obtained,\n\nTherefore, the system heat transfer time constant (\\({\\tau }_{{{\\rm{s}}}}\\)) at 1064\u2009nm is 242.25\u2009s (Fig.\u00a03f). In addition, m is 1\u2009g and c is 4.2\u2009J\u2009g\u22121. Therefore, hs can be determined from Eq.\u00a08. The laser power (I) used here can be determined as 1\u2009W. Then the photothermal conversion efficiency (\u03b7) of the PtNP-shell at 1064\u2009nm can be calculated to be 73.7% by substituting hs into Eq.\u00a06.\n\nAll animal experiments were approved by the Animal Care and Use Committee of Renmin Hospital of Wuhan University (WDRM20230805A). All experimental procedures were in accordance with the Declaration of Helsinki for animals and were conducted according to the guidelines established by the National Institutes of Health. All adult male beagle dogs (8\u201312\u2009kg) were anesthetized intravenously with 3% sodium pentobarbital (30\u2009mg\u2009kg\u22121 induction dose, 2\u2009mg\u2009kg\u22121 maintenance dose/h) and respiration was maintained by endotracheal intubation using a ventilator. Arterial blood pressure was continuously monitored through femoral artery catheterization with a pressure transducer attached. ECG and blood pressure data were recorded throughout the procedure. A heating pad was used to maintain the core body temperature at 36.5\u2009\u00b1\u20090.5\u2009\u00b0C.\n\nThe cells were cultured in a humid incubator containing 5% CO2 at a temperature of 37.0\u2009\u00b0C. The cell-specific medium was prepared by mixing Dulbecco\u2019s modified Eagle\u2019s medium (DMEM), fetal bovine serum, and penicillin\u2013streptomycin mixture at 89%, 10%, and 1%, respectively.\n\nWe performed separate experiments for each biological sample in our in vitro and in vivo experiments, performing duplicate experiments with different subjects to ensure the reproducibility and accuracy of our studies.\n\nWestern blotting was used to assess the expression of TRPV1 and TREK1 in neuronal cells and ganglion tissues. HT-22 cells or HEK-293T cells were cultured in six-well plates for 24\u201348\u2009h, then lysed and centrifuged (12000\u2009\u00d7\u2009g, 10\u2009min) to collect cells. Ganglion tissues were obtained from deceased animals and frozen in liquid nitrogen or stored at \u221280.0\u2009\u00b0C. Total protein was determined using BCA protein assay reagent after tissue grinded and cells lysed. Afterward, the procedure was followed according to the manufacturer\u2019s instructions. Primary antibodies were anti-TRPV1 (dilution ratio 1:1000) and anti-TREK1 (dilution ratio 1:1000). Expression levels of specific proteins were normalized to GAPDH (dilution ratio 1:20000).\n\nThe effect of PtNP-shell photothermal modulation on ion channels in HT-22 cells was explored through calcium imaging experiments. HT-22 cells were incubated in 35\u2009mm confocal dishes for 24\u2009h. Cells were washed three times with PBS and then stained with 5\u2009\u03bcM Fluo-4 AM (dilution ratio 1:500) for 30\u2009min in a cell incubator at 37.0\u2009\u00b0C, protected from light. To induce activation of TRPV1 and TREK1 ion channels, the culture dish was exposed to a 1064\u2009nm laser (0.75\u2009W\u2009cm\u22122, TRPV1: 50\u2009s, TREK1: 80\u2009s), resulting in an elevation of temperature. TRPV1, being a calcium channel, exhibited observable changes in the flow of calcium ions upon activation, while TREK1 as a potassium channel did not display such behavior. Therefore, the effect of PtNP-shell photothermal modulation on neuronal cells via TREK1 was observed by introducing a 15\u2009mM KCl solution prior to NIR-II irradiation. Fluorescence signals at 525\u2009nm were recorded using a confocal microscope with 488\u2009nm as the excitation wavelength. XYT images in the region of 1064\u2009nm illumination were acquired and collected under a 20\u00d7 objective lens. The average fluorescence intensity of the cells was analyzed using ImageJ software (Fiji). The normalized fluorescence change was calculated as follows: \u0394F/F = (F\u2009\u2212\u2009F0)/F0, where F is the original fluorescence signal; F0 is the average baseline intensity before irradiation with NIR-II laser.\n\nThe cytotoxicity of PtNP-shell on neuronal cells was evaluated by Cell Counting Kit-8 (CCK-8) assay. HT-22 cells were seeded in 96-well plates at a density of 1\u2009\u00d7\u2009104\u2009well\u22121 and cultured for 24\u2009h. HT-22 cells were then treated with different concentrations (10, 25, 50, 100, 150, 200\u2009\u03bcg\u2009mL\u22121) of PtNP-shell for another 24\u2009h. Cell viability was determined by CCK-8 assay after incubating with the CCK-8 reagent for 1\u2009h. To investigate the impact of PtNP-shell\u2019s photothermal effect on neuron cell viability, HT-22 cells were co-cultured with PtNP-shell (50\u2009\u03bcg\u2009mL\u22121) for 12\u2009h followed by irradiation with a 1064\u2009nm laser (0.75 and 1\u2009W\u2009cm\u22122) for various durations (0, 30, 60, and 90\u2009s). After incubation again for 12\u2009h, the absorbance at 450\u2009nm was recorded using a microplate reader. Cell survival (%)\u2009=\u2009(ODsamples\u2009\u2212\u2009ODblank)/(ODcontrol\u2009\u2212\u2009ODblank)\u2009\u00d7\u2009100%.\n\nPart 1: exploring the in vivo effects of precise photothermal stimulation of the parasympathetic nervous system by PtNP-shell under NIR-II irradiation. Twelve beagles were randomly assigned to the control group (100\u2009\u03bcL phosphate-buffered saline (PBS) was microinjected into the NG, n\u2009=\u20096) and the PtNP-shell group (100\u2009\u03bcL PtNP-shell (50\u2009g\u2009mL\u22121) was microinjected into the NG, n\u2009=\u20096). NG nerve activity, heart rate (HR), and ventricular electrophysiological parameters were recorded at baseline and at multiple consecutive time points after NIR-II irradiation (Fig.\u00a04b).\n\nPart 2: the protective effect of PtNP-shell activation of the parasympathetic nervous system against myocardial I/R injury was investigated. The same grouping pattern as in Part 1 was used, with NIR-II irradiation (heating stage: 0.8\u2009W\u2009cm\u22122, 12\u2009\u00b1\u20093\u2009s; equilibrium stage: 0.45\u2009W\u2009cm\u22122, 5\u2009min) of the NG before opening the occluded LAD coronary vessel. Afterward, ventricular electrophysiological parameters, heart rate variability (HRV), and ECG data were recorded and analyzed (Fig.\u00a05b).\n\nPart 1: the in vivo effects of precise photothermal stimulation of the sympathetic nervous system by PtNP-shell under NIR-II irradiation (heating stage: 0.8\u2009W\u2009cm\u22122, 25\u2009\u00b1\u20095\u2009s; equilibrium stage: 0.6\u2009W\u2009cm\u22122, 5\u2009min) were explored. Twelve beagles were randomly assigned to the control group (100\u2009\u03bcL PBS microinjected into the LSG, n\u2009=\u20096) and the PtNP-shell group (100\u2009\u03bcL PtNP-shell (50\u2009g\u2009mL\u22121) microinjected into the LSG, n\u2009=\u20096). LSG nerve activity, SBP, and ventricular electrophysiological parameters were recorded at baseline and at multiple consecutive time points after NIR-II irradiation (Fig.\u00a06b).\n\nPart 2: to investigate the protective effect of PtNP-shell inhibition on the sympathetic nervous system in improving MI, we used the same grouping pattern as in Part 1, with 5-min NIR-II irradiation of the LSG before ligation of LAD vessels. Finally, ventricular electrophysiological parameters, HRV, and ECG data were also recorded and analyzed (Fig.\u00a07b).\n\nWe selected NG and LSG as targets for modulation in the autonomic nervous system to explore the multimodality of the PtNP-shell photothermal strategy. A \u201cC\u201d incision is made behind the left ear, and the angle between the occlusal and trapezius muscles serves as the access approach37. The tissue is bluntly separated to expose the carotid sheath and identify the parasympathetic nerve. Moving upstream along the nerve, a distal expansion is observed as NG (Fig.\u00a04a). LSG can be visualized and localized by left-sided thoracotomy (Fig.\u00a06a)34. PtNP-shell (50\u2009\u03bcg\u2009mL\u22121) or PBS was slowly injected into two sites within the NG and LSG tissues to achieve homogeneous photothermal conversion. Initial vertical irradiation of NIR-II laser (1064\u2009nm) at 0.80\u2009W\u2009cm\u22122 was performed on NG and LSG surfaces. The power density of the NIR-II laser was reduced to 0.45\u2009W\u2009cm\u22122 for continuous irradiation when the temperature of the NG reached 42.0\u2009\u00b0C, and was reduced to 0.6\u2009W\u2009cm\u22122 for continuous irradiation when the temperature of the LSG reached 46.0\u2009\u00b0C. The NIR-II laser irradiation remains stable with a spot size maintained at 1.0\u2009cm\u22122. Dual temperature monitoring using a thermal imager and T-type thermocouple was performed to plot the temperature-time curve.\n\nThe NG is a ganglion located upstream of the cervical parasympathetic nerve and can significantly inhibit HR after receiving direct electrical stimulation26. The LSG, as an important peripheral sympathetic ganglion, can rapidly elevate blood pressure when activated by electrical stimulation. Based on the functional properties of different autonomic ganglia, we assessed the function of NG and LSG. A pair of special electrodes made with silver wires were directly connected to the surfaces of NG and LSG for stimulation. High-frequency electrical stimulation (HFS: 20\u2009Hz, 0.1\u2009ms) was applied to the ganglion. The voltage was set to five levels in continuous increments (level 1: 0\u20132\u2009V; level 2: 2\u20134\u2009V; level 3: 4\u20136\u2009V; level 4: 6\u20138\u2009V; level 5: 8\u201310\u2009V), while keeping the stimulation voltage values consistent with the baseline at different time points during the experiment. The percentage of sinus rate or AV conduction (measured by the A\u2013H interval) slowing down constructed voltage level/degree of HR decrease curves reflecting NG function. On the other hand, the percentage increase in SBP built the voltage level/degree of SBP increase to reflect LSG function.\n\nTwo specially designed microelectrodes were inserted into the NG and LSG, respectively, while a grounding wire was connected to obtain signals from the autonomic nerves. These electrical signals were recorded by a Power Lab data acquisition system, filtered through a band-pass filter (300\u20131000\u2009Hz) and amplified 30\u201350 times by an amplifier. Finally, the signals were digitized and analyzed in LabChart software (version 8.0, AD Instruments).\n\nThe left anterior descending coronary occlusion (LADO) method was used to establish the MI model27. The ligation site was located beneath the first diagonal of the LAD, and the successful MI model was confirmed by observing ST-segment elevation on the ECG. After ensuring cardiac electrophysiological stabilization, the junction was released to reperfuse the occluded coronary arteries, completing the construction of the myocardial I/R injury model38.\n\nThe ERP was measured at three locations: LVA, LVB, and LVM (located between the LVA and LVB). Malignant arrhythmic events occurring within 1\u2009h of MI and I/R injury were assessed by electrocardiographic recordings in a canine model using a Lead 7000 Computerized Laboratory System. VAs were classified according to Lambeth Conventions as VPBs, VT (three or more consecutive VPBs), and VF39. The sVT is defined as continuous VT for more than 30\u2009s. In addition, arrhythmia inducibility was further assessed by programmed ventricular stimulation at the right ventricular apex (RVA). Eight consecutive stimuli (S1S1) were performed at intervals of 330\u2009ms, followed by additional stimuli until VT/VF occurred. Arrhythmia inducibility was assessed based on a modified arrhythmia scoring system40. If VF occurs during the evaluation, a defibrillator is required to restore sinus rhythm, followed by a waiting period of 30\u2009min to restore cardiac electrophysiological stability. The VF threshold was assessed in the perimyocardial infarction region. Pacing was initiated using a Grass stimulator with a voltage of 2\u2009V (20\u2009Hz, 0.1\u2009ms duration, 10\u2009s). The stimulation voltage was increased in 2\u2009V increments until VF was induced. The lowest voltage that induced VF was regarded as the VF threshold41.\n\nThe ECG data were recorded using the Power Lab data acquisition system. And the ECG segments recorded more than 5\u2009min before modulation and after MI or I/R injury were analyzed by LabChart software with the Lomb\u2013Scargle periodogram algorithm42. Frequency domain metrics of HRV were calculated, including LF (0.04\u20130.15\u2009Hz, reflecting sympathetic tone), HF (0.15\u20130.4\u2009Hz, reflecting parasympathetic tone), and LF/HF (reflecting autonomic balance). The results were expressed in standardized units.\n\nThe ganglions were rapidly dissected for histopathological staining after the experimental animals died. Tissues were fixed with 4% paraformaldehyde, embedded in paraffin, and cut into 5\u2009\u03bcm-thick sections. NG was stained with multiple immunofluorescence staining using anti-VAChT (dilution ratio 1:100), anti-c-Fos (dilution ratio 1:100), and anti-TRPV1 (dilution ratio 1:100) antibodies. VAChT is an essential protein for the transport of acetylcholine and can therefore localize parasympathetic neurons belonging to cholinergic neurons. Expression of c-Fos serves as a marker for neural cell activation. And LSG was stained by multiple immunofluorescences using anti-TH (dilution ratio 1:100), anti-c-fos (dilution ratio 1:200), and anti-TREK1 (dilution ratio 1:100) antibodies. Sympathetic neurons can be localized by TH protein expression. Cell nuclei were stained with DAPI. Images were taken at 100\u00d7 magnification and analyzed using ImageJ software (Fiji).\n\nIn myocardial I/R injury model experiments, 5\u2009mL of venous blood was obtained from the jugular vein of each beagle 4\u20135\u2009h after ligation of the coronary vessels (3\u20134\u2009h after reperfusion treatment). After standing for 1\u2009h, the blood was centrifuged at 1237\u2009\u00d7\u2009g for 15\u2009min. The upper serum layer was collected and stored at \u221280.0\u2009\u00b0C. Myocardial injury levels were detected by c-TnI and MYO. Standard process analyses were performed according to the instructions of each ELISA kit. To evaluate the long-term biosafety and biocompatibility of PtNP-shell in vivo, beagle dogs and rats were randomly divided into PtNP-shell and PBS groups.\n\nTo evaluate the long-term biosafety and biocompatibility of PtNP-shell in vivo, beagle dogs and rats were randomly divided into two groups: a PtNP-shell group (n\u2009=\u20096) and a PBS group (n\u2009=\u20096). In the PtNP-shell group, 200\u2009\u03bcL PtNP-shell (50\u2009\u03bcg\u2009mL\u22121) was microinjected into canine ganglion tissue and tail vein of rats to explore long-term biosafety. Blood and tissue samples were collected from each dog and rat 1 month after injection. One month after injection, blood samples were collected from the jugular vein of dogs as well as from the inferior vena cava of rats for analysis of serum biochemical indices, including alanine transaminase (ALT), aspartate aminotransferase (AST), urea, creatinine (Crea) and lactate dehydrogenase-1 (LDH1). Tissue H&E staining was also performed on major organs, including the heart, liver, spleen, lung, and kidney.\n\nAll graphical data are presented as mean\u2009\u00b1\u2009standard error of the mean (S.E.M.), and the distribution of data was assessed by the Shapiro\u2013Wilk test. Differences between groups were determined using the Student\u2019s t-test or Mann\u2013Whitney U test. Data distribution was assessed by the Shapiro\u2013Wilk test for normality. Differences between the two means were tested using unpaired Student\u2019s t-test for Gaussian distributed data. Analysis of variance followed by one-way ANOVA with Dunnett test (one independent variable) or two-way ANOVA with Tukey test (two independent variables) were used as appropriate and indicated in the figure legend. The P values of <0.05 were considered statistically significant. Data were analyzed and plotted using GraphPad Prism 9.0 software (GraphPad Software, Inc., La Jolla, CA, USA) and Oringinpro 2018 (OriginLab, USA).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data underlying this study are available from the corresponding author upon request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Virani, S. S. et al. Heart disease and stroke statistics\u20142020 update: a report from the American Heart Association. 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Res. 118, 1821\u20131834 (2022).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "The research was supported by the National Natural Science Foundation of China (Grants 22025303: L.F., 82241057: L.L.Y., 82270532: L.L.Y., and 82200556: L.P.Z.); and the National Key Research and Development Program of China (Grant 2023YFC2705705: L.F.); and Foundation for Innovative Research Groups of Natural Science Foundation of Hubei Province, China (Grant 2021CFA010: L.L.Y.); and the Interdisciplinary\u00a0Innovative Talents Foundation from Renmin Hospital of Wuhan University (Grant JCRCFZ-2022-005: H.J.). We thank the Core Facility of Wuhan University for their substantial support in sample characterization, including SEM, XPS, DLS, and XRD. We thank the Center for Electron Microscopy at Wuhan University for their support of STEM, HRTEM, and EDX characterization. We also thank Meimei Zhang in the Institute for Advanced Studies of Wuhan University for their assistance in TEM characterization.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Chenlu Wang, Liping Zhou, Chengzhe Liu.\n\nCollege of Chemistry and Molecular Sciences, Wuhan University, Wuhan, China\n\nChenlu Wang,\u00a0Luyang Wang,\u00a0Yaxi Liu,\u00a0Bi Xu,\u00a0Mengqi Zeng\u00a0&\u00a0Lei Fu\n\nDepartment of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China\n\nLiping Zhou,\u00a0Chengzhe Liu,\u00a0Jiaming Qiao,\u00a0Xinrui Han,\u00a0Qinfang Qiu,\u00a0Zizhuo Zhang,\u00a0Jiale Wang,\u00a0Xiaoya Zhou\u00a0&\u00a0Lilei Yu\n\nHubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China\n\nLiping Zhou,\u00a0Chengzhe Liu,\u00a0Jiaming Qiao,\u00a0Xinrui Han,\u00a0Qinfang Qiu,\u00a0Zizhuo Zhang,\u00a0Jiale Wang,\u00a0Xiaoya Zhou\u00a0&\u00a0Lilei Yu\n\nCardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China\n\nLiping Zhou,\u00a0Chengzhe Liu,\u00a0Jiaming Qiao,\u00a0Xinrui Han,\u00a0Qinfang Qiu,\u00a0Zizhuo Zhang,\u00a0Jiale Wang,\u00a0Xiaoya Zhou\u00a0&\u00a0Lilei Yu\n\nHubei Key Laboratory of Cardiology, Wuhan, China\n\nLiping Zhou,\u00a0Chengzhe Liu,\u00a0Jiaming Qiao,\u00a0Xinrui Han,\u00a0Qinfang Qiu,\u00a0Zizhuo Zhang,\u00a0Jiale Wang,\u00a0Xiaoya Zhou\u00a0&\u00a0Lilei Yu\n\nCardiovascular Research Institute, Wuhan University, Wuhan, China\n\nLiping Zhou,\u00a0Chengzhe Liu,\u00a0Jiaming Qiao,\u00a0Xinrui Han,\u00a0Qinfang Qiu,\u00a0Zizhuo Zhang,\u00a0Jiale Wang,\u00a0Xiaoya Zhou\u00a0&\u00a0Lilei Yu\n\nTaikang Center for Life and Medical Sciences, Wuhan University, Wuhan, China\n\nLiping Zhou,\u00a0Chengzhe Liu,\u00a0Jiaming Qiao,\u00a0Xinrui Han,\u00a0Qinfang Qiu,\u00a0Zizhuo Zhang,\u00a0Jiale Wang,\u00a0Xiaoya Zhou,\u00a0Lilei Yu\u00a0&\u00a0Lei Fu\n\nInstitute of Molecular Medicine, Renmin Hospital of Wuhan University, Wuhan, China\n\nLiping Zhou,\u00a0Chengzhe Liu,\u00a0Jiaming Qiao,\u00a0Xinrui Han,\u00a0Qinfang Qiu,\u00a0Zizhuo Zhang,\u00a0Jiale Wang,\u00a0Xiaoya Zhou,\u00a0Lilei Yu\u00a0&\u00a0Lei Fu\n\nThe Institute for Advanced Studies, Wuhan University, Wuhan, China\n\nLei Fu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nL.F., L.L.Y., and M.Q.Z. conceived the research concept. L.F., L.L.Y., M.Q.Z., and X.Y.Z. supervised the research; C.L.W., L.P.Z., C.Z.L., J.M.Q., X.R.H., B.X., Q.F.Q., Z.Z.Z., and J.L.W. performed the experiments; C.L.W., L.P.Z., C.Z.L., L.Y.W., and Y.X.L. discussed the results; C.L.W., L.P.Z., and C.Z.L. analyzed the data and co-wrote the manuscript. All authors commented on the manuscript.\n\nCorrespondence to\n Xiaoya Zhou, Lilei Yu or Lei Fu.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks David J. Lundy, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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Pt nanoshells with a high NIR-II photothermal conversion efficiency mediates multimodal neuromodulation against ventricular arrhythmias.\n Nat Commun 15, 6362 (2024). https://doi.org/10.1038/s41467-024-50557-w\n\nDownload citation\n\nReceived: 01 March 2024\n\nAccepted: 16 July 2024\n\nPublished: 28 July 2024\n\nVersion of record: 28 July 2024\n\nDOI: https://doi.org/10.1038/s41467-024-50557-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"Microtopography-induced changes in cell nucleus morphology enhance bone regeneration by modulating the cellular secretome", + "pre_title": "Micropillar-induced changes in cell nucleus morphology enhance bone regeneration by modulating the secretome", + "journal": "Nature Communications", + "published": "11 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_MOESM6_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_MOESM7_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_MOESM8_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_MOESM9_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE286676", + "https://www.ebi.ac.uk/pride/archive/projects/PXD059752", + "/articles/s41467-025-60760-y#Sec37" + ], + "code": [], + "subject": [ + "Biomedical materials", + "Mesenchymal stem cells", + "Regenerative medicine" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5530535/v1.pdf?c=1752318487000", + "research_square_link": "https://www.researchsquare.com//article/rs-5530535/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60760-y.pdf", + "preprint_posted": "06 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Nuclear morphology, which modulates chromatin architecture, plays a critical role in regulating gene expression and cell functions. While most research has focused on the direct effects of nuclear morphology on cell fate, its impact on the cell secretome and surrounding cells remains largely unexplored, yet is especially crucial for cell-based therapies. In this study, we fabricated implants with a micropillar topography using methacrylated poly(octamethylene citrate)/hydroxyapatite (mPOC/HA) composites to investigate how micropillar-induced nuclear deformation influences cell paracrine signaling for osteogenesis and cranial bone regeneration. In vitro, cells with deformed nuclei showed enhanced secretion of proteins that support extracellular matrix (ECM) organization, which promoted osteogenic differentiation in neighboring human mesenchymal stromal cells (hMSCs). In a mouse model with critical-size cranial defects, nuclear-deformed hMSCs on micropillar mPOC/HA implants elevated Col1a2 expression, contributing to bone matrix formation, and drove cell differentiation toward osteogenic progenitor cells. These findings indicate that micropillars not only enhance the osteogenic differentiation of human mesenchymal stromal cells (hMSCs) but also modulate the secretome, thereby influencing the fate of surrounding cells through paracrine effects.Biological sciences/Biotechnology/Tissue engineeringBiological sciences/Stem cells/RegenerationHealth sciences/Medical research/Translational research", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryTable1.xlsxSupplementary Table 1SupplementaryTable2.xlsxSupplementary Table 2SupplementaryTable3.xlsxSupplementary Table 3SupplementaryTable4.xlsxSupplementary Table 4SupplementMicrotopographyinducedchangesincellnucleusmorphologyenhanceboneregenerationbymodulatingthecellularsecretome.pdfMicrotopography-induced changes in cell nucleus morphology enhance bone regeneration by modulating the cellular secretome", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Nuclear morphology plays a critical role in regulating gene expression and cell functions. While most research has focused on the direct effects of nuclear morphology on cell fate, its impact on the cell secretome and surrounding cells remains largely unexplored. In this study, we fabricate implants with a micropillar topography using methacrylated poly(octamethylene citrate)/hydroxyapatite (mPOC/HA) composites to investigate how micropillar-induced nuclear deformation influences cell secretome for osteogenesis and cranial bone regeneration. In vitro, cells with deformed nuclei show enhanced secretion of proteins that support extracellular matrix (ECM) organization, which promotes osteogenic differentiation in neighboring mesenchymal stromal cells (MSCs). In a female mouse model with critical-size cranial defects, nuclear-deformed MSCs on micropillar mPOC/HA implants elevate Col1a2 expression, contributing to bone matrix formation, and drive cell differentiation toward osteogenic progenitor cells. These findings indicate that micropillars modulate the secretome of hMSCs, thereby influencing the fate of surrounding cells through matricrine effects.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The nucleus is a dynamic organelle that changes its morphology in response to the cell\u2019s status1. Its morphology has a critical influence on nuclear mechanics, chromatin organization, gene expression, cell functionality and disease development2,3,4,5. Abnormal nuclear morphologies, such as invagination and blebbing, have functional implications in several human disorders, including cancer, accelerated aging, thyroid disorders, and different types of neuro-muscular diseases6,7. In addition, severe nuclear deformation is also observed during tissue development, cell migration, proliferation, and differentiation2.\n\nTo manipulate nuclear morphology, various biophysical tools have been developed, including atomic force microscopy (AFM) nanoindentation, optical, magnetic, and acoustic tweezers, microfluidic devices, micropipette aspiration, plate compression, substrate deformation, and surface topography modulation, referred to as microtopography engineering8,9,10,11,12,13,14,15. Among these methods, microtopography engineering of materials is can be readily applicable to implantable medical devices and has broad implications for regenerative engineering. One commonly used approach is the fabrication of pillar structures, which are employed to deform cell nuclei and study nuclear properties such as mechanics and deformability16. These micropillar designs have been utilized to manipulate various cell functions, including migration, adhesion, proliferation, and differentiation17,18,19,20. A design featuring 5 \u00d7 5\u2009\u03bcm\u00b2 micropillars with 5\u2009\u03bcm spacing has been shown to significantly enhance the osteogenic differentiation of MSCs, highlighting the considerable potential of surface engineering for advancing bone regeneration20,21.\n\nA wide range of materials can be used to create micropillar structures, such as poly-L-lactic acid (PLLA), poly(lactide-co-glycolide) (PLGA), OrmoComp (an organic-inorganic hybrid polymer), and methacrylated poly(octamethylene citrate) (mPOC)20,21,22,23. Among these options, mPOC is particularly suitable for bone regeneration due to its major component, citrate, which acts as a metabolic factor to enhance the osteogenesis of mesenchymal stromal cells (MSCs)24. Additionally, a series of products made from citrate-based biomaterials (CBBs), including Citrelock, Citrefix, and Citregraft, have been cleared by the FDA for musculoskeletal regeneration in patients, further demonstrating the clinical efficacy of CBBs. Implantation of mPOC micropillars in a mouse cranial defect model demonstrated its bone regenerative potential in vivo21. However, the volume of regenerated bone remains limited, highlighting the need for further development of implant to enhance the efficacy of bone regeneration. More importantly, the majority of the new bone does not directly contact the implants; instead, it forms with a noticeable gap between the implant and the regenerated tissue. This observation inspired us to consider that nuclear deformation on micropillar implants may influence surrounding cells through the modulation of their secretomes.\n\nBioactive molecules secreted by cells are crucial for intercellular communication, affecting various biological processes such as inflammation, cell survival, differentiation, and tissue regeneration25,26. The success of many cell and exosome-based therapies depends on the cellular secretome27, which can be modulated by surface topography. For example, surfaces featuring grooves, roughness, or spiral patterns have been shown to influence the secretory profile of MSCs, primarily affecting immune regulation28. Additionally, the cytokine secretion profile of stromal cells, including MSCs and kidney-derived perivascular stromal cells (kPSCs), is closely linked to cell morphology, which is regulated by the unique surface structures29. Despite reports highlighting the influence of surface topography on secretion, the impact of nuclear morphogenesis, regulated by topography, on cellular secretion remains unclear. Additionally, in vivo testing of regeneration is necessary to advance the clinical application of surface engineering.\n\nHydroxyapatite (HA) is a naturally occurring mineral form of calcium apatite, widely utilized in bone regeneration due to its exceptional biocompatibility, osteoconductivity, and structural similarity to the mineral component of bone30. The incorporation of HA with mPOC potentially combines the advantages of both materials in bone repair, thereby enhancing bone formation and offering a promising clinical option for future orthopedic implants. In this study, we fabricate micropillars to manipulate nuclear morphology and investigate their effects on the secretome of human mesenchymal stromal cells (hMSCs), as well as test their regenerative efficacy for bone tissue in vivo. Our results show that mPOC/HA micropillars facilitate osteogenic differentiation of hMSCs compared to flat mPOC/HA samples in vitro. Secretome analysis reveals that hMSCs with deformed nuclei exhibite higher expression levels of bioactive factors associated with extracellular matrix (ECM) components and organization, as well as ossification. In vivo, both mPOC/HA flat and micropillar scaffolds seeded with hMSCs result in new bone formation; however, the micropillar group demonstrates significantly greater new bone volume and regenerated tissue thickness. Spatial transcriptomic analysis further confirms elevated expression of genes related to the regulation of ECM structures, consistent with the secretome analysis results. These findings suggest that the influence of nuclear deformation on the osteogenesis of hMSCs operates through similar mechanisms in both in vitro and in vivo environments. Therefore, using microtopography engineering of scaffolds to control nuclear morphology and materials science approaches to mimic native bone composition is a promising approach to enhance bone regeneration.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "mPOC prepolymer was synthesized according to our previous report31, and its successful synthesis was confirmed via the nuclear magnetic resonance (1H NMR) spectrum (Supplementary Fig.\u00a01a\u2013c). The size of HA nanoparticles is around 100\u2009nm, as characterized by dynamic light scattering (DLS) (Supplementary Fig.\u00a01d). To mimic the nature of bone composition32, 60% (w/w) HA was mixed with mPOC, and the slurry was used to fabricate flat and micropillar implants using a combination of UV lithography and the contact printing method (Fig.\u00a01a). The square micropillars, with dimensions of 5 by 5 in side length and spacing, were fabricated (Fig.\u00a01b). The height of the micropillars is around 8\u2009\u03bcm, which can cause significant nuclear deformation (Fig.\u00a01c, d)22. Fourier transform infrared (FTIR) spectrum shows a similar typical peak of functional groups in mPOC and mPOC/HA implants (Supplementary Fig.\u00a01e). The surface roughness of the implants was scanned using an atomic force microscope (AFM) (Fig.\u00a01e). The analysis result indicates that the topography didn\u2019t affect the surface roughness of the implants (Fig.\u00a01f). Additionally, we tested the hydrophilicity of flat and micropillar implants via water contact angle measurement (Supplementary Fig.\u00a02). Although, at the initial state, the flat surface was more hydrophilic, there was no significant difference in the water contact angle after a 5-minute stabilization process.\n\na Illustration shows the combination of UV lithography and contact printing to fabricate free-standing mPOC/HA micropillars. b SEM image shows the micropillar structures made of mPOC/HA. c Optical microscope image and d cross-section analysis of mPOC/HA micropillars. e Surface scanning of flat and micropillar implants by AFM. f Surface roughness of flat and micropillar implants. N.S., no significant difference, n\u2009=\u20093 biological replicates. g Degradation test and h calcium release of flat and micropillar mPOC/HA implants. N.S., no significant difference, n\u2009=\u20094 biological replicates, insert plot shows the initial release of calcium within 24\u2009h. i. Representative images of flat and micropillar implants at different time points after accelerated degradation. Data are presented as mean\u2009\u00b1\u2009SD. Values from two groups were compared using a non-paired Student\u2019s t-test (two-sided). Source data is provided as a Source Data file.\n\nThe mechanical properties of the implants were tested using the nano-indentation method. The force-indentation curve of the flat sample has a sharper slope, indicating it is stiffer than the micropillar sample (Supplementary Fig.\u00a03a). The Young\u2019s Modulus of the flat sample (0.95\u2009\u00b1\u20090.12\u2009GPa) is significantly higher than that of the micropillars (0.48\u2009\u00b1\u20090.02\u2009GPa) and the lateral modulus of the micropillars (46.88\u2009\u00b1\u20091.49\u2009MPa) (Supplementary Fig.\u00a03b, c). However, based on a previous report, the high modulus of the substrates is beyond the threshold that cells can distinguish and does not have an influence on nuclear morphology manipulation33,34. Accelerated degradation and calcium release tests of the implants were performed in DPBS at 75\u2009\u00b0C with agitation35. There is a burst weight loss and calcium release of both flat and micropillar samples at day 1, followed by a gradual change until day 10, and another increase in the degradation and calcium release rate from day 10 to 14 (Fig.\u00a01g, h). The micropillar structure enhanced the degradation and calcium release, but not significantly. According to the images of the samples captured at different time points, the initial burst degradation and calcium release can be attributed to the fast surface erosion of both scaffolds, as many small pores can be observed on their surfaces (Supplementary Fig.\u00a04). From day 10 to 14, scaffolds started break into pieces that may lead to another burst degradation and calcium release (Fig.\u00a01i). The micropillars exhibited slight deformation in both the xy and z directions after degradation, though the changes were not significant (Supplementary Fig.\u00a05). Additionally, the structures transformed from outward convex to inward concave shapes.\n\nhMSCs were cultured on the flat and micropillar mPOC/HA surfaces in osteogenic medium and stained for F-actin and nuclei after 3 days (Fig.\u00a02a). Noticeable deformation in both the nucleus and cytoskeleton was observed, consistent with mPOC micropillars21. The Nuclear shape index (NSI) was calculated to assess the degree of nuclear deformation22. A significantly lower NSI value, indicating more severe deformation, was found in the micropillar group (Fig.\u00a02b). Confocal images were then employed to evaluate the 3D geometry of cell nuclei (Fig.\u00a02c). 3D reconstruction analysis revealed that several geometric parameters, including nuclear volume, surface area, and project area, were significantly decreased on micropillars, while nuclear height was significantly increased (Fig.\u00a02d and Supplementary Fig.\u00a04).\n\na Staining of nucleus (green) and F-actin (red) of hMSCs on flat and micropillar mPOC/HA surfaces. Insert: high magnification of cell nucleus. Dashed lines indicate micropillars. b Analysis of nuclear shape index of hMSCs. n\u2009=\u2009117 (flat) and 132 (pillar) collected from 3 biological replicates, ****p\u2009<\u20090.0001. c Orthogonal view of cell nucleus on flat and micropillar surfaces. d Nuclear volume analysis based on 3D construction of the confocal images of cell nuclei. n\u2009=\u200935 cells collected from 3 biological replicates, ****p\u2009<\u20090.0001. e Initial cell adhesions on flat and micropillar surfaces. n\u2009=\u20095 biological replicates, N.S., no significant difference. f SEM images show the cell adhesions on flat and micropillar mPOC/HA surfaces. g Live/dead staining of hMSCs on flat and micropillar surfaces at 72\u2009h in osteogenic medium. h Cell metabolic activity of cells on flat and micropillar surfaces tested by a MTT assay. n\u2009=\u20095 biological replicates, ****p\u2009<\u20090.0001. i Cell proliferation tested via DNA content after 72\u2009h induction. n\u2009=\u20095 biological replicates, N.S., no significant difference. j ALP staining of hMSCs on flat and micropillar surfaces after 7\u2009d induction. k ALP activity test of cells after 7\u2009d osteogenic induction. n\u2009=\u20093 biological replicates. l Blot images of osteogenic marker OCN and RUNX2 in cells cultured on flat and micropillar implants. GAPDH is shown as a control. Quantification (m) OCN and (n). RUNX2 according to Western blot tests. n\u2009=\u20093 biological replicates, ****p\u2009<\u20090.0001. Data are presented as mean\u2009\u00b1\u2009SD. Values from two groups were compared using a non-paired Student\u2019s t-test (two-sided). Source data are provided as a Source Data file.\n\nWe then investigated the impact of micropillars on cell adhesion, a crucial aspect for manipulating cell function36. Initial cell attachment tests revealed that the micropillar structure did not influence cell attachment on the implants (Fig.\u00a02e). SEM imaging of cell adhesion demonstrated that cells formed lamellipodia on flat surfaces but exhibited more retraction fibers on micropillars (Fig.\u00a02f). The retraction fibers were observed on the top, side, and bottom of micropillars, indicating that cells were sensing the 2.5D environment using these antennae-like structures17. The majority of cells were found to be viable on both flat and micropillar substrates, as evidenced by live/dead staining (Fig.\u00a02g and Supplementary Fig.\u00a05). While the micropillars reduced cell metabolic activity (Fig.\u00a02h), there was no significant impact on cell proliferation after 3 days of culture (Fig.\u00a02i).\n\nTo assess the impact of mPOC/HA micropillars on the osteogenesis of hMSCs, we stained ALP (alkaline phosphate) on substrates with both flat and micropillar structures (Fig.\u00a02j). Quantification results demonstrated a significant increase in ALP activity on the micropillars (Fig.\u00a02k). Furthermore, additional osteogenic differentiation markers of hMSCs, including RUNX2 and osteocalcin (OCN), were quantified through western blot analysis (Fig.\u00a02l). The quantification of these proteins revealed a significant increase in both RUNX2 and OCN in cells on micropillars, confirming that the structures can effectively promote the osteogenic differentiation of hMSCs (Fig.\u00a02m, n)20,21,22.\n\nPreviously, we demonstrated the ability of micropillar implants to enhance in vivo bone formation21. However, the newly formed bone was not in close contact with the implant. Consequently, we hypothesized that nuclear deformation on micropillars might impact cellular secretion, thereby influencing osteogenesis through the secretome. To test this hypothesis, secretome analysis was conducted using medium collected from flat and micropillar samples. Differences in protein secretion levels between the two groups were depicted through a volcano plot, revealing a significant influence of nuclear deformation on the secretome (Fig.\u00a03a, b). Gene ontology (GO) analysis was performed to annotate the significantly altered proteins in relevant processes37. Top changes in cellular component, molecular functions, biological processes, and biological pathways indicated that micropillars predominantly affected extracellular matrix (ECM)-related processes (Fig.\u00a03c and Supplementary Figs.\u00a08\u201310). Moreover, ossification and collagen fibril organization were identified as biological processes significantly overrepresented by differentially expressed proteins (Fig.\u00a03d). The heatmap plot of proteins associated with collagen-containing extracellular matrix and ossification showed predominant upregulation on micropillars (Fig.\u00a03e). The linkages of proteins and GO terms in biological process highlighted that ECM organization forms the largest cluster and is closely associated with the ossification process (Fig.\u00a03f).\n\na PCA plot of differentially expressed proteins secreted by hMSCs on flat and micropillars. Cyan: flat; Red: micropillar. b Volcano plot of proteins secreted by hMSCs seeded on micropillars compared to the flat surface. Blue and orange dots indicate significantly downregulated and upregulated proteins secreted by cells on micropillars compared to those on flat surface. Grey dots indicate non-significantly changed proteins. A threshold of expression greater than 2 times fold-change with p\u2009<\u20090.05 was considered to be significant (non-paired Student\u2019s t-test (two-sided)). Proteins that are related with collagen-ECM pathways are labeled. c Top 4 significantly enriched GO terms and Pathways identified through over-representation analysis using the one-sided Fisher\u2019s exact test. Significance was determined based on adjusted p-values\u2009<\u20090.05 (FDR\u2009<\u20095%, Benjamini-Hochberg). ***p\u2009<\u20090.001.\u2009d The most significantly enriched Biological Processes (one-sided Fisher\u2019s exact test, adjusted p-values\u2009<\u20090.05 (FDR\u2009<\u20095%, Benjamini-Hochberg)). e Heatmap of proteins that are related to collagen-containing extracellular matrix and ossification. F indicates flat samples and P indicates pillar samples, n\u2009=\u20093 biological replicates for each group. f The linkages of proteins and GO terms in biological processes related to collagen fibers, ECM, and ossification as a network. g Heatmap of the top 15 enriched terms plotted based on Reactome pathway analysis. Source data are provided as a Source Data file.\n\nReactome pathway analysis was further conducted to assess potential downstream effects of secretome changes on micropillars38. Results indicated that pathways related to ECM organization, ECM proteoglycans, and collagen fibril crosslinking were among the top 15 pathways significantly overrepresented by differential expressed pathways (DEP), predominantly showing upregulation (Fig.\u00a03g and Supplementary Fig.\u00a011). We also noticed an upregulation in the degradation of the ECM on micropillars, indicating enhanced ECM remodeling which a crucial factor for tissue regeneration39. These findings suggest that micropillars can influence the ECM formation of hMSCs through matricrine effects. Additionally, we performed proteomic analysis using cells cultured on flat and micropillar mPOC/HA scaffolds (Supplementary Fig.\u00a012). PCA and volcano plots indicated significant influences of nuclear deformation on protein expression. Pathway analysis revealed significant changes in many cell proliferation-related processes, consistent with previous transcriptomic tests on micropillars21.\n\nSince the micropillar surfaces can modulate the secretome of hMSCs, we investigated whether the deformed cells could influence the osteogenic differentiation of undeformed cells using a transwell assay (Fig.\u00a04a). The flat and micropillar mPOC/HA surfaces were fabricated at the bottom of cell culture plates to manipulate the nuclear morphology of hMSCs, while undeformed hMSCs were seeded on a transwell membrane with 400\u2009nm nanopores, allowing the exchange of growth factors. After cell attachment, all samples were cultured in osteogenic induction medium. ALP staining of the cells on the transwell membrane showed a higher number of ALP-positive cells when co-cultured with nuclear-deformed cells, indicating enhanced osteogenic differentiation (Fig.\u00a04b, c). Additionally, Alizarin Red S (ARS) staining confirmed increased calcium deposition\u2014a key step in osteogenesis\u2014when the cells were cultured above the micropillar-treated cells (Fig.\u00a04d, e). Based on the secretome analysis, hMSCs on micropillars appear to promote osteogenesis in the transwell culture by secreting proteins that enhance ECM structure and organization. Collagen staining revealed higher coverage, stronger staining intensity, and more interconnected collagen network structures in the transwell co-cultured with micropillar-treated cells (Fig.\u00a04f, g). In addition, energy dispersive X-ray spectroscopy (EDS) images showed more Ca and P deposition in the transwell co-cultured with micropillar-treated cells (Fig.\u00a04h). Together with the secretome analysis, these findings suggest that the proteins secreted by cells with deformed nuclei improve ECM organization in undeformed cells, thereby promoting osteogenesis.\n\na Schematic illustration of the experiment setup. b ALP staining and (c). quantification of ALP-positive cells on transwell membrane incubated with undeformed and deformed MSCs (n\u2009=\u20093 biological replicates). d ARS staining and e. quantification of cells on transwell membrane incubated with undeformed and deformed MSCs (n\u2009=\u20096 biological replicates). (f) Immunofluorescence staining images of collagen in the ECM of cells on the transwell membrane incubated with undeformed and deformed MSCs. g The coverage of collagen was analyzed according to the staining images (n\u2009=\u20094 biological replicates). h EDS images showing Ca, P, and SEM images of cells on the transwell membrane incubated with undeformed and deformed MSCs. Data are presented as mean\u2009\u00b1\u2009SD. Values from two groups were compared using a non-paired Student\u2019s t-test (two-sided). Source data is provided as a Source Data file.\n\nTo test the in vivo regeneration efficacy of mPOC/HA scaffolds, we created a critical size cranial defect model in nude mice. Two 4\u2009mm diameter critical defects were made on the left and right sides of the skull tissue for the implantation of flat and micropillar scaffolds, respectively (Fig.\u00a05a). The scaffolds were seeded with hMSCs for 24\u2009h to allow for cell attachment and nuclear deformation (Fig.\u00a05b). After 12 weeks, micro CT was performed to evaluate the bone formation in the living animals. Based on the images, newly formed bone can be observed in the defect area with both flat and micropillar mPOC/HA implants (Fig.\u00a05c and Supplementary Fig.\u00a011). Furthermore, larger bone segments were observed with the micropillar implant treatment. Quantification results confirmed a significantly increased bone volume with micropillar implant treatment (Fig.\u00a05d).\n\na Image shows implantation of hMSC seeded flat and micropillar mPOC/HA scaffolds. b Staining images of nuclei (green) and F-actin (red) of cells on the implants. c Representative \u03bcCT images of a typical animal implanted with hMSC-seeded flat (left) and micropillar (right) scaffolds at 12-weeks post-surgery. d Regenerated bone volume in the defect region (n\u2009\u2009=\u2009\u20095 animals). e Trichrome staining of the defect tissue treated with flat and micropillar implants. f Average thickness of regenerated tissues with implantation of flat and micropillar scaffolds (n\u2009\u2009=\u2009\u20095 animals). IHC staining of osteogenic marker, g OPN and h. OCN, in regenerated tissues with flat and micropillar implants. Data are presented as mean\u2009\u00b1\u2009SD. Values from two groups were compared using non-paired Student\u2019s t-test (two-sided). Source data is provided as a Source Data file.\n\nHistology analysis was further performed to evaluate the influences of flat and micropillar mPOC/HA implants on bone regeneration. Trichrome staining images revealed that defects treated with micropillar implants exhibited more osteoid tissue (Fig.\u00a05e and Supplementary Fig.\u00a012). Moreover, both flat and micropillar mPOC/HA implants showed evidence of newly formed bone tissue, indicating enhanced bone regeneration compared to the mPOC alone scaffold. As no bone segment was observed with flat mPOC implant treatment21. The thickness of the regenerated tissue was quantified, and the results demonstrated a significant enhancement with micropillar implant treatment (Fig.\u00a05f). Positive staining of osteogenesis markers, including osteopontin (OPN) and osteocalcin (OCN), was observed throughout the regenerated tissues with both flat and micropillar implants, indicating osteoid tissue formation (Fig.\u00a05g, h). The tissue appeared more compact in the micropillar group compared to the flat group. Furthermore, regenerated bone segments were more frequently observed with micropillar implant treatment. It has been reported that athymic nude mice retain an innate immune system, including macrophages, which contribute to bone regeneration40. Therefore, we further assessed macrophage activation in the regenerated tissue by staining for three markers: F4/80 (a pan-macrophage marker), CD86 (an M1 macrophage marker), and CD163 (an M2 macrophage marker), to evaluate macrophage polarization (Supplementary Fig.\u00a015)35. The results indicate a slight increase in overall macrophage expression and a decrease in the M1/M2 ratio; however, these changes were not statistically significant.\n\nHistological analyses showed more new bone formation with micropillar implants, although the new bone tissue did not directly interact with the micropillar surfaces. To further investigate the transcription profile of the regenerated tissue, we performed spatial transcriptomics (ST) analyses with both flat and pillar samples (Supplementary Fig.\u00a016). ST represents a powerful tool to investigate the cellular environment and tissue organization by providing a detailed map of gene expression within the native tissue context41. Differential gene expression (DGE) analysis revealed changes in expression levels between the two groups. Although only a few genes showed significant differences, all of them were related to ECM structure or organization (Supplementary Fig.\u00a016). Notably, the expression of Col1a2, critical for type I collagen formation (comprising 90% of the bone matrix), was enhanced in the micropillar group (Fig.\u00a06a). This expression showed a gradient, increasing toward the dura layer, possibly due to the osteogenic contribution of dura cells42. We then plotted a heatmap showing the top 10 up-regulated and down-regulated differentially expressed genes (pillar vs. flat) in comparison with those in native skull bone (Fig.\u00a06b). The heatmap indicated that the tissue regenerated with micropillar implants had expression patterns more similar to native skull bone than the flat group. Gene Ontology (GO) analysis of DGEs was further performed to annotate their relevant biological processes (Fig.\u00a06c). Protein localization to extracellular matrix and crosslinking of collagen fibrils were among the top 5 up-regulated processes in the micropillar group. These results are consistent with the secretome test, all indicating that micropillar structures can influence ECM organization via matricrine effects.\n\na Spatial plot of Col1a2 expression profile in tissues regenerated with flat mPOC/HA implant and micropillar mPOC/HA implant. Arrow indicates enhanced expression around dura layer. b The heatmap showing the top ten up- and down-regulated DEGs (pillar vs flat) in tissues regenerated with flat mPOC/HA implant, micropillar mPOC/HA implant, and native skull tissue. c Gene Ontology analysis results based on the top 100 up-regulated genes (pillar vs flat). The results are colored by q, false-discovery-rate-adjusted p-value. d Deconvoluted cell types in each spatial capture location in flat and micropillar groups. Each pie chart shows the deconvoluted cell type proportions of the capture location. e Bar plots of the cell type proportions in tissues regenerated with flat mPOC/HA implant and micropillar mPOC/HA implant. LMPs, MSCs, and fibroblasts are the predominant cell types. f Violin plot of the proportion of LMPs in flat (100 capture locations) and micropillar (69 capture locations) groups. The boxplots display medians and quartiles, with whiskers extending to 1.5 times the interquartile range, and the violin plot outlines represent the kernel probability density. The p-value from a two-sided Wilcoxon rank-sum test is shown. g Top enriched processes associated with LMP compared with other cell lineages. LMP: late mesenchymal progenitor cells; MSC: mesenchymal stromal cells; OLC: MSC-descendant osteolineage cells. The results are colored by q, false-discovery-rate-adjusted p-value. Source data are provided as a Source Data file.\n\nTo further investigate the relationship between cell type composition and the regenerated tissues, we performed cellular deconvolution on the ST data using single-cell RNA sequencing (scRNA-seq) references from previously published studies43,44,45. Several major cell lineages involved in bone regeneration were considered when deconvoluting the data (Fig.\u00a06d). The most abundant cell type in regenerated tissues was late mesenchymal progenitor cells (LMPs), followed by MSCs and fibroblasts (Fig.\u00a06e). There were also small proportions of MSC-descendant osteolineage cells (OLCs), osteocytes, osteoblasts, and chondrocytes. LMPs are identified as the late stage of MSCs through osteogenic differentiation43,46. Among all cell types, the proportion of LMPs, which have high expression of marker genes associated with osteoblasts, was significantly increased in regenerated tissues with micropillar implants, indicating that these deformed cells facilitate the differentiation of MSCs toward the osteolineage (Fig.\u00a06f). Additionally, GO analysis of DGEs (LMP versus other cell types) was performed to investigate the roles of LMPs in regenerated tissue. The results suggest that LMPs do not directly contribute to osteogenesis, a role performed by osteoblasts and osteocytes. Instead, LMPs can affect ECM formation, as the process of extracellular matrix organization is one of the top involved pathways (Fig.\u00a06g).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60760-y/MediaObjects/41467_2025_60760_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Micropillars, as a typical topographical feature, have been extensively studied for their ability to regulate cell functions. Recent studies have shown that rigid micropillars can deform nuclear morphology, which in turn promotes the osteogenic differentiation of mesenchymal stromal cells (MSCs), generating significant interest for bone regeneration applications20,22. Our previous work demonstrated that mPOC micropillars enhanced bone regeneration in a mouse cranial defect model21. The mPOC, a citrate-based biomaterial (CBB), is an excellent candidate for bone regeneration because citrate, an important organic component of bone, plays key roles in skeletal development and bone healing by influencing bone matrix formation and the metabolism of bone-related cells47. In this study, hydroxyapatite (HA) was incorporated into mPOC to further enhance its regenerative potential, leveraging HA\u2019s well-known osteoconductive properties48. A 60% HA content was used to fabricate the implant, mimicking the composition of native bone49. Both in vitro and in vivo experiments confirmed that the addition of HA significantly improved bone regeneration compared to mPOC alone21. Moreover, several products made from CBB/HA composites have recently received FDA clearance, highlighting the promising clinical potential of mPOC/HA micropillars for bone regeneration applications50.\n\nPillar structures, a common topographic cue, have been extensively used to study various cell behaviors, including migration, mechanics, engulfment, proliferation, and differentiation19,22,51,52,53. Depending on the material properties and pattern design, cells may either reside on top of or between the pillar structures, and in some cases, the pillars can even penetrate through the cells17,34. In this study, due to the stiffness and design of the mPOC/HA micropillars, the nuclei predominantly settle between the micropillars and adopt shapes such as \u2018T\u2019 or \u2018X\u2019. Following accelerated degradation, the micropillars showed slight morphological changes but remained effective in inducing nuclear deformation. The slow degradation may account for the minimal differences in weight loss and calcium release between flat and pillar implants, despite the overall increase in surface area of the micropillars. Based on our previous study, the restricted cell spreading on micropillars may limit the impact of the increased surface area, as the expression of vinculin remained similar on both flat and micropillar surfaces21.\n\nDespite recent intensive investigations into nuclear morphogenesis, little is known about its influence on cellular secretion, which can regulate neighboring cells and is critical for regenerative engineering. Previous studies have shown that nuclear mechanotransduction, activated by substrate stiffening or cellular compression, can impact cell secretions that regulate changes in the osteolineage phenotype54,55,56. Here, we found that cells with deformed nuclei exhibited higher expression levels of ECM components and binding proteins that support collagen-enriched ECM organization. This may be related with the changes of chromatin packing induced by nuclear deformation21. Additionally, soluble proteins secreted by these deformed cells were able to diffuse and modulate ECM secretion and organization in neighboring cells, as demonstrated by a transwell assay. The ECM is a complex, dynamic environment with tightly regulated mechanical and biochemical properties that affect essential cell functions, including adhesion, proliferation, and differentiation57. ECM fiber alignment increases local matrix stiffness, which promotes higher force generation and increases cell stiffness, creating a positive feedback loop between cells and the matrix58. Furthermore, the organized ECM enhances calcium recruitment and accelerates mineralization, contributing to effective bone regeneration.\n\nImplantation of the flat and micropillar mPOC/HA scaffolds seeded with MSCs resulted in larger new bone volume formation in vivo compared to previous studies using mPOC alone, a finding likely due to the osteoconductive properties of HA. Compared to flat implants, mPOC/HA micropillars promoted bone formation and the expression of osteogenic markers in regenerated tissues, consistent with the results observed for mPOC scaffolds21. This result suggests that nuclear deformation induced by the micropillars can enhance bone regeneration, regardless of the implant material, provided it is not toxic. This could be attributed to the osteogenic differentiation of cells in direct contact with the micropillars, as well as their secretion, which promotes ECM protein expression. Histological staining further supports this, showing a thicker layer of collagen-enriched regenerated tissue in the presence of the micropillar implant, consistent with the secretome results. Macrophage activation showed a slight, though not statistically significant, difference between the two groups. Given the compromised immune response in athymic nude mice, additional testing in normal mice may be necessary to fully assess the impact of micropillar implants on immune modulation.\n\nST analysis revealed a significant upregulation of genes encoding cartilage oligomeric matrix protein (COMP) and fibromodulin (FMOD) in the micropillar group, consistent with the secretome analysis. COMP binds to matrix proteins like collagen, enhancing ECM organization and assembly59. As an ECM protein, COMP also promotes osteogenesis by binding to bone morphogenetic protein 2 (BMP-2), increasing its local concentration and boosting its biological activity60. FMOD, with a strong affinity for the HA matrix, helps attenuate osteoclast precursor maturation, thereby influencing osteoblast\u2013osteoclast crosstalk61. These results suggest that nuclear deformation induced by micropillars may promote osteogenesis in neighboring cells via matrricrine effects.\n\nDespite the enhanced bone regeneration observed, mPOC/HA implants did not achieve complete healing of the cranial defect, likely due to the limited interaction surface of the film scaffold. The influence of the implants, whether through direct chromatin reprogramming guidance or secretome activity, was restricted to cells at the tissue-scaffold interface. Future efforts should focus on the design and fabrication of 3D micropillar implants using additive manufacturing and composite materials to create a more comprehensive 3D cellular microenvironment that promotes bone regeneration. Additionally, the application of micropillars as a platform for delivering bioactive factors could be explored as a strategy to achieve complete cranial bone healing.\n\nIn summary, we investigated the effects of nuclear deformation on the cellular secretome using micropillar implants fabricated from an mPOC/HA composite. The mPOC/HA micropillars demonstrated similar properties to a flat substrate in terms of roughness and degradation but had a substantial impact on cellular and nuclear morphology, cell adhesion, cytoskeletal development, and osteogenic differentiation in hMSCs. Nuclear-deformed cells showed increased secretion of proteins and RNA transcriptions that regulate ECM components and organization, promoting osteogenesis in neighboring cells both in vitro and in vivo. These findings suggest that microtopography engineering of implants holds significant promise for enhancing bone regeneration. This study offers valuable insights for the future design and fabrication of bioactive implants for regenerative engineering and regenerative medicine applications.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The mPOC pre-polymer were synthesized according to a previous report31. Briefly, the POC pre-polymer was firstly synthesized by reaction of equal molar of citric acid (Sigma-Aldrich, 251275) and 1,8-octandiol (Sigma-Aldrich, O3303) at 140\u2009\u00b0C oil bath for 60\u2009min. The product was then purified by precipitation in DI water. After lyophilization, 66\u2009g POC pre-polymer was dissolved in 540\u2009ml tetrahydrofuran (THF) and reacted with 0.036\u2009mol imidazole (Sigma-Aldrich, I2399) and 0.4\u2009mol glycidyl methacrylate (Sigma-Aldrich, 151238) at 60\u2009\u00b0C for 6\u2009h. The final product was then purified by precipitation in DI water and lyophilized for storage at \u221220\u2009\u00b0C. Successful synthesis of mPOC pre-polymer was characterized using proton nuclear magnetic resonance (1H-NMR, Bruker A600).\n\nSU-8 micropillar structures (5\u2009\u00d7\u20095\u2009\u00d7\u20098 um3) were fabricated according to our previous study21. PDMS molds were then fabricated to replicate the inverted structures. HA nanoparticles (Sigma-Aldrich, 677418) were mixed with mPOC pre-polymer at weight ratio of 6:4. The 60% HA was selected to mimic composition of native bone62. Photo-initiator (5\u2009mg/ml camphorquinone and ethyl 4-dimethylaminobenzoate) was added to the mPOC/HA slurry. The mixture was then added onto PDMS mold and pressed onto cover glass to prepare free-standing scaffold under exposure with laser (1\u2009W, 470\u2009nm). Post-curing of the scaffold was performed in 80\u2009\u00b0C oven over night. The size of HA nanoparticles was characterized using Dynamic Light Scattering (DLS). The topography of micropillars was observed using scanning electron microscope (SEM, FEI Quanta 650 ESEM) and characterized using 3D optical microscope (Bruker). Surface roughness of flat and micropillar scaffolds was characterized using atomic force microscope (AFM, Bruker ICON system). The water contact angle was tested using VCA Optima XE system. The compressive modulus of the scaffolds was characterized using a Tribioindenter (Bruker). Based on a previous report63, the lateral modulus of micropillars was calculated according to the following equations:\n\nThe \u2018kL\u2019 is the lateral stiffness, \u2018E\u2019 is the measured modulus, \u2018I\u2019 is the moment area of inertia, and \u2018L\u2019 is the micropillar height. For square micropillars, \u2018I\u2019 can be described as:\n\nwhere \u2018a\u2019 is the side length of the micropillars. Thus, the lateral modulus of the micropillars \u2018EL\u2019 equals to:\n\nwhere \u2018A\u2019 is the cross-section area of micropillars.\n\nTo test the degradation of the mPOC/HA scaffold, the dry weight of mPOC/HA scaffolds at day 0 was recorded as the initial weight. Then the scaffolds were merged in 1\u2009ml DPBS solution in 75\u2009\u00b0C oven. At each designed time point (1, 2, 3, 5, 7, 10 and 14\u2009d), the scaffolds were rinsed with DI water followed by drying at 60\u2009\u00b0C. The weight was recorded to calculate the weight loss percentage. The calcium release test was also performed with 75\u2009\u00b0C DPBS (no calcium, no magnesium). At the designed time points, the elution solution was collected and replaced with fresh DPBS (1\u2009ml). The released calcium was detected with inductively coupled plasma mass spectrometry (ICP-MS, ThermoFisher Element 2). Quantification of calcium (Ca) was accomplished using ICP-MS of acid digested samples. Specifically, 100 uL of the PBS elution was digested in 250 uL nitric acid (HNO3, >69%, Thermo Fisher Scientific, Waltham, MA, USA) at 65\u2009\u00b0C for 4\u2009h. Ultra-pure H2O (18.2 M\u03a9\u2219cm) was then added to produce a final solution of 2.5% nitric acid (v/v) in a total volume of 10\u2009mL. A quantitative standard was made using a 1000 ug/mL Ca elemental standard (Inorganic Ventures, Christiansburg, VA, USA) which was diluted to create a 1000\u2009ng/g Ca standard in 2.5% nitric acid (v/v) in a total sample volume of 50\u2009mL. A solution of 2.5% nitric acid (v/v) was used as the calibration blank. ICP-MS was performed on a computer-controlled (QTEGRA software) Thermo iCapQ ICP-MS (Thermo Fisher Scientific, Waltham, MA, USA) operating in KED mode and equipped with a ESI SC-2DX PrepFAST autosampler (Omaha, NE, USA). Nickle skimmer and sample cones were used from Thermo Scientific (part numbers 1311870 and 3600812). Internal standard was added inline using the prepFAST system and consisted of 1\u2009ng/mL of a mixed element solution containing Bi, In, 6Li, Sc, Tb, Y (IV-ICPMS-71D from Inorganic Ventures). Each sample was acquired using 1 survey run (10 sweeps) and 3 main (peak jumping) runs (40 sweeps). The isotopes selected for analysis were 44Ca and 45Sc (chosen as an internal standard for data interpolation and machine stability). Instrument performance is optimized daily through autotuning followed by verification via a performance report (passing manufacturer specifications). Accumulated calcium amount was calculated based on the sum of released calcium at each time point measured by ICP-MS from the same sample.\n\nHuman mesenchymal stromal cells (hMSCs, PCS-500-012) were purchased from the American Type Culture Collection (ATCC) and cultured with the growth medium acquired from ATCC. hMSCs with the passage 4-6 were seeded onto the flat and micropillar mPOC/HA substrates. To test cell attachment, hMSCs were seeded at 5000 cells/cm2 and cultured for 3\u2009h followed by PBS rinsing to remove unattached cells. The attached cells were then trypsinized and collected for cell counting. To check cellular and nuclear morphology, hMSCs were seeded at 5000 cells/cm2 and cultured in growth medium for 1\u2009d before fixation.\n\nTo check cell viability, hMSCs were seeded at 5000 cells/cm2 and cultured in growth medium for 3\u2009d. Live/dead staining (Thermo Fisher, L3224) was performed to assess the viability of hMSCs on flat and micropillar surfaces. Briefly, a mixture of 2\u2009\u00b5M calcein AM and 4\u2009\u00b5M EthD-1 working solution was added to the cells and incubated for 30\u2009minutes at room temperature, followed by rinsing with PBS. The cells were then imaged using a Nikon Eclipse Ti2 microscope. The MTT assay (Thermo Fisher, V13154) was used to evaluate the metabolic activity of the cells. Cells cultured on flat and micropillar surfaces in a 24-well plate were incubated with 500\u2009\u03bcL of 1.1\u2009mM MTT solution (diluted in medium) at 37\u2009\u00b0C for 3\u2009h. An empty well without cells served as the background reading. After incubation, 125\u2009\u03bcL of solution was removed from each well, and 250\u2009\u03bcL of DMSO was added with thorough mixing. After a 10-minute incubation at 37\u2009\u00b0C, 50\u2009\u03bcL of the solution from each well was transferred to a 96-well plate, and absorbance was measured at 540\u2009nm using a Cytation 5 cell imaging multimode reader (Biotek). The Picogreen assay (Thermo Fisher, P7589) was performed to assess cell proliferation according to the manufacturer\u2019s protocol. Briefly, a standard curve ranging from 10\u20131000\u2009ng/mL dsDNA was prepared to calculate DNA content in the samples. Cells on flat and micropillar surfaces, fabricated in a 24-well plate, were lysed using 200\u2009\u03bcL lysis solution (10\u2009mM Tris pH 8, 1\u2009mM EDTA, and 0.2% Triton X-100). The solution was then diluted with TE buffer (10\u2009mM Tris-HCl, 1\u2009mM EDTA, pH 7.5) to a final volume of 300\u2009\u03bcL. Next, 100\u2009\u03bcL of the Quant-iT\u2122 PicoGreen\u2122 dsDNA Reagent working solution was added to each sample. The samples were incubated for 5\u2009minutes at room temperature, protected from light. Finally, 50\u2009\u03bcL of the final solution from each well was transferred to a 96-well plate, and fluorescence was measured using the Cytation 5 (ex/em: 480/520). DNA content in each sample was then calculated using the standard curve.\n\nAfter one day of culture, the cells were fixed with 4% paraformaldehyde, and cell nuclei were stained using SYTOXTM Green (ThermoFisher, S7020) according to the manufacture\u2019s instruction. The nuclear shape index (NSI) was analyzed to evaluate 2D nuclear deformation22. The stained cells were then imaged using a confocal microscope (Leica SP8) to acquire their 3D morphology. Cell nuclei were reconstructed using the Fiji ImageJ software (https://imagej.net/Fiji). Cell nuclear volume, surface area, project area, height, and the ratio of surface area to volume were measured using 3D objects counter plugin. More than 30 nuclei from 3 biological replicates were imaged and analyzed to calculate the statistics.\n\nF-actin fibers were stained according to previous report64. Briefly, cells cultured on flat and micropillar surfaces were fixed with 4% paraformaldehyde and rinsed with PBS. The cells were then permeabilized with 0.2% Triton X-100 and rinsed with PBS. Blocking was performed using a 1% BSA solution. Cell nuclei were stained with 1\u2009\u03bcM SYTOX\u2122 Green (Thermo Fisher, S7020), and F-actin was stained with Alexa Fluor\u2122 594 conjugated phalloidin (Invitrogen, A12381, 1:40 dilution). For collagen staining, cells were fixed, permeabilized, and blocked as described above. They were then incubated overnight at 4\u2009\u00b0C with an anti-collagen antibody (Abcam, ab36064, 1:100 dilution). The following day, after rinsing with PBS, the samples were stained with goat anti-rabbit IgG secondary antibody (Invitrogen, A11034, 1:1000 dilution) and DAPI (1:1000 dilution) at room temperature for 1\u2009h. After additional PBS rinsing, the samples were ready for imaging. All immunofluorescent images were acquired using a Nikon Eclipse Ti2 microscope.\n\nTo visualize cell adhesion on mPOC/HA scaffolds, cells were fixed with 3% glutaraldehyde (Electron Microscopy Sciences) and rinsed with DI water. Subsequently, the cells underwent dehydration using a series of ethanol concentrations (30%, 50%, 70%, 90%, and 100%) for 5\u2009min each, followed by drying using a critical point dryer (Tousimis Samdri) as per the manual. The dehydrated cells were coated with a 5\u2009nm osmium layer and imaged using a scanning electron microscope (SEM, FEI Quanta 650). Captured images were further enhanced for visualization of cellular architecture using Photoshop. Additionally, cells on transwell plates were imaged using SEM, and EDS analysis was performed to evaluate the calcium and phosphate deposition. Briefly, the transwell samples underwent the same dehydration and coating procedures as described above, followed by SEM imaging. Calcium and phosphate were selected for EDS analysis using AZtec software (Oxford Instruments). Elemental mapping was performed under the following conditions: 20\u2009kV acceleration voltage, 30\u201350% deadtime, 1 frame count, 2048 channels, 256 resolution, and 100\u2009\u03bcs pixel dwell time.\n\nhMSCs were seeded onto both flat and micropillar mPOC/HA substrates at a density of 5000 cells/cm2 with growth medium. One-day post-seeding, osteogenic induction medium (Lonza) was applied to prompt the osteogenic differentiation of hMSCs. After 7 days of induction, cells were washed with PBS buffer and fixed with 4% paraformaldehyde for 10\u2009minutes. Subsequently, the samples were immersed in a solution of 56\u2009mM 2-amino-2-methyl-1,3-propanediol (AMP, pH~9.9), containing 0.1% naphthol AS-MX phosphate and 0.1% fast blue RR salt to stain alkaline phosphatase (ALP). Bright-field images were acquired using a Nikon Eclipse TE2000-U inverted microscope. ALP activity was assessed using the ALP assay kit (K422-500, Biovision) following the provided manual. Briefly, cells cultured in induction medium for 7 days were homogenized using ALP assay buffer. Subsequently, the non-fluorescent substrate 4-Methylumelliferyl phosphate disodium salt (MUP) was mixed with the homogenized samples to generate a fluorescent signal through its cleavage by ALP. Fluorescence intensity was measured using a Cytation 5 imaging reader (BioTek) at (Ex/Em\u2009=\u2009360/440\u2009nm). Enzymatic activity was calculated based on the standard curve and normalized to total DNA content, determined by the Quant-iT PicoGreen dsDNA assay (Invitrogen). The expression levels of OCN and RUNX2 were quantified through Western Blot analysis. In brief, cell lysis was performed using radioimmunoprecipitation assay (RIPA) buffer. The relative protein quantities were measured using a Cytation 5 imaging reader. Equal amounts of proteins extracted from flat and micropillar samples were loaded onto a NuPAGE 4\u201312% Bis-Tris Gel (Invitrogen) and subsequently transferred to nitrocellulose membranes (Bio-rad). Afterward, membranes were blocked with 5% milk and incubated with primary antibodies (including GAPDH from Abcam, ab181602, 1:5000 dilution; OCN from Cell Signaling, 59757\u2009T, 1:500 dilution; and RUNX2 from Santa Cruz, sc-390715, 1:200 dilution) overnight at 4\u2009\u00b0C with gentle shaking. Following this, secondary antibodies, diluted at a ratio of 1:5000, were applied and incubated with the membranes at room temperature for 1\u2009h. Protein bands were visualized using the Azure 600 gel imaging system. The acquired images underwent analysis through the \u2018Gel Analyzer\u2019 tool in ImageJ. The intensity of all target protein bands was initially compared to the corresponding GAPDH, and then normalized against a flat surface, which was set as 1. Statistical calculations were based on three biological replicates.\n\nFor secretome testing, hMSCs were seeded at 20,000 cells/cm2 and cultured in osteogenic induction medium for 3 weeks, followed by serum-free medium treatment for 2\u2009d. Then the cell culture medium was collected for analysis. We developed an optimized protocol for processing large-volume secretome samples (\u226515\u2009mL) to increase the dynamic range of protein coverage by removing residual serum and concentrating low-abundance secreted proteins for LC-MS analysis65. Secretome samples were first processed using a 50\u2009kDa molecular weight cutoff (MWCO) Amicon Ultra-15 centrifugal filter Ultracel, Merck (UFC905008) to separate the sample into two fractions: the filtrate containing proteins smaller than 50\u2009kDa and the concentrate with proteins larger than 50\u2009kDa as per the manufacturer\u2019s protocol65,66. The concentrate was depleted using High-Select Midi Spin Columns (A36367, Thermo Fisher Scientific), and the depleted flowthrough was recovered by centrifugation as per the protocol provided by Thermo67,68. Both the filtrate and the depleted concentrate were subjected to acetone/trichloroacetic acid (TCA) protein precipitation to isolate the proteins65,66. The resulting protein pellets were solubilized (8\u2009M urea and 400\u2009mM ammonium bicarbonate), combined, and quantified using the BCA and micro-BCA protein assay kits (Thermo Scientific, Ref: 23227, Ref: 23235)69. Disulfide bonds were reduced by 4\u2009mM dithiothreitol and incubated for 45\u2009minutes at 55\u2009\u00b0C. Sulfhydryl groups were alkylated by addition of 16\u2009mM iodoacetamide and incubated for 45\u2009minutes at 25\u2009\u00b0C shielded from light. Samples were diluted 4-fold with ammonium bicarbonate to reduce the urea concentration below 2\u2009M. Protein digestion was performed by addition of trypsin (MS-grade, Promega) at a 1:50 ratio (enzyme:substrate) and incubated overnight at 37\u2009\u00b0C. Digestion was halted with the addition of 10% formic acid (FA) to a final concentration of 0.5%. Peptides were desalted with Pierce C18 spin columns (Ref:89870), dried by vacuum centrifugation, and stored at \u221220\u2009\u00b0C. Peptides were resuspended in 5% ACN(Acetonitrile) / 0.1% FA for LC-MS/MS analysis.\n\nFor proteomic testing, hMSCs were seeded at 20,000 cells/cm2 and cultured in osteogenic induction medium for 3 weeks followed by serum-free medium treatment for 2\u2009d. Cells were lysed using a cell lysis buffer containing 0.5% SDS, 50\u2009mM ammonium bicarbonate (AmBic), 50\u2009mM NaCl, and Halt protease inhibitor. Protein precipitation was performed using acetone/TCA, and the resulting protein pellets were quantified using the BCA and Micro BCA protein assay kits (Thermo Scientific, Catalog No. 23227, 23235), and 100ug protein per sample was subjected in-solution digestion69. The pellets were resuspended in 100\u2009\u03bcl of re-suspension buffer (8\u2009M urea, 400\u2009mM ammonium bicarbonate). Disulfide bonds were reduced by adding 4\u2009mM dithiothreitol (DTT), followed by incubation at 55\u2009\u00b0C for 45\u2009minutes. Sulfhydryl groups were alkylated by adding 16\u2009mM iodoacetamide, and the reaction was incubated for 45\u2009minutes at 25\u2009\u00b0C, shielded from light. To reduce the urea concentration below 2\u2009M, the samples were diluted 4-fold with ammonium bicarbonate. Trypsin (MS-grade, Promega) was then added at a 1:50 enzyme-to-substrate ratio, and digestion was carried out overnight at 37\u2009\u00b0C. The digestion was terminated by the addition of 10% formic acid to a final concentration of 0.5%. Peptides were desalted using Pierce C18 spin columns (Ref:89870), dried by vacuum centrifugation, and resuspended (1\u2009\u03bcg/\u03bcl) in 5% acetonitrile (ACN) and 0.1% formic acid (FA) in preparation for LC-MS analysis.\n\nPeptides were analyzed using a Vanquish Neo nano-LC coupled to an Exploris 480 hybrid quadrupole-orbitrap mass spectrometer (Thermo Fisher Scientific, USA). The samples were loaded onto the trap column of 75 \u03bcm internal diameter (ID) x 2\u2009cm length (Acclaim PepMapTM 100, P/N 164535) and analytical separation was performed using a UHPLC C18 column (15\u2009cm length x 75\u2009\u00b5m internal diameter, 1.7\u2009\u00b5m particle size, Ion Opticks, AUR3-15075C18). For each run, 1\u2009\u00b5g of peptide sample was injected. Electrospray ionization was performed using a Nanospray Flex Ion Source (Thermo Fisher, ES071) at a positive static spray voltage of 2.3\u2009kV. Peptides were eluted from the analytical column at a flow rate of 200 nL/min using an increasing organic gradient to separate peptides based on their hydrophobicity. Buffer A was 0.1% formic acid in Optima LC-MS grade water, and buffer B was 80% acetonitrile, 19.9% Optima LC-MS grade water, and 0.1% formic acid: The method duration was 120\u2009minutes. The mass spectrometer was controlled using Xcalibur and operated in positive polarity. The full scan (MS1) settings used were: mass range 350\u20132000\u2009m/z, RF lens 60%, orbitrap resolution 120,000, normalized AGC target 300%, maximum injection time of 25 milliseconds, and a 5E3 intensity threshold. Data-dependent acquisition (DDA) by TopN was performed through higher-energy collisional dissociation (HCD) of isolated precursor ions with charges of 2+ to 5+ inclusive. The MS2 settings were: dynamic exclusion mode duration 30\u2009seconds, mass tolerance 5 ppm (both low and high), 2\u2009second cycle time, isolation window 1.5\u2009m/z, 30% normalized collision energy, orbitrap resolution 15,000, normalized AGC target 100%, and maximum injection time of 50 milliseconds.\n\nThe samples were acquired on mass spec and the data were searched against a human database using the MaxQuant application70. Label-Free Quantification (LFQ) was obtained by LFQ MS1 intensity. Data was filtered to accept proteins with a minimum of 2 unique peptides. Search parameters included a fixed modification of cysteine carbamidomethylation, and variable modifications of methionine oxidation, deamidated asparagine and aspartic acid, and acetylated protein N-termini.\n\nThe \u2018proteinGroups\u2019 output file from MaxQuant was imported into Perseus software for data preprocessing and statistical analysis71. Intensities were Log2 transformed to achieve a normal distribution of the data and scaled using median subtraction normalization. Differentially expressed proteins were determined by doing a non-paired Student t-Test. Proteins quantified in all samples (i.e., with non-missing values) with p\u2009<\u20090.05 and FC\u2009\u2265\u20092 were considered significant. Downstream analyses and visualizations were done using RStudio software (R version 4.3.2, RStudio version 2024.09.0). Principal component analysis (PCA) was done using \u2018prcomp\u2019 R function to visualize the ability of the differential protein expression to distinguish between biological conditions. Heatmap plot was built using \u2018ComplexHeatmap\u2019 R package. GO and Pathways enrichment analysis was done using \u2018clusterProfiler\u2019 R package, and annotations with adjusted p-values (FDR, Benjamini-Hochberg)\u2009<\u20090.05 were considered significant72. Additional R packages used included \u2018org.Hs.eg.db\u2019 for human gene annotations and \u2018enrichplot\u2019 for visualization. This analysis considered the entire set of human protein-coding genes as the reference background.\n\nThe flat and micropillar mPOC/HA surfaces were fabricated in a 24-well plate. The hMSCs were seeded onto the surfaces with 40,000 cells per well. Then a transwell was put in each well, and additional hMSCs were seeded inside the transwell (Costar, 0.4\u2009\u03bcm polyester membrane) at a density of 5000 cells/cm2. After cell attachment, osteogenic medium was used to induce osteogenic differentiation of the cells. At 7 days post-induction, the cells on the transwell were fixed, followed by ALP staining and quantification to investigate the secretion profile of deformed and undeformed cells on osteogenesis. At 3 weeks post-induction, additional transwells were collected for Alizarin Red S (ARS) staining and quantification to show the calcium deposition influenced by the secretome. At 4 weeks post-induction, collagen, which is one of the major components in ECM and significantly affected according to the secretome analysis, was stained to investigate the influence of nuclear deformation on ECM organization.\n\nThe animal study was approved by the University of Chicago Animal Care and Use Committee following NIH guidance (ACUP#71745).\n\nEight-week-old female athymic nude mice obtained from Harlan Laboratories were used for the study. The animals were housed in a separately air-conditioned cabinet at a temperature of 24\u201326\u2009\u00b0C with 12:12 light:dark cycle. The surgeries were performed according to the previous report61. Briefly, animals were treated with 2% isoflurane delivered by 100% O2 and maintained with 1\u20131.5% isoflurane for anesthesia. Two critical-sized defects were created on the left and right sides of the skull of each animal using a 4\u2009mm trephine under continuous normal saline irrigation to prevent tissue thermal injury (Dremel\u00ae USA, Robert Bosch Tool Corp), followed by the implantation of hMSCs seeded onto flat and micropillar scaffolds, respectively. The implants were seeded with 20,000 cells per implant (approximately 160,000 cells/cm\u00b2) in growth medium for 1 day before being implanted into the defects. After implantation of scaffolds, a larger mPOC film (1\u2009\u00d7\u20091.5\u2009cm2) was attached to the skull with thrombin/fibrinogen to prevent displacement of implants. Skin tissue was closed with 5\u20130 nylon interrupted sutures and removed after 2\u2009weeks. The animals were monitored after anesthesia hourly until recovery. Buprenorphine 50\u2009\u00b5g\u2009kg\u22121 and meloxicam 1\u2009mg\u2009kg\u22121 were used for pain relief.\n\nMicro-CT images of cranial were performed on the XCUBE (Molecubes NV) by the Integrated Small Animal Imaging Research Resource (iSAIRR) at The University of Chicago. The animal was sedated with 1\u20131.5% isoflurane inhalation during the microCT scanning. Spiral high-resolution computed tomography acquisitions were performed with an X-ray source of 50\u2009kVp and 440\u2009\u00b5A. Volumetric computed tomography images were reconstructed by applying the iterative image space reconstruction algorithm (ISRA) in a 400\u2009\u00d7\u2009400\u2009\u00d7\u2009370 format with voxel dimensions of 100\u2009\u00d7\u2009100\u2009\u00d7\u2009100\u2009\u00b5m3. The same animal was scanned at multiple time points to monitor the regeneration of the skull bone. An Amira software (Thermo Scientific) was used for 3D reconstruction of the skull tissue and to analyse the bone formation in the defect area. Scale bars were used to standardize the images. Baseline imaging and defect volume calculations were performed 48\u2009h postoperatively, serving as a standard for comparing all subsequent measurements of residual defect volume. Defect recovery is defined as (Vi\u2009\u2212\u2009Vd)/Vi\u2009\u00d7\u2009100%, where Vi and Vd represent defect volume at initial and designed timepoints, respectively.\n\nSkull samples were fixed and decalcified in Cal-EX II (Fisher Scientific) for 24\u2009h, rinsed with PBS, and embedded in paraffin. Tissue sections containing defect sites were cut to 5\u2009\u03bcm thickness and stained with H&E and trichrome to assess tissue regeneration. Regenerated tissue thickness was measured using ImageJ, and osteogenesis was evaluated via IHC staining for key osteogenic markers, including OCN (Cell signaling, 59757\u2009T, 1:200 dilution) and OPN (Santa Cruz, sc-21742, 1:100 dilution). Mouse skin tissue served as a negative control for all IHC staining. Macrophage activation was evaluated by staining of F4/80 (Cell signaling, D2S9R, 1:100 dilution), CD86 (Invitrogen, 14-0862-82, 5\u2009\u03bcg/ml), and CD163 (Abcam, ab182422, 1:100).\n\nTo confirm the RNA quality of each FFPE tissue block, 1\u20132 curls (10um thickness each) were used for RNA extraction using Qiagen RNeasy FFPE kit (Qiagen 73504) according to manufactures\u2019 protocol. Extracted RNA was examined by Agilent Bioanalyzer RNA pico chip to confirm the DV200\u2009>30%. Simultaneously, the tissue morphology was examined on HE-stained slide to identify the region of interest.\n\nFor each FFPE sample, 1 section (5um thickness) was placed on Visium slides. Each slide was incubated at 42\u2009\u00b0C for 3\u2009h followed by overnight room temperature incubation. Then, the slide was stored a desiccated slide holder until proceeding to deparaffinization.\n\nThe deparaffinization, HE staining and imaging, and decrosslinking of tissue slides were performed according to 10x Genomics protocol (CG000409 and CG000407) specific for Visium spatial gene expression for the FFPE kit. Then, the slides were proceeded to human probe (v2) hybridization and ligation using 10x Genomics Visium spatial gene expression, 6.5\u2009mm kit (10x Genomics, PN-1000188). The probes were released from tissue slide and captured on Visium slide, followed by probe extension. Sequencing libraries were prepared according to manufacturer\u2019s protocol. Multiplexed libraries were pooled and sequenced on Novaseq X Plus 10Bflowcell 100 cycles kit with following parameter: 28nt for Read 1 and 90nt for Read 2.\n\nWe visually identified the implant region in each sample. To exclude low-quality capture locations, we removed the capture locations with fewer than 500 unique molecular identifiers, fewer than 500 genes, or \u2265 25% mitochondrial reads73. We also filtered out the genes that are expressed in fewer than five capture locations73. After quality control, the flat group had 101 capture locations and 12,701 genes, whereas the micropillar group had 73 capture locations and 13,371 genes.\n\nTo identify the genes differentially expressed in flat and micropillar groups, we performed Wilcoxon rank-sum tests on the merged dataset (174 capture locations) using the FindAllMarkers function in Seurat V374. Our testing was limited to the genes present in both implants, detected in a minimum 1% of cells in either implant, as well as showing at least 0.1 log-fold difference between the two implants.\n\nTo perform cell typing on our data, we first identified three publicly available bone single-cell RNA sequencing (scRNA-seq) references with annotated cell types43,44,45. The scRNA-seq references were processed, quality controlled, and merged using Seurat V3. Since our samples are nude mice, we excluded all the immune cells from the merged reference. The final merged scRNA-seq dataset contained a total of 12,717 cells and represented all major cell types present in bone tissues.\n\nIn 10x Visium data, each capture location contains a mixture of cells75. Therefore, we performed cell type deconvolution to predict the cell type proportions in each capture location using BayesPrism, a Bayesian deconvolution method shown to work on spatial transcriptomics data76,77. We excluded chromosomes X and Y, ribosomal, and mitochondrial genes from the analysis to reduce batch effects. We also removed the outlier genes with expression greater than 1% of the total reads in over 10% of capture locations. To improve cell typing accuracy, we only used the cell type signature genes for deconvolution analysis. The cell type markers were identified based on the differential expression analysis results on the merged scRNA-seq reference. The predicted cell type proportions with above 0.5 coefficient of variation were clipped to zero to reduce noise.\n\nWe performed Wilcoxon rank-sum tests using the deconvoluted cell type proportions to test if certain cell types are more prevalent in one implant than the other. We further examined the association between cell type proportions and gene expression levels in the two implants through Kendall\u2019s correlation analyses. All the p-values were adjusted for multiple testing through the false discovery rate approach. The proportions of three cell types (chondrocyte, OLC, and osteocyte) had over 50 significantly positively correlated genes. For each of these cell types, we performed pathway enrichment analysis of the significantly positively correlated genes using Metascape78.\n\nThe results are shown as mean\u2009\u00b1\u2009standard deviation (SD) using violin super plots or bar graphs. Statistical analysis was performed using Kyplot software (version 2.0 beta 15). Statistical significance was determined by Student\u2019s t-test (flat versus micropillar, two-sided). In Supplementary Data\u00a01 and 2, non-paired Student t-Test (two-sided) were used for statistical analysis. In Supplementary Data\u00a03 and 4, the functional enrichment analysis p-values were obtained from Metascape using hypergeometric tests, and q-values represent Benjamini-Hochberg-adjusted p-values to account for multiple testing. All experiments presented in the manuscript were repeated at least as two independent experiments with replicates to confirm the results are reproducible with similar results.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data supporting the findings described in this manuscript are available within the paper, the Supplementary Information, and the Source data file. The raw imaging datasets generated during the study are too large to be publicly shared but are freely available on request from the corresponding author. All the sequencing data are available from the Gene Expression Omnibus (GEO) under the accession code GSE286676. 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This work made use of the EPIC facility, the NUFAB facility, and the BioCryo facility of Northwestern University\u2019s NUANCE Center, which has received support from the SHyNE Resource (NSF ECCS-2025633), the International Institute for Nanotechnology (IIN), and Northwestern\u2019s MRSEC program (NSF DMR-1720139). Metal analysis was performed at the Northwestern University Quantitative Bulk-Elemental Information Core (QBIC). Proteomics services were performed by the Northwestern Proteomics Core Facility, generously supported by NCI CCSG P30 CA060553 awarded to the Robert H Lurie Comprehensive Cancer Center, instrumentation award (S10OD025194) from NIH Office of the Director, and the National Resource for Translational and Developmental Proteomics supported by P41 GM108569. We also thank the help from Ms. Rebecca A. Sponenburg at QBIC, Northwestern University, for ICP-MS analysis and Dr. Hsiu-Ming Tsai at the Department of Radiology, The University of Chicago for microCT imaging. This work also made use of the Northwestern University NUSeq Core and the Biological Imaging Facility (BIF).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Center for Advanced Regenerative Engineering, Northwestern University, Evanston, IL, USA\n\nXinlong Wang,\u00a0Maya Joshi,\u00a0Yugang Liu,\u00a0Huifeng Wang,\u00a0Amy B. Zun,\u00a0Chongwen Duan,\u00a0Bin Jiang,\u00a0Vadim Backman,\u00a0Tong-Chuan He,\u00a0Russell R. Reid\u00a0&\u00a0Guillermo A. Ameer\n\nDepartment of Biomedical Engineering, Northwestern University, Evanston, IL, USA\n\nXinlong Wang,\u00a0Yugang Liu,\u00a0Huifeng Wang,\u00a0Amy B. Zun,\u00a0Vasundhara Agrawal,\u00a0Cody L. Dunton,\u00a0Chongwen Duan,\u00a0Bin Jiang,\u00a0Vadim Backman\u00a0&\u00a0Guillermo A. Ameer\n\nQuerrey Simpson Institute for Regenerative Engineering at Northwestern University, Northwestern University, Chicago, IL, USA\n\nXinlong Wang,\u00a0Bin Jiang,\u00a0Vadim Backman,\u00a0Yuan Luo\u00a0&\u00a0Guillermo A. Ameer\n\nDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA\n\nYiming Li,\u00a0Zitong Lin\u00a0&\u00a0Yuan Luo\n\nProteomics Center of Excellence, Northwestern University, Evanston, IL, USA\n\nIndira Pla,\u00a0Raju Gajjela\u00a0&\u00a0Basil Baby Mattamana\n\nMolecular Oncology Laboratory, Department of Orthopedic Surgery and Rehabilitation Medicine, The University of Chicago Medical Center, Chicago, IL, USA\n\nHao Wang\u00a0&\u00a0Tong-Chuan He\n\nCenter for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA\n\nChing-Man Wai\n\nCenter for Physical Genomics and Engineering, Northwestern University, Evanston, IL, USA\n\nVasundhara Agrawal,\u00a0Cody L. Dunton,\u00a0Vadim Backman\u00a0&\u00a0Guillermo A. Ameer\n\nDepartment of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA\n\nBin Jiang\u00a0&\u00a0Guillermo A. Ameer\n\nChemistry of Life Process Institute, Northwestern University, Evanston, IL, USA\n\nVadim Backman\n\nLaboratory of Craniofacial Biology and Development, Section of Plastic and Reconstructive Surgery, Department of Surgery, The University of Chicago Medical Center, Chicago, IL, USA\n\nRussell R. Reid\n\nNorthwestern University Clinical and Translational Sciences Institute, Northwestern University Feinberg School of Medicine, Chicago, IL, USA\n\nYuan Luo\u00a0&\u00a0Guillermo A. Ameer\n\nCenter for Collaborative AI in Healthcare, Institute for AI in Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA\n\nYuan Luo\n\nInternational Institute for Nanotechnology, Northwestern University, Evanston, IL, USA\n\nGuillermo A. Ameer\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nX.W. and G.A. designed the experiments. X.W. performed most experiments and analyzed the data. Yiming L., Z.L., and Yuan L. analyzed the spatial transcriptomic results. I.P., R.G., and B.M. performed the secretome experiment and analyzed the results. M.J., Huifeng W., A.Z., Chongwen D. and B.J. helped with material preparation and characterization. Yugang L., Hao W., T.H., and R.R. helped with animal work. C.W. performed the spatial transcriptomic experiments. V.A., Cody D., and V.B. helped with data analysis. X.W., Yiming L., I.P., R.G., B.M., Yuan L., and G.A. wrote the manuscript. All the authors discussed the results and reviewed the manuscript.\n\nCorrespondence to\n Guillermo A. Ameer.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "An Invention Disclosure has been filed for the mPOC micropillar scaffold through Northwestern University (X.W., V.A., V.B., and G.A.A.). G.A.A. is the inventor of US Food and Drug Administration-approved citrate-based biomaterials. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Wang, X., Li, Y., Lin, Z. et al. Microtopography-induced changes in cell nucleus morphology enhance bone regeneration by modulating the cellular secretome.\n Nat Commun 16, 6444 (2025). https://doi.org/10.1038/s41467-025-60760-y\n\nDownload citation\n\nReceived: 25 December 2024\n\nAccepted: 03 June 2025\n\nPublished: 11 July 2025\n\nVersion of record: 11 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60760-y\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"Invariance-based Mendelian Randomization Method Integrating Multiple Heterogeneous GWAS Summary Datasets", + "journal": "Nature Communications", + "published": "18 August 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM7_ESM.xlsx" + }, + { + "label": "Supplementary Data 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM8_ESM.xlsx" + }, + { + "label": "Supplementary Data 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM9_ESM.xlsx" + }, + { + "label": "Supplementary Data 8", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM10_ESM.xlsx" + }, + { + "label": "Supplementary Data 9", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM11_ESM.xlsx" + }, + { + "label": "Supplementary Data 10", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM12_ESM.xlsx" + }, + { + "label": "Supplementary Data 11", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM13_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM14_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_MOESM15_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-62823-6#MOESM13", + "/articles/s41467-025-62823-6#Fig2", + "/articles/s41467-025-62823-6#Fig4", + "/articles/s41467-025-62823-6#MOESM3", + "/articles/s41467-025-62823-6#MOESM5", + "/articles/s41467-025-62823-6#MOESM6", + "/articles/s41467-025-62823-6#MOESM7", + "/articles/s41467-025-62823-6#MOESM8", + "/articles/s41467-025-62823-6#MOESM9", + "/articles/s41467-025-62823-6#MOESM10", + "/articles/s41467-025-62823-6#MOESM11", + "/articles/s41467-025-62823-6#MOESM12" + ], + "code": [ + "https://github.com/hhoulei/MREILLS", + "https://doi.org/10.5281/zenodo.15951617", + "https://github.com/hhoulei/MREILLS_Simul", + "https://doi.org/10.5281/zenodo.15951779" + ], + "subject": [ + "Genetic association study", + "Population genetics", + "Statistical methods" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5602368/v1.pdf?c=1755601680000", + "research_square_link": "https://www.researchsquare.com//article/rs-5602368/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-62823-6.pdf", + "preprint_posted": "16 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Various geographical landscapes, diverse lifestyles and genetic structures may lead the heterogeneity among the GWAS summary datasets from distinct populations, especially different ethnic groups. This increases the difficulty in inferring global causal relationships from exposures on the outcome when integrating multiple GWAS summary datasets. We proposed a mendelian randomization (MR) method called MR-EILLS, which leverages the Environment Invariant Linear Least Squares (EILLS) to deduce the global causal relationship that invariant in all heterogeneous populations. The MR-EILLS model works in both univariate and multivariate scenarios, and allows the invalid instrumental variables (IVs) violating exchangeability and exclusion restriction assumptions. In addition, MR-EILLS shows the unbiased causal effect estimations of one or multiple exposures on the outcome, whether there are valid or invalid IVs. Comparing with traditional MR and meta methods, MR-EILLS demonstrates the highest estimation accuracy, the most stable type I error rates, and the highest statistical power. Finally, MR-EILLS is applied to explore the independent causal relationships between 11 blood cells and lung function, using GWAS summary statistics from five ancestries (African, East Asian, South Asian, Hispanics Latinos and European). The results cover most of the expected causal links which have biological interpretations and several new links supported by previous observational literatures.Biological sciences/Computational biology and bioinformatics/Data integrationHealth sciences/Medical research/Genetics researchBiological sciences/Computational biology and bioinformatics/Statistical methodsBiological sciences/Genetics/Genetic association studyunivariate mendelian randomizationmultivariate mendelian randomizationGWAS summary datasetsheterogeneous populationsmultiple ancestries", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5Figure 6", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-5602368/v1/7ef0ccee6f32bc29ec5795df.png", + "https://assets-eu.researchsquare.com/files/rs-5602368/v1/175374a4d6a852453e57396b.png", + "https://assets-eu.researchsquare.com/files/rs-5602368/v1/b0d5709b5cf9ffcc4f040477.png", + "https://assets-eu.researchsquare.com/files/rs-5602368/v1/00db4fcbe31eb689cc0d0ff1.png", + "https://assets-eu.researchsquare.com/files/rs-5602368/v1/c85f026cda6595040e1354d7.png", + "https://assets-eu.researchsquare.com/files/rs-5602368/v1/7bc05a810f92ba7b4a239de5.png" + ] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryTable.xlsxSupplementary TablesSupplementaryMaterials1208.docxSupplementary Materials", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Diverse genetic structures can lead to heterogeneity among GWAS summary datasets from distinct populations. This makes it more difficult to infer causal effects of exposures on the outcome when multiple GWAS summary datasets are integrated. Here, we propose a Mendelian randomization method called MR-EILLS, which leverages environment invariant linear least squares to establish whether there is a causal relationship that is invariant in all heterogeneous populations. The MR-EILLS model works in both univariate and multivariate scenarios and allows for invalid instrumental variables that violate the exchangeability and exclusion restriction assumptions. In addition, MR-EILLS shows the unbiased causal effect estimations of one or multiple exposures on the outcome, whether there are valid or invalid instrumental variables. Compared to traditional Mendelian randomization and meta methods, MR-EILLS yields the highest estimation accuracy, the most stable type I error rates, and the highest statistical power. Finally, we apply MR-EILLS to explore the independent causal relationships between 11 blood cells and 20 disease-related outcomes, using GWAS summary statistics from five ancestries (African, East Asian, South Asian, Hispanic/Latino and European). The results cover most of the expected causal links that have biological interpretations as well as additional links supported by previous observational studies.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "In recent years, with the increasing number of genome-wide association study (GWAS) investigations, there has been a notable increase in the public availability and utilization of GWAS summary data by researchers1,2. This inclusive dataset encompasses information from diverse populations and ethnic backgrounds3,4,5,6, a development that researchers find valuable, thus making it a current focal point of research interest. Owing to a range of influences, including geographical landscapes and varied lifestyles, genetic structures exhibit significant diversity among distinct populations7,8, also called population stratification, potentially leading to heterogeneity in GWAS summary data across different ethnic groups, such as those of European, Asian, and American descent.\n\nMendelian randomization (MR)2,9 is a methodology that relies on the utilization of publicly available GWAS summary data for causal inference. It uses genetic variants as instrumental variables (IVs) to infer the causal effect of one or multiple exposures on an outcome, that is, univariable or multivariable MR10,11, respectively. A valid IV must satisfy the following three assumptions: (A1) Relevance (IV is strongly associated with at least one of the exposures), (A2) Exchangeability (IV is independent of confounders between exposures and outcomes), and (A3) Exclusion restrictions (IV affects the outcome only through exposures)9. These assumptions are also mentioned in the Methods section. When we consider heterogeneous populations, one valid IV in a population may be an invalid IV in another population due to various genetic structures. For example, \\({G}_{1}\\) is a valid IV in population I; it may be correlated with the confounder \\(U\\) between exposure and outcome in population II, whereas \\(U\\) is not the confounder in population I. In this case, \\({G}_{1}\\) violates the exchangeability in population II. In addition, \\({G}_{1}\\) may be correlated (linkage disequilibrium (LD))12 with another SNP \\({G}_{2}\\), which directly affects the outcome in population II, but \\({G}_{1}\\) is independent of \\({G}_{2}\\) in population I. In this case, \\({G}_{1}\\) violates the exclusion restriction in population II because the LD references in different populations are different. In addition, the effects of some traits, such as body mass index, educational attainment or depression, on various disease outcomes are mediated or modified by social and environmental factors, which lead to inconsistent causal relationships in different societies or population groups. Therefore, the heterogeneity among populations includes genetic and nongenetic differences, such as social or environmental factors, also creating distinct challenges for IV validity across groups. This complexity increases the difficulty of deducing a purely causal relationship by integrating multiple heterogeneous GWAS summary datasets.\n\nTherefore, the aim of this paper was to explore the pure causal effect, in which \u201cpure\u201d means that we focus on the causal effect that is not affected by social and environmental factors, i.e., the pure causal effect is invariant across heterogeneous populations. One straightforward way to infer pure causal relationships using MR is to first conduct MR analysis separately using valid IVs in different populations, obtain causal effect estimations in each population, and then combine all estimations by meta-analysis13,14. Even if there are invalid IVs in the first step, many MR methods15,16,17,18 have been proposed to eliminate the influence of invalid IVs on causal effect estimation. However, the accuracy of meta-analysis results depends on the robustness of different MR methods, and these MR methods require different assumptions15,16,17,18, which may be difficult to satisfy or cannot be tested. This may induce inconsistent causal effect estimation in different populations and make inferring pure causal relationships difficult (see Application section). Another idea is to first conduct GWAS meta-analysis for heterogeneous populations, and then select valid IVs to infer causal relationships via MR. The difficulty with this strategy is that only a short number of independent SNPs (no LD) can be selected because the LD reference panels in different populations are different8,19. These two strategies are both two-step processes, and result in doubled statistical errors, which results in a lower accuracy of causal effect estimation. In addition, meta-analysis is a statistical technique used to combine and analyze results from multiple studies20; if one result is inaccurate, the meta-analysis results are also incorrect. Meta-analysis is not a causal method in itself and does not necessarily provide causal evidence that holds true in every population included in the analysis. Therefore, we proposed a one-step method that integrates all the information, not only the MR results for each population, and provides causal evidence that holds true (also called invariant effects) in each population.\n\nIn this paper, we introduce an MR method called MR-EILLS, which utilizes the environment invariant linear least squares (EILLS)21 to integrate multiple heterogeneous GWAS summary datasets and then infer pure causal relationships. The MR-EILLS model works in both univariate and multivariate scenarios and allows for invalid IVs that violate exchangeability and exclusion restriction assumptions. In addition, MR-EILLS shows the unbiased causal effect estimation of one or multiple exposures on the outcome, whether there are valid or invalid IVs. Compared with traditional MR and meta methods, MR-EILLS yields the highest estimation accuracy, the most stable type I error rates, and greater statistical power. Finally, MR-EILLS was applied to explore the independent causal relationships between 11 blood cells and 20 disease-related outcomes, using GWAS summary statistics from five ancestries: African, East Asian, South Asian, Hispanic/Latino, and European.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The MR-EILLS model integrates the GWAS summary statistics from multiple heterogeneous populations, and identifies the causal exposures that have invariant effects on the outcome. The GWAS summary statistics for \\({E}\\) heterogeneous populations include the \\({G}_{j}-X\\) association \\({\\hat{\\theta }}_{p,j}^{({e})}\\) and its standard error \\({\\sigma }_{{G}_{j}{X}_{p}}^{({e})}\\), as well as the \\({G}_{j}-Y\\) association \\({\\hat{\\Gamma }}_{y,j}^{({e})}\\) and its standard error \\({\\sigma }_{y,j}^{({e})}\\), where the subscript \\(p\\) represents the p-th exposure (\\(p\\in \\{1,2,{\\mathrm{..}}.,P\\}\\)), \\(j\\) represents the j-th IV (\\(j\\in \\{1,2,{\\mathrm{..}}.,J\\}\\)) and the superscript \\(e\\) represents the e-th population (\\(e\\in {\\mathcal E},{\\mathcal E}=\\{1,2,{\\mathrm{..}}.,E\\}\\)). We assume that the causal effects of causal exposures (\\(\\{{X}_{p}\\},p\\in P\\ast \\)) on \\({Y}\\) are invariant in different populations, that is \\({\\beta }_{0p}^{(1)}={\\beta }_{0p}^{(2)}={\\mathrm{.}}..={\\beta }_{0p}^{(E)}={\\beta }_{0p}^{\\ast }\\) for \\(p\\in P\\ast \\), where \\(P\\ast \\) is the set of causal exposures, whereas the genetic associations between SNPs and exposures/outcome/confounders may be different, and confounders between exposures and the outcome are also different. The aim of the MR-EILLS model (Fig. 1) is to explore the pure causal effect of causal exposures on the outcome by minimizing the following objective function\n\nwhere \\({{\\rm{E}}}\\) is the expectation, \\({w}_{j}^{(e)}\\) is the weight of IV \\({G}_{j}\\) on the causal effect estimation in the e-th population, and \\({w}^{(e)}\\) is the weight of e-th population on the pure causal effect estimation. The first part of the objective function (1) is the empirical \\({L}_{2}\\) loss, which is the multiple-population version of ordinary least squares in one population, and \\({\\hat{\\varepsilon }}_{j}^{(e)}={\\hat{\\Gamma }}_{y,j}^{(e)}-{\\sum }_{p}{\\hat{\\theta }}_{p,j}^{(e)}{\\beta }_{0p}^{(e)}\\) denotes the pleiotropic effect. Motivating simulation (Fig. 1a and Supplementary Fig. 1a) demonstrated that as the pleiotropic effect increased, the absolute value of \\({\\hat{\\varepsilon }}_{j}^{(e)}\\) increased. The pleiotropic effect includes correlated and uncorrelated pleiotropy. When only the A3 assumption (exclusion restriction) is violated, we say that an uncorrelated pleiotropic effect exists; when the A2 assumption (exchangeability) is violated, a correlated pleiotropic effect emerges. The second part of the objective function (1) is the empirical focused linear invariance regularizer, which discourages the selection of exposures with strong correlations between \\({\\theta }_{p,j}^{(e)}\\) and \\({\\varepsilon }_{j}^{(e)}\\) in some populations because these correlations represent correlated pleiotropy, which would distort causal effect estimation. The results of the motivating simulation (Fig. 1b and Supplementary Fig. 1b) demonstrated that as the correlated pleiotropic effect increased, the correlation between \\({\\hat{\\varepsilon }}_{j}^{(e)}\\) and \\({\\hat{\\theta }}_{p,j}^{(e)}\\) became stronger. \\(\\gamma > 0\\) is the hyperparameter. In addition, we added the following restriction\n\nto select valid IVs, that satisfy the three assumptions and have no pleiotropy. The first part of Eq. (2) represents the total pleiotropic effect for the \\(j-th\\) IV, and the second part of Eq. (2) represents the correlated pleiotropic effect, that is, the correlation between \\({\\theta }_{p,j}^{(e)}\\) and \\({\\varepsilon }_{j}^{(e)}\\) for the \\(j-th\\) IV. \\(\\lambda > 0\\) is the hyperparameter controlling the strictness of filtering IVs. When there are invalid IVs, the ridge plot of \\({\\sum }_{e\\in {\\mathcal E} }|{\\varepsilon }_{j}^{(e)}|+{\\sum }_{p\\in P}{\\sum }_{e\\in {\\mathcal E} }|{\\hat{\\theta }}_{p,j}^{(e)}{\\varepsilon }_{j}^{(e)}|\\) has at least two peaks (Fig. 1c), whereas the ridge plot has only one peak when there is no invalid IV. The corresponding abscission value at the lowest point between the two peaks is the optimal \\(\\lambda \\). Thus, Eq. (2) removes the invalid IVs with pleiotropic effects larger than \\(\\lambda \\). Details for motivating the simulation are shown in Supplementary Note 1.\n\nThe MR-EILLS model aims to infer the causal relationships between one or multiple exposures and one outcome, integrating multiple GWAS summary datasets from heterogeneous populations. There are different pleiotropic effects and IV strengths for the same IVs in heterogeneous populations. The objective function of the MR-EILLS model considers both correlated and uncorrelated pleiotropy and removes invalid IVs. Panels a\u2013c show the results of the motivating simulation. Panel a shows the point plot for the absolute value of \\({\\hat{\\varepsilon }}_{j}^{(e)}\\) in different populations, where a larger point indicates a larger value of \\(|{\\hat{\\varepsilon }}_{j}^{(e)}|\\). As the pleiotropic effect increases, \\(|{\\hat{\\varepsilon }}_{j}^{(e)}|\\) increases; thus, the first part of the MR-EILLS model minimizes the pleiotropic effect between different populations. Panel b shows the correlation between \\({\\hat{\\varepsilon }}_{j}^{(e)}\\) and \\({\\hat{\\theta }}_{p,j}^{(e)}\\), which represents the correlated pleiotropic effect or the violation of the InSIDE assumption. As the correlated pleiotropic effect increases, this correlation becomes stronger. This corresponds to the second part of MR-EILLS, the empirical focused linear invariance regularizer, which discourages the selection of exposures with strong correlations between \\({\\theta }_{p,j}^{(e)}\\) and \\({\\varepsilon }_{j}^{(e)}\\) in some populations because this correlation would distort the causal effect estimation. Panel c shows the ridge plot of \\({\\sum }_{e\\in {{\\rm E}}}|{\\varepsilon }_{j}^{(e)}|+{\\sum }_{p\\in P}{\\sum }_{e\\in {{\\rm E}}}|{\\hat{\\theta }}_{p,j}^{(e)}{\\varepsilon }_{j}^{(e)}|\\) when there are different proportions of invalid IVs. When there are invalid IVs, the ridge plot has two peaks, whereas the ridge plot has only one peak when there is no invalid IV. The corresponding abscission value at the lowest point between the two peaks is the optimal \\(\\lambda \\). The third part of the MR-EILLS model removes the invalid IVs by \\(\\lambda \\).\n\nWe generated GWAS summary statistics for heterogeneous populations with varying edge effects, IV strengths, and pleiotropy values under both the UVMR and MVMR scenarios. We then compared MR-EILLS with nine published methods: IVW, MR-Egger, MR-Lasso, MR-Median, MR-cML, MRAID, Cause, MR-BMA, and MR-Horse. For these MR methods, we implemented two analytical strategies: (1) metaMR, which first performs meta-analysis of GWAS summary statistics across all datasets for each variable, followed by MR analysis; and (2) mrMeta, which first conducts MR analysis separately for each dataset, followed by meta-analysis of the MR results. Both random effects and fixed effects meta-analysis approaches were employed.\n\nIn the UVMR scenario (a), with both correlated and uncorrelated pleiotropy (30% invalid IVs), MR-EILLS, Cause, and MR-cML with metaMR demonstrated unbiased causal effect estimation, whereas other methods exhibited bias (Fig. 2 and Supplementary Data 1, 2). Compared with Cause and MR-cML with metaMR, MR-EILLS showed superior performance with higher accuracy, more stable type I error rates when the causal effect was null, and greater statistical power for nonnull effects. When 80% of the IVs were invalid, all the MR methods, including MR-cML and Cause, produced biased estimates, whereas MR-EILLS maintained unbiased estimation (Supplementary Figs. 8, 9,18, 19). Notably, MR-EILLS displayed robust performance across all evaluation metrics: it maintained appropriate type I error rates for null effects and achieved >90% power with 300 IVs for nonnull effects (Supplementary Figs. 10, 11 and Supplementary Figs. 20, 21). In scenario (b), MR-IVW and MR-Egger showed significant bias, whereas the other methods performed better with smaller bias. Additionally, all methods yielded more accurate estimates under balanced pleiotropy conditions than under unbalanced pleiotropy conditions (Supplementary Figs. 22\u201329). For scenario (c), all methods performed similarly well, exhibiting three key characteristics: unbiased effect estimation, well-controlled type I error rates for null effects, and high power (>80%) for detecting nonnull effects (Supplementary Figs. 30\u201333). The complete simulation results are presented in Supplementary Figs. 2\u201333 (Supplementary Note 2\u20135).\n\na, b Results of the causal effect estimation and type I error rate when the causal effect is zero. c, d Results of causal effect estimation and statistical power when the causal effect is 0.1. The number of IVs is 100 and the proportion of invalid IVs is 30%. The number of populations is \\(E=3\\). 200 repeated datasets were generated in all simulations. Data in boxplots are presented as median values and interquartile range.\n\nFor MVMR analyses with eight exposures and 30% of IVs exhibiting either correlated or uncorrelated pleiotropy (case (a)), Fig. 3 (Supplementary Data 3) demonstrates that MR-EILLS provides unbiased causal effect estimates for all exposures, whereas other methods show varying degrees of bias, including slight biases observed with MR-cML using metaMR for certain exposures. Notably, MR-EILLS achieves the highest estimation accuracy among all methods. Figure 4 (Supplementary Data 3) presents type I error rates under null effects and statistical power under nonnull effects, revealing that MR-EILLS maintains the highest statistical power for nonnull effects while showing the most stable type I error control, albeit with marginally lower than nominal rates (0.05) for some exposures\u2014a phenomenon that diminishes with increasing population sizes (Supplementary Figs. 52, 53). Similar performance patterns are observed when \\(P=3\\) (Supplementary Figs. 46, 47). Under more extreme conditions with 80% invalid IVs, all MR methods produce biased estimates except MR-EILLS, which maintains unbiased estimation (Supplementary Figs. 48, 49). Furthermore, MR-EILLS demonstrates robust type I error control and achieves >90% statistical power with 300 IVs for nonnull effects (Supplementary Figs. 50, 51). For case (b), the results mirror those from UVMR analyses regardless of pleiotropy balance status (Supplementary Figs. 54\u201357). In the absence of pleiotropy (case (c)), all methods performed comparably well, yielding unbiased estimates, appropriate type I error rates, and high statistical power (>90%) (Supplementary Figs. 58, 59). Comprehensive simulation results are provided in Supplementary Figs. 38\u201359 (Supplementary Notes 6\u20139). When evaluating \\(P=15\\), Fig. 5 displays the mean F1 score, recall, and precision across methods, with MR-EILLS consistently outperforming all alternatives on these metrics.\n\nThe number of IVs is 100, and the proportion of invalid IVs is 30%. The number of populations is \\(E=3\\). Among a total of eight exposures, two (\\({X}_{1}\\) and \\({X}_{2}\\)) are causal exposures (with a causal effect of 0.2), and the other six (\\({X}_{3},{\\mathrm{}}...,{X}_{8}\\)) are spurious exposures (with a causal effect of 0). 200 repeated datasets were generated in all simulations. Data in boxplots are presented as median values and interquartile range.\n\nThe number of IVs is 100, and the proportion of invalid IVs is 30%. The number of populations is \\(E=3\\). Among total of eight exposures, two (\\({X}_{1}\\) and \\({X}_{2}\\)) are causal exposures (with a causal effect of 0.2), and the other six (\\({X}_{3},{\\mathrm{}}...,{X}_{8}\\)) are spurious exposures (with a causal effect of 0). This figure displays the type I error rates for \\({X}_{1}\\) and \\({X}_{2}\\), the statistical power for \\({X}_{3},{\\mathrm{}}...,{X}_{8}\\).\n\nThe number of IVs is 100 and the proportion of invalid IVs is 30%. The number of populations is \\(E=3\\).\n\nOur analyses further revealed significant heterogeneity in causal effect estimates across different populations. Supplementary Figs. 34, 60 summarizes the variation in \\({I}^{2}\\) across all the simulations. To illustrate this heterogeneity, we randomly selected one simulation, and the detailed forest plots of causal effect estimates for each MR method and dataset are presented in Supplementary Figs. 36, 37, 61\u201363. These plots demonstrate substantial inconsistencies in effect estimates among populations.\n\nOur simulation studies were conducted under weak IV conditions, with conditional F-statistics in simulations mirroring those observed in the applied analysis (Supplementary Data 4). Compared with alternative MR methods, our proposed approach exhibits minimal variance inflation while preserving superior statistical power (Supplementary Figs. 44, 45). These findings proved robust across different genetic correlation scenarios, maintaining consistent performance when accounting for nonzero genetic correlations between populations (Supplementary Figs. 4\u20137, 14\u201317, 40\u201343). In terms of computational efficiency, MR-EILLS maintains high performance regardless of the number of exposures. Conversely, methods such as MR-cML and MR-BMA\u2014particularly MR-Horse\u2014exhibit progressively increasing computational requirements, demanding significantly greater memory allocation and processing time as the number of exposures increases (Table 1).\n\nWe explored the causal relationships between a total of 11 blood cells (five red blood cells: hemoglobin (HGB) concentrations, hematocrit (HCT), mean corpuscular hemoglobin (MCH) concentrations, mean corpuscular volume (MCV), and the mean corpuscular hemoglobin concentration (MCHC); five white blood cells: white blood cell (WBC) counts, neutrophil (Neutro) counts, monocyte (Mono) counts, basophil (Baso) counts, and eosinophil (Eosin) counts; one platelets: platelet (PLT) counts) and 20 disease-related outcomes (asthma, body fat percentage, body mass index, waist circumference, weight, fasting blood glucose, HbA1c measurement, total cholesterol, type 2 diabetes, cardioembolic stroke, ischemic stroke, large artery stroke, stroke, heel bone density, pneumonia, schizophrenia, forced expiratory volume (FEV), forced vital capacity (FVC), the FEV/FVC ratio, and peak expiratory flow (PEF)) using GWAS summary statistics from five ancestries: African, East Asian, South Asian, Hispanics Latinos, and European.\n\nFirst, we conducted traditional MR analysis in five ancestries, and performed heterogeneous analysis for each MR method. The results are shown in Fig. 6 (Supplementary Data 5). We found that there were large heterogeneities (\\({I}^{2}\\)\u2009>\u20090.75) for blood cells among five ancestries. We subsequently conducted multivariable MR-EILLS analysis to explore the independent causal effects of 11 blood cells on 20 disease-related outcomes. We plot ridge plots for each outcome in five ancestries, and the results are shown in Supplementary Figs. 64\u201366. We used the ridge plot to set the \\(\\lambda \\) for MR-EILLS (Supplementary Data 6).\n\nThe heterogeneity of MR estimations in each population using MVMR-IVW, MVMR-Egger, MVMR-Lasso, and MVMR-Median, whereas MVMR-cML and MVMR-Horse cannot output MR estimations owing to the substantial memory consumption and computational time.\n\nIn Fig. 7 (Supplementary Data 7), we found that higher counts of some white blood cells, red blood cells or platelets were independently associated with decreased lung function. For FEV, higher WBC, Neutro counts and HGB concentrations causally induced a lower FEV (WBC: beta\u2009=\u2009\u22120.14, 95%CI: [\u22120.24, \u22120.04]; Neutro: beta\u2009=\u2009\u22120.17, 95%CI: [\u22120.24, \u22120.04]; HGB: beta\u2009=\u2009\u22120.29, 95%CI: [\u22120.54, \u22120.03]). The counts of Neutro and HCT were negatively associated with the level of FVC (Neutro: beta\u2009=\u2009\u22120.09, 95%CI: [\u22120.18, \u22120.01]; HCT: beta\u2009=\u2009\u22120.06, 95%CI: [\u22120.13, \u22120.002]). In addition, increases in the PLT and Neutro counts were associated with a decreased FEV/FVC ratio (PLT: beta\u2009=\u2009\u22120.26, 95%CI: [\u22120.49, \u22120.02]; Neutro: beta\u2009=\u2009\u22120.16, 95%CI: [\u22120.30, \u22120.02]). Higher MCH concentrations might result in a lower PEF level (beta\u2009=\u2009\u22120.08, 95%CI: [\u22120.16, \u22120.004]). James et al. reported that an increased WBC count is associated with lower levels of lung function and provided biological explanations22. A 15-year longitudinal study demonstrated that higher blood neutrophil concentrations were associated with accelerated FEV decline23. The inverse relationships of FEV and FVC with red blood cell counts were also supported by observational studies24,25. A prospective longitudinal analysis revealed that a higher baseline neutrophil count predicted a lower serially obtained FVC26. Moreover, a retrospective study revealed a strong correlation between the PLT count and the FEV/FVC ratio27.\n\nCausal effect estimations of 11 blood cells on disease-related outcomes via the MVMR-EILLS method. Data are presented as mean values and 95% confidence intervals. All the P value and confidence interval is estimated via the bootstrap method, and it is two-sided.\n\nOur study provides compelling evidence for causal relationships between cytokine profiles and stroke pathogenesis. Elevated PLT counts were independently correlated with increased overall stroke risk (OR\u2009=\u20091.93, 95%CI: [1.37, 2.71]), particularly for the cardioembolic stroke subtype. Increased Mono levels were positively associated with stroke susceptibility (OR\u2009=\u20091.39, 95%CI: [1.24, 1.57]), whereas elevated MCHC specifically amplified cardioembolic stroke risk (OR\u2009=\u20091.25, 95%CI: [1.03, 1.53]). MCH levels exhibited a marginal yet statistically significant association with ischaemic stroke incidence (OR\u2009=\u20091.06, 95%CI: [1.01, 1.12]). Notably, reduced WBC counts conferred protection against overall stroke risk (OR\u2009=\u20090.63, 95%CI: [0.47, 0.84]), with enhanced preventive efficacy observed in the large artery stroke subtype (OR\u2009=\u20090.55, 95%CI: [0.40, 0.75]). Protective associations were further identified across multiple hematological parameters: Eosin (OR\u2009=\u20090.38, 95%CI: [0.34, 0.44]), Neutro (cardioembolic: OR\u2009=\u20090.74, 95%CI: [0.58, 0.95]; large artery: OR\u2009=\u20090.54, 95%CI: [0.39, 0.76]), Baso (OR\u2009=\u20090.39, 95%CI: [0.31, 0.49]), HGB (OR\u2009=\u20090.64, 95%CI: [0.57, 0.72]), and HCT (OR\u2009=\u20090.69, 95%CI: [0.63, 0.76]).\n\nThese findings align with established mechanisms of inflammation-mediated vascular pathology. Mansfield et al.28 demonstrated that cytokine-driven endothelial dysfunction occurs through the IL-6/TNF-\u03b1 pathway, providing mechanistic support for our observed mono-stroke associations. The neuroprotective neutrophil subset (N2) identified by ref. 29 offers a plausible explanation for the reduced stroke risk at lower Neutro counts through inflammatory modulation. Rodhe et al.30 further substantiated cytokine-mediated vascular injury through IL-8/TNF-\u03b1 correlations in geriatric populations, whereas ref. 31 delineated cytokine polarization patterns affecting vascular homeostasis, which is consistent with our PLT and HGB findings. Tyagi et al.32 established chemokine-specific inflammatory cascades (e.g., CXCL10), reinforcing our conclusions that Eosin and Baso count reductions attenuate stroke risk.\n\nOur analysis demonstrated that elevated PLT counts independently increased body fat percentage (beta\u2009=\u20090.01, 95%CI: [0.005, 0.02]), although no significant causal associations were detected between blood cell indices and body mass index or waist circumference. Higher MCHC values were positively related to weight (beta\u2009=\u20090.02, 95%CI: [0.004, 0.02]), whereas lower HCT levels were causally linked to increased weight (beta\u2009=\u2009\u22120.01, 95%CI: [\u22120.002, \u22120.02]). Our findings align with studies linking hematological indices to metabolic outcomes. Zhang et al.33 demonstrated that elevated HCT in obese individuals with dyslipidaemia correlates with worsened lipid profiles (higher TC, TG, LDL-C; lower HDL-C), supporting our observation that lower HCT levels are causally associated with increased weight. Similarly, ref. 34 established HCT as a key determinant of blood viscosity, which impairs insulin sensitivity and promotes adiposity\u2014mechanisms consistent with our positive association between MCHC and body mass. These studies reinforce the role of red blood cell parameters in metabolic regulation, although causal relationships remain nuanced compared with BMI/waist circumference.\n\nElevated PLT (beta\u2009=\u20090.57, 95%CI: [0.26, 0.89]) and MCHC (beta\u2009=\u20090.3, 95%CI: [0.01, 0.58]) values were independently positively associated with fasting blood glucose. Conversely, Baso (beta\u2009=\u2009\u22120.18, 95%CI: [\u22120.29, \u22120.08]) and Mono (beta\u2009=\u2009\u22120.14, 95%CI: [\u22120.17, \u22120.007]) counts demonstrated inverse correlations with glucose levels. For lipid metabolism, the PLT (beta\u2009=\u20090.001, 95%CI: [0.002, 0.023]), WBC (beta\u2009=\u20090.01, 95%CI: [0.001, 0.02]), and Neutro (beta\u2009=\u20090.01, 95%CI: [0.002, 0.02]) counts exhibited positive dose-response relationships with total cholesterol. In contrast, HGB (HGB, beta\u2009=\u2009\u22120.01, 95%CI: [\u22120.013, \u22120.0001]) and HCT (beta\u2009=\u2009\u22120.01, 95%CI: [\u22120.014, \u22120.0007]) levels displayed protective associations against cholesterol elevation. Our findings align with the established literature linking hematological indices to metabolic outcomes. Elevated PLT and MCHC levels predict fasting blood glucose, which is consistent with studies demonstrating the role of platelet activation in insulin resistance35 and the impact of erythrocyte rigidity on endothelial function36. Lower Baso and Mono counts are correlated with increased glucose levels, supporting their anti-inflammatory roles in glucose metabolism37,38. For lipid metabolism, the PLT, WBC, and Neutro counts are positively associated with total cholesterol, mirroring leukocyte-driven atherosclerosis mechanisms39 and haemorheological contributions to dyslipidaemia40. Conversely, lower HGB and HCT levels have protective effects against hypercholesterolemia, which aligns with the role of iron metabolism in lipid regulation41.\n\nElevated HGB and HCT independently elevated asthma risk (HGB: OR\u2009=\u20091.22, 95%CI: [1.04, 1.41]; HCT: OR\u2009=\u20091.19, 95%CI: [1.01, 1.41]). Higher Eosin counts were positively associated with heel bone density (beta\u2009=\u20090.23, 95%CI: [0.02, 0.45]), whereas reduced PLT and mono counts were inversely correlated (PLT: beta\u2009=\u2009\u22120.25, 95%CI: [\u22120.47, \u22120.04]; Mono: beta\u2009=\u2009\u22120.1, 95%CI: [\u22120.17, \u22120.02]). HGB and HCT elevations were linked to pneumonia risk (HGB: OR\u2009=\u20091.63, 95%CI: [1.04, 2.54]; HCT: OR\u2009=\u20091.48, 95%CI: [1.06, 2.03]). Schizophrenia risk exhibited dose-dependent relationships with Eosin (OR\u2009=\u20091.27, 95%CI: [1.14, 1.42]), Baso (OR\u2009=\u20091.56, 95%CI: [1.21, 2.05]), and Mono (OR\u2009=\u20091.15, 95%CI: [1.04, 1.28]) counts, in contrast with the protective effect of a lower MCHC (OR\u2009=\u20090.54, 95%CI: [0.39, 0.75]). The independent elevation of asthma risk by HGB and HCT aligns with studies showing that iron deficiency anemia (proxied by low HGB/HCT) impairs lung function and increases airway hyperresponsiveness42. For bone health, the positive correlation between Eosin counts and heel bone density supports the role of eosinophils in osteoblast activation, as demonstrated by the fact that eosinophil-derived growth factors promote bone formation43. Conversely, lower PLT and Mono counts may reflect anti-inflammatory environments that reduce asthma exacerbations44. The links between HGB/HCT elevations and pneumonia risk correspond with evidence that iron overload (high HGB/HCT) promotes bacterial adherence and pulmonary inflammation45. With respect to psychiatric outcomes, the dose-dependent relationships of Eosin, Baso, and Mono counts with schizophrenia risk align with immune dysregulation theories, where proinflammatory monocytes and basophils exacerbate neuroinflammation46. Finally, the protective effect of low MCHCs against schizophrenia may reflect iron homeostasis disruption in schizophrenic patients, as elevated MCHCs are correlated with oxidative stress and dopamine dysregulation47. The details of the results are shown in Supplementary Data 5\u20139 and Supplementary Note 10.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62823-6/MediaObjects/41467_2025_62823_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this paper, we proposed an MR method MR-EILLS, which works in both univariable and multivariable frameworks, and it outputs the pure causal effect estimation of multiple heterogeneous populations using only GWAS summary statistics. The results of the simulation revealed the superior performance of MR-EILLS and its application in exploring causal relationships from 11 blood cells to 20 disease-related outcomes, which covered most of the expected causal links.\n\nMR-EILLS integrates GWAS summary datasets from heterogeneous populations, and for each population, GWAS summary datasets for exposure and outcome can be from either the same individuals or different but heterogeneous individuals. This assumption is the same as that in traditional two-sample MR analysis, which requires two homogeneous but nonoverlapping samples. MR-EILLS assumes that the GWAS summary datasets for each population are from homogeneous but nonoverlapping samples. In the application, we assume that the individuals in each ancestry are homogeneous, and that the genetic diversity in different ancestries leads to heterogeneity among ancestries (different IV strengths and pleiotropy). The heterogeneity among populations includes genetic and nongenetic differences, also creating distinct challenges for IV validity across groups. Genetic architecture differences include allele frequency variations, population-specific linkage disequilibrium (LD) patterns, divergent selection pressures and unique mutation histories, etc. In addition, nongenetic modifiers\u2014such as socioeconomic status, environmental exposures, cultural practices, and disparities in healthcare access\u2014also play a significant role in shaping population heterogeneity. These contextual differences contribute to inconsistent causal relationships across different populations, highlighting the complex interplay between genetic predispositions and external determinants of health.\n\nIn our context, we assume that given the causal exposures, the conditional expectation of the outcome is invariant, that is, the causal effects of causal exposures on the outcome are invariant across different populations. The joint distribution of the candidate exposures and outcome may vary across different populations due to population heterogeneity. Therefore, the conditional expectation invariance structure assumption of the original EILLS method is satisfied. Another two identification conditions for identifying pure causal effect are: (1) it is necessary for identifying pre causal effect to select heterogeneous populations with large differences in social and environment factors including genetic difference; (2) a minimal identification condition related to the heterogeneity of the populations: for a exposures\u2019 set, if \\({\\hat{\\theta }}_{p,j}^{(e)}\\) and \\({\\hat{\\varepsilon }}_{j}^{(e)}\\) is not independent in at least of one population, then there must be two populations with different causal effects. However, both of these identification conditions are untestable in practice. Therefore, when applying this method, practitioners must rely on prior knowledge to satisfy the identification assumptions as much as possible.\n\nMR-EILLS is specifically designed for estimating causal effects in biological pathways where variables and pathway effects remain independent of social/environmental factors\u2014either unaffected by or unmodified through these contextual influences. Taking the causal effect of hemoglobin concentration on stroke risk as an example, MR-EILLS tend to estimation the causal effect on the biological pathway like Hemoglobin level \u2192 Impaired oxygen transport \u2192 Elevated blood viscosity \u2192 Thrombosis \u2192 Stroke risk, or Hemoglobin level \u2192 Anemia development \u2192 Compensatory cardiac mechanisms \u2192 Left ventricular hypertrophy \u2192 Stroke risk, etc., but not hemoglobin level \u2192 oxygen transport efficiency \u2192 fatigue symptoms \u2192 reduced physical activity \u2192 elevated stroke risk. MR-EILLS methodology excludes such socio-environmentally mediated pathways when estimating invariant causal effects, as the mediator (\u201cexercise\u201d) inherently exhibits environmental variability. MR-EILLS aims to eliminate the influence of socioeconomic factors on the causal invariant effect from exposures to the outcome. The methodology does not require study populations to be globally representative, as it remains applicable to specific observational study cohorts. This is justified because socioeconomic confounders or effect modifiers inherently exist within any defined study population in observational research. Similar to MR, MR-EILLS requires no interaction assumptions for causal exposures and environmental variables. If there is an interaction between exposure and environmental variables, then this exposure tends to be considered a spurious exposure. If there is an interaction between the environmental variable and the mediator variable in the mediation pathway from exposure to outcome, then the pathway where this mediator variable is located is not included in the invariant causal effect. The additional explanation for the pure causal effect is shown in Supplementary Note 11.\n\nMR-EILLS allows different IVs to be set in different populations. However, the strategy for metaMR, that is, first conducting GWAS meta-analysis and then performing MR analysis, requires the SNPs that are independent (i.e., no LD detected) in all populations, which reduces the number of IVs substantially; however, GWAS meta-analysis helps researchers identify more significant SNPs (i.e., \\(P < 5\\times {10}^{-8}\\)). In addition, only a few MR methods allow the SNP set to have high LD. MR-EILLS solves this tricky issue and only requires that the IV set in each population is independent without LD.\n\nThe MR-EILLS model has two hyperparameters, which require researchers to set appropriate values to estimate the causal effects of exposures on the outcome. For \\(\\gamma \\), we recommend \\(\\gamma > 0.4\\) in UVMR and \\(\\gamma > 0\\) in MVMR. The larger \\(\\gamma \\) is, the stronger the role of the empirical focused linear invariance regularizer is. For \\(\\lambda \\), we suggest that researchers construct ridge plots to find the optimal value. In Model (2), we keep the SNP for which the pleiotropic effect in all populations is lower than \\(\\lambda \\). When the scales of different populations are different, the Model (2) can be modified to the following Model (2-1)\n\nResearchers can set different \\({\\lambda }_{e}\\) values for different populations. For example, in our applications, we set different \\({\\lambda }_{e}\\) values for five ancestries and five ridge plots are shown for each outcome. MR-EILLS works only if there are at least \\({J}\\ge {P}\\) valid IVs in the IV set; this assumption is less strict than the plurality assumption17, which requires the valid IVs from the largest group of IVs sharing the same causal parameter value.\n\nThere are several limitations for MR-EILLS. The first is that MR-EILLS does not yet work in the high-dimensional case. One future key research direction is to extend MR-EILLS to high-dimensional exposure scenarios, especially for high-dimensional omics biomarkers; for this purpose, correlated IVs are also important issues to be solved. Another point is that inappropriate settings of hyperparameters may lead to incorrect inference of causal relationships between exposures and outcomes. It is important to choose the appropriate hyperparameters, especially for \\(\\lambda \\). The value of \\(\\lambda \\) determines whether the invalid IVs are removed, and if \\(\\lambda \\) is too large, the causal effect estimation would be distorted. If \\(\\lambda \\) is too small, the number of remaining IVs is small; thus, in the future, it is necessary to extend MR-EILLS to correlated IV scenarios. Our empirical application of MVMR to analyse 11 potentially correlated blood cell traits presents notable methodological challenges. The high degree of intertrait correlation, combined with the difficulty of identifying truly independent IVs for each exposure, renders this analysis particularly vulnerable to weak instrument bias. While acknowledging these fundamental limitations inherent in analyzing highly correlated traits, our proposed methodology demonstrates improved robustness by substantially reducing variance inflation compared to conventional approaches. This enhancement enables more reliable inference despite the analytical challenges posed by trait correlations.\n\nIn conclusion, we propose the MR method MR-EILLS, which integrates multiple heterogeneous GWAS summary datasets to infer the pure causal relationships between exposures and outcomes. This study has important guiding significance for the discovery of new disease-related factors. We look forward to offering constructive suggestions for disease diagnosis and applying our method beyond the scope considered here.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Since our research solely employs de-identified, publicly accessible GWAS summary datasets, ethical approval and participant consent procedures were addressed in the primary source publications. We have therefore omitted this information from our manuscript in accordance with secondary analysis guidelines.\n\nFor one population, assume \\({P}\\) exposures \\({X}_{p},p\\in \\{1,{\\mathrm{..}}.,{P}\\}\\) and one outcome \\({Y}\\). The \\({J}\\) independent IVs \\({G}_{j},{j}\\in \\{1,{\\mathrm{..}}.,{J}\\}\\) satisfy the following three assumptions:\n\nA1. Relevance, \\({G}_{j}\\) is associated with at least one of the \\({P}\\) exposures;\n\nA2. Exchangeability, \\({G}_{j}\\) is not associated with the confounder between \\({P}\\) exposures and the outcome;\n\nA3. Exclusion restriction, \\({G}_{j}\\) affects the outcome only through exposures.\n\nThe MR model based on the individual data is as follows:\n\nwhere \\({\\varepsilon }_{{X}_{U}},{\\varepsilon }_{{X}_{p}},{\\varepsilon }_{Y} \\sim N(0,1)\\). \\({\\gamma }_{j}\\) represents the uncorrelated pleiotropic effect and \\({\\omega }_{j}\\) represents the correlated pleiotropy. \\({X}_{k}\\in pa({X}_{p})\\) is the father node of \\({X}_{p}\\), which is the direct cause of \\({X}_{p}\\), and where \\({\\beta }_{{X}_{k}{X}_{p}}^{(e)}\\) represents the effect of \\({X}_{k}\\) on \\({X}_{p}\\). \\({\\beta }_{0p}\\) denotes the causal effect of \\({X}_{p}\\) on \\({Y}\\). We call the exposures with \\({\\beta }_{0p}\\ne 0\\) causal exposures, which we want to discover, whereas the exposures with \\({\\beta }_{0p}=0\\) are the spurious exposures, which are not the true cause of the outcome. We define the set of causal exposures as \\(\\{{X}_{p}\\},p\\in P\\ast \\subseteq \\{1,{\\mathrm{..}}.,{P}\\}\\). When \\({P}\\)\u2009=\u20091, the above model is called UVMR, whereas when \\({P}\\)\u2009>\u20091, it is called MVMR. To simplify the expression, our model below uniformly uses \\({P}\\) exposures, both of which are applicable to UVMR and MVMR.\n\nThe GWAS summary statistics include the \\({G}_{j}-{X}_{p}\\) association \\({\\hat{\\theta }}_{p,j}\\) and its variance \\({\\sigma }_{p,j}^{2}\\), as well as the \\({G}_{j}-Y\\) association \\({\\hat{\\Gamma }}_{y,j}\\) and its variance \\({\\sigma }_{y,j}^{2}\\). Based on Model (3), we have\n\nWhen \\({G}_{j}\\) is a valid IV (no pleiotropy), that is, \\({\\gamma }_{j}={\\omega }_{j}=0\\), then \\({\\varepsilon }_{j}={\\Gamma }_{y,j}-{\\sum }_{p}{\\theta }_{{p},j}{\\beta }_{0p}\\) is zero and is dependent on \\({\\theta }_{{p},j}\\). For \\({j}\\in \\{1,{\\mathrm{..}}.,{J}\\}\\), we can identify \\({\\beta }_{0p}\\)\\((p\\in \\{1,{\\mathrm{..}}.,{P}\\})\\) by the system of linear equations \\({\\Gamma }_{y,j}={\\sum }_{p}{\\theta }_{p,j}{\\beta }_{0p}\\) if and only if \\({J}\\ge {P}\\). The causal effects of exposures on the outcome \\({\\beta }_{0p}\\) can be estimated by a weighted version of ordinary least squares (OLS), that is, the IVW regression\n\nwhich sets the intercept equal to zero. This model minimizes the empirical \\({L}_{2}\\) loss objective function\n\nwhere \\({w}_{j}\\) represents the weight of IV \\({G}_{j}\\) in the causal effect estimation. If \\({G}_{j}\\) has uncorrelated pleiotropy (\\({\\gamma }_{j}\\ne 0\\)), that is, if \\({G}_{j}\\) is causally associated with \\({Y}\\) not through any \\({X}_{p}\\), then \\({\\varepsilon }_{j}={\\gamma }_{j}\\) is no longer equal to zero, and it represents the uncorrelated pleiotropic effect. MR-Egger regression18 is proposed to solve this problem by allowing the intercept term \\(\\bar{\\gamma }\\) in Model (5), and the intercept represents the mean pleiotropic effect. The causal effect can be estimated by minimizing\n\nMR-Egger regression requires the InSIDE assumption, which means that the pleiotropic effect is independent of \\({\\theta }_{p,j}\\). If \\({G}_{j}\\) has correlated pleiotropy (\\({\\omega }_{j}\\ne 0\\)), that is, if \\({G}_{j}\\) is causally associated with the unmeasured confounding \\(U\\) between \\({X}_{p}\\) and \\({Y}\\), then the pleiotropic effect \\({\\varepsilon }_{j}={\\omega }_{j}{\\beta }_{2}+{\\gamma }_{j}\\) is not independent of \\({\\theta }_{p,j}\\) because of the common term \\({\\omega }_{j}\\). This is a violation of the InSIDE assumption. Equations (5\u20136) and MR-Egger require that \\({\\varepsilon }_{j}\\) is independent of \\({\\theta }_{p,j}\\) because the correlation between the intercept term and the independent variables would distort the causal effect estimation. The results of the motivating simulation for correlated and uncorrelated pleiotropy are shown in Supplementary Fig. 1.\n\nThe MR-Lasso method applies lasso-type penalization to the direct effects of IVs on the outcome. The causal estimate is described as a post-lasso estimate and is obtained via the IVW method, which uses only those IVs that are identified as valid by the lasso procedure. The objective function is as follows:\n\nIf \\({\\gamma }_{j}\\) shrinks to zero, the IV is treated as a valid IV. The MR-median estimator is defined as the 50th percentile of either an unweighted or IVW empirical density function of the Wald ratio, and it is consistent even when up to 50% of the information comes from an invalid IV. The aforementioned methods are classified into a category of MR methods, namely, the Wald ratio-based MR method.\n\nThe MR-cML and MRAID methods are representative of the likelihood-based MR method. MR-cML infers causal relationships using constrained maximum likelihood. It is robust to both uncorrelated and correlated pleiotropic effects, under the plurality assumption. MRAID models an initial set of candidate IVs that are in potentially high linkage disequilibrium with each other and automatically selects among them the suitable IVs for causal inference, under the joint likelihood framework. MRAID also explicitly models both uncorrelated and correlated horizontal pleiotropy.\n\nThe MR-Horse and cause methods produce unbiased causal effect estimates under the framework of Bayesian inference, while avoiding inflated false positive rates, and can account for both correlated and uncorrelated pleiotropy. The disadvantage of these two methods is that when the number of exposures is large, they require a significant amount of memory and computation time.\n\nMR-BMA is an MVMR method used to select likely causal risk factors from high-throughput experiments. It uses Bayesian model averaging and computes the posterior probability for all possible combinations of risk factors, finally estimating the model-averaged causal estimate (MACE) by weighting and summing the causal effect estimation of each model. To some extent, MR-BMA avoids pleiotropy by considering as many risk factors as possible.\n\nThese methods are all based on a single dataset, and only information from one dataset can be used. Therefore, we proposed a method that fully exploits the information from multiple existing datasets to identify causally invariant factors, comprehensively improves the estimation precision and enhances the statistical power.\n\nWhen there are \\({E}\\) heterogeneous populations, GWAS summary statistics include \\({\\hat{\\theta }}_{p,j}^{({e})}\\), \\({\\sigma }_{{G}_{j}{X}_{p}}^{({e})2}\\), \\({\\hat{\\Gamma }}_{y,j}^{({e})}\\) and \\({\\sigma }_{y,j}^{({e})2}\\) for \\(e\\in {\\mathcal E} \\). We define \\({\\varepsilon }_{j}^{(e)}={\\Gamma }_{y,j}^{(e)}-{\\sum }_{p}{\\theta }_{p,j}^{(e)}{\\beta }_{0p}^{(e)}\\) and \\({\\hat{\\varepsilon }}_{j}^{(e)}={\\hat{\\Gamma }}_{y,j}^{(e)}-{\\sum }_{p}{\\hat{\\theta }}_{p,j}^{(e)}{\\beta }_{0p}^{(e)}\\) in the version of multiple populations. We use the superscript \\(({e})\\) to denote the e-th population. We assume that the pleiotropic effect, IV strength and relationships among exposures are different in heterogeneous populations, whereas the causal effects of causal exposures on \\({Y}\\) are invariant, that is \\({\\beta }_{0p}^{(1)}={\\beta }_{0p}^{(2)}={\\mathrm{.}}..={\\beta }_{0p}^{(E)}={\\beta }_{0p}^{\\ast }\\) for \\(p\\in P\\ast \\); this assumption is called the structure assumption21. These assumptions are rational because the IVs satisfying A1\u2013A3 control only the unmeasured confounders between \\({X}_{p}\\) and \\({Y}\\), whereas other unmeasured confounders between IVs and exposures, or between IVs and outcomes, or between exposures are not controlled, and these unmeasured confounders are also the reason for heterogeneity between populations.\n\nNote that a valid IV in one population may be an invalid IV in the other heterogeneous populations. On the other hand, an IV may be associated with exposure in all heterogeneous populations, while it may have different uncorrelated or correlated pleiotropy in different populations. This leads to inconsistent independence relationships between \\({\\theta }_{p,j}^{(e)}\\) and \\({\\varepsilon }_{j}^{(e)}\\) across different populations and inconsistent causal effect estimation of exposures on the outcome in different heterogeneous populations. Therefore, we leveraged the EILLS21, the multiple heterogeneous population version of OLS, to construct the MR-EILLS model. The MR-EILLS model integrates GWAS summary statistics from multiple heterogeneous populations and finds causal exposures that have invariant effects on the outcome in heterogeneous populations. The MR-EILLS model aims to minimize the following objective function\n\nwhere\n\n\\({w}_{j}^{(e)}\\) is the weight of IV \\({G}_{j}\\) on the causal effect estimation in the e-th population, and \\({w}^{(e)}\\) is the weight of the eth population on the final causal effect estimation. The first part of the objective function (1) is the empirical \\({L}_{2}\\) loss, which is the multiple-population version of the objective function (6) in one population. The second part of the objective function (1) is the empirical focused linear invariance regularizer, which discourages the selection of exposures with strong correlations between \\({\\theta }_{p,j}^{(e)}\\) and \\({\\varepsilon }_{j}^{(e)}\\) in some populations because this will distort the causal effect estimation. \\(\\gamma > 0\\) is the hyperparameter. In addition, we added the following restriction\n\nto select the valid IVs. The first part of Eq. (2) represents the uncorrelated pleiotropic effect for the \\(j-th\\) IV, and the second part of Eq. (2) represents the correlated pleiotropic effect for the \\(j-th\\) IV. \\(\\lambda > 0\\) is the hyperparameter controlling the strictness of filtering IVs. Equation (2) removes the invalid IVs with pleiotropic effects above \\(\\lambda \\).\n\nThe causal effects \\({\\beta }_{0p}^{\\ast }\\) can be identified under the assumption21 that there are at least \\({P}\\) valid IVs in the IV set, that is\\({J}\\ge {P}\\). We use a limited-memory modification of the BFGS quasi-Newton method48 to find the optimal solution \\({\\beta }_{0p}^{\\ast }\\) of the objective function (1) under the restriction of Eq. (2). The confidence interval is estimated via bootstrap method.\n\nWe generate the GWAS summary statistics of \\(E\\) heterogeneous populations via the following process:\n\nwhere \\({X}_{k}\\in pa({X}_{p})\\) is the father node of \\({X}_{p}\\), which is the direct cause of \\({X}_{p}\\), and where \\({\\beta }_{{X}_{k}{X}_{p}}^{(e)}\\) represents the effect of \\({X}_{k}\\) on \\({X}_{p}\\). In total \\(P\\) exposures, the causal exposures are the top 30% of all exposures (e.g., \\(P=8\\), \\(floor({P}\\times 30\\%)=2\\), then the top two (\\({X}_{1}\\) and \\({X}_{2}\\)) are the causal exposures). The effect of causal exposure on \\(Y\\) (\\({\\beta }_{0p}^{(e)},p\\in P\\ast \\)) is 0.2 for MVMR (\\(P\\)\u2009>\u20091) and 0.1 for UVMR (\\(P\\)\u2009=\u20091), and the effect of other spurious exposures on \\(Y\\) (\\({\\beta }_{0p}^{(e)},p\\notin P\\ast \\)) is 0.\\({\\beta }_{{X}_{k}{X}_{p}}^{(e)} \\sim U(-1,1)\\) for the effect of edge \\({X}_{k}\\to {X}_{p}\\).The structure between the exposures is randomly generated, and the parameter \\({\\beta }_{{X}_{k}{X}_{p}}^{(e)}\\) represents the effect from \\({X}_{k}\\) to \\({X}_{p}\\). We set IV strength \\({\\alpha }_{p,j}^{(e)} \\sim N(0,0.2)\\) for the eth population and \\({X}_{p}\\); \\({\\xi }_{p,j}^{(e)} \\sim N(0,{\\sigma }_{p,j}^{({e})2})\\) for eth population and \\({X}_{p}\\), \\({\\sigma }_{p,j}^{({e})2} \\sim U(0.01,0.05)\\) for \\({X}_{p}\\); \\({\\xi }_{{y},j}^{(e)} \\sim N(0,{\\sigma }_{y}^{({e})2})\\) for \\(E=e\\), \\({\\sigma }_{y,j}^{({e})2} \\sim U(0.05,0.1)\\) and different variances represent different sample sizes; \\({\\beta }_{1p}^{(e)} \\sim U(0.5,0.8)\\) for \\({X}_{p}\\); and \\({\\beta }_{2}^{(e)} \\sim U(0.5,0.8)\\). We consider three scenarios:\n\nuncorrelated and correlated pleiotropy effects, \\({\\gamma }_{j}^{(e)} \\sim U(0,0.5)\\) and \\({\\omega }_{j}^{(e)} \\sim U(0,0.5)\\);\n\nuncorrelated pleiotropy effect (balanced pleiotropy: \\({\\gamma }_{j}^{(e)} \\sim U(-0.5,0.5)\\), unbalanced pleiotropy: \\({\\gamma }_{j}^{(e)} \\sim U(0,0.5)\\));\n\nNo pleiotropy.\n\nThe parameters of edge effects, IV strength and pleiotropy were randomly selected from a uniform distribution; thus, they are different in different datasets and represent heterogeneous datasets. We varied the number of populations to be \\(E=3\\) or 8; the number of IVs was 100 or 300; and the number of exposures was \\(P\\)\u2009=\u20091, 3, 8, or 15, including the cases of univariable and multivariable MR. In addition, we considered the situation in which genetic correlations among different populations are nonzero, and we generated the IV strength \\({\\alpha }_{p,j}^{(e)}\\) in different populations using multiple normal distributions, with correlations in the covariance matrix of 0.2 or 0.6. Finally, as the number of exposures increases, weak IVs are more likely to emerge; thus, we observed the performance of our method in the presence of weak IVs, by setting the IV strength ranging from 0.01 to 0.05, and the corresponding conditional F-statistics are shown in Supplementary Data 4.\n\nWe conducted 200 repeated simulations to evaluate the performance of MR-EILLS. We also compare nine methods, including IVW, MR-Egger, MR-Lasso, MR-Median, MR-cML, MR-BMA, MARID, Cause and MR-Horse. For \\(P=1\\), we compare eight methods in the UVMR version except MR-BMA; for \\(P\\)\u2009=\u20093, 8, and 15, we compare seven methods in the MVMR version except MARID and Cause, because these two methods have only the UVMR version. For these MR methods, we considered two strategies: (1)metaMR, first meta all the GWAS summary statistics of \\(E\\) datasets for each variable and then conduct the MR analysis; and (2) mrMeta, first conduct the MR analysis (excluding MR-BMA, MARID and Cause because they cannot output the standard errors) in \\(E\\) datasets separately and then meta all the MR results. Meta methods include random effects and fixed effects meta-analyses.\n\nWe evaluated the performance of all methods via box-violin plots for causal effect estimation, histograms for type I errors when the causal effect is zero and statistical power when the causal effect is not zero. In addition, we calculated the \\({I}^{2}\\) statistics in each simulation to evaluate the heterogeneity of causal effect estimation among different datasets for each MR method. We constructed the violin plot of the \\({I}^{2}\\) statistics for the estimations of each variable, randomly selected three simulations to demonstrate the quartiles of estimation, and then constructed the forest plot of the estimations for each method and each variable. For \\(P\\)\u2009=\u200915, we calculated the means of the F1 score, recall and precision for each method. Recall (i.e., power, or sensitivity) measures how many relationships a method can recover from the true causal relationships, whereas precision (i.e., 1-FDR) measures how many correct relationships are recovered in the inferred relationships. The F1 score is a combined index of recall and precision. We also summarize the computing time for different methods to assess the computational efficiency.\n\nWe recommend that practitioners determine the value of \\(\\lambda \\) by constructing a ridge plot. The abscissa is the value of \\({\\sum }_{e\\in {\\mathcal E} }|{\\varepsilon }_{j}^{(e)}|+{\\sum }_{p\\in P}{\\sum }_{e\\in {{\\rm E}}}|{\\hat{\\theta }}_{p,j}^{(e)}{\\varepsilon }_{j}^{(e)}|\\) for each IV in Eq. (8). We constructed the ridge plot in the simulations in Supplementary Figs. 67\u201371. These plots demonstrate that when there is no pleiotropy, the figure has only one peak, and \\(\\lambda \\) only takes the value of abscission after the first peak. When there is pleiotropy, the figure has two peaks, and the corresponding abscission value at the lowest point between the two peaks is the optimal \\(\\lambda \\). We provide the function of the ridge plot in the R package MR-EILLS.\n\nIn addition, we evaluated the root mean square error (RMSE) of causal effect estimation using a grid search: \\(\\gamma \\) ranges from 0.1 to 200, and \\(\\lambda \\) ranges from 0.1 to 1. The results are shown in Supplementary Figs. 72\u201380. We present the ranges of hyperparameters when the RMSE <0.1 in Supplementary Data 10. For UVMR, we recommend \\(\\gamma > 0.4\\). When \\(\\gamma > 0.4\\), the RMSE is less than 0.1, especially for the case of correlated and uncorrelated pleiotropy, whereas in other cases, the RMSE is less than 0.05. For MVMR, \\(\\gamma > 0\\) is recommended. Compared with all valid IVs, invalid IVs increased the RMSE of causal effect estimation, regardless of whether correlated or uncorrelated pleiotropy was used. Therefore, \\(\\gamma \\) is loosely valued, especially when \\(P > 1\\). The larger \\(\\gamma \\) is, the stronger the role of the empirical focused linear invariance regularizer. Details are shown in Supplementary Note 12.\n\nIn our simulations, we employed a grid search approach to identify the optimal lambda value that minimizes the RMSE. In addition, we set \\(\\gamma=0.5\\) for MVMR and \\(\\gamma=3\\) for UVMR.\n\nWe explored the causal effect of 11 blood cells on 20 disease-related outcomes using GWAS summary statistics from five ancestries: African, East Asian, South Asian, Hispanic/Latino, and European. GWAS summary statistics for blood cells were extracted from ref. 49, who conducted transethnic and ancestry-specific GWASs in 746,667 individuals from five populations (15,171, 151,807, 8189, 9368, and 563,947 individuals for five ancestries, respectively). GWAS summary statistics for 20 disease-related outcomes50 were extracted from the MR-Base and GWAS Catalog platforms. The details are shown in Supplementary Data 11.\n\nFirst, we selected IVs for MR analysis. We separately selected SNPs with \\(P < 5\\times {10}^{-8}\\) and clumped the LD with \\({r}^{2} > 0.01\\) in each population (Supplementary Data 8). In addition, we calculated the conditional F-statistics for IVs to assess the IV strength for each exposure (Supplementary Data 9). Then, we extracted the summary statistics for the IVs and conducted MR-EILLS and MR analysis for each population. We also calculated the \\({I}^{2}\\) statistic to evaluate the heterogeneity of causal effect estimation among different populations for each MR method. For MR-EILLS, we constructed the ridge plot in each population, and set \\(\\gamma=0.5\\). The settings of \\(\\lambda \\) are shown in Supplementary Data 6. The confidence interval is estimated via the bootstrap method, and details are shown in Supplementary Note 13.\n\nFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The GWAS summary statistics for 20 disease-related outcomes and 11 blood cell traits are publicly available through MR-Base and the GWAS Catalog. All dataset accession IDs are comprehensively listed in Supplementary Data 11. Data points in Figs. 2\u20134 are shown in Supplementary Data 1\u20133. Conditional F-statistics in simulation are shown in Supplementary Data 4. Results of heterogeneity analysis in the application are shown in Supplementary Data 5. Settings of parameters in the application are shown in Supplementary Data 6. Results of the MR-EILLS analysis in the application are shown in Supplementary Data 7. IVs and their GWAS summary statistics in the application are shown in Supplementary Data 8. Conditional F-statistics in the application are shown in Supplementary Data 9. Optimal ranges of hyperparameters when RMSE <0.1 in the simulation are shown in the Supplementary Data 10.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "R package MR-EILLS are available in the GitHub repository at https://github.com/hhoulei/MREILLS under the MIT license (https://doi.org/10.5281/zenodo.15951617). All the codes for simulation are available in the GitHub repository at https://github.com/hhoulei/MREILLS_Simul under the MIT license (https://doi.org/10.5281/zenodo.15951779). All the analyses in our article were implemented in R software (version 4.3.2). 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This work for L.H. was partially supported by 2021 Shandong Medical Association Clinical Research Fund\u2014Qilu Special Project (grant YXH2022DZX02008), and Key R&D Program of Shandong Province (2024CXPT085).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Hao Chen, Xiao-Hua Zhou.\n\nHealthcare Big Data Research Institute, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, P. R. China\n\nLei Hou\u00a0&\u00a0Hao Chen\n\nDepartment of Medical Dataology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, P. R. China\n\nLei Hou\n\nDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, P. R. China\n\nHao Chen\n\nBeijing International Center for Mathematical Research, Peking University, Beijing, P. R. China\n\nXiao-Hua Zhou\n\nDepartment of Biostatistics, Peking University, Beijing, P. R. China\n\nXiao-Hua Zhou\n\nChongqing Big Data Research Institute, Peking University, Chongqing, P. R. China\n\nXiao-Hua Zhou\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nL.H. and X.-H.Z. conceived the study. L.H. contributed to the theoretical derivation with assistance from X.-H.Z. L.H. and H.C. contributed to the data simulation and application. L.H. and X.-H.Z. wrote the manuscript with input from all the other authors. L.H. and H.C. revised the manuscript. All the authors reviewed and approved the final manuscript.\n\nCorrespondence to\n Hao Chen or Xiao-Hua Zhou.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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MR-EILLS: an invariance-based Mendelian randomization method integrating multiple heterogeneous GWAS summary datasets.\n Nat Commun 16, 7668 (2025). https://doi.org/10.1038/s41467-025-62823-6\n\nDownload citation\n\nReceived: 08 December 2024\n\nAccepted: 30 July 2025\n\nPublished: 18 August 2025\n\nVersion of record: 18 August 2025\n\nDOI: https://doi.org/10.1038/s41467-025-62823-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Networks Require Explicit Error Coding for Gain Recalibration", + "journal": "Nature Communications", + "published": "17 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63817-0/MediaObjects/41467_2025_63817_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63817-0/MediaObjects/41467_2025_63817_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63817-0/MediaObjects/41467_2025_63817_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63817-0/MediaObjects/41467_2025_63817_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://github.com/LIMBSlab/secer2025_expliciterror.git", + "/articles/s41467-025-63817-0#Sec24" + ], + "code": [ + "https://github.com/LIMBSlab/secer2025_expliciterror.git" + ], + "subject": [ + "Dynamical systems", + "Network models", + "Perception" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4209280/v1.pdf?c=1760785600000", + "research_square_link": "https://www.researchsquare.com//article/rs-4209280/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63817-0.pdf", + "preprint_posted": "14 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Representations of continuous variables are crucial to create internal models of the external world. A prevailing model of how the brain maintains these representations is given by continuous bump attractor networks (CBANs) in a broad range of brain functions across different areas, such as spatial navigation in hippocampal/entorhinal circuits and working memory in prefrontal cortex. Through recurrent connections, a CBAN maintains a persistent activity bump, whose peak location can vary along a neural space, corresponding to different values of a continuous variable. To track the value of a continuous variable changing over time, a CBAN updates the location of its activity bump based on inputs that encode the changes in the continuous variable (e.g., movement velocity in the case of spatial navigation)---a process akin to mathematical integration. This integration process is not perfect and accumulates error over time. For error correction, CBANs can use additional inputs providing ground-truth information about the continuous variable's correct value (e.g., visual landmarks for spatial navigation). These inputs enable the network dynamics to automatically correct any representation error. Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground-truth inputs also fine-tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bump's location. However, existing CBAN models lack this plasticity, offering no insights into the neural mechanisms and representations involved in the recalibration of the integration gain. In this paper, we explore this gap by using a ring attractor network, a specific type of CBAN, to model the experimental conditions that demonstrated gain recalibration in hippocampal place cells. Our analysis reveals the necessary conditions for neural mechanisms behind gain recalibration within a CBAN. Unlike error correction, which occurs through network dynamics based on ground-truth inputs, gain recalibration requires an additional neural signal that explicitly encodes the error in the network's representation via a rate code. Finally, we propose a modified ring attractor network as an example CBAN model that verifies our theoretical findings. Combining an error-rate code with Hebbian synaptic plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables.Biological sciences/Neuroscience/Computational neuroscience/Dynamical systemsBiological sciences/Neuroscience/Computational neuroscience/Network modelsBiological sciences/Neuroscience/Cognitive neuroscience/Perception", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "appendix.pdfAppendix", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Continuous bump attractor networks (CBANs) are a prevailing model for how neural circuits represent continuous variables. CBANs maintain these representations by temporally integrating inputs that encode differential (i.e., incremental) changes to a given variable. The accuracy of this computation hinges on a precisely tuned integration gain. Experiments have shown that the brain can recalibrate this gain using ground-truth sensory information, yet existing CBAN models rely on biologically implausible or currently unknown plasticity rules for recalibration. Here, we demonstrate that ring-type CBANs can recalibrate their integration gain through two mechanisms that rely on well-established, biologically plausible forms of plasticity. In the first mechanism, the spatially distributed synapses conveying incremental information to the attractor are plastic, allowing the integration gain to become transiently inhomogeneous during recalibration. In the second, plasticity is implemented in other components of the network, keeping the gain homogeneous during recalibration. Both mechanisms require explicit error signals that drive plasticity. We instantiate each mechanism within a CBAN, demonstrating their potential for biologically plausible, adaptive coding of continuous variables.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The brain\u2019s ability to represent continuous variables, such as location, time, and sensory information, is fundamental to our understanding and interaction with the external world. A compelling theoretical framework for how the brain constructs these representations is provided by continuous bump attractor networks (CBANs) in a diverse range of brain functions, such as orientation tuning in visual cortex1, working memory2,3, evidence accumulation and decision-making4,5,6,7, and spatial navigation8,9,10,11,12.\n\nThe CBAN is a class of recurrent neural network that maintains persistent patterns of population activity through interactions among its neurons. This persistent activity typically forms the shape of a \u2018bump\u2019 when visualized on an appropriate topological arrangement of neurons (known as a low-dimensional manifold), such as a plane, circle, or torus13. Although the shape of the activity bump is constrained by network dynamics, its center location can vary along this low-dimensional manifold, corresponding to different values of the encoded continuous variable. Neural activity consistent with these key properties of CBANs, namely, the activity bump and the low-dimensional manifold, have been observed in recordings from various regions of the mammalian brain that encode continuous variables14,15,16. More conclusive and direct evidence for CBANs has been found in the central complex of the fly brain, where a biological CBAN encoding the fly\u2019s heading angle, a continuous variable, has been identified based on the connectome and a combination of calcium imaging and optogenetics17,18,19,20. While these experimental findings support the idea of brain circuits employing CBANs to represent continuous variables, the neural mechanisms that enable CBANs to accurately update their representations in response to changes in continuous variables remain incompletely understood.\n\nCBANs update their representations of a continuous variable based on two distinct types of inputs. The first type provides \u2018absolute\u2019 information, namely, the true value of a continuous variable, such as spatial location relative to visual landmarks or the item to be held in working memory. When this absolute information is available, it provides input to a specific location on the CBAN\u2019s low-dimensional manifold that is associated with the true value of the continuous variable. In response to this localized input, the internal dynamics of the CBAN create a basin of attraction on its low-dimensional manifold toward which the activity bump gravitates, bringing the representation into close alignment with the actual value of the continuous variable21,22,23. This theoretical phenomenon has been experimentally observed in the biological CBAN within the fly central complex17 and, more indirectly, in the spatially tuned neurons of the hippocampus and entorhinal cortex\u2014two regions modeled as CBANs in the mammalian brain24,25,26,27,28.\n\nIn contrast to the first type of inputs providing absolute information to the CBAN, the second type provides \u2018differential\u2019 information, namely, the changes in the continuous variable. Sources of such inputs may be, for instance, self-generated movements providing velocity information in the context of spatial navigation or sensory cues serving as pieces of evidence in the context of decision-making. In response to these inputs, the internal dynamics of the CBAN shift the activity bump along its low-dimensional manifold\u2014in a process akin to mathematical integration\u2014such that the bump\u2019s location reflects the value of the continuous variable. However, the encoding accuracy of this integration process depends critically on an additional factor, namely, the integration gain of the network that relates the cumulative change in the continuous variable to the updating of the bump location in a proportional manner29,30. If this gain factor is miscalibrated, the result of the CBAN\u2019s integration process begins drifting away from the true value of the continuous variable; simply stated, it accumulates error. In the presence of absolute information sources such as visual landmarks in the context of spatial navigation, the aforementioned \u2018basin\u2019 mechanism corrects errors, preventing them from accumulating. However, without absolute information, error accumulation continues due to the miscalibrated integration gain. The error accumulation may eventually cause, for example, a CBAN that integrates evidence to reach a decision threshold (associated with activation of a specific neuron above a certain level4) either too soon or too late. Likewise, a miscalibrated CBAN integrating an animal\u2019s angular head velocity may overestimate or underestimate the correct head direction. Thus, a finely tuned integration gain is crucial for a CBAN to accurately encode a continuous variable based on inputs with only differential information.\n\nRecent data from time cells and place cells of the rodent hippocampal formation, hypothesized to rely on CBANs31, showed that the brain\u2019s integration gain is indeed a plastic variable whose value is adjusted based on the feedback from absolute information sources32,33. In the first study that demonstrated this phenomenon on place cells33, the virtual visual landmarks, which provided the absolute information, were moved as a function of the animal\u2019s movement on a circular track. This manipulation induced persistent errors between the encoded location, derived from angular path integration, and the actual location relative to the moving landmarks. Consequently, the brain recalibrated its integration gain, adjusting it in both direction and magnitude to reduce the positional encoding error. The recalibration was most evident after the landmarks were extinguished: The space encoded by place cells during pure path integration either expanded or contracted, depending on the direction of the preceding landmark manipulation. Present CBAN models treat the integration gain either as a constant set via carefully chosen, hard-wired model parameters (e.g., synaptic weights)11,12,34,35,36 or as a variable learned via plasticity rules that are not biologically plausible37 or are unproven38. Although these models showed the possibility of gain tuning, fundamental insights into the error-based neural mechanisms underlying this tuning are missing. Therefore, given the biological relevance and theoretical importance of this recalibration phenomenon, an open question remains: What are the critical factors enabling a CBAN to recalibrate its integration gain based on feedback from absolute information sources?\n\nIn the present paper, we aim to address this question and generate testable physiological predictions about the neural mechanisms underlying gain recalibration in brain circuits that encode continuous variables. As a representative problem, we focus on hippocampal place coding and theoretically investigate how visual landmarks\u2014an absolute information source\u2014might recalibrate the integration gain of a CBAN encoding an animal\u2019s position on a circular track33. We identify two distinct gain recalibration mechanisms within a ring attractor. The first mechanism involves Hebbian plasticity in the synaptic connections between the differential inputs and the attractor, allowing the integration gain to become spatially inhomogeneous during recalibration. This inhomogeneity can be transient, fading away as the system continually uses the feedback from absolute information sources for recalibration. In contrast, the second mechanism features nonplastic differential-input synapses and maintains spatially homogeneous integration gain during recalibration, with path-integration gain plasticity arising through nonsynaptic mechanisms. Importantly, we provide strong theoretical evidence that in CBAN models, both recalibration mechanisms depend on explicit error signals at the neuronal level, unlike error correction that can occur automatically without explicit error signaling (Fig.\u00a01A). Our findings predicts a previously untested functional role for the absolute information sources within the putative CBAN circuits of the brain and highlight a critical modification to prior CBAN models, which lack an explicit error signal. Finally, we propose modified CBAN models incorporating explicit error signals to recalibrate their integration gains. Our approach and the organization of the paper can be found in Fig.\u00a01B.\n\nA Conceptual illustration of the main question addressed in the present paper: If a CBAN (Continuous Bump Attractor Network) has an inaccurate integration gain, its representation of a continuous variable accumulates errors. A classical finding from decades of CBAN research is that these representational errors are automatically corrected by ground truth signals\u2014a process known as the \u201cground truth fix\"91,92\u2014without requiring an explicit code of the error at the level of single neurons. This raises the question of whether a CBAN can automatically learn from these errors and recalibrate its integration gain without an explicit error code. In this paper, we present theoretical evidence that an explicit error signal, in the form of a rate code, plays a crucial role in the recalibration of the CBAN's integration gain. Because classical CBANs without an explicit error code lack the ability to recalibrate their integration gain, their representational errors continue to accumulate at the same rate until the ground truth signals become available, at which point they are corrected (red line). In contrast, a CBAN equipped with an explicit error code does not only correct representational errors but also recalibrates its integration gain, gradually reducing the rate at which errors accumulate over time (green line). B Our technical approach and how it is organized across the paper.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63817-0/MediaObjects/41467_2025_63817_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "A CBAN is a recurrently connected neural network where neighboring neurons excite one another and inhibit distant neurons according to a connectivity pattern known as local excitation and global inhibition1,39. This connectivity gives rise to a persistent bump of activity as a stable equilibrium state of the system. For large networks (i.e., in the limit as the number of neurons goes to infinity; but see40) where this connectivity pattern remains consistent across the network, the equilibrium states form a continuum, known as attractor states39,41,42,43. The arrangement of neurons and the exact pattern of the recurrent connectivity determine the topology of this attractor. In the case of a ring attractor, neurons are arranged as a topological ring11. By sustaining an activity bump whose location can be shifted around the ring based on external inputs (i.e., the differential and absolute information sources), a ring attractor network is well-suited to represent a variable on a closed curve (e.g., the angular location of an animal on a one-dimensional (1D) circular track). Because integration-gain recalibration has been demonstrated in place cells of rats running on a circular track, we choose to model this process with a ring attractor, a computationally tractable framework naturally suited for encoding circular variables.\n\nVarious architectures have been proposed for ring attractors8,10,11,12. From this set of possible architectures, we restrict our analysis to the three-ring architecture because of its consistency with the anatomy of the fly central complex18,19 and its generalizability to higher dimensions36,44. For example, augmenting the 1D ring-arrangement of neurons in this architecture to a 2D toroid naturally leads to an attractor that is well-suited to represent two continuous variables13, as in grid and place cells36,44. As the name suggests, the three-ring attractor consists of three groups of neurons, each ordered in a ring arrangement: a central ring, a clockwise (CW) rotation ring, and a counter-clockwise (CCW) rotation ring8,11. Neurons within these three rings are interconnected via intrinsic connections. Additionally, they receive inputs via extrinsic connections from upstream neurons that encode the velocity information (i.e., a \u2018differential\u2019 type of input) and from a fourth ring that encodes the positional feedback from visual landmarks (i.e., an \u2018absolute\u2019 type of input) (Fig.\u00a02A).\n\nA Schematic representation of the model. The central ring forms the main body of the model based on its recurrent connections (labeled with \u2460). Its reciprocal offset connections with the rotation CW and CCW rings (labeled with \u2461 and \u2462) create a push-pull mechanism that modulates the intrinsically controlled neural activity of the central ring based on external inputs from the CW and CCW velocity neurons (labeled with \u2463). An additional external input is provided to the central ring from the visual ring (labeled with \u2464), corresponding to a set of sensory neurons that are tuned to visual landmarks. B Synaptic weight function \\({W}_{{{{\\rm{c-c}}}}}:{S}^{1}\\to {\\mathbb{R}}\\) that describes the recurrent connections within the central ring according to the well-known local excitation and global inhibition pattern. C Numerical demonstration of how recurrent connectivity within the central ring can autonomously maintain a persistent activity bump. Simulation of the central ring neurons was started with initial conditions that were assigned pseudo-randomly (light green line labeled with (i)). Within \u0303 100 milliseconds, a bump of activity emerges (medium green line labeled with (ii)). Eventually, the firing rates converge to an equilibrium, forming a persistent bump of activity (dark green line labeled with (iii)). D Tuning curves of CCW and CW velocity neurons are shown with blue-dashed and red-dashed lines, respectively.\n\nOur ultimate goal is to garner insights into how the three-ring attractor network\u2019s integration gain\u2014hereafter referred to as the path-integration (PI) gain\u2014can be recalibrated by visual landmarks. Intuitively, the PI gain determines how much the activity bump shifts around the central ring for a given amount of the animal\u2019s movement in physical space. To attain our goal, we will first reduce the complex network dynamics into a simplified, 1D differential equation39. Prior work derived such models for networks wherein the synaptic weights are constrained to be spatially uniform and static45,46. Here, we relax some of these constraints and develop a simplified model of the ring attractor network, progressively, starting from the central ring (Section \u201cControl theory reveals algorithmic conditions for PI-gain recalibration\u201d). We next include rotation rings to model the response of the network dynamics to self-movement inputs, which reveals an analytical expression of PI gain and its spatially distributed structure (Section \u201cThe need for an explicit error code to meet the algorithmic conditions for PI-gain recalibration\u201d). Finally, we extend the simplified model to include the positional feedback from visual landmarks, where we observe how the strength of this feedback may be modulated by changes in the PI gain during recalibration (Section \u201cThe visual ring provides gain-dependent positional feedback that corrects path integration\u201d). This simplified model allows us to rigorously identify algorithmic conditions (Section \u201cControl theory reveals algorithmic conditions for PI-gain recalibration\u201d) for PI-gain recalibration, from which we derive the mechanistic requirements for a network to implement the recalibration (Section \u201cThe need for an explicit error code to meet the algorithmic conditions for PI-gain recalibration\u201d).\n\nParameterizing a neuron based on its angle \u03c8 \u2208 S1 in the circular neural space, we can express the dynamics of the central ring as follows:\n\nHere, rc(t,\u00a0\u03c8) denotes the firing rate of the central ring neuron \u03c8 at time t, \u03c4c denotes the synaptic time constant of central ring neurons, \u229b denotes the circular convolution operation, \u03c3 denotes an activation function (chosen as a rectified linear unit) in our current study), Iext(t,\u00a0\u03c8) denotes external synaptic inputs to the central ring, and \\({W}_{{{{\\rm{c-c}}}}}:{S}^{1}\\to {\\mathbb{R}}\\) denotes a rotationally invariant synaptic weight function that describes the recurrent connections (\u2460 in Fig. 2A) (Fig.\u00a02B). At the limit, as the network size increases39,41,42, the recurrent connectivity leads to stabilization of a persistent \u201cbump\u201d of neural activity, a hallmark of the CBANs.\n\nThe activity bump is constrained by the network dynamics to take nonzero values in a limited range (i.e., compact support) and to have a symmetric shape with a single peak \u03b8 (i.e., even symmetry about the peak) (Fig.\u00a02C). This peak location, which corresponds to the internal representation of the animal\u2019s position, can vary along the central ring in response to external synaptic inputs Iext. Even though we are unable to obtain an exact analytical solution to the central-ring dynamics in Eq. (1) to fully describe this dynamic response, we can \u2018guess\u2019 a solution form that can describe its general properties without relying on a specific function as follows:\n\nHere, \\({\\hat{r}}_{{{{\\rm{c}}}}}(\\psi -\\theta (t))\\) denotes a function, such as a thresholded Gaussian or a sinusoid11,21,47,48,49,50, describing the persistent activity bump with the previously mentioned symmetry and width properties (see Supplementary Note\u00a03 for further details), and \u03c8\u00a0=\u00a0\u03b8(t) denotes the bump location associated with its peak (Fig.\u00a03A). The guess we make with the function \\({\\hat{r}}_{{{{\\rm{c}}}}}\\) is termed an ansatz solution. Assuming that the activity bump\u2019s symmetric shape and compactly supported width remain fixed, the ansatz solution enables us to derive a 1D differential equation that governs the dynamics of the bump location \u03b8 in response to external inputs Iext. The external inputs are provided by neurons of the rotation rings and the visual ring (see Supplementary Note\u00a01 for an analytical expression of these inputs). We progressively incorporate these parts into our model in the following subsections.\n\nA The representation \u03b8, decoded from the peak location of an ansatz solution \\({\\hat{r}}_{{{{\\rm{c}}}}}\\). B Implementation of angular PI based on the push-pull mechanism formed by reciprocal connections between the central (green) and rotation rings (CCW: blue, CW: red). The top row shows the balanced inputs from these rotation rings about the central ring\u2019s current activity-bump location \u03b8 when the animal is stationary (v\u00a0=\u00a00). The middle and bottom rows show the imbalance in these synaptic inputs in the direction of the animal\u2019s movement (middle: v\u00a0>\u00a00; bottom v\u00a0<\u00a00), which in turn shifts the activity bump in the same direction. C Cartoon illustration of neural dynamics given in Eq. (3). Top left: Circular track. Top right: Internal representation of this track with a spatially inhomogeneous PI gain k(\u03b8) ranging from 0.6 at \u03b8\u00a0=\u00a0\u03c0 to 1.4 at \u03b8\u00a0=\u00a00. The bump location \u03b8 corresponding to the network\u2019s position representation is visualized by the pale brown rat. As the rat moves through physical space with velocity v, the representation moves through neural space according to k(\u03b8)v. Middle: The relationship between the bump velocity \\(\\frac{d\\theta }{dt}\\) and the animal\u2019s velocity v. Bottom left: Firing rate map of uniformly distributed cells in a `traditional' network model with a global PI gain k(\u03b8)\u00a0=\u00a01. Bottom right: The same rate map for a network model with spatially inhomogeneous PI gain k(\u03b8). D Left shows the central ring\u2019s activity bump (green) and the bump-shaped synaptic input onto the central ring from the visual ring (pink). Right shows the model \u03b2 of the stabilizing visual feedback emerging from the interaction between these two bumps as a function of the error\u2014namely, the discrepancy \u03b8\u22c6\u00a0\u2212\u00a0\u03b8. As indicated by the opacity of the lines, this feedback may depend on the value of the PI gain. E Cartoon illustration of neural dynamics given in Eq. (5). The pale brown rat symbolizes the internal representation \u03b8 as in C, whereas the medium brown rat symbolizes the actual location \u03b8\u22c6 as represented by the visual drive. The temporal change in \u03b8 is controlled by the PI term k(\u03b8)v and the visual feedback term \u03b2(\u03b8\u22c6\u00a0\u2212\u00a0\u03b8,\u00a0k0) as visualized by two arrows acting on the pale brown rat. Left shows a case where the PI has a low gain k(\u03b8)\u00a0<\u00a01, thus underestimating the position relative to the landmarks. Right shows the overestimation case due to \u201chigh\u201d PI gain.\n\nWe begin with the rotation rings that combine positional information and self-movement velocity information based on inputs from two afferent sources: (i) The central ring provides the positional information, represented by its bump location, to both rotation rings through the synaptic weight functions \\({W}_{{{{\\rm{c-cw}}}}},\\,{W}_{{{{\\rm{c-ccw}}}}}:{S}^{1}\\to {\\mathbb{R}}\\) (\u2461 in Fig. 2A). (ii) Velocity-dependent differential firing of CW and CCW \u2018velocity\u2019 neurons, namely, ucw\u00a0=\u00a0u0\u00a0\u2212\u00a0\u03b1cwv and uccw\u00a0=\u00a0u0\u00a0+\u00a0\u03b1ccwv (Fig.\u00a02D), signal the animal\u2019s velocity v to the respective rotation rings through synaptic weights \\({W}_{{{{\\rm{v-cw}}}}},\\, {W}_{{{{\\rm{v-ccw}}}}}:{S}^{1}\\to {\\mathbb{R}}\\) (\u2463 in Fig. 2A). Combining these inputs, we obtain ansatz solutions \\({\\hat{r}}_{{{{\\rm{cw}}}}}\\), \\({\\hat{r}}_{{{{\\rm{ccw}}}}}\\) to the CW and CCW rotation rings\u2019 firing rates under the assumption that the synaptic dynamics of rotation rings are sufficiently fast as described in Supplementary Note\u00a03. These solutions describe an activity bump within the rotation rings, derived from the ansatz \\({\\hat{r}}_{{{{\\rm{c}}}}}\\) for the central ring.\n\nThe rotation rings project reciprocally back to the central ring via offset connections, where a CW/CCW rotation-ring neuron at a specific location in the angular neural space connects to a central ring neuron at a slightly offset location in the same direction (\u2462 in Fig. 2A), giving rise to a feedback push-pull structure. How does this connectivity structure respond to external inputs from the velocity neurons? If we assume that the CW and CCW components are symmetric in both the synaptic weights of the push-pull connectivity and the baseline firing rates of the velocity neurons (a commonly made assumption), then the rotation rings fire equally when the animal is immobile (i.e., v\u00a0=\u00a00). As a result, they provide balanced inputs to the central ring, keeping the bump location \u03b8 unchanged \\((\\frac{d\\theta }{dt}=0)\\). In contrast, inputs from the CW and CCW velocity neurons modulate the activity of the respective rotation rings differentially when the animal is moving (v\u00a0\u2260\u00a00). This modulation causes the input from the rotation rings to become imbalanced about the central ring\u2019s current activity bump. In response to this imbalance, the central ring shifts its activity bump at a rate proportional to the animal\u2019s velocity (Fig.\u00a03B). This process is known as angular path integration; hereafter, we refer to it simply as path integration (PI).\n\nUnder the classical assumptions (e.g., the movement causes a weak differential change in the inputs to the central ring), the ansatz solutions, \\({\\hat{r}}_{{{{\\rm{c}}}}},{\\hat{r}}_{{{{\\rm{cw}}}}},{\\hat{r}}_{{{{\\rm{ccw}}}}}\\) can be used to derive a simplified model for the PI process\u2014specifically, a 1D differential equation governing the temporal evolution of the position representation \u03b8. Applying the dimensionality reduction protocol detailed between Supplementary Notes\u00a02 to 3.2, this model takes the following form:\n\nThis equation shows that the PI process updates the bump location \u03b8 in proportion to the animal\u2019s velocity v with a gain factor k(\u03b8). This factor simply represents the ring attractor network\u2019s PI gain, and its analytical expression takes the following form:\n\nHere, i denotes the index of the summation, representing either the CW or CCW rotation ring, \u03b1 denotes the absolute value of the slope of the velocity neurons\u2019 tuning curves, b denotes the value of the offset in the connections between rotation rings and the central ring, and \u2225 \u22c5 \u2225 denotes the magnitude (i.e., root-mean-square) of an expression. Intuitively, this equation shows that the PI gain emerges from the interaction between external velocity inputs and the attractor\u2019s recurrent dynamics, which together process and transform the animal\u2019s movement into the bump\u2019s movement. In this interaction, if the synaptic processes operate more quickly (lower time constant \u03c4c), the network responds more rapidly to the velocity inputs, resulting in an increased PI gain. Similarly, larger synaptic weights in Wi\u2212c (corresponding to rotation-to-central ring synapses) or in Wv\u2212i (corresponding to velocity-to-rotation ring synapses), or steeper velocity-neuron slopes \u03b1 enhance the velocity input onto the central ring, accelerating the bump movement, thereby increasing the PI gain. The attractor\u2019s bump magnitude, however, has the opposite effect. A larger bump magnitude resists movement, requiring stronger inputs, hence resulting in a reduced PI gain. A detailed account of how every parameter in Eq. (4) influences the PI gain will be given in Section \u201cThe need for an explicit error code to meet the algorithmic conditions for PI-gain recalibration\u201d, where we explore how temporal changes in network parameters can recalibrate the PI gain over time.\n\nBeyond showing how network parameters relate to the value of PI gain, our derivation of Eq. (4) uncovers a previously unknown property of the PI gain within a ring attractor model: The PI gain is locally distributed across the network, as governed by the spatially distributed synaptic weights in the pathway from the velocity neurons onto the central ring (\u2461 and \u2463 in Fig. 2A). When these weights are spatially uniform (i.e., having the same profile and magnitude), as has always been assumed in previous work11,36,45,46, the PI gain is independent of the bump location, resulting in an ideal model with a single, global PI gain under all conditions. If the synaptic weights ever become heterogeneous (a possibility we revisit later), however, this ideal state is no longer maintained; instead, path integration occurs with a PI gain k that varies as a function of the bump location \u03b8 (Fig.\u00a03C). One may wonder why we even care about this seemingly strange possibility. As will be evident later, for a subset of biologically plausible gain recalibration mechanisms (e.g., tuning the distributed synaptic weights from the velocity neurons to the rotation rings), such inhomogeneities in the PI gain are inevitable\u2014though they may diminish with repeated use of feedback from landmarks. By contrast, for other mechanisms (e.g., tuning the slope \u03b1 of the velocity inputs), the system can always maintain a single, global PI gain. Nevertheless, our derivation of Eq. (4) applies to both cases and forms the foundation of our analytical investigation of the PI-gain recalibration.\n\nNext, we include in our simplified model the influence of external inputs from the visual ring (Fig.\u00a02A). The visual ring receives no explicit inputs; instead, its neurons are presumed to autonomously fire at specific locations of the animal relative to landmarks, capturing the absolute positional information received from visual landmarks available at each position (modeling how egocentric visual processes can calculate position from landmarks is beyond the scope of this paper). Through the synaptic weight function \\({W}_{{{{\\rm{vis-c}}}}}:{S}^{1}\\to {\\mathbb{R}}\\) (\u2464, in Fig. 2A), this firing of the visual ring provides the central ring with a bump-like synaptic input encoding the animal\u2019s \u201ctrue\u201d position \u03b8\u22c6 relative to landmarks51,52.\n\nIn the ring attractor model, the network\u2019s position representation \u03b8 is anchored to this bump-like synaptic input that encodes the positional feedback \u03b8\u22c6 from landmarks, as observed in numerous experimental studies on head direction and place cells24,25,26,27. To determine a simplified, approximate model for how \u03b8 varies under this anchoring effect, we again apply the dimensionality reduction. Assuming that the visual ring provides a weak and narrow bump-like input, this application leads to the differential equation\n\nwhere k(\u03b8)v denotes the PI inputs as in the previous section and \\(\\beta :{S}^{1}\\times {\\mathbb{R}}\\to {\\mathbb{R}}\\) is a function modeling the influence of visual inputs on \u03b8. See Supplementary Note\u00a03.3 for details and the assumptions.\n\nOne might think that the influence of the visual ring would depend exclusively on the mismatch between the ring attractor bump and the visual ring bump. However, according to our derivation, the visual input, \u03b2, influences the system through two mechanisms: a direct effect, which depends on the discrepancy \u03b8\u22c6\u00a0\u2212\u00a0\u03b8, and an indirect effect, mediated through the PI gain k(\u03b8). The direct effect aligns the sign of \u03b2 with the discrepancy \u03b8\u22c6\u00a0\u2212\u00a0\u03b8, forming a negative feedback loop that pulls \u03b8 toward \u03b8\u22c6 (Fig.\u00a03D). Indeed, this mechanism alone is sufficient to explain landmark correction in traditional ring attractor models, where the PI gain is fixed due to static network parameters. In a ring attractor model capable of recalibrating its PI gain, however, the direct effect alone can be insufficient because some network parameters that may be updated to recalibrate the PI gain k(\u03b8) may also modulate the landmark correction \u03b2, altering its ability to correct discrepancies \u03b8\u22c6\u00a0\u2212\u00a0\u03b8 (these parameters are explored in Section \u201cThe need for an explicit error code to meet the algorithmic conditions for PI-gain recalibration\u201d below). To account for this dual impact, the simplified model in Eq. (5) incorporates dependence of \u03b2 on the indirect effect k(\u03b8). That is, the model can capture dynamic changes in the landmark correction when there is a change in a network parameter associated with a change in the PI gain. When present, this indirect effect based on the PI gain k(\u03b8) modulates only the amplitude of \u03b2 without altering its sign (different lines in Fig.\u00a03D). See Supplementary Note\u00a03.3 for details and an alternative form of the \u03b2.\n\nTogether, these direct and indirect effects model the \u03b2 function and how it anchors the attractor\u2019s representation \u03b8 to the \u201ctrue\" value \u03b8\u22c6 measured relative to landmarks. When the animal is stationary (i.e., v\u00a0=\u00a00), the direct feedback provided by \u03b2 ensures \u03b8\u00a0\u2192\u00a0\u03b8\u22c6. During the animal\u2019s movement, however, the PI-related term, k(\u03b8)v updates \u03b8 based on the animal\u2019s velocity, while \u03b2 continues to anchor \u03b8 toward \u03b8\u22c6. A well-balanced combination of these terms anchors \u03b8 around \u03b8\u22c6 (Fig.\u00a03E). Therefore, Eq. (5) provides a simplified framework that captures the combined influence of PI and visual feedback on the ring attractor network\u2019s position representation, forming the basis for our subsequent analysis of the algorithmic and mechanistic conditions necessary for successful PI-gain recalibration.\n\nExperiments showed that the PI gain of the rodent hippocampal system is a plastic variable that can be recalibrated by visual landmarks33. In Section \u201cControl theory reveals algorithmic conditions for PI-gain recalibration\u201d\u00a0below, we leverage the analytical tractability of Eq. (5), a simplified model of the ring attractor network, to identify the algorithmic conditions required for recalibration of its PI gain. Then, in the subsequent\u00a0Section \u201cThe need for an explicit error code to meet the algorithmic conditions for PI-gain recalibration\u201d, we use Eq. (4), the analytical expression of the ring attractor\u2019s PI gain, to map these algorithmic conditions from the simplified model back to the high-dimensional network dynamics as mechanistic prerequisites for implementing PI-gain recalibration.\n\nTo understand the computations required at an algorithmic level for PI-gain recalibration within a ring attractor network, we first revisit the experimental conditions that led to this recalibration phenomenon33; In those experiments, an animal moved on a circular track while an array of visual landmarks was rotated around the track as a function of the animal\u2019s velocity and an experimentally controlled, visual gain factor, k\u22c6. When k\u22c6\u00a0<\u00a01, the landmarks moved in the same direction as the animal, decreasing the perceived speed; when k\u22c6\u00a0>\u00a01, the landmarks moved in the opposite direction as the animal, increasing the perceived speed; when k\u22c6\u00a0=\u00a01 (veridical condition), the landmarks remained stationary. To model these experimental conditions in a ring attractor network, we assume that the visual drive, represented by its bump location \u03b8\u22c6, moves through the circular neural space at a rate equal to the animal\u2019s velocity v times the visual gain k\u22c6, namely, \\(\\frac{d{\\theta }^{\\star }}{dt}={k}^{\\star }v\\). The experiments in ref. 33 showed that prolonged exposure to these visual conditions recalibrated the animal\u2019s PI gain, resulting in a strong correlation between the average value of the PI gain measured over many laps after the landmarks were removed and the final value of the visual gain k\u22c6 before the landmark removal. What does this result imply in the context of a ring attractor model where the PI gain k is a spatially distributed network parameter? Because the experiments measured the recalibrated PI gain from neural activity over many laps, the results suggest that the ring attractor network must adjust its PI gain such that the PI gain\u2019s spatial average\\({k}_{0}\\triangleq \\frac{1}{2\\pi }\\int_{0}^{2\\pi }k(\\theta )d\\theta\\) converges to the visual gain k\u22c6, namely, \\({\\lim }_{t\\to \\infty }{k}_{0}(t)={k}^{\\star }\\). We refer to this exact convergence as complete recalibration. At this stage, the mechanistic details of how recalibration can occur remain unclear\u2014specifically, whether the ring attractor adjusts its PI gain uniformly across the neural space, maintaining the same value everywhere, or whether some degree of spatial inhomogeneity emerges spontaneously during the recalibration process. By focusing on the PI gain\u2019s spatial average k0, nevertheless, we ensure that our subsequent analysis remains robust to such mechanistic differences, as long as any potential spatial fluctuations in the PI gain, defined as kac(\u03b8) \u225c k(\u03b8)\u00a0\u2212\u00a0k0, remain within a reasonable range, an assumption that will be further clarified in the following paragraphs.\n\nWe proceed by posing a question: What variables in the ring attractor network are important for updating k0? We searched for a general equation that can model these updates based on neural activity levels within the network and the current value of the synaptic weights, assuming an environment with spatially homogenous feedback from visual landmarks (See Supplementary Note\u00a04.1 for further details and assumptions). This search led to a surprisingly simple equation\n\nwhere \\({g}_{0}:{\\mathbb{R}}\\times {S}^{1}\\times {\\mathbb{R}}\\to {\\mathbb{R}}\\) denotes a function that instantiates the instantaneous change in k0 based on three variables: the current gain k0, the animal\u2019s velocity v and the difference between the visual drive\u2019s position representation \u03b8\u22c6 and the ring attractor\u2019s position representation \u03b8. By contrast, the update does not directly depend on the specific values of \u03b8 or \u03b8\u22c6 because of the assumed spatially uniform visual feedback across the environment. Although there are infinitely many g0 functions of the form in Eq. (6), some of them may fail to result in PI-gain recalibration, i.e., k0 not converging to the visual gain k\u22c6. We thus ask what are the necessary and sufficient properties of the PI-gain update rule g0 for k0\u00a0\u2192\u00a0k\u22c6 (i.e., the fundamental features shared by all successful update rules)?\n\nTo seek these fundamental properties, we revisit Eq. (5) along with the positional feedback \\(\\frac{d{\\theta }^{\\star }}{dt}={k}^{\\star }v\\) from landmarks and with the fact that the PI gain\u2019s spatial average k0 varies according to Eq. (6). Perfect convergence of k0 to k\u22c6 through this update rule would imply that the error between these two gains, namely \\(\\tilde{k}\\,\\triangleq\\, {k}^{\\star }-{k}_{0}\\), approaches zero. We refer to this error \\(\\tilde{k}\\) in the PI gain\u2019s average component as the gain error.\n\nWhen the gain error \\(\\tilde{k}\\) is not zero, the influence of path integration on the attractor\u2019s position representation \u03b8 causes some positional error \\(\\tilde{\\theta }\\,\\triangleq\\, {\\theta }^{\\star }-\\theta\\) relative to the visual drive \u03b8\u22c6. If the gain error were reduced, this positional error would also be reduced, aligning the attractor\u2019s representation more closely with the visual drive. This coupling between the gain and positional errors\u2014namely \\(\\tilde{k}\\) and \\(\\tilde{\\theta }\\)\u2014prompts us to analyze their temporal progression to garner insights into PI-gain recalibration. To this end, we consider a constant visual gain (as was the case during the first and last epochs of the experiments in ref. 33, when landmarks were present). We then analyze the local stability of the errors \\(\\tilde{k}\\) and \\(\\tilde{\\theta }\\), assuming that fluctuations in kac decrease following the gain error \\(\\tilde{k}\\), while also making assumptions on the properties of g0\u2019s derivatives. This analysis identifies the algorithmic conditions for complete recalibration, where k0 converges exactly to k\u22c6, as follows:\n\nFormal results\n\nThe animal cannot be stationary, otherwise complete recalibration (k0\u00a0\u2192\u00a0k\u22c6) is not possible.\n\nAssuming that the animal remains in motion (v\u00a0\u2260\u00a00), we found that complete recalibration requires the PI-gain update rule g0 to share the same sign as the product of the animal\u2019s velocity v and the ring attractor\u2019s positional error \\(\\tilde{\\theta }\\) in some neighborhood of \\(\\tilde{\\theta }=0\\):\n\nAssuming that the animal\u2019s speed is constant and \u2223kac\u2223 \u2248 0, we additionally found that the system is guaranteed to achieve complete recalibration if this sign condition is satisfied.\n\nThe proofs of these formal results along with the specific assumptions made are provided in Supplementary Note\u00a04.2. The first result states a trivial necessary condition: if the animal is stationary, visual landmarks will fully correct positional error \\(\\tilde{\\theta }\\), making gain errors \\(\\tilde{k}\\) imperceptible. The second and third results establish the sign condition in Eq. (7) as both necessary and sufficient. Simply stated, recalibrating the PI gain of a ring attractor is equivalent to increasing it when positional error and velocity align, and decreasing it when they oppose each other. To understand the intuition behind this condition, imagine walking along a circular track with a slightly miscalibrated PI gain k0. Suppose your PI gain is under-calibrated (k0\u00a0<\u00a0k\u22c6\u00a0=\u00a01), meaning that you consistently underestimate how far you have traveled. In this case, if you walk CCW (v\u00a0>\u00a00), your internal position estimate \u03b8 lags behind your true position \u03b8\u22c6 in the CCW direction, resulting in a growing positive positional error (\\(\\tilde{\\theta } > 0\\)). Conversely, if you walk CW (v\u00a0<\u00a00), the same underestimation instead causes \u03b8 to lag behind in the CW direction, leading to a negative positional error (\\(\\tilde{\\theta } < 0\\)). The crucial point is that in both movement directions, the product \\(\\tilde{\\theta }v\\) remains consistently positive because the sign of both \\(\\tilde{\\theta }\\) and v flip together with movement direction. Likewise, if the PI gain is over-calibrated (k0\u00a0>\u00a0k\u22c6), the same reasoning shows that the product \\(\\tilde{\\theta }v\\) remains negative regardless of movement direction (CCW or CW). Thus, the sign of \\(\\tilde{\\theta }v\\) precisely indicates whether the PI gain should be increased or decreased, and Eq. (7) simply formalizes this principle. Crucially, our formal analysis only establishes that this sign condition needs to hold within a specific range of errors around zero, rather than for arbitrarily large positional and gain errors. This follows from the local nature of our stability analysis, which investigates convergence within a neighborhood of zero error. However, if the sign condition in Eq. (7) never holds for any range of errors, then k0 simply cannot converge to k\u22c6. On the other hand, as long as it holds within some range, recalibration is guaranteed\u2014at least under the assumption of constant velocity. Later, we will numerically demonstrate that this constant-velocity assumption is likely not required, though a formal proof relaxing this assumption remains as future work.\n\nOur analysis so far provided insights into complete recalibration of the PI gain. However, the data from33 showed that, on average, the recalibration was only partial (75%). In such a case, the average PI gain k0 may converge to a value \\({k}_{0}^{\\infty }\\), which is biased towards, but not necessarily equal to, the visual gain k\u22c6. In turn, if \\({k}_{0}^{\\infty }\\,\\ne\\, {k}^{\\star }\\), the system may operate under some persistent, residual positional error \\(\\tilde{\\theta }={\\tilde{\\theta }}^{\\infty }\\) that is not equal to zero. The question is whether the conditions for complete recalibration also apply to partial recalibration. Assuming that the landmark-correction \u03b2 does not depend on the PI-gain, we found that the same necessary and sufficient condition (Eq. (7)) must still be satisfied, but now with respect to the residual positional error at steady-state \\(\\tilde{\\theta }={\\tilde{\\theta }}^{\\infty }\\) rather than with respect to the zero error as was the case of complete recalibration. See Supplementary Note\u00a04.3 for details.\n\nBelow, we simulate Eqs. (5) and (6) along with two example PI-gain update rules (g0) to numerically illustrate our analytical findings, especially how specific functional forms of g0 determine the recalibration outcome. Additionally, these simulations offer intuition and insights that inform the design of ring attractor networks capable of PI-gain recalibration, as instantiated in Section \u201cImplementing PI-gain recalibration in a ring attractor\u201d.\n\nThe simplest PI-gain update rule satisfying the sign condition (Eq. (7)) is\n\nwhere \u03bc denotes a positive learning rate. Because adjustment of the PI gain ceases at zero positional error \\(\\tilde{\\theta }\\) under this rule, the system achieves complete PI-gain recalibration by reaching \\({k}_{0}^{\\infty }={k}^{\\star }\\) (top row, Fig.\u00a04B). This numerical example demonstrates that the sign condition (Eq. (7)) is sufficient for PI-gain recalibration even when the animal\u2019s velocity is not constant.\n\nConsider now a slightly more complex PI-gain update rule\n\nwhere \u03bc denotes a positive learning rate as before, and \u03b7 denotes a constant that controls the magnitude of the additional velocity-dependent term \u03b7k0v2. This term practically acts as a positive bias on top of the simplest PI-gain update rule (Eq. (8)) that satisfies the sign condition. As we will see in Section \u201cImplementing PI-gain recalibration in a ring attractor\u201d, when we introduce a modified ring attractor model, this update rule provides a more biologically plausible representation of PI-gain recalibration in a ring attractor network than the simplest update rule in the previous example.\n\nNote that with the modified update rule in Eq. (9), the sign condition in Eq. (7) remains satisfied\u2014but now relative to a steady-state positional error that may be nonzero depending on the velocity-dependent bias term \u03b7k0v2. The degree of recalibration also depends on the magnitude of this term. If \u03b7\u00a0=\u00a00, for example, the bias is zero, and the update rule reduces to the rule in Example 1, resulting in complete recalibration (i.e., \\({k}_{0}^{\\infty }={k}^{\\star }\\)). Otherwise, recalibration is only partial (bottom row Fig.\u00a04B) with a steady-state gain error that is proportional to both \u03b7 and the animal\u2019s velocity v (see Eq. (132) and the analysis afterward in Supplementary Note\u00a07.2 for a derivation).\n\nFor both simulations, we set the initial condition k0(0)\u00a0=\u00a01, the landmark stabilization function \\(\\beta (\\tilde{\\theta },{k}_{0})=0.66\\times \\sin (\\tilde{\\theta })\\), the visual gain k\u22c6\u00a0=\u00a01.5, and the learning rate \u03bc\u00a0=\u00a00.02. The gain choices imply that the initial value of gain error is \\(\\tilde{k}(0)=0.5\\). Additionally, we chose the constant \u03b7\u00a0=\u00a00.12 for the second example. A A smoothed velocity profile of an animal from an experiment in ref. 33. B It shows trajectories of the network\u2019s positional and gain errors (left column), their interrelationship (middle column), and how the animal\u2019s velocity influences this relationship (last column), all obtained from the simulation under the example PI-gain update rules: Eq. (8) (Example 1, top row) and Eq. (9) (Example 2, bottom row). When the animal begins moving at t\u00a0=\u00a00, the positional error \\(\\tilde{\\theta }\\) (black line, left y-axis) quickly increases because of the nonzero gain error \\(\\tilde{k}\\) (red line, right y-axis). As the PI gain is modified, the gain error (\\(\\tilde{k}\\)) and, consequently, the positional error diminishes gradually, eventually converging to steady state. Their steady-state values are zero for Example 1 (complete gain recalibration) while being nonzero for Example 2 (partial recalibration). The middle column shows that although positional error strongly depends on gain error, it is also influenced by other factors. This additional influence can also be observed in the positional error progression in the left column. Although there is a general gradual, convergent trend of the gain and positional errors, the positional error goes through many fast, transitory changes around this trend. Close inspection reveals that these fast changes are influenced by changes in the animal\u2019s velocity. For example, as animal slows down (v\u2193) around minute 5, the positional error decreases (\\(\\tilde{\\theta }\\downarrow\\)), eventually becoming zero (\\(\\tilde{\\theta }=0\\)) with the animal coming to a stop (v\u00a0=\u00a00). This behavior of the positional error is best explained by the multiplication of the gain error with the animal\u2019s velocity. The right column verifies this analytical expectation using simulation results.\n\nHow can a ring attractor network mechanistically implement Eq. (7), the algorithmic sign condition for PI-gain recalibration? To address this question, we investigate an analytical expression of the spatial average of the ring attractor network\u2019s PI gain, k0, which can be simply obtained by averaging the PI gain k(\u03b8) in Eq. (4) over \u03b8. The resulting expression identifies a number of terms as possible neural loci for updating k0: (i) the synaptic time constant \u03c4c, (ii) the offset b in the central-to-rotation ring connections, (iii) the synaptic weight functions Wv-cw,Wv-ccw of the velocity-to-rotation ring connections, (iv) the synaptic weight functions Wcw-c,Wccw-c of the rotation-to-central ring connections, (v) the slope parameters \u03b1cw,\u03b1ccw quantifying the absolute value of the tuning slopes of velocity neurons, (vi) the function \\({\\hat{r}}_{{{{\\rm{c}}}}}\\) describing the central ring\u2019s persistent activity bump, and (vii) the functions \\({\\hat{r}}_{{{{\\rm{cw}}}}}\\), \\({\\hat{r}}_{{{{\\rm{ccw}}}}}\\) describing solutions to the rotation ring\u2019s persistent activity bump. Out of the seven terms, we consider the last five (iii-vii) as candidates driving the PI-gain recalibration within the ring attractor model via temporal changes, implicitly assuming that the first two terms, the synaptic time constant \u03c4c and the offset b are \u201chardwired\u201d (i.e., time-invariant).\n\nThe rationale behind excluding the first two terms arises, in part, from the limitations of our modeling approach. First, the rate-based model of the ring attractor network does not include any cellular and receptor details to capture possible temporal changes in the synaptic time constant \u03c4c. Instead, our model includes \u03c4c as a \u201clumped parameter\u201d reduction of complex phenomena that govern the changes in membrane potential with ion flux through receptors; future work could use biophysical modeling (e.g., ion channel kinetics) to investigate how changes in \u03c4c could contribute to PI-gain recalibration, but that is beyond the scope of the present study. Second, our model employs a simplified one-to-one connectivity between rotation and central rings, where each neuron in a rotation ring connects to only one neuron in the central ring with a fixed offset b. This contrasts with a one-to-all connectivity, which would be necessary to capture plasticity in b through gradual modulation of weights along the neural space.\n\nWe then analyzed the relationship between the temporal change in each of the remaining five candidate terms and the resulting temporal change in the PI gain k0. Regardless of which term drives the changes in the PI gain, we find that rate-based encoding of the positional error \\(\\tilde{\\theta }\\) is critical for the ring attractor network to implement the sign condition (Eq. (7)), which is both necessary and sufficient for PI-gain recalibration. However, the specific nature of the error code depends on the driver term. As we shall show, if the PI-gain recalibration is implemented by plastic changes in the velocity pathway of the circuit (iii-iv), then the error signal must take the form of a rate code for the instantaneous difference between \u03b8\u22c6 and \u03b8. In contrast, if the recalibration is implemented elsewhere in the circuit (v-vii), then the error signal must take the form of a rate code for the time integral of the error between \u03b8\u22c6 and \u03b8. Finally, it should be noted that these findings are derived using our previous analytical results (Eqs. (4\u20137)) and are therefore subject to the same assumptions.\n\nAs previously implied in Section \u201cThe need for an explicit error code to meet the algorithmic conditions for PI-gain recalibration\u201d, the PI gain can be altered by modifying the strength of the velocity-dependent synaptic inputs onto the central ring. To this end, we first consider a mechanism that adjusts the ring attractor\u2019s PI gain through Hebbian plasticity in the pathway from velocity neurons to the central ring. This pathway includes the synaptic weight pair Wv-cw,\u00a0Wv-ccw, describing the strength of velocity-to-rotation ring connections (\u2463 in Fig. 2A), and the pair Wcw-c,\u00a0Wccw-c, describing the strength of rotation-to-central ring connections (\u2462 in Fig. 2A). According to Eq. (4), the CW and CCW components of these weight pairs influence the PI gain in an additive manner. Because of the aforementioned CW\u2013CCW symmetry requirement in Section \u201cThe need for an explicit error code to meet the algorithmic conditions for PI-gain recalibration\u201d (i.e., the inputs to the central ring must be balanced for stability of PI during immobility periods), however, we assume that the CW and CCW components undergo the same temporal changes, ensuring that their individual contribution to PI remains symmetric. This symmetry assumption implies that if the value of k0 changes as per the algorithmic sign condition in Eq. (7), then the individual contribution of CW and CCW components must be in the direction of the product of the animal\u2019s velocity v and the positional error \\(\\tilde{\\theta }\\). To identify the mechanistic underpinnings of such symmetric recalibration of the PI gain\u2019s spatial average k0, we revisit Eq. (4). By differentiating this equation with respect to time and considering Hebbian plasticity in the velocity pathway, we find that the algorithmic condition translates into a mechanistic constraint as follows:\n\nHebbian plasticity of the velocity-to-rotation ring connections (Wv-cw,\u00a0Wv-ccw): Controlling the strength of velocity inputs onto the ring attractor, these weights directly affect the movement speed of the activity bump for a given movement speed of the animal. This effect is locally instantiated as the weights are spatially distributed across the ring. Thus, when Hebbian plasticity modifies the weights Wv-cw,\u00a0Wv-ccw in a spatially inhomogeneous manner\u2014due to unequal activation of neurons across the bump\u2014movement speed of the bump begins to exhibit local variations depending on its location along the ring. As a result, the PI gain is updated non-uniformly. Despite these local variations, however, the spatial averages of the weights Wv-cw, Wv-ccw always remain positively correlated with the spatial average\u00a0k0 of the PI gain.\n\nThe fact that k0 is proportional to average strengths of these synapses constrains the mechanisms by which their Hebbian plasticity can achieve recalibration. Specifically, satisfying Eq. (7)\u2014a necessary and sufficient condition for PI-gain recalibration\u2014requires that the rate of change of the average strengths of these synapses must be in the same direction as the product of the animal\u2019s velocity (v) and the network\u2019s positional error (\\(\\tilde{\\theta }\\)), namely:\n\nBecause the pre-synaptic side of these synapses consists of velocity neurons whose firing rates vary monotonically with the animal\u2019s velocity v, Hebbian plasticity can satisfy the above equality only if the firing rates of the post-synaptic neurons, namely, the rotation rings, similarly exhibit a monotonic relationship with the instantaneous positional error, \\(\\tilde{\\theta }\\) (Fig.\u00a05B-1). This required error tuning within the CW and CCW rotation rings must also reflect the differential sign of velocity tuning within the CW and CCW velocity neurons (previously shown in Fig.\u00a02D) to ensure that both CW and CCW pathways satisfy Eq. (10).\n\nA Temporal progression of\u00a0the animal's velocity v (from an experiment33) and other variables: gain\u00a0(\\(k_{0}\\)), positional error\u00a0(\\(\\tilde{\\theta}\\), and the time-integral of the error. Model parameters and initial conditions are the same as Example 1 in Fig.\u00a04B. Similar to that example, the positional error reduces, as the system recalibrates its PI gain. B Mechanistic constraints for recalibration through plasticity in the velocity pathway. (B-1) and (B-2) correspond to plasticity in the velocity-to-rotation ring and in the rotation-to-central ring connections, respectively (for simplicity, only one rotation ring is shown.). The weight profiles of these connections\u2014Wv-rot and Wrot-c (solid lines)\u2014may be modified through Hebbian plasticity (dashed lines). The average PI gain k0 remains positively correlated with their average strengths. B-1: If Hebbian plasticity modifies Wv-rot to drive k0 toward the visual gain k\u22c6, then the mean firing rates of CCW (blue) and CW (red) rotation rings must vary over time, inversely with one another, to encode the instantaneous positional error (bottom row of B-1). B-2: Alternatively, Wrot-c is modified, the mean firing rates of either the rotation or the central rings must vary to encode the same positional error (bottom row of B-2). Unlike B-1, our analysis does not determine the direction of this error-dependent variation in B2, so the direction of the depicted tuning curve is arbitrary. C Mechanistic constraints for recalibration via other mechanisms; each row follows a similar schema as the mechanisms in B: (C-1) Velocity neurons' slopes as the locus of plasticity. (C-2) Rotation rings' widths as the locus of plasticity. (C-3) The central ring\u2019s bump magnitude as the locus of plasticity. If the average PI gain k0 converges to k\u22c6 through changes in any of these neural loci, then the time-integral of the error must be encoded in the mean firing rates of a ring population. In the case of C-1 and C-2, CCW (blue) and CW (red) rotation rings are the sources of this integral-of-error rate code. In the case of C-3, the central ring (green) is the source. Note that the directions of these error codes (i.e., the tuning-curve slope) flip if the animal moves in the the opposite direction than panel A.\n\nIntuitively, this error-rate code within the rotation rings enables the network to detect whether it is lagging behind or advancing ahead of reality, which in turn recruits Hebbian plasticity to adjust the strength of the synapses controlling the movement speed of the bump, effectively \u201cspeeding it up\u201d or \u201cslowing it down\u201d as needed. Over time, this adaptive adjustment will result in recalibration of the PI gain. See Supplementary Note\u00a05.1 for mathematical details.\n\nHebbian plasticity of the rotation-to-central ring connections (Wcw-c,Wccw-c): Just as the velocity-to-rotation ring synapses can recalibrate the PI gain through locally occurring Hebbian plasticity, the rotation-to-central ring connections (Wcw-c, Wccw-c) can also contribute to its recalibration. In both cases, plasticity occurs in a spatially inhomogeneous manner due to the non-uniform activation of neurons across the bump. Fortunately, just as we saw for the velocity-to-rotation ring connections, the average strength of the rotation-to-central ring synapses remains positively correlated with the spatial average k0 of the PI gain.\n\nTherefore, as in the previous case, if the network satisfies Eq. (7)\u2014a necessary and sufficient condition for recalibration\u2014via Hebbian plasticity of rotation-to-central ring synaptic weights Wcw-c, Wccw-c, then it must modify their average strengths in the same direction as the product of the network\u2019s positional error and the animal\u2019s velocity. In the case of velocity-to-rotation ring synapses, meeting this requirement necessitated a rate code for error on the postsynaptic side, since the pre-synaptic neurons were assumed to encode only velocity. Here, however, neither side of the rotation-to-central ring connections is inherently constrained in this way, meaning that the positional error could, in principle, be encoded on either the pre- or post-synaptic side, provided that the velocity is also encoded. This encoding can occur in two ways: either the firing rate of a single ring (rotation or central) varies monotonically with both negative and positive instantaneous errors (Fig.\u00a05B-2), or each ring exhibits monotonic tuning for only one direction of error, such that together they cover the full range. Mathematical details are provided in Supplementary Note\u00a05.2.\n\nAs we prove in the relevant\u00a0Supplementary Notes (referenced above), Hebbian plasticity in a component of the velocity pathway cannot modify the PI gain k0 as required by the algorithmic sign condition in Eq. (7), unless the firing rates of either the rotation rings or the central ring encode the instantaneous positional error via monotonic changes. Since this sign condition was previously identified as both necessary and sufficient for PI gain-recalibration in a ring attractor network (Section \u201cControl theory reveals algorithmic conditions for PI-gain recalibration\u201d), it follows that a rate-coded representation of the network\u2019s instantaneous error is equally essential for PI-gain recalibration through Hebbian plasticity in the velocity pathway. Once the network includes both a rate code of the instantaneous positional error and Hebbian plasticity in the velocity pathway, synaptic weights undergo activity-dependent modifications that integrate this error signal over time. Consequently, the weights gradually track the time integral of the positional error, which serves as a proxy for the PI gain (Fig.\u00a05A), ultimately recalibrating the PI gain. Put differently, the synaptic weights continuously accumulate past discrepancies, enabling the ring attractor network to fine-tune its PI gain dynamically.\n\nWe next consider the scenario where the synaptic weights along the velocity pathway are hardwired (i.e., constant). Instead, PI-gain recalibration is driven by temporal changes in one of the three firing-rate related terms. These terms include the parameters \u03b1cw, \u03b1ccw describing the absolute value of the velocity neurons\u2019 tuning slope, the ansatz functions \\({\\hat{r}}_{{{{\\rm{cw}}}}}\\),\\({\\hat{r}}_{{{{\\rm{ccw}}}}}\\) describing the rotation rings\u2019 activity bumps, or the ansatz \\({\\hat{r}}_{{{{\\rm{c}}}}}\\) describing the central ring\u2019s activity bump. In the previous case, where plasticity occurred in the velocity pathway, synaptic weights tracked the time-integral of the positional error through Hebbian modifications, ultimately recalibrating the ring attractor\u2019s PI gain. But how can the ring attractor achieve the same outcome in the absence of synaptic plasticity? As we show below, firing rates themselves varying according to the time-integral of the positional error is equally effective for PI-gain recalibration. Unlike synaptic plasticity in the velocity pathway, which recalibrates the PI gain through locally accumulating modifications, temporal changes in the firing-rate-related terms act globally across the neural space, ensuring that recalibration occurs uniformly, without introducing any spatial inhomogeneities into the PI gain.\n\nChanges in the slopes of velocity neurons\u2019 tuning curves (\u03b1cw, \u03b1ccw): The slopes of velocity neurons\u2019 tuning curves determine how strongly they respond to movement. If the network detects that its position estimate is consistently off, adjusting these slopes would allow it to scale its velocity signals accordingly, effectively \u201cspeeding up\u201d or \u201cslowing down\u201d to better align with reality. This indicates a direct relationship between the PI gain and the slopes of velocity neurons. Indeed, such a relationship can be seen in Eq. (4) where the velocity neurons\u2019 absolute slope parameters \u03b1cw, \u03b1ccw act as a positive multiplicative factor on the value of the PI gain. Therefore, a change in these slope parameters leads to a commensurate change in the entire profile of the PI gain, thereby its spatial average k0 without introducing any inhomogeneities. Because this one-to-one positive relationship is similar to the ones studied in the previous section (despite the difference in inhomogeneities), we can again infer that satisfying the algorithmic sign condition (Eq. (7)) for the PI-gain recalibration is subject to the slope parameters \u03b1cw, \u03b1ccwvarying in the direction of the product of the animal\u2019s velocity (v) and the network\u2019s positional error (\\(\\tilde{\\theta }\\)), namely,\n\nThis requirement implies that, when the animal is moving in one direction (as was the case in the recalibration experiments33), the change in the slope parameters is monotonically related to the positional error, reflecting its value on a moment-to-moment basis with a sign additionally depending on the sign of the velocity. As the velocity signals are transmitted to the rotation rings via synaptic connections, gradual changes in their tuning slopes\u2014driven by instantaneous positional error\u2014accumulate over time, leading to cumulative adjustments in the firing activity of the rotation rings. Consequently, the mean firing rate of the rotation rings reflects the accumulated positional error over time, varying monotonically with the time-integral of the error (Fig.\u00a05C-1). Mathematical details are provided in Supplementary Note\u00a05.3.\n\nChanges in the persistent activity bump of the rotation rings (\\({\\hat{r}}_{{{{\\rm{cw}}}}}\\),\\({\\hat{r}}_{{{{\\rm{ccw}}}}}\\)): The rotation rings are responsible for providing the central ring with the movement signals from the velocity neurons, which in turn shifts the activity bump in tandem with the animal\u2019s movement. Intuitively, each rotation ring neuron acts like a pulley, transmitting the \u2018force\u2019 from a velocity neuron to its counterpart in the central ring, which eventually moves the activity bump. Hence, as more rotation ring neurons become active (i.e., wider rotation-ring bump \\({\\hat{r}}_{{{{\\rm{cw}}}}}\\), \\({\\hat{r}}_{{{{\\rm{ccw}}}}}\\)), they provide broader synaptic input to the central ring, effectively amplifying the \u2018pulling force\u2019 on the activity bump. This results in a greater movement speed of the bump overall, leading to a commensurate increase in the PI gain globally without introducing any spatial inhomogeneities. By analyzing Eq. (4) in Supplementary Note\u00a05.4, we indeed find a positive relationship between the widths of the rotation rings\u2019 activity bumps and the average PI gain k0. This positive relationship is similar to the previous case regarding the slope parameters \u03b1cw,\u03b1ccw. Thus, like in the previous case, satisfying Eq. (7) requires the rotation rings\u2019 activity widths to vary monotonically with the product of the animal\u2019s velocity and the positional error. Consequently, when the animal is traveling in one direction (say forward), the widths of the rotation rings\u2019 activity bumps must increase monotonically with the time-integral of the error (Fig.\u00a05C-2). All else being equal, this implies a similar monotonic increase in the average firing rate of rotation rings with the time-integral of the error. The direction of this monotonic relationship is reversed if the animal moves in the other direction. See Supplementary\u00a05.4 for mathematical details. Despite its similarity to the previous mechanisms in requiring a rate code of error, the present mechanism slightly differs in its capacity to recalibrate the PI gain. Unlike previous mechanisms, which in principle has no obvious limits on the range of values PI gain can be recalibrated to, the present mechanism, which involves changes to the rotation rings\u2019 bump widths, is inherently constrained. The maximum bump width is limited by the circular topology and its assumed relation with the central ring\u2019s bump width (Assumption 2 in SI).\n\nChanges in the persistent activity bump of the central ring (\\({\\hat{r}}_{{{{\\rm{c}}}}}\\)): Consider as an example that there are two networks with the same Gaussian bump profile but one has a higher peak firing rate. In this case, if all else is equal, the network with the higher firing requires higher velocity inputs to shift its activity bump from point A to point B at the same time as the other network (analogous to the greater force required to move a more massive object). This need for higher velocity inputs independent of the bump location indicates an inversely correlated relationship between the central ring\u2019s bump magnitude and the PI gain\u2019s value at all locations, thus its average k0. This relationship can also be verified from Eq. (4) wherein the denominator includes a term proportional to the bump magnitude, which itself is positively correlated with the central ring\u2019s mean firing rate. Thus, satisfying the algorithmic condition for PI-gain recalibration is subject to a mechanistic constraint that is similar to the previous case in spirit but slightly different due to the inverse effect: When the animal is traveling in the positive direction, the central ring\u2019s average firing rate must decrease monotonically with the time-integral of the error (Fig.\u00a05C-3). Change in the movement direction reverses the direction of this relation. See Supplementary Note\u00a05.5 for details.\n\nAs proved in the relevant\u00a0Supplementary Notes (referenced above), a ring attractor network with a hardwired velocity pathway and a fixed synaptic time constant \u03c4c and connection offset b cannot modify its PI gain k0 as required by the algorithmic sign condition in Eq. (7), if none of its rings encode the time-integral of the error as described above (i.e., if they all remain invariant relative to the time-integral of error). Given that the sign condition is necessary for PI-gain recalibration, this finding indicates that a rate code of the time-integral of the positional error is equally essential for PI-gain recalibration in a ring attractor network lacking plasticity in its velocity pathway, synaptic time constant, and connection offset. While our analysis does not determine the precise mechanisms for generating such a rate code or the specific temporal variations in related terms (e.g., the slopes of velocity neurons), our findings do not rule out the possibility that some form of plasticity elsewhere in the network may be necessary to achieve them. Regardless of the underlying mechanism, however, the PI gain is no longer encoded in the synaptic weights: instead, it is encoded within the firing rates that track the time-integral of the error, a proxy of the PI gain (Fig.\u00a05A). Taken together with our previous findings, we conclude that a rate code of the instantaneous positional error or its time integral is crucial to recalibrate the PI gain of a ring attractor network with a fixed synaptic time constant \u03c4c and connection offset b.\n\nIn this section, we propose a ring attractor network model to achieve PI-gain recalibration through a mechanism developed from our theoretical findings. Briefly, the model utilizes plasticity in the velocity pathway and its mechanistic prerequisite: a rate code for the instantaneous positional error. Like classical models, the proposed model also corrects accumulated PI errors based on feedback from landmarks. As described in the following subsections, we develop this model from the classical ring attractor network by introducing two specific modifications to its extrinsic connectivity. These modifications enable gain recalibration by inducing inhomogeneous synaptic weight changes that gradually fade as the PI gain approaches its target value. Additionally, we present a conceptual model in Supplementary Note\u00a06 that recalibrates its PI gain without relying on any synaptic plasticity. Instead, it adjusts the activity bump\u2019s magnitude based on the time-integral of the error, as illustrated in Fig.\u00a05C-3, that is encoded\u00a0by\u00a0a line attractor53,54,55. Unlike the detailed model described in the rest of the present section, this conceptual model applies gain adjustments globally, ensuring that the PI gain remains spatially homogeneous at all times. Together, these models demonstrate how explicit error coding at the level of individual neurons can support PI-gain recalibration and highlight the robustness of our theoretical results across different implementation approaches.\n\nWe begin by adding Hebbian plasticity into the velocity-to-rotation ring connections of the classical ring attractor network (M1 in Fig.\u00a06A). Based on our theoretical results, specifically Fig.\u00a05B-1, we know that recalibrating PI gain through this plasticity requires CCW and CW rotation rings to respectively increase and decrease their firing rates monotonically with the instantaneous positional error in the network\u2019s representation.\n\nA Schematic representation of the model. Solid and dashed lines denote hardwired and plastic connections, respectively. Arrow and circle terminals denote excitatory and inhibitory connections, respectively. The labels M1 and M2 correspond to the two modifications made to the classical model. B Illustration of how CCW rotation ring\u2019s firing rate (blue) varies monotonically with the positional error (\u03b8\u22c6\u00a0\u2212\u00a0\u03b8). Middle column: Zero error (\u03b8\u22c6\u00a0=\u00a0\u03b8). Although the visual ring\u2019s activity bump is aligned with that of the central ring (top row) in this error-free state, the CCW offset in the visual-to-CCW rotation ring connections introduces some misalignment between the inputs to the CCW rotation ring (middle row), resulting in moderate activity levels (bottom row). Left column: CW error (\u03b8\u22c6\u00a0\u2212\u00a0\u03b8\u00a0<\u00a00). In this case, the visual-ring bump associated with \u03b8\u22c6 is shifted CW relative to the central-ring bump associated with \u03b8 (top row). Because of the CCW offset in the visual-to-CCW rotation ring connections, the inhibition from the visual ring becomes more aligned with the excitation from the central ring at the level of synaptic inputs (middle row), thereby reducing the firing rate of the CCW rotation-ring bump (bottom row). Right column: CCW error (\u03b8\u22c6\u00a0\u2212\u00a0\u03b8\u00a0>\u00a00). Here, the visual-ring bump associated with \u03b8\u22c6 is shifted CCW relative to \u03b8 (top row), and the CCW offset in the visual-to-CCW rotation ring connections places inhibition further away from the CCW rotation ring\u2019s active neurons (middle row), thereby increasing the firing rate of the rotation-ring bump (bottom row). C Tuning curve depicting the relationship between the rotation rings' mean firing rate and the positional error for a given velocity. The color coding is the same as in panel A. D Illustration of how CW (left) and CCW (right) rotation rings' firing rates depend conjunctively on the animal\u2019s velocity and the positional error.\n\nIn the classical ring attractor, the visual drive\u2014a necessary component for computing these error codes\u2014is provided to the central ring via topographic excitatory connections. However, this setup is incompatible with the required properties of the error codes in two aspects: First, because the CCW and CW rotation rings derive their activity bump from the central ring via symmetric connections, the classical model\u2019s visual drive cannot change them in distinct directions (e.g., an increase in CCW accompanied by a decrease in CW with error), as required by the aforementioned error code. Second, because topographic connections within the classical model align the visual drive symmetrically with the attractor\u2019s activity bump in the absence of errors, positive and negative positional errors affect the attractor\u2019s average firing rate similarly (e.g., both error directions lead to an increase), failing to induce the required monotonic changes.\n\nTo overcome these limitations of the classical ring attractor, we remove the topographic connections between the visual and the central rings and introduce offset inhibitory connections from the visual ring onto the rotation rings (M2 in Fig.\u00a06A). Specifically, we connect the visual ring to the CCW rotation ring with a CCW offset and to the CW rotation ring with a CW offset. Note that these offset connections are distinct from the existing offset connections between the rotation and central rings. Unlike the existing rotation-to-central ring connections that implement PI, the newly added equal and opposite offset connections between the visual and rotation rings enable the CCW and CW rotation rings to increase and decrease their firing rates, respectively, as a monotonic function of the network\u2019s positional error (Fig.\u00a06B, C). However, in networks with a narrow visual ring bump, this monotonic relationship is maintained only within a limited range of positional errors: once the positional error exceeds a certain threshold (which increases with the visual ring\u2019s bump width), the inhibitory visual inputs escape the excitatory input from the central ring\u2019s activity bump, rendering the rotation rings\u2019 firing rates insensitive to further errors (Supplementary Fig.\u00a06A-B). Nevertheless, as we show next the system automatically corrects errors as they occur, preventing them from accumulating, thereby helping the rotation rings maintain their error code within the monotonic range.\n\nA well-known feature of classical ring attractor networks is their ability to correct PI errors based on the excitatory visual drive onto the central ring as we model in Section \u201cThe visual ring provides gain-dependent positional feedback that corrects path integration\u201d (remember the \u03b2 function). With the inhibitory offset connections onto the rotation rings replacing the original excitatory ones onto the central ring, an important question is whether our model retains its landmark correction capability. As shown in Fig.\u00a06C, this replacement results in differential modulation of the rotation rings\u2019 firing rates by the positional error. This modulation resembles how velocity neurons affect the rotation rings: when the animal moves, the firing rate of one rotation ring increases while the other decreases, thereby shifting the activity bump along the central ring.\n\nThus, by encoding the positional error in directionally distinct rate codes within the rotation rings, our model effectively converts positional error into a \u201cvirtual velocity signal\u201d which in turn shifts the activity bump along the central ring in a manner that reduces this error. We confirm this error correction mechanism in a numerical simulation of our model. Following an abruptly introduced positional error between the activity bumps of the central and visual ring, the differential changes in the rotation rings\u2019 firing rates successfully realign the central ring\u2019s activity bump with that of the visual ring, analogous to a \u201cvisual fix\u201d (Fig.\u00a07A). By continuously providing such a \u2018fix\u2019, this mechanism helps ensure that the system operates not only within the dynamic range of its error-rate code but also with minimal representational error. However, there are certain cases where this error correction mechanism may fail. For instance, if the positional error suddenly becomes too large\u2014exceeding the width of the visual-ring bump\u2014firing rates of CW and CCW rotation become nearly equal. In this case, they fail to generate an appropriate virtual velocity signal for realignment (Supplementary Fig.\u00a06C). Interestingly, this apparent limitation of our model resembles experimental data from the rodent head direction and place cell systems, where cue conflicts exceeding a threshold (typically reported to be 45\u00b0\u201390\u00b0) led to a failure in realignment of the activity bump27,56,57,58.\n\nSee Supplementary Note 7.3 for details.\u00a0A Correction of positional error by visual landmarks. The top panel illustrates the convergence of the central ring\u2019s bump location (green) toward that of the visual-ring bump (pink), while the bottom panel shows the mean firing rates of CW (red) and CCW (blue) rotation rings during this correction process. B Recalibration of the PI gain. The top panel shows the convergence of the average PI gain (green) toward the visual gain (pink), as the rat runs 100 laps according to the same velocity profile in Fig.\u00a05A. The bottom panel shows the positional errors (moving-averaged) driven by the discrepancy between the PI gain k0 and the visual gain k\u22c6 during this process. At steady-state, k0 slightly overshoots k\u22c6, leading to a small negative positional error, like Fig.\u00a04B. C Emergence of spatial inhomogeneity during PI-gain recalibration. The left panel shows the evolution of the normalized weights of the velocity-to-rotation ring connections, with four representative samples (solid lines) taken as the PI gain approaches steady state (opacity increases with time t\u00a0\u2265\u00a00). The dashed line shows the ideal weight profile under perfect recalibration with no spatial inhomogeneity (k(\u03b8)\u00a0=\u00a0k\u22c6\u00a0=\u00a01.4). The right panel quantifies the spatial inhomogeneity throughout the recalibration process with the coefficient of variation (CoV) across the circular neural space (black line; with moving average in red). At the start of the simulation, weights near the bump\u2019s initial location are upregulated locally, increasing inhomogeneity. As the bump moves, synaptic changes propagate to other regions, gradually reducing inhomogeneity. However, because the bump cycles through the neural space, this process repeats each lap, creating periodic fluctuations in spatial inhomogeneity (black line) at the same frequency as the bump\u2019s traversal. As recalibration progresses and the PI gain nears its target, the magnitude of these synaptic adjustments diminishes, leading to a gradual stabilization of inhomogeneity. D The relationship between the PI and visual gains (green line) in comparison to the perfect recalibration case (dashed pink line). Observe the subtle asymmetry in this simulated relationship. The simple algorithmic model previously simulated based on Eq. (9) as well as our analyses of the modified network model in Supplementary Note\u00a07.2 and Supplementary Fig.\u00a07 predict a similar asymmetry due to the asymmetric influence of the squared velocity term on the steady-state PI gain. Interestingly, similar asymmetries have been observed experimentally23,33,94.\n\nIn addition to correcting positional errors via error-rate codes within the rotation rings, our model is also capable of recalibrating its PI gain when these error-rate codes are paired with Hebbian plasticity in the velocity-to-rotation ring connections. Hebbian plasticity adjusts the synaptic weights based on the correlated activity of pre- and post-synaptic neurons. In the velocity-to-rotation ring connections, this adjustment occurs positively with the product of the animal\u2019s velocity v (encoded by both pre- and post-synaptic neurons) and the positional error \\(\\tilde{\\theta }\\) (encoded only by post-synaptic neurons), ensuring that the PI gain\u2019s spatial average k0, which depends linearly on these plastic weights, varies according to \\(\\tilde{\\theta }\\times v\\) as required by Eq. (7), the algorithmic condition for its recalibration.\n\nWhile this algorithmic condition establishes PI-gain recalibration, it is unclear whether the model can reach a complete recalibration, where the PI gain\u2019s spatial average k0 would converge exactly to the visual gain k\u22c6, or a partial recalibration, where k0 would converge to a value only biased towards k\u22c6. As illustrated by the algorithmic Examples 1 and 2 in Section \u201cControl theory reveals algorithmic conditions for PI-gain recalibration\u201d, complete PI-gain recalibration is achieved if no additional variable other than the product of the animal\u2019s velocity v and the network\u2019s positional error \\(\\tilde{\\theta }\\) influences the temporal change in the average PI gain k0. However, in our modified model, the pre-synaptic side of the velocity-to-rotation ring connections solely encodes the animal\u2019s velocity v, whereas the rotation rings on the post-synaptic side additively encode the positional error \\(\\tilde{\\theta }\\) along with v (Fig.\u00a06D). As a result, Hebbian plasticity modifies the synaptic weights not only by the product \\(\\tilde{\\theta }\\times v\\) but also by the squared velocity v2. As previously shown in Example 2 in Section \u201cAlgorithmic and mechanistic requirements for PI-gain recalibration\u201d, this additional v2 term implies that our model achieves only partial recalibration.\n\nHow close k0 gets to the visual gain k\u22c6 at the end of this partial recalibration depends on the relative contributions of v2 and \\(\\tilde{\\theta }\\times v\\)\u2014trade-off between the influence of error and velocity on the firing rates of the rotation rings. Specifically, as the positional error modulation of rotation rings\u2019 firing rates becomes more dominant relative to their velocity modulation, the influence of the \\(\\tilde{\\theta }\\times v\\) term increases relative to the v2 term, enhancing the extent of the recalibration. To achieve such dominance of the error modulation, we propose two minor refinements in Supplementary Note\u00a07.2 to the design of our modified ring attractor, based on computational and theoretical analyses: (i) using a sharper activity bump for the visual ring and (ii) incorporating weak inhibitory connections from the visual ring to the central ring. These minor refinements enhance gain recalibration without compromising error correction or path integration and are therefore employed in our simulation studies.\n\nIn simulation, we test the performance of PI-gain recalibration within our modified ring attractor network model for a simulated rat running on a circular track while visual landmarks were moved as per the visual gain k\u22c6. The simulation confirms our theoretical expectations, showing that the network\u2019s PI gain k0 partially recalibrates to the visual gain k\u22c6. During this recalibration, the PI gain inevitably and quickly becomes spatially inhomogeneous because of non-uniform weight changes from Hebbian plasticity across the neural space. As the animal continues to move under spatially uniform visual feedback from landmarks, however, these inhomogeneities within the PI gain gradually reduce, eventually reaching a minimal level at which point the PI gain closely approximates, though does not exactly match, the visual gain k\u22c6 at all locations in the neural space (Fig.\u00a07B, C). Therefore, our modified ring attractor is capable of learning and maintaining a reasonably well-tuned PI gain for a range of visual gains based on feedback from landmarks (Supplementary Fig.\u00a06D), unlike the classical ring attractor, which lacks any such recalibration behavior. That said, the recalibration may fail in our model if the discrepancy between the PI gain k0 and the visual gain k\u22c6 suddenly becomes very large, leading to positional errors \\(\\tilde{\\theta }\\) (recall from Fig.\u00a04B that positional error is correlated with the gain error) greater than the dynamic range of the error-encoding scheme within the rotation rings. In such cases, our model breaks free from landmark feedback, unable to recalibrate its PI gain (Supplementary Fig.\u00a06D). This simulated behavior aligns closely with the response of CA1 place cells under large gain changes as reported in ref. 33.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63817-0/MediaObjects/41467_2025_63817_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63817-0/MediaObjects/41467_2025_63817_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63817-0/MediaObjects/41467_2025_63817_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63817-0/MediaObjects/41467_2025_63817_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63817-0/MediaObjects/41467_2025_63817_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63817-0/MediaObjects/41467_2025_63817_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Fine-tuning the gain factor of a neural integration computation is crucial to maintain accurate representations of continuous variables since the relationship between the sensing of the relative change in a continuous variable and its actual value can fluctuate on both developmental (e.g., changes in body size59) and behavioral (e.g., changes in locomotion effort due to a change in locomotion surface60,61) time scales and even due to dynamic biological processes, such as circadian rhythms, that can alter synaptic transmission and intrinsic electrical properties of neurons62,63. Building upon previous behavioral work on perceptual plasticity of human locomotion30,64, experiments showed that a persistent conflict between self-motion and external visual cues recalibrates the integrator gain of hippocampal place cells, demonstrating the first physiological evidence for such fine-tuning33.\n\nIn the present paper, we investigated the algorithmic and mechanistic requirements for gain recalibration in a ring attractor network with a three-ring structure, a prevailing CBAN-type model for encoding circular continuous variables. In CBAN models, when the integration gain is inaccurate, an internal representation of a continuous variable slightly drifts relative to its actual value, resulting in encoding errors. When absolute \u2018ground-truth\u2019 information (e.g., feedback from visual landmarks in the present study) is present, the representational errors are corrected automatically through the CBAN\u2019s internal dynamics, without any need for an explicit code of the error at the level of single neurons. In contrast to this automatic error correction, our findings, conceptually summarized in Fig.\u00a08, provide strong theoretical evidence that fine-tuning the integration gain of a CBAN may critically depend on an explicit error signal encoded within the firing rates of individual neurons. Overall, our results suggest a new role for brain circuits hypothesized to form a CBAN and highlight a critical modification to prior CBAN models, which lack such an explicit error signal. Beyond gain recalibration, this type of explicit error signal could serve a more generalized function supporting novelty detection, assessing the reliability of absolute information from external cues, and building an internal sense of confidence in the accuracy of the encoded continuous variable\u2014potentially informing planning and decision-making in complex behavioral tasks.\n\nWe modeled the spatially tuned activity of hippocampal place cells on the circular track surrounded by landmarks (LM, green objects) as a classical ring attractor network. This classical model lacks an explicit error code and the ability to recalibrate its integration gain, referred to as path-integration (PI) gain in the context of spatial coding. To garner insight into the neural mechanisms that can achieve this recalibration, we first performed a dimensionality reduction in Section \u201cModel setup: ring attractor network\u201d. This reduction led to an analytical expression of the PI gain k along with a simple dynamical model of how the location \u03b8 of the network\u2019s activity bump is controlled by LM and PI. In contrast to previous work implicitly assuming that CBANs' integration gain is a global parameter, we found that a ring attractor network\u2019s integration gain is a spatially distributed parameter. Under certain conditions outlined in Section \u201cThe need for an explicit error code to meet the algorithmic conditions for PI-gain recalibration\u201d, this spatially distributed parameter can become inhomogeneous, varying as a function of the bump location. We then employed control theory techniques in Section \u201cControl theory reveals algorithmic conditions for PI-gain recalibration\u201d to dissect the algorithmic conditions for how the spatial average k0 of this distributed integration gain can be recalibrated to a target value k\u22c6 and how zero positional error can be achieved, together forming a 2D stable dynamical system as exemplified by the phase portrait. Mapping these conditions from the abstract, algorithmic level to the network level in Section \u201cThe need for an explicit error code to meet the algorithmic conditions for PI-gain recalibration\u201d, we found strong theoretical evidence that, under many conditions, PI-gain recalibration in a ring attractor network requires some neurons' firing rates to encode either the instantaneous positional error or its time integral. Finally, in Section \u201cImplementing PI-gain recalibration in a ring attractor\u201d, we propose a ring attractor network with modified extrinsic connectivity and Hebbian plasticity as an example CBAN model that can recalibrate its PI gain based on such an explicit error code.\n\nAlthough our findings have been obtained through a detailed analysis of a ring attractor, certain assumptions were made for mathematical tractability, introducing limitations to our study. First, we examined a ring attractor network with a three-ring topology8, a choice supported by strong experimental evidence18,19 and its adoption as the classical model for spatial representations in 1D and 2D36,44. Although our findings can be readily generalized to CBANs with the same topology in higher dimensions, they may not extend as readily to CBANs with different topologies, such as double-ring attractors12. Second, we used a dimensionality reduction technique to derive a simplified model of the ring attractor, based on which we identified the algorithmic conditions for PI-gain recalibration. This reduction provides an accurate approximation when external inputs to the attractor are relatively weak compared to its internal dynamics and when the activity bump is symmetric and lives on a continuum. If these assumptions fail, the accuracy of the reduced model degrades, in which case our findings may become less relevant to the network\u2019s actual dynamics. For instance, smaller networks, such as the putative attractor of the fly head direction system, may violate some of these assumptions. Third, we carried out local stability analysis to derive the necessary conditions for PI-gain recalibration. Although our analysis established a rate code of the attractor\u2019s positional error as such a condition, it did not identify the dynamic range of this encoding scheme because of its local nature, except that the dynamic range must encompass zero error. Fourth, our search for the mechanistic requirements for PI-gain recalibration was based on an exhaustive analysis of the relationship between the model parameters of a ring attractor and the value of its PI gain. In doing so, however, we excluded two parameters, namely the synaptic time constant and the value of the offset in the central-to-rotation ring connections, because of our model\u2019s inability to include details about how they may change, as explained in Section \u201cThe need for an explicit error code to meet the algorithmic conditions for PI-gain recalibration\u201d. Thus, while it remains an open question, PI-gain recalibration may be influenced by the plasticity of the excluded parameters, in which case our findings may not be applicable. We leave the investigation of these limitations as future work.\n\nTo identify algorithmic requirements for recalibration of the integration gain, we simplified the dynamics of the ring attractor through a dimensionality reduction protocol described in39,45,46. Similar approaches have recently been applied to explore the evolution of high-dimensional neural data within low-dimensional structures6,15,65. In our case, the dimensionality reduction yielded a simplified 1D model of the ring attractor, capturing its response to both differential inputs (e.g., velocity) and absolute positional feedback from landmarks45,46,66.\n\nPrevious studies demonstrated that when multiple cues are presented as inputs, the ring attractor network can, under certain conditions and mechanisms, approximate Bayes-optimal cue fusion22,67,68,69,70. If one of the cues provides only differential information, such as an animal\u2019s velocity, the network recursively integrates and fuses the information, performing a Kalman-like filtering process at each integration step45. In standard Kalman filtering, such integration depends on a fixed internal model that assumes stable parameters (see ref. 67 for a neural implementation of the standard Kalman filter). In contrast, our analysis suggests that a ring attractor with PI-gain recalibration acts as an adaptive Kalman filter, continuously tuning its integration gain over time. To achieve this adaptive tuning, our model follows a specific algorithmic rule: the integration gain must change in the same direction as the product of the animal\u2019s velocity and the model\u2019s positional representation error relative to the external reference. This rule aligns closely with principles from adaptive control in engineered systems, where similar multiplicative mechanisms, like those in the MIT rule71, guide parameter adaptation.\n\nOur theoretical analysis provided evidence that rate-based encoding of the instantaneous value or the time-integral of the representational error at the level of single neurons may play a critical role in the recalibration of the integration gain within CBAN models. Intuitively, without such explicit error-rate codes, the network does not have a teaching signal that can guide the tuning of its integration gain. This implies that, for CBAN models, learning the integration gain from errors can be a very different neural process than correcting the errors, which can occur automatically through network dynamics.\n\nThe hypothesized explicit error signal resembles reward and sensory prediction error signals within the mammalian brain. Midbrain dopamine neurons encode error in the internal predictions of reward via monotonic changes in their firing rates72,73; they elevate their activity with more reward than predicted, remain at baseline activity for fully predicted rewards, and exhibit depressed activity with less reward than predicted. Climbing fiber inputs to Purkinje cells of the cerebellum encode errors in the predicted sensory consequences of motor commands via changes in the rate and duration of complex spikes74,75. Both midbrain and cerebellar rate-based error codes are thought to act as teaching signals that recalibrate the internal models, just like how a rate-based error code can act as a teaching signal that recalibrates the integration gain of a CBAN.\n\nTo demonstrate the practical relevance of our theoretical findings, we implemented explicit error codes in two distinct ring attractor models. The first is a detailed model, for which we showed through systematic simulations that it can recalibrate its integration gain based on a rate code of the instantaneous error combined with Hebbian plasticity. The second is a conceptual model describing a potential recalibration mechanism based on the time-integral of error without any synaptic plasticity. These biologically plausible models, inspired by our theoretical findings, represent advances over prior work, where biologically implausible or unproven plasticity rules were used for gain recalibration37,38. Moreover, unlike the prior work, which offered limited mechanistic insights, our work makes a concrete experimental prediction: errors between representations in a CBAN and absolute teaching signals must be encoded by the firing rates of some neurons in the circuit. Testing for the presence of such error signals in brain circuits that are thought to form a CBAN remains future work.\n\nPrior CBAN models implicitly assumed the integration gain to be a single, global parameter of the network, independent of the value of the encoded continuous variable11,36. Although the idea of different, hard-wired integration gains has previously been suggested in the context of location coding to explain the changes in the spatial scale of place coding along the dorsal-ventral axis of the hippocampus29, it has largely been assumed that the integration gains are constant at all locations within an environment (but see ref. 45). In contrast, our analysis of a CBAN network showed that the integration gain is a spatially distributed parameter instantiated in the network\u2019s array of synaptic weights. If plasticity occurs in the synapses between the differential (e.g., velocity) inputs and the attractor during recalibration, this distributed gain may temporarily become inhomogeneous, taking on different values across different locations in the neural space (corresponding to different values of the encoded continuous variable). This transient inhomogeneity, which is contingent upon local synaptic plasticity in the pathway between differential inputs and the attractor, is a strong experimental prediction of our study. Under this prediction, we expect some regions of the hippocampal map to compress while others stretching, subtly warping how distances are represented across the neural manifold throughout the recalibration process (similar distance warping in place cells can be caused by the presence of texture boundaries on the local surface76).\n\nTheoretically, spatial inhomogeneity may be a stable state of the system if the teaching signal (e.g., feedback from absolute \u2018ground-truth\u2019 information sources) is available nonuniformly across the values of the continuous variable (unlike the case we studied in Fig.\u00a07B, C). As a result, a CBAN becomes capable of adjusting its representation metric locally (as in Fig.\u00a03B), which promises flexibility in representing different values of the continuous variable with uneven resolutions, depending on, for instance, their behavioral significance77. This representational flexibility, driven by the attainment of the inhomogeneous integration gain as a stable state, may offer a mechanistic explanation for some experimental findings from spatial navigation and decision-making literature. In the context of spatial navigation, a CBAN can employ inhomogeneous integration gains to \u201coverrepresent\u201d certain locations, for instance nearby rewards or boundaries, as is seen in recordings from hippocampus and entorhinal cortex78,79,80,81,82,83,84,85,86,87. In the context of decision-making, a CBAN with such inhomogeneities can accumulate early or late evidence unevenly, reproducing the so-called primacy and recency effects in the decision-making literature88,89,90. Complementing the mechanisms proposed in prior work4,45, our finding regarding the inhomogeneous integration gain of CBANs offers an alternative explanation to an array of seemingly complex responses in spatial navigation as well as other brain functions.\n\nIn this manuscript, we investigated whether a CBAN can automatically recalibrate its integration gain without relying on an explicit error code-just as it does for error correction (Fig.\u00a01). Our findings provide strong theoretical evidence that, unlike error correction, which emerges spontaneously from network dynamics, recalibration likely requires an explicit rate code of error at the level of individual neurons. This distinction highlights a fundamental difference between these two processes\u2014error correction vs. integration gain recalibration\u2014and underscores the general importance of explicit error coding in adaptive neural computation.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63817-0/MediaObjects/41467_2025_63817_Fig8_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Detailed derivations, proofs, and implementation details are provided in the\u00a0Supplementary Information materials, summarized below.\n\nSupplementary Notes\u00a01 to 3 cover derivation of the reduced ring-attractor models introduced in Section \u201cModel setup: ring attractor network.\n\nSupplementary Note\u00a04 derives the general PI-gain recalibration rule and proves the necessary and sufficient conditions for its stability (Section \u201cControl theory reveals algorithmic conditions for PI-gain recalibration\u201d).\n\nSupplementary Note\u00a05 proves the neural-mechanism requirements discussed in Section \u201cThe need for an explicit error code to meet the algorithmic conditions for PI-gain recalibration\u201d.\n\nSupplementary Note\u00a06 introduces the conceptual ring-attractor model referred in Section \u201cImplementing PI-gain recalibration in a ring attractor\u201d.\n\nSupplementary Note\u00a07.2 details the design of the modified ring-attractor model analyzed in Section \u201cImplementing PI-gain recalibration in a ring attractor\u201d.\n\nSupplementary Note\u00a07.3 describes the simulation procedures and parameter values.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data that support the findings of this study are publicly available at https://github.com/LIMBSlab/secer2025_expliciterror.git.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All code that support the findings of this study are publicly available at https://github.com/LIMBSlab/secer2025_expliciterror.git.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Ben-Yishai, R., Bar-Or, R. 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We thank Ravikrishnan Jayakumar, Kathryn Hedrick, Bharath Krishnan, Manu Madhav, Francesco Savelli, and Kechen Zhang for their helpful comments during the development of this work.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors jointly supervised this work: James J. Knierim, Noah J. Cowan.\n\nLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA\n\nGorkem Secer\u00a0&\u00a0Noah J. Cowan\n\nZanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA\n\nGorkem Secer\u00a0&\u00a0James J. Knierim\n\nKavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA\n\nGorkem Secer,\u00a0James J. Knierim\u00a0&\u00a0Noah J. Cowan\n\nSolomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA\n\nJames J. Knierim\n\nDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA\n\nNoah J. Cowan\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nG.S., J.J.K., and N.J.C. conceptualized the study. G.S. and N.J.C. performed the theoretical and computational analyses. J.J.K. provided critical feedback. G.S., J.J.K., and N.J.C. wrote the paper.\n\nCorrespondence to\n Gorkem Secer or Noah J. Cowan.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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the weak exchange coupled van der Waals antiferromagnet", + "journal": "Nature Communications", + "published": "28 March 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58306-3/MediaObjects/41467_2025_58306_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58306-3/MediaObjects/41467_2025_58306_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.6084/m9.figshare.28557785" + ], + "code": [], + "subject": [ + "Spintronics" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5308219/v1.pdf?c=1743246332000", + "research_square_link": "https://www.researchsquare.com//article/rs-5308219/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58306-3.pdf", + "preprint_posted": "27 Oct, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Spin Seebeck effect (SSE) refers to the creation of spin currents due to a temperature gradient in the magnetic materials or across magnet-normal metal interfaces, which can be electrically detected through the inverse spin Hall effect (ISHE) when in contact with heavy metals. It offers fundamental insights into the magnetic properties of materials, including the magnetic phase transition, static magnetic order, and magnon excitations. However, the SSE in van der Waals antiferromagnet is still elusive, especially across the spin-flip transition. Here, we demonstrate the SSE in the weak exchange coupled van der Waals antiferromagnet CrPS4. The SSE increases as the magnetic field increases before the spin-flip transition due to the enhancement of the thermal spin current as a function of the applied field. A peak of SSE is observed at the spin-flip field, which is related to the magnon mode edges across the spin-flip field. Our results extend SSE research to van der Waals antiferromagnets and demonstrate an enhancement of SSE at the spin-flip transition.Physical sciences/Physics/Condensed-matter physics/SpintronicsPhysical sciences/Materials science/Condensed-matter physics/Spintronics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupportingInformation.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Spin Seebeck effect refers to the creation of spin currents due to a temperature gradient in the magnetic materials or across magnet-normal metal interfaces, which can be electrically detected through the inverse spin Hall effect when in contact with heavy metals. It offers fundamental insights into the magnetic properties of materials, including the magnetic phase transition, static magnetic order, and magnon excitations. The behavior of the spin Seebeck effect across the spin-flop transition has been extensively studied, whereas the spin Seebeck effect across the spin-flip transition remains poorly understood. Here, we demonstrate the spin Seebeck effect in a weakly exchange-coupled van der Waals antiferromagnet CrPS4. The spin Seebeck effect increases as the magnetic field increases before the spin-flip transition due to the enhancement of the thermal spin current as a function of the applied field. A peak of spin Seebeck effect is observed at the spin-flip field, which is related to the magnon mode edges across the spin-flip field. Our results extend spin Seebeck effect research to van der Waals antiferromagnets and demonstrate an enhancement of spin Seebeck effect at the spin-flip transition.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Thermoelectricity combines heat transfer and electric voltage in solid materials, presenting a promising option for green energy production by harnessing waste heat with a simple device design1. In particular, the thermal spintronics effect utilizes nonequilibrium magnon transport phenomena in the presence of a heat gradient, enabling magnetic insulators to serve as effective thermoelectric devices2. The spin Seebeck effect (SSE) has therefore drawn significant interest, where a temperature gradient (\u2207T) in magnetic materials leads to the generation of spin currents (Js). SSE can be subsequently detected via the inverse spin Hall effect (ISHE) in a heavy metal contact with strong spin-orbit coupling3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24.\n\nIn ferromagnet/heavy metal bilayers, the SSE observed below the Curie temperature is associated with the spin current generated by thermally excited magnons that exhibit only right-handed chirality19. The SSE mechanism in antiferromagnetic heterostructures is more complex due to two magnetic sublattices, which result in different magnon modes20,21,22,23. In a uniaxial antiferromagnet, there are two magnon branches with opposite chirality carrying opposite angular momentum. These modes degenerate at zero magnetic fields, meaning there is no net magnon current until a field is applied to lift this degeneracy. A change in the sign of the SSE was observed during the spin-flop transition14,15, which is attributed to the change in the chirality of the thermally excited magnon mode, which dominates. Additionally, the interfacial N\u00e9el coupling and spin conductance can influence the sign and magnitude of the SSE21,23. Recent studies have explored magnon transport and interfacial spin transport in MnPS325, as well as spin caloritronics in CrBr326, CrI3/NiCl227. More recent work has also investigated magnon transport in CrPS428,29,30. Especially, in Ref. 29, in addition to discovering nonlocal magnon transport of CrPS4/Pt with in-plane magnetic field across the spin-flip transition, a distinct peak of SSE signal around the spin-flip transition and a sign change of non-local SSE signal below 15\u2009K were observed but the mechanism remains to be understood29. Although the SSE is not the main focus of Ref. 29, it is nonetheless a pioneering study on the magnetic field dependence (especially spin flip) of this effect in CrPS4. These latest results indicate that the spin Seebeck effect in van der Waals antiferromagnets, especially across the spin-flip transition, remains an area requiring further investigation31,32. This is particularly relevant for van der Waals systems with interlayer antiferromagnetic coupling, where the weak exchange coupling and low spin-flip fields are typically observed.\n\nCrPS4 is an antiferromagnetic van der Waals material constituted of chains of chromium octahedra interconnected through phosphorus33,34,35,36,37,38,39 as shown in Fig.\u00a01a. Due to the chemical composition and multi-bonded crystal structures, CrPS4 is a comparably air-stable material that makes the device fabrication easier compared with other van der Waals materials40. It shows a sizeable N\u00e9el temperature (TN=36 K) and A-type antiferromagnetic ordering33. Unlike the conventional bulk antiferromagnetic materials, CrPS4 with a layered structure exhibits extremely weak interlayer interactions between sublattice spins, where spins within each monolayer are aligned ferromagnetically out of the plane, subsequently leading to the weak spin-flop field (0.8\u2009T at 15\u2009K) and spin-flip field (7\u2009T at 15\u2009K) as shown in Fig.\u00a01b. This characteristic also significantly lowers the frequency of antiferromagnetic magnons to the GHz range41. As a result, it provides easier access to antiferromagnetic dynamics. Notably, it improves the efficiency of the thermal magnon population compared to traditional antiferromagnets with a large magnon gap, making CrPS4 an excellent candidate for investigating the mechanism of SSE in antiferromagnets.\n\na Crystal structure of CrPS4. The red and blue arrows indicate the direction of the magnetic moment. b The magnetic measurements at 15\u2009K are taken both along and perpendicular to the c-axis. The spin-flop and spin-flip transitions appear when the magnetic field is aligned with the c-axis. In contrast, only the spin-flip transition occurs when the field is applied perpendicularly to the c-axis, c Schematic of the Hall bar devices for the longitudinal spin Seebeck effect. The alternating current heats the sample, creating a vertical heat gradient and generating a spin current perpendicular to the sample plane. d Angular dependence (in the xz plane) of Rxy2\u03c9 at different fields at a temperature of 15\u2009K and an applied current of 1\u2009mA (peak value). e Applied current dependence of Rxy2\u03c9 (at 9\u2009T) at 15\u2009K. The dashed-dot line is the linear fit.\n\nHere, we investigate the SSE in CrPS4 in contact with a heavy metal. A vertical temperature gradient in CrPS4 drives the magnon current in the longitudinal SSE configuration. The SSE increases as a function of the applied field before the spin-flip transition. The enhancement of the canted magnetization leads to a pronounced magnon pumping. At the spin-flip field, a peak of SSE is observed that further disappears above the N\u00e9el temperature. A theoretical model indicates that the linear increase of SSE with increasing magnetic field below the spin-flip transition is dominated by the spin canting angle, while the decrease of SSE with further increasing magnetic field above the spin-flip transition is dominated by increased energy of magnon modes.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58306-3/MediaObjects/41467_2025_58306_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "High-quality CrPS4 single crystals are used to fabricate the devices, with magnetic properties shown in Supplementary Fig.\u00a0S1. To obtain CrPS4/Pt heterostructures for the SSE measurements, we deposited 5\u2009nm Pt on top of exfoliated CrPS4 flakes and subsequently fabricated Hall bar devices (see methods for details and schematic in Fig.\u00a01c). The structure and phase of the CrPS4 are characterized with X-ray Diffractometer and Raman spectroscopy (details see Supplementary Fig.\u00a0S2). The microscopic picture of the CrPS4/Pt Hall bar device can be found in Supplementary Fig.\u00a0S3, where one could obtain the thickness of the CrPS4 flake to be 75\u2009nm. The high-quality interface of the CrPS4/Pt heterostructure has been clearly demonstrated through cross-sectional transmission electron microscopy, as shown in Supplementary Fig.\u00a0S4. An alternating current (I~) is applied to the Hall bar to generate vertical temperature gradient \u2207T, leading to the population of spin current Js=\u2212S\u2207T. S is the SSE coefficient. By further applying a magnetic field, it is possible to observe the SSE detected via the inverse spin Hall effect. The resultant electric field EISHE is given by3\n\nWhere \u03b8SH is the spin Hall angle.\u03c3 is the spin polarization direction, which is parallel to the equilibrium magnetization M. Since the temperature gradient results from the heating power of Pt, which is proportional to I~2=I0sin2(\u03c9t), it is expected that the thermal signal can be detected through the second harmonic response Rxy2\u03c9=Vxy2\u03c9/I0.\n\nIn the magnetic material/Pt bilayer system, Rxy2\u03c9 typically involves different factors, including current-induced torque and thermal effects, which encompass the Nernst and spin Seebeck effects42. The electric field induced by the Nernst effect can be expressed as ENE\u221d\u2207T\u00d7M43, which shares the same symmetry as SSE in the longitudinal configuration. When a strong magnetic field is applied, the current-induced torque is suppressed42, leaving only thermal effects in the second harmonic response Rxy2\u03c9. Figure\u00a01d illustrates the angular dependence of Rxy2\u03c9 in the xz plane under different applied fields with the applied current of 1\u2009mA (peak value) and the ambient temperature (chamber temperature) of 15\u2009K. Rxy2\u03c9 reaches the maximum when the magnetic field is aligned with the x-axis and disappears when aligned with the z-axis (or c-axis), and the angular dependence data can be fitted well using the sine function following the Eq. (1). By applying an in-plane magnetic field, the Zeeman splitting lifts the degeneracy of the two magnon eigenmodes, resulting in the spin current that induces the SSE signal. In the canted phase, the SEE increases with the strength of the applied magnetic field, similar to the local SSE signal in Ref. 29. This increase is generally attributed to the larger canted magnetization resulting from a strong magnetic field23,44 or the increased SSE coefficient in response to the magnetic field45. This fundamentally differs from the SSE in ferromagnets, where an increased applied field would open the magnon gap, causing a decrease in SSE due to the reduction of the thermal magnon population4. Additionally, the magnitude of Rxy2\u03c9 is proportional to the applied current as shown in Fig.\u00a01e, demonstrating a thermoelectric nature similar to previous findings46. It is important to note that CrPS4 has a semiconducting characteristic with an energy gap of Ea=1.4eV33, which yields a very high resistivity and prevents the electrical conduction through CrPS4 at low temperature28, allowing us to safely rule out the Nernst effect from conducting electrons in CrPS4.\n\nTo better distinguish the SSE from other spurious effects, we utilize Pt and Ta in the two Hall bar devices (Fig.\u00a02a and b). Due to the opposite spin Hall angles47, the thermally generated spin current should yield SSE signals with opposite polarities in Pt and Ta samples. In contrast, other magnetic thermoelectric effects, such as the Nernst effect arising from the proximity effect48, retain the same polarity in both Pt and Ta. As illustrated in Fig.\u00a02c and d, the Rxy2\u03c9 shows the opposite polarities in Pt and Ta samples, suggesting that the phenomenon originates from the SSE. As the temperature increases, the strength of the SSE decreases, and the SSE remains present even at temperatures exceeding the TN of CrPS4. A more apparent trend is illustrated in Fig.\u00a02e. Although the propagation of spin waves without magnetic interactions is not permitted in the paramagnetic phase, short-range magnetic interactions still facilitate short-wavelength magnetic excitations, resulting in the paramagnetic SSE16. In addition to the increase in Rxy2\u03c9 with the applied field, peaks of Rxy2\u03c9 are observed in both samples at varying temperatures. Similar effects are observed in the sample with a different Pt thickness (see Supplementary Fig.\u00a0S5 for details) and also seen in local and non-local SSE signals in Ref. 29, which confirm the robustness of this effect. The magnetic field at which the Rxy2\u03c9 peak occurs aligns with the spin-flip field of CrPS4, as illustrated in Fig.\u00a02f, suggesting a strong connection between the Rxy2\u03c9 peak and the magnetic phase transition induced by the magnetic field. We note that the proximity effect could potentially lead to magnetization of the Pt layer, which might contribute to the Nernst effect. However, since the peak in Rxy2\u03c9 disappears above TN, while the proximity effect would still be present, we conclude that the proximity effect does not contribute to the observed peak in Rxy2\u03c9. The longitudinal resistances for CrPS4/Pt and CrPS4/Ta are ~ 600 \u03a9 and 1560\u03a9 respectively, with applied currents of 1\u2009mA and 0.6\u2009mA for the two samples. This results in a higher heating power in CrPS4/Pt, causing a larger temperature difference between the sample and the variable temperature insert (VTI) chamber. There is expected to be a shift in the spin-flip field for the samples with and without heating at the same VTI chamber temperatures, and this discrepancy will become more pronounced at lower temperatures (see Fig.\u00a02f and also Supplementary Fig.\u00a0S5 for the current dependence of SSE). A numerical simulation of the temperature distribution in the device can be seen in Supplementary Fig.\u00a0S6. The simulation shows that a temperature change of about 4\u2009K is induced in CrPS4 via Joule heating under the experimental conditions, in good agreement with the observed change in the spin-flip field. The simulation also shows a prominent temperature gradient in the CrPS4 layer (~4.2\u2009\u00d7\u2009106\u2009K/m), which is the basis for the observed SSE.\n\na, b The schematics of spin Seebeck effect in CrPS4 in contact with Pt and Ta, the differing signs of the spin Hall angle result in a change in the sign of the SSE. c, d Field dependence (\u03bc0Hx) of Rxy2\u03c9 at various temperatures for both CrPS4/Pt (5\u2009nm) (applied current of 1\u2009mA) and CrPS4/Ta (11\u2009nm) (applied current of 0.6\u2009mA). e Temperature dependence of the SSE effective resistance in CrPS4/Pt at 9\u2009T, along with the magnetization as a function of temperature under a 50 mT applied field. The N\u00e9el temperature (TN) is identified as 36\u2009K, however, the SSE signal continues to be present even above TN. f The field of the Rxy2\u03c9 peak decreases with increasing temperature (blue star and red square), which is similar to the temperature dependence of the spin-flip transition field (black circle).\n\nThe peak of Rxy2\u03c9 observed at the spin-flip field is intriguing, as it is not associated with the static magnetic moment, which would not exhibit an increased canted magnetization during the spin-flip transition, as illustrated in Fig.\u00a03a. In Fig.\u00a03b, the angular dependence (in the xz plane) of Rxy2\u03c9 is shown as the applied field approaches the spin-flip transition at a temperature of 15\u2009K and an applied current of 1\u2009mA in the CrPS4/Pt. The curves can be well-fitted with the sine function according to Eq.(1), with a maximum observed at 6.8\u2009T, indicating that the peak originates from the SSE. Additionally, the SSE continues to be present above TN, while the peak of Rxy2\u03c9 disappears beyond TN (see Fig.\u00a02c, d). Although the paramagnetic phase could exhibit a SSE, the loss of long-range ordering above the TN causes the spin-flip transition to disappear. This highlights the significant connection between the peak of SSE and the spin-flip transition.\n\na Comparison of the field dependence of Rxy2\u03c9 in CrPS4/Pt (obtained at 15\u2009K) and magnetic moment CrPS4 flake (measured at 20\u2009K). b Angular dependence (in the xz plane) of Rxy2\u03c9 when the applied field approaches the spin-flip field at a temperature of 15\u2009K and an applied current of 1\u2009mA. c Magnon mode edges (k\u2009=\u20090) as a function of the applied field perpendicular to the c axis. The inset shows the simulated magnetic moment as a function of the magnetic field. d The canted magnetization of \u03c9\u03b1 mode processes around the applied field, while that of \u03c9\u03b2 mode oscillates in the direction of the applied field.\n\nThe SSE signal involves the following three physical processes: first, the temperature gradient excites the magnetization dynamics, leading to a non-equilibrium magnon current; second, the magnon current is transformed into a conduction-electron spin current through the s-d interaction, which travels across the interface connected to the metal; finally, the spin current is converted into a charge current via the ISHE. We highlight the efficient spin transport at the interface of CrPS4 and sputtered Pt, as recent studies on CrPS4/Pt have demonstrated effective magnon transport28,29,30,49. Notably, detecting the spin current is not crucial for the SSE peak, as both the CrPS4/Pt and CrPS4/Ta samples exhibit peaks (see Fig.\u00a02c, d). The only remaining likely mechanism for the SSE peak is related to the pumped spin current Js from the antiferromagnet into heavy metals which includes the effect of both thermal magnon excitation and interfacial spin mixing conductance49.\n\nConsidering the canted magnetic phase, the magnetic field dependence of magnon frequency can be obtained by diagonalizing the spin Hamiltonian50 with eigenfrequencies51. Before the spin-flip field, \u03bc0H\u22642\u03bc0HE+\u03bc0HA,\n\nAfter the spin-flip field, \u03bc0H>2\u03bc0HE+\u03bc0HA,\n\nwhere \u03bc0H, \u03bc0HE, and \u03bc0HA represent the applied in-plane field, interlayer exchange field, and anisotropic field along the c-axis, respectively. The simplified model only considers the anisotropic field along the c-axis. \u03c9\u03b1 and \u03c9\u03b2 are the two magnon modes. \u03b3 is the gyromagnetic ratio and \u03c6 is the canted angle along the c-axis applied in the plane field, \u03c6=arcsin\u2061\u03bc0H2\u03bc0HE+\u03bc0HA.\n\nThe field dependence of the magnon mode frequency is plotted in Fig.\u00a03c with parameters \u03bc0HE=3.5T and \u03bc0HA=0.12 T41. The \u03c9\u03b1 mode has the potential to transport angular momentum due to the canted magnetization of the mode rotating around the applied magnetic field. This mode is similar to the quasi-ferromagnetic mode that emerges following a spin-flop transition when a magnetic field is applied along the c-axis14. Moreover, the SSE in CrPS4/Pt has the same sign as that in YIG/Pt (see Supplementary Fig.\u00a0S7 for details), suggesting that right-handed magnons (\u03c9\u03b1 mode) are responsible for the SSE signal. In contrast, the \u03c9\u03b2 mode oscillates in the direction of the applied field (see Fig.\u00a03d).\n\nWe further calculate the spin current in the heavy metal following Ref. 23 using a minimal model where the CrPS4 sample is modeled as a one-dimensional antiferromagnetic chain with periodic boundary conditions. The model has an interfacial s-d coupling that couples the localized spins in the antiferromagnet with the itinerant electrons in the heavy metal. Using Fermi\u2019s Golden rule to calculate the transition probability for the spins to be pumped from the antiferromagnet into the heavy metal, the thermal spin current density polarized along the x-axis in the heavy metal is given by Tang & Bauer23\n\nwhere \u039b is a constant depending on the interface and the density of states for the electrons in the heavy metal, \u0394T is the temperature difference across the interface, k is the wave vector of the one-dimensional chain, and \u0394 parametrizes the degree of compensation at the interface; \u0394=0 corresponds to a compensated interface and \u0394=\u00b11 corresponds to a fully uncompensated interface where only one of the two sublattices couple to the heavy metal. The \u03c9\u03b2 mode only contributes to the spin current for an uncompensated interface, reflecting the linearly polarized nature of the mode (see Supplementary Fig.\u00a0S8 for the calculation of the spin current as a function of the applied field).\n\nThe effect of the in-plane magnetic field on the pumped spin current in the heavy metal is twofold: first, the magnetic field increases the canting angle \u03c6, causing a linear increase of the factor sin\u2061\u03c6 in Eq. (6). Physically, this can be interpreted by noting that each of the two sublattices pumps a spin current that on average is polarized along the sublattice equilibrium direction, thus, the measured spin current is given as the projection on the x-axis, which is proportional to sin\u2061\u03c6. Second, the magnetic field changes the magnon frequencies of both magnon modes. Above the spin-flip critical field, the energy of the \u03c9\u03b1 mode and the \u03c9\u03b2 mode increases with the in-plane field. This causes a decrease in the terms inside the sum in the above equation. Importantly, the increase due to the change in canting angle is proportional to sin\u2061\u03c6\u223cH below the critical field and constant above the critical field since the canting angle has reached its maximum at this point. In total, these two effects explain the observed peaks and saturation in SSE of CrPS4/Pt at the spin-flip field.\n\nThe gap closure of the \u03c9\u03b2 mode frequencies at the critical field could further increase the peak observed in the spin Seebeck effect at the critical field for systems with an uncompensated interface. However, to probe the low-frequency excitations, the temperature needs to be smaller than or comparable to the gap energy, which for CrPS4 is 0.4K in units of temperature. Therefore, a sharper peak is expected for temperatures approaching this value (see Supplementary Fig.\u00a0S8 for details).\n\nThe nonlocal configuration is further introduced to explore the SSE in CrPS4/Pt as shown in Fig.\u00a04a (see method and Supplementary Fig.\u00a0S3 for details). An in-plane heat gradient is created by passing current through one of the Pt strips, resulting in a nonequilibrium distribution of magnons. At the detection part, the magnon spin current is injected into Pt, which leads to the SSE. It is worth noting, in this configuration, that the temperature gradient \u2207T is oriented along the x-axis, while the spin current Js flows along the z-axis, differing from the longitudinal SSE previously discussed. Figure\u00a04b shows the field dependence of SSE at different angles (\u03b8) at 5\u2009K with the applied current of 1\u2009mA. By applying the in-plane field (\u03b8=0\u2218), the SSE as a function of the applied field is similar to the longitudinal configuration, and a peak of SSE is also observed at the spin-flip field.\n\na Schematics of nonlocal SSE measurement. b Field dependence of SSE at different angles at 5\u2009K with the applied current of 1\u2009mA. Inset shows the field dependence SSE when the applied field is slightly off the c-axis (z-axis).\n\nA weak SSE response occurs when the applied field is close to the z-axis, with nominal angles of \u03b8\u2009=\u200992\u00b0 and 87\u00b0. Typically, the SSE should not be present when the field is directed along the z-axis, as the parallel alignment of spin polarization \u03c3 and spin currents Js does not generate a SSE voltage. However, a slight deviation from the z-axis in the direction of the applied field results in a finite value of Js\u00d7\u03c3, since the spin polarization aligns with the canted magnetization. This accounts for the observed positive and negative SSE at strong positive fields when \u03b8\u2009=\u200987\u00b0 and 92\u00b0, respectively. The plateau in the SSE is observed before the spin-flop transition, as there is no x-component of the canted magnetization. In particular, one could also find a peak of SSE at the spin-flop field, which is attributed to the divergence of spin conductance as the magnon gap closes approaching the spin-flop transition49. Similar effects are also observed in the longitudinal SSE configuration (see Supplementary Fig.\u00a0S5 for details).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58306-3/MediaObjects/41467_2025_58306_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58306-3/MediaObjects/41467_2025_58306_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58306-3/MediaObjects/41467_2025_58306_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We report evidence of the SSE in a weakly interlayer exchange-coupled van der Waals antiferromagnet CrPS4 in contact with the heavy metal. We showed how the SSE is substantially enhanced by tuning the magnetic field. In particular, we observe a peak of SSE which shares the same temperature dependence as the spin-flip transition of CrPS4 when applying magnetic field perpendicular to the c-axis. By considering the thermal spin current density into the heavy metal, we conclude that the SSE peak is related to the magnon mode edges as a function of the applied field across the spin-flip field.\n\nField-induced peaks in SSE were also observed in Y3Fe5O12/Pt52, Lu2BiFe4GaO12/Pt53, Fe3O4/Pt54, and Cr2O3/Pt55 bilayers. These peaks in SSE arise when the magnetic field adjusts the magnon energy to the point of anticrossing between the magnon and phonon dispersion curves, creating magnon-polarons52. The combined magnetoelastic excitation couples the long-lasting acoustic phonons in single crystals with the short-lived magnons, increasing the magnon lifetime and the associated SSE53. The SSE peak in CrPS4/Pt (Ta) exhibits similar field-like behaviors, but it arises from a mechanism involving the magnon mode and spin conductance. Given that the SSE peak in CrPS4/Pt (Ta) is observed at low temperatures where the phonon population is frozen, we do not expect the magnon-polarons to dominate the signal our samples.\n\nThe SSE is a sensitive tool for investigating the interfacial spin conductance and magnon population across various materials. Our findings indicate that the magnon spin transport in CrSP4/Pt(Ta) can be effectively modulated through adjustments in temperature and applied magnetic field, particularly at the spin-flip field. This approach paves the way for innovative magnonic devices that utilize weakly exchange-coupled van der Waals antiferromagnetic materials.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "CrPS4\u00a0single crystals are synthesized\u00a0using\u00a0chemical vapor transport technique. Chromium (Aladdin,99.99%), red phosphorus (Aladdin,99.999%), and sulfur (Aladdin,99.999%) powders were measured in a stoichiometric ratio of 1:1:4 and combined with 5% more sulfur as transport agents. The mixed powders were sealed in a quartz tube and placed in a two-zone furnace, where the temperatures at the source and sink ends were maintained at 923\u2009K and 823\u2009K for a duration of 7 days. The atomic structure was analyzed using X-ray diffraction (XRD) with Cu K\u03b1 radiation (\u03bb\u2009=\u20091.54056\u2009\u00c5). The magnetic properties were measured using a Superconducting Quantum Interference Device (SQUID). The CrPS4 flakes were mechanically exfoliated from the single crystals using adhesive tape and transferred onto a SiO2/Si substrate. CrPS4/Pt(Ta) samples were prepared with the magnetron sputtering in a vacuum of approximately 6\u2009\u00d7\u200910\u22128 torr. The thickness of the Pt layer is 5\u2009nm, while the Ta layer is 15\u2009nm; 5\u2009nm of Ta will oxidize in air, leaving 10\u2009nm of Ta to facilitate the inverse spin Hall effect for detecting spin current generation. The Hall bar with 10\u2009\u03bcm in width and 25\u2009\u03bcm in length was fabricated using photolithography followed by ion beam etching. The width of the heater and the detection Pt strips are designed to be 1.4\u2009\u03bcm and 2.3\u2009\u03bcm, the distance of the two stipes is 1.6\u2009\u03bcm in the nonlocal device. An atomic force microscopy image of the samples is provided in Supplementary Information Fig. S3, showing the thickness of the CrPS4 flake in the Hall bar device\u00a0to be 75\u2009nm.\n\nThe SSE is measured at different temperatures by varying the magnetic field in the Physical Properties Measurement System (PPMS-9T). 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M.K. acknowledges support by the German Research Foundation (CRC TRR 288\u2014422213477 Project A12 and CRC TRR 173\u2014268565370 Projects A01 and B02). A.B. acknowledges the Research Council of Norway through its Center of Excellence 262633 \u201cQuSpin\u201d.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Spin-X Institute, School of Physics and Optoelectronics, State Key Laboratory of Luminescent Materials and Devices, and Guangdong-Hong Kong-Macao Joint Laboratory of Optoelectronic and Magnetic Functional Materials, South China University of Technology, Guangzhou, China\n\nXue He,\u00a0Jicheng Wang\u00a0&\u00a0Rui Wu\n\nDepartment of Materials, ETH Z\u00fcrich, Z\u00fcrich, Switzerland\n\nShilei Ding\u00a0&\u00a0Mingxing Wu\n\nCenter for Quantum Spintronics, Norwegian University of Science and Technology, Trondheim, Norway\n\nHans Gl\u00f8ckner Giil,\u00a0Mathias Kl\u00e4ui\u00a0&\u00a0Arne Brataas\n\nInstitute of Physics, Johannes Gutenberg-University Mainz, Mainz, Germany\n\nMona Bhukta\u00a0&\u00a0Mathias Kl\u00e4ui\n\nCenter for Electron Microscopy, South China University of Technology, Guangzhou, China\n\nWen Shi\n\nState Key Laboratory for Mesoscopic Physics, School of Physics, Peking University, Beijing, China\n\nZhongchong Lin,\u00a0Zhongyu Liang\u00a0&\u00a0Jinbo Yang\n\nSchool of Materials, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China\n\nYanglong Hou\n\nSchool of Materials Science and Engineering, Beijing Key Laboratory for Magnetoelectric Materials and Devices, Peking University, Beijing, China\n\nYanglong Hou\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.D. and R.W. conceived the experiments. X.H. fabricated the devices. X.H., S.D., J.W. and R.W. carried out the transport and magnetic measurements. Z.C.L., Z.Y.L. and J.Y. made the single crystal samples and carried out basic characterizations. H.G.G. and A.B. contributed to the theoretical calculation. X.H., S.D., R.W., and M.K. contributed to data analysis. W.S. contributes to the Transmission Electron Microscopy characterization. M.B. and M.W. contribute to the temperature distribution simulation. S.D. drafted the manuscript and all authors contributed to the reviewing and revising of the manuscript. Y.H. and R.W. supervised the research and contributed to the acquisition of the financial support for the project leading to this work.\n\nCorrespondence to\n Shilei Ding, Jinbo Yang, Yanglong Hou or Rui Wu.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Shaojie Hu, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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Spin Seebeck in the weakly exchange-coupled Van der Waals antiferromagnet across the spin-flip transition.\n Nat Commun 16, 3037 (2025). https://doi.org/10.1038/s41467-025-58306-3\n\nDownload citation\n\nReceived: 22 October 2024\n\nAccepted: 18 March 2025\n\nPublished: 28 March 2025\n\nVersion of record: 28 March 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58306-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"Molecular Manipulation of Polyamide Nanostructures Reconciles the Permeance\u2013Selectivity Threshold for Precise Ion Separation", + "journal": "Nature Communications", + "published": "04 August 2025", + "supplementary_0": [ + { + "label": "Supplementary information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_MOESM3_ESM.docx" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_MOESM6_ESM.txt" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_MOESM7_ESM.txt" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_MOESM8_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-62376-8#Sec16" + ], + "code": [], + "subject": [ + "Chemical engineering", + "Polymer synthesis", + "Polymers" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5431568/v1.pdf?c=1754391994000", + "research_square_link": "https://www.researchsquare.com//article/rs-5431568/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-62376-8.pdf", + "preprint_posted": "16 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Membrane nanofiltration (NF) has emerged as a prominent energy-efficient separation technology for widespread applications related to the water\u2012energy nexus. However, state-of-the-art polyamide (PA) NF membranes are markedly constrained by a ubiquitous, pernicious tradeoff between water permeance and selectivity. Leveraging the prestigious structure-determining performance rationale, this work conceives a facile and robust molecular engineering approach that enables simultaneous improvements in water permeance and co-cation selectivity through synthetic molecular construction of a PA nanofilm with unique cationic triazolyl heterocyclic polyamide (CTHP) structures during scalable interfacial polymerization. Experimental data in conjunction with molecular simulations reveal that the CTHP structures instigate exquisite regulation of the PA subnanometer pore architecture and the specific binding affinity with water and ions, which not only affords precise ion sieving ability and advanced Donnan exclusion selectivity but also energetically facilitates the partitioning and transport of water molecules. The exemplified PA membrane exhibits unparalleled divalent cation rejections of over 99%, accompanied by a 9-fold increase in monovalent/divalent cation sieving selectivity, which is substantially greater than that of the pristine benchmark, a superior water permeation rate, and excellent chemical and operational stability, circumventing the permeance/selectivity threshold. We believe that the molecular engineering strategy implemented in this work holds broad prospects for the rational design and fabrication of semipermeable polymeric NF membranes for sustainable and precision separations.Physical sciences/Engineering/Chemical engineeringPhysical sciences/Chemistry/Polymer chemistry/NanocompositesNanofiltrationPolyamide membraneInterfacial polymerizationIon separationPermselectivity", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupportinginformationNov112024.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Membrane nanofiltration (NF) has emerged as a prominent technology for efficient separations of ions, but state-of-the-art polyamide (PA) NF membranes are constrained by a pernicious tradeoff between water permeance and selectivity. This work conceives a versatile molecular engineering strategy to simultaneously improve water permeance and co-cation selectivity through molecular construction of cationic triazolyl heterocyclic polyamide (CTHP) nanofilms via scalable interfacial polymerization. Experimental data and molecular simulations reveal that the CTHP structures precisely regulate the subnanometer pore architecture and binding affinity with water and ions, affording advanced size-sieving and Donnan exclusion while facilitating water partitioning and transport. The exemplified PA membrane exhibits ultrahigh divalent cation rejections of over 99% with a 9-fold increase in monovalent/divalent cation sieving selectivity relative to the pristine benchmark, exceptional water permeance, and good fouling resistance. The implemented molecular engineering strategy holds broad prospects for the rational design of high-performance polymeric membranes for sustainable and precision separations.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Precision discrimination of target ions and molecules from complex aqueous mixtures of similar species remains a considerable challenge in widespread applications such as water, clean energy, and resource reclamation1,2,3. Membrane nanofiltration (NF), featuring phase-free conversion separation, has evolved into a premier tool for sustainable water separation because of its high energy efficiency, low carbon footprint, compact design, and manufacturing scalability4,5. The rapid dissemination of NF technology relies on high-performance membranes that ideally have high values of both water permeance and selectivity to fully exploit the prominent process advantages, but such a combination is exceedingly difficult to achieve particularly for polymeric membranes, as the material properties that affect solute transport would, in turn, exert influence on water permeation6,7,8.\n\nPolyamide (PA) thin-film composite membranes are state-of-the-art NF membranes that are particularly attractive for water filtration in practical modules across all scales9,10,11,12. However, the deleterious tradeoff between water permeance and membrane selectivity consistently poses a stumbling block for further performance advancement, where increasing water permeance is inevitably accompanied by a diminished ability to selectively reject solutes13,14. According to the prevailing membrane separation mechanisms, effective strategies for rationally regulating mass transport across the PA membranes hinge on well-defined pore sizes with a narrow size distribution, finely tuned interactions with the permeants of interest, and a thin PA selective layer15,16,17. Innovative materials and fabrication methods that can precisely regulate PA chemistry and nanostructures have therefore become essential pursuits of academic research.\n\nA prevalent approach that has been widely adopted to increase the permselectivity of PA membranes toward charged species involves surface charge optimization to strengthen electrostatic exclusion via in situ and/or post-synthetic modifications18,19. However, most of the approaches reported thus far have primarily focused on promoting solute rejection or selectivity rather than overcoming the permeance/selectivity tradeoff threshold20,21. Recently, exceptional size sieving ability and co-cation selectivity have been achieved by trailblazing studies that use interfacial modulators to narrow the pore size distribution of the PA layer22,23,24,25. Unfortunately, a significant decrease in water permeance is usually accompanied by a concomitant increase in water transport resistance23,24. Many studies have also focused on exploring advanced membrane materials, ranging from biological ion channels and aquaporins to two-dimensional nanomaterials such as vermiculite and graphene, as well as emerging microporous materials including zeolites, metal-organic frameworks (MOFs), covalent organic frameworks (COFs), polymers of intrinsic microporosity (PIM), conjugated microporous polymers (CMPs), macrocycles, and porous organic cages, some of which achieved remarkable permselectivity with unprecedented combinations of high permeance and selectivity26,27,28. Although the practicality of these intriguing materials is markedly restricted by many daunting limitations that vary from inherent low structural stability to inferior material availability and the feasibility of membrane fabrication on a large scale, their distinct structural features underscore the importance of well-defined pore sizes and finely regulated mutual interactions in achieving exceptional molecular sieving capabilities and water transport rates29,30,31. Hereupon, we speculate that multifunctional monomers with synthetically engineered chemistry that enable the formation of a PA structure that not only imparts subnanometer pores with a narrow size distribution but also provides low resistance for water transport are likely to achieve disruptive improvements in both water permeance and solute selectivity. Unfortunately, there is currently a lack of rational material design and feasible membrane fabrication strategies to accomplish this arduous undertaking.\n\nHerein, we demonstrate a facile and effective molecular engineering approach for precise regulation of the mass transfer behavior of PA membranes to circumvent the formidable permeance/selectivity tradeoff in nanofiltration for ion discrimination. Our strategy is contingent on molecular-level control over the nanoporous structure of the PA nanofilm and its interactions with water and ions through in situ construction of cationic triazolyl heterocyclic polyamide (CTHP) structures via scalable interfacial polymerization (IP) using rationally designed quaternary triazole ammonium monomers. Experimental data and molecular simulations revealed that the CTHP structures endow the PA nanofilm with well-defined subnanometer pores with a narrow size distribution and abundant positive charge but low intrinsic water transport resistance, which not only synergistically enhances steric hindrance sieving and Donnan exclusion but also facilitates the permeation of water. The advantages of this molecularly engineered PA structure were demonstrated by its exceptional performance within precise co-cation sieving (Fig.\u00a01a), achieving a 9-fold increase in monovalent/divalent cation selectivity with a tripled water flux relative to the benchmark, successfully reconciling the tradeoff threshold. Considering the diverse array of monomer chemistries, the implemented design strategy provides a promising gateway to advance the development of effective PA membranes with exceptional permselectivity for precise ion separations toward clean water and renewable energy.\n\na Working principle of precision co-ion separation via nanofiltration. b Schematic diagram of the interconnected subnanometer-sized pores in a desired PA nanofilm with high selectivity and low water transport resistance, and three-dimensional view of an amorphous cell of the PA polymer (cell size: 65\u2009\u00d7\u200965\u2009\u00d7\u200965\u2009\u00c53). c Synthetic reaction formula of DAT-NH2 isomers and the visualized conformation of their atomic electrostatic potential. d 1H NMR spectra and liquid chromatography (upper left inset) of DAT-NH2 isomers. e Schematic illustration of interfacial polymerization between DAT-NH2/PEI and TMC at the water/hexane interface to form a PA nanofilm. f Molecular structure of the DP-M PA nanofilm (left) and its corresponding polymer chain structure derived from an amorphous cell generated by molecular dynamic (MD) simulations. g FT-IR spectra of the DP-M and P-M PA nanofilms. h N 1\u2009s XPS spectra of the DP-M PA nanofilm. The N1s core level spectrum was deconvoluted into three components located at 399.7, 400.4, and 401.7\u2009eV corresponding to N\u2009\u2212\u2009(C\u2009=\u2009O)\u2009\u2212\u2009, N(H)\u2009\u2212\u2009C\u2009\u2212\u2009, and N+\u2009\u2212\u2009C\u2009\u2212\u2009, respectively.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "Figure\u00a01b shows the conceptual illustration of the ideal PA structure that we intend to construct to circumvent the permeance/selectivity tradeoff in precision nanofiltration (NF). Specifically, the PA layer is synthetically designed with well-defined permeate-PA binding affinity and steric sieving selectivity to simultaneously manipulate the enthalpy and entropy barriers for water and solute transport32,33. To realize this design strategy, we molecularly constructed a multifunctional monomer with primary amine dangling and a highly polarized triazolyl heterocyclic ring bearing quaternary ammonium such as 3,5-diamino-4/1-(2-aminoethyl)\u22121,2,4-triazole (DAT-NH2, Fig.\u00a01c). Our pursuit of such a molecular architecture was inspired by seminal studies revealing that nitrogen-containing heterocycles, like imidazole derivatives, could induce energetically preferential interactions with water molecules to facilitate their permeation during membrane filtration, while the primary amine pendants with concentrated electron density concurrently provide highly reactive sites to crosslink with trimesoyl chloride (TMC) to form tightly crosslinked PA network with enhanced hydrophilicity and interconnected subnanometer pores. We speculate that these effects are likely to be maximized in triazole-derived compounds, given its five-membered aromatic ring that contains three sp2-hybridized nitrogen atoms and two carbon atoms. Electrostatic potential mapping and quantitative analysis of the key molecular and electronic descriptors of a series of five-membered rings with various numbers of nitrogen heteroatoms by density functional theory (DFT) revealed that the triazole unit possesses the most asymmetric charge distribution and the largest dipole moment (3.974 Debye) (Supplementary Fig.\u00a01). These features suggest that the triazole moiety may introduce localized hydrophilic domains within the PA polymer matrix due to its high polarity and electron delocalization, which could enhance water affinity and thus facilitate transport. DFT-based electronic structure analyses of the DAT-NH2 isomers and PEI fragments further demonstrated that the former exhibits significantly higher molecular polarity, enhanced nucleophilicity, and denser positive electrostatic potentials than the latter (Supplementary Fig.\u00a02, Supplementary Table\u00a01 and 2), further substantiating our hypothesis that the DAT-NH2 isomers can enable the formation of highly ionizable cationic PA structures with preferential water transport pathways.\n\nThe molecularly designed DAT-NH2 monomer was synthesized via a one-pot quaternization reaction between 3,5-diamino-1,2,4-triazole and 2-bromoethylamine in dimethylformamide (DMF) and then purified by nonsolvent-induced precipitation (Fig.\u00a01c and Supplementary Fig.\u00a03 and 4). Intriguingly, liquid chromatography\u2013mass spectrometry (LC\u2013MS) measurements reveal that a mixture of 3,5-diamino-4/1-(2-aminoethyl)\u22121,2,4-triazole isomers was obtained from the synthesis. As shown in Fig.\u00a01d, two distinct peaks with almost the same intensity and area ratio were observed in the LC chromatogram pattern. Subsequent MS analysis of these two peaks showed one coincident peak at m/z 143 (Supplementary Fig.\u00a05), which is in good agreement with the molecular weight of the DAT-NH2 isomers (C4N6H11+, MW\u2009=\u2009143\u2009Da). Proton nuclear magnetic resonance (1H NMR) spectra corroborate these results, where four 1H NMR peaks corresponding to the two types of protons in each isomer are spotted at 3.11 (labeled H I), 3.30 (labeled H I), 3.35 (labeled H II), and 3.99 (labeled H II) ppm. The area ratios of the two peaks (H I/H II) in the 1H NMR spectrum were measured to be 0.88 and 1.04 for the isomers, which are close to the theoretically expected values based on the DAT-NH2 chemical structure. The visualized atomic electrostatic potential image of DAT-NH2 intuitively proclaims the positive charge characteristics of the triazole ring, and quantitative analysis of the molecular van der Waals surface electrostatic potential (ESP) shows that the distribution area and intensity of the positive ESP of the DAT-NH2 isomers are greater than the negative values (Fig.\u00a01c and Supplementary Fig.\u00a06).\n\nA self-sustaining PA thin film with good stability immediately formed when DAT-NH2 was brought in contact with TMC at the water/hexane interface (Supplementary Fig.\u00a07), indicating a high polymerization rate between DAT-NH2 and TMC. DFT calculations further confirmed the high nucleophilic substitution reactivity of the amino groups on DAT-NH2 toward acyl chloride (Supplementary Figs.\u00a08 and 9). Comprehensive ESP and average local ionization energy (ALIE) analyses reveal that the backbone and chemical functional moieties of the PA structures formed by the two isomers with TMC are almost identical (Supplementary Fig.\u00a010). Therefore, the DAT-NH2 isomers were directly used for PA membrane preparation without further purification. A continuous PA nanofilm can also be in situ synthesized via a similar interfacial polymerization (IP) procedure on top of a polyethersulfone (PES) substrate to prepare robust PA thin-film composite membranes for NF tests (Supplementary Fig.\u00a011). Unfortunately, we observed that the obtained DAT-NH2/TMC PA membrane experienced severe water swelling and thus relatively low permselectivity were obtained in nanofiltration (Supplementary Fig.\u00a012), likely owing to the enhanced hydrophilic nature of the cationic triazolyl heterocyclic structures. We thereby further modified the PA chemical structure by using polyethyleneimine (PEI) as the comonomer during IP to increase the membrane stability and separation performance (Fig.\u00a01e, f). PEI is a benchmark monomer that is widely used for the synthesis of positively charged PA NF membranes (Supplementary Fig.\u00a013). Synthesis condition optimization experiments confirmed that the DP-M membrane fabricated with a 0.06\u2009wt% DAT-NH2 shows an optimum combination of water permeance and ion selectivity in addition to the exceptional robustness (Supplementary Figs.\u00a014 and 15), substantially exceeding those of the PA membrane formed solely by PEI (denoted as the P-M benchmark). The Fourier transform infrared spectroscopy (FT-IR) peaks observed at 1621\u2009cm\u22121, 1806 cm\u22121, and 1705\u2009cm\u22121 are associated with the amide I band, which arises from the stretching vibration of C\u2009=\u2009O and the coupling with the bending of N\u2013H in subtly different chemical environments (Fig.\u00a01g and Supplementary Fig.\u00a016), validating the formation of PA structure. The quaternary ammonium peak at 401.8\u2009eV in the X-ray photoelectron spectroscopy (XPS) spectrum of DP-M (Fig.\u00a01h and Supplementary Figs.\u00a017 and 18) confirms the presence of DAT-NH2 moieties in the PA layer. It is noteworthy that a significant decline in the O/N ratio was observed by DP-M compared with that of the P-M benchmark, where the O/N ratio decreases from ~1.50 to 0.96 (Supplementary Table\u00a03), suggesting a substantial increase in the PA crosslinking degree of DP-M. The elevated crosslinking degree constricts the space between the stacked polymer chains, thus diminishing the pore sizes and augmenting the mechanical strength of the PA nanofilm (Supplementary Fig.\u00a015). According to the chemical characterization and molecular simulations, the DAT-NH2 modulated PA layer of DP-M has a semirigid 3D polyamide network with a large amount of intrinsic positive charges and smaller chain space compared to the P-M benchmark (Fig.\u00a01f and Supplementary Figs.\u00a019 and 20).\n\nField emission scanning electron microscopy (FESEM) images corroborate the uniformity and integrity of the formed PA thin layer at the macroscopic scale (Fig.\u00a02a, b and Supplementary Fig.\u00a021). At a finer scale, the PA layer of DP-M appears a smooth and compact surface, whereas the counterpart of the P-M benchmark shows a crumpled surface with numerous ridged wrinkles unequivocally seen on top. This surface morphological discrepancy was further manifested by the atomic force microscopy (AFM) data, where the surface roughness of DP-M (Rq = 3.44\u2009nm) is distinctly lower than that of P-M (Rq = 9.09\u2009nm) (Fig.\u00a02c, d and Supplementary Fig.\u00a022). A smooth surface is conducive to alleviating the fouling tendency. Cross-sectional transmission electron microscopy (TEM) images showcase that DP-M has an low PA thickness of 59\u2009\u00b1\u20092\u2009nm (Fig.\u00a02e, f and Supplementary Fig.\u00a023), which is much thinner than that of the P-M benchmark (i.e., 88\u2009\u00b1\u20092\u2009nm). The significantly reduced PA thickness might be attributed to the rapid formation of a relatively dense nascent PA film mediated by DAT-NH2 at an initial stage of IP (Supplementary Fig.\u00a024), which subsequently stymies the diffusion of aqueous monomers at the interface and thus suppresses the subsequent growth of the PA nanofilm34,35. On the other hand, the positively charged structure of DAT-NH2 may slow down the diffusion of PEI towards the interface via H-bonding interactions, further retarding the growth of the PA film36,37,38,39,40. In the context of membrane filtration, a thinner PA selective layer spontaneously confers shorter transport pathways and lower water penetration resistance, which is favorable for achieving high water permeance.\n\na, b Surface FESEM images. c, d 2D and 3D AFM images. e, f Cross-sectional TEM images. Top: P-M. Bottom: DP-M. g Pore radius distribution of PA membranes obtained by PEG rejection tests (the inset illustrates the geometric standard deviation). h Molecular dynamics (MD) simulations of the fractional free volume (FFV) of PA layers (left). The dark blue and gray colors represent the voids between the polymer chains and the space occupied by the polymer skeleton, respectively. Representative porous molecular structures of the DP-M and P-M PA networks (right). i MD simulations of the pore radius distribution of the PA nanofilms. j Zeta potential as a function of pH. k Summary of the MWCO, water contact angle (CA), root mean square roughness (Rq), FFV, polyamide layer thickness, and zeta potential (ZP) at pH = 6 for DP-M and P-M.\n\nThe rejection tests with neutral solutes of polyethylene glycol (PEG) indicate that DP-M has a molecular weight cutoff (MWCO) of 245\u2009Da, almost two times smaller than that of the P-M benchmark (MWCO\u2009=\u2009479\u2009Da, Supplementary Fig.\u00a025). Correspondingly, a small effective mean pore radius of 2.8\u2009\u00c5 accompanied by a narrow size distribution was achieved by DP-M (Fig.\u00a02g), whereas P-M shows a relatively larger mean pore radius of 3.1\u2009\u00c5 and broader pore size distribution, in accordance with our design strategy and the XPS-derived crosslinking degree (Fig.\u00a01b, h). Molecular dynamics (MD) simulations were further performed to construct realistic structural models to glean molecular-level insights into the microporous structure of the PA layer. As shown in Fig.\u00a02h, the fractional free volume (FFV) of DP-M and P-M PA layers are ~10.3% and 21.6%, respectively. The pore size survey conducted by MD molecular simulations substantiates that the majority of pores within the DP-M PA layer are ~2.75\u2009\u00c5 in radius, which is significantly smaller than that of the P-M benchmark (i.e., 3.38\u2009\u00c5). Further analyses of the interior cavity radius disclose a narrower distribution of pore sizes within DP-M (Fig.\u00a02i and Supplementary Fig.\u00a026), signifying the compact structure of the PA layer mediated by DAT-NH2 (Fig.\u00a01c). Notably, the microscopic pore features derived from molecular simulations coincide well with those experimentally obtained from neutral solute rejection tests (Fig.\u00a02g, i). The tight nanostructure of DP-M constricts membrane pores to dimensions more favorable for size sieving, with precise ionic and molecular sieving capabilities and a threshold of 5.6\u2009\u00c5 in pore diameter. Furthermore, aligning with the chemical features of the CTHP structures in DP-M (Fig.\u00a01g), the cationic DAT-NH2 moieties elevate the membrane hydrophilicity and positive charge density, as manifested by its smaller surface water contact angle (CA) and higher zeta potential (ZP) than that of the P-M benchmark (Supplementary Fig.\u00a025 and Fig.\u00a02j). In the realm of NF applications, the enhanced hydrophilicity facilitates surface partitioning and interior diffusion of water molecules, whereas the ameliorated positive charge density reinforces the electrostatic repulsion selectivity. Collectively, the advanced characteristics gained by the DP-M membrane resonate with our intended design strategy illustrated in Fig.\u00a01b, which underpins the significance of synthetic molecular engineering in precisely regulating the nanoporous structure and chemical features of the PA selective layer (Fig.\u00a02k).\n\nThe well-defined subnanometer pores with a sharped size distribution and the inherent positive charges of the DP-M membrane would afford prominent molecular sieving and electrostatic repulsion selectivity in NF applications. We subsequently examined the mass transport behavior of a wide spectrum of inorganic salts through DP-M using a crossflow filtration system. In contrast to the acquiescent expectation that a decrease in membrane pore size along with a downscaled FFVs generally accompanied by a concomitant decline in the water permeation rate, a substantial increase in the water permeance was achieved by DP-M, where the water permeation flux of DP-M is almost tripled at the same pressure compared to the P-M benchmark (Fig.\u00a03a), corresponding to an approximately 3-fold increase in pure water permeance (PWP). The incongruence between the enhanced water permeance and the reduced pore sizes and FFVs likely stems from the molecularly constructed CTHP structures and the low thickness of the PA layer, which provide facilitated water transport pathways with low resistance.\n\na Pure water flux (PWF) of DP-M and P-M at different operation pressures. b Water flux and ion rejections of DP-M for filtrating different cation solutions. (Feed salt concentration: 1000 ppm; test pressure: 6.0\u2009bar). c MgSO4 and MgCl2 rejections of P-M and DP-M (feed salt concentration: 1000 ppm, test pressure: 6.0\u2009bar). d Effect of operation pressure on the water permeance and MgCl2 rejection of DP-M (feed: 1000 ppm MgCl2). e Effect of pH on the water flux and LiCl rejection of DP-M (feed: 1000 ppm LiCl, test pressure: 6.0\u2009bar). f Effect of operation time on the water flux and MgCl2 rejection of DP-M (feed: 1000 ppm MgCl2, test pressure: 6.0\u2009bar). g Effect of operation pressure on the water permeance and SLi+/Mg2+ of DP-M (feed: 2000 ppm binary mixture of MgCl2 and LiCl with a MgCl2/LiCl mass ratio of 20). h Effects of the Mg2+/Li+ ratio on the water flux and SLi+/Mg2+ ratio of DP-M (feed: 2000 ppm binary mixture of MgCl2 and LiCl with various MgCl2/LiCl mass ratios; test pressure: 6.0\u2009bar). i Performance comparison of DP-M with state-of-the-art PA NF membranes operated under cross-flow nanofiltration. The corresponding references for the data points in (i) are specified in Supplementary Table\u00a04.\n\nAt the same time, DP-M shows a sharp size-exclusion cutoff of ~2.5\u2009\u00c5 in the Stokes radius of cations (Fig.\u00a03b), adhering to its densified catatonic PA molecular structure, which thereby facilitates the transport of smaller monovalent cations (i.e., Rb+, K+, Na+, and Li+) while sufficiently blocks larger divalent cations (i.e., Ni2+, Ca2+, Mg2+, and Zn2+), with rejection rates greater than 98.0% and high water flux of 50\u2009L\u2009m\u22122 h\u22121 (LMH). It is noteworthy that ultrahigh rejections of up to 99.1% towards MgCl2 and MgSO4 were specifically achieved by DP-M (Fig.\u00a03c), exceeding most state-of-the-art NF membranes, and similar rejections and water permeance were maintained over a wide pressure range of 2\u201316\u2009bar (Fig.\u00a03d). In contrast, LiCl rejection monolithically increased from 45.7% to 81.2% when the pressure was raised from 2 to 16\u2009bar (Supplementary Fig.\u00a027). As a result, an ideal Li+/Mg2+ selectivity of 35.8\u201342.9 was obtained based on single salt rejections (Supplementary Fig.\u00a028), demonstrating its promising capability for precise cation sieving. In the same vein, the P-M benchmark is inferior in terms of both salt rejection and cation differentiation selectivity (Fig.\u00a03b and Supplementary Fig.\u00a029).\n\nThe separation performances of conventional NF membranes are generally susceptible to the feed salt concentration and pH due to the electrostatic screening effects. Interestingly, DP-M consistently retains its water flux, salt rejection, and co-cation selectivity across a wide range of feed salt contents and pH values. As demonstrated by the cycling performance tests, the Mg2+ rejection of the DP-M membrane decreased slightly from 99.1% to 95.7% while the Li+ rejection dropped from 59.8% to 23.3% as the feed salt content spanned from 1000 to 7000\u2009mg/L, while the rejections were almost fully recovered to the initial values when the feed content was switched back to 1000\u2009mg/L (Supplementary Fig.\u00a030), substantiating good electrostatic shielding resistance toward high ionic strength. There were also no obvious deteriorations in salt rejections and water flux as the feed pH escalated from 1 to 13 (Fig.\u00a03e), underscoring the ability of DP-M to maintain high separation performance in both acidic and alkaline environments. The exceptional pH and salinity stabilities of DP-M are consistent with the inherent structural features of CTHP and the enhanced size-sieving ability endowed by the narrow pore size distribution (Fig.\u00a02g, i). The exceptional hydrophilicity and positive surface charge imparted by the catatonic CTHP structures also affords DP-M with exceptional fouling resistance to positively charged foulants (i.e., DTAB, dodecyltrimethylammonium bromide) (Supplementary Fig.\u00a031). Moreover, DP-M shows exceptional structural durability and performance stability throughout long-term filtration for 120\u2009h (Fig.\u00a03f), where MgCl2 rejection consistently surpassed 99%, with a stable water flux of ~51 LMH being retained. Notably, the DP-M membrane maintained a high MgCl2 rejection of above 99% and a water flux of greater than 50 LMH even after being stored in water for one month (Supplementary Fig.\u00a032). In addition, the DP-M membrane exhibits exceptional chlorine resistance. As shown in Supplementary Fig.\u00a033, relatively stable MgCl2 rejections and water flux were observed throughout the 175\u2009h exposure to 200 ppm sodium hypochlorite (NaClO), while the MgCl2 rejection of the P-M benchmark decreased dramatically from 92.4% to 29.5% with a concomitant rapid increase in water flux from 20.4 to 149.3 LMH. FT-IR analysis (Supplementary Fig.\u00a033c, d) further reveals that the characteristic peak of the amide bond (~1660\u2009cm\u22121) in P-M substantially decreased with the extension of exposure time, whereas the counterpart peak in the DP-M membrane remained nearly unchanged, demonstrating the exceptional structural durability of the DP-M membrane under oxidative conditions.\n\nThe co-cation sieving ability of DP-M was further illuminated by binary salt filtration tests using a mixture of LiCl and MgCl2 as the probe solutes. Similar to the single-salt tests (Supplementary Fig.\u00a028), DP-M shows high Li+/Mg2+ selectivity with a separation factor of greater than 39.0 (SLi+/Mg2+) for the binary mixtures at different operation pressures (Fig.\u00a03g), which signifies a 9-fold greater magnitude than that achieved by the P-M benchmark (SLi+/Mg2+ = 4.3), accompanied by an approximately 2.5-fold increase in water permeance. The persistently high rejections toward divalent cations and co-cation selectivity are presumably ascribed to the advanced molecular sieving and electrostatic repulsion effects afforded by the regulated subnanometer pores and inherent positive charges of DP-M. Furthermore, slight fluctuations in the co-cation selectivity were observed when the feed Mg2+/Li+ mass ratio altered from 1 to 120, where the SLi+/Mg2+ oscillated between 33.5 and 44.3 and the water flux was consistently higher than 51.4 LMH (Fig.\u00a03h). Compared with other reported PA NF membranes with similar chemical and structural properties, DM-P exhibits upper-level water permeance and co-cation sieving selectivity (i.e., Li+/Mg2+) (Fig.\u00a03i), which potentially contributes to a more streamlined separation process with diminished energy consumption (Supplementary Fig.\u00a034), underscoring its remarkable potential for energy-efficient and cost-effective NF separations. To validate the uniformity and scalability of the developed PA membranes, a large-sized DP-M membrane (1\u00d70.3\u2009m) was fabricated via the same interfacial polymerization (Supplementary Fig.\u00a035). Membrane coupons randomly selected from different regions exhibited consistent performance, with the MgCl2 rejection rate stably maintained at 98% while the water flux remained around 50 LMH, indicating the exceptional scalability and stability. Furthermore, the generality of NF performance enhancement by triazole derivatives was demonstrated by extending to other quaternary ammonium triazole analogs (i.e., 3,5-diamino-4/1-(3-aminopropyl)\u20131,2,4-triazole, DAAT-NH2) and benchmarking against non-triazole monomers (Supplementary Fig.\u00a036 and Fig.\u00a037). The successful breakthrough of the permeance/selectivity tradeoff underpins our membrane design strategy and exemplifies the feasibility of synthetic molecular engineering in rational membrane design.\n\nGiven that the DP-M membrane possesses strong intrinsic positive charges and an average micropore diameter of 5.6\u2009\u00c5 with narrow size distribution (i.e., from 1.1 to 5.9\u2009\u00c5) (Fig.\u00a02g), we hypothesize that its high rejections toward divalent cations and enhanced co-cation sieving ability lean upon the on-demand tuning of the PA chemistry and nanoporous structure according to the size and valence differences between cations, which instigates unusual differences in energy barriers that cations need to overcome for dissolution and migration. As illustrated in Fig.\u00a04a, the average pore size of DP-M (5.6\u2009\u00c5) falls between the Stokes diameter (4.8\u2009\u00c5) and hydrated diameter (7.6\u2009\u00c5) of Li+. Given the substantial difference in hydration energy between Mg2+ and Li+ (1828 vs. 474\u2009kJ/mol), Li+ ions are more susceptible to undergo partial dehydration and subsequent selective permeation through the well-defined micropores of DP-M, enabling effective discrimination between Li+ and Mg2+ cations. This energy discrepancy is further corroborated by DFT calculations (Supplementary Fig.\u00a038), which estimate the hydration energies of Mg2+ and Li+ to be \u22121922.45 and \u2212563.52\u2009kJ/mol, respectively, indicating that the hydrated Mg2+ encounters a significantly higher energy barrier than Li+ to dehydrate at identical transmembrane pressure.\n\na Pore size distributions of P-M and DP-M membranes and the sizes of Li+ and Mg2+ cations. Schematic illustration of transmembrane transport of hydrated Li+ and Mg2+ through the subnanometer pores in DP-M. b Analysis of the binding configurations between P-M/DP-M molecular fragments and hydrated Li+ and Mg2+ using the independent gradient model based on Hirshfeld partition (IGMH). c, d Differential scanning calorimetry (DSC) thermogram derived crystallization and melting behavior of the DP-M membrane after being equilibrated with 1000 ppm c MgCl2 and (d) LiCl aqueous solutions. The lower right inset shows the melting enthalpy (\u0394Hm), crystallization enthalpy (\u0394Hc), and their difference (\u0394Hm\u2212c). e The binding energies between the hydrated Li+/Mg2+ and the PA molecular fragments of P-M/DP-M. The numbers represent the calculated energy gaps between Li+ and Mg2+ in P-M and DP-M. f Interaction region indicator analysis of the PA fragments interacting with Mg2+. The effects of the hydration layer and TMC were shielded (the arb. units here represents energy, and 1 arb. units is approximately 27.21\u2009eV). g Interaction region indicator analysis of the PA fragments derived from DAT-NH2 and the visualized structure diagram. h van der Waals surface area ratio corresponding to various electrostatic potential intervals of the PA molecular fragments.\n\nTo gain fundamental insights into the influences of the pore environment on cation dehydration behavior, dynamic molecular simulations were performed to examine the interactions between PA fragments and hydrated Li+ and Mg2+ ions (Fig.\u00a04b and Supplementary Fig.\u00a039). Compared with P-M, the independent gradient model based on Hirshfeld partitioning (IGMH) analysis shows that DP-M has weaker hydrogen bonding and van der Waals interactions with water molecules in the hydration shell of Mg2+ ions, which is unfavorable for stabilizing the configuration. In addition, the lower intrinsic charge number of Li+ relative to Mg2+ leads to the weaker binding energy of its hydration layer (Supplementary Fig.\u00a040), while the positively charged CTHP structures in DP-M further promote the escape of polar water molecules from the Li+ hydration shell by exerting extensive van der Waals and hydrogen bonding interactions, as demonstrated in the DP-M/hydrated Li+ configuration (Fig.\u00a04b), thereby aggrandizing the dehydration of Li+ during transmembrane permeation. These effects of promoting Li+ dehydration expedite its transport through DP-M, which plays an important role in enhancing Li+/Mg2+ selectivity, especially when the membrane pore sizes are diminished and the size distribution is constricted. Differential scanning calorimetry (DSC) measurements were also performed to experimentally explore the hydration/dehydration behavior of Li+ and Mg2+ within the polyamide polymer matrix. As shown in Fig.\u00a04c, d, the obtained crystallization enthalpy reveals that Mg2+ (\u0394Hc = \u2212303.0\u2009J/g) tends to form a more structured and strongly bound hydration shell within the polyamide matrix of DP-M compared to Li+ (\u0394Hc = \u2212188.2\u2009J/g). Furthermore, the melting enthalpy (\u0394Hm, 314.8 vs. 237.8\u2009J/g) indicates that Mg2+ is more likely to establish stable coordination interactions with the functional groups and chemical bonds of the polyamide network, thereby requiring greater energy to disrupt the hydration structure. These thermodynamic distinctions disclose the underlying rationale for the enhanced Li+/Mg2+ selectivity of the DP-M membrane, which is in good agreement with the modeling results. Contrarily, for the P-M benchmark (Supplementary Fig.\u00a041), the melting (\u0394Hm = 338.9 vs. 357.4\u2009J/g) and crystallization (\u0394Hc = \u2212303.0 vs. \u2212320.3\u2009J/g) enthalpies associated with hydrated Mg2+ and Li+ are comparable, indicating that the P-M benchmark lacks the thermodynamic ability to discriminate between hydrated Mg2+ and Li+, which sheds light on its low Li+/Mg2+ selectivity.\n\nDFT calculations were further performed to illuminate the mutual interactions between PA and cations by performing configuration optimization and cation\u2013PA binding energy calculations (Supplementary Table\u00a05). As displayed in Supplementary Fig.\u00a042, the negative binding energies of hexahydrated Mg2+ with the DP-M PA fragments are consistently lower than those with the P-M fragments, suggesting that the binding interactions between Mg2+ and P-M are relatively more stable. On the contrary, the similar binding energies of hydrated Li+ to DP-M and P-M PA fragments (\u201328.83 vs. \u201329.65\u2009kcal/mol) acknowledge that the transport of hydrated Li+ through the two membranes is subjected to similar energetic penalties despite the diminished and narrowed pore sizes of the former (Fig.\u00a04e). However, the binding energy gaps between Li+ and Mg2+ in the DP-M fragments are 16.80 and 9.90\u2009kcal/mol, respectively, which are markedly larger than those in the P-M fragments (i.e., 2.82 and 6.66\u2009kcal/mol) (Fig.\u00a04e), implying that DP-M has an overwhelming advantage over P-M to differentiate Li+ and Mg2+ from the energy perspective. The interaction region indicator (IRI) was subsequently applied to conduct an in-depth analysis of the specific types of interactions between the PA fragments and hexahydrated Mg2+41 (Supplementary Fig.\u00a043). The corresponding scatter plots reveal that the interaction forces involved are intricate and hard to distinguish (Supplementary Fig.\u00a044). Therefore, the hydration layer of Mg2+ and the influence of TMC were shielded to better disclose the contributions of DAT-NH2 moieties in the PA (Fig.\u00a04f). Examining their respective IRI scatter plots, eminent peaks appear near sign(l2)r values of \u22120.05 and 0.06 arb. units in the DP-M PA molecular fragments. From the electron density point of view, the peak at \u22120.05 arb. units corresponds to a weak interaction of higher strength, whereas the peak at 0.06 arb. units stems from a stronger spatial repulsion. Notably, the scatter plot of the weak interaction portion ascertains an anomalous peak at approximately 0.013 arb. units in DP-M (Supplementary Fig.\u00a045), which indicates that the CTHP structures derived from DAT-NH2 may generate proprietary steric hindrance at the molecular level. The two distinct peaks in the scatter plot obtained from interaction decomposition confirm that CTHP instigates both attractive and repulsive interactions (Fig.\u00a04g). Further analysis of the IRI visualized isosurface shows that the peak near \u22120.05 arb. units is attributed to intramolecular H-bonding interactions. These H-bonds account for the in situ formation of the cyclic conformations in the CTHP structures (Fig.\u00a04g), which dictate additional steric hindrance at approximately 0.013 arb. units (the peak near 0.013 is retained in Supplementary Fig.\u00a046). Moreover, the peak at approximately 0.06 arb. units is associated with the strong repulsion induced by the overlapping of the triazole rings in CTHP driven by van der Waals surfaces. Other than the narrowed subnanometer pore sizes, these anomalous intramolecular H-bonding structures provide additional steric hindrance at the molecular level, further amplifying the permeation energy barrier acting on divalent cations.\n\nIn addition to the non-Coulombic interactions, long-range Coulombic electrostatic forces (i.e., Donnan exclusion) also play an imperative role in the cation\u2013PA interactions, particularly considering the strongly charged PA structure of DP-M. Qualitative and quantitative analyses of the electrostatic potential (ESP) were conducted to acquire the van der Waals surface ESP distributions of the PA molecular fragments of DP-M and P-M. As illustrated in Fig.\u00a04h and Supplementary Fig.\u00a047\u201348, DP-M exhibits a exceptional positive potential and this observation was reaffirmed by the quantitative calculation of the ESP region proportions, where DP-M shows a large positive potential region proportion of 97% and an average ESP value of 55.55\u2009kcal/mol, far exceeding the respective value of P-M. The pronounced electrostatic repulsion between DP-M and the positively charged hexahydrated cations is unequivocally conferred by the CTHP structures of the PA layer. The ESP of hydrated Mg2+ is nearly twice as high as that of hydrated Li+ (193.29 vs. 98.53\u2009kcal/mol, Supplementary Fig.\u00a040), which inevitably invokes formidable Donnan exclusion selectivity towards the positively charged DP-M (55.55\u2009kcal/mol). Overall, the intriguing co-cation sieving ability of DP-M proceeds through a cooperative mechanism of steric hindrance and electrostatic exclusion, as corroborated by comprehensive modeling and experimental characterization.\n\nWater molecules need to overcome certain energy barriers when dissolving and diffusing through the PA membrane, which is primarily governed by the chemical features and nanoporous structure of the PA selective layer (Fig.\u00a05a). The mutual interactions of water molecules with the binding sites on the PA network thereby have substantial impacts on water permeation rate. To gain a fundamental understanding of the mechanisms governing the facilitated permeation of water through the DP-M membrane, DFT atomistic calculations and MD simulations were performed. DFT was employed to specifically evaluate the molecular interactions between water and the cationic DP-M PA fragments by calculating the surface free energy (SFE, Supplementary Table\u00a06) and the molecular polarity index (MPI), which reflect membrane hydrophilicity from the perspectives of interfacial thermodynamics and quantum chemistry. As displayed in Fig.\u00a05b, DP-M shows a higher MPI value than that of P-M (i.e., 56.0 vs. 19.7), which is consistent with the lower surface water contact angle of the former. Meanwhile, the SFE value of DP-M is greater than that of P-M (i.e., 50.5 vs. 41.4\u2009kJ/m2), indicating a clear preference of polar water molecules for wetting and partitioning into the PA fragments of DP-M (Fig.\u00a05c and Supplementary Table\u00a06). To further elucidate the diffusion behavior of water molecules within the PA layer, MD simulations were subsequently conducted to acquire the molecular-level binding affinities of water cluster to the PA fragments (Fig.\u00a05d). The radial distribution functions (RDFs) plots with respect to the PA fragments labeled in green (I), blue (II), and orange (III) reveal that the peak of I-H2O in the first coordination layer is significantly higher than those of II-H2O and III-H2O (Fig.\u00a05e), closely aligned with the DFT calculations and experimental data. Furthermore, the water bonding (WB) capacity calculated by RDFs follows an order of WBI\u2009>\u2009WBII\u2009>\u2009WBIII, while the computed binding energies (BE) of fragments I, II, and III with water are in the order of |BEI\u2009|\u2009> |BEII\u2009|\u2009> |BEIII\u2009|\u2009(Fig.\u00a05f). Hereupon, the cationic triazolyl heterocyclic PA structures derived from DAT-NH2 in DP-M have a relatively lower affinity to water clusters. Such energy metrics accentuate a thermodynamic inclination of DP-M to facilitate the transport of water by providing favorable water binding sites with moderate resistance, aligned with our design strategy and the ideal PA nanostructure we intend to construct (Fig.\u00a05g).\n\na Schematic representation of the advanced structure of an ultrapermeable PA membrane with precise co-cation sieving capability. b DFT atomistic calculations of polarity differences between DP-M and P-M molecular fragments. c ESP distributions of van der Waals surfaces of the water molecules obtained by DFT calculations. d Three representative PA molecular fragments of DP-M and P-M (left) and the schematic illustration of water cluster distribution in each fragment (right). e The radial distribution functions (RDFs) between water and the three PA fragments. f Binding energy (BE) and the number of water molecules (NW) around the three PA fragments. All the information and data described in (d\u2013f) were obtained from MD simulations. g Schematic illustration of the working principle of the semipermeable DP-M for ultrafast co-cation nanofiltration (yellow and blue spheres represent monovalent and divalent cations, respectively).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62376-8/MediaObjects/41467_2025_62376_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Advanced nanofiltration membranes with exceptional permselectivity offer promising solutions to address pressing challenges associated with water scarcity and renewable energy. This study demonstrates a feasible molecular engineering strategy to reconcile the tradeoff between water permeance and selectivity in state-of-the-art PA nanofiltration membranes for ultrafast co-cation sieving. Our approach is contingent on the fine-tuned regulation of the porous nanostructure and the mutual interactions of the PA layer with water molecules and ions by molecular construction of cationic triazolyl heterocyclic polyamide (CTHP) structures via in situ interfacial polymerization using synthetic DTA-NH2 isomers. The obtained PA membranes exhibited simultaneously enhanced water permeance and selectivity for cation separation, achieving high divalent cation rejections of over 99%, accompanied by a 9-fold increase in monovalent/divalent cation sieving selectivity and tripled water permeance in comparison with the pristine benchmark, as well as outstanding chemical stability and fouling resistance. Experimental data in conjunction with molecular simulations confirm that the intriguing permselectivity springs from the advanced molecular structures of the CTHP-modulated PA layer, which not only investigates a substantial decrease in the membrane pore size and narrows the size distribution but also affords high positive charge density and polarity. Coincidentally, the CTHP structures also provide preferential water binding sites with low energy barriers, energetically facilitating the accommodation and diffusion of water molecules within the PA layer, which eliminates the increased water transport resistance caused by pore size shrinkage. The developed synthetic engineering strategy sheds light on the rational design and fabrication of high-performance polymer membranes for precision NF separations affiliated with water\u2012energy nexuses.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Polyethyleneimine (PEI, MW\u2009=\u200970000\u2009Da, 50\u2009wt% aqueous solutions), trimesoyl chloride (TMC, 98.0%), 1,2,4-triaminobenzene (95%), 3-bromopropylamine (97%), and sodium dodecyl sulfate (SDS, 99.0%) ordered from Macklin (Shanghai, China) were used to synthesize the polyamide benchmark membrane via interfacial polymerization. Hexane (99.0%) purchased from Macklin was used as the solvent for the TMC monomer. 3,5-Diamino-1,2,4-triazole (DAT, 98%), 2-bromoethylamine (98%), acetonitrile (99%), and N,N-dimethylformamide (DMF, 98%) were obtained from Macklin for the synthesis and purification of DAT-NH2 isomers. Ethylene glycol (62\u2009Da), diethylene glycol (106\u2009Da), glucose (180\u2009Da), and polyethylene glycols of different molecular weights (i.e., PEG200, PEG400, PEG600, and PEG1000) were provided by Aladdin (Tianjin, China) for membrane pore size characterization. Sodium sulfate (Na2SO4), lithium sulfate (Li2SO4), magnesium sulfate (MgSO4), magnesium chloride (MgCl2), lithium chloride (LiCl), sodium chloride (NaCl), rubidium chloride (RbCl), potassium chloride (KCl), nickel chloride (NiCl2), calcium chloride (CaCl2), and zinc chloride (ZnCl2) were obtained from Macklin for salt rejection tests. Dodecyltrimethylammonium bromide (DTAB) used in the fouling study was purchased from Macklin (Shanghai, China). Sodium hydroxide (NaOH, 99.9%) was supplied by Shanghai Aladdin Biochemical Technology Co., Ltd. Hydrochloric acid (HCl, AR) was purchased from Fuchen (Tianjin) Chemical Reagent Co., Ltd. Unless otherwise indicated, all chemicals and reagents were used as received without further purification. Polyether sulfone (PES) ultrafiltration membrane with a molecular weight cutoff of 20\u201330\u2009kDa from Weihua Technology Co., Ltd., China was used as the substrate for preparing the polyamide membranes for nanofiltration tests. Deionized water was supplied by a Millipore-D 24 UV ultrapure water integrated system (Millipore Instruments, 18.2 M\u03a9 cm).\n\n3,5-Diamino-4/1-(2-aminoethyl)\u22121,2,4-triazole (DAT-NH2) isomers were synthesized via a one-step quaternization reaction following the reaction path shown in Supplementary Fig.\u00a03. In a typical synthesis, 4.24\u2009g of 3,5-diamino-1,2,4-triazole (DAT, 42.8\u2009mmol) and 8.77\u2009g of 2-bromoethylamine (42.8\u2009mmol) were dissolved in 90\u2009mL of N,N-dimethylformamide (DMF) in a 150\u2009mL round-bottom flask. The flask with the reaction mixture was then heated to 45\u2009\u00b0C in a water bath and reacted at this temperature for 24\u2009h under vigorous stirring. A pale green solution rapidly formed with increasing reaction time. When the reaction was complete, the resulting mixture was immediately transferred into a 500\u2009mL beaker, and 180\u2009mL of acetonitrile was then added to obtain a milky white suspension. The obtained flocculent precipitates were subsequently redissolved in 10\u2009mL of DMF and then precipitated with 180\u2009mL of acetonitrile. The white solids were collected via high-speed centrifugation. The above dissolution and precipitation treatment was repeated three times. Finally, the as-synthesized DAT-NH2 was vacuum dried at 40\u2009\u00b0C overnight and then stored in a sealed container for subsequent characterization and membrane fabrication.\n\nFor the fabrication of the PA thin-film composite NF membrane, the polyether sulfone (PES) substrate was first immersed in an amine monomer aqueous solution with 0.1\u2009wt% sodium dodecyl sulfate (SDS) and 0.1\u2009wt% Na2CO3 for 5\u2009min. After the excess water on the top surface was removed via filter paper, the amine-monomer saturated PES substrate was sandwiched into a homemade frame with the top surface facing upward. Interfacial polymerization was initiated by carefully adding excessive 0.3\u2009wt% trimesoyl chloride (TMC) solution into the frame to cover the surface, which was allowed to react for 1\u2009min. When the reaction was complete, the excess hexane solution was drained, and the resulting membrane was dried at 60\u2009\u00b0C for 30\u2009min. Specifically, DP-M represents a PA membrane that was prepared following the above synthesis procedure using a mixture of DAT-NH2 (0.06\u2009wt%) and polyethyleneimine (PEI) (0.44\u2009wt%) as the amine monomer. The P-M benchmark membrane was fabricated solely by using PEI as the amine monomer. All the as-synthesized PA membranes were stored in deionized water at 5\u2009\u00b0C for further characterization and performance tests. The detailed preparation process of the PA nanofilm is included in the\u00a0Supplementary Information (Supplementary Note\u00a01). Two model membranes were prepared by replacing DAT-NH2 with 3,5-diamino-4/1-(3-aminopropyl)\u22121,2,4-triazole (DAAT-NH2) and 1,2,4-triaminobenzene, respectively, under identical interfacial polymerization conditions. Large-sized DP-M membranes (1\u00d70.3\u2009m) were fabricated using a custom-made interfacial polymerization apparatus under the same reaction conditions as the laboratory-scale preparation.\n\nThe successful synthesis of DAT-NH2 isomers was confirmed by mass spectrometry (MS, MSQ Plus, USA) and high-performance liquid chromatography (HPLC, Ultimate 3000 RS, USA). The chemical structure of DAT-NH2 was characterized by proton nuclear magnetic resonance (1H NMR) spectroscopy (Bruker AVANCE AV400, USA). The chemical features of the PA membranes were also analyzed via Fourier transform infrared spectroscopy (FT-IR, Nicolet IN10, Thermo Fisher, USA) and X-ray photoelectron spectroscopy (XPS, Escalab 250Xi, Thermo Fisher, USA). The membrane surface morphology and roughness were examined via field emission scanning electron microscopy (FESEM, Quanta 250 FEG, FEI, USA) and atomic force microscopy (AFM, Nano Wizard 4, Bruker, Germany). The membrane cross-sectional morphology was identified via high-resolution transmission electron microscopy (TEM, FEI Tecnai G2 F30, FEI, USA). Surface hydrophilicity was assessed via water contact angle measurements on a contact angle goniometer (HARKE-SPCA, HARKE, China). The surface zeta potential was measured via a SurPASS electrokinetic analyzer (Anton Paar, GmbH, Austria). The molecular weight cutoff (MWCO) and pore size distribution of the membrane were obtained via solute retention tests using polyethylene glycol (PEG) probes with different molecular weights. The thermal behavior related to the hydration state of the ions within the membrane matrix was characterized using a TA Q2000 differential scanning calorimeter (TA Instruments, USA). The detailed procedures for each measurement are included in the\u00a0Supplementary Information (Supplementary Notes\u00a02\u20133).\n\nThe separation performance of the PA membranes was characterized in nanofiltration mode at 23\u2009\u00b0C via a cross-flow filtration apparatus with an effective membrane filtration area of 6.0\u2009cm2. Before data collection, the membrane sample was conditioned at a pressure of 2\u2009bar greater than the intended test pressure until the water flux stabilized. The pure water flux (Jw, L m\u22122 h\u22121, abbreviated as LMH) was measured using deionized water as the feed, and the water permeance (A, LMH/bar) was calculated via Eq. (1).\n\nwhere \u2206P (bar) is the trans-membrane hydraulic pressure, \u2206V (L) is the volume of permeate water collected during a time interval of \u2206t (h), and S (m2) is the effective membrane filtration area.\n\nThe membrane ion sieving ability was examined via rejection tests that were conducted under various conditions using a wide spectrum of inorganic salts as solutes. Specifically, Na2SO4, Li2SO4, MgSO4, MgCl2, LiCl, NaCl, RbCl, KCl, NiCl2, CaCl2, and ZnCl2 solutions with different concentrations and compositions were used as the feed. The single salt rejection (R, %) was calculated via Eq. (2).\n\nwhere Cp and Cf are the salt contents of the permeate and feed, respectively. The salt concentration was determined via conductivity measurement via a SevenCompact\u2122 S230 (Mettler Toledo) conductivity meter. Binary mixtures of MgCl2 and LiCl with different mass ratios were used to evaluate the membrane selectivity for co-cation fractionation. The Li+/Mg2+ separation factor (SLi+/Mg2+) was calculated via Eq. (3).\n\nwhere Cf Mg2+ and Cf Li+ and where Cp Mg2+ and Cp Li+ represent the concentrations of Mg2+ and Li+ in the feed and permeate, respectively. An inductively coupled plasma optical emission spectrometer (ICP\u2012OES, iCAP 7000, Germany) was used to quantify the ion contents of the solution. Each data point was tested three times under the same conditions using randomly selected membrane samples, and the average value was reported.\n\nThe long-term stability of the membrane was evaluated by monitoring the water flux and salt rejection for up to 120\u2009h at 6\u2009bar using 1000 ppm MgCl2 solution as the feed. The pH stability of the membrane was assessed by measuring the water flux and salt rejection in a feed pH range of 1\u2009\u2212\u200913 using 1000 ppm LiCl solution as the probe feed, during which the solution pH was adjusted via HCl and NaOH. The concentrations of the single salt solution and binary mixed solution (MgCl2/LiCl mass ratio of 20) involved in the experiment were 1000 and 2000 ppm, respectively. All nanofiltration tests were performed at room temperature (25\u2009\u00b0C), and no additional pH adjustment was made to the feed except for the pH influence tests. The chlorine resistance of the membrane was evaluated by immersing the membrane samples in aqueous sodium hypochlorite solution (NaClO, 200 ppm, pH = 6) under continuous stirring. To ensure that the chlorine concentration remained consistent throughout the exposure period, the NaClO solution was replaced every 24\u2009h. After a specified exposure duration, the membranes were thoroughly rinsed with deionized water to eliminate residual chemicals and then subjected to nanofiltration performance tests. The chlorine tolerance was assessed by measuring the MgCl2 rejection and water flux using a 1000 ppm MgCl2 solution as the probe feed under a transmembrane pressure of 6\u2009bar. Antifouling performance was evaluated using a cross-flow filtration system at 6\u2009bar. A baseline water flux was recorded over 2\u2009h using a 1000 ppm MgCl2 solution as feed. The membrane was then exposed to 200 ppm DTAB in a 1000 ppm MgCl2 solution for 8\u2009h to monitor flux degradation. After three 10-minute deionized water rinse cycles, the recovered flux was evaluated using a 1000 ppm MgCl2 solution for 2\u2009h.\n\nDensity functional theory (DFT) atomistic calculations were conducted via ORCA quantum chemistry software (version 5.0.4)42,43,44. The binding energy and ion hydration energy were obtained via single-point energy calculations. Electrostatic potential (ESP)45,46, average local ionization energy (ALIE)47, interaction region indicator (IRI)41, independent gradient model based on Hirshfeld partition (IGMH)48, and molecular polarity index (MPI)49 analyses were performed with the Multiwfn software package to gain insights into molecular electronic properties and interaction patterns50. LAMMPS and GROMACS were employed for molecular dynamics (MD) simulations51,52, where LAMMPS was used to calculate the free volume fraction of the PA nanofilm, whereas GROMACS was applied to analyze the distribution and binding energy of water molecules around specific PA molecular segments. These simulations enabled a detailed exploration of water\u2012PA interactions, which is crucial for understanding the hydration behavior and separation performance of the membrane. 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Commun. 271, 108171 (2022).\n\nArticle\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (22125603 & 92475205 to G.H.), the Tianjin Applied Basic Research Diversified Investment\u2013Urban Fire Protection Project (Grant No. 24JCQNJC00010 to G.H.), the General Program of Tianjin Natural Science Foundation (Grant No. 24JCYBJC01550 to G.H.), and the Fundamental Research Funds for the Central Universities (040-63253198 & 040-63243125 to G.H.). Special thanks are also made to the Han Gang Research Lab members for their helpful suggestions related to the characterization of materials.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "College of Environmental Science and Engineering, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Nankai University, 38 Tongyan Road, Tianjin, China\n\nZhenxiang Pan,\u00a0Yalong Lei,\u00a0Tiange Yan,\u00a0Fuxin Zheng,\u00a0Yu Liao,\u00a0Jiang Zhan,\u00a0Tong Zhang\u00a0&\u00a0Gang Han\n\nState Key Laboratory of Urban Water Resource and Environment, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, China\n\nLu Shao\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nZ.X.P., Y.L.L., and G.H. designed research; Z.X.P. and Y.L.L. performed research; Z.X.P., Y.L.L., T.G.Y., F.X.Z., Y.L., J.Z., and T.Z. analyzed data; Z.X.P., Y.L.L., L.S. and G.H. wrote the paper.\n\nCorrespondence to\n Lu Shao or Gang Han.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Niveen Khashab and the other, anonymous, reviewers for their contribution to the peer review of this work. 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carrier creation in colloidal gold", + "pre_title": "The Dynamics of Plasmon-Induced Hot Carrier Creation in Colloidal Gold", + "journal": "Nature Communications", + "published": "07 March 2025", + "supplementary_0": [ + { + "label": "Supplementary information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57657-1/MediaObjects/41467_2025_57657_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57657-1/MediaObjects/41467_2025_57657_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57657-1/MediaObjects/41467_2025_57657_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-57657-1#Sec8" + ], + "code": [], + "subject": [ + "Chemical physics", + "Nanoparticles", + "Photochemistry" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3799527/v1.pdf?c=1741352742000", + "research_square_link": "https://www.researchsquare.com//article/rs-3799527/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-57657-1.pdf", + "preprint_posted": "08 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "There is an increasing interest in nonequilibrium \u201chot\u201d carrier generation, created by the decay of collective electronic oscillations on metals known as surface plasmons. Despite extensive efforts, direct observation of the mechanism responsible for generating hot carriers due to plasmon decay has proven challenging. Here, the dynamics of hot carrier generation on gold nanoparticles (Au NPs) are followed with unparalleled detail through ultrafast X-ray absorption spectroscopy (XAS) at the X-ray free-electron laser (XFEL). In Au NPs, the plasmon dephases after 25 fs and the hot carrier population peaks within 105 fs, reaching thermal equilibrium within 1.5 ps. The nonequilibrium carriers display an energy dispersion governed by the density of states of the metal, with some carriers possessing energies surpassing that of a single photon, consistent with the involvement of an Auger heating mechanism distinct from the expected impact excitation that dominates the carrier multiplication step. The most energetic carriers exhibit relatively shorter lifespans, a property that may be critical for exploiting them in applications. This study substantiates hot carrier formation through nonradiative decay as the main decay channel of plasmon resonance. The proposed methodology provides a straightforward approach for real-time tracking of plasmon-induced hot carrier dynamics.Physical sciences/Nanoscience and technology/Nanoscale materials/NanoparticlesPhysical sciences/Chemistry/Physical chemistry/Excited states", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-799527/v1/335931065146afc12fc8303d.png", + "https://assets-eu.researchsquare.com/files/rs-799527/v1/6c5506bf27d18fdb0e275ccf.png", + "https://assets-eu.researchsquare.com/files/rs-799527/v1/82885d5a1665bb86650b4e0a.png" + ] + }, + { + "section_name": "Introduction", + "section_text": "Surface plasmons, the collective oscillations of conduction electrons in metallic nanostructures, have emerged as an essential elementary excitation in condensed matter, giving rise to multiple practical applications. They can capture distant radiation and focus it within subwavelength regions, defying diffraction limits,1,2 resulting in potent near-fields and profound field amplifications.3 These attributes have propelled innovative applications of plasmonics, such as highly sensitive biosensing,4 photothermal therapy for cancer,5 photovoltaics,6,7 and photocatalysis. 8\u00a0\nSurface plasmons exhibit finite lifetimes, decaying either by photon emission (radiatively) or the creation of electron-hole pairs (nonradiatively). Over the past decade, the radiative decay pathway has been researched extensively, yielding the development of efficient nanoantennas that amplify and steer emissions from individual emitters.9,10 Recent research has focused on leveraging nonradiative decay for applications.11 Hot carriers can initiate chemical reactions in adjacent molecules, even those that demand high energy under conventional thermal conditions.12,13 Moreover, plasmon-induced hot carriers offer a potent means to transform light into electrical currents,14 fostering novel solar energy converters15 and circumventing the bandgap limitations of traditional photodetectors.16\u00a0\nWhile the direct excitation of hot carriers on metal surfaces using high-intensity laser pulses has been a longstanding practice in surface femtochemistry, exploiting surface plasmon decay to amplify hot carrier generation is a recent development. This significant advance stems from the remarkably boosted light harvesting ability of collective plasmon excitations, combined with the substantial enhancement of the plasmon-induced field when metals are nano-confined. Comprehending the underlying physical mechanisms driving plasmon-induced hot carrier generation is essential to leverage these benefits fully. Although theoretical frameworks elucidating this phenomenon exist,17-18[i]19[ii]20[iii]21[iv]22 a suitable experimental methodology to validate these models still needs to be developed.\nX-ray absorption spectroscopy (XAS) provides a way to investigate the interplay between X-ray photons and matter, simultaneously unveiling unparalleled insights into a material\u2019s electronic and chemical characteristics. When X-ray photons are directed toward a material, they can be absorbed by core electrons, resulting in these electrons shifting to higher energy states. The precise energy at which this absorption occurs depends on the specific element's electronic structure and its local environment. Hot carriers emerge from the interaction between external electric fields and valence electrons, creating electrons and holes with energies above and below the Fermi level (EF).\nTransient XAS (aka time-resolved XAS (TR-XAS)) probes empty states around theFermi energy and, in the case of d10 metals with the L3-edge transition, provides direct information about the amount of carrier participation and their nonequilibrium energy distributions.23 At synchrotrons, such dynamical measurements are typically hampered by limited temporal resolution (~ 5 ps) and photon density,24 impeding real-time observations of the hot carrier generation process.8 However, this limitation has been surpassed by the advent of hard X-ray free electron lasers (XFELs),25 capable of delivering intense and ultrashort hard X-ray pulses (up to 30 keV at the European XFEL26 and 12 keV at SwissFEL (used in this study) 27) of less than 50 fs in duration.27,28 With this unique combination of high photon energies and ultrashort pulses, time-resolved XAS has become an exceptionally valuable experimental probe of dynamical processes. Typical time-resolved measurements are implemented in a pump-probe scheme, where an optical-frequency pump laser triggers electron dynamics, and the X-ray probe captures the evolving nonequilibrium electron distribution. Over the past few years, femtosecond TR-XAS studies have been used to probe photoinduced electronic and structural changes in photoexcited transition metal oxides29 and complexes.30 In this study, TR-XAS was used to observe the generation and relaxation of plasmon-induced hot carriers in gold nanoparticles directly.31,32\u00a0\nThe widely accepted understanding of how localised surface plasmon resonance (LSPR) excitation leads to hot carrier formation and subsequent thermalisation, including the hypothesised timescales for each process, is summarised in Figure 1A.8,22 Briefly, the light electric field induces a coherent excitation of Au valence electrons. The excited electrons' coherence dephases due to Landau damping after the light excitation elapses. The process is expected to take 10-100 fs, resulting in a non-Fermi-Dirac distribution of hot carriers. The carriers undergo multiplication, eventually reaching a Fermi-Dirac distribution and thermal relaxation after ~1 ps. This description of hot carrier formation has been deduced from physical models that underpin our understanding. Still, it has never been validated experimentally due to the lack of element-specific techniques with sufficient temporal resolution. However, the attempts from Bigot et al.33 and Lehmann et al.34 with femtosecond optical pump-probe investigations with ionising probe pulses, which provided earlier evidence for hot electrons and their dynamics, should be mentioned. Nevertheless, no information could be extracted about the hot holes.\nFigure 1B illustrates the TR-XAS approach for tracking the density of states (DOS) changes induced by LSPR excitation. More specifically, the study focuses on the X-ray absorption near edge structure (XANES) part of the XAS spectrum, which contains the electronic changes in the element, i.e., information on LSPR-induced hot carrier formation. The transient data was collected using the classic pump-probe methodology for optical spectroscopy. The technique involves \"pumping\" a sample with an initial laser pulse and then \"probing\" it with a delayed pulse to observe the changes induced by the pump pulse. In the present case, the probe is an fs X-ray pulse from the XFEL.\u00a0To prevent the excitation of damaged Au NPs induced by intense XFEL pulses, a liquid jet was employed to circulate the Au NPs and the solution was refreshed every four hours.\nSince nanoparticle measurements at XFELs are uncommon, it was essential to validate that the XANES spectra collected with this radiation represent the sample. Figure S1 shows the steady-state XANES spectra of Au foil and nanoparticles measured at the Au L3-edge transition (2p3/2\u00e0\u00a05d) at the synchrotron (Solaris synchrotron, Poland). Au has a [Xe] 4f14 5d10 6s1 electronic structure, i.e., with a filled d-shell, which results in a slight absorption edge only visible due to some level of s-d shell hybridisation. For comparison purposes, the signal was plotted against Pt ([Xe] 4f14 5d9 6s1) (Fig. S2), revealing the method sensitivity to empty states within the metal 5d shell and, to some extent, the s-shell due to this hybridisation. In this study, Au NPs with an average particle size of 8 \u00b1 2 nm were used, as confirmed by atomic force microscopy (AFM) and dynamic light scattering (DLS) (Figs. S3 and S4). The Au NPs have a LSPR centered at nominally 520 nm (2.38 eV) according to UV-vis spectroscopy (Fig. S5).\u00a0\nThe steady-state XANES analysis established that the Au NPs exhibit an electronic structure resembling bulk gold, as reported elsewhere.22,35 This agreement is further corroborated by our theoretical calculations, showing the evolution of the DOS as function of particle size (Fig. S6). The unexcited XANES spectrum of the Au NPs, measured at XFEL (SwissFEL, Switzerland) and the synchrotron, displayed a consistent shape. This consistency supports the applied methodology's ability to capture the transient alterations in the electronic structure of gold before the sample gets damaged, i.e., probe-before destruction concept.36, 37 XFELs have only recently provided access to hard X-ray energies, allowing one for the first time to probe the Au L3-edge.\nUltrafast time-resolved XANES data were acquired with the XFEL source as a probe, following the excitation of 5 mM Au NPs at 532 nm (~ 2.33 eV), utilising a 15 nm full width at half maximum (FHWM) bandwidth, a pulse duration of approximately 75 fs, and a power density of 98 mJ/cm2 (equivalent to 4 \u00b5J within a 60 x 60 \u00b5m2 spot). The choice of this precise plasmon excitation energy was to induce LSPR through intra-band s- to s-shell excitation while minimising interband excitation (d- to s-shell excitation). The centre of the Au d-shell is located at 2.5-2.58 eV (~\u00a0496-480 nm) from the metal Fermi level (EF),38,39 meaning that the laser pulse with 2.33\u00b10.13 eV (15 nm FHWM) photon energy can only excite the low energy tail of the d-shell at best. Figure 1C compares the XANES spectra of unexcited (unpumped spectrum) and excited (pumped spectrum) recorded at Dt =100 fs time delay after excitation at 532 nm. Optical excitation induced a spectral downshift in energy and decreased XANES whiteline intensity, corroborating that it induced changes in the gold electronic structure around its Fermi-level energy, and the TR-XAS can track the changes.\u00a0\nTo better illustrate the results, the XANES difference spectrum (pumped-unpumped XANES spectra) is also shown in Figure 1C. The difference spectrum is dominated by the positive signal below and a negative signal above the Au EF.\u00a0Transient L3-edge XANES readily capture changes in the density of unoccupied states, particularly those induced in the d-shell, either directly or through processes like hybridisation with the s-shell. Accordingly, a positive signal correlates with an increase in density of states (DOS); conversely, a negative signal (i.e., a bleached signal) indicates a decrease in empty states. Therefore, the positive signal below the Au EF is ascribed to the formation of a hot hole population induced by the plasmon optical excitation. In contrast, hot electrons give rise to the negative signal above the Au EF, consistent with empty states filling. The transient signal directly demonstrates the generation of hot carriers through LSPR decoherence via Landau damping (the non-radiative pathway dominant in small nanoparticles).21,24,40 Most notably, the hot hole and electron signals are neither symmetric nor have the same integrated magnitude. This is related to the XANES higher sensitivity to empty states formation and the L3-edge transition changes in the d-shell that is part of the valence, where the hot holes are formed.\nTo establish the time scales for plasmon damping (g) and the average lifetime of carriers (t), kinetic traces were extracted at the maximum of the hot hole intensity (11916 eV, 2.5 eV below Au EF (Figure 2B)) and the excited electron intensity (11922 eV, 3.0 eV above Au EF (Figure 2C)) populations, as depicted in (Figure 2A). The kinetic data from the time scans were fitted by a model published elsewhere,41 described in SI equations S1 and S2. In brief, the data collected at 11916 and 11922 eV were fitted with a convolution of temporal instrument response function (Gaussian) with a monoexponential decay (with a time constant ). The resulting fit is the solid green in Figure 2B. Due to the low signal-to-noise ratio for the hot electron data, the error bars are relatively large. However, qualitatively, it is possible to see that the signal has dynamics similar to the hot holes.\u00a0\nThe \u03b3 time can be extracted from the transient signal onset time because it is the point at which the Au DOS starts to change, i.e., the fingerprint for hot carrier formation. In this particular case, it was estimated to be 24.6 \u00b1 6 fs, corroborating that plasmon decoherence occurs between 10-100 fs.24 Following plasmon damping, the hot carriers undergo a carrier multiplication reaching a maximum at 105 \u00b1 8 fs, estimated from rising edge analysis. The lifetimes of the hot carriers were determined from a single exponential decay to be 498 \u00b1 35 fs with complete carrier thermalisation occurring within 1.5 ps. These time constants align with previous postulations24 but are here substantiated through direct measurement. The confirmed ultrafast hot carrier dynamics in plasmonic nanoparticles are the primary bottleneck in plasmonic applications.\u00a0\nTo estimate the number of electrons engaged when exciting 5 mM Au NPs at 532 nm, utilising a 15 nm full width at half maximum (FHWM) bandwidth, a pulse duration of approximately 75 fs, and a power density of 98 mJ/cm2, the positive signal variance at 0 and 100 fs were integrated. This integrated signal was then juxtaposed with the signal difference between the Au and Pt L3-edges (Fig. S2). Note that the signal difference between Au and Pt relates to 1e- less in Pt valence states, i.e., the integrated positive signal of the difference between Pt and Au corresponds to the equivalent of having 1e- from each Au atom participating in the resonance. Employing this simple methodology, we estimated that each gold atom contributed with 0.19e- at the start of the resonance, which underwent multiplication until 105 fs, reaching a maximum of 0.46e- from each Au atom contributing to hot carrier formation at this excitation power.\nAssuming an excitation volume of 60 x 60 x 100\u00b5m3 and considering the Au solution concentration (5 mM), one can expect 1.5 x 108 nanoparticles in the excited volume. An 8 nm Au NP has \u00bb12000 atoms, equating to about 1.8 x 1012 Au atoms in the excited volume. The photon density in the optical pulses is about 1013, from which 20% is absorbed according to UV-Vis, implying that the excited volume absorbs around 2x1012 photons. This suggests an excitation of about 1e- per atom of Au, from which 19% are converted into hot carriers at the onset, multiplying to about 46% within 100 fs. The observation suggests that hot carrier generation is a prime decay channel of Au LSPR and undoubtedly the most significant mechanism in nonradiative decay.\nAfter verifying the generation of hot carriers, the next step is the investigation of the dynamics of their energy distribution - a significant yet elusive aspect in the realm of plasmonic hot carriers, particularly when it comes to holes. Our understanding is derived mainly from theoretical studies20,4243 and indirect techniques.22,33,34,44 For example, internal quantum efficiency measurements have inherent limitations as they solely quantify carriers injected into an acceptor layer, like a semiconductor, failing to provide insights into the dynamic behaviour of the carriers in the metal. While the hot electrons can only populate the empty states within the sp-shells, the holes can be in sp- and d-shells, confirmed by valence band \u2013 X-ray photoelectron spectroscopy (VB-XPS) shown in Figure 3A. It is evident when the VB-XPS is overlapped with the transient XANES spectrum (recorded at time zero) that the generated holes are indeed located throughout the entire valence, including the d-shell, despite the optical pulse energy allowing primarily sp-shell excitation.\nFigure 3B shows the energy distribution and population of the carriers at different time delays after excitation. As expected, the plasmonic excitation depopulates and populates states below and above the Fermi energy. The ultrafast carrier-carrier interactions during dephasing and multiplication determine their energy and respective population. The hot carrier energy distribution goes beyond single photon energy for hot electrons and holes. Moreover, it is noticeable that both carrier populations and the width of their energy distributions increase until about 100 fs, decreasing asymptotically after that. \u00a0A slight asymmetry exists between hot electron and hot hole populations, which cannot be fully explored here due to the probe's lower sensitivity to hot electrons.\nThe rapid depopulation of electrons in the d-shell is expected due to the broad energy overlap between the d and sp-band, which provides a high density of d-electrons that couples with the plasmonic resonance and dissipates its energy.43 However, this does not explain the observation of carriers having energies above the photon energy, even considering that the Au core-hole lifetime broadening is 5.41 eV at the L3-edge,45 which inevitably broadens the energy scale. Achieving precise energy distributions of carriers requires high-resolution measurements,46 which implies extended acquisition times rarely offered at XFEL facilities. Nonetheless, hot holes are distributed across the entire valence electronic structure, and their energy distribution increases up to 250 fs (Figure 3C) before relaxing. These two observations indicate the involvement of carrier multiplication mechanisms that can increase the carrier population and their energy distribution, an effect that has yet to be reported.43 Note that the low optical laser fluency and short pulse duration used in this experiment make it highly unlikely that multiphoton excitation of single electrons occurs.\nRegarding carrier multiplication, there are two scattering mechanisms: impact excitation and Auger heating,47,48 The predominant mechanism in carrier multiplication is impact excitation, where an excited electron (hole) undergoes Coulomb scattering, losing energy and momentum and giving rise to an additional electron-hole pair. The distinctive feature of impact excitation is a rise in the number of carriers and a simultaneous reduction in their energy. Conversely, Auger heating characterises the non-radiative recombination of an electron with a hole, where the energy and momentum are transferred to an electron (hole) within the same shell. The hallmark of Auger heating is a decline in the number of carriers and an increase in their energy. \u00a0\nTo enhance the visualisation and comprehension of the hot hole multiplication process, the shape of different spectra regarding charge width and charge energy was analysed (see Figure 3C). The details of the data analysis procedure are outlined in the SI. Commencing with the average hot carrier distribution energy, it remained constant until 100 fs before exhibiting a subsequent decrease. This implies the LSPR dephasing process extends to 100 fs, increasing the nonequilibrium hot carrier population through the impact excitation scattering mechanism. However, examining the hot carrier distribution width, reflecting the energy distribution of the hot holes unveils a relative surge in the energy distribution beyond the time when the hole population is at its maximum (approximately 100 fs), i.e., the energy distribution of hot holes increases up to 250 fs. This observation is noteworthy, especially considering this process competes with hole thermalisation, occurring within tens of femtoseconds in metals. The broadening induced by the core hole relaxation cannot account for the increase in distribution width, as it should have smeared the energy resolution from the outset, preventing the difference signal from accurately reflecting the valence state of gold. This suggests the involvement of a mechanism that generates carriers with higher energy than the dephasing process produces - specifically, the participation of Auger heating. This mechanism has yet to be considered in plasmon relaxation dynamics, altering the current understanding of hot carrier formation, multiplication, and relaxation in plasmonic materials.\nIn this work, we presented the results from an ultrafast X-ray absorption experiment conducted at the XFEL involving citrate-capped gold nanoparticles excited at their LSPR with minimum intraband excitation. This experiment enabled the real-time observation of the generation and subsequent relaxation of hot carriers. The plasmon damping was determined to be 25 fs, with a maximum hot carrier population of 0.46e- from each Au atom detected at 105 fs after excitation. The lifetimes of the hot carriers were estimated to be 498 fs, with complete carrier thermalisation occurring within 1.5 ps. Energy scans conducted at varying delay times revealed that the energies of these carriers conform to the density of states of the metal, with some carriers possessing energies that exceed the photon energy, consistent with an Auger heating scattering mechanism. The observation impacts hot carrier applications, particularly those that are based on the energy of the hot carriers, such as photocatalysis and photovoltaics. For instance, without the Auger process, chemical reactions with redox windows larger than photon energy could not be catalysed. Similarly, the open circuit voltage of photovoltaic devices could not exceed the voltage offered by a single photon. The novel insight into plasmon induced hot carrier generation and dynamics provided here is likely to significantly impact applications for years to come.\u00a0\n\n[i]. Kornbluth, M., Nitzan, A., Seideman, T. Light-Induced Electronic Non-Equilibrium in Plasmonic Particles. J. Chem. Phys. 138, 174707 (2013).\n\n\n[ii]. Govorov, A. O., Zhang, H., Demir, H. V., Gun'ko, Y. K. Photogeneration of Hot Plasmonic Electrons with Metal Nanocrystals: Quantum Description and Potential Applications. Nano Today 9, 85\u2013101 (2014).\n\n\n[iii]. Manjavacas, A., Liu, J. G., Kulkarni, V., Nordlander, P. Plasmon-Induced Hot Carriers in Metallic Nanoparticles. ACS Nano 8, 7630\u20137638 (2014).\n\n\n[iv]. Rossi, T. P., Erhart, P., Kuisma, M. Hot-Carrier Generation in Plasmonic Nanoparticles: The Importance of Atomic Structure. ACS Nano 14, 9963-9971 (2020).\u00a0\n", + "section_image": [] + }, + { + "section_name": "Declarations", + "section_text": "Acknowledgements\nWe acknowledge the Paul Scherrer Institut, Villigen, Switzerland, for providing beamtime at the Alvra beamline of the SwissFEL facility. We also acknowledge SOLARIS National Synchrotron Radiation Centre, Krakow, Poland, for the access to the ASTRA and PHELIX beamline. The simulations were performed using computational resources provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX and NSC, for which we want to thank.\nFunding: J.Sa acknowledges funding from Olle Engkvists Stiftelse (grant no. 210-0007), Knut & Alice Wallenberg Foundation (Grant No. 2019-0071) and Swedish Research Council (grant no. 2019-03597). A.W. acknowledges funding from the European Union\u2019s Horizon 2020 research and innovation program under Marie Sk\u0142odowska-Curie grant agreement no. 884104 (PSI-FELLOW-III-3i). N.J.H. and P.N. acknowledge support from the Robert A. Welch Foundation under grants C-1220 and C-1222 and\u00a0the Air Force Office of Scientific Research via the Department of Defense Multidisciplinary University Research Initiative under AFOSR Award No. FA9550-15-1-0022. The work is partially supported under the Polish Ministry and Higher Education project: \u201cSupport for research and development with the use of research infrastructure of the National Synchrotron Radiation Centre SOLARIS\u201d under contract nr 1/SOL/2021/2. The work is partially funded by the National Science Centre in Poland under grant number 2020/37/B/ST3/00555.\u00a0\nAuthor contributions: Conceptualization and methodology: A.W., J. Sz. and J.Sa; formal data analysis: A.W., J. Sz. and J.Sa; experimental investigations: A.W, C.B., C.C., P.J.M.J., R.G.C., V.R.S., P-B., J.K., A.M., T.S., E.P.-J. and J.Sa; data visualisation concepts: A.W., J. Sa, J.Sz and N.J.H., draft preparation: A.W., N.J.H., J. Sz. and J.Sa; writing-review and editing: all the authors. All authors have read and agreed to the published version of the manuscript.\nCompeting interests: The authors declare that they have no competing interests.\u00a0\nData and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "\nNovotny, L., Hecht, BPrinciples of Nano-OpticsCambridge University Press: New York, (2006).\nMaier, SAPlasmonics: Fundamentals and ApplicationsSpringer: New York (2007).\nHalas, NJ., Lal, S., Chang, W., Link, S., Nordlander, PPlasmons in Strongly Coupled Metallic NanostructuresChemRev111, 3913\u20133961 (2011).\nXu, H., Bjerneld, EJ., K\u00e4ll, M., B\u00f6rjesson, LSpectroscopy of Single Hemoglobin Molecules by Surface Enhanced Raman ScatteringPhysRevLett83, 4357\u20134360 (1999).\nO'Neal, DP., Hirsch, LR., Halas, NJ., Payne, JD., West, JLPhoto-Thermal Tumor Ablation in Mice Using Near Infrared-Absorbing NanoparticlesCancer Lett209, 171\u2013176 (2004).\nAtwater, HA., Polman, APlasmonics for Improved Photovoltaic DevicesNatMater9, 205\u2013213 (2010).\nGeng, X., Abdellah, M., Vadell, RB., Folkenant, M., Edvinsson, T., 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conditionsNatNanotechnol13, 953\u2013958 (2018).\nGarc\u00eda de Arquer, FP., Mihi, A., Kufer, D., Konstantatos, GPhotoelectric Energy Conversion of Plasmon-Generated Hot Carriers in Metal-Insulator-Semiconductor StructuresACS Nano 7, 3581\u20133588 (2013).\nSchwede, JW., Bargatin, I., Riley, DC., Hardin, BE., Rosenthal, SJ., Sun, Y., Schmitt, F.; Pianetta, P., Howe, RT., Shen, Z.-X.; et alPhoton-Enhanced Thermionic Emission for Solar Concentrator SystemsNatMater9, 762\u2013767 (2011).\nKnight, MW., Sobhani, H., Nordlander, P., Halas, NJPhotodetection with Active Optical AntennasScience 332, 702\u2013704 (2011).\nWhite, TP., Catchpole, KRPlasmon-Enhanced Internal Photoemission for Photovoltaics: Theoretical Efficiency LimitsApplPhysLett101, 073905 (2012).\nKornbluth, M., Nitzan, A., Seideman, TLight-Induced Electronic Non-Equilibrium in Plasmonic ParticlesJChemPhys138, 174707 (2013).\nGovorov, AO., Zhang, H., Demir, HV., Gun'ko, YKPhotogeneration of Hot Plasmonic Electrons with Metal Nanocrystals: Quantum Description and Potential ApplicationsNano Today 9, 85\u2013101 (2014).\nManjavacas, A., Liu, JG., Kulkarni, V., Nordlander, PPlasmon-Induced Hot Carriers in Metallic NanoparticlesACS Nano 8, 7630\u20137638 (2014).\nRossi, TP., Erhart, P., Kuisma, MHot-Carrier Generation in Plasmonic Nanoparticles: The Importance of Atomic StructureACS Nano 14, 9963-9971 (2020)\nKhurgin, JBFundamental limits of hot carrier injection from metal in nanoplasmonicsNanophotonics 9, 453\u2013471 (2020).\nS\u00e1, J., Tagliabue, G., Friedli, P., Szlachetko, J., Rittmann-Frank, MH., Santomauro, FG., Milne, CJ., Sigg, HDirect observation of charge separation on Au localized surface plasmonEnergy EnvironSci6, 3584\u20133588 (2013).\nHe, J., Liu, M., Yin, C., Liu, Z., Dong, X., Zhang, Z., Wang, JExperimental studies on the X-ray single-pulse jitter at the SSRFNuclear InstMethPhysRes1025, 166038 (2022).\nPellegrini, CThe history of X-ray free-electron lasersEurPhysJH 37, 659\u2013708 (2012).\nhttps://www.xfel.eu/news_and_events/news/index_eng.html?openDirectAnchor=1772 (Accessed on 2023/11/28).\nhttps://www.psi.ch/en/swissfel/accelerator (Accessed on 2023/11/28).\nAlonso-Mori, R., Sokaras, D., Cammarata, M., Ding, Y., Feng, Y., Fritz, D., Gaffney, KJ., Hastings, J., Kao, C.-C., Lemke, HT., et alFemtosecond electronic structure response to high intensity XFEL pulses probed by iron X-ray emission spectroscopyScieRep10, 16837 (2020).\nPark, SH., Katoch, A., Chae, KH., Gautam, S., Miedema, P., Cho, SW., Kim, M., Wang, R.-P., Lazemi, M., de Groot, F., Kwon, SDirect and real-time observation of hole transport dynamics in anatase TiO2 using X-ray free-electron laserNatCommun13, 2531 (2022).\nJay, RM., Banerjee, A., Leitner, T., Wang, R.-P., Harich, J., Stefanuik, R., Wikmark, H., Coates, MR., Beale, EV., Kabanova, V., et alTracking C\u2013H activation with orbital resolutionScience 380, 955 (2023).\nNarang, P., Sundararaman, R., Atwater, HAPlasmonic hot carrier dynamics in solid-state and chemical systems for energy conversionNanophotonics 5, 96\u2013111 (2016).\nBrown, AM., Sundararaman, R., Narang, P., Schwartzberg, AM., Goddard III, W., Atwater, HAExperimental and Ab Initio Ultrafast Carrier Dynamics in Plasmonic NanoparticlesPhysRevLett118, 087401 (2017).\nBigot, J.-Y., Merle, J.-C., Cregut, O., Daunois, AElectron Dynamics in Copper Metallic Nanoparticles Probed with Femtosecond Optical PulsesPhysRevLett75, 4702-4705 (1995).\nLehmann, J., Merschdorf, M., Pfeiffer, W., Thon, A., Voll, S., Gerber, GSurface Plasmon Dynamics in Silver Nanoparticles Studied by Femtosecond Time-Resolved Photoemission PhysRevLett85, 2921-2924 (2000).\nZamponi, F., Penfold, TJ., Nachtegaal, M., L\u00fcbcke, A, Rittmann, J., Milne, CJ., Chergui, M., van Bokhoven, JAProbing the dynamics of plasmon.excited hexanethiol-capped gold nanoparticles by picosend X-ray absorption spectroscopyPhysChemChemPhys16, 23157-23163 (2014).\nNeutze, R., Wouts, R., van der Spoel, D., Weckert, E., Hajdu, JPotential for biomolecular imaging with femtosecond X-ray pulsesNature 406, 752\u2013757 (2000).\nKern, Jet alSimultaneous femtosecond X-ray spectroscopy and diffraction of photosystem II at room temperatureScience 340, 491\u2013495 (2013).\nJohnson, PB., Christy, RWOptical constants of the noble metalsPhysRevB 6, 4370- 4379 (1972).\nDreesen, L., Humbert, C., Celebi, M, Lemaire, JJ., Mani, AA., Thiry, PA., Peremans, Ainfluence of the metal electronic properties on the sum-frequency generation spectra of dodecanethiol self-assemble monolayers on Pt (111), Ag (111) and Au(111) single crystalApplPhysB 74, 621-625 (2002).\nEscande, DF., Doveil, F., Elskens, YBody description of Debye shielding and Landau dampingPlasma PhysControlled Fusion 58, 014040 (2016).\nBacellar, C., Kinschel, D., Mancini, GF., Ingle, RA., Rouxel, J., Cannelli, O., Cirelli, C., Knopp, G., Szlachetko, J., Lima, FAet alSpin cascade and doming in ferric hemes: Femtosecond X-ray absorption and X-ray emission studiesProcNatlAcadSciUSA 117, 21914-21920 (2020).\nLiu, JG., Zhang, H., LinkS., Nordlander, PRelaxation of Plasmon-Induced Hot CarriersACS Photon5, 2584-2595 (2018).\nDouglas-Gallardo, OA., Berdakin, M., Frauenheim, T., S\u00e1nchez, CGPlasmon-induced hot carrier generation diffrences in gold and silver nanoclustersNanoscale 11, 8604-8615 (2019).\nTagliabue, G., Jermyn, AS., Sundararaman, R., Welch, AJ., DuChene, JS., Pala, R., Davoyan, AR., Narang, P., Atwater, HAQuantifying the role of surface plasmon excitation and hot carrier transport in plasmonic devicesNatCommun9, 3394 (2018).\nKrause, MO., Oliver, JHNatural widths of atomic K and L levels, K( X-ray lines and seberal KLL Auger linesJPhysChemRefData 8, 329-338 (1979).\nH\u00e4m\u00e4l\u00e4inen, K., Siddons, DP., Hastings, JB., Berman, LEElimination of the Inner-Shell Lifetime Broadening in X-ray-Absorption SpectroscopyPhysRevLett67, 2850-2853 (1991).\nGierz, I, Calegari, F., Aeschlimann, S., Ch\u00e1vez Cervantes, M., Cacho, C., Chapman, RT., Springate, E., Link, S., Starke, U., Ast, CR., Cavalleri, ATracking primary thermalization events in graphene with photoemission at extreme time scalesPhysRevLett115, 086803 (2015).\nSidiropoulos, TPH., Di Palo, N., Rivas, DE., Severino, S., Reduzzi, M., Bauerhenne, B., Krylow, S., Vasileiades, T., Danz, T., Elliot, P., Sharma, S., et alProbing the energy conversion pathways between light, carriers, and lattice in real time with attosecond core-level spectroscopyPhysRevX 11, 041060 (2021).\n", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Supportinginformation.pdfSupporting information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The generation and dynamics of plasmon-induced hot carriers in gold nanoparticles offer crucial insights into nonequilibrium states for energy applications, yet the underlying mechanisms remain experimentally elusive. Here, we leverage ultrafast X-ray absorption spectroscopy (XAS) to directly capture hot carrier dynamics with sub-50 fs temporal resolution, providing clear evidence of plasmon decay mechanisms. We observe the sequential processes of Landau damping (~25\u2009fs) and hot carrier thermalization (~1.5\u2009ps), identifying hot carrier formation as a significant decay pathway. Energy distribution measurements reveal carriers in non-Fermi-Dirac states persisting beyond 500\u2009fs and observe electron populations exceeding single-photon excitation energy, indicating the role of an Auger heating mechanism alongside traditional impact excitation. These findings deepen the understanding of hot carrier behavior under localized surface plasmon resonance, offering valuable implications for applications in photocatalysis, photovoltaics, and phototherapy. This work establishes a methodological framework for studying hot carrier dynamics, opening avenues for optimizing energy transfer processes in nanoscale plasmonic systems.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Surface plasmons, the collective oscillations of conduction electrons in metallic nanostructures, are recognized as an essential elementary excitation in condensed matter, giving rise to multiple practical applications. They can capture far-field radiation and focus it within subwavelength regions, defying diffraction limits1,2, resulting in potent near-fields and significant field amplifications3. These characteristics have propelled innovative applications of plasmonics, such as highly sensitive biosensing4, photothermal therapy for cancer5, photovoltaics6,7, and photocatalysis8.\n\nSurface plasmons exhibit finite lifetimes, decaying either by photon emission (radiatively) or the creation of electron-hole pairs (nonradiatively). Over the past decade, the radiative decay pathway has been researched extensively, yielding the development of efficient nanoantennas that amplify and steer emission from individual emitters9,10. Recent research has focused on leveraging nonradiative decay for applications11. Hot carriers can initiate chemical reactions in adjacent molecules, even those that demand high-energy under conventional thermal conditions12,13. Moreover, plasmon-induced hot carriers offer a potent means to transform light into electrical currents14, fostering original solar energy converters15 and circumventing the bandgap limitations of traditional photodetectors16.\n\nWhile the direct excitation of hot carriers on metal surfaces using high-intensity laser pulses has been a longstanding practice in surface femtochemistry, exploiting surface plasmon decay to intensify hot carrier generation is a recent development. This significant advance stems from the remarkably boosted light-harvesting ability of collective plasmon excitations, combined with the substantial enhancement of the plasmon-induced field when metals are nano-confined. Comprehending the underlying physical mechanisms driving plasmon-induced hot carrier generation is essential to fully leveraging these benefits. Although theoretical frameworks elucidating this phenomenon exist17,18,19,20,21,22, suitable experimental methodologies are still needed to validate these models.\n\nX-ray absorption spectroscopy (XAS) provides a way to investigate the interactions between X-ray photons and matter, simultaneously providing insight into a material\u2019s electronic and chemical characteristics. When X-ray photons are directed toward a material, they can be absorbed by core electrons, resulting in these electrons\u2019 transitions to higher energy states. The precise energy at which this absorption occurs depends on the specific material\u2019s electronic structure and local environment, making the technique element-specific and highly sensitive.\n\nTransient XAS (or time-resolved XAS (TR-XAS)) probes empty states around the Fermi energy and, in the case of d10 metals with the L3-edge transition, provides direct information on the number of carriers involved in electronic transitions and their nonequilibrium energy distributions23. At synchrotrons, such dynamical measurements are typically hampered by limited temporal resolution (\u223c50-100\u2009ps) and photon density24, impeding real-time observations of the hot carrier generation process8. However, this limitation has been surpassed by the advent of hard X-ray free electron lasers (XFELs)25, capable of delivering intense and ultrashort hard X-ray pulses (up to 30\u2009keV at the European XFEL and 12\u2009keV at SwissFEL (used in this study) of less than 50\u2009fs in duration26. With this combination of high photon energies and ultrashort pulses, TR-XAS has become an exceptionally valuable experimental probe of dynamical processes. Typical time-resolved measurements are implemented in a pump-probe scheme, where an optical-frequency pump laser triggers electron dynamics, and the X-ray probe captures the evolving nonequilibrium electron distribution. Over the past few years, femtosecond TR-XAS studies have been used to probe photoinduced electronic and structural changes in photoexcited transition metal oxides27,28,29,30 and complexes31. In this study, TR-XAS was used to observe the generation and relaxation of plasmon-induced hot carriers in gold nanoparticles (Au NPs) directly because this is an element-specific technique with sufficient temporal resolution. Hot carriers emerge from the interaction between external electric fields and valence electrons, creating electrons and holes with energies above and below the Fermi level (EF). Notably, previous attempts from Bigot et al32. and Lehmann et al33. with femtosecond optical pump-probe investigations with ionising probe pulses provided earlier evidence for hot electrons and their dynamics but could provide no information about hot holes. On the other hand, Pelli Cresi et al.34. investigated the electron transfer process in a hybrid plasmonic/semiconductor system (Ag/CeO2) following photoexcitation of the LSPR in the silver NPs by time-resolved soft X-ray absorption spectroscopy. Their findings reveal that the electronic structure of the cerium atoms undergoes an ultrafast change within the first few hundred femtoseconds and persists for at least up to about 1\u2009ps delay time. Their work focused on the plasmon-mediated charge transfer process, however, it did not provide information about hot carrier formation.", + "section_image": [] + }, + { + "section_name": "Results and discussion", + "section_text": "The mechanism for hot carrier formation and thermalization following localized surface plasmon resonance (LSPR) excitation is illustrated in Fig.\u00a01A8,22. In brief, the light\u2019s electric field coherently excites the valence electrons in gold, and the subsequent decoherence of this plasmonic excitation leads to non-thermal electron distributions. This occurs via intraband transitions, often aided by phonon scattering or transitions from Landau damping and surface collisions22, with a time scale of 10\u2013100\u2009fs8,21. Initially, photon absorption produces an out-of-equilibrium electron energy distribution, which resembles a double-step function in electron occupancy. Over hundreds of femtoseconds, energy redistribution among the electrons progresses until a high-temperature Fermi-Dirac distribution is reached. Finally, electron-phonon interactions lower the electron temperature over the course of a few picoseconds. This process has been well-supported by theoretical models35,36,37,38,39 and confirmed experimentally through pump-probe optical spectroscopy40,41,42,43,44,45, wherein a visible/near-infrared pump excites conduction band carriers, and a probe pulse tracks the time-dependent changes in transmission or reflectivity as the carriers generate and relax. The non-radiative decay of the plasmon resonance further relaxes through phonon-phonon scattering over hundreds of picoseconds, eventually releasing the generated heat to the surroundings over tens of nanoseconds. These latter stages are beyond the scope of this study.\n\nA Illustration of the theorised plasmonic resonance decay mechanism, including hypothesized time constants for each process. B Transmission electron microscopy (TEM) image of a characteristic Au NP of this study. Inset shows a higher-resolution TEM image, revealing the particle\u2019s high crystallinity. C Transient absorption spectroscopy of Au NPs excited at 535\u2009nm with 1.67\u2009mJ/cm2, depicting the characteristic bleach signal and the two positive winglets. D Excitation power-dependent bleach recovery dynamics. Kinetic traces extracted at 500\u2009nm (horizontal dashed line in (C) with double-exponential fits, shown across varying laser fluences.\n\nAu NPs with an average particle diameter of 6.8\u2009\u00b1\u20090.9\u2009nm were used, as confirmed by transmission electron microscopy (TEM, Figs.\u00a01B,\u00a0S1) and dynamic light scattering (DLS) measured in H2O (solvent) (Fig.\u00a0S2). The Au NPs have an LSPR centered at 520\u2009nm (2.38\u2009eV) according to UV-Vis spectroscopy (Fig.\u00a0S3), consistent with previous reports46. The UV-Vis spectra of the solution had an optical absorption at the LSPR maximum of\u2009\u223c\u20090.2, which was used in conjunction with the DLS to validate the homogeneity of concentration and particle size of the colloidal solutions used in the experiments. Transient absorption spectroscopy (TAS) data were acquired using pump-probe methodology, with an excitation (pump) at 535\u2009nm (~2.32\u2009eV) and a white light (400\u2013800\u2009nm) as a probe. A 5 \u00d710-3\u2009M Au NPs aqueous solution was excited with a pump pulse duration of approximately 40\u2009fs and a maximum power density of 1.67\u2009mJ/cm2.\n\nThe TAS data are shown in Fig.\u00a01C. Laser excitation of LSPR using an ultrashort pump pulse initiates the generation of excited electrons and holes above and below the Fermi level. Notably, under standard laser fluences, numerous excited carriers are produced, collectively inducing a bleaching effect on the plasmon resonance observed in the transient absorption spectrum (see Fig.\u00a01C)47. This initial alteration in electron occupation triggered by the pump pulse is usually referred to as a nonthermal distribution, which subsequently transitions through electron-electron scattering into a thermally equilibrated distribution following Fermi-Dirac statistics48. The initial electron-electron scattering contributes to the rising phase of the transient absorption signal, which subsequently diminishes as electron-phonon coupling commences, leading to the equalization of electron and lattice temperatures within a few picoseconds. Electron-phonon coupling plays a crucial role in lowering the electron temperature due to the difference in the heat capacities of electrons and the lattice. Subsequent cooling towards room temperature occurs via phonon-phonon coupling with the surroundings (\\({\\tau }_{{ph}-{ph}}\\)), typically within hundreds of picoseconds46. The primary decay constant (\\({\\tau }_{e-{ph}}\\)) measured by ultrafast transient absorption spectroscopy for Au NPs corresponds to electron\u2212phonon coupling, which happens when electrons and holes are still \u201celectronically hot\u201d rather than just \u201cthermally hot\u201d46. This term can be used to estimate the average electron temperature, as outlined in the following sections. The kinetic traces extracted at 500\u2009nm were fitted with a double-exponential decay model to obtain the \\({\\tau }_{e-{ph}}\\) for the short-lived electron-phonon scattering component and \\({\\tau }_{{ph}-{ph}}\\) for the longer-lived phonon-phonon scattering component, as described in the supplementary information (SI).\n\nThe relationship of the TAS data with excitation power is evaluated by fitting kinetic traces extracted in the bleach region, in this case, at 500\u2009nm (Fig.\u00a01D). This relationship can be understood through the two-temperature model49, which describes the concurrent changes in electron (\\({T}_{e}\\)) and lattice (\\({T}_{l}\\)) temperatures over time (\\(t\\)) by coupled differential equations. This interaction is governed by the electron-phonon coupling constant (\\(g\\))50,51,52,53:\n\nHere, Ce and Cl are the electron and lattice heat capacities. Importantly, Ce is temperature-dependent according to Ce (Te)\u2009=\u2009\u03b3Te, where \\(\\gamma \\) is the electron heat capacity constant. For bulk gold, \u03b3\u2009=\u200966 Jm\u22123K\u22122 and \\(g\\)\u2009=\u20092.5\u2009\u00b1\u20090.5\u2009\u00d7\u20091016\u2009Wm\u22123K\u2212150. The variation in electron heat capacity with temperature leads to the observed dependence on excitation power because the excitation power modulates the initial electron temperature. When the increase in electron temperature remains modest, the linear relationship of electron heat capacity with temperature persists36,54. Consequently, the interconnected equations can be reformulated to establish an electron-phonon relaxation time (\\({\\tau }_{e-{ph}}\\)):\n\nHere, \\({T}_{0}\\) is the ambient temperature (291\u2009K) and \u2206Te is the pump-induced temperature change of the electrons. According to Eq.\u00a02, the \\(\\Delta {T}_{e}\\) was estimated for each laser fluence from the \\({\\tau }_{e-{ph}}\\), extracted from the first exponential decay of the plasmonic resonance TAS data. This analysis is summarized in the Table in Fig.\u00a02B. It is noticeable that the increase in laser fluence leads to a rise in the \\({\\tau }_{e-{ph}}\\) and, consequently, the \\(\\Delta {T}_{e}\\), as anticipated53. However, it is also observed that at high fluencies, the linearity of the process starts to disappear and the \\(\\Delta {T}_{e}\\) saturates (Fig.\u00a02C). This is consistent with the two-temperature model predictions, which establish the electron temperature threshold for gold to be lower than\u2009\u223c\u20093000\u2009K36,55. In practice, this means that further increases in fluence primarily affect the number of hot carriers rather than their energy. This temperature analysis demonstrates that the pump fluence used at the XFEL falls within this stagnation regime, leading to the generation of many hot carriers rather than an increase in carrier energy. This insight is critical for understanding the population of hot carriers under these experimental conditions and provides a basis for analyzing the subsequent electron relaxation dynamics.\n\nA Au NPs hot carrier generation, multiplication and thermalisation were investigated using pump-probe ultrafast transient XANES with colloidal nanoparticles in water, circulated in a liquid jet. The expected changes due to optical excitation are schematically represented. B Tabulation of the best fitting parameters for the LSPR decay channels. C Calculated change in the electron temperature versus laser fluence. The TAS data is combined with the TR-XAS signal at an incident energy of 11916\u2009eV and a pump-probe delay of 100\u2009fs. D Superimposed L3-edge spectra of steady-state (black trace) and excited-state (red trace) Au NPs with the excited spectrum recorded at \u0394t\u2009=\u2009100\u2009fs time delay after excitation at 532\u2009nm. The transient XAS spectrum (blue trace) is the difference between excited (pumped) and steady-state (unpumped) spectra. A positive signal in the difference spectrum equates to an increase in empty states (holes) and vice versa. The steady-state XAS spectrum of the Au NPs measured at the synchrotron (grey, dashed line) is shown for comparison.\n\nThe limitation of transient optical measurements is that the nonthermal and thermal carrier populations in plasmonic systems are merely inferred by making assumptions about the functional form of the initial energy distribution or by using indirect monitoring methods, such as localized plasmon frequency shifts56. Fig.\u00a02A illustrates the TR-XAS approach for tracking the changes in the metal density of states (DOS) induced by LSPR excitation, i.e., the direct detection of hot carrier energy distribution. This study specifically focuses on the X-ray absorption near edge structure (XANES) part of the XAS spectrum, which directly monitors the electronic changes in the material, i.e., information on LSPR-induced hot carrier formation. The transient data was collected using the analogous pump-probe methodology that was adopted for the TAS measurements. The technique involves \u201cpumping\u201d the sample with an optical laser pulse, then \u201cprobing\u201d with an XFEL fs X-ray pulse. Transient data is acquired by varying the relative time delay between optical and X-ray pulses. To prevent the excitation of damaged Au NPs induced by intense XFEL pulses, a liquid jet was employed to circulate the Au NPs suspended in water. The colloidal solution was refreshed every four hours. The experiments were performed in a climate-controlled laboratory, which, in conjunction with sample circulation (i.e., reduction of local heat deposition), ensured that experiments were performed under isothermal conditions.\n\nAu NPs are commonly utilized as ideal test models in XFEL studies on diffraction and imaging57. However, to our knowledge, ultrafast XAS studies on Au nanoparticles have yet to be conducted at these facilities, partly because access to the hard X-ray energies necessary to probe the Au L3-edge transition has only recently become available. Hence, it is essential to validate the XANES spectrum of the unpumped sample to confirm that it is truly representative of the Au NPs. Figure\u00a0S4 shows the steady-state XANES spectra of Au foil and nanoparticles measured at the Au L3-edge transition (2p3/2 \u2192 5\u2009d) at the synchrotron. Au has a [Xe] 4f14 5\u2009d10 6s1 electronic structure, i.e., with a filled d-shell, which results in a weak absorption edge only visible due to some level of s-d shell hybridization. For comparison purposes, the signal was plotted against Pt ([Xe] 4f14 5d9 6s1) (Fig.\u00a0S5), illustrating that this method is sensitive to empty states within the metal 5d shell and, to some extent, the s-shell due to this hybridization.\n\nThe unexcited XANES spectrum of the Au NPs, measured at XFEL and the synchrotron, displayed a consistent shape (Fig.\u00a0S4). The consistency between the synchrotron and XFEL data validates that the XANES acquired at XFEL accurately reflect the electronic structure of Au atoms. Additionally, incorporating time-resolved measurements into XAS enhances the method\u2019s ability to capture transient alterations in the electronic structure of gold prior to any detectable sample damage, i.e., probe-before destruction concept58,59. Moreover, the unexcited XANES of the Au NPs used were identical to the bulk gold spectrum, consistent with previous reports23,60,61. The observation that the electronic structure of the Au NPs used in this study matches that of bulk Au was further supported by theoretical calculations of the Au DOS as a function of particle size. These calculations indicate that for particles above 3\u2009nm, the electronic structure of Au NPs begins to resemble that of bulk gold (Figure\u00a0S6). Consequently, the similarity in the XAS spectral shapes observed for the Au NPs and bulk Au foil (Fig.\u00a0S4) is consistent with these findings.\n\nUltrafast time-resolved XANES data were acquired with the XFEL source as a probe, following the excitation of a 5\u2009\u00d710\u22123\u2009M Au NP aqueous solution at 532\u2009nm (~ 2.33\u2009eV) (i.e., slightly to the red of the LSPR maximum), utilising a 15\u2009nm full width at half maximum (FHWM) bandwidth, a pulse duration of ~75\u2009fs, and a power density of 4\u2009\u00b5J within the 60\u2009\u00d7\u200960\u2009\u00b5m2 spot used for all time-resolved XANES experiments. The choice of this precise plasmon excitation energy was to induce LSPR excitation while minimising interband excitation62. The center of the Au d-shell is located at 2.5\u20132.58\u2009eV (~496\u2013480\u2009nm) from the metal EF63,64, meaning that the laser pulse with a 2.33\u2009\u00b1\u20090.13\u2009eV (15\u2009nm FHWM) photon energy can only excite the low-energy tail of the d-shell at best. Therefore, the optical photon energy reduces to a high degree the interband excitations with an absorption onset at 2.38\u2009eV65 if one ensures that only fundamental dipole LSPR transitions of Au NPs are excited66, as controlled by laser fluence.\n\nSince completely avoiding interband excitation when exciting close to the LSPR maximum is not feasible62, it was crucial to demonstrate that its contribution to the overall signal remains minimal when exciting to the red of the LSPR peak67. To investigate this, optical TAS measurements were performed at different excitation wavelengths: 450\u2009nm (predominantly interband excitation, below the LSPR peak), 520\u2009nm (resonance excitation, at the LSPR peak maximum), and 532\u2009nm (intraband excitation, above the LSPR peak). A comparison of the kinetic traces extracted near the excitation wavelength (Fig.\u00a0S7) reveals that pure interband transitions (450\u2009nm) are significantly less efficient than intraband transitions (532\u2009nm) in generating hot carriers. Additionally, the observed decrease in signal amplitude when exciting beyond the LSPR peak, compared to excitation at the LSPR maximum, further supports the conclusion that interband excitation has a diminished contribution when excitation wavelengths are chosen to the red of the LSPR maximum67.\n\nFigure\u00a02D compares the XANES spectra of unexcited (unpumped spectrum) and excited (pumped spectrum) recorded at \u0394t\u2009=\u2009100\u2009fs time delay after excitation at 532\u2009nm. Optical excitation induced a spectral broadening, with the low-energy edge becoming more extended and the white line weakening, corroborating the presence of light-induced changes in the gold electronic structure around its Fermi-level energy and confirming that TR-XAS can track these changes. To better illustrate these results, the XANES difference spectrum (pumped-unpumped XANES spectra) is shown in Fig.\u00a02D. The difference spectrum is dominated by the positive signal below and the negative signal above the Au EF (11,920.3\u2009eV). Transient L3-edge XANES clearly captures changes in state occupancy, particularly those induced in the d-shell, either directly or through processes like hybridization with the s-shell. Accordingly, a positive signal correlates with an increase in density of states (DOS); conversely, a negative signal (i.e., a bleached signal) indicates a decrease in empty states. Thus, the positive signal observed below the Au EF is attributed to the formation of a hot hole population from the non-radiative decay of the plasmon. Conversely, the negative signal observed above the Au EF is due to hot electrons filling empty states, as expected.\n\nTo exclude additional effects during the time-resolved XAS experiment, we performed additional test measurements at two different X-ray fluxes, with a two-fold increase in flux. The average X-ray flux at the sample position at 11,900\u2009eV (monochromatic beam) was about 5\u2009\u00d7\u2009109 photons/pulse, corresponding to 2.5\u2009\u00d7\u20091013\u2009W/cm2 at applied experimental conditions, i.e., 75\u2009fs X-ray pulse length and 60\u2009\u00d7\u200960\u2009\u00b5m2 spot size. The transient XAS spectra measured at 100\u2009fs time delay and two different X-ray fluxes equal to c.a. 3.7\u2009\u00d7\u2009109 and 7.5\u2009\u00d7\u2009109 photons/pulse, respectively, are plotted in Fig.\u00a0S8. No detectable differences are observed between the spectra, indicating the absence of any observable nonlinear or multiphoton X-ray interactions. To induce any nonlinear interactions in the hard X-ray regime, fluences in the 1018\u20131020\u2009W/cm2 range would be required, as reported in the literature68,69,70,71. Therefore, our experimental tests align well with previously reported studies, indicating that our specific experimental conditions were orders of magnitude lower than those reported to be required for multiphoton ionization or nonlinear processes.\n\nAn important consideration in the TR-XAS experiments is the pump laser fluence. Figure\u00a0S9 presents the fluence dependence of the TR-XAS signal at an incident energy of 11,916\u2009eV with a pump-probe delay of 100\u2009fs. While TR-XAS \u0394A at 100\u2009fs shows an increase with rising laser fluence, the rate of this increase is relatively modest (slope\u2009=\u200911.1\u2009\u00b1\u20093.5), particularly in comparison to the TAS \u0394A versus laser fluence slope 383\u2009\u00b1\u200995 (see Fig.\u00a0S10). This indicates that the observed rise in TR-XAS \u0394A does not significantly alter the overall electron temperature. Consequently, the TR-XAS signals remain within the ~2000\u2009K electron temperature plateau, achieved at ~5\u2009mJ/cm\u00b2 of laser excitation. Thus, to balance optimal signal intensity with minimal saturation effects, a fluence of 98 mJ/cm\u00b2 was selected for the TR-XAS measurements.\n\nThe TR-XAS signal directly indicates hot carrier generation via LSPR non-radiative decay (Fig.\u00a02D). The chosen laser pump energy minimizes interband excitation, ruling out direct vertical interband transitions as a significant decay pathway. Furthermore, the carriers\u2019 average energy distribution is larger than \u210f\u03c9/4 (\u210f\u03c9\u2009=\u2009photon energy), excluding EE Umklapp scattering-assisted transitions. Therefore, one is left with phonon (or defect) scattering or diagonal transitions caused by Landau damping or surface collisions as potential electron decay channels. Notably, the hot hole and electron signals are neither symmetric nor have the same integrated magnitude. The carriers\u2019 wider and asymmetric distribution suggests that Landau damping72 is the dominant electron decay process in smaller absolute space confinement, i.e., small nanoparticles21,22,56,73. This conclusion is further substantiated by a) the high crystalline quality of the Au colloids (see HRTEM Fig.\u00a01B), suggesting a very low number of crystal defects present to induce scattering events, and b) the average particle size (ca. 7\u2009nm) is significantly larger than the quantum limit of the plasmon (ca. 2\u2009nm) and the intermediate regime (ca. 4\u2009nm)74, considerably reducing the rate of surface collisions. The signal asymmetry and finite-size effects are discussed further below.\n\nTo establish the time scales for plasmon Landau damping and the average lifetime of carriers, kinetic traces were extracted at the maximum of the hot hole intensity (11,916\u2009eV, 4.3\u2009eV below Au EF) and the excited electron intensity (11,922\u2009eV, 1.7\u2009eV above Au EF populations, as depicted in (Fig.\u00a03A). The kinetic data (Fig.\u00a03C) from the time scans were fitted by a model published elsewhere75 and described in the SI by equations S2 and S3. In brief, the data collected at 11,916 and 11922\u2009eV were fitted with a convolution of a temporal instrument response function (Gaussian) with a monoexponential decay. The resulting fit is shown as the solid green curve in Fig.\u00a03C. Due to the low signal-to-noise ratio for the hot electron data, the error bars are relatively large. However, it is possible to appreciate that the signal has dynamics similar to the hot holes. Note that the position of the Fermi level did not change significantly over the measured time scale (Fig.\u00a03B), corroborating that the carrier transient changes are due to their relative populations.\n\nA The difference spectrum (pumped-unpumped signal) shows kinetic traces of energy extraction points. B Transient changes of the zero intercept with energy scale (Fermi level). C Time traces showing intensity versus time delay extracted at X-ray photon energies of 11,916\u2009eV (representing hot holes, green trace) and 11,922\u2009eV (representing hot electrons, orange trace). The solid line represents the fit obtained using the methodology detailed in ref. 75 and described in the SI. The signal of the hot electrons was inverted in order to be plotted on the same y-axis.\n\nRossi et al. divided the total energy stored in the excited electronic system into the energy of nonresonant electron-hole transition contributions constituting screened plasmon excitation occurring in <10\u2009fs and resonant transition contributions, comprising mainly hot carriers with a 17\u2009fs lifetime (a.k.a Landau damping)21. The dephasing time of nonresonant electron-hole transition contributions is determined by analysis of changes in the spectral line shape caused by surface plasmon resonance. The induced broadening (\u0393hom, being the homogeneous linewidth of the surface plasmons resonance) can be determined by Lorentzian line fitting, and the dephasing time (T2) is estimated from T2\u2009=\u20092\u210f/\u0393hom44, with \u210f being Planck\u2019s constant. Dephasing experiments on single particles using scanning near-field optical microscopes or nonlinear photoemission electron microscopy estimate the dephasing times between 5\u20139\u2009fs65,76, consistent with the calculations of Rossi et al.21. The plasmon Landau damping time can be extracted from the onset of XAS spectral shape changes caused by hot carrier formation, which start at \u223c24.6\u2009\u00b1\u200910\u2009fs after optical excitation. This value is close to the previously calculated value21. Landau damping time and its associated error were estimated from the average of the onset of the rising function fitting (i.e., time zero) hot electrons and hot holes.\n\nFollowing plasmon Landau damping, the hot carriers reach a maximum carrier population at 105\u2009\u00b1\u20098\u2009fs, estimated from the rising edge analysis performed for hot electrons and hot holes. This value is consistent with optical measurements that revealed that the initial electron-electron scattering convolved with the rise of the transient absorption signal occurs within\u2009\u223c\u2009110\u2009fs, corresponding to the average value of the five measured fluences77. The initial electron-electron scattering occurs after the Landau damping process, with the maximum populations expected to occur shortly thereafter.\n\nThe lifetimes of the hot carriers were determined from a single exponential decay to be 498\u2009\u00b1\u200935\u2009fs and 505\u2009\u00b1\u200965\u2009fs for hot holes and electrons, respectively. The ability to fit the data with a single exponential decay further supports the argument that interband excitations are largely avoided, as these excitations exhibit distinct kinetic decays (see Fig.\u00a0S11). If interband excitations contributed significantly to the signal, a more complex decay behavior would be expected. Note that, to improve the fit for the hot electron data, which has a significantly lower signal-to-noise ratio compared to the hot hole data, the fitting was focused on the decay portion of the process. The rising parameters, derived from the hole kinetic data, were applied to aid in fitting the electron trace, as these parameters should be consistent across both datasets. The complete electronic thermalisation occurring within\u2009\u223c\u20091.5\u2009ps, consistent with the \u03c4e-ph of about 4\u20135\u2009ps established with TAS, confirmed the ultrafast hot carrier relaxation as the primary bottleneck limiting plasmonic applications. The estimated thermalization time for hot carriers is consistent with previous studies where LSPR transitions are excited without significant interband excitation78 and the predictions of the classic two-temperature model49.\n\nA recent study reported slow relaxation kinetics in TAS measurements alongside a predominant fast decay within 4\u2009ps79, which accounts for most of the observed signal. This slower, minor component was extracted through deconvolution of the optical signal under conditions where both interband and intraband transitions were excited. However, optical deconvolution offers limited ability to separate contributions from hot carriers versus phonons and does not provide direct insights into carrier energy. This limitation is particularly relevant as the measurements were conducted on larger Au nanoparticles (approximately 25\u2009nm), which increases the likelihood of multipole excitation, potentially leading to longer-lived, lower-energy carriers80. Our TAS measurements also show a small, longer-lived component (Fig.\u00a0S10), especially when exciting at the LSPR maximum, which we primarily attribute to phonon-phonon scattering81 rather than an additional electron-phonon contribution. Given that the energy resolution of our transient XANES does not respond to phonon modes and that the signal fully decays within 1.5\u2009ps, we conclude that the reported longer-lived signal is most likely attributable to phonons rather than hot carriers.\n\nTo estimate the number of electrons engaged when exciting 5\u2009mM Au NPs at 532\u2009nm, utilising a 15\u2009nm full width at half maximum (FHWM) bandwidth, a pulse duration of approximately 75\u2009fs, and a power density of 98 mJ/cm2, the positive signal variance at 0 and 100\u2009fs was integrated. This integrated signal was then juxtaposed with the signal difference between the Au and Pt L3-edges (Fig.\u00a0S5). Note that the signal difference between Au and Pt relates to 1e\u2212 less in Pt valence states, i.e., the integrated positive signal of the difference between Pt and Au corresponds to the equivalent of having 1e\u2212 from each Au atom participating in the resonance. Employing this simple methodology, we estimate that each gold atom contributes with 0.19e- at the start of the resonance, which undergoes multiplication until 105\u2009fs, reaching a maximum of 0.46e\u2212 from each Au atom contributing to hot carrier formation at this excitation power. An Au NP has\u2009\u2248\u200910,000 atoms, equating to about 1.4\u2009\u00d7\u20091012 Au atoms in the excited volume. The photon density in the optical pulses is about 1013, from which 20% is absorbed according to UV-Vis, implying that the excited volume absorbs around 2\u2009\u00d7\u20091012 photons. This suggests an excitation of about 1e- per atom of Au, of which 19% are converted into hot carriers at the onset, multiplying to about 46% within 100\u2009fs. The observation suggests that hot carrier generation is a prime decay channel of Au NP LSPR and undoubtedly the most significant mechanism in nonradiative decay.\n\nAfter verifying the generation of hot carriers, we proceeded to investigate the dynamic behavior of the hot carrier energy distribution\u2014a significant yet elusive aspect in the realm of plasmonic hot carriers, particularly when it comes to holes32,33. The current understanding is derived mainly from theoretical studies20,82,83 and indirect techniques23,32,33,84. For example, internal quantum efficiency measurements have inherent limitations as they solely quantify carriers transferred to an acceptor layer, like semiconductors, not their energy. Moreover, internal quantum efficiency does not provide information about the carrier's dynamic behavior within the metal. In Au, hot electrons can only populate the empty states within the sp-shells, but the holes can be in sp- and d-shells, confirmed by valence band\u2014X-ray photoelectron spectroscopy (VB-XPS) shown in Fig.\u00a04A. It is evident when the VB-XPS is overlapped with the transient XANES spectrum (recorded at time zero) that photogenerated holes are located throughout the entire valence band of the metal, including the d-shell, despite the optical pulse energy allowing primarily sp-shell excitation.\n\nA Comparison of the valence band photoelectron spectrum (VB-XPS) with the Au L3-edge transient XANES spectrum collected at time zero. The zero of the energy scale corresponds to the Fermi level. The optical pulse in green is depicted as a Gaussian band centered at 2.33\u2009eV with a full width at half maximum (FWHM) of 15\u2009nm, corresponding to an energy envelope of \u00b10.13\u2009eV; B The transient XANES measured at the Au L3-edge absorption spectra collected at different pump-probe time delays (0\u2009fs corresponds to the best possible overlap between pump and probe). C Relative changes in hot holes mean energy distribution (blue trace) and population (red trace). D The temporal evolution of the hot holes with different energies indicates that the Fermi-Dirac distribution is not reached within 500\u2009fs. Note that the energy of the holes is affected by the Au core-hole lifetime.\n\nFigure\u00a04B illustrates the temporal evolution of the hot carrier population and their energy distribution, resulting from the non-radiative decay of optically excited LSPR transitions. As expected, this non-radiative deexcitation of the plasmon depopulates states below the Fermi energy and populates states above it. The ultrafast carrier-carrier interaction during carrier multiplication determines their energy and respective population. The hot carrier energy distribution exceeds single photon energy for hot electrons and holes. Furthermore, it is evident that the carrier populations and their energy distributions do not peak simultaneously. Additionally, an asymmetry is observed between the hot electron and hot hole populations.\n\nThe temporal evolution of hot carrier populations and their energy distribution following optical excitation can be understood without invoking finite-size effects. As shown in the DOS plots (Fig.\u00a0S6), electron and hole occupations vary with particle size, with smaller nanoparticles (Au25) exhibiting more distinct peaks than larger ones (Fig.\u00a0S12). This highlights that finite-size effects become significant only below the plasmon quantum limit (<2\u2009nm)74. Using particles with an average diameter of 7\u2009nm avoids finite-size effects, maintains dipole resonance dominance, and ensures stability under the XFEL beam. Our findings thus apply to nanoparticles above the plasmon quantum limit and below the multipole resonance threshold.\n\nThe signal asymmetry between hot electrons and holes can be partly attributed to the higher sensitivity of the L3-edge XANES transition to the formation of empty states in the d-shell, i.e., the hot holes. However, the shape asymmetry between hot electrons and holes is also anticipated because of the difference in electron and hole density of states, consistent with experimental56 and theoretical reports74. Temporal analysis of the relative changes in mean energy distribution and population for hot holes (Fig.\u00a04C) and electrons (Fig.\u00a0S13) reveals a contrasting behavior. Despite the significant experimental errors related to the lower sensitivity of the XANES probe to occupied states, the hot electron energy distribution and population have similar dynamics, peaking in intensity around 100\u2009fs (Fig.\u00a0S14). Electron-nuclear dynamics calculations within the Ehrenfest ansatz, implemented in the DFTB+ code85, were performed to rationalise the observed asymmetry. Figure\u00a0S12 shows an apparent asymmetry in the electron and hole dynamics, owing to the asymmetric DOS around the Fermi level within the range of the laser energy. These observations are in qualitative agreement with the experimental measurements. The higher localisation of electrons concerning the holes favors carrier multiplication, which increases the number of carriers and simultaneously reduces their energy86.\n\nThe rapid depopulation of electrons in the d-shell is expected due to the overlap between the d and sp-shells. Consequently, a high density of d-electrons will couple with the plasmonic resonance to dissipate its energy83. This is consistent with the time-resolved calculations of electron and hole localisations shown in Fig.\u00a0S12. However, this does not explain the observation of carriers with energies above the photon energy, even considering the energy broadening induced by the 5.41\u2009eV Au L3-edge core-hole broadening87, which limits the experimental energy resolution88. Nonetheless, hot holes are distributed across the entire valence electronic structure, and their energy distribution increases up to 250\u2009fs (Fig.\u00a04C) before starting their relaxation. These two observations imply the involvement of carrier-carrier coupling mechanisms that both increase carrier population and its energy distribution, an effect that has yet to be reported76. Note that the experimental conditions preclude the possibility of multiphoton excitation of single electrons.\n\nWhen it comes to carrier multiplication, there are two possible scattering mechanisms: impact excitation and Auger heating89,90. The predominant mechanism in carrier multiplication is impact excitation. In the impact excitation mechanism, an excited electron (hole) undergoes Coulomb scattering, losing energy and momentum and giving rise to an additional electron-hole pair. The distinctive feature of impact excitation is a rise in the number of carriers and a simultaneous reduction in their energy. Conversely, Auger heating characterizes the non-radiative recombination of an electron with a hole, where the energy and momentum are transferred to an electron (hole) within the same shell. The hallmark of Auger heating is a decline in the number of carriers and an increase in their energy.\n\nTo enhance the visualisation and comprehension of the hot hole multiplication process, the integrated hole population and the energy distribution (aka energy width (3\u03c3)) are plotted versus delay time (see Fig.\u00a04C) using the data analysis procedure outlined in the SI. Commencing with the average hot hole population, it peaked at 100\u2009fs and decreased subsequently. This implies the maximum nonequilibrium non-Fermi-Dirac hot carrier population occurs after Landau damping (early hot carrier population), indicating the involvement of the impact excitation scattering mechanism. When examining the hot carrier energy distribution width, it is noticeable that it increases up to 250\u2009fs. This observation indicates the involvement of Auger heating in the carrier multiplication, but, more importantly, the mechanism involvement extends beyond the 10\u2009s of fs90, hugely significant for hot carrier applications.\n\nA final aspect of plasmon carrier relaxation that can be observed is the time when the Fermi-Dirac distribution is reached. For extended metal surfaces (e.g., Au thin films), time-resolved studies suggest forming a Fermi-Dirac-like distribution characterized by a sizeable effective electron temperature within 1\u2009ps56,91. However, in Au NPs, the electron gas is expected to thermalise very fast to a Fermi-Dirac distribution over a time scale \u03c4e ~100\u2009fs92,93. In such a scenario, one would expect carriers with different energies to decay at different rates, with the ones with the highest energies decaying more rapidly. Figure\u00a04D (for hot holes) and S14 (for hot electrons) illustrate carrier decay across different energy levels over a 500-fs period. Interestingly, the decay rates are similar regardless of carrier energy, suggesting that achieving a Fermi-Dirac-like distribution with a high effective electron temperature takes longer than the expected 100\u2009fs. To determine the precise time scale for this thermalization, time-resolved, high-resolution XAS and XES would be required, which were not accessible during this measurement.\n\nThe participation of the Auger heating mechanism in carrier multiplication helps explain early reports concerning hydrated electron formation with Cu NPs94 and near-infrared plasmon-assisted water oxidation, which used photons with insufficient energy to drive such processes95. More critical is the finding that the Auger mechanism extends up to 250\u2009fs and the nonequilibrium Fermi-Dirac distribution extends beyond 500\u2009fs, meaning that the hottest carriers are available and able to perform work, according to the Franck-Condon principle and Marcus theory. This is transformational for photocatalysis, photovoltaics, solar redox flow batteries, and phototherapy with hydrated electrons because it enables low-energy photons to do work on applications requiring voltages beyond the ones attained with single photon energy. In photocatalysis, one can foresee driving chemical reactions with redox windows that are more extensive than the photon energy, avoiding the use of detrimental high-energy photons. Similarly, one can generate hydrated electrons in situ with lower-energy photons with a higher penetration depth. In photovoltaics, one could create devices with larger open circuit voltages than a single photon permits, enabling effective photon energy use and quite possibly circumventing the Shockley-Queisser limit for single junction solar cells.\n\nAs a final remark, it is important to reiterate that while the excitation wavelength used for transient XANES may induce a small fraction of interband transitions due to plasmonic near-field enhancement, our experimental design was carefully chosen to minimize this effect. By selecting an excitation wavelength slightly red-shifted from the LSPR maximum62 and positioned relative to the expected Au d-band onset63,64, we effectively reduce the likelihood of significant interband contributions. The validity of this approach is further supported by the similar dynamics reported by Sun et al.78, who utilized 900\u2009nm excitation to completely suppress interband transitions. Therefore, the collective evidence strongly indicates that interband transitions do not play a dominant role in the observed signal at 532\u2009nm, even when accounting for potential near-field enhancements.\n\nIn this work, the dynamic behavior of plasmon hot carrier formation, multiplication and thermalisation on gold nanoparticles upon LSPR excitation is reported. The methodology is intrinsically sensitive to the metal electronic structure, permitting real-time observation of the entire process. The plasmon Landau damping was determined to be\u2009\u223c\u200925\u2009fs, with a maximum hot carrier population detected at 105\u2009fs after excitation. At this time point, there is\u2009\u223c\u20090.46e- per Au atom as a hot carrier, establishing hot carrier formation as a significant decay pathway of plasmon excitation. Complete thermalisation of the hot carriers occurs\u2009\u223c\u20091.5\u2009ps. Energy scans at variable delay times reveal that carriers do not reach a Fermi-Dirac distribution within 500\u2009fs, signalling that the high-energy carriers can be realistically harnessed to do work. Importantly, carriers with energies exceeding the single photon excitation energy were detected, suggesting the involvement of the Auger heating scattering mechanism in the carrier multiplication apart from the expected impact excitation mechanism. This observation opens perspectives for plasmon hot carrier applications in fields where the carrier energy defines the device\u2019s potential for work, such as photocatalysis, phototherapy with hydrated electrons and photovoltaics. These insights into plasmon-induced hot carrier generation and dynamics provided here further the fundamental understanding of plasmon hot carriers and are likely to impact applications for years.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57657-1/MediaObjects/41467_2025_57657_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57657-1/MediaObjects/41467_2025_57657_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57657-1/MediaObjects/41467_2025_57657_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57657-1/MediaObjects/41467_2025_57657_Fig4_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "All details regarding the experimental methods are provided in the Supplementary Information.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data needed to evaluate the conclusions in the paper are present in the paper/Supplementary Information/Source Data file. Source data are provided with this paper. Additional data supporting this study\u2019s findings are available from the corresponding author upon request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Change history", + "section_text": "A Correction to this paper has been published: https://doi.org/10.1038/s41467-025-58580-1", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Novotny, L., Hecht, B. Principles of Nano-Optics. (Cambridge University Press: New York, 2006).\n\nMaier, S. A. Plasmonics: Fundamentals and Applications. (Springer: New York, 2007).\n\nHalas, N. J., Lal, S., Chang, W., Link, S. & Nordlander, P. Plasmons in strongly coupled metallic nanostructures. Chem. Rev. 111, 3913\u20133961 (2011).\n\nArticle\u00a0\n PubMed\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nXu, H., Bjerneld, E. J., K\u00e4ll, M. & B\u00f6rjesson, L. 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Funding Olle Engkvists Stiftelse grant 210-0007 (J.Sa); Knut & Alice Wallenberg Foundation grant 2019-0071 (J.Sa); Swedish Research Council grant 2019-03597 (J.Sa); European Union\u2019s Horizon 2020 research and innovation program under Marie Sk\u0142odowska-Curie grant 884104 (PSI-FELLOW-III-3i) (A.W.); Robert A. Welch Foundation grants C-1220 (N.J.H.) and C-1222 (P.N.); Air Force Office of Scientific Research via the Department of Defense Multidisciplinary University Research Initiative under AFOSR grant FA9550-15-1-0022 (N.J.H., P.N.); National Science Center in Poland grant 2020/37/B/ST3/00555 (J.Sz.); Polish Ministry and Higher Education project: \u201cSupport for research and development with the use of research infrastructure of the National Synchrotron Radiation Center SOLARIS\u201d grant 1/SOL/2021/2.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open access funding provided by Uppsala University.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "SOLARIS National Synchrotron Radiation Centre, Jagiellonian University, Krakow, Poland\n\nAnna Wach,\u00a0Alexey Maximenko,\u00a0Tomasz Sobol,\u00a0Ewa Partyka-Jankowska\u00a0&\u00a0Jakub Szlachetko\n\nPaul Scherrer Institut, Villigen PSI, Switzerland\n\nAnna Wach,\u00a0Camila Bacellar,\u00a0Claudio Cirelli\u00a0&\u00a0Philip J. M. Johnson\n\nInstitute of Physical Chemistry, Polish Academy of Sciences, Warsaw, Poland\n\nAnna Wach\u00a0&\u00a0Jacinto S\u00e1\n\nDepartment of Chemistry-\u00c5ngstr\u00f6m, Physical Chemistry division, Uppsala University, Uppsala, Sweden\n\nRobert Bericat-Vadell,\u00a0Vitor R. Silveira\u00a0&\u00a0Jacinto S\u00e1\n\nMax Planck Institute for Chemical Energy Conversion, M\u00fclheim an der Ruhr, M\u00fclheim an der Ruhr, Germany\n\nRebeca G. Castillo\n\nMaxepartment of Chemistry-\u00c5ngstr\u00f6m, Structural Chemistry division, Uppsala University, Uppsala, Sweden\n\nPeter Broqvist\u00a0&\u00a0Jolla Kullgren\n\nDepartment of Electrical and Computer Engineering, Rice University, Houston, TX, USA\n\nPeter Nordlander\u00a0&\u00a0Naomi J. Halas\n\nDepartment of Physics and Astronomy, Rice University, Houston, TX, USA\n\nPeter Nordlander\u00a0&\u00a0Naomi J. Halas\n\nDepartment of Chemistry, Rice University, Houston, TX, USA\n\nNaomi J. Halas\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization and methodology: A.W., J.Sz. and J.Sa.; formal data analysis: A.W., J.Sz. and J.Sa.; experimental investigations: A.W, R.B.-V., C.B., C.C., P.J.M.J., R.G.C., V.R.S., P-B., J.K., A.M., T.S., E.P.-J. and J.Sa.; data visualisation concepts: A.W., J.Sa., J.Sz., and N.J.H., draft preparation: A.W., N.J.H., P.N., J.Sz. and J.Sa.; writing-review and editing: all the authors. All authors have read and agreed to the published version of the manuscript.\n\nCorrespondence to\n Jakub Szlachetko or Jacinto S\u00e1.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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b/0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9/metadata.json @@ -0,0 +1,153 @@ +{ + "title": "Route-centric ant-inspired memories enable panoramic route-following in a car-like robot", + "pre_title": "Continuous Visual Navigation with Ant-Inspired Memories", + "journal": "Nature Communications", + "published": "24 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_MOESM1_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_MOESM2_ESM.mp4" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.6084/m9.figshare.27708105", + "/articles/s41467-025-62327-3#ref-CR77" + ], + "code": [ + "https://doi.org/10.5281/zenodo.15783472", + "/articles/s41467-025-62327-3#ref-CR78" + ], + "subject": [ + "Computational models", + "Mechanical engineering" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5505975/v1.pdf?c=1758798400000", + "research_square_link": "https://www.researchsquare.com//article/rs-5505975/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-62327-3.pdf", + "preprint_posted": "04 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Solitary foraging ants excel in following long visual routes in complex environments with limited sensory and neural resources\u2014an ability that remains challenging for robots with minimal computational power. Here, we introduce a self-supervised, insect-inspired neural network that enables robust route-following on the compact, low-cost Antcar robot. The robot leverages key aspects of ant brain and behavior: (i) continuous, one-shot visual route learning using panoramic encoding in a mushroom body-inspired network, (ii) categorization of low-resolution egocentric panoramas via oscillatory movements, (iii) opponent-process control of angular and forward velocities based on visual familiarity, (iv) recognition of places of interest along routes, and (v) motivation-based memory modulation. Antcar autonomously followed routes between indoor or outdoor destinations, forward or backward, while remaining stable in both theoretical analysis and real-world testing despite occlusions and visual changes. Across 1.3 km of autonomous travel, Antcar achieved challenging route-following with sub-20 cm lateral error at speeds up to 150 cm/s, requiring only 148 kilobits of memory and processing panoramas every 62 ms. This efficient, brain-inspired architecture stands out from more sensor-intensive and computationally demanding methods, presenting a neuromorphic approach with valuable insights into insect navigation and practical robotic applications.Biological sciences/Computational biology and bioinformatics/Computational modelsPhysical sciences/Engineering/Mechanical engineering", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "ContinuousvisualnavigationSupplementaryInformation.pdfSupplementary NotesContinuousvisualroutefollowingVF.mp4Continuous Visual Navigation with Ant Inspired Memories", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Solitary foraging ants excel at route following using minimal neural resources, Robots don\u2019t. Recent biological studies proposed lateralized, nest-centric memories to explain ants\u2019 direct visual homing but did not address how ants follow curved visual routes away from their nest. We present a biologically inspired neuromorphic model for one-shot panoramic route learning and continuous route following, implemented on a compact car-like robot, Antcar. We demonstrate that route-centric lateralized memories, inspired by the insect mushroom body, enable Antcar to achieve bi-directional route-following, with motivation-driven recognition of route extremities and familiarity-based velocity control. With rigorous Lyapunov-based stability analysis and an empirical memory scalability evaluation, the model was tested over 1.6 km across 113 challenging real-world trials. The system achieves less than 25 cm median lateral error using minimal resources (800-pixel input, 300 MB RAM, 500 mW power, and 18.75 kB memory per 50 m route), offering insights into insect cognition and advancing autonomous robotics under strict resource constraints.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Insect navigation has long intrigued researchers across various fields, from biology to robotics, driving the development of cutting-edge technologies for autonomous mobile robots1,2,3. Autonomous navigation remains a demanding and interdisciplinary challenge with applications ranging from space exploration to last miles delivery4,5, especially in scenarios where robots cannot rely on satellite systems6. Simultaneously, robots serve as valuable tools for studying insects navigation and brain structure, advancing neuromorphic engineering7,8,9,10,11.\n\nIn Robotics, visual teach-and-repeat methods combined with dead-reckoning techniques have gained in popularity12,13,14,15. However, experienced solitary foraging ants navigate along familiar routes using only visual memories, without relying on dead reckoning (called path integration in insect literature)16,17,18. This behavior has inspired various robotic models, although current implementations are generally limited to short-range experiments of about ten meters, with modest computational efficiency, precision, and accuracy19,20,21,22,23,24. While ant-inspired models achieve results comparable to conventional computer vision approaches13,25, they struggle in dynamic environments where computational efficiency must be balanced with resource use.\n\nThese challenges are partly due to early navigation models that emphasized hymenopteran behavior rather than underlying brain processes. Early models, referred to as perfect memory models, stored periodic snapshots at specific waypoints26,27. Then, during autonomous route following (or exploitation), forced scanning movements compared acquired views to an image bank, using rotational image differences to establish the most familiar image and desired heading, a process known as the insect-based visual compass28,29,30,31,32,33. However, these approaches has revealed two main limitations when applied in robotics.\n\nThe first limitation involves the cumulative storage of snapshots, which significantly increases memory and computational demands as the route lengthens, making it unsuitable for long-distance navigation. This issue was partially addressed by the Infomax neural network34, which enables efficient encoding of increasing numbers of images without a corresponding rise in memory load20,32,35. However, Infomax requires substantial adjustments to synaptic weights for each input through a non-local learning mechanism, limiting its biological plausibility.\n\nIn parallel, research on the Mushroom Body (MB), a key part of the insect brain, has highlighted its essential role in olfactory and visual learning36,37. In the MB, learning occurs through synaptic depression between thousands of Kenyon Cells (KCs) \u2013 intrinsic neurons that sparsely encode sensory input \u2013 and a few Mushroom Body Output Neurons (MBONs), which modulate behavioral responses based on learned associations. These processed signals are then transmitted to downstream neural circuits, influencing decision-making38. The first MB model simulating visual route following used a Spiking Neural Network with 20,000 KCs and one MBON to compute familiarity39. Despite this advancement, a second limitation remains: navigation requires forced, systematic scanning, which slows robotic movement due to non-continuous command updates21. Also, this limitation does not reflect natural ant behavior, where scanning occurs only occasionally40,41,42.\n\nTo address the second limitation, an early robotic implementation combined a klinokinesis model with perfect memory, enhancing short-distance route-following by replacing cumbersome scanning with alternating, ballistic left and right turns where familiarity adjusted turn amplitude19 (later also observed in ants43).\n\nTo move beyond random, undirected kinesis, an early visual homing strategy was proposed that simulates directed movement toward a goal (e.g., a nest). In this model, tested in a particle-based simulation, the firing activity of KCs\u2014which encode visual input\u2014is categorized based on the path integration vector into two pairs of lateralized MBONs. Specifically, views acquired when the nest lies to the left are assigned to the left MBON, and vice versa44,45. This approach mirrors how insects, through continuous lateral body oscillations, sample multiple directions around their nest position43,46.\n\nIn this paper, we focus on route following, which is considered distinct from visual homing in the ant navigation framework,\u00a0and aligns\u00a0with\u00a0Visual Teach & Repeat\u00a0paradigms in robotics47. While visual homing is goal-directed and attractor-based, route following relies on on-route visual memories48. Some recent robotic models have attempted to extend lateralization to route following by splitting the field of view into left and right memories. However, these methods have shown limited success in real-world conditions23,49, and they diverge from biological evidence: in ants, the entire panoramic field is processed binocularly by the MB50. Our approach, therefore, uses the full panoramic view and categorizes visual input with right and left MBON in a route-centric manner, i.e., with respect to a local route orientation, without splitting the field-of-view.\n\nBuilding on our earlier one-MBON model for route learning in an indoor robot21,22, and the lateralization principle for nest-centric homing in simulation45, we present a fully-embodied, biologically realistic, and route-centric lateralized MB architecture implemented and tested on a real-world robot. This work introduces several contributions that significantly advance the field:\n\n(i) Route-centric lateralized learning: We replace the nest-centered categorization with a local, route-aligned reference frame, enabling two lateralized MBONs (right and left) to memorize panoramas as seen on the right or left of the route. This supports curved and goal-directed navigation without requiring a global home vector. (ii) Bi-directional navigation with start/end recognition: Two additional MBONs allow the robot to recognize route extremities, enabling stop and back-and-forth route following, a behavior observed in ants51,52,53,54. (iii) Familiarity-based velocity control: The robot adapts its speed based on visual familiarity, accelerating on known routes and decelerating in novel environments, mirroring ant behavior40. (iv) Analytical stability via oscillatory learning: We show that oscillatory heading movements during learning lead to stable route following, supported by a Lyapunov-based proof of convergence in the local route frame. (v) Empirical memory scalability analysis: We propose a metric to quantify memory capacity in relation to network hyperparameters, providing insights into the scalability of Mushroom Body-based architectures for longer and more complex routes. (vi) Extensive real-world validation: Embedded in the compact Antcar robot (Fig.\u00a01, 2a), our model was tested across 113 autonomous trajectories covering 1.6 km in both indoor and outdoor settings with different environmental conditions and challenges. The system achieved median lateral and angular errors of 24\u2009cm and 4\u00b0, with processing rates of 16\u2009Hz during exploitation and 38\u2009Hz during learning \u2014 all using a single 32\u2009\u00d7\u200932-pixel panoramic camera and minimal computational resources: 300 megabytes of RAM, 500\u2009mW power consumption, and just 18.75 kilobytes of memory. Overall, this paper delivers a biologically grounded and resource-efficient implementation of the proposed panoramic, route-centric and lateralized visual route-following on a physical robot. It integrates neural plausibility, theoretical robustness, and practical feasibility, bridging the gap between insect-inspired models and real-world autonomous robotics.\n\nAn ant in the foreground symbolizes nature\u2019s efficient navigational strategies, while the Antcar robot in the background integrates these principles into a neuromorphic system. The blurred image captures only the large masses of the environment, similar to the low-pass spatial filter in the ant\u2019s visual system, which retains these large features even when objects obstruct the view between the robot and the building. \u24d2Tifenn Ripoll - VOST Collectif / Institut Carnot STAR.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_Fig1_HTML.jpg" + ] + }, + { + "section_name": "Results", + "section_text": "Our proposed MB model emulates ant visual processing by encoding panoramic images as low resolution neural representations (800-pixel input), enabling efficient learning and route recognition with minimal computational demands (see Methods for details, Fig.\u00a02b). The model operates in two main phases: learning (Fig.\u00a02c) and exploitation (Fig.\u00a02d). During the learning phase, our self-supervised model encodes the route using two route-centric MBONs, with a in silico scanning, and stores place-specific memories for the nest and feeder as route extremities (see \u201cMethods\u201d, Fig.\u00a02c). In the exploitation phase, the robot processes each view through all memory pathways, yielding four familiarity values (left, right, nest and feeder MBON activities). The lateralized difference of route familiarities (\u03bbdiff) directs steering, while the maximum familiarity value modulates forward speed. In addition, a motivational control modulates motor gain, allowing the robot to stop or reverse based on a familiarity threshold set by place-specific MBONs (see \u201cMethods\u201d, Fig.\u00a02d).\n\nThis figure illustrates the processing pipeline from image encoding to navigation control, spanning both the learning and exploitation phases. a The Antcar robot: a compact car-like platform equipped with a panoramic camera and a GPS-RTK (Global Positioning System - Real-Time Kinematic) system for ground truth data. b Image encoding: this process mimics the ant\u2019s visual system. Panoramic images (I) are captured, blurred, sub-sampled, and edge-filtered to produce a low-resolution 32\u2009\u00d7\u200932 pixel panorama (IS). The resulting image is then transformed into Projection Neurons (PN), expanded into Excitatory Post-Synaptic Projections (EP), and reduced into Action Potentials (AP) via a \u03ba-WTA function, forming the sparse Kenyon Cell (KC) representation. c Learning phase: the robot follows a route (C) from a start point (N). An in silico scan (simulated image rotation) generates images with a defined angular error (\\(\\hat{{\\theta }_{{{\\rm{e}}}}}\\)), used to continuously categorize views at 38\u2009Hz in a route-centric manner---i.e., as facing to the right or left of the local route orientation. These continuous categorizations drive the update of synaptic weights in the corresponding Mushroom Body Output Neurons (MBONs), allowing visual inputs to be associated with specific route memories in a self-supervised fashion. Joystick inputs are used to define learning boundaries at the start and end of the route, modulating plasticity. d Exploitation phase: the robot seeks to minimize lateral (d) and angular (\u03b8e) errors with respect to the learned route. The encoded image continuously activates MBONs at 16Hz based on previously learned synaptic weights, enabling the robot to infer route orientation and adjust its steering and speed accordingly. MBON familiarity indices (\u03bb) operate in an opponent process: differences in familiarity between left and right MBONs guide steering, while the overall familiarity magnitude modulates speed. Special MBONs associated with route extremities affect motivational states, enabling route polarity correction or halting movement.\n\nThis study begins with an offline analysis of the proposed self-supervised, route-centric MB model using two route MBONs to assess stability (Fig.\u00a03), followed by experimental route-following tasks in challenging indoor and outdoor environments (Figs.\u00a04, 5 and 6). Next, a homing task is described, in which the robot follows a long outdoor route in reverse toward the starting area, designated as the nest (N), and stops nearby, utilizing three MBONs (Fig.\u00a07). Finally, a shuttling task is introduced, where the robot, after a single learning trial with two route MBONs and two extremities MBONs for the nest and feeder, autonomously shuttles to and fro between these two locations, driving both forward and backward (Fig.\u00a07).\n\nThis figure illustrates the familiarities differentiation and maximum value of route Mushroom Body Output Neurons (MBONs) during offline analysis of panoramic images and positional data from indoor (Mediterranean Flight Arena) and outdoor (Luminy Campus, Marseille, France) environments. The mapping was performed using an oscillation amplitude (A) of 45\u00b0. a, b Raw familiarities from the left and right MBONs. c, f Familiarity difference index (\u03bbdiff) mapped in the route\u2019s frame of reference, showing variations with both lateral (d) and angular (\u03b8e) errors. The defined operating area is highlighted in pink. d, g familiarity maximum index (\\({\\lambda }_{\\max }\\)) mapped similarly. e Overview of the indoor environment with the learned route highlighted in red. h Overview of the outdoor environment i, j Cross-sectional view of \u03bbdiff and \\({\\lambda }_{\\max }\\), respectively, plotted against \u03b8e when d is zero. Indoor conditions are represented by a solid line, and outdoor conditions by a dotted line. k Pearson correlation coefficient illustrating the linear relationship between \u03bbdiff and \u03b8e as a function of oscillation amplitude A. This plot also indicates the learning time required for a single oscillation cycle per image captured by the robot.\n\nEffects of steering direction and lighting variation with artificial visual cues. Detailed environmental configurations and familiarity data are shown in Supplementary Fig.\u00a0S4 and the accompanying video. a Autonomous route following experiments for two distinct learned routes (route 1: solid black line, route 2: dashed black line), each approximately 8 m long and represented independently by separate pairs of route MBONs. Learned trajectories are indicated in red. b Kidnapped robot tests performed independently for each learned route, assessing robustness to positional uncertainty. c, d Route following tests performed under bright (c) and dim (d) lighting conditions, while routes were learned under standard office lighting. e\u2013g Overview images of the indoor experimental environment.\n\nAntcar robot navigating indoor and outdoor routes under challenging conditions after a single learning phase. a, b Indoor experiments along an 8\u2009m route marked with artificial visual cues, featuring walking pedestrians (a) and large dynamical occlusions (b). c, d Outdoor experiments along a narrow (1.5\u2009m) forest-like footpath of 21\u2009m length, without (c) and with (d) static occlusions. e, g Third-person views of indoor and outdoor experimental environments. f, h Robot\u2019s first-person views illustrating dynamic and static occlusions in the frontal field of view.\n\na First-day experiments: learning and autonomous route following with several cars along the 55\u2009m road. b, f Overviews of the outdoor environment on the first and second days, respectively. c, d, g, h Familiarities difference (\u03bbdiff) and maximum (\\({\\lambda }_{\\max }\\)) values plotted against distance traveled on the first and second days, respectively. e Second-day experiments conducted at the same hour demonstrate autonomous route following using day-one memories in an altered, car-free environment.\n\na Outdoor homing experiments along a 50\u2009m L-shaped route in a cloudy environment. Two route MBONs and one motivation MBON guided an autonomous return route in the opposite direction. b Indoor shuttling experiments with artificial visual cues, using two route MBONs and two place MBONs. Autonomous routes swing back (blue) and forth (black). In both figures, the inset shows the robot\u2019s point of view. c Familiarity nest index (\u03bbN) plotted against distance traveled under a fixed stopping condition (p\u2009=\u20090.2). d Familiarity nest (\u03bbN) and feeder (\u03bbF) indices plotted against the traveled distance, with an inset zooming in on the values to highlight the backward and forward movements.\n\nThe continuous in-silico rotation of the panoramic image, along with the route-centric hypothesis and the robot\u2019s assumption that each captured image corresponds to the route direction, drives a self-supervised route-learning mechanism. We first evaluated the self-supervised model for route learning (using only two MBONs) with a dataset of indoor and outdoor parallel routes (Fig.\u00a03e, h). Results demonstrated that, with a controlled oscillation amplitude during learning, the model accurately estimated its heading error based on the differential familiarity \u03bbdiff, handling angular deviations up to 135\u00b0 indoors and 90\u00b0 outdoors (Fig.\u00a03c, f, i). Furthermore, the maximum familiarity index \\({\\lambda }_{\\max }\\), used as feedback for speed control, increased proportionally with heading error, enabling the robot to slow down when misaligned with the route. This behavior was consistent even when the robot was moved laterally off-route. Outdoors, these gradients were steeper (Fig.\u00a03c, d, f and g), indicating a higher visual contrast with larger landmarks.\n\nThe model\u2019s ability to identify heading error accurately across training oscillation amplitudes up to 135\u00b0 (Fig.\u00a03k, see also Supplementary Note\u00a01 and Supplementary Fig.\u00a0S1) suggests that this parameter may not require further tuning below this threshold. However, larger oscillation amplitudes increased computation time, especially on the Raspberry Pi platform (0.4s for \u00a0\u00b1\u200945\u00b0, Fig.\u00a03k). Notably, the familiarity difference index (Fig.\u00a03i) closely matched the spatial derivative of the maximum familiarity index, corresponding to the catchment area and turn rate amplitude observed in ants (Fig.\u00a03j, Supplementary Note\u00a01, 2, Supplementary Figs.\u00a0S1 and S244).\n\nThis analysis helped establish the operational limits of our MB model, maintaining stable behavior within a lateral error (d) of 2 meters and an angular error (\u03b8e) within the learning oscillation amplitude, set here at 45\u00b0. For asymptotic stability (i.e., the system\u2019s ability to return to equilibrium), we assumed a proportional relationship between \u03bbdiff and \u03b8e, supported by the Pearson correlation coefficient being close to 1 (Fig.\u00a03k) and expressed as Kdiff \u22c5 \u03bbdiff\u2009=\u2009\u2212\u2009\u03b8e, where Kdiff is a tuned negative gain. Integrating this relationship into the robot\u2019s motion equations, we applied a Lyapunov function for stability analysis. Results confirmed that the system converged to equilibrium points at de\u2009=\u20090 and \\({\\theta }_{{{\\rm{e}}}}^{e}=0\\), effectively correcting small deviations and enabling the robot to remain aligned with the learned route. The full derivation of these equations and Lyapunov stability proof are provided in the Methods (section 6) and Supplementary Note\u00a03, 4 and Supplementary Fig.\u00a0S3.\n\nThe proposed self-supervised approach for route learning was validated through a series of indoor and outdoor route-following tasks in fully autonomous mode, with only two MBONs. After a first outbound route with online learning, where images were captured continuously to update synaptic weights in real-time, the robot demonstrated robust route-following in various configurations (Figs.\u00a04, 5 and 6). First, the Antcar robot successfully navigated two routes in a cluttered indoor environment of approximately 8 meters (median lateral error \u2009\u00b1\u2009median absolute deviation (MAD)\u2009=\u20090.21\u2009\u00b1\u20090.09 m, angular error \u2009\u00b1\u2009MAD\u2009=\u20093.4\u2009\u00b1\u20096.2\u00b0, Fig.\u00a04a, e and Fig.\u00a08a). Moreover, the robot showed resilience in a kidnapped robot scenario, realigning with the learned route after being displaced from 1 to 2\u2009m away and oriented from 0 to 50 degree, (lateral error\u2009\u00a0\u00b1\u2009MAD\u2009= 0.26 \u2009\u00b1\u20090.14\u2009m, angular error\u2009\u00b1\u2009MAD\u2009=\u20096.45\u2009\u00b1\u20094.19\u00b0, Fig.\u00a04b, and Fig.\u00a08a). Only one predictable crash occurred when the robot exceeded theoretical angular limits (see Supplementary Fig.\u00a0S5).\n\na Detailed error analysis for each experiment. b Weighted bi-variate distribution for lateral (d) and angular errors (\u03b8e) across 13 experimental configurations.\n\nFurther tests assessed the robot\u2019s adaptability to bright and dim light conditions (Fig.\u00a04c, f, d, g). Despite a single learning trial under standard lighting (815 Lux), the robot accurately followed its route in bright (1340 Lux) and dim (81 Lux) lighting, with similar lateral and angular errors across tests (Fig.\u00a08). This indicates that the route-centric MB-based control system is robust to significant changes in illumination.\n\nIn indoor dynamic conditions with pedestrians and camera occlusions (Figs.\u00a05a, b), the robot maintained reliable route-following performance. When encountering pedestrians, the lateral error was \u00a0\u00b1\u2009MAD\u2009=\u20090.27 \u2009\u00b1\u20090.15 m and the angular error was \u00a0\u00b1\u2009MAD\u2009=\u20094\u2009\u00b1\u20092.8\u00b0 (Figs.\u00a05a, e and \u00a08a). Similarly, in the presence of dynamic occlusions, the robot achieved a lateral error of \u00a0\u00b1\u2009MAD\u2009=\u20090.22\u2009\u00b1\u20090.13\u2009m and an angular error of \u00a0\u00b1\u2009MAD\u2009=\u20094.7\u2009\u00b1\u20093.3\u00b0 (Figs.\u00a05b, f and 8a). The presence of pedestrians and occlusions resulted in a reduction of maximum familiarity, leading to slower speeds and increased oscillatory motion\u2014approximately 15% slower than in experiments without these disturbances (Fig.\u00a05a, b, Supplementary Figs.\u00a0S5 and Supplementary Movie\u00a01). Across all seven autonomous routes with dynamic occlusions, occlusions occupied on average 11% of the image area, with peaks up to 56% (see Methods and Supplementary Fig.\u00a0S4 for occlusion detection using YOLOv1155).\n\nOutdoor experiments demonstrated the model\u2019s robustness in diverse settings\u2014from a narrow 1.5\u2009m pathway in a tree-like environment to a wider 5 m semi-urban route\u2014while maintaining stable performance under static occlusions and altered conditions. On a semi-cloudy day, a 20 m route was learned and accurately recapitulated with low errors (lateral error \u00a0\u00b1\u2009MAD\u2009=\u20090.3 \u2009\u00b1\u20090.12\u2009m; angular error \u00a0\u00b1\u2009MAD\u2009=\u20094\u2009\u00b1\u20092.3\u00b0; Figs.\u00a05c and 8a). Then, a static occlusion was introduced, consisting in a fixed black spot in the frontal field of view, covering 4 to 15% of the entire visual field (and 8 to 30% of the front view), the performance remained robust (lateral error \u00a0\u00b1\u00a0MAD\u2009=\u20090.38\u2009\u00b1\u20090.10\u2009m; angular error\u2009\u00b1\u2009MAD\u2009=\u20095.6 \u2009\u00b1\u20093\u00b0; Figs.\u00a05d and 8a).\n\nOver a 55 m route under altered conditions, the robot first navigated a sunny day with low errors (lateral error \u2009\u00b1\u2009MAD\u2009=\u20090.39 \u2009\u00b1\u20090.13\u2009m; angular error \u00a0\u00b1\u2009MAD\u2009=\u20095.8 \u2009\u00b1\u20092.8\u00b0; Figs. 6a and 8a). The following day, at the same hour, with parked cars removed and a higher speed gain (cruising at 1.5\u2009m/s versus 1\u2009m/s), errors increased slightly (lateral error \u00a0\u00b1\u2009MAD\u2009=\u20091.3\u2009\u00b1\u20090.5\u2009m; angular error \u00a0\u00b1\u2009MAD\u2009=\u20096. \u2009\u00b1\u20093.2\u00b0; Figs.\u00a06e and 8a) yet remained within acceptable limits. During exploitation, the difference and maximum index values were marginally higher on the second day (Fig.\u00a06g, h) than during the first day (Fig.\u00a06c, d), likely due to the increased lateral error. These results underscore the system\u2019s resilience and adaptability under challenging conditions (See environment\u2019s picture taken at similar place from day one Fig.6b to day two Fig.\u00a06f).\n\nBuilding on the validated route-following strategy, further tests refined the robot\u2019s behavior, focusing on ant-like homing. Homing, by definition, include graphicsis the ability to return to a specific location after displacement. To test this, we evaluated the robot\u2019s ability to follow a 50 m outdoor route in reverse, stopping at a designated nest area (point N in Fig.\u00a07a). During learning, a 180\u00b0 shift in the visual oscillation pattern simulated the \u201cturn back and look\u201d behavior observed in ants and led to homeward route following.\n\nThe robot successfully followed the 50\u2009m route in reverse under cloudy outdoor conditions (lateral error \u00a0\u00b1 MAD = 0.9 \u00a0\u00b1 0.5 m, angular error \u2009\u00b1\u2009MAD\u2009=\u20096.3\u2009\u00b1\u20094.2\u00b0, Figs,\u00a06a, 8a). Although maximum familiarity was higher than in previous outdoor experiments (see Supplementary Note\u00a06, Supplementary Fig.\u00a0S5 and Supplementary Table\u00a0S1), overall accuracy remained stable and emerging oscillatory movements was demonstrated (see Supplementary Movie\u00a01).\n\nTo enable autonomous stopping at the nest, a place-specific MBON was used to learn \u2018nest-views\u2019 at the starting point of the route. Subsequent \u2018recognition\u2019 in this MBON, based on a familiarity threshold, acted as a motivational cue to halt route-following behavior and reduce the robot\u2019s linear velocity. This mechanism was sufficient for the robot to successfully reach and stop at the nest area in 4 out of 5 trials, with a median stopping distance of 1.4 m (Fig.\u00a07c, see also Supplementary Fig.\u00a0S7b for detailed familiarities values over distance).\n\nReverse route-following is also commonly observed in ants and was successfully replicated on board Antcar. Homing ants can pull food items backward when it is too large to carry forward, maintaining body alignment with the outbound route learned forward, and using outbound memories with an opposite valance53. Shuttling tests show the robot\u2019s ability to switch movement direction and drive backward while maintaining alignment with the outbound route (Fig.\u00a06b).\n\nThis foraging behavior was made possible by incorporating two additional place MBONs, which learned a series of panoramic views defining each endpoint of the route (feeder and nest). During shuttling, the model triggered a switch in motor gain polarity upon recognizing these panoramic views corresponding to the feeder or nest areas. In a cluttered indoor environment along a 6-meter learned route, the robot autonomously shuttled to and fro between the feeder and the nest, covering a total distance of 160 meters without interruption. Using a similar familiarity threshold on the two route-extremity MBONs, the robot detected the endpoints 22 times, achieving a median stopping distance of 0.31 m (Fig.\u00a07d) (See Supplementary Fig.\u00a0S7a for detailed familiarities values over distance).\n\nThis continuous shuttling revealed distinct differences in error profiles between forward and backward movement (Fig.\u00a07b). During forward motion, the robot maintained stable control with minimal deviations (lateral error\u2009\u00b1\u2009MAD\u2009=\u20090.1\u2009\u00b1\u20090.03\u2009m, angular error\u2009\u00b1\u2009MAD\u2009=\u20091.26\u2009\u00b1\u20090.83\u00b0, Fig.\u00a07b). However, during backward motion, the traction-driven setup amplified steering effects, resulting in slightly larger deviations from both accuracy and precision, though overall performance remained acceptable (lateral error\u2009\u00b1\u2009MAD\u2009=\u20090.19\u2009\u00a0\u00b1\u20090.08\u2009m, angular error\u2009\u00b1\u2009MAD\u2009=\u20092.7\u2009\u00b1\u20092.1\u00b0, Figs.\u00a07b and 8a). The increased \u2018motor\u2019 variability led to a lower visual recognition signal and thus usefully affected speed, which decreased by 14% compared to forward motion (see Supplementary Note\u00a06, Supplementary Fig.\u00a0S5 and Supplementary Table\u00a0S1). Nonetheless, the robot consistently realigned with the correct path after such minor deviations. These results highlight the model\u2019s versatility across different driving dynamics, capability to implement inverted steering, and adaptability to variations in motor kinematics and propulsion.\n\nAcross all experiments, including both indoor and outdoor route-following, homing and shuttling tasks, the model demonstrated robust and stable navigation performance, completing 113 autonomous trajectories with a total of 1.6 km traveled. The theoretical limits of the system were validated, with convergence toward equilibrium points consistently achieved under various environmental conditions, even in the presence of noise (lateral error\u2009\u00b1\u2009MAD\u2009=\u20090.24\u2009\u00b1\u20090.13\u2009m, angular error\u2009\u00b1\u2009MAD\u2009=\u20094\u2009\u00b1\u20092.7\u00b0, Fig.\u00a08b). Lateral errors were within acceptable margins for both indoor and outdoor contexts, aligning within the standard widths of roads in France (5\u2009m) and typical indoor corridor (1.5\u2009m).\n\nIn addition, statistical analysis showed no significant differences in the lateral or angular errors across the eleven test scenarios (Kruskal-Wallis test, H\u2009=\u20092.10 for lateral error, p\u2009=\u20090.99; H\u2009=\u20098 for angular error, p\u2009=\u20090.76), underscoring the system\u2019s reliability across diverse conditions (see Statistical Information). These results highlight the robustness and adaptability of the route-centric MB model in dynamic environments, confirming its potential applicability in a variety of navigation contexts.\n\nIn classical single-MBON familiarity models, memory capacity (m) is defined by an error probability (Perror), the chance of confusing an unfamiliar visual pattern with a learned one39. With our chosen parameters (N\u2009=\u200915,\u00a0000,\u00a0\u03ba\u2009=\u20090.01), theoretical estimates predict a memory capacity of m\u2009=\u2009346 KC activity patterns stored for a 1% error probability equivalent to roughly 38m of route in a cluttered environment39. However, such binary classification metrics fail to capture the complexity of our route-centric lateralized MBON steering mechanism.\n\nTo better quantify our model\u2019s steering-specific memory performance, we propose an empirical metric (Plerror), defined as the proportion of visual patterns incorrectly activating the opposite MBON (e.g., images associated with a leftward orientation producing lower familiarity on the left MBON than on the right MBON, and vice versa). Therefore, we define a steering-specific memory capacity (see Fig.\u00a09, Methods section and Supplementary Fig.\u00a0S8).\n\na Error proportion Plerror (defined as cases where the current visual pattern activates the incorrect lateralized MBON) as a function of route length and visual patterns learned, from outdoor experiments conducted on day one. b Route length that our MB architecture can store, maintaining an error proportion below 1%, plotted against the number of neurons and k-WTA parameter. The dataset is from a 250\u2009m outdoor route (real), and extrapolation is made on two couples of parameter (because it did not reach the 1% before 250\u2009m, see Supplementary Fig.\u00a0S8). Under our current network parameters (N\u2009=\u200915,\u00a0000,\u00a0\u03ba\u2009=\u20090.01), our route memory capacity with a 1% error probability corresponds to approximately 65\u2009m.\n\nUnder our metric, during extended route-following experiments in a semi-urban setting (55\u2009m, 430 unique KC activity patterns, Fig. 6), our network achieved 0.7% of error, below the critical threshold of 1% confusion probability (Fig. 9a). Further analysis of memory scalability using a larger dataset (up to 250\u2009m; Supplementary Fig.\u00a0S8) indicated that under our current network parameters (N\u2009=\u200915,\u00a0000,\u00a0\u03ba\u2009=\u20090.01), a 1% error probability yields an estimated 500 patterns, corresponding to about 65\u2009m of stored route in this conditions. A basic linear extrapolation indicated that with a larger network (N\u2009=\u2009100,\u00a0000, \u03ba\u2009=\u20090.005), routes as long as 600 m could be reliably encoded at the error threshold of 1% (Fig.\u00a09b).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_Fig8_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_Fig9_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We introduced a fully embodied, biologically grounded route-following system that unifies route-centric lateralized visual memory, panoramic input encoding, and neuromorphic control within a real-world robotic platform. Unlike prior ant-inspired models, which remained limited to short-range tasks or simulations, our model delivers continuous, one-shot learning and stable bi-directional navigation of visual routes \u2014 using only a single 800-pixel sensor, four MBONs, and light computational resources. These results mark a significant step forward in insect-inspired embodied cognition for real-world robotic autonomy.\n\nThe angular error between the agent\u2019s head direction and the dynamic local route orientation (i.e., route-centric, defined in the Methods as the Frenet frame56) emerged as both a challenge during exploitation\u2014where the system tends to minimize this error\u2014and a cue during learning, where the categorization process depends on its polarity. Our model demonstrated homing behavior using either a 180\u00b0 shift in visual oscillation or by inverting motor gains, thus enabling forward and backward movements with only a single foodward learning route. In addition, visual place memories stored in supplementary MBONs, paired with a motivational control system, allowed the robot to recognize route endpoints and modulate motor gains, halting movement or reversing foraging motivation. With a single learning pass in one direction, the agent could follow the route forward, backward, and in reverse, controlled by oscillation parameters and motivational cues. Only motivational rules required adjustment to switch between route following, homing, and shuttling, underscoring the model\u2019s flexibility.\n\nCompared to earlier ant-inspired familiarity-based models, generally limited to short, indoor routes or stop-and-scan strategies, our system demonstrates practical advantages in scalability, efficiency, and real-world adaptability19,20,21,22,23,24. Our memory footprint and command computation time are significantly lower than those reported in panoramic visual route-following methods13, which require 3 megabytes per kilometer and 400\u2009ms per control update. In contrast, our model operates at 0.3 megabytes per kilometer and produces commands within 75\u2009ms on a lightweight embedded board\u2014enabling real-time operation in constrained platforms. In addition, it achieves competitive performance compared to snapshot-based visual route-following methods that use odometry15.\n\nThis route-centric lateralized MB model distinguishes itself through reduced time and space complexity for route direction processing compared to perfect memory, snapshot, and insect-based visual compass approaches. Whereas time and space complexity increase with the number of images in perfect memory or snapshot models, our MB model maintains constant space complexity, relying only on the synaptic matrix size KCtoMBON. In addition, in contrast to visual compass approaches, where computational complexity scales with in-silico scan range and resolution during exploitation (\\({{\\mathcal{O}}}(n)\\)), our MB model maintains a constant factor (\\({{\\mathcal{O}}}(1)\\)) since in-silico scanning is only required during learning. For instance, while an insect-based visual compass scanning a \u2009\u00b1\u200945\u00b0 range at 1\u00b0 resolution requires 90 comparisons per image, our model requires only two comparisons, eliminating the need for angular scanning in exploitation. Notably, our model produced commands five times faster than the insect-based visual compass approach on the same robot platform21,22.\n\nOur contribution also aligns well with current biological observations, particularly highlighting the effectiveness of latent learning, where continuous learning bypass the need to control \u201cwhen to learn\u201d32,45. The opposed event-triggered and snapshot-based learning models producing place learning15,27 where used here only to recognize the place of interests, such as the nest and the feeder, to switch motivation, but were not engaged for route guidance. Also, our MB model prioritized body orientation within the route frame rather than splitting the visual field23,49, aligning with biological observations in ants with unilateral visual impairment, showing that these insects store and recognize fundamentally binocular views50. Interestingly, the linear relationship observed between familiarity measures and route-centric angular error during exploitation closely mirrors experimental findings in ants with nest-centric models44. This relationship enabled us to demonstrate the asymptotic stability of the system within a defined domain, ensuring the consistent and predictable behavior essential for a robotic navigation model57.\n\nFurthermore, oscillatory learning behavior mirrors ant behavior, where initial routes involve slow, rotational movements, transitioning to direct paths on subsequent journeys40. These oscillations typically fall within \u2009\u00b1\u2009100\u00b0, with peaks around \u2009\u00b1\u200945\u00b0 in unfamiliar terrain41,43. The robot\u2019s ability to slow down and produce emerging mechanical oscillation upon entering unfamiliar areas (see Fig.\u00a05a, b and Supplementary Movie\u00a01) are consistent with such naturalistic behaviors. Finally, Antcar\u2019s homing capability was maintained even when navigating backward, closely mirroring ant behavior while dragging food51,52,53,58. Overall, our attempt to integrate multiple MBONs, oscillations, \u201cturn back and look\u201d behavior, and motivational control mechanisms echoes insect mechanisms2,38, and the resulting expression when implemented in the robot echoes insect behaviors.\n\nThis study addresses several core needs identified in research on embodied neuromorphic intelligence6,8, such as robustness to visual changes, adaptability to real-world environments, and support for extended route learning. Our algorithm\u2019s efficiency allows computational power for additional tasks, making it valuable in GPS-compromised or SLAM-disrupted scenarios (Simultaneous Localization And Mapping). The robot\u2019s low-resolution, wide-angle vision proves resilient against moving objects that often disrupt SLAM. Our model is particularly effective in dynamic environments or scenarios where traditional odometry\u2014whether visual, inertial, step-counting, or wheel-rotation\u2014is unreliable. It can serve as a low-resource backup system (running on a 4-core CPU) alongside other processes, or be integrated with additional sensors to enhance state estimation robustness59,60.\n\nInterestingly, the semi-random encoding process, specifically the PNtoKC synaptic projections, introduces a \u201cfail-secure\u201d memory-sharing mechanism. If synaptic weights for encoding differ, memory sharing becomes inaccessible, an advantageous feature for swarm robotics or cross-robot memory sharing. Future research could enhance this approach but also transitioning this model to a spiking neural network on neuromorphic hardware could further enhance computational efficiency and biological fidelity11. In addition, incorporating obstacle avoidance61,62, would improve performance in dynamic environments.\n\nOur upward-facing fisheye camera provides a 360\u00b0 view at minimal cost, and in silico rotation keeps the learning route straight. However, if the ground is not planar, in silico rotation may not accurately represent physical rotation, thus learn an image that the robot will never encounter. Moreover, bypassing image unwrapping and calibration speeds computation but reduces the influence of distal cues, especially in large open fields where the horizon is severely deformed and can appear uniform. Interestingly, salt lake ants in such environments have developed enhanced horizontal resolution and accuracy, and build artificial visual cues near the nest entrance63.\n\nA reduced visual field, as seen in more general cases, may preclude in silico scanning, necessitating an estimation of the angular error between the road frame and the agent. Collett et al.64 proposed that ants use route segment odometry for navigation, suggesting a potential alternative. In insects, dopaminergic neurons (DAN) modulate MBON synapses from Kenyon cell inputs in response to motor stimuli, effectively acting as a \u201csupervisor\u201d for sensory cue categorization65. There are plenty of feedback from motor areas towards the MB (both DANs and MBON)66 that could enable to orchestrate visual memory formation, for instance, based on whether the current body orientation is left or right from a goal compass direction45. Similarly, our model\u2019s left-right classification functionally mimics the effect of dopaminergic feedbacks (onto MB), which activity would be based on whether the current body orientation is left or right from the local route heading.\n\nOur approach does not cover beeline homing post-foraging, or search behaviors near points of interest, although these could be added by adding path integration mechanisms67 or using the current visual mechanism but adding \u201clearning walk\u201d behaviors around places of interest45. Although anti-Hebbian learning effectively improves familiarity discriminability by sparsifying KCtoMBON synapses, there is a theoretical limit to this benefit. Excessive sparsification would lead to memory saturation (dramatic forgetting), where the MBON output no longer discriminates between familiar and unfamiliar inputs. Therefore, it suggests an opportunity for further exploration by adjusting KCs numbers or connectivity by testing different MB learning mechanisms with feedback or prediction error68.\n\nFinally, our system\u2019s memory capacity metric (500 patterns) is higher than the theoretical estimation from Ardin et al. (346 patterns)39. This discrepancy may arises because the theoretical model assumes random, independent KC activations, whereas in continuous route learning, KC activations are correlated and structured. In addition, our empirical error calculation depends on our visual preprocessing, hardware and the semi-urban environment. Also, the memory capacity error computed here can be linked to the sparsity of the KCtoMBON synaptic weight, avoiding a possible dramatic forgetting when all the KCs are used (i.e., the KCtoMBON vector is 0). To extend route length while reducing error, we could increase the number of KCs (see Fig.\u00a09); however, this may slow processing due to larger matrices\u2014though the impact may be minimal on a GPU. Alternatively, incorporating additional pairs of MBONs for different route segments could enable longer and multiple route memories without affecting processing time and, therefore, considering an extrapolated memory footprint of only 0.3 megabytes per kilometer. Also, expanding the number of MBONs, akin to the 100 in honey bees69, along with a mechanism (such as a context-based selection) to dynamically activate the appropriate MBON pair would enable more complex motivational states, multi-route memory storage, and broader navigational abilities70.\n\nInspired by the neuroethology of ants, our route-centric, lateralized MB model provides an effective bridge between theoretical insights and practical application in insect-inspired robotic navigation. This egocentric architecture demonstrates how biologically plausible neural circuits can support robust, scalable, and adaptive behavior in real-world robots\u2014using accessible hardware, panoramic input, and minimal computing resources. These results reinforce the promise of neuromorphic approaches for embodied autonomy and broaden opportunities for both robotic systems and experimental studies in comparative cognition.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "This section describes the methodology used in the present study, focusing on the Encoding, Learning, and Exploitation processes of the proposed MB model (Figs.\u00a02b\u2013d and 10). We also provide details on the hardware setup, control architecture, and stability analysis.\n\nThe network includes four MBONs fitted during the exploration phase. For visualization, the EP, AP, and KCtoMBON connections are reshaped into matrix-like formats. KCtoMBON synapses are initially fully connected prior to learning.\n\nInspired by the visual system of ants71, the model encoded real-world images into sparse, binary neural representations to efficiently handle visual input.\n\nThe encoding function (Figs.\u00a02b, 10 and Supplementary Note\u00a05) processed panoramic images from a camera with a 220\u00b0 vertical and 360\u00b0 horizontal field of view. This wide field of view enabled the camera to capture from slightly below the horizon to nearly directly below itself. To enhance natural contrast, the green channel of each image was selected71, followed by Gaussian smoothing (\u03c3\u2009=\u20093 pixels) to reduce noise. The chosen \u03c3 corresponds to an acceptance angle of about 4.\u00a04\u00b0 (\u03c3\u2009+\u2009FOV/r), slightly larger than the estimated visual acceptance angle of the Melophorus species72. The image was then downsampled to a low-resolution 32\u2009\u00d7\u200932 pixel thumbnail (0.145 pixel per degree), approximating the visual resolution of ants at 7.1\u00b0 between adjacent photoreceptors63. Next, a Sobel filter extracted edges, mimicking lateral inhibition as seen in insect optical lobes73. These processed images were flattened into n\u2009=\u2009800 Visual Projection Neurons (\\({{\\bf{PN}}}\\,\\in \\,{{\\mathbb{R}}}^{n}\\)), comparable to the number of ommatidia in Cataglyphis ants. These processing steps, with the aim of mimicking the output of the optic lobes, are described in detail in Supplementary Note\u00a05 and in Fig.\u00a010.\n\nThe PNs were further expanded into Kenyon Cells (KCs) using a fixed, sparse pseudo-random binary synaptic matrix (PNtoKC, equation (1)). Each KC received input from four PNs, enhancing the visual encoding\u2019s discriminative power within the MB, forming an Excitatory Post Synaptic Projection (EP) vector (see equation (2) and Fig.\u00a010).\n\nSo, the product\n\nwill be an N-dimensional vector, i.e., \\({{\\bf{EP}}}\\in {{\\mathbb{R}}}^{N}\\). The EP vector size was set to N\u2009=\u200915,\u00a0000 for the route MBONs (MBONR and MBONL), while for place-specific MBONs (MBONN and MBONF), which required fewer images, N was set to 5000.\n\nA \u03ba-Winner-Take-All (WTA) mechanism was applied to capture the highest contrasts, creating a high-dimensional, sparsified binary vector (see Fig.\u00a010). This vector, referred to as the Action Potential (AP, equation (3)), consequently activated only 1% of KCs (\u03ba\u2009=\u20090.01), giving Nu\u2009=\u2009N*\u03ba active neurons, such that:\n\nWhere i is the neurons number, and AP \u2208 {0, 1}N. This final binary representation served as the encoded visual input to be learned. All parameters were predefined by literature and experimental tests, but not further optimized.\n\nThe learning process is governed by synaptic depression through anti-Hebbian learning. For each MBONs, their synaptic weight vector (KCtoMBON) dynamically adjusted their binary weight based on input from the AP layer.\n\nWhere KCtoMBON \u2208 {0, 1}N. At the beginning, for each MBONs, their KCtoMBON vector are fully connected (i.e., all AP neuron are connected to the MBONs). During learning, each time the KCtoMBON have the same weight as the current AP (1), the respective synaptic connection is depressed (weight from 1 to 0) in the designed KCtoMBON (equation (4)). For the route, the KCtoMBON vector is designed (therefore depressed) according to the route-centric left or right looking direction (equation (4)). The model assumed the robot perfectly aligned to the route being learned. The body rotation was estimated as \\({\\hat{\\theta }}_{e}={\\theta }_{{{\\rm{e}}}}+{\\theta }_{c}\\), where therefore \u03b8e\u2009=\u20090 during learning, and \u03b8c is the image rotation angle. The encoded binary image (AP) was learn in one MBONs based on the polarity of \\({\\hat{\\theta }}_{e}\\), such that:\n\nWhere Learn() is the function in equation (4). The simulated oscillatory movements during learning were obtained by rotating each captured image in steps, creating a sweep of rotations (\u03b8c) described by the following function:\n\nwhere A represents the oscillation amplitude, \u0394\u03b8 the step size, and \u03d5 the phase shift. The step size was fixed at \u0394\u03b8\u2009=\u20095\u00b0, with A\u2009=\u200945\u00b0 for route MBONs and A\u2009=\u200930\u00b0 for place MBONs. The phase shift was \u03d5\u2009=\u2009180\u00b0 only for the homing task (Fig.\u00a07). The place MBONs learning was triggered by an external stimulus, specified by the robot operator.\n\nSynaptic weights (KCtoMBON) were stored in CSR format (Compressed Sparsed Row), achieving significant data compression to 148 kilobits independently of the route length (from 6\u2009m to 55\u2009m), reducing memory requirements by 99.97% from cumulative image storage. This self-supervised model continuously learned visual input at high throughput without memory overload, as only novel views (i.e., newly recruited KCs) modulated synapses. Several panoramic views were learned to define the start and finish areas in their respective MBONs, serving as motivational cues.\n\nDuring exploitation, the model calculated familiarity scores (\u03bb) by comparing the current input (AP) with each MBON\u2019s synaptic weight matrix (KCtoMBON):\n\nThis familiarity score, ranging from 0 (unfamiliar) to 1 (familiar), was used to assess route alignment. The lateralized difference in familiarities between the left and right MBONs (\u03bbdiff, equation (8) and Fig.\u00a010), which indicates whether the current view is more oriented to the left or right of the route, guided the robot\u2019s steering angle (\u03c6). Meanwhile, the maximum familiarity (\\({\\lambda }_{\\max }\\)), representing how familiar the current view is, modulated its speed (v).\n\nThus, the control input U was defined as:\n\nHere, Kv and K\u03c6 are proportional gains that control linear and angular velocities, while the saturation function (sat()) establishes a minimum throttle level, ensuring minimum speed even at low familiarity levels to allow the robot to turn (due to his mechanical constraint). The motivational state (M) regulated transitions between behaviors, based on familiarity thresholds within place-specific MBONs. During route following, M was consistently set to 1. In homing experiments, where the objective was to stop at the nest, M initially started at 1 and switched to 0 once the familiarity of the nest-specific MBON (\u03bbN) fell below a fixed threshold (p\u2009=\u20090.2), signaling arrival at the nest. For shuttling tasks, M alternated between values of 1 and \u00a0\u2212\u20091 as the robot reached each route extremity, driven by a familiarity threshold of the two place-specific MBONs (\u03bbN and \u03bbF).\n\nStability in mobile agents, biological or robotic, is essential for reliable, predictable behavior. In non-linear\u00a0control theory, an agent\u2019s motion is generally modeled as \\(\\dot{x}=f({{\\bf{x}}},{{\\bf{U}}})\\), where x is the state vector (e.g., position or velocity), U is the control input, and f describes system dynamics. A desired equilibrium point xe is achieved by defining a control input Ue such that f(xe,\u00a0Ue)\u2009=\u20090, allowing the system to maintain stability and return to equilibrium after disturbances. Stability is typically assessed using a Lyapunov function57, which ensures the system converges to a stable state over time.\n\nIn contrast to conventional control approaches, we applied a neuroethologically inspired control input derived from ant behavior and assessed its stability via an a posteriori Lyapunov analysis. The robot\u2019s motion was modeled in a Frenet frame, a moving reference frame coincident with the nearest point on the route, to minimize lateral and angular errors, defined by x\u2009=\u2009[d,\u00a0\u03b8e]. Empirical data for stability assessment was collected in indoor and outdoor environments (paths of approximately 6 meters with 855 learned images each), providing distinct visual contexts (Figs.\u00a02, 3). The robot\u2019s equations of motion from a global to the Frenet frame are ref. 74:\n\nwhere s is the arc length along the route, d is the lateral error, and \u03b8e is the angular error.\n\nThis kinematic model, along with by empirical observations (Fig.\u00a03), enabled us to establish an asymptotically stable domain for lateral and angular errors (d and \u03b8e), ensuring reliable route-following performance even with minor disturbances. The full theoretical stability proof and derivations of the model in the Frenet frame are provided in the Supplementary Note\u00a03 and 4.\n\nThe experiments were conducted using Antcar (Figs.\u00a01 and 2a), a PiRacer AI-branded car-like robot. Antcar features four wheels, with two rear drive wheels powered by 37-520 DC motors (12V, 1:10 reduction rate) and a front steering mechanism controlled by an MG996R servomotor (9kg/cm torque, 4.8V). The robot\u2019s chassis measures 13\u2009\u00d7\u200924\u2009\u00d7\u200919.6\u2009cm and is powered by three rechargeable 18650 batteries (2600\u2009mAh, 12.6\u2009V output). Antcar\u2019s primary sensor is a 220\u00b0 Entaniya\u2122 fisheye camera, mounted upward to capture panoramic images at 160\u2009\u00d7\u2009160px\u2009\u00d7\u20093 resolution, processed using OpenCV on a Raspberry Pi 4 Model B (Quad-core Cortex-A72, 1.8\u2009GHz, 4GB RAM), running Ubuntu 20.04. Note that there was no closed-loop control on the wheel rotation speed. Raspberry Pi manages real-time performance and controls the motors through a custom ROS architecture.\n\nReal-time communication is facilitated by ROS Noetic, either via Wi-Fi (indoor) or a 4G dongle (outdoor). The robot can be controlled manually using a keyboard, joystick or with a GPS waypoint, but in autonomous visual-only mode, it follows its own internal control law. Control inputs\u2014steering angle (\u03c6) and throttle (v) are processed using the PyGame library. Real-time data visualization and post-experiment monitoring are achieved via Foxglove.\n\nAntcar has a maximum velocity of 1.5 m/s and a maximum steering angle of 1 rad, with a wheelbase of 0.15\u2009m. The robot\u2019s configuration states q\u2009=\u2009(x,\u00a0y,\u00a0\u03b8) were tracked using different systems. Indoor experiments utilized eighteen Vicon\u2122 motion capture cameras, with infrared markers on Antcar providing precise tracking at 50\u2009Hz with 1\u2009mm accuracy. Outdoor experiments employed a GPS-RTK system with a SparkFun GPS-RTK Surveyor, providing 14\u2009mm accuracy at 2\u2009Hz (GPS-RTK stands for Global Positioning System - Real-Time Kinematic). Ground speed and angular speed were calculated through position differentiation. The base station used for GPS corrections was a Centipede LLENX station located at 24\u2009km (Marseille Provence Airport) from the experiment site in Marseille. Note that the ground truth acquisition system was run on the Raspberry Pi along with the MB model.\n\nLateral error was calculated by finding the nearest point on the learning route using the Euclidean distance, with the shortest distance representing the absolute lateral error. Angular error was defined as the absolute difference in heading between the nearest learning route point and the current position. The euclidean distance between the agent and the nest or feeder areas was calculated to estimate the distance when the robot switched behavior (i.e familiarity dropped below the threshold).\n\nTo compute our memory capacity and Plerror, we used a 250\u2009m dataset (Supplementary Fig.\u00a0S8). The metric was computed over the operating area defined by the learning oscillation. The computation was performed incrementally: for each new learned image (rotated from \u00a0\u2212\u200945\u00b0 to 45\u00b0 in 5\u00b0 steps), all previously encountered images were tested (rotated from \u00a0\u2212\u00a045\u00b0 to 45\u00b0 in 1\u00b0 steps), we didn\u2019t take into account the value between \u2212\u20091\u00b0 and 1\u00b0. For each tested image i, the familiarity difference \u03bbdiff\u2009[i] was calculated. An error indicator ei was then defined by comparing the sign of \u03bbdiff\u2009[i] with that of the heading error \u03b8e[i]:\n\nFinally, the cumulative mean error up to test image i is given by:\n\nThe errors used for statistics were recorded at each command decision timing. Due to non-normality in error values (with outliers retained), Box-Cox transformations were applied to stabilize variance across experiments, reducing the impact of outliers caused by indoor obstacles that hid the robot from the motion capture system or by GPS-RTK inaccuracies outdoors. The groups was compared using the Kruskal-Wallis test75, and median values are reported with median absolute deviation (MAD), as median \u00a0\u00b1 MAD. The package Python SciPy76 was used for the statistics. The overall medians and bivariate distribution plots were weighted by the number of measurements per experiment for the Fig.\u00a08.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62327-3/MediaObjects/41467_2025_62327_Fig10_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "The dataset, including image banks and experimental data, is available at Figshare: https://doi.org/10.6084/m9.figshare.2770810577.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code for the MB model, dataset analysis, and figure generation is available on GitHub: https://doi.org/10.5281/zenodo.1578347278. 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Code for \u201croute-centric ant-inspired memories enable panoramic route following\u201d. https://doi.org/10.5281/zenodo.15783472 (2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "The authors thank David Wood for revising the English in this study, Guillaume Caron for providing the camera reference,\u00a0St\u00e9phane Viollet for the GPS, and Thomas Gaillard, Cl\u00e9ment Serrasse, and Hamidou Diallo for their assistance during the robotic tests. G.G.G. was supported by a doctoral fellowship grant from Aix Marseille University (amU) and the French Ministry of Defense (AID - Agence Innovation D\u00e9fense, agreement #A01D22020549 ARM/DGA /AID). G.G.G., J.R.S. and F.R. were also supported by Aix Marseille University and the CNRS Institutes (Biology, Informatics, as well as Engineering). A.W. was supported by ERC Consolidator Grant RESILI-ANT no. 101125881. The facilities for the experimental tests has been mainly provided by ROBOTEX 2.0 (Grants ROBOTEX ANR-10-EQPX-44-01 and TIRREX ANR-21-ESRE-0015).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Aix Marseille Univ, CNRS, ISM, Marseille, France\n\nGabriel G. Gattaux,\u00a0Julien R. Serres\u00a0&\u00a0Franck Ruffier\n\nUniv Toulouse, CRCA, CBI, UMR CNRS-UPS 5169, Toulouse, France\n\nAntoine Wystrach\n\nInstitut Universitaire de France, IUF, Paris, France\n\nJulien R. Serres\n\nENSTA|IP Paris, CNRS, Lab-STICC, Brest, France\n\nFranck Ruffier\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nG.G.G., A.W., J.R.S., and F.R. designed this research work; G.G.G., A.W., J.R.S., and F.R. got funding for this study; G.G.G. performed experiments, collected and visualized the data; G.G.G., A.W., J.R.S., and F.R. analyzed data; G.G.G. wrote the first full draft. All authors reviewed the results and approved the final version of the manuscript.\n\nCorrespondence to\n Antoine Wystrach or Franck Ruffier.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Philippe Gaussier, and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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Route-centric ant-inspired memories enable panoramic route-following in a car-like robot.\n Nat Commun 16, 8328 (2025). https://doi.org/10.1038/s41467-025-62327-3\n\nDownload citation\n\nReceived: 22 November 2024\n\nAccepted: 14 July 2025\n\nPublished: 24 September 2025\n\nVersion of record: 24 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-62327-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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brain\u2013computer interface and mind control of smart devices enabled by space-time-coding metasurface", + "pre_title": "Secure wireless communication of brain-computer interface and mind control of smart devices enabled by space-time-coding metasurface", + "journal": "Nature Communications", + "published": "25 August 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63326-0/MediaObjects/41467_2025_63326_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63326-0/MediaObjects/41467_2025_63326_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63326-0/MediaObjects/41467_2025_63326_MOESM3_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63326-0/MediaObjects/41467_2025_63326_MOESM4_ESM.mp4" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63326-0/MediaObjects/41467_2025_63326_MOESM5_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Electrical and electronic engineering", + "Metamaterials" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4860006/v1.pdf?c=1756206415000", + "research_square_link": "https://www.researchsquare.com//article/rs-4860006/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63326-0.pdf", + "preprint_posted": "22 Aug, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Brain-computer interface (BCI) provides an interconnected pathway between human brain and external devices and paves a potential route for mind manipulations. However, most of existing BCI technologies are based on simple signal transmission and independent of other interface devices, owing to the considerations of reliability and safety of human brain\u2019s information interaction in the complicated wireless environment. To address the formidable limitation, we present a brain space-time-coding metasurface (BSTCM) system to deeply fuse the visual stimulation and electromagnetic manipulation for reliable and secure information transfer between human brain and external devices. Here, we innovatively integrate the BCI flashing frame and electromagnetic encoding sequence in the BSTCM system, and the STC metasurface ensures the secure wireless communications by using the harmonic-encrypted beams. We design and fabricate a proof-of-principle demonstration system and experimentally show that the proposed wireless BCI scheme could establish a remote but safeguarded paradigm for human-machine interactions, intelligent metasurfaces, and potential applications in metaverse, as a prominently scrutinized domain in the future 6G wireless communications.Physical sciences/Engineering/Electrical and electronic engineeringPhysical sciences/Optics and photonics/Optical materials and structures/MetamaterialsPhysical sciences/Materials science/Materials for optics/MetamaterialsSpace-time-coding metasurfacebrain-computer interfacesecure wireless communicationhuman-machine interactions", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementMaterials.pdfSupplementaryVideos.zipSupplementary Videos", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Brain\u2013computer interface (BCI) provides an interconnected pathway between the human brain and external devices and paves a potential route for mind manipulations. However, most existing BCI technologies are based on simple signal transmission and are independent of other interface devices, with limited consideration for the reliability and security of the human brain\u2019s information interaction in complicated wireless environments. Here, we propose a deep fusion coding scheme that combines the BCI visual stimulation coding with metasurface space-time coding at the physical layer, enabling reliable and secure information transfers between the human brain and external devices. A brain space-time-coding metasurface platform is designed to implement a secure wireless communication system by using harmonic-encrypted beams. We design and fabricate a proof-of-principle prototype and experimentally show that the proposed wireless BCI scheme can establish a remote but safeguarded paradigm for human\u2013machine interactions and intelligent metasurfaces, providing a potential direction in future secure wireless communications.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Brain\u2013computer interface (BCI) has emerged as a cutting-edge technology in human\u2013machine interaction and demonstrates promising applications such as brain-controlled spelling input1,2, medical rehabilitation, and equipment control3,4. Electroencephalography (EEG) signals remain the predominant input signal modality in BCI systems, with notable implementations including motor imagery5, P3006, and steady-state visually evoked potential (SSVEP)7. The SSVEP-based BCI systems utilize the brain\u2019s SSVEP response to the fixed-frequency visual stimuli for mind recognition and interaction8, offering a significant advantage of a high information transfer rate. Recently, some high-performance BCI systems have been proposed9,10,11,12,13,14, and the advancement of 6G wireless communication technology has significantly expanded the potential applications of BCIs. Hence, ensuring high information security and privacy preservation15 for the BCI users and devices becomes imperative when facilitating intelligent interactions within the constructed communication environment.\n\nHowever, most existing BCI systems lack in-depth research in terms of security. Wireless transmission of brain signals in the BCI systems is vulnerable to theft and attacks, potentially leading to inaccurate control commands and unauthorized privacy breaches. Although some methodologies have been proposed to enhance the security and privacy in BCIs16,17,18, the encryption mechanisms specifically tailored to these systems remain largely unexplored. Moreover, visual stimulation is often isolated from back-end information processing, lacking deep integration and interaction. With the increasing demand for secure BCI systems, it is essential to develop intelligent interactions in a secure and reliable communication environment. The frequency-dependent SSVEP responses and programmable harmonic characteristics of space-time-coding (STC) metasurfaces have notable similarities. Therefore, the STC metasurfaces can be used as a promising method that not only provides visual stimulation but also enhances the security of the BCI systems at the physical layer, owing to their powerful ability to flexibly manipulate electromagnetic (EM) waves in both time and space domains19.\n\nThe metasurfaces are composed of specific unit structures arranged in periodic or quasi-periodic arrays, which can flexibly control the EM waves at the subwavelength scale and yield a large number of unusual physical phenomena and novel devices20,21,22,23. The proposal of digital coding and programmable metasurfaces has established a profound connection between the EM fields and digital information under the control of a high-speed field programmable gate array (FPGA)24. Recently, the exploration of STC metasurfaces has sparked a surge of research interest due to the excellent ability to manipulate the EM waves and process digital information in both temporal and spatial dimensions25,26,27,28,29, resulting in many novel physical phenomena that cannot be realized by the traditional spatially modulated metasurfaces. More importantly, the utilization of STC metasurfaces holds the capability to precisely control the amplitudes, phases, and polarizations at different harmonic frequencies independently by specially designing the STC matrices30, which opens up avenues to develop advanced communication schemes with enhanced efficiency and reliability31,32,33. Hence, the STC metasurface is a potential candidate for deep information modulation and interaction in the SSVEP-based BCI system owing to its capability to flexibly manipulate the EM waves and interact with the frequency-dependent visual stimulation.\n\nHere, we propose a deep fusion coding scheme that combines SSVEP visual stimulation coding with metasurface coding for secure information interactions at the physical layer. To implement this scheme, we present a brain space-time-coding metasurface (BSTCM) platform that seamlessly integrates the visual stimulation for SSVEP-based BCIs with the information interaction to the external environment, improving the system\u2019s compactness and reliability. To enhance BCI security, we propose a secure, encrypted wireless communication system that integrates the physical layer security with the cryptography encryption method. Specifically, the transmitted information is encrypted into two ciphertexts, which are respectively sent to two users through two independent harmonic frequency channels constructed by the BSTCM system. Experimental results demonstrate a high bit error rate (BER) of nearly 50% for the Eves and a secrecy capacity of approximately 1.9\u2009dB, validating the security of the encrypted wireless communication system. Finally, smart device control is presented by using the BSTCM system, realizing intelligent human\u2013machine interactions. The proposed BSTCM establishes a paradigm of human\u2013machine interactions and secure wireless communications.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The proposed deep fusion coding method combines the BCI visual stimulation coding with the metasurface coding for secure information interactions at the physical layer, as presented in Fig.\u00a01a. The brain signals can be induced by low-frequency flickering visual stimuli (4\u201350\u2009Hz), while the STC metasurface produces a series of harmonic frequencies, revealing notable similarities in the frequency domain. The visual stimulation is crucial for eliciting the distinct oscillatory patterns necessary for user-intent decoding, and the BCI system would be inoperative without it. Since the metasurface\u2019s switching frequency is significantly higher than the flicker frequency, we fuse the STC signals into high-level intervals of low-frequency visual stimuli, where two signals are temporally synthesized to generate the cohesive BSTCM signals that support both visual flicker and EM manipulation. This integration enables the seamless fusion of the two coding mechanisms, as illustrated in Fig.\u00a01a. To implement this fusion coding, an integrated BSTCM platform for both visual stimulation and EM-wave modulation is presented in Fig.\u00a01, which comprises an STC metasurface and a BCI device. The metasurface element includes a meta-structure for EM-wave regulation and a light-emitting diode (LED) stimulator for visual stimuli. The corresponding EEG signals can be accurately captured by a head-worn EEG cap when the user focuses on the LEDs flickering at distinct frequencies. Then, the brain\u2019s intention can be recognized as different commands through the classification process of EEG signals, and the commands are transmitted to the STC metasurface, where the metasurface coding is fused with the visual stimuli to generate BSTCM control signals. This simultaneously supports visual stimulation and EM-wave modulation, opening up a wide range of potential applications.\n\na The schematic diagram of fusing visual stimulation coding with metasurface coding. The STC metasurface can correctly support the visual stimuli for the SSVEP-based brain computer interface (BCI) and facilitate information interaction with the external environment. Here, \\({f}_{{SSVEP}}\\) represents the four target visual stimulation frequencies, and \\({f}_{0}\\) denotes the switching frequency of STC signals. b The BSTCM system integrates the STC metasurface into the SSVEP-based BCI systems, and an encrypted wireless communication system is realized. In this way, smart devices can be controlled by the human mind.\n\nThe detailed flow diagram of the BSTCM system is shown in Fig.\u00a02. The target flickering stimuli, characterized by four distinct frequencies (8.5\u2009Hz, 10\u2009Hz, 11.5\u2009Hz, and 7\u2009Hz), are supported by the STC metasurface integrated with LEDs. The interaction process begins with the user wearing an EEG cap and sitting in front of the STC metasurface. Four visual flickering stimuli with distinct frequencies are presented to the user for selection. These visual stimuli are employed to elicit specific BCI signals, while an EEG amplifier collects the BCI signals (see Supplementary Note\u00a01 for details). Then, the BCI signals are classified into four different interaction commands, each corresponding to one of the aforementioned visual stimulation frequencies. The interaction commands are sent to the STC metasurface, and the corresponding STC signals are generated. A fusion operation is conducted to fuse the STC signals and visual stimulation signals, hence generating the corresponding BSTCM signals. These BSTCM signals drive the STC metasurface to simultaneously maintain the visual stimuli of the fixed frequency and manipulate the EM waves. Finally, the BSTCM system is used to construct independent physical channels at harmonic frequencies, enabling secure wireless communication and remote control of smart devices. This integrated platform not only ensures high mobility and system compactness by reducing the external connections but also achieves real-time bidirectional interaction. This approach highlights a fusion of BCI and STC metasurface technologies to realize secure, efficient, and intelligent human\u2013computer interaction systems. By deeply fusing the visual encoding and metasurface STC signals, the BSTCM platform ensures that every module fulfills a specific function while remaining inseparable from the rest. Specifically, the extracted complex EEG signals can be precisely recognized using a machine learning method, an FPGA can fuse the low-frequency visual stimuli with high-frequency STC signals, and the STC metasurface can be controlled to generate the harmonic-encrypted EM beams. They collectively form a cohesive framework that seamlessly handles the human brain's intention and secure data transmission, ensuring real-time adaptation, robust security, and user-driven interaction. Ultimately, the BSTCM system allows individuals to customize and modulate EM waves via cognitive intent, expanding the EM functionalities while enhancing the flexibility and interactivity in the human-machine systems.\n\nThe user generates the BCI signals by gazing at visual stimuli, which are processed and classified into different commands by the machine learning method. These commands are then sent to the STC metasurface, which fuses the low-frequency visual stimuli signals and high-frequency STC signals to form different BSTCM signals in Fig.\u00a01a. The fusion method can simultaneously maintain the visual stimuli of the fixed frequency and manipulate the EM waves. The BSTCM platform supports brain-driven interactive functions with the external environment, enabling secure wireless communications and remote smart-device controls via the frequency- and space-multiplexed physical channels.\n\nThe STC metasurface enables precise control of the propagation direction and harmonic power distribution of the EM waves in both spatial and frequency domains. By harnessing the abundant harmonic modulation capabilities and integrating the advantages of human brain intelligence, the BSTCM system facilitates the implementation of a secure, encrypted wireless communication system and enables smart device control by the human mind. In the encrypted wireless communication system, the XOR-based encryption encoding method is employed to encrypt the target information34,35,36. In fact, the target information is encrypted into two secret ciphertexts. As schematically demonstrated in Fig.\u00a01b, a legal transmitter (Alice) equipped with an EEG cap intentionally transfers two secret ciphertexts to two legal receivers (Bob and Carol) at two different harmonic frequency channels based on the encrypted communication. The eavesdropper (Eve) cannot decrypt the transmitted information unless she simultaneously obtains two ciphertexts, and knows the encryption mechanism, which are nearly impossible. Hence, the proposed system ensures high security and concealment by encrypting the secret information into two ciphertexts and transmitting these ciphertexts through two harmonic frequency channels. Ingeniously camouflaged as an LED stimulator, the STC metasurface can effectively obstruct the Eve from detecting the information interaction. The proposed encrypted wireless communication system integrates the physical layer security with the cryptography encryption method, enhancing the system security. Furthermore, the proposed system facilitates the remote control of smart devices, enabling users to control devices directly through the user\u2019s brain intention without the need for physical actions.\n\nConventional SSVEP classification and recognition algorithms include canonical correlation analysis37, filter bank canonical correlation analysis38, task-related component analysis1, support vector machine39, and convolutional neural network (CNN)40. However, some limitations still exist in the above-mentioned algorithms. In this study, we present a robust deep learning-based algorithm for the recognition of the SSVEP component, which is seamlessly integrated into the signal recognition stage of the proposed system (refer to Supplementary Note\u00a02). As depicted in Fig.\u00a03a, the raw BCI signals undergo a comprehensive pre-processing pipeline to extract the key frequency features for SSVEP signal recognition. This pipeline filters the signals using a filter bank consisting of four Chebyshev Type I bandpass filters, isolating four distinct sub-bands with specified frequency ranges to mitigate the interference and enhance the signal quality. Subsequent application of the fast Fourier transform (FFT) to these sub-bands reveals their amplitude spectra, highlighting the frequency components of the SSVEP signals. To further refine the spectral information, a weighted summation process is used to assign weight factors to each sub-band based on their indices, emphasizing the contribution of lower frequency components. The resulting signal amplitude spectrum (SigSpec) encapsulates the essential frequency characteristics of the SSVEP signals, serving as the foundation for subsequent feature extraction and classification processes.\n\na The comprehensive workflow from raw BCI signal acquisition to the final signal recognition outcome. b The conversion of signal spectra (SigSpec) into eight distinct feature graphs through the outer product operation with the pre-calculated reference spectra (RefSpec). c The lightweight classification model, in which four convolutional and two fully connected layers were employed. d An example of frequency responses of the SSVEP signals for the four target frequencies, showing distinctive amplitude peaks corresponding to different frequencies.\n\nFurthermore, we employ an innovative feature extraction technique to perform outer product operations between the SigSpec and pre-computed reference spectra (RefSpec), derived from the SSVEP signals with known frequencies, as described in Fig.\u00a03b. This operation yields eight feature graphs (sized by 160\u2009\u00d7\u2009160 pixels), each representing the interaction between two input SSVEP signal channels (O1 and O2) and the reference signals corresponding to the four target frequencies. A discernible square grid pattern appears in the feature graph corresponding to the frequency of the anticipated classification result (refer to Supplementary Notes\u00a03\u20135). These feature graphs are then processed by a lightweight CNN-based classification model, which incorporates four convolutional layers and two linear layers, as shown in Fig.\u00a03c and Supplementary Fig.\u00a012. The feature graphs are initially processed through a convolutional input layer. Subsequently, a MaxPooling operation with a 2\u2009\u00d7\u20092 kernel is applied after the data are activated by the ReLU function. To facilitate the propagation of original data, a residual connection is strategically integrated between the second and third convolutional layers. Each convolutional layer is followed by the same 2\u2009\u00d7\u20092 MaxPooling operation and ReLU activation. Once the convolutional layers extract spatial feature vectors from the feature graphs, a series of two consecutive linear layers is employed to classify the feature vectors into four distinct target frequencies. Finally, an output layer with sigmoid activation reshapes the model\u2019s output into four confidence scores. After training 300 epochs, the model achieved a classification accuracy of 96.67% on the validation set, with precision and recall rates over 90%. These results highlight the effectiveness of the proposed deep learning-based algorithm for the SSVEP signal recognition (see Supplementary Notes\u00a06 for additional details).\n\nFigure\u00a04a showcases the detailed configuration of the 1-bit STC metasurface element integrated with an LED, which consists of three metallic layers and multiple substrate layers. The top metallic layer has two slotted rectangular patches with a width of w\u2009=\u20097.3\u2009mm and a length of l\u2009=\u200911\u2009mm, in which the middle slot has a width of w1\u2009=\u20091.5\u2009mm and a length of l1\u2009=\u20097\u2009mm. The outer metal frame with a width of 0.15\u2009mm is used to weaken the coupling effect between elements. The top F4B dielectric substrate has a relative permittivity of 2.65 and a loss tangent of 0.003 with the thickness h\u2009=\u20093\u2009mm and period p\u2009=\u200919\u2009mm. Two PIN diodes are serially loaded in the middle of two rectangular patches, and the two rectangular patches are connected with metallic bars, serving as positive and negative poles, in which two inductors are used as radio-frequency (RF) chokes to effectively isolate the RF signal from direct current (DC). To enable simultaneous visual stimulation and EM modulation in Fig.\u00a04a, an LED serving as visual stimulation is placed outside the metal patch to avoid affecting the scattering properties of meta-atoms and share the same voltage signals with the PIN diode. To evaluate the performance of the 1-bit meta-atom, full-wave simulations are conducted using the commercial software CST Microwave Studio. As shown in Fig.\u00a04b, c, the scattering characteristics of the meta-atom can be altered by switching the states of two PIN diodes corresponding to distinct RLC equivalent circuit models, hence realizing the 1-bit phase regulation. The diodes with the OFF state are assigned as coding \u201c0\u201d, and the ON state as coding \u201c1\u201d. When the meta-atom is subjected to normal illumination by a y-polarized plane wave, a phase difference of 180\u00b0 between \u201c0\u201d and \u201c1\u201d states is observed at approximately 6.9\u2009GHz, and both states exhibit reflective amplitudes exceeding \u22121 dB, indicating excellent performance of the 1-bit programmable meta-atom. Hence, the designed meta-atom can be used to build the 1-bit STC metasurface. The STC metasurface is composed of 32\u2009\u00d7\u200932 elements, and segmented into four partitions, with each region consisting of 16\u2009\u00d7\u200916 elements. In these partitions, LEDs are operated at four distinct frequencies (8.5\u2009Hz, 10\u2009Hz, 11.5\u2009Hz, and 7\u2009Hz) and implemented to evoke four distinct SSVEP signals. The detailed prototype design, modeling, and characterization are given in Methods and Supplementary Note\u00a09.\n\na The geometrical structure of the STC metasurface element. b, c The reflective amplitude and phase of the element, respectively. d\u2013f The optimized STC matrices for different scattering angles at different harmonic frequencies. g\u2013i The far-field results correspond to the optimized STC matrices. j\u2013m The encoding schemes for the transmitting symbol \u201c0\u201d and \u201c1\u201d for two users: the symbols \u2018\u201c0\u201d and \u201c1\u201d for Users 1 (j, k); the symbols \u201c0\u201d and \u201c1\u201d for Users 2 (l, m). n The detailed configuration of four visual stimulation regions on the STC metasurface.\n\nTo establish a dual-user encrypted wireless communication system, we implement an amplitude-shift keying (ASK) modulation scheme using the STC metasurface, enabling simultaneous encrypted transmissions via space and frequency multiplexing. The control voltages, originating from the FPGA, are applied to the PIN diodes, enabling periodic switching to the reflection phases of the 1-bit STC element with a time period of \\({T}_{0}=1/{f}_{0}\\). Consequently, each STC element possesses a periodic time-coding sequence and constitutes part of an STC matrix, making it possible to flexibly control the EM waves in both space and time domains. The flexibility of STC matrices can generate a series of new harmonic spectra at the frequency interval of \\({f}_{0}\\), which are further optimized with specific requirements (see Supplementary Notes\u00a07 and 8). The space-time modulation enables abundant harmonic characteristics to construct harmonic-based encrypted wireless communications. Using the 2D STC matrices, we can modulate the EM waves to steer to the desired directions for target users. The STC matrices encompass 16 rows of elements, each possessing periodic time-coding sequences with 11 intervals. As illustrative examples, three radiation patterns corresponding to three STC matrices (M0, M1, and M2) are shown in Fig.\u00a04. The radiation pattern of the optimized STC matrix M1 in Fig.\u00a04e is strategically manipulated to yield a main beam with a deflected angle of \u03b8\u2009=\u2009\u221215\u00b0 at the +1st harmonic frequency, as showcased in Fig.\u00a04h. Similarly, the radiation pattern of the STC matrix M2 (Fig.\u00a04f) is optimized to direct the beam towards the angle \u03b8\u2009=\u2009+30\u00b0, in correspondence with the \u22121st harmonic frequency, as demonstrated in Fig.\u00a04i. Furthermore, the time-invariant STC matrix M0 (Fig.\u00a04d) facilitates the positioning of main beam towards a direction of \u03b8\u2009=\u20090\u00b0, associated with the fundamental frequency, as illustrated in Fig.\u00a04g. The harmonic energy could be further enhanced in the future by optimizing STC coding strategies using advanced algorithms. As an example of ASK modulation scheme, the left panels (\\({f}_{1}\\)\u2009=\u20098.5\u2009Hz and \\({f}_{2}\\)\u2009=\u200910\u2009Hz) of the STC metasurface are utilized to send the digital symbols \u201c0\u201d and \u201c1\u201d to User 1 at the position of \u03b8\u2009=\u2009\u221215\u00b0 when the transmitting information is encoded as binary data streams. The right panels (\\({f}_{3}\\)\u2009=\u200911.5\u2009Hz and \\({f}_{4}\\)\u2009=\u20097\u2009Hz) of the STC metasurface are utilized to send the digital symbols \u201c0\u201d and \u201c1\u201d to User 2 at the position of \u03b8\u2009=\u2009+30\u00b0. Experiments are carried out in a microwave anechoic chamber to measure the far-field radiation patterns of the STC metasurface, as illustrated in Supplementary Notes\u00a010 and 11. Hence, the good experimental results demonstrate that two frequency-dependent communication channels are constructed, providing a solid foundation for the physical layer security.\n\nConstrained by the requirement of the SSVEP-based BCI to gather data and detect EEG signals at a single frequency, a viable encoding strategy is proposed to reduce the symbol error rate (SER), as depicted in Fig.\u00a04j\u2013m. The transmission of digital symbols \u201c0\u201d and \u201c1\u201d is achieved by encoding a sequence of repeated data frames, with every set of 4 frames serving as the synchronous frames. In the encoding scheme, the first bit \u201c1\u201d serves as the start bit, signaling the initiation of the transmission process. The second bit signifies the harmonic frequency (+1st or \u22121st) used for transmission, and the third bit corresponds to the transmission of the data itself. Finally, the fourth bit functions as the stop bit, indicating the end of transmitting one data frame. Based on the encoding process, the digital symbols \u201c0\u201d and \u201c1\u201d for User 1 are encoded as the repeated data frames \u201cM1\u2013M0\u2013M0\u2013M0\u2013M1\u2013M0\u2013M0\u2013M0\u2026\u201d and \u201cM1\u2013M0\u2013M1\u2013M0\u2013M1\u2013M0\u2013M1\u2013M0\u2026\u201d. Similarly, the digital symbols \u201c0\u201d and \u201c1\u201d for User 2 are encoded as the repeated data frames \u201cM2\u2013M2\u2013M0\u2013M0\u2013M2\u2013M2\u2013M0\u2013M0\u2026\u201d and \u201cM2\u2013M2\u2013M2\u2013M0\u2013M2\u2013M2\u2013M2\u2013M0\u2026\u201d, respectively. Hence, the STC matrix M1 operating with a switching frequency \\({f}_{0}\\), is precisely injected into the region of high amplitude associated with the flicker frequencies \\({f}_{1}\\) and \\({f}_{2}\\), as illustrated in Fig.\u00a04j, k. The STC matrices are updated when the information is sent to User 1 via the BSTCM system. Similarly, the STC matrix M2 with a period switch frequency \\({f}_{0}\\), undergoes injection into the high amplitude attributed to the flicker frequencies \\({f}_{3}\\) and \\({f}_{4}\\), as displayed in Fig.\u00a04j, k, representing the transmission of information to User 2. Remarkably, the update of STC matrices does not affect the flickering frequency of LEDs used for SSVEP stimuli, ensuring that BCI signals can be synchronously detected and recognized. Hence, the BCI operator can freely transmit different information by staring at different visual stimuli of the STC metasurface according to the above-mentioned encoding method.\n\nIn the proposed encrypted wireless communication system, the transmitted information is randomly encrypted into two ciphertexts using a fully algorithmic method without human involvement. These ciphertexts are then securely sent to two designated users through different harmonic communication channels controlled by the BSTCM platform. Under this encrypted method, the original information is recovered by XOR-based stacking of two visual shared keys (VSKs), wherein each pixel of the secret message is encrypted into two sub-pixels for two distinct users. As illustrated in Fig.\u00a05a, VSK1 is randomly generated, and VSK2 is subsequently derived through the XOR operation. Notably, neither share alone contains any discernible details regarding the original secret, thereby ensuring that the disclosure of a single VSK does not compromise the confidentiality of the underlying message. As an illustrative example in Fig.\u00a05a, the secret image \u201cH\u201d, consisting of a grid of 5\u2009\u00d7\u20095 black pixels (digital \u201c1\u201d) and white pixels (digital \u201c0\u201d), is encrypted into two VSKs composed of 5\u2009\u00d7\u20095 sub-pixels based on the presented encryption method, where VSK1 is randomly generated and VSK2 is subsequently derived through the XOR operation. Consequently, neither VSK1 nor VSK2 alone contains sufficient information to reveal the original secret information. When the BCI operator (Alice) sequentially transmits the two bitstream sequences of two VSKs to two legal users (Bob and Carol) by the human mind, she freely gazes at the visual stimuli of the four STC metasurface regions based on the properties of the SSVEP-based BCI system. As a result, different EEG signals are generated and classified into four commands, which are then sent to the FPGA to produce the corresponding four BSTCM signals, as illustrated in Fig.\u00a04j\u2013m. Each gaze from the BCI operator corresponds to transmitting a single pixel of the VSK. Due to the neural response time constraints and the required high classification accuracy, the system\u2019s data transmission rate is about 0.25\u2009bps, which can be further improved with advanced technology (refer to Supplementary Note\u00a017). By employing the proposed BSTCM system to construct two harmonic frequency-dependent physical channels (+1st and \u22121st harmonics), two VSKs (VSK1 and VSK2) are sequentially delivered to the respective legal users through these channels. During decryption, the two VSKs corresponding to each secret are XOR-based to recover the original information. The details of encryption and decryption methods can be found in Supplementary Note\u00a012. The target image in question can be decrypted through the XOR operation of two VSKs, realizing a robust level of information security. In the future, the encryption scheme can be expanded to include more users, further enhancing the BSTCM system\u2019s security.\n\na The encrypted encoding scheme for the secret image \u201cH\u201d. b The experimental scenarios of the encrypted wireless communication system, in which the operator, equipped with an EEG cap, transmits the encrypted information to two users via the BSTCM platform, respectively. c The receiving signals corresponding to the encoding scheme in Fig.\u00a04j\u2013m. The top panel displays the raw received signals, whereas the bottom panel shows the corresponding signals processed by the FFT method. d, e The decoded information of VSKs (VSK1 and VSK2) from the receiving signals is based on the encrypted coding scheme. f The correct transmitting information is decoded by the acquired VSK1 and VSK2.\n\nTo validate the encrypted communication scheme, we experimentally set up an encrypted wireless communication system using the BSTCM, as demonstrated in Fig.\u00a05b. The prototype of 1-bit STC metasurface is used to construct the encrypted communication system, as shown in Supplementary Fig.\u00a015. A commercial BCI device (actiCHamp of Brain Product GmbH company) composed of an EEG cap and amplifier is used to acquire the EEG signals when the operator stares at different partitions on the STC metasurface. The corresponding EEG signals can be accurately recognized by the proposed deep learning algorithm. The detailed description of the algorithm is provided in Supplementary Note\u00a06. A linearly polarized horn antenna, connected to a signal generator (Keysight E8267D), is placed at a vertical distance of 4\u2009m away from the metasurface center to excite a monochromatic plane wave at the frequency of 6.9\u2009GHz. The receiving terminals are composed of two horn antennas (serving as two users) and a spectrum analyzer (Keysight N9040D), which is used to demodulate the received signal. Two receiving horn antennas are located at the angles of \u03b8\u2009=\u2009\u221215\u00b0 and \u03b8\u2009=\u2009+30\u00b0 with relative to the metasurface normal, respectively. In the transmission process, the information undergoes encryption, resulting in the generation of two harmonic-based ciphers texts according to the encryption scheme. The transmission process is achieved by ensuring that the BCI operator maintains direct visual focus on the STC metasurface in alignment with the binary cipher texts. In addition, the system enables prompt acquisition, recognition, and translation of EEG signals into appropriate control signals of the FPGA, which in turn drive the STC metasurface. As a result, the accurate transmission of binary cipher texts is accomplished, facilitating reliable and efficient communications between the BCI operator and the intended recipients.\n\nIn the receiving process, the radiated signals propagating through free space are received individually by the two horn antennas, and these signals are subsequently demodulated using the spectrum analyzer. As shown in Fig.\u00a05c, the demodulated signals represent four encoding schemes that correspond to different digital symbols (\u201c0\u201d or \u201c1\u201d) for the two users and are then processed to the decoded results shown in Fig.\u00a05d by the FFT method. The decoded results clearly exhibit four repeated data frames, providing strong evidence to support the capability of the proposed system to recover the transmitting information successfully. Hence, two binary cipher texts are transmitted to two specific users by the BCI operator based on the proposed wireless communication system. The transmitted information is received, and the measured results are comprehensively showcased in Supplementary Note\u00a013 and Supplementary Video\u00a01. By employing the encoding method described earlier, the data streams corresponding to the specific users are extracted from the received signals, as depicted in Fig.\u00a05e, f. Subsequently, these data streams are decrypted accurately by the method outlined in Supplementary Note\u00a012. The deciphered information is displayed in Fig.\u00a05g. To verify the security performance of the BSTCM system, we measured the signal-to-noise ratio (SNR) and BER results. The SNR for two legal users is measured as approximately 22\u2009dB, while the SNR for an illegal user is less than 5\u2009dB. The security capacity is calculated as approximately 1.9\u2009dB. Furthermore, the BERs of the illegal user are measured to be approximately 50% when the illegal user eavesdrops separately from both the spatial and frequency domains (refer to Supplementary Note\u00a015 for additional details). These results validate the robustness and effectiveness of the proposed system in ensuring a high level of security and confidentiality, demonstrating its resilience against unauthorized interception and its capability to safeguard the secret information during transmission.\n\nTo experimentally verify the performance of the proposed BSTCM system, we conducted experiments to show the remote control capabilities of smart devices through human intention, which holds significant potential to enhance the quality of life for individuals with disabilities. The system flowchart depicted in Fig.\u00a06a outlines the key steps involved in the experimental setup. Initially, distinct EEG signals can be generated when the BCI operator stares at different visual stimuli of the STC metasurface. The EEG signals are acquired and subsequently processed by the BSTCM system for command recognition. The recognized commands are then translated to update the STC matrices of the metasurface by an FPGA. The four partitions of the STC metasurface are controlled by an FPGA to generate different beams at four deflected angles corresponding to the four harmonic frequencies, enabling the remote control of smart devices. Notably, the main beam at the \u22122nd harmonic frequency exhibits a pronounced peak in the direction of \u03b8\u2009=\u2009+10\u00b0, corresponding to the device assigned to User 3. Conversely, the main beam at the +2nd harmonic frequency demonstrates a strong peak at \u03b8\u2009=\u2009\u221245\u00b0, corresponding to the device assigned to User 1. As displayed in Fig.\u00a06c, d, the measured radiation patterns exhibit good consistency with theoretical predictions corresponding to the STC matrices. Additionally, the main beams were deflected at the other two angles (\u221215\u00b0 and 30\u00b0) using the +1st and \u22121st harmonic frequencies (Users 2 and 4), as shown in Fig.\u00a04h, i. The excellent performance of harmonic beam manipulation demonstrates that the system can effectively utilize harmonic frequencies to construct four independent physical channels, significantly enhancing the physical layer security. The receiving terminals are composed of four horn antennas, four RF energy detectors (LMH2110), four Microcontroller Unit (MCU) modules (Arduino), and four LED modules (serve as smart devices), as depicted in Fig.\u00a06b. The RF energy from harmonic beams captured by the detectors can be converted into DC voltages, which are subsequently fed into the MCU modules. Once the corresponding DC voltage is detected, the MCU modules send the control commands to activate the corresponding LED module, thereby implementing the remote control of the smart devices. The experimental setup is shown in Fig.\u00a06b, where the STC metasurface is illuminated by a monochromatic frequency signal of 6.9\u2009GHz.\n\na The system architecture of wireless remote control. b The experimental scenarios of the wireless remote-control system by the human brain. c, d The theoretical and experimental results for far-field patterns at \\(\\pm\\)2nd harmonic frequencies. e The variations between the input power of the detectors and the output voltage with respect to the different distances between the receiving antenna and the metasurface center. f The temporal waveform of the output voltages from four detectors when the operator lights up four devices in sequence.\n\nTo verify the robustness of the brain-control smart devices system, the position of the receiving antenna is sequentially placed at different distances from the metasurface center while maintaining specific harmonic direction angles with respect to the metasurface normal. The detection of RF energy from the emitted harmonic beams is carried out across varying distances, and the corresponding DC voltages can be accurately converted by the RF detectors, as illustrated in Fig.\u00a06e (see Supplementary Note\u00a014 for details). We observe that the RF energies of four users relatively decrease, and the converted DC voltages also decrease accordingly as the distance increases. To guarantee the attainment of the far-field region and maintain system stability, the four receiving antennas are positioned at a distance of approximately 300\u2009cm from the metasurface. Consequently, the detection voltage threshold is set as 0.15\u2009V, 0.4\u2009V, 0.4\u2009V, and 0. 2\u2009V in the MCU modules, respectively. We recorded a video of the brain-controlled multiple smart devices, as presented in Supplementary Movie\u00a02. The sequence diagrams shown in Fig.\u00a06f provide a clear visual representation of the sequential control process, where the smart devices (LED modules) are successively controlled through human intention using the BSTCM system. The experiment proves the feasibility of brain-controlled smart devices. By harnessing the power of BCI technology, individuals can exert control over smart devices without needing physical interaction. The BSTCM system enables seamless and intuitive manipulations of various smart devices, facilitating greater independence and convenience for people in daily life. The experimental results serve as a compelling validation supporting the significant benefits that the BSTCM can offer in improving the quality of life for individuals facing physical challenges.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63326-0/MediaObjects/41467_2025_63326_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63326-0/MediaObjects/41467_2025_63326_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63326-0/MediaObjects/41467_2025_63326_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63326-0/MediaObjects/41467_2025_63326_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63326-0/MediaObjects/41467_2025_63326_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63326-0/MediaObjects/41467_2025_63326_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We presented a deep fusion coding scheme that combines the visual stimulation coding with the metasurface coding, and developed an integrated BSTCM platform to enable simultaneous visual stimulation and EM manipulation. The proposed system harnesses the human brain's intelligence and the flexible EM-control capabilities of the STC metasurface, enabling the information interaction within the EM field to establish reliable and stable communication environments. Furthermore, we demonstrated a secure, encrypted wireless communication system that combines the physical layer security and the cryptography encryption method, enhancing the system security. Finally, remote control of smart devices was illustrated by the BSTCM system, realizing intelligent human\u2013machine interactions. The proposed BSTCM, integrating the EM manipulation capability with the intelligence of the human brain, provides a paradigm for the interactions among human-machine interfaces and the future 6G wireless communication applications.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "A metasurface prototype was fabricated by the standard printed circuit board (PCB) process. The entire STC metasurface is composed of 32\\(\\times\\)32 digital elements and an effective size of 608\\(\\times\\)608\u2009mm2. For the convenience of processing and welding, the entire metasurface was disassembled into 8 sub-metasurfaces with 8\\(\\times\\)16 digital elements and an effective size of 152\\(\\times\\)304\u2009mm2. A total number of 2\\(\\times\\)32\\(\\times\\)32 PIN diodes (SMP1320-079LF from SKYWORKS) and RF inductors of 10 nH were elaborately welded on the metasurfaces with the surface mount technology, which is a mature engineering method to weld small components on PCB using the batch solder-reflow processes in a dedicated machine. Meanwhile, an LED is embedded into each metasurface element.\n\nAs depicted in Fig.\u00a02a, the SSVEP-BCI is delineated into three discrete distinct stages: the preparation stage, the signal acquisition stage, and the signal recognition stage. The EEG data in this study were acquired using the actiCHamp EEG amplifier manufactured by Brain Products GmbH. This amplifier offers a maximum capacity of 64 channels, 24-bit precision, and a sampling rate of 100\u2009kHz. It exhibits a high common mode rejection ratio (CMRR) of 100, making it suitable for capturing and detecting SSVEP signals effectively. This study uses a standard 10\u201320 EEG cap to obtain the signal. Since SSVEP is a local potential that predominantly originates from the occipital lobe7, the O1 and O2 electrodes located on the occipital region were selected for EEG data acquisition, with a reference electrode placed at the vertex (Cz) region. The sample time in this study was set to 4\u2009s. With a sampling rate of 512\u2009Hz and excluding the reference electrode data, each sample yielded 4096 sampling points (4\u2009s\u2009\u00d7\u2009512\u2009Hz\u2009\u00d7\u20092 channels). Square wave signals were utilized to present visual stimuli on the LEDs of the STC metasurface units, which emitted the red light, thereby ensuring adequate visual contrast. To minimize the harmonic interference, four target visual stimulus frequencies were: top-left at 8.5\u2009Hz, bottom-left at 10\u2009Hz, top-right at 11.5\u2009Hz, and bottom-right at 7\u2009Hz. To mitigate mutual interference between two closely spaced flickering stimuli pairs, the frequencies 7\u2009Hz and 8.5\u2009Hz, as well as 10\u2009Hz and 11.5\u2009Hz, were placed diagonally opposite to one another. The example of the collected brain wave signal data for four target frequencies is shown in Supplementary Fig.\u00a01. Data processing and model training were conducted in Python and PyTorch environments. Signal processing primarily involved utilizing the scipy package for filtering and FFT calculations. Given that neither the absolute value of the EEG signal nor its temporal characteristics were pertinent to this study, all sampled signals were linearly transformed to have a mean of zero and a standard deviation of one.\n\nInitially, a dataset comprising 240 instances of raw SSVEP signals was collected from two participants. Subsequently, a subset consisting of 80 instances of randomly selected raw signals was employed to create RefSpec. The remaining 160 instances of raw signals were randomly selected to serve as training and validation datasets for classification model training. The two subsets have an equal number of signals across four stimulus frequencies. As for the training and validation dataset, a subset of 60 instances was extracted from the initial subset of 160 instances to serve as the validation set. The remaining 100 instances underwent a data augmentation process, which resulted in an expanded dataset of 1700 instances. These augmented instances constituted the training set, which was then utilized to train the model for 300 epochs. The validation operation was executed every two training epochs.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data supporting the findings of this study are presented in the paper and the Supplementary Information file.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code that supports the findings of this study are available from the corresponding author upon request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Nakanishi, M. et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis. IEEE Trans. Biomed. Eng. 65, 104\u2013112 (2018).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nBin, G. et al. 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Eng. 29, 2615\u20132624 (2021).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "The work is supported by the National Natural Science Foundation of China (62288101, T.J.C., 92167202, T.J.C., 62301147, T.J.C., and 72171044, Y.F.N.), the National Key Research and Development Program of China (2022YFA1404903, T.J.C.), the Major Project of Natural Science Foundation of Jiangsu Province (BK20212002, T.J.C., and BK20210209, T.J.C.), Special Fund for Key Basic Research in Jiangsu Province (BK20243015, T.J.C.), the Natural Science Foundation of Jiangsu Province (BK20230822, T.J.C.), the State Key Laboratory of Millimeter Waves, Southeast University, China (K201924, T.J.C.), the Fundamental Research Funds for the Central Universities (2242023K5002, T.J.C., 2242018R30001, T.J.C., and 2242022R20017, T.J.C.), the 111 Project (111-2-05, T.J.C.), Young Elite Scientists Sponsorship Program by CAST (2022QNRC001, Q.M.), Jiangsu joint laboratory of multidimensional perceptual information technology (BM2022017, T.J.C.), the China Postdoctoral Science Foundation (2021M700761, Q.M.), and ZhiShan Scholar Program of Southeast University (2242022R40004, Y.F.N.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Qiang Xiao, Lin Han Fan, Qian Ma.\n\nState Key Laboratory of Millimeter Waves and Institute of Electromagnetic Space, Southeast University, Nanjing, China\n\nQiang Xiao,\u00a0Qian Ma,\u00a0Yu Ming Ning,\u00a0Ze Gu,\u00a0Long Chen,\u00a0Jian Wei You\u00a0&\u00a0Tie Jun Cui\n\nJiangsu Joint Laboratory of Multidimensional Perceptual Information Technology, Nanjing, China\n\nQiang Xiao\u00a0&\u00a0Qian Ma\n\nSchool of Mechanical Engineering, Southeast University, Nanjing, China\n\nLin Han Fan\u00a0&\u00a0Ya Feng Niu\n\nState Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronics, Peking University, Beijing, China\n\nLianlin Li\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nT.J.C. suggested the designs and supervised the work, in consultation with Q.M., Y.F.N., Q.X., and J.W.Y. conceived the idea and carried out the theoretical analysis and numerical simulations. Q.X., L.H.F., Y.M.N., and Z.G. built the system and performed the experimental measurements. Q.X., L.H.F., and L.C. performed the data analysis. Q.X. and L.H.F. wrote the manuscript. Q.M., L.L., J.W.Y, Y.F.N., and T.J.C. reviewed the manuscript. All authors discussed the theoretical aspects and numerical simulations, interpreted the results, and reviewed the manuscript.\n\nCorrespondence to\n Qian Ma, Ya Feng Niu or Tie Jun Cui.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Chung-Tse Wu and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Xiao, Q., Fan, L.H., Ma, Q. et al. Secure wireless communication of brain\u2013computer interface and mind control of smart devices enabled by space-time-coding metasurface.\n Nat Commun 16, 7914 (2025). https://doi.org/10.1038/s41467-025-63326-0\n\nDownload citation\n\nReceived: 06 August 2024\n\nAccepted: 15 August 2025\n\nPublished: 25 August 2025\n\nVersion of record: 25 August 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63326-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"Phosphorylation-Driven Epichaperome Assembly: A Critical Regulator of Cellular Adaptability and Proliferation", + "journal": "Nature Communications", + "published": "16 October 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_MOESM7_ESM.xlsx" + }, + { + "label": "Supplementary Data 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_MOESM8_ESM.xlsx" + }, + { + "label": "Supplementary Data 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_MOESM9_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_MOESM10_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_MOESM11_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_MOESM12_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.6084/m6089.figshare.27075415", + "/articles/s41467-024-53178-5#ref-CR113", + "/articles/s41467-024-53178-5#MOESM3", + "https://doi.org/10.6084/m6089.figshare.26662333", + "/articles/s41467-024-53178-5#ref-CR114", + "http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD050251", + "https://www.uniprot.org/", + "https://doi.org/10.5281/zenodo.10800912", + "/articles/s41467-024-53178-5#ref-CR115", + "/articles/s41467-024-53178-5#Sec47" + ], + "code": [], + "subject": [ + "Chaperones", + "Mass spectrometry", + "Mechanisms of disease", + "Phosphorylation" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4114038/v1.pdf?c=1729163153000", + "research_square_link": "https://www.researchsquare.com//article/rs-4114038/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53178-5.pdf", + "preprint_posted": "02 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The intricate protein-chaperone network is vital for cellular function. Recent discoveries have unveiled the existence of specialized chaperone complexes called epichaperomes, protein assemblies orchestrating the reconfiguration of protein-protein interaction networks, enhancing cellular adaptability and proliferation. This study delves into the structural and regulatory aspects of epichaperomes, with a particular emphasis on the significance of post-translational modifications in shaping their formation and function. A central finding of this investigation is the identification of specific PTMs on HSP90, particularly at residues Ser226 and Ser255 situated within an intrinsically disordered region, as critical determinants in epichaperome assembly. Our data demonstrate that the phosphorylation of these serine residues enhances HSP90's interaction with other chaperones and co-chaperones, creating a microenvironment conducive to epichaperome formation. Furthermore, this study establishes a direct link between epichaperome function and cellular physiology, especially in contexts where robust proliferation and adaptive behavior are essential, such as cancer and stem cell maintenance. These findings not only provide mechanistic insights but also hold promise for the development of novel therapeutic strategies targeting chaperone complexes in diseases characterized by epichaperome dysregulation, bridging the gap between fundamental research and precision medicine.Biological sciences/Cell biology/Mechanisms of diseaseBiological sciences/Cell biology/Post-translational modifications/PhosphorylationBiological sciences/Biological techniques/Mass spectrometryBiological sciences/Biochemistry/Proteins/Chaperones", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nMemorial Sloan Kettering Cancer Center holds the intellectual rights to the epichaperome portfolio. G.C., A.R. and S.S. are inventors on the licensed intellectual property. All other authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryData1.xlsxDataset 1SupplementaryData2.xlsxDataset 2SupplementaryData3.xlsxDataset 3SupplementaryData4.xlsxDataset 4SupplementaryData5.xlsxDataset 5SupplementaryData6.xlsxDataset 6SupplementaryInformation03.01.2024.pdfSourceData.xlsxSource Data", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The intricate network of protein-chaperone interactions is crucial for maintaining cellular function. Recent discoveries have unveiled the existence of specialized chaperone assemblies, known as epichaperomes, which serve as scaffolding platforms that orchestrate the reconfiguration of protein-protein interaction networks, thereby enhancing cellular adaptability and proliferation. This study explores the structural and regulatory aspects of epichaperomes, with a particular focus on the role of post-translational modifications (PTMs) in their formation and function. A key finding is the identification of specific PTMs on HSP90, particularly at residues Ser226 and Ser255 within an intrinsically disordered region, as critical determinants of epichaperome assembly. Our data demonstrate that phosphorylation of these serine residues enhances HSP90\u2019s interactions with other chaperones and co-chaperones, creating a microenvironment conducive to epichaperome formation. Moreover, we establish a direct link between epichaperome function and cellular physiology, particularly in contexts where robust proliferation and adaptive behavior are essential, such as in cancer and pluripotent stem cell maintenance. These findings not only provide mechanistic insights but also hold promise for the development of novel therapeutic strategies targeting chaperone assemblies in diseases characterized by epichaperome dysregulation, thereby bridging the gap between fundamental research and precision medicine.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Conventional wisdom, as crystallized in Beadle and Tatum\u2019s 1941 paradigm of \u201cone gene\u2013one enzyme\u2013one function,\u201d has traditionally delineated targets as outcomes of protein expression changes or point mutations within proteins. However, it is increasingly apparent that protein dysfunctions in the context of many disorders, including cancer, neurodegenerative disorders, among others, are predominantly shaped by changes in interaction strengths and cellular mislocalization. These factors, in turn, can be modulated by variations in post-translational modifications (PTMs), stabilization of disease-associated protein conformations, and other protein-modifying mechanisms1,2. Within this complex context, Heat Shock Protein 90 (HSP90) emerges as a compelling exemplar, transcending the boundaries of conventional understanding3.\n\nPositioned as a versatile chaperone, often referred to as the guardian of the proteome, HSP90 assumes a pivotal task in the realm of maintaining cellular equilibrium by facilitating protein folding, stabilization, and degradation4. Under the canonical folding paradigm, HSP90 functions as a homodimer. Each protomer is composed of an N-terminal domain (NTD), a middle domain (MD), and a C-terminal dimerization domain (CTD)4,5. The NTD contains a nucleotide-binding pocket, where ATP binding and hydrolysis take place6. The chaperone cycle of HSP90 is coupled to a series of dynamic conformational changes accompanying its ATPase cycle. Beginning with NTD/MD and MD/CTD interdomain rotations and cross-monomer dimerization7, HSP90 transitions from open to closed conformational states, while folding client proteins8,9. HSP70 and HOP (HSP70\u2013HSP90 organizing protein) bring client proteins to HSP90 and form the loading complex10. Other co-chaperones participate at different stages of the HSP90 chaperone cycle and regulate its conformational changes along the chaperone and ATPase cycle4. Co-chaperones may have different preferences for client proteins, fine-tuning subcellular pools of HSP90 to mitigate stressors and maintain proteostasis11. These assemblies are further shaped by PTMs in HSP90, co-chaperones and client proteins12. Overall, the highly orchestrated interactions among these proteins\u2014both chaperones and clients\u2014are transient in the chaperone cycle under physiological conditions.\n\nWhile this classical understanding portrays HSP90 as a dimeric entity that interacts dynamically with co-chaperones and client proteins, research has uncovered a spectrum of multimeric HSP90 forms, each sculpted by the cellular milieu and the presence of stress-inducing factors3. These multimers, whether homo-oligomeric or hetero-oligomeric, expand HSP90\u2019s functional repertoire, blurring the boundaries between traditional chaperone functions and newfound roles as holdases or scaffold proteins. In disease contexts, such as cancer and neurodegenerative disorders, HSP90\u2019s conformational adaptability gives rise to epichaperomes\u2014distinctive hetero-oligomeric formations of tightly bound chaperone, co-chaperones and other factors13,14,15. This phenomenon goes beyond mere biochemical curiosity; it represents a fundamental mechanism by which cells respond to stressors, whether of genetic, proteotoxic or environmental nature3,16,17,18. Unlike chaperones which help proteins fold or assemble, epichaperomes exert a maladaptive influence, reshaping the assembly and connectivity of proteins pivotal for sustaining pathological traits. For example, in cancer, epichaperomes take on scaffolding functions not found in normal cells, altering the assembly and connectivity of proteins important for maintaining a malignant phenotype and enhancing their activity, which provides a survival advantage to cancer cells and tumor-supporting cells13,19. In Alzheimer\u2019s disease epichaperomes rewire the connectivity of, and thus negatively impact, proteins integral for synaptic plasticity, brain energetics, and immune response15.\n\nThe revelation of HSP90\u2019s maladaptive multimeric epichaperomes has also profound implications for therapeutic interventions, including in the treatment of diverse disease states including cancers and of neurodegenerative disorders. Rather than a blanket inhibition of all HSP90 pools, targeting specific pathologic conformations of HSP90 as found in epichaperomes while sparing normal HSP90 functions holds the promise of enhancing the safety as well as the immunostimulatory and anticancer effects of HSP90 inhibitors3.\n\nDespite these important mechanistic and therapeutic implications, key factors facilitating HSP90 incorporation into epichaperomes\u2014namely, the conformations that enable epichaperome formation and the structural elements that support the enrichment of such conformations\u2014remain unknown. In this study, we use a combination of chemical biology, unbiased mass spectrometry techniques, and molecular dynamics simulations to elucidate the conformation of HSP90 populated in epichaperomes and to characterize the structural and molecular factors that support and favor the enrichment of such conformation, and in turn, the formation of epichaperome assemblies. Beyond these structural revelations, our findings demonstrate how these factors directly influence cellular behaviors, particularly in contexts where robust proliferation and adaptation are crucial, such as cancer and stem cell maintenance. This direct link between epichaperome function and fundamental cellular processes has translational relevance for therapeutic development.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Epichaperomes nucleated through enhanced interactions between HSP90 and HSP70, namely the heat shock cognate 70 (HSC70) isoform, are a distinct feature of cancer cells13,19. Epichaperomes containing HSP90 are detected in induced pluripotent stem cells (iPSCs)20, in leukemia stem cells21,22, and in glioma cancer stem cells (CSCs)23. Hyperactivation of the transcription factor c-MYC required in generating iPSCs24, maintaining embryonic stem cells (ESCs)25 and CSCs26, is also a driving factor in epichaperome formation in tumors, irrespective of the tumor type13,27. Notably, these epichaperomes are all sensitive to and can be disrupted by small molecules such as PU-H71 (zelavespib) or PU-AD (icapamespib) that bind to HSP9013,23,28, suggesting that a similar composition, facilitated by a specific conformation of HSP90, may characterize epichaperomes in these distinct cellular contexts.\n\nTo test this hypothesis, we initially explored the composition of epichaperomes in selected cellular models, encompassing pluripotent stem cells and cancer cells. For pluripotent stem cells, we examined two mouse ESCs (E14 and ZHBTc4) and a human induced pluripotent cell line (hiPSC). Additionally, two cancer cell lines, well-characterized in terms of epichaperome composition and function, were chosen as representative epichaperome-positive (MDA-MB-468) and -negative/low (ASPC1) cancer cells (Fig.\u00a01a\u2013f and Supplementary Figs.\u00a01, 2).\n\na Schematic illustrating the biochemical and functional distinctions between epichaperomes, defined as long-lasting hetero-oligomeric assemblies composed of tightly associated chaperones and co-chaperones, and traditional chaperones. Unlike chaperones, which assist in protein folding or assembly, epichaperomes sequester proteins, reshaping protein\u2013protein interactions, and consequently altering cellular phenotypes. The schematic also outlines key principles for the use of PU-probes\u2014PU-beads and PU-clicked to a fluorophore such as cy5\u2014in epichaperome analysis. b Detection of epichaperome components (chaperones and co-chaperones) through SDS\u2013PAGE (bottom, total protein levels) and native PAGE (top), followed by immunoblotting. See also Supplementary Fig.\u00a01. c Visualization of HSP90 in epichaperomes using the PU-TCO probe clicked to cy5. See also Supplementary Fig.\u00a02. Gel images are representative of three independent experiments. d Epichaperome constituent chaperones and co-chaperones identified through mass spectrometry analyses of PU-beads cargo. Representative data of two independent experiments. See Supplementary Fig.\u00a03 for the GA cargo. HSP7C is HSC70, STIP1 is HOP, and AHSA1 is AHA1. e Illustration of an isobaric, discriminant peptide pair from ESC lysate samples and HSP90 captured by PU- and GA-beads. Representative data of two independent experiments. f Schematic summary. Both cancer cells and pluripotent stem cells harbor epichaperomes. These epichaperomes undergo disassembly during differentiation processes. Source data are provided in Supplementary Data\u00a01 and as a Source data file.\n\nIn contrast to folding chaperone complexes, which are inherently dynamic and short-lived6, epichaperomes represent long-lasting hetero-oligomeric assemblies composed of tightly associated chaperones, co-chaperones, and various other factors. HSP90 is a major component found within epichaperomes along with other chaperones, co-chaperones, and scaffolding proteins like HSP70 (especially HSC70), CDC37, AHA1, and HOP13. Consequently, when we analyzed cell homogenates containing epichaperomes using native PAGE followed by immunoblotting with antibodies specific to epichaperome constituent chaperones and co-chaperones, we observed a range of high-molecular-weight species, both distinct and indistinct, in addition to the primary band(s) characteristic of chaperones. This observation held true for both pluripotent stem cells and cancer cells (Fig.\u00a01b, Supplementary Fig.\u00a01a\u2013d, and refs. 13,19,20). Notably, HSP90 immunoblotting revealed the presence of species comprising HSP90 in epichaperome assemblies in cancer cells and pluripotent stem cells, in addition to the prominent 242\u2009kDa band, which is a characteristic of non-transformed cells13,19,29.\n\nEpichaperomes undergo disassembly during iPSC differentiation20 or when cancer cells are treated with PU-H71 or PU-AD15,23,28,30. Therefore, next we induced the differentiation of the pluripotent stem cells under investigation. In the ZHBTc4 cell line, Oct4 expression is controlled by a Tet (tetracycline)-off oct4 regulatory system31. Downregulation of Oct4 in ZHBTc4 cells has been reported to induce trophoblast differentiation, which is characterized by changes in cell morphology, specifically, cells flattening into epithelial-like cells, and is associated with slower growth32. Mouse embryonic E14 stem cells undergo spontaneous differentiation into embryoid bodies (EB) when cultured in suspension without antidifferentiation factors such as leukemia inhibitory factor (LIF)33 and iPSCs differentiate into mature dopaminergic neurons using a floor plate-based differentiation protocol34. We confirmed that the differentiation of these pluripotent stem cells was correlated with the disassembly of epichaperomes, as observed through native PAGE immunoblotting. This disassembly is evident by a reduction in high-molecular-weight chaperone species on native PAGE observed when immunoblotting for epichaperome constituent chaperones (see HSP90\u03b1/\u03b2, HOP, HSC70, CDC37, AHA1, HSP110 in Fig.\u00a01b and Supplementary Fig.\u00a01), with minimal changes observed in total chaperone levels on SDS\u2013PAGE. Notably, for HSP90, a decrease in bands other than those in the ~242\u2009kDa range was observed upon differentiation, supportive of epichaperome disassembly (see HSP90 immunoblotting).\n\nPU-H71 serves as an epichaperome probe that, in contrast to the tested antibodies which indiscriminately detect epichaperomes and other HSP90 pools, exhibits a preference for HSP90 when it is integrated into epichaperomes13. Labeled derivatives of PU-H71 can, therefore, be employed to detect HSP90 within epichaperomes, distinguishing it from other HSP90 pools (as illustrated in Fig.\u00a01c and Supplementary Fig.\u00a02a\u2013c). To achieve this, we generated lysates from ZHBTc4, E14 cells, and MDA-MB-468 cells under conditions that preserve native protein assemblies. Subsequently, we labeled these homogenates with a clickable PU-probe (PU-TCO, refs. 19,35). After running these labeled samples on native PAGE gels, we conjugated the PU-probe with a Cy5 dye and visualized epichaperomes, confirming the presence of epichaperomes in both the ESCs and the cancer cells. These epichaperomes were characterized by multimers observed at and above ~300\u2009kDa (Fig.\u00a01c). Moreover, the labeling of epichaperomes by the PU-probe decreased upon ESC differentiation, supportive of epichaperome disassembly (Fig.\u00a01c and Supplementary Fig.\u00a02b).\n\nAdditionally, we conducted labeling experiments using live E14 ESCs, instead of homogenates, employing a PU-CW800 probe (a derivative of PU-H71 conjugated with an 800\u2009nm near-infrared dye) or a control derivative (an inactive PU-derivative that does not interact with epichaperomes) (see Supplementary Note\u00a01). The most responsive target of the PU-probes, but not the control probe, was an HSP90 assembly of ~300\u2009kDa, thus above the major 242\u2009kDa band preferred by the anti-HSP90 antibody. This species was detected on native PAGE in PU-probe treated cells but not in control treated cells (Supplementary Fig.\u00a02c).\n\nIn summary, the predominant HSP90 band characteristic of epichaperomes is a 300\u2009kDa assembly, distinctly differing from the typical ~242\u2009kDa band observed in non-transformed cells13,19,32 when analyzed on native PAGE gels. Mass spectrometric (MS) analysis of the ~300\u2009kDa assembly confirmed the presence of HSP90 and HSC70 as the primary protein components of this multimeric complex (Supplementary Data\u00a01, 300\u2009kDa LC\u2013MS). This finding aligns with the composition of core epichaperome complexes previously reported in cancer cells13. Consequently, these findings combined confirm that both cancer cells and pluripotent stem cells share HSP90 and HSC70 as integral constituents of their core epichaperomes.\n\nTo gain further insights into epichaperome assemblies, we employed resin-based affinity purification experiments. Specifically, we utilized resins with immobilized PU-H71, referred to as PU-beads, and an inert control molecule on control beads, following established procedures13 (Fig.\u00a01d). As an additional control, we employed a resin containing immobilized geldanamycin (GA), known for its ability to bind and isolate predominantly un-complexed HSP90 (GA-beads, Supplementary Fig.\u00a03 and ref. 36). Subsequently, we subjected the protein cargo isolated by these probes to unbiased MS analysis. To precisely determine the protein components of the cargo, we conducted in-gel digestion of the entire gel lanes and employed liquid chromatography/mass spectrometry (LC\u2013MS/MS) in conjunction with the semi-quantitative spectra-counting method37,38 for the identification and quantification of cargo proteins (Supplementary Data\u00a01).\n\nWe observed that the cargo isolated by PU-beads from ESCs contained 26 of the 42 major chaperone and co-chaperones identified prior in cancer cells13 as being epichaperome components (Fig.\u00a01d). The identity of all components identified in ESCs is found in Supplementary Data\u00a01. The interaction between PU-beads and epichaperomes was specific towards PU-H71, because control resins did not purify noticeable protein complexes. Similarly, GA-beads precipitated HSP90 but few co-purifying proteins and epichaperome components (Supplementary Fig.\u00a03 and Supplementary Data\u00a01) consistent with previous results that GA isolates largely an un-complexed HSP9039.\n\nIn mammalian cells, HSP90 exists in two paralogs, HSP90\u03b1 and HSP90\u03b240, both of which have been reported to play roles in epichaperome formation in cancer cells13. To assess the isoform composition of HSP90 within epichaperomes, we exploited the subtle difference between one pair of isobaric peptides, namely 88Thr-Lys100 in HSP90\u03b1 and 83Thr-Lys95 in HSP90\u03b2, where a single amino acid distinguishes them (Ile in HSP90\u03b1 and Leu in HSP90\u03b2) (Supplementary Fig.\u00a04a). The assignment of HSP90 isoforms relied on co-eluting peptides obtained from the isobaric peptide present in purified HSP90\u03b2 (Supplementary Fig.\u00a04b, c). Extracted ion chromatograms of the peptide mass revealed an ~1.5 \u03b2/\u03b1 ratio in the ESC lysate and the cargo isolated by PU-beads (Fig.\u00a01e), while the GA-beads cargo exhibited an ~1.0 \u03b2/\u03b1 ratio. Similar findings were obtained through spectra counting, with the HSP90\u03b2/HSP90\u03b1 ratio determined using spectral counting consistent with ratios obtained through MS intensity calculations (Supplementary Data\u00a01: 708/540\u2009=\u20091.31 for the PU-beads cargo; 219/235\u2009=\u20090.93 for the GA-beads cargo). This validation of spectra counting as an effective semi-quantitative method supports the conclusion that epichaperomes isolated from ESCs exhibit a predominantly unbiased HSP90 paralog composition, akin to what has been reported for cancer cells13.\n\nIn summary, the wealth of complementary biochemical experiments presented here lends strong support to the idea that both cancer cells and pluripotent stem cells harbor epichaperomes that are compositionally similar. Notably, HSP90 and HSC70 emerge as major constituents of the core epichaperome structure, serving as a scaffold for recruiting various co-chaperones to create specific epichaperome assemblies. This shared architectural similarity between epichaperomes in ESCs and cancer cells underscores the existence of a common epichaperome-enabling HSP90 conformer that is enriched in both biological contexts.\n\nMS identification of cross-linked residues that are in spatial proximity but not necessarily close in primary sequence, provides valuable distance restraints that can be employed for computational modeling of proteins and protein complexes41,42,43. Therefore, to determine the conformation of HSP90 in epichaperomes, we used a chemical cross-linking and mass spectrometry (CX\u2013MS) approach to identify and quantify cross-linked peptides of PU-H71-favored HSP90 pools.\n\nTo ensure the capture of the epichaperome-enabling conformation, we first cross-linked cellular lysates using the amine-reactive cross-linker disuccinimidyl suberate (DSS) prior to HSP90 capture on the PU-beads13,36 (Fig.\u00a02a). DSS crosslinking stabilizes the conformation of proteins by covalently linking residues that are in close proximity, effectively \u201cfreezing\u201d their relative positions. While crosslinking could potentially affect key residues and the binding of the assembly to the chemical inhibitor-attached resin, the DSS cross-linker primarily targets solvent-accessible surface lysine residues, minimizing the likelihood of introducing extensive conformational changes or directly perturbing the binding pocket on HSP90. Given PU-H71\u2019s preference for binding HSP90 in its epichaperome conformation, any significant alteration of HSP90\u2019s structure by DSS would likely reduce PU-H71\u2019s binding affinity. Therefore, the structure captured by PU-beads is more likely to reflect the native HSP90 conformation found in epichaperomes rather than any altered state. By applying DSS before introducing PU-H71, the experimental setup increases the likelihood that the observed conformation is representative of the functional epichaperome, prior to any potential conformational changes or epichaperome disassembly induced by PU15,19,28. We used SDS\u2013PAGE to separate proteins after crosslinking and capturing HSP90 with the beads, specifically analyzing the major ~80\u201390\u2009kDa band that corresponds to the HSP90 monomer (Fig.\u00a02a). In addition, the cross-linked peptides identified were predominantly intra-monomeric, as they fit within the expected spatial constraints of the DSS cross-linker43.\n\na Experiment outline. DSS disuccinimidyl suberate crosslinker. b Plot comparing cross-linking propensity of Lys residues in HSP90 bound to PU-H71 or geldanamycin (GA). Average cross-linking percentage of PU-H71 (x-axis) and GA (y-axis) bound HSP90 cross-linked pairs are shown. Pairs with similar cross-linking propensity are shown along the dotted line with a slope of 1. Outlier cross-linked peptides are those with cross-linked Lys residues eight amino acids away and a cross-linking percentage difference \u22651.5 standard deviations of replicates. Statistically significant outliers (p\u2009\u2264\u20090.05) were determined by two-sample t-test with equal variances, n\u2009=\u20093 replicate measurements. c Homology model illustrating the HSP90 dimer in the open conformation (template PDB: 2IOQ), favored by GA, and the closed conformation (template PDB: 2CG9), favored by PU-H71. One HSP90 protomer is colored to indicate the N-terminal domain (NTD), the middle domain (MD), and the C-terminal domain (CTD). Cross-linked residues are shown as dashed lines between labeled residues. d NTD structures of PU-H71 (top, PDB: 2FWZ) and GA (bottom, PDB: 1YET)-bound HSP90. Source data are provided in Supplementary Data\u00a02.\n\nParallel experiments were conducted using GA-beads, corresponding to solid-support immobilized GA, as a control13,36. The identity of cross-linked HSP90 peptides purified by PU- or GA-beads pull-down can be found in Supplementary Data\u00a02. Notably, the alpha carbon distances between all cross-linked residues, as identified with high confidence, fell below the maximal span of DSS (30\u2009\u00c5). This suggests that proteins retained their native states without significant conformational perturbations during the cross-linking process.\n\nWe calculated the cross-linking percentage for each pair of cross-linked PU- or GA-bound HSP90 residues. This calculation involved normalizing the MS ion intensity of cross-linked peptides by the sum of all cross-linked peptides and cross-linker-modified peptides containing the cross-linked residues. By doing so, we could mitigate the impact of variations in the reactivity of cross-linked residues, allowing us to primarily assess the influence of the distance between cross-linked residues and their local secondary structures44.\n\nMost cross-linked pairs from both PU- and GA-bound samples exhibited similar cross-linking percentages, with data points evenly distributed around a trend line with a slope of 1 (dotted line, Fig.\u00a02b). This observation suggests a broad similarity in secondary and tertiary structures between these HSP90 populations. However, clear differences emerged, revealing conformational distinctions between the PU- and GA-favored HSP90 subpopulations (highlighted by orange circles, Fig.\u00a02b).\n\nNotably, residues Lys58\u2013Lys112 in HSP90\u03b1 and Lys53\u2013Lys107 in HSP90\u03b2, situated within the ligand-binding pocket, displayed a higher cross-linking percentage in PU-bound HSP90 populations compared to their GA-bound counterparts (Fig.\u00a02b). This observation aligns with distinct pocket configurations preferred by each ligand, as previously observed through X-ray crystallography45,46,47,48,49. Specifically, crystal structures show the bulkier GA binds more superficially, causing helices 4 and 5 (Fig.\u00a02d) to move away from the nucleotide-binding site, thereby preventing full closure of the ATP lid. Moreover, the side-chain amino functional group of Lys112 forms a hydrogen bond with a benzoquinone oxygen of GA. This pocket configuration aligns with the reduced cross-linking activity of the lysine pair mentioned above. Conversely, PU-H71 binds deeply within the pocket. In this configuration, helices 4 and 5 are packed against helix 2 with Lys112 and Lys58 in HSP90\u03b1 (or Lys107 and Lys53 in HSP90\u03b2) positioned more favorably for cross-linking. This arrangement of lysine residues is more likely to be found in the closed conformation of HSP90 (Fig.\u00a02c), as proposed by crystallographic studies (PDB: 2CG9)50.\n\nIt is essential to reiterate that the cross-linking experiments were conducted to \u201clock\u201d HSP90 conformations with covalent bonds before resin-based affinity purification experiments using the PU- or GA-beads. Consequently, the X-ray structures of PU- or GA-bound HSP90 NTD closely reflect a preferred pocket configuration that each ligand may capture in the cell, and in this case, for PU-H71, it is indicative of the pocket configuration of HSP90 in the epichaperomes.\n\nFurthermore, differences in HSP90 conformation were corroborated by cross-linked pairs located at the interfaces between NTD/MT (HSP90\u03b1: Lys293\u2013Lys363) and MD/CTD (HSP90\u03b1: Lys444\u2013Lys616; HSP90\u03b2: Lys435\u2013Lys607) (Fig.\u00a02b). These interfaces undergo significant reorientation during the HSP90 conformational cycle, implying a distinct HSP90 conformation favored by PU-H71 compared to GA. Lys444 in HSP90\u03b1 (Lys435 in HSP90\u03b2) and Lys616 in HSP90\u03b1 (Lys607 in HSP90\u03b2) are positioned either within the middle of the MD or in proximity to the central axis of the HSP90 homodimer (Fig.\u00a02c). The distance between these lysine residues can provide insights into the relative placement of the monomer arms in specific HSP90 conformations (e.g., 20\u2009\u00c5 in closed-like conformations; 29\u2009\u00c5 in open-like conformations). The lower cross-linking percentage observed for Lys444 and Lys616 in HSP90\u03b1 (Lys435 and Lys607 in HSP90\u03b2) in GA-favored HSP90 suggests a longer distance (29\u2009\u00c5) between them, supporting GA\u2019s preference for binding to an open-like conformation. In contrast, the moderate cross-linking percentage detected for these residues in PU-H71-favored HSP90 implies a medium distance (20\u2009\u00c5) between them, favoring a closed-like conformation enriched in epichaperomes (Fig.\u00a02c).\n\nAdditionally, a third pair of cross-linked residues (Lys293 and Lys363 in HSP90\u03b1) supports this notion. Located near the interface between the NTD and the MD, their positions are sensitive to the ligand-binding state of the NTD, leading to changes in the relative positioning of secondary structures near the NTD/MD interface and altering the distance between Lys293\u03b1 and Lys363\u03b1. Consistent with the cross-linked pair at MD/CTD interface, a closed-like conformation (16\u2009\u00c5) in PU-H71 bound HSP90 will be more amenable than an open-like conformation (13\u2009\u00c5) in GA-bound since the short distance might have limited the location of side chains for cross-linking reactions.\n\nIn summary, our CX\u2013MS data, supported by several cross-linked residue pairs situated in structurally distinct regions, the nucleotide-binding pocket, and the NTD/MD and MD/CTD interfaces, shed light on the conformation adopted by HSP90 within epichaperomes. These findings underscore the notion that an enrichment of the closed-like conformation of HSP90 in specific cellular environments favors the formation of epichaperomes.\n\nTo uncover the factors that facilitate the enrichment of the epichaperome-favoring HSP90 conformation, we conducted a comprehensive examination of the HSP90 pools isolated by PU-H71 and GA, searching for potential differences. Notably, we identified several peptides phosphorylated on Ser231 and Ser263 in HSP90\u03b1 (Ser226 and Ser255 in HSP90\u03b2) exclusively in the PU-H71 cargo from ESCs (Fig.\u00a03a, b and Supplementary Data\u00a03). High-quality MS/MS spectra (illustrated for Ser226 and Ser255 phosphopeptides in HSP90\u03b2, Fig.\u00a03b) coupled with precise mass accuracy allowed for the unequivocal identification of the peptide sequences and the phosphorylation sites. In contrast, these phosphorylated peptides were notably absent in substantial quantities in the GA cargo (Supplementary Data\u00a03).\n\na Experiment outline and expected outcomes. b Tandem mass spectrometry (MS) spectra of HSP90 Ser226 (bottom) and Ser255 (top) phosphorylated peptides are presented, supporting the sequence and phosphorylation site identification. c Comparison of the extracted ion chromatogram of HSP90 Ser255 phosphopeptide in the PU-bead cargo and ESC lysate (bottom) with a representative unmodified tryptic peptide in the PU-bead cargo and ESC lysate (top). d Ion intensity values of all identified phosphopeptides and the ratio of mean peptide intensity for each phosphosite in the samples described in (a) (i.e., n\u2009=\u20094 Ca and n\u2009=\u20092 NT cell lines). Each data point represents an individual phosphopeptide, and data are presented as mean\u2009\u00b1\u2009s.e.m. to illustrate variability between peptides across the cell lines. e Ratio of individual peptide intensity for each phosphosite in the samples described in the schematic (graph: mean\u2009\u00b1\u2009s.e.m., S255 n\u2009=\u20095; S226 n\u2009=\u20094; S263 n\u2009=\u20098; S231 n\u2009=\u20095). Source data are provided as a Source Data file and in Supplementary Data\u00a03 and 4.\n\nSubsequently, we performed label-free quantitation of these phosphopeptides using ion intensity measurements and observed a significant enrichment in the PU-beads cargo, particularly in the case of Ser255 of HSP90\u03b2. For instance, the Ser255 phosphopeptide displayed a nearly threefold enrichment in the PU-H71 cargo compared to the lysate, after protein loading normalization using a representative tryptic peptide (Fig.\u00a03c).\n\nTo gain further insights, we leveraged previously reported MS datasets of PU-H71-isolated cargo from epichaperome-positive cancer cells13,19, including MDA-MB-468 (triple negative breast cancer), Daudi (Burkitt\u2019s lymphoma), IBL-1 (AIDS-related immunoblastic lymphoma), and NCI-H1975 (non-small cell lung carcinoma), as well as from non-transformed (NT) proliferating cells in culture (e.g., MRC5, lung fibroblast and HMEC, mammary epithelial cells) (Supplementary Data\u00a04). This analysis revealed that phosphorylation of these serine residues is also enriched in cancer cells when compared to NT cells (Ca:NT S255\u2009=\u200916; S226\u2009=\u20098; S263\u2009=\u200912, Fig.\u00a03d) establishing it as a hallmark of both ESC and cancer epichaperomes. This observation further supports the idea of a shared structural and architectural foundation for epichaperomes among ESCs and cancer cells.\n\nAs HSP90 is found alongside HSC70 in epichaperomes, we conducted an additional confirmatory experiment. Here, we used YK5-B, a biotinylated probe that binds to HSC70 in epichaperomes, and thus captures HSP90 in epichaperomes via HSC7019. PU-H71 and YK5-B probes were used to isolate cargo from epichaperome-positive cancer cells, including MDA-MB-468 and OCI-Ly1 (breast cancer and diffuse large B-cell lymphoma, respectively), as well as from CCD-18Co colon cells in culture (i.e., non-transformed proliferating cells in culture) (Supplementary Data\u00a04). We found that the Ser255 and S226 phosphopeptides of HSP90\u03b2 were nearly four to five times more abundant in epichaperome-positive cancer cells compared to non-transformed proliferating cells in culture, for both the PU-cargo and the YK5-B cargo. Similar enrichment was noted for Ser263 and Ser231 in HSP90\u03b1 (Fig.\u00a03e). This analysis, thus, using both PU-H71 and YK5-B probes across diverse cell types, underscores the robustness of our observations and reinforces the role of phosphorylation in the acidic linker in shaping HSP90 within epichaperomes.\n\nIn light of these findings, made with two distinct probes and observed in ESCs, five cancer cell lines, each representative of a distinct cancer type, and of three non-transformed, but proliferating, cells in culture, it is evident that the epichaperome-specific agents target a subpopulation of HSP90 characterized by high phosphorylation levels in the acidic linker between the NTD and the MD, and this subpopulation predominantly assumes a closed-like conformation. In conjunction with PU\u2019s preference for HSP90 within epichaperomes, and substantiated by YK5-B, a probe that binds epichaperomes via HSC70, these results strongly indicate that phosphorylation at these two serine residues is a key driver for HSP90 incorporation into epichaperomes and, consequently, for epichaperome formation.\n\nTo explore whether the phosphorylation of these serine residues plays a pivotal role in driving, rather than merely resulting from, epichaperome formation, we next studied the phosphomimetic (HSP90\u03b2S226E,S255E) and the non-phosphorylatable (HSP90S226A,S255A) mutants.\n\nNotably, these serine residues are located within an intrinsically disordered region (IDR) of HSP90 (Supplementary Fig.\u00a05). IDRs are pivotal elements in the intricate network of protein\u2013protein interactions (PPIs). These regions lack a fixed three-dimensional structure, granting them exceptional flexibility. This structural adaptability enables proteins containing IDRs to assume various conformations in response to specific cellular contexts or binding partners. Such adaptability plays a crucial role in facilitating context-dependent involvement in distinct PPIs. In the case of HSP90, these serine residues within the IDR may alter the dynamics and structure of the charged linker, contributing to stabilizing the epichaperome-enabling conformation of this chaperone, and in turn facilitating epichaperome formation.\n\nTo explore this hypothesis, we conducted computational analyses to investigate the impact of each mutation on the flexibility of the charged linker (Fig.\u00a04a\u2013c and Supplementary Figs.\u00a06, 7). We constructed a model of the putative epichaperome core\u2014namely the ~300\u2009kDa assembly, see Fig.\u00a01\u2014based on the cryo-EM structure of a multimeric HSP90 assembly (PDB: 7KW7). This structure represented 2xHSP90\u03b1, protomer A and B, bound to 2xHSP70 and 1xHOP (i.e., HSP70(A)\u2013HSP90(A)\u2013HSP90(B)\u2013HSP70(B)\u2013HOP). To create the model, we substituted HSP90 with human HSP90\u03b2 using the closed-state cryo-EM structure (PDB: 8EOB). Additionally, we computationally inserted the charged linker, which was missing in the cryo-EM structures (Fig.\u00a04a).\n\na Model of the HSP90\u2013HSP90\u2013HSP70\u2013HSP70\u2013HOP assembly used for the molecular dynamics simulations. A and B, protomers A and B, respectively. b Protein secondary structure elements (SSE) like alpha-helices and beta-strands of the charged linker of protomer A of ATP-bound HSP90 monitored throughout the molecular dynamics simulation. WT (HSP90S226/S255), phosphomimetic (HSP90S226E/S255E), and non-phosphorylatable (HSP90S226A/S255A) mutants were analyzed. Each pentameric assembly was simulated three times for 100\u2009ns, yielding similar results across simulations. The plot on the left reports SSE distribution by residue index throughout the charged linker and the plot on the right monitors each residue and its SSE assignment over time. Schematic illustrating the primary structure of the full-length HSP90 with color-coded domains is also shown: NTD N-terminal domain; MD middle domain and CTD C-terminal domain. The charged linker (CL) and the location of the two key serine residues are also shown (top inset). The gray bar indicates the CL segment encompassing residues 218\u2013232. c Cartoon representation of ATP-bound HSP90 protomer A in assemblies with either the phosphomimetic (HSP90S226E/S255E) or non-phosphorylatable (HSP90S226A/S255A) mutants. The figure depicts the reference trajectory and representative trajectories from n\u2009=\u20091000 simulation\u00a0frames. The inset illustrates the surfaces available for the interaction between HSP90 A and HSP70 A when the CL is in the up conformation. The arrow indicates the location of the key beta-strand in the charged linker. See also Supplementary Figs.\u00a05\u20139.\n\nWe conducted all-atom molecular dynamics simulation of this pentameric protein assembly, with each system containing all the components along with either the EE, AA, or WT HSP90\u2014in both protomers. These simulations are intended to qualitatively explore the immediate response of the assembly to the perturbation induced by mutations and not to provide an extensive characterization of the assemblies\u2019 dynamics. By using a comparative MD-based approach we explore how short-term changes in the structural dynamics of different components within a large assembly may influence the emergence of states relevant for assembly stabilization. The underlying premise is that nanosecond timescale residue fluctuations in regions specifically responsive to certain states may facilitate large-scale rearrangements that underlie functional changes.\n\nThese simulations revealed that the structure and conformation of the charged linker were sensitive to the phosphorylation of the serine residues. In the pentameric assembly containing the phosphomimetic EE mutant (i.e., HSP90S226E/S255E), the linker of HSP90 protomer A (i.e., HSP90(A)), had a high probability of forming a \u03b2-strand bordering the Ser226Glu residue (2.1% of \u03b2-strand A). This strand remained stable over the duration of the simulation. This \u03b2-strand\u2019s formation significantly decreased in the pentameric assembly containing the wild-type (WT, i.e., HSP90S226/S255) protein (0.4% of \u03b2-strand A), with no secondary structure element (SSE) found in the assembly containing the AA (i.e., HSP90S226A/S255A) mutant (Fig.\u00a04b). Notably, ATP binding, but not ADP binding, favored a charged linker with a high content of \u03b2-strand A formation (2.1% vs 0.3%, respectively, in the EE mutant) (Fig.\u00a04b and Supplementary Fig.\u00a06a). This finding emphasizes that the observed changes in the EE mutant were not merely due to the addition of charged residues; they were intricately tied to the phosphorylation status and the specific context, including the nucleotide environment permissive of the specific HSP90 conformation (i.e., closed-like). Intriguingly, the strategic formation of \u03b2-strand A not only stabilized the charged linker but also induced a conformational switch, flipping it into an up conformation, thereby fully exposing the MD of HSP90(A), where HSP70(A) binds (Fig.\u00a04c, see HSP90 protomer A\u2014HSP70(A) interface). While other SSEs were observed in the analyzed assemblies containing either the WT or the mutant HSP90s, no other had a similar conformational effect on the charged linker as we observed for the \u03b2-strand A (see the effect of \u03b1-helices 1 through 6 in Supplementary Fig.\u00a06a, b). Intriguingly, the behavior observed for the charged linker of protomer A (HSP90(A)) was not mirrored in protomer B (HSP90(B)). In the assembly, the presence of HSP70(B) and HOP results in stabilizing intermolecular hydrogen-bond interactions with the charged linker of HSP90(B). These interactions effectively lock the linker into a specific conformation, thereby limiting its potential for SSE formation and conformational rearrangement compared to protomer A (Supplementary Fig.\u00a07).\n\nWe conducted dynamical residue cross-correlation analyses to explore how different protein units or subdomains in the pentameric HSP70(A)\u2013HSP90(A)\u2013HSP90(B)\u2013HSP70(B)\u2013HOP assemblies, featuring either the WT (HSP90S226/S255) or mutant (HSP90S226E/S255E or HSP90S226A/S255A) HSP90s, correlate in their motions throughout the simulation (Fig.\u00a05a, b). This analysis aimed to reveal how individual components move in relation to each other. Positive dynamical cross-correlations spanning different components of the assembly within the large epichaperome core may indicate enhanced cooperative motions, suggesting increased interactions that contribute to the stability of the assembled structure. Previous studies have employed similar analyses to investigate how ligand-induced modulations influence the overall flexibility of HSP90 assemblies, facilitating progress along the chaperone cycle, thereby supporting feasibility of this approach51.\n\na Dynamic cross-correlation matrix of C\u03b1 atoms for 100\u2009ns molecular dynamics simulations of ATP-bound assemblies containing WT (HSP90S226/S255), phosphomimetic (HSP90S226E/S255E) or non-phosphorylatable (HSP90S226A/S255A) HSP90. Correlated and anti-correlated motions are shown in the matrix and represented in the cartoon. The color of the arrows in the cartoon corresponds to the colors shown in the correlation index bar, with darker blue indicating stronger co-movement (positive correlation) and darker red indicating stronger opposite movement (negative correlation). The assembly contains two full-length HSP90\u03b2 proteins (protomer A and protomer B). The two HSP70 proteins (HSP70 A and HSP70 B) and the HOP protein are of sizes reported, and as per the constructs used in 7KW7. b Cartoon showing assemblies that are preferentially formed when the HSP90 charged linker is either phosphorylated (as in the EE mutant) or not phosphorylated (as in the WT protein). c The plot depicts the root mean square fluctuation (RMSF) values for each residue within the ATP-bound HSP90 assemblies across different conditions. Each point along the x-axis corresponds to a specific residue in the protein sequence of HSP70A, HSP70B, and HOP. The y-axis represents the RMSF value in angstroms (\u00c5), indicating the average flexibility of each residue. Higher RMSF values suggest greater flexibility, while lower values indicate rigidity. Arrowheads highlight areas where the structural dynamics diverge significantly. See Supplementary Fig.\u00a08 for the full assembly. d The plot depicts the combined global coordinated motions of all C\u03b1 atoms in ATP-bound assemblies within the PC1 and PC2 component space, representing the major directions of variance in the simulations. Each point corresponds to a frame in the simulation, illustrating the assembly\u2019s conformational state. Different sub-spaces for WT and EE mutants have been merged here for comparison. a\u2013d Each condition was simulated three times with similar results. Source data are provided in Supplementary Data\u00a05 and as a Source data file.\n\nIndeed, we observed the highest correlation among the components in assemblies containing the HSP90 EE phosphomimetic, mimicking the case where the charged linker is phosphorylated, followed by the WT, and then the non-phosphorylatable HSP90 AA mutant (Fig.\u00a05a). The AA mutant does not fully mimic the WT in MD simulations because substituting serine with alanine alters interaction capabilities and structural dynamics. This substitution is commonly used to create non-phosphorylatable mutants because alanine\u2019s small, non-polar side chain minimizes steric hindrance and structural alteration. However, alanine lacks the hydroxyl group needed for phosphorylation and hydrogen bonding, affecting local structural dynamics and protein conformation. Thus, while the AA mutant serves as a useful non-phosphorylated baseline, it may not fully replicate the WT\u2019s behavior. Comparing WT, EE, and AA mutants, however, provides insights into how phosphorylation modulates HSP90\u03b2 dynamics and epichaperome assembly. Notably, the coordinated movements observed in the assemblies containing the HSP90 phosphomimetic strongly support the idea that the HSP70(A)\u2013HSP90(A)\u2013HSP90(B)\u2013HSP70(B) or HSP70(A)\u2013HSP90(A)\u2013HSP90(B)\u2013HSP70(B)\u2013HOP assemblies can be preferentially stabilized when the HSP90 charged linker is phosphorylated (Fig.\u00a05b). This observation aligns with the prominent ~300\u2009kDa band observed for the epichaperome core in native PAGE (see Fig.\u00a01 showing HSP90 assemblies favored by PU-H71).\n\nIn contrast, in the WT HSP90 assembly, coordinated movements were primarily observed between the two HSP90 protomers, within HSP90, and between HSP90 and HSP70 and HOP, specifically through HSP90 protomer B (Fig.\u00a05a, b). These movements are more consistent and favorable in the context of HSP90(A)\u2013HSP90(B)\u2013HSP70(B) or HSP90(A)\u2013HSP90(B)\u2013HOP assemblies (Fig.\u00a05b). This observation implies that the major, broad ~242\u2009kDa band detected by the HSP90 antibody\u2014representing the primary HSP90-containing assembly observed in differentiated ESCs (Fig.\u00a01) and in non-transformed cells13,14,15,17,20\u2014may consist of such assemblies, along with HSP90 homo-oligomers.\n\nAs the correlation in movement is only one part of the bigger picture regarding dynamics, we next calculated the root mean square fluctuation (RMSF) and principal component analysis (PCA) to provide more quantitative information on the amplitude of fluctuations among the different assemblies52,53.\n\nRMSF offers a detailed view of the flexibility of individual residues by measuring the average deviation of each residue from its mean position over the simulation period. To gain insights into the dynamics of specific components within the pentameric assemblies, we calculated the RMSF as an average of all residues for each protein component, allowing us to pinpoint regions (or components) within the assemblies that exhibit higher flexibility or rigidity (Fig.\u00a05c and Supplementary Fig.\u00a08).\n\nIn our ATP-bound assembly analysis, we observed that there was no significant difference in fluctuation at the regions encompassing the HSP90(A)\u2013HSP90(B) components between the WT and phosphomimetic (EE) assemblies (WT: 1.79\u2009\u00b1\u20091.24\u2009\u00c5 vs EE: 1.77\u2009\u00b1\u20091.32\u2009\u00c5) (Supplementary Fig.\u00a08). This indicates that phosphorylation does not significantly alter the core\u2019s flexibility. However, notable differences were detected in the regions encompassing binding to HSP70(A) and HSP70(B) (Fig.\u00a05c). Specifically, for HSP70(A), the WT assembly showed higher flexibility (2.32\u2009\u00b1\u20090.94\u2009\u00c5) compared to the EE assembly (2.02\u2009\u00b1\u20090.90\u2009\u00c5). Similarly, in the region binding to HSP70(B), the WT assembly exhibited more fluctuation (2.57\u2009\u00b1\u20091.53\u2009\u00c5) than the EE assembly (2.38\u2009\u00b1\u20090.90\u2009\u00c5). The AA mutant had a slightly destabilizing effect on the HSP90(A)\u2013HSP90(B) core (AA: 1.97\u2009\u00b1\u20091.22\u2009\u00c5 vs WT: 1.79\u2009\u00b1\u20091.24\u2009\u00c5). Importantly, in the ADP-bound assembly, phosphorylation\u2014as in the EE mutant\u2014had a destabilizing effect on the HSP90 core (WT: 1.76\u2009\u00b1\u20091.33\u2009\u00c5 vs EE: 1.95\u2009\u00b1\u20091.33\u2009\u00c5), supporting the notion that phosphorylation does not favor the open-like conformation of HSP90. RMSF data supporting these analyses are found in Supplementary Data\u00a05.\n\nThus, the RMSF data reveal that phosphorylation has a significant impact on the flexibility and stability of the pentameric assembly, particularly at the HSP70 binding sites. While the core region between HSP90 protomers A and B shows no significant difference in flexibility between WT and EE assemblies, indicating stable core dynamics, the regions interacting with HSP70(A) and HSP70(B) demonstrate notable changes. At the HSP70(A) binding site, the EE assembly shows reduced flexibility compared to the WT. This reduced flexibility suggests that phosphorylation stabilizes the interaction of HSP70(A) with other assembly components, enhancing cooperative stability. This stabilization aligns with the increased correlated motions observed in the simulations, indicating that phosphorylation promotes tighter and more coordinated interactions at this site. Similarly, at the HSP70(B) binding site, the EE assembly exhibits reduced fluctuations, suggesting tighter binding. Thus, protomer B\u2019s interactions with HSP70(B) and HOP impose intrinsic rigidity, which is further stabilized by phosphorylation of the charged linker, resulting in reduced flexibility and increased cooperative stability.\n\nPCA is used to reduce the complexity of the data by identifying the main modes of movement (principal components, PCs) in the protein\u2019s trajectory. By analyzing the movement of the C\u03b1 atoms, one can understand how different regions or entire proteins in the assembly move relative to each other over time. Our PCA analysis revealed distinct differences between the WT and EE mutant HSP90 assemblies (Fig.\u00a05d). These movements are captured along the first two PCs (PC1 and PC2), which represent the major directions of movement in the protein structure during the simulation.\n\nIn the ATP-bound state, we observed that the span of PC1 is significantly larger in the WT HSP90-containing assemblies compared to the EE HSP90 mutant assemblies. PC1 captures the direction of the greatest variance in the data, correlating with the most substantial conformational changes in the protein assembly. The larger PC1 span in the WT suggests that this assembly experiences greater conformational flexibility, allowing it to adopt a broader range of structural conformations.\n\nPC2 captures the second most significant direction of variance, orthogonal to PC1. Although the differences in PC2 are not as pronounced as in PC1, the larger PC2 span in the WT indicates additional modes of flexibility or movement. This suggests that the WT assembly not only explores more conformational space but also possesses more varied structural dynamics, which are essential for its function in protein folding. On the other hand, the EE mutant\u2019s narrower spans suggest more restricted dynamics, indicating that phosphorylation of the charged linker enhances structural stability, contributing to a robust epichaperome assembly.\n\nTo further support the stability differences observed through RMSF and PCA analyses, we examined the potential energy of the assemblies (Supplementary Fig.\u00a09a, b). In the ATP-bound state, the EE-containing assembly exhibited the most favorable potential energy (i.e., the largest negative value), indicating greater stability compared to the WT and AA assemblies. While potential energy values can be approximations and should not be viewed as definitive evidence on their own, they are consistent with the observed reduced flexibility and tighter binding in the EE assembly. This stabilizing effect was specific to the ATP-bound state and was not observed in the ADP-bound state.\n\nIn summary, both MS evidence and computational models converge to support the conclusion that phosphorylation of the charged linker is a crucial contributor to epichaperome assembly, emphasizing its role in shaping not only HSP90, but also the stability and dynamics of the epichaperome structure. These findings highlight how phosphorylation modulates the structural dynamics and functional roles of HSP90 within the epichaperome assembly, promoting a scaffolding function through enhanced stability and reduced flexibility at critical interaction sites. Asymmetry and stabilization through intermolecular interactions lead to distinct dynamic behaviors for the charged linkers of protomers A and B. While protomer A\u2019s linker remains more flexible and capable of rearranging into different conformations\u2014modulated by its phosphorylation status\u2014protomer B\u2019s linker is constrained by its interaction with HSP70 and HOP. Despite these distinct local impacts of phosphorylation on the structure and conformation of individual linkers, both linkers contribute to modulating the overall stability and dynamics of the pentameric assembly. This dynamic interplay highlights the complex regulatory mechanisms at play within the epichaperome, illustrating how each protomer\u2019s interaction with its partners can influence the system\u2019s overall behavior and stability.\n\nNext, we carried out an extensive biochemical and functional analysis to reinforce these findings. Given the well-established tight association between HSP90 and other chaperones and co-chaperones in epichaperomes13,19,20,54, our focus shifted to a comprehensive evaluation of chaperone and co-chaperone proteins co-purified with the phosphomimetic (HSP90\u03b2S226E,S255E) and non-phosphorylatable (HSP90S226A,S255A) mutants. Our strategy involved the purification of protein complexes containing N-terminally mCherry-tagged HSP90\u03b2 in ESCs while retaining the endogenous WT HSP90 proteins. Distinctly labeled ESCs (i.e., labeled with heavy or light isotope lysine and arginine) expressing either the phosphomimetic or non-phosphorylatable mutant were subjected to immunoprecipitation (IP), followed by SDS\u2013PAGE separation and quantitative analysis via MS to determine protein abundance (Supplementary Fig.\u00a010a\u2013d and Supplementary Data\u00a06). It is worth noting that we performed IP separately for the phosphomimetic and non-phosphorylatable mutants to minimize subunit exchange during IP55, thereby enhancing our ability to detect changes in co-chaperone binding more accurately than previous studies56.\n\nWe found co-chaperones were among the most abundant co-purifying proteins, and most co-chaperones reported to participate in epichaperome formation13,19 displayed prominent changes in the phosphomimetic mutant (Supplementary Fig.\u00a010a\u2013d). The increased presence of epichaperome-specific co-chaperones (such as AHA1 and FKBP4)13 in phosphomimetic complexes compared to non-phosphorylatable complexes highlights a stronger association with Ser226P/Ser255P HSP90 as opposed to the non-phosphorylatable protein. However, we observed a slight reduction in the levels of HSC70 and HOP within phosphomimetic complexes. This decrease is potentially associated with specific subpopulations of HSP90 complexes that become more prevalent when the non-phosphorylatable Ala mutant is overexpressed in cells.\n\nWhile the phosphomimetic mutant (EE) increases the formation of epichaperomes, the non-phosphorylatable mutant (AA), which as we show in Fig.\u00a06c\u2013e can incorporate into the endogenous non-tagged HSP90 assemblies, is potentially altering the cellular composition of folding chaperone assemblies. This alteration could lead to a higher prevalence of assemblies involving HSC70 and HOP distinct from epichaperomes. Since the immunopurification experiment reports on a ratio of EE to AA, as captured by the antibody, the AA component, which may contain the non-epichaperome HSP90\u2013HSC70 or HSP90\u2013HOP assemblies, will skew the ratio to make it appear that there is less HSC70 and HOP in the EE component (i.e., in the epichaperomes). In fact, this is not true, as the apparent reduction is due to the presence of other HSP90 assemblies that incorporate these chaperones. Therefore, the observed reduction in HSC70 and HOP in the phosphomimetic\u2019s immunopurification does not necessarily contradict the computational findings, but rather highlights the complexity of HSP90\u2019s interactions within the cell, where multiple forms and assemblies coexist, each with distinct roles and interactions. The introduction of two Ala residues in the unstructured linker region of HSP90 may prompt the recruitment of HSC70 and HOP, chaperones recognized for their ability to bind unstructured unfolded protein stretches57. It is important to note that these assemblies are distinct from epichaperomes. Due to the anti-mCherry antibody capturing the entirety of the tagged HSP90, differentiation between specifically epichaperome-related HSP90 and a mixture of epichaperomes and other pools becomes challenging.\n\na Overview of the experimental design and expected outcomes. b Analysis of transfection efficacy in cells transfected with HSP90\u03b2 mutants, as indicated in (a). c Detection of epichaperome components (chaperones and co-chaperones) through SDS\u2013PAGE (bottom, total protein levels) and native PAGE (top), followed by immunoblotting. Brackets indicate the approximate position of epichaperome-incorporated chaperones. Data are presented as mean\u2009\u00b1\u2009s.e.m., n\u2009=\u20093, one-way ANOVA with Sidak\u2019s post-hoc, EE vs AA. d Visualization of HSP90 in epichaperomes using the PU-TCO probe clicked to Cy5 (left) and the mCherry tag (middle). Right, merged images. MWM molecular weight marker. e Detection and quantification of epichaperome components through PU-beads capture as indicated in (a). Protein amount loaded for input represents 2% of the protein amount incubated with the beads. Data are presented as mean\u2009\u00b1\u2009s.e.m., n\u2009=\u20093, unpaired two-tailed t-test. Gel images are representative of three independent experiments. Source data are provided as a Source data file.\n\nTo address these limitations, we adopted a multi-pronged approach. First, we utilized immunoblotting with native cognate antibodies for chaperone assemblies retained on native PAGE, coupled with chemical blotting using PU-probes. Additionally, we employed affinity capture with PU-probes to quantify the amount of epichaperome components under each condition (Fig.\u00a06a). For these experiments, we transfected cells with the phosphomimetic (HSP90\u03b2S226E,S255E, EE mutant) and with the non-phosphorylatable (HSP90S226A,S255A, AA mutant) mutants, as well as with HSP90\u03b2 WT or mCherry tag only for control purposes. In this study, we chose human embryonic HEK293 cells as our cell model since they exhibit intermediate epichaperome expression levels (i.e., medium expressor, Supplementary Fig.\u00a011), making them suitable for studying epichaperome dependence. We confirmed comparable transfection efficiency for each construct, with the tagged HSP90\u03b2 protein expressed in addition to the endogenous HSP90\u03b2 (Fig.\u00a06b).\n\nOur findings revealed that cells expressing the EE mutant exhibited higher levels of epichaperomes compared to those expressing the AA mutant, as evidenced by immunoblotting of various epichaperome components (including HSP90\u03b1, HSC70, CDC37, AHA1, HOP, and HSP110) (Fig.\u00a06c, native PAGE) and chemical blotting with the PU-Cy5 epichaperome probe (Fig.\u00a06d). Notably, there was no significant change in the overall concentration of these proteins in association with their incorporation into epichaperomes (Fig.\u00a06c, SDS\u2013PAGE).\n\nEpichaperome isolation using PU-beads as an affinity purification probe also revealed significantly greater incorporation of chaperones, including mCherry-HSP90\u03b2, and co-chaperones into epichaperomes in cells expressing the EE mutant compared to those containing the AA mutant HSP90 (Fig.\u00a06e), with no substantial alterations observed in cells containing the control vectors (Supplementary Fig.\u00a012a). In contrast, overexpression of wild-type HSP90 in HEK293 cells had a minimal impact on endogenous epichaperomes (Fig.\u00a06c, native PAGE and Supplementary Fig.\u00a012a, PU-beads capture). This observation aligns with previous reports13 suggesting that factors beyond chaperone concentration play a pivotal role in driving HSP90 incorporation into epichaperomes. Notably, cargo isolated on the control probe (control beads, Supplementary Fig.\u00a012b) showed no detection of HSP90.\n\nSupporting the hypothesis that phosphorylation of HSP90 shifts its role from a folding function (as in chaperone complexes) to a scaffolding role (as in epichaperomes), we found that the refolding activity was impaired in cell lysates containing the phosphomimetic EE mutant (Supplementary Fig.\u00a013). We conducted an experiment using denatured luciferase as a substrate to assess the refolding capabilities of different HSP90 mutants present in HEK293 cell lysates. We prepared cell extracts from HEK293 cells transfected with cherry-HSP90\u03b2 constructs, specifically the WT, AA (non-phosphorylatable), and EE (phosphomimetic) mutants. Denatured luciferase was mixed with equal amounts of these lysates to determine whether the distinct HSP90 species in each lysate could facilitate the refolding of luciferase. Denatured luciferase was chosen as the substrate because its spontaneous refolding is inefficient, and because its refolding is HSP90 dependent58, providing a sensitive assay to measure the chaperone activity of HSP90. MDA-MB-468 cells and CCD-18Co cells were selected as control cell lines with endogenously high- and low-epichaperome levels, respectively.\n\nLysates containing WT and AA HSP90 had effective protein-folding activity (Supplementary Fig.\u00a013). The WT lysates regained ~50% of luciferase activity, while the AA lysates regained 31.6% of activity after 60\u2009min, demonstrating that these forms of HSP90 can support the refolding process, similar to the CCD-18Co lysates, which regained 44% of luciferase activity. However, lysates from cells expressing the EE mutant showed significantly impaired refolding activity, with only 0.2% of luciferase activity recovered after 60\u2009min. This lack of refolding capacity was similar to what we observed in lysates from MDA-MB-468 cells, which contain high levels of epichaperomes (Supplementary Fig.\u00a013). These findings demonstrate that phosphorylation of HSP90 at key serine residues impairs its ability to refold denatured proteins, supporting the transition from a protein-folding chaperone to a scaffolding platform role within the epichaperome.\n\nThe impaired refolding activity observed with the EE mutant (and in the epichaperome-high MDA-MB-468 cells), despite the presence of endogenous HSP90, is highly intriguing. The phosphorylated HSP90 form (EE mutant) could act as a dominant negative, interfering with the normal function of endogenous HSP90. By sequestering co-chaperones or client proteins away from native HSP90, the phosphorylated form could effectively disrupt the entire chaperone system within the lysate. This disruption would reduce the overall folding capacity despite the presence of endogenous HSP90, a hypothesis that warrants future investigation.\n\nWe further established the dependency of epichaperome function, beyond its formation, on the phosphorylation of HSP90 serine residues (Figs.\u00a07 and 8). A key characteristic shared among high epichaperome-expressing cells in PSC, CSC, and cancer cells is the hyperactivity of the transcription factor c-MYC13,25,26,27. In cancer, c-MYC is frequently overexpressed or mutated, resulting in sustained activation, which drives uncontrolled cell proliferation59. In ESCs, c-MYC plays a crucial role in maintaining pluripotency and self-renewal, crucial for preserving the undifferentiated state of ESCs60. We therefore investigated the impact of HSP90\u03b2 Ser226P/Ser255P on cellular behaviors such as self-renewal and proliferation.\n\na ESC proliferation at 60\u2009h post-transfection in E14 cells transfected with either the phosphomimetic HSP90\u03b2S226E,S255E (EE) or the non-phosphorylatable HSP90S226A,S255A (AA) mutant. Medium (1\u00d7) or high (2\u00d7) plasmid concentrations were employed. Data are presented as mean\u2009\u00b1\u2009s.e.m., n\u2009=\u20096, one-way ANOVA with Sidak\u2019s post-hoc, EE vs AA. b Representative spectra (n\u2009=\u20093 independent experiments) of phosphopeptides, S255P (left) and S226P (right), and a representative unmodified tryptic peptide (middle) in mCherry-tagged WT HSP90\u03b2 affinity-purified from ESC or differentiated trophoblast (T) cells. c Representative spectra (n\u2009=\u20093 independent experiments) of a tryptic peptide from Oct4 protein co-purified from ESCs labeled with heavy or light isotope lysine and arginine expressing either the EE or the AA HSP90 mutant. Quantitative analysis via mass spectrometry (MS) to determine protein abundance is shown. d Overview of the experimental design and expected outcomes (for e and f). e, f Detection and quantification of Oct4 protein expressed in cells transfected with the indicated HSP90 mutants or vector control (e) and sequestered into the epichaperome platforms (identified through PU-beads capture, f). Data are presented as mean\u2009\u00b1\u2009s.e.m., n\u2009=\u20095 AA, n\u2009=\u20095 EE, n\u2009=\u20093 WT, n\u2009=\u20093 empty vector, one-way ANOVA with Dunnett\u2019s post-hoc, EE vs AA, WT vs AA, empty vector vs AA (for e) and as mean\u2009\u00b1\u2009s.e.m., n\u2009=\u20093, unpaired two-tailed t-test (for f). Source data are provided as a Source Data file and in Supplementary Data\u00a06 and 7.\n\na Overview of the experimental design and expected outcomes. b Detection and quantification of proteins involved in transducing signaling events that lead to cell proliferation, survival, and protein synthesis control. See Supplementary Fig.\u00a014 for total protein levels and levels sequestered into epichaperomes. Data are presented as mean\u2009\u00b1\u2009s.e.m., p-S6 n\u2009=\u20098; p-mTOR n\u2009=\u20093; p-MEK1/2 n\u2009=\u20096; p-AKT n\u2009=\u20095, unpaired two-tailed t-test. c Confocal microscopy shows morphological differences between the cells transfected with either the AA or the EE HSP90 mutant. Micrographs are representative of 96 cells for EE and 62 cells for AA. Scale bar, 10\u2009\u00b5m. Data are presented as mean\u2009\u00b1\u2009s.e.m., n\u2009=\u20098 wells for EE, n\u2009=\u200914 wells for AA, unpaired two-tailed t-test. Source data are provided as a Source data file.\n\nTo assess proliferation, ESCs were transfected with plasmids containing either the phosphomimetic (HSP90\u03b2S226E,S255E) or non-phosphorylatable (HSP90\u03b2S226A,S255A) mutant. Notably, ESCs transfected with the HSP90\u03b2 phosphomimetic mutant displayed a significantly higher proliferative rate (P\u2009<\u20090.0001, >25%) compared to those transfected with the non-phosphorylatable variant, regardless of whether medium (1\u00d7) or high (2\u00d7) plasmid concentrations were employed (Fig.\u00a07a). This observation lends support to the notion that HSP90\u03b2 Ser226P/Ser255P, and consequently, epichaperomes, play a crucial role in ESC proliferation.\n\nDifferentiation of ESCs results in a decreased proliferative rate, as indicated by the doubling time of ZHBTc4 ES cells (~12\u2009h) and trophoblast-differentiated cells (~25\u2009h)32. Since differentiation is also closely associated with the disassembly of epichaperomes, we next examined the phosphorylation levels of HSP90\u03b2 at Ser226 and Ser255 in cells with varying self-renewal capacities. We utilized the TET-repressible oct4 mouse ESC line ZHBTc4, where the Oct4 expression is suppressed in the presence of doxycycline for ESC differentiation into trophoblast-like cells (Troph)31. In this experiment, we expressed WT mCherry-HSP90\u03b2 in ZHBTc4 cells and quantified phosphopeptides in both ESCs and trophoblast cells following ESC differentiation (Fig.\u00a07b and Supplementary Data\u00a07). After normalizing the data to mCherry-HSP90\u03b2 protein loading (middle panel, ES/Troph\u2009=\u20090.44), we observed a 30% higher phosphorylation of HSP90\u03b2 at Ser255 in stem cells compared to differentiated cells (left panel, ES/Troph\u2009=\u20090.57). Phosphorylation levels of HSP90\u03b2 at Ser226 appeared to remain unchanged under these experimental conditions after normalizing to protein loading (right panel, ES/Troph\u2009=\u20090.45).\n\nPluripotency hinges on crucial transcription factors like Oct4. Oct4 is widely recognized as one of the principal transcription factors governing the self-renewal of both pluripotent stem cells and cancer cells61. We find Oct4 interacts with epichaperomes in ESCs (Supplementary Data\u00a01) and exhibits significant enrichment in the cargo captured with the Ser226/Ser255 phosphomimetic compared to the non-phosphorylatable HSP90 (Supplementary Data\u00a06 and Fig.\u00a07c, 1.4-fold EE: AA). To validate the reliance of Oct4 on epichaperomes, we examined Oct4 levels in both MDA-MB-468 cancer cells and HEK293 cells transfected with the various HSP90 plasmids. Additionally, we utilized affinity capture with PU-probes (Fig.\u00a07d\u2013f and Supplementary Fig.\u00a014a). Notably, we observed that cells expressing the phosphomimetic EE mutant showed significantly elevated levels of Oct4, both overall (Fig.\u00a07e) and within epichaperomes (i.e., those sequestered within the epichaperomes, Fig.\u00a07f), compared to cells expressing the HSP90 AA mutant. No detectable differences were observed under control conditions (WT HSP90 and empty vector only) (Supplementary Fig.\u00a012a). Additionally, Oct4 was sequestered by epichaperomes in MDA-MB-468 cells, supporting the idea that epichaperomes play a role in regulating pluripotency through both direct and indirect regulation of Oct4.\n\nEpichaperomes play a pivotal role in supporting enhanced proliferation by altering the regulation of various proteins involved in cell signaling3,13,19. Higher epichaperome levels translate to a greater number of proteins being affected, resulting in increased signaling output13,17,62. We therefore next assessed the signaling output of cells transfected with the various HSP90 mutants. We observed a significantly heightened epichaperome-dependent impact on key signaling effector proteins involved in cell growth and proliferation (i.e., MEK, AKT, and mTOR) in cells expressing the HSP90 EE mutant compared to those expressing the AA mutant. This was evident in both the increased phosphorylation status of these effector proteins (Fig.\u00a08a, b) and their enhanced recruitment to epichaperome platforms (Supplementary Fig.\u00a014a\u2013c) in cells expressing the EE mutant, as compared to those expressing the AA mutant. Importantly, these effects occurred without notable changes in the expression levels of the proteins (Supplementary Fig.\u00a014a, b). No measurable differences were observed under control conditions (WT HSP90 and empty vector only) (Fig.\u00a08b and Supplementary Fig.\u00a014a, b).\n\nEpichaperome formation fuels aggressive behaviors in cells54,63. Indeed, when observed under a microscope, we noted that, in comparison to cells expressing the non-phosphorylatable AA mutant (HSP90\u03b2S226A,S255A), those expressing the phosphomimetic EE mutant (HSP90\u03b2S226E,S255E) displayed a higher prevalence of cells with an elongated phenotype and several protrusions (Fig.\u00a08c), supportive of a mesenchymal-like phenotype64. These morphological changes suggest a shift towards a more stem cell-like state, or a more aggressive phenotype in the context of cancer, in cells harboring the EE HSP90 mutant (i.e., with a high epichaperome load), a feature not observed in cells carrying the AA HSP90 mutant (i.e., not permissive of epichaperome formation).\n\nOne intriguing question is which kinase could phosphorylate HSP90 at these serine residues? A likely candidate is casein kinase II (CK2)65,66. Ser226 fits well within CK2\u2019s phosphorylation consensus sequence and is a likely phosphorylation target, whereas Ser255 has a potential but weaker consensus match. CK2 is sequestered to epichaperomes in ESCs and in cancer cells13. Notably, CK2 is overexpressed in highly proliferative cells67 and plays a role in phosphorylating numerous protein substrates involved in cell proliferation and survival68. Moreover, the mutation of CK2 has been shown to abolish the viability of both PSCs69 and tumor cells70,71, indicating a potential direct link between epichaperome function and cellular physiology, possibly mediated by CK2 phosphorylation.\n\nTo investigate this potential link, we explored the impact of two CK2 inhibitors, CX4945 and CIBG-300, on epichaperome formation (Fig.\u00a09a, b). Treatment of MDA-MB-468 epichaperome-high cancer cells with these inhibitors resulted in a dose-dependent decrease in epichaperomes, as observed by native PAGE coupled with immunoblotting against epichaperome components such as HSP90\u03b1, HSP90\u03b2, HSC70, CDC37, HOP, and HSP110. This treatment also led to a similar decrease in the levels of HSP90 phosphorylated at Ser226. Importantly, no substantial change was observed in the total concentration of chaperones, indicating that the inhibitors specifically affected the phosphorylation state of HSP90, and in turn, epichaperome formation, rather than the expression levels of the epichaperome constituents. siRNA knockdown of CK2\u03b1\u2014the catalytic subunit of CK2\u2014recapitulated the effects on epichaperomes observed with CK2 inhibitors (Fig.\u00a09c).\n\na, b The gels illustrate the resulting epichaperome formation and the phosphorylation status of HSP90 in lysed MDA-MB-468 epichaperome-positive cancer cells treated with CK2 inhibitors. Detection of epichaperome components was done through SDS\u2013PAGE (total protein levels) and native PAGE followed by immunoblotting. a The effect of CX4945 treatment, while b depicts the effect of CIBG-300 treatment. Vehicle-treated cells serve as controls. CK2\u03b1 levels are shown to verify that inhibitor treatment effects are independent of changes in CK2\u03b1 expression levels. c Same as in (a, b) for CK2\u03b1 knockdown using dose-dependent siRNAs in MDA-MB-468 cells. CK2\u03b1 levels, knockdown efficiency control. d The indicated CK2 constructs were used to transfect HEK293 cells. CK2\u03b1, catalytic subunit; CK2\u03b2, regulatory subunit; kinase-dead mutant CK2 K68M \u03b1. HA tag and CK2\u03b1 levels, transfection efficacy control. a\u2013d Gel images are representative of three independent experiments. Source data are provided as a Source data file. e Schematic summary. CK2\u2019s phosphorylation activity directly influences the stability and assembly of epichaperomes. These findings confirm the functional role of HSP90 phosphorylation at these specific serine residues in epichaperome formation and posit CK2 as a likely physiological candidate behind epichaperome formation.\n\nCK2 is a tetrameric enzyme consisting of two catalytic CK2\u03b1 subunits and two regulatory CK2\u03b2 subunits72. The \u03b1 subunit serves as the catalytic unit responsible for substrate phosphorylation, while the \u03b2 subunit acts as the regulatory unit, controlling the specificity and activity of CK2. Without the regulatory \u03b2 subunit, the \u03b1 subunit can randomly phosphorylate substrates, highlighting the importance of testing CK2 activity with both subunits present to accurately reflect its physiological role. Additionally, CK2 K68M \u03b1 is known as the kinase-dead mutant, lacking catalytic activity73. Transfection with HA-tagged CK2\u03b1 or a combination of HA-tagged CK2\u03b1 and Myc-tagged CK2\u03b2 resulted in an increase in epichaperome levels (Fig.\u00a09d), suggesting that the presence of active CK2 enhances epichaperome assembly. Conversely, transfection with the HA-tagged kinase-dead mutant CK2 K68M \u03b1 along with Myc-tagged CK2\u03b2 led to a complete disruption of epichaperomes (Fig.\u00a09d), underscoring the necessity of CK2\u2019s catalytic activity for maintaining epichaperome integrity. No effects were observed in cells transfected with an empty vector, confirming the specific role of CK2 activity in modulating epichaperome levels.\n\nThese findings confirm the functional role of HSP90 phosphorylation at these specific serine residues in epichaperome formation and posit CK2 as a likely physiological candidate behind epichaperome formation (Fig.\u00a09e). By demonstrating how CK2\u2019s phosphorylation activity directly influences the stability and assembly of epichaperomes, our study highlights a crucial regulatory mechanism in cellular proliferation and survival.\n\nPrevious studies have found that irrespective of the tumor type, 60\u201370% of tumors contain HSP90\u2013HSC70 epichaperomes13,19. Additionally, epichaperomes are known to specifically form in diseased tissue3. To assess whether our observations regarding the impact of the HSP90 charged linker, derived from cell models, extend to human patients and are not artifacts specific to cultured cells, we obtained surgical specimens from breast and pancreatic cancer surgeries (n\u2009=\u200918 tissues from 9 patients, Fig.\u00a010a\u2013d). Both tumor (n\u2009=\u20099) and tumor-adjacent (n\u2009=\u20099) tissues, determined by gross pathological evaluation to be potentially non-cancerous, were analyzed for epichaperome levels using native PAGE. Additionally, total HSP90\u03b2 and phosphorylated HSP90\u03b2 at Ser226 were assessed by SDS\u2013PAGE and immunoblotting with specific antibodies. To mitigate potential biases arising from varying HSP90 levels, each pair was normalized based on HSP90 concentration. Despite challenges in obtaining high-quality epichaperome profiles from surgical samples, a robust correlation emerged between epichaperome expression and Ser226 phosphorylation (Fig.\u00a010c, d). Tissues positive for epichaperomes exhibited p-Ser226 HSP90\u03b2 positivity, and conversely, those negative for epichaperomes showed no or negligible p-Ser226 signal.\n\na Cartoon illustrating the processing of human tissue for biochemical analyses. Both tumor (T) and tumor-adjacent (TA) tissues, determined by gross pathological evaluation to be potentially non-cancerous, were harvested and analyzed. b MDA-MB-468 breast cancer cells (epichaperome-high) and ASPC1 pancreatic cancer cells (epichaperome-low) served as controls for assessing p-Ser226 HSP90 levels. Gel images are representative of three independent experiments. c The graph presents the relationship between epichaperome positivity and HSP90 Ser226 phosphorylation for tissues described in (a). Data represent mean\u2009\u00b1\u2009s.e.m., with n\u2009=\u20099 tumor (T) and n\u2009=\u20099 paired tumor-adjacent (TA) tissues classified based on epichaperome positivity or negativity, as determined by native PAGE (see d); unpaired two-tailed t-test. d Detection of epichaperomes through native PAGE (top), and of p-Ser226 HSP90 (middle) and total HSP90 (bottom) by SDS\u2013PAGE, followed by immunoblotting, in tissues from the indicated patient specimens, as in (a). Brackets indicate the approximate position of epichaperome-incorporated HSP90. Note: obtaining genuinely \u201cnormal\u201d tissue adjacent to tumors presents challenges, especially in the case of pancreatic tissue. The relatively small size of the organ and the nature of surgical procedures for pancreatic cancer often lead to the collection of normal samples in close proximity to the tumor. It\u2019s crucial to acknowledge that, due to these challenges, we designate potentially normal tissue as tumor-adjacent tissue, recognizing that it may not entirely reflect a truly normal tissue state. PDAC pancreatic ductal adenocarcinoma, IDC invasive ductal carcinoma, ILC invasive lobular carcinoma, ER estrogen receptor, PR progesterone receptor. Source data are provided as a Source data file.\n\nCollectively, these multifaceted biochemical and functional lines of evidence establish a compelling connection between structural features in HSP90 and the processes of epichaperome formation and function. These findings lend robust support to the hypothesis that the regulation of epichaperome processes in ESC and cancer cells\u2014encompassing critical factors such as proliferative potential, self-renewal capacity, plasticity, and signaling output\u2014crucially relies on the specific phosphorylation events taking place at key residues within HSP90\u2019s charged linker.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_Fig8_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_Fig9_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53178-5/MediaObjects/41467_2024_53178_Fig10_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The intricate network of protein\u2013chaperone interactions within cells plays a critical role in maintaining protein homeostasis and cellular function. In recent years, the discovery of epichaperomes as specialized chaperone assemblies in both cancer cells and pluripotent stem cells has opened new avenues for understanding chaperone biology. This investigation offers valuable insights into the structural and regulatory intricacies of epichaperomes, with particular attention to the pivotal role played by PTMs of HSP90 in orchestrating their formation and function.\n\nA central discovery in this investigation is the recognition of specific PTMs on HSP90, especially at Ser226 and Ser255, as critical factors governing the assembly of epichaperomes. Our data reveal that phosphorylation of these serine residues enhances the association of HSP90 with other chaperones and co-chaperones, creating a microenvironment conducive to epichaperome formation. This PTM within the charged linker region reduces flexibility at critical interaction sites, such as those between HSP90, HSP70, and HOP, thereby enhancing the overall stability of the epichaperome assembly. Consequently, chaperones in epichaperome structures exhibit a more rigid and stable conformation with reduced variability, distinguishing them from the dynamic nature of typical chaperone complexes, where flexibility and rapid assembly/disassembly are essential for protein-folding activities. This finding underscores the significance of PTMs in regulating chaperone assemblies and highlights the potential of targeting these modifications for therapeutic intervention.\n\nChaperones appear to be highly susceptible to structural and functional regulation by a spectrum of PTMs. The concept of the \u201cHSP90 PTM code\u201d was introduced to highlight the nuanced regulation of HSP90 function by specific modifications, which transform its activity and interactions within the cell12. Understanding this code provides valuable insights into the mechanistic shifts that enable HSP90 to transition between different structures, assemblies and functions. For example, PTMs of HSP90 provide an important regulatory element, modulating co-chaperone and client protein binding74,75,76,77,78, ATPase activity79, conformational cycle75,78,79,80, turnover81, and small molecule affinity12,39. Similar to minor changes in primary sequence, these PTMs likely regulate the access to and occupancy of key conformational states of HSP90 for in vivo processing of some essential clients. Our investigation pinpoints crucial PTMs that remodel the functional profile of HSP90, metamorphosing it from a protein-folding entity into epichaperomes, a platform orchestrating the reorganization of PPI networks for heightened cellular adaptability and proliferation.\n\nOur study uncovered a fascinating aspect of PTMs in HSP90 within epichaperomes\u2014phosphorylation events occur in an IDR of the protein. The strategic placement of these PTMs in the IDR holds profound significance, suggesting that they influence HSP90\u2019s conformation and function beyond the traditional structured regions. This adaptability is crucial for HSP90\u2019s participation in distinct PPIs, allowing it to stabilize the epichaperome-enabling conformation and restructure the interactions of numerous proteins in response to cellular stressors. Intriguingly, previous studies in yeast82, where the IDR was substituted with glycine\u2013glycine\u2013serine residues, align with our findings. These studies suggested that the charged linker (encompassing the IDR), influenced by the N-domain of HSP90, can adopt a structured form. This structured form, in turn, can stabilize interactions between specific HSP90 domains, influencing HSP90 dynamics, co-chaperone binding, and overall biological function, especially in conditions of cellular stress.\n\nChanges in PPI networks play a fundamental role in cellular responses to stressors and the coordination of various biological processes18. These alterations, often induced by external stressors, are vital for the cell\u2019s ability to adapt and function under different conditions. Notably, <10% of human PPIs remain unaffected by stress-induced perturbations, highlighting the widespread impact of cellular stress on the interactome. These changes, influenced by factors such as PTMs and protein conformation, are essential for species-specific adaptation and contribute to PPI network malfunctions observed in diseases18.\n\nOur findings position CK2 as a physiological regulator of HSP90 phosphorylation that drives epichaperome formation. CK2 is a constitutively active kinase, and its overexpression has been reported in various diseases, including cancers, infectious diseases, neurological disorders, and cardiovascular conditions83. Future studies will be crucial in exploring whether the link between CK2 and epichaperome formation extends to these other diseases as well.\n\nThe implications of our study go beyond providing structural and mechanistic insights. We present compelling evidence that phosphorylation of HSP90 at Ser226 and Ser255 not only promotes epichaperome formation but also influences cellular behaviors, including proliferation and self-renewal. This suggests a direct link between epichaperome function and cellular physiology, particularly crucial in contexts such as cancer and pluripotent\u00a0stem cell maintenance, where robust proliferation and adaptation are vital. The shared composition of epichaperome complexes between ESCs and cancer cells suggests a possible commonality in their functional roles. In both contexts, the epichaperome may facilitate rapid cellular proliferation and adaptability to environmental stress, characteristics crucial during development and tumorigenesis. This raises intriguing questions about whether the epichaperome contributes to the aberrant growth and survival of cancer cells by reactivating developmental pathways. The epichaperome might allow cancer cells to hijack developmental pathways typically active in ESCs, enabling them to maintain high proliferation rates and resist cell death.\n\nPlasticity, a key characteristic associated with both ESCs and cancer cells84, is also implicated in our findings. The morphological changes observed in cells expressing the phosphomimetic HSP90 mutant\u2014specifically, the higher prevalence of cells with an elongated phenotype and several protrusions\u2014hint at a mesenchymal-like phenotype64. This phenotypic shift is often associated with increased plasticity and is indicative of a more stem cell-like state. Our findings suggest a potential role for epichaperomes in modulating this dynamic process of cellular transition between different phenotypic states.\n\nThe link between pluripotency and cancer is particularly intriguing. Cellular stress is increasingly recognized as a pivotal factor that can shift the balance between cellular pluripotency and the development of malignancies. The process of dedifferentiation, observed in regeneration in plants and some vertebrates, involves the deactivation of genes responsible for cell-specific functions, re-entry into the cell cycle, proliferation, and activation of pluripotency-associated genes85. Tumors also undergo dedifferentiation, where cancer cells revert to a less differentiated state, re-express stem cell genes like Oct4, leading to the emergence of cancer stem-like cells with enhanced metastatic potential and treatment evasion86. Our study proposes epichaperomes as significant mediators of changes in cellular identity, partly through Oct4.\n\nThe revelation of HSP90\u2019s dysfunctional multimeric states carries implications for therapeutic interventions3,16. Instead of universally inhibiting all HSP90 pools, a paradigm shift comes to the fore with precision medicine strategies. The prospect of targeting specific pathologic conformations while preserving normal HSP90 functions emerges as a promising direction. This shift beckons researchers to navigate the intricate interplay of HSP90 conformations as they forge ahead in the quest for innovative therapeutic approaches. Our study also confirms the notion that small molecule HSP90 binders have distinct preference for HSP90 conformers in cells, reinforcing the finding that not all HSP90 inhibitors act equally well or equally selectively on specific disease-promoting HSP90 conformations or disease-associated HSP90 assemblies in comparison with HSP90 conformers found in normal cells. The first feature determines drug efficacy, whereas the latter influences the safety profile during administration.\n\nIn conclusion, our study unravels the intricate interplay between PTMs, conformational regulation, and biological functions of HSP90 within epichaperomes. These findings have implications for the development of novel therapeutic strategies targeting chaperone complexes in diseases characterized by epichaperome dysregulation, such as in cancers and neurodegenerative disorders. By deciphering the regulatory mechanisms underlying epichaperomes, we move one step closer to harnessing their potential for precision medicine and therapeutic intervention.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "This research complies with all relevant ethical regulations for research involving human participants. Surgical specimens were obtained in accordance with the guidelines and approval of the Institutional Review Board at Memorial Sloan Kettering Cancer Center, under the following approved protocols: Biospecimen Research Protocol# 09-121, project title: Ex-Vivo Testing of Breast Cancer Tumors for Sensitivity to Inhibitors of Heat Shock Proteins and Signaling Pathway Inhibitors, S. Modi, PI, and Biospecimen Research Protocol# 14-091, project title: Establishment and Characterization of Unique Mouse Models Using Patient-Derived Xenografts, E. de Stanchina, PI. The source of samples consisted of unused portions of surgical specimens taken for reasons other than research (i.e., for patients undergoing the procedures for medical reasons unrelated to need for research samples or to the nature of the research). No individuals were excluded on the basis of age, sex or ethnicity. Because breast cancer is a disease which overwhelmingly affects women, and is a disease that is generally not seen in children, the vast majority of breast cancer patients enrolled on protocol# 09-121 were females >18\u2009years of age. In the case of pancreatic cancer samples (n\u2009=\u20093 patients), sex information was not available. Patient tissue samples were obtained with written informed consent and were de-identified prior to use in the studies. No compensation was provided for participation in this research.\n\nAll commercial chemicals and solvents were purchased from Sigma-Aldrich or Fisher Scientific and used without further purification. The identity and purity of each product was characterized by MS, HPLC, TLC, and NMR. Purity of target compounds has been determined to be >95% by LC/MS on a Waters Autopurification system with PDA, MicroMass ZQ and ELSD detector and a reversed phase column (Waters X-Bridge C18, 4.6\u2009\u00d7\u2009150\u2009mm, 5\u2009\u00b5m) eluted with water/acetonitrile gradients, containing 0.1% TFA. Stock solutions of all inhibitors were prepared in molecular biology grade DMSO (Sigma-Aldrich) at 1000\u00d7 concentrations. The PU-TCO, PU-CW800, and YK5-B probes and relevant control probes, and the PU-beads and the control probes were generated using published protocols13,19,36,87,88,89,90,91,92 or as described in Supplementary Fig.\u00a015 and Supplementary Note\u00a01. The GA-biotin probe was purchased from Sigma (SML0985). DSS was acquired from Thermo Fisher (21655). CX4945 (Silmitasertib) was purchased from MedChemExpress (Cat No. HY-50855), and CIBG-300 was obtained from Sigma-Aldrich (Cat No. SML3143).\n\nCell line selection was not based on gender, sex, or ethnicity. Cell lines were cultured according to the providers\u2019 recommended culture conditions. Cells were authenticated using short tandem repeat profiling and tested for mycoplasma. The MDA-MB-468 (female, breast cancer cell line, HTB-132, RRID: CVCL_0419), ASPC1 (female, pancreatic cancer cell line, CRL-1682, RRID: CVCL_0152), NCI-H1975 (female, non-small cell lung cancer cell line, CRL-5908, RRID: CVCL_1511), Daudi (male, B lymphoblast cell line, CCL-213, RRID: CVCL_0008), MRC5 (male, lung fibroblast cell line, CCL-171, RRID:CVCL_0440), CCD-18Co (female, colon fibroblast cell line, CRL-1459, RRID: CVCL_2379) and the Human Embryonic Kidney 293 (HEK293) cell line (CRL-1573, RRID: CVCL_0045), of female origin as determined by sequencing, were purchased from ATCC. IBL-1 (RRID:CVCL_9638) was derived from a male AIDS-related immunoblastic lymphoma patient93. Human mammary epithelial cells HMEC (PCS-600-010) isolated from adult female breast tissue were purchased from Lonza. B-cell lymphoma cell line OCI-LY1 (RRID:CVCL_1879), of male origin as determined by sequencing, was obtained from the Ontario Cancer Institute. E14 mouse ES cells94 were received as frozen ampules from TG Fazzio (U Mass Med School). Cells were feed-free and verified as of male mouse origin through sequencing. ZHBTc4 mouse ES cells derived from a male mouse31 were received from D. Levasseur (U of Iowa). Cells were cultured as ESCs without feeder cells in the absence of doxycycline. hiPSC were a gift from the Studer lab (MSKCC) and were derived from fibroblasts from a healthy male donor purchased from Coriell (#AG16146) and reprogrammed using CytoTune Sendai viruses34.\n\nMouse feeder-free ESCs (E14 or ZHBTc4 line) were grown on tissue culture plates coated with 0.2% gelatin. ESCs were cultured in Dulbecco\u2019s Modified Eagle Medium (DMEM; Gibco 10829018) media supplemented with 10% fetal bovine serum (FBS, HyClone SH30070.03HI), 2\u2009mM l-glutamine, 0.1\u2009mM nonessential amino acids (Gibco 11140050), 100\u2009U\u2009mL\u22121 penicillin/streptomycin (Gibco 15140122), 0.1\u2009mM beta-mercaptoethanol (Sigma M6250), and 103\u2009U\u2009mL\u22121 LIF. Cells are grown in 37\u2009\u00b0C/5% CO2 incubator with media change every 2 days, passaged or harvested when 60\u201380% confluent. After harvesting, cell pellets are washed with phosphate-buffered saline (PBS, GenClone 25-508) and flash frozen before storing in \u221280\u2009\u00b0C. For pull-down and chemical cross-linking experiments, frozen cells are thawed and lysed in Felts lysis buffer (20\u2009mM HEPES pH 7.4, 50\u2009mM KCl, 5\u2009mM MgCl2, 0.01% NP-40) in the presence of protease inhibitors, phosphatase and deacetylase inhibitors.\n\nZHBTc4 cells were differentiated into trophoblasts through Oct4 repression. Cells were seeded at a density of 2\u2009\u00d7\u2009105 cells mL\u22121 and grown in media with added doxycycline at a final concentration of 200\u2009ng\u2009mL\u22121 for 96\u2009h before harvest. E14 cells were spontaneously differentiated using attached EB culture. Briefly, cells were seeded at a density of 5\u2009\u00d7\u2009104\u2009cells/mL in sterile bacteriological petri dishes in differentiation media (ES media without LIF) and cultured in 37\u2009\u00b0C/5% CO2 incubator for 4 days to aggregate into EBs. When turned orange, media were changed. On day 4, EBs were transferred into tissue culture dishes (without gelatin) at a density of 100\u2013200 EBs per 10\u2009cm tissue culture dish. Attached EBs were cultured in differentiation media in 37\u2009\u00b0C/5% CO2 incubator for 14\u201318 days before harvest. hiPSC differentiated in midbrain dopaminergic neurons were a gift from Dr. Lorenz Studer. Cells were differentiated into midbrain dopamine (mDA) neurons by a modified dual-SMAD inhibition protocol as described20. Briefly, hiPSCs were dissociated into single cells using Accutase and plated at high density on Matrigel (BD). The cells were subjected to timed exposure to LDN193189 (100\u2009nM, Stemgent), SB431542 (10\u2009\u03bcM, Tocris), SHH C25II (100\u2009ng\u2009mL\u22121, R&D), Purmorphamine (2\u2009\u03bcM, Stemgent), FGF8 (100\u2009ng\u2009mL\u22121, R&D), and CHIR99021 (CHIR; 3\u2009\u03bcM, Stemgent) to induce midbrain floor plate precursors. For mDA neuron induction, floor plate precursors were maintained in mDA differentiation media containing Neurobasal/B27/L-Glut (NB/B27; Invitrogen) supplemented with CHIR (until day 13) and with brain-derived neurotrophic factor (BDNF, 20n\u2009mL\u22121; R&D), ascorbic acid (0.2\u2009mM, Sigma), glial cell line-derived neurotrophic factor (GDNF, 20\u2009ng\u2009mL\u22121; R&D), transforming growth factor type \u03b23 (TGF\u03b23, 1\u2009ng\u2009mL\u22121; R&D), dibutyryl cAMP (0.5\u2009mM; Sigma), and DAPT (10\u2009\u03bcM; Tocris). On day 20, cells were dissociated using Accutase and replated on dishes pre-coated with polyornithine (PO; 15\u2009\u03bcg\u2009mL\u22121)/laminin (1\u2009\u03bcg\u2009mL\u22121)/fibronectin (2\u2009\u03bcg\u2009mL\u22121) in differentiation medium (NB/B27\u2009+\u2009BDNF, ascorbic acid, GDNF, dbcAMP, TGF\u03b23, and DAPT). On day 30 of differentiation, cells were dissociated using Accutase and replated on dishes pre-coated with polyornithine (PO; 15\u2009\u00b5g\u2009mL\u22121)/laminin (1\u2009\u00b5g\u2009mL\u22121)/fibronectin (2\u2009\u00b5g\u2009mL\u22121) in differentiation medium (NB/B27\u2009+\u2009BDNF, ascorbic acid, GDNF, dbcAMP, TGF\u03b23, and DAPT) supplemented with 10\u2009\u00b5M Y-27632 (until day 32). Two days after plating, cells were treated with 1\u2009\u00b5g\u2009mL\u22121 mitomycin C (Tocris) for 1\u2009h to kill any remaining proliferative contaminants. The mDA neurons were fed every 2\u20133 days and maintained without passaging until they were assayed at day 65. To prevent neurons from lifting off, laminin and fibronectin were supplemented into the media every 7\u201310 days.\n\nMonolayer cultures of MDA-MB-468 and HEK293 cells were grown in high glucose (4.5\u2009g\u2009L\u22121) DMEM containing 10% FBS and 1\u00d7 antibiotic and antimycotic (100\u00d7 antibiotic-antimycotic (ABAM), GIBCO) in a 37\u2009\u00b0C incubator supplied with 5% oxygen\u2013air atmosphere. For native electrophoresis, and in-gel fluorescence studies, 1\u2009\u00d7\u2009107 cells were seeded in 100\u2009mm dishes (Corning) at 70% confluency in DMEM supplemented with 10% FBS and 1\u00d7 ABAM. Next day, spent medium was changed with fresh serum and antibiotic-free DMEM for 1\u2009h before performing transfections. Cells were transfected using Lipofectamine 3000 (Invitrogen) with 4\u2009\u00b5g of mCherry empty vector, mCherry-HSP90\u03b2-Wild type (mCherry-HSP90\u03b2-WT), mCherry-HSP90\u03b2-S226A, S255A mutant (mCherry-HSP90\u03b2-AA) or mCherry-HSP90\u03b2-S226E, S255E mutant (mCherry-HSP90\u03b2-EE) plasmids. See Supplementary Tables\u00a01\u20133 for plasmid sequences. Transfection mixtures were prepared in Opti-MEM (Gibco). Post 6\u2009h of transfection, medium was changed with 10% FBS and 1\u00d7 ABAM supplemented DMEM. Cells were harvested in native lysis buffer for future analyses.\n\nFrozen tumor and matched tumor-adjacent tissues were cut into small pieces using surgical blades and weighed using a precision balance. Seventy-four milligrams of tissue was homogenized in 200\u2009\u00b5L of 1\u00d7 native lysis buffer in 1.5\u2009mL microtube homogenizer for each sample. Homogenization was performed on dry ice. Post homogenization samples were incubated on ice for 30\u2009min followed by centrifugation at 12,000\u2009\u00d7\u2009g at 4\u2009\u00b0C for 15\u2009min. Supernatant was collected, and protein quantification was done using BCA method. Samples were normalized using total HSP90\u03b2 levels for each tissue pairs. An initial SDS\u2013PAGE was run using 5\u2009\u00b5g of total protein for each sample. Total protein loads were adjusted to ensure equal levels of total HSP90\u03b2 in tumor and corresponding matched adjacent tissue. Samples were then processed for native PAGE and SDS\u2013PAGE to check for HSP90\u03b2 and p-Ser226 HSP90\u03b2 as described below. A specific analysis of sex or gender was not conducted as part of this study because the primary focus was on the molecular mechanisms of epichaperome formation in disease states, irrespective of sex or gender. The sample set used did not include sufficient representation of different sexes to allow for meaningful sex-based comparisons. For breast cancer, the majority of samples were from female patients, while for the pancreatic cancer samples, sex data were unavailable.\n\nNative gel electrophoresis was performed as reported95. Namely, 1\u2009\u00d7\u2009107 cells were lysed in 20\u2009mM Tris pH 7.4, 20\u2009mM KCl, 5\u2009mM MgCl2, 0.01% NP-40, and 10% glycerol buffer containing protease and phosphatase inhibitors (native lysis buffer), by a freeze\u2013thaw procedure. Protein concentrations were measured by using the BCA assay according to the manufacturer\u2019s protocol (Pierce\u2122 BCA Protein Assay Kit, Thermo Fisher Scientific, Waltham, MA). One hundred micrograms (100\u2009\u00b5g) of protein were loaded in 4\u201310% native gel and run using native 1\u00d7 Tris\u2013Glycine buffer (25\u2009mM Tris, 192\u2009mM glycine) at 4\u2009\u00b0C in a cold room at 125\u2009V. Following electrophoresis, proteins were transferred to PVDF membrane, by wet transfer (25\u2009mM Tris, 192\u2009mM glycine, 20% (v/v) methanol, 0.02% SDS) at 100\u2009V in the cold room. Membranes were then blocked for 1\u2009h in 5% BSA in TBS/0.1% Tween 20. The blots were then probed with the following antibodies: HSP90\u03b2 (SMC-107; RRID:AB_854214; 1:2000) and HSP110 (SPC-195; RRID:AB_2119373; 1:1000) from Stressmarq; HSC70 (SPA-815; RRID:AB_10617277; 1:1000), and HOP (SRA-1500; RRID:AB_10618972; 1:1000) from Enzo; HSP90\u03b1 (ab2928; RRID:AB_303423; 1:6000), AHA1 (ab56721, RRID:AB_2273725, 1:1000) from Abcam; CDC37 (4793; RRID:AB_10695539; 1:1000), HOP (5670; RRID:AB_10828378; 1:1000), from Cell Signaling Technologies. The blots were washed with TBS/0.1% Tween 20 and incubated with appropriate HRP-conjugated secondary antibodies: goat anti-mouse (1030-05, RRID: AB_2619742, 1:5000), goat anti-rabbit (4010-05, RRID: AB_2632593, 1:5000), and goat anti-rat (3030-05, RRID: AB_2716837, 1:5000) (Southern Biotech, Birmingham, AL, USA). The chemiluminescent signal was detected with enhanced chemiluminescence (ECL) reagent according to manufacturer\u2019s instructions and visualized using ChemiDoc (Bio-Rad) and analyzed using Image Studio Lite Version 5.2. (LI\u2010COR Biosciences). NativeMark unstained protein standard (Invitrogen, LC0725) was used to estimate the molecular weight of protein complexes in native gel electrophoresis and western blotting.\n\nProteins were extracted in 20\u2009mM Tris pH 7.4, 20\u2009mM KCl, 5\u2009mM MgCl2, 0.01% NP-40, and 10% glycerol buffer containing protease and phosphatase inhibitors (native lysis buffer), by a freeze\u2013thaw procedure. Protein concentrations were measured by using the BCA assay according to the manufacturer\u2019s protocol (Pierce\u2122 BCA Protein Assay Kit, Thermo Fisher Scientific, Waltham, MA). Ten to thirty micrograms (10\u201330\u2009\u00b5g) of total protein were subjected to SDS\u2013PAGE, transferred onto PVDF membrane, by wet transfer (Towbin buffer: 25\u2009mM Tris, 192\u2009mM glycine, 20% (v/v) methanol) at 100\u2009V in cold room. Membranes were then blocked for 1\u2009h in 5% BSA in TBS/0.1% Tween 20 and incubated overnight with the indicated antibodies. HSP90\u03b2 (SMC-107; RRID:AB_854214; 1:2000) and HSP110 (SPC-195; RRID:AB_2119373; 1:1000) from Stressmarq; HSC70 (SPA-815; RRID:AB_10617277; 1:1000), HSP70 (ADI-SPA-810, RRID:AB_10616513, 1:2000) and HOP (SRA-1500; RRID:AB_10618972; 1:1000) from Enzo; HSP90\u03b1 (ab2928; RRID:AB_303423; 1:6000), AHA1 (ab56721, RRID:AB_2273725, 1:1000) and anti-HA tag (ab9110, RRID:AB_307019; 1:1000) from Abcam; p-MEK1/2 (S217/221) (9154; RRID:AB_2138017; 1:1000), MEK1/2 (9122; RRID:AB_823567; 1:1000), p-mTOR (S2448) (5536; RRID:AB_10691552; 1:500), mTOR (2983; RRID:AB_2105622; 1:1000), CDC37 (4793; RRID:AB_10695539; 1:1000), HOP (5670; RRID:AB_10828378; 1:1000), p-S6 ribosomal protein (Ser235/236) (4858; RRID:AB_916156; 1:2000), S6 ribosomal protein (2217; RRID:AB_331355; 1:3000), Oct4 (2840, RRID:AB_2167691, 1:2000), p-AKT (S473) (9271, RRID:AB_329825, 1:2000), AKT (4691, RRID:AB_915783, 1:3000), CK2\u03b1 (2656, RRID: AB_2236816, 1:2000) from Cell Signaling Technologies, \u03b2-actin (A1978, RRID: AB_476692, 1:3000) from Sigma-Aldrich, and mCherry (PA5-34974, RRID:AB_2552323, 1:2000) and p-Ser226 HSP90\u03b2 (PA5-105480, RRID:AB_2816908, 1:1000) from Fisher Scientific. The blots were washed with TBS/0.1% Tween 20 and incubated with appropriate HRP-conjugated secondary antibodies: goat anti-mouse (1030-05, RRID: AB_2619742, 1;5000), goat anti-rabbit (4010-05, RRID: AB_2632593, 1:5000) and goat anti-rat (3030-05, RRID: AB_2716837, 1:5000) (Southern Biotech, Birmingham, AL, USA). The chemiluminescent signal was detected with ECL reagent according to manufacturer\u2019s instructions and visualized using ChemiDoc MP imaging system (Bio-Rad) and analyzed using Image Studio Lite Version 5.2. (LI\u2010COR Biosciences). Thermo Scientific PageRuler Plus prestained protein ladder (Fisher Scientific, 26619) or Precision Plus protein standards (Bio-Rad, 161-0375) were used as size standards in protein electrophoresis and western blotting.\n\nWhere indicated, gels after native PAGE or SDS\u2013PAGE were washed with deionized water three times for 5\u2009min and incubated with Coomassie G-250 stain (Bio-Rad) for 1\u2009h. The gels were washed with water after to remove the excess of the dye and imaged. Where indicated, membranes after protein transfer were incubated with Ponceau S solution (Sigma) for 10\u2009min, then were washed with water to remove the excess of the dye and imaged.\n\nSpecimens were harvested as previously reported96. Briefly, the surgical team delivered specimens in tightly sealed, sterile, leak-proof bags without fixatives. This maintained specimens in their fresh state, crucial for downstream analyses. Fresh specimens underwent sterile harvesting by the pathologist or assistant, using laminar flow hoods. Harvesting times were meticulously recorded, kept under 30\u2009min post-surgery to mitigate cold ischemia effects. Primary breast tumor specimens were selectively obtained from the index lesion\u2019s periphery, avoiding central necrosis. Recognition criteria for necrotic tissue included color loss, softness, and demarcation from viable tissue. Normal breast tissue samples (e.g., normal dense/fibrous breast parenchyma) are taken from distant locations, at least 1\u2009cm grossly away from the target lesion if feasible. In contrast, due to the relatively small size of the pancreas and the nature of surgical procedures, normal pancreas samples collected were typically in close proximity to the tumor. Whipple procedures typically involve the resection of the head of the pancreas, while distal procedures focus on the resection of the tail. Samples were initially stored in tubes with MEM and antibiotics and transported on wet ice to the laboratory immediately after procurement. Upon reaching the laboratory, samples were transferred to cryovials, snap frozen, and stored at \u221280\u2009\u00b0C for future molecular analyses.\n\nFor in-gel blotting using PUTCO, cells were harvested in 20\u2009mM Tris pH 7.4, 20\u2009mM KCl, 5\u2009mM MgCl2, 0.01% NP-40, and 10% glycerol buffer containing protease and phosphatase inhibitors (native lysis buffer), by a freeze\u2013thaw procedure. Protein concentrations were measured by using the BCA assay according to the manufacturer\u2019s protocol (Pierce\u2122 BCA Protein Assay Kit, Thermo Fisher Scientific, Waltham, MA). One hundred micrograms (100\u2009\u00b5g) of protein were incubated with 1\u2009\u00b5M of PUTCO in a total volume of 42\u2009\u00b5L. Post 3\u2009h of incubation samples were loaded in 4\u201310% native gel and run using native 1\u00d7 Tris\u2013Glycine buffer at 4\u2009\u00b0C in cold room at 125\u2009V. Following electrophoresis, the gel was incubated in 30\u2009mL of 700\u2009nM Cy5-Tetrazine containing ice cold 1\u00d7 Tris\u2013Glycine buffer at room temperature (RT) for 15\u2009min for the click reaction to occur. After 15\u2009min, the gel was washed thrice (5\u2009min each) with ice cold 1\u00d7 Tris\u2013Glycine buffer. The gel was then imaged using ChemiDoc MP imaging system (Bio-Rad). Alexa 546 channel (illumination: Epi-green, 520\u2013545\u2009nm excitation, Filter: 577\u2013613\u2009nm filter for green-excitable fluorophores and stains) was used to visualize mCherry-tagged species, and native page ladder (NativeMark\u2122 Unstained Protein Standard, Cat. No. LC0725, Invitrogen\u2122). The Cy5 channel (illumination: Epi-far red, 650\u2013675\u2009nm excitation, Filter: 700\u2013730\u2009nm filter for far red-excitable fluorophores and stains) was used for imaging PUTCO staining. Post capturing, the images from the two channels were merged to get the alignment of the bands with respect to the molecular weight ladder in Image Lab 6.1 (Bio-Rad). For in cell blotting using PU-CW800, E14 cells were plated at a seeding density of 1\u2009\u00d7\u2009106/10\u2009cm plate and grown for 44\u2009h before treatment with either PU-CW800 or control fluorophore (C-CW800) at a concentration of 1\u2009\u03bcM in culture media for 4\u2009h while incubating at 37\u2009\u00b0C, 5% CO2. Following the treatment, cells were harvested and lysed by dounce homogenization in Felts lysis buffer (20\u2009mM HEPES at pH 7.4, 50\u2009mM KCl, 2\u2009mM EDTA, and 0.01% NP-40) supplemented with protease, phosphatase, and deacetylase inhibitors. Cell lysates were buffer exchanged with fresh Felts lysis buffer containing supplements to remove any unbound drug before loading into a native gel. For visualization of PU-CW800 fluorescence and total protein, 200\u2009\u03bcg of cell lysate was loaded onto a 4\u201310% native gradient gel and resolved at 4\u2009\u00b0C for 5\u2009h. Fluorescence was visualized on LI-COR Odyssey CLx using Image Studio\u2122 Software (LI-COR Biosciences, v5.2) and then total protein was visualized on the same gel using Coomassie Brilliant Blue R250 stain. Band(s) with observable fluorescent signal were then processed by in-gel digestion and analyzed for LC\u2013MS/MS to identify major proteins.\n\nFor metabolic labeling with SILAC (stable-isotope labeling of amino acid in cell culture), ESCs were cultured and passaged five times at 48\u2009h intervals in media containing SILAC DMEM (Thermo Fisher 88364) supplemented with 13C- and 15N-labeled heavy l-arginine (84\u2009mg\u2009L\u22121, Cambridge isotope CNLM-539-H) and l-lysine (146\u2009mg\u2009L\u22121, Cambridge isotope CNLM-291-H) or supplemented with 12C- and 14N-labeled light l-arginine (Fisher BP2505100) and l-lysine (Fisher J6222522) amino acids for five passages to ensure complete stable-isotope incorporation. For heterologous expression of HSP90 AA or EE mutants, cells were then reverse transfected with plasmid DNA using Lipofectamine\u2122 3000 Transfection Kit (Invitrogen #L3000015) and incubated at 37\u2009\u00b0C, 5% CO2 for 72\u2009h at which point they were harvested.\n\nE14 cells were transfected and incubated in 37\u2009\u00b0C/5% CO2 incubator for 24\u2009h. Cells were then replated to 6-well plate at the same dilution factor for each transfection treatment condition and then returned to incubator. At 60\u2009h post-transfection, cell proliferation was determined via cell count for all conditions.\n\nMDA-MB-468 cells (1\u2009\u00d7\u2009106) were treated with either 10, 25, or 50\u2009\u00b5M of Silmitasertib (Cat No. HY-50855, MedChemExpress) or CIBG-300 (SML3143, Sigma-Aldrich), with DMSO serving as the control. After 24\u2009h of incubation, cells were harvested and lysed in native lysis buffer supplemented with protease inhibitors (PI) and phosphatase inhibitor cocktail (PIC). The lysates were then processed for native and SDS\u2013PAGE.\n\nMDA-MB-468 cells were seeded in 6\u2009cm dishes at a density of 1\u2009\u00d7\u2009106 cells. The following day, the spent medium was replaced with fresh, serum-free, and antibiotic-free DMEM for 1\u2009h before transfection. Cells were transfected using Lipofectamine 3000 (Invitrogen) with 50 or 100 picomoles of SignalSilence\u00ae CK2\u03b1 siRNA I (CST Cat No. 6389S) for 24\u2009h. After transfection, the medium was replaced with fresh DMEM supplemented with 10% FBS and 1\u00d7 ABAM. After 72\u2009h, cells were harvested and lysed in native lysis buffer containing PI and PIC, and samples were prepared for both native and SDS\u2013PAGE assays.\n\nThe following constructs were purchased from Addgene and purified using the QIAprep Spin Miniprep Kit (Cat No. 27104): pZW6, CK2\u03b1 (ID: 27086), pZW12, CK2\u03b2 (ID: 27088), pGV15, CK2\u03b1 K68M (kinase-dead mutant, ID: 27089). HEK293 cells were cultured in high glucose (4.5\u2009g\u2009L\u22121) DMEM supplemented with 10% FBS and 1\u00d7 ABAM (100XABAM, GIBCO) in a 37\u2009\u00b0C incubator with a 5% CO2 atmosphere. HEK293 cells were seeded in 100\u2009mm dishes (Corning) at 70% confluency in DMEM supplemented with 10% FBS and 1\u00d7 ABAM. The following day, the medium was replaced with fresh, serum-free, and antibiotic-free DMEM for 1\u2009h before transfection. Cells were transfected using Lipofectamine 3000 (Invitrogen) with 5\u2009\u00b5g CK2\u03b1, co-transfected with either 5\u2009\u00b5g of CK2\u03b1\u2009+\u20095\u2009\u00b5g of CK2\u03b2, or 5\u2009\u00b5g CK2\u03b1 K68M\u2009+\u20095\u2009\u00b5g CK2\u03b2 plasmids, following the manufacturer\u2019s protocol. Transfection mixtures were prepared in Opti-MEM (Gibco). After 6\u2009h of transfection, the medium was replaced with DMEM supplemented with 10% FBS and 1\u00d7 ABAM. Cells were harvested in native lysis buffer for downstream experiments.\n\nRecombinant firefly luciferase (QuantiLum\u00ae Recombinant Luciferase, Promega, Cat No. E1701) was heat-denatured at 42\u2009\u00b0C for 15\u2009min in refolding buffer (25\u2009mM HEPES/KOH pH 7.6, 100\u2009mM KOAc, 10\u2009mM Mg(OAc)2, 2\u2009mM ATP, 5\u2009mM DTT). Refolding reaction mixtures were prepared by reconstituting 10\u2009\u00b5M of heat-denatured luciferase with 50\u2009\u00b5g of native extracts from MDA-MB-468, HEK293-WT, HEK293-AA, HEK293-EE, or CCD-18Co cells in refolding buffer, and then incubated at 30\u2009\u00b0C. At the indicated time points, 1\u2009\u00b5L of the sample was taken from each tube and added to 124\u2009\u00b5L of assay buffer (100\u2009mM K-phosphate buffer pH 7.6, 25\u2009mM glycylglycine, 100\u2009mM KOAc, 15\u2009mM Mg(OAc)2, 5\u2009mM ATP), then mixed with 125\u2009\u00b5L of 80\u2009\u00b5M d-luciferin (Sigma-Aldrich, L6882). The final luciferase concentration for detection was 40\u2009nM. Luminescence was measured for 10\u2009s using a Perkin Elmer EnVision 2104 Multilabel Reader.\n\nHEK293 cells transfected with mCherry-HSP90\u03b2-AA or mCherry-HSP90\u03b2-EE plasmids were seeded at a density of 1.8\u2009\u00d7\u2009106 cells mL\u22121 on coverslips in a monolayer in 6-well plates and then grown overnight for the cells to attach. Coverslips were mounted with ProLong\u2122 Gold antifade mountant with DAPI. Imaging was done using Leica SP8 Stellaris microscope. Images were analyzed using Image J (version 1.54f) and Leica LAS X lite (version 2.6.0) software. Cell morphology was manually inspected, and the percentage of cells exhibiting an elongated phenotype and several protrusions was calculated. Specifically, cells transfected with mCherry were assessed, and those displaying the described features were counted. The percentage was then determined based on the total number of mCherry-transfected cells observed.\n\nThe GA-affinity beads were prepared by incubating GA-biotin (Sigma SML0985) with Dynabeads M-280 Streptavidin (Thermo Fisher 11205D) at 4\u2009\u00b0C for 2.5\u2009h. The GA-bound beads were then incubated with cleared cell lysates or cross-linked cell lysates overnight at 4\u2009\u00b0C. For PU-beads affinity capture, cell lysates were incubated with PU-beads or control beads at 4\u2009\u00b0C for 3.5\u2009h. Following incubation, bead conjugates were washed three times in lysis buffer before elution with sample buffer. The chemical cross-linking and HSP90 purification experiments were carried out in >3 replicates for both ligands. Samples were analyzed separately, and statistical significance was assessed.\n\nCells were harvested in 20\u2009mM Tris pH 7.4, 20\u2009mM KCl, 5\u2009mM MgCl2, 0.01% NP-40, and 10% glycerol buffer containing protease and phosphatase inhibitors (native lysis buffer), by a freeze\u2013thaw procedure. Protein concentrations were measured by using the BCA assay according to the manufacturer\u2019s protocol (Pierce\u2122 BCA Protein Assay Kit, Thermo Fisher Scientific, Waltham, MA). PU-beads and control beads were washed with the native gel buffer three times prior use. Post washing, 40\u2009\u00b5L aliquots of the beads were distributed into the sample tubes. Five hundred micrograms (500\u2009\u00b5g) of total protein in 300\u2009\u00b5L final volume, adjusted with native lysis buffer were added. Samples were incubated for 3\u2009h at 4\u2009\u00b0C on a rotor, followed by washing with native lysis buffer four times. Post washing, 30\u2009\u00b5L of 5\u00d7 Laemmli buffer was added to the beads and boiled at 95\u2009\u00b0C for 5\u2009min. Ten micrograms (10\u2009\u00b5g) of the lysates (2%) was used as input for the pull-down experiment. Samples were then centrifuged at 13,000\u2009\u00d7\u2009g for 20\u2009min and supernatant collected was loaded on to SDS\u2013PAGE. The protein transfer and western blotting procedures were performed as described in SDS\u2013PAGE and western blot section.\n\nThe primary amino acid sequence of human HSP90\u03b2 (P08238) and HSP90\u03b1 (P07900) were extracted in FASTA format. These sequences served as the input for subsequent disorder prediction using the IUPred algorithm97.\n\nThe IUPred algorithm utilizes energy potentials derived from pairwise amino acid interactions to assess the local structural propensities of each residue in the protein sequence. For each residue, IUPred computes a disorder score within the range of 0\u20131. A score of 0 suggests a higher likelihood of being ordered, while a score of 1 indicates a higher likelihood of being disordered.\n\nTo classify residues as either ordered or disordered, a threshold was applied to the calculated disorder scores. A common threshold of 0.5 was employed, designating residues with scores above 0.5 as disordered. The output of the IUPred analysis consisted of a disorder profile, providing disorder scores for each residue in the input protein sequence. Residues were categorized based on the applied threshold, facilitating the identification of regions with a high probability of disorder. All analyses were performed with the default parameters of the IUPred algorithm. The results presented here are based on the specific sequence input and the applied threshold for disorder classification.\n\nThe structure comprising HSP90\u03b2\u2013HSP70(2)\u2013HOP proteins was developed using the molecular comparative modeling technique, employing Modeller v10.4, the Modeller Python script98, and experimental template structures (PDB codes: 7KW7, 8EOB)10,99. The cryo-EM structure of human HSP90\u03b2 (8EOB) served as the basis for obtaining coordinates for HSP90\u03b2 (protomers A and B) in the developing model. To construct the assembly involving HSP70 and HOP, we utilized the sequences and atomic cryo-EM structure from the HSP90\u2013HSP70\u2013HOP\u2013GR (7KW7) template. As these structures lacked certain residues, including those in the charged linker (Glu222\u2013Lys273), we incorporated them as intrinsic loops during computational processing. The target sequence for each HSP90\u03b2 protomer was extracted from UniProt ID: P08238. After model generation, we selected the optimal model based on the Discrete Optimized Protein Energy (DOPE) score. The final model included full-length HSP90 (excluding a ten-residue N-terminal disordered segment). For HOP and HSP70, we maintained the sequences provided in PDB:7KW7. The validated model, equipped with co-crystal ligands on each HSP90\u03b2 protomer, was imported into Maestro v13.3 (Schr\u00f6dinger LLC, 2022-3). Mutagenesis was performed to substitute Ser226/Ser255 with phosphomimetic conditions (Glu226/Glu255) and de-phosphorylated conditions (Ala226/Ala255) in both protomers of HSP90\u03b2. The preparation of all complexes utilized the Protein Preparation Wizard, a module for creating reliable, all-atom protein models. This involved restraining the assignment of bonds and bond orders, adding hydrogens, correcting formal charges, and filling missing side chains. Pre-processing steps included generating hetero states, H-bond assignment, and energy minimization using the optimized potentials for liquid simulations (OPLS3) force field, with a maximum root-mean-square deviation of 0.30\u2009\u00c5, employing the molecular mechanics engine Impact v9.6. Essential water atoms within 5\u2009\u00c5 of the binding pocket were retained, while remaining waters were deleted. Structural refinement at neutral pH was carried out through the Epik v6.1 module100. The final refined structure served as the receptor for docking simulations. Ligands, such as ATP and ADP, underwent preparation with the LigPrep node, where the optimized ligand minimization algorithm yielded more conformers with numerous rotatable bonds, enhanced efficiency, and robustness. Different possible protonation states based on machine learning were generated, and ligand structures were minimized at pH values within the range of 7.0 and \u00b12.0, to guide the selection of protonation states on acidic/basic groups on ligands consistent with their pKa values, using the OPLS_3 force field, Premin, Truncated Newton Conjugate Gradient (TNCG), and Epik v6.1 nodes. Subsequently, a receptor grid was generated around the co-crystal ligand with default parameters. Docking experiments were executed on the nucleotide-binding pockets of both protomers using the XP (extra-precision) Glide program (Glide v9.6) and Prime-MMGBSA (molecular mechanics generalized born surface area) modules, respectively. The best poses in the resulting docked complexes served as the initial complex structure for MD simulations101.\n\nThe pentameric assemblies were prepared in the following combinations: 2xHSP90(Ser226Ser255)-2xHSP70-HOP, 2xHSP90(Glu226Glu255)-2xHSP70-HOP-, 2xHSP90(Ala226Ala255)-2xHSP70-HOP, each bound to either ATP or ADP. For molecular dynamics simulations, the wild-type HSP90\u03b2 (Ser226Ser255) assembly bound to ATP was simulated for 100 nanoseconds (ns) with three independent replicas, resulting in a total simulation time of 300\u2009ns. Similarly, the phosphomimetic mutant HSP90\u03b2 (Glu226Glu255) assembly bound to ATP was also simulated for 100\u2009ns with three independent replicas, providing a total simulation time of 300\u2009ns. The non-phosphorylatable mutant HSP90\u03b2 (Ala226Ala255) assembly bound to ATP underwent the same simulation duration of 100\u2009ns with three replicas, contributing to another 300\u2009ns of total simulation time. For the simulations involving ADP-bound assemblies, the wild-type HSP90\u03b2 (Ser226Ser255) assembly was simulated for 100\u2009ns with three independent replicas, yielding a total simulation time of 300\u2009ns. The phosphomimetic mutant HSP90\u03b2 (Glu226Glu255) assembly bound to ADP was likewise simulated for 100\u2009ns with three replicas, again leading to 300\u2009ns of total simulation time. Lastly, the non-phosphorylatable mutant HSP90\u03b2 (Ala226Ala255) assembly bound to ADP was simulated under the same conditions, resulting in another 300\u2009ns of simulation time. In total, each assembly condition was run for 100\u2009ns across three independent replicas, with a total simulation time of 300\u2009ns for each condition (Supplementary Tables\u00a04, 5). These complexes underwent molecular dynamics simulations using the Desmond v7.1 module of the MAESTRO Suite from Schrodinger (www.schrodinger.com). Before simulations, each assembly was built by embedding water molecules, adjusting temperature and pressure closer to the physiological environment through the OPLS3 force field and TIP4PEW water model. The system was neutralized with counter ions (Na+/ Cl\u2212) to balance the net charge in the simulation box. The particle mesh Ewald method102 was used for electrostatics with a 10\u2009\u00c5 cut-off for Lennard-Jones interactions, and the SHAKE algorithm103 was applied to restrict the motion of all covalent bonds involving hydrogen atoms. The complex system underwent a six-step relaxation protocol before productive MD simulations. The solvated system was initially minimized with solute restraints and then without solute restraints, utilizing a hybrid method of steepest descent and the LBFGS (limited memory Broyden\u2013Fletcher\u2013Goldfarb\u2013Shanno) algorithm104,105. The energy-minimized system underwent a brief 12\u2009ps simulation within the NVT canonical ensemble at a temperature of 10\u2009K, followed by a similar simulation in the isothermal-isobaric (NPT) ensemble at 10\u2009K, with restraints on nonhydrogen solute atoms. Subsequently, the system was simulated for 24\u2009ps in the NPT ensemble at 300\u2009K with limited restraints on nonhydrogen solute atoms. In the final equilibration step, the system was simulated for 24\u2009ps in the NPT ensemble at 300\u2009K without constraints to reach an equilibrium state. The minimized and equilibrated system without restraints was then subjected to a 100\u2009ns NPT simulation for production. The temperatures and pressures of the system in the initial simulations were controlled by Berendsen thermostats and barostats, respectively104,105. The relaxed system underwent productive simulations using the Nose\u2019\u2013Hoover thermostat at 300\u2009K and the Martyna\u2013Tobias\u2013Klein barostat at 1.01325\u2009bar pressure. Atomic-coordinate data for each receptor\u2013ligand complex and system energies were recorded every 1000\u2009ps. Residue-pair correlations were calculated along the MD trajectory using the script trj_essential_dynamics.py available in the Schr\u00f6dinger suite. Additionally, the unexplored cryptic motions, distribution of secondary structural elements, and the array of protein folding in intrinsic disordered regions were thoroughly examined using the extracted meta-trajectory data from 1000 trajectories throughout the simulation period. The SSE index was computed to illustrate the percentage occurrence of alpha-helices (\u03b1) and beta-strands (\u03b2) during the simulation period, delineated by residue. The RMSF calculations were performed for the C\u03b1 atoms of each residue in the pentameric assemblies (HSP90(A)\u2013HSP90(B)\u2013HSP70(2)\u2013HOP) in both ATP and ADP-bound states. These assemblies included WT, AA-mutant, and EE-mutant HSP90. The analysis was conducted using 1000 trajectory simulations loaded into the Simulation Interactions Diagram (SID) program within the Schr\u00f6dinger software suite. Similarly, PCA was performed on the full assemblies, assessing three PC modes for each combination of WT, AA-mutant, and EE-mutant bound with ATP or ADP. The PCA was executed using a Python script available from the Schr\u00f6dinger site. PCs that reflect dynamic (slow) global motions, derived from 1000 simulation frames, were utilized to generate PCA plots. Potential and total energies were calculated using Desmond v7.7 in the Schr\u00f6dinger software suite to assess the stability of the large assemblies with phosphorylated and non-phosphorylated mimic variants (mutant-EE, mutant-AA, and WT) in the presence of ATP and ADP.\n\nRFP Selector (NanoTag #N0410) resins were equilibrated with lysis buffer to prepare the resin. Cell lysates were then added and incubated with the resins at 4\u2009\u00b0C with head-over-tail rotation for 90\u2009min. Following incubation, resins were washed twice with lysis buffer and once with PBS before elution with 2\u2009\u00d7\u2009sample buffer and incubated at 95\u2009\u00b0C for 5\u2009min. Eluents were then run on a 12.5% SDS\u2013PAGE. For SILAC samples, heavy and light replicates (n\u2009=\u20093) were immunoprecipitated separately, then combined and separated by SDS gel electrophoresis.\n\nCell lysates, with a concentration of ~3\u2009\u03bcg\u2009\u03bcL\u22121, underwent cross-linking using DSS (Thermo Fisher# 21655) at a concentration of 2.5\u2009mM. This process occurred at RT for 1\u2009h. To terminate the reaction, 0.8\u2009M NH4OH (Sigma# 09859) was added, reaching a final concentration of 25\u2009mM, and incubated at RT for an additional 15\u2009min. The lysates were clarified through two rounds of centrifugation at 16,200\u2009\u00d7\u2009g for 15\u2009min at 4\u2009\u00b0C before proceeding to separate HSP90 using immobilized PU-H71 or GA.\n\nAfter elution from PU- or GA-beads, samples were loaded into 12.5% SDS\u2013PAGE gel for separation. The entire lanes were cut into 10\u201315 bands and processed by in-gel digestion as described previously19. Briefly, gel bands were cut into small cubes, washed with 25\u2009mM NH4HCO3/50% acetonitrile, reduced with 10\u2009mM DTT (in 25\u2009mM NH4HCO3) at 56\u2009\u00b0C for 1\u2009h, alkylated with 55\u2009mM iodoacetamide (in 25\u2009mM NH4HCO3) in darkness for 45\u2009min. Gel pieces were washed again with 25\u2009mM NH4HCO3/50% acetonitrile and evaporated in a speed-vac to complete dryness. The dried gel samples were proteolyzed using varied volumes of trypsin (0.6\u20131.0\u2009\u00b5g depending on the intensity of the gel bands) at 37\u2009\u00b0C for 4\u2009h, before the extraction of tryptic peptides by 50% acetonitrile/2% acetic acid. Tryptic peptide mixture was concentrated down to ~7\u2009\u00b5L before LC\u2013MS/MS analysis. This experiment was done twice with similar results. For validation experiments in Fig.\u00a03d, e, chemical precipitation and sample preparation for PTM analyses were performed as follows. For in-cell YK-B bait affinity purification, cells were plated in 10\u2009cm plates at 6\u2009\u00d7\u2009106 cells per plate and treated with 50\u2009\u00b5M YK5-B for 4\u2009h. Cells were next collected and lysed in 20\u2009mM Tris pH 7.4, 150\u2009mM NaCl, and 1% NP-40 buffer. Five hundred micrograms (500\u2009\u00b5g) of total protein were incubated with streptavidin agarose beads (Thermo Fisher Scientific) for 1\u2009h and beads were washed with 20\u2009mM Tris pH 7.4, 100\u2009mM NaCl, and 0.1% NP-40 buffer (washing buffer). For in-lysate YK5-B bait affinity purification, cells were lysed in the above-mentioned lysis buffer. Streptavidin agarose beads were incubated with 50\u2009\u00b5M YK5-biotin for 1\u2009h, washed and added to 500\u2009\u00b5g of total protein and incubated overnight. The beads were then washed with the washing buffer. For PU-H71 beads pull-down, 250\u2009\u00b5g of the same protein lysates were incubated with 40\u2009\u00b5L PU-H71 beads for 3\u2009h and washed. Three different cell lines were used: MDA-MB-468 and OCI-Ly1 (epichaperome-positive cancer cell lines) and CCD-18Co (non-transformed but proliferating cell lines). For each cell line, three conditions were used: PU-beads incubated with lysates, YK5-B beads incubated with lysates, or YK5-B applied directly to cells in culture, resulting in a total of nine samples (three for PU and six for YK across three cell lines, n\u2009=\u20099). The nine samples were applied onto SDS\u2013PAGE. Resulting gels were washed three times in distilled deionized H2O for 15\u2009min each and visualized by staining overnight with Simply Blue Coomassie stain (Thermo Fisher Scientific). Stained protein gel regions were typically excised into six gel sections per gel lane, and completely destained as described19. In-gel digestion was performed overnight with MS-grade trypsin (Trypsin Gold, Mass spectrometry grade, Promega) at 5\u2009ng\u2009mL\u22121 in 50\u2009mM NH4HCO3 digestion buffer and incubation at 37\u2009\u00b0C. After acidification with 10% formic acid (final concentration of 0.5\u20131% formic acid), peptides were extracted with 5% formic acid/50% acetonitrile and resulting peptides were desalted using hand-packed, reversed phase Empore C18 Extraction Disks (3\u2009M, Cat#3M2215), following an established method106. Each of the six sections per sample, per gel lane, was excised and separately digested in-gel, at the same time, using the same batch and amount of trypsin. The peptides from each of these gel sections were purified and analyzed by nano-LC\u2013MS/MS separately. For CCD-18Co samples, each experimental condition was run in technical replicates, resulting in six total MS batches. For MDA-MB-468 and OCI-Ly1 (epichaperome-positive cancer cell lines), each sample was run once, resulting in six MS batches. Each MS batch involved six gel sections, yielding a total of 72 LC\u2013MS/MS runs (36 for CCD-18Co and 36 for the epichaperome-positive cancer cells).\n\nBriefly, digestion mixtures were injected into a Dionex Ultimate 3000 RSLCnano UHPLC system (Thermo Fisher Scientific), and separated by a 75 \u03bcm\u2009\u00d7\u200925\u2009cm PepMap RSLC column (100\u2009\u00c5, 2\u2009\u00b5m) at a flow rate of ~450\u2009nL\u2009min\u22121. The eluant was connected directly to a nanoelectrospray ionization source of an LTQ Orbitrap XL mass spectrometer (Thermo Fisher Scientific). LC\u2013MS data were acquired in a data-dependent acquisition (DDA) mode, cycling between a MS scan (m/z 315\u20132000) acquired in the Orbitrap, followed by low-energy collision-induced dissociation (CID) analysis on three most intense multiply charged precursors acquired in the linear ion trap. The centroided peak lists of the CID spectra were generated using PAVA and searched against the Swiss-Prot protein database (version 2021.06.18; 17,089/565,254 entries searched for Mus Musculus), using Batch-Tag, a program of the University of California, San Francisco (UCSF) Protein Prospector software, version 6.5.2. For identification of proteins in pull-down experiments, a precursor mass tolerance of 15\u2009ppm and a fragment mass tolerance of 0.5\u2009Da were used for protein database searches (trypsin as enzyme; 1 miscleavage; carbamidomethyl [C] as constant modification; acetyl [protein N-term], acetyl+oxidation [protein N-term M], Gln->pyro-Glu [N-term Q], Met-loss [protein N-term], Met-loss\u2009+\u2009acetyl [protein N-term], oxidation [M] as variable modifications). Protein hits were reported with a Protein Prospector protein score\u2009\u2265\u200922, a protein discriminant score\u2009\u2265\u20090.0, and a peptide expectation value\u2009\u2264\u20090.01107. This set of thresholds of protein identification parameters does not return any substantial false positive protein hits from the randomized half of the concatenated database. We used a label-free, spectral counting-based quantitation strategy to estimate the abundance relationship between identified proteins108,109. Protein abundance index values were determined as a ratio of the number of detected peptides (Nobsd) and observable peptides (Nobsbl)108. The number of observable peptides were calculated using MS-Digest module of the UCSF Protein Prospector version 6.5.2 in the Mr range 700\u20132800, trypsin as enzyme, and 0 miscleavage allowed. After protein identification, PTM search was carried out with S/T/Y phosphorylation included in variable modifications among the identified proteins. A threshold of SLIP score >6 was imposed for false phosphorylation site assignment <5%110. Identified phosphopeptides were manually inspected by confirming the quality of MS/MS spectra and mass accuracy. Cross-linked peptides were identified using an integrated module in Protein Prospector, based on a bioinformation strategy developed in the UCSF Mass Spectrometry Facility42,43,111,112. Key cross-linked peptides were identified and confirmed by manually examining the returned spectrum, peptide scores (>20), FDR (<5%), mass accuracy (<10 ppm), and absence from uncross-linked samples. For validation experiments in Fig.\u00a03e, MS data acquisition and processing were performed as follows. Desalted peptides were concentrated to a very small droplet by vacuum centrifugation and reconstituted in 10\u2009mL 0.1% formic acid in H2O. Approximately 90% of the peptides were analyzed by nano-LC\u2013MS/MS. A Q Exactive HF mass spectrometer was coupled directly to an EASY-nLC 1000 (Thermo Fisher Scientific) equipped with a self-packed 75\u2009mm\u2009\u00d7\u200918\u2009cm reverse phase column (ReproSil-Pur C18, 3M, Dr. Maisch GmbH, Germany) for peptide separation. Analytical column temperature was maintained at 50\u2009\u00b0C by a column oven (Sonation GmBH, Germany). Peptides were eluted with a 3\u201340% acetonitrile gradient over 60\u2009min at a flow rate of 250\u2009nL\u2009min\u22121. The mass spectrometer was operated in DDA mode with survey scans acquired at a resolution of 120,000 (at m/z 200) over a scan range of 300\u20131750\u2009m/z. Up to 15 of the most abundant precursors from the survey scan were selected with an isolation window of 1.6 Th for fragmentation by higher-energy collisional dissociation with normalized collision energy of 27. The maximum injection time for the survey and MS/MS scans was 20 and 60\u2009ms, respectively; the ion target value (Automatic Gain Control) for survey and MS/MS scan modes was set to 3e6 and 1e6, respectively.\n\nManually confirmed, high-confidence phosphopeptides and cross-linked peptides were quantified by the peak height of the extracted ion chromatogram of each peptide monoisotope mass. For phosphopeptide quantitation, the protein loading of HSP90 peptides in lysates or from pull-down experiments was normalize to a representative, isoform-specific tryptic peptide, ELISNSSDALDK for HSP90\u03b1 and ELISNASDALDK for HSP90\u03b2. Phosphopeptides with different charge state or miscleavages were considered as different measurements for quantitation of each phosphosite. To assess the relative phosphorylation levels of different phosphosites in cancer cells and non-transformed cells, the ion intensity values of all phosphopeptides for each phosphosite were summed. The average ion intensities of each phosphosite between cancer and non-transformed cells were compared. Cross-linked peptides were identified using an integrated module in Protein Prospector, based on a bioinformation strategy developed in the UCSF Mass Spectrometry Facility42,43,111,112. Key cross-linked peptides were identified and confirmed by manually examining the returned spectrum, peptide scores, mass accuracy and absence from uncross-linked samples. Cross-linked peptides identified from various samples were pooled together, and the cross-linking propensity of each cross-linked peptide was assessed by its cross-linking percentage44. Cross-linking percentage for each peptide pair was calculated using the following formula:\n\nwhere the peak height is the apex peak height in LC\u2013MS/MS runs. Dead-end XLs are cross-linker modified peptides where only one NHS-ester function of DSS is cross-linked to a Lys residue and the other NHS-ester function is hydrolyzed by water.\n\nThe mouse HSP90 sequences for both alpha and beta isoforms were aligned and the models were built using an open conformation template (PDB: 2IOQ), a closed conformation template (PDB: 2CG9), and an HSP70-bound model (derived from a cryo-EM structure of HSP90\u2009\u00b7\u2009HSP70\u2009\u00b7\u2009GR complex10 using UCSF Modeller (version 10.4)). Structural visualization and analysis were carried out using UCSF Chimera (version 1.18).\n\nUnless as specified above under protein identification and bioinformatics analyses, statistics were performed, and graphs were generated, using Prism 10 software (GraphPad). Statistical significance was determined using Student\u2019s t-tests or ANOVA, as indicated. Means and standard errors were reported for all results unless otherwise specified. Effects achieving 95% confidence interval (i.e., p\u2009<\u20090.05) were interpreted as statistically significant. No statistical methods were used to pre-determine sample sizes, but these are similar to those generally employed in the field. No samples were excluded from any analysis unless explicitly stated.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The source data underlying all main and Supplementary Figs.\u2014raw data, statistical analyses and uncropped gels\u2014are provided with this paper as a Source Data file and were deposited in the Figshare repository under accession code 27075415113. Datasets and analytics associated with epichaperomics and proteomics analyses are available in the Supplementary Information as Supplementary Data\u00a01 through 7 and were deposited in the Figshare repository under accession code 26662333114. LC\u2013MS data (i.e., proteomics and epichaperomics raw mass spectrometry data, peak lists, and results) that support the findings of this study are deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD050251. Protein sequences (FASTA files) were obtained from UniProt (https://www.uniprot.org/). MD simulations data were deposited in Zenodo entry 10800912115.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Bludau, I. & Aebersold, R. Proteomic and interactomic insights into the molecular basis of cell functional diversity. Nat. Rev. Mol. 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S.S. would like to acknowledge funding support from BrightFocus Foundation (Award ID: A2022020F). We thank Dr. David A. Agard for providing the model of HSP90\u00b7HSP70\u00b7GR complex derived from a cryo-EM density map116, Dr.\u00a0Thomas G. Fazzio (U Mass Med School) for the E14 cells, Dr. Lorenz Studer for the human iPSCs and iPSC-derived neurons, and\u00a0Dr. Dana Levasseur (U Iowa) for the ZHBTc4 cells. We thank the Molecular Cytology Core, the Antitumor Assessment Core, and our colleagues in the Departments of Surgery and Medicine at Memorial Sloan Kettering for providing the biospecimens for research.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Adriana Corben\n\nPresent address: Maimonides Medical Center, Brooklyn, NY, USA\n\nMary L. Alpaugh\n\nPresent address: Rowan University, Glassboro, NJ, USA\n\nThese authors contributed equally: Tanaya Roychowdhury, Seth W. McNutt, Chiranjeevi Pasala.\n\nThese authors jointly supervised this work: Gabriela Chiosis, Feixia Chu.\n\nChemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA\n\nTanaya Roychowdhury,\u00a0Chiranjeevi Pasala,\u00a0Sahil Sharma,\u00a0Chander S. Digwal,\u00a0Suhasini Joshi,\u00a0Palak Panchal,\u00a0Souparna Chakrabarty,\u00a0Sadik Bay,\u00a0Sun Young Chung,\u00a0Pengrong Yan,\u00a0Mary L. Alpaugh,\u00a0Anna Rodina\u00a0&\u00a0Gabriela Chiosis\n\nDepartment of Molecular, Cellular & Biomedical Sciences, University of New Hampshire, Durham, NH, USA\n\nSeth W. McNutt,\u00a0Hieu T. Nguyen,\u00a0Daniel T. Thornton,\u00a0Luke Botticelli,\u00a0Nan Yang\u00a0&\u00a0Feixia Chu\n\nAntitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA\n\nVladimir Markov,\u00a0Charlene Kwong,\u00a0Jeanine Lisanti\u00a0&\u00a0Elisa De Stanchina\n\nDepartments of Psychiatry, Neuroscience & Physiology & the NYU Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, USA\n\nStephen D. Ginsberg\n\nCenter for Dementia Research, Nathan Kline Institute, Orangeburg, NY, USA\n\nStephen D. Ginsberg\n\nDepartment of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA\n\nAdriana Corben\n\nDepartment of Medicine, Division of Solid Tumors, Memorial Sloan Kettering Cancer Center, New York, NY, USA\n\nShanu Modi\u00a0&\u00a0Gabriela Chiosis\n\nDepartment of Chemistry, University of Pavia, Pavia, Italy\n\nGiorgio Colombo\n\nDepartment of Neuroscience and Physiology and Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, USA\n\nHediye Erdjument-Bromage\u00a0&\u00a0Thomas A. Neubert\n\nMass Spectrometry Facility, University of California, San Francisco, CA, USA\n\nRobert J. Chalkley,\u00a0Peter R. Baker\u00a0&\u00a0Alma L. Burlingame\n\nHubbard Center for Genome Studies, University of New Hampshire, Durham, NH, USA\n\nFeixia Chu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nT.R. performed biochemical and functional studies in human cells and tissues. S.W.M. performed the MS studies and biochemical and functional studies in mouse ESCs. C.P. performed the MD simulations. H.T.N. and D.T.T. performed MS studies of cargos and cross-linking experiments. S.S. performed chemical synthesis, compound identity and purity evaluations for the epichaperome probes. L.B. and N.Y. generated ESC culture samples and MS sample preparation. A.R., P.P., S.J., S.C., S.B., and H.E.-B. performed experiments. C.S.D. provided reagents. V.M., C.K., J.L., P.Y., E.deS., A.C., S.M., and M.L.A. were involved in various aspects of biospecimen handling, including recruitment, procurement, or processing at different stages from surgery to delivery to the laboratory. R.J.C. and P.R.B. provided Protein Prospector and supported data analysis. F.C., T.A.N., G.Chiosis, and A.L.B. participated in the design and analysis of various experiments. H.E.-B., A.R., S.D.G., G.Colombo, and T.A.N. assisted with manuscript writing and data analysis. F.C. and G.Chiosis. developed the concept and wrote the paper.\n\nCorrespondence to\n Gabriela Chiosis or Feixia Chu.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "Memorial Sloan Kettering Cancer Center holds the intellectual rights to the epichaperome portfolio. G.Chiosis., A.R., and S.S. are inventors on the licensed intellectual property. All other authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Yue Chen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Roychowdhury, T., McNutt, S.W., Pasala, C. et al. Phosphorylation-driven epichaperome assembly is a regulator of cellular adaptability and proliferation.\n Nat Commun 15, 8912 (2024). https://doi.org/10.1038/s41467-024-53178-5\n\nDownload citation\n\nReceived: 16 March 2024\n\nAccepted: 04 October 2024\n\nPublished: 16 October 2024\n\nVersion of record: 16 October 2024\n\nDOI: https://doi.org/10.1038/s41467-024-53178-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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+ "title": "Intrinsic metal-support interactions break the activity-stability dilemma in electrocatalysis", + "pre_title": "Intrinsic metal-support interactions break the activity-stability dilemma in electrocatalysis", + "journal": "Nature Communications", + "published": "01 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63397-z/MediaObjects/41467_2025_63397_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63397-z/MediaObjects/41467_2025_63397_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63397-z/MediaObjects/41467_2025_63397_MOESM3_ESM.txt" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63397-z/MediaObjects/41467_2025_63397_MOESM4_ESM.mp4" + }, + { + "label": "Supplementary Code 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63397-z/MediaObjects/41467_2025_63397_MOESM5_ESM.txt" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63397-z/MediaObjects/41467_2025_63397_MOESM6_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-63397-z#ref-CR69" + ], + "code": [ + "/articles/s41467-025-63397-z#MOESM5" + ], + "subject": [ + "Electrocatalysis", + "Heterogeneous catalysis", + "Hydrogen energy" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5208867/v1.pdf?c=1759403192000", + "research_square_link": "https://www.researchsquare.com//article/rs-5208867/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63397-z.pdf", + "preprint_posted": "26 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Electrocatalysis plays a central role in clean energy conversion and sustainable technologies. However, the trade-off between activity and stability of electrocatalysts significantly hinders their practical applications, notably in the oxygen evolution reaction (OER) for producing hydrogen and solar fuels. Here we report a steam-assisted synthesis armed with machine learning screening of an integrated ruthenium-titanium-manganese electrode, featuring intrinsic metal-support interactions. These atomic-scale interactions with self-healing capabilities radically address the activity-stability dilemma across all pH levels. Consequently, our electrode achieved high mass activities, 48.5, 112.8 and 74.6 times those of benchmark ruthenium oxides in acidic, neutral and alkaline conditions, respectively; and stable operation for up to 3,000 hours, a multi-fold improvement in stability over the reported advanced catalysts. The breakthrough in activity-stability limitations highlights the potential of intrinsic metal-support interactions for enhancing electrolysis and other heterogeneous catalysis.Physical sciences/Materials science/Materials for energy and catalysis/ElectrocatalysisPhysical sciences/Chemistry/Electrochemistry/Electrocatalysis", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformation.pdfSupplementary InformationSupplementaryVideo1.mp4Supplementary Video 1", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Electrocatalysis plays a central role in clean energy conversion and sustainable technologies. However, the trade-off between activity and stability of electrocatalysts largely hinders their practical applications, notably in the oxygen evolution reaction for producing hydrogen and solar fuels. Here we report a steam-assisted synthesis armed with machine learning screening of an integrated Ru/TiMnOx electrode, featuring intrinsic metal-support interactions. These atomic-scale interactions with self-healing capabilities radically address the activity-stability dilemma across all pH levels. Consequently, the Ru/TiMnOx electrode demonstrate enhanced mass activities\u201448.5\u00d7, 112.8\u00d7, and 74.6\u00d7 higher than benchmark RuO2 under acidic, neutral, and alkaline conditions, respectively. Notably, it achieves stable operation for up to 3,000\u2009h, representing a multi-fold stability improvement comparable to other state-of-the-art catalysts. The breakthrough in activity-stability limitations highlights the potential of intrinsic metal-support interactions for enhancing electrocatalysis and heterogeneous catalysis in diverse applications.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The global energy crisis and climate issues urgently call for the development of sustainable green energy technologies1,2,3. Electrocatalysis is key to clean energy conversion, driving multiple sustainable processes for future technologies1,2,3,4,5. However, the dilemma between the catalytic activity and the stability imposes fundamental limitations on the practical applications6,7,8,9. Typically, the oxygen evolution reaction (OER) is crucial in various renewable energy conversion and storage systems7,8,9,10,11,12,13,14, especially for hydrogen (H2) production through water electrolysis (WE)2,3,4,5,7,8,9,10,12. The activity-stability tradeoff in OER, arising from the formation of highly active yet soluble Mx+ species, greatly hinders large-scale WE deployment15,16,17. Therefore, overcoming these conventional constraints has been a long-pursued goal in OER and other catalysis research, yet remains extremely challenging.\n\nTo date, supported metal catalysts (SMCs) have been regarded as promising candidates for electrocalysis and heterogeneous catalysis17,18,19. By manipulating active metal species at nanoscale or even atomic scale, maximal intrinsic activity can be achieved17,18,19,20,21,22. Unfortunately, the inherently high surface free energy in atomic-scale metal species often drives atom aggregation and activity loss17,18,21,23. Previous studies have focused on modulating the metal-support interactions to prevent metal dissolution in SMCs17,18,19,20,21,22,23. Strategies such as post-treatment24,25,26,27, defect engineering28,29,30,31,32,33 and cation exchange4,15,34,35 have been employed to strengthen these interactions. However, these strategies typically involve support growth and metal loading through a stepwise bond-breaking and reformation process15,24,28,29,30,31,32. The resultant extrinsic metal-support interactions still fails to address the activity-stability dilemma fundamentally. Furthermore, most reported SMCs are in powder form and require binder coating for electrode preparation, thereby compromising catalyst-substrate adhesion and charge transfer efficiency4,15,28,29,30,31,32,33,34,35. Growing SMCs directly on the substrate offers potential for achieving both intrinsic metal-support and catalyst-substrate interactions, leading to a radical enhancement in stability without compromising activity. Prior studies have not experimentally resolved atomic-scale metal-support interactions, particularly with regard to OER under pH-universal conditions.\n\nIn this work, we develop a one-step chemical steam deposition (CSD) strategy to fabricate a Ru/TiMnOx electrode with atomic-scale metal-support interactions. This integrated structure overcomes the activity-stability trade-off in pH-universal OER. The key to the CSD strategy was the introduction of potassium permanganate (KMnO4) to obtain gaseous Ru species, ensuring atomic/molecular-scales reaction for achieving intrinsic interactions. Using machine learning, the optimal composition balancing both activity and stability metrics has been screened. The best-performing Ru0.24/Ti0.28Mn0.48O electrode exhibited high mass activities, ~49, 113, and 75 times those of RuO2 under acidic, neutral, and alkaline conditions, respectively; and outstanding long-term durability for up to 3,000\u2009h. Our findings demonstrate a machine learning-guided steam deposition strategy for atomic-level synthesis of nano-metal oxides, enabling tunable interfacial properties applicable across pH-universal conditions", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "A one-pot chemical steam deposition (CSD) strategy was developed to fabricate an integrated Ru/TiMnOx electrode (Fig.\u00a01a, Methods, Supplementary Figs.\u00a01 and 2, and Supplementary Discussion\u00a01). Under hydrothermal conditions, solution-derived RuO4 and KMnO4 volatilized into gas phases. These precursors then reacted with the Ti substrate, enabling Ru embedding at nanoscale within TiMnOx lattices. KMnO4 played a pivotal role in the CSD strategy, not only as a Mn source but also as a strong oxidant that oxidizes Ru3+ into RuO4 gas precursors. RuO4, acting as a gaseous reactant, directly diffused to the Ti substrate surface in molecular form and rapidly accumulated through physical adsorption or chemical interactions. Upon reaching local supersaturation at the Ti interface, RuO4 underwent immediate reduction and nucleated on the substrate surface, forming an interlayer dominated by Ru nanoclusters36,37. As the reaction progressed, the RuO4 concentration near the substrate decreased due to rapid consumption, shifting the reaction mechanism from diffusion-controlled kinetics (fast reduction under high-concentration conditions) to surface reaction-controlled kinetics (slow deposition at low concentrations)38,39. Concurrently, KMnO4 participated as a co-deposition agent under critical conditions, competing with RuO4 for reduction40,41. This phenomenon could potentially explain the suppression of Ru nanocluster formation while enabling atomic-level incorporation of Ru into the TiMnOx lattice. Consequently, these collective processes likely contributed to the formation of the catalytic layer with atomically dispersed Ru species, which constituted the dominant component of Ru/TiMnOx. To validate the CSD synthesis mechanism, we devised an apparatus that ensures the exclusive production of gas-phase products throughout the hydrothermal process. By positioning the Ti substrate onto apparatuses of diverse dimensions, Ru/TiMnOx with distinct electrode areas were successfully fabricated (Supplementary Fig.\u00a02). This strategy provides the initial demonstration of scalable steam-assisted hydrothermal synthesis for nanomaterials.\n\na Schematic illustration of the one-step synthesis of Ru/TiMnOx. b X-Ray diffraction (XRD) patterns of Ru/TiMnOx with different Ru, Ti, and Mn ratios. c Overall machine learning diagram, showing the steps of experimentation, feature extraction, and validation. d, e Predicted OER overpotential (\u03b7) and deactivation rate (\u0394E) with various ternary compositions.\n\nEmploying the CSD strategy, we synthesized Ru/TiMnOx electrodes with varying compositions. As the Ru ratio increases (Fig.\u00a01b, from bottom to top), the peak of Ru/TiMnOx located at 59.3\u00b0 shifts to a lower angle. This low-angle shift aligns with Bragg\u2019s law, indicating an expansion of the lattice parameters due to the substitutional incorporation of Ru atoms into the TiMnOx matrix, where Ru ions replaced smaller Mn and Ti ions42,43. Subsequently, we evaluated their OER performance based on both experimental overpotential (\u03b7) and deactivation rate (\u0394E) indicators (Supplementary Fig.\u00a03 and Supplementary Table\u00a01). Given the requirement for an ideal catalyst to exhibit concurrently high activity and stability, we employed machine learning analysis to predict the OER performance of Ru/TiMnOx using both activity and stability indicators as inputs (Fig.\u00a01c, Supplementary Fig.\u00a04 and Supplementary Table\u00a02). The OER \u03b7 and \u0394E of catalysts with different Ru-Ti-Mn ratios are presented in a ternary composition diagram with the molar ratios among the constituent elements on the vertices (Fig.\u00a01d, e). The regions exhibiting the lowest \u03b7 and \u0394E were highlighted in yellow circles, with their overlapping areas indicating the optimal composition range, specifically (Ru-Ti-Mn: 0.20\u20130.50, 0.20\u20130.30, 0.25\u20130.50). The predicted lowest overpotential to drive a current density of 10\u2009mA\u2009cm\u22122 is 163.0\u2009mV, corresponding to a ratio of 0.26: 0.26: 0.48 (Ru: Ti: Mn).\n\nBased on machine learning predictions, an optimized Ru-Ti-Mn electrode was obtained via the CSD strategy. The crystal structure of Ru/TiMnOx was characterized by X-ray diffraction (XRD) analysis (Fig.\u00a02a and Supplementary Table\u00a03). The absence of characteristic peaks corresponding to Ru species in the XRD pattern suggests that Ru species are highly dispersed on the support15. The Ru-Ti-Mn ratio of the Ru/TiMnOx was determined from the refined occupancies to be ~0.24:0.28:0.48, which is consistent with the machine learning prediction and the data obtained from inductively coupled plasma optical emission spectrometry (ICP-OES; Supplementary Fig.\u00a05). Transmission electron microscopy (TEM) image and corresponding elemental mapping (Fig.\u00a02b) verified a homogeneous distribution of Ru, Mn and Ti. Subsequently, focused ion beam (FIB) milling was employed to prepare cross-sectional slices of the Ru/TiMnOx on the Ti substrate, enabling direct observation of interfacial structures (Supplementary Figs.\u00a06\u20138). Cross-sectional spherical aberration-corrected high-angle annular dark-field scanning TEM (HAADF\u2013STEM) imaging combined with elemental mapping revealed two layers: (i) an interlayer adjacent to the Ti substrate, where Ru predominantly existed as nanoclusters embedded within Ti-rich oxide domains, and (ii) a catalytic layer (accounting for ~80% of the total thickness) with highly dispersed Ru single-atoms uniformly distributed throughout the TiMnOx matrix. Notably, Mn species exhibited preferential surface enrichment, while Ru and Ti demonstrated complete penetration across the entire oxide film.\n\na Rietveld refinement analysis of XRD patterns of Ru/TiMnOx and its crystal structure (inset). b TEM image of Ru/TiMnOx and corresponding elemental mapping profile. c, e Aberration-corrected HAADF-STEM images of Ru/TiMnOx. d Inverse-fast Fourier-transform (IFFT) image for (c). f 3D surface plot for (c). g, h Magnified images of (f) and the corresponding STEM intensity profiles presented to directions labeled with \u03b1 and \u03b2 boxes.\n\nFurthermore, HADDF-STEM characterization was performed on ultrasonically dispersed Ru/TiMnOx fragments, which were preferentially exfoliated from the outer catalytic layer (Fig.\u00a02c\u2013e and Supplementary Fig.\u00a09). The analysis confirmed the atomic-level incorporation of Ru into the TiMnOx lattice. Additionally, lattice fringes with interplanar spacings of 0.23\u2009nm and 0.25\u2009nm were observed, corresponding to the (020) and (111) planes of cubic Ru/TiMnOx, respectively. The observed crystal structure agreed well with the XRD Rietveld refinement results. The 3D surface plots displayed varying peak heights, where relatively higher peaks correlated with Ru atoms (Fig.\u00a02f\u2013h). The line intensity and weight fraction profiles (Fig.\u00a02g, h and Supplementary Fig.\u00a09c,d) further demonstrated the atomic-level embedding of Ru into the TiMnOx lattice within the dominant catalytic layer, which is a prerequisite for achieving intrinsic metal-support interactions. To highlight the significance of these interactions for OER performance, Ru/MnOx and TiMnOx catalysts were synthesized (Supplementary Figs.\u00a010\u201312 and Supplementary Table\u00a04).\n\nThe OER performances of the as-prepared Ru/TiMnOx electrodes were evaluated in electrolytes with different pH values, including pH=0 (0.5\u2009M H2SO4), pH=7 (0.5\u2009M PBS), and pH=14 (1.0\u2009M KOH). Ru/MnOx, TiMnOx and commercial RuO2 (Com. RuO2) were used as references. The OER activities in these three electrolytes across the full pH range showed the same trend: Ru/TiMnOx\u2009>\u2009Ru/MnOx > Com. RuO2\u2009>\u2009TiMnOx (Fig.\u00a03a and Supplementary Fig.\u00a013). Notably, the overpotential of Ru0.24/Ti0.28Mn0.48O under 10\u2009mA\u2009cm\u22122 in acidic conditions (165.2\u2009mV) closely aligns with the prediction of Ru0.26/Ti0.26Mn0.48O (163.0\u2009mV) by machine learning, affirming the validity of the established model. Tafel analysis reveals fast reaction kinetics of Ru/TiMnOx across the investigated pH range (Supplementary Fig.\u00a013d\u2013f). Furthermore, Ru/TiMnOx showed high mass activities, 48.5, 112.8 and 74.6 times those of benchmark Com. RuO2 in acidic, neutral and alkaline conditions, respectively (Supplementary Fig.\u00a014 and Supplementary Table\u00a05). The same trend was also confirmed when the activity was evaluated using the electrochemically active surface area (ECSA) (Supplementary Figs.\u00a015\u201317), further highlighting the advantages of atomically dispersed Ru species in reducing the noble metal loading amount. The electrochemical impedance spectroscopy (EIS) analysis determined the enhanced charge transfer capabilities of Ru/TiMnOx (Supplementary Fig.\u00a018), revealing notably low charge transfer resistance. The radar charts (Fig.\u00a03b and Supplementary Fig.\u00a019) showed the outstanding OER performance of Ru/TiMnOx under pH-universal conditions from multiple perspectives. Its notable activities are attributed to optimized kinetics from atomically dispersed sites and enhanced mass transfer from an integrated structure. As illustrated in Supplementary Movie\u00a01, the Ru/TiMnOx electrode served directly as the anode for electrocatalytic water splitting, bypassing the use of binders. The rapid generation and detachment of oxygen bubbles on the electrode surface visually demonstrated the notable activity of Ru/TiMnOx.\n\na Linear sweep voltammetry (LSV) curves of Ru/TiMnOx, Ru/MnOx, TiMnOx, and commercial RuO2 (Com. RuO2) in 0.5\u2009M H2SO4 (pH = 0.3\u2009\u00b1\u20090.02), 0.5\u2009M PBS (pH = 7.1\u2009\u00b1\u20090.10) and 1.0\u2009M KOH (pH = 13.9\u2009\u00b1\u20090.03). Scan rate: 10\u2009mV\u2009s-1. b Radar plot comparing the OER performance of Ru/TiMnOx and the reference samples in 0.5\u2009M H2SO4 (pH\u00a0= 0.3\u00a0\u00b1\u20090.02). c\u2013e Chronopotentiometry curves of Ru/TiMnOx for OER at 10\u2009mA\u2009cm\u22122 in 0.5\u2009M H2SO4 (c), 0.5\u2009M PBS (d) and 1.0\u2009M KOH (e), respectively. The potentials of chronopotentiometry tests are not iR-corrected.\n\nIn addition to high OER activity, good stability is another crucial prerequisite for catalysts to realize practical applications. The OER stability in acidic and neutral conditions is particularly challenging5,7,8,44. We investigated OER durability at 10\u2009mA\u2009cm\u22122geo (Fig.\u00a03c\u2013e and Supplementary Fig.\u00a020), a widely adapted benchmark criterion in literature. Specifically, in 0.5\u2009M H2SO4, the overpotential (without iR compensation) of Ru/TiMnOx increased from 175\u2009mV to 185\u2009mV in a 1000\u2009h test, to 198\u2009mV in a 2000\u2009h test, and 205\u2009mV in a 3000\u2009h test (Fig.\u00a03c). The average increase in overpotential was 0.01\u2009mV\u2009h-1; this is over 1698 times slower than that of the Com. RuO2 (16.9\u2009mV\u2009h\u22121) (Supplementary Fig.\u00a020a). To date, no catalyst, especially Ru-based materials, has matched such performance of Ru/TiMnOx under acidic OER conditions (Supplementary Table\u00a06). This stability was also notable in both neutral and alkaline conditions (Fig.\u00a03d, e, Supplementary Tables\u00a07 and 8), particularly under the challenging neutral conditions5,44, where it attained a duration of 1500\u2009h (Fig.\u00a03d). Furthermore, Ru/TiMnOx demonstrated notable long-term stability under high current density across the entire pH range. It can stably drive a current density of 100\u2009mA\u2009cm\u22122 for 220\u2009h under acidic conditions, 120\u2009h under neutral conditions, and 140\u2009h under alkaline conditions, respectively (Supplementary Fig.\u00a021). These results demonstrate its potential for practical applications in water electrolysis devices. As a proof of concept, both the integrated proton exchange membrane water electrolyzer (PEMWE) and anion exchange membrane water electrolyzer (AEMWE) utilizing Ru/TiMnOx electrodes were constructed to evaluate the performance under conditions that are representative of practical applications. The devices achieved industrial-grade OER performance, delivering a current density of 1.0\u2009A\u2009cm\u22122 at cell voltages of 1.66\u2009V (PEMWE) and 1.72\u2009V (AEMWE), respectively (Supplementary Figs.\u00a022 and 23). These operational efficiencies outperformed those reported for state-of-the-art catalysts and commercial RuO2 benchmarks, as evidenced by the substantially higher potentials (1.94\u2009V and 1.99\u2009V) to reach equivalent current densities under comparable conditions (Supplementary Table\u00a09). Besides, RuO2-based systems exhibited activity decay within 3000\u2009min (PEMWE) and 6000\u2009min (AEMWE) at 200\u2009mA\u2009cm\u22122, whereas Ru/TiMnOx-based electrolyzers maintained stable operation for over 11,000\u2009min and 10,000\u2009min, respectively. These findings further highlights the competitive advantage of Ru/TiMnOx in OER at high current densities, demonstrating its great potential for scalable water electrolysis technologies. In summary, the performance of Ru/TiMnOx for pH-universal OER demonstrated a multi-fold improvement over other advanced OER electrocatalysts (Supplementary Tables\u00a06\u20139). Surprisingly, we observed periodic increases and decreases in voltage during the long-term stability tests (see inset in Fig.\u00a03c\u2013e, Supplementary Figs.\u00a021 and 24). We believe that periodic potential fluctuations are linked to the long-term stability of Ru/TiMnOx. Next, a thorough examination of the intrinsic metal-support interactions was conducted to elucidate its impact on OER performance.\n\nThe electronic configuration and the coordination structure of Ru, Ti and Mn in Ru/TiMnOx were investigated using X-ray photoelectron spectroscopy (XPS) and synchrotron X-ray absorption spectroscopy (XAS). Deconvolution of the Ru 3d spectra revealed oxidation states of metallic Ru0 and oxidized Ru4+ in Ru/TiMnOx and Ru/MnOx45,46,47. For Ru/TiMnOx, the Ru0 component accounted for only ~14% of the total Ru content, whereas in Ru/MnOx, the Ru0 fraction was ~60% (Supplementary Table\u00a010). The minimal Ru0 content in Ru/TiMnOx likely originated from trace Ru nanoclusters, while the predominant Ru0 in Ru/MnOx indicated extensive aggregation of Ru nanoparticles due to weaker metal-support interactions. Notably, compared to RuO2, the Ru 3d5/2 binding energies in Ru/TiMnOx and Ru/MnOx exhibited negative shifts of 0.05\u2009eV and 0.10\u2009eV, respectively (Supplementary Fig.\u00a025a and Supplementary Table\u00a010). This observation indicates a lower Ru oxidation state in both Ru/TiMnOx and Ru/MnOx compared to the stoichiometric Ru(IV) in RuO2, with Ru/TiMnOx displaying a much higher Ru valence than Ru/MnOx. In the Mn 2p spectra, the electronic structure of TiMnOx aligned closely with MnO2, suggesting a Mn(IV)-dominant configuration15,16 (Supplementary Fig.\u00a025b and Supplementary Table\u00a011). Conversely, the Mn 2p3/2 binding energies of Mn3+ for Ru/TiMnOx and Ru/MnOx showed negative shifts of 0.79\u2009eV and 0.11\u2009eV relative to TiMnOx, respectively. These shifts correlate with a reduced Mn oxidation state in the ternary oxides, with Ru/MnOx exhibiting a marginally higher Mn valence compared to Ru/TiMnOx. Ti oxidation states were further elucidated through high-resolution Ti 2p spectra. Both Ru/TiMnOx and TiMnOx exhibited dual Ti3+/Ti4+ character, indicating mixed-valence Ti species in these oxides (Supplementary Fig.\u00a025c and Supplementary Table\u00a012)48,49,50,51,52. Collectively, the XPS results reveal strong electronic interactions among Ru, Mn, and Ti in Ru/TiMnOx. The observed binding energy shifts reflected synergistic charge transfer processes, where Ru served as an electron reservoir while Mn and Ti participated in redox-active cycling. These findings provide critical insights into the electronic structure-property relationships governing the catalytic behavior of these multi-metallic oxides, particularly in electrochemical energy conversion applications. Additionally, in-depth XPS analysis was performed to verify changes in elemental composition and valence states within Ru/TiMnOx (Supplementary Fig.\u00a026). As the etching depth increased, a negative shift in the Ru 3\u2009d binding energy was observed (Supplementary Fig.\u00a026a), concomitant with an increase in the Ru0/Ru4+ ratio from 0.17 (catalytic layer) to 0.26 (interlayer) (Supplementary Fig.\u00a026b and Supplementary Table\u00a013). This trend confirmed the accumulation of metallic Ru0 species at the catalyst-substrate interface, as corroborated by HAADF-STEM cross-sectional imaging, which revealed Ru0 nanoclusters within the interlayer (Supplementary Fig.\u00a08). Concurrently, the elemental depth profile demonstrated a gradual increase in Ru content and a decrease in Mn/Ti concentrations. These observations aligned with cross-sectional elemental mapping, further validating the elemental distribution within the catalytic layer. The Mn species in the lattice are known to enhance the OER activity of the oxide3,4,34, while the Ti species are believed to improve the OER stability, especially in acidic conditions32,53. This metal distribution potentially facilitates bulk Ru migration to replenish surface active sites upon dissolution, contributing to enhanced stability. The cross-sectional imaging (Supplementary Figs.\u00a06\u20138) and depth-resolved XPS analysis (Supplementary Fig.\u00a026 and Supplementary Table\u00a013) collectively provided evidence supporting the as-proposed growth mechanism of Ru/TiMnOx: RuO4 supersaturation first triggered Ru0 nanocluster formation at the interlayer, followed by atomic Ru incorporation into TiMnOx upon precursor depletion, ultimately forming the dominant component of catalytic layer.\n\nNext, the structural and electronic structure stability of Ru/TiMnOx was investigated. HADDF-STEM images demonstrated that the structure of Ru/TiMnOx remained stable after long-term OER stability test in 0.5\u2009M H2SO4 (Supplementary Fig.\u00a027). Subsequent ICP-MS analysis confirmed that the Ru/TiMnOx sample retained over 90% of Ru and Mn content after stability testing, exhibiting only trace ion leaching (Supplementary Fig.\u00a028). Notably, the Ru retention rate in Ru/TiMnOx was 1.67 times and 2.29 times higher than those of Ru/MnOx and Com. RuO2 reference samples, respectively. The electronic structure stability was then verified through XPS, as no marked changes in the valence states of Ru and Mn in Ru/TiMnOx were observed after OER stability testing (Supplementary Figs.\u00a029 and 30). In contrast, a distinct positive shift in the Ru 3\u2009d binding energy was observed in both Ru/MnOx and Com. RuO2. Notably, the Ru0/Ru4+ ratio in Ru/TiMnOx remained nearly unchanged (from 0.17 to 0.16), whereas that in Ru/MnOx drastically decreased from 1.48 to 0.41 (Supplementary Table\u00a010). These results, combined with ICP-MS findings, indicated that the active sites in Ru/TiMnOx were protected from excessive oxidation-induced dissolution, whereas Ru in the comparison samples underwent severe oxidation and leaching deactivation.\n\nTo elucidate the origin of the high OER performance in Ru/TiMnOx, we systematically conducted XAS analysis on Ru, Mn and Ti to clarify its coordination structure (Fig.\u00a04, Supplementary Figs.\u00a031-40 and Supplementary Tables\u00a014-17). The K-edge X-ray absorption near-edge spectroscopy (XANES) spectra of Ru, Mn, and Ti reveal that the oxidation states of Ru, Ti, and Mn in Ru/TiMnOx lay between those of metal foils and +4-valent metal oxides (Fig.\u00a04a, Supplementary Figs.\u00a031, 33 and 35). Notably, the oxidation state of Ru in Ru/TiMnOx was determined to be +3.0, which is higher than that observed in Ru/MnOx (+\u20091.1). Conversely, the oxidation state of Mn exhibited an opposing trend, with a valence of +3.1 in Ru/TiMnOx, slightly lower than that in the Ru/MnOx (\u2009+\u20093.8). These findings are in good agreement with the oxidation state trends identified through XPS analysis, further validating the strong interactions between Ru, Mn and Ti in Ru/TiMnOx. The extended X-ray absorption fine-structure (EXAFS) analysis further elucidated the metal coordination environment in Ru/TiMnOx. The Ru K-edge Fourier-transform (FT) EXAFS spectra (Fig.\u00a04b) indicated dominant coordination of Ru centers with oxygen (Ru-O), while the Ru-Ru scattering path, arising from interlayer Ru nanoclusters36,54, is consistent with the metallic Ru0 component identified in XPS analysis. Notably, a unique Ru-O-M signal (where M represents Ru, Mn or Ti) emerged at ~3.3\u2009\u00c5, distinct from both RuO2 reference and Ru/MnOx control sample (Supplementary Table\u00a014). The FT-EXAFS and wavelet transform (WT) patterns of Mn and Ti also confirmed the formation of M1-O-M2 (where M1 and M2 represent different metallic species among Ru, Mn, or Ti) bridging bonds, demonstrating the presence of strong intrinsic interactions within Ru/TiMnOx (Fig.\u00a04c and Supplementary Figs.\u00a032\u201336). Since Ti-based bridging bonds are conducive to stabilizing active species32,53, Ti stabilizes the active sites through Ti-O-M bonds, underscoring its critical role in maintaining structural integrity and catalytic performance.\n\na, b Ru K-edge XANES spectra (a) and Fourier transforms (FT) of k2-weighted Ru K-edge of EXAFS spectra (b) of Ru/TiMnOx and Ru/MnOx, with Ru foil and RuO2 as references. c, FT k2-weighted Mn K-edge EXAFS spectra of Ru/TiMnOx and Ru/MnOx, with Mn foil, Mn2O3 and MnO2 as references. d FT k2-weighted Ru K-edge EXAFS spectra of Ru/TiMnOx, Ru/TiMnOx-OER-10 and Ru/TiMnOx-OER-20, with Ru foil and RuO2 as references. e The plot of coordination number (CN) and radial distance (R) fitted from Ru K-edge EXAFS spectra (see Supplementary Table\u00a014 for details). f Wavelet transforms for the k2-weighted EXAFS signals at Ru K-edge of RuO2, Ru/TiMnOx, Ru/TiMnOx-OER\u221210 and Ru/TiMnOx-OER-20. k (\u00c5\u22121): Wave vector in momentum space; R (\u00c5): radial distance of coordination shells.\n\nTo elucidate the evolution of the active site, in-situ XAS measurements of Ru/TiMnOx were conducted in the 0.5\u2009M H2SO4 electrolytic environment (Supplementary Fig.\u00a039 and 40, Supplementary Table\u00a017). The Mn K-edge XANES spectra demonstrated a reversible behavior of unoccupied Mn orbitals: upon applying a positive voltage bias (\u2009+\u2009V), the white-line intensity exhibited an initial increase followed by complete recovery to its original state upon voltage reversal (-V), indicative of dynamic reconstruction of Mn 3\u2009d electronic states (Supplementary Fig.\u00a039a). Furthermore, the Mn-O bond length displayed reversible structural modulation: starting from the initial value of ~1.89\u2009\u00c5, it underwent elongation to ~1.90\u2009\u00c5 under +V bias and subsequent contraction to ~1.85\u2009\u00c5 upon -V restoration (Supplementary Fig.\u00a039c and Supplementary Table\u00a017). This reversible bond length variation, combined with its orbital state recovery, provided evidence for the self-healing ability of the Mn-O bond.\n\nWe hypothesized that this reversibility guarantees the stability of Ru/TiMnOx. To investigate the self-healing behavior in the OER process, we firstly monitored changes in electrolyte concentration during electrolysis at 10\u2009mA\u2009cm\u22122 in 0.5\u2009M H2SO4. Nearly no Ti was detected in the time-dependent ICP-OES tests (Supplementary Fig.\u00a041). By contrast, Ru and Mn concentrations initially increased for 600\u2009min then stabilized at ~20 and ~25 ppb, respectively, correlating with potential fluctuations period. The fluctuation period of the potential driving OER during the stability test is ~20\u2009h (with the voltage increasing during the first ~10\u2009h and then decreasing over the subsequent ~10\u2009h, see Supplementary Fig.\u00a024 for details). To elucidate the structural self-healing mechanism of Ru/TiMnOx, we comprehensively characterized samples collected at 10.83\u2009h (denoted as Ru/TiMnOx-OER-10) and 20.63\u2009h (denoted as Ru/TiMnOx-OER-20) using XAS (Fig.\u00a04d\u2013f, Supplementary Figs.\u00a042 and 43). XANES analysis revealed a reversible oxidation state modulation of Ru: starting from +3.0 in the fresh catalyst, the valence state increased to +3.7 after ~10\u2009h of electrolysis, then decreased to +2.7 by ~20\u2009h (Supplementary Fig.\u00a042). This dynamic redox cycling suggested inherent self-healing capability of Ru centers, effectively maintaining the high catalytic activity. It is noteworthy that the strong interaction between the bridging O atom and proton induced local crystal lattice distortion55. This structural perturbation manifested as an initial elongation of the Ru-O-M bond length (from 3.31\u2009\u00c5 to 3.34\u2009\u00c5), accompanied by a simultaneous reduction in coordination number (CN) from 3.9 to 2.6 within the first ~10\u2009h, followed by complete recovery to the original geometric parameters after ~20-h electrolysis (Fig.\u00a04e and Supplementary Table\u00a014). The temporary decrease in CN reflects partial lattice distortion56, while the subsequent recovery indicates structural self-healing through dynamic bond reconfiguration. Such dynamic behavior demonstrated the reconstruction of long-range coordination order, thereby re-establishing its catalytic activity for OER in the subsequent period. By combining time-dependent ICP and XAS characterizations, the self-healing mechanism of Ru/TiMnOx was proposed to involve the following processes: Ti stabilizes the crystalline framework, trace amounts of Ru/Mn dissolve and redeposit, accompanied by the restoration of both compositional and coordination structures (Fig.\u00a05a). This self-healing processes not only explain the observed potential oscillations but also fundamentally guarantee Ru/TiMnOx\u2019s long-term stability in OER applications.\n\na Schematic illustration for the self-healing behavior of integrated Ru/TiMnOx electrode. b In-situ Raman spectra of integrated Ru/TiMnOx electrode in 0.5\u2009M H2SO4 electrolyte under different external applied potential (OCP\u22121.75\u2009V vs. RHE). c 2D in-situ Raman contour image enlarged from (b). d, e In-situ ATR-FTIR spectra recorded in the potential range of OCP\u22121.85\u2009V vs. RHE for Ru/TiMnOx in 0.5\u2009M H2SO4 electrolyte. f, g In-situ differential electrochemical mass spectrometry (DEMS) signals of O2 products for Ru/TiMnOx in 0.5\u2009M H2SO4 (H218O) (f) and 0.5\u2009M H2SO4 (H216O) (g).\n\nThen, the structural evolution of catalyst was tracked by in-situ electrochemical Raman spectroscopy (Fig.\u00a05b, c and Supplementary Fig.\u00a044). As the potential increased from open circuit potential (OCP) to 1.75\u2009V vs. RHE, a blue-shift was observed in the Mn-O vibrational peaks (Fig.\u00a05b). According to Hooke\u2019s law57,58, this blue-shift are attributed to the substitution of Mn by Ru during the OER. Notably, the A1g mode at ca. 666\u2009cm-1 gradually vanished as the potential increased, indicating the deprotonation of Ru-O-Mn59,60. When the potential cycled back to OCP, this peak intensity returned to initial level (Fig.\u00a05c). The combined time-dependent ICP-OES and in-situ spectroscopy results coincide with the hypothesis that the Ru/TiMnOx can self-repair during the OER process, thereby ensuring long-term catalytic efficiency.\n\nTo experimentally investigate the OER mechanism, in-situ attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy was conducted on Ru/TiMnOx, allowing for tracking the changes in surface reaction intermediates (Fig.\u00a05d, e, Supplementary Figs.\u00a045 and 46). In addition to an absorption peak at ca. 3,619\u2009cm-1 (Fig.\u00a05e, O-H stretching mode), distinctive absorption peaks at 1117\u2009cm-1 and 1100\u2009cm-1 were also observed (Fig.\u00a05d and Supplementary Fig.\u00a046). The central peak (1089\u2009cm-1) and shoulder peak (1100\u2009cm-1) signify the formation of O-O bonds15,61. Specifically, the linearly bonded superoxol species (*-O-O) and the oxygen bridges between metal sites (*-O-O-*), respectively15,62,63. This feature agrees with the advanced oxide path mechanism (OPM) pathway which break through the theoretical activity limit in the traditional adsorbate evolution mechanism (AEM) and avoid the structure collapse in the lattice oxygen oxidation mechanism (LOM)7,15,34,35,61. Additionally, these peak intensities returned to their initial levels when the potential decreased to OCP, reinforcing the reversibility of Ru/TiMnOx. To experimentally confirm this OPM pathway, in-situ differential electrochemical mass spectrometry (DEMS) with isotopic labeling measurements was conducted (Fig.\u00a05f, g, Supplementary Figs.\u00a047 and 48). Initially, the Ru/TiMnOx were subjected to three LSV cycles in 0.5\u2009M H2SO4 (H218O) (Supplementary Fig.\u00a048a). For the OPM-type OER, the adsorbed 16O species on neighboring metal sites may couple to form 32O2, which were detected during the initial 18O-labeling process for Ru/TiMnOx (Fig.\u00a05f). Next, the electrocatalyst was thoroughly washed with H216O deionized water and then operated in the 0.5\u2009M H2SO4 (H216O) electrolyte (Supplementary Fig.\u00a048b). The captured 36O2 signals (Fig.\u00a05g), resulting from the coupling of adjacent 18O adsorbates, corroborate the OPM-related pathway in Ru/TiMnOx15.\n\nThe density functional theory (DFT) calculations were conducted to gain insight into the origin of the enhanced OER performance for Ru/TiMnOx. According to the XRD refinement and TEM analysis, Ru/TiMnOx (100) and Ru/MnOx (110) slab models were constructed with formulas of Ru0.24/Ti0.28Mn0.48O and Ru0.56/Mn0.44O2, respectively (Fig.\u00a06a, Supplementary Figs.\u00a049 and 50, Supplementary Data\u00a01). Based on the Bader charge analysis, the average charge of Ru in Ru/TiMnOx was found to be +0.55 e, which is lower than that of RuO2 (+1.68 e). The charge density difference of Ru/TiMnOx and Ru/MnOx further indicated a shift of electron cloud from O to M in the M-O covalent bond (Fig.\u00a06b and Supplementary Figs.\u00a051\u201353). This observation indicates that the electron transfer from Mn/Ti to Ru through the bridging O atom (Obri) results from the intrinsic metal-support interactions. To quantify the covalency of the M-O bond, the partial density of states (PDOS) calculations were employed (Fig.\u00a06d and Supplementary Fig.\u00a054). The energy of the Ru d-band center (\u025bd) for Ru/TiMnOx (-1.167\u2009eV) was more appropriate for adsorbate-catalyst surface interaction compared to Ru/MnOx (-1.936\u2009eV) and RuO2 (-1.110\u2009eV)43. Furthermore, the gap between the Ru/Mn \u025bd and O 2p band center (\u025bp) is greatly enlarged in Ru/TiMnOx, with values of 2.31\u2009eV and 0.98\u2009eV for Ru-O and Mn-O, respectively. Consequently, the covalency of M-O bonds in Ru/TiMnOx is lower than in Ru/MnOx and RuO2. It is well-established that weaker M-O covalency boosts catalyst stability by inhibiting the LOM mechanism during OER, thereby accounting for the long-term stability of Ru/TiMnOx64,65,66. The enhanced stability of Ru/TiMnOx was also evidenced by its high de-metallization energies for Ru and Mn (Fig.\u00a06c and Supplementary Table\u00a018). Specifically, the de-metallization energy of Ru in Ru/TiMnOx is 0.174\u2009eV greater than in RuO2 and 0.462\u2009eV greater than in Ru/MnOx. Next, we conducted mechanistic studies targeting the OER process. The preferred reaction pathways were determined by investigating the OER paths based on OPM and AEM (Fig.\u00a06e and Supplementary Figs.\u00a055\u201358). For Ru/MnOx, the formation of *OOH was identified as the rate-determining step (RDS), characterized by a significant free energy barrier of 1.957\u2009eV. Comparatively, the RDS for Ru/TiMnOx via the OPM pathway is 1.511\u2009eV, 0.952\u2009eV lower than that of the AEM, indicating a thermodynamic preference for the OPM. Therefore, theoretical calculations reveal that Ru-Obri-Ti/Mn bonds in Ru/TiMnOx modulate charge distribution and orbital overlap, optimizing the OER pathway.\n\na Ru0.24/Ti0.28Mn0.48O structure with the value of computed bader charge and bond length. b Filled contour plot of the cross-section of differential charge density for Ru0.24/Ti0.28Mn0.48O (100). c Demetallization energies of Ru from RuO2; Ru and Mn from Ru/MnOx and Ru/TiMnOx, respectively. d Schematic diagram of the band structures of Ru/TiMnOx, Ru/MnOx and RuO2 based on PDOS analysis. e The free energy diagrams for preferred OER paths on the surfaces of Ru/MnOx and Ru/TiMnOx. RDS denotes rate-determining step.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63397-z/MediaObjects/41467_2025_63397_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63397-z/MediaObjects/41467_2025_63397_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63397-z/MediaObjects/41467_2025_63397_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63397-z/MediaObjects/41467_2025_63397_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63397-z/MediaObjects/41467_2025_63397_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63397-z/MediaObjects/41467_2025_63397_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In summary, the concept of intrinsic metal-support interactions has been established and validated, addressing a critical challenge in breaking the activity-stability tradeoff for electrolysis. The Ru/TiMnOx exhibited low overpotential of 165.2\u2009mV at 10\u2009mA\u2009cm\u22122, aligning well with machine learning predictions and thereby avoiding traditional trial-and-error approaches. Its mass activities exceed benchmark Ru oxides by ~49, 113, and 75 times under acidic, neutral, and alkaline conditions, respectively; while maintaining 3000-h stability at pH=0. Our findings offer a potential pathway to modulate intrinsic metal-support interactions, thereby mitigating the activity-stability tradeoff in electrocatalysis.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The integrated Ru/TiMnOx electrode was prepared through a chemical steam deposition (CSD) strategy which combines the concept of chemical vapor deposition (CVD) and the conditions of hydrothermal reaction (see Fig.\u00a01a, Supplementary Figs.\u00a01 and 2, and Supplementary Discussion\u00a01 for details). In detail, the entire CSD process can be completed in one step, and its mechanism were dissected into the following three steps: firstly, potassium permanganate (KMnO4, Aladdin 99.99%, AR, grade) in aqueous solution oxidized ruthenium(III) chloride hydrate (RuCl3\u00b7xH2O, Alfa Aesar, 99.99%, AR, grade) to RuO4 gas. Secondly, under hydrothermal conditions, RuO4 gas, along with KMnO4 steam, underwent spontaneous redox reactions with the Ti foam (TF, thickness 600 \u03bcm, Tianjin Eilian Electronics & technology Co. ltd). Finally, the resulting Ru/TiMnOx deposited in-situ on the Ti substrate. Before synthesis, a Ti foam piece (1.0\u2009\u00d7\u20096.0\u2009cm) was etched in hydrochloric acid (HCl, 18\u2009wt%, Sinopharm Chemical Reagent Co., Ltd.) at 90\u2009\u00b0C for 15\u2009min. Synthesis of Ru0.24/Ti0.28Mn0.48O involved the following steps: (1) Preparation of a redox-active precursor by dissolving 27.0\u2009mg RuCl3\u2022xH2O and 79.0\u2009mmol KMnO4 in 10.0\u2009mL deionized water; (2) Immersion of a pre-etched TF substrate into the solution within a Teflon-lined autoclave; (3) Hydrothermal reaction at 140\u2009\u00b0C (3\u2009h) to concurrently generate the Ti-Mn-O matrix and immobilize Ru species, uniquely yielding gas-phase reaction products from the critical interaction between KMnO4 and RuO4 during hydrothermal nucleation; (4) Post-synthesis washing (deionized water) and vacuum drying (60\u2009\u00b0C) of the TF-supported Ru0.24/Ti0.28Mn0.48O. The material obtained from the reaction occurring above the liquid level was identified as Ru0.24/Ti0.28Mn0.48O, and its physical representation corresponds to the black portion on the Ti substrate (Supplementary Fig.\u00a02a). To validate the CSD synthesis mechanism, we devised an apparatus that ensures the exclusive production of gas-phase products during the hydrothermal process (Supplementary Fig.\u00a02). Specifically, the Ti substrates were placed on hollow supports with different heights (4.5, 5 and 5.5\u2009cm) to ensure sufficient gas reaction. Furthermore, we successfully scaled up the support size in proportion to the dimensions of the hydrothermal reactor, thereby obtaining integrated Ru/TiMnOx electrodes with surface areas of 4.00, 6.25, and 12.25\u2009cm\u00b2, demonstrating the scalability of the CSD. The Ru mass loading of Ru/TiMnOx was 0.14\u2009mg\u2009cm\u22122.\n\nFor comparison, Ru/MnOx, TiMnOx and commercial RuO2 (denoted as Com. RuO2) samples were synthesized. The synthesis conditions for TiMnOx were identical to those employed for Ru/TiMnOx, with the sole exception being that TiMnOx was derived via a liquid-phase reaction occurring within the liquid medium during the hydrothermal process. TiMnOx corresponds to the gray portion on the Ti substrate depicted in Supplementary Fig.\u00a02a. The Ru/MnOx powder was prepared using the same hydrothermal method as Ru/TiMnOx, without adding a Ti substrate. The RuO2 catalyst on TF was fabricated by the following steps: a dispersion was prepared by mixing 50\u2009\u03bcL of ethanol, 5\u2009\u03bcL of nafion, 15\u2009\u03bcL of deionized water, and 10\u2009mg of RuO2 (Alfa Aesar). Subsequently, the dispersion was dropwise added and dispersed onto the TF, with the added quantity determined according to the loading mass of Ru. The Ru mass loadings of Ru/MnOx and Com. RuO2 were 0.35\u2009mg\u2009cm\u22122 and 0.52\u2009mg\u2009cm\u22122, respectively.\n\nTo find the optimal ratio combination of Ru, Mn, and Ti, a powerful and widely used machine learning model, the back-propagation neural network, was employed. In this experiment, five-fold cross-validation was used to optimize the number of layers and the initial learning rate. Three-layer, five-layer, and seven-layer back-propagation networks were considered (excluding the input layer). The activation function of the last layer is linear and all the other layers are rectified linear unit. The number of neurons for the three-layer neural network was (128, 32, 2), for the five-layer network was (128, 64, 32, 16, 2), and for the seven-layer network was (128, 64, 64, 32, 32, 16, 2). The initial learning rates considered were 1e-2, 1e-3, 1e-4, and 1e-5. The loss function was the weighted mean squared error of \u03b7 and the degradation rate (\u0394E), with the weight of \u03b7 set to be twice that of the \u0394E in order to emphasize the importance of \u03b7. The Adaptive Moment Estimation optimizer, also known as Adam, was used with 1000 epochs. Finally, based on the cross-validation results, a five-layer back-propagation network with an initial learning rate of 1e-4 was selected. To minimize the impact of randomness, ten training runs were performed, and the one with the smallest mean squared error was chosen as the final model. The data sources, training process and the detailed parameters are shown in Supplementary Figs.\u00a03 and 4, Supplementary Tables\u00a01 and 2, and Supplementary Code\u00a01.\n\nMultiscale structural and chemical profiling of the samples included: (1) Topographical and elemental distribution analysis via field-emission scanning electron microscopy (FE-SEM, JSM-7500) integrated with energy-dispersive X-ray spectroscopy (EDS); (2) Atomic-resolution lattice imaging by aberration-corrected transmission electron microscopy (FE-TEM, Titan Themis G2F20) in high-angle annular dark-field scanning TEM mode (HAADF-STEM); (3) Nanoscale compositional mapping using scanning transmission electron microscopy (STEM, Tecnai G2F30) at 300\u2009kV accelerating voltage; (4) Crystalline phase identification through powder X-ray diffraction (XRD, Rigaku SmartLab) with Cu K\u03b1 radiation (\u03bb\u2009=\u20091.5418\u2009\u00c5), scanning at 2\u00b0/min; (5) Surface speciation and oxidation states quantified by X-ray photoelectron spectroscopy (XPS, ESCALAB 250Xi) with monochromatic Al K\u03b1 source (1486.6\u2009eV), calibrated against adventitious carbon (C 1\u2009s, 284.8\u2009eV). Depth profiling was further conducted via alternating cycles of argon ion sputtering (Ar+) and XPS spectral acquisition, enabling layer-by-layer characterization of composition gradients from the surface to the substrate interface. The focused ion beam (FIB) was used for Ru/TiMnOx cross-sectional imaging using FEI Helios G4. The metal concentration in electrolytes were characterized by the inductively coupled plasma optical emission spectrometry (ICP-OES) measurement (iCAP 7400, Thermo, Waltham, USA). Since Ru/TiMnOx was grown in situ on a Ti substrate, the dissolution of the solid did not allow for the measurement of the Ti content in the Ru/TiMnOx. To ascertain the relative elemental proportions within the Ru/TiMnOx material, we implemented an ultrasonic dispersion technique to disperse the Ru/TiMnOx in an aqueous medium, subsequently quantifying the elemental composition of the resultant solution through ICP-OES. This approach circumvented interference from the Ti substrate, enabling accurate measurement of Ru and Mn concentrations. Synchrotron-based X-ray absorption spectroscopy studies were conducted at the Shanghai Synchrotron Radiation Facility (SSRF, beamline BL14W1). Ru K-edge and Mn K-edge spectra were collected using a 4-channel silicon drift detector (Bruker 5040). Samples were prepared as 1\u2009cm diameter pellets sealed with Kapton tape. Reference standards (Ru foil, RuO2, Mn foil, Mn2O3, MnO2) were measured in transmission mode for valence state calibration. Data processing, including background subtraction, normalization, and EXAFS fitting, was executed using the IFEFFIT package (Athena and Artemis modules)67,68. Fourier transforms of \u03c7(k) data employed a Hanning window (dk\u2009=\u20091.0\u2009\u00c5-1), with structural parameters refined via least-squares fitting for conversion into radial distance space (R-space). By taking the first derivative (d\u03bc/dE) of the normalized-XAS of Ru and Mn K-edge, the E0 value was obtained corresponding to the first maximum point. Subsequently, the linear relationship between E0 and valence state is established using references with known valence states (Ru foil and RuO2 for Ru; Mn foil, Mn2O3 and MnO2 for Mn). Finally, the valence state of the target elements in unknown samples were determined by comparing their measured E0 values to the relationship.\n\nIn-situ XAS for Mn K-edge was performed at the Canadian Light Source. Before the in-situ XAS test, a micro-electrochemical cell was fabricated with Ru/TiMnOx as the anode (\u2009~\u20091.0\u2009cm2), 0.5\u2009M H2SO4 as electrolyte. The micro-electrochemical cell was connected to an electrochemical station (Biologic). The working electrode was firstly tested without H2SO4 electrolyte and applied voltage, named as ex-situ. Then, the voltages (1.45\u2009V vs. RHE and -0.13\u2009V vs. RHE) were applied, corresponding to current densities of 15\u2009mA\u2009cm\u22122 and -15 mA cm\u22122, respectively. The catalyst was stabilized for 15\u2009min prior to XAS measurements at each potential. The in-situ XAS spectra of Mn were recorded in transmission mode for 120\u2009seconds, and then the X-ray beam was blocked for 20\u2009min to minimize potential beam damage. During the test, all obtained spectra were calibrated using Mn foil as a reference. The spectra were analyzed using Athena software. In-situ Raman spectroscopy was performed using a Renishaw inVia Raman microscope and a CHI650E electrochemical workstation. The working electrode of Ru/TiMnOx was immersed in the 0.5\u2009M H2SO4 electrolyte through the wall of the in-situ cell, with its plane perpendicular to the laser. Pt and Ag/AgCl were used as the counter electrode and reference electrode, respectively. The potential was gradually increased from OCP to 1.75\u2009V vs. RHE and then gradually decreased back to the initial value, with a stabilization period of 300\u2009seconds at each potential level. For the electrochemical in-situ attenuated total reflectance Fourier transform infrared (ATR-FTIR) measurements, a silicon crystal coated with an Au film was used in the internal reflection mode. The spectra were recorded on a Thermo Nicolet Nexus 670 spectrometer, with a CHI650E electrochemical workstation employed. Pt and Ag/AgCl were used as the counter electrode and reference electrode, respectively. Before data collection, a voltage was applied to the working electrode for 20\u2009min to test for receiving a reliable signal. Then, the potential was gradually increased from OCP to 1.85\u2009V vs. RHE and then gradually decreased back to the initial value. The in-situ differential electrochemical mass spectrometry (DEMS) system is similar to the system recently reported by the Lee group15. The in-situ DEMS system consisted of two interconnected vacuum chambers, including a mass spectrometer chamber with a high vacuum and a second chamber with a mild vacuum. The working electrode was an Au film sputtered on a porous polytetrafluoroethylene membrane. After obtaining powder catalysts through ultrasonic treatment of integrated Ru/TiMnOx electrode, the catalyst ink was prepared and dropped onto the Au film. The electrochemical cell was a three-electrode system with volume of ~3\u2009mL. Before the electrochemical measurements, all the electrolytes were purged with high-purity Ar to remove the dissolved oxygen. Before data collection, a voltage was applied to the working electrode for 718\u2009s to test for receiving a reliable signal. The Ru/TiMnOx were subjected to three LSV cycles in the potential range of 1.17-1.72\u2009V vs. RHE at a scan rate of 10\u2009mV\u2009s-1 in 0.5\u2009M H2SO4 (H218O), while the mass signals of 32O2, 34O2 and 36O2 were recorded. Then, five consecutive CV cycles (1.17-1.72\u2009V vs. RHE) were applied for labeling the catalyst surface with 18O. The catalysts were thoroughly washed with H216O deionized water to remove surface-adsorbed H218O and then operated in the 0.5\u2009M H2SO4 (H216O) electrolyte. Before data collection, a voltage was applied to the working electrode for 767\u2009s to test for receiving reliable signals. Again, the gaseous products were monitored by the mass spectrometer.\n\nAll electrochemical experiments were conducted using a BioLogic VSP-300 potentiostat equipped with iR compensation module. A conventional three-electrode cell was employed, comprising: working electrode (as-synthesized integrated RuTiMnOx electrode: geometric area: 1\u2009cm2; thickness: 600 \u03bcm), counter electrode (Pt plate: 1\u2009cm2 effective area; positioned parallel to the working electrode) and reference electrode (Ag/AgCl in saturated KCl). The Ag/AgCl reference electrode was calibrated against a secondary pre-validated master electrode (Tianjin Eilian Electronics & technology Co. ltd) using a dual-reference-electrode setup. Both electrodes were immersed in saturated KCl solution, and the open-circuit potential (OCP) was recorded, ensuring potential stability within \u00b1 5\u2009mV. The electrolytes were freshly prepared and promptly utilized. Linear sweep voltammetry (LSV) was performed at a scan rate of 10\u2009mV\u2009s-1 from 0.0 to 2.0\u2009V vs. RHE. The potential was converted to the reversible hydrogen electrode (RHE) scale using the Eq. (1):\n\nThe LSV curves were corrected with 95 % iR (i, current; R, resistance) compensation. The chronopotentiometric tests, conducted under iR-free conditions at a constant current density of 10\u2009mA\u2009cm\u22122, were performed in an H-type water electrolysis cell with the anode and cathode separated by a Nafion 117 membrane. A Nafion 117 membrane (DuPont, thickness: 183 \u03bcm) was sequentially washed by 5\u2009wt% H2O2, electrolyte, and deionized water for 30\u2009min, 60\u2009min, and 30\u2009min, respectively. The active area of Nafion 117 membrane is 19.6\u2009cm\u22122. Nyquist plots derived from electrochemical impedance spectroscopy (EIS) were acquired using a BioLogic VSP-300 potentiostat under open-circuit conditions, with a sinusoidal perturbation amplitude of 5\u2009mV applied across the frequency domain from 100\u2009kHz to 0.01\u2009Hz. Measurements were conducted in 0.5\u2009M H2SO4 (pH = 0.3\u2009\u00b1\u20090.02), 0.5\u2009M PBS (pH = 7.1\u2009\u00b1\u20090.10) and 1.0\u2009M KOH (pH = 13.9\u2009\u00b1\u20090.03) electrolytes.\n\nFor PEMWE tests, a cation exchange membrane (DuPont, Nafion\u2009117) was used as the membrane electrolyte. The membrane electrode assembly (MEA) was prepared by pressing the cathodes (20% Pt/C sprayed on the Nafion 117 membrane) and anodes (Ru/TiMnOx). During the test, the cell was maintained at 60\u2009\u00b0C, and the pre-heated deionized water was fed to the anode by a peristaltic pump. For AEMWE tests, the anion exchange membrane (FAA-3\u201350) was pre-treated in 1.0\u2009M KOH for over 24\u2009h and cleaned with deionized water. The commercial Raney Ni mesh was used as the cathode while the Ru/TiMnOx was used as the anode. 1.0\u2009M KOH was used as an electrolyte at a temperature of 60\u2009\u00b0C. All the data of PEMWE and AEMWE were not iR corrected and displayed as raw data.\n\nThe electrochemically accessible surface area (ECSA) was quantified using the double-layer capacitance method, as defined by (2):\n\nWhere Cdl is electrode-specific double-layer capacitance and Cs is the specific capacitance of the sample. Cdl was derived from cyclic voltammetry (CV) scans in the non-faradaic region (0.2\u2009~\u20090.3\u2009V vs. RHE) with six scan rates (20, 40, 60, 80, 100, 120\u2009mV\u2009s-1). The charging current (ic) was measured at 0.25\u2009V vs. RHE and plotted against scan rate (v). The slope of the linear regression yielded Cdl according to Eq. (3):\n\nThe mass activity (jmass) was determined using Eq. (4):\n\nWhere mRu is the calculated Ru mass loading based on the results of ICP-OES analysis, Ageo is the geometric area and jgeo is the geometric current density.\n\nAll DFT calculations were conducted by Vienna Ab initio Simulation Package (VASP) with projected augmented wave (PAW) pseudopotentials. The generalized gradient approximation (GGA) functional of Perdew-Burke-Ernzerhof (PBE) was applied as the exchange-correlation functional. The kinetic cutoff energy of plane wave was set at 520\u2009eV. The DFT\u2009+\u2009U approach was applied with Ueff\u2009=\u20093.9 for Mn to treat the strong on-site Coulomb interaction of localized electrons.The supercell of Ru/MnTiOx, Ru/MnOx and RuO2 contained 64, 96, 48 atoms respectively. The Brillouin zone in reciprocal space was sampled by a gamma-centered k-point grid of 2\u2009\u00d7\u20092\u2009\u00d7\u20092 during structural optimization. The slab model of Ru/MnTiOx had four layers, containing 18 Ru atoms, 34 Mn atoms, 20 Ti atoms and 72\u2009O atoms. The Ru/MnOx (110) slab model had 96 atoms, including 18 Ru atoms, 14 Mn atoms and 64\u2009O atoms. The slab models are used for calculations of OER and demetallization energies. The demetallization energies were obtained by the following Eq. (5):\n\nwhere Etot is the total energy of the slab model, Eatom is the energy of a single atom and Evac is the slab model energy with a vacancy formed by removing the single atom.\n\nThe computational hydrogen electrode (CHE) approach was applied to calculate the energy barrier in OER process. Adsorbate evolution mechanism (AEM) and oxide path mechanism (OPM) were both considered. The overpotential in AEM was evaluated using the following steps (6)\u2013(9):\n\nOPM was shown in the following steps (10)\u2013(14):\n\n*OH, *O, and *OOH are the reaction intermediates adsorbed on the catalyst surface. The catalyst surface was represented by slab models in calculation. Then, the free energy of each OER step was calculated according to the following Eq. (15):\n\nwhere \u0394ZPE is the zero-point energy, \u0394E is the change in the total ground-state energy, T is temperature (298\u2009K), \u0394S is the change in entropy and \u0394GU\u2009=\u2009eU, U is the electrode potential. The zero point energies and entropies of the intermediates were obtained by calculating the vibrational frequencies.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data supporting this study is available in the article and the Supplementary Information. Source Data file has been deposited in Figshare under accession code DOI link69.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The Python code written in this paper for screening the most appropriate catalyst metal ratio is provided in the Supplementary Code\u00a01.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Seh, Z. W. et al. Combining theory and experiment in electrocatalysis: insights into materials design. Science 355, eaad4998 (2017).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nRam, R. et al. Water-hydroxide trapping in cobalt tungstate for proton exchange membrane water electrolysis. Science 384, 1373\u20131380 (2024).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nChong, L. et al. La-and Mn-doped cobalt spinel oxygen evolution catalyst for proton exchange membrane electrolysis. 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Lv, Intrinsic metal-support interactions break the activity-stability dilemma in electrocatalysis, Figshare, https://doi.org/10.6084/m9.figshare.27233385 (2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work is supported by the National Natural Science Foundation of China (Grant No. 52371228 (R.L.) and 52302302 (M.Y.)), the National Key Research and Development Program of China (Grant No. 2021YFA1200800 (R.L.)), Xishan-Tsinghua Program for Deep Integration of Industry-University-Research (Grant No. 20242002205) and Tsinghua University-Toyota Joint Research Center for Hydrogen Energy and Fuel Cell Technology of Vehicles (grant to R.L.). We thank R. X. Zhou and Y. F. Li for technical support, and Q. M. Yuan and G. S. Liao for experimental assistance.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Lingxi Zhou, Menghao Yang.\n\nState Key Laboratory of New Ceramic Materials, School of Materials Science and Engineering, Tsinghua University, Beijing, China\n\nLingxi Zhou\u00a0&\u00a0Ruitao Lv\n\nShanghai Key Laboratory for R&D and Application of Metallic Functional Materials, School of Materials Science and Engineering, Tongji University, Shanghai, China\n\nMenghao Yang\u00a0&\u00a0Yihong Liu\n\nInstitute of Materials Research and Shenzhen Geim Graphene Center, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China\n\nFeiyu Kang\n\nKey Laboratory of Advanced Materials (MOE), School of Materials Science and Engineering, Tsinghua University, Beijing, China\n\nFeiyu Kang\u00a0&\u00a0Ruitao Lv\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nR.L., and L.Z., conceived the original idea. R.L., supervised the project. L.Z., carried out machine learning, catalyst synthesis, materials characterization, catalytic tests and related data processing. M.Y., and Y.L., carried out DFT calculations. L.Z., M.Y., Y.L., and F.K., co-wrote the manuscript. All authors contributed to data analysis, scientific discussion and commented the manuscript.\n\nCorrespondence to\n Ruitao Lv.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Abuzar Khan, Yan-Gu Lin who co-reviewed with Chun-Kuo Peng; and Yongwen Tan for their contribution to the peer review of this work. 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Intrinsic metal-support interactions break the activity-stability dilemma in electrocatalysis.\n Nat Commun 16, 8739 (2025). https://doi.org/10.1038/s41467-025-63397-z\n\nDownload citation\n\nReceived: 14 November 2024\n\nAccepted: 19 August 2025\n\nPublished: 01 October 2025\n\nVersion of record: 01 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63397-z\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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corporate climate strategies", + "journal": "Nature Communications", + "published": "10 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62970-w/MediaObjects/41467_2025_62970_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62970-w/MediaObjects/41467_2025_62970_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62970-w/MediaObjects/41467_2025_62970_MOESM3_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62970-w/MediaObjects/41467_2025_62970_MOESM4_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62970-w/MediaObjects/41467_2025_62970_MOESM5_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-62970-w#MOESM3", + "https://github.com/n-stolz/nature_comms_negligible_role_carbon_offsetting.git", + "https://github.com/n-stolz/nature_comms_negligible_role_carbon_offsetting.git", + "https://doi.org/10.5281/zenodo.15634074" + ], + "code": [ + "https://github.com/n-stolz/nature_comms_negligible_role_carbon_offsetting.git" + ], + "subject": [ + "Business", + "Industry" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5355499/v1.pdf?c=1757674135000", + "research_square_link": "https://www.researchsquare.com//article/rs-5355499/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-62970-w.pdf", + "preprint_posted": "24 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Carbon credits feature prominently in corporate climate strategies and have sparked public debate about their potential to delay companies' internal decarbonisation. While industry reports claim that credit purchasers decarbonise faster, rigorous evidence is missing. Here, we provide an in-depth analysis of 89 multinational companies\u2019 historical emissions reductions and climate target ambitions. Based on self-reported sustainability data and more than 400 sustainability reports, we find no significant difference between companies that purchased credits and those that did not. Voluntary offsetting is not a central part of most companies\u2019 climate strategies, and many pass these credits' costs and purchase decisions directly onto their customers. While the companies within our sample retired 1/4th of all carbon credits in 2022, the top five offsetters' expenditures on voluntary emission offsetting are, on average, only 1 percent relative to their capital expenditures. For most companies, carbon credits are, therefore, unlikely to crowd out internal decarbonisation measures. Yet, we document that for large-scale offsetters like Delta Air Lines or easyJet, carbon credit purchases competed with financing internal decarbonisation efforts.Scientific community and society/Business and industry/IndustryScientific community and society/Business and industry/Business", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Carbon credits feature prominently in corporate climate strategies and have sparked public debate about their potential to delay companies\u2019 internal decarbonisation. While industry reports claim that credit purchasers decarbonise faster, rigorous evidence is missing. Here, we provide an in-depth analysis of 89 multinational companies\u2019 historical emission reductions and climate target ambitions. Based on self-reported environmental data and more than 400 sustainability reports, we find no significant difference between the climate strategies of companies that purchased credits and those that did not. Voluntary offsetting is not a central part of most companies\u2019 climate strategies, and many pass credit costs directly onto their customers. While the companies within our sample retired one-fourth of all carbon credits in 2022, the top five offsetters\u2019 expenditures on voluntary emission offsetting are, on average, only 1 percent relative to their capital expenditures. For most companies, carbon credits are, therefore, unlikely to crowd out internal decarbonisation measures. Yet, we document that for large-scale offsetters in the airline industry, carbon credit purchases competed with financing internal decarbonisation measures.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Companies have faced increasing pressure from policymakers, consumers, civil society, and investors to reduce emissions. In response to the Paris Agreement, governments have consistently tightened their climate policies1. This regulatory shift has increased investors\u2019 concerns about transition risk, prompting them to demand credible climate strategies from the companies in their portfolio2. However, media and non-governmental organisations (NGOs) that monitor corporate emission targets3 frequently expose cases of greenwashing4,5.\n\nIn response to the growing pressure, half of the Forbes Global 2000 companies have set net zero targets3. However, no globally accepted, binding standard for setting credible net zero targets exists. This gap has been filled by private initiatives like the Science-Based Targets initiative (SBTi), which provides standards on the emission coverage and ambition of net zero targets. SBTi also offers guidelines for how companies can compensate for residual emissions once they have reduced most of their emissions6.\n\nMany companies use or plan to use carbon credits from the voluntary carbon markets to offset parts of their emissions on their path to net zero7. Carbon credits are the \u201creduction, avoidance or removal of a unit of greenhouse gas (GHG) emissions by one entity, purchased by another entity to counterbalance a unit of GHG emission by that other entity\"8. These voluntary emission practices and the claims derived from emission offsetting (e.g. carbon neutral, net zero)9 are prominently featured in the public debate about decarbonisation10,11,12. However, carbon credits\u2019 role in corporate climate strategies remains unclear.\n\nInsights from political economy, stakeholder theory, and legitimacy theory suggest that when civil society, politicians, and other stakeholders pressure companies, they may engage in voluntary social or environmental disclosure to shore up support13,14. However, these strands of literature typically do not predict or assess whether such action translates into improved corporate climate performance. Companies may engage in voluntary environmental disclosure to achieve a variety of objectives: they may aim to inform the public about superior environmental performance relative to peers, to reshape the company\u2019s image without substantial change, to shift the focus of the discourse, or to influence stakeholder expectations about the company\u2019s actions13,15. In the context of voluntary carbon offsetting, companies may retire carbon credits to signal superior environmental performance or enhance their public image without substantially advancing internal decarbonisation. Alternatively, corporate emission offsetting, as a form of corporate social responsibility, may be driven by altruistic motives16 that have not been previously captured in the literature.\n\nPurchasing carbon credits instead of pursuing\u00a0potentially more effective internal decarbonisation can be conceptualised as a moral hazard. Moral hazard occurs when actors take on higher risks or engage in socially suboptimal behaviour because they are shielded from the consequences of their actions17. Therefore, there is a risk that emission offsetting leads to moral hazard when companies neglect internal and value chain emission reductions because the improved public perception achieved through emission offsetting shields them from the risk of reputational damage, public scrutiny, or governmental regulation.\n\nThere is mixed evidence regarding the relationship between corporate carbon management practices and subsequent emission reductions. While some studies demonstrate a positive relationship between corporate carbon disclosure and emission reductions18,19, others find this link only among emission-intensive companies20. Conversely, to our knowledge, no study to date has established a significant relationship between adopting reporting guidelines, such as the Global Reporting Initiative (GRI) and improved corporate emission performance21,22. Further, the impact of corporate climate strategies on emission reductions remains ambiguous. For example, there is limited evidence around the relationship between the mere presence of corporate climate targets and subsequent decarbonisation23, though more ambitious targets are associated with greater emission reductions23,24. Recent findings suggest that only a comprehensive mix of corporate climate instruments (e.g., absolute emission targets, internal carbon prices, value chain engagement) is linked to absolute emission reductions25.\n\nWhile research on companies\u2019 use of carbon credits is still nascent, research on renewable energy attributes (REAs), another market-based carbon accounting tool, is more advanced. REAs allow companies to verify and claim the purchase of renewable energy, directly reducing market-based scope 2 emissions under the Greenhouse Gas Protocol and Science-Based Targets initiative (SBTi)26. Unlike voluntary carbon credits, REAs can be counted towards SBTi goals. Ascui et al.27 show that companies using REAs tend to increase their scope 1 and 2 emissions without improving energy efficiency compared to peers who do not use them, which indicates their potential to induce moral hazard27. Additionally, setting targets for market-based scope 2 emissions and achieving them by purchasing REAs might undermine the integrity of SBTi because these certificates do not lead to real emission reductions28,29,30.\n\nIn contrast to the well-documented role of REAs in corporate climate performance, research on carbon credits is still nascent. Recent industry reports claim that companies engaging in emission offsetting decarbonise faster than their peers31,32,33. These industry reports argue that by voluntarily offsetting emissions, companies put a price on greenhouse gas emissions, creating a financial incentive for faster internal decarbonisation33. In addition, Engler et al.34 show that emission offsetting does not crowd out investment in decarbonisation for German small- to medium-sized companies but complements pro-environmental activities34.\n\nHowever, growing evidence challenges voluntary carbon markets\u2019 overall positive climate effect. A range of studies show that emission reductions associated with carbon credits are systematically overestimated35. Also, large-scale offsetters tend to source cheap and low-quality carbon credits36. Beyond these issues, the industry reports supporting the claim that emission offsetters decarbonise faster than their peers do not control for company size and do not examine whether carbon credit spending might crowd out investments in internal decarbonisation31,32,33. Further, scientific findings on the corporate usage of carbon credits are not based on observed corporate emission performance but capture explanatory variables like perceived climate-related risks, presence of any climate target and environmental management systems34. However, these findings cannot be generalised to multinational companies in hard-to-abate sectors, which constitute the largest buyer group37.\n\nHere, we analyse the emission offsetting behaviour of 89 multinational companies in the oil and gas (O&G), automobile manufacturing, and airline sectors. The companies in the sample constitute 24% of carbon credits retired in the voluntary carbon market in 2022. We focus on multinational companies in hard-to-decarbonise sectors since those companies are characterised by high emissions, low profits per ton of emitted carbon dioxide38, and weak climate targets37. We compiled a dataset on the corporate usage of voluntary carbon credits by combining several sources. First, we utilised data from CDP on corporate greenhouse gas emissions, carbon credit retirements, and emission targets. Second, we cross-verified the carbon credit retirement data with the largest registries (Verra, Gold Standard, CDM). Third, we compiled a dataset of qualitative information on corporate emission offsetting between 2014 and 2023, analysing over 400 corporate sustainability and annual reports. Our analysis proceeds in two stages. First, we evaluate if emission offsetting is associated with more ambitious decarbonisation efforts by companies compared to peers that do not engage in emission offsetting. We measure decarbonisation ambition with two metrics: the change in scope 1 emissions between the CDP reporting cycles 2018 and 2023 as a retrospective indicator and the ambition of emission reduction targets as a forward-looking metric. Second, we examine the role of emission offsetting in corporate climate strategies by analysing carbon credit expenditures and their usage details.\n\nWe find no significant difference in climate strategy between companies that offset emissions and those that do not. The reason for the non-significant relationship is likely the low expenditures on emission offsetting relative to companies\u2019 capital expenditures, the low overall share of offset emissions relative to total company emissions, and the non-binding nature of voluntary emission offsetting. However, we find that carbon credit expenditures compete with financing internal decarbonisation efforts for large-scale offsetting campaigns. We argue that voluntary emission offsetting is not associated with accelerated corporate decarbonisation and that the role of carbon credits in corporate climate strategies is overstated in the public discourse.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We find no significant relationship between the number of carbon credits a company retired in CDP\u2019s 2023 reporting period and the historical change in scope 1 emissions or with the ambition of their emission targets (see Fig.\u00a01).\n\nThe graph displays the estimated regression coefficients (\\({\\hat{\\beta }}_{i}\\)), with error bars representing their 95% confidence intervals (CI) with \\({\\text{CI}}_{{\\beta }_{i},0.95}=\\left[{\\hat{\\beta }}_{i}-{t}^{*}\\cdot \\,\\text{SE}\\,({\\hat{\\beta }}_{i}),{\\hat{\\beta }}_{i}+{t}^{*}\\cdot \\,\\text{SE}\\,({\\hat{\\beta }}_{i})\\right]\\) and t* the critical value from the t-distribution. In (a), positive regression coefficients indicate a negative relationship between the explanatory variables (on y-axis) and decarbonisation speed (x-axis), suggesting that as the explanatory variables increase, we observe a decreased decarbonisation speed. In (b), positive regression coefficients indicate a positive relationship between the explanatory variables (y-axis) and climate target ambition (x-axis), suggesting that as the explanatory variables increase, we observe an increased climate target ambition. The sectoral categorical variables are relative to the aviation sector, and the geographic categorical variables are relative to headquarters in Asia. The label of retired carbon credits is written in bold as it represents the study\u2019s primary outcome variable of interest.\n\nThe only significant association with the change of scope 1 emissions over the study period is that companies in the oil and gas sector reduced their emissions more slowly than companies in the aviation sector (significance level \u03b1\u2009=\u20090.05, Fig.\u00a01a). However, this relationship is not robust to leaving single observations out of the regression (see Supplementary Information Fig.\u00a0S1a, b). Nevertheless, there is no significant association between the number of carbon credits retired and the change in scope 1 emissions.\n\nConversely, the variation in climate target ambition until 2050 is significantly correlated with sector and location. Companies headquartered in Europe set more ambitious climate targets than their peers in other regions. Conversely, oil and gas companies are associated with setting targets that are less ambitious than those of companies in other sectors. However, we find no significant association between the number of retired carbon credits and climate target ambition.\n\nThese findings suggest that the number of carbon credits a company retires is unrelated to its past emission reductions and climate target ambition. This conclusion holds across various robustness checks (see Supplementary Information Tables\u00a0S3\u2013S8). Below, we explore the reasons for these findings.\n\nThere are two opposing lines of argument about how the purchase of carbon credits can influence companies\u2019 emission trajectories. Some argue that buying carbon credits diverts resources from internal decarbonisation, thereby delaying it39. Others argue that the cost of carbon credits is a voluntary penalty for greenhouse gas emissions, which incentivises companies to accelerate decarbonisation33. Our findings support none of these views since we find a non-significant relationship between offsetting and emission trajectories and climate target ambition.\n\nTo delve deeper into potential reasons behind our findings, we estimate the magnitude of emission offsetting costs relative to companies\u2019 investments in their assets and mandatory emission costs. Therefore, we estimate companies\u2019 carbon credit and emission trading scheme costs and compare them to their reported capital expenditures. Compared to their capital expenditures (CAPEX), companies allocate relatively small amounts of funds to emission offsetting (see Fig.\u00a02). The companies with the largest CAPEX share spent on carbon credits are easyJet (2.7%) and Delta Air Lines (1.8%). In the oil and gas sector, Eni (0.14 \u22120.38%) and Shell (0.10 \u22120.25%) follow. In the automobile manufacturing sector, Volkswagen Group (0.13\u22120.20%) and Mercedes-Benz Group (0.07 \u22120.13%) spend the largest share of funds on offsetting. Overall, the distribution of corporate spending on emission offsetting is long-tailed, meaning that few companies spend a lot on credits while the majority spend little.\n\nMinimum estimates (blue) are based on the lowest reported carbon credit price among companies in the sample (easyJet) for 2022, and maximum estimates (red) are based on data from Ecosystem Marketplace (2023)60. Reported CAPEX shares (green) indicate that companies reported total spending for carbon credits in 2022. The figure includes the 15 companies with the highest share of funds spent on carbon credits relative to their CAPEX.\n\nSimilarly, European companies face much higher costs under compliance Emission Trading Schemes (ETS) - such as EU-ETS, UK-ETS, and Switzerland-ETS - compared to their spending on carbon credits (see Fig.\u00a03). However, there is substantial variation between sectors. For example, easyJet, despite being an outlier in carbon credit spending, spent 9.9\u2009times more money on compliance emission trading than on voluntary emission offsetting. In the oil and gas sector, Eni had to allocate ~33.1\u2009times more to compliance emission trading, while Shell only spent ~6.7\u2009times more. An exception to this trend is the BMW Group, which spent more on voluntary offsetting - 2.3\u2009times their compliance emission trading costs - since they could cover their emissions with excess allowances from past years40.\n\nSources: Retired carbon credits and ETS allowances from the CDP database40, average ETS prices from World Bank46, Carbon credit price estimates from Ecosystem Marketplace (2023)60. *Easyjet directly reports spending on carbon credits in their 2022 annual report.\n\nIn addition to the financial commitment of offsetting emissions, which emissions a company offsets, who pays for the carbon credits, and the permanence of the decision to offset emissions likely affect the role of carbon credits in companies\u2019 decarbonisation strategies. We find that the role of emission offsetting in corporate climate strategies varies between companies (see Fig.\u00a04). Companies that purchase large quantities of carbon credits typically follow one of two approaches: they either offset a fixed portion of their emissions (e.g. Delta Air Lines, easyJet, and Volkswagen Group) or offset residual emissions to meet specific emission targets (e.g. Shell, Eni, and Inpex). In contrast, companies that purchase fewer carbon credits typically only offer them to their customers during the checkout (e.g. Deutsche Lufthansa, Air France - KLM, Ryanair Holding, International Consolidated Airlines Group) or offset their business travel (e.g. Deutsche Lufthansa, International Consolidated Airlines Group).\n\nOverview of offsetting purpose for companies that reported >100,000 retired credits in CDP's 2023 survey between 2014 and 2023.The information is based on companies' sustainability and annual reports.\n\nWe find that all airlines and oil and gas companies that reported the retirement of >100,000 carbon credits in the CDP 2023 survey either provide customers the option to voluntarily offset their purchases at checkout or to buy pre-offset fossil products. It is common practice for airlines to offer the option to offset emissions during the flight booking process (e.g. easyJet post-2022, Deutsche Lufthansa, International Consolidated Airlines, Ryanair Holdings, Air France - KLM). Similarly, oil and gas companies regularly sell pre-offset fossil products (e.g. Shell, BP, Eni, Inpex, TotalEnergies). Therefore, despite their relatively low expenditure on carbon credits, many companies pass these credits\u2019 costs and purchase decisions directly onto their customers.\n\nBesides large variability in the usage of carbon credits for emission offsetting, companies regularly change their offsetting strategy. Over the past decade, many companies have expanded the use cases for emission offsetting. However, the largest two emission offsetters, Delta Air Lines and easyJet, stopped their large-scale offsetting campaign in 2022. Also, BP changed its offsetting strategy in 2020, ceasing to use carbon credits to reach its zero net growth target41.\n\nGiven the large variety in companies\u2019 approaches to emission offsetting (Fig.\u00a04), it is unclear how substantial the number of retired carbon credits is compared to companies\u2019 overall greenhouse gas emissions. We find that only easyJet (78.2%) and Delta Air Lines (43.7%) retire carbon credits accounting for substantial portions of their total scope 1, 2, and 3 emissions (see Fig.\u00a05a). Deutsche Lufthansa offsets the third highest share of scope 1, 2 and 3 emissions (1.5%) despite ranking 11th in the number of retired carbon credits in the sample and only offsetting employee business travel and offering voluntary offsets to customers (see Fig.\u00a04). Among oil and gas companies, Eni (1.4%) and BP (0.7%) offset the largest shares of their emissions, while in the automobile manufacturing sector, Volkswagen Group (1.1%) and BMW (0.7%) offset the most.\n\nShare of scope 1, 2, and 3 emissions (a) and of scope 1 and 2 emissions (b) that companies voluntarily offset based on emission reporting during the 2023 CDP reporting cycle40. Location-based scope 2 emissions are used.\n\nThe picture changes substantially when focusing solely on scope 1 and location-based\u00a0scope 2 emissions. The three automobile manufacturers that offset their emissions, Volkswagen Group (50.3%), BMW (45.2%), and Mercedes-Benz Group (40.3%), emerge among the largest five offsetters of scope 1 and 2 emissions (see Fig.\u00a05b). These offset shares rise further when considering market-based scope 2 emissions since BMW and Mercedes-Benz Group offset their full scope 1 and market-based scope 2 emissions. However, even limited to scope 1 and 2 emissions, oil and gas companies offset minor emission shares. Shell (9.8%) has the largest share of offset scope 1 and 2 emissions in the oil and gas sector, followed by Eni (7.5%) and BP (7%).\n\nDespite clear indications that voluntary emission offsetting contributes, on average, little to meaningful decarbonisation efforts (see Fig.\u00a01) due to low investment levels (see Fig.\u00a02) and low persistence of emission offsetting in corporate climate strategies (see Fig.\u00a04), it is unclear if voluntary emission offsetting competes with internal decarbonisation investment in some cases. Among companies engaging in emission offsetting, we identify two key pathways through which offsetting may compete with internal decarbonisation efforts.\n\nThe first pathway is the investment effect. In this pathway, companies allocate a fixed budget for decarbonisation, which includes both the purchase of carbon credits and investments in internal emission reduction measures. Every dollar spent on carbon credits reduces the funds available for investments in operations or value chain decarbonisation. As a result, carbon credit purchases compete with investments in operations or value chain decarbonisation, potentially replacing internal decarbonisation efforts.\n\nThe second pathway is the target effect. Here, companies use carbon credits as part of their strategy to meet emission reduction targets. Since purchasing carbon credits is often cheaper and easier than implementing structural changes, companies may opt to purchase carbon credits instead of implementing more substantial internal decarbonisation initiatives to meet their targets. Therefore, as for the investment effect, the target effect might indirectly lead to competition between investments in internal decarbonisation and carbon credit expenditures.\n\nWe find evidence for the investment effect, where investments in decarbonisation are crowded out due to the allocation of funds to carbon offsetting for Delta Air Lines and easyJet. These two companies are the outliers, spending the largest CAPEX ratios on carbon credits (see Fig.\u00a02). Delta Air Lines set a fixed budget of USD 1\u2009billion over 10\u2009years for decarbonisation measures, including both carbon credits and investments in internal decarbonisation initiatives42. After 3\u2009years, Delta spent USD 284\u2009million on carbon credits, leaving only USD 16\u2009million for internal decarbonisation initiatives, assuming an annual budget of USD 100\u2009million over 10\u2009years (see Table\u00a01). Although easyJet did not communicate a fixed decarbonisation budget, after stopping their large-scale offsetting campaign, they announced that they would use the funds formerly committed to emission offsetting for internal decarbonisation measures, indicating that large-scale offsetting competed with internal decarbonisation investments.\n\nIn the oil and gas sectors, we observe the target effect, where emission offsetting is treated as a substitute for emission reductions. Shell, Eni, and Inpex include carbon credits in their emission reduction targets (see Table\u00a01), allowing them to meet their targets either by reducing emissions or by retiring carbon credits. While Eni communicates how many carbon credits they plan to use to achieve their emission targets43, it is unclear how many carbon credits Shell44 and Inpex45 plan to use for achieving their emission targets.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62970-w/MediaObjects/41467_2025_62970_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62970-w/MediaObjects/41467_2025_62970_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62970-w/MediaObjects/41467_2025_62970_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62970-w/MediaObjects/41467_2025_62970_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62970-w/MediaObjects/41467_2025_62970_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our analysis of 89 multinational companies, covering around one-fourth of all carbon credits retired in 2022, reveals no significant difference in climate performance or ambition between companies that offset their emissions and those that do not. This conclusion holds for historical emission performance (CDP 2018-2023 survey) and forward-looking climate target ambition. Contrary to the findings by industry reports31,32,33 and Engler et al. (2023)34, companies in hard-to-decarbonise sectors appear to purchase and retire carbon credits primarily in response to external pressure. Given that most companies in our sample spend little and inconsistently on carbon credits, it appears that carbon credit purchases are a strategy to maintain or restore legitimacy without accelerating decarbonisation relative to their peers.\n\nCompared to their overall capital expenditures, companies allocate very little funds to emission offsetting (Fig.\u00a02). Even the two outliers, easyJet and Delta Air Lines, spend less than 3% relative to their CAPEX on carbon credits. Also, compared to compliance emission trading schemes, the costs of voluntary carbon offsetting are relatively low where such schemes exist. For instance, the five European companies that retired the most carbon credits spent, on average, 5.7% of their compliance carbon expenditures on voluntary carbon credits. Therefore, even reallocating these funds to internal decarbonisation efforts would likely have minimal impact on overall emission reductions. This illustrates that despite its prominence in the public debate on corporate climate strategies, voluntary emission offsetting plays only a minor role in shaping these strategies. Hence, its importance is overstated in public discourse since it imposes minimal financial burden on companies and requires little commitment to climate change mitigation since companies can cease these activities at any time.\n\nConversely, companies operating under European emission trading schemes already pay substantial costs for their emissions. Unlike voluntary carbon markets, these compliance schemes are mandatory, and carbon allowances trade at substantially higher prices than carbon credits on the voluntary carbon markets46. Consequently, they are more suitable for making a business case for corporate decarbonisation.\n\nAlthough, on average, there is no statistically significant association between voluntary emission offsetting and companies\u2019 environmental performance, qualitative evidence suggests two potential ways emission offsetting could compete with internal decarbonisation. The first is the investment effect, which arises when companies allocate a shared decarbonisation budget for both carbon credit purchases and internal decarbonisation. Here, internal decarbonisation initiatives directly compete with spending on carbon credits. The second is the target effect, which occurs when companies allow emission offsetting to reach climate targets, offering a low-cost alternative to internal decarbonisation. However, the long-term impact of these effects on emission trajectories remains unclear. In practice, companies may increase their decarbonisation budgets once they realise that more funds are necessary. For instance, after committing USD 1 billion to decarbonisation in 2020, Delta Air Lines acknowledged in 2023 that additional resources would be required to achieve their emission reduction targets47. Moreover, access to emission offsetting could allow companies to achieve more ambitious climate targets than companies that do not use offsets for residual emissions.\n\nThe lack of significant correlation between the number of retired carbon credits and environmental performance suggests that voluntary carbon offsetting has historically not been associated with moral hazard. However, there is qualitative evidence that some oil and gas companies, like Shell, Eni, and Inpex, plan to integrate voluntary emission offsetting into their climate targets. Although we cannot assess whether these companies genuinely plan to reduce their emissions, this approach could result in moral hazard if companies purchase carbon credits that do not entail promised emission reductions rather than actively reduce their emissions. In such a scenario, the use of carbon credits could reflect the same type of moral hazard previously observed in the context of renewable energy attributes, where the claimed benefits fall short of real emission reductions. Lastly, some compliance pricing mechanisms, such as the Colombian and South African carbon taxes or the Korean and Californian ETSs, permit companies to use carbon credits sourced from voluntary carbon markets to meet part of their obligations48. Hence, if companies reduce their compliance emission costs by retiring carbon credits, this can be associated with moral hazard.\n\nOur findings entail several recommendations for policymakers. Voluntary emission offsetting is not associated with positive corporate environmental performance. Therefore, it is not a reliable alternative to regulatory measures, such as compliance carbon pricing. Policymakers should, therefore, focus on strengthening regulatory mechanisms to ensure substantial corporate contributions to emission reductions. Moreover, companies in emission-intensive sectors typically offset only small portions of their total scope 1, 2, and 3 emissions and allocate minimal funds to purchasing carbon credits. This highlights the importance of policies such as the European Union\u2019s directive to empower consumers for the green transition through better protection against unfair practices and through better information49, which aims to prevent companies from making excessive environmental claims.\n\nWhile we find clear indications that carbon credits play a minor role in corporate climate strategies, the study has potential limitations. First, we only consider a limited number of companies in three hard-to-abate sectors. Therefore, we might not identify small effect sizes of carbon credit usage on the change in scope 1 emissions and climate target ambition. However, few companies in hard-to-decarbonise sectors purchase substantial quantities of carbon credits and report on the details of their credit purchases and emissions. In hard-to-decarbonise sectors like cement, steel, and cargo shipping, no major company offset substantial shares of their emissions during the latest CDP reporting year40. Therefore, the effect of voluntary offsetting cannot be evaluated for those sectors. Second, we rely on self-reported company data, which are often criticised for their lack of credibility and comparability between companies. To limit the influence of reported data, we validated offsetting data with publicly available carbon credit registries and manually checked if outliers in our dataset were plausible.\n\nOverall, the system of voluntary offsetting emissions is not associated with improved corporate sustainability performance. Most companies allocate minimal funds to voluntary carbon credits, which, even if redirected toward internal decarbonisation, would likely have little impact on their overall environmental performance. Further, the flexibility for companies to discontinue or alter their offsetting strategies at any time indicates that emission offsetting does not entail a solid commitment to decarbonising operations and value chains. Therefore, voluntary carbon offsetting should not be considered an indicator of superior corporate environmental performance. Instead, the public discourse on corporate decarbonisation should focus on progress in internal and value chain decarbonisation initiatives.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Our main data source for numerical sustainability data is the CDP (formerly known as Carbon Disclosure Project) reporting cycle 2023. CDP has collected climate impact disclosure from companies since 2002. Scholars frequently use CDP data to study corporate climate performance over time50,51,52,53. In addition, we use financial data from S&P Capital IQ.\n\nThe study includes 89 oil and gas, airlines, and automobile manufacturing companies. These sectors are well-suited for investigating the role of voluntary emission offsetting, as they are characterised by high emissions and include some of the largest emission offsetting companies. In contrast, other hard-to-decarbonise industries, such as steel, cement, and maritime shipping, rarely engage in voluntary offsetting40. The sample includes all passenger airlines and automobile manufacturers that disclose their emissions to CDP. Further, we selected the 40 companies with the highest scope 1 emissions from the oil and gas sector due to the large number of companies in the CDP dataset. PJSC Lukoil was excluded from the analysis due to substantial structural changes in the Russian gas industry during the study period. Consequently, the final sample consists of 39 oil and gas companies, 27 passenger airlines, and 23 automobile manufacturers.\n\nWe use data from CDP\u2019s 2023 survey wave to obtain (1) companies\u2019 scope 1, 2 and 3 emissions, (2) carbon credit retirement data (number of credits and project types), (3) emission targets, and (4) purchased allowances under emission trading schemes. Companies\u2019 reporting years in the 2023 CDP survey wave end between 31. March 2022 and 31. March 2023, with the majority (70 companies) reporting for the calendar year 2022. In addition to the 2023 survey wave, we use CDP\u2019s 2018 survey wave to obtain historical scope 1 and 2 emission data. Since 36 companies in our sample did not participate in CDP\u2019s emission disclosure in 2018, we complement the data with information from corporate sustainability or annual reports. For nine companies (Ko\u00e7 Holding, San Miguel Corporation, Copa Holding, Kinder Morgan, Wizz Air Holding, Grupo Aeromexico, NFI Group, Hawaiian Holding, and Chorus Aviation), we have not found scope 1 emission data for 2018.\n\nTo improve data reliability, we cross-validate carbon credit retirement data with the largest voluntary carbon market registries of Verra, Gold Standard, and CDM. While the CDM was introduced to enable industrialised countries to reach their emission targets under the Kyoto Protocol, CDM credits can also be retired for voluntary purposes. If a company\u2019s cumulative credit retirements in those carbon market registries are larger than what the company reports to CDP, we use the registry data. Discrepancies between CDP data and registry data happen predominantly in cases where companies offset emissions for fossil products sold to clients (e.g. Inpex Corporation and PETRONAS).\n\nWe define the historical emission performance as the ratio of scope 1 emissions reported in the CDP survey waves 2023 and 2018. We do not include scope 2 emissions since 20 companies in the sample did not report scope 2 emissions in 2017, and 13 further companies reported scope 2 emissions in 2017 without disclosing whether they used the location-based or market-based accounting approach. Further, location-based scope 2 emissions are heavily influenced by grid-emission factors, while market-based scope 2 emissions can be lowered through the purchase of renewable energy attributes that have been shown to have little effect on renewable energy expansion28,29. To ensure robustness, we present findings that include changes in combined scope 1 and 2 emissions in the supplementary information (Table\u00a0S4). Further, we do not consider scope 3 emissions since current reporting practices do not allow for meaningful cross-organisational comparison54. We exclude Mercedes-Benz Group from the emission analysis due to the spin-out of Daimler Truck in December 2021. Further, we exclude Inpex Corporation from the emission analysis since it is an outlier with a 1085% increase in scope 1 over the study time. In the\u00a0supplementary information we illustrate with a leave-one-out cross-validation that Inpex Corporation is the only data point that influences the results substantially (see Supplementary Information Fig.\u00a0S1).\n\nWe perform ordinary least squares (OLS) regression using the python package statsmodels (version 0.14.1) to estimate the association between retired carbon credits and the change in historical emission reductions. We control for companies\u2019 sizes, industrial sectors, and continents of headquarters. We estimate:\n\nwhere i indexes the companies, Yi is a company\u2019s ratio of scope 1 emissions reported in the CDP survey waves 2023 and 2018, Xi is the number of carbon credits a company retired in CDP\u2019s 2023 reporting cycle, Ci are the control variables, and \u03f5i represents the error term, capturing unobserved factors affecting Yi. We control for revenue, sector, and continent of headquarters. We convert the categorical variables for the sector (automobile, oil and gas, and airlines) and the continent of headquarters (Asia, Europe, Latin America, and North America) into binary indicator columns (i.e. one-hot encoding). That means in the regression, each categorical variable equals 1 if a company belongs to a specific sector or is headquartered in a particular region and 0 otherwise. To avoid multicollinearity, we excluded the sectoral category \u201cAirlines\" and the geographical category \u201cAsia\" from the regression. These omitted categories serve as the reference groups against which the effects of other categories are compared.\n\nIt is difficult to compare climate target ambition due to differences in scope, base years and target years. Therefore, corporate climate targets must be harmonised before they can be directly compared across companies55. Here we calculate the ambition for each emission scope (scope 1, 2, and 3) and then add the ambitions weighted with the relative importance of that scope for a specific industry (relative importance\u2009=\u2009avg. share of total emissions for scope n in industry X).\n\nWe use the following assumptions and simplifications to construct the target emission trajectories between 2020 and 2050 in line with previous studies that compared climate targets across organisations:\n\nBetween intermediate targets, emission trajectories are linear55.\n\nEmissions that are not covered by any target remain unchanged55,56\n\nWe only consider company-wide targets, not product targets - e.g. when a company sells oil and gas but only has a target for their oil operation, we do not regard it since a reduction in oil emissions could be compensated by increased gas production. The only exception is when there are product-level targets for all main products of a company (e.g. separate targets for oil and gas operation). This assumption helps to avoid the difficulties of aggregating product-level emission-intensity targets for integrated energy companies that often sell different energy and non-energy products56 by constructing a company-wide intensity metric.\n\nIn line with SBTi\u2019s net zero standard6, we accept both absolute and intensity targets. If, for the same year, intensity and absolute targets are given, we use the absolute target. While other studies also accept absolute and intensity targets55,56, in contrast to Bolay et al. (2022), we do not use the associated expected change in absolute emissions for intensity targets that companies need to report to CDP. Instead, we directly use the targeted change in emission intensity to construct a company\u2019s emission trajectory. The deviation from previous studies is due to low data quality for the expected change in absolute emissions for intensity targets. Often, it is unclear if companies correctly use positive and negative numbers to indicate expected increases or decreases in absolute emissions. Further, it is not possible to verify the data since this data is typically not reported in annual or sustainability reports. To verify that companies that use intensity targets do not systematically set more ambitious climate targets, we show in the\u00a0Supplementary Information that the share of a company\u2019s target that is covered by intensity targets is not significantly correlated with its target ambition (Table\u00a0S5) and, therefore, does not bias the result reported in the regression analysis.\n\nWe assign an ambition score for each subtarget by comparing planned emission trajectories with emission reductions in line with the annual average of all 1.5 degree Celsius warming emission scenarios from IPCC\u2019s Sixth Assessment Report57 Studies that evaluated climate targets compare target ambitions either to emission trajectories58 or to targeted average annual change in emissions55,56. We choose to compare companies based on the targeted cumulative emissions until 2050 instead of targeted average annual changes in emissions since not only the average change in emissions but also the shape of the emission trajectory is directly linked to global warming. The choice of the reference emission trajectory is not relevant for the regression results since it is the same constant that is deducted from each observation and, hence, does not influence the regression coefficients. Further, we do not use different reference emission trajectories per sector or geography to avoid biasing our regression results. Instead, we explicitly control for geography and sector in the regression.\n\nFigure\u00a06 illustrates how we translate emission targets to emission trajectories and target ambition for the example company A. Company A\u2019s emission target only covers 90% of its emissions. Therefore, we assume that 10% of emissions remain unchanged. First, we construct the emission trajectory between the base year and target years, assuming emissions decline linearly. Second, we set 2020 as the reference year where emissions are at 100%. Finally, we quantify the target ambition as the area between the targeted company emission trajectory and the average of IPCC\u2019s 1.5 degree Celsius warming emission scenarios:\n\nIllustration of emission target quantification for company A. The red area is the magnitude of ambition. Since the targeted emission trajectory is higher than the 1.5\u00b0 degree trajectory, the ambition is negative.\n\nPositive target ambition values indicate targets surpassing the reference scenario\u2019s ambition, negative target ambition values fall short of the reference scenario\u2019s ambition, and values of zero indicate an exact match with the average of IPCC\u2019s 1.5 degree Celsius warming emission scenarios.\n\nWe perform OLS using the python package statsmodels (version 0.14.1) to estimate the effect size of using carbon credits on the companies\u2019 climate target ambitions. We estimate:\n\nwhere i indexes the companies, Yi is a company\u2019s climate target ambition (eq. (2)), Xi is the number of carbon credits a company retired in CDP\u2019s 2023 reporting cycle, Ci are the control variables, and \u03f5i represents the error term, capturing unobserved factors affecting Yi. We control for revenue, sector, continent of headquarters, and share of emissions covered by intermediate targets on the path to net zero. We convert the categorical variables for the sector (automobile, oil and gas, and airlines) and the continent of headquarters (Asia, Europe, Latin America, and North America) into binary indicator columns (i.e. one-hot encoding). That means in the regression, each categorical variable equals 1 if a company belongs to a specific sector or is headquartered in a particular region and 0 otherwise. We include the share of emissions covered by intermediate targets (i.e. emission targets that are no net zero targets) to avoid systematically favouring companies that set only long-term net zero targets without intermediate goals. For instance, a company with a net zero target for 2050 but no intermediate targets would appear to have higher target ambition than the exemplary company depicted in Fig.\u00a06, as the red area decreases without Target 1.\n\nBesides the quantitative evaluation of how many carbon credits companies retire, we qualitatively evaluate how companies use carbon credits over time using the qualitative data analysis software Atlas.ti. We manually scanned through 488 corporate sustainability and annual reports for the years 2014\u22122023 using the search words \u201coffset\", \u201ccarbon credit\", \u201ccarbon market\", \u201ccompensate\", and \u201ccompensation\". We assigned codes when the text passage revealed the purpose of voluntary carbon credit retirement. We tagged 522 text passages on carbon credit usage in 238 distinct documents.\n\nWe manually classify the use case of carbon credits into five categories:\n\nRetirement to meet emission targets: Companies plan to use carbon credits to reach an emission target. The quantity of retired carbon credits depends on the gap to target completion.\n\nOffsetting fixed portion of emissions: Companies define a fixed portion of their emissions to offset (e.g. scope 1, scope 2, specific product).\n\nEmployees\u2019 business travel\n\nCustomer Offsetting: Companies either offer customers the service to purchase carbon credits during checkout (mostly business to consumer) or offer pre-offset products (both business to consumer and business to business).\n\nOther: Offset usage that does not fit in other categories. These are often pilot projects to source carbon credits in preparation for compliance schemes or fixed-term marketing events.\n\nPrices companies pay for carbon credits are not publicly available at a granular level. Therefore, we estimate feasible price ranges by using the lowest prices reported by companies (USD 3.72 by easyJet59) in our sample as the low end of the price range and average credit prices 2022 by project type reported by Ecosystem Marketplace60 as the upper end of the price range. Using Ecosystem Marketplace\u2019s data as an upper bound is a feasible assumption for the upper end of the feasible price range since companies in our sample are multinational companies purchasing relatively large quantities of carbon credits. Therefore, we assume these companies do not pay prices above the market average. We estimate the lower (boundarylow) and upper (boundaryhigh) boundary of the feasible carbon credit cost range for company i purchasing carbon credits from project type n as:\n\nTo put the costs of carbon credits in perspective, we compare them with companies\u2019 CAPEX and costs of emission allowances under compliance emission trading schemes. CAPEX, which represents the funds a company spends to buy or improve assets, helps assess whether carbon credit costs are substantial enough to compete with investments in internal decarbonisation. Additionally, by comparing these costs to compliance emission pricing mechanisms, we evaluate whether carbon offsetting incentivises companies to accelerate decarbonisation efforts beyond existing regulation.\n\nWe estimate the costs for emission allowances in European emission trading schemes (EU-ETS, UK-ETS, and Switzerland-ETS) by multiplying the number of allowances a company purchased under a specific ETS with average allowance prices in 2022. Companies disclose the number of allocated allowances, purchased allowances, and overall emissions under ETSs in the CDP survey40. Average ETS prices in 2022 are based on the World Bank\u2019s carbon pricing database (EU ETS: USD 86.52, UK ETS: USD 98.99; Switzerland: 65.59 USD)46. The only exception is easyJet, which does not disclose the number of purchased allowances in the CDP report. Here we determine the number of purchased emission allowances as the difference between scope 1 emissions under the different ETSs disclosed to CDP40 and publicly available information on allocated emission allowances61,62,63.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62970-w/MediaObjects/41467_2025_62970_Fig6_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "All relevant data to reproduce plots can be found in our\u00a0supplementary data and under https://github.com/n-stolz/nature_comms_negligible_role_carbon_offsetting.git. The data used in this article includes data points from CDP. The reproduction of any part of the CDP data by any third party is prohibited.\n\nThe data can be cited as: Niklas Stolz and Benedict Probst, The negligible role of carbon offsetting in corporate climate strategies, https://github.com/n-stolz/nature_comms_negligible_role_carbon_offsetting.git, https://doi.org/10.5281/zenodo.15634074, 2025.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All relevant code can be found under https://github.com/n-stolz/nature_comms_negligible_role_carbon_offsetting.git.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Stechemesser, A. et al. Climate policies that achieved major emission reductions: global evidence from two decades. Science 385, 884\u2013892 (2024).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nvan Benthem, A. A., Crooks, E., Giglio, S., Schwob, E. & Stroebel, J. 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We would like to express our gratitude to Lambert Schneider, Malte Toetzke, and Volker Hoffmann for their valuable input and insightful discussions that contributed to the development of this work.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open access funding provided by Swiss Federal Institute of Technology Zurich.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Group for Sustainability and Technology, ETH Zurich, 8092, Zurich, Switzerland\n\nNiklas Stolz\u00a0&\u00a0Benedict S. Probst\n\nNet Zero Lab, Max Planck Institute for Innovation and Competition, Munich, Germany\n\nBenedict S. Probst\n\nCambridge Centre for Environmental, Energy and Natural Resource Governance, University of Cambridge, Cambridge, UK\n\nBenedict S. Probst\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nN.S. designed and led the implementation of the study and collected and analysed the data. N.S. and B.S.P. developed the methodology and wrote the manuscript.\n\nCorrespondence to\n Niklas Stolz.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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The negligible role of carbon offsetting in corporate climate strategies.\n Nat Commun 16, 7963 (2025). https://doi.org/10.1038/s41467-025-62970-w\n\nDownload citation\n\nReceived: 04 November 2024\n\nAccepted: 05 August 2025\n\nPublished: 10 September 2025\n\nVersion of record: 10 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-62970-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"journal": "Nature Communications", + "published": "19 June 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49399-3/MediaObjects/41467_2024_49399_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49399-3/MediaObjects/41467_2024_49399_MOESM2_ESM.pdf" + }, + { + "label": "Lasing Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49399-3/MediaObjects/41467_2024_49399_MOESM3_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49399-3/MediaObjects/41467_2024_49399_MOESM4_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.10839570" + ], + "code": [ + "https://www.vasp.at/", + "https://doi.org/10.5281/zenodo.10839570" + ], + "subject": [ + "Nanowires", + "Quantum optics", + "Semiconductor lasers", + "Silicon photonics" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3875137/v1.pdf?c=1718881689000", + "research_square_link": "https://www.researchsquare.com//article/rs-3875137/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-49399-3.pdf", + "preprint_posted": "09 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Silicon is indisputably the most advanced material for scalable electronics, but it is a poor choice for active photonic applications, due to its indirect band gap. The recently developed hexagonal (hex-)Si1\u2212xGex semiconductor features a direct bandgap at least for x > 0.65, and the realization of quantum heterostructures would unlock new opportunities for advanced optoelectronic devices based on the SiGe system. Here, we demonstrate the synthesis and characterization of direct bandgap quantum wells (QW)s realized in the hex-Si1\u2212xGex system. Photoluminescence experiments on hex-Ge/Si0.2Ge0.8 QWs demonstrate quantum confinement in the hex-Ge segment with type-I band alignment, showing light emission up to room temperature. Moreover, the tuning range of the QW emission energy can be extended using hex-Si1\u2212xGex/Si1\u2212yGey QWs with additional Si in the well. These experimental findings are supported with ab initio bandstructure calculations. A direct bandgap with type-I band alignment is pivotal for the development of novel low-dimensional light emitting devices based on hex-Si1\u2212xGex alloys, which have been out of reach for this material system until now.Physical sciences/Nanoscience and technology/Nanoscale materials/NanowiresPhysical sciences/Optics and photonics/Optical materials and structures/Silicon photonicsPhysical sciences/Nanoscience and technology/Nanoscale devices/Nanophotonics and plasmonicsPhysical sciences/Optics and photonics/Lasers, LEDs and light sources/Semiconductor lasers", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "HexGeSiGeQWsExtendedData.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Silicon is indisputably the most advanced material for scalable electronics, but it is a poor choice as a light source for photonic applications, due to its indirect band gap. The recently developed hexagonal Si1\u2212xGex semiconductor features a direct bandgap at least for x\u2009>\u20090.65, and the realization of quantum heterostructures would unlock new opportunities for advanced optoelectronic devices based on the SiGe system. Here, we demonstrate the synthesis and characterization of direct bandgap quantum wells realized in the hexagonal Si1\u2212xGex system. Photoluminescence experiments on hex-Ge/Si0.2Ge0.8 quantum wells demonstrate quantum confinement in the hex-Ge segment with type-I band alignment, showing light emission up to room temperature. Moreover, the tuning range of the quantum well emission energy can be extended using hexagonal Si1\u2212xGex/Si1\u2212yGey quantum wells with additional Si in the well. These experimental findings are supported with ab initio bandstructure calculations. A direct bandgap with type-I band alignment is pivotal for the development of novel low-dimensional light emitting devices based on hexagonal Si1\u2212xGex alloys, which have been out of reach for this material system until now.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Electronic devices based on silicon have been the driver for the revolution in information technology witnessed today. However, with their standard cubic-diamond crystal structure, silicon, germanium, and SiGe-alloys are all indirect band gap semiconductors, impeding the use of silicon-based materials for lasers and optical amplifiers for integrated photonics1. Several strategies have been investigated for integrating light emitting materials on silicon, including III-V2,3, GeSn4,5,6,7,8,9, strained Ge7,10, and SiGe quantum wells and dots11,12,13,14,15,16,17, but remain challenging due to various reasons. When transformed into the hexagonal crystal structure, the hex-Si1\u2212xGex alloys18 are direct bandgap semiconductors with the fundamental bandgap at the \u0393-point. The hex-Si1\u2212xGex compositional family shows tunable light emission from 1.8\u2009\u03bcm to 3.4\u2009\u03bcm and features a nanosecond radiative lifetime18. As such, hex-Si1\u2212xGex stands out in the field of group IV photonics as a direct bandgap semiconductor with a relatively large energy difference between the direct and indirect conduction band minima, up to 0.3 eV for hex-Ge19,20. Additional favorable properties of hex-Si1\u2212xGex include its low surface recombination velocity21, large theoretical Land\u00e9 g-factor of 1822, and the potential to fabricate structures from nuclear spin-free isotopes23, which is important for applications in quantum information.\n\nQuantum confinement in direct bandgap semiconductors has stood at the cradle of many photonic devices such as single photon quantum dot (QD) emitters24,25,26,27, quantum well (QW) lasers28,29 and colloidal QD LED display technology30,31,32. These direct bandgap low dimensional structures have been responsible for major advances in science and constitute a toolbox for many optoelectronic and quantum photonic devices33,34, allowing for tunable and narrow band emission, and the concentration of charge carriers.\n\nHere, we show the synthesis of hex-SiGe quantum wells, and we demonstrate quantization of the energy levels with type-I band alignment between the hex-Si1\u2212xGex well (0.9\u2009<\u2009x\u2009<\u20091.0) and the hex-Si1\u2212yGey barrier (0.7\u2009<\u2009y\u2009<\u20090.8). We observe broad tunability of the QW emission from 3.4\u2009\u03bcm for hex-Ge/Si0.2Ge0.8 to 2.0\u2009\u03bcm for hex-Si0.1Ge0.9/Si0.3Ge0.7, which may be further extended down towards 1.5\u2009\u03bcm, the limits of which are a subject of future investigations. Most notably, we confirm direct bandgap emission from the QWs by observing a subnanosecond photoluminescence lifetime, comparable with direct bandgap emission in bulk hex-SiGe. Our experimental data are complemented by ab initio density functional theory and quasiparticle calculations of the bandstructure of hex-Ge/Si0.25Ge0.75 QWs, showing a direct bandgap with a large directness, defined to be the separation between the \\(\\bar{\\Gamma }\\) minimum and the nearest indirect conduction band minimum. Theory confirms a type-I heterostructure and carrier confinement in the hex-Ge layers, with almost identical valence and conduction band offsets. Our hex-Ge/Si0.2Ge0.8 QWs thus can serve as a textbook example demonstrating quantum confinement.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We have embedded coaxial hex-Ge quantum wells in hex-Si0.2Ge0.8 barriers, grown epitaxially on the \\(\\{1\\overline{1}00\\}\\) m-plane facets of wurtzite (WZ) GaAs core nanowires (NWs)18,35, as shown in Fig.\u00a01a. The goal is to create a QW of hex-Ge, as shown in Fig.\u00a01b. A Scanning Electron Microscopy (SEM) image in Fig.\u00a01c illustrates the dimensions of the resulting structures. The Ge/Si0.2Ge0.8 shells in these NWs are doped with arsenic, at a doping level below 2.5\u2009\u00d7\u20091018\u2009cm\u22123 (See \u201cMethods\u201d for details about the growth).\n\na Schematic illustration of the GaAs/Si0.2Ge0.8/Ge/Si0.2Ge0.8 core-multishell nanowires. All interfaces are orthogonal to \\(\\langle 1\\overline{1}00\\rangle\\) directions. b Schematic band alignment of the different materials. The electrons and holes are confined in the hex-Ge layer due to type-I alignment with the surrounding hex-Si0.2Ge0.8, as will be proven in this manuscript. Approximate values of the bandgap and offsets are given. c 30-degree tilted scanning electron micrograph of a NW array. Within these NWs, a (12\u2009\u00b1\u20093) nm Ge/Si0.2Ge0.8 QW is embedded.\n\nThe Ge/Si0.2Ge0.8 QWs are characterized by cross-sectional Scanning Transmission Electron Microscopy (STEM) along two different zone axes. When imaged along the [0001] zone axis, the Ge/Si0.2Ge0.8 QW is visible as a hexagon, an example is given in Fig.\u00a02a, and other data is shown in Fig.\u00a0S2. We note that the Si0.2Ge0.8 barrier has composition fluctuations, Si-rich spokes connect the corners of the GaAs with the outer corners of the NW36. Moreover, as highlighted in the inset of Fig.\u00a02a, the thickness of the Ge QW varies between the different facets. Fluctuations in QW thickness on different facets have also been reported for other material systems37,38, possibly resulting in charge carrier localization in the thickest well39. The QW thickness varies between 10 and 30\u00a0nm by changing the growth time, as shown in Fig.\u00a02b, while the Si0.2Ge0.8 barrier thickness always exceeds 50\u00a0nm. For each sample, we observe a distribution of thicknesses, mainly due to the facet-to-facet fluctuation within one NW, which is larger than the deviation in average QW thickness between different NWs of the same sample. The probability distribution is bimodal for some samples, with two different Ge QW thicknesses that are most likely. However, the bimodal distribution does not appear for all samples, and therefore the average is taken as a measure of the QW thickness.\n\na False-colored HAADF-STEM image of a cross-sectional lamella, viewing the Ge QW along the [0001] zone axis. Inset shows that Ge QWs on neighboring facets have different thicknesses. b Growth rate curve for Ge/Si0.2Ge0.8 QWs. The thicknesses of individual facets, all measured in images acquired along the [0001] zone axis, are indicated with the colored data points. Colored areas show approximate probability distributions, obtained from these data points by Kernel smoothing. c False-colored HAADF-STEM image of a cross-sectional lamella, viewing the QW along the \\([11\\overline{2}0]\\) zone axis. The core of the NW is on the left. Locations with local hexagonal (ABABA, blue), cubic (ABCA, green), and twinned cubic boundary (ABCBA, pink) stacking are indicated with circles. The pink arrow highlights a defect that starts in the Ge QW. d X-ray diffraction reciprocal space map around the hexagonal \\([10\\overline{1}5]\\) reflection. The peak position does not match Vegard\u2019s rule (dashed line), indicating pseudomorphic strain relaxation.\n\nWhen imaged along the \\([11\\overline{2}0]\\) zone axis, the Ge/Si0.2Ge0.8 QW is visible as a vertical stripe in TEM (Fig.\u00a02c). The thickness of the QW is not constant along the length of the NW (Fig.\u00a0S3), and the roughness on the \\(\\{1\\overline{1}00\\}\\) interface between Ge/Si0.2Ge0.8 is estimated from Fig.\u00a02c to be a few nm. Additionally, the \\([11\\overline{2}0]\\) zone axis allows to distinguish between hexagonal and cubic stacking. The hexagonal stacking is not continuous along the [0001] direction but is segmented due to the inclusion of cubic defects. Most of these are I3 defects, which nucleate either on the GaAs-Si0.2Ge0.8 interface or at random positions in the shell40,41. An example is indicated with the arrow in Fig.\u00a02c. A statistical analysis of the atomic stacking shows a broad distribution in the length of segments with the hexagonal stacking (Fig.\u00a0S4a, b). In contrast, only narrow segments of coherent cubic stacking are observed.\n\nX-ray diffraction (XRD) is used to study the crystalline quality and lattice constants from a large ensemble of NWs. The diffraction spectra of all samples are similar, indicating comparable crystalline quality between samples (Fig.\u00a0S4c, d). A reciprocal space map around the hexagonal \\([10\\overline{1}5]\\) reflection shows a single peak (Fig.\u00a02d), despite the 0.8% lattice mismatch between Ge and Si0.2Ge0.8. Increasing the Ge thickness does not significantly influence the lattice parameters of the NWs (Fig.\u00a0S5a). Instead, the c-lattice constant depends on the thickness of the Si0.2Ge0.8 barriers (Fig.\u00a0S5b). These observations indicate that there is pseudomorphic strain relaxation in the Ge/Si0.2Ge0.8 structures.\n\nThe Si0.2Ge0.8 barriers have smaller lattice constants than the Ge QW, and the Ge is therefore compressed along the \\(\\langle 11\\overline{2}0\\rangle\\) and \u30080001\u3009 directions. Pseudomorphic strain relaxation in the Ge QW results in an increased lattice constant along the \\(\\langle 1\\overline{1}00\\rangle\\) direction. This radial relaxation becomes more pronounced if the Ge thickness is increased, as confirmed by the Geometric Phase Analysis (GPA) of TEM images (Fig.\u00a0S5c, d).\n\nThe optical properties of the Ge/Si0.2Ge0.8 QW samples have been studied by low-temperature photoluminescence (PL) as a function of the QW thickness in Fig.\u00a03a. We observe that the emission energy consistently blueshifts with decreasing QW growth time demonstrating increasing quantum confinement with decreasing thickness. Moreover, all QW emission peaks are positioned between the emission originating from the bulk hex-Ge and hex-Si0.2Ge0.8 reference samples, thus providing experimental evidence for type-I band alignment. We note that for type-II band alignment, one would expect emission below the energy of (strained) bulk hex-Ge42. The width of the QW emission peaks is larger than that of the reference samples, and for some samples, multiple peaks have been observed; this is probably due to fluctuations in QW thickness and, for the wider QWs, the presence of the second confined level. The intensity of the QW emission exceeds that of the reference sample (see Fig.\u00a0S6a), indicating that many carriers diffuse towards the QWs. The relation between emission energy and QW thickness is shown in Fig.\u00a03b, showing a blueshift with decreasing thickness, consistent with a shift due to confinement energy in a QW.\n\na Ge/Si0.2Ge0.8 PL spectrum for varying growth time at low temperature (T\u2009\u2248\u20094 K) and low excitation density (P\u226465\u2009W cm\u22122), b The PL emission versus the QW thicknesses tQW determined from TEM, together with the confinement energy predicted from theory shifted up by 60 meV to account for the difference in the theoretical and experimental bandgap of the hex-Ge. The dashed line shows the confinement energies using a simple finite QW model. We also include the reference spectra of bulk-Ge and the bulk Si0.2Ge0.8 barrier as horizontal lines with the FWHM of the spectra shown as horizontal gray bars. Error bars in tQW are the standard deviations presented in Fig.\u00a02b and error bars in the peak energy indicate the FWHM of the emission spectrum.\n\nThe optoelectronic properties of the Ge/Si0.2Ge0.8 QWs are investigated in more detail by power- and temperature-dependent photoluminescence spectroscopy. We focus here on two specific samples: (i) a relatively thin (10\u2009\u00b1\u20094) nm QW showing single peak emission with strong confinement and (ii) a thick (24\u2009\u00b1\u20097) nm QW with small confinement energy and a large separation between the confinement level in the QW and the barrier, as shown in Fig.\u00a04. Besides the emission being between the hex-Ge reference and the Si0.2Ge0.8 barrier, as mentioned before, we observe that the emission peak energy of the (10\u2009\u00b1\u20094) nm QW is nearly independent of both the excitation density in Fig.\u00a04a and the temperature in Fig.\u00a04b. We highlight the (absence of) shift with excitation density in Fig.\u00a04c. At low excitation densities a minor\u2009<\u20095 meV blueshift is observed, followed by a redshift at high excitation. These shifts are likely due to Burstein-Moss band-filling (blueshift) and bandgap renormalization (redshift). Importantly, we do not observe the significant blueshift with increasing excitation density expected for a type-II QW structure43. The absence of such a blueshift provides additional evidence for a type-I band offset. Similar trends have been observed for the other QW samples. The spectra of the thick (24\u2009\u00b1\u20097) nm QW sample are plotted in Fig.\u00a04d as a function of excitation density. At low excitation density, we observe a single emission peak, while with increasing excitation density, the sample evolves from a single to a double peak shape. We attribute the presence of the second peak at increased excitation density to either distinct QW thicknesses e.g., at different facets of the nanowire shells or to the observation of the HH2-C2 transition within the wide quantum well. The behavior of the high energy peak becomes dominant at intermediate excitation densities, while the lower energy peak increases at the highest excitation densities. This could indicate a different density of states of the subbands44, but a detailed analysis is beyond the scope of the present paper. The light-in light-out (LILO) curves for the QWs and the Si0.2Ge0.8 barrier reference sample are introduced in Fig.\u00a04g. While we observe sublinear behavior, the slopes of (0.69\u2009\u00b1\u20090.01) and (0.66\u2009\u00b1\u20090.01) for the (24\u2009\u00b1\u20097) nm and (10\u2009\u00b1\u20094) nm QWs respectively exceed the slope of the barrier reference sample (0.59\u2009\u00b1\u20090.02) (observed for all QW samples shown in Fig.\u00a0S6b). Pure radiative (non-radiative) recombination is expected to yield a slope of 1 (2). A LILO slope below unity is due to an increasing loss of carriers at high excitation, which is most likely due to carrier overflow into cubic insertions, or due to Auger recombination. This behavior deserves further study.\n\na The (10\u2009\u00b1\u20094) nm (2.5 min) low temperature (T\u2009\u2248\u20094\u2009K) QW photoluminescence spectrum as a function of excitation density showing a constant lineshape over two orders of magnitude with the peak position in between the bulk-Ge and Si0.2Ge0.8 barrier reference measurements, b The (10\u2009\u00b1\u20094) nm QW showing a near constant lineshape through temperature with the tail states becoming slightly more significant as the peak intensity quenches at higher temperatures. c The emission peak energy of the (10\u2009\u00b1\u20094) nm QW shows a nearly constant magnitude through excitation density. Initially the peak blueshifts due to band-filling of the QW and then redshift around 100\u2009W cm\u22122, likely due to Bandgap renormalization. d The (24\u2009\u00b1\u20097) nm (9 min) QW spectrum evolves from a single to a double peak with increasing excitation density due to band-filling. Additionally, if the lowest and highest excitation density spectra are compared, we observe no significant shift in the position of the low energy peak. e The (24\u2009\u00b1\u20097) nm QW sample as a function of temperature showing emission up to room temperature. f The Arrhenius plot of the QWs and Si0.2Ge0.8 barrier reference samples measured at an excitation density of 0.88 kW cm\u22122. It can be seen that the temperature behavior of the QWs exceeds the bulk hex-Si0.2Ge0.8 reference. g The Light-In Light-Out (LILO) curves of the QWs and SiGe barrier reference samples measured at 4 K. The slopes of (0.69\u2009\u00b1\u20090.01) and (0.66\u2009\u00b1\u20090.01) for the (24\u2009\u00b1\u20097) nm and (10\u2009\u00b1\u20094) nm QWs respectively exceed the (0.59\u2009\u00b1\u20090.02) of the bulk hex-Si0.2Ge0.8 reference.\n\nWe present the PL as a function of temperature in Fig.\u00a04e. Notably, room temperature emission from an ensemble of NWs with a single coaxial hex-Ge/Si0.2Ge0.8 QW is demonstrated. In the range T\u2009=\u20092.4\u2013100\u2009K, the relative magnitude of the higher energy peak increases, which is likely due to the de-trapping of carriers from the potential landscape due to alloy fluctuations in the Si0.2Ge0.8 barrier, allowing more carriers to diffuse to the QW, while the lower energy QW level is already fully occupied. Above 250 K the low energy peak again becomes more dominant, which is likely due to a higher probability of thermal emission from the higher energy QW level into the barrier, while also allowing the carriers to be even more mobile to find the lowest energy states. The temperature dependence of the integrated PL intensity is shown in Fig.\u00a04f and shows a monotonous decay of the intensity with temperature. This shows that the emission is not phonon-activated, which is a strong indication for direct bandgap emission18. Moreover, the intensity of the QW emission outperforms the emission of the bulk hex-Si0.2Ge0.8 reference sample at elevated temperatures (observed for all QW samples shown in Fig.\u00a0S6c), which is an important advantage for devices e.g., a hex-Ge/Si0.2Ge0.8 QW laser. From the thermal quenching results we estimate the band offset and effective mass of the most shallow confined charge carrier from the activation energies in Fig.\u00a0S6d of three of the widest (approximately infinite) QW samples which are found to be Eoffset\u2009=\u2009(100\u2009\u00b1\u200930)meV and m*\u2009=\u2009(0.03\u2009\u00b1\u20090.02)m0 respectively, which is close to the predicted band offset and effective mass of our ab initio bandstructure calculations presented below.\n\nHaving confirmed quantum confinement and wavelength tunability of emission from the hex-Ge/Si0.2Ge0.8 QWs, we subsequently like to demonstrate type-I confinement in hex-Si0.1Ge0.9/Si0.3Ge0.7 QWs that emit light at even higher energy by making use of alloys with a larger bandgap18. These hex-Si0.1Ge0.9/Si0.3Ge0.7 QWs are realized as coaxial nanowire shells, similar to those presented in Fig.\u00a01. A cross-sectional view of the (5\u2009\u00b1\u20091) nm Si0.1Ge0.9/Si0.3Ge0.7 QW is presented in Fig.\u00a05a, and an overview of all studied Si0.1Ge0.9/Si0.3Ge0.7 QWs is presented in Fig.\u00a0S7. There are two main differences compared to the Ge/Si0.2Ge0.8 system studied. Additional radial contrast lines, which do not terminate at the NW corners, are recognizable in the TEM image. These lines correspond to dislocations, whose occurrence is correlated with the lattice mismatch between the WZ GaAs core and the hex-Si1\u2212xGex shell. Secondly, there is a compositional gradient in the Si1\u2212xGex barrier, where the Si concentration increases with increasing distance to the GaAs core (see Fig.\u00a0S8). Both effects arise from the lattice mismatch in this system, which is either relaxed through dislocations or mitigated by forming a self-assembled compositional gradient buffer layer.\n\na False-colored HAADF-STEM of a cross-sectional lamella, viewing the (5\u2009\u00b1\u20091) nm (5 min) Si0.1Ge0.9/Si0.3Ge0.7 QW in the [0001] zone axis. b Background corrected photoluminescence spectra for varying QW growth time at low temperature (\u2009\u2248\u20094 K) and high excitation density\u2009<\u20090.88 kW cm\u22122. Reference spectra of bulk Si0.1Ge0.9 and Si0.3Ge0.7 are included. c The PL emission versus the QW thicknesses tQW determined from TEM. Spectra of the Si0.1Ge0.9 well and Si0.3Ge0.7 barrier alloys are included as horizontal lines with the FWHM of the spectra as horizontal gray bars. A simple finite QW model is calculated for this heterostructure which shows reasonable agreement with the experiment. Error bars in tQW are the standard deviations presented in Fig.\u00a0S7e and error bars in the peak energy indicate the FWHM of the emission spectrum. d Initial QW lifetime measured using TCSPC for the (5\u2009\u00b1\u20091) nm QW for varying laser fluence with the error bars indicating the standard deviation determined fitting the initial decays presented in Fig.\u00a0S9b.\n\nThe photoluminescence emission from the hex-Si0.1Ge0.9/Si0.3Ge0.7 QW is between the emission of the bulk Si0.1Ge0.9 well material, and the barrier material, as shown in (Fig.\u00a05b), signifying a type-I band offset also for these compositions. We again fit the observed QW emission energies with the conventional finite QW model, showing qualitative agreement in Fig.\u00a05c. This suggests that the band alignment of the broader family of the hex-Si1\u2212xGex/Si1\u2212yGey QWs is of type-I nature.\n\nWe emphasize that the observation of efficient direct bandgap emission is not obvious since theoretical DFT calculations predict18 a radiative lifetime of 20\u2009\u03bcs for hex-Ge. If true, this would comprise the well material of our hex-Ge/SiGe QWs. To obtain experimental evidence for direct bandgap emission, we measure the carrier recombination lifetime using a Time-Correlated Single Photon Counting (TCSPC) system employing a Superconducting Nanowire Single Photon Detector (SNSPD) for the (5\u2009\u00b1\u20091) nm QW (Single nanowire spectrum shown in Fig.\u00a0S9a). We measure the PL lifetime at a lattice temperature of 4 K where the nonradiative recombination rate is expected to vanish since the nonradiative recombination is a thermally activated process by \\({\\tau }_{nr}^{-1}={\\tau }_{nr}^{-1}{e}^{-{E}_{a}/kT}\\). For our QWs, this behavior is experimentally observed as a constant PL-intensity below a temperature of 10 K and at an excitation density of 0.88 kW cm\u22122 in Fig.\u00a04f. We measure the PL decay time under pulsed excitation conditions where the radiative limit is maintained up to much higher temperature as shown by Fadaly et al.18, implying that the measured PL decay time should be equal to the radiative lifetime at 4 K. We present the carrier recombination lifetime in Fig.\u00a05d for varying laser fluence. Importantly, we observe an initial carrier lifetime of\u2009\u2248\u20091 ns for the lowest fluence (Full time decays are provided in Fig.\u00a0S9b), confirming direct bandgap emission. We note that the observation of a decreasing recombination lifetime with increasing excitation density provides additional evidence for radiative recombination governed by 1/\u03c4rad\u2009=\u2009B(n0\u2009+\u2009\u0394n)(p0\u2009+\u2009\u0394p)/\u0394p\u2009\u2248\u2009B\u0394n for high excitation (\u0394n\u2009=\u2009\u0394p\u2009>\u2009>\u2009n0,\u2009p0), in which B is the coefficient for radiative recombination, n0,\u2009p0 are the doping concentrations and \u0394n,\u2009\u0394p are the photoexcited carrier concentrations. On the other hand, the observations in Fig.\u00a05d cannot be explained by a nonradiative recombination mechanism since nonradiative recombination centers get saturated at high excitation, thus increasing the lifetime. We conclude that the observed nanosecond radiative recombination lifetime falls within the same range as that reported by Fadaly et al.18,45 for bulk hex-SiGe nanowires and confirms direct bandgap emission in Si0.1Ge0.9/Si0.3Ge0.7 QWs.\n\nTo examine the band alignment of the experimentally realized hex-Ge/Si0.2Ge0.8 and Si0.1Ge0.9/Si0.3Ge0.7 single QWs, we first calculate the electronic band structure of hex-Ge/Si0.25Ge0.75 multi-quantum well (MQW) structures, with \\((1\\overline{1}00)\\) interfaces, as superlattices (see Fig.\u00a06a). The ab initio calculations are based on Density Functional Theory (DFT) for optimized atomic geometries and an approximate quasiparticle (QP) electronic structure approach to the band structures (see \u201cMethods\u201d for details). The band structures of the different materials and heterostructures are aligned employing their branch points (BPs)46. The Ge/Si0.25Ge0.75 MQW system is the closest approximation of the experimentally realized Ge/Si0.2Ge0.8 QWs, which still allows modeling of the alloy barriers by ordered arrangements of a single Si and three Ge atoms in one Lonsdaleite unit cell. The increase of the average Si incorporation by 5% compared to the experiment increases the barrier heights by approximately 0.05 eV, but has a vanishing effect on the confinement for both carrier types. Within the calculations, the Si0.25Ge0.75 barrier thickness is kept constant at 2\u00a0nm, i.e., 12 monolayers along the \\([1\\overline{1}00]\\) direction, while the Ge well thickness is varied between 4 and 15\u00a0nm. This barrier thickness is sufficient to prevent tunneling of electron and hole wave functions through the barriers47. As a consequence, the Ge layers in the MQW system are electronically decoupled, and the Ge layers can thus be treated as isolated single QWs. The use of thin Si0.25Ge0.75 barriers in the modeling only affects the strain distribution, which is different for the thick Si0.2Ge0.8 barriers in the experiment. This effect is accounted for by applying an external biaxial strain to the Ge/Si0.25Ge0.75 MQW structure of -0.6% and -0.91% along the \\([11\\overline{2}0]\\) and [0001] directions respectively, based on the X-ray diffraction experiments on the realized Ge/Si0.2Ge0.8 QWs (Fig.\u00a0S5a). The studied heterostructure is allowed to relax along the \\([1\\overline{1}00]\\) direction, tending towards\u00a0an \u22480.3% expansion in the well and \u22480.1% contraction in the Si0.25Ge0.75 barrier.\n\na Hexagonal Ge/Si0.25Ge0.75 heterostructure with \\((1\\overline{1}00)\\) interfaces. b Bulk hexagonal Brillouin zone (BZ) and its projection onto the two-dimensional BZ of the \\((1\\overline{1}00)\\) interface. c Direct bandgap band structure of hexagonal 4 nm Ge/ 2 nm Si0.25Ge0.75 multiple quantum well structure (black lines) and bulk Si0.25Ge0.75 (gray area) projected onto the two-dimensional Brillouin zone. The horizontal red line indicates the branching points of the two systems used as energy zero for alignment. d Energies of the lowest electron and highest hole subband at \\(\\bar{\\Gamma }\\) versus Ge thickness in the Ge/Si0.25Ge0.75 heterostructures studied. They are compared with the lowest conduction and highest valence band of the bulk Si0.25Ge0.75 barrier material, see the \u201cMethods\u201d section for an explanation. Dashed lines indicate the extrapolated band-states at infinite Ge well thickness. For comparison, also the energy position of the lowest indirect conduction band minimum outside \\(\\bar{\\Gamma }\\) (dot-dashed line) is given.\n\nThe QP band structure of a (superlattice with a) 4\u00a0nm thick Ge layer is displayed in Fig.\u00a06b, c, clearly showing a direct bandgap with a \\(\\bar{\\Gamma }\\) minimum approximately 0.3 eV below the lowest indirect conduction band minimum which appears near the corner point \\(\\overline{{{{{{{{\\rm{M}}}}}}}}}\\) of the Brillouin zone boundary. We plot the band structure of the MQW together with a background illustrating the projected band structure of the strained Si0.25Ge0.75 bulk. The two band structures are aligned by their BPs. The bands of the Ge/Si0.25Ge0.75 MQW, within the fundamental gap of the projected Si0.25Ge0.75 band structure, describe subbands of electrons and holes, whose wave functions are both localized in the Ge layers. The localization of both the electron and hole wave functions in the Ge well (Fig.\u00a0S10a) clearly indicates type-I band alignment. The type-I behavior is confirmed by the energies for the highest hole subbands and lowest-energy electron subbands at the \\(\\bar{\\Gamma }\\) point, which are presented versus the Ge layer thickness in Fig.\u00a06d. Corresponding band structures for MQW structures with thicker Ge layers are displayed in Fig.\u00a0S10b. Combining this data with the calculated band structure for bulk (strained) hex-Ge, serving as infinitely thick QW, allows us to extract the quantization effects, more precisely the confinement energies of the lowest n\u2009=\u20091 electron and hole levels directly from ab initio band structure calculations. While only one level appears in the narrow QW with a thickness of 4\u00a0nm, a second and third confined level appear in finite QWs starting from a thickness of 8 nm (Fig.\u00a0S10b).\n\nThe band offsets in the conduction band and the valence band of 0.13\u20130.15 eV are nearly equal (Fig.\u00a0S10c). The band offsets can be employed as barrier heights in simplified rectangular finite QW models for electrons and holes. The ab initio confinement energy of electrons (holes) in the QW vary from 72 (36) to 31 (8) meV for thicknesses of 4 and 15\u00a0nm, respectively. These values are much smaller than the offsets, and one therefore may approximate the system as an infinite QW. For the lowest n\u2009=\u20091 levels, the finite band offsets \u0394Ec/h, and the mentioned ab initio confinement energies \u03f5e/h in the finite rectangular-well model allows the extraction of the effective electron/hole masses according to48\n\nas me\u2009\u2248\u20090.05\u2009m0 and mh\u2009\u2248\u20090.13\u2009m0 averaged over all studied QWs. These values are close to those as 0.076 m0 and 0.055 m0 which have been calculated for unstrained bulk hex-Ge along the \\([1\\overline{1}00]\\) direction19. Computations without external strain result in much smaller confinement energies, which indeed are closely related to the bulk effective masses of unstrained hex-Ge.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49399-3/MediaObjects/41467_2024_49399_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49399-3/MediaObjects/41467_2024_49399_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49399-3/MediaObjects/41467_2024_49399_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49399-3/MediaObjects/41467_2024_49399_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49399-3/MediaObjects/41467_2024_49399_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49399-3/MediaObjects/41467_2024_49399_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The experimental values for the bandgaps of the 4, 6, 8, 11 and 15\u00a0nm Ge/Si0.2Ge0.8 QWs are compared with the calculated results (black dots) in Fig.\u00a03b. For properly comparing theory with experiment, the theoretical bandgaps are shifted with +60 meV to match the calculated bandgap of the hex-Ge well (\u2009\u2248\u20090.30 eV)19 with the experimentally observed bandgap of bulk hex-Ge (\u2009\u2248\u20090.36 eV)18. This shift remains within the error margin of the ab initio DFT calculations (\u2009\u2248\u20090.1 eV or 25%49). Based on the theoretically calculated band offsets and effective masses19,50,51, the emission energy versus thickness is also calculated using a conventional finite QW model (dashed line)52. This simple model is useful to calculate the emission energies for any QW thickness and composition when reasonable values for the band offsets and carrier masses are available and detailed QP calculations are computationally unfeasible.\n\nA qualitative agreement between theory and experiment is obtained, but the experimental emission energies are all higher than the theoretical values. We identify three possible reasons for the deviation between experiment and theory. (1) The Ge QW thicknesses, measured from TEM images, are slightly overestimated (see Fig.\u00a0S3). (2) We do not include an additional confinement energy due to quantization along the length of the NW, due to the inclusion of cubic stacking faults. The alignment between cubic and hexagonal stacking is expected to be of type-I47,53, and every hexagonal segment with a direct bandgap is thus bound by cubic barriers with larger bandgap (Fig.\u00a0S4a). The exact increase of confinement due to the cubic insertions is ambiguous and subject of future investigations. (3) Likely a few percent Si is incorporated in the wells due to interdiffusion of Si between the Si0.2Ge0.8 and the Ge wells, which elevates their bandgap since the bandgap of hex-Si1\u2212xGex alloys is larger than that of hex-Ge18. Moreover, interdiffusion of Si results in a less steep potential at the QW-Barrier interface, which might also increase the confinement energies.\n\nIn conclusion, we have grown coaxial hex-Ge/Si0.2Ge0.8 and Si0.1Ge0.9/Si0.3Ge0.7 QWs showing direct bandgap light emission. We experimentally confirm efficient direct bandgap emission by the temperature dependence of the integrated PL versus temperature as well as by the observed carrier lifetime of\u2009\u2248\u20091 ns at 4 K, where the recombination is purely radiative. The direct bandgap is confirmed by ab initio DFT and approximate quasiparticle calculations showing a high directness, implying that the indirect minima are 0.3 eV above the \\(\\bar{\\Gamma }\\) minimum. In addition, we observe clear quantum confinement combined with type-I band alignment. Importantly, both analyses of the thermal quenching observed in the Arrhenius pots as the theoretical calculations demonstrate nearly equal conduction and valence band offsets. Although our hex-Ge/Si0.2Ge0.8 QWs are lattice mismatched and feature strongly anisotropic effective masses, our results can still be properly described by a simple finite QW model. In this paper, we studied hex-Si1\u2212xGex/Si1\u2212yGey nanowire QWs, but our findings are expected to equally apply to future planar hex-Si1\u2212xGex/Si1\u2212yGey QWs compatible with Si-photonics circuits. Our results are unlocking the hex-Si1\u2212xGex/Si1\u2212yGey system for different low-dimensional devices for photonics and quantum information, such as quantum well lasers, optical amplifiers and single photon sources using Si1\u2212xGex alloys.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The nanowires are grown on commercially available GaAs (111)B oriented (n-doped with Si) substrates. The substrates are cleaned with an NH4OH treatment before e-beam exposure, for which an PMMA-A2 photoresist is used. The e-beam exposure patterns 300\u2009x\u2009300\u00a0nm squares with a pitch of 4 \u03bcm. The resist is developed after e-beam exposure with a solution of MIBK/IPA, followed by an Au deposition of 6\u00a0nm by e-beam evaporation. The resist is removed in a lift-off process with PRS3000, acetone and IPA. Final steps before growth involve an oxygen plasma treatment to remove organic residues, followed by an NH4OH treatment to remove surface oxides. The epitaxial growth of WZ GaAs NWs is done in a close-coupled shower head Metal Organic Vapor-Phase Epitaxy (MOVPE) reactor and follows the recipe detailed in Fig.\u00a0S1a. The total flow through the reactor is 8.2 liters per minute. The obtained wurtzite (WZ) GaAs NWs can have stacking fault densities as low as 4 per \u03bcm.\n\nRemoval of the Au catalyst is done by wet-chemical etching in a diluted cyanide solution (KCN:H2O - 1:10) for 17 min. KCN residues are removed by rinsing in H2O for 20 min. The rinsing is immediately followed by a NH4OH treatment to remove oxides from the NW sidewalls. The samples are immersed in IPA for 30 seconds, after which the samples are ready to be dried through centrifugation. The samples with GaAs NWs are placed inside the MOVPE reactor to clean the sidefacets of the NWs, according to the recipe detailed in Fig.\u00a0S1b. The GaAs shell growth during this step is negligible.\n\nThe samples are taken out of the reactor, and the reactor kit is changed to a kit dedicated to the growth of Si1\u2212xGex alloys. Extensive coating recipes are used to ensure minimal contamination from previous GaAs runs. Earlier work on hex-Si1\u2212xGex showed a high As-doping level in the order of 9\u2009\u00d7\u20091018\u2009cm\u22123 as deduced by Atom Probe Tomography (APT) measurements18. Equivalent samples as those presented in this study still have unintentional doping from arsenic, but the level is reduced below the detection limit of APT, so it is below 2.5\u2009\u00d7\u20091018\u2009cm\u22123, likely due to the extensive coating runs.\n\nSamples with WZ GaAs cores are reintroduced in the reactor. Different recipes are used for hex-Ge/Si0.2Ge0.8 and Si0.1Ge0.9/Si0.3Ge0.7 samples, as detailed in Fig.\u00a0S1c\u2013d. For hex-Ge/Si0.2Ge0.8 QWs, the first step is an anneal in an H2 atmosphere, which improves the GaAs-Si0.2Ge0.8 interface. The initial Si0.2Ge0.8 shell is grown in 90 min. However, a large fraction of these 90 min is the incubation time, which is a growth delay before the shell starts to grow. The initial shell typically has a thickness of 10\u201320 nm. The precursor flows to the reactor are stopped, leaving the sample in an H2 atmosphere for 5 min, and this time is used to lower the flows of GeH4 and Si2H6. Growth of the Ge QW follows a similar procedure. Both flows are stopped, and the reactor is flushed for 5 min with H2 to remove residual Si2H6.\n\nFor hex-Si0.1Ge0.9/Si0.3Ge0.7 QWs, the initial barrier is grown in a single step (Fig.\u00a0S1d). The remainder of the recipe is comparable to the hex-Ge/Si0.2Ge0.8 QWs.\n\nTransmission Electron Microscopy (TEM) studies were performed using a probe corrected JEOL ARM 200F, operated at 200\u2009kV. All images were acquired at low camera length (8 cm, 68-280 mrad) to minimize the contribution of strain and diffraction contrast.\n\nEnergy dispersive X-ray Spectroscopy (EDS) studies were performed using a 100 mm2 Centurio EDS silicon drift detector. Quantification of the EDS spectra was done using the Cliff-Lorimer model. The accuracy of EDS quantification was previously confirmed by determining the composition of a single sample, corresponding to MOVPE input Si0.10Ge0.90, with both EDS-STEM and Atom Probe Tomography (APT)18.\n\nCross-sectional TEM samples of nanowires were prepared using a Focused Ion Beam (FIB) FEI Nova Nanolab 600i Dualbeam system. For this, the NWs were initially swiped from the growth substrate to a piece of Si and then arranged to lie parallel to each other with the aid of a micromanipulator. These NWs were covered with the use of electron-beam induced C and Pt deposition to minimize the ion beam damage in following steps. Afterwards, the NWs were embedded in ion-beam induced Pt deposition. The lamella was cut out by milling with 30 kV Ga ions and thinned down with subsequent steps of 30, 16, and 5 kV ion milling in order to minimize the Ga-induced damage in the regions imaged with TEM.\n\nThe QW thickness is mainly determined from images along the [0001] zone axis. QWs of which the thickness could not be measured accurately, due to varying QW position or width within the thickness of the TEM lamella, are excluded from the analysis.\n\nThe stacking sequence within the Ge/Si0.2Ge0.8 QWs is obtained from Scanning Transmission Electron Microscopy (STEM) images. Within each image, we count the number of planes that have surrounding hexagonal segments. A segment of i\u2009=\u20091,\u20092,\u20093 planes would represent segments of 2,\u20093,\u20094 consecutive neighboring monolayers (ABA,ABAB,ABABA) respectively. Over multiple images, we count how many times we observe a segment that contains i hexagonal stacked planes, which we call \\({N}_{i}^{Hex}\\). Similar reasoning holds for the segments with coherent cubic stacking. The distribution of the hexagonal and cubic segment lengths (\\({N}_{i}^{Hex}\\) and \\({N}_{i}^{Cub}\\)) respectively, are shown in Fig.\u00a0S4a.\n\nThe hexagonality \\({F}_{i}^{Hex}\\), i.e., the percentage of the NW that has local hexagonal stacking of at least i planes, as\n\nFor i\u2009=\u20091, above equation calculates the fraction of the NW that is made from hexagonal segments that are at least 1 plane long. Longer segments are also included, and weighted according to their length. Higher-order degrees of hexagonality are calculated using larger values of i, which are shown in Fig.\u00a0S4b. The minimum length of a segment with hexagonal stacking, to still have a direct bandgap, is not yet precisely determined.\n\nLocal variations of the lattice constant are measured with Geometric Phase Analysis (GPA), utilizing STEM images at atomic resolution. We used a custom, in-house developed toolbox to perform the GPA analysis. The GPA tool calculates the local diffraction pattern, using a 2D-Fourier transformation. Changes of the diffraction spots, due to changes in the local lattice constant, are used to calculate the strain with respect to a reference area. With the 2D-Fourier transformation, it is possible to measure the strain in the horizontal and vertical direction of each image. If the QW is imaged along the [0001] zone axis, this corresponds to the strain in the azimuthal and radial directions of the NW geometry. This reference area is defined within each TEM image, in this case, to be within the inner Si0.2Ge0.8 layer.\n\nThe X-ray diffraction measurements were made with a Bruker Discover D8. The incidence beam is filtered with a Ge monochromator for the Cu K-\u03b1 radiation (1.5406 \u00c5). The incidence beam is collimated with a nozzle of 2 mm in diameter. The diffracted beam is measured with a 2D detector, without any optics in between. The 2D detector is used to collect diffracted X-rays with an in-plane angle perpendicular to 2\u03b8 of\u2009\u00b1\u20090.36\u00b0.\n\nReciprocal space maps (RSMs) covering the cubic twin [331] until the hexagonal \\([10\\bar{1}6]\\) reflection are measured in a single scan. The RSMs are aligned such that the angular coordinates [\u03c9,\u20092\u03b8] of the GaAs [224] substrate reflection correspond exactly to the theoretical values of [61.3474\u00b0,\u200983.7524\u00b0].\n\nThe hexagonal lattice constants of the NWs are obtained by fitting the RSMs around the \\([10\\bar{1}5]\\) reflection with a 2D Gaussian profile. The uncertainty in the peak position of this Gaussian is used to calculate the uncertainty in the lattice constants.\n\nAsymmetrical crystal truncation rods are obtained by taking a line scan along Qz through the RSMs. The intensity at Qx\u2009=\u20091.816 \u00c5\u22121 is integrated along the \u03c9-direction within a region of \u03c9\u2009\u00b1\u20091.\u20095\u00b0. The range is chosen to collect both the substrate and NW reflections, which occur at slightly different Qx due to the difference in the in-plane lattice constant.\n\nThe asymmetrical crystal truncation rod allows the separation of the hexagonal and the cubic reflections. Hence, it is used as a probe for the amount of hexagonal stacked material within the NWs. One of the main problems with XRD is that it is quite insensitive to the I3 stacking fault, which is the most common defect in the hex-Si1\u2212xGex. Consider two hexagonal stacked domains ABAB and BABA, which are aligned along the [0001] axis, separated by either a single \u201cA\u201d plane, i.e., perfect hexagonal stacking, or by a single \u201cC\u201d plane, corresponding to the I3 stacking fault. The only difference between the two configurations is that the I3 defect transforms the local stacking from ABABABABA to ABABCBABA. The two hexagonal domains separated by an I3 defect still interfere constructively, since the I3 defect has no burgers vector40. Therefore, we believe that an I3 defect does not broaden any peak in XRD54. The I3 stacking, however, should result in a lower intensity of the diffraction signal since there are fewer lattice planes contributing to constructive interference. The relative intensity of the hexagonal peaks between samples is therefore used as a probe for the amount of I3 defects.\n\nTo do so, peaks with a Voigt profile are fitted to the asymmetrical crystal truncation rods. Near the hex-\\([10\\overline{1}5]\\) peak, two peaks are fitted. One around Qz\u2009\u2248\u20094.82 \u00c5\u22121, which we attribute to signal coming from the core-shell NWs, and one around Qz\u2009\u2248\u20094.78 \u00c5\u22121, which we attribute to bulk-like WZ GaAs, that parasitically grows on the GaAs substrate around the base of the NW. After Si1\u2212xGex shell growth, this bulk-like WZ GaAs maintains a lattice constant close to WZ GaAs, while the lattice constant from the NW is shifted towards Si1\u2212xGex. The obtained hex-\\([10\\overline{1}5]\\) peak areas are normalized to the [224] substrate reflection, to account for small imperfections in the alignment between the samples. Moreover, the \\([10\\overline{1}5]\\) peak areas are divided by the volume of the NWs. These volumes are calculated from the length and diameter, as extracted from SEM images. When normalized in this manner, all GaAs-Si1\u2212xGex core-shell NW samples give a similar number within a factor of 1.5 (Fig.\u00a0S4d).\n\nThe (macro) PL measurements were performed using a Thermo Scientific iS50R step-scan Fourier Transform InfraRed Spectrometer (FTIR). The as-grown NW samples are introduced to the setup by placing them in a LHe cooled Oxford Instruments HiRes2 continuous-flow cryostat which can be temperature controlled using the integrated heater governed by an Oxford Instruments MercuryiTC. The samples are excited using a Quasi-continuous wave (Quasi-CW) 976\u00a0nm laser, focused on the sample by a 2.1\u2009cm focal distance off-axis parabolic Au mirror to an\u2009\u2248\u2009100\u2009\u03bcm spot and the collected photoluminescence is measured using the internal Mercury Cadmium Telluride (MCT) detector of the FTIR. The excitation laser was filtered out using a germanium window (1950\u00a0nm) or a 1650\u00a0nm long pass filter. To extract the NW response from the black-body radiation background, the laser is modulated using a 38\u2009kHz square wave generated by a Siglent SDG1032X Arbitrary Waveform Generator (AWG) and the signal is finally demodulated using a Zurich Instruments MFLI Lock-in Amplifier (LIA). To improve the stability of the modulation frequency, the AWG was locked to the oscillator in the LIA using the 10 MHz clock signal reference.\n\nFor Fig.\u00a03, the QW and reference samples were measured at the lowest excitation density that still gave an acceptable Signal-to-noise ratio, being 3, 13, 50, 39, 6, 9 and 13\u2009W\u2009cm\u22122 for the 9, 6, 4, 3, 2.5, 2 and 1.5\u2009min QWs and 64 and 2\u2009W\u2009cm\u22122 for the bulk hex-Si0.2Ge0.8 and hex-Ge reference samples respectively and lightly smoothed for clarity using a 21 point, linear Savitzky-Golay filter. The finite quantum well model added to Fig.\u00a03b was calculated using the bulk effective masses for the well and interpolated effective masses between bulk hex-Ge (me\u2009\u2248\u20090.079\u2009m0,\u2009mh\u2009\u2248\u20090.055\u2009m0)19 and hex-Si for the barrier (me\u2009=\u20090.122\u2009m0,\u2009mh\u2009=\u20090.213\u2009m0)50. The bandgap energy EWell was determined from the experimental 0.354 eV emission peak of the hex-Ge reference spectrum increased by 13 meV to account for the shift due to strain from the QP calculations and EBarrier\u2009=\u20090.570\u2009eV was determined from the peak energy of the hex-Si0.2Ge0.8 reference spectrum, the reference spectra are shown in Fig.\u00a03a. The band-offsets were assumed to be symmetrical as indicated by the QP calculations Fig.\u00a0S10c and the experimental estimation Fig.\u00a0S6d.\n\nFor Fig.\u00a05, the QW and reference samples were measured at 0.88\u2009kW cm\u22122 for the 5 and 15\u2009min QWs and 0.88 and 0.42\u2009kW cm\u22122 for the bulk Si0.1Ge0.9 and Si0.3Ge0.7 reference samples respectively. The spectra were background corrected by fitting the sum of an exponential Urbach tail55,56 from the GaAs epitaxial substrate and a Gaussian peak spectrum for each spectrum, after which the exponential is subtracted. As the Si0.3Ge0.7 reference had a very low intensity even at high excitation density it was smoothed for clarity after the baseline correction using an 81 point, quadratic Savitzky-Golay filter. The spectra of the 5 min QW and Si0.3Ge0.7 reference after baseline correction are in agreement with the \u03bcPL spectra of single NWs mechanically transferred onto an Aluminum-Nitride (AlN) substrate shown in Fig.\u00a0S9. The finite quantum well model added to Fig.\u00a05c was calculated using interpolated bulk effective masses between hex-Ge (me\u2009=\u20090.076\u2009m0,\u2009mh\u2009=\u20090.055\u2009m0)19 and hex-Si (me\u2009=\u20090.122\u2009m0,\u2009mh\u2009=\u20090.213\u2009m0) for both the barrier and the well material, the band-offsets were assumed to be symmetric and determined from the experimental emission energies of the well and barrier reference samples shown in Fig.\u00a05b.\n\nThe single nanowire spectrum is investigated using a Time-Resolved Fourier-Transform-Infrared-Spectroscopy setup (TR-FTIR). This setup allows us to study the spectrally-resolved time decay of the photoluminescence of a sample. The as-grown hex-SiGe NWs samples are mechanically transferred on a planar AlN substrate and are introduced to the setup by placing them in a LHe cooled Oxford Instruments HiRes2 continuous-flow cryostat. The temperature is set to 4\u2009K using an Oxford Instruments MercuryiTC. The samples are optically excited using a femto-second pulsed mode-locked fiber laser (NKT ORIGAMI 10\u201340) with a wavelength of 1032\u00a0nm and repetition rate of 40\u2009MHz. A 36x/0.40NA Cassegrain objective is used to excite and collect the signal from the sample. The excitation/collection spot diameter on the sample is 3\u2009\u03bcm. The PL signal from the sample is sent through the Nireos GEMINI birefringent Fourier transform interferometer to acquire spectrally resolved photoluminescence and finally collected by a Superconducting Nanowire Single-Photon Detector (SNSPD) with a measurement window up to 2.35\u2009\u03bcm (Single Quantum EOS110). A 1350\u00a0nm long-pass filter is placed before the GEMINI module to block the excitation laser reflected on the sample. For the single NW lifetime measurement, the GEMINI interferometer is kept fixed at the zero path distance and the measurement is performed without acquiring spectral information from the NW signal.\n\nAll calculations were performed within the framework of Density Functional Theory (DFT) using the VASP software57,58 and the projector-augmented wave method59, with a plane-wave cutoff of 500 eV. The shallow 3d levels of Ge were treated as valence states. Geometry relaxations employed the Perdew-Becke-Ernzerhof exchange-correlation (XC) functional PBEsol60. Brillouin zone integrations were carried out with a \u0393-centered 12\u2009\u00d7\u200912\u2009\u00d7\u20096 k-point grid for lonsdaleite (2H) crystals. Quasiparticle band structures were computed using the MBJLDA XC potential of Tran and Blaha61, which combines the modified Becke-Johnson (MBJ) exchange62 with correlation in the local density approximation (LDA)63. Spin-orbit coupling (SOC) was consistently considered, as the resulting corrections to the band structure are crucial for Ge and alloys with a substantial Ge content. Branch point energies were calculated following the method of reference46, and they were applied whenever necessary to align energy levels of different materials and heterostructures. This approach was already validated for [0001] interfaces in reference47. The resulting band structures of hex-Ge and hex-SiGe alloys are consistent with previously published results19,20,64. Numerical differences between the reported findings here and those published earlier stem from the additional biaxial strain applied in this work to replicate experimental conditions, as discussed in the main text.\n\nIn our approach, the Ge layer thickness affects the lowest conduction band of the bulk Si0.25Ge0.75 barrier material (red solid line in Fig.\u00a06d). The structural optimization within the DFT approach of the studied MQW structures gives rise to mutual biaxial strains in the hex-Ge well layers as well as in the SiGe barrier layers in dependence of the layer thicknesses in addition to the significant \u201cexternal\u201d biaxial strain taken from the measurements. Despite this strong biaxial strain due to the assumed pseudomorphic growth of the hex-Ge/SiGe heterosystems on the wurtzite-GaAs core wires, the additional small strain distribution in the heterosystem only slightly affects the actual strong strain situation in the barrier material resulting in small band edge variations made visible in Fig.\u00a06d by a red (blue) line for electrons (holes). The accompanying changes of the QW barrier heights of less than 0.015\u2009eV hardly influence the carrier confinement in the lowest n\u2009=\u20091 levels in the QWs.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The raw data generated in this study have been deposited in Zenodo: https://doi.org/10.5281/zenodo.10839570.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The VASP code used for electronic structure calculations can be acquired from the VASP Software GmbH at https://www.vasp.at/. 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This project received funding from the European Union\u2019s Horizon 2020 research and innovation program under grant agreement number 964191, Opto Silicon (W.H.J.P., M.M.J., S.B., F.B., J.E.M.H., and E.P.A.M.B.), the Dutch Organization for Scientific Research (NWO) in the Zwaartekracht Project, Grant No. 024.002.033 (M.A.J.T.), 739.017.002, (V.T.L.), and OCENW.M.21.052 (R.F.) and Solliance and the Dutch province of Noord-Brabant for funding the TEM facility.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Wouter H. J. Peeters, Victor T. van Lange, Abderrezak Belabbes.\n\nDepartment of Applied Physics, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands\n\nWouter H. J. Peeters,\u00a0Victor T. van Lange,\u00a0Max C. van Hemert,\u00a0Marvin Marco Jansen,\u00a0Riccardo Farina,\u00a0Marvin A. J. van Tilburg,\u00a0Marcel A. Verheijen,\u00a0Jos. E. M. Haverkort\u00a0&\u00a0Erik P. A. M. Bakkers\n\nDepartment of Physics, Sultan Qaboos University, P.O. Box 123, Muscat, Oman\n\nAbderrezak Belabbes\n\nInstitut f\u00fcr Festk\u00f6rpertheorie und -optik, Friedrich-Schiller-Universit\u00e4t Jena, Jena, Germany\n\nAbderrezak Belabbes,\u00a0Silvana Botti\u00a0&\u00a0Friedhelm Bechstedt\n\nEurofins Materials Science Netherlands BV, Eindhoven, The Netherlands\n\nMarcel A. Verheijen\n\nResearch Center Future Energy Materials and Systems of the University Alliance Ruhr and Interdisciplinary Centre for Advanced Materials Simulation, Ruhr University Bochum, Universit\u00e4tsstra\u00dfe 150, Bochum, Germany\n\nSilvana Botti\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nW.H.J.P., M.M.J. carried out the growth of hex-Si1\u2212xGex/Si1\u2212yGey quantum wells. W.H.J.P. analyzed the data. V.T.L. and M.C.H. carried out the photoluminescence spectroscopy and analyzed the optical data. R.F. and M.A.J.T. performed time-resolved spectroscopy on single quantum well nanowires. A.B. and F.B. performed the DFT calculations. W.H.J.P. performed the XRD measurements. W.H.J.P. and M.M.J. performed the FIB cuts, and M.A.V. performed the TEM analysis. S.B., F.B., J.E.M.H., and E.P.A.M.B. supervised the project. F.B. contributed to the interpretation of data, and W.H.J.P., V.T.L., F.B., J.E.M.H., and E.P.A.M.B. contributed to the writing of the manuscript. All authors discussed the results and commented on the manuscript.\n\nCorrespondence to\n Erik P. A. M. Bakkers.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Peeters, W.H.J., van Lange, V.T., Belabbes, A. et al. 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Duchenne muscular dystrophy mouse through YY1-CCL5 axis", + "pre_title": "Skeletal Muscle Stem Cells Modulate Niche Function in Duchenne Muscular Dystrophy through YY1-CCL5 Axis", + "journal": "Nature Communications", + "published": "03 February 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_MOESM7_ESM.xlsx" + }, + { + "label": "Supplementary Data 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_MOESM8_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_MOESM9_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_MOESM10_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-56474-w#Sec31" + ], + "code": [ + "https://github.com/Hannah-bioinfo/Scripts_for_YY1_paper" + ], + "subject": [ + "Muscle stem cells", + "Regeneration", + "Stem-cell niche" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3919531/v1.pdf?c=1738674556000", + "research_square_link": "https://www.researchsquare.com//article/rs-3919531/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-56474-w.pdf", + "preprint_posted": "22 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Stem cell activity is known to be tightly regulated by both intrinsic and extrinsic pathways but less is known about whether and how stem cells modulate their niche microenvironment. Adult skeletal muscle stem cells (MuSCs) are indispensable for muscle regeneration and also tightly regulated by macrophages (MPs) and fibro-adipogenic progenitors (FAPs) in the niche. Deregulated MuSC/MP/FAP interactions and the ensuing inflammation and fibrosis are hallmarks of dystrophic muscle. Here in this study we demonstrate that intrinsic deletion of transcription factor YY1 in MuSCs exacerbates dystrophic pathologies by altering the cellular composition and heterogeneity of MPs and FAPs. Further analysis reveals that the YY1 loss induces the expression of immune genes in MuSCs, including Ccl5. Augmented secretion of CCL5 from MuSCs promotes the recruitment of MPs via CCL5/CCR5 mediated crosstalk, which subsequently hinders the apoptosis and clearance of FAPs through elevated TGF\u03b21 accumulation. Maraviroc mediated pharmacological blockade of the CCL5/CCR5 axis effectively mitigates muscle dystrophy and improves muscle performance. Lastly, we further demonstrate that YY1 represses Ccl5 transcription in MuSCs by directly binding to its enhancer thus facilitating promoter-enhancer looping. Altogether, our study has demonstrated the previously unappreciated role of MuSCs in actively shaping their niche microenvironment through secreting immunomodulatory cytokines and has also provided novel insight into the therapeutic intervention of muscle dystrophy.Biological sciences/Stem cells/Muscle stem cellsHealth sciences/DiseasesMuSCYY1DMDnichemacrophagefibro-adipogenic progenitorCCL5", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-919531/v1/0ac001fad94d66928c44719b.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-919531/v1/8d302c36f2ee3f309b715d4d.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-919531/v1/aeaeb64f4c5a91fe1e4f0baa.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-919531/v1/fd451dda093142b5712bd402.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-919531/v1/4ed81cd66a230b170798c37b.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-919531/v1/76f2c3cea4f9d4f3e62fc9b9.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-919531/v1/399789f07acd6b4a255f6adb.jpg%3FmaxDims%3D150x150&w=256&q=75.png" + ] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.\nSupplementary Tables are not available with this version.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplInfo.pdfSupplementary materials", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Adult skeletal muscle stem cells (MuSCs) are indispensable for muscle regeneration and tightly regulated by macrophages (MPs) and fibro-adipogenic progenitors (FAPs) in their niche. Deregulated MuSC/MP/FAP interactions and the ensuing inflammation and fibrosis are hallmarks of dystrophic muscle. Here we demonstrate intrinsic deletion of transcription factor Yin Yang 1 (YY1) in MuSCs exacerbates dystrophic pathologies by altering composition and heterogeneity of MPs and FAPs. Further analysis reveals YY1 loss induces expression of immune genes in MuSCs, including C-C motif chemokine ligand 5 (Ccl5). Augmented CCL5 secretion promotes MP recruitment via CCL5/C-C chemokine receptor 5 (CCR5) crosstalk, which subsequently hinders FAP clearance through elevated Transforming growth factor-\u03b21 (TGF\u03b21). Maraviroc-mediated pharmacological blockade of the CCL5/CCR5 axis effectively mitigates muscle dystrophy and improves muscle performance. Lastly, we demonstrate YY1 represses Ccl5 transcription by binding to its enhancer thus facilitating promoter-enhancer looping. Altogether, our study demonstrates the critical role of MuSCs in actively shaping their niche and provides novel insight into the therapeutic intervention of muscle dystrophy.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Skeletal muscle has a robust regenerative capacity, with rapid re-establishment of full power occurring even after severe damage that causes widespread myofiber necrosis1,2. The cells responsible for muscle regeneration are adult muscle stem cells (MuSCs, also called satellite cells) which are located in a niche beneath the ensheathing basal lamina on the surface of the myofibers in a quiescent stage under normal conditions3,4,5. Upon injury, MuSCs are rapidly activated to become myoblasts, undergo proliferative expansion, and eventually differentiate into myotubes and fuse to form new myofibers3,4,5. A subset of MuSCs undergoes self-renewal and returns to the quiescent state to replenish the adult stem cell pool6,7,8. Each phase of the activities is tightly orchestrated at two levels. First, through intrinsic preprogrammed mechanisms and, second, through extrinsic regulations imposed by the stem cell microenvironment or so-called stem cell niche9,10. The intrinsic regulatory mechanisms have been relatively well defined. For example, it is widely accepted that gene regulation at transcriptional level by transcription factors (TFs) plays crucial roles in instructing MuSC regenerative responses9,11. Among many key TFs, Yin Yang 1 (YY1) is ubiquitously expressed but possesses unique transcriptional regulatory functions in MuSCs based on findings from our group and others12,13,14,15,16,17. For example, we recently elucidated the function of YY1 in acute injury-induced muscle regeneration as a key regulator of MuSC activation/proliferation. We found that intrinsic YY1 deletion in MuSCs impairs muscle regeneration by regulating MuSC activation/proliferation through its dual roles in modulating metabolic pathways12. Moreover, we also observed that the deletion in MuSCs in chronically injured muscle of mdx (a mouse model for Duchene muscular dystrophy, DMD) aggravates muscle dystrophy but the in-depth dissection is yet performed12.\n\nMuSC functionality is also tightly controlled by the crosstalk between MuSCs and other cell types within their niche18. The MuSC niche is relatively static under homeostatic conditions to maintain MuSC quiescence and undergo dynamic remodeling following injury through a spatiotemporally tightly coordinated flux of different cell types such as inflammatory, vascular, and mesenchymal cells10,19. The reciprocal functional interactions among these cell types are crucial in coordinating the repair of injured muscles. In particular, optimal regeneration entails a sequence of events that ensure temporally coordinated interactions among MuSCs, macrophages (MPs), and fibro-adipogenic progenitors (FAPs)20,21,22. An initial recruitment of MPs is typically followed by the sequential activation of FAPs and MuSCs. The key immune and non-immune functions that MPs exert in muscle regeneration are well accepted23,24. The pro-inflammatory response following acute muscle injury usually begins with infiltration of neutrophils, followed by pro-inflammatory MPs featured by Ly6ChighF4/80low, which produce mostly inflammatory cytokines, such as TNF\u03b1, IL-1\u03b2, and IFN\u03b3 to promote MuSC activation and proliferation. Afterward, at later stages of regeneration, a distinct population of Ly6ClowF4/80high pro-regenerative (anti-inflammatory) MPs become more prevalent, which produce different cytokines, such as IL-4, IL-10 and TGF\u03b21, to promote myoblast differentiation and tissue repair23,25. Although much is known about the impact of MPs on MuSCs, the possible reciprocal regulation of MuSCs on MPs remains largely unexplored despite a pioneer work long time ago demonstrating that human myoblasts can indeed secrete an array of chemotactic factors to initiate monocyte chemotaxis in culture26. FAPs are a muscle interstitial mesenchymal cell population which can differentiate into fibroblasts, adipocytes, and possibly into osteoblasts and chondrocytes. Emerging evidence solidifies FAPs\u2019 critical role in efficient muscle repair21. Upon muscle injury, FAPs become activated, proliferate and expand, and provide a transient favorable microenvironment to promote MuSC-mediated regeneration. It is now known that heterogenous FAP subsets have distinct functions in muscle regeneration. For example, Wisp1+ FAPs expansion is critical during regeneration to sustain MuSC proliferation and differentiation in a paracrine manner and maintain the MuSC pool27 while mTie2High/mTie2Low FAPs play a role in promoting angiogenesis28. As regeneration proceeds, the timely removal of FAPs from the regenerative niche through apoptosis is necessary to prevent pathological accumulation and muscle dysfunction. Accumulated fibrogenic FAPs lead to fibrosis29,30 and adipogenic FAPs result in fat deposition31. Again, it remains largely unknown if FAPs receive any reciprocal signaling from MuSCs. Nevertheless, it is widely accepted that finely tuned molecular interactions of FAPs and MPs are essential for successful regeneration. Expansion and decline of FAP cell populations following injury are determined by MPs and disrupted MP dynamics can result in aberrant retention of FAPs in muscles following acute or chronic injuries20,21. For example29, in acutely damaged skeletal muscle, infiltrating MPs, through their expression of TNF\u03b1, directly induce apoptosis and the timely clearance of FAPs, thus preventing the occurrence of fibrosis.\n\nTherefore, the finely orchestrated functional interactions among MuSCs, MPs and FAPs are crucial to instruct the proper progress of damage-induced muscle repair. Conditions that compromise the functional integrity of this network can skew muscle repair toward pathological outcomes, for example in the case of DMD20,22, which is a lethal progressive pediatric muscle disorder caused by the genetic mutations in the dystrophin gene. In the absence of dystrophin protein, DGC (dystrophin-glycoprotein complex) assembly on the muscle member is impaired which weakens the muscle fibers, rendering them highly susceptible to injury. Muscle contraction-induced stress results in constant cycles of degeneration and regeneration, resulting in chronic inflammation and fibro-fatty tissue replacement. Recent advances in single cell RNA-sequencing (scRNA-seq) have enabled us to characterize cell compositions and interactions in both human and mouse DMD muscles32,33. Compared to muscles underlying acute injury-regeneration, more intricate cellular dynamics and interactions are observed in dystrophic muscles. An increased prevalence of MPs and FAPs was confirmed and correlated with disease severity29,34,35. MPs are highly heterogeneous and partially pathogenic in dystrophic muscle36. A plethora of MP-derived factors are critically involved in inflammation and fibrosis of the muscle. It is speculated that the heterogeneous and pathogenic activation of MPs in dystrophic muscle is likely induced by the asynchronous regeneration altered microenvironment but the underlying signaling molecules and cellular sources remain unexplored37. Evidence from the scRNA-seq profiling also revealed evident heterogeneity of FAPs and their correlation with disease severity32. FAPs undergo uncontrolled expansion and resistance to clearance in dystrophic muscle, which plays a prominent role in intramuscular fat deposition and fibrosis38. Increased TGF\u03b2 signaling is believed to prevent FAP apoptosis and induce their differentiation into matrix-producing cells to cause fibrosis29; the main source of TGF\u03b21 in injured muscle niche is MPs. While pro-regenerative MPs are predominantly contributor to TGF\u03b21 secretion in acutely injured muscle niche, most MPs in chronically injured muscle can secret TGF\u03b21 to foster a TGF\u03b21 enriched microenvironment29.\n\nOverall, cellular communications among the main pathogenic cells in dystrophic muscle warrant further investigation; in particular, it is unknown if MuSCs play an active role in initiating crosstalk with MPs and FAPs to manipulate their own niche microenvironment. In fact, it is safe to state that in general our understanding about if and how tissue adult stem cells impact their niches is very limited. Across a variety of well-studied adult stem cells such as mesenchymal stem cells, hematopoietic stem cells, and neural stem cells etc., we have learned plenty about the essential roles of the stem cell niche in regulating stem cell behavior and functionality39, including how alteration in the stem cell niche causes cellular damage and impairs the regenerative capacity of stem cells. In principle the stem cells are perceived as the passive recipient of niche signals and impact. There is relatively less understanding of whether and how stem cells can actively contribute to the niche integrity in homeostasis and how the intrinsic changes in stem cells are connected to extrinsic niche alterations in pathological conditions. Here in this study, we discovered that intrinsic deletion of YY1 in MuSCs of dystrophic mdx mice (dKO) exacerbated fibrosis and inflammation. Analysis of cellular compositions uncovered elevated numbers of MPs and FAPs accompanying a decrease in MuSCs in dKO vs. control mice; moreover, scRNA-seq profiling revealed altered cellular heterogeneity of MPs and FAPs. Furthermore, we found that YY1 deletion in MuSCs induced up-regulation of immune genes thus rendering MuSCs immunogenic. Notably, CCL5 was identified as a critical factor in facilitating the recruitment of MPs via the CCL5/CCR5 axis mediated MuSC-MP interaction; Escalated MP accumulation subsequently prevented FAP apoptosis and clearance via increased TGF\u03b21 accumulation. Consistently, treatment of the dKO mice with a CCR5 antagonist Maraviroc (MVC) significantly ameliorated the dystrophic pathologies and muscle function in dKO mice. Lastly, we elucidated that Ccl5 induction in dKO MuSCs resulted from an altered enhancer-promoter loop interaction. Altogether our findings demonstrate an active role of MuSCs in orchestrating cellular interactions with other niche cells and highlight their capacity in modulating niche microenvironment through their immune-secretory function.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Our prior study12 has hinted the possible YY1 involvement in chronic degeneration/regeneration occurring in dystrophic mdx mouse, which promotes us to further investigate the functional and mechanistic role of YY1 in dystrophic muscle in this study. We conducted a comprehensive characterization of the phenotypes of the Yy1/mdx double knockout (dKO) mice that were generated by inducible deletion of YY1 in MuSCs of mdx mice (Supplementary Fig.\u00a01A). Five days of consecutive intraperitoneal (IP) administration of tamoxifen (TMX) was performed in 1.5-month-old (1.5 M)\u00a0Ctrl and dKO mice to induce YY1 deletion in MuSCs and the muscles were harvested when mice were 2.5\u2009M, 3.5\u2009M and 8.5\u2009M old for subsequent analyses (Fig. 1A\u2013D). Consistent with our prior observations12, YY1 deletion caused severe impairment in muscle regeneration in both limb (tibialis anterior, TA) and diaphragm (DP) muscles as evidenced by the abnormal fiber size distribution with increased larger fibers in the dKO muscles as assessed by H&E (Fig.\u00a01E) and eMyHC (Supplementary Fig.\u00a01B, C) staining at 2.5\u2009M. Moreover, the dKO muscles showed a significantly decreased number of fibers with centrally located nuclei (CLN) (Fig.\u00a01F) and a reduced number of fibers per area (Fig.\u00a01G). Additionally, a decreased number of MuSCs (Supplementary Fig.\u00a01D) caused by impaired proliferation (measured by in vivo EdU incorporation assay in Supplementary Fig.\u00a01E) was also confirmed. Moreover, the dKO mice displayed striking fibrotic- and immune-phenotypes. An excessive amount of fibrosis was detected in dKO by both Masson\u2019s trichrome staining (Fig.\u00a01H) and increased expression of a panel of fibrotic markers (Supplementary Fig.\u00a01F, G). The level of fibrosis appeared to arise from the increased number of PDGFR\u03b1\u2009+\u2009FAPs as a positive correlation was detected between the main fluorescence intensity (MFI) of PDGFR\u03b1 and COL1a1 protein staining in both TA (Fig.\u00a01I) and DP (Fig.\u00a01J) of Ctrl and dKO mice. This was accompanied by an elevated level of inflammation as indicated by increased expression of inflammatory markers (Supplementary Fig.\u00a01H, I) and an augmented number of macrophages (Fig.\u00a01K, L, Supplementary Fig.\u00a01J, K). Altogether, the above phenotypic characterization demonstrates that YY1 deletion in MuSCs leads to exacerbation of dystrophic pathologies in mdx mice. The phenotypes persisted in dKO mice into elder age by contrasting with the Ctrl mdx mice alleviated at around 3.5-month-old (Supplementary Fig.\u00a01L\u2013Q). As a result, the dKO mice were overall very fragile displaying much smaller body size (Fig.\u00a01M, 8.5 M) and significantly lower body weight and muscle weight (Fig.\u00a01N, 3.5 M); the TA muscle mass decreased by 50.97% (Fig.\u00a01O) and the thickness of DP muscle shrunk by 39.51% (Fig.\u00a01P). When the 3.5-month-old mice were subject to treadmill (Fig.\u00a01Q) or voluntary wheel-running (Fig.\u00a01R) exercises, these dKO mice exhibited poor performance compared to the Ctrl mice. As expected, the survival rate of the dKO mice was significantly reduced and half of them died before 6 months (Fig.\u00a01S). Therefore, the YY1 loss in MuSCs worsens the dystrophic manifestations in the mdx mouse.\n\nA Schematic of the experimental design for analyzing dystrophic phenotypes in Ctrl and dKO mice. Validation of YY1 ablation in dKO MuSCs by RT\u2013qPCR (B), Western blot (C), and IF staining (D), n\u2009=\u20095 mice. p\u2009=\u20090.0000014 (B). E H&E staining of TA and DP muscles collected from the Ctrl and dKO mice at the age of 2.5\u2009M. Distribution of fiber size is shown. Scale bar: 50 \u03bcm, n\u2009=\u20095 mice. p\u2009=\u20090.034, 0.022, 0.044, 0.00015. F, G Quantification of fibers with central-localized nuclei (CLN) and fiber numbers, n\u2009=\u20095 mice. p\u2009=\u20090.0041 (F), 0.0047 (G). H Masson\u2019s Trichrome staining on the above muscles. Quantification of fibrotic areas is shown. Scale bar: 50\u2009\u03bcm, n\u2009=\u20095 mice. p\u2009=\u20090.0000076, 0.00075. I, J IF staining of Collagen1a1 (COL1a1, red) and PDGFR\u03b1 (green) on the above muscles. Mean fluorescence intensity (MFI) and correlation of COL1a1 and PDGFR\u03b1 MFI were calculated. Scale bar: 50\u2009\u03bcm, n\u2009=\u20094 mice. p\u2009=\u20090.028 (I), 0.0055 (J). K, L IF staining of DAPI (blue), Laminin (green) and CD68 (red) on the above muscles. Quantification of the number of CD68+ cells per area. Scale bar: 50\u2009\u03bcm, n\u2009=\u20095 mice. p\u2009=\u20090.000074 (K), 0.00013 (L). M Representative images of Ctrl and dKO mice at the age of 8.5\u2009M. N\u2013P Quantification of body weight, TA muscle weight and DP muscle thickness in the 3.5\u2009M mice, n\u2009=\u20095 mice. p\u2009=\u20090.00075 (N), 0.0000014 (O), 0.0000051 (P). Q The running distance until exhaustion of the above mice subject to treadmill running, n\u2009=\u20095 mice. p\u2009=\u20090.038. R The daily running distance of the above mice subject to voluntary wheel-running, n\u2009=\u20094 mice. p\u2009=\u20090.0094, 0.018, 0.033, 0.014, 0.0069. Created in BioRender.com. S Survival rate of Ctrl and dKO mice before 6-month-old, n\u2009=\u200910 mice. All the bar graphs are presented as mean\u2009\u00b1\u2009SD, paired two-sided Student\u2019s t test (B, I, J) and unpaired two-sided Student\u2019s t test (E\u2013H, K, L, N\u2013R) were used to calculate the statistical significance: *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, n.s. no significance. Source data are provided as a Source Data file.\n\nWe hypothesized that YY1 deletion in MuSCs exacerbates dystrophic phenotypes possibly by increasing the numbers of MPs and FAPs in the niche microenvironment while shrinking the MuSC pool. To test this hypothesis, MuSCs, FAPs and MPs were isolated from limb muscles at 5, 21, and 60 days after TMX administration (Fig.\u00a02A) following established FACS sorting protocols12,40,41 (Supplementary Fig.\u00a02A\u2013C). As expected, we observed a progressive decline of MuSCs in the dKO but not in the Ctrl (Fig.\u00a02B). Meanwhile, FAPs (Fig.\u00a02C) and MPs (Fig.\u00a02D) were increased at later stages (21- and 60-days post TMX injection), particularly at 21 days. The above results suggest that the intrinsic deletion of YY1 in MuSCs leads to skewed cellular composition in mdx mouse muscle.\n\nA Schematic of the experimental design for analyzing MuSC, FAP, and MP populations in Ctrl and dKO mice. B\u2013D The percentages of isolated MuSCs, FAPs, and MPs from Ctrl and dKO mice at 5, 21 and 60 days after TMX administration, n\u2009=\u20099 mice. p\u2009=\u20090.0034, 0.0055 (B); 0.0085 (C); 0.0013 (D). E Schematic of scRNA-seq experimental design (created in BioRender.com). Mononuclear cells from three pairs of Ctrl and dKO mice were combined for FACS sorting and living cell selection. A total of 4435 and 3329 living cells from Ctrl and dKO mice were identified respectively. F scRNA-seq was performed in the whole muscle from three pairs of Ctrl and dKO mice. Uniform manifold approximation and projection (UMAP) is shown to visualize variation in single-cell transcriptomes. Unsupervised clustering resolved at least 14 cell types (color coded). G Dot plot showing the expression signatures of representative marker genes for each cell type. H Top: Sankey plots showing the distribution of Ctrl and dKO cells across different cell types. Bottom: pie plots showing the relative cell proportion between Ctrl and dKO groups across different cell types. I, J Left: pseudotime trajectory inference of the identified FAP subpopulations in Ctrl and dKO. Right: pie charts showing the relative cell proportion of each subtype. K Ridge map showing the global distribution density of anti-apoptotic of Ctrl and dKO FAPs. The corresponding dashed line represents the peak position of each group. p\u2009=\u20090.0000028. L, M Left: pseudotime trajectory inference of the identified MO/MP subpopulations in Ctrl and dKO. Right: pie charts showing the relative cell proportion of each subtype. N Ridge map showing the global distribution density of inflammatory scores of Ctrl and dKO MO/MP. The corresponding dashed line represents the peak position of each group. p\u2009=\u20090.00000000000000022. All the bar graphs are presented as mean\u2009\u00b1\u2009SD, unpaired two-sided Student\u2019s t test was used to calculate the statistical significance (B\u2013D, K, N): *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, n.s. no significance. Source data are provided as a Source Data file.\n\nTo further illuminate the altered muscle niche we performed single cell (sc) RNA-seq. As shown in Fig.\u00a02E, hind limb muscles were collected from three pairs of Ctrl or dKO mice (1 month after TMX injection); a mixed single-cell suspension from the three mice was subject to droplet-based scRNA-seq on a 10x Chromium platform. After filtering (cells with fewer than 200 genes and 1000 unique molecular identifiers (UMIs) detected or with more than 5% of UMIs mapped to mitochondrial genes, Supplementary Fig.\u00a02D\u2013H) and doublet removal (Supplementary Fig.\u00a02I), 4335 and 3329 cells were obtained from Ctrl and dKO groups (Supplementary Fig.\u00a02J) for subsequent analyses. A comprehensive definition of cellular atlas uncovered a total of 14 different cell populations including monocyte/macrophage (MO/MP), FAP, endothelial cell (EC), tenocyte, T cell (TC), B cell, smooth muscle cell, dendritic cell, MuSC, neutrophil, cycling basal cell\u00a0(CBC), myocyte, pericyte and Schwann cell (Fig.\u00a02F) based on normalized gene expression levels and canonical cell type-specific markers (Fig.\u00a02G, Supplementary Fig.\u00a01K, and Supplementary Data\u00a02). Notably, FAP, MO/MP were the major cell populations in the niche, making up over 60% of the total cells in both Ctrl and dKO (Fig.\u00a02H). Consistent with the results from Fig.\u00a02B, C, a significantly increased population of FAPs was detected in dKO vs. Ctrl (35.9% vs. 26.4%) accompanied by a reduced population of MuSCs (1.2% vs. 3.3%). Interestingly, no obvious increase in the number of MO/MP was observed (38.0 % vs. 38.2%). The ratio of most cell populations, including EC (7.2% vs. 12.6%), neutrophil (1.1% vs. 2.6%), and pericyte (0.9% vs. 2.1%), displayed a decrease in dKO vs. Ctrl (Fig.\u00a02H, Supplementary Data\u00a02).\n\nTo further illuminate the dynamic shifts of FAPs, pseudotime trajectory was utilized to reveal the cellular heterogeneity and fate determination. Unbiased SNN clustering42 uncovered five subpopulations of FAPs in Ctrl group and designated as 0-Activated, 1-Stressed, 2-Fibrogenic, 3-Adipogenic and 4-Transitional according to differentially expressed genes (DEGs) (Fig.\u00a02I, Supplementary Fig.\u00a02L, Supplementary Data\u00a02). Activated FAPs localized toward the starting point of the trajectory, which were marked by the expression of Cxcl5, Cxcl3, Ccl7, and Ccl2; Stressed FAPs did not appear to have any spatial bias, and were enriched with stress-responsive genes such as heat shock genes (Hspb1, Hspd1, Hspe1) and AP-1 family transcription factors (Fos, Atf3, Jun); Fibrogenic (enriched with ECM factors, Cxcl14, Lum, Smoc2, Podn) and Adipogenic FAPs (enriched with adipogenic factors, Pi16, Dpp4, Igfbp5) (Supplmentary Fig.\u00a02L) highlighted the main divergent fates of two FAP subpopulations in the trajectory. Transitional FAPs appeared on the path from the Activated to Fibrogenic/Adipogenic FAPs, marked with Apod, Ptx3, Myoc and Mt1 genes. As a comparison, above-described five subsets of FAPs were also identified in dKO muscles including 0-Activated, 1-Stressed, 2-Fibrogenic, 3-Adipogenic and 4-Inflammatory (Fig.\u00a02J, Supplementary Fig.\u00a02M, and Supplementary Data\u00a02). And a similar trajectory was adopted: starting from the Activated state, dKO FAPs were arranged along a trajectory that diverged into two distinct branches, which coincided with the two subpopulations, Fibrogenic and Adipogenic (Fig.\u00a02J). Notably, the proportion of the Activated subset increased significantly in dKO vs. Ctrl (32.1% vs. 15.1%), while the Stressed subset displayed a decrease (23.2% vs. 31.0%) (Fig.\u00a02I, J). According to a previous study43, activated FAPs emerge in the early stage of muscle injury, therefore the phenomenon was in line with the delayed regeneration in dKO. Meanwhile, stressed FAPs were enriched with heat shock genes, which were closely related to cellular apoptosis44,45, suggesting resistance to apoptosis in the dKO FAPs. Indeed, a significantly higher anti-apoptotic score (defined by anti-apoptotic gene set, Supplmentary Data\u00a02) was measured in dKO vs. Ctrl FAPs (0.16 vs. 0.08) (Fig.\u00a02K) alongside a decreased apoptotic score (Supplementary Fig.\u00a02N, Supplementary Data\u00a02). The above results suggest that enhanced apoptosis resistance may explain the elevated FAP population in dKO muscle niche. It is also interesting to point out that an inflammatory FAP subset, marked by elevated expression of phagosome and chemokine pathway related genes, was identified exclusively in dKO but not in Ctrl group (Fig.\u00a02I, J, Supplementary Fig.\u00a02L, M).\n\nWhen examining MO/MP subpopulations and heterogeneity, we identified five subsets in Ctrl muscle defined as 0-Monocyte, 1-Inflammatory, 2-Restorative, 3-MHC II, 4-Proliferating (Fig.\u00a02L, Supplementary Fig.\u00a02O, Supplementary Data\u00a02)43,46. Monocytes were characterized by highly expressed Cxcl3, Vcan and Chil3; Inflammatory MPs were enriched for Spp1, Fabp5 and Cd36; Restorative MPs expressed high levels of C1qa, C1qb, and C1qc; MHC II MPs harbored an abundance of H2-Aa, H2-Eb1, and H2-Ab1; The Proliferating subset was distinguished by highly expressed cell cycle and cell division related genes such as Ccna2, Ccnb2, Cdk1, Cdc20, Cdca3, Cdca8 (Supplementary Fig.\u00a02O). In dKO, Monocyte, Inflammatory, Restorative, MHC II but not Proliferating subsets were identified; interestingly, a unique subpopulation with highly induced CCL family genes (Ccl7, Ccl4, Ccl2, Ccl8, Ccl6, Ccl24, Ccl3) was detected and named CCL+ MPs (Fig.\u00a02M and Supplementary Fig.\u00a02P). When the pseudotime trajectory was analyzed, we found that in Ctrl (Fig.\u00a02L) Monocytes plotted tightly together at the initial position of the trajectory line, followed by bifurcation into Restorative and Proliferating MPs. Inflammatory and MHC II MPs were distributed along the line between Monocyte and Restorative MPs. A distinct trajectory was plotted in dKO (Fig.\u00a02M): starting from Monocyte, the line diverged into three branches, Restorative, MHC II, CCL+; Inflammatory mainly located along the two lines between Monocyte and Restorative/CCL+. The proportions of both Inflammatory and Restorative were increased in dKO vs. Ctrl (23.6% vs. 22.3%, 38.2% vs. 26.4%), but the MHC II MPs showed a decline (14.3% vs. 19.2%) (Fig.\u00a02L, M); the dKO specific CCL+ cells displayed enhanced inflammatory features (enriched for Rsad2, Ifit1 and Tnf) while the Ctrl specific Proliferating subset exhibited neutrophil-like scavenger characteristics (enriched for S100a8, Camp and Ngp). Altogether, the above results support that the inflammatory niche is skewed in dKO muscle. Consequently, by comparing the inflammatory score (defined by inflammatory gene set, Supplementary\u00a0Data\u00a02), a significant elevation was detected (0.16 vs. \u22120.08) (Fig.\u00a02N). Altogether, scRNA-seq results show that the intrinsic deletion of YY1 in MuSCs leads to niche remodeling primarily by altered FAP and MP compositions.\n\nTo delineate the molecular mechanism underlying the cellular composition changes in dKO muscles, bulk RNA-seq was performed on freshly isolated MuSCs from six pairs of Ctrl or dKO mice (Fig.\u00a03A, Supplementary Data\u00a03). A total of 1090 genes were up-regulated and 1527 down-regulated in dKO compared with Ctrl (Fig.\u00a03B and Supplementary Data\u00a03). Strikingly, Gene Ontology (GO) analysis revealed that the up-regulated genes were highly enriched for immune related terms such as \u201cimmune system process\u201d, \u201cinnate immune response\u201d etc. (Fig.\u00a03C and Supplementary Data\u00a03), suggesting an immune-like nature of dKO cells. Conversely, genes associated with \u201ccell cycle\u201d and \u201ccell division\u201d were predominantly down-regulated (Fig.\u00a03D and Supplementary Data\u00a03), aligning with the detected reduced proliferative capacity of dKO MuSCs (Supplementary Fig.\u00a01D, E and Supplementary Data\u00a03). A detailed examination of the up-regulated genes highlighted the induction of numerous pro-inflammatory mediators, particularly those facilitating MPs infiltration into injured tissue, such as a panel of chemoattractants from CCL family, Ccl5, Ccl25, Ccl2, Ccl7, Ccl3 (Supplementary Fig.\u00a03A, B and Supplementary Data\u00a03)26,47,48. Among these factors, Ccl5 gene induction was robustly detected in dKO MuSCs (Fig.\u00a03E, 2.58-fold, Supplementary Fig.\u00a03C\u2013E) accompanied by the protein induction (Fig.\u00a03F, 72.89% vs. 3.55%) and secretion (Fig.\u00a03G); a gradually increased amount of CCL5 protein was also detected in dKO muscles after TMX injection (Fig.\u00a03H). It is known that enriched ligands can promote the up-regulated receptor expression in cells49; expectedly, we found an escalated amount of Ccl5 receptor Ccr5 mRNA and protein in dKO muscles (Fig.\u00a03I). Moreover, co-localization of CCL5 and CCR5 proteins were detected (Fig.\u00a03J). CCL5/CCR5 axis is known to play an important role in chemoattracting MPs50,51,52, consistently, we found Ccr5 was mainly expressed by MPs (Supplementary Fig.\u00a03F) and a significantly increased expression was detected in dKO MPs (Supplementary Fig.\u00a03G) by analyzing the scRNA-seq data, which was further confirmed in the isolated MPs from TMX-1M muscle (Fig.\u00a03K). Moreover, IF staining showed that most of the Ctrl and dKO MPs were CCR5+ cells (>95%, Supplementary Fig.\u00a03H). Altogether, these findings suggest that CCL5 induction in dKO MuSCs enhances MP recruitment via a CCL5/CCR5 axis. To further test this, transwell assay was performed by seeding MuSCs from Ctrl or dKO underneath the transwell insert and bone marrow derived macrophages (BMDMs, Supplementary Fig.\u00a03I) on the top (Fig.\u00a03L). As expected, a much higher number of migrated BMDMs were detected in dKO vs. Ctrl (113.3 vs. 79.4, Fig.\u00a03L), indeed confirming the enhanced recruiting ability of dKO MuSCs. To validate the role of CCL5/CCR5 axis in mediating the recruitment, we found that BMDM attraction by dKO cells was significantly attenuated by neutralization of CCL5 (Fig.\u00a03M) or CCR5 (Fig.\u00a03N) with antibodies. Altogether, the above findings demonstrate that intrinsic loss of YY1 transforms MuSCs into immune-secretory and endows its heightened ability to crosstalk and recruit MPs to the niche microenvironment via the CCL5/CCR5 axis.\n\nA Schematic of the experimental design for testing MuSC/MP interaction in Ctrl and dKO mice. B Differentiability expressed genes (DEGs) were identified from the RNA-seq profiling in Ctrl vs dKO MuSCs using Log2FC\u2009>\u20090.5 as a cut-off. C, D GO analysis of the above identified 1090 up and 1527 down-regulated DEGs. E RT-qPCR detection of Ccl5 mRNA in freshly isolated MuSCs form Ctrl and dKO, n\u2009=\u20095 mice. p\u2009=\u20090.0086. F IF staining of CCL5 protein in freshly isolated MuSCs from Ctrl and dKO. Scale bar: 50\u2009\u03bcm, n\u2009=\u20095 mice. p\u2009=\u20090.0000000031. G ELISA detection of secreted CCL5 protein from Ctrl and dKO MuSCs, n\u2009=\u20095 mice. p\u2009=\u20090.00000036. H Western blot detection of CCL5 protein in TA muscles of Ctrl and dKO at the designated times after TMX administration. I RT-qPCR detection of Ccl5 and Ccr5 mRNAs in Ctrl and dKO TA muscles, n\u2009=\u20095 mice. p\u2009=\u20090.0015, 0.0498. J IF staining of CCL5 and CCR5 proteins on TA muscle sections of Ctrl and dKO. Co-localization of CCL5 and CCR5 is shown in the red frame. Scale bar: 50\u2009\u03bcm, n\u2009=\u20095 mice. K RT-qPCR detection of Ccr5 mRNA in MPs freshly isolated from Ctrl and dKO, n\u2009=\u20094 mice. p\u2009=\u20090.032. L BMDMs were isolated from mdx mice and co-cultured with MuSCs from Ctrl and dKO in transwell. Quantification of migrated BMDMs is shown, n\u2009=\u20096 mice. p\u2009=\u20090.015. M, N CCL5 or CCR5 antibody was added to the above transwell. Quantification of migrated BMDMs is shown, n\u2009=\u20096 mice. p\u2009=\u20090.0026, 0.00000073 (M); 0.035, 0.0016 (N). All the bar graphs are presented as mean\u2009\u00b1\u2009SD, unpaired one-sided Student\u2019s t test (B), paired two-sided Student\u2019s t test (E, I, K), unpaired two-sided Student\u2019s t test (F, G, L\u2013N) and one-sided Fisher\u2019s exact test (C, D) were used to calculate the statistical significance: *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, n.s. no significance. Source data are provided as a Source Data file.\n\nTo further elucidate the underlying cause for increased FAPs in dKO muscle, we examined if the dKO FAPs possess apoptosis resistance as suggested by Fig.\u00a02K. PDGFR\u03b1\u2009+\u2009FAPs were isolated from Ctrl and dKO mice and used for TUNEL staining (Fig.\u00a04A). As expected, a significant reduction (32.5%) of TUNEL+ cells was observed in dKO vs. Ctrl (Fig.\u00a04B), confirming the resistance to apoptosis of dKO FAPs. This was further supported by co-staining of TUNEL and PDGFR\u03b1 on TA (Fig.\u00a04C) or DP (Fig.\u00a04D) muscles, showing a substantial decrease (26.3%, 9.3%) of apoptotic FAPs in dKO. We also examined the proliferative ability of FAPs by EdU staining (Fig.\u00a04A); no significant difference of EdU+ cells was detected when the assays were conducted on in vitro cultured (Fig.\u00a04E, Supplementary Fig.\u00a04A, B) and in vivo isolated (Fig.\u00a04F, Supplementary Fig.\u00a04C, D) FAPs from Ctrl or dKO mice. Moreover, the fibrogenic and adipogenic differentiation abilities of FAPs were examined. Elevated \u03b1-SMA protein (Supplementary Fig.\u00a04E) and mRNA (Supplementary Fig.\u00a01F) were detected in dKO TA muscle, indicating increased fibrogenic differentiation propensity. However, adipogenic assessment by Oil red O staining or marker gene expressions showed no significant difference (Supplementary Fig.\u00a04F, G). Altogether, these results demonstrate that the accumulation of FAPs in dKO muscle mainly arises from the mitigated apoptosis thus the enhanced survival and the increased fibrogenic differentiation may exacerbate the fibrosis. To assess if direct crosstalk from MuSCs impacts FAP apoptosis and proliferation in dKO muscle niche (Fig.\u00a04A), MuSCs from Ctrl or dKO mice were co-cultured with FAPs (isolated from Ctrl\u00a0mice) for 24\u2009hours (Fig.\u00a04G) and there were no significant changes in apoptosis by TUNEL staining (Fig.\u00a04H) or proliferation by Ki67 staining (Fig.\u00a04I), suggesting that MuSCs may exert negligible direct impact on FAPs.\n\nA Schematic of the experimental design for testing FAP/MuSC interaction in Ctrl and dKO mice. B TUNEL staining of FAPs isolated from Ctrl and dKO muscles. The percentage of TUNEL+ cells is shown. Scale bar: 50 \u03bcm, n\u2009=\u20096 mice. p\u2009=\u20090.0000041. C, D TUNEL and PDGFR\u03b1 staining of TA or DP muscles from Ctrl and dKO mice. The percentage of TUNEL\u2009+\u2009PDGFR\u03b1+ cells is shown. Scale bar: 50\u2009\u03bcm, n\u2009=\u20095 mice. p\u2009=\u20090.0084 (C), 0.0095 (D). E EdU staining of 1D-in vitro cultured FAPs. The percentage of EdU+ cells is shown. Scale bar: 50\u2009\u03bcm, n\u2009=\u20095 mice. p\u2009=\u20090.70. F EdU and PDGFR\u03b1 staining of freshly isolated FAPs from TMX-5D mice. The percentage of EdU+ PDGFR\u03b1+ cells is shown. Scale bar: 50\u2009\u03bcm, n\u2009=\u20095 mice. p\u2009=\u20090.28. G Schematic of MuSCs and FAPs co-culture experiment. H TUNEL staining of above co-cultured FAPs. The percentage of TUNEL+ cells is shown. Scale bar: 100\u2009\u03bcm, n\u2009=\u20095 mice. p\u2009=\u20090.64. I Ki67 staining of the above co-cultured FAPs. The percentage of Ki67+ cells is shown. Scale bar: 100 \u03bcm, n\u2009=\u20095 mice. p\u2009=\u20090.088. J, K RT-qPCR and Western blot detection of TGF\u03b21 mRNA and protein in TA muscles, n\u2009=\u20095 mice. p\u2009=\u20090.030. L IF staining of TGF\u03b21 and quantification of MFI in TMX-2M TA muscles. Scale bar: 50\u2009\u03bcm, n\u2009=\u20095 mice. p\u2009=\u20090.041. M Bar plot showing the Tgf\u03b21 expression in cells. N RT-qPCR detection of Tgf\u03b21 expression in Ctrl and dKO MPs, n\u2009=\u20095 mice. p\u2009=\u20090.996. O ELISA detection of secreted TGF\u03b21 protein from Ctrl and dKO MPs, n\u2009=\u20096 mice. p\u2009=\u20090.38. P Schematic of MPs and FAPs co-culture experiment. Q TUNEL staining of above co-cultured FAPs. The percentage of TUNEL+ cells is shown. Scale bar: 50\u2009\u03bcm, n\u2009=\u20095 mice. p\u2009=\u20090.996. All the bar graphs are presented as mean\u2009\u00b1\u2009SD, paired two-sided Student\u2019s t test (J, L, N) and unpaired two-sided Student\u2019s t test (B\u2013F, H, I, O, Q) were used to calculate the statistical significance: *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, n.s. no significance. Source data are provided as a Source Data file.\n\nWe then sought to test the possibility that enhanced FAP survival in dKO muscle may be caused by increased TGF\u03b21 level since it was recently demonstrated that highly expressed TGF\u03b21 prevented FAP apoptosis and promoted their differentiation into matrix-producing cells, contributing to fibrosis in dystrophic muscle29. No obvious difference in TGF\u03b21 expression was detected in dKO vs. Ctrl muscles at TMX-7D (Supplementary Fig.\u00a04H), but a significant induction was observed at later stages (TMX-14D, Supplementary Fig.\u00a04I; TMX-2M, Fig.\u00a04J\u2013L), indicating the gradual accumulation of TGF\u03b21 in the muscle niche after YY1 deletion which is concomitant with FAP increase. According to previous reports29,40,53, the predominant contributor of TGF\u03b21 in dystrophic muscle is MPs, indeed, analyzing our scRNA-seq data uncovered MPs as the main source of TGF\u03b21 (Fig.\u00a04M). To further investigate whether the elevated TGF\u03b21 is a result of increased level of secretion or number of MPs, we found no difference in TGF\u03b21 expression (Fig.\u00a04N) or secretion (Fig.\u00a04O) when the same number of MPs from Ctrl or dKO were analyzed. To further test the impact of\u00a0MP secreted TGF\u03b21 on FAP apoptosis, the same number of MPs from Ctrl or dKO mice were co-cultured with FAPs (isolated from Ctrl) for 24\u2009h (Fig.\u00a04P) and no significant change in apoptosis was detected by TUNEL staining (Fig.\u00a04Q); but when a higher ratio of dKO cells (5:3, dKO: Ctrl, mimicking the in vivo situation) were used in the assay, FAPs displayed significantly decreased apoptosis (Supplementary Fig.\u00a04J), suggesting that the enriched TGF\u03b21 accumulation in dKO muscle is more likely a direct result of the increased number of MPs. Altogether, these findings demonstrate that the heightened MP population results in elevated TGF\u03b21 level, which inhibits FAP apoptosis thus causes FAP accumulation to exacerbate fibrosis in dKO muscle.\n\nNext, to investigate if inhibiting above-defined CCL5/CCR5 signaling can mitigate dystrophy in dKO mice, we treated Ctrl and dKO mice with MVC, a well-documented CCR5 antagonist54,55. IP administration of MVC in Ctrl and dKO mice was repeated every other day over 30 days at 2\u2009mg/kg after which the TA muscles were isolated for subsequent analysis (Fig.\u00a05A). The treatment did not affect the expression of CCL5 in MuSCs (Supplementary Fig.\u00a05A, B) or TA muscles (Supplementary Fig.\u00a05C), but significantly ameliorated the muscle phenotypes in dKO mice: the regeneration was elevated, evidenced by decreased abnormally larger fibers (Fig.\u00a05B), increased fibers with CLN (Fig.\u00a05C) and fiber numbers (Fig.\u00a05D), accompanied by significantly tampered amount of fibrosis (Fig.\u00a05E). As expected, a concomitant decline of the numbers of FAPs and MPs was observed in the dKO muscle niche (Fig.\u00a05F\u2013H), accompanied by decreased TGF\u03b21 expression (Supplementary Fig.\u00a05D, E), but the number of MuSCs did not show any obvious increase (Fig.\u00a05F).\n\nA Schematic of the DMSO or MVC treatment and assessment in six pairs of male Ctrl and dKO mice. B H&E staining of TA and DP muscles collected from the Ctrl and dKO mice at the age of 2.5\u2009M. Distribution of fiber size is shown. Scale bar: 50 \u03bcm, n\u2009=\u20096 mice. p\u2009=\u20090.00000000061, 0.000051, 0.000061, 0.032, 0.0048, 0.000016. C Quantification of CLN+ fibers in the above-stained muscles, n\u2009=\u20096 mice. p\u2009=\u20090.0000049, 0.00000036. D Quantification of fiber numbers per area of the above-stained muscles, n\u2009=\u20096 mice. p\u2009=\u20090.0096, 0.013. E Masson\u2019s Trichrome staining on the above muscles. Quantification of fibrotic areas is shown. Scale bar: 50 \u03bcm, n\u2009=\u20096 mice. p\u2009=\u20090.0000000044. F Flow cytometry detection of MuSC, FAP, and MP population in Ctrl and dKO mice after the treatment, n\u2009=\u20095 mice. p\u2009=\u20090.038, 0.0056. G IF staining of DAPI (blue), PDGFR\u03b1 (green), and Laminin (red) was performed on TA muscles of Ctrl and dKO mice after the treatment. The quantification of MFI of PDGFR\u03b1 is shown. Scale bar: 50 \u03bcm, n\u2009=\u20096 mice. p\u2009=\u20090.00078. H IF staining of DAPI (blue), F4/80 (red), and Laminin (green) was performed on TA muscles of Ctrl and dKO mice after the treatment. The quantification of F4/80+ cell number per area is shown. Scale bar: 50 \u03bcm, n\u2009=\u20096 mice. p\u2009=\u20090.000015. I The running distance until exhaustion of the above mice subject to treadmill running is shown, n\u2009=\u20096 mice. p\u2009=\u20090.00037, 0.037. J The daily running distance of the above mice subject to voluntary wheel-running is shown, n\u2009=\u20096 mice. K Body weight of Ctrl and dKO mice after the treatment, n\u2009=\u20095 mice. p\u2009=\u20090.0010, 0.031. All the bar graphs are presented as mean\u2009\u00b1\u2009SD, unpaired two-sided Student\u2019s t test was used to calculate the statistical significance (B\u2013I, K): *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, n.s. no significance. Source data are provided as a Source Data file.\n\nConsistently, the treated mice displayed significantly improved muscle function when subject to treadmill or voluntary wheel-running exercises. The endurance of dKO mice was notably recovered in the treadmill experiment (Fig.\u00a05I). The treated dKO mice also demonstrated increased engagement in voluntary wheel-running, with a consistent increase in running distance over time (Fig.\u00a05J). The overall morphology and health state of the dKO mice were improved by the treatment showing a significantly restored body weight (Fig.\u00a05K). Altogether, these results further underpin the importance of CCL5/CCR5 signaling axis in muscle dystrophy and highlight the possibility of targeting this axis to modulate muscle niche and decelerate disease progression.\n\nTo answer how intrinsic YY1 deletion in MuSCs induces Ccl5 expression, YY1 ChIP-seq was employed to map YY1 bound genes in freshly isolated MuSCs from mdx mice (Fig.\u00a06A). A total of 4681 YY1 binding sites were identified with a canonical YY1 binding motif \u201cAANATGGC\u201d (Fig.\u00a06B and Supplementary Data\u00a04). Over half of the binding occurred in promoter regions (59%) while 29% and 12% in gene body and intergenic regions (Fig.\u00a06C). GO analysis revealed YY1 bound genes were enriched for a wide range of terms such as \u201chistone modification\u201d and \u201cmRNA processing\u201d etc. (Fig.\u00a06D). Further integration with the RNA-seq identified DEGs (Fig.\u00a03B) uncovered a total of 181 and 226 YY1 bound genes were up- and down-regulated in dKO vs. Ctrl MuSCs (Fig.\u00a06E and Supplementary Data\u00a04). GO analysis revealed that the up-regulated targets were highly enriched for \u201cNucleus\u201d, \u201cNucleoplasm\u201d and \u201cDNA binding\u201d etc. while the down-regulated targets were related to \u201cNucleus\u201d, \u201cNucleoplasm\u201d and \u201cCell cycle\u201d etc. (Supplementary Fig.\u00a04A, B and Supplementary Data\u00a04). Next, we took a close examination of the Ccl5 locus and found no YY1 binding at its promoter region, instead, a binding site was identified at a distal site which was a potential enhancer by analyzing our previously published H3K27ac ChIP-seq data56 (Fig.\u00a06F). Considering YY1 is emerging as a 3D chromatin organizer that can facilitate enhancer-promoter (E-P) looping57,58,59, we tested if YY1 modulated Ccl5 transcription via orchestrating E-P looping in MuSCs. To this end, we performed in situ Hi-C60 to interrogate the 3D genome organization in Ctrl and dKO MuSCs. We found that YY1 knock-out in MuSCs had limited effect on global 3D genome organization as no difference in the contact frequency was detected between Ctrl and dKO at both short-\u00a0and long-distance levels (Supplementary Fig.\u00a04C). The YY1 knock-out did not alter the overall genome organization at compartment level either (Supplementary Fig.\u00a04D), indicated by infrequent locus switching between A and B compartments, with only 2.8% B to A and 2.2% A to B switching (Fig.\u00a06G). At the TAD level, however, we noticed an evident TAD boundary remodeling: 9393 new boundaries formed while 4033 out of 13416 remained unchanged (Fig.\u00a06H); and a significant decline in the TAD size was detected in dKO vs. Ctrl (Fig.\u00a06I). Additionally, a slight increase in the number of TADs was observed (9557 in dKO vs. 8965 in Ctrl) (Fig.\u00a06J). Furthermore, at the looping level, we found the loop size remained largely unaltered (Fig.\u00a06K) while a declined number of looping events occurred in dKO vs. Ctrl (1056 vs. 1753) (Fig.\u00a06L). These results suggest that YY1 may play a role in regulating the 3D genome in MuSCs via looping modulation.\n\nA Schematic of the experimental design for dissecting YY1 regulation of Ccl5 expression. B Enrichment of YY1 motif in YY1 ChIP-seq binding regions. C Genomic distribution of 4681 YY1 binding peaks. D GO analysis of YY1 ChIP-seq target genes. E The overlapping between the above identified YY1 ChIP-Seq targets and the down/up-regulated genes. F Genomic snapshots on Ccl5 locus showing co-binding of H3K27ac and YY1. G A/B compartment switch between Ctrl and dKO. H\u2013J Comparison of TAD boundaries, size and number between Ctrl (n\u2009=\u20098965 TADs) and dKO (n\u2009=\u20099557 TADs). K, L Comparison of loop size and number between Ctrl (n\u2009=\u20091753 loops) and dKO (n\u2009=\u20091056 loops). The boxes (I, K) indicate median (center), Q25, and Q75 (bounds of box), the smallest value within 1.5 times interquartile range below Q25 and the largest value within 1.5 times interquartile range above Q75 (whiskers). M Heatmap and genomic snapshots showing the Hi-C interactions encompassing Ccl5 locus (yellow box indicates TAD). N, O 3C-qPCR detection of E-P interaction on Ccl5 locus, n\u2009=\u20093 cells. P Schematic of YY1 tethering experiment design in C2C12 cells. Q ChIP-qPCR detection of YY1 enrichment on the tethered site in dCas9-YY1 vs. dCas9 cells, n\u2009=\u20093 cells. p\u2009=\u20090.029, 0.0034, 0.000086, 0.10, 0.019. R 3C-qPCR detection of E-P interaction on Ccl5 locus in the above cells, n\u2009=\u20093 cells. S RT-qPCR detection of Ccl5 expression in the above cells, n\u2009=\u20093 cells. p\u2009=\u20090.00049, 0.000085, 0.023, 0.022, 0.57. T Left: Luciferase reporter assay design. Right: Relative fluorescence unit (RFU) of reporter activity is shown, n\u2009=\u20093 cells. p\u2009=\u20090.000015. U Left: Luciferase reporters assay design. Right: RFU of reporter activity is shown, n\u2009=\u20093 cells. p\u2009=\u20090.047. All the bar graphs are presented as mean\u2009\u00b1\u2009SD, unpaired one-sided Student\u2019s t test (I, K), paired two-sided Student\u2019s t test (Q, S), unpaired two-sided Student\u2019s t test (T, U), one-sided Fisher\u2019s exact test (D) were used to calculate the statistical significance and adjustments were made for multiple comparisons: *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, n.s. no significance. Source data are provided as a Source Data file.\n\nNext, we took a close examination of the Ccl5 locus, attempting to elucidate the cause behind high Ccl5 in dKO MuSCs. Interestingly, two evident TADs were observed encompassing the Ccl5 locus in Ctrl; and a newly gained third one was found in dKO and the above-identified YY1 bound enhancer was located within this gained TAD (Fig.\u00a06M). Further analysis of E-P looping defined one strong looping event occurring between this enhancer and the Ccl5 promoter; moreover, the interacting strength measured by interaction frequency was significantly lower in dKO vs. Ctrl (7.6 vs. 16.5) (Fig.\u00a06M), suggesting an interesting scenario where YY1 mediated E-P looping functions to suppress Ccl5 induction in Ctrl and the attenuation of the looping relieves the suppression in dKO. To test this notion, the above-identified E-P interaction was validated by Chromosome Conformation Capture (3C) assay in freshly isolated MuSCs from Ctrl and dKO using the Ccl5 promoter as an anchor (Fig.\u00a06N). Expectedly, a prominent interaction peak was detected at the enhancer region in both Ctrl and dKO groups and the quantified interaction frequency by 3C-qPCR weakened as the genomic distance from the enhancer site increased. In addition, the interaction frequency at the enhancer site was reduced in dKO vs. Ctrl (0.16 vs. 0.10) (Fig.\u00a06O). These results indicate that the YY1 deletion in MuSCs indeed attenuates the E-P interaction at the Ccl5 locus.\n\nTo further substantiate the direct function of YY1 in facilitating the E-P interaction, artificial tethering of YY1 protein to the Ccl5 enhancer region (Fig.\u00a06P) was performed in C2C12 myoblast cell line where no direct binding of YY1 on the Ccl5 enhancer locus was detected by analyzing the existing H3K27ac and YY1 ChIP-seq data from C2C12 myoblast16,61 (Supplementary Fig.\u00a04E). A dCas9-YY1 plasmid and five sgRNAs targeting the enhancer region were harnessed to generate five stable clones expressing increased level of dCas9-YY1; the transfection efficiency was confirmed in both mRNA (Supplementary Fig.\u00a04F) and protein levels, with no significant influence on endogenous YY1 expression (Supplementary Fig.\u00a04G). As a result, YY1 enrichment at the Ccl5 enhancer region was markedly strengthened after tethering (Fig.\u00a06Q) (detected by YY1 ChIP-qPCR using five primers targeting the enhancer region) and an increased contact frequency (0.0036 vs. 0.0012) was detected between the promoter and the enhancer by 3C-qPCR (Fig.\u00a06R). Accordingly, the expression level of Ccl5 was significantly reduced in four of the five stable clones (Fig.\u00a06S). Altogether, the above results demonstrate an active role of YY1 in facilitating E-P interaction to curb the activation of Ccl5 locus in Ctrl MuSCs. The regulatory role of YY1 and the upstream enhancer was further confirmed by transfecting a luciferase reporter containing the promoter and enhancer regions of Ccl5 in Ctrl and dKO MuSCs (Fig.\u00a06T). A significantly higher level of luciferase activity was detected in dKO vs. Ctrl (8.73 vs. 1.00) (Fig.\u00a06T). Moreover, a reporter devoid of the enhancer sequence displayed elevated reporter activity (20.86 vs. 1.00) in Ctrl MuSCs (Fig.\u00a06U), solidifying the repressive function of the enhancer element. Altogether, these results have confirmed the direct role of YY1 in repressing the Ccl5 transcription by enhancing the E-P interaction.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we leverage the Yy1/mdx dKO mouse strain to elucidate how MuSCs impact their niche microenvironment in dystrophic muscle. We demonstrate the MuSCs are capable of modulating their niche via cellular interactions with MPs and FAPs. Intrinsic deletion of YY1 in MuSCs leads to unexpected aggravation of inflammation and fibrosis in dKO which is attributed to altered niche microenvironment characterized by skewed dynamics and heterogeneity of MPs and FAPs. Further investigation demonstrates that conveying the dKO\u2019s intrinsic change to niche alterations stems from the induction of many immune related genes causing the conversion of MuSCs into competent secretory cells. In particular, we identify a CCL5/CCR5 signaling axis that mediates the MuSC-MP cellular interaction which enhances MP recruitment and exacerbates inflammation. As a result, elevated TGF\u03b21 secreted by MPs leads to FAP accumulation and aggravated fibrosis. Treatment with the CCR5 antagonist MVC reduces dystrophic disease manifestations and improves muscle function. Furthermore, we also illuminate the intrinsic mechanism of how YY1 loss causes Ccl5 induction in MuSCs via YY1\u2019s ability to modulate enhancer-promoter looping interaction at Ccl5 locus\u00a0(Fig. 7).\n\nIn Ctrl MuSCs, YY1 binding to a Ccl5 regulatory element orchestrates the E-P formation to suppress Ccl5 expression. Upon YY1 loss in dKO MuSCs, Ccl5 is highly activated and augmented secretion of CCL5 from MuSCs promotes the recruitment of MPs via CCL5/CCR5 mediated cell crosstalk, which subsequently causes increased accumulation of TGF\u03b21 to hinder the apoptosis and clearance of FAPs therefore the aggravated fibrosis. MVC-mediated pharmacological blockade of the CCL5/CCR5 axis effectively mitigates muscle dystrophy and improves muscle performance in dKO mice.\n\nOur study provides compelling evidence to demonstrate the important active role of MuSCs in modulating niche microenvironment. As the small population of residents in skeletal muscle, MuSCs are often portrayed as the passive recipient of niche regulation. Mounting efforts are focused on dissecting how MuSC behavior is tightly regulated by spatiotemporal signaling from the niche and how niche-derived growth factors and signaling molecules, metabolic cues, the extracellular matrix and biomechanical cues, and immune signals exert their effects on MuSCs62. However, sporadic evidence hinted the possible reciprocal effect that MuSCs exert on MPs. For example, a study from 2013 demonstrated that human myoblasts can indeed secrete an array of chemotactic factors to initiate monocyte chemotaxis in culture26. A recent study from Oprescu et al. 43 profiled transcriptomic dynamics at various stages of acute damage-induced muscle regeneration by scRNA-seq; a myoblast subpopulation enriched for immune genes were identified to be capable of active communication with immune cell populations. More recently, a study from Nakka K. et. al. demonstrates MuSCs can initiate the production of hyaluronic acid to modulate the ECM in the niche which in turn directs MuSCs to exist quiescence state for repair of injured muscle63. Moreover, emerging reports reveal the presence of senescent MuSCs in regenerating and aging muscles; these senescent MuSCs are characterized by SASPs (senescence-associated secretory phenotypes) and could have paramount roles in remodeling niche microenvironment64. Therefore, following our study, much effort will be needed to elucidate the functional interactions between MuSCs and the niche. Moreover, we also posit that similar niche-regulatory functions might be a feature of other adult stem cells; the tremendous advances in single cell profiling and spatial transcriptomic technologies are poised to revolutionize our understanding of stem cell niche dynamics in unparalleled detail.\n\nOur findings uncovered a novel MuSC/MP interaction that offers insights into the pathogenic recruitment of MPs in dystrophic muscle. In acutely injured skeletal muscle, infiltrating MPs are mainly Ly6Chi CCR2+CX3CR1lo monocytes that are recruited through CCR2 chemotaxis signaling by myoblasts, ECs and resident macrophages36. These monocytes then differentiate into Ly6Chi inflammatory MPs. After phagocytosing necrotic muscle debris, intramuscular Ly6Chi MPs can switch into Ly6Clo MPs. It has been shown that recruitment of Ly6hi monocytes in mdx mouse muscles is also dependent on CCR251. Nevertheless, the induction of other CCL class chemokine ligands including CCL5, CCL6, CCL7, CCL8, and CCL9 and receptors CCR1, and CCR5, has also been observed in mdx mouse muscle65. Here we demonstrate that MuSCs can also contribute to MP recruitment via the CCL5/CCR5 axis. MuSC-MP interactions may have commenced promptly following TMX injection, evidenced by immediately elevated expression and secretion of CCL5 in MuSCs and an increased population of MPs in dKO. Nevertheless, MuSCs may not be the only source of CCL5, which can also be a product of T cells, MPs and muscle fibers65. Additionally, it will be interesting to further explore whether the MuSC-MP crosstalk has impact on the heterogeneous presence of MPs in dystrophic muscles. It is also necessary to point out that mdx mice in fact display a milder phenotype compared to DMD patients66. Muscle inflammation starts about 3 weeks of age, persists into 2\u20133 months of age, and then gradually subsides in limb muscles but not diaphragm. Our Yy1/mdx dKO mice however display more severe dystrophic pathologies mimicking human DMD. Therefore, CCL5/CCR5 axis constitutes a potential target for DMD treatment. Although gene and molecular therapies targeting the primary defect of Dystrophin gene remain the most promising approach for DMD treatment, therapeutic strategies targeting the complex secondary mechanisms responsible for DMD pathogenesis are also being developed in parallel22. Drugs aiming at reducing inflammation and fibrosis are proven to be effective in mitigating the disease progression. In our study, MVC treatment leads to notable amelioration of the dystrophy pathologies; both inflammation and fibrosis were reduced while muscle performance was improved, which encourages us to perform trials in human DMD patients in the future. In addition, our findings also reinforce the previously known pro-fibrotic role of TGF\u03b21 in dystrophic muscle highlighting it as a potential therapeutic target. Indeed, targeting TGF\u03b2 signaling by intramuscular injection of an inhibitor leads to reduced FAP accumulation and fibrosis in dystrophic mice53.\n\nLastly, in search of reasons underlying Ccl5 induction upon YY1 deletion, we demonstrate that YY1 acts on Ccl5 enhancer to orchestrate E-P interactions thus reinforcing its role as a structural protein. Emerging evidence demonstrates YY1 contributes to E-P structural interactions in a manner analogous to DNA interactions mediated by CTCF58. It is shown that YY1 binds to active enhancers and promoter-proximal elements and forms dimers that facilitate the interaction of these DNA elements. Deletion of YY1 binding enhancer sites or depletion of YY1 protein disrupts E-P looping and gene expression58. These findings demonstrate the importance of enhancers in cell state transition and cell abundance67. In line with this, reduced E-P interaction frequency on Ccl5 locus was observed in dKO MuSCs which was reversed by artificially tethering YY1 protein to the Ccl5 enhancer region. Interestingly, our results suggest that the YY1-facilitated E-P interaction suppresses but not enhances Ccl5 induction. Studies from others and ours in fact also demonstrate that binding of certain TFs renders an enhancer element to become suppressive in gene expression58,68,69. Additionally, we have only focused our investigation on Ccl5, it will also be interesting to elucidate how YY1 loss induces the expression of other immune genes and transforms the intrinsic epigenetic signaling to extrinsic regulation of the MuSC niche.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56474-w/MediaObjects/41467_2025_56474_Fig7_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "All animal handling procedures, protocols, and experiments ethics approval was granted by the CUHK AEEC (Animal Experimentation Ethics Committee) under Ref No. 21-080-GRF and 19-220-MIS. The mice were maintained in an animal room with 12\u2009h light/12\u2009h dark cycles, temperature (22\u201324\u2009\u00b0C), and humidity (40\u201360%) at the animal facility in CUHK, fed with PicoLab\u00ae Select Mouse Diet 50 IF/9F Diet and provided with plenty of fresh clean water at all times. For all animal-based experiments, at least three pairs of littermates or age-matched mice were used.\n\nYy1f/f and C57BL/10 ScSn DMDmdx (mdx) mouse strains were purchased from The Jackson Laboratory (Bar Harbor, ME, USA). The YY1-inducible conditional KO (YY1iKO) strain (Ctrl: Pax7CreERT2/R26YFP; Yy1+/+, Yy1iKO: Pax7CreERT2/R26YFP; Yy1f/f mice) was generated by crossing Pax7CreERT2/R26YFP mice with Yy1f/f mice. The Yy1/mdx double KO (YY1dKO) strain (Ctrl: Pax7CreERT2/R26YFP; Yy1+/+; mdx, YY1dKO: Pax7CreERT2/R26YFP; Yy1f/f; mdx) was generated by crossing YY1iKO with mdx mice. Primers used for genotyping are shown in Supplementary Data\u00a01.\n\nInducible deletion of YY1 was administrated by IP injection of TMX (Sigma-Aldrich, T5648) at 100\u2009mg/kg (body weight). For EdU (Sigma-Aldrich, 900584-50MG) incorporation assay in vivo, 5\u2009mg EdU (diluted in 100\u2009\u03bcl PBS) injection via IP per day was performed for 3 consecutive days, followed by FACS isolation of MuSCs or FAPs 12\u2009h later. MVC (Sigma-Aldrich, PZ0002-25MG) treatment was administrated by IP injection at 2\u2009mg/kg every 2 days for 30 days. For voluntary wheel-running test, mice were housed individually for 7 days in polycarbonate cages with 12-cm-diameter wheels equipped with optical rotation sensors (Yuyan Instrument, ARW). For treadmill test, mice were adapted to a treadmill (Panlab, Harvard Apparatus, 76-0895) with a 5\u00b0 incline at an initial speed of 10\u2009cm/s, followed by a stepwise increase of 5\u2009cm/s every two min until their exhaustion. For each animal experiment, Ctrl and YY1dKO mice of the same sex and age were used.\n\nMouse C2C12 myoblast cells (CRL-1772) and 293T cells (CRL-3216) were obtained from American Type Culture Collection and cultured in DMEM medium (Gibco, 12800-017) with 10% fetal bovine serum (Invitrogen, 16000044), 100 units/ml of penicillin and 100\u2009\u03bcg of streptomycin (P/S, Gibco,15140-122) at 37\u2009\u00b0C in 5% CO2. All cell lines were tested negative for mycoplasma contamination.\n\nMuSCs, fibro-adipogenic progenitors and macrophages were sorted based on established method56,70,71,72,73,74,75. Briefly, entire hindlimb muscles from mice were digested with collagenase II (Worthington,\u00a0LS004177, 1000 units per 1\u2009ml) for 90\u2009min at 37\u2009\u00b0C, the digested muscles were then washed in washing medium (Ham\u2019s F-10 medium (Sigma-Aldrich,\u00a0N6635) containing 10% heat-inactivated\u00a0horse serum (HIHS, Gibco,\u00a026050088, 1% P/S)) before MuSCs were liberated by treating with Collagenase II (100 units per 1\u2009ml) and Dispase (Gibco17105-041, 11 unit per 1\u2009ml) for 30\u2009min. The suspensions were passed through a 20\u2009G needle to release cells. Mononuclear cells were filtered with a 40 \u03bcm cell strainer and sorted by BD FACSAria Fusion with the selection of the GFP+ (MuSCs); FITC-(CD45-, CD31-, ITGA7-) APC\u2009+\u2009(SCA1+) (FAPs); FITC-(Cd45-) APC-(Ly6G-) eFluor450\u2009+\u2009(CD11b+) (MPs). Flowjo V10.8.1 was used for analysis of flow cytometry data. MuSCs were cultured in Ham\u2019s F10 medium with 20% FBS, 5\u2009ng/ml \u03b2-FGF (Thermo Fisher Scientific,\u00a0PHG0026) and 1% P/S, on coverslips and culture wells which were coated with poly-D-lysine solution (Sigma-Aldrich,\u00a0p0899) at 37\u2009\u00b0C overnight and then coated with extracellular matrix (ECM) (Sigma-Aldrich,\u00a0E-1270) at 4\u2009\u00b0C for at least 6\u2009h. FAPs and MPs were cultured in DMEM medium with 10% FBS and 1% P/S.\n\nIsolating BMDM was performed according to literature40,76 with slight modification. Briefly, bone marrow from adult mdx mouse was obtained by flushing femur and tibiae with DMEM and cells were cultured in DMEM containing 20% FBS and 20\u2009ng/mL M-CSF (Thermo Fisher Scientific, PMC2044) for 6\u20137 days. The culture DMEM medium was changed on the 3rd day. After 7 days of culturing, cells were carefully washed by PBS twice and collected for experiments.\n\nFor EdU incorporation assay, freshly sorted MuSCs or FAPs were seeded into prepared coverslips and harvest immediately after adherence (in vivo) or cultured for days and added EdU to the culture medium for 6\u2009h before harvest (in vitro). EdU staining was performed following the instruction of Click-iT\u00ae Plus EdU Alexa Fluor\u00ae 594 Imaging Kit (Thermo Fisher Scientific,\u00a0C10639). Growing cells on coverslips were incubated with 10\u2009\u03bcM EdU for a designated time before the fixation with 4% PFA for 20\u2009min. EdU-labeled cells were visualized using \u201cclick\u201d chemistry with an Alexa Fluor\u00ae 594\u2010conjugated azide and cell nuclei were stained by DAPI (Life Technologies, P36931). Fluorescent images were captured with a fluorescence microscope (Leica). TUNEL (Terminal deoxynucleotidyl transferase dUTP nick end labeling) assay was performed following our previous publication12,77 and the instruction of In Situ Cell Death Detection Kit TMR red (Roche, 12156792910). Cells were cultured on coverslips for 36\u2009h, followed by washing twice with PBS and fixing with 4% PFA for 15\u2009min. TUNEL staining was carried out by adding reaction mixture of label solution for 30\u2009min at dark. The coverslips were mounted with DAPI solution to stain the cell nucleus. Fluorescent images were captured with a fluorescence microscope (Leica).\n\nBMDMs or FAPs were re-suspended at the appropriate concentration in DMEM culture medium and seeded in the 24-well inserts (Corning, 353097, 353095). The bottom chamber was coated and seeded with MuSCs or MPs 1 day before the seeding of BMDMs or FAPs. After seeding and assembling, the co-cultured cells were incubated at 37\u2009\u00b0C 5% CO2 for 12\u201324\u2009h and then inserts were harvested for experiments.\n\nFor constructing the dCas9-YY1 plasmid, pAW91.dCas9 (Addgene, 104372) and pAW90.dCas9-YY1 (Addgene, 104373) plasmids were purchased from Addgene. Five sgRNAs were designed by CRISPOR78 to target the Ccl5 upstream enhancer region and cloned into a lentiguide-puro vector at the BsmbI site(lentiguide-puro-sgRNA1,2,3,4,5). For constructing the Ccl5-enhancer/promoter luciferase report plasmid, a 2456\u2009bp DNA fragment of Ccl5 enhancer region and 1500\u2009bp Ccl5 promoter were cloned into a pGL3-basic (purchased from Promega) vector between MluI and HindIII sites.\n\nMuSCs were co-transfected with the Ccl5-enhancer/promoter luciferase reporter plasmid and internal control Renilla reporter plasmid. Cells were harvested 48\u2009h after transfection through adding 100\u2009\u03bcl lysis buffer and gently shaking for 15\u2009min at room temperature. Luciferase activity was measured by the Dual-Luciferase kit (Promega, E1910) according to our previous publication56,77. The luminescent signal was recorded by Elmer VICTOR TM X multilabel reader. The ratio of the reporter signal and the Renilla control signal was compared between different samples for further analysis.\n\nFor virus production, HEK293T cells were grown to 50\u201375% confluency on a 15\u2009cm dish and then transfected with 15\u2009\u03bcg of pAW91.dCas9 or pAW90.dCas9-YY1, 11.25\u2009\u03bcg psPAX2 (Addgene, 12260), and 3.75\u2009\u03bcg pMD2.G (Addgene, 12259). Viral supernatant was cleared of cells by filtering with 0.2\u2009\u03bcm filter membrane (Pall Corporation, 4612). 5\u2009mL of vital supernatant was mixed with 5\u2009mL DMEM medium and added to C2C12 cells in the presence of polybrene (Santa Cruz Biotechnology, sc-134220) at 8\u2009\u03bcg/mL. After 24\u2009h, viral media was removed and fresh media containing blasticidin (Thermo Fisher Scientific,\u00a0R21001) at 10\u2009\u03bcg/mL. Cells were selected until all cells on non-transduced plates died, to obtain the blasticidin+ dCas9 C2C12 and dCas9-YY1 C2C12 cell lines. The tethering guide RNAs were packaged by the virus as described above using the lentiguide-puro-sgRNA1,2,3,4,5 plasmids and were transduced into dCas9 C2C12 and dCas9-YY1 C2C12 cells respectively. After 24\u2009h, viral media was removed and fresh media containing puromycin (Thermo Fisher Scientific, A1113802) at 2.5\u2009mg/mL. Cells were selected until all cells on non-transduced plates died. Double-positive cells (blasticidin+, puromycin\u2009+) were identified and expanded.\n\nTotal RNAs were extracted using TRIzol reagent (Invitrogen, 15596026) following the manufacturer\u2019s protocol. For quantitative RT-PCR, cDNAs were reverse transcribed using HiScript III First-Strand cDNA Synthesis Kit (Vazyme, R312-01). Real-time PCR reactions were performed on a LightCycler 480 Instrument II (Roche Life Science) using Luna Universal qPCR Master Mix (NEB, M3003L). Sequences of all primers used can be found in Supplementary Data\u00a01.\n\nFor Western blot assays, according to our prior publication79,80,81, cultured cells were washed with ice-cold PBS and lysed in cell lysis buffer. Whole cell lysates were subjected to SDS\u2013PAGE and protein expression was visualized using an enhanced chemiluminescence detection system (GE Healthcare, Little Chalfont, UK) as described before74. The following dilutions were used for each antibody: YY1 (Abcam ab109237; 1:1000), CCL5 (Abcam ab189841; 1:500), CCR5 (Abcam ab65850; 1:500), TGF-\u03b21 (Abcam ab92486; 1:1000), \u03b1-TUBULIN (Santa Cruz Biotechnology sc-23948; 1:5000), GAPDH (Sigma-Aldrich G9545-100UL; 1:5000), CAS9 (CST 14697\u2009T; 1:1000). For immunofluorescence staining, cultured cells were fixed in 4% PFA for 15\u2009min and blocked with 3% BSA within 1\u2009h. Primary antibodies were applied to samples with indicated dilution below and the samples were kept at 4\u2009\u00b0C overnight. For immunofluorescence staining12,75, cultured cells were fixed in 4% PFA for 15\u2009min and permeabilized with 0.5% NP-40 for 10\u2009mins. Then cells were blocked in 3% BSA for 1\u2009h followed by incubating with primary antibodies overnight at 4\u2009\u00b0C and secondary antibodies for 1\u2009h at RT. Finally, the cells were mounted with DAPI to stain the cell nucleus and images were captured by a Leica fluorescence microscope. Primary antibodies and dilutions were used as following PAX7 (Developmental Studies Hybridoma Bank; 1:50), YY1 (Abcam ab109237, 1:200), CCL5 (Abcam ab189841; 1:200), F4/80 (Abcam ab6640, 1:200), PDGFR\u03b1 (R&D BAF1062; 1:200), Ki67 (Santa Cruz Biotechnology, sc-23900; 1:200). For immunohistochemistry12,72,75, in brief, slides were fixed with 4% PFA for 15\u2009min at room temperature and permeabilized in ice-cold methanol for 6\u2009min at \u221220\u2009\u00b0C.Heat-mediated antigen retrieval with a 0.01\u2009M citric acid (pH 6.0) was performed for 5\u2009min in a microwave. After 4% BSA (4% IgG-free BSA in PBS; Jackson, 001-000-162) blocking, the sections were further blocked with unconjugated AffiniPure Fab Fragment (1:100 in PBS; Jackson, 115-007-003) for 30\u2009min. The biotin-conjugated anti-mouse IgG (1:500 in 4% BBBSA, Jackson, 115-065-205) and Cy3-Streptavidin (1:1250 in 4% BBBSA, Jackson, 016-160-084) were used as secondary antibodies. Primary antibodies and dilutions were used as follows: CCL5 (Abcam ab189841; 1:200), CCR5 (Abcam ab65850; 1:200), TGF-\u03b21 (Abcam ab92486; 1:200), PDGFR\u03b1 (R&D BAF1062; 1:200), Collagen Ia1(Novus NBP1-30054; 1:200), F4/80 (Abcam ab6640), CD68 (Biorad MCA1957GA; 1:200), CD206 (Abcam ab64693;1:200), Laminin (Sigma-Aldrich L9393-100UL, 1:800), \u03b1-SMA (Invitrogen, 14-9760-82; 1:200), Ki67 (Santa Cruz Biotechnology sc-23900; 1:200); PAX7 (Developmental Studies Hybridoma Bank; 1:50), eMyHC (Leica NCL-MHC-d; 1:200) for staining of muscle cryosections. Images were slightly modified with ImageJ in which background was reduced using background subtraction and brightness and contrast were adjusted. H&E (Hematoxylin and eosin), was performed as previously described12,74,82. Masson\u2019s trichrome staining was performed according to the manufacturer\u2019s (ScyTek Laboratories, Logan, UT) instructions. Oil Red O staining was performed according to the manufacturer\u2019s (Abcam, ab150678) instructions.\n\nSingle-cell RNA-seq was performed on 10x genomics platform. Briefly, whole muscle cells were isolated with an additional step of viability validation by Propidium Iodide (PI) staining. Red blood cells were eliminated by ACK buffers (150\u2009M NH4Cl, 100\u2009mM KHCO3, 10\u2009mM EDTA-2Na) before sorting. After sorting, live cells were washed with 0.04% BSA in PBS twice and resuspended in the BSA solution at an appropriate concentration (300\u20131200 cells/\u03bcl). Suspended cells were counted under a microscope and Typan blue was used to examine the cell viability. After the confirmation of cell number and viability, library construction was performed following the manufacturer\u2019s instructions for generation of Gel Bead-In Emulsions (GEMs) using the 10x Chromium system. The single cell RNA library was sequenced by Illumina HiSeq X Ten instrument. CellRanger 7.0.0 and Seurat 4.3.0 were used to analyze the single-cell data. Ctrl and dKO groups were initially merged together and filtered with quality control parameters (cells with more than 10% expression on mitochondrial genes, or fewer than 500 total features expressed were filtered out and 7.5% doublets were removed according to captured cell numbers; Supplementary Fig.\u00a02D\u2013I). The two groups were integrated using FindIntegrationAnchors and IntegrateData function wrapped in Seurat package to minimize batch effects32,43,46. Dimensionality reduction was performed through Principal Component Analysis. UMAP embedding parameters were based on the top 40 PCs and embedded in 2-dimensions to visualize the data. Cells from Ctrl and dKO groups were relatively evenly distributed in all clusters, indicating no major batch effect after treatment (Supplementary Fig.\u00a02J). To annotate different cell types, differentially expressed genes among cell clusters were identified using FindAllMarkers function. Genes were identified as significantly differentially expressed if FDR\u2009<\u20090.05 and expression in at least 20% of cells. To dissect the subtypes of FAPs and macrophages, we conducted second-round clustering (sub-clustering) within each cell type. Differentially expressed gene markers were also examined for sub-clusters for manual subtype annotation. Monocle 2.22.0 was used for pseudotime trajectory analysis83. The raw counts data from FAP and macrophage populations were used for trajectory inference and the top sub-cluster DEGs were used as input gene lists for trajectory construction analysis. AddModuleScore function wrapped in Seurat was used to calculate the average expression levels of each gene set of interest (Supplementary Data\u00a02) at single-cell level, yielding module scores named as anti-apoptotic, apoptotic score and inflammatory score.\n\nFor RNA-seq (polyA\u2009+\u2009mRNA)72,73, total RNAs were subjected to polyA selection (Ambion, 61006) followed by library preparation using NEBNext Ultra II RNA Library Preparation Kit (NEB, E7770S). Libraries were paired-end sequenced with read lengths of 150\u2009bp on Illumina HiSeq X Ten or Nova-seq instruments. The raw reads of RNA-seq were processed following the procedures described in our previous publication56. Briefly, the adapter and low-quality sequences were trimmed from 3\u2019 to 5\u2019 ends for each read, and the reads shorter than 36\u2009bp were discarded. The clean reads were aligned to mouse (mm10) reference genome with STAR. Next, Cufflinks was used to quantify the gene expression. Genes with an expression level change greater than 1.5-fold and a p value of <0.01 were identified as DEGs between two stages/conditions. GO enrichment analysis was performed using R package clusterProfiler.\n\nYY1 ChIP was performed following our previously described protocol12. 10\u2009\u03bcg of antibodies against YY1 (Santa Cruz Biotechnology, sc-1703), or normal mouse IgG (Santa Cruz Biotechnology, sc-2025) were used for immunoprecipitation. Immunoprecipitated genomic DNA was resuspended in 20\u2009\u03bcl of water. For ChIP-seq DNA library construction, a NEBNext\u00ae Ultra\u2122 II DNA Library Prep Kit for Illumina\u00ae (NEB, E7645S) was used according to the manufacturer\u2019s instructions. Bioanalyzer analysis and qPCR were used to measure the quality of DNA libraries including the DNA size and purity. Finally, DNA libraries were sequenced on the Illumina Genome Analyzer II platform. The raw data were first pre-processed by initial quality assessment, adapter trimming, and low-quality filtering and then mapped to the mouse reference genome (mm10) using bowtie284, and only the non-redundant reads were kept. The protein DNA-binding peaks (sites) were identified using MACS293 with an input (IgG) sample as the background. During the peak calling, candidate peaks were compared with the background, dynamic programming was used to determine \u03bb of Poisson distribution, and the P value cutoff was set to 0.0001 for YY1 ChIP-Seq experiment.\n\nHi-C was performed according to previously described protocols56. Libraries were prepared by on-bead reactions using the NEB Next Ultra II DNA Library Preparation Kit (NEB, E7645S). The beads were separated on a magnetic stand, and the supernatant was discarded. After washes, the beads were resuspended in 20\u2009\u03bcl of 10\u2009mM tris buffer and boiled at 98\u2009\u00b0C for 10\u2009min. The elute was amplified for 10 to 13 cycles of PCR with Phanta Master Mix (Vazyme, P511-01), and the PCR products were purified using VAHTS DNA Clean Beads (Vazyme, N411-01). The Hi-C libraries were paired-end sequenced with read lengths of 150\u2009bp on an Illumina HiSeq X Ten instrument. Data were analyzed by Hi-C Pro, juicer box software and mapping to mouse genome mm10. Raw Hi-C data were processed as previously described71. Briefly, the in-situ Hi-C data was processed with a standard pipeline HiC-Pro85. First, adapter sequences and poor-quality reads were removed using Trimmomatic (ILLUMINACLIP: TruSeq3-PE-2.fa:2:30:10; SLIDINGWINDOW: 4:15; MINLEN:50). The filtered reads were then aligned to mouse reference genome (mm10) in two steps: (1) global alignment was first conducted for all pair-end reads, (2) the unaligned reads were split into prospective fragments using restriction enzyme recognition sequence (GATCGATC) and aligned again. All aligned reads were then merged and assigned to restriction fragments, while low quality (MAPQ\u2009<\u200930) or multiple alignment reads were discarded. Invalid fragments including unpaired fragments (singleton), juxtaposed fragments (re-ligation pairs), un-ligated fragments (dangling end), self-circularized fragments (self-cycle), and PCR duplicates were removed from each biological replicate. The remaining validated pairs from all replicates of each stage were then merged, followed by read depth normalization using HOMER and matrix balancing using iterative correction and eigenvector decomposition (ICE) normalization to obtain comparable interaction matrix between different stages.\n\nFollowing previous procedure86, to separate the genome into A and B compartments, the ICE normalized intra-chromosomal interaction matrices at 100-kb resolution were transformed to observe/expect contact matrices, and the background (expected) contact matrices were generated to eliminate bias caused by distance-dependent decay of interaction frequency and read depth difference56,71. Pearson correlation was then applied to the transformed matrices and the first principal component (PC1) of these matrices was divided into two clusters. The annotation of genes and the expression profile were used to assign positive PC1 value to gene-rich component as compartment A and negative PC1 value to gene-poor component as compartment B.\n\nNormalized contact matrix at 10\u2009kb resolution of each time point was used for TAD identification using TopDom87. In brief, for each 10-kb bin across the genome, a signal of the average interaction frequency of all pairs of genome regions within a distinct window centered on this bin was calculated, thus TAD boundary was identified with local minimal signal within certain window. The falsely detected TADs without local interaction aggregation were filtered out by statistical testing. Invariant TADs were defined using following criteria: (1) the distance of both TAD boundaries between two conditions is no more than 10\u2009kb; (2) the overlapping between two TADs should be larger than 80%; stage-specific TADs were defined otherwise. Loops are identified by using HiCCUPS module of Arrowhead with default parameter at 10\u2009kb resolutions.\n\n3C-qPCR was performed following published protocols88. The chromatin was cut by HindIII restriction enzyme to obtain DNA fragments. The promoter region of Ccl5 was set as the anchor to detect the designed E-P interactions. Primers were designed targeting the nearby site of Ccl5 enhancer region. 18s rRNA was used as an internal control for quantification normalization. Sequences of the oligos used in the study were included in Supplementary Data\u00a01.\n\nData represent the average of at least three independent experiments or mice\u2009\u00b1\u2009s.d. unless indicated. No statistical method was used to predetermine sample size. \u201cNo data were excluded from the analyses. The statistical significance of experimental data was calculated by the two-sided paired Student\u2019s t test (Fig.\u00a01B, I, J; Fig.\u00a03E, I, K; Fig.\u00a04J, L, N; Fig.\u00a06Q, S), two-sided unpaired Student\u2019s t test (Fig.\u00a01E\u2013H, K, L, N\u2013R; Fig.\u00a02B\u2013D, K, N; Fig.\u00a03F, G, L\u2013N; Fig.\u00a04B\u2013F, H, I, O, Q; Fig.\u00a05B\u2013I, K; Fig.\u00a06E\u2013H, K, L, N\u2013R), one-sided unpaired Student\u2019s t test one-sided (Figs.\u00a03B; 6I, K), one-sided Fisher\u2019s exact test and adjustments were made for multiple comparisons (Fig.\u00a03C, D; Fig.\u00a06D; Supplementary Data 2\u20134): *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001 and n.s.: no significance p\u2009\u2265\u20090.05. Specifically, a single zero-truncated negative binomial distribution was fitted to the input data and each region was assigned a P value based on the fitted distribution. Representative images of at least five independent experiments are shown in FigS.\u00a01D, E, H, I\u2013L O, P; 3F, J; 4B\u2013F, H, I, L, Q; 5B, E, G, H; and Supplementary Fig.\u00a01B, C, J\u2013L, O; 3H; S4A\u2013J; 5A, E.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "In situ, Hi-C, ChIP-seq, bulk RNA-seq, and scRNA-seq data reported in this paper are deposited in the Gene Expression Omnibus database under accession GSE250204. All data supporting the findings of this study are provided with Source data.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code used in this study is available at the GitHub repository https://github.com/Hannah-bioinfo/Scripts_for_YY1_paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Charg\u00e9, S. B. & Rudnicki, M. A. Cellular and molecular regulation of muscle regeneration. Physiol. Rev. 84, 209\u2013238 (2004).\n\nTidball, J. G. Mechanisms of muscle injury, repair, and regeneration. Compr. Physiol. 1, 2029\u20132062 (2011).\n\nArticle\u00a0\n PubMed\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nAziz, A., Sebastian, S. & Dilworth, F. J. The origin and fate of muscle satellite cells. Stem Cell Rev. 8, 609\u2013622 (2012).\n\nArticle\u00a0\n CAS\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nBentzinger, C. F., Wang, Y. X. & Rudnicki, M. A. 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This work was supported by\u00a0National Key R&D Program of China [2022YFA0806003\u00a0to H.W.]; National Natural Science Foundation of China [82172436 to H.W.];\u00a0General Research Fund (GRF) from the Research Grants Council (RGC) of the Hong Kong Special Administrative Region, China [14106521, 14100620, 14105823 and 14115319\u00a0to H.W.;14103522, 14105123 and 14120420 to H.S.]; Theme-based Research Scheme (TRS) from RGC [T13-602/21-N to H.W.]; Strategic Topics Grant (STG) from RGC [STG1/E-403/24-N\u00a0to H.W.]; Area of Excellence Scheme (AoE)\u00a0from RGC\u00a0[AoE/M-402/20 to H.W.]; Health and Medical Research Fund (HMRF) from Health Bureau of the Hong Kong Special Administrative Region, China [10210906 and 08190626 to H.W.]; the research funds from Health@InnoHK program launched by Innovation Technology Commission, the Government of the Hong Kong SAR, China [to H.W.];\u00a0Chinese University of Hong Kong (CUHK) Strategic Seed Funding for Collaborative Research Scheme (SSFCRS) [to H.W.].", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Orthopaedics and Traumatology, Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong SAR, China\n\nYang Li,\u00a0Chuhan Li,\u00a0Qiang Sun,\u00a0Yeelo Cheung\u00a0&\u00a0Huating Wang\n\nCenter for Neuromusculoskeletal Restorative Medicine Limited, Hong Kong Science Park, Hong Kong SAR, China\n\nYang Li,\u00a0Qiang Sun\u00a0&\u00a0Huating Wang\n\nDepartment of Chemical Pathology, Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong SAR, China\n\nXingyuan Liu\u00a0&\u00a0Fengyuan Chen\n\nMolecular Cancer Research Center, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, China\n\nYu Zhao\n\nCenter for Tissue Regeneration and Engineering, Division of Life Science, Hong Kong University of Science and Technology, Hong Kong SAR, China\n\nTing Xie\n\nUnit\u00e9 Physiopathologie et G\u00e9n\u00e9tique du Neurone et du Muscle, UMR CNRS 5261, Inserm U1315, Universit\u00e9 Claude Bernard Lyon 1, Lyon, France\n\nB\u00e9n\u00e9dicte Chazaud\n\nWarshel Institute for Computational Biology, Faculty of Medicine, Chinese University of Hong Kong (Shenzhen), Guangdong, China\n\nHao Sun\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nYang Li designed and performed most of experiments; Fengyuan Chen and Yeelo Cheung performed and helped with animal experiments; Chuhan Li and Qiang Sun analyzed RNA-seq, ChIP-seq, Hi-C, and scRNA-seq data; Yu Zhao supervised Hi-C assay; Xingyuan Liu helped the revision of the experiments and results; B\u00e9n\u00e9dicte Chazaud provided constructive suggestions and supervised co-culture experiments; Ting Xie contributed to the manuscript writing; Hao Sun supervised computational analyses; Yang Li and Huating Wang wrote the manuscript, with inputs from all authors.\n\nCorrespondence to\n Hao Sun or Huating Wang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Skeletal muscle stem cells modulate niche function in Duchenne muscular dystrophy mouse through YY1-CCL5 axis.\n Nat Commun 16, 1324 (2025). https://doi.org/10.1038/s41467-025-56474-w\n\nDownload citation\n\nReceived: 18 March 2024\n\nAccepted: 15 January 2025\n\nPublished: 03 February 2025\n\nVersion of record: 03 February 2025\n\nDOI: https://doi.org/10.1038/s41467-025-56474-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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amplified ovarian and endometrial cancers by combined inhibition of PKMYT1 and ATR", + "pre_title": "Targeting CCNE1 amplified ovarian and endometrial cancers by combined inhibition of PKMYT1 and ATR", + "journal": "Nature Communications", + "published": "01 April 2025", + "supplementary_0": [ + { + "label": "Supplementary Figs.", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_MOESM3_ESM.pdf" + }, + { + "label": "Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_MOESM4_ESM.mov" + }, + { + "label": "Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_MOESM5_ESM.mov" + }, + { + "label": "Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_MOESM6_ESM.mov" + }, + { + "label": "Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_MOESM7_ESM.mov" + }, + { + "label": "Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_MOESM8_ESM.mov" + }, + { + "label": "Movie 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_MOESM9_ESM.mov" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_MOESM10_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_MOESM11_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-58183-w#Sec31" + ], + "code": [], + "subject": [ + "Cancer therapy", + "Ovarian cancer", + "Targeted therapies" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3854682/v1.pdf?c=1743591977000", + "research_square_link": "https://www.researchsquare.com//article/rs-3854682/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58183-w.pdf", + "preprint_posted": "15 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Ovarian cancers (OVCAs) and endometrial cancers (EMCAs) with CCNE1-amplification are often resistant to standard of care treatment and represent an unmet clinical need. Previously, synthetic-lethal screening identified loss of the CDK1 regulator, PKMYT1, as synthetically lethal with CCNE1-amplification. We hypothesized that CCNE1-amplification associated replication stress will be more effectively targeted by combining the PKMYT1 inhibitor, lunresertib (RP-6306), with the ATR inhibitor, camonsertib (RP-3500/RG6526). Low dose combination RP-6306 with RP-3500 synergistically increased cytotoxicity more in CCNE1 amplified compared to non-amplified cells. Combination treatment produced durable antitumor activity and increased survival in CCNE1 amplified patient-derived and cell line-derived xenografts. Mechanistically, low doses of RP-6306 with RP-3500 increase CDK1 activation more so than monotherapy, triggering rapid and robust induction of premature mitosis, DNA damage and apoptosis in a CCNE1-dependent manner. These findings suggest that targeting CDK1 activity by combining RP-6306 with RP-3500 is a novel therapeutic approach to treat CCNE1-amplifed OVCAs and EMCAs.Health sciences/Oncology/Cancer/Gynaecological cancerBiological sciences/Cancer/Cancer therapy/Targeted therapiesPKMYT1ATRCCNE1 amplification", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-854682/v1/0b5629ae3c7d09314c066e6c.png", + "https://assets-eu.researchsquare.com/files/rs-854682/v1/34aee92460360d8d39360a77.png", + "https://assets-eu.researchsquare.com/files/rs-854682/v1/46c57af21f2ba1a191f5fcfc.png", + "https://assets-eu.researchsquare.com/files/rs-854682/v1/c8967b08ef575a5aa335b4db.png", + "https://assets-eu.researchsquare.com/files/rs-854682/v1/7039e4596cfe8ba312b3d498.png", + "https://assets-eu.researchsquare.com/files/rs-854682/v1/260d19b30577f833e338f5fc.png", + "https://assets-eu.researchsquare.com/files/rs-854682/v1/c5b0afe68fd2ceae56c559f9.png" + ] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nF.S. serves on scientific advisory boards for AstraZeneca, GSK and Zentalis Pharmaceuticals. She has received institutional research funding from AstraZeneca, Repare Therapeutics, Instill Bio and Sierra Oncology.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryFigures20240111Final.pdfSupplementoryFigureLegends.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Ovarian cancers (OVCAs) and endometrial cancers (EMCAs) with CCNE1-amplification are often resistant to standard treatment and represent an unmet clinical need. Synthetic-lethal screening identified loss of the CDK1 regulator, PKMYT1, as synthetically lethal with CCNE1-amplification. We hypothesize that CCNE1-amplification associated replication stress will be more effectively targeted by combining PKMYT1 inhibitor lunresertib (RP-6306), with ATR inhibitor camonsertib (RP-3500/RG6526). Low dose combination RP-6306 with RP-3500 synergistically increases cytotoxicity more so in CCNE1-amplified compared to non-amplified cells. Combination treatment produces durable antitumor activity, reduces metastasis and increases survival in CCNE1-amplified patient-derived OVCA and EMCA xenografts. Mechanistically, low doses of RP-6306 with RP-3500 increase CDK1 activation more so than monotherapy, triggering rapid and robust induction of premature mitosis, DNA damage, and apoptosis in a CCNE1-dependent manner. These findings suggest that targeting CDK1 activity by combining RP-6306 with RP-3500 is an effective therapeutic approach to treat CCNE1-amplifed OVCAs and EMCAs.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Despite significant progress in the last twenty years in ovarian cancer (OVCA) treatment and improved mortality rates1, this disease remains the most lethal gynecologic malignancy2. Such progress has particularly favored patients with germline or somatic BRCA1/2 pathogenic mutations or those with tumors exhibiting homologous recombination (HR) deficiency3,4. On the other hand, patients with HR-proficient tumors, particularly those with CCNE1 gene amplification, exhibit de novo or rapid emergence of chemotherapy resistance and poor survival5,6. Endometrial cancer subtypes share molecular profiles with HGSOC, such as CCNE1 amplification and TP53 mutations. There is an increasing incidence in high-risk EMCA histologic subtypes such as uterine serous carcinoma and carcinosarcoma7, and these subtypes are among the cancers with the highest incidence of CCNE1 gene amplification8. Further, mortality rates for endometrial cancer (EMCA) are overall increasing, and the two-fold higher risk of death from ovarian cancer compared to endometrial cancer in the early 1990s has virtually been eliminated by oppositional mortality trends1. To date, there are no FDA-approved drugs for CCNE1 amplified cancers, including OVCA and EMCA, despite preclinical data showing CCNE1 amplification is targetable therapeutically9,10,11,12. Given this unmet clinical need, we sought to identify a treatment strategy that targets critical survival pathways for CCNE1-amplified dependent gynecological cancers.\n\nCyclin E1 binds and activates CDK2, a key regulatory element that promotes initiation of DNA replication and G1/S cell cycle progression13,14. CCNE1 amplification and cyclin E1 overexpression prematurely activate CDK2 and initiation of DNA replication before cells have time to sufficiently license pre-replication complexes at origins15. Unscheduled origin firing leads to replication stress and ensuing DNA damage and genome instability9,11,16. Cyclin E-CDK2 complexes directly activate the MYBL2-MuvB-FOXM1 (MMB) transcriptional network causing early activation of the G2/M transcriptional network and accumulation of cytoplasmic cyclin B in S-phase17. To slow the onset of mitosis and allow time to compete faithful genome duplication, CCNE1 amplified cells activate the S and G2/M cell cycle checkpoints to suppress cell cycle transitions, allowing time to complete DNA replication11. We reasoned that increased dependence on S and G2/M checkpoints for survival in CCNE1 amplified cells represents a therapeutic vulnerability which is the focus of this study.\n\nMembrane-associated tyrosine- and threonine-specific Cdc2-inhibitory kinase (PKMYT1) and WEE1 kinase are each critical G2/M checkpoint regulators that inhibit cell cycle progression by catalyzing inhibitory phosphorylation of CDKs18,19,20,21. WEE1 restricts both CDK1 and CDK2 activity by phosphorylating Tyr15, while PKMYT1 selectively restricts CDK1 activity by phosphorylating Thr1422. WEE1 is localized to the nucleus, while PKMYT1 is tethered to the cytoplasmic face of the ER/Golgi, where it physically interacts with cyclin B21,23. PKMYT1 and WEE1 inhibitors (PKMYT1i and WEE1i, respectively) are each reported to show preclinical efficacy as monotherapy in CCNE1-amplified and cyclin E overexpressing models by activating CDK1, which forces cells into mitosis prematurely with under replicated and damaged DNA17,24,25.\n\nReplication stress caused by CCNE1 amplification also activates the ataxia telangiectasia and Rad3-related (ATR) kinase to promote the DNA damage response (DDR), stabilizing DNA replication forks from collapse into DNA double-strand breaks and preventing dormant origin activation11. ATR also activates CHK1, that limits mitotic progression by phosphorylating and inhibiting the CDC25 family phosphatases required for dephosphorylation and activation of CDK126. CCNE1 amplification also sensitizes cells to ATR inhibition27.\n\nPrevious studies indicate that targeting S and G2/M checkpoint kinases selectively kill CCNE1-amplified and overexpressing cells. WEE1 inhibitors (WEE1i) are reported to show preclinical efficacy as monotherapy in CCNE1-amplified and overexpressing models17,24,25. In certain contexts, ATR inhibitors (ATRi) are also reported to show cytotoxic activity in CCNE1 overexpressing models with high levels of replication stress27. We have previously reported that low doses of WEE1i and ATRi synergize in CCNE1-amplified OVCA and EMCA preclinical models by increasing replication fork collapse and DNA damage11. Intriguingly, a recent CRISPR-Cas9 genome-wide screen using an isogenic pair of cell lines that stably overexpress cyclin E from a CCNE1-2A-GFP fusion integrated into the genome of RPE1-hTERT TP53\u2212/\u2212 Cas9 cells identified PKMYT1 as a top hit for synthetic lethality in the CCNE1 overexpressing cells. This study further showed that PKMYT1 inhibition (PKMYT1i) alone induces cell death by forcing cells from the S-phase into mitosis with under-replicated DNA, resulting in mitotic catastrophe17. Given CCNE1-amplified cells rely on the G2/M checkpoint to attenuate lethal levels of replication stress-induced DNA damage by upregulating the ATR axis for DNA repair24, we hypothesized that dual inhibition of PKMYT1 and ATR (PKMYT1i-ATRi) will further enhance CDK1 activation and cytotoxicity, especially towards CCNE1 amplified or overexpressing tumor cells allowing lower dosing strategies and potentially alleviate toxicity.\n\nThere is currently one PKMYT1i and several ATRi in clinical development. Lunresertib (RP-6306), is a novel, first in class, selective and orally bioavailable PKMYT1i28. RP-6306 is currently in clinical development as monotherapy or in combination with chemotherapy or targeted therapies (e.g., camonsertib) for the treatment of patients with solid tumors harboring CCNE1 amplification or FBXW7/PPP2R1A inactivating mutations (NCT04855656, NCT06107868, NCT05147272)29. There are currently seven ATR inhibitors in clinical development (RP-3500,AZD6738, BAY1895344, ATRN119, M1774, ART0380, IMP9064; NCT04497116, NCT01955668, NCT03188965, NCT04905914, NCT04170153, NCT04657068, NCT05269316). Camonsertib (RP-3500) is a potent, selective, and orally bioavailable ATR inhibitor currently in clinical development as monotherapy and in combination with other targeted therapies, including PARPi, for the treatment of patients with solid tumors harboring selected DDR alterations, including BRCA1/2 or ATM inactivating mutations (NCT04972110, NCT05405309)30,31.\n\nIn this study, we report that combining RP-6306 with RP-3500 (PKMYT1i-ATRi) increases CCNE1 level-dependent cytotoxicity in a large panel of OVCA and EMCA cell lines. The increased cytotoxicity translates to significantly improved activity and overall survival in mice bearing CCNE1-amplified cell lines and patient-derived xenografts. Increased cytotoxicity and activity is attributed to synergistic CDK1 activation in the S-phase of CCNE1 overexpressing or amplified cells triggering unscheduled mitosis before completion of DNA replication, leading to lethal levels of DNA damage and increased apoptosis. We find that CCNE1 amplification is a robust biomarker predictive of sensitivity towards low doses of PKMYT1i-ATRi, with limited effects observed in CCNE1LOW or BRCA1/ATM mutated cells. Taken together our studies support the clinical development of PKMYT1i-ATRi combinations for treatment of tumors with CCNE1 amplification and/or overexpression.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To investigate the dependence of PKMYT1i on CCNE1 level in OVCA cells (OVCAR3, FUOV1, COV318, OVCAR8, OVSAHO, Kuramochi, WO-20, SKOV3) and EMCA cells (KLE, MFE280, SNU685), RP-6306 efficacy in cancer cells with CCNE1 amplification (CCNE1AMP), CCNE1 copy gain (CCNE1GAIN), or CCNE1 neutral (CCNE1LOW) was investigated. RP-6306 was most active in CCNE1AMP cells, with reduced activity in CCNE1GAIN cells and very limited effect in CCNE1LOW cells, as evident by the difference in median IC50 between these groups (Fig.\u00a01A, B and Supplementary Fig.\u00a0S1A). Since PKMYT1 inhibition can force cells into unscheduled mitosis by preventing CDK1 phosphorylation, we hypothesized that this effect could be enhanced by activation of CDC25 via inhibition of ATR. We therefore tested the ATR inhibitor (ATRi), RP-3500, as monotherapy and in combination with RP-6306. Similar to RP-6306, high CCNE1 expression levels did enrich for RP-3500 single-agent activity, as evident by the difference in median IC50 between these groups (Fig.\u00a01A, B and Supplementary Fig.\u00a0S1A).\n\nA, B Detection of cells response to monotherapy of PKMYT1i, RP6306 (A) and ATRi, RP-3500 (B) with MTT assay. CCNE1AMP in blue, CCNE1GAIN in orange, and CCNE1LOW in black. Absolute CN and IC50, and median IC50 for all CCNE1AMP, CCNE1GAIN, and CCNE1LOW cell lines with fold shift relative CCNE1LOW lines (table). C Cell viability analysis of the indicated CCNE1 amplified, gain, and low/neutral cell lines after treatment at indicated doses. Monotherapy for PKMYT1i, RP6306, is highlighted in blue, and ATRi, RP3500, is highlighted in red. Combinations are highlighted in purple. Assays were normalized by doubling time such that cells doubled at least twice. n\u2009=\u20093; Mean was presented. The dose highlighted in red in the table (bottom right) was selected as the most synergistic dose in the SNU685 CCNE1 inducible cell line (Fig.\u00a02F) and compared across all cell lines tested with the combination. n\u2009=\u20093; Mean\u2009\u00b1\u2009S.D. D Coefficient of drug interaction (CDI) relative to the fraction affected (Fa) plot of the indicated cell lines from the MTT assay was calculated for each dose combination. The red dot corresponds to the red highlighted dose combination in (C). CCNE1AMP in Green, CCNE1GAIN in orange, and CCNE1LOW in black. CDI\u2009<\u20091 synergy with CDI\u2009<\u20090.7 significant synergy, CDI\u2009=\u20091 additive, CDI\u2009>\u20091 antagonistic. Plot of CDI versus CCNE1 copy number at indicated dose highlighted in red (right). R2 value shown (P\u2009=\u20090.0064). E Colony formation Analysis of PKMYT1i-ATRi combination in CCNE1 amplified OVCA and EMCA cells with RP-6306 (31.3\u2009nM), RP-3500 (6.25\u2009nM) or combination for 10 days. F Quantification of colony formation assay in (E). G WO-58 organoids were developed from CCNE1 amplified, BRCA1 mutant HGSOC WO-58 and characterized with ovarian cancer marker PAX8 and epithelial marker CK7 by immunofluorescence (IF) and immunohistochemistry (IHC). P53 expression was detected by IHC. Scale bar: 50\u2009\u00b5m. H Cell viability detection of PKMYT1i-ATRi combination on CCNE1 amplified HGSOC organoids. WO-58, WO-19, and WO-77 organoids were treated with RP-6306 (250\u2009nM), RP-3500 (50\u2009nM), or both for 10 days and measured with CCK8 assay. n\u2009=\u20093 for WO-58 organoids, n\u2009=\u20094 for WO-19 and WO-77 organoids; Mean\u2009+\u2009SD. Significance determined by two-way ANOVA followed by Tukey\u2019s multiple comparisons test for (C) and (F). Simple linear regression calculated for (D). One-way ANOVA for followed by Tukey\u2019s multiple comparisons test for (H).\n\nCombination of RP-6306 with RP-3500 showed a much stronger inhibitory effect than the respective monotherapy treatments, with significant synergistic effect by coefficient of drug interaction (CDI) in CCNE1AMP cells, less so in CCNE1GAIN cells, and very limited to no effect in CCNE1LOW cells (Fig.\u00a01C). CCNE1 copy number (CN) significantly correlated with CDI (R2\u2009=\u20090.87, P\u2009=\u20090.0064 Fig.\u00a01D). Fraction affected (Fa) cells and CDI calculation clearly illustrates that the synergistic effect of the combination decreases viability in a CCNE1-dependent manner by lower CDI values with higher fractions of cells affected (Fig.\u00a01D). Colony formation assays yielded similar results, with the combination of RP-6306 and RP-3500 significantly inhibiting colony formation in CCNE1AMP cells, somewhat less effectively in CCNE1GAIN cells, with minimal effects in CCNE1LOW cells (Fig.\u00a01E-F, Figure\u00a0S1B-C). In the cell lines investigated, we found CCNE1 copy number correlated with mRNA and Cyclin E1 protein expression levels (Figure\u00a0S1E).\n\nTo evaluate the efficacy of these compounds in a more clinically relevant models, we established patient-derived organoids from a BRCA1 mutant, CCNE1AMP (CN\u2009=\u20097) homologous recombination (HR) proficient PDX model with acquired PARP inhibitor resistance, designated WO-58, CCNE1AMP WO-19 (CN\u2009=\u200911-23) and WO-77 (CN\u2009=\u20099) models. The organoid model is characterized by P53 mutation (p53 overexpression), ovarian marker PAX8, and epithelial marker CK7 (Fig.\u00a01G). The combination of RP-6306 and RP-3500 showed increased growth inhibition than the respective monotherapies in WO-58, WO-19 and WO-77 organoids (Fig.\u00a01H and Supplementary Fig.\u00a0S1D). Taken together, these results suggest that the PKMYT1i-ATRi combination is more effective in OVCA and EMCA with higher levels of CCNE1 copy number.\n\nTo further investigate whether the PKMYT1i-ATRi combination is dependent on Cyclin E1 protein level, we established and then tested drug effects in immortalized fallopian tube cells and ovarian and endometrial cancer cell lines, with and without CCNE1 overexpression (FT282 with CCNE1 overexpression, WO-20 with inducible CCNE1 and SNU685 with inducible CCNE1)11. Strong synergy was observed in both parental and CCNE1 overexpressing FT282 cells (Fig.\u00a02A). However, the concentration of RP-6306 required for synergy was about 60-fold lower, and for RP-3500 about 4-fold lower in CCNE1 overexpressing cells (Fig.\u00a02A). While 938\u2009nM RP-6306 and 7.8\u2009nM RP-3500 showed no effect in combination on parental FT282 cells, FT282-CCNE1 overexpressing cells treated with only 31\u2009nM RP-6306 and 7.8\u2009nM RP-3500 showed almost 80% growth inhibition (Fig.\u00a02B). In SNU685 cells, induction of CCNE1 increased sensitivity to RP-6306 alone by 4.8-fold, but had a limited effect on sensitivity to RP-3500 (Fig.\u00a02C, D and Supplementary Fig.\u00a0S2A). The PKMYT1i-ATRi combination showed limited activity in SNU685 parental cells, but with CCNE1 induction, the combination was much more effective and demonstrated synergy (Fig.\u00a02E, F). The effect of CCNE1 induction on enhancing combination sensitivity in the OVCA WO-20 cell line was similar to that observed in SNU685 (Fig.\u00a02G). The PKMYT1i-ATRi combination also inhibited colony formation in WO-20 and SNU685 cells with CCNE1 induction, but very limited effect was observed in matched Cyclin E1 low cells (Fig.\u00a02H, I and Supplementary Fig.\u00a0S2B). To test that RP-6306 and RP-3500 effects are not off target, we knocked down PKMYT1 and ATR genes in OVCAR3 cells. The combination of PKMYT1 and ATR siRNAs showed decrease in viability compared to single siRNA treatment (Supplementary Fig.\u00a0S2C). These results demonstrate that combination inhibition of PKMYT1i-ATRi clearly relies on cyclin E1 expression level for a cytotoxic effect.\n\nA ZIP synergy scores at various dose combinations of RP-6306 and RP-3500 in FT282-hTERT p53R175H parental (WT) and CCNE1-overexpressing (CCNE1-O/E) cells. Score \u2265\u200910 (red color) represents synergy, \u2264\u2009\u2212\u200910 (green) represents antagonism. Values were obtained by analyzing mean data from 3 independent biological replicates with SynergyFinder. B Growth inhibition relative to DMSO control of parental and CCNE1-overexpressing cells after treatment with the indicated dose of RP-6306, RP-3500, or the combination of both. n\u2009=\u20093; Mean\u2009+\u2009SD. C, D Cell viability detection of SNU685 cells in response to RP-6306 monotherapy (C) and RP-3500 monotherapy (D). n\u2009=\u20093; Mean\u2009\u00b1\u2009SD. Highlighted dose showing the statistical difference in CCNE1-induced SNU685 cells with or without CCNE1 induction. E, F Cell viability analysis of the indicated SNU685 CCNE1 inducible cells \u00b1 doxycycline lines after treatment at the indicated doses. Doxycycline: 1\u2009\u00b5g/ml. Monotherapy for PKMYT1i, RP6306, is highlighted in blue, for ATRi, RP3500, is highlighted in red, Combinations are highlighted in purple for PKMYT1i-ATRi. Assays were normalized by doubling time such that cells doubled at least twice. n\u2009=\u20093; Mean\u2009\u00b1\u2009SD. Growth inhibition relative to DMSO control of parental and CCNE1-overexpressing cells after treatment with indicated doses in pink (middle). The coefficient of drug interaction (CDI) relative to the fraction affected (Fa) plot of the indicated cell lines is indicated to the right of each bar graph. CDI\u2009<\u20091 synergy with CDI\u2009<\u20090.7 significant synergy, CDI\u2009=\u20091 additive, CDI\u2009>\u20091 antagonistic. The red dot corresponds to doses highlighted in pink. G Measurement of drug combinations in WO-20 CCNE1inducible cells with or without Cyclin E1 induction. n\u2009=\u20094; Mean\u2009+\u2009SD. H, I Colony formation analysis (Upper panels) and quantification (Lower panels) of WO-20 CCNE1inducible cells (H) and SNU685 CCNE1inducible (I) in response to RP-6306 (31.3\u2009nM), RP-3500 (6.25\u2009nM) or combination for 10 days. Significance determined by two-way ANOVA followed by Tukey\u2019s multiple comparisons test for (B, E, G) and two-way ANOVA followed by Tukey\u2019s multiple comparisons for (C, D).\n\nTo study the in vivo anti-tumor activity of the PKMYT1i-ATRi combination, three patient-derived xenograft (PDX) models with CCNE1 amplification were utilized, including two OVCA (WO-19, WO-77) and one EMCA (WU-115) model (Supplementary Fig.\u00a0S3A, B). First, tolerability and anti-tumor activity were explored using RP-6306 and RP-3500 monotherapy in NSG mice, and mice bearing WO-19 xenograft tumors, respectively. Both RP-6306 and RP-3500 monotherapy were tolerated, as indicated by stable body weights at their maximum tolerated doses (MTDs), of 20\u2009mg/kg for both compounds (Supplementary Fig.\u00a0S4A). Both RP-6306 and RP-3500 monotherapy yielded limited single-agent anti-tumor activity in WO-19 PDX (Supplementary Fig.\u00a0S4B), necessitating evaluation of the PKMYT1i-ATRi combination.\n\nTo first evaluate the tolerability of the combination, non-tumor-bearing NSG mice were treated with a combination of RP-6306 and RP-3500, using multiple dose levels, in order to identify doses for evaluation in a follow-up efficacy study using tumor-bearing mice. There were no treatment cessations required for the mice in the combination RP-6306 at 5\u2009mg/kg or 10\u2009mg/kg (Day 1\u20135) with RP-3500 using intermittent dosing at 5\u2009mg/kg (Day 1\u20133). There were only two dose reductions in the combination RP-6306 at 10\u2009mg/kg with RP-3500 at 5\u2009mg/kg (Supplementary Fig.\u00a0S4A), yielding tolerable doses for evaluation next in tumor-bearing efficacy studies. Next, mouse plasma pharmacokinetic studies were performed to evaluate drug-drug interactions and overall free fraction exposure. The free plasma concentrations of RP-6306 and RP-3500 were similar when dosed as single agents or when used in combination, suggesting no significant drug-drug interactions (Supplementary Fig.\u00a0S4C), and exposures were at or above levels previously associated with meaningful target pathway engagement28,29.\n\nThe combination was next evaluated in OVCA and EMCA PDX models. RP-6306 at 5\u2009mg/kg or 10\u2009mg/kg with RP-3500 (5\u2009mg/kg) resulted in significant tumor regressions compared to RP-6306 monotherapy at 10\u2009mg/kg (P\u2009=\u20090.0240, P\u2009=\u20090.0025, respectively) and RP-3500 monotherapy in the CCNE1 amp EMCA model (WU-115) model (P\u2009=\u20090.0032, P\u2009=\u20090.002, respectively, Fig.\u00a03A and Supplementary Fig.\u00a0S5A). There was over a 6-fold improvement in median overall survival compared to control and an approximate 3.5-fold improvement in median overall survival relative to monotherapies (Fig.\u00a03A). Combination treatment also decreased metastasis (organs with metastasis) compared to monotherapy or control but this was nonsignificant in this overall not a highly metastatic model (Fig.\u00a03A). Combination PKMYT1i-ATRi also increased tumor suppression in the CCNE1AMP OVCA WO-19 and WO-77 PDX models, which are resistant to standard-of-care platinum chemotherapy (Fig.\u00a03B, C and Supplementary Fig.\u00a0S5B, C)11. For WO-19, a combination of RP-6306 at 10\u2009mg/kg with RP-3500 (5\u2009mg/kg) significantly increased tumor suppression relative to RP-6306 at 10\u2009mg/kg (P\u2009<\u20090.0001) and RP-3500 (P\u2009<\u20090.0001) monotherapy and improved median overall survival relative to monotherapy (P\u2009=\u20090.0007, P\u2009=\u20090.0006, respectively; Fig.\u00a03B). Notably, there was a significant decrease in metastases relative to control with combination treatment (P\u2009=\u20090.0401, P\u2009=\u20090.0122 for combination with 5\u2009mg/kg and 10\u2009mg/kg RP-6306, respectively; Fig.\u00a03B). For WO-77, combination RP-6306 at 5\u2009mg/kg with RP-3500 (5\u2009mg/kg) significantly increased tumor suppression relative to RP-6306 at 5\u2009mg/kg (P\u2009<\u20090.0001) and RP-3500 (P\u2009=\u20090.0063) monotherapy and improved median overall survival relative to monotherapy (P\u2009<\u20090.0001 and P\u2009=\u20090.0003, respectively). Also, there was a statistically significant decrease in metastases with combination treatment relative to control (P\u2009=\u20090.0002, P\u2009<\u20090.0001 for combination with 5\u2009mg/kg and 10\u2009mg/kg RP-6306, respectively; Fig.\u00a03C) and a significant decrease in ascites score relative to control (P\u2009=\u20090.0261, P\u2009=\u20090.0102, respectively, Supplementary Fig.\u00a0S4D). Neither the WU-115 of WO-19 models made a significant amount of ascites (Supplementary Fig.\u00a0S4D).\n\nA\u2013C Tumor volume growth was measured weekly in (A) EMCA WU-115 (B) OVCA WO-19, and (C) OVCA WO-77 CCNE1 amplified PDX models treated with the indicated drugs. RP-6306 was given oral BID on days 1\u20135, and RP-3500 was given oral QD on 3 days on / 4 days off schedule until tumor progression (tumor volume >\u20091000\u2009mm3). Survival rate (middle panel) and metastases (right panel) was analyzed at the end of each experiment. Metastases was defined as the number of organs with metastatic disease in each mouse. D The toxicity of drugs was revealed by the mice body weights changes. E Tumor growth (left) and body weight change (right) of OVCAR3 xenografts in mice treated with either RP-6306, RP-3500, or both. RP-3500 was given oral QD, and RP-6306 was given oral BID, both given on a 3-day on / 4-day off schedule for 24 days (n\u2009=\u20098). Tumor growth and percent body weight change shown is mean\u2009\u00b1\u2009SEM. Longitudinal tumor growth was analyzed by linear mixed effects modeling with type II ANOVA and pairwise comparisons across groups. Data were analyzed for overall survival using the Mantel-Cox log-rank test. Metastases were compared with one-way ANOVA followed by Tukey\u2019s multiple comparisons. The body weight shown is mean\u2009\u00b1\u2009SEM.\n\nWith all three PDX models, there was limited toxicity, as evident by body weight for both monotherapy and combination treatments (Fig.\u00a03D). While the combined RP-6306/RP-3500 treatment groups exhibit significant tumor regressions and improved overall survival compared to vehicle and monotherapy groups, we eventually observe tumor outgrowth after several weeks of treatment. We believe this is likely due to acquired resistance mechanisms such as loss of CCNE1 CN or downregulation of MMB-FOXM1 transcription17. We also tested the combination PKMYT1i-ATRi in the CCNE1AMP OVCAR3 cell line xenograft and found superior tumor growth inhibition (TGI) with the combination compared to RP-6306 and RP-3500 monotherapy (P\u2009<\u20090.001) and showed limited toxicity (Fig.\u00a03E and Supplementary Fig.\u00a0S5D). These data collectively demonstrate that combination inhibition of PKMYT1 and ATR significantly suppresses tumor growth and improves median overall survival in CCNE1AMP OVCA and EMCA.\n\nWe investigated if a combination PKMYT1i-ATRi treatment causes defective cell cycle progression and DNA damage leading to cell death. To measure cell cycle progression and DNA damage, we used quantitative imaging-based cytometry (QIBC) with 5-ethynyl-20 -deoxyuridine (EdU) incorporation to label S-phase cells, 4\u2019,6-diamidino-2-phenylindole (DAPI) to measure DNA content and \u03b3H2AX as a marker of DNA damage after PKMYT1i-ATRi treatment using FT282 CCNE1-overexpressing cells (Fig.\u00a04A and Supplementary Fig.\u00a0S6A, B). The combination of RP-6306 with RP-3500 led to a substantial increase in DNA damage in CCNE1 overexpressing FT282 cells based on the appearance of pan-\u03b3H2AX-positive (pan-\u03b3H2AX+) cells compared to parental cells (Fig.\u00a04A). Most of the pan-\u03b3H2AX+ cells had <2\u2009C DNA content and were EdU-, suggesting the cells with DNA damage were unable to complete DNA replication (Supplementary Fig.\u00a0S6A). Consistent with a defect in DNA replication, the proportion of EdU+ CCNE1-overexpressing cells was dramatically decreased when treated with both RP-6306 and RP-3500 compared to RP-6306 or RP-3500 alone (Supplementary Fig.\u00a0S7C). Notably, the most dramatic increase of DNA damage in CCNE1-overexpressing cells was observed at RP-6306 and RP-3500 concentrations that have little effect as a single agent or in parental FT282 cells (Fig.\u00a04A). Combination RP-6306 with RP-3500 treatment increased \u03b3H2AX levels in CCNE1AMP (OVCAR3, KLE, and FUOV1) and CCNE1GAIN (OVCAR8) but not CCNE1LOW (WO-20) cells indicating that tumors with CCNE1 amplification are susceptible to DNA damage induced by combination PKMYT1i-ATRi (Fig.\u00a04B\u2013D). Combination of very low dosage RP-6306 (31.3\u2009nM) with RP-3500 at (6.25\u2009nM) also show similar trend in increasing \u03b3H2AX levels in a time-dependent manner (Supplementary Fig.\u00a0S6D, E).\n\nA QIBC quantitation of FT282-hTERT p53R175H parental (WT, left) and CCNE1-overexpressing (CCNE1-O/E, right) EdU-/pan-\u03b3H2AX+ cell in response to the indicated RP-6306/RP-3500 combinations treated for 48\u2009h. n\u2009=\u20093; Mean\u2009+\u2009SD. B Detection of \u03b3H2AX+ cells by flow cytometry in indicated cells after treated with RP-6306 (250\u2009nM), RP-3500 (50\u2009nM), or a combination of both treated for 24\u2009h. n\u2009=\u20093; Mean\u2009+\u2009SD. C, D Whole cell lysates of OVCAR3 (C) and KLE (D) cells were treated with RP-6306 (250\u2009nM), RP-3500 (50\u2009nM), or both for the indicated times and immunoblotted with \u03b3H2AX and Actin antibodies. Actin is loading control. E Representative micrographs (left) of metaphase spreads from FT282 parental (WT) and CCNE1-overexpressing cells left untreated or following treatment with combination of RP-6306 (125\u2009nM) and RP-3500 (25\u2009nM) for 24\u2009h and quantitation of cells (right) after 24\u2009h treatment with the indicated RP-6306 (125\u2009nM) and RP-3500 (25\u2009nM) conditions with at least 40 metaphases counted per replicates. n\u2009=\u20093; Mean\u2009+\u2009SD. F OVCAR3 tumor-bearing mice were administered RP-6306 (5\u2009mg/kg) orally BID, RP-3500 (5\u2009mg/kg) orally QD or a combination of both for 3 days, sacrificed 2\u2009h post last treatment, and tumor tissue was prepared for FFPE. Tumor tissues were stained with \u03b3H2AX antibodies (left), and the percentage of \u03b3H2AX 3\u2009+ strong positive tissue (right) present in the tumor area was quantified by HALO software, n\u2009=\u20096,5,5,5;); Mean\u2009+\u2009SD. Scale bar: 100\u2009\u00b5m. G WU-115 were administered RP-6306 (10\u2009mg/kg) orally BID, RP-3500 (5\u2009mg/kg) orally QD or a combination of both for 10 days, sacrificed 2\u2009h post last treatment and tumor tissue was prepared for FFPE. Tumor tissues were stained with \u03b3H2AX antibodies (left), and the percentage of \u03b3H2AX 3\u2009+ strong positive tissue (right) present in the tumor area was quantified by HALO software. n\u2009=\u20096,5,5,5; Mean\u2009+\u2009SD. Scale bar: 100\u2009\u00b5m. Red arrows illustrate \u03b3H2AX 3\u2009+ strong positive cells quantified (H) Flow cytometry quantification of apoptotic cells with Annexin V and propidium iodide (PI) staining of the indicated cells after treated with drugs RP-6306 (250\u2009nM), RP-3500 (50\u2009nM), or both for 72 hrs. n\u2009=\u20093; Mean\u2009+\u2009SD (I) SNU685 CCNE1 inducible cells were treated and detected with apoptotic cells same as (G). n\u2009=\u20093; Mean \u2009+\u2009SD. J, K Whole cell lysates of OVCAR3 (J) and KLE (K) cells were treated with RP-6306 (250\u2009nM), RP-3500 (50\u2009nM), or both for the indicated times and immunoblotted with cleaved PARP (cPARP), cleaved caspase 9 (cCas9), cleaved caspase 93 (cCas3), and Actin antibodies. Actin is loading control. Significance determined by two-way ANOVA followed by Tukey\u2019s multiple comparisons test for (A, B, E, H, I) and one-way ANOVA followed by Tukey\u2019s multiple comparisons for OVCAR3 and WU-115 xenograft in (F, G).\n\nTo further probe the source of DNA damage we measured micronucleation and chromosome pulverization, phenotypes associated with RP-6306 treatment in CCNE1-overexpressing cells17. Similarly, a combination of RP-6306 and RP-3500 both increased micronucleation (Supplementary Fig.\u00a0S6F) and chromosome pulverization (Fig.\u00a04E) in CCNE1-overexpressing cells with little effect in the wildtype parental cells. We also observed increased \u03b3H2AX in OVCAR3 and WU-115 xenografts with the RP-6306/RP-3500 combination compared to either single agent alone, suggesting that DNA damage accumulates in PKMYT1i-ATRi treated tumors (Fig.\u00a04F, G). DNA damage was accompanied by apoptosis in PKMYT1i-ATRi treated CCNE1-amp and CCNE1-overexpressing cells, as demonstrated by increased Annexin V staining (Fig.\u00a04H, I and Supplementary Fig.\u00a0S7A) and elevated cleaved PARP, caspase-9 and caspase-3 (Fig.\u00a04J, K). PKMYT1i-ATRi combination treatment-induced apoptosis is dependent on caspases as pan- caspase inhibitor, Z-VAD-FMK, abrogated PKMYT1i-ATRi induced apoptosis (Supplementary Fig.\u00a0S7B). Importantly, high levels of apoptosis were dependent on CCNE1 amplification (Fig.\u00a04H and Supplementary Fig.\u00a0S7A) or overexpression (Fig.\u00a04I and Supplementary Fig.\u00a0S7A). Together, these results suggest PKMYT1i-ATRi induces lethal amounts of DNA damage in cells with elevated CCNE1 copy number or cyclin E expression.\n\nPrevious studies using ATRi in combination with WEE1i or agents that induce replication stress established that irreversible levels of DNA damage arise from DNA replication defects and exhaustion of the available pool of replication protein A (RPA), leading to replication catastrophe from the conversion of single-stranded DNA (ssDNA) to double-strand breaks (DSBs)11,32. We tested if the source of pan-\u03b3H2AX in PKMYT1i-ATRi treated CCNE1-overexpressing or CCNE1-amplified cells originated from replication catastrophe by simultaneously measuring chromatin-bound RPA and \u03b3H2AX using QIBC (Supplementary Fig.\u00a0S8A\u2013D). Cells treated with RP-3500 and hydroxyurea to induce replication stress showed the characteristic replication catastrophe profile with the emergence of cells with pan-RPA preceding those with pan-\u03b3H2AX (Supplementary Fig.\u00a0S8A\u2013D). In contrast, the majority of PKMYT1i and PKMYT1-ATRi treated CCNE1-overexpressing FT282 and OVCAR3 cells accumulated pan- \u03b3H2AX before the appearance of pan-RPA suggesting that RPA exhaustion is not causing DNA damage (Supplementary Fig.\u00a0S8A\u2013D). We note there is slight induction of pan-RPA+/\u03b3H2AX- cells in ATRi-treated cells at later time points, indicating a small contribution of replication catastrophe. We conclude the predominant mechanism of pan-\u03b3H2AX induction in PKMYT1i-ATRi treated CCNE1-overexpressing or CCNE1-amplified cells do not result from replication fork breakage or replication catastrophe.\n\nPKMYT1 inhibition in CCNE1-overexpressing or CCNE1-amplified cells activates CDK1 in S-phase triggering premature mitotic entry leading to chromosome pulverization and cell death17. Considering ATR inhibits cell cycle progression and CDK1 activation during S-phase33,34, we examined if premature mitotic entry is causing DNA damage in PKMYT1i-ATRi treated cells by measuring the proportion of EdU-positive (EdU+) cells marked by histone H3 Ser10 phosphorylation (H3-pS10, Supplementary Fig.\u00a0S8E). PKMYT1i treatment alone increased premature mitotic entry of CCNE1-overexpressing FT282 and OVCAR3 cells based on the emergence of histone H3 Ser10 phosphorylation (H3pS10+) in EdU+ cells (Fig.\u00a05A). Importantly, the addition of ATRi at doses that have no single agent effect increased the proportion EdU+/H3pS10+ cells. Increased H3-pS10 expression was also observed in PKMYT1i-ATRi treated CCNE1AMP (KLE and FUOV1) cells but not in CCNE1GAIN (OVCAR8) or CCNE1LOW (WO-20) cells indicating a conserved mechanism-of-action in tumor-derived CCNE1-amplified models (Fig.\u00a05B). To further understand the effect of combined PKMYT1-ATRi on mitotic entry we conducted time-lapse microscopy of cells expressing a PCNA chromobody fused to TagRFP17. We define a premature mitotic event when there is nuclear envelope breakdown in a cell with PCNA puncta, a marker for active DNA replication and S-phase. In concordance with the QIBC results, we observed that combined RP-6306 with RP-3500 treatment increased the frequency of premature mitosis in either the first or second S-phase of CCNE1-overexpressing cells compared to either single agent alone or the combination in wildtype parental cells (Fig.\u00a05C, supplemental videos 1\u20136). Finally, we measured DNA replication fork progression with a combination of RP-6306 and RP-3500 in OVCAR3 cells using DNA fiber assays35. OVCAR3 cells were treated with RP-6306, RP-3500, or the combination and sequentially labeled with the nucleotide analogs CldU and IdU. Consistent with an interruption of DNA replication for progression, we observe shorter CIdU+IdU track lengths in OVCAR3 cells treated with combination RP-6306 and RP-3500 compared to monotherapy or untreated controls (Fig.\u00a05D). Taken together, these results suggest that ATR is reducing the CDK1 activation potential of RP-6306 and limiting induction of premature mitosis in CCNE1-overexpressing or CCNE1-amplified cells.\n\nA QIBC quantitation of FT282-hTERT p53R175H parental (WT, left) CCNE1-overexpressing (CCNE1-O/E, middle) and OVCAR3 (right) cells with percent of EdU+/ pHH3+ as a function of time after addition of RP-6306 (250\u2009nM), RP-3500 (100\u2009nM) or combination of both. * P-values reveal the comparison of groups at 8\u2009h. B Measurement pHH3+ cells in the indicated cells after treated with RP-6306 (250\u2009nM), RP-3500 (50\u2009nM), or combination for 24\u2009h. n\u2009=\u20093; Mean\u2009+\u2009SD. C Quantitation of the number of nuclear envelope breakdowns (NEBDs) observed during the 1st or 2nd observed S-phase using time-lapse imaging of FT282-hTERT p53R175H PCNA-chromobody-TagRFP (WT) and CCNE1-overexpressing (CCNE1) cells treated with the indicated RP-6306 (125\u2009nM) or RP-3500 (25\u2009nM) for 47\u2009h. n\u2009=\u20093; Mean\u2009+\u2009SD. D DNA fiber assay were performed to detect DNA fiber progression. The OVCAR3 cells were treated with RP-6306 (250\u2009nM), RP-3500 (50\u2009nM), or a combination for 1\u2009h, then pulsed with CIdU (red) and IdU (green) for 25\u2009min. Significance determined by one-way ANOVA followed by Tukey\u2019s multiple comparisons test in (A, D), and two-way ANOVA followed by Tukey\u2019s multiple comparisons test for (B, C).\n\nWe postulated that cyclin B-CDK1 activation in the S-phase precedes premature mitotic entry in CCNE1-overexpressing or CCNE1-amplified cells treated with PKMYT1i-ATRi. At the onset of prophase, cyclin B-CDK1 complexes are rapidly activated and imported into the nucleus marked by CDK1 autophosphorylation of cyclin B on Ser12636,37 (cyclin B-pS126+). In PKMYT1i treated CCNE1-overexpressing FT282 and OVCAR3 cells, cyclin B-pS126 accumulated in the nucleus of EdU+ cells (Fig.\u00a06A and Supplementary Fig.\u00a0S9A), and the addition of ATRi increased the proportion of EdU+/cyclin B-pS126+ cells. Only a mild increase in EdU+/cyclin B-pS126+ cells was observed at later time points in FT282 parental cells, indicating that high CCNE1 expression underpins the robust and premature CDK1 activation by PKMYT1i-ATRi.\n\nA QIBC quantitation of FT282-hTERT p53R175H parental (WT, left) CCNE1-overexpressing (CCNE1-O/E, middle) and OVCAR3 (right) cells with percent of EdU+/cyclin B-pS126+ as a function of time after addition of RP-6306 (250\u2009nM), RP-3500 (100\u2009nM) or combination of both. B Whole cell lysates of FT282-hTERT p53R175H CCNE1-overexpressing (CCNE1-O/E) cells treated with RP-3500 (100\u2009nM), RP-6306 (250\u2009nM) or both for the indicated times were immunoblotted with CDK1, CDK1-pT14, CHK1, CHK1-pS345, CDC25B, CDC25B-pS151 and Actinin specific antibodies Actinin is used as loading control. C, D Whole cell lysates of KLE (C) and OVCAR3 (D) cells treated with RP-6306 (250\u2009nM), RP-3500 (50\u2009nM), or both for the indicated times were immunoblotted with CDK1, CDK1-pT14, CHK1, CHK1-pS345 and Actin specific antibodies. E, F Tumor tissue from WO-77 (E) tumor-bearing mice from Fig.\u00a03C at end of treatment or WU-115 (F) tumor-bearing mice administered RP-6306 (10\u2009mg/kg) orally BID, RP-3500 (5\u2009mg/kg) orally QD or combination of both for 10 days and sacrificed 2\u2009h post last treatment was prepared for FFPE Tumor tissues were stained with CDK1-pT14 antibodies (left) and the percentage of CDK1-pT14 strong-positive tissue (right) present in the tumor area was quantified by HALO software. n\u2009=\u20093,3,3,3,4,4 (E), n\u2009=\u20096,5,5,5 (F) Mean\u2009\u00b1\u2009SD. Scale bar: 200\u2009\u00b5m. G Growth inhibition relative to DMSO control of RPE1-hTERT TP53-/- parental, BRCA1-/-, ATM-/- and CCNE1-overexpressing (CCNE1-2A-GFP) cells after treatment with the indicated dose of RP-6306, RP-3500 or the combination of both. n\u2009=\u20093; Mean\u2009+\u2009SD. Significance determined by one-way ANOVA followed by Tukey\u2019s multiple comparisons test in for (A, E, G), and Students\u2019 t test in (F).\n\nTo investigate how PKMYT1 and ATR inhibition are cooperating to activate CDK1, we measured levels of the CDK1 inhibitory phosphorylation at Thr14 (CDK1-pT14) in cell lines and tumor xenografts. As expected, PKMYT1i treatment reduced CDK1-pT14 levels in CCNE1-overexpressing/CCNE1-amplified cell lines (Fig.\u00a06B\u2013D and Supplementary Fig.\u00a0S9B) and CCNE1-amp OVCAR3, WO-77 and WU-115 xenografts (Fig.\u00a06E, F and Supplementary Fig.\u00a0S9C\u2013E). Remarkably, the addition of ATRi to PKMYT1i facilitated a greater reduction in CDK1-pT14 levels compared to PKMYT1i alone in cell lines (Fig.\u00a06B\u2013D and Supplementary Fig.\u00a0S9B), suggesting that ATRi is bolstering PKMYT1i-dependent dephosphorylation and activation of CDK1. We also see decreased CDK1-pT14 levels in WO-77 and WU-115 xenografts (Fig.\u00a06E, F and Supplementary Fig.\u00a0S9D, E). In response to DNA damage, ATR activates CHK1, which halts G2/M progression by catalyzing inhibitory phosphorylation of CDC25B/C phosphatases38. CDC25B and CDC25C phosphatases act in a sequential and coordinated manner to activate CDK1 by dephosphorylating CDK1. We reasoned that the DNA damage generated by PKMYT1i treatment leads to ATR-CHK1 activation and CDC25B inhibition, which is blocked by ATRi treatment. We investigated this by monitoring the activating phosphorylation of Ser345 on CHK1 (CHK1-pS345) and inhibitory phosphorylation of Ser151 on CDC25B (CDC25B-pS151). In CCNE1-overexpressing FT282 cells both CHK1-pS345 and CDC25B-pS151 levels increased upon PKMYT1i treatment and were partially suppressed by the addition of ATRi (Fig.\u00a06B and Supplementary Fig.\u00a0S9B). CHK1-pS345 levels were also reduced by ATRi addition to PKMYT1i in CCNE1-amp OVCAR3 and KLE cells (Fig.\u00a06C, D). Finally, co-treatment of CCNE1-overexpressing FT282 cells with the CDK1 inhibitor RO-3306 significantly reduced PKMYT1i-ATRi-dependant pan-\u03b3H2AX induction (Supplementary Fig.\u00a0S9F). Together, these results suggest that the addition of ATRi to PKMYT1i permits rapid and robust CDK1 activation by stimulating CDK1 dephosphorylation.\n\nOur current studies identified a strong synergistic interaction between low doses of PKMYT1i-ATRi in CCNE1-overexpressing or CCNE1-amplified preclinical models. We compared the sensitivity of PKMYT1-ATRi combination in an isogenic panel of RPE1-hTERT TP53-/- parental, ATM-/-, BRCA1-/- and CCNE1-overexpressing cells (Supplementary Fig.\u00a0S10A). The concentration of PKMYT1i that attained the highest synergy was lower in CCNE1-overexpressing compared to parental, ATM-/- and BRCA1-/- cells, indicating the synthetic lethal window of RP-6306 in CCNE1-overexpressing cells was exacerbated by the addition of ATRi (Fig.\u00a06G and Supplementary Fig.\u00a0S9B). For example, combining 24.7\u2009nM PKMYT1i with 12.3 nn ATRi was cytotoxic in CCNE1-overexpressing but not in the parental, ATM-/- or BRCA1-/- counterparts (Fig.\u00a06G). These results suggest that CCNE1-amplification, rather than ATM or BRCA1/2 inactivation, may associate with increased tumor sensitivity to PKMYT1i-ATRi combinations.\n\nIn summary, we propose a model where the synergistic cytotoxicity of PKMYT1i-ATRi originates from the ability of ATR assisting PKMYT1 in keeping CDK1 activity low during S-phase in CCNE1-overexpressing cells (Fig.\u00a07). PKMYT1i causes DNA damage that activates ATR-CHK1 and represses CDC25 phosphatases limiting CDK1 activation potential. Combined PKMYT1i-ATRi increases CDC25 phosphatase activity, allowing deeper dephosphorylation of CDK1-pT14, which rapidly drives S-phase cells into mitosis resulting in catastrophic DNA damage and cell death (Fig.\u00a07).\n\nA, B Model of synergy between PKMYT1 and ATR inhibition in CCNE1-amplified or overexpressing cells. A CCNE1 amplification or overexpression in cells causes replication stress and S-phase elongation. To delay induction of mitosis until DNA replication is complete, CDK1 activity is inhibited by increased PKMYT1 inhibitory phosphorylation on CDK1-Thr-14 and decreased CDC25 phosphatase activity via ATR-CHK1 signaling. B Inhibition of PKMYT1 (lunresertib) reduces CDK1 The14 phosphorylation and inhibition of ATR (camonsertib) increases CDC25 activity resulting in rapid and robust S-phase CDK1 activation and premature mitosis with synergistic induction of DNA damage and cell death.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58183-w/MediaObjects/41467_2025_58183_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "CCNE1 is commonly amplified in gynecological cancers such as OVCA and EMCA, and effective treatments exploiting this genomic alteration are lacking5,6,39. Given that this subset of cancers are typically resistant to standard-of-care platinum chemotherapy and associated with poor overall survival, we sought to address this clinical unmet need by identifying a treatment strategy that targets critical survival pathways for CCNE1-driven cancers5,6. We previously identified PKMYT1 as a new synthetic lethal target for CCNE1 amplified cells using a genome-wide CRISPR screen approach17,28. PKMYT1 inhibition with RP-6306 is a selective and potent CDK1 activator, leading to mitotic catastrophe, especially in CCNE1 amplified models17. Because the emergence of resistance to monotherapy for oncogene-addicted cancers is essentially universal40, a combination strategy was investigated. Further, combination strategies that exploit genomic vulnerabilities can permit the utilization of drug concentrations lower than required to be active as monotherapy, thereby decreasing toxicity41. Considering CCNE1 amplification causes replication stress that activates the DNA replication fork stabilizer and regulator of G2/M checkpoint kinase ATR, we sought to further improve anti-tumor efficacy by combining PKMYT1 inhibition (RP-6306) with ATR inhibition (RP-3500).\n\nHere, we demonstrate that combined inhibition of PKMYT1 and ATR is synergistic in CCNE1 amplified OVCA and EMCA cells compared to non-amplified models and effects are CCNE1 copy number dependent (Fig.\u00a01C\u2013F, Supplementary Fig.\u00a0S1C, D and Fig.\u00a02C, E\u2013I). We demonstrate a significant increase in anti-tumor activity and overall survival compared to monotherapy alone in CCNE1 amplified OVCA and EMCA PDX models using a low-dosing strategy justifying further evaluation in the clinic (Fig.\u00a03 and Supplementary Fig.\u00a0S5). Notably, especially in highly metastatic PDX models, a combination of PKMYT1i and ATRi decreased metastasis in OVCA and EMCA PDX models which is clinically meaningful as metastatic disease is what ultimately kills our patients. We observe that a combination of PKMYT1 with ATR inhibition leads to defective DNA replication (Fig.\u00a05C, D) and induces lethal amounts of DNA damage in cells with elevated CCNE1 copy number or cyclin E expression as evidence by increased \u03b3H2AX (Fig.\u00a04A\u2013D and Supplementary Fig.\u00a0S6A\u2013E) and Annexin V (Fig.\u00a04H, I) as well as cleaved caspase 3 (Fig.\u00a04J, K and Supplementary Fig.\u00a0S7A). Notably, the most dramatic increase in DNA damage and cytotoxicity in CCNE1- overexpressing/amplified cells is observed at PKMYT1i and ATRi concentrations that have little effect as a single agent or in immortalized fallopian tube cells (FT282) without CCNE1 overexpression (Figs.\u00a02 and \u00a04), thus suggesting clinical tolerability.\n\nOther effective combination studies targeting CCNE1 amplification and or expression have been identified preclinically. We recently showed that CCNE1 amplification is a biomarker of response to the combination WEE1 with ATR inhibition (WEE1i-ATRi)11. Mechanistically, the WEE1i-ATRi combination anti-tumor effects differ from the PKMYT1i-ATRi combination. We previously showed that combination WEE1i-ATRi treatment results in irreversible levels of DNA damage that arise from DNA replication defects and exhaustion of the available pool of RPA, ultimately leading to replication catastrophe from conversion of single-strand DNA to double-strand breaks (DSBs)11,32. In this study, we demonstrate that the major mechanism of action of PKMYT1i-ATRi does not result from replication fork breakage or replication catastrophe, as evidenced by the accumulation of pan-\u03b3H2AX before the appearance of pan-RPA (Supplementary Fig.\u00a0S8A\u2013D). The differential mechanism of action between PKMYTi-ATRi and WEE1i-ATRi can potentially be attributed to the observations that WEE1i increases origin firing and DNA replication stress via activation of Cyclin E-CDK2 leading to greater reliance on ATR to stabilize and restrict replication fork progression42,43, whereas PKMYT1i has little effect on CDK2 activation17. Monotherapy of both WEE1 and ATR inhibitors are both associated with myelosuppression, suggesting an overlapping toxicity profile6,7. Conversely, RP-6306 monotherapy does not lead to substantial myelosuppression42 suggesting a more favorable toxicity profile for combination with ATR inhibitors (or WEE1). Taken together, this study indicates that CCNE1 amplified OVCA and EMCA cells are specifically vulnerable to CDK1 activation, and strategies to increase CDK1 activation offer an attractive therapeutic avenue for this unmet need in the clinic. Preclinically, other combinations targeting CCNE1 overexpression include CDK2 inhibition (e.g., dinaciclib) with AKT inhibition, CDK2/9 and PIK3CA inhibition, and WEE1 with PKMYT1 inhibition44,45,46,47.\n\nThere are several strategies targeting CCNE1 amplification or CCNE1 overexpressing solid tumors that are being evaluated in the clinic in Phase I/II trials. Targeting WEE1 with ZN-c3, CHK1/2 with LY2606368, and CDK2 with INX-315, BLU-222, INCB123667, or ARTS-021 are all in early phase I/II monotherapy clinical trials (clinicaltrials.gov). Thus far, CDK2 inhibitors have largely failed in clinical trials due to insufficient selectivity10. Clinical trials of combination regimens targeting CCNE1 are also in development. Given the results of this study showing that CCNE1 amplification or overexpression represents a strong biomarker for sensitivity to low-dose combination PKMYT1i (RP-6306) with ATRi (RP-3500), this combination has moved forward into the clinic as a phase 1 dose escalation study in advanced solid tumors with CCNE1 amplification (NCT04855656; MYTHIC). PKMYT1i, in combination with the chemotherapies gemcitabine and FOLFIRI, is also being explored given preclinical data showing this combination is synergistic and similarly resulted in mitotic catastrophe (MAGNETIC and MINOTAUR: clinical trials.gov)17. Chemotherapy combinations with targeted agents such as older generation WEE1i have demonstrated activity but have been overall intolerable because of toxicity43.\n\nOur data demonstrates that CCNE1 gene amplification and overexpression are important biomarkers for sensitization to PKMYT1 combined with ATR inhibition. We show that mutation of BRCA1 or ATM, the clinical biomarkers for ATRi or ATRi-PARPi sensitivity, show little to no sensitivity to low doses of PKMYT1i-ATRi. Considering the synthetic lethal relationship between CCNE1 -BRCA1 and the near mutually exclusivity of CCNE1 amplification and BRCA1 mutations in tumors44, results from our work support the inclusion of tumors with CCNE1 amplification and exclusion of those with homologous recombination or ATM deficiencies for combination PKMYT1 with ATR inhibition. However, there is a clinical need to determine if CCNE1 overexpression, either by gene copy number (CN) or protein level better correlates with response to agents targeting this oncogene. The optimal copy number threshold for CCNE1 amplification and the role of cyclin E protein levels as predictive biomarkers of response is currently being investigated across preclinical and clinical studies. Further, it is possible that other biomarkers exist to predict sensitivity to this drug combination. Low molecular weight Cyclin E1 has been shown to facilitate replication stress tolerance and DNA damage repair, suggesting sensitivity to drugs targeting the ATR/CHK1 pathway45,46. We have previously identified both full-length and low molecular weight isoforms in the Cyclin E1 overexpressing cell lines used in this study11,17. The scope of our study was limited to full-length Cyclin E1 and does not exclude the possibility that other biomarkers could enhance response46. Other mutations have also been identified that may predict sensitivity to PKMYT1i or the combination, such as FBXW7 (which encodes a substrate adapter for the E3 ligase that targets cyclin E for ubiquitin-dependent proteolysis)47, or PPP2R1A (a serine/threonine phosphatase and tumor suppressor)17. Cells evaluated such as KLE (CCNE1AMP) and SKOV3 (CCNE1LOW) have a likely pathogenic mutation in FBXW7 and OVCAR3 (CCNE1AMP), a PPP2R1A mutation which has not been further characterized; such alterations all potentially increasing the sensitivity to PKMYT1i or combination48.\n\nThere are limitations to 2-dimensional cultures, which does not fully recapitulate the complex cell-cell and cell-environment interactions of a 3-dimensional or in vivo systems, which may affect the efficacy and synergy of drug combinations. Cancer\u2019s molecular landscape, heterogeneity, and tumor microenvironment all influence tumorigenesis, metastasis, response to treatment, and emergence of drug resistance49. Further, DNA damage repair inhibitors may influence immune system response50, which is lacking in the models used in this study.\n\nIn summary, we identify a potential treatment option for an aggressive subset of OVCA and EMCA patients who have poor prognosis and limited treatment options. By exploiting oncogene-addicted cell-cycle checkpoints and DNA repair mechanisms with a combination of PKMYT1 with ATR inhibition, normal cells should be spared, allowing lower dosing strategies, thereby limiting toxicity. Translational endpoints in ongoing and future clinical trials with this drug combination and additional preclinical studies are crucial to define the optimal CCNE1 CN (or protein) level to predict sensitivity to this drug combination.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "NSG mice (NOD/SCID IL2R\u03b3\u2009\u2212\u2009/\u2212) were purchased from the Stem Cell and Xenograft Core (SCXC) at the University of Pennsylvania (UPENN, Philadelphia, PA). All mice experiments were performed in adherence to the policies of the NIH Guide for the Care and Use of Laboratory Animals and approved by the Institutional Animal Care and Use Committee (IACUC). Patients were consented, and tumors were obtained from ovarian cancer debulking surgeries conducted at the Hospital of the UPENN and Pennsylvania Hospital (IRB# 702679).\n\nOrthotopic PDX models were generated by surgically engrafting of patient tumor chunks (3\u20134 pieces, 2 mm3 each) to the ovary/oviduct of five eight-week-old female mice as previously described11,51. The harvested PDX tumors were either retransplanted to NSG mice for further expansion or cryopreserved for future use.\n\nWe used in this study two high-grade serous ovarian cancer (HGSOC, WO-19, WO-77) PDX models and one endometrial cancer (EMCA, WU-115) PDX model. For preclinical trials, cryopreserved PDX tumor tissue was thawed and transplanted. After tumors were palpable (~\u20093-4\u2009mm), tumor volume was measured weekly by ultrasound (SonoSite Edge II Ultrasound System) by a trained sonographer. The tumor volume criteria for randomization to treatment arms was 50-100 mm3. Animals were randomized in a blinded manner into 6 treatment groups: vehicle (0.5% methyl cellulose); RP6306 (10mgkg, BID/day 1\u20135 weekly), RP6306 (5\u2009mg/kg, BID/day 1\u20135 weekly), RP3500 (5\u2009mg/kg, QD/day 1\u20133 weekly), combination RP6306 (10\u2009mg/kg, BID/day 1\u20135)\u2009+\u2009RP3500 (5\u2009mg/kg, QD/day 1\u20133), combination RP6306 (5\u2009mg/kg, BID/day 1\u20135)\u2009+\u2009RP3500 (5\u2009mg/kg, QD/day 1\u20133). Drugs were dosed by oral gavage. In all the models, the percentage change in body weight during treatment was used as a marker for toxicity and dose level adjustments. Significant treatment toxicity was defined as a 15% drop in body weight, and the mice require treatment reduction at a 25% dose and supplements supportive. For mice with a 20% drop in body weight, treatment was stopped and supportive measures (i.e., food supplement and subcutaneous fluid) were provided. The body weights and condition scores of mice were monitored and recorded weekly. Once improved, treatment was restarted with a 25% dose reduction. If body weight was not regained after one week, the animal was sacrificed in accordance with the Institutional Animal Care and Use Committee (IACUC) protocols. Trial endpoints were defined as tumor volume >\u20091000\u2009mm3 for all orthotopic PDX models. The humane endpoints for all mouse experiments were never exceeded. At the end of each experiment, mice were also assessed for the presence of ascites (0 no ascites, 1 small ascites, 2 medium ascites, 3 large ascites) and metastases at the time of necropsy. In the WU-115 model, two mice in the RP-6306 (10\u2009mg/kg)\u2009+\u2009RP-3500 group were monitored without detectable tumors starting at week 38. Their tumors were palpated weekly, but not body weight not measured, which leads to unchanged body weight in from week 38 to week 56 Fig.\u00a03A.\n\nTargeted DNA sequencing and sequencing analysis, variant calling, and copy Number Profiling for PDX tumors was previously reported11,52.\n\nOVCAR3, FUOV1, and KLE cell lines were purchased from ATCC (Manassas, Virginia); FUOV1 was obtained from Leibniz Institute DSMZ; OVCAR8 was obtained from NCI-DTP; Kuramochi, OVSAHO and OVKATE obtained from the Japanese Collection of Research Bioresources Cell Bank (JCRB). SNU685 from AcceGen Biotech (Fairview, NJ). CCNE1Amp lines (copy number [CN]\u2009>\u20095) were: OVCAR3, FUOV1, COV318, KLE; CCNE1Gain (CN 2-5): OVCAR8, OVSAHO, Kuramochi; CCNE1 copy neutral (CCNE1Low): OVKATE, WO-20, SNU685. Ovarian cancer cell lines included: OVCAR3, FUOV1, COV318, OVCAR8, OVSAHO, WO-20, OVKATE. Endometrial cancer cell lines included: KLE SNU685. OVCAR3, OVCAR8, Kuramochi, OVSAHO, OVKATE, and SNU685 cells were maintained in RPMI 1640 media with 10% fetal bovine Serum (FBS; Thermo Fisher) and 1% penicillin/streptomycin (P/S; Thermo Fisher). FUOV1 and KLE cells were cultured in Dulbecco\u2019s Modified Eagle\u2019s Medium (DMEM)/F12 media with 10% FBS and 1% P/S. RPE1-hTERT p53-/- Cas9, RPE1-hTERT p53-/- Cas9 BRCA1-/-53, RPE1-hTERT p53-/- Cas9 ATM-/-29 and RPE1-hTERT p53-/- Cas9 CCNE1 overexpressing cells17 were grown in DMEM (Life technologies # 11965-092) with 10% FBS (Wisent #080150) and 1% Pen/Strep (Wisent #450-201-EL). FT282-hTERT p53R175H WT (empty vector) and CCNE1 overexpressing cell lines were obtained from Ronny Drapkin13 and cultured in DMEM: F-12(1:1) (Life technologies # 11330-032) with 5% FBS, 1% UltroserG (Pall Life Sciences #15950-017) and 1% Pen/Strep.\n\nThe WO-20 primary ovarian cancer tumor cultures were generated in Simpkins laboratory as previous11. WO-20 CCNE1 inducible and SNU685 CCNE1 inducible cells were established by lentivirus stable infection11. Cell lines were authenticated by short tandem repeat (STR) analysis at the Oncogenomics Core at Wistar Institute and confirmed mycoplasma negative by end-point PCR at the Cell Center Service at the University of Pennsylvania. For established cell lines, CCNE1 copy number data from the Cancer Cell Line Encyclopedia (CCLE) was used. These data are available online, at https://depmap.org. An absolute copy number of \u22656 was considered CCNE1 amplified, CN\u2009>2 and <6 copy gain, and \u22642 copy neutral / low.\n\nAnimals were housed, and experiments were performed at Repare Therapeutics (Admare Bioinnovations Montreal site, St-Laurent, Canada), which is a CCAC (Canadian Council on Animal Care) accredited vivarium. Studies were conducted under a protocol approved by the Admare Animal Care Committee (AACC). Mice were inspected upon arrival, and group-housed (3\u20135 per cage) in individual HEPA ventilated autoclaved cages (Blue Line, Techniplast, Buguggiate, Italy) in a temperature-controlled environment (22\u2009\u00b1\u20091.5\u2009\u00b0C, 30\u201380 % relative humidity, 12\u2009h light/dark). Animals were provided with autoclaved corncob bedding, irradiated food (Harlan Teklad, Montreal, Canada), and filtered, autoclaved water ad libitum. They were also provided with nesting material and a plastic shelter as enrichment. Fresh bedding, nesting material, and water was replenished/replaced on a weekly basis. Mice were acclimatized in the animal facility for at least 5 days prior to use and were identified with indelible ink. Experiments were performed during the light phase of the cycle.\n\nOVCAR3 cells were implanted at 5\u2009\u00d7\u2009106 cells per mouse into the right flanks of female SCID-beige mice respectively (5-7 weeks old; Charles River), in 1:1 Matrigel:media (ECM gel Sigma, cat# 1270; media Corning RPMI 1640 cat #10-41-CM). When tumors reached the average target size of ~150 mm3 (between ~\u2009100 and 200 mm3), (n\u2009=\u20098) mice were randomized to treatment groups according to tumor volume and body weight using the \u201cstratified\u201d method in Studylogv4.4 software, and treatment with lunresertib and camonsertib was initiated. Lunresertib was formulated in 0.5% methylcellulose and orally administered twice daily (BID, 0-8\u2009h) for cycles of 3 days on/4 days off, for 28 days (4 cycles). Camonsertib was formulated in 0.5% methylcellulose and 0.02% SLS (pH 6.00) and orally administered once daily (QD) for cycles of 3 days on/4 days off, for 28 days (4 cycles). Statistical significance relative to vehicle control and other test groups was established by one-way Brown Forsyth and Welch ANOVA tests followed by unpaired t with Welch\u2019s correction, with individual variances computed for each comparison for multiple groups and unpaired t-test for two group comparisons (GraphPad Prism v9.0).\n\nUnder isoflurane anesthesia, whole blood was collected by cardiac puncture and transferred to tubes containing 0.1\u2009M citric acid (3:1 citric acid:blood) and stored at \u2212\u200920\u2009\u00b0C for LC-MS/MS analysis. Tumors were removed from mice flanks and cleared of surrounding mouse stroma. Tumor pieces between 50\u2009mg and 100\u2009mg were collected in a pre-weighed pre-filled bead mill tube (Fisher Scientific, Cat# 15-340-154) and then flash-frozen in liquid nitrogen. Other tumor fragments from vehicle- and compound-treated mice were placed in 10% neutral buffered formalin (NBF) within 2-3\u2009min of surgical excision, fixed in NBF for 24\u2009hours at room temperature, and embedded in paraffin.\n\nThe extraction of whole blood samples was performed by protein precipitation using four volumes of acetonitrile. The sample extracts were analyzed using a Transcend LX2 / Ultimate 3000 liquid chromatography system coupled to a Thermo Altis triple quadrupole electrospray mass spectrometer (Thermo Fisher Scientific) operated in positive mode. Separations were performed using a 2\u2009\u00d7\u200950\u2009mm, 2.8\u2009\u00b5m Pursuit XRS C8 HPLC column (Agilent). A reversed-phase linear gradient of water\u2009+\u20090.1% formic acid and 1:1 acetonitrile:MeOH was used to elute RP-6306, RP-3500, and the internal standards. Samples were quantified against a 12-point linear standard curve and 5 levels of quality control samples. Whole blood concentrations of RP-6306 and RP-3500 were converted to free unbound plasma concentrations using an in vitro derived blood / plasma ratio\u2009=\u20091.2 and fraction unbound (fu) plasma\u2009=\u20090.185 for RP-6306 and blood / plasma ratio\u2009=\u20090.613 and fraction unbound (fu) plasma\u2009=\u20090.00665 for RP-3500 from the CD-1 mouse strain.\n\nHistology in Figs.\u00a04F, 6E, and Supplementary Fig.\u00a0S9C was performed by HistoWiz Inc. Briefly, the formalin-fixed tissues were dehydrated through a 20, 80, 95 and 100 % ethanol series, cleaned in Histoclear, embedded in paraffin then sectioned at 4\u2009\u03bcm. Immunohistochemistry for \u03b3H2AX and CDK1pT14 were performed on a Bond Rx autostainer (Leica Biosystems) with heat antigen retrieval. Bond polymer refine detection (Leica Biosystems) was used according to the manufacturer\u2019s protocol. After staining, sections were dehydrated and film coverslipped using a TissueTek-Prisma and Coverslipper (Sakura). Whole slide scanning (40x) was performed on an Aperio AT2 (Leica Biosystems). Image quantification analysis was performed using HALO software (Indica Labs). The percentage of tumor cells staining at each of the following four levels was recorded: 0 (no staining), 1\u2009+ (weak staining), 2\u2009+ (moderate staining,) and 3\u2009+ (strong staining). Thresholds for staining intensity were optimized for each xenograft model. Histology in Supplementary Fig.\u00a0S3B was performed by Mosaic Laboratories (A CellCarta Company). Immunohistochemistry for Cyclin E1 (rabbit clone EP126) was performed in according to Mosaic\u2019s standard operating procedures. This assay was designed and validated to be a laboratory-developed test. After heat-induced epitope retrieval, staining was performed on a Bond-RX autostainer (Leica Biosystems) and visualized with DAB chromogen. Slides were then removed from the instrument, dehydrated, cleared and coverslipped. Stained slides were evaluated by a board-certified pathologist on a semi-quantitative scale, and the percentage of tumor cells staining at each of the following four levels was recorded: 0 (no staining), 1\u2009+ (weak staining), 2\u2009+ (moderate staining), and 3\u2009+ (strong staining). H-Score was calculated based on the summation of the product of the percent of cells stained at each staining intensity using the following equation: (3 x % cells staining at 3\u2009+)\u2009+\u2009(2 x % cells staining at 2\u2009+)\u2009+\u2009(1 x % cells staining at 1\u2009+).\n\nTumor tissue samples were set on a sterile petri dish, and necrotic tissue were removed. The tumor was dissected to a 5\u2009mm square under sterile conditions and washed with HBSS. Cleaned tissues were placed in a new petri dish and then minced. The minced tissues were mixed with enzymatic digestion buffer containing HBSS, collagenase 4 (1\u2009mg/ml), and Rock Inhibitor (Y-27632). The mixture was placed in a 50\u2009mL tube in a water bath at 37\u2009\u00b0C for 15\u2009min. The mixture was collected and dripped through a cell strainer on a new 50\u2009mL tube to remove any residual tissue. The suspension was centrifuged at 300\u2009\u00d7\u2009g for 5\u2009min at room temperature, the supernatant was removed. In case of a visible red pellet, erythrocytes were lysed in RBC Lysis buffer for 5\u2009min at room temperature, followed by two wash steps with 10\u2009mL of HBSS and centrifugation at 300\u2009\u00d7\u2009g for 5\u2009min. The cell pellet was suspended in Matrigel, and 50\u2009\u03bcL drops of matrix cell suspension were allowed to solidify on a pre-warmed 6-well plate at 37\u2009\u00b0C for 15\u2009min. On stabilization of the Matrigel, we added the organoid medium cocktail54. The culture media is Advanced DMEM/F12 (Thermo Fisher Scientific, Cat#12634010), containing 2\u2009mM Glutamax (Thermo Fisher Scientific, Cat# 35050061), 10\u2009mM HEPES(Sigma-Aldrich, Cat# H0887-100ML), 100unit Pen Strep (Gibco, Cat# 15140-122), 100\u2009ng/ml Noggin (PeproTech, Cat#120-10C-100ug),100\u2009ng/ml R-Spondin-1 (PeproTech, Cat# 120-38-100ug), 1X B27(Thermo Scientific, Cat#17504001), 1.25mM N-Ace-L-Cys (Sigma, Cat#A9165-5G), 100 ug/ml Primocin (Invivogen, Cat# ant-pm-1), 10\u2009mM Nicotinamids (Sigma, Cat#N0636-500G), 500\u2009nM A83-01 (Tocris, Cat#2939), 10\u2009ng/ml FGF10 (PeproTech, Cat#100-26-50ug), 10\u2009ng/ml FGF2 (PeproTech, Cat#100-18B), 10uM SB202190 (Sigma-Aldrich, Cat#S7076-5MG),1uM PGE2(Tocris, Cat#2296-10\u2009mg), and 50\u2009ng/ml EGF(PeproTech, Cat#AF-100-15-500ug). The medium was changed every 3\u20134 days, and the organoids were passaged at a 1:2\u20133 dilution every 2\u20134 weeks. For passaging, organoids were mechanically and enzymatically dissociated into small clusters. Matrigel-embedded organoids were suspended in Cell Recovery Solution (Corning, 500\u2009\u00b5L/well). The organoid suspension was occasionally mixed with gentle pipetting for 30\u2009min on ice to completely solubilize the Matrigel. The tube was then placed on ice to precipitate the organoids. The supernatant was removed, and organoids were washed with 1\u2009ml cold PBS. The organoid was suspended in Matrigel and plated on 6-well plate.\n\nAfter established, organoids were processed for paraffin sectioning using standard protocols for characterization. Matrigel-embedded organoids were suspended in Cell Recovery Solution (Corning, 500\u2009\u00b5L/well). The organoid suspension was occasionally mixed with gentle pipetting for 30\u2009min on ice to completely solubilize the Matrigel. The tube was then placed on ice to precipitate the organoids. The supernatant was removed, and organoids were washed with cold PBS. Organoids were fixed with 4% paraformaldehyde (PFA) for 20\u2009min at room temperature and solidified using histogel before embedding in paraffin. 5\u2009\u03bcm sections were stained with hematoxylin\u2013eosin (H&E) and Antibodies (p53, PAX8, CK7).\n\nWO-58, WO-19, and WO-77 organoids were established and passaged as described above. To detect the drugs\u2019 effect on organoids, the organoids were mechanically and enzymatically dissociated into small clusters, and resuspended in 500\u2009\u00b5l organoid culture media in an EP tube (See above). 500\u2009\u00b5l Matrigel was added into the EP tube and mix. Organoids in Matrigel were seeded in 96 well plates at 45\u2009\u00b5l per well. After solidifying at 37 degrees, 50\u2009\u00b5l organoid culture media were added to each well. The organoids are cultured for 3 days and then treated with RP-6306 (250\u2009nM), RP-3500 (50\u2009nM), or both for 10 days and measured with CCK8 assay. The cells were incubated with CCK8 solution for 12 hrs and then measured with absorbance at 450\u2009nm.\n\nCells were seeded into 96-well plates at 5000 cells/well. Cells were treated with control (DMSO), RP-6306, RP-3500, or a combination at indicated concentrations for 5 days. The drugs were clinical grade and obtained from Repare Therapeutics. At the end of the treatment period, an MTT colorimetric assay was performed to detect the cell viability. Cells were incubated with 10\u2009mL of MTT at 5\u2009mg/ml (Sigma Chemical Co., St Louis, MO) for 4\u2009h at 37 degrees. The supernatant was removed, and 100\u2009mL DMSO (Fisher Scientific, Hampton, NH) was used to dissolve the MTT formazan. Absorbance was measured in a microplate reader at a wavelength of 570\u2009nm. Relative cell viability was calculated, with the non-treatment group as a control.\n\nFor colony formation assay, cells were plated onto 24-well at 5000 cells/well and cultured overnight in triplicate. They were then treated with DMSO vehicle, RP-6306, RP-3500, or combination as indicated every 3 days for a total of 10 days. Cells were then fixed and stained with 0.1% Crystal violet in 20% methanol solution. The plates were washed, air-dried, scanned, and quantified in ImageJ (National Institutes of Health, Bethesda, MD).\n\nCells were treated and collected at the indicated time, then washed and incubated with Laemmli Sample Buffer (4% SDS, 20% Glycerol, 0.12\u2009M Tris-HCl at pH 6.8 in distilled water) containing a protease and phosphatase inhibitor cocktail (EMD Millipore, Billerica, MA). After measured protein concentration with BCA kit (BioRad, Hercules, CA), whole cell lysates (15\u2009mg) were separated on reducing 4\u201315% SDS-PAGE gels, electrotransferred to PVDF membrane (Bio-Rad, Hercules, CA), blocked with 5% BSA (ThermoFisher) in 1x Tris-buffered saline (ThermoFisher) with 0.1% Tween20 (ThermoFisher) (1x TBST), and immunoblotted with respective primary antibodies including anti-pCHK1(S345) (1:1000, Cell Signaling Technology, Danvers, MA, cat.#2348), anti-CHK1 (1:1000, Cell Signaling Technology, cat.#2360), anti-CHK1 (Santa Cruz G-4 sc-8408, 1:500), anti-CHK1-phosphoS345 (Bethyl, cat#2348, 1:500), anti-Alpha Actinin (Millipore Sigma 05-384, 1:10000), anti-CDC25B (Thermofisher OTI6H9 TA8-12352, 1:500), anti-CDC25B-phosphoS151 (Thermofisher PA5-104568, 1:500), anti-pCDK1(T14) (1:1000, Abcam, Cambridge, UK, cat.# ab58509), anti-CDK1(1:1000, Cell Signaling Technology, cat.#9116), anti-\u03b3H2AX(1:2000, Cell Signaling Technology, cat.#9178), anti-cleaved caspase 3(1:500, Cell Signaling Technology, cat.#9664), Cyclin E1(1:2000, Cell Signaling Technology, cat.#4129), anti-Actin(1:50000, Cell Signaling Technology, cat.#3700), anti-ATM (Cell Signaling Technology,cat#2873, 1:500,), anti-BRCA1 (Dan Durocher, University of Toronto, 1:500). After that, membranes were washed and blotted with species-appropriate horseradish peroxidase conjugated anti-rabbit (1:3000, catalog 7074, Cell Signaling Tech), anti-mouse (1:3000, catalog 7076, Cell Signaling Tech) secondary antibodies or anti-mouse Irdye 800CW (LiCOR 926-32210, 1:10000), anti-mouse Irdye 680CW (LiCOR 926-68072, 1:10000), anti-rabbit Irdye 800CW (LiCOR 925-32213, 1:10000) and anti-rabbit Irdye 680CW (LiCOR 926-68073, 1:10000) in 5% BSA in 1x TBST for 1\u2009h, followed by chemiluminescent substrate (Thermo Scientific, Rockford, IL) incubation and film development. Actin or Actinin was used as loading control for the whole cell.\n\nCells were seeded in triplicate and then treated with RP-6306, RP-3500, or combination for indicated time. Cells were then trypsinized, fixed, washed and incubated with blocking buffer. Cells were then stained with the following primary antibodies diluted in blocking buffer at 1:300: gH2AX (Cell Signaling Technology, cat# 9718), pRPA32 (S33, Bethyl Laboratories, cat#A300-246A) or phospho-histone H3 (Ser10, Cell Signaling Technology, cat# 53348). The cells were washed, and incubated with secondary antibody goat anti-Rabbit IgG (H\u2009+\u2009L), Alexa Fluor 647 (ThermoFisher Scientific) for 30\u2009min. The cells were then incubated with 50\u2009mg/mL propidium iodide (Sigma-Aldrich) and subjected to flow cytometry acquisition on BD LSRII (BD Biosciences) and data analysis with FlowJo (Tree Star, Inc., Ashland, OR).\n\nTo evaluate the off target effect of PKMYT1 and ATR inhibitors, OVCAR3 cells were transfected with 10\u2009nM PKMYT1 siRNA (Thermo Fisher Scientific, Assay ID: s194984) and/or 10\u2009nM ATR siRNA (Thermo Fisher Scientific, Assay ID 82, AM51331) with Lipofectamine RNAiMAX reagent (Thermo Fisher Scientific) following the transfection protocol. The cell viability and protein expression were detected 48\u2009h and 24\u2009hr post-transfection respectively.\n\nCells were plated, incubated overnight, and treated with DMSO vehicle, 250\u2009nM RP-6306, 50\u2009nM RP-3500, or a combination for 72\u2009h. For low-dosage combination studies, the cells were treated with DMSO, 31.3\u2009nM RP-6306, 6.25\u2009nM RP-3500, or combination for 5 days or 7 days. For detecting caspase-dependent apoptosis, the cells were pretreated with 20\u2009\u00b5M Z-VAD-FMK (HY-16658, Medchem Express) for 1\u2009h, and then treated with DMSO or combination RP-6306 (250\u2009nM)\u2009+\u2009RP-3500 (50\u2009nM) for 72\u2009h. Apoptosis assay was performed with eBioscience Annexin V Apoptosis Detection Kit APC (Invitrogen, 88-8007-74), according to the manufacturer\u2019s instructions. Annexin V-APC and propidium iodide labeled cells were detected by BD Accuri C6 Cytometer (BD Biosciences, San Jose, CA). The acquired data was analyzed with FlowJo (Tree Star, Inc., Ashland, OR).\n\nHigh-throughput analysis of nuclear \u03b3-H2AX, Histone H3-phosphoS10, and Cyclin B1-phosphoS126 cells was done as preciously described17. Briefly, cells were seeded in 96-well plates (3000 cells/well for FT282-hTERT p53R175H) and cultured for up to 24\u201348\u2009h depending on the experiment. Prior to harvesting, cells were pulsed with 20\u2009\u03bcM EdU (5-ethynyl-2-deoxyuridine, Life Technologies #A10044) for 30\u2009min followed by the addition of paraformaldehyde (PFA) in PBS to a final concentration of 4% and incubated for 15\u2009min at room temperature (RT). Cells were then rinsed with PBS and permeabilized using 0.3% Triton X-100/ PBS for 30\u2009min. For chromatin-bound \u03b3H2AX and RPA measurements, cells were pre-extracted for 15\u2009min on ice with CSK buffer (300\u2009mM sucrose, 100\u2009mM NaCl, 3\u2009mM MgCl2, 10\u2009mM PIPES pH 7.0, 0.5% v/v Triton-X 100) before PFA fixation. Cells were rinsed with PBS and incubated with EdU staining buffer (150\u2009mM Tris-Cl pH 8.8, 1\u2009mM CuSO4, 100\u2009mM ascorbic acid, and 10\u2009\u03bcM AlexaFluor 488 azide (Life Technologies, #A20012) for 30\u2009min. Cells were washed with PBS and incubated in blocking buffer (10% goat serum (Sigma #G6767), 0.5% NP-40 (Sigma-Aldrich, #I3021), 5% w/v Saponin (Sigma-Aldrich, #84510), diluted in PBS) for 30\u2009min. Fresh blocking buffer containing primary antibodies was added for 2\u2009h. Primary antibodies including histone H2A.X (phospho-S139, Millipore Sigma #05-636, 1:500 IF), RPA32 (Abcam ab2175, 1:500 IF), Histone H3-phosphoS10 (Cell Signaling Technology #9706, 1:500 IF), Cyclin B1-phosphoS126 (Abcam ab55184, 1:500 IF). Cells were rinsed three times with PBS and then blocking buffer, with secondary antibodies including AlexaFluor488 goat anti-mouse IgG (Thermo Fisher Scientific A11029, 1:1000), AlexaFluor647 goat anti-rabbit IgG (Thermo Fisher Scientific A21244, 1:1000) and AlexaFluor555 goat anti-mouse IgG (Thermo Fisher Scientific A28180, 1:1000). Then 0.4\u2009\u03bcg/mL DAPI (4,6-diamidino-2-phenylindole, Sigma-Aldrich, #D9542) was added for 1\u2009h. After rinsing with PBS, immunocomplexes were fixed again using 4% PFA/PBS for 5\u2009min. Cells were rinsed with PBS, wells were filled with 200\u2009\u03bcl PBS, and images were acquired at the Network Biology Collaborative Center (LTRI) on an InCell Analyzer 6000 automated microscope (GE Life Sciences) with a 20X objective. Image analysis was performed using Cellprofiler 3.1.955 and RStudio v1.2.5019 in a similar manner as previously described17.\n\nOVCAR3 cells were treated with DMSO, 250\u2009nM RP-6306, 50\u2009nM RP-3500 or combination for 1\u2009h, pulse-labeled with 300\u2009\u03bcM 5-chloro-2\u2032-deoxyuridine (CldU; cat. # C6891, Sigma-Aldrich, St. Louis, MO) followed by 100\u2009\u03bcM 5-iodo-2\u2032-deoxyuridine (IdU; cat. # I7125, Sigma-Aldrich, St. Louis, MO) for 25\u2009min each treatment, in the presence of drug. Aspirate the media and wash with cold PBS twice. The cells are trypsinized and resuspended to 1*106/ml in cold PBS. 2\u2009\u00b5l cells were put on one edge of the silane-coated slides (5070, Newcomer Supply). The cells were processed as previously35. The cells were lyzed with lysis buffer (200\u2009mM Tris\u2013HCl pH 7.4, 50\u2009mM EDTA, and 0.5% SDS), and stretch alongside the slide slowly. The cells are fixed with Methanol:Acetic acid at 3:1 for 10\u2009min after air-drying. The slide immersed in 2.5\u2009M HCl for 1\u2009hr at room temperature and neutralized with 400\u2009mM Tris-HCl pH 7.4 for 10\u2009mins. The slides were blocked with blocking buffer (5% BSA\u2009+\u200910% Goat serum), and then stained with rat anti-CIdU antibody (1:200, ab6326, Abcam) overnight at 4 degree, AlexaFluor 647-conjugated anti-rat IgG secondary antibody (1:100, A-21247, ThermoFisher Scientific) for 1\u2009h at room temperature. The slides were further incubated with mouse anti-IdU antibody (1:40, 347580, BD Pharmigen) for 1\u2009hour and AlexaFluor 488-conjugated anti-mouse IgG secondary antibody (1:100, A-11001, ThermoFisher Scientific) for 1\u2009hour. After staining, the slides were mounted with Prolong Gold antifade mountant (P36930, Thermo Fisher Scientific). The labeled fibers were imaged with a Nikon Eclipse 80i microscope. The fiber length were quantified with ImageJ with at least 150 fibers in each group.\n\ncb-PCNA-TagRFP expressing cells were maintained at 37\u2009\u00b0C and 5% CO2 while line scanning confocal microscopy was performed using the Nikon Biopipeline live high-content system equipped with an NA 0.45 20x ELWD objective (Nikon) and a Nikon A1 LFOV imaging system. A single field was acquired every 10\u2009min over 48\u2009h with a single z-stack (1.244\u2009\u03bcm/pixel).\n\n2\u2009\u00d7\u2009106 FT282-hTERT p53R175H cells were seeded in 10-cm dishes. 24\u2009h later RP-6306 (125\u2009nM), RP-3500 (25\u2009nM), or combination of both was added. 22\u2009h later, 100\u2009ng/mL KaryoMAX colcemid (Thermo Fisher Scientific #15212-012) was added to the media for 2 additional hours and cells were harvested as follows: Growth medium was stored in a conical tube. Cells were treated twice for 5\u2009min with 1\u2009mL of trypsin. The growth medium and the 2\u2009mL of trypsinization incubations were centrifuged (250\u2009\u00d7\u2009g 5\u2009min, 4\u2009\u00b0C). Cells were then washed with PBS and resuspended in 75\u2009mM KCl for 15\u2009min at 37\u2009\u00b0C. Cells were centrifuged again, the supernatant was removed, and cells were fixed by drop-wise addition of 1\u2009mL fixative (ice-cold methanol: acetic acid, 3:1) while gently vortexing. An additional 9\u2009mL of fixative was then added, and cells were fixed at 4\u2009\u00b0C for at least 16\u2009h. Once fixed, metaphases were dropped on glass slides and air-dried overnight. To visualize mitotic cells, slides were mounted in a DAPI-containing ProLong Gold mounting medium (Invitrogen, #P36930). Images were captured on a Zeiss LSM780 laser-scanning confocal.microscope with ZEN 2.3 SP1 software\n\nIn vitro studies were performed using at least 3 biological replicates per sample and 3 independent experiments. Two-tailed unpaired t tests were used when comparing two groups. One-way ANOVA followed by Tukey\u2019s post hoc comparison was performed for multiple group comparisons. p\u2009<\u20090.05 was considered statistically significant. Drug interaction between RP-6306 and RP-3500 was analyzed using the coefficient of drug interaction (CDI)56. CDI\u2009=\u2009AB/(AxB); AB is the ratio of a two-drug combination group to control, and A or B is the ratio of a single drug to control. CDI\u2009<\u20091 indicates synergism, CDI\u2009<\u20090.7 indicates significant synergism, CDI\u2009=\u20091 indicates additivity, and CDI\u2009>\u20091 indicates antagonism. GraphPad Prism (GraphPad Software version 10.0.2, San Diego CA) was used for statistical analyses.\n\nFor statistical power for in vivo studies, there were 4-10 mice/arm. Weekly ultrasound measurements, weights, and condition scores were obtained. Longitudinal analysis of tumor growth was carried out by linear mixed-effect modeling with type II ANOVA and pairwise comparisons across groups on log pre-processed tumor sizes using the TumGrowth web tool (https://kroemerlab.shinyapps.io/TumGrowth/)57 Natural log-transformed tumor volume was used to better satisfy normal distribution. Survival data was analyzed by the Mantel-Cox log-rank test. Survival data was analyzed by the Mantel-Cox log-rank test.\n\nFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All the data supporting the findings in this work are included in the main article, supplementary information, or source data file. Source data are provided in this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Giaquinto, A. N., Broaddus, R. R., Jemal, A. & Siegel, R. L. 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Oncoimmunology 7, e1462431 (2018).\n\nArticle\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "The authors thank the Penn Cytomics and Cell Sorting Shared Resource Laboratory and the Stem Cell & Xenograft Core (SCXC) at the University of Pennsylvania for their support in providing support to flow cytometry and animal studies and M. Hasegan at the Network Biology Collaborative Center for microscopy support. This work was supported by NIH (5R37CA215436-06 (F.S.), 5-P50-CA-228991-04 SPORE and CEP in ovarian cancer (F.S. and H.X.), 1U54CA283759-01 (F.S.), 1 R50CA283807-01A1 (H.X.)); Foundation for Women\u2019s Cancer (H.X.), REPARE Therapeutics institutional grant for research.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Erin George\n\nPresent address: Department of Gynecologic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA\n\nDavid Gallo\n\nPresent address: Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA\n\nThese authors contributed equally: Haineng Xu, Erin George, David Gallo.\n\nPenn Ovarian Cancer Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA\n\nHaineng Xu,\u00a0Erin George,\u00a0Sergey Medvedev,\u00a0Xiaolei Wang,\u00a0Fang Liu,\u00a0Matthew Anderson,\u00a0Hyoung Kim\u00a0&\u00a0Fiona Simpkins\n\nDepartment of Obstetrics and Gynecology, Division of Gynecologic Oncology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA\n\nHaineng Xu,\u00a0Erin George,\u00a0Sergey Medvedev,\u00a0Xiaolei Wang,\u00a0Fang Liu,\u00a0Matthew Anderson,\u00a0Hyoung Kim\u00a0&\u00a0Fiona Simpkins\n\nRepare Therapeutics, Inc., 7171 Frederick-Banting, Ville St-Laurent, QC, Canada\n\nDavid Gallo,\u00a0Rosie Kryczka,\u00a0Jimmy Fourtounis,\u00a0Rino Stocco,\u00a0Shou Yun Yin\u00a0&\u00a0Ariya Shiwram\n\nDepartment of Cancer Biology, Penn Center for Genome Integrity, Basser Center for BRCA, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA\n\nArindam Datta\u00a0&\u00a0Roger A. Greenberg\n\nRepare Therapeutics, Inc., 101 Main St, Cambridge, MA, USA\n\nMarc L. Hyer,\u00a0Elia Aguado-Fraile,\u00a0Adam Petrone\u00a0&\u00a0C. Gary Marshall\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConception and design: F.S., G.M., H.X., E.G., and D.G. Development of methodology: F.S., G.M., H.X., E.G., D.G., S.M., and M.L.H. Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): H.X., E.G., D.G., S.M., X.W., R.K., J.F., R.S., E.A., A.P., S.Y., A.S., A.D., H.K., and F.L. Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): F.S., G.M., E.G., D.G., S.M., E.A., A.P., and M.L.H. Writing, review, and/or revision of the manuscript: F.S., G.M., H.X., E.G., D.G., S.M., X.W., R.K., J.F., R.S., E.A., A.P., S.Y., A.S., H.K., F.L., M.L.H., A.D., and R.G. Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): H.X., E.G., D.G., and S.M. Study supervision: F.S. and G.M.\n\nCorrespondence to\n Fiona Simpkins.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "F.S. serves on scientific advisory boards for AstraZeneca, GSK, and Zentalis Pharmaceuticals. She has received institutional research funding from AstraZeneca, Repare Therapeutics, Instill Bio, and Sierra Oncology. R.K., J.F., R.S., E.A., S.Y.Y., C.G.M. are, and D.G., M.L.H., A.P., A.S. were employees of Repare Therapeutics. R.A.G. serves on the scientific advisory board for Dong-A ST. All other authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Xavi Dolcet, and the other anonymous reviewer(s) for their contribution to the peer review of this work. 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Targeting CCNE1 amplified ovarian and endometrial cancers by combined inhibition of PKMYT1 and ATR.\n Nat Commun 16, 3112 (2025). https://doi.org/10.1038/s41467-025-58183-w\n\nDownload citation\n\nReceived: 07 February 2024\n\nAccepted: 14 March 2025\n\nPublished: 01 April 2025\n\nVersion of record: 01 April 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58183-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"Non-linear elasticity, earthquake triggerring and seasonal hydrological forcing along the Irpinia fault, Southern Italy", + "journal": "Nature Communications", + "published": "13 November 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54094-4/MediaObjects/41467_2024_54094_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54094-4/MediaObjects/41467_2024_54094_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54094-4/MediaObjects/41467_2024_54094_MOESM3_ESM.gif" + }, + { + "label": "Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54094-4/MediaObjects/41467_2024_54094_MOESM4_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "http://isnet.unina.it/", + "https://eida.ingv.it/it/", + "http://ring.gm.ingv.it", + "https://www.mathworks.com/products/matlab.html", + "https://www.python.org/" + ], + "code": [ + "/articles/s41467-024-54094-4#ref-CR25", + "/articles/s41467-024-54094-4#ref-CR32" + ], + "subject": [ + "Geophysics", + "Seismology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4042694/v1.pdf?c=1731589582000", + "research_square_link": "https://www.researchsquare.com//article/rs-4042694/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54094-4.pdf", + "preprint_posted": "13 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Pump-probe experiments investigate the strain sensitivity of crustal elastic properties, showing nonlinear variations during the strain cycle. In the laboratory, pre-seismic reductions in seismic velocity indicate that asperity contacts within the fault zone begin to fail before macroscopic frictional sliding. The recognition of such effects in natural seismic-cycles has been challenging. By exploiting seasonal hydrological strains, we used a quasi-static natural pump-probe experiment to investigate the nonlinear response of crustal rocks and its role in seismic failure along the tectonically-active Irpinia Fault System (Southern Italy). By comparing 14-years-long series of spring discharge, strain, seismic velocity variations and earthquakes rate, we find that seismicity peaks during maximum hydrological forcing and minimum seismic velocity. Seasonal strains of ~10-6 are required for both earthquakes triggering and significant nonlinearity effects arising from modulus reduction. We suggest that, for faults in a critical state, cyclical softening may lead to failure and seasonal seismicity.Earth and environmental sciences/Solid Earth sciences/GeophysicsEarth and environmental sciences/Solid Earth sciences/Seismology", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryMaterial.docxSupplementary MaterialanimationFRAMES6.gifContinuos strain evolution", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Pump-probe experiments investigate the strain sensitivity of crustal elastic properties, showing nonlinear variations during the strain cycle. In the laboratory, pre-seismic reductions in seismic velocity indicate that asperity contacts within the fault zone begin to fail before the macroscopic frictional sliding. The recognition of such effects in natural seismic-cycles has been challenging. Here we exploit seasonal hydrological strains, performing a natural analogue to a quasi-static laboratory pump-probe experiment to investigate the nonlinear strain sensitivity of crustal rocks and its role in seismic failure along the tectonically-active Irpinia Fault System (Southern Italy). By comparing 14-years-long series of spring discharge, strain, seismic velocity variations and earthquakes rate, we find that seismicity peaks during maximum hydrological forcing and minimum seismic velocity. Seasonal strains of ~10\u22126 are required for both earthquake triggering and significant nonlinearity effects arising from modulus reduction. We suggest that, for faults in a critical state, cyclical softening may lead to failure and seasonal seismicity.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "While linear elasticity is widely employed in Earth science applications1, the heterogenous nature of geomaterials makes them to behave in a non-linear way, as it can be revealed by variations in elastic modulus2, hysteresis3, slow dynamics4, when rocks are subjected to strain perturbations5. Pump-probe experiments are nowadays widely used to quantify the non-linear behavior of rocks in laboratory2,5,6,7 and provide fundamental insights about the relationships between non-linear elastic parameters and physical properties of materials, such as damage7,8 or presence and amount of fluids9,10,11, among others2,6,12,13,14,15. All these variables have a key role in controlling the seismic cycle16,17 and the physics of earthquakes nucleation18,19. It has been suggested that progressive loading produces distributed microcracks that at some stage begin to coalesce onto a volumetric region of concentrated damage that, when a critical level is exceeded, experiences instability that leads to rupture17. In laboratory20 accelerated fault creep causes elastic moduli (and seismic velocity) reductions during the preparatory phase preceding failure, as asperity contacts begin to fail before macroscopic frictional sliding. This can be also related to rock damage. A temporary reduction in the fault core modulus could induce fault slip for a fault already near failure18. It is thus crucial to assess the non-linear response of crustal rocks by investigating behaviors associated with stress, pore pressure, permeability, material failure, and rock damage in the Earth in the same way as in laboratory2,20,21,22. The advent of ambient noise seismology23 offers to scientists the possibility to develop ad-hoc pump-probe experiments, measuring in-situ the velocity changes (\u03b4v/v) from estimates of the Green\u2019s function23 (the probe), excited by (the pump) natural (mainly tidal strains12,24) or man-made strain. These studies revealed significant non-linearity for shallow crustal rocks13, enhanced in complex settings such as fault systems8,25 and volcanic regions9,13.\n\nFollowing previous works on non-linearity8,12,24, \u03b2 represents the second order, quadratic, coefficient of the stress-strain elastic relationship5:\n\nwhere \u03c3 is the stress, M is the elastic modulus, \u03b5 is the strain and f indicates the hysteretic components dependent on strain and strain rate. The strain sensitivity \u03b25,24 of seismogenic rocks can be estimated through a quantitative comparison of strain \u03b5 vs \u03b4v/v curves5,24:\n\nwhere \u03b1 is an offset.\n\nA value of \u03b2 different from zero indicates non-linearity13,26 and increases with the degree of the material damage and density of cracks7. Negative values of the strain sensitivity \u03b2 have been interpreted in terms of decrease (increase) of seismic velocities under dilatational (contractional) strain operated by opening (closing)27 of preexisting cracks in the crust13.\n\nIn this study we perform a 14-years long-term analysis based on a natural analog of a pump-probe experiment to assess the non-linear behavior of the seismogenic volume in the Irpinia Fault Zone (IFZ, Fig.\u00a01), which hosted the largest (Ms 6.9) instrumentally recorded earthquake in Italy28. Here we measured \u03b4v/v induced by annual strain variations originating from recharging groundwater in karst aquifers29,30, that significantly extends the temporal tidal forcing employed in previous works14,24. We show that hydrological processes induce nearly periodic, anisotropic fluctuations of horizontal strain31,32 (which we used as a pump) associated with seasonal modulation of seismicity. We employ ambient seismic noise correlation23 to track the temporal evolution of \u03b4v/v in two sites (MCRV and CAFE, Fig.\u00a01; see \u201cMethods\u201d), representative of the variable hydrological forcing conditions, that we used as the probe to measure the non-linear elastic response of crustal rocks to strain fluctuations. Our analysis reveals a negative value for \u03b2 in agreement with previous natural12,13,24 and laboratory experiments2,7,21, related to seasonal cycling of material damage along the IFZ and associated with seasonal modulation of seismicity. Our results highlight the dependence of seasonal seismicity on the non-linear rock behavior, which calls to improve our understanding of the role of elastic non-linearity and its effect on frictional properties in controlling the triggering of earthquakes along major seismogenic faults8,33,34,35,36,37,38,39,40.\n\na Map of the study area. Global Positioning System velocities in a Tyrrhenian reference frame43. The trace of Irpinia70 is shown in red. Carbonate rocks are represented with green overlays. Background color-coding shows the secular tectonic strain rate (second invariant of the strain rate tensor). The inset shows the location of the study area in Italy. b Enlarged map (area enclosed by the black line in a) showing 2008\u20132022 seismicity from the Irpinia Seismic Network. Colored dots represent the considered declustered seismicity within the first 12\u2009km of depth and above the completeness magnitude, white dots represent other not-considered seismic events. Global Centroid Moment Tensor focal solution and surface projections of the three faults28 responsible for Ms 6.9, 1980 Irpinia earthquake with its focal mechanism; green patches enclose shallow carbonates rocks; c Time series of hydrological, seismological and geodetic observations. The upper panel shows the discharge measured at Caposele spring. Lower panels show velocity variations (green circles, inverted sign) for coda waves time lapse using empirical Green\u2019s functions reconstructed by autocorrelation of seismic noise recorded at MCRV and CAFE in the frequency band of 0.5\u2010 to 1\u2010Hz (blue dots) together with East components of displacement at the same stations (red circles); d conceptual model of the modulation of crustal deformation induced by variable hydraulic head in karst aquifers. \u03b5xx indicates the horizontal strain in the ENE-WSW direction.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54094-4/MediaObjects/41467_2024_54094_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "The Caposele spring29 provides a unique, 100-years-long, hydrological dataset of discharge (average ~4\u2009m3/s) from the karst aquifer of the Picentini Mountains (Fig.\u00a01) hosted in extensively fractured, 2\u20133\u2009km-thick, allochthonous, Mesozoic carbonates41. As no man-made modifications occur in its catchment, this spring is strictly controlled by climate trends29 and is regionally-representative of the variations of hydrological forcing operated by the karst aquifers with maximum/minimum recharge in May-July and November-January, respectively29,30. To investigate the effects of hydrological forcing on crustal elastic properties, we calculated the velocity variations \u03b4v/v from 14 years (January 2008-December 2021) of seismic data recorded at MCRV and CAFE stations (Fig.\u00a01). Both sites (with co-located seismic and GPS receivers) are located within the tectonically deforming belt along the Apennines (Fig.\u00a01a) but at variable distances from the IFZ and the karst aquifer. The recent microseismicity (local magnitude \u22120.4\u2009\u2264\u2009ML\u2009\u2264\u20093.7) occurring in the investigated region is characterized by normal fault mechanism42, in agreement with the regional strain field43 and the geometry and slip of the faults involved in the Ms 6.9, 1980 Irpinia earthquake28. We used ambient noise correlations from single station measurements to apply the Coda Wave interferometry method23 (CWI, see Methods) in a time-lapse of 10\u201350\u2009s after zero time (ballistic waves arrival time). The time series of \u03b4v/v (Fig.\u00a01c) at MCRV reveal up to ~0.2% velocity variations which closely tracks the evolution of spring discharge and the GPS horizontal displacement (Fig.\u00a01c). At MCRV we found that peaks of eastward displacement, which correspond to a transient, recoverable dilatational strain in the shallow crust across the karst aquifer31,32, are correlated with negative peaks in the velocity of seismic waves, similarly to observations in other Apenninic regions25,44. The opposite occurs for negative Eastward displacements with contractional strains and significant increase of seismic velocities. At CAFE, located outside the karst aquifer region but inside the actively-deforming tectonic belt (Fig.\u00a01a), we observe an order of magnitude smaller fractional seismic velocity change (~0.03%), and a GPS time series insensitive to seasonal variations, which document the absence of any relevant hydrological forcing. The seasonal velocity variations observed at MCRV are higher than those observed in other regions in the same tectonic environment25,44 and comparable to those occurring in volcanic regions9,45, where pronounced seasonal velocity variations9 are similarly detected, and where the presence of large amount of preexisting microcracks can be a common condition.\n\nTo assess the depth at which seismic velocity variations occur, we computed the depth sensitivity of surface waves46 for a local 1D velocity model47. The sensitivity of surface wave (see Methods) in the analyzed frequency band (0.5\u20131.0\u2009Hz25,) is concentrated in the first few kilometers of the crust (depth\u2009<\u20091.5\u2009km, see Fig.\u00a0S1), which include the thickness of highly-permeable, fractured carbonate rocks41 forming the karst aquifer. However, the theoretical depth sensitivity of the scattered body waves, computed as in ref. 25 considering a 3D sensitivity kernel formulation48 (see \u201cMethods\u201d), shows that the kernel is sensitive to a deeper volume in the crust than for surface waves (Fig.\u00a0S1). The uncertainty in the nature of scattered waves challenges a precise attribution of the velocity variations observed at MCRV, which could be related to both the shallow highly-permeable, fractured carbonate rocks and the deeper seismogenic volume. Improving the reliability of sensitivity kernels is crucial to address and solve the depth resolution problem49. Additionally, independent information is provided by time-dependent P and S wave tomographic models, with poorer intensity and temporal sensitivity but higher spatial resolution. These analyses show significant temporal variations of Vp/Vs ratio below 6\u2009km depth correlated with hydrological forcing50 supporting the hypothesis of significant nonlinear effects at similar depths.\n\nWe estimated the strain variations (positive extensional strain) induced by the hydrological cycle (Fig.\u00a02), by computing a 14 years-long time series of horizontal strain, that we modeled with a 3.5\u2009km-thick layer of elementary cuboid sources51 in the IFZ area (similar approach as in ref. 32, see \u201cMethods\u201d). To remove the long-term tectonic component and mitigate the temporal high-frequency noise and daily scatter, raw GPS time series have been initially detrended and low-pass filtered using a Gaussian filter (full-width 180 days). Two snapshots illustrating the regional NNE-SSW extension during wet periods and contraction in dry periods are reported in Fig.\u00a02a, b, respectively, while the horizontal dilatational strain at MCRV is reported in Fig.\u00a02c. The \u03b4v/v follows the evolution of strain, with positive \u03b4v/v for contractional strains and negative \u03b4v/v associated with extension (Fig.\u00a02c), consistent with previous natural12,24,52 and laboratory experiments2,21,27. We remark that in the two episodes (Fig. 2a, b) the displacement at MCRV is oriented respectively NE (Fig. 2a) and SW (Fig. 2b), suggesting that the horizontal dilatation induced by hydrological forcing occurs along a single axis during both the recharge/discharge phases. Figure\u00a02d represents the frequency of the compressional (left) and extensional (right) axis azimuth, coupled with the sign of the temporally coincident strain (top) and velocity variations (bottom) series. In particular, the frequency of the compressional (extensional) axis is counted when the dilatation is negative (positive). During summer, when strain is positive (red color), the extensional axis is oriented mostly W-NW, while during winter, when strain is negative (blue color), a similar orientation is shown by the compressive axis. The same happens when looking at velocity variations; during summer (winter) the velocity changes are negative (positive) and the extensional (compressional) axis is oriented mostly W-NW. We attribute the uniaxial strain fluctuation to an anisotropic poroelastic response53 of the crust due to hydrological forcing32 (hydraulic head \u2206h in karst aquifers). The horizontal strain \u03b5xx in the ENE-WSW direction (taken here as the x axis, see Supplementary Material for the complete derivation) can thus be expressed as:\n\nwhere \u03b1 is the Biot\u2019s coefficient, \u03c1w is water density, g is the gravity acceleration, E is the Young\u2019s modulus and \u03bd is the Poisson\u2019s coefficient. We test the validity of our model considering a realistic water table variation \u2206h\u2009=\u200920\u201340\u2009m32 and a reasonable range of upper crust poroelastic parameters for carbonate rocks (\\(E=60-80{GPa}\\), \\(\\nu=0.2-0.3\\), \\(\\alpha=0.6-0.8\\)54;). We estimate a strain variation in the order of \\(2-10\\times {10}^{-6}\\), whose lower bound is consistent with the range of observed non-tectonic strain variations within the decade-long time series (Fig.2c).\n\na, b Maps of distributed hydrological strain with calculated/observed GPS displacements. The complete, continuous strain evolution is shown in Supplementary Movie 1. The two frames display opposite conditions of groundwater recharge in karst aquifers (shown as green lines) and opposite dilatational patterns. Red, green, and blue circles in (a) indicate data points where series of dilatational strain, velocity variations, and spring discharge (shown in (c)) have been respectively calculated. c Time series of dilatational strain (red) calculated at MCRV compared with the co-located velocity variations (green), earthquake rate (black), and Caposele spring discharge (blue); d frequency of the azimuth of compressional axis (left) and extensional axis (right) coupled with the sign of the synchronized scalar of the strain (top panel) and velocity changes (bottom panel).\n\nThe observations summarized in Fig.\u00a02, together with our simplified poroelastic model, suggest a strong coupling between \u03b4v/v and strain in saturated crustal rocks. The oscillatory change in velocity with strain (Fig.\u00a02) is similar to observations in laboratory-scale experiments2,21, and can be exploited to study the nonlinear elastic response of crustal rocks following equation [2]. A comparison between \u03b4v/v and strain values (Fig.\u00a03a) shows a general inverse relationship (\u03b2\u2009=\u2009\u22120.64\u2009\u00d7\u200910\u22123) indicating that, on average, seismic waves are slower when rocks are extended during maximum hydrological forcing (May-July), that when compressed during minimum hydrological forcing (December-January). This mechanism is consistent with the opening and closing of cracks and pores6,13,52 with increasing stiffness of their internal contacts during compression27.\n\na seismic velocity variations (\u03b4v/v) plotted against dilatational strain, color-coded for annual phase (capital letters in the colorbar indicate each month). The black dashed line shows the best-fit regression line whose slope (\u03b2) represents the strain sensitivity of velocity variations. b same as (a) but color-coded for dilatational rate. c\u2013h specific annual cycles (year labeled on top left) color-coded for annual phase.\n\nThe observed sensitivity \u03b2 aligns with the values observed in previous works (\u22121\u2009\u00d7\u2009103\u2013105)12,13,24. We also observe a complex, non-unique correspondence between velocity variations and strain, attributed to the superposition of seasonal and multiyear forcing cycles31. This superposition results in annual trajectories with similar slopes but different cycle means along the \u03b4v/v axis (Fig.\u00a03c\u2013h). Additional information on the strain-rate dependency of seismic velocity variations can be inferred from the hysteretic behavior of \u03b4v/v and strains3,8. For annual cycles defining closed or semi-closed loops (Fig.\u00a03c\u2013h), we observe prevalent clockwise loops but no evidence that seismic velocities are systematically higher during extension (Fig.\u00a03b). We also do not observe a dependency of the cycles pattern upon strain magnitude.\n\nIt has been suggested3 that the mechanism controlling quadratic nonlinearity (i.e., \u03b2) is distinct from the mechanisms controlling additional nonlinear parameters (i.e., average softening and hysteresis). This distinction3,26 may depend on the primary deformation mode across the interfaces (cracks or pores) within the bond system. Longitudinal deformation perpendicular to cracks or low-aspect ratio pores would control \u03b2, whereas shear deformation across the interfaces being responsible for average softening and hysteresis.\n\nPossible factors arising from the calculation of strain that may affect the estimation of \u03b2 are two folds. The first is the spacing of geodetic stations (tens of kilometers), larger than the dimension of the karst aquifer (Fig.\u00a01), likely providing a lower boundary on the estimated strain. The second is the use of surface measurements of strain as representative of the actual values at depths (likely to be lower) where seismic velocity changes occur. These two factors, which cannot be easily quantified, have opposite effects and will probably affect the value of \u03b2 but not its sign.\n\nIt has been previously reported that seismicity along the IFZ is modulated at seasonal and multiannual time scales by hydrological forcing from the karst aquifers32. The low values of estimated hydrologically-induced stress variations32, corresponding to the observed seismicity modulation, suggest a critical state of stress for the faults within the actively deforming area, as observed in other actively-deforming regions where seismicity is triggered by small stress changes55. To investigate the relation between rock properties modulated by the hydrological forcing (strain and seismic velocity variations) and earthquake nucleation we declustered the seismic catalog along the IFZ (color-coded circles in Fig.1 c) and calculated the daily seismicity rates within 90-days moving windows (see Methods). This modulation is clearly displayed in the multiyear time series of Fig.\u00a02c (where all the different observables have been resampled and synchronized at a 0.04-year step), showing in-phase peaks of seismicity rate, \u03b4v/v, strain and spring discharge. We also observe that the three largest peaks of seismicity rates in 2009, 2013 and 2021 (Fig.\u00a02c), correspond to large seasonal increments in spring discharge (>2\u2009m3/s) and dilatational strain (>1\u2009\u00d7\u200910\u22126). We test the relationship between background seismicity and hydrological forcing (expressed as variations of dilatational strain or seismic velocity), following the approach of ref. 56 using a modified quantile-quantile plot (QQP). QQP tests for a specific relationship between hydrological forcing and seismicity rate rather than simply asking the question if earthquake occurrence is correlated with such forcing. In our case we plot the normalized cumulative fraction of earthquake rates that occurs at or below a given level of strain or seismic velocity against the normalized cumulative fraction of time that is spent at or below the same given level. To facilitate interpretation, we follow ref. 56 and modify the QQP plots to show excess or deficit of earthquakes by removing the 1:1 trend line. In the case of no correlation between the earthquake rate and hydrological forcing (either the strain or velocity variations), we expect to see a horizontal line because the fraction of earthquakes below a given level should be equivalent to the fraction of time spent at or below that level. If a point falls below the horizontal line, then there is a deficit of earthquakes up to the corresponding value compared with what is expected for time spent up to the corresponding value. If the point falls above, the opposite is true, which means we have an excess of earthquakes. We found (red lines in Fig.\u00a04a) a deficit of earthquakes for low values of the strain compared to the case in which there would be no correlation with the strain (e.g., in the case of a Poisson process). This indicates that seismicity and hydrologically-related strains are correlated with each other. Moreover, the curve is slightly skewed to the right (Fig.\u00a04a), showing that seismicity rate responds non-linearly to increasing strains explaining also the large response of seismicity to the largest strain increase in 2009, 2013 and 2021. As expected, the same analysis with velocity variations (Fig.\u00a04b), shows an opposite behavior and an excess of earthquakes during low values of seismic velocity. Therefore, earthquakes occurrence and velocity variations are anti-correlated. In this second case, the curve is also skewed to the left (Fig.\u00a04b), showing that seismicity rate also responds non-linearly to increasing velocity variations. The gray lines shown in Fig.\u00a04a, b display 100 random permutations of the observed catalog which are cloudily disposed around the horizontal line showing that, when the catalog is randomized, no correlation is present with either strain (Fig.\u00a04a) or seismic velocity variations (Fig.\u00a04b). By comparing the observed seismicity rate with synthetic rate histories containing both random and hydrological components, we could estimate the level of hydrological forcing in the real catalog (see Methods). Figure\u00a04(c\u2013f) shows that the observed nCDFs of the observed seismicity require ~40% contribution of hydrological forcing and an increasing effect of strain or seismic velocity variation to reproduce the observed skewness, i.e., a non-linear influence of strain or seismic velocity variations on the seismicity rate.\n\nThe vertical axis is a measure of excess or deficit of earthquakes at a given level of strain (in red, plots a, c, e) or velocity (in blue, plots b, d, f) based on the amount of time spent at or below that level. The horizontal axis is the normalized Cumulative Density Function (nCDF) of time-strain (or time-velocity). Gray lines show synthetic realizations (mean shown in yellow) with variable contribution and increasing effect of hydrological forcing.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54094-4/MediaObjects/41467_2024_54094_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54094-4/MediaObjects/41467_2024_54094_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54094-4/MediaObjects/41467_2024_54094_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We measured hydrologically-modulated velocity changes \u03b4v/v in the order of ~0.2% near the karst aquifers close to IFZ. Those variations are one order of magnitude smaller (~0.03%) at around 25\u2009km distance, where the amplitudes of horizontal transient displacements are negligible too (Fig.\u00a01c). Our primary observation shows that velocity is systematically slower during extension and higher during compressional strains resulting in a seasonally-controlled variation of the state of rock damage. Variations of elastic properties and their non-linear sensitivity to strain arise from the stiffness modification of the grain and fracture contacts. We thus propose that seasonal variations induce a weakening/healing process that cyclically affects the crust along the IFZ. The process we observe seems to be reversible, with a restoration of the initial conditions (and of the elastic properties), like a fault healing process57 by which the crust retrieves its original characteristics prior to the new damage episode. Geodetic strain allows us to precisely track the mechanism underlying the velocity variations, which is the cyclical crack opening/closing of NW-SE-oriented crack system in the direction of the regional direction of minimum horizontal stress.\n\nWe observe that significant non-linear seasonal variations of elastic properties of the crust along the IFZ are statistically correlated with earthquakes rate. We propose a model in which hydrological deformations promote earthquake failure by a mechanism involving dynamic nonlinear elasticity22. Seasonal variations of elastic properties cause a weakening that triggers the observed micro-seismicity, alternated to a healing process as the strain amplitudes decrease and seismic wave velocity increases with frictional contacts ageing20. In terms of frictional contacts, we may also interpret triggering as the onset of sliding of the faults, resulting from an abrupt decrease in the shear strength of the fault gouge by softening of frictional contacts. Necessary physical characteristics for this triggering mechanism require a weak fault in a critical state and dynamic strain amplitudes greater than about 10\u2212618, regardless of their frequency content58. Both requirements are satisfied along the IFZ where active tectonic strain is likely to keep faults close to failure and hydrological forcing provides sufficient oscillatory strains. For the recent background micro seismicity, no significant localization along the segments responsible for the 1980 Ms 6.9 earthquake is observed59 suggesting that a diffused triggering mechanism in the volume interested by hydrological forcing and resulting nonlinear reduction of elastic properties is more likely than an accelerated aseismic slip along major fault zones.\n\nLaboratory experiments22 show that modulus reduction increases progressively as the effective stress is reduced, implying that the system\u2019s elastic nonlinearity is strongly sensitive to increase in pore pressure. In agreement with the observed depth distribution of seismicity (Fig.\u00a0S1), the deeper parts of the crust, where pore pressure may be greater than hydrostatic, may thus be more sensitive to hydrological forcing with respect to the shallow crust where extensive fracturing maintains a hydrostatic profile. The observed relationship between seismicity triggering and modulus reduction (Fig.\u00a04e, f) also agrees with laboratory experiments18 showing the existence of an approximate strain threshold above which significant elastic nonlinearity is observed and earthquakes are easily triggered. Thus, our study suggests that the elastic nonlinearity of fault cores close to a critical state plays a major role in earthquake triggering and offers an alternative perspective beyond models based solely on the evolution of pore pressure or Coulomb stress. We speculate that regional weakening of active fault zones, over time scales relevant for earthquake nucleation, may also increase the likelihood of higher-magnitude ruptures on large fault systems.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "We computed the three-component (ZZ, EE, NN) autocorrelation using continuous seismic data recorded at the MCRV station located in the IFZ area, in Southern Italy (Fig.\u00a01a). The considered time window for this study is from January 2008 to December 2021. We rejected daily traces if they don\u2019t contain more than 20\u2009h of data available and to reduce transient signals, we carried out a one\u2010bit normalization.\n\nWe measured the velocity variations using the stretching technique60, which provides stable measurements25, by stacking 90 days using an 89-day overlap of correlations. The stack of the autocorrelation over the full-time period was used as the reference signal to compute relative velocity variations. This operation was performed for each component in the coda wave window starting at 10\u2009s from the arrival of ballistic waves and with a duration of 40\u2009s. The choice of this window allows to resolve changes of \u03b4v/v in the shallow crust25. Velocity variations of the three components are, then, combined weighting with squared correlation coefficients estimated after stretching. We then select the velocity variations with correlation coefficients above 0.85.\n\nIn Fig.\u00a01c we represent the comparison of the seismic velocity variation for the stations MCRV and CAFE, this latter being outside the carbonates and characterized by much smaller variations (around 34% compared to MCRV), demonstrating that the response of crust around MCRV is very peculiar and associated with the local characteristics of the complex IFZ.\n\nWe computed the depth sensitivity of surface waves (Fig.\u00a0S1c, d) as in ref. 46 for a local velocity model47 and for the frequency band 0.5\u20131.0\u2009Hz at which we computed the velocity variations. This frequency band has been selected as it is the optimal band used in the Apennines to have a good temporal resolution with high quality correlation while limiting the number of stacked days25,44,61. For autocorrelations, the better outcomes are observed for frequencies exceeding 0.5\u2009Hz62, because in Southern Europe seasonal variations in the distribution of noise sources reduce the quality of the correlations and thus limit time dependent analysis63, while frequencies higher than 1\u2009Hz are too much contaminated by anthropogenic noise. We also computed the theoretical depth sensitivity of the scattered body waves (Fig.\u00a0S1e) as in ref. 25, considering a 3D sensitivity kernel formulation48. We solved for the body wave depth sensitivity normalized to 30\u2009km depth with each layer 1\u2009km thick layer25. In our case we considered a coda time lapse 30\u2009s, representative of the time window 10\u201350\u2009s, and two free path (10,100\u2009km) as reference.\n\nContinuous surface displacements have been measured by permanent GPS stations of the RING network (http://ring.gm.ingv.it). For our analysis, we considered the time series of horizontal components corrected for the long-term tectonic trend and instrumental offset from January 2008 through December 2021 obtained following the procedure outlined in ref. 32. We calculate the time-dependent horizontal strain rate tensor at the surface by modeling the observed displacement with elementary cuboid sources extending to a depth of 3.5\u2009km51 following the approach described in ref. 32. The use of this modeling approach (fully described in ref. 51) to calculate the horizontal components of the \u201chydrological\u201d strain rate field is required by the need to regularize the sparse density coverage of the GPS stations and incorporate the geometry of the karst aquifers. The second invariant of the horizontal long-term, tectonic strain rate field shown in Fig.\u00a01a, has been obtained from the secular GPS velocity field of ref. 64 and using the VISR code65 to calculate a regular grid of the horizontal strain rate tensor.\n\nIFZ is monitored since 2007 by the Irpinia Near Fault Observatory (INFO), which includes the Irpinia Seismic Network (ISNet, http://isnet.unina.it, network code IX), composed of a total of 31 co-located tri-axial strong motion accelerometers and three-components short period or broad-band seismometers and 8 INGV seismic stations (https://eida.ingv.it/it/, virtual network _NFOIRPINA, network code IV). The period analyzed in the present study ranges from January 2008 through December 2021. The original data set consisted of 1898 events with local magnitude \u22120.4\u2009\u2264\u2009ML\u2009\u2264\u20093.7, and the seismicity is mostly concentrated at depths between 8 and 12\u2009km (Fig.\u00a01c, S1). To avoid biases associated to aftershocks, we declustered the earthquake catalog using the approach described in ref. 66, and its windowing technique, where a scan of the catalog within distance and time is performed with spatiotemporal windows as a function of the magnitude66. In this procedure, the first shock is not necessarily the largest shock in the sequence; thus, a small foreshock is the first event of an aftershock sequence. If a largest shock occurs in the series, this enlarges the window beyond the value used for the first shock66. Since the recorded seismicity may be affected by the detection capability of the network, we select events shallower than 12\u2009km with magnitude above the minimum magnitude of completeness (MC), i.e., the magnitude above which the network is assumed to reliably record all the events occurring in the region of interest, estimated at ML 1.1 for ISNet67. Then we computed the seismicity rate as the number of earthquakes occurring in 90-days moving windows. We also tested the sensitivity of the seismicity rate to the parameters of the windowing technique66 used for the declustering and we found that seismicity rate is not changed at all by varying the windows used (Fig.\u00a0S2, Supplementary Materials). We constructed the synthetic catalogs starting from the observed number of events occurring in the considered time interval and considering this number as resulting from the sum of an homogeneous Poisson process and a nonhomogeneous hydrological forcing. The nonhomogeneous contribution has been produced using the inversion method68 which involves firstly obtaining the cumulative distribution for the variable to be sampled (strain or seismic velocity). Cumulative distribution functions of the strain and seismic velocity variations (simple or squared to consider the case of linear and non-linear effects, respectively) are calculated from the respective time series and rearranged to give an expression for the variable of interest (strain or seismic velocity) in terms of its probability. The variable can then be sampled by inserting uniform random values between 0 and 1 into this expression for each event contributing to the nonhomogeneous part of the synthetic catalog. The same approach has been used to calculate the homogeneous Poissonian contribution to the synthetic catalogs randomly sampling the cumulative function of the exponential distribution which controls the distribution of time intervals between successive independent events in a Poisson process68. Synthetic seismicity rate histories (90-days moving windows) are then computed from the synthetic catalogs.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The seismic catalog can be downloaded at http://isnet.unina.it. Velocimetric continuous data are available at https://eida.ingv.it/it/. Raw GPS data (rinex files) are available at http://ring.gm.ingv.it. The time series of the discharge of Caposele spring has been provided by Approvviggionamento Idrico (DIRAP), Acquedotto Pugliese, S.p.a., Bari. The dataset of seismic velocity changes, geodetic strain, and seismicity rate, generated during the current study, is available from the corresponding author on reasonable request. Analysis was made using MATLAB (release 2023a, https://www.mathworks.com/products/matlab.html) and Python (https://www.python.org/). Correspondence and material requests should be addressed to S.T. at the following address: stefania.tarantino@ingv.it.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "This study was performed using the Python package Obspy and uses workflows provided in ref. 25 and ref. 32 for velocity variations measurement and strain computation respectively.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Zener, C. Elasticity and Anelasticity of Metals (The University of Chicago Press, 1948).\n\nRenaud, G., Le Bas, P.-Y. & Johnson, P. A. Revealing highly complex elastic nonlinear (anelastic) behavior of Earth materials applying a new probe: dynamic acoustoelastic testing. J. Geophys. Res. Solid Earth 117, n/a-n/a (2012).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nSimpson, J., Malcolm, A. E. & van Wijk, K. 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P.P. has been supported by the European Research Council (ERC) under the European Union Horizon 2020 Research and Innovation Program (grant agreements 802777\u2014MONIFAULTS). Figures were generated with Generic Mapping Tools69.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Istituto Nazionale di Geofisica e Vulcanologia, L\u2019Aquila, Italy\n\nStefania Tarantino\u00a0&\u00a0Maurizio Vassallo\n\nDepartment of Geoscience, University of Padova, Padova, Italy\n\nPiero Poli\n\nIstituto Nazionale Geofisica e Vulcanologia, Rome, Italy\n\nNicola D\u2019Agostino\n\nDepartment of Physics, University of Napoli, Napoli, Italy\n\nGaetano Festa\u00a0&\u00a0Aldo Zollo\n\nApprovvigionamento Idrico (DIRAP), Acquedotto Pugliese S.p.A, Bari, Italy\n\nGerardo Ventafridda\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.T., P.P., and N.D. conceived the methodology and wrote the first draft of the manuscript. S.T., P.P., N.D., G.F., and M.V. contributed to data preparation and analysis and to the interpretation of results. G.V. provided data of the Caposele spring. A.Z. contributed to the interpretation of results. All authors revised the final draft of the manuscript.\n\nCorrespondence to\n Stefania Tarantino.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Andrew Delorey and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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mechanisms", + "pre_title": "The mitochondrial mRNA stabilizing protein, SLIRP, regulates skeletal muscle mitochondrial structure and respiration by exercise-recoverable mechanisms", + "journal": "Nature Communications", + "published": "13 November 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54183-4/MediaObjects/41467_2024_54183_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54183-4/MediaObjects/41467_2024_54183_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54183-4/MediaObjects/41467_2024_54183_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54183-4/MediaObjects/41467_2024_54183_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-54183-4#Sec47" + ], + "code": [], + "subject": [ + "Diabetes", + "Energy metabolism", + "Musculoskeletal development", + "RNA metabolism" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3900062/v1.pdf?c=1731589535000", + "research_square_link": "https://www.researchsquare.com//article/rs-3900062/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54183-4.pdf", + "preprint_posted": "29 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Decline in mitochondrial function associates with decreased muscle mass and strength in multiple conditions, including sarcopenia and type 2 diabetes. Optimal treatment could include improving mitochondrial function, however, there are limited and equivocal data regarding the molecular cues controlling muscle mitochondrial plasticity. Here we uncover the mitochondrial-mRNA-stabilizing protein SLIRP, in complex with LRPPRC, as a PGC-1\u03b1 target that regulates mitochondrial structure, respiration, and mitochondrially-encoded-mRNA pools in skeletal muscle. Exercise training effectively counteracted mitochondrial defects induced by loss of LRPPRC/SLIRP, despite sustained low mitochondrially-encoded-mRNA pools, via increased mitoribosome translation capacity. In humans, exercise training robustly increased muscle SLIRP and LRPPRC protein content across exercise modalities and sexes, yet this increase was less prominent in subjects with type 2 diabetes. Our work identifies a mechanism of post-transcriptional mitochondrial regulation in skeletal muscle through mitochondrial mRNA stabilization. It emphasizes exercise as an effective approach to alleviate mitochondrial defects by possibly increasing mitoribosome capacity.Biological sciences/Physiology/Metabolism/Mitochondria/Energy metabolismBiological sciences/Cell biology/Organelles/Mitochondria/Energy metabolismSRA stem-loop interacting RNA-binding proteinmitochondrial dysfunctionskeletal muscleexercise trainingDrosophila", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Decline in mitochondrial function is linked to decreased muscle mass and strength in conditions like sarcopenia and type 2 diabetes. Despite therapeutic opportunities, there is limited and equivocal data regarding molecular cues controlling muscle mitochondrial plasticity. Here we uncovered that the mitochondrial mRNA-stabilizing protein SLIRP, in complex with LRPPRC, is a PGC-1\u03b1 target that regulates mitochondrial structure, respiration, and mtDNA-encoded-mRNA pools in skeletal muscle. Exercise training effectively counteracts mitochondrial defects caused by genetically-induced LRPPRC/SLIRP loss, despite sustained low mtDNA-encoded-mRNA pools, by increasing mitoribosome translation capacity and mitochondrial quality control. In humans, exercise training robustly increases muscle SLIRP and LRPPRC protein across exercise modalities and sexes, yet less prominently in individuals with type 2 diabetes. SLIRP muscle loss reduces Drosophila lifespan. Our data points to a mechanism of post-transcriptional mitochondrial regulation in muscle via mitochondrial mRNA stabilization, offering insights into how exercise enhances mitoribosome capacity and mitochondrial quality control to alleviate defects.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Mitochondrial homeostasis and function are vital for skeletal muscle physiology, primarily influenced by the ability to meet energy demands through oxidative phosphorylation (OXPHOS) in mitochondria1. A decline in mitochondrial function is associated with decreased muscle mass and strength in multiple conditions, including sarcopenia, type 2 diabetes (T2D), and cancer. Reduced muscle function adversely impacts health and quality of life1,2,3,4,5,6,7,8,9 and is associated with increased all-cause mortality. Mounting evidence suggests that improvements in mitochondrial metabolism contribute to the exercise-induced functional benefits, as mitochondrial preservation protects against the age-associated decline in skeletal muscle mass and performance10,11. The beneficial effects of exercise training are more potent than any drug in preserving muscle mass and function, positioning exercise training as a frontline strategy for the prevention and treatment of sarcopenia, T2D, and cancer12,13,14. Viewing mitochondrial control through the lens of exercise biology is a strategy to gain new mechanistic insights into mitochondrial regulation, which is currently incompletely understood. With an aging population and no FDA/EMA-approved drugs to treat muscle functional decline, there is an urgent unmet need to identify potential treatment targets.\n\nOXPHOS proteins are of dual origin and transcribed in spatially distinct cellular compartments. Nuclear (n)DNA encodes for the majority of OXPHOS proteins. Yet, 13 essential OXPHOS proteins are encoded by the small circular high-copy number mitochondrial DNA (mtDNA)15,16. In non-muscle systems, a protein complex comprised of steroid receptor RNA activator stem-loop interacting RNA-binding protein (SLIRP) and the leucine-rich pentatricopeptide repeat containing protein (LRPPRC) mediates mitochondrial-mRNA (mt-mRNA) stability and polyadenylation of most mtDNA-encoded OXPHOS transcripts17,18,19,20,21. SLIRP and LRPPRC, encoded by nuclear DNA, are primarily targeted to mitochondria. In addition, SLIRP may also regulate nuclear receptors by binding to steroid receptor RNA activator22, and LRPPRC may activate nuclear genes through interaction with PGC-1\u03b123. Stabilization of mt-mRNA is critical for protein synthesis by the mitochondrial ribosome (mitoribosome). Yet, the mitoribosome comprises 82 mitoribosomal proteins encoded by nuclear genes, and 12S and 16S rRNAs, highlighting the requirement for intricate coordination between the processes of cytosolic and mitochondrial protein synthesis18,19.\n\nDespite the high mitochondrial abundance in skeletal muscle, the complex interaction between LRPPRC/SLIRP-mediated posttranscriptional processes, mitoribosomal translation, and mitochondrial function has not previously been studied in skeletal muscle. Elucidating the underlying mechanisms for stabilizing mt-mRNA could not only facilitate our understanding of the fundamental energy metabolism in muscle, but also aid intervention strategies for diseases associated with skeletal muscle mitochondrial defects.\n\nEndurance exercise training potently increases mitochondrial mass in skeletal muscle, in part due to the increased synthesis of nDNA\u2010 and mtDNA\u2010encoded mitochondrial proteins1,24. Importantly, exercise training retains its ability to improve oxidative capacity not only in healthy individuals but also in patients with mtDNA mutations, with common PGC-1\u03b1-mediated mitochondrial adaptive responses shared between both groups13. These results suggest that exercise training induces adaptations to reinforce the mitochondria\u2019s ability to efficiently respond to increased energy demands, even in the presence of mtDNA mutations, which may negatively affect mitochondrial transcription and translation. However, a notable knowledge gap persists in understanding the role of mitochondrial posttranscriptional processes, specifically in mt-mRNA stabilization and translation, in skeletal muscle biology and following exercise training. Yet, that knowledge is needed to gain a comprehensive understanding of the adaptive responses to exercise training.\n\nSince SLIRP, a key player in mitochondrial posttranscriptional gene expression, is markedly upregulated in mouse skeletal muscle by exercise training25, we hypothesized that SLIRP would regulate mitochondrial function in skeletal muscle at rest and in response to exercise training. Our findings illuminate SLIRP\u00b4s role in regulating mt-mRNA transcript levels in skeletal muscle, downstream of PGC-1\u03b11. Knockout (KO) of SLIRP led to damaged and fragmented mitochondria alongside lowered respiration. Intriguingly, exercise training could compensate for the absence of SLIRP, leading to improvements in mitochondrial integrity and respiratory capacity. Our findings imply the activation of complex exercise-induced molecular signaling in skeletal muscle which bypasses mt-mRNA defects, possibly through enhanced mitoribosomal translation and capacity of mitochondrial quality control.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Protein profiling of five different skeletal muscle tissues showed that SLIRP content was highest in the oxidative soleus compared to glycolytic extensor digitorum longus (EDL, Fig.\u00a01A, Supplementary Fig.\u00a01A), in accordance with the potential critical role for SLIRP in oxidation. Moreover, SLIRP was present across a diverse array of tissues in mice and was highly abundant in energy-demanding tissues such as liver, kidney, brown adipose tissue, heart, and skeletal muscle (Fig.\u00a01A, Supplementary Fig.\u00a01A), in line with previously reported Slirp mRNA levels22.\n\nA SLIRP protein content across different wild-type tissues (n\u2009=\u20093\u20134, female C57BL/6\u2009J). B SLIRP and LRPPRC protein content in tibialis anterior muscle of Slirp knockout (KO) and littermate wildtype (WT) mice; WT, n\u2009=\u200926; Slirp KO\u2009=\u200935. C SLIRP and LRPPRC protein content in tibialis anterior muscle of WT mice injected with recombinant adeno\u2010associated virus serotype 6 encoding SLIRP (rAAV6:SLIRP) or rAAV6:eGFP in the contralateral leg as control (n\u2009=\u20096). D Confocal microscopy of mitochondrial network structure in flexor digitorum brevis muscle fibers of Slirp KO and WT mice (n\u2009=\u20094, 7\u201310 fibers per mouse). Corresponding fragmentation index are presented as super plots105; small symbols, each fiber; large symbols, mean of fibers per mouse and color- and symbol-coded for each sex (\u25cb male, \u25c7 female). E TEM images of Slirp KO and WT gastrocnemius muscle (n\u2009=\u20094; 7\u20139 fibers per sample). F, G Quantification of percentage of damaged intramyofibrillar (IMF) mitochondria within total mitochondria and relative volume of mitochondria. Small symbols, damaged or total mitochondria per fiber; large identical symbols, average of fibers per biological replicate (female Slirp KO and WT gastrocnemius muscle, n\u2009=\u20094/group). H Mitochondrial respiration in of Slirp KO and WT gastrocnemius muscle (male/female: WT, n\u2009=\u20094/4; Slirp KO, n\u2009=\u20095/5); \u25cb (blue) male, \u25c7 (red) female. I Fatty acid oxidation in isolated Slirp KO and WT soleus muscle at rest and in response to contraction (male/female: WT, n\u2009=\u20093/4; Slirp KO, n\u2009=\u20094/8); \u25cb (blue) male, \u25c7 (red) female. J RT-qPCR analysis of mitochondrial transcript levels and corresponding immunoblots in gastrocnemius of male Slirp KO and WT mice (mRNA: WT, n\u2009=\u20096; Slirp KO, n\u2009=\u20096; protein: WT, n\u2009=\u20096; Slirp KO, n\u2009=\u20097). K qPCR analysis of mtDNA levels in male Slirp KO and WT mice (WT, n\u2009=\u20096; Slirp KO, n\u2009=\u20097). L, M Climbing assay and life span of control, SLIRP1 and SLIRP2 knockdown flies (n\u2009=\u20093\u20139, 10 flies per sample for climbing assay; n\u2009=\u200910, 10 flies per vial for lifespan assay). Data are shown as mean\u2009\u00b1\u2009SEM, including individual values, where applicable. Geno, main effect of genotype; substrate, main effect of substrate addition; Substrate X Geno, interaction between genotype and substrate; Contraction, main effect of contraction. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, as per Two-tailed Mann Whitney test (D, F, G) on average values, Two-way RM ANOVA with \u0160\u00edd\u00e1k\u2019s multiple comparisons test (H, I), Two-tailed unpaired Student\u00b4s t test (J, K), ordinary one-way ANOVA with Dunnett\u2019s multiple comparisons test (L), Log-rank (Mantel-Cox) test (M). Source data are provided as a Source Data file.\n\nIn skeletal muscle of global Slirp KO mice, SLIRP protein was undetectable. Moreover, SLIRP\u00b4s binding partner LRPPRC showed 90% reduction in protein content (Fig.\u00a01B), indicative of their co-stabilization. To confirm co-stabilization, we administered intramuscular injections of recombinant adeno-associated viral serotype 6 (rAAV6):Slirp into the tibialis anterior (TA) muscle of wild-type (WT) mice, while injecting a control vector into the contralateral TA muscle. As expected, in the absence of injecting vectors to concomitantly upregulate LRPPRC, total SLIRP protein content did not increase because endogenous SLIRP protein was degraded (Fig.\u00a01C). These findings show that SLIRP and LRPPRC stabilize each other in skeletal muscle, aligning with findings from non-muscle tissue17,18,20.\n\nWe next determined the role of SLIRP for mitochondrial structure in skeletal muscle, using the membrane potential probe, tetramethylrhodamine ethyl ester (TMRE\u2009+\u2009), in intact flexor digitorum brevis (FDB) muscle fibers of Slirp KO and littermate control wild-type (WT) mice. Interestingly, the finely interconnected mitochondrial network, obvious in WT fibers, was disrupted in Slirp KO muscle fibers, evident by a 30% increased mitochondrial fragmentation index (Fig.\u00a01D). Transmission electron microscopy (TEM) analysis of gastrocnemius muscle provided further insights into the ultrastructural changes of mitochondrial morphology in Slirp KO (Fig.\u00a01E). Half of the Slirp KO gastrocnemius muscles displayed reduced density of the matrix, disarray of the cristae, and substantial enlargement and vacuolation of intermyofibrillar (IMF) mitochondria (Fig.\u00a01E). Quantitative analysis of the mitochondria showed that the percentage of damaged IMF mitochondria was increased (Fig.\u00a01F). However, there were no significant alterations in the total number of IMF mitochondria (Fig.\u00a01G). The percentage of damaged mitochondria in Slirp KO muscle varied between 2\u201320% of total mitochondria compared to <2% of damaged mitochondria in WT muscle (Fig.\u00a01E, F). Other structural parameters of IMF mitochondria, such as mitochondrial area, aspect ratio, elongation, convexity, perimeter, sphericity, and diameter did not display any significant changes in Slirp KO muscles, likely due to heterogeneity in the extent of damage (Fig.\u00a01F, Supplementary Fig.\u00a01B\u2013H).\n\nIn agreement with the observable abnormalities in mitochondrial structure, respiratory capacity was reduced in permeabilized gastrocnemius muscle fibers of Slirp KO mice compared to WT (Fig.\u00a01H). This was particularly evident when adding glutamate to assess complex I linked respiratory activity (\u221224%) and succinate to assess complex I\u2009+\u2009II linked respiratory capacity (\u221221%). These results indicate an important role for SLIRP selectively\u00a0in skeletal muscle respiration, as in isolated mitochondria from liver and heart tissues, Slirp KO did not compromise mitochondrial respiration18. In agreement with lower respiration, fatty acid oxidation tended (p\u2009=\u20090.0718) to be lower in intact incubated soleus muscle of Slirp KO mice (Fig.\u00a01I). However, electrically-induced muscle contraction increased fatty acid oxidation similarly in both genotypes, suggesting that fatty acid utilization for fuel during muscle contraction does not depend on SLIRP (Fig.\u00a01I).\n\nWith the suggested role of SLIRP as an mt-mRNA stabilizing protein in non-muscle cells18, we determined mtDNA-encoded mRNA transcripts. Intriguingly, we observed a 60\u201380% reduction in mt-Nd1, mt-Nd5/Nd6 (Complex I), mt-CytB (Complex III), mt-Co1 (Complex IV), and mt-Atp6 (ATP synthase) in gastrocnemius muscle (Fig.\u00a01J) with concomitant reductions in protein content for MTCO1 and MTATP6. Additionally, there was an increased mtDNA copy number, estimated by measuring Nd1 and Nd6 (Fig.\u00a01K). Those results indicate that SLIRP stabilizes mt-mRNA, and its loss adversely affects MTCO1 and MTATP6 protein content in skeletal muscle.\n\nTogether, these results suggest that mt-mRNA stabilization via SLIRP is required for proper mitochondrial network structure and morphology, and respiration in mouse muscle.\n\nTo determine the long-term consequences of SLIRP muscle\u00a0deficiency on the whole organism, we utilized the UAS-GAL4 system26 for muscle-specific (Mef2-GAL4\u2009>\u2009) knock-down (KD) of SLIRP1 and SLIRP2 in Drosophila (D.) melanogaster. Only one SLIRP gene is present in mouse and human, while two SLIRP genes exist in D. melanogaster (Flybase annotation symbols (http://flybase.org): CG33714 and CG8021) likely to have originated from gene duplication events27. This consequently gives rise to two fly orthologue proteins of the human and mouse SLIRP, denoted SLIRP1 (CG33714) and SLIRP2 (CG8021), respectively27. The functions of the mammalian SLIRP are carried out by two proteins in flies: SLIRP1 and SLIRP2, which interact with the fly orthologue proteins LRPPRC1 and LRPPRC2, respectively, to regulate mt-mRNA polyadenylation and maturation, and coordinating mitoribosomal translation27,28,29. To test physical functionality, we subjected the flies to a negative geotaxis (climbing) assay30 and found that SLIRP2 KD, but not SLIRP1 KD, flies climbed 23% slower than control flies, indicative of impaired muscle function (Fig.\u00a01L). The detrimental effects of SLIRP KD in the muscle became further apparent following starvation. Median fasting survival of SLIRP1 KD flies was 41\u2009h, compared with 44\u2009h for control and SLIRP2 KD flies (Supplementary Fig.\u00a01I, p\u2009=\u20090.0681). Muscle-specific SLIRP deficiency, irrespective of orthologue, had a deleterious effect on lifespan. While the median survival of control flies was 77 days, median survival of SLIRP1 and SLIRP2 KD flies was only 55 and 63 days, respectively, and accordingly both SLIRP1 and SLIRP2 KD significantly reduced lifespan (Fig.\u00a01M).\n\nTaken together these findings show that SLIRP KD can have detrimental long-term consequences such as impaired muscle function, starvation intolerance, and reduced life span.\n\nHaving established critical functional roles of SLIRP in skeletal muscle mitochondrial morphology and respiratory capacity with detrimental effects on lifespan, we next investigated the upstream regulation of SLIRP protein content. Our recent work pinpointed SLIRP as a protein responsive to exercise training25; yet the mechanisms governing its induction and functions remained elusive. Knowing that exercise potently increases PGC-1\u03b1 protein and mRNA levels in both mouse and human skeletal muscle31,32,33,34, and that PGC-1\u03b1 is an important regulator of mitochondrial biogenesis, respiration, and quality control35, we explored the regulation of SLIRP by PGC-1\u03b1. The Ppargc1a gene encodes several PGC-1\u03b1 isoforms, including isoform 1 (PGC-1\u03b11) and isoform 4 (PGC-1\u03b14), with distinct regulation and biological functions36,37. Overexpression (OE) of PGC-1\u03b11 in mouse skeletal muscle36,38, associating with endurance exercise training-like adaptations36,38,39, resulted in approximately 8.3-fold higher muscle SLIRP protein content, without a concomitant change in Slirp\u00a0mRNA levels (Fig.\u00a02A). On the contrary, PGC-1\u03b14 OE40, inducing resistance-type exercise training adaptations such as muscle hypertrophy and strength36, had no effect on SLIRP protein content in mouse gastrocnemius muscle (Fig.\u00a02B). This suggests that SLIRP is an unrecognized player in PGC-1\u03b11-regulated oxidative metabolism.\n\nA RT-qPCR analysis of Slirp transcript levels relative to Actb levels and Western blot analysis of SLIRP protein abundance in quadriceps with skeletal-muscle-specific transgenic expression of PGC-1\u03b11 (\u03b11 OE; mRNA: n\u2009=\u20098/group; protein: WT, n\u2009=\u20099; \u03b11 OE, n\u2009=\u200911). B Western blot analysis of SLIRP protein abundance in gastrocnemius with skeletal-muscle-specific transgenic expression of PGC-1\u03b14 (\u03b14 OE; WT, n\u2009=\u20099; \u03b14 OE, n\u2009=\u20096). C RT-qPCR analysis of Slirp and Ppargc1\u03b1 transcript levels relative to Hprt levels in gastrocnemius of muscle-specific PGC-1\u03b1 knockout (PGC-1\u03b1 mKO) and littermate control (WT) mice at rest and 3\u2009h after acute exercise bout (WT, rest/3\u2009h, n\u2009=\u20098/7; PGC-1\u03b1 mKO, rest/3\u2009h, n\u2009=\u20095/6). D, E RT-qPCR analysis of Slirp, Ppargc1\u03b1, mt-Nd1, mt-Co1, mt-Co2, and mt-Atp6, transcript levels relative to Actb levels in WT quadriceps at rest, immediately after, 2\u2009h, 6\u2009h or 24\u2009h after acute exercise bout (n\u2009=\u20098/group). F Western blot analysis of SLIRP protein content in whole-quadriceps lysate, and SLIRP and COX4 protein content in cytosolic and mitochondrial fractions, isolated from WT quadriceps at rest and 2\u2009h after in situ contraction (n\u2009=\u20094/group). G Western blot analysis of SLIRP protein abundance in quadriceps of sedentary (SED) and 12-week ET PGC-1\u03b1 mKO and WT mice (WT SED/ET, n\u2009=\u200910/10; PGC-1\u03b1 mKO SED/ET, n\u2009=\u20098/10). H Western blot analysis of SLIRP protein abundance in quadriceps of muscle-specific AMPK\u03b11 and -\u03b12 double KO (mDKO) and WT mice treated with/without AICAR (WT, saline/AICAR, n\u2009=\u20095/4; mDKO, saline/AICAR, n\u2009=\u20096/7). Data are means\u2009\u00b1\u2009SEM, including individual values. Geno, main effect of genotype; Ex, main effect of acute exercise; Geno x Ex, interaction between genotype and acute exercise. *p\u2009<\u20090.05, ***p\u2009<\u20090.001, as per Two-tailed unpaired Student\u00b4s t test (A, B, F), Two-way ANOVA with \u0160\u00edd\u00e1k\u2019s multiple comparisons test (C, G, H), and ordinary one-way ANOVA with Dunnett\u2019s multiple comparisons test (D, E). Source data are provided as a Source Data file.\n\nTo test whether PGC-1\u03b1 regulates Slirp mRNA in skeletal muscle following exercise, we determined Slirp and Ppargc1a (Exon 3\u20135) mRNA levels in TA muscle 3\u2009h into recovery from one exercise bout in mice lacking PGC-1\u03b1 in skeletal muscle (PGC-1\u03b1 mKO) and WT control littermates. Slirp mRNA was not upregulated 3\u2009h post-exercise (Fig.\u00a02C), in contrast to the expected34 increase in Ppargc1a (+6.8-fold; Fig.\u00a02C) in WT mice. Interestingly, Slirp mRNA levels were only modestly 14% reduced in PGC-1\u03b1 KO skeletal muscle (Fig.\u00a02C), in line with the dissociation between Slirp mRNA levels and SLIRP protein levels observed in PGC-1\u03b11 OE skeletal muscle (Fig.\u00a02A).\n\nThese findings align with reports describing only moderate correlations between mRNA and protein levels following exercise41,42,43. Yet, with the interpretative limitations of a single post-exercise time point, we aimed to acquire a more detailed record of the temporal changes in Slirp transcripts in recovery from exercise. We subjected WT mice to an acute exercise bout at 60% of their maximal running capacity and harvested quadriceps muscles at rest, immediately after, 2\u2009h, 6\u2009h, and 24\u2009h post-exercise. We found that Slirp mRNA levels were 1.4-fold elevated 6\u2009h into the recovery period in quadriceps muscle (Fig.\u00a02D). This is a time-point not included in our prior cohort (Fig.\u00a02C), and partially concurrent with Ppargc1a mRNA levels that were elevated 2\u2009h (+3.0-fold) and 6\u2009h post-exercise (+\u20093.3-fold; Fig.\u00a02D). Concomitant with Slirp and Ppargc1a mRNA levels, mtDNA-encoded mt-Nd1 mRNA transcripts were increased 1.5-fold 6\u2009h post-exercise, but not mt-Cox1, mt-Cox2, and mt-Atp6 (Fig.\u00a02E). These results indicate a coordinated regulatory mechanism linking SLIRP, PGC-1a and the simultaneous upregulation of mt-Nd1 mRNA transcripts.\n\nSince SLIRP is a nuclear-encoded protein primarily targeted to mitochondria by a specific signal sequence, we next determined if muscle contractions increased the mitochondrial localization of SLIRP in cytosolic and mitochondrial fractions. To investigate this, we investigated quadriceps muscles from WT mice 2\u2009h after electrically-induced in situ contraction, using the contralateral leg muscle as a rested control, and performed an adapted subcellular fractionation assay on frozen tissue44,45 to isolate cytosolic and mitochondrial fractions. COX4 protein was used as mitochondrial marker, and GAPDH as cytosolic marker, respectively, to assess the purity of the fractions.\n\nOn a whole-tissue lysate level, SLIRP protein remained unchanged in response to contraction (Fig.\u00a02F). SLIRP protein content tended to be 1.2-fold and 1.3-fold enriched in the cytosolic and mitochondrial fraction, respectively, following in situ muscle contraction (Fig.\u00a02F), indicating that muscle contractions increase protein synthesis in the cytosolic and mitochondrial localization of SLIRP. Interestingly, and with the same sample input, we were able to detect SLIRP protein in the cytosolic fraction, indicating that SLIRP may not be exclusive to mitochondria in skeletal muscle. These data suggest that muscle contraction elicits a subcellular redistribution of SLIRP towards the mitochondria.\n\nGiven that SLIRP might be regulated by PGC-1\u03b1, we next turned to investigate the long-term dependency of exercise training-induced regulation of SLIRP protein by PGC-1\u03b1. In contrast to acute exercise, exercise training increased SLIRP protein content 4.8-fold in WT mice (Fig.\u00a02G), recapitulating our earlier observations in mice engaged in voluntary wheel running25. The effect of exercise training on SLIRP protein content was markedly blunted by 70% in mice lacking PGC-1\u03b1 in muscle compared to trained WT mice (Fig.\u00a02G). Expectedly, in WT mice, exercise training potently upregulated the steady-state levels of multiple OXPHOS proteins including SDHB, UQCRC2, and MTCO1 in skeletal muscle (Supplementary Fig.\u00a01I\u2013K). In mice lacking PGC-1\u03b1 in muscle, SDHB (Supplementary Fig.\u00a01I) and MTCO1 protein (Supplementary Fig.\u00a01K) was 50\u201360% reduced in sedentary (SED) mice. Moreover, the effect of exercise training on SDHB (Supplementary Fig.\u00a01I) and UQCRC2 protein content (Supplementary Fig.\u00a01J) was blunted by 30% and 65%, respectively, in PGC-1\u03b1 mKO compared to WT mice. In contrast to PGC-1\u03b1, the exercise-sensitive metabolic sensor AMPK46 did not seem to regulate SLIRP expression, as SLIRP protein abundance was comparable to controls in muscle-specific double KO of AMPK\u03b11 and AMPK\u03b12 and WT mice47,48 treated with or without the AMPK-activator AICAR for 4 weeks (Fig.\u00a02H).\n\nPGC-1\u03b1 has marked effects on muscle endurance, partially attributed to its pivotal role in mitochondrial biogenesis, and respiration38,49. Our results suggest SLIRP as an unrecognized downstream target of PGC-1a in such exercise adaptations and oxidative metabolism.\n\nHaving identified SLIRP as an exercise training responsive protein downstream of PGC-1a, we next determined SLIRP\u00b4s mechanistic involvement in exercise training-mediated organismal adaptations. To this end, we conducted a 10-week voluntary wheel-running training study in Slirp KO and WT littermate mice of both sexes commencing at ~18 weeks of age. The experimental design is shown in Fig.\u00a03A.\n\nA Experimental design of 10-week exercise training (ET) intervention. Graphical illustration created in BioRender. Pham, T. (2023) BioRender.com/i39k213. B Average running distance of male and female ET Slirp KO or littermate control (WT) mice measured for 6 weeks (male/female: WT ET, n\u2009=\u20098/6; Slirp KO, n\u2009=\u20097/10); \u25cb (blue) male, \u25c7 (red) female). C Average food intake of male and female sedentary (SED) and ET Slirp KO or WT mice measured for 2 weeks after 4 weeks of running (male/female: WT SED, n\u2009=\u20095/3; WT ET, n\u2009=\u20096/4; Slirp KO SED, n\u2009=\u20095/8; Slirp KO ET, n\u2009=\u20095/7), \u25cb (blue) male, \u25c7 (red) female). D\u2013K Blood glucose levels following a 4-hour fasting period before the glucose tolerance test (GTT). Glucose tolerance of male and female SED and ET Slirp KO or WT mice after 7-weeks of ET. iAUC of glycemic excursion in response to bolus of glucose 2\u2009g\u2009kg\u22121 body weight (BW). Insulin response before (0\u2009min) and following (20\u2009min) the oral glucose challenge (male/female: WT SED, n\u2009=\u20096/6; WT ET, n\u2009=\u20097/6; Slirp KO SED, n\u2009=\u20097/11; Slirp KO ET, n\u2009=\u20097/10. \u25cb male, \u25c7 female). Data are means\u2009\u00b1\u2009SEM, including individual values where applicable. Geno, main effect of genotype; ET, main effect of exercise training; Acute exercise, main effect of acute exercise bout; Time X Geno ET, interaction between glucose bolus and genotype in the ET groups. *p\u2009<\u20090.05, **p\u2009<\u20090.01; E WT ET vs. KO ET, *p\u2009<\u20090.05; WT SED vs. WT ET, #p\u2009<\u20090.05, as per Two-tailed unpaired Student\u00b4s t test (B), Two-way RM ANOVA with \u0160\u00edd\u00e1k\u2019s multiple comparisons test in ET groups (E, I), Two-way ANOVA (C, D, F, G, H, J, K). Source data are provided as a Source Data file.\n\nThe daily running distance recorded over 6 weeks of the 10-week study was 2.4\u2009km/day on average for male mice of both genotypes, whereas it was 4.8\u2009km/day for female exercise-trained (ET) WT mice and 3.0\u2009km/day for female KO ET mice (Fig.\u00a03B). It is important to consider that the running distance observed in our study of 18-week old mice is shorter than those reported in other exercise training studies using ~ 10-week-old mice, which typically run 6\u20138\u2009km/day on average25,50,51. Despite the higher food intake in ET mice (Fig.\u00a03C), exercise training lowered body weight in both trained genotypes (Supplementary Fig.\u00a02A). Moreover, irrespective of genotype, exercise training reduced fat mass/body weight ratio (Supplementary Fig.\u00a02B) and increased lean mass/body weight ratio compared to sedentary (SED) groups (Supplementary Fig.\u00a02C). Exercise training had no effect on gastrocnemius or TA muscle weight relative to tibia length in either sex (Supplementary Fig.\u00a02D). However, we noted that female Slirp KO SED and both ET groups had elevated heart weight relative to WT SED (Supplementary Fig.\u00a02D).\n\nExercise capacity increased similarly with exercise training, independently of genotype (+\u20091.2-fold for WT ET, +1.3-fold for KO ET; Supplementary Fig.\u00a02E) and sex (females shown in Supplementary Fig.\u00a02G). Post-exercise blood lactate levels tended to be reduced (-1.7-fold; p\u2009=\u20090.067) by ET in WT male mice (Supplementary Fig.\u00a02F). The training-induced lowering of blood lactate post-exercise was absent in male and female Slirp KO mice (Supplementary Fig.\u00a02F, H).\n\nExercise training is a powerful preventative intervention against many metabolic disorders12, partially due to its remarkable effects in improving glucose tolerance and insulin sensitivity documented in both animal models and humans25,50,52. Accordingly, male WT ET mice displayed reduced fasting blood glucose (Fig.\u00a03D) and improved blood glucose control evidenced by a downshifted glucose response curve compared to WT SED mice. This effect was not observed in trained male Slirp KO mice (Fig.\u00a03E). Thus, trained Slirp KO mice displayed similar glucose tolerance as both SED groups. The incremental area under the curve (iAUC) of blood glucose was similar between groups (Fig.\u00a03F), suggesting that the lowered plasma glucose excursion during the GTT was driven by lower basal glucose levels. Yet, exercise training reduced the levels of plasma insulin in both WT and Slirp KO mice, suggesting that insulin sensitivity was increased by exercise training in both groups (Fig.\u00a03G). There was no effect of exercise training or genotype on fasting glucose, glucose tolerance or insulin levels in female mice (Fig.\u00a03H\u2013K).\n\nOur results demonstrate that SLIRP is largely dispensable for organismal adaptations to exercise training, yet, in male mice SLIRP is\u00a0required for training-induced improvement in blood glucose regulation.\n\nExercise training can counteract mitochondrial damage, arising from excessive accumulation of reactive oxygen species (ROS) or impaired assembly of OXPHOS complex following mtDNA mutations11,13,53,54,55. As we established that loss of SLIRP had marked negative implications for mitochondrial structure and respiration in skeletal muscle (Fig.\u00a01E\u2013H), we asked whether exercise training could rescue these defects.\n\nSkeletal muscle mitochondrial network structure was qualitatively investigated in TMRE+ stained FDB fibers by confocal microscopy. Remarkably, exercise training completely rescued the derangements in mitochondrial network structure and mitochondrial fragmentation observed in Slirp KO SED mice, quantitatively corroborated by restoration of the fragmentation index (Fig.\u00a04A).\n\nA Confocal microscopy of mitochondrial network structure using a mitochondrial membrane potential probe (TMRE\u2009+\u2009) in flexor digitorum brevis muscle fibers of sedentary (SED) and 10-week ET Slirp knockout (KO) mice and littermate controls (WT) (male/female: WT SED, n\u2009=\u20091/3; WT ET, n\u2009=\u20092/2; Slirp KO SED, n\u2009=\u20093/2; Slirp KO ET, n\u2009=\u20092/2. 7\u201310 fibers per mouse, \u25cb male, \u25c7 female), and corresponding fragmentation index. SED data also depicted in Fig. 1D. B Mitochondrial respiration measured by Oroboros respirometry system in gastrocnemius of SED and ET Slirp KO and WT (male/female: WT SED, n\u2009=\u20094/4; WT ET, n\u2009=\u20095/2; Slirp KO SED, n\u2009=\u20095/5; Slirp KO ET, n\u2009=\u20096/5. \u25cb male, \u25c7 female). SED data is also depicted in Fig. 1H. C Western blot analysis of SLIRP and LRPPRC protein abundance in gastrocnemius of male SED and 10-week ET Slirp KO and WT mice (WT SED/ET, n\u2009=\u20096/8; Slirp KO SED/ET, n\u2009=\u20097/6). D Schematic of mtDNA- and nuclear DNA-encoded oxidative phosphorylation (OXPHOS) proteins analysed. Graphical illustration created in BioRender. Pham, T. (2022) BioRender.com/n94e933. E RT-qPCR analysis of mitochondrial transcript levels in gastrocnemius of male SED and ET Slirp KO and WT mice (WT SED/ET, n\u2009=\u20096/7; Slirp KO SED/ET, n\u2009=\u20097/6). SED data also depicted in Fig.\u00a01J. F\u2013H Western blot analysis of (F) mtDNA-encoded proteins, MT-ND3 (CI), MT-CO1 (CIV), MT-ATP6 (ATPase), (G) nDNA-encoded proteins NDUFB8 (CI), SDHB (CII), UQCRC2 (CIII) and ATP5A (ATPase), (H) MTCO1/ATP5A ratio as readout of mitonuclear balance, and (I) representative blots in gastrocnemius of male SED and ET Slirp KO and WT mice (WT SED/ET, n\u2009=\u20096/8; Slirp KO SED, n\u2009=\u20097/6). Data are means\u2009\u00b1\u2009SEM, including individual values. Geno, main effect of genotype; ET, main effect of exercise training; Geno x ET, interaction between genotype and exercise training; Substrate, main effect of substrate. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.01 as per Two-way ANOVA with \u0160\u00edd\u00e1k\u2019s multiple comparisons test (A, B, C, E, F\u2013H). Source data are provided as a Source Data file.\n\nConcomitantly with improved mitochondrial structure, exercise training restored the respiratory flux in Slirp KO mice to the level of WT muscle (Fig.\u00a04B). WT mice did not improve respiratory flux with exercise training, which was consistent with no effect of exercise training on fatty acid oxidation of contracting isolated WT soleus muscle (Supplementary Fig.\u00a02I) and aligns with other studies56,57. Exercise training in Slirp KO mice improved fatty acid oxidation in contracting Slirp KO soleus muscles (Supplementary Fig.\u00a02I), in alignment with improved respiratory flux in Slirp KO muscles by exercise training. Thus, exercise training counteracted the structural alterations in Slirp KO SED mice and had a positive impact on mitochondrial respiratory capacity, suggesting restored mitochondrial function.\n\nTogether, these results support that exercise training exploits the remarkable adaptive plasticity that mitochondria retain even in the event of mitochondrial network disruptions and impaired respiratory flux in skeletal muscle.\n\nWe next aimed to investigate the molecular underpinnings for the defects in mitochondrial structure and function in Slirp KO and their correction by exercise training. We primarily focused our analyses on male mice, as our findings revealed a stronger propensity for adaptive response to exercise training in male Slirp KO mice. The corresponding analyses for female mice are included in Supplementary Fig.\u00a03.\n\nFirst, we verified that both SLIRP (+\u20091.2-fold) and concomitantly LRPPRC (+\u20091.4-fold) were upregulated by exercise training in the gastrocnemius muscle in WT, but not Slirp KO mice (Fig.\u00a04C). These findings reinforce the co-stabilizing relationship of SLIRP and LRPPRC not only at sedentary17,18,20,58 but also during exercise trained conditions.\n\nSLIRP, together with LRPPRC, has been shown to maintain mt-mRNA stability and aid mitoribosomal translation in non-muscle tissues17,18,19,20,58. Indeed, the marked downregulation of mt-mRNA transcripts in SED Slirp KO muscle, also shown in Fig.\u00a01J, remained largely reduced in ET Slirp KO mice relative to WT ET mice (Fig.\u00a04E). Visually, there appeared to be a partial rescue of mt-Nd5/6 and mt-Co1 in ET Slirp KO mice, though this observation was not statistically confirmed. The sustained reduction in mt-mRNA transcripts upon Slirp KO is clearer in female mice, where exercise training significantly increased mt-mRNA transcript levels in WT mice, a response completely abrogated in Slirp KO mice (Supplementary Fig.\u00a03B). The differences in mt-mRNA transcript levels in response to exercise training between sexes are likely due to the variation in running volume, with female WT mice running twice as much as male WT mice (4.8\u2009km/day vs. 2.4\u2009km/day; Fig.\u00a03B). In contrast, mtDNA copy number, measured using quantitative PCR and primers for mt-Nd1 (Supplementary Fig.\u00a02J) or mt-Nd5/mt-Nd6 (Supplementary Fig.\u00a02K), were unaltered across groups or elevated only in Slirp KO SED mice. This suggests a compensatory response to mitigate muscle mitochondrial defects by increasing mtDNA copy number59 or decreasing its degradation. Accordingly, these findings suggests that SLIRP is crucial for mitochondrial transcript stability in skeletal muscle. We were intrigued to find that exercise training still restored protein content of mtDNA-encoded OXPHOS subunits, MT-ND3, MT-CO1 and MT-ATP6 in Slirp KO mice (Fig.\u00a04F). Thus, exercise training completely counteracted the sustained reduction in the mt-mRNA levels of these proteins in Slirp KO ET mice. Interestingly, exercise training markedly upregulated nuclear-encoded OXPHOS subunits SDHB (Complex II) and ATP5A (ATPase complex, also known as complex V) protein content in Slirp KO mice, both 1.4-fold more than in the trained WT mice (Fig.\u00a04G). Other nuclear-encoded OXPHOS subunits, such as NDUFB8 (Complex I) and UQCRC2 (Complex III) also upregulated by exercise training (Fig.\u00a04G, representative blots shown in Fig.\u00a04I). Intriguingly, the trained Slirp KO mice had a more pronounced increase in all OXPHOS protein content in response to exercise training compared to the WT mice (Supplementary Fig.\u00a04A\u2013G), suggesting a stronger propensity for adaptive responses to exercise training due to loss of SLIRP. Mitonuclear imbalance in Slirp KO mice also appeared to be restored by exercise training (Fig.\u00a04H), evaluated by determining the ratio between mtDNA- (MTCO1) and nDNA-encoded (ATP5A) OXPHOS proteins60. Citrate synthase (CS) activity correlates with mitochondrial content in human61 and mouse skeletal muscle62. CS activity, indicative of mitochondrial content, was increased similarly in Slirp KO and WT mice after exercise training (Supplementary Fig.\u00a02L), suggesting that improvements in mitochondrial content following exercise training are independent of SLIRP.\n\nThese results underline a substantial skeletal muscle mitochondrial plasticity, which is retained despite a 60\u201380% reduction in mt-mRNA pools. These findings point towards unidentified mechanisms by which exercise training modulates translational efficiency or protein degradation pathways to dictate final protein content.\n\nWe next sought to determine the potential mechanism by which sustained reductions of mitochondrial transcript levels can be bypassed by exercise training. Enhanced mitochondrial protein synthesis mediates exercise training adaptations in muscle mitochondrial function in both young and elderly individuals42. Thus, we assessed pathways associated with protein synthesis in mitochondria, facilitated by the resident mitoribomes. We observed training-induced increases in 12S ribosomal RNA (12S rRNA), 16S ribosomal rRNA (16S rRNA, Fig.\u00a05B) and several mitoribosomal proteins, essential components of mitoribosome translation63,64 (MRPL11, MRPL12, MRPS18B, MRPS35, Fig.\u00a05C). MRPL11, MRPL12, MRPS18B protein content was 86%, 59%, and 48%, respectively, more elevated in response to exercise training in Slirp KO than WT mice (Supplementary Fig.\u00a04H\u2013J). Exercise training increased cytosolic ribosomal protein S6 protein content 68% in WT mice and 34% in Slirp KO mice (rpS6, Fig.\u00a05C; representative blots in Fig.\u00a04L).\n\nA Schematic of proteins associated with mitoribosomal biogenesis and mitochondrial quality control analysed. Graphical illustration created in BioRender. Pham, T. (2024) BioRender.com/y84d051. B RT-qPCR analysis of 12S rRNA and 16S rRNA in gastrocnemius of male sedentary (SED) and exercise trained (ET) Slirp knockout (KO) and control littermate (WT) mice (WT SED/ET, n\u2009=\u20096/7; Slirp KO SED/ET, n\u2009=\u20097/6). C\u2013L Western blot analysis of mitoribosomal proteins (MRPL11, MPRL12, MRPS18B, MRPS35; C), rpS6 (D), LONP1 (E), YME1L1 (F), phospho-EIF2\u03b1 S51 (G), PRDX3 (I), PRDX3 dimer/monomer ratio (J), PRDX2 (K), and representative blots (H, L) in gastrocnemius of male sedentary (SED) and exercise trained (ET) Slirp knockout (KO) and control littermate (WT) mice (WT SED/ET, n\u2009=\u20096/8; Slirp KO SED/ET, n\u2009=\u20097/6). Data are means\u2009\u00b1\u2009SEM, including individual values. Geno, main effect of genotype; ET, main effect of exercise training; *p\u2009<\u20090.05, **p\u2009<\u20090.01, as per Two-way ANOVA (B\u2013G, I\u2013K). Source data are provided as a Source Data file.\n\nGiven the predominant impact of Slirp KO on mitochondria, training-induced adaptive changes might also manifest directly within the affected cellular compartment. Here, we investigated mitochondrial quality control systems involving the mitochondrial Lon protease 1 (LONP1), YME1-like ATPase (YME1L1), the p-eIF2\u03b1\u2013ATF4\u2013CHOP axis65, and PRDX3.\n\nLONP1 and YME1L1 are the frontline defense against mitochondrial damage and remove accumulated unfolded or damaged proteins66. Exercise training increased both LONP1 and YME1L1 protein content (Fig.\u00a05E, F), with a 1.7-fold greater response in Slirp KO mice (Supplementary Fig.\u00a04K), suggesting an enhanced mitochondrial quality control induced by exercise training that is augmented in Slirp KO muscles with low mt-mRNA levels.\n\nRecent research identifies mitochondrial damage as a key activator of the p-eIF2\u03b1\u2013ATF4\u2013CHOP axis, known as integrated stress response (ISR)65,66. Since ISR is involved in mitochondrial quality control, we measured EIF2\u03b1 phosphorylation at S51 to investigate the impact of SLIRP loss and exercise training on ISR. The loss of SLIRP tended to increase p-EIF2\u03b1 1.4-fold, indicating the potential presence of mitochondrial damage. Intriguingly, exercise training markedly reduced phospho-EIF2\u03b1 by 58% selectively in Slirp KO mice, and below the level of WT SED and WT ET mice. As exercise training had no such effect in the WT mice (Fig.\u00a05G, Supplementary Fig.\u00a04L), exercise training may reduce phospho-EIF2\u03b1 specifically in the context of mitochondrial damage induced by SLIRP loss to promote protein translation.\n\nAnother mitochondrial quality control system involves a mitochondrial scavenger enzyme, peroxiredoxin 3 (PRDX3). PRDX3 plays a role in balancing redox environment specifically\u00a0in mitochondria67,68, possibly contributing to the preservation of factors critical for translation during conditions of mitochondrial dysfunction and exercise training. Interestingly, PRDX3 protein content was 50% lower in Slirp KO SED compared to WT SED mice, indicative of a lower mitochondrial oxidative stress defense capacity in Slirp KO muscle. This was restored by exercise training, bringing PRDX3 up to levels observed in trained WT mice (Fig.\u00a05I). We further noted that ET lowered the PRDX3 dimer to monomer ratio in both genotypes, indicative of a lower basal mitochondrial-derived peroxide accumulation likely due to an enhancement of scavenger activity (Fig.\u00a05J). The cytosolic peroxidoxin 2 (PRDX2) was unaffected by genotype and exercise training (Fig.\u00a05K, representative blots in Fig.\u00a05L).\n\nCollectively, our findings indicate a complex interplay of spatially distinct molecular muscle adaptations in response to Slirp KO and ET. Our findings suggest that exercise training induces mechanisms to increase mitochondrial protein synthesis capacity and improve mitochondrial quality control. Specifically, exercise training elevated mitoribosome mass and restored mitochondrial quality control to powerfully circumvent the Slirp KO-induced depletion of mtDNA-encoded transcript levels.\n\nOur intriguing results from mouse and fly models prompted us to test whether our results provide translational value for humans in health and disease. SLIRP and LRPPRC protein content were determined in human skeletal muscle in four independent exercise cohorts employing different exercise modalities in conjunction with or without aging or diabetes and in males and females. Participant characteristics and study designs for the different ET modalities have been published previously for all the clinical studies69,70,71,72.\n\nSkeletal muscle SLIRP and LRPPRC protein abundances were 70% increased following 14-weeks of controlled and supervised aerobic and strength exercise training intervention in healthy young women69 (Fig.\u00a06A). Also, a 6-week high-intensity interval training (HIIT) intervention elevated muscle protein content of SLIRP (+\u200980%, Fig.\u00a06B) and LRPPRC (+\u200950%, Fig.\u00a06B) in healthy young men72. Thus, the exercise training response of SLIRP and LRPPRC was highly consistent, independent of sex, and responsive to various exercise modalities. The 12-week progressive resistance exercise training70 increased SLIRP and LRPPRC muscle protein content in the entire group of young and old individuals but had no significant effect on LRPPRC abundance in neither young nor elderly subjects separately (Fig.\u00a06C). The mtDNA-encoded transcript levels of MT-ND6, MT-CYB, MT-CO1 and MT-ATP6 were comparable across all groups (Fig.\u00a06D), indicating that mitochondrial gene expression was not significantly affected by either aging or the training regimen in this study\u00a0at the time of sampling. Similarly to our observations in mice, we observed a marked 1.9 and 2.3-fold increase in MRPL11 in elderly and young individuals, respectively (Fig.\u00a06E; representative blot in Fig.\u00a06C), supporting that the increase in mitoribosomal biogenesis and mitochondrial translation capacity in response to exercise training is conserved in humans42.\n\nA Western blot analysis of SLIRP and LRPPRC protein in the vastus lateralis muscle of healthy young women (n\u2009=\u20099/group, (\u25cb Pre ET, \u25a1 Post ET)) before and after a 14-week controlled aerobic and strength exercise training (ET) intervention69. B Immunoblotting of SLIRP and LRPPRC protein in the vastus lateralis muscle of healthy young men (n\u2009=\u20096/group, (\u25cb Pre ET, \u25a1 Post ET)) before and after a 6-week high intensity interval training (HIIT)72. C, E Immunoblotting of SLIRP and LRPPRC (C), MRPL11 (E) protein in the vastus lateralis muscle of male young and older individuals before and after 12-week progressive resistance training70 (Young, Pre/Post, n\u2009=\u200910/6; Old, Pre/Post, n\u2009=\u200911/9. \u25cb Pre ET, \u25a1 Post ET). D RT-qPCR analysis of mitochondrial transcript levels in the vastus lateralis muscle of male young and older individuals before and after 12-week progressive resistance training70 (Young, Pre/Post, n\u2009=\u20099/7; Old, Pre, n\u2009=\u200910/8. \u25cb Pre ET, \u25a1 Post ET). F, H Immunoblotting of SLIRP, LRPPRC (F), and MT-CO2 (H) protein in the vastus lateralis muscle of glucose-tolerant lean, obese males, and males with type 2 diabetes before and after high-intensity interval training71 (Con, n\u2009=\u200916; OB, n\u2009=\u200915, T2D, n\u2009=\u200913; \u25cb pre ET, \u25a1 post ET). Con, control; OB, obese; T2D, Type 2 diabetes. G RT-qPCR analysis of mitochondrial transcript levels in the vastus lateralis muscle of glucose-tolerant lean, obese males, and males with type 2 diabetes before and after high-intensity interval training71 (Con, Pre/Post, n\u2009=\u200913/10; OB, Pre/Post, n\u2009=\u200910/8; T2D, Pre/Post, n\u2009=\u20099/6; \u25cb pre ET, \u25a1 post ET). Data presented as individual before-and-after values. ET, main effect of exercise training, ET x T2D, Interaction between exercise training and presence of type 2 diabetes in Con and T2D group; *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, as per two-tailed paired Student\u00b4s t test (A\u2013C), Mixed-effects, REML model (D, E, G), Two-way RM ANOVA with \u0160\u00edd\u00e1k\u2019s multiple comparisons test (F, H). Source data are provided as a Source Data file.\n\nExercise training-mediated improvements in muscle mitochondrial content and function may be associated with improved glucose metabolism in individuals with obesity and T2D73,74, although this association is not always clear75. The training response of muscle SLIRP and LRPPRC to 8-weeks HIIT was conserved in individuals who were lean (+\u2009150% and +54%) or obese (+\u200985% and +45%, Fig.\u00a06F). However, the exercise response for LRPPRC, but not SLIRP, was blunted in muscle of patients with T2D in comparison to lean individuals (Fig.\u00a06F), suggesting impaired sensitivity of muscle to some mitochondrial adaptations to ET in T2D. At the mRNA level, mt-DNA encoded transcripts of MT-ND1, MT-CYB, MT-CO1 and MT-ATP6 were reduced with HIIT across all groups (Fig.\u00a06G). Yet, protein content of MT-CO2 (Fig.\u00a06H, representative blot in Supplementary Fig.\u00a05A) and other nuclear-encoded OXPHOS proteins (Supplementary Fig.\u00a05A) were significantly upregulated with HIIT. In line with previous findings42, the upregulation in OXPHOS protein abundance occurred despite lower levels of mRNA, and demonstrate a disassociation between the transcriptome and proteome76. Our correlation analysis in this cohort revealed that SLIRP protein content was positively associated with LRPPRC (r\u00b2 = 0.2901) and several OXPHOS proteins, specifically NDUFB8 (r\u00b2 = 0.1487), SDHB (r\u00b2 = 0.4020), UQCRC2 (r\u00b2 = 0.1110), MT-CO2 (r\u00b2 = 0.0528), and ATP5A (r\u00b2 = 0.1141, Supplementary Fig.\u00a05).\n\nTogether, we show that SLIRP and LRPPRC are robustly upregulated in skeletal muscle across various exercise training modalities in men and women, lean and obese. However, the exercise training response of LRPPRC was blunted in patients with T2D. This suggests that T2D is associated with decreased sensitivity to some of the adaptive mitochondrial responses normally elicited by exercise training, possibly influenced by exercise intensity or volume. Thus, our clinical data in humans provide strong translational value of the SLIRP-deficient fly and mouse models and point towards a conserved requirement of SLIRP for skeletal muscle health, and potentially the SLIRP-independent mechanisms that compensate during exercise training.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54183-4/MediaObjects/41467_2024_54183_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54183-4/MediaObjects/41467_2024_54183_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54183-4/MediaObjects/41467_2024_54183_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54183-4/MediaObjects/41467_2024_54183_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54183-4/MediaObjects/41467_2024_54183_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54183-4/MediaObjects/41467_2024_54183_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our investigation led to six key findings that bridge substantial knowledge gaps concerning the role of mitochondrial posttranscriptional mechanisms on skeletal muscle biology and their contributions to exercise training adaptations.\n\nFirst, lack of Slirp led to mild mitochondrial network disruption, mitochondrial fragmentation, and reduced respiratory capacity in skeletal muscle in mice. Second, SLIRP deficiency impaired muscle functionality, and reduced lifespan in flies. Third, SLIRP was identified as an exercise training-responsive downstream target of PGC1-\u03b1. Fourth, SLIRP was needed for improving blood glucose regulation after exercise training in male mice. Fifth, exercise training restored the mitochondrial defects elicited by the lack of SLIRP on mitochondrial structure, respiration, and mtDNA-encoded OXPHOS, via mechanisms likely involving the upregulation of mitoribosome content and enhanced mitochondrial quality control. Finally, we established a translational foundation for our findings by substantiating the conservation of SLIRP\u00b4s response to exercise training in four independent human cohorts integrating different exercise modalities, sex, health, and age conditions.\u00a0These\u00a0findings not only unravel a delicate interplay between the mt-mRNA-stabilizing protein SLIRP, and the integrity of skeletal muscle mitochondria structure and performance, but also underscore the potential of exercise training in promoting selective mitochondrial translation capacity and quality control to circumvent these mechanisms\u00a0(graphical summary depicted in Fig. 7).\n\nOur first finding indicates that SLIRP is a hitherto unrecognized player in regulating mitochondria content, structure, and respiration in skeletal muscle. Our results are partially consistent with reports in heart, liver, and kidney Slirp KO17,18,58 showing that SLIRP forms a complex with LRPPRC and is crucial for the maintenance of the mt-mRNA reservoir. Accordingly, our observation of mitochondria with abnormal cristae in Slirp KO muscle aligns with TEM micrographs of Lrpprc KO heart tissue19. However, despite the stark similarities, mitochondrial respiration was mildly compromised in Slirp KO skeletal muscle, but not in heart or liver18, underscoring SLIRP\u2019s functional importance within skeletal muscle relative to other tissues. In alignment, previous research further showed that Slirp KO skeletal muscle fibers have reduced sarcoplasmic reticulum Ca2+ storage capacity77. Interestingly, the young full-body Slirp KO mice only have a mild molecular phenotype and appear overall healthy, despite the significant decrease in mt-mRNA levels, respiration, and mitochondrial morphology. The effects of Slirp KO on mitochondrial morphology further seemed to be isolated to IMF mitochondria, whereas subsarcolemmal mitochondria were unaffected at this age. Yet, detrimental physiological long-term consequences are suggested by the flies lacking SLIRP1 given their much shorter life span. Thus, our present findings underscore the importance of SLIRP across various tissue types, with emphasis on its importance for normal skeletal muscle mitochondrial morphology and respiration, and overall mitochondrial health.\n\nOur second finding that SLIRP depletion compromised climbing ability, impaired starvation tolerance, and reduced lifespan in flies, illustrates the detrimental functional consequences of lacking SLIRP long-term in the muscle tissue. SLIRP1 has been demonstrated to interact with the fly orthologue protein LRPPRC1 to regulate mitochondrial mRNA polyadenylation and maturation, while SLIRP2 interacts with LRPPRC2 to coordinate mitochondrial translation19,27,28. It has been shown that SLIRP1/LRPPRC1 and SLIRP2/LRPPRC2 complexes in flies\u00a0have distinct essential roles in the regulation of mtDNA expression. This distinction likely accounts for the varying impacts on climbing ability, starvation tolerance, and lifespan observed when SLIRP1 or SLIRP2 is lost. The effects of mitochondrial proteins on modulating life span are equivocal. Some report that the depletion of specific OXPHOS genes reduces life span, whereas others report no effect or an increase on life span in model organisms78,79,80. The \u201cMitochondrial Threshold Effect Theory\u201d81 posits that mild mitochondrial dysfunction allows normal physiology in model organisms until a threshold is reached. Beyond that, severe mitochondrial dysfunction can lead to premature aging, developmental arrest or even death82,83,84. In support, introducing a heteroplasmic pathogenic mtDNA mutation in the tRNAAla gene into Slirp KO mice caused an aggravated mitochondrial translation defect, resulting in embryonic lethality and impaired growth of mouse embryonic fibroblasts85. Our findings support an important role for SLIRP and mitochondrial function in maintaining normal lifespan because we observed reduced survival in muscle-specific SLIRP KD flies.\n\nOur third finding was that PGC-1\u03b11 modulated SLIRP protein, providing evidence for a new training-responsive PGC-1\u03b1 target in skeletal muscle. Interestingly, training-induced adaptations in mitochondrial proteins can occur independently of PGC-1a1 as the main regulator86. In agreement with that, our fifth finding was that the training-induced improvements in mitochondrial function could bypass SLIRP deletion. These findings agree with other studies showing that exercise training can rescue mitochondrial dysfunction and elicit improvements in glucose homeostasis in mice and humans13,53,86,87. Interestingly, SLIRP was required for the training-induced improvements in blood glucose regulation in trained male Slirp KO mice. The mechanism by which SLIRP influences glucose metabolism was not established in the present study, although this could be due to decreased intramyocellular insulin signalling88, reduced insulin-independent glucose uptake14, capillarization, and/or blood flow89. Female mice did not improve their glucose tolerance in response to exercise training. Sexual dimorphism in response to metabolic challenges, including exercise training, has been frequently reported in mouse models90,91. In both sexes, exercise training circumvented SLIRP to induce mitochondrial and metabolic adaptations in skeletal muscle, despite sustained low mitochondrial transcript levels. However, we observed differences in mechanisms by which male and female mice compensated for the lack of SLIRP, illuminating an exciting area for further investigation.\n\nWe were intrigued by the observation that exercise training could circumvent even 60\u201380% loss in mitochondrial-mRNA pools to upregulate mtDNA-encoded OXPHOS proteins. In other words, the beneficial effect of exercise training on mitochondrial oxidative phosphorylation and ATP provision is independent of a correction of mt-mRNA transcript levels and LRPPRC/SLIRP protein content. As mtDNA copy number was either increased in SED Slirp KO or unchanged in ET Slirp KO mice, these findings give rise to the possibility that mt-mRNAs are produced in excess in vivo18. Excess mt-mRNA may allow for rapid engagement of mitochondrial protein synthesis in the event of sudden changes in energy demand. In response to exercise training, the mitoribosomal components, MRPL11, MRPL12, MRPS18B, 12S rRNA, and 16S rRNA were upregulated, indicating increased mitoribosome biogenesis and capacity for mitochondrial protein synthesis. These adaptations to exercise training may help sustain protein synthesis and normal physiology, even in the presence of low mt-mRNA abundance. The greater increase in OXPHOS, mt-rRNA, and mitoribosomal proteins in Slirp KO ET mice indicates that exercise training may be more effective in the presence of mitochondrial dysfunction, providing exciting avenues for exploring the use of exercise training in conditions of mitochondrial dysfunction. Exercise training also upregulated LONP1 and YME1L1, both important regulators of mitochondrial quality control and proteostasis, and PRDX3, linked to peroxide scavenging. While mitoribosomal protein content, 12S rRNA, 16S rRNA, LONP1, YME1L1, and PRDX3 may not directly control translation efficiency, their roles in the mitoribosomal makeup and improved mitochondrial quality control can influence the overall cellular environment where protein synthesis occurs. Albeit limited by not directly measuring mitochondrial translational rate, this working hypothesis is supported by findings in human skeletal muscle showing that mitochondrial protein synthesis and upregulation of mitoribosome biogenesis is at the core of exercise training-induced benefits42. That mitochondrial stress was effectively relieved via exercise training is suggested by the marked reduction in phosphorylation of EIF2a at S51, an integral marker of the ISR selectively in Slirp KO mice. This suggests a unique interaction between mitochondrial damage and the ISR, highlighting the heightened sensitivity of Slirp KO mice to exercise training.\n\nFinally, our results are prospectively relevant to humans, as SLIRP and LRPPRC were consistently upregulated in human skeletal muscle in response to multiple exercise training modalities in both sexes. The effect of HIITwas blunted for LRPPRC in patients with T2D. Interestingly, the age-related decline in muscle mitochondrial protein synthesis92 can be reversed by exercise training in elderly42, corroborating our findings that ET bypasses SLIRP to increase mitoribosomal translation. Importantly, our findings demonstrate that the synthesis of mtDNA-encoded OXPHOS proteins is not limited by transcript abundance in skeletal muscle \u2013 a feature that seems to be conserved as a fundamental mechanism27,42.\n\nTaken together, our findings not only imply that mt-mRNA stabilization via SLIRP/LRPPRC is needed for the regulation of basal mitochondrial function in skeletal muscle, but also highlight an incredible exercise training \u2013stimulated plasticity of mitochondria in skeletal muscle facing mitochondrial defects (graphical summary depicted in Fig.\u00a07). Our findings underscore exercise training as a therapeutic intervention to combat mitochondrial dysfunction in genetic and lifestyle-induced muscle pathologies, including T2D.\n\nGraphical illustration created in BioRender. Pham, T. (2023) BioRender.com/m15m010.\n\nIn this work, we uncover a conserved role for mitochondrial transcript stability via SLIRP in mitochondrial structure and respiratory capacity in skeletal muscle. We identify SLIRP as an\u00a0exercise-responsive downstream target of PGC-1\u03b1 that is conserved in human skeletal muscle and critical for lifespan in Drosophila. Because the defects in Slirp KO muscle could be reversed by ET, these findings add to the clinical relevance and add impetus to the important health-promoting role of ET in pathologies characterized by mitochondrial defects.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54183-4/MediaObjects/41467_2024_54183_Fig7_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Clinical experiments were approved by the Ethics Committee of Copenhagen (H-17004045, H-2-2010-100, H-1-2013-034) or by the Regional Scientific Ethical Committees for Southern Denmark (S-20170142) and performed in accordance with the Helsinki Declaration II and with informed consent was obtained from all human research participants. No compensation was given to the study participants, apart from NCT02429128 and NCT03317704 where subjects received a small compensation for discomforts related to the study and were reimbursed for travel expenses. Studies including human study participants are described in refs. 69,70,71,72 and registered at ClinicalTrials.gov (NCT03500016, NCT02429128, NCT01252381, and NCT03317704). All mouse experiments complied with the European Convention for the protection of vertebrate animals used for experimental and other scientific purposes (No. 123, Strasbourg, France, 1985; EU Directive 2010/63/EU for animal experiments) and were approved by the Danish Animal Experimental Inspectorate (License number: 2016-15-0201-01043, 2021-15-0201-01104).\n\nIn standard conditions, the flies were maintained on a SYA medium containing 0.5% (w/v) agar, 2.4% (v/v) nipagin, 0.7% (v/v) proprionic acid, 10% (w/v) dry baker\u2019s yeast and 5% (w/v) sucrose. The experiments took place at +25\u2009\u00b0C, 65% humidity under a 12\u2009h:12\u2009h light:dark cycle. RNAi lines for SLIRP1 (51019 GD), SLIRP2 (23675 GD), and the GD library control line (w1118, 60000 GD) were obtained from Vienna Drosophila Resource Center. Mef2-GAL4 line was obtained from Bloomington Stock Center to generate muscle-specific SLIRP KD flies.\n\nDrosophila has a natural tendency to climb upwards, also known as negative geotaxis. The negative geotaxis, or climbing, assay was performed as previously described93. Briefly, 20 male Drosophila flies aged 5\u20136 days were transferred into empty vials (25 \u00d7 95\u2009mm), and another empty vial was taped on top to create a 25 \u00d7 190\u2009mm cylinder. Six cylinders were set side by side into an apparatus. The apparatus was tapped down 5 times to make all the flies fall to the bottom of the cylinder. The climbing assay was measured in technical triplicate for each biological replicate, with 90\u2009s allowed for each climb. The assay was recorded with a camera and videos were analysed to determine the time for half of the flies in the vial to reach halfway point (95\u2009mm) in the cylinder.\n\nFour-day old male flies were individually housed in monitor tubes with an outside diameter of 5\u2009mm (PPT5x65, Trikinetics, Waltham, MA). For starvation analysis, the tubes contained 1% agar in water. Thirtytwo tubes per monitor (DAM2 Drosophila Activity Monitor, Trikinetics, Waltham, MA) were set up, one monitor per genotype. For the starvation analysis, flies were kept in the DAM system until the last fly had died.\n\nFor lifespan assays, flies were mated at a controlled density of 25 female and 10 male flies, respectively. One-day old male flies were collected into vials (10 male flies per vial, with 10 vials per genotype). The flies were kept on a SYA diet in Drosoflippers (http://www.drosoflipper.com/). Flies were flipped onto fresh food every second day and deaths were scored during the transfer.\n\nThe generation of the model, quality, and specificity of the overexpression has been described previously38, of which we analyzed quadriceps muscle samples of 12-week-old male WT and PGC-1\u03b11 transgenic mice, kept on C57/Bl6 background.\n\nThe generation of the model, quality, and specificity of the overexpression has been described previously36, of which we analyzed gastrocnemius muscle samples of 12-week-old male WT and PGC-1\u03b14 transgenic mice, kept on C57/Bl6 background.\n\nThe generation of the model, quality, and specificity of the KO has been described previously49. Male muscle-specific PGC-1\u03b1 MKO mice and littermate control mice homozygous for loxP inserts (lox/lox) were generated by crossbreeding myogenin-Cre mice with loxP flanked-Pgc-1\u03b1 mice. Mice were housed in a 12:12\u2009h light/dark photocycle at 22\u2009\u00b1\u20092\u2009\u00b0C with nesting material.\n\nFor the acute exercise bout, tibialis anterior muscles of PGC-1\u03b1 MKO mice and littermate control mice were collected 3\u2009h after one bout of equal distance treadmill running (1.4\u2009km) at 10\u00b0 incline and 60% of their individual maximal running speed achieved by a graded treadmill running test94.\n\nFor the ET intervention, mice were single-housed with or without access to in-cage running wheels from 8 to 20 weeks old. Running distance and duration were monitored by a regular cycle computer. PGC-1\u03b1 MKO mice tended to run less than lox/lox, thus running wheels of lox/lox mice were occasionally blocked to ensure equal running distance. On average, mice ran 25\u2009km/week. The running wheels were blocked for all mice 24\u2009h prior to euthanization and quadriceps muscle was harvested in the morning in the fed state.\n\nThe AAV6 vector for SLIRP overexpression and ultra-purified eGFP control AAV6 virus were manufactured by VectorBuilder Inc. (Shenandoah, Texas, USA; AAV6SP(VB190219-1010jvy)-C).\n\nViral particles were diluted in Gelofusine (B. Braun, Germany) to a dosage of 5 \u00d7 109 vector genomes at a volume of 30 uL per injection. For muscle\u2010specific delivery of rAAV6 vectors, 12\u2010week-old wildtype mice (n\u2009=\u20096) were placed under general anaesthesia (2% isofluorane in O2). and injected intramuscularly with rAAV6:SLIRP in the TA muscle of one leg and control rAAV6:eGFP (empty vector) in the contralateral leg. Muscles were harvested 4 weeks after rAAV6 administration.\n\nAll mice were maintained under a 12:12\u2009h light/dark photocycle at 22\u2009\u00b1\u20092\u2009\u00b0C with nesting material. The female mice were group-housed. All mice received a rodent chow diet (Altromin no. 1324; Chr. Pedersen, Denmark) and water ad libitum.\n\nTwelve-week old female C57BL/6\u2009J mice (n\u2009=\u20098 for each time point) were subjected to an acute exercise bout (1\u2009h running, 60% of maximal running intensity, 15\u00b0 incline). Quadriceps muscle was harvested from rested and exercised mice immediately after, 2\u2009h, 6\u2009h, and 24\u2009h after the acute exercise bout.\n\nThe generation of the model, quality, and specificity of the KO has been described previously18. Sperm of homozygous Slirp KO mice kindly provided by Nils-G\u00f6ran Larsson and re-derived offspring was backcrossed to C57BL/6\u2009N background in our own animal facilities.\n\nAll mice were maintained under a 12:12\u2009h light/dark photocycle at 22\u2009\u00b1\u20092\u2009\u00b0C with nesting material. The female mice were group-housed (except during the ET intervention), whereas the male mice were single-housed. All mice received a rodent chow diet (Altromin no. 1324; Chr. Pedersen, Denmark) and water ad libitum.\n\nMouse genotyping of Slirp KO and WT mice was performed as previously described95 using qPCR on DNA from ear punches with the following primers: WT; 5\u2019-AGAAGGGAAT CCACAGGATA GGACA-3\u2019 and 5\u2019-GCTTTATTCC TAGTGCTGGC CTTGTT-3\u2019, KO; 5\u2019-AGAAGGGAAT CCACAGGATA GGACA-3\u2019 and 5\u2019-CGCCGTATAA TGTATGCTAT ACGAAGTT-3\u2019.\n\nFor 10-week voluntary wheel-running exercise training interventions, 16\u201318-week-old Slirp KO and littermate mice were randomized into test groups with or without access to in-cage running wheels (Tecniplast activity cage, wheel diameter: 23\u2009cm; Tecniplast, Buguggiate VA, Italy). The experimental design is schematically illustrated in Fig.\u00a03A. Running distance was monitored by a regular cycle computer prior to the metabolic tests for 6 weeks. Running wheels were locked 12\u2009h prior to terminal procedures to avoid effects of acute exercise bouts.\n\nTotal, fat, and lean body mass was measured by nuclear magnetic resonance using an EchoMRI\u2122 (USA).\n\nWe subjected the Slirp KO and WT mice to a GTT at 7 weeks of the training intervention study (as illustrated in Fig.\u00a03A). The GTT was executed after a 5\u2009h fasting period (7:00 a.m.\u201312:00 p.m.). Resting blood samples were taken from the tail 30\u2009min prior to the intraperitoneal injection of D-mono-glucose (2\u2009g/kg body weight). Tail blood glucose was measured after 0, 20, 40, 60, and 90\u2009min of injection.\n\nTo measure glucose-stimulated plasma insulin concentration at time-point (min) 0 and 20, tail vein blood samples were collected in a capillary-tube (50\u2009\u00b5l), centrifuged at 14,200\u2009g for 5\u2009min at 4\u2009\u00b0C, plasma collected and stored at \u221280\u2009\u00b0C. Insulin concentration was determined in duplicates using the Ultra-Sensitive Mouse Insulin ELISA Kit (#80\u2010INSTRU\u2010E10; ALPCO Diagnostics) to the manufacturer\u2019s instructions. The incremental area under the curve (iAUC) from the basal blood glucose concentration was determined using the trapezoid rule.\n\nSlirp KO and WT mice were acclimatized to the treadmill on 3 consecutive days by running at a speed of 0.16\u2009m/s and incline of 10\u00b0 for 5\u2009min the first day and 10\u2009min the following days. Prior to the running test and with a 1\u2009h delay, 2 blood samples were drawn from the tail before the test for pre-exercise blood glucose and blood lactate measurements. Subsequently, the mice ran at 0.16\u2009m/s for 5\u2009min followed by a gradual increase in speed every minute with 0.02\u2009m/s until exhaustion. Once the mouse reached its maximal running capacity, 2 blood samples were immediately drawn from the tail for post-exercise blood glucose and blood lactate measurements. The test was stopped when the mouse failed to keep up with the treadmill despite motivational efforts by the researcher.\n\nThis protocol was described elsewhere96. The mice were anaesthetized by inhalation of 2% isoflurane during the entire procedure. Acupuncture needles (0.2\u2009mm, TAI-CHI; B.C. Medical, Nykoebing SJ, Denmark) were inserted into both of the proximal and distal part of the mouse quadriceps femoris muscle of male 45\u201349-weeks old WT mice. The in situ contraction protocol consisted of nine sets of contraction bouts of 1\u2009min in duration, with a 30\u2009s break between bouts. The contraction bouts consisted of 3\u2009s of 10\u2009V stimulations of pulses with a duration of 0.1\u2009ms at a frequency of 100\u2009Hz, repeated every 10\u2009s. The contralateral leg served as a resting control. Two hours after the contraction, the mice were anaesthetized using 1:10 lidocaine:pentobarbital (6\u2009mg of pentobarbital sodium 100\u2009g\u22121 body weight) by intraperitoneal injection and given a retro-orbital injection of 21.75\u2009mg\u2009kg\u22121 body weight puromycin (Calbiochem, San Diego, CA, USA) in saline. Exactly 15\u2009min after puromycin injection, the mice were euthanized by cervical dislocation and Quad muscles were collected and snap frozen in liquid nitrogen.\n\nFlexor digitorum brevis muscles (FDB) were dissected and then incubated for 2\u2009h in serum-free \u03b1-MEM (22571-020, Gibco) containing 1.9\u2009mg/ml collagenase type 1 from clostridium histolyticum (C0130, Sigma-Aldrich) and 1\u2009mg/ml bovine serum albumin (9048-46-8, Sigma-Aldrich) at 37\u2009\u00b0C on a rotator. After collagenase treatment, muscles were incubated in \u03b1-MEM containing 10% fetal bovine serum (26050-70, Gibco) and subjected to mechanical dissociation using fire-polished Pasteur pipettes. Single muscle fibers were seeded in 35x14mm glass-bottom microwell dishes (P35G-1.5-14-C, MatTek Corporation) coated with 4 uL of Engelbreth-Holm-Swarm murine sarcoma ECM gel (E1270, Merck). Muscle fibers were kept in \u03b1-MEM containing 5% fetal bovine serum in a cell incubator (37\u2009\u00b0C, 5% CO2) for at least 16\u2009h prior to the experiments.\n\nFor determination of mitochondrial membrane potential (\u0394\u03a8mitochondrial) and mitochondrial network morphology, the fibers were incubated in 20\u2009nM tetramethylrhodamine and ethyl ester (TMRE\u2009+\u2009, Life Technologies) dissolved in Krebs Ringer buffer (145\u2009mM NaCl, 5\u2009mM KCl, 1\u2009mM CaCl2, 1\u2009mM MgCl2, 5.6\u2009mM glucose, 20\u2009mM HEPES, pH 7.4) for 30\u2009min before imaging. Confocal images were collected using a C-Apochromat \u00d740, 1.2 NA water immersion objective lens on an LSM 980 confocal microscope (Zeiss) driven Zeiss Zen Blue 3.\n\nMitochondrial network analysis was performed semi-automatically in ImageJ (National Institute of Health, USA). At least two nucleus-free regions per fiber were analyzed blindly. Before segmentation, the background was subtracted, and the pixels were averaged using a value of mean=2. The images were segmented, and particle analyses revealed the relative area of the TMRE+ signal compared to the total area (% mitochondrial area). The fragmentation index was calculated as the number of objects in relation to the total area covered by the dye. The ImageJ \u2018Red Hot\u2019 lookup table was used to visualize the images.\n\nA small longitudinal section (<2\u2009mm) of the red portion of the gastrocnemius muscle tissue was fixed by immersion 2% glutaraldehyde in 0.05\u2009M Phosphatebuffer, pH 7.4 and stored at 4\u2009\u00b0C. The samples were rinsed four times in 0.1\u2009M sodium cacodylate buffer, pH 7.4, and post-fixed with 1% osmium tetroxide (OsO4) and 1.5% potassium ferrocyanide [K4Fe(CN)6] in 0.1\u2009M sodium cacodylate buffer, pH 7.4 for 90\u2009min at 4\u2009\u00b0C. The samples were then rinsed twice and dehydrated through a graded mixture of alcohol at 4\u201320\u2009\u00b0C, infiltrated with graded mixtures of propylene oxide and Epon at 20\u2009\u00b0C, and embedded in 100% Epon at 30\u2009\u00b0C, as previously described (Nielsen et al., 2011). Longitudinally oriented sections, 60\u2009nm thick, were obtained with a Leica UC7 ultramicrotome. The sections were contrasted with uranyl acetate and lead citrate, and subsequently examined and image recorded in a CM100 TEM (Philips, Eindhoven, The Netherlands) operated at 100\u2009kV, and equipped with a Veleta camera and the iTem software package (Olympus, Hamburg, Germany) at a resolution of 2048 \u00d7 2048 pixels.\n\nA mean of eight fibers per sample (7\u20139) was included from the sectioned samples, and from each fiber, 24 images were obtained at x13,500 magnification. The imaging was performed in a randomized systematic order including 12 images from the subsarcolemmal (SS) region, and 6 from both the superficial and central region of the intermyofibrillar (IMF) space. The Z-disc width was measured once on all IMF images and the mean from all fibers within one sample was calculated to determine the fiber type. The categorization of fiber types was based on previous reports from observations48. The images were analyzed by two genotype-blinded investigators and all further analyses were performed by the same blinded investigator. The quantification of mitochondrial morphology was done using the Radius EM Imaging Software (Emsis GmbH, Radius 2.0).\n\nSeveral measurements were included in this study: 1) Mitochondrial number: Total number of mitochondrial profiles (count). 2) Mitochondrial volume fraction: IMF mitochondrial volume is annotated as percentage of mitochondria covering the IMF space. 3) Damaged mitochondria: Mitochondria were categorized as damaged when they were swollen, vacuolated, or empty.\n\nIn a subset of mice, mitochondrial respiratory capacity was measured in permeabilized gastrocnemius skeletal muscle fibers as previously described97. In brief, gastrocnemius muscle was rinsed from fat and connective tissue and separated into small fiber bundles. Fiber bundles were permeabilized with saponin (50\u2009\u03bcg/ml) in BIOPS buffer for 30\u2009min, followed by a 20\u2009min wash in MiR05 buffer on ice. Mitochondrial respiration was measured in duplicate under hyperoxic conditions at 37\u2009\u00b0C using high resolution respirometry (Oxygraph-2k, Oroboros Instruments, Innsbruck, Austria). The following protocol was applied: Leak respiration was assessed by addition of malate (5\u2009mM) and pyruvate (5\u2009mM), followed by adding three different concentrations of ADP (0.01\u2009mM, 0.25\u2009mM and 5\u2009mM; for the final concentration of ADP, 3\u2009mM of magnesium (Mg) was added as well) to measure complex I linked respiratory capacity. 10\u2009mM Glutamate was added to measure complex I linked respiratory capacity followed by 10\u2009mM succinate to measure complex I\u2009+\u2009II linked respiratory capacity. Finally, 5\u2009\u03bcM Antimycin A was added to inhibit complex III in the electron transport chain. Pooled data for both sexes are shown as no sex-specific differences were detected.\n\nContraction-stimulated exogenous palmitate oxidation in isolated soleus muscle from Slirp KO and WT mice was measured as previously described98. In brief, excised soleus muscles from mice anesthetized with pentobarbital were mounted at resting tension (\u223c5 mN) in 15\u2009ml vertical incubation chambers with a force transducer (Radnoti, Monrovia, CA) containing 30\u2009\u00b0C carbonated (95% O2 and 5% CO2) Krebs-Henseleit Ringer buffer (KRB), pH = 7.4, supplemented with 5\u2009mM glucose, 2% fatty acid-free BSA, and 0.5\u2009mM palmitate. After \u223c20\u2009min of pre-incubation, the incubation buffer was refreshed with KRB additionally containing [1\u201314\u2009C]-palmitate (0.0044 MBq/ml; Amersham BioSciences, Buckinghamshire, U.K.). To seal the incubation chambers, mineral oil (Cat. No. M5904, Sigma\u2013Aldrich) was added on top. Exogenous palmitate oxidation was measured simultaneously at rest and during 25\u2009min contractions (18 trains/min, 0.6\u2009s pulses, 30\u2009Hz, 60\u2009V). After incubation, incubation buffer and muscles were collected to determine the rate of palmitate oxidation as previously described97,99,100. Palmitate oxidation was determined as CO2 production (complete FA oxidation) and acid-soluble metabolites (representing incomplete FA oxidation). As no difference was observed in complete and incomplete FA oxidation between genotypes, palmitate oxidation is presented as a sum of these two forms.\n\nThe subcellular fractionation assay for frozen muscle was adapted44,45 and performed on ice or at 4\u2009\u00b0C, where applicable. The frozen and pulverized muscle samples were homogenized for 2\u2009min at 17.5\u2009Hz using a TissueLyser II bead mill (QIAGEN, USA) in cold isolation buffer solution, herein referred to as ISO buffer, containing 880\u2009mM sucrose, 20\u2009mM HEPES (pH 7.4), 50\u2009mM NaCl, 5\u2009mM MgCl2, 5\u2009mM EGTA, protease inhibitor and phosphatase inhibitor (cOmplete\u2122 Protease Inhibitor and PhosStop\u2122, Roche, Germany). The homogenate was then rotated end-over-end for 30\u2009min and centrifuged at 1000\u2009g for 10\u2009min. The pellet was washed and centrifuged twice at 1000\u2009g for 10\u2009min in ISO buffer. The resulting pellet was used to prepare the nuclear fraction (not shown), while the supernatant was used to prepare the cytosolic and mitochondrial fractions.\n\nFor the cytosolic and mitochondrial fractions, the first supernatant fraction was centrifuged twice for 10\u2009min at 1000\u2009g. The resulting pellets were discarded, and the supernatant was centrifuged for 20\u2009min at 20,000\u2009g. From here the resulting supernatant and pellet were used to obtain the cytosolic and mitochondrial fractions, respectively.\n\nFor the cytosolic fraction, the supernatant was centrifuged twice for 20\u2009min at 20,000\u2009g and the resulting pellets were discarded. The final supernatant contained the cytosolic fraction.\n\nFor the mitochondrial fraction, the pellet was resuspended in 200 ul ISO buffer, centrifuged twice for 20\u2009min at 20 000\u2009g and the resulting supernatants were discarded. The resulting pellet was resuspended in 75\u2009\u03bcL mitochondrial lysis buffer [50\u2009mM Tris HCl (pH 6.8), 1\u2009mM EDTA, 0.5% Triton X-100, and protease and phosphatase inhibitor] and rotated end-over-end for 20\u2009min. The final suspension contained the mitochondrial fraction.\n\nThe total protein content of each subcellular fraction was determined colorimetrically, using the bicinchoninic acid method and bovine serum albumin (BSA) as a protein standard (Pierce BCA Protein Assay Kit, Thermo Fisher Scientific, Rockford, IL, USA). For subsequent immunoblotting, the same amount of protein was loaded onto each lane.\n\nLysate preparation and immunoblotting of vastus lateralis muscles samples of HIIT in young, healthy men were performed as described by Hostrup et al. 101, whereas lysate preparation and immunoblotting of vastus lateralis muscles of HIIT in individuals, who were lean, obese or T2D were performed as described by Kruse et al. 102.\n\nTo preserve and assess PRDX3 dimer to monomer ratio, freshly harvested mouse gastrocnemius muscle tissue was incubated for 10\u2009min in ice-cold 100 mM N-Ethylmaleimide (NEM) diluted in PBS. NEM was subsequently aspirated and the tissue homogenized for 1\u2009min at 30\u2009Hz using a TissueLyser II bead mill (QIAGEN, USA) in ice-cold homogenization buffer [10% glycerol, 1% NP-40, 20\u2009mM sodium pyrophosphate, 150\u2009mM NaCl, 50\u2009mM Hepes (pH 7.5), 20\u2009mM \u03b2-glycerophosphate, 10\u2009mM NaF, 2\u2009mM phenylmethylsulfonyl fluoride, 1\u2009mM EDTA (pH 8.0), 1\u2009mM EGTA (pH 8.0), 2\u2009mM Na3VO4, leupeptin (10\u2009\u03bcg\u2009ml\u22121), aprotinin (10\u2009\u03bcg\u2009ml\u22121), and 3\u2009mM benzamidine].\n\nLysate preparation and immunoblotting of all remaining mouse tissues and human vastus lateralis muscle samples of aerobic and strength ET in women69, and resistance training in the young and elderly cohorts103 were performed as previously described25. Immunoblotting of the NEM-treated samples was performed under non-denaturing conditions. The primary antibodies used are listed in Supplementary Table\u00a01.\n\nCoomassie Brilliant Blue staining was used as a control to assess total protein loading and transfer efficiency104 by quantifying the whole lane and for each sample set, a representative membrane from the immunoblotting is shown. The same Coomassie brilliant blue staining is presented for proteins analyzed when derived from the same sample set. Band densitometry was carried out using Image Lab (version 6.0.1). For each set of samples, a standard curve was loaded to ensure quantification within the linear range for each protein probed for. Uncropped and unprocessed scans of all blots are included in the Source Data file.\n\nFigures\u00a01J, 4F: RNA was extracted from ~20\u2009mg of pulverized whole mouse gastrocnemius muscle using TRIzol\u2122 reagent (Invitrogen) following the manufacturer\u2019s instructions (tissue homogenization was performed using the MP Bio lysis system) and treated with the TURBO DNA\u2010free\u2122 Kit (Ambion) to remove contaminating DNA. For RT\u2013qPCR expression analysis, cDNA was reversed transcribed from 0.85\u2009\u03bcg total RNA using the High\u2010Capacity cDNA Reverse Transcription Kit (Applied Biosystems) in the presence of RNase Block (Agilent). The qPCR was performed in a QuantStudio 6 Flex Real\u2010Time PCR System (Life Technologies), using TaqMan\u2122 Universal Master Mix II, with UNG (Applied Biosystems) to quantify mitochondrial transcripts (mitochondrial\u2010rRNAs and mt\u2010mRNAs). The gene expression levels were determined using the \u0394\u0394Ct method, comparing the Ct values of mitochondrial transcripts to that of the beta-actin reference gene for normalization.\n\nFigure\u00a02A, C\u2013E: Total RNA was extracted from pulverized quadriceps muscle using an adapted guanidinium thiocyanate-phenol-chloroform extraction method. Reverse transcription to cDNA was performed as previously described51. Real\u2010time qPCR was performed in triplicate for 2\u2009A, C, D and duplicate for 2E with QuantStudio 7 Flex Real\u2010Time PCR System (Applied Biosystems, Waltham, MA). Cycle threshold (Ct) was converted to a relative amount using a standard curve derived from a serial dilution of a representative pooled samples run together with the samples of interest. Beta-actin or Hprt mRNA was used for normalization of target mRNA levels.\n\nFigure\u00a06D: Total RNA extraction from vastus lateralis muscle and reverse transcription to cDNA was performed as previously described70. The qPCR was performed in a QuantStudio 6 Flex Real\u2010Time PCR System (Life Technologies), using TaqMan\u2122 Universal Master Mix II (Applied Biosystems) to quantify mt-mRNAs. The gene expression levels were determined using the \u0394\u0394Ct method, comparing the Ct values of mitochondrial transcripts to that of the 18S rRNA reference gene for normalization.\n\nFigure\u00a06G: Total RNA was extracted from skeletal muscle biopsies using TRI Reagent (Sigma-Aldrich) following the manufacturer\u2019s instructions. cDNA was reversed transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems), while the qPCR was performed on an Aria Mx (Agilent) using a TaqMan\u2122 Universal Master Mix II (Applied Biosystems). The gene expression levels were determined using the \u0394\u0394Ct method with the mRNA levels being normalized to the geometric mean of PPIA and B2M.\n\nPrimers or Taqman probes or Taqman expression assays used for mRNA levels measured in Figs.\u00a02, 4 and 6 are shown in Supplementary Table\u00a02.\n\nTotal DNA was extracted from ~20\u2009mg mouse gastrocnemius muscle using the Dneasy Blood and Tissue Kit (Qiagen) according to the manufacturer\u2019s instructions and treated with RNase A. Levels of mtDNA were measured by qPCR using 2.5\u2009ng of DNA in a QuantStudio 6 Flex Real\u2010Time PCR System using TaqMan\u2122 Universal Master Mix II, with UNG. The mt-Nd1 and mt-Nd6/Nd5 TaqMan gene expression assays were used. 18S rRNA was used for normalization.\n\nA detailed description of the study participants, research design and methods has been previously published69,70. In the present study, we included vastus lateralis muscle lysate for immunoblotting obtained from 9 healthy young women (age, 33\u2009\u00b1\u20096 years; body mass index (BMI), 23.2\u2009\u00b1\u20092.6\u2009kg\u2009m\u22122) before and after 14-week of aerobic and strength exercise training.\n\nA detailed description of the study participants, research design and methods has been previously published72. In the present study, we included vastus lateralis muscle lysate for immunoblotting obtained from 6 healthy young men before and after 6-week HIIT training.\n\nA detailed description of the study participants, research design and methods has been previously published70. The original study reported no additive effect of vitamin D intake during the 12 weeks of resistance exercise training on muscle hypertrophy or muscle strength69,70. Accordingly, in the present study, samples from young or older participants were considered as one group, irrespective of vitamin D intake. We included vastus lateralis muscle samples (~10\u2009mg wet weight) for immunoblotting obtained from a subset of the original sample set due to lack of sample material. For the young participants, we included 10 samples before and 6 samples after resistance exercise training. For the older participants we included 11 before and 9 samples after resistance exercise training.\n\nA detailed description of the study participants, eligibility criteria, research design and methods has been previously published71. In brief, 15 middle-aged men with T2D and obesity, 15 age- and BMI-matched glucose-tolerant men with obesity, and 18 age-matched glucose-tolerant lean men were recruited. All participants, except four (two men with T2D and obesity and two lean men), completed the study. The training protocol consisted of 8-weeks with three weekly training sessions consisting of supervised HIIT in combination with biking and rowing. HIIT-sessions consisted of 5 \u00d7 1\u2009min exercise blocks interspersed with 1\u2009min rest, and shifted between blocks on cycle and rowing ergometers. The volume was increased from two to five blocks during the 8 weeks. In the present study, we included vastus lateralis muscle samples that were obtained 4\u20135 days after the last HIIT session but 48\u2009h after the last physical activity (a VO2 max test). The number of samples included for immunoblotting were as follows: 16 before and after HIIT from lean glucose-tolerant men, 15 before and after HIIT from glucose-tolerant men with obesity, and 13 before and after HIIT from men with T2D and obesity.\n\nGraphical illustrations were created in \u00a9BioRender - biorender.com, as indicated in the figure legends.\n\nResults are presented as mean\u2009\u00b1\u2009SEM with individual values shown, when feasible. Statistical differences were analyzed by ordinary one-way ANOVA, repeated/ordinary two-way ANOVA, or Log-rank (Mantel-Cox) test, and Mann-Whitney test as applicable. Dunnett\u2019s multiple comparisons test or \u0160\u00edd\u00e1k\u2019s multiple comparisons test were used to evaluate significant interactions in ANOVAs. Percentage change analysis was performed by Mean difference and 95% CI of difference calculated by Uncorrected Fisher\u00b4s LSD. Pearson correlation coefficients were calculated. As statistical tests varied according to the dataset being analyzed, the respective tests utilized are specified within the figure legends. 0.05\u2009\u2264\u2009p\u2009<\u20090.1 were considered a tendency and p values\u2009<\u20090.05 were considered significant.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The authors declare that all data supporting the findings of this study are available within the paper (and its supplementary information files).\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Romanello, V. & Sandri, M. The connection between the dynamic remodeling of the mitochondrial network and the regulation of muscle mass. Cell Mol. 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Cell Biol. 219, e202001064 (2020).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We acknowledge the technical assistance of Betina Bolmgren and Irene Nielsen, Martin Thomassen, and Roberto Meneses-Valdes (Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Denmark), Anja Jokipii-Utzon (Institute of Sports Medicine, Bispebjerg Hospital, Copenhagen, Denmark), Michala Carlsson and Katrine Brantbjerg Mosegaard (Department of Biomedical Sciences, University of Copenhagen, Denmark). We thank Vivian Shang for her assistance with the Drosophila experiments (Charles Perkins Centre, The University of Sydney, Australia). We acknowledge Cristiano di Benedetto for preparing muscle samples for TEM analysis (Core Facility for Integrated Microscopy, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark). We thank Professor Rudolf J. Wiesner for generously providing us with the MRPL12, MRPS18B, MRPS35 and YME1L1 antibodies (Institute of Vegetative Physiology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany). The study was supported by the Novo Nordisk Foundation (grant NNF16OC0023418, NNF18OC0032082, and NNF20OC0063577 to L.S.; grant NNF22OC0074110 to A.M.F.), by Independent Research Fund Denmark to L.S. (#0169-00013B), by the European Union\u2019s Horizon 2020 research and innovation programme (Marie Sk\u0142odowska-Curie grant agreement No 801199 to T.C.P.P. and E.A.R.), grants by Danish Council for Independent Research - Medical Sciences (4181-00078) and the Augustinus Foundation to H.P. and grants from the Independent Research Fund Denmark (2030-00007\u2009A) and Lundbeck Foundation (R380-2021-1451) to S.H.R.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Copenhagen, Denmark\n\nTang Cam Phung Pham,\u00a0Carlos Henriquez-Olgu\u00edn,\u00a0Andreas M\u00e6chel Fritzen,\u00a0Nicoline Resen Andersen,\u00a0Solvejg Hansen,\u00a0Anders Krogh Lemminger,\u00a0Thomas Elbenhardt Jensen,\u00a0Bente Kiens,\u00a0Morten Hostrup,\u00a0J\u00f8rgen Frank Pind Wojtaszewski,\u00a0Erik Arne Richter\u00a0&\u00a0Lykke Sylow\n\nDepartment of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark\n\nTang Cam Phung Pham,\u00a0Steffen Henning Raun,\u00a0Emma Frank,\u00a0Andreas M\u00e6chel Fritzen,\u00a0Mona Sadek Ali,\u00a0Andrea Irazoki,\u00a0Steen Larsen,\u00a0Klaus Qvortrup\u00a0&\u00a0Lykke Sylow\n\nStem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland\n\nEssi Havula\n\nExercise Science Laboratory, Faculty of Medicine, Universidad Finis Terrae, Av. Pedro de Valdivia 1509, Santiago, Chile\n\nCarlos Henriquez-Olgu\u00edn\n\nDivision of Molecular Metabolism, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden\n\nDiana Rubalcava-Gracia\n\nMolecular and Cellular Exercise Physiology, Department of Physiology and Pharmacology, Karolinska Institutet, SE-17177, Stockholm, Sweden\n\nPaulo R. Jannig\u00a0&\u00a0Jorge L. Ruas\n\nSteno Diabetes Center Odense, Odense University Hospital, Odense, Denmark\n\nRikke Kruse,\u00a0Maria Houborg Petersen,\u00a0Martin Eisemann de Almeida\u00a0&\u00a0Kurt H\u00f8jlund\n\nDepartment of Biology, University of Copenhagen, Copenhagen, Denmark\n\nJens Frey Halling,\u00a0Stine Ringholm\u00a0&\u00a0Henriette Pilegaard\n\nCharles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia\n\nElise J. Needham\n\nInstitute of Sports Medicine Copenhagen, Department of Orthopaedic Surgery M, Bispebjerg Hospital, Copenhagen, Denmark\n\nPeter Schjerling,\u00a0Steen Larsen\u00a0&\u00a0Michael Kj\u00e6r\n\nCenter for Healthy Aging, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark\n\nPeter Schjerling,\u00a0Steen Larsen\u00a0&\u00a0Michael Kj\u00e6r\n\nDepartment of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark\n\nMartin Eisemann de Almeida,\u00a0Niels \u00d8rtenblad\u00a0&\u00a0Joachim Nielsen\n\nClinical Research Centre, Medical University of Bialystok, Bialystok, Poland\n\nSteen Larsen\n\nDepartment of Clinical Research, University of Southern Denmark, Odense, Denmark\n\nKurt H\u00f8jlund\n\nInstitute for Mitochondrial Diseases and Aging, Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD) and Center for Molecular Medicine (CMMC), Medical Faculty, University of Cologne, Cologne, Germany\n\nAleksandra Trifunovic\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nT.C.P.P.: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing - Original Draft, Visualization, Project administration, Funding acquisition; S.H.R.: Conceptualization; Investigation, Writing - Review & Editing; E.H: Methodology, Investigation, Writing - Review & Editing; C.H.-O.: Methodology, Investigation, Writing - Review & Editing; D.R.-G.: Methodology, Investigation, Writing - Review & Editing; E.F.: Methodology, Investigation, Writing - Review & Editing; A.M.F.: Methodology, Investigation, Writing - Review & Editing; P.J.: Methodology, Investigation, Writing - Review & Editing; N.R.A.: Investigation, Writing - Review & Editing; R.K.: Investigation, Writing - Review & Editing; M.S.A.: Investigation, Writing - Review & Editing; A.I.: Investigation, Writing \u2013 Review & Editing; J.F.H.: Resources, Writing - Review & Editing; S.R.: Resources, Writing - Review & Editing; E.J.N.: Methodology, Writing \u2013 Review & Editing; S.H.: Resources, Writing - Review & Editing; A.K.L.: Resources, Writing - Review & Editing; M.H.P.: Resources, Writing - Review & Editing; M.E.de.A.: Resources, Writing - Review & Editing; T.E.J.: Resources, Writing - Review & Editing; B.K.: Resources, Writing - Review & Editing; M.H.: Resources, Writing - Review & Editing; S.L.n: Methodology, Investigation, Writing - Review & Editing; N.\u00d8.: Resources, Writing - Review & Editing; K.H.: Resources, Writing - Review & Editing; P.S.: Methodology, Investigation, Resources, Writing - Review & Editing; M.K.: Resources, Writing - Review & Editing; J.R.: Resources, Writing - Review & Editing; A.T.: Resources, Writing - Review & Editing; J.W.; Resources, Writing - Review & Editing; J.N.: Methodology, Investigation, Writing - Review & Editing; K.Q.: Resources, Writing - Review & Editing; H.P.: Resources, Writing - Review & Editing; E.A.R.: Resources, Investigation, Writing - Review & Editing, Supervision, Funding acquisition; L.S.: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing - Original Draft, Project administration, Supervision, Funding acquisition.\n\nCorrespondence to\n Lykke Sylow.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "Since the study concluded, Solvejg Hansen, Jens Frey Halling and Anders Krogh Lemminger have become employees and shareholders of Novo Nordisk A/S, Denmark. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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The mitochondrial mRNA-stabilizing protein SLIRP regulates skeletal muscle mitochondrial structure and respiration by exercise-recoverable mechanisms.\n Nat Commun 15, 9826 (2024). https://doi.org/10.1038/s41467-024-54183-4\n\nDownload citation\n\nReceived: 06 February 2024\n\nAccepted: 04 November 2024\n\nPublished: 13 November 2024\n\nVersion of record: 13 November 2024\n\nDOI: https://doi.org/10.1038/s41467-024-54183-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 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b/0e6062296811ed0247c4338e0381fd53a7dc3cfb8287474a87040085029684fc/metadata.json @@ -0,0 +1,189 @@ +{ + "title": "Molecular and functional profiling of G\u03b1i as an intracellular pH sensor", + "pre_title": "Molecular and Functional Profiling of G\u03b1i as an Intracellular pH Sensor", + "journal": "Nature Communications", + "published": "11 April 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_MOESM4_ESM.zip" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_MOESM5_ESM.zip" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_MOESM6_ESM.zip" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_MOESM7_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_MOESM8_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_MOESM9_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.2210/pdb1GP2/pdb", + "https://doi.org/10.2210/pdb1CIP/pdb", + "https://doi.org/10.2210/pdb7S0F/pdb", + "https://doi.org/10.2210/pdb3ohm/pdb", + "https://doi.org/10.2210/pdb1azt/pdb", + "https://doi.org/10.2210/pdb1zca/pdb", + "https://doi.org/10.13018/BMR30078", + "/articles/s41467-025-58323-2#Sec24" + ], + "code": [ + "https://www.gromacs.org", + "https://manual.gromacs.org", + "/articles/s41467-025-58323-2#ref-CR38", + "https://pymol.org", + "/articles/s41467-025-58323-2#ref-CR39", + "https://www.cgl.ucsf.edu/chimera/", + "http://plasma-gate.weizmann.ac.il/Grace/" + ], + "subject": [ + "Computational biophysics", + "Membrane proteins", + "Molecular conformation", + "Solution-state NMR" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4203924/v1.pdf?c=1744456039000", + "research_square_link": "https://www.researchsquare.com//article/rs-4203924/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58323-2.pdf", + "preprint_posted": "29 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Heterotrimeric G proteins (G\u03b1, G\u03b2 and G\u03b3) act downstream of G-protein-coupled receptors (GPCRs) to mediate signaling pathways that regulate various physiological processes and human disease conditions. Previously, human G\u03b1i and its yeast homolog Gpa1 have been reported to function as intracellular pH sensors, yet the pH sensing capabilities of G\u03b1i and the underlying mechanism remain to be established. Herein, we identify a pH sensing network within G\u03b1i, and evaluate the consequences of pH modulation on the structure and stability of the G-protein. We find that changes over the physiological pH range significantly alter the structure and stability of G\u03b1i-GDP, with the protein undergoing a disorder-to-order transition as the pH is raised from 6.8 to 7.5. Further, we find that modulation of intracellular pH in HEK293 cells regulates G\u03b1i-G\u03b2\u03b3 release. Identification of key residues in the pH-sensing network allowed the generation of low pH mimetics that attenuate G\u03b1i-G\u03b2\u03b3 release. Our findings, taken together, indicate that pH-dependent structural changes in G\u03b1i alter the agonist-mediated G\u03b2\u03b3 dissociation necessary for proper signaling.Biological sciences/Biochemistry/Proteins/G protein-coupled receptorsBiological sciences/Biochemistry/Structural biology/NMR spectroscopy/Solution-state NMR", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Supplementaryfigures.pdfSUPPLEMENTAL MATERIAL", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Heterotrimeric G proteins (G\u03b1, G\u03b2 and G\u03b3) act downstream of G-protein-coupled receptors (GPCRs) to mediate signaling pathways that regulate various physiological processes and human disease conditions. While human G\u03b1i and its yeast homolog Gpa1 were previously postulated to function as intracellular pH sensors, the pH\u2013sensing capabilities of G\u03b1i and the underlying mechanism remain to be established. Our research shows that variations in pH significantly affect the structure and stability of G\u03b1i-GDP. Specifically, at the lower end of the physiological pH range, the protein undergoes an order-to-disorder transition due to the loss of electrostatic interactions within the G\u03b1i Switch regions, resulting in a reduction in agonist-mediated G\u03b1i-G\u03b2\u03b3 release. Further, we identified key residues within the G\u03b1i Switch regions that form the pH\u2013sensing network. Mutation of these residues in G\u03b1i gives rise to \u2018low pH mimetics\u2019 that abolish pH-dependent thermostability changes and reduce G\u03b1i-G\u03b2\u03b3 release. Overall, our findings suggest that pH-sensitive structural changes in G\u03b1i impact the agonist-mediated dissociation of G\u03b2\u03b3, which is essential for proper signaling.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Within the complex milieu of living cells, intracellular pH (pHi) is maintained within a narrow range, as even small changes in pH can affect a myriad of cellular processes including membrane potential, ion transport, cellular growth and metabolism1,2,3. Unsurprisingly, disruptions in pH regulation can contribute to the development of pathological conditions such as ischemic heart disease, cancer and neurological disorders4,5,6. While various proton pumps and transporters play a role in regulating the flow of protons across the membrane to uphold pHi homeostasis, there are intracellular proteins (termed pH sensors) that sense and transmit pH signals, thus orchestrating the regulation of biochemical processes. Among these pH sensors are signal-transducing proteins.\n\nNotably, a study by Isom et al.3 provided evidence that a subset of signal-transducing heterotrimeric G proteins may serve as intracellular pH sensors3. These membrane-associated proteins form a heterotrimeric complex (G\u03b1, G\u03b2 and G\u03b3) and act downstream of G-protein coupled receptors (GPCRs). GPCRs represent the most extensive group of membrane proteins that are targeted by approved drugs. They play a crucial role in orchestrating the majority of cellular responses to hormones and neurotransmitters through several signaling pathways mediated by different isoforms of G protein (G\u03b1i, G\u03b1s, G\u03b112/13 and G\u03b1q) that receive and transduce signals through diverse pathways7,8. The G\u03b2 and G\u03b3 subunits associate to form a G\u03b2\u03b3 heterodimer3, whereas the G\u03b1 subunit binds GDP or GTP and catalyzes GTP hydrolysis. The G\u03b1 subunit is comprised of two distinct domains: a helical domain and a Ras-like domain. Within the Ras-like domain, there are three key \u2018Switch\u2019 regions, namely SW-I, SW-II and SW-III, which play an essential role in its activity. In the GDP-bound state, which is an inactive form of G\u03b1 protein, these Switch regions exhibit dynamic behavior. In contrast, in the GTP-bound state (the active form of G\u03b1), they become more structured and less dynamic9. In the GDP-bound state, the G\u03b1 subunit is associated with the G\u03b2\u03b3 complex. Upon GPCR-stimulated G\u03b1 GTP-loading, the G\u03b2\u03b3 subunits dissociate from G\u03b1-GTP and along with Ga, promote activation of downstream signaling pathways.\n\nIsom et al.3 developed a computer algorithm, pHinder, which predicted that both the mammalian G\u03b1i isoform and yeast homolog Gpa1 contain a core of residues between the Ras-like and helical domains that may promote pH\u2013sensing properties3. In support of this prediction, both proteins showed pH-dependent changes in thermostability over a pH range from 5.5 to 8. They also found that Gpa1 undergoes phosphorylation under acidic conditions to attenuate pheromone-dependent stimulation of mitogen-activated protein kinases in the yeast3. While these findings, taken together suggest that mammalian G\u03b1i may function as a pH sensor, the molecular mechanism, and the biological consequence of pH sensing through G\u03b1i remain unknown.\n\nTo expand on past observations, we characterize the pH-dependent biochemical and structural/dynamic properties of\u00a0G\u03b1i-GDP, elucidate the underlying pH-dependent electrostatic network, and assess the functional consequences on cellular G\u03b1i activity. We find that the structure, stability, and dynamics of G\u03b1i in the GDP-bound state are highly dependent on pH over the physiological pH range due to a pH-responsive network within the Ras-like domain. These findings differ from a previous report where larger pH-dependent changes in thermostability were observed for the GTP-bound state with ionizable residues predicted to lie at the interface between the Ras-like and helical domains3. Our NMR, biophysical and computational analyses indicate that changes in the ionization state of residues within the pH\u2013sensing network promote a disorder-to-order transition in G\u03b1i over the physiological pH range, to populate a less ordered state that enhances G\u03b1i-G\u03b2\u03b3 association in HEK293 cells at the lower end of the physiological pH range. Identification of the pH-sensing network allowed for the identification and generation of low pH mimetics that reduce pH-dependent G\u03b2\u03b3 release from the G\u03b1i-G\u03b2\u03b3 complex. Of note, the G\u03b1i Switch III region plays a key role in pH\u2013sensing, suggesting a potential role for this understudied Switch region in agonist-mediated G\u03b2\u03b3 release, a key step for both G\u03b1i and G\u03b2\u03b3 activation. Taken together, our studies indicate that G\u03b1i undergoes a pH-dependent disorder-to-order transition that modulates G\u03b1i-G\u03b2\u03b3 interactions and G\u03b1i activation over the physiological pH range.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Earlier investigations into mammalian G\u03b1i demonstrated changes in structure and stability in response to pH variations3. While these findings suggest a potential role for G\u03b1i as a pH sensor3, the molecular basis and functional relevance have yet to be established. Herein, we apply comprehensive and multidisciplinary approaches to evaluate how pH changes in the physiological range affect structure, stability and G\u03b1i activity in vitro and in cells. We first conducted CD experiments on G\u03b1i-GDP\u00a0to monitor pH-dependent changes in thermal unfolding, stability and secondary structure. Further, to monitor thermal unfolding and stability, we collected CD spectra at 222\u2009nm as a function of pH and temperature. As shown in Fig.\u00a01A, a striking and gradual increase in thermal stability (\u0394Tm ~ 25\u2009\u00b0C) of G\u03b1i-GDP was observed over the pH range from 5 to 7.3. Notably, the thermal unfolding transition for G\u03b1i-GDP appears cooperative at low pH but shifts to a multi-state unfolding transition over the physiological pH range (Fig.\u00a01A). G\u03b1i contains two subdomains, a Ras-like and helical domain. To evaluate differential unfolding at higher pH (above pH 7.1), we conducted CD scans (200\u2013250\u2009nm) for G\u03b1i-GDP as a function of temperature at pH 7.2. As shown in Figs.\u00a01B and 1C, CD spectra revealed a significant reduction in alpha-helical propensity, but not the beta-sheet propensity as the temperature is raised from 45-55\u00b0C. These findings indicate that the helical domain melts first, followed by the Ras-like domain, with the Ras-like domain showing the greatest change in thermal stability at higher pH.\n\nA Representative CD thermal melt (222\u2009nm, 30\u201395\u2009\u00b0C) of G\u03b1i-GDP (15\u2009\u00b5M) demonstrates enhanced thermostability at higher pH (N\u2009=\u20093). B Representative CD spectral scans (200\u2013250\u2009nm) of G\u03b1i-GDP collected at 45 and 55\u2009\u00b0C and pH 7.2 (N\u2009=\u20093). C Bar graph derived from temperature-dependent CD spectral scans shows a loss of alpha-helical but not beta-sheet secondary structure. Data are averages of N\u2009=\u20093 independent experiments (\u2009\u00b1\u2009SE). D Representative intrinsic tryptophan fluorescence spectra of G\u03b1i-GDP (2\u2009\u00b5M, excitation = 280\u2009nm, emission = 300\u2212400\u2009nm) show enhanced fluorescence as the pH is increased from 5 to 7.2 (N\u2009=\u20092 independent experiments, n\u2009=\u20096 total replicates).\n\nGiven the enhanced stability observed at higher pH, we employed intrinsic tryptophan fluorescence experiments using tryptophan (W211) in the SW-II region as a fluorescence probe to monitor differences in solvent exposure as a function of pH, which indirectly indicates pH-dependent switch conformational changes. It has previously been shown that the more dynamic and less ordered GDP-bound state of G\u03b1i promotes enhanced exposure of W211, resulting in a fluorescence decrease relative to that of the G\u03b1i-GTP state9,10,11. As shown in Fig.\u00a01D, intrinsic G\u03b1i-GDP tryptophan fluorescence increases over the pH range from 5 to 7.2, suggesting that higher pH promotes decreased solvent accessibility, possibly due to enhanced interactions and structural order, consistent with the greatly enhanced stability observed by CD. Taken together, these results support a disorder-to-order transition at higher physiological pH for G\u03b1i in its GDP-bound form.\n\nTo further probe whether a pH-dependent disorder-to-order transition occurs in G\u03b1i-GDP, we conducted 2D NMR analyses. For these studies, we prepared 15N enriched G\u03b1i-GDP and collected a 2D 1H-15N Heteronuclear Single Quantum Coherence (HSQC) NMR spectra over a physiologically relevant pH range (6.4 - 7.6). Enrichment with 15N allows the detection of backbone and sidechain NH resonances within G\u03b1i and provides a site-specific probe for every amino acid except proline. The 2D HSQC overlay of G\u03b1i-GDP at pH 6.4, 6.8 and 7.2 is shown in Fig.\u00a02A, with an HSQC overlay comparing pH 6.4 versus pH 7.6 shown separately in Supplementary Fig.\u00a01. Spectra acquired at lower pH show significant chemical shift changes, line broadening and loss of several peaks in comparison to those obtained at higher pH values, suggesting the protein is more dynamic at lower pH (Fig.\u00a02A). This data correlates well with CD and tryptophan fluorescence data which suggests a less thermostable structure of G\u03b1i-GDP at lower pH. The pH-dependent HSQC changes observed are consistent with an earlier report which showed extensive broadening of NH peaks in the 1H-15N HSQC spectrum of G\u03b1i-GDP at pH 6 relative to pH 712. Residue-specific chemical shift perturbation and peak intensity changes associated with changes in pH between 6.4 and 7.2 are plotted in Figs.\u00a02B and 2C, respectively. Most pH-dependent spectral changes are localized to \u03b11, \u03b15, \u03b21, \u03b22 and the \u03b22-\u03b23 loop within the Ras-like domain as mapped onto the G\u03b1i-GTP structure (PDB: 1CIP)13 in Fig.\u00a02D. Of note, several resonances within these key regions are undetectable in the GDP-bound state at all pH values (Fig.\u00a02D, black) which somewhat limits NMR analyses. These findings are consistent with previous work showing that residues associated with the Switch regions in the GDP-bound state of G\u03b1i exhibit enhanced backbone dynamics and are broadened and undetectable compared to the resonances associated with the GTP-bound state9. Taken together, 2D NMR analyses, CD data and tryptophan fluorescence data indicate that the Switch regions of G\u03b1i-GDP are more dynamic and less structured at pH 6.4 - 6.8 compared to pH 7.2 - 7.5.\n\nA Representative 2D 1H-15N TROSY-HSQC spectral overlay of 2H, 13C, 15N-enriched G\u03b1i-GDP (230\u2009\u00b5M) at pH 6.4, 6.8 and 7.2, highlighting peak shifts and line broadening. Spectra were acquired on a Bruker Avance III 850\u2009MHz instrument at 25\u2009\u00b0C (N\u2009=\u20092). B Chemical shift perturbation (CSP) and (C) peak intensity ratio (IntpH 6.4/IntpH 7.2) for pH 6.4 and 7.2. The striped horizontal line represents the mean value of \u0394\u03b4 amide. Most of the residues that show significant CSP, and broadening lie within the Ras-like domain (\u03b11, \u03b15, \u03b21, \u03b22 and \u03b22-\u03b23 loop). D Spectral differences are highlighted on a ribbon diagram of GTP-bound G\u03b1i (PDB: 1CIP). NH residues with CSP greater than 0.03 ppm are represented in red. Residues with decreased peak intensity (line broadening) at pH 6.4 relative to pH 7.2 are shown in green. Residues missing or unassigned are shown in black color, while unaffected residues are shown in blue and gray color for the Ras-like domain and helical domain respectively.\n\nG\u03b1i, when bound to GDP, adopts a conformational ensemble and dynamic properties distinct from that of the GTP-bound state9. This in turn drives recognition of regulatory factors and downstream targets. Given our findings that pH modulates G\u03b1i-GDP structure, stability and dynamics, we asked if pH could alter GDP binding to G\u03b1i protein. For that purpose, we performed Mant-GDP dissociation assays. For these assays, the rate of GDP dissociation was determined by adding excess GDP to Mant-GDP loaded G\u03b1i and monitoring Mant fluorescence changes (by FRET upon tryptophan excitation) as a function of time at different pH values (Fig.\u00a03A-B). As shown in Fig.\u00a03C-D, small differences in GDP dissociation rates were observed over the pH range (pH 6.8-7.4), indicating that GDP binding is not significantly altered at physiological pH. Interestingly, enhanced rates of GDP dissociation were observed at pH values below the cytosolic physiological pHi regime.\n\nA Structure of G\u03b1i (PDB: 1GP2) highlighting the position of tryptophan (W211) in Switch II and bound Mant-GDP. B Diagram displaying experimental setup of FRET-based determination of G\u03b1i nucleotide association and dissociation rates. C Rate of GDP dissociation from G\u03b1i as a function of pH by monitoring the time-dependent decrease in FRET emission of G\u03b1i-loaded Mant-GDP at 445\u2009nm upon the addition of 7.5\u2009\u00b5M GDP. D The rate of Mant-GDP dissociation decreases as the pH increases. Data are averages of N\u2009=\u20092 independent experiments with n\u2009=\u20094 total replicates. Figure\u00a03B was created in BioRender. Prakash, A. (2025) https://BioRender.com/b57r381.\n\nTo elucidate the molecular basis for pH-dependent stability and conformational dynamic changes in G\u03b1i-GDP, we sought to identify key residues involved in pH sensing. As extensive broadening of resonances in GDP-bound spectra prevented pKa determination by NMR, we examined and identified two networks of charged residues in regions, designated as the \u2018GDP release network\u2019 (Fig.\u00a04A) and \u2018Switch network\u2019, that showed pH-dependent changes in the NMR spectra (Fig.\u00a02D). The GDP release network contains residues within \u03b11, \u03b15, \u03b22 and \u03b23, and was previously shown to be important for GDP release during the GPCR-mediated activation of the G\u03b1i14. We postulated that if this network plays a key role in pH sensing, mutation of charged residues within this network (e.g., H57, H188, K192, D193, H195 and D337) would reduce pH-dependent G\u03b1i thermostability due to protonation and loss of electrostatic interactions that destabilize G\u03b1i tertiary structure.\n\nA Ribbon diagram of GTP-bound G\u03b1i (PDB: 1CIP) highlighting electrostatic network near GDP release network. The helical domain, Ras-like domain and the Switch regions are shown in gray, blue and green, respectively. Charged residues are shown as yellow sticks. B Rates of GDP dissociation are compared for WT G\u03b1i and G\u03b1i variants (H57T and K192Q) as a function of pH. Data are averages of\u00a0N\u2009=\u20092 independent experiments with n\u2009=\u20094 total replicates. C Representative CD melt profile of GDP-bound WT and variant G\u03b1i (N\u2009=\u20092), G\u03b1i H57T (D) and K192Q (E) proteins. G\u03b1i release network variants retain pH-dependent thermal unfolding profiles similar to that of WT G\u03b1i.\n\nTo examine whether this network modulates pH-dependent changes in stability and nucleotide binding, we generated several G\u03b1i variants and conducted pH-dependent CD thermal melt and nucleotide dissociation assays. To select neutral or uncharged amino acid substitutions that retain or have a minimal effect on G\u03b1i structure, we employed the Rosetta modeling suite. Rosetta replaces a desired amino acid within the protein with all possible amino acid substitutions and predicts associated free energy changes15. Substitutions that minimally perturb free energy are predicted to retain protein structure. Using this strategy, we identified four variants (H57T, H188V, K192Q and H195N) predicted to retain G\u03b1i structure and stability. Further, we performed a GDP dissociation assay to evaluate whether the G\u03b1i variants alter nucleotide binding. Both H57T and K192Q variants showed pH-dependent GDP dissociation rates similar to WT G\u03b1i (Fig.\u00a04B). Also, to evaluate pH-dependent thermostability associated with these G\u03b1i GDP release network variants, we acquired CD thermal melts at pH 6 and 7.2. Of note, all four variants retained pH-dependent thermostability similar to WT G\u03b1i (Fig.\u00a04C-E and Supplementary Fig.\u00a02A-C). These findings indicate that the GDP release network does not significantly modulate pH-dependent stability or nucleotide binding.\n\nGiven our observations that several charged residues in the Switch regions form stabilizing electrostatic interactions in the active G\u03b1i-GTP bound state, we postulated that the decreased stability of G\u03b1i-GDP at lower pH may result from protonation of pH-dependent Switch network (SW-I, SW-II, SW-III and \u03b13) residues. To test this hypothesis, we selected residues from the \u2018Switch network\u2019 shown or predicted to be important for Switch stability in the GTP-bound form of G\u03b1i16. Then, based on Rosetta prediction, we mutated a subset of charged residues within this network (e.g. R205N, R208Q, E236L, D237G, R242Q and E245N) predicted to least perturb G\u03b1i structure (Fig.\u00a05A). As shown in Fig.\u00a05B-E, two of the variants located in SW-III (E236L and D237G) and \u03b13 (E245N), respectively, showed a reduction in pH-dependent thermostability change between pH 6 and 7.2 relative to WT G\u03b1i. Moreover, a \u2018double variant\u2019 consisting of two substitutions (E236L\u2009+\u2009E237G) from SW-III showed further reduction in pH-induced stability but not complete abolishment of pH dependence (Fig.\u00a05F). Yet, a \u2018triple variant\u2019 containing all three substitutions (E236L\u2009+\u2009D237G\u2009+\u2009E245N) effectively eliminated pH-induced stability changes relative to WT G\u03b1i (Fig.\u00a05G), suggesting a key role for these three Switch network residues (E236, D237 and E245) in forming a \u2018pH\u2013sensing network\u2019 and stabilizing G\u03b1i at higher pH. To further confirm the role of these residues in the pH-sensitive network, we generated a compensatory variant of G\u03b1i (E236D\u2009+\u2009D237E). Notably, this G\u03b1i-GDP compensatory variant retains pH-dependent thermostability changes (Supplementary Fig.\u00a03) similar to WT G\u03b1i-GDP. We also monitored tryptophan fluorescence of the GDP-bound G\u03b1i \u2018triple variant\u2019 as a function of pH to examine changes in solvent accessibility of SW-II residue W211. Consistent with the loss in pH-dependent thermal stability, we found that the \u2018double variant\u2019 and \u2018triple variant\u2019 reduce and abolish pH-dependent fluorescence intensity changes relative to WT G\u03b1i-GDP, respectively (Fig.\u00a05H and Supplementary Fig.\u00a04). As the CD thermal and fluorescence profiles associated with the \u2018double variant\u2019 and \u2018triple variant\u2019 mimic WT G\u03b1i at lower pH (pH 6), we refer to these variants as \u2018low pH mimetics\u2019. To further confirm that the identified residues participate in pH\u2013sensing, we performed NMR analyses for the \u2018double variant\u2019 at pH 6.4 and pH 7.2 in comparison to WT G\u03b1i-GDP. As shown in Supplementary Fig.\u00a05, pH-dependent changes in peak intensity and chemical shift perturbations are significantly reduced in the \u2018double variant\u2019 with respect to WT G\u03b1i. Taken together, our results point to key residues in the Switch network that regulate pH-dependent stability and conformational dynamic properties.\n\nA The putative pH-dependent electrostatic network (red) within the Switch regions (green) is highlighted on the ribbon diagram of GTP-bound G\u03b1i (PDB: 1CIP). B Comparison of representative CD melt profiles obtained from two independent experiments (N\u2009=\u20092) at pH 6.0 and 7.2 for WT G\u03b1i-GDP, (C) G\u03b1i-GDP E236L, (D) G\u03b1i-GDP D237G, (E) G\u03b1i-GDP E245Q (F) G\u03b1i-GDP \u2018double variant\u2019 (E236L\u2009+\u2009D237G), and (G) a G\u03b1i-GDP \u2018triple variant\u2019 (E236L\u2009+\u2009D237G\u2009+\u2009E245Q). The CD thermal profile for G\u03b1i single and double variants shows decreased pH-dependent thermal stability while the \u2018triple variant\u2019 shows a complete loss of pH-dependent thermal stability compared to WT G\u03b1i. H Representative intrinsic tryptophan fluorescence spectra of G\u03b1i-GDP \u2018triple variant\u2019 (2\u2009\u00b5M, excitation = 280\u2009nm, emission = 300\u2212400\u2009nm) as a function of pH (N\u2009=\u20092 independent experiments, n\u2009=\u20096 total replicates). Consistent with the CD results, pH-dependent intrinsic tryptophan (W211) fluorescence observed for WT G\u03b1i-GDP is abolished for the G\u03b1i \u2018triple variant\u2019 supporting a role for E236, D237 and E245 in pH-dependent stability and structural changes.\n\nWe identified three residues in G\u03b1i-GDP, including two SW-III residues (E236, D237) and one \u03b13 residue (E245), that appear to play a key role in pH-dependent stability and conformational dynamics. To evaluate how these residues form pH-dependent electrostatic interactions that stabilize the Switch regions at higher physiological pH, we employed molecular dynamics (MD) simulations. We generated G\u03b1i structures using the G\u03b1i-GDP crystal structure (PDB: 1GP2) as a starting point and then changed the protonation state of side chains associated with residues E236, D237 and E245 to simulate a low pH and high pH state, respectively.\n\nAnalysis of MD simulation trajectories of G\u03b1i-GDP\u00a0collected for 1000\u2009ns in \u2018charged\u2019 versus \u2018uncharged\u2019 states revealed higher root mean square deviation (RMSD) in the protonated or \u2018uncharged\u2019 state. As shown in Fig.\u00a06A, the RMSD of the \u2018uncharged\u2019 state indicated higher dynamics, with RMSD values around 3.12\u2009\u00c5 \u00b1 0.41\u2009\u00c5, while the \u2018charged\u2019 state exhibited lower RMSD values of 2.42 \u00b1 0.37\u2009\u00c5. Based on analyses of the MD trajectories, we attribute the RMSD reduction associated with the \u2018charged\u2019 state to the formation of salt-bridge interactions within the Switch regions. To further probe the dynamic properties of the G\u03b1i-GDP Switch regions, we calculated residue-specific root mean square fluctuation (RMSF) in the \u2018uncharged\u2019 state (Fig.\u00a06B) and mapped RMSF changes onto the G\u03b1i-GTP crystal structure (PDB:1CIP)13 (Fig.\u00a06C). As shown in Figs.\u00a06B and C, our simulations suggest increased dynamics of SW-I, SW-II and SW-III in the \u2018uncharged\u2019 state compared to the \u2018charged\u2019 state, consistent with reduced thermal stability at lower pH. Overall, these findings suggest that at lower pH (pH 6.4-7), there is a loss of electrostatic interactions within the Switch regions of GDP-G\u03b1i that promotes enhanced dynamics, consistent with observations from NMR and CD data.\n\nTo mimic the low pH state of G\u03b1i-GDP (\u2018uncharged\u2019 state), E236, D237 and E245 side chains were protonated using the G\u03b1i-GDP crystal structure (PDB: 1GP2) as a starting point. MD simulations of G\u03b1i-GDP in both \u2018charged\u2019 (deprotonated) and \u2018uncharged\u2019 (protonated) states were performed for 1000\u2009ns in triplicate. A Root means square deviation (RMSD) of G\u03b1i C\u03b1 atoms in \u2018charged\u2019 (black) and \u2018uncharged\u2019 (red) states, as determined by superposing each frame of the trajectory to the corresponding starting structure. G\u03b1i-GDP shows higher RMSD in the \u2018uncharged\u2019 state compared to the \u2018charged\u2019 state, indicative of enhanced dynamics and consistent with NMR line broadening at lower pH. B Residue-wise root mean square fluctuation (RMSF) derived from MD trajectory (1000\u2009ns) of \u2018charged\u2019 (black) and \u2018uncharged\u2019 (red) G\u03b1i-GDP, highlights enhanced Switch dynamics in the \u2018uncharged\u2019 state. C Mapping of residues that display higher RMSF (yellow) on the G\u03b1i-GTP crystal structure (1CIP) indicates that both SW-II, SW-III and \u03b1D become more dynamic in the \u2018uncharged\u2019 state compared to the \u2018charged\u2019 state, consistent with the reduced thermal stability and intrinsic tryptophan fluorescence at lower pH. D Salt-bridge distances for critical interactions between E236-R205, E245-R208 and E245-K248 in the \u2018charged\u2019 (top) and \u2018uncharged\u2019 (bottom) states. In the \u2018charged\u2019 state, the E245-K248 interaction remains stable throughout the trajectory, while the E245-R208 interaction stabilizes in the final 350\u2009ns. In contrast, none of these interactions induce stable salt-bridges in the \u2018uncharged\u2019 state. The 3.2\u2009\u00c5 salt-bridge distance cutoff is depicted with a dotted line. E Gibbs Free Energy plots for \u2018charged\u2019 and \u2018uncharged\u2019 states. The 2D and 3D free energy landscapes illustrate the distribution of G\u03b1i conformational states. In the global minimum energy conformation of the \u2018charged\u2019 state, salt-bridge interactions between E236-R205, E245-R208 and E245-K248 are present. However, these interactions are absent in the \u2018uncharged\u2019 state. The star symbol indicates the position of the minima on the projection.\n\nRepresentative snapshots extracted from the MD trajectories of G\u03b1i-GDP provide insights into the dynamic behavior of G\u03b1i as a function of pH. In the \u2018charged\u2019 or higher pH state of G\u03b1i-GDP, we observe the transient formation of three critical salt-bridge interactions: E236 interacting with R205, E245 with R208 and E245 with K248 (top panel of Fig.\u00a06D). Notably, these residues form electrostatic interactions as observed in crystal structures of active G\u03b1i-GTP state (PDB: 1CIP)13 and play a pivotal role in the stabilization of the Switch regions. In particular, E236 and D237 side chains from SW-III interact with R205 and R208 in SW-II, whereas \u03b13 residue E245 interacts with R20816. In the \u2018uncharged\u2019 state of G\u03b1i-GDP (lower pH), conformations extracted from MD simulation trajectories exhibit a notable absence of these salt-bridge interactions (bottom panel of Fig.\u00a06D and Supplementary Table\u00a01), which we attribute to the destabilization of the SW-III and SW-II region (Fig.\u00a06E). As a result, the protein displays increased dynamics, as evidenced by higher RMSD and RMSF values, suggesting greater structural fluctuations. The protonation of key residues interferes with the formation of electrostatic interactions, leading to their destabilization. Consequently, the protein becomes more dynamic and less thermally stable under more acidic conditions. The 2D and 3D Gibbs Free Energy plots illustrate the distribution of G\u03b1i conformational states in both the \u2018charged\u2019 and \u2018uncharged\u2019 states. In the \u2018charged\u2019 state, the global minimum energy conformation shows the presence of key salt-bridge interactions, specifically E236-R205, E245-R208 and E245-K248, which are absent in the \u2018uncharged\u2019 state, reflecting a different conformational energy profile (Fig.\u00a06E).\n\nTo predict how a pH-dependent disorder-to-order transition in G\u03b1i-GDP affects the interaction of G\u03b1i with binding partners, we analyzed interactions with GPCRs or G\u03b2\u03b3. Agonist-simulated GPCRs interact with G\u03b1i-GDP through \u03b15, which is part of the GDP release network. As inspection of the crystal structure of \u03b21-adrenergic receptor with G\u03b1i and G\u03b2\u03b3 (PDB: 7S0F)17 indicates that GPCR engagement does not alter the structure of SW-II and SW-III17, we predict that pH changes over the physiological pH range do not significantly modulate GPCR interactions with G\u03b1i. Our NMR data indicates that key residues within \u03b15 (e.g., F336, T340, I343, K345, etc.), which directly interact with GPCRs17, lack pH-dependent chemical shifts or peak intensity changes, suggesting that the interaction of G\u03b1i with GPCRs is not altered over the 6.4-7.2\u2009pH range (Fig.\u00a02D). In support of these observations, the MD-derived RMSD (Supplementary Fig.\u00a06) and residue-wise RMSF (Fig.\u00a06B) of \u03b15 in \u2018charged\u2019 and \u2018uncharged\u2019 states of G\u03b1i-GDP are similar, indicating that the conformational dynamic properties of \u03b15 are unaffected by pH changes. Overall, the NMR and computational analyses strongly suggest that pH-dependent structural changes in G\u03b1i do not affect \u03b15 and likely interactions with GPCRs. However, these observations do not entirely rule out the possibility that the interaction between G\u03b1i and GPCRs could be indirectly influenced by pH. Conversely, G\u03b2\u03b3 binds to G\u03b1i-GDP through the SW-II region, which is more dynamic in the GDP-bound form than the GTP-bound form. We propose, consistent with a recent study16, that residues from the Switch network (including the three identified pH-sensing residues) provide stabilization of SW-II in the GTP-bound form of G\u03b1i, which in turn prevents interaction with G\u03b2\u03b3. Since our MD data show enhanced dynamics in the protonated or lower pH state of G\u03b1i-GDP, we predict that at the lower end of the physiological pH range, G\u03b1i engages G\u03b2\u03b3 with higher affinity, resulting in the downregulation of G\u03b1i and G\u03b2\u03b3 mediated downstream signaling. To evaluate this hypothesis computationally, MD simulations were performed on the G\u03b1i-G\u03b2\u03b3 trimeric complex in both \u2018charged\u2019 and \u2018uncharged\u2019 states of G\u03b1i. The results show an increased number of H-bonds between G\u03b1i and G\u03b2\u03b3 in the \u2018uncharged\u2019 state of G\u03b1i with respect to the \u2018charged\u2019 state, indicating increased binding between G\u03b1i and G\u03b2\u03b3 (Fig.\u00a07A). To directly assess binding interactions between G\u03b1i and G\u03b2\u03b3, we estimated the binding energy (\u0394G) of G\u03b1i in \u2018charged\u2019 versus \u2018uncharged\u2019 states with G\u03b2\u03b3 using MM/PBSA analysis. In the \u2018uncharged\u2019 state, G\u03b1i exhibits a stronger binding affinity (-74.99 \u00b1 20.04\u2009kcal/mol) compared to its \u2018charged\u2019 state (-60.13 \u00b1 12.97\u2009kcal/mol), suggesting that protonation of residues E236, D237 and E245 in G\u03b1i promotes its interaction with G\u03b2\u03b3 (Fig.\u00a07B). Residue-wise decomposition of binding energy contributions over time for both the \u2018charged\u2019 (Fig.\u00a07C) and \u2018uncharged\u2019 states (Fig.\u00a07D) of G\u03b1i further shows enhanced interaction associated with several surface residues between G\u03b1i and G\u03b2\u03b3 in the \u2018uncharged\u2019 state. Specifically, Figs.\u00a07E and F highlight stronger interactions between G\u03b1i residues E207, K210 and K211 with residues D186 and D228 of G\u03b2\u03b3 in the \u2018uncharged\u2019 state compared to the \u2018charged\u2019 state. MD simulations were also performed for the G\u03b1i \u2018triple variant\u2019 in complex with G\u03b2\u03b3. Similar to the \u2018uncharged\u2019 state of G\u03b1i, the \u2018triple variant\u2019 possesses stronger binding to G\u03b2\u03b3 (Supplementary Fig.\u00a07). Taken together, our computational analyses suggest that G\u03b1i-G\u03b2\u03b3 interactions are modulated by pH, with lower pHi enhancing G\u03b1i-G\u03b2\u03b3 trimeric complex formation.\n\nMD simulations of the G\u03b1i-G\u03b2\u03b3 complex (PDB: 1GP2) in G\u03b1i \u2018charged\u2019 and \u2018uncharged\u2019 states were performed for 1000\u2009ns. A A higher number of H-bonds were formed for the \u2018charged\u2019 state (black) of G\u03b1i compared to the \u2018uncharged\u2019 state (red) suggesting stronger binding affinity of G\u03b1i with G\u03b2\u03b3 in the \u2018uncharged\u2019 state of G\u03b1i. B Comparison of the binding energy (\u0394G) between G\u03b1i and G\u03b2\u03b3 in \u2018charged\u2019 versus \u2018uncharged\u2019 states using MM/PBSA analysis. In the \u2018uncharged\u2019 state, G\u03b1i possesses stronger binding affinity, indicating that protonation of E236, D237 and E245 in G\u03b1i enhances its interaction with G\u03b2\u03b3. Residue-wise decomposition of binding energy contributions of G\u03b1i over time for both the \u2018charged\u2019 (C) and \u2018uncharged\u2019 (D) states of G\u03b1i. Structural mapping of the per-residue binding energy decomposition of G\u03b1i and G\u03b2\u03b3 onto the G\u03b1-G\u03b2\u03b3 trimeric complex (PDB: 1GP2) in the \u2018charged\u2019 (E) and \u2018uncharged\u2019 (F) states of G\u03b1i. The color gradient reflects the per-residue energy contribution, where blue indicates stronger, favorable interactions and red portrays weaker or unfavorable contributions. Blue residues contribute favorably to binding, whereas red residues contribute less favorably.\n\nG\u03b2\u03b3 interacts with the dynamic SW-II region of G\u03b1i in the GDP-bound state. Upon upstream activation by GPCRs, G\u03b1i becomes activated through the exchange of GDP for GTP. In the GTP-bound state, the SW-II region becomes rigid and G\u03b1i is unable to adopt a conformation compatible with binding to G\u03b2\u03b3. Given our findings that G\u03b1i adopts a more dynamic state at the lower end of the physiological pH range (6.8-7.0), we hypothesized that G\u03b1i-GDP engages G\u03b2\u03b3 with a higher affinity at lower pH, whereas higher pH (7-7.5) promotes G\u03b2\u03b3 release from G\u03b1i-GDP. Consistent with this hypothesis, our MD analyses indicate that pH-mediated changes in the protonation state of key Switch residues alter electrostatic interactions and the dynamic properties of Switch regions of G\u03b1i in its GDP-bound form which may, in turn, modulate G\u03b1i-G\u03b2\u03b3 interactions and thus the downstream signaling. To experimentally evaluate the pH dependence of G\u03b1i-G\u03b2\u03b3 interactions in a cellular context, we conducted bioluminescence resonance energy transfer (BRET) assays in human HEK293T cells. For these assays, WT G\u03b1i was tagged with Renilla luciferase (RLuc) and co-transfected with G\u03b3 tagged with GFP2, G\u03b2 and the neurotensin receptor.\n\nUpon stimulation with neurotensin (agonist), the G\u03b1-RLuc (energy donor) and G\u03b2\u03b3-GFP2 (energy acceptor) dissociate with receptor-catalyzed dissociation of the G\u03b1i-G\u03b2\u03b3 complex measured by comparing energy transfer from donor to acceptor (Fig.\u00a08A). To directly evaluate whether lower intracellular pH (pHi) reduces G\u03b1i-G\u03b2\u03b3 dissociation, we employed two distinct approaches to alter and monitor pHi changes in HEK293 cells. Intracellular pH was determined by treating HEK293 cells with BCECF dye and determining the ratio of emission intensity (detected at 535\u2009nm) when the dye is excited at \u223c490\u2009nm and \u223c440 nm18. To convert the fluorescence ratios to pHi, a calibration curve of HEK293 cells treated with 5\u2009\u00b5M nigericin was generated (Supplementary Fig.\u00a08A). Treatment of cells with nigericin makes the pHi equivalent to extracellular pH (pHe) thus the calibration curve for pHe vs. fluorescence can be used to convert fluorescence into cellular pHi. To alter the pHi, we compared results using two different strategies. One common method to reduce pHi is the addition of Carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (FCCP), an uncoupler of oxidative phosphorylation in the mitochondria19. We determined that cells treated with 1\u2009\u00b5M FCCP generated a pHi of ~7.04, while at 5\u2009\u00b5M the pHi was further reduced to 6.85 (Supplementary Fig.\u00a08B). As one drawback of FCCP is that it can alter mitochondrial energetics, we employed a second method to alter pHi by modulating extracellular pH. As shown in Supplementary Fig.\u00a08C, lowering extracellular pH to 6 and 5 caused a reduction in pHi to 6.9 and 6.7, respectively, which is consistent with a previous report20. To evaluate the pH dependence of G\u03b1i-G\u03b2\u03b3 dissociation in cells, we conducted the BRET assay by expressing WT G\u03b1i in HEK293 cells and inducing acidosis in the cytoplasm by either altering extracellular pH (Fig.\u00a08B) or by the addition of varying concentrations of FCCP (Fig.\u00a08C). Results obtained from the BRET assays show that lowering pHi (pHi 7.2 - 6.85 by FCCP or 7.2 - 6.7 by changing pHe) reduces agonist-mediated G\u03b2\u03b3 release from G\u03b1i, indicating that G\u03b1i-G\u03b2\u03b3 interactions are highly dependent on pH over the physiological pH range. Similarly, the basal (agonist unstimulated) BRET shows enhanced G\u03b1i-G\u03b2\u03b3 association at lower pHi (Supplementary Fig.\u00a09). Further, to confirm that the observed changes in the BRET signal are due to pH\u2013sensing properties of G\u03b1i but not the GFP2 (attached to G\u03b3), we examined GFP2 fluorescence over a range of intracellular pH values. The results suggest that GPF2 is not pH sensitive within the tested pH range (pHi 6.7-7.2) (Supplementary Fig.\u00a010).\n\nA Schematic diagram illustrating the BRET assay used to monitor receptor-mediated dissociation of G\u03b1i from G\u03b2\u03b3. For this assay, the GPCR neurotensin receptor, G\u03b1 Renilla luciferase 8 (Rluc8), G\u03b23 and \u03b39-GFP2 fusion constructs were co-transfected in HEK29T cells and fluorescence (515\u2009nm) was monitored after the addition of the substrate and agonist (neurotensin). Net BRET (dissociation) plotted as \u03b39-GFP2 (acceptor) over G\u03b1-Rluc (donor) ratio as a function of log doses of neurotensin (B). Neurotensin receptor-mediated BRET shows that lowering pHi (from 7.2 to 6.7) either by altering extracellular pH (B) or by the addition of FCCP (C), decreases G\u03b1i dissociation from G\u03b2\u03b3 in HEK29T cells. D WT G\u03b1i shows significantly higher dissociation from G\u03b2\u03b3 relative to both \u2018double variant\u2019 and \u2018triple variant\u2019 (low pH mimetics of WT G\u03b1i). E The difference in net BRET signal as a function of FCCP concentration (0\u2009\u00b5M in solid line and 2\u2009\u00b5M in dotted line) for \u2018double variant\u2019 and \u2018triple variant\u2019 is reduced with respect to WT G\u03b1i, confirming key roles for E236, D237 and E245 in pH-mediated G\u03b2\u03b3 dissociation for G\u03b1i-G\u03b2\u03b3 complex. All BRET data are averages N\u2009=\u20092 independent experiments with n\u2009=\u20094 total replicates. Figure\u00a08A was created in BioRender. Prakash, A. (2025) https://BioRender.com/b57r381.\n\nTo further confirm that the modulation of G\u03b2\u03b3 release by pH occurs through the identified 3 residues of G\u03b1i, we performed the BRET assay using the double or triple G\u03b1i variants. As shown in Fig.\u00a08D, agonist-stimulated G\u03b1i dissociation from G\u03b2\u03b3 is significantly reduced for the \u2018double variant\u2019 and further reduced for the \u2018triple variant\u2019 with respect to WT G\u03b1i. These variants appear to serve as low pH mimetics, as they produce BRET similar to lower pHi. This observation was further validated by BRET assays performed for \u2018double variant\u2019 and \u2018triple variant\u2019 at different pHi (Fig.\u00a08E). Taken together, these findings suggest that enhanced G\u03b1i-G\u03b2\u03b3 association at lower pH is due to the loss of electrostatic interactions within the Switch network needed for G\u03b1i-G\u03b2\u03b3 formation.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The recognition of pH sensors among biomolecules is crucial in understanding cellular processes, as they play a pivotal role in responding to changes in the proton concentration within the physiological range. While many biomolecules experience alterations in their protonation state, only a specific subset serves as pH sensors, exhibiting pH-dependent functional changes that influence cellular processes.\n\nOne of the first pH-sensing proteins to be characterized is hemoglobin. In acidic environments, hemoglobin exhibits a reduced affinity for oxygen, a phenomenon known as the Bohr effect. This effect is due to the protonation of specific amino acid residues, including histidine 146, located in the \u03b1 subunit of hemoglobin21. The protonation of H146 promotes the formation of a salt bridge with a nearby aspartate residue (D94) on the \u03b2 subunit. This interaction stabilizes the deoxygenated or T-state conformation of hemoglobin, reducing its affinity for oxygen and facilitating the release of oxygen to tissues where it is needed22. After the identification of hemoglobin, proton-sensing ion channels and receptors governing cytosolic pH have been a focal point in research.\n\nMore recent structural informatics calculations have shown that buried ionizable networks are a structural hallmark of pH sensitivity3 and the large pKa shifts exhibited by buried sidechains can be harnessed by proteins to drive pH-dependent changes in structure, stability and function. Indeed, networks of buried ionizable amino acids are conserved in nearly all of the 100\u2009+\u2009G\u03b1 protein structures in the Protein Data Bank (PDB)3. On that basis, the cell signaling GTPases, G\u03b1i and its yeast homolog Gpa1, were proposed to function as pH-sensing proteins. The study indicated that the stability of heterotrimeric G\u03b1 proteins exhibits a significant dependence on pH levels across a broad range from 5.0 to 8.0. Furthermore, alterations in yeast intracellular pH (pHi) were shown to promote Gpa1 phosphorylation and subsequently dampen the mitogen-activated protein (MAP) kinase signaling pathway3. In addition to our findings that G\u03b1i serves as a pH sensor to regulate G\u03b1-G\u03b2\u03b3 interactions, recent studies indicate that select GPCRs possess pH-sensing properties that modulate extracellular signaling to G\u03b1 proteins23,24,25. These and other GPCRs may work independently or synergistically with G\u03b1i proteins to transduce extracellular pH-dependent signals to changes in intracellular pH20,26,27.\n\nHerein, we find that as pH is raised from 6.8 to 7.3, the thermostability of G\u03b1i-GDP is greatly enhanced (\u0394Tm ~25oC) due to the formation of an electrostatic network within the Ras-like domain. Since thermostability changes for G\u03b1i-GDP\u00a0were significantly larger (~20o), relative to the GTP-bound form (~8o), we focused on characterizing pH-dependent structural and dynamic changes associated with the GDP-bound form of G\u03b1i in this study. Using pH-dependent NMR analyses and thermal stability profiling on WT and mutant proteins, we identified three key pH-sensing residues that drive pH-dependent thermostability changes. Two of the residues (E236 and D237) reside in SW-III of G\u03b1i, while the third residue (E245) lies in the neighboring \u03b13 helix. Mutation of E236 and D237 in SW-III promotes G\u03b2\u03b3 release in HEK293 cells, supporting the role of these residues in pH-dependent electrostatic interactions that allosterically regulate heterotrimer formation. While both Ras and heterotrimeric G-proteins contain SWI and SWII regions, Ras proteins lack the SW-III region found in G\u03b1 proteins. The presence of this unique SW-III region in heterotrimeric G-proteins may explain the distinct pH\u2013sensing properties of G\u03b1i, suggesting a potential role of the SW-III region in pH-dependent G\u03b2\u03b3 release from the G\u03b1i-G\u03b2\u03b3 complex. Of note, the pH\u2013sensing network identified in this study is distinct from the network predicted by Isom et al.3. This computationally predicted pH\u2013sensing network consists of residues at the Ras/helical domain interface (e.g. K46, D150, D200, D229, R242, K270 and K277). However, our NMR and CD data do not support this interaction network, as observable NMR resonances associated with K270 and K277 do not show pH-dependent perturbations and mutation of K46 and R242 did not alter pH-dependent thermostability changes measured by CD.\n\nLarge changes in the stability of GTP-bound G\u03b1i have previously been attributed to the formation of an interaction triad (termed as G-R-E Triad) involving residues G203 and R208 from SW-II and E245 from \u03b1316. This interaction triad is absent from the less stable and more dynamic GDP-bound state9,16. Interestingly, E245 is also one of the residues identified in the pH\u2013sensing network. Moreover, E236 and D237 from SW-III interact with SW-II via residue R205 to stabilize both Switches in the GTP-bound forms of the protein. This nucleotide-dependent disorder-to-order transition is predicted to facilitate the release of G\u03b2\u03b3 from G\u03b1i resulting in the activation of G\u03b1i. Consistent with these findings, SW-III residues E236 and D237 are involved in pH-dependent electrostatic interactions that appear to allosterically regulate G\u03b2\u03b3 release.\n\nWhile several pH-sensing proteins contain titratable histidines28,29,30, others such as EmrE contain aspartate and glutamate residues that titrate in the physiological range due to the formation of electrostatic networks24,31. In these systems, even a small reduction in pH can alter the side chain protonation state, leading to the neutralization of charge. In the case of G\u03b1i, we identified a network of three charged residues (E236, D237 and E245) that regulate pH-dependent thermostability changes in the G\u03b1i-GDP. To investigate how the protonation/deprotonation of these residues alters G\u03b1i structure and dynamics in the GDP-bound state, we performed MD simulations. As starting points for the simulations, residues E236, D237 and E245 were generated in both \u2018charged\u2019 and \u2018uncharged\u2019 states to represent higher (deprotonated) and lower pH (protonated) states, respectively. Results from these analyses suggest that the protonation of residues E236, D237 and E245 enhances G\u03b1i-GDP dynamics due to the loss of electrostatic interactions between SW-II, SW-III and \u03b13. Moreover, we find that these residues make transient electrostatic interactions in G\u03b1i-GDP at higher (~>pH 7.2), but not at lower pH. MD simulations performed for the G\u03b1i-G\u03b2\u03b3 trimeric complex in the \u2018charged\u2019 or \u2018uncharged\u2019 state of WT G\u03b1i-GDP showed increased binding energy with G\u03b2\u03b3 in the \u2018uncharged\u2019 state, suggesting stronger binding. Based on these MD simulation data, we propose that lower physiological pH promotes G\u03b2\u03b3 binding to G\u03b1i resulting in attenuation of both G\u03b1i and G\u03b2\u03b3 mediated signaling pathways (Fig.\u00a09). Consistent with this premise, our findings indicate that at the lower end of the intracellular pHi range, agonist-mediated G\u03b2\u03b3 release from G\u03b1i is attenuated due to the loss of pH-dependent electrostatic networks involving key residues within SW-III and \u03b13. This, in turn, is expected to downregulate G\u03b1i and G\u03b2\u03b3 downstream signaling.\n\nActivation of G\u03b1i by GPCRs leads to the release of G\u03b1i from G\u03b2\u03b3 and stimulation of signaling pathways. A distinct mechanism of signaling regulation may occur by intracellular pH regulation. G\u03b1i proteins may act as intracellular pH sensors to regulate G\u03b1i-G\u03b2\u03b3 interactions with lower pHi, enhancing G\u03b1i-G\u03b2\u03b3 interactions to inhibit G\u03b1i signaling. This figure was created in BioRender. Prakash, A. (2025) https://BioRender.com/b57r381.\n\nWhile the current investigation is centered on the G\u03b1i isoform, sequence and structural alignment with other G\u03b1 isoforms (such as G\u03b1s, G\u03b1o and G\u03b1q) reveals conservation within the core pH\u2013sensing network (Supplementary Fig.\u00a011)3. This analysis suggests the potential for pH-sensing properties in other G\u03b1 isoforms. The development of pH-insensitive forms of G\u03b1i will facilitate investigations of hormone and neurotransmitter signaling during physiological stresses, such as occur during glucose or oxygen deprivation, leading to changes in cellular pH32.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58323-2/MediaObjects/41467_2025_58323_Fig9_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "A bacterial pET-SUMO vector containing the human G\u03b1i1 gene (GenBank accession no. BC026326) with the first 31 amino acids deleted, N-terminal 6xHis, and SUMO-tag were employed for in vitro analyses. Full-length human G\u03b1 containing Renilla luciferase (G\u03b1-RLuc8), G\u03b2 and G\u03b3 containing green fluorescence protein 2 (G\u03b3-GFP2), and neurotensin receptor (NTR) constructs were employed for cell-based BRET assays33,34.\n\nG\u03b1i variants were generated using the Q5 Site-Directed Mutagenesis kit (NEB). Polymerase chain reaction (PCR) primers were designed using the NEBaseChanger (https://nebasechanger.neb.com), an online mutagenesis primer design tool powered by New England Biolabs from Eton Bioscience Inc. Mutagenesis was performed as described33. The variant constructs were sequenced by Sanger Sequencing (Genewiz) to confirm successful mutagenesis. Sequencing results for G\u03b1i variants were aligned with published sequences of wild-type (WT) G\u03b1i using Clustal Omega (European Molecular Biology Laboratory, Cambridgeshire, UK).\n\nG\u03b1i proteins were overexpressed in E. coli Agilent BL21-codon plus (DE3)-RP-X competent cells. Cells were grown at 37\u02daC for 3-4\u2009hours to achieve an optical density at 600\u2009nm (OD 600) of 0.60 and then induced with 500\u2009\u00b5M isopropylthio-\u03b2-galactoside (IPTG). The temperature was then reduced to 18\u02daC and the cultures were left to grow overnight. After 20\u2009hours, bacteria cells were harvested by centrifugation at 10,000 x g and then resuspended in 50\u2009mL Dialysis buffer (10\u2009mM KH2PO4, 20\u2009mM K2HPO4, 0.15\u2009M KCl, 1\u2009mM MgCl2, 10\u2009\u00b5M GDP, 1\u2009mM \u03b2-mercaptoethanol (BME), pH 7.0). Resuspended cells were lysed using a sonicator (Fisher Scientific, #CL-334) with an amplitude of 80 and 5\u2009seconds on/off time ratio for 10\u2009mins/L cells. Clarified lysate was passed through the Ni-NTA column equilibrated with 1 column volume (CV) (50\u2009ml) of Dialysis buffer, washed with Wash buffer (10\u2009mM KH2PO4, 20\u2009mM K2HPO4, 0.15\u2009M KCl, 1\u2009mM MgCl2, 10\u2009\u03bcM GDP, 1\u2009mM BME, 30\u2009mM imidazole, pH 7.0) and eluted using the Elution buffer (10\u2009mM KH2PO4, 20\u2009mM K2HPO4, 0.15\u2009M KCl, 5\u2009mM MgCl2, 10\u2009\u00b5M GDP, 1\u2009mM BME, 250\u2009mM imidazole, pH 7.0). The elution fraction was dialyzed overnight in the presence of a ubiquitin-like-specific protease 1 (ULP1) to cleave the SUMO tag on G\u03b1i. After dialysis, the protein was again passed over a Ni-NTA column equilibrated with 3 CVs of water and 1 CV dialysis buffer. The protein flow-through was further purified by gel filtration chromatography using a Fast Protein Liquid Chromatography (FPLC) system (\u00c4kta Primeplus) and Superdex 75 column (Cytiva). The purity of the protein was verified by SDS PAGE gel electrophoresis, and the concentration of the purified protein was estimated by Thermo Scientific NanoDrop.\n\nG\u03b1i proteins were exchanged in CD buffer (10\u2009mM K2HPO4, 500\u2009\u03bcM MgSO4, 500\u2009\u03bcM tris(2-carboxyethyl)phosphine (TCEP)) using an Amicon centrifugal filter unit (MilliporeSigma, #UFC901024) at 3800 x g, diluted to 15\u2009\u00b5M (pH 6.0, 6.4, 6.8, 7.2, or 7.6) and centrifuged (16,000 x g) for 10\u2009mins at 4 \u00b0C. Temperature dependent CD experiments were performed on a Jasco J815CD spectrometer using 15\u2009mM WT or G\u03b1i variants protein in a 1\u2009mm path-length quartz cuvette (Hellma Analytics). Thermal melts were obtained at 222\u2009nm, over a temperature range of 20\u201395 \u00b0C, using a temperature increment of 1\u2009\u00b0C/min. The CD spectral scans were collected for G\u03b1i proteins at different fixed temperatures (e.g. 45\u2009\u00b0C, 55\u2009\u00b0C and 65\u2009\u00b0C) by taking CD measurements every 1\u2009nm from 200-250\u2009nm, and secondary structure evaluated using the online server BeStSel (Beta Structure Selection)35.\n\nIntrinsic tryptophan fluorescence assays were conducted using 2\u2009mM purified WT or variant GDP-loaded G\u03b1i proteins in 200\u2009ml of assay buffer (20\u2009mM HEPES, 50\u2009mM NaCl, 5\u2009mM MgCl2, 2\u2009mM DTT) at different pH (pH 5, 6 and 7.2) in the 96 well plate. Ga proteins were excited at 290\u2009nm and intrinsic tryptophan fluorescence was measured from 300 to 400\u2009nm wavelength using a SpectraMax M4 Series Microplate Reader.\n\nTriple labeled 2H/13C/15N G\u03b1i samples were generated by expressing G\u03b1i proteins in BL21(DE3)-RIPL E. coli cells and growing in minimal medium supplemented with 1\u2009g/L 15NH4Cl, 3\u2009g/L 13C-glucose, 99% D2O (Cambridge Isotope Labs)9, and purified as described above. Triple labeled G\u03b1i was prepared in 20\u2009mM potassium phosphate (pH=7.0), 50\u2009mM potassium chloride, 5\u2009mM DTT (dithiothreitol), 5\u2009mM magnesium chloride, and 50\u2009\u00b5M GDP containing 5% (v/v) D2O. For 15N-enriched WT and G\u03b1i variants, bacterially expressed proteins were grown in minimal media containing 1\u2009g/L 15NH4Cl as the sole nitrogen source and purified as described above.\n\n2D NMR 1H-15N HSQC spectra of G\u03b1i were acquired on a Bruker Avance 850\u2009MHz (14.1\u2009T field strength) NMR spectrometer at 25\u2009\u00b0C, with a cryogenic (TCI) 5\u2009mm triple resonance probe equipped with a z-axis gradient. The 1H-15N HSQC 2D NMR experiments were recorded with 2048 and 224 complex points, 16 and 36 ppm spectral width, 75.36 and 36.10\u2009ms acquisition times in the direct and indirect dimensions, respectively, 16 scans per increment and a recovery delay of 1.25\u2009s. The 1H-15N HSQC spectra of G\u03b1i-GDP were assigned using a combination of triple resonance experiments, including 3D HNCA, HN(CO)CA, HN(CA)CO and HNCO9. TROSY-based pulse sequences were used for sensitivity enhancement. Bruker-TopSpin 4.1.1 was used to process the NMR data and NMRFAM-SPARKY was used to visualize and analyze the NMR spectra36. For assignments, BMRB 30078 was used as a reference spectrum for G\u03b1i-GDP37. pH titration studies were performed by calculating the chemical shift perturbation (CSP) of G\u03b1i-GDP over a pH range from 6.4, 6.8, 7.0, 7.2, 7.4 and 7.6. Average 1H-15N CSP were determined using the formula \u0394\u03b4\u2009=\u2009[(\u03941HN)2\u2009+\u2009(\u039415N/5)2]0.5. PyMOL38 (https://pymol.org/2/) and UCSF ChimeraX39 (https://www.cgl.ucsf.edu/chimera/) was used to generate all images of molecular structures.\n\nG\u03b1i proteins were exchanged into nucleotide association buffer (20\u2009mM HEPES, 50\u2009mM NaCl, 5\u2009mM MgCl2, 2\u2009mM DTT) using an amicon concentrator (3800\u2009g, 15\u2009min, 3 rounds). In the cuvette, 0.75\u2009\u03bcM (2\u2019-(or-3\u2019)-O-(N-Methylanthraniloyl) Guanosine 5\u2019-Diphosphate, Disodium Salt (Mant-GDP, purchased from Thermofisher) was added to 1\u2009ml of association buffer for the assay. The intrinsic tryptophan (W211) was excited at 280\u2009nm and the Mant-GDP fluorescence intensity at 425\u2009nm was measured as a function of time using a PerkinElmer LS55 luminescence spectrometer. After collecting data for a few seconds to obtain baseline fluorescence, purified 1\u2009\u03bcM G\u03b1i-GDP was added to the solution to initiate GDP association. Once the fluorescence reached saturation, 10 x (7.5\u2009\u00b5M) GDP was added to the solution to initiate the GDP dissociation.\n\nG\u03b1i-GDP coordinates were extracted from the G\u03b1i-G\u03b2\u03b3 complex structure (PDB: 1GP2)40 with missing residues and atoms introduced using MODELLER 9v21.241. The pdb2gmx module from the GROMACS-2020.3 package was used to generate both the \u2018charged\u2019 and \u2018uncharged\u2019 states for residues E236, D237 and E24542 to represent the \u2018charged\u2019 state as higher pH (above 7.2) and the \u2018uncharged\u2019 state as low pH (below 6.4). Following side-chain optimizations, minimum energy conformations of \u2018charged\u2019 and \u2018uncharged\u2019 G\u03b1i-GDP\u00a0states were identified. Subsequently, MD simulations were performed to investigate conformational and dynamic differences compared to WT GDP-bound G\u03b1i. The CHARMM36 forcefield was used to parametrize the protein and GDP, and simulations were run using GROMACS-2020.343 (Supplementary Table\u00a02). G\u03b1i proteins were solvated in a cubic box containing approximately 22,541 TIP3P water molecules and maintained a salt concentration of 150\u2009mM through the addition of an appropriate number of Na+ and Cl- ions. The solvated systems were energy minimized using the conjugate gradient algorithm and subsequently equilibrated for 1000\u2009ns using the V-rescale thermostat and the Parrinello-Rahman barostat44. Long-range electrostatic interactions were evaluated using the Particle-Mesh Ewald sum45, and all bonds involving hydrogen atoms were constrained using the LINCS algorithm46. Simulations were run for 1000\u2009ns and were repeated in triplicate for reproducibility. Structural figures were generated using PyMOL38 (https://pymol.org/2/) and UCSF ChimeraX39 (https://www.cgl.ucsf.edu/chimera/). Graphical plots were created using Xmgrace (http://plasma-gate.weizmann.ac.il/Grace/).\n\nMD simulations of the trimeric G\u03b1i-G\u03b2\u03b3 complex were initiated using the G\u03b1i-G\u03b2\u03b3 complex structure (PDB: 1GP2) as the starting point. The protonation states of residues E236, D237 and E245 were adjusted to simulate \u2018uncharged\u2019 (\u20180\u2019) and \u2018charged\u2019 (\u2018-1\u2019) states, corresponding to different pH conditions. Additional MD simulations were also run by introducing the E236L\u2009+\u2009D237G\u2009+\u2009E245Q triple mutation into G\u03b1i to evaluate how changes in salt-bridge interactions within the Switch regions influence G\u03b1i-G\u03b2\u03b3 interactions. The MD simulations were run for 1000\u2009ns in triplicate for robustness and reproducibility (Supplementary Table\u00a02).\n\nMolecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis was performed using the gmx_MMPBSA tool47 to compute the binding free energies between G\u03b1i and G\u03b2\u03b3 by analysis of contributions from electrostatic, van der Waals, and polar and non-polar solvation energies. In addition, residue-based energy decomposition from gmx MMPBSA was performed to evaluate pH-dependent contributions from individual residues to the overall binding energy of the trimeric complex.\n\nHEK293T [American Type Culture Collection (ATCC), CRL-11268] cells were maintained, passaged, and transfected in Dulbecco\u2019s Modified Eagle Medium (DMEM, Gibco) containing 10% sterilized Fetal Bovine Serum (FBS, Gibco), 100 Units/mL penicillin, and 100\u2009\u03bcg/mL streptomycin (Gibco-ThermoFisher, Waltham, MA) in a humidified atmosphere at 37\u2009\u00b0C and 5% CO2. Transfection was carried out using a lipid-polymer-based transfection agent (Mirus Bio, MIR 5400, Madison, WI). After transfection, cells were plated in DMEM (Dulbecco\u2019s Modified Eagle Medium) containing 10% FBS (Fetal Bovine Serum), 100 Units/mL penicillin, and 100\u2009\u03bcg/mL streptomycin for BRET assays.\n\nThe intracellular pH of HEK293 cells was altered by two different methods. In the first method, HEK293 cells were treated with 1-5\u2009\u00b5M of trifluoromethoxycarbonylcyanide phenylhydrazone (FCCP) for 5\u2009min. In the second method, intracellular pH was modulated by incubating HEK293 cells with Hanks\u2019 Balanced Salt Solution (HBSS) buffer at different pH for 15\u2009min. Intracellular pH was quantified using the intracellular pH indicator, 2\u2019,7\u2019-bis-(2-carboxyethyl)-5-(and-6)-carboxyfluorescein-acetoxymethyl ester (BCECF-AM) as described18. To convert BCECF-AM fluorescence to the intracellular pH values, HEK293 cells were treated with 20\u2009\u00b5M nigericin and a calibration curve for pHi and BCECF-AM fluorescence was generated.\n\nBRET assays were carried out as described by Olsen et al.34. Briefly, HEK293T cells were seeded in six-well dishes in DMEM containing 10% FBS, 100 Units/mL penicillin, and 100\u2009\u03bcg/mL streptomycin at a density of 300,000 cells/well and allowed to grow to 80-90% confluency. Cells were then transfected using a 1:1:1:1 DNA ratio of NTR:G\u03b1-RLuc:G\u03b2:G\u03b3-GFP2 (1000\u2009ng/construct for six-well dishes). TransIT-2020 (Mirus Biosciences, Madison, WI) was used to complex the DNA at a ratio of 3\u2009\u03bcL Transit/\u03bcg DNA, in OptiMEM (Gibco-ThermoFisher, Waltham, MA) at a concentration of 1\u2009\u03bcg DNA/\u03bcL OptiMEM. The next day, cells were harvested from the plate using trypsin (0.25% EDTA, Gibco, #25200056) and plated in poly-D-lysine-coated white, clear bottom 96-well assay plates (Greiner Bio-One, Monroe, NC) in DMEM containing 1% dialyzed FBS, 100 Units/mL penicillin, and 100\u2009\u03bcg/mL streptomycin at a density of 50,000 cells/well. One day after seeding in 96-well assay plates, white backings (Perkin Elmer, Waltham, MA) were applied to the plate bottoms, and the growth medium was aspirated. Cells were washed three times with Assay buffer (1X HBSS, 20\u2009mM HEPES, pH 7.4), then 60\u2009\u03bcL of Assay buffer was added immediately, followed by a 10\u2009\u03bcL addition of freshly prepared 50\u2009\u03bcM coelenterazine 400a (1-bisdeoxycoelenterazine) (Nanolight Technologies, Pinetop, AZ) in each well. After a five-minute equilibration period, cells were treated with 30\u2009\u03bcL of 3x neurotensin (3 \u00d710-5M) for another 5\u2009minutes. Plates were then read in a plate reader (Clariostar, BMG Labtech, Ortenberg, Germany) at 410\u2009nm (Rluc8-coelenterazine 400a) and 515\u2009nm (GFP2) with a slit width of 8\u2009nm using 10 flashes per spiral well scan. Plates were read serially five times, and measurements from the third read were used in all analyses. BRET ratios were computed as the ratio of the GFP2 emission to Rluc8 emission. For GFP control assays, G\u03b2 and G\u03b3-GFP2 (200\u2009ng each) were transfected into HEK293 cells. Assay buffer (100\u2009\u03bcL) was added following wash steps (lacking coelenterazine 500a or neurotensin) with fluorescence measured at 515\u2009nm. The raw data was used to compare GFP fluorescence at different pH values.\n\nCD melt-curves were fit to Boltzmann sigmoidal equation in Prism 9.3.1 (GraphPad Software, San Diego, CA). Concentration-response curves for BRET assays were fit to a three-parameter logistic equation. Raw BRET concentration-response curves were normalized to the best-fit maximum within a data set. BRET data were represented as mean\u2009\u00b1\u2009SEM. Data analysis was carried out using Prism 9.3.1 (GraphPad Software, San Diego, CA).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "PDB codes of previously published structures used in this study are: 1GP2 (GDP bound G\u03b1i-G\u03b2-G\u03b32), 1CIP (GppNHp bound G\u03b1i), 7S0F (Isoproterenol bound beta1 adrenergic receptor in complex with heterotrimeric G\u03b1i protein), 3OHM (G\u03b1q bound to phospholipase C beta 3), 1AZT (GTP\u03b3S bound G\u03b1s), and 1ZCA (G\u03b112 in complex with GDP). Reference assignments were taken from BMRB 30078 (G\u03b1i subunit in complex with GDP). The co-ordinates of G\u03b1i-GDP\u00a0in \u2018charged\u2019 and \u2018uncharged\u2019 states in complex with G\u03b2\u03b3 are provided as supplementary data files. All remaining data are available in the Article, Supplementary and Source Data files. All data reported in this paper will be shared by the lead contacts upon request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Molecular dynamics simulations were performed using GROMACS-2020.3 (https://www.gromacs.org) with the CHARMM36 force field for protein and GDP parametrization. Simulations were carried out using standard GROMACS tools and the pdb2gmx module to prepare the \u2018charged\u2019 and \u2018uncharged\u2019 states of the G\u03b1i protein. Structural and energy analyses were performed using the gmx_MMPBSA tool (https://manual.gromacs.org) for binding free energy calculations. Structural figures were rendered using PyMOL38 (https://pymol.org) and UCSF ChimeraX39 (https://www.cgl.ucsf.edu/chimera/). Xmgrace (http://plasma-gate.weizmann.ac.il/Grace/) was used for graphical plotting. 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Research reported in this publication was supported by National Institutes of Health (NIH) grants R35GM134962 (to S. L. Campbell) and R35GM118105 (to H. G Dohlman) and UNC Cancer Center Core Support Grant P30CA016086 (to V. R. Chirasani).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Zijian Li, Venkat R. Chirasani.\n\nDepartment of Biochemistry & Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA\n\nAjit Prakash,\u00a0Zijian Li,\u00a0Venkat R. Chirasani,\u00a0Juhi A. Rasquinha,\u00a0Garrett B. Hubbard,\u00a0Aspen T. Hawkins,\u00a0Luca J. Montore\u00a0&\u00a0Sharon L. Campbell\n\nR. L. Juliano Structural Bioinformatics Core, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA\n\nVenkat R. Chirasani\n\nDepartment of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA\n\nNatalie Hewitt\u00a0&\u00a0Henrik G. Dohlman\n\nThe Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, 518107, China\n\nGuowei Yin\n\nLineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA\n\nSharon L. Campbell\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: A.P., G.Y., H.G.D., S.L.C. Methodology: A.P., Z.L., J.R., N.H., G.B.H., G.Y., Experimentation: A.P., Z.L., J.R., N.H., G.B.H., G.Y., A.T.H., L.J.M., Computational\u00a0Modeling and MD simulations: V.R.C. Writing\u2014original draft: A.P., Z.L.,V.R.C., H.G.D, S.L.C. Writing\u2014review & editing: G.Y., J.R., N.H. Analysis: A.P., Z.L., J.R., V.R.C.,\u00a0N.H., G.B.H. Revision: A.P., V.R.C, A.T.H., L.J.M., Supervision: H.G.D., S.L.C.\n\nCorrespondence to\n Sharon L. Campbell.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Robert Prosser, and the other, anonymous, reviewers for their contribution to the peer review of this work. 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Cell-Free RNA as a Predictor of Early and Late-Onset Preeclampsia Throughout Pregnancy", + "journal": "Nature Communications", + "published": "20 October 2025", + "supplementary_0": [ + { + "label": "Supplementary information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64215-2/MediaObjects/41467_2025_64215_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64215-2/MediaObjects/41467_2025_64215_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data File 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64215-2/MediaObjects/41467_2025_64215_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data File 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64215-2/MediaObjects/41467_2025_64215_MOESM4_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64215-2/MediaObjects/41467_2025_64215_MOESM5_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64215-2/MediaObjects/41467_2025_64215_MOESM6_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [ + "https://github.com/marinaigual/ipremom-cfrna-preeclampsia-predictor/tree/main" + ], + "subject": [ + "Pre-eclampsia", + "Predictive markers", + "RNA sequencing" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5684050/v1.pdf?c=1761044762000", + "research_square_link": "https://www.researchsquare.com//article/rs-5684050/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-64215-2.pdf", + "preprint_posted": "16 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Early-onset (EOPE) and late-onset preeclampsia (LOPE) pose significant challenges to maternal and child health, highlighting the need for early, non-invasive risk identification. In this prospective longitudinal study, we followed 9,586 pregnant women, collecting blood samples each trimester: 9-14 weeks (T1), 18-28 weeks (T2), and after 28 weeks or at preeclampsia diagnosis (T3). Plasma cell-free RNA (cfRNA) signatures were analyzed in women who developed EOPE (n=42) or LOPE (n=43) and compared to matched normotensive controls (n=75). Mapping cfRNA origins and performing differential abundance analysis provided insights into multi-organ impacts, revealing distinct transcriptional features of EOPE and LOPE. We developed a first-trimester EOPE predictive model using 36 transcripts, achieving 83% sensitivity, 88% specificity, and an AUC of 0.85, detecting risk 18.0 weeks before onset. A second-trimester model based on 87 cfRNA transcripts, predicted EOPE 8.5 weeks prior to onset with 87% sensitivity, 84% specificity, and an AUC of 0.85. For LOPE model, detecting risk 14.9 weeks before onset, used 92 cfRNAs, with 86% sensitivity, 89% specificity, and an AUC of 0.88. EOPE models were enriched for decidua-associated transcripts, highlighting the maternal involvement in this subtype, while LOPE models showed diverse tissue responses, paving the way for improved subtype differentiation and tailored interventions to mitigate preeclampsia risks.Health sciences/Biomarkers/Predictive markersBiological sciences/Biological techniques/Sequencing/RNA sequencingBiological sciences/Computational biology and bioinformatics/Predictive medicine", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Table 1 is available in the Supplementary Files section.\nYes there is potential Competing Interest. N.C-M., M.I., T.G-G., C.S. are inventors on a patent application (EP24383276.3) covering methods for determining the risk of preeclampsia. N.C-M., T.C., M.I., C.G-A., N.B-G., A.G-D., E.O-D., A.V., T.G-G. are employees of iPremom Pregnancy Healthcare Diagnostics. C.S. is a founder of iPremom Pregnancy Healthcare Diagnostics", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Table1.xlsxTable 1. Maternal characteristics and pregnancy outcomes for the selected subset of participants.Supp.Table1.xlsxSupplementary Table 1. Gestational age at blood sample for patients and controls in the selected \u00a0subset of participants.Supp.Table2.xlsxSupplementary Table 2. Differentially abundant cfRNA transcripts at diagnosis in early-onset \u00a0preeclampsia patients compared to normotensive controls.Supp.Table3.xlsxSupplementary Table 3. Differentially abundant cfRNA transcripts at diagnosis in late-onset preeclampsia patients compared to normotensive controls.Supp.Table4.xlsxSupplementary Table 4. Gene ontology analysis for increased abundant cfRNA in early-onset preeclampsia and late-onset preeclampsia.Supp.Table5.xlsxSupplementary Table 5. cfRNAs composing the predictive models for early-onset preeclampsia and late-onset preeclampsia at the first and second trimester of pregnancy.Supp.Table6.xlsxSupplementary Table 6. Summary of predictive model performance metrics for early-onset \u00a0preeclampsia and late-onset preeclampsia during the first and second trimesters of pregnancy", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Early- and late-onset preeclampsia (EOPE and LOPE) pose serious maternal-fetal risks, yet non-invasive early prediction remains challenging. In a prospective cohort of 9,586 pregnancies, we analyze trimester-specific plasma cell-free RNA (cfRNA) profiles from 42 EOPE and 43 LOPE cases versus 131 normotensive controls. Organ-specific transcriptomic shifts distinguish EOPE from LOPE. Predictive models based on cfRNA signatures identify EOPE up to 18.0 weeks before clinical onset in the first-trimester (T1) (AUC\u2009=\u20090.88), and 8.5 weeks in the second trimester (T2) (AUC\u2009=\u20090.89). LOPE is predicted 14.9 weeks in advance using T2 data (AUC\u2009=\u20090.90), while T1 performance is lower (AUC\u2009=\u20090.68). External validation confirms robust EOPE prediction (AUC\u2009=\u20090.87 at T1; 0.81 at T2) and acceptable LOPE performance (AUC\u2009=\u20090.63 at T1; AUC\u2009=\u20090.77 at T2). EOPE models are enriched for decidual transcripts, suggesting early maternal involvement; LOPE models reflect broader tissue contributions. These findings offer a path to early, non-invasive, subtype-specific preeclampsia risk stratification and prevention.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Maternal and infant mortality during pregnancy and labor are critical indicators of community and national health1,2. Most pregnancy complications arise from disorders that develop during the periconceptional phase, particularly during embryonic implantation and early placentation3.\n\nPreeclampsia\u2014a life-threatening obstetric syndrome\u2014is characterized by new-onset of hypertension after 20 weeks of gestation, accompanied by signs of kidney, liver, or brain damage4. Each year, preeclampsia contributes to 14% of maternal deaths worldwide, leaving a lasting impact on survivors\u2019 health5. It also constitutes a significant public health burden, incurring $1.03 billion in maternal healthcare costs and an additional $1.15 billion for neonatal care in infants born to mothers affected by preeclampsia within the first year after birth in the United States6.\n\nThe heterogeneity of preeclampsia is notable, differentiated by the timing of onset and severity of symptoms. Early-onset preeclampsia (EOPE) arises before 34 weeks of gestation, necessitating emergency delivery to mitigate risks to maternal and fetal health7,8. In contrast, late-onset preeclampsia (LOPE) manifests after 34 weeks and can lead to severe maternal organ damage such as kidney, liver, or brain damage8,9,10,11. Therefore, there is an urgent need for straightforward, non-invasive methods for early diagnosis of preeclampsia in the first trimester to implement preventive strategies effectively12,13,14.\n\nSince the maternal decidua regulates the initial steps of maternal-embryo communication, decidualization resistance (DR)\u2014characterized by defective endometrial cell differentiation\u2014results in abnormal placentation, which has been associated with the etiology of major obstetric syndromes, including preeclampsia15,16,17,18,19, even though symptoms may manifest later in gestation15,16,17,18,19. Recently, we provided an in-depth multi-omics characterization of DR in former EOPE patients, further underscoring the uterine contribution to this pathological condition20.\n\nAnalyzing plasma cell-free RNA (cfRNA) through liquid biopsy (i.e., from a blood sample) has emerged as a promising non-invasive tool for molecular monitoring in pregnancy, offering insights into physiological and pathological events21,22. However, previous cfRNA studies on preeclampsia prediction have faced limitations such as small EOPE sample size23, lack of clear subtype distinction23,24, or sampling at later gestational ages24,25. Our study builds on these foundations and addresses these gaps by including a large, prospectively collected cohort of EOPE cases with strict first-trimester sampling, clearly differentiating EOPE and LOPE, and employing longitudinal sampling. This comprehensive design has allowed us to develop and validate predictive models with improved early and subtype-specific risk stratification.\n\nSpecifically, in this case-control study, we prospectively analyzed the cfRNA profiles in pregnant women across the three trimesters of pregnancy, comparing EOPE and LOPE with normotensive controls. This approach facilitated the characterization of the circulating transcriptome by mapping the tissue origins and transcriptional changes associated with EOPE and LOPE, revealing that both subtypes display distinct transcriptional differences compared to controls. Our research identified cfRNA profiles that exhibited robust predictive performance for EOPE in both the first (averaging 18.0 weeks before diagnosis) and second trimesters (averaging 8.5 weeks prior to clinical onset), as well as for LOPE in the second trimester (14.9 weeks prior to clinical onset). Monitoring cfRNA profiles not only aids in predicting the risk of developing preeclampsia but also allows the differentiation of both subtypes of preeclampsia and the evaluation of different organ damage in affected patients, providing insights into their prognosis.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "A total of 9586 pregnant women with singleton pregnancies were enrolled in this prospective and longitudinal case-control study in fourteen tertiary hospitals in Spain (ClinicalTrials.gov Identifier: NCT04990141). Blood samples were collected prospectively across all three trimesters and at the time of EOPE or LOPE diagnosis. Each participant was followed until delivery, ensuring the availability of obstetrical outcome and the creation of a curated database with comprehensive clinical data. Uncomplicated pregnancies that progressed to term (>\u200937 weeks) were classified as normotensive controls, while those diagnosed with EOPE or LOPE, were categorized according to current established ACOG4 and FIGO24 clinical guidelines.\n\nOf the 9586 pregnant women enrolled, 7142 were eligible for analysis after excluding participants for selection failure, loss to follow-up, and obstetric complications other than preeclampsia. We included all EOPE cases (n\u2009=\u200942) and randomly selected a subset of LOPE cases (n\u2009=\u200943). The number of LOPE cases was established to match the number of EOPE cases, ensuring that both groups had the same control-to-case ratio of 1:3, which is optimal for model development. Normotensive controls (n\u2009=\u2009131) were randomly selected from the 6,905 uncomplicated pregnancies and matched to both EOPE and LOPE cases for key epidemiological variables including gestational age at sampling, maternal age, parity, BMI and ethnicity (Supplementary Fig.\u00a01a, b, and Supplementary Table\u00a01). Then, a subset of 216 participants composed by preeclampsia cases (EOPE and LOPE) and normotensive controls was selected for total cfRNA sequencing to characterize cfRNA profiles throughout the progression of pregnancy (Fig.\u00a01). For the development of predictive models, the cohort was randomly stratified into a discovery set (70% of patients) and a validation set (30% of patients). The discovery set was used to build the predictive model and the validation set to assess its performance in a hold-out group of samples (Supplementary Fig.\u00a01c).\n\nA total of 9586 pregnant participants were recruited. After excluding participants due to selection failure and loss to follow-up, 8991 remained. Within this cohort, 237 (2,6%) individuals were diagnosed with preeclampsia, including 42 EOPE and 195 LOPE cases, while 1849 (20.6%) individuals had other pregnancy-related pathologies, and 6905 (76,8%) participants had no obstetric complications. For cfRNA analysis, we included all 42 EOPE cases, a subset of 43 LOPE cases and 131 normotensive controls, randomly selected from the matched cohort based on gestational age at sample collection, maternal age, parity, ethnicity, and BMI.\n\nFrom each participant, we collected three peripheral blood samples between 9 and 14 weeks of gestation (T1), 18-28 weeks (T2), and at the time of diagnosis of EOPE and LOPE or after 28 weeks (T3) (Fig.\u00a02). Data on the gestational weeks of blood sample collection are summarized in Supplementary Table\u00a02. Due to clinical emergencies necessitating immediate termination of pregnancy, T3 could not be collected from fourteen EOPE patients and seven LOPE patients.\n\nBar graph illustrating the number of samples collected at each gestational week for the EOPE (a), LOPE (c) and control (e) groups. Color represents the time point of sample collection: T1 (9-14 gestational weeks); T2 (18\u201328 gestational weeks); T3 (at the time of preeclampsia diagnosis or >28 gestational weeks). Density plot showing the relative frequency of preeclampsia diagnosis and delivery across gestational weeks for the EOPE (b), LOPE (d) and control (f) groups.\n\nMaternal characteristics, clinical symptoms, and birth outcomes are summarized in Table\u00a01. There were no significant differences in maternal age, parity, ethnicity, BMI index or smoking habits between patients and controls (p\u2009>\u20090.05). Natural conception rate was statistically lower in EOPE patients compared to controls (p\u2009=\u20090.0003) but did not differ significantly in LOPE (p\u2009=\u20090.105). Aspirin prophylaxis (150\u2009mg) was prescribed to 30 EOPE (71.4%), 23 LOPE cases (53.5%), and 10 normotensive controls (7.6%). EOPE was diagnosed at 30.0\u2009\u00b1\u20093.4 weeks, with severe symptoms in 76.2% of patients; LOPE was diagnosed at 36.5\u2009\u00b1\u20091.8 weeks, with severe symptoms in 41.9% of patients. Severe symptoms were considered the presence of severely elevated blood pressure (systolic \u2265160\u2009mm Hg or diastolic \u2265100\u2009mm Hg), thrombocytopenia, impaired liver function, progressive renal insufficiency, pulmonary edema, or neurological complications such as cerebral or visual disturbances4.\n\nBirth outcomes for EOPE and LOPE included higher rates of small for gestational age, preterm birth, cesarean delivery, and lower fetal weight (p\u2009<\u20090.001). Specifically, preterm deliveries occurred in 87.8% of EOPE patients and in 41.9% of LOPE patients, with cesarean sections required in 69.0% and 44.2% of patients, respectively. In contrast, all deliveries in the control group occurred at term, and only 19.8% involved cesarean sections. Fetal sex did not differ between groups. EOPE patients had significantly higher rates of stillbirth (11.9%) and post-delivery complications (p\u2009<\u20090.001), with 35.2% of mothers and 50.0% of neonates requiring intensive care. In comparison, among patients with LOPE, 18.6% of mothers and 16.3% of newborns required intensive care, whereas no mothers and 0.8% of neonates in the control group needed intensive care.\n\nWe analyzed a total of 29,871 cfRNA transcripts after applying quality filtering and normalization processes. To determine the tissue origins of the identified transcripts, we compared our cfRNA dataset to the Human Protein Atlas database26, focusing on transcripts classified as \u201cenriched\u201d or \u201cenhanced\u201d in specific tissues or organs. In this analysis, we examined tissues and organs that are directly involved in the pathophysiology of preeclampsia and contribute to its clinical manifestations. Our experimental protocol detected over 90% of these classified transcripts for each targeted organ or tissue of interest (Fig.\u00a03a), indicating a robust coverage of tissue-specific cfRNA signatures in our dataset.\n\na Number and proportion of cfRNA transcripts from organs/tissues implicated in preeclampsia, relative to Human Protein Atlas reference. b Box plots show cfRNA abundance scores by tissue of origin at each time point, calculated as the sum of log-transformed CPM-TMM normalised counts. Color indicates group. Horizontal lines represent medians; boxes, 25th\u201375th percentiles; whiskers extend to 1.5x interquartile range. Sample sizes for each time point and group are as follows: T1 (EOPE, n\u2009=\u200941; LOPE, n\u2009=\u200943; control, n\u2009=\u2009129); T2 (EOPE, n\u2009=\u200940; LOPE, n\u2009=\u200941; control, n\u2009=\u2009120); T3 (EOPE, n\u2009=\u200919 vs. control, n\u2009=\u200934; LOPE, n\u2009=\u200924 vs. control, n\u2009=\u200939). P-values were determined by Wilcoxon rank-sum test with two tails. Exact P-values for all comparisons are provided in Supplementary Table\u00a03. *P\u2009<\u20090.05, **P\u2009<\u20090.01, ***P\u2009<\u20090.001, ****P\u2009<\u20090.0001.\n\nWe then calculated the organ/tissue-specific signature score for patients and controls at three time points during pregnancy (T1, T2 and T3) (Fig.\u00a03b and Supplementary Table\u00a03). In EOPE patients, a significant increase in cfRNA transcripts from the liver, kidney, and decidua was identified at T2 (p\u2009<\u20090.01), indicating tissue specific damage approximately eight weeks before diagnosis. At T3, when clinical symptoms appear, EOPE patients displayed a significantly higher signature score (p\u2009<\u20090.0001) for additional organs including brain, lungs, placenta, and lymphoid tissues, signaling widespread organ injury. In contrast in LOPE patients, tissue-specific transcripts suggesting organ damage was only observed at T3 (p\u2009<\u20090.01), with lower levels of significance than those in EOPE.\n\nTo decode cfRNA dynamics throughout pregnancy, we performed a differential abundance analysis at each time point, elucidating molecular changes in the circulating transcriptome associated with disease progression and offering insights into underlying mechanisms. At the time of diagnosis (T3), we identified 24,336 transcripts with significantly altered abundance in EOPE patients compared to controls (FDR\u2009<\u20090.05) (Supplementary Fig.\u00a02a and Supplementary Data File\u00a01). In contrast, LOPE patients exhibited 11,859 differentially abundant transcripts (FDR\u2009<\u20090.05) (Supplementary Fig.\u00a02b and Supplementary Data File\u00a02). Notably, 8,127 cfRNAs showed differential abundance in T2 for EOPE patients (FDR\u2009<\u20090.05), whereas no differentially abundant cfRNAs were detected in T1 for either EOPE or LOPE patients, nor in T2 for LOPE. These findings suggest that transcriptomic alterations emerge as EOPE progresses, while LOPE remains largely unchanged.\n\nGene ontology overrepresentation analysis within the differentially abundant cfRNAs at diagnosis revealed biological processes indicative of fetal and maternal organ-specific damage (FDR\u2009<\u20090.05) (Supplementary Fig.\u00a02c and Supplementary Table\u00a04). Both, EOPE and LOPE patients displayed significant enrichment in key biological processes, including transport across the blood-brain barrier, renal water homeostasis, regulation of blood pressure and cognition, which are hallmark processes of the pathology. Importantly, signatures of fetal tissue damage were identified in both EOPE and LOPE, with a notably greater impact in EOPE patients. Distinct biological processes were associated with either EOPE or LOPE. In EOPE, overrepresentation analysis revealed significantly enriched pathways related to neuronal death, renal filtration, and immune dysfunction \u2500including interleukin-8 production, response to interleukin-4, neutrophil-mediated immunity, and antimicrobial humoral immune response. In contrast, LOPE cfRNA profile showed signatures linked to heart and brain function (FDR\u2009<\u20090.05), suggesting significant damage to these organs.\n\nThus, cfRNA profile analysis at diagnosis (T3) indicates more extensive transcriptomic alterations in EOPE compared to LOPE, highlighting an exacerbated proinflammatory state as a defining feature. These findings underscore the impacts of the disease on multiple organ systems and suggest that cfRNA profiling may provide valuable insights into the molecular distinctions between preeclampsia subtypes. Additionally, the identification of distinct biological processes linked to each preeclampsia subtype emphasizes the need for tailored therapeutic approaches targeting specific dysfunctions observed in EOPE and LOPE.\n\nGiven the evidence that cfRNA profiles reflect molecular changes throughout pregnancy, disruptions in these pathways may help identify pregnancies at risk for EOPE or LOPE. Here, we developed a model for EOPE risk assessment based on plasma cfRNA profiles in the first trimester (T1), approximately 18.0 weeks before clinical onset.\n\nOur optimal predictive model for EOPE utilized 36 cfRNA transcripts (Supplementary Table\u00a05) and was evaluated in a hold-out validation set. The model achieved a sensitivity of 83% and specificity of 90%, with an area under the receiver operator characteristic curve (AUC) of 0.88 (Fig.\u00a04a and Supplementary Table\u00a06). Nearly all samples were correctly classified, with minimal misclassifications observed reinforcing the model\u2019s robustness and indicating no evidence of overfitting (Fig.\u00a04b). Relative contribution of individual cfRNA transcripts to the model\u2019s performance are detailed in Fig.\u00a04c. We further evaluated the same cfRNA signature in an independent external dataset23 (Fig.\u00a04a,b and Supplementary Table\u00a06), confirming consistent performance (sensitivity 78%, specificity 90%; AUC 0.87), despite cross-cohort variability in protocols and data origin.\n\nReceiver operating characteristic (ROC) curves for EOPE (a) and LOPE (d) models across internal validation (validation 1) and external validation23 (validation 2). The X-axis represents the False Negative Rate; the Y-axis, the True Positive Rate. Violin plots showing correctly and misclassified patients and controls based on the classifier score obtained from the predictive model for EOPE (b) and LOPE (e). The X-axis shows the real obstetric outcome; the Y-axis, the predicted outcome. Bar plot illustrating each cfRNAs contribution to EOPE (c) and LOPE (f) models. The X-axis shows the feature importance scores, which quantify the relative contribution of each cfRNA to the model\u2019s predictions, with higher scores indicating features that play a more significant role in discriminating between outcomes. CfRNAs associated with DR are marked with an asterisk. AUC, area under the curve.\n\nFurther analysis of these 36 transcripts revealed that 17 (47.2%) were identified as markers of DR in women with a history of severe preeclampsia, including CBR3, MMP7, MDK, TRIB1, PAEP20. The model also incorporates cfRNA transcripts known to be disrupted in preeclamptic placentas, such as RFLBN27, and CD7428, as well as others associated with fetal growth restriction, such as CCL4L229 and MYL630.\n\nUsing the same computational approach, we developed a predictive model for LOPE in the first trimester (T1), with predictions averaging 24.9 weeks before clinical onset. However, the model\u2019s performance in the validation set was limited, achieving a sensitivity of 72%, specificity of 64%, and an AUC of 0.68 (Fig.\u00a04d and Supplementary Table\u00a06). Consistent with these findings, the model showed similarly limited performance when applied to an independent external cfRNA dataset23 (sensitivity 39%, specificity 58%, AUC 0.63) (Fig.\u00a04d, e and Supplementary Table\u00a06), underscoring the challenges in early LOPE prediction. Misclassified samples are shown in Fig.\u00a04e, and the relative contribution of individual cfRNAs to predictive accuracy detailed in Fig.\u00a04f. While predictive capability was limited, analysis of the selected cfRNAs offers insights into LOPE mechanisms.\n\nFurther exploration revealed that several of these cfRNAs map to protein-coding genes with known roles in cardiovascular, hepatic, and immune functions, including PRR23D1, SnoRD126, CD52, TRDV3. Unlike EOPE, no cfRNA transcripts in this model were associated with decidua, underscoring distinct pathophysiological pathways for EOPE and LOPE.\n\nIn conclusion, our findings demonstrate the effectiveness of cfRNA signatures in predicting EOPE during the first trimester, while LOPE prediction remains challenging, likely reflecting fundamental differences in pathophysiology between EOPE and LOPE.\n\nWe next investigated the potential for early detection of EOPE and LOPE in the second trimester (T2). The most effective predictive model for EOPE was based on 87 cfRNA transcripts (Supplementary Table\u00a05), achieving a sensitivity of 89% and specificity of 86%, with an AUC of 0.89 in the validation set (Fig.\u00a05a and Supplementary Table\u00a06). Misclassified samples are shown in Fig.\u00a05b, and importance scores for each transcript are illustrated in Fig.\u00a05c. This model reliably identifies patients at risk for EOPE between 18 and 28 weeks of gestation, approximately 8.5 weeks before clinical onset. When applied to an independent external cfRNA dataset23, the signature demonstrated good performance (sensitivity 67%, specificity 78%, AUC 0.81), with a moderate reduction likely influenced by the small sample size of EOPE cases (n\u2009=\u20095) (Fig.\u00a05a, b and Supplementary Table\u00a06).\n\nReceiver operating characteristic (ROC) curves for EOPE (a) and LOPE (d) models across internal validation (validation 1) and external validation23 (validation 2). The X-axis represents the False Negative Rate; the Y-axis, the True Positive Rate. Violin plots showing correctly and misclassified patients and controls based on the classifier score obtained from the predictive model for EOPE (b) and LOPE (e). The X-axis shows the real obstetric outcome; the Y-axis, the predicted outcome. Bar plot illustrating each cfRNAs contribution to EOPE (c) and LOPE (f) models. The X-axis shows the feature importance scores, which quantify the relative contribution of each cfRNA to the model\u2019s predictions, with higher scores indicating features that play a more significant role in discriminating between outcomes. cfRNAs associated with DR are marked with an asterisk. AUC, area under the curve.\n\nFurther investigation into the tissue-specific origin of these transcripts revealed that 32 (36.8%) are associated with DR signature previously described in endometrial tissue from women with a history of severe preeclampsia, including CCL20, CXCR4, IGF1, RBP4, SQSTM1, WNT5A20. The persistence of decidual contributions as EOPE approaches underscores the maternal decidua\u2019s role in its pathophysiology. The model also includes inflammatory mediators such as SQSTM1, IL1B, CCL20, FASLG and TREM1, as well as transcripts encoding T cell receptors (e.g. TRAV21, TRBV27, TRBV5-7). Additionally, it incorporates anti-inflammatory mediators like ALOX5AP, an immunosuppressive gene linked to recurrent miscarriage31, and IL19. Transcripts such as RBP4, which directly influences blood pressure regulation32, NRBF2 involved in autophagy and liver protection33, and WNT5A, a key regulator of placental growth34, further support the model\u2019s clinical relevance.\n\nThe top-performing predictive model for LOPE at T2 included 92 cfRNAs (Supplementary Table\u00a05), achieving a sensitivity of 88%, specificity of 92%, and an AUC of 0.90 in the validation cohort (Fig.\u00a04d and Supplementary Table\u00a06). An analysis of misclassified samples is shown in Fig.\u00a05e, with the contributions of individual cfRNAs to predictive accuracy detailed in Fig.\u00a05f. Further validation using an independent external dataset23, performance was lower (sensitivity 60%, specificity 92%, AUC 77%) (Fig.\u00a05d, e and Supplementary Table\u00a06), likely due to differences in sample timing. While all samples were annotated as collected after 23 weeks, some were obtained near symptom onset or during the third trimester, and metadata limitations precludes precise identification of those cases.\n\nPathway enrichment analysis revealed that this model included cfRNAs related to immune function, such as CFHR1 and CFHR3, involved in complement activation, and immunoglobulin transcripts (e.g., IGKV3D-20, IGKV3D-11, IGHV5-10-1, IGHV3-69-1), and CXCR5, linked to B-cell migration35. Additionally, the model incorporates a cfRNA corresponding to HISLA, highly expressed in the liver36, and LINC0141937. Notably, most predictive cfRNAs were classified as non-coding RNAs or pseudogenes with no annotated function. In contrast to the EOPE model, this LOPE model includes only two cfRNAs related to DR, HES4 and SPEF1.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64215-2/MediaObjects/41467_2025_64215_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64215-2/MediaObjects/41467_2025_64215_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64215-2/MediaObjects/41467_2025_64215_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64215-2/MediaObjects/41467_2025_64215_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64215-2/MediaObjects/41467_2025_64215_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Previous efforts to develop screening tests for preeclampsia have primarily focused on circulating biomarkers related to placental dysfunction, such as sFLT1 and PlGF38. These tests have been validated for use starting at 23 weeks of gestation, with their strongest predictive accuracy typically observed within two weeks of symptom onset. Consequently, they are recommended for patients with suspected preeclampsia39,40. While these tests are particularly useful for short-term prediction, placental dysfunction-based tests are also utilized as early as the first trimester. They are often combined with maternal epidemiological factors and ultrasound or Doppler parameters. However, they face significant limitations in their effectiveness and application41,42,43,44. In settings where guidelines from the National Institute for Health and Care Excellence (NICE) and the ACOG are applied, screening primarily relies on pregnancy-related factors and maternal characteristics. While this approach minimizes additional costs, it has low sensitivity (<\u200941%)45,46.\n\nPredictive models based on cfRNAs from liquid biopsy, grounded in biological plausibility and applicable early in pregnancy, offering potential improvements for the clinical management of preeclampsia22,23,24,25, yet they have not been clinically applied. Building on this foundation, we prospectively collected blood samples from 9586 pregnant women across three gestational trimesters (9\u201342 weeks). Then, we selected a subset of 216 participants composed by preeclampsia cases and normotensive controls to generate a comprehensive longitudinal dataset of cfRNA profiles related to EOPE or LOPE progression. The performance metrics demonstrate substantial advancements in leveraging cfRNA signatures for early detection of EOPE in both the first and second trimesters, as well as LOPE in the second trimester.\n\nOur study stands out from previous research by addressing several key limitations in the field. First, we report the largest prospective cohort of EOPE cases with first-trimester sampling strictly between 9 and 14 weeks of gestation. This early and consistent inclusion window allowed us to capture cfRNA signatures an average of 18 weeks before clinical onset. Second, our carefully curated dataset enabled a clear distinction between EOPE and LOPE, classified according to established clinical guidelines. Unlike many previous studies that analyze preeclampsia as a single entity or rely on later gestational time points, our approach allows for a more precise molecular characterization of disease subtypes. This also suggests that cfRNA reflects a time-specific pathological status rather than a fixed disease signature. Third, the longitudinal design with multiple sampling points across gestation provided a dynamic view of disease progression, from preclinical stages to diagnosis.\n\nThe clinical implications of early risk stratification warrant consideration. While low-dose aspirin remains the primary intervention and is already recommended for many at-risk patients, earlier and more precise identification of individuals at high risk for preeclampsia allows for tailored clinical management strategies47. A shared approach to surveillance\u2014including frequent blood pressure monitoring, renal and liver function assessment, and fetal growth evaluations\u2014has been recommended to mitigate complications in high-risk pregnancies. Furthermore, structured lifestyle interventions, such as calcium and vitamin D supplementation, aerobic exercise, and improved sleep hygiene, may complement pharmacological strategies. A cfRNA-based screening tool offers potential advantages over existing multimarker approaches, which require strict quality control and trained operators, potentially reducing costs and improving accessibility48.\n\nIn addition, our analysis highlighted the tissue-specific origins of the detected cfRNAs, offering further insight into the pathophysiology of both subtypes of preeclampsia. For EOPE patients, early signs of tissue distress were observed in the liver, kidney, and decidua at T2, suggesting that these organs may be affected up to eight weeks before clinical diagnosis. By the time of the clinical onset (T3), cfRNA levels associated with critical organs such as the placenta, heart, brain and lungs showed marked elevation in EOPE, indicating widespread organ involvement likely due to apoptotic processes releasing cfRNA into circulation. In LOPE patients, although cfRNA levels also increased by T3, levels were lower compared to EOPE. Furthermore, differential abundance analysis at diagnosis revealed distinct transcriptomic profile in EOPE, with more pronounced cfRNA changes than in LOPE reflecting potential differences in severity and inflammatory response between both preeclampsia subtypes.\n\nThese distinctions between the subtypes extended to the biological roles of cfRNAs included in the predictive models. In models predicting EOPE, a substantial proportion of cfRNA transcripts were associated with genes involved in decidualization and DR, along with some placental-related transcripts. In contrast, cfRNA transcripts associated with LOPE prediction reflecting broader systemic contributions including placental malfunction. This molecular characterization provides opportunities for the development of targeted interventions. For instance, transcriptomic profiling, such as that presented in this study, could facilitate in silico drug repurposing by identifying dysregulated pathways as potential therapeutic targets49,50. Experimental approaches using siRNA or mRNA-based interventions to modulate key regulators of preeclampsia pathogenesis are already being explored51, highlighting the translational potential of cfRNA-driven insights. Furthermore, the progression of cfRNA changes throughout pregnancy could also play a pivotal role in the development of novel therapies. By tracking how cfRNA levels shift in response to disease onset and progression, new therapeutic windows can be explored and more effective treatment targets identified. This approach could pave the way for the development of interventions that could not only prevent the disease but also modify its course, thereby improving maternal and fetal outcomes.\n\nWhile external validation is crucial to confirm the diagnostic performance of our models, we have already conducted validation using an independent external dataset, which supported the relevance of the predictive signature across datasets for both EOPE and LOPE. We acknowledge, however, that the case\u2013control design may limit generalizability compared to case-cohort studies. To address this, a large-scale external validation is currently underway (ClinicalTrials.gov Identifier: NCT06716242), as part of the iPregnostic study, which will assess the performance of these models across a wider range of clinical backgrounds, thereby enhancing their applicability to diverse patient populations.\n\nOverall, by analyzing circulating RNAs from a single blood sample at T1 or T2, our approach provides a reliable, standardized diagnostic measure that minimizes subjective interpretation and reduces variability in clinical decision-making. This streamlined strategy simplifies risk stratification, improving both the accuracy and efficiency of preeclampsia screening and facilitating personalized patient monitoring.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "This prospective, multicenter case-control study was conducted between September 2021 and June 2024 in fourteen hospitals across Spain (ClinicalTrials.gov Identifier: NCT04990141) in compliance with all relevant ethical regulations. Given the incidence rate of preeclampsia, the cohort size was designed to capture a minimum of 30 patients of EOPE over the course of the study. Approval was obtained from the following Clinical Research Ethics Committees in Spain: Comit\u00e9 de \u00c9tica de la Investigaci\u00f3n con medicamentos del Hospital General Universitario de Castell\u00f3n (Castell\u00f3n); CEIm - Hospital Universitario y Polit\u00e9cnico La Fe (Valencia); CEIm Hospital General de Alicante (Alicante); CEIm Hospital Virgen de la Arrixaca (Murcia); CEI Hospital Universitario Sta. M\u00aa del Rosell (Cartagena); CEIm de la Gerencia de Atenci\u00f3n Integrada de Albacete (Albacete); CEIm Hospital Puerta del Hierro de Majadahonda (Madrid); Comisi\u00f3n de Investigaci\u00f3n del Hospital de Torrej\u00f3n (Madrid); CEIc Arag\u00f3n (Zaragoza); CEIm de Euskadi (Bilbao); CEIm \u00c1rea de Salud Valladolid Oeste (Valladolid); CEI Provincial de C\u00f3rdoba (C\u00f3rdoba); CEIm Complejo Hospitalario Universitario de Canarias (Tenerife); and CEIm del Hospital Universitario de Gran Canaria Dr. Negr\u00edn (Las Palmas). Written informed consent was collected from all participants prior to blood collection and sample anonymization. A total of 9,586 pregnant women were enrolled based on the following criteria: signed informed consent, age over 18, singleton pregnancy, and first blood sample collection within 9\u201314 gestational weeks. Each participant provided 20\u2009mL of peripheral blood in the three trimesters of pregnancy, coinciding with routine clinical follow-up: (T1) 9\u201314 weeks, (T2) 18\u201328 weeks, and (T3)\u2009>\u200928 weeks or at the time of preeclampsia diagnosis. Gestational age was confirmed via ultrasound during the first trimester. Clinical data for each participant were recorded in an electronic data capture system. All blood samples were processed to isolate plasma and stored at \u221280\u2009\u00b0C until pregnancy outcomes were available. Preeclampsia patients were diagnosed following ACOG4 and FIGO52 guidelines, as per the clinical protocol of each hospital involved.\n\nTo develop predictive models for EOPE and LOPE, a subset of participants was selected from the cohort. All EOPE patients (n\u2009=\u200942), a randomly selected subset of LOPE patients (n\u2009=\u200943) and a subset of normotensive pregnant women with uncomplicated pregnancies were included as controls (n\u2009=\u2009131). Control participants were randomly selected from the 6905 uncomplicated pregnancies and matched to both EOPE and LOPE cases for key clinical variables including gestational age at sampling, maternal age, parity, BMI and ethnicity. Participants in the control group were selected based on matching gestational age at the time of blood collection, maternal age, and parity, utilizing Euclidean distance for optimal pairing. Patients and controls were randomly stratified following a 70:30 proportion into two sets: discovery and validation. Sample sizes for the EOPE, LOPE, and control groups in each set are detailed in Supplementary Table\u00a06. The discovery set was used for feature selection, model training, and optimization, with model performance assessed by leave-one-out cross-validation. For feature selection, a 1:2 case-to-control ratio was used, as it is optimal for identifying distinct patterns between the groups. For model training, the case-to-control ratio was increased to 1:3 to ensure a larger sample size, which supports better learning of the patterns by the model and improves predictive accuracy. The optimal model from this process was then applied to the validation set to assess the predictive performance, yielding metrics based on an unexposed sample set. The bioinformatic workflow is detailed in Supplementary Fig.\u00a03.\n\nPeripheral blood samples (20\u2009mL) were collected in Streck Cell-Free DNA BCT tubes (Illumina, 15073345), stored, shipped at room temperature, and processed within seven days to obtain the plasma fraction. All blood samples were centrifuged for 15\u2009min at 1600 x g and 4\u2009\u00b0C. Plasma was transferred to a new collection tube and stored at -80\u2009\u00b0C until use.\n\nPlasma supernatant samples (n\u2009=\u2009548) from the study patients (n\u2009=\u2009216) were centrifuged for 10\u2009min at 13,000 x g. Following the manufacturer\u2019s protocol, cfRNA from 2\u2009mL of plasma was isolated using MiRNeasy Serum/Plasma Advanced Kit (Qiagen, 217204). According to the manufacturer\u2019s protocol, cDNA libraries from total cfRNA samples were prepared using Illumina RNA Prep with Enrichment (L) Tagmentation (Illumina, 20040537). cDNA libraries were quantified using an Agilent D1000 ScreenTape in a 4200 TapeStation system (Agilent Technologies Inc, 5067-5582). Libraries were normalized to 10\u2009nM and pooled in equal volumes. The pool concentration was quantified by qPCR using the KAPA Library Quantification Kit (Roche, 7960336001) and an Agilent D1000 ScreenTape in a 4200 TapeStation system (Agilent Technologies Inc, 5067-5582). The mean value was used to establish pool concentration, which was then sequenced in a NextSeq 500/550 High Output kit with 2.5 cartridges of 150 cycles (Illumina, 20024907).\n\nRaw reads were aligned to the human reference genome (GRCh38 Gencode v38 Primary Assembly) using STAR (2.7.10a). The SAM/BAM files were further processed using SAMtools (v.1.6). Only reads with mapping quality more significant than 90% were maintained (MAPQ score obtained from the alignment). The duplicated reads were removed with Picard MarkDuplicates (v.2.27.4). The mapping and the quantification of the reads were done using featureCounts (v.2.0.1). Read statistics were estimated using FastQC (v.0.11.9) and RseqQC (v.5.0.1) and summarized using MultiQC (v.1.13).\n\nThree key quality parameters related to the sequencing process were estimated for each analyzed sample: RNA degradation, DNA contamination, and rRNA fraction as previously defined21,53 (Supplementary Fig.\u00a04a-c). Samples were retained for further analysis if they met the established cut-off values for each parameter: RNA degradation (cut-off: 40%), DNA contamination (cut-off ratio: 3), and rRNA fraction (cut-off: 15%). Principal Component Analysis (PCA) was used as an additional quality control measure (Supplementary Fig.\u00a04d, e). Samples deviating by more than 3 standard deviations from the mean of the first and second components for each dataset were excluded from the analysis. In total, 12 samples were removed\u20142 due to a high rRNA fraction and 10 due to PCA-based exclusion.\n\nCfRNAs were filtered based on their detection value, and only cfRNAs with levels over more than 0.5 counts per million reads (CPMs) in \u226570% of discovery samples after removing outlier samples were kept. Discovery set CPMs were normalized using the \u201cdeseq median ratio normalization\u201d with pydeseq2 (v0.4.1). The validation set were then normalized with the same algorithm using size factors from discovery set as described in MLSeq package54,55. Batch effect and other possible confounding factors were assessed using PCA, hierarchical clustering with Spearman correlation as a distance metric, and variance component analysis. Finally, the normalized counts of each sample of discovery and validation sets were re-scaled to 0-1 range with a min-max scaling process.\n\nCfRNAs differentially abundant between EOPE or LOPE patients and controls at each time point (T1, T2, T3) were identified using the limma-Voom method from the Bioconductor package limma (v3.60.5). For the T3 samples, comparisons only included patients whose samples were collected at the time of EOPE or LOPE diagnosis and gestationally matched control samples collected during routine medical appointments. Genes with False Discovery Rate (FDR) less or equal to 0.05 were considered statistically significant.\n\nGene Ontology (GO) analyses were performed to identify biological processes using the enrichGO function from the clusterProfiler R package (v4.2.2). The input consisted of cfRNAs that were differentially abundant between EOPE and controls, as well as LOPE and controls (FDR\u2009<\u20090.05). The p-value adjustment method used was FDR, with a significance threshold set at 0.05 (FDR\u2009<\u20090.05).\n\nGene sets for each tissue of interest \u2500those directly involved in the pathophysiology of preeclampsia\u2500 were derived from the Human Protein Atlas database26, which includes gene expression data across tissues, focusing specifically on transcripts classified as either \u201cenriched\u201d or \u201cenhanced\u201d within those tissues, but only if they were additionally categorized as \u201cdetected in single\u201d to ensure higher tissue specificity. The signature score in our dataset was calculated by summing the log-transformed, normalized counts of each gene in the set. For the T3 samples, comparisons included only those patients whose samples were collected at the time of EOPE or LOPE diagnosis and gestationally matched control samples collected during routine medical appointments, to avoid potential bias. Differences between groups were assessed using the Wilcoxon rank-sum test.\n\nOur study cohort was divided into discovery and validation sets to develop and evaluate the predictive models, following best practices to prevent overfitting in artificial intelligence. Using stratified sampling based on obstetric outcomes (patient/control groups) and the scikit-learn library (v1.5.1) in Python, 70% of participants were allocated to the discovery set.\n\nFor feature selection, a 1:2 case-to-control ratio was used, as it is optimal for identifying distinct patterns between the groups. The Lasso regression model was used to select the more relevant cfRNAs to discriminate between patients and controls. The discovery dataset was used with a lasso regression algorithm (v1.5.2, sklearn.linear_model.Lasso) with a penalty term (alpha) of 0.5 and the case condition as a dependent part and the cfRNA abundance levels as the independent components, resulting in a regression formula that assigns a coefficient to each cfRNA variable, indicating the correlation between the condition and each variable. The number of cfRNAs selected was determined by a minimum coefficient threshold, which determined whether a cfRNA was relevant or not. Different minimum coefficient thresholds, ranging from 0 to the maximum coefficient in increments of 0.05, were tested to determine the optimal set of cfRNAs. The F1-score was calculated for each set of cfRNAs using the strategy of leave-one-out cross-validation, and the set that yield the highest F1-score metric was selected. Lasso regression was chosen over other feature selection methods due to the relatively small sample size, which can lead to model overfitting. The penalty term of the model helps to counteract overfitting by shrinking and selecting features with less importance56.\n\nFor the development of the optimal predictor, the discovery set was used, with cfRNA selection performed using the lasso regression method as previously described. Six different algorithms were tested with Python (v3.10.6): Support Vector Machine (v1.5.2, sklearn.svm.SVC), Elastic Net Linear Regression (v1.5.2, sklearn.linear_model.ElastcNet), Lasso Linear Regression (v1.5.2, sklearn.linear_model.Lasso), Random Forest (v1.5.2, sklearn.ensemblel.RandomForestClassifier), XGBoost (v1.7.6 xgboost. XGBClassifier) and TabPFN (v0.1.10 tabpfn.TabPFNClassifier). Each algorithm was trained with the best parameters calculated with a grid search applied with a cross-fold strategy. The evaluation of the predictive capacity of each model was done with a leave-one-out cross-validation with the discovery samples. The algorithm providing the best F1-score was selected for each group of samples: EOPE in the first trimester (EOPE T1), in the second trimester (EOPE T2), LOPE in the first trimester (LOPE T1) and LOPE in the second trimester (LOPE T2). The resulting chosen algorithms were TabPFN for EOPE (T1 and T2) and Lasso Linear Regression for LOPE (T1 and T2).\n\nFor each model (EOPE T1, LOPE T1, EOPE T2, LOPE T2), the ML algorithm showing the highest F1-score and its best parameters was trained with the discovery dataset, which was based on a 1:3 case-to-control ratio. To evaluate the predictive capacity with the discovery data, a strategy of leave-one-out was performed. The selected algorithm was trained N number of times. In each iteration, one sample was isolated, and the rest were used to fit the model. The fitted model was used to predict the label of the isolated sample, and the result of the prediction was added to a pool of predicted labels that were used to calculate the discovery leave-one-out metrics. Finally, the algorithm was fitted with all the discovery samples, and the obtained trained model was used to predict the labels in the validation dataset and evaluate the performance with never seen samples.\n\nWe evaluated the predictive performance of each model (EOPE T1, LOPE T1, EOPE T2, LOPE T2) using three approaches: (1) leave-one-out cross-validation on the discovery dataset; (2) predictions on the hold-out validation dataset using the final model (validation 1); and (3) external validation of the predictive cfRNA signature using an independent dataset23 (Gene Expression Omnibus: GSE192902), which includes cfRNA profiles of EOPE and LOPE collected during pregnancy (n\u2009=\u2009190) (validation 2). For external validation, we retuned the model architecture to account for technical differences in cfRNA processing between datasets. Since the external cohort did not distinguish between EOPE and LOPE, we constructed separate balanced case\u2013control subsets for each subtype. All available PE cases were included, and controls were selected using an agnostic downsampling approach based on cfRNA profiles and gestational age (T1 or T2). Within each timepoint, the largest available subset of controls was retained using a reproducible selection criterion. Model performance was assessed using leave-one-out cross-validation. Performance across the three validations was assessed with key metrics, including accuracy, sensitivity, specificity, AUC, and F1-score.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Data supporting the findings described in this manuscript are available in the article and in the Supplementary Information, or as Source Data available from the corresponding authors upon reasonable request and under controlled access. Source Data contain individual-level results derived from human samples; access is restricted to comply with data privacy laws and the ethical approvals governing these samples. The cell-free RNA-sequencing data are available under controlled access to comply with data privacy laws and the ethical approvals governing these samples. Access can be granted upon reasonable request, subject to approval by the Institutional Review Board of the Spanish hospitals involved and execution of a Data Transfer and Use Agreement. Inquiries for data usage can be directed to C.S. (carlos.simon@uv.es) or T.G. (tgarrido@fundacioncarlossimon.com), and requests will be reviewed within 30 days.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The custom code used to perform the analysis presented here is available on GitHub under the repository name ipremom-cfrna-preeclampsia-predictor [https://github.com/marinaigual/ipremom-cfrna-preeclampsia-predictor/tree/main].", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Organization WH. 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). (WHO, 2018).\n\nSay, L. et al. Global causes of maternal death: a WHO systematic analysis. Lancet Glob. 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Carbajo and C. Montagud for their assistance with the experimental analyses. We also acknowledge the PREMOM Consortium and all clinical collaborators involved in patient recruitment, clinical data and sample collection at participating centers. Most importantly, we are especially grateful to all the participants in the PREMOM study for their generous contribution to this research.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Carlos Simon Foundation, Valencia, Spain\n\nNerea Castillo-Marco,\u00a0Teresa Cordero,\u00a0Irene Mu\u00f1oz-Blat,\u00a0Sheila Ortega-Sanch\u00eds,\u00a0Petr Volkov,\u00a0Carlos Sim\u00f3n\u00a0&\u00a0Tamara Garrido-G\u00f3mez\n\nR&D Department, iPremom Pregnancy healthcare Diagnostics, Valencia, Spain\n\nNerea Castillo-Marco,\u00a0Teresa Cordero,\u00a0Marina Igual,\u00a0Carla G\u00f3mez-\u00c1lvarez,\u00a0Neus Bernat-Gonz\u00e1lez,\u00a0\u00c1ngela Gaspar-Dom\u00e9nech,\u00a0\u00c9rika Ortiz-Domingo,\u00a0Alba Vives,\u00a0Carlos Sim\u00f3n\u00a0&\u00a0Tamara Garrido-G\u00f3mez\n\nDepartment of Obstetrics, University Hospital La Fe, Valencia, Spain\n\nRogelio Monfort-Ortiz,\u00a0Alfredo Perales-Mar\u00edn,\u00a0Beatriz Marcos-Puig,\u00a0S. Florez-Perez,\u00a0M. Montesinos-Albert,\u00a0M. Nieto-Tous,\u00a0A. Orive-Boluda\u00a0&\u00a0E. Satorres-P\u00e9rez\n\nFetal Medicine Unit Murcia, IMIB Arrixaca, Murcia, Spain\n\nJuan Luis Delgado,\u00a0Laura Hernandez-Hernandez,\u00a0Esther Canovas,\u00a0JL Delgado-Gonzalvez,\u00a0P. Diaz-Lozano,\u00a0A. Jimenez-Mendez,\u00a0Y. Mico,\u00a0A. Salinas\u00a0&\u00a0E. Sanchez-Martinez\n\nObstetrics and Gynecology Department, Hospital Universitario de Torrej\u00f3n, Madrid, Spain\n\nMaria del Mar Gil,\u00a0Bel\u00e9n Santacruz,\u00a0S. Alonso-Men\u00e9ndez,\u00a0MC Casanova,\u00a0D. Cuenca-G\u00f3mez,\u00a0E. Ferrer\u00a0&\u00a0A. Martin-Arias\n\nObstetrics and Gynecology Department, Hospital Universitario La Paz, Madrid, Spain\n\nMaria del Mar Gil\n\nSchool of Medicine, Universidad Francisco de Vitoria, Madrid, Spain\n\nMaria del Mar Gil,\u00a0Bel\u00e9n Santacruz,\u00a0S. Alonso-Men\u00e9ndez,\u00a0MC Casanova,\u00a0D. Cuenca-G\u00f3mez,\u00a0E. Ferrer\u00a0&\u00a0A. Martin-Arias\n\nDepartment of Obstetrics and Gynecology, University of La Laguna, Tenerife, Spain\n\nNieves Luisa Gonzalez-Gonzalez\n\nDepartment of Obstetrics and Gynecology, Hospital Universitario de Canarias, Tenerife, Spain\n\nWalter Plasencia,\u00a0M. Hern\u00e1ndez-Su\u00e1rez\u00a0&\u00a0E. Padr\u00f3n-P\u00e9rez\n\nDepartment of Pediatrics, Obstetrics, and Gynecology, University of Valencia, Valencia, Spain\n\nAlfredo Perales-Mar\u00edn\u00a0&\u00a0Carlos Sim\u00f3n\n\nObstetrics and Gynecology Department, Dr. Balmis General University Hospital, Alicante, Spain\n\nAna Mar\u00eda Palacios-Marqu\u00e9s,\u00a0P. Baviera-Royo,\u00a0J. Franc\u00e9s-Ferr\u00e9,\u00a0V Garc\u00eda-Sousa\u00a0&\u00a0E. P\u00e9rez-Pascual\n\nAlicante Institute for Health and Biomedical Research (ISABIAL), Alicante, Spain\n\nAna Mar\u00eda Palacios-Marqu\u00e9s\n\nDepartment of Gynecology, Miguel Hern\u00e1ndez University, Alicante, Spain\n\nAna Mar\u00eda Palacios-Marqu\u00e9s\n\nObstetrics and Gynecology Service, BioCruces Health Research Institute, Hospital Universitario Cruces (Basque Country University), Biscay, Spain\n\n\u00cd\u00f1igo Melchor,\u00a0A. Aiartzaguena,\u00a0MJ Barbaz\u00e1n,\u00a0J. Burgos,\u00a0A. Del Campo,\u00a0E. Gibbone,\u00a0H. Goiti,\u00a0A. Meabe,\u00a0JC Melchor,\u00a0V Recio\u00a0&\u00a0L. Rodr\u00edguez\n\nDepartment of Obstetrics and Gynecology, Complejo Hospitalario Universitario Insular, Materno Infantil, Las Palmas, Spain\n\nAlicia Martin-Martinez,\u00a0Taysa Benitez-Delgado\u00a0&\u00a0S. De Leon-Socorro\n\nDepartment of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA\n\nCarlos Sim\u00f3n\n\nINCLIVA Health Research Institute, Valencia, Spain\n\nCarlos Sim\u00f3n\u00a0&\u00a0Tamara Garrido-G\u00f3mez\n\nDepartment of Obstetrics and Gynecology, University Hospital Complex of Albacete, Albacete, Spain\n\nA. Amezcua,\u00a0A. Gomez\u00a0&\u00a0E. Gonzalez\n\nDepartment of Obstetrics and Gynecology, University General Hospital, Castell\u00f3n, Spain\n\nC. Andrada-Ripolles,\u00a0R. Batalla-Urrea,\u00a0CR Cabrera-Leon,\u00a0A. Lozano-Moreno,\u00a0C. Reula-Blasco,\u00a0L. Rubert\u00a0&\u00a0E. Villar-Graullera\n\nDepartment of Obstetrics and Gynecology, Rio Hortega Hospital, Valladolid, Spain\n\nEM Arias-Vald\u00e9s,\u00a0AM Arnal-Burr\u00f3,\u00a0RM Lobo-Valent\u00edn,\u00a0MJ Mac\u00edas-Alonso,\u00a0EM Mart\u00edn-Medrano\u00a0&\u00a0A. Moreno-Reviriego\n\nInstituto de Investigaci\u00f3n Sanitaria de Arag\u00f3n (IISA), Hospital Cl\u00ednico Universitario Lozano Blesa, Zaragoza, Spain\n\nM. Bond\u00eda,\u00a0M. Fabre,\u00a0D. Oros,\u00a0C. Paules\u00a0&\u00a0S. Ruiz-Martinez\n\nDepartment of Obstetrics and Gynecology, University Hospital Miguel Servet, Zaragoza, Spain\n\nJM Campillos-Maza,\u00a0C. De Bonrostro-Torralba,\u00a0SE Gregorio-Gonz\u00e1lez,\u00a0R. Herrero-Serrano,\u00a0A. Lasierra-Beamonte\u00a0&\u00a0AC Ruiz-Pe\u00f1a\n\nDepartment of Obstetrics and Gynecology, Santa Lucia Hospital, Cartagena, Murcia, Spain\n\nO. Garc\u00eda-Izquierdo,\u00a0MA Jodar-Perez,\u00a0A. L\u00f3pez-Soto,\u00a0JP Mart\u00ednez-Cend\u00e1n\u00a0&\u00a0I. Martinez-Rivero\n\nDepartment of Clinical Biochemistry, University Hospital Miguel Servet, Zaragoza, Spain\n\nMM Larrea-Ort\u00edz Quintana\n\nDepartments of Obstetrics and Gynecology. Hospital Universitario Reina Sof\u00eda, C\u00f3rdoba, Spain\n\nSM Pe\u00f1a-Lobo\u00a0&\u00a0MI Romero\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nC.S., and T.G.-G. conceptualized the study. R.M.-O., J.L.D., L.H.-H., E.C., M.M.G., B.S., N.L.G.-G., W.P., A.P.-M., B.M.-P., A.M.P.-M., I.M., A.M.-M., T.B.-D. and all PREMOM consortium members recruited patients, collected samples, and contributed clinical data to the central research database. C.G.-\u00c1., \u00c9.O.-D., and A.V. were responsible for study monitoring and data entry into the central research database. N.C.-M., T.C., I.M.-B., N.B.-G., \u00c1.G.-D., and S.O.-S. conducted the laboratory experiments. N.C.-M., M.I., and P.V. performed the bioinformatic analyses. N.C.-M., C.S., and T.G.-G. wrote the original draft of the manuscript. C.S. and T.G.-G. supervised the work and reviewed and edited the final version of the paper. All authors have substantively revised the paper and approved the submitted version.\n\nCorrespondence to\n Carlos Sim\u00f3n or Tamara Garrido-G\u00f3mez.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "N.C-M., M.I., T.G-G., C.S. are inventors on a patent application (EP24383276.3) covering methods for determining the risk of preeclampsia. N.C-M., T.C., M.I., C.G-A., N.B-G., A.G-D., E.O-D., A.V., T.G.-G. are employees of iPremom Pregnancy Healthcare Diagnostics. C.S. is a founder of iPremom Pregnancy Healthcare Diagnostics. The remaining authors declare no conflict of interest.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Castillo-Marco, N., Cordero, T., Igual, M. et al. Maternal plasma cell-free RNA as a predictor of early and late-onset preeclampsia throughout pregnancy.\n Nat Commun 16, 9208 (2025). https://doi.org/10.1038/s41467-025-64215-2\n\nDownload citation\n\nReceived: 17 April 2025\n\nAccepted: 09 September 2025\n\nPublished: 20 October 2025\n\nVersion of record: 20 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-64215-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Multi-Gram Access in a Two-step Synthesis to Soluble Nine-atomic Silylated Silicon Clusters", + "journal": "Nature Communications", + "published": "23 December 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55211-z/MediaObjects/41467_2024_55211_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55211-z/MediaObjects/41467_2024_55211_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "http://www.ccdc.cam.ac.uk/data_request/cif" + ], + "code": [], + "subject": [ + "Synthetic chemistry methodology", + "Organic\u2013inorganic nanostructures", + "Synthesis and processing" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4073358/v1.pdf?c=1735045538000", + "research_square_link": "https://www.researchsquare.com//article/rs-4073358/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-55211-z.pdf", + "preprint_posted": "28 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Silicon is by far the most important semiconducting material. However, solution-based synthetic approaches for unsaturated silicon-rich molecules require less efficient multi-step syntheses. We report on a straightforward access to soluble, polyhedral Si9 clusters from the binary phase K12Si17, which contains both [Si4]4- and [Si9]4- clusters. [Si4]4- ions, characterised by a high charge per atom ratio, behave as strong reducing agents, preventing [Si9]4- from directed reactions. By the here reported separation of [Si4]4-, Si9 clusters are isolated as monoprotonated [Si9H]3- ions on a multi-gram scale and further crystallised as their 2.2.2 Cryptate salt. 20 grams of the product can be obtained through this two-step procedure - a new starting point for silicon Zintl chemistry such as the first isolation and structural characterisation of a trisilylated [MeHyp3Si9]- cluster.Physical sciences/Chemistry/Inorganic chemistry/Organometallic chemistryPhysical sciences/Chemistry/Chemical synthesis", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "NCOMMS2414778SupportingInformation.pdfAn Efficient Multi-Gram Access in a Two-step Synthesis to Soluble Nine-atomic Silylated Silicon ClustersNCOMMS24147781.cifCrystal structure 1NCOMMS24147782a.cifCrystal structure 2a", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Silicon is by far the most important semiconducting material. However, solution-based synthetic approaches for unsaturated silicon-rich molecules require less efficient multi-step syntheses. We report on a straightforward access to soluble, polyhedral Si9 clusters from the binary phase K12Si17, which contains both [Si4]4\u2212 and [Si9]4\u2212 clusters. [Si4]4\u2212 ions, characterised by a high charge per atom ratio, behave as strong reducing agents, preventing [Si9]4\u2212 from directed reactions. By the here reported separation of [Si4]4\u2212 by means of fractional crystallisation, Si9 clusters of the precursor phase K12Si17 are isolated as monoprotonated [Si9H]3\u2212 ions on a multi-gram scale and further crystallised as their 2.2.2-Cryptate salt. 20 grams of the product can be obtained through this two-step procedure - a new starting point for silicon Zintl chemistry, such as the isolation and structural characterisation of a trisilylated [MeHyp3Si9]\u2212 cluster.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "With the progressing technologisation of our society and the accompanying miniaturisation of electronic devices, physicists and chemists face new challenges. Silicon stands out as the most important semiconducting material by far. However, traditional manufacturing methods, such as lithography and etching of crystalline silicon (top-down) or Chemical Vapour Deposition (CVD) of volatile silanes for producing nanostructured components, are reaching their limits. Nevertheless, not only manufacturing these materials requires different approaches. Bulk materials and semiconducting materials in the nanometre range significantly differ in their optical and electronic properties (quantum confinement). Molecular precursors could provide an answer to new manufacturing methods and the necessity for targeted investigations of quantum confinement effects in low-dimensional materials (quantum dots, wires and wells). Alongside the targeted synthesis of silicon nanoparticles, defined molecular (silicon) clusters are also considered model systems for studying physical and chemical processes in nanomaterials.\n\nThe targeted synthesis of saturated cage oligosilanes1,2, unsaturated siliconoids3,4,5 and Zintl clusters6,7,8 has been intensively studied in the past decades. In 1970, West et al. achieved the preparation of a cage oligosilane under reductive conditions starting from chlorosilane precursors for the first time9. Such Wurtz-type couplings or metathesis reactions also provide access to paradigmatic clusters like silaprismanes10,11,12, -cubanes13,14,15,16, and -tetrahedranes17,18 (Fig.\u00a01; II), primarily impressive by their structural beauty. Following West\u2019s initial hints towards the synthesis of a permethylated sila-adamantane19, Marschner et al. established the synthetic pathway to the sila-adamantane derivative\u00a0I (Fig.\u00a01), representing a molecular fragment of the diamond structure of elemental silicon. Via a simple and elegant cascade of silyl abstraction and silylation steps, coupled with subsequent Lewis acid mediated isomerisation, this molecule was obtained in a stepwise synthesis from readily available TMS4Si20. Later, targeted functionalised sila-diamondoid derivatives were reported as well21. Apart from the synthesis of perchlorocyclohexasilane, the disproportionation of the versatile precursor Si2Cl6 also enables the synthesis of an endohedral, chloride-decorated silafullerane (Fig.\u00a01; III)22,23,24. In addition to the described saturated silicon clusters, Breher, Scheschkewitz, and Lips report on unsaturated so-called siliconoid clusters, a term introduced by Scheschkewitz11. Silapropellane IV (Fig.\u00a01) is obtained via the co-reduction of Mes2SiCl2 and Si2Cl6, exhibiting a biradicaloid character of the transannular interaction between both bridgeheads25. The structurally related,\u00a0bridged silapropellane V (Fig.\u00a01)26\u00a0can be derived from an aromatic dismutational isomer of hexasilabenzene in a thermal or photochemical rearrangement and serves as a starting point for a rich cluster functionalisation and expansion chemistry27,28,29,30. Furthermore, the suitability of amido ligands31,32 has been demonstrated in stabilising six-atomic silicon clusters (Fig.\u00a01; VI), accessible via both the reductive coupling of corresponding bromosilane precursors and the thermal transformation of a zwitterionic tetrasilane33.\n\nExamples I\u2013III represent saturated, IV\u2013VI siliconoid and VII and VIII Zintl-type clusters. Silicon is depicted as blue, SiMe2 units as grey and Si-SiCl3 units as red circles. TMS Trimethylsilyl, Dis TMS2HC, Mes 2,4,6-Trimethylphenyl, Tip 2,4,6-Tri-iso-propylphenyl, Dipp 2,6-Di-iso-propylphenyl, MeHyp TMS3Si, Cy Cyclohexyl.\n\nWhile all discussed routes require multi-step syntheses to build up molecules with multiple Si\u2013Si bonds, an alternative method involves the direct formation of bare silicon clusters in a one-step synthesis by the solid-state reaction of silicon with alkali metals. This approach has yielded considerable success in the case of heavier Ge9 clusters. For instance, the solid-state phase M4Ge9 (M\u2009=\u2009Na\u2013Cs)34 can easily be obtained and readily dissolves in various polar solvents. In recent years, a rich chemistry has been established around the nine-atomic [Ge9]4\u2212 cluster anion such as (organo)functionalisation35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50, metalation43,51,52,53,54,55,56,57,58, and cluster growth59,60,61,62,63,64,65. In contrast to the heavier tetrel elements (Ge-Pb), [Si9]4\u2212 cannot be selectively obtained in a solid-state reaction. Instead, only phases with the composition M12Si17 (equivalent to {(M+)12([Si4]4\u2212)2([Si9]4\u2212); M\u2009=\u2009Na\u2013Cs) are accessible, which\u00a0also contain four-atomic [Si4]4\u2212 clusters in addition to the desired nine-atomic clusters66. Due to the highly reductive character of these tetrahedral ions, direct reactions of K12Si17 with electrophilic reagents like chlorosilanes are not feasible. As a result, silicon-based Zintl cluster chemistry\u00a0has lagged behind the heavier homologues up to now and is limited to reactions and studies in liquid ammonia. Apart from bare anions [Si9]x\u2212 (x\u2009=\u20092\u20134)67,68,69 and protonated ions like [Si4H]3\u221270 and [Si9Hn](4-n)\u2212 (n\u2009=\u20091, 2)71,72,73 there are only six examples of metalated silicon-based Zintl ions, such as [PhZnSi9]3\u221274, [{Ni(CO)2}2(\u03bc-Si9)2]8\u221275, [NHCDippCu(\u03b74-Si9)H]2\u221276, and [(NHCtBuAu)6(\u03b72-Si4)]2\u221277, as well as [(MesCu)2Si4]4\u2212 (Fig.\u00a01; VII)78 and [NHCDippCu(\u03b74-Si9)]3\u221279. Both the removal of the highly reactive four-atomic silicon clusters and the avoidance of liquid ammonia as a reaction medium are crucial for further developing this kind of chemistry and overcoming synthetic limitations.\n\nIn a first step, we recently made the nine-atomic [Si9]4\u2212 clusters available for reactions in organic solvents by dissolving K12Si17 in ammonia with 2.2.2-cryptand and subsequent solvent removal. Starting from this so-called activated precursor phase, the extraction of bis-protonated [Si9H2]2\u2212 clusters in pyridine and the transformation into disubstituted dianions of the form [2R2Si9]2\u2212 (Fig.\u00a01; XIII; 2R\u2009=\u2009MeHyp, tBu2HSi, Cy3Sn) in thf was achieved72,80,81. However, the four-atomic clusters continue interfering with the respective electrophilic reagents, causing limited functional group tolerance, poor product purity and low yields.\n\nIn this work, we report on wet chemical access to a synthetic K4Si9 analogue via separation of four- and nine-atomic clusters in\u00a0liquid ammonia through fractional crystallisation. Obtaining such a precursor compound represents a key step in the still largely unexplored chemistry of nine-atomic silicon clusters and allows for the selective synthesis of trisilylated [K(2.2.2-crypt)][(R3Si)3Si9] cluster salts.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55211-z/MediaObjects/41467_2024_55211_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "Our previous work shows that the solid-state phase K12Si17 can be converted into an activated form, accessible for follow-up reactions like silylation. This conversion is done by dissolving K12Si17 in liquid ammonia with 2.2.2-cryptand as\u00a0a sequestering agent and subsequent solvent removal80,81. Nevertheless, interfering four-atomic clusters might still\u00a0be present in this activated phase.\n\nIn order to separate [Si4]4\u2212 and [Si9]4\u2212,\u00a0we exploit their different solubilities in liquid ammonia. Keeping the ammonia extract of K12Si17 for ~12\u2009h at \u221240\u2009\u00b0C, we observe the formation of a bright red solid under a reddish-brown solution (Fig.\u00a02a). After filtration and\u00a0solvent removal,\u00a0an orange, coarse solid\u00a0(Fig. 2b) was isolated\u00a0from the filtrate. In contrast, the former red filtration residue changed into a grey, finepowder (Fig.\u00a02c). Surprisingly, the Raman measurements of the filtrate and filtration residue after solvent removal (Fig.\u00a03a, b) show a clear separation of the four- and nine-atomic cluster species. For the residue, the most intense resonances at 477\u2009cm\u22121, 287\u2009cm\u22121, and 272\u2009cm\u22121 can be assigned by comparison with the solid-state phase K4Si4 (Fig.\u00a03c), which contains exclusively four-atomic [Si4]4\u2212 clusters. The slight shift in the resonances is due to the non-identical chemical environment within the crystalline solid and the amorphous filtration residue. In contrast, the Raman spectrum of the filtrate does not show any Si4 band, as can be detected in K4Si4 and K12Si17 (Fig.\u00a03d). The resonances at 294\u2009cm\u22121 and 384\u2009cm\u22121 agree with the Raman data already described by Schnering for Cs4Si9,\u00a0which was obtained by the thermal decomposition of Cs4Si482. The third resonance at 248\u2009cm\u22121 indicates a further mode due to the different symmetry with respect to the C4v symmetric [Si9]4\u2212 ion.\n\na Ammonia solution of K12Si17 and 2.2.2-cryptand after storage at \u221240\u2009\u00b0C for 12\u2009h; b K1-x[K(2.2.2-crypt)]2+x[Si9] (x\u2009=\u20090.2) obtained from the filtrate after solvent removal; c Highly reactive filtration residue after solvent removal.\n\na Filtrate, b filtration residue, c K4Si4, and d K12Si17.\n\nAlthough we can clearly demonstrate that four- and nine-atomic clusters are separated by this procedure, the exact chemical composition of the dried filtrate cannot be conclusively determined with respect to the number of sequestered cations. We chose the minimum amount of expensive 2.2.2-cryptand, and elemental analysis of the solid shows a composition corresponding to K1-x[K(2.2.2-crypt)]2+x[Si9] (x\u2009=\u20090.2). The exact 2.2.2-cryptand content in this intermediate after filtration may vary around an ideal composition of K1[K(2.2.2-crypt)]2[Si9], which, however, had no impact on the follow-up chemistry.\n\nSingle crystals as orange blocks suitable for SC-XRD were obtained by vapour diffusion of Et2O into an ammonia solution of the filtrate by adding additional 2.2.2-cryptand to sequester all cations. The structure determination results in the composition of [K(2.2.2-crypt)]3[Si9H]\u00b78.5 NH3 (1). The crystal structure analysis unambiguously shows the formation of a threefold negatively charged cluster. The exceptionally good data quality allows for further structure refinement and the localisation of a hydrogen atom at the cluster from the difference Fourier map and a free refinement of the position of the H atom. The asymmetric unit contains a monoprotonated, threefold negatively charged [Si9H]3\u2212 cluster, three [K(2.2.2-crypt)]+ counter ions and 8.5 equivalents of co-crystallised ammonia. The co-crystallised ammonia primarily occupies the voids between the [K(2.2.2-crypt)]+ units and the cluster. This results in a particular thermal and mechanical sensitivity of the crystals. The refinement shows the presence of an orientationally disordered Cs symmetric [Si9H]3\u2212 cluster (for details, see the Supplementary Discussion). For the main orientation \u03b1 (Fig.\u00a04), the refinement allows for the localisation of the proton H1A at the Si1A position with a bond length of 1.55(3)\u2009\u00c5, which is in the range of typical Si\u2013H bond distances. The cluster framework shows the expected involvement of the substituted Si1A position of the open square plane. Thus, the Si1A-Si2B (2.347(3)\u2009\u00c5) and Si1A-Si4 (2.322(2)\u2009\u00c5) distances are significantly shortened compared to the Si2B-Si3B (2.580(2)\u2009\u00c5) and Si3B-Si4 (2.532(2)\u2009\u00c5) distances of the open square plane. The ratio of the square diagonals (Si2B-Si4/Si1A-Si3B) of 1.20 clearly shows the deviation from the ideal C4v symmetry of the parent [Si9]4\u2212 ion69. The structural characteristic of shorter Si-Si bonds at cluster atoms with ligands supports the existence of an H atom at Si1A and agrees with findings for the solvate [K(DB-18-crown-6)][K(2.2.2-crypt)]2[Si9H]\u00b7NH371,73.\n\na Front view; b top view. Anisotropic displacement ellipsoids of silicon (blue) are drawn at 50% probability. The hydrogen atom (red) is displayed as\u00a0a sphere of an arbitrary radius. Silicon and hydrogen atoms of minor disorder components are omitted for clarity. Selected bond length (\u00c5): Si1A-H1A: 1.55(3); Si1A-Si2B: 2.347(3); Si1A-Si3B: 3.121(4); Si1A-Si4: 2.322(2); Si1A-Si5: 2.4244(10); Si1A-Si6: 2.4342(9); Si2B-Si3B: 2.580(2); Si2B-Si4: 3.755(3); Si2B-Si6: 2.5193(12); Si2B-Si7: 2.4655(12); Si3B-Si4: 2.532(2); Si3B-Si7: 2.4553(9); Si3B-Si8: 2.4325(9); Si4-Si5: 2.4905(7); Si4-Si8: 2.4268(7); Si5-Si9: 2.4474(8); Si6-Si9: 2.4326(8); Si7-Si9: 2.4348(8); Si8-Si9: 2.4539(7); Si2B-Si4/Si1A-Si3B: 1.20.\n\nThe crystallographic data are supported by mass spectra of acetonitrile (MeCN) solutions of the filtration residue showing Si9 species (Fig.\u00a05), confirming the presence of nine-atomic clusters in the dried filtration product. Additionally, 1H NMR (Fig.\u00a06) of K1-x[K(2.2.2-crypt)]2+x[Si9] in DMF-d7 verifies the existence of monoprotonated cluster species. The prominent signal in the high-field region at \u22121.80\u2009ppm aligns with the spectral range reported for [Si9H2]2\u221272. The characteristic satellite pattern emerges from scalar coupling to all nine cluster atoms. This pattern displays the superposition of all possible isotopologues caused by the low natural abundance of NMR active 29Si (Natural abundance\u2009=\u20094.7%) in the cluster framework. Consequently, the intense main singlet (indicated in grey) results from all non-NMR active isotopologues ([28/30Si9H]3\u2212). While the first set of satellites (indicated in red) is due to a doublet splitting of the isotopologues [28/30Si829Si1H]3\u2212, the second\u00a0set is due to a triplet splitting (indicated in blue) of the isotopologues [28/30Si729Si2H]3\u2212. The satellite signals of higher isotopologues are not detectable due to the low natural abundance of 29Si. A full overview of the statistical intensity distribution for the superposition of all isotopologues is given in the Supplementary Information (Supplementary Fig.\u00a017 and Supplementary Table\u00a07) and in accordance with previous work72. The exceptionally small coupling constant of J(1H, 29Si)\u2009=\u200919.5\u2009Hz and the interaction of the proton with all nine silicon atoms of the cluster framework paints the picture of a highly dynamic system at room temperature. At \u221250\u2009\u00b0C, however, a doublet with a significantly increased coupling of 152\u2009Hz is observed (see Supplementary Fig.\u00a018), falling within the typical range of localised 1J(1H, 29Si) couplings. Thus, proton migration becomes slow on the 1H NMR timescale at the transition from the high- to the low-temperature limit of proton migration, allowing for direct detection of a localised Si\u2013H unit. This spectroscopic behaviour perfectly aligns with previously reported data in liquid ammonia71. Similar ligand migrations have also been described for [Sn9R3]3\u2212 (R3\u2009=\u2009H83, SnCy384) at room temperature.\n\na {[K(2.2.2-crypt)][Si9]\u2009+\u20092H}\u2212 (m/z\u2009=\u2009670.37); b {[K(2.2.2 crypt)][Si9]\u2009+\u20092H\u2009+\u2009mecn}\u2212 (m/z\u2009=\u2009711.42).\n\nMain resonance of non-coupled isotopologues [28/30Si929Si0H]3\u2212 is indicated in grey, doublet splitting of [28/30Si829Si1H]3\u2212 in red and visible part of triplet splitting of [28/30Si729Si2H]3\u2212 in blue. (400\u2009MHz, DMF-d7, 300\u2009K).\n\nIn order to clarify the origin of the cluster attached proton, we repeated the whole synthetic protocol of separation of [Si4]4\u2212 and [Si9]4\u2212 in ND3 instead of NH3 as solvent. After this, a signal at \u22121.62\u2009ppm for [Si9D]3\u2212 can only be detected in the 2H NMR (Supplementary Fig.\u00a019). This shows that the proton of [Si9H]3\u2212 originates from ammonia.\n\nThe access to isolated Si9 clusters on a multi-gram scale, in the absence of highly reductive [Si4]4\u2212 clusters, provides promising and well-defined conditions for follow-up reactions of nine-atomic silicon clusters. Silylation reactions of K1-x[K(2.2.2-crypt)]2+x[Si9] in tetrahydrofuran (thf) lead to the isolation and the structural characterisation of trisilylated cluster salts [K(2.2.2-crypt)][(R3Si)3Si9] (2a\u20132d; Fig.\u00a07) in good yields as orange-brown solids. All compounds were characterised by NMR and ESI-MS analyses. Yellow block-shaped single crystals of [K(2.2.2-crypt)][MeHyp3Si9]\u00b7thf were grown from a thf solution at \u221232\u2009\u00b0C over two weeks. The crystal structure of the molecular anion in 2a is depicted in Fig.\u00a08. 2a crystallises in the monoclinic space group P21/n (14) (a\u2009=\u200915.0913(4)\u2009\u00c5, b\u2009=\u200924.7859(6)\u2009\u00c5, c\u2009=\u200923.9571(6)\u2009\u00c5, \u03b1\u2009=\u200990\u00b0, \u03b2\u2009=\u200990.959(2)\u00b0, \u03b3\u2009=\u200990\u00b0, V\u2009=\u20098959.9(4)\u2009\u00c53) with one trisilylated [MeHyp3Si9]\u2212 cluster anion, one disordered [K(2.2.2-crypt)]+ unit and one disordered thf molecule in the asymmetric unit (for more details see Supplementary Discussion). Analogously to the homologous germanium cluster85, the present silicon cluster can also be described as a D3h symmetric threefold capped trigonal prism. The attachment of a further hypersilyl group to the C2v symmetric dianion [MeHyp2Si9]2\u221281 leads to a closure of the planar square plane Si1-Si4-Si8-Si5 by shortening of the Si1-Si8 bond from 3.770(7)\u2009\u00c5 to 3.2565(10)\u2009\u00c5. At the same time, the remaining prism edges (Si2-Si9 and Si3-Si7) are elongated by 0.526\u2009\u00c5 and 0.430\u2009\u00c5, respectively. The attachment of the third silyl substituent at the Si9 cluster induces the same geometric changes that have been described for the homologous [MeHypnGe9](4-n)\u2212 (Supplementary Table\u00a06)41,85 and [MeHypnSn9](4-n)\u2212 clusters (n\u2009=\u20092, 3)86,87. As expected, the cluster framework undergoes a significant contraction from tin and germanium to silicon. Similar to the homologous germanium cluster [MeHyp3Ge9]\u221247, the UV-VIS spectra of 2a and 2d in thf (Supplementary Figs.\u00a033 and 34) exhibit intense, overlapping signals below 400\u2009nm. The attachment of the electron-withdrawing silyl ligand tBu2FSi in 2d results in a hypsochromic shift compared to 2a.\n\nSilicon is depicted as blue circles. RHyp\u2009=\u2009(R3Si)3Si.\n\nAnisotropic displacement ellipsoids of silicon (blue) are drawn at 50% probability. Carbon (grey) is displayed as spheres of an arbitrary radius and hydrogen atoms are omitted for clarity.\n\nAs expected from the crystal structure and analogously to [MeHyp3Ge9]\u221285, 2a also\u00a0behaves D3h symmetric on NMR time scale in thf-d8 at room temperature. Hence, the three hypersilyl groups collapse to two 29Si resonances at \u2212130.03\u2009ppm and \u22128.71\u2009ppm. These signals can be attributed to the exo-bonded silicon atoms (SiTMS3) and the TMS groups, respectively. Further, in the high field\u00a0region at \u2212175.3\u2009ppm, the three capping positions show one signal, while the six equivalent prism positions exhibit a strongly shielded signal at \u2212360.8\u2009ppm. The NMR data are consistent with the data described in our previous studies81. The remaining derivatives 2b\u2013d exhibit similar behaviour, with the cap and prism signals falling within the characteristic range of \u2212160\u2009ppm and \u2212350\u2009ppm. The high quality of the data enables the determination of the 1J(29Si, 29Si) homonuclear couplings between the cap and prism atoms (Table\u00a01). The coupling constants of the sterically demanding hypersilyl groups (40.1\u2009Hz for 2a and 42.7\u2009Hz for\u00a02b) are significantly higher than those of the sterically less demanding silyl groups in 2c (24.4\u2009Hz) and 2d (23.2\u2009Hz). These couplings differ from localised Si\u2013Si bonds as in cyclic oligosilanes (1J(29Si, 29Si)\u2009\u2248\u200950\u201370\u2009Hz)88 and may indicate possible dynamic processes in the cluster framework, which have not yet been described in homologous silylated clusters. The access to NMR active cluster frameworks could reveal processes that have remained hidden from us so far.\n\nThe presence of strongly reductive [Si4]4\u2212 clusters in the solid-state phase K12Si17 limits the directed conversion of nine-atomic [Si9]4\u2212 silicon clusters. However, in this work, we have shown that the separation of both cluster species is easily possible on a multi-gram scale in liquid ammonia and provides valuable synthetic access to [Si9H]3\u2212 ions.\n\n[Si9H]3\u2212 shows a pronounced tautomerisation tendency, in which the proton rapidly migrates over the entire nine-atomic cluster framework. In addition, those monoprotonated silicon clusters in the form of the crude product K1-x[K(2.2.2-crypt)]2+x[Si9] represent a synthetic equivalent to [Si9]4\u2212 ions that are still not accessible in an isolated form via a solid-state approach. Thus, the trisilylated cluster salts [K(2.2.2-crypt)][(R3Si)3Si9] (2a\u2013d) are obtained in good yields and high purity by direct silylation of K1-x[K(2.2.2-crypt)2+x[Si9H] with the corresponding chlorosilanes. The spectroscopic behaviour and the crystallographic characterisation of 2a prove the strong similarities between silicon- and germanium-based Zintl clusters. With the present work, we were able to close a significant gap in the chemistry of group 14 Zintl ions. Studies on the further reactivity of the obtained trisilylated monoanions are underway.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55211-z/MediaObjects/41467_2024_55211_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55211-z/MediaObjects/41467_2024_55211_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55211-z/MediaObjects/41467_2024_55211_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55211-z/MediaObjects/41467_2024_55211_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55211-z/MediaObjects/41467_2024_55211_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55211-z/MediaObjects/41467_2024_55211_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55211-z/MediaObjects/41467_2024_55211_Fig8_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "All reactions and manipulations were performed in oven dried glassware under a purified argon atmosphere using standard Schlenk and glove box techniques unless otherwise mentioned. NMR solvents were purchased from Sigma\u2013Aldrich and stored over molecular sieve (3\u2009\u00c5) for at least one day. Dichloromethane, Tetrahydrofuran (THF), and pentane were dried by using a solvent purificator (MBraun MB-SPS) and stored over molecular sieve (3\u2009\u00c5). Ammonia was liquified in a dry ice/iPrOH bath and dried over sodium metal for one night prior to use. ND3 was prepared from D2O and Mg3N2. Triethylene glycol bis(p-toluenesulfonate) was prepared by a modified literature procedure89.\n\nA mixture of potassium (1.49\u2009g, 38.0\u2009mmol, 12.0 eq.) and silicon (1.51\u2009g, 53.9\u2009mmol, 17.0 eq.) was sealed in a tantalum ampule and heated up to 800\u2009\u00b0C with a rate of 2\u2009K/min. After 18\u2009h, the reaction mixture was cooled down to room temperature (1\u2009K/min) yielding K12Si17 (2.91\u2009g, 97%) as a dark grey microcrystalline solid. The analytical data (Supplementary Fig.\u00a09) agree with the literature72.\n\nA mixture of 350\u2009mg potassium (8.95\u2009mmol, 1.00 eq.) and 251\u2009mg silicon (8.95\u2009mmol, 1.00 eq.) was sealed in a tantalum ampule and heated up to 600\u2009\u00b0C with a rate of 2\u2009K/min. After ten hours, the reaction mixture was cooled down to room temperature (1\u2009K/min) yielding K4Si4 (589\u2009mg, 98%) as a dark grey microcrystalline solid. The analytical data (Supplementary Fig.\u00a010) agree with the literature72.\n\n45.1\u2009g triethylen glycol (300\u2009mmol, 1.00 eq.) was dissolved in 300\u2009mL CH2Cl2 under non-inert conditions. After addition of 114\u2009g TsCl (600\u2009mmol, 2.00 eq.), the mixture was cooled to 0\u2009\u00b0C and 135\u2009g powdered KOH (2.40\u2009mol, 8.00 eq.) was carefully added in small portions (Caution: Can cause strong heat evolution). After stirring for three hours at 0\u2009\u00b0C, 300\u2009mL CH2Cl2 and 600\u2009mL ice-water were added. The organic layer was separated and the aqueous phase was extracted with CH2Cl2 (3\u2009\u00d7\u2009200\u2009mL). The combined organic layers were washed with water (2\u2009\u00d7\u2009100\u2009mL), dried over Na2SO4 and rotary evaporated. Triethylene glycol bis(p-toluenesulfonate) (118\u2009g, 258\u2009mmol, 86%) was obtained as a white solid. 1H NMR (400\u2009MHz, CDCl3, 298\u2009K): \u03b4 7.77 (d, J\u2009=\u20098.0\u2009Hz, 4 H), 7.33 (d, J\u2009=\u20098.0\u2009Hz, 4 H), 4.12 (t, J\u2009=\u20094.8\u2009Hz, 4 H), 3.63 (t, J\u2009=\u20094.8\u2009Hz, 4 H), 3.50 (s, 4 H), 2.42 (s, 6 H); 13C{1H} NMR (100\u2009MHz, CDCl3, 298\u2009K): \u03b4 145.0, 133.0, 129.9, 128.0, 70.7, 69.3, 68.8, 21.7. The analytical data agree with the literature89.\n\nA mixture of 31.0\u2009g triethylene glycol bis(p-toluenesulfonate) (67.0\u2009mmol, 2.00 eq.), 62.1\u2009g of Na2CO3 (586\u2009mmol, 17.5 eq.) and 4.89\u2009mL 2,2\u2032-(ethylenedioxy)bis(ethylamine) (33.5\u2009mmol, 1.00 eq.) was refluxed in 1000\u2009mL MeCN for five days under non-inert conditions. After cooling to room temperature, the mixture was filtrated and rotary evaporated. The resulting orange oil was redissolved in 375\u2009mL EtOH and 50.0\u2009mL citric acid (1.8\u2009m), heated to 85\u2009\u00b0C for three hours and filtrated again. After adjusting the pH of the filtrate to 14 with aqueous tetramethylammonium hydroxide solution, the mixture was rotary evaporated. The resulting residue was redissolved with Celite in CH2Cl2 and rotary evaporated again. After Soxhlet extraction with cyclohexane overnight and recrystallisation from CH2Cl2:Et2O (1:4), 2.2.2-cryptand was obtained as a white crystalline solid (5.29\u2009g, 14.1\u2009mmol, 42%). Further purification was achieved via sublimation (0.1\u2009mbar, 130\u2009\u00b0C). The analytical data match with an authentic sample of 2.2.2-cryptand. 1H NMR (400\u2009MHz, CDCl3, 298\u2009K): \u03b4 2.62 (t, 3J\u2009=\u20095.56\u2009Hz, 12H, NCH2CH2), 3.56 (t, 3J\u2009=\u20095.56\u2009Hz, 12H, NCH2CH2), 3.66 (s, 12H, CH2). Elemental Analysis: (calcd., found for C18H36N2O6) C (57.42, 57.52), H (9.64, 9.64), N (7.44, 7.44).\n\nK12Si17 (25.0\u2009g, 26.4\u2009mmol, 1.00 eq) and 2.2.2-cryptand (18.9\u2009g,50.2\u2009mmol, 1.90 eq) were dissolved in 250\u2009mL NH3(l) at \u221278\u2009\u00b0C under inert atmosphere leading to a deep red dispersion. The reaction was stirred for one hour before it was stored at \u221240\u2009\u00b0C for twelve hours. After filtration under continuous cooling with dry ice, the ammonia of the deep red filtrate was evaporated. The resulting red-orange solid was weighted (20.4\u2009g) and used without further purification for silylation experiments. According to the elemental analysis, the solid consist of K1-x[K(2.2.2-crypt)]2+x[Si9H] (x\u2009=\u20090.2). We\u00a0would like to point out that the number of non-sequestered and sequestered potassium ions may not always have the same ratio and the exact composition of the crude product might slightly vary with respected to the number of sequestered cations. The amount of 2.2.2-cryptand was optimised in order to reach the best separation of Si9 and Si4 clusters also considering using the minimum amount of 2.2.2-cryptand to reduce the costs. We found that the follow-up chemistry of the anion [Si9H]3\u2212 is not influenced by the amount of 2.2.2-cryptand. Caution: The grey, dried filtration residue reacts explosively with air and protic solvents. Isolation is strongly discouraged. Even Raman measurements conducted within airtight glass capillaries have sometimes resulted in the detonation of these capillaries. Hence, it is strongly recommended to carefully quench the undried residue with iPrOH at \u221278\u2009\u00b0C overnight! The solid passivates in iPrOH. After one night, a bright red reactive solid may remain in the flask. Do not quench this solid with water or iPrOH at room temperature under any circumstances! Even small amounts of this residue can react explosively.\n\nRed orange block-shaped single crystals (20%) of [K(2.2.2-crypt)]3[Si9H]\u22198.5NH3 (1) were obtained by vapour diffusion of Et2O into an ammonia solution of K1-x[K(2.2.2-crypt)]2+x[Si9H] (1.00 eq.) and cryptand (1.00 eq.) at \u221240\u2009\u00b0C after one week. 1H NMR (400\u2009MHz, DMF-d7, 298\u2009K): \u03b4 \u22121.80 (s, Si\u2013H). ESI-MS (negative mode, 3500\u2009V, 300\u2009\u00b0C): m/z 670.37 ({[K(2.2.2-crypt)][Si9]+2H}\u2212), 711.42 ({[K(2.2.2-crypt)][Si9]+2H+mecn}\u2212); Elemental Analysis (calcd., found for K1-x[K(2.2.2-crypt)]2+x[Si9H]; x\u2009=\u20090.2; C39.6H80.2K3N4.4O13.2Si9): C (39.70, 39.66), H (6.78, 6.74), N (5.47, 5.14).\n\n1.50\u2009g TMS4Si (4.68\u2009mmol, 1.00 eq.) and 551\u2009mg KOtBu (4.91\u2009mmol, 1.05 eq.) were dissolved in 7.50\u2009mL THF and stirred for 5\u2009h at room temperature. The resulting yellowish solution was slowly added to a solution of 442\u2009mg Me2SiHCl (4.68\u2009mmol, 1.00 eq.) in 5.00\u2009mL THF at \u221278\u2009\u00b0C. After complete addition, the reaction mixture was stirred at room temperature overnight before quenched with sat. aqueous NH4Cl solution. The mixture was extracted with Et2O (3\u2009\u00d7\u200925\u2009mL). The combined organic layers were washed with brine and dried over Na2SO4. After filtration rotary\u00a0and evaporation of the solvent, MeHypMe2SiH (1.32\u2009g, 4.34\u2009mmol, 93%) was obtained as colourless solid. 1H NMR (400\u2009MHz, CDCl3, 298\u2009K): \u03b4 4.02 (sept, 3J(1H, 1H)\u2009=\u20094.4\u2009Hz, dsept, 1J(1H, 29Si)\u2009=\u2009177\u2009Hz, 3J(1H, 1H)\u2009=\u20094.4\u2009Hz, 1H, Si\u2013H), 0.26 (d, 3J(1H, 1H)\u2009=\u20094.4\u2009Hz, 6H, SiMe2), 0.21 (s, 27H, MeHyp); 13C{1H} NMR (101\u2009MHz, CDCl3, 298\u2009K): \u03b4 2.63 (MeHyp), \u22121.95 (SiMe2); 29Si{1H} INEPT (79.5\u2009MHz, CDCl3, 298\u2009K): \u03b4 \u22129.42 (TMS), \u221233.51 (SiMe2), \u2212136.59 (TMS3Si).\n\n1.00\u2009g MeHypMe2SiH (3.26\u2009mmol, 1.00 eq.) and 277\u2009mg TCCA (1.19\u2009mmol, 0.37 eq.) were stirred in 2.00\u2009mL CH2Cl2 overnight under formation of a white suspension. After solvent removal under reduced pressure, the resulting white solid was extracted with pentane (3\u2009\u00d7\u200915\u2009mL). The combined solutions were evaporated under reduced pressure, giving MeHypMe2SiCl as colourless solid. 1H NMR (400\u2009MHz, MeCN-d3, 298\u2009K): \u03b4 0.61 (s, 6H, SiMe2), 0.26 (s, 27H, MeHyp). The analytical data agree with the literature90.\n\nK1-x[K(2.2.2-crypt)]2+x[Si9H] (x\u2009=\u20090.2) (1.00 eq.) and chlorosilane (3.10 eq.) were dissolved in thf and stirred at room temperature under formation of a red-brown solution. After filtration and removing of the solvent in vacuo, the resulting solid was washed with pentane. After drying under reduced pressure, the trisilylated cluster salts [K(2.2.2-crypt)][(R3Si)3Si9] (2) were obtained as orange-brown solids.\n\nCrystal preparation was carried out under a continuous flow of cold nitrogen in perfluorinated ether (Galden\u00ae LS 230, Solvay Specialty Polymers Italy SpA). For single-crystal data collection, the crystals were fixed on a glass capillary and positioned in a cold stream (150\u2009K) of dried N2 gas. Single-crystal data collection was performed with a STOE StadiVari diffractometer (Mo K\u03b1 radiation; \u03bb\u2009=\u20090.71072\u2009\u00c5) equipped with a DECTRIS PILATUS 300\u2009K detector.\n\nThe X-Area 1.9 software package (Stoe) was used for data reduction and absorption correction91. Structures were solved by Direct Methods (SHELXS-2014) and refined by full-matrix least-squares calculations against F2 (SHELXL-2014)92,93. The positions of the hydrogen atoms were either refined from the difference Fourier map or calculated and refined using a riding model. Unless otherwise stated, all non-hydrogen atoms were treated with anisotropic displacement parameters. The silicon cluster in compound 1 (CCDC 2338275) shows orientational disorder over three orientations and disorder of non-coordinated ammonia molecules. In compound 2 (CCDC 2232604) the disorder of [K(2.2.2-crypt)]+ and thf has been refined by a split layer refinement. For more details see the Supplementary Information (section Crystallographic Data). The crystal structures have been visualised with CrystalMaker\u00ae 11.1.194 and Diamond 3.295.\n\nThe data were collected at room temperature on a STOE Stadi P diffractometer (Ge(111) monochromator, Cu K\u03b11 radiation, \u03bb\u2009=\u20091.54056\u2009\u00c5) with a DECTRIS MYTHEN 1\u2009K detector in Debye\u2013Scherrer geometry. For the measurements, the samples were sealed in glass capillaries (\u00d8\u2009=\u20090.3\u2009mm). The raw data were processed with WinX-POW96. OriginPro 2023 (OriginLab Corporation) was used for visualisation97.\n\n1H, 2H, 13C, 19F and 29Si NMR spectra were recorded on a Bruker AVIII Ultrashield 400 and AVIII HD 500 Cryo. The signals of the 1H NMR spectra were referenced to the residual proton signal and the 13C-NMR spectra on the 13C signal of the deuterated solvent. 2H (\u03b4 (Me4Si-d12)\u2009=\u20090\u2009ppm), 19F (\u03b4 (CFCl3)\u2009=\u20090\u2009ppm), and 29Si (\u03b4 (Me4Si)\u2009=\u20090\u2009ppm) were referenced to external standards. Chemical shift values are given in \u03b4 values in parts per million (ppm). The coupling constants J are given in Hz. Signal multiplicities are abbreviated as follows: s\u2014singlet, d\u2014doublet, t\u2014triplet, q\u2014quartet, sept\u2014septet, dsept\u2014doublet of septet, b\u2014broad. The spectra were processed and visualised with MestReNova 15.0.098 and OriginPro 2023 (OriginLab Corporation)97.\n\nRaman measurements were performed with a Renishaw inVia Reflex Raman System with a CCD Detector (Renishaw 266n10 detector) and a 785\u2009nm laser of 500\u2009mW max. power (Software WiRE 5.3 Renishaw) in sealed glass capillaries (\u00d8\u2009=\u20090.5\u2009mm)99. The spectra were visualised with OriginPro 2023 (OriginLab Corporation)97.\n\nESI-MS spectra were measured on an HCT instrument (Bruker Inc). The data were processed with Bruker Compass Data Analysis 4.0 SP 5. The dry gas temperature was adjusted to 573\u2009K and the injection speed to 270\u2009\u03bcL/s. Data visualisation of the spectra was carried out with the programs OriginPro 2023 (OriginLab Corporation)97.\n\nUV\u2010VIS spectra were recorded on an Agilent Cary 60 UV\u2010Visible spectrophotometer (Agilent Technologies). The absorption spectra were recorded in 1\u2009mm quartz cuvettes (Hellma Analytics) in thf at room temperature. OriginPro 2023 (OriginLab Corporation) was used for visualisation97.\n\nElemental analyses were performed by the microanalytical laboratory at the Catalysis Research Center (CRC) of the Technical University of Munich\u00a0(TUM). The elements C, H, and N were determined by a combustion analyser (EURO-EA, HEKATech).", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data generated or analysed during this study are available in this published article, its Supplementary Information files or from the corresponding authors on request. The X-ray crystallographic coordinates for the structures reported in this study have been deposited at the Cambridge Crystallographic Data Centre (CCDC), under the deposition numbers 2338275 (1) and 2232604 (2a). 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Tr\u00e2n for their assistance in the synthesis of 2.2.2-cryptand.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Chemistry, TUM School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstra\u00dfe 4, D-85748, Garching, Germany\n\nKevin M. Frankiewicz,\u00a0Nicole S. Willeit,\u00a0Viktor Hlukhyy\u00a0&\u00a0Thomas F. F\u00e4ssler\n\nWacker Institute of Silicon Chemistry, Technical University of Munich (TUM), Lichtenbergstra\u00dfe 4, D-85748, Garching, Germany\n\nKevin M. Frankiewicz,\u00a0Nicole S. Willeit\u00a0&\u00a0Thomas F. F\u00e4ssler\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nK.M.F. conceived and performed the syntheses of cluster compounds and collected the single-crystal X-ray data of 1 and 2a, solved and refined the structure of 1 and 2a, performed the ESI-MS and NMR measurements and prepared samples for further analyses. N.S.W. performed the Raman measurements. V.H. reviewed the structural refinement of 2a. T.F.F. supervised the work. K.M.F. wrote the paper. K.M.F. and N.S.W. conceived and performed the synthesis of 2.2.2-cryptand. All authors approved the submission of the manuscript.\n\nCorrespondence to\n Thomas F. F\u00e4ssler.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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An efficient multi-gram access in a two-step synthesis to soluble, nine-atomic, silylated silicon clusters.\n Nat Commun 15, 10715 (2024). https://doi.org/10.1038/s41467-024-55211-z\n\nDownload citation\n\nReceived: 11 March 2024\n\nAccepted: 04 December 2024\n\nPublished: 23 December 2024\n\nVersion of record: 23 December 2024\n\nDOI: https://doi.org/10.1038/s41467-024-55211-z\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"Serotonin 2A Receptor Attenuates Psoriatic Inflammation by Suppressing IL-23 Secretion in Monocyte-derived Langerhans Cells", + "journal": "Nature Communications", + "published": "29 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_MOESM4_ESM.xlsx" + }, + { + "label": "Source data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_MOESM5_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc=GSE274449", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc=GSE274941", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE222197", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151177", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE162183", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13355", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE34248", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109248", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41664", + "/articles/s41467-025-63971-5#Sec33" + ], + "code": [ + "https://zenodo.org/records/16366824" + ], + "subject": [ + "Autoimmunity", + "Chronic inflammation", + "Langerhans cells", + "Mucosal immunology", + "Neuroimmunology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5628384/v1.pdf?c=1759230345000", + "research_square_link": "https://www.researchsquare.com//article/rs-5628384/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63971-5.pdf", + "preprint_posted": "05 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Anecdotal evidence has suggested an association between psychiatric drugs and psoriasis but consensus is absent due to contradicting reports and the mechanism remains poorly defined. Here, we investigated the role played by serotonin 2A receptor (HTR2A), a receptor commonly targeted by psychiatric drugs, in regulating psoriasis. HTR2A antagonistic drugs worsened psoriatic outcome and HTR2A modulation reduced psoriatic inflammation. Using Imiquimod-induced psoriasiform model, HTR2A-deficient mice experienced exacerbated inflammation. Hematopoietic cells, particularly monocyte-derived Langerhans cells (moLC), were responsible for this phenotype. Mechanistically, the exacerbated inflammation is due to increased interleukin-23 (IL-23) secretion and HTR2A suppresses it by inhibiting activation of the non-canonical NFkB pathway. Serotonin is the putative agonist modulating HTR2A attenuating psoriatic inflammation. Lastly, our findings in mice were also validated clinically. Our data demonstrate serotonin modulates HTR2A, attenuating psoriatic inflammation by suppressing IL-23 secretion via inhibiting non-canonical NFkB pathway in moLCs.Biological sciences/Immunology/AutoimmunityBiological sciences/Immunology/Innate immune cells/Dendritic cells/Langerhans cells", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nTsen-Fang Tsai has conducted clinical trials or received honoraria for serving as a consultant for AbbVie, AnaptysBio, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Galderma, GlaxoSmithKline-Stiefel, Janssen-Cilag, Leo-Pharma, Merck, Novartis, PharmaEssentia, Pfizer, Sanofi, Sun Pharma and UCB. The remaining authors state no conflict of interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "GEODataset.pdfGEO Dataset Information20241209Supplementalinformation.pdfSerotonin 2A Receptor Attenuates Psoriatic Inflammation by Suppressing IL-23 Secretion in Monocyte-derived Langerhans Cells Supplemental InformationNCOMMS2482399rs.pdfReporting Summary", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Anecdotal evidence has suggested an association between psychiatric drugs and psoriasis, but consensus is absent due to contradicting reports, and the mechanism remains poorly defined. Here, we investigate the function of serotonin 2A receptor (HTR2A), a receptor commonly targeted by psychiatric drugs, in regulating psoriasis. HTR2A antagonistic drugs worsen psoriatic outcome, and HTR2A modulation reduces psoriatic inflammation. Using the Imiquimod-induced psoriasiform model, HTR2A-deficient mice manifest exacerbated inflammation. Hematopoietic cells, particularly monocyte-derived Langerhans cells (moLC), are involved in this phenotype. Mechanistically, the exacerbated inflammation is due to increased interleukin-23 (IL-23) secretion, and HTR2A suppresses this by inhibiting activation of the non-canonical NF\u03baB pathway. Serotonin is the putative agonist modulating HTR2A, attenuating psoriatic inflammation. Lastly, our findings in mice are also validated clinically. Our data demonstrate that serotonin modulates HTR2A, attenuating psoriatic inflammation by suppressing IL-23 secretion via inhibiting the non-canonical NF\u03baB pathway in moLCs.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Psoriasis is a chronic immune-mediated inflammatory skin condition that affects millions of people worldwide1. Despite the identification of the IL-23/IL-17 axis playing a key role in psoriasis, much remains unknown about psoriasis, as patients exhibit varied responses despite receiving identical treatment2. Of late, the psychological component of psoriasis has been given increased attention, as emerging evidence suggests mental health influences disease outcomes3,4. High rates of psychiatric comorbidities are observed in psoriatic patients, leading to the classification of psoriasis as a psychosomatic disease3,5. For many patients, exacerbation of psoriatic episodes is often preceded by stressful life episodes, underscoring the need to unravel the complexities between psychological and physiological factors in psoriasis6,7.\n\nInterestingly, studies have shown mixed results regarding the impact of mood-altering medications on psoriasis. While some selective serotonin reuptake inhibitors (SSRIs) appear to improve psoriatic conditions8,9,10. Others have been linked to worsening psoriasis11,12,13. This conflicting evidence highlights the need for a closer examination of serotonin\u2019s role in psoriasis. Thus, here we focused on second-generation antipsychotics- serotonin and dopamine receptor antagonists, which have strong antagonism for serotonin receptors14. In contrast, SSRIs have unpredictable effects on local serotonin levels depending on the duration of treatment15. Clozapine and risperidone, both second-generation antipsychotics, with their direct antagonism of serotonin receptors, particularly serotonin 2A receptor (HTR2A), facilitate a clear interpretation of results, making them ideal candidates for further studies14,16.\n\nHTR2A is a G protein-coupled receptor (GPCR) of the Gq subtype and is primarily activated by serotonin17. HTR2A can be activated by naturally occurring compounds like psilocybin or synthetic compounds like lysergic acid diethylamide and (R)\u22121-(2,5-dimethoxy-4-iodophenyl)\u22122-aminopropane (DOI)18,19. In particular, DOI is widely used to study HTR2A as it is a HTR2A-specific agonist19. Studies have reported that DOI suppresses inflammation by blocking tumor necrosis factor-alpha (TNF)-induced cytokine and chemokine expression both in vivo and in vitro, yet we lack a detailed understanding of how HTR2A regulates immune cells and cytokines in the context of psoriasis20,21.\n\nWe previously found HTR2A to be highly expressed in Langerhans cells (LC), indicating HTR2A could impact LC functions affecting psoriatic outcome22. LCs are skin macrophages with dendritic cell functions and are known to play a role in the exacerbation of psoriatic symptoms involving the cytokine IL-2323,24; however, some reports suggest otherwise25, indicating that LCs might play a role in immune tolerance. This schism could be due to the existence of two subsets of LCs, identified as embryonic LC (eLC) and monocyte-derived LC (moLC)26. eLCs originate from erythro-myeloid progenitors, which were formed from the yolk sac, while moLCs originate from monocyte-like precursors, which are derived from definitive hematopoiesis26. moLCs can be identified by their expression of CD64 and have been known to play a role in exacerbating psoriasiform inflammation27,28,29. It remains to be investigated how each of the LC subsets is affected by HTR2A.\n\nWhile HTR2A\u2019s role in regulating cytokine-mediated inflammation is documented, no studies have yet linked it directly to autoimmune diseases like psoriasis. Addressing this gap, our study investigates how HTR2A expression influences psoriasiform inflammation. We found that HTR2A expression on moLCs attenuates psoriasiform inflammation by reducing IL-23 secretion through the non-canonical NF\u03baB pathway.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To determine the association between psoriasis and serotonin receptors, we performed a retrospective cohort study to examine serotonin-related drug use and changes in psoriasis treatment received as a surrogate marker for psoriasis severity. Our algorithm for recruiting patients is as depicted, with patients having to meet the psoriasis diagnosis, age 18 and above, and have a psoriasis diagnosis for more than 90 days (Fig.\u00a0S1). The categories of psoriasis treatment are as defined by the Taiwan Dermatological Association, with topical treatment for patients with mild psoriasis symptoms, followed by systemic agents, combination therapy, and biologics for patients with the most severe symptoms30. We defined worsening psoriasis as a change from mild treatment to severe treatment, for example, from receiving topical treatment to receiving biologics. We found that patients taking anti-psychotics were more likely to have worsening psoriasis as compared to control patients not taking anti-psychotics or SSRIs (Figs.\u00a01A, S2C). Correspondingly, patients on anti-psychotics did not show any significant improvement in psoriasis (Fig.\u00a0S2A, S2C). Of note, patients on SSRIs were found to have a significant worsening but no improvement in psoriasis (Fig.\u00a0S2B, S2C). The patients\u2019 baseline clinical characteristics were not too different among the three groups (Table\u00a0S1).\n\nA Kaplan-Meier plot of anti-psychotic use and worsening psoriatic outcome. B Forrest plot showing breakdown of each anti-psychotic drug and their respective hazard ratios on worsening psoriatic outcome. Data and statistical analysis obtained from (A). C Table showing ranking of anti-psychotic drug\u2019s affinity for HTR2A among serotonin receptors. D Schematic depicting the experimental plan of ex vivo experiment from human samples. E Venn diagram showing the number of genes overlapping between differentially expressed genes of healthy non-treated versus lesioned non-treated and differentially expressed genes of lesioned non-treated versus lesioned DOI-treated. F Heat map showing 107 overlapping genes and their expression levels from (E) (n\u2009=\u20093). G Box plot showing HTR2A expression levels in psoriatic cases as compared to control in publicly available GEO datasets. H Immunohistochemistry staining of HTR2A on the skin of healthy volunteers, non-lesioned skin of psoriatic patients, and lesioned skin of psoriatic patients with quantification of intensity (H-score) between lesioned and non-lesioned skin of psoriatic patients (n\u2009=\u200920). Data are a summary of two independent experiments (E, F). p values determined by Cox proportional hazard test (A), two-sided unpaired Student\u2019s T-test (G) and two-sided paired Student\u2019s T-test (H). Mean\u2009\u00b1\u2009SEM (B, G). All box plots include the median line, the box denotes the interquartile range (IQR), whiskers denote the rest of the data distribution and outliers are denoted by points greater than \u00b11.5\u2009\u00d7\u2009IQR. Source data are provided as a Source Data file. Created in BioRender. Tan, Y. (2025) https://BioRender.com/0hxttks.\n\nWe then looked at the hazard ratio of each anti-psychotic and found patients on aripiprazole, chlorpromazine, prochlorperazine, quetiapine, risperidone, sulpiride and anti-psychotics categorized as Others (brexipriprazole, fluphenazine, loxapine, lurasidone, perphenazine, pimozide, thioridazine, and trifluoperazine) were more likely to have worsening psoriasis as compared to controls as they had statistically significant hazard ratios that were greater than one (Fig.\u00a01B). We did not find statistically significant hazard ratios in patients taking amisulpride, clozapine, haloperidol, and olanzapine. We then checked the binding affinity of each anti-psychotic to each serotonin receptor on the PDSP Ki database31. We found most of these anti-psychotics bound most strongly to HTR2A among the serotonin receptors, indicating HTR2A might be responsible for the worsening psoriasis observed (Figs.\u00a01C, S2D).\n\nTo determine whether HTR2A plays a role in psoriasis, we modulated HTR2A via the specific agonist DOI and wondered if it causes transcriptional changes in psoriatic genes. We obtained skin biopsies from lesioned sites of three psoriatic patients who have yet to receive any psoriatic treatment and skin from three healthy participants as controls. The biopsies were halved, with half of them reserved for RNA sequencing without treatment (NT) and the other half treated with DOI (T) for 16\u2009h. All samples were subjected to RNA sequencing (Fig.\u00a01D) (GSE274449). We determined the differentially expressed genes (DEGs) of lesioned skin T versus lesioned skin NT and identified 653 genes. A total of 1475 psoriatic DEGs were identified when comparing NT healthy skin versus NT lesioned skin. We then compared these two sets of DEGs and found 107 DEGs that overlapped (Fig.\u00a01E). 94 of these genes were upregulated in psoriasis, but were downregulated following DOI treatment, and 13 of these genes were downregulated in psoriasis, but with DOI treatment, they were upregulated. In healthy skin, irrespective of treatment, the identified genes share a similar pattern with the lesioned treated group, indicating HTR2A activation on lesioned psoriatic skin reversed the psoriasis transcriptomic profile (Fig.\u00a01F). We then determined HTR2A expression in psoriatic cases using publicly available datasets on the GEO database and found lower HTR2A expression in psoriatic cases (Fig.\u00a01G). Our immunohistochemical staining of lesioned, non-lesioned, and healthy skin of patients revealed that lesioned skin has lower HTR2A expression as compared to non-lesioned and healthy skin. The difference between lesioned and non-lesioned skin is statistically significant (Fig.\u00a01H). In all, these data strongly suggest that HTR2A plays a role in affecting psoriatic outcomes.\n\nTo investigate HTR2A\u2019s role in psoriasis further, we treated HTR2A-deficient (KO) and age-matched, gender-matched wild-type (WT) mice with Imiquimod (IMQ) for 7 days (Fig.\u00a02A). There was a significant increase in ear thickness in IMQ-treated KO mice as compared to WT mice. In contrast, no differences were observed in mice treated with Vaseline irrespective of HTR2A expression (Fig.\u00a02B). By visual inspection, we found the ears of IMQ-treated KO mice to be thicker with more scaling (Fig.\u00a02C). Histologically, acanthosis along with hyperkeratosis, parakeratosis, Munro\u2019s microabscesses, and immune infiltrate are hallmarks of psoriasis and reflect the severity of psoriasis32. There was obvious acanthosis (thickening of the epidermis) and immune infiltrate in hematoxylin and eosin (H&E)-stained slides of IMQ-treated KO mice. There was also a significant increase in epidermis thickening in IMQ-treated KO mice as compared to IMQ-treated WT mice (Fig.\u00a02D). It has been reported that \u03b3\u03b4 T cells will respond to IL-23, a key cytokine in the pathogenesis of psoriasis, and differentiate into IL-17-producing cells33. Of note, the V\u03b34 subset is one of the major IL-17-producing \u03b3\u03b4 T cells34,35. Circulating neutrophils are also normally recruited into inflammatory sites, and in psoriasis, Munro\u2019s microabscesses are filled with neutrophils36. Flow cytometry of cells according to our gating strategy revealed an increase in IL-17- and IL-22-producing V\u03b34\u2009+\u2009T cells and an increase in neutrophils (Figs.\u00a02E, S3). However, there were no statistically significant differences in IL-17- and IL-22-producing \u03b1\u03b2 CD4 T cells. Key pro-inflammatory cytokines like Il17a and Il23a were also increased in HTR2A-deficient mice (Fig.\u00a02F). In all, HTR2A deficiency exacerbates psoriasiform inflammation.\n\nA Schematic showing the experimental plan for Imiquimod-treatment of mice. B Line chart showing changes in ear thickness of mice. (n\u2009=\u20094 mice for Vas groups, n\u2009=\u200910 mice for IMQ groups) C Pictures showing ears of mice of various groups on Day 7. D H&E-stained slides with quantification of epidermal thickness. E Flow cytometry of IL17\u2009+\u2009IL22\u2009+\u2009V\u03b34\u2009T cells and neutrophils in the respective groups with quantifications. The cells were isolated from the ears of mice. The detailed method is in the Methods section. F qPCR of pro-inflammatory cytokines of respective groups. G Schematic showing the experimental plan for developing bone marrow chimeric mice. H Line chart showing changes in ear thickness of mice. (n\u2009=\u20095 for WT to KO and KO to WT, n\u2009=\u20098 for WT to WT and KO to KO). I Pictures showing ears of mice of various groups on Day 7. J H&E-stained slides with quantification of epidermal thickness. K Flow cytometry of IL17\u2009+\u2009IL22\u2009+\u2009V\u03b34\u2009T cells and neutrophils in the respective groups with quantifications. L qPCR of pro-inflammatory cytokines of respective groups. Data are representative of three independent experiments (B\u2013F and H\u2013L). The first day of significant differences was marked # comparing WT IMQ and KO IMQ (B) and comparing WT to KO versus KO to KO, WT to WT versus KO to WT, and WT to WT versus KO to KO (H). p values determined by two-way ANOVA (B, H) followed by Tukey\u2019s post-hoc test and one-way ANOVA (D\u2013F and J\u2013L) followed by Tukey\u2019s post-hoc test. Mean\u2009\u00b1\u2009SEM (B, D\u2013F, H, and J\u2013L). Source data are provided as a Source Data file. Created in BioRender. Tan, Y. (2025) https://BioRender.com/qx9hedj.\n\nTo investigate which cell was responsible for the exacerbated inflammation, we performed reciprocal bone marrow transplants to generate chimeric mice with WT bone marrow (representing hematopoietic stem cells (HSCs) and KO somatic cells (representing non-HSCs) and vice versa. KO bone marrow was transplanted to KO mice, and WT bone marrow was transplanted to WT mice as controls (Fig.\u00a02G). We checked the composition of cells 11 weeks post-transplant, and ~65\u201370% of CD45+ cells were replaced with cells from donor bone marrow (Fig.\u00a0S4A). A statistically significant increase in ear thickness was observed in mice with KO bone marrow irrespective of their somatic cells (Fig.\u00a02H). Visually, the ears of mice with KO bone marrow appeared thicker with some scaling (Fig.\u00a02I). Histologically, the dermis did not vary much between the groups, but the epidermis was significantly thicker in mice with KO bone marrow than in mice with WT bone marrow (Fig.\u00a02J). Flow cytometry of immune cells revealed an increase in IL-17- and IL-22-producing V\u03b34\u2009+\u2009T cells in mice with KO bone marrow. Paradoxically, neutrophil levels were lower in mice with KO bone marrow (Fig.\u00a02K). Il17a and Il23a were increased in mice with KO bone marrow (Fig.\u00a02L). Our findings suggest that HTR2A deficiency in HSCs is responsible for the exacerbated inflammation.\n\nSince HTR2A deficiency in HSCs exacerbates inflammation and immune cells make up a large part of HSCs, we wondered are the adaptive immune cells or the innate immune cells causing this exacerbated inflammation. To answer this, we generated mice with conditional deletion of HTR2A in B and T cells (HTR2ARag1) by crossing Rag1-cre mice with HTR2A floxed mice (Fig.\u00a0S5A)37. We found no increase in ear thickness in IMQ-treated-HTR2ARag1 mice as compared to IMQ-treated-HTR2Afl/fl mice (Fig.\u00a0S5B). Visual inspection of mice ears found minimal scaling and no differences between the groups (Fig.\u00a0S5C). Histologically, there was no increase in the thickening of the epidermis or the dermis between the groups. When epidermal thickening was quantified, there were no significant differences (Fig.\u00a0S5D). There were also no differences in pro-inflammatory immune cells and pro-inflammatory cytokines between IMQ-treated-HTR2ARag1 mice and IMQ-treated-HTR2Afl/fl mice (Fig.\u00a0S5E, F). The data here indicates that HTR2A deficiency in B and T cells, which are the adaptive immune cells, does not cause exacerbated inflammation.\n\nAs adaptive immune cells were not responsible for the exacerbated inflammation, we turned our attention to innate immune cells. Monocyte-derived inflammatory Langerhans cells have been reported to cause psoriasis-like inflammation28. Hence, we decided to generate mice with conditional deletion of HTR2A in Langerhans cells (HTR2ACD207) by crossing CD207-cre mice with HTR2A floxed mice (Fig.\u00a03A)38. We found that conditional deletion of HTR2A in Langerhans cells (LC) recapitulated the inflammatory levels observed in HTR2A-deficient mice with increased ear thickness, increased scaling, increased epidermal thickening, increased IL-17- and IL-22-producing V\u03b34\u2009T cells, and increased mRNA levels of Il17a and Il23a (Fig.\u00a03B-F). However, the increase in neutrophil recruitment is not statistically significant (Fig.\u00a03E).\n\nA Schematic showing strategy for developing conditional knockout mice with HTR2A knocked out in Langerhans cells. B Line chart showing changes in changes in ear thickness of mice. (n\u2009=\u20094 for HTR2ACD207 Vas, n\u2009=\u20096 per group for all other groups). C Pictures showing ears of mice of various groups on Day 7. D H&E-stained slides with quantification of epidermal thickness. E Flow cytometry of IL17\u2009+\u2009IL22\u2009+\u2009V\u03b34\u2009T cells and neutrophils in the respective groups with quantifications. F qPCR of pro-inflammatory cytokines of respective groups. G Schematic showing treatment schedule on CD207-DTR mice for the depletion of different subsets of Langerhans cells. H Line chart showing changes in ear thickness of mice (n\u2009=\u20098 for both groups). I Pictures showing ears of mice of various groups on Day 7. J H&E-stained slides with quantification of epidermal thickness. K Flow cytometry of IL17\u2009+\u2009IL22\u2009+\u2009V\u03b34\u2009T cells and neutrophils in the respective groups with quantifications. L) qPCR of pro-inflammatory cytokines of respective groups. Data are representative of two independent experiments (B\u2013F and H\u2013L). The first day of significant differences was marked # comparing HTR2Afl/fl IMQ and HTR2ACD207 IMQ (B) and comparing PBS\u2009+\u2009DOI versus 3XDT\u2009+\u2009DOI (H). p values determined by two-way ANOVA followed by Tukey\u2019s post-hoc test (B), two-way ANOVA followed by Sidak\u2019s test (H), one-way ANOVA (D-F) followed by Tukey\u2019s post-hoc test and two-sided Student\u2019s T-test (J\u2013L). Mean\u2009\u00b1\u2009SEM (B\u2013F and H\u2013L). Source data are provided as a Source Data file. Created in BioRender. Tan, Y. (2025) https://BioRender.com/xo0d23l.\n\nTo further validate HTR2A\u2019s influence on LC exacerbating inflammation, we used a Langerhans cell depletion model, whereby LCs were depleted by administering diphtheria toxins to CD207-diphtheria toxin receptor (CD207-DTR) mice28. We validated that this method could lead to the depletion of LCs (Fig.\u00a0S6A\u2013S6D). All the mice were administered 0.1\u2009mg/kg of DOI intraperitoneally (Fig.\u00a03G). In terms of changes in ear thickness, we found DOI-suppressed inflammation in mice with LCs but not in mice with depleted LCs (Fig.\u00a03H). A similar trend was observed visually and histologically (Fig.\u00a03I and J). IL-17- and IL-22-producing V\u03b34\u2009T cells were low in mice with no depletion but high in mice with LC depletion, and the differences were statistically significant (Fig.\u00a03K). Neutrophil levels follow a similar trend, but there was no statistical significance (Fig.\u00a03K). mRNA of Il17a and Il23a is high in mice with LC depletion and low in mice with no depletion; however, only differences in Il17a reach statistical significance (Fig.\u00a03L). The data here strongly support that HTR2A deficiency in LCs causes exacerbated inflammation.\n\nWe have found that HTR2A deficiency in both cells of bone marrow origin and LCs leads to exacerbated inflammation, but these two findings contradict each other, as LCs are not replaced during bone marrow transplantation28. To reconcile the differences, we suspect HTR2A affects LCs derived from monocytes, which are of bone marrow origin, causing exacerbated inflammation. To test our hypothesis, we differentiated bone marrow cells into monocyte-derived Langerhans cells (moLC) as published previously (Fig.\u00a04A)39. These differentiated cells are considered of monocyte origin due to their CD64 expression and Irf8 expression (lineage defining transcription factor for monocytes) (Fig.\u00a0S7A\u2013S7C)27,39. They are considered LCs due to their expression of Id2 and Runx3 which are lineage defining transcription factors for LCs (Fig.\u00a0S7C)39,40. Cells expressing MHC II and CD207 are considered moLCs whereas all the other cells are considered non-moLCs (Fig.\u00a0S7B). These cells were then adoptively transferred into the ear pinna of LC-depleted mice (Fig.\u00a04A). Exacerbated inflammation was observed in mice with HTR2A-deficient moLCs, whereas a lower-grade inflammation was observed in mice with WT moLCs (Fig.\u00a04B\u2013D). Mice adoptively transferred with non-moLCs irrespective of HTR2A expression experienced minimal inflammation (Fig.\u00a04B\u2013D). A similar inflammatory trend was observed in IL-17- and IL-22-producing V\u03b34\u2009T cells and mRNA levels of Il17a; however, this trend was not observed in neutrophil levels and mRNA levels of Il23a (Fig.\u00a04E, F).\n\nA Schematic showing the experimental design for developing moLCs and adoptively transferring them (1\u2009\u00d7\u2009105 per ear) into Langerhans cells depleted mice. B Line chart showing changes in ear thickness of mice. (n\u2009=\u200912 per group). C Pictures showing ears of mice of various groups on Day 7. D H&E-stained slides with quantification of epidermal thickness. E Flow cytometry of IL17\u2009+\u2009IL22\u2009+\u2009V\u03b34\u2009T cells and neutrophils in the respective groups with quantifications. F qPCR of pro-inflammatory cytokines of respective groups. G Schematic showing strategy for developing conditional knockout mice with HTR2A knocked out in moLCs. H Line chart showing changes in ear thickness of mice. (n\u2009=\u20096 for HTR2Afl/fl IMQ and HTR2AMs4a3 Vas, n\u2009=\u20097 for HTR2AMs4a3 IMQ, n\u2009=\u200910 for HTR2Afl/fl Vas). I Pictures showing ears of mice of various groups on Day 7. J H&E-stained slides with quantification of epidermal thickness. K Flow cytometry of IL17\u2009+\u2009IL22\u2009+\u2009V\u03b34\u2009T cells and neutrophils in the respective groups with quantifications. L qPCR of pro-inflammatory cytokines of respective groups. Data are representative of two independent experiments (B\u2013F and H\u2013L). The first day of significant differences was marked # comparing WT moLC and KO moLC (B) and comparing HTR2Afl/fl IMQ and HTR2AMs4a3 IMQ (H). p values determined by two-way ANOVA (B, H) followed by Tukey\u2019s post-hoc test and one-way ANOVA (D\u2013F and J\u2013L) followed by Tukey\u2019s post-hoc test. Mean\u2009\u00b1\u2009SEM (B\u2013F and H\u2013L). Source data are provided as a Source Data file. Created in BioRender. Tan, Y. (2025) https://BioRender.com/xo0d23l.\n\nTo further validate the role of LCs of monocyte origin being responsible for the exacerbated inflammation seen in HTR2A-deficient mice, we generated mice with conditional deletion of HTR2A in cells of monocyte lineage (HTR2AMs4a3) by crossing Ms4a3-cre mice with HTR2A floxed mice (Fig.\u00a04G)41. Exacerbated inflammation was observed in IMQ-treated-HTR2AMs4a3 mice in terms of ear thickening, visually, and histologically (Fig.\u00a04H\u2013J). When quantified, there were significant increases in ear thickness and epidermal thickening (Fig.\u00a04H, J). IL-17- and IL-22-producing V\u03b34\u2009T cells but not neutrophils were significantly increased (Fig.\u00a04K). mRNA levels of Il17a and Il23a were increased significantly in IMQ-treated-HTR2AMs4a3 mice (Fig.\u00a04L). In all, we conclude that HTR2A deficiency in LCs of monocyte origin exacerbates psoriasiform inflammation.\n\nWe turned our focus on the cytokine affected by HTR2A deficiency in moLCs. We determined the mRNA levels of commonly secreted cytokines by Langerhans cells (Tnf, Il1b, Il23a, Il12a, Il6, Il2, Il10 and Tgfb1) after stimulating them with Imiquimod22,39,42. We found Il23a to be drastically increased in IMQ-stimulated HTR2A-deficient moLCs as compared to IMQ-stimulated WT moLCs (Fig.\u00a05A). To validate it, we performed IL-23p19 ELISA on stimulated and unstimulated, WT or HTR2A-deficient moLCs and found IL23A levels to be increased significantly in stimulated HTR2A-deficient moLCs as compared to stimulated WT moLCs (Fig.\u00a05B). We then determined IL-23 secretion among different cell types in IMQ-treated-HTR2A-deficient mice, and the cells were gated as published (Fig.\u00a0S8)28. We found that moLCs have the highest IL-23 mean fluorescence intensity (MFI) and moLCs have the most IL-23-producing cells among all the other cell types (Fig.\u00a05C). We have also isolated conventional dendritic cell 1 and conventional dendritic cell 2 from mouse spleens, embryonic Langerhans cells from mouse skins, and moLCs developed from bone marrow. We treated these cells in vitro with Imiquimod (10\u2009\u03bcg/mL) and DOI (10\u2009\u03bcg/mL) and found the reduction in IL23+ cells was significant in moLCs only (Fig.\u00a0S9). These data strongly suggest HTR2A-deficient moLCs have increased secretion of IL-23.\n\nA Heatmap showing mRNA levels of key cytokines in wild type or HTR2A deficient moLCs. (3 biological replicates) The cells were stimulated for 24\u2009h with Imiquimod (10\u00a0\u03bcg/mL). B IL-23 ELISA of stimulated and unstimulated 1\u2009\u00d7\u2009105 wild type or HTR2A deficient moLCs (n\u2009=\u20094 for WT moLC and KO moLC with Imiquimod added, n\u2009=\u20093 for WT moLC and KO moLC with no Imiquimod added). C Representative histogram of IL-23a MFI among different cell types in cells isolated from ears of HTR2A-deficient mice treated with Imiquimod for 7 days. (n\u2009=\u20094) The cells were isolated and treated with PMA, Ionomycin, and GolgiStop before antibody staining and flow cytometry analysis. The quantification of IL-23a MFI and the percentage of IL-23a positive cells is included. D Schematic showing the experimental design for treating mice with DOI and determining IL-23a expression. E Line chart showing changes in ear thickness of mice. (n\u2009=\u200914 per group). F Picture of ears on Day 7 of the respective groups. G H&E-stained slides of the respective groups with quantification of epidermal thickness. H Flow cytometry showing the percentage of IL-23+ cells among CD45+ cells in the respective groups with quantification. I Flow cytometry showing the percentage of IL-23+ cells among moLCs in the respective groups with quantification. J Histogram of moLCs\u2019 IL-23a MFI in the respective groups with quantification. K Schematic showing the experimental design of IL-23r blocking experiments. L Line chart showing changes in ear thickness of mice in the respective groups. (n\u2009=\u20096). M Picture of ears on Day 7 of the respective groups. N H&E-stained slides of the respective groups with quantification of epidermal thickness. O Flow cytometry of IL17\u2009+\u2009IL22\u2009+\u2009V\u03b34\u2009T cells and neutrophils in the respective groups with quantifications. P mRNA levels of pro-inflammatory cytokines of the respective groups. Data are representative of two independent experiments (B, E\u2013J, and L\u2013P). The first day of significant differences was marked # comparing WT IMQ versus WT IMQ\u2009+\u2009DOI (E) and comparing WT Iso and KO Iso (L). p values determined by two-way ANOVA (E) followed by Sidak\u2019s post-hoc test, two-way ANOVA (L) followed by Tukey\u2019s post-hoc test, one-way ANOVA (B and N\u2013P) followed by Tukey\u2019s post-hoc test, one-way ANOVA (C) followed by Dunnett post-hoc test, and two-sided Student\u2019s T-test (G\u2013J). Mean\u2009\u00b1\u2009SEM (B, C, E, G\u2013J, L, and N\u2013P). Source data are provided as a Source Data file. Created in BioRender. Tan, Y. (2025) https://BioRender.com/twstqhl.\n\nWe performed an in vivo experiment where we injected DOI or PBS intraperitoneally into WT mice and looked at inflammatory and IL-23 levels (Fig.\u00a05D). We found mice injected with DOI had less inflammation, as seen in changes in ear thickness, gross evaluation, and epidermal thickness (Fig.\u00a05E\u2013G). Importantly, we found IL-23-positive cells decreased in the DOI-treated group as compared to the control group (Fig.\u00a05H). The percentage of IL-23-positive cells among moLCs is lower in DOI-treated as compared to the control group, and the difference is statistically significant (Fig.\u00a05I). IL-23 MFI of DOI-treated moLCs is also significantly lower as compared to control moLCs (Fig.\u00a05J).\n\nTo further validate IL-23\u2019s role in exacerbating inflammation, we performed an in vivo IL-23 neutralizing experiment (Fig.\u00a05K). In mice injected intradermally with isotype antibody, the inflammation was more severe in HTR2A-deficient mice than in WT mice. The inflammation levels were minimal as observed in terms of ear thickness, visual inspection, histological assessment, and quantification of epidermal thickness in \u03b1-IL23r-treated mice irrespective of HTR2A deficiency (Fig.\u00a05L\u2013N). IL-17- and IL-22-producing V\u03b34\u2009T cells were reduced in \u03b1-IL23r-treated mice in both WT and HTR2A-deficient mice; however, neutrophil levels were not significantly different (Fig.\u00a05O). mRNA levels of pro-inflammatory cytokines were reduced in both WT and HTR2A-deficient mice treated with neutralizing antibodies, with a reduction in Il17a levels reaching statistical significance (Fig.\u00a05P). With that, we conclude that IL-23 plays a pivotal role in exacerbating psoriasiform inflammation in HTR2A-deficient moLCs.\n\nTo determine the transcription factor involved in the exacerbated inflammation, we performed bulk RNA-sequencing on HTR2A-deficient moLCs isolated from IMQ-treated ears (GSE274941) and compared it with WT moLCs isolated from IMQ-treated ears as published previously (GSE222197). A total of 140 DEGs were identified, 47 genes were downregulated, and 93 genes were upregulated in HTR2A-deficient LCs (Fig.\u00a06A). The DEGs were analyzed by GAGE pathway analysis on the TRRUST Transcription Factor Database, and we found the Nfkb1 pathway to be the top-ranked statistically significant transcription factor (Fig.\u00a06B). The key genes requiring Nfkb1 as a transcription factor were listed on the heat map (Fig.\u00a06A).\n\nA Heatmap showing 140 differentially expressed genes (DEGs) between wild-type moLCs (GSE222197) treated with Imiquimod and KO moLCs treated with Imiquimod (GSE274941) with genes affected by NF\u03baB shown on the right. The experiments generating the datasets of GSE222197 and GSE274941 were done in parallel. MoLCs were pooled from the ears of five mice treated with IMQ for two days, constituting a single biological sample. B Bar chart showing transcription factors enriched among the DEGs from (A) as analyzed using Generally Applicable Gene-set/pathway Analysis (GAGE) on the TRRUST Transcription Factor database. C Western blot of cultured WT and KO moLCs lysate treated with or without DOI and stained with anti-p100/p52, anti-RelB, and anti-GAPDH. D IL23p19 ELISA of 2\u2009\u00d7\u2009105 WT or KO moLCs (5 biological replicates) stimulated for 24\u2009h with Imiquimod (10\u00a0\u03bcg/mL) and treated with either DOI (10\u2009\u03bcg/mL) or EVP 4593 (0.1\u2009mM). E Differentiation of V\u03b34\u2009T cells into IL-17\u2009+\u2009IL-22+ cells after coculture with WT or KO moLCs (5 biological replicates) for 3 days. F Intracellular cytokine staining of IL23A of WT moLCs treated with Imiquimod (10\u2009\u03bcg/mL) or CD40 stimulating antibody (CD40L) (1\u00a0\u03bcg/mL) with or without DOI (10\u2009\u03bcg/mL) treatment. G Intracellular cytokine staining of IL23A of WT or KO moLCs, stimulated with Imiquimod (10\u2009mg/mL) and treated with DOI (10\u2009\u03bcg/mL) or B022 (5\u2009\u03bcM). H Intracellular cytokine staining of IL23A of human moLCs (from 3 different individuals) treated with Imiquimod (10\u2009\u03bcg/mL), serotonin (10\u2009\u03bcM) or DOI (10\u00a0\u03bcg/mL). Data are a summary of two independent experiments (C\u2013G). p values were determined by one-way ANOVA followed by Dunnett\u2019s post-hoc test (D, E, and H) and one-way ANOVA followed by Tukey\u2019s post-hoc test (F, G). Mean \u00b1 or SEM (D\u2013H). Source data are provided as a Source Data file.\n\nIt has been reported that IL-23a secretion involves both canonical and non-canonical NF\u03baB pathways43. We performed Western blot on key molecules within the non-canonical NF\u03baB pathway and found that processing of p100 unit is increased in HTR2A-deficient moLCs, as p100 levels were low, and p52 (the processed subunit of p100) is increased in HTR2A-deficient moLCs. The addition of DOI to WT moLCs led to a reduced level of p52, even though p100 levels were rather similar. There were, however, no changes to RelB levels in HTR2A-deficient moLCs as compared to WT moLCs (Fig.\u00a06C).\n\nWe then investigated the role of NF\u03baB using the in vitro assays of IL-23p19 ELISA and coculture of V\u03b34\u2009T cell with moLCs, DOI, and antagonist of NF\u03baB (EVP 4593)44. We found IL-23 levels to be lower in WT moLCs stimulated with Imiquimod than stimulated HTR2A-deficient moLCs. When DOI, the HTR2A-specific agonist, and EVP 4593 were added to WT moLCs, IL-23 levels were decreased (Fig.\u00a06D). Differentiation of V\u03b34\u2009T cells into IL-17- and IL-22-producing cells was lower when cultured with stimulated WT moLCs than with stimulated HTR2A-deficient moLCs. The addition of DOI or EVP 4593 into WT moLCs reduced the differentiation of cocultured V\u03b34\u2009T cells when compared to stimulated WT moLCs only (Fig.\u00a06E).\n\nWe validated HTR2A\u2019s role in suppressing the non-canonical NF\u03baB pathway by stimulating WT moLCs with a \u03b1-CD40 stimulating antibody (CD40L)45. After intracellular cytokine staining, we found that the IL23+ cells percentage is higher in cells stimulated with \u03b1-CD40 stimulating antibody (1\u00a0\u03bcg/mL) or Imiquimod (10\u00a0\u03bcg/mL), but when these cells are treated with DOI, the percentage of IL23+ cells dropped in a statistically significant manner (Fig.\u00a06F). We also blocked the non-canonical NF\u03baB pathway with B022 (a NIK inhibitor) in WT moLCs and found that it reduces IL23+ cells to a similar level as the DOI-treated group46. Treatment of KO moLCs with B022 also reduced IL23+ cells in a statistically significant manner, and there were no statistically significant differences between WT moLCs and KO moLCs treated with B022 (Fig.\u00a06G). We then differentiated human monocyte-derived Langerhans cells from CD34+ hematopoietic stem cells by using human recombinant GM-CSF, TGF-\u03b2, and IL-34. Human monocyte-derived Langerhans cells are defined by Live CD45\u2009+\u2009HLA-DR+ CD11c\u2009+\u2009CD1a\u2009+\u2009CD14+ cells (Fig.\u00a0S10). After intracellular cytokine staining, we found that the IL23+ cell percentage was reduced when they were treated with serotonin (10\u00a0\u03bcM) or DOI (10\u00a0\u03bcg/mL) (Fig.\u00a06H). In all, we found that HTR2A suppresses IL-23 secretion involving the non-canonical NF\u03baB pathway.\n\nWe then wondered what the putative agonist of HTR2A is in physiological conditions. Since serotonin is the known agonist of HTR2A, we wondered if there were any changes to their levels in psoriasis47. We performed immunohistochemistry (IHC) staining of serotonin on skin samples of healthy volunteers, non-lesioned skin, and lesioned skin of psoriatic patients. We found serotonin levels to be high in healthy volunteers and non-lesioned skin. Serotonin levels were low in lesioned skin (Fig.\u00a07A). We quantified the serotonin levels by determining its intensity in terms of H-score and found serotonin levels to be significantly higher in the skin of healthy volunteers than lesioned skin of psoriatic patients, irrespective of their psoriasis area and severity index (PASI) score. The difference in serotonin levels of psoriatic skin between low PASI (\u2009<\u200930) and high PASI (\u2009\u2265\u200930) was not significant (Fig.\u00a07B). Serotonin levels were higher in non-lesioned skin than in lesioned skin, even though both are from the same patient (Fig.\u00a07C).\n\nA Immunohistochemistry staining of serotonin on the skin of healthy volunteers, non-lesioned skin of psoriatic patients, and lesioned skin of psoriatic patients. B Quantification of serotonin intensity in terms of H-score using DensitoQuant (3DHISTECH LTD) between the skin of healthy volunteers (n\u2009=\u200913), lesioned skin of psoriatic patients with low PASI score (\u2009<\u200930) (n\u2009=\u200915) and high PASI score (\u2009\u2265\u200930) (n\u2009=\u200912). C Quantification of serotonin intensity in terms of H-score between lesioned and non-lesioned skin of psoriatic patients (n\u2009=\u200927). D Schematic showing strategy for treating wild type or HTR2A deficient mice with serotonin with their respective control. E Line chart showing changes in ear thickness of mice of the respective groups (n\u2009=\u20097). F Pictures showing the ears of mice in different groups. G H&E-stained slides of respective groups with quantification of epidermal thickness. H Flow cytometry of IL17\u2009+\u2009IL22\u2009+\u2009V\u03b34\u2009T cells and neutrophils of respective groups with quantifications. I mRNA levels of pro-inflammatory cytokines of the respective groups. Data are a summary of two independent experiments (D\u2013I and L\u2013R). The first day of significant differences was marked # comparing WT versus WT\u2009+\u20095HT and WT\u2009+\u20095HT versus KO\u2009+\u20095HT (E). p values were determined by two-way ANOVA (E), one-way ANOVA followed by Tukey\u2019s post-hoc test (B and G\u2013I) or paired Student\u2019s T-test (C). Mean \u00b1 or SEM (B, C, E, G\u2013I). Source data are provided as a Source Data file. Created in BioRender. Tan, Y. (2025) https://BioRender.com/01gxdwy.\n\nTo further validate serotonin\u2019s role as the putative agonist of HTR2A in psoriasis, we injected serotonin intraperitoneally into WT or HTR2A-deficient mice (Fig.\u00a07D). In terms of changes in ear thickness, visual inspection, and histological assessment, we found WT mice injected with serotonin to have lower inflammatory levels than WT control mice. On the other hand, HTR2A-deficient mice injected with serotonin experienced exacerbated inflammation (Fig.\u00a07E\u2013G). A similar trend was observed during the quantification of epidermal thickness (Fig.\u00a07G). IL-17- and IL-22-producing V\u03b34\u2009T cells were highest in HTR2A-deficient mice injected with serotonin, followed by WT control mice and WT mice injected with serotonin. The differences were significant (Fig.\u00a07H). However, when we looked at neutrophil levels, mRNA levels of Il17a and Il23a, we found a similar trend, but the differences were not significant (Fig.\u00a07H, I). The data here support serotonin as the putative agonist of HTR2A, attenuating psoriatic inflammation.\n\nWe then wondered where this serotonin originates from. Platelet is known to store peripheral serotonin; hence, we determined plasma and platelet serotonin levels of psoriatic patients and healthy controls48. We found no difference in plasma serotonin levels but found a significant difference in platelet serotonin levels of psoriatic patients and healthy controls (Fig.\u00a0S11A, S11B). We then used an established method to deplete platelet serotonin levels by pre-treating the mice for 14 days with fluoxetine (Fig.\u00a0S11C)15. Mice treated with fluoxetine were found to have very low platelet serotonin levels (Fig.\u00a0S11D). In terms of changes in ear thickness, visual inspection, histological assessment, and quantification of epidermal thickness, we found mice depleted of platelet serotonin have exacerbated inflammation (Fig.\u00a0S11E\u2013G). IL-17- and IL-22-producing V\u03b34\u2009T cells, neutrophils, mRNA of Il17a and Il23a were higher in platelet serotonin-depleted mice but only changes in neutrophil levels and changes in mRNA levels of Il17a achieved significance (Fig.\u00a0S11H\u2013I). In all, serotonin is the putative agonist of HTR2A attenuating psoriatic inflammation and our preliminary data suggests it originates from platelets.\n\nTo ensure our findings in mice are recapitulated in humans, we reanalyzed single cell data from publicly available datasets. We combined two different psoriatic datasets with healthy controls (GSE151177 and GSE162183). We identified 27 cell clusters in the combined dataset (Fig.\u00a08A). Importantly, we identified embryonic Langerhans cells, monocyte-derived Langerhans cells, and dendritic cell type 3 (DC3). The embryonic Langerhans cell cluster has high Id2 and Runx3 (lineage-defining transcription factors for Langerhans cells) expression but low Irf8 (lineage-defining transcription factor for cells of monocyte lineage) expression39. MoLCs have high Irf8 expression and ITGAM (CD11b). Dendritic cell type 3 was identified by its expression of CD163 but not CD11b (Fig.\u00a08B)49. We then looked at the expression levels of HTR2A between psoriatic moLCs and control moLCs and found HTR2A expression on psoriatic moLCs to be significantly lower (Fig.\u00a08C). Among the cytokines, Il23a and Il1b are highly expressed in moLCs (Fig.\u00a08D). Expression of NF\u03baB-related subunits was increased in psoriatic moLCs (Fig.\u00a08E). We then performed immunofluorescence staining of psoriatic lesioned and non-lesioned skin samples for CD1a and HTR2A. CD1a represents Langerhans cells, and we found HTR2A expression to be lower in Langerhans cells of psoriatic lesioned skin as compared to non-lesioned skin (Fig.\u00a08F). Our findings in mice closely resemble clinical conditions, as HTR2A deficiency in moLCs leads to exacerbated inflammation in mice mirrors low HTR2A expression in psoriatic moLCs. IL-23 was found to be a key mediator, and NF\u03baB is enriched in psoriatic moLCs, leading to exacerbated inflammation similar to HTR2A-deficient moLCs in mice.\n\nA UMAP of reanalyzed single cell RNA-sequencing data from GSE151177 and GSE162183 showing 27 different clusters. B Violin map showing lineage defining transcription factors, receptors, and markers of embryonic Langerhans cells (eLC), monocyte-derived Langerhans cells (moLC), and dendritic cell type 3 (DC3). C Box plot comparing HTR2A expression levels of control and psoriatic monocyte-derived Langerhans cells. D Dot plot showing expression of various cytokines by eLC, moLC, and DC3. E Dot plot showing expression of various NF\u03baB pathway-related genes by psoriatic moLCs and control moLCs. F Immunofluorescence staining of HTR2A on Langerhans cells (marked by CD1a) in lesioned and non-lesioned psoriatic skin with quantification (n\u2009=\u200927). p values were determined by paired Student\u2019s T-test (C, F). Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63971-5/MediaObjects/41467_2025_63971_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In our retrospective cohort study, we investigated how anti-psychotics, which antagonize dopamine and serotonin receptors, affect psoriatic outcomes50. Patients on anti-psychotics have worsened psoriatic outcomes, and these anti-psychotics have a very strong binding affinity for HTR2A16. This indicates that HTR2A antagonism by anti-psychotics worsens psoriatic outcomes. While this association is far from ideal, we confirmed the pivotal role played by HTR2A in regulating psoriatic inflammation by treating human psoriatic skin with an HTR2A-specific agonist ex vivo. Besides, our in vivo mouse psoriasiform model further proved HTR2A\u2019s pivotal role in suppressing psoriatic inflammation.\n\nWe also found that chronic SSRI use led to worsening psoriatic outcomes. In congruence with our findings, there are reports showing that patients who had taken fluoxetine (a commonly used SSRI) for extended periods experienced worsened psoriasis11,13. In contrast to our findings, a retrospective study done in Sweden demonstrated an improved outcome with a decreased need for systemic psoriatic treatment following two SSRI dispensations within six months9. There is a lack of information on the length of treatment, dosage, and adherence reported in the Swedish study, making comparisons between their study and ours impractical. Nevertheless, the conflicting findings could be due to the paradoxical effect of SSRIs on peripheral serotonin levels. Since SSRI targets serotonin transporter (SERT), it could lead to a temporary increase of serotonin in between synaptic clefts, but chronic use will lead to depletion of platelet serotonin stores51,52,53,54. The major source of serotonin in the periphery is in platelets55. While platelet serotonin depletion following chronic SSRI use is established in mice, much remains unknown about its impact on humans.\n\nWe demonstrated that HTR2A deficiency exacerbated psoriasiform inflammation. This is congruent with a previous study that showed hypermethylation of HTR2A, which decreases HTR2A expression, resulting in exacerbated inflammation in rheumatoid arthritis (a disease that involves type 17 immunity)56,57. We noticed increased IL-17- and IL-22-producing V\u03b34\u2009T cells in our mouse psoriasiform model, as expected, since V\u03b34 T cells are the dominant type 17 immune responders in acute models, particularly the acute psoriatic model35,58. The human equivalent of mouse V\u03b34 is V\u03b39\u2009V\u03b42 and they are the dominant \u03b3\u03b4 T cells in adults59. This particular subset of cells has been reported to secrete IL17A; their numbers were increased in psoriatic patients as compared to healthy controls and atopic dermatitis patients, and following successful treatment of psoriasis, their numbers decreased accordingly60. During the initial stages of psoriasis, V\u03b39\u2009V\u03b42 are the primary source of IL17A; however, in the later stages, Th17 (of \u03b1\u03b2 subtype) and \u03b3\u03b4 T17 are both significant sources of IL17A59. Changes in ear thickness and epidermal thickness, which are associated with IL-22, were also increased, reflecting acanthosis commonly seen in psoriasis61. Increases in mRNA levels of Il17a and Il23a were observed, suggesting the IL-23/IL-17 axis plays a key role in exacerbating inflammation. We also observed increased neutrophil levels as predicted, which may be due to neutrophil recruitment at lesioned sites under psoriatic conditions36. However, we did not observe any increase in IL-17- and IL-22-producing CD4 T cells, which may be due to two reasons: i) conventional CD4 T cells in the periphery will only express IL23R upon activation; and ii) IMQ is an acute model with no antigen limiting the expansion of antigen-specific Th17 cells62,63.\n\nWe delineated the cell type exacerbating inflammation in HTR2A-deficient mice, as it is crucial in unravelling the complex interplay between HTR2A and type 17 inflammation. HTR2A was found to have minimal impact on B and T cells, as seen in our conditional knockout mice with HTR2A-deficient B and T cells. Nevertheless, we are aware that our seven-day model will not be able to reveal and reflect the true extent of HTR2A\u2019s impact on these cells. Among the innate cells, it was established previously that monocyte-derived inflammatory Langerhans cells play a pivotal role in modulating psoriasiform inflammation28. In agreement with previous findings, we demonstrated that HTR2A deficiency in monocyte-derived Langerhans cells (moLC) played a key role in exacerbating inflammation. We validated this using multiple models, including Langerhans cells depletion, adoptive transfer, and two conditional knockout mouse models. Our chimeric bone marrow transplant model further supports this, as HTR2A deficiency in hematopoietic cells, which includes moLCs but not embryonic Langerhans cells, exacerbates inflammation28.\n\nHTR2A affects the function of moLCs and embryonic Langerhans cells (eLC) to varying extents. Under inflammatory conditions, monocytes are recruited in waves to the inflammatory site and they serve as end-type killer cells by secreting chemokines and cytokines, exacerbating the inflammation64. This means moLCs inherently secrete more IL-23 than eLCs. HTR2A serves as a brake to inhibit cytokine secretion rather than inducing increased cytokine secretion in moLCs. This may explain why HTR2A deficiency appears to affect moLCs more than eLCs.\n\nWe identified moLCs by their expression of MHC II, CD207, and CD64 markers. We found it hard to fit our moLCs neatly into any of the P1 to P5 dermal macrophages identified by Tamoutounour et al.65 Rather, our cells closely resemble the mouse monocyte-derived macrophage identified by McGovern et al., reflecting heterogeneity in this population29. Importantly, moLCs identified in our study have high expression of Irf8, a lineage-defining transcription factor for monocytes, and mutations in this gene will lead to genetic deficiency of monocytes and dendritic cells39,66. The moLCs also expressed Runx3 and Id2, lineage-defining transcription factors for Langerhans cells, as Langerhans cell development requires TGF-\u03b2, and Runx3 and Id2 are critical mediators downstream of TGF-\u03b239,67,68. MoLCs and eLCs identified in the single-cell RNA-sequencing data reflect this as they have high expression of Runx3 and Id2. The variation in Irf8 expression levels helps us differentiate them into moLCs and eLCs.\n\nIL-23 is a key cytokine in the pathogenesis of psoriasis, and drugs targeting IL-23, like guselkumab and risankizumab, are successful in treating psoriatic symptoms69. However, to date, no studies have reported any association between HTR2A and IL-23 levels. We found IL-23 secretion to be increased drastically in HTR2A-deficient moLCs, and this is observed in both ELISA and intracellular cytokine staining of IL-23. Stimulation of HTR2A with DOI, a HTR2A-specific agonist, led to a significant decrease in IL-23 secretion by moLCs, further supporting the notion that HTR2A expression on moLCs modulates psoriatic inflammation via IL-23. Contradicting our study, Wohn et al. found that IL-23 was exclusively produced by Langerin-negative DCs in vivo post-IMQ-painting70. We believe this could be explained by differences in methodology, as we did not inject Brefeldin A into the mice directly; instead, we isolated the cells, treated them with PMA, Ionomycin, and GolgiStop (containing Brefeldin A) and found Langerhans cells, particularly moLCs, secrete elevated levels of IL-23.\n\nFurther downstream, IL-23 has been reported to induce innate IL-17 production, especially among \u03b3\u03b4 T cells, and IL-23 has been known to play a cardinal role in mediating Imiquimod-induced psoriasiform inflammation71,72. While IL-1\u03b2 has also been proposed to play a role in inducing innate IL-17 production, our qPCR of IL-1\u03b2 did not reveal a drastic increase in IL-1\u03b2 levels with HTR2A deficiency71. Besides, it has been reported that the stimulation of peritoneal exudate cells with IL-23 alone but not IL-1\u03b2 is sufficient to induce IL-17 production by \u03b3\u03b4 T cells73. IL-23 is known to play a central role in T cell differentiation instead of T cell survival and expansion74. Using our V\u03b34 T cell coculture assay, we demonstrated increased V\u03b34 T cell differentiation into IL-17- and IL-22-producing cells after coculture with moLCs. This highlights that IL-23 is the key cytokine mediating exacerbated inflammation in HTR2A-deficient moLCs.\n\nIL-23 transcription requires NF\u03baB, and HTR2A has been suggested to affect NF\u03baB levels20,75,76. The NF\u03baB pathway is found to be enriched in HTR2A-deficient Langerhans cells. It was previously reported that both canonical NF\u03baB and non-canonical NF\u03baB pathways are involved in IL-23 secretion. The canonical NF\u03baB pathway is involved in early-phase induction, and the late-phase induction, peaking at 12\u2009h, requires the non-canonical NF\u03baB p100 subunit43. Our findings are congruent with previous findings since the immunoblot of our cells post-24-hour treatment showed enrichment in the non-canonical NF\u03baB pathway. Notably, we also found enriched NF\u03baB pathways, both canonical and non-canonical, in psoriatic moLCs as compared to control moLCs in our reanalysis of single-cell RNA-sequencing data.\n\nWe found serotonin levels to be raised in healthy skin and non-lesioned skin samples of psoriatic patients as compared to lesioned skin via immunohistochemical staining. In contrast, there are two reports via immunohistochemical staining indicating serotonin levels were raised in psoriatic lesioned skin77,78. The discrepancy could be due to the difference in controls used, as in their studies, they used the skin of healthy individuals as controls, whereas we used both non-lesioned paired samples and healthy skin as controls. There is also a lack of information on the method for collecting and preserving samples, making comparisons difficult. Our findings are further supported by our in vivo mouse model, where serotonin injected intraperitoneally significantly inhibited type 17 inflammation. Congruent with our findings, a study reported that serotonin modulates M2 macrophage polarization, and another reported that serotonin decreases Th1 and Th17 cytokines in multiple sclerosis patients79,80.\n\nHowever, the problem with peripheral serotonin, as pointed out by Gershon, is that \u201cit is able to do too much\u201d81. Serotonin at different concentrations can have the opposite effect even on the same cell type. A case in point, at high doses, serotonin suppressed interferon-\u03b3 (IFN-\u03b3)-induced phagocytosis, but it had stimulatory effects at physiological concentrations82. We noted a previous study demonstrating serotonin at various concentrations inhibited lipopolysaccharide-induced TNF secretion but serotonin at concentrations of 10\u03bcM and above induced IL6, IL8, IL12p40, and IL-1\u03b2 secretion83,84. Besides taking into account the dose of serotonin, we have to pay attention to the cells\u2019 expression of Mono Amine Oxidase (MAO), particularly in in vitro conditions, as low expression of MAO by a specific cell type will lead to low serotonin metabolism and accumulation of serotonin85. Besides MAO, we need to pay attention to Serotonin Transporter (SERT). Cells expressing SERT will uptake serotonin in the medium, leading to lower concentrations of serotonin in the medium86. All these could lead to results that are different from in vivo conditions.\n\nSerotonin in other skin conditions like atopic dermatitis (AD) has been reported to worsen itch, and serotonin 2\u2009A receptor antagonist has been reported to alleviate itch87. However, upon further investigation by the same team, they found that serotonin-evoked itch is dependent on transient receptor potential vanilloid type-4 (TRPV4)88. Another mediator of itch is serotonin receptor 7 (HTR7); however, the itch triggered requires both HTR7 and transient potential receptor cation channel 1 (TRPA1)89. Serotonin has also been reported to induce hyperproliferation of keratinocytes. However, during diseased conditions, hyperproliferation of keratinocytes will involve multiple factors like keratinocyte growth factor (KGF) and epidermal growth factor (EGF)90. Given the many confounding factors involving serotonin, we are mindful not to generalize our findings, and we must emphasize that our findings regarding serotonin are highly context-dependent. Our data suggest that serotonin at a very specific concentration is able to suppress Il23a expression in monocyte-derived Langerhans cells, but it may not be applicable in other skin conditions.\n\nWe further found platelet serotonin levels but not serum serotonin levels to be decreased in psoriatic patients. A study in Indonesia, however, found psoriatic patients had significantly lower serum serotonin levels as compared to healthy individuals91. Their sample size was larger than ours, giving their study more power as compared to ours. Using an in vivo model where we depleted platelet serotonin with chronic fluoxetine pre-treatment, we found exacerbated inflammation in platelet serotonin-depleted mice, even though more experiments are needed to validate this finding15. It has been previously reported that platelet serotonin accumulated at inflamed sites will cause increased vascular permeability and increased pro-inflammatory immune infiltrate92. Clinically, SSRI use in psoriatic patients, which has an impact on platelet serotonin levels, has resulted in conflicting outcomes8,9,10,11,12,13. Our efforts to clarify the role of platelet serotonin are hampered by the lack of tools to directly measure serotonin levels at inflamed sites, particularly during the initial release of serotonin by platelets. As pointed out above, serotonin at different concentrations could have the exact opposite effect on the functions of immune cells, and the exact concentration of serotonin is critical for the interpretation of results. Much remains unknown about the role of platelet serotonin in psoriasis; we need to be cautious on this matter and avoid making hasty generalizations.\n\nOur study provides evidence that drugs antagonizing HTR2A and HTR2A deficiency led to exacerbated psoriatic inflammation. On the other hand, the HTR2A-specific agonist of DOI attenuates inflammation. IL-23 secretion by HTR2A-deficient moLCs was increased, as HTR2A, functioning as brakes of the immune system via its interference with the NF\u03baB pathway, was absent in HTR2A-deficient moLCs. Serotonin is the putative agonist of HTR2A, modulating inflammation and critically, our findings were recapitulated in clinical samples. Overall, our study provides insights into the role of HTR2A in regulating psoriatic inflammation, with the cells, cytokines, and transcription factor involved identified. Thus, our findings could lead to the development of novel therapeutic interventions, providing new avenues for treating psoriasis.\n\nOur study has been limited by our Imiquimod-induced psoriasiform mouse model as it is an acute inflammatory model. Application of Imiquimod for more than 7 days will lead to resolution of psoriasiform inflammation even without external intervention93. Due to the short-term nature of our model, we cannot recapitulate the chronic component of psoriatic inflammation like the involvement of CD4 \u03b1\u03b2 T cells.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The study protocol was approved by the institutional review board for human studies at university hospitals and complied with the principles outlined in the Declaration of Helsinki. In this paper, we have conducted a retrospective cohort study from 2015/01/01-2021/12/31 by using data from the National Health Insurance Research Database (NHIRD). The NHIRD contains healthcare costs and detailed claim records of all beneficiaries participating in the Taiwan National Health Insurance (NHI) program. We have obtained approval from the Institutional Review Board for Biomedical Sciences Research, Academia Sinica (AS-IRB01-23014). The collection of skin samples was approved by Academia Sinica\u2019s Institutional Review Board for Biomedical Science Research with IRB number (AS-IRB-BM-20062). Informed consent was provided by the participants, a blank copy of the consent form in Traditional Chinese is included. We followed the regulations of the Ministry of Health and Welfare, Taiwan. The patient\u2019s sex was self-reported. Sex is not considered in the study design as psoriasis affects both sexes.\n\nA retrospective cohort study was conducted to investigate the relationship between SSRI or anti-psychotic use and change in treatment received for psoriasis. This cohort study used population-based data from Taiwan\u2019s National Health Insurance Research Database (NHIRD). The NHIRD contains healthcare costs and detailed claim records of all beneficiaries participating in the Taiwan National Health Insurance (NHI) program. We have recruited patients from 2015/01/01 to 2021/12/31. This study only used secondary data from this database for academic purposes. We have obtained approval from the Institutional Review Board for Biomedical Sciences Research, Academia Sinica (AS-IRB01-23014), and followed the regulations of the Ministry of Health and Welfare, Taiwan.\n\nSSRIs included in this study were citalopram, escitalopram, fluoxetine, paroxetine, sertraline, and fluvoxamine. Anti-psychotics included in this study were aripiprazole, chlorpromazine, prochlorperazine, quetiapine, risperidone, sulpiride, amisulpride, clozapine, haloperidol, olanzapine, and anti-psychotics categorized as Others (brexipriprazole, fluphenazine, loxapine, lurasidone, perphenazine, pimozide, thioridazine, and trifluoperazine). This study used Anatomical Therapeutic Chemical codes to identify SSRI or anti-psychotic exposure.\n\nPsoriatic cases were identified using the codes 696.1 and 696.8 in the International Classification of Diseases 9 (ICD-9). The primary outcome is a change in the categories of psoriatic treatment received within 180 days from the index date (the date starting SSRI or anti-psychotic). Psoriatic treatments were divided into four categories as defined by the Taiwan Dermatological Association, namely topical for the mildest symptoms, followed by systemic agents, combination therapy, and biologics for patients with the most severe symptoms. A worsened psoriatic outcome is defined as a change of treatment from treatment of mild symptoms to treatment of severe symptoms, for example, from using systemic agents to using biologics. Improved psoriatic outcome is defined as a change of treatment from treatment for severe symptoms to treatment of mild symptoms, for example, from biologics to using topical treatment.\n\nData from 23,415 psoriatic persons were included in this study, and they were checked for anti-psychotic or SSRI exposure. 9,570 people were controls, 11,315 people were on anti-psychotics, and 2,530 persons were on SSRIs. We performed multinomial logistic regression to adjust for age and gender. We then performed a Cox proportional hazard model to calculate the hazard ratios and 95% confidence intervals for worsened psoriatic outcomes and improved psoriatic outcomes, between anti-psychotic users versus controls and SSRI users versus controls.\n\nOf the 23,415 individuals recruited, 14,022 are males and 9393 are females. The patient\u2019s sex was self-reported. Sex is not considered in the study design as psoriasis affects both sexes.\n\nHuman CD34+ hematopoietic stem cells were purchased (STEMCELL Technologies,70002.1). The cells were then resuspended in DMEM with 1000\u2009mg/L D-Glucose (STEMCELL Technologies, #36150) containing 20\u2009ng/ml of recombinant human GM-CSF (PeproTech, 30003100UG), 5\u2009ng/ml of TGF-\u03b2 (BioLegend, 781802), and 8\u2009ng/ml of IL-34 (BioLegend, 577902), plated on a 6 well plate, and incubated for 8 days at 37\u2009\u00b0C. The medium was changed every 48\u2009h. Monocyte-derived Langerhans cells were isolated by a FACSAria IIIu Sorter with L/D eFluor 506, \u03b1-CD45, \u03b1-HLA-DR, \u03b1-CD11c, \u03b1-CD1a, and \u03b1-CD14 staining. LCs were stimulated with 10 \u03bcg/mL Imiquimod (Enzo, ALX-420-039-M100) and 10\u2009\u03bcg/ml of DOI (Merck, D101) or 10\u2009\u03bcM of Serotonin (Sigma-Aldrich, H9523) were added for 24\u2009h to investigate IL-23 expression.\n\nHtr2a floxed (Htr2aflox/flox) mice in C57BL/6\u2009J background were generated by the CRISPR/Cas9 technology. Selection of the sgRNA sequences followed the online resources, the sgRNA Designer: CRISPRko and Cas-OFFinder94,95. The 5\u2019 loxP sequence was inserted in the upstream of exon 2, and 3\u2019 loxP in the 3\u2019UTR after stop codon 134\u2009bp. The two sgRNA target sequences with PAM sites (NGG) were 5\u2019- GTTTGATTGTGAGACATCGG -3\u2019, and 5\u2019- GTCCGGACAGCATTTGAACT -3\u2019, for inserting the 5\u2019-loxP and the 3\u2019-loxP DNA fragments, respectively. SgRNA and Cas9 protein for electroporation were purchased from Synthego Corporation. Electroporation was performed on fertilized eggs from C57BL/6\u2009J mice. To minimize off-target effects in CRISPR mice, Htr2aflox/flox mice were first backcrossed to C57BL/6JNarl mice for 2 generations before breeding into cell-specific Htr2a knockout mice. Genotyping of founder mice was performed by PCR. PCR condition for genotyping of offspring was the initial denaturation at 95\u2009\u00b0C 5\u2009min, followed by 45 cycles of 95\u2009\u00b0C for 30\u2009s, 58\u2009\u00b0C for 30\u2009s and 72\u2009\u00b0C for 30\u2009s, and the final extension at 72\u2009\u00b0C for 7\u2009min. All techniques for the production of the Htr2aflox/flox mice were provided by the \u201cTransgenic Mouse Models Core Facility of the National Core Facility for Biopharmaceuticals, National Science and Technology Council, Taiwan\u201d and the \u201cGene Knockout Mouse Core Laboratory of National Taiwan University Center of Genomic and Precision Medicine\u201d.\n\nOthers mice used: C57BL/6\u2009JNarl, 129S/Sv-Tg(Prm-cre)58Og/J (Jax 003328), B6.SJL-Ptprca Pepcb/BoyJ (Jax 002014), C57BL/6-Tg(Rag1-RFP,-cre/ERT2)33Narl/Narl (RMRC 13191), STOCK Tg(CD207-cre/ERT2)1Dhka/J (Jax 028287), B6.129S2-Cd207tm3.1(HBEGF/EGFP)Mal/J (Jax 016940), C57BL/6J-Ms4a3em2(cre)Fgnx/J (Jax 036382).\n\nFor the generation of whole-body knockout mice, Prm-cre mice were crossed with Htr2aflox/flox mice. The double gene offspring were then crossed with wild-type mice, producing heterozygous offspring. The heterozygous mice were then inbred with a 25 per cent probability of producing homozygous HTR2A-deficient mice. For the generation of conditional knockout mice, Rag1-RFP-cre mice were crossed with Htr2aflox/flox mice. The offspring were then backcrossed with Htr2aflox/flox mice. The offspring that are Htr2aflox/flox mice-Rag1-RFP-cre (conditional knockout) will be bred with their littermate that are Htr2aflox/flox mice, producing only Htr2aflox/flox mice-Rag1-RFP-cre (HTR2ARag1) or Htr2aflox/flox mice, which serve as a littermate control. A similar breeding strategy was adopted for CD207-cre and Ms4a3em2(cre)Fgnx mice.\n\nAll experiments involving animals were conducted following experimental protocols approved by the Academia Sinica Institutional Animal Care and Utilization Committee, Academia Sinica, Taipei, Taiwan (20-11-1539). Male and female mice used for this study were aged 8-12 weeks. Mice were sacrificed by exposing them to high concentrations of carbon dioxide in a chamber, the mice become unconscious when exposed to a CO2 concentration of 70%. Further exposure for 5\u2009min will asphyxiate thereby euthanizing the animals. Sex is not considered in the study design as psoriasis affects both sexes. Mice were housed in individually ventilated cages under specific pathogen-free (SPF) conditions with a 12\u2009h light/dark cycle (lights on at 07:00), at a controlled ambient temperature of 22\u2009\u00b1\u20092\u2009\u00b0C and relative humidity of 50\u2009\u00b1\u200910%.\n\nSkin-punched biopsies were obtained from the lesioned sites of patients, and healthy foreskin was collected from male patients undergoing circumcision. The skins were halved, with half untreated and the other half treated with 100\u2009nM of DOI for 16\u2009h. Total RNA was isolated with Direct-zol RNA Miniprep Kit (Zymo Research) according to the manufacturer\u2019s instructions. NGS was performed by NGS High Throughput Genomics Core at Biodiversity Research Centre, Academia Sinica, Taipei, Taiwan.\n\nWe used the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) to search for HTR2A gene expression in skin tissue between lesioned sites versus non-lesioned sites, as well as psoriasis patients versus healthy controls. The GSE13355 includes Affymetrix HU133 Plus 2.0 microarrays for lesion skin tissues and non-lesion skin tissues from 58 psoriasis patients96. GSE34248 includes Affymetrix HU133 Plus 2.0 microarrays for lesion skin tissues and non-lesion skin tissues from 13 psoriasis patients97. GSE109248 includes Illumina HumanHT-12 V4.0 for skin tissues from 17 psoriasis patients and 12 healthy controls98. GSE41664 includes Affymetrix Human Genome U133 Plus 2.0 Array of lesioned and non-lesioned skin tissues of 38 psoriasis patients97. The differences in gene expression levels between the two groups were calculated by a two-tailed paired Student\u2019s T-test.\n\nFor mice to develop psoriasis, we applied 15\u2009mg of 5% Imiquimod (Aldara cream, iNova Pharmaceuticals, Australia) for 7 days on the ears of mice. The ears were harvested the day after.\n\nCells were isolated from the ears by incubating overnight at 37\u2009oC with Dispase II (Thermo Scientific). The next day, the ear is then cut by scissors into small pieces and then immersed in RPMI (Gibco) containing Collagenase IV (Gibco) and DNase I (Bio Basic) for 90\u2009min. The sample is then filtered using a 70\u2009\u03bcm cell strainer. The isolated single cells were then stained directly with fluorescent antibodies or treated with PMA (Merck), Ionomycin (BioGems), and GolgiStop (BD) if intracellular cytokine staining was required. Cells were incubated with LIVE/DEAD Fixable Blue Dead Cell Stain Kit (Thermo Fisher Scientific) for the exclusion of dead cells. The Fc-blocking antibody was then used to prevent non-specific binding, followed by incubation with specific antibodies against surface antigens. For intracellular cytokine staining, cells were stimulated with PMA (Merck) and Ionomycin (BioGems) in the presence of GolgiStop (BD) at 37\u2009\u00b0C for four hours. The cells were fixed and permeabilized with Cytofix/Cytoperm buffer (BD) according to the manufacturer\u2019s instructions. For all samples, acquisition was performed on LSRII (BD).\n\nTotal RNA from tissue was immersed in TRIzol (Invitrogen, 15596026) overnight, homogenized using steel beads together with Bullet Blender Tissue Homogenizer (Next Advance), and total RNA was extracted using RNA MiniPrep Kit (Zymo Research, R2050) according to manufacturer\u2019s instructions. The quantity of the total RNA was measured by NanoDrop Spectrophotometer (Thermo Scientific). For qPCR analysis, equivalent amounts of RNA were reverse-transcribed by Maxima H Minus First Strand cDNA Synthesis Kit (Thermo Scientific, K1652). cDNAs were mixed with indicated primers and PowerUp SYBR Green Master Mix (Applied Biosystems, A25742), and RT-qPCR was performed on Quant Studio 5 Real-time PCR System (Applied Biosystems). cDNAs were normalized to equal amounts using primers against Gapdh. The primer sequences can be found in the supplementary table (Table\u00a0S2).\n\nCD45.1 mice (Jax 002014) were fed with water added with Trimethoprim and Sulfamethoxazole (Merck, SML3191, with a final concentration 1.92\u2009mg/ml) for two weeks before they were lethally irradiated (1200 rads) in split doses, with three hours of rest in between, one day before the experiment. Bone marrow from HTR2A-deficient mice was harvested on the day of the experiment. 5\u2009mL of ACK Lysing Buffer (Gibco, A1049201) was added to the cells for five minutes to lyse red blood cells. The cells were then washed with Phosphate Buffered Saline and quantified. A total of 5\u2009x\u2009105 bone marrow cells were intravenously injected into the lethally irradiated mice. The mice were continuously fed antibiotic-laced water for another 14 days before experiments were carried out on them 12 weeks after transplantation. We also transplanted the bone marrow of CD45.1 mice to HTR2A-deficient mice and the bone marrow of HTR2A-deficient mice to HTR2A-deficient mice.\n\nBone marrow cells were harvested from the femur and tibia of wild-type or HTR2A-deficient mice. The cells were lysed with ACK Lysing Buffer (Gibco, A1049201). The cells were then resuspended in RPMI (Gibco) containing 20\u2009ng/ml of recombinant mouse GM-CSF (PeproTech, 315-03), 5\u2009ng/ml of TGF-\u03b2 (BioLegend, 763104), and 8\u2009ng/ml of IL-34 (BioLegend, 577604), plated on a 6 well plate, and incubated for 8 days at 37\u2009\u00b0C. The medium was changed every 48\u2009h. Monocyte-derived Langerhans cells were isolated by a FACSAria IIIu Sorter with \u03b1-CD207, \u03b1-CD64, and \u03b1-MHC II staining. LCs were stimulated with 10 \u03bcg/mL Imiquimod (Enzo, ALX-420-039-M100) and 10\u2009\u03bcg/ml of DOI (Merck, D101) or 0.1\u2009\u03bcM of EVP 4593 (MCE, HY-13812) were added for 24\u2009h to investigate IL-23 expression and its ability to induce differentiation of V\u03b34\u2009T cells in a coculture assay.\n\nIL-23p19 expression was measured using Mouse IL-23p19 ELISA Kit (Elabscience, E-EL-M0731) according to the manufacturer\u2019s instructions. Mouse and human serotonin levels were measured using a Serotonin ELISA Kit (Enzo, ADI-900-175) according to the manufacturer\u2019s protocol. The absorbance at 450\u2009nm (Mouse IL-23 ELISA kit) or 405\u2009nm (Serotonin ELISA Kit) was measured with an ELISA reader (Infinite M1000 PRO, Tecan).\n\nSpleens of wild-type mice were surgically removed and mechanically disrupted by meshing it against a 70\u2009\u03bcm cell strainer (Jet Biofil, CSS013070). ACK Lysing Buffer (Gibco, A1049201) was added to lyse red blood cells. The cells were then isolated by a FACSAria IIIu Sorter with \u03b1-CD45, \u03b1-CD3, \u03b1-\u03b3\u03b4, and \u03b1-V\u03b34 staining. Monocyte-derived Langerhans cells post-stimulation with DOI or EVP 4593 were incubated with these V\u03b34\u2009T cells for 72\u2009h in an \u03b1-CD3-coated plate. \u03b1-CD28 was then added to further stimulate V\u03b34\u2009T cells. Post-72\u2009h, the cells were then stimulated with PMA and Ionomycin, and incubated with GolgiStop for 4\u2009h. The cells were then stained for IL-17A and IL-22. The acquisition was performed on LSRII (BD).\n\nThe experiments generating datasets of GSE222197 and GSE274941 were done in parallel. MoLCs (defined as CD45\u2009+\u2009MHCII+ CD11c\u2009+\u2009CD207\u2009+\u2009CD64\u2009+\u2009) were pooled from the ears of five mice treated with IMQ for two days, constituting a single biological sample. Cells were sorted and total RNA was isolated with a Direct-zol RNA Microprep Kit (Zymo Research) according to the manufacturer\u2019s instructions. NGS was performed by NGS High Throughput Genomics Core at Biodiversity Research Centre, Academia Sinica, Taipei, Taiwan.\n\nTotal cell lysates were prepared using RIPA (20\u2009mM Tris HCl, pH8.0, 150\u2009mM NaCl, 1\u2009mM EDTA, 1\u2009mM EGTA, 1% TritonX-100, 0.5% deoxycholate, 0.1% SDS) supplemented with protease inhibitors (Halt Protease Inhibitor, Thermo Scientific, 78430), or used directly with 1X protein sample loading buffer (50\u2009mM Tris-HCl, pH 6.8, 10% glycerol, 2% SDS, 0.1\u2009M DTT, 0.04% Orange G). The protein concentrations of clarified supernatants were measured by a BCA Protein Assay Kit (Pierce, 23227). Proteins in lysates were separated by Bolt 4\u201312% Bis-Tris Plus gels (Invitrogen) and transferred to a PVDF membrane (Thermo Scientific, 88520) by a Mini Trans-Blot cell system (BioRad). Transferred membranes were blocked for 1\u2009h in 5% Bovine Serum Albumin (Merck, A7030) diluted in TBS (20\u2009mM Tris-HCl, pH 7.4, 150\u2009mM NaCl) and then incubated with primary antibodies in TBS with 0.1% Tween-20 (TBST) overnight at 4\u2009\u00b0C. Primary antibodies used were as the key resources table. After washing with TBST, membranes were incubated with secondary antibodies in 3% BSA in TBST for one hour at room temperature. A secondary anti-mouse antibody conjugated with HRP (1:10000, Thermo Scientific, 31430) or anti-rabbit antibody conjugated with HRP (1:10000, Thermo Scientific, 31460) was used. Membranes were washed in TBST and then incubated in SuperSignal West Pico PLUS Chemiluminescent Substrate (Thermo Scientific, 34580) for 2\u2009minutes. Membranes were immediately imaged on UVP ChemStudio Western Blot Imaging System (Analytik Jena, 849-97-0847-03).\n\nParaffin-embedded tissue sections were deparaffinized with xylene and rehydrated with serial passage through changes of graded ethanol. Slides were subjected to heat-induced epitope retrieval in EDTA solution (10\u2009mM Tris, 1\u2009mM EDTA, 0.05% Tween 20, pH 8) followed by blocking with 5% goat serum or 3% H2O2. Tissues were incubated with primary \u03b1-serotonin (YC5/45, Abcam) Abs at 4oC overnight. Slides were washed and incubated with HRP-carried Abs (Leadgene) for 1\u2009h at room temperature. In the immunohistochemistry assay, DAB (Roche, 11718096001) was used as a chromogen to visualize peroxidase activity, and the reaction was stopped by the addition of PBS. The preparations were lightly counterstained with hematoxylin (Sigma, H3136), mounted with Fluoromount Aqueous mounting medium (Sigma, F4680), and examined by Pannoramic 250 FLASH II (3DHistech).\n\nFlowJo v10 was used to quantify flow cytometry data, and GraphPad Prism 9 was used for statistical analysis. Student\u2019s T-test was used to analyze experiments with two conditions, Student\u2019s paired T-test was used to analyze experiments with paired samples, one-way ANOVA was used for experiments with three or more conditions, and two-way ANOVA was used for changes in ear thickness. Statistical details of experiments, including the number of animals and corresponding analyses are reported in the figure legends.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data, code, and materials used in the analysis are available upon request. Mice are available upon request and subject to materials transfer agreements (MTAs). The human skin sample data generated in this study have been deposited in the GEO database under accession code GSE274449. The raw and processed sequencing data are available at GEO database. The GSE274449 data generated in this study are provided in the Supplementary Information/Source Data file. The HTR2A-deficient monocyte-derived Langerhans Cells data generated in this study have been deposited in the GEO database under accession code GSE274941. The raw GSE274941 data are protected and are not available due to data privacy laws. The processed GSE274941 data are available at GEO database. The GSE274941 data generated in this study are provided in the Supplementary Information/Source Data file. Other published data used in this study GSE222197 (RNA-sequencing data with wild type monocyte-derived Langerhans cells treated with Imiquimod), GSE151177 (psoriatic single-cell RNA sequencing data with healthy controls), GSE162183 (psoriatic single-cell RNA sequencing data with healthy controls), GSE13355 (microarray data of gene expression data of skin from psoriatic patients and normal controls), GSE34248 (microarray data of gene expression profiling in psoriatic lesional and non-lesional skin), GSE109248 (microarray data of genome-wide analysis of gene expression of cutaneous lupus and cutaneous psoriasis lesions), and GSE41664 (microarray data of comparison of gene expression in psoriatic skin from different sources). All other data are available in the article and its Supplementary files or from the corresponding author upon request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The specific version of the code associated with the publication is archived in Zenodo and is accessible via [https://zenodo.org/records/16366824].", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Rendon A., Schakel K. Psoriasis pathogenesis and treatment. Int. J. Mol. Sci. 20, 1475 (2019).\n\nDi Cesare, A., Di Meglio, P. & Nestle, F. O. The IL-23/Th17 axis in the immunopathogenesis of psoriasis. J. Investig. Dermatol. 129, 1339\u20131350 (2009).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nRieder, E. & Tausk, F. Psoriasis, a model of dermatologic psychosomatic disease: psychiatric implications and treatments. Int. J. 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Invest 128, 2966\u20132978 (2018).\n\nArticle\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We would like to thank the Academia Sinica Core Facility and Innovative Instrument Project (AS-CFII108-113) for providing cell sorting services. We would also like to thank the Laboratory Animal Facility, Light Microscopy Core Facility, and Pathology Core Facility of the Institute of Biomedical Sciences, Academia Sinica, for their technical assistance. We would also like to thank the technical services provided by the Transgenic Mouse Model Core Facility of the National Core Facility for Biopharmaceuticals, the National Science and Technology Council (NSTC), Taiwan and the Animal Resources Laboratory of National Taiwan University Centers of Genomic and Precision Medicine. This study was supported by grants UN108-015 and UN110-032 from National Taiwan University Hospital (acquired by T.F.T. and Y.L.L.), and grants AS-BRPT-112-01 and AS-IDR-112-01 from Academia Sinica (acquired by Y.L.L.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Taiwan International Graduate Program in Molecular Medicine, National Yang Ming Chiao Tung University and Academia Sinica, Taipei, Taiwan\n\nYeh Fong Tan\n\nInstitute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan\n\nYeh Fong Tan,\u00a0Chen-Yun Yeh,\u00a0Sheng-Yun Hsu,\u00a0Chun-Hao Lu,\u00a0Ching-Hui Tsai,\u00a0Pei-Chuan Chiang\u00a0&\u00a0Yungling Leo Lee\n\nGraduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan\n\nSheng-Yun Hsu\n\nDepartment of Dermatology, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan\n\nHao-Jui Weng\n\nGraduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan\n\nHao-Jui Weng\n\nDepartment of Dermatology, National Taiwan University Hospital, Taipei, Taiwan\n\nTsen-Fang Tsai\n\nCollege of Public Health, China Medical University, Taichung, Taiwan\n\nYungling Leo Lee\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: Y.F.T., Y.L.L.; Methodology: Y.F.T.; Investigation: Y.F.T., S.Y.H.; Visualization: Y.F.T.; Funding Acquisition: T.F.T., Y.L.L;. Project Administration: Y.L.L.; Resources: C.Y.Y., P.C.C., H.J.W., T.F.T.; Software: Y.F.T., C.H.L., C.H.T.; Supervision: Y.L.L.; Writing- Original Draft: Y.F.T.; Writing- Review and Editing: Y.F.T., Y.L.L.\n\nCorrespondence to\n Yungling Leo Lee.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "T.F.T. has conducted clinical trials or received honoraria for serving as a consultant for AbbVie, AnaptysBio, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Galderma, GlaxoSmithKline-Stiefel, Janssen-Cilag, Leo-Pharma, Merck, Novartis, PharmaEssentia, Pfizer, Sanofi, Sun Pharma and UCB. The remaining authors state no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Kexiang Yan, Huaping Zheng and the other anonymous reviewer(s) for their contribution to the peer review of this work. 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Serotonin 2A receptor attenuates psoriatic inflammation by suppressing IL-23 secretion in monocyte-derived Langerhans cells.\n Nat Commun 16, 8544 (2025). https://doi.org/10.1038/s41467-025-63971-5\n\nDownload citation\n\nReceived: 12 December 2024\n\nAccepted: 29 August 2025\n\nPublished: 29 September 2025\n\nVersion of record: 29 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63971-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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maintains O-GlcNAcylation homeostasis to restrain endometrial malignancy", + "pre_title": "Impairment of FBXO31-mediated Ubiquitination of OGT Upregulates O-GlcNAcylation to Advance Endometrial Malignancy", + "journal": "Nature Communications", + "published": "02 February 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM7_ESM.xlsx" + }, + { + "label": "Supplementary Data 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM8_ESM.xlsx" + }, + { + "label": "Supplementary Data 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM9_ESM.xlsx" + }, + { + "label": "Supplementary Data 8", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM10_ESM.xlsx" + }, + { + "label": "Supplementary Data 9", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM11_ESM.xlsx" + }, + { + "label": "Supplementary Data 10", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM12_ESM.xlsx" + }, + { + "label": "Supplementary Data 11", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM13_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM14_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM15_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_MOESM16_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://download.cncb.ac.cn/gsa-human/HRA007070", + "/articles/s41467-025-56633-z#Sec36" + ], + "code": [ + "https://doi.org/10.5281/zenodo.14292333" + ], + "subject": [ + "Endometrial cancer", + "Glycosylation" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4019799/v1.pdf?c=1738587950000", + "research_square_link": "https://www.researchsquare.com//article/rs-4019799/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-56633-z.pdf", + "preprint_posted": "11 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Aberrant O-GlcNAc cycling of the cancer proteome is a manifestation of its metabolic plasticity. As one of the most common cancer of the female genital tract associated with metabolic syndrome, endometrial cancer (EC) tissues often bear altered O-GlcNAcylation patterns. However, integration of O-GlcNAc status with existing histomorphologic and molecular subtypes of EC in large cohorts and identification of molecular modules controlling the O-GlcNAc homeostasis remain to be accomplished. Here we establish a positive correlation of O-GlcNAcylation with histologic grade of EC in a Chinese cohort containing 219 tumors and consolidate it in The Cancer Genome Atlas (TCGA) EC dataset. Higher O-GlcNAc level is associated with less pathological differentiation and poorer prognosis. Functionally, increasing O-GlcNAcylation promotes proliferation and stem-like cell properties in normal endometrial epithelial organoids (EE-Os), whereas decreasing O-GlcNAcylation limits the growth of endometrial cancer organoids (EC-Os). Using genome-wide CRISPR screen, we further identify that the F-box only protein 31 (FBXO31), whose loss of heterozygosity is frequently observed in cancer, regulates O-GlcNAc homeostasis. FBXO31 acts as a substrate receptor of the SCF ubiquitin ligase complex to ubiquitinate the O-GlcNAc transferase OGT. Loss of FBXO31 results in accumulation of OGT and upregulation of O-GlcNAcylation in EC. Our study highlights the O-GlcNAcylation as a useful stratification marker and potential therapeutic target for the advanced, poorly differentiated EC cases.Biological sciences/Cancer/Gynaecological cancer/Endometrial cancerBiological sciences/Cell biology/Post-translational modifications/Glycosylation", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Supplementarytable1.xlsxSupplementary Table 1Supplementarytable2.xlsxSupplementary Table 2Supplementarytable3.xlsxSupplementary Table 3Supplementarytable4.xlsxSupplementary Table 4Supplementarytable5.xlsxSupplementary Table 5Supplementarytable6.xlsxSupplementary Table 6Supplementarytable7.xlsxSupplementary Table 7Supplementarytable8.xlsxSupplementary Table 8Supplementarytable9.xlsxSupplementary Table 9", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Protein O-GlcNAcylation is a post-translational modification coupled to cellular metabolic plasticity. Aberrant O-GlcNAcylation has been observed in many cancers including endometrial cancer (EC), a common malignancy in women. However, clinical characterization of dysregulated O-GlcNAcylation homeostasis in EC and interrogating its molecular mechanism remain incomplete. Here we report that O-GlcNAcylation level is positively correlated with EC histologic grade in a Chinese cohort containing 219 tumors, validated in The Cancer Genome Atlas dataset. Increasing O-GlcNAcylation in patient-derived endometrial epithelial organoids promotes proliferation and stem-like cell properties, whereas decreasing O-GlcNAcylation limits the growth of endometrial cancer organoids. CRISPR screen and biochemical characterization reveal that tumor suppressor F-box only protein 31 (FBXO31) regulates O-GlcNAcylation homeostasis in EC by ubiquitinating the O-GlcNAc transferase OGT. Downregulation of O-GlcNAcylation impedes EC tumor formation in mouse models. Collectively, our study highlights O-GlcNAcylation as a useful stratification marker and a therapeutic vulnerability for the advanced, poorly differentiated EC cases.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Post-translational modifications (PTMs) endow the proteome with functional plasticity to cope with intrinsic and extrinsic perturbations under various developmental and disease conditions. Protein O-GlcNAcylation, catalyzed by a pair of evolutionarily conserved enzymes O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), is a PTM involving the covalent addition of single O-linked N-acetylglucosamine (O-GlcNAc) modification to serine or threonine residue of intracellular proteins1. The GlcNAc moieties are supplied by a metabolite uridine diphosphate N-acetylglucosamine (UDP-GlcNAc), whose synthesis via the hexosamine biosynthesis pathway (HBP) requires fructose-6-P, acetyl-CoA, glutamine, and UTP, substrates from all major cellular metabolic processes. As a result, O-GlcNAcylation is sensitive to nutrient availability and intrinsic metabolic reprogramming. Meanwhile, O-GlcNAcylation is highly responsive to a wide range of extrinsic stimuli, including osmotic, oxidative, hyperthermic, and genotoxic stresses1,2,3, making it an important cellular stress sensing mechanism. O-GlcNAcylation is required for the maintenance of pluripotency of embryonic stem cells (ESCs). Its level declines as ESCs differentiate, alongside the cellular metabolic switch from glycolysis to oxidative phosphorylation4. Cancer cells often hijack embryonic programs to support their cell fate transition and abnormal proliferation, adopting a metabolic lifestyle relying on aerobic glycolysis (Warburg effect). Altered O-GlcNAcylation has been observed in cell lines of many cancers5,6,7,8, probably as a result of their increased nutrient consumption, or imbalanced enzymatic activity of OGT and OGA due to somatic mutations or altered protein stability9,10,11,12,13. To date, systemic assessment of O-GlcNAcylation level in major cancer cohorts and functional dissection of its homeostasis in patient-derived organoids haven\u2019t been conducted.\n\nEndometrial cancer (EC), the incidence of which has increased over 50% during the past two decades, is the most common cancers within the female reproductive system in developed countries14. In China, as of 2022, there were ~77,700 newly diagnosed EC cases and 13,500 estimated EC cancer deaths15. EC comprises a panel of tumors that are clinically and biologically heterogeneous, with obesity and conditions associated with metabolic syndrome such as diabetes being its risk factors16. It can be grouped into type I or type II tumors according to the clinical and endocrine features17, or classified as endometrioid carcinoma, serous carcinoma, carcinosarcoma, or clear-cell carcinoma based on its histopathological characteristics18. The Cancer Genome Atlas Research Network (TCGA) study of uterine corpus endometrial carcinoma (UCEC) has established a more precise genomic classification including four molecular subtypes: POLE-mutated, microsatellite instable (MSI), copy-number low, and copy-number high tumors19. More recently, integration of proteomic analysis to the genomic classification has accelerated the identification of clinically actionable molecular targets in EC20,21. Yet, PTMs, which add tremendous functional complexity to the proteome, remain to be comprehensively characterized in EC samples and complemented into the current classification system. O-GlcNAcylation as an important PTM responsive to cellular metabolism and stress has been linked to the molecular etiology of EC. Both OGT and OGA manifested highest alterations, mainly gene mutation and amplification, in EC among female reproductive cancer types22. The mRNA levels of both OGT and OGA were increased in EC samples of higher histologic grade23. More recently, elevated O-GlcNAcylation level in EC tissues was observed using a small tissue microarray24. O-GlcNAcylation was reported to promote proliferation, migration, and epithelial-mesenchymal transition (EMT) in cultured EC cell lines by regulating Wnt/\u03b2-catenin and Hippo-YAP signaling pathways22,24,25,26. These observations suggest that altered O-GlcNAcylation may contribute to EC progression, and it is worthy of thorough interrogation in large EC cohorts to determine whether O-GlcNAcylation can be utilized both as a stratification factor and a potential druggable target.\n\nIn this study, utilizing a Chinese EC cohort containing 219 tumors and the TCGA UCEC dataset, we uncover that O-GlcNAcylation level correlates with histologic grade, International Federation of Gynecology and Obstetrics (FIGO) stage, and patients\u2019 prognosis. Moreover, we experimentally demonstrate that upregulation of O-GlcNAcylation promotes proliferation and stem-like cell properties in non-cancerous endometrial epithelial organoids (EE-Os), whereas downregulation of O-GlcNAcylation impedes the proliferation of endometrial cancer organoids (EC-Os). Furthermore, we identify FBXO31 as a key regulator of O-GlcNAcylation homeostasis, by controlling the ubiquitin-dependent protein degradation of OGT. Using subcutaneous xenograft mouse models, we show that treatment with small molecular inhibitor targeting OGT restrains tumor formation of EC cells. Our findings highlight that O-GlcNAcylation is a useful factor complementary to the current classification system to better stratify EC patients, and targeting the dysregulated O-GlcNAcylation homeostasis is a promising differentiation therapeutic strategy worthy of clinical exploitation for high grade EC patients.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To get a glimpse of global O-GlcNAcylation level in EC tissues, we obtained an EC tissue array from Xinchao Biotech (Shanghai, China) containing 23 peritumoral and 31 tumoral endometrial specimens, and performed immunohistochemistry (IHC) analyses with the anti-O-GlcNAc monoclonal antibody RL2, which was raised against the nuclear pore complex-lamina fraction of rat liver and is widely used to detect O-GlcNAcylation in a broad range of species in different applications24,27,28,29,30,31,32,33,34, as well as the antibodies of OGT and OGA (Fig.\u00a01a). The amount of O-GlcNAcylation and OGT expression were significantly higher in epithelial cells of EC tissues relative to the control (Fig.\u00a01b\u2013e), consistent with a previous report24. The expression of OGA however showed no significant difference between peritumoral and tumoral endometrial tissues (Fig. s1a, b).\n\na A flowchart illustrating the process of clinical sample selection, data collection, and analysis. All samples were derived from patients receiving their initial treatment, and none of the patients had concurrent or previous tumors. Paraffin-embedded (FFPE); Immunohistochemistry (IHC). b, c Representative images depicting IHC staining of O-GlcNAcylation (RL2) and OGT in EC tumoral and peritumoral tissues in the FFPE tissue array. O-GlcNAcylation (RL2) and OGT immunostaining were intense in the glandular epithelium of the tumor. Scale bars: 50\u2009\u00b5m. d, e Quantitative analysis of the levels of O-GlcNAcylation (RL2) and OGT in the EC tissue arrays. The levels of O-GlcNAcylation and OGT were assessed semi-quantitatively based on both the intensity and area of the staining. The product of proportion and intensity score was used as the final IHC score (0\u201312). Tumoral tissue (n\u2009=\u200931); peritumoral tissue (n\u2009=\u200923). The results are presented as mean\u2009\u00b1\u2009SD. Statistical significance was calculated using unpaired two-tailed Student\u2019s t-test. f Representative images of IHC staining showing varying levels of O-GlcNAcylation in serial sections of EC tissues of different histologic grades (well differentiated G1, moderately differentiated G2, and poorly differentiated G3. G1, n\u2009=\u200971; G2, n\u2009=\u2009106; G3, n\u2009=\u200942). Scale bar: 50\u2009\u00b5m. g Percentage of samples with high or low level of O-GlcNAcylation in different histologic grade groups. High and low categories were determined using a scoring system (high score: 8\u201312; low score: 0\u20136). (G1, n\u2009=\u200971; G2, n\u2009=\u2009106; G3, n\u2009=\u200942). Statistical significance between groups was calculated using two-sided Fisher\u2019s exact test. h, i Kaplan\u2013Meier survival curves of PFS and OS of the EC patients stratified by the level of O-GlcNAcylation derived from their IHC scores. (Patients in high-RL2 group, n\u2009=\u200989; Patients in low-RL2 group, n\u2009=\u2009115). Statistical significance was determined by the log-rank test. The source data for (d-e, g, h, i) are provided in the Source Data file.\n\nTo elaborate the relationship between the O-GlcNAcylation level and clinical characteristics of EC, we expanded the analyses to an EC cohort containing 219 tumor patients who received hysterectomy in the Department of Gynecology, Xiangya Hospital, Central South University (Fig.\u00a01a). The paraffin-embedded EC tissue sections were subjected to IHC analysis, and the O-GlcNAcylation level revealed by the RL2 antibody staining for each specimen was semi-quantified to categorize the patients. Specifically, the IHC results were quantified by two independent assessors, and confirmed by a pathologist, based on both the proportion of positively stained tumor cells which was assessed by a value of 0\u20134 (0: negative; 1: 1\u201325%; 2: 26\u201350%; 3: 51\u201375%; 4: 76\u2013100%), and the intensity of the staining which was scored using a value of 0 to 3 (0: negative; 1: weak; 2: medium; 3: strong). The product of the proportion and the intensity values was used as the final IHC score for each sample35,36,37. The patients were then divided into high O-GlcNAcylation (High-RL2) and low O-GlcNAcylation (Low-RL2) groups according to their IHC scores (High-RL2: 8\u201312; Low-RL2: 0\u20136). This high- and low-O-GlcNAcylation status exhibited significant association with histologic grade, FIGO stage, and distant metastasis of EC (Supplementary table\u00a01). Consistently, the level of O-GlcNAcylation manifested a marked increase in EC tissues from patients with more advanced histologic grade (Fig.\u00a01f), and the high O-GlcNAcylation cases were significantly enriched in the histologic grade 3 (G3) patients\u2019 group (Fig.\u00a01g). Further statistical analysis established a positive correlation between the O-GlcNAcylation level and tumor histologic grade (Goodman-Kruskal gamma statistic p\u2009\u2264\u20090.0001; 2-sided gamma-knife gamma\u2009=\u20090.473), as well as distant metastasis (Goodman-Kruskal gamma statistic p\u2009=\u20090.003; 2-sided gamma-knife gamma\u2009=\u20091). Kaplan-Meier analysis indicated that patients in the high O-GlcNAcylation group exhibited significantly shorter progression-free survival (PFS) and overall survival (OS) than that in the low O-GlcNAcylation group (Fig.\u00a01h, i). Univariate analysis revealed that O-GlcNAcylation level, alongside age, FIGO stage, and myometrial invasion, was significantly associated with PFS. Subsequent multivariate Cox regression analysis using all the statistically significant variables (p\u2009<\u20090.05) identified O-GlcNAcylation level and age as independent predictors of the clinical outcome of EC patients (Supplementary table\u00a02).\n\nWe wanted to validate the correlations observed in our EC cohort using the TCGA UCEC dataset. Kaplan-Meier analysis of the OS based on either OGT or OGA expression showed no statistical difference (Fig. s1c, d), suggesting that the mRNA abundance of OGT or OGA alone is insufficient to reflect the O-GlcNAcylation level and the amount of OGT protein in EC might be regulated translationally or post-translationally. To better estimate the O-GlcNAcylation level, we sent 40 high O-GlcNAcylation and 15 low O-GlcNAcylation frozen EC samples according to their corresponding RL2 IHC scores for RNA-seq (Fig. s1e). Gene set enrichment analysis (GSEA) revealed that the high O-GlcNAcylation group was enriched for expression of genes involved in EMT and angiogenesis (Fig. s1f, g, Supplementary data\u00a01). Next, we calculated the Pearson\u2019s correlation coefficient (r) of the transcripts level of each gene with the O-GlcNAcylation IHC scores, and included the top 1000 genes with r\u2009>\u20090.3 in the O-GlcNAcylation correlated gene set (Supplementary data\u00a02). Gene ontology (GO) analysis revealed that they were highly enriched in biological processes related to cilium (Fig. s1h, Supplementary data\u00a03). This result was in agreement with the observed correlation between O-GlcNAcylation level and the histologic grade of EC, because multiciliogenesis is a marker of differentiation of endometrial epithelial cells38.\n\nWe subsequently constructed a mathematical model based on the expression matrix of the O-GlcNAcylation correlated gene set using machine learning algorithms in R to calculate a virtual O-GlcNAc index for each sample in the TCGA UCEC cohort (Fig. s1e, Supplementary data\u00a04). The calculated O-GlcNAc index in the TCGA dataset exhibited a significant association with histologic grade and FIGO stage (Fig.\u00a02a, Supplementary data\u00a05), consolidating the observations made in our EC cohort. Patients in the advanced histologic grade G3 group had a higher O-GlcNAc index in comparison to that in the grade 1 (G1) or grade 2 (G2) group (Fig.\u00a02b). Similarly, EC patients at FIGO stages II, III, or IV demonstrated an increased O-GlcNAc index than that at stage I (Fig.\u00a02c). Of note, analysis of the relationship between the O-GlcNAc index and EC molecular subtypes revealed that patients of the copy-number high molecular subtype, which had the worst clinical outcome among all EC cases39, exhibited a significantly higher O-GlcNAc index than that of other molecular subtypes (Fig.\u00a02d). The O-GlcNAc index also increased with age, an independent predictor of the clinical outcome of EC patients (Fig.\u00a02e). We further stratified the EC patients in the TCGA cohort into high O-GlcNAcylation and low O-GlcNAcylation groups using the median O-GlcNAc index as the cutoff. Patients from the high O-GlcNAcylation group experienced significantly shorter progression-free interval (PFI) and OS than that from the low O-GlcNAcylation group (Fig.\u00a02f, g).\n\na Heatmap displaying the expression profiles of the 1000 O-GlcNAcylation correlated genes in the TCGA UCEC RNA-seq dataset (n\u2009=\u2009589). The EC samples are annotated by clinical parameters, including Body Mass Index (BMI), menopause status, diabetes, histologic grades, molecular subtypes (integrative cluster), International Federation of Gynecology and Obstetrics (FIGO) stage, age, and primary diagnosis. Patients were categorized into O-GlcNAcylation high or O-GlcNAcylation low group using the median of the calculated O-GlcNAc index as the threshold. The symbol (*) indicates a statistically significant difference of the calculated O-GlcNAc index among the patients\u2019 groups according to the indicated clinical parameter. Statistical significance was determined by two-sided Wilcoxon test, **p\u2009<\u20090.01, ****p\u2009<\u20090.0001. b\u2013e The O-GlcNAc index in different EC groups stratified by histologic grade, FIGO stage, integrative cluster, or age in the TCGA UCEC dataset. For histologic grade (b): G1 (n\u2009=\u200999), G2 (n\u2009=\u2009119), and G3 (n\u2009=\u2009324). For FIGO stage (c): Stage I (n\u2009=\u2009335), Stage II (n\u2009=\u200951), Stage III (n\u2009=\u2009127), and Stage IV (n\u2009=\u200929). For integrative clusters (d): POLE (n\u2009=\u200917), copy number low (n\u2009=\u200990), microsatellite unstable (n\u2009=\u200965), and copy number high (n\u2009=\u200961). For age (e), n\u2009=\u2009177 and 362. The box bounds the interquartile range divided by the median, with the whiskers extending to a maximum of 1.5\u2009times the interquartile range beyond the box. Outliers are shown as dots. Statistical significance was determined by two-sided Wilcoxon test. f, g Kaplan\u2013Meier survival curves for Progression-free interval (PFI) and OS of EC groups with high or low O-GlcNAc index in the TCGA UCEC dataset. Statistical significance was determined by the log-rank test.\n\nIn summary, the elevated O-GlcNAcylation level in EC tissues is correlated with more advanced histologic grade and poorer clinical outcome of the patients, both in our EC cohort and the TCGA UCEC dataset.\n\nEndometrial organoids mirror many molecular and functional traits of the in vivo endometrial tissues, manifesting glandular self-organization, apicobasal polarity, mucus production, and responsiveness to sex hormones40,41. To dissect the functional impact of altered O-GlcNAcylation level on endometrial tissues, we generated endometrial organoids from biopsy or surgical samples, including three eutopic endometrial epithelial organoids (EE-Os) from patients with endometriosis as non-cancerous controls and three endometrial cancer organoids (EC-Os) (Supplementary data\u00a06).\n\nThe EE-Os were usually monocystic, with well-polarized epithelial cells forming hollow spheres in the three-dimensional (3D) extracellular matrix (Fig. s2a). IHC analysis showed that the EE-Os retained many characteristics of endometrial epithelium, including production of mucins, expressions of estrogen receptor (ER) and progesterone receptor (PR) (Fig. s2b). The EC-Os however manifested more irregular cell organizations, often with dense and polycystic phenotypes (Fig. s2a). The EC-Os faithfully reflected the histopathological characteristics of their primary EC tissues, such as the presence or absence of P53 and FOXA2, expressions of ER and PR (Fig. s2c). Additionally, the EC-Os demonstrated substantial proliferative activity as indicated by the Ki-67 staining (Fig. s2c). We performed IHC analysis with RL2 antibody on the paraffin-embedded endometrial organoids and the matched primary tissues, and confirmed that the O-GlcNAcylation status remained unchanged (Fig. s2d). In accordance with their primary tissues, the EC-Os manifested significantly higher O-GlcNAcylation level than the EE-Os (Fig. s2e).\n\nSmall molecular inhibitors have been developed to modulate the activities of O-GlcNAc cycling enzymes OGT and OGA, and are widely used to dissect the functions of O-GlcNAcylation in vitro and in vivo42,43,44,45,46,47,48,49,50,51,52. We treated the EE-Os with the OGA inhibitor Thiamet-G (TMG) to increase the cellular O-GlcNAcylation level (Fig.\u00a03a, b). The addition of TMG resulted in enhanced colony formation and organoid growth of EE-Os (Fig.\u00a03c, d), along with a rise in the number of mitotic cells within each EE-O (Fig.\u00a03e, f). Acetylated alpha-tubulin (Ac-tubulin) and PAEP are differentiation markers for multiciliated epithelial cells and secretory cells respectively in the endometrium41. TMG treatment reduced the number of both PAEP positive cells and Ac-tubulin labeled multiciliated cells (Fig.\u00a03g, h), suggesting that the elevated O-GlcNAcylation level caused de-differentiation of the endometrial cells in the EE-Os. We further examined the expression levels of a panel of stemness markers of the endometrium, including SSEA-1, SOX9, ALDH1, OCT4, CD133, and SOX2. In contrast to PAEP whose mRNA level was decreased upon TMG treatment, all the examined stemness markers showed upregulated expressions (Fig.\u00a03i). Given that the GSEA results suggested that high O-GlcNAcylation could promote EMT and angiogenesis (Fig. s1f, g), we also examined the expression of genes involved in these two processes in the TMG-treated EE-Os. While the expression of two epithelial markers E-cadherin and ZO-1 showed no difference, four out of six of the mesenchymal markers analyzed, namely, FN-1, Snail1, TWIST2, and MMP1, exhibited elevated expression after TMG treatment (Fig. s3a). Among the analyzed angiogenesis-related genes, increase of O-GlcNAcylation in EE-Os by TMG treatment upregulated the expression of PDGFA and VEGFC. Yet, the expression of the rest of the angiogenesis genes was unaltered or downregulated (Fig. s3b). These findings suggest that elevated O-GlcNAcylation can promote de-differentiation of endometrial epithelial cells, as well as their EMT and angiogenesis capacities to varying degrees.\n\na Bright-field images of endometrial epithelial organoids (EE-Os) depicting responses to Thiamet G (TMG) at day 1 and day 3. Representative images from control dimethyl sulfoxide (DMSO) and 10\u2009\u00b5M TMG treated EE-O groups are presented. Scale bar: 50\u2009\u00b5m. b Immunoblot with RL2 antibody assessing O-GlcNAcylation level in EE-Os treated with DMSO, 5\u2009\u00b5M, or 10\u2009\u00b5M TMG for 48\u2009h. Actin was used as the loading control. c Comparison of the EE-O numbers at day 3 of culture after 10\u2009\u00b5M TMG or DMSO treatment. d Measurement of cross-sectional area of EE-Os at day 3 after treatment with 10\u2009\u00b5M TMG versus DMSO. (Organoids derived from 6 biological replicates, n\u2009=\u200994 and 165 organoids). e Representative immunofluorescence images of control and TMG-treated EE-Os stained with PH3 (red), Tubulin (green), DAPI (blue), and F-actin labeled by Phalloidin (magenta). Scale bar: 5\u2009\u00b5m. f Quantification of phospho-histone H3 (PH3) positive cells in each EE-O. (n\u2009=\u200931 and 22 organoids). g Representative immunofluorescence images of control and TMG-treated EE-Os. Ciliated epithelium is labeled by acetylated alpha-tubulin (Ac-tubulin, green), secretory cells by PAEP (red), DAPI (blue), and F-actin (magenta). Scale bar: 50\u2009\u00b5m. Insets show magnification of the area in the white box, scale bar: 5\u2009\u00b5m. h Quantification of the number of ciliated cells (Ac-tubulin\u2009+\u2009) in each EE-O (n\u2009=\u200923 and 25 organoids). i qPCR analysis of stemness markers\u2019 expression in EE-Os treated with TMG or DMSO, normalized to actin mRNA level. j Minimum-Distortion Embedding (MDE) projection of scRNA-seq data of DMSO and TMG treated EE-Os. k Subclustered epithelial populations of EE-Os (left), and the proportion of each subcluster in control and TMG-treated groups (right). Results in (a, b) show a representative example from n\u2009=\u20093 independent experiments. Results in (c, d) were derived from n\u2009=\u20096 biologically independent experiments, and results in (f\u2013i) were derived from n\u2009=\u20093 biologically independent experiments, with p-values calculated by unpaired two-tailed Student\u2019s t-test and data presented as mean\u2009\u00b1\u2009SD. The source data for (b\u2013d, f, h, i, k) are provided in the Source Data file.\n\nTo further characterize the influence of TMG treatment on different cell subtypes in the EE-Os, the control and TMG treated EE-Os were subject to single-cell RNA-seq analysis (Fig.\u00a03j). The cells were clustered and classified into six major subtypes according to the specific expression of known markers38,53: pre-ciliated, ciliated, stem, proliferative, O-GlcNAc-related stem-like, and inflammatory (Fig. s3c-f, Supplementary data\u00a07). Of note, we identified an O-GlcNAc-related stem-like subtype in which the cells displayed activated signaling pathways regulating the pluripotency of stem cells, as well as the O-glycan biosynthesis (Fig. s3d). The TMG treatment of EE-Os resulted in a substantial decrease of cells in the ciliated and pre-ciliated subtypes, and a concurrent increase of cells in the proliferative and O-GlcNAc-related stem-like subtypes (Fig.\u00a03k). This result supports that upregulation of O-GlcNAcylation level promotes proliferation and stemness of endometrial epithelial cells.\n\nFor comparison, we treated the EE-Os with the OGT inhibitor OSMI-1 to analyze the effects of downregulation of O-GlcNAcylation on the non-cancerous endometrial epithelial cells. OSMI-1 treatment only mildly impacted the growth of EE-Os, and the number of organoids formed was unaffected (Fig. s4a\u2013c). TUNEL staining revealed no significant apoptosis in the OSMI-1 treated EE-Os, and the number of differentiated cells with multiple cilia remained comparable (Fig. s4d, e). We also examined the expression of the marker genes for stemness, EMT, and angiogenesis in the EE-Os treated with OSMI-1, and no concordant changes were observed (Fig. s4f\u2013h). These results suggest that inhibition of OGT only has minimal influence on the cells in EE-Os.\n\nTo investigate whether a decrease of O-GlcNAcylation can inhibit the growth of tumor cells in EC-Os, we treated the EC-Os with OSMI-1, a chemical inhibitor of OGT (Fig.\u00a04a, b). The addition of OSMI-1 impeded the formation and growth of EC-Os. A significant fraction of the EC-Os displayed darkening and cell lysing in the presence of OSMI-1, resulting in reductions in both the number and size of the EC-Os compared to time-matched control (Fig.\u00a04a\u2013d). TUNEL staining revealed that many cells in the OSMI-1 treated EC-Os underwent apoptosis (Fig.\u00a04e). We performed immunofluorescence on the remaining EC-Os with relatively normal size and morphology. Mitotic cells as visualized by phospho-histone H3 (PH3) staining became barely detectable in EC-Os after OSMI-1 treatment (Fig.\u00a04f, g). Meanwhile, the population of both the PAEP positive secretory cells and Ac-tubulin labeled multiciliated cells increased in these EC-Os (Fig.\u00a04h, i), suggesting that OSMI-1 treatment promoted differentiation. Consistently, the expression of stemness markers, including SSEA-1, SOX9, ALDH1, OCT4, CD133, and SOX2, was significantly downregulated in the OSMI-1 treated EC-Os, accompanying the upregulation of the differentiation marker PAEP (Fig.\u00a04j). The expression of all the mesenchymal markers, including FN-1, Vimentin, Snail1, TWIST2, TGFB1, and MMP1, was downregulated (Fig.\u00a04k). Many of the angiogenesis markers also manifested reduced expression in the EC-Os treated with OSMI-1 (Fig.\u00a04l).\n\na Representative bright-field images of endometrial cancer organoids (EC-Os) treated with 50\u2009\u00b5M OSMI-1 at day 1 and day 3. Scale bar: 50\u2009\u00b5m. b Immunoblot assessing O-GlcNAcylation level in EC-Os treated with 25\u2009\u00b5M or 50\u2009\u00b5M OSMI-1 for 48\u2009h. Actin was used as the loading control. c Comparison of the numbers of EC-Os at day 3 after treatment with OSMI-1 versus the control DMSO. d Cross-sectional area of EC-Os at day 3 after OSMI-1 treatment compared to control (DMSO). (n\u2009=\u2009101 and 57 organoids). e TUNEL staining showing apoptotic cells in EC-Os after 50\u2009\u00b5M OSMI-1 treatment. Nuclei are visualized with DAPI (blue). Scale bar: 50\u2009\u00b5m. f Representative immunofluorescence images of control and OSMI-1 treated EC-Os. Mitotic cells (PH3, red), tubulin (green), DAPI-labeled nuclei (blue), and Phalloidin labeled F-actin (magenta). Scale bar: 10\u2009\u00b5m. g Quantification of the number of PH3+ cells. (n\u2009=\u200942 and 46 organoids). h Representative immunofluorescence images of control and OSMI-1 treated EC-Os showing ciliated epithelial cells (Ac-tubulin, green), secretory cells (PAEP, red), DAPI (blue), and F-actin (magenta). Scale bar: 50\u2009\u00b5m. Insets show magnification of the area in the white box, scale bar: 5\u2009\u00b5m. i Quantification of the number of Ac-tubulin+ cells. (n\u2009=\u200940 and 28 organoids). (j\u2013l) qPCR analyses of stemness (j), EMT (k), and angiogenesis (l) markers in EC-Os treated with OSMI-1 or DMSO, normalized to actin mRNA. m Measurement of cell viability in patients-derived endometrial organoids with the indicated inhibitors. RLU represents relative light units. 5 replicates per each patient-derived organoid. Results in (a, b) show a representative example from n\u2009=\u20093 independent experiments. Results in (c, d) represent n\u2009=\u20096 biologically independent experiments, and results in (g, i-l) represent n\u2009=\u20093 biologically independent experiments, with p-values calculated by unpaired two-tailed Student\u2019s t-test and data presented as mean\u2009\u00b1\u2009SD. The source data for (b\u2013d, g, i\u2013m) are provided in the Source Data file.\n\nTo ascertain the effects observed after OSMI-1 treatment in the EC-Os was on-target, we directly knocked down the expression of OGT using short hairpin RNAs (shRNA) and repeated the analyses. Similar to the observations made with OSMI-1, knockdown of OGT by shRNA also downregulated O-GlcNAcylation level and significantly inhibited the formation and growth of EC-Os (Fig. s5a\u2013d). The cells in EC-Os after OGT knockdown exhibited increased apoptosis (Fig. s5e), and the remaining ones were often multiciliated (Fig. s5f, g), with upregulated expression of the differentiation marker PAEP and downregulated expression of the stemness genes (Fig. s5h). These results suggest that a decrease of O-GlcNAcylation level induced by different means invariably leads to growth limitation and enhanced differentiation and apoptosis of tumor cells in EC-Os.\n\nWe also treated the EC-Os with the OGA inhibitor TMG to check if there was a tumor-promoting effect. TMG treatment led to a marked increase in the number and the size of EC-Os (Fig. s5i\u2013k). The expression of stemness genes such as SOX9, ALDH1, CD133, and SOX2 was also upregulated, while the differentiation marker PAEP was downregulated (Fig. s5l). Additionally, all the mesenchymal markers and many of the angiogenesis genes manifested increased expression in the TMG treated EC-Os (Fig. s5m, n), suggesting that inhibition of OGA further enhanced malignancy.\n\nThe experiments with OGT and OGA inhibitors in the endometrial organoids showed that the EE-Os were less sensitive to the downregulation of O-GlcNAcylation than the EC-Os. To validate this, we conducted a 3D cell viability assay using EC-Os from the two EC patients with relatively high O-GlcNAcylation level, as well as two EE-Os from non-EC patients. In addition to OSMI-1, we also included its derivative, OSMI-454, in the assay. While treatment with TMG promoted the proliferation of both EE-Os and EC-Os, inhibition of OGT by OSMI-1 or OSMI-4 demonstrated a reduction of viability in EC-Os than the control EE-Os (Fig.\u00a04m). These findings indicate that OGT inhibitors can effectively restrain the expansion of tumor organoids in vitro, particularly that with an inherently high O-GlcNAcylation level.\n\nTo identify crucial factors regulating O-GlcNAcylation homeostasis in EC, we conducted a comprehensive genome-wide CRISPR-Cas9 knockout screen. A lentiviral single guide RNA (sgRNA) library targeting 19,050 genes (6 sgRNAs/gene) was transduced into 293T cells, along with 1000 nontargeting control sgRNAs, at a multiplicity of infection (MOI) of 0.3 to ensure each cell expressed only one sgRNA. Following cell staining with the anti-O-GlcNAc antibody RL2, we isolated the top 5% RL2-positive cells via fluorescence-activated cell sorting (FACS) and conducted deep sequencing of the sgRNAs from this cell population (Fig.\u00a05a, b and Fig. s6a). The sgRNA abundance was then used to calculate a robust rank aggregation (RRA) score for each gene using MAGeCK55, and the genes were ranked accordingly, with a smaller RRA score indicated greater essentiality (Supplementary Data\u00a08). We reviewed the literatures and collected known regulators whose inactivation could impact cellular O-GlcNAcylation homeostasis56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71. Genes that negatively regulate O-GlcNAcylation, such as TSC2, SIRT1, and TP53, had smaller RRA scores and were enriched in the first half of the gene list, comparing to the known positive regulators of O-GlcNAcylation (Fig.\u00a05c). We performed Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on the 1038 high-confidence genes (p\u2009<\u20090.05) from the genome-wide screen. These genes were enriched in pathways including ECM-receptor interaction, thermogenesis, histidine metabolism, proteoglycan in cancer, and maturity onset diabetes of the young (Fig.\u00a05d, Supplementary Data\u00a09).\n\na Schematic representation of the FACS-based genome-wide CRISPR-Cas9 screen for putative regulators of O-GlcNAcylation homeostasis. Fluorescence-Activated Cell Sorting (FACS); Immunofluorescence (IF); CRISPR: Clustered Regularly Interspaced Short Palindromic Repeats; Human genome-wide CRISPR/Cas9 knockout (GeCKO). The elements in this figure were created using BioGDP.com (https://BioGDP.com). b Validation of the sensitivity of RL2 staining (red) with 293T cells transfected with OGT (green). Nuclei are labeled with DAPI (blue). Scale bar: 5\u2009\u00b5m. c Genes plotted according to their relative ranking analysis (RRA) enrichment scores, with known O-GlcNAcylation regulators highlighted in red and blue. Knockdown (KD). d Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showing enrichment of putative O-GlcNAcylation regulators in the indicated pathways (statistical analysis was performed using a hypergeometric test to calculate p-values). Analysis was performed on the 1038 high-confidence genes (p\u2009<\u20090.05). e Venn diagram showing the overlap between the 526 human UCEC Tumor Suppressor Genes (TSGs) and the 1038 high-confidence genes from the O-GlcNAcylation screen. Source data are provided in the Source Data file. f Immunofluorescent detection of O-GlcNAcylation level by RL2 (red) in WT (Wild Type) and FBXO31-KO (FBXO31-Knockout) 293T cells. Nuclei were stained with DAPI (blue). Scale bar: 5\u2009\u00b5m. g Kaplan-Meier analysis of the OS of the EC patients stratified by the expression level of FBXO31 (http://kmplot.com/analysis/). EC cases were stratified using the median cut-off, and statistical significance was determined using the log-rank test. Results in (b, f) show a representative example from n\u2009=\u20093 independent experiments.\n\nTo further pinpoint key regulators that impact O-GlcNAcylation level in EC tissues, we cross-referenced the 1038 positive hits in the screen with 526 putative tumor suppressor genes of EC72,73. As a result, 18 overlapping genes were identified, including ACVR1C, AGTR1, CADM2, PRKAA1, CDKN1C, CMTM3, DIRAS3, SIK1, EPHB4, GATA5, ITGAV, KLF10, MAP3K8, PLA2G2A, PTPN11, RNASEL, SPARCL1, and FBXO31 (Fig.\u00a05e). We conducted Kaplan-Meier analysis using the TCGA UCEC dataset for each gene, and found that only FBXO31 showed downregulated expression in EC that was associated with poor survival (Figs. s6b and 5g). Therefore, we generated FBXO31 knockout (FBXO31-KO) 293T cells using CRISPR (Fig. s6c). Immunostaining with RL2 antibody confirmed that the O-GlcNAcylation level was significantly increased in the FBXO31-KO cells (Fig.\u00a05f).\n\nFBXO31 functions as a substrate recognition component in the SCF ubiquitin E3 ligase complex to control the degradation of many proteins74,75,76,77,78,79. Accordingly, FBXO31 might regulate O-GlcNAcylation level by directly binding and ubiquitinating the O-GlcNAc transferase OGT. To confirm the interaction between FBXO31 and OGT, we performed a pull-down assay using bacterially purified GST-OGT to incubate with lysates of 293T cells expressing GFP-FBXO31. Western blot showed that GST-OGT pulled down a significant amount of GFP-FBXO31 relative to GST control (Fig.\u00a06a). We further validated the interaction using co-immunoprecipitation in 293T cells overexpressing Flag-OGT and GFP-FBXO31. GFP-FBXO31 was co-immunoprecipitated with Flag-OGT, and both the amounts of Flag-OGT and GFP-FBXO31 in the immunoprecipitant were increased in the presence of the proteasome inhibitor MG132 (Fig.\u00a06b).\n\na Immobilized recombinant GST-OGT protein but not GST control absorbed GFP-FBXO31 from 293T cell lysates. GST and GST-OGT were detected by Coomassie brilliant blue (CBB) staining, and FBXO31 was detected by western blotting with FBXO31 antibody. b Co-immunoprecipitation of GFP-FBXO31 with Flag-OGT in 293T cell lysates. The presence of MG132 enhanced the interaction between Flag-OGT and GFP-FBXO31. c Western blotting assessing the protein level of OGT as well as the global O-GlcNAcylation (RL2) level in 293T cells transfected with increasing amount of GFP-FBXO31. d Western blotting quantification of the protein level of endogenous OGT in 293T cells transfected with GFP-FBXO31. MG132 was added to inhibit the ubiquitination-mediated proteasome degradation. e Western blotting detecting the protein level of endogenous OGT and its ubiquitination in 293T cells transfected with different amount of HA-Ub and GFP-FBXO31. f In vitro ubiquitination of His-OGT by the SCF complex together with FBXO31. HA-tagged SCF components (Skp1, Cul1, and Roc1) and HA-FBXO31 were affinity-purified using anti-HA-conjugated magnetic beads from 293T cell lysates. The purified protein complex was incubated with E1 (UBA1), E2 (UBE2D1), Ub, and His-OGT in ubiquitination buffer. The reaction was halted by the addition of SDS sample buffer, and the samples were subjected to western blotting using the indicated antibodies. g In vivo ubiquitination assay was performed to evaluate the ubiquitination levels of exogenous Flag-OGT in 293T cells transfected with HA-tagged Ub and GFP-FBXO31 or its F-box domain deletion mutant GFP-FBXO31\u0394F. h Western blotting detecting the O-GlcNAcylation (RL2) and OGT levels in WT and FBXO31-KO 293T cells. i Western blotting quantitation of OGT protein level following cycloheximide (CHX) treatment in WT and FBXO31-KO 293T cells. Results in (d, i) represent n\u2009=\u20093 independent experiments, with p-values calculated by unpaired two-tailed Student\u2019s t-test and data presented as mean\u2009\u00b1\u2009SD. Samples derive from the same experiment and gels were processed in parallel. (a\u2013c, e\u2013h) show representative examples from n\u2009=\u20093 independent experiments. The source data for results in (a\u2013i) are provided in the Source Data file.\n\nTo assess whether the interaction with FBXO31 controlled the protein homeostasis of OGT, we transfected 293T cells with increasing amounts of GFP-FBXO31 and detected the levels of OGT as well as O-GlcNAcylation by western blot. Both the OGT protein and cellular O-GlcNAcylation levels demonstrated a negative correlation with the amount of GFP-FBXO31 (Fig.\u00a06c). Additionally, the downregulation of OGT induced by GFP-FBXO31 overexpression was significantly reversed by MG132, suggesting that FBXO31 controlled the OGT level via the ubiquitin-dependent proteasome degradation process (Fig.\u00a06d). To ascertain that FBXO31 could induce ubiquitination of OGT, we immunoprecipitated OGT from 293T cell lysates overexpressing GFP-FBXO31 and HA-ubiquitin. Western blot detected strong polyubiquitination of OGT in the presence of GFP-FBXO31 (Fig.\u00a06e). We further tested whether FBXO31 could ubiquitinate OGT in vitro. The SCF complex was affinity-purified with anti-HA magnetic beads from 293T cells expressing HA-tagged Skp1, Cul1, and Roc1 with or without FBXO31, and then incubated with bacterially purified E1, E2, ubiquitin, and His-OGT. Polyubiquitination signals of His-OGT were detected, suggesting that the FBXO31-containing SCF complex could directly ubiquitinate OGT (Fig.\u00a06f). Skp1 in the SCF complex recruits F-box proteins via their F-box motif. We mutated the F-box of FBXO31 (FBXO31\u2206F) and assessed its ability to induce polyubiquitination of OGT in 293T cells. Overexpression of HA-ubiquitin and GFP-FBXO31 resulted in strong polyubiquitination of the immunoprecipitated Flag-OGT, which was significantly reduced when GFP-FBXO31 was replaced with the GFP-FBXO31\u2206F mutant (Fig.\u00a06g). These results confirmed that FBXO31, together with other components of SCF complex, possessed a ubiquitin E3 ligase activity toward OGT. We further evaluated the impact of FBXO31 in controlling the cellular OGT homeostasis using FBXO31-KO 293T cells. Both the OGT and O-GlcNAcylation levels were increased in FBXO31-KO cells (Fig.\u00a06h). Cycloheximide (CHX) treatment, which blocked new protein synthesis, uncovered that the half-life of OGT was significantly extended in FBXO31-KO cells relative to control (Fig.\u00a06i), indicating that FBXO31 is indispensable for limiting the cellular OGT level.\n\nWe wanted to confirm if the observed regulation of OGT by FBXO31 also held true in endometrial cancer cells. To this end, we overexpressed GFP-FBXO31 in the Ishikawa cells. Immunofluorescent staining showed that both OGT and the O-GlcNAcylation level visualized by RL2 were markedly decreased in the GFP-FBXO31 positive cells (Fig. s7a). We subsequently generated FBXO31 knockout (FBXO31-KO) Ishikawa cells using CRISPR, and western blot indicated that OGT and O-GlcNAcylation were upregulated in these cells (Fig. s7b). Co-immunoprecipitation assay with endogenous FBXO31 and OGT in Ishikawa cells detected only weak interaction (Fig. s7c). However, this interaction became evident with exogenous GFP-FBXO31 and Flag-OGT that were overexpressed in Ishikawa cells (Fig. s7d). To test if FBXO31 could similarly mediate ubiquitination of OGT in Ishikawa cells, we immunoprecipitated OGT from the Ishikawa cell lysates overexpressing GFP-FBXO31 and HA-ubiquitin. Substantial polyubiquitination signal of OGT was detected in the presence of GFP-FBXO31 (Fig. s7e), confirming that FBXO31 could also control OGT protein level via the ubiquitin-dependent proteasome degradation process in endometrial cancer cells.\n\nO-GlcNAcylation was reported to increase the stability of several target proteins, such as YAP, \u03b2-catenin, and c-Myc, therefore promoting tumor progression25,29,80. To test if the FBXO31-OGT regulatory axis impacted the homeostasis of these proteins in EC, we examined their protein levels in WT and FBXO31-KO Ishikawa cells. While YAP and \u03b2-catenin showed minimum changes, c-Myc was markedly increased in FBXO31-KO cells, and this increase was largely dependent on OGT, as knockdown of OGT could restore its level to that seen in WT (Fig. s7f). We further investigated whether c-Myc was a bona fide O-GlcNAcylated target in EC. To this end, c-Myc was immunoprecipitated from Ishikawa cell lysate, and its O-GlcNAc modification was detected by western blot with RL2 (Fig. s7g). The amount of O-GlcNAcylated c-Myc was significantly higher in the FBXO31-KO Ishikawa cells than in the WT (Fig. s7h). These results indicate that c-Myc is an important cellular target downstream of FBXO31 and OGT to promote EC progression.\n\nWe next investigated the clinical relevance of the regulation of O-GlcNAcylation homeostasis by FBXO31 using the endometrial specimens in our EC cohort. IHC staining revealed that the protein level of FBXO31 was significantly downregulated in EC relative to normal endometrial tissues, often manifesting an anti-correlation pattern to that of O-GlcNAcylation (Fig.\u00a07a, b). We semi-quantified the expression level of FBXO31 based on the IHC signals, and found that the FBXO31 protein level in the low O-GlcNAcylation EC group was markedly higher than that in the high O-GlcNAcylation group (Fig.\u00a07c). We further analyzed the relationship between the calculated virtual O-GlcNAc index and the expression level of FBXO31 in the TCGA UCEC cohort, and observed a significant negative correlation (Fig.\u00a07d). Western blot uncovered that the FBXO31 protein level was decreased, accompanying the increase of OGT level, in EC-Os comparing to the EE-Os (Fig.\u00a07e), suggesting that the elevated O-GlcNAcylation in EC tissues was due to upregulation of OGT. We categorized the cases in our EC cohort into FBXO31-low and FBXO31-high groups. The FBXO31 expression exhibited significant association with the O-GlcNAcylation status and histologic grade (Supplementary Table\u00a03).\n\na Representative IHC images of FBXO31 in EC and peritumoral tissues from an FFPE tissue array. Scale bar: 50\u2009\u00b5m. b Quantitative analysis of FBXO31 levels in the EC tissue array. FBXO31 expression was semi-quantified based on staining intensity and area. Tumoral tissue (n\u2009=\u200931); peritumoral tissue (n\u2009=\u200923). Results are presented as mean\u2009\u00b1\u2009SD. Statistical significance was calculated using unpaired two-tailed Student\u2019s t-test. c Percentage of samples with high or low FBXO31 level by IHC in the two different O-GlcNAcylation level groups (Patients in high-RL2 group, n\u2009=\u200983; Low-RL2 group, n\u2009=\u200938). Statistical significance was calculated using two-sided Fisher\u2019s exact test. d Spearman two-sided correlation analysis between the calculated virtual O-GlcNAc index and the expression of FBXO31 (Transcripts Per Million, TPM), n\u2009=\u2009542. e Protein levels of OGT and FBXO31 were assessed by western blotting in EC-Os and EE-Os derived from different patients. f Immunofluorescence detection of O-GlcNAcylation (RL2, green) and FBXO31 (red) in control and shFBXO31 infected EE-Os. The nuclei were stained with DAPI (blue) and F-actin with Phalloidin (magenta). Scale bar: 50\u2009\u00b5m. g qPCR analysis of stemness markers\u2019 expression in control shNT and shFBXO31 infected EE-Os, normalized to actin mRNA level. h Quantification of organoid numbers of the control and shFBXO31 infected EE-Os after 3D culture. Representative bright-field images are provided on the left. Scale bar: 300\u2009\u00b5m. i Quantification of organoid numbers in shFBXO31 treated EE-Os, with OSMI-1 or DMSO treatment at day 3. Representative bright-field images are shown on the left. Scale bar: 150\u2009\u00b5m. j Quantification of organoid numbers of EC-Os overexpressing GFP or GFP-FBXO31. Bright-field and fluorescent images of the treated EC-Os are shown on the left. Scale bar: 50\u2009\u00b5m. Results in (g) represent n\u2009=\u20093 biologically independent experiments, and results in (h\u2013j) represent n\u2009=\u20096 biologically independent experiments, with p-values calculated by unpaired two-tailed Student\u2019s t-test and data presented as mean\u2009\u00b1\u2009SD. e, f show a representative example from n\u2009=\u20093 independent experiments. The source data for results in (b\u2013e, g\u2013j) are provided in the Source Data file.\n\nTo elucidate the functional impact of FBXO31 alterations in endometrial tissues, we knocked down the expression of FBXO31 using lentivirus-mediated expression of shRNAs in EE-Os. Downregulation of FBXO31 resulted in an increased amount of O-GlcNAcylation in EE-Os (Figs.\u00a07f and s8a). Particularly, the growth of EE-Os was significantly enhanced by FBXO31 knockdown, in alignment with upregulated expression of the stemness markers SSEA-1, SOX9, ALDH1, OCT4, CD133, and SOX2 (Fig.\u00a07g, h). This enhanced growth of EE-Os after FBXO31 knockdown could be inhibited by OSMI-1 treatment, indicating that it was a result of elevated O-GlcNAcylation (Fig.\u00a07i). Reciprocally, given that FBXO31 was downregulated in EC-Os, we supplemented the EC-Os with GFP-FBXO31 or GFP control using lentivirus-mediated transduction. Overexpression of GFP-FBXO31 downregulated O-GlcNAcylation and significantly impeded the formation of EC-Os (Figs.\u00a07j and s8b).\n\nIn summary, our results identify FBXO31 as one of the key rheostats that control the O-GlcNAcylation homeostasis by ubiquitinating OGT. FBXO31 is frequently downregulated in EC, resulting in stabilization of OGT and elevation of cellular O-GlcNAcylation level that advance endometrial malignancy.\n\nThe in vitro characterization of the impact of O-GlcNAcylation on EC cells suggested that targeting OGT to decrease O-GlcNAcylation level is a promising therapeutic strategy. We therefore investigated the antitumor effects of the OGT inhibitor OSMI-1 in a xenograft mouse model using Ishikawa cells. Ten days after the subcutaneous transplantation of Ishikawa cells, tumor-bearing mice were randomly divided into three groups and administered either DMSO (vehicle solvent), TMG (20\u2009mg/kg/day), or OSMI-1 (10\u2009mg/kg/day) via intraperitoneal injection (Fig.\u00a08a). The TMG treatment resulted in increased tumor growth and shortened lifespan, while OSMI-1 treatment reduced tumor volume compared to the control, and the mice exhibited increased survival (Fig.\u00a08b, c). IHC staining of the dissected tumor tissues confirmed that OSMI-1 treatment decreased O-GlcNAcylation level compared to TMG or the control DMSO (Fig.\u00a08d). Consistently, the expression of the differentiation marker PAEP in the tumor tissues was increased, and many of the stemness genes were mildly downregulated after OSMI-1 treatment (Fig. s8c). In contrast, TMG treatment resulted in downregulation of PAEP expression and upregulation of the stemness markers in the dissected tumor tissues (Fig. s8d).\n\na Schematic representation of the treatment schedule in the Ishikawa cells xenograft model. On day 10 after subcutaneous injection of EC cells, mice were treated daily with DMSO, TMG, or OSMI-1 for 15\u2009days. Tumor growth and survival were monitored till the endpoint. The mouse elements in this figure were created using BioGDP.com (https://BioGDP.com). b Tumor growth curves of Ishikawa xenografts in different treatment groups as indicated (DMSO group, n\u2009=\u20098 mice; TMG group, n\u2009=\u20097 mice; OSMI\u22121 group, n\u2009=\u20098 mice). The results are presented as mean\u2009\u00b1\u2009SEM. c Survival curves for mice bearing Ishikawa xenografts across different treatment groups. (DMSO group, n\u2009=\u20098 mice; TMG group, n\u2009=\u20097 mice; OSMI-1 group, n\u2009=\u20098 mice). Statistical significance was determined by log-rank test. d Representative Hematoxylin and Eosin (HE) and IHC staining of mouse tumor tissues from different treatment groups. Scale bar: 50\u2009\u00b5m. e Schematic representation of the treatment schedule in the WT and FBXO31-KO Ishikawa cells xenograft model. On day 10 of tumor growth, mice with WT or FBXO31-KO cells xenografts received daily treatment with DMSO or OSMI-1 for 15\u2009days. Tumor growth was assessed till the endpoint. The mouse elements in this figure were created using BioGDP.com (https://BioGDP.com). f Tumor growth curves of WT and FBXO31-KO Ishikawa cells xenografts in different treatment groups as indicated (WT DMSO control group, n\u2009=\u200910 mice; WT OSMI-1 treatment group, n\u2009=\u20099; FBXO31-KO DMSO group, n\u2009=\u200910 mice; FBXO31-KO OSMI-1 treatment group, n\u2009=\u20099 mice). The results are presented as mean\u2009\u00b1\u2009SEM. Statistical significance was calculated using unpaired two-tailed Student\u2019s t-test. g Photograph of the excised tumors from different treatment groups as indicated. h Immunofluorescence detection of CD31 (red) in tumor tissues from the indicated treatment groups. Nuclei were stained with DAPI (blue). Scale bar: 50\u2009\u00b5m. i Quantitative analysis of CD31-positive blood vessel areas. The results are presented as mean\u2009\u00b1\u2009SD. Statistical significance was calculated using unpaired two-tailed Student\u2019s t-test, n\u2009=\u20096 mice. The source data for results in (b\u2013i) are provided in the Source Data file.\n\nNext, we investigated whether the deletion of FBXO31 promoted EC tumor formation in the mouse model and sensitized the tumors to OSMI-1 treatment. We subcutaneously injected WT and FBXO31-KO Ishikawa cells into nude mice and monitored tumor growth. Ten days after the transplantation, tumor-bearing mice were randomly divided into groups and administered either DMSO (vehicle solvent) or OSMI-1 (10\u2009mg/kg/day) via intraperitoneal injection (Fig.\u00a08e). The tumors formed by the FBXO31-KO Ishikawa cells grew much faster than that of the WT cells (Fig.\u00a08f), and they upregulated the expression of many stemness genes such as SOX9, ALDH, OCT4, CD133, and SOX2 (Fig. s8f). OSMI-1 treatment decreased the O-GlcNAcylation level in these tumor tissues (Fig. s8e), downregulated the expression of the stemness genes (Fig. s8g), and significantly limited the tumors\u2019 growth in both the WT and FBXO31-KO groups (Figs.\u00a08f and s8e). Moreover, compared to the WT, the FBXO31-KO tumors showed increased sensitivity to the OSMI-1 treatment (Fig.\u00a08f, g). In addition, we observed that the tumors formed by the FBXO31-KO Ishikawa cells harbored richer vasculature as indicated by the CD31 immunofluorescent staining (Fig.\u00a08h). The OSMI-1 treatment significantly blocked angiogenesis and the formation of blood vessels in the FBXO31-KO tumors (Fig.\u00a08i).\n\nTogether, these findings confirm that loss of FBXO31 promotes EC tumor formation in vivo by enhancing the stemness as well as angiogenesis, and downregulation of O-GlcNAcylation by inhibiting OGT can suppress these tumors, indicating that targeting the dysregulated O-GlcNAcylation homeostasis in EC is a promising therapeutic strategy worthy of further clinical exploitation.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "This study delves deeply into the intricate relationship between O-GlcNAcylation homeostasis and the progression of EC, elucidating the clinical significance of abnormal O-GlcNAcylation level and unveiling an important regulatory module controlling its homeostasis in endometrial tissues (Fig.\u00a09).\n\nFBXO31-mediated ubiquitination of OGT maintains a relatively low level of O-GlcNAcylation in the non-cancerous endometrium. Inactivation of FBXO31 in endometrial cancer tissues results in accumulation of OGT and concurrent increase of O-GlcNAcylation that promote endometrial malignancy. The uterus elements in this figure were created using BioGDP.com (https://BioGDP.com).\n\nIntegrative analysis of O-GlcNAcylation in a large clinical EC cohort to assess its relationships with current histomorphologic and molecular subtypes of EC had not been conducted till this study. A previous report using 76 EC samples revealed that the two executing enzymes of O-GlcNAcylation, OGT and OGA, manifested increased mRNA levels in ECs of higher histologic grade relative to the well-differentiated tumors23. A more recent IHC analysis on a tissue microarray containing 28 EC specimens showed that both the OGT and O-GlcNAcylation levels were increased in EC tissues than the adjacent normal endometrial tissues24. This pilot study indicated that increased O-GlcNAcylation was associated with histologic grade, clinical stage, and lymph node metastasis. However, when repeating the IHC analysis using the same tissue microarray, we only observed increased OGT and O-GlcNAcylation levels in ECs but failed to associate O-GlcNAcylation level with any of the clinical parameters, probably due to the differences in inclusion and exclusion criteria and the limited sample size. Nonetheless, when we expanded the analysis to our EC cohort containing 219 patients, the clinical significance of O-GlcNAcylation became invariable. The O-GlcNAcylation level shows strong association with histologic grade, FIGO stage, and poor prognosis. The caveat of our EC cohort is that it only contains endometrioid carcinoma cases and lacks information of molecular subtypes. Therefore, we further included the TCGA UCEC dataset in our analysis, by building a mathematical model to calculate an estimated O-GlcNAc index for each TCGA UCEC case. This computational analysis not only confirmed the observations made with our EC cohort, but also revealed that EC patients belonging to the copy-number high molecular subtype group have significantly higher O-GlcNAcylation level than that in the other groups. Agreeingly, one feature of the copy-number high molecular subtype is TP53 mutations18, and p53 is a known negative regulator of O-GlcNAcylation level not only identified in our genome-wide screen but also reported in a previous study56. Loss-of-function of p53 in tumor cells increases glucose uptake, aerobic glycolysis, and pentose phosphate pathway (PPP) flux, thereby promoting O-GlcNAcylation level56. These findings suggest that O-GlcNAcylation is a useful factor complementary to the current classification system to better identify EC patients with poor clinical outcomes.\n\nOur understanding on the molecular circuitry controlling the cellular O-GlcNAcylation homeostasis is incomplete. Given that O-GlcNAcylation is dependent on nutrient availability, metabolic factors such as GFPT1, POLDIP2, and PPM1K have been reported to influence O-GlcNAcylation level by modulating the metabolic flux of the HBP pathway56,59,64,70,71. However, emerging evidence indicates that O-GlcNAcylation may also be regulated by non-nutrient dependent mechanisms, particularly at the protein level of OGT57,58,62,66,67,68. OGT is regulated by the balance of ubiquitination and deubiquitination9,10,12,13. The E3 ligases XIAP and E6AP have been reported to promote the ubiquitin-dependent proteasome degradation of OGT10,11. The histone demethylase LSD2 displays an atypical ubiquitin E3 ligase activity toward OGT in the A549 cells9. However, we found that the expression of these reported E3 ligases of OGT has no clinical relevance in ECs. Instead, our unbiased screen uncovered that FBXO31, together with other components in the SCF complex, functions as an E3 ligase for OGT. Loss of FBXO31 stabilizes OGT and increases cellular O-GlcNAcylation level, thereby promoting the progression of endometrial malignancy. Consistently, EC patients with low FBXO31 expression exhibited more advanced histologic grade and poor survival. FBXO31 is a tumor suppressor gene located in the 16q24.3 region, with frequently observed loss of heterozygosity in several cancers, including breast, ovarian, hepatocellular, and prostate cancers81,82. The FBXO31-OGT regulatory axis reported in this study is worthy of investigation in these cancers as well.\n\nHow the aberrant O-GlcNAcylation downstream of the FBXO31-OGT regulatory module promotes EC progression is not fully understood and can be complex. Loss of FBXO31 leads to accumulation of OGT that can increase O-GlcNAcylation on thousands of nuclear and cytoplasmic proteins, including many EC-related oncogenes and tumor suppressors, such as PI3K, PTEN, ARID1A83, p5384, c-Myc85, YAP24,29,37, and \u03b2-catenin86. Our study revealed that c-Myc protein level was increased in FBXO31-KO EC cells in an OGT-dependent manner, suggesting that c-Myc can be one of the key tumor-promoting factors controlled by the FBXO31-OGT axis. Additionally, proteins involved in the regulation of pluripotency, such as Oct487, Sox288, and Sox989, are able to be modified by O-GlcNAcylation, which may contribute to the observation that increased O-GlcNAcylation promotes stemness of EC cells. It is noteworthy that in addition to OGT, the SCFFBXO31 complex can also ubiquitinate other protein substrates, including cell cycle regulators, such as cyclin D174, Cdt175, MDM277, and cyclin A90; signaling molecules, such as c-Myc91, \u03b2-catenin92, and MKK676; as well as EMT factors, Snail193 and Slug94. Considering how FBXO31 recognizes its substrates remains unclear and FBXO31 can physically interact with OGT, it is compelling to speculate that O-GlcNAcylation can modulate the substrates recognition of the SCFFBXO31 complex. This hypothesis warrants future molecular and structural characterizations.\n\nLast but not least, our pilot experiments in mouse xenograft models validated that the OGT inhibitor OSMI-1 has anti-tumor activity in vivo. The administration of OSMI-1 reduced O-GlcNAcylation level in tumors formed by the subcutaneously injected Ishikawa cells, downregulated the expression of many genes involved in stemness, EMT, and angiogenesis, inhibited the formation of blood vessels, and significantly limited the tumors\u2019 growth. These results suggest that hyper O-GlcNAcylation is a shared vulnerability for the FBXO31 mutated as well as many high histologic grade EC cases, and targeting this dysregulated O-GlcNAcylation homeostasis holds promising therapeutic significance. Future elaboration of the spatiotemporal dynamics of the O-GlcNAcylation landscapes during the progression of ECs with high-throughput, tissue-specific proteomic profiling methods will further consolidate the foundation of targeting O-GlcNAcylation to develop new therapeutic strategies in clinical settings.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56633-z/MediaObjects/41467_2025_56633_Fig9_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "This research complies with all relevant ethical regulations approved by the Ethics Committee of Central South University, including the use of human tissues and mouse experiments.\n\nAll fresh tissues and paraffin-embedded (FFPE) tissues were prospectively obtained from patients with endometrial diseases at Xiangya Hospital, Central South University. Clinical data and histopathological characteristics were retrieved from patient records and routine pathology reports. The study was approved by the Medical Ethics Committee of Central South University (No. 202103076, No. 201910255), and all participating patients provided informed written consent. The study was registered with and approved by the Human Genetics Resource (HGR) office of the Minister of Science and Technology of China (No. 2024BAT00742). Surgical tumor tissue from consented patients with a confirmed diagnosis of EC was included in this study. The inclusion criteria for patient enrollment were as follows: no prior anticancer therapies, no diagnosis or history of other concurrent malignancies, and availability of follow-up data. For this study, all pathology reports were reviewed by two pathologists. Patients whose original biopsies did not indicate endometrial cancer or whose histology was insufficiently informative were excluded. The endometrial tissues for generating the EE-Os were obtained from endometriosis patients who underwent hysteroscopic biopsy for endometrial polyps without prior drug treatment. Samples were selected based on tissue availability without bias toward any specific parameters.\n\nThe endometrial organoids were generated as previously described95. Tumor tissues and non-cancerous control tissues were isolated and stored in ice-cold serum-free DMEM medium supplemented with 1% penicillin-streptomycin. The tissues were then washed in ice-cold DPBS (Biological Industries) supplemented with penicillin-streptomycin and minced into small pieces. The tissues were digested by collagenase IV (1-2\u2009mg/mL; 17104019, Thermo Fisher Scientific) in the presence of ROCK inhibitor (10\u2009\u00b5M; SCM075, Merck Millipore) and penicillin-streptomycin for 1\u2009h on a shaker at 37\u2009\u00b0C, then incubated for 15\u2009min in TrypLE (1 \u00d7; 12604013, Thermo Fisher Scientific) supplemented with ROCK inhibitor and penicillin-streptomycin. Subsequently, the tissue digests were stopped by ice-cold serum-free DMEM/F12 and after centrifugation, a 100\u2009\u03bcm cell strainer was used to obtain cell pellets. The strainers were inverted over a Petri dish, and the glandular elements were backwashed, transferred to a centrifuge tube, and pelleted by centrifugation. Larger undigested tissue fragments retained on the strainer were collected for further digestion. Finally, the cell pellets were resuspended in 70% Matrigel mixed with 30% DMEM/F12 (356231, Corning and 11039021, Gibco, respectively) and seeded in 50\u2009\u03bcL droplets in non-treated 24-well plates. After incubation at 37\u2009\u00b0C and 5% CO2 in a cell culture incubator for 20\u201330\u2009min, the pre-warmed organoid complete medium (DMEM/F12 supplemented with 1% penicillin-streptomycin, 2% B27 supplement minus vitamin A (12587010, Gibco), 5% R-spondin-1 conditioned medium, 1% chemically defined lipid concentrate (11905031, Gibco), recombinant human Noggin 100\u2009ng/mL (HY-P7051A, MCE), 1% N2 (17502048, Gibco), N-acetyl-L-cysteine 1.25\u2009mM (A7250, Sigma Aldrich), Nicotinamide 10\u2009\u00b5M (73240, Sigma Aldrich), recombinant human EGF 50\u2009ng/mL (236-EG-01M, R&D Systems), Y-27632 10\u2009\u00b5M (SCM075, Sigma Aldrich), 17-\u03b2 estradiol 10\u2009nM (E8872, Sigma Aldrich), SB202190 0.1\u2009\u00b5M (S7067, Sigma Aldrich), A83-01 0.25\u2009\u00b5M (SML0788, Sigma Aldrich), recombinant human IGF 40\u2009ng/mL (100-11, Peprotech), recombinant human HGF 20\u2009ng/mL (100-39, Peprotech), IL-6 5\u2009ng/mL (200-06, Peprotech)) was added. The organoid medium was changed every 2\u2009days, and the organoids were passaged after 7-10\u2009days of culture. Organoids of low passage number (P3-P6) were used for the experiments described. To assess clonogenic capacity, organoids were dissociated into single cells with TrypLE supplemented with Y-27632, filtered through a 40-\u00b5m cell strainer and resuspended in 70% Matrigel / 30% DMEM/F12 supplemented with Y-27632 at 1000 cells per well in 96-well plates. The organoids formed were counted after 10\u201315\u2009days. A randomly selected field of view at 10\u2009\u00d7\u2009magnification in each well was used to count organoids and measure their cross-sectional area.\n\nAn endometrial cancer tissue array was purchased from Xinchao Biotechnology Company (HUteA060CS01, Shanghai, China), consisting of 26 pairs of tumoral and peritumoral tissue specimens, along with an additional 8 cases of cancerous tissue without paired peritumoral tissue. After removing the incomplete tissue spots, 31 cases of cancer tissue and 23 cases of peritumoral tissue were included in IHC analysis. IHC was performed as previously described96, with primary antibody incubation overnight after antigen retrieval and endogenous peroxidase activity blocking on paraffin sections. The IHC staining signal levels were blindly scored by two independent assessors without knowledge of clinical parameters, and confirmed by a pathologist. Based on the proportion of positive stained-tumor cells which was assessed on a value of 0\u20134: 0 (negative), 1 (1\u201325%), 2 (26\u201350%), 3 (51\u201375%), or 4 (76\u2013100%) and the intensity of staining which was scored on a value of 0-3: 0 (negative), 1 (weak), 2 (medium), or 3 (strong). The final IHC score was calculated by multiplying the staining intensity score (0\u20133) and the proportion of positively stained tumor cells score (0\u20134), resulting in a staining index (SI) ranging from 0 to 12. Low and high expression were defined as SI 0\u20136 and SI 8\u201312, respectively36. Immunofluorescence was performed for CD31 staining, a marker of endothelial cells. Briefly, the slides were de-paraffinized, and avidin and biotin were added with the blocking agents. The blocking agent was blotted off, and the first antibody was added to incubate overnight at 4\u2009\u00b0C. The slides were washed and incubated with the second antibody at room temperature for 30\u2009min. DAPI was applied to the slides and coverslip was placed, and the slides were kept in dark97. All the antibodies used in this study were listed in Supplementary Data\u00a010.\n\nProgression-free survival (PFS) was calculated as the time between the surgery that procured the sample and the date of disease progression or a new metastatic event in a different location. Overall survival (OS) was defined as the interval between the date of surgery and the date of death or last follow-up. Progression-free interval (PFI) was defined as the duration from surgery to the first occurrence of disease progression or death after treatment. The curves were stratified based on the O-GlcNAcylation level. Log-rank test was used to compare the two groups over a follow-up time of 61\u2009months. Kaplan-Meier survival curves were generated and compared using GraphPad Prism (version 8.0.2).\n\nThe RNA-seq data of 15 low O-GlcNAcylation level (RL2 by IHC) tumor tissues and 40 high O-GlcNAcylation level tumor tissues were processed to identify the O-GlcNAcylation correlated genes. The gene expression matrix of these 55 EC samples was correlated with the O-GlcNAcylation IHC staining index using the Pearson correlation method in the mlr3.filters package within the mlr3 framework in R. The top 1000 genes with a correlation coefficient >0.3 were included in the O-GlcNAc correlated gene set. Subsequently, mlr3 learners including six regression model-based approaches (regr.lm, regr.glmnet, regr.kknn, regr.ranger, regr.rpart, regr.svm) were applied to the expression matrix of the 1000 O-GlcNAcylation correlated genes. The O-GlcNAc indices for the 55 EC tissues were calculated, subjecting to 5-fold cross-validations of training and ranking based on predefined performance metrics. The reliability of the prediction model was assessed by comparing the calculated O-GlcNAc indices with actual IHC SI scores. The regr.glmnet demonstrated the lowest mean squared error (MSE) and was selected for the establishment of the final prediction model. The O-GlcNAc indices were then calculated using the prediction model for the 589 EC samples in TCGA. The patients were categorized into high and low O-GlcNAcylation groups using the median of the calculated O-GlcNAc indices. Wilcoxon Mann-Whitney tests were used to assess differences between the two groups in terms of histologic grade, FIGO stage, molecular subtype, age, and diabetes. Log-rank tests were employed to compare the OS and PFI differences between the high and low O-GlcNAcylation groups, and Kaplan-Meier survival curves were generated and compared using R (version 4.03).\n\nImmunofluorescence staining experiments were performed on organoids as previously described98. When the organoids reached a size of ~100\u2009\u03bcm, they were selected for staining. After washing twice with pre-cooled DPBS, 500\u2009\u03bcL of cell recovery solution (354253, Corning) was added to each well, and the Matrigel was dissolved on ice to ensure that the morphology of the organoids was not disrupted. After 30\u2009min, all the organoids were collected into a 15\u2009mL centrifuge tube, fixed with 4% paraformaldehyde for 30\u2009min, and then centrifuged to remove the supernatant. Next, 10\u2009mL of 1% PBST was added to stop the tissue fixation. After blocking with Organoid Washing Buffer (OWB, 0.1% Triton X-100, 0.2% BSA in DPBS), the primary antibody was added and incubated overnight at 4\u2009\u00b0C with shaking at 60\u2009rpm. On the following day, the organoids were washed three times with OWB for 2\u2009h each time, and then the corresponding fluorescent secondary antibody was added. The organoids were incubated overnight on a shaker in the dark. On the third day, 4\u2032,6-Diamidino-2-phenylindole dihydrochloride (DAPI, D9542, Sigma) at 10\u2009mg/mL was added for 30\u2009min. After washing with OWB, the samples were spun down at 70\u2009\u00d7\u2009g for 5\u2009min at 4\u2009\u00b0C. Finally, the organoids were resuspended with fructose-glycerol clearing solution (60% glycerol and 2.5\u2009M fructose in ddH2O) and imaged using an LSM880 confocal microscope (Zeiss) and a CSU-W1 spinning disk field scanning confocal system (Nikon). A cell death detection (TUNEL) kit (Roche) was used to identify dead cells in accordance with the company\u2019s description. All the antibodies used in this study were listed in Supplementary Data\u00a010.\n\nFor organoid lentiviral transduction, pLKO.1-puro vectors and TK-PCDH-copGFP-T2A-Puro vectors were used. The organoids were washed twice with pre-cooled DPBS, and 500\u2009\u03bcL of TrypLE (12604013, Thermo Fisher Scientific) was added to each well for 10\u2009min at 37\u2009\u00b0C. The Matrigel was disrupted by pipetting the mixture up and down repeatedly during digestion. TrypLE was inactivated by adding 10\u2009mL of ice-cold serum-free DMEM/F12, and the mixture was centrifuged for 5\u2009min at 200\u2009\u00d7\u2009g. After digestion, the organoids were made into single cells or cell mass and resuspended in virus infection solution containing ROCK inhibitor, polybrene, and concentrated lentivirus in organoid culture media. The cell suspension was added to a 6-well plate, spun at 2000\u2009rpm for 1\u2009h, and then incubated at 37\u2009\u00b0C for 5-6\u2009h. The cells were then transferred to a 15\u2009mL centrifuge tube, washed twice with serum-free DMEM/F12, and seeded in a prewarmed 24-well plate with 70% Matrigel. Then, 500\u2009\u00b5L of organoid medium was added to each well, followed by incubation at 37\u2009\u00b0C with 5% CO2 for 20\u2009min. The medium was changed every 2\u2009days. Puromycin selection (1\u2009\u03bcg/mL) in organoid culture was conducted for 3\u20134\u2009days to establish stably infected organoids. The stable organoids were validated by western blot or quantitative RT-PCR.\n\nTumor organoids were recovered from the Matrigel and dissociated. 2000 cells were seeded in 96-well plates and allowed to form organoids for 7\u2009days. Then, TMG (10\u2009\u00b5M, Selleck; S7213), OSMI-1 (50\u2009\u00b5M, Selleck; S9835), OSMI-4 (20\u2009\u00b5M, MCE; HY-11436), or DMSO (0.1%, Sigma Aldrich, D2650) was added and viability was measured after 72\u2009h using Cell Titer Glo 3D cell (Promega, Cat# G9681) following the manufacturer\u2019s instructions95. The Cell Titer-Glo\u00ae 3D Cell reagent was thawed and equilibrated at room temperature for 30\u2009min. The reagent was mixed 1:1 with organoid complete medium and added to the plate. After a 30\u2009min incubation at 37\u00b0C, the luminescence was measured on the PerkinElmer Envision.\n\nIn this in vivo study, 4-week-old female nude mice without a thymus (BALB/C) were purchased from Hunan SJA Laboratory Animal Co., Ltd (Changsha, China). All animal experiments were conducted in accordance with the Animal Welfare Law and were approved by the Ethics Committee of Central South University (No. 202103076, No. 202411200). The mice were housed at a maximum of five per cage under a 12\u2009h light/dark cycle at 22\u201325\u00b0C and 50\u201370% humidity. The animals were provided with standard growth maintenance chow (GMCF, purchased from Beijing Keao Xieli Feed Limited, Beijing, China; Product ID: 24083213) and allowed free access to water. Ishikawa or Ishikawa FBXO31-KO endometrial cancer cells were suspended in 100\u2009\u00b5L DPBS and injected subcutaneously into the left flank (5\u2009\u00d7\u2009106 cells per mouse). For the tumor TMG or OSMI-1 treatment study, after 10\u2009days, upon tumor onset, mice were randomly divided into three groups: the DMSO group (vehicle solvent, n\u2009=\u20098), the TMG group (20\u2009mg/kg/day, n\u2009=\u20097), and the OSMI-1 group (10\u2009mg/kg/day, n\u2009=\u20098). The vehicle solvent, comprising 5% DMSO, 40% PEG300 (Selleck), and 5% TWEEN80 (Selleck), was prepared according to the manufacturer\u2019s instructions to improve the solubility of OSMI-1 and TMG for intraperitoneal injections in mice99. The mice received daily intraperitoneal injections over 15\u2009days. Tumor growth, body weight, and survival of the animals were monitored twice a week. The maximal tumor volume allowed by the Ethical Committee for Animal Experiment of the Central South University is 2000\u2009mm3, which was not exceeded in this study. Experimental endpoints were reached when tumors exceeded 20\u2009mm in diameter or ruptured, or when mice became moribund, showing signs of lateral recumbency, cachexia, lack of response to noxious stimuli, or observable weight loss (\u226520% of body weight)100. For the FBXO31-KO tumor study, upon tumor onset, nude mice were randomly divided into four groups: the WT DMSO control group (vehicle solvent, 5% DMSO\u2009+\u200940% PEG300\u2009+\u20095% TWEEN80, Selleck, n\u2009=\u200910), the WT OSMI-1 treatment group (10\u2009mg/kg/day, n\u2009=\u20099), the FBXO31-KO DMSO group (vehicle solvent, 5% DMSO\u2009+\u200940% PEG300\u2009+\u20095% TWEEN80, Selleck, n\u2009=\u200910), and the FBXO31-KO OSMI-1 treatment group (10\u2009mg/kg/day, n\u2009=\u20099). Tumor growth and body weight of the animals were monitored three times a week. Tumor volume was calculated by measuring the short (l) and long (L) diameters (volume\u2009=\u2009l2\u2009\u00d7\u2009L/2). Mice were sacrificed for examination 28\u2009days after tumor inoculation. At the end of the study, mice were euthanized, and the tumors were processed for further analyses.\n\nRNA extraction was performed using TRIzol (87804, Life Technologies) according to the manufacturer\u2019s protocol for all samples, including cells, organoids, and primary tissues. The extracted RNA was then reverse transcribed to cDNA using the PrimeScript RT Reagent Kit (RR037A, Takara). The cDNA was used as a template for qPCR, which was performed using the SYBR Green qPCR Master Mix (QST-100, SolomonBio) on the QuantStudio 3 Real-Time PCR system (Applied Biosystems). All the primers were listed in Supplementary Data\u00a011.\n\nCells were lysed in sample buffer (2% SDS, 10% glycerol, and 62.5\u2009mM Tris-HCl, pH 6.8) supplemented with 1\u2009\u00d7\u2009protease inhibitor cocktail (P8340, Sigma). The protein concentration was measured using a BCA kit (P0009, Beyotime). Cell lysates were separated by SDS-PAGE and transferred onto a nitrocellulose membrane. The membrane was then blocked with 5% non-fat dry milk for 1\u2009h at room temperature and probed with the indicated primary antibodies overnight at 4\u00b0C. Antigen-antibody complexes were detected by incubating with horseradish peroxidase secondary antibodies followed by ECL substrates (WBKLS0500, Millipore). For immunoprecipitation experiments, cells were washed twice with ice-cold PBS and then lysed in lysis buffer (20\u2009mM Tris-HCl (pH 8.0), 137\u2009mM NaCl, 1% NP-40, 2\u2009mM EDTA) on ice for 30\u2009min. Cell lysates were gently mixed with specific antibodies overnight at 4\u00b0C under gentle rotation, then incubated with protein A/G beads (SC-2003, Santa Cruz) for 1-2\u2009h at 4\u00b0C Immunoprecipitants were washed three times with lysis buffer. After the final wash, the supernatant was aspirated and discarded, and the pellet was resuspended in 2\u2009\u00d7\u2009SDS sample buffer (0.125\u2009M Tris HCl (pH 6.8), 4% SDS, 20% glycerol, 2% \u03b2-mercaptoethanol, 0.02% bromophenol blue). The sample was then subjected to reducing SDS-PAGE and western blot. All the antibodies used in this study were listed in Supplementary Data\u00a010.\n\nHA-R-Spondin1-Fc 293T cell line (3710-001-01, R&D Systems) was used to produce R-spondin-1 conditional media. HEK293T (ATCC, CRL-3216) and Ishikawa cells (Sigma, 99040201) were maintained in DMEM (06-1055-57-1 ACS, Vivocell) supplemented with 10% FBS. All cells were cultured at 37\u2009\u00b0C in a humidified incubator with 5% CO2 and periodically screened for Mycoplasma contamination. Human FBXO31 knockout cell lines were generated according to previously published protocol79. To generate 293T and Ishikawa FBXO31-KO cell lines, the cells were transfected with LentiCRISPR-V2 plasmid carrying sgFBXO31 (Supplementary Data\u00a011) and further selected with 1\u2009\u03bcg/mL puromycin (s7417, Selleck) for 3 days. The cells were then plated at single-cell density in 100\u2009mm Petri dishes, and the individual clones that emerged were picked and replated into 24-well plates. The loss of FBXO31 expression was confirmed by western blot and Sanger sequencing. Genomic DNA was extracted using QuickExtract (Epicenter). Genotyping PCRs were performed with KOD FX DNA Polymerase (KFX-101, Toyobo) using primers flanking the genomic target site. The resulting PCR products were purified and sequenced to confirm the presence of indel events. To further validate the mutational status of candidate clones, the PCR products underwent TA cloning (Invitrogen) and were sequenced to distinguish the amplified products of distinct alleles. Clones with confirmed insertion or deletion events were also validated by western blot analysis.\n\nFor detection of ubiquitinated proteins in vivo, 293T and Ishikawa cells were co-transfected with expression vectors for HA-ubiquitin and the indicated proteins. Polyubiquitinated OGT was detected by immunoprecipitation of OGT with ANTI-FLAG\u00ae M2 Affinity Gel (A2220, Merck Millipore) or OGT antibody under denaturing conditions followed by western blot with an anti-HA antibody. In vitro ubiquitination was performed as previously described101. The SCF-FBXO31 (E3) complexes were immunopurified from the cell lysate using Pierce\u2122 Anti-HA Magnetic Beads (88836, Thermo Fisher Scientific) and incubated with His-OGT fusion protein expressed and purified from E. coli as previously reported102 in the presence of recombinant purified E1 (UBA1; 11990-H20B, sinobiological), E2 (UBE2D1; 11432-H07E, sinobiological), recombinant human ubiquitin protein (U-100H, Boston Biochem), and ubiquitination buffer (20\u2009mM Tris-HCl, pH 7.5, 5\u2009mM MgCl2, 0.5\u2009mM DTT, 2\u2009mM ATP). The reaction was stopped by adding 2\u2009\u00d7\u2009SDS sample buffer and boiling for 10\u2009min.\n\nThe human genome-scale CRISPR knockout library (GeCKO v2, Addgene #1000000048) in the lentiCRISPR v2 vector (Addgene, #52961) consists of 123,411 sgRNAs that target 19,050 protein-coding genes (6 sgRNAs per gene) and 1000 nontargeting control sgRNAs was used103,104. The human GeCKO v2 library was transduced into 293T cells by lentivirus at a multiplicity of infection of 0.3. Cells were selected with puromycin for 7\u2009days followed by fluorescence-activated cell sorting (FACS) based on their O-GlcNAcylation staining intensities. An unsorted sample was used to assess sgRNA library coverage, and the sorted RL2 high population was subjected to genomic DNA extraction. The inserted sgRNA library was amplified by two steps of PCR for next-generation sequencing. Each screen was performed twice. For data analysis, reads from the fastq files generated by sequencing were tallied for each guide by taking the first 20\u2009bp from each read and mapping to the identical short gRNA sequence. For each screen, a table of reads per guide that includes the counts from the RL2 high population of both replicates was generated and loaded into MAGeCK55. Top genes were determined based on their mean log2 fold change, FDR, and robust ranking aggregation (RRA) score.\n\nThe TCGA UCEC dataset used in this study, including the gene raw count data (htseq-count files), and the annotated somatic simple nucleotide variation files (MuTect2 VCF), were downloaded using the gdc-client v1.6.0. The clinical OS and PFI information were obtained from Liu et al.105.\n\nTotal RNA was isolated from EC tissues, and libraries were generated using the NEBNext UltraTM RNA Library Prep Kit (New England Biolabs) for the Illumina system. Sequencing was conducted using the Illumina Novaseq 6000 platform (Novogene). Trim Galore v0.6.4 was employed to eliminate adapter sequences and remove reads of poor quality. Subsequently, the reads from each RNA-seq data were aligned to the hg38 genome assembly using STAR v2.7.2\u2009d. The key alignment parameters were set as follows: \u2018--outFilterMismatchNoverLmax 0.04 --outSAMtype BAM SortedByCoordinate --outFilterMultimapNmax 500 --outMultimapperOrder Random --outSAMmultNmax 1\u2019. Gene expression was quantified using featureCounts v2.0.0. Heatmaps were created using R package heatmap v1.0.12. Differential expression analysis was conducted using the negative binomial distribution with the \u2018DESeq\u2019 and \u2018results\u2019 functions from DESeq2, applying cut-off values of adjusted p-value\u2009<\u20090.05 and |log2 FC\u2009|\u2009>\u20091. All GSEA analyses presented in this study were based on hallmark gene sets and performed using the R package \u2018clusterProfiler\u2019. The GO enrichment analysis was performed using the function \u2018enrichGO\u2019 from the R package clusterProfiler v3.10.1106.\n\nEE-Os were treated with 10\u2009mM TMG or vehicle control (0.1% DMSO). Following treatment, the organoids were dissociated into single cells using TrypLE digestion, and the mixture was passed through a 40-\u03bcm cell strainer. The cells were then counted and viability assessed. Single-cell suspensions (2 \u00d7 105 cells/mL) in PBS (HyClone) were loaded onto microwell chip using the Singleron Matrix\u00ae Single Cell Processing System. Barcoding beads were subsequently collected from the microwell chip, followed by reverse transcription of the mRNA captured to obtain the cDNA. After PCR amplification, the amplified cDNA was then fragmented and ligated with sequencing adapters. The scRNA-seq libraries were constructed according to the protocol of the GEXSCOPE\u00ae Single Cell RNA Library Kits (Singleron)107. Individual libraries were diluted to 4\u2009nM, pooled, and sequenced on Illumina Novaseq 6000 with 150\u2009bp paired end reads. Raw reads were processed to generate gene expression profiles using CeleScope v2.0.7 (Singleron) with default parameters. Briefly, barcodes and UMIs were extracted from R1 reads and corrected. Adapter sequences and polyA tails were trimmed from R2 reads and the trimmed R2 reads were aligned to the hg38 transcriptome using STAR (v2.6.1b). Uniquely mapped reads were then assigned to exons with featureCounts (v2.0.1). Successfully assigned reads with the same cell barcode, UMI and gene were grouped to generate the gene expression matrix. Omicverse V1.5.4 was used for quality control, dimensionality reduction and clustering under Python 3.8. The following criteria were used to filter the expression matrix: (1) cells with gene count <500 were excluded; (2) cells detected genes <250 were excluded; (3) cells with mitochondrial content >15% were excluded; (4) genes expressed in <3 cells were excluded. After filtering, 20,736 cells were retained for the downstream analyses. The raw count matrix was normalized by total counts per cell and logarithmically transformed into normalized data matrix. The top 3000 highly variable genes were selected by setting flavor\u2009=\u2009\u2018seurat\u2019. Principal Component Analysis (PCA) was performed on the scaled variable gene matrix, and 50 principal components were used for clustering and dimensional reduction. \u2018Harmony\u2019 was employed to integrate samples. Cells were separated into 6 clusters using Leiden algorithm with the resolution parameter at 0.25. Subsequently, omicverse was used to calculate the ranking of highly differential genes in each cluster to identify marker genes. Cell clusters were visualized using Minimum-Distortion Embedding (mde). Cell types were annotated based on the cell type auto-annotation tool SCSA, and the known cellular markers from the literature38,53,108,109,110: epithelial cells (EPCAM, KRT8, KRT18), stem cells (LGR5, SOX9, POU5F1, PROM1, AXIN2), proliferative cells (MMP7, TOP2A, MKI67), ciliated cells (PIFO, FOXJ1, TPPP3), pre-ciliated cells (CDC20B, DYDC2, CCNO), and inflammatory cells (IL4I1, IL32, S100A9, CD14, IL1RN). The O-GlcNAc-related stem-like cells annotation was mainly based on the results of KEGG pathway enrichment. The expression of markers used to identify each cell type was visualized using a violin plot.\n\nKyoto Encyclopedia of Genes and Genomes (KEGG) pathways were collected and used as functional genesets for AUCell scoring. AUCell scores of gene sets were visualized using sc.pl.embedding. p-values from t-tests were used for estimating the statistical significance between cell types and groups.\n\nThe experiments were conducted in at least three independent biological replicates, and the data were presented as mean\u2009\u00b1\u2009SD. If not specified, the Student\u2019s t-test was used to perform a statistical significance test between different groups, and p\u2009<\u20090.05 was considered significant. Overall survival curves were estimated by the Kaplan-Meier method and Cox proportional hazards model. All statistical and correlation analyses were performed using the GraphPad Prism 8.0 software (GraphPad Software) and SPSS 26.0 (SPSS Software).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The sequencing data generated in this study, including both RNA-seq and scRNA-seq raw data, have been deposited in the Genome Sequence Archive for Human (GSA) under the accession number HRA007070. These raw data files can be accessed at https://download.cncb.ac.cn/gsa-human/HRA007070. These data have been registered with the Human Genetics Resource Office in China, with the registration number: 2024BAT00742. The sequencing raw data of the genome-scale CRISPR knockout screen generated in this study have been deposited in the Gene Expression Omnibus (GEO) database under the accession code GSE287788. These data are publicly available. Source data are provided with this paper. All the other data are available within the article and its Supplementary Information.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The original code for the mathematical model of the virtual O-GlcNAc index has been deposited in Zenodo (https://doi.org/10.5281/zenodo.14292333).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Yang, X. & Qian, K. Protein O-GlcNAcylation: emerging mechanisms and functions. Nat. Rev. Mol. Cell Biol. 18, 452\u2013465 (2017).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nZachara, N. E. & Hart, G. W. O-GlcNAc a sensor of cellular state: the role of nucleocytoplasmic glycosylation in modulating cellular function in response to nutrition and stress. Biochim. Biophys. 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Daan van Aalten, Kum Kum Khanna, Xiaowei Yang, Timothy Mitchison, Xuebiao Yao, Chao Xu, Cuiting Yong, Lisha Wu, Wenqing Yang, and Hongqiang Qin for reagents or inspiring discussions. This project has been supported by the National Natural Science Foundation of China (grants 92153301, 32170821, and 32370821 to K.Y, 32101034 to F.C), National Key Research and Development Program of China (2021YFC2701200), Department of Science & Technology of Hunan Province (grants 2023RC1028, 2023SK2091, and 2021JJ10054 to K.Y).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Hunan Key Laboratory of Molecular Precision Medicine, Department of Oncology & Department of Gynecology, Xiangya Hospital, Central South University, Changsha, 410000, China\n\nNa Zhang,\u00a0Song Mao,\u00a0Huiling Ni,\u00a0Canhua Huang,\u00a0Licong Shen,\u00a0Kun Fu,\u00a0Lu Lv,\u00a0Chunhong Yu,\u00a0Piyanat Meekrathok,\u00a0Chunmei Kuang,\u00a0Fang Chen,\u00a0Yu Zhang\u00a0&\u00a0Kai Yuan\n\nCenter for Medical Genetics, School of Life Sciences, Central South University, Changsha, 410008, China\n\nYang Meng,\u00a0Huiling Ni\u00a0&\u00a0Kai Yuan\n\nSchool of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China\n\nYang Meng\n\nFurong Laboratory, Changsha, 410008, China\n\nKai Yuan\n\nNational Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410000, China\n\nKai Yuan\n\nThe Biobank of Xiangya Hospital, Central South University, Changsha, 410000, China\n\nKai Yuan\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: K.Y.; Methodology: N.Z., Y.M., H.N., C.H., L.S., L.L., C.Y., P.M., C.K., S.M., F.C., Y.Z., K.Y.; Validation: H.N., C.H., L.S., K.F.; Software: N.Z., Y.M., S.M.; Formal Analysis: N.Z., Y.M., K.Y.; Investigation: N.Z., Y.M., H.N., S.M., K.Y.; Resources: Y.Z., K.Y.; Data Curation: N.Z., Y.M., H.N.; Writing-Original Draft: N.Z., K.Y.; Writing-Review & Editing: K.Y.; Visualization: N.Z., Y.M., S.M., K.Y.; Supervision: K.Y.; Project Administration: L.L., K.Y.; Funding Acquisition: Y.Z., K.Y.\n\nCorrespondence to\n Kai Yuan.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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FBXO31-mediated ubiquitination of OGT maintains O-GlcNAcylation homeostasis to restrain endometrial malignancy.\n Nat Commun 16, 1274 (2025). https://doi.org/10.1038/s41467-025-56633-z\n\nDownload citation\n\nReceived: 06 March 2024\n\nAccepted: 24 January 2025\n\nPublished: 02 February 2025\n\nVersion of record: 02 February 2025\n\nDOI: https://doi.org/10.1038/s41467-025-56633-z\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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b/19a49b5392b6ecc9b2b1cb7e9c4673f0cfa1d11b956f8b9bd67664c341dc49f0/metadata.json @@ -0,0 +1,144 @@ +{ + "title": "Experimental determination of giant polarization in wurtzite III-nitride semiconductors", + "pre_title": "Experimental Determination of Giant Polarization in Wurtzite III-Nitride Semiconductors", + "journal": "Nature Communications", + "published": "24 April 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58975-0/MediaObjects/41467_2025_58975_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58975-0/MediaObjects/41467_2025_58975_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58975-0/MediaObjects/41467_2025_58975_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-58975-0#Sec13" + ], + "code": [], + "subject": [ + "Ferroelectrics and multiferroics", + "Semiconductors" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5438804/v1.pdf?c=1745579149000", + "research_square_link": "https://www.researchsquare.com//article/rs-5438804/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58975-0.pdf", + "preprint_posted": "24 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Polarization engineering has revolutionized the photonic and electronic landscape of III-nitride semiconductors over the past decades. However, recent revelations of giant ferroelectric polarization in wurtzite III-nitrides challenge the long-standing paradigms. Here, we experimentally elucidate the polarization, including its magnitude and orientation, and its relationship to lattice polarity in III-nitrides. Those experimentally determined polarizations exceeding 1 C/m2 with an upward orientation in metal-polar wurtzite nitride compounds align with recent theoretical predictions. This unified framework redefines the polarization landscape in contemporary GaN heterostructures, quantum structures, and ferroelectric heterostructures. Furthermore, we predict significant tunability and a dramatic increase in sheet electron concentration in ferroelectric ScAlN/GaN heterostructures, heralding advancements in high-power, high-frequency, and reconfigurable transistors, and non-volatile memories. This work bridges the critical gap in the understanding of polarization in both conventional and ferroelectric wurtzite nitrides, offering fundamental insights and paving the way for next-generation photonic, electronic, and acoustic devices.Physical sciences/Physics/Condensed-matter physics/SemiconductorsPhysical sciences/Materials science/Condensed-matter physics/SemiconductorsPhysical sciences/Physics/Condensed-matter physics/Ferroelectrics and multiferroicsPhysical sciences/Materials science/Condensed-matter physics/Ferroelectrics and multiferroics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SIIIINpolarization.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Polarization engineering has revolutionized the photonic and electronic landscape of III-nitride semiconductors over the past decades. However, recent revelations of giant ferroelectric polarization in wurtzite III-nitrides challenge the long-standing paradigms. Here, we experimentally elucidate the polarization, including its magnitude and orientation, and its relationship to lattice polarity in III-nitrides. Those experimentally determined polarizations exceeding 1 C/m2 with an upward orientation in metal-polar wurtzite nitride compounds align with recent theoretical predictions. To reconcile these findings, a unified polarization framework is established based on the centrosymmetric layered-hexagonal reference structure. This unified framework redefines the polarization landscape in contemporary GaN heterostructures, quantum structures, and ferroelectric heterostructures. Furthermore, we predict significant tunability and a dramatic increase in sheet carrier concentration in ferroelectric ScAlN/GaN heterostructures, heralding advancements in high-power, high-frequency, and reconfigurable transistors, and non-volatile memories. This work bridges the critical gap in the understanding of polarization in both conventional and ferroelectric wurtzite nitrides, offering fundamental insights and paving the way for next-generation photonic, electronic, and acoustic devices.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "III-nitride semiconductors, characterized by their non-centrosymmetric wurtzite (WZ, P63mc) structure, inherently exhibit nonvanishing spontaneous polarization and piezoelectric polarization1,2. The pronounced polarization discontinuities at the III-nitride heterointerfaces result in the formation of bound sheet charges. These charges generate an internal polarization field, leading to phenomena such as the formation of two-dimensional electron or hole gases (2DEG or 2DHG)3,4,5, polarization-induced doping6, quantum-confined Stark effect7, etc. in III-nitride heterostructures. Over the past decades, polarization engineering has remarkably boosted the photonic and electronic ecosystem of III-nitrides8,9,10. However, recent discoveries of wurtzite nitride ferroelectrics, such as scandium aluminum nitride (ScAlN)11,12,13,14,15, which exhibit a giant spontaneous polarization compared to traditional expectations3,16, challenge the well-established paradigms. Therefore, refining and unifying the cognition of polarization within the III-nitride semiconductor family is crucial to ensuring continued progress in material and device development.\n\nExperimentally determining the polarization of a solid, particularly the spontaneous polarization, faces significant challenges. In crystalline solids, people generally measure the changes in polarization rather than the absolute values17. Consequently, theoretically predicted spontaneous polarizations are often relied upon to simulate and interpret the experiments. Within the framework of the Modern Theory of Polarization (MTP), the spontaneous polarization of a crystal is defined as the difference in the formal polarization between the material of interest and a reference structure: \\({{{{\\boldsymbol{P}}}}}_{{{{\\rm{SP}}}}}^{{{\\mathrm{int}}},{{{\\rm{ref}}}}}={{{{\\boldsymbol{P}}}}}_{{{{\\rm{f}}}}}^{{{\\mathrm{int}}}}-{{{{\\boldsymbol{P}}}}}_{{{{\\rm{f}}}}}^{{{{\\rm{ref}}}}}\\), where \\({{{{\\boldsymbol{P}}}}}_{{{{\\rm{f}}}}}^{{{\\mathrm{int}}}}\\) and \\({{{{\\boldsymbol{P}}}}}_{{{{\\rm{f}}}}}^{{{{\\rm{ref}}}}}\\) are the formal polarization of the material and its reference structure, respectively, \\({{{{\\boldsymbol{P}}}}}_{{{{\\rm{SP}}}}}^{{{\\mathrm{int}}},{{{\\rm{ref}}}}}\\) is the spontaneous polarization of the material relative to the reference structure17,18,19,20. Normally, the reference structure should have vanished formal polarization. The reference structure for wurtzite crystals can be either a zinc-blende (ZB, \\({{\\mbox{F}}}\\bar{4}3{\\mbox{m}}\\)) or a h-BN like layered-hexagonal (LH, P63/mmc) structure, as shown in Fig.\u00a01a and detailed in Supplementary Note\u00a01. The polarization properties of WZ III-nitrides were firstly investigated by Bernardini et al. in 1997 using the MTP framework, with ZB serving as the reference structure1. Their predictions, with polarization values below 0.1\u2009C/m2, have been extensively used to predict the net polarization bound charge at GaN-based heterointerfaces. In 2016, Dreyer et al. revised the polarization constants within WZ III-nitrides using a LH reference structure21, predicting giant spontaneous polarizations exceeding 1\u2009C/m2\u2014more than an order of magnitude larger than the initial ones with the ZB reference structure. This divergence garnered attention following the report of ferroelectric polarization switching in ScAlN films by Fichtner et al. in 2019, where remnant polarizations over 1\u2009C/m2 were measured11,22,23,24. Nevertheless, the polarization values and their orientations in III-nitride semiconductors are still open questions, in particular, convincing experimental evidences remain elusive.\n\na Crystal structures of III-nitrides, including WZ, ZB, and LH configurations (top), and atomic schematics illustrating the adiabatic and gap-preserving displacement between WZ and its ZB and LH reference structures along the c-axis (bottom). b Electron localization function (ELF) of WZ AlN projected along the \\([11\\bar{2}0]\\) zone-axis, showing both the ionic (Pion) and electronic (Pel) contributions to polarization. c Diagram of the local polarization measurement method based on a simulated ABF-STEM image of WZ AlN. Atomic displacement (\u0394r) and lattice constants (a and c) are measurable. The inset shows a quadrilateral prism representing the unit cell with volume of \u03a9. d\u2013k ABF-STEM images of WZ M-polar (d) AlN, (e) GaN, (f) InN, and (g) ScAlN, and N-polar (h) AlN, (i) GaN, (j) InN, and (k) ScAlN, each overlaid with vector map of the measured local polarization.\n\nIn this work, we establish a unified perspective on the polarization of III-nitride semiconductors through systematic experimental examinations. The inherent correlation between the spontaneous polarization and lattice polarity are determined via comprehensive local and macroscopic polarization characterizations. These experimental findings corroborate that the understanding of the spontaneous polarization, ferroelectric polarization, and lattice polarity in WZ III-nitride family can be much more accurately unified by referencing the LH structure, rather than the long-standing ZB reference structure. Accordingly, the polarization landscape in the advanced AlGaN/GaN and ferroelectric ScAlN/GaN heterostructures has been reevaluated using the newly unified polarization framework. This approach not only reconciles the existing experimental observations, but also predicts significant tunability and a dramatic increase of sheet carrier concentration in the ferroelectric ScAlN/GaN heterostructures, providing new insights into the development of high-performance electronic and ferroelectric devices.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58975-0/MediaObjects/41467_2025_58975_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "As illustrated in the top panel of Fig.\u00a01a, III-nitride semiconductors exhibit a wurtzite ground state structure, featuring with an in-plane lattice a for the basal hexagon, an out-of-plane lattice c for the hexagonal prism, and an internal parameter u, which is defined as the ratio of the metal-nitrogen (M-N) bond length along the c-axis to the lattice constant c. Wurtzite is a non-centrosymmetric structure where metal and nitrogen atoms occupy two separate atomic planes. A sublattice displacement, \\(d=\\frac{1}{2}-u\\), represents the smallest interplanar spacing between the metal and nitrogen atomic planes in unit of c. The lack of spatial inversion symmetry results in a non-zero spontaneous polarization along the c-axis. Two opposite crystallographic lattice polarities exist along this axis, defined as M-polarity and N-polarity, corresponding to the [0001] and \\(\\left[000\\bar{1}\\right]\\) directions, respectively3. In the M-polarity configuration, metal atoms are positioned above the nearest nitrogen atom plane, resulting in d\u2009>\u20090 (Fig.\u00a01a), whereas in the N-polar one, the order is reversed. Although spontaneous polarization plays a crucial role in the development of III-nitride semiconductors, direct polarization measurements have been experimentally challenging. Therefore, the reported values of polarization are basically theoretically predicted ones1,2,21,25. The MTP modeling is usually used for such theoretical prediction17,20,26, where it suggests that both ionic and electronic contributions to polarization can be calculated from the atomic displacement (\u0394r) using the Born effective charges (Z *) (Fig.\u00a01c and Supplementary Note\u00a02). In this approach, \u0394r is the atomic displacement of a crystal compared to its centrosymmetric structure. The bottom panel of Fig.\u00a01a shows the atomic chains for the WZ phase and the corresponding ZB and LH reference structures along their polar axes, specifically the \\(\\left\\langle 0001\\right\\rangle\\), \\(\\left\\langle 111\\right\\rangle\\), and \\(\\left\\langle 0001\\right\\rangle\\) axes, respectively. Obviously, the centrosymmetric reference structure for WZ is the LH phase, where both metal and nitrogen atoms share the same atomic plane. Moreover, an adiabatic, gap-preserving atomic displacement path can only be established between the WZ and LH reference structure for III-nitrides.\n\nIn the WZ structures, as shown in Figs.\u00a01a, c, the smallest atomic plane spacing along the c-axis is the atomic displacement used for polarization calculation, i.e., \u0394r = dc. This displacement is possible to be directly measured using atomically resolved scanning transmission electron microscope (STEM) by identifying the positions of metal and nitrogen atom columns27,28. Furthermore, for III-nitride semiconductors, \u0394r determines the polarization orientation21,25,28: positive (d\u2009>\u20090) and negative (d\u2009<\u20090) \u0394r correspond to upward and downward polarizations, which can be defined as +P and \u2212P, respectively, as indicated in Fig.\u00a01a. Therefore, local polarization measurements utilizing STEM provide a direct and accurate way for determining the intrinsic polarization at the unit cell scale. While STEM has been widely used to investigate microstructure and lattice polarity in III-nitrides15,29, its application for local polarization measurements remains limited.\n\nIn this work, we conducted comprehensive local polarization measurements on III-nitrides, including M-polar and N-polar AlN, GaN, InN, and ScAlN by using annular bright field STEM (ABF-STEM). Figure\u00a01d\u2013g displays ABF-STEM images of M-polar AlN, GaN, and InN films, respectively, which were grown on sapphire substrates. The lattice parameters of these films, measured by X-ray diffraction (XRD), are consistent with previous theoretical and experimental reports, showing negligible strain (Fig.\u00a0S4 and Table SII). The spatial positions of atom columns were determined by using a two-dimensional Gaussian fitting process on the ABF-STEM images, enabling precise measurement of \u0394r across the entire image (Fig.\u00a0S6). Polarization was then quantified using Born effective charges derived from the density-functional theories (DFT) (Supplementary Note\u00a02 and Table SII). Vector maps of the calculated polarization per unit cell were superposed on the corresponding ABF-STEM images (Fig.\u00a01d\u2013g). The measured average polarization for M-polar AlN, GaN, and InN is +1.37\u2009\u00b1\u20090.11, +1.29\u2009\u00b1\u20090.11, and +1.04\u2009\u00b1\u20090.15\u2009C/m2, respectively, all exhibiting upward-orientated spontaneous polarization. The error bar arises from the measurement uncertainty resulting from the atomic position fitting process.\n\nWe further conducted local polarization measurements on a M-polar ScAlN layer which was grown on GaN/sapphire template by molecular beam epitaxy (MBE)30. The ScAlN has a monocrystalline wurtzite structure with 18% Sc content, which is nearly\u00a0lattice-matched to the underlying GaN, minimizing strain effect on the polarization measurements16,31,32. More details can be found in the Methods and Supplementary Fig.\u00a0S5. Figure\u00a01g shows the ABF-STEM image of the ScAlN, overlaid with the measured polarization vector map. We obtained an average polarization of +1.38\u2009\u00b1\u20090.17\u2009C/m2. To further validate the accuracy and reliability of this approach and obtain a systematic understanding of polarization in WZ III-nitrides, the same measurements were performed on the N-polar counterparts of these materials. As illustrated in Fig.\u00a01h\u2013k, the measured average polarization for N-polar AlN, GaN, InN, and ScAlN is \u22121.34\u2009\u00b1\u20090.20, \u22121.28\u2009\u00b1\u20090.24, \u22121.09\u2009\u00b1\u20090.26, and \u22121.38\u2009\u00b1\u20090.21\u2009C/m2, respectively, all showing downward-orientated spontaneous polarization.\n\nSince ScAlN is a ferroelectric material, we then conducted macroscopic electrical measurements on the MBE-grown ferroelectric ScAlN/GaN heterostructure to determine its polarization value. Figure\u00a02a shows the high angle annular dark field (HAADF) STEM image captured from the ScAlN/GaN sample, exhibiting atomically sharp interface. Lithographically patterned circular Ti/Au pads served as top electrodes, while the n+-GaN layer functioned as the bottom electrode, with voltages applied from the top electrode, as schematically shown in the inset of Fig.\u00a02b. Figure\u00a02b shows a typical polarization versus electric field (P-E) hysteresis loop measured from the ferroelectric ScAlN/GaN heterostructure, yielding a remanent polarization of approximately 1.48\u2009C/m2, consistent with the locally measured polarization value of 1.38\u2009C/m2. The slightly higher remanent polarization is attributed to overestimation due to electric leakage at high voltage. This issue can be partially mitigated by using positive-up-negative-down (PUND) measurements (Fig.\u00a0S7). The remanent polarization obtained from PUND analysis using a triangular pulse sequence is 1.11\u2009C/m2.\n\na HAADF-STEM image of the as-grown ScAlN/GaN heterostructure, exhibiting an atomically sharp interface. b Typical P-E hysteresis loop recorded from a ScAlN capacitor at 1\u2009kHz without subtracting electric leakage. The inset shows a schematic of the ScAlN metal-ferroelectric-semiconductor (MFS) capacitor structure. c, d ABF-STEM images of ScAlN, captured from (c) positively and (d) negatively biased MFS capacitors, as indicated in (b). The superimposed metal (pink) and nitrogen (blue) atoms highlight the atomic stacking sequence, revealing N- and M-polar lattices in (c) and (d), respectively. e Monopolar measurements executed on ScAlN MFS capacitors with opposite initial polarization states: (i, ii) fresh M-polar ScAlN, exhibiting upward spontaneous polarization, and (iii, iv) switched N-polar ScAlN, presenting downward spontaneous polarization. The insets depict the applied voltage pulses and the evolution of polarization orientation under an external electric field, with arrows in the ScAlN/GaN heterostructures indicating the polarization orientation.\n\nThe ferroelectric nature of ScAlN also enables local polarization measurement in III-nitrides after reversing the lattice polarity, which was previously unattainable. Figure\u00a02c, d shows ABF-STEM images captured from two ScAlN devices with opposite polarization states, corresponding to the positive and negative biases indicated in Fig.\u00a02b. By comparing the stacking order of metal (Sc/Al) and nitrogen atoms in these ABF images with the atomic model in Fig.\u00a01a, the lattice polarity was confirmed as N-polarity for positively-biased state (Fig.\u00a02c) and M-polarity for negatively-biased one (Fig.\u00a02d). The measured average polarizations are \u22121.34\u2009\u00b1\u20090.14 and +1.43\u2009\u00b1\u20090.11\u2009C/m2, respectively. The excellent agreement of these STEM quantified and electrically measured polarization magnitudes for as-deposited and lattice-reversed ScAlN films unambiguously identifies that this material possesses a polarization over 1\u2009C/m2. Notably, recent studies have demonstrated ferroelectric polarization switching in sputter-deposited AlN films33,34, showing a remanent polarization of 1.5\u2009C/m2, which aligns well with the polarization values obtained for AlN in Fig.\u00a01. Given that conventional III-nitrides and ferroelectric III-nitrides share identical crystal symmetry, as well as comparable Born effective charges and atomic displacements (Table SII), we can infer, based on the MTP, that the polarization landscape of the WZ III-nitride family should be similar. In other words, conventional III-nitrides possess strong polarization effects akin to those observed in ferroelectric nitrides. Thus far, both local and macroscopic polarization measurements corroborate that WZ III-nitrides exhibit spontaneous polarization values exceeding 1\u2009C/m2.\n\nSubsequently, we examined the intrinsic orientation of the spontaneous polarization in WZ III-nitrides, an ongoing topic of debate, through macroscopic electrical measurements. The wake-up-free nature and highly controllable, uniform lattice polarity of MBE-grown monocrystalline ferroelectric ScAlN allowed us to perform monopolar measurements on fresh devices without pre-cycling treatments. This is essential for examining intrinsic polarization orientation. In ferroelectrics, once the orientation of the spontaneous polarization is reversed by an external electric field, the redistribution of the polarization-compensated charges gives rise to an instantaneous current flow known as \u201cdisplacement current\u201d35,36. Displacement current, therefore, serves as an indicator of changes in polarization orientation. For monopolar measurements, a single positive or negative triangular pulse exceeding the coercive field was applied to the top electrodes of fresh ScAlN capacitors. The corresponding current density versus electric field (J-E) curves are plotted in Fig.\u00a02e. A distinct displacement current is observed following the application of a positive pulse bias on a fresh M-polar ScAlN capacitor, while no displacement current is detected after executing a negative pulse bias, as displayed in the top panel of Fig.\u00a02e. This behavior indicates that the orientation of the spontaneous polarization in M-polar ScAlN can be inverted by a downward external electric field (towards the ScAlN/GaN interface), but not by an upward one (towards the ScAlN surface), as depicted in the insets of Fig.\u00a02e(i, ii).\n\nThe same measurements were conducted on N-polar ScAlN/GaN capacitors, shown in the bottom panel of Fig.\u00a02e. Those N-polar ScAlN/GaN capacitors were obtained by converting the original M-polar to N-polar lattice via applying positive voltage pulses (Fig.\u00a02c). In contrast to the M-polar capacitors, the N-polar ones exhibited negligible response to a positive voltage pulse, while a definite displacement current was generated under a negative pulse. This phenomenon suggests that it is an upward external electric filed\u2014rather than a downward one\u2014that induces inversion of the spontaneous polarization orientation in N-polar ScAlN, as illustrated in the insets of Fig.\u00a02e(iii, iv). According to the theory of dielectric polarization, the spontaneous polarization of a ferroelectric aligns with the external electric field after polarization reversing17,36,37. Based on this, above monopolar analyses suggest that M-polar ScAlN exhibits an upward spontaneous polarization, while N-polar ScAlN has a downward one. These findings, when combined with the local polarization measurements shown in Fig.\u00a01, provide unambiguous experimental confirmation that M-polar III-nitrides possess an upward spontaneous polarization (+P), while N-polar III-nitrides exhibit a downward one (\u2212P).\n\nThe spontaneous polarization of wurtzite ScAlN with Sc content in a range of 0 to 0.375 was calculated by using first-principles DFT (Fig.\u00a0S3a). See Supplementary Note\u00a01 for details. The theoretically predicted spontaneous polarizations align well with both local and macroscopic measurements, as well as the experimentally reported remanent polarization for ScAlN, in terms of both magnitude and evolution trend (Fig.\u00a0S3b). Table\u00a01 lists our results alongside previously reported experimental measurements34,38,39,40 and theoretical calculations1,16,21,25,41 of spontaneous polarizations in III-nitride semiconductors. Notably, while the experimentally determined local and macroscopic polarizations in our data are well consistent, they show significant discrepancies when compared to the widely adopted polarization values for III-nitride semiconductors over the past two decades, which were obtained using ZB reference structure1,16,21,38. These discrepancies are evident in two key aspects: (i) the experimentally determined spontaneous polarization values are an order of magnitude higher than the theoretical predictions using ZB reference structure; (ii) for the same lattice polarity, such as M-polar AlN, our experimental findings indicate an orientation that is exactly opposite to the previous theoretical predictions.\n\nThe reason for the significant discrepancy lies in the reference structure used when calculating the polarization within the MTP framework (Supplementary Note\u00a01)17,26. In the MTP modeling, polarization is a periodic multiple-valued lattice, where only the polarization differences between two states, such as the material of interest and the reference, taken from the same branch, are uniquely defined17,18,20,21. The multivalued formal polarizations for WZ, ZB, and LH III-nitrides within the MTP modeling are illustrated in Fig.\u00a03a. The spontaneous polarization relative to the ZB (\\({P}_{{{{\\rm{SP}}}}}^{{{{\\rm{WZ}}}},{{{\\rm{ZB}}}}}\\)) and LH (\\({P}_{{{{\\rm{SP}}}}}^{{{{\\rm{WZ}}}},{{{\\rm{LH}}}}}\\)) references can be calculated by taking the difference in formal polarization from the same branch17,19,20,26. Obviously, both magnitude and orientation of the polarization are strongly linked to the reference structure. For example, as shown in Fig.\u00a03b and Table\u00a01, M-polar AlN has a spontaneous polarization of \u22120.090\u2009C/m2 when referenced to the ZB structure, whereas a spontaneous polarization of +1.351\u2009C/m2 is obtained using the LH reference structure. The latter one agrees well with our experimentally measured polarization of +1.37\u2009C/m2.\n\na Multivalued formal polarization Pf, predicted by the MTP modeling as a function of the atomic displacement d. Each black dashed line represents a branch of multivalued function, while the difference between two branches is the modulo of the polarization quantum Pq. The blue, brown, and red spots correspond to the Pf values for a WZ structure and its ZB and LH reference structures, respectively. b Formal polarization of WZ, ZB and LH III-nitrides (AlN, GaN, InN and ScAlN) with M-polar lattice (d\u2009>\u20090) taking from the \u201c0\u201d branch in (a). Black arrows represent spontaneous polarizations of WZ III-nitrides relative to the ZB and LH reference structures, showing downward and upward orientations, respectively.\n\nThere is no inherent issue with utilizing either the ZB or LH reference structure to define the spontaneous polarization in WZ crystals. Nevertheless, when using the spontaneous polarization to evaluate the polarization difference at a WZ heterointerface, such as an AlN/GaN (\\(\\Delta {P}_{\\inf }^{{{{\\rm{AlN}}}}/{{{\\rm{GaN}}}}}={P}_{{{{\\rm{f}}}}}^{{{{\\rm{AlN}}}}}-{P}_{{{{\\rm{f}}}}}^{{{{\\rm{GaN}}}}}\\)), a correction term accounting for the polarization difference between the corresponding reference structures (\\(\\Delta {P}_{{{{\\rm{corr}}}}}^{{{{\\rm{AlN}}}}/{{{\\rm{GaN}}}},{{{\\rm{ref}}}}}={P}_{{{{\\rm{f}}}}}^{{{{\\rm{AlN}}}},{{{\\rm{ref}}}}}-{P}_{{{{\\rm{f}}}}}^{{{{\\rm{GaN}}}},{{{\\rm{ref}}}}}\\)) must be incorporated21,25, as shown in Fig.\u00a03b and Supplementary Note\u00a01. For the AlN/GaN heterostructure, the correction term for ZB reference structure is clearly nonzero: \\(\\Delta {P}_{{{{\\rm{corr}}}}}^{{{{\\rm{AlN}}}}/{{{\\rm{GaN}}}},{{{\\rm{ZB}}}}}=\\frac{e\\sqrt{3}}{2}\\left(\\frac{1}{{a}_{{{{\\rm{AlN}}}}}^{2}}-\\frac{1}{{a}_{{{{\\rm{GaN}}}}}^{2}}\\right)\\), where e is the electronic charge, \\({a}_{{{{\\rm{AlN}}}}}\\) and \\({a}_{{{{\\rm{GaN}}}}}\\) are the in-plane lattice constant of WZ AlN and GaN, respectively. In contrast, the correction term for LH reference structures (\\(\\Delta {P}_{{{{\\rm{corr}}}}}^{{{{\\rm{AlN}}}}/{{{\\rm{GaN}}}},{{{\\rm{LH}}}}}\\)) is just zero because all LH reference structures exhibit vanished formal polarization. Therefore, the spontaneous polarization of WZ III-nitrides determined by using the LH reference structure can be directly used to calculate the polarization difference at heterointerfaces without requiring a correction term. Moreover, spontaneous polarizations derived using the ZB reference are not possible for understanding the reversible giant polarization of wurtzite ferroelectrics (Fig.\u00a02 and Supplementary Note\u00a01). It is thus clear that the discrepancies between theoretically predicted and experimentally determined spontaneous polarization are fairly addressed by using the LH reference structure.\n\nThe above findings contribute to a refined understanding of polarization in GaN-based heterostructures, where strain induced piezoelectric polarization should also be considered. WZ III-nitrides are typically hetero-grown on substrates along the polar c-axis. Consequently, an in-plane biaxial strain (\\({\\varepsilon }_{1}\\), where \\({\\varepsilon }_{1}=\\frac{a-{a}_{0}}{{a}_{0}}\\), with \\({a}_{0}\\) and \\(a\\) being the in-plane lattice constants for the relaxed and strained layers, respectively) is present in the top layer, leading to a strain induced piezoelectric polarization (\\({P}_{{{{\\rm{PE}}}}}\\))3. \\({P}_{{{{\\rm{PE}}}}}\\) is defined as the formal polarization difference between the strained and relaxed structure. It can be expressed in terms of the in-plane biaxial strain (\\({\\varepsilon }_{1}\\)), piezoelectric coefficients (\\({e}_{31}\\), \\({e}_{33}\\)), and elastic constants (\\({C}_{13}\\), \\({C}_{31}\\))3,16,21,42,43,44. See details in Supplementary Note\u00a01.\n\nWe use a state-of-the-art M-polar AlGaN/GaN high electron mobility transistor (HEMT) structure as an example to assess the spontaneous, piezoelectric, total polarization, as well as the interface polarization difference (\\(\\Delta {P}_{\\inf }\\)). Calculation details are discussed in Supplementary Note\u00a03. To quantitively compare the difference arising from the divergent reference structures, we set the Al composition to 0.3 (Al0.3Ga0.7N), a commonly used barrier in AlGaN/GaN HEMT structures10. The biaxial strain effect in Al0.3Ga0.7N is evaluated by considering two extreme cases: fully relaxed (\\({\\varepsilon }_{1}=0\\)) and fully strained (\\({\\varepsilon }_{1}=0.0073\\)) one. The left panel of Fig.\u00a04a(i-iii) presents the polarization schematic in M-polar AlGaN/GaN heterostructures derived using the LH reference, exhibiting a significant difference compared to the conventional understanding based on the ZB reference, as shown in Fig.\u00a04a(iv-vi).\n\na Spontaneous polarization, piezoelectric polarization, interface polarization difference, and sheet carriers in (i, iv) relaxed, (ii, v) tensile-strained, and (iii, vi) compressive-strained AlGaN/GaN heterostructures. The schematics in (i-iii) and (iv-vi) are drawn with the LH and ZB reference structures, respectively. Black arrows indicate the polarization orientation. b\u2013d Polarization charge distribution and corresponding band diagrams for (b) M-polar AlGaN/GaN, (c) M-polar GaN/AlGaN, and (d) N-polar GaN/AlGaN heterostructures, illustrating the formation of 2DEG or 2DHG at the interface.\n\nFor fully relaxed AlGaN/GaN heterostructures, a positive \\({\\Delta P}_{\\inf }^{{{{\\rm{AlGaN}}}}/{{{\\rm{GaN}}}},{{{\\rm{LH}}}}}\\) (+0.012\u2009C/m2) is obtained, suggesting a negative net polarization charge at the interface. Therefore, as shown in Fig.\u00a04a(i), the fully relaxed AlGaN/GaN interface tends to attract free holes, rather than electrons, to compensate the negative net polarization charge. However, the fully relaxed structure is a hypothetical scenario, as strain is typically present in real systems. For fully strained AlGaN/GaN heterostructures, a negative \\({\\Delta P}_{\\inf }^{{{{\\rm{AlGaN}}}}/{{{\\rm{GaN}}}},{{{\\rm{LH}}}}}\\) (\u22120.021\u2009C/m2) is obtained, which is comparable with \\({\\Delta P}_{\\inf }^{{{{\\rm{AlGaN}}}}/{{{\\rm{GaN}}}},{{{\\rm{ZB}}}}}\\) (\u22120.030\u2009C/m2). The similarity between these two values results from the cancelation of the correction terms, which has been discussed in detail by Dreyer et al.21. As shown in Fig.\u00a04a(ii), the negative polarization difference leads to a positive net polarization charge, which tends to accumulate free electrons at the tensile strained AlGaN/GaN heterointerface. This phenomenon underlies the formation of 2DEG in AlGaN/GaN HEMTs.\n\nThe polarization discontinuities at other M-polar and N-polar AlGaN/GaN heterostructures can be deduced using the same method. Figure\u00a04a presents the polarization schematics for several representative heterostructures. Based on this understanding, the polarization charge distribution and band profiles for three typical III-nitride heterostructures, including M-polar AlGaN/GaN, M-polar GaN/AlGaN, and N-polar GaN/AlGaN, are redrawn in Fig.\u00a04b\u2013d. These heterostructures have attracted tremendous attention in high-frequency and high-power electronics due to their potential to form high-mobility 2DEG or 2DHG. Although there are no significant differences in band profiles between the two reference structures, the refined polarization distribution and formation mechanisms provide a deeper and more solid foundation for the understanding and engineering of polarization in advanced nitride heterostructures. A comprehensive comparison of our predictions with previously reported experimental results and theoretical studies for AlGaN/GaN HEMT structures is provided in Fig.\u00a0S9 and further discussed in Supplementary Note\u00a03. Referring to the pioneering explanation of III-nitride polarization and 2DEG/2DHG formation, this new understanding aligns well with existing experimental observations. Furthermore, by comparing the fully relaxed and strained AlGaN/GaN heterostructures within the unified polarization framework, we conclude that: (i) piezoelectric polarization is oriented opposite to spontaneous polarization under tensile strain while in the same orientation under compressive strain; and (ii) piezoelectric polarization plays a crucial role in the formation of 2DEG or 2DHG at the interface.\n\nWe then turn to ScAlN/GaN heterostructures, which are more attractive since ScAlN has a ferroelectric polarization, and the strain in ScAlN can be tuned from tensile to compressive through modifying the Sc content and the orientation of the piezoelectric polarization can thus be manipulated as well11,12,13,16,31,45. Figure\u00a05a illustrates the polarization schematics of ferroelectric ScAlN/GaN heterostructures, where the ScAlN layer is in M- (left panel) and N-polarity (right panel), while the underlying GaN layer remains M-polarity in both cases. In Fig.\u00a05a, the evolution of the in-plane lattice parameter a can be divided into three regimes with increasing Sc content from top to bottom: \\({a}_{{{{\\rm{ScAlN}}}}} \\, < \\, {a}_{{{{\\rm{GaN}}}}}\\) (panel i, tensile strain), \\({a}_{{{{\\rm{ScAlN}}}}}={a}_{{{{\\rm{GaN}}}}}\\) (panel ii, fully relaxed), and \\({a}_{{{{\\rm{ScAlN}}}}} \\, > \\, {a}_{{{{\\rm{GaN}}}}}\\) (panel iii and iv, compressive strain). The spontaneous and piezoelectric polarizations, as well as the polarization differences, are indicated with black arrows in Fig.\u00a05a. The net polarization charge at the ScAlN/GaN interface is plotted in Fig.\u00a05b (solid curve), with the corresponding maximum sheet carrier concentration indicated on the right axis. Further details are provided in Supplementary Note\u00a03.\n\na Spontaneous polarization, piezoelectric polarization, interface polarization difference, and sheet carriers in as-grown M-polar (left) and polarization switched N-polar (right) ferroelectric ScAlN/GaN heterostructures with varying Sc content. The strain transitions in ScAlN undergo three regimes along with Sc content increasing: (i) tensile strain, (ii) fully relaxed, and (iii, iv) compressive strain. Black arrows indicate the polarization orientation. b Net polarization charge and corresponding maximum sheet carrier density of ScAlN/GaN heterostructures as a function of Sc content. The red and blue curves represent sheet charge density before and after polarization switching, respectively. While solid and dashed curves correspond to the values calculated using LH and ZB reference structures, respectively. c Schematic of a ScAlN/GaN Fe-HEMT with opposite polarization orientations for ScAlN (left) and its typical transfer characteristic curves (right), showing counterclockwise ID\u2013VGS hysteresis loops with tunable Vth.\n\nAs shown in Fig.\u00a05a, the initial tensile strain in the ScAlN layer gradually evolves into compressive strain with increasing Sc content, leading to a reversed orientation of the piezoelectric polarization. Consequently, the sheet carriers at the heterointerface transform from 2DEG to 2DHG (Fig.\u00a05a, b). As the Sc content increases from 0 to 0.25, the positive polarization bound charge initially rises slightly and then lowers down to zero, followed by a gradual increase in negative polarization bound charge. This evolution trend reflects the trade-off between spontaneous and piezoelectric polarizations. For comparison, the net polarization charge calculated using ZB reference structure is plotted as a blue dashed line in Fig.\u00a05b, revealing a larger transition point at the Sc content of 0.4.\n\nThe polarization bound charge in the switched ScAlN/GaN heterostructure is\u00a0also plotted in Fig.\u00a05b (red curve). In this scenario, the top ScAlN layer exhibits N-polar lattice while the underlying GaN remains M-polar one, forming a head-to-head polarization domain wall at the ScAlN/GaN interface, as illustrated in the right panel of Fig.\u00a05a. This configuration results in a significant increase in positive polarization sheet charge, at least 30 times higher than typically observed, which is completely contradict to the predictions by using ZB reference structure, indicated with red dashed curve in Fig.\u00a05b. In idealized configurations, assuming sharp, clean polarization reversal interfaces without dead layer, dangling bonds, defects, alloy composition fluctuations, or charge compensation, this high positive polarization charge leads to pronounced downward band bending (Fig.\u00a0S10). This facilitates the formation of 2DEG with ultra-high sheet carrier densities exceeding 1 \u00d7 1015\u2009cm\u22122 in the polarization switched ScAlN/GaN heterostructures. Such high carrier density has never been observed at any semiconductor heterointerfaces. This unprecedented phenomenon reveals a significant potential for advancing high-frequency and high-power electronic applications.\n\nFurthermore, since ScAlN is a ferroelectric semiconductor, the threshold voltage (Vth) of ScAlN/GaN ferroelectric HEMTs (Fe-HEMTs) can be tuned by ferroelectric polarization through either the quantity or sign of the polarization bound charge, resulting in a counterclockwise ID\u2013VGS hysteresis loop, as shown in Fig.\u00a05c. The initial net polarization charge at the ScAlN/GaN interface determines the highest Vth, while the giant positive polarization charge induced by ferroelectric switching governs the lowest Vth. Therefore, Vth can be precisely modulated by controlling the ferroelectric switching process. Additionally, a steep-slope ID\u2013VGS curve with a reduced subthreshold swing is likely observed during backward (positive-to-negative) sweeps, benefiting from the negative capacitance effect caused by ferroelectric switching46,47. It should be noted that the substantial positive polarization charge induced by polarization reversal at the interface can significantly enhance vertical conductivity. This enhancement contributes to the low-resistance state observed in ferroelectric ScAlN/GaN memristors48,49. Such a phenomenon could also facilitate the formation of low-resistance drain and source Ohmic contacts in ScAlN/GaN Fe-HEMTs and photodetectors.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58975-0/MediaObjects/41467_2025_58975_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58975-0/MediaObjects/41467_2025_58975_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58975-0/MediaObjects/41467_2025_58975_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58975-0/MediaObjects/41467_2025_58975_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Although the above analyses are promising, experimental demonstration and precise determination of the electron density and mobility of the 2DEG formed at the ferroelectric switched ScAlN/GaN interface remain significant challenges. The primary difficulty lies in the presence of ferroelectric dead layers, as well as dangling bonds, defects, and roughness at the polarization reversal interface in practical ferroelectric heterostructures15,29,50. In contrast, polarization modeling normally assumes an idealized polarization reversal interface. Those undesirables in practical systems significantly suppress the modulation capability of ferroelectric polarization on the ScAlN/GaN interface carriers, for instance, through mitigated field effects and partial charge compensation50, as discussed in detail in Supplementary Note\u00a03. Nevertheless, the increased vertical conductivity48,49 and the negatively shifted pinch-off voltage (Supplementary Note\u00a03) or Vth51 observed in ferroelectric ScAlN/GaN heterostructures after polarization reversal confirm the enhancement of interfacial carrier concentration. These experimental observations suggest that the potential applications of the extreme sheet charges predicted in Fig.\u00a05b are already emerging. We believe that ongoing advancements in ferroelectric nitride technologies, such as mitigating or eliminating dead layers, achieving sharp switching, and creating clean polarization-reversal interfaces, will gradually bring experimental results closer to the theoretical predictions presented here.\n\nIn summary, the magnitude and orientation of polarization in wurtzite III-nitrides have been identified through quantitative local polarization analysis at the unit cell scale as well as macroscopic electrical measurements. Giant upward (downward) spontaneous polarizations exceeding 1\u2009C/m2 have been experimentally determined in as-grown M-polar (N-polar) wurtzite III-nitride films, including conventional AlN, GaN, and InN, as well as the emerging ferroelectric ScAlN. These findings align well with theoretical predictions based on the centrosymmetric LH reference structure, establishing a unified perspective on the polarization of III-nitride semiconductors with strong support from both theoretical and experimental evidence. Furthermore, the polarization landscape and band profile in the cutting-edge AlGaN/GaN and ferroelectric ScAlN/GaN heterostructures have been reevaluated within this unified polarization framework. The potential for achieving 2DEG with ultra-high sheet carrier densities in ferroelectric ScAlN/GaN heterostructures has been highlighted. The proposed and validated new understanding not only bridges the knowledge gap in polarization engineering when integrating ferroelectric nitrides with contemporary GaN device architectures, but also remains consistent with existing experimental observations. This work provides essential insights into the understanding and modulation of polarization in III-nitride heterostructures, setting the stage for the development of advanced photonic, electronic, optoelectronic, and acoustic devices by incorporating ferroelectricity into the III-nitride ecosystem.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "M-polar InN and ScAlN films were grown on commercial GaN/sapphire templates, while N-polar AlN, GaN, InN, and ScAlN films were grown on C-face 4H-SiC substrates utilizing an SVTA MBE system. M-polar GaN and AlN films were prepared using metal organic chemical vapor deposition (MOCVD) on sapphire substrates. The MBE system was equipped with a Veeco Unibulb radio frequency (RF) N-plasma source, dual-filament Knudsen cells for the Al (purity 99.99995%), Ga (purity 99.99999%), and In (purity 99.99999%) sources, and a high-temperature Knudsen cell for the Sc source (purity 99.999%). The epitaxy conditions for MBE-grown AlN, GaN, and InN films have been previously reported52,53,54. For ScAlN growth, an N2 (purity 99.9999%) gas flow rate of 1 sccm with an RF power of 400\u2009W was maintained throughout the growth process, corresponding to a deposition rate of 300\u2009nm/h for GaN under slightly Ga-rich conditions. The GaN/sapphire template was degassed at 300\u2009\u00b0C for 1\u2009h in the MBE preparation chamber, followed by outgassing at 750\u2009\u00b0C for 10\u2009min in the growth chamber prior to deposition. A 100-nm-thick Si-doped n+-GaN (with an electron concentration of 5 \u00d7 1019\u2009cm-3) was first grown, followed by the deposition of an 80-nm-thick ScAlN under N-rich growth conditions. In order to match the underlying GaN lattice, the Sc content was controlled to be about 18%, confirmed utilizing X-ray photoelectron spectroscopy (XPS) and electron dispersive spectroscopy (EDS) in STEM. For standard metal/ScAlN/GaN capacitor fabrication, 50/100\u2009nm thick Ti/Au circular pads with a diameter of 20\u2013100 \u03bcm were lithographically patterned on the ScAlN surface as top electrodes, with the n+-GaN layer serving as the bottom electrode.\n\nThe surface morphology of ScAlN was characterized using a Dimension ICON atomic force microscope (AFM). XRD analysis was performed with a Philips PANalytical X\u2019pert high-resolution XRD system equipped with a 1.54\u2009\u00c5 Cu \u039a\u03b11 X-ray source. Ferroelectric measurements were conducted with a Radiant Premier II Ferroelectric Tester. The P-E hysteresis loop was measured using a triangular wave with a frequency of 1\u2009kHz. The sample lamellae used for STEM measurements were prepared with a ThermoFisher Helios G4 UX focused ion beam (FIB). HAADF- and ABF-STEM images were captured using a JEM-ARM300F2 aberration-corrected STEM at an acceleration voltage of 300\u2009kV, with pixel integration times ranging from 0.5 to 2 \u03bcs.\n\nSee details in\u00a0Supplementary Information.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data in this work are available within the main text and Supplementary Information files, as well as available from the corresponding author. Source data are provided in this paper.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Fiorentini, V., Vanderbilt, D. & Bernardini, F. Spontaneous polarization and piezoelectric constants of III-V nitrides. Phys. Rev. B. 56, R10024\u2013R10027 (1997).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nFiorentini, V., Vanderbilt, D. & Bernardini, F. Accurate calculation of polarization-related quantities in semiconductors. Phys. Rev. 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Sci. 5, 1800844 (2018).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by the National Key Research and Development Program of China (No. 2023YFB3610400 to P.W.), the National Natural Science Foundation of China (NSFC) (No. 62321004 to X.W.; 62374002 to P.W.; 62304008 to R.W.; 62374010 to T.W.; 12304218 to W.T.), the Natural Science Foundation of Beijing Municipality (No. Z230024 to P.W.), the Research Fund of Suzhou Laboratory (No. SK-1202-2024-012 to P.W.), the China Postdoctoral Science Foundation (No. 2023T160016 to R.W.), Shanghai Pujiang Program (No. 23PJ1402200 to C.D.), and the State Key Laboratory of Artificial Microstructure and Mesoscopic Physics at Peking University.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-Optoelectronics, School of Physics, Peking University, 100871, Beijing, China\n\nHaotian Ye,\u00a0Ping Wang,\u00a0Rui Wang,\u00a0Jinlin Wang,\u00a0Xifan Xu,\u00a0Ran Feng,\u00a0Tao Wang,\u00a0Fang Liu,\u00a0Bowen Sheng,\u00a0Wenjie Ma,\u00a0Bingxuan An,\u00a0Hongjian Li,\u00a0Zhaoying Chen,\u00a0Weikun Ge,\u00a0Bo Shen\u00a0&\u00a0Xinqiang Wang\n\nElectron Microscopy Laboratory, School of Physics, Peking University, 100871, Beijing, China\n\nTao Wang\n\nKey Laboratory of Polar Materials and Devices (MOE), School of Physics and Electronic Science, East China Normal University, 200241, Shanghai, China\n\nWen-Yi Tong\u00a0&\u00a0Chun-Gang Duan\n\nShanghai Center of Brain-Inspired Intelligent Materials and Devices, East China Normal University, 200241, Shanghai, China\n\nChun-Gang Duan\n\nPeking University Yangtze Delta Institute of Optoelectronics, 226010, Nantong, Jiangsu, China\n\nBo Shen\u00a0&\u00a0Xinqiang Wang\n\nCollaborative Innovation Center of Quantum Matter, 100871, Beijing, China\n\nBo Shen\u00a0&\u00a0Xinqiang Wang\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.Y., P.W., and X.W. conceived the original concept and initiated the project. H.Y., P.W., R.F., and B.A. conducted the material growth and performed the AFM, XRD and XPS analyses. R.W. was responsible for the device fabrication and electrical measurements. J.W., X.X., and T.W. conducted the STEM measurements, with H.Y., P.W., J.W., X.X., and T.W. analyzing the STEM data. X.X. and H.Y. carried out the local polarization measurements. W.T. and C.D. performed the DFT calculations. H.Y., P.W., W.T., F.L., B. Sheng, W.M., H.L., Z.C., W.G., B. Shen, and X.W. contributed to the polarization analysis. H.Y., P.W., and X.W. drafted the manuscript, with all authors contributing to the discussions of the results and providing feedback on the manuscript at all stages. P.W. and X.W. supervised the research.\n\nCorrespondence to\n Ping Wang, Tao Wang or Xinqiang Wang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous, reviewer(s) for their contribution to the peer review of this work. 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Experimental determination of giant polarization in wurtzite III-nitride semiconductors.\n Nat Commun 16, 3863 (2025). https://doi.org/10.1038/s41467-025-58975-0\n\nDownload citation\n\nReceived: 12 November 2024\n\nAccepted: 08 April 2025\n\nPublished: 24 April 2025\n\nVersion of record: 24 April 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58975-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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-0,0 +1,150 @@ +{ + "title": "Observation of sub-relativistic collisionless shock generation and breakout dynamics", + "pre_title": "Obervation of subrelativistic collisionless shocks generation and breakout dynamics", + "journal": "Nature Communications", + "published": "28 April 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58867-3/MediaObjects/41467_2025_58867_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58867-3/MediaObjects/41467_2025_58867_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58867-3/MediaObjects/41467_2025_58867_MOESM3_ESM.zip" + }, + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58867-3/MediaObjects/41467_2025_58867_MOESM4_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-58867-3#Sec9" + ], + "code": [ + "https://github.com/Warwick-Plasma/epoch" + ], + "subject": [ + "Astrophysical plasmas", + "Laser-produced plasmas" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4950329/v1.pdf?c=1745924945000", + "research_square_link": "https://www.researchsquare.com//article/rs-4950329/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58867-3.pdf", + "preprint_posted": "14 Oct, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Relativistic collisionless shocks1, which are ubiquitous in the cosmos, play a significant role in various astrophysical phenomena such as gamma-ray bursts2\u20134, PeVatrons5\u20137, and shock breakouts accompanying a supernova explosion8. To unravel these processes happening at enormous spatial and energy scales, the most convenient and feasible approach so far \u2013 laser \u2013 has been long sought to access such extreme conditions in an ultrafast and volume-efficient manner9\u201313. Yet observing a relativistic collisionless shock in a laboratory has remained out of reach. Here, we demonstrate that sub-relativistic collisionless shocks with velocities\u2009~\u20090.03c of astrophysical significance can be produced and characterized in a laboratory using a table-top femtosecond \u201claser engine\u201d. We attribute the shock formation to a rapidly growing Weibel instability in a carefully adjusted low-density preplasma environment, which resembles the interstellar media near an astrophysical central engine. Owing to this Weibel instability, a magnetic field as high as ~\u20095000 T is developed within 2.7 ps, leading to the formation of a collisionless shock and shock breakout is finally observed at the preplasma edge. Our results not only pave the way for further exploration of astrophysics related to relativistic collisionless shocks but also enable a new regime of ultrahigh magnetic field physics on a tabletop scheme14.Physical sciences/Physics/Plasma physics/Astrophysical plasmasPhysical sciences/Physics/Astronomy and astrophysics/Laboratory astrophysics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformations.docx", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Relativistic collisionless shocks, which are ubiquitous in the cosmos, play a significant role in various astrophysical phenomena such as gamma-ray bursts, PeVatrons, and supernova shock breakouts. Here we present a demonstration using a compact femtosecond laser system to generate sub-relativistic collisionless shocks (0.03c) under astrophysically relevant conditions. We attribute the shock formation to a rapidly growing Weibel instability in a precisely tuning low-density preplasma environment, which resembles the interstellar media near an astrophysical central engine. Owing to this Weibel instability, a 5000 Tesla magnetic field is developed within 2.7\u2009ps, leading to the collisionless shock formation and subsequent breakout at the preplasma boundaries. This platform enables direct investigation of astrophysics related to relativistic collisionless shocks. The achieved parameters bridge the gap between astrophysical observations and controlled laboratory experiments, offering unprecedented opportunities to validate cosmic shock models.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "In astrophysics, relativistic collisionless shocks can happen in the expansion of intense plasmas into interstellar or intergalactic media1,2,3,4. Examples of such phenomena include the shocks observed in gamma-ray bursts, supernova explosions, and pulsar wind nebulae2,5,6. The formation of collisionless shocks could depend on a turbulent magnetic field induced by Weibel instability which can generate a magnetic field from unmagnetized plasmas7,8,9. When the plasmas are highly anisotropic, Weibel instability occurs, and the enhanced magnetic field could divert the trajectory of the charged particles, thereby converting the plasma into isotropic10,11. In this process requiring enormous energy, black holes, and supernova explosions function as the central engine for driving the collisionless shocks12. Consequently, various phenomena such as PeVatrons13 and their relevant energetic radiations from synchrotron radiation or inverse Compton scattering14,15 are observed. The high energy gamma ray from these PeVatrons also serves as an important tool for positioning these central engines such as those discovered by HESS and LHAASO telescopes16,17.\n\nRecent exploration of astrophysics has seen a new trend in the laboratories. During recent decades, a succession of experiments has been conducted to investigate the Weibel instability-mediated collisionless shocks by utilizing large laser facilities of OMEGA18,19, and NIF7. With driving energy in the range of about kilojoule, the characteristic expanding plasma plumes have showcased velocities up to ~106\u2009m/s and temperatures around 1\u2009keV, demonstrating a platform for studying extraordinary phenomena in astrophysics. Meanwhile, in contrast to experimental approaches that aimed at stimulating intense filamentary magnetic fields by counter-streaming two laser-produced plasma plumes, a theoretical prototype design utilizing a laser piston-driven scheme to induce forward near-relativistic collisionless shocks has been proposed20,21. However, this kind of forward-propagating relativistic collisionless shock experiment does not seem realizable very shortly.\n\nFor collisionless shocks to form, the mean free path li of the ion7\n\nshould be greater than the length of the interacting region Lint, where vi is the ion velocity, ni is the density, Ai is the atomic number of the ion, qi is the charge state of the ion and ln\u03bb is the coulomb logarithm. In Weibel instability, due to energy conservation, the amplification of the magnetic field is accompanied by the deceleration of the charged particles. As a result, ions that drives the instability will be scattered and trapped in the interaction region by the magnetic field, decelerating without collisions. This will result in the pile-up of the charged ions and the formation of a collisionless shock. For a typical interaction region of ~100\u2009\u03bcm, a magnetic field >1000\u2009T is needed. Nevertheless, since the growth rate of the Weibel instability is proportional to the anisotropic parameter and the shock formation time is \u03c4shock\u2009~\u2009(\u03b2d\u03c9p)\u22121\u2009~\u2009(\u03b3F)\u22121, which is approximately an e-folding time of the ion Weibel instability, where \u03b2d is the normalized velocity of the accelerated ions, \u03c9p is ion plasma frequency and \u03b3F is the growth rate of the Weibel instability. The formation length of the shock wave can be greatly reduced if a highly directional ion beam with low energy divergence is used for driving the instability. Unlike picosecond or nanosecond laser pulses for producing collisionless shocks, compressing the pulse duration to femtosecond provides unprecedented intensity for creating extreme plasma environments that are naturally collisionless. Particularly, the interaction scale of ~100\u2009\u03bcm allows for a forward propagating collisionless shock by using merely a tabletop 10-mJ-level femtosecond laser beam.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Our design draws on the idea of a laser engine. Specifically, energetic expanding plasmas are induced in the laser-plasma interaction which then leads to collisionless shocks, analogous to the black hole engine. As shown in Fig.\u00a01a, a customized laser pulse is used. The highly controllable laser beams are analogous to those applied in inertial confinement fusion experiments whose temporal profiles could tune between a high-foot and low-foot pulse mode (Methods), allowing for precise density control of the preplasmas.\n\na The experimental setup: By illuminating a customized laser pulse (which can be tuned between the low-foot and high-foot modes by the two Pockels cells in the regeneration cavity) onto a solid target, sub-relativistic collisionless shocks are driven in the vicinity of the target. The magnetic field and the plasma density are monitored by the Faraday rotation method and the interferometry. OAP, off axis parabola. GL, Glan polarizer. CCD, Charge-Coupled Device. BS, Beam Splitter b A characteristic filamentary magnetic structure observed in front of the target and the formation of a collisionless shock c Five consecutive frames of the experimental result at preplasma density ~ 0.1nc are displayed, which shows the temporal evolution of the magnetic field after the action of the laser engine. A shock breakout is observed at a time delay of ~ 4\u2009ps.\n\nThe first-stage ignitor (leakage pulses from the regeneration cavity) is employed to generate an expanding plasma within a nanosecond time scale. This highly reproducible plasma with a background magnetic field of ~ 10\u2009T is homogeneous. As shown in Fig.\u00a01b, the electromagnetic energy of the main laser beam functions as a Laser engine, which drives the expanding energetic plasma out of the target as well as the subsequent collisionless shock22.\n\nAs demonstrated in Fig.\u00a01c, the experiment reveals a Weibel instability-mediated collisionless shock with a velocity of ~ 0.03c at preplasma density ~ 0.1nc(To see the shock is collisionless, for ions with a velocity ~ 0.03c, the Al+8-Al+8 collision mean free path is \\({\\lambda }_{A{l}^{+8}-A{l}^{+8}}\\)\u2009~\u200930\u2009m), almost on par with the expanding plasmas at the boundary of an exploding supernova23,24. With a velocity of ~10000\u2009km/s, we can study the shocks relevant to the youngest supernova remnant (SNRs) (~ 100year) in the laboratory, such as the SNR G1.9\u2009+\u20090.3 which25 has a shock velocity of ~ 13000\u2009km/s. The magnetic field peaks at ~ 2.7\u2009ps and lasts for about 5\u2009ps, and the formation time of the shock \\({\\tau }_{{shock}}\\)\u2009~\u2009\\({({\\beta }_{d}{\\omega }_{p})}^{-1} \\sim \\,{({\\gamma }_{F})}^{-1}\\) is estimated to be ~1\u2009ps and can be clearly seen in the density profile measured in experiment. The topological structures of the magnetic field experienced an abrupt change after a time delay of ~ 4\u2009ps, indicating the breakout of the shock wave.\n\nWe note in Fig.\u00a01c that for time delays smaller than 2\u2009ps, magnetic tube structures emerge through Weibel instability self-organization. Soon later, at time delays of ~ 2.7\u2009ps, the magnetic filaments demonstrate a curved structure, which is regarded as a precursor for the nonlinear stage of the collisionless shock. The energy spectrum of the magnetic field is displayed in Fig. 2a, b. Filamentary peaks are apparent in the figures as indicated by the blue shaded area which is indicative of the Weibel instability. From time delays ~ 2.7\u2009ps to ~ 4.0\u2009ps, the scaling law of the magnetic energy spectrum evolves from \\({k}_{s}^{-8/3}\\) to \\({k}_{s}^{-2}\\), suggesting the magnetic energy cascade during the interaction.\n\nThe magnetic energy spectrum Fs at time delays (a) blue: 1.3\u2009ps and yellow: 2.7\u2009ps, (b) dark blue: 4.0\u2009ps and orange: 5.3\u2009ps. The dashed lines shows a fit to the scaling law of the magnetic energy spectrum. The blue-shaded area in both figures represents the range of the most unstable scale; Particle-in-cell simulation of the time evolution of the magnetic field space distribution at (c) 2.7\u2009ps and (d) 4.0\u2009ps; The density of the preplasma are ~ 0.1\\({n}_{c}\\).\n\nFor shock formation, the time required can be expressed26 as \u03c4shock\u2009=\u2009n\u03c4i\u2009+\u2009\u03c4e, where \u03c4e and \u03c4i are the saturation times of electron and ion Weibel instability respectively, and n accounts for the time needed for the current filaments to merge. However, for our specific experiment conditions, a revision of the equation is required20. Since the expanding plasma is ion-dominated, electron Weibel instability can be neglected, as is the time needed for the merging of the current filaments. Thus, as the initial filament distance is already near its maximum, only \u03c4i is taken into consideration for the shock formation. In fact, an e-folding time of the ion Weibel instability is needed for the shock to form, which is \u03c4shock\u2009~\u2009(\u03b3F)\u22121\u2009~\u2009(\u03b2d\u03c9p)\u22121\u2009~\u20091\u2009ps and is consistent with our experimental observation (See Supplementary Fig.\u00a01). In the later stage of nonlinear ion Weibel instability, the deceleration of ions and the pile-up of ions at the front of the expanding plasma contribute to the formation of the collisionless shock27. A \\({k}_{s}^{-8/3}\\) law of plasma spectrum at scales smaller than the ion gyroradius can be induced due to structure formations28 and turbulent motion of electrons, since electron gyroradius is ~ 1\u03bcm. Electrons can be heated during the interaction, leading to a change in the scaling law from \\({k}_{s}^{-8/3}\\) to \\({k}_{s}^{-2}\\), which implies that magnetic field is dominant in the collisionless shock29.\n\nUnder our experimental conditions, the steady shock velocity is ~ 0.03c and the maximum plasma expanding velocity observed in the experiment is ~ 0.07c before shock formation. The peak magnetic field estimated from the experiment is ~ 5000\u2009T. After the formation of the shock wave, by assuming an effective degree of ionization Z\u2009=\u20098 for the aluminum target, the gyroradius of the accelerated Al+8 ions in the target can be calculated to be \\({\\widetilde{R}}_{g}={p}_{i}/{q}_{i}B\\)\u2009~\u200935\u03bcm, which is comparable to the filament distance \u03bbF\u2009=\u200931\u2009\u03bcm. Since the particles are all ionized, the influence of neutral particles, which could potentially have a cooling effect, can be neglected. Based on the magnetic bouncing mechanism, the maximum magnetic field is determined by Bsat\u2009~\u2009\\({\\gamma }_{F}^{2}{m}_{i}c/{q}_{i}v{k}_{{sat}}\\). Where \u03b3F is the growth rate of the instability and qi is the charge of the ion, v is the shock velocity, ksat\u2009=\u20092\u03c0/\u03bbsat is the saturation wave vector, this gives a maximum magnetic field of ~ 4000\u2009T. It indicates that the Weibel instability-induced magnetic field is already saturated which takes ~ 2-3 e-folding time of the instability. The magnetic field energy density reaches ~14% of equipartition with the plasma kinetic energy density (ek =(B2/8\u03c0)/(nimiv2/2)\u2009~\u20090.14, where ni is the density of the ions), which indicates that a magnetized collisionless shock is induced.\n\nThe plasma dynamics and the formation of the collisionless shocks are investigated using Particle-in-cell (PIC, EPOCH)30 (See in Supplementary\u00a0Information 3). Our simulation results feature a laser-heated electron expanding time of about 100\u2009fs, after when the ions are drawn out of the target by the sheath field. Since the sheath field is perpendicular to the surface of the target, ions can be accelerated to velocities as high as ~ 0.07c. This acceleration leads to the occurrence of Weibel instability within 1\u2009ps, as well as the formation and amplification of a filamentary magnetic field. Turbulent magnetic islands are then created, as demonstrated in the temporal dynamics of the magnetic fields in Fig.\u00a02c, d. A peak magnetic field as high as ~ 5000\u2009T is approached in the simulation and is consistent with our experimental results (Fig.\u00a03a).\n\na The time evolution of the maximum magnetic field in front of the target at different time delays. The solid lines show the experimental results, whereas the dashed lines display our simulation results. As indicated by the legend, the curves with different colors correspond to pre-plasma densities of 0.4nc, 0.2nc, and 0.1nc respectively. The error bar represents the standard deviation of multiple shots. b The leading-edge position of the expanding plasma; The red solid line presents the experimental results measured when the preplasma density is ~ 0.2nc. The blue line presents the front of the expanding plasma when the density of the preplasma is ~ 0.1nc. The yellow dashed line is a linear fit of the leading edge of the expanding plasma when the preplasma density is ~ 0.1nc; the equation for the fitted curve is kt3/5, where k\u2009=\u200927 is used; The yellow-shaded area delineates the shock wave impact region associated with the fitted curve; The error bar represents the standard deviation of multiple shots; (c) PIC simulation results of the time evolution of the density of the plasma along the line y\u2009=\u20090. The horizontal solid red line indicates the position of shock formation; The vertical dashed yellow line indicates the time of shock breakout; The dashed white curve shows the fitted curve with a function R(t) = 27t3/5. d The catastrophe model: density of the plasma at shock front.\n\nAfter the shock formation, the otherwise hardly accessible dynamics of the collisionless shock wave become detectable by pump-probe recording of its propagation processes. We have tracked the leading-edge position of the expanding plasma by monitoring the plasma density and the magnetic field (Fig.\u00a01c). The leading-edge position can be fitted by a function of R(t)=27t3/5, t in ps and R in \u03bcm, which describes well the experimental results, as shown in Fig.\u00a03b. We note that this function is slightly different from a 2/5 power law that is expected with a spherical shock. This discrepancy could arise from the finite size of the laser focus spot which leads to an ellipsoidal expanding wavefront rather than strictly spherical. By comparing Fig.\u00a03a and b, we observe that an increase in magnetic field energy is accompanied by a decrease in shock velocity. Some portion of the ion energy is lost during shock formation due to the driving of the return current and the amplification of the magnetic field. This results in a magnetic field-dominated collisionless shock that resembles what happens in a supernova remnant25. The fitting function for the shock front is consistent with our PIC simulations and follows a curve R(t) ~ 27t3/5. Meanwhile, the density jump npk/nbg measured in the experiment is ~ 5 as shown in Supplementary Fig.\u00a01, where npk and nbg is the peak plasma density and background plasma density measured in the experiment respectively. Since for a strong shock30 the density jump is determined by npk/nbg\u2009~\u2009(\u03b3g\u2009+\u20091)/(\u03b3g\u22121), this gives an adiabatic index \u03b3g\u2009~\u20091.5. The adiabatic index indicates the distribution of energy among different degrees of freedom, which implies that the magnetic field is crucial to the formation of the density jump, and the eventual collisionless shock mediated by Weibel instability.\n\nThe shock wave observed in experiment resembles that observed in the supernova remnant, such as SNRs G1.9\u2009+\u20090.3 like SNRs. Though the total scale of the shock wave is ~70\u2009\u03bcm, the radius\u2014time relation for our experiment and SNRs G1.9\u2009+\u20090.3 like SNRs are both approximately R(t) ~ t0.6 which can be seen as the selfsimilar solution of a central engine-driven spherical collisionless shock. The characteristic dimensionless parameters in our experiment, such as the Sonic Mach number(vsh/cs) and the Alfven Mach number(vsh/va) are 40 and 300 respectively. Meanwhile, for the dimensionless magnetic energy (magnetization), ek =(B2/8\u03c0)/(nimiv2/2)\u2009~\u20090.14, which is comparable with that observed in the SNRs G1.9\u2009+\u20090.3 like SNRs. Thus our experimental results can be used for the probe of the dynamics related to the SNRs G1.9\u2009+\u20090.3 like SNRs. A comparison between plasma parameters in our experiments and SNRs G1.9\u2009+\u20090.3 type young supernova remnant is listed in Table\u00a01.\n\nIn the cosmos, a shock breakout is initiated when the shock reaches the edge of a star31,32, such as that during a supernova explosion. Interestingly, our experiment observations have shown a resemblance to the collisionless shock breakout: the shock structure experienced a catastrophic breakdown at about 4.0\u2009ps after the illumination of the pump pulse. Meanwhile, the magnetic field also vanishes in accompany with the shocks (Fig.\u00a01c) and this may indicate a release of electromagnetic energy. The sudden disappearance of the magnetic field will cause the accumulated high-density shock front to explode. The Weibel-mediated collisionless shock breakout is reproduced in a laboratory. Since the trapped ions can expand almost isotropically inside the intense magnetic field, the duration of the shock breakout can be roughly estimated by the ions\u2019 expanding velocities. Specifically, the steady shock velocity of ~0.03c in our experiment suggests that approximately 1\u2009ps is required for the plasma to expand to a density of <0.01nc, where the plasma\u2019s volume has increased by six-fold (plasma radius raised by ~10\u2009\u03bcm) and the shock breakout has stopped.\n\nThe experimental results are supported by our PIC simulations. Figure\u00a03c shows the density profile of the plasma density at different time delays. It shows that the Al+8 ions continue to expand after 4\u2009ps at an almost constant velocity, which is consistent with our basic estimation of the shock breakout time and is a hallmark of the breakout of a shock wave. This ultrafast release of energetic ions from the target surface, as revealed by the shock breakout process, has important implications not only for astrophysics but also for multiple plasma and radiation phenomena. Specifically, the detachment of the ions in the acceleration process directly indicates the basic mechanisms behind the laser-driven ion acceleration, which has yearned for further explorations.\n\nGiven the complexity of the breakout process, a simple catastrophe model is developed to show the basic working principles33. By assuming a Gaussian curve of the shock front density,\n\nwe assume that the width of the shock, \u03c3(t), expands with a small velocity and is on the order of the ion skin depth before reaching the edge of the preplasma, and we take n0 to be a constant. The shock front expands with the fitted curve 27t3/5 before breakout and the expanding velocity of the width of the shock front \u03c3(t) is set to ~0.03c while reaching the plasma edge. As shown in Fig.\u00a03d the plasmas show an abrupt decrease in density which eventually leads to the disappearance of the shock. To compare with shock breakouts observed in astrophysics, we have listed several characteristic duration times of the shock breakouts34 in Table\u00a02. Besides the catastrophic disappearance of the shock front observed in the experiment, an electromagnetic pulse with a duration of ~1\u2009ps, which indicates the occurrence of the breakout, should be detectable in the experiment and this calls for further exploration.\n\nGiven the complexity of the interaction between the intense laser pulse and the plasma, a proper preplasma density that depends on the laser\u2019s contrast ratio is decisive for the shock formation. This is because high preplasma densities will prohibit the ion dynamics as electron Weibel instabilities will dominate the plasma expansion. In our experiment, this specific threshold for the collisionless shock formation is below 0.1nc, corresponding to high contrast (or the low-foot mode) of the laser conditions (see Methods). Otherwise, at high preplasma densities of ~ 0.4nc or ~ 0.2nc, filamentary magnetic tubes are formed at the boundary of the preplasma region, and no shock waves are visible.\n\nTo qualitatively characterize the underlying interaction processes, we now examine other two particular cases of preplasma densities of 0.4nc, and 0.2nc\u2014each representative of the Weibel instability in the unsaturated and saturated regime. Figure\u00a04 compares the spatiotemporal evolution of the magnetic fields at the target front in these two scenarios, respectively. At the preplasma density of ~ 0.4nc, it is clear a concentration of the filamentary structures appears in the middle of the plasma region with three recognizable magnetic filaments. The physical origin of these ion Weibel instability-related filaments is seeded by electron Weibel instabilities. Since ions cannot undergo adequate acceleration for preplasma densities of 0.4nc, the expanding hot electrons from the target surface account primarily for the filamentary structures, which are then amplified by the insufficient accelerated ion beams. To account for this process, a hot electron expanding model is developed in Supplementary\u00a0Information 2 (A sketch map of the model is shown in Supplementary Fig.\u00a02, and the calculated anisotropic parameter is shown in Supplementary Fig.\u00a03.).\n\nSpatiotemporal evolution of the magnetic field in front of the target when the preplasma density npre is (a) npre\u2009=\u20090.4nc; (b) npre\u2009=\u20090.2nc, \u03c4d is the time delay; The target surface is located at x\u2009=\u20090 which is indicated by an arrow in each picture.\n\nWith declining preplasma density, the peak sheath field gradually migrates from the preplasma boundary to the target surface. The ions thus can gain greater energy, leading to a nearly saturated magnetic field induced by the ion Weibel instability at the preplasma density of ~0.2nc (Fig.\u00a03b). The gyroradius of an Al+8 ion in the magnetic field can be estimated as \\({\\widetilde{R}}_{g} \\sim 100\\mu m\\). The filament distance, dF, as determined by the experiment is ~15\u2009\u03bcm, indicating that the Weibel instability is nearly at the saturation point.\n\nIn comparison with the higher preplasma density situation, a major difference between 0.2nc and 0.4nc is the trajectory of the Al+8 ions. For the preplasma density of ~ 0.2 nc, the deflection angle \u03b8 of Al+8 ions in the magnetic field can be calculated as,\n\nwhich indicates that the Al+8 ions can propagate somewhat freely in the magnetic field. The expanding ions hence can be regarded as a Weibel instability-mediated rarefactive wave. Near the boundary of the preplasma, the magnetic field structures stop moving, as being frozen because the return currents cannot be supported outside of the preplasma region.\n\nFigure\u00a03a presents the temporal evolution of the peak magnetic field for three different preplasma densities. Notably, for preplasma density ~ 0.1nc, the magnetic field increases to its maximum value of B\u2009~\u20095000\u2009T at around 2.7\u2009ps. It suggests that the growth rate \\({\\gamma }_{F}\\) of the magnetic field is approximately three times greater than in the case of a low-foot laser and is estimated to be ~ 1\u2009\u00d7\u20091012 s\u22121, which is compatible with our estimation of the growth rate of the Weibel instability: \u03b3F\u2009~\u2009\u03b2d\u03c9p.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58867-3/MediaObjects/41467_2025_58867_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58867-3/MediaObjects/41467_2025_58867_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58867-3/MediaObjects/41467_2025_58867_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58867-3/MediaObjects/41467_2025_58867_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We have obtained the unambiguous experimental evidence of sub-relativistic collisionless shock driven by a table-top femtosecond laser using a concept which we termed laser engine. In this concept, a carefully adjusted laser temporal profile is used to create a low-density isotropic preplasma and the ensuing ion acceleration by the second femtosecond main laser pulse. Afterward, a strong flow of highly directional ion beams develops, which in turn drives a rapidly growing Weibel magnetic field and leads to the formation of a collisionless shock wave. Much different from the amplification of the Weibel magnetic fields observed in ref. 18, a real Weibel instability mediated forward propagating collisionless shock with subrelativistic velocity is observed in the experiment. When the shock propagates out of the plasma region, collisionless shock breakout is observed in the experiment. Importantly, our experimental results identify the Weibel instability as an efficient mechanism for escalating the magnetic field and could provide an essential boost in the formation of relativistic collisionless shocks in the universe. Meanwhile, femtosecond laser beams can provide far larger energy densities and, concomitantly, significantly reduced time and space scales required for studying the laser-plasma interactions. As a general application, we anticipate the use of femtosecond laser systems for studying collisionless shock formation, propagation, and breakout, as an alternative to large laser facilities that are beyond the reach of common researchers. This simple scheme, conducted with only a mJ-level laser system, is flexible and can, in principle, be extended to much higher laser energies to escalate the collisionless shock velocity beyond our current observations. Looking ahead, since vshock\\(\\,\\propto \\,\\)E for a spherical shock, relativistic collisionless shock mediated with a turbulent magnetic field as high as 105 \u2013 106 T with picosecond-scale duration can be induced with currently available petawatt-level femtosecond laser facilities35. With this high magnetic field, the atomic properties and the behaviors of matter will be changed fundamentally. Extreme physics resembling that observed on the surface of neutron stars and similar compact objects can be explored36.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "A laser system with 30-fs pulse width and central wavelength of ~800\u2009nm is used in the experiment. The laser beam is split into two parts with a beam splitter, the laser beam with about ~ 90% of the total energy is named the pump pulse. This beam with ~ 4-mJ energy was focused at 90\u00b0 with an off-axis parabola mirror onto the surface of fused quartz that is coated with ~30\u2009\u03bcm thick aluminum film. The peak intensity of the laser beam is ~ 1\u2009\u00d7\u20091017\u2009W\u2009cm\u20132. Another beam with ~10% of the total energy is frequency doubled to 400\u2009nm to be used as a probe beam. The off-axis parabola and the targets were held in a vacuum chamber with ambient air pressure ~10\u22123\u2009Pa. The relative time delay of the main beam and the probe beam is controlled by an electric stepper motor.\n\nThe laser temporal contrast is defined as the ratio of the peak intensity of the main pulse to the prepulse intensity. The temporal contrast can be controlled by the relative time delays between different Pockels cells in the Chirped Pulse Amplification system (CPA system). The laser consists of a Ti:sa oscillator, a repetition amplifier, and a single-pass amplifier. The repetition rate of the oscillator is 80\u2009MHz, i.e., the time interval between two neighboring oscillator pulses is 12.5\u2009ns. The cavity length of the regeneration amplifier is 3\u2009m, which corresponds to a round-trip time of 10\u2009ns. A schematic drawing of a time series of laser pulses is shown in Fig.\u00a05. As the extinction ratio of the Pockels cell is ~1000:1, when the selected oscillator pulses (PC I in Fig.\u00a05) enter the regeneration cavity, leakage pulses are accompanying with the selected ones. When the cavity is dumped at a second Pockel cell, the leakage pulses are output with the selected pulses (PC II in Fig.\u00a05) which affects the contrast in nanoseconds. And the contrast can be controlled by the switching time of the second Pockel cell.\n\nThe temporal separation between consecutive seed pulses is 12.5 ns. Within the regeneration cavity (Regen), the round-trip time is 10 ns. When the selected seed pulses are injected into the regeneration cavity (via Pockels cell I), they are accompanied by leakage pulses. The\u00a0contrast of the amplified laser pulse can be controlled by\u00a0the switching time\u00a0of the second Pockels cell (Pockels cell II).\n\nThe prepulse approaches the target ~2.8\u2009ns before the main pulse, and a preplasma is induced. Due to the long-time delay between the main pulse and the prepulse, when the main pulse illuminates on the target the preplasma density distribution is almost homogeneous in front of the target. The density of the preplasma is determined by the intensity of the prepulse, thus the temporal contrast of the laser beam can be easily monitored by the density of the preplasma at the zero delay.\n\nTo diagnose the plasma density in front of the target, complex laser interferometry is used in the experiment37. The interference fringes are used to reconstruct the phase changes due to the presence of plasma in front of the target. This phase change can then be used for the extraction of the density distribution of the plasma.\n\nThe magnetic field is measured by the classical Faraday rotation method. To overcome the difficulties meeting in two-channel Faraday rotation experiments, we follow a method developed in ref. 37, in which only one CCD camera is needed, thus overcoming the difficulties of perfectly matching two images from different CCD channels. Glan-type polarizing prisms are used in the experiment to measure the rotation of the polarization plane of the probe beam transmitted through the plasma region. By comparing this rotation angle with the background polarization state of the probe beam the magnetic field can be extracted.\n\nPrecise control of the time delay between the main pulse and the prepulse, as well as adjustment of the prepulse intensity, can be used for the regulation of the preplasma density situated ahead of the target. This control facilitates achieving a preplasma density on the order of near critical density, such as ~0.1nc. In the target normal sheath acceleration mechanism (TNSA), ~ MeV level proton beams (vi\u2009~\u20091\u2009\u00d7\u2009107\u2009m/s) can be easily induced with a millijoule level femtosecond laser system as it is demonstrated in ref. 22. When the accelerated ion beams penetrate into the preplasma in front of the target, the mean free path of the ion beam is ~30\u2009m which ensures that the ion plasma interaction is collisionless and can be used for the study of the collisionless shocks.\n\nWhile hot electrons are stimulated by the ultrashort laser beam expanding out of the target an electrostatic sheath field is formed at the boundary of the target which can accelerate the charged ions. However, if the density of the preplasma is very high, this acceleration field can be shielded by the preplasma, which can lead to low efficiency in the acceleration of ion beams. There must be some critical density ncrt, below which the ion beams can be efficiently accelerated. Based on a model developed in ref. 38, in TNSA the energy gained by an ion beam in a plasma with two electron temperatures Th and Tc can be written as Eion\u2009~\u2009Ze(nhTh + ncTc)/(nh + nc). The kinetic energy of the accelerated ions in our experiment is ~ MeV. The growth rate \u03b3F of the ion Weibel instability is \u03b3F\u2009~\u2009\u03b2i\u03c9i. Based on PIC simulations, the maximum ion velocity can reach ~ 0.07c which is consistent with the experiment. The growth rate of the Weibel instability decreases exponentially with increasing preplasma density because the sheath field also decreases exponentially with preplasma density. As shown in the article, just like in high harmonic generation, a \u201cphase transition-like\u201d behavior should be observed, and this transition has been observed in the experiment.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58867-3/MediaObjects/41467_2025_58867_Fig5_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "The data supporting the findings in this study are available within the article and its Supplementary Information/Source Data file. All other relevant data are available from the corresponding author upon request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The EPOCH code used in this study is publicly available for download from https://github.com/Warwick-Plasma/epoch.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Pelletier, G., Bykov, A., Ellison, D. & Lemoine, M. Towards understanding the physics of collisionless relativistic shocks. Space Sci. Rev. 207, 319\u2013360 (2017).\n\nArticle\u00a0\n ADS\u00a0\n \n Google Scholar\u00a0\n \n\nMedvedev, M. V. & Loeb, A. Generation of magnetic fields in the relativistic shock of gamma-ray burst sources. Astrophys. J. 526, 697 (1999).\n\nArticle\u00a0\n ADS\u00a0\n \n Google Scholar\u00a0\n \n\nSilva, L. O. et al. Interpenetrating plasma shells: near-equipartition magnetic field generation and nonthermal particle acceleration. Astrophys. 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E 69, 026411 (2004).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "Y.T. acknowledge the support of the National Natural Science Foundation of China under grant numbers 12388102, 12325409, U2267204 and Shanghai Pilot Program for Basic Research, Chinese Academy of Sciences, Shanghai Branch. Y.B. acknowledge the support of the National Natural Science Foundation of China under grant numbers 12474350, the Youth Innovation Promotion Association of Chinese Academy of Sciences, CAS Project for Young Scientists in Basic Research under grant number YSBR-060, the Natural Science Foundation of Shanghai, China under grant number 24ZR1493000 and Strategic Priority Research Program of Chinese Academy of Sciences under grant number XDB0890203.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "State Key Laboratory of Ultra-intense Laser Science and Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, 201800, China\n\nYafeng Bai,\u00a0Dongdong Zhang,\u00a0Yushan Zeng,\u00a0Jiakang Mao,\u00a0Liwei Song,\u00a0Ye Tian\u00a0&\u00a0Ruxin Li\n\nCenter of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China\n\nYafeng Bai,\u00a0Yushan Zeng,\u00a0Jiakang Mao,\u00a0Liwei Song,\u00a0Ye Tian\u00a0&\u00a0Ruxin Li\n\nZhangjiang Laboratory, Shanghai, 201210, China\n\nRuxin Li\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY.T. and R.L. conceived and supervised the project. Y.B., J.M. conducted the experimental measurements. Y.B. and Y.T. developed the theory. Y.B. and D.Z. performed the data analyses. Y.T., Y.B., D.Z. and Y.Z. wrote the manuscript. All authors reviewed and discussed the manuscript and made substantial contribution to it.\n\nCorrespondence to\n Ye Tian or Ruxin Li.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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Observation of sub-relativistic collisionless shock generation and breakout dynamics.\n Nat Commun 16, 3770 (2025). https://doi.org/10.1038/s41467-025-58867-3\n\nDownload citation\n\nReceived: 24 September 2024\n\nAccepted: 31 March 2025\n\nPublished: 28 April 2025\n\nVersion of record: 28 April 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58867-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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fibrosis.", + "journal": "Nature Communications", + "published": "05 December 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132914", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE282002", + "/articles/s41467-024-54997-2#Sec25" + ], + "code": [], + "subject": [ + "Adult stem cells", + "Regeneration", + "Respiratory tract diseases" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4177351/v1.pdf?c=1733490565000", + "research_square_link": "https://www.researchsquare.com//article/rs-4177351/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54997-2.pdf", + "preprint_posted": "21 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Idiopathic pulmonary fibrosis (IPF) is a progressive scarring disease arising from the maladaptive differentiation of lung stem cells into bronchial epithelial cells rather than into alveolar type 1 (AT1) cells, which are responsible for gas exchange. Here, we report that healthy lungs maintain their stem cells through tonic Hippo and \u03b2-catenin signaling, which promote Yap/Taz degradation and allow for low level expression of the Wnt target gene Myc. Inactivation of upstream activators of the Hippo pathway in lung stem cells inhibits this tonic \u03b2-catenin signaling and Myc expression and promotes their Taz mediated differentiation into AT1 cells. Vice versa, increased Myc in collaboration with Yap promotes the differentiation of lung stem cells along the basal and myoepithelial like lineages allowing them to invade and bronchiolize the lung parenchyma in a process reminiscent of submucosal gland development. Our findings indicate that stem cells exhibiting the highest Myc levels become supercompetitors that drive remodeling, whereas loser cells with lower Myc levels terminally differentiate into AT1 cells.Biological sciences/Stem cells/RegenerationHealth sciences/Diseases/Respiratory tract diseasesHealth sciences/Medical research/Stem-cell research", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Idiopathic pulmonary fibrosis (IPF) is a progressive respiratory scarring disease arising from the maladaptive differentiation of lung stem cells into bronchial epithelial cells rather than into alveolar type 1 (AT1) cells, which are responsible for gas exchange. Here, we report that healthy lungs maintain their stem cells through tonic Hippo and \u03b2-catenin signaling, which promote Yap/Taz degradation and allow for low-level expression of the Wnt target gene Myc. Inactivation of upstream activators of the Hippo pathway in lung stem cells inhibits this tonic \u03b2-catenin signaling and Myc expression and promotes their Taz-mediated differentiation into AT1 cells. Vice versa, increased Myc in collaboration with Yap promotes the differentiation of lung stem cells along the basal and myoepithelial-like lineages allowing them to invade and bronchiolize the lung parenchyma in a process reminiscent of submucosal gland development. Our findings indicate that stem cells exhibiting the highest Myc levels become supercompetitors that drive remodeling, whereas loser cells with lower Myc levels terminally differentiate into AT1 cells.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Idiopathic pulmonary fibrosis (IPF) pathogenesis encompasses alveolar and fibrotic remodeling, inflammation, and eventual loss of lung architecture1 resulting in progressive loss of pulmonary function, respiratory failure, and death often within 5 years of diagnosis2,3. Accumulating genetic data implicate impaired epithelial maintenance and function as drivers of pulmonary fibrosis4,5,6,7,8.\n\nThe alveolar epithelium is primarily comprised of alveolar type 2 stem cells (AT2s) and alveolar type 1 cells (AT1s) responsible for gas exchange. Club stem cells and AT2 stem cells are capable of self-renewal and differentiation into AT2 and/or AT1 cells through a pre-AT1 transitional cell state (PATS) that has only recently been appreciated9,10,11. Hallmarks of ineffectual repair include the aberrant accumulation of PATS9,10,12 and ectopic airway differentiation, called bronchiolization, a prominent feature of interstitial lung disease12,13,14,15,16,17,18,19. In vivo, there is no evidence for AT2 stem cells and some evidence for Club cells contributing to bronchiolization20,21. In fact, upon H1N1 influenza injury, the stem cells driving this bronchiolization have been demonstrated to be intralobular serous cells22, intralobular airway-resident basal p63+ progenitors21 and to a limited extent preexisting basal cells (BCs)23 all of which depend on Trp63. For the purpose of this manuscript, we will group these together as basal-like cells (BLCs). Once established bronchiolization is difficult to resolve and this persistence of bronchial epithelial cells incapable of gas exchange ultimately leads to death. However, genetic interventions have suggested that it may be possible to reprogram these bronchiolized areas into alveolar epithelium and potentially cure this disease20,24.\n\nInterestingly, whether bronchiolization occurs seems to largely depend on the level of injury, e.g., catastrophic injury to the lung parenchyma which wipes out the majority of AT2s, AT1s and Club cells, suggesting that some form of cell competition may be at play. Indeed, BLCs are resistant to influenza virus24 and SARS-CoV-225 infection. Therefore, one possibility is that BLCs under normal conditions are kept at bay by \u201cmore competitive\u201d Club cells or AT2 stem cells. Interestingly, upon Sendai virus infection which only destroys Club cells and AT2 cells but not AT1 cells26, BLCs have been shown to outcompete and replace surviving AT1 cells and bronchiolize the lung parenchymal regions devoid of AT2 stem cells27.\n\nIn tissues harboring a mosaic imbalance in Myc protein levels, cells with higher Myc levels expand at the expense of cells with lower levels by eliminating them through apoptosis, inducing senescence, promoting autophagy, or directing them to terminal differentiation and sloughing28. Cells measure their Myc content relative to their neighbors, and cells with lower Myc levels are eliminated by neighbors with higher Myc29,30. This process is known as cell competition28,31,32,33,34,35. Cells that grow faster and eliminate less-fit cells are called super-competitors. Cells become super-competitors when their levels of Myc expression are two-fold higher than that of their neighbors29,31,36,37. This process may reflect a selection for fit cells, since Myc maintains stemness, eliminating cells with lower Myc may guard against premature differentiation.\n\nHere, we demonstrate that lung epithelial stem cell competitiveness/fitness levels are determined by their Myc levels, which are tuned by the Hippo pathway. Healthy lungs maintain their stem cells through tonic Hippo and \u03b2-catenin signaling, which promote Yap/Taz degradation and allow for low-level expression of the Wnt target gene Myc. Inactivation of upstream activators of the Hippo pathway, Mst1/2 (encoded by Stk3/4), in AT2 or Club stem cells stabilizes Taz, which inhibits Myc expression by promoting \u03b2-catenin degradation and allows for Taz to translocate to the nucleus and drive AT1 cell differentiation. Vice versa, increased Myc in collaboration with Yap promotes the differentiation of lung stem cells along the basal and myoepithelial-like lineages allowing them to invade and bronchiolize the lung parenchyma in a process reminiscent of submucosal gland development. Our findings indicate that stem cells exhibiting the highest Myc levels become supercompetitors that drive remodeling, whereas loser cells with lower Myc levels terminally differentiate into AT1 cells.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "After catastrophic injury to the lung parenchyma by influenza infection, bronchial epithelial stem cells (BESCs) in the airway have been proposed to undergo a binary response to reconstitute epithelial barriers giving rise to either alveolar epithelium or generate more airway epithelium and \u201cbronchiolize\u201d the lung parenchyma. However, it has been unclear whether one particular BESC subpopulation undergoes this binary response or whether there is competition between different BESC populations capable of either promoting alveolar epithelial regeneration or bronchiolization. This is largely because most lineage tracing experiments to target BESCs rely on Sox2CreERT2;mTmG mice which lineage labels all bronchial epithelial cells.\n\nIt is well known that subsets of Club cells (e.g., BASCs) can give rise to both airway and alveolar epithelium38,39,40 but do not contribute in a significant way to bronchiolization of the lung parenchyma after catastrophic injury mediated by H1N1 influenza21. However, because Club cells and especially BASCs are also destroyed by H1N1 influenza it has been difficult to assert whether they can contribute to alveolar epithelial regeneration if they survive the initial injury. To investigate this, we used Scgb1a1CreERT;mTmG mice to lineage label Club cells, including BASCs and performed H1N1-mediated injury. Our experiments confirm previous reports that Club cells do not participate in the bronchiolization of the lung parenchyma after H1N1-mediated injury (Fig.\u00a01A\u2013D) which is known to be mediated by BLCs under this condition21. However, we find that if Club cells survive the initial assault they can contribute to alveolar epithelial regeneration after injury (Fig.\u00a01B\u2013D). Remarkably, Club cells regenerating alveolar epithelium or BLCs bronchiolizing the lung parenchyma are mutually exclusive events i.e. a binary response, suggesting that when Club cells survive the initial assault, they compete with BLCs, preventing them from invading and bronchiolizing the lung parenchyma.\n\nScgb1a1CreERT;mTmG and Scgb1a1CreERT;Mycf/f;mTmG were placed on tamoxifen containing chow at 8 weeks of age for 3 weeks to inactivate Myc and permanently label all Club cells/BASCs and their offspring with GFP. After a 3 week wash-out period, mice were infected with H1N1 influenza virus, and lungs were harvested at 6 weeks post injury. Coimmunostaining for GFP (lineage label), Keratin 5 (Krt5; basal and BLCs), and Keratin 8 (Krt8; BLCs and transitional cells) on Scgb1a1CreERT;mTmG (A) and Scgb1a1CreERT;Mycf/f;mTmG (E) lung sections. Coimmunostaining for GFP (lineage label), surfactant protein C (Sftpc; AT2 cells), Rage (AT1 cells) on Scgb1a1CreERT;mTmG (B, C) and Scgb1a1CreERT;Mycf/f;mTmG (F, G) lung sections. White boxes in B and F are enlarged in (C, G), respectively. D Diagram demonstrating that normal Club cells inhibit BLCs and give rise to alveolar epithelial cells. H Diagram demonstrating that Myc deficient Club cells are outcompeted by Myc sufficient BLCs. Created in BioRender. Warren (2024) https://BioRender.com/ k46i357. I Nanostring nCounter analysis on RNA from Scgb1a1CreERT;mTmG (n\u2009=\u20094) and Scgb1a1CreERT;Mycf/f;mTmG (n\u2009=\u20097) lungs for BLC genes Keratin 17 (p\u2009=\u20090.03, Log2 fold change\u2009=\u2009\u22121.87), Sox9 (p\u2009=\u20090.03, Log2 fold change\u2009=\u2009\u22121.04), and Krt5 (p\u2009=\u20090.047, Log2 fold change\u2009=\u2009\u22121.86). Data are Log2 normalized. J Lineage tracing analysis on immunostaining in B (n\u2009=\u20097), E (n\u2009=\u20092) using Aivia machine learning software (Sftpc/gfp p\u2009=\u20090.003, RAGE/gfp p\u2009=\u20090.6, Sum/gfp p\u2009=\u20090.03). K Hydroxyproline analysis on Cre- controls (n\u2009=\u200917) and Scgb1a1CreERT;Mycf/f;mTmG (n\u2009=\u200920) lungs normalized to control (p\u2009=\u20090.04). L Image analysis of total area of basal cells in control (n\u2009=\u200911) and Scgb1a1CreERT;Mycf/f;mTmG (n\u2009=\u20099) lung sections (p\u2009=\u20090.03). Data are presented as mean values +/\u2212 SEM. Scale bar: 250 \u03bcm. Two two-tailed unpaired T-test was used to determine significance. F test was used to determine equal variances and unreported F values indicate equal variance. *p\u2009<\u20090.05, **p\u2009<\u20090.01.\n\nIn tissues harboring a mosaic imbalance in Myc protein levels, cells with higher Myc levels expand at the expense of cells with lower levels by eliminating them through apoptosis, inducing senescence, promoting autophagy or directing them to terminal differentiation and sloughing28. To investigate whether Club cells compete with BLCs using the classic cell competition model we inactivated Myc in Club cells specifically using Scgb1a1CreERT;Mycf/f;mTmG mice while simultaneously lineage tracing them. We find that upon H1N1 injury alveolar epithelial regeneration by Club cells in Scgb1a1CreERT;Mycf/f;mTmG mice is impaired with the majority being outcompeted by BLCs (Fig.\u00a01E\u2013L) and the remainder giving rise preferentially to AT1 rather than AT2 cells compared to Scgb1a1CreERT;mTmG control mice, that feature normal Myc levels. Scgb1a1CreERT;Mycf/f;mTmG lungs featured increased bronchiolization mediated by BLCs (Fig.\u00a01E, I, L) and increased pulmonary fibrosis as measured by hydroxyproline content (Fig.\u00a01K). Together these findings indicate that stem cell competition in the lung is governed by Myc levels.\n\nWe next wanted to investigate how Myc levels in BLCs affect bronchiolization. To do this we performed immunostaining for Myc on lungs after H1N1 injury or severe bleomycin injury. We show that after catastrophic H1N1 or severe bleomycin injury some BESC offspring at the periphery or leading edge of the BC-pods feature high Myc levels (Fig.\u00a02A\u2013C, Supplementary Fig.\u00a01A\u2013H). Interestingly, these leading edge BLCs unlike trailing cells in the BC pods also express high levels of Sox9 and Acta2 (Fig. 2D, E), reminiscent of myoepithelial cells (MECs) in the submucosal gland41,42 (SMG). Immunostaining and scRNAseq analysis of human IPF tissue demonstrate that subsets of BCs in honeycomb cysts of IPF lungs also feature high levels of Myc, Sox9 and/or Acta2 expression reminiscent of myoepithelial cells present in microdissected proximal airways of human donors confirming the proximalization of human IPF containing myoepithelial-like cells in the distal epithelium (Fig.\u00a02F, G, Supplementary Fig.\u00a01I\u2013M).\n\nA\u2013E Coimmunostaining for myoepithelial cell markers Myc (A\u2013C) or Sox9 (D, E), Krt5, and Acta2 (smooth muscle actin) on honeycomb regions in mouse lungs 17 days after bleomycin (A\u2013D) and 14 or 21 days after H1N1 injury (B, C, E). F Coimmunostaining for Myc, Krt17, and Krt5 on honeycomb regions in human IPF tissue. (G) scRNAseq analysis of myoepithelial cell genes Krt5, Krt17, Myc, and Acta2 expression in human IPF vs donor lungs. Uniform Manifold Approximation and Projection (UMAP) of 10x scRNAseq data on human control (Donor Distal) and control including microdissected proximal airways (donor proximal) and IPF distal lungs demonstrating high Myc, Krt5, Krt17, Krt15, Acta2 expression in bronchiolized epithelium in IPF and proximal airway basal cells. Scale bar: 50 \u03bcm (A\u2013C), 100 \u03bcm (D\u2013F).\n\nTo investigate if the MEC-like cells at the leading edge of the BC pods are derived from MECs in the SMG, we lineage labeled the latter prior to injury using a Nkx2.1Flpo;Acta2-Frt-STOP-Frt-CreERT2;mTmG43 intersectional mouse model in which we can specifically lineage label lung epithelial cells that co-express the lung epithelial cell marker Nkx2.1 and the mesenchymal Acta2 (\u03b1-SMA) marker. The Acta2-Frt-STOP-Frt-CreERT2 knock-in mouse line, possesses a CreERT2 cassette, inserted in the Acta2 locus, which is preceded by a STOP codon, flanked by Frt sites. As such, when crossed with Nkx2.1Flpo expressing mice, Acta2-Frt-STOP-Frt-CreERT2 mice permanently express CreERT2 in Acta2 and Nkx2.1Flpo co-expressing cells as well as their offspring, due to removal of the STOP codon. This then allows for the lineage labeling of MECs in Nkx2.1Flpo;Acta2-Frt-STOP-Frt-CreERT2;mTmG after tamoxifen treatment.\n\nUsing this mouse model we find that SMG MECs do not migrate and give rise to BC pods after H1N1 injury (Fig.\u00a03A, B). However, we can label de novo myoepithelial like cells in BC-pods by treating this same intersectional mouse model with tamoxifen after H1N1 injury (Fig.\u00a03C, D), suggesting that BLCs, other than the MECs in the SMG, can acquire MEC-like characteristics upon catastrophic H1N1 or bleomycin injury. We were able to confirm these findings using two additional intersectional mouse models Trp63DreERT2;Acta2CreERT2;RLTG and Nkx2.1Flpo; Acta2CreERT2;FLTG (Fig.\u00a03E, G\u2013L).\n\nA, B Nkx2.1Flpo;Acta2-Frt-STOP-FrtCreERT2;mTmG mice were place on tamoxifen containing chow for 3 weeks. Following a 3 week washout period, mice were infected with H1N1. At 6 weeks after injury, left lung lobes and trachea were inflation fixed, embedded in paraffin, and sectioned. Coimmunostaining for myoepithelial cell markers Acta2, Krt5, and lineage label GFP on Nkx2.1Flpo;Acta2-Frt-STOP-FrtCreERT2;mTmG trachea (E) and lung (F). C, D Nkx2.1Flpo;Acta2-Frt-STOP-FrtCreERT2;mTmG mice were intranasally administered H1N1. At 2 weeks after injury mice were placed on tamoxifen containing chow. At 6 weeks after injury, left lung lobes and trachea were inflation fixed, embedded in paraffin, and sectioned. Coimmunostaining for Krt5, Krt8, and GFP on Nkx2.1Flpo;Acta2-Frt-STOP-FrtCreERT2;mTmG lungs. E Trp63DreERT2;Acta2CreERT2;RLTG mice were intranasally administered H1N1 and placed on tamoxifen containing chow at 2 weeks after injury. tdTomato is induced in only Trp63DreERT2 expressing cells and GFP is induced only when both Trp63DreERT2 and Acta2CreERT2 are expressed (myoepithelial cells). Coimmunostaining for RFP (tdtomato) and GFP on lungs at 6 weeks after injury. F Sox9CreERT2;tdTomato mice were intranasally administered H1N1 and placed on tamoxifen containing chow at the injury. Coimmunostaining for RFP (tdTomato) and Krt5 on lungs at 6 weeks after injury. G\u2013L Nkx2.1Flpo; Acta2CreERT2;FLTG mice were intranasally administered H1N1 and placed on tamoxifen containing chow at 2 weeks after injury with additional tamoxifen shots at 14 and 16 days after injury. tdTomato is induced in only Nkx2.1Flpo expressing cells and GFP is induced only when both Nkx2.1Flpo and Acta2CreERT2 are expressed (myoepithelial cells). G Coimmunostaining for GFP and Krt5 on lungs at 6 weeks after injury and (H) image analysis using Aivia machine learning software (n\u2009=\u20094 animals). Coimmunostaining for GFP and (I) Dclk1 (tuft cells), (J) Foxj1 (ciliated cells), and (K, L) Scgb1a1 (secretory cells) and Krt5 (BLCs). Data are presented as mean values +/\u2212 SEM. Scale bar: 200 \u03bcm (A) 100 \u03bcm (D, F), 50 \u03bcm (B, G), 25 \u03bcm (C, E, I\u2013L).\n\nUsing Trp63DreERT2;Acta2CreERT2;RLTG we can specifically target MEC-like cells that co-express the basal cell transcription factor Trp63 and the mesenchymal Acta2 (\u03b1-SMA) marker. Tamoxifen exposure results in Dre-mediated excision of a polyA signal (STOP) from the RLTG dual recombinase reporter allele (Dre/Cre recombinase reporter) within Trp63 expressing intralobular basal cells, with subsequent Cre-mediated excision of tdTomato-STOP within Acta2-expressing MECs. Outcomes of these recombination events include tracing of Trp63+-intralobular basal cells by expression of tdTomato, and Trp63+/Acta2+ MECs by expression of eGFP (Fig.\u00a03E).\n\nUsing Nkx2.1Flpo; Acta2CreERT2;FLTG we can specifically target MEC-like cells that co-express the lung epithelial-specific transcription factor Nkx2.1 and the mesenchymal Acta2 (\u03b1-SMA) marker. Flpo-mediated excision of a polyA signal (STOP) from the FLTG dual recombinase reporter allele (Flpo/Cre recombinase reporter) occurs within Nkx2.1 expressing lung epithelial cells, with subsequent tamoxifen-induced Cre-mediated excision of tdT-STOP within Acta2-expressing MEC like cells. Outcomes of these recombination events include tracing of Nkx2.1+-lung epithelial cells by expression of tdTomato, and Nkx2.1+/Acta2+ myoepithelial-like cells by expression of eGFP (Fig.\u00a03G\u2013L).\n\nFinally, to investigate whether all cells in BC pods may be derived from these MEC like cells that lead the invasion we gave tamoxifen chow to Sox9CreERT2;mTmG mice after H1N1 injury and found that all cells in the BC pods were lineage labeled (Fig.\u00a03F), indicating that all cells in basal cell pods either induced Sox9 expression at some point during the invasion of BC pods or are all derived from the MEC-like stem cells at the leading edge of the invasion. Indeed, treating Nkx2.1Flpo; Acta2CreERT2;FLTG mice with tamoxifen 2 weeks after H1N1 injury, lineage labels MEC-like cells and their offspring and demonstrates that myoepithelial-like cells do differentiate into conducting airway epithelial cells such as Dclk1+ tuft cells (Fig.\u00a03I), Foxj1+ ciliated cells (Fig.\u00a03J) and Scgb1a1+ club cells (Fig.\u00a03K, L). This is interesting as it indicates that super-competitor myoepithelial like cells may give rise to all trailing cells in the basal cell pods. Our findings further suggest that the process of bronchiolization is reminiscent of the process that drives submucosal gland development, suggesting that BC pods may be considered as de novo submucosal glands44,45.\n\nTo investigate if Myc levels in BESCs affect stem cell competition after severe bleomycin or H1N1 injury we generated Sox2CreERT2;Mycf/f;mTmG mice in which we can inactivate Myc in all BESCs, in order to level fitness levels, while simultaneously lineage labeling them. When we perform bleomycin (Fig.\u00a04B\u2013K) or H1N1 (Fig.\u00a04L\u2013U) injury on Sox2CreERT2;Mycf/f;mTmG mice, in which we inactivated Myc in BESCs prior to injury (Fig.\u00a04A), BESCs fail to acquire MEC-status and fail to bronchiolize the lung parenchyma, as demonstrated by impaired basal cell pod generation (Fig.\u00a04B, E, K, L, O, U) and reduced expression of bronchial epithelial markers Muc5b, Muc5ac and Krt5 by Nanostring nCounter RNA analysis (Fig.\u00a04I), qPCR analysis (Fig.\u00a04S) and 10x Visium spatial transcriptomics (Supplementary Fig.\u00a02). Interestingly, BESCs in Sox2CreERT2;Mycf/f;mTmG lungs, presumably Club cells/BASCs, give rise to more alveolar epithelium after severe bleomycin injury (Fig.\u00a04C, D, F, G, J) but less alveolar epithelium after H1N1 injury (Fig.\u00a04M, N, P, Q, T) compared to control mice. This is likely because inactivation of Myc in Club cells prevents them from giving rise to basal cells and allowing them only to give rise to alveolar epithelium after bleomycin injury resulting in a net increase in regeneration, whereas decreasing Club cell fitness through Myc inactivation likely also makes them more vulnerable to H1N1 infection. As a corollary, Sox2CreERT2;Mycf/f;mTmG lungs feature reduced pulmonary fibrosis based on hydroxyproline content (Fig.\u00a04H) after bleomycin injury, but increased pulmonary fibrosis after H1N1 injury (Fig.\u00a04R). However, in the H1N1 injury model in which most BASCs and AT2 stem cells are destroyed we find that that the inability of BLCs to robustly participate in the immediate regenerative response, even though maladaptive, is detrimental to survival (Supplementary Fig.\u00a06I) and likely contributes to the increased pulmonary fibrosis (Fig.\u00a04R) compared to littermate controls. Our theory is that de novo submucosal gland development (basal cell pod development) near terminal bronchioles and lung parenchyma that have been catastrophically damaged, is a way to seal off or plug those damaged conducting airways preventing more air from entering the lung via that route, which would cause further mechanical damage to the lung parenchyma.\n\nA Mice were placed on tamoxifen chow for 3 weeks to inactivate Myc and permanently lineage label bronchial epithelial cells and their offspring. Following a 3-week washout period, mice were injured with intratracheal administration of bleomycin (B\u2013K) or intranasal administration of H1N1 (L\u2013U) and lungs were harvested at 6 weeks post injury. (B, E, L, O) Immunostaining for Krt5 (basal and BLCs) and Acta2 (smooth muscle actin; myofibroblasts) on bleomycin (B, E) and influenza (L, O) injured Sox2CreERT2;mTmG and Sox2CreERT2;Mycf/f;mTmG. (C, F, M, P) Immunostaining for Rage (AT1 cells), GFP (lineage label), and Sftpc (AT2 cells) on Sox2CreERT2;mTmG and Sox2CreERT2;Mycf/f;mTmG. (D, G, N, Q) Models demonstrating that Myc sufficient Club cells inhibit BLCs and give rise to alveolar epithelial cells after bleomycin (D) and influenza (N) injury but Myc insufficient BLCs fail to give rise to basal cell pods (G, Q). Created in BioRender. Warren (2024) https://BioRender.com/j04r129. H Hydroxyproline analysis on bleomycin injured Cre- controls (n\u2009=\u200926) and Sox2CreERT2;Mycf/f (n\u2009=\u200919) (p\u2009=\u20090.04). I Nanostring nCounter analysis on RNA from bleomycin injured Cre- controls (n\u2009=\u200917) and Sox2CreERT2;Mycf/f;mTmG (n\u2009=\u20096) lungs BLC genes (Muc5ac p\u2009=\u20090.01, Muc5b p\u2009=\u20090.003, Krt5 p\u2009=\u20090.046). Data are Log2 normalized. J Lineage tracing analysis on bleomycin injured lungs from immunostaining in (C) (n\u2009=\u20092), (F) (n\u2009=\u20095) using Aivia machine learning software (p\u2009=\u20090.008). K Image analysis of the total area of basal cells in bleomycin injured control (n\u2009=\u20093) and Sox2CreERT2;Mycf/f;mTmG (n\u2009=\u20093) lung sections (p\u2009=\u20090.04). R Hydroxyproline analysis on influenza injured Cre- controls (n\u2009=\u20098) and Sox2CreERT2;Mycf/f (n\u2009=\u20095) (p\u2009=\u20090.004). S qPCR analysis on RNA from influenza injured Cre- controls (n\u2009=\u200912) and Sox2CreERT2;Mycf/f;mTmG (n\u2009=\u20097) lungs for BLC genes (Krt5 (p\u2009=\u20090.04, F\u2009=\u20093.32\u00d710\u22129) and Tp63 (p\u2009=\u20090.01, F\u2009=\u20096.29\u00d710\u22128)). T Lineage tracing analysis on influenza-injured lungs from immunostaining in M (n\u2009=\u20094), (P) (n\u2009=\u20095) using Aivia machine learning software (p\u2009=\u20090.04). U Image analysis of total area of basal cells in influenza injured control (n\u2009=\u20095) and Sox2CreERT2;Mycf/f;mTmG (n\u2009=\u200913) lung sections (p\u2009=\u20090.007). Data are presented as mean values +/\u2212 SEM. Scale bars: 100\u2009\u00b5m. Magnification insets are 400x larger. Two tailed unpaired T-test was used to determine significance. F test was used to determine equal variances and unreported F values indicate equal variance. *p\u2009<\u20090.05, **p\u2009<\u20090.01.\n\nSo far, our findings suggest that Myc levels in lung stem cells determine their fitness levels and that cells with the lowest Myc levels differentiate into AT1 cells. Since BC-pods are known to persist in the lung long after H1N1 infection, we wondered whether Myc is required for their maintenance and/or expansion post H1N1 injury. To investigate this we infected Krt5CreERT2;mTmG and Krt5CreERT2;Mycf/f;mTmG mice with H1N1 influenza, and lineage labeled their BLCs with or without simultaneous inactivation of Myc, starting at 2 weeks after injury (Fig.\u00a05C). Interestingly, we find that upon inactivation of Myc in BC pods in Krt5CreERT2;Mycf/f;mTmG mice, BC-pods are reduced in size, as indicated by less GFP RNA per Krt5 transcript, and fewer basal cells in Krt5CreERT2;Mycf/f;mTmG lung sections (Fig.\u00a05A, B, F) compared to H1N1 injured Krt5CreERT2;mTmG control mice. In addition, we find that compared to H1N1 injured Krt5CreERT2;mTmG mice, fibrosis is reduced in Krt5CreERT2;Mycf/f;mTmG mice, in which we inactivated Myc in BLCs and myoepithelial-like cells after injury (Fig.\u00a05G). More strikingly we find that inactivation of Myc in BC pods post H1N1 injury affects BC stem cell maintenance over time and allows for their differentiation towards the AT1 lineage by 12 weeks after H1N1 injury (Fig.\u00a05I\u2013Q).\n\nC, K Krt5CreERT2;mTmG and Krt5CreERT2;Mycf/f;mTmG were infected with H1N1 at 8 weeks of age. At 2 weeks after injury, mice were placed on tamoxifen chow to inactivate Myc and permanently label all BLCs and their offspring with GFP and lungs were harvested at 6 (A-H) or 12 (I-Q) weeks post injury. A, B Coimmunostaining for Keratin 8 (Krt8; BLCs and transitional cells), GFP (lineage label), and Keratin5 (Krt5; basal and BLCs), (D, E) and coimmunostaining for Muc5b (mucus-producing secretory cells) and GFP (lineage label) on Krt5CreERT2;mTmG (A, D) and Krt5CreERT2;Mycf/f;mTmG (B, E). F qPCR analysis for Gfp, Krt5, and Muc5b Cre- control (n\u2009=\u20095) and Krt5CreERT2;Mycf/f (n\u2009=\u20095). Values are graphed as ratios (GFP/Krt5 p\u2009=\u20090.046, GFP/Muc5b p\u2009=\u20090.79, Krt5/Muc5b p\u2009=\u20090.006). G Hydroxyproline analysis on Cre- control (n\u2009=\u200910) and Krt5CreERT2;Mycf/f (n\u2009=\u200916) (p\u2009=\u20090.01). H Image analysis of total area of basal cells in influenza injured control (n\u2009=\u20096) and Krt5CreERT2;Mycf/f;mTmG (n\u2009=\u20098) lung sections (p\u2009=\u20090.02). I, J Coimmunostaining for Rage (AT1 cells) and GFP (lineage label) on Krt5CreERT2;mTmG and Krt5CreERT2;Mycf/f;mTmG lungs at 12 weeks post injury with magnification in O\u2013Q. L Lineage tracing analysis on influenza injured lungs from immunostaining in I and (J) (n\u2009=\u20093). Area of GFP (p\u2009=\u20090.02), Sftpc (p\u2009=\u20090.03), and Rage (p\u2009=\u20090.02) were determined using Aivia machine learning software. M, N Diagram depicting that Myc deficient basal cells pods are smaller than Myc sufficient basal cell pods and can differentiating into Rage+ AT1 cells at 12 weeks after influenza injury. Created in BioRender. Warren. (2024) https://BioRender.com/ w28w946. Data are presented as mean values +/\u2212 SEM. Scale bars: 250\u2009\u00b5m (A, B, D, E), 125\u2009\u00b5m (I, J), 25\u2009\u00b5m (O\u2013Q). The two-tailed unpaired T-test was used to determine significance. F test was used to determine equal variances and unreported F values indicate equal variance. *p\u2009<\u20090.05, **p\u2009<\u20090.01.\n\nWe next wondered what would happen if we boosted the fitness of Club cells by overexpressing Myc in Club cells after a less severe bleomycin injury. Overexpression of a dominant active version of the Hippo transcriptional effector Yap1S112A in Club cells has been shown to direct their differentiation along the BLC lineages46, and Myc and Yap have both been shown to be important for cell competition29,31,36,47,48.\n\nInterestingly, when we overexpress Myc in Club cells/BASCs after bleomycin injury (Fig.\u00a06A), Club cells/BASCs massively acquire a super-competitor myoepithelial cell (SCMC) like status, coexpressing Krt5, Acta2, Sox9 and Myc (Fig.\u00a06B\u2013M), resulting in the hyper-invasion and apparent destruction of the lung parenchyma including AT2 cells and its replacement with bronchial epithelial cells demonstrated by increased expression of bronchial epithelial markers Krt5, Krt17, and Muc5b, increased pulmonary fibrosis based on hydroxyproline content and reduced expression of alveolar epithelial marker Sftpc by Nanostring nCounter RNA analysis (Fig.\u00a06B\u2013P) and 10x Visium spatial transcriptomics (Supplementary Fig.\u00a03). This suggests that the cell competition program may converge onto a SCMC plastic like state that can be acquired by different BESC populations.\n\nA Mice were placed on tamoxifen chow for 3 weeks and following a 3-week washout period, mice were injured with intratracheal administration of bleomycin and placed on doxycycline containing chow to induce Myc overexpression and harvested at 6 weeks post injury. B\u2013D, H\u2013J Coimmunostaining for markers associated with fibrosis and bronchiolization (Col1a1, Krt5, and Acta2) on control (B\u2013D) and Scgb1a1CreERT;LSL-rtTA;Tet-Myc (H\u2013J). Coimmunostaining for myoepithelial cell-like markers Sox9, Krt5, and Myc on control (E, F) and Scgb1a1CreERT;LSL-rtTA;Tet-Myc (K, L). G, M Diagram demonstrating that Myc sufficient Club cells inhibit basal cells and give rise to alveolar epithelial cells after bleomycin injury while Myc overexpressing Club cells dedifferentiate into basal cells and promote their invasion into the alveolus. Created in BioRender. Warren, R. (2024) https://BioRender.com/ l07p577. N Immunostaining for Muc5b (mucus producing secretory cells), Krt5 (BLCs), and Scgb1a1 (Club cells/BASCs) on Scgb1a1CreERT;LSL-rtTA;Tet-Myc. O Hydroxyproline analysis on Cre- control (n\u2009=\u200931) and Scgb1a1CreERT;LSL-rtTA;Tet-Myc (n\u2009=\u200930) normalized to control (p\u2009=\u20090.003). P Log2 normalized values for RNA expression for BLC (Krt5, Krt17, Scgb3a2, Muc5b) and AT2 cell (Sftpc) genes from NanoString analysis on control (n\u2009=\u20099 Krt5 p\u2009=\u20090.0000008, Krt17 p\u2009=\u20090.000001, Scgb3a2 p\u2009=\u20090.02, Sftpc p\u2009=\u20090.02\u2009F\u2009=\u20090.02, Muc5b p\u2009=\u20090.002), Scgb1a1CreERT;LSL-rtTA;Tet-Myc (n\u2009=\u20095), and Scgb1a1CreERT;Yapf/f;LSL-rtTA;Tet-Myc (n\u2009=\u20098 Krt5 p\u2009=\u20090.0001, Krt17 p\u2009=\u20090.00006, Scgb3a2 p\u2009=\u20090.0007, Sftpc p\u2009=\u20090.04,Muc5b p\u2009=\u20090.02). Values are normalized to control and p values are compared to Scgb1a1CreERT;LSL-rtTA;Tet-Myc. Data are presented as mean values +/\u2212 SEM. Scale bars: 500\u2009\u00b5m (B, G, L) and 100\u2009\u00b5m (C\u2013F, H\u2013K). Two tailed unpaired T-test was used to determine significance. F test was used to determine equal variances and unreported F values indicate equal variance. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, ****p\u2009<\u20090.0001.\n\nIt is well known that Hippo pathway plays an important role in cell competition, and Yap and Myc are thought to work together in this process29,31,36,47,48. It is also well known that Myc is a quintessential target gene of the canonical Wnt signaling pathway49 and that the Hippo pathway controls \u03b2-catenin stabilization and nuclear localization50,51. However, how the Hippo pathway affects Myc levels seems to be context dependent.\n\nInterestingly, the Hippo pathway is active in AT252 and Club cells, demonstrated by Merlin expression, phospho-Yap and phospo-Mst1/2 staining (Supplementary Fig.\u00a05A\u2013E), resulting in the degradation and cytoplasmic retention of Yap and Taz the nuclear effectors of the pathway. To investigate how increased Yap and/or Taz levels may affect Myc expression in BESCs cells we inactivated the Hippo kinases Mst1/2 (encoded by Stk4/3) in BESCs, and found that this resulted in decreased Myc expression, decreased bronchiolization, increased AT1 cell regeneration and reduced pulmonary fibrosis based on hydroxyproline content upon severe bleomycin injury (Fig.\u00a07A\u2013E, L, N, O, Supplementary Fig.\u00a04). Interestingly, inactivation of the Hippo pathway via deletion of Nf2 in Club cells or AT2 stem cells in the absence of injury is sufficient to drive their spontaneous differentiation into AT1 cells, consistent with previous reports53,54 (Supplementary Fig.\u00a05F, G & 8A\u2013H, Q).\n\nA Mice were placed on tamoxifen chow for 3 weeks to inactivate Yap1 and/or Wwtr1 or Stk3/4 and permanently label all Sox2+ cells and their offspring with GFP. Following a 3 week washout period, mice were injured with intratracheal administration of bleomycin and harvested at 6 weeks post injury. Coimmunostaining for Rage (AT1 cells), GFP (lineage label), and Sftpc (AT2 cells) on Sox2CreERT2;mTmG (B), Sox2CreERT2;Stk3f/f;Stk4f/f;mTmG (D), Sox2CreERT2;Yap1f/f;mTmG (F), Sox2CreERT2;Wwtr1f/f;mTmG (H), and Sox2CreERT2;Yap1f/f;Wwtr1f/f;mTmG (J) lungs. C, E, G, I, K diagrams illustrating the direction of airway epithelial cell differentiation in figures (B, D, F, H, J). Created in BioRender. Warren (2024) https://BioRender.com/ a02e401. L Lineage tracing analysis on bleomycin injured lungs from immunostaining in B (n\u2009=\u20093) and D (n\u2009=\u20094) using Zeiss Zen Intellesis machine learning software to trace (p\u2009=\u20090.57). M Lineage tracing analysis on bleomycin-injured lungs from immunostaining in B (n\u2009=\u20093), F (n\u2009=\u20095, p\u2009=\u20090.92), H (n\u2009=\u20094, p\u2009=\u20090.002), and J (n\u2009=\u20094, p\u2009=\u20090.004) using Aivia machine learning software. N Hydroxyproline analysis on Cre- control, Sox2CreERT2;Yap1f/f;Wwtr1f/f (n\u2009=\u200919, p\u2009=\u20090.04), Sox2CreERT2;Yap1f/f (n\u2009=\u200925, p\u2009=\u20090.01), Sox2CreERT2;Wwtr1f/f (n\u2009=\u20099, p\u2009=\u20090.04), and Sox2CreERT2;Stk3f/f;Stk4f/f (n\u2009=\u200918, p\u2009=\u20090.03). O qPCR analysis for bronchiolization and fibrosis genes (Krt5, p63, Col1a1, Col3a1 and Muc5b) on Cre- control (n\u2009=\u200922), Sox2CreERT2;Yap1f/f;Wwtr1f/f (n\u2009=\u20098, Krt5 p\u2009=\u20090.03, F\u2009=\u20090.00001, p63 p\u2009=\u20090.03, F\u2009=\u20090.01, Col1a1 p\u2009=\u20090.2, Col3a1 p\u2009=\u20090.008, and Muc5b p\u2009=\u20090.05), Sox2CreERT2;Yap1f/f (n\u2009=\u20095, Krt5 p\u2009=\u20090.01, F\u2009=\u20090.00007, p63 p\u2009=\u20090.002, F\u2009=\u20090.005, Col1a1 p\u2009=\u20090.002, Col3a1 p\u2009=\u20090.0005 and Muc5b p\u2009=\u20090.8), Sox2CreERT2;Wwtr1f/f (n\u2009=\u200910, Krt5 p\u2009=\u20090.27, p63 p\u2009=\u20090.05, Col1a1 p\u2009=\u20090.03\u2009F\u2009=\u20090.02, Col3a1 p\u2009=\u20090.004 and Muc5b p\u2009=\u20090.05), and Sox2CreERT2;Stk3f/f;Stk4f/f (n\u2009=\u200910, Krt5 p\u2009=\u20090.009, F\u2009=\u20095.7 \u00d7 10-8, p63 p\u2009=\u20090.01\u2009F\u2009=\u20090.05, Col1a1 p\u2009=\u20090.95, Col3a1 p\u2009=\u20090.16 and Muc5b p\u2009=\u20090.00009\u2009F\u2009=\u20090.003). qPCR for Myc on control (n\u2009=\u200923), Sox2CreERT2;Yap1f/f;Wwtr1f/f (n\u2009=\u200914, p\u2009=\u20090.05) and Sox2CreERT2;Stk3f/f;Stk4f/f (n\u2009=\u200910, p\u2009=\u20090.04). Values are represented as 2-\u0394\u0394Ct normalized to Control. Values are normalized to control. Data are presented as mean values +/\u2212 SEM. Scale bar: 200\u2009\u00b5m. Two tailed unpaired T-test was used to determine significance. F test was used to determine equal variances and unreported F values indicate equal variance. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001.\n\nTo investigate how decreased Yap/Taz levels may affect Myc expression in BESCs, we inactivated Yap1 and Wwtr1 in BESCs, using Sox2CreERT2;Yap1f/f;Wwtr1f/f;mTmG mice, and found that this resulted in increased Myc expression, decreased AT1 cell regeneration and increased pulmonary fibrosis based on hydroxyproline content upon severe bleomycin injury (Fig.\u00a07J, K, M\u2013O). Even though Sox2CreERT2;Yap1f/f;Wwtr1f/f;mTmG mice feature increased Myc levels, their lack of Yap prevents them from BLC-mediated bronchiolization (Fig.\u00a07F, J, K, O). Instead, Sox2CreERT2;Yap1f/f;Wwtr1f/f;mTmG mice featured increased goblet cell differentiation based on Muc5b expression (Fig.\u00a07O), something we also observed in the absence of injury (Supplementary Fig.\u00a05H, I, L), and which is consistent with previous reports55 demonstrating the spontaneous differentiation of BESCs into goblet cells upon simultaneous inactivation of Yap1 and Wwtr1.\n\nTogether these findings suggest that the Hippo pathway controls Myc levels and therefore stem cell competitiveness by controlling Yap and/or Taz levels. This is consistent with a role for cytoplasmic Taz inhibiting Wnt/\u03b2-catenin signaling50,51. Interestingly, overexpression of a dominant active \u03b2-catenin in the airway epithelium has been shown to result in excessive goblet cell differentiation56.\n\nThe fact that inactivation of upstream Hippo kinases (Nf2, Stk3/4, Lats1/2) in Club57 (Supplementary Fig.\u00a06F, G) or AT2 (Fig.\u00a08A\u2013H)52 cells results in their spontaneous differentiation into AT1 cells is intriguing since we and others have previously demonstrated that Yap is required for tracheal BC maintenance46,58 and overexpression of a dominant active version of the Hippo transcriptional effector Yap1S112A in Club cells is able to drive their differentiation towards a BLC lineage in cooperation with p6346. Together all these findings suggest a role for Yap-Myc-Trp63 in the acquisition of SCMC state whereas an increase in Taz or a lack of Yap-Myc-Trp63 promotes AT1 cell differentiation.\n\nA\u2013P SftpcCreERT2;mTmG, SftpcCreERT2;Nf2f/f;mTmG, SftpcCreERT2;Nf2f/f;Yap1f/f;mTmG, and SftpcCreERT2;Nf2f/f;Wwtr1f/f;mTmG were placed on tamoxifen containing chow at 8 weeks of age for 3 weeks to inactivate Nf2 and/or Yap1 and/or Wwtr1 and permanently lineage label AT2 stem cells and their offspring. At 9 weeks after being placed on normal chow left lung lobes were inflation fixed, embedded in paraffin, and sectioned. Coimmunostaining for GFP (lineage label), Rage (AT1 cells), and Sftpc (AT2 cells). Q Image analysis for lineage labeled AT1 and AT2 cells in A-P using Aivia machine learning software (SftpcCreERT2;mTmG (n\u2009=\u20096), SftpcCreERT2;Nf2f/f;mTmG (n\u2009=\u20098, p\u2009=\u20090.00003), SftpcCreERT2;Nf2f/f;Yap1f/f;mTmG (n\u2009=\u20095, p\u2009=\u20090.0009), and SftpcCreERT2;Nf2f/f;Wwtr1f/f;mTmG (n\u2009=\u20096, p\u2009=\u20090.13)). Data are presented as mean values +/\u2212 SEM. Two tailed unpaired T-test was used to determine significance. F test was used to determine equal variances. Scale bar: 200\u03bcm. *p\u2009<\u20090.05.\n\nInterestingly, though we have long favored a role for Taz and not Yap in AT1 cell differentiation and maintenance, some reports seem to suggest a role for Yap in AT1 cell differentiation59,60. To definitively answer this question we generated SftpcCreERT2;Nf2f/f;Wwtr1f/f;mTmG and SftpcCreERT2;Nf2f/f;Yap1f/f;mTmG mice to investigate which nuclear effector of the Hippo pathway is required for the spontaneous differentiation of AT2 cells into AT1 cells upon Nf2 inactivation. We now demonstrate that the simultaneous inactivation of Nf2 and Yap1 in AT2 cells does not inhibit their spontaneous differentiation into AT1 cells (Fig.\u00a08A\u2013L, Q), whereas the simultaneous inactivation of Nf2 and Wwtr1 in AT2 cells completely blocks this process (Fig.\u00a08A\u2013Q).\n\nIf Taz promotes AT1 cell differentiation and Yap promotes BLC differentiation, inactivation of Wwtr1 in bronchial epithelium using Sox2CreERT2;Wwtr1;mTmG mice should mainly impair AT1 cell differentiation but not bronchiolization, whereas inactivation of Yap1 in airways using Sox2CreERT2;Yap1f/f;mTmG mice should mainly impair bronchiolization. Interestingly, Sox2CreERT2;Wwtr1;mTmG mice not only feature impaired AT1 cell differentiation but also increased bronchiolization (Fig.\u00a07B, C, H, I, M, O). The latter is likely because Yap and Taz compete to bind to Tead transcription factors, with a loss of Taz resulting increased Yap/Tead binding. Vice versa, Sox2CreERT2;Yap1f/f;mTmG mice fail to generate BLCs and to bronchiolize the lung parenchyma upon severe bleomycin injury or H1N1 injury, with increased AT1 cell differentiation likely due to increased Taz/Tead, and increased pulmonary fibrosis as measured by hydroxyproline content as well as increased mortality (Fig.\u00a07B, C, F, G, M, O & Supplementary Fig.\u00a06A\u2013I).\n\nSince overexpression of dominant active Yap1S112A in BESCs is sufficient to drive Club cell to BLC differentiation46, we wondered if overexpression of dominant active Yap1S112A alone in BESCs and their offspring after bleomycin injury, using Sox2CreERT2;LSL-rtTA;Tet-Yap1S112A mice, is sufficient to promote bronchiolization. Interestingly, while overexpression of a dominant active Yap1S112A in BESCs is sufficient to drive BLC differentiation and prevent alveolar epithelial differentiation after bleomycin injury (Supplementary Fig.\u00a06H\u2013N), overexpression of dominant active Yap1S112A did not induce Myc expression, and BESCs failed to acquire SCMC status and as such did not amplify nor invade the lung parenchyma nor destroy the remaining alveolar epithelium (Supplementary Fig.\u00a06H\u2013N).\n\nThis is interesting as we have just demonstrated that overexpression of Myc in Club cells after bleomycin injury causes them to acquire a super-competitor myoepithelial cell (SCMC) like status, coexpressing Krt5, Acta2, Sox9 and Myc (Fig.\u00a06). Therefore, to specifically investigate the requirement for Yap and Myc in the acquisition of MEC-status we generated Scgb1a1CreER;Yap1f/f;LSL-rtTA;Tet-Myc mice in which we could simultaneously inactivate Yap1 and overexpress Myc in Club cells after bleomycin injury and found that BCs and MEC-like cells development was impaired, compared to Scgb1a1CreER;LSL-rtTA;Tet-Myc mice, indicating that both Myc and Yap are required for obtaining SCMC status (Fig.\u00a06P).\n\nFinally, to investigate if AT2 stem cells have the capacity to acquire a SCMC state and to bronchiolize the lung parenchyma we generated SftpcCreERT2;LSL-rtTA;Tet-Myc, SftpcCreERT2;LSL-rtTA;Tet-Yap1S112A and SftpcCreERT2;LSL-rtTA;Tet-Myc;Tet-Yap1S112A mice in which we could respectively overexpress Myc, a dominant active Yap1S112A or both in combination in AT2 stem cells and their progeny after bleomycin injury. We found that simultaneous overexpression of both Myc and Yap1S112A in AT2 cells allowed them to adopt SCMC state and to bronchiolize the lung parenchyma (Supplementary Fig.\u00a07A\u2013D, M\u2013O). However, overexpression of Myc or Yap1S112A alone was not sufficient (Supplementary Fig.\u00a07E-L).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this manuscript, we set out to investigate and clarify several unresolved issues on lung Hippo signaling, ARDS and pulmonary fibrosis. We find that different lung stem cell populations compete with one another to regenerate or remodel the lung and that this stem cell competition follows the classical cell competition model originally identified in Drosophila61,62. Our model in which bronchiolization of the lung parenchyma is reminiscent of the development of submucosal glands, has wide ranging implications for the early diagnosis of pulmonary fibrosis as well development of new treatments for this devastating disease. Especially, our findings about the distinct roles for Yap and Taz in bronchiolization vs alveolar epithelial regeneration will allow for the development of targeted therapies\u00a0(Fig. 9).\n\nStem cells exhibiting the highest Myc and Yap1 levels become supercompetitors that drive remodeling, whereas Taz promotes terminal differentiation into AT1 cells by inhibiting Myc. Created in BioRender. Warren, R. (2024) https://BioRender.com/d35d050.\n\nIt is thought that Yap and Myc coordinately regulate genes required for cell proliferation, where activation of Myc leads to extensive association with its genomic targets, most of which are prebound by TEAD63. At these loci, recruitment of Yap is thought to be Myc-dependent and required for full transcriptional activation. This cooperation between Yap and Myc is thought to be critical for cell cycle entry, organ growth, and tumorigenesis63. Cells can become super competitors through intrinsic (e.g., somatic mutations) or extrinsic mechanism64. At the molecular level, future studies will need to explore the impact of genetic perturbations on the ability of winner cells to contribute to cellular populations, both in vitro and in vivo. However, changes to gene expression shown to drive cell competition need not involve genetic engineering or mutations to the DNA itself. Cells may receive signals from their microenvironment, including cell-cell interactions, that converge on the cellular processor and drive cell competition behavior by affecting gene expression65,66,67. Similarly, epigenetic changes can also drive cell competition-relevant gene expression changes.\n\nUnder normal conditions, cell competition will select against the emergence of altered cells with disruptive behavior toward tissue integrity and/or tissue pattern formation. However, upon catastrophic organ injury this molecular machinery involved in the winner/loser interaction could be hijacked to maintain organism survival. In acute respiratory distress syndrome (ARDS), such as experienced after influenza or SARS-CoV-2 infection, the \u201cdominant\u201d stem cell populations (club cells and AT2 cells) are vulnerable to infection and normal alveolar tissue is steadily being replaced by bronchial/conducting airway epithelial cells which survive the infection but cannot participate in gas exchange. Stem cells in the conducting airway are sometimes considered a \u201creserve stem cell\u201d population that only participates in alveolar epithelial repair after catastrophic injury to the lung parenchyma. As such, these \u201creserve stem\u201d cells only win the fitness battle upon loss or destruction of alveolar type 2 (AT2) stem cells, considered the \u201cdominant\u201d stem cell population in the alveolar compartment27. We recently also reported increased bronchiolization in mice in which the fitness of AT2 cells was compromised52. Therefore, it appears that lowering AT2 stem cell fitness can be sufficient to cause conducting airway epithelial stem cells to acquire a competitive advantage and drive bronchiolization.\n\nWe demonstrate that active cell competition is a feature of pulmonary fibrosis/ARDS and its underlying mechanisms can be manipulated to help prevent and treat this disease. Lowering the fitness of BESCs can reduce and even reverse pulmonary fibrosis progression. Boosting the fitness/survival of AT2 stem cells or BASCs, could also prevent bronchiolization. Therefore, cell competition can be exploited to maximize the potential of healthy tissue replacement.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54997-2/MediaObjects/41467_2024_54997_Fig9_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "All mice were bred and maintained in a pathogen-free environment on a 12\u2009hr light/dark cycle with free access to food and water. Ambient temperature was maintained at 21\u2009\u00b0C\u201324\u2009\u00b0C. Both male and female mice were used for all experiments. Sox2CreERT2 (JAX 017593; RRID:IMSR_JAX:017593), Krt5CreERT2\u200968, Scgb1a1CreERT (JAX 016225; RRID:IMSR_JAX:016225), SftpcCreERT2\u200969, Trp63DreERT2 (Shanghai Model organisms Center NM-KI-190029; RRID:IMSR_NM-KI-190029), Acta2CreERT2\u200970, Sox9CreERT2 (JAX 035092; RRID:IMS9R_JAX:035092), mTmG (JAX 007676; RRID:IMSR_JAX:007676), Rosa26-tdTomato (JAX 007909; RRID:IMSR_JAX:007909), RLTG (JAX 026931; RRID:IMSR_JAX:026931), FLTG (JAX 026932; RRID:IMSR_JAX:026932), Stk3/4f/f (JAX 017635; RRID:IMSR_JAX:017635), Yap1f/f\u200971, Wwtr1f/f\u200971,Nf2f/f\u200972, Mycf/f\u200973, Rosa26-CAGs-LSL-rtTA3 (LSL-rtTAf/f; JAX 029617; RRID:IMSR_JAX:029617), Tet-Yap1-H2BGFP (JAX 031279; RRID:IMSR_JAX:031279), Tet-myc (JAX 019376; RRID:IMSR_JAX:019376), Acta2-Frt-STOP-Frt-CreERT243, Nkx2.1Flpo (JAX 028577; RRID:IMSR_JAX:028577) mice were previously described.\n\nFor bleomycin injury, adult 8- to 12-week-old mice were intratracheally instilled with 50 uL bleomycin (0.8\u20132 U/kg body weight optimized for each strain, batch of bleomycin, and gender) as described previously74. The following reagent was obtained through BEI Resources, NIAID, NIH: Influenza A Virus, A/Puerto Rico/8-9VMC3/1934 (H1N1), NR-29028. Mice were infected via intranasal route with a sublethal dose of H1N1 (100,000-162,500 viral forming units (VFU) optimized for each strain) diluted in 50\u2009\u00b5L of saline. For tamoxifen induction, SftpcCreERT2, Scgb1a1CreER and Sox2CreERT2 mice were placed on tamoxifen-containing chow (rodent diet with 400\u2009mg/kg tamoxifen citrate; Harlan Teklad TD.130860) for 3 weeks and SftpcCreERT2 and Sox2CreERT2 mice received an additional intraperitoneal tamoxifen injection (0.20\u2009mg/g body weight, Millipore Sigma) in the last week of tamoxifen citrate feed. Following a 3-week tamoxifen washout period, mice were either injured with bleomycin or H1N1. Mice containing LSL-rtTA3 were placed on doxycycline containing chow (rodent diet with 625\u2009mg/kg doxycycline; Harlan Teklad TD.09761) on the day of bleomycin. Sox9CreERT2, Trp63DreERT2;Acta2CreERT2, and Krt5CreERT2 mice were placed on tamoxifen containing chow beginning at 2 weeks following injury. All experiments were approved by the Mayo Clinic Institutional Animal Care and Use Committee.\n\nAll staining was done on paraffin sections of formalin-fixed lungs. Immunofluorescent staining was performed with the following primary antibodies: rabbit anti-Merlin (NF2; 1:250; clone A-19; sc-331; RRID:AB_2298548; Santa Cruz Biotechnology), rabbit anti-phosporylated-Mst1(Thr183)/2(Thr180) (1:200; 3681; RRID:AB_330269; Cell Signaling Technologies), rabbit anti-phosphorylated Yap (Ser127) (1:200; 4911; RRID:AB_2218913; Cell Signaling Technologies), goat anti-Scgb1a1 (1:200; clone T-18; sc-9772; RRID:AB_2238819; Santa Cruz Biotechnology Inc.), goat anti-Sox9 (1:500; AF3075; RRID:AB_2194160; R&D Systems), mouse anti-Keratin 17 (1:50; clone Ks17.E3; sc-101461; RRID:AB_2234376; Santa Cruz Biotechnology, Inc.), chicken anti-GFP (1:500; GFP-1020; RRID:AB_10000240; Aves Labs Inc.), rabbit anti-Keratin 5 (1:200; clone EP1601Y; MA5-14473; RRID:AB_10979451; Thermo Fisher Scientific), chicken anti-Keratin 5 (1:500; 905901; RRID:AB_2565054; BioLegend), rabbit anti-SFTPC (1:200; WRAB-9337; RRID:AB_2335890; Seven hills bioreagents), rat anti-RAGE (1:500; Clone 175410; MAB1179; RRID:AB_2289349; R&D Systems), goat anti-RAGE (1:500; AF1145; RRID:AB_354628; R&D Systems), rat anti-Keratin 8 (1:100; TROMA-I; RRID:AB_531826; Developmental Studies Hybridoma Bank), Syrian hamster anti-podoplanin (PDPN, T1a; 1:500; 8.1.1; RRID:AB_531893; Developmental Studies Hybridoma Bank), rabbit anti-mucin 5b (Muc5b; 1:250; clone H-300; sc-20119; RRID:AB_2282256; Santa Cruz Biotechnology Inc.), mouse anti-alpha actin (smooth muscle actin (SMA), Acta2; 1:500; Clone 1A4; sc-32251; RRID:AB_262054; Santa Cruz Biotechnology Inc.), rabbit anti-myc (1:200; clone Y69, ab32072; RRID:AB_731658; Abcam), rabbit anti-human Myc (1:200; clone C-19; sc-788; RRID:AB_631277), rabbit anti-collagen I (1:500; ab34710; RRID:AB_731684; Abcam), mouse anti\u2013beta-tubulin (1:500; clone 3F3-G2; LMAB-3F3; RRID:AB_451728; Seven Hills Bioreagents), and rabbit anti-p63 (1:500; clone poly6190; 619002; RRID: AB_2207170; BioLegend).\n\nAfter deparaffinization, slides were rehydrated through a series of decreasing ethanol concentrations, antigen unmasked by either microwaving in citrate-based antigen unmasking solution (Vector Labs, H-3300) or by incubating sections with proteinase K (7.5\u2009\u03bcg/ml) (Invitrogen, 25530-049) for 7\u2009min at 37\u2009\u00b0C. Tissue sections were then washed in TBS with 0.1% Tween-20 and blocked with 3% Bovine Serum Albumin (BSA), 0.4% Triton X-100 in TBS for 30\u2009min at room temperature followed by overnight incubation of primary antibodies diluted in 3% BSA, 0.1% Triton X-100 in TBS. The next day, slides were washed in TBS with 0.1% Tween-20 and incubated with secondary antibodies diluted in 3% BSA, 0.1% Triton X-100 in TBS for 3\u2009h at room temperature. All fluorescent staining was performed with appropriate secondary antibodies from Jackson Immunoresearch. Slides were mounted using Vectashield (Vector Labs, H-1000).\n\nTissue was imaged using a micrometer slide calibrated Zeiss LSM800 Laser scanning confocal microscope using ZEN imaging software or Leica Stellaris 5 confocal microscope with LASX imaging software. Lungs were imaged using tiled stitched 20x images covering the entire cross-section of the left or lower right lung lobe from \u22656 different lungs. Representative images were chosen. Images were processed and analyzed using Zen blue (Zeiss), LASX (Leica), and Adobe Photoshop 2024 (Adobe) software.\n\nDifferentiation of GFP-positive cells was determined using machine learning and machine learning image segmentation with Aivia software. The total area of GFP and GFP overlapping with different cell-specific antibody stains (Sftpc or RAGE) was determined. Image quantification and analysis were performed in a double-blinded fashion. Each quantification was \u22653 different mouse lungs.\n\nTotal mRNA was extracted from lung accessory lobes stored in RNALater (Invitrogen, AM7021) and using Total RNA Kit I (Omega Biotek, R6834-02) according to the manufacturer\u2019s instructions. RNA concentration was determined by spectrophotometry. cDNA was generated using Maxima\u2122 First Strand cDNA Synthesis (Fisher Scientific, FERK1642) according to the manufacturer\u2019s instructions. Gene expression was analyzed by quantitative RT-PCR using Taqman Gene Expression Assays (Applied Biosystems, 4369016) directed against the mouse targets \u03b2-glucuronidase (Mm00446953_m1), Krt5 (Mm01305291_g1), Trp63 (Mm00495788_m1), Muc5b (Mm00466391_m1), Col1a1 (Mm00801666_g1), Col3a1 (Mm01254476_m1), Myc (Mm00487803_m1). Quantitative real-time PCR was performed using a StepOne Plus system (Applied Biosystems). Data were presented as 2-\u0394\u0394Ct with \u03b2-glucuronidase as the internal sample control normalized to control group. Each experiment was repeated with samples obtained from \u22653 different lung preparations.\n\nRNA was isolated from lung accessory lobes as described above. 100\u2009ng of RNA was hybridized with a custom RNA probe panel designed by NanoString (NanoString Technologies; DL_1206_C9662) for 16\u2009hours according to manufacturer\u2019s instructions. The RNA-probe hybridization was loaded on a NanoString cartridge and processed in a NanoString nCounter. Data was analyzed with Rosalind.bio (Rosalind, Inc) and Log2 Fold Changes were calculated and graphed. Each experiment was repeated with samples obtained from \u22653 different lung preparations.\n\nExplant tissue was obtained from patients undergoing transplantation for end-stage IPF, fixed and paraffin-embedded in compliance with consent procedures accepted by the Internal Review Board at Mayo Clinic.\n\nEpithelial cells from donor distal samples and IPF fibrotic samples were subsets from GSE132914 for this analysis75. Standard data integration workflow from the Seurat V3 package was applied to integrate and combine data sets for unsupervised clustering. The batch correction was processed with PCA (Principal Component Analysis) using the 5000 most variable genes, and the first 30 independent components were used for downstream unbiased clustering with a resolution of 0.4. The UMAP (Uniform Manifold Approximation and Projection) method was used for visualization of unsupervised clustering and basal cell subset with the first 30 independent PCA components. The cell type of each cluster is determined by known markers of individual cell types. Gene expression levels were shown using the FeaturePlot function from the Seurat Package.\n\nRNA was isolated from formalin fixed paraffin-embedded (FFPE) tissue sections using E.Z.N.A FFPE RNA Kit (Omega Bio-Tek). The RNA integrity in FFPE blocks was determined on an Agilent TapeStation. 5\u03bcm FFPE lung sections that had a DV200% above 50 were placed within the frames of the capture areas on the active surface of the Visium spatial slide (10x Genomics) and processed according to manufacturer\u2019s instructions. Tissues were stained with podoplanin (PDPN, T1a) and GFP and imaged with fluorescent secondary antibodies. Final library preparations and sequencing were completed by the Mayo Genomics Research Core according to manufacturer\u2019s instructions on an Illumina NextSeq. Count matrices were generated using the \u2018spaceranger count\u2019 function in Space Ranger 1.0.0. The resulting data were processed in Scanpy and Squidpy.\n\nThe right lobes were flash-frozen in dry ice at the time of harvest and stored at \u221280\u2009\u00b0C. For acid hydrolysis, the lobes were baked in a 70\u2009\u00b0C oven without lids for 2 days until completely dry. The weights of dry lobes were measured and 500\u2009\u00b5l of 6\u2009N HCl were added to each sample. The lungs were then hydrolyzed in an 85\u2009\u00b0C oven for 2 days with occasional vortexing. The hydrolysates were cooled at room temperature and centrifuged at maximum speed for 10\u2009minutes. The supernatants then were transferred to fresh 1.5\u2009mL tubes and centrifuged at maximum speed for 10\u2009minutes. Each sample or standard was diluted with citrate-acetate buffer (5% citric acid, 1.2% glacial acetic acid, 7.24% sodium acetate, and 3.4% sodium hydroxide) in a 96-well plate. Chloramine-T solution (1.4% chloramine-T, 10% N-propanol, and 80% citrate-acetate buffer) was added, and the mixture was incubated for 20\u2009minutes at room temperature. Then, Ehrlich\u2019s solution (1.27\u2009M p-dimethylaminobenzaldehyde, 70% N-propanol, 20% perchloric acid) was added to each sample and the samples were incubated at 65\u2009\u00b0C for 20\u2009minutes. Absorbance was measured at 550\u2009nm. Standard curves were generated for each experiment using the reagent hydroxyproline (Sigma H-1637) as a standard. The amount (\u00b5g) of hydroxyproline were calculated by comparison to the standard curve.\n\nAll results are expressed as mean values\u2009\u00b1\u2009SEM. The \u2018n\u2019 represents biological replicates and can be found in the figure legends. The significance of differences between 2 sample means was determined by two-tailed unpaired T-test (assuming unequal or equal variances as determined by the F-test of equality of variances). All datasets followed a normal distribution and P values less than 0.05 were considered statistically significant. The number of samples to be used was based on the number of experimental paradigms multiplied by the number in each group that is necessary to yield statistically significant results (based on power analysis, to reject the null hypothesis with 80% power (type I error = 0.05).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Epithelial cells from donor proximal and distal samples and IPF fibrotic samples were subset from GSE132914. 10x Genomics Visium Spatial Transcriptomics generated in this study have been deposited in the GEO Repository under GSE282002. Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Stijn De Langhe: delanghe.stijn@mayo.edu.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Barkauskas, C. E. & Noble, P. W. Cellular mechanisms of tissue fibrosis. 7. New insights into the cellular mechanisms of pulmonary fibrosis. Am. J. Physiol. Cell Physiol. 306, C987\u2013C996 (2014).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nKing, T. E. Jr., Pardo, A. & Selman, M. Idiopathic pulmonary fibrosis. Lancet 378, 1949\u20131961 (2011).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nSteele, M. P. & Schwartz, D. A. Molecular mechanisms in progressive idiopathic pulmonary fibrosis. Annu. Rev. 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De Langhe\n\nDepartment of Medicine, Division of Pulmonary, Allergy & Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL, USA\n\nTingting Yuan\n\nWomen\u2019s Guild Lung Institute, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA\n\nChangfu Yao\u00a0&\u00a0Barry Stripp\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nR.W. designed and performed experiments, interpreted the data, and prepared the manuscript. K.K., H.L., J.K., and T.Y. performed experiments. C.Y. and B.S. performed and analyzed single-cell sequencing data. S.P.D-L conceived, designed, and supervised the study, analyze the data, and prepared the manuscript.\n\nCorrespondence to\n Stijn P. De Langhe.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Tien Peng, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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transmission value and its drivers in United States power markets", + "pre_title": "Electric transmission value and its drivers in United States power markets", + "journal": "Nature Communications", + "published": "28 August 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Energy economics", + "Energy policy", + "Energy supply and demand", + "Social sciences" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3957695/v1.pdf?c=1756465774000", + "research_square_link": "https://www.researchsquare.com//article/rs-3957695/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63143-5.pdf", + "preprint_posted": "28 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Electric transmission infrastructure serves a critical role during extreme weather and supply disruptions and can enable low-cost, low-carbon electricity systems. This paper contributes to a more complete understanding of transmission\u2019s value, cost-effectiveness, and market barriers. By studying wholesale energy market prices in the United States during 2012-2022, we find that additional transfer capacity between regions was especially valuable (median of $116M/GW-year) and directionally balanced. Transmission\u2019s market value was highly influenced by a small fraction of time: 5% of hours typically capture at least 45% of value. These peak periods were driven primarily by unforeseen changes in conditions within one day of operations. Transmission infrastructure cost estimates were less than market values for most studied locations, including all links crossing regional seams where value-to-cost ratios were frequently above 4, suggesting barriers to the development of valuable infrastructure. These results complement forward-looking modeling studies and aid efforts to improve modeling practices.Scientific community and society/Energy and society/Energy economicsScientific community and society/Energy and society/Energy policyScientific community and society/Energy and society/Energy supply and demand", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementalInformation.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Electric transmission infrastructure plays a vital role during extreme weather and supply disruptions and can enable low-cost electricity systems. This paper contributes to a more complete understanding of the value and cost-effectiveness of transmission, as well as barriers to its development. By studying wholesale energy market prices in the United States between 2012 and 2022, we find that additional transfer capacity between regions would have been especially valuable, with a median value of $116 million per GW per year. This capacity would often have provided balanced benefits to each region. The market value of transmission was highly influenced by a small fraction of time: 5% of hours typically captured at least 45% of the total value. These peak periods were primarily driven by unforeseen changes in conditions within one day of operations. Annualized transmission infrastructure cost estimates were lower than the average market value for most locations, including all links crossing regional seams, where the value-to-cost ratio was often greater than 4. This suggests that there are barriers to developing valuable grid infrastructure. These results complement forward-looking modeling studies and support efforts to improve modeling practices.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Recent events have put electric transmission in the spotlight. In the United States, extreme winter storms have caused electric system outages, leading to discussion of transmission as a possible solution1,2. In Europe\u2019s 2021\u20132022 energy crisis, existing well-connected electricity networks increased supply security3, yet the value of more cross-border transmission capacity was also apparent4. Looking beyond individual events, discussion around transmission development has also been elevated by industry trends during the 2010s and early 2020s, including generation fleets shifting away from coal and nuclear toward gas, wind, and solar, record infrastructure investments by China\u2019s State Grid5, and continued siting challenges such as local opposition6,7 and complex permitting8. In the US, a slowdown in construction of new high-voltage lines9 suggests there may be pent-up demand for transmission, while transmission also serves an increasing role in accessing generation resources in new locations. Transmission is also considered a key enabler of a low-cost and low-carbon electricity system10,11,12,13. Motivated by this confluence of factors, improving transmission planning processes and expanding transmission is a focus of regulators14, legislators3,15,16,17, and private sector analyses4,18,19.\n\nTransmission development in the United States typically occurs through one of three channels: centrally planned through a regional system operator (most common), independently constructed by a merchant transmission developer (rare), or generator interconnection processes (frequent, mostly incremental upgrades). In the latter channel, development is narrow in scope, including only those upgrades required to maintain grid safety, reliability, and often deliverability after adding specific new resources. In the first two channels, development decisions come down to a cost/benefit trade-off, though system operators and merchants may assess different sets of costs and benefits. Traditionally in centralized planning processes a need for transmission is established to either ensure reliability or satisfy public policy directives. Economically driven transmission projects have also been pursued and are typically evaluated on their ability to reduce production costs of the system. However, modeled production cost benefits alone rarely outweigh transmission costs20. In recognition of the many ways transmission can be beneficial, including lower generation capital costs, reduced pollution, risk mitigation, and reducing unserved demand, in addition to traditional production cost savings21, some system planners have moved to a multi-value study framework22.\n\nTo account for system changes expected during a transmission asset\u2019s lifetime, cost/benefit analyses are typically performed by simulating the electricity system under assumptions about future system conditions. A weakness of such models is that often they do not replicate real-world conditions that affect the benefits of transmission, conditions such as forecast errors, extreme weather, and infrastructure outages18,19,23,24. Because transmission infrastructure takes many years to plan and build and, by nature, affects multiple locations, the impacts of misestimating transmission value during the planning phase could be long lasting and widespread.\n\nThe purpose of this paper is to provide insight into transmission value by offering a direct contrast to forward-looking modeling. We use observed wholesale market prices, along with load and renewable data, to assess where, when, and why transmission is valuable, focusing on 70 location pairs across the contiguous US from 2012 through 2022. We also conduct a scoping-level analysis to compare historical energy market values of transmission to the costs of transmission infrastructure projects while accounting for market depth, i.e., the extent to which prices would change as a result of new capacity. These comparisons contextualize the empirical value estimates and highlight where market barriers including, but not limited to, current planning processes are greatest.\n\nSpecifically, we analyze locational marginal prices (LMPs) that measure the marginal cost of serving the next increment of demand at a specified pricing node and reflect the sum of three components: system-wide marginal energy cost, marginal cost of losses, and marginal cost of congestion. LMPs are defined by the seven independent system operators (ISOs) and regional transmission organizations (RTOs) in the US (i.e., CAISO, ERCOT, ISO-NE, MISO, PJM, and SPP) at over 80,000 nodes, typically for both a forward day-ahead market and a spot real-time market. Concurrent price differences between market nodes indicate network congestion and reflect challenges due to actual generator and infrastructure outages. Market prices serve as an investment signal to market actors and are a rich data source that have been used by researchers to advance our understanding of the value of renewable energy25,26, efficient levels of subsides for energy efficiency27, and the impact of specific transmission investments that are already online28,29,30, for example.\n\nWhile our approach is not meant to replace models, it is intended to help identify key mechanisms that may lead to biased model estimates of transmission value. Future efforts could use these insights to improve transmission planning processes. There are several categories of societal benefits that transmission may provide, including resource adequacy, resilience, risk mitigation, and reductions in emissions, market power, capital costs and production costs21. These concepts are priced into wholesale electricity markets to varying degrees, and similarly reflected in market values of transmission. Centralized system planners, however, separately quantify each benefit they consider and typically use production cost savings as the main economic benefit. We do not offer a one-to-one comparison between transmission\u2019s market value and each of its modeled benefit types, instead using market value as an aggregate signal. Empirical prices precisely reflect actual system conditions and market participant behavior as they occurred in the past, providing insights that cannot be gained from system models.\n\nPrior to this work, studies of transmission value using historical market price data focused on select severe weather1,2 or geopolitical4 events, the specific benefits of increased competition31,32, measuring the impact of a specific transmission investment that is already online28,29,30, or congestion on existing lines within a limited footprint (ISO/RTO annual reports, such as33,34,35). The authors of this paper have previously employed a subset of the methods used here and published the findings in refs. 36,37,38. This paper contributes to a more complete understanding of transmission\u2019s value, cost-effectiveness, and market barriers and aids efforts to improve modeling practices by identifying key patterns and value drivers for use in model validation.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The transmission value analyzed in this section is a pairwise quantity between two wholesale market pricing nodes (i.e., a \u201clink\u201d) defined as the mean absolute locational marginal price difference between the nodes over time (in units $/MWh). It represents the marginal energy value of spatial price arbitrage within a specific market interval. A link is uniquely defined by two pricing nodes; it does not correspond to a specific transmission line, and there may be zero, one, or multiple existing transmission paths between a link\u2019s nodes. This paper primarily focuses on the real-time market, because we are most interested in transmission\u2019s ultimate value to the system and real-time prices reflect physically binding dispatch decisions under the actual operating conditions of the system. Further, only real-time prices exist for all 70 links depicted in Fig.\u00a01, though day-ahead prices, derived from a forward financial market, exist for many of the links. We will later offer a comparison to value based on prices from the day-ahead timeframe. The marginal value is useful as it can be directly compared to market prices, but it can also be converted to an equivalent total value per link, such as millions $/GW. Total value metrics are useful when compared to transmission costs. When considering total value metrics, it is important to also consider market saturation effects (e.g., in each hour, is the full GW of transmission capacity needed, or would prices converge with less capacity?).\n\na Map of mean marginal transmission market values for all 70 analyzed links over the entire study period (real-time market). The line segments depict which pairs of wholesale market pricing nodes are analyzed and do not portray existing transmission lines. b Distribution of mean marginal transmission market values (real-time market) across the set of 70 analyzed links. Each point represents one link, and there are the following number of links in each category: cross-interconnect: 9, interregional: 31, within-region: 30. The horizontal lines on each box plot show, from low to high, the smallest data point lying within 1.5x the inter-quartile range (IQR) from the 25th percentile, the 25th percentile, the 50th percentile (median), the 75th percentile, and the largest data point lying within 1.5x the IQR from the 75th percentile.\n\nSeventy hypothetical transmission links between major market regions in the contiguous U.S. are considered; 30 links are contained within a balancing authority (\u201cwithin-region\u201d), 31 links are interregional within the same interconnection (\u201cinterregional\u201d) and 9 links span an interconnection seam (\u201ccross-interconnect\u201d). In the contiguous U.S. there are three interconnections \u2013 the Western Interconnection, the Eastern Interconnection, and the Electric Reliability Council of Texas (ERCOT) \u2013 that operate almost independently with separate frequency synchronization and limited cross-interconnect transfer capacity. Each line segment in Fig.\u00a01a and each point in Fig.\u00a01b show the marginal transmission market value for an individual link, averaged over the study period. Relatively high value links are found within and between many regions.\n\nGenerally, links crossing market seams have greater potential to arbitrage high and low prices: The market value for the median cross-interconnect and interregional links are $30/MWh and $15/MWh, respectively, compared to $8/MWh for a within-region link. For context, if the marginal value of $15/MWh was maintained for the entire capacity of a 1\u2009GW line, the line\u2019s market value would be $131 million per year \\(\\left(\\frac{{{{\\rm{\\$}}}}15}{{{{\\rm{MWh}}}}}*\\frac{1000{{{\\rm{MW}}}}}{1{{{\\rm{GW}}}}}*\\,\\frac{8760{{{\\rm{h}}}}}{{{{\\rm{year}}}}}\\right)\\). Note that market saturation effects will be quantified and discussed later. Interregional and cross-interconnect links may have higher value due to more diversity of weather, load profiles, and generator resources than is found within regions and due to a historical focus by transmission system operators on development within their own geographic footprint39.\n\nLinks connecting ERCOT to any of its neighbors (SPP, MISO, non-ISO West) register the highest values, driven by exceptionally high values since 2018. Links bridging the western and eastern interconnections (i.e., between the West and SPP or MISO) and connecting NYISO and ISO-NE are also among the most valuable. ERCOT, SPP, and NYISO contain the highest value within-region links. Not all links have substantial value, however. For example, the average hourly price difference between ISO-NE\u2019s Massachusetts and Maine hubs is just $2/MWh, consistent with ISO-NE\u2019s statement that \u201ctransmission system upgrades have nearly eliminated congestion costs in the New England energy market\u201d40.\n\nPersistent pricing gradients often underlie this value, but do not fully capture it since the lower-priced location of the pair can alternate over time. The transmission value suggested by differences in annual average prices is typically only 18\u201345% of the value suggested by our methodology based on hourly differences in real-time prices for cross-interconnect links, 26\u201361% for interregional links and 35\u201381% for within-region links. Similarly, only considering the value in one direction (e.g., when the price on side A of the link is higher than on side B) captures just 59\u201367%, 63\u201380% and 67\u201390% of the value for cross-interconnect, interregional and within-region links, respectively. All ranges reported in this paragraph reflect the 25th-75th percentiles of the studied transmission links. As shown in Fig.\u00a02a, cross-interconnect and interregional links tend to be fairly balanced in terms of which direction power flow would be valuable over time. Figure\u00a02b shows an example of a balanced link in a specific year. Studying directionality has important implications for concerns over winners and losers in transmission planning41,42. Balanced directional value could suggest comparable benefits from transmission development for multiple parties.\n\na Consistency of high-low price direction (real-time market). Each point represents one link, and there are the following number of links in each category: cross-interconnect: 9, interregional: 31, within-region: 30. The horizontal lines on each box plot show, from low to high, the smallest data point lying within 1.5x the inter-quartile range (IQR) from the 25th percentile, the 25th percentile, the 50th percentile (median), the 75th percentile, and the largest data point lying within 1.5x the IQR from the 75th percentile. Supplementary Fig.\u00a01 provides analogous information for the day-ahead market. b Example of a link-year with balanced value of power flow in each direction. The graph shows the distribution of marginal transmission market value (real-time market) across the months of 2022 and by which location had the higher price in any particular hour, for the link between southern Texas and Louisiana. For this example, the maximum share of value in a single direction \u2013 the metric used in (a) \u2013 is 0.54.\n\nThe market value of transmission varies over the course of years, but also between the day-ahead and real-time markets and from hour to hour. The bars in Fig.\u00a03 show the trend in transmission value for each year 2012\u20132022. In 2022, large locational price differences were a broad phenomenon across most of the U.S., resulting in the highest median (left) and mean (right) transmission value of any calendar year since at least 2012, the earliest year in-scope of this study. The increase in transmission value across so many locations is suggestive of a cause that is national in scope, such as overall increased energy prices (see the lines in Fig.\u00a03). In contrast, high mean values without corresponding high median values, such as in 2018 and 2021, indicate events that drove extremely high transmission value in isolated regions. In 2021, for example, winter storm Uri drove high values for interregional transmission into SPP and ERCOT but had less impact on other regions of the U.S. This pattern underscores the importance of considering a long time horizon when analyzing transmission investments, which have lifespans of 50-80 years, while also considering possible value drivers that derive from national, regional, and local conditions.\n\nNote that the set of links in the early years is smaller due to data constraints, as explained in the Methods. Supplementary Fig.\u00a02 provides analogous information for the day-ahead market.\n\nFocusing on shorter, operational time scales, a typical link had a transmission value 30% greater in the real-time market than in the day-ahead market. While electricity prices are positively correlated with transmission market value across years, the same is not true when looking at day-ahead and real-time markets: Real-time transmission value is greater than day-ahead value on average (Fig.\u00a04, left panel) and for the vast majority of links each year (Fig.\u00a04, center panel), yet average electricity prices are lower in the real-time market (Fig.\u00a04, left panel). It is the magnitude, not frequency, of the change in transmission value between markets that primarily drives greater real-time value: Our analysis finds that a link\u2019s market value is approximately equally likely to increase or decrease from the day-ahead to the real-time market for any given hour (Fig.\u00a04, center panel), but the mean increase is far greater than the mean decrease (Fig.\u00a04, right panel). Because an increase in transmission value often coincides with a price increase at one or both nodes, this finding is consistent with the real-time nodal price(s) being on more inelastic portions of the supply curve(s) when transmission value increased relative to day-ahead than when it decreased. Transmission value is also more volatile in the real-time market, with a standard deviation of $42/MWh compared with $16/MWh in the day-ahead market for a typical link.\n\nExcludes links connected to the non-ISO West where there is not a day-ahead market. The set of links in the early years is smaller due to data constraints. Left: Average in the real-time market relative to average in the day-ahead market for (1) transmission market value and (2) wholesale electricity prices. Center: Prevalence of greater real-time transmission value in terms of (1) the share of links where annual real-time value was greater than day-ahead and (2) the share of hours across all links in which real-time value was greater than day-ahead. Right: Magnitude of the average hourly difference between day-ahead and real-time transmission value presented separately for hours in which (1) real-time value was greater than day-ahead value and (2) day-ahead value was greater than real-time value.\n\nGiven the volatility of congestion, aggregating to calendar year intervals offers an incomplete view of how a link\u2019s value potential is distributed over time. Instead, we consider how the value is distributed within the entire study horizon. This perspective is presented in Fig.\u00a05 and reveals that, for real-time markets, at least 45% of total value is accounted for by only 10% of hours for all links and by just 5% of hours for the majority of links. Narrowing further, the top 1% of hours by real-time market value typically account for 20\u201330% of a link\u2019s potential, but it can reach over 50% in some cases. Transmission value in day-ahead markets is not only lower than in real-time markets, as discussed earlier, but also more dispersed. Figure\u00a05 shows that a smaller share of total day-ahead market value is concentrated in the hours with the highest day-ahead value as compared to real-time. Still, transmission value in day-ahead markets is not distributed evenly across all hours and the majority of day-ahead transmission value is usually found in just 10% of hours. Since transmission\u2019s marginal market value is heavily influenced by a relatively small collection of key hours with exceptionally large price differences, capturing the tails of market outcomes is important for transmission planners aiming to estimate the value of a transmission line.\n\nSample sizes: RT market: 70, DA market: 57. The horizontal lines on each box plot show, from low to high, the smallest data point lying within 1.5x the inter-quartile range (IQR) from the 25th percentile, the 25th percentile, the 50th percentile (median), the 75th percentile, and the largest data point lying within 1.5x the IQR from the 75th percentile.\n\nTo explore conditions during times of high transmission value and therefore assess potential drivers of these large geographic price spreads, in this section, we focus on the hours where the real-time transmission value is in the 95th percentile or above for each link and refer to these 5% of hours as the peak value hours. Due to net load data availability and the absence of a day-ahead market in the non-ISO West, this section focuses on the 52 studied links within or between ISO or RTO regions, excluding those in the non-ISO West and Southeast.\n\nUnforeseen intraday variance, high net load, cold weather, or high renewable power generation conditions are present in over 75% of the hours with peak transmission value. This is not simply because these four conditions are pervasive and present most of the time. Rather, these conditions disproportionately coincide with the peak value hours.\n\nUnforeseen intraday variance is identified by a large change in the LMPs between the day-ahead and real-time markets on either side of the link (see Methods for a precise definition). Such a change between markets reflects sizeable forecast errors in the day-ahead timeframe, for example, errors in estimated supply, demand, weather, or infrastructure outages, and limited flexibility to adapt to unexpected operational circumstances. These large price changes are a price increase 64% of the time and a price decrease the other 36% of the time. Of the transmission value resulting from peak value hours, 74% overlaps with a detected unforeseen event, as shown in Fig.\u00a06a (i.e., the entire blue bar covers 74% of the left gray bar). Remarkably, 43% of all hours with an unforeseen intraday variance detected are in the 5% of hours with greatest transmission value. Figure\u00a06b(i) shows how the distribution of hours with an unforeseen intraday variance are skewed toward the times of highest transmission value. This result directionally supports the simulation-based conclusion in ref. 18 that the benefits of adding transmission depend on the uncertainty between day-ahead scheduling and real-time operations. Unforeseen intraday variances sometimes occur during times of high net load, high renewable power generation and/or cold weather: Of the peak transmission value coinciding with large day-ahead to real-time price differences, 43% overlaps with one or more of these conditions, as shown by the lighter blue labeled segments in Fig.\u00a06a.\n\nAggregate results for all 52 studied links within or between ISO or RTO regions, excluding those in the non-ISO West and Southeast. a Contribution of key system conditions to transmission market value during peak value hours (top 5%). This figure should be read from left to right. When multiple conditions are present at the same time, the associated value appears in the first (i.e., leftmost) applicable column and is identified by lighter segments and \u201c& [overlapping condition]\u201d labels. b Distributions of hours relative to transmission value when a key condition is present (blue) compared to when it is not (orange). The local peaks in the left half of each distribution represent a mass of hours each with $0 transmission value and therefore the same value rank for each link.\n\nCold weather\u2014defined here as the coldest 5% of days in each location \u2013 overlaps with 25% of the value resulting from the peak hours (Fig.\u00a06a). Often this value overlaps with unexpected events, net load, or both, but cold weather is the only studied driver present for 5% of the peak transmission value (dark orange segment). High net load \u2013 defined here as the 5% of hours in each location with the highest net load \u2013 is rarely the only studied condition affecting a peak value hour (dark yellow segment; 2% of peak value). However, it is still an important factor because 20% of the value resulting from the peak value hours comes from times of high net load (Fig.\u00a06a). High net load is the result of high electricity demand and/or low variable renewable energy generation. The distributions of high net load and cold weather periods in Fig.\u00a06b(ii,iii) show a clear skew toward the highest value hours, though to a lesser degree than was observed for unforeseen intraday variance. While a disproportionate amount (13%) of cold weather and high net load hours coincide with the peak value hours, it is still much more common for a period with one of these conditions to pass without encountering a peak transmission value. Thus, peak value periods are difficult to predict, even if you are expecting very cold weather or high net load.\n\nWhile high levels of renewable generation make a period less likely to have high net load, it can still be a driver of peak transmission value due to geographically varying renewable penetration rates and weather patterns. Focusing on the 5% of hours with greatest combined wind and solar generation in each region, Fig.\u00a06b(iv) shows that hours with high renewable generation coincide more often with high transmission value than low value, but they do not have the same presence in the very highest transmission value hours as the other conditions discussed so far. Still, high renewable generation impacts a portion of peak transmission value which unforeseen intraday variance, high net load, and cold weather do not (green segment in Fig.\u00a06a; 2% of peak value).\n\nSupplementary Table\u00a01 details the prevalence of each condition discussed here and its relationship with the top 1%, 5%, and 10% value hours. Further, the Supplementary Information includes analysis on the roles of hot weather and designated storms or grid reliability events on peak transmission value, which were found to be nearly subsumed under the four conditions analyzed here.\n\nThe conditions analyzed above do not affect all links equally. Some conditions are more relevant for interregional links or within region links and others have an outsized impact on certain regions of the country. Figure\u00a07 shows the distribution of links in terms of the share of peak value with each condition. In general, interregional and cross-interconnect links see a greater share of peak transmission value explained by these conditions. This is consistent with greater weather differences across longer distances and our use of regional-level load and renewable generation totals; local condition differences that are not investigated here may have more impact on within-region links. Unforeseen intraday variation is a key condition for all studied links, but especially for interregional links. Interregional transfer quantities are often scheduled several hours or more in advance, with less flexibility to adjust compared to within-region flows. These results point to the lack of interregional operational flexibility within 24\u2009h of operation as a driver of transmission value. The two links with the greatest share of peak value during cold weather are SPP South\u2009<\u2009>ERCOT West and MISO\u2009<\u2009>\u2009ERCOT, but all ISO/RTOs other than CAISO are connected to at least one link where cold weather is present for at least 35% of peak value. High renewable generation rarely coexists with peak transmission value for some links, but for some regional and interregional links, particularly those within or between MISO and SPP, there is considerable overlap.\n\nEach point represents one of the 52 studied links (3 cross-interconnect, 19 interregional, 30 within-region) within or between ISO or RTO regions, excluding those in the non-ISO West and Southeast. The three cross-interconnect links are between ERCOT and either SPP or MISO. The horizontal lines on each box plot show, from low to high, the smallest data point lying within 1.5x the inter-quartile range (IQR) from the 25th percentile, the 25th percentile, the 50th percentile (median), the 75th percentile, and the largest data point lying within 1.5x the IQR from the 75th percentile.\n\nCase studies investigating differences in the conditions affecting transmission value for two links in more detail are provided in the Supplementary Information, including in Supplementary Figs.\u00a03 and 4.\n\nThis analysis has so far focused on transmission\u2019s marginal market value, which is defined precisely by market-clearing prices. If one is instead interested in the energy market value of a finite, larger transmission capacity addition, under the counterfactual that the line existed during 2012\u20132022, there are several factors to consider in addition to the observed marginal market value. One such factor is market depth; each increment of transmission capacity has the ability to reduce the price difference across its span, leading to an average value for the line that is less than the marginal value without it. The impact of market depth is accounted for in the following analysis through the use of statistically-based rolling supply curves and assumes a static market. That is, it does not account for generator entry and exit29,30, demand changes, shifts in market power31,32,43, or other dynamic effects that transmission expansion may create.\n\nHere, we summarize the analytical approach that is detailed in the Methods section. First, we infer supply curves from the data by fitting a non-decreasing third-degree polynomial to the relationship between regional net load and market price at each node for every bi-monthly period during the study horizon, similar to ref. 44. Second, we screen the fitted supply curves to identify which models represent the data sufficiently well and which models produce large-magnitude residuals for a substantial share of the hours. Models that fail to pass the screening are not used to estimate transmission value. Third, the supply curve models and hourly data are analyzed for each link to establish hourly transmission value estimates. In most hours, both (net load, price) observations are near their respective supply curves and the difference between these two supply curves forms a transmission demand curve. The area under this transmission demand curve, yet above the horizonal axis, between zero and the additional transmission capacity is the transmission value estimate (see Fig.\u00a08). When an observation is not near the supply curve or the corresponding supply curve did not pass screening, we use alternative approaches that lead to lower and higher value estimates. Finally, hourly values are summed over the study horizon.\n\nLeft and center panels: Each black point is an hourly observation from the half-month period that starts on the listed date. The red curve is the supply curve fitted to these points, and the blue curve is the red curve shifted to pass through the hour currently being analyzed. Right panel: The blue curves in the left and center panels combine to the transmission demand curve shown here. The area under the curve is the transmission value for the specified hour. In the upper example, transmission value declines slowly and the value is close to the marginal value. In the bottom example, transmission value declines quickly and prices converge before 1\u2009GW of capacity is fully utilized. Supplementary Fig.\u00a09 offers three more detailed illustrations of the same methodology.\n\nApplying this approach, we estimate the transmission value accounting for market depth of a 1\u2009GW increase in transmission capacity individually for each of the 40 interregional and cross-interconnect links in this study. Unlike the directly observable marginal transmission value metric, this value is an estimate for a hypothetical situation. 1\u2009GW of transmission capacity represents a relatively small addition for some links, such as those between PJM and MISO North where there is already 21.7\u2009GW of transfer capacity, and a substantial increase in other regions, such as between PJM and New York and between New York and New England where it represents a 50% increase20. We find values typically 72% to 93% (25th percentile of lower estimate to 75th percentile of higher estimate) of the marginal market value, as shown in Fig.\u00a09. Based on these findings, we conclude that, at a realistic line size of 1\u2009GW, saturation effects are not dominant and instead represent a discount to marginal transmission value that is occasionally moderate but typically modest. Supplementary Fig.\u00a05 presents results for a more aggressive test of market depth that assumes the market size (as defined by net demand) is 50% smaller than the market used here. In that case, transmission value accounting for market depth of a 1\u2009GW capacity increase is typically 59% to 87%2 of the marginal market value.\n\nGray and white bands are used to improve readability; they do not communicate information about the results.\n\nTo better contextualize our market value estimates, we compiled data on the costs to construct individual transmission projects across different regions of the United States. In total, the 26 recent and proposed transmission lines we evaluated correspond to over 87\u2009GW of potential transfer capability (details for each individual line are provided in Table\u00a01) and can be loosely matched, on a regional basis, to a subset of the transmission link values we estimate. Such value-to-cost comparisons should not be used to assess the full cost and value of any individual, specific transmission investment. However, the value-to-cost ratios we present in Fig.\u00a010 do provide some indication of the net economic value of transmission development, focusing here solely on energy market value and excluding other possible benefits of transmission investment.\n\nSee Table\u00a01 for details on the project cost data. See Supplementary Figs.\u00a06 and 7 for versions of this figure comparing multiple value and cost assumptions. a Value-to-cost ratios where the value is the midpoint of the value estimate range in (b) and the cost estimate assumes 60-year depreciation. The horizontal dashed line at 1 represents the break-even ratio. b Range of market value (real-time) estimates accounting for market depth and annualized project cost estimates.\n\nFigure\u00a010 shows the value estimate accounting for market depth and the cost of geographically similar transmission projects for the 26 projects (22 unique links). The figure breaks out our results for the three types of links we study: cross-interconnect, interregional, and within-region transmission lines. Figure\u00a010b shows the range of cost and value estimates in units of $/MW-yr and shows variation both across the types of links as well as within a given link. Across all 26 projects studied, only 3 projects had cost ranges that exceeded the value estimate range. Figure\u00a010a converts the absolute cost and value estimates into a value-to-cost ratio. We find that all cross-interconnect projects have value-to-cost ratios that exceed 4, suggesting that energy market value alone could motivate transmission development to link these areas. All these lines are in fact being privately proposed by merchant developers. The failure and challenge of developing some of these lines imply non-economic constraints. For instance, merchant developers often must seek customers to subscribe to their proposed lines, but those customers can be utilities that might own and operate their own transmission lines and thus not want to participate in such a competitive development process. For interregional lines within a common interconnection, value-to-cost ratios are smaller (median value of 1.6) with all of the ten lines having value-cost ratios > 1. Within-region lines have the lowest value-to-cost ratios with four of the eleven lines assessed having value-to-cost ratios <1 and the seven others > 1. Supplementary Figs.\u00a06 and 7 present results for sensitivity cases under different depreciation rates and where the value estimate range is based on a market size (as defined by net demand) that is 50% smaller than the market used here.\n\nProjects that have higher investment costs compared to historical market value may still have merit, especially if grid conditions are anticipated to change in ways that increase value over time. Additionally, energy arbitrage value is only one of many potential benefits of transmission: other drivers like renewable integration, electric reliability/resiliency, and avoided capacity investments may motivate investment and are not fully captured by wholesale energy prices. Furthermore, several of the lines we analyze came into service during the time period of market value estimation. Figure\u00a010a shows that these lines tend to have lower value-to-cost ratios, especially for within-region projects. Such results imply that while market value still exists for those links, some of the market value may already have been absorbed by the completed transmission line.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_Fig8_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_Fig9_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63143-5/MediaObjects/41467_2025_63143_Fig10_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "For transmission, the number of merchant projects that have been built in a decentralized manner to capture the value of spatial arbitrage in U.S. electricity markets is small. For instance, in PJM, the largest electricity market in the U.S., only 1% of its total transmission capacity is made up by merchant lines45,46. This could be because all lines that would be valuable to the system are built centrally through regulated ISO, RTO, or utility planning processes. Alternatively, it could be because of barriers to capturing the value provided to the system and/or to financing and constructing the project. These results suggest the latter: that there is valuable transmission infrastructure not being built in a centralized or decentralized fashion.\n\nWe find that transmission links often have substantial economic value in their ability to increase energy trade and, in turn, reduce congestion. Interregional links (those crossing market or grid seams) are especially valuable and see fairly balanced trade in each direction, emphasizing the broad value of such investments and the importance of interregional planning that jointly accounts for benefits to multiple regions. Further, our scoping-level comparison of transmission infrastructure costs to historical energy market values finds greater value than cost for majority of links, including all cross-interconnect links. At the same time, the limited actual investment in interregional transmission over the last decades suggests that barriers are effectively preventing development of otherwise valuable infrastructure.\n\nMany types of barriers prevent an efficient buildout of transmission. For example, Joskow and Tirole47, discuss how deviations away from perfectly competitive markets can interfere with efficient buildout of merchant transmission. Pfeifenberger et al.19 add discussion on barriers presented by limited, localized transmission planning and the challenge of expanding both the size of the planning region and the types of benefits assessed during planning processes. FERC8 focuses on barriers related to (1) regulatory review, including navigation of processes across different states, (2) limits to the locations at which transmission can be built, and (3) the length of time for new transmission to progress from plan to operation, which can take over a decade. The high value of transmission found in our work serves to highlight the cost of these barriers, that is, the potential net value is equal to missed savings. Each of the above citations discuss possible solutions to and implications of these barriers, including institutional reforms to planning, permitting, and cost allocation procedures.\n\nOur findings also have implications for modelers, investors, planners, and policymakers and may serve to motivate and guide change to the processes through which transmission value is estimated. We find that the market value of transmission is highly influenced by the small fraction of time during which transmission is extremely valuable. In this sense, transmission can be thought of as having insurance value. Peak transmission value periods are primarily driven by events that are unforeseen or mis-forecast ~12\u201336\u2009h before the operating window. Secondary causes include high net load, cold weather, and/or high levels of wind and solar generation. Unforeseen intraday variance can occur due to demand and generation forecast errors, or unexpected outages of generators or transmission infrastructure. For modelers, improving the representation of these four drivers in planning scenarios may help improve performance. There is a wide variety of strategies for assessing these conditions in models. For example, models could benchmark simulated transmission market values against empirical market outcomes both in terms of their average magnitude and distribution of values over time. Additional research is warranted to help refine the types of conditions that have driven high transmission market value in the past, compare the results of the empirical analysis in this paper with power-sector planning models, and to identify the highest priority areas for improvement.\n\nFor investors, historical pricing patterns can help identify areas where additional transmission investment may be warranted. Historical data should be paired with modeling simulations to properly judge cost-value tradeoffs over the expected 30+ year life of new infrastructure. However, many of the conditions that drove past transmission value are likely to persist for years to come, e.g., the benefits of weather, load, and generation diversity across large geographic regions. Historical patterns of transmission value can therefore be a useful complement to model results. For example, both forward looking and historical analysis were used by the United States Department of Energy (DOE) to develop a preliminary list of National Interest Electric Transmission Corridors20,48. These corridors are important because they allow transmission developers access to federal financing and permitting tools. The corridors reflect the key high-level findings in this paper, in that 8 of the 10 corridors are interregional, with 3 of those designed to facilitate trade across interconnections, and further, many of the corridors address high value links identified here.\n\nFor planners and policymakers, several important implications derive from our results beyond the aforementioned model improvements that will have derivative value to planners. First, and most simply, they suggest that barriers have been thwarting otherwise valuable transmission investment and thereby provide a signal to policymakers and planners that more effort is needed to identify and mitigate those barriers. Second, the results highlight where additional planning and policy effort may bear the most fruit. In the United States, recently finalized federal regulations have focused on generator interconnection to local transmission networks and within-region transmission planning (refs. 49,50). As has been noted by others (e.g., ref. 51), and as supported by our analysis, a critical next step is to address barriers to interregional and cross-interconnection transmission. Finally, among these barriers has been the perception that such investments primarily benefit consumers in one region at the expense of consumers in the other. On the contrary, the results presented here indicate that in many cases the benefits are bi-directional, and even more balanced for cross-interconnection and interregional lines. Such a finding should further motivate planners and policymakers to overcome the hurdles to new transmission investment.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "In electricity markets that employ spatially-differentiated marginal pricing (i.e., locational and, to some extent, zonal pricing), prices signal where investment is needed in the system. High prices incentivize the deployment of lower-cost generation resources and energy efficiency measures. Large variations in prices over time at the same node provide incentives for demand response and energy storage investments. Concurrent price differences between nodes indicate network congestion and signal that additional transmission capacity would be valuable. A price difference between two nodes could be caused by congestion exclusively on a line directly connecting them, but it typically is the result of simultaneous limits on multiple pieces of transmission infrastructure. These market signals of transmission value are the focus of this paper. While markets with a single market clearing price (e.g., Germany) provide location-agnostic signals for some of these investment types and indicators of transmission value may be visible to system operators (e.g., as shadow prices in optimal dispatch models), the presence of market-facing signals reflecting the value of specific transmission capacity is unique to markets with spatially-differentiated marginal pricing (e.g., the liberalized electricity markets in the U.S.: CAISO, ERCOT, ISO-NE, MISO, NYISO, PJM, SPP)52.\n\nThis study utilizes wholesale locational marginal price differences as a market signal for the value of additional transmission between two locations. The following equation defines the transmission market value quantity used throughout the paper,\n\nEquation\u00a01: Definition of the mean marginal transmission market value between two nodes from time t\u2009=\u20091 to time t\u2009=\u2009n.\n\nwhere \\(n\\) is the number of hours under consideration, the market price at node \\(X\\) during hour \\(h\\) is denoted \\({{{\\rm{pric}}}}{{{{\\rm{e}}}}}_{h}^{X}\\), and \\(\\left|\\cdot \\right|\\) is the absolute value operator. The marginal transmission market value at a fixed time (i.e., \\(\\left|{{{\\rm{pric}}}}{{{{\\rm{e}}}}}_{h}^{A}-{{{\\rm{pric}}}}{{{{\\rm{e}}}}}_{h}^{B}\\right|\\)) is sometimes referred to as the locational spot price of transmission53.\n\nThese are real economic signals, but they are distinct from other measures of transmission\u2019s economic impact to the system and should be interpreted with care. One interpretation is that the mean marginal day-ahead transmission market value between nodes A and B is equivalent to the average hourly payment from holding two financial transmission right (FTR) options of equal size over the entire study period: one from A to B and one from B to A. FTR options are financial instruments that pay the LMP difference between two nodes if that difference is positive28. Another interpretation is that, if a link were an existing transmission line, the average congestion (i.e., shadow) price of the line\u2019s transmission constraint would equal the link\u2019s mean marginal transmission market value.\n\nIf a new transmission line were to have been built by 2012 between two nodes linked in this study (hypothetically), the mean marginal transmission market value multiplied by the number of hours is the marginal impact of the line on the total cost of energy bought by loads. The total change in energy costs for a static market is\u00a0at most\u00a0the line\u2019s marginal impact multiplied by its rated capacity for several reasons, including: (1) The transmission market value is marginal. That is, the absolute nodal price difference with the line is typically less than the price difference without the line and so, while the first unit of incremental transmission reduces costs at the rate of the transmission market value, the line as a whole impacts energy costs at a lower rate due to saturation effects. This is addressed in the next section. (2) The line may not be fully utilized even when there is congestion due to security constraints and power flow dynamics. The latter refers to the fact that the path of power flow on alternating current (AC) networks cannot be arbitrarily prescribed, because the electricity flows across transmission facilities according to Kirchoff\u2019s Laws based on the power consumption of loads and the voltage magnitude and real power injection established by generators. While direct current (DC) lines offer greater control over power flows, the surrounding AC system dynamics could still limit utilization. (3) Transmission expansion can further lower energy costs by increasing supply-side competition, reducing the ability of generators to exert market power31,32,43. On the other hand, while the marginal market value reflects elements of transmission\u2019s system value (e.g., savings to electricity production costs and the costs of ensuring reliability) it does not fully capture all benefits (e.g., impacts on generation capacity sharing, reduction in unpriced or mispriced emissions) and it includes generator rents, a reduction in which is not necessarily a net economic benefit but rather a redistribution. Further, there are dynamic impacts of transmission expansion on generator entry and30 found that \u201cignoring this dynamic effect (\u2026) would substantially understate the benefits of transmission investments.\u201d Therefore, a link\u2019s empirical marginal market value cannot be treated as a direct measure of, or strict upper or lower bound on, the total economic impact of expanded transmission capacity along the link. Still, we contend that market outcomes are a meaningful tool for measuring transmission value because concurrent price differences between market nodes indicate network congestion and serve as an investment signal to market actors and because empirical prices precisely reflect actual system conditions and market participant behavior.\n\nIn our empirical analysis, the prices used are the observed market prices as established by the corresponding Independent System Operator (ISO), Regional Transmission Operator (RTO), or energy imbalance market. Real-time prices (averaged within each hour) are used as the default, since they most closely reflect the actual operating conditions of the system. Day-ahead prices are also used selectively as a point of comparison, and values based on day-ahead prices are clearly labeled as such. All results dependent on day-ahead prices exclude links connected to the non-ISO West, as the Western Energy Imbalance Market did not operate a day-ahead market over this period.\n\nThe locational spot price of transmission is the value of the first increment of additional transmission capacity. As more energy is transferred, the prices on either side of the link will begin to converge and may equalize, if there is sufficient transfer capacity. In this paper, we estimate the effect of such price convergence and the depth of markets on transmission value for each link individually. That is, we only consider the change in prices for two nodes at a time, and we assume each link is the only transfer capacity addition when analyzing it. We do not consider a case where the capacities of all, or a group of several, analyzed links are expanded simultaneously.\n\nTo account for the impact of market depth on transmission value, we first computed supply curve functions representing the relationship between regional net load and market price at each node over each bi-monthly (i.e., 1st\u201315th and 16th-end of month) period during the study horizon. Robust polynomial regression with shape constraints was used to find the best-fit cubic function that is non-decreasing on its domain, where best-fit is defined according to the Huber loss54. Each function\u2019s domain was defined as the range of net load values during the period extended in both directions by 10% of this range to allow for some extrapolation without encountering negative slopes. Huber loss uses the squared loss for samples with smaller residuals and the absolute loss for samples with larger residuals, making the model robust to outliers without ignoring their effect. The choice of period length and polynomial order was informed by ref. 44. Our Python implementation of this method was based on ref. 55. Demand is modeled as perfectly inelastic, because demand responsiveness is already reflected in the observations and therefore captured as a supply resource in the fitted supply curves.\n\nSecond, we screened the fitted supply curves to identify which models represent the data sufficiently well and which models produce large-magnitude residuals for a substantial share of the hours. Algorithm 2 in the Supplementary Information details this screening procedure. To summarize Supplementary Algorithm 2, we define an adaptive tolerance level for residuals in each bi-monthly period and a model passes the screening if at least two-thirds of the hours during the period are within the tolerance level. The tolerance level is always at least $25/MWh and can be greater during periods of high price volatility, as determined by the median absolute deviation from the median price. Models that fail to pass the screening, such as the one depicted in Supplementary Fig.\u00a08, are not used to estimate transmission value. For the 5256 total node-period supply curve models computed for the comparison of transmission value to cost, 99.8% passed the screening and at most 2 failed per node.\n\nThird, the supply curve models and hourly data are analyzed for each link to establish hourly transmission value estimates. Here, we will describe the general methodology. In the Supplementary Information, Algorithm 1 details the complete procedure and Fig.\u00a09 visualizes this methodology with several examples. For most link-hours \u2013 those with a model that passed the screening and a residual magnitude within the tolerance level described above on both ends of the link\u2014a single value estimate is found. It is rare that the (net load, price) observation for an hour falls directly on the supply curve, since the model represents the best fit over ~360 observations. The true market might behave similarly to the model at the observed net load or to the model at the observed price. So, the value estimate assumes prices at each node change at rates determined by the shape of each demand curve at the midpoint between these two points, i.e., the observed net load and the net load that the model predicts would produce the observed price. The difference between these two supply curve translations (e.g., the yellow curves in the left and center panels of Supplementary Fig.\u00a09a,b) forms a transmission demand curve, and the area under this transmission demand curve yet above the horizonal axis between zero and the additional transmission capacity is the transmission value estimate. (This area is shaded in the right panels Supplementary Fig.\u00a09a,b). If a node-hour has a successfully screened model but a large residual, we use two approaches to produce two estimates (see Supplementary Fig.\u00a09c). The first approach simply uses a vertical translation of the supply curve; it assumes prices change as the model predicts around the observed net load. The second approach is more complex and assumes prices will change quickly with small changes in net load due to transmission. How quickly prices change is node-specific and determined using the steepest slopes found across all of the node\u2019s fitted models (excluding those that failed screening) on their observed net load domains, as detailed in Supplementary Algorithm 3. These are the steepest slopes across 264 models, for a node whose study period is 2012\u20132022. Finally, when a node-hour corresponds to a model that failed screening, we use only the latter, \u201csteepest slope,\u201d method. This should produce a conservative value estimate in most hours because, typically, most hours in a 15-day period are on a relatively flat portion of the supply curve. Finally, any link-hours where prices are at or above the price cap for each node are treated as supply-constrained and assumed to have no trade on additional transmission capacity. Hourly value estimates for each link are aggregated into a lower estimate that is the sum of all singular hourly estimates plus the lower estimate when there are two estimates and a higher estimate that is the sum of all singular hourly estimates plus the higher estimate when there are two estimates.\n\nWe analyze several environmental and market conditions that affect transmission value. Each of these conditions is defined based on a combination of measurements and criteria, as defined in Table\u00a02. The unforeseen intraday variation criteria were designed to reflect large day-ahead-real-time price spreads while capturing a similar amount of time as the high net load, cold weather, and high renewable generation drivers. If a condition is present at one or both of a link\u2019s terminal nodes at a given time, we consider the condition to be present for the link.\n\nA survey of large transmission project costs was taken from refs. 56, 57. See Table\u00a01 for details of the projects and corresponding cost estimates. These sources provide both a total cost estimate for construction of the lines (converted to 2022 dollars with the Bureau of Economic Analysis price deflators) as well as the total transfer capacity of the lines (in MW). However, they ignore operational costs for managing transmission lines. We apply the below equation to convert these capital cost estimates to annualized values to facilitate comparison to our transmission market value estimates.\n\nWhere\n\nC = capital cost of transmission investment ($2022)\n\nr = real interest rate (%)\n\nn = transmission asset lifetime (years)\n\nK = incremental capacity of transmission infrastructure (MW)\n\nSimilar to ref. 56, we assume either a 60- or 30-year transmission line lifetime and a 4.44% real interest rate based on a 55/45 debt to equity ratio, 3.6% debt cost, 11.3% ROE, 26% tax rate, and 2% inflation rate. There is uncertainty in the transmission costs identified here, especially for projects that have only been proposed and not completed, as they may incur cost overruns, not represent the full all-in project cost, or not reflect differences in construction schedules.\n\nFinally, we select a specific link from our market value analysis to correspond, as best as possible, with the geographic scope of the actual transmission line path. To perform this linkage, we compare the key market hubs used in our market value analysis to the planned electric linkages created by our actual transmission projects. This process is typically straightforward for cross-interconnect and interregional lines, where transmission projects will explicitly state their plan to electrically link two distinct market regions, oftentimes with HVDC transmission technology. This process is less straightforward for within-region lines, where the inherent nature of the AC connection makes it more difficult to assess transfer capability of new lines.\n\nEach pair of nodes analyzed is referred to as a link. 70 hypothetical transmission links are considered; 30 links are contained within a balancing authority (\u201cwithin-region\u201d) and 40 links are interregional. Over 75% of pricing nodes at which links terminate are hub, zonal, or aggregate nodes that reflect the price signal for a group of buses in the same geographic area. Each of the remaining nodes is either a load, generator, tie generator, interface, or external node. The non-ISO Southeast nodes are of the latter two types as reported by MISO and PJM. Prices in the non-ISO West are Western Energy Imbalance Market prices reported by CAISO. Links between pairs of these nodes were established based on geographic relevance (e.g., a direct link between Arizona and Massachusetts was not included). Note that markets occasionally change their price node definitions over time. When the 2022 node does not exist for a given year, the geographically closest node of a similar type in the same balancing authority is used, if one exists. There are six nodes that experience a definition update during the study period.\n\nThe study period is 2012\u20132022 with the following exceptions: links involving SPP are 2015\u20132022 and the Pacific Northwest (PNW) link is also 2015\u20132022. All links have hourly real-time price data available for at least 90% of hours in each studied year and over 99% of studied link-years have at least 98% of data available.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "We purchased data on market prices, load, renewable generation and weather from a commercial vendor; the product is called Velocity Suite, by Hitachi. This information is also publicly available.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Goggin, M. \u201cTransmission Makes The Power System Resilient to Extreme Weather,\u201d Grid Strategies, LLC; American Council on Renewable Energy, Jul. 2021. [Online]. 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The US Government retains, and the publisher, by accepting the article for publication, acknowledges, that the US Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Lawrence Berkeley National Laboratory, Berkeley, CA, USA\n\nJulie Mulvaney Kemp,\u00a0Dev Millstein,\u00a0Will Gorman,\u00a0Seongeun Jeong\u00a0&\u00a0Ryan Wiser\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJulie Mulvaney Kemp: Methodology, Software, Formal Analysis, Writing \u2013 Original Draft, Review & Editing, Visualization; Dev Millstein: conceptualization, writing - review & editing, funding acquisition, supervision; Will Gorman: data curation, formal analysis, writing - original draft; Seongeun Jeong: data curation, visualization; Ryan Wiser: writing - review & editing, funding acquisition.\n\nCorrespondence to\n Julie Mulvaney Kemp.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Audun Botterund, Paul Joskow and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Electric transmission value and its drivers in United States power markets.\n Nat Commun 16, 8055 (2025). https://doi.org/10.1038/s41467-025-63143-5\n\nDownload citation\n\nReceived: 19 March 2024\n\nAccepted: 08 August 2025\n\nPublished: 28 August 2025\n\nVersion of record: 28 August 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63143-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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naturalized as aliens abroad have also become more common at home during the Anthropocene", + "journal": "Nature Communications", + "published": "05 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63293-6/MediaObjects/41467_2025_63293_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63293-6/MediaObjects/41467_2025_63293_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63293-6/MediaObjects/41467_2025_63293_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63293-6/MediaObjects/41467_2025_63293_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.6084/m9.figshare.25487209", + "/articles/s41467-025-63293-6#ref-CR73", + "/articles/s41467-025-63293-6#Sec17" + ], + "code": [ + "https://doi.org/10.24433/CO.1618280.v1", + "https://doi.org/10.6084/m9.figshare.25487209", + "/articles/s41467-025-63293-6#ref-CR73" + ], + "subject": [ + "Biogeography", + "Invasive species", + "Macroecology" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4177196/v1.pdf?c=1757156746000", + "research_square_link": "https://www.researchsquare.com//article/rs-4177196/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63293-6.pdf", + "preprint_posted": "09 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Changes in species' native range size and occupancy have been dramatically accelerated by anthropogenic pressures in the last centuries. At the same time humans have introduced thousands of species beyond their historic range limits, and some of these have established self-sustaining populations (i.e. become naturalized). It is known that particularly common plant species have become naturalized, but how dynamics in native distributions relate to global naturalization is unknown. We retrieved data on grid-cell occupancy of native vascular plant species for 10 European regions, for at least two periods, to calculate for each species an occupancy-change index. For nine of the ten regions, we found a significant increase in global naturalization with both the early period occupancy and occupancy-change index. This finding shows that many of the plant species expanding globally as naturalized aliens are also expanding within their native ranges and suggests that the same drivers underlie both processes.Biological sciences/Ecology/Invasive speciesBiological sciences/Ecology/Macroecologydeclining speciesexpanding speciesEuropean native rangeglobal naturalizationnaturalization extent", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryinformationPaudeletal.pdfPlants that have naturalized as aliens abroad have also become more common at home during the Anthropocene", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Due to anthropogenic pressure some species have declined whereas others have increased within their native ranges. Simultaneously, many species introduced by humans have established self-sustaining populations elsewhere (i.e. have become naturalized aliens). Previous studies have shown that particularly plant species that are common within their native range have become naturalized elsewhere. However, how changes in native distributions correlate with naturalization elsewhere is unknown. We compare data on grid-cell occupancy of native vascular plant species over time for 10 European regions (countries or parts thereof). For nine regions, both early occupancy and occupancy change correlate positively with global naturalization success (quantified as naturalization in any administrative region and as the number of such regions). In other words, many plant species spreading globally as naturalized aliens are also expanding within their native regions. This implies that integrating data on native occupancy dynamics in invasion\u00a0risk assessments might help prevent new invasions.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Natural processes such as tectonic upheavals and glaciations or evolutionary innovations have driven dynamics in species distributions throughout the history of life1,2. In the last centuries, however, and particularly since the start of the Anthropocene in the mid-20th century3, human pressures, such as land-use change, habitat fragmentation and eutrophication, have accelerated these dynamics dramatically4. In particular, human pressures have caused rapid declines in populations and range sizes of many native species5, resulting in drastic declines in occupancy (i.e., the number of locations of occurrence) in many regions where those species are native e.g.6,7,8. However, while many species are in decline, and c. 25% of assessed animal and plant species are threatened with extinction4, there are also species that benefit from the rapid changes occurring during the Anthropocene and are on the rise9.\n\nData on temporal changes in regional occupancy for large numbers of species are still relatively rare and restricted to European regions. Nevertheless, the few studies that have analyzed such data consistently show that native species with increasing occupancy are typically tall, habitat generalists, classified as competitors in Grime\u2019s CSR-strategy framework, and with high values of the Ellenberg indicator for nitrogen6,10,11,12. This corroborates the idea that these species have benefited from anthropogenic environmental changes, such as atmospheric nitrogen deposition6 and the creation of novel anthropogenic habitats13 within their native ranges.\n\nConcurrent with anthropogenic changes to the environment, humans have intentionally and unintentionally transported many species from their native ranges across major geographical barriers to new regions14. Though most of these introduced alien species have failed to establish self-sustaining populations outside captivity or cultivation, a small percentage have succeeded in becoming naturalized, occasionally in hundreds of regions (e.g., countries, states and provinces) around the globe15,16,17. Among the vascular plants, more than 16,000 species have already become naturalized somewhere on Earth18, provisionally accepted]. Most of these naturalizations happened after the 1950s, i.e., during the Anthropocene19, and predominantly in habitats with high levels of anthropogenic disturbances e.g.20. Previous analyses of the characteristics of naturalized plants or the subset of invasive alien plants (i.e., naturalized plants that have spread rapidly and frequently have ecological and/or socio-economic impacts) have shown that they are typically tall, habitat generalists, classified as competitors in Grime\u2019s CSR-strategy framework, and with high values of the Ellenberg indicator for nitrogen e.g.12,21,22,23,24,25. \u2014 just like the species that are increasing within their native ranges26,27. However, whether species that have increased their occupancy in regions of their native range and species that have become naturalized elsewhere are largely the same species has never been tested explicitly. If this is the case, it would imply that information on native occupancy dynamics could inform invasion\u00a0risk assessments.\n\nUltimately, occupancy dynamics of species, both within and outside their native range, are likely to depend on intrinsic features of the species. Unfortunately, despite the many studies that have measured plant functional traits, for most traits, data are available only for a small proportion of the global flora28. A notable exception is woodiness, which is indicative of both growth form (i.e., woody species are usually shrubs or trees) and habitat affiliation (i.e., woody species typically occur in forests and other closed habitats). On the one hand, the tall stature of woody species allows them to have greater dispersal capacities and a higher competitive dominance, which could facilitate range expansion in both native and non-native regions. However, despite these potential advantages, woody species generally have a lower probability of naturalization than non-woody species29. This may be because woody plants usually are less successful in frequently disturbed habitats, and because they have longer generation times and therefore require more time to become naturalized and to spread after introduction30,31. So, species features, such as woodiness, might mediate the relationship between global naturalization success and occupancy dynamics in the native range.\n\nIt has been suggested that extinction risk of native species and invasion success of alien species might represent two sides of the same coin32. Jeschke and Strayer33 did not find this to be the case for birds and freshwater fish. However, this concept not yet been assessed in plants. The findings that naturalized plants and those spreading in their native range share a common set of traits suggests that it may be the case6,21,22,23,24,25,26,27. Accordingly, numerous studies have shown that common species with large native ranges are more likely to naturalize elsewhere34,35. However, range size is just one dimension of commonness. Other dimensions include habitat breadth and local abundance, as proposed by Rabinowitz36, and occupancy, as recently proposed by Crisfield et al.37. These different dimensions of commonness are frequently positively correlated38,39, and some studies have shown that regional occupancy in the native range correlates positively with naturalization success elsewhere21,22,25. However, in addition to these static measures of commonness, the change in occupancy over time could be considered a further dimension, similar to spread rate, which has been proposed as one of the dimensions of invasiveness for alien species40. If species that increase their occupancy within their native range over time are largely the same species that spread as naturalized aliens elsewhere, this would suggest that similar mechanisms may underlie both processes.\n\nHere, we test the hypothesis that the plant species that have become widely naturalized across the globe are also increasing in occupancy (i.e., in the proportion of grid cells in which they have been recorded) within their native regions. To test this hypothesis, one would ideally have time series data of grid-cell occupancies for the entire native range of the species. However, as such data is not available, we instead retrieve data on grid-cell occupancies of vascular plant species during an early period (i.e., early occupancy) and a later period, each usually covering multiple decades, for 10 regions in Europe (Fig.\u00a01). For each of these 10 native regions, which correspond to countries or parts thereof, we calculate for each native species an occupancy-change index according to Telfer et al.41. This index quantifies the degree to which the change in the proportion of grid cells in which a species has been recorded between the early and later period is higher or lower than expected based on the early\u00a0occupancy41. Consequently, the occupancy-change index is not correlated with early occupancy. We then use hurdle models to analyze how global naturalization success \u2014 a combination of naturalization incidence (i.e. whether or not a species has become naturalized, which is modeled using a Bernoulli distribution) and naturalization extent (i.e. the number of regions where a naturalized species has become naturalized, which is modeled using a zero-truncated negative binomial distribution) \u2014 correlates with occupancy in the early period and the occupancy change within the species\u2019 native regions. Given that the available data for the 10 native regions vary in many aspects (Table\u00a01), the analyses are done for each region separately.\n\nFor these regions, we have information on occupancies of native plant species for an early and a later time period. Polygons were obtained from GADM, the GloNAF database, or were\u00a0created using Google Earth Pro (Data SIO, NOAA, U.S. Navy, NGA, GEBCO; Image Landsat / Copernicus).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63293-6/MediaObjects/41467_2025_63293_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "As we had data on woodiness of all 3920 species in our 10 datasets (see Supplementary Methods for details), we ran hurdle models with and without woodiness of the species as an additional predictor. However, as the results for our two main predictors of interest, occupancy in the early period and occupancy change within the species\u2019 native regions, remained largely the same, we focus here on the models without woodiness. Results for the models with woodiness are provided in Table\u00a0S1 (also see Tables\u00a0S2\u2013S11).\n\nOur hurdle models showed that global naturalization success was associated with occupancy in the early period for all 10 native regions (Table\u00a02 (also see Tables\u00a0S12\u2013S21), Fig.\u00a02). This was true for both the likelihood of being naturalized outside the native range (i.e. for the Bernoulli part of the hurdle model) and for being naturalized in more regions (i.e., for the zero-truncated count part of the hurdle model; Table\u00a02, Fig.\u00a0S1, S2).\n\nThese models combined naturalization incidence (i.e., whether or not a species has become naturalized; Bernoulli distribution) and naturalization extent (i.e., the number of non-native regions where the species has become naturalized; zero-truncated negative binomial distribution). To illustrate how global naturalization success depends on early occupancy in the native region, the data points are colored according to whether they are in the upper, middle or lower third of the early occupancy distribution. Accordingly, the predicted relationships are plotted for early occupancy values set equal to the 5/6th quantile, the median and the 1/6th quantile. Significant relationships between global naturalization success and occupancy change (either for the Bernoulli or zero-truncated count part) are plotted with solid lines, and non-significant relationships are plotted in dashed lines. The regions for which the interaction between early occupancy and occupancy change was significant are marked with an asterisk (*) next to the region names. Source data are provided as a Source Data file.\n\nSpecies with high occupancy-change indices also had a higher likelihood of being naturalized for seven of the 10 native regions and were naturalized in more regions globally for nine of the 10 native regions (Table\u00a02, Fig.\u00a02, Fig.\u00a0S1, S2). The Thi\u00e9rache region was the only region where the occupancy-change index was not significantly associated with any of the two components of global naturalization success (Table\u00a02, Fig.\u00a02j, Fig.\u00a0S1j, S2j). So, overall, our findings indicate that species that have increased in occupancy in their native regions relative to other species with similar early occupancy are also more likely to have become widely naturalized. For three of the 10 native regions (the Czech Republic, the Netherlands and Switzerland), the positive association between global naturalization success (either measured as the likelihood of being naturalized or as being naturalized in more regions) and the occupancy-change index was strongest for species that already had high occupancies in the early period (significant early occupancy \u00d7 occupancy change interactions in Table\u00a02, Fig.\u00a02b, h, i, Fig.\u00a0S1b, h, i, S2h, i). These results thus indicate that recent increases in occupancy within these three native regions are particularly strongly associated with global naturalization success for species that were already common decades ago.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63293-6/MediaObjects/41467_2025_63293_Fig2_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our analyses show that global naturalization success is highest not only for species that already had high occupancies in their native European regions decades to centuries ago \u2014when many of them were first introduced to new regions19 \u2014 but also for species that have since then increased in occupancy within their native regions (Table\u00a02). Furthermore, for three of the 10 native regions, the positive association between global naturalization success and the occupancy-change index was particularly pronounced for species that had already a high occupancy in the native region during the early time period (Table\u00a02, Fig.\u00a02b, h, i). Our results thus show that many species that have increased their occupancy within their native regions have also increased their ranges abroad, whereas species that are declining in their native regions are less likely to successfully naturalize elsewhere. This strongly suggests that global naturalization success and the changes of species\u2019 native distributions are driven, at least in part, by similar processes.\n\nSpecies with high occupancies in their native regions decades ago (i.e., in the early period) were more likely to naturalize and to do so in many regions around the globe. This could reflect that such common species were more likely to encountered, transported and introduced elsewhere22,25. It could also reflect that the selective pressures that have made certain species common in their native regions also have preadapted these species for success as invaders. In order to be able to occur at many locations in its native region, a species has to be able to coexist with a variety of biotas and persist in a wide range of climatic and other environmental conditions42. Therefore, species with high occupancies in their native regions are likely to be ecologically versatile, which should also increase the likelihood of naturalization when introduced elsewhere25,43. Furthermore, species that are common in their native regions usually have high dispersal abilities26,44 and are capable of autonomous self-fertilization45, which are characteristics that also facilitate spread within regions where they are not native35,46. For most of the 3920 species in our datasets, information on these traits is not available, so we could not test their importance. However, as woodiness of species is indicative of their growth forms as well as habitat affiliation and is available for all species in our 10 datasets, we ran additional analyses for this variable (see Supplementary Methods for details). While woodiness was not significantly associated with early occupancy in any of the 10 native regions (Table\u00a0S22), it was positively associated with occupancy change in six native regions (Table\u00a0S23). The latter might reflect that many woody species are very long lived and therefore might, in contrast to non-woody species, still occur in grid cells even when the environment is no longer optimal. Inclusion of woodiness in the hurdle models slightly increased the explained variation in global naturalization success (Table\u00a0S24), but did not change the overall conclusions regarding the effects of early occupancy and occupancy change (compare Table\u00a02 and Table\u00a0S1). When woody species did naturalize, they did so in fewer regions than non-woody species, and this was significant for eight of the 10 datasets (Tables\u00a0S1, S2\u2013S11). Similarly, Dong et al.29 recently showed that among introduced plants in China, woody species were less likely to naturalize than non-woody species. Woody species, particularly trees, have longer juvenile periods and/or are probably introduced in lower numbers than non-woody species, and therefore require more time before they spread into multiple regions. Whatever the exact reason, the additional analyses including woodiness indicate that not all of the characteristics associated with species that increase in their native regions are also associated with species that have become widely naturalized globally.\n\nNumerous studies have analyzed how naturalization relates to static measures of commonness in species\u2019 native distributions, such as native range size and grid-cell occupancy e.g.21,34,47. However, we are not aware of any previous study that also considered temporal changes in measures of commonness within native regions. In line with the idea that the drivers of expansion in native regions and naturalization elsewhere should be largely the same, we found that the global naturalization success of species\u2014measured either as naturalization incidence or extent\u2014was positively associated with occupancy change in nine of the 10 native regions for which we had data (Fig.\u00a02). Although these associations were highly significant, and inclusion of occupancy change increased the explained variation in naturalization success (Table\u00a0S24), the standardized effect estimates were always smaller for the occupancy-change index than for occupancy in the early period (Tables\u00a0S12-S21). The association between occupancy-change and global naturalization success, however, may be an underestimate because many species may already have started to change in occupancy within their native regions before the early census periods, most of which were in the second half of the 20th century. Additionally, for species that occurred in almost all grid cells of the early period, an increase in occupancy was hardly possible (Fig.\u00a0S3, Table\u00a0S25). So, although such common species may also be widely naturalized, they would neverthless have low occupancy-change values. This would have weakened the strength of the association between global naturalization success and the occupancy-change index. In addition, it might also have reduced the likelihood of detecting synergistic effects between early occupancy and occupancy change, as was found for the Czech Republic, the Netherlands and Switzerland. Therefore, the associations between global naturalization success and native\u00a0range occupancy change might be even stronger than indicated by our analyses.\n\nSpecies that have been reported previously to increase in occurrence frequencies within their native regions have been found to be adapted to disturbed and anthropogenic habitats48, to be strong competitors and to have a preference for nutrient-rich habitats6,11,27. Similarly, a higher naturalization success has been reported for species that grow in anthropogenic, nutrient-rich and more productive habitats in their native regions24,49. It has also been shown that naturalized and invasive species frequently capitalize more on increases in nutrient availability than less successful alien species50,51. The similar characteristics of species that have become more common in their native regions and those that have successfully naturalized elsewhere strongly suggests that similar processes drive both phenomena. The Thi\u00e9rache region in northern France (Table\u00a02, Fig.\u00a02j, Fig.\u00a0S1j, S2j) was the only region in which the change in occupancy was not significantly associated with global naturalization success. This might reflect that Thi\u00e9rache was the smallest region with the\u00a0fewest number of species, and that the early period was not in the second half of the 20th century, but at the end of the 19th century. Although the latter would better capture the occupancies of species prior to the Great Acceleration (i.e., the period of marked increases in human activity, which started in the mid-20th century), the occupancies in the early period for the Thi\u00e9rache were coarse estimates based on verbal descriptions of the commonness of the species10. Consequently, the data for Thi\u00e9rache were less precise than for the other nine regions, and the analysis had less statistical power because of the fewer number of species.\n\nThe relative consistency between the Bernoulli and zero-truncated count parts of the hurdle models suggests that species with high early occupancies and occupancy-change values were both more likely to naturalize and to do so in many regions. Furthermore, with the exception of Thi\u00e9rache, our results were also consistent across the native regions, despite the large variation in time periods and intervals covered by the datasets (Table\u00a01). This indicates that our results are robust. For Great Britain and Ireland, the original data sources actually provided occupancy data for three different periods (see Supplementary Methods for details). However, irrespective of which period was assigned as an early or later period for calculating the occupancy-change index, the results were largely similar (Tables\u00a0S26, S27, S29, S30). For the Netherlands, we extracted occupancy data using two different years of split. In Fig.\u00a02 and Table\u00a02, we\u00a0present the data for the periods before and after 1990, but if we instead used data for the periods before and after 2000, the results were generally the same (Table\u00a0S32). Furthermore, when we added woodiness and its interactions with early occupancy and occupancy change to the models, the results were, with a few deviations, largely the same (Tables\u00a0S1, S2\u2013S11). The main deviations were for Flanders and Germany, in which the positive main effect of occupancy change on the likelihood of being naturalized was no longer significant, but the interaction between occupancy change and woodiness was significant (Table\u00a0S5, S6). This suggests that for these two native regions the positive effect of occupancy change on the likelihood of naturalization is mainly accounted for by the woody species. Nevertheless, the additional analyses show that the positive associations of global naturalization success with early occupancy and occupancy change in the native regions are robust.\n\nOverall, our analyses show that species with high values of both early occupancy and occupancy change in their native regions are also successful as naturalized species globally. This suggests that data on occupancy and changes therein in the native range could inform invasion\u00a0risk assessments. However, it would ultimately be interesting to unravel which characteristics distinguish the group of widespread and expanding species from other species in the native range. For most traits, data are available for only a small proportion of the global flora28. Indeed, a recent analysis showed that there are only 10 traits with data available for more than 50 percent of naturalized species52. As mentioned above, a notable exception is woodiness, which we therefore also included in an alternative set of hurdle models (Tables\u00a0S1 and S2\u201311). Still, as previous studies found that expanding species are typically strong competitors that take advantage of additional resources, we ran additional analyses using Grime\u2019s CSR strategy53 and Ellenberg environmental indicator\u00a0values54 for the subsets of species for which these data were available (see Supplementary Methods for details). We found that the competitor scores of the species with high values of both early occupancy and occupancy change were significantly higher than those of all other species in nine of the 10 native regions (Fig.\u00a0S4, Table\u00a0S33), whereas the stress-tolerator and ruderal scores were frequently lower (Fig.\u00a0S5, S6, Table\u00a0S33). Furthermore, widespread and expanding species had significantly lower Ellenberg indicator values for light (Fig.\u00a0S7, Table\u00a0S34), and higher Ellenberg indicator values for nutrients (Fig.\u00a0S8, Table\u00a0S34), in all 10 native regions. On the other hand, indicator values for moisture (Fig.\u00a0S9, Table\u00a0S34) and temperature (Fig.\u00a0S10, Table\u00a0S34) differed significantly between the two groups of species only in some of the native regions, and not in a consistent pattern. Overall, these supplementary analyses align with the findings of previous studies that competitively strong species preferring nutrient-rich habitats tend to be widespread and have recently further increased in occurrence frequencies within their native regions6,11,27.\n\nA strength of our study is that we had multiple datasets on temporal changes in the occupancy of native species. However, the data also comes with limitations that should be kept in mind when interpreting the results. First, we only found suitable data for calculating the occupancy-change index for regions in central and north-western Europe. This means that we cannot generalize our findings to species that are native to other continents or to their whole native range. On the other hand, given that Europe is one of the major donors of naturalized plants globally55, the anthropogenic changes that drive the naturalization of European plant species in other continents are likely to similarly affect the occupancy change of native species in these other continents. As many regions around the globe now have plant\u00a0distribution atlas data e.g.56,57, (https://anpsa.org.au/, https://plants.usda.gov/, https://data.canadensys.net/), future reassessments of these distributions will allow for the calculation of occupancy changes in these regions. Second, there might be biases in the recording of different species and in the intensity of recording in different parts of the same region. Moreover, it could be that some species are increasing in part of their native distribution, for example in high-latitude regions, but decreasing in other parts of their native distribution, for example in low-latitude regions. Nevertheless, for species present in multiple datasets, the occupancy-change indices were generally positively correlated between the regions (of the 44 pairwise Pearson correlation coefficients, 41 were significantly positive and only two were significantly negative; Fig.\u00a0S11). This indicates that the species that are increasing or decreasing in one of their native regions usually also do so in the other ones.\n\nRabinowitz36 proposed three dimensions of rarity and commonness \u2014geographic range, habitat specificity and local abundance. Here, we only considered occupancy within native regions, which is arguably only one component of Rabinowitz\u2019s geographic range dimension. However, Crisfield et al.37 recently proposed to add occupancy as another dimension and to remove habitat specificity as a dimension, because the latter is rather a cause of rarity. Native geographic range size is unlikely to have dramatically changed for most species, at least not when quantified as the number of regions (mostly countries) in which a species is native. In our 10 datasets, early occupancy was always positively correlated with native range size (Pearson r\u2009=\u20090.182-0.404, all P\u2009<\u20090.001, Table\u00a0S35). Local abundance, on the other hand, is likely to have changed for many species in the last decades due to anthropogenic environmental change. The local abundance of species is usually positively related to the other measures of a species\u2019 distribution e.g.58,59. Previous studies across various spatial scales and taxonomic groups have also shown that species increasing their occupancy are also very likely to increase in abundance at the sites where they occur60,61. However, quantitative data for changes in local abundances for large numbers of species in areas comparable in size to our 10 datasets are rare. A notable exception is a recent study by Jandt et al. 62, who analyzed data on changes in the local abundances (cover) of plant species in vegetation plots in Germany over the period 1927\u20132020. The change in local abundance was positively correlated with the occupancy-change index that we calculated for the native species in Germany (Pearson r\u2009=\u20090.185, n\u2009=\u20091214, P\u2009<\u20090.001). This suggests that, overall, species that have become more widespread in Germany have also become locally more abundant. Thus, when more such data become available from resurveys of vegetation plots63, it will be worthwhile to look at the interactive effect of changes across different dimensions of commonness in future studies.\n\nIn conclusion, our analyses provide strong evidence that many plant species that are spreading as naturalized aliens around the globe also have high occupancies and/or are increasing in occupancy in their native regions. These findings have several major implications. First, if both phenomena are largely driven by anthropogenic environmental changes and the species\u2019 characteristics that preadapt them to these changes, this could explain why studies that compared widely naturalized aliens with widespread native species did not find differences in e.g., their responses to nutrient addition64, soil heterogeneity65 and competitive abilities66. Second, although the objective of our study was not to build a predictive model of drivers of plant naturalization, our findings suggest that measures of commonness and changes therein in the native regions could provide important insights into the likelihood that these species may naturalize after introduction elsewhere. While national assessment schemes for potential invasion risk, such as the Australian weed\u00a0risk assessment67, consider whether the assessed alien species is known to be naturalized or invasive in other regions, these risk assessment schemes do not yet consider commonness and its dynamics in the species\u2019 native regions. Considering these factors might help policymakers and managers enhance the accuracy and effectiveness of invasion risk assessments, ultimately leading to more informed and proactive conservation strategies.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "As a metric of commonness within species\u2019 native regions, we used occupancy (i.e., the proportion of grid cells across a region in which a species has been recorded). To obtain datasets on native species\u2019 occupancies for multiple distinct periods, we searched the scientific literature and online plant atlases and contacted curators of national plant species distribution databases. We found such data for 10 European regions (countries or parts thereof): Austria (number of native species: n\u2009=\u20092419), the Czech Republic (n\u2009=\u20091834), southeastern Denmark (n\u2009=\u2009921), Flanders (including the capital region of Brussels) in Belgium (n\u2009=\u2009861), Germany (n\u2009=\u20091715), Great Britain (including the Isle of Man and the Channel Islands) (n\u2009=\u20091355), Ireland (including the Republic of Ireland and Northern Ireland) (n\u2009=\u2009910), the Netherlands (n\u2009=\u20091115), Switzerland (n\u2009=\u20092307) and the Thi\u00e9rache region in northern France (n\u2009=\u2009775) (Fig.\u00a01, Table\u00a01). Across the\u00a010 regions, there were 3920 unique taxa, with 288 occurring in all 10 regions, 1261 present in only a single region, and the remaining ones shared across combinations of two to nine regions (Table\u00a0S36). Although some are subspecies or varieties, we refer to all taxa as species for simplicity.\n\nFor each native species in the 10 datasets (i.e., native regions), we extracted the number of grid cells where it has been recorded in two or more distinct time periods, where each period usually lasted multiple years or decades (Table\u00a01). Most of the data sources provide data for only two time periods, limiting us to the temporal splits available in the original datasets. However, for Great Britain and Ireland, the data sources provide data for three time periods. In addition, for the Netherlands, we could extract grid-cell numbers from the original data source choosing different years of split. We chose 1990 and 2000, as these years were also used by some of the other datasets. To see how robust the results were with regard to the chosen year of split, we analyzed the data for Great Britain, Ireland and the Netherlands using multiple splits.\n\nThe regions varied in the number of species, the early and later periods, the durations of these periods and the interval between the periods, as well as in the size and total number of grid cells (Table\u00a01). For example, for the Thi\u00e9rache region, which had data on 775 native species and their occurrences in 129 grid cells of 4\u2009\u00d7\u20094\u2009km, the early and later periods were 1880\u20131900 and 1957\u20132005, whereas for Germany, which had data on 1715 native species and their occurrences in 12,024 grid cells of 5\u2032 \u00d7 3\u2032, the early and later periods were 1960\u20131987 and 1997\u20132017. For most of the native regions, the data were actual grid-cell frequency counts, and they covered the entire region. However, for the Thi\u00e9rache region, the grid-cell frequencies for the early period were only coarse estimates based on verbal descriptions of how widespread the species were during that period10. Furthermore, for southeastern Denmark, the data do not cover one contiguous region but 11 subregions that do not all border one another (Fig.\u00a01). The data source for southeastern Denmark7 provides regional abundance data for each of the 11 subregions, where each species\u2019 abundance in a subregion was calculated by dividing the number of grid cells occupied by the species by the total number of grid cells for the subregion. From these data, we back-calculated the numbers of grid cells occupied by a species in each subregion, and then combined them across the 11 subregions to get one single occupancy value. Further details on the data for each of the 10 native regions are provided in the Supplementary Methods.\n\nTo select native plant species for inclusion in the final datasets and to harmonize the taxonomic names, we applied one common workflow to all 10 datasets. First, we excluded species that, according to the original data source, are not native to the respective region. When the native status was not provided or not entirely clear, we checked the native status of the species in the corresponding region in the Plants of the World Online database (POWO; https://powo.science.kew.org/ accessed in April 2023). Second, we harmonized the species names according to the taxonomic backbone of the World Checklist of Vascular Plants (WCVP version 11, which is integrated in POWO). This was done in R version 4.2.368, initially with the TNRS package version 0.3.369, and later, after it became available, the rWCVP package version 1.0.370. Species that did not have exact matching names in WCVP version 11 or that had multiple matching names were checked manually, and corrections were made when necessary. Species that did not match an accepted name were removed from the datasets. If multiple species in the original dataset were assigned to the same accepted name in WCVP, we kept the one that had the largest number of occupied grid cells. We could not merge the grid cell data because most datasets only provide the number of occupied grid cells, and not the identities of the grid cells occupied by each species.\n\nTo calculate an occupancy-change index, defined as the change in the proportion of grid cells between the two time periods covered by the data, relative to the expected change based on the occupancy in the early period (i.e., early occupancy), we followed the method developed by Telfer et al.41. We chose this method because the resulting occupancy-change index corresponds to the residuals of a weighted regression of the logit-transformed occupancy in the later period on the logit-transformed occupancy in the early period. As a consequence, the occupancy-change index is, compared to other change indices, less sensitive to differences in collection effort between the early and later periods. This is because the index does not quantify the absolute change but quantifies how much larger or smaller the magnitude of a species\u2019 occupancy change is relative to species with the same early occupancy. Another advantage is that the occupancy-change index is not correlated with early occupancy, and therefore does not cause multicollinearity issues when both are included in the same statistical model. However, like for any other occupancy-change index, we cannot rule out the possibility that the accuracy of recording certain groups of species might have changed for some regions between the periods.\n\nFollowing the protocol of Telfer et al.41, we first calculated the proportion of occupied grid cells in a native region for each species in each time period as (x\u2009+\u20090.5)/(n\u2009+\u20091), where x is the number of grid cells in which the species has been recorded and n is the total number of grid cells in the respective region. As recommended by Telfer et al.36, we added 0.5 to x and 1 to n to avoid proportion values of zero and one. This was necessary because in the second step, the proportion values were logit transformed, and the logit of zero would be undefined and the logit of one would be infinite. Next, we performed a weighted least-squares linear regression of the logit-transformed proportions of occupied grid cells during the later period as a function of the logit-transformed proportion of occupied grid cells during the early period. The weights were equal to the reciprocal of the variance of the logit-transformed proportions. Data visualization using scatterplots showed that species with low occupancy proportions in the early period deviated from the linear relationship. Therefore, as recommended by Telfer\u00a0et al.41, we excluded species occupying fewer than five grid cells during the early period. The resulting regression plots are shown in Fig.\u00a0S3. The values of the occupancy-change index then correspond to the standardized residuals (i.e., the deviations of the logit-transformed proportions of grid cells occupied by the species in the later period from the regression line). Therefore, for each early occupancy value, we have a more or less symmetrical distribution of negative and positive occupancy-change index values (i.e., standardized residuals; Fig.\u00a0S3). It should be noted that a positive value of the change index does not necessarily mean that the occupancy of a species has increased between the two periods. Instead, it means that the species occupied a higher proportion of grid cells in the later period than expected based on its occupancy in the early period.\n\nTo quantify global naturalization success of each species in the 10 native\u00a0region datasets, we used the Global Naturalized Alien Flora (GloNAF) database. GloNAF is the most comprehensive compendium of lists of naturalized alien vascular plant species for regions around the globe [18, provisionally accepted] and is continuously being updated. We used version 2.0 (extracted in January 2024), with data for 920 non-overlapping regions around the globe, including both mainland regions and islands (Fig.\u00a0S12). GloNAF regions mostly follow geopolitical boundaries, including countries, states, provinces and individual islands. The GloNAF regions range in size from 0.045 to 2336618\u2009km2 (the median size is 34382.71\u2009km2). Global naturalization of a species was quantified as the number of GloNAF regions in which it has naturalized, which is strongly correlated with the cumulative area of these regions15. As the species names in GloNAF version 2.0 follow the WCVP taxonomic backbone, the species names in the native occupancy datasets could be directly matched to the GloNAF database.\n\nBecause the datasets of the 10 regions differed in many aspects (Table\u00a01), we\u00a0analyzed each region separately. The separate analyses also ensure that we only make comparisons of changes in occupancy of species that occurred over the same time-period interval. Global naturalization of a species, quantified as the number of GloNAF regions in which the species is naturalized, was the response variable in our analyses. Because large proportions of the species in the 10 datasets have not naturalized in any region (median proportion: 0.300, range: 0.108\u20130.443), the count data are zero inflated. Therefore, we analyzed global naturalization success as a combination of naturalization incidence (naturalized vs. not naturalized) and naturalization extent (the number of regions where naturalized) using a hurdle model71 with the hurdle function of the pscl package version 1.5.571 in R. The hurdle model consisted of a generalized linear model (GLM) with a Bernoulli distribution with the logit link function for naturalization incidence (comparing the non-zeros to the zeros), and a GLM with a zero-truncated negative binomial distribution with the log link function for naturalization extent. We used a negative binomial distribution for the zero-truncated count part instead of a Poisson distribution to account for overdispersion. All the statistical tests in the hurdle model were two-sided. In both parts of the model, we included occupancy in the early time period (i.e., early occupancy; EO) the occupancy-change index (i.e., OC) and their interaction as predictor variables. Early occupancy and its interaction with the occupancy-change index were included in the model to test whether species that were widespread within the native regions decades ago were more likely to become naturalized elsewhere, and whether this is particularly true when these widespread species have further increased in native\u00a0region occupancy. To illustrate whether the relationship between global naturalization success and occupancy change varied depending on the occupancy in the early period, we fitted the predicted relationship between naturalization success (as a combination of the Bernoulli and zero-truncated count part) and the occupancy-change index for the median value of early occupancy, as well as for the 1/6th quantile (i.e., species that were relatively rare in the early period) and 5/6th quantile (i.e., species that were relatively common in the early period; Fig.\u00a02, Fig.\u00a0S1, S2). To facilitate interpretation and comparison of model estimates across the\u00a010 native regions, early occupancy was scaled to a mean of zero and a standard deviation of one72. This was not necessary for the occupancy-change index because it corresponds to standardized residuals, which are already scaled to a mean of zero and a standard deviation of one.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All the datasets used in this study have been deposited in the Figshare database under the https://doi.org/10.6084/m9.figshare.2548720973.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The R code used for the statistical analysis is available in Code Ocean with the identifier https://doi.org/10.24433/CO.1618280.v1 and in the Figshare database under the\u00a0identifier https://doi.org/10.6084/m9.figshare.2548720973.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Levin, D. 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M.C. and J.D. were supported by the Czech Science Foundation (project no. 19-28491X). K.G. and W.Y.G. acknowledge funding from the Natural Science Foundation of China (grant no. 32301386 to K.G. and 32171588 and 32471676 to W.Y.G.). P.P. and J.P. were supported by EXPRO grant no. 19-28807X (Czech Science Foundation) and, together with J.D., Z.K. and J.W., by long-term research development project RVO 67985939 (Czech Academy of Sciences). F.E. acknowledges funding from the Austrian Science Foundation FWF (Global 458 Plant Invasions, grant no. I 5825-B). H.K. acknowledges funding of research unit FOR2716 DynaCom (379417748) and Biodiversa+ BioMonI (533271599) from the German Research Foundation (DFG). M.W. acknowledges funding from the German Research Foundation (via iDiv, FZT 118, 202548816). We thank Fang-Wei Lin for help with data extraction. This Open Access publication was supported by the Publication Fund of the University of Konstanz.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Ecology, Department of Biology, University of Konstanz, Konstanz, Germany\n\nRashmi Paudel,\u00a0Trevor S. Fristoe,\u00a0Nicole L. Kinlock,\u00a0Amy J. S. Davis,\u00a0Weihan Zhao\u00a0&\u00a0Mark van Kleunen\n\nInternational Max Planck Research School for Quantitative Behaviour, Ecology and Evolution (IMPRS-QBEE), Max Planck Institute of Animal Behaviour, Konstanz, Germany\n\nRashmi Paudel\u00a0&\u00a0Weihan Zhao\n\nDepartment of Biology, University of Puerto Rico \u2013 Rio Piedras, San Juan, Puerto Rico\n\nTrevor S. Fristoe\n\nBiometry, Methodology and Quality Assurance, Research Institute for Nature and Forest, Brussel, Belgium\n\nHans Van Calster\n\nDepartment of Botany and Zoology, Faculty of Science, Masaryk University, Brno, Czech Republic\n\nMilan Chytr\u00fd\u00a0&\u00a0Ji\u0159\u00ed Danihelka\n\nDepartment of Taxonomy, Institute of Botany, Czech Academy of Sciences, Pr\u016fhonice, Czech Republic\n\nJi\u0159\u00ed Danihelka\u00a0&\u00a0Zden\u011bk Kaplan\n\nEcologie et Dynamique des Syst\u00e8mes anthropis\u00e9s (UMR CNRS 7058 EDYSAN), University of Picardie Jules Verne, Amiens, France\n\nGuillaume Decocq\n\nDepartment of Botany and Biodiversity Research, University of Vienna, Vienna, Austria\n\nLuise Ehrendorfer - Schratt\u00a0&\u00a0Franz Essl\n\nState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China\n\nKun Guo\n\nZhejiang Tiantong Forest Ecosystem National Observation and Research Station, Institute of Eco-Chongming, Research Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China\n\nWen-Yong Guo\n\nZhejiang Zhoushan Island Ecosystem Observation and Research Station, Zhoushan, China\n\nWen-Yong Guo\n\nState Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China\n\nWen-Yong Guo\n\nDepartment of Botany, Faculty of Science, Charles University, Prague, Czech Republic\n\nZden\u011bk Kaplan\n\nDepartment of Agricultural and Environmental Sciences (DiSAA), University of Milan, Milan, Italy\n\nSimon Pierce\n\nDepartment of Geoecology, Institute of Botany, Czech Academy of Sciences, Pr\u016fhonice, Czech Republic\n\nJan Wild\n\nDepartment of Evolution, Ecology and Behaviour, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK\n\nWayne Dawson\n\nCentre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Stellenbosch, South Africa\n\nFranz Essl\n\nBiodiversity, Macroecology and Biogeography, University of G\u00f6ttingen, G\u00f6ttingen, Germany\n\nHolger Kreft\n\nCentre of Biodiversity and Sustainable Land Use (CBL), University of G\u00f6ttingen, G\u00f6ttingen, Germany\n\nHolger Kreft\n\nCampus-Institut Data Science (CIDAS), University of G\u00f6ttingen, G\u00f6ttingen, Germany\n\nHolger Kreft\n\nDepartment of Invasion Ecology, Czech Academy of Sciences, Institute of Botany, Pr\u016fhonice, Czech Republic\n\nJan Pergl\u00a0&\u00a0Petr Py\u0161ek\n\nDepartment of Ecology, Faculty of Science, Charles University, Prague, Czech Republic\n\nPetr Py\u0161ek\n\nGerman Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany\n\nMarten Winter\n\nZhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, Taizhou University, Taizhou, China\n\nMark van Kleunen\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nM.v.K. designed research; R.P. performed research; M.v.K & R.P. compiled the data for calculating native range occupancy change; H.V.C., M.C., J.D., G.D., L. E-S., K.G., W.Y. G., Z.K., S.P., J.W., W.D., F.E., H.K., J. P., P.P., M.W. & M.v.K. contributed data; R.P., T.S.F., N.L.K., A.D., W.Z. & M.v.K. prepared the data; R. P. & M.v.K analyzed data; R.P., M.v.K. and T.S.F. wrote the first paper draft and all other coauthors contributed to writing.\n\nCorrespondence to\n Rashmi Paudel.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "All the authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Dov Sax and the other anonymous reviewer(s) for their contribution to the peer review of this work. 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Many plants naturalized as aliens abroad have also become more common within their native regions.\n Nat Commun 16, 8227 (2025). https://doi.org/10.1038/s41467-025-63293-6\n\nDownload citation\n\nReceived: 27 March 2024\n\nAccepted: 15 August 2025\n\nPublished: 05 September 2025\n\nVersion of record: 05 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63293-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"Influence of individual models and studies on quantitative mitigation findings in the IPCC Sixth Assessment Report", + "journal": "Nature Communications", + "published": "02 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64091-w/MediaObjects/41467_2025_64091_MOESM1_ESM.pdf" + }, + { + "label": "Description of Addtional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64091-w/MediaObjects/41467_2025_64091_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1-2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64091-w/MediaObjects/41467_2025_64091_MOESM3_ESM.zip" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64091-w/MediaObjects/41467_2025_64091_MOESM4_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-64091-w#ref-CR3" + ], + "code": [ + "/articles/s41467-025-64091-w#ref-CR45" + ], + "subject": [ + "Climate-change mitigation", + "Climate-change policy", + "Research management", + "Socioeconomic scenarios" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5698716/v1.pdf?c=1759489766000", + "research_square_link": "https://www.researchsquare.com//article/rs-5698716/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-64091-w.pdf", + "preprint_posted": "13 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Quantitative mitigation findings based on emissions scenarios submitted to the Intergovernmental Panel on Climate Change (IPCC) play an authoritative role in climate policy and decision making. We analyse the impact of the uneven representation of models and modelling studies in the IPCC Sixth Assessment Report (AR6) on statistical values that are used to present quantitative mitigation findings. We find that several key AR6 findings are influenced considerably by the model with the most scenarios, including emissions reductions by 2030 and the decline in fossil fuels consistent with 1.5\u00b0C, and we find that the year of net-zero greenhouse gas emissions is influenced considerably by both the model and the study with the most scenarios. We find that weighting by model- or study does not provide a straightforward solution and discuss three issues related to the use of database statistics to present emissions scenarios findings. Based on our findings and informed by the purpose of the IPCC and the kinds of insights that can be obtained from emissions scenarios, we suggest improvements to the current practices used in IPCC assessments.Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigationEarth and environmental sciences/Environmental social sciences/Climate-change mitigationEarth and environmental sciences/Environmental social sciences/Climate-change policyEarth and environmental sciences/Environmental social sciences/Socioeconomic scenariosScientific community and society/Scientific community/Research management", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SINCom20241223.pdfSupplementary Information to \u2018Influence of individual models and studies on quantitative mitigation findings in the IPCC Sixth Assessment Report\u2019SupplementaryData1.xlsxSupplementary Dataset 1SupplementaryData2.xlsxSupplementary Dataset 2", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Quantitative mitigation findings based on emissions scenarios submitted to the Intergovernmental Panel on Climate Change (IPCC) play an authoritative role in climate policy and decision making. We analyse the impact of the uneven representation of models and modelling studies in the IPCC Sixth Assessment Report (AR6) on statistical values that are used to present quantitative mitigation findings. We find that several key AR6 findings are influenced considerably by the model with the most scenarios, including emissions reductions by 2030 and the decline in fossil fuels consistent with 1.5\u2009\u00b0C, and we find that the year of net-zero greenhouse gas emissions is influenced considerably by both the model and the study with the most scenarios. We find that weighting by model- or study does not provide a straightforward solution and discuss three issues related to the use of database statistics to present emissions scenarios findings. Informed by the purpose of the IPCC and the kinds of insights that can be obtained from emissions scenarios, we suggest improvements to the assessment of emissions scenarios.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Quantitative findings in the Intergovernmental Panel on Climate Change (IPCC) Working Group III (WGIII) reports on climate mitigation rely strongly on scenarios in the IPCC scenarios databases1,2,3,4,5,6. Descriptive database statistics, including median values and interquartile or 5th-95th percentile ranges, are used to report key findings, including emissions reductions over time, the year of net-zero emissions, and the reductions in fossil fuels consistent with different temperature targets2. These findings play authoritative roles and have been used to inform climate negotiations and climate policy at international and national levels7. Increasingly, they are also used by other actors, including local governments, private companies, banks, and financial regulators to inform net-zero strategies8, evaluate alignment with climate targets8, and to assess and disclose climate-related financial risks8,9,10.\n\nThe emissions scenarios in the IPCC scenarios databases are generated almost exclusively by process-based Integrated Assessment Models (IAMs). The collection is based on the voluntary submission of scenarios that have been published in individual modelling studies or in multi-model studies11,12. Because some modelling groups publish and submit more scenarios, the number of scenarios from different models and studies in the database is not even12,13. For this reason, the IPCC scenarios databases are referred to as \u201censembles of opportunity\u201d14. In the Sixth Assessment Report (AR6), the four models with the most scenarios are responsible for two-thirds of the scenarios that passed vetting and received a climate assessment, and the study with the most scenarios is responsible for almost half of the scenarios (Fig.\u20091).\n\na Number of scenarios by model. b Number of scenarios by study. All the scenarios that passed vetting and received a climate assessment are shown (1202 scenarios, out of 2304 submitted global scenarios12). Models are grouped into \u2018model families\u2019 representing potentially several versions of a single model core. The studies cover all scenarios in that study, though often from single papers. Scenario-based mitigation findings in the AR6 WGIII report are based on these scenarios (see \u201cMethods\u201d). Data: IPCC AR6 Scenarios Database3.\n\nBecause scenario outcomes depend on model and study assumptions15,16,17,18, median values and ranges may be sensitive to the sampling of models and studies in the database. Several recent studies have found model differences to be an important driver of scenario outcome variation19,20, and have identified distinct \u2018model fingerprints\u201921. Yet, the impacts of the uneven representation of models and studies on findings presented in the IPCC reports have not yet been quantified. Although it is noted in Chapter 3 of the IPCC WGIII report that uncorrected database statistics may be misleading11, such statistics are still used for headline AR6 findings.\n\nWe first analyse the influence of individual models and studies on database statistics that are used to report key findings in the AR6 WGIII Summary for Policymakers22 (SPM). Secondly, we assess the impact of dominant models on median outcomes in the AR6 scenarios database overall. Third, we compute model- and study-weighted medians and show that the results depend on the choice of weighting, in addition to the representation of models and scenarios. Finally, based on our findings, we discuss more fundamental issues with the use of database statistics to present emissions scenarios findings. Informed by the purpose of the IPCC and of emissions scenarios, we suggest ways forward for the IPCC authors to improve the assessment and communication of emissions scenarios findings.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64091-w/MediaObjects/41467_2025_64091_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "To assess the influence of individual models and studies on key AR6 findings, we calculate the impact on findings reported in the WGIII SPM from removing each model and study one-by-one. WGIII SPM findings are reported using primarily median values, with either interquartile or 5-95th percentile ranges in parenthesis (see e.g., SPM Table SPM.2, or SPM paragraphs C.1.2 or C.3.2 in the AR6 WGIII report). Since the WGIII SPM is focused on scenarios that achieve 1.5\u2009\u00b0C without overshoot (C1 category) and 2\u2009\u00b0C (C3 category), we focus on 1.5\u2009\u00b0C without overshoot and use 2\u2009\u00b0C scenarios as a reference to assess the magnitude of the impacts. We analyse a total of 27 WGIII SPM findings (see Methods for the selection) and measure the impact of each individual model and study in two different ways: First, we measure the impact of removing the individual model and study on the 1.5\u2009\u00b0C median relative to the difference between the 1.5\u2009\u00b0C and 2\u2009\u00b0C medians, and second, we measure how close the 1.5\u2009\u00b0C median is to the individual model and study relative to the median of\u00a0all the other models and studies. The first measure captures the impact of scenario sampling compared to the impact of switching from one climate target (1.5\u2009\u00b0C) to another (2\u2009\u00b0C). The second measure captures the influence of individual models and studies relative to all other models and studies within the same climate category (see Methods for details).\n\nThe impact on median values is substantial for several key AR6 findings (Fig.\u20092, Table\u20091). From removing just one model from the AR6 ensemble, median 2030 greenhouse gas (GHG) reductions in 1.5\u2009\u00b0C scenarios (relative to 2019)\u2014a widely recognised target23 written into the 2022 Sharm el-Sheikh Implementation Plan24\u2014 shifts from 43% to 50%. Median 2030 CO2 reductions shift from 48% to 56%. Median coal and gas reductions in 2050 shift from 95% to 83% and 43% to 29%, respectively. Net-zero GHG year shifts from 2098 to 2086 when removing one model, to 2084 when removing one study, and to after 2100 (the year of net-zero GHG is not specified in these scenarios) when removing several other models and studies.\n\nChanges in median values for a Year of net zero GHG emissions by model, b Year of net zero GHG emissions by study, c GHG emissions in 2030 by model, d Coal in 2050 by model, and e Gas in 2050 by model. Boxes show the minimum and maximum, the interquartile ranges, and the median of each model/study. The number of scenarios from each model/study is shown at the top of each box, and the data points are shown to the left. Models and studies are ordered according to the number of scenarios, with the model/study with the most scenarios furthest to the left. Long, solid horizontal lines show median values when models (orange) and studies (yellow) are removed one-by-one, with letters at the end of each line indicating the model/study that has been removed. Bolded and starred letters show the model/study whose removal leads to the biggest shift in the median value. Dashed grey horizontal lines show ensemble medians. The findings are selected based on a combination of policy relevance and impact (shown in Table\u20091) to illustrate how individual models and studies affect median values (more findings are shown in Supplementary Figs.\u20091\u20133). All variables are from scenarios that limit global warming to 1.5\u2009\u00b0C (>50%) (C1 category). Values above 2100 on the y-axis indicate \u2018after 2100\u2019, which means zero was not reached before 2100 (and may not be reached). The model acronyms are R: REMIND, M: MESSAGE, W: WITCH, I: IMAGE, GC: GCAM, A: AIM, P: POLES, CR: C-ROADS, GE: GEM-E3, CO: COFFEE and the study acronyms are E: ENGAGE, vV: van Vuuren 2021, E33: EMF33, N: NGFS2, C: CD-LINKS, K21: Kikstra 2021, O21: Ou 2021, SSP: SSP, S18: Strefler 2018, S21a: Strefler 2021a, S21b: Strefler 2021b, A: ADVANCE, B21: Baumstark 2021, H18: Holz 2018, K18: Kriegler 2018, So21: Soergel 2021, L21: Luderer 2021, B18: Bertram 2018, F20: Fujimori 2020, G18: Grubler 2018, S21: Schultes 2021. Data: IPCC AR6 Scenarios Database3.\n\nFor most of the assessed WGIII SPM findings, individual models have a much larger impact than individual studies (Table\u00a01, Fig.\u20093 and Supplementary Figs.\u20091\u20133). One exception to this is the net-zero GHG year. For the net-zero GHG year, the ENGAGE study, with 25 of 97 scenarios in the C1 category, has a large influence because many ENGAGE scenarios do not reach net-zero GHG emissions before 2100 by design (Fig.\u20092 and Supplementary Note\u20091). The net-zero GHG year is also sensitive to the removal of models and studies because few scenarios reach net-zero around the median year (2098) (Fig.\u20094). The reason why median values are more sensitive to individual models is partly because the dominant model is responsible for a larger share of the C1 scenarios (41 of 97 scenarios) than the dominant study (25 of 97 scenarios). Models with fewer than 25 scenarios also have a smaller impact (Supplementary Figs.\u20091\u20133). In addition to this, models have a larger impact on median values because some WGIII SPM findings are more model-dependent than they are study-dependent. As seen in the cases of coal, oil, gas, CH4, N2O, and F-Gases, the level of disagreement across models is larger than the level of disagreement across studies (Supplementary Figs.\u20091\u20133). This is consistent with other studies that have found model differences to be larger than other scenarios differences15,18,20,21.\n\na Year of net zero GHG emissions, b Cumulative net-negative CO2 emissions between the year of net zero and 2100, c CO2 emissions in 2030, d GHG emissions in 2030, e CH4 emissions in 2040, f CH4 emissions in 2050, g N2O emissions in 2050, h F-gas emissions in 2050, i Coal in 2050, j Coal without CCS in 2050, k Oil in 2050, l Oil without CCS in 2050, m Gas in 2050. The selection represents the findings that are most impacted according to the \u2018Between\u2019 measure for either models or studies, as shown in Table\u20091. The different years and ranges correspond to what is reported in the SPM: For fossil variables, interquartile ranges are shown; for other variables, 5th-95th percentile ranges are shown. The grey bars show statistics for all scenarios that limit global warming to 1.5\u2009\u00b0C (>50%) (category C1, including only vetted scenarios that received a climate assessment). The other bars show statistics when the model with the most scenarios is removed (orange bars) and when the study with the most scenarios is removed (yellow bars). The blue diamonds show median values of all scenarios that limit global warming to 2\u2009\u00b0C (\u2009>\u200967%) (category C3, including only vetted scenarios that received a climate assessment). The model with the most scenarios is the REMIND model, and the study with the most scenarios is the ENGAGE study for all the findings. \u2018>2100\u2019 means \u2018after 2100\u2019 (the year is not specified for scenario that reach net zero GHG emissions after 2100), \u2018CCS\u2019 stands for Carbon Capture and Storage. Data: IPCC AR6 Scenarios Database3.\n\na Year of net zero GHG emissions by model, b Year of net zero GHG emissions by study, c GHG emissions in 2030 by model, d Coal in 2050 by model, and e Gas in 2050 by model. Dominant model/study scenarios are shown in blue, and all other scenarios are shown in orange/yellow. The findings are selected based on a combination of policy relevance and impact (shown in Table\u20091). All variables are from scenarios that limit global warming to 1.5\u2009\u00b0C (\u2009>\u200950%) (C1 category). The boxes at the top of each histogram show the 5th, 25th, 50th, 75th, and 95th percentiles of all scenarios (in grey), the median of the dominant model/study (in blue), and the median without the dominant model/study (in orange/yellow). \u2018>2100\u2019 means after 2100 (net zero is not reached before 2100 and may not be reached in these scenarios). Data: IPCC AR6 Scenarios Database3.\n\nThe model with the largest impact on most median values is the model with the most scenarios, which, for all the assessed findings, is the REMIND model (Table\u20091). The strong influence on median values for this model is explained in large part due to its substantial share of the 1.5\u2009\u00b0C scenarios in AR6 (41 of 97 scenarios). Because REMIND uses less coal and more oil compared to other models, median coal is higher and median oil is lower when REMIND is removed. And because near-term emissions reductions in REMIND are lower than in\u00a0most other models, median GHG and CO2 reductions in 2030 are higher when REMIND is not included. The model with the second largest impact after REMIND is the model with the second most scenarios, MESSAGE (20 of 97 scenarios). Except for the net-zero GHG year, however, the impact of removing MESSAGE is relatively small (Table\u20091). The magnitude of the shift when removing models depends on both the number of scenarios and the position of those scenarios relative to other scenarios (Fig.\u20094).\n\nThe study with the largest impact on median values is also the study with the most scenarios, ENGAGE18 (Table\u20091). And the study with the largest influence after ENGAGE is also the study with the second most scenarios, van Vuuren 202125,26. This study also happens to contain all the IMAGE scenarios in the C1 category, and removing it is therefore equivalent to removing the IMAGE model. The impact of van Vuuren 2021 on median values is, however, relatively small (Table\u20091).\n\nInterquartile and 5-95th percentile ranges are also influenced by the dominant model and the dominant study, with the interquartile ranges being more sensitive than the 5-95th percentile ranges, and with the dominant model having a larger impact than the dominant study (Fig.\u20093).\n\nThe median values that are least impacted by individual models and studies (relative to the differences in 1.5\u2009\u00b0C and 2\u2009\u00b0C medians) include peak CO2 and GHG emissions years and net-zero CO2 year (Table\u00a01). Because almost all scenarios have the same peak GHG and CO2 year (2020), removing individual models and studies has no impact. For the net-zero CO2 year, inter-model variation is still considerable, but the two models with the most scenarios happen to both be very close to the ensemble median (Supplementary Table\u20091). Therefore, removing these two models has almost no impact on the median in this case. Models that give significantly different net-zero years (such as GCAM, POLES, C-ROADS, and COFFEE) do not have enough scenarios to influence the median net-zero year on their own. This highlights that it is not only the numbers of scenarios that matter, but also their positioning relative to the median.\n\nOverall, the largest influence on the assessed WGIII SPM findings comes from the dominant model (Table\u20091). Compared to the dominant model, the dominant study and other models have much less influence (Supplementary Figs.\u20091\u20133). For 16 of the 27 SPM findings, the reported median is closer to the median of the dominant model than to the median of\u00a0all the other models combined (Table\u20091). For coal reductions, year of net-zero GHG emissions, CH4 and F-gas emissions, the impact from the dominant model on the median is as large as or larger than the distance between reported median values for 1.5\u2009\u00b0C and 2\u2009\u00b0C scenarios. This shows that the uneven representation of models can be as or more important than the climate category for median scenario outcomes.\n\nWe have so far focused on the scenario variables reported in the WGIII SPM. To assess the influence of dominant models on the AR6 scenarios database findings overall, which is relevant for users of the database more broadly7,10,13,27,11, we compute the impact on the median values of all the variables in the database (see \u201cMethods\u201d). In each climate category, we compute the number of variables for which the median (in 2050) is closer to the median of the dominant model than to the median of all the other models combined. We do this for all scenario variables reported by at least two models.\n\nMost median values in the AR6 database are closer to the median of the dominant model than to the median of the other models\u00a0taken together (Fig.\u20095). In scenarios that limit global warming to 1.5\u2009\u00b0C (C1 category), 79% of median values are closer to the median of the dominant model than to the median of all the other models. In scenarios that limit global warming to 2\u2009\u00b0C (C3 category), 64% of median values are closer to the median of the dominant model than to the median of all the other models. Across all climate categories, 67% of median values in the AR6 scenarios database are closer to the median of the dominant model than to the median of all the other models.\n\nThe left column shows the number of variables with median values closer to the dominant model than to the median of all the other models, by climate category and by model (a, c, e). The right column shows the total number of variables across all climate categories with median values closer to the dominant model than to the median of all the other models, by model (b, d, f). Results are shown for all variables (a, b), Tier 1 variables (c, d), and Tier 2 variables (e, f) (see \u201cMethods\u201d). Percentages above bars in (a, c, e) show the proportion of variables in each climate category with median values closer to the dominant model than to the median of all other models. The colours show what model is the dominant model. Percentages above bars in (b, d, f) show the proportion of variables for which the median is closer to the dominant model than to all the other models, by the dominant model. Median values are in 2050. Only variables reported by at least two models in the AR6 Scenarios Database are included (see Methods). Data: IPCC AR6 Scenarios Database3.\n\nWithin each climate category, the dominant model depends on the scenario variable. This is because different models report different scenario variables (Supplementary Fig.\u20094 and Supplementary Data\u20091). In scenarios that limit global warming to 1.5\u2009\u00b0C and 2\u2009\u00b0C, the most common dominant model across variables is REMIND, followed by MESSAGE and IMAGE.\n\nDifferent models dominate across variables in different climate categories (Fig.\u20095). Whereas REMIND is the most common dominant model in the C1, C3, C7, and C8 categories, MESSAGE is the most common dominant model in the C2, C4, C5, and C6 categories. This means that when comparing median outcomes of variables across climate categories\u2014which is often done to show the implications of different climate targets\u2014the differences may in some cases be more reflective of differences in model sampling than of the climate target. For example, comparing the implications of different levels of \u2018overshoot\u2019 (C1 versus C2) may be more about differences in the REMIND and MESSAGE models than about overshoot.\n\nOverall, the models with the most scenarios in the AR6 database have a much larger impact on median values than the models with fewer scenarios. The two models with the most scenarios (47% of all vetted and climate-assessed scenarios), REMIND and MESSAGE, are responsible for 77% of the cases where the median is closer to the dominant model than to the median of all the other models. For 96% of the cases, the responsible model is one of the four models with the most scenarios in the AR6 scenarios database (REMIND, MESSAGE, IMAGE, and WITCH). The distribution of dominant models (Fig.\u20095b) is thus even more uneven than the distribution of scenarios per model (Fig.\u20091a).\n\nThe results are similar if we limit the analysis to include only Tier 1 and Tier 2 variables, which are considered the more important variables (see Methods and Supplementary Data\u20092). All models should submit Tier 1 variables (of which there are 82), suggesting that the uneven submissions across variables should be less common. In the C1 category, 63% of Tier 1 and 73% of Tier 2 variables have median values that are closer to the median of the dominant model than to the median of all the other models (Fig.\u20095). In the C3 category, 48% of Tier 1 and 59% of Tier 2 variables have median values that are closer to the median of the dominant model than to the median of all the other models. Across all climate categories, 47% of Tier 1 variables and 65% of Tier 2 variables have median values that are closer to the median of the dominant model than to the median of all the other models. In most cases, the dominant model is one of the four models with the most (vetted and climate-assessed) scenarios in the AR6 scenarios database. Models other than REMIND, MESSAGE, IMAGE and WITCH are the dominant model in less than 5% of all cases.\n\nWe expect models that report more variables (Supplementary Fig.\u20094) to be the dominant model for more scenario variables. However, models with more scenarios tend to also report more variables, which means that these two effects go in the same direction (Supplementary Fig.\u20095 and Supplementary Data\u00a01). Overall, the number of vetted scenarios per model (Fig.\u20091 and Supplementary Fig.\u20096) is a better predictor of model dominance (Fig.\u20095) than the number of variables reported (Supplementary Fig.\u20094 and Supplementary Note\u20092).\n\nThe median values presented in the AR6 WGIII report are computed by giving each scenario equal weight. A simple way to counteract the uneven number of scenarios from different models and studies is to instead give each model or study equal weight (see Methods for details). For the assessed WGIII SPM findings, the direction of change in median values when each model or each study is given equal weight is usually the same as when the dominant model or study is removed (Fig.\u20096). This is not surprising, given the scenarios from the dominant model and study are given much less weight in model- and study-weighted medians. When each model is given equal weight, median coal reductions in 2050 changes from 95% to 83% in 1.5\u2009\u00b0C scenarios, which is the same as when removing the dominant model, and median gas reductions changes from 43% to 33%, which is close to the change from removing the dominant model (29%). Median GHG reductions in 2030 changes from 43% to 45%, which is less than when removing the dominant model (50%), because the models are evenly distributed on each side of the median for this variable.\n\na Year of net zero GHG emissions by model, b Year of net zero GHG emissions by study, c GHG emissions in 2030 by model, d Coal in 2050 by model, and e Gas in 2050 by model. Boxes show the minimum and maximum, the interquartile ranges, and the median of each model/study. The number of scenarios from each model/study is shown at the top of each box, and the data points are shown to the left. Models and studies are ordered according to the number of scenarios, with the model/study with the most scenarios furthest to the left. Long, solid horizontal lines show median values when models (orange) and studies (yellow) are removed one-by-one, with letters at the end of each line indicating the model/study that has been removed. Bolded and starred letters show the model/study whose removal leads to the biggest shift in the median value. Dashed grey horizontal lines show ensemble medians. Longer dashed horizontal lines show medians weighted by model (orange) and by study (yellow). The findings are selected based on a combination of policy relevance and impact (shown in Table\u20091) to illustrate how individual models and studies affect median values (more findings are shown in Supplementary Figs.\u20091\u20133). All variables are from scenarios that limit global warming to 1.5\u2009\u00b0C (>50%) (C1 category). Values above 2100 on the y-axis indicate \u2018after 2100\u2019, which means zero was not reached before 2100 (and may not be reached). The model acronyms are R: REMIND, M: MESSAGE, W: WITCH, I: IMAGE, GC: GCAM, A: AIM, P: POLES, CR: C-ROADS, GE: GEM-E3, CO: COFFEE and the study acronyms are E: ENGAGE, vV: van Vuuren 2021, E33: EMF33, N: NGFS2, C: CD-LINKS, K21: Kikstra 2021, O21: Ou 2021, SSP: SSP, S18: Strefler 2018, S21a: Strefler 2021a, S21b: Strefler 2021b, A: ADVANCE, B21: Baumstark 2021, H18: Holz 2018, K18: Kriegler 2018, So21: Soergel 2021, L21: Luderer 2021, B18: Bertram 2018, F20: Fujimori 2020, G18: Grubler 2018, S21: Schultes 2021. Data: IPCC AR6 Scenarios Database3.\n\nWeighted medians, however, do not offer a straightforward solution to the problem of model and study representation. There are two reasons for this. First, model- and study-weighted medians often move in opposite directions, leading to large differences for certain key findings (Fig.\u20096 and Supplementary Figs.\u20097\u20139). The median net zero GHG year varies by almost two decades or more, depending on whether you give each scenario equal weight (2098), each study equal weight (2085), or each model equal weight (after 2100). This is partly because the dominant model and the dominant study sit on opposite sides of the median. This shows that the choice of weighting scheme can be a key determinant of median values. But it is not clear what weighting scheme is more appropriate. Scenario outcomes can depend on model or study assumptions, or both, and it is not clear whether it is models or studies (or scenarios) that should be given equal weight in the calculation of database statistics. Second, model- and study-weighted medians may be dependent on what models and studies are included and not included in the AR6 scenarios database. While model- and study-weighted medians are insensitive to the number of scenarios, they are not insensitive to the representation of models and studies. Not all models submitted scenarios to the AR6 database, and of the more than 50 models that submitted scenarios, only 13 models submitted scenarios that passed vetting and received a climate assessment11 (Supplementary Note\u20091 and Supplementary Table\u20092). Furthermore, because not all models report all variables in all climate categories, most findings are based on even fewer than those 13 models (Supplementary Fig.\u200910). Thus, the models that are used to derive AR6 findings represent only a small subset of the models in the scenarios literature.\n\nThe problem of uneven sampling is not unique to the IPCC scenarios database. Unbalanced samples are common in social sciences, and several weighting methods have been developed to deal with this. These methods, however, rely on knowing the target population (see e.g.,28,29.). When the target population is known, weights can be assigned to ensure that the distribution of chosen characteristics in the sample, such as age, gender, education, and geographical location, reflects the target population. For the IPCC scenarios ensemble, however, the target population is unclear. Should it be all plausible scenarios, including ones that have never been modelled, or should it be all scenarios that have been published? And what scenario characteristics should one be ensuring a good representation of? While median fossil fuel reductions depend mostly on model representation, the net-zero year depends on both model and study representation. This suggests that the relevant characteristics depend on the scenario outcome in question.\n\nWeighting methods have also been discussed extensively in relation to physical climate modelling30,31,32,33. Most likely climate outcomes (contingent on forcing levels) are most often computed in structured experiments where each climate model is given equal weight34. In the literature, it has been argued that greater weight should be given to climate models that have been shown to have greater skill and to models that are more independent 31,33. That is, climate models should be weighed based on both performance and model independence, where dependencies may stem from the sharing of ideas for parametrization or simplifications, or from sharing of computer code, which may lead to similar model biases31. Because different climate models have greater skill at simulating different climate outcomes, and because different outcomes depend on different assumptions, both performance and independence depend on the outcome in question33.\n\nDespite valuable suggestions for how to evaluate IAMs35 there are no agreed upon performance metrics for IAMs. While climate model performance is based on comparing model projections with historical observations33, observations that can be used to assess the performance of IAMs are not available in the same way (Supplementary Note\u00a03). And research into IAM dependencies is lacking.\n\nIn lieu of performance and independence metrics, weighting by model might represent an improvement to the current approach of weighting by scenario, as it removes duplication of model outcomes that may result from known model fingerprints. Giving each model equal weight is also the most common approach in climate modelling. But unlike climate model ensembles, where each model is run once for each scenario in a structured experiment34 (such as in Coupled Model Intercomparison Projects (CMIPs)), the IPCC IAM ensembles contain a diversity of different scenarios, run by different sets of models, under different assumptions, to answer different questions. In this case, differences in outcomes do not represent different answers to the same question, which may be interpreted as the uncertainty of the answer, but how the answers change when the questions change. In this case, weighting by model remains an arbitrary approach, which may also remove important variation captured by different studies run by the same model.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64091-w/MediaObjects/41467_2025_64091_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64091-w/MediaObjects/41467_2025_64091_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64091-w/MediaObjects/41467_2025_64091_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64091-w/MediaObjects/41467_2025_64091_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64091-w/MediaObjects/41467_2025_64091_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We have shown that several WGIII SPM median values are influenced considerably by the model, and in some cases study, with the most scenarios in the AR6 database. Additionally, we have shown that weighting does not offer a straightforward solution to the uneven representation of models and studies. The median will, in any case, depend on the weighting choice and the representation of models and studies. This brings up more fundamental questions regarding the use of database statistics to present emissions scenarios findings. Informed by the purpose of the IPCC assessment, we discuss three issues and make recommendations based on our findings and the kinds of insights that can be obtained from IAM scenarios.\n\nFirst, median values and percentiles do not convey the level of agreement in findings, which is key to informing confidence and robustness. The IPCC is set up to \u201ctell policymakers what we know and don\u2019t know\u201d and \u201cwhere there is agreement in the scientific community, where there are differences of opinion, and where further research is needed\u201d36. As part of this, assessment findings with stronger agreement and multiple lines of evidence can be assigned a higher degree of confidence37. While interquartile and 5th-95th percentile ranges show the spread in scenario outcomes, they don\u2019t provide information about whether different models and studies agree or disagree. There is relatively low agreement, for example, on the precise reductions of emissions, coal, and gas in specific years, and on the net-zero GHG year. This is because these outcomes clearly depend on choices and assumptions that differ across models and studies (Figs.\u20092, 3 and 5). As our findings show, the reporting of descriptive statistics to the nearest percentage point for emissions reductions, the nearest 5% for coal and gas, and the closest five-year interval for the net-zero year is not robust to the sampling of models and studies. The reporting of median peak CO2 and GHG emissions years is (Table\u20091). If the point is to show differences in implications between different climate targets, robust scenario findings can be defined as those that vary more across climate categories than they do across models and studies20. This robustness could be evaluated, for example, by assessing the sensitivity of outcomes to removing single models and studies or to giving models and studies equal weight, as is done in this paper.\n\nRather than focusing on median values and percentiles, which may not only be sensitive to sampling but can also be misinterpreted as probabilistic confidence intervals, the IPCC could report the full ranges of scenario outcomes and focus more on how outcomes depend on assumptions, including model and study assumptions. Many different strategies are consistent with 1.5\u2009\u00b0C and 2\u2009\u00b0C, and scenario analysis is largely about showing the different implications and trade-offs associated with different choices38. Scenario analysis is less suited to providing precise outcomes for specific variables38. In addition to this, several models and studies fall entirely outside of the interquartile, and in some cases, 5th-95th percentile, ranges that are used to present findings (Supplementary Figs.\u20091\u20133). But given the non-probabilistic nature of the emissions scenario ensemble2 and the role of emissions scenarios in exploring different strategies and trade-offs, these results are no less important. An improved assessment of how scenario outcomes depend on choices and assumptions could be informed by several recent studies that have analysed how the model used, the scenario assumptions, and the climate target affect scenario outcomes19,20,21. A focus on such dependencies could provide policy-relevant insights regarding, for example, how the net-zero emissions year depends on key assumptions, including the climate target definition, which is defined differently in different studies18, and the discount rate, which may be defined differently in different models or studies39.\n\nSecond, the reliance on database statistics to present key scenarios findings give a lot of weight to the subset of scenarios that are submitted to the database, pass vetting, and receive a climate assessment. This comes at the exclusion of findings that are captured by models and studies that are not included in this subset but still contribute to the literature. This is an issue because the IPCC is meant to assess the full scenarios literature, and the scenarios database is meant to aid this assessment, not replace it. With the construction and use of the scenarios database to derive mitigation findings, the IPCC is essentially conducting a meta-analysis of an \u201censemble of opportunity\u201d2 that includes only a subset of the scenarios literature12. But the AR6 is meant to40 \u201ctake all available literature on emissions scenarios fully into account independently of whether underlying emissions scenarios are submitted to the AR6 scenario database\u201d. To serve this goal, the IPCC should assess whether the subset of scenarios used to derive findings gives a good representation of the scenarios literature. Guidelines for the use of database statistics that address the issues of over- and underrepresentation could also be developed. Making this an integral part of the use of the IPCC scenarios database may help reduce biases in key findings that are\u00a0based on the database.\n\nThird, database statistics may not be very meaningful given the varied representation of different research questions and assumptions. According to AR6, \u201cscenarios are neither predictions nor forecasts, but are used to provide a view of the implications of developments and actions\u201d2. These views are provided through specific modelling studies that employ specific models and scenario assumptions to answer specific research questions, such as: What are the cost implications of meeting stringent climate targets without overshoot18? What are the impacts on electrification if renewable energy costs continue to decline?41 How might changes in energy service provision affect global energy demand and supply, and the achievement of climate and development goals42? Although the scenarios database has an advantage in terms of the number of models and studies and therefore diversity of assumptions, this diversity also means that distributions of scenario outcomes are difficult to interpret6 and that database statistics may not be very meaningful2,27. More readily available information on model and scenario assumptions1,13, greater openness and transparency in the curation of the IPCC scenarios database including on vetting12, and more research into the different causes of scenario outcome variability (e.g17.) would enable users to get a better understanding of the representation of key assumptions in the database, which could help them make more meaningful comparisons and draw more relevant insights suited to their needs. But this information does not in itself make the scenarios in the database more comparable43 or statistical values more meaningful. Modelling is for \u201cinsights not numbers\u201d44 and model outcomes carry less meaning when they are not interpreted with respect to the choices and assumptions under which they were generated, and the research questions they were designed to answer. Because of this, the insights that IAM scenarios offer are best understood and appreciated by assessing scenario papers directly.\n\nThe use of scenario database statistics to present key scenario findings, and giving each scenario equal weight, is an easy choice, but it is not a neutral choice as it gives more weight to choices and assumptions embedded in models and studies that have a large number of scenarios in the database. While the uneven representation of studies in the AR6 ensemble might mean that certain questions, and therefore certain answers, are overrepresented, the uneven representation of models might mean that certain mitigation strategies are overrepresented. Our results confirm that different models tend to choose different strategies for reaching the same climate target15,19,20,21 and thus provide different views of the implications of mitigation. REMIND 1.5\u2009\u00b0C scenarios, for example, use less coal and gas, but more oil, and they mitigate more slowly in the beginning, but faster later on, compared to scenarios from other models.\n\nMore research is needed to understand what a representative sample of emissions scenarios might look like and how different models and studies contribute to this. Given that the IPCC scenarios database has a diversity of uses, there might not be a one-size-fits-all method for analysing it, and a transparent discussion of how to leverage it for different purposes, contexts and outputs is important. One way to remedy the uneven representation of models and studies in the database is to use the scenarios literature to better inform and guide the interpretation of different scenario outcomes. Even though the scenarios literature is also prone to an uneven representation of model and study assumptions, the weight given to different insights in the literature does not reflect a simple counting of scenarios and thus does not suffer in the same way.\n\nThe current use of database statistics to present key scenario findings in IPCC reports might mean that targets and decisions are based on findings that are more reflective of idiosyncratic model or study assumptions than of the chosen climate target and the scenarios literature. Moving away from descriptive statistics that are difficult to interpret and sensitive to sampling might be more in line with both the goal of the IPCC, to assess the full scenarios literature, and the purpose of integrated assessment modelling, to provide insights, not numbers.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The statistical values that are reported in the AR6 WGIII report and that are analysed in this paper are based on the subset of global scenarios in the AR6 scenarios database that passed vetting and that received a climate assessment (which means they were categorised into one of the C1-C8 climate categories). A total of 2304 global scenarios were submitted to the AR6 scenarios database. Of these, 1686 passed vetting and 1202 passed vetting and received a climate assessment3.\n\nThe analysis of the influence of individual models and studies on key AR6 findings (Figs.\u20092, 3 and 5) includes scenarios only in the C1 and C3 climate categories, as these categories (1.5\u2009\u00b0C with no or limited overshoot and 2\u2009\u00b0C) are the focus in the WGIII SPM. It includes a select number of scenario variables based on what is reported in the WGIII SPM (see below). The analysis of the impact of dominant models on the AR6 scenarios database overall includes scenarios from all climate categories (C1-C8), and it includes all the scenario variables reported in the database (more detailed information on this is provided further below).\n\nThe AR6 WGIII SPM findings mainly cover scenarios that limit global warming to 1.5\u2009\u00b0C (\u2009>\u200950%) with no or limited overshoot (C1 climate category) and scenarios that limit global warming to 2\u2009\u00b0C (\u2009>\u200967%) (C3 climate category). We analyse 27 global 1.5\u2009\u00b0C scenario (C1 category) variables whose median values are reported in the AR6 WGIII SPM. The list of all the 27 variables and the impacts of removing the models and studies with the largest impact and the models and studies with the most scenarios, is shown in Table\u20091.\n\nThe impacts of removing individual models and studies are measured in two different ways, both shown in Table\u00a01. \u2018Between\u2019 measures the impact relative to the differences between 1.5\u2009\u00b0C and 2\u2009\u00b0C scenarios: it is a unitless measure of the change in median value when an individual model/study is removed relative to the difference between the 1.5\u2009\u00b0C and 2\u2009\u00b0C medians. A value of 100% means that the change in median value is the same as the (absolute) difference between the 1.5\u2009\u00b0C and 2\u2009\u00b0C medians. A value of 0% means that there is no change.\n\nwhere X is an individual model or study. \u2018Within\u2019 measures the impact relative to other models and studies within the same (1.5\u2009\u00b0C) climate category: it is a unitless measure of how close the reported median is to the median of the individual model/study that is removed versus the median of all the other models/studies\u00a0combined. A value of 100% means that the median coincides with the median of the dominant model/study, and a value of 0% means that the ensemble median coincides with the median of all the other models/studies. A value above 50% means that the reported median is closer to the median of the dominant model/study than to the median of all the other models/studies taken together.\n\nwhere X is an individual model or study.\n\nWe use the climate categories as defined in the AR6 WGIII report2. While the analysis of WGIII SPM findings includes scenarios only from the C1 and C3 categories, the analysis of the overall impact of dominant models on the AR6 scenarios database includes scenarios from all (C1\u2013C8) categories.\n\nCategory C1 includes scenarios that limit warming to 1.5\u2009\u00b0C in 2100 with a likelihood of greater than 50% and that reach or exceed warming of 1.5\u2009\u00b0C during the 21st century with a likelihood of 67% or less. These scenarios are referred to in the AR6 report as scenarios that limit warming to 1.5\u2009\u00b0C (\u2009>\u200950%) with no or limited overshoot. Limited overshoot means exceeding 1.5\u2009\u00b0C by up to about 0.1\u2009\u00b0C for up to several decades. Category C3 includes scenarios that limit peak warming to 2\u2009\u00b0C throughout the 21st century with a likelihood of greater than 67%. These scenarios are referred to in the AR6 report as scenarios that limit warming to 2\u00b0C (\u2009>\u200967%).\n\nCategory C2 includes scenarios that limit warming to 1.5\u2009\u00b0C in 2100 with a likelihood of greater than 50% and exceed warming of 1.5\u2009\u00b0C during the 21st century with a likelihood of greater than 67%. These scenarios are referred to in AR6 as scenarios that return warming to 1.5\u2009\u00b0C (\u2009>\u200950%) after a high overshoot. High overshoot refers to temporarily exceeding 1.5\u2009\u00b0C global warming by 0.1\u2009\u00b0C\u20130.3\u2009\u00b0C for up to several decades. Categories C4, C5, C6 and C7 include scenarios that limit warming to 2\u2009\u00b0C, 2.5\u2009\u00b0C, 3\u2009\u00b0C, 4\u2009\u00b0C, respectively, throughout the 21st century with a likelihood of greater than 50%. Category C8 includes scenarios that exceed a warming of 4\u2009\u00b0C during the 21st century with a likelihood of 50% or more.\n\nTo capture core model structure, we group what would generally be considered different versions of the same model (in model documentation and publications) together. For the scenarios that passed vetting and received a climate category, this results in 13 unique models. The list of unique model names and corresponding model versions is provided in Supplementary Table\u20093, together with the model acronyms used in tables and figures.\n\nDue to differences in naming conventions across modelling groups, it is difficult to know how large the differences between different model versions are. Some modelling groups give their model a different name for each modelling study that the model takes part in (e.g., POLES), and other groups do not (e.g., AIM). Some modelling groups use long and detailed model names to distinguish different versions and sub-versions, not only for the core model, but also for sub-models, from each other (e.g., MESSAGE and REMIND, linked to the land use models GLOBIOM and MagPIE).\n\nVisual inspection of box plots (e.g., Figure\u20093 or Supplementary Figs.\u20091\u20133) suggests that the unique model names that we use capture core structural differences: there is a clear correlation between variable values and models.\n\nStudy groupings are based on the \u2018Project_study\u2019 category in the AR6 scenarios database metadata3. \u2018Project_study\u2019 describes the parent project or individual study from which each scenario derives. This category is kept intact, as given in the metadata. This results in a total of 30 studies for the scenarios that passed vetting and received a climate category. The list of studies is provided in Supplementary Table\u20094, together with the acronyms used in tables and figures.\n\nScenarios are first assigned to models and studies from which they derive, according to Supplementary Tables\u20093, 4 (and as explained above). All the scenarios from each model/study are then removed from the ensemble, one-by-one, to calculate the median value without that model/study. The impacts from the models and studies with the largest impact are shown in Table\u00a01, together with the impacts from the models and studies with the most scenarios (when these are different).\n\nThe analysis of the overall impact of dominant models is complicated by the fact that i) each model reports different scenario variables (both from other models and across its own scenarios), and ii) each model has a different number of scenarios in each climate category. What model is the dominant model, therefore, depends on both the scenario category and the scenario variable: the same variable can be dominated by different models in different climate categories, and different variables within the same climate category can be dominated by different models.\n\nTo assess the overall impact of dominant models on the AR6 scenarios database, we compute the impact on each variable in each climate category of the model that (for that variable, in that climate category) has the most scenarios. More specifically, we compute the number of variables for which the median (in 2050) is closer to the median of the dominant model than to the median of all the other models combined, corresponding to a value above 50% for the \u2018Within\u2019 measure (Eq.\u2009(2) above). We do this for all scenario variables reported by at least two models, in each climate category. We exclude variables that are reported by only a single model because the median in this case is closer to the median of the dominant model than to the median of all other model scenarios (of which there are none) by default. Had we also included variables that are reported by only a single model, the percentage of median values that are closer to the median of the dominant model than to the median of all other models would be higher. If there is more than one model that has the most scenarios (for a given variable in a given climate category), we calculate the shift in median value of removing each one at a time and select the model whose removal leads to the greatest shift in the median to be the dominant model.\n\nBecause each climate category contains different scenarios from different models, and because different models report different variables (in different scenarios), the number of variables in each climate category differs (Supplementary Data\u20091). Only a subset of these, again, are reported by more than one model. There are 967 variables reported by at least two models in the C1 category, 978 variables in the C2 category, 1001 variables in the C3 category, 892 variables in the C4 category, 963 variables in the C5 category, 942 variables in the C6 category, 990 variables in the C7 category, and 518 variables in the C8 category. This amounts to a total of 7251 different median values, from at least two models, in the AR6 scenarios database.\n\nAll global scenario data were submitted to the AR6 scenarios database using a common scenario reporting template. In the template, all variables are listed in a common format and assigned a Tier: \u201cTier 1 variables define a core set of information that would enable assessing the scenario in a meaningful way\u201d, \u201cTier 2 variables are important for enabling more specific analyses\u201d40, and remaining variables are not assigned a Tier. In case of constraints for providing scenario data, modelling teams were asked to consider the ranking of importance (Tier 1 and Tier 2).\n\nDifferent Tiers were assigned by different chapters in WGIII. Of the 1871 variables that are listed in the common scenario reporting template, 303 are marked as \u2018core\u2019. Of these core variables, 82 are Tier 1 and 221 are Tier 2. Of these, again, 79 Tier 1 variables and 201 Tier 2 variables are included in the AR6 scenarios database (Supplementary Data\u20092). These are the variables analysed in Fig.\u20095. The AR6 WGIII call for scenarios and templates is available at https://data.ene.iiasa.ac.at/ar6-scenario-submission/#/about.\n\nThe model- and study-weighted medians, shown in Fig.\u20096 and Supplementary Figs.\u20097\u20139, give each model and study equal weight. 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Zenodo https://doi.org/10.5281/zenodo.10975768 (2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "I.S. and G.P.P. were supported by the European Union\u2019s Horizon Europe research and innovation programme under grant agreement no. 101056306 (IAM COMPACT)\u00a0and\u00a0no.\u00a0101081179 (DIAMOND), by the Norwegian Research Council, Finance Market Fund, project no. 309613 (Using scenarios to assess climate risks in the financial sector, StressTest), and the Norwegian Research Council project no. 334811 (Tracking Risks in Future Emission, Climate and Technological Assumptions, TRIFECTA).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "CICERO Center for International Climate Research, Oslo, Norway\n\nIda Sognnaes\u00a0&\u00a0Glen P. Peters\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nI.S. conceived of and designed the analysis and wrote the initial draft. I.S. and G.P.P. interpreted the data and wrote, read, and approved the final article.\n\nCorrespondence to\n Ida Sognnaes.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks B\u00e9atrice Cointe, Ruben Pr\u00fctz and the other anonymous, reviewer(s) for their contribution to the peer review of this work. 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Influence of individual models and studies on quantitative mitigation findings in the IPCC Sixth Assessment Report.\n Nat Commun 16, 8343 (2025). https://doi.org/10.1038/s41467-025-64091-w\n\nDownload citation\n\nReceived: 23 December 2024\n\nAccepted: 08 September 2025\n\nPublished: 02 October 2025\n\nVersion of record: 02 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-64091-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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syndrome", + "journal": "Nature Communications", + "published": "03 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60574-y/MediaObjects/41467_2025_60574_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60574-y/MediaObjects/41467_2025_60574_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1-5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60574-y/MediaObjects/41467_2025_60574_MOESM3_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60574-y/MediaObjects/41467_2025_60574_MOESM4_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60574-y/MediaObjects/41467_2025_60574_MOESM5_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60574-y/MediaObjects/41467_2025_60574_MOESM6_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://sharing.nih.gov/accessing-data/accessing-genomic-data/how-to-request-and-access-datasets-from-dbgap", + "/articles/s41467-025-60574-y#Sec26" + ], + "code": [ + "https://github.com/davemcg/nr6a1/releases/tag/1.3", + "/articles/s41467-025-60574-y#ref-CR58", + "https://github.com/NIH-NEI/variant_prioritization/releases/tag/v0.1", + "/articles/s41467-025-60574-y#ref-CR59" + ], + "subject": [ + "Disease model", + "Hereditary eye disease", + "Medical genetics" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5375105/v1.pdf?c=1751627264000", + "research_square_link": "https://www.researchsquare.com//article/rs-5375105/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60574-y.pdf", + "preprint_posted": "14 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Colobomatous microphthalmia is a potentially blinding congenital ocular malformation that can present either in isolation or together with other syndromic features. Despite a strong genetic component to disease, many cases lack a molecular diagnosis. We describe a novel autosomal dominant oculo-vertebral-renal (OVR) syndrome in six independent families characterized by colobomatous microphthalmia, missing vertebrae and congenital kidney abnormalities. Genome sequencing identified six rare variants in the orphan nuclear receptor gene NR6A1 in these families. We performed in silico, cellular and zebrafish experiments to demonstrate the NR6A1 variants were pathogenic or likely pathogenic for OVR syndrome. Knockdown of either or both zebrafish paralogs of NR6A1 results in abnormal eye and somite development, which was rescued by wild-type but not variant NR6A1 mRNA. Illustrating the power of genomic ascertainment in medicine, our study establishes NR6A1 as a critical factor in eye and vertebral development and a pleiotropic gene responsible for OVR syndrome.Health sciences/Medical research/Genetics researchBiological sciences/Genetics/Development", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementalTables.xlsxSupplementary Tables", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Colobomatous microphthalmia is a potentially blinding congenital ocular malformation that can present either in isolation or together with other syndromic features. Despite a strong genetic component to disease, many cases lack a molecular diagnosis. We describe an autosomal dominant oculo-vertebral-renal (OVR) syndrome in six independent families characterized by colobomatous microphthalmia, missing vertebrae and congenital kidney abnormalities. Genome sequencing identified six rare variants in the orphan nuclear receptor gene NR6A1 in these families. We performed in silico, cellular, and zebrafish experiments to demonstrate the NR6A1 variants were pathogenic or likely pathogenic for OVR syndrome. Knockdown of either or both zebrafish paralogs of NR6A1 results in abnormal eye, kidney, and somite development, which was rescued by wild-type but not variant NR6A1 mRNA. Illustrating the power of genomic ascertainment in medicine, our study establishes NR6A1 as a critical factor in eye, kidney, and vertebral development, and a pleiotropic gene responsible for OVR syndrome.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Uveal coloboma is a congenital ocular malformation caused by failure of the ventral optic fissure to close during early eye morphogenesis and is usually considered on a phenotypic continuum with microphthalmia and anophthalmia1,2,3,4,5. A rare condition6,7,8,9,10,11, coloboma may nonetheless account for up to 10% of childhood blindness12. Although significant progress has been made in identifying genes associated with syndromic and non-syndromic coloboma, the yield of diagnostic testing remains low, especially for isolated, non-syndromic coloboma, suggesting other genes are yet to be discovered13,14,15. To identify novel coloboma genes, the National Eye Institute has conducted a natural history study since 2006, on the genetics of coloboma that includes systematic deep phenotyping of probands and first-degree family members. We have previously identified a syndrome characterized by missing vertebrae (in the thoracic and/or lumbar spine), congenital kidney abnormalities, and uveal coloboma, inherited in an autosomal dominant fashion with incomplete penetrance and variable expressivity16.\n\nWe identified structural and sequence variants in the transcription factor gene NR6A1 (Nuclear receptor subfamily 6, group A, member 1, OMIM*602778) in three families by genome sequencing (GS). These results were extended via analysis of the Genomics England 100,000 Genomes Project (UK100KGP), where three additional individuals with microphthalmia/anophthalmia/coloboma were identified17.\n\nOriginally termed germ cell nuclear factor (GCNF)/retinoid receptor-related testis-associated receptor (RTR), NR6A1 is an orphan member of the nuclear hormone receptor family of transcription factors, often acting as a transcriptional repressor. NR6A1 is highly expressed in embryonic and other stem cells from various tissues (especially testes) and is repressed upon differentiation. NR6A1 plays an important role in somite and subsequent vertebral development in mice, and in livestock species it is correlated with vertebral number18,19,20,21. To our knowledge, there are no reports on the role of NR6A1 in eye or kidney development.\n\nHere we described an autosomal dominant oculo-vertebral-renal (OVR) syndrome caused by variants in the orphan nuclear receptor gene NR6A1, supporting the pathogenicity of variants through a combination of in silico, in vitro, and in vivo investigations. To our knowledge, this is an undescribed Mendelian trait in humans characterized by missing vertebrae.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We identified three rare NR6A1 variants in three families affected by uveal coloboma (COL005, COL034, COL171) with or without microphthalmia, cataract, and missing vertebrae through genome sequencing. In cases where multiple generations are affected, transmission is autosomal dominant with incomplete penetrance and variable expressivity (Fig.\u00a01a). Clinical data for all the participants with a positive molecular result is shown in Table\u00a01. No other candidate pathogenic variants in NR6A1 were identified in the NEI coloboma cohort consisting of a total of 224 probands (66 analyzed by genome sequencing, 57 by exome sequencing, and 101 by amplicon sequencing).\n\na Pedigrees of three families (COL005; COL034; COL171) from the NEI cohort demonstrating coloboma with or without microphthalmia and cataract, missing vertebrae, and congenital renal anomalies. Inheritance is autosomal dominant with incomplete penetrance and variable expressivity. b Linear pigmentary disturbance representing a forme fruste of coloboma (arrow) in COL005.1 (right eye). c Larger chorioretinal coloboma in the left eye of COL005.1 demonstrating a retinal tear in the far periphery (arrowhead). d Iris coloboma of the left eye of COL005.10. e Microphthalmia of the left eye in COL034.1. f Retro-illumination image of the left eye of COL171.1 demonstrating iris coloboma and posterior subcapsular cataract (open arrow). g Spine x-ray of COL005.4 demonstrating 11 thoracic (normal 12) and 4 lumbar (normal 5) vertebrae. h Schematic of NR6A1 variants detected in the NEI and UK Genomics England cohorts. + individual with variant, \u2212 individual without variant. DNA binding domain (DBD) and putative nuclear receptor ligand binding domain (NR-LBD) are noted (Q15406; InterPro).\n\nThe proband of the family (COL005.1) presented at age 14, with bilateral uveal colobomas (Fig.\u00a01b, c). Family history was notable for a younger brother (COL005.4), a second cousin (COL005.10), and a first cousin once removed (COL005.17) with uveal coloboma. The deletion breakpoints were in intron 2 and 6 removing the coding sequence for amino acids (aa) Ile48-Gly275 and likely causing a frameshift (p.Ile48Asnfs*3, Fig.\u00a01h). The status of the heterozygous deletion was determined by breakpoint PCR among family members available, which revealed complete segregation with the missing vertebrae with an estimated LOD score of 3.6 (Fig.\u00a01a, Supplementary Fig.\u00a01). Four family members were also affected by coloboma in addition to missing vertebra, of which one (COL005.17) also had unilateral renal agenesis by report. The proband of the family (COL034.1) presented at age 11 months with bilateral uveal colobomas and microphthalmia OS (Fig.\u00a01e). Prenatal history was remarkable for the inability of an ultrasound at 18 weeks to visualize the left kidney. Systemic testing demonstrated 10 thoracic vertebrae and left renal agenesis. Genome sequencing revealed a heterozygous c.274C>T p.(Arg92Trp) variant in NR6A1, which was found in the affected mother and unaffected grandfather (Fig.\u00a01a, h). The proband of the family (COL171.1) presented at age 36 with bilateral colobomatous microphthalmia affecting the iris, retina/choroid, and optic nerve. Slit lamp exam was notable for bilateral microcornea, bilateral posterior subcapsular and nuclear cataracts, and missing zonules inferiorly OU (Fig.\u00a01f). Genome sequencing revealed a heterozygous c.1306C>T p.(Arg436Cys) variant in the proband which was absent in his unaffected mother (Fig.\u00a01h). Detailed study of the probands and their family members available for evaluation, were described in clinical vignettes in the\u00a0Supplementary Notes. No convincing pathogenic variants in known coloboma genes were identified in any of these subjects.\n\nWe performed an unbiased disease association analysis of rare pLoF variants using the UK100KGP dataset17. After removing variants resulting from calling artifacts or mis-annotation, only three pLoF variants were found in the cohort with approximately 126,700 alleles (Supplementary Data\u00a01, Supplementary Fig.\u00a02). We found three probands, Proband (A1) presented at age 30, with bilateral chorioretinal coloboma (forme fruste OD) and OS coloboma of the optic nerve (Supplementary Fig.\u00a02b). Genome sequencing revealed a heterozygous c.965_980del p.(Ser322Ter), present in both the proband and her unaffected father. Proband (B1) presented at the age of 29, with a severe form of bilateral microphthalmia with a vestigial remnant of eyes, delayed motor development, intellectual disability, abnormal behavior, and schwannoma. This proband carried a heterozygous c.902G>A p.(Trp301Ter) variant. These two nonsense variants are expected to cause loss of protein function either through nonsense-mediated decay or truncation of the putative nuclear receptor ligand binding domain (NR-LBD, Fig.\u00a01h). Proband D (Supplementary Data\u00a01) had a disorder of sex development, carried variant c.288dup p.(Cys96TrpfsTer4), which was absent in either parent. His father was also affected with a disorder of sex development, suggesting that the NR6A1 variant is likely not associated with the condition.\n\nThe UK100KGP MAC cohort, which consists of 215 probands, was queried for rare missense and in-frame insertion/deletion variants. Proband C1, presented at the age of 25 with bilateral microcornea and coloboma affecting the iris, choroid/retina, and optic nerve. One brother had a similar condition by report. Both parents and the two other siblings of the proband had no history of coloboma by report. Genome sequencing revealed a heterozygous variant c.227_229del p.(Ser76del) present in the proband (Fig.\u00a01h, Supplementary Data\u00a01). This variant leads to an in-frame deletion of a serine within the Zn-finger motif. Within the three MAC patients we report, no candidate pathogenic variants were found in the known MAC genes present in the current Genomics England PanelApp (ocular coloboma v1.47, anophthalmia or microphthalmia v1.51, structural eye disease v3.79). Thus, these cases further support that rare variants in NR6A1 can cause MAC with reduced penetrance.\n\nThe NR6A1 amino acid sequence is well-conserved between human, mouse, and zebrafish; specifically, the residues Ser76, Arg92, and Arg436 are conserved across multiple species (Supplementary Fig.\u00a03). To understand the effects, missense variants had on protein stability and function, we created an in-silico model of a complex of NR6A1 with DNA (Supplementary Fig.\u00a04a). The AlphaFold model of NR6A1 is shown by the composition of Zn-finger (residues 60\u2013172) and NR_LBD (residues 246\u2013480) domains shown in orange and green, respectively. The rest of the model shown in gray is predicted as an irregular structure by AlphaFold. In wild-type (WT) NR6A1, a positively charged arginine residue 92 is predicted to interact with negatively charged DNA based on a zinc-finger protein model (Supplementary Fig.\u00a04b). The R92W variant replaces the R92 residue with hydrophobic tryptophan (W), possibly interrupting the electrostatic interaction with DNA. The R436C variant affects the putative nuclear receptor ligand binding domain NR_LBD. In NR6A1, hydrogen atom 1HH2 of arginine R436 closely interacts with the oxygen atom of glutamic acid E388 (Supplementary Fig.\u00a04c). The variant R436C breaks this bond and creates a cysteine residue which could form abnormal disulfide bridges in the variant protein, since residues C443, C391, and C422 are distanced at 8\u201312\u2009\u00c5 from C436 in this variant domain as compared to 14\u201319\u2009\u00c5 (C443\u2013C391), 11.09\u2009\u00c5 (C443\u2013C422) and 4.52\u2009\u00c5 (C91\u2013C422) in the WT protein model.\n\nTo study the functional impact of the missense variants on protein localization in the cell, the R92W and R436C mutations were introduced in WT NR6A1 cDNA fused to a GFP coding sequence. All experiments were performed in context to the NR6A1 isoform NM_033334.4 and repeated at least three times. Transfection efficiencies were between 50-60% for the WT and variant constructs as analyzed by flow-cytometry and Western blotting (Supplementary Figs.\u00a05, 6). The WT-NR6A1 when over-expressed in HEK293 cells was consistently observed to localize in the nucleus (Fig.\u00a02a, b), consistent with a previous report22. The R92W variant, although nuclear, was not uniformly distributed across the nucleus. To study the localization of R92W variant puncta to the nucleolus, we performed immunofluorescence staining of NR6A1 wild-type and mutant isoforms with nucleolar marker FIBRILLARIN. As shown in supplementary Fig.\u00a07, the punctae do not colocalize with FIBRILLARIN staining suggesting that the variant is not mis-localized to the nucleolus. In contrast, the R436C variant localized exclusively in the cytoplasm (Fig.\u00a02a, b). The above-described localization pattern of the WT and variant isoforms was consistent in all transfected cells and across multiple rounds of transfection. Taken together these results suggest that both missense variants likely interfere with NR6A1 function due to improper subcellular localization.\n\nNR6A1 variant localization pattern was studied by overexpression in HEK293 cells and representative high magnification (63X) images are shown from three different trials (a) Scale bar\u2009=\u200910\u2009\u00b5m. Low magnification images (b) scale bar 100\u2009\u00b5m. The localization pattern for the WT and the two variant isoforms was observed to be consistent across three transfection experiments. (Cells counted: WT\u2009=\u2009387, R92W\u2009=\u2009350 and R436C\u2009=\u2009217).\n\nAnalysis of bulk RNA-Seq datasets from ocular and non-ocular tissues demonstrates modest expression of NR6A1 in most tissues and relatively higher levels of expression in embryonic stem cells/induced pluripotent stem cells (compared to adult ocular tissues) and in bone marrow and testis systemically (Fig.\u00a03a, d)23,24. Consistent with this observation, bulk RNA-Seq data from human fetal tissue shows that NR6A1 expression is highest in early stages of development, including the time of optic fissure closure in the first trimester25,26 (Fig.\u00a03b). In the Human Retinal Cell Atlas single nucleus RNA-Seq dataset, NR6A1 is highly expressed in adult horizontal cells and low in microglia and RPE (Supplementary Fig.\u00a08)27. Expression of NR6A1 is strongly correlated (>5 fold enrichment, p\u2009=\u20090.0024) with that of other coloboma-associated genes in fetal ocular tissues (Fig.\u00a03c). This strength of enrichment was not seen in Genotype-Tissue Expression (GTEx) body tissue (p\u2009=\u20090.361) or adult eye tissue (p\u2009=\u20090.451)23,24. We note that several of the highly correlated genes-SALL4 (Duane-Radial Ray Syndrome), PAX2 (Papillorenal syndrome), ACGT1 (Baraitser-Winter Syndrome 2), SALL1 (Townes-Brocks Syndrome 1) can also present with congenital renal anomalies.\n\na Comparative levels of NR6A1 from publicly available bulk human tissue RNA-sequencing (RNA-Seq) datasets accessed on the eyeIntegration website (https://eyeintegration.nei.nih.gov/). On average, expression is higher in embryonic and induced pluripotent stem cells (ESC, iPSC, respectively) than in adult ocular tissues. b Bulk RNA-Seq data in human retinal fetal tissue from two studies suggests NR6A1 expression is highest in early stages of development, including the window of optic fissure closure (lavender box). NR6A1 expression is plotted against the tissue age (days post conception, dpc). A linear regression analysis was added for each paper\u2019s data from the 40 to 80\u2009dpc and 80 to 160\u2009dpc samples. c The density correlation plot (closer to \u22121 and 1 is more negatively or positively correlated, respectively) shows ten notable coloboma associated genes with highly ranked correlations with NR6A1 expression across eyeIntegration curated fetal retina and RPE tissues. This enrichment was not seen in adult tissues. d Among systemic tissues, NR6A1 is expressed most highly in bone marrow and testes. The boxplots display the median, 25th and 75th percentiles, and 1.5 * interquartile range (IQR). Any data outside the 1.5 * IQR are plotted. In panels a and d the number of samples is given above the boxplots.\n\nTo establish plausible causation for NR6A1 variants, we studied the embryonic expression of its orthologs in mouse and zebrafish model systems at developmentally relevant time points. Previous work has demonstrated widespread expression of Nr6a1 in mouse at E8.5 and E9.5 (including the optic vesicle) that becomes nearly undetectable by E12.521. To study expression in the optic cup around the time of optic fissure closure, we used a probe that detects all validated transcripts of mouse Nr6a1 at embryonic day 10.5 (E10.5, early optic cup) and E11.5 (time of optic fissure closure). The manufacturer\u2019s control probe against a bacterial sequence was used for reference (Supplementary Fig.\u00a09). At E10.5, we noted diffuse low-level expression throughout the early optic cup and surrounding tissues that becomes significantly decreased by the time optic fissure closure commences (E11.5) (Supplementary Fig.\u00a09). The level of expression in the optic cup is comparable to that observed in the brain vesicle, but higher than that observed in some areas of the surrounding eye mesenchyme.\n\nIn zebrafish, nr6a1 has two paralogs, nr6a1a and nr6a1b, both of which are maternally expressed28,29. At 11\u2009hpf, when the optic vesicle evaginates, nr6a1a is widely expressed throughout the embryo, especially rostrally, showing less expression towards the posterior embryo axis (Fig.\u00a04a, Supplementary Fig.\u00a010a). Expression of nr6a1a is seen in heart and periocular tissue at 14\u2009hpf (Supplementary Fig.\u00a011); expression in the heart reduced by 16\u2009hpf and is absent by 19\u2009hpf (Fig.\u00a04b, c). At 16\u2009hpf, nr6a1a remains widely expressed, while becoming restricted to the ventral regions of the brain, epiphysis, periocular tissues, heart and in the notochord and neural tube (Fig.\u00a04b); expression in the developing eye is reduced compared to the adjacent developing brain (Supplementary Fig.\u00a010b). Notably, nr6a1a expression is absent from the neural-mesodermal progenitor region in the tail of zebrafish embryos, consistent with its role in the trunk differentiation program21. By 19\u2009hpf the expression appears to decrease overall but remains present in the ventral brain regions, notochord, somites, and the pronephric duct (Fig.\u00a04c). At 24\u2009hpf, expression is prominent in the anterior diencephalon, tegmentum, midbrain, and along most of the length of the embryo in the neural tube; interestingly, expression is nearly absent from the neural retina and retina pigmented epithelia but is prominent in the lens (Fig.\u00a04d\u2013d\u201d). After 26\u2009hpf and up to 72\u2009hpf we observed no detectable nr6a1a expression, consistent with published single-cell mRNA expression during zebrafish development28,29.\n\nnr6a1a is expressed ubiquitously at 11\u2009hours post-fertilization (hpf) (a). By 16\u201319\u2009hpf (b, c) expression is present in multiple structures including the somites (S), neural tube (NT) and notochord (N). At 24\u2009hpf, expression remains in the NT but is decreased in the S and N. Expression in the lens (L) is first noted at 19\u2009hpf and is particularly prominent by 24\u2009hpf (d\u2013d\u201d\u2018\u2019). nr6a1b expression at 11\u2009hpf (e) is in\u00a0anterior trunk, localizing to neural tube and somites from 16\u2009hpf (f) and 19\u2009hpf (g). At 24hpf (h\u2013h\u201d) it remains expressed in the neural tube and somites, with faint expression can be seen in the lens. All embryos are oriented in a lateral view, anterior to the left and dorsal up, except (d\u2019, d\u201d, h\u2019, and h\u201d) shown in dorsal views. Scale bar\u2009=\u2009100\u2009\u00b5m. e-epiphysis, l-lens, p-pronephros, h- heart, n-notochord, s-somite, nt-neural tube, ad-anterior diencephalon, tg-tegmentum.\n\nUnlike nr6a1a, nr6a1b expression at 11\u2009hpf is limited to a patch in the posterior neuroectoderm of the embryo but excluded from the most caudal region (Fig.\u00a04e). At 16\u2009hpf and 19\u2009hpf, nr6a1b expression is prominent in the neural tube, somites, and pronephric duct and, like nr6a1a, is excluded from the neural-mesodermal progenitor region in the tail (Fig.\u00a04f, g). By 24\u2009hpf, expression is decreased in most tissues but remains in the tegmentum, cranial ganglia, neural tube, and somites in the distal region of the trunk (Fig.\u00a04h-h\u201d). By 36\u2009hpf and through 72\u2009hpf, nr6a1b is notably expressed in the developing lens, brain, and cranial ganglions. (Supplementary Fig.\u00a012). At 72\u2009hp we also note faint expression in the retina and in the presumed RPE (Supplementary Fig.\u00a012d, e).\n\nAll MO experiments were carried out following the guidelines set forth for their use in zebrafish30,31,32. These guidelines include: 1) use of two non-overlapping MOs (one translation blocking (TB), one splice blocking (SB)); 2) observation of a consistent phenotype with both TB and SB MOs for each paralog; 3) a correlation between dose of MO and phenotype, with lower concentrations of MO causing a milder phenotype; 4) validation of the efficacy of SB MOs by RT-PCR analysis; 5) lack of a phenotype with injection of a control MO; and; 6) partial rescue of the MO phenotype with co-injection of the corresponding human mRNA.\n\nTo test the functional consequences of nr6a1a and nr6a1b knockdown, we designed TB and SB MOs for each paralog of the gene. The sequence of the TB morpholinos does not overlap with the human mRNA sequence and is therefore unlikely to interfere with mRNA rescue experiments (Supplementary Fig.\u00a013). Morphants were divided into four phenotypes: normal, mild (normal/near normal body axis with microphthalmia), moderate (slightly shortened and mildly curved body axis, microphthalmia\u2009\u00b1\u2009coloboma and heart edema) or severe (significantly shortened and curved body axis, microphthalmia\u2009\u00b1\u2009coloboma, heart edema) (Fig.\u00a05a\u2013d\u2019, Supplementary Figs.\u00a014, 15). Embryos were scored at 72\u2009hpf (after optic fissure closure and initial stages of eye growth are normally completed) to ensure microphthalmia/coloboma represents a true phenotype and not because of developmental delay or undergoing growth compensation.\n\nControls (a, a\u2019) have a straight body axis and the optic fissure (OF) is closed. The nr6a1+nr6a1b morphants that have a mild phenotype (b, b\u2019) have close to a normal body with microphthalmia and heart edema; a moderate phenotype (c, c\u2019) with a slightly bent body axis with smaller eyes, coloboma and a severe heart edema; and severe morphants (d, d\u2019) have a curved body axis with smaller eyes, coloboma and heart edema. The morphant phenotype was rescued when the morpholinos were co-injected along with the human-NR6A1-wild-type mRNA. However, there was no significant rescue in the morphant phenotype when the morpholinos were injected with either R92W or R436C human disease-causing variants (e). Morpholinos were injected at 0.75\u2009ng each (1.5\u2009ng total). Embryos were imaged at 72\u2009hpf. Scale bar\u2009=\u2009100\u2009\u00b5m. Statistical significance was calculated using Chi-square test and Fisher\u2019s test. P value of nr6a1-TB-MO(a\u2009+\u2009b) V hWT-NR6A1 is <0.0001 and between nr6a1-TB-MO(a\u2009+\u2009b) V hR92W and hR436C are 0.0266 and 0.5169 respectively. Source data file has been provided for (e).\n\nKnockdown of nr6a1a (Supplementary Fig.\u00a014e) or nr6a1b (Supplementary Fig.\u00a015e) with either TB-MO or SB-MO resulted in a significant number of moderate and severe phenotypes with few mild phenotypes. Although the effect of TB-MO and SB-MO were similarly potent for nr6a1b knockdown, the SB-MO had a stronger effect than the TB-MO for nr6a1a. SB-MO knockdown of the gene was validated for both paralogs by reverse transcription-PCR experiments (Supplementary Figs.\u00a014f, 15f). MOs can elicit p53-activated cell death and cause non-specific defects33. We therefore tested MOs for both nr6a1 paralogues at all concentrations used with and without co-injection of a p53-MO. The phenotypic spectrum was not affected by co-injection of this p53 MO, suggesting widespread cell death was not the primary cause of our observations (Supplementary Fig.\u00a016).\n\nOverexpression of 100\u2009pg of human NR6A1 mRNA in zebrafish shows no overt phenotype (Supplementary Fig.\u00a017a, b). Co-injection of 2\u2009ng and 1.25\u2009ng of nr6a1a and nr6a1b, TB-MO respectively along with 100\u2009pg of WT human mRNA (hWT-NR6A1), resulted in a rescue, with over 60% embryos exhibiting a normal/control-injected phenotype, thus validating our TB-MO\u2019s (Supplementary Figs.\u00a014g, 15g). In contrast, co-injection with either hR92W or the hR436C missense variants of NR6A1 identified in coloboma patients were significantly less effective in rescuing the zebrafish nr6a1a/b knockdown, indicating that the missense variants are deleterious (Supplementary Figs.\u00a014g, 15g).\n\nTo study the effect of knocking down of both nr6a1a and nr6a1b zebrafish paralogues, we co-injected 0.75\u2009ng of TB-MO for each paralog (1.5\u2009ng total), resulting in a similar spectrum of eye and body axis phenotypes compared to the knockdown of individual paralogues (Fig.\u00a05a\u2013d, a\u2019\u2013d\u2019). At 48\u2009hpf, double morphants exhibited abnormal expression of nphs1/nephrin and nphs2/podocin, both of which are required for the formation, maturation and maintenance of kidneys34,35,36 (Fig.\u00a06a\u2013j). The spectrum of expression patterns included reduced, absent, midline and asymmetric expression of nphs1/2 (Fig.\u00a06d, h) which persisted at 72\u2009hpf (Supplementary Fig.\u00a018).\n\nAt 48\u2009hpf, control embryos demonstrate bilateral expression of nphs1 (nephrin) and nphs2 (podocin) (a, f), markers of kidneys. Knockdown of nr6a1(a\u2009+\u2009b) resulted in a range of abnormal expression patterns including mildly reduced expression (b, g), moderately reduced expression (c, h), unilateral or midline expression (d, i) or absent expression (e, j) of nphs1 and nphs2. Somites (the early precursors of vertebrae) have a sharp, chevron shape with an approximately 90-degree angle in control zebrafish at 24\u2009hpf (k). Knockdown of nr6a1a and nr6a1b resulted in a range of abnormal expression patterns including chevron shapes with obtuse angles (l), as well as mildly rounded (m) or flattened (n) somites. The sclerotome marker pax9 is expressed in a continuous angular pattern in the ventromedial portion of somites along the length of 24\u2009hpf control embryos (o). Double morphant embryos exhibit a spectrum of abnormal patterns of pax9 expression including reduced levels of expression (p), patchy expression extending beyond the caudal tip of the yolk (q) and patchy expression ending near the caudal end of the yolk sac (r). nr6a1(a\u2009+\u2009b) morphants have reduced number of somites (s), statistical significance is calculated using two tailed t-test. Std-MO n\u2009=\u200920 and nr6a1-TB-MO(a\u2009+\u2009b) n\u2009=\u200950. Data is represented as difference between means of nr6a1-TB(a\u2009+\u2009b)\u2013Std-MO\u2009\u00b1\u2009SEM (\u22124.490\u2009\u00b1\u20090.5629). P value is <0.0001. 0.75\u2009ng of each paralogue is used for knock-down. Numbers of each representative pattern are given in each panel. Scale bar\u2009=\u2009100\u2009\u00b5m. Source data file has been provided for (s).\n\nVertebrae develop from the sclerotome of somites during development37,38. At 24\u2009hpf, control embryos have chevron shaped somites, while morphants exhibit a spectrum of abnormal morphologies ranging from blunting of the chevron angle to more severe U-shaped or flattened somites (Fig.\u00a06k-n). Double morphants also exhibit a significantly decreased number of somites, consistent with the missing vertebrae phenotype in several patients (Fig.\u00a01, Fig.\u00a06s). Similarly, the sclerotome marker pax939,40 is expressed in a uniform and regular pattern in the ventromedial region of somites of control embryos at 24\u2009hpf (Fig.\u00a06o). By comparison, double morphants show varying degrees of decreased and/or patchy pax9 expression (Fig.\u00a06p-r). Overall, our results indicate that it is likely that nr6a1a\u2009+\u2009b morphant embryos have defective vertebrae development.\n\nInjection of TB-MO\u2019s, nr6a1(a\u2009+\u2009b), resulted in 16% and 49% embryos having severe and moderate phenotypes respectively; co-injection of 100\u2009pg hWT-NR6A1 mRNA, resulted in >50% embryos having straight body and normal eye, however 19% of these embryos show, heart edema. Breaking down each phenotype separately, we rescued approximately 55% embryos for coloboma, 53% for body axis and 44% for heart edema. Neither the hR92W nor hR436C NR6A1 mRNAs resulted in significant rescue, of any of the described phenotypes, confirming the pathogenicity of these variants (Fig.\u00a05e, Supplementary Data\u00a05). Injection of 0.75\u2009ng of either nr6a1a TB-MO or nr6a1b TB-MO resulted in a significantly milder phenotype, suggesting that co-injection of these had at least an additive phenotypic effect in the combined MO injection experiment (Supplementary Fig.\u00a019).\n\nA prior study reported that both overexpression and loss-of-function of nr6a1 can result in developmental phenotypes in Xenopus laevis41, we also evaluated the effect of injection of human NR6A1 mRNA on zebrafish development. Overexpression of 150\u2009pg of hNR6A1 mRNA resulted in microphthalmia and heart edema with a straight body axis (n\u2009=\u200991/108) (Supplementary Fig.\u00a017c, c\u2019). At 200\u2009pg, overexpression of hNR6A1 mRNA, phenotypes were more severe including colobomatous microphthalmia, heart edema and a bent body axis (n\u2009=\u200960/92), with 26% (n\u2009=\u200924/92) (Supplementary Fig.\u00a017 d-f\u2019), exhibiting noticeable shortening and loss of chevron-shaped somites (Supplementary Fig.\u00a020); a minority of embryos (n\u2009=\u20098/92) developed no discernible eyes (Supplementary Fig.\u00a017f, f\u2019). Taken together, these experiments demonstrate that normal zebrafish eye development is sensitive to nr6a1 dosage and both reduced and increased nr6a1 expression result in developmental phenotypes analogous to human colobomatous microphthalmia.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60574-y/MediaObjects/41467_2025_60574_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60574-y/MediaObjects/41467_2025_60574_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60574-y/MediaObjects/41467_2025_60574_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60574-y/MediaObjects/41467_2025_60574_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60574-y/MediaObjects/41467_2025_60574_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60574-y/MediaObjects/41467_2025_60574_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Here we describe six NR6A1 variants that cause an autosomal dominant syndromic form of colobomatous microphthalmia and missing vertebrae with or without congenital kidney abnormalities, which we term OVR syndrome. As with many other cases of syndromic and non-syndromic microphthalmia/coloboma, the OVR syndrome show, incomplete penetrance and variable expressivity1. By 2015 ACMG/AMP variant interpretation criteria, we considered chr9:g.124536516_124643457del pathogenic (criteria: PVS1, PP1_Strong, PM2) and other MAC-associated variants likely pathogenic (criteria: Ser76del, PM1, PM2, PM4, PP3; Arg92Trp, PS3, PM1, PM2, PP3; Arg436Cys, PS3, PM2, PP3; Ser301Ter & Ser322Ter, PVS1, PM2). Thus, NR6A1 variants were causative among 1.3%\u20131.4% families in two independent patient cohorts (3 out of 224 in the NEI coloboma cohort and 3 out of 215 in the MAC cohort in the UK100KGP).\n\nThe NEI study, which specifically recruits patients with coloboma/microphthalmia, performs extensive phenotypic analysis on probands including complete eye examination, kidney ultrasound, neuropsychological testing, physical exam/dysmorphology exam, spine x-ray, routine bloodwork/urinalysis, ECHO (in the presence of a murmur), and audiology. Additional testing (e.g., brain MRI) may be performed on an as needed basis. In addition, all available first-degree relatives undergo a complete dilated fundus exam. As such, we have greater certainty that a patient is truly unaffected, say, by coloboma, rather than being simply asymptomatic. Indeed, the mother of the proband in family COL034 (COL034.2), for example, was visually asymptomatic and unaware of a forme fruste of coloboma or a missing thoracic vertebra prior to her exam with us. Conversely, the Genomics England database spans an entire population in a gene and phenotype-agnostic manner but may contain incomplete or unrelated phenotypic information. As such, phenotypes such as intellectual disability (Individual B1, Supplementary Data\u00a01) may be spurious associations or may be uncommon manifestations of an NR6A1-related syndrome. Confirmation of these and other possible phenotypes awaits description of additional cases. We include congenital renal disease as part of this syndrome not only because two individuals in two separate pedigrees exhibited these phenotypes, but also because Rasouly et al. have simultaneously identified presumed loss-of-function variants in thirteen individuals with congenital renal abnormalities, with or without congenital eye abnormalities, providing further validation of our findings42. Using a combination of imaging and genetic data, Sun et al. recently reported that NR6A1 was a key gene associated with differences in vertebral number43. In addition, Jacquinet et al. subsequently noted congenital kidney, uterine, and vertebral anomalies in three patients and in zebrafish mutant line44. None of our patients reported uterine abnormalities or difficulty with pregnancy; however, pelvic ultrasounds were not generally performed, so subclinical phenotypes cannot be ruled out. Because none of our affected patients displayed heart defects, we currently do not include this as part of the syndrome, even though heart edema was noted in the morphants. However, we cannot rule out that congenital heart anomalies will be found in subsequent patients as more are identified.\n\nAdditional studies are required to understand the detailed disease mechanisms of NR6A1 variants. Deletion and presumed truncating variants are generally associated with a haploinsufficiency mechanism such as nonsense-mediated decay. This may be tested by expression profiling in patient cells. However, the subcellular localization defects of the two missense variants in NR6A1 hint at more than one mechanism of disease. The early expression of NR6A1 homologs in mouse and zebrafish are consistent with the previous data21 and suggest that the colobomatous microphthalmia observed in our patients may result from effects on early eye morphogenesis rather than a defect in optic fissure closure per se. Given the known roles of Nr6a1 in stem cell biology, we posit that the developmental trajectory of the optic cup neuroepithelium is altered in a way not consistent with optic fissure closure. However, given the expression of nr6a1a/nr6a1b in the lens vesicle in zebrafish and mouse, a non-cell autonomous effect on optic fissure closure cannot be excluded. In fact, evidence from Mexican surface and cave fish (Astyanax mexicanus) experiments show that early neural retina development and maintenance relies on a healthy lens45,46.\n\nRecently, NR6A1 has been shown to be important for somite development and, consequently, vertebral number, thus strengthening the phenotyping link with missing vertebrae we describe in humans18,19,20,21,47. Vertebrae differentiate from somites which develop their stereotyped segmentation pattern in an anterior to posterior progression during early development, with successive HOX genes specifying different regions of the spine via a process called temporal collinearity. Homozygous germline inactivation of Nr6a1 in mice results in embryonic lethality around E10.5 with cardiovascular, neural tube and hindgut abnormalities as well as fewer somites (13, rather than the normal 25)21,48. In Sus domesticus (pig), NR6A1 was identified as a quantitative trait locus for vertebral number, which is known to vary between breeds18,20. In Equus assinus (donkey), an NR6A1 intronic polymorphism is associated with body size/vertebral number and a single nucleotide polymorphism in exon 8 is associated with the number of lumbar vertebrae in Kazakh sheep19,47. In developing Xenopus, NR6A1 is expressed in late tailbud and neurula stages; overexpression results in posterior defects and disturbed somite formation, while expression of a dominant negative form of the receptor results in abnormal neural tube differentiation, loss of head structure including eyes41, and downregulation of a retinoic acid receptor (RAR\u03b32) anteriorly49. Retinoic acid treatment of embryos upregulates expression of NR6A1, increasing primary neurogenesis via factors such as NeuroD, XDelta1 and x-ngnrl. Retinoic acid is a known and important regulator of both ocular and kidney development50,51; whether retinoic acid receptor signaling is disrupted in model systems of Nr6a1/nr6a1 is currently under investigation. However, all phenotypes previously observed when modulating the activity of NR6A1 in animal models are consistent with the developmental defects in the eyes, kidneys, and vertebrae that we observe in patients carrying deleterious mutations in NR6A1.\n\nIn this study, we identified novel NR6A1 variants in three unrelated families with an OVR syndrome; these findings were further corroborated in an independent cohort using a genome first approach. Using in silico prediction and molecular studies we demonstrated that these highly conserved variants disrupt NR6A1 protein structure leading to mis-localization at the cellular level. We further demonstrated enrichment of coloboma-associated genes with NR6A1 in fetal, but not adult tissues. Expression of NR6A1 homologs in mouse and zebrafish embryos suggests disease relevant tissue-specific gene expression pattern. This was further confirmed by in vivo experiments where the knockdown of zebrafish nr6a1a and nr6a1b resulted in ocular, renal and vertebral phenotypes that were partially rescued with WT human NR6A1 mRNA but not with the two variants tested. This data implicates the human NR6A1 gene variants with the OVR syndrome.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The design and the conduct of the study complied with all relevant regulations governing research on human subjects and according to the principles of the Declaration of Helsinki. Complete eye examinations and genetic testing at the National Eye Institute (NEI) were conducted after informed consent from all participants under National Institutes of Health-Internal Review Board-approved clinical protocols (NCT01778543, NCT01087320, NCT02077894, www.clinicaltrials.gov). This consent included permission to publish deidentified data. No compensation was provided to the participants. Probands underwent systemic testing as clinically indicated, which included physical exam, kidney ultrasound, routine blood chemistries, audiology, and spine x-ray. Eye examinations included age-appropriate testing of visual acuity, refraction, ocular motility/alignment, slit lamp exam, dilated fundus exam, and ophthalmic photography. Specific informed consent for exome/genome sequencing was obtained under an IRB-approved protocol, along with pre- and post-test genetic counseling (NCT02077894). Family COL005 and COL034 were previously reported as Family 1 and 2, respectively, without molecular characterization and detailed individual phenotyping data16. For patients and relatives recruited from the Genomics England 100,000 Genomes Project (UK100KGP), informed consent for whole genome sequencing (GS) was obtained in accordance with approval from the HRA committee East of England-Cambridge South (REC 14/EE/1112)17. Sex was not considered as an independent variable as OVR syndrome is autosomal dominant, affecting both males and females, and due to the relatively small size of the studied families. Sex reported in Table\u00a01 is self-identified by the participants.\n\nGenomic DNA samples prepared from blood or saliva from NEI patients, and their family members were subjected to short-read Next-generation sequencing (NGS) using Illumina platforms. In total, 101 proband samples were subjected to amplicon sequencing of the NR6A1 gene (Supplementary Data\u00a02) using a MiSeq sequencer (2\u2009\u00d7\u2009300\u2009bp paired-end), 57 samples subjected to exome sequencing (2\u2009\u00d7\u2009150\u2009bp paired-end, xGen exome v1 supplemented with additional probes, Blueprint Genetics), 66 samples subjected to GS (2\u2009\u00d7\u2009150\u2009bp paired-end, PCR-free library, NIH Intramural Sequencing Center). Reads were aligned to the GRCh38 reference genome, small variants and structural variants were then called, annotated, and prioritized using a custom NGS analysis pipeline (https://github.com/NIH-NEI/NGS_genotype_calling & https://github.com/NIH-NEI/variant_prioritization).\n\nSanger sequencing was performed to confirm select variants in probands and family members using the BigDye-direct sequencing kit (Thermo Fisher) using primers provided in Supplementary Data\u00a02. The deletion breakpoint in family COL005 was also determined by PCR and Sanger sequencing (Supplementary Data\u00a02). Breakpoint PCR was further used for genotyping of the COL005 family. The logarithm of the odds (LOD) score in family COL005 was estimated using the formula log10(1/0.5Segregations).\n\nAdditional patients and family members underwent Genome Sequencing (GS) as part of the UK100KGP including the clinical variant interpretation pipeline (The National Genomics Research Library v5.1, Genomics England. doi:10.6084/m9.figshare.4530893/7. 2020.). Genome data from affected individuals recruited with a clinical phenotype in keeping with microphthalmia, anophthalmia or coloboma were interrogated for rare (minor allele frequency <0.001, gnomAD v3.1 dataset) biallelic or de novo protein altering variants across the genome. Candidate variants underwent manual curation including in silico prediction, literature search and pathway analysis to establish biological plausibility as a pathogenic variant in developmental eye disease. Additional analyses of all rare protein altering variants in NR6A1 across the entire UK100KGP was performed to identify any individuals outside of the ophthalmology cohort who harbored a candidate pathogenic variant. All variants were manually inspected in the Integrative Genomics Viewer (IGV) after loading sample bam files. Variants appeared to be artifacts were not reported.\n\nThe 2015 ACMG/AMP sequence variant interpretation guidelines were followed for variant classification52,53. The PM1 (functional domain) criterion was applied to variants in part of the DNA binding domain, a.a. Thr68-Lys119, as the region is highly constrained for missense variations in gnomAD (v2.1.1, missense observed/expected\u2009=\u20090.19, p value\u2009=\u20096\u2009\u00d7\u200910\u22126). The PP3 criterion was applied to missense variants based on a collection of in-house in silico prediction tools (https://github.com/NIH-NEI/variant_prioritization) and the inframe deletion variant based on five in silico prediction tools (CAPICE, FATHMM-indel, MutationTaster, MutPred-Indel, and SIFT).\n\nA structural model of NR6A1 was generated using the AlphaFold server, AF-Q15405-F1-model_v4). The Zn-finger domain (ZFD) and nuclear receptor ligand binding domain (NR_LBD) were saved as two PDB files. The binding of DNA to the ZFD of NR6A1 was modeled using a single ZFD domain of the retinoid X receptor alpha-liver X receptor beta (PDB ID: 4NQA) in a complex with DNA. Two variants (R92W and R436C) were generated using the Edit\u2009>\u2009Swap\u2009>\u2009Residue function on the respective domain PDB files in YASARA. Variant models were optimized and minimized using gradient descent. All two minimized mutants and the two WT, ZFD and NR_LBD models were subjected to 10\u2009ns of Molecular Dynamics (MD) using YASARA\u2019s \u201crun.mcr\u201d macro. Ion concentration was added as a mass fraction with 0.9% NaCl. The simulation temperature was set to 310\u2009K with a water density of 0.997\u2009g/mL. For each domain, the cell size extended to 10\u2009\u00c5 beyond each side of the protein in the shape of a cube. Dimensions were 90.2\u2009\u00c5\u2009\u00d7\u200990.2\u2009\u00c5\u2009\u00d7\u200990.2\u2009\u00c5 and 82.5\u2009\u00c5\u2009\u00d7\u200982.6\u2009\u00c5\u2009\u00d7\u200982.6\u2009\u00c5 for the nuclear receptor Zn-finger and ligand-binding domains, respectively. Each simulation was run in YASARA using an AMBER14 forcefield, with a timestep of 2.5\u2009fs. Simulation snapshots were outputted for every 0.1\u2009ns, resulting in 100 simfiles for each simulation.\n\nDanio rerio were maintained under standard conditions. Embryos were staged according to Kimmel et al., 199554. ABTL stocks were used for all the experiments, which were carried out in accordance with National Eye Institute, Animal Care and Use Committee Protocol Number NEI-648 and NIH Animal Research Advisory Committee.\n\nEmbryo were fixed in 4% paraformaldehyde (PFA) overnight at 4\u2009\u00b0C and dehydrated in methanol for 1\u2009h at \u221230\u2009\u00b0C. The embryos were rehydrated, treated with proteinase-K and re-fixed with 4% PFA. Pre-hybridization and hybridization were carried out at 65\u2009\u00b0C. RNA probes were synthesized using a DIG labeling kit (Millipore-Sigma, 112770739) following manufacturer\u2019s protocol. nr6a1a RNA probe was synthesized from a CDS clone in TOPO TA vector (ThermoFischer Scientific), while nr6a1b was synthesized using PCR product as a template. Primers are noted in Supplementary Data\u00a03. Samples were hybridized overnight with RNA probes at 65\u2009\u00b0C, washed, incubated with Anti-DIG antibody (Millipore-Sigma, 1109327490); color was developed using BCIP/NBT substrate (Millipore-Sigma, 11681451001) in alkaline phosphatase buffer. Embryos were imaged with Leica DM6 dissecting microscope.\n\nAll morpholinos (MO) were obtained from Gene Tools LLC. MOs used to target zebrafish nr6a1a and nr6a1b are given in Supplementary Data\u00a04. Human NR6A1-wild-type, variants NR6A1-R92W and NR6A1-R436C DNA fragments were synthesized and cloned in pCS2+ (Azenta Life Sciences). Plasmids were linearized with Not I restriction enzyme and capped mRNA was synthesized using mMessage mMachine T7 Transcription kit (ThermoFischer Scientific). MOs and mRNA were co-injected into zebrafish embryos at single cell stage. nr6a1a and nr6a1b translation blocking (TB) MOs were used at 2\u2009ng and 1.25\u2009ng respectively. Nr6a1a and nr6a1b, SB-MOs were injected at 2\u2009ng and 1\u2009ng respectively. Human NR6A1-wild-type was used at 100\u2009pg and 150\u2013200\u2009pg for RNA rescue and over expression studies respectively. NR6A1-R92W and NR6A1-R436C RNAs were used at 100\u2009pg for rescue experiments. For over-expression experiments, doses of 100\u2009pg\u2013200\u2009pg hNR6A1 mRNA were injected at the single cell stage. Embryo phenotypes were scored and imaged at 72\u2009hours post-fertilization (hpf) using Leica DM6 dissecting microscope.\n\nHEK293T cells maintained in DMEM with 10% FBS and 1% penicillin-streptomycin were seeded onto 4-well chamber slides, maintained for 24\u2009h and transiently transfected with GFP tagged WT and/or mutant NR6A1 constructs (Azenta Life Science, Burlington, MA, USA) using X-treme Gene HP (Roche, Indianapolis, IN, USA) following manufacturers\u2019 instructions. After 24\u201348\u2009h of transfection, transfected cells were fixed for 15\u2009min in 4% paraformaldehyde (PFA) in PBS. After washing with 1 \u00d7 PBS cells were incubated for 1\u2009h at room temperature with Hoechst33342 (1:250 dilution in PBST). Subsequently, the slides were washed and mounted with Fluoromount-G\u00ae (SouthernBiotech, Birmingham, AL, USA). Zeiss confocal microscopes 880 coupled with an Airyscan\u00ae detector was used for confocal imaging. The images were analyzed using ZEN Software (Carl Zeiss Microscopy LLC, Thornwood, NY). The cell culture experiments were repeated at least three times for each for variant localization studies. Co-transfected cells with both WT and mutant forms of the NR6A1 constructs were fixed for 15\u2009min in 4% paraformaldehyde (PFA) in PBS. After washing with 1 \u00d7 PBS and permeabilization and blocking in ICC buffer (0.5% BSA 0.5% Tween and 0.1% triton X100 1 \u00d7 PBS). Cells were then incubated overnight at 4\u2009\u00b0C with the Fibrillarin antibody (MA3\u221216771, Thermo Fisher Scientific) in ICC buffer. After multiple washes in PBS, the cells were incubated for 1\u2009hr at room temperature in Alexa Fluor\u2122 555 conjugated goat anti-mouse antibody (A-21422, Thermo Fisher Scientific) and Hoechst33342 (1:1000 dilution in ICC buffer). Cells were then washed in PBST before mounting with Fluoromount-G\u00ae (SouthernBiotech, Birmingham, AL, USA) imaging.\n\nTransfection efficiency was determined by measuring the expression of GFP after 48 hrs post transfection. HEK293 cells were detached from the plates using Trypsin for 5\u2009min followed by neutralization with serum containing media. The cells were then fixed for 15\u2009min in 4% paraformaldehyde (PFA) in PBS and then collected in 1\u00d7 PBS containing 2% FBS (FACS buffer) and washed 2 times by centrifugation. The cell suspension was filtered through a 50\u2009\u00b5m cell strainer. Data was acquired with a CytoFlex NUV instrument (Beckman Coulter, Brea CA) using the blue light excitation and 525\u2009nm emission to detect GFP and violet light excitation and 450\u2009nm emission to detect DAPI detection. Data analysis was done using CytExpert software Version 2.5 (Beckman Coulter, Brea CA). Interesting cells were identified as DAPI negative, in the whole cell cluster in a FSC vs. SSC plot and being in a single cell state in the FSC-A vs. FSC-Width. Transfection efficiency was quantified as the Stain Index of GFP fluorescence intensity, which was calculated using the median fluorescent intensity and robust Standard Deviation as described. The cell culture experiments were repeated at least three times for each for variant localization studies.\n\nC57Bl/6 mice (The Jackson laboratory, Strain #:000664) were housed in individually ventilated cages (five per cage) under conditions of a 14\u2009h/10\u2009h light/dark cycle and ambient temperature of 22\u2009\u2009\u00b1\u2009\u20092\u2009\u00b0C with 30\u201370% humidity. Experiments were carried out in accordance with National Eye Institute, Animal Care and Use Committee Protocol Number NEI-605 and NIH Animal Research Advisory Committee. Nr6a1 mRNA expression in mouse was assayed by RNA in situ hybridization with Nr6a1(Cat: 1314941-C1) probe using the RNAScope Assay, Multiplex fluorescent Reagent Kit V2 (Advanced Cell Diagnostics (ACD), Newark, CA, USA) on E10.5 and E11.5 cryosections55. The manufacturer\u2019s control against a bacterial sequence was used for comparison, RNAscope\u2122 3-plex Negative Control Probe (Cat. #320871). Exposure settings were identical for all samples to facilitate comparisons in expression levels. Expression levels in the eye were quantitated relative to brain and periocular tissue using a free open source bioimaging QuPath software56 for comparison. A \u201cbest fit\u201d model was applied to all samples analyzed.\n\nFIBRILLARIN antibody from Invitrogen with Cat#-MA1-91878, Lot#-ZA389553, Clone#- DYKDDDDK Tag Monoclonal Antibody (FG4R) raised in mouse was used at 1:1000 dilution.\n\nThe h5ad (d27a79a1-8a5f-404d-8063-52e19122ef49.h5ad for adult and 88444d73-7f55-4a62-bcfe-e929878c6c78.h5ad for fetal) from the HRCA project were downloaded from cellxgene.cziscience.com and the raw counts were summed at the sample and cell type level to create a pseudobulk matrix with the python package ADPBulk (https://github.com/noamteyssier/adpbulk). The eyeIntegration (which includes GTEx) gene counts and sample level metadata were downloaded from eyeIntegration.nei.nih.gov (https://hpc.nih.gov/~mcgaugheyd/eyeIntegration/2023/gene_counts.csv.gz and https://hpc.nih.gov/~mcgaugheyd/eyeIntegration/2023/eyeIntegration23_meta_2023_09_01.built.csv.gz). The pseudobulk and bulk RNA-seq counts were normalized by counts per million (CPM) and transformed in R/4.3 to have a mean of zero and a standard deviation of one. Plots were created in R/4.3 with the ggplot2, cowplot, and ggbeeswarm packages.\n\nThe same data used in the plotting was used to create separate gene expression matrices that for either fetal primary tissue (retina and RPE) or adult tissue (retina and RPE). We used spatial quantile normalization (spqn) analysis to derive gene correlation matrices that were not biased by higher gene expression.\u00a0The same data used in the plotting was used to create separate gene expression matrices that for either fetal primary tissue (retina and RPE) or adult tissue (retina and RPE). We used spatial quantile normalization (spqn) analysis to derive gene correlation matrices that were not biased by higher gene expression57. Briefly, the first four principal components were removed with the WGCNA tool removePrincipalComponents. The correlation matrices were created with the base R \u201ccor\u201d function. The correlation scores were expression transformed with the spqn package\u2019s \u201cnormalize_correlation\u201d function with the parameters \u201cngrp\u2009=\u200920, size_grp\u2009= 300, ref_grp\u2009=\u200918\u201d. The correlation matrix was filtered to only contain correlations between NR6A1 and all other genes. Code for all bioinformatic analyses have been deposited in 10.5281/zenodo.14.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The genome sequencing data from NEI participants generated in this study have been deposited in the dbGaP database under accession code phs003996.v1. The genome data are available under controlled access due to restriction in participant informed consent; access can be obtained by following dbGaP data access policy. The application process, time frame for review of requests will be according to the published governance structure: https://sharing.nih.gov/accessing-data/accessing-genomic-data/how-to-request-and-access-datasets-from-dbgap. Data for all bioinformatic analyses have been deposited in 10.5281/zenodo.14757568. All other data are provided in the main text or in the Supplementary Information. Source data files are provided with this research article.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Code for scRNA-seq and Multiple Sequence Alignment can be found at GitHub (https://github.com/davemcg/nr6a1/releases/tag/1.3)58. NGS analysis code can be found at GitHub (https://github.com/NIH-NEI/variant_prioritization/releases/tag/v0.1)59.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Williamson, K. A. & FitzPatrick, D. R. The genetic architecture of microphthalmia, anophthalmia and coloboma. Eur. J. Med. Genet. 57, 369\u2013380 (2014).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nChang, L., Blain, D., Bertuzzi, S. & Brooks, B. P. Uveal coloboma: clinical and basic science update. Curr. Opin. 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Variants in NR6A1 cause a novel oculo vertebral renal syndrome. https://doi.org/10.5281/zenodo.14757568 (2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We are grateful to the patients and their families for participation in this research, which spanned over 20 years. We thank the staff at the NIH Clinical Center and the NEI Eye Clinic for patient phenotyping, ophthalmic testing, and ophthalmic imaging. This work utilized the computational resources of the NIH HPC Biowulf cluster (https://hpc.nih.gov). This study was supported by the Intramural Research Program of the NIH. This research was made possible through access to data in the National Genomic Research Library, which is managed by Genomics England Limited (a company owned by the Department of Health and Social Care). The National Genomic Research Library holds data provided by patients and collected by the NHS as part of their care and data collected as part of their participation in research. GA is supported by a Fight For Sight (UK) Early Career Investigator Award (5045/46), the National Institute of Health Research Biomedical Research Centre (NIHR-BRC) at Moorfields Eye Hospital, and the UCL Institute of Ophthalmology, and Moorfields Eye Charity (Stephen and Elizabeth Archer in memory of Marion Woods) and NIH-P20GM139769. RY was supported by the Moorfields Eye Charity Career Development Award and Springboard (GR001155 and GR001210), Medical Research Council (MR/X001067/1) and FODNECYT (1221843). MIM was supported by Moorfields Eye Charity PhD Studentship (GR001661).", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open access funding provided by the National Institutes of Health.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Uma M. Neelathi, Ehsan Ullah.\n\nThese authors jointly supervised this work: Rodrigo M. Young, Bin Guan, Brian P. Brooks.\n\nOphthalmic Genetics & Visual Function Branch, National Eye Institute, National Institutes of Health, Bethesda, MD, USA\n\nUma M. Neelathi,\u00a0Ehsan Ullah,\u00a0Aman George,\u00a0Elangovan Boobalan,\u00a0Daniel Sanchez-Mendoza,\u00a0Chloe Adams,\u00a0David McGaughey,\u00a0Yuri V. Sergeev,\u00a0Ranya AI Rawi,\u00a0Amelia Naik,\u00a0Chelsea Bender,\u00a0Delphine Blain,\u00a0Robert B. Hufnagel,\u00a0Bin Guan\u00a0&\u00a0Brian P. Brooks\n\nUCL Institute of Ophthalmology, University College London, London, UK\n\nMara I. Maftei,\u00a0Michel Michaelides,\u00a0Siying Lin,\u00a0Gavin Arno\u00a0&\u00a0Rodrigo M. Young\n\nHarkness Eye Institute, Columbia University, New York, NY, USA\n\nIrene H. Maumenee\n\nMoorfields Eye Hospital, NHS Foundation Trust, London, UK\n\nMichel Michaelides\u00a0&\u00a0Siying Lin\n\nTorbay Hospital, Torbay and South Devon NHS Foundation Trust, Devon, UK\n\nTun Giap Tan\n\nFlow Cytometry Core, National Eye Institute, Bethesda, MD, USA\n\nRafael Villasmil\n\nCenter for Integrated Health Care Research, Kaiser Permanente Hawai\u2019i; Hawai\u2019i Permanente Medical Group, Honolulu, HI, USA\n\nRobert B. Hufnagel\n\nGreenwood Genetic Center, Greenwood, SC, USA\n\nGavin Arno\n\nCenter for Integrative Biology, Universidad Mayor, Santiago, Chile\n\nRodrigo M. Young\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nE.U., R.B.H., M.I.M., G.A., and B.G. performed exome/genome analysis and variant confirmation in the NIH and UK100KGP populations. R.A., A.N., and C.B. performed variant confirmation, including confirmation of deletion breakpoints. I.H.M., D.B., S.L., T.G.T., M.M., and B.P.B. performed patient examination/phenotyping. Y.V.S. performed molecular dynamic modeling. D.M. performed bioinformatic analyses of NR6A1. E.B. preformed RNA scope analysis. U.M.N. designed, performed and analyzed zebrafish morpholino knockdown, mRNA rescue experiments and imaging. U.M.N. and D.S.M. performed zebrafish in situ hybridization. R.Y. assisted in interpretation of zebrafish data. A.G. and C.A. performed cell culture and transfection experiments. A.G. performed confocal microscopy. R.V. performed flow cytometry. All co-authors provided draft language and experimental design for their portions of the manuscript, as well as a critical review of the entire manuscript. BPB provided overall project conception and experimental design, drafting of the manuscript and project support.\n\nCorrespondence to\n Brian P. Brooks.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Edwina McGlinn and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Neelathi, U.M., Ullah, E., George, A. et al. Variants in NR6A1 cause a novel oculo vertebral renal syndrome.\n Nat Commun 16, 6111 (2025). https://doi.org/10.1038/s41467-025-60574-y\n\nDownload citation\n\nReceived: 09 November 2024\n\nAccepted: 29 May 2025\n\nPublished: 03 July 2025\n\nVersion of record: 03 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60574-y\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 41.25c-9.8 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disruption of PRC1.1 complex enhances bone remodeling", + "journal": "Nature Communications", + "published": "08 May 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_MOESM5_ESM.csv" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_MOESM7_ESM.csv" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_MOESM8_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_MOESM9_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE280429", + "/articles/s41467-025-59638-w#Sec39" + ], + "code": [], + "subject": [ + "Diseases", + "Drug discovery", + "Histone post-translational modifications", + "Musculoskeletal development" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5432206/v1.pdf?c=1746788954000", + "research_square_link": "https://www.researchsquare.com//article/rs-5432206/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-59638-w.pdf", + "preprint_posted": "03 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Polycomb repressive complexes (PRCs) are pivotal epigenetic regulators preserving cell identity by restricting the transcription responsiveness to sub-threshold levels of extracellular signals. Their roles in osteoblast function and bone formation remain largely unexplored. Here in aging osteoblasts, we observed a selective activation of PRC1.1 complex, with KDM2B acting as a chromatin-binding factor and BCOR and PCGF1 serving as essential catalytic partners for histone H2A monoubiquitylation (H2AK119ub1). Using genetic models, we found that osteoblast-specific KDM2B inactivation significantly enhances bone remodeling under steady-state conditions and in scenarios of bone loss. This enhancement is attributed to H2AK119ub1 downregulation which leads to the derepression of Wnt signaling. Furthermore, we developed a small molecule termed iBP, that specifically inhibits the interaction between BCOR and PCGF1, thereby suppressing PRC1.1 activity. Notably, iBP promotes bone formation in mouse models of bone loss. Therefore, our findings suggest that targeting PRC1.1 augments cellular responses to Wnt signaling and may offer a promising strategy to counteract bone deterioration.Health sciences/DiseasesBiological sciences/Drug discovery", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryinformationTargeteddisruptionofPRC1.1complexenhancesboneremodeling.pdfsupplementary figures and methods", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Polycomb repressive complexes (PRCs) are pivotal epigenetic regulators that preserve cell identity by restricting transcription responses to sub-threshold extracellular signals. Their roles in osteoblast function and bone formation remain unclear. Here in aging osteoblasts, we found marked activation of PRC1.1 complex, with KDM2B acting as a chromatin-binding factor and BCOR and PCGF1 enabling histone H2A monoubiquitylation (H2AK119ub1). Osteoblast-specific Kdm2b inactivation significantly enhances bone remodeling under steady-state conditions and in scenarios of bone loss. This enhancement is attributed to H2AK119ub1 downregulation and subsequent Wnt signaling derepression. Furthermore, we developed a small molecule termed iBP, that specifically inhibits the interaction between BCOR and PCGF1, thereby suppressing PRC1.1 activity. Notably, iBP administration promotes bone formation in mouse models of bone loss. Therefore, our findings identify PRC1.1 as a critical epigenetic brake on bone formation and demonstrate that therapeutic targeting of this complex enhances Wnt pathway activation, offering a promising strategy against skeletal deterioration.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The adult skeleton undergoes continuous bone remodeling, involving the resorption of mineralized bone by osteoclasts (OCs) and the subsequent formation of bone matrix by osteoblasts (OBs), which then become mineralized. This dynamic process is vital for maintaining skeletal integrity and ensuring mineral homeostasis. An imbalance between OBs and OCs leads to defective ossification, which in turn can result in conditions such as skeletal dysplasia or an increased risk of fractures1,2. The process of ossification is influenced by various factors, including nutrition, pharmaceuticals, hormones, inflammation, and mechanical stress on the bones, etc. Consequently, disorders such as trauma, infection, rheumatoid arthritis, postmenopausal osteoporosis and disuse osteoporosis often present with bone loss. The deterioration of skeletal integrity significantly raises the risk of pathological fractures and negatively impacts overall well-being.\n\nOBs, as the primary bone-forming cells, play a crucial role in bone formation, maintenance, and remodeling through functions such as synthesizing the bone matrix, mineralization, producing signaling molecules and interacting with OCs3,4. To develop effective and safe therapeutics to modulate bone formation, it is crucial to decipher the molecular mechanisms that govern the differentiation and functionality of OBs.\n\nOver the past two decades, genetic studies in humans and mice have consistently highlighted Wnt signaling as a critical driver for bone formation and regeneration5,6. Upon binding of Wnt ligands to their receptors, \u03b2-catenin is stabilized, translocated to the nucleus, and forms a complex with transcription factors such as TCF/LEF, thereby initiating the transcription of target genes in mesenchymal stem cells (MSCs) or OBs for osteogenesis7. TCF/LEF, in conjunction with lineage-specific factors like RUNX2 and OSTERIX/SP7, modulates OB differentiation and the expression of bone matrix genes8,9,10. Deficiency or mutations in Wnt ligands or their corresponding FZD and LRP receptors lead to significant impairments in bone formation5,6,11,12. Although numerous therapeutic strategies have been developed to target Wnt signaling components5,6, their efficacy is contingent on the chromatin environment\u2019s responsiveness13,14. The epigenetic mechanisms that govern the transcriptional response to Wnt/\u03b2-catenin signaling and OB functions are not yet fully understood.\n\nAmong epigenetic repressors, Polycomb group (PcG) proteins have been demonstrated to restrict transcription response to extracellular cues15 including Wnt signaling16,17,18,19. Through forming two biochemically distinct Polycomb Repressive Complexes (PRC1 and PRC2), they maintain a close chromatin environment through distinct activities. PRC2 catalyzes the methylation of histone H3 at lysine 27 (H3K27me), while canonical or non-canonical PRC1 complexes compact chromatin or catalyze monoubiquitylation of histone H2A at lysine 119 (H2AK119ub1) respectively20,21. PRC1 complexes share RING1 proteins (RING1A and RING1B) and are categorized into PRC1.1-PRC1.6 according to the composition of PcG ring-finger domain proteins (PCGF1-PCGF6). Canonical complexes PRC1.2 and PRC1.4 contribute minimally to H2AK119ub1 but play significant roles in chromatin compaction22,23. Non-canonical PRC1 bind chromatin through their unique DNA binding factors. In PRC1.1, FBXL10/KDM2B is responsible for recruiting the complex to CpG-rich promoters through its -CxxC domain24,25, with BCOR and PCGF1 being essential accessory factors for H2AK119ub1 activity26,27. In PRC1.6, MAX/MGA contribute to chromatin binding while PCGF6 plays diverse roles in gene silencing or activation28,29,30. The specific expression patterns and functions of these PRCs in OBs remain largely unknown.\n\nIn this study, we observed an excessive activation of PRC1.1 complex in aging bones. Through generation of OB-specific KDM2B-deficient mouse models, we found that KDM2B inactivation leads to a\u00a0significant increase of bone mass under physiological or ossification-defective conditions through enhanced bone remodeling. These osteogenic effects are dependent on the activation of Wnt signaling, associated with a decrease of H2AK119ub1 deposition at the Wnt/\u03b2-catenin target genes. Subsequently, we identified a compound specifically targeting PRC1.1 that limits H2AK119ub1 activity and activates Wnt signaling pathway. Furthermore, this compound promotes bone formation in vivo, presenting a promising epigenetic therapy for disorders characterized by bone deterioration.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Aging is a prevalent risk factor for bone loss, characterized by a continuous decline in OB activity. By comparing femurs from aged (18 months old) and young (3 months old) mice, we observed impaired ossification in the aged group, as evidenced by reduced trabecular bone revealed through micro-computed tomography (\u03bcCT) analysis (Fig.\u00a01a).\n\na Representative images of scanning sections of mouse femurs at ages of 3 and 18 months. b Volcano plot of DEGs in OBs (n\u2009=\u20093 YOUNG and n\u2009=\u20093 OLD). Upregulated genes are labeled in red and downregulated genes in blue (Wald test, p\u2009<\u20090.05; |Fold\u00a0change|\u2009>1.5). OBs were collected after 14 days of culture by removing the bone fragments. c Heatmap showing mRNA levels of osteocyte aging-associated genes between the two groups, colorscale: Z-Score. d GO enrichment analysis of the biological processes in downDEGs in aged OBs (Fisher\u2019s exact test, p\u2009<\u20090.05). Number of genes and statistical significance are shown. e Heatmap showing mRNA levels of ossification-related genes between the two groups. Colorscale: Z-Score. f, g WB analyses comparing the levels of PRC members and associated histone modifications in OBs from young and aged mice. ACTIN and H3 were used as loading controls, respectively. h Heatmaps showing H2AK119ub1 signals in chromatin of YOUNG and OLD OBs around the peak center (\u00b1\u20095\u2009kb) of H2AK119ub1 target genes. Colors represent CUT&Tag RPM, and rows were ranked by CUT&Tag signals in young group (n\u2009=\u20092 YOUNG and n\u2009=\u20092 OLD). OBs were collected after 14 days of culture by removing the bone fragments. i Boxplots showing quantification of differential H2AK119ub1 enrichment at downDEGs (OLD vs YOUNG, n\u2009=\u2009386, Wilcoxon matched-pairs signed rank test, two tailed, p\u2009<\u20090.0001). The boxplots indicate the median (centre line), the third and first quartiles (box limits) and 1.5\u2009\u00d7\u2009IQR above and below the box (whiskers). j, k The IGV view of increased H2AK119ub1 enrichment at\u00a0the promoter of representative ossification-related genes and their decreased mRNA levels in aged OBs compared with young group, with Actin serving as a negative control. Signals representing CUT&Tag and RNA-seq RPM. 2\u2009M: 2 months; 3\u2009M:3 months;\u00a08\u2009M:8 months; 18\u2009M:18 months. Source data of (f, g, i) are provided as a Source Data file.\n\nTo assess changes in gene expression, we performed RNA sequencing (RNA-Seq) analysis on primary OBs cultured in vitro for two weeks, focusing on the differentially expressed genes (DEGs). In aged OBs, 201 upregulated and 386 downregulated genes were identified relative to the young group (Fig.\u00a01b and Supplementary Data\u00a01). In line with these findings, the expression of numerous genes associated with aged OCs, as previously defined by Zhang et al.31, was altered in aged OBs. These alterations included downregulation of genes such as Alpl, Bglap, Bmp2 and upregulation of genes including Ccr1, Ccl6 (Fig.\u00a01c). Gene Oncology (GO) analysis of downregulated genes in aged OBs (P\u2009<\u20090.05) indicated a significant enrichment in ossification processes (Fig.\u00a01d), underscoring the regulatory roles of identified genes in bone formation. The heatmap in Fig.\u00a01e illustrates the downregulation of leading genes in ossification. Considering individual variability among mice, future studies should control for variables including genetic background, specific age, and environmental factors, to ensure reproducibility. These findings suggest that the reduction in bone mass is intimately linked to the diminished ossification capabilities of OBs. Investigating the specific contributions of these mouse-specific features will also be crucial for translating our findings to broader biological contexts.\n\nRecognizing the established suppressive roles of PRCs on osteogenic signaling during OB differentiation32,33, we compared the protein levels of PRC components in OBs isolated from mice of varying ages. As expected, Western Blot (WB) analysis revealed a significant rise in P16 expression levels with aging, a hallmark of cell senescence34. For reasons that remain to be elucidated, there was a noticeable decrease in the protein levels of EZH2 and SUZ12, which are core components of PRC2. Among the principal PRC1 members, RING1B levels showed a slight increase with age, while the levels of NSPc1/PCGF1 and KDM2B25,35, were significantly elevated. In alignment with these findings, there was a specific and substantial increase in H2AK119ub1 levels and a modest decrease in H3K27me3 levels in aging OBs. The gain of H2AK119ub1 is primarily attributed to elevated levels of PCGF1, as other key members of non-canonical PRC1, such as PCGF3 and PCGF6, show either unchanged or only minor downregulation in expression (Fig.\u00a01f, g). These data link the marked activation of PRC1.1 to bone aging.\n\nGenome-wide profiling through Cleavage Under Targets and Tagmentation (CUT&Tag) analysis with proper spike-in normalization demonstrated a widespread increase of H2AK119ub1 signals in aged OBs compared to the\u00a0young group (Fig.\u00a01h and Supplementary Data\u00a02). Among the 386 downregulated genes in aged OBs, we observed a significant elevation of H2AK119ub1 enrichment (Fig.\u00a01i). Ossification genes serve as illustrative examples while the unaffected gene Actin serves as a negative control (Fig.\u00a01j, k). Collectively, these findings suggest that PRC1.1 activation raises the threshold for transcriptional activation by osteogenic signals, thereby suppressing the ossification process.\n\nTo investigate whether PRC1.1 limits OB functions, we managed to generate a mouse model of conditional ablation of PRC1.1. Accordingly, we created the conditional knockout allele of Kdm2b by flanking exon13 that encodes the CxxC-domain with two loxP sequences (Supplementary Fig.\u00a01a)18. These floxed mice Kdm2bflox/flox were then crossed with the widely used OB-targeting Ocn-Cre mice to generate Kdm2bfl/flOcnCre conditional knockout mice (referred to as CKO mice) (Fig.\u00a02a and Supplementary Fig.\u00a01b). This crossbreeding enabled the specific deletion of the KDM2B-CxxC domain, thereby abrogating its chromatin regulatory activity25,36 in OBs. Notably, CKO mice showed no discernible differences in body size or weight compared to Ocn-Cre mice, referred to as control (CON) at various ages, including 9 and 10 weeks and between 6 to 12 months (Supplementary Fig.\u00a01c).\n\na Schematic diagram of construction of CKO mice. b Statistical analysis of femur length of 9-week-old mice (n\u2009=\u20093 per genotype). c Representative images of 3D reconstruction and scanning sections of femurs from control and CKO mice at the age of 9 weeks. d \u03bcCT analysis of trabecular parameters (n\u2009=\u20093 per genotype). e Representative images of H&E staining of the whole femur (left) and metaphysis (right) from CKO and control mice. f Representative images of femurs from control and CKO mice, the yellow areas represent trabecular bone and the gray areas represent cortical bone. g \u03bcCT analysis of cortical bone parameters (n\u2009=\u20093 per genotype). h, i Schematic diagram of the timeline of the calcein double labeling experiment. Representative images (h) and quantification of calcein double labeling parameters (i) (n\u2009=\u20093 mice per genotype). j Three-point bending experiment, with blue arrows indicating the CKO group and yellow arrows indicating the CON group. k Statistical analysis of the maximum load bearing capacity of fresh unfixed femurs from three-point bending tests (n\u2009=\u20093 mice per genotype). Statistical significance was assessed using Student\u2019s t tests, two-tailed, in (b, d, g, i, k), error bars are presented as mean values\u2009\u00b1\u2009SD. Scale bar, 1\u2009mm in (e) and 100\u2009\u03bcm in (h). CON: Ocn-Cre CKO: Kdm2bfl/fl Ocn-Cre, (a) and (h) created with Biorender (https://biorender.com/w07p252, https://BioRender.com/ibb92gm). Source data of (b, d, e, g, i, j, k) are provided as a Source Data file.\n\nStrikingly, at around 10 months of age, the femur length of CKO mice was significantly increased (Fig.\u00a02b), suggesting potential alterations in the bone\u2019s internal structure. \u03bcCT analysis revealed a substantial enhancement in trabecular bone volume in CKO mice at various ages, with three-dimension (3D) reconstruction at 9 weeks of age (Supplementary Figs.\u00a01d and 2c). Bone metrological analysis demonstrated significant increases in trabecular number (Tb.N), thickness (Tb.Th), and bone volume/total volume (BV/TV) ratio, along with a significant reduction in trabecular separation (Tb.Sp) in CKO mice (Fig. 2d). Hematoxylin-Eosin (H&E) staining of distal femur sections confirmed the increased bone mass in CKO mice (Fig.\u00a02e). Heterozygous (HE) mice, however, showed no apparent differences in trabecular bone architecture compared to the control group (Supplementary Fig.\u00a01e). In contrast to trabecular bone, no significant differences in cortical bone were observed between CKO and control mice, as evidenced by the lack of statistical variation in absolute or relative Cortical bone Area (Ct.Ar) (Fig.\u00a02f, g). Thus, these data reveal a role for KDM2B in the negative regulation of trabecular bone mass.\n\nTo directly assess new bone formation, we administered calcein injections 13 days and 3 days prior to the mice sacrifice, followed by the visualization of fluorescent bands (Fig.\u00a02h). According to the distance between double labels of mineralization, the bone formation rate/bone surface (BFR/BS) and bone formation rate/total volume (BFR/TV) were significantly increased in CKO mice (Fig.\u00a02i). Additionally, we compared the structural integrity of femurs from CKO and control mice using a three-point bending test. By comparing the compression loading, we noticed that the CKO femurs could withhold significantly higher forces (Fig.\u00a02j, k), indicating enhanced bone stiffness compared to controls.\n\nNotably, tartrate-resistant acid phosphatase (TRAP) staining of femurs unveiled a significant increase in the number of OCs in CKO mice as measured by OC per tissue area (N.Oc/T.Ar) (Supplementary Fig.\u00a01f, g). Moreover, the serum levels of RANKL, a well-recognized bone resorption factor were significantly elevated in CKO mice (Supplementary Fig.\u00a01h). These data support that the enhanced bone density observed in CKO mice is not due to decreased bone resorption but attributed to elevated OB function and augmented bone formation. Consistent with this, OB-secreted RANKL has\u00a0been shown to induce OC differentiation13. Therefore, these findings indicate that KDM2B limits bone remodeling under steady states.\n\nNext, we wondered how KDM2B-inactive OBs respond to diverse microenvironments of defective ossification. Initially, we compared the bone architecture of the\u00a0femur between CKO mice and WT mice at 18 months of age. \u03bcCT scanning unveiled that the trabecular bone loss observed in control mice was significantly mitigated in CKO mice (Fig.\u00a03a). A substantial increase in BV and Tb.N, along with a decrease in Tb.Sp, was noted in CKO mice relative to the control group, though Tb.Th remained unchanged (Fig.\u00a03b). Furthermore, to model postmenopausal osteoporosis, we performed ovariectomy (OVX) on both CON and CKO mice. \u03bcCT analysis indicated that CKO mice demonstrated significant resistance to OVX-induced bone loss (Fig.\u00a03c, d). Thus, KDM2B-inactivated OBs effectively counteract bone loss associated with aging or hormonal deficiency.\n\na Representative images of 3D reconstruction and scanning sections of femurs from control and CKO mice at the age of 18 months. b \u03bcCT analysis of trabecular parameters (n\u2009=\u20093 mice per genotype). c Representative images of three-dimensional reconstruction and scanning sections of trabecular\u00a0bone from OVX-CON, OVX-CKO, sham-CON, sham-CKO. d Statistical analysis to compare the BV/TV ratio in designated groups (n\u2009=\u20093 mice per genotype). e Representative images of scanning sections in coronal and sagittal plane of femur of bone defects (three columns on the left) and images of 3D reconstruction of repaired circular bone defect (column on the right) from control and CKO mice. f \u03bcCT analysis of BV/TV ratio of newly formed bone between control and CKO group (n\u2009=\u20093 mice per genotype). g Representative images of H&E staining of femur of repaired bone defect from control and CKO mice, box areas shown at a higher magnification. (C cortex, BM bone marrow). Statistical significance was assessed using Student\u2019s t tests, two-tailed, in (b, f),\u00a0error bars are presented as mean values\u2009\u00b1\u2009SD. Statistical significance was assessed using Two-Way ANOVA, two-tailed, in (d), error bars are presented as mean values\u2009\u00b1\u2009SD. Scale bar, 1\u2009mm in low-power field and 100\u2009\u03bcm in high-power field of 3\u2009g. sham, sham operation. OVX, Ovariectomy. CON Ocn-Cre, CKO Kdm2bfl/fl Ocn-Cre. Source data of (b, d, f, g) are provided as a Source Data file.\n\nTo determine how KDM2B-deficient OBs react to acute stimuli, we created a circular bone defect (1-mm diameter) in the midshaft of the femur using low-speed dental handpieces in both CON and CKO mice (Supplementary Fig.\u00a02a, b). As shown in Fig.\u00a03e, CKO mice exhibited a greater presence of bone-like substances around the circular defects compared to the CON group. The BV/TV value of newly formed bone was approximately three times higher in CKO mice than in\u00a0the CON group (Fig.\u00a03f). Sagittal plane H&E staining clearly confirmed increased osteoid and new bone formation in CKO mice following acute bone trauma (Fig.\u00a03g). Moreover, TRAP staining revealed a significant increase in the number of OCs in new bone, suggesting an enhanced bone turnover in CKO mice (Supplementary Fig.\u00a02c, d). Consequently, the heightened OB functionality due to KDM2B inactivation not only prevents bone loss but also expedites the bone repair process after acute trauma.\n\nSubsequently, we confirmed that KDM2B deficiency indeed results in the suppression of PRC1.1 activity. To accurately quantify the transcriptomic and epigenomic alterations following KDM2B inactivation, we utilized highly purified OBs for high-throughput sequencing analysis. To achieve this, we crossed OcnCre or Kdm2bfl/fl OcnCre mice with Ai14 reporter mice, which enables OB-specific expression of tdTomato following Cre-mediated recombination (Fig.\u00a04a).\n\na Schematic of tdTomato-positive mouse generation. b Fluorescent images of trabecular and cortical bone sections from tdTomato mice. White triangles highlight Tomato-positive OBs in the trabecular region (green dashed line, ~400\u2009\u03bcm\u00b2), while yellow triangles indicate OBs in the cortical region (yellow box, ~100\u2009\u03bcm\u00b2). c Quantification of tdTomato-positive cells in periosteum of trabecula and cortex bone (n\u2009=\u20093 mice per genotype). d Workflow for isolation, sorting and analysis of tdTomato-positive OBs. e Volcano plot of DEGs in OBs isolated from 3-months-old mice (n\u2009=\u20093 CON and n\u2009=\u20093 CKO). Upregulated genes are labeled in red and downregulated genes in blue (Wald test, p\u2009<\u20090.05; |Foldchange|\u2009>1.5) OBs were collected after 14 days of culture by removing the bone fragments. f and h GO enrichment analysis for biological processes and KEGG enrichment analysis in upDEGs (CKO vs CON (YOUNG), Fisher\u2019s exact test, p\u2009<\u20090.05). Number of genes and statistical significance are shown. g and j RT-qPCR analysis of representative genes in CKO and control OBs. i The heatmap showing the levels of leading genes in Wnt/\u03b2-catenin signaling pathway. Gene expression levels are shown as relative Z-Scores between two groups. k WB analysis of \u03b2-catenin levels in total protein lysates\u00a0of femur tissue and tdTomato-positive OBs of control and CKO mice. GAPDH was used as loading control. l WB analysis of cytoplasmic and nuclear \u03b2-catenin in tdTomato-positive OBs from control and CKO mice. TUBULIN and H3 were used as fraction-specific controls. m WB analysis of KDM2B levels in KDM2B-WT and KDM2B-CxxC deletion mutant overexpressing MC3T3-E1 cells. n ChIP-qPCR analysis comparing \u03b2-catenin enrichment at target genes in differentiated MC3T3-E1 cells (n\u2009=\u20093/group). o Representative images of 3D reconstruction of trabecula bone from CKO mice treated with PBS or LGK974 for 1 month. p \u00b5CT analysis of trabecular parameters (n\u2009=\u20094 mice/group). Statistical significance was assessed using Student\u2019s t tests, two-tailed, in (c, g, j, p), error bars are presented as mean values\u2009\u00b1\u2009SD. Statistical significance was assessed using Two-Way ANOVA, two-tailed, in (n), error bars are presented as mean values\u2009\u00b1\u2009SD. Scale bar, 100\u2009\u03bcm. CON: Ocn Cre td-Tomato, CKO: Kdm2bfl/fl Ocn Cre td-Tomato in (b\u2013i, o, p); CON: empty vector in (m). a and d created with Biorender (https://biorender.com/w07p252, https://biorender.com/p69f867). Source data of (c, g, j, k, l, m, n, p) are provided as a Source Data file.\n\nAfter genotyping (Supplementary Fig.\u00a03a), the phenotype of the femur in CKO-tdTomato mice was confirmed by \u03bcCT (Supplementary Fig.\u00a03b). The numbers of tdTomato-positive OBs were increased in both the trabecular and cortical bone of CKO mice (Fig.\u00a04b, c), suggesting that the enhanced OB function also stimulates OB differentiation as positive feedback. Femurs from CON and CKO-tdTomato mice were minced into small fragments for OB culture. Within a few days, tdTomato-positive cells were observed migrating out of the cultured bone fragments (Supplementary Fig.\u00a03c). Immunofluorescence (IF) staining confirmed the expression of RUNX2 and ALP, two osteogenic markers, in tdTomato-positive cells (Supplementary Fig.\u00a03d). The adherent cells were subsequently harvested and collected after 14 days of culture, sorted by flow cytometry, and subjected to RNA-Seq analysis (Fig.\u00a04d).\n\nIn either young or aged CKO OBs, hundreds of DEGs were identified compared to their respective controls (Fig.\u00a04e, Supplementary Fig.\u00a03e and Supplementary Data\u00a03). Notably, 43% of the aged OB-downregulated genes (166 out of 386) are upregulated in aged CKO group (Supplementary Fig.\u00a03f). GO analysis indicated the involvement of upregulated genes in young and old CKO OBs with skeletal system morphogenesis, embryonic skeletal system development, and skeletal system development (Fig.\u00a04f and Supplementary Fig.\u00a03g). RT-qPCR analysis confirmed the upregulated expression of osteogenic genes (Fig.\u00a04g) in CKO OBs. Collectively, these analyses demonstrate that KDM2B ablation significantly reverses the deterioration of OB functions. Given the partial reversing effect, further studies are warranted to elucidate the interplay between PRC1.1 and other regulatory mechanisms in the context of aging.\n\nKyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that upregulated genes were enriched in Wnt signaling pathways (Fig.\u00a04h and Supplementary Fig.\u00a03g). As illustrated by heatmap and confirmed RT-qPCR analysis, multiple components of the Wnt pathway, including ligands, receptors, and signal transducers, were significantly activated upon KDM2B loss (Fig.\u00a04i, j). Moreover, protein levels of \u03b2-catenin were increased in both femur tissues and tdTomato-positive OBs from CKO mice (Fig.\u00a04k). To quantify the subcellular distribution of \u03b2-catenin, we separated nucleoplasmic and cytoplasmic fractions of OBs. WB analysis showed that \u03b2-catenin levels were significantly increased in the nucleus and slightly decreased in the cytoplasm (Fig.\u00a04l).\n\nRunx2 and Sp7 recognized as osteogenic target genes downstream of \u03b2-catenin8,13, were further investigated for their regulation by KDM2B. We overexpressed KDM2B-WT and KDM2B-\u0394CxxC mutant in 3T3-E1 cells, a pre-OB cell line (Fig.\u00a04m), and induced OB differentiation. Chromatin Immunoprecipitation (ChIP)-qPCR analysis of the differentiated cells revealed a significant increase of \u03b2-catenin binding at the Runx2 and Sp7 promoter regions in the KDM2B-\u0394CxxC mutant group when compared to the KDM2B-WT group (Fig.\u00a04n). These data suggest that KDM2B ablation strengthens the Wnt signaling pathway.\n\nTo substantiate the roles of activated Wnt signaling in OB functions, we treated the CKO mice with LGK974, a potent and specific inhibitor blocking WNT secretion37,38. After two weeks of LGK974 intraperitoneal injection (3\u2009mg/kg), IF staining confirmed decreased \u03b2-catenin intensity in OBs (Supplementary Fig.\u00a03h). \u03bcCT scanning showed that trabecular bone was significantly reduced by LGK974 treatment. Statistical analyses for trabecular bone microarchitecture unveiled a significant decrease in BV/TV, Tb.Th, and Tb.N and unchanged Tb.Sp (Fig.\u00a04o, p). These data confirm the essential role of Wnt signaling in the enhanced OB functions resulting from KDM2B inactivation.\n\nTo find out whether the enhanced Wnt signaling in KDM2B-deficient OBs is attributed to the dampened function of PRC1.1, we simultaneously performed H2AK119ub1 CUT&Tag analysis (Fig.\u00a04d). As expected, H2AK119ub1 levels at the promoters of 763 genes were significantly reduced in CKO OBs (Fig.\u00a05a, b and Supplementary Data\u00a04). Integrating these results with RNA-seq data revealed significant upregulation of the associated genes in CKO OBs (Fig.\u00a05c). KEGG and GO analysis revealed that these genes are significantly enriched in the Wnt signaling pathway and are involved in skeletal system morphogenesis and development (Fig.\u00a05d, e). Decreased H2AK119ub1 enrichment and corresponding expression patterns in CKO OBs were observed at representative Wnt regulator genes, such as Wnt10b, Fzd10, with an irrelevant intergenic region as a negative control (Fig.\u00a05f). Collectively, these data suggest that PRC1.1-mediated H2AK119ub1 accounts for the repression of Wnt/\u03b2-catenin target genes in OBs.\n\na Heatmaps represent the enrichment for H2AK119ub1 in chromatin of CON and CKO mice around the TSS (\u00b1\u20095 Kb) of H2AK119ub1 target genes. (n\u2009=\u20092 per group). Colors represent CUT&Tag RPM, and rows \u00a0are ranked by CUT&Tag signals in CON. b Box plot showing peak intensity of CUT&Tag reads for H2AK119ub described in panel (a). (Wilcoxon matched-pairs signed rank test, two tailed, p\u2009<\u20090.0001) c Quantification of log2-transformed fold change of expression levels of H2AK119ub1 decreased genes (CKO vs CON, n\u2009=\u2009759) in promoter region of OBs (n\u2009=\u20093 per group, Wilcoxon matched-pairs signed rank test, two tailed, p\u2009<\u20090.0001). The box plots in (b) and (c) indicate the median (centre line), the third and first quartiles (box limits) and 1.5\u2009\u00d7\u2009IQR above and below the box (whiskers). (d, e) KEGG enrichment analysis and GO enrichment analysis for biological processes of genes described in (c), (n\u2009=\u2009763, Fisher\u2019s exact test, p\u2009<\u20090.05). Number of genes and statistical significance are shown. (f) The IGV view of decreased H2AK119ub1 enrichment at representative Wnt/\u03b2-catenin target genes and increased gene expression levels in control and CKO OBs. An intergenic region serves as a negative control. CON: Ocn Cre td-Tomato CKO: Kdm2bfl/fl Ocn Cre td-Tomato. Source data of (b, c) are provided as a Source Data file.\n\nNext, we investigated the possibility of pharmacologic targeting of PRC1.1 and the potential impact on bone formation. To develop specific PRC1.1 inhibitors, we examined the available structural data. We recently identified an electrostatic interaction formed between the linker region of BCOR and the positively charged patch on the F-box and LRRs of KDM2B39. However, this interaction surface is rather shallow, lacking a desirable binding pocket for inhibitor design. Here, we focused on the interaction between the BCORL1 PUFD domain and the RAWUL domain of PCGF1, which creates an extended interface for associating with KDM2B and\u00a0is unique to the PRC1.140. Based on previous studies, a pocket on PCGF1, which packs against two anti-parallel \u03b2 strands on BCOR (Fig.\u00a06a), is essential for PCGF1-BCOR complex formation41. Thereby, inhibitors targeting this pocket would potentially impair PCGF1 binding to BCOR/BCORL1, eventually disrupting the activity of PRC1.1. Based on this analysis, we performed high-throughput virtual screening of 231,000 compounds from ZINC database (Fig.\u00a06b). Based on Docking Scores and visual inspection, 11 hits were selected for activity validation (Supplementary Fig.\u00a04).\n\na Structural model of the binding interface between PCGF1-BCOR complex (PDB: 4HPL). The RAWUL domain of PCGF1 is shown as a surface (carbon in white, oxygen in red, nitrogen in blue, sulfur in yellow), while the PUFD domain of BCOR is depicted as a green cartoon. b Flowchart of screening and verification of PRC1.1 inhibitors. c AlphaScreen assay to show the inhibition of PCGF1RAWUL L238/F242 interaction with BCORPUFD by selected compounds. d Split-GFP system construction. Representative images of each group of transfected 293\u2009T cells are shown. Scale bar, 100\u2009\u03bcm. e Quantification of GFP/Hoechst fluorescence intensity in 293\u2009T cells co-transfected with GFP1-10-PCGF1 and GFP11-BCOR, subsequent to treatment with candidate inhibitors. Statistical significance was assessed using One-Way ANOVA, two-tailed, error bars are presented as mean values\u2009\u00b1\u2009SD (n\u2009=\u20093 per group). f Chemical structure of iBP (Left). Docking model of PCGF1RAWUL (surface) and iBP (orange stick). g AlphaScreen determination of iBP\u2019s IC50 value for inhibiting the interaction between PCGF1RAWUL L238A/F242A and BCORPUFD. h Biolayer interferometry (BLI) analysis of iBP binding to PCGF1RAWUL L238A/F242A. Representative data and 1:1 binding model fit shown as blue and red lines, respectively. i, j Co-IP assay examining how iBP treatment at various concentrations affects the interaction between PCGF1 and BCOR. k ChIP-qPCR analysis comparing H2AK119ub1, PCGF1 and RING1B enrichment at PRC1-target promoters and non-targeted gene bodies as control in control and iBP-treated 293\u2009T cells. Statistical significance was assessed using Two-Way ANOVA, two-tailed, error bars are presented as mean values\u2009\u00b1\u2009SD (n\u2009=\u20093\u2009~\u20096 per group). Source data of (e, g\u2013k) are provided as a Source Data file.\n\nTo determine the inhibitory potency of these hit compounds on the PCGF1-BCOR interaction in vitro, we conducted a bead-based AlphaScreen assay with recombinant PCGF1RAWUL protein and BCORPUFD. As the purification of wild type PCGF1RAWUL protein turned out to be rather challenging, eventually we obtained purified PCGF1RAWUL mutant (PCGF1RAWUL L238A/F242A) by mutating hydrophobic residues (L238 and F242) on the surface. In the structure of PCGF1RAWUL/BCORPUFD complex, these two residues are away from the predicted inhibitor binding pocket (Supplementary Fig.\u00a05), and are surrounding the Leu cage of BCORPUFD that is less important for the complex formation41. AlphaScreen assay showed that, among these selected compounds, Compound 1, 6, 8, 9 exhibited more than 50% inhibition rate for the interaction between PCGF1RAWUL L238A/F242A and BCORPUFD, with Compound 8 (C8) achieving an\u00a0inhibition rate about 95% at concentration of 200\u2009\u03bcM (Fig.\u00a06c).\n\nMeanwhile, we assessed the cellular efficacy of these candidate compounds using a Split-GFP system (Fig.\u00a06b), which relies on the self-association of GFP10 and GFP11 with GFP1-9 to reconstitute a functional GFP upon interaction of the fused proteins42. We fused PCGF1 and BCOR into plasmids with GFP1-10 and GFP11 fragments, respectively, and co-transfected the plasmids into 293\u2009T cells (Fig.\u00a06d). High-content imaging and analysis revealed that among the compounds tested, C6 and C8 significantly downregulated GFP fluorescence intensity, with C8 exhibiting the most potent efficacy (Supplementary Fig.\u00a06a and e). Based on these in vitro and intracellular screenings, we selected C8 as the optimal candidate and named it as \u201ciBP\u201d, the inhibitor of BCOR-PCGF1 (Fig.\u00a06f and Supplementary Fig.\u00a06b). The IC50 of iBP, as determined by AlphaScreen, was 12.21\u2009\u00b1\u20090.88\u2009\u03bcM (Fig.\u00a06g). In addition, affinity measurements revealed that iBP bound to PCGF1RAWUL L238A/F242A with a dissociation constant (Kd) of 6.42\u2009\u00b1\u20090.75\u2009\u03bcM (Fig.\u00a06h).\n\nHigh-performance liquid chromatography (HPLC) analysis showed that 92.2% and 86.9% of iBP remained after 12 and 24\u2009h (hrs) respectively in cell culture media, indicating iBP\u2019s inherent stability (Supplementary Fig.\u00a06c). According to the luminescence cell viability assay in 293 cells, the IC50 of iBP was 42.91\u2009\u03bcM (Supplementary Fig.\u00a06d). To determine a safe and effective concentration of iBP, we conducted Co-Immunoprecipitation (Co-IP) assays in 293\u2009T cells. Titrating iBP to 5 and 10\u2009\u03bcM, we found that 10\u2009\u03bcM was sufficient to inhibit the interaction between PCGF1 and BCOR, without disrupting PRC2 or PRC1.4 integrity (Fig.\u00a06i, j and Supplementary Fig.\u00a07a, b). WB analysis showed a reduction of total H2AK119ub1 levels in 293\u2009T cells following treatment with 10\u2009\u03bcM iBP (Supplementary Fig.\u00a07c). ChIP-qPCR analysis further demonstrated a significant reduction in the enrichment of PCGF1, RING1B and H2AK119ub1 levels specifically at target promoter regions following iBP treatment (Fig.\u00a06k). These findings confirm iBP as a potent and selective PRC1.1 inhibitor.\n\nTo delineate the functional impact of iBP treatment on aged OBs, we conducted RNA-seq analysis. Strikingly, 1786 genes upregulated in aged OBs exhibited significant enrichment in osteogenic morphogenesis, skeletal development, and Wnt/\u03b2-catenin signaling pathways (Fig.\u00a07a, b). Notably, 386 genes downregulated during aging showed marked upregulation in both CKO and iBP-treated aged OBs compared to age-matched controls (Fig.\u00a07c). These findings collectively demonstrate that genetic ablation of KDM2B or pharmacological inactivation of PRC1.1 rescues the transcriptional signature of bone aging.\n\na Volcano plot of DEGs in OBs (n\u2009=\u20093 CON and n\u2009=\u20093 CON-iBP). Upregulated genes are labeled in red and downregulated genes in blue (Wald test, p\u2009<\u20090.05; |Fold\u00a0change|\u2009>1.5). The x-axis shows the log2 (fold change) in gene expression between CKO and control OBs, and the y-axis shows the statistical significance of the differences. OBs were collected after 14 days of culture by removing bone fragments. b GO enrichment analysis for biological processes and KEGG enrichment analysis of upDEGs in (a), (CKO-iBP vs CON, n\u2009=\u20091786, Fisher\u2019s exact test, p\u2009<\u20090.05). Number of genes and statistical significance are shown. c Boxplots showing log2-transformed fold change of expression levels of downDEGs in aged OBs (OLD vs YOUNG, n\u2009=\u2009386) following KDM2B ablation or iBP treatment (n\u2009=\u20093 per group, Wilcoxon matched-pairs signed rank test, two tailed, p\u2009<\u20090.0001). The boxplots indicate the median (centre line), the third and first quartiles (box limits) and 1.5\u2009\u00d7\u2009IQR above and below the box (whiskers). d, e Images of 3D reconstruction of trabecula bone of OVX mice treated with iBP or PBS, with sham group as controls. CT analysis of the distal femur metaphysis. Statistical significance was assessed using One-Way ANOVA, two-tailed, error bars are presented as mean values\u2009\u00b1\u2009SD, n\u2009=\u20095 per group. f H&E staining of femur of the designated groups of mice. g IHC staining of WNT2, WNT3A and \u03b2-catenin in femurs of the designated groups of mice. Scale bar, 1\u2009mm. OVX, Ovariectomy. Source data of (e, f, g) are provided as a Source Data file.\n\nWe also tested iBP\u2019s effects on osteogenic differentiation in MC3T3-E1 cells. After 14 days in osteogenic medium (OM), we observed a significant upregulation in the expression of osteogenic marker genes and Wnt/\u03b2-catenin target genes, which was further enhanced by iBP treatment (Supplementary Fig.\u00a07d, e). Additionally, transfecting MC3T3-E1 cells with the TOP-FLASH reporter plasmid, containing six TCF-binding motifs upstream of a luciferase gene, revealed that iBP treatment significantly activated the Wnt reporter. This activation was reversed by LGK974 (Supplementary Fig.\u00a07f). These data demonstrate that iBP treatment enhances OB functions as well as KDM2B ablation.\n\nTo evaluate the drug\u2019s toxicity and metabolic profile in vivo, pharmacokinetic (PK) studies were conducted. Following an intraperitoneal administration of 20\u2009mg/kg iBP to C57/BL6 mice, the maximum concentration (Cmax) of iBP reached 3209\u2009ng/mL (9.66\u2009\u03bcM) at 0.083\u2009h (Tmax) (Supplementary Fig.\u00a08a), approaching the effective concentration of 10\u2009\u03bcM noted in 293\u2009T cells. Moreover, iBP exhibited acceptable plasma exposure (AUC0-t, 1973 h\u2009\u00d7\u2009ng/mL) and half-life (T1/2, 1.05\u2009h). Importantly, iBP was well tolerated by the tested mice, indicating its suitability for in vivo efficacy studies. Therefore, a 20\u2009mg/kg dose was selected for further experiments.\n\niBP was administered intraperitoneally to OVX mice for one month (20\u2009mg/kg daily). During this period, OVX mice exhibited weight gain compared to the control sham group, which was modestly restricted by iBP treatment (Supplementary Fig.\u00a08b). Histological examination of heart, liver, spleen, lung, and kidney tissues showed no apparent abnormalities (Supplementary Fig.\u00a08c). Bone metrology analysis following \u03bcCT scanning revealed that iBP treatment markedly reversed the loss of trabecular bone in OVX mice, as evidenced by the recovery of BV/TV, Tb.Th, and Tb.N, with no alterations in Tb.Sp (Fig.\u00a07d, e). Increased trabecular bone was also confirmed by sagittal plane H&E staining (Fig.\u00a07f). Immunohistochemistry (IHC) assay demonstrated that the expression levels of WNT2, WNT3a and \u03b2-catenin were increased in iBP-treated femur (Fig.\u00a07g). These data indicate that iBP treatment effectively activates Wnt signaling and prevents osteoporosis.\n\nThen we continued to examine whether iBP could foster bone repair in bone trauma models. To enhance local repair efficiency, we formulated an iBP-loaded hydrogel for targeted and controlled drug delivery. F127, a thermosensitive hydrogel known for its temperature responsiveness, biodegradability, and excellent biocompatibility43, was selected for this purpose. It solidifies at room temperature (RT) (25\u00b0), as shown in Supplementary Fig.\u00a08d.\n\nAnalysis of particle size and surface charge revealed iBP particles to be around 162\u2009nm in diameter (Fig.\u00a08a and Supplementary Fig.\u00a08e). Scanning electron microscopy showed the hydrogel voids to be approximately 10\u2009\u03bcm in diameter (Fig.\u00a08b), implying that the nanosized iBP would result in an initial burst release. Gelatin, known to reduce the burst release of F127, is clinically utilized for bone wound hemostasis44,45, making it a promising candidate for iBP delivery. We cut gelatin sponges to a specific size (2\u2009\u00d7\u20092\u2009\u00d7\u200910\u2009mm3) (Supplementary Fig.\u00a08f) and injected cold (4\u2009\u00b0C) liquid iBP-loaded F127 into them before quickly transferring them to a 37\u2009\u00b0C incubator to facilitate solidification within the sponge (Fig.\u00a08c). Based on the UV absorbance spectrum of iBP, which peaks at 300\u2009nm, we established a standard curve (Supplementary Fig.\u00a08g, h) and found that a 20% F127@gelatin sponge loaded with 2\u2009mg/ml iBP exhibited a sustained release over 14 days, with an average drug release concentration of 20\u2009\u03bcM (Fig.\u00a08d). Thus, we successfully developed a sustained-release delivery system for iBP.\n\na Images obtained by transmission scanning microscope of iBP. b Images of scanning electron microscope of F127. c Schematic diagram of construction of iBP loaded gelatin sponge -F127 hydrogel. d Statistical analysis of sustained release efficiency of iBP for 14 days. e Representative images of scanned sections and sagittal plane views of femur defects in the designated treatment groups for two weeks. Red triangles indicate the new callus at the sites of circular bone defect. f BV/TV quantification of the osteotylus in (e). Statistical significance was assessed using One-Way ANOVA, two-tailed, error bars are presented as mean values\u2009\u00b1\u2009SD, n\u2009=\u20093 per group, Mock: A model representing femur defects, F127-Gelatin: femur defects area treated with F127-Gelatin for two weeks, iBP@F127-Gelatin: femur defects area treated with iBP@F127-Gelatin for two weeks. c created with Biorender (https://biorender.com/j89n671). Source data of (a, b, d, f) are provided as a Source Data file.\n\nWith this system, we evaluated the healing effects of iBP. Notably, after 14 days of healing, mice that received local iBP treatment exhibited a significantly larger amount of new callus formation compared to mice in the mock or hydrogel-only control groups, as revealed by \u03bcCT scanning (Fig.\u00a08e, f). These findings indicate that iBP treatment promotes new bone formation in vivo.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59638-w/MediaObjects/41467_2025_59638_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Epigenetic regulation plays critical roles in tissue homeostasis, extending beyond lineage differentiation and development. In this study, we provide genetic evidence that KDM2B inactivation in OBs enhances bone homeostasis by derepressing Wnt/\u03b2-catenin target genes due to impaired PRC1.1 activity. Based on this finding, we have developed a specific PRC1.1 inhibitor, iBP, which enhances osteogenic functions in mouse models of osteoporosis and acute trauma (Supplementary Fig.\u00a09).\n\nThe processes of cell signal transduction and epigenetic regulation are intricately linked in OB differentiation, bone formation, and homeostasis. Current strategies to increase bone density, such as administering Wnt ligands, Vitamin D, Parathyroid Hormone (PTH), or Bone Morphogenetic Proteins (BMPs), rely heavily on extracellular factors46. However, the effectiveness of these approaches depends on the systemic or local microenvironment, which influences chromatin states and thus responsiveness to signaling cascades. This underscores the importance of identifying chromatin regulators that shape the chromatin landscape, facilitating osteogenic lineage commitment and bone remodeling.\n\nThe biochemistry and epigenomic regulation of PRCs have been well characterized. In stem cells, PRCs are known to preserve their differentiation potential by silencing key developmental genes, keeping them in a poised state ready to respond to signaling cues15,20,22. For example, the Wnt10b pathway promotes mesenchymal stem cell (MSC) differentiation towards OBs47,48, a process that PRCs restrict. Accordingly, EZH2 inhibition stimulates OB differentiation32,49. However, the composition and chromatin binding of PRCs vary dynamically across different developmental stages22,23,36,50,51, during aging52,53, disease progression or under drug treatment54,55,56,57. The context-dependent regulation of PRCs and their functions will be an interesting direction for future research. Our unexpected observation of increased PRC1.1 activity in aging bones raises intriguing questions about potential upstream modulators. While our study delineates the functional consequences of PRC1.1 activation in aged OBs, the molecular triggers driving its enhanced activity remain incompletely resolved. We postulate that unidentified signaling cascades or post-translational modifications of KDM2B-PRC1 components may fine-tune its chromatin-binding affinity or enzymatic robustness in aging contexts.\n\nDetailed phenotypic analyses in mouse models reveal that PRC1.1 inactivation not only enhances OB function but also promotes OB differentiation. This increase in OB differentiation is supported by the elevated OB counts in CKO mice and the activation of Wnt/\u03b2-catenin and osteogenic genes in PRC1.1-inactive pre-OB cells. Consistently, BCOR deficiency in MSCs enhances osteo-dentinogenic differentiation. BCOR loss-of-function mutations are associated with oculo-facio-cardio-dental (OFCD) syndrome, which includes characteristic dental and neural crest defects58. In contrast, the inactivation of PR-DUB complex as evidenced by Asxl1 deletion causes\u00a0gain of H2AK119ub1 activity and significant OB dysfunction and bone loss59. These\u00a0evidence highlight a critical role of H2AK119ub1 activity in the epigenetic regulation of OB lineage differentiation and functions. Additionally, KDM2B inactivation also enhances OC activity (Supplementary Figs.\u00a01f, g and\u00a02c, d), suggesting a new equilibrium in which bone formation and resorption are heightened upon PRC1.1 inhibition.\n\nOur transcriptomic and epigenomic analyses indicate that excessive PRC1.1 accumulation raises the activation threshold for Wnt/\u03b2-catenin target genes, a crucial regulatory mechanism for bone homeostasis. PRC1.1 inactivation lowers this threshold, enhancing the transcriptional response to Wnt signaling, particularly under bone-loss conditions associated with PRC1.1 gain, such as aging. Additionally, PRC1.1 inhibition may increase chromatin accessibility to various stimuli, indicating that combining PRC1.1 targeting with extracellular osteogenic factors could provide synergistic therapeutic benefits.\n\nExisting PRC1 inhibitors like PRT-416560, RB-361, etc. target RING1B\u2019s E3 ligase activity or the stability of key partner proteins, broadly affecting PRC1 complexes. In contrast, iBP selectively targets PRC1.1, potentially minimizing side effects. In our OVX mouse models, it showed no significant toxicity over one month of treatment. Our study provided the core structure of iBP, which could function as a lead compound for future optimization to improve its bioavailability. Additional long-term studies in aged mice with varying doses are necessary to confirm its safety and efficacy. Beyond bone disease, iBP could offer insights into PRC1.1\u2019s role in transcriptional regulation and might be applicable to other conditions, such as PRC1.1-overactive cancers62,63.\n\nHowever, our study has some limitations. The factors driving PRC1.1 gain during aging and its occurrence in other pathological conditions are still unknown. Additionally, the detailed regulatory mechanisms by which PRC1.1 influences OB differentiation and OC activity require further elucidation. Moreover, clinical applicability will depend on assessing PRC1.1 expression patterns and relevant epigenomic profiles in human bone deterioration disorders.\n\nIn summary, our genetic mouse models and chemical screenings reveal a promising epigenetic therapeutic strategy to increase bone density. iBP may serve as a candidate for treating other conditions characterized by aberrant PRC1.1 function, offering a novel approach to address bone loss and related diseases.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "All C57BL/6 mice were maintained in specific pathogen-free barrier facilities and housed at 20\u201322\u2009\u00b0C with 12\u2009h:12\u2009h light: dark cycles at 50\u201360% humidity, animals were used in accordance with protocols approved by the institutional animal care and user committee (Approval No. TMUaMEC 2020008) at the Tianjin Medical University (SYXK(JIN)\u22122020-0010). The details of mice age and sex are provided in the main text and figure legends. Kdm2bfl/fl mice were reported previously18. Ocn-Cre mice were purchased from Cyagen. Ai14(Rosa-CAG-LSL-tdTomato) mice were a gift from Dr. Zhiyong Liu, Institute of Neuroscience, CAS. Kdm2bfl/fl mice were cross-bred with Ocn-Cre mice to specifically delete KDM2B-CxxC domain OBs (Kdm2bfl/fl Ocn-Cre). The genotyping primers are listed in Supplementary Table\u00a01. Age- and sex-matched littermates Ocn-Cre mice were used as control.\n\nTo isolate OBs, femurs from 3-month-old and 18-month-old C57BL/6 mice were cleaned of surrounding tissue. After the bone marrow was flushed out with phosphate-buffered saline (PBS), the bone tissue was cut into small pieces. These bone fragments were then digested overnight at 37\u2009\u00b0C using Type II collagenase (C8150, Solarbio) and subsequently cultured in \u03b1-MEM medium supplemented with 10% FBS (LONSERA, Suzhou Shuangru Biotechnology) and 1% penicillin-streptomycin (P/S) (Sigma) in a humidified incubator at 37\u2009\u00b0C with 5% CO2. Adherent OBs were collected after 14 days of culture by removing the bone fragments64. For tdTomato-reporter mice, OBs were processed similarly and subsequently sorted using flow cytometry based on tdTomato expression, which was used as a marker for OBs.\n\nMC3T3-E1 cells are grown in Dulbecco\u2019s Modified Eagle\u2019s Medium (DMEM, Gibco) medium (GM) supplemented with 10% FBS, 10\u2009mM HEPES (Sigma), 1% P/S. The cells were maintained in a humidified incubator at 37\u2009\u00b0C with an atmosphere of 5% CO2. The culture medium was changed every 2 and 3 days to ensure cell growth and proper maintenance. For osteogenic differentiation, the MC3T3-E1 cells were induced with an osteogenic medium (OM), which consists of 10% FBS in DMEM, 50\u2009\u03bcM ascorbic acid, 10\u2009mM \u03b2-glycerol phosphate, and 100\u2009nM dexamethasone.\n\nFor the extraction of total proteins from OBs, cells were lysed in RIPA buffer supplemented with a phosphatase and protease inhibitor cocktail (PIC, HY-K0010, MCE). For bone tissues, femurs were isolated from the surrounding soft tissues, and the mid-shaft portions were carefully dissected and ground into a fine powder under liquid nitrogen and a mortar and pestle. These powders were then lysed in RIPA buffer containing the same inhibitor cocktail for 30\u2009min (min) at 4\u2009\u00b0C. After centrifugation at 12,000\u2009\u00d7\u2009g for 15\u2009min at 4\u2009\u00b0C to remove cellular debris, the supernatant containing the total protein extract was collected, and the proteins were denatured and separated by SDS-PAGE on a 10\u201312% polyacrylamide gel, depending on the protein size. The resolved proteins were transferred onto polyvinylidene fluoride (PVDF) membranes at 300\u2009mA for 2\u2009h. The membranes were blocked with 5% non-fat dry milk in Tris-buffered saline with 0.1% Tween-20 (TBST) for 1\u2009h at RT to reduce non-specific binding. After blocking, membranes were then probed with specific primary antibodies which are listed in Supplementary Table\u00a02. Following incubation with appropriate secondary antibodies, the membranes were washed three times for 5\u2009min each with TBST. The blots were visualized using the NcmECL High Reagent (P2300, New Cell & Molecular Biotech) and captured with a Uvitec Cambridge Imaging System. The protein bands were quantified using ImageJ software by normalizing to an internal control.\n\nThe library preparation and sequencing were conducted by Novogene, using an Illumina Novaseq platform to generate 150\u2009bp paired-end reads. The raw sequencing data were first cleaned to remove low-quality reads. Clean reads were aligned to the mm39 reference genome with Hisat2 (v2.2.1), and Samtools (v1.19) was used to convert sam files to bam files. Deeptools (v3.5.4) was employed to generate bigwig files, with visualization done via Integrative Genomics Viewer. Gene counts were calculated by Stringtie (v2.2.1), and differential expression analysis was performed using the R package DESeq2 (v1.34.0). Genes were considered significantly different if they met the criteria of P\u2009<\u20090.05 and |log2FoldChange|\u2009> 0.585. Volcano plots and heatmaps were generated with R packages ggplot2 (v3.4.3) and pheatmap (v1.0.12). Pathway enrichment analysis was performed using Metascape, and selected pathways were visualized with R packages ggplot2 (v3.4.3)65.\n\nCUT&Tag assay was performed using the Hyperactive Universal CUT&Tag Assay Kit (TD904, Vazyme) as previously described36. Briefly, 100,000 cells were harvested, washed, and resuspended in wash buffer before incubation with pre-activated concanavalin A beads. Primary anti-H2AK119ub1 antibody (1:1000, 8240S, CST) and secondary antibodies were added sequentially, followed by incubation with Dig-wash buffer and pA/G-Tnp Pro was added to initiate tagmentation. Once tagmentation was complete, DNA was extracted from the beads, and an equal amount of E. coli DNA was added as a spike-in control. After DNA extraction and PCR amplification, sequencing was performed on an Illumina Novaseq6000 platform (Annoroad Gene Technology, Beijing). Raw data underwent quality evaluation with Fastqc (v0.12.1) and Multiqc (v1.19). Adapters were removed with Trim-galore (v0.6.10). Clean reads were aligned to the mm39 reference genome and \u03bbDNA (TD904, Vazyme), normalized based on scale-factor, and duplicates removed with Sambamba (v1.0). Peak calling was conducted with Macs2 (v2.2.9.1), and bigwig files were generated with Deeptools (v3.5.4). Peaks were annotated with R packages ChIPseeker (v1.30.3), and pathway enrichment analysis was performed using Metascape (https://metascape.org/gp/index.html). Visualization of selected pathway were generated by R packages ggplot2 (v3.4.3)65.\n\nTo observe and evaluate bone microstructure, \u00b5CT analysis was performed. Briefly, femurs were harvested from mice, fixed overnight in 4% paraformaldehyde (PFA), and scanned with an isotropic voxel size of 8\u2009\u00b5m and a peak tube voltage of 55\u2009kV and current of 100\u2009\u03bcA (SKYSCAN 1276; Bruker). Three-dimensional images were reconstructed and regions of interest (ROI) were analyzed using NReconServer, CTAn, and CTvox softwares (GE). Trabecular bone parameters were measured from 100 consecutive slices of the distal metaphysis, devoid of epiphyseal structures.\n\nFor calcein double labeling, mice were injected with calcein at 10\u2009mg/kg on day 13 and day 3 prior to femur harvest. Femurs were fixed, embedded in methylmethacrylate, and sectioned at 5\u2009\u03bcm. Images were acquired using a fluorescence confocal microscope, and bone formation rates (BFR/BS and BFR/TV) were calculated from fluorochrome double labels at periosteal and endocortical surfaces using OsteoMeasure software. The three-point bending test was conducted on fresh femurs to assess structural and material strength using an Instron electromechanical tester (Instron 3367). Load-displacement curves were recorded until fracture, and the maximum force at failure was calculated.\n\nSamples including femurs, heart, liver, spleen, lung, and kidney were collected and sectioned at 5\u2009\u03bcm for H&E staining. Paraffin sections were dewaxed, stained with hematoxylin and eosin, dehydrated, and sealed66. For IHC, slides were stained with specific antibodies which are listed in Supplementary Table\u00a02. Sections were deparaffinized, rehydrated, and antigen retrieval was performed with 3% hydrogen peroxide. After blocking with 3% BSA, primary and secondary antibodies were applied, and DAB substrate was used to develop signals. For TRAP staining, sections were deparaffinized, rehydrated, and stained with a TRAP staining kit (G1050, Servicebio). Images were scanned at \u00d7100 magnification (Pannoramic 250 FLASH, 3DHISTECH), and OB/OC counts were performed using ImageJ software.\n\nFemurs were fixed in 4% PFA and decalcified in EDTA solution before embedding in OCT compound and sectioning at 8 \u03bcm. Sections were stained with an anti-\u03b2-Catenin antibody. For OBs, cells were fixed in 4% PFA, permeabilized with 0.2% Triton X-100, and blocked with 3% BSA67. Sections were then incubated with primary antibodies: RUNX2 (1:100, 12556S, Cell Signaling Technology), ALP (1:100, AB_2838191, Affinity Biosciences Cat# DF6225) and the sections were incubated with corresponding secondary antibodies conjugated to fluorescent dyes. DAPI were used to stain the nuclei. Images were captured using a fluorescence microscope.\n\nCells (1\u2009\u00d7\u2009106) were used for each immunoprecipitation following a previously described protocol36. In brief, cells were crosslinked with 1% formaldehyde at RT in medium for 10\u2009min, after which glycine was added to a final concentration of 0.125\u2009M to quench the crosslinking and incubate at RT for 5\u2009min. Cells were washed twice with PBS and lysed with SDS buffer (100\u2009mM NaCl, 50\u2009mM\u2009pH 8.1 Tris-HCl, 5\u2009mM\u2009pH 8.0 EDTA, 0.02% NaN3, 0.5% SDS, PIC) at RT for 5\u2009min. Cell lysates were harvested, and samples were thawed in a water bath to ensure complete dissolution of SDS. After centrifugation at 4\u2009\u00b0C 1200\u2009\u00d7\u2009g for 6\u2009min, the supernatant was discarded and 1\u2009mL of prechilled IP buffer (a mixture of SDS buffer and Triton Dilution Buffer in a 2:1 ratio, supplemented with PIC) was added. Samples were then sonicated to produce 0.2\u20130.5 Kb DNA fragments, centrifuge sonicated chromatin 13,000\u2009\u00d7\u2009g for 30\u2009min.\n\nEqual amount of lysates was taken, and the volume was adjusted to 1\u2009mL with IP buffer. Primary antibodies as listed in Supplementary Table\u00a02 were added for IP overnight at 4\u2009\u00b0C with rotation. Protein A/G beads were then added and incubated on a rotating wheel at 4\u2009\u00b0C for 2\u2009h. Beads were washed once with 1\u2009mL of 150\u2009mM wash buffer (containing 1% TritonX-100, 0.1% SDS, 150\u2009mM NaCl, 2\u2009mM EDTA pH 8.0, 20\u2009mM Tris-HCl pH 8.0), and then twice with 500\u2009mM wash buffer (containing 1% TritonX-100, 0.1% SDS, 500\u2009mM NaCl, 2\u2009mM EDTA pH 8.0, 20\u2009mM Tris-HCl pH 8.0). Finally, 120\u2009\u03bcL of de-crosslinking buffer was added to both input and IP samples and incubated at 65\u2009\u00b0C overnight at 1200\u2009\u00d7\u2009g to elute the complexes from beads. QIAGEN PCR purification kit was used for ChIP-DNA purification, and the samples were quantified by real-time PCR using primers listed in Supplementary Table\u00a01.\n\nTotal RNA was isolated using kit (M050, New Cell & Molecular Biotech) following the manufacturers\u2019 instruction and reverse transcribed using HiScript III All-in-one RT SuperMix (R333-01, Vazyme). The resulting cDNA was used for PCR which was conducted in 10-\u03bcl reactions using SYBR Green Master Mix (Q711-03, Vazyme) with the LightCycler480 system. The relative expression of target genes was calculated using method of 2 \u2212\u0394\u0394CT and calculated after normalization to the expression levels of rp0 in each sample. Primers used in qRT-PCR was listed in Supplementary Table\u00a01.\n\nTo detect relative luciferase activity of TOP reporter, 3T3-E1 cells were co-transfected with 100\u2009ng TOP-FLASH plasmid harboring six TCF-binding motifs (Millipore), and 2\u2009ng of the renilla luciferase control vector pGL4.74 (Promega). Following a 7-day incubation in OM, cell lysates were prepared using the lysis buffer from the TransDetect\u00ae Double-Luciferase Reporter Assay Kit (FR201, TransGen Biotech, China). Luciferase activity was subsequently quantified using a GloMax 20/20 luminometer (Promega)68.\n\nCompounds from ZINC database were subject to docking by using AutoDock Vina. Structure of PCGF1RAWUL was taken from crystal structure of PCGF1RAWUL \u2013BCORPUFD complex (PDB code 4HPL). pdbqt file of PCGF1RAWUL was generated usinig MGL Tools. PCGF1RAWUL protein was prepared by adding hydrogen atoms and Kolloman charges. The pocket on PCGF1RAWUL, that packs against two anti-parallel \u03b2 strands on BCORPUFD, was defined as a potential ligand binding site. The grid box was generated using grid-box option from MGL Tools. Each ligand was docked using exhaustiveness value of 16, and energy_range value of 0.1.\n\nThe cDNA encoding PCGF1RAWUL L238A/F242A mutant (amino acids 166-255) was cloned into pGEX 6P-1 vector with N-terminal GST and hexa-histidine tag followed by PreScission Protease cleavage site. The cDNA encoding BCORPUFD (residues 1580-1696) was cloned into pET-28a vector with N-terminal hexa-histidine tag. Both PCGF1RAWUL L238A/F242A mutant and BCORPUFD were expressed in E. coli BL21 (DE3) strain, respectively. Cultures were grown at 37\u2009\u00b0C to OD600 of 0.6\u20130.8 before induction with 0.5\u2009mM IPTG, and incubated for an additional 20\u2009hrs at 16\u2009\u00b0C to promote protein expression.\n\nFor the purification of PCGF1RAWUL L238A/F242A mutant, cells were harvested and resuspended in lysis buffer (20\u2009mM Tris pH 8.0, 1\u2009M NaCl, 7\u2009mM \u03b2-mercaptoethanol, 5% glycerol). The recombinant protein was purified using Ni2+ affinity chromatography, followed by cleavage of the GST and His tags with PreScission Protease. The mutant PCGF1 RAWUL was further refined by additional Ni2+ affinity chromatography and gel filtration on a Superdex 75 (16/60) column pre-equilibrated with a buffer comprising 20\u2009mM HEPES pH 7.5, 150\u2009mM NaCl, 10% glycerol, and 0.5\u2009mM TCEP. For the purification of His-BCORPUFD, cells were harvested and resuspended in lysis buffer (20\u2009mM Tris pH 8.0, 300\u2009mM NaCl, 7\u2009mM \u03b2-mercaptoethanol, 5% glycerol). His-BCORPUFD was purified using a Ni2+ affinity column with standard protocol, and followed by gel filtration on a Superdex 75 (16/60) column pre-equilibrated with a buffer comprising 20\u2009mM HEPES pH 7.5, 150\u2009mM NaCl, 10% glycerol, and 0.5\u2009mM TCEP.\n\nThe PCGF1RAWUL L238A/F242A mutant was biotinylated using Biotinylation kit (G-MM-IGT, Genemore, Shanghai, China) according to manufacture\u2019s instructions. A concentration of 200\u2009nM biotinylated PCGF1RAWUL L238A/F242A mutant was mixed with inhibitor at indicated concentration in the buffer containing 50\u2009mM MOPS pH 7.4, 0.05\u2009mM CHAPS, 50\u2009mM NaF and 0.1\u2009mg/mL BSA, and incubated for 15\u2009min at room temperature. After adding 7.5\u2009\u00b5g/mL Nickerl chelate beads, 7.5\u2009\u00b5g/mL Streptavidin beads and 200\u2009nM His-BCOR, the mixture was then incubated for additional 1.5\u2009h at 20\u2009\u00b0C. Finally, the mixture was transferred to 384-well plate and analyzed by a EnVision 2105 (PerkinElmer). Data were plotted using GraphPad Prism software.\n\nBiolayer interferometry equipment (Gator Bio) was used to determine the binding affinity between iBP and the PCGF1RAWUL L238A/F242A mutant. Biotinylated PCGF1RAWUL L238A/F242A at concentration of 50\u2009\u03bcg/mL was immobilized on a SA XT biosensor. To remove non-specific bound, the biosensors were washed with assay buffer (50\u2009mM MOPS pH 7.4, 0.05\u2009mM CHAPS, 50\u2009mM NaF, 0.1\u2009mg/mL BSA and 0.5% DMSO). After obtaining a baseline reading in assay buffer, the biosensors were dipped into reference well or wells containing the various concentration of iBP for 5\u2009min. Then, the biosensors were washed with assay buffer for 2\u2009min. Binding kinetics were analyzed using 1:1 binding model with on-board software. Data were plotted using GraphPad Prism software.\n\niBP (Vitas-M, STL373868) stability (400\u2009\u03bcM) was assessed using HPLC following incubation in DMEM supplemented with 10% FBS at 37\u2009\u00b0C for 0, 12, or 24\u2009h. To facilitate the precipitation of proteins from FBS, the incubation mixture was diluted with a fourfold volume of acetonitrile and centrifuged. The resulting supernatant was then subjected to HPLC analysis to determine the remaining iBP concentration.\n\nHPLC analysis for iBP was conducted using a Shimadzu LC-20AT system equipped with an SPD-20A UV\u2013VIS detector. The chromatographic separation was achieved on a 4.6\u2009\u00d7\u2009150\u2009mm Agilent Eclipse XDB-C18 5\u2009\u00b5m column. The mobile phase consisted of solvent A (water with 0.1% trifluoroacetic acid) and solvent B (acetonitrile) at a flow rate of 1.0\u2009mL/min. The gradient elution program was as follows: 10% B (0\u20132\u2009min), 10\u2013100% B (2\u201316\u2009min), 100% B (16\u201318\u2009min), 100\u201380 % B (18\u201319\u2009min) and 80% B (19\u201320\u2009min).\n\nA standard pharmacokinetic study was conducted on iBP using C57BL/6J mice (n\u2009=\u20093). The intraperitoneal injection formulation consisted of a 4\u2009mg/mL solution of iBP prepared with a ratio of 10% DMSO, 40% PEG300, 5% Tween 80, and 45% Saline. The mice were administered iBP intraperitoneally at a dosage of 20\u2009mg/kg, and plasma samples were collected at intervals of 5\u2009min, 15\u2009min, 30\u2009min, 1, 2, 4, 8, and 24\u2009h post-injection. The harvested supernatant was diluted with water at a 1:2 ratio, and 2\u2009\u00b5L of the diluted supernatant was subjected to LC/MS/MS for quantitative analysis.\n\nThe surface structure of the F127 hydrogel (20% W/V) was examined using SEM69. Briefly, samples were freeze-dried using an LGJ-12A freeze dryer (China), gold-coated, and imaged with a Zeiss Gemini 300 SEM. The morphology of iBP was observed under a HT7700 TEM (Hitachi High-Tech, Tokyo, Japan). The size and size distribution of iBP particles were further analyzed using a Zetasizer Nano ZS automatic particle size detector (Malvern, UK).\n\nThe release profile of iBP from the F127 hydrogel-gelatin sponge was assessed using a dialysis membrane with a molecular weight cut-off of 3500\u2009Da. Sponges loaded with either 2\u2009mg/mL (100\u2009\u03bcL) or 4\u2009mg/mL (100\u2009\u03bcL) of iBP (measuring 2\u2009\u00d7\u20092\u2009\u00d7\u200910\u2009mm3) were placed in 100\u2009mL of PBS at 37\u2009\u00b0C with continuous stirring at 100\u2009\u00d7\u2009g. At each time point, 1\u2009mL of the solution was removed and replaced with 1\u2009mL of PBS to maintain a constant volume. The concentration of iBP in the solution was determined using a standard curve established at OD300 nm, recorded with a U-3310 spectrophotometer (Hitachi High-Tech, Tokyo, Japan).\n\nFor establishing OVX models, mice were randomly assigned to either the OVX or Sham group. Under isoflurane inhalation anesthesia, a lateral dorsal skin incision was made to expose the ovaries. In the OVX group, the ovaries were removed after ligating with bilateral sutures, while in the Sham group, the ovaries were exposed but not removed. Penicillin was administered postoperatively to prevent infection, and the muscle and skin incisions were closed.\n\nTo evaluate the therapeutic efficacy of iBP in countering bone loss, 3-month-old virgin female mice were subjected to either ovariectomy or sham surgery (n\u2009=\u20094 per group). A subset of ovariectomized mice received daily intraperitoneal injections of iBP at a dose of 20\u2009mg/kg, starting two days post-surgery and continuing for four weeks. A comparative group of ovariectomized mice received PBS injections under the same conditions.\n\nTo evaluate the pharmacological effect of iBP on bone healing, a unilateral circular bone defect was created in the right femur of CON and CKO mice. Using a low-speed dental drill with a 1\u2009mm diameter bit, a defect was made in the femur. A gelatin sponge, dimensioned at 2\u2009\u00d7\u20092\u2009\u00d7\u200910\u2009mm3 and preloaded with 0.2\u2009mg of iBP in F127 hydrogel, was positioned onto the defect site and secured with sutures. Two weeks later, the healing progress in both treated and untreated groups was monitored using \u03bcCT imaging.\n\nStatistical analysis One- or two-way ANOVA followed by Sidak\u2019s test for multiple com parisons (with repeated measures for time series data) was used in all studies, as noted in figure legends. For comparison between two groups, Student\u2019s t tests were performed. All tests used the software GraphPad Prism (GraphPad). The exact p-value is provided in the corresponding figure. For data presented without statistics, the experiment was repeated at least twice to ensure reproducibility, unless otherwise stated.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The RNA-seq, and CUT&Tag raw dataset in this study have been deposited in the GEO dataset with accession numbers GSE280429. The remaining data are available within the Article, Supplementary Information or Source Data file.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Hadjidakis, D. J. & Androulakis, I. I. Bone remodeling. Ann. N. Y. Acad. Sci. 1092, 385\u2013396 (2006).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nEriksen, E. F. 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The Wnt reporter construct was provided by Dr. Chunsheng Kang, TMU General Hospital. This study was supported by by the National Key R&D Program of China (2024YFA1802300 to X.W., 2019YFA0112100 to S.F., 2022YFA1303101 to J.L.), National Natural Science Foundation of China (32320103009, 82473964 to X.W.; 82071079 to D.L.), The Key Program of Tianjin Natural Science Foundation (23JCZDJC00210 to D.L.), Jinmen Medical Talents Project of Tianjin Health Commission (TJSJMYXYC-D2-025 to D.L.), Basic Research Project of Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences (GIBHBRP24-04 to Y.X. and J.X.) and National Youth Yalent Support Program (X.W.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Liangyu Xing, Jinxin Xu, Meihan Gong, Yunzhi Liu.\n\nState Key Laboratory of Experimental Hematology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Department of Endodontics, Tianjin Medical University School and Hospital of Stomatology & Tianjin Key Laboratory of Oral Soft and Hard Tissues Restoration and Regeneration, Tianjin Medical University, Tianjin, China\n\nLiangyu Xing,\u00a0Meihan Gong,\u00a0Yunzhi Liu,\u00a0Xuanyuan Li,\u00a0Ying Zhou,\u00a0Zhaoguang Ouyang,\u00a0Xu Liu,\u00a0Shaofei Tao,\u00a0Yuxin Cao,\u00a0Chunyi Liu,\u00a0Feng Gao,\u00a0Ruohui Han,\u00a0Lei Sui,\u00a0Dayong Liu\u00a0&\u00a0Xudong Wu\n\nDepartment of Cell Biology, Tianjin Medical University, Tianjin, China\n\nLiangyu Xing,\u00a0Meihan Gong,\u00a0Yunzhi Liu,\u00a0Xuanyuan Li,\u00a0Yingying Zhao\u00a0&\u00a0Xudong Wu\n\nChina-New Zealand Joint Laboratory on Biomedicine and Health, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China\n\nJinxin Xu\u00a0&\u00a0Yong Xu\n\nInstitute of Drug Discovery, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China\n\nJinxin Xu,\u00a0Lingyu Meng,\u00a0Ruyue Du,\u00a0Yan Dong\u00a0&\u00a0Yong Xu\n\nState Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China\n\nLingyu Meng,\u00a0Ruyue Du,\u00a0Hui Shen,\u00a0Yan Dong,\u00a0Yong Xu\u00a0&\u00a0Jinsong Liu\n\nDepartment of Medicinal Chemistry, Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, China\n\nTao Li,\u00a0He Chen\u00a0&\u00a0Yingying Zhao\n\nInternational Science and Technology Cooperation Base of Spinal Cord Injury, Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China\n\nBaoyou Fan,\u00a0Shiqing Feng\u00a0&\u00a0Xudong Wu\n\nThe Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China\n\nShiqing Feng\n\nShanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology & Department of Endodontics, Shanghai Tongji Stomatological Hospital and Dental School, Tongji University, Shanghai, China\n\nDayong Liu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: D.L., X.W., J.L. Methodology: Z.O., X.L. (Xuan Li), S.T., Y.C., B.F. Investigation: L.X., J.X., M.G., Y.L., L.M., R.D., Y.Z., X.L. (Xu Liu), C.L., F.G., R.H., H.S., Y.D., Y.X., T.L., Y.Z., and L.S. Analysis of data: L.X., H.C. Funding acquisition: S.F. Supervision: S.F., J.L., D.L., X.W. Writing \u2013 original draft: L.X., X.W. Writing \u2013 review & editing: J.X., M.G., Y.L., D.L., X.W.\n\nCorrespondence to\n Shiqing Feng, Jinsong Liu, Dayong Liu or Xudong Wu.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.\n\nWe support inclusive, diverse, and equitable conduct of research.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Xing, L., Xu, J., Gong, M. et al. Targeted disruption of PRC1.1 complex enhances bone remodeling.\n Nat Commun 16, 4294 (2025). https://doi.org/10.1038/s41467-025-59638-w\n\nDownload citation\n\nReceived: 14 November 2024\n\nAccepted: 29 April 2025\n\nPublished: 08 May 2025\n\nVersion of record: 08 May 2025\n\nDOI: https://doi.org/10.1038/s41467-025-59638-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 41.25c-9.8 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Acute Toxicity Assessment", + "journal": "Nature Communications", + "published": "01 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60989-7/MediaObjects/41467_2025_60989_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60989-7/MediaObjects/41467_2025_60989_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60989-7/MediaObjects/41467_2025_60989_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60989-7/MediaObjects/41467_2025_60989_MOESM4_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-60989-7#ref-CR25", + "https://toxric.bioinforai.tech/home", + "/articles/s41467-025-60989-7#ref-CR40", + "https://pubchem.ncbi.nlm.nih.gov/", + "https://doi.org/10.6084/m9.figshare.27195339.v5", + "/articles/s41467-025-60989-7#ref-CR80", + "/articles/s41467-025-60989-7#Sec29" + ], + "code": [ + "https://github.com/LuJiangTHU/Acute_Toxicity_FSL", + "/articles/s41467-025-60989-7#ref-CR81", + "https://toxacol.bioinforai.tech/" + ], + "subject": [ + "Biochemistry", + "Biotechnology", + "Computational models", + "Toxicology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5513730/v1.pdf?c=1751454572000", + "research_square_link": "https://www.researchsquare.com//article/rs-5513730/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60989-7.pdf", + "preprint_posted": "09 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Chemical safety assessment is critical in the early stages of drug discovery and ecological risk assessment. Multi-species acute toxicity assessment is typically conducted as the initial phase to determine whether chemicals can proceed to industrial use or clinical trials. Although deep learning has shown promise in acute toxicity evaluation, inherent challenges such as diverse experimental conditions, imbalanced endpoint data, and scarce target endpoint data are often overlooked. This hinders existing methods from revealing associations among multi-condition endpoints, and leads to poor predictive performance for data-scarce target endpoints, especially those related to humans. Here we propose a novel machine learning paradigm, Adjoint Correlation Learning, for multi-condition acute toxicity assessment (ToxACoL) to address these challenges. ToxACoL models biological associations among multi-species, multi-condition toxicity endpoints via graph topology and achieves knowledge transfer via graph convolution. An adjoint correlation mechanism encodes compounds and endpoints synchronously, enabling an endpoint-aware and task-focused representation learning for compounds. Comprehensive analyses demonstrate that ToxACoL successfully balances performance across multi-condition endpoints, yielding 43%-115% improvements for data-scarce human-related endpoints, while reducing required training data by approximately 70% to 80%. Furthermore, investigation into the visualization and interpretability of the top-level representation learned by ToxACoL elucidates the structural alert mechanisms behind acute toxicity and highlights the potential for extrapolating animal test results to humans when integrated with the filled-in toxicity values.Biological sciences/BiotechnologyBiological sciences/Drug discovery/ToxicologyBiological sciences/Computational biology and bioinformatics/Computational modelsBiological sciences/Biochemistry", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SI.pdfSupplementary Information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Multi-species acute toxicity assessment forms the basis for chemical classification, labelling and risk management. Existing deep learning methods struggle with diverse experimental conditions, imbalanced data, and scarce target data, hindering their ability to reveal endpoint associations and accurately predict data-scarce endpoints. Here we propose a machine learning paradigm, Adjoint Correlation Learning, for multi-condition acute toxicity assessment (ToxACoL) to address these challenges. ToxACoL models endpoint associations via graph topology and achieves knowledge transfer via graph convolution. The adjoint correlation mechanism encodes compounds and endpoints synchronously, yielding endpoint-aware and task-focused representations. Comprehensive analyses demonstrate that ToxACoL yields 43%-87% improvements for data-scarce human endpoints, while reducing training data by 70% to 80%. Visualization of the learned top-level representation interprets structural alert mechanisms. Filled-in toxicity values highlight potential for extrapolating animal results to humans. Finally, we deploy ToxACoL as a free web platform for rapid prediction of multi-condition acute toxicities.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Today, explosive growth, massive production, widespread application, and long-term emissions of various chemicals have induced a huge threat to human health and the environment1,2,3. Multi-species acute systemic toxicity assessment forms the basis for chemical classification, labeling and risk management4,5. Acute toxicity assessment is typically conducted as the initial phase of the entire safety assessment6,7,8,9 and is usually mandatory to the regulatory procedures of new chemicals, directly determining whether the chemicals can enter subsequent industrial use or clinical trials10,11,12. Acute toxicity evaluates the unwanted effects that occur either immediately or at a short time interval after a single or multiple administration of a substance13. In some cases, acute systemic toxicity data may be used to establish doses for longer-term studies, identify target organs for toxicity, and assess the hazard of accidental ingestions of chemical contaminants14. Due to ethical and legal restrictions, large-scale experimental testing on humans or certain unconventional species, such as wildlife, is not feasible. Conventional animal testing remains the primary method for acute toxicity assessment15,16. These tests involve diverse conditions, such as various species, administration routes, and assessment indicators17. However, the experimental results for the same compound vary significantly under different species and different testing conditions. When extrapolating test doses and toxicity from experimental species to humans and other unconventional species, significant gap in physiological structures and metabolic mechanisms often lead to inaccurate toxicity assessments18. Especially in drug discovery, toxicity to the human body often appears in clinical trials or after market launch although no toxicity was found in preclinical animal testing, leading to the failure of drug discovery. A classic example is troglitazone. No toxicity was found in vitro and in animal testing. Shortly after it was approved for the treatment of type 2 diabetes, it was withdrawn from the market due to its hepatotoxicity. This emphasizes the importance of accurately predicting or extrapolating the toxicity of compounds to humans.\n\nModern toxicology emphasizes the 3Rs (replacement, reduction, and refinement) principle in animal testing, seeking alternative methods for toxicity assessment19,20. Computational methods have emerged as powerful tools for replacing animal testing. Among them, machine learning (ML) and deep learning (DL) have demonstrated significant advantages over traditional quantitative structure\u2013activity relationship (QSAR) statistical methods in terms of prediction scope and accuracy, especially in the context of rapidly growing toxicity data21,22,23,24. For the convenience of ML modeling, several ML-ready toxicity databases have been established, providing extensive acute toxicity data resources. For example, in our previous work, TOXRIC database25, we collected 59 multi-species, multi-endpoint acute toxicity values, covering over 80,000 compounds. Luechtefeld et al.4,26 constructed a database of 10,000 chemicals and 800,000 toxicological studies, focusing on acute oral toxicity among other endpoints.\n\nThere were several reports indicating that ML trained on masses of toxicity data is so good at predicting some toxicities and sometimes outperforms expensive animal studies16,27,28,29. Studies have developed ML algorithms for acute toxicity assessment, encompassing single-task learning (STL), multi-task learning (MTL) methods, and consensus modeling. In acute toxicity prediction under STL paradigms, existing studies have reported that random forest (RF) exhibits optimal performance among traditional ML algorithms including support vector machines (SVM), artificial neural networks (ANN), message-passing neural networks (MPNN), gradient boosting (GB), Xgboost (XGB), and generalized linear models (GLM)30,31,32. Luechtefeld et al.28 developed read-across structure\u2013activity relationship (RASAR) models that employ binary fingerprints and Jaccard similarity to construct chemical adjacency matrices, with data Fusion RASAR improves predictive accuracy by integrating multi-property features to train RF. While graph-based algorithms employing molecular graph inputs, such as graph convolution network (GCN) and attentive fingerprint (Attentive FP)33, have shown better performance over RF and other traditional ML algorithms34,35. However, they lack the ability to integrate multi-condition acute toxicity data for training, resulting in poor performance on small-sized endpoints with insufficient data. MTL models recognize the correlations between multiple endpoints and train on multi-condition toxicity data. They extract molecule representations via a shared encoder and use a multi-channel regression module to simultaneously predict multi-condition acute toxicity. Previous studies have proven that multi-task deep neural network (MT-DNN) and multi-task graph convolution network (MT-GCN) can effectively improve average performance across all endpoints compared to all STL implementations for acute toxicity prediction17,32,36,37. Their superiority over STL indirectly indicates that the shared learning strategy among diverse endpoints can mutually boost each other38. Additionally, consensus models integrate multiple model (STL or MTL) outputs, demonstrating better performance compared to individual models. Jain et al.17 developed a consensus framework named DLCA that integrates multiple MT-DNNs with different feature inputs, showing improved performance compared to individual models. Mansouri et al.5 constructed consensus models for five endpoints of acute toxicity, and proposed a weight-of-evidence (WoE) approach with weighted integration to generate consensus predictions of the five endpoints. Effective performance improvement was achieved at single endpoints.\n\nHowever, due to the inherent complexities of acute toxicity endpoints, such as diverse experimental conditions, scarce target endpoint data, and imbalanced endpoint data, existing ML methods struggle to reveal relationships among multi-condition endpoints, fail to accurately predict data-scarce endpoints (especially in humans), and have not explored the extrapolation patterns between species. Firstly, acute toxicity in vivo experiments involve various species (e.g., mouse, rabbit, and dog, etc.), administration routes (e.g., intravenous, skin, oral, etc.), and measurement indicators like median lethal dose (LD50), lethal dose low (LDLo) and toxic dose low (TDLo). These diverse experimental conditions across studies make it difficult to model the relationships among multi-condition endpoints using a unified mathematical logic or general methodology. Additionally, ethical and legal restrictions make it difficult to obtain training data for target endpoints, such as humans and certain unconventional species. Large-scale reference data for expensive experimental species are also scarce, resulting in extreme data imbalance across endpoints. While existing MTL models effectively improve the average performance across all endpoints, they struggle to make accurate predictions for data-scarce target endpoints. This may be due to the varying toxicity intensity of compounds across species, which creates a significant gap between endpoints39. This further emphasizes the importance of identifying and modeling the relationships between multi-condition endpoints. Moreover, such modeling supports the exploration of extrapolation patterns between species. Understanding the differences in toxicity responses across species and identifying which species exhibit toxicity patterns most similar to humans are key questions that warrant further exploration.\n\nTo address these challenges, We introduce a machine learning paradigm, Adjoint Correlation Learning, for multi-species acute toxicity assessment of compounds, named as ToxACoL. We first collect multi-species, multi-condition acute toxicity data from some public chemical databases, such as TOXRIC25 and PubChem40, which involve various test species, administration routes, and measurement indicators. Based on these data, ToxACoL is designed to apply graph topology to model relationships between multi-condition endpoints, incorporating an adjoint correlation mechanism to process and integrate multi-endpoint information with compound representations in parallel. In ToxACoL, graph convolution is employed to propagate information on multi-endpoints and their relationships, while a feed-forward network with residual connections is used to propagate compound embeddings. The two branches interact through a correlation operation to learn endpoint-aware compound representations. Comprehensive analyses on a 59-endpoints acute toxicity dataset demonstrate that ToxACoL effectively captures relationships among multi-condition endpoints and achieves balanced performance across them. By learning the relationships across endpoints, ToxACoL significantly improves prediction accuracy for data-scarce endpoints, including human-oral-TDLo, women-oral-TDLo, and man-oral-TDLo, with performance increases of 56%, 87%, and 43%, respectively, compared to state-of-the-art methods. Meanwhile, ToxACoL reduces the required training data for sparse endpoints by ~70\u201380%, aligning with the global 3Rs principle of reducing animal testing. Then, tested on two additional benchmark datasets (115-endpoint dataset and study of Mansouri et al.5), ToxACoL demonstrates robust performance compared to representative acute toxicity prediction models. AD analysis depicts the chemical space where ToxACoL achieves reliable predictions. Furthermore, analysis of ToxACoL\u2019s top-level representations assists in identifying structural alerts, uncovering potential mechanisms of acute toxicity, and providing insights for extrapolating animal test results to humans. Finally, an online platform for the prediction of acute toxicity was developed that integrates the ToxACoL model, serving as a freely accessible and useful resource for regulatory applications.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "ToxACoL introduces the adjoint correlation mechanism to parallelly learn multi-condition labels and multi-type sample information (Fig.\u00a01a), achieving good performance in multi-condition acute toxicity assessment. This paradigm insists on bidirectional learning from compounds (samples) and multi-condition endpoints (labels) simultaneously. By capturing endpoint-to-endpoint dependencies, the relationships between multi-condition endpoints are captured and a multi-condition endpoint graph is constructed. The adjoint correlation mechanism facilitates interaction between the two learning branches, i.e., compounds and endpoints, at each layer, enabling the extraction of endpoint-aware and task-focused compound representations.\n\na ToxACoL workfolow. The training toxicity measurements were leveraged to explore pairwise dependencies between endpoints and the acute toxicity endpoint graph was constructed based on these dependencies. Each adjoint correlation layer comprising a residual network layer and a graph convolution layer was designed to process compound embeddings and endpoint embeddings parallelly, and the two branches internally interact via a correlation operation. After a cascade of multiple adjoint correlation layers, the embedding of each endpoint outputted by the topmost graph convolution layer will serve as the toxicity regressor for the corresponding endpoint, and then perform the toxicity regression with the top-level compound embedding, finally outputting toxicity intensity value concerning the corresponding endpoint. b Illustration of data imbalance and data sparsity of the large-scale multi-condition acute toxicity dataset. c Two examples for calculating pairwise dependencies between endpoints, which were based on the training compounds shared by the two endpoints. The dependency was evaluated via a two-sided Pearson correlation coefficient (PCC) analysis. There exists a significant correlation between mouse-intravenous-LD50 and rabbit-intravenous-LDLo, as well as for mouse-intravenous-LD50 and mouse-skin-LD50. The center line in the correlation plots represents the regressed line and the error band denotes the confidence interval of 0.95 for linear regression. d The one-hot entity encoding strategy encompassing three endpoint attributes was developed for initializing endpoint embeddings in graph. Credits: the icons of bottles, chemicals, and animals including mouse, rabbit, cat, and man, along with illustrations of administration tools including spoon, syringe, and dropper, are sourced from https://creazilla.com/. Source data are provided as a Source Data file.\n\nThe construction of the endpoint graph is based on existing studies about large-scale acute toxicity datasets, that involve multi-condition acute toxicity endpoints, and each endpoint provides information of test species, administration route, and measurement indicator. Taking our previous study, the TOXRIC database25 for example, which includes 59 various toxicity endpoints with 80,081 unique compounds represented using SMILES strings, and 122,594 usable toxicity measurements described by continuous values with a unified toxicity chemical unit: \u00a0\u2212log(mol/kg). The 59 acute toxicity endpoints involve a total of 15 test species, 8 administration routes, and 3 measurement indicators, ensuring comprehensive information coverage. However, the size of samples across 59 endpoints is highly imbalanced and sparse (Fig.\u00a01b). Some endpoints involve tens of thousands of available measurement samples, while some endpoints only contain about 100 samples, with a high rate of missing data (Supplementary Table\u00a01). To address this issue, we constructed an endpoint graph to capture inter-endpoint relationships and leverage multi-condition endpoint information to enhance performance on data-scarce endpoints. For each pair of endpoints, we first count their shared training compounds, and then calculate the Pearson correlation coefficients (PCC)41 of toxicity measurements based on their shared compounds if the quantity of these shared compounds is acceptable (Fig.\u00a01c). Two endpoints are considered dependent, and thus an edge is formed between them, only if the shared compound count exceeds a certain threshold and their toxicity measurements are highly correlated (see the \u201cMethods\u201d section). Based on this deduction, we can construct an acute toxicity endpoint graph, where nodes represent toxic endpoints and edges represent the dependency between two endpoints. Considering that each endpoint contains three attributes: test species, administration route, and measurement indicator, we separately encodes each attribute into a one-hot subvector and then concatenates the three subvectors to initialize graph node features, serving as the initial endpoint embeddings (Fig.\u00a01d).\n\nNext, the adjoint correlation learning is applied between the compounds and the acute toxicity endpoint graph. Avalon fingerprints is adopted as the initial representation for compounds, as previous studies17,42 have shown that it is the cutting-edge feature for predicting acute toxicity. The adjoint correlation layer is designed to process the compounds and the endpoint graph in parallel, where compound embeddings are processed by feed-forward layers with residual connections and endpoint embeddings are processed by graph convolution. Critically, the new endpoint embeddings are correlated with the compound embeddings, and then accumulated onto the previous compound embeddings in the form of residuals. The compound embeddings after the residual operation, as well as the new endpoint graph after graph convolution, are used together as inputs for the next adjoint correlation layer. In this way, multiple adjoint correlation layers are cascaded sequentially. The endpoint embeddings outputted by the topmost graph convolution are treated as toxicity regressor weights for the corresponding endpoints. These weights are then combined with a top regression layer to assess the toxicity intensity for multiple endpoints.\n\nWe first evaluate the predictive performance of ToxACoL and existing state-of-the-art methods under benchmark setting of the 59-endpoint acute toxicity dataset from TOXRIC, which include single-task deep neural networks (ST-DNN)17, single-task random forest (ST-RF)17, graph attention network (GAT)43, graph convolution network (GCN)44, attentive fingerprint (Attentive FP)33,43, multi-task deep neural networks (MT-DNN)17, multi-task graph convolution network (MT-GCN)17, and deep learning consensus architecture (DLCA)17,42. These baseline models have been extensively studied in17,42,43,44, and the MTL and consensus models among them still maintain the leading performance for acute toxicity assessment to this day. We maintained the same experimental settings as these baselines to ensure fairness, such as selecting the same Avalon fingerprints as our molecular features and adopting the same 5-fold dataset split for cross-validation. The performance is evaluated using the metrics of determination coefficient (R2) and root-mean-squared error (RMSE), which respectively reflect the fitness or deviation between the predicted toxicity intensity by computational models and the ground-truth intensity.\n\nWe first compared the average performance of these models on all the 59 endpoints via 5-fold cross-validation (Fig.\u00a02a, Supplementary Tables\u00a03 and 4). The results indicated that ToxACoL yielded an improvement in overall average performance to other baseline models. Concretely, ToxACoL achieved an averaged R2 of 0.5843 and a smaller averaged RMSE of 0.6396, surpassing the previously best-performing algorithm (DLCA). Considering the acute toxicity data is very imbalanced, we investigated the performance of different models in dealing with the dilemma of data imbalance between endpoints. A ridge diagram (Fig.\u00a02b) fitting the performance of each model on all 59 endpoints was drawn based on kernel density estimation (KDE)45, to intuitively present the overall distribution of toxicity estimation performance of each model on all endpoints. On the one hand, the overall performance of ToxACoL is superior to other baselines. On the other hand, the performance distribution of ToxACoL is more concentrated with a significantly smaller standard deviation than other models, indicating that ToxACoL can more robustly and evenly handle the data-imbalanced multi-condition acute toxicity evaluation tasks.\n\na Average R2 and RMSE on all toxic endpoints via 5-fold cross-validation. The two-sided Wilcoxon signed-rank test was selected to compute the significant difference between ToxACoL and other baselines across all endpoints. It can be seen that the p-values are small, indicating that the improvements by our ToxACoL are statistically significant. The five dots on each box plot represent the results of five cross-validation experiments; the center line in the box represents the median among the five results, excluding outliers; the lower and upper bounds of the box represent the first (Q1) and third (Q3) quartiles, respectively; the lower and upper bounds of the whiskers represent the minima and maxima, excluding outliers, respectively. b Overall performance distribution of different models on 59 endpoints, fitted using Kernel density estimation (KDE). The 59 dots in the ridge plot represent the endpoint-wise performance of the corresponding method on the 59 endpoints. The more concentrated their distribution and the smaller their standard deviation, the more balanced the model\u2019s performance on all endpoints. c The proportion of different models in performance rankings on all 59 toxic endpoints. d The Friedman and Nemenyi test with the critical difference (CD) for all models. The CD diagrams illustrate the average performance ranking of each model on 59 endpoints, calculated based on R2 and RMSE. The length of the horizontal thick line segments is shorter than the CD value, indicating that the differences between the two models covered by these thick line segments are not significant. e The heatmap of endpoint-wise performance achieved by all models. All endpoints were arranged from left to right in ascending order of their sample sizes of toxicity measurements. Source data are provided as a Source Data file.\n\nTo further compare these models on single endpoints, we statistically analyzed their performance rankings on each endpoint and visualized the proportion of these rankings via stacked histograms (Fig.\u00a02c). Our ToxACoL ranked in the top two on the vast majority of endpoints. Based on these endpoint-wise rankings, we further made a Friedman and Nemenyi test46 to intuitively display the averaged performance ranking gap of different models on 59 endpoints via the critical difference (CD) diagram (Fig.\u00a02d). It can be seen that ToxACoL ranked ahead of other baseline models concerning both R2 and RMSE, demonstrating its holistic superiority to other baseline models. Finally, the endpoint-wise performance of all models were summarized in increasing order of their toxicity measurement samples (Fig.\u00a02e), and it can be observed that ToxACoL surpassed other baseline models on most toxic endpoints.\n\nTo verify the indispensability of the adjoint correlation mechanism in ToxACoL, we provided an ablation study to compare the standard ToxACoL and its variant. In this variant model, the feed-forward layer and graph convolution layer in ToxACoL no longer interact at any layer, but independently handle compound embeddings and endpoint embeddings, respectively. We tested six sets of network structures with different depths and recorded the average R2 through 5-fold cross-validation. We found that the standard ToxACoL consistently outperformed its variant regardless of network depth (Supplementary Fig.\u00a01). Moreover, as the network becomes deeper, the performance of the variant model has sharply declined, which is mainly caused by the inherent over-smoothing problem existing in conventional GCN47,48,49. On the contrary, the standard ToxACoL appears less sensitive to increasing network layers, and thus the performance gap between the two models widens. It is because the adjoint correlation mechanism can guide ToxACoL to learn better endpoint-aware representations at feed-forward layers, and the extra gradients from adjoint correlation can regularize the learning of endpoint embeddings in GCN. In brief, introducing the adjoint correlation mechanism can effectively overcome the intractable over-smoothing of graph convolution at deeper layers.\n\nIn real world, the primary focus of acute toxicity assessments for compounds is on humans and unconventional species (such as specific wildlife). However, toxicity data for these species are difficult to obtain, resulting in severe data scarcity for these target endpoints. This challenge is frequently overlooked in existing studies, leading to poor predictive performance for human-related endpoints. Next, we specifically assess ToxACoL\u2019s competitiveness in addressing data-scarce endpoints, with a particular focus on human-related endpoints. To explicitly distinguish between small/large-sized endpoints, we quantitatively classified all endpoints, where endpoints with less than 200 toxicity measurements were considered small-sized endpoints, and those with more than 1000 measurements were treated as large-sized endpoints. In total, there are 21 small-sized endpoints and 11 large-sized endpoints in the 59-endpoint dataset from TOXRIC. The three human-related endpoints, human-oral-TDLo, women-oral-TDLo, and man-oral-TDLo, are typical small-sized endpoints, with only 140, 156, and 163 available toxicity measurement samples, respectively. In addition, the assessment indicator inside the three endpoints is TDLo, which only appears in them, while the other 56 animal endpoints do not contain it. Therefore, the three human endpoints have significant semantic and biological gaps with other endpoints, making it difficult to effectively transfer knowledge learned from animal endpoints to the three human endpoints, which is why other baseline models perform poorly on the human endpoints.\n\nWe first compared the average R2 of these models after 5-fold cross-validation on the human-related endpoints (Fig.\u00a03a, c, and e). ToxACoL brought significant performance improvements in the three human endpoints. The R2 achieved by ToxACoL on the three endpoints are 0.50, 0.43, and 0.40, respectively, outperforming the previous state-of-the-art results by a large margin (56% for human-oral-TDLo, 87% for women-oral-TDLo, and 43% for man-oral-TDLo). We selected one of the 5-fold cross-validation experiments to directly present the acute toxicity estimation results of ToxACoL on the test fold (Fig.\u00a03b, d, and f). The toxicity intensity values predicted by ToxACoL can maintain a good consistency with the ground-truth toxicity intensity of the test compounds, and for some certain compounds, ToxACoL predicted them accurately, despite the scarce training measurement samples and the isolated endpoint attributes for the three human endpoints.\n\na, c, and e Average R2 of different models over 5-fold cross-validation on human-oral-TDLo, women-oral-TDLo, and man-oral-TDLo. Their significant differences were analyzed on the basis of a two-sided Student t-test. b, d, and f Acute toxicity estimation curves of ToxACoL for testing compounds at three human-related endpoints. Here, ToxACoL was trained using four folds of the whole toxicity dataset, and the testing compounds are all from the remaining one test fold. g Comparison between ToxACoL and advanced baseline methods on more small-sized endpoints. Taking the first subgraph for example, it considered the 4 endpoints (n\u2009=\u20094) with sample size of measurements <130, and so on for the following three subgraphs. The dots on the bar represent R2 values at single endpoints, and the bar with the error bar denotes the mean R2 value with standard deviation over the n small-sized endpoints (from left to right, n\u2009=\u20094, 8, 14, 21, respectively). h Comparison between ToxACoL and advanced baseline methods on 11 large-sized endpoints (n\u2009=\u200911). The bar with the error bar represents the mean R2 value with standard deviation over the 11 large-sized endpoints. Source data are provided as a Source Data file.\n\nThe performance of ToxACoL on more small-sized endpoints was further examined (Fig.\u00a03g, Supplementary Fig.\u00a02a). We set different thresholds for the sample size of toxicity measurements to observe the average R2 of different models on all the small-sized endpoints whose sample size of measurements is less than the threshold. The improvement from ToxACoL on these small-sized endpoints is universal and significant, leading by nearly 15% on the average R2 over 21 small-sized endpoints. Moreover, the R2 achieved by ToxACoL is more balanced over all small-sized endpoints (with a more concentrated dot distribution and smaller standard deviation on histograms). We also compared the average R2 on the 11 large-sized endpoints and found that ToxACoL had little performance gaps to the other advanced baseline models (Fig.\u00a03h, Supplementary Table\u00a05, Supplementary Fig.\u00a02b), as the large sample size of toxicity measurements was sufficient to saturate all models. This further indicates that the advantages of our adjoint correlation learning mechanism over other baseline models mainly focus on small-sized acute toxic endpoints.\n\nCollecting acute toxicology measurements via in vivo animal testing is time-consuming, labor-intensive, and expensive12, and its sample size will not be too large due to the 3Rs principle, especially for data-scarce experimental subjects such as humans. Therefore, reducing the number of training measurements required for AI models on these small-sized endpoints is of great significance. This section mainly focuses on the acute toxicity estimation performance of ToxACoL on fewer training measurements for data-scarce endpoints.\n\nIn this test, the quantity of training measurements for the 21 small-sized endpoints was randomly reduced at one certain reduction ratio, while their test sets remained unchanged. We evaluated six different reduction schemes, which reduced the sample size of training measurement of small-sized endpoints to 80%, 50%, 40%, 30%, 20%, and 10% of their original size, respectively. Correspondingly, we retrained and evaluated our ToxACoL six times based on these reduced training datasets. Meanwhile, we also evaluated the performance of other state-of-the-art methods when the sample size of the small-sized endpoints was reduced to 30%.\n\nAn exciting experimental conclusion is that compared to other methods, ToxACoL only needs to use 20\u201330% of their training measurements on small-sized endpoints to match the previously optimal performance for the state-of-the-art methods (Fig.\u00a04, Supplementary Tables\u00a06 and 7, Supplementary Fig.\u00a03), showing its strong potential in reducing animal toxicity testing. Taking the human-oral-TDLo for example (Fig.\u00a04a), the original sample size of training measurements averaged over 5-fold cross-validation was 95, and ToxACoL achieved an R2 of 0.38 with only 30% of the training measurements (\u224829 measurements), surpassing the previous best R2 of 0.32 achieved by MT-DNN. Even with only 10% of the training measurements (\u224810 measurements), ToxACoL can significantly outperform baseline models such as ST-DNN, ST-RF, GAT, GCN, and MT-GCN. Similar conclusions can also be observed for women-oral-TDLo (Fig.\u00a04b) and man-oral-TDLo (Fig.\u00a04c). At these two endpoints, ToxACoL only requires 30% (\u224832 measurements) and 40% of the training samples (\u224844 measurements), respectively, to surpass previously best-performing baseline models.\n\na\u2013c The performance at the three human-related endpoints, including human-oral-TDLo, women-oral-TDLo, and man-oral-TDLo, with the toxicity measurement samples used for training reduced proportionally. d The performance over all the 21 small-sized endpoints (n\u2009=\u200921) as the toxicity measurement samples used for training reduced proportionally, where the bar with the error bar represents the mean R2 value with standard deviation over the 21 small-sized endpoints. Source data are provided as a Source Data file.\n\nStatistically, we investigated the average R2 on 21 small-sized endpoints concerning training measurement reduction (Fig.\u00a04d). The original sample size of training data per endpoint was about 120, and ToxACoL achieved an average R2 of 0.51 with only 30% of the training samples (\u224836 measurements per endpoint), surpassing the previously best average R2 of 0.50 achieved by DLCA. With only 10% of the training measurements (\u224812 measurements per endpoint), ToxACoL also reached an average R2 of 0.43, greatly surpassing most of the state-of-the-art methods (ST-DNN: 0.08, ST-RF: 0.28, GAT: 0.17, GCN: 0.21, Attentive FP: 0.33) and highly competitive with MTL or consensus models (MT-DNN: 0.48, MT-GCN: 0.44, DLCA: 0.50). We can also observe that when the training samples of the small-sized endpoints were reduced to 30%, the performance of other comparison methods significantly declined and falls far behind our ToxACoL. These results highlight the prospect of ToxACoL in few-shot learning and its robustness to the reduction of toxicity measurements at data-scarce endpoints. In addition, the reduction of the training measurements for data-scarce endpoints does not weaken the performance of ToxACoL on large-sized endpoints (Supplementary Fig.\u00a04).\n\nNext, to assess the robustness of ToxACoL\u2019s prediction, we conducted comparative experiments against other methods on two additional benchmark datasets. First, to explore the performance of the models on a broader range of acute toxicity endpoints, we collected and constructed a brand-new 115-endpoint acute toxicity dataset based on PubChem database40. Compared with the previous 59-endpoint acute toxicity dataset, this new dataset is more challenging for all current acute toxicity prediction models, since it has a more abundant and comprehensive number of toxicity endpoints, a larger number of small-sized endpoints with fewer available measurements, and more severe data imbalance and sparsity rate (see the \u201cMethods\u201d section). Similarly, the random 5-fold cross-validation was adopted.\n\nOn this more challenging dataset, the advantage of ToxACoL compared to other state-of-the-art methods is more significant (Table\u00a01, Supplementary Figs.\u00a05 and 6). First, the average R2 of ToxACoL across all 115 endpoints is ~31% higher than that of DLCA (ranked second) with a lower standard deviation, indicating more balanced performance across all endpoint tasks (Supplementary Fig.\u00a05c). Importantly, the average R2 of ToxACoL across 68 small-sized endpoints is ~57% higher than that of the second-best DLCA, and it also outperforms all other methods on the 11 human-related endpoints. In addition, judging from the performance rankings of all methods across the 115 endpoints (Supplementary Fig.\u00a05b, d), ToxACoL ranks first in terms of performance at the vast majority of endpoints. These results once again strongly demonstrate the superiority of ToxACoL and its powerful few-shot learning ability for acute toxicity prediction.\n\nIn addition, the U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup developed a five-endpoint prediction project. The five acute toxicity-related endpoints include LD50 value, U.S. Environmental Protection Agency (U.S. EPA) hazard (four) categories, Globally Harmonized System for Classification and Labeling (GHS) hazard (five) categories, very toxic chemicals (LD50\u2009\u226450\u2009mg/kg), and nontoxic chemicals (LD50\u2009\u2265\u20092000\u2009mg/kg). We next test ToxACoL with the representative models on the reported LD50 datasets.\n\nFor the five endpoints, Mansouri et al.5 proposed a consensus modeling framework, Collaborative Acute Toxicity Modeling Suite (CATMoS), which consolidates predictive models from all participating teams, effectively leveraging the strengths of each model. To address conflicts in results across these five endpoints, the authors proposed a WoE approach with weighted integration to generate consensus predictions. Specifically, for the LD50 endpoint (rat-oral-LD50), the WoE method refined the original predictions, significantly enhancing performance. To demonstrate the superiority of ToxACoL, we compared it with CATMoS and the improved WoE method on the LD50 dataset using the same data partitioning strategy (6398 molecules for training and 2196 molecules for evaluation). The results show that ToxACoL achieved performance improvements of 8.24% and 21.54% in R2 metrics on the training and evaluation sets, respectively, compared to the WoE optimization proposed by Mansouri et al. (Table\u00a02). This breakthrough highlights the significant advantages of ToxACoL in terms of prediction accuracy and generalization capability.\n\nTo explore the effectiveness of the molecular representations learned by ToxACoL, we visualized the top-level embeddings generated by ToxACoL in the latent space. Specifically, we randomly selected one of the 5-fold cross-validation experiments and applied the well-trained ToxACoL via the 59-endpoint dataset from TOXRIC to the test fold to extract the top-level embeddings for the testing compounds. The t-SNE algorithm50 is used to visualize these embeddings, showcasing their local relationships and distribution patterns in low-dimensional space. The clear clustering manifolds concerning toxicity intensity were shown across different acute toxicity endpoints including both large- and small-sized endpoints (Fig.\u00a05a). For large-sized endpoints, the model learns excellent molecular representations due to sufficient training data. Despite the limited data for small-sized endpoints, their representations still form distinct clusters and clear manifolds, demonstrating the strong predictive and representation learning capabilities of ToxACoL.\n\na and b The t-SNE visualization of top-level embeddings learned by ToxACoL concerning various acute toxic endpoints. Here, ToxACoL was trained using four-fold data of the 59-endpoint acute toxicity dataset, and the displayed compounds are all from the remaining test fold. The darker the dot\u2019s color, the greater its toxicity intensity. Visualization of single endpoints (a) and multiple endpoints belonging to the same species (b). c Several compound examples of structural alerts discovered by ToxACoL from the high-toxicity clusters at different endpoints, including quaternary ammonium cation, aromatic nitro, and halogenated dibenzodioxin, which have been highlighted by masks.\n\nWe selected data-scarce experimental species, such as cats, dogs, and guinea pigs, to perform joint embedding visualizations under different conditions within the same species (Fig.\u00a05b). These compound embeddings of the same species can still be distributed in a well-defined manifold, forming similar clustering patterns and distributions regarding varying toxicity intensities, although they are tested under different conditions. This phenomenon also conforms to our toxicology priors, that is, for a certain type of compound that has strong toxicity to the same animal, regardless of the administration route and measurement indicator, its toxicity results are generally relatively strong. This demonstrates that ToxACoL can indeed capture potential dependency relationships between endpoints and guide the learning of more scientific compound representations.\n\nTo further investigate the structural patterns of compounds with high acute toxicity and uncover their potential mechanisms, we performed an in-depth analysis of the embedding manifold\u2019s morphology. It is well-established that a compound\u2019s structure is closely linked to its biological activity and toxicity. Certain highly reactive molecular fragments, which can be presented in the parent compound or its metabolites after biochemical transformation by human enzymes (bioactivation), have been confirmed to possess strong toxic potential51,52. The clustering within ToxACoL\u2019s latent space embeddings could potentially reveal key molecular fragments or functional groups shared by highly toxic compounds, which is critical for understanding the mechanisms behind acute toxicity.\n\nFor the testing compounds, we focused on clusters representing highly toxic compounds and analyzed the molecular characteristics within the same cluster in all the endpoints (Fig.\u00a05a). The results showed that compounds in the same region typically shared molecular fragments, which may play a dominant role in influencing acute toxicity intensity. These molecular fragments are commonly referred to as structural alerts. Extensive studies show that structural alerts are effective markers for toxicity assessment and mechanistic understanding across diverse endpoints, with their presence indicating high toxic potential in compounds53,54,55,56. In high-toxicity clusters across multiple endpoints, we identified well-known structural alerts (Fig.\u00a05c).\n\nFor example, in the endpoints such as mouse-intraperitoneal-LD50, mouse-subcutaneous-LD50, and mouse-oral-LD50, we identified that ~10\u201325 compounds within the high-toxicity clusters contain quaternary ammonium cations, which are considered typical structural alerts. The structure of quaternary ammonium cation includes one hydrophobic hydrocarbon chain linked to a positively charged nitrogen atom, along with other alkyl groups, predominantly consisting of short-chain substituents such as methyl or benzyl groups57. Long-term exposure to low doses of quaternary ammonium compounds may lead to acute toxicity in aquatic organisms or humans58,59,60. In addition to the aforementioned endpoints, a considerable proportion of compounds containing quaternary ammonium cation structures, ranging from approximately 10% to 30%, was also observed in endpoints with fewer high-toxicity molecules, such as rabbit-intravenous-LDLo, mouse-oral-LDLo, and rabbit-subcutaneous-LDLo. Notably, we found that in nearly all mouse-related endpoints, as well as other mammal-related endpoints, compounds containing quaternary ammonium cation structures account for a significant proportion of high-toxicity clusters. In contrast, such molecules were nearly absent in avian species, including birds, chickens, and quails. This discrepancy may be attributed to interspecies differences in physiological structures, metabolic capacities, and excretion mechanisms.\n\nIn various mammal- and avian-related toxicity endpoints, such as mouse-oral-LD50, rat-oral-LD50, dog-intravenous-LDLo, and chicken-oral-LD50, compounds containing nitro groups and aromatic nitro structures were found to have a high prevalence in high-toxicity clusters. It is reported that Nitro-containing compounds exhibit a broad range of toxic effects, including acute toxicity, carcinogenesis61, and systemic impacts on various biological systems such as the reproductive, immune, nervous62, digestive, respiratory, cardiovascular, as well as specific organs like the liver63,64, kidney, and stomach65. Nitroaromatic compounds are widely used as pesticides, explosives, pharmaceuticals, and chemical intermediates in industrial synthesis66. They are potent toxic or carcinogenic compounds, presenting a considerable danger to the human population66,67,68.\n\nAdditionally, halogenated dibenzodioxins are identified as a structural alert in the guinea pig-oral-LD50 endpoint. Halogenated dibenzodioxins, as a subclass of dioxins, are recognized as persistent environmental pollutants characterized by extreme toxicity69. Their primary emission sources include waste incineration facilities and industrial combustion processes70. Although the detection frequency of this structure across multiple toxicity endpoints is lower than the structural alerts discussed earlier, this discrepancy may stem from the limited samples of environmental contaminants in the current dataset. Notably, the ToxACoL successfully identified the high toxic potential of this structure through representation learning, demonstrating its capability to uncover latent hazardous properties even in sparsely populated data domains.\n\nIn summary, we believe that the latent space clustering patterns predicted by ToxACoL can help identify structural alerts responsible for acute toxicity, facilitating the analysis of structural differences across species. This offers valuable insights for further exploration into the underlying mechanisms of acute toxicity.\n\nThe preceding discussion illustrates the similarities and differences in the distribution of acute toxicity values, latent space representations, and alert structures among endpoints of different species. This suggests that interspecies extrapolation may follow specific patterns in both toxicity values and latent space distributions71. Furthermore, to more thoroughly assess the toxicity effects of compounds on data-scarce species such as humans, this section delves into the extrapolation patterns from experimental species to humans. We used all the acute toxicity data from TOXRIC to train the ToxACoL model and took three human-related endpoints and the compounds associated with these endpoints as the research references. In TOXRIC, the number of available compounds for the human-oral-TDLo, woman-oral-TDLo, and man-oral-TDLo endpoints were 140, 156, and 163, respectively. The trained ToxACoL was applied to predict and fill in the missing acute toxicity values for these selected reference compounds across all endpoints. Based on these filled-in toxicity values and previously existing toxicity values, we explored the toxicity responses of compounds across different species and the extrapolation patterns.\n\nWe applied PCC values to quantify the proximity of the toxicity values of all experimental species-related endpoints to those of humans (Fig.\u00a06a and Supplementary Figs.\u00a07\u20139). The endpoints with the highest PCC values associated with human-oral-TDLo are the other two human-related endpoints: woman-oral-TDLo (0.912) and man-oral-TDLo (0.903) (Fig.\u00a06a). Similar results were observed for the other two human endpoints, which aligns with theoretical expectations and validates the reliability of the model\u2019s predictions. Beyond human endpoints, the cat-intravenous-LDLo, frog-subcutaneous-LDLo, and mouse-intravenous-LDLo showed the highest PCC values among all animal endpoints when compared to the three human endpoints. This demonstrates that these three animal endpoints have a stronger linear correlation with human endpoints, making them more suitable for transferring and extrapolating to human oral toxicity responses, indicating potentially robust extrapolation models. Additionally, we extracted the latent space representations for cat-intravenous-LDLo, human-oral-TDLO, woman-oral-TDLo, and man-oral-TDLo to examine their latent space patterns (Fig.\u00a06b). The data distributions and trends in toxicity intensity for the human endpoints and cat-intravenous-LDLo are consistent, with highly toxic compounds being closely situated in the embedding space, further demonstrating the potential of extrapolating cat-intravenous-LDLo results to human endpoints.\n\na Pearson correlation coefficient (PCC) values between human-oral-TDLo and the remaining 58 endpoints. Note that there are a total of 140 compounds in the dataset that have available toxicity measurement values at human-oral-TDLo endpoint. The missing toxicity intensity values of these 140 compounds at the other 58 endpoints were filled in by the predicted intensity values of ToxACoL. Thus, the PCC value between the two endpoints was calculated based on the two groups of toxicity intensity values of the 140 compounds concerning the two endpoints. The Pearson correlation analysis is two-sided. The center line in the correlation plots represents the regressed line and the error band denotes the confidence interval of 0.95 for linear regression. b Latent space representation distribution for cat-intravenous-LDLo, human-oral-TDLo, woman-oral-TDLo, and man-oral-TDLo. Here, ToxACoL was trained using four-fold data of the whole acute toxicity dataset, and the displayed compounds are all from the remaining test fold. c Performance metrics (R2,\u00a0\u2009RMSE) of in-AD and out-of-AD samples under varying thresholds within the AD defined in this study, averaged across 59 endpoint tasks. The X-axis represents the AD threshold ST corresponding to different Z parameters. The left Y-axis (blue lines) indicates metric values, while the right Y-axis (red lines) denotes the proportion of extracted samples relative to the total (Coverage). Blue lines and shaded areas represent the mean and standard deviation of five-fold cross-validation results. Source data are provided as a Source Data file.\n\nOther mammalian species such as guinea pig, rabbit, and dog also show relatively high PCC values with human endpoints. In contrast, avian species like duck, chicken, and quail exhibit results that are significantly different from the human endpoints. Regarding the administration route, intravenous measurements are more concordant with human oral results, whereas other administration routes show less consistency with the target endpoints. Exploring the reasons for this phenomenon, intravenous delivers drugs directly into the bloodstream, allowing them to quickly reach effective concentrations while avoiding digestion and metabolism processes. This may make intravenous data better reflect how oral medications ultimately work in the human body. We also highlight two endpoints with poor consistency to human endpoints, quail-oral-LD50 and rat-subcutaneous-LD50 (Fig.\u00a06a), which may be attributed to differences in species, administration routes, and measurement indicators.\n\nBased on Eq. (11) (see the \u201cMethods\u201d section), we defined the AD of ToxACoL to delineate the chemical space where the model achieves reliable predictions. We systematically evaluated the dynamic changes in prediction performance (R2,\u00a0\u2009RMSE) and coverage rates inside and outside the AD under varying parameters (k\u2009=\u200910, Z\u2009\u2208\u2009[1, 9.5]) (Fig. 6c). As the threshold increases, the coverage of in-AD samples decreases sharply from 99.96% (Z\u2009=\u20091, ST\u2009=\u20090.247) to 4.62% (Z\u2009=\u20099.5, ST\u2009=\u20090.927), while prediction accuracy improves significantly: R2 rises from 0.58 to 0.75, and RMSE decreases from 0.64 to 0.46. This demonstrates that stricter AD thresholds effectively screen high-confidence predictions by narrowing the chemical space. Notably, the predictive performance for in-AD samples at all Z-values significantly outperforms the full-dataset test results, validating the necessity of AD demarcation for enhancing reliability. By balancing prediction accuracy and coverage, Z\u2009=\u20096 (ST\u2009=\u20090.647) was selected as the globally optimal parameter, achieving 69.33% in-AD coverage, R2\u2009=\u20090.60, and RMSE\u2009=\u20090.61, thereby addressing both predictive robustness and practical applicability.\n\nWe further analyzed out-of-AD samples. The R2 for out-of-AD samples increases from 0.16 to 0.55 with higher ST thresholds (Fig.\u00a06c). When out-of-AD molecular coverage reaches 51.82%, R2 attains 0.51 and continues to rise, indicating ToxACoL\u2019s partial generalization capability for extrapolation beyond the AD. This phenomenon likely stems from ToxACoL\u2019s latent feature extraction capability enabled by deep representation learning, which may capture cross-chemical-space patterns to achieve reliable predictions in certain out-of-AD regions, offering useful perspectives for expanding the model\u2019s applicability boundaries.\n\nIn order to enable all researchers to directly use our pre-trained ToxACoL, we have integrated ToxACoL into an online web platform (https://toxacol.bioinforai.tech/). Users can simply upload single or multiple molecular SMILES, and obtain predictions for various toxic endpoints and GHS classifications, with results downloadable in multiple formats. Notably, acute toxicity in practice is used to determine the potency of a substance (usually as GHS classes). Thus, the ToxACoL online web platform also provides the predicted GHS classification of chemicals as a toxicity endpoint. In alignment with GHS hazard classification criteria, ToxACoL converts predicted rat-oral-LD50 and rat-skin-LD50 values into corresponding oral and dermal hazard categories. Among them, Category 1 is the highest hazard category, while Category 5 applies to substances of lower acute toxicity but may still pose risks to vulnerable populations under specific conditions. Full classification criteria are provided at ToxACoL online web.\n\nFigure\u00a07 illustrates the interfaces of the ToxACoL online prediction web platform. On the homepage (Fig.\u00a07a), users can manually input or upload a CSV file containing single or multiple SMILES strings through the \u201cInput SMILES Structures\u201d module, followed by clicking the \u201cSubmit\u201d button to initiate the analysis. Upon completion within seconds, the prediction results comprise two functional modules (Fig.\u00a07b): the \u201cDownload\u201d section for downloading and viewing all predictions, and the \u201cSingle SMILES Analysis\u201d module for individual compound results. The \u201cDownload\u201d module allows users to download comprehensive prediction results for all input SMILES in three formats: \u201cPredicted values (mg/kg)\u201d provides toxicity values in mg/kg units \u201cPredicted values (\u2212log(mg/kg))\u201d offers dimensionless normalized scores, and \u201cPredicted values (GHS class)\u201c supplies GHS classification outcomes. Below this, the \u201dSingle SMILES Analysis\u201d module enables users to click on any valid SMILES to view its detailed predictions across 59 toxicity endpoints and GHS classifications. Toxicity endpoints are classified by species. GHS classes are divided into two exposure routes, oral and dermal (Fig.\u00a07c). A \u201cDownload\u201d button within the individual SMILES result section facilitates the download of all predictions for the selected compound. We believe this platform can offer a new path for validation processes, in the hope of becoming a useful resource for regulatory applications.\n\na Homepage, where users can input molecular SMILES in the \u201cInput SMILES Structures\u201d section and click \u201cSubmit\u201d to initiate the acute toxicity analysis. b The result page of acute toxicity prediction. c The result page of GHS classification. Credits: the icons of molecular structure and animals are sourced from https://creazilla.com/. Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60989-7/MediaObjects/41467_2025_60989_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60989-7/MediaObjects/41467_2025_60989_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60989-7/MediaObjects/41467_2025_60989_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60989-7/MediaObjects/41467_2025_60989_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60989-7/MediaObjects/41467_2025_60989_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60989-7/MediaObjects/41467_2025_60989_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60989-7/MediaObjects/41467_2025_60989_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Acute toxicity assessment is commonly the initial step in evaluating the safety of compounds and plays a crucial role in determining whether a compound can proceed to chemical development and industrial application. The emergence of ML algorithms offers an excellent solution for implementing the 3Rs principle, providing an alternative to animal testing for acute toxicity. By leveraging existing animal experimental data, ML can predict acute toxicity across various conditions. However, the complexity of toxicity testing including spanning different species, administration routes, and measurement indicators, combined with imbalanced data, presents significant challenges, particularly in predicting toxicity for data-scarce species like humans.\n\nIn this study, we model the relationships between endpoints under various experimental conditions by constructing a multi-condition endpoint graph. An adjoint correlation learning paradigm is designed to simultaneously learn both sample representations and multi-condition label information. This embeds experimental condition data into the representation learning process, allowing the model to capture fine-grained toxicity patterns. Learning the relationships between species and conditions enables the model to transfer knowledge effectively when data is scarce. For example, although human experimental data is limited, the model can improve its prediction accuracy by learning the relationships between humans and other species like mice and rats. Beyond the focus of this study on acute toxicity prediction, the adjoint correlation learning framework also offers valuable insights for other multi-task learning problems with multi-label settings, rich label information, and significant data imbalance.\n\nBased on the designed Adjoint Correlation Learning, we propose a multi-condition acute toxicity prediction model, ToxACoL. Before our work, the learning process of most QSAR models72,73 employed for computational toxicity prediction was unidirectional from compound data to toxic endpoints. Taking the MTL paradigm as an example (Supplementary Fig.\u00a010), it uses a deep network to extract high-level compound representations and a multi-channel linear regression layer to predict toxicity intensity at various endpoints. In ToxACoL, we propose synchronous bidirectional learning from compound data and toxic endpoints. We use graph topology to model endpoint associations and graph convolution for cross-correlation and develop the adjoint correlation mechanism to process compound and endpoint embeddings simultaneously, maintaining their interaction. Importantly, we use the top-level endpoint embeddings as toxicity regressor weights, which incorporate the learned relationships between endpoints into the final prediction step. Unlike the MTL models\u2019 independent regressors, the multi-endpoint toxicity regressors generated by ToxACoL are interdependent due to graph convolution, incorporating multi-endpoint biological priors. This enables parameter propagation, reducing training data reliance. Also, the top-level feed-forward layer\u2019s compound embeddings are endpoint-aware, facilitating a more task-focused compound representation learning. These designs are the key factors that enable the performance improvement of small-sized endpoints.\n\nToxACoL\u2019s effectiveness and application value are validated through various experimental scenarios. These scenarios include comprehensive multi-endpoint performance evaluation, performance improvement for scarce species endpoints, reduced training data testing, latent space manifold analysis, structural alerts clustering analysis, species extrapolation pattern exploration, AD analysis, and online prediction platform development. First, ToxACoL balanced performance across multi-condition endpoints. Due to the highly imbalanced training dataset, existing algorithms typically perform well on large-sized endpoints but poorly on small-sized ones. ToxACoL demonstrated robust handling of imbalanced multi-task datasets. Second, the performance improvement evaluation for scarce species endpoints focused on three small-sized human-related endpoints (100\u2013200 samples), thus confirming its practical value. Results showed remarkable improvements over baselines, with R2 performance increases of 56%, 87%, and 43% for the three endpoints, respectively. Third, in real-world applications, human and unconventional animal experimental data often do not exceed 100 samples. We further reduced training data for scarce species endpoints to observe model performance. Results indicated that ToxACoL requires only 20\u201330% of training data (~20\u201340 samples) to outperform state-of-the-art methods, demonstrating strong competitiveness in few-shot learning. Fourth, the embeddings learned by ToxACoL\u2019 revealed clear clustering manifolds based on toxicity intensity, consistent across both large and small endpoints. Fifth, further analysis of ToxACoL\u2019s learned representations revealed that compounds in the same clusters shared common substructures. High-toxicity clusters contained well-known structural alerts, suggesting that ToxACoL can assist in identifying unknown structural alerts while predicting acute toxicity for unseen compounds or endpoints, thus aiding the exploration of toxicity mechanisms. Sixth, since it is difficult to obtain experimental data for scarce species like humans, we explored interspecies extrapolation patterns by completing toxicity values across multiple endpoints. The results showed that toxicity values in cats were closer to humans than those of other species. Additionally, intravenous administration in animal experiments showed closer results to human oral than other routes. These findings may offer valuable insights for toxicity extrapolation research. Seventh, AD analysis of ToxACoL provides a detailed analysis of in-AD and out-of-AD samples and depicts the chemical space where the model achieves reliable predictions. Finally, for the convenience of researchers in using ToxACoL, we have developed an online web platform. Users can simply upload single or multiple molecular SMILES, and obtain predictions for various toxic endpoints and GHS classifications.\n\nIn future work, ToxACoL should be extended to accommodate a broader spectrum of acute toxicity tasks even other chemical-related tasks. First, Mansouri et al.5 proposed that regulatory agencies generally require three types of acute toxicity outcomes: (1) an LD50 value estimate, (2) a binary outcome based on a single threshold, and (3) a multiclass scheme based on different thresholds. In this study, we have developed predictive models for LD50 estimation across multiple species and under various administration routes. Based on the LD50 estimates for oral and dermal routes, we applied GHS classification criteria to achieve GHS class-based multiclass prediction. Both prediction results are accessible on the ToxACoL online platform. While the other two binary classification scenarios should be supported, including the prediction of whether it is very toxic or nontoxic. This classification requirement can also be addressed by setting appropriate thresholds on the predictions generated by ToxACoL. Additionally, Mansouri et al. have provided ML-ready datasets for LD50 estimation, binary classification, and multiclass classification based on GHS and US EPA classification criteria. These datasets can be used to train models separately and then combined through weighted averaging to produce integrated prediction results, thereby improving the accuracy of chemical hazard predictions.\n\nSecond, ToxACoL has the potential to be adapted for other chemical assessment tasks beyond acute toxicity. ToxACoL is specifically designed for predictive tasks involving multi-condition labels and heterogeneous sample types. Its innovative dual-path learning paradigm, adjoint correlation learning, combined with multi-condition endpoint graph modeling capabilities, renders it particularly suitable for complex MTL architectures. Additionally, the modular design of ToxACoL demonstrates significant transfer learning potential across diverse chemical assessment scenarios. For instance, in multi-objective optimization for drug discovery, the dual-path interaction architecture can be extended to incorporate optimization targets like target inhibition potency, therapeutic indication prioritization, and pathway activation modulation as multi-condition endpoints. By constructing multi-modal endpoint graphs comprising drug\u2013target\u2013disease-pathway nodes, ToxACoL will systematically encode efficacy\u2013endpoint correlations, thereby providing a computational framework to accelerate lead compound optimization. This adaptability highlights ToxACoL\u2019s capacity to transcend traditional toxicity prediction paradigms and fosters cross-domain applications in computational chemistry.\n\nThird, these multi-endpoint-shared, high-weight structural alerts identified by ToxACoL enable early-stage toxicity flagging during virtual screening. By automatically filtering candidates containing known alerts, ToxACoL may accelerate hit-to-lead pipelines while minimizing downstream attrition risks. In future work, ToxACoL\u2019s multi-endpoint toxicity predictions can be integrated as penalty terms in optimization objectives of molecule generation, prioritizing structures with alert replacement.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "This work involves two large-scale acute toxicity datasets (59-endpoint dataset, 115-endpoint dataset) and a benchmark dataset from Mansouri et al.5. Unlike typical toxicity classification datasets, these acute toxicity datasets are more complex as they focus on fitting acute toxicity intensity values.\n\nThe 59-endpoint dataset is sourced from the TOXRIC database25, a public and standardized toxicology database for compound discovery. This dataset includes 59 various toxicity endpoints with 80,081 unique compounds represented using SMILES strings, and 122,594 usable toxicity measurements described by continuous values with a unified toxicity chemical unit: \u00a0\u2212log(mol/kg). The larger the measurement value, the stronger the toxicity intensity of the corresponding compound towards a certain endpoint. The proportion of \u201cvery toxic\u201d (LD50\u2009\u226450\u2009mg/kg) molecules ranged from 3.58% (mouse-oral-LD50) to 85.06% (cat-intravenous-LD50) across endpoints, while \u201cnon-toxic\u201d (LD50\u2009\u2265\u20092000\u2009mg/kg) molecules varied between 2.83% (mouse-intravenous-LD50) and 67.63% (rabbit-skin-LD50). These classification criteria are based on studies by Mansouri et al., Tran et al., and GHS5,74. The 59 acute toxicity endpoints involve 15 different species including mouse, rat, rabbit, guinea pig, dog, cat, bird wild, quail, duck, chicken, frog, mammal, man, women, and human, 8 different administration routes including intraperitoneal, intravenous, oral, skin, subcutaneous, intramuscular, parenteral, and unreported, and 3 different measurement indicators including LD50, LDLo, and TDLo. In this dataset, each compound only has toxicity measurement values concerning a small number of toxicity endpoints, so this dataset is very sparse with nearly 97.4% of compound-to-endpoint measurements missing (Fig.\u00a01b, Supplementary Table\u00a01). Meanwhile, this dataset is also data-unbalanced with some endpoints having tens of thousands of toxicity measurements available, e.g., mouse-intraperitoneal-LD50 has 36,295 measurements, mouse-oral-LD50 has 23,373 measurements, and rat-oral-LD50 has 10,190 measurements, etc., while some endpoints contain only around 100 measurements like mouse-intravenous-LDLo, rat-intravenous-LDLo, frog-subcutaneous-LD50, and human-oral-TDLo, etc. The sparsity and unbalance of this dataset present acute toxicity evaluation as a challenging issue. Among the 59 endpoints, 21 endpoints with <200 measurements were considered small-sized endpoints, and 11 endpoints with more than 1000 measurements were treated as large-sized endpoints. Three endpoints targeting humans, human-oral-TDLo, women-oral-TDLo, and man-oral-TDLo, are typical small-sized endpoints, with only 140, 156, and 163 available toxicity measurements, respectively.\n\nThe 115-endpoint dataset is built based on PubChem database40, a publicly available and free chemical database. First, we collected the acute toxicity effects of 120K chemicals from the PubChem database, involving information on species, administration route, measurement indicators, and corresponding toxicity values. Second, we obtained the data of a total of 636 acute toxicity endpoints. However, most of these endpoints only contained 1\u20133 available compounds. In order to properly conduct the design of the AI model and implement the 5-fold cross-validation, we only retained the acute toxicity endpoints with no less than 30 available samples for each endpoint. Eventually, a new acute toxicity dataset with 115 endpoints was formed. Third, similar to the operation of acute toxicity data in the TOXRIC database, we also unified the various units of all toxicity measurement values of the above new data into\u2014log(mol/kg), so that the AI model can better regress the acute toxicity values. Overall, the \u201cvery toxic\u201d proportions spanned 4.71% (mouse-oral-LD50) to 84.38% (cat-intravenous-LD50), with \u201cnon-toxic\u201c proportions ranging from 0.68% (guinea pig-intravenous-LD50) to 42.88% (rabbit-skin-LD50). Compared with the previous 59-endpoint acute toxicity dataset from TOXRIC, the number of acute toxicity endpoints in this new dataset has doubled, adding more possible species (like goat, monkey, hamster, etc.), administration routes (like intracerebral, intratracheal), and measurement indicators (like LD10, LD20). It should be emphasized that the sample imbalance among endpoints and the data missing rate of this dataset are more severe. Its sparsity rate reaches 98.7%, and it contains 68 small-sized acute toxicity endpoints (i.e., endpoints with <200 toxicity measurement data), among which the endpoint with the fewest samples has only 30 available measurement data (Supplementary Table\u00a02). Therefore, this dataset is more challenging for all current acute toxicity prediction models.\n\nThe benchmark dataset is collected from the study of Mansouri et al.5. To adapt to ToxACoL\u2019s regression framework, we exclusively utilized the LD50 dataset reported in this study (6398 molecules for training and 2196 molecules for evaluation), which was collected from rat oral experiments with units of mg/kg. Then, we standardized the provided toxicity values into\u2014log(mol/kg) using the same method applied to the aforementioned two datasets, enabling compatibility with ToxACoL for experimental validation.\n\nThis operation is performed on training data. Taking the experiments on the 59-endpoint dataset, for example, the graph includes 59 nodes, denoting 59 acute toxicity endpoints. The edges represent the dependency between two endpoints. For each pair of endpoints (i,\u00a0j), we count the total number of compounds shared by the ith endpoint and the jth endpoint, denoted by NUM(i,\u00a0j), and then calculate the PCC of the corresponding toxicity measurements between the two endpoints based on their shared compounds (if necessary), denoted by PCC(i,\u00a0j). Only if their shared compounds are enough and the toxicity measurements of these shared compounds at both endpoints are highly correlated, can the two endpoints be considered dependent, and thus an edge is considered to exist between them:\n\nwhere \u03bb and \u03c4 are two predefined hyperparameters (we set \u03bb\u2009=\u200915 and \u03c4\u2009=\u20090.75). After traversing all possible edges, we can construct the adjacency matrix A of the acute toxicity endpoint graph.\n\nEach adjoint correlation layer takes compound embeddings and endpoint embeddings from the previous adjoint correlation layer as inputs, and outputs the new compound embeddings and endpoint embeddings as inputs for the next adjoint correlation layer:\n\nwhere \\({{{{\\bf{E}}}}}_{{\\rm {c}}}^{(l)}\\in {{\\mathbb{R}}}^{B\\times {d}_{l}}\\) denotes the compound embeddings outputted by adjoint correlation layer l (B is the batch size when training), and \\({{{{\\bf{E}}}}}_{{\\rm {c}}}^{(l-1)}\\in {{\\mathbb{R}}}^{B\\times {d}_{l-1}}\\) denotes the compound embeddings outputted by previous layer l\u22121. \\({{{{\\bf{E}}}}}_{{\\rm {e}}}^{(l)}\\in {{\\mathbb{R}}}^{N\\times {d}_{l}}\\) denotes the N endpoint embeddings outputted by the graph convolution in adjoint correlation layer l, and \\({{{{\\bf{E}}}}}_{{\\rm {e}}}^{(l-1)}\\in {{\\mathbb{R}}}^{N\\times {d}_{l-1}}\\) denotes the N endpoint embeddings of previous layer l\u22121. Next, we explain in detail the implementation of the adjoint correlation layer. Based on endpoint graph A, a graph convolution layer is designed to process the endpoint embeddings in each adjoint correlation layer. In this way, the embedding of each endpoint is a mixture of the embeddings of its neighbor endpoints from the previous layer. We adopted the following operation to perform the graph convolution on endpoint embeddings:\n\nwhere \u2297 denotes matrix multiplication, \\(\\hat{{{{\\bf{A}}}}}\\in {{\\mathbb{R}}}^{N\\times N}\\) is the normalized adjacency matrix of the N-endpoint graph, \\({{{{\\bf{W}}}}}_{{\\rm {G}}}^{(l)}\\in {{\\mathbb{R}}}^{{d}_{l-1}\\times {d}_{l}}\\) is a transformation matrix, and is learnable in the training phase, \u03c3( \u22c5 ) denotes a non-linear activation operation (here we used Leaky ReLU with a negative slope of 0.1). Note that \\({{{{\\bf{E}}}}}_{{\\rm {e}}}^{(0)}\\in {{\\mathbb{R}}}^{N\\times {d}_{0}}\\) is the initial embeddings of the N endpoints and an entity encoding strategy produces it. Concretely, three attributes of the endpoint (species, administration route, and measurement indicator) were separately encoded into three one-hot subvectors and then concatenated to form the initial endpoint embedding. The 59-endpoint dataset involved 15 species, 8 routes, and 3 indicators, so the dimension of initial endpoint embeddings is d0\u2009=\u200915\u2009+\u20098\u2009+\u20093\u2009=\u200926. Critically, endpoint embeddings \\({{{{\\bf{E}}}}}_{\\rm {{e}}}^{(l)}\\) will interact with the compound embeddings:\n\nwhere \u22a4 is a matrix transpose operation. \\({f}_{{{{{\\mathbf{\\theta }}}}}^{(l)}}(\\cdot ):{{\\mathbb{R}}}^{{d}_{l-1}}\\to {{\\mathbb{R}}}^{{d}_{l}}\\) is the feed-forward layer in adjoint correlation layer l, which consists of Linear layer, BatchNorm layer, Dropout layer, and ReLU activation, connected in series. \u03b8(l) denotes the learnable parameters in the feed-forward layer. \\({{{{\\bf{W}}}}}_{\\rm {{L}}}^{(l)}\\in {{\\mathbb{R}}}^{N\\times {d}_{l}}\\) is the learnable parameter of the Linear layer (green block in Fig.\u00a01a) in adjoint correlation layer l, aiming at restoring the dimension of compound embeddings. The operation of \\({f}_{{{{{\\mathbf{\\theta }}}}}^{(l)}}({{{{\\bf{E}}}}}_{{\\rm {c}}}^{(l-1)})\\otimes {({{{{\\bf{E}}}}}_{{\\rm {e}}}^{(l)})}^{\\top }\\) achieves the correlation calculation between compound embeddings and N endpoint embeddings, and the addition term in Eq. (4) achieves a residual connection. Note that \\({{{{\\bf{E}}}}}_{{\\rm {c}}}^{(0)}\\in {{\\mathbb{R}}}^{B\\times {d}_{0}^{{\\prime} }}\\) is initial compound embedding, and we adopted the 1024-dimensional Avalon fingerprints as \\({{{{\\bf{E}}}}}_{{\\rm {c}}}^{(0)}\\in {{\\mathbb{R}}}^{B\\times 1024}\\), which can be generated from the SMILES of compounds.\n\nAfter connecting multiple adjoint correlation layers in series, a top regression layer was devised to output the final estimation results concerning multiple endpoints. Specifically, assuming there are a total of L adjoint correlation layers (we selected L\u2009=\u20094 in our experiments), then the final predictive results \\({{{{\\bf{Y}}}}}_{\\rm {{e}}}\\in {{\\mathbb{R}}}^{B\\times N}\\) of the B compounds in a mini-batch can be computed as\n\nwhere \\({{{{\\bf{W}}}}}_{{\\rm {T}}}\\in {{\\mathbb{R}}}^{{d}_{{\\rm {L}}}\\times N}\\) denotes the weights of the top linear layer. \\({{{{\\bf{W}}}}}_{{\\rm {R}}}={(\\hat{{{{\\bf{A}}}}}\\otimes {{{{\\bf{E}}}}}_{{\\rm {e}}}^{(L-1)}\\otimes {{{{\\bf{W}}}}}_{{\\rm {G}}}^{(L)})}^{\\top }\\in {{\\mathbb{R}}}^{{d}_{{\\rm {L}}}\\times N}\\) denotes the N pre-nonlinear endpoint embeddings outputted by final layer L, which is treated as the N endpoint-wise regressors to fit the toxicity intensity at various endpoints.\n\nWe assume that in a data mini-batch containing B compounds, the ground-truth toxicity intensity value of the ith compound with respect to the jth endpoint is yi,j,\u00a0\u20091\u2009\u2264\u2009i\u2009\u2264\u2009B,\u00a01\u2009\u2264\u2009j\u2009\u2264\u2009N. Due to the extreme sparsity of the dataset, yi,j only has available values on a few (i,\u00a0j) pairs, and in most cases, it is NULL. Certainly, each compound has the ground-truth toxicity intensity value at least at one endpoint, i.e., \u22111 \u2264 j \u2264 N I(yi,j\u2009\u2260\u2009NULL) \u2265\u2009 1, \u2009\u2200i. So, we designed the following mini-batch loss function to train ToxACoL:\n\nwhere Ye[i,\u00a0j] is the estimated toxicity intensity of the ith compound with regard to the jth endpoint. I(yi,j\u2009\u2260\u2009NULL) is an indicator function, equal to 1 if yi,j is an available value and 0 when it is missing. This loss filters out the missing compound-to-endpoint measurements and fully utilizes the existing supervision information provided by the sparse acute toxicity dataset.\n\nFor each endpoint, the determination coefficient, R2, is used as the main evaluation metric:\n\nwhere n is the total number of compounds at this endpoint, yi and \\({\\hat{y}}_{i}\\) denote the ground-truth toxicity value and the estimated toxicity intensity value for the ith compound, 1\u2009\u2264\u2009i\u2009\u2264\u2009n, respectively, while \\(\\bar{y}\\) is the average toxicity intensity value over all the n compounds at this endpoint. We also calculated the RMSE for each endpoint as an additional evaluation metric: \\({{{\\rm{RMSE}}}}=\\sqrt{\\frac{1}{n}\\mathop{\\sum }_{i=1}^{n}{({\\hat{y}}_{i}-{y}_{i})}^{2}}\\). Unless otherwise specified, the R2 or RMSE metric values we reported are the average results after 5-fold cross-validation.\n\nThe Friedman test and Nemenyi test46 were employed to analyze the performance difference among various methods over all N acute toxic endpoints. The Friedman test is a nonparametric test equivalent to the analysis of variance (ANOVA), used to ascertain whether there are statistically significant differences in the means of three or more methods tested on identical endpoints. Assuming \\({{{{\\rm{Rank}}}}}_{j}=\\frac{1}{N}\\mathop{\\sum }_{i=1}^{N}{{{{\\rm{rank}}}}}_{j}^{i}\\) denotes the average performance ranking of the jth method over N endpoints (\\({{{{\\rm{rank}}}}}_{j}^{i}\\) is the ranking of the jth method on the ith endpoint), The null hypothesis believes that the performances of all methods are equal. The Friedman statistic in the following equation obeys the \\({{{{\\rm{\\chi }}}}}_{{\\rm {F}}}^{2}\\)-distribution with (K\u22121) free degrees:\n\nwhere K is the total number of methods. Furthermore, the statistic in the following equation obeys the F-distribution with free degrees of (K\u22121) and (K\u22121)(N\u22121):\n\nNext, the Nemenyi test was used to evaluate the performance difference between pairwise methods, deeming the pairwise methods significantly different if their average ranking gap exceeds the critical difference (CD):\n\nwhere q\u03b1 is based on the Studentized range statistic divided by \\(\\sqrt{2}\\) and \u03b1 is the corresponding confidence level.\n\nIn addition, we adopted kernel density estimation45, a powerful non-parametric statistical method used to estimate unknown probability density functions, to fit the overall performance distribution of different models on all endpoints and then compare the performance balance of different methods on all endpoints. Two-sided Wilcoxon signed-rank test75 was used to compute the significant difference between the two methods.\n\nSeveral state-of-the-art STL, MTL, and consensus models for acute toxicity prediction were compared with ToxACoL. ST-DNN: training a deep neural network for each endpoint and taking the Avalon fingerprints as inputs. The complexity of the single-task neural network for each endpoint varies with the sample size of the available toxicity measurement about this endpoint. ST-RF: building a random forest for each endpoint, and the total number of trees varies with the sample size concerning this endpoint. It also takes the Avalon fingerprints as inputs. GAT: combining a graph neural network with an attention mechanism that determines the relative importance of neighboring nodes. GCN: operating on the molecule graph structure of compounds using convolutional neural networks rather than on fingerprint vectors. Attentive FP: a graph architecture that represents molecules using molecular fingerprints and a graph attention mechanism. MT-DNN: a multi-channel deep neural network for all endpoints, where the anterior feature encoder in this network for all endpoints is shared but the regressors for various endpoints are independent. Its input is also the 1024-dimensional Avalon fingerprints. MT-GCN: a multi-task graph convolution network, where the input is the molecule graph structure of compounds and the molecule graph learning module is shared across all endpoints while the final regressors towards endpoints are independent. DLCA: a consensus learning architecture that averages the outputs of separate multi-task networks, including four descriptor-based networks and one descriptor-free network. The four descriptor-based networks were trained using four different types of molecular representation (Avalon, Morgan, AtomPair fingerprints, and RDKit descriptors). The one descriptor-free network was realized based on SMILES strings, consisting of a convolutional network with 1D convolutional layers and GlobalMax pooling layers followed by fully connected layers and multi-channel regression layers.\n\nThe split of the training and testing sets in the dataset is completely consistent with the baseline models17, which was randomly divided 5 times for 5-fold cross-validation. The results we reported mostly came from a ToxACoL model with four adjoint correlation layers, where the embedding dimensions are designed as d1\u2009=\u2009768,\u00a0\u2009d2\u2009=\u2009512,\u00a0\u2009d3\u2009=\u2009384, and d4\u2009=\u200964, respectively. The channel number of the top regression layer was designed to match the total number of toxic endpoints. The initial compound embeddings are the 1024-dimensional Avalon fingerprints, and the initial endpoint embeddings are the 26-dimensional (59-endpoint dataset) or 35-dimensional (115-endpoint dataset) binary vectors concatenated by three one-hot subvectors corresponding to the three attributes of the endpoints. The dropout rate in feed-forward layers was maintained at 0.1. The non-linear activation function of graph convolution was set to Leaky ReLU with a negative slope of 0.1. The SGD with Nestrerov momentum76 was used as the optimizer, where the momentum factor is 0.9, the weight decay is 5e\u22124 and the initial learning rate is 0.01. Our ToxACoL model was trained for 120 epochs with a batch size of 32, and the optimal model and another nine models obtained from its nine nearest epochs were saved. For inference on testing compounds, the average outputs from the 10 well-trained models were treated as the final estimation results.\n\nThe model was implemented via Python 3.10, and the main dependent packages only include Torch, Pandas, and Rdkit. When training, the codes were run on an Intel I9-13900KF processor and an NVIDIA RTX4090 GPU, with the configuration deployed on an Ubuntu 22.04 LTS system. The training takes ~15\u2009min. During the inference phase, the toxicity of chemical compounds can be inferred entirely using the CPU environment. We conducted tests on a laptop equipped with an ordinary Intel Core i5 processor, and the model only takes about 0.8\u2009s to predict the toxicity of a single molecule on various acute toxicity endpoints. Such low-demand computing resources can enable many researchers to use our model.\n\nFor the experiments of reducing training measurements for the 59-endpoint dataset, the data reduction was made on the 21 small-sized endpoints, where the available measurements for each small-sized endpoint in the training set were randomly discarded according to a certain ratio, like 80%, 50%, 40%, 30%, 20%, and 10%. This was done by randomly replacing the ground-truth toxicity intensity of some available measurements with NULL. Technically, we can achieve it just by modifying the indicator function value of the randomly selected training measurements from I(\u2009yi,j\u2009\u2260\u2009NULL)\u2009=\u20091 into I(\u2009yi,j\u2009\u2260\u2009NULL)\u2009=\u20090 during the ToxACoL training process.\n\nAccording to the Organization for Economic Co-operation and Development (OECD) guidelines, QSAR modeling requires defining the AD to ensure reliable predictions. The reliability of a model\u2019s prediction depends on whether the queried compound falls within the AD defined by the model77,78,79. We quantify the AD of ToxACoL using Tanimoto similarity based on molecular fingerprints. First, we calculate the Tanimoto similarity matrix between the test set and training set using chemical Avalon fingerprints. The AD threshold ST is calculated using the equation:\n\nwhere \u03b3 and \u03c3 represent the mean and the standard deviation of the Tanimoto similarities between all compounds in the training set, reflecting the overall similarity level within the training set. Z is an adjustable confidence parameter. Next, for each compound in the test set, we calculate its Tanimoto similarity to all compounds in the training set. The top k compounds from the training set with the highest similarity to the test compound are selected (k is set to 10 as a predefined value). If the average similarity of these k compounds exceeds the threshold ST, the compound is considered within the AD otherwise, it is classified as outside the AD.\n\nDifferent from the previous 5-fold cross-validation experiment, we used all the data in the 59-endpoint acute toxicity dataset to train our ToxACoL in the extrapolation experiment. Since we need to study the potential associations of toxicity responses between animal endpoints and human endpoints, we selected the three human-related endpoints in TOXRIC as the control benchmarks and took the compounds with existing toxicity values for these three endpoints as research objects. Among them, the number of available compounds for the human-oral-TDLo, woman-oral-TDLo, and man-oral-TDLo endpoints were 140, 156, and 163, respectively. Taking the human-oral-TDLo as an example, we used the trained ToxACoL to predict the acute toxicity of these 140 compounds at the other 58 endpoints and filled in the missing toxicity values in the dataset. After this operation, we can obtain the acute toxicity values of the same set of compounds at different endpoints. Based on this, we can conduct Pearson correlation analysis and statistical tests to explore the extrapolation relationships between different animal endpoints and the human-oral-TDLo endpoint.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The 59-endpoint acute toxicity dataset used in our study is freely available on the published data platform TOXRIC25 at https://toxric.bioinforai.tech/home, and the 115-endpoint acute toxicity dataset is freely available on the PubChem database40 at https://pubchem.ncbi.nlm.nih.gov/. The two datasets have also been organized by us and published at https://doi.org/10.6084/m9.figshare.27195339.v5with DOIs and citations80.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code used to develop the model, perform the analyses, and generate results in this study is publicly available and has been deposited in GitHub at https://github.com/LuJiangTHU/Acute_Toxicity_FSL, under CC-BY 4.0 license. The specific version of the code associated with this publication is archived in Zenodo and is accessible via 10.5281/zenodo.1506359581. In addition, to enable all researchers to directly use our pre-trained ToxACoL, we have integrated our ToxACoL into an online software using the Docker containerization platform. This online software can accurately predict the acute toxicity values and the GHS classes of chemical compounds on different acute toxicity endpoints. 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Zenodo https://doi.org/10.5281/zenodo.15063595 (2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by the National Key R&D Program of China (2023YFC2604400 to S.H. and 2024YFA1307700 to X.B.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Jiang Lu, Lianlian Wu.\n\nAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People\u2019s Republic of China\n\nJiang Lu,\u00a0Lianlian Wu\u00a0&\u00a0Xiaochen Bo\n\nDepartment of Advanced & Interdisciplinary Biotechnology, Academy of Military Medical Sciences, Beijing, People\u2019s Republic of China\n\nJiang Lu,\u00a0Lianlian Wu,\u00a0Ruijiang Li,\u00a0Song He\u00a0&\u00a0Xiaochen Bo\n\nInstitute of Advanced Technology and Equipment, Xi\u2019an Jiaotong University, Xi\u2019an, People\u2019s Republic of China\n\nJiang Lu\u00a0&\u00a0Xiaochen Bo\n\nShanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, People\u2019s Republic of China\n\nMengxuan Wan\u00a0&\u00a0Peng Zan\n\nDepartment of Cell Biology, School of Life Sciences, Central South University, Changsha, People\u2019s Republic of China\n\nJun Yang\n\nClinical Translational Research Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, People\u2019s Republic of China\n\nHui Bai\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.L., L.W. and S.H. conceived and designed the study. J.L. developed the methodology and code, carried out the evaluation, achieved the comparison with baseline models, and created the visualizations. L.W. built the acute toxicity dataset, analyzed the model interpretability, designed experiments for structural alerts and toxicity extrapolation, and explored the model\u2019s utility in chemical toxicology. R.L. and P.Z. provided insights into toxicity representation. M.W. collected the acute toxicity data and achieved the comparison with several graph-based models. J.Y. carried out data preprocessing and organization. J.L., L.W. and S.H. contributed to analyzing the results and writing and improving the manuscript. S.H., H.B. and X.B. supervised the study. All authors reviewed and approved the final manuscript.\n\nCorrespondence to\n Hui Bai, Song He or Xiaochen Bo.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Thomas Hartung and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment.\n Nat Commun 16, 5992 (2025). https://doi.org/10.1038/s41467-025-60989-7\n\nDownload citation\n\nReceived: 24 November 2024\n\nAccepted: 30 May 2025\n\nPublished: 01 July 2025\n\nVersion of record: 01 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60989-7\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Cryptosporidium by genetic crossing", + "pre_title": "SKSR1 identified as key virulence factor in Cryptosporidium by genetic crossing", + "journal": "Nature Communications", + "published": "20 May 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60088-7/MediaObjects/41467_2025_60088_MOESM1_ESM.pdf" + }, + { + "label": "Reporting summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60088-7/MediaObjects/41467_2025_60088_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60088-7/MediaObjects/41467_2025_60088_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60088-7/MediaObjects/41467_2025_60088_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/sra/", + "/articles/s41467-025-60088-7#Sec32" + ], + "code": [ + "https://github.com/tyhou/BSA.Cpar.IId.GDxHLJ", + "https://codeocean.com/capsule/0441727/tree/v1" + ], + "subject": [ + "Parasite biology", + "Parasite genetics" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3993483/v1.pdf?c=1747825684000", + "research_square_link": "https://www.researchsquare.com//article/rs-3993483/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60088-7.pdf", + "preprint_posted": "29 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Cryptosporidium is a major cause of severe diarrhea. Although Cryptosporidium isolates exhibit significant differences in infectivity and virulence, the genetic determinants for these traits are not clear. In this study, we used classical genetics to cross two Cryptosporidium parvum isolates of different virulence and used bulked segregant analysis of whole-genome sequence data from the progeny to identify quantitative trait loci (QTL) associated with Cryptosporidium infectivity and virulence. Of the 26 genes in three QTL, two had loss-of-function mutations in the low-virulence isolates. Deletion of the SKSR1 gene or expression of the frame-shift mutant sequence reduced the pathogenicity of infection in vivo. SKSR1 is a polymorphic secretory protein expressed in small granules, secreted into the parasite-host interface, and may cooperate with other secretory proteins in pathogenesis. These results demonstrate that SKSR1 is an important virulence factor in Cryptosporidium, and suggest that the extended SKSR protein family, encoded by variant subtelomeric genes, may contribute to pathogenesis.Biological sciences/Microbiology/Parasitology/Parasite biologyBiological sciences/Microbiology/Parasitology/Parasite geneticsCryptosporidiumgenetic crosslinkage mappingbulked segregant analysisvirulence factorSKSR1", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "CryGeneticCrossBSASFigure20230227.pdfSupplementary figuresCryGeneticCrossBSAStable120240227.pdfSupplementary table1CryGeneticCrossBSAStable220240227.pdfSupplementary table2", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Cryptosporidium is a major cause of severe diarrhea. Although Cryptosporidium isolates exhibit significant differences in infectivity and virulence, the genetic determinants for these traits are not clear. In this study, we use classical genetics to cross two Cryptosporidium parvum isolates of different virulence and use bulk segregant analysis of whole-genome sequences from the progeny to identify quantitative trait loci (QTL) associated with Cryptosporidium infectivity and virulence. Of the 23 genes in three QTL, two have loss-of-function mutations in the low-virulence isolates, including the SKSR1 gene encoding a variant secretory protein. Deletion of the SKSR1 gene or expression of the frame-shifted sequence reduces the pathogenicity of the virulent isolate. SKSR1 is expressed in small granules and secreted into the parasite-host interface during invasion. These results demonstrate that SKSR1 is an important virulence factor in Cryptosporidium, and suggest that the extended SKSR protein family, encoded by clusters of subtelomeric genes, may contribute to pathogenesis.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Cryptosporidiosis is a leading cause of severe diarrhea, leading to death and malnutrition in many children in low- and middle-income countries1. It is also a major cause of food- and waterborne disease in high-income countries2. The disease is particularly severe in young children and immunocompromised individuals, leading to malnutrition and significant mortality3. The pathogenesis of cryptosporidiosis is poorly understood.\n\nCryptosporidium hominis and Cryptosporidium parvum are the dominant species responsible for more than 90% of human cases of cryptosporidiosis4. The two differ significantly in their host range, with the former being predominantly an anthroponotic species in low- and middle-income countries and the latter being a zoonotic species and the dominant species in both humans and calves in high-income countries4. The two species share ~96% sequence identity in whole genome sequences5. Due to the lack of convenient culture and animal models for C. hominis, most biological studies of Cryptosporidium spp. have been conducted with C. parvum6.\n\nC. parvum isolates vary widely in infectivity and virulence. In experimental infections of healthy adults, three C. parvum isolates differed in half infectious doses (ID50), infection intensity, attack rates, and duration of diarrhea7. Differences in virulence among C. parvum isolates have also been observed in experimental infections of mice and young livestock8,9,10. For example, at the subtype level, IIaA15G2R1 has become the dominant C. parvum in both humans and animals in most high-income countries4,11. However, the genetic determinants of Cryptosporidium virulence and infectivity are not clear, although the results from comparative genomic analysis have provided some clues10.\n\nIn Plasmodium spp. and Toxoplasma gondii, linkage mapping of genetic crosses of isolates has been effective in studies of virulence factors12,13. It has led to the identification of several rhoptry proteins as major determinants of virulence in T. gondii14 and point mutations in the erythrocyte binding like protein that impact red blood cell invasion and virulence in Plasmodium yoelii15. However, traditional linkage analysis requires the comparative analysis of numerous recombinant progeny. In recent years, a more efficient linkage mapping technique, bulk segregant analysis (BSA), has been used to characterize genetic crosses of Plasmodium falciparum isolates16,17. It measures changes in allele frequencies in cross progeny under different selection pressures, allowing the identification of single nucleotide polymorphisms (SNPs) associated with specific phenotypic traits18.\n\nIn this report, we describe the identification of genes underlying the virulence differences using BSA of progeny from genetic crosses of two C. parvum isolates. The role of candidate genes in infection and virulence is validated using gene depletion and replacement techniques. The results show that the small granule (SG) protein SKSR1 is a key virulence factor in C. parvum.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We used two C. parvum isolates of different subtypes of the 60\u2009kDa glycoprotein (GP60) gene in our cross studies, including IIdA20G1-HLJ (HLJ) and IIdA19G1-GD (GD)10. They differ in infectivity (ID50\u2009=\u20090.6 and 5.1 oocysts, respectively) and infection pattern (HLJ produces over 107 oocysts per gram of feces, OPG, for more than 8 weeks compared to a peak OPG of 106 for less than 2 weeks for GD) in interferon-\u03b3 knockout (GKO) mice. HLJ is highly virulent in this animal model, causing arched backs, lethargy, weight loss, and significant mortality. In contrast, GD is avirulent in GKO mice10.\n\nTo create a genetic cross between the virulent HLJ and the avirulent GD, we endogenously tagged the two isolates with different fluorescent proteins using CRISPR/Cas9 as previously described19. In the HLJ isolate, we replaced the uracil phosphoribosyltransferase (UPRT) gene on chromosome 1 with a sequence encoding the tdTomato protein. Similarly, in the GD isolate, we replaced the thymidine kinase (TK) gene on chromosome 5 with a sequence encoding the mNeonGreen protein (Supplementary Fig.\u00a01a). Fluorescence microscopy of purified oocysts showed that all (100/100) oocysts from HLJ and GD contained mNeonGreen (green) and tdTomato (red) fluorescent proteins, respectively (Supplementary Fig.\u00a01b). PCR analysis showed correct integration of the replacement cassette (Supplementary Fig.\u00a01c).\n\nWe examined the difference in pathogenicity between fluorescently tagged GD (GD-mNeonGreen) and HLJ (HLJ-tdTomato) lines. When treated with paromomycin (PRM) after infection with transgenic lines carrying the neomycin resistance (neo) gene, the HLJ-tdTomato line showed higher oocyst shedding (Supplementary Fig.\u00a01d), caused significant clinical signs of infection (Supplementary Fig.\u00a01e), and resulted in 100% mortality of the GKO mice (Supplementary Fig.\u00a01f). In contrast, GD-mNeonGreen is avirulent in GKO mice (Supplementary Fig.\u00a01e, f).\n\nWe performed four genetic crosses of GD-mNeonGreen and HLJ-tdTomato in GKO mice (Fig.\u00a01a). Prior to infection, GKO mice were pretreated with antibiotics to increase the infection rate and the intensity of oocyst shedding of the parasite20. Co-infection of the two isolates resulted in the generation of recombinant oocysts expressing both mNeonGreen and tdTomato fluorescent proteins. We purified the progeny oocysts from the intestinal contents of infected mice on day 4 post infection (DPI) (Fig.\u00a01b). In the first cross, we found 19% colorless oocysts in the cross progeny (Supplementary Fig.\u00a02a), possibly due to the presence of some colorless sporozoites in newly tagged parental oocysts. Subsequent crosses of parental oocysts from subsequent passages showed that approximately 32.6% of the purified oocysts showed both tdTomato and mNeonGreen fluorescence (yellow in merged images), while the remaining oocysts showed predominantly tdTomato fluorescence, with a small number of oocysts showing mNeonGreen fluorescence or being colorless (Fig.\u00a01c). We enriched and collected oocysts expressing two fluorescent proteins by flow cytometric sorting (Supplementary Fig.\u00a02b). Microscopic examination of 100 sorted oocysts and sporozoites showed that all oocysts and sporozoites had two fluorescent proteins (Supplementary Fig.\u00a02c), some of which may be residues of the parental protein21.\n\na Diagram of genetic crosses of HLJ and GD in mice and flow cytometric sorting of the progeny. b Image of oocysts harvested from the intestinal mucosa 4 days after coinfection of oocysts tagged with different fluorescent proteins and the sporozoites excysted from them. Scale bars\u2009=\u20092\u2009\u00b5m. c Ratio of oocysts of each color in intestinal content of mice 4 days after coinfection, as determined by microscopy of samples (mean\u2009\u00b1\u2009SD) from three independent experiments (the 2nd, 3rd, and 4th genetic crosses). d Confirmation of genetic recombination in single oocysts of progeny by read mapping of whole genome sequences. W587 and W595 represent the IDs of the sequenced single oocyst genomes. Two polymorphic sites relative to the parental sequences on the genomes in oocyst W595 are marked, indicating crossover of sequence types in this region. Source data are provided as a Source Data file.\n\nPrior to analysis, we generated a chromosome-level HLJ genome using PacBio in combination with Illumina sequencing. The whole genome assembly together with full annotation has been deposited in DDBJ/ENA/GenBank databases under accession number JBJGDY000000000. The new HLJ genome is comparable in completeness to several other C. parvum genomes sequenced using third-generation sequencing technology22, with 13 telomeric ends. Three missing telomeric regions are located at the 5\u2032 end of chromosome 1 and the 3\u2032 ends of chromosomes 7 and 8 (Supplementary Fig.\u00a02d). There are 1065 SNP differences distributed between the HLJ and GD genomes (Supplementary Fig.\u00a02e). Based on PCR analysis of polymorphic loci, we confirmed the presence of mixed sequence types in oocysts (DPI 4 progeny pool) expressing two fluorescent proteins (Supplementary Fig.\u00a02f), indicating the presence of genomic sequences from both HLJ and GD. Whole genome sequencing (WGS) was performed on 12 individual oocysts, yielding good sequence data from 11 of them. Allele frequencies and mapping depth of TK and UPRT locus analysis showed that the single oocyst genomes were all heterozygous (Supplementary Fig.\u00a03a and Supplementary Fig.\u00a04), confirming the recombinant nature of the progeny as in previous studies19. Mosaic sequences at polymorphic loci were found in comparative genomic analyses of sequencing data from two single oocysts (Fig.\u00a01d), suggesting that the yellow oocysts contained pools of recombinant progeny of HLJ and GD. Frequent crossovers of the HLJ and GD sequences were observed along the eight chromosomes of the 11 single-oocyst genomes (Supplementary Fig.\u00a03a). However, due to the heterozygosity of the single-oocyst genomes, we used allele frequency differences >0.8 between neighbouring SNP loci to identify sequence crossovers. The lack of a consistent pattern of genetic recombination across the eight chromosomes suggests that the current threshold setting may underestimate the number of crossover events, resulting in some recombination not being detected (Supplementary Fig.\u00a03b).\n\nTo identify genes associated with parasite growth, we performed BSA of the progeny from genetic crosses of the HLJ and GD isolates. Fecal samples were collected from GKO mice every 6 days after infection with the progeny pool for oocyst purification and WGS analysis (Fig.\u00a02a). Microscopic examination of oocysts collected at different time points revealed that although only yellow oocysts were used to inoculate mice and PRM was used to maintain fluorescence tagged parasites, there was a rapid decrease in the proportion of yellow oocysts during the early course of infection. This change was accompanied by the appearance of tdTomato, mNeonGreen fluorescence, and colorless oocysts (Fig.\u00a02b, c). G-statistics analysis of the WGS data was used to identify SNPs enriched in recombinant C. parvum progeny over the course of infection. Calculation of G\u2032 values for different batches and replicates resulted in a threshold value of 91.4 for the identification of quantitative trait loci (QTL). Gradual increases in G\u2032 values were observed in the subtelomeric regions of chromosomes 1, 7, and 8 as infection progressed, suggesting an increased frequency of some alleles in these regions (Fig.\u00a02d and Supplementary Fig.\u00a05a).\n\na Diagram showing the design of the BSA study in GKO mice and the time of sample collection for WGS analysis. b Image of purified oocysts at different time points of the second BSA infection with PRM. Images of purified oocysts at DPI 6, DPI 36, and DPI 48 are shown. Scale bars\u2009=\u20095\u2009\u00b5m. c Ratio of oocysts of different colors at different time points of infection as determined by microscopy. d Distribution of G\u2032 values of Cryptosporidium genomes at DPI 6, DPI 36, and DPI 48 of the BSA study. e Distribution of the SNP index of Cryptosporidium genomes collected at DPI 0 (progeny pool) and DPI 36. The delta SNP indices in the subtelomeric regions of chromosomes 1, 7, and 8 are close to \u22121, indicating the enrichment of HLJ alleles (n\u2009=\u20093 mice; data from one representative replicate at DPI 36 are shown). The first row is the delta SNP index of the DPI 36 (W438) and DPI 0 (W389) genomes, which is the result after removing the background noise, i.e., the pre-screening interference (the SNP index of DPI 0). The second and third rows are the SNP indices of the DPI 36 and DPI 0 genomes, respectively. W389 and W438 are the IDs of the genomes. Data in (b\u2013e) are from the second BSA infection study with the progeny pool from the second genetic cross. Altogether, four BSA studies were performed using progeny from two genetic crosses. f Identification of three regions on chromosomes 1, 7, and 8 as theQTLs underlying the growth differences between HLJ and GD, based on a 95% confidence interval of data from four BSA studies. The shaded areas are overlapping QTL regions obtained from different BSA studies after Venn analysis of the data. g Identification of 23 candidate genes associated with growth using physical and Venn plots of data from four BSA studies. Source data are provided as a Source Data file.\n\nIn addition, the delta SNP index in these regions was close to \u22121, suggesting an enrichment of HLJ alleles (Fig.\u00a02e and Supplementary Tables\u00a01\u20134). The SNP index is another algorithm used to localize QTL in BSA23. It creates a segregating population from the progeny, selects individuals with extreme phenotypes to form mixed pools, and then genotypes these pools for SNPs. The allele frequencies within each mixed pool are analyzed and compared to one of the parental genotypes. The SNP index at a given locus is determined by the proportion of genotypes that differ from the parent. A larger delta SNP index (the difference in SNP index between two mixed pools) indicates a stronger association of the SNP with the trait of interest.\n\nIn total, we performed four infection studies with two genetic crosses in GKO and neonatal mice with and without the use of PRM (Supplementary Figs.\u00a06\u20138). BSA of WGS data collected at different time points in the infection courses identified similar enrichment of HLJ alleles in three regions on chromosomes 1, 7, and 8 (Fig.\u00a02f). The GKO mouse is an appropriate model for Cryptosporidium infection, as our isolates show differences in virulence in this model10. However, it uses immunodeficient mice, which may support Cryptosporidium growth differently than immunocompetent mice. Therefore, we used neonatal C57 BL/6 mice to validate the BSA results obtained from GKO mice. Interestingly, the same three regions were identified as QTL associated with differences in virulence between the two C. parvum isolates. However, the use of no PRM selection during progeny pool infection of GKO mice identified an additional enrichment of HLJ alleles on chromosome 5 (Supplementary Fig.\u00a07), which was not present in the other three BSA experiments. In total, three QTL were identified in all four BSA studies, encompassing and contained 23 genes on chromosomes 1, 7, and 8. They were associated with the difference in parasite growth between HLJ and GD (Fig.\u00a02g and Supplementary Fig.\u00a05b).\n\nThe 23 genes in the three QTL regions were closely examined for sequence differences between HLJ and GD and for the level of effect of these sequence variations. Most of these genes had only 1\u20132 SNPs in the coding regions, which were judged by SnpEff to have a low to moderate effect on the function of the proteins they encode (Supplementary Table\u00a05). However, three genes had nucleotide insertions or substitutions that resulted in the formation of stop codons that are likely to significantly affect the function of the proteins they encode (Fig.\u00a03a). These included cgd1_140 (encoding the protein SKSR1), a newly predicted paralog (gene ID: CPCDC_7g4512) of the cgd5_4510 gene (encoding a hypothetical protein; gene ID: CPCDC _7g4511) on chromosome 7 of the IIa-IOWA 43IA8 genome22, and cgd8_550 (encoding a hypothetical protein). Allele frequency analyses of these genes showed a rapid enrichment of the HLJ sequence during the BSA infection (Supplementary Fig.\u00a09).\n\na Distribution of different types of SNPs in 23 genes. b Alignment of the partial nucleotide and amino acid sequences of 3 candidate virulence genes. Insertion of base A in cgd1_140 results in premature termination of SKSR1-GD transcription (the arrowhead). The other two arrowheads indicate the position of the base mutation causing a termination codon in cgd8_550-GD and CPCDC_7g4512-HLJ. c Box and violin plots of the expression of potential virulence genes. The CPCDC_7g4512 gene is not expressed in all life cycle stages examined. Each dot represents one gene (n\u2009=\u20094 for the RNA-seq analysis of each culture point). The bounds and horizontal bar of the box in each plot represent quartile and median expression level, while the density curves in the violin plot shows the distribution of gene expression levels. Source data are provided as a Source Data file, and the RNA-seq data are available at NCBI under BioProject No. PRJNA1011005.\n\nAmong these candidate QTL genes, SKSR1-GD had an A-base insertion after nucleotide 1753 compared to SKSR1-HLJ, resulting in premature termination of SKSR1-GD translation (Fig.\u00a03b). In contrast, the CPCDC_7g4512 gene in virulent HLJ had a G to T substitution, resulting in the formation of a stop codon 51\u2009bp after the start codon. The CPCDC_7g4512 gene had high sequence identity to CPCDC_7g4511, which differed by one nucleotide between HLJ and GD and had no stop codon-generating nucleotide substitution (Supplementary Table\u00a05 and Fig.\u00a03b). Similarly, there was only one nucleotide difference between HLJ and GD in the cgd8_550 gene resulting in the formation of a termination codon in cgd8_550-GD (Fig.\u00a03b).\n\nSince CPCDC_7g4512 is a recently predicted gene, we analyzed its expression level. RNA-seq analysis of the transcriptome across all developmental stages of HLJ in HCT-8 cultures and sporozoites showed no evidence of CPCDC_7g4512 expression. In contrast, its paralog CPCDC_7g4511 showed modest expression in sporozoites and at 36 and 48\u2009h in culture (Fig.\u00a03c). Although the cgd8_550 gene was not expressed in sporozoites, its expression gradually increased in HCT-8 culture and remained at high levels during 6\u201348\u2009h. In contrast, the expression of the SKSR1 gene was high in sporozoites and at 3, 12, and 24\u2009h in culture (Fig.\u00a03c). The appearance of the stop codon in the virulent HLJ isolate and the absence of transcription suggested that CPCDC_7g4512 was unlikely to be involved in the virulence differences between HLJ and GD. Therefore, our subsequent validation studies had focused on the SKSR1 and cgd8_550 genes.\n\nWe investigated the expression and function of the hypothetical protein encoded by the cgd8_550 gene. We generated cgd8_550-tagged (cgd8_550-3HA), C-to-G mutant (cgd8_550m(C-G)-3HA), and gene deletion (\u0394cgd8_550) lines of the virulent HLJ strain using CRISPR/Cas9 (Supplementary Figs.\u00a010a, c and 11a). PCR and fluorescence analysis confirmed the successful deletion of the cgd8_550 gene in the \u0394cgd8_550 line, with oocysts from this line being green due to the replacement of the gene with the mNeonGreen sequence (Supplementary Fig.\u00a010e). Immunofluorescence analysis (IFA) showed no cgd8_550 expression in sporozoites and high expression near the nuclei in meronts (Supplementary Fig.\u00a012a). We investigated the role of cgd8_550 in Cryptosporidium growth and host pathogenicity using HCT-8 culture and GKO mouse models. Deletion of the cgd8_550 gene did not significantly affect C. parvum growth in vitro and in vivo (Supplementary Fig.\u00a012b, c), and GKO mice infected with the cgd8_550-3HA and \u0394cgd8_550 lines had similar weight gain and survival (Supplementary Fig.\u00a012d, e). Because the cgd8_550 gene was not associated with the high virulence of the HLJ strain, we did not subsequently validate the effect of the C-to-G substitution (cgd8_550m(C-G)-3HA) on virulence.\n\nThe cgd1_140 gene is a subtelomeric gene on chromosome 1, encoding the Cryptosporidium-specific secretory protein SKSR1. Comparison of cgd1_140 sequences from C. parvum showed that the gene is polymorphic, with 21\u201323 SNPs between IIa and IId subtypes in the 2892\u2009bp coding sequence. Within the IId subtypes, the A insert between NT1743 and NT1744 was seen in all IIdA19G1 isolates, but was absent from the sequences of other IId isolates. This insert was also not detected in the IIa and IIc isolates analyzed. The sequence polymorphism resulted in the formation of two subclades within the IId cluster and three subclades within the IIa cluster (Supplementary Fig.\u00a013a).\n\nWe generated SKSR1-tagged (SKSR1-3HA), A-base inserted mutant (SKSR1m(+A)-3HA), and SKSR1 deletion (\u0394sksr1) lines of the HLJ isolate using CRISPR/Cas9 (Supplementary Figs.\u00a010b, d and 11b). Fecal luciferase monitoring of mice infected with these lines and PCR analysis of purified oocysts confirmed successful integration of the replacement templates (Supplementary Figs.\u00a010d and 13b). SKSR1 expression was observed near the nucleus of sporozoites and merozoites in a pattern distinct from canonical dense granules (Fig.\u00a04b and Supplementary Fig.\u00a013c). In the SKSR1m(+A)-3HA line, due to the insertion of an A in the SKSR1 gene, there was no detection of SKSR1 in IFA analysis of all stages examined (Supplementary Fig.\u00a013c). This was confirmed by Western blot analysis, showing the expression of SKSR1-3HA but not SKSR1m(+A)-3HA (Supplementary Fig.\u00a013d). Fluorescence microscopy further confirmed the successful deletion of the SKSR1 gene in the \u0394sksr1 line, as oocysts of the mutant line all contained the replacement tdTomato protein (Supplementary Fig.\u00a010f). This finding was supported by read mapping of the WGS data from the \u0394sksr1 line (Supplementary Fig.\u00a013e).\n\na Identification of SKSR1-3HA expression in 8 egressing merozoites. SKSR1(red) is localized near the nucleus; EF1a in green; nuclei (stained with Hoechst) in blue; scale bar\u2009=\u20095\u2009\u00b5m. b Ultrastructure expansion microscopy (U-ExM) showing SKSR1 in green relative to NHS ester-stained organelles (red); nuclei in blue; scale bar\u2009=\u20095\u2009\u00b5m. c Dynamics of SKSR1 expression during invasion. SKSR1 is translocated from small granules to the apical region of C. parvum sporozoites during the late phase of the host cell invasion. The cartoon images to the left of IFA panels illustrate the process of sporozoite invasion. Scale bar\u2009=\u20095\u2009\u00b5m. d Immunofluorescence localization of SKSR1 at the stage when sporozoites form a cup-like structure (side view) with the host cell membrane. Scale bar\u2009=\u20095\u2009\u00b5m. e Immunoelectron microscopic localization of SKSR1. SKSR1 is localized at the parasite-host interface. Scale bars\u2009=\u2009500\u2009nm. a\u2013e Each experiment was performed at least twice with similar results. f A model of SKSR1 secretion during Cryptosporidium invasion. The SKSR1 protein is shown in red. Source data are provided as a Source Data file.\n\nWe performed immunoelectron microscopy (IEM) of the ileal tissue from mice infected with the SKSR1-3HA line and SG1-3HA lines, and the results showed the accumulation of gold particles for SKSR1 in small vesicles near the nucleus that matched the location, shape, and size of SG1-3HA-positive vesicles (mean\u2009\u00b1\u2009SD\u2009=\u2009113.9\u2009\u00b1\u200917.3 and 118.3\u2009\u00b1\u200928.2\u2009nm, respectively; Supplementary Fig.\u00a014a, b) instead of the dense granules (mean\u2009=\u2009202\u2009\u00b1\u200922.7\u2009nm)24. In addition, ultrastructure expansion microscopy (U-ExM) confirmed SKSR1 expression in SG around the nucleus, away from dense granules (Fig.\u00a04b). Consistent with the gene expression pattern of SG proteins24, SKSR1 expression was detected in sporozoites as well as at most time points of the asexual development of C. parvum (Fig.\u00a03d).\n\nWe used immunofluorescence to follow SKSR1 secretion during sporozoite invasion. The results showed that SKSR1 was not secreted during sporozoite gliding. SKSR1 remained in the SG during host cell invasion until the parasite was completely engulfed by the host cell membrane (Supplementary Fig.\u00a015a). Once a cup-like structure was formed, SKSR1 was detected at the apical side of the parasite (Fig.\u00a04c). The secretion of SKSR1 at the apical region of sporozoites was confirmed by generating 3D rendered Z-stacks (Fig.\u00a04d). In addition, SKSR1 was detected at low levels at the host-parasite interface by IEM (Fig.\u00a04e). In the intracellular stages, confocal microscopy revealed the aggregation of SKSR1 on the parasite surface (Supplementary Fig.\u00a014c). SKSR1 was further localized to the parasite-host interface by U-ExM and IEM (Supplementary Fig.\u00a014d, e). Taken together, these results suggest that SKSR1 is transported from the SG to the host-parasite interface during the late phase of the invasion process (Fig.\u00a04f). This pattern is similar to the secretion of SG1 during host cell invasion24.\n\nWe used an attachment-invasion assay to determine whether SKSR1 is involved in host cell invasion by C. parvum sporozoites25. HCT-8 cells were infected with sporozoites from the SKSR1-HA, SKSR1m(+A)-3HA and \u0394sksr1 lines for 2.5\u2009h. After the removal of unattached sporozoites by washing, samples were fixed and probed with rabbit anti-C. parvum antigens (anti-Cp) followed by goat anti-rabbit IgG-Alexa Fluor 488. The specimens were then permeabilized and reprobed with rabbit anti-Cp and goat anti-rabbit IgG-Alexa Fluor 594. Attached but non-invasive parasites were stained yellow, reflecting both detection steps, while intracellular parasites were stained red only. The number of intracellular and extracellular parasites was quantified by image analysis, and data from SKSR1m(+A)-3HA, and \u0394sksr1-infected cultures were normalized to the SKSR1-HA control. The data obtained showed that host cell invasion in cultures infected with SKSR1m(+A)-3HA and \u0394sksr1 parasites was comparable to that in cultures infected with SKSR1-HA parasites (Supplementary Fig.\u00a015b), indicating that SKSR1 is not involved in parasite invasion.\n\nWe investigated the role of SKSR1 in C. parvum growth and virulence using in vitro and in vivo infection models. Compared to the SKSR1-3HA line, deletion of the SKSR1 gene (\u0394sksr1) significantly reduced C. parvum growth in vitro. and the A base insertion in the gene (SKSR1m(+A)-3HA) also had a similar effect (Fig.\u00a05a). We examined the differences in growth and pathogenicity between the \u0394sksr1, SKSR1-3HA, and SKSR1m(+A)-3HA lines in three independent experiments using the GKO mouse model, with five mice per group in each experiment. There was no significant difference in oocyst shedding among the three groups during the early stage of infection (DPI 6). However, the oocyst shedding intensity of the \u0394sksr1 and SKSR1m(+A)-3HA groups was significantly lower than that of the SKSR1-3HA group from DPI 8 to DPI 12. After the peak of oocyst shedding at DPI 12, there was no significant difference in oocyst shedding intensity among the three infection groups (Fig.\u00a05b). When the intestinal tissues of infected mice were analyzed in two infection studies, the parasite load on the villus surface was significantly different between the SKSR1-3HA and \u0394sksr1 groups, with the SKSR1m(+A)-3HA group having a parasite load intermediate between the two groups (Fig.\u00a05c, d). SKSR1-3HA and SKSR1m(+A)-3HA caused severe intestinal damage, whereas \u0394sksr1 caused only mild villous atrophy (Fig.\u00a05c). Mice infected with the \u0394sksr1 line had a higher ratio of villus height to crypt depth ratio than those infected with the SKSR1-3HA and SKSR1m(+A)-3HA lines (Fig.\u00a05e).\n\na Growth pattern of SKSR1-3HA, SKSR1m(+A)-3HA, and \u0394sksr1 lines in HCT-8 cultures. The \u0394sksr1 and SKSR1m(+A)-3HA lines had significantly slower growth at 24\u2009h and 48\u2009h post infection (dots represent data from a total of six replicates in three independent experiments, and each bar represents the mean\u2009\u00b1\u2009SD). b Oocyst shedding pattern of \u0394sksr1, SKSR1\u22123HA, and SKSR1m(+A)-3HA lines in GKO mice. Dots represent data from three independent experiments with a total of 15 mice per group, and each bar represents the mean\u2009\u00b1\u2009SD. c Hematoxylin and eosin (H&E) microscopic images. Scale bar\u2009=\u2009100\u2009\u03bcm. d Parasite load per villus on the ileal surface under H&E microscopy. e Villus length and crypt depth ratio of the ileum of uninfected and infected mice. Data in (d and e) are the mean\u2009\u00b1\u2009SD from two independent experiments (n\u2009=\u200915 villi per mouse), and different color shades represent two independent experiments. f Differences in clinical score (left) and the area under the curve (AUC) (right) (mean\u2009\u00b1\u2009SD) between groups. At 14 days post infection, in three independent experiments, six mice in the SKSR1\u22123HA-infected group were moribund, which we defined as animals with the worst clinical signs. g Body weight gain and AUC (mean\u2009\u00b1\u2009SD) during the course of infection with different lines. Dead mice were not included in the AUC statistics for body weight gain. Statistical analysis in (a\u2013g) was performed using the Kruskal\u2013Wallis test. h Survival curves of infected mice. The time of death of mice in the SKSR1-3HA group was significantly different from that in the \u0394sksr1 and SKSR1m(+A)\u22123HA groups by the Gehan-Breslow-Wilcoxon test (two-tailed). Data in (b and f\u2013h) are from three independent infection studies with five mice per group in each experiment, the different dots represent data from three independent experiments. One mouse in the SKSR1m(+A)-3HA group died of a non-infection-related cause during the first infection study and its data were not included in subsequent analyses. Source data are provided as a Source Data file.\n\nWe used a scoring system to rank the severity of clinical signs of infected mice in each group, with higher scores representing worse conditions (Fig.\u00a05f). Mice in the SKSR1-3HA group showed significant clinical signs and weight loss at DPI 8, with all animals moribund by DPI 21, whereas mice in the SKSR1m(+A)-3HA group had a significantly delayed onset of clinical signs and weight loss (starting at DPI 12) and showed less weight loss than those in the SKSR1-3HA group (Fig.\u00a05f\u2013h). In contrast, \u0394sksr1 mice show no clinical signs and weight loss by DPI 14, and all mice in these groups survived to the end of three infection studies at DPI 45 (Fig.\u00a05f\u2013h).\n\nTo validate the involvement of SKSR1 in parasite growth, we performed a direct head-to-head competition experiment between SKSR1-3HA and SKSR1m(+A)-3HA in vivo. Equal numbers of SKSR1-3HA and SKSR1m(+A)-3HA oocysts were used to infect three GKO mice. Analysis of the trace files from Sanger sequencing of the PCR products from the fecal samples showed a clear double peak in the A-base insertion region, but due to the presence of competing nucleotides in this region, we were unable to determine the temporal changes of the two lines during the course of infection (Supplementary Fig.\u00a016a). Two mice became moribund at DPI 11 and the remaining mouse succumbed to infection at DPI 15 (Supplementary Fig.\u00a016b). The survival curves of the co-infections were similar to those of the SKSR1-3HA infections, suggesting that the SKSR1-3HA parasites may outcompeted the SKSR1m(+A)-3HA parasites in vivo in the co-infection, leading to the lack of delay in death of infected mice.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60088-7/MediaObjects/41467_2025_60088_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60088-7/MediaObjects/41467_2025_60088_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60088-7/MediaObjects/41467_2025_60088_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60088-7/MediaObjects/41467_2025_60088_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60088-7/MediaObjects/41467_2025_60088_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Virulence factors play a key role in the pathogenesis of microbial pathogens26. They are often involved in the initial pathogen-host interactions, induce cellular damage, or alter host cellular responses. Different pathogens use different strategies to mediate virulence. In apicomplexan parasites, T. gondii uses a family of rhoptry-secreted polymorphic kinases as key virulence factors13. They are injected into host cells and mediate immune evasion and other changes in host cell signaling26,27. In contrast, P. falciparum uses a family of variant PfEMP1 surface antigens encoded by the subtelomeric var genes. In addition to inducing immune evasion, these proteins mediate the adhesion of infected erythrocytes to endothelial cells in various organs, leading to different types of severe malaria28. Linkage mapping of genetic crosses of isolates of different virulence has played a major role in the identification of virulence factors12,13.\n\nIn the present study, we have successfully used linkage mapping of genetic crosses to identify one virulence factor in C. parvum. We have generated genetic crosses of fluorescently tagged C. parvum isolates HLJ and GD, which differ significantly in infectivity, infection pattern, and virulence in GKO mice10. Linkage mapping using BSA of genomes collected during infection with progeny pools has identified three QTL containing 23 genes that potentially control the growth difference between the two isolates. Reverse genetic studies of the two best candidates have confirmed the involvement of SKSR1, a secretory SG protein encoded by a polymorphic subtelomeric gene, in C. parvum virulence.\n\nThe generation of genetic crosses of C. parvum is greatly facilitated by the endogenous fluorescent tagging of isolates. Previous attempts at genetic crosses of Cryptosporidium isolates have been hampered by the lack of genetic tagging tools, making it difficult to isolate recombinant progeny difficult without fluorescent tagging and drug selection29,30. Recent advances in genetic manipulation of Cryptosporidium spp. allow for endogenous tagging of isolates with fluorescent proteins and incorporation of selectable markers and luciferase sequences for easy detection and enrichment of recombinant progeny19. This approach has been successfully used to generate genetic crosses of a C. parvum isolate tagged with two different fluorescent proteins21,31,32. Research has also shown that genetic crosses between species are possible. Crosses between C. parvum and C. tyzzeri have resulted in progeny with a recombinant genome derived from both species that continue to reproduce vigorously sexually33. Despite the fact that these two species hybridize, large fragments or even entire chromosomes have been involved in genetic recombination, limiting the ability to identify genes related to host infectivity. In the present study, we have tagged two C. parvum isolates of different virulence and generated genetic crosses in mice, and purified the recombinant progeny using flow cytometric sorting. WGS analysis of individual oocysts has confirmed the presence of frequent crossovers in the genomic sequences of the progeny, thus facilitating genetic mapping and gene identification.\n\nThe identification of virulence determinants in Cryptosporidium spp. in the present study is achieved by a new approach to perform linkage mapping of recombinant progeny using BSA of WGS data. The technique examines genomic changes during the course of infection using pools of progeny from genetic crosses. Alleles enriched in late infection are identified by read mapping of WGS data18. This eliminates the need to clone the progeny of genetic crosses, which is difficult to perform for Cryptosporidium spp. due to the lack of effective culture systems and the high ID50 of isolates in laboratory animal models. This strategy has been used in combination with crosses to map genes associated with drug resistance, virulence, and other phenotypic traits in P. falciparum16,18. In the present study, by BSA of 68 sets of WGS data collected from GKO and neonatal mice infected with progeny pools of two genetic crosses of C. parvum isolates of low and high virulence, we have identified the evolution toward the more virulent parent as the infection persisted. This is consistent with the observation in a previous study of genetic crosses of two C. parvum isolates with different host preferences30. Using this approach, we have identified three QTL containing 23 genes on chromosomes 1, 7, and 8 that are potentially involved in the growth and virulence difference between the two C. parvum isolates studied.\n\nWhile the strategy used in our BSA studies is designed to enrich for genes involved in rapid parasite growth, there is an established relationship between the severity of cryptosporidiosis and the intensity of infection in humans and animals. For example, in human volunteer studies of C. parvum infection, individuals with diarrhea had significantly higher oocyst shedding levels than those without diarrhea34, and one high virulence isolate induced a higher intensity of oocyst shedding than two low virulence isolates7. Similarly, immunodeficient mice infected with virulent strains of C. parvum shed significantly more oocysts than those infected with avirulent strains, even when a lower number of oocysts from the virulent strain was used to establish infection9,10. In calves experimentally infected with C. parvum, there is a high correlation between the severity of diarrhea and the intensity of oocyst shedding35,36. This is supported by a recent treatment study in calves experimentally infected with C. parvum in which elimination of oocyst shedding with a Cryptosporidium PI(4)K inhibitor resulted in rapid resolution of diarrhea37.\n\nMost of the 23 candidate genes identified by BSA have only a small number of SNPs that are less likely to significantly affect the functions of the proteins they encode, making it difficult to intuitively determine their effect on the difference in virulence between the two C. parvum isolates studied. However, three of these genes, including cgd1_140 (SKSR1), CPCDC_7g4512, and cgd8_550, have premature stop codons due to single base insertion and loss-of-function SNPs. Among them, CPCDC_7g4512 has the deleterious nucleotide substitution in the highly virulent HLJ. Since this newly predicted gene is not expressed at different developmental stages, CPCDC_7g4512 is unlikely to be involved in virulence. In contrast, cgd1_140 (SKSR1) and cgd8_550 have premature stop codons in the low virulence isolate GD, making them candidate virulence determinants.\n\nResults from gene tagging and deletion studies suggest that the cgd8_550 gene is unlikely to be involved in virulence. The sequence characteristics of the gene are not related to those of other QTL identified in the study. In addition, the protein it encodes is a cytosolic protein that is predominantly expressed in the asexual stages rather than in the invasive sporozoites, suggesting that it is unlikely to interact with host cells as expected for known virulence factors in apicomplexans. Indeed, depletion of the gene in the virulent HLJ has minimal effect on the growth and pathogenicity of the isolate.\n\nStudies using genetically modified lines of HLJ have confirmed the involvement of SKSR1 in C. parvum virulence. Deletion of the SKSR1 gene significantly reduced C. parvum growth in vitro, and attenuated parasite virulence in vivo. In particular, mice infected with the \u0394sksr1 line had significantly reduced infection intensity and no weight loss and mortality. More importantly, replacement of the SKSR1 gene in HLJ with GD also reduced the infection intensity and pathogenicity of the wild-type isolate, resulting in reduced weight loss and improved survival of infected mice. SKSR1 shares some characteristics of virulence factors in other apicomplexans, including being a secretory protein encoded by a polymorphic subtelomeric gene, and being secreted into the parasite-host interface during invasion and early development38,39. The SKSR1 family is one of a small number of multigene families in Cryptosporidium spp. They are secreted proteins encoded by 8\u221211 subtelomeric genes. These genes are polymorphic within and between Cryptosporidium species, and are mainly found in species closely related to the human-pathogenic C. parvum and C. hominis40. In C. parvum, gain and loss of SKSR genes is associated with the host range of isolates41. However, very few studies have investigated the biological function of SKSR proteins. One study genetically tagged SKSR7 and SKSR8, but their locations and functions remain unknown42.\n\nThe preliminary characterizations performed in this study may shed light on the biological function of SKSR1. First, we have identified SKSR1 as a member of the secretory proteins in the newly identified SG. Recently, two SG proteins were identified in C. parvum using spatial proteomics and gene tagging. Although the functions of these proteins remain unclear, SG1 has been shown to be secreted into the parasite-host interface soon after invasion and thus may be involved in host-pathogen interactions24. Second, SKSR1 is secreted into the parasite-host interface during the late phase of the invasion process. In T. gondii, several dense granule proteins are secreted into the PVM to modulate host cellular responses as part of parasite immune evasion43. Third, SKSR1 is one of several variant secretory SKSR proteins encoded by two gene clusters, suggesting that it may function in concert with other putative C. parvum virulence factors44. Whether SKSR1 acts as a virulence factor by regulating host cellular responses will require future studies.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The GD and HLJ isolates of C. parvum used in this study were originally obtained from dairy calves in Guangdong (GD) and Heilongjiang (HLJ) provinces, China, respectively10 and propagated in interferon-\u03b3 knockout (GKO) C57BL/6J mice. C. parvum oocysts were purified from the feces of infected mice by sucrose flotation and cesium chloride gradient centrifugation as described10.\n\nGKO mice were purchased from The Jackson Laboratory (Bar Harbor, USA). Mice were housed in clean filter-top cages with a 12:12 light-dark cycle, 50\u201360% humidity, and room temperature (22\u2009\u00b0C) according to standard protocols under the regulations of the Laboratory Animal Center of the South China Agricultural University. This study was approved by the Institutional Animal Care and Use Committee of South China Agricultural University (No. 2021C076). Animals used in the study were housed and handled in accordance with established ethical principles. Female and male mice (4\u20136 weeks old) were housed in individually ventilated cages (one mouse per cage) and used in a 1:1 ratio to generate and propagate stable transgenic parasites.\n\nHomologous repair templates and CRISPR/Cas9 plasmids were generated as previously described32. The CRISPR/Cas9 plasmids were constructed by inserting an sgRNA targeting the gene of interest into the C. parvum Cas9/U6 linear plasmid amplified from pACT1:Cas9-GFP, U6:sgTK using Gibson assembly cloning. To generate the GD-\u0394tk-mNeonGreen and HLJ-\u0394uprt-tdTomato lines, we used mNeonGreen and tdTomato tags to replace GFP and mCh in TK-GFP-Nluc-P2A-neo-TK and UPRT-mCh-Nluc-P2A-neo-UPRT plasmids, respectively. To generate gene knockout lines (\u0394sksr1 or \u0394cgd8_550), homology repair fragments were generated by PCR amplification of TK-mNeonGreen-Nluc-P2A-neo-TK and UPRT-tdTomato-Nluc-P2A-neo-UPRT. These fragments were assembled with corresponding regions of the gene of interest, including the 3\u2019 and 5\u2019 UTR sequences. To tag genes with the 3\u2009\u00d7\u2009HA epitope, we modified the pINS1-3HA-Nluc-P2A-neo45 by replacing its INS1 C-terminal and 3\u2019 UTR sequences with regions of the gene of interest using a four-fragment Gibson assembly. In addition, to prevent erroneous cleavage of the repair plasmid by the CRISPR/Cas9 plasmid, we modified the gRNA or protospacer adjacent motif sequence at the 5\u2019 homology arm of the repair plasmid using a two-fragment Gibson assembly. In addition, we constructed repair templates and CRISPR/Cas9 plasmids for SG1 as previously described24, which were subsequently used to generate an SG1-3HA line. The primers were synthesized by Sangon Biotech (Shanghai, China) and are listed in Supplementary Table\u00a06.\n\nTransgenic parasites were generated using CRISPR/Cas9 as previously described19,46. Briefly, oocysts were treated with 25% Clorox (1.3% sodium hypochlorite) on ice for 10\u2009min and excysted by treatment with sodium taurodeoxycholate at 37\u2009\u00b0C for 60\u2009min. The excysted sporozoites were transfected with an appropriate CRISPR/Cas9 plasmid containing a parasite-specific guide RNA (gRNA) and a targeting plasmid containing the desired genetic manipulation flanked by DNA homologous to the regions surrounding the gRNA cut site. The transfected sporozoites were administered by oral gavage to GKO mice pretreated with oral sodium bicarbonate to neutralize gastric acid. Transgenic parasites in infected mice were selected by continuous administration of 16\u2009g/L PRM in the drinking water at 18\u2009h post infection (HPI 18).\n\nTo quantify transgenic parasites expressing luciferase, fecal pellets from infected mice were weighed, placed in microfuge tubes containing 1\u2009mL luciferase lysis buffer and glass beads, and vortexed for 3\u2009min. The tubes were briefly centrifuged at 10,000\u2009\u00d7\u2009g to pellet debris, and 100\u2009\u03bcL of the supernatant was transferred to a 96-well round-bottom plate (Costar, USA). Next, 100\u2009\u03bcL of a 1:50 mixture of nanoluciferase substrate:buffer (Promega, USA) was added to each sample, and luminescence was measured using a BioTek Synergy H1 Hybrid plate reader (BioTek, USA).\n\nDNA was extracted from oocysts of transgenic parasites using the QIAGEN DNeasy Blood and Tissue Kit (QIAGEN, Germany). PCR primers were designed to anneal outside the 5\u2032 and 3\u2032 homology arms directing homologous recombination, the luciferase reporter gene and the neomycin selection marker. In addition, primers were designed for the coding and mutant regions of the gene of interest to verify successful knockout and mutation of genes.\n\nTwo GKO mice were treated orally with a suspension containing 10,000 each of HLJ-\u0394uprt-tdTomato oocysts and GD-\u0394tk-mNeonGreen oocysts. Infected mice were treated continuously with 16\u2009g/L PRM via drinking water at HPI 18. They were monitored for fecal luciferase at DPI 4. If the mice were positive, they were euthanized and the small intestine of the mice was collected. The small intestine was rinsed several times with chilled PBS, and the suspension was filtered through 70\u2009\u03bcm and 40\u2009\u03bcm filters (Falcon, Cat#352350 and Cat#352350). The filtrate was centrifuged at 10,000\u2009\u00d7\u2009g for 10\u2009min, and the pellet was resuspended in 500\u2009\u03bcL PBS for fluorescence microscopy on a BX53 microscope (Olympus, Japan). The oocyst suspension was also analyzed by flow cytometry on a FACSAria III (BD Biosciences, USA), with recombinant oocysts of dual color harvested by flow sorting. Two genetic crosses of the two isolates were performed during this study.\n\nThe progeny pool was diluted to a final concentration of 1 oocyst/\u03bcL, and 1\u2009\u03bcL of the oocyst suspension was placed on a low adsorption slide for light microscopy. After confirming the presence of a single oocyst, the droplet was transferred to a PCR tube with 2.5\u2009\u03bcL PBS form the droplet wash. The harvested single oocyst was repeatedly freeze-thawed in liquid nitrogen and a 55\u2009\u00b0C water bath to lyse the oocysts. The released genomic DNA was amplified using the REPLI-g Single Cell Kit (QIAGEN) and analyzed by PCR targeting three genes that were polymorphic between the GD and HLJ isolates. Positive DNA from individual oocysts was sequenced for whole genomes as described below. The PCR sequences used are listed in Supplementary Table\u00a06.\n\nIn the first infection study, GKO mice (n\u2009=\u20093) were orally gavaged with 1000 yellow oocysts from the progeny pool of the genetic cross and treated with PRM as described above. Fecal samples were collected from the infected mice every 6 days for 36 days starting at DPI 6. In the second infection study, two infection groups were established with and without PRM treatment, and mice (n\u2009=\u20093) in each group were infected with progeny pool from the second cross. A total of 49 samples were collected at multiple time points of infection as described in the first infection experiment. In the third infection study, one liter of 5-day-old C57BL/6J mice were infected as in the second infection study and 4 samples were collected at DPI 12, DPI 18, DPI 24, and DPI 30. Oocysts in fecal samples collected during the infection studies were purified by cesium chloride and sucrose gradient centrifugation for WGS.\n\nGenomic DNA was extracted from the purified oocysts using the QIAGEN DNA Mini Kit and amplified using the REPLI-g Midi Kit (QIAGEN). Whole-genome amplification products were purified using the EasyPure PCR Purification Kit (TransGen Biotech) and randomly sheared into fragments of ~350\u2009bp. Libraries were constructed using the Rapid Plus DNA Lib Prep Kit for Illumina (RK20208) and sequenced using Illumina 150\u2009bp paired-end technology on a Novaseq S4 or Hiseq X sequencer to achieve >100-fold coverage of the Cryptosporidium genome. In addition to sequencing 12 single oocysts from the progeny pool, 68 oocyst pools from the progeny pool infection studies were sequenced during the study. To generate a chromosome-level reference genome, the parental HLJ strain was also sequenced using standard Pacific Biosciences (PacBio) ccs procedures after whole-genome amplification of the extracted DNA as described above.\n\nSequence reads from PacBio were filtered for chimeras using 3rd-ChimeraMiner47. Hybrid de novo assembly was performed on the PacBio and Illumina reads of HLJ using wengan v0.2 with M model. The primary assembly underwent two rounds of polishing with PacBio reads and four rounds of polishing with Illumina sequencing data using NextPolish48. The sequencing data were manually checked against the assembled genome to obtain the final genome assembly. AUGUSTUS and GeneMark-ES v4 were used for de novo gene prediction, and homology-based prediction GeMoMa v1.9 and the rapid annotation transfer tool Miniprot v 0.13 were used to transfer gene annotation information from the C. parvum IOWA-43IA8 reference genome to the HLJ genome. Orthofinder v 2.5.5 was used to analyze the annotated amino acid sequences of HLJ with the amino acid sequences of C. parvum IOWA-43IA822 and IOWA-ATCC49 to identify orthologous genes and gene families to obtain the final genome annotation file. To identify the structural differences among the C. parvum strains, we aligned the fully assembled genomes of IOWA-43IA8, IOWA-ATCC, IOWA-BGF, IIa-KWI52, and HLJ using Mauve (https://darlinglab.org/mauve/mauve.html).\n\nFastq files from WGS were checked for quality using FastQC-v0.11.05, and sequence reads were trimmed for poor quality and adapters using Trimmomatics v0.3950 (MINLEN:60, SLIDINGWINDOW:4:15, ILLUMINACLIP:Truseq3-PE.fa:2:30:10). The cleaned reads were aligned to the C. parvum HLJ genome using BWA-MEM2 v2.251. The resulting alignments were sorted and duplicate reads were removed using SAMtools v1.7 and Sambamba v0.8.252,53. Read mapping results were viewed using the Integrative Genomics View54. We identified sequence crossovers from single oocysts in the progeny pool using variant allele frequency (VAF)\u2009=\u2009AD/DP (AD stands for allele depth, DP stands for total depth); the difference of VAF\u2009>\u20090.8 between adjacent SNPs in single-oocyst genomes indicates that a sequence crossover has occurred in the region.\n\nFor BSA, the previously described approach was used16. Briefly, variants were called and filtered using BCFtools v1.1252 and annotated using SnpEff v4t55, which provides a simple assessment of the putative impact of the variant (e.g., HIGH, MODERATE or LOW impact). The BCFtools filter was used to remove variants with Phred scores <30 and average read depth \u226425. The modules runQTLseqAnalysis and runGprimeAnalysis in the R package QTLseqr were used to calculate the delta SNP index and G\u2032 values56. We calculated the G-statistic for each locus of the genome using data from each BSA experiment and used the mean plus three standard deviations of the G\u2032 values as the cutoff for each locus. Because the average G-statistic value for different BSA studies was used to identify QTL, some regions with marginal G-statistic values in individual BSA studies may have been missed. Regions with G\u2032 > the threshold were identified as extreme QTL. Once a QTL was detected, genes within the QTL regions were extracted based on the intersection of the QTL regions obtained from the BSA replicate experiments using jvenn (https://jvenn.toulouse.inra.fr/app/example.html). The expression patterns of the candidate genes were determined based the transcriptomic data we previously described57.\n\nHuman ileocecal adenocarcinoma cells (HCT-8; ATCC CCL-244) were seeded onto 48-well plates and grown to 80% confluence. Bleach-treated transgenic parasite oocysts (10,000 oocysts per well) with identical luminescence levels were then used to infect the HCT-8 monolayer for various time periods: 3, 12, 24, 36, and 48\u2009h. Cultures infected with transgenic oocysts were then washed twice with PBS at HPI 3 and replenished with fresh medium containing 2% fetal bovine serum. At multiple time points, the culture medium was removed from the wells, 100\u2009\u00b5L of lysis buffer was added to the wells, and the plate was incubated at 37\u2009\u00b0C for 10\u2009min. After incubation at 37\u2009\u00b0C for 10\u2009min, the lysates were collected by centrifugation at 15,000\u2009\u00d7\u2009g for 3\u2009min and subjected to luciferase assay as described previously.\n\nFor IFA, 12-mm diameter glass coverslips (Thermo Fisher Scientific) were placed in 24-well plates and seeded with HCT-8 cells. The cells were washed with PBS, fixed by incubation with 4% paraformaldehyde (Thermo Fisher Scientific), and permeabilized by treatment with 0.5% Triton X\u2212100 (Bio-rad) for 15\u2009min. Coverslips were then blocked with 1% bovine serum albumin (BSA; Sigma-Aldrich) and incubated with antibodies diluted in the blocking solution, including a rabbit monoclonal antibody against HA (1:800; Cell Signaling Technology) as the primary antibody and a goat anti-rabbit polyclonal Alexa Fluor 594 (1:400; Invitrogen) as secondary antibody, along with direct staining of parasite stages using Vicia Villosa Lectin (1:1000; VVL, Vector). Host cell and parasite nuclei were probed with Hoechst (5\u2009\u03bcg/mL, Thermo Fisher Scientific). For sporozoite IFA, sporozoites were resuspended in 10\u2009\u00b5L PBS and spread on sterile coverslips pretreated with poly-L-lysine (Sigma-Aldrich). After fixation and permeabilization, they were treated with mouse anti-Cp23 (1:200; Laboratory Preparation) or anti-EF1a (1:200; Laboratory Preparation) and rabbit monoclonal anti-HA (1:800; Cell Signaling Technology) as primary antibodies and goat anti-rabbit polyclonal Alexa Fluor 594 (1:400; Invitrogen) and goat anti-mouse polyclonal Alexa Fluor 488 (1:400; Invitrogen) as secondary antibodies. Each analysis was performed in duplicate for at least two biological replicates. Slides were examined using either a Leica STELLARIS 5 or an Olympus BX53 microscope.\n\nFor the glidingassay58, 100\u2009\u03bcL of poly-L-lysine was added to 12\u2009mm diameter glass coverslips and incubated for 30\u2009min at room temperature. After washing with PBS, 50\u2009\u03bcL of 1\u2009\u00d7\u2009106 SKSR1-HA sporozoite suspension was added to coverslips and incubated at 37\u2009\u00b0C for 30\u2009min, and the liquid on the coverslips was washed with PBS. After fixation and permeabilization, SKSR1 protein was identified using rabbit monoclonal anti-HA (1:800) as the primary antibody and goat anti-rabbit polyclonal Alexa Fluor 594 (1:400) as the secondary antibody; mouse anti-Cp23 (1:200) was used as the primary antibody (1:200) and goat anti-mouse polyclonal Alexa Fluor 488 (1:400) as the secondary antibody to detect the parasite and glide track.\n\nAn attachment and invasion assay25 was used to evaluate the effect of SKSR deletion on C. parvum invasion of host cells. Briefly, a suspension of 2\u2009\u00d7\u2009105 sporozoites was added to HCT-8 cells in 24-well plates. After 2.5\u2009h of infection, all wells were washed three times with PBS and fixed. Extracellular parasites were labeled with rabbit anti-Cp and goat anti-rabbit IgG Alexa Fluor 488. The wells were then permeabilized with 0.05% Triton X\u2212100. After permeabilization, all parasites were labeled with rabbit anti-Cp and goat anti-rabbit IgG Alexa Fluor 594 and nuclei were stained with Hoechst 33342 nuclear stain. Slides were examined under an Olympus BX53 microscope to count the number of green fluorescent parasites (extracellular parasites) and two-color fluorescent parasites (extracellular parasites). The invasion efficiency of the parasites was calculated using previously described methods.\n\nUltrastructure-expansion microscopy (U-ExM) was performed on Cryptosporidium sporozoites and meronts as described for T. gondii59. Briefly, excysted sporozoites were added to poly-D-lysine-coated coverslips and fixed with 1.4% formaldehyde and 2% acrylamide in PBS. The samples were embedded in a water-based gel and denatured at 95\u2009\u00b0C. The gels were then probed with rat monoclonal antibody (3F10) against HA (1:200; Roche) as the primary antibody and rabbit anti-rat IgG AF488 (1:200; Invitrogen) as the secondary antibody, along with direct staining of parasite organelles using NHS ester (10\u2009\u03bcg/mL; Sigma-Aldrich). Hoechst was used to stain host cell and parasite nuclei. The gels were imaged using STELLARIS 5 (Leica Microsystems).\n\nPurified transgenic oocysts (5\u2009\u00d7\u2009106) were treated with bleach, washed with cold PBS, and resuspended in lysis buffer (ThermoFisher Scientific) containing protease inhibitors (Sigma-Aldrich). The mixture was incubated overnight at 4\u2009\u00b0C, combined with protein loading buffer, and boiled for 10\u2009min. Proteins in the lysate were then fractionated by SDS-PAGE and transferred to a PVDF membrane (Merck Millipore). After blocking with 1% nonfat milk overnight at 4\u2009\u00b0C, the membrane was incubated with a rabbit monoclonal antibody against HA (1:2000; Cell Signaling Technology) for 1\u2009h, and washed three times with PBS containing 0.1% Tween 20 (PBST). After incubation with HRP-conjugated anti-rabbit IgG (H\u2009+\u2009L) (1:10,000; Cell Signaling Technology), washed three times with PBST, and treated with High-sig ECL Western Blotting Substrate (Tanon), the membrane was analyzed using a Tanon 5200. The membrane was further probed with a mouse polyclonal antibody (1:2000) against CP23 of C. parvum and HRP-conjugated goat anti-mouse IgG (H\u2009+\u2009L) (1:10,000; Cell Signaling Technology).\n\nFor studies of the biological significance of virulence-related genes, one mouse from each experimental group was selected and euthanized on DPI 14 at the peak of oocyst shedding. The small intestine was dissected and washed with cold PBS. The ileum was collected for conventional hematoxylin-eosin (H&E) and IEM. For H&E microscopy, 30 villi were randomly selected for measurement of villus length and crypt height and calculation of villus length/crypt height ratio. Parasite burden was also determined in 15 intestinal villi. For IEM, rabbit anti-HA (1:20; Cell Signaling Technology) and goat anti-rabbit IgG conjugated with 10\u2009nm colloidal gold (1:20; Sigma-Aldrich) were used as primary and secondary antibodies, respectively, as described45. Processed sections were examined on a Talos L120C (ThermoFisher Scientific) by a technician blinded to ileal tissue group assignment.\n\nTo assess the biological significance of each candidate gene, GKO mice in each infection group were orally gavaged with 10,000 oocysts of transgenic C. parvum, and received PRM via drinking water as described above. To assess the relevance of SKSR1 for virulence, we performed three independent experiments with six mice per group in each experiment. Intestinal tissues from one mouse per experimental group in each experiment were used for histological and immunoelectron microscopic analyses as described above. After infection, fecal luciferase activity and body weight of each mouse were determined every other day, and mortality and other clinical signs were recorded daily. A scoring system was used to evaluate the severity of the clinical signs in infected mice, and this scoring was blinded: 1: mice were in good health and physically active; 2: mice appeared lethargic and moved less frequently; 3: mice were obviously lethargic, had arched backs, and moved only when touched by hand; 4: mice were hunched, had rough hair, and were immobile even when touched; 5: mice are in extremely poor mental condition. Survival curves were plotted at the end of the infection study.\n\nGraphPad Prism (https://www.graphpad.com/) was used for all statistical analyses. Group means were obtained from at least two independent experiments or three biological replicates. Unless otherwise noted, two-tailed Mann-Whitney test was used to assess differences between two groups, and Kruskal\u2013Wallis test was used to assess differences between three or more groups. The Gehan-Breslow-Wilcoxon test was used to compare the survival curves. P values\u2009<\u20090.05 were considered statistically significant.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All whole-genome sequence data generated in this study have been deposited in the NCBI Short Read Archive (https://www.ncbi.nlm.nih.gov/sra/) under the BioProject accession numbers PRJNA1069297. The fully assembled and annotated genome of the IIdA20G1-HLJ strain is deposited in DDBJ/ENA/GenBank databases under the accession number JBJGDY000000000. All data necessary to evaluate the conclusions of the paper are included present in the paper and/or in the Supplementary Materials.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code used in analysis and data analyzed are available at GitHub and Code Ocean through the following links: https://github.com/tyhou/BSA.Cpar.IId.GDxHLJ; https://codeocean.com/capsule/0441727/tree/v1.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Kotloff, K. L. et al. Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): a prospective, case-control study. 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Biol. 2369, 121\u2013137 (2021).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported in part by the National Natural Science Foundation of China (32030109 to L.X., 31972697 to Y.G., 31820103014 to L.X. and 32150710530 to L.X.), Guangdong Major Project of Basic and Applied Basic Research (2020B0301030007 to L.X.), 111 Project (D20008 to L.X.) and Double First-class Discipline Promotion Project (2023B10564003 to L.X.). We thank Jilei Huang, Chuanhe Liu, and Xiaoxian Wu of the Instrumental Analysis & Research Center, South China Agricultural University for assistance with electron microscopy. The authors also thank the Institute of Hematology, Jinan University, for technical assistance in flow cytometric sorting.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Wei He, Lianbei Sun, Tianyi Hou.\n\nState Key Laboratory for Animal Disease Control and Prevention, Center for Emerging and Zoonotic Diseases, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China\n\nWei He,\u00a0Lianbei Sun,\u00a0Tianyi Hou,\u00a0Zuwei Yang,\u00a0Fuxian Yang,\u00a0Shengchen Zhang,\u00a0Tianpeng Wang,\u00a0Xinran Wang,\u00a0Na Li,\u00a0Yaqiong Guo,\u00a0Yaoyu Feng\u00a0&\u00a0Lihua Xiao\n\nSchool of Biology and Agriculture, Shaoguan University, Shaoguan, China\n\nWei He\u00a0&\u00a0Tianpeng Wang\n\nDepartment of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, USA\n\nL. David Sibley\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: L.X., Y.F. and L.D.S.; Methodology and investigation: W.H., L.S., T.H., Z.Y., F.Y., S.Z., T.W., X.W., N.L. and Y.G.; Formal Analysis: W.H., L.S. and T.H.; Supervision: L.X., Y.F. and L.D.S.; Writing\u2014original draft: W.H., L.X. and Y.F.; Writing\u2014review and editing: All authors.\n\nCorrespondence to\n L. David Sibley, Yaoyu Feng or Lihua Xiao.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Jessica Kissinger, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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and TLR stimulation contribute to induction of a durable HIV-1-specific neutralizing antibody response", + "pre_title": "Antigen Dose and Persistence Contribute to Induction of a Durable HIV-1-Specific Neutralizing Antibody Response", + "journal": "Nature Communications", + "published": "03 June 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60481-2/MediaObjects/41467_2025_60481_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60481-2/MediaObjects/41467_2025_60481_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60481-2/MediaObjects/41467_2025_60481_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60481-2/MediaObjects/41467_2025_60481_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-60481-2#MOESM1", + "/articles/s41467-025-60481-2#Fig1", + "/articles/s41467-025-60481-2#Fig7", + "/articles/s41467-025-60481-2#MOESM1", + "/articles/s41467-025-60481-2#MOESM1", + "/articles/s41467-025-60481-2#MOESM1", + "/articles/s41467-025-60481-2#MOESM1", + "https://www.ebi.ac.uk/emdb/EMD-49656", + "/articles/s41467-025-60481-2#Sec25" + ], + "code": [], + "subject": [ + "Antibodies", + "HIV infections", + "Protein vaccines", + "Viral infection" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5589251/v1.pdf?c=1749035206000", + "research_square_link": "https://www.researchsquare.com//article/rs-5589251/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60481-2.pdf", + "preprint_posted": "22 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The immunogenicity of HIV-1 Env glycoprotein (Env) is limited in part by its structural instability and extensive glycan shielding and is likely the greatest obstacle to an HIV-1 vaccine. Stabilized Env trimers can elicit serum neutralizing antibodies but often require many immunizations and the response is short-lived. To understand the parameters that confer a durable neutralizing antibody response, we used a Newcastle Disease Virus-like particle (NDV-VLP) platform to present stabilized versions of HIV-1 Env at high valency and varied the conformational stability, adjuvants, dose, and antigen persistence. Influenza virus hemagglutinin (HA), or SARS-CoV2 Spike (S)-bearing VLPs were used as controls. HA or S bearing VLPs rapidly induced neutralizing antibodies, whereas they were not induced by those bearing Env. A replicating adenovirus type 4 expressing Env rapidly induced autologous neutralizing antibodies, suggesting glycan shielding or the na\u00efve B cell repertoire were not barriers. We then tested the parameters that might approximate a replicating virus infection. Only when multiple features of a virus infection were combined did we observe durable neutralizing antibodies, with the largest impact attributable to dose and escalating dose. Our results suggest that numerous features of a replicating virus infection, including stabilization, spike density, TLR stimulation, total dose, and persistence of antigen, can be combined to improve the immunogenicity of HIV-1 Env.Biological sciences/Immunology/Vaccines/Protein vaccinesBiological sciences/Immunology/Adaptive immunity/Humoral immunity/AntibodiesBiological sciences/Immunology/Infectious diseases/HIV infectionsBiological sciences/Immunology/Infectious diseases/Viral infection", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "HIV-1 Env glycoprotein (Env) immunogenicity is limited in part by structural instability and extensive glycan shielding and is likely the greatest obstacle to an HIV-1 vaccine. Stabilized Env trimers can elicit serum neutralizing antibodies, but the response is short-lived. Here we use Newcastle Disease Virus-like particle (NDV-VLP) platform to present stabilized versions of HIV-1 Env at high valency and in the context of varied conformational stability, adjuvants, dose, and antigen persistence. Influenza virus hemagglutinin, or SARS-CoV2 Spike-bearing VLPs rapidly induce neutralizing antibodies, in contrast, they were not induced by those bearing Env. A replicating adenovirus type 4 expressing Env rapidly induces autologous neutralizing antibodies. However, durable neutralizing antibodies are induced only when multiple features of a replicating virus infection are combined, with the largest impact from dose and escalating dose. In summary, we show here immunogenicity of HIV-1 Env could be improved by reproducing features of virus infection.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Induction of a durable antibody response capable of neutralizing diverse isolates is among the highest priorities for the development of vaccines for viruses such as HIV-1, SARS-CoV-2, or the influenza virus. In the case of HIV-1, the induction of neutralizing antibodies in humans has been extraordinarily challenging1. HIV-1 Envelope (Env), the only neutralizing determinant on HIV-1 virions, has features making it a particularly poor immunogen and difficult target of neutralizing antibodies, including its extraordinary diversity, structural instability, and extensive shielding by glycans that are sensed as self by the immune system. In many cases, the lack of induction of serum neutralizing antibodies in humans could be attributed to HIV-1 Env not being presented in the appropriate native-like conformation or the use of overly attenuated vector platforms. In recent years, critical advances have been made in using structural biology to produce stabilized native-like Env trimers and other immunogens that open the possibility of inducing trimer-binding neutralizing antibodies with non-replicating immunogens produced in vitro2,3. However, these immunogens do not typically result in rapid development of heterologous neutralizing antibodies4 and may require many immunizations with adjuvants over months to years, and durability remains an obstacle. Although great progress has been made in producing stable native-like antigens, replicating viral vectors are often more immunogenic, particularly regarding durability5,6,7,8. This may be due to several factors such as conformation, valency, stimulation of the innate immune response, or persistence of the antigen. However, how these individual factors contribute to immunogenicity remains poorly understood.\n\nHere, we present data on the parameters that might recapitulate features of a replicating virus infection and contribute most to the induction of a neutralizing antibody response. We use stabilizing mutations in Env and a virus-like particle (VLP) system to present Env in a high valency particle. We observe that this display is sufficient to induce neutralizing antibody responses of high magnitude and with durability for antigens such as influenza virus H5 HA and SARS-CoV2 S. However, for HIV-1 Env, this display does not recapitulate responses observed with a replicating vector. To induce responses observed with a replicating vector, we observed that RNA packaging and adjuvants increased response magnitude. However, durability was only improved with high doses and with the use of an escalating dose. These data demonstrate that there are numerous features of a replicating vector that contribute to the rapid development of neutralizing antibodies against HIV-1 that might be used to engineer improved vaccines for HIV-1.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Env is considered a poor immunogen largely because of the difficulties in generating neutralizing antibodies to HIV-1. However, much of the early work was performed with subunits or non-stabilized forms that might not elicit neutralizing antibodies. Some greater success has followed the development of technologies to stabilize Env9,10. Some of these technologies require the removal of the membrane-proximal external region (MPER) to avoid aggregates or express only a portion of the gp120 subunit. However, there are little to no data on head-to-head comparisons of Env to non-HIV-1 surface glycoproteins within a single platform11,12. This is particularly true for full-length Env expressed bound to the cell\u00a0membrane.\n\nTo explore the parameters that most contribute to immunogenicity, we used a Newcastle Disease Virus-like particle (NDV-VLP) system that permits the expression of the full-length Env ectodomain on the surface bound to the membrane. This system has been previously shown to be highly immunogenic by inducing higher levels of serum neutralizing antibodies than natural infection in pre-clinical models of RSV infection13. We designed VLPs expressing HIV-1 Env, or influenza virus H5 HA or SARS-CoV-2 S as controls (Fig.\u00a01A)14. The transmembrane domain (TM) and cytoplasmic tail (CT) of HIV-1 Env were replaced with the corresponding region of NDV fusion protein (F) for incorporation into NDV-VLPs15. In this study, the Env sequence of subtype C HIV-1 1086C16 was used. We generated two HIV-1 Env-NDV chimeric protein constructs with either a covalent disulfide bond and proline stabilization (SOSIP) or native flexible linker trimer-derived (NFL-TD) stabilization to understand how this might impact immunogenicity3,9,17,18. For the influenza virus-NDV chimeric construct, we utilized the H5 HA sequence (A/Vietnam 1194/2004)19. For the SARS-CoV-2 NDV chimeric construct, only the CT region of SARS-CoV-2 Delta+ S protein was replaced with the corresponding region of NDV F to increase the surface expression of the protein15. The expression of these chimeric proteins was confirmed in lysates of transfected cells by western blot (Fig.\u00a01B).\n\nA Schematics of chimeric NDV F expressing HIV-1 Envelope (1086\u2009C, 1086\u2009C SOSIP, 1086\u2009C NFL-TD), Influenza HA or SARS-CoV2 spike proteins designed for this study. B Western blots of lysates of A549 cells transfected with the indicated chimeric NDV F chimeric proteins. C Detection of indicated chimeric NDV F and NP proteins in purified VLPs by western blot. D The ratio of indicated chimeric NDV F protein chimeras to NDV nucleoprotein measured by western blot (1086\u2009C constructs, n\u2009=\u20097; H5 and CoV2, n\u2009=\u20096). The samples derive from the same experiment, and the blots were processed in parallel. E Quantification of protein spikes expressed on the VLP surface performed by negative stain electron microscopy. F Representative EM images of VLP variants. The scale bars correspond to 50\u2009nm.\n\nWe next produced NDV-VLPs and examined incorporation of chimeric proteins into the particles (Fig.\u00a01C, D). NDV nucleocapsid protein (NP) was detected in the same preparations with HIV-1 Env, influenza virus HA, or SARS-CoV-2 S proteins, indicating that chimeric proteins are successfully incorporated into the VLPs. When the ratio of Env to NP signal was assessed, stabilized HIV-1 Envs showed higher levels of Env signal, suggesting that the shedding of gp120 is prevented in stabilized Env designs. We further optimized the incorporation of chimeric proteins by co-expressing NDV hemagglutinin-neuraminidase (HN) spike (Supplementary Fig.\u00a01)14. The signal of HA protein increased when NDV HN was co-expressed, whereas the signal of HIV-1 Env protein decreased. This result suggested NDV HN protein interacted with the ectodomain of the co-expressed antigens and positively impacted the incorporation of HA protein, but negatively impacted HIV-1 Env and SARS-CoV-2 S proteins. For this reason, the NDV-VLP H5 used in this study contains NDV HN spikes to maximize the amount of chimeric H5 HA incorporation, whereas in the Env and SARS-CoV-2 constructs, it was omitted.\n\nThe valency of the expressed antigens on the VLP surface was then assessed by negative-stain electron microscopy (Fig.\u00a01E). Although there was some variability in the size of particles (60\u2013120\u2009nm in diameter), most particles were covered with the spikes with the expected shape, with the exception of VLPs with non-stabilized 1086\u2009C. The computational analysis of images indicated the estimated number of chimeric stabilized spikes was 149 for VLP H5, 104 for VLP CoV2, 120 for VLP SOSIP and 160 for VLP NFL-TD. There were no intact non-stabilized chimeric F/Env spikes detectable on the VLP 1086\u2009C (Fig.\u00a01F), consistent with the western blot result in Fig.\u00a01C. Based upon these densities, the distances between spikes were approximately 233\u00c5 for VLP H5, 186\u00c5 for VLP CoV2, 158\u00c5 for VLP SOSIP and 134\u00c5 for VLP NFL-TD. It is notable that the propeller-like shaped HIV-1 Env trimer protein was more readily detected, and the valency was greater on VLP NFL-TD than that of SOSIP stabilized version. Nonetheless, both stabilizing designs successfully prevented the shedding of gp120 subunit and permitted expression at high valency.\n\nThe conformational integrity and preservation of broadly neutralizing antibody (bNab) epitopes were confirmed by staining the Env/NDV chimeric proteins expressed on the transfected cell surface with a panel of monoclonal antibodies (Fig.\u00a02A, B, Supplementary Fig.\u00a02A). These included the Env apex (PGT145, PG16), CD4 binding site (CD4bs) (VRC01 and b12), or gp120/41 interface (35O22, 8ANC195, PGT151) and a MPER bNab (10E8). The interface and MPER antibodies were included because they do not bind maximally when native-like Env is membrane-bound due to steric constraints, and binding may suggest some conformational change. In addition, we included antibodies whose epitopes are exposed upon conformational change of Env after CD4 binding; the CD4bs antibody F105, and the V3 antibody 447-52D. Cells infected with an adenovirus type 4 expressing a non-stabilized 1086\u2009C Env (Ad4 FDE3 Env150), a non-enveloped virus that displays the transgene product on the cell surface, were similarly stained as a comparator. Data were normalized to the median fluorescence intensity (MFI) of VRC01 binding, which was used as a measure of the prevalence of Env.\n\nA Binding of HIV-1 Envelope monoclonal antibodies to chimeric NDV F/1086\u2009C protein on transfected A549 cells by flow cytometry. B Summary of HIV-1 Envelope monoclonal antibodies binding to chimeric NDV F/1086\u2009C proteins on transfected, or Ad4 FDE3 Env150 infected A549 cells. MFI is normalized to VRC01. C Binding of VRC01 and PGT145 to stabilized 1086\u2009C NFL-TD and SOSIP proteins on chimeric NDV F VLPs by flow virometry.\n\nThe non-stabilized F/1086\u2009C chimera showed a similar binding pattern to the unmodified full-length 1086\u2009C. The F/1086\u2009C SOSIP construct showed increased binding to some interface antibodies (35O22, 8ANC195, and PGT151), and the MPER antibody (10E8) compared to non-stabilized designs. In addition, the F/1086\u2009C SOSIP construct bound less F105 than non-stabilized designs, consistent with a more native-like conformation. The F/1086\u2009C SOSIP is also bound to the V3 antibody, suggesting some conformational heterogeneity and/or exposure of the V3. The F/1086\u2009C NFL-TD showed no evidence of this conformational change and had markedly reduced binding of antibodies targeting all CD4-inducible epitopes (F105 and 447\u201352D), all interface epitopes (35O22, 8ANC195, and PGT151), consistent with prior reports20. The F/1086 C NFL-TD design did not stain with the 10E8 antibody consistent with\u00a0the lack of\u00a0MPER in this design.\u00a0Although the magnitude of binding differed from the wildtype and non-stabilized F/1086\u2009C, all chimeric constructs successfully formed trimers, indicated by binding of PGT145 and PG16. The level of Env chimeras on the surface of VLPs was further confirmed by flow virometry, which can better detect heterogeneity of incorporation in larger numbers of particles than electron microscopy (Fig.\u00a02C, Supplementary Fig.\u00a02B). Although there was modestly greater heterogeneity in staining of VLP NFL-TD compared to VLP SOSIP, both had overall high levels of staining, indicating good incorporation into VLPs.\n\nWe compared the immunogenicity of VLP H5, VLP CoV2, VLP SOSIP and VLP NFL-TD in groups of 6 New Zealand White (NZW) rabbits, a model of immunogenicity for Env vaccines21. Animals were immunized intramuscularly with 150\u2009\u03bcg of purified VLPs four times, and blood samples were collected periodically to assess the serum neutralizing antibody activities. The neutralizing activity against influenza virus H5 was detected as early as 4 weeks after the first immunization in all animals immunized with VLP H5 (Fig.\u00a03A). The median neutralization titer peaked at 14 weeks (median ID50\u2009=\u20095747; 2 weeks after the 3rd immunization) and decreased to 937.3 ID50 by 28 weeks. Similarly, neutralization activity to SARS-CoV-2 variant pseudoviruses was detected in VLP CoV2 immunized animals four weeks after priming (Fig.\u00a03B). The pseudovirus neutralization was high against all tested strains (Peak Delta+ median\u2009=\u200915,156 ID50, Wuhan=14266, Omicron B.1\u2009=\u200913,758, and Omicron B.2\u2009=\u200911,876) and remained above 1000 at 28 weeks. In contrast, we only detected sporadic neutralizing antibody responses in VLP SOSIP or VLP NFL-TD immunized animals after the 3rd immunization at 14 weeks (Fig.\u00a03C, D). Neutralizing activity was tested against a pseudovirus expressing a heterologous neutralization-sensitive (tier 1) subtype B SF162 that adopts an open conformation22,23. It was detected in 3 animals at low levels in VLP SOSIP immunized animals. Most of these activities were diminished by 28 weeks post-immunization. Similarly, neutralizing activity against SF162 and autologous (tier 2) 1086\u2009C was detectable in 3 VLP NFL-TD immunized animals at 14 weeks. To determine the level of immunogenicity that could be achieved with a replicating virus vector expressing the same Env, we also immunized rabbits intramuscularly with 1011 TCID50 of an adenovirus type 4 with a full deletion of the E3 region replaced by the coding sequence for a non-stabilized 1086\u2009C Env with a truncation of the CT (Ad4 FDE3 Env150). Rabbits rapidly developed autologous neutralizing antibodies to 1086\u2009C by 8\u201312 weeks after this immunization and lower levels of neutralization of SF162 (Fig.\u00a03E, F). This serum neutralizing antibody response showed little, if any, decay over the 28-week study period. These results indicate that Env, even when expressed as a full-length protein bound to the membrane, is poorly immunogenic relative to other viral glycoproteins, and this is not overcome by a native-like conformation and high valency display. They also suggest that some features of the cell surface displayed by the Ad4 viral vector, beyond conformation and valency, can overcome the relatively poor immunogenicity of Env.\n\nA\u2013D Immunogenicity of 150\u2009\u03bcg intramuscular (IM) dose administered NDV VLP variant expressing influenza virus HA (A), SARS-CoV2 Spike (B), SOSIP stabilized HIV-1 envelope protein (C), or NFL-TD stabilized HIV-1 envelope protein (D). E Immunogenicity of replication-competent Ad4 FDE3 Env150 recombinant (1011, IM) expressing non-stabilized 1086\u2009C HIV-1 Envelope protein. Serum HIV-1 neutralizing antibody titers (C\u2013E) of each rabbit (n\u2009=\u20096) are shown in red squares (SF162) and blue dots (1086\u2009C). F Serum neutralization breadth (ID50) of rabbits (n\u2009=\u20096) immunized with Ad4 FDE3 Env150 at the indicated weeks against pseudoviruses of HIV-1 Env SF162, 1086\u2009C and SIV Env mac256. Blue arrows indicate the week of IM-administered immunization. Serum neutralizing antibody titers (ID50) of each rabbit (n\u2009=\u20096) are shown in dots, and the median value for each time point is connected with a solid line.\n\nOne potential mechanism by which presentation of Env by a viral vector might be more immunogenic is through stimulation of innate immunity, enhancing the magnitude of antibody responses24. To assess the importance of activation of innate immunity for this Env immunogen, we first co-formulated VLPs with AS0125,26,27, a potent stimulator of toll-like receptor (TLR) 4. Rabbits were immunized with 150\u2009\u03bcg of VLP SOSIP (Fig.\u00a04A) or VLP NFL-TD (Fig.\u00a04B) formulated with AS01. Serum neutralizing activity against SF162 was detected in 3 animals at 8 weeks in the group immunized with VLP SOSIP. Transient increases in neutralization activity were detected after each boost and peaked at 16 weeks (Median\u2009=\u2009302.2 ID50). However, these responses fell to undetectable levels in most animals by 24 weeks. Neutralizing antibodies emerged against SF162 at 4 weeks and 1086\u2009C at 8 weeks in one animal immunized with VLP NFL-TD. More animals developed neutralizing activities over time, and these neutralization titers transiently increased after each boost, although they remained well below 100 ID50. Overall, the addition of AS01 did not result in a statistically significant increase in neutralization magnitude for either construct against the SF162 or 1086\u2009C pseudoviruses (p\u2009>\u20090.05 for all comparisons) (Supplementary Table\u00a01).\n\nA\u2013F Immunogenicity of 150\u2009\u03bcg intramuscular (IM) dose of chimeric NDV VLP variant expressing the indicated protein with AS01 adjuvant (A, B) or TLR agonist encapsidated RNA40 (C\u2013F). Animals immunized with constructs without RNA40 are shown in gray. G Heatmap of serum neutralizing activity (ID50) against a panel of 10 HIV-1 Env pseudoviruses from clades (A\u2013C), and SIVmac256. Blue arrows indicate the week of the IM-administered immunization. Serum neutralizing antibody titers (ID50) of each rabbit (n\u2009=\u20096) are shown in dots, and the median value for each time point is connected with a solid line.\n\nIn a viral infection, stimulation of TLRs 7/8 takes place in the endosome of antigen-presenting cells. Some data suggest that much of the AS01 adjuvant may wash through the lymph node and may not be temporally and spatially associated with the vaccine antigen24. Therefore, we sought to assess the impact of encapsidating a TLR agonist into the VLP like a virus. RNA40 is a short RNA sequence originally found in the long terminal repeat region of the HIV-1 genome and is known to potently activate TLR7/8 in the endosome of antigen-presenting cells28. In NZW rabbits, TLR7 is a pseudogene, although they are known to respond to TLR7/8 agonists29. We designed this short RNA fragment such that it would package in VLP particles by flanking it with the NDV leader and trailer sequences (Supplementary Fig.\u00a03A)30. The surface Env spike density and conformation of RNA40 incorporated VLPs were like those without RNA40 based upon EM (Supplementary Fig.\u00a03B). Incorporation of RNA in VLP particles was confirmed by qPCR in the presence and absence of RNase during the purification step (Supplementary Fig.\u00a03C). We first assessed the impact of this RNA40 incorporation on the magnitude of neutralizing activity in VLP H5 and VLP-CoV2 immunized rabbits (Fig.\u00a04C, D). Neutralization activity in serum was detected by 4 weeks after immunization and peaked at 14 weeks for VLP H5 and 16 weeks for VLP CoV2, with detectable increases in neutralization after each boost. There was only an increase in neutralizing activities due to RNA40 incorporation in animals immunized with VLP CoV2 and only against the Delta strain (Fig.\u00a04D, C; p\u2009=\u20090.0321 for Delta; p\u2009>\u20090.05 for all other comparisons). When RNA40 containing Env particles were used, neutralizing activity was observed in two animals immunized with VLP SOSIP RNA40, mostly after the 3rd immunization at 12 weeks (Fig.\u00a04E). However, there were no notable changes in the response rate or magnitude of serum neutralizing activities in the VLP NFL-TD RNA40 immunized group (Fig.\u00a04F, Supplementary Table\u00a01).\n\nTo test whether activation of innate responses would induce some breadth of serum neutralizing activity, we tested sera collected at 28 weeks against a panel of 10 HIV-1 strains from subtypes A, B, and C (Fig.\u00a04G). Homologous and heterologous neutralization was low in magnitude. There were only 2 animals with neutralizing activity against more than two strains of HIV-1 in the two groups immunized with VLP SOSIP or VLP NFL-TD formulated with AS01. There was one animal that neutralized 9 strains in the group immunized with VLP SOSIP RNA40. However, no neutralizing activity was detected in the group immunized with VLP NFL-TD RNA40. Overall, there was no statistically significant increase in neutralization magnitude or breadth induced by encapsidation of RNA40 into the Env constructs (p\u2009>\u20090.05 for all comparisons). These data suggested that activation of TLR 4 alone or TLR 7/8 may modestly enhance immunogenicity of HIV-1 Env, but this was not sufficient to induce antibody responses comparable to those of VLP H5, VLP CoV2, or replicating Ad4-1086c.\n\nThe total amount of antigen presented to the immune system during viral infection is thought to be much greater than that typically achieved by non-replicating vaccines5,31. Thus, we assessed the impact of increased dose on the magnitude of neutralizing antibody responses. Rabbits were immunized with 500\u2009\u03bcg of VLP SOSIP RNA40 or VLP NFL-TD RNA40 (Fig.\u00a05A, B). Surprisingly, neutralization activity against SF162 was detected at 4 weeks in 4 animals immunized with 500\u2009\u00b5g of VLP-1086C SOSIP RNA40. Furthermore, this activity peaked at 14 weeks (Median\u2009=\u2009208.2 ID50) and did not show a significant decline as seen in the groups immunized with 150\u2009\u03bcg of VLP. The serum neutralizing activity at the time of euthanasia (41 weeks) was unchanged from 24 weeks, a considerable improvement in durability compared to lower dose regimens. Although there was an increase in the response rate and the magnitude of neutralizing activity against SF162 this did not achieve statistical significance (p\u2009>\u20090.05). No neutralizing antibody against 1086\u2009C was detected in this group of animals, except in one animal at 22 weeks at a very low level. In the VLP NFL-TD RNA40 group, only two animals showed neutralizing activity against 1086C, first detected at 8 weeks. These neutralizing activities were undetectable by 41 weeks post-immunization.\n\nA\u2013D Immunogenicity of\u00a0TLR agonist RNA40 encapsidated chimeric NDV VLP variants expressing the indicated stabilized HIV-1 envelope protein with or without AS01 adjuvant and IM administered at high doses (500\u2009\u03bcg). E Heatmap of neutralization activity (ID50) in rabbit serum against a panel of 10 HIV-1 Env pseudoviruses from clades (A\u2013C), and SIVmac256. Blue arrows indicate the week of IM-administered immunization. Serum neutralizing antibody titers (ID50) of each rabbit (n\u2009=\u20096) are shown in dots, and the median value for each time point is connected with a solid line.\n\nNext, we assessed the impact of activating TLRs 4, 7 and 8 in combination with a high dose (Fig.\u00a05C, D). Some prior data suggest that neutralizing antibody responses increase when adjuvants with these activities are combined32. The addition of AS01 alone did not increase the response rate or magnitude of neutralizing activities in high-dose VLP SOSIP RNA40 immunized rabbits (Fig.\u00a05C, Supplementary Table\u00a01) (p\u2009>\u20090.05). Although the addition of AS01 only yielded modest improvements in magnitude compared to the 500\u2009\u00b5g with RNA40, there were significant increases in neutralization of SF162 in the VLP SOSIP RNA40 group (p\u2009=\u20090.0360) and of 1086\u2009C in the VLP NFL group (p\u2009=\u20090.0329) when compared to the respective groups immunized with unadjuvanted 150\u2009\u00b5g of VLPs (Supplementary Table\u00a01). The neutralizing activities were still detectable at the time of euthanasia (34 weeks). Some increase in breadth of serum neutralizing antibody responses was observed although at a low level (Figs.\u00a04G and 5E). None of the animals immunized with 500\u2009\u03bcg of VLP SOSIP RNA40 or VLP NFL-TD RNA40 showed serum neutralizing activities against more than two strains. On the other hand, serum from a total of 5 animals (2 in VLP SOSIP RNA40 and 3 in VLP NFL-TD RNA40 groups) achieved low-level neutralizing activities against more than two strains when the same immunogens were combined with AS01 and given at a high dose. Although some animals developed neutralization of the tier 1B viruses SF162 or BaL.01, in most cases, this broadening of the response was associated with low-level neutralization of the SIV pseudovirus, which is a specificity control. These results suggested that the total amount of the antigen presented to the immune system has a significant impact on the magnitude as well as durability of the antibody response, but repeated immunization at high doses with adjuvant can induce some nonspecific neutralization.\n\nAnother characteristic of a viral infection is in vivo amplification of the antigen. It has been reported that immunization in a dose-escalating manner increases the immunogenicity of soluble trimer-based vaccines33. This is thought to work by early immunogen-specific binding antibodies causing improved lymph node retention of antigen and germinal center formation. To test the impact of dose escalation, we split 500\u2009\u03bcg of VLP SOSIP RNA40 or VLP NFL-TD RNA40 formulated with AS01 into 7 doses and immunized rabbits in a dose-escalating manner over two weeks. Most VLP SOSIP RNA40 immunized rabbits developed serum neutralizing activities against SF162 at 4 weeks, and continued to increase over time, peaking at 24 weeks (after the 4th immunization) (Fig.\u00a06A). Neutralizing antibody responses against 1086\u2009C were detected in most animals by 22 weeks (Median\u2009=\u200919.6 ID50), although most of these responses were below 50 ID50. The increase in neutralization provided by dose escalation was statistically significant for 1086\u2009C (p\u2009=\u20090.0038). An effect of dose escalation was also observed in the animals immunized with VLP NFL-TD RNA40 (Fig.\u00a06B). There was a higher response rate against both SF162 and 1086\u2009C, peaking at 24 weeks (Median; SF162\u2009=\u200932.6 ID50, 1086\u2009C\u2009=\u200970.8 ID50). However, the serum neutralizing antibody response against these viruses was not durable and waned to below the level of detection by week 41 in all but 1 animal and did not achieve statistical significance (p\u2009>\u20090.05, for SF162 and 1086\u2009C).\n\nA, B Immunogenicity of TLR agonist RNA40 encapsidated chimeric NDV VLP variants expressing the indicated HIV-1 envelope formulated with AS01 adjuvant given at an escalating dose of 500\u2009\u03bcg. The immunogen was split into 7 IM immunizations in a dose-escalation manner. Four separate weeks of dose-escalation IM immunizations are indicated by the blue histogram. Serum neutralizing antibody titers of each rabbit are shown in dots, and the median value for each time point is connected with a solid line. C Heatmap of rabbit serum (Week 28) neutralization activity (ID50) against a panel of 10 HIV-1 Env pseudoviruses from clades (A\u2013C) and SIVmac256.\n\nThere was also an increase in the number of animals showing breadth in neutralizing activity in both groups (Fig.\u00a06C, Supplementary Table\u00a02). Five out of six animals immunized with an escalating dose of VLP SOSIP RNA40 showed neutralization against more than 2 strains (no dose escalation vs escalation; p\u2009=\u20090.0289). Three of these animals were able to neutralize all tested strains, albeit at low titers. Similarly, 3 of 6 animals immunized with VLP NFL-TD RNA40 were able to neutralize all the tested strains, including some difficult to neutralize tier 2 and 3 strains. Although the response magnitude for most animals was low, the result is surprising given that this was induced with a single strain immunogen and did not require heterologous boosting. However, this low-level response again extended to the SIV control pseudovirus, indicating a lack of specificity. Prior work suggests that neutralization of Tier 1 viruses such as SF162 might be dominated by antibodies with specificity for V334. To evaluate this possibility, we performed competition experiments with V3 peptide (Supplementary Fig.\u00a04). The neutralizing activity against SF162 induced by VLP SOSIP was competed (p\u2009=\u20090.0146), suggesting that this activity was V3 mediated, likely induced by V3 loop exposure within the immunogen, consistent with the surface staining in Fig.\u00a02B.\n\nWe next sought to further understand the mediators of the low magnitude, broad, but nonspecific responses observed in the dose escalation experiments. We first purified immunoglobulins from the serum of animals in Fig.\u00a06C to determine if the response was mediated by the humoral immune response or by some other non-specific factor in the sera. Overall, the level of neutralization in sera tracked with the IC50 observed after purification (Supplementary Fig.\u00a05A, B). This purified immunoglobulin also mediated neutralization of pseudoviruses bearing murine leukemia virus Env and vesicular stomatitis virus G proteins (Supplementary Fig.\u00a05C, D). These results suggested that the broad, low-level neutralization we observed was immunoglobulin-mediated, but not Env-specific.\n\nThe induction of binding antibodies was also examined in sera. Binding was measured on week 28 sera using a Meso Scale Discovery assay against 3 trimers; the heterologous (clade A) BG505 that has additional disulfide (DS) stabilizing mutations (201\u2009C, 433\u2009C) (BG505 DS-SOSIP)35, and the homologous 1086\u2009C DS-SOSIP and 1086\u2009C NFL-TD (Fig.\u00a07A). Binding antibodies to the BG505 DS-SOSIP trimer were primarily induced by the 1086\u2009C SOSIP immunogen and largely paralleled the neutralization results against SF162 shown in Figs.\u00a03\u20136. The previous V3 competition result suggests that this binding might be largely mediated by V3-targeting antibodies. However, high levels of binding antibodies were induced to both the DS-SOSIP stabilized and NFL-TD trimers by the NFL-TD immunogen but were not induced by SOSIP immunogens (Fig.\u00a07A). Taken together with the neutralization data, the VLP regimens using the SOSIP stabilized 1086\u2009C induced neutralizing antibodies that target V3 that may be responsible for SF162 neutralization, in addition to other specificities responsible for the heterologous Tier 2 virus neutralization. The VLP regimens that were stabilized with NFL-TD induced high levels of trimer-binding antibodies but relatively low levels of serum neutralizing activity.\n\nA Sera from week 28 from rabbits (n\u2009=\u20096) immunized with the indicated vaccine regimen was assayed against the indicated stabilized trimer protein. The area under the curve (AUC) of serial dilutions is displayed. B Negative-stain EMPEM 3D reconstruction of polyclonal antibody fragment antigen-binding (pFab) from rabbit 142 (RB142) at week 31 in complex with 1086\u2009C NFL trimer (left). The map is segmented and colored by component. Docking of an Env gp140 protomer model into the map reveals that the polyclonal antibodies bind near the V3 and V1 regions of gp120, near N301 (middle and right panels). Relevant potential N-linked glycosylation sites in 1086\u2009C NFL are labeled in green, and those commonly present in other HIV-1 genotypes but not in 1086\u2009C are labeled in red.\n\nWe also attempted to map the specificities of binding antibodies that might mediate the broad, low-level responses using electron microscopy polyclonal epitope mapping (EMPEM). Sera from animals at day 41 were used to bind the 1086\u2009C NFL, BG505 SOSIP2 or the PVO.04 NFL23,36 trimers. Binding of antibody binding fragments (Fabs) from the sera of most animals with low-level neutralization was not detected by EMPEM (Supplementary Fig.\u00a06A). Binding was only detected in sera from rabbit RB142, which had the greatest breadth and magnitude of serum neutralization. In this NFL-TD immunized rabbit, binding was only observed to 1086\u2009C NFL, and not to BG505 SOSIP or PVO.04 trimers (Fig.\u00a07B, Supplementary Fig.\u00a06A). Most trimers bound up to 3 polyclonal antibody binding fragments (Fabs) against the V3/V1 region, centered on N301 (HxB2 numbering). It is notable that 1086\u2009C lacks N295 and has a potential N glycosylation site at N334, potentially reducing steric hindrance in this area and increasing the likelihood of detection of antibody binding. In addition, the pFab-bound 1086\u2009C NFL did adopt a more open conformation compared to other trimers (Supplementary Fig.\u00a06B). This raises the possibility that these antibodies require a more open conformation and were not captured on the BG505 SOSIP or PVO.04 trimers that adopt a more closed conformation and are less likely to expose these epitopes.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60481-2/MediaObjects/41467_2025_60481_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60481-2/MediaObjects/41467_2025_60481_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60481-2/MediaObjects/41467_2025_60481_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60481-2/MediaObjects/41467_2025_60481_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60481-2/MediaObjects/41467_2025_60481_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60481-2/MediaObjects/41467_2025_60481_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60481-2/MediaObjects/41467_2025_60481_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we explored the factors that contribute to immunogenicity that might be exploited to engineer better immunogens. The NDV-VLP system permitted variation of many of the features of a live virus infection thought to contribute to immunogenicity, including the surface glycoprotein and its conformation, spike density, adjuvant, packaging of RNA as a TLR agonist, total and escalating doses. We observed that HIV-1 Env is markedly less immunogenic than H5 or SARS-CoV2 Spike in the NDV VLP system, and this observation adds to the notion that HIV-1 is exceptionally poorly immunogenic11,12. This lower immunogenicity was not attributable to the number of glycoprotein spikes, given that the valency of the Env VLPs was intermediate between VLP H5 and VLP CoV2. Although overall glycoprotein spacing was higher than the 50\u2013100\u2009\u00c5 spacing thought to be important for HPV VLP vaccine immunogenicity37, the spacing was greater for VLP H5 and VLP CoV2 compared to Env VLPs, suggesting this was not a cause of the differences in immunogenicity. We also observed that there was some modest improvement in the induction of neutralizing antibodies with the incorporation of a TLR 4-stimulating adjuvant and the packaging of a TLR 7/8 agonist RNA. However, by far the largest impact on the durability of the neutralizing antibody response was observed with large increases in total dose or use of an escalating dose scheme. It is important to note that poor immunogenicity was specific to HIV-1 Env. The inclusion of adjuvants or very high doses, were not required to induce magnitude or durability to H5, S, or in prior work, RSV F13. This suggests that poor immunogenicity is not simply a product of the NDV system. Rather, this is possibly a result of the closed conformation of Env primary isolates, closed conformation of highly stabilized Envs, their extensive coating with self-glycans, or other factors.\n\nAt first examination, it may seem somewhat surprising that dose would play such a prominent role in the induction of durability of the neutralizing antibody response. However, there are some examples in human clinical trials that are consistent with these results. In a prior trial, we observed that when Ad4 H5-Vtn was given by the oral route, the serum neutralizing antibody response waned by 6 months6. However, when the same vaccine was given by the intranasal route, where there is greater replication, neutralizing antibodies were unchanged from their peak at 3\u20135 years after immunization. In another example, the half-life of antibodies to vaccinia virus is measured in hundreds of years5. However, serum responses to the modified vaccinia Ankara vaccine, which does not replicate in human cells, last less than 1 year even after 2 doses38. Even with non-replicating vaccines, such as the inactivated Hepatitis A vaccine, large increases in dose can extend the durability of the serum antibody response to more than 7 years39. In addition, there was a recent demonstration that the use of self-amplifying RNA vaccine for SARS-CoV-2 can dramatically increase the durability of serum neutralizing antibodies compared to conventional mRNA vaccines40. It should be noted that the doses used here for induction of a neutralizing antibody response to Env when scaled to a 70\u2009kg human, are ~18\u2009mg per dose. This compares to only 100\u2009mcg of Env trimers in recent clinical trials41 and 40\u201350\u2009mcg of total protein for the HPV VLP vaccine. Such doses would likely be prohibitive for widespread use in humans. Not only was the total dose an important factor in driving durable antibody responses, but also the use of an escalating dose. At present, the mechanism of the effect of higher doses in inducing long-lived plasma cells is incompletely understood. These might be related to greater persistence of antigen in draining lymph node and driving of germinal centers, or presentation of antigen by alternative modes or cells33.\n\nThe finding that the addition of adjuvants increased the magnitude but did not result in improvements in durability is consistent with several published reports. For example, formulation of chimeric influenza split vaccines with AS01 dramatically increases the magnitude of the antibody response but does not dramatically improve the poor durability of the response42. A similar increase in magnitude but lack of improvement in durability was observed with split H5 influenza vaccines formulated with AS0343. This is in contrast to the pattern of durability of responses we observed to a replication-competent Ad4-H5-Vtn given intranasally, where a single dose stimulated B cell expansions, somatic hypermutation and affinity maturation that continued for 6 months to one year44. When vaccinees returned for boosting at 3\u20135 years, the anti-H5 serum neutralizing titers were unchanged from their peak6. The short-lived nature of the serum antibody response to SARS-CoV2 S induced by currently licensed mRNA vaccines, and prior experience with mRNA vaccines for H7, H1045 and Rabies gp15046, and some protein-based vaccines43 underscore the need to better understand the parameters that guide antibody durability.\n\nIt was also surprising that high valency display in a native-like conformation, even with the addition of adjuvants, only induced very modest titers of neutralizing antibodies and very limited durability. High valency display is thought to improve the stimulation of naive B cells by overcoming a low-affinity interaction with a highly avid one that engages multiple B cell receptors per cell47. This display may also enhance antigen uptake by B cells and presentation to CD4+ T follicular helper cells (TFH)48. Induction of CD4\u2009+\u2009TFH cells is thought to be a critical determinant of the induction of long-lived plasma cells in the bone marrow and thus antibody durability. In large part, this is thought to be the mechanism for the success of the human papillomavirus VLP vaccine that induces neutralizing antibodies that remain above the protective threshold for the life of the vaccinee5,47. Critical to this activity is the rigidity of the immunogen and spacing of surface spikes. However, high valency display of HIV-1 Env in a native-like conformation appears insufficient, under the current experimental conditions, to induce a durable neutralizing antibody response.\n\nThere are some important limitations to this study. First, we were unable to fully understand the mechanism of the low-level non-HIV-1-specific neutralization that we observed in highly immunized animals, although it appears immunoglobulin-mediated. It is highly unlikely that such low-level responses, if achieved in humans, would meaningfully impact HIV acquisition49. In addition, we should underscore that even with high valency display, adjuvants and an escalating high dose, we did not achieve the magnitude of autologous neutralization that we observed with a replicating Ad4-1086c. Of the available vaccine platforms for presenting viral glycoproteins to the immune system, replicating vectors have several advantages over most non-replicating vaccines. They can express viral surface glycoproteins on the host cell at high valency, over a prolonged period, and in the appropriate conformation and glycosylation state. Antibodies induced by the host cell, which produce glycoproteins, in contrast to those produced in cell lines or eggs, may better target virions during natural infection. Replicating vectors may also directly or indirectly stimulate B cell proliferation and differentiation through nucleic acid stimulation of toll-like receptors in B cells or antigen-presenting cells and induce pro-inflammatory cytokines. Some combination of these factors likely leads to the extraordinary durability of the neutralizing antibody response to replicating vectors5,6,7. Although replicating vectors offer numerous advantages, the level of immunogenicity is often modulated by the level of replication of the vector, transgene expression, pre-existing immunity, and route of administration. In addition, development can be complicated by safety or transmission concerns. One path forward is to further develop replicating vectors and address these issues, and in parallel, address the reasons for the differences in immunogenicity between replicating and non-replicating approaches. In theory, it should be possible to achieve the magnitude and durability of neutralizing antibodies induced by a live virus with a non-replicating immunogen. Additional modifications to the stabilization strategy or glycan shielding could further improve Env immunogenicity for both approaches. It is also possible that there are other factors that contribute to immunogenicity that were not explored here, such as tissue injury or T follicular help, that might be pursued in further work. The parameters that approximate a replicating virus infection found here may therefore serve as an important starting point for such work. However, the rapid generation of 1086-specific neutralizing antibodies by the Ad4 vector suggests that poor immunogenicity is not due to factors\u00a0solely intrinsic to Env, such as glycosylation, or lack of engagement of\u00a0the naive B cell repertoire. Rapid induction of autologous neutralization by an Ad4 recombinant suggests that there are features of a live virus infection that can overcome the poor immunogenicity of HIV-1 Env, and if better understood, might further the development of vaccines, replicating or non-replicating, that might accelerate the induction of neutralizing antibodies.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "All animal experiments were conducted according to the animal study proposal (ASP LIR21/LIRID9) approved by National Institutes of Health (NIH), National Institute of Allergy and Infectious Diseases (NIAID), Animal Care and Use Committee (ACUC) that meets all federal requirements, as defined in the Animal Welfare Act (AWA), the Public Health Service Policy (PHS), and the Humane Care and Use of Laboratory Animals in AALAC accredited facilities. This study used 6\u20138-week-old female NZW rabbits (Charles Rivers Laboratories, Wilmington, MA, USA) that were co-housed, all of which were processed for terminal bleed collections under general anesthesia and euthanized by exsanguination as approved by the AVMA (American Veterinary Medical Association) and adopted by NIH-NIAID ACUC.\n\nThe Env sequence for VLP and adenovirus constructs was derived from the clade C 1086 isolate (name: 1086-B2 C, GenBank accession number: FJ444395, from Malawi 2004)16. The original 1086\u2009C was modified to introduce a K160N mutation to permit binding of antibodies specific for the apex (HxB2 numbering). The SOSIP version was generated by adding the following changes: a TPA signal sequence (MDAMKRGLCCVLLLCGAVFVSPSQEIHARFRRGAR), A501C and T605C (gp120-gp41ECTO disulfide bond), I559P in gp41ECTO (trimer-stabilizing), H66R and T316W (trimer-stabilizing), Q543N in gp41ECTO (improved trimerization) and REKR to RRRRRR (R6) in gp120 (furin cleavage enhancement)2,50,51 and a stop codon after residue 704. A second intermolecular disulfide bond was added by introducing mutations A73C in gp120 and A561C in gp4152. Eight BG505 Trimer Derived (TD) mutations were also introduced (E47D, K49E, V65K, E106T, E429R, R432Q and E500R; the sequence already contains a L at position 165)10,53 and MD39 mutations (T106E, R304V, A319Y, P363Q, F519S, L568D, V570H and R585H; the sequence already contains an I at position 271 and a L at position 288)54 resulted in 1086c SOSIP.v8.2 gp145.\n\nTo generate an NFL trimer design, the furin cleavage site REKR (HIV-1 Env residues 508\u2013511) was replaced by a flexible linker (GGGGSGGGGS) to covalently link the gp120 and gp41 Env subunits20. The natural HIV-1 Env leader sequence was replaced by the CD5 leader sequence to increase expression. The following HIV-1 Env TD substitutions were made to generate highly stable and homogeneous NFL trimers: E47D, K49E, V65K, E106T, E429R, R432Q, E500R; helix-destabilizing gp41 mutations, I559P, L568G, N636G; V3 and Fusion peptide stabilizing mutations, N302Y, T320M, F519R, L520R and V513Y. To further enhance sensitivity to the V2-apex antibodies, the K166R and H170Q mutations were also introduced3,10. Finally, a second linker GGGGS was incorporated to replace the MPER, residues 664\u2013683, to covalently link the 1086c NFL to the NDV TM. The Ad4 FDE3 Env150 was constructed as previously described except that a stop codon was introduced at position 732 to enhance surface expression, and the REKR furin cleavage site was restored to improve antigenicity55. The 1086c Env also included the K160N mutation.\n\nPlasmids for the production of NDV VLP were generated as previously described14. Briefly, B1 strain of NDV cDNA sequences encoding NP, M and HN were subcloned into the mammalian expression vector pCAGGS to generate plasmids for co-transfection in the construction of VLPs. Chimeric constructs were constructed by combining protein coding regions of either Influenza H5 HA, HIV-1 Env 1086\u2009C, HIV-1 Env 1086\u2009C SOSIP, HIV-1 Env 1086\u2009C NFL-TD or SARS-CoV2 S Delta+ with the TM and CT from NDV fusion (F) protein. F/H5, F/1086\u2009C, F/1086\u2009C SOSIP, F/1086\u2009C NFL-TD and F/CoV2 Delta+ plasmids were generated by synthesizing the chimeric codon optimized F protein sequence containing ectodomain sequence from Influenza H5 HA (A/Vietnam/1203/2004; GenBank accession EF541402), HIV-1 1086\u2009C Env, SOSIP stabilized HIV-1 1086\u2009C Env, or NFL-TD stabilized HIV-1 1086\u2009C Env respectively (Genscript, Piscataway, NJ, USA). The SARS-CoV-2 Delta+ full-length spike protein sequence (GenBank accession number OK098887) included additional mutations from other circulating Delta strains (R21T, T77K, E154K, Q216H, E482Q and H1099D) and retained the TM from SARS-CoV2 Spike but maintained the NDV/F CT, which was truncated to increase surface Spike. All chimeric constructs were subcloned into pCAGGS mammalian expression vector. RNA40D6 plasmid was generated by synthesizing the DNA fragment containing RNA40 sequence28 between NDV leader and trailer sequences56, and subcloned into the pCAGGS mammalian expression vector. An extra 3 nucleotides downstream of the NDV leader sequence were included to adjust the total number of NDV/RNA40 nucleotides divisible by 6.\n\nVLPs were produced as previously described with some modifications14. The transfection was performed using the Expi 293 Expression System Kit [A14635] (Thermo Fisher Scientific, Waltham, MA, USA). Expi293F [A14527] (Thermo Fisher Scientific) cells were transfected using Expifectamine transfection reagent (Thermo Fisher Scientific) as recommended by the manufacturer. Briefly, 60\u2009\u03bcg of plasmid mixture (For VLP H5: NP, M, HN and F/H5-vtn at the molar ratio of 1:1:1:1. For VLP-1086C-SOSIP or NFL-TD: NP, M, F/1086\u2009C SOSIP or NLF-TD at the molar ratio of 1:1:1. For VLP CoV2: NP, M and F/CoV2 Delta+ at the molar ratio of 1:1:1 with 0.5\u2009\u00b5g of TMPRSS2 plasmid) was transfected into 1.5\u2009\u00d7\u2009108 Expi 293\u2009F cells. For all VLPs with TLR agonist incorporated, RNA40D6 plasmid was included in each plasmid panel at the same molar ratio. Expifectamine 293 transfection enhancers and 10\u2009\u00b5g/mL of heparin were added to the culture 24\u2009h post-transfection. Media containing VLPs were collected at 48 and 72\u2009h post-transfection (72 and 96\u2009h for VLP SOSIP or VLP NFL-TD) and purified by a series of discontinuous sucrose gradients, as previously described14. The media was centrifuged at 38,500\u2009\u00d7\u2009g for 18\u2009h at 4\u2009\u00b0C using a SW32 rotor in an Optima L-100K Ultracentrifuge (Beckman Coulter, Brea, CA, USA) to obtain a VLP pellet. The VLP pellet was then resuspended in TNE buffer (25\u2009mM Tris-HCl, 150\u2009mM NaCl and 5\u2009mM EDTA, pH 7.4) and layered on top of a discontinuous sucrose gradient containing 20% and 65% sucrose (w/v) cushion, followed by centrifugation at 100,000\u2009\u00d7\u2009g for 6\u2009h at 4\u2009\u00b0C in a SW41 Ti rotor (Beckman Coulter). The VLP fraction collected between the 20% and 60% sucrose interphase was adjusted to 60% sucrose concentration and layered between a 50% and 80% sucrose gradient. The tube was then topped up with 10% sucrose and centrifuged at 200,000\u2009\u00d7\u2009g for 16\u2009h at 4\u2009\u00b0C. The interphase between 10% and 50% sucrose containing purified VLPs was collected, diluted in TNE buffer, and pelleted by centrifugation at 145,000\u2009\u00d7\u2009g for 6\u2009h at 4\u2009\u00b0C. The VLP pellet was resuspended in TNE buffer and stored at \u221220\u2009\u00b0C until further use.\n\nCell lysates or purified VLPs were heat-denatured at 95\u2009\u00b0C for 10\u2009min in sample buffer under reducing conditions. Samples were resolved on 10% Tris-Glycine SDS-PAGE and transferred on a nitrocellulose membrane for Western blot analysis using the following antibodies: chicken anti-Newcastle Disease Virus polyclonal antibody [ab34402] (Abcam, Cambridge, United Kingdom) and goat anti-chicken IgY H&L- HRP conjugated secondary antibody [ab6877] (Abcam) for detection of Newcastle Disease Virus antigen; rabbit anti-HIV-1 gp120 Env (Clade B, IIB) antibody [ABL#5414] (Advanced Bioscience Laboratories, Rockville, MD, USA) and donkey anti-rabbit IgG (H\u2009+\u2009L) cross-absorbed- HRP conjugated antibody [SA1-200] (Thermo Fisher Scientific) for detection of HIV-1 Env protein; mouse anti-influenza A virus (H5N1/HA1) antibody [ab135382] (Abcam) and horse anti-mouse IgG-HRP conjugated antibody [7076S] (Cell Signaling Technology, Danvers, MA, USA) for detection of influenza HA protein; rabbit anti-spike (SARS-CoV2) antibody [scv2-SA-200] (eEnzyme LLC, Gaithersburg, MD, USA) and donkey anti-rabbit IgG (H\u2009+\u2009L) cross-absorbed-HRP conjugated antibody [SA1-200] (Thermo Fisher Scientific) for detection of SARS-CoV-2 spike proteins. Signals were detected using the SuperSignal West Pico Plus Chemilluminescent substrate [34580] (Thermo Fisher Scientific) with the ChemiDoc MP Imaging system (Bio-Rad Laboratories) and analyzed by ImageJ (Version 2.1.0/1.53c).\n\nTo examine the preservation of native-like Env conformation, HIV-1/NDV chimeric proteins were expressed in mammalian cell lines, and the binding of anti-Env antibodies was measured by flow cytometry. One day before transfection, 4.5\u2009\u00d7\u2009106 A549 human adenocarcinoma cells [CCL-185] (ATCC, Manassas, VA, USA) were seeded in T-75 flasks with F-12K medium [30\u20132004] (ATCC) containing 1% Penicillin-Streptomycin-Glutamine [10378016] (Thermo Fisher Scientific) and 10% Fetal Bovine Serum [100\u2013106] (Gemini Bio-Products, West Sacramento, CA, USA). Cells were transfected with 15\u2009\u00b5g of NDV-F/Env chimeric plasmid DNA, 75\u2009\u00b5l of DNA-In\u00ae A549 Transfection Reagent [73772] (MTI-Global Stem, Gaithersburg, MD, USA), and 150\u2009\u00b5l of Opti-MEM [31985070] (Thermo Fisher Scientific) and cultured for 48\u2009h at 37\u2009\u00b0C with 5% CO2. To detect expression of Env, cells were collected 48\u2009h post-transfection with 0.01\u2009M EDTA in phoshpate-buffered saline (PBS) and stained with 50\u2009\u00b5l of anti-Env monoclonal antibodies PGT145, PG16, VRC01, b12, PGT151, 8ANC195, 35O22, 10E8, F105, or 447-52D (BEI Resources, Manassas, VA, USA) at 1\u2009\u00b5g/ml in PBS containing 0.01\u2009M HEPES and 0.09% bovine serum albumin [A7979] (Millipore Sigma, Burlington, MA, USA) for 1\u2009hr at 37\u2009\u00b0C. A secondary antibody, goat anti-human IgG Fab2-phycoerythrin (PE) [109-116-097] (Jackson ImmunoResearch, West Grove, PA, USA) was used at a 1:100 dilution for 1\u2009hr at 37\u2009\u00b0C. To differentiate live and dead cells, a Live/Dead Fixable Violet Dead Cell Stain Kit [L34964] (Thermo Fisher Scientific) was used at a 1:250 dilution for 30\u2009min at room temperature. Cells were fixed with 250\u2009\u00b5l of Cytofix/Cytoperm [554722] (Becton Dickinson, Franklin Lakes, NJ, USA) for 20\u2009min on ice. Alternatively, A549 cells were infected with Ad4-Env at an MOI of 0.1, harvested at 48\u2009h post-infection, and processed for surface staining. Cells were then permeabilized overnight in Perm/Wash buffer [554723] (Becton Dickinson) and intracellularly stained with 50\u2009\u00b5l of anti-Hexon (adenoviral capsid protein) antibody 8C4-allophycocyanin (APC) [NB600-413APC] (Novus Biologicals, Centennial, CO, USA) at a 1:700 dilution in Perm/Wash buffer for 30\u2009min on ice. Cells were analyzed by flow cytometry on a BD FACS Aria with FACSDiva software (Becton Dickinson).\n\nTo assess incorporation of viral glycoproteins, anti-Env antibodies, VRC01 or PGT145 were custom conjugated to PE (Becton Dickinson). VLPs were mixed with a diluted fluorescent primary anti-Env antibody in PBS containing bovine serum albumin (BSA) in 4.5-ml V-bottom polystyrene tubes and incubated for 30\u2009min in the dark at 4\u2009\u00b0C. After incubation, the sample was diluted 10\u00d7 in PBS/BSA. VLPs were transferred to 5-ml polystyrene round-bottom tubes and were analyzed with a FACSymphony S6 cell sorter with FACSDiva software (version 10.9.0) (Becton Dickinson). VLPs not containing HIV-1 Env were stained with the fluorescent anti-Env antibodies to use as controls. The cytometer was set to trigger on both forward scattering (FSC) and side scattering (SSC) lights. VLPs were detected by FSC and SSC, and then the population of gated virions was determined to be expressing Env using fluorescence emitted from the anti-Env PE conjugated antibodies. To confirm that events were indeed VLPs, the FSC and SSC thresholds and voltages were adjusted to discriminate buffer particulates from VLPs using PBS/BSA without VLPs. Cleaning with BD Detergent Solution [660585] (Becton Dickinson) was performed as needed between each sample to ensure fewer than 50 events were detected in a tube of PBS/BSA collected over a minute at the maximum flow rate.\n\nqPCR was performed using the QuantStudio 3 System (Thermo Fisher Scientific) to measure the levels of RNA40 incorporated into VLP variants. RNA was extracted from VLPs by QIAamp Viral RNA Mini Kit [52904] (Qiagen, Venlo, Netherlands) with and without RNase treatment, then reverse transcribed by SuperScript III First-Strand Synthesis System [18080051] (Thermo Fisher Scientific) using random hexamers according to the manufacturers\u2019 instruction. Synthesized cDNA product was then combined with TaqMan Fast Advanced Master Mix [4444556] (Thermo Fisher Scientific). The RNA40D6 transcript was amplified using the following synthesized primers: forward primer 5\u2032-CCAAAGAGTCGGAATTTAACGC-3\u2032, reverse primer 5\u2032-TGTGAGGTACGATAAAAGGCG-3\u2032, and TaqMan probe labeled with a 5\u2032 reporter dye (FAM) and 3\u2032 fluorescent quencher (TAMRA dye): 5\u2032 (6-FAM)-ACGGAGTCACACAACAGACGGG- (TAMRA-Sp) 3\u2032. The reaction conditions were as follows: one 20\u2009s period at 95\u2009\u00b0C, followed by 40 cycles of 1\u2009s at 95\u2009\u00b0C and 20\u2009s at 60\u2009\u00b0C. The Cq values were used to report the level of transcripts detected in copies/\u00b5g. Data were analyzed using the QuantStudio 3/5 Real-Time PCR software and Thermo Fisher Connect Platform (Thermo Fisher Scientific).\n\nA 4.8-\u00b5l drop of the sample was applied to a freshly glow-discharged carbon-coated copper grid for ~15\u2009s and removed using blotting paper. The grid was washed with three drops of buffer containing 0.01\u2009M HEPES, 150\u2009mM NaCl, pH 7.0, followed by negative staining with three drops of 0.75% uranyl formate. Micrographs were acquired using a Talos F200C transmission electron microscope (Thermo Fisher Scientific) operated at 200\u2009kV and equipped with a Ceta CCD camera (Thermo Fisher Scientific). The nominal magnification was 57,000, corresponding to a pixel size of 2.53\u2009\u00c5. To estimate the number of visible spikes per VLP, micrographs were high-pass filtered to 250\u2009\u00c5 using SPIDER57 to suppress the signal corresponding to the VLP, followed by low-pass filtration to 15\u2009\u00c5 to eliminate high-frequency noise. Spikes were then detected automatically in Relion 3.058 using a Laplacian-Gaussian filter with a minimal diameter of 90\u2009\u00c5 and a maximal diameter of 150\u2009\u00c5. When the elongated shape of the spikes and their high density prevented reliable automatic quantification, visible spikes were counted manually instead.\n\nFemale NZW rabbits [Crl: KBL(NZW), stock number 052] (6\u20138 weeks old: 6 animals per group) (Charles River Laboratories) were immunized intramuscularly with 150\u2013500\u2009\u00b5g of purified VLPs in TNE buffer at 0, 4, 12, and 20 weeks and 1011 TCID50 of Ad4 FED3 Env150 at 0 and 4 weeks for bolus immunization studies. One-fifth of a human dose of AS01 adjuvant (containing 10\u2009\u00b5g of 3-O-desacyl-4\u2019monophosphoryl lipid A (MPL) from Salmonella minnesota and 10\u2009\u00b5g of QS-21, a saponin, combined in a liposomal formulation (GlaxoSmithKline Biologicals, London, United Kingdom) was formulated with VLPs for the groups tested to assess the impact of the adjuvant. For dose escalation immunization studies, a total of 500\u2009\u00b5g of VLP was split into 7 doses (1, 2, 5.8, 15.8, 42.9, 116.5 and 316\u2009\u00b5g) and administered intramuscularly in 48-h intervals. The adjuvant was also split proportionally to the VLP in these groups. The blood samples were collected at 0, 4, 8, 12, 14, 16, 20, 22, 24, and 28 weeks post-immunization.\n\nSerum Ig was purified using a 1:1 mix of rProtein A Sepharose\u2122 Fast Flow and Protein G Sepharose\u2122 4 Fast Flow resins [17-1279-02 and 17061802] (Cytiva, Marlborough MA, USA) according to manufacturer\u2019s instructions. Briefly, 1\u2009ml of rabbit serum from each animal was diluted (1:1) in Pierce\u2122 Protein A/G IgG binding buffer [54200] (Thermo Fisher Scientific) and passed through a Poly-Prep\u00ae Chromatography gravity flow column [7311550] (Bio-Rad Laboratories, Hercules, CA, USA) packed with 500\u2009\u00b5l of Protein A/G Sepharose. Columns were washed with PBS to remove non-specific binding and eluted with IgG Elution Buffer (Thermo Fisher Scientific). Purified Ig was dialyzed in PBS and concentrated by centrifugation at 3000\u2009\u00d7\u2009g at 4\u2009\u00b0C using a 30\u2009kDa MWCO Amicon ultra centrifugal filter [UFC903008] (Millipore Sigma). Pierce\u2122 BCA Protein Assay Kit [A55864] (Thermo Fisher Scientific) was used to quantify concentrated purified Ig and stored at \u221280\u2009\u00b0C until use.\n\nHIV-1 Env-specific antibodies were assayed using the 384-well Streptavidin SECTOR Plate [L21SA-1] (Meso Scale Discovery, Rockville, MD, USA). To reduce non-specific binding signals, plates were blocked using a 5% MSD Blocker A solution [R93BAA] (Meso Scale Discovery) for 1 h with shaking using an Orbi-Shaker MP (Benchmark Scientific Inc., Edison, NJ, USA) at room temperature. Following blocking, plates were washed with 1\u00d7 MSD Wash Buffer [R61TX-1] (Meso Scale Discovery) and incubated with the biotinylated capture protein with shaking for 1\u2009h at room temperature. Plates were washed with 1\u00d7 MSD Wash Buffer, and then serial dilutions of samples and controls were prepared in 1% MSD Blocker A [R93BA] (Meso Scale Discovery) in DPBS with 0.05% Tween-20, and then added to the plate and incubated for 1 h with shaking at room temperature. Samples were tested using serial dilutions starting at a minimum dilution of 1:100. After sample incubation, plates were washed with 1\u00d7 MSD Wash Buffer, and 1\u2009\u03bcg/mL goat anti-rabbit SULFO-TAG\u2122 conjugated detection antibody [R32AB-1] (Meso Scale Discovery) was added for 1\u2009h with shaking at room temperature. To detect signals 1\u00d7 MSD Read Buffer was applied and analyzed using the MSD Sector Imager S600 (Meso Scale Discovery). All samples were tested in duplicate. Samples with a replicate coefficient of variation >30% were retested. Serial dilutions of the sample were used to assign an area under the curve (AUC) value as the primary readout. Results were plotted and analyzed using Prism version 9 or newer (GraphPad, San Diego, CA).\n\nThe details of serum and sample preparation to obtain polyclonal antigen-binding fragments (Fabs) for EMPEM were previously described59. Briefly, IgG was isolated from 1\u2009mL rabbit sera (drawn at week 31 post-first immunization) using a self-packed FPLC column containing 5\u2009mL CaptureSelect Fc multispecies resin [2942852005] (Thermo Fisher Scientific) on an AKTA Pure system (Cytiva). Polyclonal IgG was eluted with 0.1\u2009M glycine pH 2.0, and buffer exchanged into TBS (50\u2009mM Tris-HCl, 150\u2009mM NaCl, pH 7.4). Papain [76216] (Millipore Sigma) was used to digest IgG to Fabs. Trimer-Fab complexes were prepared and incubated overnight by mixing 15\u2009\u00b5g of 1086\u2009C NFL, BG505 SOSIP or PVO.04 NFL trimers with 1\u2009mg of Fab mixture (containing Fc and residual papain). The complexes were purified using a Superdex 200 Increase 10/300 GL gel filtration column [28990944] (Cytiva). Purified complexes were concentrated and diluted to a final concentration of 0.03\u2009mg/mL, which were adsorbed on glow-discharged carbon-coated copper mesh grids and stained with 2% (w/v) uranyl formate. Electron microscopy images were collected on an FEI Tecnai Spirit T12 equipped with an FEI Eagle 4k\u2009\u00d7\u20094k CCD camera (120\u2009keV, 2.06\u2009\u00c5/pixel) or a FEI Thermo Fisher Scientific Glacios equipped with a Thermo Fisher Scientific Falcon IV direct electron detector (200\u2009keV, 1.89\u2009\u00c5/pixel) and processed using Relion 3.060 following standard 2D and 3D classification procedures. UCSF Chimera61 was used to generate the composite maps and estimate epitope contacts by fitting the atomic coordinates of a BG505 SOSIP protomer into the map.\n\nHIV-1 Env, Flu-HA and SARS-CoV-2 S pseudoviruses were generated as previously described44. To produce HIV-1 Env pseudovirus, HEK293T cells [CRL-3216] (ATCC) were co-transfected with plasmids encoding an Env-deficient backbone (pSG3\u0394Env) and HIV-1 Env (BG505, Q769.d22, KER 2018, SF162, BaL.01, PVO.04, Du156.12, ZM106.9, 16055, 1086\u2009C) or SIV Env (SIVmac256) at a ratio of 3:1. For influenza virus HA pseudovirus, HEK293T cells (ATCC) were transfected with the following plasmids: 1\u2009\u00b5g of HA, 0.1\u2009\u00b5g of matched NA (A/Vietnam/1203/2004), 17\u2009\u00b5g of pCMV-Luc and 0.1\u2009\u00b5g of TMPRSS2. SARS-CoV2 pseudoviruses were generated by transfection of HEK293T/17 cells [CRL-11268] (ATCC) with the following plasmids: 0.53\u2009\u00b5g of SARS-CoV-2 S (Wuhan, Delta+ S, Omicron B.1, or Omicron BA.2), 9.2\u2009\u00b5g of lentiviral backbone (VRC5602), 9.2\u2009\u00b5g of pCMV-Luc and 0.16\u2009\u00b5g of TMPRSS2. Supernatants containing pseudoviruses were harvested 48-72\u2009h post-transfection and processed by centrifugation at 2000\u2009\u00d7\u2009g for 10\u2009min at room temperature and filtration through a 0.2\u2009\u00b5m filter for titration. Processed pseudoviruses were titered using the following cell lines: TZM-bl [HRP-8129] (BEI Resources) for HIV-1, HEK293A [R70507] (Thermo Fisher Scientific) for Influenza, and HEK293T/ACE [631289] (Takara Bio Inc., Kusatsu, Shiga, Japan) for SARS-CoV2 and stored at \u221280\u2009\u00b0C until further use.\n\nHIV-1, Influenza and SARS-CoV-2 neutralization activities of sera from VLP-HIV-1/Flu/CoV2 immunized rabbits were tested using a single-round pseudovirus infection of TZM-bl, HEK293A or HEK293T/ACE cells, respectively, as described previously23. For testing HIV-1 and Influenza neutralization, heat-inactivated rabbit serum or purified serum Ig was serially diluted five-fold with Dulbecco\u2019s modified Eagle medium (Thermo Fisher Scientific) supplemented with 10% FCS (GeminiBio), and 10\u2009\u03bcl of serum, purified Ig, or mAb was incubated with 40\u2009\u03bcl of pseudovirus in a 96-well plate at 37\u2009\u00b0C for 30\u2009min. TZM-bl (for HIV-1), HEK293A (for Influenza) or HEK293T/17-Ace (for SARS-CoV-2) cells at 1\u2009\u00d7\u2009104 per well were then added, and plates were incubated at 37\u2009\u00b0C with 5% CO2 for 48\u2009h (for HIV-1 and Influenza) or 72\u2009h (for SARS-CoV-2). Luciferin signals were then detected using a Luciferase Assay System (Promega), and the relative light units (RLU) were read on a Victor X2 luminometer (Perkin Elmer, Waltham, MA, USA). All neutralization assays were performed in duplicate, and means are reported. All neutralization assays were repeated at least once with similar results.\n\nTo competitively inhibit V3-mediated SF162 pseudovirus neutralization, 10\u2009\u03bcl of serially diluted heat-inactivated rabbit serum was co-incubated with 20\u2009\u03bcl of solubilized V3 peptide or control peptide (EIYKRWII) at a concentration of 75\u2009\u03bcg/\u03bcl for 90\u2009min at 37\u2009\u00b0C. Pseudovirus (20\u2009\u03bcl) was then added before incubation at 37\u2009\u00b0C for 30\u2009min. TZM-bl cells (1\u2009\u00d7\u2009104 cells/well) were then added to 96-well cell culture plates and incubated, as described above.\n\nWelch\u2019s two-sample t test (unequal variances) was used for all comparisons unless otherwise noted. ArUC was calculated by multiplying neutralizing magnitude at each timepoint by the number of timepoints using trapezoidal integration using R ve4.4.0. In cases where experiment length differed, the duration was shortened to match for comparison. To assess V3-mediated neutralization a paired t test was used to compare control peptide to V3 peptide.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data supporting the findings in this study are available within the article and its\u00a0Supplementary Information. The source data underlying Figs.\u00a01\u20137, Supplementary Figs.\u00a01\u20136 and Supplementary Tables\u00a01 and 2 are provided as a Source Data file. The negative-stain EMPEM map has been deposited in the Electron Microscopy Data Bank under accession code EMD-49656.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Haynes, B. F. & Mascola, J. R. The quest for an antibody-based HIV vaccine. Immunol. 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This research was also supported in part by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health Award Number P01 AI157299 (A.B.W., R.W.). The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open access funding provided by the National Institutes of Health.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "HIV-Specific Immunity Section of the Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA\n\nKenta Matsuda,\u00a0Mitra Harrison,\u00a0Eleanor Wettstein,\u00a0Jessica Pederson,\u00a0Alyssa A. Pullano,\u00a0Lyuba Bolkhovitinov,\u00a0Breanna Kim,\u00a0Isabel Steinberg,\u00a0Trevor Griesman,\u00a0Sarah Stuccio,\u00a0Daniel Rogan,\u00a0Andy Patamawenu,\u00a0Tulley Shofner,\u00a0Nathaniel E. Wright,\u00a0Jonathan D. Webber,\u00a0Freya van\u2019t Veer,\u00a0Rachel Roenicke,\u00a0Emma Koory,\u00a0Peyton M. Roeder,\u00a0Ellison Ober,\u00a0Benjamin Leach\u00a0&\u00a0Mark Connors\n\nElectron Microscopy Laboratory, Cancer Research Technology Program, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA\n\nYaroslav Tsybovsky\u00a0&\u00a0Tyler Stephens\n\nDepartment of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, the Netherlands\n\nIvan Del Moral-Sanchez,\u00a0Ilja Bontjer\u00a0&\u00a0Rogier W. Sanders\n\nDepartment of Microbiology and Physiological Systems, Sherman Center, University of Massachusetts Medical School, Worcester, MA, USA\n\nLori W. McGinnes-Cullen\u00a0&\u00a0Trudy Morrison\n\nDivision Of Clinical Research, Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA\n\nEric Chu\u00a0&\u00a0Jason Liang\n\nDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA\n\nJonathan L. Torres,\u00a0Ryan N. Lin,\u00a0Andy S. Tran,\u00a0Gabriel Ozorowski\u00a0&\u00a0Andrew B. Ward\n\nVaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA\n\nGabrielle Dziubla,\u00a0Leonid Serebryannyy,\u00a0Sandeep Narpala,\u00a0Bob Lin,\u00a0Mike Castro\u00a0&\u00a0Peter D. Kwong\n\nInternational AIDS Vaccine Initiative Neutralizing Antibody Center, Department of Immunology and Microbiology, The Scripps Research Institute, San Diego, CA, USA\n\nJavier Guenaga\u00a0&\u00a0Richard Wyatt\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nK.M., P.D.K., R.W., and M. Connors led the design and execution of the study. K.M., M.H., E.W., J.P., A.A.P., L.B., B.K., T.G., S.S., D.R., I.S., A.P., T.S., N.E.W., J.D.W., F.V.V., R.R., E.K., P.M.R., E.O., and B.L. constructed, purified and characterized VLPs, constructed and purified adenovirus, and performed neutralization assays. Y.T. and T.S. performed electron microscopy and spike quantitation. I.D., M.S., I.B. and R.W.S. designed the VLP SOSIP constructs. L.W.M., M. Connors, and T.M. designed and managed the production of NDV VLPs. E.C. and J.L. performed statistics. J.L.T., R.N.L., A.S.T., G.O., and A.B.W. performed EMPEM analyses. G.D., L.S., S.N., B.L., and M. Castro performed antibody binding assays. J.G. and R.W. designed the VLP NFL-TD constructs. K.M. and M. Connors wrote the manuscript.\n\nCorrespondence to\n Mark Connors.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Carmen Gomez, Bin Ju and Robert J. O\u2019Connell for their contribution to the peer review of this work. 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Antigen persistence and TLR stimulation contribute to induction of a durable HIV-1-specific neutralizing antibody response.\n Nat Commun 16, 5162 (2025). https://doi.org/10.1038/s41467-025-60481-2\n\nDownload citation\n\nReceived: 05 December 2024\n\nAccepted: 23 May 2025\n\nPublished: 03 June 2025\n\nVersion of record: 03 June 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60481-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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functions for accelerated molecular dynamics on near-exact electronic surfaces", + "journal": "Nature Communications", + "published": "26 February 2025", + "supplementary_0": [ + { + "label": "Supplementary information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57134-9/MediaObjects/41467_2025_57134_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57134-9/MediaObjects/41467_2025_57134_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57134-9/MediaObjects/41467_2025_57134_MOESM3_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-57134-9#MOESM3", + "https://doi.org/10.5281/zenodo.14532437", + "/articles/s41467-025-57134-9#ref-CR101", + "/articles/s41467-025-57134-9#Sec15" + ], + "code": [ + "https://github.com/BoothGroup/evcont", + "/articles/s41467-025-57134-9#ref-CR102" + ], + "subject": [ + "Computational chemistry", + "Method development", + "Molecular dynamics", + "Quantum chemistry" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3976196/v1.pdf?c=1740661576000", + "research_square_link": "https://www.researchsquare.com//article/rs-3976196/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-57134-9.pdf", + "preprint_posted": "15 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "While there have been many developments in computational probes of both strongly-correlated molecular systems and machine-learning accelerated molecular dynamics, there remains a significant gap in capabilities in simulating accurate non-local electronic structure over timescales on which atoms move. We describe a practical approach to bridge these fields by interpolating the correlated many-electron state through chemical space, whilst avoiding the exponential complexity of these states. With a small number of accurate correlated wave functions as a training set, we demonstrate provable convergence to near-exact potential energy surfaces for subsequent dynamics with propagation of a valid many-body wave function and inference of its variational energy whilst retaining a mean-field computational scaling. This represents\r\na profoundly different paradigm to the direct interpolation of properties through chemical space in established machine-learning approaches. We combine this with modern electronic structure to systematically resolve molecular dynamics, highlighting qualitative improvements from traditional machine-learned potentials or dynamics on mean-field surfaces.Physical sciences/Chemistry/Theoretical chemistry/Computational chemistryPhysical sciences/Chemistry/Theoretical chemistry/Molecular dynamicsPhysical sciences/Chemistry/Theoretical chemistry/Quantum chemistryPhysical sciences/Chemistry/Theoretical chemistry/Method development", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SuppInfo.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "While there have been many developments in computational probes of both strongly-correlated molecular systems and machine-learning accelerated molecular dynamics, there remains a significant gap in capabilities in simulating accurate non-local electronic structure over timescales on which atoms move. We develop an approach to bridge these fields with a practical interpolation scheme for the correlated many-electron state through the space of atomic configurations, whilst avoiding the exponential complexity of these underlying electronic states. With a small number of accurate correlated wave functions as a training set, we demonstrate provable convergence to near-exact potential energy surfaces for subsequent dynamics with propagation of a valid many-body wave function and inference of its variational energy whilst retaining a mean-field computational scaling. This represents a profoundly different paradigm to the direct interpolation of potential energy surfaces in established machine-learning approaches. We combine this with modern electronic structure approaches to systematically resolve molecular dynamics trajectories and converge thermodynamic quantities with a high-throughput of several million interpolated wave functions with explicit validation of their accuracy from only a few numerically exact quantum chemical calculations. We also highlight the comparison to traditional machine-learned potentials or dynamics on mean-field surfaces.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The quantum fluctuations of interacting electrons represent the critical interaction between atoms which underpin all atomic bonding, dynamics, and reactivity. Computational approaches for systems with strongly interacting electrons have undergone a number of major developments in recent decades, as emerging methods enable a description of correlated electronic structure for ever larger and more realistic systems with unprecedented accuracy1,2,3. These modern approaches across both chemical and materials science include those based on tensor networks4,5,6,7,8, stochastic methods9,10,11,12, selected configuration interaction13,14,15 and machine-learning-inspired wave function ansatze16,17,18,19,20,21,22,23,24,25. This has allowed for the near-exact solution to the quantum many-electron problem in these systems, providing high-accuracy insights for a few fixed atomic configurations, but have in general had little or no impact on our understanding of the physics and chemistry on the timescales of atomic and molecular motion.\n\nThe reasons for this are obvious; while a small number of single-point calculations with fixed nuclei are possible, the different timescales of atomic dynamics and electronic quantum fluctuations mean that on the order of at least thousands of sequential electronic structure calculations are required. This is essential to propagate the atoms in molecular systems to relevant timescales, entailing generally prohibitive computation expense for these high-accuracy methods. This is particularly challenging for these emerging methods which can lack a \u201cblack-box\u201d use, requiring care to ensure reliable convergence at each point, while often also lacking analytic atomic forces to propagate the nuclear coordinates in time26. Important developments have been made in recent years in extending the application of established ground-state quantum chemical models to atomic dynamics27,28,29,30,31,32, while \u201cactive space\u201d methods are also increasingly widely used for stronger correlation or excited state molecular dynamics33. However, the additional cost of these approaches has meant that \u201cab-initio Born-Oppenheimer molecular dynamics\u201d (AI-BOMD), where the atoms are classically propagated according to the potential energy surface of the electrons, is almost synonymous with a more empirical density functional description of the electronic structure which lacks systematic improvability and has many well documented deficiencies34,35,36. These include an over-stabilization of delocalized electronic states, as well as often inaccurate descriptions of dispersion forces, transition states, or bond-breaking among others37. These are critical parts of the phase space in real chemical dynamics, and the acute need for more reliable potential energy surfaces which build on the developments in accurate electronic structure is clear.\n\nThe most widespread and successful resolution to this need has come from a machine-learning approach to force fields38,39,40,41. These interpolate across chemical space between accurate single-point estimates of the electronic energy, based on local descriptors of the environment of each atom42. While this approach to straddling the electronic and atomic timescales has been arguably one of the most successful contributions of machine learning to quantum-level simulations to date, it is not without its own drawbacks43. In particular, the local nature of the descriptors can lead to difficulty describing long-range interactions44, as well as \u201choles\u201d where non-variational inferred energy estimates can lead to a collapse in the statistical sampling of phase space to these unphysical minima45. On a more fundamental level, since these approaches integrate out the electronic structure, there is in general no fundamental electronic variable at each sampled point (such as the wave function), meaning that the electronic properties which can be extracted are limited to the ones which correspond to the model definition. If the evolution of e.g., the dipole moment or charge distribution across a trajectory was desired this would not be accessible from a force field, and extensions to non-adiabatic effects are also far from straightforward in this framework, noting however significant recent research in these directions46,47,48,49.\n\nWe take a different perspective and show that rather than interpolating observables such as the potential energy, we can instead interpolate the many-body electronic wave function itself through the phase space of molecular conformations. Importantly, despite the many-body wave function of each training point being in general exponentially complex, inference of properties from the model can be achieved in a scaling which is the same as (hybrid) density functional theory, rendering this a practical scheme. This decouples the unfavorable scaling of high-accuracy single-point electronic structure calculations from the evaluation of the interpolated potential energy surface, and thus allows for the use of these electronic structure methods for molecular dynamics on realistic timescales. We show that the resulting potential energy surfaces and molecular dynamics are systematically improvable to near-exactness via interpolation between highly accurate training configurations. Since a valid correlated many-electron state is propagated through the sampled phase space, this paradigm enables all electronic properties of interest to be simultaneously accessible within the same model, without relying on local or low-rank descriptors. Furthermore, since the energy is computed as a rigorous quantum expectation value over this inferred state, it provides a fully variational potential energy estimate (precluding \u201choles\u201d) for all atomic configurations, allows for clear evidence of systematic improvability to exactness as the training set is enlarged, an inductive bias of the model away from poorly described regions of phase space, and simple access to analytic atomic forces of the model for efficient propagation of dynamics.\n\nWe combine this approach for interpolating wave functions with modern density matrix renormalization group (DMRG) methods, allowing convergence of the strongly correlated potential energy surfaces to near exactness within the employed basis4,5. We demonstrate that this can provide a fully correlated electronic description of reactive molecular dynamics beyond traditional parameterized or machine-learned force fields, and ensembles of thermalized trajectories for equilibrated quantities over time scales which would be inaccessible without this acceleration. We show this can result in qualitative differences in behavior for a number of proto-typical molecular dynamics simulations compared to both density functional and traditional machine-learned force field approaches50. Finally, we compute both thermalized expectation values from canonical emsembles and reactive high-energy dynamical trajectories on a near-exact potential surface for the Zundel cation, a key intermediate for the Grotthuss mechanism for hydrogen diffusion through aqueous solutions29,51,52. With explicit validation of the accuracy of the surface, we compare the dynamics to both density functional theory results and other quantum chemical methods for both structural and electronic quantities, highlighting marked differences which can result from the quality of the surface.", + "section_image": [] + }, + { + "section_name": "Results and discussion", + "section_text": "We first consider how to interpolate a single many-electron wave function between two different atomic configurations. We assume that we have an exact (FCI) correlated many-electron state defined within an atom-centered basis set of L functions, for a specific set of atomic coordinates R53. This wave function is a linear superposition over exponentially many electron configurations (Slater determinants) spanning the Hilbert space, as\n\nwhere \\({C}_{{n}_{1},{n}_{2},\\ldots,{n}_{L}}\\) is the rank-L tensor of probability amplitudes over the electronic configurations, and ni indexes the four local Fock states of the ith orbital; either unoccupied, spin-up, spin-down, or doubly occupied with electrons for each orbital. In general, both the probability amplitudes, and the single-particle orbitals defining each electronic configuration \\(\\left\\vert {n}_{1},{n}_{2},\\ldots,{n}_{L}\\right\\rangle\\) will change with atomic configuration R. However, we aim to represent an approximation to the correlated electronic state at a different atomic configuration (and therefore electronic Hilbert space) with the same tensor of probability amplitudes over these electronic states. We exploit the fact that the properties of the exact state are invariant to orthogonal rotations of the single-particle orbitals, but that the probability amplitudes themselves will vary with this choice. Therefore, to enable transferrability between chemical environments, we seek a choice of orbital representation in which the probability amplitudes of the exact many-electron state change least between atomic configurations of interest.\n\nA plausible choice is a basis of local atomic-like functions, appealing to the fact that a large portion of the electronic fluctuations among atomic-local orbitals will remain qualitatively similar as atoms are moved by small amounts. Similarly, regions of similar chemical bonding will also have common features in their probability amplitudes defining e.g., covalent fluctuations between neighboring atoms54. However, for reasons which will become clear, we also require that the orbitals represent an orthonormal set for all atomic configurations. To ensure this, while (in a least-squares sense) optimally preserving this atomic-like character of the orbitals, we symmetrically (L\u00f6wdin) orthonormalize the atomic-orbital basis (see Methods), defining orthonormal \u201cSAO\u201d orbital sets for each atomic configuration55,56. We can then choose to interpolate the state (and all resulting properties) between atomic configurations without re-optimizing the many-electron state by simply transferring the probability amplitudes, while ensuring the consistent SAO basis definition.\n\nThis simple approach is limited, since the many-body amplitudes will in general change as the atoms move. However, we can generalize the state while retaining a valid wave function by linearly combining probability amplitude tensors in this transferable SAO representation from a larger \u201ctraining\u201d set optimized at other atomic configurations. We then variationally optimize the relative contributions of each of the N training states for any test atomic configuration. This is achieved in closed form as the diagonalization of a generalized eigenvalue problem in the basis of the training states. By projecting the Hamiltonian at the desired test geometry R into this many-body basis, we get\n\nwith the eigenvectors, X(R), giving the amplitudes of the training states defining the interpolated wave functions at the test geometry, with inferred energy spectrum E(R). The electronic Hamiltonian of the test geometry, \\({{\\boldsymbol{{{\\mathcal{H}}}}}}({{\\bf{R}}})\\), is found by projecting the Hamiltonian operator into the many-body basis defined by the fixed probability amplitudes of the training states. This can be found in compact form as\n\nwhere n denotes the many-electron configurations in the SAO basis of the test geometry, with \\(\\left\\vert {{\\bf{n}}}\\right\\rangle \\equiv \\left\\vert {n}_{1},{n}_{2},\\ldots,{n}_{L}\\right\\rangle\\), and with \\({C}_{{{\\bf{n}}}}^{(a)}\\) and \\({C}_{{{{\\bf{n}}}}^{{\\prime} }}^{(b)}\\) the fixed SAO probability amplitudes of the training wave functions at atomic geometries a and b respectively. This definition therefore implicitly transfers the probability amplitudes between the Hilbert spaces of the training and test geometries. The Kijkl(R) tensor is the two-electron reduced Hamiltonian defined in the SAO basis of the test geometry (given explicitly in the \u201cMethods\u201d section) with second-quantized Fermionic operators acting in this basis as \\({\\hat{c}}^{({\\dagger} )}\\) shown.\n\nSince the Hamiltonian is a sum of only two-electron interactions, the contraction over the exponential many-electron configurations, n, is performed for all pairs of training states to give the transition two-body density matrices \u0393ijkl defined in Eq. (3). Crucially, since the training probability amplitudes are defined not to change with test geometry, this contraction is only performed once on the training states, rather than for each test geometry. The construction of the subspace Hamiltonian at a test geometry therefore only requires the \\({{\\mathcal{O}}}[{L}^{4}]\\) contraction of Eq. (3), with only the Kijkl(R) term changing with test configuration. The overlap between the training states, \\({{\\boldsymbol{{{\\mathcal{S}}}}}}\\) of Eq. (2), can similarly be precomputed during the training phase, as\n\ndue to the orthonormality of the SAO basis at all test geometries. This ensures that the overlaps of the training states do not change with geometry, despite the physical training wave functions changing with atomic rearrangements as they transform between Hilbert spaces. In this way, the exponential complexity of the many-electron states are completely avoided in the inference of wave functions at new test points, by representing the training states in the polynomially-compact tensors \u0393ijkl and their overlaps. The inference of the model requires a computational scaling of non-iterative \\({{\\mathcal{O}}}[{N}^{2}{L}^{4}]\\) after the density matrices of the training states have been precomputed in the training stage\u2014the same formal scaling with system size as traditional (hybrid) density functional theory. This scaling for evaluation of the model at test geometries could also be further lowered with factorizations exploiting the low-rank nature of the \\({\\Gamma }_{ab}^{ijkl}\\) tensors57,58,59,60.\n\nThe lowest energy eigenvector from the diagonalization of this Hamiltonian (whose dimensionality scales only with the number of training points and is independent of system size) defines the specific variationally optimal linear combination of training probability amplitudes for the state, which can subsequently be used to predict any electronic property at this test geometry. Due to the variationality we have the desirable properties that the inferred state at a point which coincides with a training geometry must necessarily be exact, as well as the fact that each additional training point must necessarily lower the inferred energy towards the exact electronic solution across all possible test atomic configurations, assuming linear independence. In this way, the method more closely resembles a reduced order method than a machine learning model, where we define the Hamiltonian in a subspace defined by a fixed set of many-body vectors taken from training wave functions at different geometries, yet both are useful viewpoints. Due to the fixed training amplitudes across geometries, as well as the variational optimization of the model, computing analytic atomic forces from the inferred state is also straightforward (see Methods), ensuring a particular relevance of this acceleration in molecular dynamics applications.\n\nThis approach also builds on the perspective of \u201ceigenvector continuation\u201d which was recently introduced in both nuclear physics and condensed matter lattice models, where an eigenstate is analytically continued to different parts of the phase diagram61,62,63,64,65,66,67,68. Even more recently this was extended to simple ab initio quantum chemistry applications, with a related scheme to the one proposed54. However, a crucial difference was the use of a non-orthogonal atomic basis, which necessitated evaluation of the test point Hamiltonian directly from the many-body states. This retained the exponential complexity of the many-body state for inference at each test geometry, which is avoided here. The method of Mejuto-Zaera et al. was therefore presented instead as an approach for quantum computers, where many-body unitary operations can be applied in polynomial complexity. In contrast, the SAO basis for the interpolation formally breaks this requirement, ensuring the approach is amenable to classical computation in the predictions at test points with tractable mean-field computational cost.\n\nA simple example of the scheme is shown in Fig.\u00a01, for the symmetric stretch of a chain of six hydrogen atoms, with up to three atomic displacements considered in the training set. The compressed intermediate representation of the overlaps and transition density matrices between the training states in the SAO basis are shown, enabling variationally optimal predictions across the whole potential energy surface as linear combinations of the many-body training basis transformed between geometries. The predictions are found to converge to near exactness for this system with only three training points, with a guarantee of smoothness on adiabatic surfaces, and exactness at any training point geometry.\n\na Three many-electron training wave functions via exact diagonalization at different geometries in a L\u00f6wdin atomic orbital (SAO) basis of a linear 6-atom hydrogen chain. The values of \u3008n\u2223\u03a8(a)\u3009 show the (exponentially many) wave function amplitudes for each training state. Geometry-agnostic one- and two-body transition density matrices (\u0393ijkl) and overlaps (\\({{\\boldsymbol{{{\\mathcal{S}}}}}}\\)) are constructed between all pairs of training states (b), which allows for fast variational prediction of the potential energy surfaces at arbitrary test geometries in the many-body basis of these training states (c). This allows efficient inference of wave functions at each test geometry, as shown in (c.1), with its associated ground state on the basis of the three training states. Plots in (c.2) show that enlarging the training space from one to three geometries systematically converges the full symmetric stretching mode of this system to the exact diagonalization result, with training data points where explicit electronic structure calculations are performed denoted by crosses. Source data are provided as a Source Data file.\n\nThis approach is invariant to translation and rigid body rotations of the chemical system, provided a consistent ordering of the SAO representation is maintained, which is straightforward to achieve. However, this is not trivial for atomic permutations or point group symmetries which would change the SAO ordering, and hence probability amplitudes of the state definition. Furthermore, in contrast with building a force field based on local descriptors, the inference requires the same dimensionality Hilbert space for the electronic state, necessitating that the training and prediction points are taken from the same sized system. This is a significant difference to force field approaches with local representations which allow for scaling the system size after training, ensuring a different scope of applicability to the proposed approach42. Future work will look to relax this constraint.\n\nWhile the proof-of-principle in Fig.\u00a01 demonstrates excellent accuracy with few training points, it is also interpolation within a simple one-dimensional phase space of geometries. We now compare to a far larger phase space, composed of averaging the errors in both the inferred energy and analytic forces on the atoms over randomly oriented three-dimensional displacements of each atom from a ten-atom linear hydrogen chain. This provides an exponentially large phase space of distorted chain configurations to test, where the radius of the displacements of each atom can be used to control the magnitude of the geometric distortions from the parent linear chain from which the training data is obtained. Only five training points from the symmetric stretch of the equidistant linear chain are used. We consider the increase in error as the magnitude of the displacements is increased in Fig.\u00a02, as the test configurations move further from these training samples. We also compare these errors to a Gaussian approximated potential (GAP); a widely used machine-learning approach based on Gaussian process regression in a space of local descriptors from the superposition of atomic potentials50,69. This models a force field directly from the same training energies, but results in a materially larger error for the energy and forces over all displacements. We note that five points would generally be a very small training set for GAP, and that improved techniques to directly train on the forces of the training data themselves or improved model definitions were not used70,71.\n\nFor each realization, a distorted chain was created by moving each atom from their position in the equilibrium geometry by a fixed displacement with a random direction. The comparison includes predictions from Hartree-Fock (\u201cHF\u201d, red, dotted), the Gaussian approximation potential framework (\u201cGAP\u201d, orange, dashed), as well as the variational continuation scheme from 5 training states of the symmetrically stretched chain (\u201cContinuation\u201d, blue, solid). Each data point corresponds to the mean over 1000 randomly generated geometries. The inset shows the mean squared force error obtained with the three methods, where the shaded area denotes the range of the errors over the random realizations. The training set of equidistant one-dimensional geometries include the equilibrium length, with an interatomic distance of \u00a0\u22481.79\u2009a0, as well as the 4 symmetric stretches of the atoms where the inter-atomic distance was increased and decreased by 0.5\u2009a0 and 1\u2009a0. Source data are provided as a Source Data file.\n\nNevertheless, a demonstration that inferring the wave function amplitudes themselves can outperform traditional machine-learning inference of the properties directly is noteworthy. Furthermore, we compare to Hartree\u2013Fock theory (HF), which neglects all correlated electron effects and has the same computational scaling as the inference of the proposed \u201ceigenvector continuation\u201d scheme. This is also significantly worse at small distortions of the chain, though outperforms the largest distortions which are far from the training geometries and deep in the extrapolation regime.\n\nWhile it is easy to envisage many applications of an interpolation scheme for accurate correlated electronic structure, an obvious target is Born-Oppenheimer molecular dynamics (MD)36. In particular, the variationality of the scheme allows for systematic and quantifiable improvability to the exact solution of the electronic Schr\u00f6dinger equation in the inferred potential energy surface at each geometry, while retaining a mean-field scaling with respect to the timescales which can be accessed. To access larger systems and basis sizes we also turn to modern electronic structure approximations for the evaluation of training states. In particular, we use DMRG to obtain training states with controllable accuracy to exactness4,5. These DMRG calculations can either be performed directly in the SAO basis or the state rotated into this basis after optimization, in advance of computation of the required overlaps and transition density matrices between the training states. As an alternative to DMRG, we are also able to approximate the training states by restricting the space of correlations to a low-energy complete active subspace (CAS) selected from the low-energy orbitals of a mean-field calculation72.\n\nWe consider these approaches for constructing training states and the subsequent MD of a water molecule in increasing basis sets in Fig.\u00a03. In particular, we consider convergence of the predicted vibrational frequency of the a1 symmetric stretching mode as the number of training points increases. For the smallest basis, we find the full vibrational dynamics converge with just three training points, where we can compare directly to exact FCI calculations of the dynamics. As we increase the basis, FCI is intractable and we restrict the training to a CAS of low-energy orbitals, where the number of training points required grows modestly to seven and thirteen training points in a cc-pVDZ and cc-pVTZ basis respectively. We validate the specific trajectories found in Fig.\u00a04, showing the difference between the inferred and reference energies at every time step. For the DMRG-based continuation in the 6-31G basis, we find that increasing the number of training states rapidly converges the full trajectory, with N\u00a0=\u00a06 training states achieving an accuracy well below 10\u22124\u2009Eh across all points.\n\na Predicted frequency of the a1 symmetric stretch. Trajectories were started from a stretched initial configuration, and predicted with increasing numbers of training data geometries. We simulate the system in increasingly large 6-31G (blue, dashed), cc-pVDZ (orange, dotted), and cc-pVTZ (red, dash-dotted) basis sets where the larger two bases use training data restricted to a complete active space (CAS) of 4 electrons in 8 Hartree--Fock orbitals. Horizontal lines give reference values from trajectories on a FCI surface in the 6-31G basis, and CAS simulations in the cc-pVDZ and cc-pVTZ basis. b Oxygen-hydrogen distance over the trajectory in the 6-31G basis, as obtained from continuation with N\u00a0=\u00a06 (blue, solid) and N\u00a0=\u00a02 (red, dotted) training points, as well as the reference trajectory from full configuration interaction (\u201cExact\u201d, black, dashed). Source data are provided as a Source Data file.\n\na Error compared to exact diagonalization of density matrix renormalization group (DMRG) trained eigenvector continuation with increasing data set (N) in a 6-31G basis. b Difference between (4,\u00a08) complete active space (CAS) trained eigenvector continuation and independently computed CAS energies at each geometry along the trajectory for N\u2009=\u20099 training points in a cc-pVDZ basis, and N\u00a0=\u00a014 training points in a cc-pVTZ basis. We stress that variationality with respect to this approximate training data is not expected, enabling the continued energies to be lower than the reference method, as shown. Source data are provided as a Source Data file.\n\nThe variationality of the method guarantees that the predicted energies are always an upper bound to the exact ground state energy, at any geometry. When the continuation is based on approximate training wave functions, the inferred linear combination of training states may result in an improved energy compared to the reference, since it can mix contributions to the test state from other training geometries. This is even true when considering a geometry corresponding specifically to a training state. This is exemplified in the bottom panel of Fig.\u00a04, detailing the energetic difference between the prediction and CASCI energies used for the training data along the trajectory in cc-pVDZ and cc-pVTZ basis sets. While the same active space sizes were used for the training states as for the computation of the reference energies, the inferred energies generally lie variationally below the CASCI reference energies. Although this improvement is small, mostly less than 1\u2009mEh, it is obtained for the majority of\u00a0geometries over the converged trajectory, ultimately improving the accuracy beyond what is obtained with the reference method. As a further noteworthy difference between the reference CASCI and interpolated results based on CASCI training, for a small path of the trajectory between 24 and 25\u2009fs in the cc-pVTZ basis of Fig.\u00a04, a much more significant improvement of the continuation results over the reference method becomes apparent. This was found to be caused by a discontinuity in the CASCI ground state energies due to a change of orbitals included in the active space for these geometries. A more careful choice of active space is likely to have alleviated this problem in the reference trajectory, but we highlight it here since it is clear that this discontinuous change does not affect the interpolated surface. In contrast to the reference method on which it is trained, the continued results necessarily change smoothly with geometry, therefore mitigating a significant challenge in the use of active space methods in molecular dynamics.\n\nIt was found important to develop an active learning scheme for the selection of appropriate atomic configurations to include in the training data for rapid convergence. In ref. 54, the energy variance was motivated as an appropriate measure for the inclusion of data points, however this is impractical in the current lower-scaling scheme as it would require the evaluation of higher-body transition density matrices between training states. Instead, we consider the addition of training points which will maximize the improvement in the MD trajectories while respecting the invariances in the model predictions. This is performed by selecting the point on the trajectory where the Hamiltonian operator in the SAO basis, Kijkl(R), has changed most (in the least squares sense) compared to the Hamiltonians employed to generate the current training data set. Since the probability amplitudes are uniquely defined by this Hamiltonian, it is a suitable measure for the addition of new data points. Furthermore, due to the variationality of the method, it is guaranteed that the potential energy with the enlarged training data will be equal or lower to the previous predictions, across the whole trajectory. This can therefore be used as a rigorous metric for the systematic convergence of the potential energy surface for the MD, with more details in the \u201cMethods\u201d section. We consider the potential energy surface over the whole MD simulation fully converged when the maximum reduction in energy for any point over the whole trajectory is less than 1\u2009mEh for two consecutive increases in the data set size.\n\nA semi-infinite symmetric one-dimensional chain of hydrogen atoms has emerged as a paradigmatic benchmark system of strongly correlated electronic structure in recent years, as a platform towards larger ab initio and extended systems. Almost all modern electronic structure methods have been applied to the system with varying success, and it has motivated further developments in both theory and understanding of its unexpectedly rich phase diagram1,73. While the symmetric stretch of this system has been considered extensively via single-point electronic structure, its full dynamics at this level have not. In Fig.\u00a05, we release the atoms to dynamically move on a tightly-converged ground state surface (see Fig.\u00a0S1 of the supplementary information for validation of the energy accuracy) of the DMRG-trained continuation scheme, starting from a \u00a0~\u00a010% symmetric stretching of thirty atoms equally from the symmetric equilibrium structure. We find that along with the vibrations of the bonds, the atoms rapidly dimerize and separate, with the overall length of the chain increasing approximately linearly with time. We are able to converge the dynamics of this dimerization and dissociation (albeit in a minimal basis) to the equivalent explicit DMRG AI-BOMD with only a small number of single-point training DMRG calculations. We note that in comparison, DFT-based AI-BOMD significantly underestimates the rate of dimerization of the chain, while Hartree\u2013Fock theory conversely results in a bond for the hydrogen dimers which is too stiff, demonstrating the importance of an accurate treatment of the electronic correlations in the dynamics.\n\nThe panels report the time-dependent Euclidean distance between two of the hydrogen atoms; a first and last atom in the chain, \u2223\u2223R1\u00a0\u2212\u00a0R30\u2223\u2223, b first and second atom, \u2223\u2223R1\u00a0\u2212\u00a0R2\u2223\u2223. This shows that the first two hydrogen atoms form a stable vibrating dimer while the overall chain lengthens. The trajectories were obtained from the eigenvector continuation (\u201cContinuation\u201d) with N\u00a0=\u00a047 (blue), N\u00a0=\u00a010 (orange) and N\u00a0=\u00a02 (red) training points, together with the trajectories from density matrix renormalization group (\u201cDMRG\u201c, black, dashed), Hartree-Fock (\u201cHF\u201d, brown, dotted) and density functional theory with PBE exchange correlation (\u201cDFT\u201d, light blue, dash-dotted) potential energy surfaces. Additional snapshots shown depict the initial and final hydrogen chain arrangements obtained from the converged eigenvector continuation (N\u00a0=\u00a047). Source data are provided as a Source Data file.\n\nWe consider the feasibility of converging faithful thermodynamic quantities and reactive chemistry on a near-exact potential energy surface for the gas-phase dynamics of a Zundel cation, comprising a water molecule and hydronium ion\u2014a system whose intricate potential energy surface poses a challenging test case for novel numerical techniques, yet is particularly important for the understanding of proton diffusion in aqueous solution29,74,75,76,77,78. We first consider a statistical ensemble of 500 different trajectories, starting from the same geometry (taken from ref. 79), and sampling initial velocities from a Maxwell\u2013Boltzmann distribution at 298.15\u2009K. The BOMD was propagated under NVT conditions to thermalize according to a Berendsen integration scheme80. We consider N\u00a0=\u00a060,\u00a080, and 100 single-point DMRG training configurations to observe the convergence of the thermodynamically equilibrated properties on a 6-31G basis. Each ensemble of trajectories at one of these training numbers involved 5\u2009\u00d7\u2009106 potential energy and force evaluations, which would be out of reach with a brute-force DMRG approach, but required a relatively modest 7500 CPU hours for the propagation of the full ensemble. Nevertheless, we can explicitly verify convergence to the accuracy of the underlying DMRG by a validation of the \u201ctest error\u201d via additional DMRG calculations for sampled geometries along the trajectories. The achieved test error, shown in Fig.\u00a0S2 of the supplementary information, demonstrates that the PES is well below chemical accuracy of the exact potential energy surface within the employed basis as the thermal equilibrium is approached, reaching relative correlation energy errors below that of both CCSD and CCSD(T) \u2013 the \u201cgold standard\u201d of quantum chemistry81.\n\nFigure\u00a06 shows this thermalization in the average distance between the central hydrogen atom and the two oxygen atoms in the explored Zundel configurations. We find this statistically equilibrated distance to be converging to a slightly shorter length than CCSD as the number of training configurations is increased. An accurate description of this multi-center bond is key for the Grotthuss mechanism of proton transfer. The differences in these quantities are in stark contrast to the much shorter distances predicted by DFT MD simulations with two widely used exchange\u2013correlation functionals, which indicate a more localized central hydrogen. We can observe this in the radial distribution function of the equilibrated configurations (bottom panel) where the distribution is far flatter than the DFT methods, indicating an increased delocalization of the hydrogen between the water subunits. This is further corroborated by considering the magnitude of the dipole moment from the thermalized ensemble (see supplementary information, Fig.\u00a0S3), which we find decreases as the level of theory is increased from DFT to CCSD to the DMRG-interpolated configurations, indicating a preference for more symmetric distributions where the central hydrogen is delocalized and less bound at any instant to an individual oxygen atom.\n\na Mean distance as a function of propagation time obtained by interpolating from N\u00a0=\u00a060,\u00a080,\u00a0100 density matrix renormalization group training geometries, as well as results from density functional theory (\u201cDFT\u201d with CAM-B3LYP103 and PBE104 exchange-correlation functionals) and coupled-cluster with singles and doubles (\u201cCCSD\u201d) trajectories. The mean corresponds to a running average of the distance between the atoms with a window of 100 timesteps (\u224860.4\u2009fs), and averaging over 500 independent trajectories and both oxygen atoms. Each emsemble of trajectories required 5 million energy and force calculations. b Thermalized radial distribution function of the oxygen from the central hydrogen, using Gaussian smearing of individual data points in a kernel density analysis105,106, with a bandwidth of \u03c3\u00a0=\u00a00.0025\u2009\u00c5. Source data are provided as a Source Data file.\n\nThe verifiably high-accuracy interpolation coupled with the high-accuracy DMRG training allows for validation in the use of CCSD for this system, with thermalized expectation values qualitatively in agreement. This is due to the lack of strong correlation in the explored molecular configurations. However, a significant advantage of this framework is the ability to also reliably converge the PES over the full phase space, including strongly correlated atomic configurations further from equilibrium where CCSD is unreliable and will potentially fail, including bond-breaking and transition state geometries.\n\nTo consider this scenario, we also propagate a single high-energy trajectory within an NVE ensemble far from the Grotthuss mechanism dynamics, where the additional proton is inserted between the water monomers, interrupting the traditional hydrogen bond framework with a four-atom bridging bond as shown in the initial snapshot of Fig.\u00a07. Increasing the number of DMRG training points to N\u2009=\u200984, we are able to observe convergence in the specific short-time MD trajectory over the 120\u2009fs of the simulation (see Fig.\u00a0S4 of the supplementary information for evidence of this convergence with training data over the trajectory). Due to the fact that an explicit representation of the electronic state is retained over the trajectory, we also extract non-energetic electronic properties of the system over time. We use this to consider the evolution of the Mulliken charge as the electron density is redistributed around the system in response to the atomic motion, beyond the physics considered in traditional polarizable force fields.\n\nPanels show the predicted Mulliken charges for the hydronium (a) and water (b) sub-units from a dynamical simulation of the reaction from eigenvector continuation with N\u00a0=\u00a040 (orange) and N\u00a0=\u00a084 (blue) training points from density matrix renormalization group. The system uses a 6-31G basis, with snapshots depicting the evolution of the molecular geometry at four evenly spaced times. Reference charges from simulations with density functional theory with a B3LYP exchange correlation (\u201cDFT\u201d, light blue, dash-dotted) and Hartree\u2013Fock (\u201cHF\u201d, brown, dotted) are included for comparison. Source data are provided as a Source Data file.\n\nThe positive charge is initially fairly evenly distributed amongst the water monomers, with the anticipation being that the water would rotate to adopt a lower-energy configuration. However, on the DMRG interpolated PES we find that before this is able to occur, a (neutral) hydrogen is ejected from the system leaving a bound hydronium and hydroxide radical system. The charges on the sub-units of this reaction show the redistribution of charge as the hydrogen oscillates a number of times before its eventual ejection from the system. This behavior is not seen on the more approximate HF or DFT electronic surfaces where the additional hydrogen remains bound over timescales enabling the water to rotate its orientation, with the HF charge distribution substantially in error even in the initial state. Comparing to CCSD across the N\u00a0=\u00a084 trajectory, we find \u00a0\u2248\u00a040 atomic configurations visited result in the CCSD energy diverging due to the presence of strong correlation effects, underlining the unreliability of the method for MD in these more unusual atomic conformations and transition states where strongly correlated electronic structure is found. This behavior is discussed further in\u00a0the \u00a0supplementary information, but underlines the applicability of the continuation across the phase space of the MD and the potential to describe dissociative dynamics68.\n\nWhile these results demonstrate an effective acceleration scheme to converge the energy surface of this system to that of high-accuracy methods, more consideration of the effects of basis size, nuclear quantum and solvent effects may be needed before predictions as to the nature of this physical reaction can be given with confidence29,30. However, the fact that qualitative changes in dynamical behavior already result from the quality of the treatment of electronic correlation effects in the determination of the potential energy surface underlines the importance of a robust and systematically improvable approach to this electronic structure. The eigenvector continuation acceleration allows computation of this surface with high-level quantum chemical methods, and extends their scope to enable them to access timescales of atomic dynamics with provable convergence.\n\nWe develop a practical approach for eigenvector continuation of many-body electronic wave functions in ab initio settings. In contrast to the traditional paradigm of machine-learning force fields from training energies, this considers the interpolation of accurate wave functions across the space of structural changes, from which all electronic properties as well as atomic forces can be efficiently computed via a variational ansatz, avoiding the exponential complexity of the many-body states themselves. Using the scheme to converge the potential surface for molecular dynamics, we find examples of qualitatively different behavior to state-of-the-art techniques, demonstrating the importance of systematically converging the electronic structure across the timescales.\n\nThe acceleration scheme therefore holds huge potential to extend the scope of modern highly accurate electronic structure to molecular dynamics applications. However, the potential of reliable wave function interpolation also goes beyond this, towards a consideration of non-adiabatic and beyond-Born\u2013Oppenheimer effects, efficient geometry optimization for ground, transition states or conical intersections, as well as a general procedure for vibrations and phonons, raising the possibility of the routine extraction of thermodynamic variables from accurate quantum chemistry. The move from single-point electronic internal energies to (thermo)dynamical quantities within correlated electronic structure theory is a long saught-after ambition82. The use of wave function interpolation with developments in solvers for the training data to extend system sizes could bring this closer to reality.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57134-9/MediaObjects/41467_2025_57134_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57134-9/MediaObjects/41467_2025_57134_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57134-9/MediaObjects/41467_2025_57134_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57134-9/MediaObjects/41467_2025_57134_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57134-9/MediaObjects/41467_2025_57134_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57134-9/MediaObjects/41467_2025_57134_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57134-9/MediaObjects/41467_2025_57134_Fig7_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "At the core of the methodology lies the prediction of the ground state electronic energy for given molecular arrangement of Nelec electrons based on few exemplary solutions of the electronic structure problem at different molecular geometries. We define the ab initio electronic Hamiltonian for a 3\u00a0\u00d7\u00a0Nnuc atomic configuration, R, in a discrete basis of electronic orbitals {\u03c7(r;\u00a0R)} as83\n\nwith Fermionic creation and annihilation operators, \\({\\hat{c}}^{{\\dagger} }\\) and \\(\\hat{c}\\) acting on the orbitals, and Enuc(R) the classical nuclear-nuclear repulsion energy. The one-electron terms, \\({h}_{ij}^{(1)}({{\\bf{R}}})\\), are matrix elements of the electron-nuclear and electronic kinetic operators, while the electron-electron repulsion integrals are\n\nA convenient reduced two-body Hamiltonian which subsumes the one-body into the two-body term can be written as84\n\nThe eigenvector continuation proceeds via the definition of a symmetrically (L\u00f6wdin) orthonormalized atomic orbital basis (SAO)55,56. This allows the training wave functions to be transferred between the Hilbert spaces of different geometries by fixing their many-body probability amplitudes in this representation. These SAOs are defined with an orbital transformation of an underlying non-orthogonal atom-centered \u201cAO\u201d orbital basis set at each geometry, {\u03d5\u03b1(r;\u00a0R)}, as\n\nwhere S(R) is the atomic orbital overlap matrix\n\nThe continuation then proceeds according to the scheme outlined in the main text, with the evaluation of the transition two-body density matrices (t-2RDMs) and overlaps between the training points in their SAO representations. Of particular importance for molecular dynamics is the evaluation of analytic forces at each test geometry, which is simplified due to the lack of response contributions from the many-body basis and the fully optimized variational nature of the interpolated states in the geometry-independent basis85,86. This therefore only required the derivatives of the electron integrals in the AO basis87,88,89, as well as the derivative of the transformation from the atomic orbitals to the SAOs with respect to nuclear positions (a \u201cPulay force\u201d90), which we evaluate via first order perturbation theory91. The specifics of this evaluation is given in\u00a0the supplementary information.\n\nRather than relying on exact (FCI) training data, we also consider modern numerically efficient approximations to the correlated electronic structure to allow for access to larger systems, which are nevertheless systematically improvable to the exact solution to the electronic Schr\u00f6dinger equation for training. These require not only the evaluation of accurate many-body wave functions at the training geometries, but also the evaluation of the t-2RDMs and overlaps between different training states.\n\nFirstly, we consider the compression of the training wave functions in the form of Matrix Product States (MPS), optimized via the density matrix renormalization group (DMRG) algorithm92. For this, we used the spin-adapted implementation from the block2 library5,93, working directly in the Fock space defined by the SAOs. We optimize the training states with a schedule for exponentially increasing bond dimension (a factor of 1.8 per increase) and decreasing noise in the MPS, a standard practice for stable ab initio DMRG4, terminating when the difference of the energy upon fully relaxing the state at an increased bond dimension is less than a specified threshold, \u03f5. For all presented results, we set \u03f5\u00a0=\u00a010\u22123\u2009Eh and start the MPS with an initial bond dimension of 34, giving training data confidently below the accepted \u201cchemical accuracy\u201d. For the reference data for the hydrogen chain evolution (Fig.\u00a05), we set the tolerance to \u03f5\u00a0=\u00a010\u22125\u2009Eh, and starting MPS bond dimension to 61.\n\nIn addition to the continuation from MPS training states optimized with DMRG, we also present the use of complete active space (CAS) solvers to access the results of Fig.\u00a03. These give an approximate ground state of the full electronic structure problem according to\n\nwhere \\(\\left\\vert {\\Psi }_{{{\\rm{AS}}}}\\right\\rangle\\) represents the fully variationally optimized state over all many-electron configurations within a chosen active subspace of orbitals and electrons, while \\({\\left\\vert 1\\right\\rangle }^{{N}_{{{\\rm{core}}}}}\\) represents fully occupied orbitals spanning the remaining space of states that are occupied in a mean-field (in this case Hartree\u2013Fock) description of the system, and \\({\\left\\vert 0\\right\\rangle }^{{N}_{{{\\rm{vir}}}}}\\) explicitly indicate that the higher-energy virtual states are unoccupied. In this way, the electronic fluctuations of a low-energy subspace are considered fully, with the choice of active space in this work selected purely based on the mean-field orbital energies about the chemical potential of the system.\n\nWhile this state can be straightforwardly optimized within a \u201cCASCI\u201d scheme implemented in the PySCF package88,89, we also require the evaluation of the overlap and the t-2RDMs between training states in their SAO basis, while the state is defined (and optimized) in a mean-field canonical basis. Therefore, it is necessary to rotate these many-body states into their respective SAO bases before the t-2RDMs and overlaps are computed. We show this for the t-2RDM as\n\nwhere \\(\\vert {\\Psi }_{{{\\rm{CAS}}}}^{(a/b)} \\rangle\\) denotes the CAS states at the different training points and \\({\\hat{U}}_{{{{\\bf{R}}}}^{(a/b)}}\\) is the unitary transformation from the state in the basis of its canonical orbitals to the SAO basis for the corresponding training point. This is evaluated efficiently as a double summation over the active space many-electron configurations (including their core) of each training state\n\nwhere \\({C}_{{{\\bf{n}}}/{{{\\bf{n}}}}^{{\\prime} }}^{(a/b)}\\) are the CASCI probability amplitudes of the active spaces. This single-particle unitary transformation \\({\\hat{U}}_{{{\\bf{R}}}}^{{\\dagger} }\\) can be formed as\n\nwhere Z\u03b1i is the transformation matrix from AO to SAO and \\({\\tilde{Z}}_{\\beta x}\\) is the transformation matrix from AO to canonical Hatree\u2013Fock orbitals, while S\u03b1\u03b2 is the AO overlap matrix. All of these quantities are dependent on the specific training geometry, R.\n\nThe inner products \\(\\langle {{\\bf{n}}}| {\\hat{U}}_{{{{\\bf{R}}}}^{(a)}}^{{\\dagger} }{\\hat{c}}_{i}^{{\\dagger} }{\\hat{c}}_{j}^{{\\dagger} }{\\hat{c}}_{k}{\\hat{c}}_{l}{\\hat{U}}_{{{{\\bf{R}}}}^{(b)}}| {{{\\bf{n}}}}^{{\\prime} }\\rangle\\) from Eq. (14) can be identified as a matrix element between two different non-orthogonal Slater determinants94. The efficient evaluation of such overlaps between different non-orthogonal Slater determinants is discussed in refs. 95,96. We utilize the libgnme package, together with its python interface pygnme, to evaluate the overlaps and t-2RDMs between CAS states for the continuation in the SAO basis. Due to the non-orthogonality of the different CAS spaces, the double contraction of Eq. (14) results in a cost scaling quadratically in the size of the active space, thus more expensive than the evaluation of expectation values of a single point CAS state, however this cost could be reduced in the future by rotating to an intermediate basis representing the co-domain of the occupied CAS orbitals in a pair of CASCI training states.\n\nWe include comparison results obtained from the prediction of potential energies via Gaussian Approximation Potentials (GAP)69\u2014a well-established framework for the prediction of potential energy surfaces and force fields. The model is extracted by fitting a data set of training geometries, \\({\\{{{{\\bf{R}}}}^{(a)}\\}}_{a=1}^{N}\\), with associated energies \\({\\{{E}^{(a)}\\}}_{a=1}^{N}\\) using a kernel model97 incorporating symmetries of atomic environments via the smooth overlap of atomic position (SOAP) descriptors42. We apply the GAP framework following standard approaches from the literature50,70,98, based on the implementation of the SOAP descriptors in the dscribe package99. Additional details of this prediction procedure can be found in the\u00a0supplementary information.\n\nThe single-trajectory Born\u2013Oppenheimer molecular dynamics of Figs.\u00a03, 5 and 7 were computed in vacuum based on a microcanonical (NVE) ensemble using the Velocity-Verlet integration implemented in PySCF36,88,89,100, according to the analytic nuclear gradients derived for the eigenvector continuation in\u00a0the supplementary information. The nuclei in these simulations were initialized at rest, and we chose a fixed timestep of \u03b4t\u00a0=\u00a05\u2009a.u.\u2009\u2248\u20090.121\u2009fs for the integration.\n\nTo extract a thermalized ensemble for the dynamics of the Zundel cation of Fig.\u00a06, we included a room temperate (298.15K) Berendsen thermostat80 as implemented in PySCF to obtain a canonical (NVT) ensemble of trajectories. This scheme relies on an additional rescaling of the velocities after each integration step to achieve an exponential convergence to the target temperature with a timescale \u03c4. Initial velocities for each trajectory were drawn from a Maxwell-Boltzmann distribution, while the nuclei positions were initialized in the ground state geometry obtained from CCSD(T) in a large basis set from ref. 79. The dynamics proceeded with a total of 10,\u00a0000 integration steps with \u03b4t\u00a0=\u00a025\u2009a.u\u00a0\u2248\u00a00.605\u2009fs, and a thermalization time constant of \u03c4\u00a0=\u00a0250\u2009a.u.\u00a0\u2248\u00a06.05\u2009fs. This required 5\u2009\u00d7\u2009106 force calculations to propagate the ensemble of 500 trajectories over the 6\u2009ps timescale considered.\n\nFor the molecular dynamics applications, we perform an active learning scheme in which we identify and select suitable molecular configurations for training the eigenvector continuation scheme on-the-fly. This scheme is based on iteratively running the MD with a given training set, and selecting an enlarged training dataset with a new molecular configuration from the sampled trajectory. A correlated electronic structure calculation is performed at the selected geometry which is then included in the training data for an improved inferred potential energy surface and resulting MD trajectory in the next step. Starting from just a single training state (the initial geometry) and iteratively adding new configurations to the dataset in this way, the number of costly electronic structure calculations can be minimized and the trajectory can be systematically and rapidly converged, noting that adding training geometries from the simulated trajectories guarantees an improved prediction in each step.\n\nTo select the new training geometry, we develop a \u201cdistance\u201d heuristic for all geometries along the trajectory, which can be used as a metric for the addition of the data, and quantifies the suitability of the current training data in describing the test state at each point. The point along the trajectory with the largest measure is added to the training data set. Since the (non-degenerate) ground states along the trajectory are uniquely defined by the ab initio Hamiltonian at each geometry, we use the differences between the Hamiltonian elements at the training points and all trajectory points in defining this measure. Defining these elements in their respective SAO basis of each geometry used for the inference also ensures that the invariances and symmetries of the eigenvector continuation are also respected in this measure. Specifically, we define this Hamiltonian distance between two geometries, \\(D({{\\bf{R}}},{{{\\bf{R}}}}^{{\\prime} })\\), as\n\nIn addition to respecting the symmetries of the model, this ensures that two geometries with similar Hamiltonians (and thus wave functions) are considered similar, even though an evaluation of the Euclidean distance between these two geometries might be large (e.g., for geometries from near a dissociated limit). To add a new configuration, we evaluate D(R(t),\u00a0R(a)) for all geometries R(t) from the trajectory and each training geometry R(a) already contained in the training set. We then pick that configuration R(tadd) from the trajectory for which the distance to the closest training configuration is maximal, i.e., where\n\nTo gauge the systematic convergence of the NVE MD single-shot trajectories, we can track the variational lowering (and hence improvement) of the potential energy surface as the dataset is enlarged. This is done by comparing the PES from the two data set sizes along the same trajectory corresponding to the larger of the two data sets. Exploiting the variationality of the method, it is guaranteed that the potential energy inference with the larger dataset will be lower or equal to the predictions with the smaller dataset, and we use the difference between the predicted energies as a convergence measure. In our applications, we terminate the simulation when the predicted energy with the enlarged data set stays within a tolerance of \u03f5\u2009=\u200910\u22123\u2009Eh along the full MD trajectory for two iterations in a row. Examples of this convergence are shown in Fig.\u00a0S4 the supplementary information.\n\nTo manage the increased data volume when generating the statistical canonical ensemble of trajectories for the NVT Zundel cation results of Fig.\u00a06, we use a somewhat coarser scheme to select the training configurations. We start with just the initial configuration in the training set, and randomly sub-sample 100 trajectories from the ensemble of 500 trajectories generated by the prior CAM-B3LYP DFT dynamics. For all timesteps of these 100 trajectories, the Hamiltonian distance metric of Eq. (16) is computed and the 19 geometries with the largest value of this metric are identified for inclusion in an enlarged training data set. The continuation scheme is then run for 500 NVT trajectories with these 20 training points. We compute the Hamiltonian metric along the full path of a new random selection of 100 of these inferred trajectories in order to identify a further set of 20 geometries to perform explicit DMRG calculations to iteratively enlarge the training data set until the desired size is reached. This training set is taken to be the same for all trajectories in an ensemble. It should be stressed that only the first batch of 19 geometries are taken from the DFT-derived trajectories, after which subsequent batches of training geometries are found self-consistently to ensure a systematically reducing bias due to the DFT paths.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The raw data in this manuscript are provided in a Source Data file. 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Y.R. also acknowledges the support of the Engineering and Physical Sciences Research Council (EP/Y005090/1).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "National Physical Laboratory, Teddington, UK\n\nYannic Rath\n\nDepartment of Physics and Thomas Young Centre, King\u2019s College London, London, UK\n\nYannic Rath\u00a0&\u00a0George H. Booth\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY.R. and G.H.B. jointly developed the methodology and wrote the manuscript. YR implemented the approach and performed the numerical experiments. G.H.B. supervised the project.\n\nCorrespondence to\n Yannic Rath or George H. 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Holographic Information Capacity through Nonorthogonal Polarization Multiplexing", + "journal": "Nature Communications", + "published": "26 July 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-50586-5/MediaObjects/41467_2024_50586_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-50586-5/MediaObjects/41467_2024_50586_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Metamaterials", + "Nanophotonics and plasmonics" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3981683/v1.pdf?c=1722078543000", + "research_square_link": "https://www.researchsquare.com//article/rs-3981683/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-50586-5.pdf", + "preprint_posted": "07 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Contemporary studies in polarization multiplexing are hindered by the intrinsic orthogonality constraints of polarization states, which restrict the scope of multiplexing channels and their practical applications. This research transcends these barriers by introducing an innovative nonorthogonal polarization-basis multiplexing approach. Utilizing spatially varied local polarization states within metaatoms, we successfully reconstruct globally nonorthogonal channels that exhibit minimal crosstalk. This method not only facilitates the generation of free-vector holograms, achieving complete degrees-of-freedom in three nonorthogonal channels with ultra-low energy leakage, but it also significantly enhances the dimensions of the Jones matrix, expanding it to a groundbreaking 10\u00d710 scale. The fusion of a controllable eigen-polarization engineering mechanism with a vectorial optical diffraction neural network culminates in the experimental creation of 55 intricate holographic patterns across these expanded channels. This advancement represents a profound shift in the field of polarization multiplexing, unlocking unprecedented opportunities in advanced holography and quantum encryption, among other applications.Physical sciences/Optics and photonics/Optical materials and structures/MetamaterialsPhysical sciences/Physics/Optical physics/Nanophotonics and plasmonicspolarization multiplexingnonorthogonal basisvector diffraction neural networkmetasurface holography", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "V6SupplementaryMateria.docx", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Contemporary studies in polarization multiplexing are hindered by the intrinsic orthogonality constraints of polarization states, which restrict the scope of multiplexing channels and their practical applications. This research transcends these barriers by introducing an innovative nonorthogonal polarization-basis multiplexing approach. Utilizing spatially varied eigen-polarization states within metaatoms, we successfully reconstruct globally nonorthogonal channels that exhibit minimal crosstalk. This method not only facilitates the generation of free-vector holograms, achieving complete degrees-of-freedom in three nonorthogonal channels with ultra-low energy leakage, but it also significantly enhances the dimensions of the Jones matrix, expanding it to a groundbreaking 10\u2009\u00d7\u200910 scale. The fusion of a controllable eigen-polarization engineering mechanism with a vectorial diffraction neural network culminates in the experimental creation of 55 intricate holographic patterns across these expanded channels. This advancement represents a profound shift in the field of polarization multiplexing, unlocking opportunities in advanced holography and quantum encryption, among other applications.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Polarization orthogonality, a fundamental principle in photonics, demands that the inner product of two output fields, u1\u2192(x,y) and u2\u2192(x,y), equals zero. This orthogonality, stemming from polarization bases p^1 and p^2, confines polarization multiplexing to a mere two channels, dictated by a 2\u2009\u00d7\u20092 Jones matrix1,2. While this orthogonality ensures perfect isolation in applications such as polarization imaging and information encryption3,4,5,6,7,8,9,10, it inherently limits the maximum number of multiplexing channels free from cross-interference. Existing methods, like space- or time-division techniques11,12,13,14,15,16,17, compromise on spatial or temporal resolution, and typically can only expand to four orthogonal polarization channels. For instance, conventional configurations using bulk optical elements like polarizers and waveplates represent this limitation18. Attempts to incorporate additional dimensions19,20,21,22,23, such as wavelength or orbital angular momentum, typically exacerbate issues of crosstalk and noise. Even with advanced eigen-polarization modulation techniques, the reliance on orthogonal polarization bases persists, constraining applications like dynamic holography and information transmission within a narrow scope17,24,25,26.\n\nRecent advancements in metasurface technology have significantly expanded the potential for holographic information capacity. For instance, Bao et al. cascaded two single-layer metasurfaces to construct a spatially varying Jones matrix, enabling the manipulation of the amplitude and phase of light for intricate holographic applications8. Furthermore, Wang et al. demonstrated high-efficiency metasurfaces capable of complex vectorial holography using polarization multiplexing, showing that engineered metasurfaces can achieve high-density information encoding9. Additionally, Xiong et al. introduced the concept of engineered noise to break the fundamental limit of polarization multiplexing capacity, demonstrating up to 11 independent holographic images using a single metasurface17. These studies demonstrate the possibility of breaking traditional limitations by employing innovative strategies, such as engineered noise and complex light manipulation techniques, to enhance polarization multiplexing. Despite these advancements, the number of achievable independent channels remains a critical area for further research, as the lack of full exploitation of metasurfaces restricts high-capacity holographic applications, particularly in fields requiring extensive data encoding and secure information transmission.\n\nHere, our work pioneers a nonorthogonal polarization-basis multiplexing technique by engineering spatially variable eigen-polarization states p^1(x,y) and p^2(x,y) at a subwavelength scale using metaatoms. We meticulously control the local eigen-polarization of each metaatom, thereby achieving a unique collective effect across multiple nonorthogonal polarization channels. This method, deviating from the norm, permits a nonzero product of u1\u2217\u2192(x,y) and u2\u2192(x,y) at each metaatom, culminating in a precise overall polarization output. Our approach\u2019s efficacy is demonstrated both theoretically and experimentally by showcasing three holograms cycled through asymmetric polarization channels, achieving full degrees-of-freedom coverage without compromising spatial, temporal, or other dimensions. Furthermore, we introduce a controllable local eigen-polarization modulation mechanism that expands the Jones matrix dimensionality to 10\u00d710. By employing a vectorial diffraction neural network (VDNN)27, we optimize multiplexing efficiency and reduce crosstalk, as evidenced by a set of 55 holograms integrating 10 input and 10 output polarization states. Notably, there is no compromise of spatial, temporal, or other dimensions in the realization of three-channel nonorthogonal polarization multiplexing. For cases involving more than three channels, the spatial dimension along the propagation direction is adopted in this work. Our work represents a substantial advance in polarization multiplexing channels and it also offers a robust and scalable solution for ultra-high capacity holographic multiplexing. This nonorthogonal strategy promises new possibilities for dynamic holography and advanced information transmission in the realm of photonics.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The polarization manipulation capabilities of a single-layer metaatom are elegantly encapsulated by the Jones matrix, represented as:\n\nHere, A, B, C, and D govern the complex amplitude control of both eigen and cross channels in standard polarization representation. The eigen-polarizations e1^ and e2^, along with the eigenvalues \u039b1 and \u039b2, define the polarization control. Equation (1) highlights that the polarization control is simultaneously constrained by space-time reciprocity and the inherent form of the eigen-polarization.\n\nTraditionally, using orthogonal basis vectors |H\u3009 and |V\u3009, the Jones matrix is decomposed into components such as |H\u3009\u3008H|, |H\u3009\u3008V|, etc. Due to the reciprocity of single-layer metasurfaces, it\u2019s established that B\u2009=\u2009C, thus limiting the polarization control to three degrees of freedom.\n\nHowever, this approach simplifies the complex relationship between the local eigen-polarization control J of a metaatom and the global output polarization from an input. For in-plane symmetric metaatoms, the inherent linearity of the eigen-polarization constrains input-output combinations to linear orthogonal polarization bases in conventional scenarios.\n\nOur work transcends these limitations by modulating the local linear eigen- polarizations of metaatoms, thus constructing globally nonorthogonal output polarization states (see Fig.\u00a01a). This fine-tuning of each metaatom\u2019s local eigen-polarization results in distinct collective effects on multiple nonorthogonal output polarization channels.\n\na Illustration of a dynamically polarized holographic metadevice. It is engineered to generate holograms for each of the 55 arbitrary polarization channels, analogous to the elements in the periodic table. b Schematic of the controllable eigen-polarization modulation mechanism. An arbitrary input polarization is initially decomposed into circular polarization bases |L\u3009 and |R\u3009. For demonstrative purposes, linear polarization is selected. Upon interaction with a local metaatom, the polarization undergoes transformations in both eigen and conjugate channels, namely ALL, BLR, CRL, and ARR. Following this, a dynamically rotating polarizer is applied, assigning aij manipulation to the \u3008L| and \u3008R| channels, respectively. This process enables the modulation of local eigen-polarization at the metaatom level, facilitating global polarization multiplexing. The cumulative and coherent interference of various metaatoms at the imaging plane realizes the envisioned nonorthogonal polarization multiplexing.\n\nFor arbitrary input pi and output pj(i, j=1, 2, \u2026, n), the responses Oij(x,y)=\u27e8pj|J|pi\u27e9 of metaatoms at different positions (x, y) differ from each other. Combining with Eq. (1), we have Oij(x,y)=pj\u2020\u22c5[e1^,e2^]\u22121\u22c5\u039b\u22c5[e1^,e2^]\u22c5pi, where \u2020 represents the transpose and conjugate operation. Since the system is unitary, [e1^,e2^]\u22121=[e1^,e2^]\u2020, thus:\n\nwhere pi\u2032, and pj\u2032 include the role of eigenvectors in their interaction with pi and pj.\n\nAt this point, we have derived the fundamental equation Oij(x,y) for the modulation of incident/output polarization along with the metaatoms, which serves as the basis for extending arbitrary polarization channel modulation. By incorporating this equation into the tools of machine learning or neural networks, it offers an efficient way to address the challenge of maximizing the isolation between channels that jointly modulate global perturbations and local controls under limited degrees of freedom. In this situation, we can achieve desired nonorthogonal multiplexing channels for any combined input and output polarization pairs.\n\nTo demonstrate this, we choose circular polarization basis vectors to rewrite Eq. (2) and get Eq. (3):\n\nIn this scenario, we can decompose the metaatom\u2019s matrix into eigen and conjugate components Jeigen=[ALL00ARR] and Jcon=[0BLRCRL0], as shown in Fig.\u00a01b, where each element represents the circular-based modulation channel, ALL/ARR and BLR/CRL are co- and cross-polarization channel respectively. |pi\u27e9=a1i|L\u27e9+a2i|R\u27e9 and \u27e8pj|=a3j\u2217\u27e8L|+a4j\u2217\u27e8R|, where a is decomposition factor, * denotes conjugation, and |L\u3009/|R\u3009 are left/right circular polarization. This decomposition enables us to explore various combinations of incident and output polarizations, showing how the global response of the metasurface and the local response of the metaatoms are intricately cross-coupled.\n\nIntuitively, the metaatom\u2019s response based on eigen-polarization modulation can be denoted by a matrix O(x, y) (Eq. (4)), in which different combinations of input and output polarizations are selective extractions of this response matrix. The out-of-plane symmetry leads to physical transport reciprocity in our system, which gives the response matrix the form of a symmetric matrix, i.e., the upper (lower) triangular form, the relevant discussion can be seen in Supplementary Note\u00a01.\n\nThe global response of metasurface for nonorthogonal polarization multiplexing is then the integral of Eq. (4) with respect to position: \u03a9=\u222cO(x,y)dxdy. In this way, the change of the response matrix due to the tuning of the input-output polarization will be fully attributed to the modulation effect of the eigenvectors on the input-output polarization. According to this integral formula and the diffraction process, we can build an efficient network and optimize the parameters so that the response matrix \u03a9 can optimally extract different channels selectively. Details can be found in Supplementary Note\u00a02.\n\nIt\u2019s worth noting that we utilize the spatially varied eigen-polarization states of the metaatoms to successfully reconstruct globally nonorthogonal channels that exhibit minimal crosstalk. This leads to a modulation capability that is no longer limited to symmetric channels (\u03a9ij, i\u2009=\u2009j), but can also be extended to asymmetric channels (\u03a9ij, i \u2260 j). Equation (4) represents a pivotal development in our study. It enables the reconstruction of the control matrix over various nonorthogonal input-output polarization channels, significantly expanding the conventional dimension of the Jones matrix.\n\nEmploying the methodology delineated, we executed a series of sample designs to empirically validate our proposed nonorthogonal polarization-basis multiplexing approach. This included an array of tri-fold cyclic nonorthogonal linear polarization multiplexes, tri-fold cyclic nonorthogonal circular and elliptical polarization multiplexes, and an extensive suite of 55 diverse nonorthogonal polarization multiplexes.\n\nIn the implementation of our triple nonorthogonal linear polarization asymmetric holography model, we simplified the metasurface to exhibit a transmittance of 1. The corresponding diffraction neural network was configured with a single hidden layer for streamlined efficiency. It should be noted that the definition of the diffraction neural network28,29,30 follows that outlined in ref. 27. However, our approach differs in that we integrate the polarization dimension of light into a single-layer metasurface, rather than using a series of cascading optical elements. The optimization is conducted using electronic neural networks31. As depicted in Fig.\u00a02a, the training process is illustrated, highlighting the incident polarization, output polarization, and the targeted holographic pattern. Operating at a wavelength of 3\u03bcm, we precisely calibrated incident polarization angles at 0, 60, and 120 degrees to correspond with output polarization angles of 60, 120, and 0 degrees, respectively. This calibration resulted in the generation of gray-scale holographic images: a puppy, a kitten, and a mouse. The distance between the metasurface layer and the output layer was meticulously maintained at 500 \u03bcm. The loss for each target pattern is evenly distributed with its respective weight during the optimization process, promoting a fair allocation of energy across all patterns. More details can be found in Supplementary Note\u00a02.\n\na Conceptual model of vectorial diffraction neural network illustrating the training process. Here, different polarized lights constitute the input layer, the metasurfaces function as the hidden layers, and the image plane serves as the output layer. b Training outcomes for the nonorthogonal linear polarization channel. This section highlights the loss maps and the correlation coefficients that measure the congruence between the output and target patterns. c Anisotropic response and orientation of the metaatoms within the systematically arranged metadevice. d Scanning electron microscope images, offering both oblique and cross-sectional views of the metadevice. Each image includes a scale bar of 2 \u03bcm to provide a sense of scale. e Depicts the optical characterization system used for evaluating the performance of the designed metadevices. LP stands for linear polarization, PR for phase retarder, and FPA for focal plane array.\n\nFigure\u00a02b offers insight into the neural network training, showcasing the loss profile and the variation in correlation coefficients across channels as a function of iteration numbers. Over successive iterations, the three patterns gradually converge towards the target patterns at varying rates, ultimately attaining a nearly flawless alignment as the iteration count rises. The vectorial diffraction neural network\u2019s output results, including the parameters \u03c6x, \u03c6y, and the metasurface orientation \u03b8, are elucidated in Fig.\u00a02c. The metasurface consisted of a 400\u2009\u00d7\u2009400-pixel array, each with a period of 1.5 \u03bcm. The nano-elliptic metaatoms, made of pure silicon, have a height of 4 \u03bcm, the length and width of the metaatom varied between 0.3 and 1.2\u2009\u03bcm. More details are in Supplementary Note\u00a03. In Fig.\u00a02d, scanning electron microscopy images provide both oblique and cross-sectional views of the metasurface, showcasing the robustness of the fabrication process and underscoring the precision achieved in metasurface manufacturing. The scale bar is 2 \u03bcm. Lastly, Fig.\u00a02e delineates the experimental setup, where the camera captures the holographic pattern following the transmission of infrared light through the polarizer, metasurface, and analyzer. The phase retarder was omitted in the linear polarization experiment. Further details on simulations and experiments can be found in the Methods section.\n\nFigure\u00a03a\u2013c displays the outcomes of the tri-fold cyclic nonorthogonal linear polarization asymmetric holography experiments. These figures are systematically organized, featuring the training targets (top left), simulation results (top right), and experimental data (middle). The asymmetric polarization channels are distinctly highlighted in purple boxes at the bottom of each figure. Notably, these results showcase vivid and crosstalk-free asymmetric holograms achieved through nonorthogonal polarization multiplexing. This alignment serves as a testament to the robustness and efficacy of the proposed methodology. Supplementary Note\u00a04 illustrates the degrees of freedom in the conventional Jones matrix for metasurface design.\n\na\u2013c Training targets (upper left), simulation results (upper right), and experimental results (middle) for the tri-fold cyclic nonorthogonal polarization holography. The bottom section of each panel, highlighted in purple boxes, denotes the distinct polarization channels. The images illustrate the holographic patterns: a puppy in (a) with polarization transitioning from 0 to 60 degrees, a kitten in (b) with polarization from 60 to 120 degrees, and a mouse in (c) with polarization from 120 to 0 degrees, all within the nonorthogonal polarization channel. d Crosstalk analysis. This panel presents a detailed crosstalk analysis, using selected metaatoms from experimental results (a)\u2013(c), as indicated by yellow (Area 1), orange (Area 2), and green (Area 3) boxes. This analysis quantifies the interference between different polarization channels. e\u2013g Longitudinal intensity statistics derived from the target patterns, simulation results, and experimental data, corresponding to (a), (b), and (c), respectively. These statistics provide a quantitative comparison between the intended patterns and both the simulated and actual experimental outcomes.\n\nTo further quantify crosstalk among these hologram channels, we adopted partitioned summation statistics across three randomly chosen, disjoint regions, as detailed in the Supplementary Note\u00a05. Figure\u00a03d presents the crosstalk computations, indicating an almost negligible interference between hologram channels, thus affirming the method\u2019s precision.\n\nFurther statistical analysis is provided in Fig.\u00a03e\u2013g, where we juxtapose the experimental results with both target and simulated images. The employed statistical approach involves summing and normalizing each image along its longitudinal axis. It can be seen that the measured results agree well with the simulations. Moreover, the curve alterations exhibit a high degree of consistency with the target variations, emphasizing the fidelity and accuracy of our experimental findings. This quantitative evaluation not only substantiates the alignment of our results with the intended outcomes but also underscores the reliability of our experimental approach. The holographic efficiency is defined as \u03b7=EholoEtotal, where Eholo refers to the measured total energy of the hologram patterns, and Etotal refers to the total incident energy received by the metasurface area. The measured holographic efficiencies are 26.04% (0\u00b0\u2009\u2192\u200960\u00b0 polarized channel), 25.25% (60\u00b0\u2009\u2192\u2009120\u00b0 polarized channel), and 27.83% (120\u00b0\u2009\u2192\u2009180\u00b0 polarized channel) respectively.\n\nTo demonstrate the extensive engineering capabilities in creating asymmetric holograms across various nonorthogonal polarization channels, we developed tri-fold cyclic nonorthogonal holograms specifically designed for arbitrary circular and elliptical polarization channels. This process necessitated meticulous amplitude manipulation during the polarization layer mapping phase, as detailed in Fig.\u00a04a.\n\na The operation mechanism that enables the extension of nonorthogonal polarization to a variety of forms. This is achieved through the strategic recombination and distribution of circular and elliptical polarization components, which facilitates the creation of a spectrum of nonorthogonal polarization channels. b Simulation and experimental results for two metasurfaces. It exemplifies the linear-circular polarization channel (using Metasurface 1, MS1) and demonstrate the mutual conversion capabilities for arbitrary elliptical polarization (using Metasurface 2, MS2). The deer image has been designed using images from Flaticon.com. c, d Quantitative analysis of the correlation coefficients between target patterns, simulation outcomes, and experimental images for both circular and elliptical polarization channels. It highlights the exceptional isolation maintained between the various distinct polarization channels.\n\nFor empirical validation, two distinct metasurface samples were rigorously designed and fabricated. The first sample was engineered to respond to incident light composed of x-, y-, and right-hand circularly polarized components. This configuration was achieved by setting \u03b2 at \u03c0/2, and \u03b1 at 0, 90, and 135 degrees within the Jones vector [cos\u2061\u03b1sin\u2061\u03b1\u22c5ei\u03b2], resulting in holographic representations of the letters \u2018A\u2019, \u2018B\u2019, and \u2018C\u2019. The second sample was tailored to process three nonorthogonal elliptical polarizations, with \u03b2 consistently at \u03c0/3, and \u03b1 at 30, 70, and 140 degrees, producing holographic depictions of a deer, squirrel, and wolf, respectively. The training, simulation, and experimental results of these implementations are presented in Fig.\u00a04b. Furthermore, the multiplexing of arbitrarily nonorthogonal linear polarizations is demonstrated in Supplementary Note\u00a06.\n\nTo quantitatively assess the quality of the reconstructed images, we employed correlation coefficients between the reconstructed, target, and simulated images, as depicted in Fig.\u00a04c, d. The correlation coefficient is defined as corr(T,R)=COV(T,R)D(T)\u22c5D(R), where T and R denote the two images, COV (T, R) is their covariance, and D(T) and D(R) are their variances. Both metasurfaces were operated at a wavelength of 3 \u03bcm, with a pixel configuration of 400\u2009\u00d7\u2009400 and a diffraction distance of 500\u2009\u03bcm. Despite the inherent challenges in metaatom determination and sample fabrication, the experimental results demonstrated a close alignment with the design targets. This congruence attests to the feasibility and effectiveness of realizing arbitrary nonorthogonal polarization multiplexed holography. A nine-channel multiplexing with the introduction of wavelength is illustrated in Supplementary Note\u00a07. The measured efficiencies of the Metasurface 1 are 19.13%, 22.16%, and 20.12%, corresponding to the letters \u2018A\u2019, \u2018B\u2019, and \u2018C\u2019, respectively, and the Metasurface 2 are 22.08%, 24.61%, and 21.4% in that order.\n\nBuilding on the innovative nonorthogonal polarization multiplexing framework, we have developed a continuous polarization-based tunable Vectorial Diffraction Neural Network (VDNN). This groundbreaking approach is designed for the full exploitation of global polarization degrees of freedom, thereby significantly enhancing the information channel capacity. By integrating the controllable eigen-polarization modulation mechanism and introducing a subtle focal plane mismatch, we have successfully generated 55 distinct holographic patterns, as showcased in Fig.\u00a05a.\n\na Schematic illustration of 55 distinct elements selected from the periodic table, each serving as a target pattern for holograms across various orthogonal polarization channels. b Correlation coefficient matrix derived from the simulation results. This matrix exemplifies the exceptional global performance achieved in optimizing metaatoms for nonorthogonal polarization multiplexing, highlighting the accuracy and fidelity of our simulation process. c Experimental results of 55 nonorthogonal polarization channels. The arrows within the outer gray box signify the input and output polarizations. This representation emphasizes our method\u2019s ability to transcend conventional boundaries in polarization multiplexing, thereby demonstrating the superior capability of our approach in facilitating nonorthogonal polarization multiplexing across multiple channels.\n\nIn this system, ten linear polarizations (at angles 0\u00b0, 18\u00b0, 36\u00b0, 54\u00b0, 72\u00b0, 90\u00b0, 108\u00b0, 126\u00b0, 144\u00b0, 162\u00b0) are chosen for each input and output, cumulatively resulting in 55 multiplexing channels. These channels are systematically arranged in a lower triangular matrix of dimension 10\u2009\u00d7\u200910, akin to a periodic table of elements, with each channel representing a unique matrix element, as depicted in Fig.\u00a05a. Relevant design process is seen in Supplementary Note\u00a08. The correlation coefficient matrix, presented in Fig.\u00a05b, confirms the high degree of effective isolation achieved between these distinct channels.\n\nAdhering to this methodology, we fabricated a metasurface comprising an array of 640,000 (800\u2009\u00d7\u2009800) metaatoms. The target pattern was tailored for experimental validation, with comprehensive details provided in the Supplementary Note\u00a09. Experimental results are illustrated in Fig.\u00a05c, where the input polarization is displayed at the bottom, the output polarization on the left, and the intersecting pattern depicting the experimentally measured channel results. The measured holographic efficiencies can be found in Supplementary Note\u00a010. It\u2019s worth noting that the images in Fig.\u00a05c are captured at slightly different spatial positions along the z-axis to ensure the best possible resolution and image quality for each holographic pattern. In addition, the nonuniform brightness distribution observed in some holographic patterns (e.g., Ni, Te, Fe) in Fig.\u00a05c become more pronounced with an increase in the number of operating channels. This phenomenon occurs because, as the number of multiplexing channels increases, the repeated use of each metaatom also increases. Consequently, the control capacity of each metaatom may not fully meet the requirements, leading to changes in the brightness of the holographic patterns due to interference at the focal plane. To mitigate this effect, several strategies can be employed: 1. Increase the height of metaatoms: This can enhance the phase control range of each metaatom, thereby reducing brightness variations. 2. Use of combined metaatoms: Implementing complicated metaatoms with more design degrees of freedom can provide better control over the holographic output. 3. Adopt multiple layered metasurfaces: Utilizing multiple layers can distribute the control load more evenly across different metaatoms, improving uniformity. 4. Increase the number of metaatoms: Adding more metaatoms can enhance the overall control precision and reduce nonuniform brightness. These methods can improve the control degree of each metaatom more effectively and regulate the distribution of the holographic target pattern, thereby minimizing the nonuniformity in brightness.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50586-5/MediaObjects/41467_2024_50586_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50586-5/MediaObjects/41467_2024_50586_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50586-5/MediaObjects/41467_2024_50586_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50586-5/MediaObjects/41467_2024_50586_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50586-5/MediaObjects/41467_2024_50586_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The nonzero analytical solution of the derived formulas in Supplementary Notes\u00a01 and 4 indicates that the largest rank of the coefficient matrix of the Jones matrix is 3. This means that for given metaatoms with similar profiles, the maximum number of channels for nonorthogonal polarization multiplexing without compromising other dimensions is three. Beyond three channels, crosstalk becomes significant, and different methods must be employed to extend the multiplexing channels. Our approach involves the use of a controllable local eigen-polarization modulation mechanism, as well as optimization through a vectorial diffraction neural network. Additionally, methods such as noise assistance, as described in ref. 17, can also be utilized to further extend the number of multiplexing channels. To achieve more multiplexing channels, it imposes a higher requirement on the metaatoms to possess stronger control abilities, such as enhanced phase coverage, more design degrees of freedom, etc. This necessitates metaatoms with larger depth-to-width ratios, higher refractive index differences, more complex shapes, or additional metasurface layers to expand their controlling capabilities. These factors can influence the efficiency, crosstalk, and the number of achievable multiplexing channels.\n\nAs for the potential to implement other types of functionalities, such as a continuous zoom lens by varying the input-output polarization, our method theoretically allows for such implementations. By precisely controlling the eigen-polarizations of the metaatoms and optimizing the metasurface\u2019s global response, we could potentially develop functionalities beyond holography, including adaptive lenses and other dynamic optical devices. This methodology offers the potential to further expand the Jones matrix to even higher dimensions. Such expansion facilitates the manipulation of more complex polarization states and channels. The compact and versatile nature of these metadevices, underpinned by the controllable eigen-polarization engineering mechanism, solidifies their role as a robust platform for advanced hyper-polarization dynamic holography.\n\nIn summary, this study marks a significant breakthrough in the field of photonics by pioneering a coupled recombination design that leverages local eigen-polarization degrees of freedom. The integration of this mechanism with a sophisticated vectorial diffraction neural network design has been instrumental in achieving a high level of polarization multiplexing holography, encompassing 55 distinct channels. This feat not only facilitates the creation of arbitrary polarization forms and asymmetric channels but also greatly expands the dimensional capacity of the Jones matrix through our controllable eigen-polarization modulation mechanism.\n\nFurthermore, by harmonizing various multiplexing techniques, our strategy significantly boosts the capacity and security of information transmission while maintaining minimal crosstalk. The introduction of nonorthogonal polarization-basis multiplexing represents a pivotal development, establishing a robust foundation for future innovations in optical communications and quantum information sciences. This work not only exemplifies state-of-the-art scientific exploration but also opens new horizons for more secure and versatile channels in the ever-evolving domain of photonics.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The fabrication of the all-silicon metasurface begins with the deposition of a 50\u2009nm chromium (Cr) layer onto a double-sided polished silicon wafer, utilizing electron beam evaporation. A photoresist layer is then spin-coated onto this chromium-coated wafer, followed by a baking process on a hot plate. The metasurface pattern is intricately defined using electron beam lithography (JBX-6300FS). Post-lithography, the samples are developed in a 300-MIF solution, rinsed thoroughly with deionized water, and dried. The fabrication is completed through the use of inductively coupled plasma (ICP) dry etching, targeting both the chromium and silicon layers. This etching process involves selective material removal using various gases, ensuring the precise realization of the desired metasurface configuration.\n\nThe experimental setup involves the generation of mid-infrared light using the Electro MIR 4.8 laser system (Leukos laser). The light is first filtered to a wavelength of 3 \u03bcm with a 200\u2009nm bandwidth, then passes through a polarizer and a full-wave liquid crystal retarder (Thorlabs, LCC1113-MIR, LCC25) for light manipulation before reaching the sample. The sample\u2019s image plane is aligned with the focal plane of an imaging lens set, comprising a 4\u2009mm aspheric lens and a 25\u2009mm lens. The transmitted light is captured and visualized using a high-resolution 640\u2009\u00d7\u2009512 pixels home-made MCT focal array, post-analyzer. Notably, the MWIR camera is cooled to approximately 80\u2009K for optimal measurement performance. In linear polarization experiments, the full-wave liquid crystal retarder is intentionally omitted, reflecting a specific experimental choice. This precise and controlled setup underlines the reliability of our measurements in the mid-infrared range.\n\nFor designing metaatoms, we employ the three-dimensional finite difference time domain (FDTD) method, provided by Lumerical Inc., to calculate transmittance and phase distributions. The holograms are constructed using Python v3.8.13 and PyTorch v1.12.1 (Facebook Inc.), with the vectorial diffraction neural network (VDNN) formulated within this environment. The Adam optimizer in PyTorch is used as the automatic differentiation library, essential for holographic design optimization, while Numpy v1.21.5 enhances data processing efficiency. The angular spectrum diffraction algorithm is incorporated for computational efficiency in diffraction calculations. For arbitrary input polarization state, neural network models were trained with a fixed learning rate of 0.015. The number of iterations varied depending on the model\u2019s complexity. For instance, the linear, elliptic, and circular polarization models underwent 300 iterations each due to their smaller sizes, while the three-wavelength nine-channel model underwent 800 iterations, and the 55-channel model underwent 1500 iterations. To train the vectorial diffraction neural network, a workstation was specifically configured with two NVIDIA GeForce RTX 3090 GPUs (Nvidia Inc.), an AMD EPYC 7513 32-Core Processor (AMD Inc.), and 256 GB of RAM, running the Windows 10 operating system (Microsoft Inc.). The training time for a diffractive model with 55 polarization channels was approximately 0.5\u2009h.\n\nFor large array hologram simulations (800\u2009\u00d7\u2009800 metaatoms), we utilize the vector integration algorithm based on Rayleigh-Sommerfeld diffraction theory, chosen to overcome the computational limitations of FDTD software, allowing for practical simulation of extensive metaatom arrays.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Relevant data supporting the key findings of this study are available in the article and Supplementary Information file. All raw data generated in this study are available from the corresponding authors upon reasonable request.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code used for data analysis during this study is available upon reasonable request from the corresponding authors.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Mueller, J. P. B., Rubin, N. A., Devlin, R. C., Groever, B. & Capasso, F. Metasurface polarization optics: Independent phase control of arbitrary orthogonal states of polarization. Phys. Rev. Lett. 118, 113901 (2017).\n\nArticle\u00a0\n ADS\u00a0\n \n Google Scholar\u00a0\n \n\nRubin, N. A., Shi, Z. J. & Capasso, F. Polarization in diffractive optics and metasurfaces. Adv. Opt. Photonics 13, 836\u2013970 (2021).\n\nArticle\u00a0\n ADS\u00a0\n \n Google Scholar\u00a0\n \n\nOvervig, A. C. et al. Dielectric metasurfaces for complete and independent control of the optical amplitude and phase. 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This work was partially carried out at the Center for Micro and Nanoscale Research and Fabrication in University of Science and Technology of China and Soft Matter Nanofab (SMN180827) in ShanghaiTech University.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Jie Wang, Jin Chen, Feilong Yu.\n\nState Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yu-Tian Road, Shanghai, 200083, China\n\nJie Wang,\u00a0Jin Chen,\u00a0Feilong Yu,\u00a0Rongsheng Chen,\u00a0Jiuxu Wang,\u00a0Zengyue Zhao,\u00a0Xuenan Li,\u00a0Guanhai Li,\u00a0Xiaoshuang Chen\u00a0&\u00a0Wei Lu\n\nCollege of Physics, DongHua University, 2999 North Renmin Road, Shanghai, 201620, China\n\nJie Wang\u00a0&\u00a0Huaizhong Xing\n\nHangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, No.1 SubLane Xiangshan, Hangzhou, 310024, China\n\nGuanhai Li,\u00a0Xiaoshuang Chen\u00a0&\u00a0Wei Lu\n\nUniversity of Chinese Academy of Science, No. 19 Yuquan Road, 100049, Beijing, China\n\nGuanhai Li,\u00a0Xiaoshuang Chen\u00a0&\u00a0Wei Lu\n\nShanghai Research Center for Quantum Sciences, 99 Xiupu Road, Shanghai, 201315, China\n\nGuanhai Li,\u00a0Xiaoshuang Chen\u00a0&\u00a0Wei Lu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nG.H.L. conceived the idea. J.W., J.C. and F.L.Y. performed the theoretical calculation and numerical simulation. G.H.L. and J.C. fabricated the sample. J.W., J.C., F.L.Y., J.X.W. and R.S.C. built the optical platform and conducted metasurface experiments. J.W., Z.Y.Z. and X.N.L analyzed the data. G.H.L., J.W., J.C., F.L.Y. and H.Z.X. prepared the manuscript with input from all authors. G.H.L., X.S.C. and W.L. initialized and supervised the project.\n\nCorrespondence to\n Guanhai Li.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Unlocking ultra-high holographic information capacity through nonorthogonal polarization multiplexing.\n Nat Commun 15, 6284 (2024). https://doi.org/10.1038/s41467-024-50586-5\n\nDownload citation\n\nReceived: 05 March 2024\n\nAccepted: 16 July 2024\n\nPublished: 26 July 2024\n\nVersion of record: 26 July 2024\n\nDOI: https://doi.org/10.1038/s41467-024-50586-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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b/2622b9014fa1412f130b9fa61d13863398aabfc717598fed487afbfcf756d2d5/metadata.json @@ -0,0 +1,150 @@ +{ + "title": "Single urinary extracellular vesicle proteomics identifies complement receptor CD35 as a biomarker for sepsis-associated acute kidney injury", + "pre_title": "Complement receptor CD35 on single urinary extracellular vesicle as novel biomarker of sepsis-associated acute kidney injury", + "journal": "Nature Communications", + "published": "29 July 2025", + "supplementary_0": [ + { + "label": "Supplementary information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62229-4/MediaObjects/41467_2025_62229_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62229-4/MediaObjects/41467_2025_62229_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62229-4/MediaObjects/41467_2025_62229_MOESM3_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://figshare.com/articles/dataset/Expression_csv/29356076", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE210622", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199321", + "http://www.huayingtangkyoto.com/202576/web_summary.html", + "/articles/s41467-025-62229-4#Sec27" + ], + "code": [], + "subject": [ + "Kidney diseases", + "Nephrology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5124447/v1.pdf?c=1753873673000", + "research_square_link": "https://www.researchsquare.com//article/rs-5124447/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-62229-4.pdf", + "preprint_posted": "09 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Sepsis-associated acute kidney injury (SA-AKI) portends severe health burden due to significant morbidity and mortality, while early diagnosis remains challenging. In this study, proximity-dependent barcoding assay (PBA) was established to profile the surface proteome of single urinary extracellular vesicle (uEV). Principle uEV clusters with unique function and origination were profiled in SA-AKI. Reduction of complement receptor CD35 on single uEV (CD35-uEV) was revealed as a novel biomarker from one of the main EV clusters with significant proportional differences. CD35-uEV demonstrated high diagnostic accuracy for SA-AKI (receiver operating characteristic-area under the curve (ROC-AUC), 0.98 in screening cohort (n=16), and 0.89 in validation cohort (n=134)). Besides, CD35-uEV enables identification of subclinical AKI (ROC-AUC, 0.84 in prospective cohort (n=72)) which was independent of other clinical parameters as validated by multivariate analysis (p<0.001). Moreover, CD35-uEV correlated closely with AKI severity which also predicts persistent AKI (ROC-AUC, 0.77), progression to AKD (ROC-AUC, 0.66), and mortality risks (ROC-AUC, 0.70). Integrative single-cell and spatial transcriptomics analysis identified that CD35-uEV originated from injured podocyte characterized with diminished CD35 expression. The combination of CD35-uEV with tubular injury biomarkers (TIMP2*IGFBP7) showed improved accuracy in identifying subclinical SA-AKI and prediction of severe stages. Overall, this study identified a novel biomarker on single uEV related to injured podocyte for early diagnosis and risk stratification of SA-AKI.Health sciences/NephrologyHealth sciences/Nephrology/Kidney diseasesSingle urinary extracellular vesicleproteome analysisbiomarkerssepsisacute kidney injury", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "PBA.docxSupplement Table 1S1.tifSupplement Figure 1S2.tifSupplement Figure 2S3.tifSupplement Figure 3S4.jpgSupplement Figure 4S5.tifSupplement Figure 5", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Sepsis-associated acute kidney injury (SA-AKI) portends severe health burden due to significant morbidity and mortality, while early diagnosis remains challenging. In this study, proximity-dependent barcoding assay (PBA) is established to profile the surface proteome of single urinary extracellular vesicle (uEV). Principle uEV clusters with unique function and origination are profiled in SA-AKI\u00a0in a\u00a0screening cohort. Complement receptor CD35 on single uEV (CD35-uEV) displays high diagnostic accuracy for SA-AKI (AUC-ROC 0.89\u00a0in validation cohort, n\u2009=\u2009134). Besides, CD35-uEV enables identification of subclinical AKI (AUC-ROC 0.84 in prospective cohort, n\u2009=\u200972). Moreover, CD35-uEV correlates closely with AKI severity which also predicts persistent AKI (AUC-ROC 0.77), mortality risks (AUC-ROC 0.70) and progression to AKD (AUC-ROC 0.66). Multi-omics profiling reveals that CD35-uEV are predominantly released from injured podocytes exhibiting diminished CD35 expression. Overall, this study identifies a single uEV biomarker related to injured podocyte for early diagnosis and risk stratification of SA-AKI.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Sepsis-associated acute kidney injury (SA-AKI) is a prevalent complication in septic patients and a leading cause of in-hospital mortality1. Approximately 30\u201350% of sepsis patients develop AKI, with mortality rates reaching up to 50%2. At present, the diagnosis of SA-AKI relies on serum creatinine levels and urine output, which are insensitive and nonspecific for kidney injury. In the last decade, several biomarkers are in developing to identify patients at risk of SA-AKI3. However, early injury markers are still lacking, leading to the challenge of timely recognition and intervention in clinic practice.\n\nExtracellular vesicles (EVs) are membrane-bound structures secreted by nearly all cell types, playing pivotal roles in diverse pathophysiological processes through mediating intercellular communication4. EVs are ubiquitously present in body fluids such as blood, urine, and cerebrospinal fluid, rendering them invaluable sources for disease biomarker discovery5,6,7. The non-invasive feature of urinary extracellular vesicles (uEVs) collection, coupled with their specific cargo from parent cells such as proteins, RNAs, underscores the significant potential in biomarker research8. A notable study from Miranda et al. identified the presence of mRNAs encoding proteins from all regions of the nephron and the collecting duct in uEVs indicating the rationality of uEVs as non-invasive injury biomarker of kidney disease9. Accordingly, uEVs biomarkers reflecting the severity of histological inflammation and fibrosis were reported in recent years10,11,12,13,14.\n\nHowever, the heterogeneity of uEV and complex cargo hinders the development of accurate biomarkers for clinic translation. This diversity makes it challenging to search for potential biomarkers from mixed clusters, especially for those rare-event related information. Hence, discrimination of subcluster constitution is crucial to minimize interference from irrelevant EVs and to realize the full potential of uEVs for precise disease diagnosis. The advancements in single-vesicle analysis have made it possible to facilitate the development of useful biomarkers on level of single EV15. Recently, it was reported that droplet digital immuno-PCR could be employed to analyze surface proteins on individual vesicle, allowing for the subcluster analysis of plasma EVs to identify biomarker of breast cancer16. Therefore, single uEV analysis may provide a promising approach for delineating the composition of uEVs to identify critical subcluster as biomarker of renal injury.\n\nProximity barcoding assay (PBA) technology captures EVs with subsequent single-round rolling circle amplification (RCA) and DNA barcodes sequencing to detect proteome of individual EV. This approach provides unique potential to discriminate uEV heterogeneity, which meanwhile elucidates the function and origin of specific subclusters17. In this work, we aim to leverage PBA technology to delineate the subcluster composition of uEVs during SA-AKI. We identified a uEV subpopulation predominantly marked by complement receptor CD35, which significantly decreases during SA-AKI and demonstrates accurate diagnostic performance in the early detection and prognostic assessment of SA-AKI. Multi-omics analysis revealed that CD35 was localized to injured podocytes, which may represent an injury marker for early diagnosis and intervention of SA-AKI.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To screening urinary biomarkers for SA-AKI at the single uEVs level, uEV from 8 SA-AKI and 8 sepsis non-AKI patients were applied for PBA proteome assay and the main uEV subpopulations with proportional difference were identified. Then, the protein biomarkers on single EVs were analyzed from the main differential subclusters which were subsequently validated in two independent cohorts: patients with clinically diagnosed SA-AKI (SA-AKI cohort, n\u2009=\u2009134) and prospective cohort of sepsis patients who had not yet been clinically diagnosed with AKI (E-AKI cohort, n\u2009=\u200972), assessing the potential of this biomarker for early diagnosis and prognosis of SA-AKI. The origin of this biomarker and its relevance to renal injury was explored through integrative single-cell RNA sequencing and spatial transcriptomics analysis. Finally, diagnostic performance was evaluated through combination of the biomarker with the well-established biomarker TIMP2*IGFBP7. A detailed workflow is provided in Fig. 1.\n\nTo explore urinary biomarkers for sepsis-associated AKI (SA-AKI) at the single uEV level, surface proteome was detected by using proximity barcoding assay (PBA) assay in the screening cohort (SA-AKI and non-AKI sepsis patients, n\u2009=\u20098 for each group). Next, the main differential uEV subpopulations in SA-AKI compared to non-AKI was characterized. Protein biomarker on single uEV was then identified and validated in two independent cohorts: a SA-AKI cohort (n\u2009=\u2009134) and a subclinical E-AKI cohort (n\u2009=\u200972). By integrating single-cell RNA sequencing and spatial transcriptomics, the cellular origin and its capacity to reflect renal cellular injury of the biomarker was explored. Finally, diagnostic performance was evaluated through combination of the useful biomarker with the well-established biomarker TIMP2*IGFBP7. (Image was Created in BioRender. Tang, T. (2025) https://BioRender.com/su096gu).\n\nTo profile the composition of uEVs, small EVs were purified from a screening set of sepsis patients, both with and without AKI, and subjected to PBA analysis. The basic clinical characteristics of these patients were presented in Supplementary Table\u00a01. The presence of uEVs was validated through the analysis of established EV protein markers, including Alix, TSG101, and CD81, as detected by WB analysis with EV samples purified from equal volume of urine. Significantly increased levels of EV-related protein markers were observed in the AKI group compared to non-AKI patients, indicating enhanced EV secretion into the urine in related to the same urine volume (Fig.\u00a02A). This finding was further supported by quantification of uEVs by acetylcholinesterase activity measurement (Fig.\u00a02B). Transmission electron microscopy (TEM) images showed typical cup-shaped morphology of the membrane structures (Fig.\u00a02C). Nanoparticle tracking analysis (NTA) revealed a typical size distribution of uEVs, with most of the vesicles ranging from 50 to 200\u2009nm in diameter (Fig.\u00a02D).\n\nA Western blot analysis confirming the presence of uEV markers Alix, TSG101, and CD81 (Non_AKI, n\u2009=\u20093, AKI, n\u2009=\u20093). uEVs samples purified from equal volume of 24-h urine were loaded. Data are presented as mean with SD (Paired two-tailed t-test); B uEV particle counts in the non-AKI and AKI groups of sepsis patients detected by SBI EV quantitation kit (Non_AKI, n\u2009=\u20096, AKI, n\u2009=\u20096, paired two-tailed t-test); Morphology and size characterization of uEV from AKI patients were characterized with transmission electron microscopy (TEM) (C) and nanoparticle tracking analysis (NTA) (D), the experiment was repeated three times; E Dimensionality reduction clustering analysis identifying 32 distinct uEV subclusters; F Distribution patterns of the 32 uEV subclusters in SA-AKI and non-AKI groups; G Protein characterization of the top two proteins for the main uEV subclusters, highlighting their unique protein signature. Source data are provided as a Source Data file.\n\nThrough establishing a target proteome analysis via PBA technology (protein tags and captured protein readings are shown in Supplementary Fig.\u00a01), dimensionality reduction and clustering of uEVs were performed to characterize the landscape of uEVs. Visualization of uEVs by t-SNE identified 32 subgroups, displaying significant differences in the composition between the AKI and non-AKI groups (Fig.\u00a02E, F). Among the 32 subclusters, clusters 0\u201310 were recognized as the main clusters, with proportions varied 1-30% among the total uEV populations. The principal proteins ranked top two were shown to display the molecular feature of each cluster, such as siglec10/CD33, CD35/CD21, and AQP1/AQP2, indicating a tubular origin or functions related to immune regulation for the specific cluster (Fig.\u00a02G). Therefore, PBA assay was successfully established for delineating the subcluster composition of uEVs.\n\nThe complement receptor-related uEV subcluster demonstrates potential in diagnosing SA-AKI among the top 10 subclusters, 5 subclusters that exhibited the most pronounced proportional differences between the AKI and non-AKI groups were identified, which showed distinct distributions as indicated by UMAP plots (Fig.\u00a03A, B). These clusters were defined as endothelial cell-derived expressing PECAM1, EMCN, and VCAM1 (cluster 0, EC-EV), tubular epithelial cell-derived expressing tight junction proteins CLDN1, CLDN10, and CLDN19 (cluster 6, TEC-EV), complement receptor-related cluster expressing CD35 and CD21(cluster2, CMR-EV), immune response-related EVs with CD38, CD26, TICAM2 (cluster1, IMR-EV) and lysosome-related cluster expressing LAMP1, LAMP2 (cluster4, LYS-EV) (Fig.\u00a03C).\n\nA UMAP plot distribution of the 5 uEV subclusters with the most significant proportional differences (endothelial cell derived EV: EC-EV, Tubular epithelial-derived EV: TEC-EV, Complement receptor related EV: CMR-EV, Lysosome related EV: LYS-EV, immune response related EVs: IMR-EV); B UMAP plot distribution of these 5 subclusters between AKI and non-AKI groups; C Key characteristic proteins of the 5 uEV subclusters; D Proportional distribution patterns of the 5 uEV subclusters across AKI stage (stage 1 vs. stages 2-3) were resolved through sample-specific proportional weighting normalization; E Proportional distribution of the 5 uEV subclusters in transient and persistent AKI; F Radar chart highlighting CMR-uEV as the most significantly different cluster between SA-AKI and non-AKI groups (Mann\u2013Whitney U two-sided test, SA-AKI, n\u2009=\u20098, Non-AKI, n\u2009=\u20098); G, H Second polynomial distribution revealed significant trends (ptrend\u2009=\u20090.047) in the proportional distribution of CMR-uEV in SA-AKI patients with different duration (G) (Non-AKI, n\u2009=\u20098; Transient-AKI, n\u2009=\u20093, Persistent-AKI, n\u2009=\u20095) and stages (H) (Non-AKI, n\u2009=\u20098; AKI-stage1, n\u2009=\u20092, AKI-stage2-3, n\u2009=\u20096). Data are presented as box plots showing the median (middle line), the 25th and 75th percentiles (box limits), the minimum and maximum values (whiskers), and outliers (individual points) Source data are provided as a Source Data file.\n\nTo determine whether the proportions of these 5 clusters are associated with the severity and prognosis of AKI, the proportions of each cluster across different AKI stages and durations were analyzed (Fig.\u00a03D). By assigning proportional weights to each sample, we quantified uEV subcluster variations across different groups, providing a comprehensive overview of their trends in transient and persistent AKI (Fig.\u00a03E). We observed that the proportions of the TEC-EV and EC-EV clusters characterized with normal cellular markers were reduced in stages 2-3 (severe AKI) and persistent AKI, probably due to the reduced expression of these markers in healthy renal tubule epithelial and endothelial cells as previously demonstrated in single-cell transcriptome studies18,19. Notably, the proportions of the CMR-EV showed a remarkable decrease in SA-AKI patients compared to Non-AKI. In contrast, the IMR-EV cluster displayed upregulated proportion in severe and persistent SA-AKI, likely reflecting the aberrant immune activation20 (Fig.\u00a03D, E).\n\nTo eliminate statistical bias caused by individual variation, we conducted a Mann\u2013Whitney U test to analyze the proportion differences of these 5 uEV clusters between AKI and non-AKI sepsis patients. The results showed that the CMR-EV cluster exhibited a significant difference between the two groups (Fig.3F). Although the IMR-EV subcluster was markedly upregulated in severe and persistent SA-AKI, it showed no significant difference when comparing SA-AKI to non-AKI. To further elucidate the association between CMR-EVs and the severity and prognosis of AKI, a quadratic polynomial model demonstrated stage-dependent (R\u00b2\u2009=\u20090.69, trend test p\u2009<\u20090.05) and duration-sensitive associations (R\u00b2\u2009=\u20090.69, trend test p\u2009<\u20090.05) of CMR-EV proportions with AKI progression trajectories (Fig.\u00a03G, H). Hence, the characterization of the main differential subclusters of uEVs was delineated, identifying the complement receptor-related uEV subcluster with diagnostic potential for SA-AKI.\n\nThrough the analysis of uEV cluster proportions, we identified a significant reduction in the CMR-EV cluster in AKI patients. This finding prompted us to explore whether proteins on individual CMR-EV cluster could serve as biomarkers of SA-AKI, providing an alternative to CMR-EV proportion for easy application. To verify this hypothesis, we used the limma package to identify differentially expressed proteins between the AKI and non-AKI groups, normalized to EVs count. The results showed that 44 proteins exhibited differential expression at the single EV level, with CD35 and CD21 ranking as the top ones, which were indeed the signature proteins of the CMR-EV cluster (Fig.\u00a04A). Principal component analysis (PCA) demonstrated that these differential proteins could effectively distinguish SA-AKI from non-AKI individuals (Fig. 4B). Two unsupervised clustering algorithms, consensus clustering and non-negative matrix factorization (NMF) were then applied to validate this finding. The results revealed that consensus clustering achieved 100% accuracy in patient grouping, whereas NMF misclassified only two AKI patients (AKI05 and AKI08) (Supplementary Fig.\u00a02). Additionally, KEGG enrichment analysis revealed that the upregulated single EV proteins were primarily enriched in inflammation and cell adhesion pathways, while the top downregulated proteins were involved in complement and coagulation pathway (Supplementary Fig.\u00a03).\n\nA Heatmap revealing differentially expressed proteins on single uEV between SA-AKI and non-AKI groups; B PCA plot illustrating the discrimination between AKI and non-AKI based on differentially expressed proteins on single uEV; C Consensus bar chart identifying CD21 and CD35 as the most characteristic differential proteins across 5 algorithms; D Single-molecule fluorescence microscopy confirming colocalization of CD21 (red) and CD35 (red) with uEV markers (CD81, green; CD63, yellow) at single vesicle; E Western blot showing significantly decreased CD35 expression in the SA-AKI compared to non-AKI group. CD21 and CD35 expression was normalized to EV marker, CD9 (n\u2009=\u20093 for Healthy control (HC), AKI and Non-AKI group respectively, pairwise comparisons between groups were performed using paired two-tailed t-test); F CD21 and CD35 were normalized to uEV protein concentration (c(uEV)) detected by BSA assay, data are presented as mean with SD (AKI n\u2009=\u20093, Non_AKI n\u2009=\u20093, HC n\u2009=\u20093, pairwise comparisons between groups were performed using paired two-tailed t-test). Source data are provided as a Source Data file.\n\nTo determine the reliability of CD35 and CD21 as single-vesicle biomarkers, we employed 4 machine learning algorithms in conjunction with the threshold from the limma package. Across all models, CD35 and CD21 were consistently identified as candidate biomarkers among the 44 differentially expressed proteins (Fig.\u00a04C, Supplementary Fig.\u00a04A\u2013F). The AUC-ROC for CD35 and CD21 reached 0.92 and 0.91, respectively (Supplementary Fig.\u00a04G, H). These findings suggest that differential proteins on single EV possess a strong discriminatory capability for AKI.\n\nTo confirm the presence of CD35 and CD21 on uEVs, single-molecule imaging was applied to verify their colocalization with CD63 and CD81 at single-vesicle level (Fig.\u00a04D). The results revealed that the proportions of CD35+ cluster (CD63+CD35+, 8.5%; CD81+CD35+, 6.5%; CD63+CD81+CD35+, 1.6%) were much higher than CD21+ cluster (CD63+CD21+,0.7%; CD81+CD35+, 0.5%; CD63+CD81+CD35+, 0.1%). Therefore, we observed variant percentage of subpopulations characterized with CD35 and distinct combinations of EV markers, which is consistent with the EV heterogeneity with distinct surface marker profiles21. Moreover, the overall count of CD21+ cluster appeared to be relatively lower compared to CD35+ cluster. To further validate protein expression, total CD35 and CD21 were measured by western blot analysis and normalized to exosome marker CD9. Decreased CD35 was observed despite the increased number of EVs in AKI group isolated from the same volume of 24-hour urine samples compared to healthy controls and sepsis non-AKI. In contrast, CD21 at single-vesicle showed no significant differences across the groups (Fig.\u00a04E). To eliminate potential biases arising from variations in individual EV marker, CD35 and CD21 levels were also normalized to total uEV protein concentration of each sample, which consistently demonstrated the reduction of CD35 expression in SA-AKI patients (Fig.\u00a04F).\n\nTo explore the diagnostic potential of CD35 expression on single uEV (CD35-uEV), urine samples were collected from 70 SA-AKI patients, 44 non-AKI sepsis patients, and 20 healthy controls across multicenters (Fig.\u00a05A). The basic clinical characteristics were shown in Table\u00a01. SA-AKI patients exhibited higher SOFA scores, elevated levels of procalcitonin and high-sensitivity CRP, increased frequency of catecholamine use, and significantly reduced 24-h urine output. Additionally, these patients showed a greater predisposition toward bacterial and fungal infections. To account for the potential influence of urine concentration, CD35 on single uEV was calculated by normalizing the total CD35 measured by ELISA to the number of EVs detected by EV quantification kit. The results showed no significant difference in CD35-uEV between the non-AKI and healthy control groups (p\u2009=\u20090.235), while a significant reduction was observed in AKI (p\u2009<\u20090.001) compared to non-AKI group (Fig.\u00a05B). The AUC-ROC for CD35-uEV in distinguishing AKI from non-AKI in sepsis patients was 0.89 (95% confidence interval (CI), 0.83\u20130.95) (Fig.\u00a05C). Moreover, to minimize methodological bias from EV quantification, single CD35-uEV levels were also normalized to the uEVs count detected by nano-flow cytometry. The results consistently demonstrated that CD35-uEV levels significantly reduced in the AKI group (p\u2009<\u20090.001) (Supplementary Fig.\u00a05A), with comparable diagnostic capacity (AUC-ROC: 0.88) (Supplementary Fig.\u00a05C).\n\nA Sample processing and detection workflow of SA-AKI cohort (n\u2009=\u2009134, collected within 24\u2009h after clinical diagnosis of AKI) (Image was created in BioRender. Li, N. (2025) https://BioRender.com/d94efgw); B Expression differences of CD35-uEV among various groups (Mann\u2013Whitney U two-sided statistics). CD35 at single EV (CD35-uEV) was calculated by total CD35 measured by ELISA normalized to EV account; C ROC curve illustrating the ability of CD35-uEV to discriminate AKI from non-AKI sepsis patients (AUC-ROC 0.89, 95% CI 0.83\u20130.95); D CD35-uEV levels across AKI severity stages were analyzed using Mann\u2013Whitney U two-sided statistics (Compared in pairs) (Non-AKI: n\u2009=\u200944, Stage1: n\u2009=\u200929, Stage2: n\u2009=\u200931, Stage3: n\u2009=\u200910); E Comparative analysis of CD35-uEV concentrations between transient versus persistent AKI was conducted with Mann\u2013Whitney U two-sided statistics (p\u2009=\u20096.1E\u22125, Transient-AKI: n\u2009=\u200927, Persistent-AKI: n\u2009=\u200943); F ROC curve for CD35-uEV in predicting persistent AKI; G Correlation between CD35-uEV and median recovery time from AKI (log-rank test, p-value: 0.037). The blue and pink translucent bands indicate the 95% confidence intervals (error bands) for the low-risk and high-risk groups, respectively; H\u2013J Correlation (general linear regression) between CD35-uEV and peak serum creatinine (Max_Scr) (r\u2009=\u2009\u22120.44, 1.5E\u22126) (H), procalcitonin levels (r\u2009=\u2009\u22120.26, p\u2009=\u20090.009) (I) and hypersensitive C-reactive protein (hs-CRP) levels (r\u2009=\u2009\u22120.27, p\u2009=\u20090.006) (J); K The General linear regression based restricted cubic spline curve revealed that CD35-uEV are an independent predictor of the lowest eGFR. Source data are provided as a Source Data file.\n\nTo investigate whether CD35-uEV correlate with the severity and adverse outcome of AKI, we analyzed their expression across different stages and durations of AKI patients. The results showed that CD35-uEV progressively decreased with advancing AKI stages (Fig.\u00a05D). Besides, CD35-uEV levels were also significantly lower (p\u2009<\u20090.001, Fig.\u00a05E) in the persistent AKI group compared to transient AKI (AUC-ROC: 0.77, 95% CI of 0.65\u20130.90) (Fig.\u00a05F).\n\nTo assess whether CD35-uEV is associated with the recovery time of AKI patients, we calculated the median expression level of CD35-uEV in the AKI group (12.05 \u00d710\u221211\u2009ng/EV) and divided the patients into high-risk (<12.05 \u00d710\u221211\u2009ng per EV) and low-risk (\u226512.05 \u00d710\u221211\u2009ng per EV) groups. The KM curve showed that the median recovery time for the low-risk group was significantly shorter than that for the high-risk group (p\u2009=\u20090.037) (Fig.\u00a05G). Correlation analysis revealed a significant negative correlation between CD35-uEV levels and the highest serum creatinine after admission (r\u2009=\u2009\u22120.44, p\u2009<\u20090.001), procalcitonin (r\u2009=\u2009\u22120.26, p\u2009=\u20090.009), and serum hs-CRP (r\u2009=\u2009\u22120.27, p\u2009=\u20090.006) (Fig.\u00a05H\u2013J).\n\nTo confirm whether CD35-uEV is an independent predictor of kidney function deterioration, we performed linear regression to examine the association between CD35-uEV and the highest serum creatinine, adjusting for age, gender, history of hypertension, diabetes, and cardiovascular disease, as well as urine output, SOFA scores, pathogen identification, and catecholamine use. The results were visualized using restricted cubic splines. After adjusting for these confounding factors, CD35-uEV remained significantly associated with the highest serum creatinine (p\u2009<\u20090.001), demonstrating that CD35-uEV is independently associated with renal function decline for SA-AKI (Fig.\u00a05K). Hence, CD35 expression on single uEV was a reliable biomarker for the diagnosis of SA-AKI, which meanwhile was associated with the severity and recovery time.\n\nGiven the capacity of CD35-uEV in the diagnosis and prognosis of AKI, we sought to determine whether this marker could also predict short and long-term adverse outcomes in AKI patients. Correlation analysis (Fig.\u00a06A) showed no significant correlation between CD35-uEV and ICU length of stay (p\u2009=\u20090.79), neither clear association with renal replacement therapy was observed (p\u2009=\u20090.503) (AUC-ROC: 0.57, 95% CI: 0.38\u20130.76) (Fig.\u00a06B, C). During the 30-day follow-up period, 10 patients died. In the deceased group, CD35-uEV expression was significantly lower (p\u2009=\u20090.043) (Fig.\u00a06D), with an AUC-ROC of 0.70 for discriminating from survivals (95% CI: 0.54\u20130.86) (Fig.\u00a06E). Based on the median CD35-uEV level (2.89 \u00d7 10\u221211\u2009ng/EV, death patients were divided into high-risk (<2.89 \u00d7 10\u221211\u2009ng/per EV) and low-risk (\u22652.89 \u00d7 10\u221211\u2009ng/per EV) groups. The KM curve indicated that the median survival tended to be lower in the high-risk group compared to the low-risk group, although without reaching statistically significance (p\u2009=\u20090.12) (Fig.\u00a06F). Intriguingly, patients progressing to AKD exhibited significantly attenuated CD35-uEV expression (p\u2009=\u20090.045; Fig.\u00a06G), demonstrating moderate predictive capacity for disease progression (AUC-ROC: 0.66, 95% CI: 0.53\u20130.79) (Fig.\u00a06H). However, no clear predictive ability was observed for the outcome progressing towards CKD (p\u2009=\u20090.234, AUC-ROC: 0.59, 95% CI: 0.45\u20130.72) (Fig.\u00a06I, J). Overall, CD35-uEV could serve as a useful biomarker for predicting the risk of death and progression to AKD for SA-AKI.\n\nA Correlation analysis (general linear regression) between CD35-uEV levels and ICU length of stay (r\u2009=\u2009\u22120.035, p\u2009=\u20090.79); B Comparison of CD35-uEV expression between the renal replacement therapy (RRT) and non-RRT groups revealed no significant difference (p\u2009=\u20090.503, Mann\u2013Whitney U two-sided statistics; RT: n\u2009=\u200914, No-RT: n\u2009=\u200951); C. ROC curve for CD35-uEV predicting the need for renal replacement therapy (AUC-ROC 0.57, 95% CI 0.38\u20130.76); D CD35-uEV expression was significantly reduced in patients who died during the study (p\u2009=\u20090.043, Mann\u2013Whitney U two-sided statistics; Death group: n\u2009=\u200911, Alive group: n\u2009=\u200953); E ROC curve for CD35-uEV predicting mortality (AUC-ROC 0.70, 95% CI: 0.54\u20130.86); F Kaplan\u2013Meier survival analysis (log-rank test) for patients stratified by high and low CD35-uEV levels revealed a trend towards longer survival in the high CD35-uEV group, though this did not reach statistical significance (p\u2009=\u20090.12). The blue and pink translucent bands indicate the 95% confidence intervals (error bands) for the low-risk and high-risk groups, respectively; G CD35-uEV expression was markedly reduced in patients who progressed to acute kidney disease (AKD) (p\u2009=\u20090.045, Mann\u2013Whitney U two-sided statistics; AKD group: n\u2009=\u200927, Non-AKD group: n\u2009=\u200939); H ROC curve for CD35-uEV predicting AKD (AUC-ROC 0.66, 95% CI 0.53\u20130.79); I Comparison of CD35-uEV expression in SA-AKI patients who progressed to chronic kidney disease (CKD, n\u2009=\u200923) and who did not (non-CKD, n\u2009=\u200954) (p\u2009=\u20090.234) (Mann\u2013Whitney U two-sided statistics); J ROC curve for CD35-uEV to predict CKD (AUC-ROC 0.59, 95% CI 0.45\u20130.72). Data are presented as box plots showing the median (middle line), the 25th and 75th percentiles (box limits), the minimum and maximum values (whiskers), and outliers (individual points). Source data are provided as a Source Data file.\n\nTo investigate whether CD35-uEV enables early identification of AKI, we collected uEVs samples from 72 sepsis patients within 12\u2009h after sepsis onset, who had not yet been diagnosed with AKI and followed them to observe their risk of developing AKI defined as E-AKI prospective cohort (Fig.\u00a07A, Table\u00a02). Patients with subclinical AKI exhibited reduced urine output, elevated levels of procalcitonin and high-sensitivity CRP, and a higher frequency of catecholamine use. However, no significant differences were observed between the groups in terms of infection agents or SOFA scores. During the follow-up period, 26 patients developed AKI. After normalizing for EV counts, single CD35-uEV levels were significantly lower in patients who progressed to AKI compared to those who did not (p\u2009<\u20090.001, AUC-ROC: 0.84, 95% CI: 0.74\u20130.94) (Fig.\u00a07B, C). Notably, similar results were observed when normalized to uEV counts quantified by nano-flow cytometry (p\u2009<\u20090.001, AUC-ROC: 0.82, 95% CI: 0.71\u20130.93) (Supplementary Fig.\u00a05B, D). Regarding AKI stages, patients who developed stage 1 AKI showed significantly lower baseline CD35-uEV levels compared to those who did not develop AKI. Additionally, patients who developed stage 2 AKI had lower CD35-uEV levels compared to those with stage 1 (Fig.\u00a07D). The AUC-ROC for distinguishing AKI stage 1 from stages 2-3 (severe AKI) was 0.69 (95% CI: 0.49\u20130.89) (Fig.\u00a07E), suggesting the predictive ability for severe AKI. Similarly, baseline CD35-uEV levels also demonstrated predictive value for patients progressing to persistent AKI (p\u2009=\u20090.04, AUC-ROC: 0.72, 95% CI: 0.52\u20130.92) (Fig.\u00a07F, G).\n\nA Workflow for sample collection and analysis of E-AKI cohort (n\u2009=\u200972), with samples collected 12\u2009h post diagnosis of sepsis, prior to a clinical diagnosis of AKI (Image was created in BioRender. Li, N. (2025) https://BioRender.com/6zj1ctq); B CD35-uEV levels were significantly reduced in subclinical AKI compared to non-AKI sepsis patients (p\u2009=\u20096.0E\u22127, Mann\u2013Whitney U two-sided statistics); C ROC curve illustrating the predictive accuracy of CD35-uEV for subclinical AKI (AUC-ROC 0.84, 95% CI 0.74\u20130.94); D CD35-uEV levels decreased progressively with increasing AKI stages (Mann\u2013Whitney U two-sided statistics (Compared in pairs), Non-AKI: n\u2009=\u200946, Stage1: n\u2009=\u200911, Stage2: n\u2009=\u200911, Stage3: n\u2009=\u20094); E ROC curve for CD35-uEV predicting AKI severity in subclinical AKI patients (AUC-ROC 0.69, 95% CI 0.49\u20130.89); F Significant reduction of CD35-uEV in patients progressing to persistent AKI compared to those with transient AKI (p\u2009=\u20090.04, Paired two-tailed t-test, Transient-AKI: n\u2009=\u200911, Persistent-AKI: n\u2009=\u200915); G ROC curve demonstrating the prediction of CD35-uEV for persistent AKI in subclinical AKI patients (AUC-ROC 0.72, 95% CI 0.52\u20130.92); H Logistic regression-based restricted cubic spline analysis identified CD35-uEV as an independent diagnostic factor for subclinical AKI (p\u2009<\u20090.001) (adjusting for age, gender, history of hypertension, diabetes, and cardiovascular disease, as well as urine output, SOFA scores, pathogen identification, and catecholamine use), the pink translucent band represents the 95% confidence interval (error bands); I Correlation (general linear regression) of CD35-uEV levels with the peak serum creatinine (Max_Scr) (r\u2009=\u2009\u22120.32, p\u2009=\u20090.008) in subclinical AKI patients post-admission. Source data are provided as a Source Data file.\n\nTo further validate whether CD35-uEV is an independent predictive biomarker for subclinical AKI, we used logistic regression (adjusting for age, gender, history of hypertension, diabetes, and cardiovascular disease, as well as urine output, SOFA scores, pathogen identification, and catecholamine use) to model the association between early CD35-uEV levels within 12\u2009h after sepsis and subsequent clinical diagnosis of AKI. The restricted cubic spline analysis showed that as CD35-uEV levels decreased, the risk of AKI significantly increased (p\u2009<\u20090.001), confirming that CD35-uEV is an independent risk factor for AKI (Fig.\u00a07H). Correlation analysis revealed that, baseline CD35-uEV levels significantly correlated with the highest serum creatinine (r\u2009=\u2009\u22120.32, p\u2009=\u20090.008) (Fig.\u00a07I). Therefore, early detection of CD35-uEV provides a valuable biomarker for identifying subclinical AKI and predicting the occurrence of severe AKI as well.\n\nTo trace the origin of CD35-uEV, regional transcriptomics data from the KPMP database was analyzed. We found that CD35 is primarily localized to the glomerulus, with decreased expression in the AKI group (Fig.\u00a08A). To further clarify the cellular changes of CD35, single-cell transcriptomics data from biopsies of pneumonia-related AKI patients (GSE21062) were analyzed, including 4 patients with AKI and 3 non-AKI patients. After normalizing and clustering the data, we identified 27 distinct cell populations (Fig.\u00a08B). Key annotation proteins for each cluster were shown in Supplementary Fig.\u00a06. Cellular localization analysis of CD35 revealed that it was\u00a0predominantly expressed in podocytes, with remarkably reduced levels in the injured population (Fig.\u00a08C). Differential analysis showed that CD35 as well as podocyte marker NPHS1 were downregulated in the AKI group, whereas inflammation and injury markers such as SPP1, GPX3, and C1R were upregulated (Fig.\u00a08D). KEGG pathway enrichment analysis indicated upregulation of inflammation-related pathways, including NK cell-mediated cytotoxicity, antigen presentation, and oxidative phosphorylation. Additionally, pathways related to ferroptosis and necroptosis were significantly upregulated (Fig.\u00a08E). Meanwhile, CytoTRACE analysis revealed a lower differentiation state in injured podocytes compared to normal podocytes population (Fig.\u00a08F), which was consistent with previous studies showing that podocytes undergo dedifferentiation following injury22. Pseudotime analysis was performed to further investigate the differentiation trajectory of podocytes. Interestingly, podocytes population transitioned to injured podocytes subgroups, accompanied by a remarkable reduction in CD35 expression (Fig.\u00a08G, H).\n\nA Regional proteomic analysis revealed CD35 localization within the glomeruli, with a marked decrease in expression in AKI groups (Kidney Precision Medicine Project database: https://atlas.kpmp.org/); B Single-cell clustering analysis of pneumonia-associated AKI identified distinct cell populations, including injured podocytes; C Localization of CD35 in podocytes which reduced in the injured population; D Differential gene expression analysis demonstrated a significant reduction of CD35 in podocytes from AKI patients (based on limma package); E KEGG pathway enrichment analysis highlighted the upregulation of inflammatory and injury-related pathways in AKI podocytes (KEGG pathway enrichment was performed via hypergeometric testing with Benjamini-Hochberg FDR correction, threshold: FDR\u2009\u2264\u20090.05); F Cytotrace analysis identified injured podocytes in a dedifferentiated state; G\u00a0Pseudotime analysis traced the trajectory of normal podocyte differentiated into injured states, H\u00a0characterized with decline of CD35 during AKI progression; I Spatial transcriptomic mapping showed spatial distribution of injured podocytes (Spatial mapping of single-cell transcriptomic annotations onto spatially resolved transcriptomic profiles). J Raincloud plot illustrating significantly reduced CD35 expression in injured podocytes at the spatial transcriptomic level (Injury podocyte, n\u2009=\u20097 data mapped cells; normal podocyte, n\u2009=\u20097 data mapped cells, Mann\u2013Whitney U statistics, p\u2009=\u20090.012). Data are presented as box plots showing the median (middle line), the 25th and 75th percentiles (box limits), the minimum and maximum values (whiskers), and outliers (individual points). Podo podocyte, Podo_inj injured podocyte. Source data are provided as a Source Data file.\n\nTo observe spatial distribution of injured podocytes, we collected additional spatial transcriptomics data from AKI patients in the KPMP database. Using Seurat\u2019s integration method, we mapped cell clusters onto the spatial transcriptomics data, scoring each spatial transcriptomics label for cell characteristics. The results demonstrated that podocytes and injured podocytes were located in the glomerulus, supporting the accuracy of our spatial cell annotations (Fig.\u00a08I). Moreover, injured podocytes were widely distributed in multiple glomeruli rather than confined to a limited area. Further analysis revealed a general downregulation of CD35 in injured podocytes compare to normal podocytes (p\u2009=\u20090.012) (Fig.\u00a08J). Therefore, multi-omic data demonstrated that podocytes are injured in SA-AKI, characterized by reduced CD35 expression. The unique population of uEVs with reduced CD35 expression may originate from these injured podocytes.\n\nNext, the source of urinary CD35 was further investigated. In normal renal tissue obtained from patients undergoing nephrectomy adjacent to renal carcinoma, CD35 expression exhibited predominant glomerular localization, demonstrating strong spatial concordance with the podocyte-specific marker synaptopodin (Supplementary Fig.\u00a07A). Correspondingly, super-resolution imaging demonstrated high colocalization of CD35 with Nephrin on individual vesicles. Among the total vesicles, approximately 13.57% were CD35+, decreasing to 3.1% in the AKI group. While Nephrin+ vesicles accounted for 15.78% in the non-AKI group and 13.35% in the AKI group. Notably, Nephrin co-expression was observed in 94.4% of CD35+ vesicles in the non-AKI group and 87.5% in the AKI group (Supplementary Fig.\u00a07B). These findings suggest that CD35-uEV is predominantly derived from podocyte.\n\nNext, we explored the presence of CD35 in other urine fractions obtained through differential centrifugation. Interestingly, the results showed that CD35 was not detectable in the fraction of neither large EV nor supernatant, proving that CD35 is exclusively localized to sEV (p\u2009<\u20090.0001) (Supplementary Fig.\u00a07C). To rule out the possibility of its origin from circulation, serum from SA-AKI and non-AKI patients were collected and no significant differences (p\u2009=\u20090.46) in circulating CD35 were noted (Supplementary Fig.\u00a07D). Collectively, these results suggest that CD35 likely originates predominantly from podocyte-derived EVs, rather than existing in soluble form or deriving from circulatory source.\n\nCurrently, several biomarkers have been validated for the early diagnosis of AKI, with TIMP2*IGFBP7 being the only FDA-approved test23. The ADQI guidelines recommend using a combination of multiple biomarkers to improve the accuracy of early AKI diagnosis in sepsis24. TIMP2*IGFBP7 is a well-established marker of tubular cell cycle arrest due to stress injury25. We aimed to explore whether the combination of TIMP2*IGFBP7 with CD35-uEV could further enhance the diagnostic accuracy for SA-AKI.\n\nImpressively, we identified that CD35-uEV displayed higher accuracy compared to TIMP2*IGFBP7 (AUC-ROC: 0.84 vs 0.75) in our sepsis cohort, while the combined biomarker yielded the best accuracy, with an AUC-ROC of 0.87 (Supplementary Fig.\u00a08A, B). The calibration curve also showed that the predicted values closely matched the actual values when CD35-uEV were combined with TIMP2*IGFBP7 (Supplementary Fig.\u00a08C). Decision curve analysis further demonstrated that the net benefit was highest when both CD35-uEV and TIMP2*IGFBP7 were used together (Supplementary Fig.\u00a08D). To visualize the combined diagnostic capacity, the scores of CD35-uEV and TIMP2*IGFBP7 to calculate the probability of AKI occurrence in sepsis patients was presented in nomogram (Supplementary Fig.\u00a08E). Besides, the risk of severe AKI could be predicted through the combined biomarker score (p\u2009=\u20090.02,\u00a0AUC-ROC: 0.78, 95% CI: 0.59\u20130.96) (Supplementary Fig.\u00a08F, G), although predicting persistent AKI was not significantly achieved (Supplementary Fig.\u00a08H, I) (p\u2009=\u20090.9754; AUC-ROC: 0.52, 95% CI: 0.26\u20130.78).\n\nFurthermore, to determine whether the combined score is an independent early diagnostic factor for AKI, a multivariable logistic regression model was established to evaluate the association with subsequent AKI diagnosis in the E-AKI cohort. The restricted cubic spline analysis showed that the risk of subsequent AKI diagnosis significantly increased with the increasing level of the combined score (p\u2009<\u20090.001) (Supplementary Fig.\u00a08J). Therefore, combination of tubular and glomerular injury markers with TIMP2*IGFBP7 and CD35-uEV enhances diagnosis accuracy of SA-AKI, which also shows potential in predicting severe AKI.\n\nTo evaluate whether CD35-uEV also serves as a biomarker for AKI caused by other etiologies, 30 patients who underwent cardiac surgery with a risk of developing ischemic AKI were enrolled. The results demonstrated no statistically significant difference in CD35-uEV levels between the AKI and non-AKI groups (AUC-ROC: 0.57, 95% CI: 0.36\u20130.79) (Supplementary Fig.\u00a09A, B). Given that CD35-uEV is proposed as a non-invasive biomarker of podocyte injury, we further explore podocyte damage in cardiac surgery-associated AKI. Single-cell RNA sequencing data from urine cells of patients following cardiac surgery (GSE19932) was analyzed through dimensionality reduction and clustering. The analysis revealed no significant changes in the expression of canonical podocyte markers, including NPHS1, NPHS2, WT1, and CD35, between the groups (Supplementary Fig.\u00a09C). Moreover, podocyte differentiation scores were comparable between AKI and non-AKI groups (Supplementary Fig.\u00a09D), indicating no substantial podocyte injury occurred. These findings suggest that CD35-uEV may represent a specific and non-invasive biomarker for podocyte injury in SA-AKI other than ischemic AKI.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62229-4/MediaObjects/41467_2025_62229_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62229-4/MediaObjects/41467_2025_62229_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62229-4/MediaObjects/41467_2025_62229_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62229-4/MediaObjects/41467_2025_62229_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62229-4/MediaObjects/41467_2025_62229_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62229-4/MediaObjects/41467_2025_62229_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62229-4/MediaObjects/41467_2025_62229_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62229-4/MediaObjects/41467_2025_62229_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "SA-AKI carries a high mortality risk and is often accompanied by severe adverse outcome. Timely intervention is crucial for improving patient outcomes. However, early and accurate biomarkers for diagnosis of AKI are still lacking. In this study, we employed single-vesicle analysis to delineate the heterogeneous populations of uEVs in sepsis patients. Notably, we identified a specific subcluster characterized by CD35 expression with excellent potential for discriminating SA-AKI. Further studies identified that CD35 on single uEV (CD35-uEV) not only enable early diagnosis of subclinical SA-AKI but also provide a reliable prediction of progressing to severe and/or persistent AKI. Impressively, CD35-uEV represents a non-invasive histological biomarker originated exclusively from the injured podocytes for SA-AKI.\n\nUtilizing PBA technology, we successfully captured surface proteome on individual uEV, allowing a complete profiling of the uEV landscape. By comparing the top 5 clusters with remarkable proportional difference, we found that proportion of complement receptor CD21+ CD35+ cluster (CMR-EV) could clearly distinguish SA-AKI from non-AKI patients. Given the key roles of CD21 and CD35 in complement system regulation, the reduced proportion of this cluster may be linked to abnormal complement activation and dysfunction in sepsis26. We also identified a distinct subcluster, IMR-EV, characterized by the expression of inflammation-regulating markers such as CD38, CD26, and TICAM2. The proportion of this subcluster was elevated in severe and persistent AKI indicating its involvement in the development of these conditions. Therefore, using PBA technology, uEV clusters was profiled, a unique uEV cluster characterized with complement receptor expression as potential biomarker of SA-AKI was identified.\n\nNext, the diagnostic potential of CD35 on single vesicles of this unique subcluster was explored in the multicenter independent validation cohorts. CD35-uEV is demonstrated as accurate diagnostic biomarker of SA-AKI, associating with the severity and duration as well. Subclinical AKI was proposed by Dialysis Quality Initiative (ADQI) group which was characterized by elevated levels of biomarkers in the absence of an increase in serum creatinine24. In this study, CD35-uEV showed remarkable ability to identify the sub-clinical AKI patients and to assess the risk of severe clinical AKI in the prospective cohort. In the past decades, the diagnostic potential of tubular injury markers was evaluated in SA-AKI. It turned out that urinary [TIMP-2]\u00b7[IGFBP7] outperformed other biomarkers for prediction of AKI with area under the curve of 0.80, while data for KIM-1 and NGAL specifically used for SA-AKI are not sufficient23. It is noteworthy that elevations of those early biomarkers for AKI occur in only one-third of subclinical AKI patients27. This presents challenges for the early and accurate diagnosis of SA-AKI. Herein we identified a useful single uEV biomarker that is capable of identifying clinical and subclinical AKI patients with good performance.\n\nPersistent AKI is associated with higher mortality and poor prognosis28,29, and patients progressing to AKD are prone to multiple organ complications30. The 23rd ADQI [23] conference recommended using biomarkers to predict the duration and recovery of AKI. Currently, only CCL14 was reported to predict persistent AKI31, and biomarkers for predicting AKD are still lacking. Impressively, in our study, CD35-uEV demonstrated potential in predicting both persistent AKI and AKD. AKI patients with higher levels of CD35-uEV had significantly shorter median recovery times. However, no significant differences were observed regarding CKD outcomes, indicating CD35-uEV is well-suited for monitoring short-term SA-AKI progression via reflecting the acute renal injury conditions. Interestingly, in the prospective E-AKI clinical cohort, CD35-uEV at 12\u2009h after sepsis onset predicted the risk of persistent AKI, indicating its potential application in early AKI screening, stratification of high-risk patients and clinical decision-making. CD35-uEV as a useful diagnostic biomarker may provide a promising approach for prognostic assessment of SA-AKI.\n\nGiven the promising potential of CD35-uEV in diagnosing SA-AKI, the cellular source of this biomarker and its implication to renal damage was further explored. Integrative analysis of multi-omic data collectively confirmed that CD35 is predominantly expressed in renal podocytes. Besides, extensive podocyte damage was noticed in SA-AKI, aligning with a early study showing podocytes present severely damaged morphology32. Despite of this finding, most previous studies have focused on vascular endothelial damage33,34 or tubular cell injury35,36 which consequently yielded biomarkers mostly derived from the injured tubule epithelial cells for SA-AKI. However, the dissociation between structural and functional changes was consistently noted in postmortem human and animal models37,38,39. Herein, we identified a notable decrease of CD35 expression in the damaged podocytes, closely paralleling the extent of podocyte injury. The dominant proportion of CD35+ Nephrin+ uEVs exclusively presented in the small EV fraction of urine collectively proved this unique cluster originated from injured podocytes. These findings were indeed consistent with previous studies showing the presence of membrane-association of complement receptor 1, CD35 in urine40. Interestingly, membrane-bound complement regulatory proteins were increasingly recognized as critical component of the complement system41,42. It is reasonable to speculate that the podocyte derived complement receptor CD35 on EVs may play an important role in the dysregulated immune responses for SA-AKI. Thus, CD35-uEV may serve as a practical non-invasive marker for podocyte injury and complement related immune dysregulation. Importantly, our findings may suggest an unrecognized pathophysiology mechanism of SA-AKI. However, podocyte injury appears to be uncommon in AKI caused by other etiologies, which may explain the lack of detectable changes in CD35-uEV levels observed in cardiac surgery-associated AKI.\n\nThe ADQI guidelines recommend using multiple biomarkers to improve the accuracy of early diagnosis of SA-AKI24. TIMP2 and IGFBP7 are cell cycle arrest markers of tubular epithelial cell injury and have been used for early diagnosis of various AKIs43. A meta-analysis23 confirmed that TIMP2*IGFBP7 significantly outperforms other biomarkers for early AKI diagnosis. Comparatively, we identified that CD35-uEV outperformed TIMP2*IGFBP7 in terms of early diagnosis of SA-AKI. This superiority may be attributed to the property of uEVs derived from injured renal cells which better reflect cellular damage compared to traditional soluble biomarkers. Further analysis identified that the combination of CD35-uEV with TIMP2*IGFBP7 improved the accuracy for early AKI diagnosis and prediction of subsequent risk of severe AKI. Therefore, the combination of tubular and glomerular injury markers may provide an approach to enhance the diagnostic accuracy of SA-AKI. Further high-quality, multicenter studies are needed to evaluate the external applicability and efficacy of this biomarker across different races, countries, and healthcare settings.\n\nOverall, through proteomic characterization of single uEV, this study identified CD35-uEV derived from injured podocyte as a practical biomarker of SA-AKI to realize early diagnosis and prognostic assessment. Integration of tubular and glomerular injury markers based on CD35-uEV and TIMP2*IGFBP7 offers a useful diagnostic approach with improving accuracy. With CD35-uEV we describe the useful marker that specifically detects podocyte injury to identify the risk, severity and prognosis of SA-AKI.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "This study adhered to the ethical principles outlined in the Declaration of Helsinki and other relevant ethical guidelines. The research protocol received approval from the Institutional Review Boards (IRBs) of Zhongda Hospital, Southeast University; Jiangsu Province Hospital of Traditional Chinese Medicine; and The Second Affiliated Hospital of Anhui Medical University. Written informed consent was obtained from all participants prior to their involvement in the study. This study enrolled ICU patients primarily diagnosed with sepsis, defined by an increase of \u22652 points from baseline in the SOFA score in the presence of infection. Between June 2022 and February 2024, we collected data from individuals of SA-AKI and E-AKI cohorts across 3 centers: Zhongda Hospital Southeast University, Jiangsu Provincial Hospital of Traditional Chinese Medicine, and the Second Affiliated Hospital of Anhui Medical University.\n\nInclusion criteria for the SA-AKI cohort were sepsis patients who developed AKI within seven days of sepsis diagnosis, based on the KDIGO criteria44 (a sudden decrease in kidney function within 48\u2009h with an absolute increase in serum creatinine of \u226526.5\u2009\u03bcmol/L, a \u226550% increase from baseline within seven days, or urine output <0.5\u2009ml/kg/h for at least six hours). Patients who did not develop AKI within 7 days post-sepsis diagnosis and healthy individuals served as controls. Participants were required to be 18 years or older. Exclusion criteria included (1) end-stage renal disease requiring dialysis, (2) kidney transplantation, (3) a history of chronic kidney disease (CKD, defined as structural and functional kidney abnormalities persisting for over three months), (4) end-stage cancer of the urinary system, (5) unwillingness to participate, and (6) urinary tract infections. Urine samples were collected within 24\u2009h of admission.\n\nThe E-AKI cohort included patients within 12\u2009h of sepsis admission, who were subsequently monitored for the risk of developing AKI. The exclusion criteria were the same as the SA-AKI cohort. Urine samples were collected within 12\u2009h of admission.\n\nTo validate biomarker expression across various AKI subtypes, we additionally enrolled post-cardiac surgery patients (AKI: n\u2009=\u200915; non-AKI: n\u2009=\u200915). Urine samples were collected within 24\u2009h of AKI diagnosis, the same inclusion/exclusion criteria were applied as in the SA-AKI cohort.\n\nFor each patient, we recorded serum creatinine levels, glomerular filtration rate (eGFR), AKI stage (AKI Stage 1 is defined by an increase in serum creatinine of \u22650.3\u2009mg/dL or a relative increase of \u226550%, or a urine output of <0.5\u2009mL/kg/h for 6\u201312\u2009h. AKI Stages 2\u20133 are defined by an increase in serum creatinine of >200% or a urine output of <0.5\u2009mL/kg/h for \u226512\u2009h), and basic clinical information. We also monitored multiple outcomes, including the risk of persistent AKI (defined as AKI lasting more than 2 days), length of ICU stay, renal replacement therapy (RT), 30-day mortality, progression to acute kidney disease (AKD, defined as AKI lasting more than 7 days45), and the risk of developing chronic kidney disease (CKD, defined as chronic renal structural and functional impairment (history of renal impairment greater than 3 months46)).\n\n24-hour urine samples were collected from each patient within the designated timeframe (SA-AKI cohort: 24\u2009h within diagnosis AKI; E-AKI cohort: within 12\u2009h of admission). Samples were immediately centrifuged at 4\u2009\u00b0C and 3000\u2009\u00d7\u2009g for 20\u2009min to remove cell debris. The supernatant was further centrifuged at 13,500\u2009\u00d7\u2009g for 30\u2009min to precipitate large vesicles and keep the supernatant. Ultracentrifugation was performed at 200,000\u2009\u00d7\u2009g for 2\u2009h to pellet the small extracellular vesicles (sEVs), which were then resuspended in PBS. Post-ultracentrifugation EV suspension was processed through a size exclusion chromatography (SEC) (Izon, Christchurch, New Zealand) column to yield purified EV suspension according to the manufacturer\u2019s protocol.\n\nIn this study, we employed PBA technology17 to detect surface proteome on individual uEV. The PBA reagent kit employed in this study was ExoSeek\u00ae Panel 260 (Secretech, Shanghai, China). Briefly, uEVs were captured through GM1 gangliosides on the membrane of uEVs using the B subunit of cholera toxin (CTB). The uEVs samples (5\u2009\u00b5L) were added in 96 well plate with CTB coating and incubated at room temperature for one hour. The plate was then washed three times with PBS containing 0.05% Tween 20 (PBST) to remove unbound materials. Specific DNA-labeled antibody probes were then added to the captured uEVs and incubated at room temperature for two hours. The plate was then washed with PBST for 3 times. Individual RCA products were added to the wells. The next step involved annealing the DNA-labeled antibody probes to their complementary sequences on the RCA products. The single EV barcode was added via extension reaction. Then DNA sequences they carried were amplified via PCR, providing labels for protein expression levels in individual uEVs. PCR amplification of these probes and adjacent RCA sequences was conducted to increase the signal strength. After amplification, sequencing was performed to read the unique barcodes representing individual probes and RCA products. Sequencing was carried out on the Illumina NovaSeq S4 platform using a PE150 sequencing strategy, which provides high-throughput, paired-end reads of 150 base pairs. The sequencing data was then processed via Evisualizer decoding package (Secretech, Shanghai, China) to decode the barcodes, allowing for the identification and quantification of protein levels in individual uEVs. This process enabled a detailed analysis of the protein composition and provided insights into the heterogeneity of the uEV population.\n\nSingle EV data was processed by clustering analysis using the Seurat package. Initially, we normalized the data to balance the features, followed by dimension reduction through principal component analysis (PCA). We then visualized the data spatially using t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) based on the PCA results. Through Seurat\u2019s FindNeighbors and FindClusters functions, we clustered the vesicles according to their expression patterns to identify distinct subclusters. To characterize the functional features of these subclusters, we identified signature proteins for each uEV cluster using the FindAllMarkers function and displayed the top signature proteins of each subgroup through a dot plot. Subgroups were categorized based on AKI staging and persistent AKI to assess the association between individual uEV cluster and AKI. Differential expression of individual uEV proteins between AKI groups was screened using the limma package. Furthermore, protein biomarkers on individual uEV were jointly selected through the integration of four supervised machine learning and regularization algorithms (Random Forest, XGBOOST, LASSO, SVM-RFE).\n\nPurified uEVs samples were placed on a 200-mesh nickel grid, stained with 2% phosphotungstic acid for 5\u2009min, air-dried, and then subjected to morphological analysis using Transmission Electron Microscopy (TEM). The particle size distribution of sEVs was determined using nanoparticle tracking analysis with the ZetaView PMX 110 (Particle Metrix)47.\n\nTo visualize single EV, dSTORM imaging was performed with super-resolution microscopy (Oxford Nanoimaging, ONI). uEVs were immobilized on a chip surface and stained with anti-CD35 (1:50, CL488-68033, Proteintech), CD21 (1:50, Abcam, ab315160), anti-Nephrin (Abcam, ab216341), anti-CD63 (Abcam, ab134045), and anti-CD81 (Abcam, ab79559) following the protocol provided by the EV Profiler Kit (EV-MAN-1.0, ONI, Oxford). For indirect staining, secondary antibodies, including anti-Ms-FITC, anti-Rab-Cy3, and anti-Rab-AF488, were incubated for 30\u2009min, followed by a wash with 40\u2009\u00b5L of W1 buffer. Fresh imaging buffer was then applied to the chip, which was immediately imaged in dSTORM mode using NimOS software (version 1.19, ONI). A minimum of six images were captured per sample and analyzed using the EV profiling App on the ONI online platform CODI. The analysis workflow encompassed drift correction, filtering by frame index and localization precision to 20\u2009nm, and cluster filtering with a circularity threshold ranging from 0.7 to 1.\n\nPurified uEVs were incubated overnight at 4\u2009\u00b0C with anti-CD35 FITC antibody (BD Pharmingen, 555452) and CD21 APC antibody (BD Pharmingen, 555422) in 100\u2009\u00b5L PBS, alongside a separate aliquot incubated with FITC-conjugated and APC-conjugated isotype control antibodies Following PBS washes to remove unbound antibodies, the uEVs were pelleted by ultracentrifugation at 200,000\u2009\u00d7\u2009g for 2\u2009h. Before measurement, calibration was performed using standard samples. Data signals were collected using Apogee analyzer, with automated discrimination of positive and negative gating for CD35 (488\u2009nm) and CD21 (638\u2009nm) using the isotype control.\n\nThe extracted pure uEVs were mixed with RIPA lysis buffer (Beyotime) at a 1:1 ratio and supplemented with protease inhibitor (Beyotime) at a 1:50 dilution. The mixture was incubated on ice for 2\u2009h for lysis. After lysis, the mixture was centrifuged at 12,000\u2009\u00d7\u2009g for 15\u2009min, and the supernatant was collected to obtain pure protein. The extracted proteins were mixed with loading buffer (Biosharp) at one-fifth of the volume and heated at 95 degrees Celsius for 5\u2009min to denature the proteins. The denatured proteins were loaded at 25\u2009\u00b5g per well and subjected to SDS-PAGE at 160\u2009V for 45\u2009min. After electrophoresis, the polyacrylamide gel was assembled in a sandwich configuration with sponges, the gel, a polyvinylidene difluoride, and another sponge, and transferred using a fast transfer device (GenScript). The membrane was then blocked with 5% milk in PBS for 2\u2009h. It was incubated overnight with primary antibodies at 4\u2009\u00b0C and with secondary antibodies at room temperature for 1\u2009h, followed by three washes with Tris Buffered Saline with Tween-20. Exposure was conducted under Electrochemiluminescence developing solution.47. EV proteins were loaded from samples extracted from equal urine volumes (50\u2009mL) of 24-h urine. The antibodies used were as follows: Rabbit anti-CD9 (1:1000, ab92726, Abcam), anti-CD21 (1:2000, ab75985, Abcam), Rabbit anti-TSG101 (1:1000, ab125011, Abcam), Mouse anti-CD81 (1:1000, ab79559, Abcam), mouse anti-Alix (1:1000, sc-53540, Santa Cruz), and anti-CD35 (1:2000, CSB-PA822164ESR1HU, Huamei). Unprocessed scans of the western blot can be referenced in the source data.\n\nCD35, TIMP2, and IGFBP7 expression was measured using an ELISA kit (AB277439, Abcam, Cambridge (CD35); E-EL-H1453, Elabscience, (TIMP2); E-EL-H6176, Elabscience, (IGFBP7)). Standards and test samples were added to a 96-well plate and incubated at room temperature for 3\u2009h. The wells were washed three times with PBST. 100\u2009\u00b5L of prepared biotinylated antibody was added to each well and incubated at room temperature for 1\u2009h. The washing step was repeated three times. Next, 100\u2009\u00b5L of prepared streptavidin solution was added, followed by incubation at room temperature for 45\u2009min. After three times washes, 100\u2009\u00b5L of TMB substrate was added and incubated at room temperature for 30\u2009min before the addition of stop solution, and absorbance was immediately measured using a spectrophotometer at 450\u2009nm.\n\nuEVs quantification was performed using an exosome quantification kit (SBI, FluoroCet, FCET96A-1). According to the kit instructions, 50\u2009\u00b5L of standards and test samples were added to each well of a 96-well polystyrene plate, followed by 50\u2009\u00b5L each of Buffer A and Buffer B, to make a total reaction volume of 150\u2009\u00b5L. The mixture was gently agitated and incubated at room temperature for 20\u2009min. Fluorescence was measured using a fluorometer at an excitation wavelength of 570\u2009nm and an emission wavelength of 590\u2009nm. uEVs were extracted from equal volume of urine (50\u2009mL) from 24-hour urine. The standard curve for quantification was in units of 1\u2009\u00d7\u200910^7EVs per 250\u2009ng of EVs. The total number of EVs applied for detection varied between 2\u2009\u00d7\u200910^10 and 4\u2009\u00d7\u200910^10EVs.\n\n10X single-cell RNA-sequencing data (scRNA-seq) were obtained from GEO database (GSE210622; GSE199321). Quality control (QC) is performed to filter out low-quality cells and potential doublets. This involves removing cells with unusual gene counts, high mitochondrial gene expression, and low UMI counts. After QC, data is normalized to account for differences in sequencing depth across cells using the \u201cNormalizeData\u201d function in Seurat. Highly variable genes are identified with the \u201cFindVariableFeatures\u201d function. Dimensionality reduction using PCA simplifies the data, and visualization is done using t-SNE or UMAP. Cells are clustered with algorithms like Louvain using the \u201cFindNeighbors\u201d and \u201cFindClusters\u201d functions in Seurat. Clusters are annotated based on known marker genes to identify cell types or states. Differential expression analysis with the \u201cFindMarkers\u201d function identifies marker genes for each cluster. Pathway and functional enrichment analyses are performed on these marker genes using tools of clusterProfiler, or GSEA to understand the biological processes and pathways enriched in each cluster. Pseudotemporal analysis and cytotrace analysis are used to evaluate the differentiation trajectory of various cells. Spatial transcriptomics data was obtained from the KPMP database (V10S14-087_XY01_21-0061). KMPM enables the visualization of gene expression directly within the database, allowing users to explore the expression patterns and distribution of genes in spatially resolved transcriptomics data online (http://www.huayingtangkyoto.com/202576/web_summary.html). Using Seurat\u2019s integration method, anchors were identified and integrated with annotated single-cell data. Mapping techniques were then employed to localize the regions of high expression within the spatial transcriptomics data.\n\nStatistical analysis was conducted using GraphPad Prism 7.00. Continuous variables were expressed as mean\u2009\u00b1\u2009SD or median with interquartile range, while categorical variables were represented as percentages or ratios. The unpaired t-test or Mann\u2013Whitney U test was utilized to evaluate significant differences between two groups. Differences among three groups were assessed using one-way analysis of variance or the Kruskal\u2013Wallis test, with subsequent multiple comparisons. Categorical variables were analyzed using the Chi-square test. The receiver operating characteristic (ROC) curve was employed to evaluate the accuracy of biomarkers. Kaplan\u2013Meier (KM) curves were used to assess survival times. Multivariate logistic regression combined with restricted cubic splines was performed to visualize the association between biomarkers and clinical information. All tests were two-tailed, and P values\u2009<\u20090.05 were considered statistically significant.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Individual EV protein expression data can be downloaded from Figshare public database (https://figshare.com/articles/dataset/Expression_csv/29356076). Single-cell RNA-sequencing (10X) data were downloaded from the GEO database (GSE210622; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE210622, GSE199321; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199321). Spatial transcriptomics data were obtained from the Kidney Precision Medicine Project (KPMP) database (accession: V10S14-087_XY01_21-0061). Interactive visualizations of spatial transcriptomics data are publicly accessible at: http://www.huayingtangkyoto.com/202576/web_summary.html. All remaining data are available within the Article and Supplementary Information files.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Poston, J. T. & Koyner, J. L. Sepsis associated acute kidney injury. BMJ Brit. Med. J. 364, k4891 (2019).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nPeerapornratana, S., Manrique-Caballero, C. L., G\u00f3mez, H. & Kellum, J. A. Acute kidney injury from sepsis: current concepts, epidemiology, pathophysiology, prevention and treatment. Kidney Int. 96, 1083\u20131099 (2019).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nBalkrishna, A. et al. Sepsis-mediated renal dysfunction: pathophysiology, biomarkers and role of phytoconstituents in its management. Biomed. 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Z.L., L.N., G.M.L., Z.T.T., T.T.T., H.R.B., Q.W.W., Z.T. and F.Y.Q. contributed participant recruitment. M.N.N. and L.N. contributed sample collection. M.N.N., L.N. and S.A.R. contributed experimental operation. L.N., S.A.R. and F.Y.Q. contributed data analysis. L.N., T.T.T. and L.L.L. contributed manuscript writing. All authors contributed manuscript review and final approval.\n\nCorrespondence to\n Lin-Li Lv.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Bendetta Bussolati and the other, anonymous, reviewer for their contribution to the peer review of this work. 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Single urinary extracellular vesicle proteomics identifies complement receptor CD35 as a biomarker for sepsis-associated acute kidney injury.\n Nat Commun 16, 6960 (2025). https://doi.org/10.1038/s41467-025-62229-4\n\nDownload citation\n\nReceived: 09 December 2024\n\nAccepted: 15 July 2025\n\nPublished: 29 July 2025\n\nVersion of record: 29 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-62229-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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productivity of a heavily fished ecosystem", + "pre_title": "Environmental Control on the Productivity of a Heavily Fished Ecosystem", + "journal": "Nature Communications", + "published": "06 June 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60453-6/MediaObjects/41467_2025_60453_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60453-6/MediaObjects/41467_2025_60453_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60453-6/MediaObjects/41467_2025_60453_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.15359627", + "https://psl.noaa.gov" + ], + "code": [ + "https://doi.org/10.5281/zenodo.15359627" + ], + "subject": [ + "Marine biology", + "Physical oceanography" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4108948/v1.pdf?c=1749294351000", + "research_square_link": "https://www.researchsquare.com//article/rs-4108948/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60453-6.pdf", + "preprint_posted": "27 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Sustainable fisheries management requires an understanding of the links between environmental conditions and fish stock populations, especially in the context of climate change. From this perspective, identifying phases where ocean climate fluctuations and changes in ecosystem productivity coincide could provide a powerful tool to help inform fisheries management. Using more than 70 years of climate and fisheries data, this study shows that the Newfoundland and Labrador (NL) ecosystem productivity, from primary producers to piscivorous fish, changes in relative synchronicity with the climate of the northern hemisphere over decadal time scales. Such correspondence between the climate and lower and higher trophic levels has not been achieved previously in the Northwest Atlantic in the context of fisheries. This work advances ideas for incorporating environmental knowledge into fisheries management on the NL shelves, or in other regions facing similar dynamics.Earth and environmental sciences/Climate sciences/Ocean sciences/Marine biologyEarth and environmental sciences/Climate sciences/Ocean sciences/Physical oceanographyClimateFisheriesEcosystemBottom-Up controlNW Atlantic Ocean", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Sustainable fisheries management requires an understanding of the links between environmental conditions and fish populations, especially in the context of climate change. From this perspective, identifying the phases in which ocean climate fluctuations and changes in ecosystem productivity coincide could provide a powerful tool to help inform fisheries management. Using more than 70 years of climate and fisheries data, we show that cyclical changes in the Newfoundland and Labrador (NL) ecosystems productivity, from primary producers to piscivorous fish, coincide with changes in the regional ocean climate and the atmospheric settings of the northern hemisphere. This broad correspondence between climate and lower and higher trophic levels advances ideas for incorporating environmental knowledge into fisheries management on the NL shelves or in other regions facing similar dynamics.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Fisheries productivity is known to be affected by broad-scale environmental processes such as those captured by the North Atlantic Oscillation (NAO)1, the Pacific Decadal Oscillation2 or the El Ni\u00f1o Southern Oscillation (ENSO)3. However, separating the effects of changes in recruitment or fishing mortality and varying ecosystem productivity due to variations in environmental conditions remains a major challenge in the evaluation of fish stocks around the world4. These considerations are even more pressing in the face of anthropogenic climate change5. Integrating environmental knowledge into stock assessment processes is a key step, along with moving beyond single-species approaches6, to achieving ecosystem-based fisheries management (EBFM)7,8. One practical limitation often put forward for not including environmental information in fisheries management is the fact that their quantitative impact on resources is often poorly understood9,10.\n\nThe Newfoundland and Labrador (NL) shelves are a broad region of the northwest (NW) Atlantic\u00a0ocean that includes the Labrador shelf, the Newfoundland shelf, and the Grand Banks of Newfoundland (Fig.\u00a01). Following centuries of exploitation, the NL Northern Cod (Gadus morhua) stock, together with other groundfish stocks, collapsed in the early 1990s11. More than 30 years later, most of these stocks have yet to recover to their pre-collapse levels, and the region\u2019s ecosystems have shown important changes in their community structure (shifting from groundfish-dominated to shellfish-dominated, and are now returning to a groundfish-dominated structure)12,13,14,15. Although the exact causes of these changes continue to be investigated, it appears that both overfishing and environmental changes contributed to the collapse of many populations16,17,18. More specifically, the collapse of groundfish stocks occurred during one of the coldest periods of the last century in the NW Atlantic19 and coincided with the collapse of capelin (Mallotus villosus), a key pelagic forage fish species for the NL ecosystems, and several groundfish species that were not subjected to directed commercial fishing13,20.\n\nThe color map shows the bathymetric features obtnained from the General Bathymetric Chart of the Oceans92. A sketch of the main surface currents is shown in black. NAFO Divisions 2H, 2J, 3K and 3L, 3N, 3O, 3M and 3Ps are shown in slate gray for reference. The red dots aligned in sections (also identified) represent the hydrographic stations where zooplankton samples used in this study were collected. Hydrographic Station 27 is shown with a red star.\n\nSeveral studies have investigated the role of environmental changes in the collapse of fish stocks on the NL shelves12,21,22,23,24,25. Less work has been directed towards the period prior to the collapse, when a recently mechanized fleet achieved record-high catches during the warmest and potentially most productive period of the last century in the NW Atlantic26,27. Could the collapse of NL fish stocks have been prevented if the changes in environmental conditions and related productivity regimes of the ecosystem (e.g., the recognition of anomalously warm and productive 1960s and anomalously cold and less productive 1980s/90s) been factored into fisheries stock assessments at the time? Although we cannot definitively answer this question, as it involves both science and policy or political considerations, we propose an approach to identify when an ecosystem is characterized by particular productivity regimes and argue that it offers a mechanism for adjusting fisheries management given changes in climatic conditions going forward.\n\nIn this work, we argue that the lack of precise quantitative knowledge on the effect of the environment on fish stocks does not preclude its integration into stock assessments and its consideration in fisheries management decisions. Using more than 70 years of data from the NL shelves, we show that the ecosystem goes through productivity phases on decadal time scales. We also show that these phases can be identified using proper environmental monitoring and be used to guide fisheries management. For example, more conservative fisheries management policies (e.g., lower extraction rates, stronger emphasis on conservation when evaluating the trade-off between stock conservation and socio-economic objectives) could be used in phases of low productivity, and more relaxed fisheries management policies (e.g., higher extraction rates, increased emphasis on socio-economic objectives) could be used in phases of higher productivity.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60453-6/MediaObjects/41467_2025_60453_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "Located in Atlantic Canada at the confluence of Arctic, sub-Arctic, and subtropical currents, the NL shelves (Fig.\u00a01) are strongly influenced by changes in ocean circulation at the scale of the NW Atlantic. These changes impact not only the regional ocean climate but also the overall composition of water masses and the immediate habitat of numerous commercial and non-commercial fish and invertebrate species.\n\nThe environmental conditions on the NL shelves and the NW Atlantic can be described using a climate index19,28. Presented as a composite graph showing the average and relative contributions of standardized anomalies from 10 environmental time series, the NL Climate Index (NLCI; https://doi.org/10.20383/101.0301 [consulted 5 May 2024]) shows annual changes in ocean climatic conditions for more than seven decades (Fig.\u00a02). Different climate phases can be identified by looking at periods where the NLCI is mostly positive or mostly negative. These phases are delimited here using the simple rule that a new phase occurs when the mean value of the NLCI (scorecard at the bottom of Fig.\u00a02) has a positive or negative run for at least three consecutive years (minimum number of years for which a linear regression can be fitted with uncertainty).\n\nThe NLCI (unitless) is the average of 10 environmental time series: the Winter North Atlantic Oscillation (NAO) index, air temperature (Air Temp), sea ice season duration and maximum area, the number of icebergs drifting on the NL shelves, sea surface temperature (SST) of the NL shelves, vertically averaged temperature (T) and salinity (S) at Station 27 (S27), cold intermediate layer (CIL) core temperature at Station 27, summer CIL area on the hydrographic sections Seal Island, Bonavista Bay and Flemish Cap, and spring and fall bottom temperatures in NAFO Div. 3LNOPs and 2HJ3KLNO (See Cyr and Galbraith, 2021 for details19). The relative contribution of each sub-index to the NLCI is proportional to the length of each bar in the stacked bar plot, while the averaged value is reported in a scorecard at the bottom of the figure. Different climate phases are identified with orange and blue shades for warm and cold, respectively. A new phase is identified when the NLCI is positive or negative for at least three successive years. Qualitative information on the fisheries during the different climate phases has been added with black arrows.\n\nEight climate phases, generally characterized by warmer or colder ocean conditions, were identified between 1951 and 2017 (orange and blue shades in Fig.\u00a02). A ninth warm and potentially more productive climate phase has emerged since 2018. Although its effect on fish stocks has yet to be quantified, some signs of improvement in the finfish community have already been detected (see Discussion). These phases are driven by large-scale atmospheric conditions, shown here with mean sea level pressure (SLP) anomalies over the northern hemisphere (Fig.\u00a03). Phases characterized by warmer climate (1951\u20131971, 1979\u20131981, 1999\u20132006, and 2010\u20132013) have mostly positive SLP anomalies above the pole and negative anomalies in the subtropics (except the 1999\u20132006 phase, where the patterns are unclear). In contrast, the phases characterized by a colder climate (1972\u20131978, 1982\u20131998, 2007\u20132009, and 2014\u20132017) all have negative SLP anomalies above the pole and positive anomalies in the subtropics.\n\nb 1951\u20131971, (i) 1972\u20131978, (d) 1979\u20131981, (k) 1982\u20131998, (f) 1999\u20132006, (m) 2007\u20132009, (h) 2010\u20132013 and (o) 2014\u20132017). Each of these periods correspond to the climate phases identified in Fig.\u00a02, recalled in a side panel showing the NLCI (gray bars, unitless) with orange/blue shades showing the warm/cold phase of interest (subfigures a, j, c, l, e, n, g and p, respectively). Anomalies in the annual primary production above the NW Atlantic and Calanus finmarchicus density on the NL shelves are shown in the top of each SLP panel (where available). The trends in capelin biomass, multi-species bottom trawl survey biomass density, and groundfish biomass are indicated in the bottom of SLP subfigures (see legend). The anomalies and trends have been highlighted in orange, blue and gray for positive, negative and non-significant, respectively.\n\nNegative SLP anomalies above the pole are associated with positive phases of the NAO and are accompanied by a strengthening of the westerlies and the atmospheric jet stream above the NW Atlantic. This in turn leads to more frigid Arctic airflow above the NW Atlantic, particularly in winter, which promotes deeper convection in the Labrador Sea29, larger volumes of cold water on the Labrador shelf30, and changes in the pathways and strength of the Labrador current31 and the subpolar gyre32,33.\n\nImportant milestones for the NL fisheries have been linked to the ocean climate (Fig.\u00a02). Following centuries of relatively small-scale fisheries, the post-World War II period saw the introduction of large mechanized fishing vessels, including factory trawlers34. Historical reconstructions suggest that cod catches remained below 300,000 tonnes/year for hundreds of years before rapidly increasing in the 1950s and 1960s, reaching a peak of more than 800,000 tonnes in 196835 (see also Supplementary Fig.\u00a06). Although the capabilities of this new fishing fleet were undeniable, it should be noted that record catches of the 1960s were achieved during one of the warmest period in (at least) seven decades on the NL shelves19. This period was characterized as uniquely favorable for groundfish productivity26.\n\nA first partial collapse of the groundfish fisheries occurred in the early 1970s34 during a relatively cold phase that was in sharp contrast to the warm climate of the 1950s and 1960s (note that 1972 is the third coldest year recorded by the NLCI, tied with 1984). This collapse on the NL shelves coincided with general decreases in Atlantic Cod stocks across the NW Atlantic and was attributed to poor environmental conditions36. A partial recovery followed in the late 1970s, a period again characterized by warmer ocean conditions and positive SLP anomalies above the pole. This partial recovery of groundfish stocks was also aided by a reduction in fishing pressure in coastal waters following international agreements that led to the establishment of exclusive economic zones of 200 miles for maritime nations36,37.\n\nFrom the early 1980s to the late 1990s, the NW Atlantic entered its coldest phase in the last 70 years (a first cold pulse was recorded in the mid-1980s and a second in the early 1990s). Multiple commercial and non-commercial fish populations successively collapsed during this period13. The biomass of the capelin stock in Northwest Atlantic Fisheries Organization (NAFO) Divisions (Div.) 2J3KL rapidly collapsed from 5800 kt in spring 1990 to 600 kt in fall 1990 \u2014 probably due to environmental drivers rather than fishing \u2014 before the collapse of the northern cod stock20. The collapse of cod led to the eventual establishment of a series of moratoria on groundfish fisheries beginning in 1992, while 1991 and 1993 were the coldest years recorded by the NLCI (Fig.\u00a02). In the following decades, some cold water shellfish stocks became more productive15, although their increases in biomass never fully compensated for losses in groundfish biomass14,38. Although some finfish stocks have shown signs of improvement in the following decades39, the recovery of many groundfish and pelagic stocks appears to have stalled during the 2014\u20132017 phase40,41,42. This period again coincides with a shift in climate towards colder conditions, with the 2014\u20132017 phase sharing some similarities with that of the early 1990s29, albeit of much shorter duration. Following a reanalysis of cod stocks using a longer time series43, the Northern cod commercial fishing moratorium ended in 2024.\n\nWe also note that in addition to 2014\u20132017, another cold phase of lesser magnitude (2006\u20132009) interrupted the otherwise positive run of the NLCI that occurred between the late 1990s and the mid-2010s. Since the late 1990s, the NL climate has exhibited alternating colder and warmer conditions over relatively short phases, and the NLCI has not returned to a positive run similar to the one observed in the 1950s and 1960s. Provided that the ocean climate is influential on fish stocks, this means that the climatic conditions and the setup preceding the record catches of the 1960s have not been replicated over the past 50 years.\n\nTo better quantify and explain the changes observed in the ecosystems, we examined, for the different phases of the climate introduced above, the evolution in primary and secondary production, forage and groundfish biomass, and the biomass density of finfish and commercial shellfish collected during scientific surveys. We treated these five time series differently (see Methods section). For primary and secondary production, we look at the mean level for each climate phase compared to the average of the entire time series (that is anomalies, because biomass cannot generally accumulate from one year to the next). For fish and finfish biomass, we look at trends during the different climate phases because surplus production can accumulate over time.\n\nPrimary production levels in the NW Atlantic, as well as the density of Calanus finmarchicus, a key zooplankton species on the NL shelves, were generally above average during warmer climate phases and below average during colder climate phases (Supplementary Figs.\u00a01 and 2).\n\nSimilarly, trends in the capelin biomass index derived from acoustic-trawl surveys, the finfish and shellfish biomass density index derived from scientific multi-species bottom trawl surveys, and the groundfish excess-biomass derived from a surplus production model accounting for density-dependent effects and fisheries catches generally increased during warm climate phases and decreased during cold phases. Here, trends refer to the slopes of linear regression fits to annual biomass, biomass index and density time series during climate phases (Supplementary Figs.\u00a03\u20135).\n\nFor nearly all climate phases identified with the NLCI, there is a good correspondence between the atmospheric large-scale SLP patterns, the regional NL climate and, where data are available, the productivity of the ecosystems determined by the mean level of primary and secondary production, as well as the trends in surplus groundfish biomass and multispecies biomass density. For example, during warmer phases of the climate (Fig.\u00a03a\u2013h), primary and secondary production levels were generally above average, and the three biomass metrics described above increased (mostly orange numbers). In contrast, during the colder phases of the climate (Fig.\u00a03i\u2013p), primary and secondary production levels were generally below average, and the three biomass metrics described above decreased (mostly blue numbers). Exceptions include above average primary production during the cold 2007\u20132009 phase (Fig.\u00a03m) and near-average abundance of Calanus finmarchicus during the warm 1999\u20132006 phase (Fig.\u00a03f). Furthermore, the evidence for the importance of climate phases in trends in capelin biomass is not as compelling as that of groundfish and multispecies biomasses. Specifically, although the capelin biomass is marked by a decline during the groundfish collapse phase (1982\u20131998; Fig.\u00a03k), its biomass increased between 1982-1990 before collapsing in 1990-1991 (Supplementary Fig.\u00a03). The reasons for this increase in capelin biomass in the 1980s are unclear. It may be associated with a reduction in predation pressure when groundfish stocks began to decrease in the mid to late 1980s13. Alternatively, capelin stocks may have had reduced spatial overlaps with their predators during the cold 1980s44, but then the capelin also collapsed during the very cold anomaly of 1990\u20131993. Trends in capelin biomass were also not significantly different from zero during the 1999\u20132006 and 2007\u20132009 climate phases, unlike the trends in the groundfish and multi-species biomasses (Fig.\u00a03f, m). However, the SLP patterns during these two phases did not exhibit clear positive or negative signals centered around the pole as seen in other phases, which corresponded to rapid fluctuations of the NLCI (especially during the 1999\u20132006 phase). This may suggest changes in predation dynamics upon capelin or that the species responds more rapidly to environmental changes.\n\nOverall, the NLCI, the SLP patterns and the productivity of the different components of the NL ecosystems track each other well. Autocorrelation in the environmental time series provides a challenge in detecting changes in climate conditions, but it also highlights the consistency in the signal for extended periods of time, particularly when consistent across multiple trophic levels. Collectively, this suggests that not only does the ocean climate, driven by large-scale atmospheric forcing, change on decadal time scales, but also that the overall productivity of the ecosystems (from primary and secondary production to forage fish and higher trophic levels) changes in relative synchronicity with climate fluctuations. These results support the description of the fisheries made in Fig.\u00a02 and suggest that productivity changes may have also occurred in periods where no fisheries-independent data were available (e.g., productive 1960s and less productive early 1970s)16. This is supported by historical Northern Cod catches reconstructed for the NW Atlantic35 that shows periods of increase and decline aligned with the different climate phases, especially before the 1990s collapse (Supplementary Fig.\u00a06). This study also supports the idea that the period preceding the late 1960s, when the highest extraction rates by fisheries occurred, was likely a period with an unusually large climatic anomaly for the NL shelves26; and that those sustained warm and productive conditions during the 1950s and 1960s have not been observed since.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60453-6/MediaObjects/41467_2025_60453_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60453-6/MediaObjects/41467_2025_60453_Fig3_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In ecology, bottom-up trophic control refers to ecosystems that are resource-driven and limited by biotic or abiotic factors (physical environment, primary and secondary production, etc.), while top-down trophic control refers to a consumer-driven cascade where the dominant control is exerted by predators such as fish, marine mammals and/or fisheries45. By demonstrating a consistent correspondence between the atmospheric setting on ocean climate and the influence of climate on primary and secondary production, forage fish, groundfish, and overall biomass density, this study provides evidence for strong bottom-up control of the NL shelves ecosystems.\n\nWith the NL shelf being located in the coldest part of the range for many groundfish species, it is not surprising to find a positive relationship between recruitment and temperature for some stocks, such as Atlantic cod46. However, it is important to acknowledge that the effects of warmer and colder climate phases go far beyond the simple physiological response of organisms to temperature. Changes in climate phases involve changes in ocean circulation, water mass composition, plankton phenology, etc. Here, a series of hypotheses linking climate and ecosystem productivity and fish biomass are reviewed.\n\nThe NL shelves are characterized by the presence of near-freezing(<\u20090\u2009\u00b0C) Arctic-origin waters in their subsurface for most of the year47. In colder climate phases, larger volumes of these cold waters are found on the NL shelves19, potentially limiting the distribution of species that are less cold tolerant48, or resulting in distributional changes of some stocks toward areas with more suitable conditions21. The different climate phases on the NL shelves also imply changes in the severity of winter and sea ice conditions, which alter the timing of post-winter ocean re-stratification and the phenology of spring phytoplankton blooms, with earlier blooms associated with warmer climate, and vice versa49.\n\nWarmer climate phases are also associated with higher densities of Calanus finmarchicus, a key copepod species in the NL ecosystems49. The increases in their density can be explained not only by increases in primary production, but also by a better match between the end of the C. finmarchicus winter diapause and the timing of the phytoplankton bloom50,51. In addition, there may be better retention of secondary production on the NL shelves during warm phases due to the weaker subpolar gyre associated with reduced wind curl over the North Atlantic32,52. The energy rich C. finmarchicus is a key prey for capelin, a keystone forage fish species that is responsible for the transfer of energy between secondary producers and higher trophic levels53,54. Thus, it is not unexpected to see capelin biomass trend upwards when C. finmarchicus abundances are high. Higher capelin biomass also has a direct effect on higher trophic levels, such as cod, a key predator of capelin55, and other groundfish species. In general, this study supports the idea that the availability of energy at the base of the food web is an important limiting factor for overall ecosystem productivity, as indicated by concurrent increases in capelin, groundfish, and other fish species biomasses in periods when C. finmarchicus densities are high (and vice versa).\n\nWe have been able to identify climate phase as an underlying mechanism that explains prolonged periods of low and high productivity for multiple trophic levels in the NL ecosystems. Unlike previous studies that have typically focused on single characteristics of the climate system (e.g., the NAO1), our approach builds on the simple concept that no single indicator can fully encapsulate all relevant environmental processes that impact ecosystem productivity. We leverage environmental indicators provided by the NLCI to uncover the relationship between the ocean climate state and ecosystem function.\n\nThe NLCI used in this study includes the winter NAO as well as nine other subindices. The advantage of integrating these additional climate indices (both on a regional scale and on a broad scale) into a climate index is that we do not rely on the trend of one index alone to identify climate phases. While we have time series of variable lengths, especially for lower trophic levels, the relative concordance between productivity trends across trophic levels supports the use of the NLCI to identify periods of low and high productivity.\n\nPrevious studies on Atlantic cod stocks across the North Atlantic basin have noted a general coherence in stock collapses during the 1980s and 1990s, suggesting that environmental forcing was an important underlying factor in addition to fishing impacts36,56,57. In the northeast Atlantic, the prevalence of warmer ocean conditions since the 1980s, combined with the application of precautionary biological reference points in applied fisheries management, has contributed to the rebuilding of the Atlantic Cod stock to record levels58. Additional work on some of these stocks has shown how changing environmental conditions affected cod through impacts in lower trophic levels59, and how the availability of a forage species like capelin emerges as a common driver for two cod stocks with very different trajectories in the northwest and northeast Atlantic55. These observations from several North Atlantic ecosystems are consistent with our finding that ecosystem productivity appears to be generally associated with the prevailing phase of ocean climate, and suggest that identifying such phases could provide information on overall ecosystem functioning more generally.\n\nIt has been hypothesized that the lack of recovery of most commercial fisheries to pre-collapse biomass levels was due to a sustained low productivity regime since the early 1990s57,60,61. This study offers a perspective on this hypothesis. First, it confirms that the climatic setting that led to record high cod catches in the 1960s \u2014 e.g., sustained warmer than average ocean conditions with potentially earlier spring blooms and higher levels of primary and secondary production \u2014 have not been observed since. Second, warm phases since 1998 provide an explanation for the modest improvements experienced by capelin and some groundfish stocks since the collapses in the early 1990s20,62,63, but the lack of further recovery to pre-collapse levels may be associated with what appears to be an increased variability in the periodicity of changes in climate phases such as the short-lived but intense cold and low productivity phases for groundfish between about 2006 and 2009, and 2014 and 201714,64 (Figs.\u00a02 and 3). The warmer phase emerging since about 2018 may, however, signify a transition to a more productive phase in the coming years, consistent with a recent improvement of the capelin fall condition index65.\n\nDisentangling fishing mortality (e.g., overfishing) and natural fluctuations of fish populations is a challenge because they are intrinsically related36,57. Favorable environmental conditions can allow for fishing levels that may not be otherwise sustainable. That may have been the case, for example, on the NL shelves in the 1960s. In contrast, fishing mortality can exacerbate the impacts of poor environmental conditions on ecosystems and accelerate the decline of fish stocks66. A stock historically fished sustainably may become over-fished if its production declines below a certain level due to changes in environmental conditions.\n\nLinking environmental fluctuations with fish stock dynamics is a key step in the implementation of EBFM67,68. Towards this goal, this study demonstrates how 70 years of fluctuations in NL ecosystems productivity and changes in fish biomass can be explained using a simple climate index. Recognizing the phases (or productivity regimes) that an ecosystem is subject to, such as the ones described in this study, provides a powerful tool to inform fisheries management. This environmental information can be taken into account either qualitatively or quantitatively.\n\nQualitatively, it allows setting more conservative extraction targets, or even ecosystem quotas, during periods when environmental conditions are deemed less favorable for productivity, or more permissive ones when these conditions appear favorable. Although conceptually a straightforward approach, real-life applications of these concepts would be expected to be more complex and likely involve the use of environmental conditions to inform risk assessments aimed at evaluating trade-offs between the conservation of the exploited stocks and socioeconomic pressures. A recent illustration of this idea is the decision of Peru to suspend the 2023 anchoveta fishery season as a trade-off for conservation69. This decision was motivated by the high number of juvenile fish in the exploratory fishery as a result of the developing El Ni\u00f1o phase of the ENSO in 2023, a situation known to impact the anchoveta fishery70.\n\nQuantitatively, environmental trend analyses could be used to adjust target levels of fishing mortality (sensu Feco)6 or these trends could be explicitly included in the operating models used in management strategy evaluations (MSE) to fully integrate environmental signals in the development of harvest control rules71. Another alternative would be to adjust ecosystem-level quotas based on a scaling factor determined by ocean climate. This is analogous to the idea of establishing ecosystem overfishing thresholds based on primary production metrics72, except this time on the basis of a climate index. This type of approach would be well suited for current applications of ecosystem indicators for total sustainable catches, such as the one implemented by NAFO that scales estimates of fisheries production potential using total biomass density, and which our study has shown responds to identified climate phases18,73,74. However, in the context of fisheries management based on maximum sustainable yield (MSY), factoring in changes in ecosystem productivity may, in some circumstances, have the unintended consequence of increasing anthropogenic pressure on a stock by lowering its limit reference point75. Climate-adaptive fisheries management strategies in the context of MSY should therefore be carefully studied if the protection of marine resources is the priority.\n\nFisheries management actions in relation to a changing climate are plagued by many unknown unknowns76 that should nevertheless be considered when setting management objectives, since these decisions can affect the future state of natural resources77. This work joins a growing body of literature that proposes pragmatic solutions to how to overcome these challenges.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Monthly SLP between 1948 and 2021 were retrieved from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) (NCEP-NCAR Reanalysis 1 data). These are provided by the National Oceanic and Atmospheric Administration Physical Science Laboratory in Boulder, Colorado, USA, and accessible on their website at https://psl.noaa.gov [consulted 4 May 2022]78. The SLP anomalies for each of the climate phases discussed above were calculated as the difference between the average for each time window (all months considered) and the average of the 1950\u20132020 period (Fig.\u00a03).\n\nThe annual net primary production (in mgC\u2009m\u22123\u2009d\u22121) over the NW Atlantic was obtained from a Mercator-Ocean biogeochemistry hindcast for the global ocean at https://doi.org/10.48670/moi-00019 [consulted 4 October 2022]. The monthly resolution data were averaged in annual means and over a geographical region covering the NW Atlantic [47-65\u00b0N; 47-65\u00b0W] (dashed box in Fig.\u00a03). The resulting time series runs from 1993 to 2020 (Supplementary Fig.\u00a01). The average primary production for each climate phase were reported as horizontal dashed-green lines with shading representing the 80% confidence interval (Supplementary Table\u00a01). The anomalies of primary production for each climate phase are calculated as the difference between the average of each climate phase and the average of the entire time series (1993\u20132020). These anomalies are reported in Fig.\u00a03 in orange when positive and blue when negative.\n\nPhysical and biogeochemical data are regularly collected on the NL shelves at standard stations along oceanographic transects since 1999 as part of Fisheries and Oceans Canada\u2019s (DFO) Atlantic Zone Monitoring Program (AZMP)79. Up to three missions (spring, summer and fall) occur annually in the NL waters as part of the AZMP. During the spring and fall, Southeast Grand Bank, Flemish Cap and Bonavista Bay transects were occupied in most years while the Seal Island transect was only occupied from 2009 to 2015 during fall missions (Fig.\u00a01). In the summer, the Flemish Cap and Seal Islands transects were occupied in most years while the White Bay transect was occupied every 1 to 3 years between 1999 and 2007.\n\nIn addition to those transects, zooplankton samples were also collected at Station 27 (47\u221832.8\u2019N, 52\u221835.2\u2019W) two to four times per month on average, from April through December. This Station is located just east of St. John\u2019s, NL and has a total depth of 176 m (Fig.\u00a01). It is downstream from the incoming flow from the Labrador shelf, and its local oceanographic conditions are considered representative of the climate of the NL shelves and the NW Atlantic19,80,81.\n\nZooplankton samples were collected at the stations described above by vertically towing a pair of conical ring nets (200\u2009\u03bcm) that were mounted side by side (e.g., \u201cbongo nets\u201d) from 10 m off the bottom to the surface at a speed of \u00a0~\u20091\u2009m\u2009s\u22121. Material collected from each of the conical ring nets was preserved separately in a buffered 2% formaldehyde solution. The material from one net was sent to a taxonomy lab for species identification and counting. For each vertical tow, the copepod density per surface area (C in ind\u2009m\u22122) was estimated by dividing the number of individuals in the tow (N) by the area of the net opening (0.44\u2009m2). Annual estimates of copepod Calanus finmarchicus (\\(\\overline{C}\\)) were obtained by fitting a linear model of the form shown in equation (1):\n\nHere \\(\\overline{C}\\) is the mean annual density of Calanus (in ind m\u22122), \u03b1 is the intercept, \u03f5 is the error, and \u03b2, \u03b4, \u03b3 are the categorical effects of the factors Year, Station and Season, respectively. \\(\\overline{C}\\) was log-transformed (ln) to normalize the skewed distribution of the observations. This model accounts for the fact that the number of stations and seasons sampled annually by the AZMP may slightly vary from year to year. To control for the order of the variables in the model, annual means were estimated using adjusted sums of squares82. The assumptions of the models were assessed, i.e., the residuals were examined for independence as well as for normality and homogeneity of variance.\n\nAnnual C. finmarchicus densities are reported in Supplementary Fig.\u00a02. The horizontal dashed brown lines correspond to the average level for each climate phase identified in the study, with shading representing the 80% confidence interval (Supplementary Table\u00a01). The anomalies in C. finmarchicus density for each climate phase are calculated as the difference between the average of each climate phase and the average of the entire time series (1999\u20132021). These anomalies are reported in Fig.\u00a03 in orange text when positive and blue text when negative.\n\nThe capelin biomass index (in kt) was estimated during the annual DFO spring capelin acoustic survey (Supplementary Fig.\u00a03). The spring capelin acoustic survey has taken place annually in its current form since 1982, except for 1983-84 and 2021, and there were no acoustic surveys in 1993\u20131995, 1997-1998, 2006, 2016, and 202060.\n\nThe acoustic survey is typically conducted in May and covers the majority of NAFO Div. 3L, and since 1996, the southern NAFO Div. 3K. Div. 3L is an area of particular importance for juvenile and non-migratory age-1\u2009+ capelin, although all age classes acoustically surveyed are included in the annual capelin biomass index. A depth-delimited stratified survey design is conducted each year, although the transect design, stratum boundaries, and areas covered have changed over time83,84. Acoustic backscatter attributed to capelin was converted to capelin biomass using biological data from directed mid-water and bottom trawls.\n\nLinear trends were calculated for each climate phase previously identified by a least squares regression with bootstrap (1000 repetitions). The mean trends, defined as the average of all repetitions, are reported in Fig.\u00a03 in orange text when positive and in blue text when negative, while the 80% confidence intervals are reported in Supplementary Table\u00a01. Slopes for which the confidence interval bounds have different signs are left gray.\n\nThe total biomass density index from DFO multi-species bottom-trawl scientific surveys (in kt\u2009km\u22122) was calculated from DFO Fall and Spring surveys for NAFO Div. 2J3KLNOPs (Supplementary Fig.\u00a04). The large marine ecosystem in the NL shelves is typically subdivided into Ecosystem Production Units (EPUs) which correspond to relatively well defined, but still interconnected, functional ecosystems85,86,87. DFO multi-species surveys have covered these EPUs differently over time. The Fall surveys have systematically surveyed the Newfoundland Shelves (NAFO Div. 2J3K) EPU since 1981, and the Grand Bank (NAFO Div. 3LNO) EPU since 1990. The Spring surveys have systematically covered the Grand Bank EPU since 1985, and the Southern Newfoundland (NAFO Div. 3Ps) EPU since 1982. The gear used in these surveys changed in 1995 for the Fall surveys and 1996 for the Spring surveys when the Campelen trawl replaced the Engel trawl. The introduction of the Campelen trawl permitted the beginning of the systematic recording of commercial shellfish species in DFO surveys. This was important given that shellfish species like Northern shrimp and snow crab had increased after the collapse of the groundfish community14,38. While these biomass increases were substantial, especially in the mid-late 1990s, they did not compensate for the losses of groundfish14. Northern shrimp was the dominant species among commercial shellfish, and available approximations of its biomass prior to the introduction of the Campelen gear indicate that it was only starting to increase in a significant way when the Campelen gear was introduced88. The available evidence88,89 suggest that commercial shellfish biomass was a relatively modest fraction of the total biomass before the collapse. It follows that considering only finfish for the Engel period, when groundfish heavily dominated the total biomass, is a reasonable approximation to the total biomass signal.\n\nFor each EPU and season, total biomass density was calculated as the total biomass for all finfish and commercial shellfish, when shellfish data is available, species divided by the total area surveyed. Total biomass was obtained as the sum of individual species biomass estimates calculated as the standard areal expansion of survey biomass based on the random-stratified survey design. Scaling factors were applied to the Engel series to provide comparability in the order of magnitude between the Engel and Campelen data14,38. These scaling factors are not available for the Southern Newfoundland EPU, so only Campelen data (i.e., 1996 forward) was considered in this analysis for this EPU. Given that the Grand Bank EPU is typically surveyed twice a year (spring and fall surveys), the biomass density signal for this EPU was summarized as the average of the estimated biomass densities from these two surveys. The total biomass density signal at the scale of the NL shelf was estimated as the median of all available total biomass densities by EPU in a given year.\n\nLinear trends were calculated for each climate phase previously identified by a least squares regression with bootstrap (1000 repetitions). The mean trends, defined as the average of all repetitions, are reported in Fig.\u00a03 in orange text when positive and in blue text when negative, while the 80% confidence intervals are reported in Supplementary Table\u00a01. Slopes for which the confidence interval bounds have different signs are left gray.\n\nGroundfish biomass was estimated using a state-space multi-species surplus production model for the NL shelves. Within each region (NAFO Div. 2J3K and 3LNO), the biomass of each species at the start of year y was given by equation (2):\n\nwhere rs is the maximum per-capita rate of change for species s, K is the carrying capacity, Cy\u22121,s is the catch through year y\u00a0\u2212\u00a01 for species s, and \u03b4y,s is process error. Process errors were modeled using a multivariate normal distribution, estimating temporal and species-to-species correlation. Relative biomass indices for species s in year y from survey i was given by equation (3):\n\nwhere qi,s is the time-invariant catchability coefficient for the survey index i, and \u03b5y,i,s is the observation error, which was assumed to be normally distributed. This model was implemented using template model builder (TMB)90 within R91.\n\nThe process errors of this model, \u03b4y,s, correspond to the variance unexplained by density-dependent processes and fishing mortality (i.e., changes presumably caused by environmental variables). The changes in biomass imposed by these errors were calculated (in kt) and averaged for all species (Supplementary Fig.\u00a05).\n\nLinear trends were calculated for each climate phase previously identified by a least squares regression with bootstrap (1000 repetitions). The mean trends, defined as the average of all repetitions, are reported in Fig.\u00a03 in orange text when positive and in blue text when negative, while the 80% confidence intervals are reported in Supplementary Table\u00a01. Slopes for which the confidence interval bounds have different signs are left gray.\n\nFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data needed to evaluate the conclusion are provided here at https://doi.org/10.5281/zenodo.15359627. In addition, updates of the Newfoundland and Labrador Climate Index (NLCI), sea level pressure and primary production data are available at 10.20383/101.0301 [consulted 5 May 2024], https://psl.noaa.gov [consulted 4 May 2022], and 10.48670/moi-00019 [consulted on 4 October 2022], respectively.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All relevant code used to generate the figures are available at https://doi.org/10.5281/zenodo.15359627.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "B\u00e1ez, J. C., Gimeno, L. & Real, R. North Atlantic Oscillation and fisheries management during global climate change. Rev. Fish Biol. Fish. 31, 319\u2013336 (2021).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nMantua, N. J. & Hare, S. R. The Pacific Decadal Oscillation. J. Oceanogr. 58, 35\u201344 (2002).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nLehodey, P. et al. 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FC thanks Flore C. for the permission to use her drawings in Fig.\u00a03.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Fr\u00e9d\u00e9ric Cyr\n\nPresent address: Centre for Fisheries and Ecosystem Research, Fisheries and Marine Institute of Memorial University, St. John\u2019s, Newfoundland and Labrador, Canada\n\nNorthwest Atlantic Fisheries Centre, Fisheries and Oceans Canada, St. John\u2019s, Newfoundland and Labrador, Canada\n\nFr\u00e9d\u00e9ric Cyr,\u00a0Aaron T. Adamack,\u00a0David B\u00e9langer,\u00a0Mariano Koen-Alonso,\u00a0Darrell Mullowney,\u00a0Hannah Murphy,\u00a0Paul Regular\u00a0&\u00a0Pierre Pepin\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nF.C. conceptualized the study and led the data analysis, writing and editing. A.A. and H.M. curated capelin data and contributed to the writing and editing. D.B. and P.P. curated zooplankton data and contributed to the writing and editing. M.K.A. curated scientific survey biomass density data and contributed to the writing and editing. P.R. curated groundfish excess biomass data and contributed to the writing and editing. D.M. curated data that did not make the final version of this study and contributed to the writing and editing.\n\nCorrespondence to\n Fr\u00e9d\u00e9ric Cyr.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Franz Mueter, \u03d5ystein Skagseth, and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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b/29120433a3a7e46c59a2c977983bb938e2fbcf67f013699862194ac0e71ec5ee/metadata.json @@ -0,0 +1,147 @@ +{ + "title": "Self-organizing neuromorphic nanowire networks as stochastic dynamical systems", + "pre_title": "Self-organizing neuromorphic nanowire networks are stochastic dynamical systems", + "journal": "Nature Communications", + "published": "13 April 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58741-2/MediaObjects/41467_2025_58741_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58741-2/MediaObjects/41467_2025_58741_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.15050217" + ], + "code": [ + "https://github.com/MilanoGianluca/Self-organizing_neuromorphic_networks_as_stochastic_dynamical_systems", + "https://doi.org/10.5281/zenodo.15174744" + ], + "subject": [ + "Electrical and electronic engineering", + "Electronic devices", + "Electronic properties and materials", + "Information technology" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4102090/v1.pdf?c=1744628724000", + "research_square_link": "https://www.researchsquare.com//article/rs-4102090/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58741-2.pdf", + "preprint_posted": "03 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Neuromorphic computing aims to develop software and hardware platforms emulating the information processing effectiveness of our brain. In this context, self-organizing neuromorphic nanonetworks have been demonstrated as suitable physical substrates for in materia implementation of unconventional computing paradigms, like reservoir computing. However, understanding the relationship between emergent dynamics and information processing capabilities still represents a challenge. Here, we demonstrate that nanowire-based neuromorphic networks are stochastic dynamical systems where the signals flow relies on the intertwined action of deterministic and random factors. We show through an experimental and modeling approach that these systems combine stimuli-dependent deterministic trajectories and random effects caused by noise and jumps that can be holistically described by an Ornstein-Uhlenbeck process, providing a unifying framework surpassing current modeling approaches of self-organizing neuromorphic nanonetworks (not only nanowire-based) that are limited to either deterministic or stochastic effects. Since information processing capabilities can be dynamically tuned by controlling the network\u2019s attractor memory state, these results open new perspectives for the rational development of physical computing paradigms exploiting deterministic and stochastic dynamics in a single hardware platform similarly to our brain.Physical sciences/Nanoscience and technology/Nanoscale devices/Electronic devicesPhysical sciences/Materials science/Nanoscale materials/Electronic properties and materialsPhysical sciences/Engineering/Electrical and electronic engineering", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformation.docxSupplementary Information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Neuromorphic computing aims to develop hardware platforms that emulate the effectiveness of our brain. In this context, brain-inspired self-organizing memristive networks have been demonstrated as promising physical substrates for in materia computing. However, understanding the connection between network dynamics and information processing capabilities in these systems still represents a challenge. In this work, we show that neuromorphic nanowire network behavior can be modeled as an Ornstein-Uhlenbeck process which holistically combines stimuli-dependent deterministic trajectories and stochastic effects. This unified modeling framework, able to describe main features of network dynamics including noise and jumps, enables the investigation and quantification of the roles played by deterministic and stochastic dynamics on computing capabilities of the system in the context of physical reservoir computing. These results pave the way for the development of physical computing paradigms exploiting deterministic and stochastic dynamics in the same hardware platform in a similar way to what our brain does.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "In the era of Artificial Intelligence (AI) and Big Data, the continuous growth of computing demand is unsustainable with currently available digital processing and storage units based on the conventional von Neumann architecture1. In the race towards future technologies, neuromorphic computing aims to take inspiration from the effectiveness and advanced functionalities our brain offers to develop energy-efficient hardware platforms2,3,4. This requires the development of radically new physical substrates as well as novel data storage and communication protocols that leverage new physical phenomena for computing in the analog domain at the matter level5 while embracing stochasticity, in a similar fashion our brain does6. With the aim of emulating the principle of self-organization typical of biological neuronal systems, self-organizing neuromorphic nanoscale networks have been recently demonstrated as feasible substrates for physical processing of information directly at the matter level7,8,9,10,11,12,13,14,15,16,17,18,19,20. Information processing and computing capabilities of these complex systems are inherently related to network dynamics, where the internal state of the system evolves over time through an adaptive behavior relying on the interaction of nano-elements driven by time-dependent external signals coming from the environment21,22,23. In these self-organizing systems, the concept of emergent behavior has been related to the collective response of a large number of nano-objects subjected to mutual interactions21,24,25. In opposition to classical algorithmic computation, where rules are explicitly given by a computer program, in these dynamical systems information processing relies on the underlying physical laws governing the connectivity of the nano elements26,27. In this context, voltage-driven deterministic dynamics occurring in memristive complex networks based on nanowires (NWs) have been exploited to emulate fundamental features of biological systems, including short-term plasticity, heterosynaptic plasticity, working memory, metaplasticity and memory engrams12,28,29. The associated deterministic dynamics have been exploited so far to solve a wide range of computational tasks including pattern recognition and time-series prediction in the framework of reservoir computing30,31,32. Moreover, stochastic spiking dynamics of self-assembled percolating networks have been considered for true random number generation33,34. However, beyond these remarkable achievements in the field, a unified mathematical framework describing both deterministic and stochastic behaviors of self-organizing neuromorphic nanoscale networks is currently missing.\n\nIn this work, we report on the modeling of nanowire networks as dynamic systems endowing deterministic and stochastic behaviors. We show that our modeling approach describing network dynamics as an Ornstein-Uhlenbeck (OU) process with random perturbations can describe the experimentally observed evolution of the system according to an external control variable, in our case the applied voltage. In particular, the proposed compact model can describe both deterministic conductance transients induced by modifications of the applied voltages as well as stochastic conductance fluctuations including noise and jumps. The model is exploited to investigate the impact of deterministic and stochastic dynamics on the information processing capabilities of the system by considering benchmark nonlinear autoregressive moving average (NARMA) and nonlinear transformation (NLT) computing tasks. The proposed description represents a step forward in the development of neuromorphic systems that, besides deterministic dynamics, endow stochasticity in a similar fashion to biological systems.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Self-assembled Ag NW networks (Fig.\u00a01a, details of fabrication in \u201cMethods\u201d) are complex dynamical systems, where the propagation of an electrical signal through the network is determined by Kirchhoff\u2019s laws and memristive nonlinear interactions among a huge number of NWs at nanoscale crosspoint junctions (Fig.\u00a01b). Here, the physical mechanism of memristive activity relies on the electric-field driven dissolution and migration of Ag+ ions across the insulating polymeric shell layer that surrounds the Ag NW cores. This forms a localized Ag conductive bridge at the crosspoint junction, as schematized in Fig.\u00a01c12. In our case, the switching mechanism is volatile-type since Ag conductive filaments can spontaneously break down after formation with a characteristic lifetime that depends on the experienced electrical excitation, as discussed in previous works35,36,37. Besides memristive behavior of NW junctions, it is worth mentioning that resistive switching in the Ag NW itself has been experimentally observed38. The interaction among a large number of memristive structures, where the conductance is regulated by the interplay between filament formation and spontaneous rupture, gives rise to a deterministic behavior of the system that was exploited in the past for neuromorphic-type data processing and unconventional computing30. The deterministic behavior is characterized by a nonlinear dynamics that, depending on the electrical stimulation and the system\u2019s initial condition, can lead to a progressive enhancement (potentiation) or decrement (relaxation) of the overall effective conductance value in between two network areas. Network conductance dynamically evolves over time due to multiple series and parallel current pathways that are sequentially formed and destroyed depending on the input stimulation and the strength of local connections23. In this context, experimental conductance time traces acquired under electrical stimulation results from collective phenomena and interactions that are hidden to the external observer19. Fig.\u00a01d reports an example of network potentiation under constant voltage stimulation of 1\u2009V, showing a progressive increase of conductance \\(G\\). On the other hand, Fig.\u00a01e illustrates an example of network relaxation under constant voltage stimulation of 0.01\u2009V, where the conductance of the network progressively decreases towards a lower value. These dynamics rely on the evolution of the system from an initial conductance state towards a new equilibrium state determined by the applied bias voltage. Besides the observed deterministic potentiation/relaxation behavior, the investigated electrical network endows a random component characterized by low-level conductance fluctuations and jumps that cannot be overlooked (see Fig.\u00a01f). This is the result of randomly distributed switching events at the memristive NW junctions caused by the local rearrangements of potential drops and the inherent stochastic nature of the conductive filament formation and rupture processes39,40. While noise effects can be attributed to conductance fluctuations in junctions distributed across the network, jumps in the conductance trace seem to correspond to transitions caused by resistive switching events in one (or few) junctions located in highly relevant topological areas of the network connecting/disconnecting entire network domains21. In this scenario, jumps are expected to have the same physical origin as the low-level fluctuations but can be considered rare events in terms of occurrence probability and magnitude.\n\na Scanning electron microscopy (SEM) image of a self-organizing neuromorphic network based on highly interconnected NWs (scale bar, 5\u2009\u03bcm) and b detail of a NW junction (scale bar, 200\u2009nm). c Schematic representation of the resistive switching mechanism at NW junctions, where the formation/rupture of a metallic Ag conductive filament connecting the metallic NW cores under the action of the applied electric field modulates the junction conductivity. d Potentiation of the neuromorphic network conductance \\(G\\) over time, characterized by the progressive enhancement of the overall NW network conductance from an initial state towards a higher conductance state under two-terminal constant voltage stimulation of 1\u2009V. e Relaxation of the neuromorphic network characterized by a progressive decrease of the overall NW network conductance from an initial state towards a lower conductance state under two-terminal constant voltage stimulation of 0.01\u2009V. f Detail of the conductance trace reported in (d), showing that, besides deterministic potentiation/relaxation, the neuromorphic behavior endows stochastic effects characterized by conductance fluctuations (noise) and jumps.\n\nA unified framework based on an OU process with jumps is here exploited for modeling deterministic and stochastic dynamics of neuromorphic nanowire networks, as described in the following. The OU process with jumps41 is described by an It\u00f4-type differential equation, which involves the combined action of deterministic and stochastic terms. The variable considered in this approach is the internal memory state of the system \\(g\\) (i.e., normalized conductance, 0\u2009\u2264 \\(g\\)\u2009\u2264\u20091) that evolves over time depending on the history of applied electrical stimulation. The evolution of \\(g\\) can be described by the stochastic differential equation (SDE):\n\nwhere \\(\\widetilde{g}\\) represents the long-term mean or equilibrium memory state (steady state), \\(\\theta\\) the reversion speed (i.e., the rate at which \\(g\\) reverts towards \\(\\widetilde{g}\\)), \\(\\sigma\\) the noise intensity (assumed independent of \\(g\\)), \\({dW}\\) the Gaussian noise (Wiener process), \\(\\varGamma\\) the jump amplitude, and \\({dq}\\) the jump occurrence rate. It is worth emphasizing that assuming a constant \\(\\sigma\\) does not mean that stochastic and deterministic dynamics are independent, since the solution of Eq. (1) relies on the noise and jumps effects in combination with the (deterministic) mean-reverting process.\n\nOriginally developed as a model for describing the velocity of a Brownian particle under the influence of friction (Langevin equation42), the OU process which is contemporarily a Gaussian and a Markov process, has been exploited to model stochastic dynamical systems in a wide range of contexts such as financial systems and natural sciences43. This represents the simplest Markov-Gaussian process that can be postulated, where coupling of deterministic and stochastic components is inherent to the assumed dynamics. In a first-order approximation, the current I that flows through the network (i.e., the physical observable) when a voltage \\({V}\\) is applied across two network areas relates to the internal memory state of the system through Ohm\u2019s law as:\n\n\\({G}_{\\min }\\) and \\({G}_{\\max }\\) are the minimum and maximum conductance values while \\(G\\) is the network conductance which depends on g.\n\nThe deterministic behavior of the memristive network has been experimentally analyzed by collecting the time traces of the conductance \\(G\\) when stimulated with a fixed voltage bias. Figure\u00a02a reports experimental traces of the time-dependent evolution of \\(G\\) from the initial network ground state (i.e., stable state when not stimulated) towards a new steady state when biased with different voltages ranging from 0.1\u2009V up to 6.6\u2009V (experimental details in \u201cMethods\u201d). The progressive increase of conductance over time while applying a constant bias voltage is related to the self-organized formation and subsequent consolidation of conductive pathways formed by activated junctions that bridge stimulated network areas, as previously investigated through both experiments29 and modeling23. The acquired experimental dataset allows us to experimentally investigate the dependence of the steady state \\(\\widetilde{G}\\) as a function of the applied voltage, as reported in Fig.\u00a02b. \\(\\widetilde{G}\\) is The value corresponding to the long-term stabilization of the network state (details in the inset). Experimental results show a sigmoidal-like transition of the steady state conductance from a low to a high \\(\\widetilde{G}\\) value as a function of the applied voltage. This deterministic trajectory of the network can be modeled using a potentiation-depression rate balance equation44 that represents the deterministic form of the memory state Eq. (1) (\\(\\sigma=0\\) and \\(\\varGamma=0\\)) expressed as:\n\na Experimental time traces of the conductance \\(G\\) under constant voltage stimulation, where each curve represents the evolution of \\(G\\) over time from the initial ground state towards the new equilibrium state for different applied voltages (from 0.1\u2009V to 6.6\u2009V, step of 0.1\u2009V). b Experimental (circles) and modeled (line) evolution of the stationary steady state (\\(\\widetilde{G}\\)) as a function of the applied voltage bias. The inset shows the time trace of the conductance after 33,000\u2009s of constant voltage bias application, showing that the conductance state is stationary. Circle colors correspond to the line colors of experimental time traces reported in the inset and in (a). c Modeling of deterministic network dynamics for different applied voltages (from 0.1\u2009V to 6.6\u2009V, step of 0.1\u2009V). Line colors correspond to the color of the experimental time traces obtained with the same applied voltage reported in (a). d Internal memory steady state \\(\\widetilde{g}\\), e reversion speed \\(\\theta\\), f potentiation rate coefficient, and (g) depression rate coefficient as a function of the applied voltage derived from modeling.\n\n\\({\\kappa }_{{{\\rm{P}}}}\\) and \\({\\kappa }_{{{\\rm{D}}}}\\) are potentiation and depression rate coefficients which exponentially depend on the applied voltage through physics-based relationships accounting for the forward/backward diffusive ionic processes occurring at the NW junctions:\n\nwhere \\({\\kappa }_{{{\\rm{P}}}0},{\\kappa }_{{{\\rm{D}}}0} > 0\\) are constants and \\({\\eta }_{{{\\rm{P}}},}{\\eta }_{{{\\rm{D}}}} > 0\\) transition rates. Since a single rate-balance equation is used to describe the dynamic behavior of the entire network, transition rates here represent effective network parameters. Notice, from Eq. (3), that the reversion speed \\(\\theta\\) and the equilibrium state of the system \\(\\widetilde{g}\\) are both functions of the applied voltage \\(V\\) according to the expressions:\n\nIn Eq. (6), \\(\\widetilde{g}\\) the internal memory of the network in steady-state conditions is represented. Importantly, Eq. (3) cannot only be solved numerically through the Euler method, but also analytically, following a recursive approach (details in \u201cMethods\u201d). The experimental dependence of the steady state reported in Fig.\u00a02b can be well interpolated by the potentiation-depression rate-balance model through Eq. (6). The interpolation of experimental steady states that enables the retrieval of rate coefficients and transition rates of the network allows also inferring the deterministic network transient dynamics, as reported in Fig.\u00a02c. By comparing modeling with experimental results reported in Fig.\u00a02a, it can be observed that the proposed model describes quite well the main features of the conductance evolution over time during transients (i.e., before reaching the steady state condition). The memory steady state \\(\\widetilde{g}\\), the reversion speed \\(\\theta\\), the evolution of the potentiation and depression rate coefficients \\({\\kappa }_{{{\\rm{p}}}}\\), and \\({\\kappa }_{{{\\rm{d}}}}\\), as a function of the applied voltage derived from interpolation of the experimental data reported in Fig.\u00a02b, are illustrated in Fig.\u00a02d, e, f, g, respectively. It is worth mentioning that in the context of system dynamics, \\(\\widetilde{g}\\) it represents the stable trajectory of the system, i.e., the statistically invariant phase the system reaches for a given voltage, irrespective of the initial conducting state (mean-reverting property of the OU process). Even if experimental observations suggest that the system\u2019s conductance tends toward the same steady state irrespective of the initial condition (details in Supplementary Fig.\u00a01), a wider variety of starting conditions would be required to demonstrate unequivocally the occurrence of hard attractor states. Notably, the proposed compact description endowing mean-reverting property enables us to describe the experimentally observed dependence of the network state only on the recent history of applied stimulation, a property that has been exploited for temporal-processing of the input signal in the framework of reservoir computing30.\n\nThe stochastic effects in the neuromorphic networks were analyzed by disentangling noise and jumps in the conductance time trace when the system operates in the stationary state45. It is worth emphasizing once again that, according to the chosen stochastic process, deterministic behavior, noise, and jumps are part of the whole system\u2019s trajectory even under steady state conditions. Figure\u00a03a reports experimental changes in the conductance \\({dG}/{dt}\\)) monitored for more than 15,000\u2009s with an applied voltage of 3.6\u2009V. As can be seen, small conductance fluctuations related to noise and large spike events related to conductance jumps occur. Consequently, the corresponding \\({dG}/{dt}\\) histogram reported in Fig.\u00a03b shows a heavy tailed distribution, as also revealed by the quantile-quantile plot assuming a normal distribution (Fig.\u00a03c). The obtained results indicate that a large proportion of the detrended data is located at the tails of the distribution in comparison with what is expected for the normal case. The disentanglement of noise and jump events was in practice performed through a thresholding algorithm that maximizes the p-value of the Gaussian distribution of the noisy component of the signal (details in \u201cMethods,\u201d the calculated threshold value is reported in Fig.\u00a03a). Figure\u00a03d reports the experimental noise component of the stationary state signal over time disentangled from jumps, where the normal distribution of the signal (Fig.\u00a03e) and the quantile-quantile plot (Fig.\u00a03f) reveals the Gaussian nature of the fluctuating component (presumably because of the central limit theorem). Remarkably, Gaussian behavior is directly related to the network activity and is not linked to the experimental setup and/or measurement protocol (details in Supplementary Fig.\u00a02). Figure\u00a03g reports the experimental raster plot of jump events and their corresponding evolution over time \\(n\\left(t\\right)\\), showing that \\(n\\left(t\\right)\\) increases almost linearly with slope \\(\\lambda\\)\u2009~\u20090.082 events per second. In case of a homogeneous stochastic Poisson process, \\(\\lambda\\) represents the event occurrence rate or intensity of the process. In this case, the interarrival times between events (interevent intervals, IEIs) are independent and identically distributed, where the density distribution of IEI can be described through the exponential distribution \\(p\\left({{\\rm{IEI}}}\\right)=\\,\\lambda {e}^{-\\lambda {{\\rm{IEI}}}}\\). As can be observed in Fig.\u00a03h, the experimental density distribution of IEIs is in good agreement with the theoretical distribution expected for a Poisson process. Together with the linear increase of the number of events over time, these results suggest that the occurrence of jumps can be well described by a homogeneous Poisson process, i.e., a stochastic process where: (i) the average rate (events per period) is constant, (ii) events are independent of each other, and (iii) two events cannot occur at the same time. It is worth mentioning that the Poisson process exploited for modeling jumps does not provide temporal correlation between jump events, as for example expected in networks operating in the critical state20,46. Note that a Poisson process has also been used to describe the probability of switching events in conventional memristive cells39. Furthermore, the experimental probability distribution of jump amplitudes reported in Fig.\u00a03i follows a power law distribution \\(p(\\varGamma )\\propto \\,{\\varGamma }^{\\alpha }\\), where \\(p(\\varGamma )\\) denotes the probability of an event with amplitude \\(\\varGamma\\). The exponent obtained from experimental data is \\(\\alpha \\sim\\) \u22122.78\u2009\u00b1\u20090.07 (details in \u201cMethods\u201d).\n\na Experimental changes in the conductance \\({dG}/{dt}\\) over time of the NW network in the stationary state sustained by an applied constant voltage of 3.6\u2009V (red dashed line represents the calculated threshold value for noise disentanglement), b histogram of \\({dG}/{dt}\\) and c corresponding quantile-quantile plot of experimental data against the theoretical quantiles of a normal distribution revealing the presence of heavy tails. d Experimental Gaussian noise component of the signal obtained by noise disentanglement, e histogram of the Gaussian component of \\({dG}/{dt}\\) fitted with a Gaussian distribution (black line), and f corresponding quantile-quantile plot of experimental data against the theoretical quantiles of a normal distribution. g Experimental raster plot of jump events (top panel) and number of jump events \\(n\\) as a function of time (low panel), where experimental data (circles) are interpolated by a straight line with slope \\(\\lambda\\) (event rate or intensity). h Experimental probability distribution of inter-event intervals (IEIs) (circles) and the theoretical exponential distribution of IEIs expected in case of a homogeneous Poisson process with intensity \\(\\lambda\\) (red line). i Experimental probability distribution for the jump amplitude \\(\\varGamma\\) that can be interpolated by a power law distribution (red line).\n\nThe stochastic behavior of the network in the stationary state (\\(g=\\,\\widetilde{g}\\)) can be described through the stochastic form of the memory state Eq. (1). While this equation has analytic solution (stochastic) in case of no jumps (\\(\\varGamma=0\\)), when jumps are included, the stochastic differential equation needs to be solved numerically through the Euler\u2013Maruyama method (details in \u201cMethods\u201d). Note that modeling noise with a Wiener process is consistent with experimental results showing Gaussian dispersion. Therefore, based on experimental results and previous discussions, stochastic jumps can be modeled through a homogeneous Poisson point process. In this case, spike generation fulfils the relationship \\(n(t)=\\lambda t\\), where \\(\\lambda\\) represents the event rate of the Poisson process (\\(\\lambda\\)\u2009~\u20090.082 events per second according to experimental results). In this context, it is possible to generate Poisson spike events on the fly, where the probability of observing a jump is given (for small \\(\\delta t\\)) by \\(p\\left\\{1 \\, {{\\rm{spike\\; during}}}\\delta t\\right\\}\\approx \\lambda \\delta t\\)43. According to experimental results, the amplitude \\(\\varGamma\\) of each jump follows a power-law distribution with exponent \\(\\alpha=\\) \u22122.78 (details in \u201cMethods\u201d). Results of modeling the stochastic behavior of the NW network experimentally reported in Fig.\u00a03 are shown in Fig.\u00a04 (details of model calibration in \u201cMethods\u201d). As can be observed by comparing Figs.\u00a03 and 4, the model correctly addresses the experimental changes of the conductance (\\({dG}/{dt}\\)) both in terms of time trace (Fig.\u00a04a, additional data in Supplementary Fig.\u00a03) and distribution (Fig.\u00a04b, c). The intertwined action of stochastic and deterministic effects endowed in our modeling approach results also in qualitative agreement with the experimental and modeled conductance time traces in the stationary state (Supplementary Fig.\u00a04). Despite further experiments are required to elucidate the coupling between deterministic and stochastic dynamics in the physical system, the OU modeling approach is in agreement with (i) the experimental observation of the reversion to the average trajectory after stochastic jumps in the experimental conductance time trace (examples in Supplementary Fig.\u00a04) and (ii) the exponential decay of the autocorrelation function with the number of lags in the stationary state as expected for an OU process (Supplementary Fig.\u00a05).\n\na Modeled changes in the conductance \\({dG}/{dt}\\) over time of the NW network in the stationary state sustained by an applied constant voltage of 3.6\u2009V, b histogram of \\({dG}/{dt}\\) and c corresponding quantile-quantile plot of modeled data against the theoretical quantiles of a normal distribution, revealing the presence of heavy tails similar to experimental data. d Experimental Gaussian noise component of the modeled signal, e histogram of the Gaussian component of \\({dG}/{dt}\\) fitted with a Gaussian distribution (black line), and f corresponding quantile-quantile plot of modeled data against the theoretical quantiles of a normal distribution. g Experimental raster plot of modeled jump events (top panel) and number of jump events \\(n\\) as a function of time (low panel), where modeled data (circles) are interpolated by a straight line with slope \\(\\varepsilon\\). h Probability distribution of inter-event intervals (IEIs) obtained from modeling (circles) and the theoretical exponential distribution of IEIs expected in case of a homogeneous Poisson process with event rate \\(\\lambda\\) (red line). i Simulated probability distribution of jump amplitude \\(\\varGamma\\) that can be interpolated by a power law distribution (red line).\n\nEven if the OU modeling approach can represent the simplest approximation of the actual behavior of the experimental system, the model statistically well describes stochastic effects, including the time-trace and distribution of the Gaussian noise component (Fig.\u00a04d\u2013f, respectively), as well as the total number of jumps at time t, \\(n\\left(t\\right)\\) (details in Supplementary Fig.\u00a06), the probability distribution of IEIs, and the jump amplitude distribution for \\(\\varGamma\\) (Fig.\u00a04g\u2013i, respectively).\n\nAs discussed in previous sub-sections, resultant dynamics of NW networks can be described through the stochastic differential Eq. (1), which is able to encompass the action of deterministic and random behaviors. As an example, Fig.\u00a05a reports the experimental trajectory of a NW network including transient effects, Fig.\u00a05b the deterministic modeling, and Fig.\u00a05c the stochastic modeling, including, besides the deterministic behavior, noise and jump events (details in \u201cMethods\u201d). While deterministic modeling with mean-reverting property can capture the average features of the conductance dynamics, including transients, the stochastic component allows addressing the deviations that arise from the local activity of the NW network\u2019s junctions.\n\na Experimental dynamics of the conductance of a NW network initially in the ground state (circles) under constant voltage stimulation of 3.6\u2009V, b dynamics by deterministic modeling, and c dynamics by stochastic modeling. Insets show a detail of conductance fluctuations.\n\nSince the neuromorphic NW network can be modeled as a stochastic dynamical system characterized by voltage-dependent trajectories, it is worth exploring its behavior in terms of the potential landscape function \\(U\\) obtained from the deterministic form of Eq. (1), where \\({dg}/{dt}=-\\partial U/\\partial g\\) (details in \u201cMethods\u201d). The potential landscape of the neuromorphic network as a function of the normalized internal state of the network \\(g\\) and applied voltage V is illustrated in Fig.\u00a06a (here, the white dashed line represents the minimum of \\(U\\) as a function of \\(g\\) and V). In this context, stable states can be conceptualized as basins in the potential landscape, where the basin depth indicates the state stability (i.e., the amount of external force needed to alter the internal memory state of the system). Importantly, Fig.\u00a06b shows that the potential landscape of the dynamical system changes according to the applied voltage. Since we are dealing with a linear dynamical system, the potential profile exhibits parabolic shape at fixed bias with its minimum (stable state) shifting progressively from 0 to 1 as the voltage is increased (phase portrait of the system in Supplementary Fig.\u00a07). This shift in the stable state is consistent with the sigmoidal-like transition of the experimental stationary conductance state (\\(\\widetilde{G}\\)) reported in Fig.\u00a02b (and corresponding evolution of the normalized internal memory state reported in Fig.\u00a02d). This means that, in the proposed description, neuromorphic dynamics of self-assembled networks arising from variations in the applied voltage results in a change of the potential profile of the dynamical system over time, driving the system towards a new stable state. Due to the parabolic shape of the potential landscape, no bifurcations or transitions among coexisting deterministic stable states are expected to occur in the proposed modeling approach. Note that more complex potential landscapes caused by the application of strategically located external biases in a multiterminal configuration would yield alternative dynamics. In our case, transient dynamics among stable states are due to changes in the applied voltage, resulting in neuromorphic potentiation/relaxation behavior of the system. By considering a stationary state, noise represents fluctuations of the stochastic dynamical system around the potential basin, while jumps correspond to sudden deviations of the internal state of the system over time from \\(U\\left(t\\right)\\) to \\(U\\left(t+\\delta t\\right)\\). This is illustrated in Fig.\u00a06c, where a stationary state sustained by 3.6\u2009V is considered (enlarged view of the time trace in Supplementary Fig.\u00a08). Here, it is possible to observe through modeling that noise and jumps allow the system to displace the internal memory states around the potential basin over time (upper and intermediate panel). The occupation probability of the equilibrium memory state is represented by the histogram shown in the bottom panel, which at the end is the stationary solution of the Fokker-Planck equation43.\n\na Potential landscape as a function of the normalized internal state of the network \\(g\\) and applied voltage V. The white dashed line represents the potential minimum, i.e., the asymptotic steady state of system. b Potential profiles at fixed voltage bias (note that y scale is optimized for visualization). c Evolution over time of the internal memory state of the stochastic dynamical system in a stationary state sustained by an applied voltage of 3.6\u2009V obtained by modeling. The upper panel represents the potential profile corresponding to the stationary state, the intermediate panel represents the evolution over time of the internal memory state, and the lower panel represents the histogram of memory state occupancy obtained by monitoring the evolution of the internal state of the system for ~15,000\u2009s. While noise represents fluctuations of the system around the potential basin, jumps are represented by sudden changes in the memory state and corresponding potential over time, as represented by the arrow.\n\nInformation processing in neuromorphic nanoscale systems occurs by encoding inputs from the environment into their internal state and processing through state dynamics. In this context, the external stimulus is usually transformed into time-dependent voltage input signals to be applied to the network, while information processing occurs by exploiting the conductance transients induced by internal voltage rearrangements and short-term memory effects. In our proposed modeling approach, this means that information processing capabilities arise from the fluctuation of the system around a steady state condition induced by the electrical input signal. However, also the internal dynamics of the modeled network relies on the applied bias voltage since different voltages lead to different reversion speeds \\(\\theta\\) of the OU process (Supplementary Fig.\u00a09). Note that a high reversion speed means that the output signal immediately responds to the input signal, while a low reversion speed is typical of a structure with a high inertia, i.e., a high resistance to changes. In this framework, the selection of an appropriate operational regime is crucial for optimizing the network response to a given input signal.\n\nFigure\u00a07 illustrates an example of how our model allows us to analyze the effect of deterministic and stochastic dynamics on the evolution of the internal memory state of the network. Figures\u00a07a and 7b report the modeled deterministic and stochastic evolution of the internal memory state of the NW network in a stationary state (i.e., following the initial transient response), when stimulated with a triangular voltage waveform (input signal), while applying different constant biases (voltage applied to sustain the network operational regime) (see \u201cMethods\u201d, transient dynamics in Supplementary Fig.\u00a010). In particular, Fig.\u00a07a shows the electrical response of the network operating in a regime sustained by a constant bias of 3.6\u2009V that drives the system near the sigmoidal-like transition of the steady state expected in stationary conditions (\\(\\widetilde{g}\\)\u2009~\u20090.5). Figure\u00a07b shows the network operating in a regime sustained by a constant bias of 5\u2009V that drives the system to a \\(\\widetilde{g}\\) value close to 1 (Supplementary Fig.\u00a011). While the applied constant bias voltage is responsible for driving the system towards a steady state, the triangular voltage waveform induces fluctuations in the system state around the corresponding steady state (detailed fluctuations of the stable state in Supplementary Fig.\u00a012). It can be observed that the network response to the same signal input is remarkably different depending on the operating regime established by the polarization bias voltage, in accordance with experimental results reported in ref. 19. Furthermore, it can be observed that enhanced dynamics of the internal memory state (i.e., higher dynamical range of \\(g\\)) is achieved when the network operates under constant bias of 3.6\u2009V in comparison with operations at 5\u2009V. A similar effect can be observed by considering deterministic and stochastic trajectories of the system under both operating regimes (Fig.\u00a07c\u2013f). Noticeably, the network dynamics is strongly affected by the stochastic effects (both noise and jumps) when operating under a 5\u2009V constant bias (refer to Fig.\u00a07f), while these effects are less influential when operating at 3.6\u2009V bias. Based on our modeling approach, these results show that it is possible to tune the dynamical response of the network to an input signal by regulating the voltage-controlled stable state representing the operating regime of the network. The magnitude of the input signal frequency in connection with the internal time response of the nanoscale system is also a factor to consider.\n\nSimulated deterministic and stochastic evolution of the internal memory state of the NW network \\(g\\) (in stationary conditions) when stimulated with a triangular voltage waveform with amplitude of 50\u2009mV (external signal), while applying a constant bias of a 3.6\u2009V and b 5\u2009V. Deterministic and stochastic trajectory of the dynamical system when operating with a voltage bias of 3.6\u2009V (c, d) and 5\u2009V (e, f).\n\nComputational capabilities of self-organizing complex networks of NWs have been evaluated in the framework of the reservoir computing paradigm, where modeling can be exploited to investigate the effect of deterministic and stochastic effects on computational performance. For this purpose, a time-multiplexed reservoir computing scheme was implemented through simulations by considering a single dynamical node with delayed feedback, following the approach proposed by Appeltant et al.47. This implementation strategy exploits the two-terminal dynamical response of the network. It basically consists in the generation of a virtual reservoir by applying masked input signals to the dynamical system and a time-multiplexing of the network state. The mask considers \\(N\\) virtual nodes with a separation time \\(\\varTheta=\\,\\tau /N\\), \\(\\tau\\) being the delay time. In this method, a linear combination of weighted signals is passed from the reservoir to the output layer to generate the response of the system. The weights are trained using a linear regression algorithm taking into account the reservoir signals according to a target function representing the desired output (details in Supplementary Fig.\u00a013). Computing capabilities were tested on standard benchmark tasks, namely the time series prediction corresponding to a nonlinear autoregressive moving average (NARMA) task48 requiring both nonlinearity and memory, and Nonlinear Transformation (NLT)49 tasks (details in \u201cMethods\u201d). For each considered task, computing performances of deterministic and stochastic network models were evaluated in terms of the normalized mean square error (NMSE) as a function of the bias voltage and the corresponding memory steady state \\(\\widetilde{g}\\,\\), for different amplitudes of the input signal (details in \u201cMethods\u201d)\n\nComputing performances for the second-order NARMA task (NARMA-2) as a function of the applied voltage and \\(\\widetilde{g}\\) are reported in Fig.\u00a08a, b, respectively. Here, it is shown that enhanced dynamics corresponding to \\(\\widetilde{g}\\)\u2009~\u20090.5 (bias ~3.6\u2009V) results in a minimum of NMSE, particularly evident in case of stochastic dynamics. In the case of deterministic dynamics, the increase of NMSE for \\(\\widetilde{g}\\,\\to 0\\) and \\(\\widetilde{g}\\,\\to 1\\) can be attributed to a saturation of the system response that lead to a progressive reduction of its fading memory capabilities. In the case of stochastic dynamics, this progressive reduction of fading memory is coupled with a progressive increase of the stochastic contribution that further increases the NMSE when moving away from \\(\\widetilde{g}\\)\u2009~\u20090.5. While there is negligible influence of the input signal amplitude on the NMSE in the case of deterministic dynamics, when considering stochastic dynamics, a higher input signal results in a lower NMSE (Fig.\u00a08c). This occurs because a higher input signal enhances the amplitude of the deterministic network dynamics, thus reducing the influence of the stochastic effects on the evolution of the memory state by enhancing the signal-to-noise ratio. By considering deterministic dynamics, the prediction obtained with optimal parameters of NARMA-2 when operating the system with a bias voltage of 3.6\u2009V (\\(\\widetilde{g}\\)\u2009~\u20090.5) is reported in Fig.\u00a08d (\\(N\\) and \\(\\varTheta\\) parameters on the NMSE is shown in Fig.\u00a08e), while the prediction when operating the system with a bias voltage of 5\u2009V (\\(\\widetilde{g}\\)\u2009~\u20090.99) is reported in Fig.\u00a08f (\\(N\\) and \\(\\varTheta\\) parameters on the NMSE is shown in Fig.\u00a08e). Considering stochastic dynamics, the prediction obtained with optimal parameters of NARMA-2 when operating the system with a bias voltage of 3.6\u2009V (\\(\\widetilde{g}\\)\u2009~\u20090.5) is reported in Fig.\u00a08h (\\(N\\) and \\(\\varTheta\\) parameters on the NMSE is shown in Fig.\u00a08i), while the prediction when operating the system with a bias voltage of 5\u2009V (\\(\\widetilde{g}\\)\u2009~\u20090.99) is reported in Fig.\u00a08j (\\(N\\) and \\(\\varTheta\\) parameters on the NMSE is shown in Fig.\u00a08k).\n\nSimulation results of the NARMA-2 task in terms of NMSE as a function of a the bias voltage, and b steady state \\(\\widetilde{g}\\) by considering deterministic and stochastic dynamics and for various amplitudes of the input signal. c NMSE as a function of the input signal amplitude for deterministic and stochastic dynamics, by considering polarization voltages of 3.6\u2009V (\\(\\widetilde{g}\\)\u2009~\u20090.5) and 5\u2009V (\\(\\widetilde{g}\\)\u2009~\u20090.99). Predictions of NARMA-2 relative to polarization voltages of 3.6\u2009V and 5\u2009V obtained with optimal parameters through deterministic dynamics in d, f, respectively ([\\(N,\\varTheta\\)] in d, f are [9,1] and [2,7], respectively). Colormaps showing task performances as a function of \\(N\\) and \\(\\varTheta\\) parameters for polarization voltages of 3.6\u2009V and 5\u2009V in e, g, respectively. Predictions of NARMA-2 relative to polarization voltages of 3.6\u2009V and 5\u2009V obtained with optimal parameters through stochastic dynamics in h, j, respectively ([\\(N,\\varTheta\\)] in d, f are [3,1] and [3,5], respectively). Colormaps showing task performances as a function of \\(N\\) and \\(\\theta\\) parameters for polarization voltages of 3.6\u2009V and 5\u2009V in i, k, respectively. Colormaps and predictions in d\u2013k refer to results obtained by stimulating the network with an input with an amplitude of 50\u2009mV.\n\nHere, by considering deterministic dynamics exclusively, it is possible to observe that a larger window of \\(N\\) and \\(\\varTheta\\) parameters lead to low NMSE (i.e., a larger portion of dark blue in colormaps reported in Fig.\u00a08) when operating the network at \\(\\widetilde{g}\\)\u2009~\u20090.5 (Fig.\u00a08d) with respect to \\(\\widetilde{g}\\,\\to 1\\) (Fig.\u00a08f). When operating the network away from \\(\\widetilde{g}\\)\u2009~\u20090.5, besides a general increase of NMSE, a substantial decrease in performance can be observed by considering a set of parameters with high \\(N\\) or high \\(\\varTheta\\) values, i.e., in cases where network dynamics are expected to be more affected by a reduction of fading memory properties (details in Supplementary Fig.\u00a014).\n\nWhen considering also stochastic effects, it can be observed that the parameters\u2019 range leading to low NMSE is further reduced with respect to the deterministic case (refer to Fig.\u00a08i, k). Notably, it can be observed that performances are highly degraded for some specific choices of \\(N\\) and \\(\\varTheta\\) values. While the choice of the mask is not substantially affecting the system in the deterministic case, results show that the specific \\(N\\) and \\(\\varTheta\\) values with degraded performances rely on the specific masking scheme adopted for time multiplexing (details in Supplementary Fig.\u00a015). This happens because each specific masking scheme combined with the input signal generates different network dynamics, driving the network to a dynamic regime that endows information processing capabilities that can be less or more resilient to stochastic effects. The worst-case scenario is obtained when considering stochastic dynamics with steady state value \\(\\widetilde{g}\\,\\to 1\\) (Fig.\u00a08k), where higher values of NMSE are observed for any \\(N\\) and \\(\\varTheta\\) value. Similar considerations apply to the results obtained from NLT tasks, as reported in Fig.\u00a09a\u2013f where sine to cosine wave transformations are reported (additional sine to triangular square, and sine to square wave transformations in Supplementary Fig.\u00a016 and 17, respectively). Also in this case, the degradation of performances for specific \\(N\\) and \\(\\varTheta\\) values when considering stochastic effects relies on network dynamics generated by the specific masking scheme (Supplementary Fig.\u00a018). Considering NLT tasks, results show that noise is less detrimental in case of the sine to triangular wave (Supplementary Fig.\u00a016), while the effect of noise in sine to square wave is to smooth transitions between minimum and maximum values of the target output (Supplementary Fig.\u00a017). However, it should be mentioned that in case of NLT tasks no degradation of the computing performances for \\(\\widetilde{g}\\,\\to 0\\) and \\(\\widetilde{g}\\,\\to 1\\) were observed in the deterministic dynamics, since the degradation of the system fading memory properties is expected to affect these computing tasks less.\n\nSimulation results of the sine to cosine waveform NLT task in terms of NMSE as a function of a the bias voltage, and b steady state \\(\\widetilde{g}\\) by considering deterministic and stochastic dynamics and for various amplitudes of the input signal. c NMSE as a function of the input signal amplitude for deterministic and stochastic dynamics, by considering polarization voltages of 3.6\u2009V (\\(\\widetilde{g}\\)\u2009~\u20090.5) and 5\u2009V (\\(\\widetilde{g}\\)\u2009~\u20090.99). Predictions of the sine to cosine waveform NLT relative to polarization voltages of 3.6\u2009V and 5\u2009V obtained with optimal parameters through deterministic dynamics in d, f, respectively ([\\(N,\\varTheta\\)] in d, f are [3,15] and [5,15], respectively). Colormaps showing task performances as a function of \\(N\\) and \\(\\theta\\) parameters for polarization voltages of 3.6\u2009V and 5\u2009V in e, g, respectively. Predictions of the sine to cosine NLT relative to polarization voltages of 3.6\u2009V and 5\u2009V obtained with optimal parameters through stochastic dynamics in h, j, respectively ([\\(N,\\varTheta\\)] in h, j are [3,1] and [1,1], respectively). Colormaps showing task performances as a function of \\(N\\) and \\(\\theta\\) parameters for polarization voltages of 3.6\u2009V and 5\u2009V in i, k, respectively. Colormaps and predictions in d\u2013k refer to results obtained by stimulating the network with a sine wave input with an amplitude of 50\u2009mV.\n\nIn this context, it is worth mentioning that a similar time-multiplexing implementation scheme has been reported in a previous simulation work, where NARMA tasks have been implemented by considering dynamics of percolating networks of nanoparticles50. Concerning NLT tasks, our implementation based on a single output-node system achieves performances that are comparable to those achieved in simulated multi-output reservoirs based on self-organizing systems51,52.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58741-2/MediaObjects/41467_2025_58741_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58741-2/MediaObjects/41467_2025_58741_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58741-2/MediaObjects/41467_2025_58741_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58741-2/MediaObjects/41467_2025_58741_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58741-2/MediaObjects/41467_2025_58741_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58741-2/MediaObjects/41467_2025_58741_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58741-2/MediaObjects/41467_2025_58741_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58741-2/MediaObjects/41467_2025_58741_Fig8_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58741-2/MediaObjects/41467_2025_58741_Fig9_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Results show that it is possible to model computing systems based on nanoscale networks as continually operating stochastic dynamical systems where signal and information processing can be performed by leveraging the evolution of the physical system. In these nanoscale networks, stimuli-dependent deterministic dynamics and stochastic effects can be holistically modeled as an OU process with jumps, i.e., an It\u00f4-type stochastic differential equation, where the evolutionary state of the system can be probabilistically described by the Fokker-Planck equation with voltage-dependent parameters53. The corresponding stationary state of this equation is nothing but the Gaussian distribution obtained in Fig.\u00a04e associated with the minimum of the potential landscape.\n\nIt is worth mentioning that the ultimate origin of the modeled network behavior is experimentally difficult to access because of the huge number of junctions involved. Indeed, understanding the origin of deterministic dynamics, noise, and jumps of the conductance time trace necessarily implies high-resolution visualization of the spatially distributed electrical activity across the whole network. Even if it has been shown that it is possible to experimentally investigate the spatial conductance distribution over the network through electrical resistance tomography29,54, this technique does not allow the spatial resolution required to unveil how switching events in single NW junctions impact the resulting network behavior. Other techniques, such as voltage contrast and resistive contrast scanning electron microscopy imaging10,55, besides usually requiring measurements in vacuum that can alter the electrical response of the system, are applicable only to networks with very limited size. Similarly, conductive AFM enables probing the conductance at the single NW junction level but only in small networks consisting of a few NWs56. In this context, experiments performed in multiterminal configuration involving synchronous recording of activity in different network areas (such as the recording of voltage maps reported in ref. 19) and the experimental investigation on how power is spatially dissipated through the network (such as through lock-in thermography11) can provide further insights on how local activity impacts the resulting network behavior. Details on the state-of-the-art of techniques for the experimental characterization of the network dynamics are reported in Supplementary Note\u00a01.\n\nThe deterministic network behavior represents the key aspect for the emulation of synaptic plasticity effects and working memory by exploiting the potentiation and spontaneous relaxation of the conductive pathway connecting stimulated areas12. These deterministic processes are fundamental for the implementation of unconventional computing paradigms where information processing occurs by exploiting the network capability of nonlinearly processing the input signal over time30. Understanding and controlling the deterministic dynamics represents a crucial aspect for rational design of the neuromorphic system, allowing for proper identification of the working conditions in terms of operating voltages. In this context, modeling results show the possibility of dynamically setting the steady conduction state through the applied bias voltage and, thus, the operating regime of the self-organizing neuromorphic network. This can enable to control and tailor the system's dynamical regime depending on the specific temporal sequence and amplitude of the input signal, fully exploiting the dynamic range of the system while avoiding operating near the saturation regime. Interestingly, the steady state and dynamics can also be experimentally tailored by controlling the network density (Supplementary Fig.\u00a019). The proposed modeling approach could be integrated into a graph representation, similarly to those reported in refs. 23,57,58, so that the dynamical regime of the system would be tuned depending also by the spatial location of multiple input signals as required for modeling the behavior of multiterminal networks. Additionally, it is worth mentioning that memristive dynamics described by the deterministic form of Eq. (1) have been exploited as dynamics of a physics-inspired recurrent neural network (RNN) computational model59.\n\nWhile our modeling approach can well describe the NW networks working in the short-term memory regime exploitable for reservoir computing, we would like to point out that the proposed model can be further extended by introducing (i) transitions between deterministic coexisting states that have been experimentally observed in certain conditions (not modeled here), and (ii) long-lasting variations in the steady states to emulate also long-term changes in the network conductance that can be experimentally observed under specific stimulation conditions29 (for these purposes, the introduction of new parameters able to account for drifts of the steady states are required).\n\nIn the framework of reservoir computing, the reported approach allows to quantify the influence of deterministic and stochastic dynamics on computing capabilities. While the reported model, based on a combination of physical foundations and mathematical properties and able to describe the behavior of the system in terms of a minimal number of assumptions, can be adopted to explore in silico different implementations of the reservoir computing paradigm through simulations, further work is required to experimentally delineate the computing capabilities of these systems. In this context, it is worth noticing that reservoir computing has been experimentally explored in a wide range of self-organizing memristive systems, including nanotubes60, nanoparticles61,62, and nanowires19,20,31, by exploiting multiterminal configurations that allow to probe the evolution of the internal state of the system as seen from different spatial locations.\n\nSimulation results show that stochastic effects limit the separability property of the system (i.e., the capability of the system to differentiate its response when processing different input signals). When little or no memory is required for the completion of the task (such as in the case of NLT), computing capabilities of the system rely mainly on the deterministic-to-stochastic (signal-to-noise) response of the network. When memory is essential for the completion of the task (such as in NARMA), computing capabilities rely also on the dynamical regime of the network and its fading memory capabilities determined by the steady state. In both cases, optimization of the computing capabilities relies on the proper selection of the input signal amplitude. For instance, operating the network at \\(\\widetilde{g}\\)\u2009~\u20090.5 enhances the system\u2019s dynamical range. Additionally, the results show that stochastic effects should be properly considered for the optimization of the time-multiplexed reservoir computing implementation, for the proper selection not only of [\\(N,\\varTheta\\)] parameters but also for the design of an appropriate masking scheme for the input signal. As a result, the masking scheme needs to be optimized to reduce degradation of computing performances for a given set of [\\(N,\\varTheta\\)] by means of, for example, the use of supervised learning algorithms. More in detail, it is important to point out that stochastic effects do not hinder the possibility of operating the system with a low number of virtual nodes \\(N\\), as required for reducing the hardware complexity of the system. Indeed, a lower N number allows the system to operate with a reduced number of weights to be trained (and a lower amount of information to be temporally stored before being analyzed by the readout).\n\nEven if at first sight the random nature of the output signal seems to be detrimental for computing when adopting a deterministic perspective, it was shown that stochastic dynamics can indeed be exploited as an additional dimension for the implementation of stochastic learning rules. This comprises the hardware realization of random number generators, physical unclonable functions, and chaotic/stochastic computing systems by taking advantage of the material substrate as the underlying source of randomness63.\n\nIn all these contexts, Eq. (1) can be exploited as an electrical transfer function to model the system's dynamical output corresponding to arbitrary input signals for a rational design of neuromorphic systems based on self-organizing nanoscale networks. This can represent a step ahead for the conceptualization of a general theory of computing with non-linear dynamics and stochastic effects through a dynamical system-oriented view27,64,65. Furthermore, we envision that dynamics of complex systems, like the one analyzed in this work, can be exploited for in materia forecasting the evolution of stochastic variables that can be generically represented by means of Orstein-Uhlenbeck processes, such as financial processes including the evolution of interest rates describable through the Vasicek or Hull-White models66. Indeed, after proper calibration, the inherent capability of the physical system to approximate the dynamic evolution of the stochastic differential equations describing specific processes (in particular OU process) could enable forecasting the evolution of stochastic trajectories by observing, given an initial condition, how the physical observables evolve over time. More in generally, these results can pave the way for the development of alternative concepts of unconventional computing paradigms that take advantage of both deterministic and stochastic dynamics on the same physical substrate, as naturally occurs in biological systems.\n\nIn summary, we showed that neuromorphic nanowire networks can be modeled as a stochastic dynamic system. We show that deterministic and stochastic dynamics of the physical system can be qualitatively and quantitatively described in a unified mathematical framework such as the Ornstein-Uhlenbeck process. This approach enables to holistically describe stimuli-dependent deterministic trajectories of the system as well as conductance fluctuations and jumps. Furthermore, the proposed compact model description can be exploited to quantitatively assess the impact of deterministic and stochastic dynamics on computing capabilities of these physical systems in the framework of reservoir computing, as shown by considering benchmark tasks. These results can pave the way for the rational and optimized development of neuromorphic systems that can fully exploit their spatio-temporal dynamics similarly to our brain.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Self-organizing Ag NW networks have been fabricated by means of drop-casting12, by dispersing Ag NWs with length of 20\u201350\u2009\u03bcm and a diameter of ~ 115\u2009nm in isopropyl suspension (from Sigma\u2013Aldrich) on an insulating SiO2 substrate. Details on structural and chemical characterization of Ag NWs are analyzed in our previous work12. The fabrication of neuromorphic NW networks consisted in drop casting Ag NWs in isopropanol solution (drop of 20\u2009\u00b5L, concentration of ~ 0.09\u2009mg\u2009mL\u22121) on a\u2009~\u20091\u2009\u00d7\u20091\u2009cm2 substrate, followed by deposition of Au metal electrodes through sputtering deposition and shadow mask. To show the dependence of the steady state on the network density (Supplementary Fig.\u00a015), results have been compared with the response of a different NW network with higher density realized by drop casting Ag NWs in isopropanol solution (drop of 20\u2009\u00b5L) with a concentration of ~0.13\u2009mg\u2009mL\u22121. The morphology of the self-organizing NW network and details of NW junctions were assessed by means of Scanning Electron Microscopy (SEM, FEI Inspect F).\n\nExperimental electrical characterization has been performed at a controlled constant temperature of 303\u2009K in a hermetically closed environment of ambient air, by contacting sample substrate with a thermocouple controlled through a Lake Shore 331 temperature controller. Electric measurements have been carried out by a Keithley 6430, connected through a preamplifier in two-terminal fashion to facing electrodes sputtered at sample edges centers (electrode distance of ~7\u2009mm). Current has been recorded in auto range mode at fixed minimum range of 1\u2009nA and with Number of Power Line Cycles (NPLC) set at 1, where voltage was sourced at 20\u2009V range. Under these conditions, the sampling rate was ~1.6\u2009Hz. Measurements were acquired in pulse-train fashion with 10\u2009h width pulses of voltage progressively increasing from 0.1\u2009V to 6.6\u2009V with 0.1\u2009V step, separated by 10\u2009mV reading intervals of 1\u2009h that ensure network relaxation to the ground state before stimulation with the subsequent voltage amplitude. The mean value of stationary state \\(\\widetilde{G}\\) reported in Fig.\u00a02b was evaluated for each voltage bias condition by considering the signal after ~18,500\u2009s from bias application to avoid transient dynamics, averaging the stationary signal over ~15,000\u2009s.\n\nThe disentanglement between Gaussian noise and jump events in the \\({dG}/{dt}\\) signal has been performed by determining the interval underlying the most Gaussian data region in a Kolmogorov-Smirnov (KS) sense. In detail, the p values of progressively larger data intervals (centered in 0) have been evaluated by using the KS test. The extreme ensuring maximum p value, i.e., most probable Gaussian distribution, has been chosen as the threshold between noise and jump events.\n\nThe probability distributions of inter-event intervals \\(p({{\\rm{IEI}}})\\) as a function of IEI reported in Figs.\u00a03h and 4h have been obtained by linear binning of data with size of 10\u2009s. Instead, the probability distributions of jumps \\(p(\\varGamma )\\) as a function of jump amplitude \\(\\varGamma\\) reported in Figs.\u00a03i and 4i have been obtained by logarithmically binning data with a density of 50 bins per decade. Power-law fittings of data reported in Figs.\u00a03i and 4i have been performed by following the procedure used for complexity analysis in ref. 67. A doubly truncated target distribution has been considered, where the lower cutoff emerges as a consequence of instrumental finite resolution, while the upper one results from the finite measurement duration and sampling. The data fraction to be fitted has been progressively shrunk and the related power-law exponent has been extracted by means of maximum likelihood estimation (MLE). The exponent has been then validated by using KS test in the following way: 500 test power-law distributions have been generated starting from the exponent under study, and their KS statistics with respect to fitting line (i.e., the maximum absolute difference between their respective cumulative distribution functions) has been evaluated. KS statistics have also been evaluated between the experimental data and the fit. The fitting has been considered acceptable if the experimental KS statistics have resulted lower than the test ones in the 20% of the cases at least. The validated fitting obtained from the least truncated data interval has been then selected as the final one. Exponent uncertainty has been obtained through the Monte Carlo method by fitting the 500\u00a0test power-law distributions used for validation and evaluating their exponent standard deviation. The experimental number of events \\(n\\) as a function of time was interpolated to extract the event rate with a straight line with intercept 0 (i.e., 0 events at \\(t=\\) 0) to obtain the experimental event rate \\(\\lambda\\).\n\nModeling was performed in Python. The deterministic balance-rate equation reported in Eq. (3) can be solved analytically, where the recursive (iterative) solution can be expressed as (assuming a simulation timestep \\({\\Delta }t\\)\u2009>\u20090 and knowing the initial value \\({g}_{0}\\))44:\n\nAs an alternative, Eq. (3) can be solved by using the Euler method as a first-order numerical procedure for solving ordinary differential equations, expressing the solution as:\n\nParameters for modeling the deterministic behavior of the network were retrieved from interpolation of the stationary state conductance value \\(\\widetilde{G}\\) over applied voltage biases reported in Fig.\u00a02b.\n\nThe stochastic differential equation describing the Ornstein-Uhlenbeck process reported in Eq. (1) can be solved analytically in case of no jumps (\\(\\varGamma=0\\)), where the recursive (iterative) solution can be expressed as:\n\nWhere \\({\\varepsilon }_{t}\\) is normally distributed with mean zero and standard deviation \\({\\sigma }_{e}\\) is:\n\nIn case of jumps (\\(\\varGamma \\ne 0\\)), the stochastic differential equation can be numerically solved through the Euler\u2013Maruyama method, where the solution can be expressed as:\n\nwhere \\(\\xi\\) is a random Gaussian variable with variance 1 (independent at each time step). The normalization factor \\(\\sqrt{\\varDelta t}\\) comes from the fact that the infinitesimal step for a Brownian motion has the standard deviation \\(\\sqrt{\\varDelta t}.\\) The stochastic model was calibrated on experimental data: (i) by selecting the \\(\\sigma\\) value that gives rise to the standard deviation of the Gaussian noise distribution that matches the experimental one, and (ii) by assigning to jump events a jump amplitude sampled from a bounded power law distribution with exponent \\(\\alpha\\) that matches experimental results and bounded values that match the minimum and maximum experimental value (\\({\\varGamma }_{\\min }\\) and \\({\\varGamma }_{\\max }\\)), where the minimum value matches with the experimental threshold for noise disentangle (i.e., modeled jump events have amplitude larger than the threshold value for noise disentangle). The jump direction during modeling is randomly assigned. While the probability of jump direction is the same when the device is in the stationary state, the probability of jump direction is assumed to be proportional to \\(\\left(\\widetilde{g}-g\\right)\\). Where the jump up probability is 0.5\u2009+ \\({A}^{*}\\left(\\widetilde{g}-g\\right)\\) and jump down probability is 0.5\u2009\u2212 \\({A}^{*}\\left(\\widetilde{g}-g\\right)\\) (\\({A}^{*}\\) is a normalization constant that depends on the maximum jump amplitude to normalize probability to 1).\n\nThe potential function\\(\\,U=\\,-\\int \\theta \\left[\\widetilde{g}-g\\right]{dg}=\\theta g\\left(\\frac{g}{2}-\\widetilde{g}\\right)+C\\), where \\(C\\) is an arbitrary constant set to 0 in our work, was obtained by integrating \\({dg}/{dt}=-\\partial U/\\partial g\\), where the deterministic form of Eq. (1) was considered as \\({dg}/{dt}\\). Information processing capabilities in Fig.\u00a07 have been analyzed by considering modeling parameters \\(\\theta \\left(V\\right),\\,\\widetilde{g}\\left(V\\right)\\) extracted from the experimental data reported in Fig.\u00a02, and \\(\\sigma,{dW},\\,\\varGamma,{dq}\\) extracted from the experimental data reported in Fig.\u00a03.\n\nBenchmarking of computing capabilities of the deterministic and stochastic network model has been performed by implementing a time-multiplexed reservoir computing scheme47, exploiting the network in two-terminal configuration as a single dynamical node with delayed feedback. In this implementation, the transient response to a masked input signal of the network in two terminal configuration is sampled \\(N\\) times (virtual nodes) with separation time \\(\\varTheta\\) during each timestep of the input signal (details on the implementation in Supplementary Fig.\u00a011). Results reported in the manuscript have been obtained by adopting the masking scheme reported in Supplementary Fig.\u00a015 panel a, if not differently specified. A similar implementation was also reported in percolating networks of nanoparticles50.\n\nThe readout function (output layer) was trained through a supervised learning algorithm. For a \\(N\\)-dimensional reservoir output \\({{\\bf{X}}}=\\left[{x}_{1},\\,{x}_{2},\\,\\ldots,\\,{x}_{N}\\right]\\), training involved the calculation of a vector of linear coefficients \\({{\\bf{w}}}=\\left[{w}_{1},\\,{w}_{2},\\,\\ldots,\\,{x}_{N}\\right]\\) such that the predicted output of the system \\(\\hat{y}\\left(k\\right)\\) well approximate the target output \\(y(k)\\):\n\nLinear coefficients were calculated through linear regression.\n\nNARMA tasks are a set of time series prediction tasks involving the emulation of nonlinear dynamical systems, representing a challenging task for computational systems because of their nonlinearity and dependence on previous time lags. The task involves learning the association between a discrete input white noise \\(u(k)\\) and the chaotic time series generated by the nonlinear dynamical system. For the second-order NARMA system (NARMA-2), the time series is given by:\n\nwith \\(\\alpha=\\) 0.4, \\(\\beta=\\) 0.4, \\(\\gamma=\\) 0.6, \\(\\delta=\\) 0.1. The white input noise was fed to the network after being scaled by the desired amplitude (amplitudes of 10, 50, and 100\u2009mV were considered). 720 timesteps of the system outputs for were used for training the readout function, while testing was performed on 180 timesteps. Performances of the system were evaluated through NMSE. In Fig.\u00a08a, b, for each polarization voltage bias (for each \\(\\widetilde{g}\\)), the NMSE represents the best NMSE obtained by evaluating computing performances for \\(N\\) and \\(\\varTheta\\) parameters in the range [0, 15]. In other words, this represents the NMSE optimized in terms of \\(N\\) and \\(\\varTheta\\) (in the selected parameter ranges), i.e., the optimized performance of the system given a \\(\\widetilde{g}\\).\n\nNLT tasks are a set of tasks involving a nonlinear transformation of a sine wave input signal49. This task requires mainly a high degree of nonlinearity. We considered the transformation of the input signal into a cosine, square, or triangular waveform of the same period. For this purpose, the sine wave input signal is fed to the network after being scaled by the desired amplitude (amplitudes of 10, 50, and 100\u2009mV were considered). Eight hundred timesteps of the system outputs were used for training the readout function, while testing was performed on 200 timesteps. Performances of the system were evaluated through NMSE. In Fig.\u00a09a, b, for each polarization voltage bias (for each \\(\\widetilde{g}\\)), the NMSE represents the best NMSE obtained by evaluating computing performances for \\(N\\) and \\(\\varTheta\\) parameters in the range [0, 15]. In other words, this represents the NMSE optimized in terms of \\(N\\) and \\(\\varTheta\\) (in the selected parameter ranges), i.e., the optimized performance of the system given a \\(\\widetilde{g}\\).\n\nTask performances were evaluated by the normalized mean square error (NMSE) between predicted output \\(\\hat{y}\\left(k\\right)\\) and target output \\(y(k)\\), through the equation:\n\nwhere the sum of square residuals is normalized by the variance of the target function \\({\\sigma }^{2}({y}_{k})\\).", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data that support the findings of this study are available on Zenodo (https://doi.org/10.5281/zenodo.15050217). All other data are available from the authors.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The codes used to generate datasets of simulations can be accessed on GitHub (https://github.com/MilanoGianluca/Self-organizing_neuromorphic_networks_as_stochastic_dynamical_systems).\u00a0The code release is available on Zenodo (https://doi.org/10.5281/zenodo.15174744).\u00a0", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Mehonic, A. & Kenyon, A. J. Brain-inspired computing needs a master plan. Nature 604, 255\u2013260 (2022).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nChristensen, D. V. et al. 2022 roadmap on neuromorphic computing and engineering. Neuromorphic Comput. Eng. 2, 0\u201331 (2022).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nHam, D., Park, H., Hwang, S. & Kim, K. Neuromorphic electronics based on copying and pasting the brain. Nat. 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This project (EMPIR 20FUN06 MEMQuD) has received funding from the EMPIR program co-financed by the Participating States and from the European Union\u2019s Horizon 2020 research and innovation program. Part of this work has been supported by NEURONE, a project funded by the European Union\u2014Next Generation EU, M4C1 CUP No. I53D23003600006, under program PRIN 2022 (prj code 20229JRTZA). Part of this work has been carried out at Nanofacility Piemonte INRiM, a laboratory supported by the \u201cCompagnia di San Paolo\u201d Foundation, and at the QR Laboratories, INRiM. E.M. acknowledges project PID2022-139586NB-C41 funded by MCIN/AEI/10.13039/501100011033, Spain, and FEDER, E.U.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), Torino, Italy\n\nGianluca Milano\u00a0&\u00a0Davide Pilati\n\nDepartment of Applied Science and Technology, Politecnico di Torino, C.so Duca degli Abruzzi 24, Torino, Italy\n\nFabio Michieletti,\u00a0Davide Pilati\u00a0&\u00a0Carlo Ricciardi\n\nDepartament d\u2019Enginyeria Electr\u00f2nica, Universitat Aut\u00f2noma de Barcelona (UAB), Cerdanyola del Vall\u00e8s, Spain\n\nEnrique Miranda\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nG.M. and E.M. generated the idea, designed the experiments, developed modeling, analyzed data, and wrote the manuscript. F.M. performed experimental activities and supported data analysis. D.P. supported modeling activities and data analysis. G.M., F.M., D.P., C.R., and E.M. participated in the discussion of results and revision of the manuscript.\n\nCorrespondence to\n Gianluca Milano.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Tomonobu Nakayama, Jianshi Tang and the other anonymous reviewer(s) for their contribution to the peer review of this work. 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changes for economically viable catalytic plastic upcycling", + "pre_title": "Unraveling the Role of Water in Mechanism Changes for Economically Viable Catalytic Plastic Upcycling", + "journal": "Nature Communications", + "published": "29 November 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54495-5/MediaObjects/41467_2024_54495_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54495-5/MediaObjects/41467_2024_54495_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54495-5/MediaObjects/41467_2024_54495_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-54495-5#Sec24" + ], + "code": [], + "subject": [ + "Catalytic mechanisms", + "Chemical engineering", + "Heterogeneous catalysis" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4284309/v1.pdf?c=1732972161000", + "research_square_link": "https://www.researchsquare.com//article/rs-4284309/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54495-5.pdf", + "preprint_posted": "22 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The surge in global plastic production, reaching to 390.7\u00a0million tons in 2021, exacerbates environmental pollution, with only 11% of plastic being recycled. Catalytic recycling, particularly through hydrogenolysis and hydrocracking, offers a promising avenue for upcycling polyolefin plastic, comprising 55% of global plastic waste. This study investigates the influence of water on polyolefin depolymerization using Ru catalysts, revealing a promotional effect only when both metal and acid sites, particularly Br\u00f8nsted acid, are present. Findings highlight the impact of Ru content, metal-acid balance, and their proximity on this interaction, as well as their role in modulating the isomerization process, affecting product selectivity. Additionally, the interaction facilitates the suppression of coke formation, ultimately enhancing catalyst stability. A comprehensive techno-economic and life cycle assessment underscores the viability and environmental benefits of the process, particularly in the presence of water. These insights advance understanding and offer strategies for optimizing polyolefin plastic recycling processes.Physical sciences/Engineering/Chemical engineeringPhysical sciences/Chemistry/Catalysis/Heterogeneous catalysis", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "WatereffectSupplementarydatafinal.docx", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The surge in global plastic production, reaching 400.3 million tons in 2022, has exacerbated environmental pollution, with only 11% of plastic being recycled. Catalytic recycling, particularly through hydrogenolysis and hydrocracking, offers a promising avenue for upcycling polyolefin plastic, comprising 55% of global plastic waste. This study investigates the influence of water on polyolefin depolymerization using Ru catalysts, revealing a promotional effect only when both metal and acid sites, particularly Br\u00f8nsted acid site, are present. Findings highlight the impact of Ru content, metal-acid balance, and their proximity on this interaction, as well as their role in modulating the isomerization process, affecting product selectivity. Additionally, the interaction facilitates the suppression of coke formation, ultimately enhancing catalyst stability. A comprehensive techno-economic and life cycle assessment underscores the viability and environmental benefits of the process, particularly in the presence of water. These insights advance understanding and offer strategies for optimizing polyolefin plastic recycling processes.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Global plastic production has surged over the years, escalating from 1.7 million tons in 1954 to a staggering 400.3 million tons in 20221. However, the management of plastic waste predominantly relies on landfill disposal (~75%) or incineration (~14%), with a mere 11% of plastic being recycled2. This concerning trend exacerbates environmental pollution, highlighting the urgent need for sustainable solutions in plastic waste management. In response to these challenges, there is a critical demand for innovative recycling technologies aimed at mitigating environmental risks3. While mechanical recycling has traditionally been utilized, it often leads to downcycling of plastics, compromising their mechanical properties4. Chemical recycling methods, particularly catalytic recycling, are increasingly recognized as promising alternatives5,6,7. Catalytic recycling enables the conversion of plastic waste into drop-in fuels at lower temperatures (250\u2013400\u2009\u00b0C) compared to conventional chemical recycling methods (500\u2013800\u2009\u00b0C)8. Furthermore, it facilitates selective cleavage of the C-C bonds in plastic wastes, yielding higher yields of liquid fuels compared to conventional methods9. These advantages reduce separation costs and energy inputs, making catalytic recycling an economically and environmentally friendly solution to the escalating concerns surrounding plastic waste management10.\n\nHydrogenolysis and hydrocracking are prominent catalytic recycling methods extensively studied for their efficacy in upcycling polyolefin plastic waste, which constitutes over half of the global plastic waste due to its short lifecycle9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35. Hydrogenolysis operates at relatively low temperatures, employing Ru or Pt supported on metal oxides such as CeO2, TiO2, Al2O3, SBA-15, and ZrO2, to produce liquid fuels with high yields13,14,15,16,17,18,19. In hydrogenolysis, both C-H bond activation and C-C bond cleavage primarily occur at metal sites, emphasizing the critical role of the geometric structure of Ru and Pt in influencing activity17,18,19,20,21. Conversely, hydrocracking involves C-H bond activation at metal sites and C-C bond cracking at acid sites, typically employing Pt and Ru metals paired with acidic supports such as WO3/ZrO2 or zeolite22,23,24. The metal-to-acid molar ratio, known as the metal-acid balance (MAB), has been reported to significantly influence hydrocracking reactivity9,23,24,25,26.\n\nPrevious studies on hydrogenolysis and hydrocracking of polyolefin plastics have mainly been conducted without solvents, but recent research has shed light on the impact of solvents on catalytic activity, particularly in HDPE hydrogenolysis over Ru/C12. Among various solvents tested, such as water, n-pentane, n-hexane, methylcyclohexane, and decalin, only n-hexane and methylcyclohexane were found to enhance HDPE hydrogenolysis activity12. Molecular dynamics simulations have revealed that the interaction between HDPE and solvent molecules changes the conformation of HDPE12. For instance, in n-hexane, HDPE tends to coil, facilitating access to the catalyst\u2019s active sites and increasing activity, whereas, in decalin, HDPE maintains extended conformations, resulting in decreased catalytic activity12. Notably, previous studies have explored the influence of water in the hydrocracking of n-hexadecane36,37,38. The addition of water was found to reduce the hydrocracking conversion of n-hexadecane over H-ZSM-5 due to competitive adsorption between water and reactants on the acidic sites of the zeolite at relatively low reaction temperatures (<350\u2009\u00b0C)37. Conversely, water addition was reported to promote the activity of Pd/NaX zeolite in the hydrocracking of n-hexadecane38. Therefore, we hypothesize that water addition enhances the depolymerization of polyolefin plastic waste in the presence of both metal and acid sites.\n\nIn this study, we investigate the influence of water on the depolymerization of polyolefin plastics by synthesizing a series of Ru catalysts supported on various materials, including SiO2, SBA-15, \u03b3-Al2O3, TiO2, and zeolite-Y. Our findings reveal that the promotional effect of water is only observed in the presence of both metal and acid sites, particularly Br\u00f8nsted acid sites, via the metal-acid interaction, leading to an increase in conversion from 51% to 82%. Additionally, we identify MAB and their proximity as crucial factors in facilitating the metal-acid interaction. Notably, the presence of water also influences the degree of isomerization, with a selectivity toward isomers of 1.6% with water compared to 72.1% without water. Through a comprehensive set of characterization techniques, including GC-MS, MS, and pyridine-DRIFTS, we propose a bifunctional depolymerization reaction mechanism, elucidating key distinctions from hydrogenolysis and hydrocracking processes. Techno-economic analysis (TEA) and life-cycle assessments (LCA) further demonstrate that the addition of water positively impacts both economic and environmental performance. This mechanistic insight not only advances our comprehension of catalytic activity but also provides strategies for regulating the degree of isomerization in polyolefin plastic waste catalytic processes.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The LDPE depolymerization reactions were conducted using various Ru catalysts supported on SiO2, SBA-15, \u03b3-Al2O3, TiO2, and zeolite-Y. In most cases, the addition of water resulted in lower or comparable activity (Table\u00a01, entries 1\u201314), consistent with previous findings12. However, 5% Ru/zeolite-Y exhibited a notable 16.3%-point increase in conversion, rising from 80.6% to 96.9% after water addition. This increase in conversion was accompanied by a shift in product distribution toward mid-range (C5-C22) alkanes, which are desirable for liquid fuel production (Fig.\u00a01a). Pyridine-DRIFTS analysis revealed that among the various Ru catalysts, only Ru/zeolite-Y exhibited Br\u00f8nsted acid sites (BAS) (Fig.\u00a01b and Table\u00a02), suggesting a potential association between enhanced activity in the presence of water and BAS.\n\na Product distributions after LDPE depolymerization reaction over 5% Ru/zeolite-Y. Raw data from GC-FID and GC-TCD spectra are shown in Supplementary Figs.\u00a014 and 15. Reaction conditions: 250\u2009\u00b0C, 3\u2009h, 30\u2009bar H2 (at 25\u2009\u00b0C), 1.70\u2009g of LDPE (Mn: ~1700, Mw: ~4000), 50\u2009mg of catalyst, 50\u2009mg of water (if needed). b. Pyridine-DRIFTS spectra of Zeolite-Y and various Ru-based catalysts. c Conversion as a function of reaction time. d. Conversion as a function of Water/Catalyst ratio. Unless otherwise mentioned, the reaction conditions are as follows: 250\u2009\u00b0C, 1.5\u2009h, 30\u2009bar H2 pressure (at 25\u2009\u00b0C), 1.70\u2009g LDPE (Mn: ~1700, Mw: ~4000), 50\u2009mg of Ru/zeolite-Y (Metal: 5\u2009wt%), and 50\u2009mg of water (if needed). e Representative STEM image and Ru particle size distribution histogram of Ru/zeolite-Y. f, g X-ray photoelectron spectra of Ru 3p3/2 of Ru/zeolite-Y before and after reaction with or without water; f 5% Ru/zeolite-Y and g 0.2% Ru/zeolite-Y. h Pore size distribution of fresh and spent Ru/zeolite Y in the presence or absence of water based on the N2 adsorption-desorption. i. Reusability test results of Ru/zeolite Y. Reaction conditions: 250\u2009\u00b0C, 3\u2009h, 30\u2009bar H2 pressure (at 25\u2009\u00b0C), 1.70\u2009g of LDPE (Mn: ~1700, Mw: ~4000), 50\u2009mg of spent Ru/zeolite Y (Metal: 5\u2009wt%), 50\u2009mg of water (if required). Error bars = standard deviation. Source data are provided as a Source Data file.\n\nTo understand the promotional effect of water over Ru/zeolite-Y, a series of experiments were conducted by changing the reaction conditions. The results revealed a time-dependent enhancement in conversion with the addition of water (Fig.\u00a01c). Additionally, the conversion displayed a bell-shaped curve as a function of the water/catalyst ratio, peaking at a water/catalyst ratio of 1.0 (Fig.\u00a01d). This observation suggests that the presence of water gradually enhances LDPE conversion as the water/catalyst ratio increases, but beyond this optimal ratio, reactivity diminishes, possibly due to a dilution effect39. Further investigations were conducted to explore the influence of reaction temperature, H2 pressure, and additional Ar (Supplementary Table\u00a01).\n\nTo further investigate the role of Ru metal, we prepared Ru/zeolite-Y catalysts with weight loadings of 1% and 0.2%. Given the potential influence of Ru dispersion and acid sites on catalytic activity, we conducted analyses to measure particle size using STEM, acid density through NH3-TPD, and BAS/LAS ratio via pyridine-DRIFTS (Table\u00a02, entries 7, 8 and Fig.\u00a01e). Similar particle size and acidic properties were observed, ruling out the potential influence of Ru dispersion and acidic properties of Ru/zeolite-Y on catalytic activity. STEM analysis of spent catalysts was further conducted to investigate the particle size changes. The spent Ru/zeolite-Y exhibited a slight increase in mean particle sizes with and without water (Supplementary Fig.\u00a03), suggesting that water did not strongly contribute to aggregation. Furthermore, XPS was conducted to determine the electronic structure of Ru/zeolite-Y. The binding energy of spent Ru/zeolite-Y remained constant at 461.7\u2009eV regardless of water and weight loadings (Fig.\u00a01f, g), a characteristic indicative of pure metallic states of Ru (Ru0)40. This suggests that the electronic state of Ru catalysts remained unchanged after the reaction, irrespective of the presence of water.\n\nWith evidence that both geometric and electronic structures of Ru catalysts remained similar regardless of the presence of water, we conducted BET analysis to characterize the pore structure of the fresh and spent catalysts. While the general shape of the hysteresis loop remained consistent (Supplementary Fig.\u00a04), the pore size distribution of the spent catalyst shifted to smaller diameters, particularly in the absence of water (Fig.\u00a01h). This shift suggests increased coke formation during the reaction, as evidenced by a decrease in both surface area and pore volume (Supplementary Table\u00a02)41. This inhibition of coke formation was further confirmed by post-calcination characterization, where the presence of water was essential for restoring the catalyst\u2019s performance (Supplementary Table\u00a02). To further assess the stability of the catalyst under different conditions, we conducted control experiments without LDPE at 300\u2009\u00b0C for 24 to 48\u2009hours. These experiments showed that the zeolite structure remained largely intact, with only minor reductions in surface area and pore volume for the 5% Ru/zeolite-Y, regardless of water exposure (Supplementary Table\u00a03). The preservation of the zeolite structure can be explained by the low n/n0 ratio in our experiments. The ratio of the amount of water present in the zeolite plus the added water to the molar number of the saturated state (n/n0) was calculated to be 0.056, well below the critical threshold of 0.3 known to cause zeolite crystal structure collapse42. Additionally, the presence of Ru metal helped mitigate structural changes in the zeolite.\n\nTo quantify the amount of coke deposited during the reaction, TGA was conducted15,26. The weight loss was approximately 12% following the reaction with water, contrasting with about 22% without water, providing compelling evidence that water suppresses coke formation (Supplementary Fig.\u00a09). Additional TGA results of 0.2% Ru/zeolite-Y and visual comparisons of the catalysts showed the similar trend, reinforcing our conclusions (Supplementary Figs.\u00a010 and 11). This observation aligns with earlier findings in methane reforming, where coke formation was higher without water (dry reforming of methane) than with water (steam reforming of methane)43. Further analysis revealed that the coke removed by water was primarily in the form of CO2, with the amount of CO2 extracted increasing with both reaction time and water/catalyst ratio (Supplementary Fig.\u00a012).\n\nThe reusability test was performed to investigate the impact of coke deposition on the reactivity of Ru/zeolite-Y in LDPE depolymerization (Fig.\u00a01i). Upon reusing Ru/zeolite-Y with water, the initial conversion was 96.9%, with subsequent 1st and 2nd regenerations yielding conversions of 94.5% and 90.1%, respectively (Table\u00a01, entries 15\u201318). These results suggest that the reactivity of the catalyst remained nearly unchanged in the presence of water. In contrast, when the reaction was conducted without water, a significant decrease in conversion was observed, dropping from 80.6% to 31.8% after the 1st regeneration and 24.1% in the 2nd. These findings indicate that the addition of water in LDPE depolymerization using Ru/zeolite-Y suppresses coke formation, resulting in improved reactivity and reusability of the catalyst.\n\nTo understand how water enhances reactivity and reusability, a control experiment was conducted with pyridine. Interestingly, the poisoned Ru/zeolite-Y catalysts exhibited a negligible promotional effect with water (Table\u00a01, entries 19 and 20), different from the fresh catalyst. Additionally, the product distribution of poisoned catalysts remained similar regardless of water (Fig.\u00a02a), unlike the fresh catalyst (Fig.\u00a01a). These findings strongly suggest the involvement of acid sites in LDPE depolymerization for the promotional effect of water. Given that pyridine poisons both Lewis acid sites (LAS) and BAS26, pyridine-DRIFTS, and NH3-TPD analyses of spent Ru/zeolite-Y were performed to discern the role of the BAS. The peaks at 1450\u2009cm-1 (LAS) and 1540\u2009cm-1 (BAS) were present over Ru/zeolite-Y before and after the reaction with water (Fig.\u00a02b). However, the pyridine-DRIFTS spectra of the spent catalyst without water showed a noticeable decrease in the peak at 1540\u2009cm-1. Indeed, the BAS of the catalyst decreased from 185.0 \u03bcmol/g to 44.1 \u03bcmol/g after the reaction without water (Supplementary Table\u00a07). Conversely, the BAS was maintained at 160.8 \u03bcmol/g after the reaction with water. This trend was also observed for the 0.2% Ru/zeolite-Y catalyst. The substantial decrease in the BAS after the reaction without water can be attributed to coke deposition on the BAS during the reaction without water23,26. This also suggests that the reaction predominantly occurs at the BAS rather than the LAS.\n\na Product distributions after the LDPE depolymerization reaction over pyridine-poisoned 5% Ru/zeolite-Y. b Pyridine-DRIFTS spectra of Ru/zeolite-Y before and after the reaction or controlled reaction in the presence or absence of water. The spent catalyst was obtained under the following conditions: 250\u2009\u00b0C, 12\u2009h, 30\u2009bar H2 pressure (at 25\u2009\u00b0C), 50\u2009mg of Ru/zeolite-Y (Metal: 5\u2009wt%), 50\u2009mg of water (if required), 1.70\u2009g of LDPE (Mn: ~1700, Mw: ~4000). LDPE was not added in the controlled reaction. c\u2013k Product distributions after LDPE depolymerization reaction. Reaction over: c 5% Ru/zeolite-Y (Si/Al=30), d 5% Ru/zeolite-Y (Si/Al=80), e 5% Ru/zeolite-Y (2.5\u2009mg Ru), f 1% Ru/zeolite-Y (0.5\u2009mg Ru), g Physical mixture of 1% Ru/zeolite-Y and zeolite-Y (0.5\u2009mg Ru), h 1% Ru/zeolite-Y (2.5\u2009mg Ru), i 0.2% Ru/zeolite-Y (2.5\u2009mg Ru), j Physical mixture of 5% Ru/SiO2 and zeolite-Y (2.5\u2009mg Ru), and k 5% Pt/zeolite-Y. l Isomer selectivity with different catalysts. Reaction conditions: 250\u2009\u00b0C, 3\u2009h, 30\u2009bar H2 (at 25\u2009\u00b0C), 1.70\u2009g of LDPE (Mn: ~1700, Mw: ~4000). Other conditions are shown in Table\u00a01. Error bars = standard deviation. Source data are provided as a Source Data file.\n\nTo discern whether the decrease in the BAS was caused by coke deposition during LDPE depolymerization or by catalyst structure deformation at the reaction temperature, additional control experiments were conducted without LDPE under identical reaction conditions. The pyridine-DRIFTS spectra of control experiments with and without water (Fig.\u00a02b) remained similar to that of the fresh catalyst (Fig.\u00a01b), indicating that the loss of the BAS without water is associated with coke formation during LDPE depolymerization. This suggests that the reduction of the BAS coincides with coking occurrences. To further evaluate the relationship between reactivity and acid strength, we synthesized and tested catalysts with SiO2/Al2O3 ratios of 30 and 80, different from our reference catalyst with a SiO2/Al2O3 ratio of 60. All catalysts showed similar levels of conversion enhancement (Table\u00a01, entries 19-22). However, differences in isomer selectivity were observed based on the concentration of the BAS, particularly in the absence of water (Table\u00a02, entries 9, 10). High Ru loading generally led to reduced isomer selectivity, with selectivity decreasing further to 3.35%, 1.97%, and 1.11% as the SiO2/Al2O3 ratio increased to 30, 60, and 80, when water was absent. In contrast, isomer selectivities remained relatively stable, ranging from 1.08% to 1.51%, in the presence of water, regardless of the SiO2/Al2O3 ratio (Fig.\u00a02c\u2013e). This behavior indicates that selectivity is influenced by both the presence of water and the density of BAS, which varies with the SiO2/Al2O3 ratio.\n\nConsidering the involvement of Br\u00f8nsted acid in LDPE depolymerization, additional experiments were performed to investigate how water influences this process (Table\u00a01, entries 23-34). As shown in Figs.\u00a02e, 5% Ru/zeolite-Y exhibited an enhanced conversion of 96.9% in the presence of water, compared to 80.6% in the absence of water after a 3-hour reaction. Conversely, 1% Ru/zeolite-Y exhibited negligible differences in conversion, with conversions of 34.3% with water and 35.3% without, indicating negligible promotional effect of water (Fig.\u00a02f). To understand this further, additional zeolite-Y was added to increase the number of acid sites. However, despite this addition, no significant water-induced enhancement occurred for 1% Ru/zeolite-Y (Fig.\u00a02g). When the quantity of 1% Ru/zeolite-Y was adjusted to match the Ru content of 5% Ru/zeolite-Y while keeping acid sites constant, a notable increase in conversion was observed\u2014from 56.8% to 94.5% with water (Fig.\u00a02h). A similar trend was observed for 0.2% Ru/zeolite-Y, where conversion increased from 54.5% to 82.8% with water (Fig.\u00a02i). These findings highlight the importance of MAB in the promotional effect of water.\n\nTo explore the influence of proximity between metal and acid sites, experiments were performed with a physical mixture of 5% Ru/SiO2 and zeolite-Y (Fig.\u00a02j). The mixture exhibited a negligible promotional effect, suggesting the pivotal role of metal-acid proximity in inducing the promotional effect of water via the metal-acid interactions in LDPE depolymerization. Additional experiments using Pt/zeolite-Y (Fig.\u00a02k) revealed minimal enhancement in conversion, but changes in isomer selectivity were observed (Fig.\u00a02l), consistent with previous findings44. This can be attributed to Pt\u2019s relatively lower C-C bond dissociation ability compared to Ru. Previous studies have shown that the ability to dissociate C-C bonds follows the order Ru > Rh > Ir > Pt45, which may explain why Pt, despite interacting with water and acid sites to modify selectivity, does not significantly enhance conversion\u2014similar to the effect seen with insufficient Ru amounts (0.5\u2009mg, Fig.\u00a02f).\n\nFurthermore, notable variations in product distribution were observed with and without water. The product distribution remained relatively consistent with water; however, significant variations were observed depending on the Ru loading without water, particularly in catalysts with lower Ru loading (Supplementary Figs.\u00a025 and 26). Considering that hydrocracking reactions typically produce shorter alkanes (C3-C4) in the presence of dominant acid sites and longer alkanes (C21+) or methane in the presence of prevalent metal sites23,24,26, this observation suggests that reactions primarily occur at Ru sites rather than zeolite sites with water, regardless of the Ru loading.\n\nTo elucidate the influence of water on the reaction mechanism, we employed deuterated water (D2O) alongside regular water (H2O). The results revealed a similar product distributions with both H2O and D2O (Fig.\u00a03a). Further analysis using mass spectrometry (MS) showed the absence of HDO and D2O peaks in the catalyst after reaction without water, while HDO peaks appeared in the catalyst after reactions with D2O, indicating the incorporation of deuterium atoms (Supplementary Fig.\u00a027). Additionally, isotopic composition analysis of gas products (Fig.\u00a03b, c) showed a selectivity of 0.3% for substituted deuterium atoms without water, which increased to 4.1% with D2O, suggesting the replacement of some hydrogen atoms by deuterium. These results support the hypothesis that water acts as a proton donor in the reaction38. Furthermore, MS and GC-MS analyses were conducted to examine the effect of water on the degree of isomerization reaction. Using n-dodecane as a surrogate, we aimed to elucidate the role of acid sites, crucial for isomerization46. Comparing reactions with and without water over 0.2% and 5% Ru/zeolite-Y catalysts (Fig.\u00a03d\u2013f), we observed significant differences in isomer selectivity. Specifically, the selectivity for isomers over 0.2% Ru/zeolite-Y decreased from 72.1% without water to 1.6% with water (Fig.\u00a03e, f), indicating a profound shift in the reaction pathway in the presence of water, especially with low metal loading. Conversely, reactions over 5% Ru/zeolite-Y exhibited an isomer selectivity of 1.2% without water and 3.0% with water, indicating a dominant role of metal sites in these conditions regardless of the presence of water (Fig.\u00a03d\u2013f). This suggests that water alters the reaction pathways in LDPE depolymerization, potentially by promoting the protonation of carbon cations rather than skeletal rearrangement, leading to the predominant production of linear alkanes. Additionally, the detection of aromatic compounds via acid sites chemistry indicates a complex interplay of reaction mechanisms30.\n\na\u2013c LDPE depolymerization reaction over 5% Ru/zeolite-Y. a Detailed carbon distributions after 3\u2009h reaction with H2O or D2O (b\u2013c). Selectivity of isotopes in gaseous products after 12\u2009h reaction: b After the reaction without water, and c After the reaction with D2O. Reaction conditions: 250\u2009\u00b0C, 30\u2009bar H2 (at 25\u2009\u00b0C), 1.70\u2009g of LDPE (Mn: ~1700, Mw: ~4000), 50\u2009mg of 5% Ru/zeolite-Y, 50\u2009mg of H2O or 55.6\u2009mg of D2O (if required). d\u2013f Dodecane decomposition reaction over: d 5% Ru/zeolite-Y, e 0.2% Ru/zeolite-Y, f total normal and isomer yield. Reaction conditions: 250\u2009\u00b0C, 3\u2009h, 30\u2009bar H2 (at 25\u2009\u00b0C), 1.0\u2009g of n-Dodecane, m(Ru): 2.5\u2009mg, Water/Catalyst ratio: 1 or 0. Error bars = standard deviation. Source data are provided as a Source Data file.\n\nGiven that MAB is widely recognized as a crucial factor in small alkane hydrogenolysis or hydrocracking24,47,48,49, we incorporated MAB as a classification criterion. An acid-catalyzed reaction tends to slow at a high MAB, whereas a low MAB shifts the rate-determining step toward hydrogenation/dehydrogenation reactions. Reflecting these shifts, the molar ratio of C21+/C4-6 was reported to increase with an elevated MAB24 (Fig.\u00a04a). To illustrate this, we plotted the molar ratio of C21+/C4-6 using Ru/zeolite-Y with varying Ru loading. In alignment with prior findings, the molar ratio of C21+/C4-6 rises with increasing MAB without water (Fig.\u00a04a)24. However, results with water show a noticeable difference in product distribution, implying a mechanism distinct from hydrogenolysis and hydrocracking. For example, the yield of gasoline is highest without water, while the yield of diesel and lubricant peaks with water over 0.2% Ru/zeolite-Y (Fig.\u00a02i). In addition to the C21+/C4-6 ratio, the ratio of the average carbon number of the gas and liquid phases was influenced by MAB24. Similar to the previous findings24, the Cliq/Cgas ratio of the average carbon number without water increases with increasing MAB (Fig.\u00a04b). However, even with similar MAB levels, the distribution differs significantly with water, resulting in different Cliq/Cgas. Therefore, we consider both the MAB and the all-product distribution to categorize the depolymerization of polyolefins into three groups (see details in Supplementary Note\u00a01).\n\na Molar ratio of C21+ to C4-6 products as a function of MAB, ref. 24. b Average carbon number ratio of the liquid (C5\u2013C35) to gaseous phase (C1\u2013C4) as a function of MAB, ref. 24. c Reactivity-mechanism map of the PE depolymerization reaction. d Average product distribution. e Color of products after PE depolymerization reactions. Reaction conditions: 250\u2009\u00b0C, 3\u2009h, 30\u2009bar H2 (at 25\u2009\u00b0C), 1.70\u2009g of PE (Mn: ~1700, Mw: ~4000), 50\u2009mg of water (if required). Error bars = standard deviation. Source data are provided as a Source Data file.\n\nConsidering both MAB and the corresponding ratios, a lower MAB and a lower Cliq/Cgas correspond to reactions closer to hydrocracking (see full details in Methods). Conversely, a higher MAB and a higher Cliq/Cgas align with reactions closer to hydrogenolysis, with moderate cases being classified as bifunctional depolymerization reactions (Fig.\u00a04c). The mechanisms of each reaction categorized in Fig.\u00a04c are described in Fig.\u00a059,22,24,50,51. The distribution shown in Fig.\u00a04d represents the average of all reaction results. In instances of high Ru loading and low concentration of acid sites, hydrogenolysis is the dominant reaction. This reaction proceeds through the dehydrogenation of the reactant, followed by the cleavage of the C-C bond at the metal site, as illustrated in the Fig.\u00a05a pathway (II). Although the introduction of water may facilitate intermediates to undergo protonation (Fig.\u00a05a, II-ii), \u03b2-scission does not occur due to the absence of, or few, acid sites even in the presence of water. Consequently, the hydrogenolysis reaction over Ru catalysts primarily produces methane (Figs.\u00a05b and 4d). The Ru/zeolite-Y catalyst, subjected to pyridine poisoning, is also categorized as a hydrogenolysis reaction due to the absence of acid sites, specifically because C-C bond dissociation occurs solely on Ru.\n\na Overall reaction mechanism for PE depolymerization reactions. b Simplified reaction mechanism for high, intermediate, and low MAB.\n\nIn instances of low Ru loading and a high concentration of acid sites, the primary reaction is hydrocracking. This process involves dehydrogenation at the metal site, followed by protonation at the acid sites, leading to skeletal rearrangement for isomerization. The protonated intermediate then undergoes \u03b2-scission to cleave the C-C bond at the acid site (Fig.\u00a05a, pathway I). Notably, the \u03b2-scission rates of the reaction intermediate differ based on the skeleton structure, with deep skeletal rearrangement showing the fastest rate, while reactions lacking branches are slower or forbidden (Fig.\u00a05a, I-iv)9. As previously shown in Fig.\u00a04d\u2013e, the quantity of iso-C12 is relatively larger than other Cn(<12) products in both 0.2% and 5% Ru/zeolite-Y, irrespective of water. This is mainly associated with the rate-determining step, \u03b2-scission, in LDPE depolymerization (Fig.\u00a05). Alkanes undergo protonation after dehydrogenation, leading to isomerization through skeletal rearrangement, followed by conversion to a short alkane via \u03b2-scission. In this series of steps, the \u03b2-scission reaction works as the rate-determining step; thus, the preceding skeletal rearrangement step produces a larger amount of iso-C12. Interestingly, products obtained from hydrocracking exhibit a yellow hue, signifying the formation of isomeric products, as previously reported (Fig.\u00a04e)32.\n\nIn cases where the MAB falls within an intermediate range, it is categorized as bifunctional depolymerization (Fig.\u00a04c). While the roles of the metal and acid sites are well-defined in hydrogenolysis and hydrocracking reactions, bifunctional depolymerization involves the simultaneous occurrence of hydrocracking and hydrogenolysis reactions. Essentially, the cleavage of C-C bonds occurs concurrently at both the metal and acid sites, resulting in superior activity compared to other mechanisms (Fig.\u00a04c). For instance, catalysts with relatively low MABs, such as 0.2% and 1% Ru/zeolite-Y, exhibited enhanced reactivity in the presence of water compared to its absence due to the bifunctional depolymerization mechanism. Water can protonate or deprotonate intermediates at the metal or acid site, as illustrated by the two-way arrow highlighted in green in Fig.\u00a05a. This process facilitates the simultaneous dissociation of C-C bonds at both the metal and acid sites, resulting in an enhancement in PE depolymerization.\n\nUnderstanding the influence of water on reaction mechanisms, we conducted depolymerization reactions using substances with varying degrees of branching. The product distribution exhibited notable differences between the more branched substance (PP) and the less branched one (HDPE) (Fig.\u00a06a, b). In substances with more branching, such as PP, \u03b2-scission occurs more rapidly due to the stabilization provided by neighboring carbons9, allowing it to occur without the need for skeletal rearrangement. Conversely, in substances like HDPE with fewer branches, skeletal rearrangement becomes essential for \u03b2-scission. Additionally, water plays a significant role by facilitating the protonation or deprotonation of the carbon cation to achieve a more stable state (Fig.\u00a06a). In the presence of water, HDPE predominantly produced methane due to the dissociation of terminal C-C bonds at Ru sites rather than undergoing skeletal rearrangement. Conversely, PP exhibited a more stable carbon cation skeleton in the presence of water, leading to the dissociation of internal C-C bonds rather than terminal ones. This resulted in higher yields of high-value materials in the gasoline and jet fuel range compared to short alkanes of C3-6.\n\nDepolymerization reaction over 0.2% Ru/zeolite-Y both with and without water of a PP, b HDPE, c Commercial LDPE, and d LDPE bottle. Reaction conditions: 300\u2009\u00b0C, 9\u2009h, 30\u2009bar H2 (at 25\u2009\u00b0C), 1.70\u2009g of reactant, 2.5\u2009mg of Ru, and a water/catalyst ratio of 1.0 for the reactions with water. Detailed reaction results are shown in Supplementary Table\u00a08. Error bars\u2009=\u2009standard deviation. Source data are provided as a Source Data file.\n\nThe promotional effect of water was also evident involving commercial LDPE and LDPE bottles. For commercial LDPE, the gas yield decreased from 32.2% without water to 2.5% with water, while the diesel yield increased from 0.7% without water to 34.3% with water (Fig.\u00a06c). Similarly, for LDPE bottles, the gasoline yield decreased from 34.1% without water to 8.8% with water, while the diesel yield increased from 9.2% without water to 21.1% with water, reflecting a noticeable shift in product distribution (Fig.\u00a06d). This shift highlights the practical feasibility of directing the depolymerization pathway toward the desired carbon range products, representing a significant advancement in plastic waste upcycling. Additionally, the observation of CO/CO2 peak only in the presence of water indicates that water not only influences product distribution but also facilitates the removal of coke (Supplementary Fig.\u00a029).\n\nTo assess the economic viability of implementing a real commercial-scale process utilizing a 0.2% Ru/zeolite-Y catalyst, a comprehensive techno-economic analysis was performed52,53,54. It was found that the LDPE fractionation, both with and without water, emerged as a major cost-driver mainly due to high material expenses and high capital cost, resulting from multiple reactors to continuously depolymerize LDPE (Figs.\u00a07a and 7b). Specifically, approximately 62.1% and 66.3% of the total costs occurred from purchasing waste LDPE, respectively. Notably, while the same amount of LDPE (240 tons/day) was supplied in both scenarios, the relatively lower contribution of LDPE cost in the process without water can be primarily attributed to a higher hydrogen consumption (192.0\u2009kg/hr)55. The reaction with water requires relatively less hydrogen (94.6\u2009kg/hr), primarily because of the production of heavier carbon-range products (C22+). However, to further enhance the value of the products, particularly those in the gasoline or diesel range, an additional fluidized catalytic cracking subsystem ($0.49/GGE) is required in the process with water to convert C22+ into more valuable fuel products, leading to an increase in the total capital investment compared to the process without water ($114.7\u2009\u00d7\u2009106 vs. $132.0\u2009\u00d7\u2009106) (see Supplementary Table\u00a012 for detailed capital cost). However, when considering the cost per gasoline gallon eq. (GGE), the process of utilizing water incurs a higher material cost ($3.10/GGE) mainly due to LDPE ($2.90/GGE) and hydrogen ($0.19/GGE). This higher cost is attributed to the lower production rate (1978\u2009gal/hr vs. 2941\u2009gal/hr) despite slightly higher energy content (30.4 kWh/gal vs. 28.7 kWh/gal) compared to that process without water (28.7 kWh/gal, 2941\u2009gal/hr). The black arrows representing carbon flows confirm that when LDPE\u2019s carbon content is 100%, the carbon efficiency of gasoline is 75% for processes without water and 54% for the process with water. Notably, although the carbon efficiency of gasoline in the process with water is lower than that in the process without water, the total carbon efficiency, considering both gasoline and diesel, is higher in the process with water (94%) than in the process without water (87%).\n\nCost contributions for a process without water and b process with water. Carbon flows (black arrows) and major three heat networks (dashed arrows) are represented. The storage ($0.04/GGE and $0.05/GGE, respectively) and utility ($0.01/GGE and $0.01/GGE, respectively) subsystems are not represented. c Comparison of economic performance between processes with and without water. Detailed economic parameters and results are specified in Supplementary Tables\u00a011 and 12. Source data are provided as a Source Data file.\n\nThe economics of the process with water is improved primarily due to higher carbon efficiency, which increases production rates and, in turn, boosts product revenues, including byproduct credits ($2.97/GGE). This occurs despite the higher total cost compared to the process without water ($5.13/GGE vs. $3.63/GGE). Specifically, the production costs, including LDPE and hydrogen costs, are the most significant factors, accounting for 75.8% and 72.1%, respectively. The MSP of gasoline for each process is determined by subtracting the income generated from diesel and electricity from the overall cost54,56,57. Consequently, the MSP of the process with water is $2.16/GGE, whereas for the process without water, it is $2.95/GGE, indicating the economic advantage of the process with water. The MSP of the processes without and with water can be increased by 10.2% to $3.25/GGE and by 9.3% to $2.36/GGE, respectively, due to changes in the hydrogen price\u2014which is one of the most influential parameters on the MSP\u2014from $3640/ton to $7280/ton. Considering the conventional retail gasoline price range ($1.66/GGE - $4.84/GGE), both processes are market-competitive even with a higher discount rate (20%) compared to a typical rate in the nth plant assumption (10%)54,58. In conclusion, despite the increased overall cost, the addition of water results in a 26.8% reduction in MSP, making it the more economically preferred.\n\nTo quantitatively evaluate the environmental impacts of the processes without water and with water, a life-cycle assessment (LCA) was conducted following the guidelines provided by ISO 14040 and 1404459,60,61,62. Based on the environmental impacts of one gallon of conventional gasoline production (100%), the relative environmental impacts of the processes are represented in Fig.\u00a08a. Unlike conventional gasoline, the environmental impacts of gasoline produced from both processes yield negative values, indicating environmental benefits. The environmental impacts of the process without water range from -2% to -42%, while those of the process with water range from -10% to -103%. This implies that the process with water offers both economic and environmental advantages. The beneficial impacts of the process with water are derived from the production of diesel. As shown in Fig.\u00a08b, the environmental impacts attributed to input materials and energy in the process with water are offset by the production of diesel, while the process without water mitigates the environmental impacts via the production of diesel and electricity. Notably, these coproducts can substitute conventional counterparts that largely rely on fossil fuels. Consequently, the production of coproducts from these processes contributes to reducing the reliance on fossil fuels, resulting in environmentally favorable outcomes.\n\na An environmental impact radar chart for the major categories, and b heatmaps of environmental contributions. The major categories include global warming potential (GWP), stratospheric ozone depletion (SOD), terrestrial acidification (TA), marine ecotoxicity (MEu), fossil resource scarcity (FRS), and fine particulate matter formation (FPMF). Additional life-cycle assessment (LCA) results are specified in Supplementary Table\u00a013. Source data are provided as a Source Data file.\n\nFurthermore, the process with water exhibits a larger offset impact in diesel production due to its higher production rate compared to the process without water. Additionally, the analysis revealed that the disposal of boiler slag, generated during the incineration of byproducts such as C22+, is the most environmentally intensive component. The major environmental impacts originated from disposal of residue are freshwater eutrophication (FWEu), freshwater ecotoxicity (FWEc), marine ecotoxicity (MEc), human carcinogenic toxicity (HCT), and human non-carcinogenic toxicity (HNCT) of both processes, respectively. Especially, disposal of residue accounts for 96% and 94% of HCT of both processes. This significant influence of disposing of residue is attributed to the cement used for its solidification.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54495-5/MediaObjects/41467_2024_54495_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54495-5/MediaObjects/41467_2024_54495_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54495-5/MediaObjects/41467_2024_54495_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54495-5/MediaObjects/41467_2024_54495_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54495-5/MediaObjects/41467_2024_54495_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54495-5/MediaObjects/41467_2024_54495_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54495-5/MediaObjects/41467_2024_54495_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54495-5/MediaObjects/41467_2024_54495_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The catalytic depolymerization of polyolefins presents a promising route for recycling plastic waste in terms of process economics and sustainability. In this study, we found the significant impact of water addition on plastic depolymerization, elevating the yield of valuable liquid fuels from 41.8% to 71.0% and effectively suppressing coke formation to preserve catalyst activity. Through our investigation into reaction mechanisms with water, we categorized reactions into three groups based on catalytic properties and product distribution including hydrogenolysis, hydrocracking, and bifunctional depolymerization. Among these, the proposed bifunctional depolymerization reaction demonstrated the highest activity, where the cleavage of C-C bonds occurs simultaneously at the metal and acid sites. This promotional effect of water extends across various waste plastics, promising implications for the recycling of plastic waste. Moreover, the addition of water not only enhances carbon efficiency, improving economic and environmental performance based on TEA and LCA analysis, but also increases conversion to gasoline and diesel. This results in a 26.8% reduction in the MSP of gasoline and enhances economic efficiency. Additionally, the increased production rate achieved by adding water substantially mitigates environmental impacts and reduces reliance on fossil fuels, thereby replacing diesel derived from fossil fuels. This opens up the possibility to advance the entire chemical recycling of plastic waste, while simultaneously addressing environmental pollution caused by plastic waste.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "All chemicals were analytical or higher grade and used as received without further treatment. RuCl3\u00b7xH2O (product no. 11043) was purchased from Alfa Aesar. SiO2 (product no. 236845) of Davisil grade 646, SBA-15 (product no. 806862), \u03b3-Al2O3 (product no. 544,833), and TiO2 (product no. 634662) of P25 were purchased from Sigma-Aldrich. Zeolite Y with SiO2:Al2O3 ratios of 30:1 (product no. 045870), 60:1 (product no. 045871), and 80:1 (product no. 045872) was purchased from Alfa Aesar. PE (Mn: ~1700, Mw: ~ 4000, product no. 427772) was purchased from Sigma Aldrich, commercial LDPE was purchased from Hanwha Total (product code: 530\u2009G), and LDPE bottle was purchased from Korea Ace Scientific. Mesitylene (product no. 12558) was purchased from Acros Organics, toluene (product no. 22903) was purchased from Alfa Aesar, and pyridine (product no. 270407) was purchased from Sigma-Aldrich. High-density polyethylene (product no. 427985), polypropylene (product no. 427888), deuterium oxide (product no. 151882), alkane standard solution C8-C20 (product no. 04070) and C21-C40 (product no. 04071) were purchased from Sigma-Aldrich. n-pentane (product no. 16787), and n-dodecane (product no. 43459) were purchased from Acros Organics. n-hexane (product no. L09938), n-heptane (product no. A19894), n-octane (product no. A13181), n-nonane (product no. A16177), n-docosane (product no. A18050) were purchased from Alfa Aesar. n-octadecane (product no. O652) was purchased from Sigma-Aldrich.\n\nSupported Ru catalysts were prepared by a wet impregnation method. The support (0.96\u2009g) and RuCl3\u00b7xH2O (133.0\u2009mg, 0.50\u2009mmol) were added to deionized water (100\u2009mL) for 5\u2009wt% Ru/support catalyst. The suspension was stirred at 600\u2009rpm for 2\u2009hours for mixing and heated to 50\u2009\u00b0C until completely dried. Subsequently, the obtained catalyst was dried 12\u2009hours at 100\u2009\u00b0C and reduced under a 10% H2/Ar flow (50\u2009mL/min) at 500\u2009\u00b0C for 3\u2009hours, with a ramping rate of 2.5\u2009\u00b0C/min. Prior to the reaction, the catalyst was ex-situ reduced for 1.5\u2009hours at 400\u2009\u00b0C under a 10% H2/Ar flow (50\u2009mL/min), with a ramping rate of 5\u2009\u00b0C/min. A similar procedure was performed using the support (2.50\u2009g) and RuCl3\u00b7xH2O (66.5\u2009mg, 0.25\u2009mmol) for 1\u2009wt% Ru/support, and the support (3.78\u2009g) and RuCl3\u00b7xH2O (19.9\u2009mg, 0.075\u2009mmol) for 0.2\u2009wt% Ru/support catalyst. To distinguish between zeolites with different SiO2:Al2O3 ratios, zeolite Y with a SiO2:Al2O3 ratio of 30:1 termed Ru/zeolite-Y (Si/Al=30), 60:1 termed Ru/zeolite-Y, and 80:1 termed Ru/zeolite-Y (Si/Al=80).\n\nA glass-coated magnetic stir bar, pretreated catalyst, water (if required), and PE were added to the glass liner in this order, and the liner was then placed into 50\u2009mL 316\u2009L stainless-steel high-pressure batch reactors fitted with a spiral wound gasket (Hanwoul Engineering CO., LTD). For depolymerization of post-consumer plastic waste, the commercial LDPE was used as received, and the LDPE bottle was cut into small pieces (3\u2009mm x 3\u2009mm) as shown in Supplementary Fig.\u00a030. The reactor was purged three times with Ar (30\u2009bar) and then three times with H2 (30\u2009bar), followed by pressurized to H2 (30\u2009bar). The pressurized reactor was placed in a heater, and heated at the reaction temperature (200 \u2013 300\u2009\u00b0C) for approximately 1\u2009h. The reaction temperature was monitored by a thermocouple, which was inserted into the reactor. Once the temperature reached 150\u2009\u00b0C, stirring at a rate of 500\u2009rpm was initiated for the target reaction time. After the reaction, the reactor was cooled down to room temperature in an ice bath. The gas and liquid products were transferred to a 2\u2009L PVC gas bag and a 50\u2009mL centrifuge tube, respectively. Mesitylene solution (125\u2009mmol/L) was used as an internal standard, and toluene was used as a washing solvent for the collection of the liquid phase. The unreacted reactant and catalyst were separated and subsequently dried in an oven set to 60\u2009\u00b0C for 48\u2009hours. The centrifuge tube, containing the reaction products and catalyst, was then weighed after drying. The weight of the initial empty tube and added catalyst were subsequently subtracted from the measurement, yielding the exact amount of unreacted reactant. The products were analyzed using a gas chromatograph (Agilent 8890 GC System) equipped with a flame ionization detector (FID) and thermal conductivity detector (TCD). A GS-Carbon PLOT (Agilent) column was used for the gas phase and an HP-1 column (Agilent) was used for the liquid phase. Analysis of gaseous products was performed using an Agilent 8890 GC System, with a ramping rate of 20\u2009\u00b0C/min from 38 to 325\u2009\u00b0C, and hold 10\u2009min. The corresponding retention times of C1-C6 hydrocarbons were identified and FID area signals were calibrated by a reference gas mixture obtained from Union gas comprising CH4, C2H4, C2H6, C3H8, and n-C4H10 in Ar. TCD area signals were calibrated by high-purity hydrogen (99.999%) obtained from Union gas. Analysis of liquid product was also performed using an Agilent 8890 GC System, with a ramping rate of 15\u2009\u00b0C/min from 50 to 325\u2009\u00b0C, and hold 45\u2009min. The corresponding retention times of C5-C40 hydrocarbons were identified and FID area signals were calibrated by n-pentane, n-hexane, n-heptane, n-octane, n-nonane, n-dodecane, n-octadecane, n-docosane and an alkane standard solution C8-C20 and C21-C40 from Sigma-Aldrich.\n\nThe spent catalyst was collected after 12\u2009h of reaction to attain full conversion to extractable. After the drying of the reaction mixture, the spent catalyst underwent a two-step process. Firstly, it was calcined at a temperature of 300\u2009\u00b0C under an air flow (80\u2009mL/min). Subsequently, the catalyst was pretreated at 400\u2009\u00b0C under a 10% H2/Ar flow (50\u2009mL/min). The preceding reaction was repeated more than twice to ensure an adequate amount of catalyst for the subsequent reaction due to a loss of approximately 5% in catalyst recovery as in a previous work15.\n\nThe reactivity mechanism map is plotted with the summation of the normalized average carbon number ratio and metal acid balance on the x-axis and the corresponding conversion on the y-axis. To consider the MAB and average carbon number ratios as equivalent ratios, each was normalized to 0 for the lowest value and 5 for the highest, and the sum was expressed from 0 to 10. From 0 to 3.3, we denoted hydrocracking, from 3.3 to 6.7, bifunctional hydrocracking, and from 6.7 to 10, hydrogenolysis. The formula is as follows:\n\nwhere \\({v}_{m}\\) is a volume occupied by an atom in bulk Ru metal63, \\({a}_{m}\\) is an area occupied by a surface atom63, and \\({d}_{{VA}}\\) is an average particle size determined by CO-chemisorption.\n\nThe catalyst underwent ex-situ reduction at a ramping rate of 5\u2009\u00b0C/min, maintaining a temperature of 400\u2009\u00b0C for 1.5\u2009h under a 10% H2/Ar flow (50\u2009mL/min). Pyridine (100\u2009mg, 1.27\u2009mmol) was added dropwise using a pipette to poison 50\u2009mg of 5% Ru/zeolite-Y, 40 times the estimated acid site density by NH3-TPD to ensure sufficient poisoning. The mixture was dried for 12\u2009hours at room temperature in a vacuum desiccator and used immediately in the reaction.\n\nThe BAS/LAS ratio was calculated as follows64:\n\nwhere IBAS and ILAS are the integrated absorbance of the BAS and LAS band (cm 1); r is the radius of the sample disk (cm); msample is the weight of the sample (mg). The molar extinction coefficient used was 1.8\u2009cm/\u03bcmol for BAS and 1.5\u2009cm/\u03bcmol for LAS65,66.\n\nConversion and yield were calculated as follows:\n\nWe developed two integrated processes to produce gasoline and diesel based on experimental data (Supplementary Fig.\u00a031): (1) one employing the depolymerization reaction with water, and (2) the other using the reaction without water (hereafter, referred to as the process with and without water, respectively). In both processes, the downstream separation system was designed to efficiently recover gasoline and diesel, considering the contaminants present in the effluent stream of the depolymerization reactor. In the process without water, since the effluent stream consists primarily of light hydrocarbons with carbon numbers below those of gasoline and diesel, a series of flash drums and distillation columns was employed for separation. On the other hand, in the process with water, heavier carbon-range products (C22+) are present in the effluent stream, requiring an additional fluidized catalytic cracking (FCC) subsystem to upgrade these C22+ into valuable fuel products.\n\nBased on the heat and mass balance of the developed process (Supplementary Fig.\u00a031 and Supplementary Tables\u00a09 and 10), capital and operating costs are computed. To calculate the capital cost, equipment costs are estimated by using the Aspen Process Economic Analyzer (V12.1) or by adopting the cost data from the literature67,68. The adopted cost data is adjusted to the capacity of the process as follows:\n\nThe variable n represents the scaling exponent that quantifies the economic impact of scale. The computed capital and operating costs are adjusted to the common basis of 2020 USD using the following formula.\n\nWith the adjusted capital and operating costs, the MSP of gasoline is determined. The MSP, which means the selling price at the breakeven point, is one of the economic indicators. Supplementary Table\u00a011 contains the detailed economic parameters and assumptions.\n\nAccording to the international standards (ISO 14040 and 14044), LCA is conducted with four interconnected steps: goal and scope definition, life-cycle inventory analysis, life-cycle impact assessment, and life-cycle interpretation. The system boundary is from waste LDPE fractionation to product separation. To compare the environmental impact of processes, both input and output materials and energy are normalized based on a functional unit of 1 GGE of gasoline production. The environmental impacts are calculated by employing SimaPro 9.1 and the ReCiPe 2016 Midpoint with a hierarchical approach. Moreover, to consider the general region, rather than focusing on a particular local location. a global region dataset from Ecoinvent 3.6 is used.\n\nPyridine adsorption spectra were acquired using a Nicolet iS20 FTIR spectrometer equipped with a Hg/Cd/telluride detector cooled using liquid nitrogen. Prior to measurements, the catalysts were reduced in situ at 400\u2009\u00b0C for 1.5\u2009hours under 10% H2/Ar, followed by cooled to ambient temperature (25\u2009\u00b0C) under an Ar atmosphere. To measure the acid sites of the catalyst after the reaction, the spent catalyst was treated with in situ reduction at 400\u2009\u00b0C for 1.5\u2009hours under 10% H2/Ar only. A baseline spectrum was recorded prior to introducing pyridine. Subsequently, the catalysts were exposed to pyridine vapor in Ar for 20\u2009min, generated by passing Ar flow through a bubbler filled with liquid pyridine24. Physically adsorbed pyridine was subsequently removed with pure Ar for 50\u2009minutes. All spectra were obtained using 128 scans with a resolution of 2\u2009cm\u22121. The peaks at 1450\u2009cm\u22121 and 1540\u2009cm\u22121 correspond to the Lewis acid sites (LAS) and the Br\u00f8nsted acid sites (BAS)65,66,69,70, respectively. The ratio of Br\u00f8nsted acid site density to Lewis acid site density (BAS/LAS) was estimated by pyridine-DRIFTS analysis69,70. The pyridine vibration bands were classified as following70: Br\u00f8nsted acid site (BAS) exhibiting peaks around 1540\u2009cm\u22121, Lewis acid site (LAS) characterized by peaks near 1450\u2009cm\u22121.\n\nScanning transmission electron microscopy (STEM) was conducted using a NEOARM (JEOL) with a Cs probe aberration corrector operated at 200\u2009kV with a spatial resolution of <0.1\u2009nm. STEM images were recorded after ex situ reduction of the fresh and spent catalysts at 400\u2009\u00b0C under 10% H2/Ar. The high-angle annular dark-field (HAADF) mode was applied to acquire images under the following conditions: HAADF detector angle: 68 to 270 mrad; probe convergence angle (2\u03b1): 1.5 to 20 mrad; probe current: 1.0\u2009nA; and probe diameter: 0.2\u2009nm. Over 200 randomly selected particles were examined to calculate the mean particle size, which exhibited a narrow Gaussian distribution in the histogram.\n\nA Thermo Scientific NEXSA XPS system equipped with a monochromatic Al K\u03b1 X-ray source was used for XPS. XPS spectra were recorded after ex situ reduction of the fresh and spent catalysts at 400\u2009\u00b0C under 10% H2/Ar. The number of scans and dwell time were 10 and 50\u2009ms, respectively. To accurately calibrate the binding energies, the reference for this calibration was the lattice oxygen (O 1\u2009s) peak at 529.9\u2009eV, instead of the C 1\u2009s peak19. This selection was primarily based on the overlap between the binding energy of the C 1s peak and the Ru 3d5/2 peak, rendering the C 1\u2009s peak unsuitable for precise calibration40.\n\nBrunauer\u2013Emmett\u2013Teller (BET) analysis were conducted using an automated gas adsorption analyzer (TriStar 3000, Micromeritics). Prior to analysis, fresh and spent catalysts weighing ~0.1\u2009g was subjected to degassing by heating at 90\u2009\u00b0C for 2\u2009h, followed by further heating at 300\u2009\u00b0C for 6\u2009h under He flow (60\u2009mL/min). BET surface area was determined through a standard multipoint analysis (P/P0\u2009=\u20090.05-0.3), while the pore volume was measured using the single-point method at P/P0\u2009=\u20090.995. The adsorption average pore width (4\u2009V/A) was calculated based on the BET results. Additionally, pore size distribution was obtained using the Barrett-Joyner-Halenda (BJH) method.\n\nAmmonia temperature-programmed desorption (NH3-TPD) spectra were collected by AutoChem II 2920 (Micromeritics) instrument. Before conducting NH3-TPD, the catalyst underwent in situ reduction at 400\u2009\u00b0C for 1.5\u2009hours under a 10% H2/Ar flow (50\u2009mL/min). Subsequently, the catalyst was purged with He and cooled down to 50\u2009\u00b0C under He flows (50\u2009mL/min). Adsorption of NH3 onto the catalyst surfaces occurred by flowing 10% NH3/He at 50\u2009\u00b0C and 50\u2009mL/min for 1\u2009hour, followed by purging with He for 1\u2009hour. The desorption process was quantified using a thermal conductivity detector (TCD) with a temperature ramp of 5\u2009\u00b0C/min, starting from 100\u2009\u00b0C and reaching 900\u2009\u00b0C. The acid site density was estimated by calculating a coefficient multiplied by the area divided by the weight, and the coefficient was obtained using a specified reference.\n\nDEPT 135 13C NMR analysis was conducted using a 600\u2009MHz Agilent DD2 NMR Spectrometer at room temperature to characterize the product structure. The product was diluted in CDCl3, and the spectra were recorded over 500 scans.\n\nTPO-MS analysis was conducted using a BEL CAT II and BEL mass analyzer (MicrotracBEL). Prior to analysis, catalysts were heated to 200\u2009\u00b0C for 2\u2009hours under a He flow (30\u2009mL/min) to remove physically adsorbed water. Thereafter, the catalyst was heated to 1000\u2009\u00b0C at a rate of 5\u2009\u00b0C/min under a 5% O2/He flow (30\u2009mL/min). The analysis was performed using thermal conductivity (TCD) and mass spectrometry.\n\nTo estimate the amount of coke in the spent catalysts, thermogravimetric analysis (TGA) was performed using a DTG-60H (Simadzu). 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W.)", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Taeeun Kwon, Byeongchan Ahn.\n\nDepartment of Chemical and Biomolecular Engineering, Seoul National, University of Science and Technology, Seoul, Republic of Korea\n\nTaeeun Kwon\u00a0&\u00a0Insoo Ro\n\nDepartment of Chemical and Biological Engineering, Korea University, Seoul, Republic of Korea\n\nTaeeun Kwon,\u00a0Byeongchan Ahn\u00a0&\u00a0Wangyun Won\n\nChemical & Process Technology Division, Korea Research Institute of Chemical Technology (KRICT), Daejeon, Republic of Korea\n\nKi Hyuk Kang\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nI.R. conceived the project idea and contributed to the experiment design. T.K. conducted the experiments, while T.K. and K.H.K. handled catalyst characterization and data analysis. B.A. and W.W. provided insights into process analysis. Project supervision was carried out by I.R. and W.W. The manuscript was collectively written by T.K., B.A., W.W., and I.R. All authors participated in result discussions and manuscript review.\n\nCorrespondence to\n Wangyun Won or Insoo Ro.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Avantika Singh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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"title": "In situ atomic observations of aggregation growth and evolution of penta-twinned gold nanocrystals", + "pre_title": "In-situ atomic observations unveil the aggregation growth and evolution of five-fold twin structures", + "journal": "Nature Communications", + "published": "25 October 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53501-0/MediaObjects/41467_2024_53501_MOESM1_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53501-0/MediaObjects/41467_2024_53501_MOESM2_ESM.pdf" + }, + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53501-0/MediaObjects/41467_2024_53501_MOESM3_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-53501-0#Sec18" + ], + "code": [], + "subject": [ + "Nanoparticles", + "Nanoscale materials" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4283157/v1.pdf?c=1729940830000", + "research_square_link": "https://www.researchsquare.com//article/rs-4283157/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53501-0.pdf", + "preprint_posted": "22 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The unique twin boundaries and inherent lattice strain of five-fold twin (5-FT) structures offer a promising and innovative approach to tune nanocrystal configurations and properties, enriching nanomaterial performance. However, due to constraints imposed by small thermodynamically stable size and complex twin configurations, gaps persist in understanding the nonclassical growth models of 5-FT nanoparticles. Here, we in-situ investigated the mechanisms underlying size-dependent and twin configuration-related aggregation growth phenomena between 5-FT and other nanoparticles at the atomic scale. The results find that surface diffusion shapes the morphology of aggregated nanoparticles, promoting symmetrical 5-FT formation, particularly involving smaller nanoparticles. Additionally, the inherent structure of 5-FT mitigates the dominance of surface diffusion in its morphological evolution, retarding the aggregation evolution process and fostering intricate twin structures. Our findings contribute to advancing our ability to manipulate the configuration of twinned particles and achieve a more predictable synthesis of novel functional nanomaterials for engineering applications.Physical sciences/Materials science/Nanoscale materials/NanoparticlesPhysical sciences/Physics/Particle physics/Experimental particle physics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformation.docx", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The twin boundaries and inherent lattice strain of five-fold twin (5-FT) structures offer a promising and innovative approach to tune nanocrystal configurations and properties, enriching nanomaterial performance. However, a comprehensive understanding of the nonclassical growth models governing 5-FT nanocrystals remains elusive, largely due to the constraints of their small thermodynamically stable size and complex twin configurations. Here, we conducted in situ investigations to elucidate the atomic-scale mechanisms driving size-dependent and twin configuration-related aggregation phenomena between 5-FT and other nanoparticles at the atomic scale. Our results reveal that surface diffusion significantly shapes the morphology of aggregated nanoparticles, promoting the symmetrical formation of 5-FT, especially in smaller nanoparticles. Moreover, the inherent structural characteristics of 5-FT mitigate the dominance of surface diffusion in its morphological evolution, retarding the aggregation evolution process and fostering intricate twin structures. These findings contribute to advancing our capacity to manipulate the configuration of twinned particles, enabling more predictable synthesis of functional nanomaterials for advanced engineering applications.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Uncovering the growth mechanisms of nanoparticles (NPs) is significant not only for elucidating the underlying formation mechanisms of minerals and comprehensively understanding the evolution of natural environments but also for its scientific and engineering significance in tuning the size, morphology, and properties of synthetic nanomaterials. Despite numerous pieces of evidence challenging the classical interpretations of crystal growth, which emphasize surface reactions and the monomer diffusion to the surface1,2, nonclassical crystal growth scenarios have only begun to be recognized gradually just over the past decade2,3,4,5. As a main nonclassical crystal growth mechanism, particle-based aggregation3,6,7 generally includes orientated attachment (OA)4,6, nearly OA (such as meso-crystal8,9 and dislocation-induced tilt attachment4), and non-OA (such as intraparticle growth10,11, aggregation and transformation of thermodynamically metastable particle to stable phases8,12, and aggregation and grain boundary/surface atom migration dominated growth10,13). These mechanisms require systematic and intensive investigation due to unresolved and challenging aspects, such as the need for high spatial and temporal resolution to study atomic diffusion and migration, particle relative movement, interfacial interactions, and grain boundary evolution.\n\nThe 5-FT structure has been extensively detected in diverse natural and synthetic systems14,15,16 and exhibits special properties attributed to its crystallographically forbidden pentagonal symmetry and inherent lattice strain, such as enhanced mechanical properties17, attractive catalytic properties18, and improved optic properties19. In addition to NP size, morphology, and composition, the twin plane and inherent lattice strain of 5-FT provide an appealing avenue to tailor the configuration and properties of nanocrystals, thus diversifying and enhancing the performance of nanomaterials. For instance, 5-FT structure holds the potential for producing hierarchical materials20,21,22, which preserve the properties of the nanoscale building blocks and may exhibit attractive performance characteristics22,23. Nevertheless, limited by complicated twin structures (5 twin units), significantly small size (3\u201314\u2009nm) under a thermodynamic stability state24,25,26, together with movement of NPs, and the only proper \\([110]\\) twin pole observation direction, revealing the atomic formation and growth mechanisms of 5-FT remains experimentally challenging. By trigging NPs aggregation through electron-beam-induced decomposition of the organic ligands surrounding the NPs, we uncovered two underlying atomic formation mechanisms of 5-FT within Au, Pd, and Pt nanomaterials6. However, nonclassical growth mechanisms of 5-FT are still elusive, particularly potentially under the coupling effect of thermodynamic and kinetics landscapes, involving but not limited to the atomic formation and evolution of complicated twin structure (twining and de-twinning processes), atom surface diffusion, and relative slip and configuration modulation of NPs. Therefore, clarifying the nanoparticle-based aggregation growth of 5-FT is scientifically significant and important from an engineering perspective.\n\nIn this work, in situ high-resolution transmission electron microscopy (HRTEM) combined with molecular dynamics (MD) simulation was utilized to study the atomic aggregation growth and evolution mechanisms involving 5-FT and diverse NPs, i.e., NPs with varied size ratios and twinned configurations (single crystal (SC) and 5-FT). Additionally, the impact of various thermodynamic and kinetic landscapes on aggregation evolution was systematically investigated.", + "section_image": [] + }, + { + "section_name": "Results and discussion", + "section_text": "Spherical Au NPs with various twin configurations (\u22484.4\u2009nm, Supplementary Fig.\u00a01) embedded in 1-dodecanethiol organic matrix were drop-casted onto a TEM grid. Then, NP aggregation was induced by decomposing organics under electron-beam irradiation (see \u201cMethods\u201d). As shown in Fig.\u00a01a, b, aggregation between a 2.1\u2009nm SC and a 3.6\u2009nm symmetrical 5-FT (S5-FT, size ratio R\u2009=\u20090.58) induces obvious growth of the twin units 1 and 5, resulting in the formation of an asymmetrical 5-FT (AS5-FT). Nevertheless, the AS5-FT evolves into a symmetrical twin structure (Fig.\u00a01c) and transforms into a stable re-entrant \\(\\left\\{111\\right\\}\\) (highlighted by white arrows in Fig.\u00a01d) S5-FT at last. Considering none visible twin boundaries (TBs) migrations, the above evolution process is supposed to be dominated by surface diffusion from the small NP to the 5-FT, which will be further discussed in Fig.\u00a02. Wherein, the formation of highly faceted structure (Fig.\u00a01d) has been proved to be associated with the balance between surface and strain energy27,28, i.e., the re-entrant \\(\\left\\{111\\right\\}\\) 5-FT can significantly release the intrinsic lattice strain energy and slightly increases the surface energy of the 5-FT structure.\n\na\u2013d Configuration evolution after a 2.1\u2009nm SC NP attached with a 5-FT NP (3.6\u2009nm) with R\u2009=\u20090.58. White arrows show the formation of the re-entrant surface. e\u2013o Configuration evolution after a 4.6\u2009nm SC NP aggregated with a 4.2\u2009nm 5-FT NP (R\u2009=\u20091.10). p The surface outlines of the aggregated NP. The migration directions of the surface outline with durations are denoted by red arrows. Twin boundaries are highlighted by red lines. Twin units are denoted by numbers ranging from 1 to 5 in (a) and (e). Yellow numbers and arrows show migration layers and directions of the TBs, compared with the immediate prior image. Partial dislocations are denoted by blue \u201c\u2514\u201d and their slip directions are denoted by the blue arrows.\n\na\u2013e Evolution of an aggregated 5-FT NP, formed by attaching of a 5.0\u2009nm symmetrical 5-FT NP (yellow, A5F) and a 2.6\u2009nm SC NP (cyan, B3S) with R\u2009=\u20090.52. Re-entrant \\(\\left\\{111\\right\\}\\) surfaces are highlighted by gray solid lines. f The biggest cross-section perpendicular to the Z-axis showing atomistic distribution of the initial SC NP. g Variation of the relative energy of the simulated system from (a) to (e). h\u2013r The aggregation evolution process after a 5\u2009nm 5-FT Au NP (A5F) attached with a 4.8\u2009nm SC NP (B5S) with R\u2009=\u20090.96. The relative slip and layers between the 5-FT and the SC are represented by cyan arrows and numbers in (i\u2013k) and (m), respectively. s Variation of the relative energy of the simulated system from (h) to (r). t TBs slip induced migrations of the twin pole from 2.5\u2009ns (i) to 4.8\u2009ns (o). u A double Thompson tetrahedron with an FCC crystal structure. Vertex-to-vertex (e.g., AB), vertex-to-orthocenter (e.g., A\u03b3), and orthocenter-to-orthocenter (e.g., \u03b1\u03b3) denote a perfect, partial, and stair-rod dislocations, respectively. v Relationship of the Thompson tetrahedron in 5-FT units verifying that dislocations within different twin units can undergo mutual transformations via various dislocation reactions. w Schematic illustration of twin pole migration (o to o\u2019). TBs before and after slip are denoted by dashed yellow lines and solid red lines, respectively. Yellow arrows and numbers show migration directions and layers of the TBs, respectively, compared with the immediate prior image. Partial dislocations are denoted by blue \u201c\u2514\u201d and their slip directions are denoted by the blue arrows. Twin units are denoted by numbers ranging from 1 to 5 in (a) and (h). Source data are provided as a Source Data file.\n\nDifferent from the above small size ratios (Fig.\u00a01a, b), the large SC (R\u2009=\u20091.10) only stimulates continuous growth of the twin unit 1 with substantially de-twinning process until the final manifestation of a SC (Fig.\u00a01e\u2013o). The detailed evolution processes are as follows: Initially, a 4.6\u2009nm SC-1 attaches with a 2.1\u2009nm SC-2 and a 4.2\u2009nm S5-FT successively, resulting in attractive growth of the twin unit 1 (Fig.\u00a01e\u2013g). Then, a partial dislocation nucleates at the TB \u03a331 from the periphery of the NP and slips toward the center of the 5-FT, leading to a single layer of migration of the \u03a331 near the periphery (Fig.\u00a01h). Subsequently, continuous nucleation and slip of partial dislocations at TBs induces de-twinning process of the 5-FT (Fig.\u00a01i\u2013o), giving rise to reduction of the twin units 2, 3, and 4. During this process, when the twin pole of the 5-FT is close to overlap (Fig.\u00a01j), specifically, it involves one layer of migrations of the four TBs (\u03a331, \u03a332, \u03a333, and \u03a334). Alternatively, there is obvious split of the twin pole. At last, when the 5-FT twin pole is close to (\u22482\u20133 layers of \\(\\left\\{111\\right\\}\\) planes away from) the periphery of the NP, 5-FT structure disappears within 0.8\u2009s, resulting in the formation of the SC NP (Fig.\u00a01n, o). To assess the influence of surface diffusion on the entire aggregation process, further analysis is conducted on the variations in surface outlines. As presented in Fig.\u00a01g\u2013p, distinct variations of the surface outlines are detected from 83.2 to 179.4\u2009s, especially at the concave surfaces and the region near the 5-FT twin pole (Fig.\u00a01p), nevertheless, the variation can be ignored from 179.4 to 220.8\u2009s (Fig.\u00a01p). Therefore, during the above aggregation induced evolution process, the surface diffusion mainly occurs at the initial stage (Fig.\u00a01g\u2013k), and the partial dislocations induced twin boundaries slip mainly happens at the subsequent stage (Fig.\u00a01i\u2013o) with an overlapping duration from 142.6 to 179.4\u2009s (Fig.\u00a01i\u2013k). In addition, surface diffusion without significant morphology variations, such as mutual diffusion, remains undetectable in Fig.\u00a01. The relative slip between the initial 5-FT and SC NPs is also indistinguishable, which can potentially induce morphological variations. These phenomena will be comprehensively analyzed in Fig.\u00a02 based on high time-resolution data and 3-D atomic evolution tracing using MD simulations.\n\nTherefore, as presented in Fig.\u00a01, after attachment between a 5-FT NP with a small (R\u2009=\u20090.58, Fig.\u00a01a\u2013d) or a relatively large (R\u2009=\u20090.73, Supplementary Fig.\u00a06) SC NP, surface diffusion dominates the aggregation growth processes, resulting in formation of a S5-FT or an asymmetrical 5-FT (AS5-FT) NP, respectively. Nevertheless, during the aggregation growth process of a 5-FT with a large (R\u2009=\u20091.10, Fig.\u00a01e\u2013o) SC NP, surface diffusion exclusively dominates the initial stage of surface modulation. Subsequent to this, the activation of partial dislocations governs the de-twinning process, resulting in the manifestation of a newly formed SC NP. Additionally, based on our previous observation6, asymmetrical 5-FTs can also evolve into symmetrical ones through NPs aggregation at small twin units. Meanwhile, with the growing of 5-FT NPs, its inherent lattice strain29 can be relieved through various mechanisms, including but not limited to the formation of plane defects in twin units30, the migration of the 5-FT twin pole30,31, and the formation of re-entrant decahedral morphology32. Thereby, the aforementioned AS5-FT, including various unstable intermediate 5-FT NPs, can be potentially stabilized via attachment with other NPs in conjunction with the modulation of the inherent lattice strain.\n\nTo uncover detailed evolution mechanisms of NPs\u2019 3D configurations, the aggregation processes between S5-FT and SC NPs are further investigated by employing high temporal resolution MD simulation (see \u201cMethods\u201d, Supplementary Fig.\u00a02 and Supplementary Table\u00a01). As shown in Fig.\u00a02a\u2013c, the SC NP (B3S, R\u2009=\u20090.52, Fig.\u00a02a) was initially designed to attach with the twin unit 1. However, a portion of the surface of the SC NP evolves into an amorphous state and its atoms diffuse symmetrically toward the S5-FT surface quickly (Fig.\u00a02b and Supplementary Fig.\u00a07), inducing significant growth of the twin units 1 and 2, resembling the phenomena detected in Fig.\u00a01a, b. The mutual diffusion of surface several layers\u2019 atoms is also detected after 4.0\u2009ns with the formation of re-entrant \\(\\left\\{111\\right\\}\\) surfaces (Fig.\u00a02c\u2013f). Additionally, significantly fewer atoms diffuse from the 5-FT to the B3S SC NP, and the atoms from the initial NP can even diffuse to the far-away twin unit 4 of the 5-FT (Fig.\u00a02f). Correspondingly, the system experiences a substantial decrease in relative energy primarily at the initial stage (0.0\u20134.0\u2009ns, Fig.\u00a02g), indicating significant configuration modifications. Therefore, the surface diffusion from the small SC NP to the big 5-FT NP dominates the aggregation growth processes, consistent with the experimental results detected in Fig.\u00a01a\u2013d. Similar mutual diffusion is also detected at the initial stage of the simulated system with a relatively large particle size ratio (0.76, Supplementary Fig.\u00a08). Nevertheless, the 5-FT structure swiftly transforms into a bi-crystal within 0.1\u2009ns (Supplementary Fig.\u00a08d, e), accompanying with significant decreasing of the relative energy (Supplementary Fig.\u00a08g). Additionally, there is also diffusion induced growth of both twin units, i.e., the twin units 1 and 5 (Supplementary Fig.\u00a08d). This verifies that the formation of initial asymmetrical configuration, i.e., significant growth of both twin units (Fig.\u00a01a, b and Supplementary Figs.\u00a06 and 8), is an inherent characteristic of surface diffusion dominated aggregation processes when R\u2009<\u20090.76.\n\nDivergent from the aforementioned surface diffusion dominated NPs aggregation evolution mechanisms (R\u2009<\u20090.76, Fig.\u00a02a\u2013f and Supplementary Fig.\u00a08), MD simulation indicates that the de-twinning process induced by TBs\u2019 migrations governs the aggregation evolution as the R increases to 0.96 (Fig.\u00a02h\u2013r). This aligns with the experimental results delineated in Fig.\u00a01i\u2013o. The aggregation evolution process can be segmented into the following three distinct stages: (1) The initial relative slip. As presented in Fig.\u00a02h, i, the interaction between the 5-FT and the SC gives rise to the relative slip of 2 layers of \\(\\left\\{111\\right\\}\\), and the slip direction of the 5-FT aligns parallel to the \u03a335 (highlighted by the cyan arrow). (2) TB migration dominated de-twinning process. This process is triggered by nucleation and slip of partial dislocations (Fig.\u00a02j\u2013o). As shown in Fig.\u00a02j, the nucleation and slip of a partial dislocation from the periphery of the concave surface to the twin pole instigate the migration of the TB \u03a335 toward the initial SC. Additionally, there are also relative slips between the 5-FT and the SC (Fig.\u00a02j, k, m). These determinations arise from the layer variation of \\(\\left\\{200\\right\\}\\) planes between the twin pole and the periphery of the initial SC (prior to the migration of the twin pole, Fig.\u00a02h, i), alternatively, the layer variations of \\(\\left\\{111\\right\\}\\) planes from the \u03a334 (without migration) to the \\(\\left\\{111\\right\\}\\) intersection plane in the twin unit 5 with the initial \\(\\left\\{200\\right\\}\\) interface between the 5-FT and the SC (Fig.\u00a02j, k, m). (3) Surface diffusion dominated de-twinning process. When the twin units are as small as 3 layers of \\(\\left\\{111\\right\\}\\) planes (Fig.\u00a02o\u2013r), surface diffusion takes precedence as the predominant mechanism during the final de-twinning process, while no discernible TB migration can be detected. This differs from the detected TB migration-dominated de-twinning process in Fig.\u00a01n, o, and is supposed to be associated with NP\u2019s configuration. That is, when the twin pole of the AS5-FT is close to the convex surface (Fig.\u00a01n, o and Supplementary Fig.\u00a010f, g), TB migration induces the fast de-twinning process. Nevertheless, when the serious AS5-FT twin structures are close to the concave surface (Fig.\u00a02o\u2013r), surface diffusion dominates the de-twinning process. Figure\u00a02s shows there is a significant increase in the system energy during the initial stage of the relative slip (Fig.\u00a02h\u2013k), followed by a substantial energy decrease attributed to the de-twinning process dominated by partial dislocations. This is mainly associated with the reduction of TB energy and lattice strain energy31,33. After that, the variation in the system energy becomes moderate. Throughout the entire evolution process, mutual diffusion was detected consistently. The surface amorphous detected in MD simulations (Fig.\u00a02b, c and Supplementary Fig.\u00a08), as opposed to experimental observation (Fig.\u00a01a\u2013c), is supposed to correlate with the relatively elevated simulation temperature employed to expedite the simulated experimental process (see \u201cMethods\u201d).\n\nEffective TBs migration layers during the TBs migrations dominated de-twinning process (Fig.\u00a02i\u2013o) are illustrated in Fig.\u00a02t. That is, TBs \u03a331 and \u03a332 migrate 4 layers, TBs \u03a333 and \u03a335 migrate 5 layers, remaining \u03a334 are unchanged. The migration of TBs is closely associated with nucleation and slip of partial dislocations, and the detailed mechanisms are depicted in Fig.\u00a02u\u2013w. A double Thompson tetrahedron is generally employed to define various dislocations in face-centered cubic crystal34, including perfect dislocation (vertex to vertex, such as AB and BD), partial dislocation (vertex to orthocenter, such as A\u03b4 and B\u03b4), and stair-rod dislocation (orthocenter to orthocenter, such as \u03b1\u03b2 and \u03b2\u03b4). Dislocations have the capability to undergo transformation into one another through reactions, guided by the principles of vector geometry addition and subtraction rules34. For instance, DB\u2009=\u2009DA\u2009+\u2009AB, \u03b2\u03b4\u2009=\u20091/3DB, DB\u2009=\u2009D\u03b2\u2009+\u2009\u03b2\u03b4\u2009+\u2009\u03b4B\u2009=\u20091/3DB\u2009+\u2009D\u03b2\u2009+\u2009\u03b4B, DB\u2009=\u20093/2(D\u03b2\u2009+\u2009\u03b4B). Here, the 5-FT NP can be regarded as a five Thompson tetrahedron (Fig.\u00a02v), and dislocations can transform into one another by diverse dislocation reactions. Based on experimental observation (Fig.\u00a01h) and MD simulation (Fig.\u00a02j), partial dislocations initially nucleate at the periphery of the NPs. For instance, a partial dislocation C\u03b2\u2032 nucleates on \u03a335 (Fig.\u00a02w) and slips toward the twin pole O, inducing one layer migration of \u03a335. Then, C\u03b2\u2032 dissociates into other two partial dislocations at the twin pole, i.e., C\u03b2\u2032\u2009\u2192\u2009C\u03b7\u2009+\u2009\u03b7\u03b2\u2032, and C\u03b7 can slip along \u03a333, resulting in one layer of migration of \u03a333. Meanwhile, \u03b7\u03b2\u2032 can be equivalent to 1/2DB (\u03b7\u03b2\u2032 serves as the median of the triangle FBD, Fig.\u00a02v). And as analyzed above, DB\u2009=\u20093/2(D\u03b2\u2009+\u2009\u03b4B), thereby, \u03b7\u03b2\u2032 can be dissociated into D\u03b2 and \u03b4B, i.e., \u03b7\u03b2\u2032\u2009=\u20091/2DB\u2009=\u20093/4(D\u03b2\u2009+\u2009\u03b4B). Correspondingly, partial dislocations D\u03b2 and \u03b4B can trigger migration of TBs \u03a331 and \u03a332, resulting in migration of the twin pole form O to O\u2032 (Fig.\u00a02w), i.e., one layer of de-twinning process of 5-FT. Based on the above dislocation reactions, one C\u03b2\u2032 only generates 3/4(D\u03b2\u2009+\u2009\u03b4B), that is, a single layer of migration of \u03a335 results in 3/4 layer of migrations of \u03a331 and \u03a332. Nevertheless, based on the geometrical relation of 5-FT, one C\u03b2\u2032 still generates approximately 0.18 more D\u03b2 and \u03b4B than that to maintain the perfect overlap of the twin pole. This residual mismatch can be mediated by various migration layers of TBs, such as 5 layers of \u03a333 and \u03a335 and 4 layers of \u03a331 and \u03a332 (5 \u00d7 0.18\u2009=\u20090.90 layer, closed to a single layer of difference, Fig.\u00a02i\u2013o, t). This observation is also consistent with the experimental observation (Fig.\u00a01h\u2013j), i.e., the TBs \u03a331, \u03a332, \u03a333, and \u03a334 migrate one layer to maintain the nearly overlapped twin pole.\n\nAggregation-induced growth and TB evolution process are also investigated between 5-FT NPs with various size ratios (Fig.\u00a03). As presented in Fig.\u00a03a, b, aggregation between a 3.7\u2009nm S5-FT and a 4.6\u2009nm S5-FT induces the formation of an AS5-FT (Fig.\u00a03d). And the AS5-FT can potentially evolve into S5-FT by aggregating with other NPs (denoted by a white arrow, Fig.\u00a03d), as discussed above. With increasing the size ratio between the two aggregated 5-FT NPs, intricate twin structures can be induced (R\u2009=\u20090.82, Fig.\u00a03e\u2013l), and the detailed evolution processes are as follows. At first, the twin unit 4 of the small S5-FT NP orientationally attaches with the twin unit 2 of the big S5-FT NP (Fig.\u00a03e, f), followed by the formation of a TBs sealed region. This is introduced by the relative slip between the two NPs (layers variation without corresponding TBs migration, highlighted by cyan arrows in Fig.\u00a03f, g), the surface diffusion toward the concave surface (denoted by red arrows, Fig.\u00a03f\u2013h), and the TBs migration (highlighted by yellow arrows, Fig.\u00a03f\u2013i). Then, the sealed region remains unchanged until the small 5-FT evolves into three-fold twin at 72.0\u2009s (Fig.\u00a03i\u2013k). This process is dominated by surface diffusion, verified by significant morphology variation and two reserved TBs, i.e., only disappearing of the twin units 1 and 2 of the S5-FT NP. Thereby, the de-twinning process (Fig.\u00a03j, k) is significantly different from the TB migration decided de-twinning process (Fig.\u00a01n, o), in which the morphology variation can be ignored. Thereby, the TBs sealed region can stabilize the 5-FT structures and modify the evolution pathway of the aggregation growth mechanism between 5-FT NPs. Nevertheless, the sealed region becomes unstable after 5-FT evolving into 3-fold twin within 1.0\u2009s (Fig.\u00a03k, l), resulting in the formation of a complicated twin structure, which can potentially evolve into AS5-FT by de-twinning of the TB at bottom left. Additionally, when the size ratios between two aggregated 5-FT are small, such as 0.47 and 0.53 (Supplementary Fig.\u00a09), S5-FT NPs ultimately formed. Besides the orientation attachment dominated NPs aggregation (with no detectable formation of newly formed GBs at attachment sites, Fig.\u00a03e, f), another scenario emerged, where a zig-zag \\(\\{200\\}\\)/\\(\\{111\\}\\) GB was induced after aggregation (R\u2009=\u20090.72, see Supplementary Fig.\u00a010 for details).\n\na\u2013d Atomic evolution process between a 3.7\u2009nm 5-FT and a 4.6\u2009nm 5-FT with R\u2009=\u20090.78. e\u2013l Atomic evolution process between a 3.3\u2009nm 5-FT and a 4.0\u2009nm 5-FT with a size ratio of 0.82. Twin units are denoted by numbers ranging from 1 to 5 in (a) and (e). TBs and unclear TBs are highlighted by red solid and dashed lines, respectively. The white arrow shows the aggregation of another NP in (d). Yellow arrows and numbers show migration directions and layers of the TBs, respectively, compared with the immediate prior image. The relative slip between the two 5-FT is represented by cyan arrows in (g). Partial dislocation is denoted by blue \u201c\u2514\u201d and its slip direction is denoted by the blue arrow.\n\nFigure\u00a04 shows aggregation growth between 5-FT NPs with various size ratios. As presented in Fig.\u00a04a, b, after orientation attachment with the twin unit 1 of the 5.0\u2009nm 5-FT, the small 5-FT NP evolves into surface amorphous state within 0.1\u2009ns (Fig.\u00a04b). There is no detectable TB migration. Then, mutual diffusion dominates the subsequent evolution process and induces the formation of a symmetrical 5-FT (Fig.\u00a04c\u2013e). Cross-section (Fig.\u00a04f) shows that atoms originating from the initial small 5-FT can even diffuse over a considerable distance to the twin units 3 and 4 (Fig.\u00a04f). The system energy variation is presented in Fig.\u00a04g. The above process analogous to that detected in Fig.\u00a01a\u2013g. Thereby, when the size ratio is small enough, such as 0.52 in Fig.\u00a02a\u2013g and 0.62 in Fig.\u00a04a\u2013g, the aggregation kinetics is governed by surface diffusion from small NPs to the big NPs, resulting in the formation of the S5-FT structure. With increasing of the size ratio to 0.82, apparent relative slip (Supplementary Fig.\u00a013a, b) and TBs migration (Supplementary Fig.\u00a013b, c) are detected, corresponding to a significant decrease of the system energy (Supplementary Fig.\u00a013h). Although relatively small 5-FT NP still evolves into surface amorphous state accompanying with surface diffusion toward the big 5-FT (Supplementary Fig.\u00a013c\u2013g), an AS5-FT ultimately forms.\n\na\u2013e, h\u2013m Evolution of the aggregated 5-FT NPs, formed by attaching of a 5.0\u2009nm S5-FT NP (yellow, A5F) with 3.2\u2009nm (B3F) and 5.0\u2009nm (B5F) S5-FT NPs (cyan), respectively. The corresponding size ratios are 0.64 and 1.00, respectively. f, n The biggest cross-sections perpendicular to the Z-axes showing atomistic distribution of the initial 5-FT NPs of B3F and B5F, respectively. g, o Variation of the relative energies of the corresponding simulated systems. TBs are highlighted by red lines. Twin units are denoted by numbers ranging from 1 to 5 in (a) and (h). The relative slip between the two 5-FT is represented by cyan arrows in (I). Source data are provided as a Source Data file.\n\nFor the large size ratio between two aggregated 5-FT NPs, MD simulation shows there is also relative slip at the initial stage (Fig.\u00a04h, i), inducing the formation of a sealed stable region S1 (Fig.\u00a04i). The region S1 can stabilize for a long time until the twin units 3 and 4 of the left 5-FT decrease to three and four layers, respectively, resulted by surface diffusion (Fig.\u00a04i\u2013l). Then, TBs migration induces \u03a332, \u03a333, \u03a334, and \u03a335 TBs to disappear within 0.02\u2009ns (Fig.\u00a04k, l), accompanied by significant decreasing of the system energy (Fig.\u00a04o). Therefore, MD simulation results are consistent with the phenomena detected during in situ observation (Fig.\u00a03e\u2013l). As for the reserved TB \u03a331, it disappears at last with the forming of an AS5-FT (Fig.\u00a03l, m). The system energy decreases slowly during the initial 69.50\u2009ns, accompanied by moderate surface modulation, and decreases seriously with de-twinning of the left 5-FT NP. This means that the system energy of the aggregated 5-FT NPs is mainly introduced by the 5-FT structures. Additionally, while the surface diffusion remains a dominated factor during 5-FT NP\u2019s aggregation, de-twinning process, i.e., TB migration, is often observed across experimental (Fig.\u00a03) and simulated (Fig.\u00a04) conditions. This phenomenon stems from the intricate and highly strained twin configuration inherent in 5-FT NPs, as opposed to the SC NPs. Due to the formation of the stable sealed region S1, a significant long simulation duration was employed in Fig.\u00a04 (80.00\u2009ns) compared with that of the previous MD simulations (30.00\u2009ns, Fig.\u00a02 and Supplementary Figs.\u00a08, 12, 13).\n\nSymmetry evolution of the aggregated 5-FT NPs is closely associated with surface diffusion and TBs migration. As observed in all experimental results (Figs.\u00a01, 3), during aggregation between various NPs, surface diffusion occurs almost throughout the whole evolution process. Surface diffusion generally promotes the generation of S5-FT (formed based on the big 5-FT NPs), especially for small particle size ratios, by transferring atoms from small NPs to the twin units far away from the attached regions (Figs.\u00a01a\u2013d, 2a\u2013f, 3a\u2013h, 4a\u2013f). Nevertheless, the effect of TBs migration on the symmetry of the aggregated NPs depends on the twin pole migration direction. The migration of the reserved 5-FT twin pole toward the periphery of the NPs results in a reduction in symmetry, and vice versa. Based on dozens of experimental observations, the size effect of the aggregation growth between 5-FT and various NPs (SC and 5-FT) is illustrated in Fig.\u00a05. Correspondingly, to obtain the S5-FT, the critical ratios should be roughly smaller than 0.83 and 0.72, respectively. Otherwise, AS5-FT (Fig.\u00a05a) or complex twin structures (Fig.\u00a05b) form. In addition, SC can be also introduced at the final stage if the size ratio between the SC and the 5-FT is larger than approximately 1.0 (Fig.\u00a05a) under experimentally observed conditions. According to the theoretical models (Supplementary Table\u00a01), the number of atoms of SC is significantly larger than that of the 5-FT with the same particle size. And even R\u2009=\u20090.76 (model 1-2, Supplementary Table\u00a01, smaller than the critical R\u2009=\u20090.83), the number of atoms of SC is also larger than that of the corresponding attached 5-FT. This indicates that: (1) under investigated particles size (several nanometers), S5-FT exhibits relative stability, maintaining its symmetrical structure after attaching with small SC NP, and even the SC possesses a slightly larger number of atoms than that of the 5-FT. That is, surface diffusion predominantly governs the aggregation evolution, rendering negligible the migration of TBs or twin poles. (2) Relatively small critical R (0.72, atoms significantly less than that of the attached 5-FT, model 2-2, Supplementary Table\u00a01) between two aggregated 5-FT also verifies the stability of the 5-FT NP, i.e., the 5-FT structure can greatly mitigate surface diffusion. (3) The mutual surface diffusion from small NP to 5-FT is markedly greater than the reverse diffusion, i.e., the surface energy plays a significant role during NP\u2019s aggregation growth.\n\na Configuration obtained after aggregation between SC and 5-FT NPs. b Configuration obtained after aggregation between two 5-FT NPs. Source data are provided as a Source Data file.\n\nTo maintain the symmetrical configuration of 5-FT NPs, the critical transfer ratio registers at \u22480.72 for 5-FT/5-FT, which is smaller than that of SC/5-FT (\u22480.83). As discussed above, the surface diffusion persists throughout the entire evolution process and facilitates to maintain the symmetrical configuration of the big 5-FT NPs. Thereby, for two aggregated 5-FT NPs, the formation of S5-FT is mainly associated with the de-twinning process of the small 5-FT. This can be introduced by TBs migration (Fig.\u00a03c, d and Supplementary Figs.\u00a012, 13) and/or surface diffusion (Fig.\u00a04a\u2013e and Supplementary Figs.\u00a012, 13). Nevertheless, the S5-FT remains relatively stable and maintains its symmetry even when attaching to SC NPs with a slightly larger number of atoms compared to the 5-FT, that is, it is relative difficult to trigger TBs migration of symmetrical 5-FT NPs. Additionally, van der Waals (vdW) force calculation (Supplementary Fig.\u00a014) shows that the force between 5-FT and SC NPs is significantly stronger than that between two 5-FT NPs. This is introduced by the structural difference between 5-FT and SC, i.e., a greater number of atoms can exhibit a close interatomic distance in the 5-FT/SC interactions compared to the 5-FT/5-FT (see Supplementary Fig.\u00a014 and related discussion). Thereby, the force exerted on the small S5-FT is minimal, which not only results in a diminished driving force to initiate TBs migration but also mitigates morphological modulation. Therefore, the relatively smaller critical transfer ratio for 5-FT/5-FT (0.72) than that of the SC/5-FT (\u22480.83) is determined by the stable twin structure and decreases the interaction force introduced by the small S5-FT. It is noteworthy that the particle size of various aggregated NPs investigated is smaller than 10\u2009nm (Fig.\u00a05). The above critical transfer ratio is supposed to vary slightly with further growth of the particle size of 5-FT, considering the size effect on 5-FT, which is stable with the size of 3\u201314\u2009nm owing to thermodynamics24,25,26.\n\nIn conclusion, our findings reveal the fundamental atomic-scale mechanisms governing the aggregation growth and evolution of 5-FT NPs. We have demonstrated that when a 5-FT attaches to small NPs (R\u2009<\u20090.83 for SC and R\u2009<\u20090.72 for 5-FT), surface diffusion of the small nanoparticles to the big 5-FT dominates the configuration evolution, resulting in the formation of S5-FT NPs and a decrease of the system energies. Nevertheless, when a 5-FT attaches to a larger SC NP, besides surface diffusion, TB migration induced de-twinning process contributes significantly to the evolution process. This dynamic interplay induces the formation of a SC or a simple twinned structure. Additionally, in the case of attachment to another larger 5-FT NP (R\u2009>\u20090.72), this results in the development of intricate 5-FT configurations.\n\nComprehending the mechanisms and dynamics underlying the process of particle aggregation growth is critical to constructing quantitative models to elucidate formation and evolution mechanisms of diverse twinned mineral materials in natural environments. Simultaneously, mastering the deterministic manipulation of NP growth holds the innovative synthesis pathways to fabricate twinned crystals endowed with precisely controlled morphologies and properties. Consequently, this endeavor provides guidance to unlock the full potential of twinned crystal structures and promotes materials design and synthesis.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53501-0/MediaObjects/41467_2024_53501_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53501-0/MediaObjects/41467_2024_53501_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53501-0/MediaObjects/41467_2024_53501_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53501-0/MediaObjects/41467_2024_53501_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53501-0/MediaObjects/41467_2024_53501_Fig5_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Synthesis of Au NPs: Gold(III) chloride trihydrate (HAuCl4\u2009\u2219\u20093H2O, \u226599.9 % trace metals basis), tetrakis(decyl)ammonium bromide (\u226599.0 %, TDAB), sodium borohydride (NaBH4, 98 %), 1-dodecanethiol (\u226598 %) and toluene (\u226599.5 %) were purchased from Sigma-Aldrich and used without further purification. The Au NPs were synthesized by the TDAB method6,35. 0.27\u2009mmol of TDAB was dissolved in 4\u2009mL toluene. The TDAB solution was mixed with 6\u2009mL of a 0.01\u2009M HAuCl4\u2009\u2219\u20093H2O aqueous solution for 1\u2009h. When the color of the toluene layer was changed to yellow due to the migration of AuCl4\u2212 ions, the only toluene layer was collected. Then, 200\u2009\u03bcL of a 26\u2009mM NaBH4 aqueous solution, which was prepared under an ice bath, was injected into the Au-TDAB toluene solution. After 20\u2009min, 800\u2009\u03bcL of 1-dodecanethiol was added to this mixture. The product was precipitated by excessive ethanol and then collected by centrifuge (3000\u00d7g).\n\nAfter centrifuge, the sample was dispersed in 5\u2009mL toluene solution, drop-cast onto a TEM grid, and labeled as the as-prepared sample. As shown in Supplementary Fig.\u00a01a\u2013c, the as-prepared sample is SC dominated Au NPs with an average size of 3.5\u2009\u00b1\u20090.6\u2009nm. To tune particles\u2019 structure and size, half (2.5\u2009mL) of the above toluene solution was sealed and kept in the dark at room temperature for approximately 2 months (Supplementary Fig.\u00a01d\u2013f), and another half (2.5\u2009mL) of the above toluene solution with centrifuged samples was cleaned four times with toluene to remove 1-dodecanethiol on the Au NP surface (Supplementary Fig.\u00a01g\u2013i). About 1/3 of NPs has evolved into 5-FT after staying for 2 months, and the particle size increases into 4.4\u2009\u00b1\u20091.4\u2009nm (Supplementary Fig.\u00a01e, f). After purifying for 4 times, besides the formation of some 5-FT NPs, significant aggregation of NPs was also detected (highlighted by yellow arrows in Supplementary Fig.\u00a01h). And the average particle size grows into 6.2\u2009\u00b1\u20092.6\u2009nm (Supplementary Fig.\u00a01i). To investigate aggregation growth and evolution of 5-FT nanoparticles, the sample after staying for 2 months was employed for further study. Particle sizes were statistically analyzed based on more than 300 particles for each sample, and the standard errors were employed (Supplementary Fig.\u00a01).\n\nTo investigate atomic-scale 5-FT NPs growth and evolution mechanisms by particle aggregation, electron-beam induced NPs aggregation method6 was employed. This method has been engaged to tune the formation kinetics of 5-FT Au nanoparticles by controlling the decomposition process of organic ligand coated on the Au nanoparticles under various electron-beam dose rates6. Here, a cold-field emission aberration-corrected TEM (Spectra 300, Thermal Fisher, USA), was employed at 300\u2009kV for the above in situ HRTEM imaging with an image frame rate of \u22480.5\u2009s. An optimized dose rate of \u2248(2\u20134) \u00d7 106 e nm\u22122 s\u22121 was applied. When the ratio between the longest TB and the shortest TB of 5-FT is smaller than 2.0, the 5-FT NT is referred to as a S5-FT. When the ratio is larger than 2.0, the 5-FT NP is referred to as an AS5-FT. Theoretically, the configuration of NPs evolves continuously under the employed electron-beam dose rate with time. Here, the terminal time was roughly determined according to the evolution rate of the NPs\u2019 configurations, including morphologies and twin structures. The ultimate configurations were obtained if their variation can be ignored within \u224820\u2009s. Correspondingly, most of the in situ experimental durations are \u2248150\u2013200\u2009s under the employed electron-beam dose rate. Slip directions of partial dislocations are determined by TBs migration directions based on the immediate prior and subsequent images.\n\nMD simulations were conducted to explore the evolution processes of both the three-dimensional configuration and energy of simulated models of Au NPs (Supplementary Figs.\u00a02, 3). Two types of models were employed in our experiment, and all invariable decahedral 5-FT NPs (left side, Supplementary Figs.\u00a02, 3) are the same, i.e., all surfaces of the big 5-FT are \\(\\{111\\}\\) planes with 1773 atoms. Model 1 was built with a 5\u2009nm decahedral 5-FT NP attached with SC NPs in a box with a side length of 20\u2009nm. The SC NPs with \u22482.6, 3.8, and 4.8\u2009nm (Supplementary Fig.\u00a02), present truncated octahedral morphologies and have corresponding numbers (Supplementary Table\u00a01) of 811, 1862, and 4033 atoms, respectively. Model 2 was built with a 5\u2009nm decahedral 5-FT NP attached with decahedral 5-FT NPs in a box with a side length of 20\u2009nm. The size and numbers of atoms of various 5-FT NPs are 3.2, 4.1, 5.0\u2009nm and 569, 1061, 1773, respectively (Supplementary Fig.\u00a03 and Supplement Table\u00a01). The models were visualized by OVITO software36.\n\nMD simulations were performed using the LAMMPS code37. The Au embedded atom potential developed by Ackland et al38. was employed in the simulation. This interatomic potential has been applied in previous investigations on the evolution of Au NPs during OA process successfully6. A Nos\u00e9\u2013Hoover thermostat39 in the canonical ensemble was used to maintain the temperature of 1100\u2009K, and the time step was configured to 1\u2009fs. The simulation temperature was determined based on the evolution kinetics of the aggregated nanoparticles and the computational resources and duration required for the MD simulation.\n\nAlthough the electrostatic force may be argued as the source of the jump connection during the orientation attachment4, in our experiment, when the distance between the two aggregated NPs is smaller than \u22482\u2009nm, the vdW force is supposed to be the determining force35, especially the evolution process after aggregation, such as the relative slip between NPs.\n\nvdW interactions between two NPs (NP1 and NP2) were performed based on Hamaker\u2019s approach. To describe the vdW interactions of heterogeneous structures such as 5-FT structures, each NP was divided into many small cubes with length (L)\u2009=\u20090.2882\u2009nm, which is the closest Au-Au separation distance. Next, the vdW interactions between the two NPs were calculated by the summations of each pairwise interaction.\n\nThe center positions of the small cubes of the NP1 and the NP2 were defined as (x1,i, y1,i, z1,i) and (x2,j, y2,j, z2,j), respectively.\n\nThe vdW interaction potentials between the small cubes of the NP1 and the small cubes of the NP2 were calculated based on the formulation shown in previous work that followed Hamaker\u2019s approach with geometrical consideration40. The formulations can produce the vdW interaction energy (V) between small cube-\u03b1,i and cube-\u03b2, j with parallel configurations at arbitrary separations.\n\nwhere A is the Hamaker constant 359.579 \u00d7 10\u221221\u2009J obtained from ref. 41, which calculated dielectric responses of Au in vacuum based on the Lifshitz theory. VR is the London-vdW interaction energy between cubes,\n\nwhere, r is the distance between an arbitrary point in the small cube-\u03b1,i and an arbitrary point in the small cube-\u03b2,j, v\u03b1,I, and v\u03b2,j are the volume of the respective bodies. For the numerical computations, the integrations of the Eq. (2) were modified by\n\nParameter am, bn, and cp were defined as below,\n\nwhere hx, hy, and hz were denoted, which are shown in Supplementary Fig.\u00a04.\n\nNext, the vdW interaction potentials (V) between NP1 and NP2 were calculated by\n\nThe interparticle distance between NP1 and NP2 (hd) was defined as the distance between closed atoms of each NC, which is shown in Supplementary Fig.\u00a05.\n\nBased on the vdW potential as a function of hd, the vdW force was calculated numerically.\n\nHere, \u0394d\u2009=\u20090.05\u2009nm was employed.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data that support the findings of this study are available from the corresponding authors upon request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Sugimoto, T. Monodispersed Particles 2nd edn (Elsevier, 2001).\n\nPolte, J. 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The support from the aberration-corrected spectra 300 in the State Key Laboratory of Powder Metallurgy is also greatly acknowledged.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "State Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan, 410083, China\n\nMiao Song,\u00a0Dingri Zhang,\u00a0Dan Leng,\u00a0Ziang Yang,\u00a0Jiaxuan Chen,\u00a0Dan Li\u00a0&\u00a0Kechao Zhou\n\nState Key Laboratory of Crystal Materials, Shandong University, Jinan, Shandong, 250100, China\n\nMiao Song\u00a0&\u00a0Lei Wang\n\nDepartment of Mechanical and Aerospace Engineering, College of Engineering, University of Missouri, Columbia, MO, 65203, USA\n\nJaewon Lee\n\nShi-changxu Innovation Center for Advanced Materials, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, Liaoning, 110016, China\n\nGang Zhou\u00a0&\u00a0Rui Yang\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nM.S. designed the experiments, conducted experiments and data analysis, wrote the manuscript, and supervised the study. D.Z. assisted with image processing and data analysis. D.Leng revised the manuscript. J.L. synthesized Au NPs and conducted force calculation. Z.Y., J.C., D.Li, Z.G., and L.W. contributed to the discussion of the results and commented on the manuscript. G.Z. conducted MD simulations and wrote the manuscript. R.Y. and K.Z. supervised the study and commented on the manuscript.\n\nCorrespondence to\n Miao Song or Gang Zhou.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Francis Deepak and the other anonymous reviewers for their contribution to the peer review of this work. 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Ida\u2019s blackout-heatwave compound risk in a changing climate", + "pre_title": "Hurricane Ida\u2019s blackout-heatwave compound hazard risk in a changing climate", + "journal": "Nature Communications", + "published": "15 May 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59737-8/MediaObjects/41467_2025_59737_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59737-8/MediaObjects/41467_2025_59737_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59737-8/MediaObjects/41467_2025_59737_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59737-8/MediaObjects/41467_2025_59737_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-59737-8#ref-CR15", + "https://doi.org/10.5281/zenodo.15012708", + "/articles/s41467-025-59737-8#Sec15" + ], + "code": [ + "https://doi.org/10.5281/zenodo.15012708" + ], + "subject": [ + "Civil engineering", + "Climate-change impacts", + "Natural hazards" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4096843/v1.pdf?c=1747393752000", + "research_square_link": "https://www.researchsquare.com//article/rs-4096843/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-59737-8.pdf", + "preprint_posted": "28 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The emerging tropical cyclone (TC)-blackout-heatwave compound hazard under climate change are not well understood. In this study, we employ future projections of TCs, sea levels, and heatwaves, in conjunction with power system resilience modeling, to evaluate historical and future TC-blackout-heatwave compound hazard risks in Louisiana, US. We find that the return period for a compound hazard event comparable to Hurricane Ida (2021), with approximately 35 million customer hours of simultaneous power outage and heatwave exposure in Louisiana, is around 278 years in the historical climate (1980-2005). Under the emissions scenario SSP5 8.5 (SSP2 4.5), this return period may decrease by a factor of ~17\u00d7(10x) to 16.2 (28.4) years in the future climate (2070-2100). The significant increase in risk can be primarily attributed to projected escalations in heatwaves, which result in an approximate 5(2)-fold decrease in compound hazard return period, and in TC activity, which cause an estimated 2(1)-fold decrease in the return period. The findings contribute to our knowledge of and adaptation to compound climate hazards.Earth and environmental sciences/Climate sciences/Climate change/Climate-change impactsPhysical sciences/Engineering/Civil engineeringEarth and environmental sciences/Natural hazards", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "smncidacompoundMarch13.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The emerging tropical cyclone (TC)-blackout-heatwave compound risk under climate change is not well understood. In this study, we employ projections of TCs, sea level\u00a0rise, and heatwaves, in conjunction with power system resilience modeling, to evaluate historical and future TC-blackout-heatwave compound risk in Louisiana, US. We find that the return period for a compound event comparable to Hurricane Ida (2021), with approximately 35 million customer hours of simultaneous power outage and heatwave exposure in Louisiana, is around 278 years in the historical climate of 1980\u20132005. Under the SSP5-8.5 emissions scenario, this return period is projected to decrease to 16.2 years by 2070\u20132100, a ~17 times reduction. Under the SSP2-4.5 scenario, it decreases to 23.1 years, representing a ~12 times reduction. Heatwave intensification is the primary driver of this increased risk, reducing the return period by approximately 5 times under SSP5-8.5 and 3 times under SSP2-4.5. Increased TC activity is the second driver, reducing the return period by 40% and 34% under the respective scenarios. These findings enhance our understanding of compound climate hazards and inform climate adaptation strategies.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "In August 2021, Hurricane Ida, a Category 4 storm, struck Louisiana with intense winds, heavy rainfall, and storm surges, resulting in widespread flooding and damage to the state\u2019s infrastructure systems. Subsequent to the hurricane\u2019s landfall, the state experienced a loss of ~200 million customer hours of electricity, affecting roughly 2.15 million customers for an average power outage duration of 96\u2009h. Data from the United States (U.S.) Department of Energy reveals that Hurricane Ida caused the most extensive power outage in Louisiana\u2019s history, largely surpassing Hurricane Katrina (Category 5; 2005) and Hurricane Laura (Category 4; 2020), which led to losses of approximately 140 million and 100 million customer hours of electricity, respectively1,2,3.\n\nFurthermore, a prolonged heatwave occurred in the aftermath of Hurricane Ida, particularly affecting households that lost power and thus had no air conditioning (3; 93% of Louisiana households used air conditioning in 2020 corresponding to ref. 4). Consequently, Louisiana residents experienced a total of 35 million customer hours of compound blackout-heatwave risk (with a heat index surpassing 37.8\u2009\u00b0C/100\u2009\u00b0F; 5). Customers exposed to the compound hazard endured an average of approximately 98\u2009h of heatwave conditions5. Prolonged heat exposure can cause hospitalization and mortality risks6, especially among vulnerable populations7. Understanding how often Ida-like compound blackout-heatwave events may occur is critical for the development of risk mitigation strategies for populations vulnerable to hurricanes.\n\nA \u201ccompound climate event\u201d can result in large impacts due to the combination of climate drivers and hazards such as floods, wildfires, heatwaves, and droughts8. Traditional risk assessment methods typically consider one hazard at a time, potentially leading to an underestimation of risk, since the physical drivers causing extreme events may exhibit spatial and/or temporal dependencies and interact to exacerbate the overall impact. Hurricanes or more generally tropical cyclones (TCs), as drivers of extreme wind, rainfall, and storm surge, inherently lead to compound impacts on coastal regions9 and are responsible for nine of the ten largest power outages in the United States over a recent two-decade period10. While extreme winds are the primary source of damage to power systems, the presence of storm surges and heavy rainfall resulted in extensive flood inundation during Hurricane Ida, which caused additional physical damage and hindered power system resilience as repair crews were unable to access affected areas2. In addition, sea-level rise (SLR) may intensify coastal flood inundation by extending and prolonging the flood coverage, further exacerbating power system damage and delaying recovery operations.\n\nDue to the seasonal peak of intense heat being ahead of that of major TCs, TC-heatwave compound events have so far been rare worldwide11. However, a previous study12 found that for Harris County (a portion of Houston situated on higher ground and affected by hurricane winds), Texas (TX), the percentage of residents experiencing 5-day TC-blackout-heatwave compound hazard conditions could increase by a factor as large as 23 over the course of the 21st century under the high emissions scenario RCP8.5. Also, in recent years, TC-heatwave compound events have happened in the Gulf Coast region. Hurricane Ida may represent the first hurricane landfall on the mainland United States associated with a long-lasting (i.e., multi-day), large-scale blackout-heatwave compound hazard (during Hurricane Laura the state-average heat index was also high but did not reach the threshold of 37.8\u2009\u00b0C/100\u2009\u00b0F). During Hurricane Ida and Laura, at least eleven and eight Louisianans, respectively, died of heat-related illnesses1,13. Investigating this emerging compound threat, enhanced by climate change, will contribute to our knowledge of and adaptation to compound climate hazards.\n\nIn this study, we integrate hazard projection and power system analysis to examine TC-blackout-heatwave compound risk for Louisiana over the 21st century under the combined influence of SLR and changes in heatwave and storm climatology. We highlight the change in the return period/recurrence interval of Ida-like compound events from the historical to future climates. We further quantify the relative importance of the change in various climatological variables (i.e., heat stress, sea level, storm frequency, storm intensity) in driving the changes in the compound risk.\n\nOur framework is an extension of the previous study in ref. 12 to incorporate multiple hazards, including storm surge, rainfall, and SLR, in addition to wind and heatwave, to more comprehensively model TC-blackout-heatwave compound risk, for the entire State of Louisiana under various climate conditions. Specifically, we combine projections of heatwaves14, TC hazards (including wind, storm surge, and rainfall15), and SLR16,17,18, driven by CMIP6 GCMs14 under a moderate (Shared Socioeconomic Pathway 2\u20134.5) and a high (SSP5-8.5) emissions scenarios, to assess both a likely scenario and an upper bound of the risk. We generate a large number of compound hazard events (~30,000 stochastic sequences) based on the projections for historical (1980\u20132005) and future (2081\u20132100) climates to estimate how hazard probabilities may change over the 21st century (Methods). Then we utilize a physics-based power outage and restoration model for Louisiana to simulate wind/surge/rainfall-induced power system failure and recovery for each hazard event, to estimate the TC-blackout-heatwave compound risk. In doing so, we extend the existing wind-impact-only simulation method (county level12,19) to a wind-rainfall-surge coupled framework for power damage and recovery process modeling to consider a larger study area including coastal regions (state level; Methods). Considering the uncertainty surrounding the impact of climate change on the frequency of TCs making landfall along the Gulf Coast, we assume a constant TC frequency but also assess the sensitivity of the compound risk to TC frequency projection. To focus on the impact of climate change, we assume that the power system, population distribution, and recovery plans in the study region will remain unchanged. However, we assume that the coastal levees will be elevated following design guidance, as this enhancement may be considered necessary to prevent the region from frequent inundation due to SLR (\u201cMethods\u201d).", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We first examine power outage simulations of the historical cases of Hurricanes Ida and Laura, which are the two major events over the last decade that caused widespread power disruptions in Louisiana1. Ida devastated the eastern half of Louisiana, which is more densely populated (including the city of New Orleans), whereas Laura grazed the western side. Ida destroyed 31,000 poles (reported by local utility company Entergy20) that carry lower-voltage distribution lines in the neighborhoods, twice as many as those in Hurricane Laura (14,000 poles) and Katrina (2005; 17,000 poles).\n\nAs shown in Fig.\u00a01a and b, the model\u2019s estimates for the overall impact of Hurricanes Ida and Laura on Louisiana compare relatively well with the observation. Hurricane Ida led to 47% (48% in simulation) of customers being out of power within the first 24\u2009h, and it took ~10 days (11 days in simulation) for 90% of customers to restore power. Meanwhile, up to 60% of Louisiana customers were under heatwave conditions within 6 days after Hurricane Ida\u2019s landfall. About 42% independent customers experienced compound power outage and heatwave hazards for at least one day after the hurricane\u2019s landfall, and the percentage of customers experiencing the compound hazard surpassed 15% during a period of time (see Supplementary Fig.\u00a01). Hurricane Laura led to 27% (29% in simulation) of customers being out of electricity initially and it took ~6 days (8 days in simulation) for 90% of customers to restore power (see Supplementary Fig.\u00a02 for spatial distribution).\n\na Ida and b Laura. The red curve shows median values, with 5% to 95% quantile range shown by shade and the blue curve shows the observation. The yellow curve in (a) shows the percent of customers impacted by heatwaves (value reads the right axis). c Comparison of observed and simulated spatial distribution of power outage for Hurricane Ida. Source data are provided as a Source Data file.\n\nTo measure the overall severity of the blackout associated with each TC, we compute the cumulative interruption hours throughout Louisiana (the total of all customers\u2019 power outage duration), a commonly used metric in evaluating power system reliability21. The model simulation estimates in total 189 (156\u2013242; \u00b13 standard deviations) million power interruption hours for Ida, which is consistent with the observed 206 million hours, and 110 (77\u2013153) million power interruption hours for Laura, which compares relatively well with the observed 99 million interruption hours. As a comparison, Hurricane Katrina led to ~140 million power interruption hours. The model estimation for the spatial and temporal distribution of power outage also correlates well with observations (the average relative error is <10% between the modeled and observed county-level power outage), as illustrated in Fig.\u00a01c for the power outages at 24\u2009h, 5 days, and 8 days after landfall at the county level for Hurricane Ida.\n\nIntegrating power outage and recovery modeling with projections of future TC, SLR, and heatwaves, we examine the TC-induced blackout-heatwave compound risk in Louisiana. We generate 10,000 simulations of synthetic hazard events for each of the historical (1980\u20132005) and future (2081\u20132100) SSP5-8.5 and SSP2-4.5 scenarios. Each stochastic simulation includes a continuous 20-year sequence of TC occurrences, along with the physical simulation of TC tracks, wind speeds, rainfall amounts, storm surge levels, and heatwaves. We track each customer\u2019s exposure (i.e., duration) to blackout or compound blackout-heatwave hazard in the power outage and recovery modeling process for each synthetic hazard event. Then, we integrate the customer-level results to obtain state-level statistics and estimate the return periods (i.e., reciprocal of annual exceedance probability) of event total interruption hours for the historical and future climates. As demonstrated by the substantial shift of the return period curves (Fig.\u00a02), the power outage risk will increase dramatically from the historical to the future climate. Specifically, the historical return period of a power outage of 206 million customer hours, as in Hurricane Ida, is 64 years (Table\u00a01). The return period of a power outage like Hurricane Ida\u2019s total outage is projected to be 35.8 years in the future under the SSP5-8.5 emissions scenario, compared to 38.2 years under SSP2-4.5. The total power outage for an event with Ida\u2019s historical return period of 64 years is approximately 413 million customer hours under SSP5-8.5 and 265 million customer hours under SSP2-4.5. The return period of a TC-blackout-heatwave compound hazard of 35 million customer hours, as in Hurricane Ida, is 278 years in the historical climate. This return period is expected to decrease in the future climate to 16.2 years under SSP5-8.5 (~17\u00d7 reduction) and 23.1 years under SSP2-4.5 (~12\u00d7 reduction). A compound hazard event with Ida\u2019s historical return period of 278 years is projected to cause about 435 million customer hours of impact under SSP5-8.5 and 138 million under SSP2-4.5. Such an event would induce an average blackout-heatwave compound hazard duration of approximately 8.8 days (SSP5-8.5) or 2.8 days (SSP2-4.5) for each of Louisiana\u2019s 2.13 million customers.\n\na interruption hours under power outage, b interruption hours under blackout-heatwave compound hazard. The red curve shows median values for SSP5-8.5 for the future climate, with the 5\u201395% quantile range shown by shade, the yellow curve represents SSP2-4.5 for the future, and the blue for the historical climate. The dashed lines highlight Hurricane Ida\u2019s power outage and compound risk levels. Source data are provided as a Source Data file.\n\nThe power outage level under SSP2-4.5 is similar to that under SSP5-8.5 at Ida\u2019s return period or shorter, although the power outage level becomes higher in SSP5-8.5 at longer return periods (Fig.\u00a02a), due to higher extreme TC hazards and SLR in SSP5-8.5. The difference between the two emissions scenarios is larger for the compound risk. Specifically, the return period of the compound hazard impact at Ida\u2019s level under SSP5-8.5 is slightly shorter than that under SSP2-4.5, and the return period of larger compound impacts becomes dramatically shorter under SSP5 8.5 (Fig.\u00a02b), due to combined effects of larger increases in extreme heatwaves, TC hazards, and\u00a0the sea level under SSP5-8.5. For example, the return period of extremely severe compound events, such as those with triple the impact of Hurricane Ida (i.e., 100 million customer-hours of compound hazards), is expected to be 2.5 times longer under SSP2-4.5 compared to SSP5-8.5. However, for less severe events, such as those with a third of Ida\u2019s impact (i.e., 10 million customer-hours of compound hazards), we do not find a statistically significant difference in the\u00a0event return period/frequency between the two emissions scenarios. These findings suggest that the combined effects of global warming and increasing hurricane intensity amplify the risk of the most extreme compound events. Nonetheless, the moderate emission scenario may still lead to a similar increase of frequency for events of Ida\u2019s impact magnitude, compared to the high emissions scenario.\n\nTo investigate the spatial distribution of the compound risk, we estimate the county-average compound hazard interruption days for each synthetic hazard event. Figure\u00a03 shows the compound hazard interruption days with Ida\u2019s historical return period of 278 years for each county in Louisiana in the historical and future climates. The coastal counties face a greater compound risk than inland counties for both historical and future climates (Fig.\u00a03a\u2013c). For example, the counties with an average compound hazard impact larger than 20 days in the future climates are mostly coastal counties. Coastal counties often face a greater compound risk since hurricane winds reach peak strength before the storm makes landfall, and storms weaken as they move inland, causing less damage to the inland power infrastructure. Moreover, the floods induced by storm surge and heavy rainfall can severely damage coastal power sectors. The flooding also hampers the recovery efforts of local contractors by submerging electrical components in water and obstructing local traffic and logistics with debris.\n\na Historical average interruption, b future SSP5-8.5 average interruption, and c future SSP2-4.5 average interruption, for each county in Louisiana for a compound hazard event with a 278-year return period. d Distribution of affected Orleans Parish customers\u2019 compound hazard duration under a 278-year event. The solid lines show the percentage of affected customers experiencing compound hazard up to a certain temporal length in the historical and future climates. Source data are provided as a Source Data file.\n\nThe general spatial disparities in compound risks are also substantial and will increase with climate change. For example, in the historical climate, the most impacted county may face on average a 1.8-day compound hazard with the return period of 278 years, and the least impacted county does not face any compound risk (Fig.\u00a03a). In the future, under the SSP5-8.5 scenario, the county with the greatest impact may face an average of 12.7 days of compound hazard exposure for an event with the return period of 278 years. Under the SSP2-4.5 scenario, this average is 3.1 days. Conversely, the least impacted area is projected to experience only 1.1 days under SSP5-8.5 and 0.1 days under SSP2-4.5 (Fig.\u00a03b\u2013c). To assess the spatial disparity in the compound risk, we use the Gini coefficient22, a statistical measure of inequality commonly applied to income, wealth, or consumption distribution. The coefficient ranges from 0 (perfect equality, where all counties face the same average compound hazard duration) to 1 (full inequality, where only one county faces the hazard). In the historical climate, the Gini coefficient is approximately 0.312. However, under future scenarios, it increases to 0.632 for SSP5-8.5 and 0.411 for SSP2-4.5. These results suggest that intensifying hurricane hazards disproportionally impact coastal regions and vulnerable communities distant from substations, and thus, climate change is likely to exacerbate existing disparities and inequalities in TC-blackout-heatwave compound risks across Louisiana.\n\nWe also examine the distribution of compound hazard durations for residents in densely populated counties. Figure\u00a03d illustrates the distribution of compound hazard interruption days for affected customers in Orleans Parish, for a\u00a0278-year\u00a0compound event. In the historical climate, only 3% of affected customers may experience a compound hazard lasting more than 120\u2009h (5 days). In the future, under the SSP5-8.5 scenario, nearly 60% of affected customers are projected to face a compound hazard exceeding 120\u2009h, with nearly 30% experiencing durations longer than 240\u2009h (10 days) and 10% encountering durations exceeding 360\u2009h (15 days). Under SSP2-4.5, 20% of affected customers may face compound hazards exceeding 120\u2009h, 3% may experience durations longer than 240\u2009h, and no customers are expected to encounter durations beyond 360\u2009hours. Hence, climate change not only increases the average compound hazard impact but also intensifies the tail risk that vulnerable residents may encounter, especially under the high emissions scenario.\n\nThe change in the compound risk is driven by the change in three climate factors: 1) heatwaves (heat index) 2) TC climatology, and 3) the sea level. As we assume that TC frequency remains unchanged in the future, the changes in TC climatology include changes in TC characteristics, particularly intensity (which drives changes in wind, storm tide, and rainfall). To determine the relative effect of the changes in these factors, we estimate the changes in the compound risk due to changes in temperature, SLR, and TC climatology, respectively, by adjusting each variable to its future value or distribution and calculating the resulting return period of Ida\u2019s compound hazard (i.e., 35 million customer hours of simultaneous power outage and heatwave impact), as shown in Fig.\u00a04a.\n\na. Relative impact of each climate change factor assuming a consistent TC frequency. b Sensitivity to TC frequency change. Note that the combined impact of all climate factors on Ida\u2019s compound hazard return period is highly non-linear, and thus, the sum of the relative impact of individual factors does not equal the total impact. Data are presented as the mean value with 10\u201390% quantile. 10,000 stochastic samples of storm sequences for each climate case are used to derive the statistics. Source data are provided as a Source Data file.\n\nAs discussed earlier, when all climate change factors are considered, Ida\u2019s return period is projected to decrease from 278 years in the historical climate to 16.2 years under SSP5-8.5 and 23.1 years under SSP2-4.5 in the future climate. Among these factors, changes in heatwaves are the largest contributors to this reduction. Due to heatwave changes alone, Ida\u2019s return period would drop to 47.6 years under SSP5-8.5 and 84.3 years under SSP2-4.5, representing approximately a five-fold and three-fold decrease, respectively. This substantial impact is driven by dramatic increases in temperature and humidity: the annual number of heatwave days (above 37.8\u2009\u00b0C) is expected to rise eightfold from the historical climate to the future climate, based on the ensemble average of the six GCMs for the study region.\n\nChanges in TC characteristics, especially intensity also contribute to the reduction, decreasing Ida\u2019s return period to 167.8 years under SSP5-8.5 and 185.2 years under SSP2-4.5. By comparison, the impact of SLR is relatively small, reducing Ida\u2019s return period to 251.2 years under SSP5-8.5 and 263.3 years under SSP2-4.5. SLR appears to have a relatively low impact because we assume the levees along the coast will be elevated. Also, the impact of SLR is limited to coastal regions, and it is averaged out when the compound hazard impact is calculated for the entire state. The contribution of the various climatological drivers to future compound risk is consistent across the two different emission scenarios.\n\nConsidering that the projection of TC frequency is subject to considerable uncertainty23,24, we assumed a constant TC frequency in above analyses. Here, we investigate the sensitivity of the estimated compound risk to the projection of TC frequency change. In one scenario, we apply the TC frequency in the applied TC\u00a0model15, which projects relatively high increases in TC frequency in the future climates. Under SSP5-8.5, Ida\u2019s return period decreases to 7.9 years, and under SSP2-4.5, it decreases to 15.2 years, compared to 16.2 years (SSP5-8.5) and 23.1 years (SSP2-4.5) when accounting for all climate change factors except TC frequency changes. In another scenario, we consider a 30% decrease in TC frequency, the lower bound of TC frequency projections ensembled in ref. 24. In this case, Ida\u2019s return period becomes 28.4 years under SSP5-8.5 and 40.6 years under SSP2-4.5. While longer than the projections with increased or constant TC frequency, these return periods represent a similarly dramatic decrease from 278 years in the historical climate. This sensitivity test indicates the relatively small impact of TC frequency change compared to the combined effects of other climate change factors.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59737-8/MediaObjects/41467_2025_59737_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59737-8/MediaObjects/41467_2025_59737_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59737-8/MediaObjects/41467_2025_59737_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59737-8/MediaObjects/41467_2025_59737_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "This analysis highlights the substantial increase in the frequency of Ida-level extreme power outage-heatwave compound hazards over time, resulting from the combined effect of temperature increase, SLR, and storm climatology change under climate change. Linear interpolation suggests that Hurricane Ida\u2019s return period has decreased from 278 years around the end of the last century to 225.6 years under SSP5-8.5 and 227.0 years under SSP2-4.5 in the 2020\u2009s, representing approximately a 19% reduction over the past two decades. This real-life observation of an emerging climate compound hazard motivates further research on projecting future compound climate risks and developing strategies to mitigate climate risks for various regions around the world.\n\nWhen examining the impact of various climate scenarios, such as the high emission scenario SSP5-8.5 and the moderate emission scenario SSP2-4.5, it appears that the risk associated with Ida-scale compound hazard events may not exhibit substantial difference. This result indicates that utility companies urgently need to prepare for the compound events to prevent major impacts, regardless of whether a moderate or high emissions scenario is considered. On the other hand, the frequency of larger compound impacts is expected to be significantly lower under the moderate emissions scenario. Moreover, the duration of interruptions caused by compound hazards will also be reduced with moderate emissions. This result highlights the importance of strengthening climate change mitigation policy to reduce the impact of extreme climate hazards.\n\nWe do not consider the potential change in the power grid or its operation in the future. In the future, localized solutions, including backup generators and solar panels, can provide temporary support to residents who lose power from the main grid, thus mitigating the impacts of compound hazards25. These solutions can help reduce the exposure of vulnerable populations to the effects of power outages and extreme heat, thereby lessening the overall impact of compound hazard events. However, backup generators and solar panels may be cost-prohibitive, limiting their effectiveness in reducing heat stress. Proactive policy measures, such as subsidies or tax breaks, may be needed to make these interventions accessible and effective for vulnerable populations. Also, equipping relief centers with reliable cooling and backup power systems can further enhance emergency response capacity. Prioritizing these interventions based on estimated compound risk can guide effective resource allocations. From the main power grid design perspective, adopting effective strategies like burying distribution networks and developing distributed power systems25 can bolster the resilience of power infrastructure against extreme weather events.\n\nWe also do not consider the potential changes in demographics and human habitat. As extreme climate events become more frequent, coastal megacities are also expected to develop rapidly26,27. Encouraging climate-resilient urban design principles that prioritize green spaces, water management systems, and heat-resistant building materials can enhance cities\u2019 resilience against compound risks28. Also, the implementation of advanced early warning systems and preparedness measures, combined with public awareness campaigns, can help minimize potential impacts on vulnerable communities29,30. Moreover, changes in population patterns, such as urbanization in low-elevation coastal zones and the concentration of populations in areas vulnerable to climate hazards, can influence the impact of compound hazards, emphasizing the need to account for these demographic shifts when devising adaptation strategies26,31.\n\nQuantifying the reliability and resilience of infrastructure systems under the impact of future compound hazards is essential for climate change adaptation. Developing an integrated risk assessment framework that combines climatology, civil and electric engineering, urban planning, and social sciences is crucial for comprehensively understanding the interconnected nature of compound risks and their societal impacts, and the formulation of effective mitigation strategies32,33. For example, conventional statistical methods may fail to detect significant changes in compound risk, especially for the most extremes. Our analysis shows that the intensity of relatively frequent hazards may not change significantly in the future, especially under moderate emissions scenarios. However, if such a conclusion for frequent, observable events is statistically extrapolated to that for extreme events, we may transform events like Ida, which could have been foreseen and prepared for, into \u201cblack swan\u201d events\u2014unpredictable extreme disasters with unimaginable losses. Only physics-based modeling integrating climate and hazards projection and infrastructure/social system analysis may provide reliable estimates of future risks. This multidisciplinary perspective is essential for capturing the complex interactions between different hazards and their cascading effects on infrastructure systems and society as a whole, ultimately enabling the development of robust and resilient strategies to mitigate the impacts of compound hazards. Also, given various uncertainties in the projections of climate change and social development, there is a need for continuous refinement and updating of risk analysis techniques as improved modeling approaches and new data become available. By adopting a comprehensive approach that integrates various disciplines and continuously enhances our understanding of compound risks, we can work towards developing effective adaptation strategies for a sustainable future in the face of a changing climate.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "We use the synthetic TC hazard dataset generated in ref. 15 for the North Atlantic basin and select the TC tracks passing within 300\u2009km of Louisiana, with a maximum wind speed of at least 22\u2009m/s. The dataset contains synthetic TC tracks generated with the statistical-deterministic TC model34, which has been applied to TC hazard assessment9,35,36. The synthetic TC tracks for the historical period (between 1980 and 2005) were generated based on the National Centers for Environmental Prediction (NCEP) reanalysis. The dataset also contains bias-corrected and weighted-average climate projections of TCs for the future period (2070 to 2100) under Shared Socioeconomic Pathway (SSP) emissions scenarios, SSP5 8.5 and SSP2 4.5. The dataset was generated based on projections from six CIMP6 models (selected given data availability and following previous studies): Canadian Earth System Model (CanESM5), Centre National de Recherches M\u00e9t\u00e9orologiques Climate Model (CNRM-CM6-1), UK Earth System Model (UKESM1-0-LL), EC-Earth3, IPSL-CM6A-LR (Institut Pierre-Simon Laplace Climate Model), and Model for Interdisciplinary Research on Climate (MIROC6). The TC storm tides were modeled in ref. 15 using the Advanced Circulation (ADCIRC) hydrodynamic model37,38. We extract peak storm tides at nodes (~1\u2009km resolution) along the coastline of Louisiana for each TC and match these to the county level. The rain fields were simulated in ref. 15 for each synthetic TC using the physics-based Tropical Cyclone Rainfall (TCR) model39. We apply area-averaged TCR estimates at the county level, and we employ the maximum 24\u2009h rainfall accumulation from each storm event, since the 24-hour storm duration is often utilized for rainfall risk assessment39,40.\n\nTo perform sequential risk analysis, we generate 10,000 stochastic samples of storm sequences for each of the historical and future climate conditions. These samples were derived by sampling storms (according to a Poisson process with a rate as the TC annual frequency) and their associated hazards from the TC hazard datasets described above. Each stochastic sequence consists of 20 consecutive years of TC activity. For the primary analysis in this study, we maintain a constant TC frequency in the future climate. For the sensitivity analysis, we consider the increased TC frequency projected by the statistical-deterministic TC model in ref. 15 and the decreased TC frequency by up to 30% projected by a range of climate models ensembled in ref. 24.\n\nFor each sampled storm, we generate the spatial-temporal wind field, employing the classical Holland wind profile41 and accounting for the effects of surface friction and large-scale background wind following ref. 42, and converting one-minute mean winds to 3\u2009s wind gusts using gust factors43. We estimate the coastal flood area by comparing the height of peak storm tide (if over levee height) to the ground surface elevation specified by the USGS 30\u2009m DEM44, assuming that areas would be inundated when the storm tide exceeds the ground elevation.\n\nIn Louisiana, the actual seawall heights vary largely along the coastline, ranging from 2 to 5 meters and often changing over short distances. Due to the difficulty in acquiring the precise data, our simulations do not incorporate partially available measurements45,46,47. We assigned the current seawall level based on estimated 100-year flood level (estimated in ref. 15) for each coastal county in Louisiana, according to the typical design guidance. For example, the 100-year flood level for New Orleans is approximately 3.4 meters above the North American Vertical Datum of 1988 (NAVD 88)9. This approximation introduces a degree of inaccuracy into our flood modeling. Acknowledging this limitation, we subsequently focused on binary flood data\u2014whether a flood occurs or not\u2014when developing our power system damage and recovery models. We observe that the occurrence of flooding is a critical factor that largely hinders the restoration efforts of the power system in coastal counties. However, the inundation depth of the flooding appears to have a less substantial impact. When compared to the areas affected by the TC\u2019s wind and rainfall, the storm surge flooded regions are generally smaller. Therefore, the majority of the structural damage to the power system may not be caused by coastal flooding.\n\nThe future coastal levee plan is uncertain. In the future climate simulations, we assume the coastal levee will be elevated by ~ 2\u2009m, based on the historical 100-year return level plus one percentile SLR. This design strategy is commonly used by governmental agencies to plan the seawall height, and it is within the framework proposed by the U.S. Army Corps of Engineers for the New Orleans Region, Lafayette, and Lake Charles47. A sensitivity test was performed on future compound risks given different elevations of the coastal levee from 0-3 meters above the current level. If the levee were not elevated, the surge impact on the compound risk would be substantially higher than estimated in this study. On the other hand, when the levee is elevated by higher than 2\u2009m, the estimated compound risk is not sensitive to the variation of the assumed levee increment up to the test\u00a0case of\u00a03 meters (see Supplementary Fig.\u00a03). The generated wind, rainfall, and coastal flood conditions from each sampled storm drive the power grid outage and recovery analysis.\n\nFollowing ref. 11, the daily HI is determined as a function of daily maximum near-surface (2 m) air temperature, daily mean specific humidity, and daily mean surface pressure. To maintain consistency with the TC simulation, we obtain these data for Louisiana from the NCEP reanalysis and the six GCMs stated above during and after landfall for each sampled synthetic storm (each synthetic storm is associated with a climatological time of occurrence and development). The future HI projected by the GCM is bias-corrected12 by adding the difference between the NCEP reanalysis and the GCM-estimated historical HI. According to the historical analysis in ref. 11, the HI will drop upon TC landfall and will recover to the ambient average within around ten days. To account for this dependence between TCs and heatwaves, we add the composite of the impact of TC passage to the meteorological variables used to calculate the HI, where the composite impact is estimated based on historical data (Fig.\u00a03a in ref. 11).\n\nWe employ sea-level projections produced by the Intergovernmental Panel on Climate Change Sixth Assessment Report16,18 (AR6) using the Framework for Assessing Changes To Sea-level17 (FACTS). Localized probabilistic SLR projections under the SSP5-8.5 and SSP2-4.5 emission scenarios with \u201cmedium confidence\u201d are incorporated in this analysis (there are two confidence levels in the datasets, which are low and medium levels). The local sea level projection takes into account ground uplift or subsidence, oceanographic effects, and spatially variable responses of the geoid and the lithosphere to shrinking land ice. The projection of SLR was developed for tide-gauge stations. For each TC sequence realization, we first sample a near-by\u00a0SLR time series (a realization) from the projection\u00a0for each county. Then, we add the SLR to the storm tide level at each time point for each county.\n\nWe apply a physics-based power system model, which explicitly simulates component-level damage to predict the total power outage, accounting for the effects of future evolving factors, e.g., climate change, infrastructure upgrade, and utility maintenance. The physics-driven modeling of the power system allows us to better understand the impact of climate change and effectiveness of risk mitigation measures compared to if we used purely data-driven models48,49.\n\nSpecifically, we extend the power grid outage and recovery model developed by refs. 12,19 to simulate TC impact on the electric power system in Louisiana (see the methodology diagram in Supplementary Fig.\u00a04, power topology in Louisiana in Supplementary Figs.\u00a05, 6, and electric utility service areas in Louisiana in Louisiana Public Service Commission50). The power grid failure model first applies probabilistic fragility functions to estimate the damage states of five main vulnerable component types of the power network: transmission substations, transmission lines, distribution nodes, distribution lines, and local distribution circuits. Component failures alter the power grid topology and may separate the power grid into disconnected sub-grids. A direct-current (DC) flow simulation is then performed to capture the power availability in each sub-grid (similar to approaches in refs. 51,52,53,54). The power system is open and connects with systems outside the study area via transmission lines; the performance of the power grid outside the study area is assumed to be under normal operation.\n\nThe fragility curves in refs. 12,19 only considered the wind damage. Here we extend the fragility functions to consider the effects of coastal floods and rainfall. For example, the probability of failure of a substation given specific wind, rainfall, and coastal flood levels is estimated based on a log-normal fragility function as Eq. (1):\n\nwhere hazard (H/h) is considered as a linear combination of wind speed (w), rainfall amount (r), and flood condition (f, flooded or not; Boolean variable) with two parameters \u03b1 and \u03b2. With the shape (\u03c3i) and location (\u03bci) parameters, the log-normal distribution describes the probability of potential damage (D) in each of four states (di), i.e., i\u2009=\u2009{low, moderate, severe, complete} damage. Fragility function refers to the latent distribution of a component\u2019s ability to withstand outer forces (hazard). Some components may not withstand any force at all, while others can withstand very large outer force. Given a certain outer force, the probability of damage to the component is equal to the integral of fragility from 0 to that force level, i.e., the probability that the strength of the component is lower than outer force. The fragility functions for other components (support structures, distribution nodes, poles, conductors, and circuits) are similarly modeled with exponential, logistic, or uniform distributions. These fragility functions are similar to those in refs. 12,19 except that the effects of rainfall and flood are incorporated. The parameters are estimated by the Markov chain Monte Carlo (MCMC) method to minimize the mean squared error between simulated and observed county-level power outages1 under Hurricanes Laura and Ida with equal weight.\n\nThe recovery model, developed based on emergency response plans and operational data, applies estimated recovery resources based on a priority-oriented strategy to repair damaged transmission substations, transmission lines, and critical facilities vital to public safety, health, and welfare before local distribution networks12,19. Debris should be removed before utilities become able to reinstate the power system. This debris-cleaning time is sampled from a uniform distribution between 48 and 72\u2009h (estimated from utility reports)2. We also account for that, without structural failure of the distribution system, residents may turn on the main power switch themselves 24\u201348\u2009h after being flooded55.\n\nThere is a chance that TCs will make landfall in sequence, and the second TC comes before the damage caused by the first TC is fully recovered56. We account for this temporal compounding effect in our power system outage and recovery analysis. For each sampled sequential hazard time series, the initial state of the power system when a TC arrives is set based on the condition of the restoration state from the previous TC. If the power system is indeed not fully recovered from the previous TC impact, the emergency response plans following the second TC are also adjusted considering the recovery process for the first TC. Specifically, the response plans will re-evaluate and prioritize the restoration tasks and redirect the repair efforts based on this updated priority list.\n\nThe power grid outage and recovery models were calibrated (to determine the model parameters) for the study area using observed power outage data for Hurricane Ida and Laura using simulated wind and observed rainfall57 and flood58. The same wind field modeling method applied to the synthetic storms is used for these two historical storms with storm characteristics (i.e., track, intensity, and size) taken from the extended best track data59.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The transmission network data is from Homeland Infrastructure Foundation Level Database (HIFLD). The distribution network data is from Louisiana Public Service Commission (LPSC). The hurricane hazard data were obtained from Ref. 15. The generated power system failure statistics are deposited to Github and Zenodo (https://doi.org/10.5281/zenodo.15012708). Source Data for figures are provided with this paper.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The codes for simulating power system failures are deposited to Github and Zenodo (https://doi.org/10.5281/zenodo.15012708).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Hurricane Response Record, Office of Cybersecurity, Energy Security, and Emergency Response, U.S. Department of Energy (2021).\n\nHurricane Ida Power Restoration, Utility Report, Entergy Link: https://www.entergynewsroom.com/article/entergy-system-hurricane-ida-update-9-2-21-9-m/ (Last visited: 11/25/2024) (2021).\n\nNicholas Bogel-Burroughs and Katy Reckdahl. 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Ouyang was supported by the National Natural Science Foundation of China (72371109,71821001), Strategic study project of Chinese Academy of Engineering (2022-JB-02), and Project of Interdisciplinary Research Support Program in HUST (2023-32). We thank the IPCC AR6 Sea Level projection team for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea Level Change Team for developing and hosting the IPCC AR6 Sea Level Projection Tool.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA\n\nKairui Feng,\u00a0Ning Lin,\u00a0Avantika Gori\u00a0&\u00a0Dazhi Xi\n\nThe National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, China\n\nKairui Feng\n\nShanghai Innovation Institute, Shanghai, China\n\nKairui Feng\n\nDepartment of Civil and Environmental Engineering, Rice University, Houston, TX, USA\n\nAvantika Gori\n\nDepartment of Earth Sciences, The University of Hong Kong, Hong Kong, China\n\nDazhi Xi\n\nSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China\n\nMin Ouyang\n\nSchool of Public and International Affairs, Princeton University, Princeton, NJ, USA\n\nMichael Oppenheimer\n\nDepartment of Geosciences, Princeton University, Princeton, NJ, USA\n\nMichael Oppenheimer\n\nHigh Meadows Environmental Institute, Princeton University, Princeton, NJ, USA\n\nMichael Oppenheimer\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nThe conceptualization of the project was carried out by K.F., N.L., M.O.Y., and M.O.P. The methodology was developed by K.F., N.L., A.G., D.X., and M.O.Y. The original draft of the writing was authored by K.F. and N.L., and K.F., N.L., A.G., D.X., M.O.Y., and M.O.P. reviewed and edited the manuscript.\n\nCorrespondence to\n Ning Lin.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Nadia Bloemendaal and the other anonymous reviewer(s) for their contribution to the peer review of this work. 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interface between metal and oxide in Zr-ZrO2 nanoparticles", + "journal": "Nature Communications", + "published": "02 September 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_MOESM4_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_MOESM5_ESM.mp4" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_MOESM6_ESM.mp4" + }, + { + "label": "Supplementary Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_MOESM7_ESM.mp4" + }, + { + "label": "Supplementary Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_MOESM8_ESM.mp4" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_MOESM9_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-52026-w#ref-CR57", + "/articles/s41467-024-52026-w#Sec18" + ], + "code": [ + "/articles/s41467-024-52026-w#ref-CR57" + ], + "subject": [ + "Nanoscale materials", + "Structural properties" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3972857/v1.pdf?c=1725361732000", + "research_square_link": "https://www.researchsquare.com//article/rs-3972857/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-52026-w.pdf", + "preprint_posted": "12 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Metal-oxide interfaces with poor coherency have unique properties comparing to the bulk materials and offer broad applications in the fields of heterogeneous catalysis, battery, and electronics. However, current understanding of the three-dimensional (3D) atomic metal-oxide interfaces remains limited because of their inherent structural complexity and limitations of conventional two-dimensional imaging techniques. Here, we determine the 3D atomic structure of metal-oxide interfaces in zirconium-zirconia nanoparticles using atomic-resolution electron tomography. We quantitatively analyze the atomic concentration and the degree of oxidation, and find the coherency and translational symmetry of the interfaces are broken. Moreover, we observe porous structures such as Zr vacancies and nano-pores and investigate their distribution. Our findings provide a clear 3D atomic picture of metal-oxide interface with direct experimental evidence. We anticipate this work could encourage future studies on fundamental problems of oxides such as interfacial structures in semiconductor and atomic motion during oxidation process.Physical sciences/Nanoscience and technology/Nanoscale materials/NanoparticlesPhysical sciences/Materials science/Nanoscale materials/Nanoparticles", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "ZrZrO2interfaceNatureSupplementaryInformation20240219final.pdfMovieS1.mp4SUPPLEMENTARY VIDEO 1MovieS2.mp4SUPPLEMENTARY VIDEO 2MovieS3.mp4SUPPLEMENTARY VIDEO 3MovieS4.mp4SUPPLEMENTARY VIDEO 4", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Metal-oxide interfaces with poor coherency have specific properties comparing to bulk materials and offer broad applications in heterogeneous catalysis, battery, and electronics. However, current understanding of the three-dimensional (3D) atomic metal-oxide interfaces remains limited because of their inherent structural complexity and the limitations of conventional two-dimensional imaging techniques. Here, we determine the 3D atomic structure of metal-oxide interfaces in zirconium-zirconia nanoparticles using atomic-resolution electron tomography. We quantitatively analyze the atomic concentration and the degree of oxidation, and find the coherency and translational symmetry of the interfaces are broken. Atoms at the interface have low structural ordering, low coordination, and elongated bond length. Moreover, we observe porous structures such as Zr vacancies and nano-pores, and investigate their distribution. Our findings provide a clear 3D atomic picture of metal-oxide interface with direct experimental evidence. We anticipate this work could encourage future studies on fundamental problems of oxides, such as interfacial structures in semiconductor and atomic motion during oxidation process.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Most metals spontaneously form an oxidation layer on their surfaces. The metal-oxide interface plays a critical role in broad applications ranging from heterocatalysis1,2, batteries3,4, and electronics5,6. The thermodynamics and kinetics of oxidation processes have been extensively studied over the years7,8,9. Plenty of research has been focused on the metal\u2019s work function10, the transport of metal or oxygen species11,12,13, and the rate of oxidation14. A number of theories have been proposed in these studies to understand the oxidation behavior. For example, the Kirkendall effect is used to explain the formation of oxidized pores15,16,17; Wagner et al. proposed the law of oxidation kinetics in which the oxidation rate is controlled by the transport of ions under electrochemical potential gradient based on several assumptions12,14,18. However, owing to the lack of direct observation of the three-dimensional (3D) metal-oxide interface at nanoscale or atomic scale, most of the theories are based on the ideal atomistic interface model. Properties of the interface, including catalytic activity, phonon dispersion, and electron transportation, are strongly related to the local atomic arrangements of the metal-oxide interface, such as coordination numbers and atomic bond lengths19,20,21,22,23,24. It is, therefore, essential to determine the 3D atomic arrangements and understand the detailed oxidation structure of metal.\n\nWith the recent development in aberration-corrected transmission electron microscopy (TEM), local structures of metal-oxide interfaces such as Cu-Cu2O25,26, Ag-Ag2O27, and Ni-NiO28 have been studied at nanoscale or atomic scale; several of them were probed in-situ by atomic-resolution imaging and theoretical simulation25,26,27,28,29. Semi-coherent and incoherent interfaces between metal and oxide have been observed at sub-\u00c5 resolution from two-dimensional (2D) projections26. Luo et al. discovered the periodic dislocation in Cu-Cu2O semi-coherent interface, suggesting the mechanism of strain release by defects between metal and oxide25. Zhu et al. tracked the formation of voids in Ni-NiO nanoparticles at nanoscale, identifying a two-stage oxidation mechanism including early-stage nucleation and then the Wagner oxidation28. However, since the oxidized interfaces are usually non-epitaxial and inherently disordered due to the lattice mismatch, the atomic arrangements between some metals and their oxidation layer cannot be clearly elucidated using high-resolution TEM or crystallography. Conventional 3D characterization methods such as atom probe tomography30,31, electron tomography28,32,33,34,35, and depth sectioning36,37 have been used to study the 3D morphological structures of the metal-oxide interfaces, and these techniques could overcome the limitation of single images, which only provide the projected information of the 3D structures in 2D. However, the resolution of these techniques is limited to nanometer scale. Thus, determining the 3D atomic arrangements of the metal-oxide interface remains a major challenge. Although it remains notoriously difficult to image and identify each of the oxygen atoms of oxides in 3D, especially in annular dark-field scanning transmission electron microscopy (ADF-STEM) mode, atomic-resolution electron tomography (AET), which is an effective tool for determining the 3D atomic structure of nanomaterials32,33,34,35, can in principle resolve the positions of heavy metal atoms in oxides and therefore give important structural information on this long-standing problem.\n\nHere, using Zr-ZrO2 as a model system, we determined the 3D atomic structure of the metal-oxide interface using AET. We chose Zr-ZrO2 for two reasons. First, Zr can form oxide spontaneously in air, and the oxidation process is moderate10; second, the Zr-O bonding is strong among all the common metal oxides, and ZrO2 has excellent chemical stability. The Zr-ZrO2 interface can maintain its atomic structures after electron irradiation at a dose rate of 6\u2009\u00d7\u2009105\u2009e \u00c5\u22122, which is essential for electron tomography experiments. By determining all the Zr atomic positions in Zr-ZrO2 nanoparticles (NPs), we obtained the 3D atomic structure of a partially oxidized Zr NP; it has an uncommon face-centered cubic (FCC) Zr metal crystal nucleus as the core and amorphous/crystalline ZrO2 as the shell. The degree of oxidation decreases while Zr packing density increases from the oxide surface to the metal core. Instead of forming a coherent interface, most of the atoms at the Zr-ZrO2 interfaces connect with each other semi-coherently or incoherently. We discovered a bidirectional distortion including bending and twisting at the semi-coherent metal-oxide interface. Moreover, we identified numbers of voids in the oxides including Zr vacancies, nano-pores, and large pores; the oxidation process is related to the distribution of the voids. These findings expand our understanding of the atomic structures of metal-oxide interfaces with poor coherency, encourage future studies on oxidation process at 3D atomic resolution, and further inspire the designing and modeling of atomic metal-oxide interface in surface engineering, heterogeneous catalysis, and semiconductors.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "NPs made of different monatomic metals with both disordered and crystalline structures can be achieved using fast-cooling vitrification process38,39. Zr NPs were synthesized using pulse laser ablation of pure Zr target (purity >\u200999.95%) in ethanol (Methods). This technique can provide a very high temperature to melt and vaporize the metal target and fast cooling to yield nanoparticles which have a large variety of morphologies38 including amorphous, partially amorphous, and crystalline metal nanoparticles (Supplementary Fig.\u00a01a). By naturally oxidizing the freshly prepared Zr NPs in air, we obtained Zr-ZrO2 NPs at different stages of the oxidation process. Some of the Zr-ZrO2 NPs have an oxidized shell and a metal core (Supplementary Fig.\u00a01c,e). To confirm the oxidation, we have employed high-resolution ADF-STEM and energy dispersive spectroscopy (EDS) to characterize the Zr-ZrO2 NPs (Supplementary Figs.\u00a01, 2); it is notable that the edges of the NPs have a high degree of oxidation since the oxygen signal of EDS at the edge are stronger while ADF-STEM intensities are weaker. The line profiles of Zr elemental intensity are particularly high at the core of the nanoparticles, also indicating the nanoparticles have a metallic Zr core and ZrO2 shell.\n\nIn this study, we selected Zr-ZrO2 particles with a suitable size (\u2248 10\u2009nm) and various of metal-oxide interfaces to perform AET experiments. We resolved the 3D atomic structures of all Zr atoms in several Zr-ZrO2 NPs using AET. In short, tomography tilt series (Supplementary Figs.\u00a03\u20138) were acquired from three Zr-ZrO2 NPs at different stages of the oxidation process using an aberration-corrected TEM in ADF-STEM mode (Supplementary Table\u00a01). After imaging processing including denoising, background subtracting, and alignment (Methods), the tilt series were reconstructed using the Real Space Iterative Reconstruction (RESIRE) algorithm40. Supplementary Fig.\u00a09 and Supplementary Movie\u00a01 show the volume rendering of raw reconstructions of these NPs. The 3D atomic coordinates of all Zr were traced from the computed reconstructions (Methods). We chose a partially oxidized Zr-ZrO2 (named Zr1) as our main interest to elucidate the metal-oxide interfaces. The other two particles are fully oxidized without obvious metal core (named as Zr2 and Zr3; Supplementary Fig.\u00a010).\n\nSince the image contrast of atoms in ADF-STEM mode is sensitive to the atomic number, the oxygen atoms are too light, and our current imaging parameters are not sufficient enough to detect oxygen (Supplementary Tables\u00a01, 2). To verify the contrast contribution of oxygen in 2D ADF-STEM images, we have constructed a slab model of cubic ZrO2 which is 8-nm-thick (similar thickness comparing to Zr NPs); and then performed the multi-slice simulation along the [100] zone axis. The contrasts of oxygen columns in the simulated images are very low, making the column of oxygen atoms barely seen (Supplementary Fig.\u00a011). To further validate our reconstruction and verify the contrast contribution of oxygen in 3D, we have performed multi-slice simulation of the tilt series and computed 3D reconstruction to show the oxygen contrast in 3D. The same number of projections have been generated from the experimental model with filling oxygens (Methods). By performing the same image processing, reconstruction, and atom tracing procedures, we obtained a multi-slice atomic model. We estimated that 97.2% of atoms were identified correctly with a 3D precision of \u2248 28\u2009pm by comparing the experimental model with the multi-slice one (Supplementary Fig.\u00a012). From either 3D reconstructed volume or traced atomic positions, we cannot distinguish the oxygen atoms in ZrO2 regions due to the low intensities of the oxygen atoms (Supplementary Figs.\u00a013, 14). These results reveal the low contrast of oxygen atoms, both in 2D ADF-STEM images and 3D AET reconstruction.\n\nFigure\u00a01a and Supplementary Movie\u00a02 show the experimental 3D atomic model of Zr1, with ordered crystalline grains and disordered structures in the particle. We calculated the normalized bond orientational order (BOO) parameters for all the atoms to quantify the disorder (Fig.\u00a01b and Methods); about 32% of all the Zr atoms are disordered. The particle has complicated phases, composed of a central metal grain, crystalline oxide grains (c-ZrO2), and an amorphous oxide phase (a-ZrO2) (Fig.\u00a01d and Supplementary Movie\u00a03). We calculated the Zr-Zr partial pair distribution functions (PDFs) of the atoms in c-ZrO2 and a-ZrO2 separately (Methods). The c-ZrO2 shows a well-matched cubic phase ZrO2 structure instead of monoclinic phase ZrO2 in both Zr1 and Zr2 (Fig.\u00a01c). The PDFs of the a-ZrO2 atoms in all three NPs exhibit similar shape; and the first peak position is located at 3.45\u2009\u00c5, which is close to the first peak position of monoclinic phase ZrO2 (Fig.\u00a01c). All the positions of main peaks and valleys in our Zr-Zr PDFs obtained from the atomic coordinates of our a-ZrO2 structures agree with those obtained from synchrotron X-ray diffraction41. The most populated Zr-Zr bond lengths in c-ZrO2 and a-ZrO2 are 3.6\u2009\u00c5 and 3.45\u2009\u00c5, respectively (Supplementary Fig.\u00a015). Interestingly, there is a small Zr metal core inside Zr1, confirmed by polyhedron template matching42 and atomic concentration analysis (Methods). Figure\u00a01e shows the atomic structure of the pure Zr metal core viewing from \u3008110\u3009 direction. The metal core has a distorted FCC structure with an averaged Zr-Zr bond length of 3.3\u2009\u00c5, slightly longer than the standard value in Zr metal (3.2\u2009\u00c5). These observations are different from the bulk behavior, where Zr typically forms hexagonal close-packed (HCP) or body-centered cubic (BCC) structures and usually forms monoclinic phase ZrO2 after natural oxidation43. This discrepancy highlights the distinctive behavior of materials at the nanoscale.\n\na Experimental 3D atomic model of the Zr1 NP with a-ZrO2 in blue, c-ZrO2 in green and metal core in red. b Normalized BOO parameters of all atoms. The red dashed curve is a criterion to distinguish the disordered atoms (32% in total, atoms below the curve) and ordered atoms (68% in total, atoms above the curve). The standard BCC, HCP and FCC parameters are marked as black dots for reference. c, d Zr-Zr PDFs of the c-ZrO2 (c) and a-ZrO2 (d), with Zr1 in red, Zr2 in blue and Zr3 in black. The gray peaks show the peak positions of the standard PDF of cubic phase ZrO2 (c) and monoclinic phase ZrO2 (d) for comparison. e The NP consists of a-ZrO2, c-ZrO2 and a metal core grain. f Magnified atomic structure of the pure Zr metal core viewing from \u3008110\u3009 direction. Source data are provided as a Source Data file.\n\nTo compare the local atomic packing density of Zr in all phases, we obtained the compactness of the NP by determining the Zr atomic concentration (\u03c1N) of all the regions present in Zr1 NP (Methods). Figure\u00a02a shows the 3D \u03c1N distribution of Zr1. The low packing density regions are not related to any voids in the NP as we exclude all the voids from consideration when performing calculation (Methods). The averaged \u03c1N of the metal core is 3.85\u2009\u00d7\u200910\u22122\u2009\u00c5\u22123 (Fig.\u00a02c), close to the \u03c1N of ideal close-packed metallic Zr (3.9\u2009\u00d7\u200910\u22122\u2009\u00c5\u22123). The \u03c1N of oxides (both c-ZrO2 and a-ZrO2 phases) are significantly lower than that of pure metal, being 2.92\u2009\u00d7\u200910\u22122\u2009\u00c5\u22123 and 2.89\u2009\u00d7\u200910\u22122\u2009\u00c5\u22123, respectively. They are comparable to the \u03c1N of ideal\u00a0cubic phase ZrO2 (3.0\u2009\u00d7\u200910\u22122\u2009\u00c5\u22123). We also observed 3D local \u03c1N heterogeneity in the oxides, particularly distributed around the metal-oxide interfaces. Figure\u00a02d shows the \u03c1N distribution as a function of the distance from the surface of the metal core (metal to c-ZrO2). The gradual decrease in \u03c1N suggests the metal-oxide interfaces are atomically smooth. The packing density gradient is attributed to the gradual change of the degree of oxidation of the Zr metal. Our PDFs and Zr-Zr bond length analysis suggest that c-ZrO2 is the cubic phase, and a-ZrO2 mainly forms the tetrahedral structure locally41; oxygen should be located in tetrahedral sites in both phases (Supplementary Fig.\u00a016). Next, we quantified the degree of oxidation by geometrically filling oxygen into the tetrahedral sites based on a reasonable Zr-specific knowledge (Methods). Since EDS measurements in other similar Zr-ZrO2 NPs suggest the oxide grain is almost fully oxidized, which is confirmed by our atomic concentration analysis, to satisfy the stoichiometric ratio of ZrO2, oxygen can be filled in eight tetrahedral sites (5.5 \u00c53) of the oxide (Supplementary Fig.\u00a016a); but those tetrahedral sites (4.2\u2009\u00c53) in Zr metal are too small (Supplementary Fig.\u00a016b). Figure\u00a02b and Supplementary Movie\u00a04 show the 3D oxidation maps of Zr1. The degree of oxidation distributions in all the phases are shown in Fig.\u00a02e and Supplementary Fig.\u00a017, where the degree of oxidation increases along with the decrease of Zr \u03c1N. The c-ZrO2 and a-ZrO2 grains are almost fully oxidized in their surfaces; and they become less oxidized as closer to their interfaces with the central Zr core (Fig.\u00a02e). The experimentally measured tetrahedral sites in the central Zr core are too small to be filled with oxygen, confirming the core is barely oxidized. There could be some heterogeneity in the degree of oxidation in some of the oxide grains, which makes the oxidation maps non-uniform. To further validate our oxygen filling method and partial oxidation, we have performed integrated differential phase-contrast STEM (iDPC-STEM) imaging on a similar nanoparticle with a known structure, the cubic ZrO2 phase (Methods, Supplementary Fig.\u00a018a, b). This technique provides improved sensitivity to light elements such as oxygens44. The line profiles of the iDPC image show the contrasts of different oxygen columns are different, indicating partial oxidation and non-uniform oxygen filling in this cubic ZrO2 grain (Supplementary Fig.\u00a018c). A cubic cutout of the 3D oxidation maps reveals that the degree of oxidation is strongly correlated to the atomic packing density of Zr; a highly oxidized region always has a lower Zr \u03c1N (Fig.\u00a02f). It\u2019s notable that some other Zr NPs are completely oxidized to cubic phase ZrO2 and/or amorphous ZrO2 (Supplementary Fig.\u00a019) even from the same batch of oxidation. These results indicate the oxidation process is kinetics controlled, in which we observed several intermediate states of oxidized Zr-ZrO2.\n\na, b Atomic concentration \u03c1N distribution (a) and the degree of oxidation (b) of all the atoms in Zr1. Each slice has a thickness of 5.3\u2009\u00c5. To increase the signal-to-noise ratio, we convolved the degree of oxidation with a 2-\u00c5-wide 3D Gaussian kernel, but this also reduces the 3D spatial resolution of oxidation map to \u2248 4\u2009\u00c5. c Distribution of \u03c1N in c-ZrO2 (blue), a-ZrO2 (red) and metal core (yellow) phase. c-ZrO2 has a slightly larger \u03c1N distribution by 1% (dashed lines) than a-ZrO2. The inset figure shows the magnified histogram of metal core. d The \u03c1N distribution of metal/c-ZrO2 as a function of the distance from the surface of metal core. The dashed lines show the standard \u03c1N in Zr metal (red) and cubic phase ZrO2 (blue). e A slice through the Zr1 NP as the red rectangle marked in (b), showing the degree of oxidation at different regions. f 3D surface rendering of local degree of oxidation and corresponding atomic concentration \u03c1N, showing the strong correlation. The cutout is 25\u2009\u00d7\u200925\u2009\u00d7\u200925 \u00c53. Source data are provided as a Source Data file.\n\nCoherency of the metal-oxide interface affects many properties, including strain, diffusion, and band structure26,45,46. It is difficult to identify the atomic arrangement of semi-coherent or incoherent metal-oxide interfaces from 2D projected images. To probe the 3D structure of metal-oxide interface at atomic level, we focus on the atomic Zr-Zr bonding of the interfaces with a range of \u2248 10\u2009\u00c5 based on the packing density between metal core and oxide phases (Fig.\u00a02d). Figure\u00a03a presents the 3D surface renderings of three major phase, showing the contour of metal core, c-ZrO2, and a-ZrO2 phase. Three slices with four atomic layers in thickness through the metal core show the Zr-Zr bonding of metal-oxide interfaces (Fig.\u00a03b\u2013d). We found several types of interfaces, including semi-coherent and incoherent interfaces between metal and c-ZrO2, and incoherent interfaces between metal and a-ZrO2. The white rectangles in Fig.\u00a03b\u2013d highlight three cutouts from the atomic structures of a semi-coherent (Fig.\u00a03e) and an incoherent interface (Fig.\u00a03k) between metal and c-ZrO2, and an incoherent interface (Fig.\u00a03l) between metal and a-ZrO2, respectively. The layer-by-layer slices of the raw reconstruction volume of these interfaces are shown in Supplementary Figs.\u00a020\u201322, indicating the consistency between the raw reconstruction and the traced atomic model.\n\na\u2013d 3D surface renderings of three major phases, showing the contour (a) of metal core (red), c-ZrO2 (green) and a-ZrO2 (blue) of Zr1. Three planes go through the Zr1 in different directions. The sliced atomic models (4-atom-layers thick) highlights three different types of interfaces, i.e., metal/c-ZrO2 semicoherent interface between (b; in blue frame), metal/c-ZrO2 incoherent interface (c; in green frame), and metal/a-ZrO2 incoherent interface (d; in orange frame). e\u2013g Experimental semicoherent interface structures specified by the rectangle region in (b). e The semicoherent interface viewing from metal [\\(1\\bar{1}0\\)] direction. There is a bending of \u2248 11\u00b0 between metal and interfacial layers in metal [112] direction (angle between red line and blue line), and a bending of \u2248 8\u00b0 between interfacial layers and c-ZrO2 in oxide [110] direction (angle in blue line and ivory line). The coordination tripods in red and ivory boxes shows the spatial crystal orientation of metal and oxide, respectively. f The semicoherent interface viewing from metal [101] direction (by rotating the cutout in (e) 120\u00b0 counter clockwise), showing a twisting of \u2248 4\u00b0 in metal [\\(1\\bar{1}0\\)] direction (angle between red line and ivory line). g One atomic plane extracted from the semicoherent interface (the highlighted area in red in e), viewing from metal [\\(\\bar{1}\\bar{1}1\\)] direction. In this direction, the oxide shows the (002) plane. The color of the atomic bonding shows the Zr-Zr bond length. h The ideal model of an interface structure between ideal FCC Zr metal and ideal cubic ZrO2, showing a 15\u00b0 of wedge if no bending exists. To minimize the interfacial energy and maintain the coherency, a bending of 15\u00b0 (i) is needed. The structure changed from metal to oxide shows in (j). The oxygen atoms are colored in red. k, l Incoherent interface structures specified by the rectangle region in (c, d), showing the metal/c-ZrO2 (k) and metal/a-ZrO2 interface (l). In (e\u2013l), the metal atoms, interface atoms and oxide atoms are colored in deep red, blue and ivory, respectively. The Zr atoms are bonded with their first-nearest Zr neighbors and linked with lines (Methods).\n\nIn the semi-coherent interface, four layers of metal Zr atoms (marked in deep red) from the metal [\\(1\\bar{1}0\\)] direction correspond to four layers of Zr atoms (marked as ivory) in the oxide (Fig.\u00a03e). To see the atomic connections in a single corresponding layer, one plane in the cutout is extracted and viewed from [\\(\\bar{1}\\bar{1}1\\)] direction of metal (Fig.\u00a03g, Supplementary Fig.\u00a023). Metal (\\(\\bar{1}\\bar{1}1\\)) plane is almost coplanar with oxide (002) plane; and the interface is about two atomic layers in thickness (blue atoms in Fig.\u00a03g) and primarily connects metal (111) face with oxide (\\(11\\bar{1}\\)) face. The Zr-Zr bond lengths increase from the metal side (\u2248 3.3\u2009\u00c5) to the oxide side (\u2248 3.6\u2009\u00c5). The interface has a long Zr-Zr distance, which is due to partial oxidation. Moreover, there is an angular mismatch of \u2248 11\u00b0 between metal planes and the interfacial planes in metal [112] direction (oxide [110] direction), making the interface bending towards the oxide (Fig.\u00a03e). To better illustrate the origin of the angular mismatch, we build an ideal model of Zr crystal grain and connect it to a cubic phase ZrO2 from the same crystal orientation (Fig.\u00a03h). Since the metal and oxide grains have different crystal orientations, there is a 15\u00b0 of wedge through direct connection (angular mismatch in Fig.\u00a03h). To minimize the interfacial energy while maintaining the coherency, the oxide has to adopt a bending of 15\u00b0 to fill the wedge (Fig.\u00a03i). At the interface, the maximum numbers of filling oxygen are four instead of eight (Fig.\u00a03j), which means the interface is partially oxidized and the maximum stoichiometric ratio is ZrO. Besides, it is notable that there is a gap angle of \u2248 8\u00b0 between the Zr (100) planes in the oxide and those in the interface (Fig.\u00a03e), alleviating the overall strain in the whole NPs. By rotating this cutout 120\u00b0 counterclockwise, we observed another angular mismatch of \u2248 4\u00b0 between the metal (\\(\\bar{1}\\bar{1}1\\)) planes and oxide (002) planes in metal [\\(1\\bar{1}0\\)] direction (oxide [\\(1\\bar{1}0\\)] direction; Fig.\u00a03f), which is perpendicular to metal [112] direction. It is considerable to have an angular mismatch when two adjacent crystal grains have different crystal plane spacing. The (111) spacing of Zr metal is 2.694\u2009\u00c5, while the (200) spacing of Zr oxide is 2.546\u2009\u00c5. To compensate for the spacing mismatch and to maintain the coherency, a certain degree (\u2248 4\u00b0) of twisting between metal and oxide is preferred (Supplementary Fig.\u00a024)47.\n\nMost of the metal-oxide interfaces are incoherent in the whole particle. Figure\u00a03k, l shows the incoherent interfaces of metal/c-ZrO2 and metal/a-ZrO2, respectively. Although the atomic bonding becomes more distorted and disordered in the incoherent interfaces between metal and c-ZrO2, most of the metal core {111} faces still correspond to oxide {111} faces (Fig.\u00a03k and Supplementary Fig.\u00a025). Zr atoms form an incoherent boundary with lower coordination number and longer bond length than crystalline region (Supplementary Figs.\u00a026, 27). Those metal-oxide incoherent interfaces introduce a number of defects which are distributed around the metal core. Many Zr defects are found in those incoherent interfaces. These observations indicate that when a semi-coherent interface forms, a significant amount of strain could occur due to lattice and/or angular mismatch during oxidation. Once the strain caused by bending or twisting is too large, some of the semi-coherent interfaces could possibly turn to disordered structures through amorphization48, where the coherency of the interface is completely broken.\n\nPorous structures of the oxide film formed on the surface of metal are usually associated with metal corrosion11,17,27,28,29. We observed numbers of porous structures in the Zr-ZrO2 particles. Figure\u00a04a shows a 2.4\u2009\u00c5-thick slice from the reconstruction volume of Zr1; in which a significant number of voids, such as Zr vacancies (triangle), nano-pores (rectangle), and the largest pore (circle), are observed in the particle. From the 3D intensity and surface renderings of three consecutive atomic layers, a single Zr vacancy defect can be clearly located (Supplementary Fig.\u00a028). To determine all the voids and evaluate their occupied volume, we employed Voronoi analysis by measuring the distance of Voronoi vertices to atoms (Methods). Figure\u00a04b shows the histogram of volume distribution of all the voids, which we define as Zr vacancies, nano-pores, and a significantly large nanoscale pore throughout the particle. The porosity is 17% and 14% in Zr1 and Zr2, respectively. Figure\u00a04c and Supplementary Movie\u00a05 show the distribution of all Zr vacancies in Zr1. No vacancy is found in the metal core. More than 110 vacancies are distributed in the particle, and all the Zr vacancies contribute 8.4% of the total porosity. Slightly more vacancies are found in a-ZrO2 than in c-ZrO2 (Fig.\u00a04d). We plot the density of Zr vacancies from the boundary of the metal core to the surface of the particle (Fig.\u00a04e). Most of the vacancies are distributed in the range of 15\u2009\u00c5 between metal core and oxide, which corresponds to the region where Zr packing density exponentially decreases (Fig.\u00a02d). It\u2019s notable that we exclude the vacancies from calculating the Zr packing density, the lower \u03c1N of interface is independent with the rich vacancies surrounding the metal core. We found 41 nano-pores in the volume range between 125 and 4500 \u00c53. They are mostly irregular and with a relatively large length-to-radius ratio. Figure\u00a04f and Supplementary Movie\u00a05 show the distribution of all nano-pores; they mostly sit at the boundaries between c-ZrO2 and a-ZrO2 regions. The largest pore is more than 34000 \u00c53 and penetrates throughout the whole particle, providing a possible pathway for further oxidation (Fig.\u00a04g and Supplementary Movie\u00a05). This pore predominantly sits in the a-ZrO2 regions, connecting and separating all three phases; it terminates at the c-ZrO2 region, releasing a large amount of strain. It\u2019s interesting that the Zr-Zr bonds are significantly distorted at the boundary between two c-ZrO2 domains (Fig.\u00a04h, i), where some of the Zr atoms turn to be completely amorphous to release strain. Several nano-pores are coincidentally observed at this region near the small amorphous ZrO2 domain. These findings indicate that when the strain reaches a certain point, possibly higher than the fracture point, the Zr-Zr crystal bonding could turn distorted and amorphous first, and then rupture to form defects to release the strain. It is generally believed that a compact layer of amorphous oxide at the micrometer scale can protect the interior of metal from further oxidation in aluminum49,50. While our results reveal that in zirconium oxide, the amorphous oxide regions are substantially more porous than those in the crystalline regions, the voids would further advance the oxidation of Zr metal. We observed a variety of voids, which are highly related to the structure of the incoherent interface in Zr NPs from the atomic scale to the nanometer scale. The rearrangements of all the atom positions, including distortion, amorphization, and the rupture of bonding, are possibly due to the massive mass transportation during oxidation at the metal-oxide interfaces facilitated by these voids.\n\na A 2.4\u2009\u00c5-thick slice from the reconstructed volume of Zr1, with vacancy (triangle), nano-pores (rectangle) and the largest pore (circle) highlight. b Volume distribution of all the voids. We define the voids with volume no larger than filling two Zr atoms (125\u2009\u00c53, Methods) as vacancies, the voids with volume between 125 and 4500\u2009\u00c53 as nano-pores. We consider the largest pore with volume of 34,000\u2009\u00c53 independently as it touches and separates all three phases. Dashed lines show the boundaries between three types of voids we define. c The surface renderings of all vacancies in c-ZrO2 (in green) and a-ZrO2 (in blue). The outline of whole NP is plotted with gray contour. d, e Statistics of vacancies. The fractions of vacancies in c-ZrO2 and a-ZrO2 show in (d). The radially normalized density distributions of vacancies as a function of distance from the surface core to the surface show in (e). f The surface renderings of all nano-pores in c-ZrO2 (in green), in a-ZrO2 (in blue) and in between c-ZrO2 and a-ZrO2 (in orange). g The surface rendering of the largest pore. The boundary atoms composed of amorphous and crystalline atoms are colored by blue and green, respectively. h One interface between two c-ZrO2 regions with distorted interfacial Zr-Zr bonds, amorphous region and nano-pores. The crystal and amorphous atoms distinguished by BOO analysis are colored as green and blue, respectively. The contour of the nano-pore is colored as orange. i One representative slice shows a 7.8\u2009\u00c5-thick (\u2248 five atomic layers) cross section of the nano-pores and surrounding atoms. Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52026-w/MediaObjects/41467_2024_52026_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In conclusion, we determined the 3D atomic structure of metal-oxide interfaces in Zr-ZrO2 NP using atomic-resolution electron tomography. We quantitatively measured the atomic packing density and the degree of oxidation from our experimental model of the metal-oxide interface. The degree of oxidation from metal to oxide increases gradually, resulting in a diffuse interface between FCC Zr core and ZrO2. The Zr metal connects with its oxide via {111} planes; and the semi-coherent interface has severe distortion, including bending and twisting. The significant stress in the interface is relieved through low coordination and defects. Numbers of defects, including vacancies and nano-pores, together leverage the mass transportation during oxidation. We anticipate that our findings will fulfill the dearth of 3D atomic structure of metal-oxide interface and advance the study of fundamental problems of metal-oxide interfaces such as oxidation kinetics, diffusion, and defect evolution in variety of materials.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The Zr NPs were synthesized in liquid using laser ablation methods. An all-solid-state ultraviolet laser with a wavelength of 355\u2009nm was employed for laser ablation in ethanol, with a pulse width of 7\u2009ps, a max pulse energy of 18\u2009\u03bcJ, a repetition frequency of 800\u2009kHz, and a beam spot diameter of 20\u2009\u03bcm. Before being placed in a clean beaker, the bulk Zr target (purity >\u200999.95%) was washed by acetone (99.5%) and ethanol (99.9%). The dissolved oxygen in liquid is eliminated to the minimization by nitrogen flow for 60\u2009min with a flow rate of 4\u2009L\u2009min\u22121. Then, the Zr target was submerged into ethanol and the laser beam was accurately focused vertically on the surface of the bulk Zr through the ethanol in a closed chamber. The laser ablation was continued for 1\u2009min, and the produced Zr NPs were dispersed using ultrasonic agitation and subsequently isolated via centrifugation to be collected in the ethanol solution. The freshly prepared Zr NPs were then placed in air for one month to obtain a naturally oxidized layer several nanometers thick. The detailed methods of synthesis are described in ref. 39. The final Zr-ZrO2 NPs were drop cast onto 7-nm-thick Si3N4 membranes using an atomizer for TEM experiment.\n\nEDS maps were collected on an aberration-corrected Thermo Fisher Scientific Themis Z microscope at 300\u2009kV in Analytical Instrumentation Center at Peking University. The iDPC-STEM imaging was conducted using aberration-corrected TEM (Thermo Fisher Themis Z) operated at 300\u2009kV. The convergence semi-angle is 15\u2009mrad. The collection angle is 6.3\u201324.7\u2009mrad. The last measured current is 19\u2009pA. The dwell time for each pixel is 10\u2009\u03bcs. The pixel size is 0.088\u2009\u00d7\u20090.088\u2009\u00c52. The iDPC-STEM images were filtered by a Gaussian filter with a sigma of 20 pixels, which is a common image-processing step directly conducted in Velox software.\n\nTomographic tilt series were acquired by an aberration-corrected Thermo Fisher Scientific Titan microscope at 300\u2009kV at the Electron Microscopy Laboratory of Peking University. The acceleration voltage was 300\u2009kV and the imaging mode was ADF-STEM mode. Detailed parameters for data acquisition are listed in Supplementary Table\u00a01. Each tomographic tilt series was acquired at the dose rate of <\u20095\u2009\u00d7\u2009105\u2009e \u00c5\u22122. For each tilt angle, three sequential images with a dwell time of 2 to 4\u2009\u03bcs were acquired and registered using normalized cross-correlation, and then averaged to enhance the signal-to-noise ratio.\n\nAcquired images were drift-corrected, denoised, and aligned before reconstruction. Linear drift from the sample or stage was corrected during the image registration. Three sequential frames of images at each tilt angle were used to estimate and compensate for the drift due to thermal vibration or instability of the stage. We computed the cross-correlation coefficient between the images to estimate the drift direction and drift speed. Then, we recovered the images by interpolating the raw images with drift-corrected pixel positions. This drift correction method has been used in many other image processing35,51. Block-matching and 3D filtering (BM3D) is employed to denoise the images after drift correction52. And then, the background was estimated using the discrete Laplacian function of MATLAB and subtracted. In the direction perpendicular to the tilt axis, the images were aligned by maximizing the cross-correlation between the common lines. Along the tilt axis, the images were aligned using the center-of-mass method.\n\nAfter image processing, the 3D reconstruction was computed from experimental tilt series using RESIRE algorithm40.\n\nAfter reconstruction, atom tracing was performed to determine the 3D atomic coordinates. First, we interpolated reconstructed volume with spline method. All the local maxima in the reconstruction were identified as the rough atomic coordinates. Then, the coordinates were optimized according to the local volume of 1.7\u2009\u00c5\u2009\u00d7\u20091.7\u2009\u00c5\u2009\u00d7\u20091.7\u2009\u00c5 with a polynomial fitting method. To separate the non-atoms from the potential atoms, K-means clustering method was employed based on the integrated intensity of the local volume (1.7\u2009\u00c5\u2009\u00d7\u20091.7\u2009\u00c5\u2009\u00d7\u20091.7\u2009\u00c5). For every potential atom, a minimum distance of 2\u2009\u00c5 to its nearest atom should be satisfied. The value of the cutoff is chosen based on the measurement deviation of atom positions. The standard error (\u03c3) of the deviation is measured by the blurring width of reconstruction volume (that is, the standard deviation of the Gaussian-shaped volume of an atom) at the atom sites. The distribution of \u03c3 is 0.357\u2009\u00b1\u20090.004\u2009\u00c5, as Supplementary Fig.\u00a029 shows. So, the cutoff is determined according to the \u201c3\u03c3\u201d criterion (in statistics) to be 3.2\u2009\u00c5 \u2212 3\u2009\u00d7\u20090.357\u2009\u00c5 \u2248 2\u2009\u00c5. The 3.2\u2009\u00c5 is the standard Zr-Zr bond length in metal. By carefully comparing the individual atom in the potential atomic models with the reconstructed volume, we manually corrected the atomic coordinates of unidentified or misidentified atoms. The manual correction has been routinely applied during the atom tracing and refinement in protein crystallography53. Specifically, we have manually removed unphysically too close atoms, which are 112 (0.73%) atoms for Zr1, 168 (0.75%) atoms for Zr2, and 93 (0.7%) atoms for Zr3.\n\nThe pixel size calibration procedure is shown in Supplementary Fig.\u00a030. A FIB sample of silicon is rotated to the [110] zone axis. Calibration images are taken at the same magnification with the same pixel size as the tomography experiments use (normally \u2248 35.8\u2009pm). To correct any potential image distortion caused by sample drift, we take 20 to 50 frames for each image. The dwell time of one pixel in each frame is only 200 to 500\u2009ns, which significantly alleviates the image stretching due to the drift. For each image, all frames are aligned and then averaged. The brightest spots of the fast Fourier transform (FFT) are precisely located using 2D gaussian fit. The distances of these points to the center of FFT are the countdowns of the lattice spacing of the image in the real space. Thus, we can get the lattice constants measured in this image. By comparing it to the standard lattice constants of Si, we can calibrate the pixel size in this magnification. We take multiple Si images at different scan rotation angles and then determine the mean value of the real pixel size.\n\nWe calculated the PDF curve from experimental 3D atomic model by\n\nwhere N is the total number of atoms; \u03b4 is the Dirac delta function; \u3008\u22c5\u3009 is the notation for expectation value; |rij| is the distance between the i-th atom and the j-th atom. To get a smoother PDF curve, a Gaussian kernel function with a \u03c3 of 1.5\u2009\u00c5 was applied to convolute with original g(r). Finally, the PDF was scaled to approach one at the large pair distances. Using this procedure, we calculate the c-ZrO2 and a-ZrO2 separately in three NPs. From the PDF, we determined the first valley position as 4.5\u2009\u00c5, corresponding to the first-nearest-neighbor shell distance. The distance was used as a cutoff for BOO and alpha shape calculation (see the sections below).\n\nWe calculated the normalized local BOO parameters to distinguish the order and disorder of all the atoms. The normalized BOO parameter is defined as \\(\\sqrt{{\\bar{Q}}_{4}^{2}+{\\bar{Q}}_{6}^{2}}/\\sqrt{{\\bar{Q}}_{4{\\mbox{FCC}}}^{2}+{\\bar{Q}}_{6{\\mbox{FCC}}}^{2}}\\), where the \\({\\bar{Q}}_{4}\\) and \\({\\bar{Q}}_{6}\\) values were computed based on the procedure described in ref. 54, using 4.5\u2009\u00c5 (the first-nearest-neighbor shell distance) as a constraint. The \\({\\bar{Q}}_{4{\\mbox{FCC}}}\\) and \\({\\bar{Q}}_{6{\\mbox{FCC}}}\\) are the reference values of the standard FCC structure. We separated the amorphous part from crystalline part according to the criterion of the normalized BOO less than 0.534.\n\nDelaunay triangulation and Voronoi tessellation were performed to determine the voids. Delaunay triangulation, Voronoi tessellation, and alpha shape were performed with the built-in functions of MATLAB (namely, \u2018delaunayTriangulation\u2019, \u2018voronoin\u2019, and \u2018alphaShape\u2019). The initial spatial region of NP was calculated by alpha shape with \u03b1\u2009=\u20094.5\u2009\u00c5 (the first-nearest-neighbor shell distance). Then, we calculated the space that accommodates at least one Zr atom in the initial particle region with the following steps:\n\nThe initial particle region was divided into tetrahedra by Delaunay triangulation.\n\nWe determined whether a tetrahedron is void. We calculated the radius of the circumscribed sphere for each tetrahedron. The radius represents the maximized sphere that can fit within the NP without intersecting with the center of any atom. Tetrahedra with a radius larger than 3.19\u2009\u00c5 were classified as voids. This criterion of radius was obtained based on the standard cubic phase of ZrO2 with one vacancy.\n\nWe grouped neighboring voids together to form larger voids. Two voids that share a common face are considered neighboring and thus combined into a single, larger void. The volume of these larger voids was calculated by summing the volumes of each void component.\n\nWe classified the voids into vacancies, nano-pores and the largest pore based on volume. We define the voids with volume filling one or two Zr atoms (45\u2013125\u2009\u00c53) as vacancies, the voids with volume between 125 and 4500 \u00c53 as nano-pores. The largest pore with volume of 34000\u2009\u00c53 was considered independently.\n\nFinally, the contours of voids were displayed with Laplacian smoothing conducted by GIBBON55.\n\nTopological bonds were determined based on the Voronoi tessellation. Two atoms are considered topologically bonded if their corresponding Voronoi polygons share a common face. In constructing the Voronoi polygons, we removed those surfaces with area less than 1% of the total area of the polygon surfaces56. Additionally, this bond must also be shorter than 4.5 \u00c5, corresponding to the first-nearest-neighbor shell distance. The atomic concentration was calculated by \u03c1N\u2009=\u20091/V, where V is the volume of Voronoi cell of an atom.\n\nOur current oxygen filling is based on geometric frustration of the tetrahedra sites formed by Zr atoms. One prior assumption for this geometric frustration is as long as the volume of any tetrahedra site is large enough (4.68\u2009\u00c53), it would be filled with oxygen atoms. This assumption is based on a fact that Zr is a highly oxygenophilic metal, it is relatively easy to form Zr-O bonds to oxidize Zr metal. Although it cannot give us the precise information about where all the oxygen atoms exactly are, we believe it could still give us a fairly good estimation of the degree of oxidation based on the reasonable Zr-specific knowledge. The oxidation state was then determined using Delaunay triangulation. First, the distortion of Delaunay tetrahedra was considered. The distortion parameter was calculated by \\(\\delta=({e}_{\\max }/{e}_{{\\mbox{avg}}})-1\\), where \\({e}_{\\max }\\) and \\({e}_{{\\mbox{avg}}}\\) are the maximum and average edge lengths of tetrahedron33. We removed the tetrahedron with a distortion parameter larger than 0.255. Then, the volume of the remaining Delaunay tetrahedra was calculated. If the volume of a tetrahedron is larger than 4.68 \u00c53 (the averaged tetrahedron volume of FCC Zr lattice and c-ZrO2 lattice), an oxygen atom was placed inside. Finally, the degree of oxidation for each Zr atom was calculated by the fraction of its surrounding tetrahedra that accommodated one oxygen atom.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data that support the findings of this study, including the raw tilt series, raw reconstructions, and traced atom positions, are available from Zenodo57 and from the corresponding authors upon request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The codes used in this study are available from Zenodo57 and from the corresponding authors upon request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Lee, S. W. et al. Controlling hot electron flux and catalytic selectivity with nanoscale metal-oxide interfaces. Nat. 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We thank the Electron Microscopy Laboratory at Peking University, Bay Area Centre for Electron Microscopy at Songshan Lake Materials Laboratory and Analytical Instrumentation Center at Peking University for the use of the aberration-corrected electron microscope. This work was supported by the National Natural Science Foundation of China (Grant No. 22172003, 52071222) and Guangdong Major Project of Basic and Applied Basic Research, China (Grant No. 2019B030302010).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Yao Zhang, Zezhou Li, Xing Tong.\n\nBeijing National Laboratory for Molecular Sciences, Center for Integrated Spectroscopy, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China\n\nYao Zhang,\u00a0Zezhou Li,\u00a0Zhiheng Xie,\u00a0Siwei Huang\u00a0&\u00a0Jihan Zhou\n\nSongshan Lake Materials Laboratory, Dongguan, 523808, China\n\nXing Tong,\u00a0Yue-E Zhang,\u00a0Hai-Bo Ke\u00a0&\u00a0Wei-Hua Wang\n\nCollege of Physics, Liaoning University, Shenyang, 110036, China\n\nYue-E Zhang\n\nInstitute of Physics, Chinese Academy of Sciences, Beijing, 100190, China\n\nWei-Hua Wang\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.Z. conceived the idea and directed the study. Z.L., Z.X., and Y.Z. performed TEM experiment and acquired the data. Y.Z. and S.H. performed the imaging processing, reconstructions, and atom tracing. Y.Z., Z.L., S.H. conducted/discussed data analysis under the direction of J.Z.; T.X. and Y.-E.Z. synthesized Zr NPs under the direction of H.-B.K. and W.-H.W.; Y.Z., Z.L., and J.Z. wrote the manuscript. All authors commented on the manuscript.\n\nCorrespondence to\n Hai-Bo Ke or Jihan Zhou.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Robert Hovden, Ryo Ishikawa, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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language model agents for automation of atomic force microscopy", + "pre_title": "Autonomous Microscopy Experiments through Large Language Model Agents", + "journal": "Nature Communications", + "published": "14 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64105-7/MediaObjects/41467_2025_64105_MOESM1_ESM.docx" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64105-7/MediaObjects/41467_2025_64105_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64105-7/MediaObjects/41467_2025_64105_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-64105-7#ref-CR37", + "https://github.com/M3RG-IITD/AILA", + "/articles/s41467-025-64105-7#Sec30" + ], + "code": [ + "/articles/s41467-025-64105-7#ref-CR37", + "https://github.com/M3RG-IITD/AILA" + ], + "subject": [ + "Atomic force microscopy", + "Computer science" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5600537/v1.pdf?c=1760526560000", + "research_square_link": "https://www.researchsquare.com//article/rs-5600537/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-64105-7.pdf", + "preprint_posted": "18 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The emergence of large language models (LLMs) has accelerated the development of self-driving laboratories (SDLs) for materials research. Despite their transformative potential, current SDL implementations rely on rigid, predefined protocols that limit their adaptability to dynamic experimental scenarios across different labs. A significant challenge persists in measuring how effectively AI agents can replicate the adaptive decision-making and experimental intuition of expert scientists. Here, we introduce AILA (Artificially Intelligent Lab Assistant), a framework that automates atomic force microscopy (AFM) through LLM-driven agents. Using AFM as an experimental testbed, we develop AFMBench\u2014a comprehensive evaluation suite that challenges AI agents based on language models like GPT-4o and GPT-3.5 to perform tasks spanning the scientific workflow: from experimental design to results analysis. Our systematic assessment shows that state-of-the-art language models struggle even with basic tasks such as documentation retrieval, leading to a significant decline in performance in multi-agent coordination scenarios.Further, we observe that LLMs exhibit a tendency to not adhere to instructions or even divagate to additional tasks beyond the original request, raising serious concerns regarding safety alignment aspects of AI agents for SDLs. Finally, we demonstrate the application of AILA on increasingly complex experiments open-ended experiments: automated AFM calibration, high-resolution feature detection, and mechanical property measurement. Our findings emphasize the necessity for stringent benchmarking protocols before deploying AI agents as laboratory assistants across scientific disciplines.Physical sciences/Materials science/Techniques and instrumentation/Imaging techniquesPhysical sciences/Materials science/Theory and computation/Computational methodsself-driving laboratoryatomic force microscopybenchmarkingAI agents", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryMaterials.pdfSUPPLEMENTARY INFORMATION", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Large language models (LLMs) are transforming laboratory automation by enabling self-driving laboratories (SDLs) that could accelerate materials research. However, current SDL implementations rely on rigid protocols that fail to capture the adaptability and intuition of expert scientists in dynamic experimental settings. Here, we show that LLM agents can automate atomic force microscopy\u00a0(AFM) through our Artificially Intelligent Lab Assistant (AILA) framework. Further, we develop AFMBench\u2014a comprehensive evaluation suite challenging LLM agents across the complete scientific workflow from experimental design to results analysis. We find that state-of-the-art LLMs struggle with basic tasks and coordination scenarios. Notably, models excelling at materials science question-answering perform poorly in laboratory settings, showing that domain knowledge does not translate to experimental capabilities. Additionally, we observe that LLM agents can deviate from instructions, a phenomenon referred to as\u00a0sleepwalking, raising safety alignment concerns for SDL applications. Our ablations reveal that multi-agent frameworks significantly outperform single-agent approaches, though both remain sensitive to minor changes in instruction formatting or prompting. Finally, we evaluate AILA\u2019s effectiveness in increasingly advanced experiments\u2014AFM calibration, feature detection, mechanical property measurement, graphene layer counting, and indenter detection. These findings establish the necessity for benchmarking and robust safety protocols before deploying LLM agents as autonomous laboratory assistants across scientific disciplines.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Scientific experimentation demands exceptional domain expertise, from exploration or hypothesis-driven experimental design to precision execution and rigorous data analysis. This complexity creates bottlenecks in scientific discovery, particularly as experimental techniques grow increasingly sophisticated. The advent of large language models (LLMs) has propelled the development of self-driving laboratories (SDLs) that integrate diverse information sources for automated planning1 and experimentation. Artificial Intelligence (AI)-agents2,3 and SDLs have already achieved several feats in materials or molecular discovery4,5,6, chemistry research7, and inorganic materials synthesis. The promise of SDLs toward achieving sustainable development8 has resulted in enormous efforts to harness their potential in high-throughput experimentation and discovery9. Efforts to streamline SDLs have resulted in orchestration architectures such as ChemOS10. Additionally, it has been demonstrated that the capability of SDLs can be enhanced by a human-in-the-loop framework that handles disambiguation, thereby enabling better planning and execution11,12. While early demonstrations of LLM-based lab assistants showed promise in chemistry and materials science1,2,3, their operational reliability remains largely uncharacterized beyond specific applications or repetitive use cases with predetermined protocols13,14,15,16,17.\n\nCurrent research predominantly addresses well-documented or predefined protocols and single-objective tasks, failing to capture the intricate interplay between experimental planning, multi-tool coordination, and result interpretation or online intervention10. While recent investigations incorporating planning elements have demonstrated success in achieving specific experimental objectives, they have not systematically evaluated SDL reliability across the broader spectrum of laboratory automation tasks13,14. Although several studies have benchmarked LLMs15,16,17,18,19,20,21,22,23 and vision language models13,14,24,25 through question-answer protocols to assess their potential as materials research co-pilots, a crucial knowledge gap persists: understanding how these AI systems handle novel experimental scenarios and their fundamental limitations.\n\nTo address this challenge, we here introduce AILA (Artificially Intelligent Lab Assistant), an LLM-powered framework augmented with specialized tools. We selected scanning probe microscopy18, specifically atomic force microscopy (AFM), as our experimental testbed, given its inherent complexity and broad applicability in materials research. There have been several efforts to automate microscopy techniques using AI and human-in-the-loop approaches due to their extensive applications in materials characterization26,27,28,29,30,31,32,33,34,35. These efforts focus exclusively on advancing specific operational aspects, such as analysing moving objects or optimizing illumination conditions, with an emphasis on improving individual steps within the broader experimental protocol. In addition to these targeted advancements, Liu et al.36 explores the integration of LLMs\u00a0with Application Programming Interface\u00a0(API) to enhance workflow preparation, instrument operation, and data reproducibility in scanning probe microscopy research. AFM operation demands expertise across multiple domains\u2014from probe calibration to parameter optimization and data interpretation\u2014making it an ideal platform for evaluating AI agents\u2019 ability to manage sophisticated experimental workflows.\n\nUsing AFM as the model system, we probe AILA\u2019s capabilities through AFMBench on five critical aspects of scientific automation: experimental workflow design, multi-tool coordination, decision-making, execution of open-ended experiments, and data analysis. Our systematic evaluation reveals key failure modes and areas requiring enhancement. We demonstrate AILA\u2019s practical utility through five real-world experiments: (1) identification and analysis of an indentation mark on a glass sample, including inference of the indenter type used; (2) detection of graphene flakes on a silicon wafer and determination of the number of graphene layers present; (3) automated microscope calibration; (4) high-resolution imaging of graphene step edges; and (5) load-dependent friction characterization on highly oriented pyrolytic graphite (HOPG).", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "AILA\u2019s architecture prioritizes modularity, enabling seamless integration with diverse experimental and analytical platforms. At its core lies an LLM-powered planner\u2014the framework\u2019s cognitive centre\u2014which orchestrates user interactions and coordinates specialized agents (Fig.\u00a01a). This planner directly takes query from a user and identifies the appropriate agent to handle the task. The agent makes tool calls to carry out the necessary steps to complete the experiment. The agent-to-agent coordination is invoked by two keywords, namely, \u201cNEED HELP\u201d and \u201cFINAL ANSWER\u201d. While the former flag invokes a routing function that transfers the unresolved task to the next appropriate agent, the latter flag results in the termination of the experiment. Thus, AILA employs a dynamic routing, exploiting available agents and tools, for completing the task given by the user (see S2.4 in Supplementary Information for additional details).\n\nAILA a System architecture of the Artificially Intelligent Laboratory Assistant (AILA). Dotted lines indicate adaptive information flow governed by AILA\u2019s decision-making, and solid lines represent deterministic information pathways with predefined routing protocols. b Image of the atomic force microscope (AFM) experimental setup showing key hardware components and control interfaces. c Representative demonstration of AILA\u2019s operation: raw transcript of a user query and AILA\u2019s unedited response sequence, showing the system\u2019s query interpretation, task planning, and execution capabilities.\n\nFor AFM operations, AILA deploys two agents: the AFM Handler Agent (AFM-HA) for experimental control and the Data Handler Agent (DHA) for analysis. The AFM-HA interfaces with a document retrieval system comprising AFM software documentation and a code execution engine that translates Python commands into experimental actions. A Python-based API establishes the hardware-software interface, enabling direct control of the AFM system through vendor-specific protocols (Fig.\u00a01b). The DHA manages image optimization and analysis through dedicated tools: an Image Optimizer that fine-tunes Proportional-integral-derivative (PID) parameters for high-fidelity imaging and an Image Analyzer that extracts targeted features from experimental data. For queries beyond agent capabilities, the planner generates alternative approaches or recommended actions.\n\nIn an AFM experiment, the workflow usually involves two key steps: capturing the image and analysing the results. The imaging part starts with choosing the right cantilever, then setting the imaging parameters. Afterwards, the tip is gently moved toward the sample surface, and the scan is carried out. For every stage, AILA creates a specific Python script and executes it, controlling the AFM instrument in real-time through an API. This connection allows the digital commands to directly translate into physical movements on the instrument. Once the scan is complete, the image is saved automatically and opened for analysis. The technical specifications and implementation details of each module are explained in the Methodology section.\n\nTo demonstrate AILA\u2019s operational workflow, we present a multi-step experiment: acquiring an AFM image of HOPG and extracting its friction and roughness parameters (Fig.\u00a01c). This open-ended task exemplifies real-world complexity, offering multiple solution pathways. Upon receiving the query, AILA dissects it into sequential objectives: image acquisition via AFM-HA followed by DHA-led analysis. AFM-HA retrieves relevant documentation, generates executable code, and captures the image. Following successful acquisition, AILA transitions control to DHA, which directs the Image Analyzer to compute the specified parameters. This orchestrated sequence exemplifies AILA\u2019s core strengths: the ability to parse complex natural language queries, develop strategic workflows, and coordinate multiple agents toward achieving experimental objectives.\n\nAFMBench comprises 100 expertly curated experimental tasks (see S3.1 in Supplementary Information for a few examples of tasks; all the tasks are available in the GitHub repo37), manually designed to rigorously evaluate autonomous AFM operations across multiple dimensions of complexity. Unlike conventional LLM benchmarks or simulation-based evaluations, AFMBench task demands physical execution on AFM hardware, introducing real-world temporal constraints and experimental variability. Analysis of the tasks reveal distinct patterns in resource utilization and operational complexity. In Fig.\u00a02a, tool coordination requirements highlight a systematic preference for sophisticated workflows, with 69% of tasks demanding multi-tool integration, while 31% operate through single-tool protocols. Agent deployment analysis reveals a distribution: 83% of operations utilize single-agent protocols, while 17% require multi-agent coordination\u2014enabling evaluation of both targeted expertise and system-wide integration capabilities.\n\na Pie charts showing the distribution of tool requirements (left, single vs. multiple) and agent requirements (right, single vs. multiple) across benchmark tasks. b Operation complexity categorization showing the proportion of basic versus advanced tasks. c Horizontal bar chart quantifying module engagement frequency across all tasks, demonstrating utilization patterns of each tool and agent. d Venn diagram illustrating the overlap between documentation, analysis, and calculation tasks. e Representative examples of basic (left) and advanced (right) tasks, demonstrating increasing complexity in experimental workflows. Source data are provided as a Source data file.\n\nIn Fig.\u00a02b, the operational landscape is divided into two primary complexity tiers: basic operations (56%) encompassing fundamental microscopy tasks and advanced procedures (44%) requiring more sophisticated experimental workflows (for example questions see Fig.\u00a02e). Core system components\u2014the AFM Handler, Document Retriever tool, and Code Executor tool\u2014demonstrate maximum engagement, each activating in 66 distinct tasks\u00a0(see Fig. 2c). The Data Handler Agent and Image Analyzer tool exhibit selective activation patterns (52 and 48 tasks, respectively), while the Image Optimizer tool engages exclusively in critical parameter optimization scenarios (4 tasks).\n\nTask distribution across functional domains reveals three primary clusters: documentation (50 standalone tasks), analysis (14 tasks), and calculation (10 tasks) (see Fig.\u00a02d). A significant overlap between these domains emerges through integrated tasks that combine multiple functional requirements, reflecting the interconnected nature of experimental workflows. This carefully constructed distribution enables systematic evaluation of AI systems across a spectrum of experimental complexity\u2014from basic instrument control to advanced multi-step procedures requiring mathematical reasoning and dynamic decision-making\u2014effectively mirroring the cognitive hierarchy of expert atomic force microscopists.\n\nSystematic evaluation of AILA using three advanced closed-source and one open-source language models\u2014GPT-4o, GPT-3.5-turbo-0125, Claude-3.5-sonnet-20241022, and Llama-3.3-70B-versatile\u2014unveils distinctive execution patterns and operational efficacies. GPT-4o exhibits exceptional proficiency in documentation-centric operations, achieving an 88.3% success rate, complemented by robust execution in analysis (33.3%) and computational tasks (56.7%)\u00a0(see Fig. 3a). The model\u2019s strength lies in its ability to navigate interconnected workflows: 23.3% success in merged documentation-analysis procedures and 36.7% in documentation-calculation sequences. These metrics highlight GPT-4o\u2019s capacity to replicate the integrative reasoning characteristic of expert microscopists.\n\nClaude-3.5-sonnet-20241022 model exhibits significantly lower performance than GPT-4o except in tasks involving standalone documentation (85.3%). While it is able to perform some cross domain tasks, we observe that the performance is notably lower than GPT-4o. These findings stand in stark contrast to previous benchmarking results in the materials domain17,20, where Claude consistently outperformed other models, suggesting that the performance advantages may not transfer across different types of scientific tasks and interaction formats. In marked contrast, GPT-3.5-turbo-0125 displays poor performance even in standalone tasks: 63.7% accuracy in documentation, and 3.3% in mathematical operations. However, its performance degrades significantly when confronted with multi-domain challenges, registering null success rates in tasks demanding simultaneous expertise across domains. This limitation suggests insufficient development of cross-functional reasoning capabilities essential for autonomous experimentation. The open-source Llama-3.3-70B-versatile model demonstrates accuracy superior to GPT-3.5 in all standalone tasks. However, it completely fails in tasks requiring cross-domain analysis or expertise.\n\nTo further investigate whether the poor performance originates from the LangGraph framework, we implemented the Model Context Protocol (MCP) to assess the performance of Claude (see Section S3.4 in the Supplementary Information for detailed results). We observe\u00a0that the results from both the frameworks are consistent, confirming that the diminished performance is inherent to the model and not a result of the framework.\n\nFor evaluation of our multi-agent\u00a0AILA framework, all successful trials were assessed across operational, token efficiency, and performance metrics (see Methodology\u00a0and Fig. 3b for details). Operational analysis revealed significant disparities in agent coordination capabilities: Llama-3.3-70B exhibited substantial tool-agent confusion, requiring an average of 10 steps per task, whereas GPT-4o demonstrated superior contextual grounding and agent selection efficiency with only 6 average steps per task. Token utilization patterns correlated directly with these operational inefficiencies, where Llama-3.3-70B consumed the highest average prompt tokens, indicating verbose or redundant intermediate reasoning processes, while GPT-4o achieved task objectives with minimal token usage, suggesting focused and deliberate reasoning pathways. Critical deficiencies in agent disambiguation and task-instruction alignment were observed in GPT-3.5 and Claude-3.5, which failed all three trials involving the Data Handler agent. For AFM Handler operations, GPT-4o demonstrated optimal efficiency with approximately 2.5 agent calls per task, contrasting with Claude-3.5, which generated the highest completion token counts and tokens-per-step ratios, indicating excessively elaborate intermediate outputs. Performance metrics revealed substantial variation in task completion success rates: GPT-4o achieved 65% success while GPT-3.5 performed inconsistently at 32.8%. Latency analysis showed Claude-3.5 suffered the highest mean response time (17.31\u2009s), whereas Llama-3.3-70B demonstrated the lowest latency (7\u2009seconds). These comprehensive metrics indicate that while Llama-3.3-70B offers reduced latency, GPT-4o provides the optimal balance between operational efficiency and execution precision, establishing it as the most suitable model for complex multi-agent coordination in autonomous laboratory environments.\n\nComponent utilization analysis reinforces these observations. GPT-4o achieves consistently elevated engagement across system modules (see Fig.\u00a03c, d). For tasks of varying complexity, GPT-4o demonstrates the highest accuracy, while GPT-3.5 performs the worst on advanced and basic tasks. Across all models, performance is generally higher for basic tasks compared to advanced ones. In multi-agent and multi-tool collaborative tasks, GPT-4o achieves the highest accuracy, whereas GPT-3.5 has the lowest. GPT-3.5 performance, in both single-agent and multi-agent collaborative task settings, is lower than that of the other models. These results highlight the fundamental importance of model architecture in autonomous experimental platforms, with GPT-4o\u2019s advanced integrative capabilities positioning it as the superior choice for sophisticated experimental automation.\n\na Venn diagrams showing accuracy metrics for GPT-4o, GPT-3.5-turbo-0125, Llama-3.3-70B-versatile and Claude-3.5-sonnet-20241022 across documentation, analysis, and calculation tasks. Numbers indicate percentage accuracy. b Evaluation metrics are grouped into three categories\u2014Operational\u00a0(left), Token Usage\u00a0(center), and Performance\u00a0(right) Metrics\u2014to assess the performance of four LLM models. c A horizontal bar chart comparing tool and agent utilization efficiency between models is expressed as a percentage of successful engagements. d Performance comparison of different models across tasks of varying complexity (Advanced/Basic) and requiring different tools (Single/Multiple) and agents (Single/Multiple). Source data are provided as a Source data file.\n\nTo assess whether direct tool integration with AILA yields equivalent performance to the multi-agent framework, we conducted a comparative analysis. A representative subset of 10 questions from the AFMBench dataset was systematically evaluated across both single-agent and multi-agent architectures, with each question assessed through three independent trials to ensure statistical reliability and account for inherent variability. The comparative analysis revealed framework-dependent performance variations: GPT-4o demonstrated superior performance in the multi-agent configuration (70% success rate) compared to direct tool integration (58% success rate). For alternative models, performance differences were minimal, as most architectures exhibited fundamental limitations in cross-domain tasks that inherently require multi-agent coordination, regardless of framework structure (see Section S6 of the supplementary material for detailed results). These findings indicate that while computational efficiency favors single-agent architecture implementations, the enhanced coordination capabilities of multi-agent architecture provide measurable performance gains for advanced models capable of complex reasoning.\n\nDetailed examination of failure cases revealed distinctive error patterns between all the language models (see Fig.\u00a04), offering insights into their operational limitations. Note that for computing\u00a0evaluation metrics,\u00a0successful tasks\u00a0are defined as those\u00a0where all three trials for a given task\u00a0are successful. Whereas, for error mode distribution, all the trials for each task are counted\u00a0individually, totalling to 300 task instances.\u00a0GPT-4o exhibits a total error rate of 29%, with errors distributed across three primary categories: code generation (21.7%), agent selection (1.3%), tool selection (0.3%), and instruction adherence (5.7%). The predominance of code generation errors suggests challenges in translating conceptual understanding into executable commands despite the model\u2019s strong performance in task comprehension.\n\nError patterns among different models: GPT-4o (top left), GPT-3.5-turbo-0125 (top right), Llama-3.3-70B-versatile (bottom left) and Claude-3.5-sonnet-20241022 (bottom right). Segments represent a proportional distribution of error types: Instruction adherence (blue), agent selection (pink), tool selection (green) and code generation (gray). Source data are provided as a Source data file.\n\nGPT-3.5-turbo-0125 demonstrates a markedly higher total error rate of 66.6%, with errors concentrated in four categories: code generation (32%) and agent selection (27.3%), tool selection (0.3%). Notably, the model shows less fundamental query interpretation errors (7.0%), indicating robust natural language processing capabilities. However, the elevated frequency of code generation errors, coupled with significant agent or tool selection failures, points to underlying deficiencies in translating comprehension into actionable experimental protocols.\n\nLlama-3.3-70B-versatile and Claude-3.5-sonnet-20241022 demonstrate substantial error rates of 60.6% and 51.6%, respectively, with distinct failure patterns. Llama-3.3-70B-versatile exhibits a notably high frequency of code generation errors (32.0%), manifesting as incorrect argument formulation for tool execution and non-functional code production. Specifically, it struggles to construct appropriate argument structures required for successful tool invocation. In contrast, Claude-3.5-sonnet\u2019s deficiencies primarily stem from agent selection errors (28.3%), where it consistently misattributes tasks between AFM-HA and DHA, resulting in the delegation of experimental procedures to inappropriate agents.\n\nA critical finding emerged regarding LLM\u2019s instruction adherence. In one of the four recorded errors, GPT-4o exceeded its designated operational limits, performing actions that were not authorized by the provided guidelines. For instance, it carried out potentially risky tip movements while it was only instructed to change the cantilever (see S3.2 in the Supplementary Information). In another case, GPT-4o was instructed to capture an image and calculate surface friction. Instead of staying within the scope of the task, it performed additional actions. This behavior was not restricted to GPT4o but was observed in other LLMs as well. Although sometimes the final\u00a0result may have been correct, the failure to follow instructions highlights concerns about AI-agent behavior and raises safety risks in automated lab environments. Similar to the observation of hallucination in LLMs38, these results present a unique challenge\u2014SDLs tend to take arbitrary actions, potentially based on memory rather than following the instructions, referred to hereafter as sleepwalking. These issues are especially critical in sensitive experimental settings, where strict protocol adherence is essential to ensure both equipment safety and the validity of results.\n\nThe AILA framework incorporates iterative debugging protocols to address code generation failures through systematic error resolution. Upon error detection, AILA captures comprehensive error logs and initiates iterative correction cycles, with a maximum threshold of 20 iterations established to optimize the balance between thoroughness and computational efficiency. Analysis of debugging outcomes reveals two distinct failure modes: (1) Iteration Limit Exhaustion, where the system terminates after 20 unsuccessful correction attempts, with persistent errors classified as code generation failures; and (2) Sleepwalking, where AILA generates functional code that exceeds the specified requirements, demonstrating functionality beyond the original instructions\u2014a phenomenon indicating instruction drift or algorithmic overfitting, categorized as instruction adherence errors. This binary classification system enables systematic characterization of failure modes while the iteration threshold ensures computational tractability without compromising debugging efficacy in autonomous laboratory operations.\n\nThis error distribution illuminates critical areas for framework enhancement. While GPT-4o\u2019s balanced error profile suggests the need for targeted improvements across multiple domains, GPT-3.5-turbo-0125\u2019s concentrated error patterns indicate fundamental limitations in experimental execution capabilities. These findings underscore the necessity for specialized training in automated experimental systems, particularly focusing on the translation of scientific protocols into executable code sequences.\n\nTo understand the safety challenges39 of AI agents, we evaluate the effectiveness of implementing a safety framework in AILA. First, we establish restricted access protocols for critical AFM functions, coupled with ethical system prompts (see S2.1 in Supplementary Information) that constrain code generation to predefined documentation40. To implement this, we classified all the operations that could be performed on an AFM as per the instrument\u2019s documentation into two categories. (i) General operations\u2014these include setting imaging parameters, controlling the tip, selecting the scanning area, and other standard tasks. (ii) Critical operations\u2014these involve sensitive adjustments such as factory calibrations, laser alignment, piezo calibration, and thermal calibration. Even a minor coding error in critical operations could seriously damage the instrument.\n\nWe restricted the access to the documentation of critical operations in AILA, while allowing complete access to the documentation of general operations. The critical functions are limited to trained human experts. Note that general operations were selected in such a fashion that any experiment that could potentially be performed on an AFM could be carried out using some combination of these operations. Thus, the predefined documentation in AILA does not restrict any new experiment to be performed on the AFM instrument. Note that an alternate approach to implement safety is to identify risky actions and involve a human-in-the-loop mechanism to review the actions. This could also enhance the robustness and overall performance of the framework. However, such a framework requires human supervision, limiting the high-throughput nature that an autonomous system can otherwise achieve, with human response becoming the bottleneck. Hence, this approach was not implemented in AILA and could be explored as part of future work.\n\nSecond, we develop strict operational boundaries that permit dynamic code generation solely for image analysis while preventing external software installation or system modifications. Evaluation of the improved protocol demonstrates the effectiveness of these safeguards\u2014AILA appropriately failed when prompted to install external Python libraries. (see S3.3 in Supplementary Information for complete validation logs). These findings underscore the critical importance of robust safety protocols in SDLs, emphasizing the necessity of comprehensive benchmarking and operational guardrails.\n\nFinally, to demonstrate AILA\u2019s capabilities in real-world scenarios, we demonstrate five experimental tasks that typically require expert intervention: automated AFM calibration, high-resolution feature detection, load-dependent friction measurement, graphene layer analysis, and indenter profiling.\n\nAFM imaging requires precise calibration of Proportional-Integral-Derivative (PID) gain values, which traditionally demand expert intervention due to the continuous nature of these parameters. This dependency on skilled operators presents a significant barrier to broader AFM adoption. We demonstrate AILA\u2019s capability to autonomously optimize these parameters by minimizing the forward-backward scan differential on standard calibration grids. To this end, after loading the calibration sample, AILA was prompted to optimize the imaging parameters (see S4 in Supplementary Information for the complete prompt and output log). A total of 45 images are generated, with 3 images produced in each of the 15 generations. Figure\u00a05a presents experimental AFM data acquired by AILA for the 1st and 15th generation of variable PID configurations, with corresponding line scan analyses that quantify trace-retrace symmetry. Initial scans with suboptimal parameters (P: 93\u2013208, I: 1747\u20136623, D: 0\u201339) exhibit poor SSIM scores (0.392\u20130.768), manifesting as visible distortions in topographic data. Note that a higher SSIM value, closer to 1, indicates a perfect match, while a value of 0 represents no similarity. Through iterative optimization, AILA achieves superior scan quality (SSIM\u2009>\u20090.81) with optimized parameters (P: 246\u2013249, I: 8676\u20138957, D: 17\u201330; see Fig.\u00a05a).\n\na Evolution of AFM image quality under varying PID parameters. The left panels show topographic images of the calibration grid; the right panels display corresponding line scan profiles (solid: trace, dashed: retrace). Structural Similarity Index (SSIM) scores quantify trace-retrace correlation, with higher values indicating superior imaging quality. Optimal parameters (Proportional (P) gain: 249, Integral (I) gain: 8957, Derivative (D) gain: 26) achieve SSIM\u2009=\u20090.818. b Large-area scan demonstrating consistent imaging quality using optimized parameters across multiple grid features. c Convergence plot showing genetic algorithm optimization efficiency. Red circles: maximum SSIM; black circles: mean SSIM per generation. d High-resolution Highly Oriented Pyrolytic Graphite (HOPG) imaging demonstrating baseline artifact challenges. Top panels: topographic images at different height (Z) ranges; bottom panels: corresponding line profiles revealing surface features. Source data are provided as a Source data file.\n\nThe genetic algorithm\u2019s convergence efficiency is demonstrated in Fig.\u00a05c, where optimal PID configurations are achieved within 15 generations. Both maximum and mean SSIM values show rapid improvement, stabilizing above 0.8, indicating robust parameter optimization. Figure\u00a05b validates the optimized parameters (P:249, I:8957, D:26) across a larger scan area, maintaining high-quality imaging across multiple grid features.\n\nSurface characterization through AFM is challenged by noise sources such as thermal drift, mechanical vibrations, and electronic interference41,42,43, which can obscure subtle topographic features like graphene step edges. In this study, we leverage the advanced analytical capabilities of AILA to address these challenges using HOPG as a model system. AILA autonomously determines the necessity for baseline correction based on feature size, recognizing that baseline artifacts predominantly affect smaller features. To further demonstrate this, we tested two different prompts with samples of distinct morphologies (see Fig.\u00a0S3 in Supplementary Information). In both cases, AILA correctly selected the appropriate baseline correction. For instance, in the raw image (Fig.\u00a05d), the graphene step edge remains indiscernible due to baseline distortions. AILA applies a fifth-order polynomial baseline correction to generate the 1st generation image (Fig.\u00a05d), which serves as the foundation for PID gain optimization. Following a process similar to the calibration grid optimization, the image is refined through iterative PID adjustments, resulting in the final optimized image in the 10th generation, where atomic steps become distinctly visible. The automated optimization process surpasses conventional manual adjustments, offering an enhanced resolution of nanoscale features. Additionally, further analysis of the processed data, including the determination of graphene step height, was facilitated through specific prompts, with the prompts and results detailed in the Supplementary Information\u00a0S5. Note that in the AILA framework, edge detection is not based on a fixed algorithm. Instead, the system generates custom code to solve the problem, whereas, for feature detection, AILA uses the built-in Image Segment tool (see Methodology) that applies Otsu\u2019s thresholding to automatically segment images by finding the most effective intensity-based thresholds.\n\nThe experiments discussed thus far are routine, with limited number of steps and hence, complexity. Now, we conduct a comprehensive load-dependent friction analysis of HOPG (see Fig.\u00a06a). The experiment requires iterative adjustments of AFM parameters, including setting a range of setpoints, capturing images, and analyzing the corresponding friction data. Manually performing this procedure is time-intensive, involving parameter modifications, image acquisition, data extraction, and result plotting, making a case for automation.\n\na Left: Highly Oriented Pyrolytic Graphite (HOPG) images obtained using setpoints of 0.2\u2009V and 0.4\u2009V, both manually captured and taken by Artificially Intelligent Lab Assistant (AILA) with GPT-4o. Right: Raw, unedited plot generated by AILA showing the relationship between setpoint and average friction. b Demonstration of AILA\u2019s workflow for real-world experimentation on graphene-coated Si sample, displaying the user\u2019s transcribed query, AILA\u2019s unedited final response, intermediate analyses, and exported images. This showcases AILA\u2019s capability to autonomously conduct real-world experimental tasks. c Demonstration of how AILA identifies an indentation mark on a glass substrate, analyses the indenter type using a horizontal line profile, and provides a final interpretation with supporting explanation. Source data are provided as a Source data file.\n\nHowever, based on the experiments conducted above and this experiment, we observed that the performance of the LLMs could be directly affected by the prompts. To evaluate this effect, we analyzed the effect of prompting (see Methodology and Table\u00a0S3) by systematically varying the prompts from simple to complex, from compact to descriptive. Our findings revealed that GPT-4o demonstrated variable task completion rates across different prompt structures, ranging from partial execution to complete fulfillment. Significantly, more elaborate and detailed prompts consistently enhanced the model\u2019s performance reliability and execution accuracy, suggesting that comprehensive contextual information improves complex task handling. Based on these experiments, we designed the prompts consistently across the experiments and ensured that prompt optimization was not carried out to arrive at desirable results.\n\nAILA was instructed (see S5 in Supplementary Information for the complete prompt and output log) to vary the setpoint voltage from 0.2\u2009V to 1.2\u2009V in increments of 0.2\u2009V. At each setpoint, AILA independently captured the AFM image, calculated the average friction value, and generated the corresponding plot. Figure\u00a06a presents the graph of average friction versus setpoint voltage for both manually obtained and AILA-captured images using the GPT-4o model. The raw image generated by AILA can be found in the Supplementary Information Fig.\u00a0S2. The entire process was conducted without additional user input regarding figure formatting or parameter settings. Remarkably, AILA autonomously develops the required Python script, executes the experimental protocol, and generates the output, including raw, unedited plots. This automation significantly reduces the time and effort compared to manual execution. The results not only validate the capability of AILA in handling AFM experiments but also demonstrate its efficiency in generating reproducible and high-quality outputs for scientific analyses.\n\nTo evaluate the performance of AILA in a real-world experimental setting, we designed two distinct experiments. In both cases, an experimentalist is required to identify a specific feature of interest, capture it, and then carry out the experiment. Further, the results need to be analyzed to draw meaningful conclusions. In the first experiment (see Fig.\u00a06b), the objective is to locate a graphene flake and determine the number of atomic layers in it. To accomplish this, AILA performs image segmentation using Image Segment tool within a user-specified region, identifies the largest visible flake, and extracts it for further analysis. Through a sequence of intermediate processing steps, AILA autonomously generates code, processes the image, and ultimately provides an estimate of the number of graphene layers present in the selected flake. The second experiment (see Fig. 6c)\u00a0involves identifying the type of indenter used to create an impression on a sample surface. The intended image is analyzed by AILA, including an inspection of the indentation line profile. Based on this analysis, it infers and concludes, along with a detailed explanation, that the indenter used was most likely of Vickers-type geometry. Thus, in both the cases, AILA performs the experiment successfully and provides analysis and conclusions similar to human experts. Specifically, in the first case, it uses the knowledge of the thickness of graphene along with numerical computation to identify the number of layers, whereas in the second, it uses the knowledge on the characteristics of Vickers and conospherical indenters along with reason to identify the appropriate indenter. The complete set of raw user inputs and unprocessed outputs is presented in (Fig.\u00a06b, c). The log files of both the\u00a0experiments are available in our GitHub repository37.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64105-7/MediaObjects/41467_2025_64105_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64105-7/MediaObjects/41467_2025_64105_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64105-7/MediaObjects/41467_2025_64105_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64105-7/MediaObjects/41467_2025_64105_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64105-7/MediaObjects/41467_2025_64105_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64105-7/MediaObjects/41467_2025_64105_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "AILA\u2019s modular design, along with AFMBench, establishes quantifiable metrics in experimental automation through systematic benchmarking. The framework\u2019s comprehensive performance metrics in AFM operations establish standards for autonomous laboratory evaluation, while AFMBench introduces reproducible protocols for systematic assessment across experimental domains. Successful execution of tasks\u2014from automated image optimization to nanomechanical measurements\u2014validates the framework\u2019s capabilities for sophisticated materials characterization.\n\nSeveral outcomes merit detailed examination. The measured limitations in tool coordination across different LLMs establish quantifiable thresholds for improving inter-module communication protocols. Our results demonstrate that multi-agent architectures systematically outperform single-agent configurations, with the primary advantage extending beyond mere instruction execution to encompass task modularization, specialized agent collaboration, independent reasoning, and dynamic decision-making regarding subtask sequencing and tool selection. These findings align with established literature demonstrating the superiority of multi-agent architecture over single-agent implementations across diverse computational domains44,45,46. These observations also establish an empirical baseline for balancing specialized and integrated operations\u2014a metric applicable to automation across analytical platforms, from mass spectrometers to X-ray diffractometers.\n\nHowever, the observed tendency of LLM agents to exceed operational boundaries through sleepwalking phenomenon during experimental execution presents critical safety concerns for autonomous laboratory deployment. This phenomenon, documented here for the first time in autonomous experimental systems, highlights urgent development priorities in instruction alignment and operational safety protocols. Additionally, despite providing direct access to comprehensive documentation and code snippets, persistent code generation errors indicate fundamental limitations in current retrieval-augmented generation frameworks. These findings necessitate the development of enhanced code generation architectures that incorporate domain-specific constraint validation and formal verification protocols to minimize coding errors\u2014representing an immediate opportunity for systematic improvement in autonomous laboratory reliability.\n\nThese findings suggest specific architectural improvements for next-generation autonomous laboratories. Enhanced integration protocols between specialized agents could address the observed limitations in multi-tool coordination. Similarly, dedicated code generation modules might mitigate the predominant error mode, potentially incorporating specialized scientific programming frameworks.\n\nThe implications of this work extend beyond materials characterization. The unexpected underperformance of Claude-3.5-sonnet-20241022 compared to GPT-4o highlights a critical insight: question-answer proficiency in a specific domain does not necessarily predict effectiveness in agentic implementations. Rather tool coordination capabilities of LLMs prove to be an important aspect for effective agentic implementation. Furthermore, the observed prompt fragility emphasizes the necessity for developing rigorous evaluation frameworks prior to deployment in research environments. Specifically, developing systematic and principled approaches to generate prompts and make systems that are robust to minor variations in prompts plays a crucial role in the wider acceptance of agentic systems. To this end, quantitative benchmarks such as AFMBench provide concrete guidance for implementing LLM-driven systems in experimental research settings where precision and reliability are paramount.\n\nApplications span pharmaceutical screening, environmental monitoring, and process optimization. For instance, documented success in parameter optimization could translate directly to automated high-throughput drug screening or catalyst discovery platforms. While current limitations in code generation and tool coordination define immediate development targets, these metrics provide clear objectives for advancing autonomous scientific platforms. The path forward requires focused development in three key areas: enhanced cross-domain reasoning capabilities, robust code generation protocols, and sophisticated multi-agent coordination mechanisms. Success in these domains would enable truly autonomous scientific platforms capable of accelerating discovery across the scientific landscape.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "AILA is constructed utilizing the LangChain software framework, incorporating components such as prompts, LLMs, memory, agents, and tools. AILA uses two categories of prompts: system prompts (see S2.1 in Supplementary Information for the system prompts) and user prompts. System prompts define ethical rules for AILA\u2019s interactions and describe the responsibilities assigned to each agent, whereas user prompts are variable inputs provided by end-users. AILA\u2019s backbone consists of LLMs, namely GPT-4o, GPT-3.5-turbo-0125, Llama-3.3-70B-versatile, and Claude-3.5-sonnet-20241022, which process user input as strings and provide string-based outputs. We used API keys from the developers for GPT-4o, GPT-3.5-turbo-0125, and Claude-3.5-sonnet-20241022. Additionally, we used API keys from Groq AI Inference for Llama-3.3-70B-versatile model. We have used a temperature value of zero for all models, with the parameters set as max tokens 2024, and max retries two. These LLMs are stateless, indicating that they do not save conversational context. Here, all interactions and agent states are stored in a Python dictionary and can be accessed by other agents. AILA consists of two specialized agents: the AFM Handler Agent and the Data Handler Agent, both equipped with unique tools to do specific tasks. These agents possess individual prompts, LLMs, and tools; however, they utilize a shared memory to store and access states, facilitating smooth interaction. The system prompts within the agents offer instructions for tool utilization and ethical guidelines, whereas the outputs from other tools or agents serve as user prompts. The framework utilizes LangGraph, a library that allows the construction of an effective multi-agent workflow, integrating all agents and tools seamlessly. AILA uses two different approaches for selecting algorithms, depending on the task. When performing standard calculations like friction or roughness analysis, AILA relies on established algorithms with adjustable input parameters. This method produces consistent and reproducible results. For exploratory work and data visualization, AILA takes a different approach by creating the code on the fly. This method adapts better to varying data formats and specific user requirements; however, this may introduce variability in the results depending on the complexity of the task.\n\nThe architecture for AILA\u2019s decision-making process is carefully designed to ensure precise information routing. Where it uses two different routing mechanism: dynamic and static routing. A detailed discussion of both dynamic and static routing is provided in the Supplementary Information. AILA can dynamically select among three primary options: AFM Handler, Data Handler, or FINISH. When AILA identifies the appropriate agent to handle a query, it routes the information to the selected option. In cases where AILA determines that none of the available agents can sufficiently address the question, it generates a well-structured response and selects the FINISH option to conclude the session effectively. The agents within this system are equipped with three distinct operational choices: utilizing their respective tools, transferring information to the next agent, or terminating the session. A system prompt has been integrated to streamline these decisions. Agents append the prefix NEED HELP to their response when transferring information to another agent. Alternatively, if they believe the query has been adequately addressed, they use the prefix FINAL ANSWER to signal the session\u2019s conclusion. By analyzing the output for these keywords, the system seamlessly routes the response to the designated agent or tool or finalizes the session. This structured approach enables efficient multi-agent collaboration, ensuring clarity, accuracy, and optimal performance across tasks while maintaining a robust and adaptive framework.\n\nAFM demands precise sequential execution of multiple experimental stages. Image acquisition requires optimization across three critical parameters: imaging conditions, probe selection, and operational mode configuration (tapping/contact). The experimental sequence encompasses surface approach protocols, scanning procedures, and standardized data acquisition\u2014with procedural deviations potentially resulting in equipment damage or data corruption. Our implementation utilizes the DriveAFM instrument (Nanosurf), which is accessed through a Python-based API architecture and designed for universal compatibility with API-enabled AFM systems. To facilitate AFM imaging experiments, we have created the AFM Handler agent, which is integrated with two specialized tools: the Document Retrieval Tool and the Code Executor Tool. Every tool has an individual role, and the AFM Handler agent can dynamically assign tasks to these tools. The agent will assign the responsibility to the Data Handler agent if it finds that neither tool can handle the task.\n\nThe documentation for the instrument offers detailed instructions on how to handle and calibrate it. However, providing full access of the documentation to an LLM entails risks, such as inadvertent alterations to factory settings or calibration data, which could potentially result in damage or malfunction of the instrument. To address this concern, we manually extracted the essential information from the AFM documentation necessary for conducting experiments while safeguarding the instrument\u2019s integrity. We consolidated all the crucial codes for regulating each parameter of the instrument into a comprehensive Python script. Since Python code relies heavily on precise indentation and line structure, we utilized the Recursive Character Text Splitter from the LangChain library, specifically designed for Python, to divide the script into manageable chunks. The chunk size was set to a maximum of 1000 characters without overlap, adhering to the token limit for embedding models. Each code chunk comprises three sections: the first includes the necessary Python libraries, the second contains the code required to load the application, and the third section features unique Python code specific to the given task. The first two sections are consistent across all chunks (see S2.2 in the Supplementary Information file for more details). These chunks were then combined to generate a document, embedded using OpenAI\u2019s text-embedding-3-large model. This model, with the capability of producing embeddings of size up to 3072 dimensions, delivers exceptional performance compared to other OpenAI embedding models, especially in multi-language retrieval benchmarks like MIRACL47. To store the embeddings, we opted for Chroma, an open-source vector database known for its reliability and efficiency in managing large-scale embedding data. We use a vector store retriever to retrieve the data from the vector store.\n\nA code executor tool has been developed to execute Python scripts generated by the AFM Handler Agent to control the AFM software. This tool is intended to run Python code, provided as a text string, directly on the local system to allow for smooth integration with the workflow of the AFM Handler Agent. The utility executes the code and sends back a success message or a detailed description of the error that occurred. If there is an error, the error message is returned to the AFM Handler agent so it can correct the error and retry executing. Otherwise, if the script runs without errors, it is considered the final result. This iterative process ensures precise control of the AFM system while systematically addressing any issues in the script.\n\nSurface tracking optimization in AFM requires precise calibration of three fundamental parameters: Proportional (P), Integral (I), and Derivative (D) gains. Optimal calibration manifests as convergence between trace and retrace signals, indicating stable scanning conditions. The Data Handler agent interfaces with specialized optimization and analysis modules; these models can access AFM image data stored in local storage systems. The agent can optimize P, I, and D gains or calculate various surface properties, such as average friction and surface roughness, using the help of modules and image files stored locally. While many AFM software packages offer basic data analysis functionalities, they present several limitations in an automated workflow as follows. (i) Most of these software solutions primarily support Windows systems, limiting cross-platform compatibility with operating systems such as macOS and Linux platforms. (ii) Commercial packages require paid licenses, restricting accessibility. (iii) Finally, most packages are not flexible to include additional functionalities beyond what is already included, limiting their customizability. Thus, to ensure broader adaptability and maintain an adaptable, flexible, modular, and open framework, we developed the Data Handler agent within AILA, which has access to several tools\u2014new functionalities can be easily integrated to this agent and the tools based on user needs. Note that this does not restrict the usage of vendor software packages, as they could also be included as a tool in AILA. The agent offers a significantly expanded suite of advanced analytical capabilities, such as:\n\nCustomizable and automated image processing workflows tailored to specific experimental needs.\n\nStatistical analysis across multiple datasets, enabling robust comparison of parameters such as average friction, surface roughness, and topographic variations.\n\nPlatform independence, ensuring compatibility across Windows, macOS, and Linux, and eliminating reliance on proprietary or licensed software.\n\nDynamic code generation via LLM integration, allowing users to automatically generate and execute scripts for plotting and analysing images.\n\nTo demonstrate the adaptability of the agent, we developed a custom function to calculate indentation volume (see Fig.\u00a0S4 in the Supplementary Information). Instructions for integrating additional functions into the Data Handler Agent are provided in a step-by-step guide available on the accompanying GitHub repository37.\n\nThe feedback system in an AFM plays a crucial role in maintaining control over the interaction between the cantilever tip and the sample surface. During scanning, variations in surface features alter the interaction forces between the tip and the sample, leading to deflections in the cantilever. These deflections are detected by a photodetector. To ensure that these deflections stay within a specified range, the feedback mechanism continuously adjusts the height of either the tip or the sample stage in real-time. This process is managed by a PID controller, which regulates the position of the z-piezo actuator. By\u00a0controlling the Z position of the AFM probe, the controller maintains a steady interaction force or adheres to a predefined setpoint, depending on the chosen mode of operation.\n\nFine-tuning the P, I, and D gain values of the controller is vital for achieving accurate control of the setpoint in AFM imaging. The integral gain is especially important for enhancing image clarity by mitigating drift and reducing steady-state errors. Once the integral gain is optimized, adjusting the proportional gain can provide further refinement. The derivative gain, on the other hand, is particularly beneficial for imaging samples with pronounced edge features. If the gains are set too low, the PID loop may fail to maintain the setpoint effectively, while excessively high gain values can introduce electrical noise into the image due to amplified feedback or overcompensation for deviations. Properly optimized PID parameters ensure that the feedback loop remains stable and responsive, enabling the AFM to accurately track surface topography, even at higher scanning speeds. This balance is especially critical when imaging delicate, irregular, or soft materials, as it preserves the integrity of tip-sample interactions.\n\nA genetic algorithm (GA) was employed for PID gain optimization. The GA parameters included a fixed population size of three and a total of 15 generations, enabling efficient tuning of the gains. Although these parameters can be manually adjusted, but excessive image scanning may degrade the AFM tip. The optimized gains ensure effective feedback control, producing comparable forward and backward images. This can be achieved by calculating the mean squared error (MSE) between forward and backward z-axis images for various PID gain settings. However, this method is sensitive to drift during scanning, and this method also depends on previously acquired images. To address this, the structural similarity index (SSIM) was adopted as the fitness function in the genetic algorithm, providing a robust measure of image similarity between the z-axis forward and backward images, independent of prior image data.\n\nThis metric offers advantages over traditional Mean Square Error (MSE) approaches by (i) addressing tip degradation challenges in contact-mode AFM by minimizing required scan cycles and enabling optimization using low-resolution images, (ii) maintaining accuracy under drift conditions, (iii) incorporating structural, brightness, and contrast variations in optimization, and (iv) providing normalized scores between 0 and 1, where 1 indicates perfect similarity.\n\nThe SSIM is defined as:\n\nwhere, \\(l(x,y)\\) is the luminance comparison, \\(c(x,y)\\) is the contrast comparison, and \\(s(x,y)\\) is the structure comparison with \\(\\alpha\\), \\(\\beta\\), \\(\\gamma\\) being the weighting parameters. Note that the individual components are defined as:\n\nwhere, \\({\\mu }_{x},{\\mu }_{y}\\) represent the mean intensities of images \\(x\\) and \\(y\\), \\({\\sigma }_{x},{\\sigma }_{y}\\) is the standard deviations of images \\(x\\) and \\(y\\), \\({\\sigma }_{{xy}}\\) is the cross-covariance between images \\(x\\) and \\(y\\), and \\({C}_{1},{C}_{2},{C}_{3}\\) are constants to avoid instability with \\(({C}_{1}={({k}_{1}L)}^{2},{C}_{2}={({k}_{2}L)}^{2},{C}_{3}={C}_{2}/2)\\) and \\(L\\) being the dynamic range of pixel values and \\({k}_{1}=0.01\\) and \\({k}_{2}=0.03\\).\n\nBaseline correction. The adaptive baseline correction employed in the step-edge detection of graphene is given by\n\nwhere, \\(B(x,y)\\) is the baseline function, \\({a}_{{ij}}\\) are the polynomial coefficients, \\(i\\) and \\(j\\) are the polynomial degrees \\((0\\le i,j\\le n)\\) with \\(n\\) being the maximum polynomial degree.\n\nAFM instrument stores the image data as a *.nid file in the local system. This *.nid file contains deflection, friction force, and z-axis images for both backward and forward scans. To further process any image from the file, exact data must be extracted from the file. To conduct this, we have used the NSFopen Python library in the Image Analyzer tool, which takes the query from the data handler agent regarding the specific image data and its location and returns the image data in an array to the data handler agent. To conduct further processing of the images, any Python script generated by the data handler agent can be executed in the Image Analyzer tool, and the result can be returned to the data handler agent. Note that there is no database available to guide the LLM model in generating the Python script. It can generate the Python script by itself. There is a total of 6 input parameters for this tool:\n\npath (str): directory path to search for the latest file (default: None).\n\nFilename (str): specific image file to display (default: None).\n\nDynamic_code (str): Python code for processing image data (default: None).\n\nCalculate_friction (bool): option to compute average friction (default: False).\n\nCalculate_mean_roughness (bool): option to compute mean roughness (default: False).\n\nCalculate_rms_roughness (bool): option to compute RMS roughness (default: False).\n\nReturns: a dictionary with the status, image data, or error details.\n\nAverage friction was calculated using the following formula:\n\nwhere \\({f}_{{ij}}\\) and \\({b}_{{ij}}\\) are the element at position \\(\\left(i,j\\right)\\) in the array of the forward and backward friction image data. We have used the formula in this tool to calculate the mean roughness and RMS roughness values\n\nwhere \\({z}_{{ij}}\\) is the element at position \\((i,j)\\) in the array, \\(\\bar{z}\\) is the mean of all elements in the array, \\(M\\) is the number of rows in the array, N is the number of columns in the array of the z-axis forward image data.\n\nAn Image Segment and an Image Scanner tool have been created to analyse AFM-scanned images. The Segment tool use the Otsu algorithm to segment images according to the various features present in the sample. Upon detection of features, the tool produces bounding boxes and allocates distinct grain IDs to each feature. This bounding box information is subsequently utilized by LLMs for additional data processing. Note that we used text-based LLM models that cannot discern any features inside the sample, whether they pertain to the material or represent alien inclusions. Following its analysis, the LLM can transmit designated grain IDs to the Image Scanner tool, which instructs the AFM instrument to meticulously scan those particular characteristics.\n\nTo evaluate the performance of the AILA, we have manually created a set of 100 questions, carefully categorized into three distinct groups. The first classification is based on whether a question requires one or multiple tools and agents to be solved. The second category assesses the complexity of the questions, distinguishing between basic and advanced levels. Lastly, the questions are grouped by their requirements, such as documentation analysis or calculations. The complexity of each question is determined by the number of agents involved and the steps required to achieve the solution. For instance, modifying a parameter in an AFM system typically requires documentation review and the use of a single agent, categorizing it as a basic task. Conversely, capturing an AFM image and analyzing its surface roughness involves multiple agents, documentation analysis, and calculations, making it an advanced task. A comprehensive JSON file has been created, encapsulating detailed metadata about each question, including its respective category, for streamlined analysis and evaluation. This file serves as a structured resource for further investigations and testing. All questions, along with their relevant classifications and details, have been made accessible on GitHub37 (https://github.com/M3RG-IITD/AILA) to support transparency and reproducibility in research.\n\nWe developed a graphical user interface (GUI) using Streamlit, an open-source Python framework, to streamline user interaction with AILA. The GUI allows users to input text-based queries, select the desired LLM model, and specify a log file name. It then executes AILA in the backend, saving the output log file locally and enabling users to observe results directly within the AFM software. Any output images or figures generated by AILA are also stored in the local system for further analysis. To ensure robustness, we manually evaluated all questions using each model, verifying the output log files and AFM software results multiple times in collaboration with different researchers to eliminate potential human errors. The evaluation of AILA\u2019s performance was categorized into two metrics: accuracy and efficiency. For accuracy, questions were divided into categories based on complexity and tool or agent usage, with a percentage of correct answers calculated for each category. For efficiency, uniform parameters were maintained across models in the AFM software, including default settings of 0.1\u2009s as time per line and 128\u00a0as lines per frame, when not specified by the user. To ensure precise efficiency measurements, scanning time for images and the time taken by questions with incorrect answers were excluded from the analysis. Average response times were computed for each category to assess AILA\u2019s overall efficiency.\n\nTo assess the evaluation of questions in terms of accuracy, we classified the answers provided by AILA into three categories: fully correct answers, incorrect and partially correct answers. A fully correct answer was considered accurate and given a score of 1, while any partially correct response was given a score of 0.5. Given that some questions require manual inspection of the AFM software to verify whether specific parameters are set correctly and whether the AFM image is captured as intended, multiple researchers were involved in verifying the results. They carefully checked the outcomes to ensure error-free results. For measurements of different properties, such as average friction, roughness, and RMS value of roughness, we used the Gwyddion software to verify the accuracy of the results. Subsequently, the questions were clustered into appropriate groups, and the corresponding average percentage of correct answers was calculated. The detailed evaluation process is provided in the Supplementary Information.\n\nAdditional evaluation metrics are defined as following. Based on the scoring, Success rate is computed as the average score (out of 100) across three iterations for all the tasks. For all the successful runs, following metrics are employed.\n\nAFM handler calls: the average number of times the AFM Handler agent was called to resolve a task (calls per task). An elevated score may signify a greater dependence on the AFM Handler for coordination.\n\nData handler calls: the average number of times the Data Handler agent was called during task solving.\n\nNumber of steps: the average number of discrete actions (represented by tool or agent call) taken by the AILA to arrive at a solution.\n\nTotal tokens: total number of LLM model tokens is processed on an average for each task, considering both input (prompt) and output (complete) tokens.\n\nPrompt tokens: number of tokens utilized in the conversation\u2019s input or instruction segment for each task.\n\nCompletion tokens: the average number of tokens that the system\u2019s answers create for all assignments.\n\nTokens per stage: this tells how many tokens were utilized on average for each stage in the process of solving a task.\n\nLatency: the average amount of time (in seconds) it takes to complete a task from the first to the final step.\n\nTime per step: the average amount of time (in seconds) spent on each step, representing the pace with which the system can perform different tasks.\n\nLatency per 1000 tokens: the average amount of time (in seconds) taken to process 1000 tokens.\n\nWe systematically investigated how prompt structure and phrasing influence GPT-4o\u2019s ability to perform load-dependent friction measurements, one of our most challenging open-ended tasks. LLMs process information hierarchically based on input format and length, making them sensitive to prompt design. Even subtle modifications can activate different training exemplars, altering response patterns and reasoning pathways. To evaluate performance within the AILA framework, we constructed multiple input prompts to assess task completion efficacy. In AILA, inter-agent collaboration is triggered when the model outputs \u201cNEED HELP\u201d, while \u201cFINAL ANSWER\u201d signals task completion. We developed and tested four distinct prompt categories: (1) concise task descriptions, (2) comprehensive task elaborations, (3) sequential task decompositions, and (4) explicit inclusions of the signaling phrases with case variations. By systematically combining these elements, we designed five system prompts, with detailed performance metrics documented in the Supplementary Information (Table\u00a0S3).", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All the data generated in this study have been deposited in the GitHub repository37 under accession code https://github.com/M3RG-IITD/AILA.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All the codes in AFMBench37, along with the complete log files of the responses for each of the tasks from all models are available at: https://github.com/M3RG-IITD/AILA.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Zhao, Z., Lee, W. S. & Hsu, D. Large language models as commonsense knowledge for large-scale task planning. Adv. Neural Inform. Process. Syst. 36, 31967\u20133198 (2024).\n\nLiu, Y. et al. 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Preprint at https://doi.org/10.48550/arXiv.2210.09984 (2022).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "N.M.A.K. acknowledges the funding support from Google Research Scholar Award and the Alexander von Humboldt Foundation. I.M. thanks University Grants Commission (UGC), Government of India for the NET-JRF fellowship (221610021768). J.S. acknowledge the funding received from the Ministry of Education, Government of India, in the form of research fellowship. M.Z. acknowledges the funding received from the PMRF award by the Government of India. M.M.S. acknowledges support from the European Union (ERC, NewGLASS, 101044664). L.W. acknowledges funding from the Carl Zeiss Foundation through its Breakthrough program. The authors thank the IIT Delhi HPC facility for computational and storage resources. We also thank Sushant Sinha for his assistance with the image segmentation.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "School of Interdisciplinary Research, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India\n\nIndrajeet Mandal,\u00a0Nitya Nand Gosvami\u00a0&\u00a0N. M. Anoop Krishnan\n\nDepartment of Materials Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India\n\nJitendra Soni\u00a0&\u00a0Nitya Nand Gosvami\n\nDepartment of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India\n\nMohd Zaki\u00a0&\u00a0N. M. Anoop Krishnan\n\nDepartment of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark\n\nMorten M. Smedskjaer\n\nLeibniz Institute of Photonic Technology, Jena, Germany\n\nKatrin Wondraczek\n\nOtto Schott Institute of Materials Research, University of Jena, Jena, Germany\n\nLothar Wondraczek\n\nYardi School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India\n\nNitya Nand Gosvami\u00a0&\u00a0N. M. Anoop Krishnan\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nI.M. developed the AILA framework, generated the visualizations for the figures and tables, and conducted the experiments. J.S. prepared figures and performed experiments. M.Z. contributed to the code development. M.M.S., K.W., and L.W. edited the draft and validated the results. All authors contributed to the conceptualization of the project, the design of the methodology. I.M. and N.M.A.K. wrote the original draft of the manuscript. All authors contributed to reviewing and editing the manuscript. N.M.A.K. and N.N.G. were responsible for funding acquisition, project administration, resource provision, and overall supervision of the project.\n\nCorrespondence to\n Nitya Nand Gosvami or N. M. Anoop Krishnan.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Evaluating large language model agents for automation of atomic force microscopy.\n Nat Commun 16, 9104 (2025). https://doi.org/10.1038/s41467-025-64105-7\n\nDownload citation\n\nReceived: 01 March 2025\n\nAccepted: 04 September 2025\n\nPublished: 14 October 2025\n\nVersion of record: 14 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-64105-7\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Filamins A and B as a Specific Mechanism Sustaining Th2 Lymphocyte Functions", + "journal": "Nature Communications", + "published": "05 December 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM5_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM6_ESM.mp4" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM7_ESM.mp4" + }, + { + "label": "Supplementary Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM8_ESM.mp4" + }, + { + "label": "Supplementary Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM9_ESM.mp4" + }, + { + "label": "Supplementary Movie 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM10_ESM.mp4" + }, + { + "label": "Supplementary Movie 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM11_ESM.mp4" + }, + { + "label": "Supplementary Movie 8", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM12_ESM.mp4" + }, + { + "label": "Supplementary Movie 9", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM13_ESM.mp4" + }, + { + "label": "Supplementary Movie 10", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM14_ESM.mp4" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM15_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM16_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_MOESM17_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ebi.ac.uk/pride/archive/projects/PXD044062", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE251847", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60680", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144586", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109737", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72005", + "/articles/s41467-024-53768-3#Sec31" + ], + "code": [], + "subject": [ + "Asthma", + "T-helper 2 cells" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3878460/v1.pdf?c=1733490407000", + "research_square_link": "https://www.researchsquare.com//article/rs-3878460/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53768-3.pdf", + "preprint_posted": "14 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Augmenting the portfolio of therapeutics for type 2-driven diseases is crucial to address unmet clinical needs and to design personalized treatment schemes. An attractive therapy for such diseases would consist in targeting the recruitment of T helper 2 (Th2) lymphocytes to inflammatory sites. Herein, we unravel the degradation of filamins (FLN) A and B by the ASB2\u03b1 E3 ubiquitin ligase as a mechanism sustaining Th2 lymphocyte functions. Low levels of FLNa and FLNb confer an elongated shape to Th2 lymphocytes associated with efficient \u03b1V\u03b23 integrin-dependent cell migration. Genes encoding the \u03b1V\u03b23 integrin and ASB2\u03b1 belong to the core of Th2-specific genes. Using genetically modified mice or the small molecule thiostrepton, we find that increasing the levels of FLNa and FLNb in Th2 lymphocytes reduces airway inflammation through diminished Th2 lymphocyte recruitment in inflamed lungs. Collectively, our results highlight ASB2\u03b1 and its substrates FLNa and FLNb to rewire Th2 lymphocyte-mediated responses.Biological sciences/Immunology/Lymphocytes/T cells/CD4-positive T cells/T-helper 2 cellsBiological sciences/Immunology/Immunological disorders/Inflammatory diseases/AsthmaAsthmaE3 ubiquitin ligaseFilaminT helper lymphocytesType 2 immunity", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nA.S. is an employee of Sanofi. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "MaireetalSupplementaryInformation.pdfSupplementary InformationSupplementaryTable1Maire.xlsxSupplementary Table 1SupplementaryTable2Maire.xlsSupplementary Table 2SupplementaryTable4Maire.xlsxSupplementary Table 4SupplementaryVideo1ctrl.mp4Supplementary Video 1SupplementaryVideo2ASB2cKO.mp4Supplementary Video 2SupplementaryVideo3DMSOtreated.mp4Supplementary Video 3SupplementaryVideo4TSTtreated.mp4Supplementary Video 4NCOMMS2402393rs.pdfReporting Summary", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Augmenting the portfolio of therapeutics for type 2-driven diseases is crucial to address unmet clinical needs and to design personalized treatment schemes. An attractive therapy for such diseases would consist in targeting the recruitment of T helper 2 (Th2) lymphocytes to inflammatory sites. Herein, we show the degradation of filamins (FLN) a and b by the ASB2\u03b1 E3 ubiquitin ligase as a mechanism sustaining Th2 lymphocyte functions. Low levels of FLNa and FLNb confer an elongated shape to Th2 lymphocytes associated with efficient \u03b1V\u03b23 integrin-dependent cell migration. Genes encoding the \u03b1V\u03b23 integrin and ASB2\u03b1 belong to the core of Th2-specific genes. Using genetically modified mice, we find that increasing the levels of FLNa and FLNb in Th2 lymphocytes reduces airway inflammation through diminished Th2 lymphocyte recruitment in inflamed lungs. Collectively, our results highlight ASB2\u03b1 and its substrates FLNa and FLNb to alter Th2 lymphocyte-mediated responses.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "T helper (Th) lymphocytes are key mediators of adaptive immune responses that, via secretion of distinct cytokine combinations, orchestrate immune responses against foreign antigens, neo-antigens, and self-antigens, respectively in the context of infection, cancer, and auto-immunity. The understanding of the molecular mechanisms involved in the regulation of specific Th lymphocyte properties and functions will pave the way to the development of new approaches to tackle immune system-mediated diseases.\n\nAsthma is a chronic inflammatory disease of the lower airways that affects nearly 400 million people worldwide. Despite the high diversity of endotypes characteristic of this pathology, half of asthmatic patients present a high type 2 inflammation initiated by the release of type 2 cytokines. Th2 lymphocytes represent a major source of type 2 cytokines and are key drivers of asthma pathogenesis because of their myriad effects on both structural and inflammatory cells in the airways1,2. Accordingly, many of the therapeutic antibodies for type 2-driven diseases are targeting type 2 cytokines and their receptors or IgE. In addition, a number of promising small-molecule drugs and vaccines are in the development pipeline3. Biologics have demonstrated encouraging results in clinical trials and some are approved for the type 2-high endotype of severe asthma3,4. These strategies may be further optimized when combined with the targeting of regulators of Th2 cell recruitment to sites of inflammation.\n\nT cell migration is essential for T cell response5. The initial step usually happens in secondary lymphoid organs with the search for cognate antigen on many antigen-presenting cells that requires rapid scanning. Once activated and fully differentiated, effector T lymphocytes exit the lymph nodes and migrate to the site of injury in peripheral tissues to find their antigen. Motility of T lymphocytes is governed by cell-intrinsic events that are regulated by their activation status as well as microenvironment cues. Beyond their distinct cytokine secretion profile, Th1 and Th2 lymphocytes harbor different scanning modes to migrate within inflamed tissues and optimize their respective effector functions6,7. In contrast to Th1 lymphocytes, Th2 lymphocytes appear to adopt a fast and wide scanning strategy that depends on \u03b1V\u03b23 integrins and is uncoupled from signals such as those triggered by chemokines7.\n\nSeveral E3 ubiquitin ligases are known to regulate differentiation and functions of Th2 lymphocytes8,9. Among them, ASB2\u03b1, the specificity subunit of a multimeric E3 ubiquitin ligase of the Cullin 5 RING Ligase (CRL5) family10, is upregulated during Th2 differentiation of naive CD4+ T lymphocytes11,12,13. Importantly, loss of ASB2 attenuated colitis-associated tumorigenesis in mice due to reduced Th2 response and enhanced type 1 antitumor immune response13. However, the molecular and cellular mechanisms whereby ASB2\u03b1 exerts its effects to positively regulate Th2 lymphocyte function remain unknown. ASB2\u03b1 triggers ubiquitylation and proteasomal degradation of filamins (FLN) a and b14. FLNa is an ubiquitously expressed actin-binding and cross-linking protein whose primary function is to organize actin filaments in an orthogonal network15. FLNa also interacts with about a hundred binding partners, many of which being involved in the regulation of signaling pathways converging towards actin cytoskeleton. Indeed, FLNa positively regulates signaling pathways downstream of the T cell receptor (TCR) and the CD28 costimulatory molecule16,17,18 and interacts with Tc-mip (truncated c-MAF inducing protein), an adapter protein involved in c-MAF-dependent Th2 signaling pathway19.\n\nOne of the most conserved activity of FLNa is to bind integrins, thereby maintaining the later in an inactive state20. Two mechanisms have been proposed. First, binding of FLNa to the C terminus of the integrin \u03b2 tail (\u03b21, \u03b22, \u03b23, or \u03b27) results in direct competition with the binding of the integrin activator talin by occupying an overlapping binding site21,22. Second, FLNa can form a ternary complex engaging the cytoplasmic tails of both integrin \u03b1IIb and \u03b23, thereby stabilizing the inner-membrane clasp and competing with talin recruitment to the \u03b2 subunit cytoplasmic tail by binding both the C-terminal and membrane-proximal regions of the \u03b23 tail23. In addition, FLNa can also associate with the \u03b1IIb cytoplasmic tail of active \u03b1IIb\u03b23 integrin to inhibit cell migration likely because of excessively enhanced integrin outside-in signaling24. We previously showed that ASB2\u03b1-mediated degradation of FLNa and FLNb regulates actin cytoskeleton remodeling and cell motility in different cell types14,25,26,27,28,29. However, the exact role and mechanisms of action of ASB2\u03b1 in Th2 lymphocytes are not well-defined.\n\nIn the present study, we ask whether ASB2\u03b1 triggers FLNa and FLNb degradation in Th2 lymphocytes and whether this is a mechanism sustaining Th2 lymphocyte functions. Furthermore, we investigate whether targeting the ASB2\u03b1-FLNa/b axis may represent a potential therapeutic opportunity in asthma using mouse models of airway inflammation. Using unbiased large-scale approaches, we show that FLNa and FLNb are the only substrates of ASB2\u03b1 in Th2 lymphocytes and that low levels of FLNa and FLNb promote an elongated cell shape conducive to a dynamic migration. We further show that ASB2\u03b1-deficient Th2 lymphocytes exhibit less \u03b1V\u03b23 integrin-dependent dynamic behavior. Accordingly, loss of ASB2\u03b1 in Th2 lymphocytes reduces their recruitment in inflamed lungs and attenuates airway inflammation.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To elucidate the roles of ASB2\u03b1 in Th2 lymphocytes, we first evaluated its expression in differentiating Th2 lymphocytes. ASB2\u03b1 transcripts are highly upregulated after 5 days of culture of naive CD4+ lymphocytes in Th2 polarizing conditions (Fig.\u00a01a) and are almost undetectable in Th2 lymphocytes generated from ASB2 cKO mice (Fig.\u00a01b). Because E3 ubiquitin ligases often ubiquitylate several substrates, we used an unbiased and broad mass spectrometry approach to identify ASB2\u03b1 substrates by comparing the whole proteomes of Th2 lymphocytes generated from ctrl or ASB2 cKO mice. As expected, ASB2 peptides were barely detected in ASB2\u03b1-deficient Th2 lymphocytes (Fig.\u00a01c, d). Deletion of ASB2 had no impact on the expression of the master transcription factors of the Th2 lineage STAT6, GATA3, and c-MAF (Fig.\u00a01d), suggesting no role of ASB2\u03b1 in the generation of Th2 lymphocytes. Out of the >6400 quantified proteins in MS experiments, FLNa and FLNb stood out as the only proteins being more abundantly expressed in ASB2\u03b1-deficient vs ctrl Th2 lymphocytes (Fig.\u00a01c, d and Supplementary Data\u00a01 and Pride database with the dataset identifier PXD044062), pointing to the selectivity of ASB2\u03b1 in controlling FLNa/FLNb levels. Enhanced expression of FLNa and FLNb at the protein levels in ASB2\u03b1-deficient Th2 lymphocytes was confirmed by western blot (Supplementary Fig.\u00a01a) and intracellular flow cytometry (Fig.\u00a01e). In contrast, levels of FLNa and FLNb transcripts were similar in ctrl and ASB2 cKO Th2 lymphocytes (Supplementary Fig.\u00a01b). Furthermore, we showed that FLNa was conjugated to ubiquitin chains in ctrl but not in ASB2 cKO Th2 lymphocytes (Fig.\u00a01f) and that FLNa degradation in ctrl Th2 lymphocytes was proteasome-dependent (Fig.\u00a01g). Notably, lower levels of FLNa and FLNb in ctrl Th2 lymphocytes compared to naive CD4+ T lymphocytes or to other T effector lymphocytes were observed by western blot (Fig.\u00a01h and Supplementary Fig.\u00a01c) and by intracellular flow cytometry (Fig.\u00a01i), while the levels of FLNa and FLNb transcripts were similar in Th1, Th2, Th17 and Treg lymphocytes (Supplementary Fig.\u00a01d). In agreement with the low levels of ASB2\u03b1 transcripts previously measured in naive CD4+ T lymphocytes, Th1 lymphocytes and Th17 lymphocytes13, loss of ASB2\u03b1 had no impact on the abundance of FLNa and FLNb in these cells (Supplementary Fig.\u00a01e, f). Protein levels of FLNa were also lower in Th2 lymphocytes than in Th1 or Th17 lymphocytes of human peripheral blood mononuclear cells (PBMCs) isolated from healthy donors (Fig.\u00a01j). ASB2\u03b1 transcript and protein are also induced during in vitro Th2 differentiation of naive human CD4+ T lymphocytes (Fig.\u00a01k, l and Supplementary Fig.\u00a01g). In contrast, ASB2\u03b1 transcripts are low in Th1 lymphocytes generated in vitro from naive human CD4+ T lymphocytes (Fig.\u00a01k). Moreover, FLNa degradation in human Th2 lymphocytes is proteasome-dependent as evidenced by increased levels of FLNa after proteasome inhibition (Fig.\u00a01m). Altogether, these results demonstrate that the FLNa and FLNb proteins are continuously and selectively degraded by ASB2\u03b1 in mouse Th2 lymphocytes and suggest that this mechanism is conserved in humans.\n\na Relative expression of ASB2\u03b1 transcripts during Th2 differentiation of naive CD4+ T lymphocytes of ctrl mice assessed by RT-qPCR. b Expression of ASB2\u03b1 transcripts in ctrl and ASB2 cKO Th2 lymphocytes assessed by RT-qPCR. c, d Mass spectrometry quantitative analyses of protein abundance differences between cell extracts of ctrl and ASB2 cKO Th2 lymphocytes. The volcano plot (c) illustrates for each protein the statistical significance of the variation as a function of the amplitude of the abundance (log2) difference between the two conditions. Dashed lines indicate cut-off values corresponding respectively to a Student t-test p value of 0.01 and a log2-transformed intensity difference of 0.5. Bar plots (d) show the intensities of ASB2, STAT6, GATA3, c-MAF, FLNa, and FLNb in each condition based on the MS measurements. Full data are presented in Supplementary Data\u00a01. e Expression of FLNa was analyzed by intracellular flow cytometry in ctrl and ASB2 cKO Th2 lymphocytes. f Cell extracts of ctrl and ASB2 cKO Th2 lymphocytes were immunoprecipitated with control (Ctrl IP) or anti-FLNa (FLNa IP) antibodies. Pre-cleared cell lysates (input), unbound, and bound fractions were immunoblotted with antibodies to FLNa. After stripping, the blot was reprobed with antibodies to ubiquitylated proteins. One experiment out of two is shown. g Expression of FLNa was analyzed by intracellular flow cytometry in ctrl Th2 lymphocytes treated at day 5 with the PS-341 proteasome inhibitor at 4\u2009nM during 20\u2009h. h Expression of FLNa and FLNb was analyzed by western blot in naive CD4+ T cells, Th1, Th2, Th17, and Treg cells generated from naive CD4+ T lymphocytes of ctrl mice. i Expression of FLNa was analyzed by intracellular flow cytometry in Th1, Th2, Th17, and Treg cells generated from naive CD4+ T lymphocytes of ctrl mice. j Representative flow cytometry and quantification of FLNa expression in Th1 (CD4+CXCR3+CCR6\u2212), Th2 (CD4+CXCR3\u2212CCR6\u2212CRTH2+) and Th17 (CD4+CXCR3\u2212CCR6+) lymphocytes of human PBMCs. k Relative expression of ASB2 transcripts in naive human CD4+ T lymphocytes and after 5 days of culture in a Th2 or Th1 polarizing medium assessed by RT-qPCR. l Expression of ASB2\u03b1 was analyzed by western blot in naive human CD4+ T lymphocytes and after 3 and 6 days of culture in a Th2 polarizing medium. m Expression of FLNa was analyzed by intracellular flow cytometry in human Th2 lymphocytes treated at day 5 with the PS-341 proteasome inhibitor at 10\u2009nM during 20\u2009h. n Gene set enrichment analyses of genes significantly up- and down-regulated in ctrl Th2 lymphocytes upon stimulation performed using transcriptomes of stimulated vs non-stimulated ASB2 cKO Th2 lymphocytes. Data are mean\u2009\u00b1\u2009SEM of biological replicates. Sample size, d1\u2009=\u20096, d2\u2009=\u20096, d3\u2009=\u200914, d4\u2009=\u200911, d5\u2009=\u20099 and d6\u2009=\u200914 for (a); ctrl\u2009=\u200928 and cKO\u2009=\u200932 for (b); ctrl\u2009=\u20099 and cKO\u2009=\u20099 for (c); ctrl\u2009=\u20099 and cKO\u2009=\u20099 for (d); ctrl\u2009=\u200922 and cKO\u2009=\u200921 for (e); ctrl\u2009=\u20098 for (g); Th1\u2009=\u20099, Th2\u2009=\u20099, Th17\u2009=\u20096 and Treg\u2009=\u20096 for (i); n\u2009=\u20098 for (j); naive\u2009=\u20094, Th2\u2009=\u20094 and Th1\u2009=\u20093 for (k); n\u2009=\u20094 for (l); and n\u2009=\u20097 for (m). p values were calculated using the two-sided Mann\u2013Whitney t-test except in (g, j, m) where the Wilcoxon matched-pairs signed rank test was used. Source data are provided as a Source Data file.\n\nBecause ASB2\u03b1-mediated degradation of FLNa and FLNb could be involved in the modulation of Th2 lymphocyte signaling and thus in the regulation of Th2-specific genes, the transcriptomic signatures of ctrl and ASB2\u03b1-deficient Th2 lymphocytes were established by RNA-seq (Fig.\u00a01n and Supplementary Fig.\u00a02). Our analyses failed to detect differentially expressed genes between ctrl and ASB2 cKO Th2 lymphocytes (Supplementary Data\u00a02). In fact, Th2-specific genes exhibited similar expression patterns in ctrl and ASB2 cKO Th2 lymphocytes, while Th1 and Th17 gene signatures were not deregulated in the absence of ASB2\u03b1 (Supplementary Fig.\u00a02a\u2013d). Collectively, these analyses strongly support that ASB2\u03b1 does not play a role in regulating the expression of Th genes. We next tested whether ASB2\u03b1-deficiency could nevertheless affect the transcriptional program in Th2 lymphocytes upon T-cell stimulation. Once again, our RNA-seq data did not reveal any significant difference between ctrl and ASB2 cKO Th2 lymphocytes (Fig.\u00a01n and Supplementary Fig.\u00a02). Altogether, these data indicate that ASB2\u03b1 does not shape Th2 lymphocyte identity at the transcriptional level.\n\nBecause FLNa has a dual role in controlling the architecture and the mechanics of the actin cytoskeleton20, we examined whether the lower levels of FLNa and FLNb in mouse Th2 lymphocytes impact cell morphology using high-content imaging. As shown in Fig.\u00a02a\u2013d, lower levels of FLNa and FLNb are associated with increased cell area and perimeter, as well as an elongated shape in Th2 lymphocytes compared to Th1, Th17, or Treg lymphocytes. In accordance with these observations, higher levels of FLNa and FLNb in ASB2\u03b1-deficient Th2 lymphocytes are associated with reduced cell area and perimeter, and a rounded shape of these cells compared to ctrl Th2 lymphocytes (Fig.\u00a02e\u2013g). In addition, low levels of FLNa in human Th2 lymphocytes are associated with increased cell area and perimeter, and an elongated shape compared to naive human CD4+ T lymphocytes (Fig.\u00a02h, i). These results demonstrate that the low levels of FLNa and FLNb due to their degradation driven by ASB2\u03b1 confer specific morphological features to Th2 lymphocytes.\n\na\u2013d Th1, Th2, Th17, and Treg ctrl lymphocytes were seeded onto VCAM-1 coated 384-well plates and analyzed by immunofluorescence with antibodies to FLNa and FLNb or phalloidin. Representative fluorescent images of one experiment out of 3 (a), FLNa MFI and FLNb MFI (b), radar plot with FLNa MFI, FLNb MFI, cell area, cell perimeter, and width to length ratio (relative to the values measured in Th2 lymphocytes) (c) and violin plots of cell area, cell perimeter and width to length ratio (d) are shown. e\u2013g Ctrl and ASB2 cKO Th2 lymphocytes were allowed to adhere in 384-well plates coated with vitronectin, fixed, and stained for FLNa, FLNb, and F-actin. Representative fluorescent images of one experiment out of 4 (e), radar plot with FLNa MFI, FLNb MFI, cell area, cell perimeter, and width-to-length ratio (relative to the values measured in ctrl Th2 lymphocytes) (f), and violin plots with FLNa MFI, FLNb MFI, cell area, cell perimeter and width to length ratio (g) are shown. h, i Human naive CD4\u2009+\u2009T lymphocytes and in vitro generated Th2 lymphocytes were seeded onto VCAM-1 coated 384-well plates and analyzed by immunofluorescence with antibodies to FLNa or phalloidin. Violin plots of FLNa MFI (h), cell area, cell perimeter, and width-to-length ratio (i) are shown. Scale bar, 20\u2009\u00b5m. Data are mean\u2009\u00b1\u2009SEM. Sample size: Th1\u2009=\u20092097, Th2\u2009=\u20093397, Th17\u2009=\u20091284 and Treg\u2009=\u20091918 for (b) (left); Th1\u2009=\u2009222, Th2\u2009=\u2009660, Th17\u2009=\u2009750 and Treg\u2009=\u2009951 for (b) (right); Th1\u2009=\u20091002, Th2\u2009=\u20092405, Th17\u2009=\u2009486 and Treg\u2009=\u2009560 for (d) (left); ctrl\u2009=\u20097189 and cKO\u2009=\u20096878 for (f) (FLNa MFI); ctrl\u2009=\u20095159 and cKO\u2009=\u20093139 for (f) (FLNb MFI); ctrl\u2009=\u20092589 and cKO\u2009=\u20091605 for (f) (width/length ratio, area, perimeter); naive\u2009=\u20091141 and Th2\u2009=\u20091137 for i. p values were calculated using the two-sided Mann\u2013Whitney t-test except in (g, j) where the Wilcoxon matched-pairs signed rank test was used. Source data are provided as a Source Data file.\n\nBecause FLNa is also a gatekeeper to integrin activation20, we next examined the expression of \u03b1 and \u03b2 integrin subunits in Th2 lymphocytes by semi-quantitative MS. As shown in Fig.\u00a03a and Supplementary Data\u00a01, the \u03b1V\u03b23 integrin together with the leukocyte-specific \u03b1L\u03b22 integrin are the main integrin proteins expressed in Th2 lymphocytes and ASB2 deficiency has no impact on their abundance. Given that transcriptional specificity is largely controlled by chromatin-based regulations in differentiating Th cells, we hypothesized that the genes encoding the \u03b1V (ITGAV) and \u03b23 (ITGB3) integrin subunits and the ASB2\u03b1 protein might be differentially controlled by epigenetic regulatory pathways in different Th cell lineages. We first conducted expression profiling of genes encoding the \u03b1 and \u03b2 subunits of integrins in naive, Th1, Th2, and Th17 lymphocytes using our previously published transcriptomic data30. ASB2, ITGAV, and ITGB3 transcripts are more expressed in Th2 lymphocytes compared to in naive, Th1, and Th17 lymphocytes (Fig.\u00a03b). Global mapping of RNApol II, and active (H3K27ac, H3K4me1) and repressive (H3K27me3) histone marks in naive, Th1, Th2 and Th17 lymphocytes indicated that ASB2, ITGAV and ITGB3 genes harbor cis-regulatory regions that are specifically active in Th2 lymphocytes. These latter are either poised or repressed through H3K27me3-dependent silencing mechanisms in the other lineages (Fig.\u00a03c). This indicates that chromatin remodeling machineries maintain these enhancers in their respective active and silent states implying that these three genes play an important role in Th2 lymphocyte identity and/or function. All these data imply that ASB2, ITGAV, and ITGB3 belong to the core of Th2-specific genes. We then wondered whether the expression of ASB2, ITGAV, and ITGB3 transcripts were upregulated in Th2 lymphocyte-dependent pathological settings. Analyses of transcriptomic data31,32 showed that ASB2, ITGAV, and ITGB3 transcripts are upregulated in both ovalbumin (OVA)-induced airway inflammation and house dust mite (HDM)-induced asthma (Supplementary Fig.\u00a03a, b). Importantly, cell surface expression of \u03b1V and \u03b23 integrin subunits was also higher in human Th2 lymphocytes compared to naive CD4+ T lymphocytes or Th1, Th17 and Treg lymphocytes of human PBMCs (Fig.\u00a03d), and in vitro generated human Th2 lymphocytes compared to naive human CD4\u2009+\u2009T lymphocytes (Fig.\u00a03e). Overall, these results indicate a coordinated regulation of ASB2\u03b1 and \u03b1V\u03b23 integrin in Th2 lymphocytes to build an efficient Th2 lymphocyte response.\n\na Mass spectrometry semi-quantitative analyses of integrin abundance in cell extracts of ctrl and ASB2 cKO Th2 lymphocytes (n\u2009=\u20099 replicates). Full data are presented in Supplementary Data\u00a01. b Heatmap showing the expression of genes encoding the \u03b1 and \u03b2 integrin subunits (ITGA and ITGB, respectively) in naive, Th2, Th1, and Th17 cells (analyzed from ref. 30). RPKM intensities were log10 transformed and are displayed as colors ranging from blue to red as shown in the key. c RNA polymerase II (Pol II), H3K27ac, H3K4me1 and H3K27me3 signals across the ASB2, ITGAV and ITGB3 loci. The identified cis-regulatory regions are highlighted (GSE14458630); d Representative flow cytometry and quantification of cell surface expression of \u03b1V and \u03b23 integrin subunits in naive CD4+ T cells and in Th1 (CD4+CXCR3+CCR6\u2212CRTH2\u2212FoxP3\u2212), Th2 (CD4+CXCR3\u2212CCR6\u2212CRTH2+FoxP3\u2212), Th17 (CD4+CXCR3\u2212CCR6+CRTH2\u2212FoxP3\u2212) and Treg (CD4+CXCR3\u2212CCR6\u2212CRTH2\u2212FoxP3+) lymphocytes of human PBMCs. e Quantification of cell surface expression of \u03b1V and \u03b23 integrin subunits in naive and in vitro generated human Th2 lymphocytes. Data are mean\u2009\u00b1\u2009SEM of biological replicates. Sample size: ctrl\u2009=\u20099 and cKO\u2009=\u20099 for (a); n\u2009=\u20096 for (d); and n\u2009=\u20094 for (e). p values were calculated using the two-sided Mann\u2013Whitney t-test except in (d) where the Wilcoxon matched-pairs signed rank test was used. Source data are provided as a Source Data file.\n\nFLNa is a negative regulator of integrin-dependent cell migration21,23,24. We, therefore, speculated that degradation of FLNa and FLNb triggered by ASB2\u03b1 in Th2 lymphocytes favors fast \u03b1V\u03b23 integrin-dependent migration. We first verified that ASB2\u03b1 loss has no effect on the total amount and cell surface expression of \u03b1V and \u03b23 integrin subunits (Fig.\u00a03a and Supplementary Fig.\u00a03c\u2013e). We then used live imaging to study the dynamics of ctrl and ASB2 cKO Th2 lymphocytes seeded onto vitronectin-coated slides (Supplementary Movies\u00a01, 2). Single-cell analysis revealed marked differences in the dynamic motility patterns of ctrl and ASB2 cKO Th2 lymphocytes (Fig.\u00a04a\u2013f). ASB2\u03b1-deficient Th2 lymphocytes exhibited a diminished track displacement length (Fig.\u00a04a, c), a diminished mean scanned area and persistence (Fig.\u00a04a, d, e), associated with a reduced cell velocity compared to ctrl Th2 lymphocytes (Fig.\u00a04f, left panel). This reduced cell velocity is not due to a defect in the initiation of the migration since cells that have a track length\u2009>\u200950\u2009\u00b5m present also a reduced velocity in the absence of ASB2 (Fig.\u00a04f, right panel). Our results demonstrated that ASB2\u03b1-deficient Th2 lymphocytes are less motile, indicating that loss of ASB2\u03b1 impacted the dynamic behavior of Th2 lymphocytes. As previously observed with fixed cells, live imaging showed that ASB2\u03b1-deficient Th2 lymphocytes have a rounded shape compared to ctrl Th2 lymphocytes (Fig.\u00a04g). Moreover, we observed a positive correlation between the elongated shape and the track displacement length or the average velocity of ctrl Th2 lymphocytes but not of ASB2\u03b1-deficient Th2 lymphocytes (Fig.\u00a04h, i). Taken together, our data demonstrate that the low levels of FLNa and FLNb due to their degradation driven by ASB2\u03b1 confer specific migratory properties to Th2 lymphocytes.\n\nCtrl and ASB2 cKO Th2 lymphocytes seeded onto vitronectin-coated slides were imaged by time-lapse microscopy. Representative images (left) and individual tracks (right) of one experiment out of 5 (a), percentages of migrating cells (b), track length (c), mean scanned area (d), persistence coefficient (e), average velocities of all the cells (left panel) or cells with a track length\u2009>\u200950\u2009\u00b5m (right panel) (f), elongation coefficient and average sphericity (g) are shown. Scatter plots showing correlation data for elongation coefficient and track length (h) or average velocity (i) of ctrl or ASB2 cKO Th2 lymphocytes. Linear regression-fit curves are shown as red lines. Scale bar, 20\u2009\u00b5m. In (a\u2013g), Data are mean\u2009\u00b1\u2009SEM of 5 biological replicates. Sample size: ctrl\u2009=\u20091070 and cKO\u2009=\u2009916 for (c); ctrl\u2009=\u20091070 and cKO\u2009=\u2009916 for (d); ctrl\u2009=\u20091070 and cKO\u2009=\u2009916 for (e); ctrl\u2009=\u20091070 and cKO\u2009=\u2009916 for (f) (left); ctrl\u2009=\u2009685 and cKO\u2009=\u2009470 for (f) (right); ctrl\u2009=\u200992,674 and cKO\u2009=\u200985,399 for (g); ctrl\u2009=\u20091070 and cKO\u2009=\u2009916 for (h); and ctrl\u2009=\u20091070 and cKO\u2009=\u2009916 for (i). p values were calculated using the two-sided Mann\u2013Whitney t-test. In (h, i), correlations between nonparametric variables were evaluated using Spearman rank correlation test (r). Source data are provided as a Source Data file.\n\nTo understand how FLNa and FLNb accumulation in ASB2\u03b1-deficient Th2 lymphocytes inhibits cell migration, we challenged the two models linking FLNa to inhibition of integrin-dependent cell motility. In the first model, FLNa maintains integrins in an inactive state21. In a second model, FLNa promotes integrin outside-in signaling leading to inhibition of dynamic cell movement24. We first studied by live imaging the dynamics of ctrl and ASB2 cKO Th2 lymphocytes treated with MnCl2 to activate integrins and allowed to migrate onto vitronectin-coated slides. Compared to untreated ctrl Th2 lymphocytes, MnCl2-treated ctrl Th2 lymphocytes exhibited a diminished track displacement length and a reduced cell velocity that were similar to those measured in untreated or in MnCl2-treated ASB2 cKO Th2 lymphocytes (Supplementary Movies\u00a03\u20136 and Fig.\u00a05a\u2013d), suggesting that integrins are more activated in ASB2\u03b1-deficient Th2 lymphocytes than in ctrl Th2 lymphocytes. We then assessed the dynamics of ctrl and ASB2 cKO Th2 lymphocytes treated with a combination of anti-\u03b1V and anti-\u03b23 integrin blocking antibodies to inhibit integrins. As expected, following \u03b1V\u03b23 integrin inhibition, ctrl Th2 lymphocytes showed decreased track displacement length and decreased cell velocity compared to untreated cells in agreement with the role of the \u03b1V\u03b23 integrin in Th2 lymphocyte migration cells (Supplementary Movies\u00a07, 8 and Fig.\u00a05e\u2013h). In contrast, following \u03b1V\u03b23 integrin inhibition, ASB2 cKO Th2 lymphocytes exhibited an enhanced track displacement length and an increased cell velocity compared to untreated cells (Supplementary Movies\u00a09, 10 and Fig.\u00a05e\u2013h), reinforcing the view that increased levels of FLNa and FLNb in ASB2 cKO Th2 lymphocytes leads to abnormal \u03b1V\u03b23 integrin-dependent cell migration.\n\nCtrl and ASB2 cKO Th2 lymphocytes were either untreated or treated with MnCl2 (a\u2013d) and treated with anti-\u03b1V and anti-\u03b23 integrin blocking antibodies (Ab) or their corresponding isotypic controls (iso) (e\u2013h), and allowed to migrate onto vitronectin-coated slides. Cells were imaged by time-lapse microscopy. Individual tracks (a, e), percentages of migrating cells (b, f), track length (c, g), average velocities of all the cells (d, h) are shown. Data are mean\u2009\u00b1\u2009SEM of 4 and 5 biological replicates for ctrl and cKO Th2 lymphocytes respectively. Sample size: ctrl\u2009=\u20091809, ctrl+MnCl2\u2009=\u20092344, cKO\u2009=\u20091611, and cKO\u2009+\u2009MnCl2\u2009=\u20091717 for (c, d); and ctrl\u00a0+ iso\u2009=\u20092768, ctrl\u2009+Ab\u2009\u2009=\u20092803, cKO\u00a0+ iso\u2009=\u20092261, and cKO\u2009+\u2009Ab\u2009=\u20091989 for (g, h). p values were calculated using the two-sided Mann\u2013Whitney t-test. Source data are provided as a Source Data file.\n\nWe next investigated the specific role of ASB2\u03b1 in Th2 lymphocytes in a mouse model of airway inflammation. Compared to ctrl mice, induction of airway inflammation with OVA injection and challenge in ASB2 cKO mice resulted in: (i) decreased cell infiltration determined by histological score after hematoxylin and eosin (HE) staining of lung sections, decreased mucus secretion assessed by the percentage of Periodic Acid Schiff (PAS) positive bronchi after PAS staining of lung sections and decreased remodeling of the airways quantified by collagen deposits after Masson\u2019s trichrome (MT) staining of lung sections (Fig.\u00a06a); (ii) reduced numbers of leukocytes in the lungs (Fig.\u00a06b); (iii) reduced percentages but similar numbers of alveolar macrophages (Fig.\u00a06c), (iv) less eosinophil recruitment in the lungs (Fig.\u00a06c) in agreement with the reduced expression of eotaxin 2 mRNA in lung lysates (Fig.\u00a06d); (v) reduced numbers of CD4+ cells and Th2 lymphocytes in the lungs (Fig.\u00a06e, f); (vi) similar percentages of CD4+ cells and reduced percentages of Th2 lymphocytes in the bronchoalveolar lavage fluids (BALF; Fig.\u00a06g); (vi) decreased mRNA levels of IL-4, IL-5 and IL-13 in lung lysates (Fig.\u00a06h); (vii) reduced percentages of IL-4+, IL-5+ or IL-13+ in CD4+ cells in the lungs and reduced percentages of IL-4+, IL-5+ or IL-13+ in ST2+CD4+ cells in the lungs (Fig.\u00a06i); and (viii) reduced IL-4, IL-5 and IL-13 secretion by LDLN cells after OVA-antigen restimulation (Fig.\u00a06j). In contrast, numbers and percentages of Th1 lymphocytes in the lungs as well as mRNA levels of TBX21 and IFNG were similar in ctrl and ASB2 cKO mice submitted to OVA-induced inflammation (Supplementary Fig.\u00a04), indicating comparable Th1 responses. FLNa is a substrate for ASB2\u03b1 in lung-infiltrating Th2 lymphocytes of mice submitted to OVA-airway inflammation as evidenced by increased FLNa intensity in ASB2 cKO mice measured by intracellular flow cytometry (Fig.\u00a06k) or mass spectrometry (Fig.\u00a06l). We also used a more clinically-relevant asthma model based on repeated exposures to HDM inhalation. As shown in Fig.\u00a07, loss of ASB2\u03b1 resulted in: (i) decreased inflammation and mucus production (Fig.\u00a07a); (ii) decreased recruitment of leukocytes in the inflamed lungs (Fig.\u00a07b); (iii) decreased recruitment of eosinophils in the lungs (Fig.\u00a07c) and in BALF (Fig.\u00a07d); (iv) similar numbers of CD4+ cells and decreased recruitment of Th2 lymphocytes in the lungs (Fig.\u00a07e); and (v) similar percentages of CD4+ cells in leukocytes and reduced percentages\u00a0of ST2+ cells in CD4+\u00a0T cells in the BALF (Fig.\u00a07f). Furthermore, intracellular flow cytometry experiments showed that FLNa is more abundant in ASB2\u03b1-deficient vs ctrl Th2 lymphocytes from the lungs of mice submitted to HDM-asthma indicating that FLNa is also a substrate of ASB2\u03b1 in Th2 lymphocytes in vivo in a clinically-relevant asthma model (Fig.\u00a07g). Collectively, our results show that loss of ASB2\u03b1 in hematopoietic cells decreases airway inflammation in both models.\n\nCtrl and ASB2 cKO mice were submitted to OVA-airway inflammation (OVA) as indicated in the online methods. Ctrl mice treated with PBS were used as controls. a Inflammation of the lungs assessed using hematoxylin and eosin (HE) staining of lung sections to analyze the infiltration of inflammatory cells (0\u201312-point scale), Masson\u2019s trichrome (MT) staining of lung sections to visualize and quantify collagen deposits (\u00b5m2/\u00b5m) (collagen area/bronchus perimeter), and periodic acid Schiff (PAS) staining to visualize and quantify mucus production. b Data represents the numbers of CD45+ cells in the lungs. c Numbers of CD45+Siglec-F+CD11c\u2212 (eosinophils, left panel) and CD45+Siglec-F+CD11c+ (alveolar macrophages; right panel). Representative flow cytometry plots for CD11c and Siglec-F within a CD45+ gated (middle panel) in the lungs of OVA-treated mice. d Relative expression of eotaxin 2 mRNA in the lung lysates assessed by RT-qPCR. e Numbers of CD45+CD4+ in the lungs. f Percentage of ST2+ cells in CD4+ cells, representative flow cytometry plots for ST2 and CD4 within a CD4+ gate, and numbers of CD45+CD4+ST2+ in the lungs. g Data represents the percentage of CD4+ in CD45+ cells and the percentage of ST2+ in CD4+ cells in the BAL fluids. h Relative expression of IL-4, IL-5 and IL-13 mRNA in the lung lysates. i Data represents the percentage of IL-4+, IL-5+ or IL-13+ in CD4+ cells and in ST2+CD4+ cells in the lungs. j Production of IL-4, IL-5, and IL-13 measured by ELISA after antigen restimulation (OVA) or not (\u2212) of cells of the lung draining lymph nodes of mice submitted to OVA-induced airway inflammation. k Expression of FLNa was analyzed by intracellular flow cytometry in CD45+CD4+ST2+ cells from the lungs of ctrl or ASB2 cKO mice submitted to OVA-induced airway inflammation. l Intensities of FLNa calculated using MaxQuant quantitative metrics in cell extracts of CD45+CD4+ST2+ living cells sorted from the lungs of control or ASB2 cKO mice submitted to OVA-induced airway inflammation. Data are mean\u2009\u00b1\u2009SEM of biological replicates. Sample size: ctrl+PBS\u2009=\u20096, ctrl\u2009+\u2009OVA\u2009=\u200914, and cKO\u2009+\u2009OVA\u2009=\u200917 for (a) (histological score); ctrl+PBS\u2009=\u20093, ctrl\u2009+\u2009OVA\u2009=\u200914, and cKO\u2009+\u2009OVA\u2009=\u200917 for (a) (PAS+ bronchi); ctrl\u2009+\u2009PBS\u2009=\u20093, ctrl\u2009+\u2009OVA\u2009=\u200910, and cKO\u2009+\u2009OVA\u2009=\u200915 for (a) (collagen deposition-left); ctrl\u2009+\u2009PBS\u2009=\u200930, ctrl\u2009+\u2009OVA\u2009=\u2009249, and cKO\u2009+\u2009OVA\u2009=\u2009275 for (a) (collagen deposition-right); ctrl\u2009+\u2009PBS\u2009=\u20096, ctrl\u2009+\u2009OVA\u2009=\u200934, and cKO\u2009+\u2009OVA\u2009=\u200933 for (b); ctrl\u2009+\u2009PBS\u2009=\u20095, ctrl\u2009+\u2009OVA\u2009=\u200926, and cKO\u2009+\u2009OVA\u2009=\u200928 for (c); ctrl\u2009+\u2009OVA\u2009=\u200919 and cKO\u2009+\u2009OVA\u2009=\u200922 for (d); ctrl\u2009+\u2009PBS\u2009=\u20096, ctrl\u2009+\u2009OVA\u2009=\u200932, and cKO\u2009+\u2009OVA\u2009=\u200931 for (e); ctrl\u2009+\u2009PBS\u2009=\u20095, ctrl\u2009+\u2009OVA\u2009=\u200925, and cKO\u2009+\u2009OVA\u2009=\u200924 for (f); ctrl\u2009+\u2009OVA\u2009=\u200921 and cKO\u2009+\u2009OVA\u2009=\u200927 for (g) (left); ctrl\u2009+\u2009OVA\u2009=\u200910 and cKO\u2009+\u2009OVA\u2009=\u200912 for (g) (right); ctrl\u2009+\u2009OVA\u2009=\u200920 and cKO\u2009+\u2009OVA\u2009=\u200924 for (h); ctrl\u2009+\u2009OVA\u2009=\u200914 and cKO\u2009+\u2009OVA\u2009=\u200914 for (i) (left); ctrl\u2009+\u2009OVA\u2009=\u200914 and cKO\u2009+\u2009OVA\u2009=\u200914 for (i) (middle); ctrl\u2009+\u2009OVA\u2009=\u20099 and cKO\u2009+\u2009OVA\u2009=\u20098 for (i) (right); ctrl\u2009+\u2009OVA\u2009=\u20097 and cKO\u2009+\u2009OVA\u2009=\u20098 for (j); ctrl\u2009+\u2009OVA\u2009=\u200920 and cKO\u2009+\u2009OVA\u2009=\u200919 for (k); and ctrl\u2009+\u2009OVA\u2009=\u20093 and cKO\u2009+\u2009OVA\u2009=\u20093 for (l). p values were calculated using the two-sided Mann\u2013Whitney t-test. Scale bar, 200\u2009\u00b5m. Source data are provided as a Source Data file.\n\na\u2013g Ctrl and ASB2 cKO mice were submitted to HDM-allergic airway inflammation (HDM) as indicated. a HE, MT, and PAS staining of lung sections to analyze the infiltration of inflammatory cells and inflammatory scores. b Data represents the numbers of CD45+ cells in the lungs. c Data represents the numbers and the percentages of Siglec-F+CD11c\u2212 in CD45+ cells in the lungs. d Data represents the percentages of Siglec-F+CD11c\u2212 in CD45+ cells in the BAL fluids. e Data represents the numbers of CD4+CD45+ cells, the percentages of ST2+ in CD4+ cells, and the numbers of ST2+CD4+CD45+ cells in the lungs. f Data represents the percentages of CD4+ in CD45+ cells and the percentages of ST2+ in CD4+ cells in the BAL fluids. g Expression of FLNa was analyzed by intracellular flow cytometry in CD45+CD4+ST2+ cells from the lungs of ctrl or ASB2 cKO mice submitted to HDM-induced asthma. h\u2013q OVA-specific Th2 lymphocytes generated from control or ASB2 cKO OT2 mice were transferred to C57Bl/6 recipients that were subsequently submitted to daily OVA inhalations to induce airway inflammation. Analysis was performed 24\u2009h after the 5th (H, O) or after the 1st OVA inhalation (p, q). h HE staining of lung sections. i MT staining of lung sections. j PAS staining of lung sections. k Data represents the numbers of CD45+ in the lungs. l Data represents the numbers and the percentages of Siglec-F+CD11c\u2212 in CD45+ cells in the lungs. m Data represents the percentages of Siglec-F+CD11c\u2212 in CD45+ cells in the BAL fluids. n Data represents the numbers of V\u03b25+V\u03b12+CD4+CD45+ cells in the lungs. o Data represents the percentages of V\u03b25+V\u03b12+ in CD4+ cells in the lungs and the BAL fluids. p Data represents the numbers of V\u03b25+V\u03b12+CD4+CD45+ cells in the lungs. q Data represents the percentages of V\u03b25+V\u03b12+ in CD4+ cells in the lungs and the BAL fluids. Data are mean\u2009\u00b1\u2009SEM of biological replicates. Sample size: ctrl\u2009=\u200912 and cKO\u2009=\u200912 for (a\u2013c); ctrl\u2009=\u200912 and cKO\u2009=\u200910 for (d); ctrl\u2009=\u200912 and cKO\u2009=\u200912 for (e); ctrl\u2009=\u200912 and cKO\u2009=\u200910 for (f); ctrl\u2009=\u200912 and cKO\u2009=\u200912 for (g); ctrl\u2009=\u200910 and cKO\u2009=\u20096 for (k); ctrl\u2009=\u20099 and cKO\u2009=\u20096 for (l); ctrl\u2009=\u200910 and cKO\u2009=\u20096 for (m, n); ctrl\u2009=\u200910 and cKO\u2009=\u20096 for (o) (left); ctrl\u2009=\u20098 and cKO\u2009=\u20094 for (o) (right); ctrl\u2009=\u200916 and cKO\u2009=\u200912 for (p); ctrl\u2009=\u200916 and cKO\u2009=\u200912 for (q) (left); and ctrl\u2009=\u20095 and cKO\u2009=\u20094 for (q) (right). p values were calculated using the two-sided Mann\u2013Whitney t-test. Scale bar, 200\u2009\u00b5m. Source data are provided as a Source Data file.\n\nTo evaluate whether ASB2\u03b1 in Th2 lymphocytes plays important roles in airway inflammation, we used a mouse model that relies on the adoptive transfer of ctrl or ASB2 cKO OVA-specific (OT2) Th2 lymphocytes to C57Bl/6 recipients followed by OVA inhalation. Deletion of ASB2 decreased cell infiltration, mucus secretion, and remodeling of the airways (Fig.\u00a07h\u2013j). Compared to mice that received ctrl OT2 Th2 lymphocytes, mice that received ASB2 cKO OT2 Th2 lymphocytes showed: (i) reduced leukocyte infiltration in the lungs (Fig.\u00a07k); (ii) reduced frequencies and reduced numbers of eosinophils in the lungs (Fig.\u00a07l); (iii) reduced frequencies of eosinophils in the BALF (Fig.\u00a07m); (iii) reduced numbers of V\u03b25+V\u03b12+CD4+CD45+ cells in the lungs (Fig.\u00a07n); (iv) reduced frequencies of OT2 Th2 lymphocytes in the lungs and BALF (Fig.\u00a07o). Altogether, our results indicate that ASB2\u03b1 expressed by Th2 lymphocytes is key to the mediation of airway inflammation and point to ASB2\u03b1-mediated degradation of FLNa and FLNb as a molecular mechanism sustaining Th2 lymphocyte functions. To further elucidate how ASB2\u03b1 loss and subsequent accumulation of FLNa and FLNb alters Th2 lymphocyte functions leading to attenuated airway inflammation, we analyzed the recruitment of transferred OT2 Th2 lymphocytes into the lungs 24\u2009h after the first OVA inhalation. The numbers of ASB2 cKO OT2 Th2 lymphocytes in the lungs (Fig.\u00a07p) and their frequencies in the lungs and BALF (Fig.\u00a07q) were lower compared to those of ctrl Th2 lymphocytes, suggesting decreased recruitment of ASB2\u03b1-deficient Th2 lymphocytes in the inflamed area. Taken together, our results demonstrate that loss of ASB2\u03b1 in Th2 lymphocytes reduces cell mobility and attenuates the recruitment of Th2 lymphocytes in inflamed areas, suggesting that increasing levels of FLNa and FLNb in Th2 lymphocytes might mitigate pathogenic type 2 immune responses.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53768-3/MediaObjects/41467_2024_53768_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we identified original targets to rewire Th2 lymphocyte-mediated responses. We revealed an unexpected role for ASB2\u03b1-mediated degradation of FLNa and FLNb in Th2 lymphocyte-specific functions and properties, and therefore in airway inflammation. Using genetically modified mice, we found that increasing the levels of FLNa and FLNb in Th2 lymphocytes attenuates airway inflammation. Collectively, our results highlight the ASB2\u03b1-FLNa/b axis as a potential therapeutic opportunity to rewire Th2 lymphocyte-mediated responses.\n\nT lymphocytes are essential for adaptive immune responses, in particular CD4+ lymphocytes, which differentiate into effector T lymphocyte subtypes depending on environmental cues. This is largely achieved through transcription factors and chromatin remodelers that initiate and maintain a heritable gene expression program. Indeed, they positively regulate genes critical to the functions one specific Th subset while repressing genes controlling the functions of the other subsets. ASB2, ITGAV and ITGB3 belong to the core set of Th2-specific genes that share common mechanisms of epigenetic and play essential roles in the establishment of the Th2 lymphocyte identity and/or in Th2 lymphocyte functions33,34,35: (i) transcripts of ASB2\u03b1, ITGAV and ITGB3 are higher in the Th2 subset of CD4+ T lymphocytes compared to in naive or to in other Th lymphocytes13,33,36 (and data herein); (ii) active marks were associated with the ASB2, ITGAV and ITGB3 loci in Th2 lymphocytes; (iii) ASB2, ITGAV and ITGB3 are upregulated in both OVA-induced airway inflammation and HDM-induced asthma31,32,37,38. Furthermore, high expression of ITGAV and ITGB3 transcripts in mouse Th2 lymphocytes correlated with high levels of the \u03b1V\u03b23 integrin protein (data herein and7). Importantly, higher expression of \u03b1V and \u03b23 integrin subunits in Th2 lymphocytes compared to the other Th subsets is conserved between humans and mice (data herein and7,39). Because Th2-specific genes including those encoding type 2 cytokines exhibited similar expression patterns in ctrl and ASB2\u03b1-deficient Th2 lymphocytes, the mechanisms linking Thy1-\u03b1V\u03b23 integrin interactions to enhanced Th2 lymphocyte differentiation and increased IL-13/IL-5 production39 is unlikely to rely on the abundance of FLNa and FLNb. Taken together, these reinforce the view that the \u03b1V\u03b23 integrin is critical to Th2 specific functions and antigen-specific lung Th2 lymphocyte responses7,39.\n\nAsthma is characterized by an exacerbated Th2 lymphocyte-mediated inflammation. Herein, we demonstrate that the loss of the ASB2 gene in hematopoietic cells attenuates OVA-induced airway inflammation and HDM-induced asthma in mice. Although we do not exclude the contribution of other immune cells that would be affected by ASB2 deficiency, we show that Th2 lymphocytes play a major contributing role to the impact of ASB2 deletion on airway inflammation using a passive airway inflammation mouse model that relies on the adoptive transfer of OVA-specific Th2 lymphocytes followed by OVA-inhalation.\n\nOur large-scale protein expression profiling based on quantitative mass spectrometry analysis shows that only FLNa and FLNb accumulated in ASB2 cKO Th2 lymphocytes. We also observed polyubiquitylation of FLNa in ctrl Th2 lymphocytes but not in ASB2 cKO Th2 lymphocytes, accumulation of FLNa in ctrl Th2 lymphocytes treated with the PS-341 proteasome inhibitor as well as accumulation of FLNa in Th2 lymphocytes of the lungs of ASB2 cKO mice submitted to OVA-induced airway inflammation or HDM-induced asthma. All these results demonstrate that FLNa and FLNb are continuously targeted to proteasomal degradation by the ASB2\u03b1 CRL5 in Th2 lymphocytes as previously shown in other cell types14,25,29, and point out to the high selectivity of ASB2\u03b1 towards FLNa and FLNb. Although FLNa was previously shown to regulate signaling pathways downstream of the TCR and the CD28 costimulatory molecule16,17,18, our data revealed that the loss of ASB2\u03b1 has no impact on the transcriptomic program of Th2 lymphocytes. These strongly suggest that the ASB2\u03b1-FLNa/FLNb axis is unlikely to play roles in cell signaling and/or transcriptional regulation in Th2 lymphocytes, at least, following TCR/CD28 engagement. Because FLNa plays key roles in the architecture and the mechanics of the actin cytoskeleton40, our results suggest that low levels of FLNa and FLNb in Th2 lymphocytes compared to in Th1, Th17, and Treg lymphocytes are likely to confer specific morphological features to Th2 lymphocytes. In line with this, compared to Th1, Th17, and Treg lymphocytes, Th2 lymphocytes harbor an elongated shape that is lost in ASB2\u03b1-deficient Th2 lymphocytes. Studies proposed that, in contrast to Th1 lymphocytes, Th2 lymphocytes adopt a fast and wide scanning strategy that depends on \u03b1V\u03b23 integrins but is uncoupled from G protein-coupled receptor signals such as those triggered by chemokines6,7. We show here that ASB2\u03b1-deficient Th2 lymphocytes exhibited a diminished track displacement length and a diminished mean scanned area, associated with a reduced cell velocity compared to their ctrl Th2 lymphocyte counterparts. Furthermore, we observed decreased in vivo recruitment of ASB2\u03b1-deficient Th2 lymphocytes in inflamed lungs of mice submitted to airway inflammation or asthma. These results together with the fact that FLNa bridges the actin cytoskeleton to integrins thereby maintaining them in an inactive state20 and that FLNa associates with active integrins to inhibit cell migration24, raise the possibility that the low levels of FLNa and FLNb driven by ASB2\u03b1 in Th2 lymphocyte favor \u03b1V\u03b23 integrin-dependent cell migration to optimize their effector functions. Since compensation by FLNb in the absence of FLNa has been observed27,41, functions of FLNa in T lymphocytes may have been missed or underestimated in assays using FLNa knockout/knockdown cells42. Our data also provide evidences that accumulation of FLNa and FLNb in ASB2\u03b1-deficient in Th2 lymphocyte inhibits \u03b1V\u03b23 integrin-dependent cell migration by enhancing \u03b1V\u03b23 integrin outside-in signaling. In line with these findings, it is tempting to speculate that fine-tuning levels of FLNa and FLNb are critical to optimal \u03b1V\u03b23 integrin-dependent cell migration of Th2 lymphocytes.\n\nInsight into the world of ubiquitin has expanded considerably over the past decade and ubiquitin conjugation to proteins is known to be involved in controlling major biological processes. E3 ubiquitin ligases are the critical components of the ubiquitylation cascade owing to their control of the substrate selectivity, making these enzymes attractive targets for therapeutic intervention43,44. Our results shed light on the key roles of ubiquitylation processes in controlling Th2 lymphocyte functions and point to E3 ubiquitin ligases as potential therapeutic intervention points for inflammatory diseases. Because Th2 lymphocytes are key drivers of type 2 inflammation, direct targeting of Th2 lymphocytes would be expected to drastically lower the burden of type 2-driven diseases. Therefore, ASB2\u03b1 and its substrates FLNa and FLNb may represent novel pharmacological targets to mitigate type 2 immunity. Taken together, our data highlight a prominent role for ASB2\u03b1-mediated degradation of FLNa and FLNb in Th2 lymphocyte functions and credential the ASB2\u03b1-FLNa/b axis as a potential therapeutic opportunity in type 2-high asthma.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Research complies with all relevant ethical regulations. Mice studies were handled according to the Centre National de la Recherche Scientifique (CNRS) and the Institut national de la sant\u00e9 et de la Recherche m\u00e9dicale (Inserm) ethical guidelines and approved by the French Ministry ethic committees (CEEA-122 & CEEA-001).\n\nPS-341 (Euromedex) was prepared in DMSO. Antibodies used are described in Supplementary Table\u00a02.\n\nAll mice were housed under specific pathogen-free conditions. Mice were housed in individually ventilated cage (500\u2009cm2) with a maximum of 5 mice per cage, at 23\u201325\u2009\u00b0C and 53\u201354% humidity with a 12\u2009h light: 12\u2009h dark with the lights being ON during the daytime. At the end of the studies, mice were euthanized by cervical dislocation. For mouse models of airway inflammation, mice were 8\u201314 weeks old. Female Asb2fl/fl (control) and VE-cadherin (VEC) -Cre;Asb2fl/fl (ASB2 cKO) transgenic mice were generated previously25,29. TCR transgenic OT2 mice were crossed with VEC-Cre;Asb2fl/fl to generate female OT2;VEC-Cre;Asb2fl/fl or OT2;Asb2fl/fl mice. Female C57BL/6\u2009J mice were purchased from Janvier Labs. Control or ASB2 cKO transgenic mice were immunized by intraperitoneal injection of 100\u2009\u00b5g ovalbumin (OVA; Sigma-Aldrich) in alum (2\u2009mg; Sigma-Aldrich) or PBS at day 0 and 7. From days 22 to 26, mice were subjected to five daily OVA (50\u2009\u00b5g/day) or PBS inhalation and analyzed at day 28. Control or ASB2 cKO transgenic mice were subjected to HDM inhalation (10\u2009\u00b5g) (Dermatophagoides pteronyssinus, Stallergenes Greer) at day 0. From days 7 to 11, mice were subjected to five daily HDM inhalation (10\u2009\u00b5g/day) and analyzed at day 16. For Th2 lymphocyte adoptive transfer experiments, 3\u2009\u00d7\u2009106 ASB2 cKO or ctrl OT2 Th2 lymphocytes were injected intravenously into C57Bl/6\u2009J recipients. Recipient mice received intranasal OVA (50\u2009\u00b5g) for 1 or 5 days. Twenty-four hours after the last OVA administration, serum, bronchoalveolar lavage fluid (BALF), lungs, and lung-draining lymph nodes (LDLN) were collected and processed. Lungs were digested for 30\u2009min at 37\u2009\u00b0C with 0.25\u2009mg/ml Liberase (Sigma-Aldrich) and 0.5\u2009mg/ml DNAse I (Roche). LDLN were digested for 25\u2009min at 37\u2009\u00b0C with 2.5\u2009mg/ml collagenase D (Roche). Single-cell suspensions were used after filtration through a 70-\u00b5m strainer.\n\nLungs were fixed in 4% paraformaldehyde (Electron Microscopy Sciences) at 4\u2009\u00b0C for 24\u2009h, placed in 70% ethanol, and paraffin-embedded. Sections (5\u2009\u00b5m) were stained with hematoxylin and eosin (HE), Masson Trichrome (MT), or Periodic Acid Schiff (PAS). Histological disease scores from 0 to 3 were attributed based on the severity of peribronchial, perivascular, and interstitial immune cell infiltration, together with thickening of peribronchial epithelium, resulting in a maximum score of 12. Collagen deposit and PAS+ bronchi were quantified using QuPath version 0.4.145. The algorithm was based on a pixel classifier and was trained on representative pictures with dedicated annotations. Bronchi were manually delineated and the respective total amount of detected collagen was obtained. The percentage of PAS+ bronchi was calculated by comparing the percentage of PAS staining in manually detected respective bronchi.\n\nPBMCs were obtained from Etablissement Fran\u00e7ais du Sang, and all human participants provided written informed consent, with appropriate ethical permission in place. Eight healthy individuals (age from 21- to 43-year-old) were enrolled in this study.\n\nMouse naive CD4+ cells were isolated from spleen immunocytes of mice using a CD4 naive T-cell Isolation Kit (#480040; BioLegend) and stimulated for 3 days with plate-bound anti-CD3\u03b5 (5\u2009\u00b5g/ml) and were differentiated in the presence of polarizing cytokines and antibody cocktails (Th1: 10\u2009ng/ml IL-2, 10\u2009ng/ml IL-12, 2\u2009\u00b5g/ml anti-CD28, and 10\u2009\u00b5g/ml anti-IL-4; Th2: 10\u2009ng/ml IL-2, 20\u2009ng/ml IL-4, 1\u2009\u00b5g/ml anti-CD28, and 10\u2009\u00b5g/ml anti-IFN\u03b3; Treg: 10\u2009ng/ml IL-2, 3\u2009ng/ml TGF\u03b2, and 2\u2009\u00b5g/ml anti-CD28; and Th17: 2\u2009ng/ml TGF\u03b2, 20\u2009ng/ml IL-6, 10\u2009ng/ml IL-23, 10\u2009ng/ml IL-1\u03b2, 1\u2009\u00b5g/ml anti-CD28, 6\u2009\u00b5g/ml anti-IFN\u03b3, and 10\u2009\u00b5g/ml anti-IL-4) in RPMI containing 10% FCS, 1% glutamine, 0.1% \u03b2-mercaptoethanol, and 1% penicillin/streptomycin in 24-well plates. For Th2 lymphocyte adoptive transfer experiments, purified naive OT2 CD4+ T lymphocytes were stimulated with irradiated splenocytes (24\u2009Gy) and OVA peptide (323\u2013339) (Sigma-Aldrich) in culture medium supplemented with 10\u2009ng/ml IL-2, 20\u2009ng/ml IL-4, 1\u2009\u00b5g/ml anti-CD28 and 10\u2009\u00b5g/ml anti-IFN\u03b3. Cells were then split on noncoated plates and cultured for 3 additional days in fresh differentiating medium without antibodies (Th1: 20\u2009ng/ml IL-2 and 10\u2009ng/ml IL-12; Th2: 20\u2009ng/ml IL-2 and 20\u2009ng/ml IL-4; Treg: 20\u2009ng/ml IL-2 and 3\u2009ng/ml TGF\u03b21; and Th17: 10\u2009ng/ml IL-2, 2\u2009ng/mL TGF\u03b21, 20\u2009ng/ml IL-6, 10\u2009ng/ml IL-23, and 10\u2009ng/mL IL-1\u03b2). Recombinant mouse cytokines were from Miltenyi Biotec (IL-2, IL-4, IL-6, IL-1\u03b2, and IL-23) and PeproTech (IL-12). TGF\u03b2 was from Active Bioscience. For RNA-seq analysis, Th2 lymphocytes were stimulated at day 5 with 3\u2009\u00b5g/ml coated anti-CD3\u03b5 and 2\u2009\u00b5g/ml soluble anti-CD28 for 24\u2009h. Naive human CD4+ T lymphocytes were isolated from PBMCs using a CD4 naive T-cell Isolation Kit (#480042; BioLegend). In Vitro, differentiation of human Th1 and Th2 lymphocytes was performed using the CellXVivo Human Th1 Cell Differentiation Kit (#CDK001) and the CellXVivo Human Th2 Cell Differentiation Kit (#CDK002), respectively, as recommended by the manufacturer (Biotechne). Freshly thawed PBMCs were cultured in RPMI 1640 supplemented with 5% FBS, 1\u2009mM sodium pyruvate, 2\u2009mM glutamine, 100\u2009U/ml penicillin, 100\u2009mg/ml streptomycin, 1\u2009mM HEPES, and 1% NEAA medium for 24\u2009h before analyses.\n\nLung draining lymph node cells were seeded into 96-well flat-bottom plates (6\u2009\u00d7\u2009105 cells per well) and stimulated with 200\u2009\u00b5g/ml ovalbumin in complete medium (RPMI 1640 supplemented with 10% FBS, 1% pyruvate, 2\u2009mM glutamine, 100\u2009U/ml penicillin, 100\u2009mg/ml streptomycin, and 50\u2009\u00b5M \u03b2-mercaptoethanol) for 72\u2009h, and cytokine production was quantified by ELISA according to the manufacturer (Invitrogen).\n\nTotal RNA was extracted with the NucleoSpin RNA kit (#740955; Macherey-Nagel) (cDNA was synthesized using the OneScript Plus cDNA Synthesis Kit G236) as recommended by the manufacturer (Applied Biological Materials). Real-time PCR was carried out with the LC480 real-time PCR system (Roche Diagnostics) using the BlasTaq 2X qPCR MasterMix (G892) (Applied Biological Materials) according to the manufacturer\u2019s instructions. Data were analyzed using the LightCycler 480 software (v1.5.1.62). The specificity of the PCR primers was confirmed by melting curve analyses. Gene expression was determined using the \u0394\u0394Ct method and data are presented as relative amounts of mRNA normalized to Rplp0 (ribosomal protein, large, P0) or YWHAZ for mouse or human cells, respectively. The primers used for qPCR are shown in Supplementary Table\u00a01.\n\nMicro-array data of ASB2 expression in naive human CD4+ T lymphocytes or cells cultured under Th2 conditions were retrieved from the a GSE6068046. Data of RNA-seq and ChIP-seq of H3K27ac, H3K4me1, H3K27me3, and RNA polymerase II in naive CD4+ T lymphocytes or in T lymphocytes cultured under Th1, Th2, and Th17 conditions were retrieved from the GSE144586 dataset30. The genomic tracks around candidate loci were generated using pyGenomeTracks (v3.8) and the expression heatmap with pheatmap R package (v1.0.12). Micro-array ASB2, ITGAV, and ITGB3 expression data in OVA-induced airway inflammation and HDM-induced asthma were retrieved from the publicly available GSE10973731 and GSE7200532 datasets, respectively.\n\nTotal RNA was extracted with the Nuclespin RNA kit (Macherey-Nagel) and subsequently used to prepare the libraries using the Stranded Total RNA Prep, Ligation with Ribo-Zero Plus kit (Illumina). Quality controls of the libraries were performed using standard methods, including quantification with Qubit spectrophotometer and assessment of size distribution with TapeStation 4150 (Agilent). Samples were indexed and sequenced (paired-end reads of 150\u2009bp) on the NovaSeq 6000 system (Illumina) from the Genomic and Transcriptomic Platform of the GeT core facility (Toulouse, France). Raw sequencing reads were processed using nf-core/RNAseq pipeline v.3.12.0 developed with Nextflow47,48. Briefly, this pipeline trims adapters and removes low-quality reads using Cutadapt v.3.449. It then aligns reads to the Ensemble GRCm39_v.107 genome using STAR v.2.7.9a50. Finally, gene expression was quantified with Salmon v.1.10.151. Only samples with more than 10\u2009M reads were kept for further analyses. Raw counts were normalized and differential expression analyses were performed using the R package DESeq2 v.1.38.352. Differentially expressed genes between activated and non-activated ctrl Th2 lymphocytes were identified as genes with an adjusted p value below 0.5 and an absolute log2 fold change greater than 2. Gene Set Enrichment analyses were performed with ClusterProfiler v.4.6.253,54. Heatmap and boxplots were made using Th1 and Th2 genve sets extracted from55 and Th17 gene set extracted from the Harmonizome database v.3.056. RNA-seq data used in this study have been deposited at GEO under accession number GSE251847.\n\nFor flow cytometry analysis and cell sorting, cells were first incubated with Zombie viability dyes (BioLegend) for 15\u2009min at room temperature and then incubated for 30\u2009min a 4\u2009\u00b0C with the appropriate combination of antibodies. IL-4, IL-5, IL-13, and IFN\u03b3 were measured by intracellular staining after stimulation for 4\u2009h with phorbol 12-myristate 13-acetate (50\u2009ng/ml, Sigma-Aldrich), ionomycin (500\u2009ng/ml, Sigma-Aldrich) and Monensin (BioLegend). Cells were fixed after extracellular staining with PBS 4% PFA 20\u2009min at 4\u2009\u00b0C and permeabilized by adding PBS 0.5% Triton X100 10\u2009min at 4\u2009\u00b0C. For quantification of FLNa expression by flow cytometry, cells were fixed with Fixation/Permeabilization buffer (Invitrogen) overnight at 4\u2009\u00b0C and were then immunostained with anti-FLNa and Alexa Fluor 488-conjugated goat anti-rabbit antibodies (Invitrogen) in Permeabilization buffer (Invitrogen). Flow cytometry analysis was performed with LSRII or Fortessa X20 Cytometers (BD Biosciences) and cell sorting on a FACS ARIA II Cytometer (BD Biosciences). Analyses of flow cytometry data were performed using FlowJo (TreeStar; v10.8.1). FACS sequential gating or sorting strategies are shown in Supplementary Fig.\u00a05-8.\n\nCellCarrier Ultra tissue culture treated 384-well plates (Perkin Elmer) were coated with 1\u2009\u00b5g/ml vitronectin (BioLegend) or 1\u2009\u00b5g/ml of VCAM-1 (BioLegend). Ten thousand human naive CD4+ or Th2 lymphocytes or mouse Th1, ctrl Th2, ASB2 cKO Th2, Treg, or Th17 cells were seeded per well and incubated 25\u2009min at 37\u2009\u00b0C to adhere in RPMI 1640 supplemented with 10% FBS, 2\u2009mM glutamine, 100\u2009U/mL penicillin and 100\u2009mg/mL streptomycin and then fixed with 4% paraformaldehyde, 60\u2009mM sucrose (Sigma-Aldrich). After fixation, cells were washed and stained with anti-FLNa, anti-FLNb, and phalloidin. Nuclei were stained with 50\u2009ng/ml of DAPI. Images were acquired on an automated Opera Phenix confocal HCS device (Perkin Elmer) with a 40\u2009\u00d7\u20091.1 NA Plan Apochromat water immersion objective and a SCMOS camera. Thirteen non-adjacent fields and 3 stacks per field (1\u2009\u00b5m step) were acquired per well. Stacks of images were combined, then assembled in sets of images per field of view corresponding to DAPI, phalloidin, FLNa, and/or FLNb. These datasets were processed, and measurements were made using the Harmony software.\n\nSix channel \u00b5-slide VI 0.4 (Ibidi) were coated overnight with 1\u2009\u00b5g/ml of vitronectin (BioLegend). Control, ASB2 cKO, and ctrl Th2 lymphocytes were labeled 15\u2009min at 37\u2009\u00b0C with 0.6\u2009\u00b5M CellTracker Green (CTG) CMFDA (Invitrogen) in HBSS without Ca2+ and Mg2+. Labeling reactions were stopped by addition of HBSS without Ca2+ and Mg2+. 8\u2009\u00d7\u2009104 Th2 lymphocytes in 0.1\u2009ml RPMI medium containing 1% BSA and 10\u2009mM HEPES were seeded per well and migration was initiated by the addition of 10% FBS. When indicated Th2 lymphocytes were treated with 0.5\u2009mM MnCl2 and a combination of 0.2\u2009\u00b5g/ml of antibodies against mouse CD51 (Clone RMV-7) or the corresponding isotypic controls (Clone HTK888), and 0.2\u2009\u00b5g/ml of antibodies against mouse CD61 (Clone 2C9.G2) or the corresponding isotypic controls (Clone HTK888). The microscope environmental chamber was maintained and monitored at 37\u2009\u00b0C and 5% CO2. Images were acquired by spinning disk confocal microscopy (Yogokawa head, Hamamatsu CMOS Flash4 camera, 20\u00d7 objective NA 0.75) using the Metamorph v7.10.1.161 software. Images were acquired every minute for both transmitted light and 491\u2009nm CTG-associated fluorescence. Multiple parameters measurements of images were performed via IMARIS v10.0.1 software (Oxford Instruments). Distance and directionality data were obtained using spot function. Shape description data were obtained using surface function. Spider plots were obtained using the MatLab (Mathworks, R2009a, v7.10) algorithm and IMARIS Translate Tracks Xtension on track distance data. Supplementary videos were encoded at 10\u2009frames/s.\n\nFor immunoblot analysis, cells were pelleted, washed in PBS, and lysed using in whole-cell extract buffer containing 50\u2009mM Tris-HCl (pH 7.9), 150\u2009mM NaCl, 1\u2009mM EDTA, 0.1% Igepal CA-630, 10% glycerol, 1\u2009mM dithiothreitol, 1\u2009mM Na3VO4, 50\u2009mM NaF, 25\u2009mM \u03b2 glycerophosphate, 2\u2009mM Na pyrophosphate and 1% protease inhibitor cocktail (P8340; Sigma-Aldrich). After three freeze-thaw cycle in liquid nitrogen, the resulting cell lysates were cleared by a 20\u2009min 20,000\u2009\u00d7\u2009g centrifugation at 4\u2009\u00b0C. The lysates were boiled with Laemmli buffer, resolved by SDS\u2013polyacrylamide gel electrophoresis, transferred to nitrocellulose membranes, and the proteins visualized by standard immunoblotting procedures. Signal acquisition was conducted using the Bio-Rad ChemiDoc apparatus and quantification of the immunoblot signal was performed with the Bio-Rad Image Lab v6.1 software. Protein quantifications were normalized to the levels of the GAPDH protein.\n\nCtrl and ASB2 cKO Th2 lymphocytes generated in vitro from naive CD4+ T lymphocytes were washed twice in ice-cold PBS, and lysed in Triton X100 lysis buffer containing 20\u2009mM Tris-HCl, pH 8, 137\u2009mM NaCl, 10% glycerol and 1% Triton X100 and supplemented with 1% protease inhibitor cocktail (Sigma-Aldrich), 1\u2009mM DTT, 1\u2009mM Na3VO4, 50\u2009mM NaF, 2\u2009mM sodium pyrophosphate and 25\u2009mM \u03b2-glycerophosphate. After a 30\u2009min incubation on ice, cell lysates were cleared by a 20-min 20,000\u2009\u00d7\u2009g centrifugation at 4\u2009\u00b0C. 100\u2009\u03bcg ctrl or ASB2 cKO Th2 lymphocyte extracts was precleared by incubating 30\u2009min in the presence of protein A-Sepharose beads (Cytiva) in a binding buffer containing 20\u2009mM Tris-HCl pH 8, 250\u2009mM NaCl, 10% glycerol and 0.1% Igepal CA-630 at 4\u2009\u00b0C. Anti-rabbit FLNa serum or rabbit pre-immune serum was added to the precleared cell extracts. After 16\u2009h of incubation, immunocomplexes were recovered with protein A-Sepharose. After three washes with binding buffer, proteins were eluted with boiling Laemmli\u2019s buffer, fractionated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE), and analyzed by immunoblotting with anti-FLNa. After incubation with the Restore western blot stripping buffer (Fisher Scientific), proteins were probed with antibodies to ubiquitylated proteins.\n\nCtrl and ASB2 cKO Th2 lymphocytes generated in vitro from naive CD4+ T lymphocytes and CD45+CD4+ST2+ living cells sorted from the lungs of control or ASB2 cKO mice submitted to OVA-induced airway inflammation were lysed in 5% SDS, 50\u2009mM ammonium bicarbonate and sonicated on a Bioruptor (Diagenode). Proteins were digested on S-trap devices (Protifi) and 50\u2009ng of the resulting peptides were analyzed by nanoLC-MS/MS using an UltiMate 3000 RS nanoLC system (ThermoFisher Scientific) coupled to a TIMS-TOF SCP mass spectrometer (Bruker). Peptides were separated on a C18 Aurora column (25\u2009cm\u2009\u00d7\u200975\u2009\u00b5m ID, IonOpticks) using a gradient ramping from 2% to 20% of B in 30\u2009min, then to 37% of B in 3\u2009min and to 85% of B in 2\u2009min (solvent A: 0.1% FA in H2O; solvent B 0.1% FA in acetonitrile), with a flow rate of 150\u2009nl/min. MS acquisition was performed in DIA-PASEF mode on the precursor mass range [400\u20131000]\u2009m/z and ion mobility 1/K0 [0.64\u20131.37]. The acquisition scheme was composed of 8 consecutive TIMS ramps using an accumulation time of 100\u2009ms, with 3 MS/MS acquisition windows of 25 Th for each of them. The resulting cycle time was 0.96\u2009s. The collision energy was ramped linearly as a function of the ion mobility from 59\u2009eV at 1/K0\u2009=\u20091.6\u2009Vs\u2009cm\u22122 to 20\u2009eV at 1/K0\u2009=\u20090.6\u2009Vs\u2009cm\u22122. Nine independent replicate samples for each condition (ctrl or ASB2 cKO), obtained from two different cell culture experiments, were analyzed in total. The raw data (18 files) was searched and quantified with DIA-NN 1.8.1, using a predicted library generated by the software from the UniProt mouse reference proteome. Validation was performed at 1% precursor and protein FDR, with a peptide length range set at 7\u201330 and precursor charge range set at 2\u20133. MS intensities measured by DIA-NN for each peptide ions (precursor matrix) were processed using the Proline software57 for calculation of protein intensities (peptide-to-protein inference and summarization of peptide intensities) and normalization. Only proteins detected and quantified in more than 2 replicates from at least one of the conditions (ctrl or ASB2 cKO), and quantified based on more than 2 peptides, were kept for statistical analysis. Normalized abundance values were log2-transformed, and missing values were imputed with a low-intensity value reflecting the noise background, defined for each analytical run as the lowest 1% percentile value of the total protein intensity distribution. A student t-test (bilateral, equal variance) was calculated based on the 9 replicates to evaluate statistical significance of the protein abundance variation between the two conditions. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE58 partner repository with the dataset identifier PXD044062.\n\nAll p values were calculated using the nonparametric Mann\u2013Whitney t-test except in Fig.\u00a01g, j, m, and in Fig.\u00a03d where the Wilcoxon\u2019s test was used. For the proteome analyses, the Student t-test was used in the statistical analysis. In Fig.\u00a04h, i, correlations between nonparametric variables were evaluated using Spearman rank correlation test (r).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The MS proteomics data generated in this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD044062. The RNA-seq data from this study have been deposited at GEO under accession number GSE251847. Data of RNA-seq and ChIP-seq from publicly accessible datasets were retrieved from GSE60680, GSE144586, GSE109737, and GSE72005. All other data are available in the article and its Supplementary files or from the corresponding authors upon request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Muehling, L. 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We are grateful to the Genotoul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul, https://doi.org/10.15454/1.5572369328961167E12) for providing computing, storage resources. We acknowledge the GeT core facility, Toulouse, France (https://get.genotoul.fr). We acknowledge the personnel of the Genomic and Transcriptomic platform of Infinity. We thank Alexandra Hicks and Lucette Pelletier for reading the article and for helpful comments. We thank P. van der Ven for the anti-FLNa rabbit serum. This work was supported by the Institut National de la Sant\u00e9 et de la Recherche M\u00e9dicale, the Centre National de la Recherche Scientifique, and the University of Toulouse. This work was also supported by grants from Inserm Transfert (CoPoC) and Sanofi (to P.G.L. and I.L.), from Sanofi Innovation Awards Europe, the Fondation ARC pour la recherche sur le cancer and the Fondation du Souffle (to P.G.L.), and from the Soci\u00e9t\u00e9 Fran\u00e7aise d\u2019Allergologie and the Comit\u00e9 Midi-Pyr\u00e9n\u00e9es de la Ligue contre le Cancer (to I.L.). K. Maire was supported by a fellowship of the French Ministry of Higher Education and Research. C. Bouisset and H. Trad were supported by fellowships of Fonroga (Fondation Roland Garrigou pour la Culture et la. Sant\u00e9). P.G.L. was laureate 2023 de la Fondation du Souffle\u2014Promotion Marina Pretolani.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors jointly supervised this work: Pierre G. Lutz, Isabelle Lamsoul.\n\nInfinity, University of Toulouse, CNRS, Inserm, UPS, Toulouse, France\n\nKilian Maire,\u00a0L\u00e9a Chamy,\u00a0Samira Ghazali,\u00a0Manon Carratala-Lasserre,\u00a0Margot Zahm,\u00a0Cl\u00e9ment Bouisset,\u00a0Lidia de la Fuente-Vizuete,\u00a0Hussein Trad,\u00a0Adeline Chaubet,\u00a0Magali Savignac,\u00a0Olivier Joffre,\u00a0Pierre G. Lutz\u00a0&\u00a0Isabelle Lamsoul\n\nInstitut de Pharmacologie et de Biologie Structurale (IPBS), Universit\u00e9 de Toulouse, CNRS, UPS, Toulouse, France\n\nArnaud M\u00e9tais,\u00a0Lucie Combes-Soia\u00a0&\u00a0Anne Gonzalez de Peredo\n\nSanofi Immunology and Inflammation Research Therapeutic Area, Cambridge, MA, USA\n\nArun Subramaniam\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nP.G.L and I.L. devised, performed, analyzed, interpreted experiments, and wrote the paper. K.M., L.C., M.C-L., C.B., A.M., L. F-V., and H.T. performed, analyzed, and interpreted experiments. Proteomic analyses were coordinated by A.G.P. performed and analyzed by L.C-S. Epigenetic analyses were coordinated by O.J. performed, and analyzed by S.G., A.C., and M.Z., M.S., and A.S. contributed important ideas. Funding was acquired by P.G.L. and I.L. All authors provided critical input into the manuscript.\n\nCorrespondence to\n Pierre G. Lutz or Isabelle Lamsoul.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "A.S. is an employee of Sanofi. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Loretta Tuosto and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Fine-tuning levels of filamins a and b as a specific mechanism sustaining Th2 lymphocyte functions.\n Nat Commun 15, 10574 (2024). https://doi.org/10.1038/s41467-024-53768-3\n\nDownload citation\n\nReceived: 19 January 2024\n\nAccepted: 22 October 2024\n\nPublished: 05 December 2024\n\nVersion of record: 05 December 2024\n\nDOI: https://doi.org/10.1038/s41467-024-53768-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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behavior of the Si dopant in Al-rich AlGaN", + "journal": "Nature Communications", + "published": "30 May 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60312-4/MediaObjects/41467_2025_60312_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60312-4/MediaObjects/41467_2025_60312_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60312-4/MediaObjects/41467_2025_60312_MOESM3_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-60312-4#Fig1", + "/articles/s41467-025-60312-4#Fig4", + "/articles/s41467-025-60312-4#Sec15" + ], + "code": [], + "subject": [ + "Applied physics", + "Electronic devices", + "Semiconductors" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5580951/v1.pdf?c=1748603303000", + "research_square_link": "https://www.researchsquare.com//article/rs-5580951/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60312-4.pdf", + "preprint_posted": "07 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "AlGaN alloys with high Al content offer the possibility to create deep ultraviolet (UV) light sources emitting at wavelengths \u2264 240 nm with enhanced quantum efficiency. However, pushing the limits of the band gap energies towards higher values leads to problems with n-type doping of AlGaN alloys with the standard choice of Si donors when the Al content surpasses \u223c80 %. This is due to the formation of the so-called negative Si DX center where the Si-N bond is ruptured, followed by a displacement of the N atom. In this paper, we show that the amphoteric nature of the Si dopant in AlGaN alloys is fundamentally controlled by the local environment and the ordering of the Ga and Al atoms in the vicinity of the Si atom. Our conclusions are based on advanced characterization sensitive to the local environment of defects and impurities: positron annihilation spectroscopy and X-ray absorption spectroscopy. Electronic structure calculations complete the picture. As our experiments show that the presence of Ga in the vicinity of Si controls the donor character of the latter in high Al content AlGaN, we propose that spatial ordering of these atoms could allow efficient n-type doping at even higher Al contents, including AlN.Physical sciences/Materials science/Materials for devices/Electronic devicesPhysical sciences/Physics/Applied physicsPhysical sciences/Physics/Condensed-matter physics/Semiconductors", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "AlGaNmanuscriptSI.pdfShort-range order controlled amphoteric behavior of the Si dopant in Al-rich AlGaN", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "AlGaN alloys with high Al content offer the possibility to create deep ultraviolet light sources emitting at wavelengths \u2264 240\u2009nm with enhanced quantum efficiency. However, increasing the band gap when the Al content surpasses \u00a0~80% leads to problems with n-type doping of AlGaN alloys with the standard choice of Si donors, due to the formation of the so-called negative Si DX center. In this paper, we show that the amphoteric nature of the Si dopant in AlGaN alloys is fundamentally controlled by the local environment and the ordering of the Ga and Al atoms in the vicinity of the Si atom. Our conclusions are based on advanced characterization sensitive to the local environment of defects and impurities, complemented by electronic structure calculations. We propose that spatial ordering of Ga and Si atoms could allow efficient n-type doping at even higher Al contents, including AlN.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Discovery and technological realization of alloying of III\u2013V compound semiconductors with different band gaps (Eg) and stacking thin films thereof opened up the field of semiconductor optoelectronics several decades ago. The family of III-nitride semiconductors AlN (Eg\u2009=\u20096.2\u2009eV), GaN (Eg\u2009=\u20093.4\u2009eV) and InN (Eg\u2009=\u20090.7\u2009eV) extends the spectrum across the whole visible range and is at present the core technology in, for example, solid-state lighting. This technology is based mainly on InGaN alloy systems, while the device heterostructures typically include also thin layers of low Al content alloys. The ultra wide band gap of AlN allows band gap engineering for the development of short wavelength opto-electronic as well as power-electronic devices, such as displays with high luminosity, lasers and high-power high-frequency chips with improved stability and energy efficiency1,2. Moreover, synthesizing AlGaN alloys with high Al content creates the possibility to fabricate deep ultraviolet (UV) light sources emitting at wavelengths \u2264240 nm with enhanced quantum efficiency3,4,5,6. Such UV light emitting devices can be applied, for example, in sanitizing systems against viruses and bacteria, which possibly can help to inhibit infectious diseases or provide more people with drinking water7.\n\nControlling the conductivity of semiconductors is key to their functionality8. Already early on with conventional III\u2013V semiconductor AlGaAs alloys, it was discovered that pushing the limits of the band gap towards higher values led to problems with n-type doping with the standard choice of Si as a dopant. The root cause for the inability to obtain highly conductive material was found to be the formation of the so-called negative Si DX center when the Al content surpassed \u00a0~\u00a020% in the AlxGa1-xAs alloy9. In this defect the Si atom normally substituting for the cation lattice site displaces strongly and undergoes a donor-to-acceptor transition10,11. A similar phenomenon occurs in Si-doped AlGaN alloys, albeit at much higher Al content: in AlGaN with Al content higher than 80%, the negatively charged Si DX state is formed by the axial Si-N bond rupturing along the c-axis followed by the displacement of the N atom12,13,14,15. Electrical compensation by the formation of cation vacancy complexes with silicon has also been suggested as the doping limiting process in high Al content AlGaN16. Interestingly, the DX center formation appears universal to compound semiconductor alloys with Ga/Al cations, as also in the \u03b2-(Al,Ga)2O3 alloys Si is an efficient donor up to a certain Al content, above which the material becomes semi-insulating irrespective of the Si doping17. In these oxide alloys, the bond-breaking associated with the Si DX formation depends on the site of the Si atom as the lattice hosts two inequivalent cation sites: either the Si atom or the adjacent O atoms are strongly displaced 18.\n\nIn this paper, we show that the amphoteric nature of the Si dopant in AlGaN alloys is fundamentally controlled by the local environment and the ordering of the Ga and Al atoms in the immediate surroundings of the Si atom. This is in contrast to the common observation in conventional III-V semiconductors, where the Si DX transformation is generally interpreted in terms of a non-local average characteristic of the material, resulting in a threshold average alloy composition as a determining factor for the phenomenon. Our conclusions are based on advanced experimental characterization sensitive to the local environment of defects and impurities on an extended set of Al0.90Ga0.10N epitaxial thin films doped with Si in the range from 1\u2009\u00d7\u20091018 cm\u22123 to 2\u2009\u00d7\u20091019 cm\u22123. Positron annihilation spectroscopy19 reveals that negatively charged acceptor ions emerge at concentrations comparable only to the Si concentration, simultaneously with the onset of the compensating character of further increase in Si doping. X-ray absorption near edge structure spectroscopy (XANES)20 shows that the local environment of the Si atoms changes from GaN-like to AlN-like at the same threshold concentration. We also perform electronic structure calculations21 that show that Si is drawn to the locally Ga-rich lattice sites, retaining its donor character when these are available. As our experiments show that the presence of Ga in the vicinity of Si controls the donor character of the latter in high Al content AlGaN, we propose that spatial ordering of these atoms could allow efficient n-type doping at even higher Al contents, including AlN.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The basic characteristics of the Si-doped Al0.90Ga0.10N epitaxial thin films discussed in this paper are shown in Fig.\u00a01a. The samples were synthesized by metal-organic vapor phase epitaxy (MOVPE) on epitaxially laterally overgrown (ELO) AlN/sapphire, with the top layer as the material of interest22. The thin films were grown in two types of conditions resulting in either a relatively high carbon content (2\u00a0\u00d7\u00a01018 cm\u22123, samples H1 \u2013 H13), or in a relatively low carbon content (2\u00a0\u00d7\u00a01017 cm\u22123, samples L1 \u2013 L5), similar to ref. 23. In set H1-H5, the highest [Si] reached 7\u00a0\u00d7\u00a01018 cm\u22123 resulting in the lowest resistivity near the possible threshold [Si] changing to the upward trend in resistivity. The set H6-H13 covers broader range of [Si] and demonstrates the predicted knee behavior of resistivity similar to samples L1-L5. We include here data on previously reported samples H1-H523 to highlight the consistency in both the resistivity and positron data across these two sets of samples. The growth conditions of H1-H5 are similar to H6-H13 with minor fluctuations in precursors flow. The Si doping was controlled by adjusting the SiH4 flow during synthesis, resulting in the Si content ranging from 1\u00a0\u00d7\u00a01018 cm\u22123 to 2\u00a0\u00d7\u00a01019 cm\u22123 in the samples. The concentrations of other impurities are all below 5\u00a0\u00d7\u00a01017 cm\u22123.\n\na The basic structure (not in scale) of the samples studied is shown in the upper panel, together with the O and H concentrations. b Resistivity as a function of Si doping concentration, with respective C concentrations in the various sample series. The curves are drawn to guide the eye.\n\nThe resistivity of the AlGaN samples as a function of Si doping, shown in Fig.\u00a01b, exhibits a similar trend in both types of synthesis conditions. First, the resistivity decreases with the increase of Si content, as expected for a dopant that should increase the free carrier concentration. Above a certain threshold, this decrease levels off and at sufficiently high Si doping concentrations the resistivity increases with further increase of Si content. This behavior where the doping efficiency is severely reduced\u2014and even excess compensation observed\u2014above a certain threshold Si concentration is a well known problem in n-type doping of AlGaN alloys15. With increasing Al mole fraction in AlGaN, both the Si doping efficiency and threshold Si concentration decrease, strongly limiting the achievable conductivity in high Al content AlGaN. The reduced doping efficiency with high Al contents is likely due to the higher donor activation energies and formation of Si DX centers that exist in AlN but not in GaN14,15. However, the reason for the vanishing and even negative doping efficiency with increasing Si concentration is not clear.\n\nFigure\u00a02 shows the normalized (S,\u00a0W) parameters measured in the Si-doped AlGaN samples at room temperature. All the measured (S,\u00a0W) data are located within the area defined by the points characterizing the GaN and AlN lattices, the typical in-grown Ga vacancies in GaN (denoted by VGa-X), and the Al vacancy in AlN23,24,25,26. Our earlier work on a subset of these samples, where we performed also temperature-dependent experiments, allowed us to conclude that the data obtained in sample H1 represents the Al0.90Ga0.10N lattice, while the average VIII found in Al0.90Ga0.10N is characterized by the data obtained in samples L1-L323. The gradual shift in (S,\u00a0W) parameters obtained in the samples H1 - H9 (red markers) with increasing Si content from the AlGaN lattice point towards the VIII point indicates that the VIII concentration in these samples increases with increasing Si concentration. The VIII concentrations\u00a0can be estimated from the positron data and are shown in Fig.\u00a02\u2013 see the\u00a0Supplementary Information (SI) for detailed analysis. It is important to note that the VIII concentrations are an order of magnitude lower than the Si concentrations, and hence too low to account for the electrical compensation in these samples.\n\nThe light green circles mark the values for the GaN lattice, AlN lattice and VAl in AlN, and the light green dashed ellipse shows those for in-grown VGa complexed with H and O impurities or VN in GaN, as shown in earlier work23. The values given above the data markers show the evolution of the cation vacancy concentration with the (S,\u00a0W) parameters. The data points in red/blue are from samples where the resistivity decreases/increases with increasing Si content. The arrows show the direction of increasing Si content. The dashed blue star shows where the data point for sample H13 shifts if the carbon effect is removed.\n\nInterestingly, the (S,\u00a0W) parameters behave very differently in samples H10\u2013H13 and L1\u2013L5 (blue markers). Instead of saturating at the point characteristic of VIII that would result from further increase of the VIII concentration with the increase of the Si content, the (S,\u00a0W) parameters shift towards the left side of Fig.\u00a02. In the L series, the endpoint of this shift is very close to the point characteristic of the AlN lattice. In the H series the end-point W parameter is close to the original AlGaN - VIII line, but still clearly below, towards the AlN-like data of the L series. As seen in the temperature-dependent data in sample L523, this dramatic shift is due to the emergence of a high concentration of negative ion type defects in the samples. Detailed analysis (see\u00a0SI) shows that the concentrations required for this magnitude of effect are very close to the Si concentration in these samples, and significantly higher than the concentration of carbon that also acts as a negative ion type defect for positrons, or of any other impurity. The less dramatic vertical effect in samples H10-H13 is indeed due to the higher C concentration in these samples compared to L1\u2013L5: removing the carbon effect from the (S,\u00a0W) data for sample H13 moves the endpoint to the location shown by the dashed star in Fig.\u00a02. The most dramatic shift of the (S,\u00a0W) parameters and hence the emergence of a high concentration of negatively charged non-open volume defects coincides with the total loss and reversal of the Si doping efficiency in Fig.\u00a01b. It is also important to note that when the positron data are dominated by VIII, there is a clear over-representation of GaN-like behavior as the VIII point is much closer to the Ga vacancies in GaN than to the Al vacancies in AlN. In contrast, when the negative ion type defects dominate the positron data the situation is reversed and the AlN-like behavior is stronger.\n\nFigure\u00a03a\u2013c shows the XANES spectra obtained at the Si K-edge in selected AlGaN samples. We identify 12 characteristic features in the experimental spectra, as denoted in the figure. Features #1, #3 and #7 are so-called isosbestic points where all the spectra coincide. The H series samples are shown in Fig.\u00a03a, and there is a clear distinction between the samples H6\u2013H9 and H10\u2013H13. First, we note that the position of the whiteline (feature #2) is the same in all samples, but its intensity is clearly higher in samples H10\u2013H13 compared to H6\u2013H9. Second, the two peaks at 1846\u20131848\u2009eV (features #4 and #5) are divided between the two sets: the #4 is clearly stronger in samples H6\u2013H9, while #5 is clearly stronger in samples H10\u2013H13. Additionally, sample H9 contains a small shoulder in the vicinity of feature #5, denoted as feature #6. Third, the samples H6\u2013H9 exhibit a relatively high intensity plateau between features #8 and #9, while the samples H10\u2013H13 exhibit two separated peaks (features #8 and #10). Fourth, and finally, the samples H6\u2013H9 exhibit a shoulder above 1860\u2009eV (feature #11), while samples H10\u2013H13 show a peak at 1868 eV (feature #12). Figure\u00a03b shows the comparison of the experimental spectra obtained in samples H13 and L2\u2013L5. It is clear that the above-discussed features are very similar in these two sets of samples.\n\na XANES spectra at the Si K-edge in H series Si doped AlGaN samples. b XANES spectra in selected H and L series Si doped AlGaN samples. Numbers 1\u201312 indicate characteristic isosbestic points and features. Simulated unconvoluted XANES spectra at the Si K-edge for SiGa in GaN and SiAl in AlN are shown for comparison. c Simulated unconvoluted XANES spectra of at the Si K-edge for various Si configurations in AlN.\n\nThe experimental XANES spectra in Fig.\u00a03b show that the local environment of Si is clearly different in samples H6\u2013H9 compared to samples H10\u2013H13 and L2\u2013L5. This difference in local environment coincides with the different behavior in resistivity. Simultaneously, it coincides with the positron annihilation data resembling either GaN-rich (H6\u2013H9) or AlN-rich environments in the AlGaN alloy.\n\nFor a quantitative assessment of the XANES spectra, we performed finite difference method near edge structure (FDMNES) simulations of Si in various environments27: SiGa in GaN, SiAl in AlN, and several SiAl in AlN complexed with substitutional C, interstitial H, interstitial Si, and the Al vacancy with and without additional H. The simulated XANES spectra at the K-edge are shown as the total density of states vs. energy in Fig.\u00a03c, and SiGa and SiAl are included for comparison in Fig.\u00a03a, b. We note that most of simulated XANES spectra show similar behavior, except for SiAl-Hi and SiAl-Sii.\n\nIn the simulated XANES spectra, interstitials near the Si atom and substitutional impurities occupying the nearest N sites contribute most significantly to the appearance of spectral features #4 and #5. Around 1847\u2009eV, most simulated systems demonstrate common behavior, characterized by the presence of two peaks with variations in intensity and slightly also in energy. However, systems containing SiAl-Hi and SiAl-Sii pairs deviate from this general trend. In SiAl-Hi complexes, a high-intensity peak (#4) appears below 1847\u2009eV, followed by a smaller peak (#5) above 1847\u2009eV, with less than one-third of the intensity of feature #4. In contrast, in the SiAl-Sii system, feature #4 is less than 70% of the intensity of feature #5, and the latter is shifted approximately 1\u2009eV to lower energies compared to all other samples. All simulated spectra exhibit a small peak at 1852\u2009eV (features #7 and #8), with slight variations in centroid position and intensity.\n\nThe spectral features #9 and #10 are primarily influenced by the atomic species occupying the cation site nearest to the absorbing Si atom. For Si-complexes in AlN, all simulated spectra display a similar trend, with a peak appearing around 1860\u2009eV, whose intensity varies depending on the composition of the system. Once again, SiAl-Hi and SiAl-Sii systems appear as outliers. In the SiAl-Hi system, the peak shifts slightly to 1859\u2009eV. In the\u00a0SiAl-Sii system, there are three peaks at 1859, 1861, and 1863\u2009eV characteristic to the introduction of a Si atom. This unique behavior is exclusive to the SiAl-Sii system, with the third peak corresponding to feature #11. For feature #12, all simulated spectra exhibit a low-intensity peak near 1867\u2009eV. However, in the experimental spectra of H6-H9, where Si is predominantly in a Ga-rich environment, feature #12 is absent, and the spectra instead show a smooth transition to the continuum. We conclude that the slight differences between the various Si-related complexes in AlN are likely to induce broadening of the various features as observed in experiments, but they cannot account for the spectral differences between H6-H9 and each of the other groups, H10-H13 and L2-L5. We also note that the concentrations of O, H and C in the samples are significantly lower than the Si doping.\n\nThe comparison between experiments and the two simulated spectra reveals that the whiteline has higher intensity for SiAl than for SiGa. In addition, the intensity of feature #5 is clearly stronger than that of feature #4 for SiGa, while there difference is not significant for SiAl. SiGa exhibits a lower intensity of the feature #8 than SiAl, and the SiGa peak at feature #9 is broad, while SiAl exhibits a narrower peak at feature #10 instead of #9. Finally, SiAl exhibits a clear peak at feature #12, while SiGa shows no higher-energy peaks at this energy range, resembling feature #11. Comparing the simulations and experiments, three out of the four main observations above are in favor of the experimental XANES spectra of samples H6\u2013H9 being dominated by cation-substituting Si in GaN-resembling local environment, and the samples H10\u2013H13 and L2\u2013L5 dominated by cation-substituting SiAl in AlN-resembling local environment. We conclude that the local environment of Si atoms is Ga-rich in samples H6\u2013H9, and Al-rich in samples H10\u2013H13 and L2\u2013L5. The observed spatial correlation of Si and Ga atoms in samples H6\u2013H9 indicates that there is a force driving Si and Ga atoms together during synthesis.\n\nState-of-the-art electronic structure calculations provide valuable insight into the electronic behavior of Si in various environments and also into the driving force behind the spatial correlation of Si and Ga atoms in the AlGaN alloys found in experiments. In binary AlN and GaN, our results on the formation of Si in the positive, neutral and stable negative DX configurations are in excellent agreement, within 0.1\u2009eV, with previous work (see\u00a0SI)14,28. In short, we calculate the (\u00b1) transition for the Si in AlN to be located at 0.17\u2009eV below the conduction band minimum (CBM) that is at 6.17\u2009eV above the valence band maximum (VBM). In GaN, the negative Si DX configuration also exists, but the (\u00b1) transition occurs 1.68\u2009eV above the CBM.\n\nFigure\u00a04 shows the results of our calculations in 6 different atomic configurations of Si in AlGaN that we investigate in more detail. In all structures, we have placed 1 Si atom, 12 Ga atoms, 131 Al atoms and 144 N atoms in a 288 atom supercell, corresponding to an atomic fraction of 8.3% of Ga (and 91.7% of Al). In the first system (Fig.\u00a04a), the 12 Ga atoms fill the second-nearest-neighbor (SNN) shell of an Al site, forming an effective Ga cluster. Two different Si configurations are considered: (1) Si substituting for the central Al atom and (2) Si substituting for an Al atom next to the Ga cluster. In the second system (Fig.\u00a04b), the SNN shell of an Al site is half-filled with 6 randomly placed Ga atoms, and the other 6 Ga atoms are distributed randomly in the rest of the supercell. Two different configurations are Si considered: (1) Si substituting for the central Al atom and (2) Si substituting for an Al atom next to the Ga half-cluster. In the third system (Fig.\u00a04c), the 12 Ga atoms are distributed randomly in the supercell. Again, two different Si configurations are considered: (1) Si substituting for an Al atom with 2 Ga atoms as SNN and (2) Si substituting for an Al atom with 1 Ga atom as SNN. We show the formation energy of Si in these configurations that correspond to the localized Kohn-Sham states within the gap for q\u00a0=\u00a0+\u00a01 and the negatively charged Si DX configuration with a broken Si-N* bond along the c-axis.\n\nThree different supercells are considered: (a) cluster of 12 Ga atoms surrounding the Al site as the second-nearest neighbor (SNN) shell, (b) half-cluster of 6 Ga atoms as SNN and 6\u2009Ga atoms distributed randomly in the supercell, and (c) 12\u2009Ga atoms distributed randomly in the supercell. In the formation energy plot, the solid vertical line followed by the blue shaded area at higher energies indicates the corresponding band gap calculated in each system without the Si atom. The dotted vertical line inside the blue shaded area indicates the band gap estimated for the alloy containing 90% Al. It is added to provide an idea on the variation of the calculated band gap value between different atomic configurations in the model, and it should not be interpreted as the absolute band edge position of the macroscopic alloy.\n\nSeveral observations are evident from the formation energies. First, the more there is Ga next to the Si atom in any of the configurations, the lower is the formation energy. Second, a lower number of Ga atoms in the SNN of Si results in the shift of the (\u00b1) transition level to the lower Fermi level energies within the gap, making it thermodynamically possible to form in highly n-type doped samples. Third, the two different Si atomic configurations in the supercell with random Ga distribution result in very similar formation energies for both charge states (Fig 4c). Fourth, the (\u00b1) transition occurs very close to the calculated CBM in all cases.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60312-4/MediaObjects/41467_2025_60312_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60312-4/MediaObjects/41467_2025_60312_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60312-4/MediaObjects/41467_2025_60312_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60312-4/MediaObjects/41467_2025_60312_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Doping high Al-content AlGaN with increasing concentrations of Si, as shown in Fig.\u00a01b, decreases the resistivity as expected up to a certain threshold concentration. Above this threshold, the resistivity increases with further increase of Si concentration. Positron annihilation results show that the concentration of compensating cation vacancy defects VIII increases with increasing Si content (Fig.\u00a02 and ref. 23), but the concentrations of these vacancy defects are an order of magnitude too low to efficiently compensate for the Si doping. Instead, at the highest Si doping concentrations where the resistivity increases with increasing Si content, the emergence of negatively charged acceptor-type defects with no associated open volume is evident, at concentrations comparable to the Si content. Importantly, the concentrations of the other impurities in the material, including carbon, are at least an order of magnitude lower. Hence the strong over-compensation is either due to a donor-to-acceptor transition of Si at high Si concentrations, or due to a so far unidentified intrinsic acceptor-type defect. We note that N vacancies have considerably higher formation energies than substitutional Si throughout the band gap of AlN, and hence are not likely to play a role, even if they also exhibit a negative charge state\u00a0when the Fermi level is close to the CBM14,29.\n\nThe details of the positron annihilation data (Fig.\u00a02) reveal that the VIII defects that are formed at increasing concentrations with increasing Si content are characterized by a strong over-representation of a GaN-like environment. In-grown cation vacancies are typically complexed with donor-like impurities and native defects. In heavily Si doped material, it is hence likely that the VIII defects observed by positrons are VIII\u2013Si complexes as the concentrations of the other impurities are not high enough. As a consequence, the Si dopants that raise the Fermi level and generate the VIII with which they are complexed are mostly in Ga-rich environments of the AlGaN alloy. In contrast, the negative acceptor defects that emerge at high Si concentrations exhibit a strong over-representation of AlN-like environments compared to the AlGaN lattice. The concentration of these negative acceptors is comparable to the Si concentration, and an order of magnitude higher than that of any other impurity. Further, the XANES spectra (Fig.\u00a03a) show that the local environment of Si is GaN-like up to a threshold Si content, and AlN-like above that threshold. The threshold concentration depends on the carbon content and the level of pre-existing compensation that dictates the Fermi level position: lower carbon content and less compensation lead to a somewhat lower Si concentration threshold. Importantly, the threshold is the same as that observed in the positron annihilation experiments and in the electrical characteristics. In short, the experimental evidence indicates that Si is predominantly incorporated in Ga-rich environments up to a threshold Si concentration and acts as a donor. Above that concentration, Si is predominantly incorporated in AlN-like environments, and acts as a negatively charged acceptor.\n\nThe experimentally observed preferential incorporation of Si in Ga-rich environments can be understood on the basis of the results of ab initio theoretical calculations presented in Fig.\u00a04a\u2013c. The formation energy of Si substituting for the cation in the AlGaN lattice is lowered by the presence of Ga atoms as second nearest neighbors (SNN), and this effect is stronger the more there are Ga atoms in the 12-atom SNN shell surrounding the Si site. While the lowering is not very large, its existence provides a driving force for the local ordering of Si and Ga atoms in AlGaN. The high growth temperature, above 1000\u2009\u2218C, allows Si to diffuse efficiently not only on the growth surface but also when buried in the film, to get stabilized at the lowest-energy lattice sites30. Assuming a random alloy with no clustering or other kind of ordering, the binomial distribution of Al/Ga atoms in Al0.90Ga0.10N leads to 2.6\u2009\u00d7\u20091019\u2009cm\u22123 cation sites with at least 50% of Ga atoms (6 out of 12) in the SNN shell. The average distance between such sites is 10\u201315 atomic jumps. Hence the Si diffusion that in AlN proceeds via the vacancy mechanism with an activation energy of about 3.5\u2009eV (ref. 30) easily allows finding the Ga-rich sites. Even if the energetic difference between Ga-rich and Al-rich cation sites for Si is relatively small\u2014roughly 0.25\u2009eV in our calculations\u2014this difference appears to be sufficient as seen in the experiments. We note that these observations are also in excellent agreement with the positron data showing that the cation vacancies that are most likely complexed with the Si dopants are in Ga-rich environments. Interestingly, this simple theoretical estimate of Ga-rich\u2014defined here as at least 50% Ga atoms as SNN\u2014lattice sites available for Si is close to the experimentally observed threshold concentration where the behavior of Si changes from donor to acceptor.\n\nThe nature of the local compositional fluctuations in a random binary alloy can be analyzed by examining the properties of the binomial distribution (see\u00a0SI for details), and this provides an additional observation. We consider 200 atoms surrounding a Si atom on the cation site, similar to the size of a typical supercell in state-of-the-art electronic structure calculations. In such a case, there are 100 Al/Ga atoms surrounding the Si atom. In Al0.90Ga0.10N, the probability of finding only 0 or 1 Ga atoms among the 100 Al/Ga atoms surrounding the Si atom on the cation site is similar to the probability of having 6 or more Ga atoms in the 12-atom SNN shell surrounding that site. It is hence possible, and even probable, that once the Ga-rich surroundings are \u201cfilled\" with Si when increasing the Si concentration, a significant fraction of the \u201coverflow\" Si atoms are incorporated in surroundings that are purely AlN-like on the scale of a few nm, the size of the supercell. On this scale31, the band structure in the vicinity of the Si atom is that of pure AlN, and the electronic behavior of Si is that found in AlN with the DX transition in the band gap. This is in good agreement with the donor activation energy being very similar in highly Si-doped AlGaN alloys with 90\u221295% Al content and in AlN, while for lower Al contents the Si donors are much more easily activated as the probability of having pure AlN on the few nm scale decreases very rapidly with Al content decreasing below 90%15. It is also important to note that these relatively large compositional fluctuations are inherent to the atomic scale, and are rapidly homogenized when the number of atoms is increased: in a 2000 atom cell that is only twice as large in diameter than the 200 atom cell, even the probability of finding a region with 95% of Al in Al0.90Ga0.10N is vanishing (see\u00a0SI).\n\nWhile the lowering of Si formation energy on the cation lattice site by the addition of Ga atoms as SNN provides the driving force for the local ordering of Si and Ga atoms in AlGaN, the ordering effects on the electronic transition levels are more subtle as shown in Fig 4. It is evident that the balance between the formation energies of various charged states is delicately influenced by the surrounding Ga atoms. Also the exact position of the CBM depends on the Ga distribution in the supercell used in the calculation. It is hence extremely important to take into account local compositional fluctuations when analyzing carrier localization phenomena in AlGaN32, similarly as recently demonstrated for InGaN alloys31. This is an important difference between the III-nitrides and the more conventional III\u2013V semiconductors where the virtual crystal approximation is a reasonable approach. Our calculations show that in each of the considered Ga configurations, the (\u00b1) transition level of Si is shallower with more Ga atoms surrounding the Si site. This supports the picture obtained from experiments: the donor character of Si is the strongest when there is a maximum amount of Ga atoms surrounding it, while the probability of Si being in a negative charge state increases when the immediate surroundings are AlN-like.\n\nCombining advanced experimental characterization and state-of-the-art electronic structure calculations allows us to show that the amphoteric behavior of Si in high Al content AlGaN alloys is controlled by short-range order with multiple Ga atoms. In addition to explaining the over-compensation at high Si doping levels, our results suggest an avenue for efficient doping by Si not only in high Al content AlGaN, but possibly also in AlN. Spatially correlating Si with multiple Ga atoms in a co-doping scheme could allow for efficient n-type doping up to much higher Si contents and carrier concentrations than so far. This could be achieved via enhanced clustering of Ga atoms in the 3D matrix, or by introducing digital superlattice structures where the Si dopants could be incorporated in the GaN or Ga-rich monolayers. Interestingly, a recent report on efficient Si doping of AlN highlights that there is excess Ga in their material33.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The samples were grown by metal organic vapor phase epitaxy (MOVPE) in a 3\u00a0\u00d7\u00a02\" close-coupled showerhead (CCS) Aixtron reactor. Epitaxially laterally overgrown (ELO) AlN layers were grown on (0001) c-plane sapphire substrates with a miscut of 0.1\u2218 towards the [1-100] sapphire direction23. The threading dislocation density in the ELO AlN/sapphire templates was 1.5\u2009\u00d7\u2009109\u2009cm-2 22,34. The layer structure from bottom to top consists of a AlN buffer layer (400 nm), a graded transition layer from AlN to Al0.90Ga0.10N (25 nm), undoped Al0.90Ga0.10N (100 nm), and a Si-doped layer of Al0.90Ga0.10N (900\u20131400\u2009nm)35. The composition of the samples was determined by high resolution X-ray diffractometry measuring reciprocal space maps near the (10\u201315) AlN reflex under consideration of the layer strain state4,36. The resistivity of the samples was determined by contactless (eddy current) resistivity measurements in a Delcom system. Impurity concentrations (Si, C, O, H) were determined by secondary ion mass spectrometry by Evans Analytical group. In H series, the impurities contents were [H]\u2009=\u20095\u2009\u00d7\u20091017\u2009cm\u22123, [O]\u2009=\u20093\u2009\u00d7\u20091017\u2009cm\u22123 and [C]\u00a0=\u00a02\u2009\u00d7\u20091018\u2009cm\u22123. In L series, the impurities contents were [H]\u2009=\u20092\u2009\u00d7\u20091017 cm\u22123, [O]\u2009=\u20092\u2009\u00d7\u20091017\u2009cm\u22123 and [C]\u2009=\u20092\u2009\u00d7\u20091017 cm\u22123.\n\nThe Doppler broadening of the positron-electron annihilation radiation was recorded with a slow positron beam at varied energies in the range of 0.5\u201325\u2009keV at room temperature in all samples19,37. A high purity germanium detector with energy resolution of 1.27\u2009keV at the 511\u2009keV annihilation line was used to collect 106 counts in the annihilation spectra. Conventional S and W parameters determined at positron implantation energies corresponding to the layer of interest were used to estimate the concentrations of negative and neutral cation vacancies in the Si-doped AlGaN samples. The S parameter is defined as the fraction of counts around the \u22640.96 keV (0.4 a.u.) central region of the peak, and the W parameter is defined in the tail of the peak in the energy range of (3.00\u22127.60\u2009keV) (1.6\u22124.0 a.u.) from the center. The measured (S,\u00a0W) parameters are shown in this paper as normalized to those obtained in a p-type GaN reference sample, representing the GaN lattice24.\n\nThe concentrations cV of the cation vacancies are determined from the trapping rate \u03baV\u00a0=\u00a0\u03bcVcV estimated by analyzing the S (W) parameter data, where \u03bcV\u2009=\u20093\u2009\u00d7\u20091015\u2009s\u22121 is the trapping coefficient at the vacancy. Importantly, we take into account the effect of negative ion defects that act as shallow traps for positrons and are efficient in positron trapping even at room temperature in AlN and Al-rich AlGaN23. When this is the case, the measured S parameter depends on the parameters of the lattice (SB) and the vacancy (SV) in the following way (a similar equation holds for the W parameter):\n\nThe negative ions whose trapping rate is denoted with \u03baion produce the same annihilation parameters as the lattice. Here, \u03bbB is the annihilation rate in the lattice, and it is the inverse of the positron lifetime in the lattice \\({\\tau }_{B}={\\lambda }_{B}^{-1}=160\\) ps in GaN/AlN23. The trapping coefficient of the negative ions is the same as that of the cation vacancies19,37,38.\n\nWe acquired fluorescence yield spectra in X-ray absorption near-edge structure region at Si K-edge (~1839\u2009eV) in the selected samples (L2\u2212L5 and H6\u2212H13) on the LUCIA beamline at the SOLEIL synchrotron facility39. The 2.75 GeV beam was operating in a hybrid refill mode at 450\u2009mA. The energy calibration was performed with a InSb(111) monochromator using the first inflection point of a Si reference spectrum, and fixed at 1841\u2009eV. Acquisitions were made in the continuous scan mode from 40\u2009eV below the silicon absorption edge (~1839\u2009eV) up to 60\u2009eV above. The varying energy steps were 1\u2009eV, 0.2\u2009eV, 0.5\u2009eV and 1\u2009eV in the corresponding energy regions 1800\u22121836\u2009eV, 1836.2\u22121850\u2009eV, 1850.5\u22121880\u2009eV and 1881\u22121900\u2009eV, respectively. With the counting time of 10\u2009s on each point, one scan recording time sums up to 2280 s. We acquired at least 3 scans to obtain a suitable signal to noise ratio.\n\nWe used Finite Difference Method Near Edge Structure (FDMNES) software to obtain total electron density of state of 9 GaN:Si and 10 AlN:Si systems27,40. The self-consistent calculations were performed with Finite Difference Method in the range 30 eV below and 80 eV above the Si K-edge with a step of 0.1 eV. We put from one to three absorbing Si atoms on metal and interstitial sites in the center of wurtzite AlN or GaN supercells and varied the first nearest neighbors of Si absorbers with different interstitial impurities. Prior to\u00a0simulations we performed ionic relaxation of all systems with conjugate gradient algorithm and Projector Augmented Wave Perdew-Burke-Ernzerhof (PAW PBE) functional on VASP 5.441,42,43,44,45. The final state and potential calculations were performed with radius values of 7 and 8 \u00c5, respectively.\n\nDefect formation energy calculations were performed with density functional theory (DFT) using VASP 5.4 code. In the calculations, we applied the projector augmented-wave (PAW) method to Perdew-Burke-Ernzerhorf (PBE) potentials from 04 January 2001, 08 April 2002, 08 April 2002, and 05 January 2001 for Al, N, Ga, and Si, respectively. The 10 3d electrons of Ga were included in the core shell. All the calculations were spin-polarized. We used lattice parameters corresponding to the wurtzite phases of AlN (a = b = 3.11 \u00c5, c/a = 1.60) and GaN (a = b = 3.20 \u00c5, c/a = 1.62)14, and cut-off energy of 400\u2009eV. The orthorhombic primitive cell contained 8 atoms, which was further scaled up to super cells containing 96 and 288 atoms. We performed optimization for lattice parameters a and c, plane wave cut-off energy and k-points meshes, and full ionic relaxation for the reference cells. We used the range-separated hybrid functional HSE06 which mixes the Hartree-Fock (HF) and generalized gradient approximation (GGA) parametrized by PBE. The fraction of exchange in a Hartree-Fock-type calculations was set to \u03b1 = 0.33 for AlN and AlGaN alloys, and \u03b1\u2009=\u20090.31 for GaN. We used the default HF screening factor. We obtained band gap values of 6.17 and 3.58 eV for AlN:SiAl and GaN:SiGa systems containing 96 atoms and calculated with 3\u2009\u00d7\u20093\u2009\u00d7\u20093 Gamma-centered k-points mesh. While our results agree with the previously reported values28,46, minor variations are due to the use of different k-point meshes and potentials. The use of k-points mesh containing Gamma point was tested for convergence against denser Gamma-centered k-points meshes in 288 atoms systems. We further used 1\u2009\u00d7\u20091\u2009\u00d7\u20091 Gamma-centered k-point mesh to reduce the computational cost for larger systems. We calculated the formation energy of a charged defect as the energy difference between the investigated system and the components in their reference states following the procedure outlined in the reviews by Van de Walle and Neugebauer47, and Freysoldt et al.21, originally formulated by Zhang and Northrup48. For \\({{{{\\rm{Si}}}}}_{{{{\\rm{III}}}}}^{0}\\) and \\({{{{\\rm{Si}}}}}_{{{{\\rm{III}}}}}^{+}\\), Si occupies the metallic cation site of the corresponding lattice, with relaxations of the nearest-neighbor nitrogen atoms. For \\({{{{\\rm{Si}}}}}_{{{{\\rm{III}}}}}^{-}\\), the lowest energy DX configuration was achieved with the broken Si-N axial bond along the c axis and large N atom displacement14. The charged states of the studied defects were modeled by changing the corresponding amount of electrons in the system. The partial density of states of introduced levels was calculated in order to determine whether the electron is localized. We determine the electrostatic local potential and dielectric constant tensors for AlN and GaN to apply finite size corrections for charged-defects49. The DFT results shown in Fig.\u00a04a\u2013c are aligned on a relative scale for direct comparison within the same atomic configuration by aligning the electrostatic potentials between the cells with and without a defect. We neglected the growth conditions (metal- or nitrogen-rich) of the systems and left the formation energy in the relative scale. Taking the chemical potentials into account will move all the lines at the same time along the vertical axis to the absolute values in relation to the specified growth condition. We note that the Si DX is metastable in all the studied cases, with the simple \\({{{{\\rm{Si}}}}}_{{{{\\rm{III}}}}}^{-}\\) configuration having a total energy lower by 0.15\u22120.50\u2009eV, but the electron is not localized in this configuration and we present the Si DX data in the figures. We also checked the effect of the Ga atoms as nearest-neighbors of the displaced N atom in the Si DX configuration in the random supercell50, and found no difference to the other configurations in this supercell (the data are not shown for the sake of clarity).", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Source data for Figs.\u00a01\u20134 in this study are provided with the paper.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All DFT calculations were performed with VASP, which is proprietary software for which the Tuomisto lab owns a license.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Amano, H. et al. The 2018 GaN power electronics roadmap. J. Phys. D: Appl. Phys. 51, 163001 (2018).\n\nArticle\u00a0\n ADS\u00a0\n \n Google Scholar\u00a0\n \n\nAmano, H. et al. The 2020 UV emitter roadmap. J. Phys. D: Appl. 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This work has been partially supported by Finnish Cultural Foundation through a personal grant (I.P.), through the Additional Million-euro Funding to Science programme grant 00231172 (F.T.), and supported by the German Federal Ministry of Education and Research (BMBF) within the \u201cAdvanced UV for Life\u201d consortium (M.K.). We acknowledge the Proposal Review Committee of SOLEIL for provision of their synchrotron radiation facilities (proposal no. 20210395) and beamtime allocation on the LUCIA beamline (R.B.). The authors wish to acknowledge CSC - IT Center for Science, Finland, for generous computational resources (project 2000028) (I.M.). The authors wish to thank S. Hagedorn, Ferdinand-Braun-Institut (FBH), Berlin, for providing the ELO AlN/sapphire templates, P. Desgardin, Universit\u00e9 Orl\u00e9ans, Orl\u00e9ans, France, for help with Doppler experiments, beamline scientist N. Trcera, LUCIA beamline, SOLEIL Synchrotron, Saint-Aubin, France, for technical assistance, and Y. Joly, Institut N\u00e9el, CNRS, Grenoble, France, and J. L. Lyons, US Naval Research Laboratory, Washington DC, USA, for fruitful discussions.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Physics and Helsinki Institute of Physics, University of Helsinki, P.O. Box 43, FI-00014, Helsinki, Finland\n\nIgor Prozheev,\u00a0Ren\u00e9 B\u00e8s,\u00a0Ilja Makkonen\u00a0&\u00a0Filip Tuomisto\n\nInstitute of Solid State Physics, Technische Universit\u00e4t Berlin, Hardenbergstr. 36, EW 6-1, 10623, Berlin, Germany\n\nFrank Mehnke,\u00a0Marcel Schilling,\u00a0Tim Wernicke\u00a0&\u00a0Michael Kneissl\n\nFerdinand-Braun-Institut (FBH), Gustav-Kirchhoff-Str. 4, 12489, Berlin, Germany\n\nMichael Kneissl\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nI.P. with F.T. designed the study. F.M., M.S., T.W. and M.K. supplied samples and characterization with SIMS, XRD and resistivity measurements. I.P. performed the positron annihilation experiments. I.P. performed the X-ray absorption near edge structure experiments and FDMNES calculations with help of R.B. I.P. performed PAW-DFT calculations with help of I.M. I.P., R.B., I.M., and F.T. analyzed the results. All authors reviewed the manuscript.\n\nCorrespondence to\n Filip Tuomisto.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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correlates of a prospective pilot trial", + "pre_title": "Safety outcomes and immunological correlates in a prospective clinical trial of immune checkpoint therapy plus debulking surgery for patients with metastatic renal cell carcinoma", + "journal": "Nature Communications", + "published": "21 February 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57009-z/MediaObjects/41467_2025_57009_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57009-z/MediaObjects/41467_2025_57009_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57009-z/MediaObjects/41467_2025_57009_MOESM3_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57009-z/MediaObjects/41467_2025_57009_MOESM4_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57009-z/MediaObjects/41467_2025_57009_MOESM5_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57009-z/MediaObjects/41467_2025_57009_MOESM6_ESM.xlsx" + }, + { + "label": "Source Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57009-z/MediaObjects/41467_2025_57009_MOESM7_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://ega-archive.org/studies/EGAS00001005667", + "https://doi.org/10.5281/zenodo.14531275", + "/articles/s41467-025-57009-z#Sec24" + ], + "code": [], + "subject": [ + "Cancer immunotherapy", + "Immunization" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4331053/v1.pdf?c=1740229553000", + "research_square_link": "https://www.researchsquare.com//article/rs-4331053/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-57009-z.pdf", + "preprint_posted": "11 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Surgical removal of primary tumors was shown to reverse tumor-mediated immune suppression in pre-clinical models with metastatic disease. However, how cytoreductive surgery in the metastatic setting modulates the immune responses in patients, especially in the context of immune checkpoint therapy (ICT)-containing treatments is not understood. Here, we report the first prospective, non-comparative clinical trial to evaluate the feasibility, clinical benefits, and immunologic changes of combining three different ICT-containing strategies with cytoreductive surgery or biopsy for patients with metastatic clear cell renal cell carcinoma (mccRCC). Based upon baseline evaluation and surgical eligibility after 6 weeks of ICT treatment, 43 patients on this trial proceeded with cytoreductive surgery, while 36 patients who had medical comorbidities preventing surgery or did not have a lesion amenable for surgical resection underwent post-ICT biopsy as specified in the clinical trial protocol, and 25 patients who discontinued study participation due to progressive disease or toxicities or withdrawal of consent did not receive either procedure (total N=104). Our data demonstrated that, in the subgroup of patients receiving the combination of ICT with cytoreductive surgery or biopsy, no additional ICT- or procedure-related toxicities were observed as compared to historical data. The median OS (overall survival) was 54.7 months for patients who received ICT-containing regimens plus cytoreductive surgery (n=43). Immune-monitoring studies with co-detection by indexing (CODEX) identified distinct tumor spatial conformation of cellular subsets as a novel and improved predictor of response to ICT. Importantly, single-cell RNA-sequencing (sc-RNA-seq) data demonstrated that surgical removal of the tumor increased antigen-presenting dendritic cell population with a concurrent reduction in KDM6B-expressing immune-suppressive myeloid cells in the peripheral blood. Together, this study highlighted the feasibility of combining ICT with cytoreductive surgery in a metastatic setting and demonstrated the potential enhancement of immune responses following ICT plus cytoreductive surgery in patients with metastatic disease.Biological sciences/Cancer/Cancer therapy/Cancer immunotherapyBiological sciences/Immunology/Immunotherapy/Immunization", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "nrreportingsummaryPS.pdfTABLES8DEGs.xlsxTable S9", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Surgical removal of primary tumors reverses tumor-mediated immune suppression in pre-clinical models with metastatic disease. However, how cytoreductive surgery in the metastatic setting modulates the immune responses in patients, especially in the context of immune checkpoint therapy (ICT), is not understood. We report the first prospective, pilot, non-comparative clinical trial (NCT02210117) to evaluate the feasibility, clinical benefits, and immunologic changes of combining three different ICT-containing strategies with cytoreductive surgery or biopsy for patients with metastatic clear cell renal cell carcinoma. Primary safety endpoint of this trial has been met, with 43 patients completing cytoreductive surgery, 36 patients undergoing post-ICT biopsy, and 25 patients without either procedure due to progressive disease or toxicities or withdrawal of consent (total N\u2009=\u2009104). Patients receiving ICT with cytoreductive surgery or biopsy, did not experience additional ICT- or procedure-related toxicities. The median overall survival was 54.7 months for patients who received ICT plus cytoreductive surgery. Immune-monitoring studies demonstrated that cytoreductive surgery increased antigen-presenting dendritic cell population and decreased KDM6B-expressing immune-suppressive myeloid cells in the peripheral blood. This study highlighted the feasibility of combining ICT with cytoreductive surgery in a metastatic setting and demonstrated the potential enhancement of immune responses following ICT plus cytoreductive surgery.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Patients diagnosed with stage IV solid tumors, characterized by multiple metastatic lesions throughout the body, have traditionally been considered ineligible candidates for surgical interventions targeting the primary tumor or any metastatic sites1,2. Nonetheless, in pre-clinical murine models bearing metastatic disease, removing the primary tumor resulted in the reversal of tumor-mediated immune suppression3, highlighting the possibility of improved response to immune-based therapy in the remaining metastatic disease. Additionally, a retrospective study of patients with metastatic melanoma treated with immune checkpoint therapy (ICT) demonstrated that patients who subsequently underwent complete surgical resection of metastases had improved survival compared to those with incomplete resection, suggesting that surgery may provide a survival benefit in the setting of ICT4. However, the concept of debulking surgery to remove a single lesion (the primary tumor-bearing organ or a metastatic lesion) to enhance anti-tumor response with continued post-surgery ICT to treat other metastatic sites of disease in the same patient has not previously been investigated in a prospective study.\n\nTo investigate the safety as well as potential clinical and biological outcomes of ICT plus cytoreductive surgery in the metastatic disease setting, we designed an open-label, pilot, non-comparative clinical trial (NCT02210117) with three different ICT-containing regimens plus cytoreductive surgery, or biopsy if a patient is not eligible for surgery, in patients with mccRCC (N\u2009=\u2009104). Each patient on this trial was given 6 weeks of systemic therapy consisting of nivolumab, nivolumab plus bevacizumab, or nivolumab plus ipilimumab, before surgery or biopsy, followed by nivolumab maintenance therapy for up to 2 years until disease progression, toxicity, or withdrawal from the protocol. The primary endpoint of the study was safety for all patients, and the secondary endpoints included best overall response, progression-free survival (PFS), overall survival (OS), and correlative immunological responses. We noted that a combination of ICT plus cytoreductive surgery or biopsy in the metastatic setting is safe. The median OS was 54.7 months for patients who received ICT plus surgery (n\u2009=\u200943). Additionally, an ad hoc non-comparative analysis showed a median OS of 23.5 months for patients who received ICT without surgery (n\u2009=\u200961).\n\nBaseline and post-treatment tissue samples and peripheral blood samples were analyzed to assess immune-genomic markers correlating with clinical benefit. Tumor interferon-gamma (IFN-\u03b3) gene signature and tertiary lymphoid structure (TLS) gene signature correlated with improved clinical response. However, we observed a cohort of patients with high IFN-\u03b3 and TLS gene signatures who did not respond to ICT. Analyzes of spatial conformation of tumor immune cell subsets in patients with high IFN-\u03b3 and high TLS gene signatures including those without clinical response to ICT delineated distinct cellular distribution and enrichment of certain cellular neighborhoods that accurately correlated with clinical response to ICT. Importantly, longitudinal analyzes of matched peripheral blood samples at a single-cell level by sc-RNA-seq demonstrated increased conventional dendritic cell population with a concurrent reduction in KDM6B expressing immune-suppressive myeloid cells in patients who underwent surgical removal of the tumor compared to patients who did not.\n\nOverall, we report herein the safety and immune correlative data from this prospective pilot trial. Our data demonstrated the feasibility of combining ICT with cytoreductive surgery in mccRCC, highlighted the importance of the spatial distribution of immune cell subsets in determining response to ICT, and demonstrated the effect of cytoreductive surgery in modulation of the immune responses in the metastatic disease setting.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Between July 2015 and March 2018, 105 patients were randomly assigned to receive nivolumab (n\u2009=\u200930), nivolumab+ bevacizumab (n\u2009=\u200945), or nivolumab \u2009+\u2009 ipilimumab (n\u2009=\u200930) for a total of 6 weeks, then underwent cytoreductive surgery or tumor biopsy, followed by maintenance nivolumab therapy for up to 2 years. Baseline and post-treatment tissue samples and peripheral blood samples were collected for immunologic and genomic analyzes (Fig.\u00a01a, Supplementary Fig.\u00a01a, b). One patient was not treated in the nivolumab arm due to inadvertent randomization prior to completing screening and ultimately was not eligible for enrollment in the trial (and thus N\u2009=\u2009104 were used for clinical outcome analysis). After enrollment into the trial, patients were concurrently evaluated by medical oncologists and urologists specialized in RCC, at baseline and after completing 6 weeks of systemic therapy, to assess the suitability of debulking surgery or biopsy. Based upon baseline evaluation and surgical eligibility after 6 weeks of ICT treatment, 43 patients on this trial proceeded with cytoreductive surgery, while 36 patients who had medical comorbidities preventing surgery or did not have a lesion amenable for surgical resection underwent post-ICT biopsy as specified in the clinical trial protocol, and 25 patients who discontinued study participation due to progressive disease (n\u2009=\u200917) or toxicities (n\u2009=\u20093) or withdrawal of consent (n\u2009=\u20095) did not receive either procedure (Supplementary Fig.\u00a01a, b). All (N\u2009=\u2009104) patients were evaluated for safety and clinical responses with a median follow-up of 76.1 months. Patient characteristics including age, gender, performance status, and international metastatic RCC database consortium (IMDC) prognostic risk group were described in Supplementary Table\u00a01. Overall, the ICT-related toxicity profile for this study (Table\u00a01 and Supplementary Table\u00a02) was expected and comparable to previously published data on ICT monotherapy or combination therapy in mccRCC5,6. For the patients who underwent cytoreductive surgery (n\u2009=\u200943), treatment with ICT did not result in any delays in surgery or wound complications. Specifically, the 90-day surgical complications among patients who received combination treatment with ICT plus surgery in this trial was 14% (6/43) as compared to historical data for cytoreductive surgical complications which ranged between 12-57%7 (Table\u00a02 and Supplementary Table\u00a03).\n\na Schema for clinical trial NCT02210117. Patients with mccRCC underwent baseline tumor biopsy and blood sample collection before being randomly assigned to receive nivolumab (n\u2009=\u200930), nivolumab + bevacizumab (n\u2009=\u200945), or nivolumab + ipilimumab (n\u2009=\u200930) for a total of 6 weeks. Four to six weeks after the ICT treatment, based upon evaluation by medical oncologists and urologists specialized on RCC, patients underwent either cytoreductive surgery or tumor biopsy. Two to four weeks after cytoreductive surgery or biopsy, nivolumab was given as maintenance therapy to each patient for up to 2 years or until disease progression or intolerable toxicities or withdrawal from the protocol. Tissue and blood samples were collected at pre-ICT treatment and at the time of surgery or biopsy (4\u20136 weeks after the initial 6 weeks of ICT treatment) for correlative studies. b Overall survival (OS) in Arm A (nivolumab). c OS in Arm B (nivolumab + bevacizumab). d OS in Arm C (nivolumab + ipilimumab).\n\nClinical responses per RECISTv1.1 criteria were assessed at 12 weeks after treatment initiation as best overall response. We assessed BOR in patients who still had metastatic lesions that could be followed by imaging studies. The BOR at 12 weeks (excluding the surgery effect) was 34% for all patients, 45% in the nivolumab arm, 36% in the nivolumab + bevacizumab arm, and 30% in nivolumab + ipilumimab arm (Supplementary Table\u00a04). Additionally, we performed ad hoc non-comparative analyzes examining outcomes for patients separately for those who received surgery and those who did not. Patients with ICT plus surgery had BOR of 79% in all ICT arms combined (Supplementary Table\u00a04). For individual ICT arms, the BOR in the group of patients who were treated with ICT plus surgery was 86% in the nivolumab arm, 81% in the nivolumab + bevacizumab arm, and 69% in the nivolumab + ipilimumab arm (Supplementary Table\u00a04). These unusually high response rates are partly because cytoreductive surgery removed target lesions in some patients. After adjusting for the effect of surgical resection of targeted lesions, the BOR was 57% in the nivolumab arm, 56% in the nivolumab + bevacizumab arm, 38% in the nivolumab + ipilimumab arm, and 51% in all arms combined in patients who received ICT plus surgery (Supplementary Table\u00a04). For patients who received ICT without surgery (n\u2009=\u200961), the BOR was 33% in the nivolumab arm, 24% in the nivolumab + bevacizumab arm, 24% in the nivolumab + ipilimumab arm, and 26% in all arms combined (Supplementary Table\u00a04). Since this study is a non-comparative clinical trial with primary endpoint of safety, no statistical comparisons were made between different treatment arms of ICT or between the surgery group and non-surgery group.\n\nWith a median follow-up time of 76.1 months, the median OS was 46.6 months (95% confidence interval [CI] 24.0, 69.2) in the nivolumab arm, 35.5 months (95% CI 20.1, 46.3) in the nivolumab + bevacizumab arm, and 30 months (95% CI 18.3, not reached [NR]) in the nivolumab + ipilimumab arm (Fig.\u00a01b\u2013d). The median PFS was 12.4 months (95% CI 5.5, 16.8) in the nivolumab arm, 7.6 months (95% CI 4.8, 9.1) in the nivolumab + bevacizumab arm, and 8.6 months (95% CI 2.1, 16.8) in the nivolumab + ipilimumab arm (Supplementary Fig.\u00a02a\u2013c). Continuing the ad hoc non-comparative analyzes of patients treated with ICT plus surgery, the median OS was 54.7 months (95% CI 40.2, NR) (Supplementary Fig.\u00a03a); and the median PFS was 12.4 months (95% CI 8.0, 18.3) (Supplementary Fig.\u00a03b). For all patients who received ICT without surgery (n\u2009=\u200961), the median OS was 23.5 months (95% CI 14.3, 35.5) (Supplementary Fig.\u00a03c) and the median PFS was 4.7 months (95% CI 2.0, 7.8) (Supplementary Fig.\u00a03d). Representative images of the clinical response to ICT plus surgery are shown in Supplementary Fig.\u00a03e.\n\nTogether, these data demonstrated the safety and feasibility of combining ICT with cytoreductive surgery or biopsy in mccRCC. Interestingly, we noted that patients who could undergo surgery appeared to have durable clinical outcomes, although this is based upon an ad hoc evaluation instead of pre-conceived analysis of randomized data and thus may be, at least in part, a reflection of selection of \u201cfit\u201d patients for surgery.\n\nAn important secondary objective for this pilot trial was to perform immune monitoring analyzes. to assess genomic and immunologic changes that correlate with clinical response, which was defined as partial response (PR), stable disease (SD) or progressive disease (PD). Among a total of 104 patients on this trial, we were able to obtain tissue samples from 94 patients for immune monitoring studies. The treatment allocation and the number of patients who had tissue available for analysis are shown in the CONSORT diagram in Supplementary Fig.\u00a04a.\n\nWhole exome sequencing (WES) of available tumor samples (n\u2009=\u200958) with paired peripheral blood mononuclear cells (PBMC), was performed to identify tumor-specific mutations correlating with clinical benefit. WES demonstrated VHL, PBRM1, and SETD2 to be the three most commonly mutated genes (Fig.\u00a02a and Supplementary Fig.\u00a04b). Most of the PBRM1 mutations were frameshift deletions and nonsense mutations, with few missense mutations whereas the majority of SETD2 mutations were nonsense mutations (Fig.\u00a02a). Importantly, mutations in either PBRM1 or SETD2 were enriched among patients with PR or SD (Fig.\u00a02b). Analysis of copy number alterations (CNA) of top 20 most commonly mutated genes in ccRCC did not show any correlation of CNA with clinical response (Supplementary Fig.\u00a04c). Next, we calculated tumor mutational burden (TMB) based on counts of somatic mutations per megabase (Mb) of the captured region. We did not note any correlation of TMB with clinical response (Supplementary Fig.4d). Additionally, we did not detect any correlation of predicted neoantigen load with clinical benefit (Supplementary Fig.\u00a04e).\n\na Oncoplot showing the somatic mutation landscape of the top 5 mutated genes [from TCGA. Kidney Renal Clear Cell Carcinoma (KIRC)]. A total of 58 tumor tissue samples were. analyzed and each column represents a patient. The color bar at the bottom shows response for. each patient (PD=progressive disease, SD=stable disease, PR=partial response). The genes are listed on the left and their respective frequencies are listed on the right of the heatmap. The colored rectangles indicate different types of somatic mutations and the key identifying each mutation type is shown at the bottom of the heatmap. The bar plot on the top shows the somatic mutation count for each patient. The bar plot on the right side shows the counts of mutations for each gene and the colors in the bar plots correspond to the colors showing mutation types in the body of the heatmap. b Stacked bar plot showing a positive association of genomic signature (mutations in PBRM1 or SETD2 genes) with clinical responses. Patients (n\u2009=\u200958) were stratified into Mut (patients with mutations in SETD2 or PBRM1, n\u2009=\u200925) and WT (patients with wild-type SETD2 and PBRM1. genes, n\u2009=\u200933) groups (p\u2009=\u20090.03). c\u2013d Box plots showing association of IFN-\u03b3 signature (c) and. TLS signature (d) with clinical responses (n\u2009=\u200983). Box plots represent the median, interquartile. range and the whiskers represent 1.5 x the upper and lower interquartile range values. Welch\u2019s. ANOVA test across the 3 groups for IFN-\u03b3 signature (p\u2009=\u20090.025) and TLS signature (p\u2009=\u20090.039). Stacked bar plot showing a positive association of IFN-\u03b3 signature (e) and TLS signature (f). with response. Patients (83) were stratified into IFNGhigh (n\u2009=\u200941) and IFNGlow (n\u2009=\u200942) groups. (p\u2009=\u20090.02) (e) or into TLShigh (n\u2009=\u200941) and TLSlow (n\u2009=\u200942) (f). Statistical significance was calculated using two-sided Welch\u2019s ANOVA, Welch\u2019s t-test and Fisher\u2019s exact test for comparing z scores in 3 or more unpaired groups, comparing z scores in 2 unpaired groups and comparing group counts, respectively. p\u2009<\u20090.05 was considered statistically significant. The following color scheme is used in all figures showing biological response groups; PD=blue, SD=yellow, and PR=red.\n\nIFN-\u03b3 gene signature is often correlated with clinical response in various tumor types, thus multiple studies are currently investigating the use of IFN-\u03b3 gene signature as a predictive biomarker8,9,10. Therefore, we performed NanoString gene expression analysis to evaluate IFN-\u03b3 gene signature in available pre-treatment (n\u2009=\u200983) samples. Pre-treatment samples were pooled from all patients since the tumor tissues were not exposed to ICTs. Analysis of pre-treatment samples demonstrated a correlation of IFN-\u03b3 gene signature with clinical responses in patients receiving ICT with nivolumab as a common backbone across the three treatment groups (Fig.\u00a02c). Similarly, patients with PR had higher TLS-gene expression scores as compared to patients with PD (Fig.\u00a02d). Next, sequential analyzes of gene expression in matched pre- and post-treatment samples (n\u2009=\u200960) also showed higher IFN-\u03b3 gene signature and TLS-gene signature in the post-treatment samples (Supplementary Fig.\u00a05a\u2013b) and differential gene expression (DEG) analysis showed upregulation of genes including CD8A, Granzyme K (GZMK), CXCL13, CCL19, CCR7, and PDCD1 (Supplementary Fig.\u00a05c) suggest a pro-inflammatory change in the tumor immune microenvironment following ICT-based therapy. Although IFN-\u03b3 and TLS gene signatures correlated with improved response, we observed a cohort of patients with high IFN- \u03b3 and TLS gene signatures who did not respond to ICT (Fig.\u00a02e, f).\n\nWe hypothesized that the lack of correlation between IFN-\u03b3 and TLS gene signatures and clinical responses in a subset of patients may be due to the fact that gene expression data do not provide information on cellular spatial distribution; therefore, we performed co-detection by indexing (CODEX) analyzes to evaluate cellular phenotype and distribution pattern in patients who had high IFN-\u03b3 and TLS gene signature associated with partial clinical response (PR, n\u2009=\u20094) as compared to patients who had high IFN-\u03b3 and TLS gene signature associated with progressive disease (PD, n\u2009=\u20094). CODEX, a multiplexed cytometric imaging approach11,12 allows spatial analyzes of single cells and their distribution within the cellular neighborhood. We assessed the cell subsets assigned by Leiden-based clustering on a staining panel consisting of 25 markers and the annotated clusters were further validated by manual inspection of multiplexed immunostains on images. This led to the identification of 15 unique cell clusters comprising of CD8 and CD4 T cells, neutrophils, tumor/epithelial cells, blood vessels, DC/APCs, macrophages, and other immune clusters. (Fig.\u00a03a\u2013c). We noted a distinct pattern of cell subsets in patients with high IFN-\u03b3 and TLS gene signatures who responded to ICT compared to patients who had high IFN-\u03b3 and TLS gene signatures but did not respond to ICT. While the percentage for B cells, CD8 T cells, and dendritic cells (DCs) was higher in PR cases, more tumor/epithelial cells were noted in the PD cases (Fig.\u00a03d). To understand the spatial organization of the cell clusters, we assessed cell neighborhoods based on the 15 annotated cell clusters and identified 6 distinct cellular neighborhoods (CNs) (Fig.\u00a03e). Enrichment of CD8 T cell neighborhood (CN1), B cell neighborhood (CN3) and DCs/APC cell neighborhood (CN5) were identified in PR while tumor cell neighborhood (CN0) was more dominant in PD (Fig.\u00a03f). To investigate cell-cell interactions, we conducted a preliminary analysis using a single pair of PR and PD cases. This analysis identified and validated 13 clusters (Supplementary Fig.\u00a06a). Among these cell types, we observed stronger intercellular interactions between CD4, CD8 T cells, B cells, and DCs in the PR case compared to the PD case within the 3\u2013100-micron range13 (Supplementary Fig.\u00a06b). Next, we expanded our initial cell-cell interaction analysis to include 25 regions of interest across all 8 patient samples (PD\u2009=\u20094, PR\u2009=\u20094), and a spatial cellular graph was constructed for each cell to its 10 closest neighbors. The volcano plots showed interactions between tumor cells and CD4 T cells as well as DCs with B cells in PR while interactions between CD8 and Tregs as well as B cells and macrophages were seen in PD (Supplementary Fig.\u00a06c). Additionally, we computed the average minimal cell distance between different pairs of cellular subsets in PR and PD cases. Our analysis showed that B cells were closely aggregated with CD8, CD4, and DCs in PR compared to PD cases (Fig.\u00a03g, Supplementary Fig.\u00a07). Together, this data demonstrated that although IFN-\u03b3 response is required to mount an immune response, the spatial organization and distribution of immune cell subsets finally dictate the outcome to ICT.\n\na Multiplex IF of immune cell aggregate for a representative partial response (PR) and progressive disease (PD) case showing staining for CD4, CD8, CD20, CD68, Ki-67, Pan CK and CD31 (PR, n\u2009=\u20094; PD, n\u2009=\u20094) (b) Heatmap of the average expression of 25 markers in the different cell clusters is shown. (c) The UMAP plots for PR and PD cases shows a total of 15 cell clusters that were validated by manual inspection of multiplexed immunostains from a 25 markers panel staining using CODEX. Color- code correspond to those for each cluster in the fig. (b). d Pie graph shows percentages of most abundant cell clusters in PR and PD cases. (e, f) Neighborhood (CN) are defined based on the presence of the 15 validated clusters. A total of six CN are identified. The stacked bar graph shows distribution of each cell neighborhood between PR (n\u2009=\u20094) and PD (n\u2009=\u20094) cases. The statistical test used is two-sided pairwise t-tests on the transformed proportions, comparing PR and PD cohorts. The resulting p-values were corrected for multiple testing by the Bonferroni method. g The bar plots show average cell distance to nearest B cells from different cell subsets when comparing 25 regions of interest from a total of eight cases (PR, n\u2009=\u20094; PD, n\u2009=\u20094), each dots represent each region of interest from all PR and PD cases. Box plots represent the median, interquartile range and the whiskers represent 1.5 x the upper and lower interquartile range values. The statistical test used is two-sided Welch Two Sample t-test on the average minimum distance metric between our two patient cohorts.\n\nBased on our adhoc analyzes, we noted that patients with mccRCC who underwent surgery had a median OS of 54.7 months. Further, metastatic models in preclinical settings previously showed that removal of the primary tumor reverts tumor-mediated immune suppression3. Therefore, to garner insight into how cytoreductive surgery in mccRCC might modulate the immune system and enhance response to ICT, we performed sc-RNA-seq analyzes on matched peripheral blood samples from mccRCC patients (n\u2009=\u200938). We assessed the changes in the immune cell subsets in patients who underwent surgery (n\u2009=\u200920, baseline=10, post-surgery=10) and compared them to patients who underwent biopsy (n\u2009=\u200918, baseline=9, post-biopsy=9). Using graph-based clustering of uniform manifold approximation and projection (UMAP), we identified 4 major immune cell clusters (T cell, NK cell, myeloid cell, and B cell) based on the expression of canonical genes (Supplementary Fig.\u00a08a, b), Cluster frequency of the major immune cell subsets showed no significant difference between post-surgery or post-biopsy group when compared to their corresponding baseline (Supplementary Fig.\u00a08c). To identify changes in T or NK cell subsets, we further sub-clustered them into 15 clusters (Supplementary Fig.\u00a09a, b) and myeloid cell subsets into 8 clusters (Fig.\u00a04a, b). Although there were no significant differences in T or NK cell subsets (Supplementary Fig.\u00a09c), we identified significant changes in the frequency of immune-stimulatory and immune-inhibitory myeloid cell subsets in the peripheral blood of patients who underwent cytoreductive surgery (Fig.\u00a04c\u2013e & Supplementary Fig.\u00a010). We noted the presence of S100A12hi monocytes (C1), HLA-DRhi monocytes (C2), KDM6B+HIF1A+ monocytes (C3), non-classical monocytes (C4 and C6), conventional dendritic cells (cDCs) (C5), plasmacytoid DCs (pDCs) (C7) and neutrophils (C8) (Fig.\u00a04a, b). Further evaluation of the myeloid cell subsets revealed that the abundance of the antigen-presenting cells including cDCs (C5) and HLA-DRhi monocytes (C2) increased significantly in post-surgery samples but not in post-biopsy samples compared to their matched baseline (Fig.\u00a04c, d). Importantly, we noted a concurrent decrease in a KDM6B+HIF1A+ immune-suppressive myeloid cell subset following cytoreductive surgery but not in patients who underwent biopsy compared to the matched baseline samples (Fig.\u00a04e). cDCs are known antigen-presenting cells, which are critical in mounting T cell-mediated immune response. Differential gene expression (DEG) analyzes further showed higher expression of HLA molecules in the cDC subsets (C5), confirming their antigen-presenting capacity (Fig.\u00a04f). We recently characterized the immune-suppressive nature of KDM6B expressing myeloid cells which inhibit T cell-mediated antitumor immunity14. DEG analyzes also demonstrated higher expression of other immune-suppressive genes such as HIF1A in this subset (Fig.\u00a04g, Supplementary Data\u00a01). Cumulatively, this data suggests that ICT treatment followed by cytoreductive surgery is associated with a pro-inflammatory skewing of the peripheral immune signature with increasing cDC population and HLA-DRhi monocytes and a concurrent reduction in KDM6B expressing immune-suppressive myeloid cells in the peripheral blood. Thus, highlighting the potential reversion of tumor-mediated immune-suppression in patients with mccRCC following cytoreductive surgery which is possibly linked to 2-year overall survival of 84% of cases seen in this cohort of patients.\n\na UMAP projection of myeloid cell subclusters in peripheral blood. b Dot plot indicating the. average expression of indicated genes as well as the percentage of cells expressing the gene to define myeloid subclusters. c\u2013e Box plots of frequency of cDCs (c), HLA-DRhi monocytes (d), and KDM6B+HIF1A+ monocytes (e), from patients who underwent surgery (n\u2009=\u200910) or biopsy (n\u2009=\u20099) comparing post-surgery or post-biopsy samples to baseline (BL). Box plots represent the median, interquartile range and the whiskers represent 1.5 x the upper and lower interquartile range values. P values indicated were analyzed using two-sided Wilcoxon signed-rank test. f, g Identity of differentially expressed genes (DEGs) in cDCs (f) and KDM6B+HIF1A+ monocytes (g) from PBMCs. P values indicated were analyzed using two-sided Wilcoxon rank-sum test and Bonferroni adjustment was performed for multiple comparisons.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57009-z/MediaObjects/41467_2025_57009_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57009-z/MediaObjects/41467_2025_57009_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57009-z/MediaObjects/41467_2025_57009_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57009-z/MediaObjects/41467_2025_57009_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Surgery for patients with mccRCC has been explored as a treatment approach through surgical removal of the primary tumor-bearing kidney (nephrectomy) and/or metastasis (metastasectomy), which has been referred to as cytoreductive or \u201cdebulking\u201d surgery. The combination of systemic therapy, such as cytokine therapy or targeted therapy, with cytoreductive surgery in mccRCC patients remains controversial due to the limited and/or conflicting evidence demonstrating a therapeutic benefit. ICT15,16,17,18,19 enhances anti-tumor T cell responses and provides durable clinical benefits in patients with mccRCC5,6. However, the value of combining ICT with debulking surgery in mccRCC is not known and currently, there is no data to prospectively evaluate the feasibility and benefits of ICT combined with surgery for patients with metastatic disease. Therefore, we designed the pilot clinical trial to test the safety and feasibility, as well as the clinical and biological outcomes of ICT with cytoreductive surgery or biopsy for patients with mccRCC.\n\nThe treatment landscape of mccRCC has changed dramatically since the initiation of this trial. While ICT agents such as nivolumab and ipilimumab were not FDA approved before initiation of this trial, bevacizumab was used in clinical practice. This trial enrolled the first patient in July 2015 before nivolumab and ipilimumab were approved by the FDA for metastatic RCC. Therefore, this trial offered patients with mccRCC the promising clinical benefits of nivolumab, bevacizumab, and ipilimumab. Although nivolumab plus ipilimumab is now FDA approved as front line and nivolumab monotherapy is approved for subsequent lines of therapy, none of this has been tested in a pre-surgical setting in combination with cytoreductive surgery in mccRCC. Therefore, this trial allowed to test the safety and feasibility of combining cytoreductive surgery (or biopsy) plus ICT with nivolumab as a common backbone across the three treatment groups. This non-comparative trial was designed to describe rather than compare clinical efficacy between different treatment arms of ICT. However, it is worth noting that in our trial the nivolumab arm has higher RR and PFS as compared to the nivolumab plus bevacizumab arm and the nivolumab plus ipilimumab arm likely due to due to the nivolumab arm has 1) younger patients; 2) more untreated patients; 3) fewer metastatic sites; and 4) less bone metastasis and more lung metastasis (Supplementary Table\u00a01). Further, it is important to highlight that clinical response was assessed by 12 weeks in our trial, whereas other studies e.g. IMMotion 15120 and JAVELIN Renal 10121 assessed clinical response at maximal response time, which could explain the difference in the CR rate between our trial and other studies.\n\nOur data demonstrated that ICT can be safely combined with cytoreductive surgery for the treatment of patients with mccRCC who have multiple metastatic lesions. In addition, this trial was not statistically designed to compare clinical outcomes for patients who received ICT plus surgery versus patients who received ICT without surgery. The ad hoc non-comparative analyzes examining outcomes for patients separately for those who received surgery and those who did not receive surgery. We noted that a median OS of 54.7 months for patients who received ICT plus surgery. For those patients who received ICT only without surgery, the median OS was 23.5 months. This is likely due to the fact that the ICT plus surgery group enriched clinically \u201cfit\u201d patients who were candidates for surgery. Similarly, we noted a generally noted a high BOR rates (38-57%, Supplementary Table\u00a04) for patients who received ICT plus surgery. For those patients who received ICT only without surgery, the BOR rates were 24-33% (Supplementary Table\u00a04). This likely due to the fact the ICT without surgery group had a higher proportion of patients with >3 metastatic sites and with previous localized and systemic therapies (Supplementary Table\u00a05).\n\nPrior to the ICT era, cytoreductive surgery has been combined with either cytokine therapy5,6,8 or targeted therapy7,22 in clinical trials for patients with metastatic ccRCC, the reported median OS in these studies was generally less than 17 months (Supplementary Table\u00a06). The prolonged survival in patients treated with ICT plus surgery in our study could be due to multiple factors including ICT as a superior therapy compared to cytokine therapy and targeted therapy, improved synergy between ICT and cytoreductive surgery, and selection bias for patients who were \u201cfit\u201d for surgery. Therefore, future prospective, randomized, controlled trials will need to test the hypothesis of whether cytoreductive surgery adds efficacy with ICT.\n\nOver the past few years, much effort has been undertaken to identify biomarkers to predict clinical response to ICT in mccRCC22,23,24,25,26,27,28. In our study, we identified both tumor intrinsic (PBRM1/SETD2 mutations) and extrinsic (IFN-\u03b3 and TLS gene signature) components correlating with response to ICT in mccRCC. Based on our TCGA analysis, no correlation of OS with TLS and IFN-G gene signatures was found in patients with mccRCC (data not shown), suggesting these biomarkers are more likely to be predictive than prognostic. However, further analyzes might be required to distinguish the predictive vs prognostic value of these biomarkers with matched samples. Importantly, we demonstrated that although IFN-\u03b3 response is required to drive an immune response, the spatial distribution of immune cell subsets finally regulate the outcome to ICT. Thus, this study provided insight demonstrating the spatial organization of immune cell subsets within the tumor microenvironment as a critical factor dictating response to ICT. Factors regulating differential spatial conformation of cellular neighborhoods within the tumor immune microenvironment will require further investigations.\n\nThe clinical data, coupled with increased peripheral antigen-presenting cells and reduction in KDM6B expressing immune-suppressive myeloid cell subsets in patients who underwent surgical removal of the tumor provided the potential mechanistic insight into immune modulation following cytoreductive surgery in a metastatic setting. This data led to the hypothesis that antigen release during cytoreductive surgery could potentially enhance anti-tumor immunity in patients, which will need to be further interrogated. Overall, our clinical and translational data may serve as a foundation to guide larger randomized, comparative clinical trials for further investigation of ICT plus cytoreductive surgery for patients with mccRCC and other tumor types as a combinatorial strategy to enhance response to ICT.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "This study complies with all relevant ethical regulations and was approved by the Institutional Review Board (IRB) at the University of Texas MD Anderson Cancer Center. This trial was registered on 08-04-2024 at: https://clinicaltrials.gov/study/NCT02210117 with a trial identification number of NCT02210117. The trial protocol is included in the Supplemental Data. The study design and conduct complied with all relevant regulations regarding the use of human study participants and was conducted in accordance with the criteria set by the Declaration of Helsinki. This trial was designed prior to SAGER guidelines and did not include sex, gender, or age in the trial design. The patients in this study included adults with histologically confirmed metastatic clear cell RCC with measurable disease who were eligible for cytoreductive nephrectomy, metastasectomy or post-treatment biopsy. In addition, patients needed to have good performance status and adequate organ functions. Patients with organ allografts, serious autoimmune diseases, active human immunodeficiency virus (HIV), acquired immunodeficiency syndrome (AIDS), hepatitis B virus (HBV), hepatitis C virus (HCV), uncontrolled hypertension, or grade 2 or higher proteinuria were excluded from this study. In addition, patients who were previously treated with anti-CTLA-4, anti-PD1, or bevacizumab were excluded from this trial. Furthermore, patients on systemic immune suppression medications such as high-dose steroids (e.g., >10\u2009mg prednisone daily or equivalent) or infliximab were also excluded from this study. This trial was a pilot, non-comparative, randomized study with a combination of three different ICT-containing strategies with cytoreductive cytoreductive surgery or biopsy for the treatment of patients with mccRCC (Fig.\u00a01a). Written informed consent was obtained from all patients for participation in the trial. In addition, all patients provided informed consent for the IRB-approved laboratory protocol MDACC PA13-0291, and all blood and tumor samples used for correlative studies were collected under this protocol. Patients on this trial underwent baseline tumor biopsy and blood sample collection for research use.\n\nPatients were randomized in a 2:3:2 fashion onto this trial to receive nivolumab (n\u2009=\u200930) [intravascular (IV) 3\u2009mg/kg every 2 weeks x3 doses], or nivolumab + bevacizumab (n\u2009=\u200945) (IV 10\u2009mg/kg every 2 weeks x3 doses), or nivolumab + ipilimumab (n\u2009=\u200930) (IV 1\u2009mg/kg every 3 weeks x2 doses) for a total of 6 weeks. The stratified randomization, including randomly sized blocks, was set up in the Clinical Trial Conduct website by the trial statistician. The website is housed on a secure server maintained by the MDACC Department of Biostatistics, allowing online access through usernames and passwords for personnel responsible for enrolling patients while maintaining allocation concealment. Of note, when this trial was designed, there were other studies with nivolumab, or nivolumab plus ipilimumab, but no combination of nivolumab plus bevacizumab at that time. We decided to allocate more patients in the nivolumab plus bevacizumab arm to gather more clinical and biological data on this combination (nivolumab plus bevacizumab) based upon the decision from our group and the sponsoring company (for clinical convenience and financial practicality). Patients were concurrently evaluated by medical oncologists and urologists specialized on RCC, at baseline and after completing 6 weeks of ICT therapy, to assess the suitability of debulking surgery or biopsy. For this purpose, imaging studies such as CT scans were performed at 12 weeks (after 6 weeks of ICT treatment) and read by a collaborating radiologist to assess clinical responses and assist the decision for cytoreductive surgery or biopsy for each patient. Two to four weeks post cytoreductive surgery or biopsy, nivolumab maintenance therapy was given to each patient for up to 2 years or until disease progression or intolerable toxicities or withdrawal from the protocol (Fig.\u00a01a). The trial underwent 2 major revisions. The initial design included only patients planning nephrectomy. To increase accrual, patients with planned metastasectomy or biopsy were added, and stratification was added to the randomization. One the original trial was complete, additional patients were added to each arm to gather more precise estimates of the endpoints. The analysis plan and methods remained the same.\n\nOf note, cytoreductive surgery was decided based upon the following criteria (in addition to consent from the patient): 1) Patients should have a resectable primary tumor; 2) ECOG performance score of 0 or 1; 3) Low surgical risk, i.e. absence of significant co-morbid illnesses; 4) Absence of uncontrolled CNS or uncontrolled spine metastasis; 5) Absence of multiple liver metastasis; 6) Absence of multiple bone metastasis; 7) Patients should be candidates for planned systemic therapy; 8) Patients should not have dominant sarcomatoid, transitional cell, or collecting duct carcinoma histology; 9) No active infection, i.e. negative culture for any previously active infection; 10) Patients should have a predicted adequate renal function after nephrectomy; 11) Patients should not have more than 2 organs involved with metastases.\n\nNinety-day surgical complications were defined according to the Clavien-Dindo classification system29. Complications were considered intraoperative if they occurred between the time from the induction of anesthesia to the time when the patient left the post-anesthesia recovery unit.\n\nSurgical approach was not mandated by the trial design and was at the discretion of the primary surgeon.\n\nThe clinical outcomes of patients were reported but the trial was not designed or powered for comparison between the treatment arms. The primary endpoint of this trial was safety and the secondary endpoints include best overall response, progression free survival (PFS), and overall survival (OS), and correlative immunologic changes. Safety stopping rules were implemented separately for each arm according to the methos of Thall30. The safety stopping endpoint was grade 3 or higher adverse events that were related to protocol therapy occurring in the first 6 weeks of therapy with 2 exceptions that only counted as events if they did not improve to grade 1 or better within 2 weeks: 1) immune-related adverse events that were treatable with steroids; or 2) any bevacizumab related events that were amenable to medical management. The proportion of patients with these events was controlled at 30% assuming an uninformative prior of beta(0.6, 1.4) which is the equivalent of adding 2 patients with a 30% safety event rate. Clinical responses were defined per RECISTv1.1 criteria and assessed at 12 weeks as complete response (CR), partial response (PR), stable disease (SD), or progression of disease (PD). BOR was defined as response status at 12 weeks. These responses were confirmed by another restaging studies about 3 months later (except in patients with apparent rapid disease progression or death or withdrawal from the trial). A response was defined as a CR or PR, and no response was defined as SD or PD. For the clinical response rate and survival analyzes, all eligible patients (N\u2009=\u2009104) were included in the analyzes. One patient was randomized prior to enrollment based upon \u201chuman error\u201d and was not followed medically, so we cannot include all randomized patients (N\u2009=\u2009105)), but this did not change our analysis plan. Of the 104 patients who received ICT, 94 patients had available tissue samples for correlation of biomarkers with clinical response (Supplementary Fig.\u00a04a).\n\nWe defined a modified BOR excluding surgery effect to correlate potential biomarkers to true biological responses without interference from cytoreductive surgery that removed target lesion(s) from some patients (Supplementary Table\u00a04). For patients (n\u2009=\u200912) who had surgery removing target lesion(s), BOR excluding surgery effect was based on assessment of the remaining target lesions with exclusion of resected target lesion(s). For those patients (n\u2009=\u20094) with only one metastatic lesion resected by metastasectomy, BOR was based upon the clinical response before surgery. Disease control was defined as a CR, PR or SD. Overall survival was defined as number of months from randomization to death (event) or last contact for patients who were alive at the final data collection. PFS was defined as the time from randomization until progression or death, whichever came first (event), or last follow-up for disease assessment among patients who were alive and free of disease at the last assessment. Estimates of OS and PFS (including those in ad hoc analyzes of patients who received ICT plus surgery and patients who received only ICT without surgery) were calculated and graphed by Kaplan-Meier methods. Patient treatment allocation and tissue analysis is shown in Supplementary Fig.\u00a01.\n\nTumor tissue samples and matched peripheral blood mononuclear cells (PBMCs, controls) from 42 patients were processed for whole exome sequencing. DNA from FFPE tissues and PBMCs was extracted using the QiaAmp DNA FFPE Tissue Kit and QiaAmp DNA Mini kit, respectively (Qiagen). Library construction was performed using manufacturer\u2019s instructions. Briefly, ~250\u2009ng genomic DNA was sheared using the Covaris S2 sonicator. KAPA Hyper Prep Kit with Agilent SureSelect XT Target Enrichment System was used for end repair, A-base addition, adapter ligation, and library enrichment PCR. Sample concentrations were measured following library construction using the Agilent Tapestation. Hybridization reaction was then performed for exon capture using the manufacturer\u2019s guidelines (Agilent SureSelect-XT Human All Exon v4). The libraries were normalized to equal concentrations using a QuantStudio 6 Flex instrument and pooled to equimolar amounts. Libraries were quantified using the Agilent Tapestation and sequenced using the Illumina HiSeq 2500 platform at a coverage of ~200X for tumor samples and ~100X for normal samples. The BWA aligner 40 was used for sequence alignment to the human reference genome, GRCh37 (UCSC genome browser: genome.ucsc.edu). The average exome- wide coverage ranges in 93.6-302.3-fold (median 200.7) in tumor samples and 54.1-294.3-fold (median 99.5) in the matched PBMC samples. SNV and indel calls were made with Mutect31 and Pindel32 respectively. The mutations were annotated by ANNOVAR33. Germline variants were filtered using germline DNA from paired blood samples. The resulting variants were filtered further by the following criteria to get the final variants: (a) dbSNPs that were \u201cnovel\u201d and the ones already existing in COSMIC were included; (b) each variant had a coverage of at least 20x for tumor and 10x for normal samples; (c) SNVs with a VAF\u2009\u2265\u20090.05 and <0.02 for tumor and normal samples, respectively and at least 3 reads to support the call at SNV in tumor sample were included; (d) for exclusion of common variants, only variants with AF\u2009<\u20090.01 in Exome Aggregation Consortium (EXAC), ESP600 and 1000 Genome (1KG) were included; (e) only variants with LOD score \u2265\u20096.3 (Mutect default) for tumor samples were included; (f) silent mutations, 5\u2019UTR and 3\u2019UTR mutations were excluded. Tumor mutational burden (TMB) was calculated based on counts of somatic mutations per Mb of captured region. Neoantigen prediction was performed using NetMHCpan (http://www.cbs.dtu.dk/services/NetMHCpan/). Briefly, all possible 8 to 12-mer peptides containing the mutated amino acid were used in the prediction. A binding affinity of less than 500\u2009nM was used as cut off of predicted neoantigen for each nonsynonymous mutation. Statistical significance was calculated using Wilcoxon rank sum test to compare TMB or neoantigen load between groups. P\u2009<\u20090.05 was considered statistically significant. Copy number alterations (CNAs) were identified using in-house algorithm as previously described34. In brief, the copy number log2 ratios of tumor versus matched normal were calculated across the entire capture regions and then subjected to segmentation using circular binary segmentation (CBS)35. A cutoff of log2 ratio <= -0.325 was applied to identify copy number loss and log2 ratio \u2265\u20090.325 was applied for copy number gain. An oncoplot plot was generated using maftools36.\n\nPre-treatment tumor tissue samples from 83 patients were processed for RNA isolation. FFPE tissues were subjected to de-waxing using deparaffinization solution (Qiagen, Valencia, CA) prior to RNA isolation. Total RNA was extracted using the RecoverALL\u2122 Total Nucleic Acid Isolation kit (Ambion, Austin, TX) for FFPE tissues and RiboPure\u2122 RNA Purification Kit (Thermo Fisher Scientific) for fresh-frozen tissues according to the manufacturer\u2019s instructions. Extracted RNA was quantified by ND Nanodrop1000 spectrometer (Thermo Scientific, Wilmington, MA, USA). For NanoString assay, 100\u2009ng of RNA was used to detect immune gene expression using nCounter PanCancer Immune Profiling panel along with custom CodeSet. Counts of the reporter probes were tabulated for each sample by the nCounter Digital Analyzer and raw data output was imported into nSolver (http://www.nanostring.com/products/nSolver, v4.0) for normalization. Negative controls were subtracted as a background correction. Positive controls and housekeeping genes were used for normalization with the default parameters. Batch effect was corrected using the R sva package37. A 24-gene TLS signature was derived using a candidate gene approach and genes were selected based on two criteria: (i) biological relevance and (ii) gene sets from published studies of TLS38,39,40,41,42. Z scores were computed for TLS signature, IFN-\u03b3 signature10. Briefly, for gene expression signature analysis, the z score standardized values of each member gene in the gene set was averaged into a combined z score by using the square root of the number of member genes as the denominator to stabilize the variance of the mean. The list of genes for the TLS and IFN-\u03b3 signatures can be found in Supplementary Table\u00a07. Statistical significance was calculated using Welch\u2019s t-test to compare Z scores between two groups and using Welch\u2019s ANOVA to compare Z scores across three or more groups. Pairwise t-test was used to compare matched pre- treatment and post-treatment samples for signature Z scores and gene expression. Benjamini- Hochberg correction was applied for multiple tests. Patients were segregated into TLShigh and TLSlow group based on the median of TLS z score. Similarly, patients were segregated into IFNGhigh and IFNGlow group based on the median of IFN-\u03b3 z score. Statistical significance was calculated using Fisher\u2019s exact test to compare patient counts in different groups. P\u2009<\u20090.05 was considered statistically significant.\n\nCodex staining assays were carried out according to the manufactured protocol. Briefly, 4\u2009\u00b5m FFPE tissue section of were placed on poly-L-lysine coated coverslip (22\u2009mm\u00d722\u2009mm). Section were stored at 4\u2009\u00b0C until use. Purified antibodies were obtained from the listed vendors (Supplementary Table\u00a08). Barcode and reporters were purchased form Akoya Bioscience (Supplementary Table\u00a08). Antibodies conjugated for CD107A, CD11C, CD20, CD21, CD31, CD44, CD45RO, CD68, CD8, E-CADHERIN, KI67 and PANCYTOKERATIN were tagged with CODEX\u00ae Barcodes at purchase (Akoya Biosciences). For CD15, CD23, CD3, CD4, CD47, EOMES, FOXP3, HLA-DR, ICOS, LAG3, MMP9, PD-1, and T-BET, 50ug of antibody (purified and free of BSA and glycerol) was conjugated in house following manufacture recommendations. Briefly, partially reduced antibodies were incubated with a unique DNA oligonucleotide (barcode), then barcode-conjugated antibody were purified using a 50-kDa centrifugal filter and collected with antibody storage solution. Purified barcoded-conjugated antibodies were stored at 4\u2009\u00b0C and used within 6 months of conjugation. The antibody conjugation reactions were validated via protein gel electrophoresis. Codex staining were validated using human tonsil tissue. Antigen retrieval Tris\u2013EDTA pH 9.0 was used during the staining protocol. Antibodies were incubated either O/N at 4\u2009\u00b0C or 3\u2009hours at room temperature, depending on the optimization protocol (Table\u00a0S8).\n\nThe regions of interest were determined using IHC and mIF staining and were representative of the microenvironment of the tissue sample. Stained sections were capture using a Keyence BZ-X810 inverted microscope with filter cubes 4900-UF1 Dapi, 49011-UF1 Alexa Fluor 488, 49004-UF1 Cy3 and 49006-UF1 Cy5 for the detection the corresponding fluorescent reporter. Exposure times for each antibody is shown in Supplementary Table\u00a08. A region of interest of 2.754\u2009\u00d7\u20092.065\u2009mm (5\u2009\u00d7\u20095 tiles) was capture using at 20x of magnification and Z-stack of pitch of 1.5\u2009\u00b5m with 9 slices.\n\nAlignment of images across cycles, stitching of tiles and subtraction of auto-fluorescence was performed using CODEX\u00ae Processor application. A neural network-based cell segmentation tool DeepCell10 was applied to pre-processed images on DAPI channels to identify nuclei, and these nuclear masks were dilated to obtain whole-cell segmented cells. Nuclear segmentation masks were stochastically dilated by flipping pixels with a probability equal to the fraction of already-assigned neighboring pixels, an algorithm that resembles a diffusion process: for rounds 1 to 9: for each nuclear mask \u201cM\u201d, for each pixel on the border of \u201cM\u201d, count the number of its neighbor pixels that are already assigned, compute the \u201cp\u201d, the fraction of neighbor pixels already assigned to cells with probability \u201cp\u201d add the pixel to mask \u201cM\u201d. The dilation was done 9 times to obtain cell masks approximating cell shapes.\n\nAll mIF analyzes were run in R-4.0.5 unless otherwise indicated. R functions are specified using the following notation: \u201c::\u201d\n\nFor cell clustering and cell neighborhood analysis, data from 8 cases (PD\u2009=\u20094, PR\u2009=\u20094) were combined using a total of twenty-five ROIs to include associated tissue heterogeneity. Possible batch effects were addressed by performing an inverse hyperbolic sine transform (\u201cbase::asinh\u201d) on cell expression values for every marker, in every ROI. The normalized values were z-scaled across both cells and markers. To cluster cells, dimensionality reduction was first performed on scaled expression values using principal component analysis with 20 components (\u201cstats::prcomp\u201d). Next, a k-nearest neighbor graph was constructed to build a similarity network between cells in principal component space (\u201cdbscan::kNN\u201d, k\u2009=\u200930). Finally, cells were clustered using the Leiden graph clustering algorithm (\u201cigraph::cluster_leiden\u201d, cluster_resolution = 1.0). To label clusters, a heatmap showing the average normalized marker expression in each cluster was plotted. Clusters were annotated using their average expression to identify cell types, and these annotations were validated by manual inspection of multiplexed immunostains on images.\n\nTo perform cell to cell interaction, a spatial graph of nearest neighbor was first constructed. Cell coordinates were derived by taking the centroid of each segmented cell nucleus relative to the corner of the ROI. A k-nearest neighbor algorithm was next used on these coordinates (\u201cdbscan::kNN\u201d, k\u2009=\u200910). This graph thus represents, for each cell, its 10 closest neighbors in 2D space.\n\nAn algorithm was computed to determine cell distance for a given cell type pair and the average minimum distance was calculated. The algorithm proceeds as follows: For each cell i of cell type A, compute distances to all cells of type B (rdist::cdist); (2) for each i, compute the shortest distance to a cell of type B (the minimum distance); and compute the average of all cells i in each ROI. Finally, to compare this metric between patient cohorts, we performed a Welch Two Sample t-test on the average minimum distance metric between our two patient cohorts.\n\nTo define cellular neighborhoods (CNs), the number of neighbors of each cell type was counted, resulting in a matrix of cells by cell clusters, with each row representing a cell, each column representing a cell annotation (cell type) from the clustering above, and each value representing the count of neighbors of the given annotation. The neighbor cell proportion was computed for each row. The resulting matrix was clustered using k-means clustering (\u201cstats::kmeans\u201d), where the optimal k was determined empirically by maximizing the silhouette score metric (\u201ccluster::silhouette\u201d). Each cluster was defined as a CN. Thus, each cell was given both a cell type annotation, which depends only on the cell\u2019s own marker expression, and a cell neighborhood annotation, which depends on the cell\u2019s type and the identities of its nearest neighbors. To compare CNs between patient cohorts, we determined the proportion of cells in each ROI belonging to each CN. Proportions were transformed using the inverse hyperbolic tangent (\u201cbase::asinh\u201d) and split by cohort. We then performed pairwise t-tests (\u201cstat::t.test\u201d) on the transformed proportions, comparing each CN between PR and PD cohorts. The resulting p - values were corrected for multiple testing by the Bonferroni method (\u201cstat::p.adjust\u201d, method = \u201cBonferroni\u201d).\n\nsc-RNA-seq was performed using the 10x Genomics Chromium Single Cell Controller. Briefly, single-cell suspensions were prepared from PBMCs. Cells were resuspended in freezing media containing 90% AB serum (derived from donors with AB blood type) and 10% dimethyl sulfoxide (DMSO) and stored in liquid nitrogen until analysis. For sc-RNA-seq analysis, cells were thawed, washed, and droplet-separated using the Chromium Single Cell 5\u2032 v.2 Reagent Kit (10X Genomics) with the 10x Genomics microfluidic system creating cDNA library with individual barcodes for individual cells. Barcoded cDNA transcripts from patients were pooled and sequenced using the NovaSeq 6000 Sequencing System (Illumina).\n\nRaw sc-RNA-seq reads generated by Illumina sequencer were demultiplexed into FASTQ and aligned to GRCh38 reference genome to generate count matrices using Cell Ranger v7.1.0 analysis pipelines (10x Genomics). Potential doublets were removed with the DoubleFinder R package (v2.0.3)43. The Seurat R package (v4.0.3)44 was used to perform the analysis including filtering out low-quality cells, normalizing the data and clustering the cells. Briefly, genes presented in less 10 cells and cells with less than 500 genes or more than 5000 genes, or with more than 20% mitochondrial gene counts were excluded from downstream analysis. A global-scaling normalization method \u201cLogNormalize\u201d was applied to the raw expression (\u201cSeurat::NormalizeData\u201d) with the default scale factor (10000). The top 2000 highly variable genes were found with the \u201cvst\u201d method (\u201cSeurat::FindVariableFeatures\u201d) and their normalized expression was scaled (\u201cSeurat::ScaleData\u201d) with regression out library size and cell cycle effects. Principal component analysis (PCA) was performed with the highly variable genes (\u201cSeurat::RunPCA\u201d) and Harmony R package (v0.1.0)45 was used to integrate the data sets with the first 50 PCA components (\u201charmony::RunHarmony\u201d). Then the first 30 components from harmony were used for constructing KNN (K-nearest neighbor) and SNN (shared nearest neighbor) graphs (\u201cSeurat::FindNeighbors\u201d). Cells were clustered with Louvain algorithm based on the SNN graph (\u201cSeurat::FindClusters\u201d) at resolution 0.4. UMAP projection was performed (\u201cSeurat::RunUMAP\u201d) with parameters (reduction = \u201charmony\u201d, dims = 1:30, n.neighbors = 20, min.dist = 0.2, spread = 1). For T and NK cells subset analysis, we selected the T and NK cells and performed the similar data analysis described above with slightly different parameters (top 1000 variable genes, first 20 harmony components for building the graphs and UMAP projection, resolution 0.8 for clustering, min. dist 0.05 and spread 2 for UMAP projection) Principal component analysis (PCA) was applied to the top 2000 highly variable genes and Harmony R package (v0.1.0)45 was used to integrate the data sets with the first 50 PCA components. Then the first 30 components from harmony were used for constructing a KNN graph, clustering and UMAP projection.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All whole exome sequencing and sc-RNA sequencing that support the findings of this study have been deposited in European Genome-phenome Archive (EGA) and are accessible through the EGA accession number EGAS00001005667. CODEX data has been deposited in Zenodo (https://doi.org/10.5281/zenodo.14531275). All other relevant data related to the current study are available in the article and its Supplementary files or from the corresponding author (Padmanee Sharma) upon request which does not include confidential patient information. Source data are provided in this paper.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "No custom codes and algorithms were used in this study. The whole exome sequencing, sc- RNA sequencing, and gene expression data (NanoString) analyzes presented in the manuscript were performed with open-source algorithms as described in Methods. Further details will be made available by the authors on request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Badwe, R. et al. Locoregional treatment versus no treatment of the primary tumour in metastatic breast cancer: an open-label randomised controlled trial. Lancet Oncol. 16, 1380\u20131388 (2015).\n\nArticle\u00a0\n PubMed\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nMetcalfe, M. J., Smaldone, M. C., Lin, D. W., Aparicio, A. M. & Chapin, B. F. Role of radical prostatectomy in metastatic prostate cancer: A review. 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Allison Institute at MD Anderson Cancer Center. We would like to thank the Immunotherapy Platform for performing immune monitoring assays, Lisa Pruitt (Data Coordinator), Jianfeng Chen, and the Advanced Technology Genomics Core at MD Anderson for performing sequencing. We would also like to acknowledge Dr. Christopher Wood, a legendary surgeon, researcher and mentor, who played a key role in the development of this manuscript but unfortunately passed away before its submission.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Sangeeta Goswami, Jianjun Gao, Sreyashi Basu.\n\nDepartment of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA\n\nSangeeta Goswami,\u00a0Jianjun Gao,\u00a0Matthew T. Campbell,\u00a0Nizar M. Tannir\u00a0&\u00a0Padmanee Sharma\n\nDepartment of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA\n\nSangeeta Goswami\u00a0&\u00a0Padmanee Sharma\n\nJames P. Allison Institute, The University of Texas MD Anderson Cancer Center, Houston, TX, USA\n\nSangeeta Goswami,\u00a0Sreyashi Basu,\u00a0Alexsandra B. Espejo,\u00a0Christian Seua,\u00a0Marc D. Macaluso,\u00a0Yulong Chen,\u00a0Wenbin Liu,\u00a0Zhong He,\u00a0Sonali Jindal,\u00a0Linghua Wang\u00a0&\u00a0Padmanee Sharma\n\nImmunotherapy Platform, The University of Texas MD Anderson Cancer Center, Houston, TX, USA\n\nSreyashi Basu,\u00a0Alexsandra B. Espejo,\u00a0Christian Seua,\u00a0Marc D. Macaluso,\u00a0Yulong Chen,\u00a0Wenbin Liu,\u00a0Zhong He,\u00a0Shalini S. Yadav,\u00a0Ying Wang,\u00a0Sonali Jindal\u00a0&\u00a0Padmanee Sharma\n\nDepartment of Urology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA\n\nDaniel D. Shapiro\u00a0&\u00a0Jose A. Karam\n\nDepartment of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA\n\nJose A. Karam\n\nDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA\n\nRebecca Slack Tidwell\u00a0&\u00a0Yu Shen\n\nDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA\n\nKamran Ahrar\n\nEnable Medicine, Menlo Park, CA, USA\n\nAlexandro E. Trevino\u00a0&\u00a0Aaron T. Mayer\n\nDepartment of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA\n\nPriya Rao\n\nDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA\n\nLi Zhao,\u00a0Jianhua Zhang,\u00a0Andrew Futreal\u00a0&\u00a0Linghua Wang\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nP.S. conceptualized clinical trial and design and supervision of immune monitoring studies. P.S. served as Principal Investigator of the clinical trial, J.G. and C.W. served as Co-PIs and supervised the clinical trial. R.S.T. and Y.S. performed statistical analysis. P.S., J.G., S.G., S.B., and S.J. contributed to the writing, reviewing, and/or revision the manuscript. D.S., J.A.K., K.A., M.T.C., A.E.T, A.T.M., A.E., C.S., M.D.M., Y.C., W.L., Z.H., S.S.Y., Y.W., P.R., L.Z., J.Z., N.M.T., A.F., L.W. provided administrative, technical or material support (reporting or organizing data and constructing databases).\n\nCorrespondence to\n Padmanee Sharma.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "Dr. Padmanee Sharma\u2019s COI disclosures are as follows as a SAB member for these companies and is not related to any of the work in this paper: Achelois, Adaptive Biotechnologies, Affini-T Akoya Biosciences, Apricity, Asher Bio, BioAtla LLC, BioNTech, Candel Therapeutics, Catalio, C-Reveal Therapeutics, Dragonfly Therapeutics, Earli Inc, Enable Medicine, Glympse, Henlius/Hengenix, Hummingbird, ImaginAb, InterVenn Biosciences, JSL Health, LAVA Therapeutics, Lytix Biopharma, Marker Therapeutics, Matrisome, Oncolytics, Osteologic, PBM Capital, Phenomic AI, Polaris Pharma, Soley Therpeutics, Sporos, Spotlight, Time Bioventures, Trained Therapeutix Discovery, Two Bear Capital, Xilis, Inc. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Ziad Bakouny and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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mechanochemistry disentangles energy density and biaxial stretchability tradeoff in composite capacitor film", + "pre_title": "Liquid metal interface mechanochemistry disentangles energy density and biaxial-stretchability tradeoff in composite capacitor film", + "journal": "Nature Communications", + "published": "06 September 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52234-4/MediaObjects/41467_2024_52234_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52234-4/MediaObjects/41467_2024_52234_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52234-4/MediaObjects/41467_2024_52234_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52234-4/MediaObjects/41467_2024_52234_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-52234-4#Sec21" + ], + "code": [], + "subject": [ + "Materials for devices", + "Materials for energy and catalysis" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3956556/v1.pdf?c=1725707343000", + "research_square_link": "https://www.researchsquare.com//article/rs-3956556/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-52234-4.pdf", + "preprint_posted": "21 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Dielectric polymer composites for film capacitors have advanced significantly in recent decades, yet their practical implementation in industrial-scale, thin-film processing faces challenges, particularly due to limited biaxial stretchability. Here, we introduce a mechanochemical solution that applies liquid metal onto rigid dielectric fillers (e.g. boron nitride), dramatically transforming polymer-filler interface characteristics. This approach significantly reduces modulus mismatch and stress concentration at the interface region, enabling polypropylene composites to achieve biaxial stretching ratio up to 450\u00d7450%. Furthermore, liquid metal integration enhances boron nitride's dielectric polarization while maintaining inherent insulation, producing high-dielectric-constant, low-loss films. These films, only microns thick yet quasi square meters in area, achieve a 55% increase in energy density over commercial biaxially-oriented polypropylene (from 2.9 to 4.5 J cm-3 at 550 MV/m), keeping 90% discharge efficiency. Coupled with improved thermal conductivity, durability, and device capacitance, this distinctive interface engineering approach makes these composites promising for high-performance film capacitors. Physical sciences/Materials science/Materials for energy and catalysisPhysical sciences/Materials science/Materials for devicescapacitive energy storagepolymer compositesfilm capacitorinterfaceliquid metal", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-956556/v1/e315646fed9d99c55af73c78.png", + "https://assets-eu.researchsquare.com/files/rs-956556/v1/e7101489cf100b071adf915c.png", + "https://assets-eu.researchsquare.com/files/rs-956556/v1/f05975886fd6f939d02619b6.png", + "https://assets-eu.researchsquare.com/files/rs-956556/v1/fc6721ffb09718cf3857e0ea.png", + "https://assets-eu.researchsquare.com/files/rs-956556/v1/2bc0d93e1bcf773034a20816.png" + ] + }, + { + "section_name": "Introduction", + "section_text": "Polymer-based dielectrics, represented by biaxially-oriented polypropylene (BOPP), is fundamental in energy storage capacitors within contemporary energy systems. Valued for low cost, high processability, and exceptional electrical properties, including high breakdown strength and rapid discharge capabilities, polymeric dielectrics are extensively utilized in the industry, with annual usage exceeding 100,000 tons in film capacitors1\u20134. However, the push towards miniaturization in evolving energy sectors5, 6, such as fast-charging piles, photovoltaic systems, and electric vehicles, poses challenges due to the size of capacitors affecting the bulkiness of power modules and inverters7, 8. Addressing this issue involves two primary strategies: enhancing the energy density of polymer materials and increasing the volume capacitance of capacitors during packaging. The material discharging energy density (Ue) is dependent on the relative dielectric permittivity (\u03b5r) and breakdown strength (Eb)1. With polymers like BOPP exhibiting high Eb (400\u2013700 MV/m), the focus shifts to increasing \u03b5r while maintaining high Eb and low dielectric loss9, 10. At the device level, maximizing capacitor volume capacitance (C), which is directly proportional to the area (S) and inversely proportional to thickness (d) of polymer dielectric film 11, necessitates processing capacitor films into larger areas and thinner dimensions.Over the past two decades, there has been significant progress in enhancing material \u03b5r while retaining high Eb, by adjusting the hierarchical structures of polymeric dielectrics through molecular structure design, surface coating, and dielectric inorganics compositing12\u201319. Yet, the processing of these advanced materials into large area/thickness ratio films remains a challenge due to loss in biaxial stretchability20\u201322. The industry-standard method, biaxial stretching, outperforms other methods like blowing, pressing, and coating in processing area, thickness, speed, and uniformity, and avoids solvent use. Molecular design strategies, such as chain structuring (e.g., cross-linking and low linearity), often compromise elastic stretchability23\u201325. Stretching polymer composites, which blends the intrinsic stretchability of polymers with the dielectric benefits of fillers, stands out as a more promising and scalable solution. However, challenges arise even with minimal filler content (1 vol%), as composite films are vulnerable to rupture at low stretch ratios (<\u2009200 \u00d7 200%). This dilemma stems from a trade-off between biaxial stretchability and dielectric functions9, 17. During biaxial stretching, polymers soften near their melting temperature, drastically lowering their modulus, while the inorganic filler phase retains very high modulus values. This modulus mismatch leads to significant interfacial stresses and deformation, causing voids and ultimately film failure under rapid industrial biaxial stretching (>\u2009100 m/min)26, 27. Current composite strategies, aimed at enhancing interface compatibility, often sacrifice dielectric permittivity and do not fully address the modulus mismatch and effective polarization of inorganic fillers28, 29.Drawing inspiration from the stretchability of biological muscle tissues, researchers have developed materials combining excellent stretchability and functional performance by using functional liquids (e.g., ionic liquids, liquid metals)30, 31. In this study, we overcome the dielectric-processing trade-off by employing wide-bandgap boron nitride (BN) and gallium-indium-tin eutectic liquid metal (LM) through mechanochemical assembly, forming a heterostructure filler (LM-BN). This structured filler not only enhances inherent dielectric polarization but also offers an ultra-soft exterior, successfully mitigating stress concentration at the polymer-filler interface and enabling high biaxial stretch ratios up to 450\u00d7450%. Moreover, the LM enhances the potential difference of BN regions, increasing \u03b5r from 2.4 in BOPP to 3.5 in BOPP composites, boosting Ue by 55% at 550 MV/m with a high discharging efficiency of 90%. Our solvent-free process aligns with production-line methods (melt extrusion, sheet forming, and biaxial stretching, Supplementary Fig.\u00a01) to produce large-area and thin capacitor film, promising advanced capacitor applications.", + "section_image": [] + }, + { + "section_name": "Interface engineering in biaxially-stretchable film fabrication", + "section_text": "In the development of biaxially-stretchable polymer composite films, this study is designed with a distinctive interface engineering strategy to impart a stretchable and highly polarized heterointerface. This is achieved by incorporating a soft and functional heterostructure filler into the polypropylene (PP) matrix, where BN serves as the core to maintain breakdown strength, and LM forms a soft, functional shell at the heterointerface, improving both stretchability and electrical polarization. A critical challenge in preparing such heterostructure filler was ensuring a stable coating of liquid and flowable LM on the chemically inert BN. We overcame this through a grinding method that initiates mechanochemical interactions between LM and BN (Fig.\u00a01a). This process alters the BN powder's color from white to homogeneous grey (Supplementary Fig.\u00a02) Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) alongside energy dispersive spectroscopy (EDS) elemental mapping reveal the atomic-level distribution of LM metal atoms on the BN sheets, even after intense sonication (Fig.\u00a01b and Supplementary Fig.\u00a03, 4). X-ray photoelectron spectroscopy (XPS, Fig.\u00a01c) validates coordination interactions between the metal atoms of LM and the nitrogen atoms of BN, as indicated by an additional peak at 397.0 eV in the N 1s orbit of LM-BN. X-ray diffraction (XRD) analyses demonstrate that the introduction of LM compresses the (002) and (004) interplanar spaces of BN, consistent with the Bragg diffraction equation (Supplementary Fig.\u00a05), indicating an LM-induced distortion of the BN crystal lattice along the Z-axis32. High-resolution transmission electron microscopy (HRTEM) images further support this, showing a reduction in the (002) interplanar spacing from 0.3366 nm in BN to 0.3303 nm in LM-BN (Supplementary Fig.\u00a06). These characterizations underscore the robustness of the coordination interaction and LM-BN interlocking structure, ensuring the morphological stability of LM-BN even during the intricate processing of the composite film capacitor.In Fig.\u00a01d, Density Functional Theory (DFT) simulations were applied to theoretically analyze the electrostatic potential distribution of LM-BN (model details in Supplementary Fig.\u00a07). In LM-BN, the contrast between positive and negative charges is significantly more pronounced than in pristine BN, suggesting an increase in dipole moment and polarization. This enhancement is expected to impart the composite film with an increased dielectric constant. Additionally, as shown in Fig.\u00a01e, the soft and stretchable nature of LM at the PP-BN interface suggests that LM-BN filler could facilitate a stress-relief interface. This interface is anticipated to mitigate the stress concentration typically observed in conventional rigid filler polymer composites, thereby ensuring a high stretching ratio during meta-stable film biaxial orientation processing. Figure\u00a01f illustrates the scalable fabrication of LM-BN filler. Emulating industrial production processes, including continuous extrusion, sheet forming, and biaxial stretching, we successfully achieved large-scale synthesis of PP/LM-BN composite pellets (Fig.\u00a01g), and obtained thin (3.4 \u00b5m, Fig.\u00a01h) and large-area (65 \u00d7 50 cm, Fig.\u00a01i) capacitor films, demonstrating the practical scalability and applicability of this all-solid fabrication method.", + "section_image": [] + }, + { + "section_name": "Characteristics of heterostructure LM-BN", + "section_text": "To elucidate the characteristics of LM-BN, we employed a probe force microscope (PFM) for in-situ analysis of its microscopic properties. The PFM's contact mode, peak-force quantitative nanomechanical mode, and piezo-force mode provide insights into the topological morphology, mechanical properties, and polarization properties of LM-BN, respectively33. LM-BN displays a unique mountain range-like surface topology, in stark contrast to the disc-like topology of pristine BN, indicative of successful LM coating (Supplementary Fig.\u00a08). This morphological shift inherently alters BN's surface from highly rigid to notably softer (Fig.\u00a02a-b and Supplementary Fig.\u00a09a). For example, modulus distribution images reveal a marked presence of low modulus regions compared to pristine BN, as further demonstrated by the cross-sectional modulus information in Fig.\u00a02a, which shows a significant decrease in modulus from 11.0 GPa in BN to 3.8 GPa in LM-BN within the 50 nm to 250 nm spectra range.Beyond surface mechanical characteristic, the presence of LM also enhances filler dielectric polarization (Fig.\u00a02c-d and Supplementary Fig.\u00a09b). The polarization amplitude distribution image and its corresponding cross-section polarization distribution illustrate two pronounced polarization peaks in LM-BN, indicating that LM's mountain-like topology contributes to heightened polarization intensity. Intriguingly, the middle valley region in LM-BN is elevated compared to pristine BN, suggesting an augmentation of BN's bulk dipole polarization by adjacent LM. This phenomenon of neighboring polarization enhancement mirrors observations in BaTiO3 heterojunctions with string-bead structures reported in previous literature34. Ultraviolet-visible diffuse reflection spectra were further used to measure the band gaps of LM-BN with varying LM contents (Supplementary Fig.\u00a010). Our findings reveal that LM-BN maintains band gaps above 5.4 eV at LM contents below 15 vol%, which is indicative of good insulative performance. Increasing the LM content adjusts the electron properties of LM-BN, as evidenced by decreasing band gaps. Therefore, the increase of BN dipole polarization by controlling LM content is beneficial for improving dielectric permittivity without increasing loss.", + "section_image": [] + }, + { + "section_name": "Biaxial stretchability of PP/LM-BN composites", + "section_text": "To examine the effect of the soft interface provided by LM-BN on the biaxial stretching capability of BOPP composite film, we prepared PP/LM-BN composites with various LM-BN filler compositions (ranging from 0 to 30 vol% LM in LM-BN) and different filler contents (0 to 4 vol%) (Supplementary Fig.\u00a011\u201312). These composites underwent biaxial stretching at 157\u00b0C, and the maximum stretching ratios before film rupture were recorded (Fig.\u00a03a). Our observations revealed that stretching ratios decreased with an increase in filler content, yet notably increased with higher LM content in LM-BN. Impressively, a stretching ratio of approximately 450% was achieved in BOPP/30-LM-BN. Even at the highest filler content of 4 vol%, where the stretching ratio of BOPP/BN significantly dropped to nearly 100%, BOPP/30-LM-BN composites maintained a high stretching ratio of around 350%. The biaxial stretching force-strain curves (Fig.\u00a03b) highlight a considerable elongation improvement and a significant stress relief effect in BOPP/LM-BN composites. In these composites, the required force was notably reduced from 3.6 N in BOPP/BN to just 1.6 N in BOPP/30-LM-BN, while simultaneously enhancing the biaxial stretchability.To elucidate the influence of the soft interface on macroscopic tensile behavior, we examined the evolution of microscopic interface morphologies during stretching, maintaining a consistent filler content of 4.0 vol%. As depicted in Fig.\u00a03c, BOPP/BN films displayed rapid development of holes and cracks at the PP-BN interface at 200\u00d7200% stretching, leading to complete rupture at 300\u00d7300% stretching. In contrast, BOPP/LM-BN films, particularly BOPP/30-LM-BN, showed minimal interface cracks and holes even at higher stretching ratios. This was confirmed by optical photographs, along with SEM and TEM imaging (Fig.\u00a03d and Supplementary Fig.\u00a013), and by observing a significantly higher visible light transmittance (Supplementary Fig.\u00a014). To understand how the soft interface mitigates interface cracks and improves tensile ratio, we employed finite element analysis (FEA) to simulate stress and crack development in composites during biaxial stretching. Cross-sectional stress distribution images (Fig.\u00a03e) indicated that interface stresses in PP/BN composites (white and red regions near BN sheet) significantly increased with strain due to the modulus mismatch between the two phases. In contrast, stresses in PP/LM-BN composites remained relatively low (dark blue regions near BN sheet) under various strains. This stress concentration difference was quantitatively assessed using the stress concentration factor (K), derived from this formula35:\\(K={\\sigma }_{max}/{\\sigma }_{matrix}\\), where \u03c3max represents the maximum stress at the filler/polymer interface, and \u03c3matrix is the average stress in the polymer matrix. For BOPP/BN, K exhibits a clear strain dependence, reaching 5.6 at a 300% stretching ratio, whereas for BOPP/LM-BN, it remains consistently below 1 (Fig.\u00a03f). Given BN's higher strength compared to PP, mechanical failures and crack growth primarily occurred at the high-stress interface region of the PP phase. Therefore, the stress-softening effect of LM at the interface crucially reduces modulus mismatch, preventing stress concentration and the resultant rupture of the PP phase. Further simulations with varying LM volumes (Supplementary Fig.\u00a015) showed a gradual decrease in interface stress with increased LM content, corroborating LM's beneficial role. This aligns with experimental observations of maximum stretching ratio changes (Fig.\u00a03a). Extensive enlargement of the simulation models to reflect the actual size of the composites (Supplementary Fig.\u00a016) consistently demonstrated a similar stress-softening effect at the interface.", + "section_image": [] + }, + { + "section_name": "Dielectric properties of BOPP/LM-BN composites", + "section_text": "Figure\u00a04a presents the dielectric properties of BOPP/LM-BN at a 300 \u00d7 300% biaxial stretching ratio and 1 kHz, as determined from dielectric relaxation frequency spectra (Supplementary Fig.\u00a017). Notably, the relative dielectric constant (\u03b5r) of all BOPP/LM-BN samples was higher than that of BOPP/BN, showing a distinct correlation with both LM and filler contents. The highest \u03b5r of 3.5, observed in BOPP/30-LM-BN at 4 vol% filler content, was 1.46 times that of pristine BOPP (\u03b5r\u2009~\u20092.4) and 1.40 times greater than BOPP/BN (\u03b5r\u2009~\u20092.5). Concurrently, the dielectric dissipation factor of BOPP/LM-BN slightly increased with filler content, ranging from 0.001 in pure BOPP to 0.002\u20130.004 in various BOPP/LM-BN compositions at the highest filler content. Figure\u00a04b illustrates the breakdown strength (Eb) of composites with different LM-BN and filler contents, calculated using Weibull distributions (Supplementary Fig.\u00a018). The Eb decreased marginally from 720 MV/m in pure BOPP to around 550 MV/m in BOPP composites. Remarkably, despite LM's non-insulative nature, BOPP/LM-BN composites retained comparable insulation to BOPP/BN composites through appropriate LM content modulation. For instance, BOPP/15-LM-BN maintained a high Eb of 566 MV/m at 4 vol% filler content. This aligns with earlier findings that LM-BN maintains high bandgaps at low LM content.To validate the insulation integrity of BOPP/LM-BN composites, leakage current tests and electron barrier analysis were further conducted. For example, BOPP/15-LM-BN displayed leakage currents similar to BOPP/BN across various electric fields (Fig.\u00a04c). The Fowler-Nordheim model was employed to evaluate the electron tunneling barrier from electrodes to dielectric materials (Supplementary Fig.\u00a019 and Fig.\u00a04d), revealing that the barrier of BOPP/LM-BN (2.51 eV) was comparable to that of BOPP/BN (2.56 eV), supporting the conclusion that BOPP/LM-BN composites effectively maintain their insulative properties36. As evidenced in the energy band structure measurements (Fig.\u00a04e and Supplementary Fig.\u00a020\u201322), LM is confirmed to exhibit a higher electron affinity (4.19 eV) compared to BN with 0.83 eV. This disparity suggests that LM can capture some excited electrons from the PP matrix, creating a trap energy level that confines these electrons. Kelvin probe force microscopy provides further insight, showing that electrons are trapped in the LM-BN phase of the composites, as indicated by the enhanced surface potential on LM-BN surfaces (purple regions in Fig.\u00a04f). Intriguingly, this increased potential may also contribute to the enhanced dipole polarization of BN and, consequently, to the increased permittivity of BOPP/LM-BN composites. This effect is attributed to the strong positive charge of transition metal atoms (Ga and In) and the intense negative charge of oxygen atoms in LM. Furthermore, the pronounced positive charge of the transition metal atoms in LM is likely responsible for the observed electrophilic trapping effect (Fig.\u00a01d).", + "section_image": [] + }, + { + "section_name": "Capacitive application in composite film capacitor", + "section_text": "Having established that proper modulation of LM-BN and filler content balances \u03b5r, Eb, and dielectric loss, we selected the optimal BOPP/15-LM-BN with 2.5 vol% filler content to assess capacitive energy storage performance above 90% discharge efficiency (\u019e) (Fig.\u00a05a and Supplementary Fig.\u00a023\u201324). The discharge energy density (Ud) of BOPP/LM-BN (4.5 J cm\u2212\u20093 at 550 MV/m) significantly surpasses that of commercial BOPP film (2.9 J cm\u2212\u20093 at 550 MV/m) and BOPP/BN film (2.9 J cm\u2212\u20093 at 500 MV/m). Additionally, BOPP/LM-BN exhibits a high \u019e of 90% at 550 MV/m. Durability tests show BOPP/LM-BN sustains 10,000 charge-discharge cycles at 400 MV/m with minimal degradation (Fig.\u00a05b), indicating long-term reliability. A fast discharge speed is a critical performance to assess the dielectric capacitors versus other electrical energy storage devices such as electrochemistry batteries. The discharge time \u03c40.9 was defined as the discharged time at the 90% energy density from the discharge tests. Under 200 MV/m, representative discharge profiles from a load resistor RL of 100 \u2126 for BOPP, BOPP/BN, and BOPP/LM-BN films show a close \u03c40.9 of 18.8, 25.4, and 20.0 ns, respectively (Fig.\u00a05c), indicating the well-maintained pulse discharge advantage of BOPP/LM-BN films. Corresponding fast Ud is increased from 0.31 J cm\u2212\u20093 of commercial BOPP to 0.42 J cm\u2212\u20093 of BOPP/LM-BN film, suggesting the greatly improved power density of BOPP/LM-BN.In Fig.\u00a05d and Supplementary Table. S1, we compare our BOPP/LM-BN dielectrics with currently representative capacitor films, including solution-casted polymers, biaxially-stretched composites, and surface-coated films at above 90% \u019e and 550 MV/m1, 4, 12, 17, 28, 37\u201341. While novel polymer strategies achieve high Ue values through elaborate molecular design, such as polyimide derivatives and ladderphane copolymer, they are limited by complex preparation and are not amenable to large-area, high-quality biaxial stretching. Surface-coated films maintain biaxial stretchability but offer limited permittivity and Ue enhancement. Composite films typically suffer from reduced stretchability; only with very low filler content (<\u20091 vol%) is stretchability preserved, but with negligible Ue improvement. Our BOPP/LM-BN, however, achieves a desirable balance in high Ue and biaxial stretching ratios, making it a promising candidate in the applications of miniaturized film capacitors.The application potential of BOPP/LM-BN in multilayer foil biaxial-orientation composite film capacitors (MFBOCFC) is demonstrated with large-area (9 \u00d7 9 cm) prototypes fabricated through stacking, pressing, leading, and sealing (Supplementary Fig.\u00a025\u201326). These prototypes can be encapsulated similarly to commercial film capacitors through methods like winding encapsulation or stacked encapsulation (Fig.\u00a05e), with dielectric film layer exhibiting a uniform thickness of around 10 \u00b5m while Al electrode foil is around 6 \u00b5m. The capacitance and dissipation factors of the BOPP, BOPP/BN and BOPP/LM-BN capacitor with the same layer numbers as a function of frequency are shown in Fig.\u00a05f. Capacitance measurements show BOPP/LM-BN capacitors outperforming BOPP and BOPP/BN capacitors in capacitance (85 nf vs. 72 nf and 65 nf, respectively) at 100 Hz, indicating potential for significant volume reduction by 31% in capacitor design. Additionally, BOPP/LM-BN capacitors exhibit excellent capacitance stability under bending (Fig.\u00a05g), highlighting their flexibility and suitability for diverse flexible applications. Meanwhile, the BOPP/LM-BN has a much higher thermal conductivity (4.1 W m \u2212\u20091 K\u2212\u20091) than that of BOPP/BN (2.6 W m\u2212\u20091 K\u2212\u20091) which can improve the high-temperature performance of devices (Supplementary Fig.\u00a027) 42 . The raw materials cost of our composite capacitor film (around $6.0/kg) is much lower than other BOPP alternating materials ($11.0/kg to 350.0/kg) (Supplementary Table. S2).In summary, our study presents a novel approach to fabricate composite capacitor films with a soft and functional interface, successfully resolving the trade-off between enhanced energy density and high biaxial stretchability. By incorporating a soft LM liquid, we effectively modulate the modulus mismatch between the soft PP matrix and the hard BN filler. This modulation reduces stress concentration and blunts crack formation, thus enabling a higher biaxial stretching ratio. The LM-BN heterostructure exhibits wide band gaps and electron traps, yet simultaneously enhances dipole polarization, which positively impacts the permittivity and breakdown strength of the composite. These unique characteristics lead to a spectrum of exceptional capacitive energy storage properties, including \u03b5r, Eb, Ud, \u019e, cyclic stability, thermal conductivity, and device capacitance, which pave the way for scaled-up fabrication of cost-effective composite dielectric films and the development of more compact capacitive energy storage devices. Moreover, we believe that this soft and functional interface approach sets a promising foundation for designing polymer-filler interface, holding potential for broad application in improving both biaxial stretchability and functionality of composite materials across various fields, including packaging, barrier films, permeable films, battery films, and optical films.", + "section_image": [] + }, + { + "section_name": "Declarations", + "section_text": "Data availability\nThe authors declare that all the published data supporting the findings of this study are available within the article and its supplementary information files.\nAcknowledgments\nThis work was financially supported by National Natural Science Foundation of China (Grant No. 52103091 and 52373042), National Key Research and Development Project of China (Grant No. 2022YFB3806900), and the International Visiting Program for Excellent Young Scholars of SCU.\nAuthor contributions\nZ. Xie, K. Wu and Q. Fu conceived the idea. Z. Xie and J. Zhu fabricated the materials and measured the material performance. Z. Xie, K. Wu and Q. Fu analyzed the data. K. Wu and Z. Xie organized the experimental data and wrote the manuscript. K. Wu and Q. Fu supervised the overall conception. All authors contributed to the discussion on the results and improved the manuscript.\nAdditional Information\nSupplementary Information accompanies this paper at \u00a0 \u00a0\nCompeting interests: The authors declare no competing financial interests.\nReprints and permission information is available online at http://npg.nature.com/reprintsandpermissions/\nPublisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "\nChen J, et al. Ladderphane copolymers for high-temperature capacitive energy storage. Nature 615, 62-66 (2023).\nQian X, Chen X, Zhu L, Zhang Q. 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Vascular smooth muscle-inspired architecture enables soft yet tough self-healing materials for durable capacitive strain-sensor. Nat. Commun. 14, 130 (2023).\nWang J, Wu B, Wei P, Sun S, Wu P. Fatigue-free artificial ionic skin toughened by self-healable elastic nanomesh. Nat. Commun. 13, 4411 (2022).\nXie Z, et al. Joint-Inspired Liquid and Thermal Conductive Interface for Designing Thermal Interface Materials with High Solid Filling yet Excellent Thixotropy. Adv. Funct. Mater. 33, 2214071 (2023).\nGuo M, et al. Toroidal polar topology in strained ferroelectric polymer. Science 371, 1050-1056 (2021).\nChen Y, et al. An All-Scale Hierarchical Architecture Induces Colossal Room-Temperature Electrocaloric Effect at Ultralow Electric Field in Polymer Nanocomposites. Adv. Mater. 32, 1907927 (2020).\nPilkey WD, Pilkey DF, Bi Z. Peterson's stress concentration factors. John Wiley & Sons (2020).\nZhu Y, Zhu Y, Huang X, Chen J, Jiang P. High Energy Density Polymer Dielectrics Interlayered by Assembled Boron Nitride Nanosheets. Adv. Energy Mater. 9, 1903062 (2019).\nYuan X, Matsuyama Y, Chung TM. Synthesis of functionalized isotactic polypropylene dielectrics for electric energy storage applications. Macromolecules 43, 4011 (2010).\nLi H, et al. Crosslinked fluoropolymers exhibiting superior high-temperature energy density and charge\u2013discharge efficiency. Energy Environ. Sci. 13, 1279-1286 (2020).\nLiu D, et al. Largely enhanced energy density of polypropylene based nanocomposites via synergistic hybrid fillers and high shear extrusion assisted dispersion. Composites, Part A 119, 134-144 (2019).\nBao Z, et al. Improved Working Temperature and Capacitive Energy Density of Biaxially Oriented Polypropylene Films with Alumina Coating Layers. ACS Appl. Energy Mater. 5, 3119-3128 (2022).\nRen M, Liu J, Sun L, Cao Y. Enhancing dielectric property of polymer films with nanoclay coatings. In: 2016 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP)). IEEE (2016).\nZhang B, et al. Exploring Trade-offs in Thermal Interface Materials: The Impact of Polymer-Filler Interfaces on Thermal Conductivity and Thixotropy. Chin. J. Polym. Sci. (2024); DOI: 10.1007/s10118-024-3101-0.\n", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Fig.pngSupportingInformation.docx", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Dielectric polymer composites for film capacitors have advanced significantly in recent decades, yet their practical implementation in industrial-scale, thin-film processing faces challenges, particularly due to limited biaxial stretchability. Here, we introduce a mechanochemical solution that applies liquid metal onto rigid dielectric fillers (e.g. boron nitride), dramatically transforming polymer-filler interface characteristics. This approach significantly reduces modulus mismatch and stress concentration at the interface region, enabling polypropylene composites to achieve biaxial stretching ratio up to 450 \u00d7 450%. Furthermore, liquid metal integration enhances boron nitride\u2019s dielectric polarization while maintaining inherent insulation, producing high-dielectric-constant, low-loss films. These films, only microns thick yet quasi square meters in area, achieve a 55% increase in energy density over commercial biaxially-oriented polypropylene (from 2.9 to 4.5\u2009J\u2009cm\u22123 at 550\u2009MV/m), keeping 90% discharge efficiency. Coupled with improved thermal conductivity, durability, and device capacitance, this distinctive interface engineering approach makes these composites promising for high-performance film capacitors.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Polymer-based dielectrics, represented by biaxially oriented polypropylene (BOPP), are fundamental in energy storage capacitors within contemporary energy systems. Valued for low cost, high processability, and exceptional electrical properties, including high breakdown strength and rapid discharge capabilities, polymeric dielectrics are extensively utilized in the industry, with annual usage exceeding 100,000 tons in film capacitors1,2,3,4. However, the push towards miniaturization in evolving energy sectors5,6, such as fast-charging piles, photovoltaic systems, and electric vehicles, poses challenges due to the size of capacitors affecting the bulkiness of power modules and inverters7,8. Addressing this issue involves two primary strategies: enhancing the energy density of polymer materials and increasing the volume capacitance of capacitors during packaging. The material discharging energy density (Ue) is dependent on the relative dielectric permittivity (\u03b5r) and breakdown strength (Eb). With polymers like BOPP exhibiting high Eb (400\u2013700\u2009MV/m), the focus shifts to increasing \u03b5r while maintaining high Eb and low dielectric loss9,10. At the device level, maximizing capacitor volume capacitance (C), which is directly proportional to the area (S) and inversely proportional to thickness (d) of polymer dielectric film11, necessitates processing capacitor films into larger areas and thinner dimensions.\n\nOver the past two decades, there has been significant progress in enhancing material \u03b5r while retaining high Eb, by adjusting the hierarchical structures of polymeric dielectrics through molecular structure design, surface coating, and dielectric inorganics compositing12,13,14,15,16,17,18,19. Yet, the processing of these advanced materials into large-area/thickness ratio films remains a challenge due to loss in biaxial stretchability20,21,22. The industry-standard method, biaxial stretching, outperforms other methods like blowing, pressing, and coating in the processing area, thickness, speed, and uniformity and avoids solvent use. Molecular design strategies, such as chain structuring (e.g., cross-linking and low linearity), often compromise elastic stretchability23,24,25. Stretching polymer composites, which blend the intrinsic stretchability of polymers with the dielectric benefits of fillers, stand out as a more promising and scalable solution. However, challenges arise even with minimal filler content (1\u2009vol%), as composite films are vulnerable to rupture at low stretch ratios (<200\u2009\u00d7\u2009200%). This dilemma stems from a trade-off between biaxial stretchability and dielectric functions9,17. During biaxial stretching, polymers soften near their melting temperature, drastically lowering their modulus, while the inorganic filler phase retains very high modulus values. This modulus mismatch leads to significant interfacial stresses and deformation, causing voids and, ultimately, film failure under rapid industrial biaxial stretching (>100\u2009m/min)26,27. Current composite strategies, aimed at enhancing interface compatibility, often sacrifice dielectric permittivity and do not fully address the modulus mismatch and effective polarization of inorganic fillers28,29.\n\nDrawing inspiration from the stretchability of biological muscle tissues, researchers have developed materials combining excellent stretchability and functional performance by using functional liquids (e.g., ionic liquids, liquid metals; LM)30,31. In this study, we overcome the dielectric-processing trade-off by employing wide-bandgap boron nitride (BN) and gallium-indium-tin eutectic LM through mechanochemical assembly, forming a heterostructure filler (LM-BN). This structured filler not only enhances inherent dielectric polarization but also offers an ultra-soft exterior, successfully mitigating stress concentration at the polymer-filler interface and enabling high biaxial stretch ratios up to 450\u2009\u00d7\u2009450%. Moreover, the LM enhances the potential difference of BN regions, increasing \u03b5r from 2.4 in BOPP to 3.5 in BOPP composites, boosting Ue by 55% at 550\u2009MV/m with a high discharging efficiency of 90%. Our solvent-free process aligns with production-line methods (melt extrusion, sheet forming, and biaxial stretching, Supplementary Fig.\u00a01) to produce large-area and thin capacitor film, promising advanced capacitor applications.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "In the development of biaxially stretchable polymer composite films, BN is selected due to its advantageous dielectric and thermal properties: a wide bandgap (5.5\u20136.0\u2009eV), two-dimensional hexagonal structure, and high thermal conductivity (>300\u2009W\u2009m\u22121\u2009K\u22121). These characteristics are expected to impart the composite material with high electrical breakdown strength, low leakage current loss, and improved high-temperature performance, respectively32. A distinctive interface engineering strategy has been implemented to impart a stretchable and highly polarized heterointerface, essential for the biaxial stretching capability. This is accomplished by incorporating a soft and functional heterostructure filler into the polypropylene (PP) matrix, where BN serves as the core to maintain breakdown strength, and LM forms a soft, functional shell at the heterointerface, improving both stretchability and electrical polarization. A critical challenge in preparing such heterostructure filler was ensuring a stable coating of liquid and flowable LM on the chemically inert BN. We overcame this through a grinding method that initiates mechanochemical interactions between LM and BN (Fig.\u00a01a). This process alters the BN powder\u2019s color from white to homogeneous gray (Supplementary Fig.\u00a02). Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) alongside energy dispersive spectroscopy (EDS) elemental mapping reveals the atomic-level distribution of LM metal atoms on the BN sheets, even after intense sonication (Fig.\u00a01b and Supplementary Figs.\u00a03 and 4). X-ray photoelectron spectroscopy (XPS, Fig.\u00a01c) validates coordination interactions between the metal atoms of LM and the nitrogen atoms of BN, as indicated by an additional peak at 397.0\u2009eV in the N 1\u2009s orbit of LM-BN. X-ray diffraction analyses demonstrate that the introduction of LM compresses the (002) and (004) interplanar spaces of BN, consistent with the Bragg diffraction equation (Supplementary Fig.\u00a05), indicating an LM-induced distortion of the BN crystal lattice along the Z-axis33. High-resolution transmission electron microscopy images further support this, showing a reduction in the (002) interplanar spacing from 0.3366\u2009nm in BN to 0.3303\u2009nm in LM-BN (Supplementary Fig.\u00a06). These characterizations underscore the robustness of the coordination interaction and LM-BN interlocking structure, ensuring the morphological stability of LM-BN even during the intricate processing of the composite film capacitor.\n\na Schematic illustration detailing the LM mechanochemistry process: Extensive milling promotes LM oxidation and BN activation, facilitating coordination between \u03b4- N atoms\u2019 pair electrons and \u03b4+ metal atoms\u2019 vacant orbitals. b TEM image and corresponding EDS image of LM-BN, illustrating LM coating on BN. c XPS N 1\u2009s spectra comparison between BN and LM-BN. d Simulated 3D electrostatic potential distribution for both BN and LM-BN, indicating LM-BN\u2019s enhanced potential difference, which promotes dipole polarization and confines electrons through electro-positivity. e Schematic illustration demonstrating composite film\u2019s interface stress relief through LM interface mechanochemistry, preventing interfacial cracks during the meta-stable biaxial-orientation process, in contrast to conventional rigid filler composites. f\u2013g Display of a large volume of LM-BN powder and PP/LM-BN composite pellets, suggesting scalability potential. h Cross-sectional SEM image showcasing a thin-film achievement of 3.4\u2009\u00b5m thickness. i Image of a large-scale BOPP composite film measuring 65\u2009\u00d7\u200950\u2009cm, demonstrating the capability for synthesizing substantial film sizes. For illustrations in these figures, the volume ratio of the LM:BN is 30:70 in LM-BN, and the LM-BN content in the composites is 2.5\u2009vol%.\n\nIn Fig.\u00a01d, density functional theory (DFT) simulations were applied to theoretically analyze the electrostatic potential distribution of LM-BN (model details in Supplementary Fig.\u00a07). In LM-BN, the contrast between positive and negative charges is significantly more pronounced than in pristine BN, suggesting an increase in dipole moment and polarization. This enhancement is expected to impart the composite film with an increased dielectric permittivity. Additionally, as shown in Fig.\u00a01e, the soft and stretchable nature of LM at the PP-BN interface suggests that LM-BN filler could facilitate a stress-relief interface. This interface is anticipated to mitigate the stress concentration typically observed in conventional rigid filler polymer composites, thereby ensuring a high stretching ratio during meta-stable film biaxial orientation processing. Figure\u00a01f illustrates the scalable fabrication of LM-BN filler. Emulating industrial production processes, including continuous extrusion, sheet forming, and biaxial stretching, we successfully achieved a large-scale synthesis of PP/LM-BN composite pellets (Fig.\u00a01g), and obtained thin (3.4\u2009\u03bcm, Fig.\u00a01h) and large-area (65\u2009\u00d7\u200950\u2009cm, Fig.\u00a01i) capacitor films, demonstrating the practical scalability and applicability of this all-solid fabrication method.\n\nTo elucidate the characteristics of LM-BN, we employed a probe force microscope (PFM) for in-situ analysis of its microscopic properties. The PFM\u2019s contact mode, peak-force quantitative nanomechanical mode, and piezo-force mode provide insights into the topological morphology, mechanical properties, and polarization properties of LM-BN, respectively34. LM-BN displays a unique mountain range-like surface topology, in stark contrast to the disc-like topology of pristine BN, indicative of successful LM coating. The thickness of this coating layer is highly dependent on the LM to BN ratio, ranging from 3.1\u2009nm (LM:BN\u2009=\u20094:96) to 7.0\u2009nm (LM:BN\u2009=\u200930:70) (Supplementary Fig.\u00a08). This LM outer layer inherently alters BN\u2019s surface from highly rigid to notably softer (Fig.\u00a02a, b and Supplementary Fig.\u00a09a). For example, modulus distribution images reveal a marked presence of low modulus regions compared to pristine BN, as further demonstrated by the cross-sectional modulus information in Fig.\u00a02a, which shows a significant decrease in modulus from 11.0\u2009GPa in BN to 3.8\u2009GPa in LM-BN within the 50\u2013250\u2009nm spectra range.\n\nIn-situ detection of topological, mechanical, and electrical properties of LM-BN was conducted via specific sensor modes of probe force microscope. a Modulus distribution information and b corresponding images of LM-BN (LM:BN\u2009=\u200930:70) as compared to BN, suggesting the soft surface nature of LM-BN. c Polarization distribution information and d corresponding PFM amplitude images of LM-BN (LM:BN\u2009=\u200930:70) as compared to the BN, suggesting enhanced surface polarization in LM-BN.\n\nBeyond surface mechanical characteristic, the presence of LM also enhances filler dielectric polarization (Fig.\u00a02c, d and Supplementary Fig.\u00a09b). The polarization amplitude distribution image and its corresponding cross-section polarization distribution illustrate two pronounced polarization peaks in LM-BN, indicating that LM\u2019s mountain-like topology contributes to heightened polarization intensity. Intriguingly, the middle valley region in LM-BN is elevated compared to pristine BN, suggesting an augmentation of BN\u2019s bulk dipole polarization by adjacent LM. This phenomenon of neighboring polarization enhancement mirrors observations in BaTiO3 heterojunctions with string-bead structures reported in the previous literature35. Ultraviolet-visible diffuse reflection spectra were further used to measure the band gaps of LM-BN with varying LM contents (Supplementary Fig.\u00a010). Our findings reveal that LM-BN maintains band gaps above 5.4\u2009eV at LM contents below 15\u2009vol%, which is indicative of good insulative performance. Increasing the LM content adjusts the electron properties of LM-BN, as evidenced by decreasing band gaps. Therefore, the increase of BN dipole polarization by controlling LM content is beneficial for improving dielectric permittivity without increasing loss.\n\nTo examine the effect of the soft interface provided by LM-BN on the biaxial stretching capability of BOPP composite film, PP/LM-BN composites with various LM-BN filler compositions (ranging from 0 to 30\u2009vol% LM in LM-BN) and different filler contents (0\u20134\u2009vol%) were prepared (Supplementary Figs.\u00a011, 12). A biaxial-stretching processing temperature of 157\u2009\u00b0C was selected, as at this temperature, both pure PP and PP composites exhibit partial melting, which ensures sufficient chain mobility and suitable film strength (Supplementary Fig.\u00a013). After these composites underwent biaxial stretching at 157\u2009\u00b0C, the maximum stretching ratios before film rupture were recorded (Fig.\u00a03a). Our observations revealed that stretching ratios decreased with an increase in filler content, yet notably increased with higher LM content in LM-BN. Impressively, a stretching ratio of ~450% was achieved in BOPP/LM-BN (LM:BN\u2009=\u200930:70). Even at the highest filler content of 4\u2009vol%, where the stretching ratio of BOPP/BN significantly dropped to nearly 100%, BOPP/LM-BN (LM:BN\u2009=\u200930:70) composites maintained a high stretching ratio of around 350%. The biaxial stretching force-strain curves (Fig.\u00a03b) highlight a considerable elongation improvement and a significant stress-relief effect in BOPP/LM-BN composites. In these composites, the required force was notably reduced from 3.6\u2009N in BOPP/BN to just 1.6\u2009N in BOPP/LM-BN (LM:BN\u2009=\u200930:70), while simultaneously enhancing the biaxial stretchability.\n\na Comparison of the maximum biaxial-stretching ratio of BOPP/LM-BN composites with different LM-BNs and different filler contents. b Biaxial-stretching force-strain curves of BOPP/LM-BN with different LM-BNs at the same filler content of 4\u2009vol% and the temperature of 157\u2009\u00b0C. c BOPP/BN film is broken after 300% biaxial-stretching. Representative cross-section SEM images suggest that interfacial holes and cracks propagate greatly from 200% strain to 300% strain. The scale bars are 500\u2009nm. d BOPP/LM-BN film maintains good integrality after 300% biaxial-stretching. Representative SEM and TEM images suggest that little interfacial holes and cracks are generated. The scale bars are 500\u2009nm. e Simulated cross-section stress distribution during the biaxial-stretching process, showing the removed interfacial stress concentration in BOPP/LM-BN (LM:BN\u2009=\u200930:70) as compared to BOPP/BN. f Calculated stress concentration factor (average interfacial stress/average matrix stress) of BOPP/BN and BOPP/LM-BN from the simulations, demonstrating the greatly suppressed interfacial stress concentration of BOPP/LM-BN at different strains.\n\nTo elucidate the influence of the soft interface on macroscopic tensile behavior, we examined the evolution of microscopic interface morphologies during stretching, maintaining a consistent filler content of 4.0\u2009vol%. As depicted in Fig.\u00a03c, BOPP/BN films displayed rapid development of holes and cracks at the PP-BN interface at 200\u2009\u00d7\u2009200% stretching, leading to complete rupture at 300\u2009\u00d7\u2009300% stretching. In contrast, BOPP/LM-BN films, particularly BOPP/LM-BN (LM:BN\u2009=\u200930:70), showed minimal interface cracks and holes even at higher stretching ratios. This was confirmed by optical photographs, along with SEM and TEM imaging (Fig.\u00a03d and Supplementary Fig.\u00a014), and by observing a significantly higher visible light transmittance (Supplementary Fig.\u00a015). To understand how the soft interface mitigates interface cracks and improves tensile ratio, we employed finite element analysis (FEA) to simulate stress and crack development in composites during biaxial stretching. Cross-sectional stress distribution images (Fig.\u00a03e) indicated that interface stresses in PP/BN composites (white and red regions near BN sheet) significantly increased with strain due to the modulus mismatch between the two phases. In contrast, stresses in PP/LM-BN composites remained relatively low (dark blue regions near BN sheet) under various strains. This stress concentration difference was quantitatively assessed using the stress concentration factor (K), derived from this formula36:\n\nwhere \u03c3max represents the maximum stress at the filler/polymer interface, and \u03c3matrix is the average stress in the polymer matrix. For BOPP/BN, K exhibits a clear strain dependence, reaching 5.6 at a 300% stretching ratio, whereas, for BOPP/LM-BN, it remains consistently below 1 (Fig.\u00a03f). Given BN\u2019s higher strength compared to PP, mechanical failures and crack growth primarily occurred at the high-stress interface region of the PP phase. Therefore, the stress-softening effect of LM at the interface crucially reduces modulus mismatch, preventing stress concentration and the resultant rupture of the PP phase. Further simulations with varying LM volumes (Supplementary Fig.\u00a016) showed a gradual decrease in interface stress with increased LM content, corroborating LM\u2019s beneficial role. This aligns with experimental observations of maximum stretching ratio changes (Fig.\u00a03a). Extensive enlargement of the simulation models to reflect the actual size of the composites (Supplementary Fig.\u00a017) consistently demonstrated a similar stress-softening effect at the interface.\n\nFigure\u00a04a presents the dielectric properties of BOPP/LM-BN at a 300\u2009\u00d7\u2009300% biaxial stretching ratio and 1\u2009kHz, as determined from dielectric relaxation frequency spectra (Supplementary Fig.\u00a018). Notably, the \u03b5r of all BOPP/LM-BN samples was higher than that of BOPP/BN, showing a distinct correlation with both LM and filler contents. The highest \u03b5r of 3.5, observed in BOPP/LM-BN (LM:BN\u2009=\u200930:70) at 4\u2009vol% filler content, was 1.46 times that of pristine BOPP (\u03b5r\u2009~\u20092.4) and 1.40 times greater than BOPP/BN (\u03b5r\u2009~\u20092.5). Concurrently, the dielectric dissipation factor of BOPP/LM-BN slightly increased with filler content, ranging from 0.001 in pure BOPP to 0.002\u20130.004 in various BOPP/LM-BN compositions at the highest filler content. The increase of permittivity arises from the polarization enhancement effect of LM, which can be understood from the following two perspectives. First, consider the dielectric interface layer. A weak dielectric interface layer, possibly air, in the BOPP/BN composite could lead to poor electric field distribution. Calculations indicate that the electric field in the BN region decreases from 405\u2009MV/m to 260\u2009MV/m when approaching an air interface (Supplementary Fig.\u00a019). However, the presence of an LM layer plays a beneficial role by allowing better penetration of the field into the high-permittivity BN, resulting in an apparent increase in material permittivity. Second, consider the inherent polarization of the heterogeneous dielectric. The atom charge distributions in BN and LM-BN (Fig.\u00a01d) show that the positivity (red region) and electronegativity (blue region) are significantly increased in LM-BN compared to BN. The increased atomic electron differences in LM-BN originate from the transition metal atoms (Ga, In, Sn), which have many vacant orbitals and exhibit strong positivity, as well as from the natural oxide layer of the LM, whose oxygen atoms display strong electronegativity. The increase in charge differences benefits the dipole moment and dipole amount in LM-BN, ultimately increasing the inherent polarity and permittivity of the filler. The LM modification might induce low-frequency, temperature-dependent phenomena related to space charges. To investigate this, dielectric properties were further studied at low frequencies and varying temperatures (Supplementary Figs.\u00a020 and 21). The results indicate that the BOPP/LM-BN composites can maintain permittivity stability over a range of frequencies (0.1\u2013103\u2009Hz) and temperatures (30\u2013120\u2009oC). However, the stability of the loss factor (related to the imaginary part of permittivity) is significantly influenced by the LM ratio. When the LM:BN ratio is at or below 15:85, the loss factor remains relatively stable (Supplementary Fig.\u00a020a\u2013c). However, increasing the ratio to 30:70 causes a surge in the loss factor at low frequencies and high temperatures (Supplementary Figs.\u00a020d and \u00a021). This suggests that by precisely controlling the LM content, continuous interface space charge conduction can be prevented in BOPP/LM-BN composites.\n\na Dielectric permittivity, dielectric dissipation factor, and b breakdown strength of BOPP/LM-BN with different LM-BN and different filler content at the stretching ratio of 300 \u00d7 300%. c Electric field-dependent current densities of BOPP, BOPP/BN, and BOPP/LM-BN (LM:BN\u2009=\u200915:85). d Fowler-Nordheim model electron barrier of BOPP/BN and BOPP/LM-BN, demonstrating the good insulation of BOPP/LM-BN. e Energy band diagram of PP, LM, and BN, suggesting the electron trap in BOPP/LM-BN composites. f Kelvin probe force microscopy phase images and potential images of BOPP/BN and BOPP/LM-BN (LM:BN\u2009=\u200915:85), showing the enhanced surface potential of LM-BN to corroborate the electron traps and polarization enhancement. g Experimental and simulated breakdown strength of the BOPP and BOPP/LM-BN (LM:BN\u2009=\u200915:85) composites at varying temperatures. The filler content is 4\u2009vol%. h Microstructural model and simulated electrostatic, strain, and joule energy density distributions in the BOPP/LM-BN composite (LM:BN\u2009=\u200915:85) at 450\u2009MV/m and 120\u2009\u00b0C.\n\nFigure\u00a04b illustrates the breakdown strength (Eb) of composites with different LM-BN and filler contents, calculated using Weibull distributions (Supplementary Fig.\u00a022). The Eb decreased marginally from 705\u2013720\u2009MV/m in pure BOPP to around 550\u2009MV/m in BOPP composites. Remarkably, despite LM\u2019s non-insulative nature, BOPP/LM-BN composites retained comparable insulation to BOPP/BN composites through appropriate LM content modulation. For instance, BOPP/LM-BN (LM:BN\u2009=\u200915:85) maintained a high Eb of 566\u2009MV/m at 4\u2009vol% filler content. This aligns with earlier findings that LM-BN maintains high bandgaps at low LM content. To further elucidate the effect of fillers during electric breakdown, phase-field simulations were conducted for an in-depth understanding18,37. Pristine BN suffers from existing voids near filler due to the biaxial stretching process (Fig.\u00a03c), the breakdown begins at the air voids region in the BOPP/BN composite. In contrast, the breakdown in the BOPP/LM-BN composite begins at the electrodes (Supplementary Fig.\u00a023). Due to the breakdown-impeding effect of the core BN, LM-BN effectively hinders the growth of the electrical breakdown phase. The Eb predicted by the phase-field analysis for the BOPP/LM-BN is consistent with experimental data, ranging from 540 to 600\u2009MV/m. The simulated electrostatic and strain energy density distribution further elucidates the breakdown mechanism of the BOPP/LM-BN (Supplementary Fig.\u00a024). Although the LM is supposed to increase electrostatic energy and strain energy near the BN, the BN core remains insulative and robust, effectively preventing both electrical and elastic breakdown.\n\nTo validate the insulation integrity of BOPP/LM-BN composites, leakage current density tests and electron barrier analysis were further conducted. For example, BOPP/LM-BN (LM:BN\u2009=\u200915:85) displayed leakage current densities similar to BOPP/BN across various electric fields (Fig.\u00a04c). The Fowler-Nordheim model was employed to evaluate the electron tunneling barrier from electrodes to dielectric materials (Supplementary Fig.\u00a025 and Fig.\u00a04d), revealing that the barrier of BOPP/LM-BN (2.51\u2009eV) was comparable to that of BOPP/BN (2.56\u2009eV), supporting the conclusion that BOPP/LM-BN composites effectively maintain their insulative properties38. As evidenced in the energy band structure measurements (Fig.\u00a04e and Supplementary Figs.\u00a026\u201328), LM is confirmed to exhibit a higher electron affinity (4.19\u2009eV) compared to BN with 0.83\u2009eV. This disparity suggests that LM can capture some excited electrons from the PP matrix, creating a trap energy level that confines these electrons. Kelvin PFM provides further insight, showing that electrons are trapped in the LM-BN phase of the composites, as indicated by the enhanced surface potential on LM-BN surfaces (purple regions in Fig.\u00a04f). The pronounced positive charge of the transition metal atoms in LM is likely responsible for the observed electrophilic trapping effect (Fig.\u00a01d).\n\nHigh-temperature characteristics are crucial for the emerging applications of dielectric capacitors, as the increase of harsh applications such as electric vehicles and underground/aerospace exploration calls for capacitor film capable of safe operation at elevated temperatures1,4,19. At elevated temperatures, the BOPP/LM-BN composites were found to retain stable dielectric performances and good Eb when the LM content in LM-BN is suitable. For instance, the BOPP/LM-BN composite with an LM:BN ratio of 15:85 maintains a stable \u03b5r of 3.2, a loss factor of 0.006 (Supplementary Fig.\u00a021), and an Eb of 463\u2009MV/m at 120\u2009\u00b0C (Fig.\u00a04g and Supplementary Figs.\u00a029 and 30). The phase-field analysis provides further insights into the breakdown performance of the BOPP/LM-BN composites at high temperatures. As revealed by the electrostatic, strain, and joule energy density distributions (Fig.\u00a04h), the electric breakdown, strain breakdown, and heat breakdown at high temperatures are effectively impeded by the LM-BN sheets. This can be attributed to the wide bandgap, high modulus of the BN core, and superior thermo-conductive functionality of the LM-BN.\n\nThe increased filler usage enhances the electric displacement and discharge energy density of the BOPP/LM-BN (LM:BN\u2009=\u200915:85) composite film, with a charging-discharging efficiency above 90% (Supplementary Fig.\u00a031). Particularly at a filler usage of 4.0\u2009vol%, the composite reaches its highest energy density (Ud) of 4.5\u2009J\u2009cm\u22123 at 550\u2009MV/m. Further optimization of the LM: BN ratio indicates that a high LM ratio (30:70) leads to a significant decrease in energy efficiency, while a low LM ratio (4:96) limits the improvement of energy storage density. Therefore, in the following discussions, we selected the optimal BOPP/LM-BN (LM:BN\u2009=\u200915:85) with 4.0\u2009vol% filler content to assess capacitive energy storage performance above 90% discharge efficiency (\u019e) (Fig.\u00a05a). The Ud of BOPP/LM-BN (4.5\u2009J\u2009cm\u22123 at 550\u2009MV/m) significantly surpasses that of commercial BOPP film (2.9\u2009J\u2009cm\u22123 at 550\u2009MV/m) and BOPP/BN film (2.9\u2009J\u2009cm\u22123 at 500\u2009MV/m). Additionally, BOPP/LM-BN exhibits a high \u019e of 90% at 550\u2009MV/m. Durability tests show BOPP/LM-BN sustains 10,000 charge-discharge cycles at 400\u2009MV/m with minimal degradation (Fig.\u00a05b), indicating long-term reliability. A fast discharge speed is a critical performance to assess the dielectric capacitors versus other electrical energy storage devices such as electrochemistry batteries. The discharge time \u03c40.9 was defined as the discharge time at the 90% energy density from the discharge tests. Under 200\u2009MV/m, representative discharge profiles from a load resistor RL of 100\u2009\u03a9 for BOPP, BOPP/BN, and BOPP/LM-BN films show a close \u03c40.9 of 18.8, 25.4, and 20.0\u2009ns, respectively (Fig.\u00a05c), indicating the well-maintained pulse discharge advantage of BOPP/LM-BN films. Corresponding fast Ud is increased from 0.31\u2009J\u2009cm\u22123 of commercial BOPP to 0.42\u2009J\u2009cm\u22123 of BOPP/LM-BN film, suggesting the greatly improved power density of BOPP/LM-BN.\n\na Discharged energy density and charge\u2013discharge efficiency of BOPP, BOPP/BN, and BOPP/LM-BN (LM:BN\u2009=\u200915:85). b Discharged energy density and charge\u2013discharge efficiency of BOPP, BOPP/BN, and BOPP/LM-BN (LM:BN\u2009=\u200915:85) over 10,000 cycles at a stress of 400\u2009MV\u2009m\u22121. c Discharge energy density and discharged time under 200\u2009MV/m from a load resistor of 100 \u03a9 for BOPP, BOPP/BN, and BOPP/LM-BN. d Comparison of the discharged energy density of BOPP/LM-BN with other representative strategies, demonstrating the exceptional balance of high energy density and high stretchability among previous studies1,4,12,17,28,39,40,41,42,43. e Discharged energy density and efficiency of BOPP composite films with different LM-BN at 120 \u00b0C. f Structure diagrams and SEM characterization of the film capacitors, showing their suitability for representative rolling and stacking methods. g Capacitance and dissipation factor as a function of frequency for the multilayer biaxial-orientated polymer foil film capacitors. h Stable bending capacitance of the BOPP/LM-BN (LM:BN\u2009=\u200915:85) film capacitors at different bending angles.\n\nIn Fig.\u00a05d and Supplementary Table\u00a0S1, we compare our BOPP/LM-BN dielectrics with currently representative capacitor films, including solution-casted polymers, biaxially stretched composites, and surface-coated films at above 90% \u019e and 550 MV/m1,4,12,17,28,39,40,41,42,43. While advanced polymer strategies achieve high Ue values through elaborate molecular design at room temperature, such as polyimide derivatives and ladderphane copolymer, they are limited by complex preparation and are not amenable to large-area, high-quality biaxial stretching. Surface-coated films maintain biaxial stretchability but offer limited permittivity and Ue enhancement. Composite films typically suffer from reduced stretchability; only with very low filler content (<1\u2009vol%) is stretchability preserved, but with negligible Ue improvement. Our BOPP/LM-BN, however, achieves a desirable balance in high Ue and biaxial stretching ratios, making it a promising candidate in the applications of miniaturized film capacitors. Moreover, at elevated temperatures, the Ud of the BOPP/LM-BN composite film, for example, with the LM:BN of 15:85, consistently exceeds that of the BOPP capacitor film while maintaining a \u019e over 90% (Supplementary Fig.\u00a032). Further optimization of the LM:BN ratio can reduce the loss and achieves the best Ud. For instance, the BOPP/LM-BN composite film with the LM:BN of 4:96 exhibits the optimum Ud of 1.21\u2009J\u2009cm\u22123 at 300\u2009MV/m, with 90% efficiency at 120\u2009\u00b0C, ~1.5 times higher than that of BOPP (0.48\u2009J\u2009cm\u22123, Fig.\u00a05e).\n\nThe application potential of BOPP/LM-BN in multilayer foil biaxial-orientation composite film capacitors is demonstrated with large-area (9\u2009\u00d7\u20099\u2009cm) prototypes fabricated through stacking, pressing, leading, and sealing (Supplementary Figs.\u00a033 and 34). These prototypes can be encapsulated similarly to commercial film capacitors through methods like winding encapsulation or stacked encapsulation (Fig.\u00a05f), with a dielectric film layer exhibiting a uniform thickness of around 10\u2009\u00b5m while Al electrode foil is around 6\u2009\u00b5m. The capacitance and dissipation factors of the BOPP, BOPP/BN, and BOPP/LM-BN capacitors with the same layer numbers as a function of frequency are shown in Fig.\u00a05g. Capacitance measurements show BOPP/LM-BN capacitors outperforming BOPP and BOPP/BN capacitors in capacitance (85 nf vs. 72 nf and 65 nf, respectively) at 100\u2009Hz, indicating potential for significant volume reduction by 31% in capacitor design. Additionally, BOPP/LM-BN capacitors exhibit excellent capacitance stability under bending (Fig.\u00a05h), highlighting their flexibility and suitability for diverse flexible applications. Meanwhile, due to both the highly oriented and extended chain conformation and favorable LM-BN orientation after the biaxial stretching process, the BOPP/LM-BN exhibits a much higher thermal conductivity (4.1\u2009W\u2009m\u22121\u2009K\u22121) than that of BOPP/BN (2.6\u2009W\u2009m\u22121\u2009K\u22121), which can improve the high-temperature performance of devices (Supplementary Fig.\u00a035)42. The raw materials cost of our composite capacitor film (~$6.0/kg) is much lower than other BOPP alternating materials ($11.0/kg to 350.0/kg) (Supplementary Table\u00a0S2), indicating its cost-effectiveness.\n\nIn summary, our study presents an effective approach to fabricating composite capacitor films with a soft and functional interface, successfully resolving the trade-off between enhanced energy density and high biaxial stretchability. By incorporating a soft LM liquid, we effectively modulate the modulus mismatch between the soft PP matrix and the hard BN filler. This modulation reduces stress concentration and blunts crack formation, thus enabling a higher biaxial stretching ratio. The LM-BN heterostructure exhibits wide band gaps and electron traps, yet simultaneously enhances dipole polarization, which positively impacts the permittivity and breakdown strength of the composite. These unique characteristics lead to a spectrum of exceptional capacitive energy storage properties, including \u03b5r, Eb, Ud, \u019e, cyclic stability, thermal conductivity, and device capacitance, which pave the way for the scaled-up fabrication of cost-effective composite dielectric films and the development of more compact capacitive energy storage devices. Moreover, we believe that this soft and functional interface approach sets a promising foundation for designing various polymer-filler interfaces. For example, it is suitable for other two-dimensional fillers like montmorillonite plate (Supplementary Fig.\u00a036a\u2013c) and spherical fillers like BaTiO3 (Supplementary Fig.\u00a036d\u2013f). This approach holds potential for broad application, improving the stretchability and functionality of composite materials across various fields, including packaging, barrier films, permeable films, battery films, and optical films.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52234-4/MediaObjects/41467_2024_52234_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52234-4/MediaObjects/41467_2024_52234_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52234-4/MediaObjects/41467_2024_52234_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52234-4/MediaObjects/41467_2024_52234_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52234-4/MediaObjects/41467_2024_52234_Fig5_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Hexagonal BN powder (<1\u2009\u03bcm, 98.0%) was purchased from Sigma-Aldrich. Galinstan alloy LM (62.5\u2009wt% gallium, 21.5\u2009wt% indium, 16\u2009wt% tin) was provided by Hunan Santech Materials, China. PP granules (HC311BF), which have a wide molecular weight distribution and low ash residuals, were offered by Borealis, Austria. Polydimethylsiloxane (PDMS, SYLGARD184) was purchased from Dow Corning Silicone Co., Ltd, USA. The water used in this experiment was deionized water. The rest materials, if not mentioned specifically, were supplied by Aladdin, China.\n\nThe LM-BN hybrid filler (LM-BN) can be prepared by a facile one-step processing. BN powder was placed together with certain amounts of LM inside a mortar and ground using an agate mortar with a speed of 30\u2009rpm at room temperature for 2.0\u2009h. BN and LM with volume ratios of 96:4, 85:15, and 70:30, respectively, were chosen to prepare different LM-BNs. Finally, the as-prepared material was sealed to prevent excessive oxidation.\n\nThe synthetic process of BOPP/LM BN heterostructure composites film (BOPP/LM-BN) was divided into two steps. Firstly, certain amounts of PP pellets and LM-BN powder were added into the high-speed pulverizer at 10,000\u2009rpm for 10\u2009s for good pre-mixing. The pre-mixed PP/LM-BN composite powders were further mixed by Haake Minilab micro-compounder at melt condition, with a screw speed of 130\u2009rpm, mixing time of 8\u2009min, and temperature of 195 \u00b0C. Then, the melting-mixed product was cut into small pellets. The product pellets were hot pressed to fabricate PP/LM-BN composite sheets (thickness 200\u2009\u00b1\u2009100\u2009\u03bcm) by putting them in between two Kapton films, with a temperature around 200\u2009\u00b0C and pressure around 10 MPa, followed by a step of cold pressing at room temperature and 10 MPa to cooling the sample sheets. Secondly, the PP/LM-BN sheets were put in the biaxial extensometer (Bruckner Karo IV) and fixed by a series of clamps on four sides of the sheets. The sheets were stretched according to the following programs: (1) preheating at 157\u2009\u00b0C for 60\u2009s; (2) stretching synchronously at a stretching temperature of 157\u2009\u00b0C and stretching speed of 10%/s; (3) cooling at room temperatures. The thickness and the lateral size of composite films can be tuned by the thickness and size of raw sheets and the stretching ratios. For most characterizations, the thicknesses of BOPP composite films, except for the ultra-thin demo, were all kept around 10\u2009\u03bcm to prevent performance interference from sample thicknesses.\n\nAs a demo, the kilogram-weighted PP/LM-BN pellets and the meter-sized BOPP/LM-BN film were also fabricated by the aforementioned steps, where the Haake Minilab micro-compounder was replaced by the continuous two-screw high-speed extruder (Leistritz ZSE18 MAXX, Germany) to examine the scalability, with a screw speed of 500\u2009rpm, extrusion temperature of 195\u2009\u00b0C, water cooling treatment and a maximum extrusion speed of 40\u2009kg/h.\n\nAs shown in Supplementary Figs.\u00a033 and 34, Al foil with a thickness of around 6\u2009\u03bcm was used as a substrate and electrode layer on the plate. Put an as-prepared capacitor film as an energy storage layer on the top of Al foil. Then, the Al foil layer and capacitor film layer were alternatively constructed layer by layer, and finally, it ended with the electrode foil layer on the top. When stacking, the position of the capacitor film was always in the middle position, while the position of each electrode foil layer was in the alternating order of the left and right position to create a 5\u2009mm margin between the foil edge and capacitor film edge to distinguish the anode and cathode. Then, the stacked multilayer sheet was pressed at a relatively low temperature of 100\u2009\u00b0C with pressure ~10\u2009MPa for 5\u2009min to remove the interspace in layers via the hot compressor, followed by cooling it to room temperature and obtaining the dense multilayer sheets. Cu wires were led as outer terminal electrodes to the left side and right side of the multilayer sheets with the help of silver paste to construct the final inner core of the capacitor. The inner core was rolled up or directly used to fabricate the rolling packaged capacitors or stacking packaged capacitors after sealing them in molds that filled with PDMS at 80\u2009\u00b0C for 1\u2009h.\n\nChanges in element valence states on the surface of BN and LM-BN were obtained using X-ray photoelectron spectroscopy (K-Alpha, Thermo Scientific, USA). The crystal structures of BN, LM, and LM-BN were obtained by X-ray diffraction (Ultima IV, Rigaku, Japan) with Cu K\u03b1 radiation (wavelength \u03bb\u2009=\u20091.5418\u2009\u00c5). SEM images were obtained by Nova NanoSEM450 (ThermoFisher Scientific, FEI, USA). TEM (F200S, Talos, USA) equipped with an EDS was used to characterize the elemental distribution and morphologies. The size distribution of BN was calculated by Nanomeasure software. The band gap of BOPP, BN, LM, and LM-BN was determined by the wave lengthen-dependent absorption changes from 200 to 700\u2009nm in UV-Vis diffuse reflection spectroscopy (Shimadzu UV-3600i Plus, Japan). The highest occupied molecular orbital (HOMO) position of BOPP, BN, LM, and LM-BN was analyzed by the ionization potential data in ultraviolet photoelectron spectra (ThermoFisher Nexsa, USA). The height distribution images, modulus distribution images, and polarization distribution images of LM-BN and BN were respectively detected by the contact mode, peak-force quantitative nanomechanical mapping mode, and piezo-force modes of PFM (Bruker Dimension ICON, USA). The in-situ surface potential distribution images of BOPP/LM-BN and BOPP/BN film were detected by the kelvin probe force mode of PFM, where the bottom of the sample film was fixed by the conductive tape on the metal pan substrate. Time-dependent stretching stresses of BOPP/LM-BN and BOPP/BN film during biaxial stretching were recorded by the force sensors of the biaxial extensometer (Bruckner Karo IV).\n\nBefore the tests of electric performances and capacitive performance of film samples, they were sputtered by Au using the sputter coater (Quorum Technologies Q300TD, UK) with a sputtering time of 300\u2009s on two sides. The thickness of the Au electrode is ~20\u201330\u2009nm, and the area is a circle with a diameter of around 4\u2009mm. Frequency-dependent and temperature-dependent dielectric properties (dielectric permittivity and dielectric dissipation factor) were acquired by broadband dielectric impedance spectrometer (Novocontrol Technologies Concept 50, Germany). Dielectric breakdown strength was measured via the electrostatic pull-down method under a DC voltage ramp of 250\u2009V/s using the voltage-withstanding tester (CJ2678, China) in insulated silicone oil. Unipolar electrical displacement\u2013electric field hysteresis loops (D-E loops) (at 100\u2009Hz) and leakage current densities (with measuring time of 10,000\u2009ms) were obtained by the ferroelectric precision materials analyzer (Radiant Technology Premier II, USA), where the samples were immersed by the insulated silicone oil to prevent the interferences from air and moisture. The test chamber was located in a relatively moist city (Chengdu) in China with an average humidity of ~60%. Samples were immersed in the insulated silicone oil to prevent interferences from air and moisture. The cycling test was carried out at room temperature. The energy storage performance is calculated by the following equation using data in hysteresis loops:\n\nwhere D, E, Dr, and Dmax are polarization, electric field strength, remanent polarization, and the maximum polarization, respectively. For linear dielectrics, the Dr approaches zero. The cyclic charge\u2013discharge tests were also performed using the Radiant Technology Premier II by a built-in program to repeat the charge\u2013discharge cycles of D-E loops. The energy tests were systematic tests ranging from 50\u2009MV/m to 550\u2009MV/m. The fast capacitive discharge performance was measured by the discharge test system (Tongguo technology CFD-003, China), where the dielectric films were charged at 200\u2009MV/m, and through a high-voltage MOSFET switch, the stored energy was discharged to a resistor RL with 100\u2009\u03a9. Characterization of the capacitance of a multilayer film capacitor device was done on the platform of a home-built system for film capacitors with a Novocontrol Concept 50 unit and a bending control unit.\n\nThe dynamic stretching process of BOPP/BN and BOPP/LM-BN film was studied by the FEA. The stretching failure was studied by observing the stress distribution evaluations at the cross-section of the films during stretching. The films were described as three-phase composites (PP, LM, and BN). The size and morphology of LM and BN were set according to Supplementary Fig.\u00a08. The linear elastic material model was used to calculate the relationship among the stress, strain, and material parameters (modulus, Poisson\u2019s ratio, and density) of each phase44. The films were biaxially stretched along the horizontal direction to 300% times the original size of the sample with a stretching speed of 10%/s at two sides (left and right) of the film. When specifically studying the effect of LM content in LM-BN (LM:BN\u2009=\u20094:96, 15:85, and 30:70), the continuous stretching setting was simplified as a constant stretching force of 2\u2009N at two sides of the film without continuous strains (according to Fig.\u00a03b) to reduce the integration calculation. The calculating unit size was set as normal.\n\nThe electrostatic potential for BN and LM-BN was studied by DFT, where the structures were first optimized via DMol3 package45. The model of BN was composed of five six-membered B/N rings constructed by the periodic structure. The model of LM-BN is composed of the BN model and the free Ga atoms, In atoms, and Sn atoms above the BN ring to simulate the amorphous structure of the LM, and an additional O atom was introduced around the metal atoms owing to the oxidation nature of LM. The generalized gradient approximation in the Perdew\u2013Burke\u2013Ernzerhof form and semi-core pseudopotential method with the double numerical basis sets plus the polarization functional were adopted46,47. A DFT-D correction with the Grimme scheme was used to account for the dispersion interaction48. The SCF convergence for each electronic energy was set as 10\u20135\u2009Ha, and the geometry optimization convergence criteria were set up as follows: 10\u20135\u2009Ha for energy, 0.004\u2009Ha\u2009\u00c5\u20131 for force, and 0.01\u2009\u00c5 for displacement, respectively. The Brillouin zone integration is performed using 3\u2009\u00d7\u20093\u2009\u00d7\u20091 Monkhorst\u2013Pack k-point sampling for a primitive cell. The electrostatic potential was also calculated by DMol3 package.\n\nContinuous phase-field variable \\(\\eta ({{{\\bf{r}}}},t)\\) is introduced to describe the evolution of the breakdown (electrical tree): \\(\\eta ({{{\\bf{r}}}},\\, t)=1\\) represents the breakdown phase, \\(\\eta ({{{\\bf{r}}}},t)=0\\) represents the non-breakdown phase. Combining the electric, thermal, and mechanical stimuli together to investigate their effects on the breakdown process. Three material features, including the dielectric permittivity, electrical conductivity, and Young\u2019s modulus, are parameterized in this model to calculate different energies. The dielectric permittivity inhomogeneity is defined according to the different dielectric permittivity of the polymer phase, filler phase, and breakdown phase. This also applies to the electrical conductivity and Young\u2019s modulus inhomogeneities. The free energy considering synergistic contributions from the phase separation, the interface, the temperature, and the electric field in a dielectric inhomogeneous system is written as:\n\nwhere the first term in the integral represents the free energy density of mixing that drives the phase separation, the second term is the gradient energy density, the third term is the electric energy density, the fourth term is the Joule heat energy density, and the last term is the strain energy density.\n\nwhere \u03b1 is a positive coefficient defining the energy barrier of the phase separation.\n\nwhere \\({\\varepsilon }_{{ij}}\\left({{{\\bf{r}}}}\\right)\\) is the spatially dependent relative dielectric permittivity tensor, \\({E}_{i}\\left({{{\\bf{r}}}}\\right){E}_{j}\\left({{{\\bf{r}}}}\\right)\\) are the electric field component, and \\({P}_{i}^{S}\\left({{{\\bf{r}}}}\\right)\\) is the spontaneous polarization, which is not zero if the local material component is ferroelectric.\n\nwhere \\({\\sigma }_{{ij}}({{{\\bf{r}}}},\\,T)\\) is the spatially and temperature-dependent electrical conductivity tensor, and dt is the operating time of applied electric field. According to the filamentary electromechanical breakdown mechanism, the effect of the electric field in inducing mechanical stress is also considered in this model.\n\nwhere Y (r) represents the Young\u2019s modulus. By using a modified Allen-Cahn equation to describe the breakdown phase evolution, the driving force from the electric term, Joule heat, and strain energy can be calculated37. A time interval \u0394t\u2009=\u20090.02 is used. The relative dielectric permittivity and the electrical conductivity of the breakdown phase are isotropic and have a value of 103 and 1\u2009\u00d7\u200910\u22125\u2009S\u2009m\u22121 to reflect its abundant space charges in the breakdown region, while Young\u2019s modulus of the breakdown phase is regarded stable in this model. The critical energy of each component in the composite is calculated by:\n\nDielectric breakdown behavior is analyzed with a two-parameter Weibull statistic described as:\n\nwhere P(E) is the cumulative probability of electric failure, E is the measured breakdown electric field strength, Eb is defined as the Weibull breakdown strength corresponding to 63.2% probability of electric breakdown, and the shape parameter \u03b2 indicates the scattering of experimental data32.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data supporting the findings of this study are included within the paper and its Supplementary Information file. 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Wang acknowledges the support by the United Laboratory of Advanced Electrical Materials and Equipment Support Technology, CSG, (Grant no. 1500002022030103GY00035).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, 610065, China\n\nZilong Xie,\u00a0Jianan Zhu,\u00a0Zhengli Dou,\u00a0Yongzheng Zhang,\u00a0Ke Wang,\u00a0Kai Wu\u00a0&\u00a0Qiang Fu\n\nMaterials Science and Engineering Program and Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA\n\nKai Wu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nZ.X., K. Wu, and Q.F. conceived the idea. Z.X. and J.Z. fabricated the materials and measured the material performance. Z.X., K. Wu, and Q.F. analyzed the data. K. Wu and Z.X. organized the experimental data and wrote the draft manuscript. K. Wu revised the manuscript. K. Wu and Q.F. supervised the overall conception. Z.D., Y. Zhang, and K. Wang contributed to the discussion on the results and improved the manuscript.\n\nCorrespondence to\n Kai Wu or Qiang Fu.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Jean-Fabien Capsal, Fei Wen, and the other anonymous reviewers for their contribution to the peer review of this work. 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The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Xie, Z., Zhu, J., Dou, Z. et al. Liquid metal interface mechanochemistry disentangles energy density and biaxial stretchability tradeoff in composite capacitor film.\n Nat Commun 15, 7817 (2024). https://doi.org/10.1038/s41467-024-52234-4\n\nDownload citation\n\nReceived: 14 February 2024\n\nAccepted: 30 August 2024\n\nPublished: 06 September 2024\n\nVersion of record: 06 September 2024\n\nDOI: https://doi.org/10.1038/s41467-024-52234-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"title": "Uncovering a widely applicable empirical formula for field emission characteristics of metallic nanotips in nanogaps", + "pre_title": "Uncovering a Universal Scaling for the Field Emission Characteristics from Metallic Nanotips in Nanogap", + "journal": "Nature Communications", + "published": "01 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60607-6/MediaObjects/41467_2025_60607_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60607-6/MediaObjects/41467_2025_60607_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60607-6/MediaObjects/41467_2025_60607_MOESM3_ESM.rar" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-60607-6#Sec11" + ], + "code": [], + "subject": [ + "Applied physics", + "Electrical and electronic engineering" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5395439/v1.pdf?c=1751456284000", + "research_square_link": "https://www.researchsquare.com//article/rs-5395439/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60607-6.pdf", + "preprint_posted": "11 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Electron field emission is a key mechanism in nanoelectronics with nanogaps, offering advantages such as high electron velocity, fast switching speeds, operation at extreme temperatures, and exceptional radiation resilience. However, traditional field emission theory inadequately describes the electron emission and charge transport behaviors at the nanoscale, as it lacks consideration of geometric asymmetry effects, quantum effects and nanosize effects. Here, we carried out an in situ investigation on the intrinsic field emission characteristics of pure tungsten nanotips across vacuum nanogaps. For the first time, we revealed a universal scaling behaviour between field emission characteristics and the ratio R/d, and demonstrated that the nonlinear geometrical effect, rather than quantum effects, is predominant. We further proposed a modified Fowler-Nordheim (FN) equation considering geometric effects, where the electric field (F) in the FN equation is substituted by F=Vexp/(k\u00d7R) with k=f(R\u2044d)=1.680\u00d7 (R/d+0.468)(-1.066), which is valid for R/d\u2009=\u20090.04 to 48. The proposed FN equation for nanoscale field emission regime is validated by well matching with the reported experimental results. These findings, grounded in theoretical insights, are essential for refining the design and performance of nanoelectronics, driving advancements in next-generation technologies.Physical sciences/Physics/Applied physicsPhysical sciences/Engineering/Electrical and electronic engineering", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Field electron emission is a key mechanism in nanoelectronics with nanogaps, offering advantages such as high electron velocity and fast switching speeds. However, nanoscale field emission, affected by geometric asymmetry including quantum tunneling near to the emitter, and quantum space charge effects, remains largely unexplored in experimental studies. Here, we in situ investigated field emission characteristics of pure tungsten nanotips across vacuum nanogaps. We revealed a widely applicable scaling behavior between field emission characteristics and the ratio of apex radius to gap length (R/d), and demonstrated that the effects of quantum tunnelling due to emitter shape are the predominant influence. We further proposed a modified field emission equation, incorporating an empirical formula for the apex shape factor, kMG (kMG\u2009=\u2009f(R/d)\u2009=\u20091.680\u2009\u00d7\u2009(R/d\u2009+\u20090.468)\u22121.066), valid for R/d\u2009=\u20090.04 to 48. These findings provide fundamental insights into the optimization of nanoelectronic device design and the advancement of future technologies.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Tremendous efforts in development of novel nanomechanical fabrication techniques and devices have witnessed a variety of emerging nanoelectronics, including field emission diodes1,2,3, field effect transistors4,5,6,7,8,9,10,11,12, ultrafast switches13,14,15,16,17, biosensors18,19,20, and molecular transistors21,22. The typical device structure has a vacuum nanogap as an electron transport channel, which is expected to show extraordinary performances in terms of the high electron traveling velocity, fast switching rates, high operating temperatures, and exceptional radiation tolerance. Specially designed with asymmetric electrode structures and operating at low voltages, these devices rely heavily on field electron emission (FE)3,6,9,17,23,24,25. The state-of-the-art ultrafast nanoelectronic switches reported in Nature has successfully achieved picosecond-level switching speeds with lower power consumption by utilizing electron field emission induced nanoplasma13,14. However, 1950s type planar FE theory, based on the 1956 Murphy-Good (MG) FE equation (see Supplementary Note\u00a01)26,27,28, does not work well for nanodevices. This is because the traditional planar potential barrier, the so-called Schottky-Nordheim barrier (Supplementary Eq. (S1)), does not adequately describe the potential-energy variation near a significantly curved surface29,30. Further, when the gap distance is at the nanometer scale, quantum effects may occur, particularly if there is significant space-charge in the gap31,32,33. These quantum effects are, firstly that it may become necessary to describe the electron motion in the gap by using the Schr\u00f6dinger equation (rather than by using classical space-charge arguments), secondly that it may become necessary to consider exchange-and-correlation interactions between electrons in the space-charge. Both effects are difficult to clearly distinguish when the size of the emitter is comparable to the nanogap spacing.\n\nGenerally, for typical sharp emitters, it is common practice to assume that the average electrostatic field F0 is enhanced by a field enhancement factor (FEF) of \u03b3 in Supplementary Eq. (S2): F\u2009=\u2009\u03b3F0\u2009=\u2009\u03b3V/d, where F is the absolute magnitude of the electrostatic field at emitter surface, and \u03b3 depends on the definition of the distance d, either as the distance between planar plates (plate FEF), particularly for a post standing on one of a pair of parallel plates, or as the distance between the emitter apex and the anode plate (gap FEF)34,35. However, this approach by itself may fail to accurately predict the field emission behavior of emitter tips with apex radii R less than 50\u2009nm, as the width of tunneling barrier is getting very sensitive to the physical dimension36,37,38,39,40,41,42,43. Previous studies have reported that during field emission from nanotips, the electron total-energy distributions sometimes display multiple well-separated peaks rather than one strong peak at Femi level, indicating a considerable variation of the structure of electron states inside the nano-emitter44,45. Currently, most FE models proposed in the literatures for nano-emitters either are focused on the corrections of the supply function and transmission coefficient due to probable quantum confinement effects of emitter with a non-planar geometry or are mainly aimed to calculate the image charge potential of space charges with cathode alone more accurately by including the geometric effects in the related mathematical formulation42,46,47,48,49. It is important to note that the supply function and transmission coefficient can be strongly geometrically dependent. This complexity suggests that it is nearly impossible to have an analytical multi-dimensional field emission model for a given emitter. This has prompted this experimental attempt to obtain an empirical scaling.\n\nMoreover, when developing a FE model for nanogaps, a sophisticated approach has been to obtain space charge limited field emitted current density by solving the coupled Schr\u00f6dinger and Poisson equations self-consistently50,51,52,53,54. Therefore, the question of whether quantum effects of this kind need to be considered turns out to be a very fundamental issue for further elucidating the intrinsic and experimental field emission characteristics at the nanoscale.\n\nHere, we studied the experimental field emission characteristics of the pure tungsten nanoelectrodes across vacuum nanogaps by utilizing a home-built in situ electrical experiment system. We explored how the field emission characteristics vary as a function of the ratio of apex radius to gap length (R/d in the range of 0.04 to 48) and revealed a consistent scaling relationship between them. This scaling is shown to recover the accepted limits at R/d \u00ab 1 (ultrasharp tip) and R/d \u00bb 1 (planar gap). Furthermore, after excluding the space charge quantum effects, we successfully proposed a modified FE equation that accounts for geometric asymmetry effects and related R/d dependence. Finally, the I-F curves and corresponding FN plots based on this FE equation are compared with previous experimental results.", + "section_image": [] + }, + { + "section_name": "Results and discussion", + "section_text": "Experimental field emission characteristics from various nanoscopic emitters in nanogaps are tested using an in situ electrical experiment system, which comprises a transmission electron microscopy (JEOL-2100F TEM) and an in situ electrically biased TEM holder (ZepTools Technology)55,56. The electrode structure is tungsten nanotip (W, cathode)\u2014gold plate (Au, anode), and the pure tungsten nanotips with different apex radii of curvature are prepared by a two-step process: the ex-situ double\u2011electrolyte electrochemical etching method57,58 and the in situ Joule melting method59. Details of the in situ electrical measurement system and the preparation of tungsten nanotips are described in the \u201cMethods\u201d section. In this work, we investigated the field emission characteristics of tungsten nanotips with apex radii of curvature Rm (measured from TEM images) ranging from 2\u2009nm to 190\u2009nm across various nanogaps (5\u2009nm\u2009\u2264\u2009dm\u2009\u2264\u2009100\u2009nm), which covers a wide range of Rm/dm\u2009=\u20090.04 to 48. Notably, in our experiments, the gap distance dm refers to the distance between the emitter apex and the anode plate. The TEM images of these tungsten nanotips are shown in Fig.\u00a01.\n\na Rm\u2009=\u20092\u2009nm. b Rm\u2009=\u20093\u2009nm. c Rm\u2009=\u20094\u2009nm. d Rm\u2009=\u20095\u2009nm. e Rm\u2009=\u20096\u2009nm. f Rm\u2009=\u200917\u2009nm. g Rm\u2009=\u200920\u2009nm. h Rm\u2009=\u200943\u2009nm. i Rm\u2009=\u200955\u2009nm. j Rm\u2009=\u200960\u2009nm. k Rm\u2009=\u200970\u2009nm. l Rm\u2009=\u2009100\u2009nm. m Rm\u2009=\u2009170\u2009nm. n Rm\u2009=\u2009180\u2009nm. o Rm\u2009=\u2009190\u2009nm.\n\nFigure\u00a02a, b presents the experimental field emission Im-Vm curves (solid lines) and the corresponding Fowler-Nordheim (FN) plots (solid lines) of the tungsten nanotip with an apex radius of 4\u2009nm, across nanogaps dm ranging from 5\u2009nm to 80\u2009nm. As depicted, the current increases rapidly as the voltage reaches the turn-on threshold, and with increasing nanogap distance, the Im-Vm curves shift towards higher voltages. Notably, all FN plots exhibit a nearly linear relationship, indicating the typical field emission behavior. The dotted lines in Fig.\u00a02a, b represent theoretical IMG-VMG-eff data generated as described in the following Section. The experimental field emission Im-Vm curves of all tungsten nanotips offer valuable insights into how both the radius of curvature Rm of nanotips and the gap spacing dm affect their emission characteristics. Figure\u00a02c\u2013e illustrate the applied voltages Vm (measured at Im\u2009=\u20092\u2009nA), the FEFs \u03b3a, and the \u03b3a\u00b7Vm as functions of R/d in logarithmic coordinates (scatterplots). Here, \u03b3a is obtained from finite element method (FEM) simulations (see \u201cMethods\u201d section and Supplementary Fig.\u00a03), where the nanotips are modeled using the sphere-on-cone approach, based on the morphology of all the tungsten nanotips in Fig.\u00a01. Notably, although the nanotip models are constructed using parameters (Rm, dm) measured from TEM images, they may not perfectly match the experimental nanotips. Thus, the radius of curvature of nanotip models and the gap distance in FEM simulations are denoted by Rt and dt, to distinguish them from the experimental nanotips, even though Rt\u2009=\u2009Rm and dt\u2009=\u2009dm. From the FEM electrostatics simulations, \u03b3a can be obtained as \u03b3a\u2009=\u2009Fa/FG, where Fa represents the electrostatic surface-field magnitude at the emitter apex, and FG is the average field amplitude in the gap between the emitter apex and the anode plate, given by FG\u2009=\u2009Vm/dt. It\u2019s important to note that our experiments are conducted in a near anode configuration, where the total emitter height Hm \u00bb Rm and Hm \u00bb dm, meaning that \u03b3 is not sensitive to H. To minimize the effect of the simulated height on the electrostatic fields, we set the height of the simulation models to be sufficiently large (at Ht/Rt\u2009=\u2009400) as shown in the \u201cMethods\u201d section and Supplementary Fig.\u00a02. The relationships \\({V}_{{{{\\rm{m}}}}}\\propto {({R}_{{{{\\rm{m}}}}}/{d}_{{{{\\rm{m}}}}})}^{-{n}_{1}}\\), \\({\\gamma }_{{{{\\rm{a}}}}}\\propto {({R}_{{{{\\rm{t}}}}}/{d}_{{{{\\rm{t}}}}})}^{-{n}_{2}}\\), and \\({\\gamma }_{{{{\\rm{a}}}}}\\cdot {V}_{{{{\\rm{m}}}}}\\propto {(R/d)}^{-{n}_{3}}\\) are used to obtain the fitted values of n1, n2, and n3 based on R/d, respectively, as shown in Fig.\u00a02c\u2013e. As expected, the required applied voltages Vm (at a fixed current) and the \u03b3a decrease with higher values of R/d. Notably, the relationship between \u03b3a and Rt/dt is nearly identical for all tungsten nanotip sphere-on-cone models with different Rt at different gap spacing dt (Fig.\u00a02d). For Rm/dm values less than 0.1, n1 increases slowly to about 0.2, rises rapidly to 0.9 as Rm/dm approaches 10, and then increases more slowly towards 1 for Rm/dm greater than 10, reflecting a transition to planar geometry with the relationship approximated as Vm \u221d dm/Rm (Fig.\u00a02c). Similarly, for Rt/dt\u2009<\u20090.1, the electrostatic field distribution between the emitter apex and the anode plane is non-uniform with n2 decreasing slowly to about 0.8, becoming more uniform as Rt/dt increases to 10 (where n2 drops rapidly to about 0.1), and approaching a planar case for Rt/dt\u2009>\u200910 with \u03b3a nearing 1 or n2\u2009=\u20090 (Fig.\u00a02d). More importantly, for all nanotips in our experiments, n3 is approximately 1 (Fig.\u00a02e), with acceptable fluctuations due to the differences between the experimental nanotips and the sphere-on-cone models. Therefore, we disregard the subscript differences \u201cm\u201d and \u201ct\u201d for R and d, obtaining the following relationship: \u03b3a\u00b7Vm \u221d d/R, which further leads to Fa\u2009=\u2009\u03b3a\u00b7FG \u221d 1/R. This finding implies that the local electrostatic field strength at the emitter apex required to obtain the same field emission current remains largely unchanged across the range of R/d\u2009=\u20090.04 to 48, with values for different nanotips depending on their apex radii of curvature. Moreover, within this range of R/d, even with a minimum R of 2\u2009nm and d of 5\u2009nm, no significant deviations from this relationship \u03b3a\u00b7Vm \u221d d/R (Fa\u2009=\u2009\u03b3a\u00b7FG \u221d 1/R) are observed in Fig.\u00a02e. This suggests that the effects due to the non-planar shape of the emitter may play a critical role in nanoscale field emission process. Notably, these relationships hold across various field emission currents in the nanoampere (nA) range. Additional details, including the logarithmic relationship between applied voltages Vm and Rm/dm, as well as between \u03b3a\u00b7Vm and R/d at different field emission currents of (10, 50, and 100\u2009nA), are provided in Supplementary Fig.\u00a04.\n\na The experimental field emission current-voltage Im-Vm curves (solid lines) of the tungsten nanotip with an apex radius of 4\u2009nm across different nanogaps. The dotted lines represent the theoretical IMG-VMG-eff data based on the Murphy-Good (MG) FE equation (VMG-eff\u2009=\u2009VMG/CMG \u2248 Vm, where CMG is correction factor). b The corresponding experimental Fowler-Nordheim (FN) plots (solid lines) and the corresponding FN plots (dotted lines) based on the MG FE equation (VMG-eff\u2009=\u2009VMG/CMG \u2248 Vm) of the tungsten nanotip with an apex radius of 4\u2009nm. c The applied voltages Vm at a field emission current (Im) of 2\u2009nA as a function of the ratio of experimental apex radius to gap length (Rm/dm) in logarithmic coordinates (scatterplots), along with the corresponding negative exponent n1 (solid line). d The field enhancement factors \u03b3a obtained from simulation as a function of simulated apex radius to gap length (Rt/dt) in logarithmic coordinates (scatterplots), along with the corresponding negative exponent n2 (solid line). e The product \u03b3a\u00b7Vm of the field enhancement factors \u03b3a and the applied voltages Vm at Im\u2009=\u20092\u2009nA as a function of R/d in logarithmic coordinates (scatterplots), along with the corresponding negative exponent n3 (solid line).\n\nNotably, the aforementioned relationships are based on experimental data or simulated data, and the specific analytical scaling (i.e., the specific analytical relationship between \u03b3a\u00b7Vm and R/d has yet to be derived (which may not be possible). Generally, the electrostatic field at the emitter apex can be expressed as Fa\u2009=\u2009\u03b2a\u00b7Vm\u2009=\u2009Vm/(kaR), where \u03b2a is the conversion factor connecting the electrostatic surface-field magnitude at the emitter apex and the applied voltages, and ka is a shape factor to be determined either experimentally or numerically60. The previous theoretical analytical scaling60 at the apex of the microtip with the prolate-spheroidal shape (which also used the assumption in Supplementary Note\u00a01) shows ka\u2009=\u20090.5\u2009\u00d7\u2009ln(4\u2009d/R) and ka\u2009=\u2009d/R, respectively, for R/d \u00ab 1 (sharp tip) and R/d \u00bb 1 (planar limit). By writing Fa\u2009=\u2009\u03b2a\u00b7d\u00b7Vm/d\u2009=\u2009\u03b3a\u00b7FG, we have \u03b3a\u2009=\u2009\u03b2a\u00b7d\u2009=\u2009d/(kaR), which gives \u03b3a\u2009=\u20092/(R/d) and \u03b3a\u2009=\u20091 (see the derivation in Supplementary Note\u00a02), respectively, for R/d \u00ab 1 (sharp tip) and R/d \u00bb 1 (planar limit), which is consistent with results shown in Fig.\u00a02d. It is important to note that the geometrical shape of our experimental tip (in Fig.\u00a01) is not exact similar to the assumed in ref. 60, but the discussion above shows qualitative agreement.\n\nTo further determine whether the space charge quantum effects are significant in our experiment, we used a self-consistent quantum model, which has been reported in refs. 53,54, to obtain the field emission characteristics JQ for the tungsten (cathode)-gold (anode) parallel-plate electrode structure50,51,52,53,54. In this model, the one-dimensional modified Poisson-Schr\u00f6dinger equation combined with the Jeffreys-Wentzel-Kramers-Brillouin tunneling model is solved numerically for calculating electron field emission current density54. The local work function (\u03d5) and Fermi Energy (EF) used for tungsten are given as 4.5\u2009eV and 5.782\u2009eV, respectively, and for gold, they are 5.1\u2009eV and 5.535\u2009eV, respectively61. Figure\u00a03 shows the normalized electron current density \u03bcQC\u2009=\u2009JQ/JCL (black lines) compared to \u03bcMC\u2009=\u2009JMG/JCL (red lines) at different distances between well-separated parallel planar plates (dsep\u2009=\u20091\u2009nm, 5\u2009nm, and 10\u2009nm) based on theoretical calculations, where JCL is the classical Child-Langmuir (CL) law62,63, and the JMG is the MG FE equation without the space charge effects (see Supplementary Note\u00a01 and Supplementary Eq. (S2)). When the gap distance dsep is greater than 5\u2009nm and the electrostatic field FP (FP\u2009=\u2009VP/dsep, VP is the applied voltage between the plates) is less than 10\u2009V/nm, the field emission characteristics considering space charge quantum effects are consistent with those obtained by MG FE equation. This indicates that the space charge quantum effects can be ignored so that we can conclude the effects due to the non-planar shape of the emitter are more important for R/d\u2009=\u20090.04 to 48 as studied in our experiment. Moreover, it should be noted that when dsep is 1\u2009nm and FP is less than 4\u2009V/nm, the emission current density \u03bcQC is higher than \u03bcMC, due to direct tunneling (see Supplementary Note\u00a03 and Supplementary Fig.\u00a05)54.\n\na The normalized electron emission current density \u03bcQC\u2009=\u2009JQ/JCL (black lines) and \u03bcMC\u2009=\u2009JMG/JCL (red lines) as a function of electrostatic fields FP\u2009=\u20091 to 12\u2009V/nm (FP\u2009=\u2009VP/dsep, VP is the applied voltage between the plates, dsep is distance between well-separated parallel planar plates) at dsep\u2009=\u20091\u2009nm for the tungsten (cathode)-gold (anode) parallel-plate electrode structure, based on theoretical calculations. Here, JCL is the classical Child-Langmuir (CL) law, JMG is the MG FE equation, and JQ is obtained by a self-consistent quantum model53,54. b \u03bcQC\u2009=\u2009JQ/JCL (black lines) and \u03bcMC\u2009=\u2009JMG/JCL (red lines) at dsep\u2009=\u20095\u2009nm. c \u03bcQC\u2009=\u2009JQ/JCL (black lines) and \u03bcMC\u2009=\u2009JMG/JCL (red lines) at dsep\u2009=\u200910\u2009nm.\n\nSince the space charge quantum effects can be ignored in our experiment, we further compare the experimental current-voltage Im-Vm characteristics with theoretically predicted characteristics. The representative geometry models used are shown in Supplementary Fig.\u00a03. For a given voltage between the emitter and the anode plate, the electrostatic field distribution over the emitter-shape-model surface is first calculated by finite-element methods (FEM), where the FEFs \u03b3a can also be obtained. Then, the total predicted emission current is obtained by integration of local emission current density (LECD) over the shape-model surface, using the planar emission approximation and the MG FE equation (see Supplementary Note\u00a01 and Supplementary Eq. (S2)). The planar emission approximation assumes that the LECD JL at any particular location \u201cL\u201d on the emitter surface is given by some specified planar emission equation, using the values at \u201cL\u201d of the local work function and the surface electrostatic field magnitude34. For a specific model and emission current, we find that theoretically predicted voltage values VMG are less than those Vm found experimentally for the same gap spacing d. This suggests that (other things being equal) MG FE theory may be over-predicting LECDs. To quantify the discrepancy, we introduced a correction factor CMG\u2009<\u20091. Specifically, due to the consistent scaling relationship at different currents (Im\u2009=\u20092\u2009nA, 10\u2009nA, 50\u2009nA, and 100\u2009nA) as shown in Fig.\u00a01 and Supplementary Fig.\u00a04, we selected the voltage at I\u2009=\u20092\u2009nA to calculate CMG, (CMG\u2009=\u2009[VMG(IMG\u2009=\u20092\u2009nA)]/[Vm(Im\u2009=\u20092\u2009nA)]), then the effective voltage for all VMG values can be expressed as: VMG-eff\u2009=\u2009VMG/CMG \u2248 Vm. The theoretical IMG-VMG-eff curves and corresponding theoretical FN plots obtained are shown in Fig.\u00a02a, b (dotted lines), which are closely aligned with the experimental data. Figure\u00a04a illustrates the numerical values of correction factors CMG as a function of R/d in logarithmic coordinates. As R/d increases, CMG initially increases from approximately 0.4 and gradually approaches 0.7. This confirms experimentally the discrepancies between the actual field emission and theoretical predictions64.\n\na The correction factors CMG as a function of the ratio of apex radius to gap length R/d in logarithmic coordinates (scatterplots). b The effective field-voltage conversion factors \u03b2eff-MG as a function of R/d in logarithmic coordinates (scatterplots), along with the corresponding exponent n4 (solid line). c The shape factors kMG as a function of R/d in logarithmic coordinates (scatterplots), along with the corresponding negative exponent n5 (solid line). The black line shows the fitting curve by kMG\u2009=\u20091.680\u2009\u00d7\u2009(R/d\u2009+\u20090.468)\u22121.066. d The field emission Im-Feff-MG curves of the tungsten nanotip with an apex radius of 4\u2009nm across different nanogaps. e The corresponding Fowler-Nordheim (FN) plots of Im-Feff-MG curves. f The effective emission area Aeff-MG obtained by finite element method (FEM) simulation.\n\nFigure\u00a04b, c show the effective field-voltage conversion factors \u03b2eff-MG (\u03b2eff-MG\u2009=\u2009CMG\u03b2a\u2009=\u2009CMG\u03b3a/d) and shape factors kMG (\u03b2eff-MG\u2009=\u20091/(kMGR)) as functions of R/d in logarithmic coordinates. The relationships \\({\\beta }_{{{{\\rm{eff}}}}-{{{\\rm{MG}}}}}\\propto {(R/d)}^{{n}_{4}}\\) and \\({k}_{{{{\\rm{MG}}}}}\\propto {(R/d)}^{-{n}_{5}}\\) are used to fit n4 and n5. For R/d\u2009<\u20090.1, n4 increases slowly to about 0.2, rises rapidly to 0.9 as R/d approaches 10, and then increases more slowly towards 1 for R/d\u2009>\u200910, following \u03b2eff-MG \u221d R/d (Fig.\u00a04b). This is consistent with the n1 trend reported in Fig.\u00a02c. Surprisingly, the scaling of kMG is valid for all tungsten nanotips with different R, which converges onto a single line with R/d (Fig.\u00a04c). Analogously, for R/d values less than 0.1, n5 rises slowly to about 0.2, increases rapidly to 0.9 as R/d approaches 10, and then rises more slowly towards 1 for R/d\u2009>\u200910, following kMG \u221d d/R (for planar geometry) and indicating a nearly uniform electrostatic field distribution between the emitter apex and the anode plane. As we can see, the kMG values ranging from 0.02 to 4 provide a clear and detailed characterization of field emission of a nanotip in a nanogap. We believe that these scaling laws should be valid for any metallic nanotip of apex radius R in a nanogap of spacing d, in the range of R/d\u2009=\u20090.04 to 48 reported experimentally in this work. For the ease of applications, the values of kMG can be fitted into a simple analytical expression depending of R/d, which is kMG\u2009=\u20091.680\u2009\u00d7\u2009(R/d\u2009+\u20090.468)\u22121.066 (based on Fig.\u00a04c, with a R-squared fitting of 0.986). Thus, the electrostatic field F in the MG FE equation (Supplementary Eq. (S2)) should be substituted by the effective electrostatic field Feff-MG\n\nwhere Feff-MG is the effective apex-field. Then, the total field emission current can be estimated by\n\nwhere Jeff-MG is the effective apex emission current density, Aeff-MG is effective emission area, a (=\u20091.54\u2009\u00d7\u200910\u22126 [A eV V\u22122]) and b (=\u20096.83 [eV\u22123/2 V nm\u22121]) are constants, y is the Nordheim parameter (where y\u2009=\u2009(1.20\u2009eV\u2009V\u22121/2 nm1/2) F1/2/\u03d5, and F is in V/nm), and t(y) and v(y) are well-known mathematical functions26,27,28.\n\nBased on the proposed equation, the applied voltage Vm in field emission characteristics of the tungsten nanotip with a radius of curvature of 4\u2009nm shown in Fig.\u00a02a, b can be replaced by the effective apex-field, Feff-MG. Figure\u00a04d, e show the field emission Im-Feff-MG curves and their corresponding FN plots. Notably, as d increases, the Im-Feff-MG curves gradually converge for R/d\u2009\u2264\u20090.27. The observed differences in the curves at d\u2009=\u20095\u2009nm (R/d\u2009=\u20090.8) and d\u2009=\u200910\u2009nm (R/d\u2009=\u20090.4) can be explained by the effective emission area Aeff-MG in Fig.\u00a04f. As the gap increases with R/d\u2009\u2264\u20090.27, the effective emission area increases and converges gradually. However, for d\u2009=\u20095\u2009nm (R/d\u2009=\u20090.8), especially or d\u2009=\u200910\u2009nm (R/d\u2009=\u20090.4), the effective emission area remains below this convergent value, indicating that the nanotip requires a higher electrostatic field to maintain the same field emission current Im. This causes the deviations of the Im-Feff-MG curves and the corresponding FN plots when R/d\u2009>\u20090.27, differing from the reported collapsing log10(I/F2)\u2009~\u20091/F curves in ref. 65, where the change of emission area is not considered. Despite this difference at R/d\u2009>\u20090.27, the collapsing I-V curves and the FN plots reported in ref. 65 are consistent with our findings for R/d\u2009<\u20090.27. To be specific, the I-V curves can collapse onto one single I-VR(d) curve, with R(d) approximating a power law R(d) \u221d d\u2212\u03bb (where \u03bb\u2009\u2248\u20090.22 for d\u2009=\u20093\u2013300\u2009nm and R \u2248 5\u2009nm). Furthermore, they found that when d\u2009>\u200910\u2009nm, V \u221d d\u03bb with \u03bb\u2009\u2248\u20090.2\u20130.35, while \u03bb towards to a larger value for d\u2009<\u200910\u2009nm65. This well matches our universal scaling \\({V}_{{{{\\rm{m}}}}}\\propto {d}^{{n}_{1}}\\) for a nanotip with R as the radius of curvature, where n1 increases slowly at approximately 0.2 for R/d\u2009<\u20090.1, which proves the validity of our proposed models for field emission of nanotip in nanogap as studied in this work.\n\nBased on the above analysis, the effects of the emitter\u2019s non-planar shape are more important for R/d\u2009=\u20090.04 to 48 as studied in our experiment. Therefore, we utilize the Kyritsakis-Xanthakis (KX) FE equation42, including surface curvature corrections for local electrostatic potential and the image charge potential which is valid only along the emitter\u2019s axis (see Supplementary Note\u00a01 and Supplementary Eq. (S4)), to derive the correction factor CKX, effective field-voltage conversion factors \u03b2eff-KX and kKX as functions of R/d, along with the corresponding power exponents, as provided in Supplementary Note\u00a04 and Supplementary Fig.\u00a06. This approach, based on the planar emission approximation, is similar to the process used for deriving CMG, \u03b2eff-MG and kMG. As shown in Supplementary Fig.\u00a06, for R/d\u2009<\u20091, CKX is greater than CMG, increasing from 0.5 (whereas CMG increases from 0.4 in Fig.\u00a04a), due to the inclusion of the curvature correction effects. For R/d\u2009>\u20091, CKX is consistent with CMG, and approaches 0.7. Therefore, despite these corrections, the KX FE equation (ref. 42), which uses an Earthed-sphere model to assess surface curvature effects, still does not accurately predict the field emission current of nanotips.\n\nInterestingly, the investigation of electron tunneling process between the planar substrate and the non-planar tips (spherical or hyperboloidal geometry) decades ago has already concluded that electron field emission current density in a nanogap is significantly regulated by the two factors including the asymmetry in the geometries of cathode and anode, and the overall image charge potential of space charge with both electrodes. Those effects on the overall image charge potential have been carefully examined in refs. 29,30 for sphere-plane electrode configuration with nanogaps using the rigorous electrodynamic models under spherical and prolate spheroidal coordinates. It was found that the non-planar geometry of cathode could directly result in the anisotropic distribution of image charge potential spatially surrounding the cathode. Most importantly, the overall image charge potential of sphere-plane system in nanogap was predicted to be less negative near the cathode, compared to that of plane-plane configuration, indicating an increasing of electron transmission barrier height in the former case due to the anticipated geometric asymmetry in electrode pair. Furthermore, the increasing of geometric asymmetry further diminishes the lowering of electron emission barrier height, because the complete multiple-image potential goes to less negative, relative to that of plane-plane geometry. For example, using the exact image charge potential model given in ref. 30 for hyperboloidal tip model (tungsten electrode with R\u2009=\u200910 nm and d\u2009=\u20091, 2, and 3\u2009nm, respectively), for R/d\u2009=\u200910, 5.0, and 3.3, the absolute values are reduced by 1.0%, 1.9%, and 2.9%, compared to those of plane-plane system, respectively. The raising of electron transmission energy barrier height with the increasing of the geometric asymmetry in nanogap for anode-cathode system can reduce the emission current density in comparison with that of plane-plane configuration under the same electrostatic field. This may be the main reason for the presence of the correction factor VMG\u2009=\u2009CMGVm\u2009<\u2009Vm between experimental field emission curves and the MG FE equation, as shown in Fig.\u00a04a. These insight inspires further theoretical exploration to refine predictions for nanoscale field emission phenomena, where the three-dimensional image charge potential between space charges and the bulk charges in the both electrodes (anode and cathode) should be especially considered30.\n\nIn this study, we delved into the experimental relationship between field emission characteristics of nanotips in nanogaps and the ratio of apex radius to gap length (R/d). We demonstrated that space charge quantum effects are not considerable in the range of R/d\u2009=\u20090.04 to 48, while the effects of quantum tunnelling due to the emitter shape are predominant. Based on our experimental results, we proposed a modified field emission (FE) equation, where the effective electrostatic field at the emitter apex is given by Feff-MG\u2009=\u2009Vm/(kMGR), with a detailed analytical expression for the shape factor, kMG\u2009=\u20091.680\u2009\u00d7\u2009(R/d\u2009+\u20090.468)\u22121.066. These findings are believed to be valid for any metallic nanotip in the range of R/d\u2009=\u20090.04 to 48. It is important to note that despite the success and limitation of 1D field emission models (many different versions), all models require some approximation to derive some scaling laws or equations to compare with experimental results. Physics-consistent 3D model for arbitrary shape of emitter is likely to be impossible. In this paper, we performed a clear measurement of field emission from an emitter of radius R\u2009=\u20092 to 190\u2009nm in a nanogap of d\u2009=\u20095\u2009nm to 100\u2009nm and provide a first experimental-proved scaling that is valid over a wide range of R/d (4 order of magnitude). Discussion of this scaling to other models can be referred to Supplementary Note\u00a04. Our work underscores the importance of emitter shape effects in dictating the emission properties, and highlights the need for precise theoretical models to accurately predict and harness these effects in practical applications, which is crucial for understanding and improving the performance of nanoelectronic devices.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60607-6/MediaObjects/41467_2025_60607_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60607-6/MediaObjects/41467_2025_60607_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60607-6/MediaObjects/41467_2025_60607_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60607-6/MediaObjects/41467_2025_60607_Fig4_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "The in situ electrical experiment system consists of the transmission electron microscopy (JEOL-2100F TEM) and an in situ electrically biased TEM holder (ZepTools Technology). This setup achieves a spatial resolution of 0.1\u2009nm and allows for nanogap adjustment with an accuracy of 0.04\u2009nm. During field emission measurements, the system can apply a direct current voltage of up to 150\u2009V across nanogaps, with a current measurement resolution of 0.1\u2009nA. The in situ electrically biased TEM holder and the schematic diagram of the field emission measurement are shown in Supplementary Fig.\u00a01a, b. All measurements are performed at room temperature and a vacuum level of ~10\u22125\u2009Pa.\n\nThe preparation of the tungsten nanotips involves two steps. The first step is an ex-situ double\u2011electrolyte electrochemical etching method57,58, and the schematic is shown in Supplementary Fig.\u00a01c. Specifically, a stepping motor (TSTA-1050, 7-star, Beijing) precisely controls the motion of a 99.97% pure tungsten wire with a diameter of 0.3\u2009mm. The tungsten wire passes through a 5\u2009M NaOH electrolyte lamella (flake, purity 97%, Aladdin) and is immersed perpendicularly in a saturated NaCl solution (AR, Aladdin). A DC power source (B2091A, Keysight, USA) supplies a voltage of 5\u2009V to a stainless-steel anode immersed in the saturated NaCl solution and a ring inert nickel-chromium cathode (diameter 10\u2009mm). The tungsten wire is etched only in the NaOH lamella, with the NaCl solution acting as a conducting element between the tungsten wire and the stainless-steel anode. Once the tungsten wire breaks, the electric circuit is automatically cut off as the lower fragment of the tungsten wire drops. However, although the apex radius of the prepared tungsten nanotip by first step is less than 50\u2009nm, there is an unavoidable oxide layer with a few nanometers thick on the tungsten nanoelectrode surface due to the ex-situ preparation, which significantly affects the field emission characteristics.\n\nThe second step is an in situ Joule melting method. Initially, the tungsten nanoelectrodes prepared by the first step and the gold plate (prepared by pressing and cutting) are mounted on the TEM electrical sample holder, as shown in Supplementary Fig.\u00a01a. Next, the interelectrode gap is adjusted in situ to less than 20\u2009nm under TEM. A ramped DC voltage is then applied across the nanogap to induce electrical breakdown, with the tungsten nanotip acting as the cathode and the gold plate as the anode. Following the breakdown, the tungsten nanotips with radii of curvature from 2\u2009nm to 200\u2009nm can be prepared in situ. The surfaces of the tungsten nanotips prepared by this method are very clean and free of contaminants such as oxide layer. This is demonstrated by the representative high-resolution TEM images and the selected area electron diffraction results of the pure tungsten nanotip with an apex radius of 5\u2009nm, as shown in Supplementary Fig.\u00a01d.\n\nThe FEFs are obtained using a commercial FEM tool (COMSOL Multiphysics). First, the sphere-on-cone nanotip models are established based on the morphology of all the tungsten nanotips shown in Fig.\u00a01. Material parameters are based on the default values provided by the COMSOL internal library. It\u2019s worth noting that the heights of practical tungsten nanotips are approximately 1\u2009cm, i.e., Hm \u00bb Rm and Hm \u00bb dm (in a near anode configuration), and in order to reduce the effect of the simulated heights on the simulated electrostatic fields, we set the height of the simulated models at Ht/Rt\u2009=\u2009400. The reason is that according to the Supplementary Fig.\u00a02, when Ht/Rt is greater than 400, the selected heights have little effect on the FEF, given that the minimum Rt/dt ratio in our experiments is 0.04. Next, the electrostatic fields are simulated using the electrostatics module. The FEF \u03b3a can be obtained as \u03b3a\u2009=\u2009Fa/FG, where Fa represents the electrostatic surface-field magnitude at the emitter apex, and FG is the average field amplitude in the gap between the emitter apex and the anode plate, given by FG\u2009=\u2009V/d. Then, the total predicted emission current is obtained by integration of LECD over the shape-model surface, using the planar emission approximation and the MG FE equation26,27,28 or KX FE equation42 (see Supplementary Note\u00a01 and Supplementary Eqs. (S2) and (S4)). The planar emission approximation assumes that the LECD JL at any particular location \u201cL\u201d on the emitter surface is given by some specified planar emission equation, using the values at \u201cL\u201d of the local work function and the surface electrostatic field magnitude34. Supplementary Fig.\u00a03 shows the electrostatic field distribution, as well as field emission current density JMG and JKX (two-dimensional linear current density) for the nanotip with an apex radius of 4\u2009nm at d\u2009=\u20095\u2009nm and V\u2009=\u200920\u2009V.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The authors declare that all relevant data supporting the findings of this work are included in the Article, with additional data provided in the Supplementary Information.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Hernandez, N. et al. 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G.M. and Y.L. are grateful to The Center for Advancing Materials Performance from the Nanoscale (CAMP-Nano) in Xi\u2019an Jiaotong University for the in situ TEM measurement.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "State Key Laboratory of Electrical Insulation and Power Equipment, Xi\u2019an Jiaotong University, Xi\u2019an, P.R. China\n\nYimeng Li,\u00a0Linghan Xia,\u00a0Nan Li,\u00a0Shilong Tang,\u00a0Yunsong Ge,\u00a0Jianyu Wang,\u00a0Bing Xiao,\u00a0Yonghong Cheng\u00a0&\u00a0Guodong Meng\n\nScience, Mathematics and Technology, Singapore University of Technology and Design, Singapore, Singapore\n\nLay Kee Ang\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nG.M., Y.C., and Y.L. conceived the project. L.K.A. contributed expertise and assistance in theoretical guidance. B.X. contributed expertise and assistance in simulation guidance. Y.L. carried out the experiments and simulations, analyzed the data, and wrote the original manuscript. L.X., S.T., Y.G., and J.W. assisted with experimentation. N.L. assisted with simulations. All authors contributed to the discussion of the data and to writing the manuscript. All authors have given approval to the final version of the manuscript.\n\nCorrespondence to\n Lay Kee Ang or Guodong Meng.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks John P. Xanthakis, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Uncovering a widely applicable empirical formula for field emission characteristics of metallic nanotips in nanogaps.\n Nat Commun 16, 5583 (2025). https://doi.org/10.1038/s41467-025-60607-6\n\nDownload citation\n\nReceived: 05 November 2024\n\nAccepted: 22 May 2025\n\nPublished: 01 July 2025\n\nVersion of record: 01 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60607-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"Topological-insulating grain boundary networks for high-performance Fe2VAl thermoelectrics", + "journal": "Nature Communications", + "published": "26 March 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57250-6/MediaObjects/41467_2025_57250_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57250-6/MediaObjects/41467_2025_57250_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-57250-6#MOESM1" + ], + "code": [], + "subject": [ + "Composites", + "Electronic properties and materials", + "Thermoelectrics", + "Topological matter" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5271025/v1.pdf?c=1743073679000", + "research_square_link": "https://www.researchsquare.com//article/rs-5271025/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-57250-6.pdf", + "preprint_posted": "14 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Decoupling charge and heat transport is essential for optimizing thermoelectric materials. Strategies to inhibit lattice-driven heat transport, however, also compromise carrier mobility, limiting the performance of most thermoelectrics, including Fe2VAl Heusler compounds. Here, we demonstrate an innovative approach, which bypasses this tradeoff: via liquid-phase sintering, we incorporate the archetypal topological insulator Bi1-xSbx between Fe2V0.95Ta0.1Al0.95 grains. Structural investigations alongside extensive thermoelectric and magneto-transport measurements reveal distinct modifications in the microstructure, a reduced lattice thermal conductivity and a simultaneously enhanced carrier mobility arising from topologically protected charge transport along the grain boundaries. This yields a huge performance boost - far beyond the effective-medium limit - and results in one of the highest figure of merits among both half- and full-Heusler compounds, z \u2248 1.6 x 10^-3 K^-1 (zT \u2248 0.5) at 295 K. Our findings highlight the potential of topological-insulating secondary phases to decouple charge and heat transport and call for more advanced theoretical studies of multiphase composites.Physical sciences/Materials science/Materials for energy and catalysis/ThermoelectricsPhysical sciences/Materials science/Condensed-matter physics/Topological matter/Topological insulatorsPhysical sciences/Materials science/Techniques and instrumentation/Design, synthesis and processingPhysical sciences/Materials science/Structural materials/Composites", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "MethodsSupplementalInformation.pdfSupplementary Information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Decoupling charge and heat transport is essential for optimizing thermoelectric materials. Strategies to inhibit lattice-driven heat transport, however, also compromise carrier mobility, limiting the performance of most thermoelectrics, including Fe2VAl Heusler compounds. Here, we demonstrate an innovative approach, which bypasses this tradeoff: via liquid-phase sintering, we incorporate the archetypal topological insulator Bi1\u2212xSbx between Fe2V0.95Ta0.1Al0.95 grains. Structural investigations alongside extensive thermoelectric and magneto-transport measurements reveal distinct modifications in the microstructure, a reduced lattice thermal conductivity and a simultaneously enhanced carrier mobility arising from topologically protected charge transport along the grain boundaries. This yields a huge performance boost, resulting in one of the highest figure of merits among both half- and full-Heusler compounds, z\u00a0\u2248\u00a01.6\u00a0\u00d7\u00a010\u22123 K\u22121 (zT\u00a0\u2248\u00a00.5) at 295 K. Our findings highlight the potential of topological-insulating secondary phases to decouple charge and heat transport and call for more advanced theoretical studies of multiphase composites.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Given the increasing global demand for efficient energy utilization, thermoelectrics (TEs) present a promising solution as they can harvest decentralized waste heat sources or function as Peltier coolers, e.g., for thermal management applications1,2. The conversion efficiency of TE devices depends on the hot- and cold-side temperatures and a material-dependent figure of merit, z \u221d \u03bcW/\u03baL. The highest achievable z in a semiconductor with optimized carrier concentration is determined by the weighted carrier mobility \u03bcW of electrons or holes, which should be maximized, and by the lattice thermal conductivity \u03baL, which should be minimized3,4. The inherent tradeoff between \u03bcW and \u03baL presents one of the most formidable challenges in the design and optimization of TE materials, requiring the decoupling of charge and heat transport, that is, the realization of a phonon-glass electron-crystal concept.\n\nSince the mid-20th century, Bi2Te3-based systems have been the gold standard for TEs operating near room temperature, and currently, they remain the only commercially available option5,6. However, the scarcity of tellurium, along with the brittle nature and poor mechanical properties of these materials limits their widespread use in everyday life and industrial applications. Therefore, it is crucial to explore alternatives that offer competitive performance and overcome the challenges related to Bi2Te3.\n\nFor n-type materials, cost-effective Mg3(Bi,Sb)2 Zintl compounds have been considered the hottest candidates as they exhibit very high z7,8,9. However, these materials, especially the Bi-rich alloys with attractive near-room temperature properties, suffer from poor chemical stability and degrade rapidly when exposed to air, presenting an ongoing challenge for practical applications.\n\nOn the other hand, Heusler compounds based on Fe2VAl, the focus of this study, exhibit excellent chemical and mechanical stability. These materials are also composed of earth-abundant, inexpensive elements with great recyclability10 \u2013 sustainability aspects that are becoming increasingly important worldwide, and particularly within the EU. Moreover, they display outstanding electronic transport properties, with weighted mobilities that are comparable to or even greater than those of other state-of-the-art TEs11. Yet, their intrinsically large \u03baL limits their potential as TE materials12. Consequently, previous studies have primarily focused on reducing \u03baL by substituting heavy elements13,14,15, lowering the dimensionality through thin-film deposition16,17,18, or reducing the grain size12,19,20. Although these strategies have resulted in enhancements of z, the overall performance remains a significant bottleneck and is too low for most practical applications.\n\nIn this study, we demonstrate that by incorporating chemically and structurally distinct Bi1\u2212xSbx at the grain boundaries, charge, and heat transport can be decoupled, resulting in a reduction of \u03baL, and simultaneously, in an unexpected increase of \u03bcW (Fig.\u00a01a). Consequently, the figure of merit is boosted by more than a factor of two, up to \\({z}_{\\max }\\approx 1.7\\times 1{0}^{-3}\\) K\u22121 at 240\u2013250\u2009K (z \u2248\u00a01.6\u2009\u00d7\u200910\u22123\u2009K\u22121 at room temperature), representing one of the largest values hitherto reported among n-type half- and full-Heusler compounds (Fig.\u00a01b).\n\na Tradeoff between weighted mobility and lattice thermal conductivity (plus bipolar term \u03baB) in Fe2VAl-based Heusler compounds at room temperature13,14,21,22,23. Data for state-of-the-art n-type Bi2Te3- and Mg3Bi2-based systems6,7 at 300\u2009K are shown for comparison. Composites in this work are found to bypass this tradeoff. b Temperature-dependent figure of merit of the best composite from this work (FVAB50), compared to other optimally doped, high-performance n-type thermoelectrics50,51,52,53,54,55, reaching one of the highest z among both half-Heusler (hH) and full-Heusler (fH) compounds. c Schematic synthesis of composites via liquid-phase sintering.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57250-6/MediaObjects/41467_2025_57250_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "The lattice thermal conductivity of Fe2VAl Heusler compounds is intrinsically large, \u03baL \u2248\u00a027\u2009W\u2009m\u22121\u2009K\u22121 at 300\u2009K21, which can be mainly attributed to a lack of structural and chemical bonding complexity as well as the absence of heavy elements, leading to high sound velocities. Upon alloying, \u03baL can be drastically reduced down to 10\u2009W\u2009m\u22121\u2009K\u22121 in Fe2VAl1\u2212xSix21, 7\u2009W\u2009m\u22121\u2009K\u22121 in Fe2VAl1\u2212xGex21, and by substituting heavy 5d elements, further down to 5\u2009W\u2009m\u22121\u2009K\u22121 in Fe2VTaxAl1\u2212x14 and 4\u2009W\u2009m\u22121\u2009K\u22121 in Fe2V1\u2212xWxAl15. As a downside, the very same point defects, which effectively inhibit heat transport by high-frequency phonons, also strongly scatter charge carriers. This is particularly true for the 5d elements like Ta and W, which are substituted for V atoms. Since V-d states dominate the electronic states of the conduction band, introducing substitutional disorder at the V site results in intense electronic scattering. We gathered TE property data from various substitution studies13,14,21,22,23 and calculated \u03bcW4. A strong tradeoff relationship between \u03bcW and \u03baL is obvious from Fig.\u00a01a (black symbols).\n\nAside from introducing point defects, \u03baL can be suppressed by reducing the grain size d and several studies attempted to enhance z by grain size reduction, e.g., via ball milling13,19 or high-pressure torsion (HPT)20,24, yielding d \u2248\u00a0100\u2009nm. Employing HPT, Fukuta et al. recently reported very low values of \u03baL down to 1.3\u2009W\u2009m\u22121\u2009K\u22121 in Fe2V0.98Ta0.1Al0.92 at 350\u2009K and zT up to 0.37 at 400 K (z \u2248\u00a00.9\u2009\u00d7\u200910\u22123\u2009K\u22121)24. These remarkable findings motivated us to (i) reproduce them and (ii) apply HPT to a variety of different samples with optimized compositions. The results of these endeavors are summarized in the\u00a0Supplementary Information (SI). While \u03baL could indeed be dramatically reduced down to \u00a0<2\u2009W\u2009m\u22121\u2009K\u22121, we concomitantly observed a huge deterioration of electronic transport in all cases (see Figs. S1 and S2 and blue symbols in Fig.\u00a01a), resulting in no enhancement of zT (Fig. S3). Similar observations have been made, e.g., for Mg3(Bi,Sb)225,26 and various half-Heuslers, where reducing grain size comes at a cost of reducing \u03bcW27,28. The discrepancy between our results and previous ones from ref. 29 suggests that setup-specific conditions during HPT are generally very important, complicating reproducibility and upscale production.\n\nInstead, we have devised a different approach wherein chemically and structurally distinct Bi1\u2212xSbx is incorporated as a secondary phase between the Heusler grains. Figure\u00a01c outlines the synthesis procedure. The starting materials were first synthesized using an induction melting furnace and then hand-ground into a fine powder. The powders were mixed in various ratios (5\u201350 vol.% Bi0.9Sb0.1), and sintered at 1373\u2009K. The much lower melting point of Bi0.9Sb0.1 causes excess liquid to be expelled during sintering. The retention of Bi1\u2212xSbx in the composite depends on the particle size of the Heusler phase and the amount of Bi0.9Sb0.1 used. Backscattered scanning electron microscopy (BS-SEM) shows that up to \u00a0\u224830 vol.%, Bi0.9Sb0.1 fills only the triple junctions of Heusler grains, while 50 vol.% Bi0.9Sb0.1 allows the liquid phase to wet and coat all grains as a grain boundary (GB) phase (Fig. S10). This produces highly dense (Fe2V0.95Ta0.1Al0.95\u2009+\u2009Bi0.9Sb0.1) composites, referred to as FVABX, with X indicating the Bi0.9Sb0.1 volume percentage added before sintering. The reference sample, without any Bi1\u2212xSbx, achieved a density of approximately 95% of its theoretical density. When about 10 vol.% Bi0.9Sb0.1 are added before sintering, the composite material shows minimal porosity. SEM micrographs confirm that any pores present in the initial sample are filled by the secondary Bi1\u2212xSbx phase, resulting in a density close to 100% for all composite samples.\n\nThe \u03bcW versus \u03baL trend (see Fig.\u00a01a) for the composite samples is unusual and cardinally different from other approaches, like alloying or grain size reduction via HPT. Moreover, the exceptionally high \u03bcW, in spite of the suppressed \u03baL, signifies a decoupling of charge and lattice-driven heat transport. In the following, we present investigations of the microstructure of these materials alongside local microscale probing of the Seebeck coefficient S. Finally, we show and discuss experimental results from extensive TE and magneto-transport measurements carried out in a broad range of temperatures and magnetic fields.\n\nThe structural properties of the sintered samples were investigated via scanning and transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). Fig. S4 shows BS-SEM images of Fe2V0.95Ta0.1Al0.95 sintered at 1373\u2009K without the addition of Bi0.9Sb0.1. Throughout the whole sample, nanoscale precipitates of a secondary Ta-rich phase are clearly noticeable at the GBs. This is in agreement with the previously established low solubility limit of Ta, x\u2009=\u20090.07 in Fe2V1\u2212xTaxAl30. Apart from that, the microstructure displays a very homogeneous phase distribution without any variations in the composition.\n\nFigure\u00a02a shows a low-magnification image of the microstructure of the FVAB50 composite, with the best TE properties. A uniform distribution of Bi1\u2212xSbx along the GBs is obvious and confirmed by compositional mapping using EDX (Fig.\u00a02b) with an estimated volume fraction of around 5\u20137 vol. %. Additionally, we find that the segregation of Bi1\u2212xSbx along the GBs goes hand in hand with two changes in the microstructure: (i) strong suppression of nanoscale Ta-rich precipitates at the GBs, (ii) diffuse brightness variations within the grains. Both these structural changes suggest an enhanced solubility limit of heavy Ta atoms, when Bi1\u2212xSbx is incorporated as a GB network during the liquid-phase sintering, contributing to a reduction of the lattice thermal conductivity as shown later. This is confirmed by EDX line scans (Fig.\u00a02d) across the grain, revealing periodic fluctuations in the Ta and V concentration, and XRD, revealing an increase of the lattice parameter of the Heusler phase (Fig.\u00a02e and inset of Fig. S12c) as larger and heavier Ta atoms are substituted. In Fig.\u00a02f, g, we focus on Bi1\u2212xSbx. Since Fe2VAl and Bi1\u2212xSbx are chemically and structurally distinct, there exists a well-defined GB without apparent interdiffusion. We find that Bi1\u2212xSbx, when embedded between Heusler grains, displays a peculiar ladder-like nanostructure with arrays of stacking fault defects. Moreover, detailed EDX analyses of different samples indicate that the Bi:Sb ratio fluctuates and that the Sb concentration is above the nominal one (although remaining within the topological-insulating regime) in our high-performance FVAB50 composite (Figs. S9 and S11).\n\na Microstructure of FVAB50 composite, where the majority of GBs are filled with Bi-Sb. b EDX analyses reveal that Bi and Sb are found at the GBs, while Fe, V, Al, and Ta are almost exclusively distributed within the grains. c BS-SEM image of a Heusler grain with periodic contrast variations, surrounded by Bi-Sb. d EDX line scan along the Heusler grain shown in (c). e Comparison of normalized X-ray diffraction peaks of the (220) plane of Fe2V0.95Ta0.1Al0.95 and FVAB50 composite. f Bright-field TEM image of the GB with ladder-like nanostructure arrays of stacking faults and (g) high-magnification image near stacking-faults. Insets in (e and g) show Heusler and Bi-Sb unit cells, respectively.\n\nThe pivotal role of understanding and investigating TE transport across grain boundaries is increasingly recognized25,29,31,32. To draw a connection between microstructure and electronic transport we employed a transient potential Seebeck microprobe (TPSM), with local property investigations performed on the same rectangular area of the sample (Fig.\u00a03a). Figure\u00a03b shows a map of the locally determined S with a spatial resolution of 3\u20135 microns. The results obtained are in excellent agreement with structural investigations revealing a rich and complex microstructure consisting of Heusler grains and a Bi1\u2212xSbx GB network. Interestingly, TPSM measurements suggest that Bi1\u2212xSbx exhibits a larger S as a secondary phase compared to its bulk form. This is emphasized by looking at line scans across Heusler grains. The plateau in Fig.\u00a03c refers to the value of Bi1\u2212xSbx within the composite, which is significantly higher than its bulk value, especially considering that TPSM measurements typically underestimate S by at least 20\u201330% due to the cold finger effect. This enhancement, which exceeds the highest S at 300\u2009K in the entire composition range of polycrystalline Bi1\u2212xSbx33, is surprising, given the near-complete immiscibility between Bi1\u2212xSbx and the Heusler phase.\n\na Microstructure, composition, and local transport probing in the same area (orange square in Fig. 2a). b, c Transient potential Seebeck microprobe (TPSM) mapping of FVAB50 at room temperature. Ta-enriched regions inside the Heusler grains and Bi1\u2212xSbx at the GBs display a smaller S than the remaining part of the matrix. c TPSM line scans across distance marked in (b). d Histogram from TPSM mapping. Solid lines are normal distributions centered around S1\u2009=\u2009\u2212140\u2009\u03bcV\u22121\u2009K\u22121 and S2\u2009=\u2009\u221278\u2009\u03bcV\u22121\u2009K\u22121, the bulk values for Fe2V0.95Ta0.1Al0.95 and Bi0.9Sb0.1, respectively.\n\nFigure\u00a03d shows the distribution histogram of the measured Seebeck coefficient. For the Heusler phase, S1 \u2248\u00a0\u2212140\u2009\u03bcV\u2009K\u22121, and for Bi0.9Sb0.1, S2 \u2248\u00a0\u221278\u2009\u03bcV\u2009K\u22121 would be expected. However, instead of two normal distributions centered around those values, the observed distribution appears much more merged with S being significantly under(over)estimated with respect to S1(S2). While the underestimation is an artifact from the cold finger effect, inherent to the TPSM and basically all microprobe measurements34, the enhanced S of the secondary phase indicates a beneficial interplay between the two components and explains why the integral value of S remains large in the composite (Fig.\u00a04c), despite being short-circuited across the GBs. We note that a similar observation has been made already several years ago by Mikami and Kobayashi in (Fe2VAl0.9Si0.1\u2009+\u2009Bi) composites with \\(z{T}_{\\max }=0.11\\)35.\n\na Temperature-dependent thermal conductivity, b electrical resistivity, c Seebeck coefficient, and (d) dimensionless figure of merit zT of Fe2V0.95Ta0.1Al0.95 composites with 20 and 50 vol.% Bi0.9Sb0.1 added before sintering (FVAB20, FVAB50) compared to pristine Fe2V0.95Ta0.1Al0.95 and Bi0.9Sb0.1. The error bars in (c) indicate the statistical variation of the Bi-Sb phase corresponding to the area shown in Fig.\u00a03b. The red solid line in (d) represents a calculation based on effective-medium theory (EMT) for FVAB50, using a volume fraction of \u00a0\u22486 vol.% Bi0.9Sb0.1, determined by EDX.\n\nIn Fig.\u00a04, we compare the temperature-dependent bulk TE properties of our FVABX (X\u2009=\u20090,\u00a020,\u00a050) composites over a broad temperature range from 4 to 523\u2009K. Measurements were performed using different setups in various laboratories at the National Institute for Materials Science (NIMS) in Japan and at TU Wien (TUW) in Austria. Additionally, extensive measurements have also been conducted on a bulk sample of Bi0.9Sb0.1 synthesized during this study, and the data have been included for comparison. The latter are in excellent agreement with those reported previously (see Fig. S13).\n\nFigure\u00a04a displays the temperature-dependent thermal conductivity \u03ba(T). At 200\u2013300\u2009K, \u03ba(T) increases due to bipolar thermal transport, consistent with the S(T) curves. Most importantly, when Bi1\u2212xSbx is incorporated as a secondary phase, \u03ba(T) decreases significantly, which we attribute to the complex microstructural evolution involving microscale Bi1\u2212xSbx GBs with an extremely large acoustic mismatch (\u22489\u2009THz) with respect to the Heusler matrix, periodic composition fluctuations within the grains, and an enhanced solubility limit of heavy Ta atoms. Moreover, \u03ba(T) of the Bi1\u2212xSbx GB network is likely reduced as well compared to the bulk values owing to the ladder-like arrays of stacking fault defects (see Fig.\u00a02f, g) and microscale composition fluctuations (Fig. S9), likely inhibiting phonon-driven heat transport along the GBs36.\n\nFrom Fig.\u00a04b, it is evident that, despite the significant reduction in \u03ba(T), electronic transport remains excellent. The temperature-dependent resistivity \u03c1(T) flattens when Bi0.9Sb0.1 is incorporated, even resulting in a decrease of \u03c1(T) at elevated temperatures. The flattening of the resistivity curves and the increased residual resistivity at low temperatures both imply a weakening of the electron-phonon coupling and enhanced disorder, aligning with the notion of an enhanced Ta solubility limit. Previous work shows that V/Ta substitution results in a softening of the Heusler lattice and a reduction of the electron-phonon deformation potential, decreasing \u03c1(T) at elevated temperatures, where acoustic phonon scattering dominates. Moreover, V/Ta substitution can expand the band gap by pushing the V-eg conduction band toward higher energies, enhancing the maximum of the Seebeck coefficient37.\n\nThe temperature-dependent Seebeck coefficient S(T) only varies moderately in the composites with \\({S}_{\\max }\\) shifting to slightly lower temperatures. As mentioned in the previous section, a simple effective-medium theory (EMT) with parallel conduction along the GB network would result in a sizeable decrease of S(T). The fact that S retains large values, is surprising and unexpected, calling for more advanced theoretical studies of TE transport in composite materials. Although it is well known that the TE properties of nanocomposites can deviate strongly from those of the individual material components38,39, deviations from the EMT in microscale composites are much rarer.\n\nThe above-listed modifications result in an extreme boost of zT by more than a factor of two (see Fig.\u00a04d) up to a \\(z{T}_{\\max }\\) of almost 0.5 at 295\u2009K. Note that, like for almost all TE studies, error bars of \u00a0\u224820% should be considered40, which were omitted for better visibility of the data. This clearly exceeds the predictions of the EMT, which, as demonstrated by Bergman and Levy in their seminal work41, states that zT in composites needs to be always smaller than the largest zT of the individual components, irrespective of the geometry. We also note that this exceeds the largest room-temperature zT of the entire binary Bi-Sb system, with \\(z{T}_{\\max }\\approx 0.3\\)33. This implies that the TE properties of the individual components change dramatically in the composite or are subject to reciprocal action of both, allowing for a decoupling of charge and heat transport. To investigate the thermal stability of our composites, transport measurements were conducted for various thermal cycles (Fig. S22), which reveal excellent reproducibility and no degradation of the properties at least up to 500\u2009K \u2013 the most relevant temperature range for the potential application of these materials.\n\nTo further elucidate transport in the best-performing composite sample (FVAB50), we measured the Hall effect in a broad temperature and magnetic field range, 4\u2013400\u2009K and \u00a0\u22129\u2009T to 9\u2009T. These results are summarized in Fig.\u00a05. The field-dependent Hall resistivity \u03c1xy, plotted in Fig.\u00a05a for various temperatures displays an extremely large anomalous Hall effect, which even increases with rising temperature up to \u00a0\u2248300\u2009K, despite the absence of any sizeable magnetization in the sample. On the contrary, the sintered Heusler compound without Bi1\u2212xSbx at the GBs exhibits a simple linear magnetic field dependence. While the observation of a giant anomalous Hall effect in various topological materials is often ascribed to huge Berry curvatures, emerging from the respective topological band structure features42,43, we interpret the complex field-dependent curves in Fig.\u00a05a as a two-channel conduction mechanism, where charge carriers can move across the sample either through topologically trivial bulk states of the Heusler grains or through topologically protected surface states of the Bi1\u2212xSbx GB network (see inset Fig.\u00a05b). Figure\u00a05b shows that the field-dependent behavior of \u03c1xy from \u00a0\u22125\u2009T to 5\u2009T can be reasonably well described by a simple two-channel transport model (details of the modeling procedure and underlying theory is presented in the\u00a0SI). The mobilities obtained for the two distinct transport channels are presented in Fig.\u00a05c alongside the values of pristine Fe2V0.95Ta0.1Al0.95 without topological-insulating GBs. The bulk values of Fe2V0.95Ta0.1Al0.95 are of the order of 10\u2009cm2\u2009V\u22121\u2009s\u22121. The mobility of the bulk channel, \u03bc1, extracted from our transport modeling is comparable, especially at low temperatures. The mobility of the Dirac-like surface states, \u03bc2, associated with the Bi-Sb network, on the other hand, is several orders of magnitude higher up to 3\u2009\u00d7\u2009105\u2009cm2\u2009V\u22121\u2009s\u22121 and 2\u2009\u00d7\u2009104\u2009cm2\u2009V\u22121\u2009s\u22121 for the Dirac holes and electrons, respectively. As a consistency check, we calculated the temperature-dependent zero-field resistivity from the obtained carrier mobilities \u03bc1,2 and carrier densities n1,2 via \\({\\rho }_{xx}(0,T)={(e{n}_{1}{\\mu }_{1}+e{n}_{2}{\\mu }_{2})}^{-1}\\), which should match temperature-dependent measurements in Fig.\u00a04b. As shown in Fig. S19, there is excellent agreement across the entire temperature range, underscoring the robustness and reliability of the fits.\n\na Field-dependent Hall resistivity of FVAB50 at different temperatures 4\u00a0\u2264\u00a0T\u00a0\u2264\u00a0400 K. Room-temperature data of Fe2V0.95Ta0.1Al0.95 are shown for comparison as black open circles. b Two-channel transport modeling of field-dependent Hall resistivity. Solid lines are least squares fits. Inset shows a sketch of the Hall effect in FVABX composites. Highly mobile Dirac-like carriers along the Bi-Sb GB network have a much larger mean free path than the Heusler bulk electrons and are deflected much easier, even in small magnetic fields. c Temperature-dependent Hall mobility of FVAB50 obtained by modeling the complex field-dependent behavior. Error bars indicate uncertainties in the fit results. The inset shows a sketch of the two transport channels (1) the bulk conduction electrons of the Heusler main phase and (2) the Dirac-like surface states of the Bi-Sb GB network.\n\nIn summary, the field-dependent Hall effect reveals a significant contribution to electronic transport from the Dirac-like surface states of the Bi1\u2212xSbx GB network, leading to a pronounced anomalous Hall effect, which can be explained by a two-channel transport model. This aligns with the colossal mobilities expected from such topologically robust carriers and the higher surface-area-to-volume ratio in the composite.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57250-6/MediaObjects/41467_2025_57250_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57250-6/MediaObjects/41467_2025_57250_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57250-6/MediaObjects/41467_2025_57250_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57250-6/MediaObjects/41467_2025_57250_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Concluding, we demonstrated that incorporating Bi1\u2212xSbx in Fe2VAl Heusler compounds can boost the zT compared to both individual materials. This is particularly surprising considering the near-complete immiscibility of both components and their chemical distinctness, which should prevent sizeable interdiffusion and changes to the individual material properties. Decoupling charge and lattice-driven heat transport in such composites is an auspicious route toward high zT, even more so in systems where reducing grain size and alloying strongly compromise carrier mobility, although, a more profound theoretical understanding of charge and heat transport in composites is required to optimally design and choose the best candidates.\n\nIn this study, we achieved heavily reduced \u03baL, and simultaneously high \u03bcW. To provide a broad comparison with other material classes for the latter, we downloaded all available TE property data from the Starrydata2 open web database44, which, as of July 2024, contains TE data from 8961 different papers and 52,020 different samples. We then calculated \u03bcW for those samples, where both S(T) and \u03c1(T) are reported. Fig. S23 shows that, near room temperature, \u03bcW of the best composite sample from this work surpasses all other reported n-type semiconductors.\n\nTo further elevate the performance of Fe2VAl systems, broad screening of secondary phases needs to be done; especially other topological insulators like Bi2Se3 could be considered. Additionally, one has to think about strategies to increase and reliably tune the volume fraction. Lastly, it is crucial to identify promising p-type compounds with competitive z. Since p-type Fe2VAl compounds inherently show much smaller Seebeck coefficients, this can only be realized via band engineering of the valence band electronic structure45,46. Only then can competitive Fe2VAl-based modules be realized, which could substitute the long-reigning Bi2Te3 systems. The present study suggests that, by proper GB engineering, Fe2VAl Heusler alloys may indeed bear the potential to rival state-of-the-art Bi2Te3 and Mg3Bi2 semiconductors. High-performance modules entirely based upon Fe2VAl alloys could open a new era for near-ambient applications, as these systems excel in terms of cost-effectiveness, excellent recyclability, and simpler device structures. Moreover, they exhibit superior mechanical, thermal, and chemical long-term stability, factors that are becoming increasingly recognized as essential assets for realizing widespread thermoelectric technology.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Bulk elements of high purity (Fe 99.99%, V 99.93%, Ta 99.95%, Al 99.999%, Bi 99.999%, Sb 99.999%) were stoichiometrically weighed and polycrystalline ingots of the starting materials (Fe2V0.95Ta0.1Al0.95 and Bi0.9Sb0.1) were synthesized by high-frequency induction melting under Ar atmosphere. The as-cast ingots were manually crushed and ground using a tungsten carbide pestle and mortar. The resulting powders from the individual starting materials were then mixed in various volume ratios. The volume percentage of Bi0.9Sb0.1 powder added was adjusted and calculated based on the theoretical densities of the respective starting materials. After the powders were thoroughly mixed, the mixture was filled into a graphite die and sintered at a temperature of 1373\u2009K, which is about 80% of the melting point of the full-Heusler phase and about 800\u2009K higher than the melting point of Bi0.9Sb0.1. Consequently, excess liquid was expelled during the sintering process. Additionally, we observed that the liquid-phase sintering led to a significant decrease in the sintering temperature of the Heusler material by up to almost 200\u2009K when Bi0.9Sb0.1 powder was added as compared to when only Fe2V0.95Ta0.1Al0.95 powder was sintered. Nonetheless, to ensure consistent and comparable processing conditions for the samples studied in this work, all specimens were sintered using exactly the same synthesis conditions, i.e., a compaction pressure of 50\u2009MPa, a maximum temperature of 1373\u2009K, and a holding time of 15\u2009min. No additional heat treatment has been applied to the samples after the sintering process.\n\nThe microstructure and elemental composition of the sintered samples were investigated using scanning electron microscopy in both secondary electron (SE) and backscattered electron (BSE) modes, complemented by energy-dispersive X-ray spectroscopy (EDX). These analyses were performed on an ultra-high-resolution field emission SEM (HRSEM SU8230, Hitachi, Japan), equipped with an X-MaxN EDS detector (Horiba, Japan). For HRSEM observations, the samples were mounted in electroconductive epoxy and polished meticulously. EDX analysis utilized an acceleration voltage of 25\u2009kV, gathering 10\u2009\u00d7\u2009106 counts per EDX map and 1\u2009\u00d7\u2009106 counts for point analysis.\n\nTo investigate the interface between the Fe2VAl matrix and the Bi0.9Sb0.1 secondary phase at the nanoscale, the sample was prepared using a conventional focused ion beam (FIB) technique. A thin section was extracted from the targeted area, attached to an Omnigrid, and thinned to approximately 90\u2009nm for electron transparency. Additionally, the FVAB50 sample was crushed into fine particles, dispersed in ethanol, and deposited on a grid to investigate the sample surface. Transmission electron microscopy (TEM) bright-field and lattice images were acquired using a JEOL JEM-3100FEF (JEOL, Japan) microscope operating at 300\u2009kV, which was also equipped with an EDS detector for detailed elemental mapping.\n\nThe X-ray powder diffraction measurements were conducted at the Institute of Solid State Physics, TU Wien, using an in-house diffractometer (AERIS by PANalytical). These measurements utilized standard Cu K-\u03b1 radiation, with data collected in the Bragg-Brentano geometry over the angular range 20\u2218\u2009<\u20092\u03b8\u2009<\u2009100\u2218. Rietveld refinements on the obtained powder patterns were performed using the program PowderCell.\n\nThermal conductivity measurements at high temperatures were performed in N2 atmosphere directly on the sintered pellets, in the direction parallel to the pressing (compaction) direction during sintering with a commercially available setup (LFA 467 by NETZSCH). The instrument makes use of a conventional laser flash method for the\u00a0diffusivity D and a differential scanning calorimeter for determining the specific heat cp. The density of the sample dm was determined via Archimedes principle and the thermal conductivity was calculated from \u03ba\u2009=\u2009D\u2009cp\u2009dm.\n\nAfter performing high-temperature thermal conductivity measurements, the samples were cut into strips 2\u22123\u2009mm in width and\u00a08\u221210\u2009mm in length using a high-speed aluminum oxide cutting wheel. The bar-shaped samples were then mounted in a commercial setup (ZEM3 by ADVANCE RIKO) and the electrical resistivity and Seebeck coefficient were measured as a function of temperature. For the best sample, the measurement was repeated to confirm reproducible and stable results.\n\nLow-temperature measurements provide valuable insights into lower-energy excited states and states near the Fermi energy. This is especially significant for samples with narrow energy gaps, such as Fe2VAl-based full-Heusler and binary Bi1\u2212xSbx systems, where the Seebeck coefficient often peaks below or near room temperature. Furthermore, when modeling temperature-dependent data (using a parabolic band model for example), it is crucial that the experimental data span a wide temperature range. The thermoelectric characterization at low temperatures was carried out at TU Wien (Austria) on the same rectangular bar-shaped sample pieces that were used for the high-temperature measurements at NIMS (Japan).\n\nThe temperature-dependent electrical resistivity was measured in a home-built bath cryostat at TU Wien. The sample was contacted in a four-probe geometry with thin gold wires, using a spot-welding device. The sample was then mounted on a sample puck using GE Varnish as an adhesive and directly inserted into the cryostat. The measurement was performed continuously every time the temperature changed by 1\u2009K.\n\nThe temperature-dependent Seebeck coefficient was also measured on the very same sample piece using a different home-built setup at TU Wien. Here, two chromel-constantan thermocouples are contacted to both ends of the sample to pick up the temperature difference and voltages. Since it is difficult to solder directly on the sample surface, a bundle of thick copper wires was first spot-welded onto the sample surface to which the thermocouples were then soldered. The high thermal conductivity of copper and the fact that the thermocouples are soldered in very close proximity to the sample surface ensures that the cold finger effect can be minimized. Furthermore, two strain gauges with a resistance of \u00a0\u2248120\u2009\u03a9 function as heaters and are fixed to the bottom of both sample ends via GE varnish. The two heaters allow switching the temperature difference (\"seesaw heating\"47) to cancel spurious voltage contributions. The measurement is carried out in an evacuated sample chamber with He exchange gas to ensure thermal coupling to the cryogen.\n\nThe thermal conductivity at low temperatures was measured by making use of a steady-state method using a home-built sample probe with a flow cryostat. Here, a heater is attached to the top surface of the sample employing a thermally conductive epoxy resin (STYCAST 2850FT). Similar to the Seebeck coefficient measurements, two bundles of copper wires are first fixed to the sample to each of which a thermocouple is then soldered. The bottom of the sample is mounted on a copper heat sink and the measurement is carried out in high vacuum (\u00a0\u224810\u22125\u2009mbar).\n\nIn a two-channel transport model for two types of charge carriers with charge q1,2, carrier density n1,2, and carrier mobility \u03bc1,2, the field-dependent longitudinal resistivity \u03c1xx(B) and Hall resistivity \u03c1xy(B) can be expressed as48\n\nand\n\nFrom the above equations, it becomes evident that non-linearities can occur in the field-dependent Hall resistivity if, for instance, there is a sizeable difference between \u03bc1 and \u03bc2. While previous models have employed four different fit parameters, i.e. the respective carrier densities and mobilities to model non-linear field dependencies, Eguchi and Paschen previously suggested a novel, more robust scheme for analyzing field-dependent magneto-transport properties49. We utilized this framework to model the non-linear field-dependent Hall effect of the FVAB50 composite. More details regarding the modeling procedure are presented in the\u00a0Supplementary Information.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data supporting the findings of this study are available within the article and its\u00a0Supplementary Information file.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Rowe, D. M. Thermoelectrics Handbook: Macro to Nano (CRC Press, 2018).\n\nHendricks, T., Caillat, T. & Mori, T. Keynote review of latest advances in thermoelectric generation materials, devices, and technologies 2022. Energies 15, 7307 (2022).\n\nArticle\u00a0\n CAS\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nZevalkink, A. et al. A practical field guide to thermoelectrics: fundamentals, synthesis, and characterization. Appl. Phys. Rev. 5, 021303 (2018).\n\nSnyder, G. J. et al. Weighted mobility. Adv. 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Furthermore, F.G. acknowledges financial support by the Lions Club Wien St. Stephan and J.d.B. acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project number 520487260\u00a0The authors also acknowledge\u00a0the TU Wien Bibliothek for financial support through its Open Access Funding Programme.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Institute of Solid State Physics, TU Wien, Vienna, Austria\n\nFabian Garmroudi,\u00a0Michael Parzer,\u00a0Alexander Riss,\u00a0Sebastian Steyrer\u00a0&\u00a0Ernst Bauer\n\nInternational Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan\n\nFabian Garmroudi,\u00a0Illia Serhiienko,\u00a0C\u00e9dric Bourg\u00e8s,\u00a0Yuya Hattori\u00a0&\u00a0Takao Mori\n\nInstitute of Materials Research, German Aeropspace Center (DLR), Cologne, Germany\n\nSanyukta Ghosh,\u00a0Pawel Ziolkowski,\u00a0Gregor Oppitz,\u00a0Eckhard M\u00fcller\u00a0&\u00a0Johannes de Boor\n\nCenter for Basic Research on Materials (CBRM), National Institute for Materials Science (NIMS), Tsukuba, Japan\n\nHieu Duy Nguyen\u00a0&\u00a0Naoyuki Kawamoto\n\nInternational Center for Young Scientists, National Institute for Materials Science (NIMS), Tsukuba, Japan\n\nC\u00e9dric Bourg\u00e8s\n\nInstitute of Materials Chemistry, University of Vienna, Vienna, Austria\n\nGerda Rogl\u00a0&\u00a0Peter Rogl\n\nFaculty of Physics, University of Vienna, Vienna, Austria\n\nErhard Schafler\n\nInstitute of Inorganic and Analytical Chemistry, Justus Liebig University Giessen, Giessen, Germany\n\nEckhard M\u00fcller\n\nUniversity of Duisburg-Essen, Faculty of Engineering, Institute of Technology for Nanostructures (NST) and CENIDE, Duisburg, Germany\n\nJohannes de Boor\n\nGraduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Japan\n\nTakao Mori\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nF.G., M.P., and A.R. conceived the idea for the study. F.G. designed the work, and, with help from I.S., C.B., and Y.H., synthesized the samples and measured the thermoelectric properties. I.S., S.G., and H.D.N. investigated the micro- and nanostructure of the material via SEM and TEM techniques. S.G., P.Z., and G.O. performed TPSM measurements, and E.M. and J.d.B. analyzed and interpreted the data. C.B., S.S., G.R., and E.S. assisted in the synthesis and experimental investigations of the samples. P.R., N.K., E.M., and E.B. discussed and improved the contents of the paper. J.d.B. and T.M. supervised the work and assisted in outlining the initial draft of the manuscript. F.G. wrote the initial draft. All authors read, discussed, and edited the manuscript.\n\nCorrespondence to\n Fabian Garmroudi, Johannes de Boor or Takao Mori.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Ajay Soni, Wenyu Zhao, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Garmroudi, F., Serhiienko, I., Parzer, M. et al. Decoupled charge and heat transport in Fe2VAl composite thermoelectrics with topological-insulating grain boundary networks.\n Nat Commun 16, 2976 (2025). https://doi.org/10.1038/s41467-025-57250-6\n\nDownload citation\n\nReceived: 08 November 2024\n\nAccepted: 17 February 2025\n\nPublished: 26 March 2025\n\nVersion of record: 26 March 2025\n\nDOI: https://doi.org/10.1038/s41467-025-57250-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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oligomer", + "journal": "Nature Communications", + "published": "17 January 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_MOESM3_ESM.txt" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_MOESM4_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_MOESM5_ESM.mp4" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_MOESM6_ESM.mp4" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_MOESM7_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_MOESM8_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_MOESM9_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://dx.doi.org/10.13018/BMR52283", + "https://doi.org/10.17617/3.0V1ODV", + "https://doi.org/10.17617/3.TXND2C", + "https://doi.org/10.2210/pdb8A4L/pdb", + "https://doi.org/10.2210/pdb8A9L/pdb", + "https://doi.org/10.2210/pdb7NCA/pdb", + "https://doi.org/10.2210/pdb7OZG/pdb", + "https://dx.doi.org/10.13018/BMR50585", + "https://dx.doi.org/10.13018/BMR16300", + "https://dx.doi.org/10.13018/BMR16904", + "https://dx.doi.org/10.13018/BMR6968", + "https://dx.doi.org/10.13018/BMR18857", + "/articles/s41467-025-55849-3#Sec32" + ], + "code": [ + "https://doi.org/10.17617/3.0V1ODV", + "/articles/s41467-025-55849-3#ref-CR84", + "/articles/s41467-025-55849-3#ref-CR63", + "/articles/s41467-025-55849-3#MOESM3", + "/articles/s41467-025-55849-3#ref-CR62" + ], + "subject": [ + "Computational biophysics", + "Molecular neuroscience", + "Solid-state NMR" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4437173/v1.pdf?c=1737205619000", + "research_square_link": "https://www.researchsquare.com//article/rs-4437173/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-55849-3.pdf", + "preprint_posted": "03 Jun, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Aggregation intermediates play a pivotal role in the assembly of amyloid fibrils, which are central to the pathogenesis of neurodegenerative diseases1,2. The structures of filamentous intermediates3 and mature fibrils4 are now efficiently determined by single-particle cryo-electron microscopy. By contrast, smaller pre-fibrillar \u03b1-Synuclein (\u03b1S) oligomers, crucial for initiating amyloidogenesis, remain largely uncharacterized. We report an atomic-resolution structural characterization of a toxic pre-fibrillar aggregation intermediate (I1) on pathway to the formation of lipidic fibrils. Super-resolution microscopy reveals a tetrameric state, providing insights into the early oligomeric assembly. Time resolved nuclear magnetic resonance (NMR) measurements uncover a structural reorganization essential for the transition of I1 to mature lipidic L2 fibrils. The reorganization involves the transformation of anti-parallel \u03b2-strands during the pre-fibrillar I1 state into a \u03b2-arc characteristic of amyloid fibrils. This structural reconfiguration occurs in a conserved structural kernel shared by a vast number of \u03b1S-fibril polymorphs including extracted fibrils from Parkinson\u2019s and Lewy Body Dementia patients. Consistent with reports of anti-parallel \u03b2-strands being a defining feature of toxic \u03b1S pre-fibrillar intermediates6, I1 impacts viability of neuroblasts and disrupts cell membranes, resulting in an increased calcium influx. Our results integrate the occurrence of anti-parallel \u03b2-strands as unique features of toxic oligomers7-9 with their significant role in the amyloid fibril assembly pathway. These structural insights have implications for the development of therapies and biomarkers.Biological sciences/Structural biology/NMR spectroscopy/Solid-state NMRBiological sciences/Neuroscience/Molecular neuroscience", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "I1supplementaryfigurestables.pdfSIGuide.docxMovieS1.mp4Transition from \u03b2-hairpin to \u03b2-arc.MovieS2.mp4Snapshots from the unrestrained MD simulation of the I1 open morphology in orientation 1 in the bilayer.MovieS3.mp4Snapshots from the unrestrained MD simulation of the I1 open morphology in orientation 2 in the bilayer.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Aggregation intermediates play a pivotal role in the assembly of amyloid fibrils, which are central to the pathogenesis of neurodegenerative diseases. The structures of filamentous intermediates and mature fibrils are now efficiently determined by single-particle cryo-electron microscopy. By contrast, smaller pre-fibrillar \u03b1-Synuclein (\u03b1S) oligomers, crucial for initiating amyloidogenesis, remain largely uncharacterized. We report an atomic-resolution structural characterization of a toxic pre-fibrillar aggregation intermediate (I1) on pathway to the formation of lipidic fibrils, which incorporate lipid molecules on protofilament surfaces during fibril growth on membranes. Super-resolution microscopy reveals a tetrameric state, providing insights into the early oligomeric assembly. Time resolved nuclear magnetic resonance (NMR) measurements uncover a structural reorganization essential for the transition of I1 to mature lipidic L2 fibrils. The reorganization involves the transformation of anti-parallel \u03b2-strands during the pre-fibrillar I1 state into a \u03b2-arc characteristic of amyloid fibrils. This structural reconfiguration occurs in a conserved structural kernel shared by a vast number of \u03b1S-fibril polymorphs including extracted fibrils from Parkinson\u2019s and Lewy Body Dementia patients. Consistent with reports of anti-parallel \u03b2-strands being a defining feature of toxic \u03b1S pre-fibrillar intermediates, I1 impacts viability of neuroblasts and disrupts cell membranes, resulting in an increased calcium influx. Our results integrate the occurrence of anti-parallel \u03b2-strands as salient features of toxic oligomers with their significant role in the amyloid fibril assembly pathway. These structural insights have implications for the development of therapies and biomarkers.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The aberrant aggregation of \u03b1-Synuclein (\u03b1S) into amyloid fibrils is a crucial step in the biochemical cascade of several neurodegenerative diseases (NDD) as evidenced by the fact that \u03b1S amyloid fibrils are a major component of Lewy bodies, the intra-cellular inclusions that are characteristic of Parkinson\u2019s disease and other synucleinopathies1,2. While fibrils are a pathological hallmark of NDDs, evidence has accumulated that oligomeric \u03b1S aggregation intermediates, in particular, exert a toxic load on neurons3,4,5,6. Further, the ability of \u03b1S to interact with and disrupt lipid bilayers is well documented3,7,8. Hence, such structures in complex with lipids, a canonical binding partner of \u03b1S9,10,11,12, are of particular interest.\n\nIn vitro preparations have been instrumental in determining characteristics of intermediate species occurring during amyloid aggregation because their low population and transient nature make it challenging to isolate them from tissues. Through these studies it has been revealed that aggregation intermediates, sometimes transient fibril like filaments13, are often composed of segments with structural features analogous to their respective fibrillar polymorphs14,15,16,17. However, atomic-resolution structures of small oligomeric \u03b1S intermediates are lacking. This impedes the understanding of nucleation, toxicity, and the effect of aggregation modulators at the molecular level.\n\nPreviously, we reported the isolation of Intermediate 1 (I1), a transient pre-fibrillar species found on pathway to the formation of the L2 fibril polymorph (Protein Data Bank (PDB) entry 8A4L in the presence of anionic lipid vesicles composed of a 1:1 molar ratio of POPA and POPC17,18 (Fig.\u00a01A). Nuclear Magnetic Resonance (NMR) chemical shifts indicated that I1 shares several segments with the L2 fibril, including residues L38-S42 in \u03b21, T44-V48 in loop 1, E57-E61 in loop 2, T72-A78 in loop 3 and \u03b2417 (Fig. 1D, E).\n\nA Transmission electron micrograph (TEM) image of I1 aggregates (left) compared to a TEM image of L2 fibrils (right) superimposed on an aggregation kinetics curve (black curve). Scale bar 100\u2009nm. The ThT kinetics curve is a schematic representation. Statistics are reported in Supplementary Fig.\u00a01E. Insets show schematics of structures and highlight unknown aspects of I1. The gray curve shows slower aggregation kinetics under conditions during NMR measurements (4\u201316\u2009\u00b0C and monomer depleted). B Backbone traces of \u03b1S fibril polymorphs that have a \u03b2-arc at T59 similar to the L2 fibrils. C Impact of \u03b1S aggregates on viability of SH-SY5Y neuroblastoma cells after 24\u2009h of incubation with 0.3\u2009\u00b5M (gray bars) or 0.6\u2009\u00b5M (black bars) \u03b1S. Error bars are shown as the \u00b1 standard error of the mean for 6 replicates. * indicates p\u2009<\u20090.05 and ** indicates p\u2009<\u20090.01, from a one-way ANOVA Tukey test. The lipid concentrations corresponding to each sample are shown in parentheses on the x-axis. Lipid concentration is shown in gray or black corresponding to 0.3\u2009\u00b5M \u03b1S or 0.6\u2009\u00b5M \u03b1S, respectively. Circles show individual datapoints. D The secondary structure as indicated by chemical shift, is shown as helix (waves), strand (arrows) or loops (lines) for Intermediate 1 (I1) and the L2 fibril. Chemical shift similarity (green-pink) mapped onto the sequence of \u03b1S. Dotted lines represent tentative assignments. White spaces indicate missing assignments. Gray lines denote assigned residues of I1 that are unassigned in the L2 fibril. E Per residue average chemical shift perturbations (CSPs), including C\u03b1, C\u03b2, Co and NH shifts, between I1 and L2 fibril (BMRB 50585). Dotted line shows the 0.7\u2009ppm cut-off for similar and dissimilar segments. Source data are provided as a Source Data file. D, E Residues with similar chemical shifts (CSPs <0.7\u2009ppm) are colored green. Residues with dissimilar chemical shifts (CSP\u2009>\u20090.7\u2009ppm) are pink. Similarity for the helical segment V16-T22 is derived from 13C correlation spectra only17. F Side-chain contacts observed for I1 (from (H)HNH and (H)HCH spectra in Supplementary Fig.\u00a03B) conflicting with the L2 fibril structure (grey trace) are marked in pink.\n\nA \u03b2-arc, which is a characteristic feature of amyloid fibrils, is found at T59 (V52-V66) in the L2 fibril. This \u03b2-arc is a structural kernel conserved in nearly half of the deposited \u03b1S fibril polymorphs (Fig. 1B), including extracted fibrils from Parkinson\u2019s disease (PD) and Lewy Body Dementia (DLB) patients (PDB 8A9L) and those seeded from Multiple System Atrophy (MSA) (PDB 7NCA) and PD patients (PDB 7OZG). Determination of the structure of I1, which clearly differs from the fibril for residues V52-V66 (Fig. 1A, D, E)17, would help elucidate the folding pathway for L2, thus far unknown7,13.\n\nHere, we report extensive NMR data for I1 that reveal an anti-parallel \u03b2-sheet with a \u03b2-hairpin at T59. Together with super-resolution fluorescence microscopy data, I1 was determined to be a tetramer. Atomistic molecular dynamics (MD) simulations reveal that the tetramer is stabilized in the context of a lipid bilayer.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "The characterization of an I1 sample, previously isolated on the folding pathway to the L2 fibril17, reveals its prolonged stability and composition. The I1 sample can be isolated for prolonged times in the rotor (several weeks), which we attribute to a reduction in temperature from 37\u2009\u00b0C during aggregation to below about 20\u2009\u00b0C during NMR measurements (Fig. 1A). Additionally, stability might be improved because I1 has been depleted in disordered monomer and membrane bound monomer via ultracentrifugation before packing. Fingerprint spectra are acquired at regular intervals to keep track of the stability of I1 (Supplementary Fig.\u00a01A). Lipid bound monomer and disordered monomer stay in the supernatant after I1 is isolated (Supplementary Fig.\u00a01B, C). Due to its transient nature, multiple freshly prepared samples of I1 have been used in the study (Supplementary Fig.\u00a01E). The spectra indicate that I1 consists of one dominant species (Supplementary Fig.\u00a01 and 2). Additionally, there was no indication of the L2 fibril in the I1 samples, as determined by the absence of characteristic L2 resonances in the I1 spectra (Supplementary Fig.\u00a01). Notably, no instance occurred where one residue was assigned to two sets of resonances (Supplementary Fig.\u00a02).\n\nConsistent with well-established behavior of amyloid aggregates6,8, I1 and the L2 fibril dramatically differ in their impact on cell viability: I1 reduces survival of SH-SY5Y neuroblastoma cells to 25%, while the L2 fibril leaves cell viability unperturbed compared to the lipid bound monomer (Fig. 1C). This is despite the remarkable similarity between the two species and further motivates a detailed characterization to link structural differences to variations in cellular impacts.\n\nClear differences are seen between I1 and the L2 fibril in morphology as well as secondary structure and topology. An I1 sample resolubilized from an MAS NMR rotor consists of particles of diameter 8\u201315\u2009nm, drastically different from longitudinal, twisted strands observed for fibrils (Fig. 1A). Comparison of C\u03b1 and C\u03b2 chemical shifts (BMRB entries 5058517 and 52283) reveals major differences between I1 and L2 in two regions of \u03b1S. Firstly, C-terminal residues, E83-K97, that are primarily structured as a \u03b2-strand in the L2 fibril, form a loop and \u03b1-helix in I1 (Fig. 1D). Differences in topology are also observed in this segment: K96 shows contacts to residues around A30 in I1. By contrast, residues V82-L97 are adjacent to \u03b23 in the L2 fibril (Supplementary Fig.\u00a03B, C). Secondly, the \u03b22 and \u03b23 strands, while retaining \u03b2-strand secondary structure in both, I1 and the L2 fibril (Fig. 1D), deviate substantially in their chemical shifts (average CSP\u2009>\u20090.7\u2009ppm) (Fig. 1E). These segments exhibit side-chain contacts in I1, that conflict with the L2 fibril, namely N65 N\u03b4 \u2013 A53 HN and V63 H\u03b3 \u2013 T54 H\u03b2, (Fig. 1F). In the L2 fibril these contacts measure greater than 10\u2009\u00c5, which is beyond the distance reached in H(H)NH NMR spectra. In the L2 fibril, \u03b22 and \u03b23 form the T59 \u03b2-arc, a shared structural kernel among various fibril polymorphs, suggesting possible commonalities in the folding pathway of other fibrils sharing the T59 \u03b2-arc.\n\nTo determine the \u03b2-strand arrangement in I1, we recorded amide proton correlation spectra19 on the 1.2\u2009GHz spectrometer to leverage improved resolution and sensitivity (Supplementary Fig.\u00a03A). Parallel-in-register (PIR) and anti-parallel (AP) \u03b2-strands produce distinct contact maps of proximity among amide moieties. \u03b21 and \u03b24 are confirmed as PIR, since only correlations to neighboring residues are observed (green labels, for example K43-T44 in Fig.\u00a02A). In contrast, the pattern of amide proton correlations for \u03b22 and \u03b23 reveals an AP arrangement (pink labels in Fig.\u00a02A). These correlations for V52-V55 on \u03b22 with V66-V63 on \u03b23 are depicted in Fig. 2B. To distinguish intra- and intermolecular amide proton contacts, we diluted the uniformly 15N-labelled \u03b1S with 50% unlabelled \u03b1S. Once normalized by the diagonal, the intensities of intermolecular contacts in the diluted labelling spectrum (blue, Fig. 2A) are reduced 2-fold compared to the fully labelled spectrum (black, Fig. 2A), while intramolecular contacts retain full intensity. In this way we identified the V66-V52 (AP) and K43-T44 (PIR) cross-peaks as intermolecular whereas the V63-V55 cross-peak was identified as intramolecular (Fig. 2C).\n\nA 15N-15N correlation spectrum of I1 with uniformly 15N- labelled \u03b1S shows that there are AP segments (pink labels) which feature intra- and inter-molecular cross-peaks. Cross-peaks from PIR segments (green labels), correlations in loops (grey labels) and helices (black labels) are also observed. On the right are one-dimensional traces of cross-peaks comparing uniformly 15N- labelled I1 (black) and 1:1 15N: unlabelled (50% dilute) I1 (blue). Intra-molecular cross-peaks are not affected by the 50% dilution (V63-V55 contact) whereas the intensity of inter-molecular cross-peaks is reduced 2-fold in the diluted spectrum (blue) (V66-V52 and K45-T44 contacts). B Contacts indicative of AP segments are indicated with pink lines on a backbone trace. C Ratios of cross-peak intensity between the fully 15N-labeled (black) and 50% 15N-labeled (blue) spectra. Color legend is the same as in panel (A). D Histograms of the number of photobleaching steps (purple) and number of polarization states per aggregate (pink) for I1 at a fluorophore labelling efficiency (p) of 25%. The grey line shows the best fit to the photobleaching and polarCOLD data with parameters p\u2009=\u200927% and m (no. of monomers per oligomer) =4 (Supplementary Fig.\u00a04I). Error bars represent standard error of the mean calculated from 1421 particles (over 5 different fields of view (FOV)) for the photobleaching experiment and 1023 particles (over 3 different FOV) for the polarCOLD experiment. Probabilities for individual FOVs are shown in circles. Source data are provided as a Source Data file. E\u2013H Examples of super-resolved polarCOLD images of different particles show the projection of aggregates with 1 (E), 2 (F), 3 (G) and 4 (H) dye molecules onto the imaging plane. The center of each spot displays the position of the fluorophore and its width represents the localization precision. The latter can vary due to the available signal-to-noise in each case, determined by the photophysics heterogeneity of fluorophores (Supplementary Fig.\u00a04H). The number of particles with states in panel E-F can be determined from the polarCOLD histogram in panel D and the source data provided in the Source Data file.\n\nAmide proton correlations indicate that I1 is a multimer, prompting further investigation with NMR and fluorescence measurements to determine oligomer size. An NMR CODEX20 (Center band only detection of exchange) measurement allows for spin counting with an upper limit of about 10\u2009\u00c5. When each molecule is labeled at a single site, CODEX can be used to determine the oligomer number, provided that the labeled sites form a cluster with the nearest intra-spin distance below 10\u2009\u00c5. For these measurements, I1 was prepared with \u03b1S containing a single 13C isotopically labeled site at H50 C\u03b5. A CODEX measurement involves the decay of initial magnetization of this single isotope labeled nucleus until the signal plateaus at the inverse of the number of spins over which magnetization can equilibrate. The CODEX curve reaches about 0.25 at long times, indicating that I1 is at least a 4-mer (Supplementary Fig.\u00a04A).\n\nStepwise photobleaching can be used to count the number of monomers in an aggregate21. In this work, we combine this approach with polarCOLD, a cryogenic super-resolution fluorescence microscopy, which can reach \u00c5ngstrom resolution22,23. We first verified that modification of a portion of molecules in I1 with a fluorophore showed no perturbations in NMR structural data and aggregation kinetics (Supplementary Fig.\u00a04C, E). Next, aggregates were immobilized on a substrate and irradiated continuously at room temperature (RT) until they photobleached (Supplementary Fig.\u00a04F). For these measurements, I1 was prepared by diluting dye labeled \u03b1S with wild-type unlabeled \u03b1S at a 3:1 ratio. Stochastic mixing of dye labeled and unlabeled \u03b1S molecules results in aggregates with varying numbers of dye-labeled molecules, leading to some aggregates being fully labeled, some with no labels and others with partial labeling. This distribution of dye molecules results in a corresponding distribution in the number of photobleaching events, which follows a binomial distribution. The number of photobleaching steps was determined by counting the intensity levels in the time-traces (Supplementary Fig.\u00a04G). The histogram of photobleaching steps at RT can be best fit to a binomial distribution for a tetramer (purple, Fig. 2D). Furthermore, polarCOLD23,24, was used to acquire\u00a0and quantify super-resolution images at a temperature of 4\u2009K by localizing individual fluorophores through their emission polarization states. Examples of such images for the projection of fluorophore positions are depicted in Fig. 2E\u2013H for different particles from a single preparation of I1 with ~27% dye labeling. We also analyzed an ensemble of individual particles to obtain a histogram of polarization states\u00a0(Supplementary Fig. 4H) per aggregate at 4\u2009K (pink in Fig. 2D), yielding very good agreement with the RT measurements. These measurements all indicate that I1 is a tetramer.\n\nAn atomic resolution model for an I1 tetramer was assembled by combining the knowledge of the L2 fibril structure (for I1 segments similar to the fibril) with experimental contacts observed for I1 that are distinct from the L2 fibril (Supplementary Table\u00a01). Detection of a single set of chemical shifts suggests a single fold for all monomers of I1, but several quaternary arrangements can be modeled to satisfy experimental restraints (Supplementary Fig.\u00a05). These include \u201copen\u201d arrangements where molecules simply stack on each other like in the fibril (Fig. 3A). We can also envisage \u201cclosed\u201d arrangements, such as a \u201cbarrel\u201d with inter-molecular H-bonds for all four molecules or a \u201cbowl\u201d morphology with intra-molecular H-bonds for all molecules except one (Supplementary Fig.\u00a05A). A comparison of the conformers reveals two distinct structural features in all morphologies (Fig. 3B, C, Supplementary Fig.\u00a05A). The first is a\u00a0fibril-like PIR arrangement of \u03b21 and \u03b24 (green, Fig. 3). The second is an AP domain that involves a \u03b2-hairpin between strands \u03b22 and \u03b23 connected by loop 2 (pink, Fig. 3). The AP domain in both open and closed models satisfies the 7\u2009\u00c5 upper-limit for side-chain contacts in I1 (Fig. 3D).\n\nA Tetramer with open AP \u03b2-strands 2 and 3 and B Schematic of a dimeric structural element of the tetramers. The fibril-like PIR domain is colored green. The AP domain is colored pink. C Close up of the PIR part. D Close up of the AP arrangement showing the contacts N65 N\u03b4\u2013A53 HN and V63 H\u03b3\u2013T54 H\u03b2 as pink lines. The structure satisfies an upper limit of 7\u2009\u00c5.\n\nA \u03b2-hairpin turn is characterized by backbone hydrogen bonds between consecutive \u03b2-strands, as seen in I1. On the other hand, a \u03b2-arc is characteristic of amyloid fibrils, and displays a distinct backbone hydrogen bonding pattern from \u03b2-hairpin turns (Supplementary Fig.\u00a06A, left). A \u03b2-arc is formed when two consecutive \u03b2-strands interact via their side-chains instead of the backbone. In such a scenario, the backbone hydrogen bonds are perpendicular to the \u03b2-arc and in fibrils are observed to form between two molecules, resulting in stacking of these molecules (Supplementary Fig.\u00a06A, right).\n\nTo convert to the L2 fibril, I1 must undergo a transition in the AP domain from a \u03b2-hairpin, with backbone H-bonds between \u03b22 and \u03b23, to a \u03b2-arc with side-chain interactions between \u03b22 and \u03b23 instead. The kinetic stability of I1 is partly attributed to the relatively high energy barrier required for breaking 28 H-bonds involved in this transition (Supplementary Movie\u00a01). Contrary to an amyloid fibril, an I1 type conformation cannot template an indefinite number of molecules. Every additional molecule that contributes one layer to the PIR domain, adds two layers to the AP domain. This causes frustration between the two domains in larger aggregates and manifests as steric clashes in the G67-V74 segment and discontinuities in AP \u03b2-strands (Supplementary Fig.\u00a05B), making it energetically unfavorable to template both the AP and PIR domain onto additional molecules (Supplementary Fig.\u00a05C).\n\nThis is consistent with the finding that Intermediate 2 (I2), the next intermediate on the pathway, features a \u03b2-arc in the V52-V66 segment, and thus a conversion from AP to PIR \u03b2-strands in this region, indicated by similar chemical shifts to the L2 fibril17 and next neighbor correlations in the amide proton correlation spectrum (Supplementary Fig.\u00a06B). Characteristic of fibrillar intermediates, I2 exhibits filamentous morphology (Supplementary Fig.\u00a06C) and coincides with a rapid increase in ThT (ThioflavinT) fluorescence (Supplementary Fig.\u00a06D) indicating fibril growth through the necessary transition from \u03b2-hairpin between the AP \u03b2-strands in I1 to \u03b2-arc between the parallel \u03b2-strands.\n\nI1 has lipid interactions spanning the entire length of the protein, consistent with its aggregation on POPC/POPA vesicles17, (Fig. 4A, B and Supplementary Fig.\u00a07). This includes the N-terminal helix and hydrophobic residues 70\u201388 (Fig. 4B, Supplementary Fig.\u00a07) consistent with previously observed \u03b1S oligomers3. Additionally, I1 also contacts lipids through Y39 and through the AP domain. The lipid contacts at Y39 are similar in the L2 fibril and I1, while L2 type lipid contacts at \u03b24 are missing in I1.\n\nA Schematic of magnetization transfer between lipid protons and protein backbone. POPC choline nitrogen atoms are highlighted in purple, terminal methyl groups in the hydrophobic core are turquoise and acyl chain protons are light orange. B lipid contacts mapped onto the sequence for I1 and the L2 fibril17. Lipid contacts are colored according to lipid protons in panel (A) and are shown together with a schematic of the I1 secondary structure along its sequence. The PIR domain is colored green, and the AP domain is pink. Residues with missing assignments are shown as white spaces and those with tentative assignments are shown as dotted lines. Snapshots from unrestrained MD simulations of an open I1 conformer with (C) both PIR and AP domains being in the same leaflet and (D) PIR and AP domains traversing both leaflets of the bilayer. The bilayer in the simulation is composed of 1:1 molar ratio of POPC and POPA. POPC choline nitrogen atoms are shown as purple spheres along with a surface map of lipids. I1 is shown as a gray ribbon colored by domain as in panel\u00a0(B). E I1\u00a0induces proton flux across liposome membranes. Adding 1\u2009M HCl lowers the pH of the external buffer. When I1 is introduced, the pH gradually increases (pink traces), indicating a leaky membrane. Without I1 (gray traces), the pH remains nearly unchanged until the addition of CCCP to uncouple proton flux. The inset shows a schematic of liposomes used in the assay. Source data are provided as a Source Data file. F Calcium influx is measured by fluorescence of Fluo-4 (F4) loaded in SH-SY5Y cells, expressed as a percentage of the maximum capacity determined by cells containing ionomycin, a calcium ionophore. I1\u00a0(pink) significantly elevates intracellular calcium levels compared to controls (gray). Error bars represent standard error of the mean obtained with 6 replicates. Inset illustrates Ca2+ flux induced by I1. The concentration of \u03b1S in I1 used for the proton and Ca2+ flux experiments was 0.6\u2009\u00b5M. Source data are provided as a Source Data file.\n\nAll-atom MD simulations were performed to probe interactions of I1 with lipid bilayers. Simulations of several orientations of I1 with respect to bilayers with a truncated G36-T81 segment identified orientations compatible with experimental lipid contacts (Supplementary Note\u00a01, Supplementary Fig. 8). Simulations that include the helical regions (V16-Q99), predict that the open morphology in orientations 1 and 2 and the bowl morphology in orientation 2 satisfy experimentally observed lipid contacts and distance restraints, as well as secondary structure propensity, with high fidelity (Fig. 4C, D, Supplementary Fig.\u00a09 and Supplementary Fig.\u00a010C, E, F).\n\nLipids appear to play a crucial role in stabilizing the AP domain. The E57-V66 segment in the AP domain contacts lipids in I1 (Fig. 4B), while in the L2 fibril, this segment forms a PIR \u03b2-sheet and does not show any lipid contacts. Notably, MD simulations of I1 with the AP domain oriented outside the lipid bilayer do not agree with experimental restraints and show a significant loss in \u03b2-strand content (orientations 3, and 5-8 in Supplementary Fig.\u00a08 and orientation 3 in Supplementary Fig.\u00a09).\n\nStrikingly, a cluster of charged residues in loop 1 (43KTKE46) and the AP domain (57EKTKEQ62) of I1 has contacts with lipid acyl chains (Fig. 4B). Simulations of the open tetramer show that lipid bending stabilizes these residues (Fig.\u00a04C, D and Supplementary Movie\u00a02 and 3), reducing the energy barrier for water and the penetration of choline and phosphate groups of the lipid headgroups into the bilayers hydrophobic core (Supplementary Fig.\u00a010B, D). This polar defect created by the stable and membrane-inserted AP domain of I1 (Supplementary Fig.\u00a010G) also facilitates cation flux across the membrane by lowering the energy barrier for Na+ and Ca2+ and ions in the hydrophobic bilayer core\u00a0in MD simulations.\n\nThe disruptive impact of I1 on lipid membranes is evident from a liposomal proton flux assay. After establishing a pH gradient across the membrane by the addition of an acid, I1 triggers a proton influx into liposomes, gradually increasing the pH of the external buffer (Fig. 4E). By contrast, in the absence of I1, liposomes are sealed and maintain a stable pH. Furthermore, I1 increases Ca2+ influx across neuroblast cell membranes, as indicated by the enhanced fluorescence of the calcium sensitive dye, Fluo-4 (Fig. 4F). Propidium Iodide fluorescence confirms that increased Fluo-4 fluorescence is not due to cell death induced membrane damage (Supplementary Fig.\u00a011E). Unlike with I1, both monomeric and fibrillar \u03b1S show no significant differences in Ca2+ influx (Supplementary Fig.\u00a011F). The influx of Ca2+ induced by I1 is independent of AMPAR channels, shown by the unchanged Ca2+ influx curves with the inhibitor,\u00a0cyanquixaline (CNQX) (Supplementary Fig.\u00a011G).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-55849-3/MediaObjects/41467_2025_55849_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Since there is no information about oligomers ex vivo, we compared the end product of our in vitro oligomer preparation, the L2 fibrils with the ex vivo Lewy fold. The latter closely resembles L2 but differs in notable aspects. The Lewy fold fibrils occur only as single filaments, whereas L2 consists of three filaments (Supplementary Fig.\u00a012A). There are also variations in the fold of individual filaments. Specifically, the Lewy fold features a near 180\u00b0 turn at G84, in contrast to L2\u2019s approximately 90\u00b0 turn. Additionally, the turn at G73-is nearly 90\u00b0 in the Lewy fold and about 160\u00b0 in L2, leading to a concave bend at G51 in the Lewy fold versus a convex bend at the same position in L2 (Supplementary Fig.\u00a012B).\n\nDespite these differences, the similarities between the Lewy fold and L2 fibrils are striking. Both share the T59-arc (\u03b22-\u03b23) that is common structural kernel in multiple \u03b1S fibrils25. There are also similarities in other segments, including the interactions between \u03b21 and \u03b24 as well as the organization of \u03b25 (Supplementary Fig.\u00a012C). These similarities make the I1 oligomer from the in vitro L2 fibril preparation a remarkable model for studying the assembly of structural features present in brain extracted \u03b1S fibrils.\n\nThe I1 structure is distinct from non-toxic oligomers which are helical and more dynamic such as the dynamic helical tetramer and those stabilized by EGCG (epigallocatechin-3-gallate)3,26,27. Additionally, I1 is distinct from stable lipoprotein particles formed by helical \u03b1S28. While non-toxic oligomers interact with lipid bilayers in an unspecific manner3,26, the I1 N-terminus contacts lipids via an amphipathic helix and many segments contact the hydrophobic bilayer core. This interaction pattern may influence the cellular fate of \u03b1S aggregates because the lipid binding sites also act as recognition motifs for protein quality control, such as for heat shock proteins (V37-K43 in \u03b1S)29,30 and ubiquitination (K21, K23, K32, K34 in \u03b1S)31.\n\nThe I1 model suggests that aggregation prone regions (APRs) are among the first to adopt \u03b2-strands in the aggregation process. Identified through factors like hydrophobicity, solubility32, and mutagenesis studies33, APRs include the Y39-S42 master controller region, and the K45-E57 segment housing several familial mutations linked to Parkinson\u2019s disease32,34. In I1 residues in these segments adopt \u03b2-strands (\u03b21 and \u03b22 in Fig. 3A) and engage in tertiary and backbone interactions with other APRs (\u03b23 and \u03b24 in Fig. 3A). All of these segments are consistent with those that lend energetic stability to \u03b1S fibrils34. Additionally, lipid bound dimers reveal helix breaks at V40 and K6035, suggesting that the destabilization of the functional helix in these segments may steer the molecule toward amyloid aggregation. G84-V95 is a hydrophobic segment; however, instead of a \u03b2-strand, it forms a lipid bound loop in I1 (Fig.\u00a04B). This deviation is attributed to its relatively lower aggregation propensity and higher solubility than the other segments32.\n\nBiophysical commonalities, namely, the ability to disrupt membranes36 and the presence of anti-parallel \u03b2-sheet8,37,38,39 have emerged among toxic amyloid oligomers. Consistent with this, the lipid defects caused by I1 (Fig. 4C\u2013E and Supplementary Figs.\u00a011A, B) are reminiscent of edge pores observed with amyloid-\u03b2 oligomers40,41 that impair membrane integrity. This impairment has been extensively studied as a mechanism involving increased influx of cations in the context of various amyloid oligomers36. The resulting increased influx of cations such as Ca2+ is an integral step in the apoptosis signaling pathway which is responsible for the death\u00a0of dopaminergic neurons42.\n\nThe I1 surface contains more hydrophobic residues, while in the L2-fibril, these are buried in its fold. Previous work3,29 has shown that \u03b1S intermediates are more hydrophobic than fibrils, promoting the absorption of the intermediates exposed hydrophobic surface into the hydrophobic region of lipid bilayers. This is consistent with the I1 structure proposed here. In the absence of lipids, AP \u03b2-strands of I1 would have two solvent-exposed interfaces. One interface has a hydrophobic ladder formed by alternating steps of V63 and V55 and the other formed by V66 and V52 (Supplementary Fig.\u00a013A). In addition, residues A69 and V71 are left exposed due to a wider loop at V74 (Supplementary Fig.\u00a013A, bottom). Hydrophobic residues in the N- and C-terminal helices, namely V15, V16, A17, A19 A89, A91, I88 and F94 would also be exposed to the solvent (Supplementary Fig.\u00a013A). The AP to PIR conversion results in the residues V52 and V66 becoming buried in the core of the \u03b2-arc formed by two PIR \u03b2-strands (Supplementary Fig.\u00a013B), decreasing the solvent exposed hydrophobic surface. Similarly, the V74 loop gets tighter upon the AP to PIR transition, bringing A69 and V71 closer to A78, and reducing their exposure to the solvent (Supplementary Fig.\u00a013B). the hydrophobic residues I88 and F94 remain exposed to the solvent until the C-terminal strand (\u03b25) folds onto the \u03b23 in the L2 fibril, which is after the intermediate 2 stage17 (Supplementary Fig.\u00a06D).\n\nAnti-parallel \u03b2-strands in toxic oligomers were first observed in bulk measurements8,37 and have since been reported to occur at different residues, featuring varied topologies such as a \u03b2-hairpin (L38-A53)43 or a steric zipper (K80-A91)38. Given the polymorphism observed in \u03b1S fibrils, multiple intermediate structures can be expected, evidenced by different residues participating in the AP \u03b2-strands. However, commonalities in the aggregation pathway are evident, as, similar to I1 oligomers (Supplementary Movie\u00a01), amyloid-\u03b2 oligomers have also been reported to undergo a 90\u00b0 turn in a \u03b2-hairpin during fibril formation15. The presence of \u03b2-arcs in most known fibrils suggests a common assembly pathway through a hairpin-to-arc transition.\n\nThe conservation of the \u03b2-arc structural kernel at T59 in a vast number of \u03b1S fibrils (Fig. 1B and Supplementary Fig.\u00a012) suggests that contacts like\u00a0in the I1 AP domain could be initiators of aggregation for these polymorphs. Tetrameric oligomers modelled from these polymorphs can accommodate I1 type AP \u03b2-strands in their otherwise distinct structural folds (Supplementary Fig.\u00a014B) and the formation of such domains appears to be energetically favorable (Supplementary Fig.\u00a015B). This suggests that I1 type AP domains may be at the heart of a common folding pathway for fibril polymorphs with a \u03b2-arc at T59 (Supplementary Fig.\u00a015A). The \u03b2-arc at G67 is implicated in the formation of MSA-type fibrils (Supplementary Fig.\u00a014A)25. Exploring \u03b1S oligomers preceding the G67 arc could offer insights into the fibrillogenesis of MSA-type fibrils.\n\nHere we have localized with atomic scale precision, the occurrence of AP \u03b2-strands in a toxic \u03b1S tetramer. We demonstrate that aggregates containing these AP \u03b2-strands precede the formation of fibrillar intermediates, and their transition into a fibril like \u03b2-arc is essential for fibril elongation. The observation that the tetramer is in contact with hydrophobic lipid chains and results in detrimental cation influx highlights its potential role in disrupting the integrity of biological membranes, such as those of presynaptic termini, where \u03b1S is known to be enriched44. Findings here underscore that the tetramer structural models can serve as a basis to investigate genesis, polymorphism and therapeutic intervention for fibrils with similar sub-structures, like the brain extracted PD/ DLB fibrils. The presence of AP strands may be an early step in triggering the amyloid aggregation process, which has been implicated in various neurodegenerative diseases.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Expression of \u03b1S was performed in the E. coli strain BL21(DE3). Unlabeled, uniformly 15N-, uniformly 13C- and uniformly 15N, 13C-labeled \u03b1S was produced in modified minimal medium. 15N-NH4Cl was used as the nitrogen source and 13C6-D-glucose as carbon source, and the protein purification was performed following a published protocol45. Cell lysis was achieved by freeze-thaw cycles followed by sonication. The lysed cells were then boiled for 15\u2009min and centrifuged at 48,000 rcf for 45\u2009minutes. Streptomycin (10\u2009mg/ml) was used to precipitate DNA from the supernatant. Following another centrifugation step, ammonium sulfate (0.36\u2009g/ml) was added to the supernatant to precipitate \u03b1S. The precipitate was resuspended in 25\u2009mM Tris/HCl, pH 7.7. The protein was further purified by anion exchange chromatography on a POROS HQ column (PerSeptive Biosystems). Mutant \u03b1S with an additional C-terminal cysteine (A140C) was generated by PCR-based site-directed mutagenesis (QuikChange 2, Agilent Technologies). The cysteine mutant was tagged with ATTO647N dye (ATTO-TEC) by overnight incubation on ice with a two-fold molar excess of the thiol-reactive maleimide of the dye in phosphate buffered saline (PBS), pH 7.4. Excess dye was removed by gel filtration on a Superdex 75 16/60 HiLoad column (Cytiva). Successful tagging was verified by electrospray ionization mass spectrometry. All protein samples were finally dialyzed against 50\u2009mM HEPES pH 7,4, 100\u2009mM NaCl. The final protein concentration was adjusted to 0.3\u2009mM.\n\nAggregation of \u03b1S was according to the protocol in Antonschmidt et al.17. Monomeric \u03b1S in 50\u2009mM HEPES and 100\u2009mM NaCl (pH\u00a07.4) was centrifuged for 1\u2009h at 160,000 rcf (TLA-100.3 rotor in an Optima MAX-TL ultracentrifuge, Beckman Coulter) at 4\u2009\u00b0C to remove any large aggregates. The supernatant was decanted and added to a solution of SUVs (small unilamelar vesicles) and NaN3 (0.02 weight %) to obtain a final protein concentration of 70\u2009\u03bcM and a molar Lipid/Protein ratio of 10:1 or 5:1. The mixture was sonicated in cycles for 30\u2009s (20\u2009kHz) with 30\u2009min of quiescence between sonication at 37\u2009\u00b0C using a Q700-110 sonication device, with a Microplate Horn Assembly (431MPX) and a Compact Recirculating Chiller (4900-110, all QSonica). The aggregation was monitored with Thioflavin-T fluorescence until the reading exceeded the background value (~6-8 a.u.) by ~2 units at which point a pellet of I1 was collected via ultracentrifugation. Maintaining temperature at ~4\u2009\u00b0C during centrifugation (160,000 rcf) and rotor packing, and ~16\u2009\u00b0C for MAS NMR measurements ensured batch-to-batch reproducibility and stability (Supplementary Fig.\u00a01).\n\nThT fluorescence of the samples was monitored continuously by taking aliquots from the aggregating solution in the sonicator and mixing with a working solution composed of 50\u2009mM glycine buffer at pH 8.5 and 2.5\u2009\u00b5M ThT. Measurements were done on the Varian Cary Eclipse fluorescence spectrometer. Fluorescence was excited at 446\u2009nm and emission was recorded from 460 to 560\u2009nm at room temperature. The aggregating solution was vortexed at\u00a0least once before collecting an aliquot for the ThT measurement. For a single 1.3\u2009mm rotor, about ~ 8\u2009mL of 70\u2009\u03bcM \u03b1S was aggregated in batches of 1.65\u2009mL. Batch to batch differences in the length of the lag phase were accounted for by measuring ThT fluorescence for each batch independently, and the increase in fluorescence was relative to the initial value of that specific batch. This approach resulted in samples with repeatable spectra (Supplementary Fig.\u00a01D) and the ThT fluorescence measurements for the samples prepared for this study are shown in Supplementary Fig.\u00a01E.\n\nAs soon as 2 units increase in fluorescence was detected, samples were placed on ice and centrifuged at 160,000 rcf (TLA-100.3 rotor in an Optima MAX-TL ultracentrifuge, Beckman Coulter) for 1\u2009h at 4\u2009\u00b0C. After decanting the supernatant, the pellet was washed with 5\u2009mM HEPES (pH 7.4 and subsequently centrifuged (10\u2009min, 175,000 rcf, 10\u2009\u00b0C) twice, each time removing excess moisture. Samples were packed into ssNMR rotors by cutting off the bottom of the centrifuged tube and centrifuging (at 4\u2009\u00b0C) the pellet directly into the rotor through a custom-made filling device made from a truncated pipette tip. The rotor was centrifuged in an ultracentrifuge packing device for 30\u2009min at 68,000 rcf in an SW 32 Ti rotor in an Optima L-80 XP Ultracentrifuge (both Beckman Coulter) at 4\u2009\u00b0C for packing the pellet46. After this step, for 1.3\u2009mm rotors, excess water was pushed out of the rotor by pushing on the bottom rubber seal before closing the rotor. All rotor packing steps were performed in the cold room, as far as possible, using tweezers, to prevent the intermediate from changing states.\n\nTo produce SUVs, POPC and the sodium salt of POPA, obtained from Avanti Polar Lipids, were dissolved in chloroform and mixed to obtain a 1:1 molar ratio of both lipids. The solvent was evaporated under a nitrogen stream and lyophilized overnight. The lipid film was rehydrated with 50\u2009mM HEPES, pH 7.4 and 100\u2009mM NaCl buffer to a total lipid concentration of 3\u2009mM. The solution was sonicated at 37\u2009kHz for four cycles of 10\u2009min sonication and 10\u2009min rest and filtered through a 0.22\u2009\u00b5m syringe filter to obtain SUVs17.\n\nAll measurements were performed on Intermediate 1 composed of u-13C,15N-labeled \u03b1S. 3D (H)CANH, (HCO)CA(CO)NH, (H)CONH, (H)CO(CA)NH and (HCA)CB(CA)NH experiments47 for protein sequence assignment and 3D H(H)NH (z-mixing) experiments for lipid-protein contacts were acquired on an 800\u2009MHz Bruker Avance III HD spectrometer at a magnetic field of 18.8\u2009T equipped with a 1.3\u2009mm magic-angle spinning (MAS) HCN probe and MAS at 55\u2009kHz and an estimated sample temperature of 16\u2009\u00b0C. The cooling gas flow was set at ~ 1500 liters per hour and temperature of the cooling gas was set to 235\u2009K. The delays for scalar carbon-carbon transfers were set based on the T2\u2032 values of 20\u2009ms for C\u03b1 and 45\u2009ms for C\u2032 as shown in Supplementary Table\u00a02. For backbone assignment experiments, an Intermediate 1 sample aggregated with 10:1\u2009L:P was used. Amine side chains for Q62 and N65 were assigned based on contacts from H(H)NH and corresponding C\u2019 assignments from (H)CONH and (H)Ca(CO)NH spectra.\n\nChemical shifts for C\u03b1, CO, C\u03b2, HN and NH were inputted in TALOS+ to obtain predictions on secondary structure and dihedral backbone angles48. Secondary structure was confirmed with secondary chemical shift differences between C\u03b1, C\u03b2 resonances and their random coil values calculated according to Schwarzinger et al.49. Chemical shift perturbations between fibril and I1 were calculated according to equations in Williamson et al.50 from 13C-13C and (H)CaNH spectra:\n\nThe H(H)NH pulse sequence used for proton-proton z-mixing measurements is similar to that reported by Najbauer et al. where longitudinal mixing drives proton-proton mixing between the protein and mobile lipid and water molecules51. To eliminate spectral overlap between protein side-chain resonances and lipid protons, a T2 filter and a J-filter of 3\u2009ms have been added after the proton excitation pulse.\n\nPartial side-chain assignments were obtained from (H)CCH experiments with 13C-13C RFDR mixing of 1.3\u2009ms. Long range contacts were obtained from H(H)NH and H(H)CH experiments with 1H-1H RDFR mixing of 0.5\u2009ms52. These experiments were acquired on a 1200\u2009MHz Bruker Avance NEO spectrometer at a magnetic field of 28.2\u2009T equipped with a 1.3\u2009mm magic-angle spinning (MAS) HCN probe and MAS at 55\u2009kHz and an estimated sample temperature of 16\u2009\u00b0C. For these measurements, 100\u2009mM Cu-EDTA was added to the sample in the rotor to an estimated final concentration of 40\u2009mM for sensitivity enhancement53. This shortened the recycle delay from 1.6\u2009s to 0.6\u2009s without causing changes to the (h)CaNH spectrum.\n\nThe (H)N(H)(H)NH MODIST spectra was acquired on a 1200\u2009MHz Bruker Avance NEO spectrometer at a magnetic field of 28.2\u2009T equipped with a 1.3\u2009mm magic-angle spinning (MAS) HCN probe and MAS of 55,555\u2009Hz with 3.46\u2009ms of 1H-1H MODIST mixing19. The sample contained Cu-EDTA at 40\u2009mM. The temperature of the cooling gas was set to 245\u2009K with a flow of 1000 liters per hour. The spectral widths were 40\u2009ppm for 1H and 38\u2009ppm on 15N. 1H and 15N hard pulses were 100\u2009kHz and 58.8\u2009kHz. The spectra were recorded for 8 days for the 100% labelled sample and 17 days for the 50% labelled sample.\n\nThe 2D 13C-13C DARR spectrum with a mixing time of 20\u2009ms was acquired on an 850\u2009MHz Avance III spectrometer with a 3.2\u2009mm MAS HCN probe at a magnetic field of 20.0\u2009T and MAS at 17\u2009kHz.\n\nFor all spectra 13.75\u2009kHz MISSISSIPPI water suppression54 (100 to 200\u2009ms), 12.75\u2009kHz Swf-TPPM proton decoupling during acquisition of the indirect dimension55 and 10\u2009kHz WALTZ-16 heteronuclear decoupling during acquisition was used.\n\nSpectra were acquired in short blocks of 12-21 hrs for linear drift correction56. The drift-corrected blocks were then averaged and processed as one spectrum in Bruker Topspin 3 or 4. Window functions used to process spectra were exponential and quadratic sine. Spectra were analyzed using CcpNmr Analysis.\n\nThe stability of the sample during solid-state nuclear magnetic resonance (ssNMR) measurements was monitored by (H)NH spectrum recorded intermittently between blocks of 3D experiment acquisitions. Measurements were halted when the intensity of the spectrum began to reduce, or new peaks appeared in the (H)NH spectrum. A new sample was prepared for further measurements and the reproducibility of the (H)NH spectrum for I1 samples is shown in (Supplementary Fig.\u00a01). I1 samples were remarkably stable (Supplementary Fig.\u00a01) even after 21 days at 55\u2009kHz magic angle spinning (MAS) and an estimated sample temperature of 16\u2009\u00b0C. This can be attributed to lack of free monomer available to polymerize I1 into higher order aggregates (Supplementary Fig.\u00a01), relatively slow diffusion in the densely packed pellet in the ssNMR rotor and a sample temperature in the magnet which was much lower than that used for aggregation (37\u2009\u00b0C).\n\nSpectra were processed and analyzed on CCPN Analysis 2.4.257 and Topspin 4.0.7 (Bruker, AXS GmBH). Signal to noise ratios were determined in Sparky58.\n\nAn I1 sample was resolubilized from a MAS ssNMR rotor and dissolved in 5% glycerol and 5\u2009mM HEPES (pH 7.4) buffer. This was diluted 1:80 times and used to blot TEM grids. Samples were bound to a glow discharged carbon foil covered 400 mesh copper grid. Samples were stained with 1% uranyl acetate aqueous solution and evaluated at room temperature using a TALOS L120C micropscope (Thermo Fisher Scientific). Images were analyzed with ImageJ software59.\n\nAn I1 sample was prepared with 13C isotopically labeled site at H50 13C\u03b5. This occurs only once in the \u03b1S sequence. Isolated I1 sample was mixed with TEMTriPol in 13C-depleted d8-Glycerol, D2O and H2O (60:30:10\u2009vol%) to a final 15\u2009mM concentration. This was packed in a 3.2\u2009mm rotor and flash frozen by plunging in liquid nitrogen. The fibril sample was exchanged with glycerol until the mass indicated 60% vol% of glycerol. Then AMUPOL powder was added to a concentration of 30\u2009mM before mixing and plunge freezing the rotor. 395\u2009GHz of microwave irradiation was applied that resulted in a 4 times enhancement for I1 and 30 times enhancement for the fibril sample. All CODEX experiments were measured on a 600\u2009MHz Bruker Avance III HD spectrometer, and a 3.2\u2009mm low temperature (LT) HCN MAS probe at 8\u2009kHz MAS. Using CODEX to count the oligomeric numbers is discussed in detail in previous literatures20,60,61.\n\nATTO647N bound to A140C \u03b1S was aggregated as outlined in the \u2018sample preparation\u2019 section with wild type (WT) \u03b1S at a ratio of 1:3 ATTO-\u03b1S: WT. Once isolated, the sample was packed in a 1.3\u2009mm rotor and a 15N-1H fingerprint spectrum confirmed that the sample indeed was structurally similar to I1 (Supplementary Fig.\u00a04).\n\nThe proteins were diluted to a stock solution of 50\u2009nM in 10\u2009mM HEPES and 10% glycerol at pH 7.8 (working buffer). The protein was further diluted into the working buffer containing 5% poly-vinyl alcohol (PVA) to obtain a final concentration of ~20\u2009pM. Then, 4\u2009\u00b5l of this diluted solution was spin-coated onto a plasma-cleaned mirror-enhanced substrate, which was prepared in-house62. Finally, the sample was immediately loaded into our custom-built cryogenic microscope62.\n\nUpon inserting the sample into our custom-built cryogenic microscope, we applied a vacuum for 5\u2009minutes to immobilize the molecules. After releasing the vacuum, we started acquiring videos from multiple fields of view (FOV) at room temperature (RT). The sample was illuminated with a 645\u2009nm wavelength laser at 2\u2009mW in a wide-field (WF) mode using an air objective (Mitutoyo 100X, 0.9 NA). Each FOV (80\u2009\u00d7\u200980\u2009\u00b5m) was recorded at a frame rate of 10\u2009Hz for up to 8\u2009min, a time point which shows complete photobleaching in the FOV. Next, we localized and clustered each molecule in the FOV to extract their intensity levels over time. The intensity time traces\u00a0(Supplementary Fig. 4F and 4G) were then fitted using the DISC algorithm to extract the number of intensity steps per molecule63. Here, we used a critical value of 15, and the minimum number of points per cluster was set to 15. To avoid any sources of artifact in the final analysis resulting from low signal-to-noise traces or traces that included high blinking events, we filtered the intensity time traces with a signal-to-noise ratio (SNR) above 5. The output from this analysis (1421 time traces) was then plotted as a histogram and fitted with a binomial model to extract the labeling efficiency or the stoichiometry of the intermediate oligomers. The binomial distribution described as:\n\nIn Eq.\u00a02, P is the probability that an oligomer contains k labeled subunits, n is the total number of monomers per oligomer and p is the labeling efficiency. Here we obtain the fit parameter (p), theoretical labeling efficiency, as a function of the total number of monomers (n) as depicted in Supplementary\u00a0Fig.\u00a04F.\n\nLabeling error is defined as:\n\nWe plotted labeling error (Eq.\u00a03) as a function of monomers per oligomer (red curve) and we found that the best model is a tetramer (black arrow in Supplementary Fig.\u00a04I). Similarly, the residual of the fit indicated that tetramer is the best model (orange curve, Supplementary Fig.\u00a04I).\n\nThe sample was prepared and imaged using the same instrument as in the fluorescence photobleaching experiment. For polarCOLD, the chamber was completely evacuated to a pressure of 1.6\u2009\u00d7\u200910\u22126\u2009mbar and then cooled down to 4.3\u2009K using liquid helium. The setup was then allowed to stabilize for 1-2\u2009h to minimize drift during recordings. Subsequently, the sample was illuminated with a 20\u2009mW laser in WF mode, utilizing the same laser source and microscope objective. The emission signal was split into two channels using a polarized beam splitter, enabling the recording of a polarization time trace23. After localizing and clustering each point spread function (PSF), we extracted the polarization time trace, which was then fitted using the DISC algorithm combined with 2D gaussian mixture model of the polarization and coordinate space to determine the number of polarization states per molecule. The number of identified polarization states (dipole orientation) in each PSF corresponds to the number of labeled monomers per oligomer, as the dipole orientation at 4\u2009K of each fluorophores is random but fixed (see Supplementary Fig.\u00a04H). This, in turn, allow us to annotate each fluorophore over time and localize it with high precision beyond the diffraction limit by clustering their coordinates accordingly (Fig. 2E\u2013H). Then, a 2D super-resolved image is reconstructed by assigning a 2D Gaussian function to each localized fluorophore with a width given by the respective localization precision. These super-resolved images demonstrate different projections of the protein molecules within the sample. The output of the number of polarizations (from 1023 traces) was plotted as a histogram (Fig. 2D), yielding results similar to those obtained from the photobleaching steps experiment.\n\nAn I1 sample from an ssNMR rotor, once confirmed to have the expected spectrum, was emptied, resuspended in buffer and aliquots were taken for concentration determination. Aliquots were incubated with 6\u2009M Guanidine Hydrochloride (GdHCl) at room temperature for 2\u20134\u2009h to dissociate aggregates. Then the sample was loaded onto a 12% SDS-PAGE gel for densitometric analysis and images of the Coomassie stained gel were obtained on a BIORAD Gel Doc XR with Image Lab software. The intensity of the band at ~15\u2009kDa was analyzed with ImageJ to determine the mass of \u03b1S loaded and converted to concentrations. To correlate intensity of the band with \u03b1S mass, a standard curve was built where the initial \u03b1S mass added to the gel was calibrated by measuring absorbance with a 0.2\u2009mm cuvette at 275\u2009nm with an extinction coefficient of 5600\u2009M\u22121\u2009cm\u22121 prior to loading the gel. An attempt was made to measure all I1 samples after GdHCl treatment with absorbance. However, the presence of a high concentration of lipids often lead to baseline distortions specially in the regions around 180\u2013300\u2009nm. Note that all concentrations are expressed as monomer equivalents.\n\nAbsorbance measurements were performed on an HP Agilent 8453 Diode array spectrophotometer to determine\u00a0the concentration of \u03b1S before using it\u00a0in aggregation assays with a cuvette of pathlength 0.2\u2009mm. The absorbance was measured at 275\u2009nm and the extinction coefficient was 5600\u2009M\u22121 cm\u22121. For ATTO647N labeled stocks, absorbance was measured at 650\u2009nm with an extinction coefficient of 150000\u2009M\u22121 cm\u22121.\n\nA standard sample of POPC was weighed out and packed in a 1.3\u2009mm rotor and measured at 55\u2009kHz on a 800\u2009MHz spectrometer. The height of the peak at 1.3\u2009ppm of the 1D 1H spectrum of this sample was used as the reference to determine the amount of lipid in I1 and L2 samples. A similar 1D 1H spectrum was acquired for all subsequent samples and compared to the reference.\n\nSH-SY5Y cells, obtained from ATCC (CRL-2266) were grown in 45% modified eagle media supplemented with L-glutamine (2\u2009mM), HAM\u2019s F-12 nutrient mixture (45%), fetal bovine serum (10%) and non-essential amino acids (1%). Cells were grown on Poly-D-lysine coated dishes and one day before treatment, cells were plated in a 96-well plate at a density of 2\u2009\u00d7\u2009104 cells/ well. Samples consisting of the intermediate 1, fibrils or monomers were added to the cells at concentrations defined in Fig. 1 and incubated at 37\u2009\u00b0C for 20\u201324\u2009h. At the end of the treatment period XTT (2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-5-carboxanilide-2H-tetrazolium) and electron coupling reagent (ThermoFisher Scientific) were added and incubated for another 4\u2009h before reading absorbance at 450\u2009nm and 660\u2009nm. Results are presented after subtraction of blank absorbance at 450\u2009nm and well as background at 660\u2009nm from the test absorbance at 450\u2009nm.\n\nSH-SY5Y cells were plated in a 96 well plate at a density of 2\u2009\u00d7\u2009104 cells/ well. The next day, media was aspirated and media containing 3\u2009\u00b5M Fluo-4 was added. Cells were incubated at 37\u2009\u00b0C for 1\u2009h. Fluo-4 AM containing media was removed and replaced with phenol red free DMEM supplemented with Glutamine and 10% FBS. The plate was incubated for another 10\u2009min to load cells with Fluo-4 and ensure complete de-esterification of Fluo-4 AM. CNQX (cyanquixaline) diluted in media or\u00a0an equivalent amount of media for control was added to cells to a concentration of 5\u2009\u00b5M. Then either I1 in 5\u2009mM HEPES, or\u00a0an equivalent amount of buffer, monomer, fibrils or ionomycin were added to the cells at a concentration of 0.6\u2009\u00b5M. The\u00a0plate was equilibrated in the BioTEK plate reader at 37\u2009\u00b0C for 15\u2009min before measurements began. Fluorescence was excited at 488\u2009nm and measured at 530\u2009nm using a filter cube. In another set of wells, after the overnight incubation, media was replaced with phenol red free media. Either I1 in 5\u2009mM HEPES, or an\u00a0equivalent amount of buffer, monomer or fibrils were added to the cells at a concentration of 0.6\u2009\u00b5M. As a dead cell control, cells were lysed with 10% SDS. Then propidium iodide was added at a concentration of 50\u2009\u00b5g/ml. Fluorescence was read in parallel to Fluo-4 with\u00a0the monochromator based fluorescence module on the BioTEK reader at excitation and emission wavelengths of 535\u2009nm and 622\u2009nm respectively.\n\nA pH-based proton flux assay was adapted from a previous protocol used for viroporins64. Liposomes were made by combining 10\u2009mg of Escherichia coli polar lipid extract (Avanti Polar Lipids) dissolved in chloroform, valinomycin solution in ethanol and methanol in a glass tube. The solvents were evaporated under continuous nitrogen gas and a thin film was obtained. The films were dissolved in chloroform again and dried down under a nitrogen stream and were left overnight in the lyophilizer to remove any solvent trace. The films were then resuspended in strongly buffered internal liposome buffer (26\u2009mM potassium citrate, 17\u2009mM citric acid, 28\u2009mM sodium citrate, 25\u2009mM K2HPO4, 25\u2009mM Na2HPO4, 6\u2009mM NaN3; pH 7.7) to form liposomes which were then extruded 11 times through 0.2\u2009M polycarbonate membrane. Buffer was exchanged on a PD-10 column (GE Health Sciences) such that the external liposome buffer was a weak buffer (4% v/v IVB, 117\u2009mM KCl, 117\u2009mM NaCl, 6\u2009mM NaN3, pH 7.7).\n\nEvery tested sample contained 5\u2009mg/mL lipids, and 0.1\u2009\u03bc\u039c valinomycin as a potassium ionophore. The external pH was decreased by the addition of 1\u2009M HCl under continuous fast stirring. Once the pH had stabilized, 0.6\u2009\u00b5M of I1 in 5\u2009mM HEPES or\u00a0an equivalent amount of buffer without I1 was added and\u00a0the pH was recorded every second. The proton uncoupler carbonyl cyanide m-chlorophenylhydrazone (CCCP) was added to determine the buffering capacity of the liposomes.\n\nContacts from Supplementary Table\u00a01 were used for the CYANA65 calculation with 250 structures and 80,000 steps. Parallel in-register hydrogen bonds were assumed for segments that show chemical shift similarity (\u2009<\u20090.7 ppm average CSP) and high fidelity in long range contacts with the L2 fibril for stretches V31-G51, E57-Q62, and G67-K80. Dihedral angles with good confidence from the TALOS+ prediction were used with one standard deviation with the exception of loop regions, where the limits for the dihedral angles for the loops were increased to three standard deviations. This resulted in the structures with a target function of ~5.\n\nTo produce an atomistic I1 structure model with restrained MD simulations the core of the \u03b1-Synuclein L2 fibril structure (8A4L, residue 33\u201383) was taken. The N-terminal part was removed. For an N- and C-terminally extended I1 structure model, residues 16\u201333 & 83\u201399 were taken from the micelle-bound \u03b1-Synuclein monomer structure (1XQ8) and fitted on residue Thr33 & GLu83, respectively. Three different tetrameric I1 AP domain morphologies (\u2019open\u2019, bowl\u2019 and \u2018barrel\u2019) were derived by MD simulations with distance restraints in a water box (Supplementary Table\u00a01 and Supplementary Fig.\u00a08, Supplementary Fig.\u00a09). In all simulation systems, the titratable amino acids were protonated according to their standard protonation states at pH 7, while also taking into account the solvent exposure and electrostatic interactions with neighboring polar groups. Thus, aspartic and glutamic side-chains were simulated with negative charge and all histidine side-chains were set to neutral. All lysine side-chains were simulated as positively charged66. The N- and C-termini of the truncated \u03b1-Synuclein molecules were capped with acetyl and N-methyl groups, respectively. All production runs were preceded by a multi-step equilibration of the system. The protein part was separately energy minimized in water. Bilayer patches with a ratio of 1:1 ratio of POPC and POPA lipids and a water slab of 3.5\u2009mm thickness of top and bottom of the bilayer were prepared using the CHARMM-GUI67 webserver. The membrane patch was relaxed for 1\u2009ns at 300\u2009K. Next, the \u03b1-Synuclein structures were either embedded into the lipid bilayer or positioned close to it (see orientations 1/8 in Supplementary Fig.\u00a08) in several different orientations. Subsequently, and if not stated otherwise, Na+ and Cl\u2212 ions (ionic strength: 150\u2009mM) were added in the aqueous phase of the periodic simulation box. Additional MD simulations of I1 in orientation 1 and 2 were carried out with Ca2+ ions (salt concentration: 100\u2009mM NaCl and 40\u2009mM CaCl2).\n\nThe total simulation size varied and amounted to roughly 65\u2009k or 230\u2009k atoms depending on the used \u03b1-Synuclein model short (G36-T81) or long (V16-Q99) construct. Specifications of all simulation systems are summarized in Supplementary Tables\u00a03 and 4. See Supplementary Note\u00a01 for further details.\n\nFor the short construct of the open morphology in different orientations, each case was simulated as triplicates of MD simulations with 1000\u2009ns length. For the longer constructs, a total of 62\u2009MD simulations of embedded structures were run for 100\u2009ns with distance restraints and an additional 500\u2009ns without restraints to collect data that are evaluated against experimental measurements (Supplementary Table\u00a04).\n\nThe GROMACS 2022 simulation68,69 software package was used to set up and carry out the MD simulations. Settings for production runs were chosen as follows: The long-range electrostatic interactions were treated using the Particle Mesh Ewald (PME) method70,71. Bonds in protein and lipid molecules were constrained using the P-LINCS algorithm72. Water molecules were constrained using SETTLE algorithm73. Neighbor lists were updated with the Verlet list scheme69,74. For production runs, the simulated systems were kept at a temperature of 300\u2009K by applying the velocity-rescaling75 algorithm. Initial velocities for the production runs were taken according to the Maxwell-Boltzmann distribution at 300\u2009K. The pressure was held constant by using the Parrinello-Rahman barostat76 with a semi-isotropic coupling in the xy-plane.\n\nAll simulations with the CHARMM36m77,78 protein force field utilized the CHARMM36 lipid79 parameters together with the CHARMM-modified80 TIP3P water model. The integration time step was set to 2\u2009fs. The neighbor lists for non-bonded interactions were updated every 20 steps. Real-space electrostatic interactions were truncated at 1.2\u2009nm. The van der Waals interactions were switched off between 1.0 to 1.2\u2009nm and short-range electrostatic interactions were cut-off at 1.2\u2009nm. For pressure coupling the scheme of Parrinello-Rahman76 was used to hold the system at a pressure of 1\u2009bar (time constant for pressure coupling,\u00a0\u03c4\u2009=\u20095).\n\nMD simulations were analyzed with the GROMACS 2022 simulation68,69 software package and post-processed with in-house scripts. For the analysis based on experimental distance restraints and lipid contacts, only the last 250\u2009ns of each simulation trajectory were used to ensure that the results are not biased by the initial equilibration of the simulation system. Data samples were collected every 250\u2009ps. Pairwise interatomic contacts were quantified for every frame using the gmx mindist and gmx hbond programs. Secondary structure analysis was carried out using the DSSP algorithm81.\n\nThe partial densities of water molecules, lipid groups and ions across the simulation box and along the membrane normal direction were computed using the gmx density tool. Histogram binning was done relative to the center of all lipid atoms. Partial density profiles were averaged over all independent trajectory replicates per simulation system. A detailed description is given elsewhere40. Structure and MD simulation renderings were produced with Chimera82 and ChimeraX83.\n\n15N-labeled \u03b1S was mixed with 50\u2009mM HEPES (pH 7.4) and 100\u2009mM NaCl to obtain samples with 10% D2O and 100\u2009\u03bcM 2,2-dimethyl-2-silapentane-5-sulfonate sodium. Experiments were recorded on a Bruker 700\u2009MHz spectrometer (Avance III HD with CP-TCI HCND probe with z-gradient) at 288\u2009K. 1H-15N-HSQC spectra were acquired using 3-9-19 watergate for water suppression using 256 increments in the indirect dimension and a relaxation delay of 1.2\u2009s. Assignment of the 1H-15N-HSQC spectrum was done by comparison to BMRB entries 16300, 16904, and 18857.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Assigned chemical shift data for \u03b1S Intermediate 1 were deposited in the BMRB under the accession number 52283. Initial coordinate and simulation input files and a coordinate file of the final output are provided through the Edmond data repository at [https://doi.org/10.17617/3.0V1ODV]. NMR spectra are deposited at Edmond under [https://doi.org/10.17617/3.TXND2C]. The PDB structures used for comparison in the article include 8A4L, 8A9L, 7NCA and 7OZG. The L2-fibril chemical shifts were obtained from BMRB accession number 50585. Monomer and C-terminal of I1 was assigned with the help of BMRB accession codes, 16300, 16904, 6968 and 18857.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Parameter files are provided through the Edmond data repository at [https://doi.org/10.17617/3.0V1ODV]. The code to identify single-molecules and extract the intensity time traces in the photobleaching experiment was done using custom written MATLAB code. However, any published codes such as ThunderSTORM can be easily used for the purpose, and available from ref. 84. The code to fit the intensity time traces is available from ref. 63. The code to fit the binomial distribution of the number of dyes per aggregate was written in MATLAB and provided as supplementary data\u00a01. The code to analyze the polarCOLD data was written by a previous lab member and described in the following B\u00f6ning et al.62. 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Belov from the facility for Synthetic Chemistry at the Max Planck Institute for Multidisciplinary Sciences, G\u00f6ttingen for synthesizing the TEMTriPol-1 radical. This work was supported by the Max Planck Society (to CG, VaS and BLdG) and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany\u2019s Excellence Strategy-EXC 2067/1-390729940 (to CG) and the Emmy Noether program to LBA (project number: 397022504).", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Kumar T. Movellan\n\nPresent address: Brown Laboratory Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, USA\n\nKai Xue\n\nPresent address: Center of High Field Imaging, Nanyang Technological University, Singapore, Singapore\n\nNMR Based Structural Biology, Max Planck Institute for Multidisciplinary Sciences, G\u00f6ttingen, Germany\n\nVrinda Sant,\u00a0Leif Antonschmidt,\u00a0Kumar T. Movellan,\u00a0Kai Xue,\u00a0Evgeny Nimerovsky,\u00a0Marianna Stampolaki,\u00a0Magdeline Nathan,\u00a0Stefan Becker,\u00a0Christian Griesinger\u00a0&\u00a0Loren B. Andreas\n\nDepartment of Theoretical and Computational Biophysics, Max Planck Institute for Multidisciplinary Sciences, G\u00f6ttingen, Germany\n\nDirk Matthes\u00a0&\u00a0Bert L. de Groot\n\nMax Planck Institute for Science of Light, Erlangen, Germany\n\nHisham Mazal,\u00a0Franz Wieser\u00a0&\u00a0Vahid Sandoghdar\n\nMax-Planck-Zentrum f\u00fcr Physik und Medizin, Erlangen, Germany\n\nHisham Mazal,\u00a0Franz Wieser\u00a0&\u00a0Vahid Sandoghdar\n\nDepartment of Physics, Friedrich-Alexander University of Erlangen-N\u00fcrnberg, Erlangen, Germany\n\nFranz Wieser\u00a0&\u00a0Vahid Sandoghdar\n\nFacility for Electron Microscopy, Max Planck Institute for Multidisciplinary Sciences, G\u00f6ttingen, Germany\n\nDietmar Riedel\n\nCluster of Excellence \u201cMultiscale Bioimaging: From Molecular Machines to Networks of Excitable Cells\u201d (MBExC), University of G\u00f6ttingen, G\u00f6ttingen, Germany\n\nChristian Griesinger\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nIntermediate1 samples were prepared by V.S. and L.A. NMR experiments were performed by V.S., L.A., K.T.M., K.X., E.N. Cell experiments performed by V.S. and M.N. Liposomal proton flux assay was performed by V.S. and M.S. Fluorescence measurements were performed and analyzed by H.M. and F.W. M.D. simulations were performed and analyzed by DM. D.R. acquired electron micrographs. Conceptualization and methodology by B.Ld.G., C.G., L.B.A., V.S. and L.A. S.B. oversaw protein expression and purification. VaS oversaw fluorescence measurements. VS prepared the figures and wrote the initial draft. B.Ld.G., C.G., and L.B.A. supervised the project. All authors contributed to the writing of the manuscript.\n\nCorrespondence to\n Bert L. de Groot, Christian Griesinger or Loren B. Andreas.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Alfonso De Simone and the other, anonymous, reviewers for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Sant, V., Matthes, D., Mazal, H. et al. 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phase transition for mitigating voltage hysteresis of iron fluoride positive electrodes in lithium-ion batteries", + "pre_title": "Guided phase transition for mitigating voltage hysteresis of iron fluoride cathode materials in lithium-ion batteries", + "journal": "Nature Communications", + "published": "29 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63676-9/MediaObjects/41467_2025_63676_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63676-9/MediaObjects/41467_2025_63676_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63676-9/MediaObjects/41467_2025_63676_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Data 1-5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63676-9/MediaObjects/41467_2025_63676_MOESM4_ESM.zip" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63676-9/MediaObjects/41467_2025_63676_MOESM5_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-63676-9#MOESM4", + "/articles/s41467-025-63676-9#MOESM4", + "/articles/s41467-025-63676-9#Sec15" + ], + "code": [], + "subject": [ + "Batteries", + "Energy" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5571992/v1.pdf", + "research_square_link": "https://www.researchsquare.com//article/rs-5571992/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63676-9.pdf", + "preprint_posted": "17 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Despite the high capacity attained by conversion-reaction-based metal-fluoride cathode materials in lithium-ion batteries through multiple electron storage, the large voltage hysteresis and low structural reversibility constrain their use. Herein, we propose guided phase transitions for designing conversion cathode materials that undergo minimal structural changes upon lithium-ion storage. This approach reduces the compositional inhomogeneity, a culprit of the voltage hysteresis, while providing high structural reversibility. Unlike the thermodynamically stable rhombohedral FeF3 (R-FeF3), which suffers from irreversible phase transitions accompanied by drastic structural evolution, tetragonal FeF3 (T-FeF3), a thermodynamically metastable phase guided by fluoride-ion incorporation into FeF2 from the electrochemical splitting of LiF, undergoes facile and reversible phase transitions during intercalation and conversion reactions by sustaining its structural integrity upon charge and discharge. Our research provides valuable insights into the significance of avoiding an irreversible reaction pathway and inducing it to minimize changes in the crystal structure for the design of conversion cathode materials with low voltage hysteresis and excellent cycle stability.Physical sciences/Materials science/Materials for energy and catalysis/BatteriesPhysical sciences/Energy science and technology/Energy storage/BatteriesPhysical sciences/Chemistry/Energy", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupportingInformation.pdfSupporting Information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Despite the high capacity attained by conversion-reaction-based metal-fluoride positive materials in lithium-ion batteries through multiple electron storage, the large voltage hysteresis and low structural reversibility constrain their use. Herein, we propose guided phase transitions for designing conversion-type positive materials that undergo minimal structural changes upon lithium-ion storage. This approach reduces the compositional inhomogeneity, a culprit of the voltage hysteresis, while providing high structural reversibility. The thermodynamically stable rhombohedral FeF3 involves irreversible phase transitions accompanied by significant structural rearrangement during lithiation. In contrast, the metastable tetragonal FeF3, electrochemically derived from a LiF-FeF2 composite, undergoes facile and reversible phase transitions by maintaining structural integrity, enabled by conversion reactions between structurally analogous phases. Our study provides valuable insights into the importance of avoiding irreversible reaction pathways and deliberately guiding them to minimize structural changes in the crystal lattice, which is critical for designing positive materials with high structural reversibility.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Lithium-ion batteries (LIBs) have been implemented in core energy-storage technology in the value chain of sustainable energy production for a wide range of applications from portable electronic devices to electric vehicles1,2,3,4. Intercalation chemistry has achieved great success in positive materials for the industrial application of LIBs through lithium and electron storage with angstrom-scale reaction in the same open-framework crystal structure. By leveraging intercalation chemistry, numerous promising positive material candidates have been developed, including NMC (nickel manganese cobalt), LCO (lithium cobalt oxide), LMO (lithium manganese oxide), LFP (lithium iron phosphate), and DRX (Disordered Rock salt)5,6,7,8,9,10,11,12,13,14. However, these intercalation-based materials have limited capacity due to the finite number of interstitial sites in the host lattice, which restricts further enhancements of the specific energy.\n\nConversion reaction chemistry, which can store multiple electrons without the constraints of an open framework crystal structure, presents a viable option to overcome the limitations in specific energy. This approach offers significantly higher specific capacity than existing intercalation materials15,16,17,18 by decoupling lithium and electron storage through the formation of lithium compounds and transition metals. Despite these advantages, pervasive issues of large voltage hysteresis and low structural reversibility19,20,21,22,23,24, widely observed in conversion-reaction materials, remain the greatest challenges.\n\nThe voltage hysteresis and low structural reversibility are understood to stem from compositional inhomogeneity caused by structural reconfigurations and phase displacement19,20,21,25 and are closely related to both reaction kinetics and mechanism. Therefore, strategies such as designing the composites with conductive materials26,27,28,29,30 or reducing the particle size to the nano-size level19,21,26,31 have been proposed to overcome the limited reaction kinetics due to the low electronic conductivity and sluggish mass transport from long-range diffusion. Despite these efforts, the compositional inhomogeneity and voltage hysteresis have not been completely resolved, which implies that they may also have reaction pathway origins. According to recent studies on the reaction mechanisms of FeF3, which is a representative conversion positive material due to its high theoretical specific energy (1922\u2009Wh\u2009kg\u22121), high voltage, and cost-competitive17,18,32,33, intermediate multi-phases with different chemical compositions are irreversibly formed during the first discharge process, with each phase following different reaction pathways upon charge and discharge21. This process deepens the compositional inhomogeneity during repeated cycling, ultimately leading to poor cyclability.\n\nCompositional inhomogeneity inevitably induces different reaction pathways in general for chemical and electrochemical reactions. In previous studies on sodium-based positive materials, for the solid-state synthesis of Na0.7CoO2, an initial phase different from the global composition (NaCoO2) forms preferentially during synthesis34. This preferential formation is caused by the local minimum of Gibbs free energy depending on the local chemical composition. Another study reported that the synthesis method can affect local compositional variations, leading to the formation of thermodynamically metastable phases depending on the synthesis approach35. These findings suggest that in situations with significant compositional variation and spatial separation, different phases can grow in distinct regions, each following different reaction pathways. As structural reformation observed during charge\u2013discharge cycles in the (re)conversion reaction can be regarded as a type of electrochemical synthesis process36,37,38,39, it is necessary to suppress thermodynamically induced compositional inhomogeneity to fundamentally address the voltage hysteresis and low structural reversibility.\n\nTo mitigate the compositional inhomogeneity from a reaction pathway perspective, it is essential to evade phase-displacement reactions accompanying long-range diffusion. In this respect, nanocomposite cathodes composed of lithium compounds and transition-metal compounds have successfully guided reversible reaction routes with minimal diffusion while maintaining the mother structure or anion framework of transition-metal compounds. For example, the LiF-FeO composite guided the formation of a new cubic FeOF host structure electrochemically while retaining the cubic FeO structure, distinct from the conventional rutile FeOF structure40. In the LiF-MnO system, the incorporation of the F anion induces a reversible phase transition from the rock-salt to spinel-like structure, which proceeds with short diffusion of Mn ions from the octahedral site to the face-shared tetrahedral site41. Both combinations avoid drastic structural changes even during charge and discharge reactions compared with typical conversion reactions. Based on these previous results40,41, it can be reasonably predicted that tetragonal FeF342 can be formed from nanocomposites of LiF-FeF2 by maintaining the structural similarity with tetragonal FeF2. Moreover, this nanocomposite can overcome kinetic limitations by ensuring efficient lithium and electron transport through nanosized particles and carbon composites. Thus, effectively utilizing the nanocomposite strategy can provide a comprehensive solution for designing advanced conversion-type positive materials with mitigated compositional inhomogeneity and voltage hysteresis.\n\nHerein, we report on the design of tetragonal FeF3 (T-FeF3) derived from a LiF-FeF2 nanocomposite to address the issue of large voltage hysteresis and low structural reversibility observed in conventional rhombohedral FeF3 (R-FeF3). We reveal that the crystalline structure of the tetragonal FeF2 in the LiF-FeF2 nanocomposite successfully guides the phase transition towards the formation of metastable T-FeF3 rather than the thermodynamically stable R-FeF3. The induced T-FeF3 exhibits structural similarity to the discharged FeF2, facilitating facile phase transitions, including insertion and conversion reactions. These phase transitions reduce compositional inhomogeneity, resulting in low voltage hysteresis and high structural and electrochemical reversibility. As a result, T-FeF3 maintained 72% of its initial capacity after 300 cycles at a specific current of 50\u2009mA\u2009g\u22121, significantly outperforming R-FeF3, which retained only 50% of its capacity. Moreover, the high structural reversibility of T-FeF3 was maintained even after the formation of LiF and Fe metal phases under the deep discharge of a conversion reaction. Our research underscores that a guided phase transition that can maintain structural similarity can open a new reaction route that can evade the reaction pathway accompanying long-range diffusion, which can mitigate the pervasive issues of large voltage hysteresis and low structural reversibility in conversion-reaction chemistry.", + "section_image": [] + }, + { + "section_name": "Results and discussion", + "section_text": "The LiF-FeF2 nanocomposite was prepared by mechanical ball milling of LiF and FeF2 following previously reported procedures43. Rietveld refinement confirmed the formation of the LiF-FeF2 nanocomposite, revealing phase fractions of 55.34% for LiF (s.g. Fm-3m) and 44.66% for FeF2 (s.g. P42/mnm) (Fig.\u00a01a and Supplementary Table\u00a0S1). A slight excess of the Li source is employed to enhance electrochemical capacity by increasing accessibility of the fluorination source, LiF, to FeF243,44 (Supplementary Fig.\u00a0S1). The transmission electron microscopy (TEM) of Fig.\u00a01b shows well-mixed nanodomains of 5\u201310\u2009nm in size, with FeF2 and LiF represented by white and green, respectively. The azimuthal integration of the FFT patterns from the TEM images also indexed LiF and FeF2 (Supplementary Fig.\u00a0S2), further verifying the successful formation of the LiF-FeF2 nanocomposite.\n\na Rietveld refinement of the X-ray diffraction (XRD) data (\u03bb\u2009=\u20091.5406\u2009\u00c5) of the LiF-FeF2. b High-resolution transmission electron microscope (TEM) image of LiF-FeF2 in a pristine state. Each domain is outlined with a dotted line. The inset shows the fast Fourier transform (FFT) pattern of LiF-FeF2. White and green represent FeF2 and LiF, respectively. c Electrochemical profile of LiF-FeF2 nanocomposite at 25\u2009\u00b0C and 20\u2009mA\u2009g\u22121 specific current. Blue depicts the evolving voltage profile of LiF-FeF2 up to the 10th cycle, while yellow represents the 10th cycle profile of R-FeF3. The right is the differential analysis of the voltage profile. d Ex situ XRD patterns of LiF-FeF2 electrodes at charged/discharged states after the 1st, 5th, and 10th cycles measured at 25\u2009\u00b0C and current density of 20\u2009mA\u2009g\u22121. Red and blue are discharge and charge states, respectively. e Crystal structures of FeF2, T-FeF3 (determined through X-ray diffraction of the 10th charge state), and R-FeF3. Brown and silver balls indicate Fe and F ions, respectively. f Fourier transformed magnitude (black), imaginary part (blue), and best fit (red) using the T-FeF3 model for the charged electrode.\n\nFirst, the voltage profile of the LiF-FeF2 nanocomposite was examined at 25\u2009\u00b0C within a voltage range of 4.8\u20132.0\u2009V. Figure\u00a01c shows the charge-discharge profiles of the LiF-FeF2 for the initial 10 cycles, compared with the 10th cycle profile of rhombohedral FeF3 (s.g. R-3c, R-FeF3), prepared by ball-milling with carbon to mitigate kinetic limitations (Supplementary Fig.\u00a0S3). During cycling, the 4\u2009V plateau in the LiF-FeF2 nanocomposite, indicated by the red-shaded area, gradually evolved, with the average discharge voltage increasing from 3.02\u2009V (1st cycle) to 3.15\u2009V (10th cycle). By the 10th cycle, the discharge capacity is 189.5\u2009mAh\u2009g\u22121, corresponding to the insertion of 0.85 Li+ (theoretical capacity of 223.75\u2009mAh\u2009g\u22121 for single-electron transfer). In contrast, such electrochemical features were absent in R-FeF3, as clearly seen in the dQ/dV analysis. Given the reversible redox reaction of iron involving fluorination, which is confirmed by X-ray absorption spectroscopy (Supplementary Figs.\u00a0S4a and S5), it is notable that the redox reaction around the 4\u2009V in the LiF-FeF2 nanocomposite represents a higher voltage for the Fe2+/Fe3+ redox couple compared to LiFeSO4F with triplite (3.9\u2009V) and tavorite structure (3.6\u2009V)45,46,47. The origin of high redox potential will be discussed later, but it implies structural evolution during electrochemical cycling, distinct from R-FeF3.\n\nTo investigate the structural evolution of LiF-FeF2 during cycling, ex situ XRD was performed after the 1st, 5th, and 10th cycles (Fig.\u00a01d). New peaks at 34.8\u00b0, 40\u00b0, and 66.7\u00b0 (indicated by arrows) in the charged state gradually became more pronounced with cycling. However, the newly formed diffraction pattern in the charged state does not match the R-FeF3 pattern. This contrasts with previous studies suggesting that LiF-FeF2 forms a rhombohedral-like FeF3 structure upon charging43,48. Instead, the evolved structure is rather consistent with the tetragonal FeF2 diffraction pattern, implying the formation of a tetragonal FeF3 phase. This discrepancy with the previous study may be due to the local probe analyses, such as XAS, which might not definitely represent the overall structure43,48. When comparing the structural evolution between LiF-FeF2 nanocomposite and R-FeF3 (Supplementary Fig.\u00a0S7), it is observed that charge/discharge progresses while maintaining the tetragonal and rhombohedral phases, respectively. This implies the formation of a tetragonal FeF3 phase.\n\nBased on the structural evolution of LiF-FeF2, which maintains the tetragonal FeF2 structure, the feasibility of tetragonal FeF3 formation was further verified with local structural analysis. Figure\u00a01e presents the crystal structures of FeF2, tetragonal FeF3 (T-FeF3)42, and R-FeF3. T-FeF3 consists of FeF6 octahedra that are edge-sharing along the c-axis and corner-sharing in the ab plane, similar to the anion framework of FeF2 but with reduced Fe occupancy, leading to a modified unit cell. In contrast, R-FeF3 has a structure based solely on corner-sharing, structurally distinct from T-FeF3. To verify the formation of T-FeF3 at the local-environment level, extended X-ray absorption fine structure (EXAFS) fitting was performed in the 10th charged state (Fig.\u00a01f). The reduced \u03c7\u00b2 and R-factor were lower for the tetragonal phase than for the rhombohedral phase, indicating better structural agreement with the tetragonal phase (Supplementary Fig.\u00a0S8). This result is attributed to the shorter Fe\u2013Fe bond distance of T-FeF3 (3.16 and 3.69\u2009\u00c5) with its edge-sharing framework, compared to R-FeF3 (~3.7\u2009\u00c5), which only has a corner-sharing framework of iron octahedra (Supplementary Fig.\u00a0S4b and Supplementary Note 3). Moreover, for the pair distribution function (PDF) analysis, the 10th charge state was more consistent with T-FeF3 than with R-FeF3 (Supplementary Fig.\u00a0S9). Conclusively, Rietveld refinement of the XRD pattern of the electrode in the 10th charged state (Supplementary Fig.\u00a0S10 and Supplementary Note\u00a04) revealed a match with the T-FeF3 phase, including residual LiF and FeF2. These results confirm that the LiF-FeF2 nanocomposite successfully leads to the gradual formation of T-FeF3 while maintaining structural similarity to the mother structure (FeF2). The efficient formation of T-FeF3 is closely governed by the interfacial contact between LiF and FeF2, which facilitates the guided phase transition during cycling (Supplementary Note\u00a05) Notably, tetragonal FeF3 derived from LiF-FeF2 nanocomposites offers practical advantages in full-cell manufacturing43 compared to the previously reported tetragonal FeF3 phase is formed via the delithiation of Li0.5FeF3, particularly due to the safety concerns and chemical instability associated with metallic lithium and lithium-containing negative electrodes4.\n\nReversible and sequential intercalation and conversion reactions were confirmed for T-FeF3 during charge and discharge. First, to investigate the reaction mechanism of T-FeF3, ex situ XRD was performed across various voltage ranges. The voltage regions, including each redox reaction around 4\u2009V and 3\u2009V, were categorized as the wide voltage range (WV, 4.8\u20132.0\u2009V), the upper voltage range (UV, 4.8\u20133.4\u2009V), and the lower voltage range (LV, 3.4\u20132.0\u2009V). Figure\u00a02a shows the ex situ XRD patterns during the charged and discharged process, including the charged state at 4.8\u2009V (red), half-discharged state at 3.4\u2009V (light red), discharged state at 2\u2009V (purple), and recharged state at 4.8\u2009V (blue). Additionally, Fig.\u00a02b shows the voltage profile and state of charge used for structural analysis, and Fig.\u00a02c displays the phase fractions at each state obtained via Rietveld refinement (Supplementary Fig.\u00a0S13). As shown in Fig.\u00a02a and Supplementary Fig.\u00a0S14, the ex situ XRD data of T-FeF3 indicate that the diffraction pattern largely retains the diffraction patterns of P42/mnm structure throughout the charge-discharge process, while certain peaks exhibit shifts and new peaks gradually emerge (Supplementary Note\u00a06). At points 1 and 2 (within the UV range), only a small expansion (0.03\u2009\u00c5, a 0.3% increase) in the c/3 lattice parameter of the tetragonal phase (LixFeFy, x\u2009<\u20090.5, and 0.5 <\u00a0y\u2009<\u20093) was observed, with no noticeable change in phase fraction or occupancy. This gradual shift in the XRD peaks, without a significant change in phase fraction, suggests that the structural evolution in this region is primarily driven by lattice parameter changes rather than a phase transformation, indicating that Li\u207a insertion occurs within the host structure of T-FeF3 in the UV region rather than triggering a phase transition (Supplementary Fig.\u00a0S15 and Fig.\u00a02c). However, as shown in Supplementary Fig.\u00a0S15, during further discharge from point 2 to point 3 (within the LV range), the a and c/3 lattice parameters of the tetragonal phase increased by 0.8% and 1.6%, respectively, and become similar to those of FeF2. Furthermore, the ratio of Fe to F significantly decreased from 1:2.96 (point 2) to 1:2.59 (point 3). At point 3, the similarity of the lattice parameters and Fe\u2013F ratio of the tetragonal phase and FeF2 as well as the phase increase of LiF and FeF2 with the consumption of the tetragonal phase indicates that the conversion reaction of the tetragonal phase to FeF2 occurs near 3\u2009V.\n\na Ex situ XRD patterns of T-FeF3 nanocomposite at different lithiation states in the wide voltage range (WV, 4.8V\u20132.0\u2009V). b Voltage profile for the 10th cycle depending on lithiation state in the WV range, measured at 25\u2009\u00b0C and a current density of 20\u2009mA\u2009g\u22121. c Phase fraction at different lithiation states determined by XRD Rietveld refinement. d The formation energy of T-FeF3 and R-FeF3 at different states of lithiation. e Experimentally measured voltage profile and DFT calculated reaction voltage for T-FeF3 at different states of lithiation.\n\nThis result is also consistently verified by the PDF analysis (Supplementary Fig.\u00a0S16). The PDF patterns at points 1 and 2 were nearly identical, implying that the host structure was maintained. In addition, a shift in the overall pattern and distinct peaks at 2.85 and 4.95\u2009\u00c5, corresponding to LiF, were observed at point 3, indicating structural changes involving LiF formation in the LV region. During the charging process, Li extraction from the lithiated phase and a reconversion reaction occur simultaneously with LiF splitting (points 4 and 5). Given the recovery of the amount of T-FeF3 phase to its initial state after the reconversion reaction, the sequential intercalation and conversion reaction appear to be highly reversible. The reversibility of the reaction mechanism of the UV and LV regimes was verified through electrochemical-cycle evaluation across various voltage ranges after the initial 10 cycles to form T-FeF3 (Supplementary Fig.\u00a0S17). At the 100th cycle, the electrode operated by only intercalation within the UV range exhibited a capacity retention of 81%, whereas the electrode that underwent the conversion reaction in the LV regime exhibited a lower capacity retention of 69%. The capacity decrease in the LV regime appears to stem from the voltage being too low to split LiF41,44,49,50,51 (Supplementary Note\u00a07) that is necessarily required for the reversible reconversion reaction.\n\nUsing the DFT calculation, the reaction mechanism of T-FeF3 was further verified by comparing the reaction voltage with experimental data and R-FeF3. Li0.5FeF3 (s.g. P42/mnm) based on a previous report42 and FeF3 (s.g. R3-c) from Materials Project52 were used as host structures for lithium intercalation into tetragonal and rhombohedral structures, respectively (Supplementary Data\u00a01\u20134). Based on previous reports21,42, Li/Fe disordering in tetragonal structures and stacking faults in rhombohedral structures were also considered. In addition, the phase diagram of the Li\u2013Fe\u2013F system was constructed to evaluate the conversion reaction (Supplementary Fig.\u00a0S16).\n\nFigure\u00a02d shows the formation energies of LixFeF3 calculated using DFT. The rhombohedral and tetragonal structures are the most stable at the fully delithiated (x\u2009=\u20090) and lithiated (x\u2009=\u20091) states, respectively. Upon considering the conversion reaction, it was found that the conversion into LiF and FeF2 is energetically more stable than maintaining the tetragonal LiFeF3 structure at the lithiated state. Notably at x\u2009=\u20090.5, the energy differences among various structures are quite small compared to those at fully delithiated and lithiated states. According to Pauling\u2019s third rule53, the structures with edge-sharing or especially face-sharing cationic octahedra are less stable than those with only corner-sharing due to longer cationic distances of corner-sharing Fe octahedra, reducing repulsion between them, a trend confirmed in our calculations (Supplementary Fig.\u00a0S20). As a result, in the fully delithiated state, the rhombohedral structure, which features exclusively corner-sharing Fe octahedra, is significantly more stable than the tetragonal structure with some edge-sharing connections, as shown in Fig.\u00a0S21. In addition, in the fully lithiated state, the tetragonal phase, lacking face-sharing Fe and Li octahedra, is more stable than other configurations (Supplementary Fig.\u00a0S22a\u2013d). Additionally, the structural characteristics of FeF2, which lacks face-sharing octahedra (Supplementary Fig.\u00a0S22e) and features longer distances between cations than the tetragonal LiFeF3 structure, enhance its stability. This contributes to the lower formation energy of LiF and FeF2 than for one of LiFeF3 structure, leading to energetically favorable decomposition of LiFeF3 into LiF and FeF2.\n\nBased on the formation energy of LixFeF3 (0\u2009\u2264\u2009x\u2009\u2264\u20091) in Fig.\u00a02d, the voltage profiles for the lithiation and delithiation reaction of tetragonal and rhombohedral LixFeF3 structures are calculated as shown in Fig.\u00a02e and Supplementary Fig.\u00a020. The anion orderings of the tetragonal and rhombohedral structures of LixFeF3 are distinctly different (Supplementary Figs.\u00a0S21\u00a0and S22). If the energy barrier for the phase transition to a more stable polymorph is high, the metastable phase can be kinetically stabilized, making the transition to a more stable polymorph less likely to occur54. Thus, assuming the host anion framework is preserved during cycling, the voltage profiles of tetragonal and rhombohedral structures are calculated based on the topotactic reaction.\n\nIn the high-voltage region of LixFeF3 (0\u2009\u2264\u00a0x\u2009\u2264\u20090.5), the calculated voltage of the tetragonal structure is 3.64\u2009V for the reaction from ordered tetragonal FeF3 to disordered tetragonal Li0.5FeF3. Based on a previous report42, the disordering between Li and Fe sites occurs in the tetragonal phase after cycling. When considering this disordering, the voltage between disordered tetragonal FeF3 and Li0.5FeF3 increases to 3.95\u2009V, which is similar to our experimental results (Fig.\u00a01c and Fig.\u00a02e). At this point, the energy difference between ordered and disordered Li0.5FeF3 is quite small (9.31\u2009meV/atom), indicating that there are no site preferences of Li and Fe at 25\u2009\u00b0C (Supplementary Fig.\u00a0S24, Supplementary Note\u00a08 and Supplementary Data\u00a05). For the rhombohedral structure, the calculated voltage is 3.27\u2009V for the reaction from the rhombohedral FeF3 to Li0.5FeF3 with stacking faults.\n\nIn the low-voltage region (0.5\u2009\u2264\u2009x\u2009\u2264\u20091), the reaction mechanisms of tetragonal and rhombohedral structures are different. In the tetragonal structure, a conversion reaction occurs from the disordered tetragonal Li0.5FeF3 to LiF and FeF2 with a reaction voltage of 3.27\u2009V. In contrast, the reaction voltage of the rhombohedral structure is 2.72\u2009V through the insertion reaction between the rhombohedral Li0.5FeF3 and LiFeF3. This difference originates from the structure of thermodynamically stable FeF2. At a fully lithiated state (x\u2009=\u20091), LiF and FeF2, as the conversion reaction products, are the most stable, and thus, the decomposition is energetically more favorable compared to the insertion reaction in both structures.\n\nThe structural characteristics of the tetragonal and rhombohedral forms further elucidate these distinct reaction pathways. For the topotactic reaction to occur, the host anion frameworks must be maintained during the reversible reaction. In the case of the tetragonal structure, its anion framework is the same as that of FeF2, only with slight differences in the occupancy and ordering of Fe between tetragonal LixFeF3 and FeF2. Therefore, the phase transition between tetragonal LixFeF3 and FeF2 may occur through the reordering of Fe ions accompanied by the formation or splitting of LiF. In contrast, the anion framework of the rhombohedral structure is distinctly different from that of FeF2. Thus, even though the conversion-reaction products, LiF and FeF2, are thermodynamically more stable than rhombohedral LiFeF3, the kinetic barrier to decomposition might be too high to overcome at room temperature. As a result, the topotactic intercalation reaction path is kinetically more favorable than the conversion reaction in the rhombohedral structure during charge and discharge. However, because the conversion reaction is thermodynamically preferred, LiF and FeF2 can also be formed from rhombohedral structures after long-term cycling21 (Supplementary Fig.\u00a0S25 and Supplementary Note\u00a09).\n\nTo evaluate the effect of maintaining the structural integrity during intercalation and conversion reaction for T-FeF3 on voltage hysteresis and compositional inhomogeneity, we first compared the voltage hysteresis in T-FeF3 and R-FeF3 with galvanostatic intermittent titration technique (GITT) analysis (Fig.\u00a03a). Charge/discharge measurements were performed at 11.2\u2009mAh\u2009g\u22121 (corresponding to 0.05 e- per formula unit) with a current of 20\u2009mA\u2009g\u22121, and each relaxation step was maintained for 3\u2009h until the voltage decay rate (dV/dt) dropped below ~0.01\u2009mV\u2009s\u22121, a criterion commonly used to approximate quasi-equilibrium (Supplementary Fig.\u00a0S26a). All the analyses were performed after 10 cycles to ensure the evolution of T-FeF3 from LiF-FeF2. Figure\u00a03b shows the voltage gap between the relaxed voltages during charge and discharge, which corresponds to reaction pathway-dependent kinetic hysteresis arising from phase-transition and bond-breaking barriers55. Figure\u00a03c presents the extent of voltage change during relaxation, which reflects conventional kinetic polarization related to ion/electron transport. The reaction pathway-dependent kinetic hysteresis was smaller for T-FeF3 than for R-FeF3, and this trend remained consistent even after extended relaxation for 48\u2009h (Supplementary Fig.\u00a0S26b\u2013d), suggesting that the major voltage gap originates not from transient transport polarization but from slow structural transformations such as phase transition and bond breaking/reformation. This voltage difference is prominent at the end of charge or discharge. Both T-FeF3 and R-FeF3 exhibit larger voltage hysteresis during the charging process than during discharge. For T-FeF3, this is mainly attributed to LiF splitting that occurs during charging (Supplementary Note\u00a07), while in the case of R-FeF3, the increased hysteresis likely results from phase transitions involving long-range diffusion. However, this difference in kinetic hysteresis between T-FeF3 and R-FeF3 is relatively insignificant during both charge and discharge states. This is due to the improved reaction rate and mass transfer in both cases using carbon composites with nano-sized particles. Taken together, despite similar particle size and carbon content, these results indicate that the reduced hysteresis in T-FeF3 compared to R-FeF3 stems from differences in reaction pathways and the reversibility of phase transitions rather than from extrinsic kinetic limitations such as transport resistance21 (Supplementary Fig.\u00a0S12 and Supplementary Note\u00a05).\n\na Galvanostatic intermittent titration technique (GITT) profiles of T-FeF3 and R-FeF3 after 10th cycle. The cells were allowed to relax for 3\u2009h after every 11.2\u2009mAh\u2009g\u22121 (corresponding to 0.05 e-/formula unit) discharging/charging at 20\u2009mA\u2009g\u22121 at 25\u2009\u00b0C. b Voltage difference (Vgap\u2009=\u2009Vrelax, charge\u2009\u2013\u2009Vrelax, discharge) between charge and discharge steps after the 3\u2009h relaxation at the same state of lithiation of T-FeF3 and R-FeF3. c Voltage changes after the 3\u2009h relaxation at different states of discharge and charge at T-FeF3 and R-FeF3. d, e, h, i, a scanning TEM (STEM)- electron energy loss spectroscopy (EELS) images of T-FeF3 and R-FeF3 in charged state and discharged state for the energy distribution of the Fe L3-edge peak. The charge state of T-FeF3 (TC) and R-FeF3 (RC). The discharge state of T-FeF3 (TD) and R-FeF3 (RD). These are for the 10th cycle measured at a current density of 20\u2009mA\u2009g\u22121 at 25\u2009\u00b0C. f,\u00a0j EELS spectra of Fe L3,2-edge for each region (1 and 2) in the charged state (TC and RC) and the discharged state (TD and RD). Regions 1 and 2 represent the most oxidized and reduced regions, respectively. g,\u00a0k Fe L3-edge peak energies and L3/L2 ratios observed in the most oxidized regions (closed symbols) and the most reduced regions (hollow symbols) for different particles (n\u2009=\u20094) at each TC, TD, RC, and RD. The distribution and spectra of the Fe L3-edge peaks for these particles are shown in Supplementary Figs.\u00a0S27\u00a0and S28.\n\nTo verify the origin of the low-voltage hysteresis of T-FeF3 regarding compositional inhomogeneity, the distribution of the oxidation state of Fe during charge/discharge was evaluated and compared with that of R-FeF3 using scanning TEM coupled with electron energy-loss spectroscopy (STEM\u2013EELS) analysis. Figure\u00a03d\u00a0and e present a color map of the energy distribution of the Fe L3-edge peak (high: blue, low: pink) for charged T-FeF3 (TC) and R-FeF3 (RC), respectively. TC shows a spatially uniform energy distribution, whereas RC exhibits a relatively inhomogeneous distribution. Figure\u00a03f displays the Fe L3,2-edge EELS spectra for the local regions having the highest (1) and lowest (2) Fe L3-edge peak energy in TC and RC. The spectra for region 1 are similar for both TC and RC. However, in region 2, TC is characterized by the copresence of peaks at both high (711.25\u2009eV) and low energies (709.5\u2009eV), indicating partial reduction, whereas only a peak at low energy is predominantly displayed for RC, indicating that it is almost fully reduced to Fe2+. The larger deviation of the iron oxidation state for the charged state, as indicated by the Fe L3-edge energy and L3/L2 intensity ratio, is commonly observed across various particles (Fig.\u00a03g and Supplementary Fig.\u00a0S27). TC has a Fe L3-edge energy variation of 0.95\u2009eV between the oxidized and reduced regions, whereas RC shows a larger energy variation of 1.16\u2009eV, which is also consistently observed in the L3/L2 intensity ratio. The presence of compositional inhomogeneity was also observed even after the discharge; however, the difference of inhomogeneity between T-FeF3 and R-FeF3 was less than that for the charged state. As shown in Fig.\u00a03h\u00a0and i, discharged T-FeF3 (TD) and R-FeF3 (RD) exhibit similar Fe L3-edge peak energy distributions. Both TD and RD have Fe L3,2-edge spectra reduced to Fe2+ in region 2 and partially oxidized spectra in region 1 (Fig.\u00a03j). The Fe L3-edge energy and L3/L2 intensity ratio observed across various particles indicate that TD and RD have similar Fe oxidation state distributions (Fig.\u00a03k and Supplementary Fig.\u00a0S28). Therefore, the comprehensive results indicate that composition inhomogeneity is more pronounced at the charged state than the discharged state, which implies that the composition inhomogeneity is governed by the reversibility of the reconversion reaction rather than the conversion reaction. This result is consistent with the larger value of mitigated voltage hysteresis at the charged state (0.28\u2009V) than the discharged state (0.12\u2009V) for T-FeF3 compared to R-FeF3 (Fig.\u00a03b). Thus, mitigated compositional inhomogeneity of T-FeF3 is expected to result in not only low-voltage hysteresis but also highly reversible cycle stability compared to R-FeF3.\n\nThe cycle stability of T-FeF3 was evaluated and compared with that of R-FeF3 to validate the reversibility of T-FeF3 intercalation and conversion sequential reaction (Fig.\u00a04a). R-FeF3 exhibited continuous capacity decay, maintaining only 50% of its capacity after 300 cycles. In contrast, T-FeF3 demonstrated improved capacity retention of 72% after 300 cycles (Fig.\u00a04b and c). Even at a higher specific current of 100\u2009mA\u2009g\u22121, stable capacity retention of 74% was maintained (Fig.\u00a04a). Figure\u00a04d displays the rate performance of T-FeF3 at various specific currents ranging from 20 to 1000\u2009mA\u2009g\u22121. At the high specific current of 1000\u2009mA\u2009g\u22121, R-FeF3 showed a capacity of 120\u2009mAh\u2009g\u22121, whereas T-FeF3 maintained an improved capacity of 136\u2009mAh\u2009g\u22121 (Supplementary Fig.\u00a0S29). Moreover, R-FeF3 exhibited capacity decay across all specific currents and a significant drop in capacity upon returning to 20\u2009mA\u2009g\u22121. However, T-FeF3 displayed stable capacity retention overall, demonstrating its reversibility under various specific current conditions. This cycling performance of T-FeF3 is attributed to the minimized structural evolution, with the analogous anion framework maintained despite undergoing both Li+ insertion and conversion reactions, which is closely linked to a recent report that maintaining the structural integrity of an amorphous structure after a conversion reaction can ensure structural and electrochemical reversibility56. Thus, minimal structural change leads to better capacity retention for T-FeF3 compared to previously reported carbon-composited R-FeF3 (Fig.\u00a04e and Supplementary Fig.\u00a0S30).\n\na Cycle stability of T-FeF3 and FeF3, measured at 25\u2009\u00b0C and a current density of 50\u2009mA\u2009g\u22121. b, c Electrochemical profile of T-FeF3 and R-FeF3 at various cycles. d Rate performance of LiF-FeF2 and FeF3. e Comparison of capacity retention of T-FeF3 and iron fluoride materials mixed with carbon. The electrochemical stability of iron fluoride materials was evaluated in the 1-electron transfer range (Discharge cutoff voltage ~2\u2009V).\n\nThe reversibility of T-FeF3 is maintained even at deep discharge of the conversion reaction to LiF and Fe metal. Figure\u00a05a shows the capacity retention when the low cut-off voltage range is continuously varied back and forth from 2 to 1.5\u2009V. The initial 10 cycles were preceded within a 4.8\u20132.0\u2009V voltage range (point 1, 10th cycle) to form T-FeF3. Then, the voltage range was changed to 4.8\u20131.5\u2009V for 10 cycles to induce drastic structural evolution, forming the Fe metal phase (point 2, 20th cycle). Subsequently, the voltage range was recovered to 4.8\u20132.0\u2009V for another 20 cycles (point 3, 40th cycle) to validate the reversibility. The characteristic 4\u2009V redox feature of T-FeF3 was absent in the differential curve, and the electrochemical profile is quite analogous to R-FeF3 at point 2 (Fig.\u00a05b). During deep discharge, Fe metal conversion occurs19,21,31,57,58 (Supplementary Figs.\u00a0S31\u201333 and Supplementary Note\u00a010), which involves long-range diffusion and could exacerbate the compositional inhomogeneity19,21. As shown in Supplementary Fig.\u00a0S34, when cycling under deep discharge conditions involving the conversion reaction to LiF and Fe, both R-FeF3 and T-FeF3 commonly experience capacity degradation. Interestingly, despite these harsh conditions (deep discharge), T-FeF3 exhibits a reversible recovery of its characteristic 4\u2009V redox process at Point 3. Consequently, the electrochemical profile of Point 3 closely resembles that observed at Point 1. This feature is repeatedly observed during cycling with a periodically altering cut-off voltage (Supplementary Fig.\u00a0S35). In contrast, R-FeF3 did not exhibit the 4\u2009V redox feature at any point (Fig.\u00a05c and Supplementary Fig.\u00a0S36). This finding indicates that T-FeF3 can be reversibly recovered even after undergoing a conversion reaction involving severe structural evolution. Furthermore, note that the lithiated state after deep discharge appears to be the same as LiF and Fe metal for both T-FeF3 and R-FeF3, given the similarity of the electrochemical profile at point 2; however, it could in fact be different.\n\na\u2013c The cycle ability and differential analysis of the voltage profile of T-FeF3 and R-FeF3 with repeated changing discharge cut-off voltage (2-1.5-2\u2009V), measured at 25\u2009\u00b0C and a current density of 20\u2009mA\u2009g\u22121. d The XRD pattern of charged states for T-FeF3(T) and R-FeF3(R) at each point in the cycle was measured at the changing cut-off voltage. Point 1 (10th cycle), point 2 (20th cycle), and point 3 (40th cycle). The tetragonal phase (arrows), LiF (inverted triangle), and Fe metal (diamond). e The cycle ability of T-FeF3 with repeated changing discharge cut-off voltage (2-1.2-2\u2009V). Changes of Differential analysis of the voltage profile of T-FeF3 and R-FeF3 from 20 to 40 cycles. f, g Blue and pink are the change conditions of 2-1.5-2\u2009V and 2-1.2-2\u2009V discharge cut-off voltage of T-FeF3, respectively. These were measured at 25\u2009\u00b0C and a current density of 20\u2009mA\u2009g\u22121. h Yellow is the change condition of 2-1.5-2\u2009V discharge cut-off voltage of R-FeF3.\n\nTo reveal the structural reversibility of T-FeF3 during the reconversion reaction even after deep discharging cycles, the charged-state structures of T-FeF3 and R-FeF3 at each point in Fig.\u00a05a are analyzed (Fig.\u00a05d). The labels T and R refer to T-FeF3 and R-FeF3, respectively, with the subsequent numbers indicating the corresponding points in Fig.\u00a05a. At point 1, each structure of T-FeF3 and R-FeF3 was well maintained respectively (T1 and R1). After deep discharge, R-FeF3 showed very broad diffraction peaks of LiF and Fe metal due to the nanosized domain caused by the previously observed phase displacement involving long-range diffusion (R2)21,59,60. At point R3, unlike at R1, not the rhombohedral phase but the tetragonal phase (arrows) was observed with LiF (inverted triangle) and Fe metal (diamond). This indicates that irreversible phase separation by long-range diffusion has occurred (Supplementary Note\u00a010), and the tetragonal phase that has resulted from this process is difficult to activate due to the compositional inhomogeneity. On the other hand, the trace of the tetragonal phase was observed for T-FeF3 even after deep discharge at T2 (26.7\u00b0, 33.4\u00b0, 51.8\u00b0, and 55.12\u00b0), and the T-FeF3 phase reversibly recovered to its original state at point T3 while maintaining high crystallinity compared to R3. This result indicates that T-FeF3 exhibits better structural reversibility than R-FeF3. Even in electrochemical evaluation protocols starting with deep discharge, the formation of the 4\u2009V redox feature was observed (Supplementary Fig.\u00a0S36). Furthermore, despite starting with a deep discharge, the capacity retention rate was better than that of R-FeF3 (Supplementary Fig.\u00a0S38). Therefore, the reversibility of T-FeF3 may be attributed to the seed of the remaining tetragonal phase even in the discharged state. To confirm this assumption, electrochemical evaluation was conducted in an over-deep discharge voltage range (4.8\u20131.2\u2009V) to remove the seeds of the tetragonal phase (Supplementary Fig.\u00a0S39). Figure\u00a05e shows the capacity retention under this over-deep discharge condition. At point 3 in Fig.\u00a05e, following the over-deep discharge, significant capacity decay and failure to clearly recover the 4\u2009V redox peak were observed (Fig.\u00a05f\u2013h and Supplementary Fig.\u00a0S40). These results imply that T-FeF3 can achieve reversible structural recovery if the phase seed remains, whereas irreversible structural changes occur in the case of R-FeF3, regardless of the presence of the phase seed due to the irreversible reactions accompanying the reaction mechanism.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63676-9/MediaObjects/41467_2025_63676_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63676-9/MediaObjects/41467_2025_63676_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63676-9/MediaObjects/41467_2025_63676_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63676-9/MediaObjects/41467_2025_63676_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63676-9/MediaObjects/41467_2025_63676_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Figure\u00a06a illustrates the reaction mechanism of the LiF-FeF2 nanocomposite as predicted from both experimental and computational studies.\n\na Crystal structures of the LiF-FeF2 nanocomposite, T-FeF3, and the intermediate phase derived from experimental data and DFT calculations. b Schematic illustration comparing the reaction mechanisms of R-FeF3 (reproduced from ref. 21 with permission from Springer Nature) and T-FeF3. Brown and silver balls indicate Fe and F ions, respectively. The schematic is based on electrochemical profiles measured at 25\u2009\u00b0C in the voltage range of 4.8\u20132.0\u2009V with a current density of 20\u2009mA\u2009g\u22121.\n\nInitial cycling: Phase transition into T-FeF3\n\nDuring initial cycling, the LiF-FeF2 nanocomposite electrochemically forms T-FeF3 via LiF splitting:\n\nAs the cycle progresses, a 4\u2009V plateau gradually forms in the electrochemical profile (Fig.\u00a01c). Correspondingly, new peaks at 34.8\u00b0, 40\u00b0, and 66.7\u00b0 are also observed; however, none of the XRD patterns in the charged state significantly deviate from those of FeF2 (Fig.\u00a01d). This finding indicates the electrochemical formation of a tetragonal phase, which demonstrates that the LiF\u2013FeF2 adopts a tetragonal structure induced by the tetragonal structure of FeF2 rather than the thermodynamically stable rhombohedral structure.\n\nPoints 1-2 (UV range): Intercalation reaction\n\nAs shown in Fig.\u00a06, the formed T-FeF3 undergoes an insertion reaction to produce LixFeFy (x\u2009<\u20090.5, 2.5\u2009<\u2009y\u2009<\u20093):\n\nDespite a 0.3% increase in the c/3 lattice parameter within the UV range, the phase fraction and PDF patterns remain almost constant (Supplementary Figs.\u00a0S15 and S16). Moreover, the capacity in the UV range after the initial 10 cycles for forming T-FeF3 demonstrates capacity retention of 93% at the 100th cycle (Supplementary Fig.\u00a0S17). These results suggest a highly reversible reaction through Li+ insertion, which is further confirmed by DFT analysis. The Li ions are inserted into T-FeF3 with Li/Fe site disordering, showing a high reaction voltage near 4\u2009V, which is consistent with the experimental results (Fig.\u00a02e).\n\nPoints 2\u20133 (LV range): Conversion reaction\n\nUpon further discharge (points 2\u20133), LixFeFy (x\u2009<\u20090.5, 2.5\u2009<\u2009y\u2009<\u20093) undergoes a conversion reaction to form LiF and FeF2:\n\nDuring further discharge from Point 2 to Point 3, the consumption of the lithiated LixFeFy phase results in the increase of the phase fraction of LiF and FeF2 (Fig.\u00a02c) and the formation of LiF peaks in the PDF pattern (Supplementary Fig.\u00a0S16), indicating the occurrence of the conversion reaction in the LV range. Additionally, DFT calculations show that the decomposition products, LiF and FeF2, are the most stable in the fully lithiated state (x\u2009=\u20091) (Fig.\u00a02d). Thus, the phase transition occurs from LixFeF3 to FeF2 through the reordering of Fe ions with LiF formation.\n\nPoints 3\u20135: Reconversion reaction\n\nDuring the charging process, T-FeF3 is gradually formed via LiF splitting:\n\nThe gradual progression of the reconversion reaction during the charging process can be confirmed by the changes in the phase fraction refined from ex situ XRD (Fig.\u00a02c). Furthermore, the reversibility in the LV region shows a capacity retention of 69%, unlike 93% in the UV region, due to the absence of a high-voltage operation necessary for LiF splitting41,44,49,50,51 (Supplementary Fig.\u00a0S17 and Supplementary Note\u00a07). This result verifies that the charging process involves a reconversion reaction along with LiF splitting from Points 3 to 5.\n\nBy comparing the reaction mechanism between R-FeF321 and T-FeF3, both materials commonly show intercalation and conversion reactions (Fig.\u00a06b). Although the final products of the conversion reaction (down to 2\u2009V) are the same in both cases (Fig.\u00a02a and Supplementary Fig.\u00a0S41), the FeF2 and LiF converted from R-FeF3 are formed in small amounts in isolated regions, resulting in poor interfacial contact (Supplementary Fig.\u00a0S42). Moreover, from a structural standpoint, T-FeF3 exhibits much higher reversibility than R-FeF3 due to its closer structural similarity to FeF2. This difference is further validated by STEM\u2013EELS analysis performed after 100 cycles. Since it was measured at 100\u2009mA\u2009g\u22121, both T-FeF3 and R-FeF3 show a mixed Fe2+/Fe3+ state in the charged state. However, R-FeF3 displays a significantly greater presence of reduced Fe2+ regions and lower Fe3+ intensity even in the most oxidized areas, indicating more pronounced compositional inhomogeneity compared to T-FeF3 (Supplementary Fig.\u00a0S43). R-FeF3 undergoes a structural change from a corner-sharing FeF6 group structure (rcp) to an edge-sharing tetragonal phase (tcp) during charging and discharge19,21,57,61 It is understood that the significant structural difference between the two structures causes an irreversible phase transition, which leads to compositional inhomogeneity21 (Supplementary Fig.\u00a0S25 and Supplementary Note\u00a09). This inherent structural mismatch and poor interfacial contact between electrochemically formed LiF and Fe species further hinder the reformation of the tetragonal FeF3 phase upon recharging. Additionally, when LiF and Fe metal of the discharge state reconvert, the fcc(LiF)\u2013tcp transition is preferred over the fcc(LiF)-rcp transition. This leads to promotion of the irreversible FeF2 phase formation and worsens the compositional inhomogeneity compared to the initial state21. Moreover, the compositional inhomogeneity can lead to localized variations in the reaction kinetics, with slower kinetics in inhomogeneous regions resulting in greater hysteresis19,20. Therefore, despite proposed strategies such as nanosizing and compositing with conductive materials to kinetically suppress the voltage hysteresis, unresolved hysteresis is still observed19,21,26,27,28,29,30,31. In contrast, T-FeF3 can easily transform into FeF2 through metal migration within the same anion framework, involving relatively shorter diffusion compared to the rcp\u2013tcp transition. Therefore, unlike R-FeF3, T-FeF3 shows high reversibility due to the structural similarity to the phase after the conversion reaction, resulting in mitigated compositional inhomogeneity and voltage hysteresis. In this context, evading the phase-displacement reaction accompanying long-distance diffusion can not only mitigate compositional inhomogeneity and voltage hysteresis but also ensure electrochemical reversibility in the conversion reaction.\n\nWe emphasize the potential of tetragonal FeF3 (T-FeF3) derived from LiF-FeF2 nanocomposites to address the issues of compositional inhomogeneity and voltage hysteresis in iron fluoride positive materials. LiF-FeF2 nanocomposite successfully guided the phase transition into metastable T-FeF3 while maintaining the structural framework of FeF2. Due to the structural integrity, lower voltage hysteresis is achieved for T-FeF3 under conversion reaction into FeF2 with mitigated compositional inhomogeneity. This result starkly contrasts with that for R-FeF3, which inevitably suffers from compositional inhomogeneity induced by irreversible phase transitions into FeF2. As a result, although T-FeF3 undergoes sequential insertion and conversion reactions, this material maintained 72% and 74% of its capacity at 50 and 100\u2009mA\u2009g\u22121, respectively, over 300 cycles. In addition, its energy efficiency improves from 81% for R-FeF3 to 87% for T-FeF3. Moreover, the reversibility of the T-FeF3 phase recovery was further validated even after conversion into LiF and Fe metal phases if the seeds of the tetragonal phase remain. Our study suggests that harnessing the conversion reaction that maintains structural integrity can resolve the chronic issues of large voltage hysteresis and low structural reversibility for conversion reaction electrode materials. Furthermore, our approach of using nanocomposites to design positive materials could offer a new direction as a model system for developing rechargeable batteries with high specific energy using conversion chemistry. Hereafter, further investigations, such as developing micron-scale particles and optimizing bulk synthesis methods, are needed to enable the practical application of designed materials.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63676-9/MediaObjects/41467_2025_63676_Fig6_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "In an Ar-filled environment, a mixture of iron (II) fluoride (anhydrous, 98%, Alfa Aesar) and lithium fluoride (\u226599.99% trace metals basis, Sigma-Aldrich) powders in a 1:1.2 molar ratio was sealed. The sealed powders were ball-milled using a Pulverisette 7 premium line high-energy ball mill at 500\u2009rpm for 48\u2009h in an 80\u2009mL silicon nitride jar with silicon nitride balls at a ball-to-powder weight ratio of 20:1. To the ball-milled mixture, 20\u2009wt.% of graphite powder (\u2212200 mesh, 99.9995% metals basis, Alfa Aesar) was added and then ball-milled at 500\u2009rpm for 12\u2009h under the same milling conditions to synthesize the LiF-FeF2 nanocomposite. R-FeF3 was prepared by sealing iron (III) fluoride (anhydrous, 97% min, Alfa Aesar) and graphite powder in a 1:0.2 mass ratio in an Ar-charged environment and then high-energy ball milling at 500\u2009rpm for 48\u2009h using the same silicon nitride jar and balls with a ball-to-powder weight ratio of 20:1.\n\nThe prepared active material, conductive agent (Super P), and binder (polyvinylidene fluoride, Solef 5130, Solvay) in a 7:2:1 weight ratio were dispersed in N-methyl-2-pyrrolidone (NMP, >\u200999.5%, Sigma-Aldrich) using a planetary centrifugal mixer (AR-100, Thinky). The prepared slurry was cast onto Al foil (99.3% purity, 20\u2009\u03bcm thickness) with a loading level of approximately 1.5\u2009mg\u2009cm\u22122, and a positive electrode with a diameter of 12.5\u2009mm (puncher, P02M, Rohtec) was subsequently prepared. Coin cells (CR2032) were assembled in an Ar-filled glove box (O2\u2009\u2009<\u2009\u20090.1 ppm, H2O\u2009\u2009<\u2009\u20090.1 ppm). A 1\u2009M LiPF6 solution in a 1:1 volume ratio of ethylene carbonate and dimethyl carbonate (Enchem) was used as the electrolyte. To fully wet the separator and electrodes, 150\u2009\u03bcL of the electrolyte was injected into each coin cell. Glass microfiber filters (Whatman GF/C, 1.2\u2009\u03bcm pore size, 0.26\u2009mm thick) served as the separators (19\u2009mm), and Li-metal foil (0.1\u2009mm thick, FMC) was used as the negative material. Electrochemical analyses were conducted at 25\u2009\u00b0C using a battery testing system (WBCS 3000, WonATech). For electrochemical measurements in a fluorine-free environment, polyacrylonitrile (PAN, average Mv 150000, Sigma-Aldrich) binder and 1\u2009M LiClO4 electrolyte (Enchem) were used instead of polyvinylidene fluoride binder and 1\u2009M LiPF6 electrolyte. GITT was conducted at 20\u2009mA\u2009g\u22121, with the cell allowed to relax for 3\u2009h after each 11.2\u2009mAh\u2009g\u22121 (equivalent to 0.05 e\u2212 per formula unit) discharge/charge step. The rate performance was tested at specific currents of 20,\u00a050, 100, 500, and 1000\u2009mA\u2009g\u22121. All the electrochemical evaluations were performed after 10 cycles at 20\u2009mA\u2009g\u22121 to ensure the formation of T-FeF3 and were verified by repeating three times to obtain reasonable data.\n\nFirst principal calculations were performed using the density functional theory (DFT) as implemented in the Vienna Ab initio Simulation Package (VASP)62. The spin-polarized generalized gradient approximation (GGA) with the Perdew-Burke-Ernzerhof (PBE) functional63 was applied. To correct the self-interaction error in iron 3\u2009d states, we added the Hubbard-type U parameter (GGA\u2009+\u2009U) of 4.0\u2009eV as employed in a previous computational study21. A plane-wave basis set was used with an energy cutoff of 520\u2009eV, and 3\u2009\u00d7\u20093\u2009\u00d7\u20093 k-point grid on the Gamma-centered mesh were used for the calculations. All structures were fully relaxed until the forces on each atom were below 0.05\u2009eV.\n\nAll distinct Li-vacancy orderings for tetragonal and rhombohedral structures within the unit cell of LixFeF3 (x\u2009=\u20090, 0.5, 1), including 16 formula units, were generated, and the 30 configurations with the lowest electrostatic energy at each Li content were conducted by GGA\u2009+\u2009U. For the tetragonal structure, the host structure was Li0.5FeF3 (s.g. P42/mnm), and Li/Fe disordering was also considered based on the previous report42. To consider the Li/Fe disordering in the tetragonal structure, the structures with Li-Fe orderings were generated using the same enumeration technique for Li-vacancy ordering, and the energies of these structures were calculated. The host structures for rhombohedral structures were FeF3 (s.g. R3-c) from Materials Project52 with stacking faulted structures that were suggested in a previous study21. To generate additionally Li inserted rhombohedral FeF3 and tetragonal Li0.5FeF3 structure, the cation insertion algorithm was applied53. The charge densities of each host structure were calculated, and the local minima sites of them were suggested through this algorithm. In addition, the ground states for tetragonal and rhombohedral LixFeF3 structures were known as antiferromagnetic state (AFM)21, thus, all tetragonal and rhombohedral structures were calculated with antiferromangetic or weak ferromagnetic response. The voltage profiles were obtained from the DFT energies of the most stable configurations at each Li contents as64\n\nwhere E(LixFeF3) and E(Li) are the DFT energy of the most stable LixFeF3 structure and bcc Li metal, and F is the Faradaic constant.\n\nFor analysis of phase stability of the Li-Fe-F system, the structures in Li-Fe-F system were obtained from Materials Project52 and formation energies of each calculated structure at their respective compositions were calculated based on the energy of pure elements such as Li, Fe, and F. The phase diagram was plotted based on the convex hull from formation energies.\n\nFor clear structural analysis, the XRD patterns of the samples were measured at the 6D UNIST-PAL beamline at the Pohang Accelerator Laboratory (PAL). The patterns were collected over a 2\u03b8 range of 10\u00b0 to 110\u00b0 with an X-ray beam wavelength of 1.5406\u2009\u00c5. For ex situ XRD pattern measurements, cells were disassembled in an Ar atmosphere at an average temperature of 24\u2009\u00b1\u20092\u2009\u00b0C after reaching specific voltages, and the electrodes were retrieved and washed with diethyl carbonate. The cleaned electrodes were collected from the Al foil, sealed in capillaries, and stored in double vacuum packaging until measurement. All XRD patterns were recorded with an exposure time of approximately 2\u2009min. Each sample was measured twice to obtain the XRD patterns, ensuring the removal of noise, outliers, and spikes caused by high-energy X-rays. Rietveld refinement of the XRD data was performed using FullProf software. Total scattering data for PDF analysis were obtained using a PANalytical Empyrean with Ag-K\u03b1 radiation source, a Rh K\u03b2 filter, and a GaliPIX3D detector. The powder sample was loaded in a 0.4-mm glass capillary in an Ar-filled glovebox, and each PDF data set was collected for 36\u2009h. The data reduction was performed using PDFGetX3 software65, and real-space data were fitted using PDFgui66. The damping factor associated with this instrumental configuration was calibrated by refining the structure of a silicon sample as a reference.\n\nThe particle size of the samples was observed through scanning Electron microscope (SEM) images taken at 10\u2009kV using a field-emission scanning electron microscope (Nova Nano SEM, FEI). To characterize the crystallographic features, transmission electron microscopy (TEM) was employed. To perform ex situ analyses, the coin cells were disassembled after reaching specific voltages, and the recovered electrodes were washed with dimethyl carbonate (DMC). Samples separated from the aluminum current collector were dispersed in DMC using ultrasonic treatment and loaded onto TEM grids. The loaded TEM sample grids were vacuum sealed until measurement to minimize air exposure. All these processes were performed under an Ar atmosphere at an average temperature of 24\u2009\u00b1\u20092\u2009\u00b0C. TEM images and STEM-EELS measurements were conducted using a Cs-corrected JEM-ARM300F (JEOL). For elemental mapping in STEM-EELS, energy dispersion was set to 0.25\u2009eV per channel at 160\u2009kV. To minimize electron beam damage to the samples and stabilize the beam, a beam shower was performed for approximately 15\u2009min before measurements.\n\nX-ray Absorption Spectroscopy (XAS) was performed to observe the local structure and oxidation state of Fe. The Fe K-edge spectra were measured in transmission mode using a Si(111) double-crystal monochromator at the PAL 6D beamline. Energy calibration was carried out through a standard iron foil, and the reference was measured simultaneously. For ex situ XAS analysis, the measured coin cells were disassembled, and the electrodes were recovered and washed with dimethyl carbonate (DMC) in an Ar atmosphere at an average temperature of 24\u2009\u00b1\u20092\u2009\u00b0C. To prevent air exposure, the electrodes were sealed with Kapton tape and stored in double vacuum packaging until measurement. The XANES and EXAFS spectra were processed using Athena software, with Fourier transforms of the EXAFS spectra performed in the k-range of 3.0\u2009~\u200911.5\u2009\u00c5\u22121 weighted by k2. EXAFS fitting was conducted using Artemis software. The F K-edge spectra were collected in total electron yield (TEY) mode at the 10D KIST bending magnet beamline of PLS-II under a base pressure of 1\u2009\u00d7\u200910\u22129\u2009Torr. The spectra were normalized to the incident photon flux with an energy resolution of 0.1\u2009eV.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data supporting the findings of this study are available in the manuscript and its Supplementary Information. The atomic coordinates of the optimized computational structures are provided as plain text files in Supplementary Data\u00a01\u20135, packaged in a single zip file. No custom code was used in this study.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Chu, S., Cui, Y. & Liu, N. The path towards sustainable energy. Nat. 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While control trained flies showed a reduction in their time of courtship when compared to control na\u00efve animals, TNTG expression in CAMEL neurons abolished this difference and impeded courtship LTM (Fig.\u00a04C), as previously shown for olfactory aversive LTM47. Overall, these findings suggest that the CAMEL tool does label putative engram cells for courtship LTM.\n\nA Example of CAMEL-GFP (yellow) mushroom body co-stained with anti-Fasciclin II (magenta). Scale bar represents 50\u2009\u03bcm. B Box plot showing CAMEL-GFP positive cells for na\u00efve and trained flies of wt and Adcy1 mutant backgrounds. Statistical significance was determined using a\u00a0two-tailed t-test, p\u2009=\u20090.015 for WT Na\u00efve (WT-N; n\u2009=\u200911) vs WT Trained (WT-T; n\u2009=\u200916), and p\u2009=\u20090.7572 for Adcy1 Na\u00efve (Adyc1-N; n\u2009=\u200911) vs Adcy1 Trained (Adcy1-T; n\u2009=\u200917). Box: 25th-75th percentile, whiskers: full data range, line: median Source data are provided as a Source Data file. C Box plot showing courtship indices (CIs) for CAMEL>eGFP (WT CAMEL) and CAMEL>TNTG for LTM assays 24\u2009h after training. Statistical significance was determined using a two-tailed Mann-Whitney test p\u2009=\u20090.004 for WT CAMEL Na\u00efve (WT-CAMEL N) vs WT CAMEL Trained (WT-CAMEL T), p\u2009=\u20090.214 for TNTG CAMEL na\u00efve (TNT-CAMEL N) vs TNTG CAMEL Trained (TNT-CAMEL T). Number of flies tested for each condition is shown under corresponding dot plot. D Experimental scheme to isolate CAMEL positive MB neurons. Three experimental groups of flies were used: WT na\u00efve (green), WT trained (blue) and Adcy1 mutant trained (red). WT and Adcy1 mutant trained animals were tested for 10\u2009min, and 2\u2009h later brains were dissected, dissociated, selected by FACs and sequenced (scRNAseq). E Box plot showing courtship indices (CIs) for the three experimental groups for LTM assays. Statistical significance was determined using a\u00a0two-tailed Mann-Whitney test p\u2009=\u20090.0214 for WT Na\u00efve vs WT Trained, p\u2009=\u20090.0459 for WT Trained vs Adcy1 Trained, and p\u2009=\u20090.3395 for WT Na\u00efve vs Adcy1 Trained.\u00a0Box: 25th-75th percentile, whiskers: full data range, line: median. Number of flies tested for each condition is shown under corresponding dot plot. Selected flies used for dissection and subsequent scRNAseq were shown as blue dots, with a Courtship Index below 0.72 or at least 3 unsuccessful courtship trials (threshold marked by a dotted line in the trained condition). Source data are provided as a Source Data file.\n\nWe performed FACS followed by single cell RNA-seq on CAMEL positive putative engram neurons under three experimental conditions: wild type trained (WTT), wild type non-trained (na\u00efve), and trained Adyc1 mutants (Fig.\u00a04D). A fraction of WTT\u00a0animals clearly showed reduced courtship behavior compared to wild type na\u00efve flies and trained Adyc1 mutants 24\u2009h after training (Fig.\u00a04E). For the WTT group, we selected only trained flies with effective memory recall, i.e., with diminished courtship towards a mated female (see Fig.\u00a04E), discarding males with a null courtship index (CI). Adcy1 mutant males also faced a mated female, with no selection afterwards. Two hours after testing (i.e., after memory reactivation, in the case of WTT), we dissected and dissociated 40\u201360 brains per condition, removing the optic lobes. CAMEL positive cells were isolated using FACS (Fig.\u00a04D). The percentage of GFP positive cells identified by FACS was approximately double for WTT (0.24%) compared to na\u00efve (0.12%) (Fig.\u00a0S3D), as expected based on confocal imaging (compare Fig.\u00a04B and Fig.\u00a0S3). We performed deep scRNA-seq (Smartseq2) on CAMEL-positive cells from all three groups. After filtering and quality control, we obtained 48, 37 and 104 cells from brains of na\u00efve, Adcy1 mutant, and WTT flies, respectively.\n\nPrincipal Components Analysis (PCA) of the transcriptomic profiles of CREB-activated MB neurons of WTT flies revealed two distinct clusters, named WTT-1 and WTT-2 (Fig.\u00a05A). To understand the identity of these cells, we determined the expression of known general MB identity genes, such as ey, Dop1R2, Oamb, mub, dac and prt (Fig.\u00a05B and Fig.\u00a0S4). These markers were expressed in most WTT-1 cells, but not in WTT-2. This suggests that the WTT-1 population likely represents CREB activated MB cells contributing to memory, while the WTT-2 population represents CREB activated non-MB cells. To understand the similarities of neurons from WTT flies with the other two experimental groups, we added cells from na\u00efve (no training) and Adcy1 mutant (ineffective training) flies to the PCA analysis (Fig.\u00a05C). This distinguished five neuronal populations, clusters 1\u20135 (Fig.\u00a05D). Visual representation of the most differentially expressed genes (DEGs) among the five clusters showed clear differentiating transcriptional signatures, although very similar between cluster 1 and 2 (Fig.\u00a05E shows the top 10 DEGs and Fig.\u00a0S5 shows the top 20 DEGs).\n\nA Principal Component Analysis (PCA) of WT trained flies with successful retrieval (K\u2009=\u200925). WTT-1 and WTT-2 are shown in light blue and purple, respectively. Each dot represents a neuron. B ey expression (white to red scale) in the PCA of WT trained flies. C PCA revealing the experimental condition for each cell: trained (WTT-1 and WTT-2), na\u00efve (green) and trained Adcy1 (red). D The five clusters as defined by PCA (K\u2009=\u200915). E Composition of the five clusters by experimental conditions (na\u00efve, Adcy1, WTT-1 and 2) by percentage. F Violin plots for general mushroom body and lobe marker genes for the five clusters. G Heatmap of the 10 most DEGs among the five clusters. The lower bar in the graph indicates the experimental condition for each sequenced neuron. H Most representative GO terms (FDR\u2009<\u20090.05) for the 4 and 5 clusters, indicating cell component (CC), biological processes (BP) and molecular function (MF).\n\nCluster 1 and 2 did not express general MB markers (ey, Dop1R2, oamb, dac, mub, and prt), suggesting that these clusters represent CREB-activated non-MB cells (Fig.\u00a05F). Regarding the cluster composition, Cluster 1\u20132 contained primarily neurons from na\u00efve flies and cluster WTT-2 of trained animals (81% and 100%, respectively) (Fig.\u00a05G). In contrast, MB neurons of the WTT-1 group were primarily seen in cluster 4 (77%) and cluster 5 (43%) (Fig.\u00a05G), and most cells from these clusters did express general MB markers (Fig.\u00a05F). Cluster 5 was comprised of cells from all conditions, but still the most represented cell population is WTT-1 (Fig.\u00a05G). Cluster 3 was also comprised of cells from all conditions, but the majority (53%) belong to trained Adcy1 mutant flies (Fig.\u00a05G). Based on the expression of MB marker genes, such as Oamb, mub and prt, cluster 3 contained mostly MB cells (Fig.\u00a05F). However, expression of other MB markers related to memory formation (such as Dop1R2 and dac) was low. To assign cells to individual MB lobes, we examined the expression of several MB lobe marker genes50. Expression of the \u03b1\u03b2 and \u03b3 cell marker sNPF was seen in most cells of cluster 3\u20135 and absent in the majority of cells from cluster 1 and 2 (Fig.\u00a05F). \u03b1\u2019\u03b2\u2019 and \u03b3 neurons express trio and mamo, which were highly expressed in Cluster 5. Clusters 1\u20132 also showed high levels of trio and mamo (Fig.\u00a05F), which was expected, since these genes are not exclusively expressed in the MB50. The \u03b1\u03b2 marker prospero is highly expressed in cells of cluster 3\u20134, while the \u03b3-specific marker ab is primarily expressed in cluster 5 (Fig.\u00a05F). We also examined an additional set of 30 markers for different MB cell types50, which overall suggests that cluster 1\u20132, 3\u20134, and 5 have a transcriptional profile reflecting non-MB, \u03b1\u03b2, and \u03b3 neurons, respectively (Fig.\u00a0S4).\n\nGO enrichment analysis of the most DEGs from Cluster 1 and 2 revealed enrichment of terms related to axons, synapses, calcium ion binding, GTPase activity, mitochondria, cellular respiration, and transmembrane transporter activity (Fig. S5 and Supplementary Data\u00a05). These genes might reflect general neuron activity that would be expected in CREB activated cells. Cluster 3, mostly composed of Adcy1 mutant cells, was enriched for ribosomal and endoplasmic reticulum components, as well as in genes related to ubiquitin and catabolic processes (Fig.\u00a0S5 and Supplementary Data\u00a05). Cluster 4, which is mostly composed of WTT-1 cells with \u03b1\u03b2 lobe identity, was enriched for the GO term \u2018learning and memory\u2019 as well as terms related to synaptic transmission (Fig.\u00a05H and Supplementary Data\u00a05). Cluster 5, which is enriched for WTT-1 cells from MB \u03b3 neurons, showed enrichment of terms related to memory signaling pathways, including \u201cG protein-coupled receptor signaling\u201d and \u201ccalmodulin binding\u201d. Both clusters 4 and 5 are therefore transcriptionally enriched in processes classically associated with LTM formation (Fig.\u00a05H and Supplementary Data\u00a05).\n\nWe also looked at the expression of the 68 core TIGs that we identified as part of the transcriptional trace of memory consolidation at 1\u2009h after courtship LTM training (Fig.\u00a03). Cluster\u00a03, which is composed mostly of MB cells from the memory deficient Adcy1 mutant, showed low expression of the 68 TIGs. Non-MB clusters\u00a01\u20132 displayed strong induction consistent with an activated-CREB response. Clusters\u00a04\u20135, representing MB cells from trained animals, exhibited renewed expression indicative of maintenance in expression of learning and memory consolidation genes (Fig.\u00a0S5).\n\nOur data suggests that the formation of Clusters 1\u20135 was driven by a combination of cell-type specific markers as well as components of the CREB-activated memory transcriptome that were not cell-type specific. To identify memory candidate genes independent of MB identity we removed 699 genes that showed enriched expression in the MB in our INTACT data (Supplementary Data\u00a06 and Fig.\u00a0S1) and other studies50,51. PCA analysis of the remaining genes revealed three clusters, A, B, and C (Fig.\u00a06A). Cluster A was mainly formed by non-MB neurons from na\u00efve and WTT-2, with 90% of cells derived from the previous Clusters 1 and 2 (Fig.\u00a06B\u2013D). Cluster B was mostly composed of \u03b1\u03b2 neurons from the Adcy1 mutant (51%), with most cells derived from the previous cluster 3 (Fig.\u00a06B\u2013D). Cluster C was mostly composed of WTT-1 cells (63%), thus merging previous cluster 4 (\u03b1\u03b2 neurons) and 5 (\u03b3 neurons) (Fig.\u00a06B\u2013D). Again, all clusters contain some na\u00efve and Adcy1 mutant cells.\n\nA The three clusters as defined by PCA (K\u2009=\u200925). Cluster A, B and C are marked in blue, yellow and green, respectively. B PCA revealing the experimental condition for each cell: WTT-1 (light blue), WTT-2 (purple), na\u00efve (green) and Adcy1 trained (red) conditions. C Percentage of cells belonging to each of the three clusters by experimental conditions. D Transition plot representing the equivalence between clusters 1\u20135 and clusters ABC. E Heatmap of the 68 core up-regulated TIGs after learning obtained by bulk RNAseq following courtship conditioning. Genes are ordered by their expression levels in cluster C.\n\nIntriguingly, the GO terms enriched among the top DEGs of Clusters A and B were very similar, including enzyme binding, synapse organization, proteolysis, and cytoplasmic vesicle, in spite of the different neuronal identity (Fig.\u00a0S6 and Supplementary Data\u00a07). Cluster C showed enrichment primarily in genes related to energy metabolism, translation, neuron projection development, and chemical synaptic transmission, which were also all enriched terms identified clusters A and B (Fig.\u00a0S6 and Supplementary Data\u00a07). Despite common GO enrichment, the genes driving the enrichment are largely different between the three clusters, suggesting the possibility of alternate functional output (Fig.\u00a0S6). Interestingly, processes such as energy metabolism, translation, and synaptic transmission were also identified to be enriched in the MB specific transcriptional trace of memory formation identified by INTACT (Fig.\u00a01). Consistently, we observed high expression of the 68 core TIGs from memory consolidation in clusters A and C, but not B (Fig.\u00a06E). To summarize, the new cluster C was mainly composed by MB \u03b1\u03b2 and \u03b3 WTT-1 cells of previous cluster 4 and 5, revealing candidate genes potentially relevant in memory processes, independent of MB cell identity.\n\nAs a proof of causality, we compiled a list of candidate genes for functional testing. We first identified the 30 most DEGs among Clusters A, B and C (Fig.\u00a0S7A). Among these, we selected genes that; (i) showed high expression levels in most of the individual neurons within each cluster, (ii) had a clear mammalian ortholog (at least a score of 4 via DIOPT v9.1 from Flybase), and (iii) had a UAS-RNAi line available from the Bloomington Stock Drosophila Center (Fig.\u00a0S7 and Supplementary Data\u00a08). Genes with known roles in memory (such as pkc53E and Pde4) or RNAi processing (such as AGO1), were discarded. We compiled a list of the highest expressed Cluster C genes that showed high expression in at least 50% of cells and selected additional candidate genes from this group (Fig.\u00a0S7 and Supplementary Data\u00a08). Since cells from all conditions (WTT, na\u00efve, and Adcy1 mutants) are present in cluster C, we also selected candidate genes that were highly expressed in more than 80% of WTT-1 neurons and in less than 20% of neurons from the other conditions, favoring uncharacterized genes (annotated with CG numbers) (Fig.\u00a0S7 and Supplementary Data\u00a08). Based on these criteria, we selected 7 genes from cluster A, 6 genes from cluster B, 18 genes from cluster C, and 10 genes from cluster C>80% WTT1. Finally, we selected 7 genes that were consistently induced across different studies using INTACT-RNAseq (Fig.\u00a03C), for a total of 48 candidate genes (Fig.\u00a07A).\n\nA Heatmap of the 48 candidate genes to be tested by courtship conditioning, from cluster A, B, C, C>80% WTT1 and INTACT RNAseq. B Schematic of approach for achieving adult-specific knockdown in the MB of Hr38 and sr. AttP2 insertions from the TRiP collection and the genetic background control strain BDSC36303, were crossed to tub-gal80ts; R14H06-gal4 and the progeny were raised at 18\u2009\u00b0C and transferred to 29\u2009\u00b0C 24\u2009h before STM and LTM training. C Box plot showing courtship indices (CIs) for na\u00efve (N) and trained (T) flies with MB-specific Hr38-RNAi, sr-RNAi, and the respective genetic background controls. Statistical significance between na\u00efve and trained flies was determined using a two-tailed Mann-Whitney test. Box: 25th-75th percentile, whiskers: full data range, line: median.\u00a0Number of flies tested for each condition is shown under corresponding box plot. D Bar graph showing corresponding memory index (MI) derived from the CI (see \u201cMethods\u201d) Statistical significance between MIs was determined using a two-tailed randomization test with 10,000 replicates. E Box plots showing courtship indices (CIs) for na\u00efve (N) and trained (T) flies for candidate genes whose down-regulation in the MB abolishes LTM. A control example is shown. Statistical significance between na\u00efve and trained flies was determined using a two-tailed Mann-Whitney test. Box: 25th-75th percentile, whiskers: full data range, line: median. Number of flies tested for each condition is shown under corresponding box plot. Source data are provided as a Source Data file. F Classification of positive hits according to function. G Percentage of positive hits versus tested candidate genes from each category of cluster (A, B, C and C>80% WTT1) as well as from INTACT RNAseq data.\n\nTo test the potential involvement of these candidate genes in courtship LTM, we performed a memory screen using UAS-RNAi lines under the control of specific MB-Gal4 drivers expressed in \u03b1\u03b2 and \u03b3 lobes; either RH1406-Gal4, or MB247-Gal4 (Figs.\u00a0S1 and S9). We combined them with either UAS-dcr2, to enhance RNAi effectiveness in the case of long hairpins vectors, or tub-Gal80ts, to avoid developmental effects (for specific details, see \u201cMethods\u201d, Fig.\u00a0S8 and Supplementary Data\u00a09). Long hairpin RNAi transgenes were not combined with UAS-dcr2 for tub-Gal80ts experiments, to avoid potentially confounding effects of high levels of dcr2 expression at 29\u2009\u00b0C. The positive hits from the screen are shown in Fig.\u00a07B\u2013E and the full set of experiments can be seen in Fig.\u00a0S8. Control flies displayed normal LTM, with a significant decrease in courtship behavior in trained animals compared to na\u00efve (Fig.\u00a07B\u2013E and Fig.\u00a0S8). RNAi knockdown of 19 candidate genes abolished LTM, with no significant difference observed in courtship behavior between trained and na\u00efve flies (Fig.\u00a07C\u2013E). To test if any memory defects were due to defective development of the MB, we stained the knocked-down MBs for each positive gene with TRIO and FasII antibodies, which label \u03b1\u2019\u03b2\u2019/\u03b3 and \u03b1\u03b2/\u03b3 lobes, respectively. For three genes, LpR1, Smox, and Teneurin-A, we saw clear defects in morphology and therefore excluded them as candidate memory genes (Fig\u00a0S9). The 16 positive hits without major structural alterations were classified according to their functions (Fig.\u00a07F), which included signaling (MAPK-CG7378, WNT-pan), transcription (Hr38, sr, cpo, fs(1)h), ubiquitination (CG2915, CG17691, CG11700), and synapses (svr and coracle). Among the tested genes, 50% from cluster C, 30% of cluster C>80% WTT-1, and 43% of tested core TIGs from INTACT RNAseq were confirmed as memory regulators (Fig.\u00a07G). In contrast, only 14% of cluster A and 17% of cluster B genes were confirmed (Fig.\u00a07G). Our RNAi approach may include false negative results due to insufficient knockdown, or false positives due to off-target effects. Nonetheless, the overrepresentation of positive genes from cluster C and from core TIGs, supports the idea that our transcriptome analyses have revealed novel memory genes participating in memory formation, storage, and possibly recall.\n\nOf the positive hits from our memory screen, two transcription factors, Hr38 and stripe (sr), were previously identified as neuron activity induced genes11,52 and therefore represent candidate ARGs that may govern the transcriptional response to courtship LTM. When Hr38 and sr RNAi were induced in the MB 1 day before training using Gal80ts, we observed defects only in LTM and not in STM compared to genetic background controls containing Gal4 and Gal80ts alone (Fig.\u00a07B\u2013D). In contrast, when we performed knockdown starting 2 days prior to training we did observe STM defects for Hr38, but not Sr (Fig.\u00a0S8I). This indicates that in the adult MB, Sr may have a very specific role in LTM. Hr38, on the other hand, seems to have a broader role, since a 2-day knockdown yielded STM and LTM defects. Two-day Hr38 knockdown in the MB also caused reduced na\u00efve courtship (Fig.\u00a0S8I), consistent with a broader role for Hr38 in memory and social behavior52. Flies that were heterozygous for the\u00a0Hr38 and Sr UAS-RNAi transgenes, with no Gal4 or Gal80, showed normal memory, demonstrating that defects are not induced by the temperature shift alone (Fig.\u00a0S8J). While off target effects are possible with RNAi, the lines used here have no homology to other genes, and have shown no phenotype when expressed with several other tissue specific Gal4 drivers53,54,55,56,57,58,59,60, indicating a low probability of off target effects.\n\nMemory ARGs are not well described for any Drosophila memory paradigm. We therefore compiled a list of known neuron activity-induced genes in Drosophila (n\u2009=\u200914)11, as well as fly orthologs of well characterized human neuron activity-induced genes, including Arc1 (human ARC), Jra (human JUN), kayak (human FOS), and Dysf (human NPAS4). These 18 genes were examined for differential induction across the MB memory transcriptome time course and in our single cell clusters (Fig.\u00a08A, B and\u00a0S10). Hr38 and Sr stood out in our memory time course analysis compared to the other candidate ARGs because they were strongly and immediately induced at a much higher level in the MB than in WH (Fig.\u00a08A, B). Indeed, Hr38 and sr are among the strongest induced transcripts of the MB-specific TIGs (Supplementary Data\u00a02). Transcript levels of Hr38 and sr decline after 1hDT but remain significantly higher than in na\u00efve MBs until 1hAT (Fig.\u00a08A, B). We validated Hr38 and sr expression at 1hAT using MB INTACT followed by qPCR. Hr38 and sr were clearly induced by LTM training (Fig.\u00a08C), but not by STM training (Fig.\u00a08D), further supporting a role in LTM for these TFs. Hr38 was also clearly induced in the memory recall cluster 5 containing WTT-1 neurons (Fig.\u00a0S5). In our cell-type-independent ABC clusters, the 16 candidate ARGs were most induced in cluster A, which is composed of non-MB CREB activated cells. However, a number of Cluster C cells also showed induction of some ARGs, including Hr38 and sr (Fig.\u00a0S10). Overall, these data suggest that the transcription factors Hr38 and sr, known ARGs, may help to shape the transcriptional trace of courtship LTM.\n\nNormalized transcript levels of A Hr38 and B sr. Significance differences identified from differential expression analysis between trained and time-of-day matched na\u00efve flies are indicated (N\u2009=\u20093, Wald\u2019s test, *FDR\u2009<\u20090.1). Black bars indicate nighttime. Real-time quantitative PCR analysis of C Hr38 and D sr induction after STM and LTM training. Statistical significance was determined using a two-tailed t-test, N\u2009=\u20093, p values are indicated. E Hr38 and sr genomic tracks displaying MB chromatin accessibility (ATAC-seq\u2014top panel), CrebB binding signal (ChIP-seq\u2014middle panel), and transcript isoforms of Hr38 and sr (bottom panel). Regions that overlap between CrebB binding signal and accessible MB chromatin are highlighted in light green. F Overlap of binding sites in regions of accessible MB chromatin for the transcription factors (TFs) Hr38, CrebB and Sr with training induced genes (TIGs) identified in the MB during LTM formation. G Relative z-score induction during LTM formation for all TF-bound MB TIGs, MB TIGs bound by two or more TFs, CrebB-bound only, or Sr-bound only. P values were determined using a two tailed t-test (n values are indicated, *p\u2009<\u20090.05). Black bars indicate nighttime. H Percentage of the top 20 genes from the clusters A, B and C that were bound by Hr38, CrebB, and/or Sr. All\u00a0error bars indicate the standard error of the mean.\n\nTo explore the potential mechanisms underlying courtship LTM training-induced gene expression, we analyzed chromatin structure using Assay for Transposase Accessible Chromatin followed by sequencing (ATAC-seq) on INTACT-isolated MB nuclei. Interestingly, both Hr38 and sr show very high accessibility near, or directly surrounding their transcriptional start sites, much higher than the accessibility of even the most highly expressed MB genes (Fig.\u00a0S11). The highly accessible chromatin landscape of Hr38 and sr promoters in the MB could facilitate their immediate and robust induction following courtship LTM training. The CrebB transcription factor has been suggested to induce memory ARGs in other systems. Interestingly, we found that CrebB binding sites, obtained using publicly available whole embryo ChIP-seq data from the ENCODE data portal61, directly overlapped with MB-specific ATAC-seq peaks in the TSSs of Hr38 and sr (Fig.\u00a08E). The presence of CrebB binding at these MB accessible sites suggests that Hr38 and sr could be direct targets of CrebB.\n\nWe next investigated whether the MB-specific transcriptional trace of courtship LTM could be mediated by Hr38, Sr, and/or CrebB. We identified Hr38, Sr and CrebB binding sites using publicly available whole organism (embryo or prepupa) ChIP-seq data61. Potential MB binding sites for Hr38, Sr and CrebB were identified by overlapping ChIP binding sites with MB-specific ATAC-seq peaks, which were highly consistent between two biological replicates (Fig.\u00a0S11 and Supplementary Data\u00a010). If experimentally identified ChIP binding sites from embryo or pupae are present at regions of MB open chromatin, it is very likely that these represent MB binding sites. It is possible that the MB has extra binding sites that are not detected in embryo, and these would be missed in our analysis. Therefore, our data may include false negatives, but our approach also has a strong advantage that it is not likely to produce false positives. Of the 756 MB TIGs that form the MB-specific courtship LTM trace, 306 were bound by either CrebB, Hr38, or Sr and more than half of these were bound by two or more of these TFs (Fig.\u00a08F and Supplementary Data\u00a011). This suggests a high level of redundancy in the activation of TIGs. Interestingly, the 306 TF-bound TIGs show late and post training activation that is significantly greater in the MB than in the WH (Fig.\u00a08G). This MB-specific memory trace is observed when looking at all 306 TF bound TIGs and those bound by two or more TFs (n\u2009=\u2009163), CrebB only (n\u2009=\u200974), or Sr only (n\u2009=\u200963) (Fig.\u00a08G). In addition, we examined TF binding sites among the top 20 differentiating genes in Clusters A, B, and C, from our cell-type-independent scRNAseq analysis. Interestingly, Cluster C genes showed a higher frequency of TF binding, with the majority bound by Sr and CrebB (55%), compared to Cluster A (25%) and Cluster B (10%) (Fig.\u00a08H). Overall, these findings suggest that CrebB, Hr38, and Sr may cooperate to activate expression of a MB-specific transcriptional trace of courtship LTM training that persists from early training, into memory recall.\n\nSeveral studies have been performed in Drosophila that analyze the transcriptome of different neuronal tissues in response to memory and social interaction25,26,27,28,29,30,31,44,62. We found a significant overlap of genes that were induced in two previous datasets produced in our laboratory compared to this study (Fig.\u00a03), however, we wanted to compare more broadly. Winbush et al., looked at gene expression changes in the WH, 24\u2009h after courtship LTM training26. Interestingly, a transcript specific analysis identified about 500 induced genes, which showed significant overlap with MB and WH TIGs identified here, including important memory genes such as svr and PKA-R1 (Supplementary Data\u00a012). Another study by Agrawal et al. compared gene expression changes in dopaminergic neurons (DANs) in group housed (GH) flies compared to solitary housed (SH)62. This social behavior paradigm partially mimics courtship conditioning since na\u00efve flies which are housed alone are compared to trained flies that experienced a social interaction. Interestingly, we found many WH and MB TIGs that were differentially expressed in DANs (Supplementary Data\u00a012). This included the ARGs Hr38, sr, and CrebA.\n\nOther Drosophila memory transcriptome studies have focused on completely unrelated behavioral paradigms including aversive olfactory conditioning. Crocker et al. analyzed transcriptome changes occurring after olfactory memory training in several different neuronal subsets isolated by patch clamp pipetting29. While the total overlap of genes was low with this study, we did find several MB and WH TIGs overlapping with memory induced genes here including Hr38, sr, svr, and sNPF (Supplementary Data\u00a012). Widmer et al. used targeted DamID to profile RNA polymerase II occupancy as a proxy for gene expression over 4 different time intervals spanning up to 72\u2009h after training28. Again, several induced genes were also found among our MB and WH TIGs. Therefore, despite the use of memory assays that are very different from courtship conditioning, we still observe induction of some key memory genes across different studies.\n\nWe also looked at the expression of memory-induced genes from different studies in our single cell clusters. Many of these genes showed high expression levels in Cluster C, representing potential MB engram cells, but were especially enriched in Cluster A, representing non-MB CREB activated cells (Figs.\u00a0S12 and S13). In contrast, activation was generally not as high in cluster B, mostly representative of Adcy1 mutant cells.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64379-x/MediaObjects/41467_2025_64379_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64379-x/MediaObjects/41467_2025_64379_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64379-x/MediaObjects/41467_2025_64379_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64379-x/MediaObjects/41467_2025_64379_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64379-x/MediaObjects/41467_2025_64379_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64379-x/MediaObjects/41467_2025_64379_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64379-x/MediaObjects/41467_2025_64379_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64379-x/MediaObjects/41467_2025_64379_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study we performed a transcriptome time course analysis in memory neurons of the Drosophila MB, during courtship conditioning. This analysis revealed a transcriptional trace of memory consolidation that is active in the MB, and not the WH, near the end of the 7\u00a0h training period and after training, during the time when LTM consolidation is occurring. The memory trace is comprised of genes that contribute to known cellular processes underlying LTM, including actin cytoskeleton remodeling, energy metabolism, translation, and cAMP signaling. In addition, single cell RNAseq analysis identified a select population of CREB activated MB cells that showed a persistent transcriptional signature of memory formation. We showed that this neuronal population is required for courtship LTM and shares some common genes with the MB-specific transcriptional trace of early learning and memory consolidation. In a proof-of-causality screen we identified 16 genes required for memory. Among these genes, we identified two candidate memory ARGs for Drosophila, Hr38 and sr. These genes are orthologs of mammalian IEGs, NR4A2 and EGR1, which have been described to be important for learning and memory63,64 Hr38 and sr have previously been characterized as ARGs induced during social interactions52,62, ethanol exposure65, and general induced neuronal activity11, but have not been characterized in the context of memory. Overall, this work significantly advances our knowledge of the transcriptional programs that are induced to facilitate LTM and identifies critical TFs that may control these programs.\n\nIn mammals, it is well established that neuronal activity induces a CREB-dependent transcriptional wave that, in turn, induces thousands of ARGs66. Among them are IEGs that are transiently expressed during LTM formation and are widely used to define cellular engrams12,46. In recent years there is evidence that IEGs vary depending on patterns of neuronal activity, stimuli, and cell type67,68. It has been proposed that information is sorted into different engrams that are determined by distinct IEGs, as demonstrated in mouse fear conditioning for two well-known IEGs63,69. This resembles what happens in Drosophila: the c-fos homolog Kayak was up-regulated in olfactory memory70, whereas in our study using courtship conditioning we only detected Hr38 and sr. Many other known ARGs did not show up-regulation under our conditions, supporting the idea of specificity in ARGs and IEGs underlying different types of memory.\n\nWe compared our memory transcriptome dataset from courtship LTM training with previous studies performed in Drosophila that analyzed the transcriptome in response to social interaction and memory25,26,27,28,29,30,31,44,62. In the context of social interaction, Agrawal et al. identified expression changes in DANs for Hr38 and sr in GH flies compared to isolated flies62. They used targeted Hr38 and sr RNAi to demonstrate that these ARGs were required in DANs for differential sleep behavior observed when flies are GH. Recently, social enrichment has been linked to memory formation48, suggesting an explanation for the overlap between genes induced by socialization, and courtship LTM training.\n\nInterestingly, the expression of memory induced genes in our scRNAseq data was strongest with Winbush et al., a study that looked at memory induced genes 24\u2009h after courtship conditioning26, very similar to the conditions from which we isolated CAMEL positive neurons. Winbush et al. identified many of the top DEGs from cluster 4\u20135 and C, as well as positive validated hits pan and cpo. Comparison with memory-transcriptome studies that used different memory paradigms rendered some common genes, such as Hr38, svr and sr28,29. However, despite the observed overlap between memory-induced genes identified here and in other studies, most genes are unique to a single dataset. These differences may be explained by several factors including the use of different memory paradigms, different tissues, and different gene expression analysis techniques. But even when conditions are similar, several factors may contribute to variability. First, all studies that we compared were done in different genetic backgrounds, and it is known that gene expression is sensitive to genetic background. Second, statistical power may vary between datasets for different genes leading to false negatives and positives. The number of biological replicates used and the variability between the replicates will affect the statistical significance of gene induction. Some genes may narrowly miss a threshold cutoff in one dataset. Indeed, we found that many genes that did not meet a statistical threshold for induction in all datasets, did show a trend toward induction in all (Fig.\u00a03B). Third, it is very likely that memory gene induction is in part stochastic in nature and dependent on the current internal state of each individual fly. For example, some flies may be predisposed to better memory formation and may already have higher expression of critical genes. Indeed, individuality does exist in Drosophila and is at least in part mediated by stochastic neurodevelopmental processes71,72,73.\n\nOne of the goals of our scRNAseq experiment was to identify a transcriptional trace of the memory engram. Due to the very low number of CAMEL positive neurons unveiled by confocal imaging, we sampled relatively low numbers of cells. Low sampling might miss rare engram neuronal subtypes, such as those related to memory reactivation. In mammals, the number of reactivated neurons after recall is about 10%46, suggesting that their number might be scarce also in Drosophila. However, despite low availability of cells, dissociation and sorting allowed us to obtain enough CAMEL positive cells from several individuals to perform robust clustering analysis which permitted to distinguish CREB activated MB neurons from non-MB cells and revealed one cluster that was enriched for cells from Adcy1 mutant animals who do not form LTM. After removal of MB enriched genes, we still identified 3 clusters, with cluster A containing non-MB neurons, cluster B containing mostly Adcy1 mutant cells and cluster C containing MB neurons mostly from trained animals. Memory engram cells are most likely contained within cluster C, however, we did not identify a specific cluster containing only cells from trained animals. All three experimental conditions were present in each cluster, although at different proportions. We functionally tested some candidate genes that were up-regulated mainly in WTT-1 and not in control conditions (Fig.\u00a0S7), but did not increase the number of positive hits under this approach (Fig.\u00a07G). Taken together, this suggests that we could not fully distinguish memory-reactivated engram neurons from neurons activated from different experiences (i.e., memory formation or learning unrelated to courtship LTM training). As stated above, it might be that memory reactivated cells are too rare to be identified under this experimental approach. Increasing the number of sequenced neurons might uncover the engram cells. However, the presence of all three conditions (trained, na\u00efve and Adcy1 mutant) in Cluster C, together with the functional analysis of genes enriched in WTT-1, suggests an alternative possibility: established engram neurons might share a similar transcriptional profile to neurons that have activated CREB during early learning or consolidation. Indeed, we find many similarities in the genes induced during early learning and social interactions, compared to consolidation and after recall.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Drosophila melanogaster stocks were reared on a standard medium (cornmeal-sucrose-agar), supplemented with the mold inhibitors methyl paraben and propanoic acid, at 25\u2009\u00b0C in 70% humidity with a 12h:12h light/dark cycle. Wild-type female flies used in this study were an in-house generated Canton-S/Oregon-R mixed genetic background called Nijmegen wild type. 5xUAS-Unc84::2xGFP flies were a gift from G.L. Henry41. RNAi lines stocks were obtained from the Bloomington Drosophila Stock Center (BDSC; Bloomington, USA), or the Vienna Drosophila Resource Center (VDRC; Vienna, Austria). R14H06-GAL4 flies express GAL4 under the control of a MB specific enhancer for Adcy1 (BDSC #48677)42,74. The CAMEL reporter tool is the result of combining 6xCRE-splitGal4AD and UAS-eGFP;R21B06-splitGal4DBD stocks, that were kindly donated by Dr Jan Pielage47. Adcy12080; 6xCRE-splitGal4AD and TNTG;6xCRE-splitGal4AD stocks were made in our laboratory and they are available upon request.\n\nWe used an isogenic heterozygous breeding strategy to produce experimental and genetic background control flies. Briefly, a common driver line containing R14H06-Gal4 or MB247-Gal4, as well as accessory transgenes such as UAS-dcr2 and temperature sensitive GAL80 (GAL80ts)75 (when relevant), was crossed to RNAi strains, and their isogenic controls (Supplementary Data\u00a09). UAS-RNAi stocks were generated by the Transgenic RNAi project (TRiP, Harvard University)76 or as part of the VDRC KK library (Supplementary Data\u00a09). Different genetic background control lines were used depending on the RNAi line (Supplementary Data\u00a09). Temporal control of GAL4 was achieved with GAL80ts, which is expressed ubiquitously under control of the \u03b1Tub84B promoter. For Gal80ts experiments, flies were raised at 18\u2009\u00b0C, which restricts GAL4 activity, preventing RNAi knockdown during development. Knockdown was then initiated 1 or 2 days prior to courtship conditioning by moving flies to 29\u2009\u00b0C. All experimental crosses, including isogenic parental and test genotypes are shown in Supplementary Data\u00a09.\n\nCourtship conditioning was performed as previously described with minor modifications39. Newly eclosed F1 male flies were collected and isolated for 5 days in individual wells of a 96-well block containing 500\u2009\u00b5L of media. F1 male flies were split into two cohorts\u2014trained and na\u00efve. For STM, male flies were trained by pairing with an unreceptive, recently-mated female fly for 2\u2009h and then placed back into isolation for 1\u2009h\u2014referred to as the rest period. For LTM, male flies were trained using a single 7-h training session, followed by re-isolation and a rest period of 20\u201324\u2009h. Following the rest period, courtship activity was measured for each individual na\u00efve and trained male fly by pairing with a new mated female. For every male-female fly pair, a CI was determined by calculating the percentage of time spent on courtship within a 10-min period. The memory index (MI), which represents the percentage reduction in courtship behavior between trained and na\u00efve flies, was calculated using the formula: MI\u2009=\u2009(CIna\u00efve\u2212CItrained)/CIna\u00efve77. Statistical analysis between na\u00efve and trained CIs was performed using a two-tailed Mann-Whitney test, with outliers removed by GraphPad Prism (v9.5.1) using the ROUT method with the false discovery rate set at the default value of 1%. Statistical comparison between the MIs of knockdown and control genotypes was performed using a randomization test with 10,000 bootstrap replicates39.\n\nMale flies homozygous for UAS-Unc84::2xGFP; R14H06-GAL4 were crossed to Nijmegen wild type virgin females. F1 heterozygote males were socially isolated for 5 days, followed by pairing with an unreceptive female for courtship LTM training. Male flies were then collected at various time-points during LTM formation by flash freezing in liquid nitrogen. Specifically, trained males were collected at three time-points during the courtship training period (DT; 1\u2009h, 3.5\u2009h, 7\u2009h) and at five time-points after training (AT; 1\u2009h, 4\u2009h, 7\u2009h, 13\u2009h, 19\u2009h). Na\u00efve male flies were also collected at five time-points (corresponding to 1hDT, 7hDT/1hAT, 7hAT, 13hAT, 19hAT) to act as time-of-day controls. For most datapoints, three individual biological replicates containing ~50 heads were collected, with no less than two for any one timepoint.\n\nTo isolate MB nuclei for downstream transcriptome and chromatin accessibility analyses, INTACT was performed as previously described27,41. Samples containing ~50 fly heads expressing UAS-Unc84::GFP under the control of R14H06-GAL4 were ground with a pestle and homogenized in buffer containing 0.3% NP40 using a Dounce homogenizer. The nuclear extract was then filtered through a 40 \u00b5m strainer. A portion of this sample was collected to represent whole-head (WH) nuclei for RNA-sequencing. MB nuclei were immunoprecipitated from the remaining sample using an anti-GFP antibody (Invitrogen: G10362) bound to magnetic beads (Invitrogen: 10004D), according to the manufacturer\u2019s instructions. MB and WH nuclei were then processed for either RNA-seq or ATAC-seq.\n\nTotal RNA was isolated from the WH nuclear fraction and immunoprecipitated MB nuclei using the Arcturus PicoPure RNA isolation kit (ThermoFisher Scientific: KIT0204) with DNase digestion performed using the RNase-free DNase kit (Qiagen: 79254) according to the manufacturer\u2019s instructions. RNA quality was assessed by visual examination of rRNA-peak integrity using the Bioanalyzer 2100 Pico RNA kit (Agilent: 5067-1513). RNA-seq libraries were prepared using the Tecan Universal Plus Total RNA-seq library preparation kit according to manufacturer\u2019s instructions. Library size and quality was assessed with the Bioanalyzer 2100 DNA high-sensitivity kit (Agilent: 5067\u20134626). Sequencing was performed with the Illumina NovaSeq 6000 at Genome Quebec with the S4 v1.5 200 cycle kit; read length was 100 bp for paired-end reads.\n\nAn average of 40,543,996 reads were generated across all MB (n\u2009=\u200938) and WH (n\u2009=\u200938) RNA-seq libraries generated. RNA-seq reads were processed on Compute Canada servers (StdEnv/2020). First, raw reads were lightly trimmed, and adapters clipped using Trimmomatic (v0.39)78. The read quality was assessed using FastQC (v0.11.9) and trimmed reads were aligned to the Drosophila melanogaster genome (Ensembl release 103, dm6) using STAR (v2.7.5a)79,80. Uniquely aligned reads with a maximum of four mismatches were counted to genes using featureCounts81. An average of 25,727,025 reads across all samples aligned to genes. Counts were then filtered for rRNA, non-coding RNA, genes mapped to the Y-chromosome or mitochondrial genome, and genes that had less than 150 normalized counts in 50 of the 76 sequenced MB and WH samples. After filtering, 6965 MB expressed genes were used for downstream differential expression analysis.\n\nDifferential expression analysis was done using DESeq2 (v1.30.1)82 in Rstudio (v4.0.3). To identify genes altered by memory training, differential expression analysis was performed between trained flies and time-of-day matched na\u00efve controls, using a cutoff of FDR\u2009<\u20090.1. To identify genes with enriched expression in the WH or MB, differential expression was performed between all MB (n\u2009=\u200938) and WH (n\u2009=\u200938) samples. With the large n values in this comparison, we used more stringent cutoffs (FDR\u2009<\u20090.05 and log2 fold change\u2009>\u20090.5) to define MB enriched genes. Further analysis of data, including gene annotation, gene ontology (GO), statistical comparison between groups of genes, was performed using the R package BinfTools (https://github.com/kevincjnixon/BinfTools). Specific commands used included: count_plot, getSym, barGene, zheat, GO_GEM and customGMT. GO analysis was performed using a custom background of 6956 expressed genes in our samples, and FDR\u2009<\u20090.05. Data was further visualized using the R packages ggplot2 (v3.4.2) and pheatmap (v1.0.12). Venn diagrams were created using BioVenn83.\n\nATAC-seq was performed as previously described84, with modifications for INTACT-isolated nuclei. MB nuclei were isolated from two independent replicates of ~50 na\u00efve male flies at a time-point corresponding to 1h after memory training onset. To generate ATAC-seq libraries, bead-bound MB nuclei were suspended in 50\u2009\u00b5L of transposase reaction mix (Tn5 Transposase, Illumina), and incubated for 30\u2009min at 37\u2009\u00b0C in a thermal cycler. DNA was then purified and eluted using a Qiagen MinElute Kit according to the manufacturer\u2019s instructions. Purified DNA was mixed with custom Nextera primers and High-Fidelity PCR Mastermix (NEB) and amplified. Amplified libraries were purified using a Qiagen PCR purification kit. Sequencing was performed with the Illumina NovaSeq 6000 at Genome Quebec with the S4 v1.5 200 cycle kit; read length was 100 bp for paired-end reads.\n\nATAC-seq reads were trimmed, and adapters clipped using Trimmomatic (v0.39). Trimmed reads were aligned to the Drosophila melanogaster reference genome (Ensembl release 103, dm6) using bowite2 (v2.4.1) with the settings -X 2000 and -very-sensitive. Reads were shifted, +4 bp for the forward strand and -5\u2009bp for the negative strand, to account for the 9-bp duplication created by the DNA repair nick of the Tn5 transpose85. Reads aligning to multiple loci, the mitochondrial genome, and scaffolds were filtered using samtools view (v1.11)86. Duplicate reads resulting from PCR amplification were identified using samtools fixmate and removed using samtools markdup, leaving 38,388,868 and 42,678,219 high-quality reads for downstream analysis. Peaks were called using MACS2 software (v2.1.2) using the settings -q 0.01 -min-length 50 and -max-gap 10087. Peak calling resulted in 15842 peaks identified uniquely between both samples, with 11705 consensus peaks, which were highly consistent between the two biological replicates (Fig.\u00a0S11), and predominantly located near transcriptional start sites (TSSs), as expected (Fig.\u00a0S11). Consensus peaks were annotated to 7488 genes using the R Package ChIPseeker (v1.26.2)88. DiffBind (v3.0.15) was used to determine the fraction of reads in peaks calculated (FRiP). The two replicates had a FRiP of >0.3 for inclusion, as per ENCODE standards89.\n\nFor visualization of ATAC data, promoter and genomic regions were extracted using the R annotation package TxDb.Dmelanogaster.UCSC.dm6.ensGene (v3.12.0) in combination with GenomicRanges (v1.42.0). Bam files were normalized using the bamCoverage function from deepTools with scale factors determined by the dba.normalize function from Diffbind. Consensus track files were generated between replicates using the mean function from the command line program wiggletools. Bandplot files for BED region subsets were generated using the computeMatrix and plotProfile functions from deepTools90.\n\nTo identify binding sites for CrebB, Hr38, and Sr, publicly available ChIP-seq data was obtained from the ENCODE project repository61. Specific files used for analysis were: CrebB (ENCFF090JJN, ENCFF655EMQ), Hr38 (ENCFF144OZH), Sr (ENCFF186BCY, ENCFF247KLE). ChIP-seq peaks for Hr38, Sr, and CrebB, were generated using optimal IDR thresholding by ENCODE89, These peaks were annotated to the nearest gene using ChIPseeker (v1.26.2)88. Genome browser tracks were generated with pyGenomeTracks91, using either control normalized or signal p value bigwig files generated by ENCODE.\n\nTo determine if translation is required during courtship memory formation, cycloheximide was fed to Nijmegen wild type male flies to block protein synthesis. Flies were fed media consisting of either 1% agarose, 5% sucrose alone (sucrose only) and with the addition of 35\u2009mM cycloheximide (sucrose\u2009+\u2009CXM), as previously described10. Flies were raised on standard media and transferred to isolation chambers containing sucrose\u2009+\u2009CXM or sucrose-only 1 day prior to STM or LTM training. For STM, flies were fed sucrose\u2009+\u2009CXM during the 1-h rest period. For LTM, flies were fed either sucrose + CXM or sucrose-only media during the ~20-h rest period.\n\nVirgin male flies carrying the CAMEL tool transgenes were collected every 4\u2009h and allowed to mature individually in tubes for 3\u20134 days. WT and Adcy1 mutant flies carrying the CAMEL tool were subjected to courtship LTM training and tested 20\u2009h later, as described40. WT na\u00efve flies carrying the CAMEL tool remained in the same tube without any female contact and were transferred to an empty test chamber. After testing, a capillary containing diluted yeast and sugar at the same concentration as standard food was placed in each test chamber and flies were left in an incubator for 2\u2009h. After that, 40\u201350 flies were dissected for each condition and brain dissociation was performed as described92. In the case of WTT flies, we did an extra replicate.\n\nTo FACS sort the CAMEL GFP positive cells from each condition we used an Influx cell sorter (Becton Dickinson) equipped with a 355\u2009nm and 488\u2009nm laser lines. We exclude aggregates using pulse processing and dead cells using DAPI as a viability dye. Cell were sorted directly into a 96-well plate with the lysis buffer to carry out single cell genomics.\n\ncDNA was generated as described93 with the following adjustments: preamplification of cDNA used 23 PCR cycles and was purified using Agencourt Ampure XP beads (Beckman Coulter) with a modified bead:DNA ratio of 0.8x. The quality of cDNA was checked using a NGS Fragment High Sensitivity Analysis Kit (Advanced Analytical) and a Fragment Analyzer (Advanced Analytical). The cDNA concentration was measured using a qubit high sensitivity dsDNA Kit. Libraries were prepared using a Nextera XT DNA Library Preparation Kit (Illumina), using a standard protocol but with all reaction volumes reduced by 1/10 to accommodate automation using the echo labcyte liquid handler (Beckman). Libraries were purified using Agencourt Ampure XP beads (Beckman Coulter). Size distribution of library pools was checked using a Fragment Analyzer and a NGS Fragment High Sensitivity Analysis Kit. Samples were pooled equimolar and the final pool quantified with the Kapa library quantification kit (Roche). The final pool was sequenced using the 150bp paired end kit on an Ilumina NovaSeq 6000 with an average read depth of 10M reads per sample.\n\nFASTQ reads were quality checked using FastQC1 (v0.11.9) software and aligned against the Drosophila melanogaster reference genome release 6 (dm6) with STAR (v2.7.9) aligner. Htseq-count (v0.11.2) was then used to count the reads mapping each annotated feature. The obtained gene expression matrix was used as input to perform downstream analyses in Seurat v4.03. We first removed potentially lysed or bad quality cells by removing cells that showed high mitochondrial gene percentage (>5%) and genes detected in less than 3 cells, as well as cells with a different transcriptional profile from the most represented population. The remaining cells were then normalized, scaled and reduced using Principal Component Analysis (PCA). The data was then clustered via an optimization model of the K nearest neighbors algorithm (k\u2009=\u200915 or K\u2009=\u200925, depending on the analysis) using the FindNeighbors and FindClusters functions in Seurat. We performed differential expression testing between clusters using the Wilcoxon rank sum statistical test. Differential marker gene lists were sorted by log2 Fold-Change, showing only those genes upregulated for each cluster and represented through heatmap plots using ComplexHeatmap R package. Removal of lobe-identity genes and the MB-enriched genes was performed directly on the Seurat object, with the same downstream processing as described above. DEGs from each single cell cluster were analyzed for GO and KEGG term enrichment using the R package ClusterProfiler, with FDR as the p value correction method.\n\nRNA was isolated from INTACT purified MB nuclei in triplicate using the PicoPure\u2122 RNA Isolation Kit (Invitrogen: KIT0204) as per manufacturer\u2019s instructions. cDNA was synthesized from the isolated RNA using the SensiFAST\u2122 cDNA Synthesis Kit (Meridian: BIO-65053) as per manufacturer\u2019s instructions. The cDNA was diluted 10-fold to be used for RT-qPCR using the SensiFAST\u2122 SYBR No-ROX Kit (Meridian: BIO-98005) as per manufacturer\u2019s instructions. Beta\u2019COP was used as the reference gene. The following primers were used:\n\nBeta\u2019COP (AACTACAACACCCTGGAGAAGG \u2013 ACATCTTCTCCCAATTCCAAAG)\n\nHr38 (TGTCGCATCCCAACAGCAG - GAAGTGGCCGTGGTAGTTGTA)\n\nSr (AAGGGCTTGAAACCCTGGTG - CGAAGCTCAGCACATTGAAGTG)\n\nTo quantify CREB-positive MB cells, primary antibodies used were rabbit anti-GFP (1/200; Invitrogen ref. A11122) and mouse anti-Fasciclin II (1/50; DSHB AB_528235). To determine neurodevelopmental defects in the MB after knocking-down positive hits, we used anti-Fasciclin II (that identifies \u2018\u03b2\u2019 and \u03b3lobes) and mouse anti-Trio (1/10; DSHB AB_528494) (that marks \u03b1\u2018\u03b2\u2019 and \u03b3 lobes). Images were taken using a Leica SP5 confocal microscopy, avoiding saturation with a 40X objective and with slices of 2.98\u2009\u03bcm. Imaging of unc84::GFP was performed using a Zeiss LSM 900 using the primary antibodies; anti-GFP (1/300; Invitrogen G10362) and, as a marker for MB nuclei, anti-Dac (1/50; DSHB mAbdac1-1). Secondary antibodies used were Alexia 488 and 568 (1/500 or 1/300; Life Technologies).\n\nCAMEL GFP positive neurons from 11-17 brains per condition were counted. Statistical significance was determined using an unpaired two tailed t-test (p\u2009=\u20090.017 for WT flies, p\u2009=\u20090.75 for Adcy1 mutant flies). Between 9 and 19 brains per experimental condition were studied to detect MB defects in Fig.\u00a0S9. Statistical significance calculated using the Fisher\u2019s exact test.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Raw and processed RNA-seq and ATAC-seq data generated in this study have been deposited in the GEO database under accession numbers GSE282414 and GSE274348. 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We thank the staff CNIO Flow Cytometry Core Unit team for their technical expertise optimizing and carrying out the flow experiments, in special Lola Mart\u00ednez and Julia Garc\u00eda-Lest\u00f3n. This research was enabled in part by high performance computational infrastructure and training provided by the Atlantic Computational Excellence Network (https://www.ace-net.ca/) and the Digital Research Alliance of Canada (https://www.alliancecan.ca). We would like to acknowledge the invaluable work of our lab technicians Robert Reid-Taylor, Carmen Rodriguez-Navas and Esther Seco. Scripts used for processing courtship conditioning were adapted with the help of Nicholas Raun. Special thanks to our colleagues Prof Alberto Ferr\u00fas, Dr Sergio Casas-Tint\u00f3, Dr Pablo M\u00e9ndez and Dr JL Trejo-P\u00e9rez for their helpful comments and suggestions on this manuscript. EASI-Genomics - This project has received funding from the European Union\u2019s Horizon 2020 research and innovation program under grant agreement No 824110. Part of the next-generation sequencing (NGS) data analysis was provided by the Genomics and NGS Core Facility at the Centro de Biolog\u00eda Molecular Severo Ochoa (CBMSO, CSIC-UAM) which is part of the CEI UAM\u2009+\u2009CSIC, Madrid, Spain-http://www.cbm.uam.es/genomica/. FAM was a recipient of a RyC-2014-14961 contract (2016-2022). Grant RyC-2014-14961 (FAM) funded by MICIU/AEI/10.13039/501100011033 and by ESF Investing in your future. Grant CNS2022-135223 (FAM) funded by MICIU/AEI/10.13039/501100011033 and by European Union NextGeneration EU/PRTR.\u00a0Grant PID2022-142742NB-I00 (FAM) funded by\u00a0MICIU/AEI/10.13039/501100011033 and by EDFR/EU.\u00a0BG-M is a recipient of a FPI-UAM predoctoral fellowship, grant number SFPI/2020/00878. This project was also funded by a Project Grant (#363723) from the Canadian Institutes of Health Research to JMK and a Nova Scotia Graduate Scholarship to SGJ.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Spencer G. Jones, Beatriz Gil-Mart\u00ed, Jamie M. Kramer, Francisco A. Martin.\n\nBiochemistry and Molecular Biology, Dalhousie University, Halifax, NS, Canada\n\nSpencer G. Jones,\u00a0Abigail C. Edison,\u00a0Emily F. Butler,\u00a0Neda Miandashti\u00a0&\u00a0Jamie M. Kramer\n\nCajal Institute, Spanish National Research Council (CSIC), Madrid, Spain\n\nBeatriz Gil-Mart\u00ed\u00a0&\u00a0Francisco A. Martin\n\nDepartment of Biology, Autonomous University of Madrid, Madrid, Spain\n\nBeatriz Gil-Mart\u00ed\u00a0&\u00a0Enrique Turi\u00e9gano\n\nBiocomputational Analysis Core Facility, Centro de Biolog\u00eda Molecular Severo Ochoa, Spanish National Research Council (CSIC), Madrid, Spain\n\nEva Sacrist\u00e1n-Horcajada\n\nDepartment of Genetics and Microbiology, Trinity College Dublin, Dublin 2, Ireland\n\nCamilla Roselli\n\nTrinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland\n\nCamilla Roselli\u00a0&\u00a0Tamara Boto\n\nSchool of Physiology Pharmacology and Neuroscience, University of Bristol, Bristol, UK\n\nTamara Boto\n\nDepartment of Biochemistry and Molecular Biology, Faculty of Biological Sciences, Complutense University of Madrid, Madrid, Spain\n\nFrancisco A. Martin\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nF.A.M., J.M.K., and S.G.J. conceptualized and designed the project; J.M.K., F.A.M., and B.G.M. supervised the project; B.G.M. performed CAMEL scRNA-seq studies, S.G.J. performed INTACT RNA-seq and ATAC-seq. B.G.M. and J.M.K. performed confocal microscopy and B.G.M. analyzed the images. E.S.H. analyzed the scRNAseq data, S.G.J. analyzed INTACT RNA-seq and ATAC-seq data. B.G.M., S.G.J., E.B., N.M., A.C.E., T.B., and C.R. participated in the loss-of-function studies; S.G.J., B.G.M., E.T., T.B., J.M.K., and F.A.M. analyzed data; J.M.K. and F.A.M. wrote the original draft; S.G.J. and B.G.M. made the figures; E.F.B. performed qPCR. E.T. made extensive editing to the manuscript and revised statistics; all authors revised the manuscript; J.M.K. and F.A.M. were responsible for funding acquisition.\n\nCorrespondence to\n Jamie M. Kramer or Francisco A. Martin.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Ann-Shyn Chiang, Simon Sprecher and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Jones, S.G., Gil-Mart\u00ed, B., Sacrist\u00e1n-Horcajada, E. et al. A memory transcriptome time course reveals essential long-term memory transcription factors.\n Nat Commun 16, 9320 (2025). https://doi.org/10.1038/s41467-025-64379-x\n\nDownload citation\n\nReceived: 01 January 2025\n\nAccepted: 12 September 2025\n\nPublished: 29 October 2025\n\nVersion of record: 29 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-64379-x\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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mirror-image monobody targeting MCP-1 generated via TRAP display and chemical protein synthesis", + "journal": "Nature Communications", + "published": "23 December 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54902-x/MediaObjects/41467_2024_54902_MOESM1_ESM.docx" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54902-x/MediaObjects/41467_2024_54902_MOESM2_ESM.docx" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54902-x/MediaObjects/41467_2024_54902_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54902-x/MediaObjects/41467_2024_54902_MOESM4_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54902-x/MediaObjects/41467_2024_54902_MOESM5_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54902-x/MediaObjects/41467_2024_54902_MOESM6_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54902-x/MediaObjects/41467_2024_54902_MOESM7_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-54902-x#Sec21" + ], + "code": [], + "subject": [ + "Antibody fragment therapy", + "Synthetic biology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4275898/v1.pdf?c=1735045547000", + "research_square_link": "https://www.researchsquare.com//article/rs-4275898/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54902-x.pdf", + "preprint_posted": "24 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Engineered protein scaffolds function as binders against various target molecules with high affinity and specificity comparable to conventional IgG antibodies. However, biologically produced protein drugs are generally degraded by proteases and often exhibit immunogenicity. To increase protease resistance and decrease immunogenicity of peptides and proteins, mirror-image peptide/protein binders consisting of D-amino acids have been developed so far mainly through the mirror-image phage display technique. Here, we develop a mirror-image protein binder derived from a monobody, one of the most promising protein scaffolds, using two notable technologies: chemical protein synthesis and TRAP (transcription-translation coupled with association of puromycin linker) display, an improved and streamlined version of mRNA display. A sequential workflow of initial screening followed by affinity maturation, facilitated by TRAP display, generates an L-monobody with high affinity (KD = 1.3 nM) against the pharmaceutically important monocyte chemoattractant protein-1 (MCP-1) D-enantiomer. By symmetry, the chemically synthesized D-monobody demonstrates strong and enantio-selective binding against L-MCP-1 and possesses pharmaceutically favorable properties such as resistance to proteolytic degradation, minimal immune response, and a potent inhibitory effect on MCP-1 binding to its cell membrane receptor. The production of a high-affinity mirror-image monobody elevates the value of mirror-image peptide/protein binders as a new modality in drug discovery.Biological sciences/Biotechnology/Biologics/Antibody fragment therapyBiological sciences/Biotechnology/Molecular engineering/Synthetic biologyBiological sciences/Evolution/Molecular evolution", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "DatafileS1Dmonover5.xlsxNucleic acid sequencesMirrorimageTRAPSIver7.1.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Biologically produced protein drugs are generally susceptible to degradation by proteases and often exhibit immunogenicity. To address this issue, mirror-image peptide/protein binders consisting of D-amino acids have been developed so far through the mirror-image phage display technique. Here, we develop a mirror-image protein binder derived from a monobody, one of the promising protein scaffolds, utilizing two notable technologies: chemical protein synthesis and TRAP display, an improved version of mRNA display. A sequential workflow of initial screening followed by affinity maturation, facilitated by TRAP display, generates an L-monobody with high affinity (KD\u2009=\u20091.3\u2009nM) against monocyte chemoattractant protein-1 (MCP-1) D-enantiomer. The chemically synthesized D-monobody demonstrates strong and specific binding to L-MCP-1 and exhibits pharmaceutically favorable properties such as proteolytic resistance, minimal immune response, and a potent inhibitory effect on MCP-1-induced cell migration. This study elevates the value of mirror-image peptide/protein binders as an alternative modality in drug discovery.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Mirror-image proteins have emerged as attractive molecular materials in the fields of structural and synthetic biology. These proteins consist of D-amino acids with an achiral glycine and were prepared exclusively by synthetic peptide chemistry1,2. Racemic or quasi-racemic protein crystallography utilizes these mirror-image proteins to aid in the crystallization of desired proteins through the formation of centrosymmetric crystals, thereby facilitating structural determination3,4. By virtue of their chiral catalytic features, mirror-image enzymes have been synthesized to evaluate and utilize their unique catalytic activities5,6,7,8. Recently, intensive studies have been conducted aiming at the creation of a mirror-image central dogma through the synthesis of mirror-image key components such as polymerases9,10 and ribosomal proteins11.\n\nSince mirror-image peptides and proteins are more resistant to proteolytic degradation and exhibit lower immunogenicity than their native counterparts12,13, these D-configured polypeptides are also considered promising drug candidates in the field of pharmaceutical sciences. While the development of L-configured peptide/protein binders against desired molecules has become relatively accessible, mainly thanks to reliable screening technologies like phage display14, discovering D-peptide/protein binders remains challenging.\n\nThe first milestone study to obtain a mirror-image polypeptide binder was established by Kim and coworkers in 1996 as a technique named mirror-image phage display (MIPD)15. In MIPD, a randomized L-peptide library displayed on phage surfaces is screened against chemically synthesized D-configured target proteins. Given that the interaction between the identified L-peptide and the target D-protein mirrors that of their enantiomeric counterparts according to the law of symmetry, a D-peptide binder can subsequently be generated. After this first report of MIPD to isolate a D-peptide binder against the Src homology 3 domain of c-Src, this strategy has been employed to obtain D-peptide binders against various mirror-image protein targets, such as amyloid peptide A\u03b216, MDM217, programmed cell death ligand 1 (PD-L1)18 and others19,20,21,22,23,24. However, the affinities of these D-peptide binders against the targets are generally moderate (i.e., KD ranging from sub-\u00b5M to double-digit \u00b5M on average, as low as double-digit nM), although additional efforts such as multimerization can make these D-peptide binders more potent25,26. One promising solution to increase target affinity is the use of protein scaffolds27,28, which usually have larger and more rigid interaction surfaces than linear peptide-based binders. Indeed, Kent and coworkers employed a 56-residue protein scaffold, the B1 domain of streptococcal protein G (GB1), in MIPD targeting the angiogenic protein vascular endothelial growth factor (VEGF-A), to obtain a mirror-image protein binder with a KD of 85\u2009nM29. Although this initial binder was not stable at physiological temperatures, extensions of both the N- and C-termini improved the thermal stability and also the binding affinity (KD\u2009=\u20096\u2009nM)30. More recently, two different three-helix bundle scaffolds, the protein G-derived GA domain (53-residue) and the protein A-derived Z domain (58-residue), were screened by MIPD against D-VEGF-A. The individually obtained binders were chemically crosslinked to form a D-configured heterodimer, which exhibited a KD of 0.08\u2009nM due to the multivalency effect and also showed inhibitory activity on tumor growth31.\n\nAnother key factor in obtaining high-affinity peptide/protein binders frequently is the library diversity used in the display selection. Generally, there is a correlation between the initial diversity of the library and the probability of acquiring low KD binders32. In this respect, mRNA display33,34 can generate an even larger diversity (~\u20091013) than phage display (~\u20091011), thereby increasing the probability of identifying high-affinity peptide/protein binders. However, conventional mRNA display requires complicated operations including stepwise transcription, puromycin linker ligation, and translation in separate tubes, leading to time-consuming experimental procedures. To address this issue, we developed an improved version of mRNA display, called TRAP (transcription\u2212translation coupled with association of puromycin linker) display, in which a polypeptide library conjugated with each mRNA sequence is automatically produced via tandem reactions and used for the isolation of macrocyclic peptide binders with double-digit nM affinities35. More recently, a fibronectin type III domain-derived protein scaffold, named monobody36, was employed in further improved TRAP display selections to generate binders with strong affinities ranging from sub-nM to single-digit nM against multiple targets including SARS-CoV-2 spike protein, epidermal growth factor receptor 1 (EGFR1), human epidermal growth factor receptor 2 (HER2)37 and optineurin38.\n\nIn this study, we establish a mirror-image TRAP display to create D-configured monobody-based binders powered by chemical protein synthesis, in which peptide segments prepared by solid-phase peptide synthesis (SPPS) are assembled by chemoselective peptide ligation reactions39,40,41. We choose monocyte chemoattractant protein-1 (MCP-1) as a target protein, which is known to be related to the pathogenesis of many disease conditions such as cancers, infectious diseases, diabetes, cardiovascular diseases, and more42. We isolate high-affinity L-monobody clones with single-digit nM KD against D-configured MCP-1 via TRAP display selection and subsequent affinity maturation experiments. Chemical synthesis using a double Cys substitution approach enables the preparation of a mirror-image monobody with high purity and minimal effort. Comparative evaluation of the mirror-image monobody with the L-monobody reveals properties such as high proteolytic resistance, undetectable immunogenicity, and potent inhibitory activity against the interaction between MCP-1 and its receptor C-C chemokine receptor type 2 (CCR2) in a cultured cell environment. Furthermore, the D-configured monobody inhibits chemotactic migration of monocytes as potently as previously developed anti-MCP-1 antibody, carlumab, which has been tested in clinical trials43. Together, these results showcase a substantial advance in the development of mirror-image peptide/protein binders. We also refer to a parallel study by Schmidt et al.44 that developed D-monobodies targeting the SH2 domain of the leukemic Bcr-Abl tyrosine kinase by utilizing MIPD. Our two studies employ distinct yet complementary approaches to achieve a common goal: the straightforward development of a functional mirror-image monobody that targets a therapeutically significant molecule. The convergence of these approaches not only underscores the robustness of the platforms for developing mirror-image binding proteins but also collectively demonstrates the viability of mirror-image binders.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Mature MCP-1 is produced after cleavage of the N-terminal 23-residue putative signal peptide45 and identified as a 76-residue protein with N-terminal pyroglutamic acid46 (Fig.\u00a01A), which is an occasionally observed post-translational modification47. For the synthesis of D-configured MCP-1, the amino-acid sequence was divided into two peptide segments at the K35/C36 junction according to the previous report48, which employed native chemical ligation (NCL)49 for peptide assembly (Fig.\u00a01B). First, we prepared the N-terminal segment 1D and the C-terminal segment 2D via Fmoc-SPPS (Supplementary Figs.\u00a01, 2). While 1D includes a C-terminal N-acyl-N\u2019-methylacylurea (MeNbz)50 as a thioester precursor, 2D contains an N-terminal Cys residue for NCL and C-terminal biotinylated Lys residue for the streptavidin pull-down used in binder selection. Segments 1D and 2D were ligated under conventional NCL conditions including mercaptophenylacetic acid (MPAA) as the thiol catalyst51, tris(carboxyethyl)phosphine (TCEP) as the reducing agent, and guanidine hydrochloride salt (Gn\u00b7HCl) as the denaturing agent. We obtained 3D in 39% isolated yield (Supplementary fig.\u00a03), which was then folded under air oxidation conditions to yield the desired D-MCP-1, 4D in 56% isolated yield after HPLC purification (Supplementary Fig.\u00a04). The folding was confirmed by a change in the HPLC retention time (Fig.\u00a01C). Following the same synthetic procedure, we also prepared L-configured MCP-1 4L and 4L'\u00a0with and without a C-terminal biotin linker, respectively (Supplementary Figs.\u00a05\u201312). Circular dichroism (CD) spectroscopic analysis of the biotinylated D- and L-MCP-1 revealed inverted CD spectra, indicating mutually mirror-imaged conformations (Fig.\u00a01D).\n\nA Amino-acid sequence of MCP-1 with N-terminal pyroglutamic acid, pE, and C-terminal biotinylated lysine. Two disulfide bonds (solid lines) and a ligation site (a dashed line) are shown. B Synthetic scheme of biotinylated D-MCP-1 via NCL followed by air oxidation. Reaction conditions: (a) 200\u2009mM MPAA, 50\u2009mM TCEP, 6\u2009M Gn\u00b7HCl, 0.2\u2009M phosphate (pH 6.5), 37\u2009\u00b0C; (b) AcOH/H2O, then NH3 aq. C Comparison of HPLC retention time between purified 3D and 4D. HPLC peaks were monitored at 220\u2009nm in the linear gradient with water/acetonitrile containing 0.1% TFA. The gradient of HPLC: acetonitrile 20\u201340% for 20\u2009min. D CD spectra of synthetic MCP-1. 4D and 4\u2009L (10\u2009\u00b5M) dissolved in PBS (pH7.4) were measured. Source data are provided as a Source Data file.\n\nPreviously, we obtained high-affinity monobody clones (sub-nM to nM KD) against several protein targets via TRAP display37,38. For the first-round selection against the biotinylated D-MCP-1 4D, we employed the same monobody mRNA library as in the previous study37, where Gly-, Ser-, Trp-, and Tyr-rich random residues were introduced into the BC (8 or 10 residues) and FG (10 or 12 residues) loops of monobody to increase the probability of obtaining high-affinity clones52. Notably, in vitro transcription (i.e., mRNA library construction) was performed separately from the in vitro translation to maximize the diversity of the monobody library. The purified library mRNAs were translated and conjugated into monobody-mRNA complexes in a reconstituted in vitro translation system53 coupled with in situ puromycin-mediated crosslinking (Supplementary Table\u00a01). After reverse transcription to generate monobody-cDNA/mRNA conjugates, non-specific binders such as bead binders and streptavidin binders were removed from the monobody-cDNA/mRNA library by treatment with streptavidin-coated magnetic beads (negative selection). The library was then incubated with 50\u2009nM of the biotinylated D-MCP-1, and the binding clones were pulled down with streptavidin beads to recover D-MCP-1 binders. cDNA of the recovered monobody conjugates was PCR-amplified and applied to the next round. From the second-round selection, consecutive transcription-translation reactions coupled with puromycin conjugation35,37 were utilized to accelerate the selection procedure. We repeated the selection procedures and observed an enrichment of the cDNA recovery rate after the fifth-round selection (Fig.\u00a02A). To obtain higher affinity clones, two additional rounds of selection were conducted with increased selection stringency (2\u2009nM biotinylated D-MCP-1). The seventh-round selection was conducted both with and without D-MCP-1, and the recovered cDNAs were sequenced by an NGS technique.\n\nA Progress of the TRAP display selection. After each round of selection, the recovered cDNA was quantified by real-time PCR. The recovery of cDNA (%) was calculated by dividing the amount of cDNA recovered after the pull-down with D-MCP-1 by the amount of PuL in the translation mixture. From the sixth round, the selection pressure was increased by decreasing the target concentration from 25\u2009nM to 2\u2009nM. B Determination of kinetic parameters of Mb5 and Mb8 by BLI. D-MCP-1 was immobilized on a streptavidin-sensor chip, and Nus-Tag fused Mb5 or Mb8 (0.062, 0.125, 0.25, 0.50, 1.0\u2009\u00b5M) were used in the kinetic analysis. The data were fitted to a 1:1 binding model. C Sequences and spatial arrangement of saturation mutagenesis libraries for affinity maturation of Mb5. Saturation mutagenesis (X) was introduced using NNK codons (N\u2009=\u2009A, C, G, T; K\u2009=\u2009G or T; 32 codons/20 aa) at six consecutive residues in the BC and FG loops. D Progress of TRAP display selection at each library in the affinity maturation. In the fifth round selection, selection stringency was increased by extending washing time was applied against mixed library derived from libraries A, B, and C. E The probability of amino acids at each position in the loops of the selected clones shown by WebLogo. F Determination of kinetic parameters of three Mb5 mutants by BLI. D-MCP-1 was immobilized on a streptavidin-sensor chip, and Mb5-9, 5-11, 5-12 (2.5, 5.0, 10, 20, 40\u2009nM) were used in the kinetic analysis. The data are fitted to a 1:1 binding model. Abbreviations: BLI, Bio-layer interferometry; Lib, library; TRAP, transcription\u2013translation coupled with association of puromycin linker. Source data are provided as a Source Data file.\n\nAmino acid sequences of the BC and FG loops of the enriched monobodies were aligned based on the order of the relative abundance after the seventh round of selection with D-MCP-1 (Supplementary Table 2). Among the top 8 clones (Mb1\u2009~\u20098), 7 clones were chosen for further studies of recombinant expression and binding tests, while Mb6 was excluded due to its low P/N value, which refers to a ratio obtained by dividing a relative abundance in positive selection by that in negative selection and could indicate a tendency of non-specific binding. The 7 clones were successfully expressed and isolated with good purity (Supplementary Fig.\u00a013A). We screened the recombinant monobodies using biolayer interferometry (BLI) analysis and found that Mb5 and Mb8 showed higher intensities than the others against D-MCP-1 immobilized on the BLI sensors (Supplementary Fig.\u00a013B). Notably, the high relative abundance values do not necessarily promise the high affinity probably because monobody molecules in TRAP display selection is surrounded by a different environment from those after E. coli expression. To determine the kinetic parameters and dissociation constants, BLI sensorgrams with different concentrations of Mb5 and Mb8 were obtained and evaluated by a global fitting algorithm. As a result, sub-\u00b5M KD values of 0.34 and 0.48\u2009\u00b5M were calculated for Mb5 and Mb8, respectively (Fig.\u00a02B). This result was unexpected for us, as high-affinity binders with nM or sub-nM KD were reproducibly selected via TRAP display in our previous studies37,38. This discrepancy might be attributed to the less favorable binding patterns between D- and L-proteins compared to those between L-proteins. To validate this hypothesis, we also conducted TRAP display selection against L-MCP-1 and obtained single-digit nM clones, which have more than 100-fold affinities than those obtained in D-MCP-1 selection (Supplementary Fig.\u00a014).\n\nTo obtain higher affinity clones against D-MCP-1, we conducted an in vitro affinity maturation of Mb5, following our previous study, which selected multiple high-affinity clones with sub-nM KD against variants of the SARS-CoV-2 spike protein54. We prepared four kinds of mRNA libraries, each incorporating saturation mutagenesis at 6 consecutive amino acid residues in the regions of the BC or FG loop (Fig.\u00a02C). Notably, the required diversity of 6 random amino acids (i.e., 6.4\u2009\u00d7\u2009107) is readily covered by our library construction. We used a low concentration of biotinylated D-MCP-1 (2\u2009nM) from the first-round selection to exclude low-affinity binders. After the fourth round of selection, increases in cDNA recovery rate were observed from libraries A, B, and C, whereas no enrichment was detected from library D (Fig.\u00a02D). As for library D, the lack of critical mutations that could improve the affinity in the last six residues of the FG loop, suggests that the original sequence of Mb5 was already near optimal. The cDNAs from libraries A, B, and C after the fourth round were equivalently mixed, and then the fifth-round selection was conducted with an extended washing time to enrich binders with slow dissociation kinetics (i.e., low koff value). The recovered cDNA was sequenced and analyzed by Weblogo55 to display the appearance ratio of 20 amino acids at each position (Fig.\u00a02E). The results not only clarified essential residues for target recognition and/or conformational stability of the monobody but also identified the mutation-tolerant sites in each loop sequence. Specifically, we observed dominant mutations at the first residue of the BC loop (Leu to Phe), the last residue of the BC loop (Lys to Pro), and the third residue of the FG loop (Gly to Pro), which could improve the affinity by stabilizing the loop structures. By selecting amino acids with a high appearance ratio from each library and combining them, we designed Mb5-9. We also designed Mb5-10, which has a Gly-to-Ser mutation at the 7th position and a Phe-to-Trp mutation at the 8th position in the BC loop, as these mutations appeared in one of the top 10 sequences from library B. Mb5-11, which includes an Arg substitution at the 4th position, was designed based on its appearance ratio as the second most abundant sequence in library C. In addition, we combined these mutations to construct Mb5-12. Mb5-9, -10, -11, and -12 exhibited significantly stronger binding signals and slower dissociation rates compared to those with fewer mutations (Mb5-1 to Mb5-8, Supplementary Fig.\u00a015), indicating that the combination of these mutations was crucial for increasing affinity. Kinetic parameters, determined by BLI with different concentrations of Mb5-9, Mb5-11, and Mb5-12, followed by fitting analysis, revealed that Mb5-11 had the best KD value of 1.6\u2009nM (Fig.\u00a02F). These results clearly demonstrate the efficacy of affinity maturation in obtaining optimized, high-affinity binders.\n\nWith the high-affinity clones against D-MCP-1 in hand, we then undertook the synthesis of the D-monobody that is supposed to bind to L-MCP-1 by symmetry. The full-length monobody sequence was divided into four peptide segments at junctions including Ala residues (Supplementary Fig.\u00a016A), which can be converted from Cys via a free radical-based desulfurization reaction56 after assembly. In our initial attempt, we conducted a C-to-N one-pot ligation employing NCL-compatible allyloxycarbonyl (Alloc) protecting groups as the N-terminal Cys protecting groups57,58 combined with allyl-protected Asp, which can be deprotected by the same organometal complex as Alloc deprotection59. However, in the first ligation between the C-terminal 2 segments, we failed to produce a detectable product peak in analytical HPLC, likely due to an invisible aggregation even in the presence of 6\u2009M Gn\u00b7HCl. Consequently, we revised the order of peptide ligation as shown in Supplementary Fig.\u00a016B, reaching the final product via seven-step reactions including four HPLC purifications. The total yield of the final product 8D\u2019 was less than 1%, likely due to the multiple purification steps, and the purity of 8D\u2019 was compromised by inseparable byproducts in the final HPLC purification step (Supplementary Fig.\u00a016C).\n\nRecently, we developed a chemically synthesized anti-GFP monobody variant containing two Cys substitutions, which simplify the synthesis of the monobody from three peptide segments and eliminate the need for a desulfurization step after peptide assembly60. Importantly, this synthetic L-configured monobody variant maintained its affinity for the target protein, GFP. Consequently, we employed this Cys substitution strategy to synthesize the mirror-image form of Mb5-11 (Fig.\u00a03). The N-terminal segment, 5D, which has a C-terminal alkyl thioester moiety, was synthesized via NaNO2-mediated hydrazide activation61,62. The middle segment, 6D, containing N-terminal thiazolidine (Thz) moiety as a protected Cys residue in addition to a C-terminal thioester, was synthesized through hydrazide activation and subsequent thioesterification63. The acetylacetone-mediated thioesterification was chosen because Thz moiety is incompatible with the standard NaNO2-mediated thioesterification conditions64. For the synthesis of the C-terminal segment, 7D, which contains an N-terminal cysteine and a C-terminal amide, we attached 6\u2009\u00d7\u2009His sequence at the C-terminal region via a Gly linker for potential solubilizing and/or affinity tag. These peptide segments were synthesized through standard Fmoc SPPS, and the purified segments were analyzed by HPLC and MALDI-TOF mass spectrometry (Supplementary Figs.\u00a0S17\u201319). The resulting peptides were assembled from the C-terminus to the N-terminus by NCL (Fig.\u00a03B). The first NCL was conducted between 6D and 7D under standard NCL conditions and a new peak corresponding to the ligation product was observed in analytical HPLC after 18\u2009h (Fig.\u00a03C). Then, the pH of the reaction mixture was adjusted to 4, and methoxyamine was added to the reaction mixture to convert the Thz into Cys in a one-pot reaction. After 2\u2009h of methoxyamine treatment, the obtained product 8D was purified and isolated in 29% yield (Supplementary Fig.\u00a020). A second NCL between 5D and 8D was then conducted to afford 10.4\u2009mg of full-length Cys mutant monobody, 9D in 14% isolated yield (4.0% total yield) (Fig.\u00a03D, E). Through the identical synthetic scheme, we also synthesized an L-configured monobody 9\u2009L, an enantiomer of 9D (Supplementary Figs.\u00a021, 22), and obtained 10.5\u2009mg of the product in 7.2% total yield. To our delight, SDS-PAGE analysis revealed that the purity of 9D and 9\u2009L was significantly improved (Fig.\u00a03F), compared to the previous synthetic scheme shown in Supplementary Fig.\u00a016C.\n\nA Target sequence derived from Mb5\u221211. BC- and FG-loop and Cys substituted sites (ligation sites) are highlighted in blue, pink, and green, respectively. B Synthetic scheme of D-monobody, 9D via C-to-N 3 segment ligation. Reaction conditions: (a) 100\u2009mM MPAA, 50\u2009mM TCEP, 6\u2009M Gn\u00b7HCl, 0.2\u2009M phosphate (pH 6.8), 37\u2009\u00b0C; (b) 200\u2009mM methoxyamine (pH 4.0) in addition to the NCL condition. C HPLC monitoring of the one-pot reaction of the 1st NCL and subsequent thiazolidine deprotection. HPLC peaks were monitored at 220\u2009nm in the linear gradient with water/acetonitrile containing 0.1% TFA. The gradient of HPLC: acetonitrile 10\u201360% for 30\u2009min. D HPLC monitoring of the 2nd NCL. HPLC peaks were monitored at 220\u2009nm in a linear gradient with water/acetonitrile containing 0.1% TFA. The gradient of HPLC: acetonitrile 10\u201360% for 30\u2009min. E MALDI-TOF/MS spectrum of peptide 9D after purification. MS(MALDI-TOF) m/z: [M\u2009+\u2009H]+ Calcd for C576H849N161O175S3 12926.1; Found 12927.3. F SDS-PAGE analysis of 9D and 9\u2009L, whose synthetic data are shown in Supplementary Fig. 21 and 22. MK: molecular weight ladder marker. Source data are provided as a Source Data file.\n\nFirst, we evaluated the binding affinity and specificity of the synthetic monobodies by BLI. Biotinylated L- or D-MCP-1, synthesized as described above, was immobilized on the sensor and different concentrations of D- or L-configured monobodies were analyzed (Fig.\u00a04A). As a result, D-monobody 9D exhibited a KD value of 1.3\u2009nM against L-MCP-1, which is almost identical to the affinity of recombinant Mb5-11 to D-MCP-1 as described in Fig.\u00a02F. Likewise, 9\u2009L also bound to D-MCP-1 with a KD value of 1.4\u2009nM. On the other hand, 9D and 9\u2009L did not show any binding to D- and L-MCP-1, respectively. These results suggest that the synthetic monobodies recognize their target proteins in enantioselective manners. Furthermore, 9D did not show significant binding against pharmaceutically important human proteins of interleukin-6 (IL-6), leukemia inhibitory factor (LIF), IL-6 receptor (IL-6R), cluster of differentiation 266 (CD266), cytotoxic T-lymphocyte associated protein 4 (CTLA4) and programmed cell death protein 1 (PD-1) (Supplementary Fig.\u00a023), indicating that the recognition of 9D against L-MCP-1 is target-selective. Notably, no folding procedures were undertaken on these synthetic monobody samples before BLI analysis. When we evaluated the binding affinity after a dialysis-based folding procedure, the calculated KD value was 1.2\u2009nM, indicating an ignorable difference from the sample without the folding procedure (Supplementary Fig.\u00a024). Therefore, we assume that the monobody established in this study could automatically fold into the proper conformation. CD spectrometry of these synthetic monobodies dissolved in PBS without a folding procedure indicated the existence of monobody-like \u03b2-sheet structure as observed in the previous study60 (Fig.\u00a04B). Importantly, 9D and 9\u2009L showed mutually inverted spectra, suggesting that 9D possesses the mirror-image conformation of 9\u2009L.\n\nA Binding affinity and specificity analyzed by BLI. L-MCP-1 or D-MCP-1 was immobilized on a streptavidin-sensor chip, and synthetic monobody 9D or 9\u2009L (2.5, 5.0, 10, 20\u2009nM) was used in the kinetic analysis. B CD spectra of synthetic monobodies. 9D and 9\u2009L (5\u2009\u00b5M) dissolved in PBS (pH7.4) were measured. C Tryptic digestion of synthetic monobodies. 9D and 9\u2009L (5\u2009\u00b5M) dissolved in PBS (pH 7.4) containing 1% PEG were incubated with trypsin (300\u2009nM) for 0.5, 1,\u00a0and 2\u2009h. The data are presented as mean\u2009\u00b1\u2009SD, n\u2009=\u20093 biological replicates. D Evaluation of the immunogenicity of 9D and 9L\u2009. A 1:1,000 dilution of immunized plasma from each mouse collected on days 0, 14, 28, and 35 was added to the 9D or 9L-coated plates. Generation of antibodies against 9D or 9\u2009L was detected using HRP-anti-mouse IgG (H\u2009+\u2009L). Bars represent mean\u2009\u00b1\u2009SD calculated from independent experiments (4 mice for 9L: 5 mice for 9D). Statistical analysis was performed by two-way ANOVA followed by Sidak\u2019s multiple comparisons test. **, p\u2009=\u20090.0047; ***, p\u2009<\u20090.0001. E MCP-1/CCR2 inhibition assay with cultured cells using the PathHunter\u00ae \u03b2-Arrestin eXpress GPCR Assay kit (DiscoverX). The cultured cells were incubated at 37\u2009\u00b0C for 90\u2009min with 9D or 9\u2009L (ranging from 0.10 to 680\u2009nM in a 3-fold serial dilution) and 7 nM L-MCP-1. Chemiluminescence was detected in a plate reader. The experiments were performed in 96-well plates with three wells for each condition (n\u2009=\u20093, biological replicates) and the bars represent mean\u2009\u00b1\u2009SD. The dotted horizontal line denotes basal RLU as shown in Supplementary Fig.\u00a0S26. F Cell migration assay was performed with and without MCP-1, and with or without the inhibitor (i.e., carlumab or D-Monobody, 9D), using three independent wells for each condition to obtain biological replicates (n\u2009=\u20093). Data are expressed as mean\u2009\u00b1\u2009SD. Inhibitor concentrations were varied at 0, 0.1, 1, 10, and 100\u2009nM. Cells that migrated to the empty chamber were quantified by Calcein-AM staining. Source data are provided as a Source Data file.\n\nTo investigate the proteolytic stability, we conducted a protease degradation assay of the synthetic monobodies using trypsin as a typical protease. After incubation at 37\u2009\u00b0C for 0.5, 1, and 2\u2009h, the tryptic digestion solution was analyzed by SDS-PAGE (Supplementary Fig.\u00a025), and the acquired band intensities were quantified by image processing. As a result, about 90% of full-length 9\u2009L was degraded within 2\u2009h, whereas 9D remained almost intact after 2\u2009h incubation (Fig.\u00a04C). This clearly demonstrated the higher protease resistance of the D-monobody compared to that of L-monobody. These results support previous studies showing that other mirror-image peptides and proteins are less degradable than L-configured native ones.\n\nNext, the immunogenicity of 9D was assessed in comparison with 9\u2009L according to the previously reported procedure60. Intraperitoneal immunization of BALB/c mice was performed with administration of 9D or 9\u2009L emulsified in Freund\u2019s complete adjuvant at day 0, and incomplete adjuvant at days 14 and 28. After plasma samples were collected at days 0, 14, 28, and 35, the level of anti-9D or anti-9L antibody was measured by ELISA (Fig.\u00a04D). Generation of anti-monobody IgG antibody was observed at day 28 from the plasma samples of 9L-immunized mice and the average antibody levels further increased at day 35. On the other hand, the plasma samples from 9D-immunized mice did not show any ELISA signals even at day 35. These results are totally consistent with the previous study showing less immunogenicity of D-monobody than that of L-monobody60. Given that antibody production begins with the degradation of protein antigens into peptides in antigen-presenting cells, the low immunogenicity of mirror-image protein could be attributed to the high proteolytic stability.\n\nIn order to represent the biological relevance of D-monobody, the inhibitory activity of 9D on the interaction between MCP-1 and CCR2 was evaluated using PathHunter\u00ae \u03b2-Arrestin eXpress GPCR Assay kit, which includes CHO-K1 cells expressing CCR2 on their cell membrane. In this assay, upon MCP-1 binding to CCR2, which is fused with a peptide fragment of \u03b2-galactosidase, \u03b2-arrestin fused with the opposite \u03b2-galactosidase fragment is recruited to the intracellular region of CCR2, followed by fragment complementation of \u03b2-galactosidase, leading to the emission of chemiluminescence. Therefore, if the monobody binding to MCP-1 competes with CCR2 binding, the luminescence signal should decrease. Initially, we investigated the synthetic L-MCP-1-mediated CCR2 activation, which led to the luminescence emission. A significant increase in luminescence signal was observed only in the presence of 7 nM L-MCP-1, indicating proper signal transduction triggered by this synthetic MCP-1 (Supplementary Fig.\u00a026). Then, we performed the assay by titrating chemically synthesized 9D and 9\u2009L with nine different concentrations in the presence of synthetic L-MCP-1 (Fig.\u00a04E). A clear decrease in luminescence signals was observed depending on the concentration of 9D, whereas nearly constant signals were detected at all concentrations of 9\u2009L. The estimated IC50 value of 9D was 2.3\u2009nM, suggesting the structural stability and MCP-1-specific inhibitory activity of the mirror-image monobody 9D against living cells in culture media.\n\nFinally, we evaluated the inhibitory effect of 9D on MCP-1-induced chemotaxis of cultured monocytes. Carlumab, an anti-MCP-1 antibody that has been tested in clinical trial43, was used as a well-developed inhibitor. THP-1 monocyte cell line was stimulated by recombinant MCP-1 in the presence or absence of the inhibitors and migrated cells were quantified by Calcein-AM staining. While, in the absence of MCP-1, neither 9D or carlumab did not affect the migration of THP-1 cells, both effectively inhibited MCP-1-induced migration in a concentration-dependent manner (Fig.\u00a04F). Therefore, we reasoned that the inhibitory effect of the synthetic D-monobody 9D is comparable to the IgG antibody.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54902-x/MediaObjects/41467_2024_54902_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54902-x/MediaObjects/41467_2024_54902_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54902-x/MediaObjects/41467_2024_54902_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54902-x/MediaObjects/41467_2024_54902_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "By virtue of chemical protein synthesis and TRAP display, we discovered a pharmaceutically promising anti-MCP-1 mirror-image monobody with high affinity, proteolytic resistance, and undetectable immunogenicity. Notably, potent inhibitory activities were observed in biologically relevant environments as comparable to carlumab, which has been used in clinical studies.\n\nDuring the clone selection process, although the affinity maturation process successfully decreased the dissociation constants to single-digit nM, the initial TRAP display selection by using a library with a diversity of over 1013 generated low-affinity monobody clones against D-MCP-1 (as low as 340\u2009nM for KD). This result was unexpected to us because several sub-nM affinity monobody clones have been obtained in the previous TRAP display selection without affinity maturation37,38. We considered two possibilities to explain the result: (1) unique character of MCP-1 protein or (2) limited patterns of interaction between D- and L-configured polypeptides. Generally speaking, the binding affinities of enriched clones from the same library would vary depending on the target characteristics such as molecular size, shape, and surface charge. Particularly, when the target protein is small, isolation of strong binders would become more challenging due to the limited binding sites available on the surface of the target protein32. Therefore, it seems reasonable that high-affinity clones were not obtained against the 76-residue MCP-1, which is relatively small compared to previously reported targets consisting of over 200 residues such as SARS-CoV-2 spike protein, EGFR1, and HER237. However, this first possibility was rejected because high-affinity clones with single-digit nM KD were obtained by the same procedure of selection using L-MCP-1 as the target (Supplementary Fig.\u00a014). Another possibility is that the interactions between D- and L-configured polypeptides could be less favorable than those found in L-configured polypeptides. Although the second hypothesis would be novel and interesting, there is currently a limited amount of data available regarding the interactions between D- and L-polypeptides. In the future, the selection of more mirror-image binders and the determination of their structures will provide insights into enantioselective binding, as demonstrated in the parallel study44.\n\nConsistent production of mirror-image peptide/protein binders is hindered by limited accessibility to reliable setups and techniques for in vitro selection and chemical protein synthesis. Given that higher library diversity tends to yield high-affinity binders against the same target, efforts to increase library diversity, such as optimizing mRNA sequences65, would be the one of promising approaches. Continuous development of methodologies for chemical protein synthesis is also necessary to efficiently prepare D-configured target and binder proteins. The results reported here demonstrate the successful generation of a mirror-image protein binder with high affinity against a pharmaceutically important target protein through chemical protein synthesis and TRAP display. While a previous study highlighted mirror-image mRNA display that produced a moderate D-peptide binder with a dissociation constant of 261\u2009nM66, the current study, by leveraging a protein scaffold and a highly diverse library, demonstrates the production of a mirror-image binder with high affinity (KD\u2009=\u20091.3\u2009nM), significant inhibitory activity on the interaction between MCP-1 and CCR2 (IC50\u2009=\u20092.3\u2009nM), and potent inhibitory effect on cell migration comparable to carlumab. We anticipate that the combinatorial use of matured screening and synthesis methodologies will lead to the revitalization of mirror-image binder development in the drug discovery field.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Oligonucleotides were purchased from Fasmac Co. and Ltd., Nippon Bio Service. The sequences of the primers and synthetic DNAs are listed in Supplementary Data\u00a01. The preparation of the E. coli reconstituted cell-free translation system, Pfu-S DNA polymerase, and Moloney murine leukemia virus reverse transcriptase (MMLV) have been described in previous reports37,53,67,68. The composition of the translation system is listed in Supplementary Table 1.\n\nFor the first-round selection, the puromycin linker (PuL)/mRNA complex was prepared by annealing HEX-mPuL (4\u2009\u00b5M) with the FN3 mRNA library (4.8\u2009\u00b5M)37 in annealing buffer (25\u2009mM HEPES-K pH 7.8, 200\u2009mM potassium acetate) by heating the solution (200\u2009\u00b5L) to 95\u2009\u00b0C for 3\u2009min and cooling to 25\u2009\u00b0C. The HEX-mPuL /mRNA complex (126\u2009\u00b5L) was added to the E. coli reconstituted cell-free translation system, resulting in a final reaction volume of 500\u2009\u00b5L. The mixture was incubated at 37\u2009\u00b0C for 30\u2009min. Subsequently, 41.7\u2009\u00b5L of 200\u2009mM EDTA (pH 8.0) was added to the translation mixture. Reverse transcription buffer (41.1\u2009\u00b5L; 0.78\u2009M Tris-HCl pH 8.4, 1.16\u2009M KCl, 0.37\u2009M MgCl2 and 0.08\u2009M DTT), 5\u2009mM dNTPs (66.7\u2009\u00b5L), 100\u2009\u03bcM FN3S.R29 (10\u2009\u00b5L), and 28.7\u2009\u03bcM HMLV (27.5\u2009\u00b5L) were added to the translation mixture, and the resulting solution was incubated at 42\u2009\u00b0C for 15\u2009min. The buffer was exchanged to HBST buffer [50\u2009mM HEPES-K pH 7.5, 300\u2009mM NaCl, 0.05% (v/v) Tween 20] using Zeba\u2122 Spin Desalting Columns. In order to remove the bead binders, the resulting solution was mixed with beads from a 100\u2009\u00b5L suspension of Dynabeads M-280/M-270 streptavidin (1:1) (Thermo Fisher Scientific) at 25\u2009\u00b0C for 10\u2009min. The supernatant was mixed with 1.75\u2009\u00b5L of 20\u2009\u00b5M D-MCP-1-biotin (f.c. 50\u2009nM) and then incubated at 25\u2009\u00b0C for 10\u2009min. After recovering the target proteins with beads from 200\u2009\u00b5L of Dynabeads M-270 streptavidin, the resulting beads were washed with 1\u2009mL of HBST buffer thrice, and PCR premix [10\u2009mM Tris-HCl pH 8.4, 50\u2009mM KCl, 0.1% (v/v) Triton X-100, 2\u2009mM MgCl2, 0.25\u2009mM each dNTP] (690\u2009\u00b5L) was added, and heated at 95\u2009\u00b0C for 2\u2009min. The amount of the eluted cDNAs was quantified by SYBR green-based quantitative PCR using T7SD8M2.F44 and FN3Lip.R20 as primers. The eluted cDNAs were PCR-amplified using T7SD8M2.F44, G5S-4Gan21-3.R42, and Pfu-S DNA polymerase and was purified by phenol/chloroform extraction and isopropanol precipitation. The DNA was dissolved in 10\u2009mM HEPES-K pH 7.8.\n\nFor the second-round selection, the resulting DNA (25\u2009nM, 4.0\u2009\u00b5L) was added to the TRAP system, and the reaction mixture (20\u2009\u00b5L) was incubated at 37\u2009\u00b0C for 30\u2009min. After the reaction, 4\u2009\u00b5L of 100\u2009mM EDTA (pH 8.0) was added to the translation mixture. A reverse transcription mixture (12\u2009\u00b5L; 150\u2009mM Tris-HCl pH 8.4, 225\u2009mM KCl, 75\u2009mM MgCl2 and 16\u2009mM DTT, 1.5\u2009mM dNTPs, 7.5\u2009\u03bcM FN3S.R29, and 3.4\u2009\u03bcM HMLV) was then added to the translation mixture, and the resulting solution was incubated at 42\u2009\u00b0C for 15\u2009min. The buffer was exchanged for HBST buffer using Zeba\u2122 Spin Desalting Columns. To remove bead binders, the resulting solution was mixed thrice with beads from 10\u2009\u00b5L of Dynabeads M-280/M-270 streptavidin (1:1) at 25\u2009\u00b0C for 20\u2009min. The half volume of the supernatant (16\u2009\u00b5L) was mixed with 0.4\u2009\u00b5L of 1\u2009\u00b5M D-MCP-1-biotin (f.c. 25\u2009nM). After incubating at 25\u2009\u00b0C for 10\u2009min, the solution was mixed with beads from 0.2\u2009\u00b5L of Dynabeads M-270 streptavidin at 25\u2009\u00b0C for 1\u2009min. The beads were washed with 10\u2009\u00b5L of HBST buffer thrice, and the PCR premix was added to the beads. Quantitation of cDNA and amplification and purification of DNA were performed using the same procedure as described for the first-round selection.\n\nFor the third to fifth-round selections, the procedure was performed in a manner similar to that of the second round, except for the volume of the reaction mixtures, which was 5\u2009\u00b5L. For the sixth to seventh-round selections, the solution for the selection was diluted 16 times with HBST to reduce the concentration of D-MCP-1-biotin to 2\u2009nM. In the seventh round, the pulldown step was performed both with and without D-MCP-1-biotin, and the recovered cDNAs were subsequently sequenced using an Ion Torrent instrument (Thermo Fisher Scientific) (Supplementary Data\u00a02).\n\nSelection against L-MCP-1-biotin was conducted with the same procedure as that of D-MCP-1-biotin.\n\nFor the first round of selection, 1\u2009\u00b5M mRNA/HEX-mPuL was added to a reconstituted translation system, and the reaction mixture (5\u2009\u00b5L) was incubated at 37\u2009\u00b0C for 30\u2009min. After the reaction, 1\u2009\u00b5L of 100\u2009mM EDTA (pH 8.0) was added to the translation mixture. A reverse transcription mixture (3\u2009\u00b5L; 150\u2009mM Tris-HCl pH 8.4, 225\u2009mM KCl, 75\u2009mM MgCl2, and 16\u2009mM DTT, 1.5\u2009mM each dNTP, 7.5\u2009\u03bcM FN3S.R29 primer, and 3.4\u2009\u03bcM MMLV) was added to the translation mixture, and the resulting solution was incubated at 42\u2009\u00b0C for 15\u2009min. The buffer was changed to HBST buffer using Zeba\u2122 Spin Desalting Columns. To remove the bead binders, the resulting solution was mixed with beads from 10\u2009\u00b5L of Dynabeads M-280/M-270 streptavidin (1:1) (Thermo Fisher Scientific) at 25\u2009\u00b0C for 20\u2009min. This step was repeated another two times. The supernatant (9\u2009\u00b5L) was diluted with the HBST buffer (81\u2009\u00b5L) and mixed with 0.9\u2009\u00b5L of 200\u2009nM D-MCP-1-biotin (f.c. 2\u2009nM); the resulting solution was incubated at 25\u2009\u00b0C for 3\u2009min. The target proteins were collected by mixing with beads from 0.5\u2009\u00b5L of Dynabeads M-270 streptavidin for 1\u2009min. The collected beads were washed with 10\u2009\u00b5L of the HBST buffer for 1\u2009min two times, and 100\u2009\u00b5L of PCR premix was added. The beads were heated at 95\u2009\u00b0C for 5\u2009min, and the amount of eluted cDNA was quantified by SYBR green-based quantitative PCR using T7SD8M2.F44 and FN3Lip.R20 as primers. The eluted cDNA was PCR-amplified using T7SD8M2.F44, G5S-4Gan21-3.R42, and Pfu-S DNA polymerase and purified by phenol/chloroform extraction and isopropanol precipitation. From the following selection, the resulting DNA (about 5\u2009nM final concentration) was added to the TRAP system, and the reaction mixture (5\u2009\u00b5L) was incubated at 37\u2009\u00b0C for 30\u2009min. The subsequent procedures were similar to those described above. In the final round of selection, after washing twice with 10\u2009\u00b5L of HBST, the beads were extensively washed by incubating them in 200\u2009\u00b5L of HBST at 25\u2009\u00b0C for 20\u2009min The sequences of the recovered DNA were determined using an Ion Torrent instrument (Thermo Fisher Scientific).\n\nAffinity measurement was performed on biotinylated D-MCP-1, or L-MCP-1 immobilized on a streptavidin biosensor (ForteBio) using the Octet system (ForteBio) as described in the manufacturer\u2019s instructions. The analyte monobody was dissolved in water to prepare a 10\u2009\u03bcM of monobody solution, and the buffer was changed to buffer D (50\u2009mM HEPES (pH 7.5), 300\u2009mM NaCl, 0.05% [vol/vol] tween 20, 0.1% [wt/vol] PEG) using Zeba\u2122 Spin Desalting Columns. The protein concentration was measured at A280, according to the molar extinction coefficient estimated from the amino acid composition. The monobody solution was stored at \u2212\u200980\u2009\u00b0C and was used for the following binding assay after dilution with buffer D. The binding assay was performed at 25\u2009\u00b0C in buffer A. Each step in the binding assay was as follows: equilibration for 60\u2009s, association for 400\u2009s or 600\u2009s, and dissociation for 600\u2009s.\n\nAutomated solid-phase peptide synthesis was performed by using Initiator\u2009+ Alstra (Biotage). Fmoc-protected D- and L-amino acids containing standard side-chain protecting groups were used in Fmoc SPPS. For Fmoc group deprotection, 20% piperidine, 0.1\u2009M HOBt in DMF was treated for 5\u2009min twice. For coupling, HBTU as an activator and DIEA as a base were used. For capping, 5% anhydrous acetic acid in DMF was used. Coupling of amino acids other than His, Arg, and Cys derivatives: Fmoc-protected amino acids (4 equiv) were activated with HBTU (3.9 equiv) and DIEA (8 equiv) in DMF and transferred to the resin (coupling time: 5\u2009min at 75\u2009\u00b0C, single coupling). Coupling of His, Arg, and Cys derivatives: Fmoc-protected amino acids (4 equiv) were activated with HBTU (3.9 equiv) and DIEA (8 equiv) in DMF and transferred to the resin (coupling time: 60\u2009min at room temperature, double coupling). The isolated yields of each peptide were estimated by using the molecular weights of TFA salt at the N-terminal amine and the sidechains of Arg, Lys, and His.\n\nAs amino-acid building blocks, Fmoc-protected monomers were used as shown below: Fmoc-D-Ala-OH, Fmoc-D-Arg(Pbf)-OH, Fmoc-D-Asn(Trt)-OH, Fmoc-D-Asp(Ot-Bu)-OH, Fmoc-D-Cys(Trt)-OH, Fmoc-D-Gln(Trt)-OH, Fmoc-D-Glu(Ot-Bu)-OH, Fmoc-Gly-OH, Fmoc-D-His(Trt)-OH, Fmoc-D-Ile-OH, Fmoc-D-Leu-OH, Fmoc-D-Lys(Boc)-OH, Fmoc-D-Met-OH, Fmoc-D-Phe-OH, Fmoc-D-Pro-OH, Fmoc-D-Ser(t-Bu)-OH, Fmoc-D-Thr(t-Bu)-OH, Fmoc-D-Trp(Boc)-OH, Fmoc-D-Tyr(t-Bu)-OH, Fmoc-D-Val-OH.\n\n2-Chlorotrityl chloride resin was swelled with DMF for 30\u2009min. Fmoc-NHNH2 (1.1 equiv) and DIEA (dissolved in DMF, 2.2 equiv) were added to the resin and reacted at room temperature for 12\u2009h. Then, MeOH was added and reacted for 10\u2009min. The resin was subsequently washed with DMF and DCM for each 3 times. After Fmoc quantification, the target peptide sequence was elongated by Initiator\u2009+ Alstra. For peptide recovery and global deprotection, the obtained resin was treated with TFA/TIS/H2O (95/2.5/2.5) for 2\u2009h at room temperature. Then, the TFA solution was obtained by filtration, and 10 times the volume of cold diethyl ether was added. The tube was vortexed well and centrifuged 10,000\u2009\u00d7\u2009g at 3\u2009\u00b0C for 5\u2009min. Ether was removed by decantation. This precipitation was washed with diethyl ether twice, and the precipitation was dried in vacuo. The solid crude peptide was dissolved in 6\u2009M Gn\u00b7HCl and 0.2\u2009M NaH2PO4 at pH 3.0 (peptide concentration: ~\u20092.5\u2009mM calculated from resin loading). The solution was cooled to \u2212\u200917\u2009\u00b0C and NaNO2 was added (10 equiv against peptide). The mixture was stirred at \u2212\u200917\u2009\u00b0C for 15\u2009min, and then 125\u2009mM MESNa aq. (50 equiv against each peptide) was added to the reaction mixture. The pH was adjusted to 6.5\u20136.8 with 6\u2009M NaOH aq. and the solution was stirred at room temperature for 30\u2009min. Then, the peptide solution was purified by preparative HPLC (Nacalai Cosmosil 5C18-AR-II column) to provide 5D (5.7\u2009mg, 0.7% isolated yield from resin). MALDI-TOF/MS spectrum of 5D after purification. MS(MALDI-TOF) m/z: [M\u2009+\u2009H]+ Calcd for C204H296N56O62S3 4622.1; Found 4621.7.\n\nThe same SPPS procedure described above was employed for resin preparation and peptide elongation. Manual synthesis was performed by the following protocol: For Fmoc group deprotection, the resin was treated with 20% piperidine, 0.1\u2009M HOBt in DMF for 5\u2009min twice. For coupling of Thz, Boc-Thz-OH (5 equiv) were activated with HATU (4.9 equiv) and DIEA (10 equiv) in DMF and transferred to the resin (coupling time; 2\u2009h at room temperature). For peptide recovery and global deprotection, the obtained resin was treated with TFA/thioanisole/EDT/anisole (90/5/3/2) for 2\u2009h at room temperature. Then, the TFA solution was obtained by filtration, and 10 times the volume of cold diethyl ether was added. The tube was vortexed well and centrifuged 10,000\u2009\u00d7\u2009g at 3\u2009\u00b0C for 5\u2009min. Ether was removed by decantation. This precipitation was washed with diethyl ether twice, and the precipitation was dried in vacuo. The peptide precipitation was dissolved in 6\u2009M Gn\u00b7HCl, 100\u2009mM MPAA, and 0.2\u2009M NaH2PO4 at pH 3.0 (peptide concentration: ~\u20091\u2009mM calculated from resin loading). Then, acetylacetone was added (2.5 equiv against peptide), and the solution was stirred at 37\u2009\u00b0C. For analysis of each reaction, an aliquot (1.0\u2009~\u20092.0\u2009\u03bcL) of each reaction mixture was diluted by water containing 0.1% TFA and injected into analytical HPLC. After the thioesterification was completed, the peptide solution was purified by preparative HPLC (Phenomenex Jupiter C4 300 column) to provide 6D (20.2\u2009mg, 6.2% isolated yield from resin). MS(MALDI-TOF) m/z: [M\u2009+\u2009H]+ Calcd for C188H290N44O63S2 4239.7; Found 4239.7.\n\nRink amide-PEG resin (0.24\u2009mmol/g, 0.20\u2009mmol, Watanabe Chemical) was swelled with DMF for 30\u2009min. The resin was subsequently washed with DMF and DCM for each 3 times. The target peptide sequence was elongated by Initiator\u2009+ Alstra. The resin was then treated with TFA/TIS/H2O/DMB (90/2.5/2.5/5) for 2\u2009h at room temperature. Then, the TFA solution was obtained by filtration, and 10 times the volume of cold diethyl ether was added. The tube was vortexed well and centrifuged 10,000\u2009\u00d7\u2009g at 3\u2009\u00b0C for 5\u2009min. Ether was removed by decantation. This precipitation was washed with diethyl ether twice, and the precipitation was dried in vacuo. Then, the peptide solution dissolved in water/acetonitrile mixture containing 0.1% TFA was purified by preparative HPLC (Nacalai Cosmosil Protein-R column) to provide 7D (64.7\u2009mg, 5.9% isolated yield from resin loading). MS(MALDI-TOF) m/z: [M\u2009+\u2009H]+ Calcd for C195H277N61O55S1 4388.7; Found 4389.5.\n\n1\u2009mM peptides 6D (17.0\u2009mg, 3.72\u2009\u03bcmol) and 1\u2009mM 7D (20.5\u2009mg, 3.72\u2009\u03bcmol) were dissolved in MPAA ligation buffer [100\u2009mM MPAA, 50\u2009mM TCEP, 6\u2009M Gn\u00b7HCl, 200\u2009mM phosphate (pH 6.5)], and the reaction mixture was incubated for 18\u2009h at 37\u2009\u00b0C. 1\u2009M HCl was added to the reaction mixture to adjust the pH into 4. Then, methoxyamine\u00b7HCl was added (200 equiv against peptide) and the solution was stirred for 2\u2009h at 37\u2009\u00b0C. For analysis of each reaction, an aliquot (1.0\u2009\u03bcL) of each reaction mixture was injected into analytical HPLC. The mixture was purified by preparative HPLC (Phenomenex Jupiter C4 300 column) to provide 8D (10.4\u2009mg, 1.06\u2009\u03bcmol, 29% isolated yield). MS(MALDI-TOF) m/z: [M\u2009+\u2009H]+ Calcd for C374H559N105O116S2 8447.2; Found 8446.9.\n\n1\u2009mM peptides 5D (5.6\u2009mg, 1.06\u2009\u03bcmol) and 1\u2009mM 8D (10.4\u2009mg, 1.06\u2009\u03bcmol) were dissolved in MPAA ligation buffer [100\u2009mM MPAA, 50\u2009mM TCEP, 6\u2009M Gn\u00b7HCl, 200\u2009mM phosphate (pH 6.5)], and the reaction mixture was incubated for 13\u2009h at 37\u2009\u00b0C. For analysis of each reaction, an aliquot (1.0\u2009\u03bcL) of each reaction mixture was diluted by TCEP (4.0\u2009\u03bcL), DTT (5.0\u2009\u03bcL) and injected into analytical HPLC. The mixture was purified by preparative HPLC (Phenomenex C4 300 column) to provide 9D (2.2\u2009mg, 0.146\u2009\u03bcmol, 14% isolated yield). MS(MALDI-TOF) m/z: [M\u2009+\u2009H]+ Calcd for C576H849N161O175S3 12926.1; Found 12927.3.\n\nFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data generated in this study are provided in the Supplementary Information/Source Data file. Supplementary Information is provided in this paper. Source Data are provided as a Source Data file. Source data are provided in this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Harrison, K., Mackay, A. S., Kambanis, L., Maxwell, J. W. C. & Payne, R. J. Synthesis and applications of mirror-image proteins. Nat. Rev. Chem. 7, 383\u2013404 (2023).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nLander, A. J., Jin, Y. & Luk, L. Y. P. D-peptide and D-protein technology: Recent advances, challenges, and opportunities. 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This was a one-time donation and not from grant funds. N.I. is grateful for JSPS Research Fellowships for Young Scientists.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Biomolecular Engineering, Graduate School of Engineering, Nagoya University, Nagoya, Japan\n\nGosuke Hayashi,\u00a0Toshinori Naito,\u00a0Sayaka Miura,\u00a0Sae Suzuki\u00a0&\u00a0Hiroshi Murakami\n\nGraduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan\n\nNaoya Iwamoto,\u00a0Yusuke Usui\u00a0&\u00a0Shinya Oishi\n\nHuman Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan\n\nMika Bando-Shimizu,\u00a0Katsuaki Higashi\u00a0&\u00a0Motohiro Nonaka\n\nLaboratory of Medicinal Chemistry, Kyoto Pharmaceutical University, Kyoto, Japan\n\nShinya Oishi\n\nInstitute of Nano-Life-Systems, Institutes of Innovation for Future Society, Nagoya University, Nagoya, Japan\n\nHiroshi Murakami\n\nResearch Institute for Quantum and Chemical Innovation, Institutes of Innovation for Future Society, Nagoya University, Nagoya, Japan\n\nHiroshi Murakami\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nG.H., M.N., S.O., and H.M. proposed the idea and designed the experiments, analyzed all the results, and wrote the manuscript. T.N., N.I., S.S., and Y.U. carried out chemical synthesis and evaluation of D- and L-proteins. S.M. performed TRAP display selection and affinity maturation. M.B.-S. and K.H. performed a cell migration assay. M.N. carried out an immunogenicity assay. All authors read and discussed the manuscript.\n\nCorrespondence to\n Gosuke Hayashi, Motohiro Nonaka, Shinya Oishi or Hiroshi Murakami.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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by territoriality and biparental care in songbirds", + "pre_title": "Sex differences in singing behaviour are predicted by territoriality and biparental care in songbirds", + "journal": "Nature Communications", + "published": "21 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60810-5/MediaObjects/41467_2025_60810_MOESM1_ESM.pdf" + }, + { + "label": "Peer review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60810-5/MediaObjects/41467_2025_60810_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60810-5/MediaObjects/41467_2025_60810_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60810-5/MediaObjects/41467_2025_60810_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-60810-5#Sec14" + ], + "code": [ + "https://doi.org/10.6084/m9.figshare.26799523" + ], + "subject": [ + "Behavioural ecology", + "Sexual selection" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4018424/v1.pdf?c=1753182373000", + "research_square_link": "https://www.researchsquare.com//article/rs-4018424/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60810-5.pdf", + "preprint_posted": "09 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Pronounced sexual dimorphism is thought to evolve through sexual selection for elaborate male traits. Increasing evidence suggests that sexual dimorphism in traits such as birdsong may also evolve through loss of elaboration in females, but the evolutionary drivers underlying this are obscure. Here we analyse ecological and natural history traits of over 1300 songbird species and show that increased female song incidence and elaboration are most directly associated with year-round territoriality, biparental care, and large body size. Moreover, phylogenetic path analysis indicates that mating system and breeding latitude have only indirect effects. Stable, tropical life histories and mating systems with biparental care promote female song, whereas evolutionary transitions to migration, reduced territoriality, and loss of male care led to losses of female song. Our results provide the first comprehensive framework to understand the drivers of sex differences in birdsong and reveal novel interactions among natural history, social, and sexual selection pressures in the evolution of avian song in both sexes.Biological sciences/Evolution/Sexual selectionBiological sciences/Ecology/Behavioural ecology", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SexDifferencesInBirdSongSupplementaryTables.xlsxrs.pdfReporting Summary", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Pronounced sexual dimorphism is generally assumed to evolve through sexual selection for elaborate male traits. However, there is increasing evidence that sexual dimorphism in traits such as birdsong may also evolve through loss of elaboration in females, but the evolutionary drivers underlying this process are obscure. Here we analyse ecological and natural history traits for over 1300 songbird species and show that increased female song incidence and elaboration are most directly associated with year-round territoriality, biparental care, and large body size. Phylogenetic path analysis indicates that mating system and breeding latitude primarily have indirect effects on female song evolution. Stable, tropical life histories and mating systems with biparental care promote female song, whereas evolutionary transitions to migration, reduced territoriality, and loss of male care led to losses or reductions of female song incidence. Our analyses provide a comprehensive framework for studying the drivers of sex differences and similarities in birdsong and reveal novel interactions among natural history and sexual selection pressures that have been hypothesized to independently shape elaborate traits.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Classic sexual selection theory posits that elaborate secondary sexual characteristics evolve if there is positive selection on traits that increase male mating success by attracting and competing for access to females1. Conversely, females are not generally considered to be under positive selection for ornaments, and many female ornaments are traditionally thought to result from correlated genetic evolution with male secondary sexual characteristics2,3. However, female ornaments are more common than previously thought4,5. Both females and males use elaborate traits not only for sexual but also for social signalling6,7,8, and in many species, elaborate female traits evolve concurrently with male traits9,10,11,12,13. Birdsong is one example of this: ancestral state reconstruction indicates that this important signalling trait in songbirds (Passeri) evolved initially in both sexes13 and that current patterns of sexual dimorphism result from multiple repeated losses of song in females, rather than predominantly from gains in males13,14,15. In contrast to the many theoretical attempts to explain how evolutionary gains and losses of elaboration in males have led to sexual dimorphism16,17,18,19,20,21, only a few large-scale studies have quantified factors associated with such evolutionary gains and losses in females (e.g., 10,12).\n\nCurrently, three main suites of non-mutually exclusive hypotheses have been proposed to explain how transitions in female singing behaviour have led to the existing sex differences in birdsong (Fig.\u00a01): (1) tropical natural history favours similar sex roles and signals in males and females8,12,21, (2) polygynous mating systems and sexual selection promote exaggerated male traits and reduced signalling in females16,22, and (3) natural selection reduces female ornamentation and elaboration when female signals pose costs to reproduction by attracting predators to the nest17,23. To assess support for each hypothesis, we examined associations between female song incidence and elaboration and multiple predictor variables tied to each hypothesis (Fig.\u00a01). Under the first hypothesis, reduced seasonality in tropical breeding latitudes leads to sedentary populations, increased competition for resources, and year-round territoriality7,8,21. Under such conditions, elaborate songs by both sexes appear to facilitate competition for year-round resources7,8. Conversely, in many migratory, temperate breeding species males compete intensely at the start of each breeding season for short-term breeding territories and mates, resulting in strong selection for elaborate song in males, but reduced selection for song in females that breed at higher latitudes21,24. The second hypothesis, the sexual selection hypothesis, predicts that in species where males compete intensely for females, such as in polygynous or lek-mating species without male care, sexual selection will result in strong sexual dimorphism, favouring elaborate song in males1,22,25. In such species, female competition, and therefore song, may also be reduced because females compete less. By contrast, when both sexes compete, song is selected for in both sexes7,8. Third, the nest predation hypothesis posits that female song should be selected against in species with high daily nest predation because of the cost to reproduction17,26. We also assessed an association between female song and body size, as it is an important variable to account for in phylogenetic analyses. A similar study found plumage elaboration in both sexes is associated with large body size in passerine birds12. We imagine female song could similarly be associated with larger body size through direct or indirect mechanisms if larger body size and female song both confer advantages.\n\nArrows represent possible causal pathways among predictor variables and how each predictor variable is associated with female song incidence. Predicted associations are labeled as positive (+) or negative (\u2212).\n\nHere we used a dataset of over 1300 songbird (Passeri) species to evaluate how variables associated with these three hypotheses are tied to three metrics of female song: incidence, elaboration, and length. We scored female song incidence, elaboration, and length ordinally, relative to male song, based on published descriptions in global, regional and taxon-specific species accounts (e.g.,27 see\u00a0Supplementary Methods for full list of references). We used ordinal categories because recordings and quantitative descriptions of female song and especially song incidence still do not exist for most species28. Female song incidence, an estimate of how often females sing, was scored as absent, rare, occasional, regular (but less than male song), of similar incidence to male song, or more common than male song. Female song elaboration and length were scored using similar ordinal scales. In addition, we examined broad associations among species with and without female song (present/absent), since we did not have ordinal scores for all species. To evaluate the relationship between these song traits and the three hypotheses detailed above, we compared our scores to the predictor variables, that are hypothesized to be directly or indirectly associated with female song evolution and sexual dimorphism in song as outlined in Fig.\u00a01: body size (mass), breeding latitude, mating system, sexual size dimorphism, biparental care, cooperative breeding, migratory behaviour, and extent of year-round territoriality. Because vocal duets may be evolutionarily and functionally a subset of female singing behaviour29,30, we also compared female song incidence to the presence or absence of duets. Phylogenetic path analysis allowed us to go beyond traditional mixed model approaches to assess how predictor variables associated with each hypothesis might be inter-related (Fig.\u00a01), thereby reconciling existing theories for sexual dimorphism in song.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60810-5/MediaObjects/41467_2025_60810_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "We found that female song is common and widespread in songbirds (Fig.\u00a02), consistent with previous studies13,14,15,31,32. Among species with sex-specific information (1309 Passeri species), females were reported to sing in 59% of species. Of the 774 species in our study with female song, species with similar female and male song incidence were most common (25%; 15% of all 1309 species), followed by species with occasional female song (18.5%; 11% of all species) and then species with regular female song (15.5%; 9% of all species), while species in which female song was rare were uncommon (<6%; 3% of all species). Only a few species have been documented with higher female song incidences than males (<1%; Fig.\u00a02). The remaining species with female song lacked information on incidence (35% of species with female song; 21% of all 1309 species). Female song elaboration and song length showed similar trends: female song length and elaboration were similar to males in the majority of species (Fig.\u00a0S1). Female song had a low phylogenetic signal under Brownian motion when we compared species with and without female song (present/absent: K\u2009=\u20090.19). Similarly, all three ordinal female song metrics also had low phylogenetic signal (song incidence: K\u2009=\u20090.24, song elaboration: K\u2009=\u20090.26, song length: K\u2009=\u20090.23, also under Brownian motion models), confirming that this is a highly labile trait that varies readily across closely related species.\n\nThis includes all species for which we have sex-specific vocal information. The bar chart shows the number and percentage of species with each degree of female song, including species with female song but no song incidence data in grey. Bird illustrations depict representative species of each song incidence level. In order, they are zebra finch (Taeniopygia castanotis), great reed-warbler (Acrocephalus arundinaceus), house wren (Troglodytes aedon), eastern bluebird (Sialia sialis), crimson-breasted shrike (Laniarius atrococcineus), streak-backed oriole (Icterus pustulatus), and Eurasian magpie (Pica pica). Only terminal branch colors are accurate; internal branch colors are not an ancestral state reconstruction. Figure illustration by Jillian Ditner. Bird illustrations are reused with permission by Lynx Edicions | Birds of the World, Cornell Lab of Ornithology. Source data are provided as a Source Data file.\n\nWe found evidence that tropical natural history (Hypothesis 1) and sexual selection (Hypothesis 2), but not daily nest predation (Hypothesis 3), shaped patterns of sexual dimorphism in song (Fig.\u00a03, S2 & S3). Hypotheses 1 and 2 were supported by two independent phylogenetic Bayesian regression models (brms and MCMCglmm; Fig.\u00a03)33,34 and phylogenetic path analysis (Fig.\u00a04; with PhyloPath)35,36. The brms and best MCMCglmm models both revealed biparental care, year-round territoriality, lack of migration, and large body size as main predictors of increased female song incidence (Table\u00a01 & S1, Fig.\u00a03). In line with the sexual selection hypothesis (Fig.\u00a01), species with female song most often had biparental care, whereas species without biparental care usually lacked female song (Table\u00a01 & S1, Fig.\u00a03). As predicted by the tropical natural history hypothesis, females sang more in species with increased territoriality, particularly year-round territoriality, and females sang less or not at all in migratory species (Table\u00a01 & S1, Fig.\u00a03). Species in which females sing to a similar extent as males also had the largest body sizes, whereas species in which females rarely sing had the smallest body sizes (Table\u00a01 & S1, Fig.\u00a03). Lastly, MCMCglmm, but not brms models, indicated that species with the highest female song incidence were more likely to breed at low latitudes and breed cooperatively (Table\u00a01 & S1, Fig.\u00a03). These patterns were largely corroborated by phylogenetic regression analyses comparing species with and without female song (present/absent): the presence of female song was associated with large body size and territorial behaviour, especially year-round territoriality, whereas female song was largely absent in migratory species (Fig.\u00a0S3, Table\u00a0S2). This shows that broadly categorizing female song as present/absent confirms our strongest results but also\u00a0illustrates the power of the ordinal categories to detect more nuanced signals.\n\na Effect size plot of female song incidence showing the scaled effect size with 95% confidence intervals and direction of association with each predictor variable based on brms results. The effect size plot colours match hypotheses in Fig.\u00a01. Significant predictor variables are bolded. Sample sizes equal the totals for each variable in b. b Distributions, mean, and standard error\u00a0plotted for variables that were found to be associated with female song incidence based on brms and MCMCglmm. Posterior means and confidence interval values are presented in Table\u00a01. For definitions of the predictor variables, see the\u00a0Supplementary Material. Source data are provided as a Source Data file.\n\nArrows represent supported paths (associations) among predictor variables and female song incidence. The thickness of each arrow represents the strength of each association, determined by the correlation coefficient next to each arrow. Blue arrows represent positive correlations, and red arrows are negative correlations. Box colours match hypotheses from Fig.\u00a01.\n\nPhylogenetic path analysis corroborated the phylogenetic regression results and uncovered a complex network of effects shaping female song evolution and consequently sexual dimorphism in birdsong (Fig.\u00a04). This analysis suggested that biparental care, increased territoriality, and increased body size were the most direct predictors of increased female song incidence (Fig.\u00a04). In addition, path analysis reconciled causal pathways among these three variables and the other factors (Figs.\u00a03 & 4). Specifically, breeding latitude was indirectly associated with female song via associations with year-round territoriality and migratory behaviour: species at lower breeding latitudes were more likely to be year-round territorial and have female song. Conversely, migratory species with temperate breeding latitudes had reduced (lower) incidence of female song. However, latitude itself did not appear to directly impact song evolution (Fig.\u00a04). Female song was also indirectly associated with mating system via biparental care: highly polygynous species often lacked biparental care and, in turn, had reduced or no female song (Fig.\u00a04). Conversely, mating system appears to also interact with sexual size dimorphism, having an inverse effect on female song incidence: highly polygynous species had higher sexual size dimorphism and lower incidence of female song.\n\nPhylogenetic path analysis also revealed three possible causal pathways from tropical natural history and mating system to body mass\u00a0and thus female song (Fig.\u00a04). First, we found support for a direct path indicating that increased territoriality\u00a0led to increased body size, with both associated with increased female song incidence. An alternative supported path suggests that migratory species have less sexual size dimorphism,\u00a0and species with sexual size dimorphism had\u00a0larger body sizes and more female song, resulting in associations among all three\u00a0traits. We also found support for an association between increased polygyny and increased sexual size dimorphism37,38. This led to overall larger body sizes and thereby indirectly increased female song incidence. However, this last pathway is inconsistent with our other findings that increased sexual size dimorphism and higher rates of polygyny are each independently associated with lower incidences of female song (Figs.\u00a03 & 4). The latter result agrees with predictions of sexual selection theory: female song incidence should be lower in polygynous species with greater sexual size dimorphism. We found evidence for this relationship, but it becomes more complex when all variables in the path analysis interact. The same best phylogenetic path model was supported when we compared species with and without female song (present/absent: Fig.\u00a0S4), indicating that our findings are robust and also explain broad patterns of female song evolution.\n\nPhylogenetic regression model results for song elaboration and length were similar to those of song incidence (Table\u00a01 & S1, Figs.\u00a0S1 & S2). Species with any length of female song were likely to have biparental care and year-round territoriality. Species with longer female songs were more likely to be larger and non-migratory (Table\u00a01 & S1, Fig.\u00a0S2). MCMCglmm, but not brms, also suggests that species with longer female songs were more likely to breed at lower, tropical latitudes, to breed cooperatively, and to be larger (Table\u00a01, Fig.\u00a0S2). Species with more elaborate female songs also bred at lower latitudes, were territorial\u00a0year-round, and were larger (Table\u00a01 & S1, Fig.\u00a0S2). MCMCglmm results suggested that female song elaboration, unlike incidence and song length, was correlated directly with mating system, such that species with the most elaborate female songs were monogamous or had low levels of polygyny (Table\u00a0S1). Path analysis recovered similar pathways among the predictor variables and all three metrics of female song (Fig.\u00a0S4 & S5). Therefore, the underlying causal associations among these predictor variables appear to be similar for song incidence, elaboration, and length.\n\nTo investigate possible interactions among the tropical natural history and sexual selection hypotheses, we also looked at interactions between territoriality and mating system for all three song metrics. We found that species with moderate levels of polygyny (5\u201320%) and seasonal or weak territoriality were more likely to have female song than other non-monogamous species (Fig.\u00a0S6). Specifically, species with moderate levels of polygyny (5\u201320%) and seasonal territoriality had higher incidences of occasional and regular female song (see mating system mosaic plot in Fig.\u00a03, Table\u00a0S3). There were no other significant interactions.\n\nWe found no associations between daily nest predation rates and female song incidence, elaboration, or length using brms (Table\u00a02 & S4; Figs.\u00a0S7 & S8). MCMCglmm results suggested that species with much shorter female than male songs had lower daily nest predation rates than other species (p\u2009=\u20090.004; Table\u00a0S4). This could be an artefact of small sample size, which was especially low for very short female songs in this analysis (n\u2009=\u20093). However, if true, this result suggests that species with shorter female songs potentially experience lower nest predation.\n\nUnsurprisingly, high incidence of female song and duetting were strongly correlated and shared similar predictors (Tables\u00a03, S5 & S6, Fig.\u00a0S9). Duetting species specifically had the largest body sizes, nearly always occupied year-round territories, and were monogamous (Table\u00a0S6, Figs.\u00a0S9). Duetting species were also generally non-migratory and bred at tropical latitudes (MCMCglmm results only; Table\u00a0S6, Fig.\u00a0S9). Conversely, species with female song but that do not duet were generally intermediately sized, bred at higher, temperate latitudes, were more likely to be seasonally territorial or migratory, and had higher rates of polygyny compared to species that duet (Tables\u00a04 & S6, Fig.\u00a0S9).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60810-5/MediaObjects/41467_2025_60810_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60810-5/MediaObjects/41467_2025_60810_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60810-5/MediaObjects/41467_2025_60810_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our analysis of over 1300 songbird species indicates that the global incidence of female song is strongly predicted by year-round territoriality and biparental care. Conversely, migration, reduced or seasonal territoriality, and loss of male care appear to have led to widespread loss of female song. These traits are, in turn, strongly influenced by latitude and mating system. Our findings are consistent with previous studies showing that sexual dimorphism in song and plumage are most common in the tropics and strongly associated with monogamous, sedentary, territorial nesting species11,14,15,21,39. However, we provide evidence that it is\u00a0not tropical latitude per se that\u00a0favours female song, but rather year-round territoriality, which is exhibited by many tropical and sub-tropical breeding species. Under the tropical natural history hypothesis, warm, stable tropical climates favour year-round residence, territoriality, similar sex roles, and care by both parents over long breeding seasons21. Consistent with this hypothesis, we see female song under conditions that favour year-round territoriality, joint territory defence, and biparental care, whereas losses of female song result from transitions to migratory or seasonally territorial natural histories with reduced male care. Our path analyses provide statistical support for the long-standing observation that biogeographic patterns influence natural history and signal evolution.\n\nOur results are also consistent with birdsong originating in both sexes in Australasia in the Late Eocene, when the Earth was warmer, and that sex differences in song incidence emerged when songbirds spread around the globe13,40. The incidence of female song is highest in tropical, year-round territorial, monogamous species with biparental care. Together with other comparative research, a plausible evolutionary scenario is that the ancestral songbird was non-migratory with biparental care and song in both sexes40,41,42, and then female song was lost in lineages that evolved seasonal migration, reduced territoriality, and/or female-only care. We suggest that birdsong initially evolved in both sexes to defend year-round territories and resources and to coordinate breeding activities among monogamous mates7,8,43, common functions of female and male song today4,44,45,46. Later, birdsong may have become sexually selected in males to help certain species compete for mates.\n\nWhile our results suggest that high incidence of female song is predominantly associated with year-round territoriality, biparental care, and monogamous mating systems (especially in the tropics), this does not exclude that female song can also be a sexually selected mating signal in females6,47. Our results also uncover patterns that may explain the persistence of female song in certain temperate species. We found that intermediate female song incidence (rare or occasional) in seasonally or weakly territorial species is associated with low levels of polygyny. This result may capture seasonally breeding, facultatively polygynous species. If males in these species provide care or continued defence of the territory, females may compete with other females for the male\u2019s presence and the direct benefits that he provides4,48,49. This is consistent with evidence that competition between females for paternal care can select for female song4,48,50. Our data also show that female solo song is most common in seasonally or weakly territorial species, whereas duets are more common in year-round territorial species, in line with previous studies30,51. Therefore, female solo song may be selected for in temperate regions when females need to compete for resources, such as nest sites or direct benefits from mates4.\n\nWe did not find associations between daily nest predation rates and female song (Hypothesis 3). A field study by Kleindorfer et al.52 found that Superb Fairy-wrens (Malurus cyaneus) that sing near or inside their nests experience significantly higher rates of egg and nestling predation than do females that sing less near the nest, probably because song attracts predators to the nest53. However, field studies54 and phylogenetic comparative analyses of elaborate female plumage and nest predation rates provide mixed support for the idea that nest predation has driven the evolution of dull female plumage in some lineages23,26,55,56. The sample sizes for our current analyses of nest predation were small. We encourage ongoing evaluation of this question with larger datasets and experimental field studies.\n\nSeveral of our analyses confirmed that female song is more common and more elaborate in large-bodied songbirds. Interestingly, large-bodied birds also tend to have more elaborate male and female plumage12. We found that larger body size is associated with greater territoriality and in turn, increased female song. Both song and body size can reliably indicate female fitness, making both traits potential targets of strong social (including sexual) selection43,57. Larger body size might also aid in territory defence and therefore, like female song, might be favored in more territorial species58. Conversely, we found that increased migration is associated with the evolution of smaller body sizes. Therefore, the correlation of female song and body size could be the result of parallel evolution for reductions in body size and female song with migration21.\n\nWe found that species with the highest incidence of female song also duet. This suggests that female song and duetting are under similar selection pressures, suggesting targeted research is needed to separate factors driving duet evolution from high female song incidence29. A recent study on southern African songbirds30 found that duetting species typically had higher incidences of female song and were also large-bodied and year-round territorial. Consistent with Mikula et al.30, we also found that female solo song was strongly associated with seasonal territoriality compared to duetting (Table\u00a0S6, Fig.\u00a0S9), whereas duetting species were territorial year-round (Fig.\u00a0S9). Furthermore, the mating system was not associated with female song incidence but was associated with duetting; most duetting species are monogamous. Lastly, species with female song but not duets had low levels of polygyny (Fig.\u00a0S9). Future work should seek to identify more non-duetting species with female song for species-specific and comparative research. Such species are probably under-represented due to female song being more detectable in duetting species.\n\nWe still lack information on sex-specific singing behaviour for most songbird species28,59,60. For nearly half of the species in which female song was reported, we were unable to score incidence, elaboration, and length. Historical and biogeographical biases mean that male song and songbirds from Northern temperate zones are over-represented in the literature and sound archives28. Female songs of duetting species may also be over-represented in this dataset, as female and male song can be readily observed during duets, whereas species with female song that do not duet may be overlooked44,51. These can be rare, occasional, or regular female singers. Our research also suggests that female song may be overlooked in monomorphic, tropical species, even though female song is particularly common in monomorphic species with elaborate female plumage32. Recent studies confirm that female song is regularly overlooked and underestimated, even in well-studied temperate breeding birds (e.g., barn swallows, Hirundo rustica61; blue tits, Cyanistes caeruleus62). This might occur in part because female song can take place at different times of the day62 or year49, or may occur in only some populations63 or in different contexts64 from male song.\n\nIn conclusion, birdsong has long been a model system for understanding the evolution of elaborate signalling traits and sexual dimorphism. With the revised understanding that losses of female song drive much song dimorphism, we sought to disentangle the factors that shape birdsong in both sexes. Our results are consistent with previous clade- and region-specific findings that year-round territoriality and residency is a major predictor of female birdsong30,31,39,51,65. In addition, we provide strong evidence that both males and females sing in species with biparental care and large body sizes. Furthermore, breeding latitude and mating system were not direct predictors of female song, but rather contributors to overall patterns of natural history that, in turn, influence female song incidence and elaboration. Our findings in combination with the current understanding of avian natural history and evolution, suggest that female song has become reduced or lost with evolutionary transitions to migration, seasonal or reduced territoriality, and loss of male care. These results are consistent with song in both sexes being selected for and maintained to compete for territories, mates, and the resources that both provide. Thus, we provide continued evidence that sexual dimorphism in birdsong evolves not only through sexual selection on males but through combined natural, social, and sexual selection pressures on both sexes. Moreover, we conclude that the incidence of female birdsong is not perfectly explained by any single hypothesis; rather, morphological, behavioural, and life-history traits all interact to shape sex differences in avian vocal signalling.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "To investigate which natural history traits are associated with female birdsong, we scored three aspects of female song: (1) song incidence, (2) song quality or elaboration, and (3) song length. The species included in the current study were compiled from previous studies on female song13,14,15,31,32, particularly Webb et al.33. We also included evidence of female song from recording collections (Macaulay Library and xeno-canto). Together, we evaluated evidence for the presence or absence of female song in over 5200 songbird species (Passeri, BirdLife taxonomy), making this the most comprehensive estimate yet of female song in songbirds. Following criteria established by Odom et al.14, we excluded species in which neither sex sings (songless species) or species without enough sex-specific vocal information to score. Note that we still do not have information about which sex sings for most songbird species29. This resulted in a final dataset of 1309 songbird species, including 774 species (59%) in which both males and females are known to sing and 535 species (41%) in which only males have been reported to sing. We used Birds of the World and regional species accounts and field guides to score female song (e.g.27, see\u00a0Supplementary Methods for full reference list). Most of these sources describe female songs in relation to male songs, meaning that our ordinal scores are a comparison of the song quality or output of females as compared to males, i.e., song dimorphism. We used ordinal categories because recordings and detailed descriptions of female song structure and especially song incidence still do not exist for many species28. We scored the female song as follows:\n\nHow often or to what extent do females sing compared to males? 0 = female song absent, 1 = female song rare (most individuals do not sing; female song has only been observed in a few individuals or certain populations some years), 2 = female song occurs occasionally (it is observed periodically in some individuals but occurs noticeably less than male song or only during truncated parts of the year), 3 = female song occurs regularly (it can be reliably observed in many or most individuals but is somewhat less obvious than male song), 4 = female song occurs to the same extent as male song, 5 = females sing more than males.\n\nTo what extent are female songs described as \u2018elaborate\u2019 compared to male songs? This often included qualitative descriptions of song complexity, amplitude, or strength (e.g., female songs were often described as softer or weaker than male song). 0 = female song absent, 1 = female song is substantially less elaborate than male song, 2 = female song is somewhat less elaborate than male song, 3 = female song is similarly elaborate to male song, 4 = female song is more elaborate than male song. Because length was scored independently of elaboration, we did not include information on song length in this elaboration score.\n\nHow does the duration of female songs compare to male song? 0 = female song absent, 1 = female song is substantially shorter than male song, 2 = female song is somewhat shorter than male song, 3 = female song is similar in length to male song, or 4 = female song is longer than male song.\n\nWe could not score female song ordinally for all species in the study due to insufficient species information. Species with female song but without more detailed ordinal data were removed from the ordinal analyses. Therefore, we assessed if our results were robust to missing data by repeating the analysis with all species that we were able to classify as female song present vs absent. Female song presence vs absence was scored according to the criteria outlined by Odom et al.13 and used by Webb et al.32. Fig\u00a0S10 shows the natural history attributes of these species, missing incidence data. Additionally, sample sizes were small for species with greater female song incidence than males (incidence score = 5, n\u2009=\u20093) and species in which females sing more elaborate songs than males (elaboration score = 4, n\u2009=\u20097). To preserve statistical power and promote model convergence, we removed these instances of rare behaviour prior to statistical analysis. This resulted in slightly different samples sizes for each female song metric.\n\nThe predictor variables used to test hypotheses associated with female song were compiled from several sources including: (1) daily nest predation rates from Unzeta et al.66, (2) the original individual variables used to create composite life-history and sexual selection scores from Dale et al.12, and (3) territoriality and duet data from Tobias et al.51. We evaluated associations of our female song scores with the following predictor variables:\n\nDaily nest predation rates (estimated from the field and literature), Breeding latitude (degrees from equator of the breeding range centroid), Body size (log mass), Sexual size dimorphism (log (male wing length) \u2212 log(female wing length)), Biparental care (0 = absent or 1 = present), Cooperative breeding (0 = absent or 1 = present), Social mating system (on a four-point scale: 0 = strict monogamy, 1 = monogamy with infrequent polygyny (\u2009<\u20095% of males), 2 = monogamy with 5 to 20% of males facultatively polygynous, and 3 = obligate or lek polygyny (\u2009>\u200920% of males)), Migratory behaviour (scored 0 to 2, with 0 = resident, 1 = partial migration, 2 = complete (full) migration), Territoriality (0 = non-territorial, 1 = seasonally or weakly territorial, or 2 = year-round territorial), Duetting (0 = absent or 1 = present). For full descriptions of the predictor variables, see\u00a0Supplementary Information.\n\nTo account for shared phylogenetic history and to incorporate phylogenetic uncertainty into our statistical analyses, we downloaded 100 trees with the Hackett backbone from http://birdtree.org67. In order to include as many species as possible in our statistical analyses, we used the trees built from all 9993 OTUs (operational taxonomic units), as opposed to the Jetz et al. trees67 generated from only molecular data. To ensure that the tree structure did not influence our results, we ran MCMCglmm phylogenetic mixed models with a consensus tree created from 100 Jetz et al. trees67 with both trees containing only species with molecular data, as well as trees with all possible OTUs. Both sets of trees were lacking species for which we had female song data, so we also evaluated the impact of missing species on our MCMCglmm results by running phylogenetic mixed models with genus and family as random effects (rather than incorporating a tree). The same final predictor variables were recovered in all three analyses, suggesting our results were robust to tree structure and missing species. We present the final analyses using a set of 100 trees from Jetz et al. (birdtree.org)67. This resulted in a final sample size of up to 1281 species included in the final phylogenetic regression and path analyses, depending on the analysis.\n\nWe conducted the following analyses to compare each female song metric as the response variable to the following predictor variables: (1) phylogenetically informed Bayesian univariate regression models comparing daily nest predation rates, (2) phylogenetically informed Bayesian multivariate regression models with all remaining predictor variables, and (3) phylogenetically informed path analyses to evaluate causal pathways or associations among predictor variables using the R package PhyloPath35. The daily nest predation rate had a much smaller sample size than the other parameters. Therefore, it was evaluated in its own model to preserve statistical power in analyses with the other predictor variables. All statistical analyses were conducted in R68. We built our phylogenetically informed Bayesian regression models with brms33 and MCMCglmm34. Both analyses produced similar results (Tables\u00a01, 2, S1 & S4), which were also supported by path analysis (Fig.\u00a04). For analyses in brms, models were replicated on the 100 trees generated by birdtree.org using the R package brmsish69 to incorporate phylogenetic uncertainty. Estimates were derived from the combined posterior distributions of the 100 models. Effect sizes are presented as median posterior estimates and 95% credibility intervals as the highest posterior density interval. R\u0302 was kept below 1.05 for all parameter estimates. MCMCglmm and brms are known to produce nuanced differences in results70. In these instances, we defer to brms results because we were able to specify a more appropriate model family for our ordinal response data and incorporate phylogenetic uncertainty in this package. For a full description of statistical analyses and parameters, see\u00a0Supplementary Methods.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data are available for download at figshare: 10.6084/m9.figshare.26799523. Source data are also provided as a Source Data file.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All associated code for statistical analyses is available for download at figshare: https://doi.org/10.6084/m9.figshare.26799523.", + "section_image": [] + }, + { + "section_name": "Change history", + "section_text": "In this article the affiliation details for Joseph A. 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Funding was provided by: the European Union\u2019s Horizon 2020 research and innovation programme (Marie Sklodowska-Curie grant no. 703999-YnotSing to KJO) and a U.S. National Science Foundation (NSF) Postdoctoral Research Fellowship in Biology (grant no. 1612861 to KJO), the Cornell Lab of Ornithology Rose Postdoctoral Fellowship Fund, and the University of Maryland, College Park.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Biological Sciences, University of the Pacific, Stockton, CA, USA\n\nKaran J. Odom\n\nDepartment of Psychology, University of Maryland, College Park, College Park, MD, USA\n\nKaran J. Odom\u00a0&\u00a0Gregory F. Ball\n\nCornell Lab of Ornithology and Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, USA\n\nKaran J. Odom\u00a0&\u00a0Michael S. Webster\n\nCentro de Investigaci\u00f3n en Neurociencias, Universidad de Costa Rica, San Jos\u00e9, Costa Rica\n\nMarcelo Araya-Salas\n\nEscuela de Biolog\u00eda, Universidad de Costa Rica, San Jos\u00e9, Costa Rica\n\nMarcelo Araya-Salas\n\nDepartment of Biological Sciences, University of Northern Colorado, Greeley, CO, USA\n\nLauryn Benedict\n\nCollege of Arts and Sciences, Cornell University, Ithaca, NY, USA\n\nKristi Lim\n\nSchool of Natural Sciences, Massey University, Auckland, New Zealand\n\nJames Dale\n\nSchool of Environmental and Animal Sciences, Unitec, Auckland, New Zealand\n\nWesley H. Webb\n\nSchool of Biological Sciences, University of Aberdeen, Aberdeen, UK\n\nCatherine Sheard\n\nDepartment of Life Sciences, Imperial College London, Ascot, Berkshire, UK\n\nJoseph A. Tobias\n\nSchool of BioSciences, The University of Melbourne, Melbourne, VIC, Australia\n\nMichelle L. Hall\n\nBush Heritage Australia, Melbourne, VIC, Australia\n\nMichelle L. Hall\n\nSchool of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia\n\nMichelle L. Hall\n\nResearch School of Biology, Australian National University, Canberra, ACT, Australia\n\nNaomi E. Langmore\n\nInstitute of Biology, Animal Sciences and Health, Leiden University, Leiden, The Netherlands\n\nKatharina Riebel\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nK.J.O., M.A.S., L.B., M.L.H., N.E.L., M.S.W. and K.R. all contributed substantially to project design, grant writing and funding, planning, execution, and writing of this manuscript. K.J.O. and M.A.S. conducted data analysis and created figures. K.L. and W.H.W. contributed substantially to female song data collection and provided feedback on written drafts of the manuscript. J.D., C.S. and J.A.T. provided predictor variable natural history datasets for comparison to female song data and feedback on the manuscript. G.F.B. contributed intellectually to writing and project feedback and provided financial support.\n\nCorrespondence to\n Karan J. Odom.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Sara Lipshutz, Jeffrey Podos and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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"Rag-GTPase-TFEB/TFE3 axis controls B cell mitochondrial fitness and humoral immunity independent of mTORC1", + "journal": "Nature Communications", + "published": "23 November 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54344-5/MediaObjects/41467_2024_54344_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54344-5/MediaObjects/41467_2024_54344_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54344-5/MediaObjects/41467_2024_54344_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54344-5/MediaObjects/41467_2024_54344_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://figshare.com/s/b5b8e00a13fba9db6caa", + "https://www.ncbi.nlm.nih.gov/bioproject?term=PRJNA1093059&cmd=DetailsSearch", + "/articles/s41467-024-54344-5#Sec32" + ], + "code": [], + "subject": [ + "Follicular B cells", + "Gene regulation in immune cells", + "Humoral immunity", + "Lymphopoiesis" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3957355/v1.pdf?c=1732453615000", + "research_square_link": "https://www.researchsquare.com//article/rs-3957355/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54344-5.pdf", + "preprint_posted": "28 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "During the humoral immune response, B cells undergo rapid metabolic reprogramming with a high demand for nutrients, which are vital to sustain the formation of the germinal centers (GCs). Rag-GTPases sense amino acid availability to modulate the mechanistic target of rapamycin complex 1 (mTORC1) pathway and suppress transcription factor EB (TFEB) and transcription factor enhancer 3 (TFE3), members of the microphthalmia (MiT/TFE) family of HLH-leucine zipper transcription factors. However, how Rag-GTPases coordinate amino acid sensing, mTORC1 activation, and TFEB/TFE3 activity in humoral immunity remains undefined. Here, we show that B cell-intrinsic Rag-GTPases are critical for the development and activation of B cells. RagA/RagB deficient B cells fail to form GCs, produce antibodies, and generate plasmablasts in both T-dependent (TD) and T-independent (TI) humoral immune responses. Deletion of RagA/RagB in GC B cells leads to abnormal dark zone (DZ) to light zone (LZ) ratio and reduced affinity maturation. Mechanistically, the Rag-GTPase complex constrains TFEB/TFE3 activity to prevent mitophagy dysregulation and maintain mitochondrial fitness in B cells, which are independent of canonical mTORC1 activation. TFEB/TFE3 deletion restores B cell development, GC formation in Peyer\u2019s patches and TI humoral immunity, but not TD humoral immunity in the absence of Rag-GTPases. Collectively, our data establish Rag-GTPase-TFEB/TFE3 axis as an mTORC1 independent mechanism to coordinating nutrient sensing and mitochondrial metabolism in B cells.Biological sciences/Immunology/LymphocytesBiological sciences/Immunology/Lymphocytes/B cells", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Germinal center (GC) formation, which is an integrant part of humoral immunity, involves energy-consuming metabolic reprogramming. Rag-GTPases are known to signal amino acid availability to cellular pathways that regulate nutrient distribution such as the mechanistic target of rapamycin complex 1 (mTORC1) pathway and the transcription factors TFEB and TFE3. However, the contribution of these factors to humoral immunity remains undefined. Here, we show that B cell-intrinsic Rag-GTPases are critical for the development and activation of B cells. RagA/RagB deficient B cells fail to form GCs, produce antibodies, and to generate plasmablasts during both T-dependent (TD) and T-independent (TI) humoral immune responses. Deletion of RagA/RagB in GC B cells leads to abnormal dark zone (DZ) to light zone (LZ) ratio and reduced affinity maturation. Mechanistically, the Rag-GTPase complex constrains TFEB/TFE3 activity to prevent mitophagy dysregulation and maintain mitochondrial fitness in B cells, which are independent of canonical mTORC1 activation. TFEB/TFE3 deletion restores B cell development, GC formation in Peyer\u2019s patches and TI humoral immunity, but not TD humoral immunity in the absence of Rag-GTPases. Collectively, our data establish the Rag GTPase-TFEB/TFE3 pathway as a likely mTORC1 independent mechanism to coordinating nutrient sensing and mitochondrial metabolism in B cells.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "During infection or immunization, B lymphocytes can be activated in a T cell-dependent (TD) or -independent (TI) manner, differentiate into plasmablast cells or form germinal center (GC) and subsequently differentiate into memory B cells and plasma cells1,2,3,4, which produce different isotypes of antibodies5. There is increasing evidence that metabolic programming underpins B cell development, quiescence, activation, and differentiation6,7,8. Specifically, glucose metabolism is vital to support the B cell function like early B cell development in bone marrow (BM), affinity maturation of GC B cells, and lymphomagenesis9,10,11. Lactate dehydrogenase-dependent glycolysis is dispensable for TI responses, but critical for TD responses, highlighting divergent metabolic requirements between TD and TI responses10. Mitochondria oxidative phosphorylation, amplified by CD40L or Toll-like receptor (TLR) ligand engagement, supports B cell survival and differentiation12,13,14,15. Nutrients modulate cellular metabolic reprogramming and subsequently affect immune responses16,17,18. Consequently, malnutrition, including Kwashiorkor, a form of severe protein inadequacy, is associated with small Peyer\u2019s patches (PP) and GC, fewer antibody-producing cells, and increased susceptibility to infection19,20. A potential mechanism through which amino acid controls immunity is the tuning of mTORC1 activation16. Yet, how B cells sense nutrients to coordinate their mitochondrial metabolism, and how metabolic pathways support B cell development and responses to TD and TI antigens, remains poorly defined.\n\nRag-GTPases are the key amino acid sensors that relay amino acid availability to modulate mTORC1 and suppress TFEB transcription factor21,22,23. As small GTPases, Rags are obligate heterodimers, configured such that RagA or RagB is bound to RagC or RagD. RagA is required for amino acid-dependent mTORC1 activation in vitro21,23. Gain of function mutations in Rag-GTPases leads to overactivation of mTORC1 in B cells, demonstrating that Rag-GTPases are sufficient for mTORC1 activation24,25. Yet, there is controversy regarding the necessity of Rag-GTPases for activation of canonical mTORC126,27,28,29,30. In fact, Rag-GTPases can even suppress mTORC1 signaling because RagA/RagB deficient macrophages exhibit highly elevated mTORC1 activity, indicating a cell type-specific, context-dependent relationship between Rag-GTPases and mTORC131. Recent studies have demonstrated that Rag-GTPases cooperate with Rheb to activate mTORC1 to support Treg functions32,33,34. However, the mechanisms through which Rag-GTPases coordinate mTORC1 and TFEB to regulate humoral immunity are currently unknown.\n\nMiTF/TFE family members, including MITF, TFEB, TFE3, and TFEC, are basic helix-loop-helix leucine zipper (bHLH-Zip) transcription factors. They share a similar structure and often express together35. Among them, TFEB and TFE3 regulate a similar set of genes involved in lysosomal biogenesis, lipid metabolism, autophagy, and stress response36,37,38. They can undergo cytoplasm-to-nucleus shuttling in response to different nutrition statuses, which is governed by the phosphorylation through kinases such as mTORC1, ERK, and GSK339,40,41,42. TFEB/TFE3 can promote autophagy and production of proinflammatory cytokines in macrophages in vitro, but it can also enhance mTORC1 activation in tumor-associated macrophages31,43,44. In adaptive immunity, TFEB/TFE3 supports CD40 ligand expression on T cells, maintains regulatory T cell functions, and may prevent B cell senescence during aging45,46,47. Their B cell-intrinsic functions in cellular metabolism and humoral immune response have not been explored.\n\nHere, we take a genetic approach to dissect the contributions of Rag-GTPases and mTORC1 to B cell development and function in vivo. Our study shows that B cell-intrinsic Rag-GTPases are essential for B cell development, GC formation in PPs, dark zone (DZ) and light zone (LZ) distribution in GC, TD, and TI antigen immunization-induced antibody responses, but largely dispensable for mTORC1 activity. Mechanistically, Rag-GTPase mediates inhibition of TFEB/TFE3 activity and prevention of abnormal mitophagy are required for optimal B cell activation, mitochondrial metabolism, humoral immunity towards TI, but not TD, antigen immunization, and GC formation in PPs. Collectively, our results demonstrate that the amino acid sensing complex Rag-GTPases maintains mitochondrial fitness by suppressing TFEB/TFE3 activity in B cells to support the humoral immune responses, in a likely mTORC1-independent manner.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Amino acids (AAs) are the key nutrients engaging Rag-GTPases and mTORC1 signaling40. To evaluate the impact of AAs on B cell activation and mTORC1 activity, we stimulated B cells in full AAs, essential AAs (EAA) or no AA condition. Among the three conditions, full AA-induced the largest cell size, highest expression of CD86, amino acid transporter CD98, and phosphorylation of ribosomal protein S6 (p-S6) and eukaryotic initiation factor 4E-binding protein 1 (p-4EBP1), markers for mTORC1 activation, while B cells in no AA condition had the lowest expression of these markers (Fig.\u00a01A). When we activated B cells in the presence of a titrated concentration of AAs, we observed that AAs promote B activation in a concentration-dependent manner (Fig.\u00a01B). Taken together, these data suggest that AAs can fine-tune B cell activation and mTORC1 activity.\n\nA, B B cells were stimulated with LPS/IL-4/BAFF and cultured with no amino acids (No AA), essential amino acids (EAA), or full amino acids (Full AA) (A), or indicated concentrations of amino acids (B) overnight. CD98, CD86, p-4EBP1, p-S6, and FSC-A levels were measured by flow cytometry. For CD98, CD86, p-S6, and FSC-A levels, n\u2009=\u20095 for each group. n\u2009=\u20093 for each group in p-4EBP1 expression. C\u2013F Tamoxifen was administered to animals intraperitoneally daily for 4 consecutive days. Splenic B cells were purified 7 days after the last tamoxifen injection and stimulated with LPS/IL-4/BAFF. C Expression of p-4EBP1, p-S6, CD98, and FSC-A was measured by flow cytometry after overnight activation. CreER control (WT) (n\u2009=\u20094), CreERRragafl/flRragbfl/fl (n\u2009=\u20094), CreERRptorfl/fl (n\u2009=\u20094). D Expression of p-4EBP1, p-S6, p-S6K, AID, and LAMP1 was measured by immunoblot. \u03b2-actin was used as the loading control. Arrow indicates non-specific bands. Data represents 3 independent experiments. E B cells were labeled with CellTrace violet (CTV) and stimulated with indicated stimuli for 72\u2009h. CTV dilution was measured by flow cytometry. F Expression of IgG1 and CTV dilution were examined by flow cytometry. Right, summary of the percentages of divided cells and IgG1+ B cells. WT (n\u2009=\u20098), CreERRragafl/flRragbfl/fl (n\u2009=\u20098), CreERRptorfl/fl (n\u2009=\u20094). G Rapamycin was added at 24\u2009h after B cell activation with LPS/IL-4/BAFF. IgG1 expression was examined by flow cytometry at 72\u2009h after activation. Right, summary of the percentages of IgG1+ B cells. The numbers indicate the fold differences of average IgG1+ percentages between WT and RagA/RagB deficient B cells. WT (n\u2009=\u20095), CreERRragafl/flRragbfl/fl (n\u2009=\u20094). Error bars represent mean\u2009\u00b1\u2009SEM. ns, not significant. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, and ****p\u2009<\u20090.0001, one-way ANOVA (A, B, and F), two-tailed/unpaired Student\u2019s t-test (G), or two-way ANOVA (C). Source data are provided as a Source Data file.\n\nGiven the contention regarding the link between Rag-GTPases and mTORC126,27,28,29,30, we directly compared the phenotypes between Rag-GTPase deficiency and mTORC1 deficiency in B cells. RagA and RagB or Raptor were acutely deleted by tamoxifen injection into CreERRragafl/flRragbfl/fl mice or CreERRptorfl/fl mice (Supplementary Fig.\u00a01A, B), which did not significantly alter mature B cell compartment in spleens, including follicular B cells and marginal zone B cells (Supplementary Fig.\u00a01C, D). Raptor deficient splenic B cells had diminished p-S6 and p-4EBP1 levels, reduced CD98 expression and cell size in either full AA medium or no AA medium (Fig.\u00a01C). In contrast, RagA/RagB deficient B cells did not exhibit reduction of any of these parameters in full AA medium. However, under no AA condition, all these parameters reduced to a largely comparable level between 3 genotypes (Fig.\u00a01C), suggesting a Rag-GTPase independent but AA dependent regulation of mTORC1 and initial B cell activation. Immunoblot analysis on B cells activated in full AA medium at different time points confirmed that Raptor deficiency nearly abolished p-4EBP1, p-S6K and p-S6 (Fig.\u00a01D and Supplementary Fig.\u00a01E), which were largely intact in the absence of RagA/RagB at early timepoint of activation (Fig.\u00a01D). Still, RagA/RagB deficient B cells had modestly (and variably) reduced p-S6 and intact p-4EBP1 at late timepoint (72\u2009h) of activation (Supplementary Fig.\u00a01F). Consistent with previous publications16,48, Raptor deficient B cells exhibited substantially reduced proliferation upon various antigenic stimulations. In contrast, RagA/RagB deletion did not affect the proliferation or apoptosis of B cells (Fig.\u00a01E and Supplementary Fig.\u00a01G). However, the class switch to IgG1-producing B cells induced by LPS was reduced in both Rag-GTPases or Raptor deficient B cells (Fig.\u00a01F), suggesting a proliferation-independent class switch defect in RagA/RagB deficient B cells. Furthermore, rapamycin treatment exacerbated the IgG1 expression defect in RagA/RagB deficient B cells in a dose-dependent manner, suggesting nonredundant roles between mTORC1 and Rag-GTPase during B cell activation (Fig.\u00a01G). Consistent with the reduced IgG1 expression, both Raptor and RagA/RagB deficient B cells showed reduced activation-induced cytidine deaminase (AID) induction (Fig.\u00a01D). Thus, amino acids modulate mTORC1 independent of Rag-GTPases. Rag-GTPases can regulate B cell class switching likely independent of canonical mTORC1 activity in vitro.\n\nConstitutive deletion of Raptor blocks early B cell development at pro-B to pre-B transition8,49. While acute deletion of Raptor or RagA/RagB did not affect mature B cells in spleens, we examined the potential impacts on early B cell development. Tamoxifen-injected CreERRragafl/flRragbfl/fl mice had relatively normal frequencies but reduced absolute numbers of B220+CD43+IgM\u2013 B cell precursors, which were largely intact in tamoxifen-injected CreERRptorfl/fl mice (Fig.\u00a02A-B). Later stage B cells, including B220loCD43\u2013 pre-B cells/immature B cells and B220hiCD43\u2013 circulating mature B cells, were mostly reduced in the absence of RagA/RagB (Fig.\u00a02A). In contrast, Raptor deficiency significantly reduced pre-B cells/immature B cells, but increased circulating mature B cells (Fig.\u00a02B). The contrasting circulating mature B cell phenotypes were associated with altered expression of CXCR4, a key chemokine receptor regulating B cell retention in bone marrow50, which increased in the absence of Raptor, but reduced in the absence of RagA/RagB (Supplementary Fig.\u00a01H). Within B cell precursors, the frequencies of fraction B and fraction C/C\u2019 were significantly reduced in the absence of either Rag-GTPases or mTORC1 (Fig.\u00a02C, D). These data indicate that acute loss of either Rag-GTPases or mTORC1 reduces frequencies of fraction B and fraction C/C\u2019 precursors. We did not observe any developmental defects in CreERRragbfl/fl mice (Supplementary Fig.\u00a01I, J). Thus, both Rag-GTPases and mTORC1 are critically required for early B cell development, but they may have different functions in the maintenance or retention of B220+CD43+IgM\u2013 B cell precursors and circulating mature B cells. In addition, RagA and RagB have redundant functions in B cell development.\n\nA\u2013E Tamoxifen was administered to animals intraperitoneally daily for 4 consecutive days. Mice were analyzed 7 days after the last injection. A Expression of B220 and CD43 in bone marrow (BM) lymphocytes from WT (n\u2009=\u200915) and CreERRragafl/flRragbfl/fl (n\u2009=\u200913) mice. Right, summaries of the percentages and numbers of B220loCD43\u2013, B220hiCD43\u2013 and B220+CD43+ cells. B Expression of B220 and CD43 in BM lymphocytes from WT (n\u2009=\u20099) and CreERRptorfl/fl (n\u2009=\u20099) mice. Right, summaries of the percentages and numbers of B220loCD43\u2013, B220hiCD43\u2013 and B220+CD43+ cells. C Expression of BP-1 and CD24 in BM B220+CD43+IgM\u2013 B cell precursors from WT (n\u2009=\u200915) and CreERRragafl/flRragbfl/fl (n\u2009=\u200913) mice. Right, summary of the percentages and numbers of fraction A (CD24\u2212BP-1\u2212), fraction B (CD24+BP-1\u2212), and fraction C/C\u2032 (CD24+BP-1+) cells. D Expression of BP-1 and CD24 in BM B220+CD43+IgM\u2013 B cell precursors from WT (n\u2009=\u20097) and CreERRptorfl/fl (n\u2009=\u20098) mice. Right, summary of the percentages and numbers of fraction A (CD24\u2212BP-1\u2212), fraction B (CD24+BP-1\u2212), and fraction C/C\u2032 (CD24+BP-1+) cells. E Expression of GL-7 and Fas in lymphocytes from the Peyer\u2019s patches of WT (n\u2009=\u200912), CreERRragafl/flRragbfl/fl (n\u2009=\u200911), and CreERRptorfl/fl (n\u2009=\u20099) mice. Right, summary of the percentages and numbers of GC (GL-7+Fas+) B cells. Data in graphs represent mean\u2009\u00b1\u2009SEM. ns, not significant. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, and ****p\u2009<\u20090.0001, two-tailed/unpaired Student\u2019s t-test (A and B), one-way ANOVA (E), two-way ANOVA (C and D). Source data are provided as a Source Data file.\n\nAcute deletion of either Rag-GTPases or mTORC1 preserves na\u00efve mature CD4+ T and B cells in spleens (Supplementary Fig.\u00a01C, D). However, we found that the frequencies and absolute numbers of GC B cells significantly decreased in PPs of both CreERRragafl/flRragbfl/fl mice and CreERRptorfl/fl mice following tamoxifen injection (Fig.\u00a02E), indicating that Rag-GTPases and mTORC1 are vital to the formation of GC in PPs. In all cases, RagB deficiency did not affect mature B cell numbers (Supplementary Fig.\u00a01K) or GC formation in PPs (Supplementary Fig.\u00a01L), again demonstrating a redundancy between RagA and RagB.\n\nTo study whether the above defects are B cell-intrinsic, we reconstituted lethally irradiated CD45.1+ congenic mice with a 4:1 mixture of bone marrow (BM) recovered from B cell-deficient (\u03bcMT) mice and either WT (CreER) or CreERRragafl/flRragbfl/fl mice51,52. Thus, tamoxifen administration achieved acute B cell-specific deletion of RagA/RagB. Tamoxifen injected \u03bcMT:CreERRragafl/flRragbfl/fl mice exhibited reduced B220loCD43\u2013 pre-B/immature B cells (Supplementary Fig.\u00a02A), modestly reduced frequencies of fraction B and C/C\u2019 B cells (Supplementary Fig.\u00a02B), intact follicular and MZ B cells (Supplementary Fig.\u00a02C), and greatly reduced GC B cells in PPs (Supplementary Fig.\u00a02D). Taken together, B cell-intrinsic Rag-GTPases are critically required for early B cell development and GC formation in PPs.\n\nTo explore the function of B cell-specific Rag-GTPases following T-dependent immune challenge, we immunized tamoxifen injected \u03bcMT:CreER (WT), \u03bcMT:CreERRragafl/flRragbfl/fl or \u03bcMT:CreERRptorfl/fl chimera mice with NP-OVA precipitated in alum. Loss of either Rag-GTPases or mTORC1 led to profound loss of GC formation in both spleen and mesenteric lymph node (mLN) (Fig.\u00a03A, Supplementary Fig.\u00a03A) and reduced generation of antigen-specific B cells in both spleen and mLN (Supplementary Fig.\u00a03B, C). Such phenotypes were confirmed with immunofluorescence (Supplementary Fig.\u00a03D). Consistent with the reduction of GC B cells, expression of Bcl6, a transcriptional factor critical for GC reaction53, was reduced in both mouse strains (Supplementary Fig.\u00a03E). GC is compartmented into LZ and DZ. DZ B cells are highly proliferative and undergo somatic hypermutation, while LZ B cells undergo selection and affinity maturation54. The ratio of DZ/LZ increased in the remaining GC B cells from \u03bcMT:CreERRragafl/flRragbfl/fl mice compared to that in control mice, however, the ratio stayed unchanged in the absence of Raptor (Fig.\u00a03B). Furthermore, plasmablast response in spleen was significantly compromised in both knockout strains, with RagA/RagB deficiency affecting a stronger reduction than mTORC1 deficiency (Fig.\u00a03C). Interestingly, reduced plasmablast frequency was found in the mLNs of immunized \u03bcMT:CreERRragafl/flRragbfl/fl mice, but not in those of \u03bcMT:CreERRptorfl/fl mice (Supplementary Fig.\u00a03F), suggesting a potential tissue specific plasmablast defect in the absence of Raptor. Active-caspase-3 staining showed that RagA/RagB deletion did not affect apoptosis in splenic CD19+, GC and CD138+ B cell subsets, and even reduced apoptosis in B cell subsets in the mLN (Supplementary Fig.\u00a03G). Hence, Rag-GTPase deficiency-induced GC and plasmablast differentiation defects are not caused by increased apoptosis. Consistent with the significant reduction of GC and plasmablast generation, total and high-affinity NP-specific antibodies of all classes were highly reduced in both knockout strains. Intriguingly, antibody titers from \u03bcMT:CreERRragafl/flRragbfl/fl mice were consistently, although not significantly, lower than those from \u03bcMT:CreERRptorfl/fl mice (Fig.\u00a03D, E), which could be a reflection of the milder plasmablast defect induced by Raptor deletion relative to that induced by RagA/RagB deletion. Finally, we accessed p-S6 and p-4EBP1 in splenic B cells, either at steady state or after overnight stimulation with anti-IgM/anti-CD40/IL-4, from immunized \u03bcMT:CreERRragafl/flRragbfl/fl mice and \u03bcMT:CreERRptorfl/fl mice. We did not observe significant reduction of mTORC1 activity in RagA/RagB deficient B cells in either condition (Fig.\u00a03F). Taken together, these data indicate an mTORC1 independent function of Rag-GTPases following immunization and illustrate a stronger dependence on Rag-GTPases than on mTORC1 for plasmablast generation.\n\nA\u2013E Tamoxifen was administered daily for 4 consecutive days followed by NP-OVA/alum immunization on day 11. A Expression of GL-7 and Fas in splenic B cells from \u03bcMT:CreER (WT), \u03bcMT:CreERRragafl/flRragbfl/fl, and \u03bcMT:CreERRptorfl/fl mice. Right, summary of the percentages and numbers of GC B cells. B Expression of CXCR4 and CD86 in GC B cells. Right, summary of the ratio between dark zone (DZ) (CXCR4+CD86\u2212) and light zone (LZ) (CXCR4\u2212CD86+) cells. C Expression of CD138 and B220 in spleens. Right, summary of the percentages and numbers of CD138+ cells. D ELISA measurement of serum NP23-specific antibodies. WT (n\u2009=\u20097), \u03bcMT:CreERRragafl/flRragbfl/fl (n\u2009=\u20098), and \u03bcMT:CreERRptorfl/fl (n\u2009=\u20097). E ELISA measurement of serum NP2-specific antibodies. WT (n\u2009=\u20095), \u03bcMT:CreERRragafl/flRragbfl/fl (n\u2009=\u20095), and \u03bcMT:CreERRptorfl/fl (n\u2009=\u20097). F Expression of p-S6 and p-4EBP1 in fresh or overnight activated splenic B cells. G Heatmap of the mRNA expression of indicated genes in mouse follicular (FO), marginal zone (MZ), and GC B cells. H\u2013L NP-OVA was administered to WT (n\u2009=\u200911), AicdaCreRragafl/flRragbfl/fl (n\u2009=\u200910), and AicdaCreRptorfl/fl (n\u2009=\u20096) mice. H Expression of GL-7 and Fas in splenic B cells. Right, summary of the GC B cell percentages. I Expression of NP and IgG1 in splenic B cells. Right, summary of the percentages of NP+IgG1+ GC B cells. J Expression of CXCR4 and CD86 on GC B cells. Right, summary of the DZ/LZ ratios. K Expression of CD138 and B220 on splenic lymphocytes. Right, summary of the percentages of CD138+ cells. L Expression of CD138 and IgG1 in plasmablasts. Right, summary of the percentages of IgG1+ plasmablasts. ELISA measurement of serum anti-NP2 (M) and anti-NP23 (N) antibodies, WT (n\u2009=\u200913), AicdaCreRragafl/flRragbfl/fl (n\u2009=\u20098), and AicdaCreRptorfl/fl (n\u2009=\u20098). Data in graphs represent mean\u2009\u00b1\u2009SEM. ns, not significant. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, and ****p\u2009<\u20090.0001, one-way ANOVA (A\u2013C, F, H, I\u2013L), two-way ANOVA (D, E, M and N). Source data are provided as a Source Data file.\n\nThe above defective GC formation in the absence of RagA/RagB or Raptor could be due to impaired B cell activation. Moreover, we found that Rraga, among all Rag family members and mTOR scaffolding molecules, preferentially expressed in GC B cells (Fig.\u00a03G), suggesting a prominent role of RagA in the GC response. To investigate the role of Rag-GTPases post B cell activation, we generated AicdaCreRragafl/flRragbfl/fl mice and AicdaCreRptorfl/fl mice to ablate RagA/RagB and Raptor after B cell activation, especially in GC B cells55. After immunization with NP-OVA/alum, we did not find any apparent alteration of the GC B cell frequencies in the spleens of either AicdaCreRragafl/flRragbfl/fl mice or AicdaCreRptorfl/fl mice compared with WT mice (Fig.\u00a03H). However, NP+IgG1+ GC B cells highly reduced in the spleens of both mouse strains (Fig.\u00a03I), indicating that Rag-GTPases and mTORC1 are both critical for antigen selection in GC B cells. Furthermore, immunized AicdaCreRragafl/flRragbfl/fl mice harbored higher DZ/LZ ratio, while a slightly reduced DZ/LZ ratio was observed in immunized AicdaCreRptorfl/fl mice (Fig.\u00a03J). We also observed substantially reduced CD138+ plasmablast frequency and IgG1 expression on plasmablasts in both AicdaCreRragafl/flRragbfl/fl and AicdaCreRptorfl/fl mice (Fig.\u00a03K, L). Measurement of NP-specific antibodies showed decreased titers of high-affinity and total IgG isotypes, but not IgM, from the sera of either mouse strain (Fig.\u00a03M, N). Altogether, these data demonstrate that both Rag-GTPases and mTORC1 are required for TD antigen-induced GC formation. But Rag-GTPases and mTORC1 likely employ distinct mechanisms to promote plasmablast formation and maintain GC dynamics.\n\nB cell activation is accompanied by extensive metabolic reprogramming including glycolytic switches and activation of mitochondrial oxidative phosphorylation56. We examined mitochondrial respiration and glycolysis in activated B cells by measuring oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), respectively. Deficiency of either RagA/RagB or Raptor led to significantly decreased OCR (Fig.\u00a04A). Raptor deficiency strongly suppressed glycolysis, while RagA/RagB deficient B cells had a slight reduction of ECAR (Fig.\u00a04B). [3-3H]-glucose labeling assay confirmed the profound glycolytic defect in Raptor deficient B cells. It showed a modest but significant reduction of glucose metabolism in RagA/RagB deficient B cells (Fig.\u00a04C). Thus, Rag-GTPases are critical for B cell metabolism, especially oxidative phosphorylation. Interestingly, despite a relatively stronger reduction of OCR in Raptor deficient B cells, RagA/RagB deficiency, but not Raptor deficiency, resulted in significant reduction of mitochondrial membrane potential, measured by tetramethylrhodamine methyl ester (TMRM) (Fig.\u00a04D) and MitoTracker Deep Red (MTDR) (Fig.\u00a04E)57,58, as well as mitochondrial reactive oxygen species (ROS) measured by MitoSox (Fig.\u00a04F), suggesting that loss of Rag-GTPases might lead to defective mitochondria and subsequent reduced mitochondrial activity, while mTORC1 deficiency compromises OCR through distinct mechanisms. Consistent with this hypothesis, rapamycin treatment did not affect TMRM and MTDR staining in WT B cells, nor did it further decrease mitochondrial membrane potential in RagA/RagB deficient B cells (Supplementary Fig.\u00a04A, B). Moreover, examination of mitochondrial phenotypes in vivo revealed that GC B cells from the immunized AicdaCreRragafl/flRragbfl/fl mice, but not AicdaCreRptorfl/fl mice, showed significantly reduced mitochondrial membrane potential and ROS level (Fig.\u00a04G-H). Previous studies indicated that mTORC1 controlled the expression of key transcription factors for mitochondrial biogenesis programs, including PGC-1\u03b1 and TFAM59,60. Indeed, expression of PGC-1\u03b1 (Fig.\u00a04I), TFAM (Fig.\u00a04J)61, and COXI (Fig.\u00a04K), a member of the mitochondrial respiratory chain were all significantly decreased in Raptor deficient B cells, but not in RagA/RagB deficient B cells. Collectively, Rag-GTPase deficiency in B cells impairs mitochondrial metabolism associated with reduced mitochondrial membrane potential and mitochondrial ROS, which are distinctive from Raptor deficient B cells.\n\nA\u2013F, I\u2013K Splenic B cells were purified from tamoxifen-treated WT, CreERRragafl/flRragbfl/fl, and CreERRptorfl/fl mice, and cultured with LPS/IL-4/BAFF for 72\u2009h. Mitostress assay (A), and glycolytic stress assay (B) were performed on a Seahorse XFe96 bioanalyzer. C Glycolytic flux was examined using LPS/IL-4/BAFF activated B cells by measuring the detritiation of [3-3H] glucose. Representative flow plots of tetramethylrhodamine methyl ester (TMRM) (D, 5 mice per group), MitoTracker Deep Red (MTDR) (E, 5 mice per group), and MitoSox (F, 5 mice per group) stainings were shown. Summaries of the mean fluorescence intensity (MFI) of each staining (relative to WT) were on the right. G Summaries of the MFIs of TMRM and MTDR staining (relative to WT) on GC B cells from the spleens of immunized mice. H Summary of the MFIs of CellRox staining (relative to WT) on GC B cells from the spleens of immunized. Representative flow plots of PGC-1\u03b1 (I), TFAM (J), and COXI (K) stainings were shown. Summaries of the MFI of each staining (relative to WT) were on the right. Data in graphs represent mean\u2009\u00b1\u2009SEM. ns, not significant. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, and ****p\u2009<\u20090.0001, one-way ANOVA (A\u2013K). Source data are provided as a Source Data file.\n\nTo probe the molecular mechanisms underlying the above metabolic defects, we conducted RNA sequencing using activated B cells and GC B cells. Gene set enrichment analysis (GSEA) identified the KEGG lysosome pathway and putative TFEB target genes as two of the top enriched pathways in RagA/RagB deficient B cells (Fig.\u00a05A-B, Supplementary Fig.\u00a04C). Many lysosomal genes were upregulated in the RagA/RagB deficient B cells (Supplementary Fig.\u00a04D), such as Lamp1, Atp6ap1, Atp6v1h and Ctsa. RNA sequencing using GC B cells from the immunized AicdaCreRragafl/flRragbfl/fl mice and AicdaCreRptorfl/fl mice demonstrated a relatively effective deletion of Rraga and Rptor (Supplementary Fig.\u00a04E). The principal component analysis (PCA) plot showed that WT and Raptor deficient cells were closer to each other, while both were distant from Rag-GTPases deficient cells (Supplementary Fig.\u00a04F). Consistent with this observation, Venn diagram of differentially expressed genes (DEG) illustrated that majority of DEGs were from the RagA/RagB KO vs WT comparison (85.7%, 1812/2114), and less than 10% of DEGs (175/2114) were identified comparing Raptor KO with WT (Fig.\u00a05C, Supplementary Fig.\u00a04G). There were more DEGs shared between RagA/RagB KO vs WT and RagA/RagB KO vs Raptor KO than between Raptor KO vs WT, indicating a stronger impact of Rag-GTPase deficiency on GC B cell transcriptome than Raptor deficiency. GSEA identified KEGG lysosome pathway as the top enriched pathway comparing RagA/RagB KO to WT GC B cells (Fig.\u00a05D), or comparing RagA/RagB KO to Raptor KO GC B cells (Fig.\u00a05E), while no significant enrichment of KEGG lysosome was observed when comparing GC B cells from AicdaCreRptorfl/fl and WT mice (Fig.\u00a05F), suggesting lysosomal activation in RagA/RagB deficient, but not in Raptor deficient, GC B cells. Therefore, Rag-GTPases and mTORC1 utilize distinct mechanisms to maintain GC reaction post B cell activation, one of which was likely TFEB regulation.\n\nA RNA sequencing was performed on 72-h activated B cells. Gene set enrichment analysis (GSEA) was conducted using differentially expressed genes (DEGs) with KEGG lysosome pathway plotted. B The enrichment of putative TFEB target genes. C RNA sequencing was performed on sorted germinal center (GC) B cells. Venn diagram of three DEG comparisons was presented. D\u2013F GSEA was performed using the DEGs between indicated genotypes with KEGG lysosome pathway enrichment between indicated genotypes presented. G qRT-PCR of Lamp1, Rragd, and Flcn expression. H Flow cytometry of LAMP1 expression, n\u2009=\u20095 mice per group. I Immunoblot of Raptor, RagA, and TFEB expression. Cytosolic and nuclear proteins were isolated from activated B cells. Lamin-B, nuclear control, tubulin, cytosol control. WT (n\u2009=\u20095), CreERRragafl/flRragbfl/fl (n\u2009=\u20097), and CreERRptorfl/fl (n\u2009=\u20094). J Expression of TFEB at indicated time-points (n\u2009=\u20095 for each condition). Right, summary of relative TFEB expression (normalized to fresh). K qRT-PCR of Lamp1, Rragd, and Flcn expression at indicated time-points. n\u2009=\u20094 for each condition. L, M Published scRNAseq dataset (E-MTAB-9478) was reanalyzed with gene signatures in na\u00efve, marginal zone (MZ), GC B cells, and plasma cells presented (L). Gene signatures in GC B cells at different time points (M). N Transmission electron microscopy (TEM) of activated B cells. Arrows indicate mitochondria surrounded by double-layer structures. Scale bar, 2\u2009\u03bcm. O GFP and mCherry expression with Mito-QC transduction. Right, summary of the normalized mCherry percentages (Relative to mCherry percentage in WT). WT (n\u2009=\u20098), and CreERRragafl/flRragbfl/fl (n\u2009=\u200910). P IgG1 expression on B cells stimulated in the presence of indicated inhibitors. Right, summary of IgG1+ B cell percentages. WT (n\u2009=\u20097), CreERRragafl/flRragbfl/fl (n\u2009=\u20096). Data represents at least 3 (G\u2013K, O, P) and 2 (N) independent experiments. Data in graphs represent mean\u2009\u00b1\u2009SEM. ns, not significant. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, and ****p\u2009<\u20090.0001, one-way ANOVA (G\u2013K), two-tailed/unpaired Student\u2019s t-test (O and P). Source data are provided as a Source Data file.\n\nTo directly examine TFEB function activity, we measured the mRNA levels of TFEB target genes, including Lamp1, Rragd, and Flcn. Consistent with the RNA sequencing data, TFEB target gene expression was significantly increased in RagA/RagB deficient B cells, but not in Raptor deficient B cells (Fig.\u00a05G). We also confirmed that LAMP1 protein expression was increased in the absence of Rag-GTPases, but not Raptor (Figs.\u00a05H,\u00a01D). Importantly, we observed increased TFEB and TFE3 nuclear localization in RagA/RagB deficient B cells, but not in Raptor deficient B cells (Fig.\u00a05I, Supplementary Fig.\u00a04H). Therefore, Rag-GTPases, but not mTORC1, constrain TFEB/TFE3 activity during B cell activation.\n\nTo further gain insight into the possible functions of TFEB during B cell activation, we first examined TFEB protein expression kinetics in in vitro activated B cells. TFEB protein had the highest expression in na\u00efve B cells and its expression declined over time (Fig.\u00a05J). Meanwhile, we observed reduced expression of Lamp1, Rragd, and Flcn (Fig.\u00a05K), indicating an association between B cell activation and reduced TFEB activity. To further investigate the TFEB activity in vivo, we re-analyzed the public database and evaluated the expression of mTORC1 signaling signatures, putative TFEB target genes62, oxidative phosphorylation, and lysosome-related genes in na\u00efve B cells, MZ B cells, GC B cells and plasma cells (Fig.\u00a05L) and GC B cells at different time points (Fig.\u00a05M) from influenza-infected mice. While mTORC1 signatures, oxidative phosphorylation, and mitochondrial complex I were highly enriched in plasma cells, TFEB targets and lysosome-related genes were enriched in na\u00efve and MZ B cells, and they had the lowest expression in GC B cells (Fig.\u00a05L). In the time-course analysis, TFEB target gene expression declined at day 14 compared to day 7 before recovering at day 28 (Fig.\u00a05M). The expression pattern and kinetics of TFEB activity were not reciprocal to mTORC1 activation, suggesting that they could be independent to each other in B cells. Taken together, our results suggest that B cell activation and differentiation are associated with the decline of TFEB activity, which might be enforced by Rag-GTPases.\n\nTFEB has been implicated in the regulation of mitophagy63,64. Yet, the function of TFEB and mitophagy during B cell activation remains unknown. Transmission electron microscopy (TEM) analysis revealed mitochondria encircled with double membrane structures, morphology consistent with mitophagy, in RagA/RagB deficient B cells (Fig.\u00a05N). To confirm the mitophagy phenotype, we introduced Mito-QC, a mitophagy reporter, which is a construct expressing an mCherry-GFP tag attached to mitochondrial fission protein 1 (FIS1, residues 101\u2013152) on the outer mitochondrial membrane65. Upon mitophagy, mitochondria are delivered to lysosomes from autophagosomes, where the GFP signal is quenched by the low lysosomal pH, resulting in only red fluorescence (mCherry signals). In accordance with the TEM data, we observed increased mCherry signals in RagA/RagB deficient B cells (Fig.\u00a05O). Thus, Rag-GTPase deficiency is associated with increased TFEB activity and mitophagy in B cells. To further gain insight into the role of mitophagy in B cell activation, we treated B cells with several putative\u00a0mitophagy activators including CMPD-39, MF095, and Urolitin A, all of which significantly reduced IgG1 expression on B cells without apparently affecting B cell proliferation (Supplementary Fig.\u00a05A, B). Conversely, putative\u00a0mitophagy inhibitors including 3-MA, IC-87114, and PRT062607 not only increased the IgG1 expression on WT B cells but also partially restored IgG1 expression on RagA/RagB deficient B cells without affecting cell proliferation (Fig.\u00a05P and Supplementary Fig.\u00a05C\u2013F). Therefore, excessive mitophagy could be detrimental to B cell activation. Abnormally increased mitophagy activity could underly some of the B cell activation defects in the absence of Rag-GTPase.\n\nTo test whether TFEB overactivation can directly control mitochondrial phenotypes and B cell activation, we retrovirally overexpressed WT TFEB or constitutive active TFEB (Ca TFEB) carrying S142/211A mutation (Supplementary Fig.\u00a06A)66, with comparable transduction efficiency (Supplementary Fig.\u00a06B). WT TFEB and Ca TFEB overexpression promoted TFEB activation in a graded manner, as measured by the expression of TFEB target genes (Fig.\u00a06A). They also reduced IgG1+ class switching (Fig.\u00a06B) and CD138+ expression (Supplementary Fig.\u00a06C) in a graded manner, without overtly affecting B cell proliferation and mTORC1 activity (Supplementary Fig.\u00a06D, E). We observed similar phenotypes when we pharmacologically stimulated TFEB using curcumin analog C1, a novel mTOR-independent activator of TFEB67 (Supplementary Fig.\u00a06F, G). Hence, TFEB overactivation suppresses class switch and CD138 expression in vitro.\n\nA qRT-PCR of Tfeb, Rragd, Rragc, and Lamp1 expression. GFP+ B cells were sorted from vector, WT TFEB, and Ca TFEB transduced B cells. B Expression of IgG1 and Celltrace violet (CTV) in transduced B cells. n\u2009=\u20094 for each group. C Venn diagrams highlight the overlapping gene numbers in downregulated DEGs (left) and upregulated DEGs (right) between Rag KO vs WT comparison and Ca TFEB vs vector comparison. D Summaries of the relative MTDR (left) and TMRM (right) MFIs. n\u2009=\u20093 for each group. E Summary of the relative MitoSox MFIs. n\u2009=\u20093 for each group. F Summary of the relative LAMP1 MFIs. n\u2009=\u20093 for each group. G Transmission electron microscope (TEM) of sorted vector or Ca TFEB transduced B cells. Arrows indicate mitochondria surrounded by double-layer structures. scale bar, 2\u2009\u03bcm. (H) ELISA of pS65-Ub levels in sorted vector or Ca TFEB transduced B cells. n\u2009=\u20095 for each group. Flow cytometry of TMRM (I) or MTDR (J) staining of vector or TFEB TDN transduced B cells. WT (n\u2009=\u20095), CreERRragafl/flRragbfl/fl (n\u2009=\u20097). K Flow cytometry of IgG1 and CTV. B cells from indicated genotypes were activated with LPS/IL-4/BAFF for 3 days. TMRM or MTDR (L), MitoSox (M), or LAMP1 (N) were measured by flow cytometry. O GFP and mCherry expression in B cells with Mito-QC transduction. P B cells from the indicated mice were purified and activated with LPS/IL-4/BAFF for 72\u2009h. Mito stress assay was performed on a Seahorse XFe96 analyzer. Right, summaries of the basal respiration and maximal respiration. Data in graphs represent mean\u2009\u00b1\u2009SEM. ns, not significant. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, and ****p\u2009<\u20090.0001, one-way ANOVA (A, B, D\u2013F, K\u2013P), two-way ANOVA (I and J), two-tailed/unpaired Student\u2019s t-test (H). Source data are provided as a Source Data file.\n\nRNA sequencing revealed the enrichment of lysosome pathway in Ca TFEB transduced B cells (Supplementary Fig.\u00a06H). There was a substantial overlap between the DEGs from the RagA/RagB deficient B cells and those from Ca TFEB expressing B cells: 70% downregulated genes (882 genes) and 75% upregulated genes (899 genes) were shared (Fig.\u00a06C), suggesting that TFEB overactivation might account for a significant portion of the DEGs in RagA/RagB deficient B cells. Like RagA/RagB deficient B cells, TFEB overactivation led to striking reductions of mitochondrial membrane potential (TMRM and MTDR, Fig.\u00a06D) and mitochondrial ROS (MitoSox, Fig.\u00a06E), and increased LAMP1 expression (Fig.\u00a06F). Importantly, TEM analysis revealed the increased mitochondrial morphology consistent with mitophagy in Ca TFEB transduced B cells (Fig.\u00a06G). Of note, TFEB overactivation promoted mitophagy likely through PINK1-PRKN/Parkin mediated pathway because we detected a significant increase of ubiquitin (Ub) phosphorylation at Ser65 (p-S65-Ub)68 (Fig.\u00a06H). Thus, TFEB activation is sufficient to suppress mitochondrial activity associated with abnormal mitophagy in B cells.\n\nTo establish whether TFEB overactivation was responsible for B cell activation and mitochondrial defects observed in RagA/RagB deficient B cells, we generated dominant negative TFEB (TFEB TDN), which contained the helix-loop-helix\u2013leucine zipper dimerization domains but lacked the DNA-binding basic region and transcription activation domains45. TFEB TDN transduction partially suppressed TFEB overactivation and partially rescued the reduced IgG1+ expression in RagA/RagB deficient B cells (Supplementary Fig.\u00a06I, J). Moreover, TFEB TDN partially restored mitochondrial membrane potential (Fig.\u00a06I-J) and MitoSox level in RagA/RagB deficient B cells (Supplementary Fig.\u00a06K). These data indicate that TFEB overactivation could be partly responsible for the mitochondrial defects induced by RagA/RagB deficiency in vitro.\n\nTo investigate if genetic inactivation of TFEB can restore B cell mitochondrial fitness in the absence of Rag-GTPases, we crossed CreERRragafl/flRragbfl/fl mice with a Tfebfl/fl allele to generate CreERRragafl/flRragbfl/flTfebfl/fl mice. As expected, loss of TFEB greatly reduced the expression of Tfeb and many TEFB target genes in RagA/RagB deficient B cells (Supplementary Fig.\u00a06L). TFEB deletion restored the reduced IgG1 class switch (Fig.\u00a06K), mitochondrial membrane potential (Fig.\u00a06L), mitochondrial ROS (Fig.\u00a06M), and reversed the increased expression of LAMP1 (Fig.\u00a06N). Importantly, TFEB deficiency prevented the abnormal mitophagy measured by Mito-QC assay (Fig.\u00a06O). Finally, the reduced mitochondrial metabolism, measured by OCR, induced by RagA/RagB loss was fully restored by TFEB deletion (Fig.\u00a06P) without apparent impact on mTORC1 activity measured by p-S6 and p-4EBP1 (Supplementary Fig.\u00a06M). Altogether, these data demonstrated that Rag-GTPases constrain TFEB/TFE3 activity to prevent abnormal mitophagy and maintain mitochondrial fitness in B cells in vitro.\n\nWe next sought to assess the impact of TFEB deletion on CreERRragafl/flRragbfl/fl mice in vivo. TFEB deletion restored the reduced frequencies of fractions B and fraction C/C\u2019 precursors, but not the blockage of the pro-B to pre-B transition, nor the reduced GC formation in PPs caused by RagA/RagB deficiency (Supplementary Fig.\u00a07A\u2013C). To evaluate the humoral immune responses against TD antigens, we immunized tamoxifen injected \u03bcMT:CreERRragafl/flRragbfl/flTfebfl/fl mice and \u03bcMT:CreERRragafl/flRragbfl/fl mice with NP-OVA/alum. TFEB deletion did not restore the reduced GC B cells, plasmablasts, NP+IgG1+ GC B cells, IgG1+CD138+ cells, and the production of NP-specific antibodies in the absence of RagA/RagB (Supplementary Fig.\u00a07D\u2013I). Thus, TFEB deletion was not sufficient to restore early B cell development, GC formation in PPs, and humoral responses towards TD antigens in the absence of Rag-GTPases.\n\nTo investigate immune response to TI-antigen, we immunized tamoxifen injected \u03bcMT:CreERRragafl/flRragbfl/flTfebfl/fl mice and \u03bcMT:CreERRragafl/flRragbfl/fl mice with TNP-LPS. RagA/RagB deletion led to significant reduction of plasmablast generation and TNP-specific antibody production, both of which were restored by TFEB deletion (Supplementary Fig.\u00a07J, K). Interestingly, we observed that plasmablasts generated by TNP-LPS immunization exhibited greater mitochondrial membrane potential, measured by TMRM and MTDR, than those generated by NP-OVA immunization (Supplementary Fig.\u00a07L), suggesting a potentially higher mitochondrial metabolism in TNP-LPS induced plasmablasts than NP-OVA induced plasmablasts. Thus, our data indicate that TFEB overactivation is responsible for the impaired TI-antigen responses, but not the reduced TD-antigen responses, pro-B to pre-B transition, or GC formation in PPs in the absence of Rag-GTPases.\n\nRagA/RagB deficiency led to overactivation of both TFEB and TFE3 (Fig.\u00a05I, Supplementary Fig.\u00a04H). To address the potential redundancy between TFEB and TFE3, we generated CreERRragafl/flRragbfl/flTfebfl/flTfe3\u2013/\u2013 mice (Supplementary Fig.\u00a01A). Strikingly, we found that TFEB/TFE3 deletion significantly restored the reduction of B220loCD43\u2013 pre-B/immature B cells and fraction A and fraction C/C\u2019 frequencies in the BM (Fig.\u00a07A, B), and GC formation in PPs (Fig.\u00a07C) in the absence of RagA/RagB, demonstrating a non-redundant role between TFE3 and TFEB during early B cell development and spontaneous GC formation in mucosal site under the control of Rag-GTPases. Like TFEB deletion, TFEB/TFE3 deletion restored most of the in vitro activation and metabolic defects in RagA/RagB deficient B cells, including reduced IgG1 expression (Fig.\u00a07D), mitochondrial membrane potential (Fig.\u00a07E), ROS level (Fig.\u00a07F) and increased LAMP1 expression (Fig.\u00a07G), as well as the reduced OCR (Fig.\u00a07H). Next, we immunized tamoxifen injected \u03bcMT:CreERRragafl/flRragbfl/flTfebfl/flTfe3\u2013/\u2013 mice and \u03bcMT:CreERRragafl/flRragbfl/fl mice with NP-OVA/alum or TNP-LPS. TFEB/TFE3 deficiency was not able to rescue the reduced GC and plasmablast formation upon NP-OVA immunization in the absence of RagA/RagB (Fig.\u00a07I\u2013J). However, the reduced responses towards TNP-LPS immunization in RagA/RagB deficient B cells were nearly restored by TFEB/TFE3 deletion (Fig.\u00a07K-L). Taken together, these data reveal non-redundant functions for TFEB and TFE3 in B cell development and GC formation in mucosal site. They support the notion that Rag-GTPase-TFEB/TFE3 axis regulates B cell development, activation, and differentiation under different immune contexts.\n\nA Flow cytometry of CD43 and B220 expression on BM cells. Below, summaries of B220loCD43\u2212, B220hiCD43\u2212 and B220+CD43+ cell frequencies. B Flow cytometry of BP-1 and CD24 expression in BM B220+CD43+IgM\u2013 B cell precursors. Right, summary of fraction A (CD24\u2212BP-1\u2212), fraction B (CD24+BP-1\u2212), and fraction C/C\u2032 (CD24+BP-1+) cell frequencies. C Flow cytometry of GL-7 and Fas expression the Peyer\u2019s patches. Right, summary of the GC B cell frequencies. For A-C, WT (n\u2009=\u20095), \u03bcMT:CreERRragafl/flRragbfl/fl (n\u2009=\u20096), and \u03bcMT:CreERRragafl/flRragbfl/flTfebfl/flTfe3\u2212/\u2212 (n\u2009=\u20096). Splenic B cells were stimulated with LPS/IL-4/BAFF for 72\u2009h. IgG1 expression (D), TMRM and MTDR staining (E), CellROX staining (F), and LAMP1 staining (G) were examined by flow cytometry. Summaries of IgG1+ B cell frequencies, the relative TMRM, MTDR, CellROX, and LAMP1 MFIs were presented. For (D\u2013G), n\u2009=\u20094 mice for each group. H Mitostress assay was performed on a Seahorse XFe96 analyzer. Right, summaries of the basal respiration and maximal respiration. WT (n\u2009=\u20096), \u03bcMT:CreERRragafl/flRragbfl/fl (n\u2009=\u20095), \u03bcMT:CreERRragafl/flRragbfl/flTfebfl/flTfe3\u2212/\u2212 (n\u2009=\u20096). I\u2013L Tamoxifen injected WT, \u03bcMT:CreERRragafl/flRragbfl/fl, and \u03bcMT:CreERRragafl/flRragbfl/flTfebfl/flTfe3\u2212/\u2212 chimera mice were immunized intraperitoneally with NP-OVA/alum (I, J) or TNP-LPS (K, L). I Flow cytometry of CD138 and B220 expression on splenic lymphocytes. Right, summary of CD138+ plasmablast frequencies. J Flow cytometry of GL-7 and Fas expression on splenic B cells. Right, summary of GC B cell frequencies. For (I and J), WT (n\u2009=\u20094), \u03bcMT:CreERRragafl/flRragbfl/fl(n\u2009=\u20093), and \u03bcMT:CreERRragafl/flRragbfl/flTfebfl/flTfe3\u2212/\u2212 (n\u2009=\u20094). K Flow cytometry of CD138 and B220 expression on splenic lymphocytes. Right, summary of CD138+ plasmablast frequencies. L ELISA measurement of serum TNP-specific antibodies. For (K and L), WT (n\u2009=\u20096), \u03bcMT:CreERRragafl/flRragbfl/fl (n\u2009=\u20097), \u03bcMT:CreERRragafl/flRragbfl/flTfebfl/flTfe3\u2212/\u2212 (n\u2009=\u20096). Data in graphs represent mean\u2009\u00b1\u2009SEM. ns, not significant. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, and ****p\u2009<\u20090.0001, one-way ANOVA (A, C, D\u2013K), two-way ANOVA (B and L). Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54344-5/MediaObjects/41467_2024_54344_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54344-5/MediaObjects/41467_2024_54344_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54344-5/MediaObjects/41467_2024_54344_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54344-5/MediaObjects/41467_2024_54344_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54344-5/MediaObjects/41467_2024_54344_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54344-5/MediaObjects/41467_2024_54344_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54344-5/MediaObjects/41467_2024_54344_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Research in childhood malnutrition has long highlighted the impact of nutrient availability and immunity. Yet, the complex interplay between systemic metabolic dysregulation and immune system hampers the mechanistic understanding of nutrient sensing and immunometabolism in adaptive immune system69. Recent studies have established Rag-GTPases as a key sensor for amino acids. Rag-GTPases primarily control mTORC1 and TFEB/TFE3 activity. In the current study, we utilized genetic models to establish the mechanisms through which Rag-GTPases suppress TFEB/TFE3 in B cell development and activation in a likely mTORC1-independent manner.\n\nThe relationship between Rag-GTPases and mTORC1 has been contentious. Rag-GTPases were initially found to be necessary and sufficient for mTORC1 activation, including in immune cells21,23,24,32. However, amino acids can engage mTORC1 independent of Rag-GTPases30. Our study demonstrated that Rag-GTPases are not necessary for mTORC1 activation in B cells. Our observations join several recent studies to illustrate the relationship between Rag-GTPases and mTORC1 is cell-type and context-dependent: Rag-GTPases can be sufficient but not necessary for mTORC1 activation in B cells24,25. Furthermore, Rag-GTPases and mTORC1 in GC B cells are critical for antigen selection and antibody affinity maturation. Yet only Rag-GTPases, but not mTORC1, modulate DZ vs LZ distribution and lysosome metabolism in GC B cells. Finally, Rag-GTPases play a more prominent role in supporting plasmablast formation than mTORC1, whose underlying mechanisms remain to be investigated. These results uncover the distinct requirements of Rag-GTPases and mTORC1 in humoral immunity. Both Rag-GTPase and mTORC1 are modulated by amino acids. Specific amino acids have been identified to promote mTORC1 in different mammalian cells32,70. Thus, it is plausible that different amino acids may provide different upstream signals and differentially regulate Rag-GTPase and mTORC1. This hypothesis awaits future investigations.\n\nWhile recent advances in immunometabolism field have identified certain metabolic requirements during mature B cell activation, much less is known about metabolic regulations during B cell development, spontaneous GC reaction in mucosal site, and humoral responses towards TD and TI antigens. Our data establish a Rag-GTPase-TFEB/TFE3-mitophagy pathway that controls mitochondrial fitness in B cells. Instead of promoting mTORC1, Rag-GTPases suppress TFEB/TFE3 activity following B cell activation. Deficiency of Rag-GTPases leads to TFEB/TFE3 nuclear accumulation and overactivation, which induces mitophagy and reduces mitochondrial membrane potential and mitochondrial oxidative phosphorylation. Importantly, our data demonstrate a key differential requirement of TFEB/TFE3 transcription factors between TD and TI responses, i.e., Rag-GTPase mediated TFEB/TFE3 suppression is critical for TI response, but a Rag-GTPase dependent but TFEB/TFE3 independent mechanism is required for TD response. The molecular mechanisms governing TI responses are much less understood compared to those regulating TD responses. A recent study showed that TD, but not TI, responses depend on LDHA-mediated glycolysis10. Here, our study demonstrated that mitochondrial integrity mediated by Rag-GTPase-TFEB/TFE3 axis is critical for TI response but may not be sufficient for TD response. The TFEB/TFE3 independent but Rag-GTPase dependent mechanisms for TD response await future study. The higher mitochondrial membrane potential in plasmablasts induced by TI-antigen than TD-antigen suggests a possibility that TI-response might have a greater reliance on mitochondrial metabolism than TD-response, consistent with a recent study71. More research is warranted to test this proposition. Thus, our investigation helps fill a critical knowledge gap and further highlight the distinct signaling and metabolic requirements between TD and TI responses.\n\nFinally, our study unveils overlapping and non-redundant functions between TFEB and TFE3 in B cells. While deletion of TFEB alone or both TFEB and TFE3 can restore the impaired antibody response to TI antigens in RagA/RagB deficient B cells, deletion of both TFEB and TFE3 is needed to rectify early B cell development defects and reduced GC formation in PPs in the absence of Rag-GTPases. Altogether, our investigations illustrate specific metabolic and signaling requirements in the lifetime of B cells at different stages and different anatomic locations coordinated by Rag-GTPase-TFEB/TFE3 signaling axis. Another known regulator of TFEB and mTORC1 is folliculin (FLCN), which also directly interacts with Rag-GTPase22,42. Its contribution to Rag-GTPase-TFEB/TFE3 mediated mitochondrial metabolism in B cells requires future investigation.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "ROSA26-Cre-ERT2, Rragafl/flRragbfl/fl, Rptorfl/fl mice have been previously described27,34. CD45.1+ (RRID: IMSR_JAX:002014), C57BL/6J (RRID: IMSR_JAX:000664), and Rag1\u2013/\u2013 (RRID: IMSR_JAX:002216), B6.129S2-Ighmtm1Cgn/J (RRID:IMSR_JAX:002288) and B6.129P2-Aicdatm1(cre)Mnz/J (RRID:IMSR_JAX:007770) mice were purchased from the Jackson Laboratory. Tfebfl/fl mouse was a gift from Dr. Andrea Ballabio (Telethon Institute of Genetics and Medicine)72. Tfe3\u2013/\u2013 mouse was a gift from Dr. Ming O. Li (Memorial Sloan Kettering Cancer Center)31. CreERRragafl/flRragbfl/fl (Rragbfl/fl denotes male hemizygous or female homozygous mice for Rragb because Rragb is located on the X-chromosome). CreERRragbfl/fl mice, CreERRptorfl/fl, CreERRragafl/flRragbfl/flTfebfl/fl, CreERRragafl/flRragbfl/flTfebfl/flTfe3\u2013/\u2013 and age- and gender-matched littermate controls were analyzed at the indicated ages. Bone marrow (BM) chimeras were generated by transferring T cell-depleted bone marrow cells into lethally irradiated (11\u2009Gy) CD45.1+ mice by mixing CreERRraga+/+Rragb+/+Rptor+/+ (WT), CreERRragafl/flRragbfl/fl, CreERRragafl/flRragbfl/flTfebfl/fl, CreERRragafl/flRragbfl/flTfebfl/flTfe3\u2013/\u2013 or CreERRptorfl/fl BM with \u03bcMT BM at a ratio of 4:1, followed by reconstitution for at least 2 months. Mice were bred and maintained in a specific pathogen-free facility in the Department of Comparative Medicine of the Mayo Clinic. The mice were euthanized by carbon dioxide according to the approved protocol. All animal protocols (A00003354-18-R23) were approved by the Institutional Animal Care and Use Committees (IACUC) of the Mayo Clinic Rochester.\n\nThe retroviral packaging Plat-E cells were a gift from Dr. Hongbo Chi (St. Jude Children\u2019s Research Hospital) and from female origin. The cells were cultured in Dulbecco\u2019s Modified Eagle Medium (DMEM, Thermo Fisher Scientific) supplemented with 10% fetal bovine serum (FBS) and 2\u2009mM glutamine, 100 U/ml Penicillin, and 100\u2009mg/ml Streptomycin and maintained at 37\u2009\u00b0C in 5% CO2. Puromycin (1\u2009mg/ml) and blasticidin (10\u2009mg/ml) antibiotics were also added into the culture medium to maintain selective pressure and were removed one day before retrovirus plasmid transfection.\n\nFor tamoxifen treatment, mice were injected intraperitoneally with tamoxifen (1\u2009mg per mouse) in corn oil daily for four consecutive days and analyzed 7 days after the last injection. For experiments involving an immune challenge, chimeras were given tamoxifen (1\u2009mg per mouse) in corn oil daily for four consecutive days via oral gavage and challenged with antigens or influenza at day 7 after the last tamoxifen administration. For NP-OVA immunization experiments, antigen for immunization was prepared by mixing NP23-OVA (23 molecules of NP linked to OVA; Biosearch Technologies), 10% KAL(SO4)2 dissolved in PBS at a ratio of 1:1, in the presence of LPS (Escherichia coli strain 055:B5; Sigma) at pH 7. Mice were immunized intraperitoneally (100\u2009\u03bcg NP-OVA and 10\u2009\u03bcg LPS) for analysis of NP-specific antibody response. Nine days after immunization, sera, spleens, and mesenteric lymph nodes were collected from the mice. Thirteen days after infection, spleens, mediastinal lymph nodes, and lungs from the mice were harvested for analysis. For T-independent immune response, mice were given 50\u2009\u03bcg TNP-LPS (Biosearch Technologies) intraperitoneally. Sera and spleens were collected on day 9 after immunization.\n\nMouse B cells were isolated from pooled single cell suspensions of spleen and peripheral lymph nodes using CD19 microbeads (Miltenyi, catalog no. 130-052-201) or EasySep Mouse B Cell Isolation Kit (Stemcell Technologies, catalog no. 19854). B cells were cultured in RPMI1640 medium supplemented with 10% (vol/vol) FBS and 1% penicillin-streptomycin and activated with 3\u2009\u03bcg/mL LPS (Sigma-Aldrich), 10\u2009ng/mL recombinant mouse IL-4 (Tonbo Bioscience) plus 20\u2009ng/mL recombinant human BAFF (Biolegend) for 3 days. Alternatively, B cells were activated with10\u2009\u03bcg/mL anti-IgM (Jackson Immunoresearch), 5\u2009\u03bcg/mL anti-CD40 (Bio X Cell) and 10\u2009ng/mL recombinant mouse IL-4, or 2.5\u2009\u03bcM CpG ODN2006 (Integrated DNA Technologies) together with 10\u2009ng/mL recombinant mouse IL-4 or 3\u2009\u03bcg/mL LPS with 10\u2009ng/mL recombinant murine IFN-\u03b3 (Peprotech) and 20\u2009ng/mL recombinant human BAFF or 5 \u03bcg/mL anti-CD40 (Bio X Cell) and 10\u2009ng/mL recombinant mouse IL-4. B cell proliferation was measured by CellTrace violet dye dilution (Thermo Fisher Scientific).\n\nWT TFEB retroviral constructs were made by inserting the cDNA of mouse TFEB isoform b into the MSCV-IRES-EGFP retroviral vector. Ca TFEB retroviral constructs were generated by mutating mouse TFEB isoform b at Ser142 and Ser211 to Ala, and the mutated form was inserted into the MSCV-IRES-EGFP vector. TFEB TDN retroviral construct was generated by deleting activation domain (AD), binding region (BR), and proline-rich region while retaining bHLH and Leucine-zipper region. HA-tag and nuclear location sequence were added at C terminal of the construct. Mito-QC reporter retroviral construct was a gift from Dr. Ping-Chih Ho (Ludwig Institute for Cancer Research), as described previously57. To obtain infectious retroviral stocks, each construct was transfected into Plat-E cells along with pCL-Eco packaging plasmid using Lipofectamine\u2122 3000 Transfection Reagent. Spleen B cells were activated with 3\u2009\u03bcg/mL LPS, 10\u2009ng/mL recombinant mouse IL-4, or 0.25 \u03bcg/ml anti-CD180 for 40\u2009h, followed by transduction with indicated viruses in the presence of 8\u2009\u03bcg/ml polybrene at 32\u2009\u00b0C, 1000\u2009\u00d7\u2009g for 90\u2009min. Transduced B cells were put in the 37\u2009\u00b0C incubator for 4\u2009h, then changed to fresh medium with 3 \u03bcg/mL LPS, 10\u2009ng/mL recombinant mouse IL-4, and 20\u2009ng/mL recombinant human BAFF for further activation. Transduced cells were analyzed after 3 days of activation.\n\nFor analysis of surface markers, cells were stained in phosphate-buffered saline (PBS) containing 1% (w/v) bovine serum albumin (BSA) with indicated antibodies. The following antibodies were used: anti-B220 (RA3-6B2, 1:300), anti-CD19 (6D5, 1:300), anti-TCR\u03b2 (H57-597, 1:300), anti-CD24 (M1/69, 1:1000), anti-BP-1 (6C3, 1:200), anti-IgD (11-26c.2a, 1:400), anti-IgG1 (RMG1-1, 1:200), anti-CD25 (PC61, 1:300), anti-CD4 (RM4-5, 1:300), anti-CD21 (7E9, 1:500), anti-CD23 (B3B4, 1:200), anti-CD93 (AA4.1, 1:300), anti-CD138 (281-2, 1:300), anti-GL7 (GL7, 1:300), anti-CD278 (C398.4A, 1:200), anti-PD-1 (J43, 1:400), anti-CD86 (GL-1, 1:300), anti-IL-7Ra (A7R34, 1:200), and anti-CD71 (R17217, 1:500) were all from Biolegend, anti-IgM (II/41, 1:200) and anti-CD184 (2B11, 1:100) were purchased Thermo Fisher Scientific. Anti-CD95 (Jo2, 1:300), and anti-CD43 (S7, 1:300) were obtained from BD Biosciences. CXCR5 was stained with biotinylated anti-CXCR5 (2G8, 1:100) and streptavidin-conjugated PE (both from BD Biosciences, 1:200) to enhance the signal. Intracellular Foxp3 (FJK-16s, 1:100), Ki-67 (SolA15, Thermo Fisher Scientific, 1:500), and anti-Bcl6 (K112-91, BD Biosciences, 1:50) were analyzed in cells fixed and permeabilized with Foxp3 staining buffers according to the manufacturer\u2019s instructions (Thermo Fisher Scientific). For phosflow staining, cells were stained with surface markers, then fixed with 1\u00d7 Lyse/Fix (BD Biosciences) buffer at 37\u2009\u00b0C for 10\u2009min, washed and permeabilized by ice-cold Perm III buffer (BD Biosciences) on ice for 30\u2009min, followed by staining with anti-phospho-S6 (S235/236, 1:400) or anti-phospho-4E-BP1 (T37/46, 1:200) (both from Cell Signaling Technology) for 30\u2009min at room temperature. Active Caspase-3 was stained according to the manufacturer\u2019s instructions (#550914, BD Pharmingen). Briefly, cells were stained with Ghost Dye\u2122 Violet 510 Fixable Viability in PBS at room temperature for 30\u2009min, and the surface markers were stained in FACS buffer on ice for 30\u2009min, then the cells were washed with cold PBS and suspended in BD Cytofix/Cytoperm\u2122 solution at a concentration of 1\u2009\u00d7\u2009106 cells/0.5\u2009ml for 20\u2009min, followed by washing with BD Perm/Wash\u2122 buffer at a volume of 0.5\u2009ml buffer/1\u2009\u00d7\u2009106 cells at room temperature and stained with active Caspase-3 diluted in BD Perm/Wash\u2122 buffer at room temperature for 30\u2009min. Cell viability was examined by Fixable viability dye (Tonbo Bioscience) or 7-AAD (Thermo Fisher Scientific) following the manufacturer\u2019s protocol. For LAMP1 staining, surface staining was done with FACS buffer on ice, followed by fixation and permeabilization using BD Cytofix/Cytoperm Fixation/Permeabilization Kit (BD Biosciences), LAMP1 antibody (1D4B, BioLegend, 1:100) was diluted in BD Perm/Was Buffer and stained at room temperature for 30\u2009min. For the dye staining, B cells were stained with 20\u2009nM MitoTracker Deep Red (ThermoFisher Scientific), 20\u2009nM MitoTracker Green (ThermoFisher Scientific), 100\u2009nM tetramethylrhodamine methyl ester (TMRM, ThermoFisher Scientific). 500\u2009nM CellROX or 1 \u03bcM MitoSOX in HBSS at 37\u2009\u00b0C for 20\u2009min. Flow cytometry was performed on a BD Fortessa X-20 or LSR II instrument or Attune NxT system (Life Technologies). Data were then analyzed by FlowJo software (Tree Star). Gating strategies were summarized in Supplementary Fig.\u00a08.\n\nLevels of phosphorylated ubiquitin at serine 65 (p-S65-Ub) were assessed in a sandwich type ELISA on a Meso Scale Discovery (MSD) platform that uses electrochemiluminescence (ECL) as a readout, which was slightly modified from Watzlawik et al.68. In brief, here we used a SULFO-TAG-labeled mouse anti-Ub antibody (clone P4D1) instead of two subsequent detecting antibodies (1. Ub (P4D1) followed by 2. SULFO-TAG-labeled anti-mouse antibody) as described previously68.\n\nA. SULFO-TAG-labeling: For SULFO-TAG-labeling of the Ub detecting antibody (ThermoFisher #14-6078-37, Ub (clone P4D1)), we first removed sodium azide by using Amicon ultra 0.5\u2009ml centrifugal filters with a 50\u2009kDa MWCO (Millipore, UFC505008) and washed 5 times with PBS, pH: 7.9 at 14,000 \u00d7 g for 2\u2009minutes. Sample recovery was done by inverting the filter in a new tube and spinning for another 2\u2009minutes at 1000 \u00d7 g. Ub (P4D1) antibody was then incubated at room temperature for 2\u2009h with SULFO-TAG NHS-Ester (MSD, #R31AA) in a challenge ratio of 20:1 (Sulfotag NHS-Ester: antibody) on a rotational shaker. Excess, non-conjugated SULFO-TAG was removed by using a 0.5\u2009ml Zeba Spin desalting column (40\u2009K MWCO) (ThermoFisher, A57760) according to the manufacturer\u2019s recommendation.\n\nB. p-S65-Ub ELISA: p-S65-Ub antibody (CST #62802) was used as a capture antibody in a concentration of 1 \u03bcg/ml in 200\u2009mM sodium carbonate buffer pH 9.7 and coated overnight at 4\u2009\u00b0C with 30 \u03bcl per well in 96-well MSD plate (MULTI-ARRAY\u00ae 96-well Plate; L15XA-3). The next morning MSD plates were washed 2 times with 0.22-micron filtered ELISA wash buffer (150\u2009mM Tris, pH 7.4, 150\u2009mM NaCl, 0.1% [v:v] Tween-20) and subsequently blocked by adding ELISA blocking buffer (150\u2009mM Tris, pH 7.4, 150\u2009mM NaCl, 0.1% [v:v] Tween-20, 1% BSA [w:v]) and incubated for 1\u2009h at 22\u2009\u00b0C without shaking. All samples were run in duplicates and diluted in blocking buffer using 10 \u03bcg of total protein per well. Antigens were incubated for 2\u2009h at 22\u2009\u00b0C on a microplate mixer (USA Scientific, 8182-2019) at 500\u2009rpm and three washing steps were then performed as described before. SULFO-TAG-labeled Ub (P4D1) antibody (1\u2009\u00b5g/ml) was added in blocking buffer in 50 \u03bcl total volume per well and incubated for 2\u2009h at 22\u2009\u00b0C on a microplate mixer at 500\u2009rpm. After three washing steps, 150 \u03bcl MSD GOLD Read Buffer (R92TG-2) were finally added to each well and the plate being read on a MESO QuickPlex SQ 120 reader.\n\nThe bioenergetic activities of B cells, displayed by both ECAR and OCR were measured by Seahorse assays according to the established protocols from Agilent Technologies. Briefly, B cells were seeded at 150, 000-300, 000 cells/well on Cell-Tak (Corning) coated XFe96 plate in indicated medium (For OCR: Seahorse XF RPMI medium containing 10\u2009mM glucose, 2\u2009mM L-glutamine, and 1\u2009mM sodium pyruvate, pH 7.4; For ECAR: Seahorse XF RPMI medium plus 2\u2009mM L-glutamine, pH 7.4; all reagents from Agilent Technologies). For the Mito stress test, OCR and ECAR were measured in the presence of Oligomycin (1.5\u2009\u03bcM, Sigma-Aldrich), FCCP (1.5\u2009\u03bcM, Sigma-Aldrich), and Rotenone (1\u2009\u03bcM, Sigma-Aldrich)/ Antimycin A (1\u2009\u03bcM, Sigma-Aldrich). For glycolysis stress, both OCR and ECAR were measured by sequential injection of Glucose (10\u2009mM, Agilent Technologies), Oligomycin (1.5\u2009\u03bcM, Sigma-Aldrich), 2-DG (50\u2009mM, Sigma-Aldrich). Glycolytic flux was also measured by detritiation of [3-3H]-glucose (Perkin Elmer) as described73. Briefly, 1 \u03bcCi [3-3H] glucose was added into the culture media, and 2\u2009h later, 500 \u03bcL media were transferred to a 1.5\u2009mL microcentrifuge tube containing 50\u2009\u03bcL of 5\u2009N HCl. The microcentrifuge tubes were then placed in 20\u2009mL scintillation vials containing 0.5\u2009ml water with the vials capped and sealed. 3H2O was separated from unmetabolized [3H] glucose by evaporative diffusion for 24\u2009h at room temperature. A cell-free sample containing 1 \u03bcCi 3H-glucose was included as a background control.\n\nB cells were cultured with LPS, IL-4 plus BAFF for 3 days, and cell pellets were harvested and fixed in 2% paraformaldehyde and 2.5% glutaraldehyde in 0.1\u2009M sodium cacodylate. Following fixation, cells were embedded and sliced for transmission electron microscopy. The Grids were imaged with a JEM1400 plus transmission electron microscope (JEOL). Damaged mitochondria with mitophagy were defined as either no visible cristae, surrounded by a phagophore, or being located inside an amphisome.\n\nThe primary data was obtained from ArrayExpress (E-MTAB-9478), followed by annotation and alignment using CellRanger (v3.0.0). All samples from spleen were included. Events with 200-5000 genes detected per cell (nFeature) and <5% mitochondrial genes were put through further analysis with the package of Seurat (v4) in R project. The original code can be found on the official (https://satijalab.org/seurat/articles/integration_introduction.html). In brief, differentially expressed genes were found in each dataset using Principal Component Analysis. Shared genes were then identified across different time points as \u201canchors\u201d to integrate all datasets. Subsequently, clustering was performed based on K-nearest neighbor (KNN) graph constructed. Clusters resembled contaminating cells (i.e., T cells, myeloid cells, etc.) were excluded. The data was re-clustered using the workflow described previously.\n\nFor cell function evaluation, the \u201cAddModuleScore ()\u201d function implanted in Seurat was utilized. Gene sets displayed in Fig.\u00a05L-M included the following: Hallmark mTORC1 signaling (MSigDB, M5924), Hallmark oxidative phosphorylation (MSigDB, M5936), Putative TFEB target genes (described previously62), KEGG Lysosome (MSigDB, M11266), GO lysosome localization (GO:0032418), mitochondrial complex I (MSigDB, M39781), Reactome mitophagy (MSigDB, M27418), and Reactome PINK1-PRKN mediated mitophagy (MSigDB, M27419).\n\nWT or RagA/RagB deficient B cells were activated in vitro for 72\u2009h with LPS, IL-4, and BAFF. RNA was isolated using a Quick-RNA Microprep kit (ZYMO research) following the manufacturer\u2019s instructions. After quality control, high-quality total RNA was used to generate the RNA sequencing library. Reads with low quality, containing the adapter (adapter pollution), or with high levels of N base were removed to generate clean data. HISAT(V2.2.1)74 was used to align the clean reads to the mouse reference genome (Mus_musculus, GCF_000001635.27_GRCm39). Bowtie2(V2.4.5)75 was used to align the clean reads to the reference genes. DEG analysis was carried out using DESeq276, and genes with log2FC\u2009>\u20090 and false discovery rate <0.05 were considered for gene cluster analysis.\n\nTwo million B cells were collected and washed with ice-cold PBS twice, discarded the supernatant, and collected the cell pellets. Gently resuspend cells in 200\u2009\u03bcl 1\u00d7 Hypotonic Buffer (20\u2009mM Tris-HCl, pH 7.4, 10\u2009mM NaCl, 3\u2009mM MgCl2) by pipetting up and down several times, then incubated on ice for 15\u2009min. 15 \u03bcl detergent (10% NP40) was added into the suspension and vortexed for 10\u2009s at the highest speed. The supernatant was collected after centrifuging for 10\u2009min at 5000\u2009rpm at 4\u2009\u00b0C. This supernatant contains the cytoplasmic fraction. The collected cell pellets were washed once with 1\u00d7 Hypotonic Buffer and resuspended in 50 \u03bcl Complete Cell Extraction Buffer (10\u2009mM Tris, pH 7.4, 2\u2009mM Na3VO4, 100\u2009mM NaCl, 1% Triton X-100, 1\u2009mM EDTA, 0% glycerol, 1\u2009mM EGTA, 0.1% SDS, 1\u2009mM NaF, 0.5% deoxycholate, 20\u2009mM Na4P2O7) for 30\u2009min on ice with vortexing at 10\u2009min intervals. The supernatant (nuclear fraction) was collected after centrifuging for 30\u2009min at 14,000\u2009\u00d7\u2009g at 4\u2009\u00b0C, and the nuclear extracts were ready for assay.\n\nB cells were lysed in radioimmunoprecipitation assay (RIPA) buffer (50\u2009mM Tris (pH 7.4), 150\u2009mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, 0.1% sodium dodecyl sulfate (SDS)) supplemented with protease inhibitor cocktail and phosphatase inhibitor cocktail (Sigma-Aldrich). Protein concentration was detected by BCA assay (Thermo Fisher Scientific), and an equal amount of protein was resolved in 4\u201312% SDS-polyacrylamide gel electrophoresis (SDS-PAGE) (Bio-Rad). Proteins were transferred to polyvinylidene difluoride membranes (Millipore) and probed overnight with the following primary antibodies: anti-p-S6K (108D2, 1:1000), anti-p-S6 (D57.2.2E, 1:1000), anti-LAMP1(C54H11, 1:1000), anti-p-4EBP1 (236B4, 1:1000), anti-Raptor (24C12, 1:1000), and anti-RagA (D8B5, 1:1000) all from Cell Signaling Technology), anti-AID (mAID-2, Thermo Fisher Scientific, 1:1000), anti-TFEB (A303-673A, Bethyl Laboratories, 1:1000), Lamin B (66095-1-Ig, Proteintech, 1:5000), tubulin (11224-1-AP, Proteintech, 1:5000), TFE3 (HPA023881, Sigma-Aldrich, 1:1000) and anti-\u03b2-actin (13E5, Sigma-Aldrich, 1:5000). The membrane was washed and incubated with indicated secondary antibody for the subsequently enhanced chemiluminescence (ECL, Thermo Fisher) exposure.\n\nFor observing the germinal center structure in the spleen, part of the spleen from the immunized or infected mice was fixed in 4% Paraformaldehyde at 4\u2009\u00b0C overnight, then the spleens were dehydrated in 20% sucrose for at least 36\u2009h. Then the spleens were embedded in OCT and sectioned at 5\u2009\u03bcm thickness. The slides were air-dried at room temperature before being fixed in cold acetone at \u221220\u2009\u00b0C for 10\u2009min. The fixed slides were washed with PBS for twice and blocked with Blocking buffer (Thermo Fisher Scientific) for 1\u2009h at room temperature. Biotinylated Peanut Agglutinin (PNA, Vector laboratories, 1:1000) was stained on the sections at 4\u2009\u00b0C overnight. The slides were washed with PBST 3 times, then the Alexa Fluor\u2122 488 Conjugated Streptavidin (1:1000), anti-mouse CD21/CD35 (Alexa Fluor\u00ae 594, Biolegend, 1:200), and anti-mouse IgD (Alexa Fluor\u00ae 647 anti-mouse IgD, Biolegend, 1:100) was applied onto the section for 2\u2009h at room temperature. After washing with PBST for 3 times, the slides were counterstained with 4\u2019, 6-diaminodino-2-phenylindole (DAPI) and mounted. The stained slides were reviewed, and representative images were acquired on Olympus DP80 digital microscope.\n\nTotal RNA was extracted using the RNeasy Micro kit (Qiagen) according to the manufacturer\u2019s instructions, and total RNA was reverse transcribed into cDNA by PrimeScript RT Reagent Kit (Takara) following the established protocol of the kit. The mRNA level of Rragd, Rragc, Bhlhe40, and Prodh2 was detected by real-time PCR with a Thermo Fisher Real-time PCR system, while \u03b2-actin was used as an internal control. Each sample was analyzed in triplicate and the relative amount of gene expression was calculated using the 2\u2212\u0394\u0394Ct method. Primer sequences were summarized in Supplementary Table.\n\nFor detecting NP-specific antibodies in sera, wells were coated with 1\u2009\u03bcg/mL NP23-BSA or NP2-BSA in coating buffer (Bicarbonate-carbonate buffer, pH 9.6) overnight. Plates were washed twice with washing buffer (0.05% Tween 20 in PBS), blocked with 5% blocking protein (Bio-Rad) at 37\u2009\u00b0C for 1\u2009h, washed twice, and incubated with indicated sera samples at 37\u2009\u00b0C for 1.5\u2009h. Horseradish peroxidase (HRP)-conjugated secondary antibodies: anti-mouse IgG1, anti-mouse IgG2b, anti-mouse IgG2c, and anti-mouse IgG3 (all from SouthernBiotech), or anti-mouse IgM (Bethyl laboratories) were developed at 37\u2009\u00b0C for 1\u2009h after washing with a buffer for four times. The reaction was further developed with tetramethylbenzidine (TMB), then stopped by 2\u2009N H2SO4, and read at 450\u2009nm.\n\nAmino acid-free (AA-) medium was prepared by RPMI 1640 powder (R8999-04A, US Biological Life Science) and sodium phosphate dibasic (5.6\u2009mM, the same concentration as commercially available RPMI 1640 medium, US Biological Life Science), supplemented with 10% (v/v) dialyzed FBS (Thermo Fisher Scientific). Amino acid-sufficient (AA\u2009+\u2009) medium was prepared by adding proper volumes of MEM amino acids solution (essential amino acids, EAA, 50\u00d7), MEM non-essential amino acids solution (NEAA, 100\u00d7), and 200 mM L-Gln (all from Sigma-Aldrich) to AA- medium to reach a final concentration of 1\u00d7EAA, 1\u00d7NEAA, and 2\u2009mM Gln. The medium was supplemented with 10% (v/v) dialyzed FBS. Medium containing single amino acids (Ala, Leu, Gln, or Arg) or their combinations was prepared with AA\u2013 medium (prepared to the same concentrations present in the AA+ medium). All media were adjusted to pH7.5 and filter-sterilized (0.2\u2009\u03bcm) before use.\n\nStatistics were performed on GraphPad Prism 8. P values were calculated with Student\u2019s t-test, one-way ANOVA, or two-way ANOVA, as indicated in the figure legends. p\u2009<\u20090.05 was considered significant. All error bars were represented as SEM.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Source data are provided with this paper and can be accessed at https://figshare.com/s/b5b8e00a13fba9db6caa. 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Li, Ping-Chih Ho, Christopher AJ. Roman for sharing mouse strains and research reagents. We acknowledge the Microscopy and Cell Analysis Core at Mayo Clinic Rochester. We acknowledge the NIH (grants R01 AI 162678 and R01 AR077518) for supporting this work in H.Z.\u2019s laboratory, and R01 AI154598, R01 AI147394, R01 AI176171 and R01 AI 112844 to J.S.\u2019s laboratory.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Division of Rheumatology, Department of Medicine, Mayo Clinic Rochester, Rochester, MN, USA\n\nXingxing Zhu,\u00a0Yanfeng Li,\u00a0Xian Zhou\u00a0&\u00a0Hu Zeng\n\nCarter Immunology Center, University of Virginia, Charlottesville, VA, USA\n\nYue Wu\u00a0&\u00a0Jie Sun\n\nDivision of Infectious Diseases and International Health, Department of Medicine, University of Virginia, Charlottesville, VA, USA\n\nYue Wu\u00a0&\u00a0Jie Sun\n\nDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, USA\n\nJens O. Watzlawik\u00a0&\u00a0Wolfdieter Springer\n\nDepartment of Immunology, Mayo Clinic Rochester, Rochester, MN, USA\n\nYin Maggie Chen,\u00a0Daniel D. Billadeau,\u00a0Virginia Smith Shapiro\u00a0&\u00a0Hu Zeng\n\nDepartment of Pathology, Microbiology & Immunology, Molecular Pathogenesis Division, Vanderbilt University Medical Center and School of Medicine, Nashville, TN, USA\n\nAriel L. Raybuck\u00a0&\u00a0Mark R. Boothby\n\nNeuroscience PhD Program, Mayo Clinic Graduate School of Biomedical Sciences, Jacksonville, FL, USA\n\nWolfdieter Springer\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nX.X.Z. and H.Z. conceived the project, designed the research, interpreted the data, and wrote the manuscript. X.X.Z. and X.Z. prepared the materials and carried out the experiments. Y.W. and Y.M.C. performed the bioinformatics analysis. Y.L. managed the mouse colony, performed molecular biology experiments and fluorescence imaging. D.D.B. provided imaging analysis. V.S.S. provided antibodies and other research materials. J.O.W. and W.S. performed part of the mitophagy analyses. A.L.R. provided tissue samples from Raptor mutant mouse line. M.R.B. interpreted the data and revised the manuscript. This work is a collaboration with J.S. and M.R.B., who provided key materials and expertize for the research.\n\nCorrespondence to\n Hu Zeng.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Hassan Jumaa and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Zhu, X., Wu, Y., Li, Y. et al. The nutrient-sensing Rag-GTPase complex in B cells controls humoral immunity via TFEB/TFE3-dependent mitochondrial fitness.\n Nat Commun 15, 10163 (2024). https://doi.org/10.1038/s41467-024-54344-5\n\nDownload citation\n\nReceived: 22 March 2024\n\nAccepted: 05 November 2024\n\nPublished: 23 November 2024\n\nVersion of record: 23 November 2024\n\nDOI: https://doi.org/10.1038/s41467-024-54344-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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numerical modeling of the seismic wavefield in the presence of solid-fluid boundaries", + "journal": "Nature Communications", + "published": "18 February 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM3_ESM.gif" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM4_ESM.gif" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM5_ESM.gif" + }, + { + "label": "Supplementary Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM6_ESM.gif" + }, + { + "label": "Supplementary Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM7_ESM.gif" + }, + { + "label": "Supplementary Movie 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM8_ESM.gif" + }, + { + "label": "Supplementary Movie 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM9_ESM.gif" + }, + { + "label": "Supplementary Movie 8", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM10_ESM.gif" + }, + { + "label": "Supplementary Movie 9", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM11_ESM.gif" + }, + { + "label": "Supplementary Movie 10", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM12_ESM.gif" + }, + { + "label": "Supplementary Movie 11", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM13_ESM.gif" + }, + { + "label": "Supplementary Movie 12", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM14_ESM.gif" + }, + { + "label": "Supplementary Movie 13", + "link": 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"https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM25_ESM.gif" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_MOESM26_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-56530-5#MOESM1", + "/articles/s41467-025-56530-5#MOESM1" + ], + "code": [ + "https://github.com/geodynamics/specfem3d_globe", + "https://figshare.com/articles/code/Efficient_hybrid_numerical_modeling_of_the_seismic_wavefield_in_the_presence_of_solid-fluid_boundaries/26956204?file=49054867" + ], + "subject": [ + "Geophysics", + "Seismology" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3960186/v1.pdf?c=1739970355000", + "research_square_link": "https://www.researchsquare.com//article/rs-3960186/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-56530-5.pdf", + "preprint_posted": "21 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Applying full-waveform methods to image small-scale structures of geophysical interest buried within the Earth requires the computation of the seismic wavefield over large distances compared to the target wavelengths. This represents a considerable computational cost when using state-of-the-art numerical integration of the equations of motion in three-dimensional earth models. \u201cBox Tomography\u201d is a hybrid method that breaks up the wavefield computation into three parts, only one of which needs to be iterated for each model update, significantly saving computational time. To deploy this method in remote regions containing a fluid-solid boundary, one needs to construct artificial sources that confine the seismic wavefield within a small region that straddles this boundary. The difficulty arises from the need to couple elastic and acoustic simulations in this region. Reconciling different displacement potential expressions used for solving the acoustic wave equation, we propose a unified framework for such hybrid simulations, a significant step towards applying \u201cBox Tomography\u201d in arbitrary regions inside the Earth, resulting in a thousand-fold computational cost reduction compared to standard approaches, without compromising accuracy. We also present examples of benchmarks of the hybrid simulations in the case of target regions at the ocean floor and the core-mantle boundary.Earth and environmental sciences/Solid Earth sciences/SeismologyEarth and environmental sciences/Planetary science/Seismology", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "suppl.pdfSF2dglobalfullxhomo60.movmov1SF2dglobalfullzhomo60.movmov2SF2dglobalfullxhete60.movmov3SF2dglobalfullzhete60.movmov4SF2dglobalresidualx60.movmov5SF2dglobalresidualz60.movmov6SF2dglobalgreenfxhomo60.movmov7SF2dglobalgreenfzhomo60.movmov8SF2dlocalfullzhomo60.movmov9SF2dlocalfullzheteulvz60.movmov10SF2dlocalfullzhetetopo60.movmov11SF2dlocalresidualzulvz60.movmov12SF2dlocalresidualztopo60.movmov13SF3dlocalfullzhomo.movmov14SF3dlocalfullzhete.movmov15SF3dlocalresidualz.movmov16SF2dlocalfullzhete30same.movmov17SF2dlocalfullzhete35same.movmov18SF2dlocalfullzhete40same.movmov19SF2dlocalfullzhete45same.movmov20SF2dlocalfullzhete50same.movmov21SF2dlocalfullzhete55same.movmov22SF2dlocalfullzhete60same.movmov23", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Applying full-waveform methods to image small-scale structures of geophysical interest buried within the Earth requires the computation of the seismic wavefield over large distances compared to the target wavelengths. This represents a considerable computational cost when using state-of-the-art numerical integration of the equations of motion in three-dimensional earth models. \u201cBox Tomography\u201d is a hybrid method that breaks up the wavefield computation into three parts, only one of which needs to be iterated for each model update, significantly saving computational time. To deploy this method in remote regions containing a fluid-solid boundary, one needs to construct artificial sources that confine the seismic wavefield within a small region that straddles this boundary. The difficulty arises from the need to combine the solid-fluid coupling with a hybrid numerical simulation in this region. Here, we report a reconciliation of different displacement potential expressions used for solving the acoustic wave equation and propose a unified framework for hybrid simulations. This represents a significant step towards applying \u2019Box Tomography\u2019 in arbitrary regions inside the Earth, achieving a thousand-fold computational cost reduction compared to standard approaches without compromising accuracy. We also present examples of benchmarks of the hybrid simulations in the case of target regions at the ocean floor and the core-mantle boundary.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Resolution in seismic tomography of the earth\u2019s mantle and crust has been progressively improving, in particular with the advent of full waveform inversion (FWI) approaches based on accurate wavefield computations in 3D earth models using the Spectral Element Method (SEM1,2).\n\nHowever, achieving higher resolution remains a significant challenge, especially for geometries where source-station distances are much larger than the minimum wavelength to be resolved. This is due to the considerable cost of the three-dimensional (3D) seismic wavefield computations, which depend on the fourth power of the target minimum period. In the global tomography case, it is also hindered by the uneven global distribution of sources and receivers, factors currently beyond our control.\n\nAt the regional scale, FWI has been successfully employed to produce high-resolution 3D seismic velocity images of the Earth\u2019s crust and upper mantle in various parts of the world, including Australia3, East Asia4, Europe5, North America6,7,8, and South America9, with adequate data coverage, and relative computational efficiency due to the comparatively small lateral and vertical size of the inversion domains. In these inversions, both the seismic sources and the receivers are located within the area of interest. We will refer to this kind of setting as the SIRI case (Source Inside and Receiver Inside). In this type of study, mostly surface waves are modeled, limiting resolution at depth.\n\nMaking use of seismic waves that originate or are recorded outside of the box can significantly improve the lateral and vertical resolution of the structure without the need to enlarge the horizontal dimensions of the model as required in the traditional SIRI setting.\n\nOver decades, geophysicists developed hybrid numerical simulations in engineering mechanics, oil and gas exploration, and ground motion10,11,12, with considerations for reducing the computational effort. Different authors have proposed hybrid approaches, making it possible to reduce the computational burden in the case where sources and/or stations are located outside of the target region, by coupling a global solver outside with a numerically local solver inside it. In this kind of approach, termed \u201cBox Tomography\u201d by Masson and Romanowicz13, the wavefield outside of the target region, or \u201cbox\u201d, is computed only once in a fixed 1D or 3D reference model, and successive iterations of the model updates are performed only inside the box. Most studies have focused on the case where sources are outside and receivers inside the box, or on the case where sources are inside and receivers outside the box. Following their work14,15, we will refer to these two scenarios as the SORI and the SIRO cases, respectively.\n\nFor example, under the SORI setting, Monteiller et al.16 coupled the 1D global Direct Solution Method (DSM17) with the 3D local solver SPECFEM3D_Cartesian1. Their work successfully imaged the deep roots of the western Pyrenees through FWI of teleseismic P waves and their coda18. In the SIRO case, Wu et al.19 implemented an SEM-DSM 3D hybrid method for modeling teleseismic waves with complicated source-side structures, using DSM to calculate the wave propagation from the box boundary to the remote receiver. Under the SORI or SIRO setting, this situation is also relevant for detailed inversion of source parameters20,21. Note that using a 1D global solver, as is frequently done, results in more affordable low-period global computations than using a 3D global solver. However, neglecting the 3D background (i.e. \u201cglobal\u201d) seismic structure may introduce some significant errors in the resulting images within the box. Following the formalism proposed in a series of papers13,22,23, Clouzet et al.24 combined the 3D global solver SPECFEM3D_GLOBE2 with the 3D local solver RegSEM25, to image upper-mantle radial anisotropy structure beneath North America, using SEMUCB_WM126 as global 3D reference model outside of the region.\n\nDifferent authors also proposed hybrid methodologies for the imaging of regions in the deep earth for which both sources and receivers are outside the target region (referred to in what follows as the SORO case). Wen and Helmberger27 utilized a 2D numerical finite-difference method (FDM28) in a localized region near the Core-Mantle Boundary (CMB) and an analytically generalized ray theory (GRT) in the 1D Preliminary Reference Earth Model (PREM29) outside of it. Lin et al.30 combined the 2D SEM in a localized domain near the CMB and the 2D global solver SPECFEM2D_GLOBE applied to the AK13531 1D reference Earth model, but without extrapolation from the boundary of the box to the receiver. Kawai and Geller32 introduced an approach where seismograms are time-shifted to account for the effect of 3D structure outside of a target region located at the base of the mantle and applied this to image several such regions33. Pienkowska et al.34 combined the SPECFEM3D_Cartesian with the AxiSEM-generated Instaseis databases35,36 in a 1D Earth background model. Recently, Adourian et al.15 further extended the Box Tomography approach13 to the SORO case, with SEMUCB_WM137 as the 3D global reference model, SPECFEM_GLOBE as the 3D global solver, and RegSEM25 as the 3D local solver. In a recent study, Li et al.38 modeled the 3D structure of the Ultra-Low Velocity Zone (ULVZ) associated with the Hawaiian mantle plume down to a period of 3\u2009s, using AxiSEM3D39 both outside and inside the target region. This solver assumes a smooth structure in the direction perpendicular to the vertical plane containing the source and the receiver, with all numerical simulations conducted throughout the entire Earth model. Due to the substantial computational demands at low periods, only four local ULVZ models were tested.\n\nA remaining challenge is to implement the Box Tomography approach in the presence of a solid-fluid interface within the target region. A similar challenge exists for oceanic regions near the earth\u2019s surface, as would be the case in studies exploiting waveform data from stations located on islands, on the ocean floor, or above it40,41,42,43. Even though the local solver SPECFEM3D_Cartesian includes solid-fluid coupling, the corresponding hybrid solution with a 3D background earth model and a target region containing solid-fluid interfaces was not implemented in the study of Pienkowska et al.34, nor in the study of Adourian et al.15.\n\nHere, we focus on implementing and validating a method for computing the 2D/3D seismic wavefield in the context of a hybrid numerical simulation with a localized domain containing a solid-fluid coupling interface (subsequently referred to as the hybrid solid-fluid coupling simulation, HSFC). In particular, we propose a unified formalism for the displacement potential in the acoustic wave equation that enables HSFC simulations in scenarios where both the source and the receiver are located outside the target domain (SORO case). We present a series of numerical experiments to validate the accuracy of the proposed method. We also explore the convergence, efficiency, and waveform completeness of the HSFC.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "In Fig.\u00a01, we illustrate two canonical scattering problems involving HSFC. The comprehensive workflow for solving the HSFC is outlined in Fig.\u00a02. In the Methods section, we outline various components essential for its implementation. We introduce the elastic and acoustic wave equations, expressing the latter using two distinct definitions of the displacement potential. Following that, we illustrate the logical relationship between these two definitions, allowing for the subsequent hybrid simulations to be expressed within a unified framework. Then, we present the nomenclature and the workflow related to the HSFC in the SORI and SORO cases, after which we present the solid-fluid coupling equations based on the two different displacement potential definitions. We derive the corresponding mathematical expressions of the hybrid input and output mirror forces used in the HSFC. Finally, we provide a brief description of the absorbing boundary conditions adopted in our approach.\n\na The source Se and receiver Ri are located outside and inside the box, respectively. The superscripts g, e, and i on \u03a9 represent the global, external, and internal domains. b Both the source Se and the receiver Re are located outside the box. The global domain \u03a9g comprises a solid part \\({{{\\mathbf{\\Omega }}}}_{{{{\\boldsymbol{s}}}}^{{{\\boldsymbol{g}}}}}\\) and a fluid part \\({{{\\mathbf{\\Omega }}}}_{{{{\\boldsymbol{f}}}}^{{{\\boldsymbol{g}}}}}\\), separated by a solid-fluid coupling interface \u0393c. The local domain \u03a9i, a confined box (yellow) within the global domain \u03a9g, contains a local solid domain \\({{{\\mathbf{\\Omega }}}}_{{{{\\boldsymbol{s}}}}^{{{\\boldsymbol{i}}}}}\\) and a local fluid domain \\({{{\\mathbf{\\Omega }}}}_{{{{\\boldsymbol{f}}}}^{{{\\boldsymbol{s}}}}}\\), with a scattering object of interest (\u0394m). Leveraging the methodology derived in this paper, after initial global wavefield computations, synthetic seismograms can be computed from an external source to both external and internal receivers by modeling wave propagation only within a compact box that contains the scatterer(s).\n\na The workflow involves three main steps, as illustrated by the numbered circles. In the initial step, a global simulation is conducted from the remote source to the box, and effective secondary sources are computed and stored at the mirror points E1/A1. In the second step, the secondary sources are imposed at mirror points E1/A1 for a local simulation, considering scenarios without a scatterer, and with a scatterer \u0394m (b) inside the inversion domain surrounded by gray lines. The resulting hybrid output mirror forces are calculated and recorded at the mirror points E2/A2. In the third step, two or three global simulations (one per component) are conducted from the remote receiver to the box, generating Green\u2019s functions that are subsequently stored at the mirror points E2/A2. The convolution between the stored hybrid output mirror forces and the Green functions produces the residual synthetic seismograms, capturing the influence of the local scatterer. Elements with black texture are used to absorb the outgoing scattered wavefields.\n\nTo verify the validity of HSFC, we conduct several 2D and 3D numerical experiments, using the 1D PREM29 and 3D SEMUCB_WM137 models as global reference models, successively, and we consider two cases, with a box containing the CMB or the ocean floor, respectively. In the following 2D simulations, both the global and local simulations are performed using a SEM solver, SPECMAT (Spectral Element Method in Matlab, Lyu et al.14), with identical spatial mesh and time steps in the global and local numerical simulations. This ensures that there is no error introduced during the spatial and temporal interpolations of the hybrid input mirror forces from the global simulation to the local simulation, resulting in hybrid waveforms with minimal error. However, in the 3D simulation, to better represent realistic scenarios, we use the SPECFEM3D_GLOBE solver for the global simulations and the SPECMAT solver for the local hybrid simulations. Additionally, the global meshing and time steps are different in the 3D cases. As a result, we observe larger waveform errors compared to the 2D simulation partially due to different intrinsic spatial and temporal dispersion errors (refer to the discussion section for how to reduce these errors for the case when global and local meshes do not match).\n\nTo verify the proposed algorithm in 2D, we construct three local models inside the local domain: the reference model, a model with an ultra-low velocity zone (ULVZ) above the CMB, and a model with an undulating CMB, as illustrated in Fig.\u00a03a, c, e. The reference \u201cglobal\u201d model is PREM29 and includes its lowermost mantle and outer Core structures (Fig.\u00a0S1a). The vertical (Z) component wavefield of the source-side global simulation is shown in Fig.\u00a0S2 for the reference model and the Z-component wavefields of the local simulations in the three local models are shown in Fig.\u00a03b, d, f. In all cases, a single-force point source with 1.667 s dominant period of Ricker wavelet is used. In the case where the local model is the same as the global model, no scattered phases are generated inside the box (Fig.\u00a03b). In the presence of a ULVZ structure located above the CMB, S-waves propagating across the box give rise to a scattered wave resembling a surface wave (Fig.\u00a03d). In contrast, in the case of an undulating CMB structure within the box, the scattered wave exhibits a relatively simpler pattern (Fig.\u00a03f), and the amplitude of the surface wave-like phase is much smaller. Note that the dispersed scattered surface waves are generated by the thin ULVZ anomaly above the CMB. In contrast, the Scholte wave, which has a lower velocity than the S wave, is produced due to the topography of the CMB. Additionally, an S-P scattered phase is generated when the S phase interacts with the topography.\n\nThe size of the local domain is (7.5\u2218, 400\u2009km) laterally and it extends 200km above and below the Core Mantle Boundary (CMB) and the local mesh consists of 120\u2009\u00d7\u00a0(40\u2009+\u200940) elements. Panels (a, b) show the local reference model and corresponding wavefield. c, d show the local target model featuring an Ultra-Low Velocity Zone (ULVZ) and the corresponding wavefield. The ULVZ extends 5\u2009km above the CMB and has a horizontal width of 1.25\u2218, with \u00a0\u221230% Vs,\u00a0\u221210% Vp, and +10% \u03c1 perturbations in elastic parameters, as shown by the mini white block (c) and the blue mesh in (d). e, f same for a local target model an undulating CMB and corresponding wavefield. The Gaussian-shaped undulating CMB is defined by its height (7.5\u2009km) and horizontal width (0.375\u2218). The Gauss-Lobatto-Legendre (GLL) points at mirror E2 within the local elastic domain and at mirror A2 within the local acoustic domain are used to obtain hybrid output mirror forces during the three different local hybrid simulations. 10 elements were used for the absorption at all four boundaries. (b, d, f) are wavefield snapshots at 80 s, also shown as dashed blue lines in Fig.\u00a0S3.\n\nFigure\u00a0S3 and Fig.\u00a04 show the accuracy of waveforms obtained in hybrid simulations for receivers Ri located inside the box (SORI case) and Re outside the box (SORO case), respectively. In the SORO case, when the local model is the same as the global reference model, and the local mesh matches the global mesh, the X- and Z-component waveforms obtained for the hybrid simulation at Re are near zero due to the absence of a model perturbation inside the box (Fig.\u00a04a, b). Figure\u00a04c, d, e, f displays the comparison of waveforms between the global and hybrid simulations in the global and local target models at the receiver Re. The relative errors for the X- and Z-components in the local ULVZ model are approximately 0.013% and 0.004%, respectively. Meanwhile, in the local undulating CMB model, the corresponding errors for the X- and Z-components are approximately 0.573% and 0.232%, respectively. The nearly negligible errors of the SORI (Fig.\u00a0S3) and SORO (Fig.\u00a04) hybrid simulations demonstrate the effectiveness of our HSFC method. Note that in the 2D SORO cases, the error is smaller than in the 2D SORI cases. The possible reason for this is that the impact of imperfect absorption is smaller in the SORO case than in the SORI case, especially when the global and local numerical simulations have identical spatial mesh and time steps. The corresponding simulations (8 global and 5 local) are shown as animations in Section\u00a07 of the Supplementary Movies\u00a01\u201313.\n\na, b X and Z component waveforms, respectively, are recorded at outside receiver Re when the local model is the reference model. These waveforms are expected to have fully zero-values. The solid red lines represent our simulated waveforms following the third-step convolution. The amplitude can be neglected in comparison to the following two SORO cases with local scatterers. c, d Same as (a, b) for the case where the local model includes an Ultra-Low Velocity Zone (ULVZ) above the CMB. (e, f) same as (c, d) for the case with an undulating CMB within the box. In panels (c, d, e, f), the solid black lines correspond to reference global simulations. The dashed red lines correspond to hybrid simulations. Residuals are shown by dashed blue lines, magnified by a factor of 100.\n\nHere the reference model is also PREM29, including its 3\u2009km ocean layer, crust, and mantle. All simulations incorporate the free surface and are performed with the SEM solver SPECMAT. The displacement is recorded at two receivers on the solid side of the ocean-crust boundary, one \\({R}_{c}^{i}\\) inside and one Re outside the box, straddling this boundary. Eleven additional regularly spaced receivers \\({R}_{f}^{i}\\) are positioned within the ocean, at a depth of 1\u2009km beneath the free surface, recording the pressure field. We introduce a localized Gaussian-shaped low-velocity structure in the mantle within the local domain. Here, we also maintain the same time step in the local and global simulations, ensuring that no errors are introduced while interpolating the hybrid input mirror forces from the global simulation to the local one.\n\nFigure\u00a0S4 shows the global reference model and the associated Z wavefield of the global simulation from the remote source Se side. Figure\u00a05a, c displays the local reference and target models and Fig.\u00a05b, d presents the Z-component wavefields for the local part of the hybrid simulation in both models. S-waves traverse this region at a velocity slower than that of the local reference model in the presence of a subsurface low-velocity body. Figure\u00a0S5 displays the Z-component wavefields of two global simulations from the remote receiver Re side. Figure\u00a06 shows waveform comparisons between global and hybrid simulations for receivers \\({R}_{f}^{i}+{R}_{c}^{i}\\) (SORI case) and Re (SORO case). In the SORI case, when the local model matches the global reference model, and their meshes are the same, the hybrid simulation produces nearly identical waveforms to those of the global simulation (Fig.\u00a06e, f and a, b). For the 11 receivers, \\({R}_{f}^{i}\\) in the ocean, the relative errors of pressure are about 0.047% and 0.302% in the local reference and target models, respectively (Fig.\u00a06e, f). For the receiver, \\({R}_{c}^{i}\\), the relative errors for X- and Z-components are approximately 0.722% and 0.565%, in the local reference and target models, respectively, with minor deviations attributable to imperfections in the absorbing boundaries (Fig.\u00a06c, d). In the SORO case, the relative errors are about 0.1% and 0.04%, for X and Z-components respectively (Fig.\u00a06e, f). Here again, the errors are negligible in both SORI and SORO settings, providing a theoretical foundation for future applications with hybrid simulations that involve the ocean-crust interface.\n\na, b represent the local reference and perturbed models, respectively. In (c), a low-velocity structure (shown by the small white elliptical shape) has been introduced 50\u2009km beneath the free surface, with approximate dimensions of 0.2\u2218\u2009\u00d7\u200911.12\u2009km, and \u00a0\u2212\u00a030%,\u00a0\u2212\u00a020% and \u00a0\u2212\u00a010% reductions in Vs, Vp, and density \u03c1, respectively. b, d Local wavefields obtained in the hybrid simulations, starting from input mirror forces recorded at mirrors E1 and A1 and computed in the global simulation from the remote source (Fig.\u00a0S4), which is the same regardless of the local model. The hybrid output mirror forces are calculated at mirrors E2 and A2. The 11 receivers \\({R}_{f}^{i}\\) are located in the ocean and one receiver \\({R}_{c}^{i}\\) is located on the ocean-crust boundary. e, f Corresponding pressure waveform comparison in the Source Outside and Receiver Inside (SORI) case for the two models (a) and (c). Same color convention as in Fig.\u00a04c. The uppermost red layer signifies the ocean.\n\nSource Outside Receiver Inside (SORI, a\u2013d) and the Source Outside and Receiver Outside (SORO, e, f) cases for the two models shown in Fig.\u00a05a, c, recorded at the two receivers \\({R}_{c}^{i}\\) and Re. a, b The local model is the same as the 1D reference model. c\u2013f The local model contains a low-velocity structure. Same color convention as in Fig.\u00a04c.\n\nIn this 3D example, the background global model is the 3D model SEMUCB_WM137 in the mantle, and the 1D global reference earth model PREM29 in the core. We consider a localized box straddling across the CMB and introduce a ULVZ at the CMB on the mantle side. The local target ULVZ model is shown in Fig.\u00a07a and details on the source and station geometry are given in Supplementary. Section\u00a04. A double-couple source is used for the 3D case. Note that we have smoothed the boundaries of the ULVZ to make it easier for accurate calculation in the global SPECFEM3D_Globe solver than with sharp boundaries. One advantage of the hybrid method is that the flexible meshing of the local domain allows us to better honor the geometry of a ULVZ with sharp boundaries, which would be difficult for global meshing. The minimum resolved period of the Heaviside source time function is 15\u2009s. The CFL condition44 results in different time steps for the global and local simulations, because of the presence of the thin low-velocity crust at the top of the global Earth model. Consequently, temporal interpolation is needed to transfer the hybrid input mirror forces from the global simulation to the local one, by taking the same Bspline compression/recovery algorithm as in Adourian et al.15. The different time steps in the global and local simulations will generate different temporal dispersion errors, which accounts for larger errors when comparing the global and hybrid waveforms than in the previously discussed 2D case.\n\na Local target 3D model includes the lower mantle portion of SEMUCB_WM1 with an Ultra-Low Velocity Zone (ULVZ) (white region at the center of the plot) and the outer core portion of PREM. Black points indicate the Core Mantle Boundary. b Corresponding local wavefield on the Z-component at 1260 seconds. Input mirror forces are recorded on mirrors E1 and A1. Output mirror forces and Green\u2019s functions are recorded at mirrors E2 and A2 for subsequent convolution. Receiver Ri inside the box is indicated by a black inverted triangle.\n\nFigure\u00a0S6b and Fig.\u00a07b display the Z-component wavefields generated within the box without and with a ULVZ, respectively. As in the 2D case, the hybrid simulation without any scatterers accurately reproduces the regional wavefield, without artificial energy leaving the box when the structure of the local model matches the background model. However, when the ULVZ is present in the local domain, outgoing scattered wavefields are generated in the hybrid simulation (highlighted by the arrow in Fig.\u00a07b). In the waveforms computed in the SORI case, the L1 difference between the global and hybrid simulations is \u00a0\u2248\u00a01.4% in the model without ULVZ (Fig.\u00a08a), which may be due to differences in element size and time step between the global and local solvers. Note that the entire local wavefield undergoes a transformation into the scattered wavefield after propagating through the mirror E1 and A1 due to the injection of hybrid input mirror forces, and the absorbing layer only works on the scattered wavefield. The incomplete absorption of scattered wavefields in the hybrid simulation with ULVZ results in an increase in waveform errors, approximately around 2% (Fig.\u00a08b). The relatively smaller error in the Z-component is due to its larger waveform amplitude compared to the E and N components (Fig.\u00a0S7b and Fig.\u00a0S8b). For the SORO case (Fig.\u00a08c, d), with recording at a distant station, the error is larger than in the SORI case due to the additional convolution operation of the hybrid output mirror forces with Green\u2019s functions (see also Supplementary. Section\u00a04). The HSFC produced a post-cursor following the S-phase due to the local ULVZ, and its waveform matches well with the results from the global simulation. Here, the error in the E component (Fig.\u00a08c, d) is smaller than in the N and Z components (Fig.\u00a0S7c, d and Fig.\u00a0S8c, d) because of its larger amplitude. Note that the increase in error at 1150\u2009s in Fig.\u00a0S7c, d could be due to PcP waves generating an outward-propagating scattered wavefield as they pass through the ULVZ. This scattered wavefield could then be back-propagated to the inside of the Box by the reflection of 660 km and 410\u2009km, as hybrid simulation cannot accurately model secondary scattering. The corresponding Z-component wavefields for the local reference/target models and their residuals are shown as animations in Section 7 of the Supplementary Movies\u00a014\u201316.\n\na, b E-component waveforms at receiver Ri correspond to reference (Fig. S6a) and target (Fig.\u00a07a) models, respectively. c, d E-component waveforms at receiver Re outside the box for the PREM and SEMUCB_WM1 models including the same local Ultra-Low Velocity Zone (ULVZ). Line colors are as in Fig.\u00a04c.\n\nNote that three factors account for the waveform error. i) the global mesh is different from the local mesh, thereby introducing spatial dispersion errors to the hybrid input and output mirror forces. ii) the different time steps used in the global and local simulations introduce different temporal dispersion errors. iii) The third source of errors comes from the imperfect absorbing boundary condition. In this study, we follow the work in Kosloff and Kosloff45 and Yao et al.46. About 10 absorbing elements assist in absorbing scattered wavefields, enabling us to achieve hybrid solid-fluid coupling. However, especially in 3D problems, they introduce additional computational overhead, and their absorption efficiency is not yet optimal. The Perfect Matched Layer (PML) absorbing condition is very efficient in absorbing the outgoing wavefields, but further development is needed and eventual integration into hybrid numerical simulations, due to its instability in anisotropic elastic models47. To minimize spatial interpolation errors, increasing the number of global elements by 1.5 times can be effective, as demonstrated by Lyu et al. (2024). Additionally, utilizing the forward and inverse time-dispersion transforms, as suggested by Lyu et al. (2021) can further reduce time dispersion errors.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We now discuss the following considerations including convergence, waveform integrity, computational efficiency, and the evolving landscape of global and local solvers of HSFC, in the context of benchmarks performed using the SEM.\n\nTo assess the convergence of HSFC, we introduce a smooth ULVZ that can be accurately represented by the various global and local meshes. New 2D global and local models incorporating CMB and a smooth ULVZ are shown in Figs.\u00a0S9 and S10a1. Discrepancies in spatial dispersion arise from variations between local and global meshing, impacting the computation of hybrid input mirror forces and Green\u2019s functions. Spatial mesh for both global and local models is initially defined based on a minimum period of 0.67 s. With this fixed spatial meshing, the minimum period of the Ricker wavelet source is gradually increased. Seven minimum periods of a Ricker source time function, ranging from 0.67 s (1.5\u2009Hz), 0.73\u2009s (1.375\u2009Hz) to 1.33 s (0.75\u2009Hz), are sampled. Snapshots of the wavefield in the local target model at the same moment with different main periods reveal distinct responses to the same anomaly (Fig. S10, a2 to a8). In the 2D SORI and SORO cases, the waveform errors of the hybrid simulation converge as a function of the period for receivers inside and outside the box (Fig.\u00a0S10b, c, d, e).\n\nIn the 3D case, for the same above local reference model, we fix both global and local meshes based on a minimum period of 8\u2009s. Subsequently, we perform five global and local simulations, respectively, using distinct minimum periods (8\u2009s, 10\u2009s, 12\u2009s, 14\u2009s, and 16\u2009s) of Heaviside source time function. In Fig.\u00a09a, b, c, we display the three-component hybrid waveforms at station Ri inside the box. The corresponding periods are increased from the bottom to the top, revealing the obvious variations in the hybrid waveforms. The associated errors as a function of period are shown in Fig.\u00a09d. The waveform errors converge at about 12\u2009s when we use the global mesh based on the 8s minimum period.\n\nBoth global and local meshes were determined based on a minimum period of 8\u2009s. Subsequently, seven different sources with minimum periods of 8\u2009s, 10\u2009s, 12\u2009s, 14\u2009s, and 16\u2009s were used to perform the corresponding reference and hybrid simulations. a\u2013c Comparison of three-component waveforms recorded on the N, E, and Z components respectively, at the same station Ri in Fig.\u00a07a, and the corresponding residuals, magnified by a factor of 10. The corresponding minimum periods increase from the bottom to the top of each plot. d Cumulative error of the hybrid simulation, on the 3 components as a function of period.\n\nBoth 2D and 3D results demonstrate the necessity of increasing the number of elements of the global mesh by approximately 1.5 times the standard for numerical simulation, to achieve very precise hybrid input/output mirror forces, ensuring the accuracy of a hybrid solid-fluid coupling simulation based on SEM, particularly in scenarios where local and global meshing configurations diverge48. To address the different temporal interpolation errors in global and hybrid simulations, the forward and inverse time-dispersion transforms can be further utilized49. This analysis validates the convergence of our HSFC method, ensuring the waveform accuracy for future Box Tomography applications everywhere on/inside the Earth using real data using SEM. The corresponding 7 local simulations (main periods from 3.333\u2009s (0.3\u2009Hz) to 1.667 s (0.6\u2009Hz) including the Z-component wavefields of the same target models, are shown as animations in Section 7 of the Supplementary Movies\u00a017-23.\n\nFor hybrid numerical simulations, accurately calculating targeted seismic phases is a crucial issue. During the standard global simulation with the long wavelength structures, waves scattered by anomalous bodies create first-order scattering, reaching surface stations. These waves reflect into the global model, causing second-order scattering. However, due to geometric spreading and intrinsic attenuation, second-order scattered waves may not be strong enough to propagate back to the surface stations. In hybrid numerical simulations, first-order scattered waves, generated due to local anomalous bodies, will be absorbed by the absorbing boundary condition, preventing the reflection phases of the scattered waves from returning to the interior of the simulation box, leading to a complete absence of second-order scattering energy for the station outside the box, if the absorbing layers work well. Note that the second-order scattering energy produced by global simulation is weaker than the first-order scattering. Consequently, in the context of actual deep subsurface structures within a long-wavelength 3D background Earth model, hybrid numerical simulations should be highly applicable. Note that except for the free surface, all the structure discontinuities or large anomalies outside of the box will also generate second-order scattering waves in the standard global simulations. It is better to define a box that contains all the nearby discontinuities and locate it far from known strong reflectors so that the target first-order scattered phase is not affected.\n\nIn Section\u00a06 of the Supplementary Info (SI), we\u2019ve modified the background models with all four boundaries as free boundaries (Fig.\u00a0S9) and performed a relatively long duration (600\u2009s). Figure\u00a0S11 shows the corresponding hybrid X- and Z-component waveforms for the SORO case. The second-order scattering phase arrives very late at about 570\u2009s, compared to the first-order scattering at about 130\u2009s, and the amplitude of the second-order scattered waves significantly diminishes due to geometric spreading, becoming much smaller than the amplitude of our target phase (SHdiff\u2019s post-cursor). In the real Earth model, we need to further consider intrinsic attenuation, which is not considered here. Therefore, in practical application scenarios, taking into account the large scale of the global long-wavelength Earth model and the effects of geometric and intrinsic attenuation, the amplitude of second-order scattered waves is expected to be significantly reduced in comparison to the primary first-order scattered phases and will exhibit a considerably delayed arrival time compared to the initial first-order scattered phase.\n\nIn the 2D HSFC case, both global and local numerical simulations were conducted on a 2020 MacBook Pro with a 2.4\u2009GHz core and 64\u2009GB memory using MATLAB Version 2023a. A global simulation using SPECMAT takes approximately 33.0\u2009min, while the corresponding hybrid simulation takes about 2.0\u2009min. The nearly 16-fold increase in computational efficiency between global and local simulations, coupled with a consistent ratio of global to local number of elements, suggests a correlation. The additional one minute observed in the global simulation compared to the theoretical factor of 16 is mainly due to the computational demand of calculating a substantial number of hybrid input mirror forces. By adopting different global and local time steps in the 3D case, we achieved even higher computational efficiency compared to the 2D case at the expense of slightly larger waveform errors. The reduction in computational time resulting from the size reduction in the hybrid simulation illustrates its efficiency and underscores the high promise of box tomography.\n\nIn the case of 3D HSFC simulations, one global simulation using SPECFEM3D_GLOBE requires approximately 11,857.9 CPU hours on the ANVIL HPC platform50, compared to 4 CPU hours for the corresponding local simulation within the target region in SPECMAT. The number of elements is 614 times larger in the global than in the local simulation, while the time step is smaller by a factor of 3.56 in the global simulation, due to a thin upper crust (refer to SI. Section\u00a04 for detailed values). The theoretical reduction value, neglecting different computation costs for solid and fluid elements in SEM, is approximately 2185.8 (614\u2009\u00d7\u20093.56). The nearly 3000-fold (11857.9/4) reduction in actual computational time underscores the efficiency and promise of hybrid numerical simulations in Box Tomography. This efficiency increases proportionally with decreasing size of the box. Note that the actual reduction in computational time is larger than the theoretical ratio, due to the nearly 50% fluid domain in volume in the 3D local domain, and it is inversely proportional to the size ratio between the global domain and the local domain. Assuming a consistent reduction ratio with decreasing periods, the significant computational cost reduction of the hybrid simulation will open opportunities for advancing higher resolution in seismic tomography, such as the plug and play (PnP) and image denoisers51.\n\nPerforming low-period hybrid numerical simulations (minimum period of a few seconds and less), relying on the SPECFEM3D_GLOBE2 for global simulations in a 3D background model, remains computationally expensive. It may be useful to explore or develop more efficient global numerical solvers. For example, AxiSEM3D39 and the SALVUS52 with the anisotropic adaptive mesh refinement offer orders of magnitude faster performance than the SPECFEM3D_GLOBE2, making them well-suited for calculating hybrid input mirror forces and Green\u2019s functions in existing 3D global reference models, at the expense of assuming that the global model is smooth in the direction orthogonal to the vertical plane containing the source and the receiver, as does anisotropic mesh refinement53. Recently, Masson54 proposed a new numerical wave propagation solver, the distributional finite difference method (DFDM), with promising efficiency and flexibility against SEM. In recent work, Masson et al.55 and Lyu et al.56 implemented the DFDM approach in spherical geometry, in elastic anisotropic 2D global and 3D regional earth models, respectively, demonstrating its potential for global seismology. Consistent displacement potential definitions can also be formulated to implement hybrid solid-fluid coupling in the target region using the SBP-SAT Finite Difference Method57. In addition, the flexible Discontinuous Galerkin Method58,59,60,61, which can naturally handle solid-fluid coupling, are also very promising in the corresponding hybrid seismic-wave numerical simulations. These advancements indicate promising avenues to enhance the efficiency and capabilities of hybrid numerical simulations as presented here. The local solver SPECMAT used here combines several features, including curvilinear mesh, anisotropy, solid-fluid coupling, and absorbing boundary conditions. However, to make the code applicable to real data, implementation of attenuation is needed. Although the code is written in Matlab, the computational efficiency is remarkably high. For a 3D regional model with elements of 56\u2009\u00d7\u200956\u2009\u00d7\u200931 and a total of 8000-time steps, the simulation can be completed in only 4 CPU hours. This high efficiency enables us to run many local simulations simultaneously, allowing for various parameter/structure explorations in the target domain.\n\nThis study presents the previously lacking theoretical steps necessary to enable hybrid numerical simulations of the seismic wavefield targeting remote regions that include solid-fluid boundaries, making it possible to image fine-scale structures anywhere within the Earth, given the availability of a sufficiently accurate background global model. Examples of potential applications of Box Tomography in this context are for seismic imaging of complex structures at the base of the mantle such as ultra-low velocity zones38,62,63,64, local solid-fluid interface fluctuations at the CMB, or the seafloor. Further parallelization and implementation of the HSFC into regional SEM or DFDM solvers will be necessary for applications at even lower periods (i.e. a few Hz) such as necessary for the study of small-scale structures near the ICB65,66,67,68,69.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56530-5/MediaObjects/41467_2025_56530_Fig9_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "In this section, we break down the essential theoretical and methodological steps for implementing the proposed hybrid solid-fluid coupling simulation method.\n\nThe propagation of seismic waves in the solid part of Earth (the Crust, Mantle, and Inner Core) is governed by the equations of motion:\n\nwhere us(x,\u00a0t) is the displacement field vector, \u03c1s(x) is the density, \u03c3(x) is the stress tensor, \u03b5(x) is the strain tensor, and f(x,\u00a0t) are the body forces at point x in the elastic domain \u03a9s; us is subject to boundary conditions (i.e., traction vanishes at the Earth\u2019s surface). The double-dot subscript indicates the second derivative in time.\n\nIn the fluid part of the Earth (e.g. the ocean and the outer core), the propagation of acoustic waves is governed with an irrotational, inviscid, and no gravity effects assumption by:\n\nand\n\nwhere uf(x,\u00a0t) is the displacement field vector, ff is the force vector, P is the pressure, \u03c1f is the fluid density, and \u03ba is the bulk modulus of the fluid70. In general, the lossless acoustic medium is fully described by only two parameters: density \u03c1f(x) and speed VP(x) such that \\(\\kappa ({{\\bf{x}}})=\\rho ({{\\bf{x}}}){V}_{P}^{2}({{\\bf{x}}})\\). uf is subject to boundary conditions (i.e., pressure vanishes at the Earth\u2019s Ocean surface).\n\nWe assume there are no sources in the fluid domain so that ff\u00a0=\u00a00 and the displacement can be expressed in terms of a scalar potential, as is done in the popular spectral element codes SPECFEM2,71. However, the SPECFEM2D_GLOBE and SPECFEM3D_GLOBE solvers71 use different displacement potential definitions, most likely because the displacement potential expression in the package SPECFEM3D_GLOBE is easier to handle when gravity is included. To focus on physical effects that are critical for the longer period band (\u2265100\u2009s), Chaljub and Valette72 decomposed the displacement in the fluid domain into two scalar displacement potentials. In this study, we focus on relatively lower periods (\u226420\u2009s) and have neglected self-gravitation, resulting in a more simplified wave equation. Both approaches result in a fully explicit fluid-solid coupling strategy. Hereafter, we focus on the displacement potential used in SPECFEM2D_GLOBE and develop the corresponding hybrid simulation workflow. Following that, we make use of the relationship between the two different displacement potentials used in SPECFEM2D_GLOBE and SPECFEM3D_GLOBE, so that the proposed workflow of hybrid simulations with solid-fluid coupling can also be used with SPECFEM3D_GLOBE.\n\nThe displacement potential \u03c6 is defined in SPECFEM2D as follows:\n\nUsing this definition in equation (2), it then follows that:\n\nwhere \\(\\ddot{\\varphi }\\) represents the second derivative of \u03c6 with respect to time. Substituting this expression into equation (3), we obtain the expression of the acoustic wave equation in terms of the first type of displacement potential:\n\nNote that this expression makes it possible to include first-order discontinuities in the acoustic medium. By employing the displacement potential and pressure definitions of equation (4), such first-order discontinuities can be seamlessly introduced while preserving the continuity of potential and pressure.\n\nIn contrast, the SPECFEM3D_GLOBE program utilizes a different definition for the displacement potential:\n\nwhich leads to the following expression for the pressure in terms of displacement potential:\n\nBy substituting equation (8) into equation (3) and using \\(\\kappa={\\rho }_{f}{V}_{p}^{2}\\), an alternative displacement potential representation of the acoustic wave equation is obtained:\n\nNote that this displacement potential definition ensures continuity in potential but the displacement is discontinuous for velocity structures with first-order discontinuities. However, the assumption is made in the SPECFEM3D_GLOBE code that the outer core is a fluid domain without any internal discontinuities. Therefore, the spectral-element discretization method remains effective in this case. The two displacement potentials in equations (6) and (9) are related by:\n\nIn Table\u00a01, we present the units of physical quantities attributed to the two different displacement potentials, clarifying relationships between various physical quantities. For the verification of the stability of the solid-fluid coupling73,74,75, refer to Section\u00a01 of SI.\n\nIn this subsection, we introduce the nomenclature related to the hybrid solid-fluid coupling simulation from a distant source to receivers situated within or outside of a specified box (SORI/SORO configurations), as also illustrated in Figs.\u00a01 and 2.\n\nGlobal domain \\({\\Omega }^{g}={\\Omega }_{s}^{g}+{\\Omega }_{f}^{g}\\): the overall domain comprising a solid part, indicated by the subscript s and a fluid part, indicated by the subscript f, separated by the solid-fluid coupling interface \u0393c.\n\nLocal domain \\({\\Omega }^{i}={\\Omega }_{s}^{i}+{\\Omega }_{f}^{i}\\): a subdomain (hereafter, a confined box) within the global domain \u03a9g, including the local solid domain \\({\\Omega }_{s}^{i}\\) and fluid domain \\({\\Omega }_{f}^{i}\\).\n\nExternal domain \\({\\Omega }^{e}={\\Omega }_{s}^{e}+{\\Omega }_{f}^{e}\\): the portion of the global domain outside the local domain, including the external local solid domain \\({\\Omega }_{s}^{e}\\) and fluid domain \\({\\Omega }_{f}^{e}\\).\n\nAbsorbing domain \\({\\Omega }^{a}={\\Omega }_{s}^{a}+{\\Omega }_{f}^{a}\\): the outermost layer in the local domain (shown with specific texture in Fig.\u00a02), necessary to prevent the outgoing scattered waves from returning to the local domain and compromising the accuracy of the hybrid numerical simulation.\n\nE/A mirrors domain: E mirror surrounds the local elastic region, while A surrounds the local acoustic region. E1/A1 consists of a layer of spectral elements. E2/A2 consists of another layer of spectral elements. The mirror forces are loaded or saved at the discrete points (e.g., GLL points in SEM) in the mirror domain.\n\nInversion domain \\({\\Omega }^{v}={\\Omega }_{s}^{v}+{\\Omega }_{f}^{v}\\): a domain inside the localized box where the model can be updated during the box tomography.\n\nNote that \u03a9i\u00a0=\u00a0\u03a9a \u222a E1 \u222a A1 \u222a E2 \u222a A2 \u222a \u03a9v. In addition to the domain definitions, models are assigned to the corresponding domains as follows. The global reference model \\({M}^{g0}={M}_{s}^{g0}+{M}_{f}^{g0}\\) is assigned to \u03a9g, including the assumed known external model \\({M}^{e0}={M}_{s}^{e0}+{M}_{f}^{e0}\\) and local reference model \\({M}^{i0}={M}_{s}^{i0}+{M}_{f}^{i0}\\). The global target model \\({M}^{g1}={M}_{s}^{g1}+{M}_{f}^{g1}\\) is also assigned to \u03a9g and includes the assumed known external model \\({M}^{e0}={M}_{s}^{e0}+{M}_{f}^{e0}\\) and the evolving local target model \\({M}^{i1}={M}_{s}^{i1}+{M}_{f}^{i1}\\). In Box Tomography13, once the initial forward global simulations from the remote source and receiver sides have been performed in the global reference model Mg0, forward and backward simulations are exclusively performed in the successively updated local target model Mi1 in the inversion domain (Fig.\u00a02), while the external model Me0 is left unperturbed.\n\nThe workflow of the hybrid solid-fluid coupling simulation of the scattering problem from a distant source to a receiver involves three main steps: from the source to the boundary of the box, within the box, and from the box boundary to the receiver, as illustrated by the numbered circles in Fig.\u00a02a. We first compute the wavefield in the global reference model Mg0 from the source to mirrors E1/A1; Once this is done we iterate the computation of the wavefield from mirrors E1/A1 to mirrors E2/A2 using the local solver in the evolving model Mi1. Then we calculate the Green\u2019s functions from the stations to the mirrors E2/A2. Note that the latter takes advantage of the reciprocity theorem76, which allows us to perform only 3 computations per station. Finally, we convolve the wavefield at E2/A2 with the stored Green\u2019s functions to reconstruct the total wavefield from source to station.\n\nFigure\u00a02 b shows the case with a local scatter in the box. More specifically, to efficiently simulate the wavefield propagation of the hybrid simulation in a localized domain with a solid-fluid coupling interface, we have extended the previous workflow15,23 as the following 5 steps.\n\nBefore performing global simulation, we need to use a local solver to calculate and record the Cartesian coordinates for GLL points on the four mirrors domain E1, A1, E2, and A2 around the local inversion region inside the closed box containing solid and fluid domains, as shown in Fig.\u00a02. Note that the local solver should support the solid-fluid coupling interface.\n\nThen we use a global solver to calculate the seismic wavefield generated by the external source Se (star in Fig.\u00a02) in a global reference model and store the displacement U at each point on mirror E1 and the displacement potential \u03c6 on A1, to calculate the hybrid input mirror forces (secondary effective sources), as well as the reference waveforms at the receivers Ri and Re (reverse triangles located inside and outside of the box on the solid side in Fig.\u00a02).\n\nSimulate the wavefield inside the box using a regional solver by imposing the secondary effective sources computed in step 2 at mirrors E1 and A1 around the solid and fluid local domains, respectively. Record the displacement U and acceleration potential \u2202tt\u03c6 wavefields on mirrors E2 and A2, calculate the hybrid output mirror forces, and record the complete waveform at the receiver Ri inside the box.\n\nUse a global solver to calculate and record the output displacement U (Green\u2019s functions) at each point on E2 and the displacement potential \u03c6 at each point on A2 from the distant receiver Re in a global reference model using a delta source time function. Two separate Green\u2019s functions are computed in 2D (single force in x and z directions, respectively) and three in 3D (single force in x, y, and z directions, respectively).\n\nTo obtain the residual waveform due to the local scatterer, we finally convolve the hybrid output mirror forces computed in step 3 with the corresponding Green\u2019s functions computed in step 4, sum the contributions over all the GLL grid points on mirrors E2 and A2. By adding the convolved time series to the reference seismogram computed in step 2, we could obtain the complete waveform at the external receiver Re (if the local model inside the box is perturbed).\n\nOn the one hand, if there are no anomalous bodies within the local solid/fluid inversion domain, i.e. when model Mi0 is used as the local model, the hybrid input mirror forces should fully recover the local solid and fluid wavefields, resulting in a zero-value wavefield outside of the local domain. The signal results obtained from convolution in the above step 5 will be a nearly zero-value time series, with some numerical error. On the other hand, if an updated local model Mi1 is used as the local model, the presence of local anomalies leads to scattered solid and fluid wavefields propagating outside of the inversion domain. In step 3, these scattered wavefields will contribute to generating the hybrid output mirror forces, acting as third sources, to be convolved with Green\u2019s functions in step 5. Then we sum the convolved waveform with the reference waveform acquired in step 2 to generate the comprehensive waveform transmitted from a distant source to a remote station. The absorbing layer \u03a9a is significantly important to maintain the accuracy and stability of the hybrid simulation. The implementation of the absorbing boundary layer will be explained in the\u00a0SI.\n\nNote that the positions of E1/A1 and E2/A2 can be either identical or distinct, with the condition that they remain between the local inversion regions and the absorbing layer. In this study, we choose to place E2/A2 inside E1/A1, slightly reducing the impact of incomplete absorption boundaries. The two layers (E1/A1 and E2/A2) are located outside of the inversion domain, and the absorbing layers are outside of the two layers of E/A mirrors. In the case when the spectral element method is used, as shown in Figure\u00a0S2 and Fig.\u00a03 both E1/A1 and E2/A2 are composed of a single layer of spectral elements with their internal Gauss-Lobatto-Legendre (GLL) points.\n\nNote that a Cartesian reference frame is used in SPECFEM3D_GLOBE, where the x-axis points East, the y-axis points North, and the z-axis points Up. After the convolution in step 5, the three component waveforms will be expressed in the same coordinate system (x, y, z) at each receiver.\n\nFor a model containing a solid-fluid interface, the normal components of both the displacement and stress should remain continuous across the solid-fluid interface70, leading to the following coupled \u03c6\u00a0\u2212\u00a0us system of equations:\n\nwhere n represents the vector normal to the solid-fluid coupling interface, pointing from the fluid domain toward the solid domain. Following the finite element method, we multiply both sides of the equations by test functions, integrate over the entire domain, and use integration by parts. We thus obtain the weak form of the elastic and acoustic wave equations: \n\nHere, w and w are the test functions in the fluid and solid domains, respectively. Because of the definition of n, a positive sign appears in front of the surface integral over the solid area, and a negative sign in front of the surface integral over the fluid region. By enforcing the continuity of the normal component of displacement and stress, naturally embedded within the third term on the left-hand side through surface integrals, we arrive at the weak form of the \u03c6\u00a0\u2212\u00a0us coupled solid-fluid wave equation system as follows:\n\nWe note that during the numerical simulation of solid-fluid coupling in SEM, we need to exchange the displacement us at the solid-fluid interface in the solid domain with the acceleration potential \\(\\ddot{\\varphi }\\) in the fluid domain at the same spatial grid points on the solid-fluid interface.\n\nAlternatively, using the second definition of the displacement potential \u03d5, the coupled solid-fluid wave equation system can be described using the second definition of the displacement potential \u03d5 by substituting \u03c6 by \u03d5 in equations (13):\n\nwhich, after multiplying by a test function and integrating, leads to:\n\nNote that there is an important difference compared to the previous equation (13). Here, we explicitly include the density of the fluid domain at the solid-fluid interface.\n\nGiven that, in SPECFEM3D_GLOBE, the calculations in the outer core rely solely on the second type of displacement potential \u03d5, we can obtain the displacement potential \u03c6 by multiplying the output displacement potential \u03d5 by the corresponding density at each mirror point of A1/A2 (e.g. equation (10)). This leads to a unified algorithm, efficiently managing solid-fluid coupling and facilitating hybrid numerical simulations for 2D and 3D cases.\n\nIn this section, we will give the explicit mathematical expressions of the hybrid input and output mirror forces obtained in steps 2 and 3 of the workflow. In what follows, we will use the first definition of displacement potential \u03c6. To transform the weak form presented in equation (12) into a matrix representation of an ordinary differential equation, we rely on the conventional spectral element discretization and assembly of the system and obtain the same equation as Equation (32) in Komatitsch and Tromp1 and Equation (23) in Cao et al.70. For the global reference model Mg0, the ordinary differential equation governing the time evolution of the global system can be expressed in a discrete \u03d5\u00a0\u2212\u00a0us formalism:\n\nwhere U represents the displacement vector of the solid domain in the global system and encompasses the displacement vector at all grid points within the global solid mesh. Additionally, \u03a6 denotes the displacement potential vector of the fluid domain in the global system. Associated global mass (Ms and Mf), and stiffness (Ks and Kf) matrices in the solid and fluid domains are defined following the definition in Komatitsch and Tromp1 and Cao et al.70. The matrices Cs and Cf, are the absorbing matrices of sponge-layer ABC, and the matrices A and AT represent the solid-fluid coupling operations. The operator [U]sf is utilized to ensure the continuity of displacement and the operator \\({[\\ddot{{{\\mathbf{\\Phi }}}}]}_{fs}\\) is employed to ensure the continuity of the normal stress along the solid-fluid coupling interface following the equation (13). For details, please refer to the same Equation (23) in Cao et al.70. From a physics perspective of implementing the solid-fluid coupling, this means that the normal displacement components are transmitted from the solid domain to the fluid domain, and in turn, normal stress components (pressure) are transmitted from the fluid domain to the solid domain.\n\nFollowing the approach introduced by Masson et al.22, and based on four discrete spatial window functions (\\({\\,{\\mbox{w}}\\,}_{s}^{hi}\\), \\({\\,{\\mbox{w}}\\,}_{f}^{hi}\\), \\({\\,{\\mbox{w}}\\,}_{s}^{he}\\), and \\({\\,{\\mbox{w}}\\,}_{f}^{he}\\)), as defined in the\u00a0SI and the discrete elastic and acoustic wave equations, we have constructed the mathematical expression of the solid and fluid hybrid input and output mirror forces:\n\nand\n\nThe detailed derivation of equations (17) and (18), is given in the SI. Note that in the formulation of the hybrid output mirror forces, we utilize the acceleration potential to compute the hybrid output mirror forces \\({{{\\bf{F}}}}_{f}^{he}\\) within the fluid domain. This differs from the mathematical expression of hybrid input mirror forces in equation (17), where we employ the potential. Due to the use of the scalar acoustic equations (6) and (9), rather than the vectorized acoustic wave equation (2), and due to the fact that the saved Green\u2019s function is not displacement but displacement potential, the hybrid input and output mirror \u201cforces\u201d in the acoustic domain are not real physical forces, but the expressions have the same mathematical form as in the elastic wave equation. In the SI, we conducted a detailed dimension analysis based on different contributions from the solid and fluid sides.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All the parameters related to the numerical simulation have been listed in the Figures and SI. All the movies are provided in the\u00a0Supplementary Data.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The open-source SPECFEM3D_GLOBAL package used in this study is available at https://github.com/geodynamics/specfem3d_globe. HSFC codes are available at https://figshare.com/articles/code/Efficient_hybrid_numerical_modeling_of_the_seismic_wavefield_in_the_presence_of_solid-fluid_boundaries/26956204?file=49054867", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Komatitsch, D. & Tromp, J. Introduction to the spectral element method for three-dimensional seismic wave propagation. Geophys. J. 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(University Science Books, 2002).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "Barbara Romanowicz and Chao Lyu acknowledge support from the National Science Foundation under Grant EAR-1758198 and from UC Berkeley core funds. Liang Zhao acknowledges support from the National Natural Science Foundation of China Grants 42488201. Chao Lyu also acknowledges partial support from the National Natural Science Foundation of China Youth Fund Grant 42004045. Computations were performed on the ANVIL system of Purdue University, funded by the National Science Foundation (NSF), through award 200563250. The authors would very much like to acknowledge Professor Daniel Peter for the discussion of the different implementations of the solid-fluid coupling in the SPECFEM2D and SPECFEM3D_GLOBE as detailed in https://github.com/SPECFEM/specfem3d_globe/issues/821.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Earth and Planetary Science, University of California, Berkeley, CA, USA\n\nChao Lyu\u00a0&\u00a0Barbara Romanowicz\n\nInstitut de Physique du Globe, Paris, France\n\nBarbara Romanowicz\n\nKey Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China\n\nLiang Zhao\n\nUniversity of Pau and Pays de l\u2019Adour, Pau, France\n\nYder Masson\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nC.L. and B.R. designed the project. All authors contributed to the discussion at various stages of the project. C.L. developed and validated the methodology and wrote the first draft of the paper, and subsequently worked with B.R. on its final version. L.Z. and Y.M. commented and contributed text to the manuscript.\n\nCorrespondence to\n Chao Lyu.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Lyu, C., Romanowicz, B., Zhao, L. et al. Efficient hybrid numerical modeling of the seismic wavefield in the presence of solid-fluid boundaries.\n Nat Commun 16, 1722 (2025). https://doi.org/10.1038/s41467-025-56530-5\n\nDownload citation\n\nReceived: 15 February 2024\n\nAccepted: 22 January 2025\n\nPublished: 18 February 2025\n\nVersion of record: 18 February 2025\n\nDOI: https://doi.org/10.1038/s41467-025-56530-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"journal": "Nature Communications", + "published": "14 August 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51262-4/MediaObjects/41467_2024_51262_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51262-4/MediaObjects/41467_2024_51262_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-51262-4#ref-CR29" + ], + "code": [ + "/articles/s41467-024-51262-4#ref-CR29" + ], + "subject": [ + "Quantum information", + "Quantum physics" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3972302/v1.pdf?c=1723720075000", + "research_square_link": "https://www.researchsquare.com//article/rs-3972302/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-51262-4.pdf", + "preprint_posted": "06 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Reservoir engineering is a powerful technique to autonomously stabilize a quantum state. Traditional schemes involving multi-body states typically function for discrete entangled states. In this work, we enhance the stabilization capability to a continuous manifold of states with programmable stabilized state selection using multiple continuous tuning parameters. We experimentally achieve 84.6% and 82.5% stabilization fidelity for the odd and even-parity Bell states as two special points in the manifold. We also perform fast dissipative switching between these opposite parity states within 1.8 \u03bcs and 0.9 \u03bcs by sequentially applying different stabilization drives. Our result is a precursor for new reservoir engineering-based error correction schemes.Physical sciences/Physics/Quantum physics/Quantum informationPhysical sciences/Physics/Quantum physics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Supplementarymaterial.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Reservoir engineering is a powerful technique to autonomously stabilize a quantum state. Traditional schemes involving multi-body states typically function for discrete entangled states. In this work, we enhance the stabilization capability to a continuous manifold of states with programmable stabilized state selection using multiple continuous tuning parameters. We experimentally achieve 84.6% and 82.5% stabilization fidelity for the odd and even-parity Bell states as two special points in the manifold. We also perform fast dissipative switching between these opposite parity states within 1.8\u2009\u03bcs and 0.9\u2009\u03bcs by sequentially applying different stabilization drives. Our result is a precursor for new reservoir engineering-based error correction schemes.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Entanglement is one major resource any quantum protocol utilizes to achieve quantum advantage1,2. Generally, the entanglement is created by unitary operations, where dissipation is considered detrimental and should be maximally avoided. Inspired by laser cooling, an alternative approach is to use tailored dissipation for stabilizing entanglement. By coupling the qubit system to some cold reservoirs, one can engineer the Hamiltonian such that the population will flow directionally to the stabilized point in the Hilbert space, and extra entropy is autonomously dumped into the cold reservoir during the process. This provides an extra route to state preparation. In a multiqubit-reservoir coupled system, dissipation engineering can enhance the capabilities of quantum simulation, as predicting the final state of a driven dissipative quantum system is more complex than its unitary counterpart3 when all local qubit and reservoir interactions are simultaneously turned on. Dissipation stabilization also inspires autonomous quantum error correction codes (AQEC)4,5,6,7,8,9 that achieve hardware efficiency in the experiment.\n\nStabilization has been theoretically proposed and experimentally realized in different platforms, such as superconducting qubits10,11,12,13,14,15,16,17 and trapped ions18,19, focusing on stabilizing a single special state per device, such as even or odd parity Bell states. Unlike universal quantum state preparation through unitary gate decomposition, dissipative stabilization requires individual Hamiltonian engineering for each stabilized state through different drive combinations or hardware. This makes the tunable dissipative stabilization a challenging task. A generalized scheme that allows one to programmatically choose stabilized states from a large class of states per device will expand the toolbox for state preparation. For instance, the ability to choose an arbitrary stabilized state can be used for the implementation of density matrix exponentiation20,21 by enabling an efficient reset of the input density matrix.\n\nIn this work, we realize an autonomous stabilization protocol with superconducting circuits that allows selection from a broad class of states, including the maximally entangled states. We use microwave-only drives with tunable parameters such as drive detunings and strengths that allow fast programmable switching between Bell states of different parities. The system is based on a two-transmon inductive coupler design8,17,22,23 that allows fast parametric interactions between qubits without significantly compromising their coherence. The readout resonators are also used as cold reservoirs, eliminating the requirement for extra components. We perform stabilization spectroscopy and demonstrate a fidelity over 78% for all stabilized states. For odd and even parity Bell pairs, we measured 84.6% and 82.5% stabilization fidelity and a stabilization time of 1.8\u2009\u03bcs and 0.9\u2009\u03bcs respectively. The current stabilization protocol cannot realize AQEC, and a larger code distance between logical states is necessary8,9 for demonstrating quantum error correction. The structure of the paper is as follows. First, we explain the Hamiltonian construction of the stabilization protocol. Then we discuss the experimental measurement of individual stabilized state and demonstrate a dissipative switch of Bell state parity.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We consider a system of two coupled qubit-resonator pairs {Q1,\u00a0Q2} and {R1,\u00a0R2}. The lossy resonators serve as both cold baths and dispersive readouts for the qubits. We label the ground and the first excited states of the qubits Q1/2 as \\(\\left\\vert g\\right\\rangle\\) and \\(\\left\\vert e\\right\\rangle\\), and of the resonators R1/2 as \\(\\left\\vert 0\\right\\rangle\\) and \\(\\left\\vert 1\\right\\rangle\\), with the full system state being represented as \\(\\left\\vert {Q}_{1}{Q}_{2}{R}_{1}{R}_{2}\\right\\rangle\\). The system Hamiltonian Hsys\u00a0=\u00a0HQQ\u00a0+\u00a0HQR1\u00a0+\u00a0HQR2 includes the dominant two-qubit interaction HQQ and qubit-resonator interactions HQRj,\u00a0j\u00a0=\u00a0{1,\u00a02} acting as perturbations. We label the four eigenstates of HQQ as \\(\\{\\left\\vert A\\right\\rangle,\\left\\vert B\\right\\rangle,\\left\\vert C\\right\\rangle,\\left\\vert D\\right\\rangle \\}\\) with eigenenergies {EA\u00a0<\u00a0EB\u00a0\u2264\u00a0EC\u00a0\u2264\u00a0ED} so that \\(\\left\\vert A\\right\\rangle\\) is the target state to stabilize. Our stabilization scheme involves engineering a one-way flow of population to \\(\\left\\vert A\\right\\rangle\\) connecting all intermediate eigenstates of the system.\n\nWe now derive the energy matching requirements for an efficient stabilization protocol in our two-qubit-two-resonator system depicted in Fig.\u00a01a. We control the form of the target stabilized state \\(\\left\\vert A\\right\\rangle\\) by choosing different two-qubit interaction strengths and detunings that control HQQ. We change the resonator photon energy in the rotating frame by detuning the QR interactions. The dynamics of Hsys are captured by considering the following set of eigenstates: \\(\\{\\left\\vert A\\right\\rangle,\\left\\vert B\\right\\rangle,\\left\\vert C\\right\\rangle,\\left\\vert D\\right\\rangle \\}\\otimes \\{\\left\\vert 00\\right\\rangle,\\left\\vert 10\\right\\rangle,\\left\\vert 01\\right\\rangle \\}\\). We neglect the resonator state \\(\\left\\vert 11\\right\\rangle\\) as the probability of simultaneous population in both resonators {R1,\u00a0R2} is extremely low when resonator decay rate \u03ba is much larger than the qubit decay rate \u03b3 (assumed identical). The central column in Fig.\u00a01(a) shows the eigenstates of HQQ with no photons in the resonators. The left column represents the same states with one photon in the left (R1) resonator and similarly for the right column is associated with the second resonator (R2). We engineer the photon energies in R1 and R2 to be EB\u00a0\u2212\u00a0EA and EC\u00a0\u2212\u00a0EA respectively through tuning the QR interactions HQRj. This condition puts two transitions \\(\\left\\vert A01\\right\\rangle \\leftrightarrow \\left\\vert C00\\right\\rangle\\) and \\(\\left\\vert A10\\right\\rangle \\leftrightarrow \\left\\vert B00\\right\\rangle\\) on resonance, shown in Fig.\u00a01a. If \\(\\left\\langle A01\\right\\vert {H}_{QR1}\\left\\vert C00\\right\\rangle\\) and \\(\\left\\langle A10\\right\\vert {H}_{QR2}\\left\\vert B00\\right\\rangle\\) are non-zero, two on-resonance oscillations between \\(\\left\\vert C00\\right\\rangle\\), \\(\\left\\vert A01\\right\\rangle\\) and between \\(\\left\\vert A10\\right\\rangle\\), \\(\\left\\vert B00\\right\\rangle\\) will be created. Since both resonators are lossy, the oscillation will quickly damp to \\(\\left\\vert A00\\right\\rangle\\). To complete the downward stabilization path, we need to also connect \\(\\left\\vert D00\\right\\rangle\\) into the flow. We further require that the following terms are non-zero so that the transfer path is not blocked: \\(\\left\\langle B01\\right\\vert {H}_{QR1}\\left\\vert D00\\right\\rangle\\), \\(\\left\\langle C10\\right\\vert {H}_{QR2}\\left\\vert D00\\right\\rangle\\). If all four interaction strengths (shown in green double-headed arrows in Fig.\u00a01a) are dominant over the qubit decay rate, populations in \\(\\left\\vert B\\right\\rangle\\), \\(\\left\\vert C\\right\\rangle\\), and \\(\\left\\vert D\\right\\rangle\\) will flow to \\(\\left\\vert A\\right\\rangle\\). From Fermi\u2019s golden rule, the interaction strength between two states is quadratically suppressed by their energy gap and maximized when on-resonance24. This imposes a simple energy-matching requirement for efficient stabilization: ED\u00a0+\u00a0EA\u00a0=\u00a0EB\u00a0+\u00a0EC. Energy degeneracy within \\(\\{\\left\\vert B\\right\\rangle,\\left\\vert C\\right\\rangle,\\left\\vert D\\right\\rangle \\}\\) will not affect the stabilization scheme, because it will not block the dissipative flow to \\(\\left\\vert A00\\right\\rangle\\) in Fig.\u00a01a.\n\na General stabilization scheme. Two qubits' eigenstates \\(\\{\\left\\vert A\\right\\rangle,\\left\\vert B\\right\\rangle,\\left\\vert C\\right\\rangle,\\left\\vert D\\right\\rangle \\}\\) are plotted in the energy level diagram. When the energy relation ED\u00a0+\u00a0EA\u00a0=\u00a0EB\u00a0+\u00a0EC is satisfied, \\(\\left\\vert A\\right\\rangle\\) is stabilized. Qubit-resonator interactions and resonator photon decay rate \u03ba are shown in blue and orange arrows. Qubit decay rate \u03b3 is assumed slowest and not plotted. b Stabilization of entangled states \\(\\left\\vert {\\Psi }_{\\theta }\\right\\rangle=\\sin \\left(\\theta /2\\right)\\left\\vert gg\\right\\rangle -\\cos \\left(\\theta /2\\right)\\left\\vert ee\\right\\rangle\\) or \\(\\left\\vert {\\Phi }_{\\theta }\\right\\rangle=\\sin \\left(\\theta /2\\right)\\left\\vert ge\\right\\rangle -\\cos \\left(\\theta /2\\right)\\left\\vert eg\\right\\rangle\\). c A special case of b that stabilizes the odd and even parity bell states \\(\\left\\vert {\\Phi }_{-}\\right\\rangle\\) and \\(\\left\\vert {\\Psi }_{-}\\right\\rangle\\). Circulating arrows are color-coded to represent red (exchange-like) and blue (two-photon-pumping) sidebands respectively. The QQ and QR sideband rates are separate \u03a9 and Wj, and the QR sideband is detuned in frequency by \u03a9/2.\n\nAs an explicit demonstration, we first stabilize a continuous set of entangled states \\(\\left\\vert {\\Psi }_{\\theta }\\right\\rangle=\\sin \\left(\\theta /2\\right)\\left\\vert gg\\right\\rangle -\\cos \\left(\\theta /2\\right)\\left\\vert ee\\right\\rangle\\), illustrated in Fig.\u00a01b. Here, \u03b8 can be regarded as a \u201cblending angle\" between the two even parity states \\(\\left\\vert gg\\right\\rangle\\) and \\(\\left\\vert ee\\right\\rangle\\). We introduce three sideband25 transitions into the system: qubit-qubit (QQ) blue sideband \\(\\left\\vert gg\\right\\rangle \\leftrightarrow \\left\\vert ee\\right\\rangle\\) with rate \u03a9 and two qubit-resonator (QR) blue sidebands \\(\\left\\vert g0\\right\\rangle \\leftrightarrow \\left\\vert e1\\right\\rangle\\) between Qj and Rj with rate Wj. In this context, \u2018sideband\u2019 refers to a two-photon process where either a single photon is exchanged at the frequency difference (known as the red sideband) or two photons are simultaneously driven at the frequency sum (referred to as the blue sideband). To ensure that HQRj act as perturbations over HQQ, we adjust the drive strengths to satisfy \u03a9 \u226b Wj. We further detune the QQ, QR1, and QR2 blue sideband by \u03b4, (\u0394\u00a0\u2212\u00a0\u03b4)/2, and (\u0394\u00a0+\u00a0\u03b4)/2 in frequencies, with \\(\\Delta=\\sqrt{{\\Omega }^{2}+{\\delta }^{2}}\\). The detuning \u03b4 determines the blending angle \\(\\theta={\\tan }^{-1}\\left(\\frac{\\delta+\\Delta }{\\Omega }\\right)\\) with a range of \\([0,\\frac{\\pi }{2})\\). In the presence of these three drives, the rotating frame Hamiltonian Hsys is\n\nHere aqj and arj are separately the j-th qubit\u2019s and resonator\u2019s annihilation operator. Anharmonicity \u03b1 is omitted from Equation (1) by treating both transmons as two-level systems. The presence of anharmonicity effectively suppresses the higher energy levels\u2019 population in either transmon. Under the combined conditions \u03a9 \u226b Wj\u00a0~\u00a0\u03ba \u226b \u03b3 and Wj\u00a0=\u00a0W, the eigenstates with zero resonator photons are \\(\\{\\left\\vert {\\Psi }_{\\theta }00\\right\\rangle,\\left\\vert ge00\\right\\rangle,\\left\\vert eg00\\right\\rangle,\\left\\vert {\\Psi }_{\\pi -\\theta }00\\right\\rangle \\}\\), with corresponding eigenenergies \\(\\{\\left(\\delta -\\Delta \\right)/2,0,\\delta,\\left(\\delta+\\Delta \\right)/2\\}\\). Assuming the lossy resonator has a Lorentzian energy spectrum, the two-step refilling rate \u0393t from \\(\\left\\vert eg00\\right\\rangle\\) to \\(\\left\\vert {\\Psi }_{\\theta }00\\right\\rangle\\) (\\(\\left\\vert eg00\\right\\rangle \\leftrightarrow \\left\\vert {\\Psi }_{\\theta }01\\right\\rangle\\), \\(\\left\\vert {\\Psi }_{\\theta }01\\right\\rangle \\to \\left\\vert {\\Psi }_{\\theta }00\\right\\rangle\\)) is24\n\nThe other two-step transitions \\(\\left\\vert ge00\\right\\rangle \\to \\left\\vert {\\Psi }_{\\theta }00\\right\\rangle\\), \\(\\left\\vert {\\Psi }_{\\theta -\\pi }00\\right\\rangle \\to \\left\\vert ge00\\right\\rangle\\), and \\(\\left\\vert {\\Psi }_{\\theta -\\pi }00\\right\\rangle \\to \\left\\vert eg00\\right\\rangle\\) also have the same rate. Therefore, the steady-state fidelity \\({{{{\\mathcal{F}}}}}_{\\infty }\\) for \\(\\left\\vert {\\Psi }_{\\theta }00\\right\\rangle\\) is (ignoring all off-resonant transitions, see Supplementary Note\u00a02 for detail)\n\nSimilarly, we can stabilize another set of entangled states with odd parity \\(\\left\\vert {\\Phi }_{\\theta }\\right\\rangle=\\sin \\left(\\theta /2\\right)\\left\\vert ge\\right\\rangle -\\cos \\left(\\theta /2\\right)\\left\\vert eg\\right\\rangle\\). We introduce three sideband interactions: QQ red \\(\\left\\vert eg\\right\\rangle \\leftrightarrow \\left\\vert ge\\right\\rangle\\), QR1 red \\(\\left\\vert e0\\right\\rangle \\leftrightarrow \\left\\vert g1\\right\\rangle\\), and QR2 blue \\(\\left\\vert g0\\right\\rangle \\leftrightarrow \\left\\vert e1\\right\\rangle\\) with rates {\u03a9,\u00a0W3,\u00a0W4} and frequency detunings {\u03b4,\u00a0(\u0394\u00a0+\u00a0\u03b4)/2,\u00a0(\u0394\u00a0\u2212\u00a0\u03b4)/2} respectively. Under this condition, four resonant interactions will appear: \\(\\left\\vert gg00\\right\\rangle \\leftrightarrow \\left\\vert {\\Phi }_{\\theta }01\\right\\rangle\\), \\(\\left\\vert ee00\\right\\rangle \\leftrightarrow \\left\\vert {\\Phi }_{\\theta }10\\right\\rangle\\), \\(\\left\\vert ee01\\right\\rangle \\leftrightarrow \\left\\vert {\\Phi }_{\\theta -\\pi }00\\right\\rangle\\), and \\(\\left\\vert gg10\\right\\rangle \\leftrightarrow \\left\\vert {\\Phi }_{\\theta -\\pi }00\\right\\rangle\\). The detuning similarly sets the blending angle \\(\\theta=\\arctan \\left(\\frac{\\delta+\\Delta }{\\Omega }\\right)\\).\n\nWith the above construction, we create a stabilization protocol that can freely tune the blending angles. As a special case, when QQ sideband detuning \u03b4\u00a0=\u00a00, the blending angle for both cases is \\(\\theta=\\frac{\\pi }{2}\\), which corresponds to the odd and even parity Bell states \\(\\left\\vert {\\Phi }_{-}\\right\\rangle=(\\left\\vert ge\\right\\rangle -\\left\\vert eg\\right\\rangle )/\\sqrt{2}\\) and \\(\\left\\vert {\\Psi }_{-}\\right\\rangle=(\\left\\vert gg\\right\\rangle -\\left\\vert ee\\right\\rangle )/\\sqrt{2}\\), shown in Fig.\u00a01c.\n\nIn fact, this stabilization protocol can be generalized to stabilize an even larger group of states, including both entangled and product states, as long as the energy matching requirement ED\u00a0+\u00a0EA\u00a0=\u00a0EB\u00a0+\u00a0EC is satisfied when engineering HQQ. The following is a list of tunable parameters to engineer HQQ: QQ sideband strength \u03a9, QQ sideband detunings \u03b4, single qubit Rabi drive strength, and single qubit Rabi drive detunings. Corresponding stabilized state \\(\\left\\vert A\\right\\rangle\\) is determined from HQQ. Details about the stabilizable manifold are discussed in Supplementary Note\u00a04.\n\nWe perform the stabilization experiment in a system with two transmons capacitively coupled to two lossy resonators (See Supplementary Note\u00a01). Two transmons are inductively coupled through a SQUID loop. All QQ sidebands and QR red sidebands are realized through modulating the SQUID flux at corresponding transition frequencies. QR blue sidebands are achieved by sending a charge drive to the transmon at half the transition frequencies. The experimentally measured qubit coherence are T1\u00a0=\u00a024.3\u2009\u03bcs\u2009(9.1\u2009\u03bcs),\u00a0\u2009TRam\u00a0=\u00a015.2\u2009\u03bcs\u2009(9.8\u2009\u03bcs),\u00a0\u2009Techo\u00a0=\u00a024.6\u2009\u03bcs\u2009(14.3\u2009\u03bcs) for Q1(Q2), and the measured resonator decay rate \u03ba/2\u03c0 are {0.33,\u00a00.43} MHz for R1 and R2 respectively.\n\nFigure\u00a02 shows the time evolution of state fidelity for the odd and even parity Bell state stabilization. To stabilize \\(\\left\\vert {\\Psi }_{-}\\right\\rangle\\), a \u03a9\u00a0=\u00a02\u03c0\u00a0\u00d7\u00a02.0 MHz QQ blue sideband, W1\u00a0=\u00a0W2\u00a0=\u00a02\u03c0\u2009\u00d7\u20090.47 MHz QR blue sidebands are simultaneously applied to the system. Both QR sidebands are detuned by \u03a9/2\u00a0=\u00a02\u03c0\u00a0\u00d7\u00a01.0 MHz in frequency to implement the stabilization scheme depicted in Fig.\u00a01(c). For each stabilization experiment, we reconstruct the system density matrix through two-qubit state tomography using 5000 repetitions of 9 different pre-rotations. The stabilization fidelity measured at 49\u2009\u03bcs (much longer than single qubit T1 and TRam) is 82.5%. To stabilize \\(\\left\\vert {\\Phi }_{-}\\right\\rangle\\), a \u03a9\u00a0=\u00a02\u03c0\u00a0\u00d7\u00a03.0 MHz QQ red sideband, W1\u00a0=\u00a0W2\u00a0=\u00a02\u03c0\u00a0\u00d7\u00a00.36 MHz QR1 red and QR2 blue sidebands are simultaneously applied to the system, with both QR sidebands detuned by \u03a9/2\u00a0=\u00a02\u03c0\u00a0\u00d7\u00a01.5 MHz. The stabilization fidelity measured at 49\u2009\u03bcs is 84.6%. The two-qubit state tomography data at 49\u2009\u03bcs after ZZ coupling correction26 are shown for both stabilization cases. Fidelities are calculated as \\(F={({\\mbox{tr}}\\sqrt{\\sqrt{\\rho }\\sigma \\sqrt{\\rho }})}^{2}\\), where \u03c3 is the target state and \u03c1 is the tomography reconstructed density matrix. Error bars (one standard deviation) for all expectation values calculated from the Maximum Likelihood Estimation(MLE) reconstructed density matrix use the Tomographer package27.\n\nExperimental demonstration of \\(\\left\\vert {\\Psi }_{-}\\right\\rangle\\) (a, b) and \\(\\left\\vert {\\Phi }_{-}\\right\\rangle\\) (c, d) stabilization with the initial state \\(\\left\\vert gg\\right\\rangle\\). Two-qubit state tomography is performed at each time point, and the reconstructed density matrix is used to calculate the target state fidelity. The density matrices reconstructed with 5000 single shot measurements at 49\u2009\u03bcs are plotted. Lab frame simulation results are shown in dash lines, which matched well in both short and long time scales. Parameters used in simulation: {\u03a9,\u00a0W1,\u00a0W2,\u00a0\u03931,\u00a0\u03932}/2\u03c0\u00a0=\u00a0{2.0,\u00a00.47,\u00a00.47,\u00a00.33,\u00a00.43} MHz for \\(\\left\\vert {\\Psi }_{-}\\right\\rangle\\) and {3.0,\u00a00.36,\u00a00.36,\u00a00.33,\u00a00.43} MHz for \\(\\left\\vert {\\Phi }_{-}\\right\\rangle\\). Qubit coherence time is chosen as \\(\\{{T}_{1}^{q1},{T}_{1}^{q2},{T}_{\\phi }^{q1},{T}_{\\phi }^{q2}\\}=\\{25,12,25,25\\}\\,\\mu\\) s. Error bars (one standard deviation) are smaller than the marker size27.\n\nNext, we introduce QQ sideband detunings \u03b4 and stabilize more general entangled states \\(\\left\\vert {\\Psi }_{\\theta }\\right\\rangle\\) and \\(\\left\\vert {\\Phi }_{\\theta }\\right\\rangle\\). We choose the same sideband strengths ({\u03a9,\u00a0W1,\u00a0W2}/2\u03c0\u00a0=\u00a0{2.0,\u00a00.47,\u00a00.47}({3.0,\u00a00.36,\u00a00.36}) MHz for \\(\\left\\vert {\\Psi }_{\\theta }\\right\\rangle\\)(\\(\\left\\vert {\\Phi }_{\\theta }\\right\\rangle\\)) case) and detune QR sideband frequencies accordingly to maximize the stabilization fidelity measured at 40\u2009\u03bcs. The experimentally measured state fidelity and state purity as a function of \u03b8 are shown in Fig.\u00a03. Under the current QR sideband color combination, \\(\\left\\vert {\\Phi }_{\\theta }\\right\\rangle\\) fails to stabilize near \u03b8\u00a0=\u00a0180\u2218. This is because the interaction strength \\(\\left\\langle gg00\\right\\vert {H}_{{{{\\rm{sys}}}}}\\left\\vert {\\Phi }_{\\theta }01\\right\\rangle\\) and \\(\\left\\langle ee00\\right\\vert {H}_{{{{\\rm{sys}}}}}\\left\\vert {\\Phi }_{\\theta }10\\right\\rangle\\) are close to 0. Swapping QR1 and QR2 sidebands\u2019 color and detuning performs a transformation \u03b8\u00a0\u2192\u00a0\u03b8\u00a0\u2212\u00a0\u03c0 in the stabilized state. This ensures a high stabilization fidelity for arbitrary stabilization angles. Details about changing sideband colors and detunings to ensure high fidelity are presented in Supplementary Note\u00a06.\n\n\\(\\left\\vert {\\Psi }_{\\theta }\\right\\rangle\\) a and \\(\\left\\vert {\\Phi }_{\\theta }\\right\\rangle\\) b are separately stabilized with a measured fidelity above 78% among different blending angle \u03b8. The fidelities are measured after 40\u2009\u03bcs of stabilization. For stabilizing \\(\\left\\vert gg\\right\\rangle\\), no external drives are applied. For \\(\\left\\vert {\\Phi }_{\\theta }\\right\\rangle\\) case, the fidelity dropped to 0 near \u03b8\u00a0=\u00a0\u03c0. The dotted lines indicate simulated fidelities for the odd and even parity Bell state stabilization. All parameters used in the simulation are the same as in Fig.\u00a02. Error bars (one standard deviation) are smaller than the marker size27.\n\nThe flexibility in our schemes and easy access to different sidebands in our device allow a further demonstration\u2014fast dissipative switching between stabilized states. Here, we implement such an operation that can flip the parity of the stabilized Bell pair by changing sideband combinations, shown in Fig.\u00a01. To quantify the stabilized parity, we measure the system\u2019s density matrix \u03c1 and define the parity signature as \\(2(| \\left\\langle ee\\left\\vert \\rho \\right\\vert gg\\right\\rangle | -| \\left\\langle ge\\left\\vert \\rho \\right\\vert eg\\right\\rangle | )\\) describing the difference in relevant coherence parameters. The results are shown in Fig.\u00a04. The scaling factor is chosen such that the ideal even and odd Bell pairs have parity signatures of \u00a0\u00b1\u00a01. Starting from the ground state \\(\\left\\vert {Q}_{1}{Q}_{2}\\right\\rangle=\\left\\vert gg\\right\\rangle\\), the stabilized state is set to even parity Bell pair \\((\\left\\vert gg\\right\\rangle -\\left\\vert ee\\right\\rangle )/\\sqrt{2}\\), and we switch the parity every 20\u2009\u03bcs. At 20\u2009\u03bcs, the stabilized state is switched to odd parity Bell pair \\((\\left\\vert ge\\right\\rangle -\\left\\vert eg\\right\\rangle )/\\sqrt{2}\\), and stabilization happens quickly with a time constant \u03c4r\u00a0=\u00a01.8\u2009\u03bcs. At 40\u2009\u03bcs, the switching from odd to even parity results in a faster stabilization with \u03c4b\u00a0=\u00a00.91\u2009\u03bcs. The switching at 60\u2009\u03bcs to odd Bell state shows a similar \u03c4r of 2.20\u2009\u03bcs. We leave the stabilization drives turned on for another 25\u2009\u03bcs to prove that the performance is not degraded after a few switching operations.\n\nThe initial state is \\(\\left\\vert gg\\right\\rangle\\), and the switch status is set to even parity between \\(\\left[0\\,\\mu {{{\\rm{s}}}},20\\,\\mu {{{\\rm{s}}}}\\right]\\) and \\(\\left[40\\,\\mu {{{\\rm{s}}}},60\\,\\mu {{{\\rm{s}}}}\\right]\\), and to odd parity between \\(\\left[20\\,\\mu {{{\\rm{s}}}},40\\,\\mu {{{\\rm{s}}}}\\right]\\) and \\(\\left[60\\,\\mu {{{\\rm{s}}}},85\\,\\mu {{{\\rm{s}}}}\\right]\\). Each experimental point is measured with the two-qubit state tomography. Stabilization time is calculated by fitting the parity signature to exponential decay after each switching event.\n\nFurther improvement of the stabilized state\u2019s fidelity is possible by reducing the transition ratio \\(\\frac{\\gamma }{{\\Gamma }_{t}}\\) (from Eq. (3)) and increasing QQ sideband rate \u03a9 for a larger energy gap. Increasing qubit dephasing time also improves stabilization fidelity (discussed in the Supplementary Note\u00a03). To speed up the stabilization, i.e., reduce time constants, we need to increase the refilling rate \u0393t. Since QR sideband rate W is bounded by the QQ sideband rate \u03a9 to ensure the validity of the perturbative approximation, given a fixed W, \u0393t is maximized when the resonator decay rate \\(\\kappa=W\\cos (\\theta /2)\\). For the even and odd parity Bell states, further increase in both resonators\u2019 \u03ba compared to our current parameters would thus be beneficial. More details about stabilization robustness are discussed in Supplementary Note\u00a03. To stabilize a more general set of states shown in Supplementary Note\u00a04, longer qubit coherence is needed to improve the experimental resolution between different stabilized states in this manifold and is a subject of future work. Details about stabilization infidelities are discussed in Methods.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51262-4/MediaObjects/41467_2024_51262_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51262-4/MediaObjects/41467_2024_51262_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51262-4/MediaObjects/41467_2024_51262_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51262-4/MediaObjects/41467_2024_51262_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In conclusion, we demonstrate a two-qubit programmable stabilization scheme that can autonomously stabilize a continuous set of entangled states. We develop an inductively coupled two-qubit device that provides access to both QQ and QR sideband interactions required. The stabilization fidelity among all stabilization angles is above 78%, specifically, we achieved high Bell pair stabilization fidelity (84.6% for the odd parity and 82.5% for the even parity) as two special points. We further demonstrate a parity switching capability between the Bell pairs with fast stabilization time constants (<\u20092\u2009\u03bcs). To realize AQEC, the refilling rate from the error state to the logical state should be much faster than the error rate. The fast switching rate between different stabilized states, which is over an order of magnitude larger than the transmons\u2019 decay and dephasing rate, is sufficient for AQEC. We believe such freedom in choosing stabilized states will inspire generalization to autonomous stabilization of larger systems, large-scale many-body entanglement3, remote entanglement28, and density matrix exponentiation20,21. While the current stabilization protocol cannot stabilize a logical manifold, it is possible to generalize this to new AQEC logical codewords by incorporating higher transmon levels8 or by utilizing additional transmons in future developments.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "To accurately simulate the real system, we sequentially introduce several error channels. After each addition, we calculate its contribution to infidelity by measuring the difference in the steady-state fidelity. We use the states \\(\\left\\vert {\\Phi }_{-}\\right\\rangle\\) and \\(\\left\\vert {\\Psi }_{-}\\right\\rangle\\) as examples, with results detailed in Table\u00a01.\n\nInitially, in an ideal case, decoherence-free simulations are conducted within a Hamiltonian of dimension 2\u2009\u00d7\u20092\u2009\u00d7\u20092\u2009\u00d7\u20092 (two levels per resonator), resulting in infidelities of 1.71% and 4.98% respectively. Subsequently, transmon T1 and T\u03d5 are incorporated into the system, revealing that transmon decoherence accounts for the majority of the stabilization infidelity observed in experiments. A higher transmon level is then added, extending the Hamiltonian to a dimension of 3\u2009\u00d7\u20093\u2009\u00d7\u20092\u2009\u00d7\u20092. The contribution of transmon ZZ coupling to the stabilization infidelities is found to be less than 1%. Other error channels contribute minimally, such as leakage to the \\(\\left\\vert f\\right\\rangle\\) state and inaccuracies in sideband frequency calibration. The discrepancy between the theoretically predicted and experimentally measured state fidelities is primarily attributed to the thermal excitation rate in the transmons when all sidebands are active. An excitation rate of 0.9 ms in both transmons sufficiently explains these deviations in the simulation.\n\nError bars (one standard deviation) for all expectation values calculated from the Maximum Likelihood Estimation(MLE) reconstructed density matrix use the Tomographer package27.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Source data are provided with the paper29. 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EK\u2019s research was additionally supported by NSF Grant No. PHY-1653820.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "James Franck Institute, University of Chicago, Chicago, IL, USA\n\nZiqian Li,\u00a0Tanay Roy,\u00a0Yao Lu\u00a0&\u00a0David I. Schuster\n\nDepartment of Physics, University of Chicago, Chicago, IL, USA\n\nZiqian Li,\u00a0Tanay Roy,\u00a0Yao Lu\u00a0&\u00a0David I. Schuster\n\nDepartment of Applied Physics, Stanford University, Stanford, CA, USA\n\nZiqian Li\u00a0&\u00a0David I. Schuster\n\nSuperconducting Quantum Materials and Systems Center, Fermi National Accelerator Laboratory (FNAL), Batavia, IL, USA\n\nTanay Roy\u00a0&\u00a0Yao Lu\n\nDepartment of Physics, Colorado School of Mines, Golden, CO, USA\n\nEliot Kapit\n\nPritzker School of Molecular Engineering, University of Chicago, Chicago, IL, USA\n\nDavid I. Schuster\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nThese authors contributed equally: Z.L., T.R. Z.L. conceived the experiment. Z.L. designed the device, and T.R. fabricated the device. Z.L. calibrated the experiment and analyzed the data with assistance from T.R. Z.L. performed the simulation with help from T.R. and Y.L. E.K. provided theoretical support and guidance throughout the experiment, and D.I.S. supervised all the aspects of the project. Z.L. and T.R. wrote the manuscript, with input from all the authors.\n\nCorrespondence to\n Ziqian Li.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Xiu-Hao Deng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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framework for end-to-end metabolomics data processing from raw files to phenotype classifiers", + "journal": "Nature Communications", + "published": "01 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60640-5/MediaObjects/41467_2025_60640_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60640-5/MediaObjects/41467_2025_60640_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60640-5/MediaObjects/41467_2025_60640_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60640-5/MediaObjects/41467_2025_60640_MOESM4_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://zenodo.org/records/14159704", + "https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=68d120fefcf243dcb83cdf5f448c31a7", + "https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=25663e4d7cc7410cbb0324b08c2c892a", + "https://www.ebi.ac.uk/metabolights/editor/MTBLS188", + "https://www.ebi.ac.uk/metabolights/editor/MTBLS620", + "https://zenodo.org/records/14159704", + "https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Project&ProjectID=PR001047", + "https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=2f73277b4e034948acebfdf1edab17ed", + "https://www.nist.gov/programs-projects/nist23-updates-nist-tandem-and-electron-ionization-spectral-libraries", + "https://zenodo.org/records/14159704", + "https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=100416b91eb24735a53eb2eecf2fd3d6", + "/articles/s41467-025-60640-5#Sec16" + ], + "code": [ + "https://github.com/huaxuyu/masscube", + "https://doi.org/10.5281/zenodo.15151320", + "https://doi.org/10.5281/zenodo.15151320", + "https://zenodo.org/records/14159704", + "/articles/s41467-025-60640-5#MOESM1" + ], + "subject": [ + "Data processing", + "Mass spectrometry", + "Metabolomics" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5530740/v1.pdf?c=1751456378000", + "research_square_link": "https://www.researchsquare.com//article/rs-5530740/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60640-5.pdf", + "preprint_posted": "06 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Nontargeted peak detection in LC-MS-based metabolomics must become robust and benchmarked. We present MassCube, a Python-based open-source framework for MS data processing that we systematically benchmarked against other algorithms and different types of input data. From raw data, peaks are detected by constructing mass traces through signal clustering and Gaussian-filter assisted edge detection. Peaks are then grouped for adduct and in-source fragment detection, and compounds are annotated by both identity- and fuzzy searches. Final data tables undergo quality controls and can be used for metabolome-informed phenotype prediction. Peak detection in MassCube achieves 100% signal coverage with comprehensive reporting of chromatographic metadata for quality assurance. MassCube outperforms MS-DIAL, MZmine3 or XCMS for speed, isomer detection, and accuracy. It supports diverse numerical routines for MS data analysis while maintaining efficiency, capable for handling 105 GB of Astral MS data on a laptop within 64 minutes, while other programs took 8-24 times longer. MassCube automatically detected age, sex and regional differences when applied to the Metabolome Atlas of the Aging Mouse Brain data despite batch effects. MassCube is available at https://github.com/huaxuyu/masscube for direct use or implementation into larger applications in omics or biomedical research.Biological sciences/Biological techniques/Mass spectrometryBiological sciences/Biological techniques/Metabolomics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "HuaxuYumasscubesupplementaryinformation11252024.pdfSupplementary information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Nontargeted peak detection in LC-MS-based metabolomics must become robust and benchmarked. We present MassCube, a Python-based open-source framework for MS data processing that we systematically benchmark against other algorithms and different types of input data. From raw data, peaks are detected by constructing mass traces through signal clustering and Gaussian-filter assisted edge detection. Peaks are then grouped for adduct and in-source fragment detection, and compounds are annotated by both identity- and fuzzy searches. Final data tables undergo quality controls and can be used for metabolome-informed phenotype prediction. Peak detection in MassCube achieves 100% signal coverage with comprehensive reporting of chromatographic metadata for quality assurance. MassCube outperforms MS-DIAL, MZmine3 or XCMS for speed, isomer detection, and accuracy. It supports diverse numerical routines for MS data analysis while maintaining efficiency, capable for handling 105 GB of Astral MS data on a laptop within 64\u2009min, while other programs took 8\u201324 times longer. MassCube automatically detected age, sex and regional differences when applied to the Metabolome Atlas of the Aging Mouse Brain data despite batch effects. MassCube is available at https://github.com/huaxuyu/masscube for direct use or implementation into larger applications in omics or biomedical research.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "MS-based nontargeted chemical analysis has rapidly advanced for lipidomics, exposome analyses and metabolomics in biomedical and environmental research1,2,3,4. Today, studies involve thousands of data files across different assays, often involving many laboratories. The UK Biobank collected 118,461 human plasma samples that await nontargeted LC-MS analyses5. Meanwhile, new generations of mass spectrometers such as the Orbitrap Astral or timsTOF instruments have led to eightfold increases in raw data file sizes, including a much higher volume of tandem mass spectra (MS/MS). This growth in size of both biological studies and MS data files requires more accurate, robust, and efficient software for data management and processing.\n\nNontargeted LC-MS data processing must report all detected chemicals with high sensitivity and robustness. Existing software has often not been thoroughly validated and benchmarked for data processing efficiency and accuracy of final reports that start from raw data imports and management to feature detection, recognition of genuine chromatographic peaks in lieu of false positives, adduct grouping including in-source fragments (ISF), chromatographic alignments, exhaustive structural annotations to statistical analysis and visualization. For example, XCMS6, MS-DIAL7, MZmine8, El-MAVEN9, OpenMS10, and TidyMass11 do not directly annotate ISFs and often report many false positive features that cannot be validated as true chromatographic peaks. Software deficiencies has led to developments of ancillary software packages to rectify problems and missing steps in nontargeted study analyses, such as CAMERA12 and NetID13 for feature grouping, GNPS14, SIRIUS15, and BUDDY16 for compound annotation, NOREVA17 and SERRF18 for normalization, and MetaboAnalyst19 for statistical analysis.\n\nTwo primary issues remained thorny challenges. First, feature detection, the basics for MS data processing, proved difficult to balance accuracy and speed. Classic software has focused on evaluating the rate of change in m/z abundance across a chromatographic peak for feature detection. However, features in LC-MS experiments can be noisy and often lack baseline separation. Rate-of-change approaches are inherently limited in robustness and are highly sensitive to noise. Consequently, automated feature detection results require extensive inspection by experienced analytical chemists, which becomes increasingly difficult as the size and number of biological studies increase. Besides accuracy, processing speed is critical for enabling data curation and downstream data evaluation, including quality controls and initial characterization of biological variance in the data. While the recent development of Flash Entropy Search20 enables ultrafast and comprehensive MS/MS investigations, chromatographic information is often overlooked due to the limitations of current feature detection algorithms in handling large-scale data processing tasks. The second major issue with MS data processing software concerns the evolution and integration of algorithms. While many method developments have achieved breakthroughs in addressing specific data processing problems, incorporating such advancements into the entire pipeline for routine research use is often difficult and time-consuming. Challenges arise due to incompatible data formats, naming conventions, or rigid software structures. Most popular metabolomics software lacks the flexibility needed for user-driven extension and modification tailored to application-oriented data processing.\n\nWe therefore present MassCube as an open-source computing library and framework for MS data processing built in Python. MassCube supports comprehensive functionalities and workflows designed for versatile data processing tasks. Inspired by the approach used in centWave21, feature detection in MassCube employs a signal-clustering strategy coupled with Gaussian filter-assisted edge detection algorithm. Compared to MS-DIAL, MZmine3, and XCMS, MassCube demonstrated superior feature detection coverage, accuracy, and speed across both synthetic and experimental MS data. We chose Python for MassCube to leverage the advancements in array programming and to take advantage of Python\u2019s extensive ecosystem of numerical libraries, its large user base, machine learning frameworks, and its balance between performance and ease of use. The object-oriented and modular architecture of MassCube facilitates the rapid implementation of algorithms contributed by the community, such as Flash Entropy Search for fast and advanced MS/MS matching.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "MassCube comprises 16 modules to handle all data processing tasks for MS-based nontargeted chemical analysis (Fig.\u00a01a). The workflow completes importing files, detecting all feature, defining peaks including adducts and ISFs, normalizing retention times and intensities, annotating compounds, performing statistics, visualization, and exporting clean results. Parallel computation enables efficient handling of large-scale data files with minimal memory overhead, allowing complex analyses to be completed even on personal laptops. MassCube is compatible with Windows, macOS, and Linux operating systems to be usable across platforms and frameworks. To support with the FAIR principles, MassCube provides a standardized metadata tracking system that records data processing steps, parameters and version of dependencies, ensuring that each data analysis can be accurately tracked and reproduced.\n\na Main functionalities in MassCube handle data import, raw data processing, feature detection and segmentation, peak grouping, peak alignment, compound annotation, statistical analysis, data visualization, and data export. b The MassCube Python framework supports object-oriented programming and flexible integration of algorithms.\n\nBy integrating processing modules, MassCube offers a choice of different workflows for users without coding skills (Supplementary Fig.\u00a01). By providing fully automated end-to-end data processing, users without substantial resources and technical training may not only obtain clean results but also overcome challenges in quality control, workflow construction or parameter selection. For advanced users and database programmers, MassCube is a ready-to-expand software platform. The modular, object-oriented design of MassCube establishes the fundamental structures for MS data, with optimizations for efficient array programming and scalability (Fig.\u00a01b). This design facilitates the flexible integration of advanced algorithms into a complete data processing pipeline for continuous improvement. For example, after publishing Flash Entropy Search in 2023, MassCube easily integrated this algorithm including multi-modal (identity search and fuzzy search) MS/MS matching. In comparison to a combination of standalone software packages, the integrated MassCube workflow design eliminates issues arising from incompatible data formats or manual data format adjustments when transferring intermediate results between different modules.\n\nMassCube feature detection empowers accurate feature evaluation, grouping and annotation by clustering all detected MS signals to unique ions (Fig.\u00a02a). Using mass resolution parameter settings, MassCube defines identical m/z values across continuous MS1 scans without imposing requirements on peak shape, signal fluctuation, or scan number, thereby minimizing empirical biases. Merging m/z signals ensures that every detected MS1 signal is assigned to a feature for 100% coverage, maximizing information gain. In difference to rate-of-change peak picking, a second advancement in MassCube is to segment features via a Gaussian filter-assisted edge detection algorithm. This method distinguishes segments from noise or from isomeric peaks. Background noise, characterized by severe signal fluctuation, is ignored by the segmentation process to prevent false positives during feature detection. Third, segmentation allows MS1 signals to be differentiated into distinct chromatographic peaks, improving detection of isomers. Importantly, the Gaussian filter, as a smoothing algorithm, is only utilized for robust peak edge detection, but not for calculating peak areas and heights. Depending on the type and parameters of algorithms, smoothing may introduce substantial changes in data structures and introduce bias. Therefore, MassCube uses the raw data instead of smoothed data for the final reports.\n\na MassCube starts by clustering MS1 signals, determines noise, and segments features into chromatographic peaks. b Schematic representation of the impact of peak parameters resolution, intensity ratio, and signal-to-noise ratio on peak detections. c Visual examples for simulated double peaks under different peak parameters. d heatmaps of true-positive peak detections for 220,000 simulated peaks for optimizing parameters during MassCube algorithm development. The averaged accuracy (right panel) determined the best balance between sensitivity and robustness. e Benchmarking double-peak, single-peak, and averaged accuracy for 27,000 simulated peaks for MassCube, MS-DIAL, MZmine3, and xcms software.\n\nThe expected true positive rate for feature detection is determined by three peak parameters: signal-to-noise ratio (S/N), peak resolution, and the peak intensity ratio relative to adjacent peaks (Fig.\u00a02b). Under conditions of low S/N, low peak resolution, and high intensity ratios, defining the true presence of peaks can be challenging, even for experienced analytical chemists (Fig.\u00a02c). These challenges are further exacerbated if S/N threshold was lowered to detect low-abundant compounds, or if signal fluctuation across peaks increases. The key to successful peak detection is to balance the trade-off between sensitivity and robustness. Ideally, the algorithm must report exactly one feature for a single peak and two features for a double peak, possibly even a shoulder peak (a partially resolved peak pair where one peak has lower intensity than the other). This definition becomes more challenging as the signal fluctuation increases. If the algorithm is overly sensitive, a single peak may be incorrectly split into multiple features; conversely, if the algorithm is too insensitive, isobaric species may not be properly distinguished.\n\nWe optimized the performance of MassCube\u2019s peak detection by tuning algorithm components critical to balancing the sensitivity-robustness tradeoff. Specifically, we focused on optimizing the segmentation algorithm by adjusting two key parameters: the sigma value (\u2320) in the Gaussian filter function, which controls noise tolerance, and the peak prominence ratio, which determines sensitivity to local minima (see \u201cMethods\u201d). For comprehensive evaluation, we designed a synthetic dataset by varying these three peak parameters, generating a total of 110,000 distinct MS signals for single peaks and another 110,000 double-peak signals. These simulations covered peak detection scenarios ranging from easy to challenging, including both single and double peaks for isomer detections. The benefit in using synthetic data is to define true positive peaks beforehand, instead of only relying on subjective judgments by analytical chemists. When \u2320 and peak prominence ratio are high, the algorithm is very robust to noise and accurate to detect single peaks, but at the expense of reduced sensitivity in distinguishing double peaks (Fig.\u00a02d). To improve double-peak accuracy, a moderate selection of \u2320 and prominence ratio is needed, ensuring that the algorithm is neither too sensitive nor too insensitive. Because the number of double-peaks and single-peaks in experimental LC-MS data is sample- and method-dependent, MassCube\u2019s configuration for peak detections was optimized to an overall best average accuracy. This process achieved an average accuracy of 96.4% with optimal settings of \u2320\u2009=\u20091.2 and prominence ratio\u2009=\u20090.1.\n\nNext, to benchmark MassCube\u2019s peak detection against other software, we generated a synthetic mzML file from a negative mode electrospray QTOF MS dataset of human urine, where 13,500 true single peaks and 13,500 true double peaks were inserted at m/z\u2009>\u20091500\u2009Da to insure that test signals were not interfering with experimental data. All synthetic signals were modeled with >10 scans, varying Gaussian noise fluctuations from 0 to 10% and peak height ratios of double peaks from 1\u20135, with peak resolution varying from 1 to 2. We then used this synthetic data to statistically compare the performance of MassCube against MS-DIAL 4.9, MZmine 3.90, and xcms R package 4.0.0 (Fig.\u00a02e). For double-peak accuracy under six noise scores ranging from 0 to 10%, MassCube achieved the highest mean accuracy of 95.2%, significantly higher than MZmine3 (mean\u2009=\u200987.0%, paired t-test p\u2009=\u20090.0011) and xcms (mean\u2009=\u200976.0%, p\u2009=\u20090.0010), but not significantly higher than MS-DIAL (mean\u2009=\u200994.3%, p\u2009=\u20090.27). MassCube also attained the second-highest mean accuracy for single peaks at 97.8%, significantly higher than MS-DIAL and MZmine3 yet with no significant difference compared to xcms (mean\u2009=\u200998.8%, p\u2009=\u20090.58). Overall, MassCube achieved the highest average accuracy of 96.5%, significantly outperforming MZmine3 (mean\u2009=\u200988.4%, p\u2009=\u20090.0029), xcms (mean\u2009=\u200987.4%, p\u2009=\u20090.016), and MS-DIAL (mean\u2009=\u200985.4%, p\u2009=\u20090.0065). Using this dataset, peak detection in MassCube was found as the most robust and sensitive software in comparison to MS-DIAL, MZmine3, and xcms.\n\nExperimental data may differ from synthetic data in unexpected ways that are hard to simulate, such as dips, tailing, insufficient scan numbers (including single scan ion detections), raising baselines or missing signals.\n\nWe started by distinguishing a feature in LC-MS datasets from genuine chromatographic peaks. A feature in MassCube is every detected unique m/z signal after clustering. A peak in MassCube is defined as a feature that is segmented by the Gaussian filters into at least one segment that is different from the total chromatographic run time with a minimum of 5 scans. All peaks have associated quality metadata to enable users to prioritize peaks in final data reports, including peak asymmetry factor, Gaussian similarity, and noise scores (Supplementary Fig.\u00a02). While peak detections by segmentation allow for missing values, such missed signals will contribute to poor quality metadata assignments. Using 200 files from 41 studies downloaded from MetaboLights22, we then profiled the metadata distribution of 1,442,223 peaks detected with \u03b55 scans per extracted ion chromatogram (Supplementary Fig.\u00a02). For example, a perfectly symmetric peak is expected with peak asymmetry factor of 1, while tailing or fronting peaks differ from that value. As expected, the number of peaks exhibiting fronting and tailing were similar. A Gaussian-shaped peak has a dot-product of 1 to the fitted Gaussian distribution. Over 50% of all peaks had a Gaussian similarity over 0.84, while the remaining features showed a nearly uniform distribution of Gaussian similarity between 0.2 and 0.8, indicating the presence of noise. The median noise score was 0.43, suggesting that signal fluctuation is common in untargeted chemical analysis. Only 25% of all peaks showed noise scores <0.20, indicating low signal fluctuation. Metadata distributions showed that hard thresholds for peak quality cannot be ascertained in a straightforward way. Yet, users can scrutinize and curtail peak detection using the combined impact of these quality scores from final MassCube reports.\n\nWe therefore benchmarked MassCube\u2019s peak detection against leading software using diverse experimental MS data. To ensure fair, objective and reproducible comparison, we implemented three key approaches (see \u201csoftware and parameters\u201d in \u201cMethods\u201d section). First, standardized key parameters were used in a consistent way for all tested software, including intensity cutoffs and mass tolerances. Second, each software used its recommended default values for all unique parameters that were not shared with other software. Because different algorithms operate under distinct mathematical assumptions, using recommended parameter settings ensure a fair use of software as intended by the software developers. Third, diverse datasets were selected to prevent bias or overfitting towards any specific software. Here, datasets ensured (a) matrix independence by selecting biologically relevant (plasma, human, mouse) and complex (fecal) matrices, (b) reusability by utilizing NIST reference materials, (c) size independence by including both small- and large-scale studies, and (d) instrument independence by testing both Orbitrap and QTOF datasets.\n\nTherefore, a total of eight LC-MS data files from samples including NIST SRM 1950 plasma, mouse plasma, NIST Human Fecal Material RGTM 10162, human serum, mouse feces, whole fruit fly (D. melanogaster strains), and human urine data, acquired on Orbitrap MS and QTOF MS with different ion mode (Fig.\u00a03a, b). LC-MS runs were acquired under data-dependent MS/MS conditions, but MS/MS signals were not used in peak detection. While for Orbitrap data >10,000 features were detected in all data files, only a fraction of these features was present in peak segments with \u03b5 5 scans (Fig.\u00a03a). Similarly, while for QTOF data >2000 features were detected in all data files, we found a similar ratio as in Orbitrap data files for the ratio of the total number of detected features over peaks with length \u03b5 5 scan as (Fig.\u00a03b). Overall, 35% of all peaks showed acceptable scan lengths, suggesting that many features in LC-MS files are spurious and cannot be associated with high quality chromatographic peak metadata defined by peak asymmetry, Gaussian similarity and noise scores (Supplementary Fig.\u00a02). This observation also justifies a distinction between LC-MS \u2018features\u2019 and chromatographic \u2018peaks\u2019. While peaks can be used for defining genuine signals that may be attributed to the presence of specific chemicals, other features may occur due to background contaminations that are caused by inconsistent electrospray patterns, or by very low abundant metabolite signals that do not reach a threshold to be defined as peaks. Interestingly, up to 28% of these spurious features with low scan numbers were associated with MS/MS data in data-dependent LC-MS/MS acquisitions, because >75% of them were present at peak intensities that were substantially higher than the background noise for both Orbitrap and QTOF data. MassCube demonstrated the ability to detect all features (including spurious signals), attributed to its ion clustering-based feature detection strategy that ensures no signal loss. When defining peaks as signals with \u03b5 5 scan lengths, MassCube reported a similar number of peaks as other software. However, the accuracy of peak reporting can be different between software packages and parameters. A true single peak might be split into multiple reported features, while a true double peak might be reported as a single feature, resulting in similar numbers of detected peaks. Figure\u00a03c illustrates an example where a true peak pair in an experimental data file was not distinguished by either MS-DIAL, MZmine3 or xcms, but accurately detected by MassCube, underscoring the accuracy of MassCube\u2019s segmentation algorithm. To comprehensively probe the accuracy of the four software tools, we randomly selected 722 locally extracted ion chromatograms with retention time window \u00b10.2\u2009min and manually labeled the number of peaks in the range (see \u201cData availability\u201d for extracted ion chromatograms). The labeled data were compared with the results from MassCube, MS-DIAL, MZmine3, and xcms. MassCube achieved the highest accuracy for double peaks (93.5%) and single peaks (97.3%), outperforming the other three software tools (Fig.\u00a03d). These results suggest that MassCube\u2019s feature detection algorithm closely approximates the results obtained by experienced analytical chemists, demonstrating the best performance for scaling human-level data processing into automated, large-scale workflows.\n\nEight ThermoFisher Orbitrap QExactive or Bruker QTOF Impact2 LC-MS/MS data files were used for metabolomics and lipidomics analyses of NIST SRM1950 human plasma, human serum, mouse plasma, NIST RGTM 10162 fecal matter, whole body of fruit flies and human urine samples. MS-DIAL vs.4.9 was used by default settings, xcms vs.4.0 with default settings 5\u201360\u2009s peak widths, MZmine3 vs. 3.90 with default settings \u03b55 non-zero consecutive scans, and MassCube with no restrictions (all features) or peaks with \u03b55 scans per segment. a Comparisons of the number of reported peaks for Orbitrap data. Grey: all detected features by MassCube; blue: MassCube reported peaks with \u03b55 scan points. Other software: MS-DIAL (green), xcms (red), MZmine3 (orange). b Comparisons of the number of reported peaks for QTOF data. c Example of a chromatographically partly resolved accurate mass pair in NIST SRM1950 plasma detected by MassCube but not detected by other software. d Benchmarking true positive peak picking. 722 features from QTOF and Orbitrap QC results were randomly selected and manually verified by visual inspection using extracted ion chromatograms. Double-peak: 46 partially resolved accurate mass peaks. Single-peak: 676 chromatographic single-peak accurate mass features. e Benchmarking data processing speed on four public datasets (plasma, urine, stool, plant leaves) with 47\u2013428 sample files using single threading. f MassCube performance for very large file sizes. Comparison of file size for 636 human plasma samples of Alzheimer\u2019s Disease patients acquired on an Orbitrap Exploris 240 (median 20.4 MB per sample) and an Orbitrap Astral mass spectrometer (median 169.5 MB per sample), using a MacBook M3 Pro laptop. Parallel processing was enabled with a batch size of 100, with a total processing time of 18\u2009min for the Orbitrap Exploris 240 data and 64\u2009min for the Orbitrap Astral data. Right panel: memory usage for Orbitrap Astral data processing; the dashed line represents the smoothed curve. Whiskers: 5\u201395 percentile; box: 25\u201375 percentile with the center indicating median.\n\nApart from accuracy of peak reporting, MS-based chemical analysis must efficiently process large numbers of files, for example, in human cohort study samples. Often, LC-MS data processing software crashes for large file numbers. MassCube was designed to maximize efficiency by incorporating Numpy-based array programming and parallel processing. In benchmarking tests across four datasets with varying sample sizes using single-threading (Fig.\u00a03e), MassCube demonstrated substantially faster feature detection speeds compared to xcms (average 6.5-fold improvement) and MS-DIAL (average fourfold improvement), and slightly faster speeds compared to MZmine3 (average 1.15-fold improvement). Despite Python\u2019s inherently slower execution speed compared to Java (used by MZmine3), these results underscore MassCube\u2019s low computational cost. We further demonstrated MassCube\u2019s capability by processing a large-scale Orbitrap Astral MS dataset consisting of 636 files with a total size of 105 GB (Fig.\u00a03f). On a MacBook M3 Pro laptop equipped with 36 GB of memory and 12 cores, MassCube completed the parallel data processing for all files in 64\u2009min, with peak memory usage reaching only 10.4 GB. This low memory usage enables a standard laptop to efficiently handle next-generation MS data with eightfold larger sizes than classic Orbitrap instruments, paving the way for the processing of increasingly complex datasets.\n\nWe demonstrated the capability and performance of MassCube by reanalyzing the Metabolome Atlas of the Aging Mouse Brain dataset23. Data encompassed a total of 702 samples with positive and negative ESI for HILIC-based metabolomics and RPLC-based lipidomics across 80 sample groups (representing ten regions, two sexes, and four age groups). MassCube handled the entire process for comprehensive data analysis (Fig.\u00a01 and Supplementary Fig.\u00a01). In particular, MassCube automatically resolves isomer peaks (Fig.\u00a04a, b), groups adducts and ISFs (Fig.\u00a04c), aligns all peaks across chromatograms and performs automatic compound annotations, including chemical classifications for unknowns. MassCube also performs data normalization to reduce retention time shift (Supplementary Fig.\u00a03) and batch effects (Supplementary Fig.\u00a04), revealing sex-specific metabolome differences in brain regions that were not reported in the original publication23.\n\na, b Examples of isomer pairs including (a) phosphatidylcholine PC 36:2 and (b) hexosylceramide HexCer 41:2;3\u2009O detected by MassCube but not resolved by MS-DIAL 4.90. Box plots illustrate the difference of statistical significance of lipid quantity between the left and right peaks when comparing adolescence (AD) and early adulthood (EA) groups with 160 biological samples in each group. FC fold change; p: two-tailed t-test p-value with unequal variance; whiskers: 5\u201395 percentile; box: 25\u201375 percentile with the center indicating median. c Automated identification of in-source fragments in MassCube. Two criteria are combined, exemplified here for the internal standards D5-glutamine (m/z 152.1078) and D5-glutamic acid (m/z 153.0918) and their corresponding endogenous metabolites. Top panels (criterion #1): Presence of in-source fragment m/z 135.081 (for the deuterated internal standards) and 130.0948 (for the endogenous metabolites) in the MS/MS spectra of the corresponding parent [M\u2009+\u2009H]+ ions. Lower panels (criterion #2): identical peak shapes of in-source and parent ion m/z values for loss of NH3 (D5-glutamine and endogenous glutamine) or loss of H2O (D5-glutamate and endogenous glutamate) to the same fragment ion m/z 135.081 (for D5-labeled internal standards) or m/z 130.0948 (for the endogenous metabolites). Peak shape identities were calculated scan-by-scan in MS1 profiles by Pearson\u2019s product-moment correlation coefficient. d, e Uniform manifold approximation and projection (UMAP) clusters of 702 sample files reprocessed by MassCube for (d) mouse brain metabolomics and (e) mouse brain lipidomics. Coloring the UMAPs by sample type revealed the age-related, sexual, and regional differences. Left panels in (d, e) show age. AD: adolescence, 3 weeks of age; EA early adulthood, 16 weeks of age; MA middle-age, 59 weeks of age; OA old age, 92 weeks. Mid panels in (d, e) show sex difference present in metabolomics but not in lipidomics data. Right panels in (d, e) show differences between the three major brain parts cerebellum, cerebrum and brainstem. Lipidomics data also revealed a specific region, the olfactory bulb (indicated by an oval), to be highly distinct from other regions in the cerebrum.\n\nWith MassCube\u2019s accurate peak detection algorithm, isomeric peaks can be effectively resolved and compared across sample groups. We demonstrated its ability to identify previously unresolved isomeric lipids, such as phosphatidylcholine (PC 36:2, Fig.\u00a04a) and hexosylceramide (HexCer 41:2;3\u2009O, Fig.\u00a04b), which were not distinguished by MS-DIAL 4.90. By analyzing mouse brain data from adolescence and early adulthood groups, we found clear differences in the statistical significance between the left and right peaks in both examples, highlighting the importance of precise peak detection and resolution. For instance, the right peak of PC 36:2 (Fig.\u00a04a) showed much better significance (p\u2009=\u20095.1\u2009\u00d7\u200910\u22125) compared to the right left (p\u2009=\u20091.1\u2009\u00d7\u200910\u22123), showing MassCube\u2019s ability to distinguish such isomers.\n\nRetention time drift is a critical challenge in large-scale MS analysis or collaborative data acquisition across laboratories. MassCube uses an automatic detection of unique model peaks that can be used to anchor chromatograms across substantial retention time shifts. This approach is different to retention time corrections that use internal reference standards (or fixed sets of matrix compounds), and it is also different from classic approaches that attempted to align chromatograms via overall similarity. The classic approach fails if studies incorporate samples with very distinct metabolome profiles, such as kidney tissues and urine samples. Similarly, brain sections in the mouse metabolome atlas were also quite different in total metabolome profiles, specifically across regions (Fig.\u00a04b). After depicting unique model peaks (defined by unique m/z segments with high peak quality and intensity) in QC pools samples, MassCube uses these model peaks via linear interpolation, specifically designed to handle nonlinear RT shifts in LC-MS experiments (Supplementary Fig.\u00a03a, b). These anchors are then split into training and testing sets for retention time correction and validation, respectively. The correction algorithm effectively reduced retention time shifts across all four analytical modes, demonstrating better performance in reverse phase mode by reducing retention time shifts in the test sets from 31.0\u2009s to 6.0\u2009s in positive mode and from 40.5\u2009s to 1.0\u2009s in negative mode (Supplementary Fig.\u00a03c\u2013e). Data normalization was performed by automatically acquiring timestamps from the raw data to define the acquisition order and applying locally weighted scatterplot smoothing algorithm to correct for systematic signal intensity drifts (Supplementary Fig.\u00a04).\n\nMassCube automatically searches all MS/MS spectra against MassBank of North America24 and the MS-DIAL metabolomics MSP spectral kit7, but it can incorporate other MSP libraries as well. For the re-analysis of the mouse brain atlas data, we also incorporated the licensed NIST23 library. With a flash entropy search similarity cutoff of 0.7, MassCube annotated a total of 1710 unique compounds, separated into 533, 286, 711, and 613 unique compounds from 7888, 7049, 12,270, and 13,984 peak groups in HILIC positive and negative, RPLC positive and negative, respectively. Hence, a substantial number of peak groups remained unidentified. Beyond classic identity search (using precursor m/z and MS/MS matching), MassCube now fully supports MS/MS fuzzy searches (also known as molecular networking14) for chemical classification of unknown compounds, combining open search, neutral loss search, and hybrid search20,25. Classifying unknown compounds enables class enrichment statistics26, and it also improved biological interpretation to generate hypotheses. This fuzzy search extended the number of chemically classified compounds two-threefold across the four assays27 (Supplementary Figs.\u00a05 and 6). Without precursor restrictions in fuzzy search, the search space increases more than 6000-fold (Supplementary Fig.\u00a07). Despite this massive increase, total search time was only <1% longer relative to the total data processing time. When stratified into chemical classes by fuzzy search, age- and regional-specific metabolic differences became statistically significant for several compound classes such as hexosylceramides and sterols that were not previously published23 (Supplementary Fig.\u00a08). To properly annotate unknown compounds that are classified by not identified, additional follow-up experiments are needed such as deuterium/hydrogen exchange studies28. Next, the re-analysis of the mouse brain atlas data validated how ISFs are confidently annotated in MassCube. Unlike isotopes and adducts, the annotation of ISFs is more challenging because no prior knowledge is available for the compound-dependent m/z difference between an ISF and its parent ion29. Current MS data processing software tools do not clearly annotate ISFs in the final report, often requiring analyses of correlations across multiple chromatograms and extra software for data curation. MassCube uses a different strategy, using only the available data within each chromatogram, systematically annotating ISFs using two key criteria (Fig.\u00a04c). The first criterion recognizes that in-source fragmentation shares similarities to low-energy MS/MS fragmentations. Here, MassCube checks if a potential ISF ion also appears in the parent MS/MS spectrum. Secondly, MassCube examines the scan-to-scan Pearson correlations between all co-eluting ions because ISFs intensity must directly depend on the intensity of the intact precursor ions. Figure\u00a04c demonstrates the proposed strategy for both internal standards and endogenous metabolites. Our proposed approach correctly annotated two ISFs from two true positive peaks, the internal standards, D5-glutamine and D5-glutamic acid. The findings were further verified by correctly annotating ISFs for both non-labeled endogenous glutamine and glutamic acid. Overall, MassCube detected 2604 ISFs in the mouse dataset, in addition to 6055 alternative adducts that were then combined to unique peak groups. For detected ISFs, 7.5% (194 out of 2604) were classic water losses, and 4.1% (107 out of 2604) were losses of ammonia. Hence, MassCube opens the door for more detailed analyses of the chemical nature of other in-source fragmentations.\n\nTo display overall metabolic phenotypes, MassCube generated unified nontargeted results from annotated compounds that were detected in > 80% of all samples. In this way, global differences in the brain metabolome (Fig.\u00a04b) and lipidome (Fig.\u00a04c) were mapped using 431 unique metabolites and 953 unique lipids, respectively. This data reduction strategy enabled focusing on metabolomics differences across three major brain regions including the cerebellum, brainstem, and cerebrum, clustered from ten subregions. Regional tissue differences were evident and aligned with the findings in the original work. Importantly, aging introduced greater variance in the brain metabolome, while sex discriminated tissues across all ages, particularly in the metabolome, though the differences in the lipidome were relatively smaller. Notably, the olfactory bulb, as part of the cerebrum, exhibited distinct lipidomic profiles compared to other cerebrum subregions.\n\nMassCube ranks chemical discriminators between classes of samples. Subsequently, it creates a phenotype classifier model directly from raw MS data, based on the selected discriminators. Python, with its enriched ecosystem for machine learning and artificial intelligence, provides MassCube with capabilities for this task. The MassCube workflow module \u2018phenotype classifier builder\u2019 enables end-to-end construction of a classifier from raw MS data without requiring user expertise for coding skills, knowledge of LC-MS data processing, or machine learning (Fig.\u00a05a). The classifier builder handles routine tasks such as raw data processing, feature selection, model validation, and more, providing users with a preliminary model for sample group prediction. During the prediction stage, users only need to provide the raw LC-MS data, while the MassCube model will automatically locate optimal metabolites used for prediction.\n\na Schema for automatic phenotype classifications with two key stages (i) the raw data-to-model utility for the training stage, and (ii) the raw data-to-phenotype utility for the prediction stage. b Querying the phenotype \u2018age\u2019 as discriminant for adolescence and middle-aged mice using 526 named metabolites annotated by m/z and MS/MS matching (HILIC, ESI-positive, QExactive, annotation >\u20090.7 entropy similarity). A random forest classification model was built leading to 32 final metabolites by filtering for MassCube peak metadata and classification strengths. c Box and whisker plot of overall metabolite intensities of the 32 selected metabolites between adolescence and middle-aged mouse brain samples with 160 biological samples in each group. Whiskers: 10\u201390 percentile; box: 25\u201375 percentile with the center indicating median. d UMAP plots depicting the global differences between adolescence and middle-aged mouse brain samples using the selected 32 metabolites. e Using the 32-dimensionsional space as classifier leads to an excellent area under the receiver-operator curve (AUC) and fivefold cross-validation accuracy of the model evaluation to distinguish adolescent (3 weeks) from middle-age mice (59 weeks).\n\nWe demonstrated the module by constructing a random forest model to predict subject age using a subset of mouse brain data (160 adolescence and 160 middle-age). After raw data processing, a sequential strategy was employed for selecting features, constructing models, and validating models. Feature selection in MassCube goes beyond conventional statistics-based approaches that focus solely on distinguishing sample groups. MassCube also considers analytical performance factors such as peak shape, feature grouping, and detection rate (Fig.\u00a05b). This strategy ensures that the selected features are analytically reliable, and the constructed model are robust. Yet, results from any single study must be viewed as preliminary outcome because underlying matrix effects may highlight specific metabolites that are unrelated to biological causes. Out of 526 annotated metabolites, the MassCube workflow selected 32 metabolites with distinct concentrations between adolescence and middle-aged mice (Fig.\u00a05c). This automatic selection concurred with previously reported alterations in the mouse brain metabolome during aging. For instance, adenosine was statistically significantly increased in middle-aged mice compared to adolescence, aligning with results published in the Metabolome Atlas of the Aging Mouse Brain23. MassCube also reported a statistically significant decrease in histidine levels during aging. The observed decrease in histidine levels may reflect its reduced strength in mitigating oxidative stress in the brain30. Similarly, cellular senescence has been associated with decreased proline synthesis, which impairs mitochondrial function and contributes to neurodegeneration31. This aligns with MassCube\u2019s findings, further supporting the link between aging and diminished proline levels. MassCube also identified pronounced effects on aging-related metabolites in the mouse brain, such as CDP-ethanolamine and homoserine lactone, whose specific biological roles in the aging process remain to be uncovered. With the automatic strategy for selecting chemical discriminators behind the phenotype, MassCube facilitates efficient preliminary filtering of protentional biomarkers, prioritizing metabolites with reliable analytical performance and biological effects for downstream biological validation.\n\nUsing the 32 most discriminatory metabolites, global differences were revealed between adolescence and middle-aged mouse brain as shown in the UMAP plot (Fig.\u00a05d). Further evaluation revealed an AUC of 1.000 in the ROC curve, with a fivefold cross-validation accuracy of 0.991\u2009\u00b1\u20090.008 (Fig.\u00a05e), indicating the power of MassCube\u2019s phenotype classification module.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60640-5/MediaObjects/41467_2025_60640_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60640-5/MediaObjects/41467_2025_60640_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60640-5/MediaObjects/41467_2025_60640_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60640-5/MediaObjects/41467_2025_60640_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60640-5/MediaObjects/41467_2025_60640_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "MassCube addresses long-standing challenge in metabolomics, starting with nontargeted peak detection. Other software typically uses \u2018rate-of-change\u2019 methods, determining the presence and boundaries of peaks by calculating derivatives of intensity changes of masses from scan to scan, or data-dependent extracted ion chromatograms (EICs)7,21,32. MassCube considers first principles, specifically how S/N, peak resolution and the intensity ratio of coeluting ions influence peak detection. At its core, MassCube balances the trade-off between sensitivity and robustness during peak detection. External validated datasets for rigorous benchmarking are still missing. Such datasets would present large-scale ground-truth data generated by different LC-MS methods and spanning biological matrices, with careful validation performed by a group of expert analytical chemists. To bridge this data gap, we used both synthetic and experimental data to develop the software, including 27,000 simulated peaks in addition to 722 randomly selected experimental peaks to test the performance of MassCube against classic MS-DIAL, MZmine and xcms software. This strategy to employ two types of benchmarking datasets has not been used before in metabolomics software development but may add confidence that MassCube will perform robustly for various LC-MS methods in different laboratories. However, convoluted experimental data also display characteristics that are difficult to explore in a systematic way using synthetic data, such as varying scan numbers across peaks, peak tailing, baseline dips, sudden peak spikes, ion saturation, and different types of background noise. The benchmarked software we compared MassCube against use different algorithms. MS-DIAL calculates the first and second derivatives of mass bins, MZmine3\u2019s ADAP algorithm32 detects peaks from data-dependent EICs, and xcms applies data-dependent EICs to a wavelet transform for peak detection. However, our benchmarking tests showed false positive and false negative peak detections remained a substantial challenge for these software programs, because nontargeted peak detection is faced with poor peak shapes, high noise levels, and partially resolved peaks. Human-defined rules for peak shapes, reflected in algorithmic parameters, cannot account for all such scenarios. Hence, rate-of-change approaches may simply be unable to robustly address such problems in experimental datasets. Instead, MassCube focuses on MS signal clustering and peak edge detection through local minimum identification. Because MassCube does not rely on the rate-of-change for peak findings, it showed consistently superior performance to benchmarked software in synthetic benchmarking data, including tests across different levels of noise added to the signals. This finding was true for both double-peaks or single-peaks (Fig.\u00a02e). Furthermore, MS-DIAL, MZmine3, and xcms performed even worse when benchmarked against complex experimental data. In contrast, MassCube maintained its accuracy against 722 experimental peaks, consistent with the results from synthetic data, achieving an average accuracy of over 95% for all tested peaks. Unlike other software, MassCube showed efficient handling of thousands of reported peaks, including peak metadata reports (such as asymmetry factor, Gaussian similarity, and noise score) that can be used for subsequent data curation. While other software tools often provide signal-to-noise information, comprehensive peak quality metadata is not comprehensively implemented in MS-DIAL, MZmine3, and xcms, sometimes even displaying erroneous reports.\n\nIn addition, parameter settings are crucial for software performance. In benchmarking, we deliberately avoided parameter tuning for any software across all test datasets. Instead, we used default, recommended settings to prevent overfitting and ensure fair comparisons. While some software might perform better with study- or matrix-specific adjustments, we argue that reliance on such tuning indicates lower robustness compared to alternatives that perform well across diverse conditions.\n\nBesides accuracy, MassCube\u2019s peak detection has high efficiency along with low memory cost. The fast computation time benefits from (a) eliminating computational overhead that is associated with dynamic peak modeling in classic rate-of-change peak detection, (b) optimized Python array programming, which is much faster than tools built on less efficient programming environments such as R, and (c) parallel computing capabilities that enable MassCube to scale efficiently for large datasets. Over the past decades, several integrated MS data processing tools have been developed in R, including XCMS, RforMassSpectrometry, and TidyMass. Using the xcms R package, we demonstrated that R exhibits lower data handling efficiency than Python, which limits its application in large-scale metabolomics research and ultra-high throughput MS data processing, such as Orbitrap Astral data. In proteomics, Python-based software has been developed like pyOpenMS33 and Dinosaur34. In metabolomics, the Python-based asari software35 is superior in feature detection compared to xcms, but it lacks comprehensive functionalities beyond MS1 data processing. Although low-level languages like C could offer even faster performance, we developed MassCube in Python to balance high efficiency with ease of use and readability. For instances requiring intensive optimization, compiled languages such as Cython and Numba can be employed to further enhance MassCube\u2019s efficiency.\n\nMassCube covers the full workflow for metabolomics and lipidomics. Using the Metabolome Atlas of the Aging Mouse Brain data, we demonstrated several core modules in MassCube, including direct annotation of ISFs and multimodal MS/MS identification that are not available in MS-DIAL, MZmine3, or xcms. MassCube utilizes multiple criteria for ISF annotation, including the presence of fragments in the MS/MS spectrum of the parent ion and the similarity of peak shapes. Consequently, MassCube provides a conservative approach to ISF annotation: if the parent ion is not detected due to complete fragmentation in the ion source, its corresponding ISFs cannot be automatically annotated using this approach.\n\nWhile MassCube does not directly perform metabolite identification, it enhances annotation accuracy in several ways. Metabolite annotation depends on accurate and comprehensive peak detection, in addition to robust in-source fragment and adduct annotation. MassCube advances the state of the art of in-source fragment and adduct annotation, thereby improving overall compound annotation accuracy. By reducing the number of false negative peak reports compared to benchmarked software, MassCube provides more robust data for statistical analyses to ensure that more biologically relevant features are used for downstream annotation.\n\nData processing in MassCube requires no coding skills and can be easily performed by analytical chemists, validated during the software beta testing. While a graphical user interface (GUI) is not available in the current version, we explicitly designed MassCube with non-programming users in mind by providing a series of command-line applications for direct usage (Supplementary Fig.\u00a01), along with step-by-step instructions on our project website (https://huaxuyu.github.io/masscubedocs/docs/quickstart/). MassCube also offers comprehensive visualization tools and automatic figure generation capabilities for in-depth data inspection. MassCube also employs direct classification tools to distinguish groups defined in the experimental design. With the advanced ecosystem of machine learning in Python, MassCube enables prediction of experimental classes with preliminarily selected chemical discriminators. The MassCube\u2019s phenotype classifier builder also implements immediate validation after model construction, ensuring model accuracy and robustness. Although the selected metabolites demonstrate reliable analytical performance based on peak metadata and show distinct concentration levels across the queried phenotypes, downstream biological or clinical experiments remain necessary to confirm their biological function and validate the generated hypotheses. Moreover, while MassCube provides reliable metabolomics data reports, confident biological insights would benefit from other software or databases explicitly design for data interpretation or integration of data from different assays or studies as well.\n\nMassCube has been tested on Windows, macOS, and Linux systems ranging from personal laptops to high-performance computing clusters. The framework with MS data objects supports the seamless integration of advanced algorithms, making it adaptable to community contributions. MassCube could also be used as a backend for cloud computing and user-interfaced applications. To support data sharing and re-analysis, the built-in metadata tracking system ensures standardized records of each data processing task with parameters. Active development is ongoing for MassCube, aiming to provide users with additional tools for data processing such as time-series analyses, SERRF data normalization, formula predictions, chemical classifications and providing a GUI.\n\nIn the next decade, MS data processing will face challenges stemming from new configurations of instrumentation. While small metabolomics projects with fewer than 100 samples do not face major computational resource constraints, the increasing complexity and scale of data processing may present challenges in the future. Mass spectrometers like Orbitrap Astral MS are already pushing the boundaries of data acquisition by parallelizing mass analyzers, a trend that is likely to continue in future developments. These advancements will introduce new concepts and data structures, necessitating the extension of conventional data objects as well as the creation of flexible and scalable computational tools. MassCube is designed to meet these demands by offering the flexibility needed to accommodate future MS data types.\n\nAs the scale of community MS data collection continues to grow, researchers are increasingly eager to integrate information from different studies for broader discoveries. With databases like GNPS, MetabolomicsWorkbench, and MetaboLights, repository-level analysis at the MS/MS level is now possible. However, a substantial amount of information is often lost when chromatograms are not fully utilized. We believe that with its advanced infrastructure and efficiency, MassCube is poised to uncover hidden insights from the vast sea of public data, leading to a deeper understanding of instrumentation, small molecules, and biology.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The Alzheimer\u2019s disease dataset used for speed benchmarking is an exploratory study coordinated by Duke University under R. Kaddurah-Daouk. The protocol was approved by the UC San Diego\u2019s Institutional Review Board (IRB) protocol #202063, Indiana University IRB study #1011003338, Kansas University IRB study #CR00020412, University of Wisconsin IRB study IORG0000056 approved 3-29-2023, New York University IRB study #i20-00427. Written informed consent was obtained from all participants.\n\nAs described in the initial study, mice were cohoused by gender groups of 4\u20135 in individually ventilated cages (Optimice IVC, Animal Care Systems, Centennial, CO) on a 12:12-h (6:00/18:00) light:dark cycle at 68\u201379\u2009\u00b0F with 40\u201360% humidity and provided water and standard rodent chow (Rodent chow, Harlan 2918) ad libitum. Brain tissue samples were collected from 3, 16, 59, and 92 weeks old male and female wild-type mice on a C57BL/6\u2009N background. All procedures were approved by the IACUC of the University of California, Davis, which is an AAALAC-accredited institution. Animal housing and euthanasia were performed in accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals.\n\nMassCube is a Python computing library with a total of 16 modules to handle a wide range of data processing tasks and fundamental objects designed for LC-MS/MS data, including raw data management, parameters, feature detection, feature evaluation, feature grouping, normalization, annotation, alignment, network analysis, statistical analysis, visualization, phenotype classifier builder, workflows and other utility functions. Documentation for MassCube is available at https://huaxuyu.github.io/masscubedocs.\n\nMassCube supports standardized raw MS data in mzML and mzXML formats. Raw MS data are read and parsed into the MSData object, which was designed to organize high-dimensional LC-MS/MS data, manage metadata, and facilitate downstream processing and visualization. Within the MSData object, scans are organized sequentially, with each scan represented as a Scan object. MassCube automatically records the timestamp of each individual file for quality control, intensity normalization, and batch effect correction. Metadata for data processing were recorded and stored as a Python dictionary object including assembly of modules, data processing parameters, and versions of dependencies. The metadata are automatically exported with timestamp for trackable and repeatable analysis. Examples were shown in Supplementary Note\u00a01.\n\nMassCube feature detection clusters MS signals from unique ions, followed by Gaussian filter-assisted feature segmentation. A region of interest (ROI) is defined as an m/z value that appears continuously across multiple MS1 scans. Let there be a total of n MS1 scans denoted as S\u2009=\u2009s1, s2, \u2026, sn. The search begins with the first scan s1, initializing a set of ROIs using all m/z values in s1. Suppose there are m1 m/z values in s1, then we have m1 ROIs initiated from s1, denoted as R\u2009=\u2009ROI1, \u2026, ROIm1. The algorithm then proceeds to the second scan s2, matching the m/z values in s2 to the initiated ROIs. If a match occurs within a given m/z tolerance (default\u2009=\u20090.01\u2009Da), the ROI will be extended. Otherwise, a gap is detected. Upon completion of each new MS1 scan, the algorithm transfers all ROIs with gap numbers exceeding a specified threshold (default\u2009=\u200930) to the final set of ROIs, which will no longer be extended in subsequent calculations. This process iterates through all MS1 scans up to sn. Subsequently, a feature segmentation algorithm was used to separate different ion species within a given ROI. For ROIi, suppose it contains k MS1 scans, resulting in k intensity values represented as an array I\u2009=\u2009int1, \u2026, intk. First, a one-dimensional Gaussian filter is applied to smooth the signal array I, generating the smoothed signal I\u2019 for edge detection. Subsequently, peaks above the baseline are captured based on peak prominence ratio calculated using the SciPy Python package. The local minimum between any two adjacent peaks are determined as the edges for segmentation. Importantly, using the scan number of detected edges, the original signal array I rather the smoothed array I\u2019 is segmented, with corresponding intensities reported by MassCube. Two parameters are critical for a successful peak segmentation that is robust to noise and can accurately separate two ion species. One parameter is the sigma (\u03c3) that controls the level of smoothing in the Gaussian filter. Over-smoothing the signal results in loss of peak shape details and inability to distinguish coeluted isobaric species, while under-smoothing makes the signal too sensitive to noise, leading to incorrect segmentation of a single peak. The other parameter is the prominence ratio, which measures how much a peak stands out from the surrounding baseline and is defined as the ratio of vertical distance between the peak and its lowest contour line to \\((I^{\\prime} )\\).\n\nMassCube systematically computes the correlations between detected features for annotating isotopes, adducts, and in-source fragments (ISFs). The algorithm considers the m/z difference, retention times, scan-to-scan correlations calculated by Pearson\u2019s product-moment correlation coefficient, and MS/MS spectra. It sequentially annotates isotopes, ISFs, and adducts. This implies that features annotated as isotopes will not be considered as ISFs or adducts, and features annotated as ISFs will not be considered as alternative adducts.\n\nAnnotation of isotopes is critical for compound annotation, determination of charge states, and determination of the presence of halogen atoms. MassCube annotates isotopes based on the m/z difference and retention times. Charge state is further determined based on isotope patterns. By default, singly and doubly charged ion species are considered, and users may define the range of charge states to be considered in MassCube. ISF is a major source of false feature annotations. MassCube annotates ISFs based on m/z, retention times, scan-to-scan correlations, and MS/MS spectra. An annotated ISF must meet the following criteria: (1) its precursor m/z presences in the parent MS/MS spectrum; (2) its scan-to-scan correlation with the parent ion at commonly detected scans is higher than tolerance (default\u2009=\u20090.7). When the number of common scans is not sufficient (less than 5) to calculate scan-to-scan correlation, retention time is matched by default tolerance\u2009=\u20090.05\u2009min. MassCube annotates adducts based on m/z, retention time, and scan-to-scan correlation. By default, [M\u2009+\u2009H]+, [M\u2009+\u2009H-H2O]+, [M+Na]+, [M\u2009+\u2009K]+, [M\u2009+\u2009NH4]+, [2\u2009M\u2009+\u2009H]+, [3\u2009M\u2009+\u2009H]+, [M\u2009+\u20092H]2+ are considered in positive ion mode; [M-H]\u2212, [M-H-H2O]\u2212, [M+Cl]\u2212, [M\u2009+\u2009CH3OO]\u2212, [M\u2009+\u2009HCOO]\u2212, [2M-H]\u2212, [3M-H]\u2212, [M-2H]2\u2212 are considered in negative ion mode. Users may choose to consider additional adduct types or define custom adduct types in MassCube. A pair of annotated adducts must meet the following criteria: (1) their m/z difference agrees with the defined value; (2) their scan-to-scan correlation is higher than tolerance (default\u2009=\u20090.7). When the number of common scans is not sufficient (less than 5) to calculate scan-to-scan correlation, retention time is matched by default tolerance\u2009=\u20090.05\u2009min.\n\nReporting chromatographic peak metadata is essential for researchers to focus on Gaussian-shaped peaks and avoid noise. Three metrics are automatically calculated and reported including asymmetry factor, Gaussian similarity, and noise score. More metrics can be easily computed using the masscube Python package, including the variance of m/z values and retention time, peak width and the average intensity of the top three scans (i.e., top average). Asymmetry factor measures how symmetrical a peak is by comparing the distances from the center to its two flanks. For a given chromatographic peak with nnn MS1 scan points, let the set of MS signal intensities be S\u2009=\u2009s1, s2, \u2026, sn, corresponding to n retention times T\u2009=\u2009t1, t2, \u2026, tn. The retention time of the apex ta is defined as\n\nMassCube then identifies the first MS1 scan point from the apex ta to the left where the intensity falls below 10% of the peak height, denoted as tleft, and similarly to the right, yielding tright. The asymmetry factor is then calculated as:\n\nIf tleft\u2009=\u2009ta, the asymmetry factor is defined as 99. This convention is used to indicate a scenario where the left side of the peak is the apex, suggesting a highly asymmetrical peak.\n\nGaussian similarity quantifies the resemblance of a given chromatographic peak to a perfect Gaussian function. The algorithm first fits a Gaussian function to the data S\u2009=\u2009s1, s2, \u2026, sn, resulting in the fitted signal S\u2019\u2009=\u2009s1\u2019, s2\u2019, \u2026, sn\u2019. The cosine similarity score is then computed between the original signal and the fitted signal:\n\nThis score ranges from \u22121 to 1, where a score closer to 1 indicates a high similarity to a Gaussian function. The noise score measures the fluctuation of the MS signal over time. For a given feature data set S\u2009=\u2009s1, s2, \u2026, sn, a data point si is considered a turning point if it meets the following condition:\n\nThe total number of turning points is counted as p, and the noise score is calculated as:\n\nWhen a peak is perfectly smooth, the noise score is 0, as there is only one turning point at the apex.\n\nMassCube requires no additional input, such as a list of internal standards or known metabolites including m/z and reference retention time for retention time correction. The algorithm is designed to be applicable even in the absence of spiked internal standards. The anchor features for retention time correction are selected using QC samples. Suppose n QC samples are analyzed. MassCube first selects the QC sample QCi with the highest total intensity as the reference. Subsequently, features are sorted by m/z values from low to high, leading to F1, \u2026, Fm. All features are examined sequentially. Let mzj and noisej refer to the m/z value and noise score of Fj, respectively. A feature Fj is considered a valid retention time anchor if the following conditions are met:\n\nwhere mztol\u2009=\u20090.01 and noisetol\u2009=\u20090.3 by default. Finally, the valid retention time anchors with the top 50 peak heights are further selected and split into training and testing data. For a given sample, the retention times of the selected anchors are located, and outliers are removed to prevent failed retention time correction. A linear interpolation model is then established for retention time correction.\n\nEvaluation of the retention time correction algorithm was performed using the mouse brain dataset conducted separately for four ion modes. The evaluation workflow is as follows: (1) Load the retention time correction models, which were automatically exported after MassCube data processing. (2) Obtain the retention times of the testing anchors for evaluation, which are different from the training features used for model construction. (3) Apply the retention time correction models to the retention times of the testing anchors. (4) Compare the corrected retention times to the reference retention times.\n\nMassCube identifies the commonly detected features across different files for alignment using m/z and retention time, with default tolerances set to 0.01\u2009Da and 0.2\u2009min, respectively, which are tunable. Gap filling is performed by forced peak-picking using raw MS data, where the highest MS signal is identified within a specified retention time window (default\u2009=\u2009reference retention time\u2009\u00b1\u20090.05\u2009min).\n\nMassCube annotates features by m/z-RT match, identity search and fuzzy search using Flash Entropy Search algorithm implemented in ms-entropy Python package (ver. 1.2.2). MS/MS spectral preprocessing is crucial for accurate spectral matching. In MassCube, preprocessing includes (a) precursor ion exclusion, (b) noise filtering using both relative and absolute intensity thresholds (default: ions with relative intensity <1% of base peak intensity are removed; ions with absolute intensity <10,000 for Orbitrap MS and <500 for QTOF MS are removed), and (c) m/z range-restricted matching. For example, if MS data was collected from m/sz\u2009=\u2009100\u2013400, then MassCube only considers fragments from 100\u2013400 for database matching. In practice, similarity scores between 0.7 and 0.8 are commonly used for putative annotations in metabolomics. A similarity score of 0.7 was chosen in MassCube by default based on its previously published entropy similarity search algorithm36. MassCube supports multiple MS/MS database formats including MSP, JSON, and pickle. Public MS/MS libraries for metabolomics and lipidomics, sourced from the MS-DIAL public MS/MS database, have been prepared in pickle format for faster loading speeds and are available at https://zenodo.org/records/11363475.\n\nMassCube offers two types of data normalization. The first type addresses differences in total sample amount or concentration, such as those found in urine samples. While Probabilistic Quotient Normalization algorithm is used in MassCube by default, the optimal normalization method can vary depending on the dataset and is often debated. We therefore provide alternative algorithms, such as summed intensity, for user selection. The second type focuses on correcting systematic MS signal drifts in large-scale data acquisition. To address this, MassCube includes QC-based normalization. Timestamps from raw data are automatically acquired and utilized to guide this normalization process, requiring no need for user input.\n\nMassCube provides univariate and multivariate models for pairwise and multi-group comparisons. For more advanced, machine learning-based statistical analysis, MassCube includes the phenotype classifier builder. This module leverages the scikit-learn package to perform a range of tasks, including data scaling, univariate feature selection, model construction, cross-validation, and evaluation metrics such as ROC curves. Moreover, the visualization module in MassCube is available for the rapid generation of publication-quality figures, facilitating both manual inspection and data overview. Examples of these visualizations are provided in Supplementary Fig.\u00a09.\n\nMass spectrometry data processing is highly application focused. MassCube is designed to support applications by integrating individual functions and modules into workflows. These pre-defined workflows simplify and accelerate data processing and analysis for specific applications. The MassCube untargeted metabolomics workflow addresses specific bottlenecks in mass spectrometry-based data processing. It integrates metadata curation, feature detection, evaluation, alignment, annotation, and statistical analysis to provide users with a comprehensive view of the data. The untargeted metabolomics workflow is implemented as a command line application. First-time users can generate results in just four steps: (1) install Python, (2) install MassCube with a single command, (3) organize raw data files in a project folder and (4) run \u201cuntargeted-metabolomics\u201d in the folder. A test data set is provided at https://zenodo.org/records/15173232. A quick start user instruction is available at https://huaxuyu.github.io/masscubedocs/docs/quickstart.\n\nMassCube 1.0.22, MS-DIAL 4.90, MZmine 3.90, and xcms R package 4.0.0 were used for performance benchmarking. Python 3.11.7 and R 4.3.0 were used for running masscube Python package and xcms R package, respectively. Complete parameters and code for data processing were provided at https://zenodo.org/records/14159704. To ensure a fair comparison, common key parameters across the compared software were standardized as follows: MS1 intensity tolerance was set to 30,000 for Orbitrap and 1000 for QTOF; m/z tolerance for MS1 scans was 0.01\u2009Da, and m/z tolerance for MS/MS scans was 0.015\u2009Da. Specific key individual parameters in each software were set by default as recommended by the respective tools:\n\nMassCube: sigma in Gaussian filter: 1.2; peak prominence ratio: 0.1; scan-to-scan Pearson\u2019s correlation coefficient tolerance for feature grouping: 0.7.\n\nMS-DIAL: mass slice width: 0.05\u2009Da; smoothing algorithm: linear weighted moving average; smoothing level: 3 scans; minimum peak width: 5 scans.\n\nMZmine: peak detection algorithm: ADAP; smoothing algorithm: Savitzky Golay algorithm; retention time smoothing: 5; chromatogram resolving: local minimum resolver; minimum consecutive scans: 5\u2009scans.\n\nxcms: peak detection algorithm: centWave; peak width: 5 to 60\u2009s; signal-to-noise threshold: 0; prefilter: 3 to 100.\n\nTwo types of synthetic MS signals were generated to represent double peaks and single peaks, respectively. For double peaks, we prepared all combinations of the three peak parameters as shown in Fig.\u00a02b including S/N, intensity ratio, and peak resolution. Different S/N were achieved by tuning the amplitude of Gaussian noise added to the normal distribution relative to the peak height (2, 4, 6, 8, 10%). For double peaks, the intensity ratio represented the peak height ratio of the two peaks (1, 2, 3, 4, 5), and peak resolution was varied (1.00, 1.25, 1.50, 1.75, 2.00). Double peaks where the smaller peak had an S/N lower than 3 were excluded, as they fell below the limit of detection (LOD). For each combination, 1000 random replicates were generated for optimizing parameters in MassCube, resulting in a total of 110,000 single peaks and 110,000 double peaks. For algorithm performance comparison, we generated a synthetic mzML file to ensure that all software tools process the same file for a fair evaluation. This allows for a direct comparison of feature detection accuracy and efficiency across MassCube, MS-DIAL, MZmine3, and xcms under identical conditions. A new set of 100 replicates for each combination was generated with an additional condition for noise score\u2009=\u20090, yielding a total of 13,500 double peaks and 13,500 single peaks. All generated signals were inserted into raw QTOF MS data of human urine samples, with distinct m/z values outside the original mass range to avoid overlapping with the real signals. The list of inserted peaks, Python code for simulation, and the generated mzML data file are available at https://zenodo.org/records/14159704.\n\nMassCube 1.0.22, MS-DIAL 4.9, MZmine 3.90, and xcms R package 4.0.0 were all used to process the same synthetic mzML data file as described above. To ensure a fair comparison, the default parameters for QTOF were used across all four software platforms, as the original data were acquired from a QTOF MS. It is important to note that the settings for mass tolerance and intensity tolerance did not affect the results since the inserted synthetic MS signals were of high intensity (on the order of 106) and exhibited no mass variation between different scans. This design ensures the focus on benchmarking the performance of different algorithms with respect to varying chromatographic peak shapes rather than other factors such as m/z variance.\n\nEight experimental LC-MS/MS data were used for benchmarking feature detection performance, including NIST SRM 1950 plasma data, acquired on Thermo Orbitrap Exploris 480 MS, reverse phase (RP) positive ion mode; NIST SRM 1950 plasma data, acquired on Thermo Q Exactive MS, RP negative ion mode; mouse plasma data, acquired on Thermo Q Exactive HF MS, hydrophilic interaction chromatography (HILIC) positive ion mode; NIST Human Fecal Material RGTM 10162 data, acquired on Thermo Q Exactive MS, HILIC negative ion mode; Human serum data, acquired on Bruker impact II QTOF MS, RP positive ion mode; Mouse feces data, acquired on Bruker impact II QTOF MS, HILIC positive ion mode; whole fruit fly (D. melanogaster strains) data, acquired on Bruker impact II QTOF MS, HILIC positive ion mode; and human urine data, acquired on Bruker impact II QTOF MS, HILIC negative ion mode. Raw data files, complete parameters and code for data processing, along with the output feature tables from all four tested software tools, are provided at https://zenodo.org/records/14159704. A total of 722 local extracted ion chromatograms (EICs) were randomly selected from the Orbitrap and QTOF data and manually labeled by experienced analytical chemists. The PNG files for labeled EICs, along with a detailed list of their corresponding m/z values and retention times, and information on whether they were identified by different software tools, are provided at https://zenodo.org/records/14159704. Speed benchmarking was performed using four metabolomics studies obtained from the MassIVE and MetaboLights repositories (see \u201cData availability\u201d). Single-threading was used during the speed benchmarking of the four metabolomics studies on a Windows Desktop with an AMD Ryzen Threadripper PRO 2945WX 12-Core CPU and 32 GB of memory. Parallel processing was enabled for processing Orbitrap Astral MS data collected from human plasma samples from Alzheimer\u2019s Disease patients on a MacBook M3 Pro laptop with 36 GB of memory. Detailed sample preparation workflow and LC-MS experimental settings for analyzing the human plasma samples were listed in Supplementary Note\u00a02. No biological metadata, including sex, was used in this study, because the dataset was solely intended for speed benchmarking and software stability demonstration.\n\nThe raw MS data files were obtained from a previous study, acquired using a ThermoFisher Q-Exactive HF with a HESI-II ion source (Thermo Scientific, Waltham, MA, USA) coupled with a Vanquish UHPLC system (Thermo Scientific, Waltham, MA, USA). The study examined cohorts of 8 male and 8 female wild-type mice at four life stages: adolescence (AD, 3 weeks), early adulthood (EA, 16 weeks), middle age (MA, 59 weeks), and old age (OA, 92 weeks). Immediately following euthanasia, brains were harvested and dissected into 10 anatomically defined regions: cerebral cortex (CT), olfactory bulb (OB), hippocampus (HC), hypothalamus (HT), basal ganglia (BG), thalamus (TL), midbrain (MB), pons (PO), medulla (MD), and cerebellum (CB). In total, 640 brain samples were analyzed using two complementary assays including untargeted metabolomics via hydrophilic interaction chromatography (HILIC) and untargeted lipidomics via reversed-phase liquid chromatography (RPLC). Sample preparation and detailed experimental configurations are provided in Supplementary Note\u00a03. Data analysis was performed using MassCube version 1.0.16. Data processing parameters and the exported feature table are available at Zenodo with accession code 14159704 [https://zenodo.org/records/14159704]. An outlier detection algorithm was developed in MassCube to automatically identify failed LC-MS injections, ensuring a clean data report and quality control. Details of the outlier detection algorithm are specified in Supplementary Note\u00a04, with examples of detected outliers provided in Supplementary Fig.\u00a010. UMAP visualization was conducted in Python using the umap package. Chemical classification was performed using the ClassyFire Batch Compound Classification tool (https://cfb.fiehnlab.ucdavis.edu). Chemical Similarity Enrichment Analysis was conducted using the ChemRICH tool (https://chemrich.fiehnlab.ucdavis.edu/).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "For software accuracy benchmarking: synthetic MS data and eight raw experimental LC-MS data files used for algorithm benchmarking are available at Zenodo with accession code 14159704. For speed benchmarking, orbitrap dataset #1 of NIST Human Fecal Material Standards was obtained from MassIVE with accession code MSV000086988 [https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=68d120fefcf243dcb83cdf5f448c31a7]; orbitrap dataset #2 of urine metabolomics storage study was obtained from MassIVE with accession code MSV000091929 [https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=25663ex4d7cc7410cbb0324b08c2c892a]; QTOF dataset #1 of plant metabolomics was obtained from MetaboLights with accession code MTBLS188; QTOF dataset #2 of Type 1 diabetes plasma lipidomics study was obtained from MetaboLights with accession code MTBLS620. For demonstration of peak evaluation criteria, 41 public datasets used for modeling the distribution of peak metadata were obtained from MetaboLights, with their study identifiers provided at Zenodo with accession code 14159704. For biological application, data of the Atlas of the Aging Mouse Brain can be accessed from the Metabolomics Workbench under Project ID PR001047. Data from the NIST human fecal material standards are available from the MassIVE repository under MSV000086989 [https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=2f73277b4e034948acebfdf1edab17ed]. For metabolite annotation, the NIST23 Tandem Mass Spectral Library used in biological applications can be purchased from NIST or other distributors [https://www.nist.gov/programs-projects/nist23-updates-nist-tandem-and-electron-ionization-spectral-libraries]; the licensed NIST23 Mass Spectral Library can only be accessed after purchase, as per NIST policy. Supplementary Data Files including raw output from MassCube by re-analyzing the data of the Atlas of the Aging Mouse Brain, eight experimental LC-MS data files used for algorithm benchmarking, EICs in.png format for manually labeled experimental data for algorithm benchmarking, MassCube\u2019s ouput of 41 metabolomics studies including 200 individual files from MetaboLights and MassCube\u2019s output of the phenotype classifier for mouse brain data are available at Zenodo with accession code 14159704. The human plasma data of Alzheimer\u2019s Disease patients collected on Orbitrap Astral MS are available on MassIVE with accession code MSV000097583 [https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=100416b91eb24735a53eb2eecf2fd3d6]. Unless otherwise stated, all data supporting the results of this study can be found in the article, supplementary, and source data files.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All the source code for the MassCube project is available on GitHub [https://github.com/huaxuyu/masscube] with https://doi.org/10.5281/zenodo.15151320 [https://doi.org/10.5281/zenodo.15151320]. Python and R code for data processing are available at Zenodo with accession code 14159704. Outline for source code is provided in Supplementary Note\u00a05.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Folz, J. et al. Human metabolome variation along the upper intestinal tract. Nat. Metab. 5, 777\u2013788 (2023).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nBar, N. et al. 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Samples from the National Centralized Repository for Alzheimer\u2019s Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG021886) awarded by the National Institute on Aging (NIA), were used in this study. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. Samples are contributed by the NIA-funded ADRCs: P30 AG072976 (PI Andrew Saykin, PsyD); P30 AG066512 (PI Thomas Wisniewski, MD); P30 AG062429 (PI James Brewer, MD, PhD); P30 AG062715 (PI Sanjay Asthana, MD, FRCP); P30 AG072973 (PI Russell Swerdlow, MD).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "West Coast Metabolomics Center, University of California Davis, Davis, CA, USA\n\nHuaxu Yu,\u00a0Tong Shen,\u00a0Min Liu,\u00a0Yuanyue Li\u00a0&\u00a0Oliver Fiehn\n\nChina CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, PR China\n\nJun Ding\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.Y. and O.F. designed the research. H.Y. developed the MassCube Python package, prepared documentation, established the data simulation algorithm, prepared benchmarking datasets, performed algorithm benchmarking, and data processing for biological application. J.D. acquired the Metabolome Atlas of the Aging Mouse Brain dataset and reviewed the data reprocessing results. T.S. acquired the Orbitrap Astral MS dataset and reviewed the data reprocessing results. M.L. performed software testing as analytical chemist without guidance. Y.L. supported implementation of the entropy search algorithm into MassCube. H.Y. and O.F. wrote the manuscript with contributions from all other authors.\n\nCorrespondence to\n Oliver Fiehn.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Wei Jia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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"Revealing an unexpectedly low electron injection threshold via reinforced shock acceleration", + "pre_title": "Revealing an Unexpectedly Low Electron Injection Threshold via Reinforced Shock Acceleration", + "journal": "Nature Communications", + "published": "13 January 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55641-9/MediaObjects/41467_2024_55641_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55641-9/MediaObjects/41467_2024_55641_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55641-9/MediaObjects/41467_2024_55641_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://lasp.colorado.edu/mms/sdc/public/", + "https://themis.ssl.berkeley.edu/data_products/index.php", + "https://spdf.gsfc.nasa.gov/pub/data/omni/", + "/articles/s41467-024-55641-9#Fig2", + "/articles/s41467-024-55641-9#Fig3", + "/articles/s41467-024-55641-9#Sec15" + ], + "code": [ + "https://github.com/spedas/pyspedas/tree/master", + "/articles/s41467-024-55641-9#ref-CR70", + "http://spedas.org/blog/", + "/articles/s41467-024-55641-9#ref-CR71", + "https://github.com/irfu/irfu-matlab/tree/master", + "/articles/s41467-024-55641-9#ref-CR72", + "https://zenodo.org/records/14048045", + "/articles/s41467-024-55641-9#ref-CR73", + "https://github.com/SavvasRaptis/Relativistic-Electrons-Foreshock", + "/articles/s41467-024-55641-9#ref-CR38", + "https://ecamporeale.github.io/codes.html", + "https://ecamporeale.github.io/software/OMNI2_classification.dat", + "https://ecamporeale.github.io/software/classify_solar_wind.m", + "https://ecamporeale.github.io/software/parameters_classification.mat", + "/articles/s41467-024-55641-9#ref-CR69" + ], + "subject": [ + "Astrophysical plasmas", + "Magnetospheric physics" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4331187/v1.pdf?c=1736860029000", + "research_square_link": "https://www.researchsquare.com//article/rs-4331187/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-55641-9.pdf", + "preprint_posted": "07 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Collisionless shock waves, found in supernova remnants, interstellar, stellar, and planetary environments, and laboratories, are one of nature's most powerful particle accelerators. This study combines in-situ satellite measurements with recent theoretical developments to establish a novel reinforced shock acceleration model for relativistic electrons. Our model incorporates transient structures, wave-particle interactions, and variable stellar wind conditions, operating collectively in a multiscale set of processes. We show that the electron injection threshold is on the order of suprathermal range, obtainable through multiple different phenomena abundant in various plasma environments. Our analysis demonstrates that a typical shock can consistently accelerate electrons into very high (relativistic) energy ranges, refining our comprehension of shock acceleration while providing new insight on the origin of electron cosmic rays.Physical sciences/Astronomy and planetary science/Astronomy and astrophysics/High-energy astrophysicsPhysical sciences/Astronomy and planetary science/Astronomy and astrophysics/High-energy astrophysicsPhysical sciences/Physics/Space physics/Solar physicsPhysical sciences/Physics/Space physics/Solar physicsPhysical sciences/Physics/Plasma physics/Astrophysical plasmas", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Collisionless shock waves, found in supernova remnants, interstellar, stellar, and planetary environments, and laboratories, are one of nature\u2019s most powerful particle accelerators. This study combines in\u00a0situ satellite measurements with recent theoretical developments to establish a reinforced shock acceleration model for relativistic electrons. Our model incorporates transient structures, wave-particle interactions, and variable stellar wind conditions, operating collectively in a multiscale set of processes. We show that the electron injection threshold is on the order of suprathermal range, obtainable through multiple different phenomena abundant in various plasma environments. Our analysis demonstrates that a typical shock can consistently accelerate electrons into very high (relativistic) energy ranges, refining our comprehension of shock acceleration while providing insight on the origin of electron cosmic rays.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Understanding how charged particles in the universe gain immense energy, reaching relativistic levels, remains a fundamental challenge in modern physics. Collisionless shocks are ubiquitously found throughout the universe (e.g., heliospheric1, astrophysical2,3, planetary4, and laboratory5) and are considered to be the primary drivers of accelerating charged particles to such an energetic regime6. Therefore, they have been the main focus of research for understanding the cosmic ray (ultra-relativistic particles) profiles observed on Earth. Shock Drift Acceleration (SDA) occurs when a charged particle encounters a collisionless shock. This mechanism can typically accelerate electrons to relatively high energies, but it is not sufficient to explain the observations of relativistic electrons7. Diffusive Shock Acceleration (DSA), known as the first-order Fermi mechanism, can accelerate particles to relativistic energies8. However, to get particles efficiently accelerated through DSA, they need to be pre-energized to a certain threshold before they can be injected into the acceleration process. Ion injection to DSA is sufficiently understood since ions have a large dynamical scale length, comparable to typical shock scales. On the other hand, electrons due to their much smaller gyroradius, have a higher injection threshold, requiring them to reach mildly relativistic energies (10\u2013100\u2009keV) before they can get further accelerated through DSA. Finding which process consistently allows these energies to be obtained in a collisionless shock is called the electron injection problem and remains one of the open questions of modern physics9,10,11.\n\nThe dynamics of a collisionless shock depend greatly on a key property, the angle between the shock normal vector and the magnetic field in the upstream plasma (\u03b8Bn)12. In a typical plasma consisting of electrons and protons, when this angle is approximately less than 45 degrees, protons (and heavy ions) can get reflected, travel far upstream, interact with the incident plasma, and cause a series of instabilities and wave activity forming an extensive region called the foreshock or precursor. The foreshock region consists of a strongly variable and dynamically evolving plasma, while characterized by temporally varying (transient) and spatially localized structures. Subsequently, this extends the shock\u2019s presence and intensifies its impact on the nearby plasma. As a result, in our Solar System, quasi-parallel shocks (\u03b8Bn\u2009\u2264\u200945\u00b0) typically emerge as notably more efficient particle accelerators compared to quasi-perpendicular (\u03b8Bn\u2009\u2265\u200945\u00b0) shocks4,13,14. Foreshock transients (or dayside/shock-generated transients) occur multiple times per day15 and form when the solar wind and its embedded, variable, and discontinuous magnetic field interact with a shock wave, like Earth\u2019s bow shock15,16. Foreshock transients are also recognized for their ability to accelerate particles to high energies13,14,17. The transients examined in this study are commonly referred to as Hot Flow Anomalies (HFAs) in heliophysics literature. However, due to their similarities with other transients, such as Foreshock Bubbles (FBs), which also play a role in particle acceleration, we use the more general term \u2019foreshock transient\u2019 throughout the text.\n\nIn this work, we propose a potential resolution to the injection problem by showing in situ observations of a comprehensive multistep process operating across various scales, allowing electrons to consistently reach relativistic energies. As we present below, our model refines the traditional shock acceleration processes by introducing findings revealing an electron injection threshold. This result is obtained through encompassing recent advancements in astrophysical plasmas and wave-particle interactions and is generalized to other stellar and interstellar environments.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The model presented in this work relies primarily on in situ observations from two NASA missions. The Magnetospheric Multiscale (MMS) mission18 provides near-Earth observations, while the Acceleration, Reconnection, Turbulence, and Electrodynamics of Moon\u2019s Interaction (ARTEMIS) mission19 describes the far upstream plasma environment close to the dayside Moon. On December 17, 2017, between 17:50 UT and 17:55 UT, the MMS spacecraft was positioned just upstream of Earth\u2019s bow shock and observed an energetic event. MMS recorded a foreshock transient associated with the highest energetic electrons ever observed upstream of the bow shock since its prime mission started in 2015, under relatively steady solar wind conditions. Electrons reached observable intensity enhancements to more than 500\u2009keV, a remarkable feat considering the typical observable range is up to only a few keV in the solar wind near Earth. This observation is unique, as there was an absence of significant solar disturbances, like a flare or a coronal mass ejection (CME), which could otherwise explain the presence of such relativistic electrons. Our findings indicate that the electron acceleration during this event manifested within a foreshock transient. This phenomenon emerged due to a disturbance (discontinuity) in the interplanetary magnetic field, interacting with Earth\u2019s bow shock and foreshock plasma. ARTEMIS, positioned in lunar orbit over one hundred thousand kilometers towards the Sun from MMS, observed this magnetic disturbance along with an associated electron seed population a few minutes before the foreshock transient formation.\n\nIn Fig.\u00a01, we present an overview plot highlighting the observations, emphasizing the presence of high-energy electrons above the rest mass energy (>\u2009511\u2009keV), as shown in panels (d) and (g). As displayed in panels (a\u2013h) around the green shaded area, a compressive edge is formed resulting in a fast collisionless quasi-perpendicular shock, which is a typical feature of foreshock transients14,20. Another vital feature of the foreshock transient is shown around the purple shaded area, where the core of the structure is infused by high-amplitude electromagnetic waves over a wide range of frequencies (illustrated in Fig.\u00a02), in a low-density plasma environment. This wave and topological environment facilitates electron scattering, enabling them to cross the shock (green shaded area) multiple times, where typical shock acceleration mechanisms occur (i.e., DSA/SDA)9,21. However, in contrast to theoretical and observational expectations of quasi-perpendicular SDA21, in panel (h), the energy power law spectral index reaches canonical values up to p\u2009=\u2009\u2212\u20092, which is the theoretical prediction for DSA at strong shocks22,23,24. This indicates that the acceleration observed upstream of the planetary bow shock results in an even harder spectrum than what would typically be produced by a classical DSA mechanism. One direct explanation for this observation is that in foreshock transients, the wave field is found in a lower background magnetic field than in a typical downstream shocked plasma, allowing particles to become more efficiently scattered and subsequent acceleration to take place25,26. Moreover, these electrons can exhibit a bouncing behavior between Earth\u2019s nearby shock environment and the foreshock transient. This interplay results in ideal confinement, enhancing the effectiveness of acceleration compared to a typical quasi-perpendicular shock13,25,26 while other factors, such as the presence of electrostatic waves can also play a role in the scattering of particles near the shock transition27,28,29.\n\na magnetic field components and magnitude in a Geocentric Solar Ecliptic (GSE) system, (b) ion and electron densities, (c) ion plasma velocity components and magnitude in GSE coordinates, (d) high-energy differential energy flux electron spectra, (e) low-mid energy differential energy flux electron spectra, (f) combined differential energy flux ion spectra, (g) integrated energy electron flux between 100 and 500\u2009keV, (h) fitted spectra index for Ep between 80\u2013200\u2009keV, (i) electron phase space density (PSD) versus energy for the whole energy range along with background noise level, (j) series of PSD versus energy line plots for high-energy measurements over different time intervals along with the background noise level limit. The colors of the panel (j) are indicated as highlighted areas in the time series plot of panels (a\u2013h). The event is observed from 17:52:45 to 17:53:25. Panels (d) and (g) show a distinct enhancement of relativistic energies in the 100\u2013500\u2009keV range. The green shaded area around 17:53:15-20 shows the area that has the strongest acceleration. The electron enhancement there can be seen compared to other times by looking at panel (j). The title shows the location of the MMS spacecraft in GSE coordinates given in Earth radius units. More information regarding the derivation of each product can be found in methods, subsection Data post-process and derivations.\n\na magnetic field components and magnitude in GSE coordinates, (b) electron density, (c) electron temperature anisotropy, (d) electric field power spectra, (e) magnetic field power spectra, (f) zoomed-in plot of magnetic field power spectra, (g) ellipticity showing the polarization of the magnetic field power spectra, (h) wave propagation angle, (i) pitch angle distribution (PAD) for electrons between 40 and 200\u2009keV averaged over 6 measurements when the wave activity is the strongest as indicated by the date at the title. Error bars represent the Standard Deviation (SD) for each measurement. Nonlinear electron whistler waves are present in the large-amplitude, banded emissions between 0.1 and 1.0 of the electron cyclotron frequency, fce, with ellipticity near 1 (red in panel g) and propagation angles parallel to the background magnetic field (blue in panel h). 0.1 and 1.0 fce are shown with the bottom-most dotted and solid white lines in the wave panels; the magenta line and the middle dashed line in the wave panels are the plasma lower hybrid frequency and 0.5 fce, respectively. Such nonlinear whistler-mode wave packets are capable of resonantly interacting with relativistic electrons, resulting in predominantly perpendicular acceleration (as shown in panel i). More information regarding the derivation of each product can be found in the methods, subsection Data post-process and derivations.\n\nIn Fig.\u00a02, a crucial factor reinforcing the shock acceleration process is unveiled. Specifically, by analyzing the compressive (shock) region of the foreshock transient, the interaction between high-frequency, high-amplitude, banded emissions of nonlinear electromagnetic waves and the local electron populations is shown. Panel (c) of Fig.\u00a02 displays a notable electron temperature anisotropy (\\({A=1-{{\\rm{T}}}_{{\\rm{per}}}/{{\\rm{T}}}_{{\\rm{per}}} < 0}\\)) known to drive large-amplitude, high-frequency electromagnetic waves, termed whistlers (or chorus in magnetospheric research nomenclature), which can experience instability and wave growth from electron betatron acceleration localized within the compression region14,30. The presence and characteristics of these waves enable higher energy electrons to cyclotron resonate with them, amplifying the electrons\u2019 energy through wave-particle interactions31, thereby further enhancing the shock acceleration process32. This phenomenon has been observed in magnetospheric environments32,33 and recently in collisionless shocks30. The nature of these waves is identified by the characteristic narrowband frequency in the magnetic field power spectra between 0.1 and 1.0 of the local electron cyclotron frequency (panels e and f), the right-hand polarization (panel g), and the propagation parallel to the magnetic field (panel h). Finally, cyclotron-resonant acceleration of relativistic electrons is expected to result in a predominantly perpendicular (with respect to the background magnetic field) acceleration32,33. As shown in panel (h), the pitch angle distribution (PAD) confirms this theoretical expectation. The electron's pitch angle distribution peaks at 90 degrees with respect to the local magnetic field as soon as the spacecraft crosses the wave acceleration region, in agreement with the wave-particle acceleration model. It should be noted that other mechanisms, such as betatron acceleration, could produce similar pitch angle distributions (PADs) and are expected to contribute to the observed acceleration. However, evaluating the contribution of each mechanism is not feasible observationally and falls outside the scope of this study.\n\nAt this point, we have confirmed the presence of multiple acceleration and confinement components taking place from small (i.e., plasma kinetic) to large (global with respect to Earth\u2019s bow shock within the solar wind) scales. Specifically, we have shown the presence of shock acceleration in the foreshock transient13,14, a strong wavefield and confining region25,26, and nonlinear wave-particle interactions and acceleration between the electromagnetic whistler waves and the electron distribution30,34. Furthermore, the environment between the primary planetary bow shock and the foreshock transient creates a larger bounded environment in which the particles can remain confined, bouncing between the regions13,25, this is particularly true for magnetic field discontinuities that intersect the bow shock under certain geometries like in our case (see \u201cMethods\u201d, subsection Statistical analysis details), these elements cannot entirely explain the distinctive presence of electrons with energies of 500\u2009keV, nor the occasional absence of such particle population during foreshock transients with similar characteristics25. Hence, we further explored the possibility of identifying whether a specific seed population, well beyond the vicinity of Earth\u2019s bow shock environment, was present, corresponding to global system size effects. From a multi-case study described below, our investigation revealed a clear increase in the suprathermal electron flux (1\u20135\u2009keV) measured by the ARTEMIS spacecraft in dayside lunar orbit, during all the foreshock transient events associated with electron acceleration, as observed by MMS. In addition, this seed electron population was associated with the faster-than-average solar wind, typical of what emerges from solar coronal holes at higher solar latitudes, resulting in high-speed plasma streams35,36,37.\n\nTo validate our model, we extensively reviewed all available observations from the operational years of MMS. While observational limitations were present (MMS required to be upstream of the shock with burst data availability and ARTEMIS in dayside far-upstream lunar orbit), we found several events aligning, either partially or fully, with our model components. Upon analysis, we noticed a distinct pattern. Whenever MMS recorded electron fluxes exceeding 100\u2009keV upstream of the bow shock, ARTEMIS detected a clear seed population of suprathermal, solar wind electrons. This trend consistently occurred when the solar wind originated from the Sun\u2019s coronal holes, typically referred to as fast solar wind35,36,37,38. Our findings were further supported by the increased occurrence of magnetic field discontinuities and overall variability (necessary ingredients for the formation of foreshock transients) in fast solar wind plasma39,40. In all events associated with fast coronal hole plasma, the suprathermal electron flux (1\u20135\u2009keV) was notably elevated, ranging from 2 to 5 times higher than the background level (see Fig.\u00a03). In contrast, slow solar wind events in which no relativistic electron acceleration occurred were lacking an enhanced seed population. As a result, despite the presence of magnetic discontinuities and consequently the formation of foreshock transients, MMS measured primarily background noise on the high-energy particle instruments. The source of the initial seed population could potentially arise from magnetic reconnection in the solar wind41, turbulence42, discontinuities impacting the solar wind plasma43, or direct jet outflow from the coronal hole environment44. Furthermore, by using the high-energy instrument on board ARTEMIS and removing events flagged by low quality, we confirm that relativistic electrons were not present in lunar orbit for the main event analyzed and for most of the seeded events. This allowed us to rule out the possibilities of pre-accelerated relativistic electrons coming from external origin. Finally, the enhanced suprathermal electron flux at ARTEMIS contains both earthward and sunward components, while the magnetic connectivity to the bow shock takes place only for a couple of cases. This indicates that a significant portion of the observed seed population appears to be of Solar origin. However, the connectivity to the shock and the presence of sunward particles in some cases hints toward an interplay between the solar wind and the Earth\u2019s bow shock under which particles from the electron foreshock may contribute to the seeding of the acceleration mechanism (see e.g., ref. 25). More details are shown in methods, subsection Statistical analysis details. Finally, we should note that while the presence of fast coronal hole (CH) solar wind and associated suprathermal energy elevation is a necessary condition for the acceleration of high-energy electrons, it is not sufficient on its own. There are instances where, despite the fast solar wind conditions, the acceleration mechanisms are limited, resulting in an absence of high-energy (100\u2009+\u2009keV) electrons.\n\nThe analysis includes six events with and without the presence of different components that embody our model. The y-axis shows the maximum ratio between the 1\u20135\u2009keV flux of the seed population with respect to the background from the initial solar/stellar wind, while the x-axis indicates the number of each event. The colorbar shows the bulk flow velocity of the stellar wind, revealing the connection between the high-speed stream and the higher maximum electron flux ratio. The marker color shows if the particles originate from fast stellar wind (blue) or slow stellar wind (red). CH refers to coronal hole plasma, SR to sector reversal, SB to streamer belts, and EJ to ejecta36,37,38. The marker shape shows the presence of the initial seed enhancement, with a diamond indicating the seeded events and a circle for the non-seeded ones. The different marker size indicates the amplitude of the nonlinear electromagnetic waves compared to the background (\u03b4B/B0), while each event is accompanied by text referring to the energy channel for which a significant electron flux was measured above the noise instrument level close to Earth. More information regarding the derivation of each product and the classification of the solar wind can be found in methods, subsection Solar wind origin determination. The exact times for which we took measurements for MMS and ARTEMIS are given in methods, subsection Statistical analysis details. Error bars represent the Standard Deviation (SD), calculated using the intervals described in methods, subsection Data post-process and derivations.\n\nTo complete our analysis, we investigated whether the energies observed at the most energetic of our events (Figs.\u00a01 and 2) are bounded by diffusion scale lengths of Earth\u2019s bow shock environment. For plasma environments with wavefields corresponding to high nonlinearity (i.e., \u03b4B ~\u2009B0) like the foreshock transient\u2019s core region (purple shaded area Fig.\u00a01), the diffusion length is approximated by assuming Bohm diffusion6. This means that the effective mean free path of electrons in these environments is approximately equal to the electron gyroradius (i.e., \u03bb\u00a0~\u2009rce)6. Using that, we recovered that for the maximum energy observed (about 0.5\u2009MeV) we obtain, a diffusion length to be in the order of 10\u2013100 Re\u2009=\u20096 \u22c5 104\u22125\u2009km (see more details in methods, subsection Diffusion time scales and spatial scale sizes) which is on the same order as foreshock transients\u2019 scale sizes observed at Earth45.\n\nThis finding is particularly interesting, as other planetary environments with larger system scale sizes (e.g., Jupiter) can sustain higher obtainable maximum energy. By using a fixed diffusion length and magnetic field, we can calculate the highest energy that an electron can obtain before the scale of its gyroradius is too large for it to remain in the shock acceleration region. Specifically, by using an approximate diffusion length of L\u2009=\u2009100\u00a0\u2212\u00a01000\u2009Re \u2248\u20096 \u22c5 105\u22126\u2009km and a background magnetic field of 1 nT, we can obtain higher relativistic energies up to 2\u2009MeV. which is in agreement with previous observations and analysis45,46. It should be noted that this upper energy can be higher in other astrophysical systems (e.g., ultrahot Jupiters47). Following the same argumentation as above, our mechanism shows that such a system can sustain electrons in the order of GeV to low TeV range (even when considering synchrotron losses), corresponding to the highest, ultra-relativistic energies of cosmic ray electrons observed at Earth48,49.\n\nFinally, in Fig.\u00a04, we provide an illustrative summary of the proposed, reinforced shock acceleration model, depicting the evolutionary pathway of two distinct events. The first event, showcased in blue on the right-hand side of the schematic, corresponds to the main seeded event detailed in Figs.\u00a01 and 2 (also referenced as #1 seeded event in Fig.\u00a03). In contrast, the left-hand side of the schematic portrays a non-seeded event (highlighted in red), identified as #1 non-seeded in Fig.\u00a03. This schematic comparison offers a clear visualization of the differences between each possible scenario highlighted by in situ measurements of both ARTEMIS and MMS mission.\n\nThe process unfolds in several steps, illuminating the distinctions between two distinct events: the primary seeded event marked in blue and a non-seeded event in red. The first step is the fueling of the process, originating directly from the Sun\u2019s activity, in which the fast solar wind plasma provides a seed population above the electron injection threshold (1\u20135\u2009keV) associated with magnetic field discontinuities as measured by the ARTEMIS mission during its dayside Lunar orbit. The next step involves a Fermi-type shock (i.e., DSA) and/or shock drift (SDA) acceleration, occurring at the sunward-facing shock within a foreshock transient. This transient arises after the discontinuity interacts with Earth\u2019s bow shock. The acceleration of electrons is further enhanced by the presence of wave-particle interactions between electrons and high-amplitude, high-frequency electromagnetic waves. Lastly, the process\u2019s efficiency is intensified by particle scattering and confinement within the local magnetic topology of the transient and its connectivity to the primary shock (i.e., Earth\u2019s bow shock in this case). Electromagnetic waves within the foreshock effectively scatter particles back to the acceleration region, while the surrounding shock\u2019s geometry acts as a confining boundary, enabling electrons to accumulate and obtain relativistic energies before reaching Earth and being lost to the downstream medium. The image of the Sun was taken during a solar eclipse, and credits are attributed to Miloslav Druckm\u00dcller, Peter Aniol, and Shadia Habbal http://www.zam.fme.vutbr.cz/~druck/eclipse/ecl2009e/0-info.htm.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55641-9/MediaObjects/41467_2024_55641_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55641-9/MediaObjects/41467_2024_55641_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55641-9/MediaObjects/41467_2024_55641_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55641-9/MediaObjects/41467_2024_55641_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our work engendered a unified shock acceleration mechanism applicable to stellar and interstellar plasma environments. First, we revealed a surprisingly low electron injection threshold, observed using in situ space plasma observations. This threshold at Earth\u2019s system is found to be in the order of suprathermal range (1\u20135\u2009keV). These suprathermal electrons occur under fast solar wind conditions and are sufficient to enable a reinforced shock acceleration mechanism. The presence of this seed population allows the foreshock transient to consistently produce relativistic electrons50. This is the result of a particularly efficient multiscale process (acceleration efficiency: \\(\\nu=\\frac{{{{{\\rm{U}}}}}_{{{{\\rm{e}}}}}}{{{{{\\rm{U}}}}}_{{{{\\rm{i}}}}}}\\approx 0.05=5\\%\\) (see more details on methods, subsection Data post-process and derivations). On ion kinetic scales, the foreshock transient, along with its compressive edge forms, allows shock acceleration to occur ahead of the primary planetary bow shock14,17. On electron kinetic scales, within the foreshock\u2019s shock, wave-particle energy transfer from the nonlinear high-frequency, high-amplitude electromagnetic waves contribute to the acceleration of suprathermal electrons to relativistic energies30,34,51. This resonance mechanism occurs alongside larger-scale processes such as betatron and Fermi acceleration, which take place within the foreshock transient scales14,16,25. Moving to even larger scales, as electrons emerge from the foreshock transient\u2019s shock acceleration region, they are confined by the presence of a highly nonlinear wavefield within the foreshock transient\u2019s core and by the presence of the primary planetary bow shock that geometrically bound the electrons. We should note that since this whole process is taking place upstream of the primary shock, the already accelerated electrons can get additional energy via shock processes at the primary shock9,21,52.\n\nUndoubtedly, the most important result we showed is that for Earth\u2019s shock system, the electron injection threshold in our model can be in the order of low 1\u201310\u2009keV range (seed population in solar/stellar wind). This relatively low level of energy requirement in electron flux can be achieved through multiple mechanisms, yet it appears to be occurring exclusively under coronal hole solar wind plasma, corresponding to relatively faster than typical solar wind conditions. As the solar activity is approaching its maximum (2024-2025), we expect solar-focused missions such as Parker Solar Probe (PSP) and Solar Orbiter in conjunction with magnetospheric missions to shed light on the nature and origin of this seed population embedded within the high-speed solar wind stream.\n\nIn addition, our results enhance the understanding of electron acceleration by providing a clear explanation for past observations of relativistic electrons on Earth. Previously, these cases were puzzling due to their unusually high-energy populations. By re-evaluating the three events discussed in ref. 17 and the event in ref. 14, we identify consistent conditions across all four events. Specifically, each event observed by THEMIS, like our findings, is associated with a distinct fast coronal hole solar wind. In the few cases where ARTEMIS provided undisturbed upstream data, an elevated suprathermal electron flux was also observed. This insight is crucial, as it clarifies the unique nature of these foreshock transients, supporting our conclusions, and providing the missing piece needed to explain previous results.\n\nMoving on, the generalization of this model is straightforward, since its ingredients are essentially fundamental astrophysical plasma processes (collisionless shocks and wave-particle interactions). The presence of the foreshock transients within our Solar System is also consistently found in all planetary systems where sufficient in situ measurements are available. Furthermore, the transients\u2019 size scales with the planetary system size, allowing more efficient acceleration to occur in larger collisionless shock systems45,46,53,54. Within our Solar System, the larger system size of gas giants (e.g., Jupiter) allows our mechanism to sustain the energization of particles up to the MeV range, consistent with previous observations. However, further evaluation by the planetary research community is crucial for the validation of our work and for the unification of Heliophysics subdomains.\n\nApart from addressing electron acceleration within our Solar System, the implications of this model are directly influencing the origin of the electron cosmic ray profile. Cosmic rays are thought to originate from high-mach number supernova shocks (MA\u2009>\u200910)55 although determining the exact details is an active research topic. However, it should be noted that even higher Mach number shock waves can be found in other planetary systems (e.g., Saturn4 and Jupiter56), while collisionless bow shock environments with similar intrinsic properties are found in the interstellar medium around young stellar systems57. More importantly, in other stellar systems, under the presence of exoplanets like ultrahot Jupiters47, the existence of massive magnetic fields enables our mechanism to potentially sustain GeV-TeV electrons. It should be noted that for young stellar systems, the stellar wind can carry even stronger magnetic fields, that could sustain electrons to even higher energy ranges. Our results, therefore, imply that a portion of the cosmic ray distribution of relativistic electrons might originate from the interaction of planetary quasi-parallel shocks with typical stellar winds. This energy range is well within what is considered to be the ultra-relativistic electron range of cosmic rays. Further investigation should be made by the stellar astrophysics and particle acceleration communities to determine whether such unique systems (young stellar environments with ultra-hot Jupiters) can have a larger contribution to the ultra-relativistic electron spectra through the utilization of the reinforced shock acceleration model. These stellar environments would, therefore, be particularly good candidates to investigate by looking for potential synchrotron emission. This implication originates directly from our model, and its validity needs to be studied in the future, especially since, up to today the main sources of these relativistic energies have been either pulsars or supernovas58,59. To fully address this impact, future endeavors will require coordinated cross-disciplinary efforts by the Heliophysics and Astrophysical communities, through the use of both remote sensing observations and computer simulations. Future research in Earth\u2019s geospace environment could benefit from incorporating solar wind monitors at the Lagrangian 1 (L1) point alongside near-Earth observations from MMS and THEMIS. This approach would provide a more direct connection between coronal hole solar wind and the seed population. In addition, large-scale global simulations that include nonlinear kinetic processes are needed to quantify the precise effects and relative contributions of each acceleration mechanism and scattering/confining process demonstrated in our observations.\n\nFinally, by reinforcing the acceleration processes at quasi-parallel shocks and by evaluating the electron injection threshold for obtaining relativistic electrons at Earth at suprathermal energy ranges, our model ultimately supports the role of quasi-parallel shocks as the source of the ultra-relativistic cosmic ray profile60.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "For Magnetospheric Multiscale (MMS) data18, we used burst resolution Level-2 data from the Fast Plasma Investigation (FPI), Flux Gate Magnetometer (FGM), Search Coil Magnetometer (SCM), Fly\u2019s Eye Energetic Particle Spectrometer (FEEPS). For the position of the spacecraft, we used the Magnetic Ephemeris Coordinates (MEC). For Acceleration, Reconnection, Turbulence, and Electrodynamics of Moon\u2019s Interaction (ARTEMIS) measurements19, we used the Flux Gate Magnetometer (FGM), Solid State Telescope (SST), and the Electrostatic Analyzer (ESA) and the state data for its position. Due to the close configuration of the MMS1-4 and ARTEMIS P1-P2, we primarily used MMS1 and ARTEMIS P2 for the analysis shown in the manuscript unless stated otherwise. In addition, for validation purposes, we used OMNIweb data to characterize the global scale of the solar wind conditions. The products used are the 1-h dataset and then propagated to the bow shock 1\u2009min one, both accessible via https://spdf.gsfc.nasa.gov/pub/data/omni/. To download the level 2 dataset used in this study, we used the software described in the code availability section.\n\nDetermining the connection between the MMS and ARTEMIS data required the use of position data for each spacecraft. We used the position of each satellite in Geocentric Solar Ecliptic (GSE) coordinates. In this system, the X-axis is on the Sun-Earth line, while the Y-axis lies on the ecliptic plane towards dusk. The Z-axis completes the coordinate system by being perpendicular to the ecliptic plane. The position of the spacecraft during the main event in GSE coordinate and in Earth Radius units (RE\u00a0\u2248\u20096371\u2009km) is given as: MMS1: [10.8, 11.1, 5.1] and ARTEMIS P2: [65.2, \u2013\u20095.3, 5.1]. Upon using the solar wind velocity (VSW) and convection along the X-GSE axis, we obtained an estimate of the time lag between the two spacecraft of about 10\u201315\u2009mins (by simply equating \\({{{{\\rm{V}}}}}_{{{{\\rm{SW}}}}}=\\frac{\\Delta {{{\\rm{x}}}}}{\\Delta {{{\\rm{t}}}}}\\), where \u0394x denotes the distance of the two spacecraft along the X-GSE axis, and \u0394t the time lag between their measurements. The rest of the MMS satellites (i.e., MMS2-4) are located in a close tetrahedron formation within a few kilometers of each other. This close formation allows multi-spacecraft methods such as timing to be used throughout our analysis, which is a typical method used in multi-spacecraft research20,21. It should be noted that the positions of ARTEMIS and MMS are very different for each event (see \u201cMethods\u201d subsection Statistical analysis details). This results in a time lag between the spacecraft that can vary from instantaneous observations to tens of minutes, depending on the orientation of the magnetic field discontinuity with respect to the plane MMS and ARTEMIS reside. Essentially, the 3D geometry of the magnetic field and the fact that discontinuities are planar structures allow any kind of time lag between the signals (see more details in the following subsections).\n\nBefore using the high-energy electron differential energy flux data, obtained from the FEEPS instrument, the data need to be cleaned from contamination. This is done by inspecting the MMS1-4 measurements. The MMS4 data typically, and also in our case, have measurable contamination, which made us choose to discard them in our study (see Supplementary Fig.\u00a01). The contamination can be clearly seen in the last panel of the left column. Looking at the MMS2 data (second panel left), one can see the unphysical high flux values appearing every 20\u2009s due to the spin of the spacecraft. The final (cleaned) MMS1-3 data are then shown in the three right panels of Supplementary Fig.\u00a01.\n\nThe energy bins of FEEPS for each of the MMS spacecraft are slightly different for the electron differential energy flux. Therefore, when combining the cleaned FEEPS electron data of MMS1-3, the energy channels need to be combined properly. Table 1 shows the individual spacecraft energy bins and the resulting combined ones.\n\nThe noise level of the FEEPS high energy electron spectra (shown in Fig.\u00a01i, j) is obtained by a 15\u2009s average window between 17:58:[10\u201325] on the 2017-12-17 UTC. This set of data is observed to have little to no counts and is located not too far away in time from our event, and is therefore ideal to use as a measure of the instrument(s) noise level. The noise level of the FPI electron flux (Fig.\u00a01i) is determined in the same way but for the FPI instrument. However, due to the consistently high count rate observed at energies below 10\u2009keV, the procedure is only done for energy channels above 10\u2009keV. For energy channels less than 10\u2009keV, the noise level is extrapolated linearly using a linear fit obtained from the observed noise data above 10\u2009keV. Fig.\u00a01 shows the combined ion spectra of the FPI (6\u2009eV\u201330\u2009keV) and FEEPS (60\u2013600\u2009keV) instruments. The gap of no data between 30\u201360\u2009keV is exponentially interpolated by assuming an exponential decay between the highest FPI channel and the lowest FEEPS channel.\n\nThe spectral index calculated in Fig.\u00a01h is calculated by a fitting process on the phase space density (PSD) profiles for energies between 80\u2013200\u2009keV. The PSD profiles are obtained from 2\u2009s averages evenly spaced between 17:52:30 and 17:52:50 using the electron differential energy flux data shown in Fig.\u00a01. The electron energy flux is converted into PSD using the following formula61:\n\nwhere E is the particle energy given in MeV and J in cm\u22122s\u22121sr\u22121.\n\nTo determine the electron acceleration efficiency, we need to estimate the total energy carried by solar wind and then which portion is transferred to the suprathermal electrons. The suprathermal electron energy density can be calculated using the expression:\n\nWhere we set the initial suprathermal energy to be E0 = 100 eV and the maximum (\\({{{{\\rm{E}}}}}_{\\max }\\)) to be the maximum energy the FEEPS instrument can measure (515\u2009keV). fe(E) denotes the phase space density of electrons at that energy, and me is the electron mass.\n\nFor the solar wind, it is easy to calculate the energy density upstream of the compressive edge of the foreshock transient structure through the expression \\({{{{\\rm{U}}}}}_{{{{\\rm{i}}}}}={{{{\\rm{n}}}}}_{{{{\\rm{i}}}}}{{{{\\rm{m}}}}}_{{{{\\rm{i}}}}}{{{{\\rm{V}}}}}_{{{{\\rm{SW}}}}}^{2}/2\\), where ni is the ion number density, VSW is the ion speed in the spacecraft reference frame, and mi is taken to be equal to the proton mass as it is the dominating population in the solar wind.\n\nTo compute these two expressions, we need to take the electron distribution when the spacecraft probes the acceleration region to estimate the suprathermal electron part, and in the upstream region for the solar wind. These regions are shown in detail in Fig.\u00a01. Specifically, they correspond to 2017-12-17 17:53:[15-17] UTC for Ue and 2017-12-17 17:53:[30-50] UTC for Ui. For the solar wind upstream region, we took the number density to be ni = 10 [cm\u22123] and VSW\u2009=\u2009600\u2009km/s which are also typical values of the fast solar wind. By using these values, we obtain the electron acceleration efficiency to be:\n\nIt should be noted that this definition aligns with what is commonly referred to as acceleration efficiency in the literature. This means that it describes the portion of particles that are above the suprathermal range (i.e., 100\u2009eV in our case). If however, we take E0 to be higher so that it corresponds to relativistic regimes (e.g., 1\u2009keV), the efficiency drops to less than 0.5%.\n\nTo determine the origin of the solar wind, we used the classification scheme described in ref. 36. In this work, the solar wind is classified into four categories. Ejecta (EJ), Coronal hole (CH), Sector reversal (SR), and Streamer belts (SB). With CH essentially consisting of fast solar wind plasma, SR and SB indicate slow solar wind conditions, and ejecta are transient phenomena such as Coronal Mass Ejections (CMEs).\n\nAt first, we evaluated the statistical properties of solar wind conditions as measured by both OMNIweb data in 1\u2009h and 1\u2009min resolution. In addition, to make sure that the classification is accurate and does not depend on instrumentation variation, we used in situ observations of the ARTEMIS satellite to re-classify the dataset. All three datasets produced identical classifications due to the limited variability of the SW between L1 and the lunar orbit.\n\nThe actual classification was initially made through visual inspection and by comparing the statistical properties of each class to each event36. However, to make our classification more robust and reproducible, we re-classified the dataset by using a supervised machine learning approach as discussed in ref. 38 and accessible through the code availability statement. Specifically, we used the same training set, which was carefully curated and analyzed in ref. 38 and we applied a machine learning Gaussian Process (GP) to classify probabilistically the dataset used. All the upstream datasets we used resulted in the same classification (i.e., the highest probability was consistently found to be in the same class). The code used to perform this operation can be found in the code availability statement.\n\nIn Table 2, we show the probabilities of each class for each event, while with bold text, one can find the highest probability which was used as the dominant class characterizing each event. Furthermore, in Supplementary Fig.\u00a02 OMNIweb 1h data are shown, describing the global picture of the solar wind conditions for the main seeded event and a non-seeded one (i.e., the #1 non-seeded of Fig.\u00a03). The main event analyzed in the manuscript occurs a few hours after the beginning of a high-speed stream.\n\nTo estimate the diffusion time scales and compare them with the system scale sizes, we have used Bohm\u2019s diffusion formula6. This formula is applicable when we have variations in the magnetic field that are in the same order as the background magnetic field values (i.e., \u03b4B\u00a0~\u2009B0). As shown in Figs.\u00a01 and 2 and Supplementary Fig.\u00a03, this is a systematic feature of the foreshock transients\u2019 core environment (i.e., the region between the trailing and leading compressive edge). Under this framework, the mean free path for pitch angle scattered electrons can be computed as \\({\\lambda }_{\\max }\\approx {{{{\\rm{r}}}}}_{{{{\\rm{ce}}}}}\\) where rce is the electron gyroradius.\n\nThe diffusion length (L) can be estimated through the expression \\({{{\\rm{L}}}}\\approx {{{\\rm{D}}}}({{{{\\rm{E}}}}}_{\\max })/{{{{\\rm{V}}}}}_{{{{\\rm{sh}}}}}\\), where \\({{{\\rm{D}}}}({{{{\\rm{E}}}}}_{\\max })\\) is the spatial diffusion coefficient for the maximum energy, and \\({{{{\\rm{V}}}}}_{{{{\\rm{sh}}}}}\\) is the velocity of the shock in the spacecraft reference frame. Finally, the diffusion coefficient can be calculated \\({{{\\rm{D}}}}({{{{\\rm{E}}}}}_{\\max })=1/3\\cdot {\\rm{v}} \\cdot {\\lambda }_{\\max }\\), where v is the velocity of the particles. Combining the above formulas, one can easily obtain an expression for the maximum diffusion length as:\n\nIn the above formula, we can use the relativistic kinetic energy expression, \\({E={(\\gamma-1)}}mc^2\\), where m is the rest mass, c is the speed of light, and \u03b3 is the Lorentz factor. By expanding the Lorentz factor, and re-writing the kinetic energy expression, one can show that:\n\nUsing that expression, since we know the maximum energy, we can calculate the equivalent velocity and translate that to an effective electron gyroradius though \\({{{{\\rm{r}}}}}_{{{{\\rm{ce}}}}}=\\frac{\\gamma mv}{e{{{{\\rm{B}}}}}_{{{{\\rm{0}}}}}}\\) where e is the electric charge, and B0 is the background magnetic field. Therefore, observationally, we can obtain a value for the electron gyroradius by measuring the energy of electrons and the background magnetic field. Finally, to compute the diffusion length, one needs to estimate the shock velocity. One can do that by timing the shock as it passes through the multiple MMS spacecraft. By performing the timing method20,21, we obtain that the shock velocity in the spacecraft reference frame is in the order of Vsh\u00a0~\u2009300\u2009km/s.\n\nThe background magnetic field at the core of a foreshock transient is in the order of 1 nT, as shown in Figs.\u00a01, 2, and Supplementary Fig.\u00a03, although in extreme cases it can reach values up to 10 nT. The maximum energy of electrons observed in our event is 523\u2009keV for the main event analyzed in the manuscript. By using the above values, we obtain a diffusion length of L\u2009=\u200910\u00a0\u2212\u00a0100\u2009Re \u2248\u20096 \u22c5 104\u22125\u2009km, which is in agreement with the scale sizes of foreshock transients that have been observed in Earth\u2019s planetary environment (with 100 Re corresponding to extreme cases)45.\n\nIn order now to estimate what are the maximum obtainable energies in other planetary systems, we can take the typical scale sizes found in other planets along with typical background magnetic field observations45. This energy corresponds to the maximum energy that an electron can have before its gyroradius gets so large that it cannot remain in the shock acceleration region and get further energized. We can assume that for giant planets like Jupiter, a foreshock transient scale size can reach values of 100\u00a0\u2212\u00a01000\u2009Re \u2248\u20096 \u22c5 105\u22126\u2009km. Using a background magnetic field value of 0.5\u22121 nT, it can be easily shown that the equivalent energy obtained from the electrons\u2019 gyroradius can be \u00a0>\u20092\u2009MeV.\n\nFinally, to generalize our results to systems outside our solar system, some assumptions need to be made. Generally, the larger the system size (planet), the larger the maximum energy that can be sustained. Similarly, the stronger the magnetic field of the stellar wind, the higher the energy. As a result, one could investigate ultra-hot Jupiters as potential candidates in which electrons can achieve very high energies. These celestial objects are Jupiter-like exoplanets with very low orbital periods due to their proximity to their star47.\n\nIf we assume a system size similar to Jupiter, we can obtain a foreshock transient scale size that can reach values of about 100\u00a0\u2212\u00a01000\u2009Re \u2248\u20096 \u22c5 105\u22126 km. Now, depending on the age of the stellar system (young stellar systems have stronger magnetic fields embedded in their stellar wind), one can estimate the background magnetic field magnitude of the stellar wind near a planet close to the star to be in the order of B\u00a0\u2248\u20090.01\u00a0\u2212\u00a01\u2009G\u2009=\u2009103\u22125 nT48,49. By following the exact argument above, we can obtain that the maximum energy in these systems can be in the order of GeV to TeV. It should be noted that even when computing the synchrotron losses for these energy ranges and magnetic fields, the loss is insignificant. As observations and modeling efforts in these systems are highly susceptible to errors, one should treat the whole discussion here not as a direct comparison to other acceleration mechanisms but rather as a consequence of our model that needs further investigation by the astrophysics and exoplanet research communities.\n\nWhile the characterization of the foreshock transients is out of the scope of this work, it should be noted that the majority of the transients shown in this work would have been classified as hot flow anomalies (HFAs) in heliophysics terminology. The exact characterization of these transients is out of the scope of this work and irrelevant to our findings as regardless of the exact nature, foreshock bubbles and HFAs have been found to be particularly efficient accelerators (see e.g.,14,17,25). Furthermore, both types of phenomena contain all the necessary ingredients for our model (i.e., shock formation, compressive edges, high-frequency nonlinear waves, large amplitude waves associated with low-density regions to facilitate scattering, etc.)16.\n\nTo characterize the shock formed at the compressive edge of the foreshock transient (highlighted in Figs.\u00a01 and 2), we used single-point and multipoint analysis techniques (see e.g., ref. 20 and code availability statements for openly accessible code to access these). All techniques, as expected from previous results, indicated that the fast shock formed at the sun-facing compressive edge of the foreshock structure is a typical quasi-perpendicular (\u03b8Bn\u00a0>\u200945\u2218) shock. Specifically, all the methods typically used in these studies (i.e., coplanarity theorem, timing, and minimum variance analysis)62 showed an angle systematically above 55 degrees similar to other similar foreshocks transient events20.\n\nFor Fig.\u00a02, we calculate the power spectral density (PSD) of the electric and magnetic fields using a wavelet transform, which allows us to observe how the power of the various oscillations is distributed in frequency and how it varies with time. Furthermore, to characterize the various wave modes excited we use singular value decomposition (SVD) to calculate the degree of polarization, ellipticity, and \u03b8kB, which are respectively a measure of the coherence of the waves, a measure of the handedness of the waves, and the angle between the wave vector (k) and the background magnetic field (B0). The ellipticity has a value in the interval [\u2212\u20091,1] where values of \u2212\u20091, 0, and 1 reflect waves that are left-hand, linearly, or right-hand polarized respectively. The propagation angle (\u03b8kB) ranges between 0 and 90 degrees, with 0/90 representing parallel/ perpendicular propagations with respect to B0.\n\nThe identification of the high-frequency whistler waves was mainly obtained by their typical frequency that lies within [0.1, 1]fce with fce being the electron cyclotron frequency. To quantify the relative power of the wave activity, we integrated the PSD in frequency space for each interval of interest (compressive region of the foreshock transient). This approach allowed us to estimate the total magnetic field variability for the specific wave mode (\u03b4B2). To evaluate how prominent the wave amplitude is compared to the background magnetic field and essentially quantify the nonlinearity of the wave, we took the square root and divided it by the background field to obtain an estimate of, \u03b4B/B0 which was used in Fig.\u00a03. The exact values obtained from this methodology are shown in Table.\u00a02.\n\nTo evaluate the cyclotron resonance between the energetic electrons and the whistler waves, we need first to write the resonance condition: \\({\\omega -{{{{\\rm{k}}}}}_{{{{\\rm{z}}}}}{{{\\rm{v}}}}}_{{{{\\rm{z}}}}}+m{\\Omega }_{{{{\\rm{ce}}}}}=0\\)42, where \u03c9 is the frequency of the wave, while kz and vz are the components of the wave vector and the electron velocity along the background magnetic field respectively, and \u03a9ce is the electron gyrofrequency. In the main event presented, we estimate \u03c9\u00a0~\u20090.3\u2009\u03a9ce which results in a frequency fce\u00a0\u2248\u2009500\u2009Hz calculated via \\(f=\\frac{\\omega }{2\\pi }\\). By using the cold plasma dispersion relations, the observed high-frequency whistlers have a wavelength of \u03bb\u2009=\u200925\u2009km. For the observed electrons of a realistic initial energy of 20\u2009keV, the total velocity of the particles is estimated to be \\({{{{\\rm{v}}}}}_{{{{\\rm{e}}}}}=8.4\\cdot 1{0}^{7}\\) km/s. For such electrons to be in cyclotron resonance with the observed HF whistlers, the pitch angle (PA) of these electrons needs to be approximately 79\u2218. Similarly, for a slightly higher energy of 50\u2009keV, their PA needs to be around 83\u2218. The interaction between the electrons is shown in Fig.\u00a03., as the nonlinear resonance of the electrons will make them gain energy in the perpendicular direction which will further increase their PAs, reaching asymptotically 90\u221832,33. As discussed in the Results section, during the strongest whistler activity, we see in the Pitch Angle distribution (Fig.\u00a02h) an enhanced flux at PA 90\u2218 which is a signature of the above-mentioned resonant energization mechanism.\n\nWe should also note the acceleration process via wave-particle interaction discussed in this work is not related to quasi-linear theory and pitch angle diffusion (e.g., ref. 63). As written in the main manuscript, the resonance we highlight is between nonlinear waves and electrons (e.g., ref. 31). The nonlinear nature of these waves is evident from their relatively high amplitude compared to the background (see Fig.\u00a03 and Table II) and their characteristic coherent narrowband emission34,64. Having said that, acceleration mechanisms through resonance are found to be more efficient for low-density plasma with high magnetic fields (e.g., radiation belts). It has been shown that a way to quantify the efficiency of the acceleration obtained through this mechanism is by looking at the ratio between the electron plasma frequency (fpe) and the gyrofrequency (fce). The ratio in the radiation belt can be close to unity (see, e.g., ref. 63) while in typical foreshock plasma, it can be on the order of magnitudes higher. However, due to the special plasma conditions of foreshock transients, the core (Fig.\u00a01, blue-shaded region) contains very low-density plasma, while the shock (Fig.\u00a01, green-shaded area) contains a magnetic field that, due to compression is higher than the typical foreshock. These combined allow this ratio for our main event to be in the order of fpe/fce\u00a0~\u200930 when considering the bulk properties. Considering partial distributions, which are more accurate in shock/foreshock environments, should decrease the density and, therefore, also the fpe/fce ratio (see e.g., ref. 65) even more. Furthermore, although the efficiency of wave-particle interactions may not be as pronounced as in very low-density plasma environments, recent studies have demonstrated that these interactions can still contribute to the energization and scattering of electrons in a foreshock transient30,66. Moreover, when moving to exoplanetary systems with stronger magnetic fields (e.g., ultra-hot Jupiters47) this ratio is decreased, which further supports the generalization of our model to other environments. Finally, while it remains uncertain whether interactions between electrons and high-frequency whistlers will average out to a quasi-linear picture, the nonlinear characteristics observed in the waves suggest deviations from classical resonance theory, given that for all events we observe a \\({\\delta {{\\rm{B}}}/{{\\rm{B}}}_0 \\approx 1-10\\%}\\).\n\nAs briefly mentioned in the main text, foreshock transient phenomena can be present in every planetary environment within a stellar system and occur very frequently15. However, obtaining actual in situ observations in our solar system of all the elements of the presented mechanism is heavily influenced by various statistical and orbital effects, qualitatively discussed below.\n\nThe first statistical issue affecting our observational capabilities is the probability of observing fast solar wind (coronal hole) plasma corresponding to the conditions we verified as necessary to have a prominent electron seed population (1\u20135\u2009keV range), highlighted throughout the main text. Coronal hole plasma consists of roughly 20% of the total solar wind measurements based on Lagrangian 1\u00a0(L1) point observations (refs. 36,37,38). In addition, fast solar wind is associated with more frequent magnetic field discontinuities39, and the occurrence rate of such a discontinuity is roughly in the order of 1\u201310 every hour40. These effects show that while foreshock transients can form with an occurrence of hours15, seeded events exhibiting significant electron acceleration like the one we describe may have an occurrence rate in the order of days.\n\nHaving said that, from an observational point of view, to measure the seeded population (ARTEMIS) and the foreshock transient (MMS), both spacecraft need to be at a specific location in their orbit.\n\nMMS spacecraft is in the ion foreshock for around 10% of its lifetime, although this heavily varies based on its orbit. Furthermore, in order to do the analysis required in this manuscript, MMS has to acquire high-time resolution measurements (i.e., burst data) that are typically unavailable during foreshock observations, making this percentage lower. Moreover, to observe these phenomena, it is not just sufficient to be in the foreshock at the right time, but also to observe the foreshock transient after it has been fully formed, which reduces the occurrence even more. Moving on to the ARTEMIS mission in lunar orbit, it can provide far upstream measurements for less than 50% of the time, as for the rest of the time it is on the nightside of the Earth or very close to the shock. This results in a net probability of observing such a foreshock transient to be less than 1\u20135% even considering the most conservative estimations. As a result, while these phenomena can occur very frequently15, the observational capabilities we currently have, allow us to observe only a few conjunction events per year.\n\nAll the effects listed above are the reason that a detailed investigation of several years of data resulted in 6 seeded cases that were analyzed in Fig.\u00a03. Our research targets the detection of high-energy electrons, enabling us to identify specific acceleration events. However, it is crucial to recognize that non-energetic events can still occur under the influence of coronal hole (CH) solar wind. Factors such as suboptimal geometry, limited wave-particle interactions, and insufficient compression can constrain substantial electron acceleration. As a result, significant\u00a0electron acceleration might not always be observed, even in the presence of CH solar wind.\n\nIn this work, we focused on analyzing the most energetic event (i.e., the one shown in Figs.\u00a01 and 2 and #1 seeded in Fig.\u00a03) which highlights all the components of the presented acceleration model. However, for the statistical verification of our work, we examined multiple cases that contained partially or fully the components of our model (Fig.\u00a03). In Supplementary Fig.\u00a03, we show an overview plot of an event that was compared with the main event (i.e., #1 non-seeded on Fig.\u00a03). There, one can see that a well-formed foreshock transient that lacks the initial seeded population from the solar wind results in no observable electron acceleration (Supplementary Fig.\u00a03d). This is also the event that was compared side by side with the main event in Fig.\u00a04.\n\nFurthermore, for verification and to make our work reproducible, a table with all the dates for each event as measured by MMS along with the associated ARTEMIS upstream observations are provided (Table 3). The intervals shown there were used to obtain the maximum flux ratio compared to the surroundings that were used in Figs.\u00a03 and 4.\n\nIt should be noted that for the determination of the magnetic field discontinuity responsible for the formation of each foreshock transient, we used a multi-step process. First, based on the position of the two spacecraft, we estimated an approximate time lag by using the solar wind velocity as measured by MMS. Then we determined whether a magnetic field rotation was observed before and after the foreshock transient at MMS. Using the rotation associated with the foreshock transient, we proceed to cross-validate our findings with the magnetic field rotations occurring within the time-lagged interval at the ARTEMIS dataset. This resulted in an associated discontinuity at ARTEMIS orbit occurring from 2 to up to 20 minutes before the MMS observations. After confirming the magnetic disturbance per event, we limited the time interval to within ~\u20095\u2009minutes of the observations and obtained the background value by evaluating steady solar wind calm conditions within a 20-minute window. As shown in Table 3, the relative position of MMS with respect to ARTEMIS can have a wide range of values. This, along with the fact that the orientation of the normal of the discontinuity can be at the same plane as the separation vector between MMS and ARTEMIS, allows the time lag in certain cases to be relatively small, or even instantaneous (e.g., event #5). Since the discontinuity can interact with Earth\u2019s bow shock and form a foreshock transient at the same time as observed by ARTEMIS, these cases are statistically expected.\n\nAt this point, we should stress that for the events occurring on 2018-12-10 (i.e., #3, #5, #6) the high-energy electron instrument of ARTEMIS (SST) observed 100s\u2009keV electrons. For the rest of the events, either the data quality of the instrument showed faulty data and/or no high-energy electrons were measured. This suggests that the electrons accelerated from the presented mechanism, as expected, can be scattered and travel both sunward and earthward.\n\nFinally, as mentioned in the main text, apart from the solar origin, the seed population can be associated with the presence of an extended electron foreshock. An exclusive foreshock origin seems unlikely since the electron foreshock is always present, and this could not explain why, in well-developed transients (see Supplementary Fig.\u00a03), with clear shock formation and wave activity there is no noticeable particle acceleration. However, electron foreshock can likely contribute to the seed population as reflected particles from the planetary bow shock can get scattered and interact with the upstream foreshock transient (see e.g., relevant articles on ions13,25).\n\nTo evaluate this possibility, we first investigated the partial distribution function for the low keV seed population observed at ARTEMIS orbit (Fig.\u00a05). For all events, we see particles between the seed energy range (~\u20091\u22125\u2009keV) to form a ring-like distribution in the XY GSE plane. This type of distribution could be considered a part of a Bi-Maxwellian like distribution that hints towards a clear solar origin. However, it could be part of the electron distribution originating from particles reflected from the nearby Earth\u2019s bow shock (i.e., electron foreshock). This, while still a possibility, is not very likely. By examining the distribution one can see that the events exhibiting these features are associated with low statistics, both because the instrument mode has a low sampling frequency, but also because of the limitation on energy range19. This is illustrated in Fig.\u00a05C, where an event with another instrument mode activated allows more frequent measurements, indicating the presence of a typical thermalized SW type distribution being present. Another test is to compare the distribution found in seeded events with the non-seeded ones (e.g., Fig.\u00a05D). As shown there, the distributions of the seeded events (panels A, B) are very similar to the ones with the non-seeded ones except for enhanced PSD indicating both earthward and sunward particles. This hints towards a complex interplay in which both solar and foreshock origin particles may be present. It is important to note that beam-like distributions are evident in the seeded events, as depicted in Fig.\u00a05A, B. This observation indicates that, in addition to potential contributions from the foreshock, instabilities within the solar wind may also contribute to the formation of the seed population67,68.\n\n2D distribution slices in velocity space in GSE coordinates, showing ARTEMIS observations for three seeded and one non-seeded event (namely, (A) for #1, (B) for #6, (C) for #3, and (D) for #2 no-seed). the X-axis shows the velocity along X GSE coordinates (Earth-Sun line). These ring-like distributions indicate that the electron population is traveling towards and from the Earth. The slices are obtained by restricting the energy between 500 and 5000\u2009eV in order to be directly associated with the seed population discussed in the manuscript.\n\nMoving on, we evaluate if a link between solar wind observations and connectivity to Earth\u2019s bow shock exists. This is done to determine whether a systematic connection to the electron foreshock exists across all seeded events. If such a consistent connection is found, it would imply that the reflected foreshock population may be the primary mechanism driving the observed seed population. Through a 3D bow shock model69, we used the magnetic field orientation for both seeded and non-seeded events and investigated whether they intersected the bow shock surface. As shown in Fig.\u00a06, 2 out of 6 seeded events and 1 out of 6 non-seeded events appear to be unambiguously mapped to the bow shock. For the seeded events, we took the magnetic field orientation during the peak of the suprathermal electron flux. For the non-seeded events, since there is no significant enhancement in electron flux, we took the magnetic field orientation before the discontinuity (taking the magnetic field data after the discontinuity produced the same results). The analysis shown in Fig.\u00a06 shows that the electron foreshock cannot be the only source of suprathermal electrons. It does show, however, that seeded events that experience significant acceleration of electrons have discontinuities for which the geometry is optimal for the formation of foreshock transients. The orientation of these field lines (Fig.\u00a06A) essentially allows the intersection point with the shock to slowly move across the bow shock as the field lines get convected with the nominal solar wind. This illustrates the geometrical effect that the foreshock transients can have on further enhancing the acceleration process discussed in the main text and illustrated in Fig.\u00a04.\n\n3D bow shock model using69 under typical solar wind conditions. A shows the location of ARTEMIS and the magnetic field lines in GSE coordinates during the maximum electron flux for the suprathermal-seed energy range (see Table 3). As shown here, only 2 out of the 6 seeded events appear to be connected to the bow shock. B the same plot but for the non-seeded event. C and D follow the same format but from a different point of view. In this case, one out of 6 magnetic field lines is connected to the Earth\u2019s bow shock.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55641-9/MediaObjects/41467_2024_55641_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55641-9/MediaObjects/41467_2024_55641_Fig6_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "The MMS data are archived at https://lasp.colorado.edu/mms/sdc/public/. The THEMIS/ARTEMIS data can be found at https://themis.ssl.berkeley.edu/data_products/index.php, while the OMNIweb data is accessed through https://spdf.gsfc.nasa.gov/pub/data/omni/. Information about the level, calibration, and instrumentation used for each quantity can be found in the method, subsection data. All calculations shown and reported in this work are done with the open-access data available from each mission as a level-2 calibrated dataset. The post-processed data supporting the findings of this study, specifically the non-time series data (i.e., Figs.\u00a02i and 3), are provided as source data to facilitate easier reproducibility. The remaining post-processed data are available from the corresponding author upon request. Source data are provided in this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The analysis of the work was done via the PySPEDAS (https://github.com/spedas/pyspedas/tree/master)70, SPEDAS (http://spedas.org/blog/71 and IRFU-Matlab (https://github.com/irfu/irfu-matlab/tree/master)72 libraries. Specifically, PySPEDAS was used to download the observations and IRFU-Matlab to analyze and process the files for the plots and the analysis shown in the manuscript. One can access the version of the codes used along with instructions on how to reproduce each figure and table of our work on the associated Zenodo repository https://zenodo.org/records/1404804573 or directly from the GitHub repository https://github.com/SavvasRaptis/Relativistic-Electrons-Foreshock. Alternatively, all the software listed above is openly available through their official repository. In addition, we made use of the machine learning code openly available on ref. 38 and on the hosting website of the corresponding author https://ecamporeale.github.io/codes.html by following the Solar Wind Classification MATLAB code. Specifically, following https://ecamporeale.github.io/software/OMNI2_classification.dat provides the training dataset for the code, https://ecamporeale.github.io/software/classify_solar_wind.m shows a working code example, and https://ecamporeale.github.io/software/parameters_classification.mat contains the parameters required to run the model. 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SR acknowledges additional funding from NASA DRIVE Science Center for Geospace Storms (CGS) - 80NSSC22M0163, and Johns Hopkins University Applied Physics Laboratory independent R& D fund. SR acknowledges the support of the International Space Sciences Institute (ISSI) team 555: Impact of Upstream Mesoscale Transients on the Near-Earth Environment, and the Archival Research Visitor Program of the European Space Agency (ESA). AL acknowledges funding from the Swedish Research Council grant 2018-05514. M.L. acknowledges the support of the International Space Science Institute (ISSI) in Bern, through ISSI International Team Project 520 and funding from the Swedish Research Council grant 2018-05514 and the Royal Society awards RF\\ERE\\210353 and RF\\ERE\\231151. D.L.T. is thankful for funding from NASA\u2019s MMS and IMAP missions. D.L.T. also acknowledges funding from the National Science Foundation (NSF) Geospace Environment Modeling (GEM) program 2225463. DC acknowledges funding from NASA H-TMS grant 80NSSC20K1273, NASA H-TMS grant 80NSSC24K0173, and NSF\u2013DoE grant PHY-2010240. We acknowledge the useful discussions with Anton Artemyev and Florian Koller. We also acknowledge Vassilis Angelopoulos, the instrument PIs and the whole team for use of data from the THEMIS/ARTEMIS mission. Finally, we acknowledge the entire Magnetospheric Multiscale team and instrument PIs for data access and support.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA\n\nSavvas Raptis\u00a0&\u00a0Drew L. Turner\n\nNorthumbria University, Newcastle upon Tyne, UK\n\nAhmad Lalti\n\nSwedish Institute of Space Physics, Uppsala, Sweden\n\nAhmad Lalti\n\nUppsala University, Uppsala, Sweden\n\nAhmad Lalti\n\nDivision of Space and Plasma Physics - KTH Royal Institute of Technology, Stockholm, Sweden\n\nMartin Lindberg\n\nDepartment of Physics and Astronomy, Queen Mary University of London, London, UK\n\nMartin Lindberg\n\nDepartment of Astronomy & Astrophysics and E. Fermi Institute, The University of Chicago, Chicago, IL, USA\n\nDamiano Caprioli\n\nSouthwest Research Institute, San Antonio, TX, USA\n\nJames L. Burch\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.R., A.L., M.L., and D.L.T. conceptualized the study and initialized the work. S.R., A.L., M.L., D.L.T., and D.C. contributed to the methodology of the work. The analysis was done by S.R., A.L., M.L., D.L.T., and D.C. Figures were generated by S.R., while visualization input was provided by S.R., A.L., M.L., D.L.T., and J.L.B. Funding acquisition was made by all the authors of the work. The original draft was written by S.R. with input from all the authors.\n\nCorrespondence to\n Savvas Raptis.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Revealing an unexpectedly low electron injection threshold via reinforced shock acceleration.\n Nat Commun 16, 488 (2025). https://doi.org/10.1038/s41467-024-55641-9\n\nDownload citation\n\nReceived: 20 May 2024\n\nAccepted: 19 December 2024\n\nPublished: 13 January 2025\n\nVersion of record: 13 January 2025\n\nDOI: https://doi.org/10.1038/s41467-024-55641-9\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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ultrahigh metal loading single-atom for significantly improved Fenton-like catalysis", + "pre_title": "Facile cascade-anchored synthesis of ultrahigh metal loading single-atom for significantly improved Fenton-like catalysis", + "journal": "Nature Communications", + "published": "02 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63858-5/MediaObjects/41467_2025_63858_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63858-5/MediaObjects/41467_2025_63858_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63858-5/MediaObjects/41467_2025_63858_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-63858-5#Sec13" + ], + "code": [], + "subject": [ + "Pollution remediation", + "Structural properties", + "Synthesis and processing" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5395627/v1.pdf?c=1759489771000", + "research_square_link": "https://www.researchsquare.com//article/rs-5395627/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63858-5.pdf", + "preprint_posted": "17 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "It is important to break the low metal loading limited by stringent conditions and reveal the catalytic behavior of single-atom catalysts (SACs) governed by individual and interacting sites. Here, a facile and universal synthesis strategy was employed to achieve the highest loading of transition metals (Fe 41.31wt%, Mn 35.13wt%), rare-earth metals (La 28.62wt%), and noble metals (Ag 27.04wt%) to date. Systematic investigation confirms that the powerful ligand-chelation between oxalic acid and metal ions, as well as the simultaneously generated entangled polymer networks are crucial for achieving high-loading SACs. High single-atoms density induced site-intensive effects and site-to-site interactions, which regulated the local electron density of the catalyst, altered the electronic structure of metal, and shifted the valence state toward the metal. As a demonstration, the activation of peroxymonosulfate (PMS) for sulfamethoxazole degradation showed a significant dependence on catalyst site density, with the rate constant at least 1-2 orders of magnitude higher than that of most current SACs. The higher metal loading increased the potential jumps in Fenton-like reaction, promoted the electron transfer and reduced the energy barrier of the rate-determining step in 1O2 generation. This material also showed promising prospect for real wastewater treatment due to its high decontamination efficiency and application stability. The cascade-anchoring synthesis strategy, which can maximize the atomically dispersed metal loadings and simultaneously enhance the reactivity, is universally applicable. It is anticipated that it will take SACs a step closer to practical applications.Earth and environmental sciences/Environmental sciences/Environmental chemistry/Pollution remediationPhysical sciences/Materials science/Nanoscale materials/Synthesis and processingSingle-atom catalysthigh metal loadingcascade-anchoringwastewater treatmentFenton-like catalysis", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Supplementaryinformation.pdfSupplementary information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "It is crucial to break the low metal-loading limitation and reveal the intersite synergy-governed catalytic behavior of single-atom catalysts (SACs). Here, a universal synthesis strategy achieves record loadings of transition metals (Fe 41.31\u2009wt%, Mn 35.13\u2009wt%), rare-earth metals (La 28.62\u2009wt%), and noble metals (Ag 27.04\u2009wt%). The strong oxalic acid-metal chelation and concurrent entangled polymer networks enable high-loading SACs. High-density single atoms induce site-intensive effects, modulating electron density and valence states to achieve peroxymonosulfate-based Fenton-like reactions with rate constants 1-2 orders of magnitude higher than conventional SACs. Elevated metal loading boosts Fenton-like potential jumps, facilitates electron transfer, and reduces the rate-limiting energy barrier in 1O2 production. This material is also proven effective in real wastewater treatment, combining high decontamination efficiency with operational stability. It is anticipated that the cascade-anchoring synthesis strategy will take SACs a step closer to practical applications.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Improving catalytic efficiency in chemical conversion and reducing energy cost and environmental impact are the central goals of heterogeneous catalytic reactions1. SACs show great potential for achieving this goal by controlling the active sites on an atomic scale and maintaining maximum utilization of active metals2,3. However, a common challenge limiting the high catalytic efficiency is the limited number of active sites in monodispersed metals, which limits the overall catalytic performance of unit catalysts in many applications4,5. It is well known that the catalyst performance is closely related to the number of intrinsic active sites6. Therefore, an intuitive solution strategy is to maximize the metal loading on the basis of guaranteeing atomic dispersion to increase the overall catalytic activity7. However, the inherent high surface energy of single atoms makes them tend to condense into thermodynamically stable particles due to the Gibbs\u2013Thomson effect8,9, which leads to the key challenge of accurately regulating the metal loading over a wide range in the field of SACs development.\n\nVarious strategies based on carrier engineering (e.g., nanostructure design and defect structure regulation)10,11, coordination geometry modulation (including first ligand and adjacent environment)12,13, and intermolecular interactions (involving precursor molecule design and molecular catalyst grafting)14,15, can be used to enhance the stability of individual metal atoms and inhibit their aggregation kinetically. However, to date, the metal loading of most reported SACs is still limited to 5\u2009wt%, with only a few studies reporting loadings greater than 10\u2009wt%16,17,18,19. Higher concentrations of metal precursors can lead to partial or complete agglomeration of the metal fraction into nanoparticles, carbides, or nitrides during pyrolysis, requiring careful design and control of the subsequent nanoparticles removal step4,5. By using graphene quantum dots with high surface-area-to-volume ratios and multi-coordination environments as substrates, the metal loading in Ir-SAC and Pt-SAC has been achieved progress16,20. However, the complex preparation processes, high equipment requirements, and low catalyst yields limit the spread of this highly loaded SACs synthesis method. The development of a simple and cost-effective synthesis strategy to achieve atomically homogeneous dispersion of dense metal and full utilization of coordination species remains a great challenge.\n\nOn the other hand, when densely distributed monodisperse metal atoms are embedded in the substrate and fully occupy the ligand species, more unique geometries and electronic structures are produced21,22. However, less attention has been paid to two important factors\u2014the changes in the intrinsic activity of metal sites at high density and their effects on the catalytic behavior of reaction substrates, which can be carefully investigated by designing SACs with wide mass loading gradients. It is evident that the increase in metal loading provides more active sites to accelerate the reaction by facilitating interactions between reaction substrates, thereby providing higher mass-specific activity (MSA)23. In addition, increasing the mass loading improves the density of active sites and shortens the distance between metal atoms, resulting in more pronounced interaction between metal sites in high-loading SACs when compared with traditional SACs24,25. Moreover, due to the site-intensive effect, adjacent metal sites simultaneously adsorb or catalyze different substrates, which cannot be achieved by conventional SACs26. Therefore, it is of great significance to develop a simple SACs synthesis method to achieve a breakthrough in metal mass loading, and systematically study the relationship between single-atom sites and their catalytic performance.\n\nIn this study, a universal cascade anchoring strategy by using a facile one-step calcination was applied to prepare Fe-SAC-x with metal loadings that can be tuned within a wide gradient of x\u2009=\u20090\u201341.31\u2009wt%, achieving the highest metal loading of Fe-based SAC to date. The mechanism for such high metal loading was systematically studied, and found that the uniform and firm anchoring of Fe3+ by oxalic acid (OA) onto the simultaneously generated entangled polymer networks is the key factor. In addition, the successful preparation of high-loading transition metal (Mn), rare earth metal (La), and noble metal (Ag) SAC demonstrates the universality of this synthesis strategy. As a demonstration, the structure-catalysis conformational relationship was systematically studied by the activation of peroxymonosulfate (PMS) for toxic organics degradation. Combining experimental analysis and theoretical calculations, the site-intensive effects, site-to-site interactions, and the dependence of catalysis on metal loading were observed. This work not only provides a synthetic strategy for breaking the low loading limitation, but also provides important guidance for controllable regulation of the electronic structure of metals, to promote the understanding of catalytic mechanisms and efficient practical applications.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "It is challenging to modulate the metal load in SACs over a wide range using a facile method. Here, a breakthrough in metal loading was achieved in Fe-SAC through a cascade anchoring strategy, obtaining the highest metal load in Fe-based SAC to date (41.31\u2009wt%). The morphology of the samples was observed by high-resolution transmission electron microscopy (HRTEM), the obtained Fe-SAC inherited the stacked-layered features of C3N4 with a rich porous structure (Fig.\u00a01a). In addition, the imaging at high magnification did not show any iron crystal-like aggregates, and the carbon substrate exhibited distinct amorphous characteristics (Supplementary Fig.\u00a01). Elemental mapping showed that Fe, N, C, and O species were uniformly distributed in density on the substrate (Fig.\u00a01b), suggesting that iron element may be anchored to the carbon substrate in an atomically dispersed form. The high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM, Fig.\u00a01c\u2013f) further revealed the dispersion imaging of Fe atoms, manifested as isolated bright spots densely distributed on the carbon substrate, which is due to the much higher electron density of Fe than that of nonmetals, proving that high-mass-loaded Fe exists in the form of single atoms. By controlling the content of iron salts in the precursors, Fe-SACs with different iron loadings were prepared. The inductively coupled plasma-atomic emission spectroscopy (ICP-AES) analysis verified the Fe content was 0\u2009wt%, 5.16\u2009wt%, 15.82\u2009wt%, 24.62\u2009wt%, and 41.31\u2009wt%, respectively (Supplementary Table\u00a01), and they were named as Carbon nitride (CN), Fe-SAC-5.16, Fe-SAC-15.82, Fe-SAC-24.62, and Fe-SAC-41.31.\n\na, b HRTEM and elemental mappings of Fe-SAC-41.31. c\u2013f HAADF-STEM images of Fe-SAC-x with metal loads was 41.31\u2009wt%, 5.16\u2009wt%, 15.82\u2009wt% and 24.62\u2009wt%, respectively. g X-ray diffraction (XRD) patterns of as-prepared Fe-SAC-x (the shaded area shows the trend of signal intensity changes with increasing Fe loading). h N2 sorption isotherms and pore size distribution (inset) for iron-anchored catalysts with different iron loadings. i Normalized XANES spectra of Fe-SAC-x samples (the inset figure is an enlarged version of the near-edge data). j The oxidation state of iron decreased with the increase of metal loading (the relationship between Fe K-edge energy and Fe valence state was fitted with a linear regression model: y\u2009=\u2009ax\u2009\u2212\u2009b.). k k3-weighted Fourier transforms. l The wavelet transform of the experimental EXAFS spectra of the indicated catalysts. Scale bars: a, b 100\u2009nm, c\u2013f 5\u2009nm.\n\nThe X-ray diffraction (XRD) spectra of all Fe-SACs samples did not reveal features associated with iron metals or iron oxides, only showing the reflection of the carrier material (Fig.\u00a01g). In pristine CN, two typical diffraction peaks at 21.4\u00b0 and 27.8\u00b0 corresponded to the repetitive heptazine structural units and interlayer stacking in carbon nitride27. It is worth noting that the signal intensities decreased and eventually disappeared with the increase in metal loading of the pyrolyzed catalyst, suggesting that the highly loaded monatomic metals disrupted the native structure of CN. In addition, the Raman peaks of all samples were relatively broad, and the peak intensities were significantly weaker after loading Fe-SAC (Supplementary Fig.\u00a02), reflecting the amorphous nature of the catalysts. When the Fe loading was elevated to 41.31\u2009wt%, it was found that the Raman spectral peaks were significantly shifted, suggesting iron atoms significantly altered the molecular vibration or the rotational energy levels of the CN substrate. As the N2 adsorption\u2013desorption isotherms and pore size distribution of different catalysts shown in Fig.\u00a01h and Supplementary Table\u00a02, all catalysts showed typical Type IV curves and H2 hysteresis curves, reflecting the presence of mesoporous pores28.\n\nThe chemical configurations and local coordination environments of Fe-SAC-x were investigated by X-ray absorption spectroscopy (XAS). It can be seen from the X-ray absorption fine structure spectroscopy (XANES) curves of Fe K-edge that the near-edge energy adsorption thresholds of all Fe-SAC-x are higher than that of FeO but lower than that of Fe2O3 (Fig.\u00a01i), suggesting that the valence states of Fe atoms in Fe-SAC-x lie between +2 and +329. Notably, the increase in the density of Fe sites promotes the shift of the absorption edge towards lower energy, suggesting that the electron density of Fe sites in high-loading Fe-SAC is higher than that in low-loading Fe-SAC30. The valence states of the catalyst with different metal loading were fitted (Fig.\u00a01j), and it was found that the valence states of Fe were 2.6, 2.4, 2.3, 2.2 in sequence as the iron loading increased. Therefore, elevating the Fe loading in Fe-SACs is beneficial to regulate the local electron density of Fe single atoms. The Fourier transform (FT) k3-weighted EXAFS spectra (Fig.\u00a01k) show that the extended-edge X-ray absorption fine spectra of all Fe-SACs display a main peak near 1.5\u2009\u00c5, corresponding to the Fe-N/C bond, suggesting the presence of isolated Fe-based species in all samples. In addition, no metal-metal distance feature between 2.1\u2009\u00c5 and 2.5\u2009\u00c5 was observed in any samples, confirming the absence of metal nanoparticles or clusters31. The wavelet transform (WT) further provides radial distance resolution, as well as K-space analysis, and the Fe-SACs show a maximum peak at ~4.4\u2009\u00c5\u22121 (Fig.\u00a01l), which is consistent with the EXAFS results in R-space. These results consistently support that Fe in Fe-SAC with different Fe loading is atomically dispersed. It is noteworthy that a decrease in k value is observed on the WT isograms of all Fe-SAC-x samples compared to FePc, which is attributed to the introduction of C in the first coordination shell layer of Fe atoms32, suggesting that in addition to N atoms, Fe atoms also coordinate with C atoms. In addition, the exact coordination structure of Fe atoms was determined by quantitative least-squares EXAFS fitting (Supplementary Table\u00a03), supporting that the coordination environment of Fe in Fe-SAC-x is similar to that of FePc, and the coordination number of the first coordination layer of Fe in Fe-SAC is 4.1\u2009\u00b1\u20090.4.\n\nWhat is the driving force behind such a high monodisperse metal load? As is well known, when metals are dispersed from particles into clusters or even individual atoms, it leads to significantly higher surface free energy and triggers higher mobility. Therefore, the successful preparation of single-atom catalysts must be premised on the realization of strong metal center-support (usually non-metallic coordination atoms such as C, N, O, and S) interactions. On the other hand, the full utilization of ligand atoms is another key to achieving high metal loading, which requires chemical control of the metal at the atomic or molecular level to achieve its homogeneous dispersion on the carrier or precursor and to fully occupy the chelation sites of the metal. Unfortunately, it is extremely challenging to simultaneously fulfill the above requirements using traditional methods, which is why the metal loading of SACs has been severely limited for a long time. At present, there is still a lack of facile and universal synthesis strategies.\n\nCarbon nitride is an excellent carrier of single atoms due to its thermal stability and high content of coordination sites, mainly provided by nitrogen atoms. Although it exhibits the homogeneity of ligand site, the chemical stability of the triazine or heptazine unit limits the effective utilization of numerous N sites in CN. Considering this limitation, a bottom-up in-situ assembled cascade anchoring mechanism was adopted in this study.\n\nDuring the preparation of Fe-SAC precursor, the metal ions are strongly assembled into the supramolecular organic skeleton (SOK) through chemical bonding, and the subsequent high-temperature pyrolysis further anchors the metal atoms in situ at the nearby N sites. Figure\u00a02a illustrates this simple synthesis strategy. Fe3+ was assembled into the SOK of oxalate-MA macromolecules using OA because it can either chelate with Fe3+ [Eq. (1)] or react with MA to form a supramolecular organic framework [Eq. (2)]. Supplementary Fig.\u00a03a further demonstrates the composite reaction between MA and OA, yielding a milky-white supramolecular organic framework. Fourier transform infrared (FTIR) spectroscopy (Fig.\u00a02b) revealed that the characteristic peaks of MA (580.8\u2009cm\u22121, 1025.2\u2009cm\u22121, 3133.7\u2009cm\u22121) or OA (713.5\u2009cm\u22121, 1686.9\u2009cm\u22121, 3388.7\u2009cm\u22121) disappeared in the supramolecular framework, while a new amide peak emerged at 3254.5\u2009cm\u22121, confirming chemical assembly rather than physical mixing between MA and OA. Upon introducing Fe3+, the resulting supramolecular polymer exhibited a homogeneous yellow-green color (Supplementary Fig.\u00a03b) with a clarified solution, indicating that OA not only chelates Fe3+ but also co-assembles with MA, thereby synchronously anchoring Fe3+ into the organic framework. In the FTIR profile, the appearance of a Fe-O bonding peak at 533\u2009cm\u22121 (Fig.\u00a02b) confirmed successful integration of Fe atoms into the skeleton via chemical bonds. The spectrum of the Fe-SAC precursor perfectly matched that of the supramolecular polymer framework, demonstrating that the chelation of Fe3+ with OA did not disrupt the skeleton structure. In stark contrast, the precursor prepared by the solvent evaporation method (Supplementary Fig.\u00a03c) displayed a spectrum nearly identical to that of pure MA, with no detectable Fe\u2013O bonding peaks (Fig.\u00a02b), confirming only physical adsorption of Fe3+ onto MA.\n\na Schematic synthesis process for Fe-SAC-x catalyst with high Fe loading. b Fourier transformed infrared (FTIR) spectra of catalyst precursors (SOK: supramolecular organic skeleton). c Elemental mappings of Fe-SAC precursor obtained by the cascade anchoring strategy. d Nonlinear correlation between Fe-SAC loading and metal salt dosage. e Nonlinear correlation between SA wt% and OA dosage. f The metal loading of SAC prepared using different metal salts (nitrate, chloride, or sulfate) and metal types (transition, rare-earth, or noble metals). g\u2013i FT of K-edge EXAFS spectra of Mn-SAC, Ag-SAC, and La-SAC, respectively.\n\nTEM characterization further demonstrated the homogeneous dispersion of Fe on the precursor, which made it possible to fully utilize the coordination species. For the conventional solvent evaporation method, the Fe-SAC precursor was a fine rod structure (Supplementary Fig.\u00a04a), surrounded by unevenly distributed metal particles, indicating physical combination and the non-uniform distribution of metal on the precursor, which led to insufficient utilization of coordination species on the carrier. This indicates either acid washing after pyrolysis or a tedious multistep annealing procedure is required to remove unbound metal species17, resulting in a low loading of catalysts. In stark contrast, the Fe-SAC-41.31 precursor synthesized via our cascade anchoring strategy displayed an irregular morphology without particle aggregation (Supplementary Fig.\u00a04b). Energy-dispersive X-ray spectroscopy (EDX) mapping further confirmed the homogeneous distribution of Fe elements across the framework (Fig.\u00a02c). This sharp contrast conclusively demonstrates the critical role of OA as a specific carrier that enables targeted delivery of Fe3+ into the supramolecular organic framework. This mechanism ensures the full utilization of coordination species on the precursor during subsequent pyrolysis, directly contributing to the ultrahigh metal loading.\n\nThe synthesis process of SAC through pyrolysis of Fe-SAC precursor was monitored by thermogravimetric analysis (TGA) (Supplementary Fig.\u00a05). The initial mass attenuation below 110\u2009\u00b0C is attributed to the volatilization of residual moisture in the sample. A pronounced mass attenuation observed in the MA sample between 250\u2009\u00b0C and 350\u2009\u00b0C corresponds to its thermal polycondensation into C3N4. In contrast, the MA-OA composite supramolecular framework exhibited significant mass attenuation at lower temperatures due to the thermal decomposition of thermally labile OA. Notably, direct pyrolysis of MA without OA yielded C3N4 with a densely packed bulk structure (Supplementary Fig.\u00a06a), demonstrating a specific surface area of merely 18.74\u2009m2\u2009g\u22121 (Supplementary Table\u00a02). The pyrolysis of the supramolecular framework produced C3N4 with a petal-like flaky architecture (Supplementary Fig.\u00a06b), exhibiting an enhanced specific surface area of 100.05\u2009m2\u2009g\u22121. This structural modification is ascribed to the liberation of CO2 from the decomposed oxalate species during calcination, which disrupts the polycondensation of MA and facilitates the formation of porous structures conducive to reactant diffusion in the catalytic processes. Remarkably, the Fe-SAC precursor anchored with Fe3+ ions displayed accelerated mass attenuation at reduced temperatures (Supplementary Fig.\u00a05), indicating the catalytic role of Fe species in promoting the volatilization of thermally unstable components. Notably, the presence of metal components also impacts the porosity and surface area of the catalyst. As shown in Fig.\u00a01h, at fixed concentrations of OA and MA, the specific surface area of the catalyst decreased with increasing metal loading. This trend arises because metal species catalyze the decomposition of unstable components and competitively scavenge nitrogen atoms from MA during pyrolysis, thereby hindering the polycondensation of the supramolecular polymer into ordered C3N4. This effect intensifies at higher metal loadings, as evidenced by the gradual disappearance of characteristic heptazine unit diffraction peaks in the XRD patterns (Fig.\u00a01g).\n\nIntriguingly, for the final synthesized SAC, its Fe mass fraction increases nonlinearly with iron salt content in precursors (Fig.\u00a02d). This indicates that a smaller incremental increase in Fe salt injection in the later stage can cause a significant rise in Fe loading. To elucidate this anomalous behavior, we systematically investigated the synthesis process of high-loading Fe-SACs. Given the critical role of OA in coordinating metal ions, the amount of OA added was adjusted while maintaining a fixed concentration of MA and Fe salt. It was found that the OA content significantly influences the yield of the Fe-SAC precursor and the ultimate metal loading of the SAC (Supplementary Table\u00a04). This demonstrates a concentration-dependent relationship between MA, OA, and Fe3+, and reveals that the supramolecular organic framework possesses a maximum metal loading capacity. Increasing the OA content generates more catalyst substrate (Supplementary Table\u00a04), resulting in a corresponding reduction in metal loading (Fig.\u00a02e). This counterintuitive phenomenon is the inverse of the trend observed in Fig.\u00a02d, where increasing the Fe salt dosage while keeping MA and OA fixed gradually approaches the maximum loading capacity of framework, resulting in a rapid surge in metal loading. This systematic investigation reveals that oxalate acts as a \u201cstructure-directing agent\u201d that governs framework integrity and metal stabilization capacity, rather than just a sacrificial template. The dosage of Fe3+ salt directly controls the loading level. The orthogonal tunability of these two parameters provides exceptional flexibility for SAC design.\n\nTo investigate the relationship between sintering temperature and the structure of SACs, the structural characteristics of the catalysts prepared at different pyrolysis temperatures (450\u2009\u00b0C, 550\u2009\u00b0C, and 650\u2009\u00b0C) were characterized. TGA analysis reveals that the pyrolysis process of Fe-SAC precursor can be divided into three stages (Supplementary Fig.\u00a05): decomposition of thermally unstable oxalate components (80\u2013250\u2009\u00b0C), polycondensation of melamine (MA) to form the catalyst substrate (250\u2013350\u2009\u00b0C), and stabilization of atomic sites via metal capture of N-coordination species (350\u2013550\u2009\u00b0C). As shown in Supplementary Fig.\u00a08a, XRD analysis proved that no diffraction peaks of the metal phase were observed for the catalyst prepared at 450\u2009\u00b0C, and no metal particle aggregates were observed by HRTEM (Supplementary Fig.\u00a08b), indicating the formation of Fe-SACs during calcination at 450\u2009\u00b0C. However, the XRD patterns exhibit distinct C3N4 diffraction peaks, indicating well-preserved periodic triazine units in the substrate. This suggests that substantial N-coordination sites remained unutilized. Therefore, the incomplete decomposition of unstable components in the substrate resulted in a relatively low metal proportion, with a loading of 15.31\u2009wt% (Supplementary Table\u00a01). In contrast, calcination at 550\u2009\u00b0C produced Fe-SACs with a high metal loading of 41.31\u2009wt%. Further increasing the temperature to 650\u2009\u00b0C led to the emergence of distinct lattice diffraction peaks in XRD patterns (Supplementary Fig.\u00a08a), indicating intensified thermal motion of metal atoms at elevated temperatures and subsequent formation of crystalline structures (Supplementary Fig.\u00a08d). Therefore, 550\u2009\u00b0C is identified as the optimal synthesis temperature for preparing high-loading single-atom catalysts.\n\nWhen densely distributed monodisperse metal atoms are embedded in the substrate and fully occupy the ligand species, more unique geometries and electronic structures are produced. The changes in chemical configuration of the catalyst under gradient iron loading were further studied by XPS (Supplementary Fig.\u00a09). The C 1s XPS spectrum of pristine CN showed a typical carbon nitride characteristic peak near 287.7\u2009eV, which is attributed to the sp2 N\u2013C=N bond in the aromatic ring. However, after the loading of monatomic Fe, this peak generally shifted toward high binding energy, and the characteristic peak of sp2 C\u2013C bond appeared at 284.8\u2009eV, with the peak intensity increasing with the increase of Fe loading. This result, combined with XRD and Raman analyses, consistently demonstrated that the high-loading Fe atoms significantly altered the skeletal structure of the CN substrate to meet the requirement for overall structural stability of the catalyst. Meanwhile, the Fe 2p spectra shifted towards lower binding energies with increasing Fe loading (Supplementary Fig.\u00a010), which echoes the XANES results and further indicates that higher loading facilitates the acquisition of Fe reaction centers with higher local electron density. Compared with the pristine CN, the anchoring of a single Fe atom resulted in the appearance of distinct Fe\u2013N characteristic peaks in the N 1s spectra (Supplementary Fig.\u00a011), further demonstrating the coordination of Fe with nitrogen atoms. Notably, the elemental content analysis (Supplementary Table\u00a05) showed that a higher Fe loading did not induce an increase in the C, N elemental percentage in the catalyst, but rather showed a decreasing trend. However, with the increase of Fe loading, the content of elemental oxygen showed an overall increasing trend, suggesting that oxygen content might play an important role in controlling SA loading. Han et al. pointed out that during the formation of highly loaded single atoms, the center metal atoms coordinate with O at low temperatures (less than 200\u2009\u00b0C) first, and the coordinated O is successively replaced by N or C at high temperatures (400\u2013600\u2009\u00b0C)33. Thus, the coordination environment of metal atoms is closely related to the terminal temperature.\n\nThe above analysis indicates that the density distribution of metal not only affects the overall structure of the catalyst, but also significantly influences the electronic structure of the metal centers. Therefore, the charge redistribution caused by the site-dense effect was further demonstrated by DFT, which is important for understanding the conformational relationship between structure and catalytic activity. Based on the Fe-CN optimization model established by EXAFS and XPS, three possible CN coordination modes are considered. As shown in Supplementary Fig.\u00a012, the isolated Fe atom has a more negative binding energy when combined with the C1N3 configuration, so this coordination configuration is more stable compared with the others. In order to theoretically better understand the effect of metal loading on the electronic structure of Fe atoms, different numbers of Fe sites were uniformly embedded into the same 4\u2009\u00d7\u20098 graphene cells to represent different metal-loaded Fe-SAC (The green area in Supplementary Figs.\u00a013\u201316. The atomic coordinates of optimized computational models for different metal loadings were provided in Source Data). Such a grid region accommodates 1, 3, 4, and 8 Fe-C1N3 sites, respectively. The calculated theoretical mass loading in the enlarged models is 6.66\u2009wt%, 18.43\u2009wt%, 23.59\u2009wt%, and 40.68\u2009wt% (Supplementary Figs.\u00a013\u201316), which is very close to the actual Fe mass loading of 5.16\u2009wt%, 15.82\u2009wt%, 24.62\u2009wt%, and 41.31\u2009wt% measured by ICP. These models with different numbers of Fe-C1N3 sites can reliably represent Fe-SACs with different mass loadings for theoretical studies, which is an important guideline for exploring the interactions between Fe sites and constructing efficient SACs.\n\nNotably, the evolution of the coordination environment occurred with the increase in the metal loading. Specifically, the Fe\u2013N/C bond lengths increase from 2.08\u2009\u00c5 (5.16\u2009wt%) to 2.13\u2009\u00c5 (41.31\u2009wt%) (Supplementary Table\u00a06), reflecting the weakening of local hybridization. Meanwhile, PDOS analysis shows diminished overlap between Fe 3d and C/N 2p states at higher loadings (Supplementary Fig.\u00a017). This reduction in orbital overlap corroborates the structural relaxation observed in bond lengths. It is worth noting that long-range electronic coupling was observed through the d-band shift. The Fe d-band center shifts negatively from \u22121.51\u2009eV to \u22122.72\u2009eV relative to the Fermi level as the loading increases, indicating enhanced metal-support charge transfer34, which is consistent with the analysis results of Bader charge (Supplementary Table\u00a06) and charge density difference (Supplementary Fig.\u00a018). These findings demonstrate that an increase in single-atom loading optimizes the proximity between adjacent Fe sites, inducing long-range Fe\u2013Fe interactions mediated by the support. Such long-range interactions modulate the electronic structure of Fe atoms and the hybridization of atomic orbitals with coordinated atoms, thereby optimizing the adsorption/desorption kinetics of reactive intermediates. Specifically, the downshifted d-band weakens the intermediate binding strength, while the enhanced electron transfer (ETP) kinetics through long-range coupling may improve catalytic efficiency, as evidenced by the reduced Rct in electrochemical impedance tests (Fig.\u00a03d). Consequently, tailoring metal site density enables precise electronic structure modulation and facilitates efficient ETP through improved hybridization and bonding interactions between metal atomic orbitals, ultimately influencing catalytic performance.\n\nInspired by the above studies, transition metal (Mn), rare earth (La), and noble metal (Ag) SACs were prepared to verify the universality of the synthesis strategy. As shown in Supplementary Fig.\u00a019, no metallic phase was observed in the XRD patterns of Mn-SAC, La-SAC, and Ag-SAC, suggesting that Mn, Ag, and La may exist in an atomically dispersed form. The HAADF-STEM images observed densely distributed isolated bright spots, further confirming the successful preparation of Mn-SAC, La-SAC, and Ag-SAC (Supplementary Fig.\u00a020). The ICP-AES results showed that the contents of Mn, La, and Ag were 35.13\u2009wt%, 28.62\u2009wt%, and 27.04\u2009wt%, respectively, which were the highest loadings recorded in the existing literatures35,36,37. XAS spectroscopy also demonstrated the separation characteristics of metal atoms. The FT of K-edge EXAFS spectra showed the main peak at ~1.5\u2009\u00c5 (Fig.\u00a02j\u2013i), corresponding to the M-N/C scattering path, and no M-M scattering signals are detected, which further proves the successful preparation of ultrahigh metal loading SACs. In addition, the choice of metal salt (e.g., chlorides or sulfate) does not interfere with the cascade anchoring process. XRD (Supplementary Fig.\u00a021) and HAADF-STEM (Supplementary Fig.\u00a022) characterizations confirm the absence of crystalline phases and the atomic dispersion of Fe. ICP analysis reveals that Fe-SACs synthesized using Fe2(SO4)3 and FeCl3\u00b76H2O achieve metal loadings of 37\u2009wt% and 40.04\u2009wt%, respectively (Fig.\u00a02f), further validating the universal applicability of this method38.\n\nPMS-based advanced oxidation processes (PMS-AOPs) have great potential in sustainable water purification. Here, sulfamethoxazole (SMX) was selected as the target toxic organic, and the dependence of Fe-SAC-x catalytic activity on metal loading was investigated in terms of PMS activation and SMX degradation kinetics. In the absence of any catalyst, PMS could only remove 13.4% of SMX within 5\u2009min (Supplementary Fig.\u00a023). The adsorption of SMX by Fe-SAC-x without the addition of PMS was also very low (6.1%). As shown in Fig.\u00a03a, Fe-SAC-x showed a significant difference in the SMX degradation upon different Fe loading, in which the degradation kinetics of SMX significantly accelerated with the increase of Fe loading. The SMX degradation rate constant of the Fe-SAC-41.31 system was 1.06\u2009min\u22121, which was 53 times higher than that of pristine CN (0.02\u2009min\u22121) (Supplementary Fig.\u00a024). The iron dissolution after degradation was positively correlated with the Fe loading of Fe-SAC (Supplementary Fig.\u00a025), but all much less than the limitation of EU and US (<2\u2009mg\u2009L\u22121)39. To examine the contribution of these dissolved iron ions to the SMX degradation, experiments were carried out using a homogeneous system with the same iron concentration, confirming that such a removal of SMX was only 26.6% after 20\u2009min (Fig.\u00a03b), whereas the Fe-SAC-41.31 system achieved 96.4% removal of SMX within just 3\u2009min. This suggests that the SMX degradation should be mainly attributed to the activation of PMS by the catalyst, rather than the dissolved Fe2+ or Fe3+. In addition, Fe-SAC-T with a metal loading of 1.61\u2009wt% was synthesized using the traditional evaporation solvent method to accentuate the promoting effect of high loading on catalytic performance. As shown in Fig.\u00a03c, Fe-SAC-41.31 has a slightly increased turnover frequency (TOF) compared with Fe-SAC-T, indicating that the activity of a single metal site did not significantly increase. However, due to the site-dense effect, the Fe-SAC-41.31 system exhibited much higher SMX and TOC removal efficiency, and the SMX removal rate constant k value was 28.65 times higher than that of the Fe-SAC-T system (0.037\u2009min\u22121) (Supplementary Fig.\u00a026). The MSA per unit mass of Fe-SAC-41.31 for SMX degradation was calculated to be 2.19\u2009mmol\u2009g\u22121, which is 24.9 times higher than that of Fe-SAC-T. In addition, the Fe-SAC-41.31 system could achieve efficient removal of SMX even if the initial concentration was 50\u2009mg\u2009L\u22121 within 20\u2009min (Supplementary Fig.\u00a027a), whereas Fe-SAC-T exhibited complete degradation of SMX only at 2\u2009mg\u2009L\u22121 within 20\u2009min (Supplementary Fig.\u00a027b). pH represents a critical determinant of aquatic matrices in practical settings. The initial solution pH fundamentally governs both the interfacial characteristics of electrocatalytic surfaces and the oxidative capacity of reactive oxygen species. Consequently, pollutant degradation efficiency across varying pH conditions serves as a pivotal metric for evaluating the real-world applicability of water treatment technologies.\n\na Differences in degradation of SMX in the PMS-based heterogeneous catalytic oxidation systems. b Contribution of homogeneous Fe ions to SMX degradation in Fe-SAC-41.31/PMS system. c Comparison of removal efficiency, k, TOF, TOC, and MSA in Fe-SAC-41.31/PMS system and Fe-SAC-T/PMS system, respectively. d Nyquist plots of indicated catalysts. e LSV curves and f corresponding Tafel plots of different catalysts with the addition of PMS. g Degradation performance of SMX by Fe-SAC-41.31/PMS system under the interference of inorganic anion and NOM. h Removal kinetic constants of personal care and pharmaceutical products (PPCPs), phenolics, and dyes, and their comparison with literature (data points are annotated with reference numbers, and full datasets are provided in Supplementary Table\u00a07). i Reuse performance of Fe-SAC-41.31. The error bars in the figures represent the standard deviations from triplicate tests.\n\nConsidering that Fe-SAC-x has significantly different catalytic activities, electrochemical performance tests were performed to explore possible catalytic mechanisms. Electrochemical impedance tests were conducted to discover the electron-transfer kinetics (Fig.\u00a03d). The metal loading showed a good negative correlation with the charge transfer resistance (Rct), indicating that increasing the single-atom loading was conducive to enhancing the charge transfer kinetics of the catalysts and promoting the catalytic performance40. This observation is echoed by the XPS fine spectroscopy analysis of C 1s, in which more sp2 C\u2013C fractions are observed in Fe-SAC-x catalysts with higher Fe loading, which is more favorable for facilitating ETP. Furthermore, linear scanning voltammetry (LSV) tests clearly indicated a higher current response as the number of monodisperse iron sites increases, supporting the most effective ETP between PMS and Fe-SAC-41.31 (Fig.\u00a03e). In addition, as shown in Fig.\u00a03f, the Tafel slope of Fe-SAC-41.31 (430\u2009mV\u2009dec\u22121) was much lower than that of pristine CN (1514\u2009mV\u2009dec\u22121) and Fe-SAC-5.16 (1330\u2009mV\u2009dec\u22121), suggesting that a higher Fe atomic loading is favorable to provide higher kinetic activity for PMS activation41, therefore Fe-SACs with different Fe loading reflected significantly different PMS catalytic activation properties.\n\nTo more intuitively reflect the superior performance of Fe-SAC-41.31 activated PMS, the influence of different anions (HCO3\u2212, H2PO4\u2212, Cl\u2212, SO42\u2212, NO3\u2212, and humic acid) on SMX degradation was investigated (Fig.\u00a03g), observing almost no effect. Therefore, the Fe-SAC-41.31/PMS system has strong environmental anti-interference ability, which indirectly explains that Fe-SAC-41.31 is mainly based on the non-free radical activation pathway of PMS as discussed in detail later. Furthermore, this system showed excellent degradation for different types of pollutants such as PPCPs (SMX, Tetracycline), phenolic (Bisphenol A, Phenol), dyes (Rhodamine B, Methyl orange), etc., and their degradation rate constant is at least 1\u20132 orders of magnitude higher than that of most current catalysts (Fig.\u00a03h and Supplementary Table\u00a07). This result should be attributed to the significantly more active sites of Fe-SAC-41.31, which provided sufficient reaction centers for the adsorption and activation of PMS, as well as the degradation of SMX. In addition, the reuse performance of Fe-SAC-41.31 was also tested, and the complete removal of SMX could be achieved after 5 times of reuse (Fig.\u00a03i), indicating that Fe-SAC-41.31 had good reuse performance.\n\nIn order to explore the dependence between the catalytic mechanism and the Fe loading, chronopotential analysis was carried out (Fig.\u00a04a). When PMS was introduced into the system, different catalyst systems showed different degrees of potential jumps (\u0394EPMS). The \u0394EPMS increased with the increase of Fe loading, up to a maximum of \u0394EFe-SAC-41.31,PMS\u2009=\u20090.898\u2009V, which was 4.01 or 2.27 times higher than that of pristine CN or Fe-SAC-5.16, respectively (\u0394ECN,PMS\u2009=\u20090.224\u2009V; \u0394EFe-SAC-5.16,PMS\u2009=\u20090.898\u2009V). This suggests that atomically dispersed Fe is the adsorption site for PMS, and Fe-SAC-41.31 with the highest Fe loading has significantly more adsorption centers, which results in the formation of more catalyst-PMS complexes with high redox potentials. When an equal dose of SMX was applied to the system, a potential decrease occurred in the different systems, suggesting that the catalyst-PMS steady-state complex extracted electrons from SMX, resulting in an overall decrease in the system potential. Among them, the potential reduction was the most obvious in the Fe-SAC-41.31 system, which corresponded to the fastest SMX degradation. Thus, the effective ETPs from SMX to Fe-SAC-PMS* are driven by the potential difference, leading to the efficient SMX degradation42.\n\na The open-circuit potential changes of Fe-SAC-x when PMS or SMX is added, respectively. b Effects of PMS or SMX addition on the i\u2013t curves at the corresponding open circuit potential. c In situ FTIR spectra of Fe-SAC, PMS alone, and Fe-SAC combined with PMS (The shaded area corresponds to the stretching vibration of the -OH bond produced by the adsorption of PMS on the catalyst surface). d Effects of quenching reactive oxygen species on the degradation of SMX. e Electron paramagnetic resonance spectra of TEMP\u22121O2 during SMX degradation in the Fe-SAC-4.31/PMS system. f Exponential Association model fit of apparent rate constant vs metal loading (R2\u2009=\u20090.99). g Adsorption energy of PMS by Fe-SAC-x and the differential charge density, Bader charge transfer after the adsorption of PMS by Fe-SAC-x with different Fe loading. h Energy profiles of 1O2 derived reaction on varying Fe density models. The error bars in the figures represent the standard deviations from triplicate tests.\n\nThe ETP process between catalyst-PMS-SMX was visualized more definitely by the amperometric i\u2013t curve (Fig.\u00a04b). The negative increase in current after the addition of PMS proves the transfer of electrons from the catalyst to PMS until a steady state is reached. Fe-SAC-41.31 shows the highest current drop \u0394IFe-SAC-41.31, PMS, proving that it has the highest ETP potentials, which also corresponds to the highest \u0394EFe-SAC-41.31,PMS in Fig.\u00a04a. In contrast, the sustained positive current rebound after the addition of SMX demonstrates the continuous electron extraction process from the pollutant by the catalyst-PMS steady-state complex. Similarly, the rapid and highest current rebound of the Fe-SAC-41.31/PMS system corresponds to its optimal SMX degradation performance.\n\nThis catalytic process was further proved by in-situ FTIR spectroscopy, as shown in Fig.\u00a04c. The infrared band at 1101\u2009cm\u22121 arises from the S\u2013O stretching vibration of HSO5\u2212 in solution43. When the catalyst was mixed with PMS solution, a new vibrational peak appeared near 3217\u2009cm\u22121, corresponding to the stretching vibration of the \u2013OH bond produced by the adsorption of PMS on the catalyst surface. In addition, the infrared band of pristine PMS located at 770\u2009cm\u22121 showed a significant red-shift, further demonstrating the strong binding of PMS on the catalyst surface, as well as the formation of catalyst-PMS* steady-state complex44. The weakening of the peak intensity over time proved the continuous activation of PMS. It is worth noting that the degradation of SMX via this ETP-mediated pathway requires strong bonding (covalent or ionic bond) between PMS and the catalyst45, so the complexation type of PMS on the catalyst was explored through ionic strength control experiments. Supplementary Fig.\u00a028 shows that increasing the concentration of ClO4\u2212 has no significant effect on SMX degradation, suggesting that there is an inner-sphere complexation between PMS and Fe-SAC-41.3146.\n\nThe open-circuit potential test was performed with only SMX addition (Supplementary Fig.\u00a029). The potential equilibrium on the catalyst surface was broken when a 10\u2009mg\u2009L\u22121 SMX was added, indicating that SMX was adsorbed onto the catalyst surface. Different from the potential decrease triggered by SMX in Fig.\u00a04a, the addition of SMX here resulted in a slight increase in the surface potential of the catalyst steady-state system, which is due to the fact that the redox potential of SMX itself is higher than that of Fe-SAC-41.31. Therefore, the steady-state potential increases when SMX is adsorbed onto the catalyst surface. However, to inspire an ETP process to degrade pollutants, the overall potential of the catalyst-PMS* steady-state complex is required to be higher than the redox potential of SMX itself, thus, the Fe-SAC-41.31/PMS system with the highest \u0394EPMS exhibited the fastest SMX degradation kinetics. Based on the above analysis, SMX and PMS are co-adsorbed on the Fe-SAC surface, and the catalyst acts as a conducting bridge to transfer electrons from SMX to the catalyst-PMS* steady-state complex with higher redox potential.\n\nIn order to verify the key role played by atomically dispersed Fe sites in SMX degradation, potassium thiocyanate (KSCN) was used to block the Fe sites to inhibit their catalytic activity47. The degradation of SMX was almost completely inhibited when 50\u2009mM SCN- was introduced (Fig.\u00a04d), suggesting that the Fe sites play a dominant role in activating PMS to degrade SMX48. Furthermore, radical quenching experiments were conducted to confirm the active species in the Fe-SAC/PMS system. Methanol (MeOH) and tert-butanol (TBA) showed high reaction kinetics with SO4\u2022\u2212 and \u2022OH, respectively (\\({k}_{{{{{\\rm{SO}}}}}_{4}^{{{{\\bullet }}}-},{{{\\rm{MeOH}}}}}\\) \u2009=\u20099.7\u2009\u00d7\u2009108\u2009M\u22121S\u22121, \\({k} _{{}^{\\bullet }{{\\rm{OH}}},{{{\\rm{TBA}}}}}=\\) 7.6\u2009\u00d7\u2009108\u2009M\u22121\u2009S\u22121), and were therefore used as selective probes for the detection of SO4\u2022\u2212 and \u2022OH in the reaction system. A high concentration of MeOH and TBA had almost no effect on the degradation of SMX, suggesting that SO4\u2022\u2212 and \u2022OH are not the main active species responsible for SMX degradation. To further verify this conclusion, time-resolved electron paramagnetic resonance (EPR) detection was performed. When 5,5-dimethyl-1-pyrroline N-oxide (DMPO) was used as a spin trapping agent, no characteristic peaks of DMPO\u2212\u2022OH and DMPO-SO4\u2022\u2212were observed in different reaction periods (Supplementary Fig.\u00a030), which further proves that Fe-SAC-41.31 does not activate PMS through SO4\u2022\u2212 and \u2022OH-dominated radical pathways.\n\nThe non-radical PMS activation pathway mediated by 1O2 has been reported in literature, with typical generation pathways including the self-decomposition of PMS and the evolution of species such as O2\u2022\u2212, dissolved oxygen, and SO549. Furfuryl alcohol (FFA) has a high reaction kinetic constant with 1O2 (kFFA,1O2\u2009=\u20091.2 \u2009\u00d7\u2009108\u2009M\u22121\u2009S\u22121)50. When 10\u2009mM FFA was introduced into the system, it was found that the quenching of 1O2 led to a decrease in SMX degradation (Fig.\u00a04d). In addition, 2,2,6,6 tetramethyl-4-piperidinol (TEMP) was used as a spin trapping agent for time-resolved EPR test, and a distinct trilinear characteristic peak after initiation was observed, which was significantly stronger than the background value (Fig.\u00a04e). This also indicates that 1O2 was produced in the Fe-SAC-41.31/PMS system. In order to clarify the 1O2 generation pathway, degradation experiments were conducted with continuous nitrogen aeration, and no effect on SMX degradation was observed (Supplementary Fig.\u00a031), suggesting that 1O2 did not evolve from O2. In addition, EPR analysis found that there was almost no O2\u2022\u2212 in the system (Supplementary Fig.32), so 1O2 in the system was generated through the activation of PMS.\n\nPrevious studies have reported that a variety of Fe-based catalysts can effectively activate PMS to produce high-valent metal-oxygen species (HV-Fe: FeV\u2009=\u2009O or FeIV\u2009=\u2009O)51. Considering the high iron loading of Fe-SAC-41.31 and the weak contribution of the radical pathway to SMX degradation, it is necessary to confirm the presence or absence of HV-Fe in the Fe-SAC-41.31/PMS system. Therefore, dimethyl sulfoxide (DMSO) was first used as a trapping agent for surface active species, and it was found that 10\u2009mM DMSO had no effect on the degradation of SMX. This indicates that HV-Fe may not be produced in the system. Furthermore, considering that methyl phenylsulfoxide (PMSO) can be oxidized by HV-Fe to produce dimethylsulfoxide (PMSO2) through an oxygen atom transfer process52,53, the conversion process of PMSO-PMSO2 in the system was monitored by HPLC (Supplementary Fig.\u00a033). However, the conversion rate of PMSO2 in the Fe-SAC-41.31/PMS system did not increase compared with the CN/PMS system, therefore no HV-Fe should be produced in the Fe-SAC-41.31/PMS system.\n\nBased on the detected intermediates (Supplementary Table\u00a08) by liquid chromatography coupled with a mass spectrometer, the possible degradation pathways of SMX in the Fe-SAC-41.31/PMS system are derived as shown in Supplementary Fig.\u00a034. P-1 is generated via electrophilic addition of hydroxyl groups on the benzene ring of SMX. In addition, the isoxazole fraction undergoes a ring-opening reaction to form P-254,55. In the other degradation pathway, the amino group of SMX is attacked by 1O2 to form P-4 after the electrophilic oxidation56. This product continues to undergo successive oxygen transfer reactions at the N site to promote the formation of nitro-SMX derivative P-5. P-8 is a hydroxylation product generated by the attack on the N site of SMX57. In contrast, the breakage of the S\u2013N bond under attack produces P-9, while the methyl group undergoes further oxidation to form an aldehyde group. In the overall degradation process, SMX and its degradation intermediates are subjected to successive attacks, first oxidized to monocyclic organic compounds, and then degraded to ring-opening molecules.\n\nNotably, Fig.\u00a04f reveals a positive correlation between k and the number of active sites. However, this positive correlation is non-linear, and the catalytic kinetics are significantly accelerated at high loadings. Assuming that each site exhibits similar intrinsic activity, the apparent rate constant k should theoretically display a linear or quasi-linear relationship with the total number of active sites. Combined with the electronic structure changes of Fe sites at high loadings, it collectively demonstrates the potential existence of long-range Fe\u2013Fe interactions at elevated metal loadings, which enhances the catalytic activity of active sites. In addition, when the metal loading exceeds 24.62\u2009wt%, the evolution of k values accelerates markedly, suggesting the existence of a critical loading threshold beyond which these long-range interactions become significantly enhanced.\n\nTo validate the above conclusions, we investigated the PMS catalytic performance at identical Fe active sites in catalyst models with varying metal loadings by DFT. It was observed that the PMS adsorption energy significantly increased when the metal loading reached 24.62\u2009wt%, and as the loading continued to rise, the adsorption energy further changed (Fig.\u00a04g). Additionally, the interaction between Fe sites and PMS exhibited enhanced differentiation due to intermetallic effects. Specifically, the electron depletion region of the Fe adsorption center expanded markedly, while the bonded O atom accumulated more electrons. Bader charge analysis further revealed an increase in ETP from isolated Fe atoms to PMS molecules under higher Fe loading (Fig.\u00a04g). This demonstrates that long-range metallic interactions at high loadings facilitate the activation of PMS by the Fe site. This result is consistent with the open circuit potential test, indicating that catalysts with higher mass loading triggered higher potential jumps (Fig.4a). Catalysts with higher metal loading simultaneously exhibited lower charge transfer resistance (Fig.\u00a03d), which collectively proves that elevated metal loading is beneficial for promoting the conductive bridging of Fe atoms to facilitate charge migration. Notably, when the metal loading surpassed the critical threshold of 24.62\u2009wt%, the energy barriers of the 1O2 generation pathway and rate-determining step (RDS) decreased substantially (Fig.\u00a04h, \u0394Erds,41.31\u2009=\u20091.16\u2009eV\u2009<\u2009\u0394Erds,24.62\u2009=\u20091.19\u2009eV\u2009<\u2009\u0394Erds,15.82\u2009=\u20091.47\u2009eV\u2009<\u2009\u0394Erds,5.16\u2009=\u20092.12\u2009eV). These theoretical analyses, combined with experimental results, including the open-circuit potential (Fig.\u00a04a), i\u2013t curves (Fig.\u00a04b), and SMX degradation kinetics (Fig.\u00a04f), collectively demonstrate that long-range Fe\u2013Fe interactions are significantly enhanced beyond the critical loading threshold of 24.62\u2009wt%. These interactions play a pivotal role in reducing energy barriers and improving ETP efficiency.\n\nBased on the above analyses, the changes in intrinsic activity of high-density metal sites and their effects on the catalytic behavior of reaction substrates were proposed (Fig.\u00a05). Obviously, due to the site-intensive effect, high-loading Fe-SAC provides more active centers for catalytic reactions. When the same dose of PMS was introduced, the Fe-SAC-41.31/PMS system underwent a higher potential jump (\u0394E\u2009=\u20090.898\u2009V) than the Fe-SAC-5.16/PMS system (\u0394E\u2009=\u20090.395\u2009V), indicating a stronger ability to capture electrons from the contaminants, thus overcoming the catalytic efficiency limitation caused by insufficient active sites. In addition, the substrate structure of the catalyst changed significantly to accommodate the needs of catalyst stability under high loading, while the valence state of the Fe atom decreased from 2.6 to 2.2, which increased the ETP of Fe to PMS from \u22120.12 e to 0.75 e, making it more conducive to the degradation of pollutants through ETP. Moreover, the energy barrier of the rate-determining step (\u0394Erds) of activating PMS to generate 1O2 by a single Fe site decreased from \u0394Erds,5.16\u2009=\u20092.12\u2009eV to \u0394Erds,41.31\u2009=\u20091.16\u2009eV due to the site-to-site interactions. This indicates that high Fe loading not only provides richer active sites, but also thermodynamically promotes the activation of PMS, thus exhibiting excellent pollutant degradation performance.\n\nElevated metal loading boosts Fenton-like potential jumps, facilitates electron transfer, and reduces the rate-limiting energy barrier in 1O2 production.\n\nThe practical application potential of the Fe-SAC-41.31/PMS system was further evaluated. The effective removal of SMX has been realized in tap water, medical wastewater, and lake water (Fig.\u00a06a), and the water quality parameters are listed in Table\u00a0S10. In addition, virtually all SMX can be completely degraded in the wide pH range of 3.0-11.0 (Fig.\u00a06b), which indicates that Fe-SAC-41.31 has good environmental adaptability. Moreover, it is found that the morphology changes in the TEM image of the catalyst are negligible (Supplementary Fig.\u00a035), no metal agglomeration particles are observed, and no diffraction peaks of metal oxides are detected by XRD (Fig.\u00a06c), which indicates that Fe-SAC-41.31 has good stability.\n\na Removal efficiency of SMX in different actual water bodies. b Effect of pH on the degradation of SMX. c XRD characterization of Fe-SAC-41.31 before and after reaction. d 3D-EEM fluorescence spectra of coal chemical reverse osmosis inlet water before treatment. e, f 3D-EEM fluorescence spectra of coal chemical reverse osmosis inlet water with the addition of 2\u2009mM PMS and 0.1\u2009g\u2009L\u22121 Fe-SAC-T (e), or 0.1\u2009g\u2009L-1 Fe-SAC-41.31 (f). g SMX removal and total iron leaching of Fe-SAC-41.31 during continuous operation before and after applying an external electric field. h SMX removal from the coal chemical reverse osmosis inlet water by Fe-SAC-41.31 and Fe-SAC-T (inset) during continuous operation after applying an external electric field. The error bars in the figures represent the standard deviations from triplicate tests.\n\nIn addition, the elimination of complex organics in an actual water body (coal chemical reverse osmosis concentrated wastewater) was assessed through a three-dimensional excitation and emission matrix (3DEEM), and the water quality parameters were listed in Supplementary Table\u00a010. As shown in Fig.\u00a06d and Supplementary Table\u00a011, the fluorescent substances in the wastewater were categorized into four regions, which were aromatic protein-like fluorophores (I), fulvic acid-like fluorophores (II), soluble microbial byproduct (III), and the humic acid-like substance (IV)58. Fe-SAC-T and Fe-SAC-41.31 were used to treat this actual water body, respectively (Fig.\u00a06e, f). It can be seen that the low Fe loading catalysts prepared by conventional methods have limited effectiveness, while the Fe-SAC-41.31 almost completely decomposes the organic pollutants. The 3DEEM results confirm that the Fe-SAC-41.31-PMS system has a promising prospect for the actual wastewater treatment due to its high efficiency decontamination performance.\n\nTo further promote the practical application of the Fe-SAC-41.31/PMS system, a flow-through module was designed to facilitate the recovery of the catalyst, and the operation parameters were investigated by using simulated laboratory wastewater (Supplementary Fig.\u00a036a). During continuous operation, the effluent flowed in from one side of the filter assembly and out from the other side. Fe-SAC-5.16 exhibited only 61% removal of SMX (5\u2009mg\u2009L\u22121) at a flow rate of 5\u2009mL\u2009min\u22121 due to the limited number of active sites, while Fe-SAC-41.31 achieved complete removal throughout the operation (Fig.\u00a06g), which proves that catalysts with high metal loading have better practical application prospects. Unfortunately, due to the higher metal loading, more total Fe leaching occurred in Fe-SAC-41.31 (1.7\u2009mg\u2009L\u22121) than in Fe-SAC-5.16 (0.02\u2009mg\u2009L\u22121). Our previous studies demonstrated that the application of a cathode electric field helps to promote the iron cycling of the catalyst and reduce metal leaching, and there is a significant synergy between electro-activation and catalytic activation to PMS50. So here the catalyst coated carbon felt (CF) was connected as the cathode, while DSA was used as the anode (Supplementary Fig.\u00a036b). As expected, the modified continuous flow reaction system achieves lower ion leaching (0.06\u2009mg\u2009L\u22121) while maintaining efficient decontamination, which enhances the stability of the system (Fig.\u00a06g). The application of electric field is cost-effective to treat saline organic wastewater, such as actual coal chemical wastewater, pharmaceutical wastewater (7000\u2009\u03bcS cm\u22121) and landfill leachate (12,770\u201325,000\u2009\u03bcS\u2009cm\u22121), etc59., without the need to add electrolytes. To demonstrate the significant performance improvement of the cascade anchoring strategy in the treatment of actual water bodies, the catalyst prepared by the traditional method was compared. Due to the complexity of organic pollutants and uncertainty of their concentration in actual water bodies, SMX (5\u2009mg\u2009L\u22121) was formulated in the actual coal chemical reverse osmosis concentrated wastewater (water quality parameters are shown in Supplementary Table\u00a010) to better quantify the treatment effect (Fig.\u00a06h). The continuous flow reactor assembled with Fe-SAC-41.31 achieved an actual wastewater treatment volume of 36.4\u2009L. During this period, the SMX removal from the wastewater remained above 95%, TOC removal was 74.35%, and the mass specific activity (MSA) of unit catalyst to remove SMX was 31.3\u2009mmol\u2009g\u22121, whereas Fe-SAC-T prepared by the conventional method showed only 50% SMX removal, 34.18% TOC removal, and 4.3\u2009L wastewater treatment volume, respectively, and the MSA was only 2.1\u2009mmol\u2009g\u22121. This further proved that the high-loading Fe-SAC-41.31 prepared by the cascade anchoring strategy has better prospects in practical wastewater treatment.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63858-5/MediaObjects/41467_2025_63858_Fig1_HTML.png", + "https:////media.springernature.com/lw399/springer-static/image/art%3A10.1038%2Fs41467-025-63858-5/MediaObjects/41467_2025_63858_Equ1_HTML.jpg", + "https:////media.springernature.com/lw400/springer-static/image/art%3A10.1038%2Fs41467-025-63858-5/MediaObjects/41467_2025_63858_Equ2_HTML.jpg", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63858-5/MediaObjects/41467_2025_63858_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63858-5/MediaObjects/41467_2025_63858_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63858-5/MediaObjects/41467_2025_63858_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63858-5/MediaObjects/41467_2025_63858_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63858-5/MediaObjects/41467_2025_63858_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, Fe-SACs with metal loading that is tunable within a gradient of 0\u201341.31\u2009wt% were prepared using a facile cascade anchoring strategy. HAADF-STEM and XAS demonstrated the successful acquisition of Fe single atoms with different mass occupancies, and they all coordinated with the substrate in the form of Fe-C2N2. The densely distributed Fe sites enhance interatomic interactions and increase the Bader charge of Fe atoms from BaderFe-SAC-5.16\u2009=\u20097.038\u2009eV to BaderFe-SAC-41.31\u2009=\u20097.120\u2009eV, which is consistent with the change in valence state of Fe confirmed by XAS. Experimental results and DFT analyses indicate that increased metal loading leads to a significant increase in catalytic activity, which facilitates the ETP pathway of PMS activation and reduces the energy barrier of the rate-limiting step during 1O2 generation. The present study overcomes the long-standing bottleneck of limited overall catalytic activity of catalysts caused by low Fe-SAC loading, which contributes to the rational design of advanced SACs.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Single-atom catalysts with different Fe mass loadings were prepared using a facile one-step calcination method. Specifically, 30\u2009mmol of MA, 30\u2009mmol of OA, and 1\u2009mmol of Fe(NO3)3\u00b79H2O were dissolved in deionized water, and the resulting mixture was stirred at 500\u2009rpm using a magnetic stirrer for 4\u2009h at room temperature. The product was collected by centrifugation, dried in a 60\u2009\u00b0C oven for 24\u2009h, and thoroughly ground into a homogeneous powder. The obtained catalyst precursor powder was annealed at 550\u2009\u00b0C for 2\u2009h under argon atmosphere to obtain Fe-SAC-x (where x referred to the Fe mass fraction in the catalyst) at a heating rate o\u2009f 5\u2009\u00b0C\u2009min\u22121. Under the same conditions, Fe-SAC-x with different Fe mass fractions were prepared by adjusting the amount of iron salts (0.3\u2009mmol, 0.75\u2009mmol, 0.9\u2009mmol, and 1\u2009mmol, respectively). The mass fractions of Fe in the catalysts were 5.16\u2009wt%, 15.82\u2009wt%, 24.62\u2009wt%, and 41.31\u2009wt%, respectively, as measured by ICP-AES, so the catalysts were named Fe-SAC-5.16, Fe-SAC-15.82, Fe-SAC-24.62, and Fe-SAC-41.31, respectively. In addition, as a control group, the catalyst substrate was prepared under the same conditions without the addition of iron salts and named CN. The chelating ligand selected for cascade anchoring-directed assembly should not only be able to coordinate with metal ions to form complexes, but also be able to co-assemble with MA to construct macromolecular organic frameworks, thereby achieving precise immobilization of metal ions within the framework. The synthesis of Mn-SAC, Ag-SAC, and La-SAC is similar to that of Fe-SAC, except that Mn ions are still ligand chelated with OA, while Ag and La are ligand chelated with CA. Reagent ratios and the rest of the procedure are consistent with Fe-SAC.\n\nFe single-atom catalyst (Fe-SAC-T) was also prepared by the traditional method. Thirty millimoles of MA and 0.3\u2009mmol of Fe(NO3)3\u00b79H2O were dissolved in deionized water at 80\u2009\u00b0C. After drying the solvent, the obtained powder was fully ground and calcined in a tube furnace under argon protection at 550\u2009\u00b0C for 2\u2009h, and the heating rate was 5\u2009\u00b0C\u2009min\u22121. After natural cooling to room temperature, the obtained Fe-SAC-T was stored for later use.\n\nTo prepare the electrode for the reactor, 20\u2009mg of catalyst was dispersed homogeneously into 5\u2009mL of ethanol, then 100\u2009\u03bcL of Nafion (5\u2009wt%) solution was added, and the mixture was fully dispersed by ultrasonic treatment for 15\u2009min. The resulting slurry was drop-coated onto commercial CF with a diameter of 4\u2009cm and left to dry naturally. According to the type of catalyst, the prepared electrode was named Fe-SAC-x/CF.\n\nIn a representative experiment of PMS activation, 10\u2009mg of Fe-SAC-x was added to a 100\u2009mL configuration of contaminant aqueous solution. The pH of the solution was adjusted with 0.1\u2009M H2SO4 and 0.1\u2009M NaOH prior to the reaction. When the suspension kept stirring reached the adsorption\u2013desorption equilibrium, 2\u2009mM of PMS was added to the system, and the timing was started. Then 2\u2009mL of suspension was extracted at predetermined time intervals. After filtration, the concentration of target pollutants was determined by high-performance liquid chromatograph (HPLC, Ultimate 3000, ThermoFisher, America) equipped with a C18 column (3\u2009\u00b5m, \u03d53.0\u2009\u00d7\u2009100\u2009mm), and the operating parameters are listed in Supplementary information, Table\u00a013.\n\nThe removal of SMX followed the pseudo-first-order kinetic model: \n\nwhere C0 is the initial concentration of SMX (mg\u2009L\u22121), t is the sampling time (min), Ct is the SMX concentration at sampling time, k is the rate constant (min\u22121).\n\nThe turnover frequencies (TOF, per Fe atom) for SMX removal were calculated based on the initial removal rate (Rd) of SMX, where Rd\u2009=\u2009k\u2009\u00d7\u2009SMX concentration60.\n\nIn order to promote the application of the Fe-SAC-x/PMS system in practical scenarios, a continuous flow filtration reactor was designed. The solution flow rate was controlled at 5\u2009mL\u2009min\u22121 by a peristaltic pump, and the concentration of SMX was 5\u2009mg\u2009L\u22121. The CF cathode was connected to a DC power supply to enhance the stability of the catalyst and reduce metal leaching. Ti/RuO2-IrO2 (DSA) disks with a diameter of 4\u2009cm were used as the anode. During the continuous degradation process, the current was set at 10\u2009mA. The preparation of the cathode is as follows: 30\u2009mg of catalyst powder was dispersed into ethanol (5\u2009mL) containing 100\u2009\u03bcL of Nafion (5\u2009wt%), which was ultrasonically dispersed homogeneously and then drop-coated onto a CF with a diameter of 4\u2009cm.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The authors declare that all data supporting the findings of this study are available within the article and the supplementary information. Any additional data are available from the corresponding author. 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Ed. 61, e202202338 (2022).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by the National Key R&D Program International Cooperation Project (2023YFE0108100), the National Natural Science Foundation of China (nos. 22376107 and U23B20165), the Natural Science Foundation of Tianjin (no. 24JCYBJC01640), the Tianjin Key Research and Development Plan of China (no. 22YFYSHZ003000), and the Key Project of Natural Science Foundation of Tianjin (no. 21JCZDJC00320). We thank beamline BL14W1 (Shanghai Synchrotron Radiation Facility) for providing the beam time and thank staff member PanZhe Qiao for his contribution to the characterization.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "MOE Key Laboratory of Pollution Processes and Environmental Criteria Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution, College of Enviromental Science and Engineering, Nankai University, Tianjin, China\n\nShuaishuai Li,\u00a0Wei Wang,\u00a0Huizhong Wu,\u00a0Xuechun Wang,\u00a0Shihu Ding,\u00a0Jingyang Liu,\u00a0Xiuwu Zhang,\u00a0Jiangli Sun,\u00a0Chunhong Fu\u00a0&\u00a0Minghua Zhou\n\nTianjin Advanced Water Treatment Technology International Joint Research Center, College of Environmental Science and Engineering, Nankai University, Tianjin, China\n\nShuaishuai Li,\u00a0Wei Wang,\u00a0Huizhong Wu,\u00a0Xuechun Wang,\u00a0Shihu Ding,\u00a0Jingyang Liu,\u00a0Xiuwu Zhang,\u00a0Jiangli Sun,\u00a0Chunhong Fu\u00a0&\u00a0Minghua Zhou\n\nCarbon Neutrality Interdisciplinary Science Centre, College of Environmental Science and Engineering, Nankai University, Tianjin, China\n\nShuaishuai Li,\u00a0Wei Wang,\u00a0Huizhong Wu,\u00a0Xuechun Wang,\u00a0Shihu Ding,\u00a0Jingyang Liu,\u00a0Xiuwu Zhang,\u00a0Jiangli Sun,\u00a0Chunhong Fu\u00a0&\u00a0Minghua Zhou\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.L. designed the study and performed the catalyst preparation, characterization, catalytic tests, data analysis, and wrote the paper. W.W. and M.Z. conceptualization, supervision, funding acquisition, resources, and writing\u2014review and editing. H.W., X.W., S.D., and J.L. writing\u2014review and editing, and data curation. X.Z., J.S., and C.F. review and edit, and perform validation.\n\nCorrespondence to\n Wei Wang or Minghua Zhou.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Liang Huang, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Facile cascade-anchored synthesis of ultrahigh metal loading single-atom for significantly improved Fenton-like catalysis.\n Nat Commun 16, 8796 (2025). https://doi.org/10.1038/s41467-025-63858-5\n\nDownload citation\n\nReceived: 23 November 2024\n\nAccepted: 27 August 2025\n\nPublished: 02 October 2025\n\nVersion of record: 02 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63858-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61805-y/MediaObjects/41467_2025_61805_MOESM4_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.10959533", + "/articles/s41467-025-61805-y#ref-CR76", + "/articles/s41467-025-61805-y#Sec40" + ], + "code": [ + "https://archive.softwareheritage.org/swh:1:dir:3f06d6d731741c4beebe901f5ef0b909c8b0b6bd;origin=", + "https://gitlab.pasteur.fr/flaurent/chloestaggers/;visit=swh:1:snp:7480ee779daabf5b5ac887096643587949adaf43;anchor=swh:1:rev:913a5bc94bab9f2ebee578ef624c92d2e46e0c19" + ], + "subject": [ + "Decision", + "Neural circuits" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4018128/v1.pdf?c=1756897626000", + "research_square_link": "https://www.researchsquare.com//article/rs-4018128/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61805-y.pdf", + "preprint_posted": "16 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Animals' feeding state changes behavioral priorities and thus influences even non-feeding related decisions. How is the feeding state information transmitted to non-feeding related circuits and what are the circuit mechanisms involved in biasing non-feeding related decisions remains an open question. By combining calcium imaging, neuronal manipulations, behavioral analysis and computational modeling, we determined that the competition between different aversive responses to mechanical cues is biased by feeding state changes. We found that this is achieved by differential modulation of two different types of reciprocally connected inhibitory neurons promoting opposing actions. This modulation results in a more frequent active type of response and less frequently a protective type of response if larvae are fed sugar compared to when they are fed a balanced diet. The information about the internal state is conveyed to the inhibitory neurons through homologues of the vertebrate neuropeptide Y known to be involved in regulating feeding behavior.Biological sciences/Neuroscience/Neural circuitsBiological sciences/Neuroscience/Sensorimotor processing/DecisionBiological sciences/Neuroscience/Feeding behaviour", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "An animal\u2019s feeding state changes its behavioral priorities and thus influences even nonfeeding-related decisions. How the feeding state information is transmitted to nonfeeding-related circuits and what circuit mechanisms are involved in biasing nonfeeding-related decisions remain open questions. By combining calcium imaging, neuronal manipulations, behavioral analysis and computational modeling, we determined that the competition between different aversive responses to mechanical cues is biased by changes in the feeding state. We found that this effect is achieved by the differential modulation of two different types of reciprocally connected inhibitory neurons promoting opposing actions. This modulation results in a more frequent active type of response and, less frequently, a protective type of response if larvae are fed sugar than when they are fed a balanced diet. Information about the internal state is conveyed to inhibitory neurons through homologs of the vertebrate neuropeptide Y, which is known to be involved in regulating feeding behavior.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Physiological states such as hunger and thirst are powerful regulators of behavior across the animal kingdom because strong homeostatic drives are critical for survival1,2,3,4,5,6,7. For example, across model systems, food deprivation has been shown to modulate responsiveness to stimuli by influencing sensory neurons and sensory pathways8,9,10,11, suggesting that food deprivation can alter the perceived value of a stimulus, which in turn affects behavioral decisions. Various studies have implicated changes in central processing12,13,14,15, which lead to changes in behavioral decisions in hungry animals. However, the detailed neural circuit mechanisms of this state-dependent flexibility of behaviors remain largely unknown.\n\nInternal drives (e.g., hunger and thirst) need to be balanced by environmental demands such as the need to avoid dangers. Avoiding danger is a critical instinctive behavior that must be balanced with finding and consuming food to ensure survival. Avoidance behaviors tend to be robust, which makes them excellent systems for studying the neural bases of behavior16,17,18,19; however, they also need to be flexible for animals to adapt their behavioral strategies to different contexts and according to different internal states17,20,21,22. Feeding states and contexts can, for example, influence both the tolerance to the level of threat and action selection during threat avoidance16,17,18,23.\n\nAt the neural circuit level, such behavioral flexibility is thought to be implemented by neuromodulation (modulation of existing synaptic connections by neuropeptides, for instance), which could bias the outcome of competition between diverse behaviors. The outcome could differ depending on the neuropeptide released24,25,26,27,28,29,30. Alternatively, information rerouting (using alternative circuit pathways)12,13,31, where information is processed differently depending on the context or state, could alter the behavioral choice in a context-dependent manner. The types of circuit motifs underlying competitive selection must allow for such flexible processing of information. Reciprocal inhibition of inhibition has been proposed to be a motif that could confer the circuits the property to be tuned to contextual/state information and thus implement flexible competitive selection32,33,34,35. However, the detailed mechanisms and implications in the case of state-dependent flexible selection have not been experimentally demonstrated. In addition, the neural circuit mechanisms underlying the feeding state-dependent modulation of behavior, the neuromodulators involved, and their mechanism of action on specific circuits, especially those that pertain directly to nonfeeding or nonwater-seeking behavior, are not well understood.\n\nThis paper addresses the understudied aspect of the influence of physiological drive on competitive selection during avoidance behaviors and investigates the neural circuit mechanisms involved in a powerful model organism for neural circuit analysis: the Drosophila larva36. Drosophila larvae are ideally suited for combining comprehensive, synaptic-resolution circuit mapping in electron microscopy (EM) across the nervous system32,37,38,39 with the targeted manipulation of uniquely identified circuit motifs at the individual neuron level, enabling the establishment of causal relationships between circuit structure and function in a brain-wide manner. In addition, evolutionarily conserved neuropeptidergic and hormonal pathways in Drosophila have been shown to regulate diverse behaviors11,18,27,40,41.\n\nPrevious studies have described the larval avoidance response to a mechanical stimulus (air puff) and detailed the neural circuit underlying the competition between Hunching, a protective or startle-like type of behavior, and Head Casting, an active exploratory type of behavior that can lead to escape32,42,43,44,45,46. We identified circuit motifs underlying competitive interactions between behavioral actions (reciprocal inhibition of inhibition) and sequence transitions (lateral and feedback disinhibition) between the two behaviors. These types of motifs based on disinhibition would allow for flexible behavioral selection in a context/state-dependent manner. By combining calcium imaging and neuronal and neuropeptide manipulation at the single-cell level with automated tracking, behavioral classification, and computational modeling, this work shows that larval responses to air puff are biased toward less protective actions and toward more active, exploratory actions upon changes in their feeding conditions. We determine that this bias is due to the differential modulation of two reciprocally connected inhibitory neurons that drive competing behaviors (Hunching and Head Casting): the activity of the neuron that promotes protective hunching is decreased, and the activity of the neuron that inhibits Hunching is increased. We also show that the modulation at the level of reciprocally interconnected inhibitory neurons results in a bias at the level of the network output toward a state that will lead to Head Casting at the expense of Hunching upon feeding on sucrose. Finally, we determine that NPF and sNPF modulate the activity of reciprocally interconnected inhibitory neurons that bias the behavioral output in a feeding state-dependent manner.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We sought to establish a short-term food-deprivation protocol to study the effects of diet on behavioral decisions as a result of cognitive control and the prioritization of needs and motivations rather than long-term physiological changes that affect circuit properties. We determined the shortest duration of food deprivation that was sufficient to induce quantifiable changes in behavior. Depriving larvae of food completely (by placing them on water-soaked filter paper) or feeding them 20% sucrose only (and therefore depriving them of proteins and increasing their sugar intake) for 90\u2009mins was sufficient to alter larval locomotion. Compared with normally fed larvae, starved larvae and larvae that fed on sucrose moved at a greater speed and spent more time crawling, eventually dispersing faster in less curved trajectories (Fig.\u00a01a\u2013d and Supplementary Fig.\u00a01a\u2013h). These changes in locomotion are consistent with increased exploration and are likely due to an increased drive to find nutrients caused by deprivation. We monitored locomotion in these larvae upon refeeding them for 15\u2009minutes to determine whether the 90\u2009min feeding deprivation protocol induced changes in behavior that were reversible. Upon refeeding, the starved larvae were slower, and their locomotor phenotype was similar to that of the larvae that were constantly fed (Supplementary Fig.\u00a01i\u2013l).\n\na\u2013d Analysis of larval locomotion. Box plots display the median, interquartile range (IQR), and whiskers up to 1.5\u2009\u00d7\u2009IQR. Outliers are plotted individually. e\u2013h, j, k Larval feeding on different substrates was quantified, in control animals fed ad libitum, in animals fed on sucrose, or subjected to starvation during 90\u2009min. Data are presented as mean values\u2009+/\u2212\u2009SEM. e Starved animals increased their feeding on a standard food medium as compared to fed animals. f Both animals fed on sucrose only and starved animals increase their yeast feeding as compared to normally fed larvae. g While starved animals consume a similar amount of water as fed ones, animals fed on sucrose only double their water consumption (h) Consumption of yeast was similar between larvae fed on normal food and those fed on food with 20% sucrose. i Place preference assay for sucrose. Larvae fed on 20% sucrose exhibit a decreased sucrose preference compared to normally fed and starved larvae. j Sucrose intake in larvae in different feeding states upon gustatory neurons inhibition (Gr43a-Gal4\u2009>\u2009TNT) and in intact larvae. k Consumption of sucralose was similar among conditions. l\u2013m Concentration of glucose and trehalose in larval hemolymph. n\u2013u Manipulating the feeding state modulates behavioral responses to mechanosensory stimuli. Behavior in response to an air-puff for larvae fed on sucrose only (n\u2013q) and starved larvae (r\u2013u). n\u2013p, r\u2013t Hunch and Static-bend are presented as population probability during the first five seconds upon stimulus onset and 95 % confidence interval, Head Cast as post-stimulus probability corrected by baseline probability. q, u Behavioral transitions over the first two seconds of stimulation in sucrose fed larvae (q) or starved (u) compared to fed larvae (Statistics: a\u2013d two-sided Mann-Whitney test with Bonferroni correction; (e\u2013g, k\u2013m) one-way ANOVA with Tukey post-hoc test (two-sided); (j) one-sided Mann-Whitney test with Bonferroni correction; (h) two-tailed T-test. (i, n, o, r, s Chi-square (one-sided) test. p, t Numerical simulation test; q, u Maximum likelihood test (one-sided, chi-square approximation); ****p\u2009<\u20090.0001, ***p\u2009<\u20090.001, **p\u2009<\u20090.01, *p\u2009<\u20090.05). See also Supplementary Fig.\u00a01.The source data and p-values are provided in Source Data 1\u20135.\n\nThe consumption rates of different foods were quantified in larvae under different feeding conditions to determine whether the larval need for nutrients was affected by the feeding protocols to which they were subjected (Fig.\u00a01e\u2013j). Compared with both normally fed larvae and larvae that were fed sucrose only, starved larvae significantly increased their intake of standard Drosophila food and yeast (rich in amino acids) (Fig.\u00a01e, f), which is consistent with a deficit in nutrients in starved larvae. Larvae fed sucrose only significantly increased their intake of yeast, which is consistent with a deficit in amino acids (Fig.\u00a01f). These larvae, however, did not increase their intake of standard Drosophila food, suggesting that the increased sugar consumption might suppress their intake of carbohydrate-rich food. To verify if larvae experience osmotic stress upon sucrose feeding, water consumption was quantified in the different states. Compared with both normally fed and starved larvae, the water consumption of larvae fed 20% sucrose was significantly increased (Fig.\u00a01g), likely due to changes in extracellular osmolality as a result of high sugar intake6. Larvae fed standard food supplemented with up to 20% sucrose, on the other hand, did not increase their yeast consumption compared with that of normally fed larvae (Fig.\u00a01h), suggesting that the increase in yeast consumption in only sucrose-fed larvae was due to the lack of protein. To test whether larvae fed on sucrose were repulsed by sugar, a choice assay was performed: larvae were added to the middle of an agar plate where half of the plate was covered with agar and the other half was covered with agar mixed with sucrose. The larvae were then monitored for 15\u2009min (Fig.\u00a01i). After 15\u2009min, more normally fed and starved larvae were found on the agar\u2009+\u2009sucrose half of the arena, with a preference index increasing over time, whereas larvae fed sucrose showed a decrease in preference for agar supplemented with sucrose. Compared with normally fed and starved larvae, larvae fed only 20% sucrose for 90\u2009min presented decreased sucrose intake (Fig.\u00a01j). Gustatory neurons inhibition (Gr43a-Gal4>\u2009TNT) decreases sucrose intake in fed larvae and starved larvae while the consumption of sucrose fed larvae remains the same (Fig.\u00a01j). The sucralose intake of sucrose fed larvae intake was similar to that of normally fed and starved larvae (Fig.\u00a01k), suggesting that their avoidance of sugar was mediated by energy-sensing pathways and not taste. The hemolymph glucose levels were quantified under different feeding conditions to determine whether the 90-minute food-deprivation or sucrose diet protocols caused changes in glucose levels. In larvae that were fed only 20% sucrose, the glucose level was significantly increased compared with that in both normally fed and starved larvae (Fig.\u00a01l, m). This high glucose level suggests that the consumption of carbohydrates by larvae fed sucrose is suppressed due to high circulating levels of glucose. We therefore tested whether rehydration would reduce the increase in exploration and locomotion observed in larvae fed sucrose. Indeed, after larvae fed sucrose only were placed on water for 15\u2009minutes, their locomotion was similar to that of normally fed larvae (Supplementary Fig.\u00a01m\u2013p). Furthermore, larvae that fed on sucralose with a similar osmolarity to that of 20% sucrose also increased their exploration and locomotion (Supplementary Fig.\u00a01q\u2013t).\n\nTaken together, these results suggest that depriving larvae of nutrients for 90\u2009min is sufficient to alter feeding and locomotion and that these changes are due to a lack of nutrients in starved larvae and could be due to both protein hunger and thirst in larvae fed sucrose. The observed increase in locomotion in larvae deprived of nutrients would thus likely result from an increase in the motivational drive to find the missing nutrients.\n\nWe monitored the sensorimotor decisions of Drosophila larva in response to an aversive mechanical stimulus, the air puff, to determine whether starvation and a sucrose-only diet can affect nonfeeding-related behaviors. In response to an air puff, larvae perform probabilistic sequences of five mutually exclusive actions that we have characterized in detail in the past32,47: Hunch, Bend, Stop, Back-up, and Crawl. We have also identified circuit motifs and characterized the neural circuit mechanisms underlying the competitive interactions between the two most prominent actions (i.e., the Hunch and the Bend) that occur in response to an air puff32. The model in that study predicted that the different activation levels of inhibitory neurons determine which actions will take place: the Hunch, Bend, or the Hunch-Bend sequence.\n\nWith characterized neurons and synaptic connectivity between the neurons, as well as the availability of driver lines that label the neurons of interest, this circuit provides an excellent system to study whether the activity of the neurons is modulated by changes in an animal\u2019s physiological state.\n\nWe compared larval behavioral responses to an air puff after subjecting them to the different feeding protocols (as described in the first section of the Results) to determine whether the feeding state modulates larval behavior in response to the mechanosensory stimulus and, by extension, whether sensorimotor decision-making in response to an air puff is an adequate behavioral paradigm to study the effects of food deprivation on neural circuit activity. For this purpose, we used automated tracking48 to monitor larval behaviors in response to an air puff and updated the supervised machine-learning-based classification method developed in our previous work42,47 to compute the probabilities of the different actions that occur in response to an air puff. The new classifiers were trained on larvae fed different diets, thus taking into account a broader range of behavioral dynamics and different phenotypes where common actions, e.g., such as Hunch, may have different features, e.g., they can exhibit slower or faster head retraction than Hunches detected previously. We also separated the large Bend behavioral category into two types of bending behavior: Static Bends, a form of protective action where the larva responds to the stimulus by being immobilized in a curved position for a period of time, and exploratory active Head Casts, where the head of the larva moves from one side to the other side, which can lead to escape42.\n\nOur analysis showed that larvae fed on sucrose Hunch less (and perform fewer Static Bends) and Head Cast more (Fig.\u00a01n\u2013q). Similarly, starved larvae performed fewer Static Bends and more Head Casts (Fig.\u00a01r\u2013u). Increasing the duration of sucrose feeding and starvation to 5\u2009h resulted in similar phenotypes (Supplementary Fig.\u00a01u\u2013z, Source data 3), suggesting that the phenotypes persist over a range of durations of food deprivation.\n\nUsing our machine learning-based classification to monitor internal state-induced behavioral changes revealed that changing feeding conditions for short periods of time alters sensorimotor decisions in response to an aversive mechanical cue and results in fewer protective types of actions and more actions consistent with active exploration and escape.\n\nAs a method to test whether changes in feeding conditions alter the perceived value of a stimulus, which in turn affects behavioral decisions, calcium responses in sensory neurons that sense air puff and chordotonal sensory neurons32,44,47,48 were monitored in response to a moderately strong mechanical stimulus under three different feeding conditions: normally fed, fed sucrose and starved (Fig.\u00a02a\u2013d and Supplementary Fig.\u00a02). We found no significant differences in chordotonal calcium responses among the different feeding states (Fig.\u00a02b\u2013d, Supplementary Fig.\u00a02a\u2013c), although the responses were slightly higher in starved larvae than in larvae fed standard food or sucrose only. GFP was genetically expressed in the neurons, and its expression level was quantified by comparing the fluorescence in larvae exposed to all three feeding conditions to ensure that the changes in feeding conditions did not influence protein expression and, by extension, GCaMP expression levels. No differences in GFP fluorescence were detected among the three feeding states (Supplementary Fig.\u00a02e\u2013h). The results, therefore, suggest that chordotonal responses are not significantly affected by starvation or sucrose feeding and that the behavioral changes observed under different feeding conditions are not due to changes in stimulus sensitivity at the level of sensory neurons. We further confirmed this result by optogenetically activating chordotonal sensory neurons in larvae in all three feeding states using a driver that labels all eight subtypes of chordotonal sensory neurons: R61D08. We found that although the activation of chordotonal sensory neurons in all three feeding states was the same due to optogenetic stimulation, larvae performed fewer Hunches and more Head Casts when they were fed only sucrose than when they were fed normally (Fig.\u00a02e, f). Similarly, the modulation of behavior when they were starved could be observed upon chordotonal optogenetic activation (Supplementary Fig.\u00a02i, j). Taken together, these results show that feeding state-dependent modulation could target neurons downstream of chordotonal sensory neurons.\n\na Organization of the circuit. b\u2013d Calcium responses to mechanical stimulations (5\u2009V) in chordotonal neurons larvae fed on sucrose only and on standard food (R61D08-Gal4/UAS-GCaMP6s). b Calcium responses of chordotonal neuron projections in the VNC from different individuals fed on different diets. c calcium response averaged during the stimulus. The white line represents the mean, the white dot represents the median, colored dots with white edges represent individual data points. Stimulus-induced activity of chordotonal mechanosensory neurons is unchanged in animals fed on sucrose as compared to larvae fed on standard food. d mean calcium trace of chordotonal neurons over time +/- SEM. The green dashed line corresponds to stimulus duration. e, f Optogenetic activation of cho in larvae fed only on sucrose. Red light (0.3\u2009mW/cm\u00b2) was used for CsChrimson activation. e Hunch probability is computed during the first 2\u2009seconds from stimulus onset. f Bend is the mean probability during the first 10\u2009s from stimulus onset, corrected by the baseline recording prior to the stimulus. (Hunch presented as population probability and 95 % confidence interval, Head Cast as post-stimulus probability corrected by baseline probability). (Statistics: c two-tailed T-test; e Chi-square (one-sided) test; f Numerical simulation test; ***p\u2009<\u20090.001, **p\u2009<\u20090.01, *p\u2009<\u20090.05). See also Supplementary Fig.\u00a02. The source data and p-values are provided in Source Data 2, 5, and 6.\n\nTo determine whether the changes in the feeding state influence the output of the network for the selection between the Hunch and the Bend, as characterized in previous work32, calcium responses of projection neurons Basin-1 and Basin-2 were monitored at different intensities of mechanical stimulation in the three feeding states (Fig.\u00a03a\u2013g and Supplementary Fig.\u00a03). Basin-1 and Basin-2 are differentially involved in Hunching and Bending, as shown in Jovanic et al., 201632: Basin-1 is required for both Hunching and Bending, whereas Basin-2 promotes Bending and inhibits Hunching. The responses of Basin-2 neurons can thus be used as a readout of the Bend state: if Basin-2 is ON, the larva will Bend, and if it is OFF, the larva will Hunch. To confirm that the circuit output and specific Basin-2 function can be mapped on Head Cast and Hunch, responses to a mechanical stimulus in larvae where Basin-2 was silenced with TNT using a Basin-2-specific driver (SS00739) were analyzed using the current classification method, where Bend was separated into Head Cast and Static Bend. We found that Basin-2 is indeed required for Head Cast and inhibited Hunch (Supplementary Fig.\u00a02k\u2013m). Hereafter, we use Hunch and Head Cast to describe the behaviors controlled by the characterized circuit.\n\na\u2013c Basin-1 (B1) calcium responses to mechanical stimulations in larvae fed standard food or sucrose (20B01-lexA;LexAop-GCaMP6s,UAS-CsChrimson-mCherry). a B1 individual calcium responses. b calcium response averaged during stimulation. c mean B1 calcium trace\u2009+/\u2212\u2009SEM. d\u2013g Basin-2 (B2) calcium responses to mechanical stimulations in fed and sucrose larvae (SS00739-UAS-GCaMP6s). d B2 individual calcium responses. e calcium response averaged during stimulation. f percentage of trials with failed responses (g) mean B2 calcium trace\u2009+/\u2212\u2009SEM. b, d, White line: mean, white dot: median, colored dots: individual data points. c, g Green dashed line: stimulation. h\u2013p Simple connectome-based rate model of the Hunch/Headcast selection circuit. h\u2013j Model 1: sucrose state - decreased maximum LNa rate. h circuit schematic: weaker LNa influence on downstream targets (i) B1 and B2 Activity state-space trajectories as a function of MCh to LNa coupling. w_ILNA: output proportion from MCh feeding into LNa, 1-w_iLNa: proportion feeding into iLNb. Fed: rmax_iLNa=20 a.u., Sucrose: rmax_iLNa=18 a.u. j phase diagram exploring multiple rmax_iLNa values. The affinity of the input signal to iLNa is identical to w_iLNa. k\u2013m Model 2: sucrose state: input current to Handle b (Hb) (k) circuit schematic: stronger Hb influence on downstream targets (l) B1 and B2 Activity state-space trajectories as a function of MCh to LNa coupling. Sucrose state: An Additional 10 a.u input current excites Hb. m phase diagram exploring multiple i_Hb values. The affinity of the input signal to iLNa is identical to w_iLNa. n\u2013p Combined model: sucrose state - 10 a.u. input current to Hb and decreased Maximum LNa rate (18 a.u). n circuit schematic, stronger Hb and weaker LNa influence on downstream targets (o) B1 and B2 activity state-space trajectories as a function of MCh to LNa coupling. p phase diagram. The threshold surface in the (rmax_iLNa, i_Hb, w_iLNa) space is shown. Gray lines: threshold levels (combinations of parameters achieving the same threshold). Red dots: fed and sucrose models simulated in o. (Statistics: b, e two-tailed T-test; f Chi-square (one-sided) test; ***p\u2009<\u20090.001, **p\u2009<\u20090.01, *p\u2009<\u20090.05). See also Supplementary Figs.\u00a03 and 5 and Source Data 5 and 6.\n\nWe found that for most stimulus intensities, the Basin-1 responses remain only mildly affected by the sucrose-only diet (Fig.\u00a03a\u2013c and Supplementary Fig.\u00a03g). Only for the very weak stimulus intensity did we observe a decrease in the Basin-1 response (Supplementary Fig.\u00a03g). Under starvation conditions, the effect on Basin-1 neurons was similar to that under sucrose-only conditions (Supplementary Fig.\u00a03a\u2013c, h). The activity of Basin-2, on the other hand, was moderately increased in larvae fed only sucrose at all intensities except at the lowest stimulus intensity (Fig.\u00a03d\u2013g and Supplementary Fig.\u00a03i), where no difference was observed between Basin-2 responses under different feeding conditions (Supplementary Fig.\u00a03i, j). However, the activity of Basin-2 in starved larvae was slightly (but not significantly) decreased compared with normally fed larvae at lower intensities of stimulation, whereas it was increased at higher intensities of stimulation (Supplementary Fig.\u00a03d\u2013f, i, j).\n\nWe then compared the distributions of Basin-2 responses in larvae fed either standard food or only sucrose to different intensities of mechanical stimulation. In response to a moderately strong mechanical stimulus (5\u2009V applied to the piezo at 1000\u2009Hz), we observed a greater probability of the absence of a neuronal response (failed response) in normally fed larvae (Fig.\u00a03f and Supplementary Fig.\u00a03k). The previous study recorded depolarization responses simultaneously from Basin-1 and Basin-2 to a mechanical stimulus and showed that Basin-1 neurons always respond, whereas Basin-2 responses are probabilistic32. In this study, we computed the probability of Basin-2 responses to a mechanical stimulus and observed a significantly greater probability of responses in larvae fed sucrose only, which is consistent with higher Head Casting and lower Hunching probabilities in these larvae (Fig.\u00a01n\u2013q, Supplementary Fig.\u00a01u\u2013w). In starved larvae, similar trends were observed only at higher stimulus intensities (Supplementary Fig.\u00a03l).\n\nBased on the circuit model simulation for selecting between two air puff-induced actions (Hunch and Head Cast)32, we predicted that the relative level of activation of the reciprocally connected feedforward inhibitory interneurons determines the outcome of the competition: Basin-1 only state (Hunch) or coactive state (Head Cast). In this study, we investigated whether changes in the feeding state affected the level of activation of two different classes of inhibitory neurons: feedforward and feedback inhibitory neurons.\n\nWe addressed this question by updating the rate model introduced in the previous study32, where the Hunch-Head Cast circuit was characterized. In this model, connections between neuron classes were derived from EM data (Fig.\u00a03h\u2013p). Coupling coefficients are proportional to the number of synapses, whereas excitatory and inhibitory connections are treated separately. Each neuron category (mechano-ch, iLNa, iLNb, fbLN-Ha, and fLN-Hb) is reduced to a single population variable, and connections are simplified to reduce small differences in the synaptic count to unique values, thus reducing parameter choices (see previous study32). The rate vector of all units is evolved according to\n\nwhere V0 sets the threshold for activation, s is the stimulus, i is an input from afferent populations, rmax sets the maximum rate, and Aexij and Ainij are the excitatory and inhibitory connection strengths from neuron j to neuron i, respectively. As previously described32, the model predicts that the level of activation of the two classes of feedforward inhibitory neurons will determine the output state of the network at the level of the Basin-1 and Basin-2 neurons (Fig.\u00a03h\u2013p and Supplementary Fig.\u00a04a).\n\nWe varied the parameter w_iLNa, which represents the balance between inputs from MCh to iLNa and inputs from MCh to iLNb. A higher w_iLNa corresponds to greater stimulation of iLNa and less stimulation of iLNb, and vice versa. We limited the source for the variation of the stimulus to this parameter. Based on the calcium imaging results (Fig.\u00a03a\u2013g and Supplementary Fig.\u00a04g\u2013p), we manipulated the activity state of the reciprocally connected feedforward and feedback inhibitory neurons in the circuit: LNa and Handle-b (Hb) neurons to explore whether the modulation of one of the neurons was sufficient to bias the state-dependent changes in neuronal activity and behaviors or the modulation of both neurons was required. In one version of the model, the sucrose state was modeled as the decreased maximum intensity of LNa neuron activity to explore the role of the feedforward inhibitory neurons in the feeding state modulation (Fig.\u00a03h\u2013j). Another version of the model was designed with the sucrose state represented by adding an input to the Handle-b neurons to explore the contribution of feedback inhibitory neurons (Fig.\u00a03k\u2013m). Finally, we constructed a combined model in which both LNa and Handle-b neurons were modulated in the sucrose-fed state (Fig.\u00a03n\u2013p and Supplementary Fig.\u00a04a\u2013f). In all versions of the model, the Hunching decreased and Head Casting increased in the sucrose-fed state compared with the normal fed state (Fig.\u00a03j, m, p), which is in good agreement with the experimental results.\n\nFigure\u00a03i, l, o shows samples of rate trajectories in the B1-B2 plane for each model. In accordance with the previous study32, the rates of the B1 and B2 populations are attracted, depending on the value of w_iLNa, either to a coactivate state (low w_iLNa) or to a B1 high/B2 low state (high w_iLNa), not only for the fed models (solid lines) but also for the sucrose models (dotted lines). For each variant of the model, the dotted lines are above the solid lines of the same color, and the transition between the two states occurs at a higher w_iLNa value in the sucrose models than in the fed models.\n\nWe investigated the sensitivity of the threshold value to the magnitude of the parameter change defining the fed and sucrose states (Fig.\u00a03j, m). The ratio of the steady-state activity of the B2 variable to the B1 variable is plotted for multiple values of the affinity of the input signal to iLNa, another name for w_iLNa, and of the parameter modeling the effect of food deprivation. The boundary between the blue and red areas corresponds to the threshold between the two end states as w_iLNa varies. In Fig.\u00a03j, we show that as rmax for iLNa decreases from 20 a.u. to 17 a.u., the threshold increases, as the boundary has a downward slope. Thus, the domain of inputs yielding a coactive response associated with Head Casts increases as the parameter modeling the effect of the diet change increases, in accordance with behavioral measurements. Similarly, in Fig.\u00a03m, the threshold increases as the exogenous input current to Hb increases, albeit less dramatically. Once again, the region of stimuli that resulted in a Head Cast increased, which was consistent with the results of the behavioral experiments.\n\nThese model results suggest that modulating both feedforward and feedback inhibitory neurons of the circuit could result in the observed behavioral changes upon sucrose feeding. To test this experimentally, calcium transients in response to a mechanical stimulus were imaged in the two different types of inhibitory neurons to which we had genetic access (Fig.\u00a04a\u2013h and Supplementary Fig.\u00a04g\u2013p). An LNa neuron type, Griddle-2 (G2), which promotes Hunching32, presents significantly lower responses to a mechanical stimulus in larvae fed sucrose than in normally fed larvae (Fig.\u00a04a\u2013d). A decrease in the response was observed at different intensities of mechanical stimulation (Supplementary Fig.\u00a04m). Similarly, in starved larvae, the responses of Griddle-2 neurons also decreased across different intensities of stimulation (Supplementary Fig.\u00a04g\u2013i, n).\n\na\u2013d Griddle-2 calcium responses to mechanical stimulations in larvae fed on standard food and sucrose (SS00918/UAS-GCaMP6s). a Imaging schematic. b Responses of different individuals, (c) mean calcium response averaged during the stimulus. d Mean Griddle-2calcium trace of over time\u2009+/\u2212\u2009SEM. e\u2013h Handle-b calcium responses to mechanical stimulations in larvae fed on standard food or on sucrose (SS00888/UAS-GCaMP6s). e Imaging schematic. f Responses of different individuals. g Mean calcium response averaged during the stimulus. h Mean Handle-b calcium trace over time\u2009+/\u2212\u2009SEM. i\u2013t Reciprocal inhibition between Handle-b and Griddle-2. i, j model simulation, trajectories of LNa activity upon Hb inactivation in fed (i, modeled as rmax_iLNa\u2009=\u200920 a.u.) and sucrose condition (j, modeled as rmax_iLNa\u2009=\u200918 a.u.) Silencing Hb increases activity in iLNa, transiently for low w_iLNa and at steady-state for high w_iLNa, more so in the sucrose than in the fed model. k Imaging schematic. l, m mean Griddle-2 calcium trace over time +/\u2212 SEM, with (SS0888-Gal4>UAS-TNT 55C05-LexA>LexAop-GCaMP6s) or without Handle-b inactivation (+/UAS-TNT 55C05-LexA>LexAop-GCaMP6s), in larvae fed on standard food (l) or on sucrose (m). n mean calcium response averaged during the stimulus. o, p model simulation, trajectories of Hb activity upon iLNa inactivation in fed (o, modeled as i_Hb\u2009=\u20090 a.u.) and sucrose condition (p, modeled as i_Hb\u2009=\u200910 a.u.). Silencing iLNa increases strongly the activity of Hb across w_iLNa values and for both models. q Imaging schematic. r, s mean Handle-b calcium trace over time\u2009+/\u2212\u2009SEM, with (55C05-LexA\u2009>\u2009LexAop-TNT 22E09-Gal4>UAS-GCaMP6s) or without Handle-b inactivation (+/LexAop-TNT 22E09-Gal4\u2009>\u2009UAS-GCaMP6s), in larvae fed larvae (r) or sucrose-fed larvae (s). t mean calcium response averaged during the stimulus. c, g, n, t White line represents the mean, white dot represents the median, colored dots represent individual data points. d,h,l,m,r,s The green dashed line corresponds to stimulation. (Statistics: c, g, n, t two-tailed T-test; ***p\u2009<\u20090.001, **p\u2009<\u20090.01, *p\u2009<\u20090.05). See also Supplementary Figs.\u00a04 and \u00a05. The source data and p-values are provided in Source Data 5 and 6.\n\nAccording to the model, a decrease in the activity of LNa neurons results in less Hunching and more Head Casting, which is consistent with the behavioral changes we observed after sucrose feeding. The model also shows that another motif in the circuit, feedback disinhibition, promotes Head Casting by amplifying the activity of Basin-2. We recorded the responses of Handle-b neurons to the mechanical stimulus in larvae in the different feeding states and found that Handle-b neurons, which inhibit hunching32, exhibit stronger responses to a mechanical stimulus in larvae fed sucrose and in starved larvae than in normally fed larvae (Fig.\u00a04e\u2013h and Supplementary Fig.\u00a04j\u2013l, o, p). This result was observed for all the different intensities of stimulation we tested (Supplementary Fig.\u00a04o, p). Therefore, the decreased response of Griddle-2 neurons (Fig.\u00a04b\u2013d) and increased response of Handle-b neurons (Fig.\u00a04f\u2013h) in larvae fed sucrose could bias the behavioral outcome, consistent with the behavioral changes observed in larvae upon sucrose feeding (less Hunching and more Head Casting) (Fig.\u00a01n\u2013q).\n\nThe EM analysis revealed that Griddle-2 and Handle-b neurons are reciprocally interconnected (Fig.\u00a04a)32. The circuit and the model predict that silencing Handle-b neurons results in an increase in Griddle-2 neuronal activity (Fig.\u00a04i, j, and Supplementary Fig.\u00a05a\u2013f). Furthermore, the reciprocal connections between Griddle-2 and Handle-b neurons were probed functionally, and the model predictions were tested experimentally; calcium responses to a mechanical stimulus were monitored in Griddle-2 neurons while inactivating Handle-b neurons using tetanus toxin in both larvae that were fed standard food and those that were fed sucrose only. The responses were greater than those of the control larvae in both feeding states (Fig.\u00a04k\u2013n and Supplementary Fig.\u00a05g), which is consistent with the inhibition of Griddle-2 neurons by Handle-b neurons. However, the increase in Griddle-2 responses upon Handle-b inactivation in larvae fed sucrose only did not reach the levels of Griddle-2 responses upon inactivation in larvae fed standard food (Fig.\u00a04k\u2013n and Supplementary Fig.\u00a05g). This result could suggest that the increase in Handle-b activity after sucrose feeding is insufficient to reduce Griddle-2 responses in larvae fed sucrose and that Griddle-2 neurons could also be modulated by internal state signals. This finding is consistent with the predictions of Models 1 and 3, where Griddle-2 neurons are modulated extrinsically (Fig.\u00a03h\u2013j, n\u2013p; Fig.\u00a04i, j and Supplementary Fig.\u00a05a\u2013c).\n\nThe circuit and the model predict that silencing Griddle-2 neurons results in an increase in Handle-B activity (Fig.\u00a04o, p). This prediction was tested by imaging Handle-b activity while inactivating Griddle-2 neurons (Fig.\u00a04q\u2013t). Handle-b responses to the mechanical stimulus were increased in normally fed larvae upon Griddle-2 inactivation (Fig.\u00a04q\u2013t, Supplementary Fig.\u00a05h), which is consistent with the inhibition of Handle-b neurons by Griddle-2 neurons. In larvae fed sucrose, the activity of the Handle-b neuron was not significantly different upon Griddle-2 inactivation and was similar to the activity upon Griddle-2 inactivation in larvae fed standard food (Fig.\u00a04q\u2013t and Supplementary Fig.\u00a05h). This lack of increase in Handle-b activity upon Griddle-2 inactivation in larvae fed sucrose could be due to the different state of the network in the larvae fed only sucrose: the activity of Griddle-2 neurons was already low in these larvae (Fig.\u00a04b\u2013d and Supplementary Fig.\u00a04m), and their inactivation may not impact Handle-b neurons significantly.\n\nOur previous work has shown that Basin-2 (B2) neurons receive input directly from the inhibitory neuron LNa and are disinhibited by the feedback inhibitory neuron Handle-b, which forms inhibitory connections with LNa and LNb neurons; the Handle-b neuron receives input primarily from Basin-2 neurons. The feedback disinhibition of Basin-2 neurons creates positive feedback that stabilizes Basin-2 ON state32. To determine whether the increase in Basin-2 responses in larvae fed on sucrose depends on feeding state-dependent changes in the activity of the inhibitory neurons, Handle-b neurons were optogenetically activated, and the activity of B2 neurons was recorded in response to a mechanical stimulus. We found that activating Handle-b in normally fed larvae increases the level of the Basin-2 response to a level similar to that observed in larvae fed sucrose (Fig.\u00a05a\u2013c and Supplementary Fig.\u00a06a). We further silenced Handle-b neurons and monitored calcium responses in Basin-2 neurons in larvae with different feeding states (Fig.\u00a05d\u2013g and Supplementary Fig.\u00a06b-d). Silencing of Handle-b neurons in larvae fed sucrose only decreased Basin-2 responses and erased the difference in Basin-2 response levels between larvae fed standard food and those fed sucrose only (Fig.\u00a05d\u2013g and Supplementary Fig.\u00a06b\u2013d). This result is consistent with the previous characterization of the Handle-b\u2019s disinhibition of Basin-2 neurons. Because Handle-b disinhibits Basin-2, silencing Handle-b therefore increases the inhibition of Basin-2 by LNa neurons and thus reduces the response of Basin-2 neurons to mechanical stimuli. In addition, in normally fed larvae, where the activity of LNa neurons is high, Basin-2 responses were almost completely abolished (Supplementary Fig.\u00a06b\u2013d).\n\na Imaging schematic. b mean calcium trace of Basin-2 over time\u2009+/\u2212\u2009SEM from individuals fed on different diets, with or without optogenetic activation of Handle-b (22E09-Gal4>UAS-CsChrimson::tdTomato 38H09-LexA\u2009>\u2009LexAop-GCaMP6s) during the first second of mechanical stimulus (red box). The green dashed line corresponds to stimulus onset. c calcium response of Basin-2 averaged during the first second of the stimulus. The white line represents the mean, the white dot represents the median, and colored dots with white edges represent individual data points. Activating Handle-b in fed larvae phenocopies the effect of sucrose feeding on Basin-2 activity. d Imaging schematic. e mean calcium trace of Basin-2 over time\u2009+/\u2212\u2009SEM from individuals fed on different diets, with (38H09-LexA\u2009>\u2009LexAop-GCaMP6s 22E09-Gal4\u2009>\u2009UAS-TNT) or without (38H09-LexA\u2009>\u2009LexAop-GCaMP6s\u2009+\u2009/\u2009>\u2009UAS-TNT) Handle-b inactivation. The green dashed line corresponds to stimulus onset. f calcium response of Basin-2 averaged during the stimulus. The white line represents the mean, the white dot represents the median, and colored dots with white edges represent individual data points. g percentage of trials that failed to elicit a calcium response in Basin-2. Inactivating Handle-b in larvae fed on sucrose only prevents the effect of sucrose feeding on Basin-2 activity. h Imaging schematic. i mean calcium trace of Basin-2 over time\u2009+/\u2212\u2009SEM from individuals fed on different diets, with (38H09-LexA\u2009>\u2009LexAop-GCaMP6s 55C05-Gal4\u2009>\u2009UAS-TNT) or without (38H09-LexA>LexAop-GCaMP6s\u2009+\u2009/\u2009>\u2009UAS-TNT) Griddle-2 inhibition. The green dashed line corresponds to stimulus onset. j calcium response of Basin-2 averaged during the stimulus. The white line represents the mean, the white dot represents the median, and colored dots with white edges represent individual data points. k percentage of trials that failed to elicit a calcium response in Basin-2. (Statistics: c, f, j one-way ANOVA with Tukey post-hoc test (two-sided); g, k Chi-square (one-sided) test; ***p\u2009<\u20090.001, **p\u2009<\u20090.01, *p\u2009<\u20090.05). See also Supplementary Fig.\u00a06. The source data and p-values are provided in Source Data 5 and 6.\n\nGriddle-2 neurons were then inactivated, and the activity of Basin-2 neurons was imaged in larvae in the two feeding states. As expected, silencing Griddle-2 neurons abolished the difference in Basin-2 responses between the two states (Fig.\u00a05h\u2212k). This result was consistent with the effect of Griddle-2 neurons on Handle-b neuronal activity (Fig.\u00a04o\u2212t). Silencing Griddle-2 neurons in fed larvae resulted in a small (but not significant) increase in Basin-2 responses (Fig.\u00a05i, j and Supplementary Fig.\u00a06e, f). However, the mean response of Basin-2 neurons in larvae fed only sucrose unexpectedly and significantly decreased upon Griddle-2 silencing. We also computed Basin-2 response probabilities. While inactivating Griddle-2 did not affect Basin-2 response probabilities in normally fed larvae, it did affect Basin-2 response probabilities in larvae fed only sucrose; the nonresponse probabilities increased significantly upon Griddle-2 inactivation in sucrose-fed larvae (Fig.\u00a05k and Supplementary Fig.\u00a06h). The mean responses of trials with only Basin-2 ON responses reveal that the level of responses of Basin-2 was increased in normally fed larvae to the level found in larvae fed only sucrose in the control. The mean responses of trials with only Basin-2 ON responses also revealed that the level of the Basin-2 response decreased in larvae fed sucrose (Supplementary Fig.\u00a06e, g) and was similar to the level of activity in normally fed control larvae.\n\nGriddle-2 is thus required for state-dependent modulation of Basin-2, possibly by mediating the disinhibition of Basin-2 by Handle-b. Inactivating Griddle-2 in normally fed larvae disinhibits Basin-2. On the other hand, inactivating Griddle-2 in larvae fed on sucrose when the activity of Griddle-2 is already low (and Griddle-2 does not strongly inhibit Basin-2 neurons) may favor other inhibitory pathways and result in lower Basin-2 responses.\n\nThese results show that two types of reciprocally connected inhibitory neurons that promote competing actions inhibit each other during the response to a mechanosensory cue and are differentially modulated by changes in the feeding state (starvation and feeding on sucrose). The activity of Griddle-2 neurons, which promotes Hunching, is decreased, whereas the activity of Handle-b neurons, which inhibits Hunching and promotes Head Casting, is increased. This differential modulation of the inhibitory neurons, in turn, affects the state of the network in a feeding state-dependent manner and biases the behavioral responses toward less Hunching and more Head Casting by increasing the activity of Basin-2 neurons when larvae are fed sucrose only.\n\nWe examined the connectome of the upstream partners of Griddle-2 and Handle-b neurons to determine the sources of feeding state-dependent modulation. Previous work has shown that the different neurons in the circuit receive input from long-range projection neurons that, as suggested by their morphology and connectivity, could bias the output of the network based on contextual/internal state information32. Interestingly, by matching the light microscopy images with the reconstructed electron microscopy images, we found that one of these neurons is an NPF-expressing neuron that synapses on Handle-b neurons (Fig.\u00a06a\u2013c). NPF (neuropeptide F), a homolog of the mammalian neuropeptide Y49, is a hunger signal, and its expression promotes feeding in both adult and larval Drosophila50. Therefore, we sought to investigate the role of NPF neurons in modulating the activity of inhibitory neurons in a state-dependent manner (Fig.\u00a06 and Supplementary Fig.\u00a07). Larvae have two pairs of NPF-expressing neurons. Both have cell bodies in the brain; the dorsolateral pair (DL-NPF) projects ipsilaterally within the brain lobes, and the dorsomedial pair (DM) sends descending projections via the suboesophageal zone (SEZ) through the ventral nerve cord (VNC) (Fig.\u00a06a, b and Supplementary Fig.\u00a07a\u2212c).\n\na, b Comparison of light microscopy images (GMR_SS01635\u2009>\u2009UAS-GFP) with electron microscopy (EM) reconstruction images of DM-NPF neurons. c EM image shows neuropeptide-containing dense core vesicles in the DM-NPF neuron near one of its synapses with Handle-b. Red arrows: presynaptic neurons, DM-NPF annotated with yellow arrow, blue arrows: postsynaptic sites, magenta arrow: Handle-b (d\u2212f) Baseline calcium fluorescence in DM-NPF ventral nerve cord projections upon 90\u2009min sucrose feeding (d), starvation for 90\u2009min (e) or 5\u2009hours (f). g, h Locomotion analysis. Box plots display the median, interquartile range (IQR), and whiskers up to 1.5\u2009\u00d7\u2009IQR. Outliers are plotted individually. g control larvae. h NPF knockdown in DM-NPF neurons. i\u2212k Handle-b calcium responses with (UAS-GCaMP6s; 22E09-Gal4, NPF-LexA\u2009>\u2009LexAop-KIR) or without (UAS-GCaMP6s; 22E09-Gal4, NPF-LexA;+) DM-NPF silencing in fed larvae (i) Handle-B calcium responses, single trial of mechanosensory stimulation. j mean Handle-B calcium trace over time +/- SEM. k mean calcium response averaged during the stimulus. l\u2212p Immunohistochemical labeling for NPFR in Handle-b (all images, scale bar\u2009=\u20095\u2009\u00b5m). GMR_SS00888 split-Gal4 drives UAS-GCaMP6s in Handle-b, and LexAop-jRGECO1a is expressed under the control of the NPFR promoter using a T2A-LexA construct (magenta). GFP and dsRed antibodies increase detection sensitivity. l 4 neurons (N1-N4), 23\u2009\u00b5m stack. The (m\u2212p) whole thickness (5 to 7\u2009\u00b5m) of each neuron (N1-N4) is shown. q\u2212s Handle-B calcium responses upon NPFR knockdown (GMR_SS00888\u2009>\u2009UAS-NPFR-RNAi; UAS-GCaMP6s) compared to control (GMR_SS00888\u2009>\u2009UAS-GCaMP6s). q mean Handle-b calcium trace over time +/- SEM in control larvae fed, fed on sucrose or starved. r mean Handle-b calcium trace over time +/- SEM upon NPFR knockdown in larvae fed, fed on sucrose or starved. s mean calcium response averaged during the stimulus. f, k, s White line: mean, white dot: the median, colored dots: individual data points. j, q, r green dashed line: stimulus. (Statistics: d\u2212f, k two-tailed T-test; g, h two-sided Mann-Whitney test with Bonferroni correction; s one-sided Mann-Whitney test; ****p\u2009<\u20090.0001, ***p\u2009<\u20090.001, **p\u2009<\u20090.01, *p\u2009<\u20090.05). See also Supplementary Figs.\u00a07 and 8. The source data and p-values are provided in Source Data 1, 5 and 6.\n\nTo study the influence of NPF neurons on Handle-b activity, the activity of the DM-NPF descending neuron was monitored in larvae in the different feeding states. Therefore, we drove GCaMP6s expression in the NPF descending neurons with NPF-GAL4 and measured its fluorescence intensity in the VNC projections of larvae fed either standard food (fed), 20% sucrose (sucrose-fed), or completely starved (administered only water for 90\u2009minutes). We showed that starvation or feeding with sucrose both increased the activity of the NPF descending neurons (Fig.\u00a06d\u2212f). The application of glucose to NPF neurons decreased their activity in the presence of tetrodotoxin (TTX), suggesting that they may sense glucose autonomously (Supplementary Fig.\u00a08a).\n\nNPF was shown to be involved in promoting feeding51. To investigate whether the increase in activity of NPF descending neurons upon 90-minute sucrose feeding or starvation had an influence on NPF release and behavior, NPF was knocked down selectively in the NPF descending neuron using a split-GAL4 driver that labels the descending pair of NPF neurons (Fig.\u00a06a) and larval locomotion was monitored in larvae that were fed standard food, larvae that were fed sucrose and starved larvae (Fig.\u00a06g, h and Supplementary Fig.\u00a08b\u2212g). The downregulation of NPF in DM-NPF neurons descending into the VNC impaired the increase in exploration observed in larvae fed sucrose and starved larvae (Fig.\u00a06g, h and Supplementary Fig.\u00a08b\u2212g).\n\nTo understand the influence of NPF neurons on Handle-b neuronal activity, the interactions between the two neurons were tested functionally by hyperpolarizing the NPF neurons with the inwardly rectifying potassium channel Kir2.1 using an NPF-LexA driver line that labels both pairs of NPF neurons and monitoring the calcium responses of Handle-b neurons to mechanical stimulation. Upon NPF neuron silencing, we observed a decrease in Handle-b responses to mechanical stimulation (Fig.\u00a06i\u2212k and Supplementary Fig.\u00a08h\u2212m). The NPF descending neuron could thus facilitate the responses of Handle-b neurons. Upon changes in the feeding state, the activity of NPF neurons increases upon starvation and sucrose feeding, which could enhance Handle-b neuronal responses to a mechanical stimulus, resulting in greater responses of these neurons (Fig.\u00a06d\u2212f, i\u2212k) that in turn would bias behavioral choice toward less Hunching and more Head Casting.\n\nIn the connectome, we observed large dense core vesicles in NPF neurons in proximity to Handle-b neurons (Fig.\u00a06c). Moreover, we showed that Handle-b neurons express NPFR1 using the T2A-LexA method52, where we drove the expression of one reporter in all NPFR1 neurons using NPFR1 T2A-Lexa and another reporter with a Handle-b-specific split-GAL4 driver. The intersection of the two expression patterns revealed that Handle-b neurons express NPFR1 (Fig.\u00a06l\u2212p). The model and functional connectivity experiments predict that Handle-b and Griddle-2 neurons could be independently modulated (Fig.\u00a04i\u2212t and Supplementary Fig.\u00a05a\u2212f). We thus investigated whether Griddle-2 neurons could also be influenced by NPF. We found that Griddle-2 neurons do not receive synaptic input from the NPF descending neurons. Since neuropeptides can act outside of synaptic sites and we found dense core vesicles characteristic of neuropeptide release along the NPF axon in proximity to Griddle-2 neurons, we also examined NPFR1 expression in Griddle-2 neurons, as well as in Basin-1 and Basin-2 neurons, using a similar approach as that used for Handle-b neurons and did not observe any NPFR1-positive cells among these neurons (Supplementary Fig.\u00a07f\u2212h).\n\nWe then downregulated the expression of NPFR1 in Handle-b neurons and imaged their calcium responses to mechanical stimulation in larvae fed standard food, larvae fed only sucrose, and starved larvae (Fig.\u00a06q\u2212s and Supplementary Fig.\u00a08n, o). The downregulation of NPFR1 in Handle-b neurons abolished its feeding-state-dependent modulation\u00a0as a significant increase in Handle-b responses was not observed upon sucrose feeding or starvation when NPFR1 was downregulated (Fig.\u00a06q\u2212s and Supplementary Fig.\u00a08n, o). This outcome was reflected in the behavioral responses to an air puff, where upon NPFR1 knockdown in Handle-b neurons, no differences in behavioral responses could be observed between the three different states, i.e., fed, sucrose only and starved, further suggesting that NPF signaling is required for the state-dependent modulation of these neurons (Supplementary Fig.\u00a09).\n\nTaken together, these results show that NPF neurons sense changes in the feeding state and that their activity increases upon starvation or sucrose-only feeding. The changes in NPF activity influence Handle-b neuronal activity, which increases upon starvation or sucrose-only feeding due to the facilitating effect of NPF. This increase in activity would then bias the sensorimotor decisions toward less Hunching and more Head Casting.\n\nThe model and calcium imaging results suggest that Griddle-2 and Handle-b neurons are independently modulated by the feeding state (Fig.\u00a04). Since Griddle-2 neurons do not express the NPFR1 receptor, other neuropeptides could influence Griddle-2 and the circuit. We therefore investigated the effect of another neuropeptide on the circuit: sNPF (the short neuropeptide F). sNPF is a second homolog of the mammalian NPY, whose receptor is widely distributed in the larval nervous system, including the VNC53, and was shown to be involved in regulating hunger-driven behaviors50 and facilitating mechano-nociceptive responses40, among other functions. Therefore, we sought to examine whether the interneurons in the circuits express the receptor for sNPF (Fig.\u00a07 and Supplementary Fig.\u00a07). By genetically coexpressing a red fluorescent reporter (jRGECO1a) under the control of LexA drivers that label either the Handle-b, Griddle-2, Basin-1 or Basin-2 neurons we identified in the literature and existing Gal4 databases (see Methods for details) and a green fluorescent reporter (GCaMP6s) under the control of the sNPFR1 promoter with a T2A-Gal4 construct, we showed that both Handle-b (Fig.\u00a07a\u2212d) and Griddle-2 (Fig.\u00a07l\u2212o) neurons express sNPFR1, whereas the two Basin neurons do not (Supplementary Fig.\u00a07i, j).\n\na\u2013d Immunohistochemistry labeling for sNPFR in Handle-b. 60E02-LexA drives LexAop-jRGECO1a in Handle-b (magenta), and UAS-GCaMP6s is expressed under the control of the sNPFR promoter using a T2A-Gal4 construct (green). Antibodies against GFP and dsRed were used to increase detection sensitivity. e\u2013g Calcium responses in Handle-b upon sNPFR knockdown (GMR_SS00888\u2009>\u2009UAS-GCaMP6s; UAS-sNPFR-RNAi) compared to a control (GMR_SS00888\u2009>\u2009UAS-GCaMP6s) in Handle-b. e calcium responses of Handle-b from different individuals and trials. One line is a trial. f mean calcium response averaged during the stimulus. g mean calcium trace of Handle-b over time\u2009+/\u2212\u2009SEM. h\u2013k Behavior in response to air-puff upon sNPFR knockdown in Handle-b (SS00888\u2009>\u2009sNPFR-RNAi) compared to the control. l\u2013o Griddle-2 immunostaining for sNPFR expression. 55C05-LexA line drives LexAop-jRGECO1a in Griddle-2(magenta), and UAS-GCaMP6s is expressed under the control of the sNPFR promoter using a T2A-Gal4 construct (green). Antibodies against GFP and dsRed were used to increase detection sensitivity. p\u2013r Calcium responses in Griddle-2 upon sNPFR knockdown (SS_TJ001\u2009>\u2009UAS-GCaMP6s; UAS-sNPFR-RNAi) compared to the control (SS_TJ001\u2009>\u2009UAS-GCaMP6s). p calcium responses of Griddle-2 from different individuals and trials. One line is a trial. q mean calcium response averaged during the stimulus. r mean calcium trace of Griddle-2 over time\u2009+/\u2212\u2009SEM. s\u2013v Behavior in response to air-puff upon sNPFR knockdown in Griddle-2 (SS_TJ001\u2009>\u2009sNPFR-RNAi) compared to the control. f, q White line represents the mean, white dot represents the median, colored dots represent individual data points. g, r The green dashed line corresponds to the stimulus. h\u2013j, s\u2013u Hunch and Static-bend are presented as population probability and 95 % confidence interval, Head Cast as post-stimulus probability corrected by baseline probability. k, v Behavioral transitions over the first ten seconds of stimulation (Maximum likelihood test (one-sided, chi-square approximation)). (Statistics: f, q two-tailed T-test; h, i, s, t Chi-square (one-sided) test; j, u Numerical simulation test; k, v Maximum likelihood test (one-sided, chi-square approximation); ***p\u2009<\u20090.001, **p\u2009<\u20090.01, *p\u2009<\u20090.05). See also Supplementary Figs.\u00a07, 9. The source data and p-values are provided in Source Data 2, 3, 5 and 6.\n\nWe determined whether sNPF signaling was involved in the response of these two types of neurons to a mechanical stimulus by monitoring their responses upon sNPFR1 downregulation. Calcium imaging recordings revealed that genetically downregulating sNPFR1 expression in Handle-b neurons increased their response to mechanical stimulation (Fig.\u00a07e\u2013g), suggesting that sNPFR1 has an inhibitory effect on Handle-b neurons. On the other hand, the responses of Griddle-2 neurons were decreased upon sNPFR1 downregulation (Fig.\u00a07p\u2013r), suggesting that sNPF facilitates Griddle-2 neuronal responses to a mechanical stimulus. Since Handle-b neurons inhibit Hunching and promote Head Casting, while Griddle-2 neurons promote Hunching and inhibit Head Casting, downregulating the sNPFR1 in these neurons would inhibit Hunching and promote Head Casting. Indeed, the behavioral experiments showed that sNPFR1 knockdown in either of these two types of neurons led to less Hunching, whereas sNPFR1 knockdown in Griddle-2 neurons resulted in significantly more transitions to Head Casting in response to an air puff (Fig.\u00a07h\u2013k, s\u2013v).\n\nsNPFR1 knockdown in Handle-b and Griddle-2 neurons leads to a modulation of their activity similar to that caused by feeding on only sucrose or starvation (lower Griddle-2 activity and higher Handle-b activity) and yields the same behavioral outcome. These findings suggest that sNPF signaling, which targets these neurons, may be downregulated in larvae fed sucrose and starved larvae and at least partially responsible for the modulation of the behavioral choices of the larvae in response to mechanical stimulation. In line with this hypothesis, the downregulation of sNPFR1 in Handle-b or Griddle-2 neurons did not significantly impact their calcium or behavioral responses to mechanical stimulation in larvae fed sucrose or starved (Supplementary Figs.\u00a010 and \u00a011).\n\nTaken together, these results show that Handle-b and Griddle-2, reciprocally interconnected inhibitory neurons that drive opposing actions, are differentially modulated by the sNPF signaling pathway to bias the response to an air puff toward less Hunching and more Head Casting.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61805-y/MediaObjects/41467_2025_61805_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61805-y/MediaObjects/41467_2025_61805_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61805-y/MediaObjects/41467_2025_61805_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61805-y/MediaObjects/41467_2025_61805_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61805-y/MediaObjects/41467_2025_61805_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61805-y/MediaObjects/41467_2025_61805_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61805-y/MediaObjects/41467_2025_61805_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Although many studies have shown that various behaviors are affected by an animal\u2019s physiological state, especially hunger, this work goes further by describing the neural circuit mechanisms by which transient changes in the physiological status impact behaviors that are not directly related to feeding itself. We took advantage of the well-characterized behavioral response of Drosophila larvae to a mechanical stimulus for which we previously dissected the circuit mechanisms underlying the selection between two main types of responses: Hunch and Head Cast, to determine whether and how changes in the feeding state affect behaviors unrelated to feeding32. The current study reveals the neural circuit mechanisms of the modulation of these sensorimotor decisions by feeding conditions. Slight changes in feeding conditions affect an animal\u2019s motivational state and bias responses to an air puff toward less Hunching and more Head Casting. Reciprocally interconnected inhibitory neurons that drive competing actions are differentially modulated by changes in the feeding state: the activity of the neuron that promotes a Hunch is decreased, and the activity of the neuron that inhibits a Hunch is increased. This modulation at the level of inhibitory neurons involving the NPF and sNPF signaling systems biases the output of the network toward promoting Head Casting and inhibiting Hunching, a modulation that is consistent with the state-dependent behavioral changes we observed.\n\nHungry animals that behave differently in nonfeeding-related contexts have been reported in various studies and across the animal kingdom7,54. However, the underlying logic and neural mechanisms are not well understood. These changes could be linked to overall changes in the behavioral strategies of hungry individuals7 to ensure survival. Risk-taking has been shown to be increased by hunger54,55,56, even in social-related decisions7,57, further raising questions about how food deprivation signals are integrated into the neural computations underlying nonfeeding-related behaviors. Animals need to be able to make any decisions flexibly depending on the need that is most critical at a given moment. Our study revealed that a circuit underlying sensorimotor decisions in response to a mechanical stimulus located at the early stages of sensory processing is influenced by changes in diet. This finding supports the idea that state-dependent flexibility of behavior could be achieved by the physiological state acting on circuits throughout the nervous system and thus reorganizing its activity in a distributed manner. In various ecological contexts, survival often involves a trade-off between avoiding danger and pursuing food- and water-seeking behaviors54. For example, increased exploration increases the likelihood of finding food but also of encountering a dangerous situation54. If animals\u2019 need for food or water increases, either due to food deprivation or thirst, they might be more likely to explore intensively despite an increasing risk of threat. Similarly, food-deprived and thirsty animals might take more risks and ignore aversive cues to increase their chances of accessing food or water sources. Hungry animals use different strategies when escaping predators than do satiated animals17,23.\n\nMonitoring locomotion in completely starved larvae or larvae fed sucrose revealed similar strategies of increased exploration in these two groups of larvae compared with normally fed larvae. These findings suggest that, despite the differences in the type of feeding state and glucose levels, the effects of nutrient deprivation on behavior were largely similar. This result could reflect an increased motivational drive to search for the nutrients the larvae are lacking, regardless of the type of nutrients missing\u00a0\u2014\u00a0it could be water in the case of sucrose-fed larvae and any food in the case of starved larvae. This increased motivation to find the missing nutrients would then translate into similar changes in behavioral strategies.\n\nMoreover, we surprisingly observed that, compared with complete starvation, sucrose feeding seemed to have a stronger effect on neuronal activity and behavioral changes in response to mechanical stimuli. Specifically, the effects of starvation on Griddle-2 and Basin-2 neurons were weaker, resulting in, for example, mildly increased activity of Basin-2 neurons only at higher stimulus intensities compared with increased activity across all intensities for Basin-2 neurons. This difference in Basin-2 responses between larvae fed only sucrose and starved larvae could explain why Hunching was not consistently significantly reduced in starved larvae, whereas it was only significantly reduced in sucrose-fed larvae.\n\nThe stronger effects of sucrose-only feeding on neuronal activity and behavior could be due to dehydration being a more critical state than hunger (after 90\u2009min of treatment). After prolonged treatment with 20% sucrose (>\u20095\u2009h), larvae start dying, whereas starved larvae continue to develop. Alternatively, the lack of water and protein and increased glycemia in sucrose-fed larvae could all contribute to stronger immediate effects on neuronal activity and behavior.\n\nThe similar effects of the two feeding states on neuronal activity and behavior point to shared pathways and mechanisms that convey information about the changes in the feeding state to the circuitry underlying locomotion and sensorimotor decisions. Thus, different neurons and circuits that sense different types of states could converge onto the same descending neurons that modulate the activity in the VNC circuits that control different behaviors.\n\nWhile various studies have shown that hunger affects behavior by altering the responses of sensory neurons9,11,58,59, others have implicated central mechanisms in feeding state-dependent behavioral flexibility13,31,60. We found that the activity of chordotonal mechanosensory neurons that sense an air puff was not significantly altered upon sucrose feeding or starvation, in contrast to the activity of downstream neurons. The changes at the level of sensory pathways tune animals\u2019 perception to increase their likelihood of finding food and feeding by increasing their responsiveness to appetitive stimuli and decreasing their responsiveness to aversive food-related stimuli. The implication of central mechanisms, on the other hand, may suggest that hunger acts as a global regulator of behavior, i.e., hunger may change brain activity in such a way that goals and behavioral strategies can be reevaluated to increase the animals\u2019 chances of survival7. Our experiment monitored the calcium responses of all the chordotonal sensory neurons together. The different inhibitory and projection neurons in the circuit receive inputs from different chordotonal subtypes. This finding could also explain the differential modulation of the different neuronal subtypes in the circuit if some subtypes of chordotonal neurons are modulated by changes in the internal state, while others are not. However, the optogenetic activation of all the chordotonal neurons under the different feeding conditions still resulted in decreased Hunching and increased Head Casting upon starvation or the consumption of a sucrose diet, strongly suggesting that downstream neurons are involved. Even if chordotonals are involved in altering behavior in a state-dependent manner, their modulation is not required to alter the behavior in response to the mechanical stimulus due to the contribution of the downstream circuitry.\n\nCalcium imaging combined with neuronal manipulations revealed that in the circuit for selecting between the Hunch and the Head Cast, inhibitory neurons (and not projection neurons) were the target of modulation by changes in the feeding conditions. Inhibitory neurons were shown to be the target of contextual modulation in other systems61,62,63. Previous work has identified the reciprocal inhibition of inhibition as a motif underlying the competition between a startle-type action and an exploratory action32,42. This motif was shown to underlie similar computations in different species and brain areas32,33,34,35,64,65,66 and was proposed to provide flexibility to the selection process32,33,35. The current study revealed that one of the reciprocally connected inhibitory neurons within this motif, LNa type Griddle-2 neurons, is modulated by changes in the feeding conditions and that this modulation contributes to biasing sensorimotor decisions. These results confirm theoretical predictions that such a motif confers the sensorimotor circuit the capacity to be tuned to other types of information, in this case, internal state information (starvation and thirst). Moreover, these findings support predictions that shaping the output of the network through disinhibition by reciprocally connected inhibited neurons allows for flexible, competitive selection32,33,35.\n\nThis work revealed that another type of inhibitory neuron, Handle-b, which is a feedback inhibitory neuron that provides positive feedback to the Hunch inhibiting Basin-2 neurons through feedback disinhibition, is also modulated by changes in internal physiology. Handle-b neurons are also reciprocally connected to the LNa neurons that participate in the reciprocal inhibition of inhibition motifs. This connectivity pattern suggests that, in addition to competition within the reciprocal inhibition of inhibition motifs, the competition between the two layers of the circuits is also modulated by changes in internal physiology. Similar to recurrent excitation, the feedback disinhibition motif provides positive feedback that stabilizes the selected output32. Using inhibitory connections rather than excitatory connections may have the advantage of allowing decisions to be influenced by contextual and state information. In addition, in this circuit architecture, the feedback inhibitory neuron Handle-b contacts both reciprocally connected inhibitory neurons in the circuit and can thus shape the circuit activity at the level of the site of competition. It is thus well suited to integrate mechanosensory information and information about an animal\u2019s state.\n\nThus, two different layers of the network are modulated by changes in the feeding state, reciprocally connected feedforward inhibitory neurons and feedback inhibitory neurons that stabilize the state of the network, resulting in the coactivation of both Basin-1 and Basin-2 neurons.\n\nThe behavioral responses of the larvae to a mechanical cue depend on the state of the circuit at the level of reciprocally interconnected inhibitory neurons, which will shape the activity of the Basin projection neurons to either give rise to a state where Basin-1 only is active or a state where Basin-1 and -2 are both active32. We showed that the feeding state-dependent modulation of Basin-2 neurons depends on the modulation of the activity of Handle-b and Griddle-2 neurons. Accordingly, genetic colabeling of Basin-1 or Basin-2 neurons with either sNPFR1 or NPFR1 using T2A GAL4/LexA technology revealed no expression of these neuropeptide receptors in the two Basin neurons. This result is consistent with the finding that the state-dependent changes in the air puff-induced sensorimotor decisions are caused by the modulation that acts on the reciprocally interconnected inhibitory neurons that integrate mechanosensory information with current internal state needs.\n\nPrevious work revealed that the inhibitory neurons in the mechanosensory circuit are in contact with long-range projection neurons32. These long-range projection neurons may carry contextual or state information to the mechanosensory circuit. We found that among these long-range projection neurons is a pair of NPF-releasing neurons that contact Handle-b neurons. NPF is a homolog of the mammalian neuropeptide Y, which is a hunger signal. NPF neurons have indeed been shown to be involved in hunger-dependent behaviors in both adult flies and larvae50,51. We found that the activity of the NPF descending pair of neurons changes as a function of the feeding state. These neurons could thus convey information about the satiation state to the mechanosensory circuit and bias sensorimotor decisions to mechanosensory cues by modulating the activity of Handle-b neurons. Additionally, the release of NPF at the proximity of the circuit by the NPF neurons (as suggested by the existence of dense core vesicles) could modulate Handle-b neurons, which express the NPFR1 (while other neurons in the circuit do not). The fact that the feedback inhibitory neurons are directly influenced by the NPF neurons supports the idea that it could gate circuit activity in a state-dependent manner. Our results indicate that both the feeding state-induced changes in the locomotor strategy (motivational locomotor state and exploration persistence) and the acute response to transient environmental stimuli are dependent on NPF signaling. The descending NPF neurons could convey information about the internal state to diverse neuronal populations in the VNC to regulate exploratory locomotion and stimulus-dependent motor responses, thus adjusting behavioral interactions with the environment according to the animal\u2019s current needs. NPF could serve as an internal state signal that couples various sensorimotor behaviors to the motivational/exploratory state of the animal and its physiological needs, thus regulating behavior across different timescales.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Flies (Drosophila Melanogaster) were raised on a standard food medium (ethanol 2%, methylhydroxybenzoate 0.4%, yeast 8%, cornmeal 8%, and agar 1%) at 18\u2009\u00b0C. Third instar larvae were collected as follows: male and female flies from the appropriate genotypes were placed together for mating, then transferred at 25\u2009\u00b0C for 12\u201316\u2009h on a petri dish containing a fresh food medium for egg laying. The petri dish was then placed at 25\u2009\u00b0C for 72\u2009h. Foraging third instar larvae were collected from the food medium by using a denser solution of 20% sucrose, scooped with a paint brush into a sieve, and gently and quickly washed with water. Larvae used for optogenetic experiments were raised at 25\u2009\u00b0C in complete darkness, on standard food supplemented with all-trans retinal at 0.25\u2009mM (R240000, Toronto Research Chemicals). The full list of genotypes used in the study can be found in the Supplementary Table\u00a01 Resource table.\n\nFor dietary treatments, larvae were placed in 60\u00d715\u2009mm circular petri dishes that contained a 45\u2009mm circular Whatman paper. Larvae were subjected to different diets: standard food without agar for fed larvae (as described in the Drosophila rearing section), 20% sucrose solution for sucrose fed larvae, standard food without agar prepared in 20% sucrose solution for food+sucrose fed larvae, 23.2% sucralose solution for sucralose fed larvae, and water for starved larvae. The Whatman paper was soaked with 0.6\u2009mL MilliQ water (starved), sucrose solution (fed on sucrose), or soaked with 0.6\u2009mL water, and 1\u2009mL of standard food medium was added on top (fed). Larvae were collected after the appropriate amount of time (90 or 300\u2009min) and rinsed in water before behavioral, imaging, or biochemistry experiments. For the behavioral experiments with rehydration, larvae were collected after 90\u2009min, rinsed in water, and placed in a petri dish with a Whatman paper soaked with 0.6\u2009mL water for 15\u2009min. Likewise, for the refeeding experiments, starved larvae were collected after 300\u2009min, rinsed, and placed for 15\u2009min in a petri dish with standard food medium and water as in the fed condition. After the treatment, larvae were once more collected and rinsed before the experiment.\n\nWe used an apparatus previously described47,48. Briefly, the apparatus comprises a video camera (Basler ace acA2040-90\u2009\u03bcm) for monitoring larvae, a ring light illuminator (Cree C503B-RCS-CW0Z0AA1 at 624\u2009nm in the red), a computer, and a hardware module for controlling air-puff, controlled through multi worm tracker (MWT) software (http://sourceforge.net/projects/mwt)48,67. The arena consisted of a 25625\u2009cm2 square of 3% Bacto agar gel (CONDALAB 1804-5) with charcoal (Herboristerie Moderne, 66000 Perpignan) in a plastic dish, and was changed for each experiment. For optogenetic experiments, plates without charcoal were used, and larvae were tracked thanks to IR light. Collected third instar larvae were washed with water, moderately dried, and spread on the agar starting from the center of the arena. We tested ~\u200930\u2013100 larvae at once during each experiment. The temperature of the behavioral room was kept at 25\u2009\u00b0C.\n\nTo assess larval locomotion in the absence of stimulation, larvae were placed on top of the agar in the arena inside the tracker, and either tracked for 5\u2009min continuously (intact attP2\u2009>\u2009UAS-TNT larvae or for 60\u2009s). The coordinates of the 11 points along the central spine and the outline of each larva were computed as described previously47,48. These 2D X-Y coordinates, structured as time series of irregular framerate, labeled by the instantaneous tracking time of the recording, comprise the raw datasets, which have been subsequently analyzed to derive all secondary metrics.\n\nAnalysis was performed in Python using the larvaworld behavioral analysis and simulation platform (https://pypi.org/project/larvaworld/). The 3-step analysis pipeline included preprocessing, computation of secondary angular, translational, and temporal metrics, and behavioral epoch detection to annotate strides, runs, and pauses, as described previously68. During preprocessing, the raw time series were adjusted to a 10\u2009Hz constant framerate by interpolating them at a 0.1\u2009s timestep. Noise reduction was achieved by applying a low-pass filter with a 2\u2009Hz cut-off frequency, a threshold high enough not to alter the crawling-related dynamics around the dominant ~\u20091.4\u2009Hz crawling frequency.\n\nFor trajectory-based spatial metrics such as pathlength and dispersal, to avoid the cumulative effect of body micromovements, the position of the 9th point along the midline was used as a proxy for the larva\u2019s position, a relatively stable rear point unaffected by lateral and translational jitter. To correct for different larval sizes, any metric measured in absolute spatial units (m or mm) can be scaled to body length, measured in dimensionless body-length units. As the instantaneous body length of an individual larva fluctuates during crawling due to subsequent stretching and contraction, individual larva length is defined as the median of the midline length across time (total length of the line connecting all 11 midline points). A trajectory\u2019s path length is the cumulative displacement of the larva during the entire track. Dispersal is the instantaneous straight-line distance relative to its initial position. Track tortuosity was quantified by the straightness index (S.I.), computed by advancing a fixed time window (20\u2009s in this study) along the track and calculating at each point the ratio of the dispersal to the actual distance traveled. This index, which varies from 0 (no movement) to 1 (straight line movement), can capture very well the complexity of the movement at various scales (set by the window time frame) throughout the track.\n\nFor the detection of peristaltic strides and crawl-pauses, the scaled crawling speed time series were used. To this end, the dominant crawling frequency for each track was extracted by applying a Fourier analysis, and its inverse was used as the expected duration of a peristaltic cycle. A stride was therefore defined as the epoch between two local speed minima, that included a local maximum of at least 0.3 body-lengths/s and lasted between 0.7 and 1.5 times the expected cycle duration. A run was defined as an uninterrupted sequence of consecutive strides, and a crawl-pause as an epoch lacking any strides during which the scaled speed was constantly below the 0.3 body-lengths/s threshold.\n\nAir-puff was delivered as described previously32,47,48 to the 25625\u2009cm2 arena at a pressure of 1.1\u2009MPa through a 3D-printed flare nozzle placed above the arena, with a 16\u2009cm\u2009\u00d7\u20090.17\u2009cm opening, connected through a tubing system to plant supplied compressed air. The strength of the airflow was controlled through a regulator downstream from the air amplifier and turned on and off with a solenoid valve (Parker Skinner 71215SN2GN00). Air-flow rates at 9 different positions in the arena were measured with a hot-wire anemometer to ensure consistent coverage of the arena across experimental days. The air-current relay was triggered through TTL pulses delivered by a Measurement Computing PCI-CTR05 5-channel, counter/timer board at the direction of the MWT. The onset and duration of the stimulus were also controlled through the MWT. Larvae were left to crawl freely on the agar plate for 60\u2009s prior to stimulus delivery. Air-puff was delivered at the 60th second and applied for 30\u2009s.\n\nFor optogenetic experiments, light was delivered using a custom-made 16\u2009\u00d7\u200916 LED panel. The arena was also illuminated from below with IR light. Light (alone or with air puff) was triggered at the 60th second and lasted for 30\u2009seconds. The light intensity was measured as the irradiance (mW/cm\u00b2) using a PM16-130 photometer (THORLABS). Irradiance was measured at 12 points across the arena and then averaged. The light intensity used for optogenetic activation experiments (red light, 617\u2009nm) was 0.3\u2009mW/cm\u00b2.\n\nBehaviors were detected thanks to a custom-made machine learning algorithm that was previously described47. Behaviors were defined as mutually exclusive actions. Larvae were tracked using MWT software, all the time series of the contours and the spine of individual larvae are obtained using Choreography. From these times series, some features are computed, the center of the larva, velocities, etc., all key features are presented in Masson et al., 202047. Behavior classification consists of a hierarchical procedure that was trained separately based on a limited amount of manually annotated data. Here, we required a more detailed definition of behavior. Hence, we extended the hierarchy with another layer to separate some Bends and Hunches between different behaviors. Bends, which were defined as a large action category encompassing all behavioral dynamics involving bending of the larva body, were separated into Static Bend and Head Cast (see the description of each behavior below). We take all the Bends, Hunches, and Back-ups obtained by the first classification algorithm, to reclassify with new annotated data on new lines.\n\nDynamic bends in which the head moves laterally from one side to the other, or sometimes from one side to the center. There are two exits from Head Cast, in both, the head moves strongly. In one, the barycenter does not move significantly, and the action at the end of the head cast is a run. In the other, the larva body moves more, and the end of the head cast is a turn (in which the larva transitions to a run with a curved trajectory).\n\nLow speed motion where the larva bends its body without its barycenter moving significantly. Static bends start with slow motion of the head and the angle between the segment between the center of mass and the head and the center of mass and the tail remains constant.\n\nStrong differences in phenotype led to features associated with larva actions being statistically too different from the original training dataset. Hence, new training data were needed to adapt the classifiers. The sets of Hunch, Bends, Backs, and Crawls were manually tagged from actions selected using the Masson et al., 2020 old behavior classification pipeline. A few numbers of tags are used for the model.\n\nThe fine-tuning procedure consisted on adding an extra layer to train on top of the main architecture. We combined features that were previously computed to provide classification within the main architecture with new features. Each characteristic is calculated for the time step we are examining and the three time steps before and after.\n\nThe three velocities include the head velocity, the motion velocity, and the tail velocity, all normalized by the length of the larva. The motion velocity is the velocity of the center of mass return by the MWT software. Head and tail are the terminal points of the spine; The averaging along the spine curve and its derivative, \\(S=\\frac{1}{2}\\left(3\\,\\left\\langle {co}{s}^{2}\\theta \\right\\rangle -1\\right)\\) with cos \u03b8 the scalar product between normalized vectors associated to a segment of the spine and the direction of the larva body.\n\nThe shape factor \\(\\lambda=\\frac{{\\lambda }_{1}-{\\lambda }_{2}}{{\\lambda }_{1}+{\\lambda }_{2}}\\) with \u03bbi the eigenvalues of the mean covariance matrix of movement, which characterizes the shape of the larva and takes a value between 0 and 1.\n\nWe have also introduced new features:\n\nThe ratio between the length of the head-center of mass and the tail-center of mass \\(\\frac{\\left||\\overline{{HG}}\\right||}{{||}\\overline{{TG}}{||}}\\) with H,T, and G respectively coordinate points of the head, the tail, and the center of the mass.\n\nThe projection of the head and tail velocity on the spine of the larva. If we note the velocity vector of the head HVh with H coordinate point of the head and Vh the coordinate point at the end of the velocity vector, the projection point satisfies the basic relationship: \\(\\left|\\left|{V}_{h}-V^{{\\prime} }_{h}\\right|\\right|=\\min \\left|\\left|{V}_{h}-x\\right|\\right|\\) with \\(x\\in \\overrightarrow{{r}_{{si}}}\\).\n\nThe cosine of the angle between the vector of the head (tail) velocity and the first (last) segment of the larva. \\(\\cos (\\theta )=\\frac{\\overrightarrow{{r}_{s1}}\\cdot \\overrightarrow{{v}_{{head}}}}{{||}\\overrightarrow{{r}_{s1}{||\\; ||}\\overrightarrow{{v}_{{head}}}{||}}}\\) with \\(\\overrightarrow{{v}_{{head}}}\\) the vector velocity of the head and \\(\\overrightarrow{{r}_{s1}}\\) the first vector of the spine.\n\nAll features are normalized by the length of the larva to ensure scale-free properties.\n\nThe fine-tuning procedure consisted on adding an extra random forest on the main classifier architecture, acting at each time point to re-classify actions that were classified as Bends, Hunches, and Backs. We conducted ten random forests on all Hunch and Back tags, along with a random selection of a thousand Bends, utilizing balanced weights. The predicted behavior represents the most probable outcome, with each random forest\u2019s confusion matrix exceeding 80% accuracy for each behavior.\n\nWe regularized anomalies of behavior lasting less than 3 time points, which is biologically unrealistic. To address actions spanning only two time steps, we introduced a preventive measure by adding five time steps before and after the behavior. In addition, to mitigate noisy results, we implemented smoothing. This involved excluding behaviors with fewer than three time steps, logically categorizing them as the behavior before or after (based on the length of the behavior and behaviors N\u2009+\u20092 and N-2). According to our knowledge, Hunch behavior initiates at the beginning of stimulation, around 60\u2009s32,42,47. Larvae typically do not exhibit multiple Hunches. In cases where they do, we classify the second Hunch as a Head Cast, a classification verified through ground truthing. We applied the same threshold as outlined in Masson et al., 2020 to the effective length change during the behavior. If a Hunch fails to surpass this threshold, the time window is assigned to the small behavior. The threshold is not the same depending on the line, some lines were slower or smaller than others (threshold between 0.6 and 0.3). The validation of these thresholds was performed through ground truthing, contributing to the enhancement of classification, particularly in cases where performance may be suboptimal for certain lines.\n\nThe distinction between Static Bends and Head Cast is determined by applying a threshold to the head velocity. If bending occurs over \u2018n\u2019 time steps, the motion velocity normalized by the length of \u2018P%\u2018 of those \u2018n\u2019 time steps must be below \u2018p\u2019 times the mean head velocity of the larva before the stimulation. The values of the two thresholds, \u2018P\u2019 and \u2018p\u2019, are line-specific, contingent upon the statistical characteristics of the velocity for that particular line; some lines are slower than others.\n\nWe computed the cumulative probabilities of actions (Stop, Hunch, Back-up) during five seconds after stimulus onset (as described in Masson et al., 2020), only in larvae that were tracked during the entire time window. For actions that occur during baseline locomotion and at high frequency (Crawl and Head Cast), we computed the mean probability over five seconds after stimulus onset and over three seconds starting one second after stimulus onset. We corrected the mean probabilities for these actions by the mean probability computed over twenty seconds of recording prior to stimulus onset. For optogenetic activation of the mechanosensory neurons, because the dynamic of the response was different to that of air-puff experiments, the time window used for computing the cumulative probability of Hunching was two seconds after the stimulus onset and that for bending probability was ten seconds after stimulus onset, by which time bending probability reached baseline levels in larvae fed on standard food. Transition probabilities were computed as the frequency of transition from one action to another over five seconds after stimulus onset, for larvae that were tracked throughout this entire duration.\n\nAs an alternative approach to mapping tracking data onto a dictionary of discrete actions, we projected the same tracking data in a 25-dimensional space to represent behavior in an unsupervised and continuous fashion.\n\nWe specifically did so for the experimental conditions that suffered from poor action identification, namely the NPFR genotype and its control.\n\nWe generated the 25D behavioral readout using an autoencoder technique known as MaggotUBA69, which takes 2-second time segments as inputs.\n\nAs a consequence, for each larva, 5 evenly-spaced time points were selected in the 60\u201365\u2009s time window (stimulus onset at 60\u2009s).\n\nA 2-second time window was centered at each of these time points.\n\nThe spatial coordinates of the 5-point spine were collected within the time window, resampled at 10\u2009Hz, and concatenated in a 200-element vector.\n\nAll such raw data vectors were projected into the common 25-dimensional space using a MaggotUBA encoder named 20230129 and publicly available as part of the LarvaTagger project70,71.\n\nThe 25D data points were used to compare between feeding states.\n\nFor each pairwise comparison, the Maximum Mean Discrepancy72 (MMD) was computed with a Gaussian kernel of width 2.\n\nA permutation test was applied to generate surrogate MMD estimates and derive a p-value.\n\nAfter inspecting a total of 45 pairwise comparisons, we show the 4 most relevant pairwise comparisons.\n\nIn particular, we only show the 60.5\u201362.5\u2009s window, as it exhibits the strongest effect and leaves a short time interval after stimulus onset, which guarantees the stimulus onset took place just before, in spite of noisy timestamps from the tracking software\n\nThe p-values were Bonferroni-corrected for the actual number of comparisons (45).\n\nTo illustrate the number of data points and groups (Supplementary Fig.\u00a09), a 2D representation of all the data points was generated using supervised UMAP73 (Python package umap-learn, with parameter n_neighbors\u2009=\u200920).\n\nThe categorical information included genotype and feeding state.\n\nAs expected, all the groups can be easily separated.\n\nHowever, interestingly, the various groups related to the control genotype, together with the fed NPFR larvae are closer neighbors than the sucrose-fed and starved NPFR larvae.\n\nNote that supervised UMAP successfully separates all the experimental conditions due to its ability to leverage local neighborhood relationships. If translated to a supervised classification approach, the domains associated with each experimental condition in the 25D latent space would exhibit complex involucrated shapes, which would be considered a sign of overfitting. The resulting 2D arrangement of the different groups of points is still informative, in spite of the complexity of the mapping. In contrast, the MMD relies on simpler and more easily interpretable patterns, as it compares two groups of points using the moments of the respective distributions. Basically, and most likely, a significant MMD indicates a difference in mean and/or variance.\n\nIn order to measure food intake, we quantified the amount of fluorescent food inside the digestive tract of larvae that were allowed to feed ad libitum for 15\u2009min on fluorescent feeding media. To this end, Rhodamine B (Sigma R6626-25G) was diluted in different feeding media (water, yeast extract, sucrose solution, or normal food medium, see in media section) to a final concentration of 20\u2009\u00b5mol/L (78\u2009\u00b5mol/L for Fig.\u00a01j). 0.8\u2009mI L of each food medium was poured on top of a circular Whatman paper (Fisher Scientific, Cytiva 1001-045) placed into a petri dish. 10 larvae of similar size were placed into each petri dish. After 15\u2009min of ad libitum feeding on the fluorescent medium, larvae were collected, rinsed in ethanol and in water, and immediately mounted under a coverslip for imaging. Intact larvae were imaged thanks to a fluorescent binocular Zeiss Discovery.V12. The surface of the digestive tract stained by the fluorescent dye was quantified for each larva thanks to custom-made Fiji and Matlab scripts. The scripts quantify the number of pixels whose intensity was above background, extracting the corresponding values of surface (area) and intensity. All area values were normalized by division by the average area of the control larvae fed on the standard food of each experimental day. The normalized area and the fluorescence intensities are multiplied to obtain the values to compare food intake between groups.\n\nTo measure the preference of larvae between a sucrose-containing agar and a water-containing agar, we performed a place-preference assay. To this end, we prepared petri dishes filled with 0.3% agar diluted in water, divided each agar in two halves, and transferred one half into a new petri dish. Then, we filled the missing half in each Petri dish with 0.3% agar diluted in a 20% sucrose solution. Therefore, each petri dish finally contained one side with 20% sucrose agar and one side with agar only.\n\nAfter cooling down, about 20 third instar larvae were put on the midline of a petri dish, and the dish was imaged every 30\u2009s in a behavioral tracker. The number of larvae on each side was then counted over time, excluding the larvae touching the limit between the two media. The preference index was then calculated as: PI\u2009=\u2009(Ns - Na)/Ntot, where Ns is the number of larvae on the sucrose side, Na is the number of larvae on the agar side, and Ntot the total number of larvae.\n\nTo measure carbohydrate concentrations inside larval hemolymph, we combined and adapted different methods already published74,75. Glucose measurements - Groups of 5 third instar larvae were rinsed in water and placed on a parafilm layer and their cuticle was cut with forceps. 2\u2009\u00b5L of the bleeding hemolymph was collected from each group, and 1\u2009\u00b5L of 0.05\u2009g/L N-phenylthiourea diluted in PBS was added to avoid darkening of the samples. Samples were heat-inactivated by a 10\u2009min incubation at 90\u2009\u00b0C and centrifuged 10\u2009min at 9500\u2009x\u2009g. 1\u2009\u00b5L of the supernatant was then mixed with 4\u2009\u00b5L of glucose assay kit (Sigma GAHK20-1KT) and incubated for 1\u2009h at 37\u2009\u00b0C. Absorbance at 340\u2009nm was measured against the blank thanks to a NanoDrop following the manufacturer\u2019s instructions. Hemolymph glucose concentration was finally calculated thanks to a standard curve of glucose concentration.\n\nTrehalose measurements - Groups of 10 third instar larvae were rinsed in water and placed on a parafilm layer, and their cuticle was cut with forceps. 3\u2009\u00b5L of the bleeding hemolymph was collected from each group, and 97\u2009\u00b5L of 0.05\u2009g/L N-phenylthiourea diluted in trehalase buffer (Tris pH 5.5, 5\u2009mM, NaCl 137\u2009mM, KCl 2.7\u2009mM) was added to avoid darkening of the samples. Samples were heat-inactivated at 70\u2009\u00b0C for 10\u2009min, centrifuged for 10\u2009min at 10,000\u2009rpm, and 5\u2009\u00b5L of the supernatant were either mixed to 5\u2009\u00b5L of trehalase (Sigma T8778-1UN) diluted in trehalase buffer (described above) to a 500 dilution. Samples were incubated at 37\u2009\u00b0C overnight. 1\u2009\u00b5L of each sample was then mixed with 4\u2009\u00b5L of glucose assay kit (Sigma GAHK20-1KT) and incubated for 1\u2009h at 37\u2009\u00b0C. Absorbance at 340\u2009nm was measured against the appropriate blank thanks to a NanoDrop following the manufacturer\u2019s instructions. Trehalose concentration was finally calculated thanks to a standard curve of glucose and trehalose concentrations, and by additionally subtracting the concentration of glucose in the sample without trehalase.\n\nTo determine the neurotransmitter identification in the interneurons, immuno-labeling was performed from the split lines or Gal4 lines crossed to UAS-myr::GFP, or LexA lines crossed to LexAop-myr::GFP. The VNC was dissected out from 3rd instar larvae and fixed with 4% PFA for 45\u2009min at room temperature. After rinsing in PBS, ten minutes of permeabilization in PBS-T and two hours blocking in PBS-T-BSA 1%, the CNS preparations were incubated at 4\u2009\u00b0C (one to three nights) in the first antibodies raised against neurotransmitter and GFP in PBS-T. Then they were incubated at 4\u2009\u00b0C (one to two nights) in fluorophore-coupled secondary antibodies in PBS-T raised against the species of the first antibodies. After rinsing, the preparations were mounted in an anti-bleaching mounting medium (SlowFade Gold, ThermoFisher S36939) under a cover slip. The confocal images were captured with a Leica SP8 confocal laser microscope. Alexa Fluor 488 was excited with a laser light of 488\u2009nm, Cy3 with a laser light of 561\u2009nm, Alexa Fluor 647 with a light of 633\u2009nm wavelength.\n\nIn order to characterize the expression pattern of sNPFR and NPFR in the circuit, expression of two genetically encoded reporter proteins of two different colors was targeted to two different subsets of neurons. To this aim, a T2A Gal-452 or LexA (for sNPFR and NPFR, respectively) was used to express LexAop-jRGECO1a or UAS-GCaMP6s (for sNPFR and NPFR, respectively) under the control of the promoter of the gene coding for that receptor, thus tagging all neurons which express the receptor transcript. A second genetic driver (LexA for sNPFR and Gal4 for NPFR) was used to individually label target neurons of the circuit with a second reporter protein (UAS-GCaMP6s for sNPFR and LexAop-jRGECO1a for NPFR). The VNC was then dissected and stained with antibodies as described in the previous section.\n\nTo the best of our knowledge, no clean or sparse LexA line exists to selectively target the Handle-b and Griddle-2 inhibitory interneuron. For sNPFR expression in Griddle-2, the LexA line L55C05 used targets many neurons in addition to Griddle-2. Griddle-2 could nevertheless be identified by comparing the cell body position and projections in the cross-section of the anterior abdominal segments of the CNS of the R55C05 LexA line and the sparse R55C05 GAL4 line that selectively labels Griddle-2 in the VNC (see Supplementary Fig.\u00a07d).\n\nFor Handle-b, we used a L60E02-LexA line for which a neuron with a cell boy in the midline resembling Handle-b is part of a very dense expression pattern. In order to confirm that the candidate enron was indeed Handle-b, we used the selective split-Gal4 line GMR_SS00888 that specifically labels only Handle-b neurons. We expressed two reporters of different colors (LexAop-GCaMP6s and UAS-Chrimson-mCherry under the control of GMR_SS00888 and L60E02 in the same larva. The colocalization confirmed the line 60E02-LexA to target Handle-b (Supplementary Fig.\u00a07e).\n\nBecause opening the cuticle might affect the larval internal state, we developed a simple preparation for the imaging of intact larvae. For this purpose, third instar larvae were rinsed in water, and mounted between a 2\u2009cm circular coverslip and a custom-made device that delivers mechanical stimulations in low melting point agarose 4% (melted in phosphate buffer saline), ventral side facing up. Larvae were gently squeezed in this position until the agar cooled down, so that the ventral nerve cord could be imaged through the cuticle.\n\nAll Gal4 and LexA drivers used for in vivo imaging are listed in the figure legends. The imaging plane was restricted to the location of the projections of neurons of interest, in particular when sparse lines that lacked specificity towards a unique neuronal type were used (R22E09).\n\nMechanical stimulations were generated by a waveform generator (Siglent sdg1032x) connected to a quick-mount extension actuator (Piezo Systems, Inc.), which was embedded in the sylgard-coated recording chamber (Sylgard Silicone Elastomer, WPI). The stimulation was set at 1000\u2009Hz, with an intensity of 1 to 20\u2009V applied to the actuator. The amplitude of the acceleration produced by the actuator was measured thanks to a triple-axis accelerometer (Sparkfun electronics ADXL313) connected to a RedBoard (Sparkfun electronics) and bound to the Sylgard surface thanks to high vacuum grease. Acceleration was 1.14\u2009m.s\u20132 at 20\u2009V, and 0.61\u2009m.s\u20132 at 10\u2009V. Mechanical stimulations were precisely triggered by the Leica SP8 software thanks to the Leica Live Data Mode and to a trigger box branched to the scanning head of the microscope. A typical stimulation experiment consisted in 5\u2009s of recording without stimulation, then 5\u2009s of stimulation, and 5\u2009s of recording in the absence of stimulation.\n\nFor optogenetic activation during in vivo imaging, larvae were mounted in the dark, with the least intensity of light possible in the room, to avoid nonspecific activation of the targeted neurons. Optogenetic stimulation of CsChrimson was achieved by a 617\u2009nm wavelength LED (Thorlabs, M617F2), controlled by an LED driver (Thorlabs, LEDD1B) connected to the waveform generator, and conveyed through a \u00d8 400\u2009\u00b5m Core Patch Cable (Thorlabs) to the imaging field. Optogenetics stimulations were triggered at 50\u2009Hz, 50% duty during 1\u2009s, concomitantly to mechanical stimulations thanks to the waveform generator. Irradiance was measured at the level of the imaging field at 500\u2009\u00b5W using a PM16-130 THORLABS photometer.\n\nImaging was achieved with a 1-photon or 2-photon scanning Leica SP8 microscope, at 200\u2009Hz, with a resolution of 512\u2009\u00d7\u2009256 pixels or 512\u2009\u00d7\u2009190 pixels. The rate of acquisition was 1 frame/s or 2 frames/s, depending on the experiment. For Basin-2 recordings, the stimulation was repeated 5 times with resting intervals of 60\u2009s in order to calculate a frequency of response. Recordings where the dF/F averaged over the whole stimulus duration did not exceed 10% were considered as failed responses. Optogenetic experiments were conducted with 2-photon imaging.\n\nWhen the projections of the neurons were not visible before stimulation (imaging of Handle-b upon NPFR knockdown), we used resonance scanning with 10 line accumulation and 6 frame averaging to increase the signal. This resulted in one image being taken each second and in the dF/F being lower than usual. With these settings, one recording was acquired per larva.\n\nNeuronal processes were imaged in the VNC at the axonal level, and fluorescence intensity was measured by manually drawing a region of interest (ROI) in the relevant areas using custom Fiji macros. Data were further analyzed using customized MATLAB scripts. F0 was defined as the mean fluorescence in the ROI during baseline recording, in the absence of mechanical stimulus or optogenetic activation. \u0394F/F0 was defined at each time step t in the ROI as: \u0394F/F0\u2009=\u2009(F(t) - F0)/F0.\n\nFor recording the baseline activity level of NPF neurons, one frame was recorded each second for 20\u2009s, and, for each larva, the frame showing the most intense fluorescence in the neuronal projections was used to evaluate its raw fluorescence level.\n\nFor imaging neurons in different feeding conditions, the effect of food treatment on the expression level of fluorescent reporter proteins was assessed by expressing GFP in the chordotonal neurons. Fluorescence was measured after exposing larvae to different food treatments.\n\nA filet preparation was used for recording baseline activity in DM-NPF neurons upon TTX applications. The filet preparation was performed as previously described in Jovanic et al, 2016. Briefly, a longitudinal incision was made along the larva\u2019s midline, carefully removing all organs, except for the nervous system. The cuticle was carefully stretched and secured at the corners for optimal exposure of the VNC. Dissections were carried out in Artificial Hemolymph-like (AHL) solution, composed of 103\u2009mM NaCl, 3\u2009mM KCl, 5\u2009mM HEPES, 1.5\u2009mM CaCl2, 4\u2009mM MgCl2\u00b76H2O, 26\u2009mM NaHCO3, 1\u2009mM NaH2PO4\u00b7H2O, 10\u2009mM trehalose, 7\u2009mM sucrose, and 10\u2009mM glucose (Boto, T. and Tomchik, S.M., 2024).\n\nTo determine whether NPF neurons respond to glucose and whether this response is mediated by synaptic communication, calcium levels in NPF axonal projections were measured following the application of 10.5% glucose (osmolarity-matched to 20% sucrose) in AHL, both before and after synaptic blockade with tetrodotoxin (TTX). Neuronal viability throughout the experiment was confirmed by assessing calcium responses to high K+ saline at the end of the experiment. NPF axonal processes in the ventral nerve cord (VNC) were sequentially imaged in the following solutions, replacing the previous solution each time: (1) AHL, (2) 100\u2009\u00b5L of 10.5% glucose in AHL, (3) 100\u2009\u00b5L of 20\u2009\u00b5M TTX in AHL (incubated for 5\u2009minutes to allow TTX action), (4) 100\u2009\u00b5L of 10.5% glucose in AHL, and (5) 100\u2009\u00b5L of high-K+ (100\u2009mM) saline (Boto, T. and Tomchik, S.M., 2024).\n\nImaging was performed using two-photon scanning on a Leica SP8 microscope at 400\u2009Hz with a resolution of 512\u2009\u00d7\u2009512 pixels. Z-sections were acquired every 2\u2009\u00b5m. Equal numbers of larvae were imaged across the Fed, Sucrose-Fed, and Starved conditions within each experimental day. Fluorescence intensity was quantified by manually defining regions of interest (ROIs) in the relevant areas on the maximum Z-projected image using custom Fiji macros. Background fluorescence in the ventral nerve cord (VNC) was subtracted for each larva.\n\nFor all boxplots and histograms, pairwise Mann-Whitney tests were used to evaluate significant differences between larva groups, with Bonferroni correction for multiple comparisons. Significance was illustrated according to the p-value by asterisks in histograms (*:<\u20090.05, **:<\u20090.01, ***:<\u20090.001, ****:<\u20090.0001) and by pairs of colored semicircles in boxplots, the left always corresponds to the group with the highest mean value. Non-significant tests were omitted for visual clarity.\n\nBehavioral probabilities were computed during the first five seconds of stimulation. Chi\u00b2 tests were used for statistical comparison.\n\nTo assess the effects of different states/neuronal manipulations on Head Casting in response to air-puff, we calculated an estimator designed to identify the emergence of behaviors at the population scale. We aim to determine the probability induced by the stimulation, so we need to subtract the stationary probability without the stimulus. We calculate the probability of larvae Bending 5\u2009s after the stimulus, on tracking larvae throughout this time window (denoted as pA). For the probability of Head Casting ing before the stimulus, we consider all larvae tracked continuously for 20\u2009seconds prior to the stimulus, between 30 and 50\u2009seconds (denoted as pB). A probability is defined as \\({p}_{k}={N}_{k}/{N}_{k,{all}}\\) with \\(k\\in \\{A,B\\}\\) and \\({N}_{k,{all}}\\) the total number of larva taking to compute probabilities. In order to quantify the effect of the stimulus we defined \\(\\chi={p}_{A}-{p}_{B}\\) as the difference in the ratio after and before the stimulus.\n\nIn order to compare the test line our estimator was defined as\n\n\\(\\varTheta (p,q)=\\chi (p)-\\chi (q)\\) with p and q the ratios of the lines and the control respectively. \u0398(p, q) takes value in [-1, 1]. The null hypothesis is \\(\\varTheta (p,q)=0\\), if there are no differences between the line tested and the control. Positive or negative values indicate an effect of neuron silencing when compared to the control.\n\nWe use numerical simulations to conduct a statistical test where \\(\\{{p}_{k},{q}_{k}\\}\\) are generated from a hypergeometric distribution, \\(X \\sim {Hypergeometric}({N}_{k,{all}},{N}_{k},1)\\). We perform \\({N}_{{sim}}=1{0}^{3}\\) repetitions, computing \\(\\varTheta (p,q)\\) each time. The p-value is determined by the number of instances when the hypothesis is not verified, divided by the total number of repetitions, (pseudo-code in Jovanic et al., 201632).\n\nNote that this estimator, \\(\\varTheta (p,q)\\), has the advantage of being able to detect the non-synchronous emergence of a behavior at a population scale. For example, Head Casting can either emerge as an immediate response to the puff or as the second response after Hunching. The statistics of the start time of Head Casting is thus widely distributed at the population scale. Time evolution of the instantaneous ratio of larva performing Head Casting ing would not exhibit a strong increase after stimuli because larvae are not all going to Head Casting immediately after stimuli. \\(\\varTheta (p,q)\\) by accumulating events during a time window allows efficient detection of a behavior even if it is widely distributed in time.\n\nGeneralized likelihood ratio tests were used for statistical analysis of behavioral transition probabilities. An in depth description of this test can be found in Masson et al.47. In brief, the test statistic is computed as \\(z=-2log\\left(\\right.(B({{\\pi }^{k}},\\,{N}_{m},\\,{{n}_{m}^{k}})B({\\pi }^{k},{N}_{0},{{n}_{0}^{k}}))/(B({{\\pi }_{m}^{k}},{N}_{m},{{n}_{m}^{k}})B({{\\pi }_{0}^{k}},{N}_{0},{{n}_{0}^{k}}))\\) with \\(\\pi=\\frac{{n}_{m}^{k}+{n}_{0}^{k}}{{N}_{m}+{N}_{0}}\\) and \\(p=\\chi^{2}(z,df=1)\\) as well as a chi\u00b2 test.\n\nWe used short time windows (2\u2009s) to assess the transition probabilities that happen immediately upon stimulus onset. For experiments where the number of transitions was low (<\u2009100) during the short time window, we computed transition probabilities in the first 10\u2009s of stimulation to make more robust comparisons between conditions.\n\nAll data in line plots are presented as mean\u2009\u00b1\u2009SEM. Violin plots show the first and third quartiles, the average of all recordings as a white line, and the median as a white dot. Comparisons of the data series between the two conditions were achieved by a two-tailed unpaired t-test. Comparisons between more than two distinct groups were made using a one-way ANOVA test, followed by Bonferroni pairwise comparisons between the experimental groups and their controls.\n\nWe reproduced and extended the rate-based system model of the circuit that was published in a previous publication32. The circuit is described as a rate model with a connection matrix derived from the larva connectome. Each neuron population (mechano-ch, iLNa, iLNb, fbLN-Ha, and fLN-Hb) was modeled by a single node (Fig.\u00a03h-p). The dynamics read:\n\nwith \u03c4 representing the vector of the characteristic time constants of the neurons, r the rate vector, V0 the threshold vector, s the sensory stimulus input vector, i the vector of inputs from other brain regions, kex a sensitivity factor to overall input, rmax the maximal rate vector, Aex and Ain respectively the excitatory and inhibitory coupling matrices. Vector multiplication denotes element-wise multiplication, also called the Hadamard product.\n\nValues in Ain and Aex were directly extracted from synaptic counts (see Jovanic et al.)32.\n\nIn order to represent the variety of stimuli larvae are subjected to, and thus the variety of behavior they elicit, we follow the approach in Jovanic et al, 2016 and vary the connection strength between Mch and iLNa populations. In the original paper, the connection strength between Mch and iLNb populations is also varied. We decided to fix the value of this connection strength at 2 in order to reduce the number of parameters to explore.\n\nWe used the solve_ivp routine of the integration package from SciPy, which internally calls the LSODA solver, able to switch between the Adams method and the BDF method, based on the stiffness of the equation. We used relative and absolute tolerances of 10-3. In addition, the solution vector is constrained to stay positive. This is obtained by replacing r by max(r, 0) and the components of r\u2019 by those of max(r\u2019, 0) whenever the corresponding component of r is smaller than 10-9.\n\nThe behavior is defined based on the steady-state rates of populations B1 and B2. We used a k-means clustering with k\u2009=\u20092 to separate the values of the output neurons for an ensemble of simulations corresponding to connection strengths spanning [0.5, 1.5] between MCh and iLNa, and [1.5, 2.5] between iLNa and iLNb. The output activations cluster strongly in a coactive state (rate(B1)\u2009\\(\\gg\\)\u20090, rate(B2)\u2009\\(\\gg\\)\u20090) corresponding to Head Casts, and a monoactive state (rate(B1)\u2009\u226b\u20090, rate(B2) \u2243 0) corresponding to Hunches. The two behaviors can also be distinguished by the single scalar rate(B2)/rate(B1), which is large for Head Casts and small for Hunches. This ratio is sometimes plotted instead of the discrete category.\n\nWe explore two models for neuromodulation, which modify the behavioral output of the network without altering its connectivity.\n\nThe first model hypothesizes that in the sucrose state, Hb receives an additional input current, modeled by a nonzero entry to the vector i. We show that as this input current increases, the range of stimuli evoking Head Casts increases, allowing us to claim that an additional input current to Hb increases the likelihood of Head Cast. We further fix the value of the input current to 10 a.u. in the sucrose state and 0 a.u. in the fed state, to perform silencing analyses.\n\nThe second model hypothesizes that in the sucrose state, the maximum rate for the iLNa neuron population is decreased, representing a saturation of the response to external stimuli. We show that as the rmax parameter for iLNa decreases, the range of stimuli evoking Head Casts s increases, consistent with experimental observations and once again despite the use of arbitrary units. For silencing analyses, we define the sucrose state as rmax\u2009=\u200918 a.u. and the fed state as rmax\u2009=\u200920 a.u.\n\nFinally, we also consider a model combining both hypotheses. In this model, every combination of a decrease in rmax for iLNa and an increase in input current to Hb results in more Head Casts. For the silencing analyses, we fix the values of those parameters. In the combined model, we define the sucrose state by setting each parameter to the value defining the sucrose state in the single-hypothesis models.\n\nWe provide here the list of parameters used in the simulations.\n\nkex\n\n2.5\n\ni\n\n[0, 0, 0, 0, 0, 0, 0] in fed state\n\n[0, 10, 0, 0, 0, 0, 0] in sucrose state for the combined model\n\nrmax\n\n[20, 20, 20, 20, 20, 20, 20] in fed state\n\n[20, 20, 20, 20, 20, 20, 18] in sucrose state for the combined model\n\nV0\n\n[0, 20, 20, 20, 20, 20, 20]\n\n\u03c4\n\n[1, 35, 35, 35, 35, 35, 35]\n\nThe excitatory and inhibitory matrices read\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data supporting the findings of this study are available within the paper, its Supplementary Information, as source data, and/or within the repository: https://doi.org/10.5281/zenodo.1095953376.\u00a0Source data are provided in this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Scripts used in this paper are available at:https://archive.softwareheritage.org/swh:1:dir:3f06d6d731741c4beebe901f5ef0b909c8b0b6bd;origin=https://gitlab.pasteur.fr/flaurent/chloestaggers/;visit=swh:1:snp:7480ee779daabf5b5ac887096643587949adaf43;anchor=swh:1:rev:913a5bc94bab9f2ebee578ef624c92d2e46e0c19.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Allen, W. 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Zenodo. https://doi.org/10.5281/zenodo.15075754 (2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by ANR PIA funding: ANR-20-IDEES-0002 (T.J.), Agence Nationale de la Recherche: ANR-17-CE37-0019-01 (T.J.), ANR-NEUROMOD (ANR-22-CE37-0027) (T.J.), ANR-21-NEUC-0002 (T.J.) in the context of the CRCNS collaboration grant with National Science Foundation CRCNS (2113179) and Department of Energy (SC0021922) (to Brian H Smith), F\u00e9d\u00e9ration pour la recherche sur le cerveau (FRC) (T.J.), Fondation pour la Recherche M\u00e9dicale: \u00c9quipe FRM EQU202303016317, Fondation des Treilles (E.T.), D.M. received a PhD fellowship from the Paris-Saclay University; Tramway, ANR-17-CE23-0016 (J.B.M.), the inception Project PIA/ANR-16-CONV-0005,OG (J.B.M.), Investissement d\u2019avenir program under the management of ANR, ANR-19-P3iA-0001 (PRAIRIE 3IA Institute (J.B.M.), German Federal Ministry of Education and Research (BMBF DrosoExpect, 01GQ2103A, M.N.), Ministry of Culture and Science of the State of Northrhine Westphalia (iBehave, Netzwerke 2021, M.N.). P.S. received a PhD stipendship from the German Research Foundation (DFG-RTG 1960, 233886668, M.N.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Marta Zlatic for sharing a Split Gal4 line for DM-NPF neurons.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Elo\u00efse de Tredern, Dylan Manceau.\n\nUniversit\u00e9 Paris-Saclay, CNRS, Institut des neurosciences Paris-Saclay, Saclay, France\n\nElo\u00efse de Tredern,\u00a0Dylan Manceau,\u00a0Abhijit Parameswaran,\u00a0Victoria Sus,\u00a0Francesca Viscido,\u00a0Perla Akiki,\u00a0Md Amit Hasan,\u00a0Sandra Autran\u00a0&\u00a0Tihana Jovanic\n\nInstitut Pasteur, Universit\u00e9 Paris Cit\u00e9, CNRS UMR 3571, Decision and Bayesian Computation, Paris, France\n\nAlexandre Blanc,\u00a0Chloe Barre,\u00a0Fran\u00e7ois Laurent\u00a0&\u00a0Jean-Baptiste Masson\n\nEpim\u00e9th\u00e9e, INRIA, Paris, France\n\nAlexandre Blanc,\u00a0Chloe Barre,\u00a0Fran\u00e7ois Laurent\u00a0&\u00a0Jean-Baptiste Masson\n\nComputational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne, Germany\n\nPanagiotis Sakagiannis\u00a0&\u00a0Martin Paul Nawrot\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nE.T. behavioral and physiology experiments and analysis, Calcium-imaging experiments and analysis; Writing: original draft,\u00a0figures; D.M. behavioral experiments and analysis, Calcium-imaging experiments and analysis, immunohistochemistry experiments; Writing: edits and revisions,\u00a0figures; A.B. modeling; A.P. calcium imaging and food quantification experiments and analysis P.S. Data analysis; C.B. behavioral classification and analysis, statistical analysis; Writing, methods; F.V. behavioral experiments; V.S. behavioral and physiology experiments; P.A. behavioral experiments, A.H. behavioral and immunohistochemistry experiments S.A. immunohistochemistry experiments; F.L. behavioral classification and analysis M.N. supervision, funding acquisition; J.B. Methodology, supervision, funding acquisition; Writing- edits and revision; T.J. Conceptualization, analysis, supervision, funding acquisition and project administration; Writing: original draft, figures, edits and revision.\u00a0These authors contributed equally as co-first authors:\u00a0D.M., E.T. These authors contributed equally as co-second authors:\u00a0A.B., A.P.\n\nCorrespondence to\n Tihana Jovanic.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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Feeding state-dependent neuropeptidergic modulation of reciprocally interconnected inhibitory neurons biases sensorimotor decisions in Drosophila.\n Nat Commun 16, 8198 (2025). https://doi.org/10.1038/s41467-025-61805-y\n\nDownload citation\n\nReceived: 12 April 2024\n\nAccepted: 27 June 2025\n\nPublished: 02 September 2025\n\nVersion of record: 02 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61805-y\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 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topological nodal lines", + "pre_title": "Dissipationless transport signature of topological nodal lines", + "journal": "Nature Communications", + "published": "21 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61059-8/MediaObjects/41467_2025_61059_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61059-8/MediaObjects/41467_2025_61059_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Topological matter" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4508425/v1.pdf?c=1753182452000", + "research_square_link": "https://www.researchsquare.com//article/rs-4508425/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61059-8.pdf", + "preprint_posted": "18 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Topological materials, such as topological insulators or semimetals, usually not only reveal the\r\nnontrivial properties of their electronic wavefunctions through the appearance of stable boundary\r\nmodes, but also through very specific electromagnetic responses. The anisotropic longitudinal magnetoresistance of Weyl semimetals, for instance, carries the signature of the chiral anomaly of Weyl fermions. However for topological nodal line (TNL) semimetals \u2013 materials where the valence and conduction bands cross each other on one-dimensional curves in the three-dimensional Brillouin zone \u2013 such a characteristic has been lacking. Here we report the discovery of a peculiar charge transport effect generated by TNLs: a dissipationless transverse signal in the presence of coplanar electric and magnetic fields, which originates from a Zeeman induced conversion of TNLs into Weyl nodes under infinitesimally small magnetic fields. We evidence this dissipationless topological response in trigonal PtBi2 persisting up to room temperature, and unveil the extensive TNLs in the band structure of this non-magnetic material. These findings provide a new pathway to engineer Weyl\r\nnodes by arbitrary small magnetic fields and reveal that bulk topological nodal lines can exhibit\r\nnon-dissipative transport properties.Physical sciences/Physics/Condensed-matter physics/Topological matterPhysical sciences/Materials science/Condensed-matter physics/Topological matter", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "2024VeyratetalAPHESI.pdfDissipationless transport signature of topological nodal lines - SM", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Topological materials, such as topological insulators or semimetals, usually not only reveal the non-trivial properties of their electronic wavefunctions through the appearance of stable boundary modes, but also through very specific electromagnetic responses. The anisotropic longitudinal magnetoresistance of Weyl semimetals, for instance, carries the signature of the chiral anomaly of Weyl fermions. However for topological nodal line semimetals\u2014materials where the valence and conduction bands cross each other on one-dimensional curves in the three-dimensional Brillouin zone\u2014such a characteristic has been lacking. Here we report the discovery of a peculiar charge transport effect generated by topological nodal lines in trigonal crystals: a dissipationless transverse signal in the presence of coplanar electric and magnetic fields, which we attribute to a Zeeman-induced conversion of topological nodal lines into Weyl nodes under infinitesimally small magnetic fields. We evidence this dissipationless topological response in trigonal PtBi2 persisting up to room temperature, consistent with the presence of extensive topological nodal lines in the band structure of this non-magnetic material. These findings provide a pathway to engineer Weyl nodes by arbitrary small magnetic fields and reveal that bulk topological nodal lines can exhibit non-dissipative transport properties.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The electronic band structure of a bulk material can feature isolated degeneracy points where electronic states with different spin, orbital or sublattice quantum numbers possess the same energy and crystalline momentum k. In materials lacking either inversion or time-reversal symmetry, such degeneracies can be simply twofold while occurring at generic points in the three-dimensional Brillouin zone. The electronic bands in the vicinity of the nodes then generally resemble the energetic dispersion of massless relativistic particles governed by the Weyl equation1. Weyl nodes represent monopoles of the Berry flux and are thus characterized by a well-defined topological charge. This non-trivial bulk topology is manifested in a very specific spectroscopic signature: the presence of surface Fermi arcs connecting Weyl points with opposite chirality2,3. The characteristic electromagnetic response of Weyl quasiparticles is instead connected to their chiral anomaly4,5. This causes a strong in-plane anisotropic magnetoconductivity that can be directly probed through measurements of the planar Hall effect (PHE)6,7,8: the appearance, in the presence of coplanar electric and magnetic fields, of a transverse voltage with \u03c0-periodic angular dependence. Weyl quasiparticles may also be evidenced in transport experiments through other effects, such as the unconventional Hall effect9.\n\nPoint-group symmetries can also stabilize twofold degenerate closed lines in the three-dimensional Brillouin zone10. When appearing at mirror-invariant planes, such nodal lines are characterized by a bulk \\({{\\mathbb{Z}}}_{2}\\) topological invariant11,12. Although they are often accompanied by \u201cdrumhead\" surface states13,14,15, topological nodal lines (TNLs) lack a genuine bulk-boundary correspondence: the relevant surfaces naturally break the protecting mirror symmetry16. Additionally, some electromagnetic responses characteristic of TNL-semimetals have been identified, but only in specific cases, such as, for instance, in the quantum limit17, making the physical consequences of the bulk topology completely hidden. Here, we unveil a peculiar charge transport effect associated with mirror symmetry-protected TNLs in trigonal crystals: an anomalous planar Hall effect (APHE) that is odd in magnetic field, does not contribute to the dissipated power18,19, and is measurable in the linear transport regime. We identify trigonal-PtBi2 as an ideal material platform because of the presence of a large number of TNLs on its three vertical mirror planes, which makes the anomalous planar Hall effect particularly robust and survives up to room temperature.", + "section_image": [] + }, + { + "section_name": "Results and discussion", + "section_text": "In low-dimensional systems, such as LaAlO3/SrTiO3 oxide interfaces, the occurrence of an APHE has been reported and is due to a Zeeman-induced modification of local concentrations of the out-of-plane Berry curvature, which, when integrated over momenta, becomes non-vanishing20. An APHE has also been reported in VS2-VS heterostructures21. The mechanism we find to be at work in TNLs is completely different in nature. It is caused by a Zeeman-induced conversion of TNLs into Weyl nodes that generalizes the fusion of Weyl nodes into nodal lines predicted to occur in mirror-symmetric systems12. The important point is that magnetic fields that break the mirror symmetry protecting the TNLs lead to a non-local conversion of the TNL into Weyl nodes of opposite chirality, meaning that they are separated in momentum space by a vector that has components parallel to the mirror plane. The extraordinary feature of this k-space separation in momentum space is that it survives even for infinitesimally small magnetic fields and can be as large as the diameter of the TNL. This conversion and its properties can be qualitatively captured using a simple two-band low-energy model (Supplementary Note\u00a0M). This generally induces large momentum regions of non-zero Chern number, thus generating an anomalous planar Hall effect (APHE) already at infinitesimal magnetic field, with a much larger amplitude than that which would be obtained solely from the Zeeman-induced displacement of Weyl nodes (Supplementary Notes\u00a0J and M). Consider, for simplicity, a single pair of TNLs related to each other by time-reversal symmetry and protected by a vertical mirror plane, which, without loss of generality, we set as \\({{{\\mathcal{M}}}}_{x}\\) (see Fig.\u00a01a). With an infinitesimally small magnetic field along the \\(\\hat{y}\\) direction, the TNLs convert into two field-induced pairs of Weyl nodes, each of which has a separation in kz comparable to the TNL dimension, generating a dissipationless (i.e., without diagonal components) antisymmetric Hall conductance \u03c3yx (i.e., an APHE). The system can be viewed in fact as a collection of two-dimensional \\(\\{{k}_{x},{k}_{y}\\}\\) insulating layers22 parameterized by kz and characterized by a local Chern number c(kz) (see Fig.\u00a01a) that is changed by the topological charge of each Weyl node. The non-local Zeeman-induced conversion of the TNLs into Weyl nodes then leads to a net \u03c3yx\u00a0\u2009=\u00a0\u2009\u222bc(kz)dkz and to Hall voltages that lie in the same plane as the applied current and the external magnetic field, precisely in a configuration where the conventional Hall effect is absent. Such APHE thus represents a characteristic electromagnetic response of TNLs.\n\na Generation of an anomalous Hall conductance in nodal line semimetal systems. In a nodal line semimetal, the nodal lines do not contribute to the Chern number at zero magnetic field (left panel). Under a finite external magnetic field (right panel, red arrow), the nodal lines split into pairs of Weyl nodes of opposite chiralities (red and blue). These pairs can appear anywhere on the nodal lines (white line), including with a significant kz separation. This leads to potential large kz ranges of non-zero c(kz) (pink color in the rectangle), inducing a large AHC at finite field. b Typical angular dependence of the conventional (top panel) and anomalous (bottom panel) planar Hall effects, in Cartesian (left) and polar (right) coordinates. For the conventional PHE, both the longitudinal (anisotropic magnetoresistance, Rxx, blue) and transverse (planar Hall effect, Ryx, red) resistances exhibit a \u03c0-periodic angular dependence, with a \u03c0/4-offset between them. The origin of the oscillation is set by the direction of the electric field (current). For the APHE, the angular dependence is 2\u03c0/3-periodic, with origin set by the crystal directions, and is not associated with any AMR. c Crystal structure of trigonal-PtBi2, with layered nature and in-plane \\({{{\\mathcal{C}}}}_{3}\\)-symmetry highlighted. d Sample configuration. The pink arrows indicate the direction of the current. The yellow arrow corresponds to a specific crystal orientation and the black arrow indicates the direction of the magnetic field. The angle \u03c6 refers to the orientation of the in-plane magnetic field B.\n\nAdditionally, Onsager\u2019s relations23 enforce the transversal APHE conductance to be odd under a magnetic field reversal and thus compatible only with an out-of-plane threefold rotational symmetry (see Fig.\u00a01b). This property, along with its non-dissipative character, makes the APHE experimentally distinguishable from a planar Hall effect. First, a PHE, such as that associated with the Berry curvature of the Weyl nodes, is characterized by a \u03c0-periodic oscillation of both the longitudinal Rxx and transverse resistance Ryx, when the magnetic field is rotated in-plane while keeping the current direction fixed, with a \u03c0/4 offset between them (see Fig.\u00a01b and \u201cMethods\u201d)6,7. The longitudinal resistance Rxx is, moreover, maximized when the magnetic field and current are aligned, while the transverse resistance Ryx vanishes in this configuration. As a result, a twofold symmetric PHE aligned with current direction can easily be disentangled from a threefold-symmetric APHE aligned with crystalline axes (see Fig.\u00a01b). We note that the APHE generally implies finite transversal conductance even for a magnetic field parallel to the electric field, a seldom-seen situation that has already been reported in the case of non-magnetic24 and magnetic materials25, the latter case exhibiting a threefold symmetry. More importantly, the APHE is non-dissipative, i.e it is not associated with any corresponding longitudinal signal. This allows it to be distinguished unambiguously from any potential threefold symmetric PHE due to magneto-crystalline anisotropies, which would be associated with a corresponding AMR.\n\nWe now show that both these effects can be probed in the layered van der Waals material PtBi2, which has recently been characterized as a non-magnetic type I Weyl semimetal. PtBi2 also exhibits sub-Kelvin 2D-superconductivity and a BKT transition in nanostructures26, as well as higher-temperature surface superconductivity27 with superconducting topological Fermi arcs3. The crystallographic point-group symmetry of PtBi2 is \\({{{\\mathcal{C}}}}_{3v}\\) that is comprised of a threefold axis and three vertical mirror planes \\({{\\mathcal{M}}}\\) (Fig.\u00a01c)28, and is compatible with the appearance of an APHE. When an in-plane magnetic field is perpendicular to a mirror plane, the anomalous Hall conductivity (AHC) \u03c3yx must vanish. Conversely, when the field is parallel to a mirror plane, \u03c3yx is maximal18. This results in a 2\u03c0/3 periodic angular dependence (see Fig.\u00a01c) that can be detected in practice with magnetotransport measurements.\n\nWe focus our study on a 70\u2009nm thick nanostructure (see magnetotransport measurement schematic in Fig.\u00a01d) investigated up to 14T, and temperatures from 5\u2009K up to 300\u2009K (two additional structures showed similar behavior, see Supplementary Notes\u00a0F and G). The results are shown in Fig.\u00a02. First, in exfoliated nanostructures of PtBi2, we systematically observed a PHE. At T\u00a0\u2009=\u00a0\u2009100\u2009K and B\u00a0\u2009=\u00a0\u200914\u2009T, a pronounced \u03c0-periodic oscillation is clearly visible in both Rxx and Ryx (Fig.\u00a02a), with the expected \u03c0/4 angular shift between them (Fig.\u00a02b). The PHE is already visible at magnetic fields as low as 1\u2009T (see Supplementary Fig.\u00a02). The angular positions of the maxima of Rxx are consistent with the expected current orientation in the sample (see \u201cMethods\u201d). The PHE is very robust with temperature, and for B\u2009\u00a0=\u2009\u00a014\u2009T it can be evidenced up to room temperature (Fig.\u00a02c). The presence of a strong PHE in the non-magnetic PtBi2 reveals the large BC present in the material, giving significant experimental indications of its Weyl nature, and confirming predictions from previous band structure calculations26.\n\na, b Angular dependence of Rxx and Ryx at 14T, 100K in Cartesian (a) and polar coordinates (b). The fits with Equation 1 (Methods) are shown in red in a and b. The radial axis has the same range as in a. The pink bars in a and the pink dashed line in b show the current direction estimated from the fits, with a \u00a0\u00b15\u00b0 width. c Angular dependence of Rxx and Ryx at different temperatures from 5 to 300\u2009K, at 14T. The curves are vertically shifted for clarity. d, e Angular dependence of the residues \u0394Rxx and \u0394Ryx from the data in a after a background removal (see Supplementary Note\u00a0B), in cartesian (d) and polar coordinates (e). A 2\u03c0/3-periodic signal is clearly visible in \u0394Ryx. The pink and green bars show the previously estimated current direction and the crystal direction estimated from the fits to Supplementary Materials eq.\u00a0S5, respectively, with a \u00a0\u00b1\u00a05\u00b0 width. In e, the fit to Supplementary Materials eq.\u00a0S5 is shown in green. f Bottom: angular dependence of \u0394Ryx at 14T for T\u2009\u00a0=\u00a0\u200920, 50, 100, 200, and 300\u2009K, with fits to Supplementary Materials eq.\u00a0S5 shown in green. The curves are vertically shifted for clarity. Top: Angular dependence of \u0394Ryx at 14\u2009T, 300\u2009K, with fit in green. The 300K data was smoothed over 31\u00b0 for visibility. The corresponding \u0394Rxx signal is plotted in a gray line with the same scale for comparison, and shows no visible periodic signal. g Field dependence of the APHE amplitude AAPHE, showing a linear dependence above Bc\u00a0\u2009~\u00a0\u20092.8\u2009T. The dashed line indicates the best linear fit for B\u2009>\u2009\u00a02.5 T. h Temperature dependence of AAPHE, showing an exponential decay above Tc\u00a0\u2009~\u00a0\u200930\u2009K, with an energy scale \u0394\u00a0~6\u2009meV. The dashed line corresponds to the best exponential fit for B\u2009\u2273\u2009 30\u2009T.\n\nThe main experimental result of this work is the evidence of an APHE in our measurements, which appears as a small deviation from the PHE in Fig.\u00a02a\u2013c. In order to evidence the APHE in our data, we remove a \u03c0- and 2\u03c0-periodic background from each measurement (in red in Fig.\u00a02a, b, and Supplementary Note\u00a0B). Removing each signal separately would be similar to antisymmetrizing (resp. symmetrizing) the data in the magnetic field, albeit with more control over exactly which terms are removed. The resulting residues are depicted in Fig.\u00a02d\u2013f. At 14T, 2\u03c0/3-periodic oscillations, which, contrary to the standard PHE signal, are antisymmetric in B, are clearly visible in the transverse resistance residues \u0394Ryx at 100\u2009K (Fig.\u00a02d, e) and can be fitted with cosine fits (Fig.\u00a02e, in green). This 2\u03c0/3-periodic signal appears above B\u00a0\u2009=\u00a0\u20094\u2009T, at a constant angular position, and its amplitude increases with magnetic field (Supplementary Note\u00a0D). Importantly, no associated 2\u03c0/3-periodic signal is visible in the longitudinal resistance residues \u0394Rxx, up to the highest fields and down to the lowest temperatures (Supplementary Fig.\u00a04). This lack of longitudinal component is critical in distinguishing the dissipationless APHE from any conventional, dissipative PHE, which would necessarily be associated with an AMR. Remarkably, the APHE is very robust in temperature, as the oscillations remain visible from 5K up to room temperature, as shown in Fig.\u00a02f.\n\nThe field and temperature dependence of the APHE signal obtained from the cosine fits are presented in Fig.\u00a02g, h. At 5\u2009K, the APHE signal is visible above 4T, and increases linearly with field. This fit yields a critical magnetic field Bc\u00a0~2.8\u2009T for the appearance of an APHE. At 14T, the APHE signal remains constant in temperature below 20\u2009K, similar to the PHE and the longitudinal resistance (see Supplementary Note\u00a0C). Above this temperature, it decreases following an exponential decay law \\({A}^{APHE}(T)-{A}^{APHE}(T=0)\\propto {e}^{-{k}_{B}(T-{T}_{c})/\\Delta }\\) with Tc\u00a0\u2009~\u00a0\u200930\u2009K and an energy scale \u0394\u2009~\u20096\u2009meV. The existence of an onset field for the APHE could be explained by the fact that, while the TNLs are already topologically gapped at infinitesimal fields, this gap vanishes at low magnetic fields, and the APHE would only become visible when it exceeds the thermal energy broadening somewhere along the TNL. Similarly, the exponential decay of the signal in temperature could come from thermal broadening through the TNL gap opened by the magnetic field (Supplementary Note\u00a0N). This is supported by the temperature shift on the onset field evidenced in sample D3 (see Supplementary Fig.\u00a08). In this case, the energy scale of the decay, \u0394, would be linked to the gap of the TNLs.\n\nWe next demonstrate that the APHE experimentally observed is consistent with the presence of TNLs in PtBi2 by performing full-relativistic electronic band structure calculations, with and without a magnetic field. In the absence of Zeeman coupling, the material features 3 groups of Weyl nodes, which, due to the concomitant presence of time-reversal symmetry and the threefold rotation symmetry, all come with multiplicity twelve. These groups of Weyl nodes appear above the Fermi energy, with the one lowest in energy being at around 45.3\u2009meV above EF, in agreement with a previous study26. In each of these groups, pairs of Weyl nodes of opposite chirality are connected by a vector perpendicular to the mirror plane. The presence of in-plane magnetic field leads to a movement of the Weyl nodes in all momentum directions due to the low residual symmetry: the system possesses at most a vertical \\({{{\\mathcal{M}}}}^{{\\prime} }={{\\mathcal{M}}}\\times \\Theta\\) symmetry (with \u0398 time-reversal) when the magnetic field is parallel to the mirror plane \\({{\\mathcal{M}}}\\). However, the Weyl node displacement is proportional to the strength of the applied magnetic field and remains relatively small at laboratory-accessible fields. The resulting anomalous planar Hall conductance from this displacement is therefore expected to be vanishingly small (Supplementary Note\u00a0J).\n\nThe situation is completely different for the three pairs of nodal loops revealed by our calculations, which lie in the vertical mirror planes of PtBi2 and are reminiscent of nodal chain semimetals12. An infinitesimal magnetic field leads to the conversion of the TNLs, each into 6 Weyl nodes [see Fig.\u00a03a]. Since in PtBi2 the degeneracy loops do not occur at fixed energies, these Zeeman-induced Weyl nodes form different groups that are separated in energy\u2014we remark that one of these groups has a complex evolution as the magnetic field is increased as it directly combines with one of the preexisting twelvefold Weyl node groups at B\u00a0\u2009=\u00a0\u20090 (see Supplementary Note\u00a0I). For a magnetic field parallel to one mirror plane, each of these groups is sixfold, with pairs of opposite chirality related by the combined \\({{{\\mathcal{M}}}}^{{\\prime} }\\) symmetry. This immediately implies that an isolated group yields a sizable contribution to the anomalous planar Hall conductance. The latter can be computed in an \u201cideal\" case by simply assuming that the Weyl nodes are all at the Fermi level, in which case each Weyl node provides a unit change to the Chern number of the insulating \\(({k}_{x},{k}_{y})\\) layers. The distribution of the Weyl nodes in two trios (see Fig.\u00a03b) at nearly opposite values of kz demonstrates that a large contribution to the anomalous planar Hall conductance can be expected from the sixfold groups of Zeeman-induced Weyl nodes. This is verified by a direct calculation of the local Chern number of the full system assuming, as before, all Weyl nodes are at the Fermi energy and a Zeeman energy EZ\u00a0\u2009=\u200914 meV from an applied magnetic field parallel to one of the vertical mirror planes of the material (see Fig.\u00a03c, in green). We note that EZ was chosen at such a value for numerical resolution purposes, and does not correspond to our experimental conditions. As expected, the twelvefold groups of precursive Weyl nodes do not give a large contribution to the anomalous planar Hall conductance.\n\na Energy gap \u0394E between HOMO and LUMO bands in the ky,\u00a0kz (mirror) plane. The nodal loops (\u0394E\u2009\u00a0=\u2009\u00a00) appear in white. When B\u00a0\u2009\u2260\u00a0\u20090, each nodal loop splits into 6 Weyl nodes (WN, yellow points), forming 6 groups of 6-WN. The signs denote the chiralities. b Two groups of WN of HOMO-LUMO for a Zeeman energy EZ\u2009\u00a0=\u00a0\u200914\u2009meV: G3 is the 12-fold set of WNs closest to EF already present at B\u2009\u00a0=\u00a0\u20090, and G8 is one of the six 6-fold groups mentioned above. The average energies of the groups are shown. Red (blue) markers denote positive (negative) chirality, while full (empty) markers denote the positive (negative) kz position of the WN (G3:\u00a0kz\u2009~\u00b1\u20090.149, G8:\u00a0kz\u2009~\u00b1\u2009\u00a00.358). Solid lines represent the mirror planes, while the dots show the high-symmetry points. c (Top) Chern number c(kz) in an ideal (green, full HOMO) and a more realistic (black, \\({E}_{F}={E}_{{G}_{3}}=45.3\\) meV) case, with a a Zeeman energy EZ\u00a0\u2009=\u2009\u00a014\u2009meV. In the ideal case, the Chern number jumps discretely by \u00a0\u00b11 at each WN, while the variation is smoothed out in the realistic case. (Bottom) Anomalous Hall conductivity\u2014\u00a0\u0394\u03c3xy(kz) calculated from the Chern signal in the realistic case (in black above). The 12 WNs from G3 at low kz contribute very little to the AHC, as the Berry curvature they generate is nearly compensated. Most of the AHC comes from 2 peaks in the Chern number at higher kz, P2, and P3 (shown in blue). A third peak at lower kz, P1, attenuates the total AHC amplitude, and is found to correspond to WNs from nodal lines below the HOMO band (see Supplementary materials sec.\u00a0K). Only the kz\u00a0\u2009>\u2009\u00a00 dependences are shown, as c(kz) is even and \u0394\u03c3xy is odd in kz.\n\nWe have also performed a realistic calculations assuming that bands are filled with a Fermi level set at the energy of the Weyl nodes group at 45.3\u2009meV above the non-magnetic EF (black line in Fig.\u00a03c). We find that the local Chern signal (see Supplemental Material) of the twelvefold groups is washed out whereas the jumps due to the sixfold groups originating from the conversion of the TNLs are smoothed. However, the realistic contribution to the anomalous planar Hall conductance (red line in Fig.\u00a03c) is not only due to these TNLs but also derives from the existence of additional nodal lines (Supplementary Note\u00a0K) that we find in lower valence bands and also undergo conversion into Weyl nodes in the presence of planar magnetic fields. Quantitatively, the contribution of the twelvefold groups to the APHE (see Supplementary Note\u00a0J) is estimated to be more than two orders of magnitude smaller than the contribution of the TNL-induced WNs. Moreover, we note that another possible origin of the APHE, the orbital intrinsic planar Hall effect, is forbidden in systems with \\({{{\\mathcal{C}}}}_{3v}\\) point-group symmetry29. This further demonstrates that the anomalous planar Hall conductance of PtBi2 is a direct electromagnetic response of TNLs.\n\nTo sum up, we measured in 3D nanostructures of the non-magnetic 3D Weyl semimetal PtBi2, beyond a conventional PHE, a very robust APHE with a signature 2\u03c0/3-periodic oscillation in \u0394Ryx and an absent dissipative signal in \u0394Rxx. This APHE is consistent with the presence of topological nodal lines in PtBi2\u2019s band structure, through a non-local conversion to Weyl nodes under a magnetic field. This mechanism can be generally used to engineer Weyl nodes in materials featuring mirror symmetry-protected nodal lines by means of arbitrarily small magnetic fields. Our observations also establish the anomalous planar Hall effect as an efficient magnetotransport tool to reveal the presence of TNLs in trigonal semimetals, which could so far only be characterized through spectroscopy measurements. Demonstrating the presence of topological features through transport is especially interesting in PtBi2, where 2D superconductivity was recently reported26, and a recent ARPES study further found the superconducting weight to be localized on the topological Fermi arcs3, opening perspectives for possible topological superconductivity.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61059-8/MediaObjects/41467_2025_61059_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61059-8/MediaObjects/41467_2025_61059_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61059-8/MediaObjects/41467_2025_61059_Fig3_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "High-quality single crystals of PtBi2 were grown using the self-flux method28. These crystals were mechanically exfoliated to obtain thin flakes, with widths exceeding 10\u2009\u03bcm and thicknesses ranging from a few dozen to a few hundred nanometers. The flakes were contacted with Cr/Au using standard e-beam lithography techniques. Prior to the metal deposition, a small Ar-etch was performed to eliminate any surface oxidation. The main sample used in this study is denoted as D1 (70\u2009nm thick), and supplementary information includes corroborating results for a second sample, D2 (126\u2009nm thick) and a third sample, D3 (320\u2009nm thick). In a previous study26, the two-dimensional superconductivity of these samples was studied in details at sub-Kelvin temperatures. Here, we focus on measurements performed above 1K, above the superconducting transition. No evidence of significant aging effects was observed between the two studies, as indicated by the similar residual resistance ratio RRR\u2009\u00a0=\u2009R(300K)/R(4K) (Supplementary Note\u00a0H).\n\nThe measurement configuration consists of a standard Hall-bar geometry. A current is injected between the source and the drain as depicted in Fig.\u00a01d. Longitudinal and transverse resistances (indicated in red and black, respectively) are measured along and across the sample relative to the current orientation.\n\nMeasurements were conducted in a Dynacool 14T PPMS using an insert equipped with a mechanical 2D rotator. By rotating the sample with the rotator, the angle \u03c6 between the fixed-axis magnetic field and the applied current can be adjusted over a full range of 360\u00b0 (with \u03c6 the angle between the magnetic field and the electric field. The resistances were measured using external lock-in amplifiers, with an AC current of 100\u2009\u03bcA at a frequency of 927.7\u2009Hz, with an integration time of 300 ms. At such low currents, no thermal effects are expected. For sample D1, for measurements taken at T\u2009\u00a0=\u2009\u00a05\u2009K and B\u00a0\u2009=\u00a0\u20091,\u00a02,\u00a03,\u00a04,\u00a05,\u00a06,\u00a07,\u00a010\u2009T, as well as at B\u00a0\u2009=\u00a0\u200914\u2009T and T\u00a0\u2009=\u00a0\u20095,\u00a010,\u00a020,\u00a050,\u00a0300\u2009K, 10 points were measured at each angular position, taking the averaged value of the resistance. The angular step for each measurement was 1\u00b0. All measurements on sample D2 were conducted with the same parameters. For sample D1, more precise measurements were taken at T\u00a0\u2009=\u2009\u00a05\u2009K and B\u2009\u00a0=\u00a0\u200914\u2009T, as well as at B\u00a0\u2009=\u00a0\u200914\u2009T and T\u2009\u00a0=\u2009100,\u00a0200\u2009K, with an averaging over 40 measurement points at each angular position. The angular step for each measurement was 0.5\u00b0, and the results were interpolated with a step of 1\u00b0, to perform the analysis in the same way for each pair of (B,T) parameters.\n\nAt low temperature (T\u2009\u2264\u200920K) the first oscillation of the APHE (0\u00b0\u2009\u2264\u2009\u03c6\u2009\u2264\u2009120\u00b0) is not fully visible even at 14T, although it is consistently observed for T\u2009\u2265\u200950\u2009K (see at T\u00a0\u2009=\u00a0\u2009100\u2009K in Fig.\u00a02a and Supplementary Notes\u00a0E and D). This partial suppression of the signal likely stems from the mechanical rotator: When the stepper-motor at the top of the measurement stick turns by a small angle (in our measurements, the angular step is 1\u00b0), the mechanical rotator in the cryostat will move by an inconsistent angle (around the target step, e.g. 1\u00b0). As we measure the angle of the rotator at the top of the stick, and not the actual angle of the sample at the bottom, this creates small deviations of the PHE signal away from a \u03c0-periodic oscillation. When a \u03c0-periodic background is removed from the data (to evidence the APHE signal), these deviations are carried to the residues, and can corrupt the signal. These artifacts are reproducible and decrease with temperature, as would be expected with mechanical rotator inconsistencies.\n\nMeasurements were conducted in a VTI equipped with a 3D-piezorotator and a large bore 14T magnet. In this work, we show in-plane rotation measurements. By rotating the sample with the rotator, the angle \u03c6 between the fixed-axis magnetic field and the applied current can be adjusted over a 180\u00b0 range (with \u03c6 the angle between the magnetic field and the electric field). In order to have the full 360\u00b0 range, we flip the orientation of the sample by 180\u00b0 along the perpendicular axis. This is equivalent to reverse the direction of the magnetic field. The resistances were measured using external lock-in amplifiers, with an AC current of 500\u2009\u03bcA at a frequency of 331\u2009Hz, with an integration time of 300\u2009ms. For sample D3, for measurements taken at T\u00a0\u2009=\u00a0\u20093,\u00a020,\u00a0100\u2009K for fields ranging between 1\u2009T and 14\u2009T. Contrary to the measurement of D1 and D2, a single point was measured at each angular position.\n\nThe contributions of the planar Hall effect/anomalous magnetoresistance to the longitudinal resistivity \u03c1xx and transverse resistivity \u03c1yx obey the following angular dependence6,7:\n\nwith \u0394\u03c1\u2009\u00a0=\u2009\u03c1\u2225\u00a0\u2009\u2212\u00a0\u2009\u03c1\u22a5 the amplitude of both the PHE and the AMR; \u03c1\u2225 and \u03c1\u22a5 the resistivities when B is respectively along and perpendicular to the electrical field (current); and \u03c6 the angle between the magnetic and electric fields (i.e., current lines) in the sample. The PHE signal is therefore characterized by \u03c0-periodic oscillations for both \u03c1xx and \u03c1yx (when rotating the magnetic field in the sample\u2019s plane, with a fixed current) with the same amplitude, with a \u03c0/4 offset between the two. The maxima of \u03c1xx correspond experimentally to the orientation of the current in the sample (Supplementary Note\u00a0A).\n\nWe performed a full-relativistic non-magnetic calculation using the full potential local orbital (FPLO) code30 version 22.01 within the generalized gradient approximation (GGA)31. The lattice parameters can be found in Supplementary Note\u00a0L. From the DFT result a 72-band Wannier function (WF) model was extracted consisting of Bi6p and Pt6s5d type WFs. A constant magnetic field Zeeman term \\({H}^{{{\\rm{Zeeman}}}}={{\\boldsymbol{B}}}{\\mu }_{B}\\left\\langle {{\\boldsymbol{S}}}\\right\\rangle\\) was added to the model using the WF representation of the spin operators \\(\\left\\langle {{\\boldsymbol{S}}}\\right\\rangle\\).\n\nThe anomalous planar Hall signal \u0394\u03c3yx can be computed by calculating the Chern signal \\(c\\left({k}_{z}\\right)=\\frac{1}{2\\pi }\\int{F}_{z}\\left({{\\boldsymbol{k}}}\\right)d{{\\boldsymbol{S}}}\\) for a number of kz-planes with subsequent integration over kz. For an assumed constant homo \\(c\\left({k}_{z}\\right)\\) can be obtained by a plaquette type integration (see Supplementary Note\u00a0J and ref. 32) and with a Fermi level by a simple Riemann-sum integral.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data that support the findings of this study are available from the corresponding authors.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code that was used to calculate the band structure is available from the corresponding authors upon request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Armitage, N. P., Mele, E. J. & Vishwanath, A. Weyl and Dirac semimetals in three-dimensional solids. Rev. Mod. Phys. 90, 15001 (2018).\n\nArticle\u00a0\n ADS\u00a0\n MathSciNet\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nWan, X., Turner, A. M., Vishwanath, A. & Savrasov, S. Y. 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Jpn. 74, 1674\u20131677 (2005).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "A.V. acknowledges funding from the European Research Council (ERC) under the European Union\u2019s Horizon 2020 research and innovation program (grant Ballistop agreement no. 833350). S.A. acknowledges the financial support of (DFG) through the grant AS 523/4-1. C.O. acknowledges support from the MAECI project \u201cULTRAQMAT\u201d. L.V. was supported by the Leibniz Association through the Leibniz Competition. This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the Sonderforschungsbereich SFB 1143 and under Germany\u2019s Excellence Strategy through the W\u00fcrzburg-Dresden Cluster of Excellence on Complexity and Topology in Quantum Matter\u2014ct.qmat (EXC 2147, project-ids 390858490 and 242021).", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Leibniz Institute for Solid State and Materials Research (IFW Dresden), Helmholtzstra\u00dfe 20, Dresden, Germany\n\nArthur Veyrat,\u00a0Klaus Koepernik,\u00a0Louis Veyrat,\u00a0Grigory Shipunov,\u00a0Iryna Kovalchuk,\u00a0Saicharan Aswartham,\u00a0Jiang Qu,\u00a0Ankit Kumar,\u00a0Nicol\u00e1s P\u00e9rez,\u00a0Romain Giraud,\u00a0Bernd B\u00fcchner,\u00a0Jeroen van den Brink\u00a0&\u00a0Joseph Dufouleur\n\nW\u00fcrzburg-Dresden Cluster of Excellence ct.qmat, Dresden, Germany\n\nArthur Veyrat,\u00a0Klaus Koepernik,\u00a0Louis Veyrat,\u00a0Grigory Shipunov,\u00a0Saicharan Aswartham,\u00a0Jiang Qu,\u00a0Ankit Kumar,\u00a0Romain Giraud,\u00a0Bernd B\u00fcchner,\u00a0Jeroen van den Brink\u00a0&\u00a0Joseph Dufouleur\n\nLaboratoire de Physique des Solides (LPS Orsay), 510 Rue Andr\u00e9 Rivi\u00e8re, Orsay, France\n\nArthur Veyrat\n\nCNRS, Laboratoire National des Champs Magn\u00e9tiques Intenses, Universit\u00e9 Grenoble-Alpes, Universit\u00e9 Toulouse 3, INSA-Toulouse, EMFL, Toulouse, France\n\nLouis Veyrat\n\nKyiv Academic University, Kyiv, Ukraine\n\nIryna Kovalchuk\n\nDepartment of Physics, University of Genoa, Genoa, Italy\n\nMichele Ceccardi\n\nCNR-SPIN Institute, Genoa, Italy\n\nMichele Ceccardi\u00a0&\u00a0Federico Caglieris\n\nUniversit\u00e9 Grenoble Alpes, CNRS, CEA, Grenoble-INP, Spintec, Grenoble, France\n\nRomain Giraud\n\nDepartment of Physics, TU Dresden, Dresden, Germany\n\nBernd B\u00fcchner\u00a0&\u00a0Jeroen van den Brink\n\nDipartimento di Fisica \u201cE. R. Caianiello\u201d, Universit\u00e1 di Salerno, Fisciano (SA), Italy\n\nCarmine Ortix\n\nCenter for Transport and Devices, TU Dresden, Dresden, Germany\n\nJoseph Dufouleur\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nA.V. and J.D. conceived the project. A.V. designed and fabricated the samples D1 and D2, conducted the measurements and analyzed the data in these samples with input from L.V., N.P. and J.D. The theory was developed by C.O., K.K. and J.v.d.B. Computation of the band structure was done by K.K. J.Q., A.K., M.C. and J.D. fabricated the sample D3, conducted the measurements and analyzed the data in this sample. The crystals were grown by G.S., I.K. and S.A. F.C., R.G. and B.B. contributed to the discussion of the data. All authors participated in the interpretation of the results and writing of the manuscript. C.O. and J.D. supervised the project.\n\nCorrespondence to\n Arthur Veyrat, Carmine Ortix or Joseph Dufouleur.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Veyrat, A., Koepernik, K., Veyrat, L. et al. Dissipationless transport signature of topological nodal lines.\n Nat Commun 16, 6711 (2025). https://doi.org/10.1038/s41467-025-61059-8\n\nDownload citation\n\nReceived: 18 December 2024\n\nAccepted: 10 June 2025\n\nPublished: 21 July 2025\n\nVersion of record: 21 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61059-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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"Cofactor-independent, photoenzymatic, reductions with water mediated by rGQDs", + "journal": "Nature Communications", + "published": "17 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61908-6/MediaObjects/41467_2025_61908_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61908-6/MediaObjects/41467_2025_61908_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61908-6/MediaObjects/41467_2025_61908_MOESM3_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-61908-6#Sec10" + ], + "code": [], + "subject": [ + "Biocatalysis", + "Enzymes", + "Photocatalysis" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5461436/v1.pdf?c=1758207137000", + "research_square_link": "https://www.researchsquare.com//article/rs-5461436/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61908-6.pdf", + "preprint_posted": "16 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Enzymatic reductions catalyzed by reductases, such as aldo/ketoreductases (AKRs), imine reductases, and ene reductases generally depend on the regeneration of nicotinamide cofactors for industrial viability. This usually involves the addition of a cosubstrate and a second enzyme, a dehydrogenase, e.g. glucose / glucose dehydrogenase. For commercial viability it would be more interesting to use water as the sacrificial cosubstrate to supply the necessary hydrogen atoms. This is possible using photocatalytic methods involving precious metal, e.g. Rh and Ru, complexes as electron mediators. A problem associated with photobiocatalysis in general is the weak penetration of ultraviolet or visible light in biological systems. Here we report the unprecedented use of near infrared (NIR) light in combination with a hybrid photoenzyme based on IR responsive reductive graphene quantum dots (rGQDs) that are non-toxic to AKRs. We envisaged that immobilization of the AKR in close proximity to the rGQDs could enable the direct transfer of hydrogen atoms from water to the prochiral ketone substrate without requiring a nicotinamide cofactor. To test this hypothesis, we immobilized rGQDs on the surface of the cross-linked AKR. The resulting rGQD/AKR hybrid photobiocatalyst mediated the synthesis of the pharmaceutical intermediate, (R)-1-[3,5-bis(trifluoromethyl)-phenyl] ethanol ((R)-3,5-BTPE) from the corresponding prochiral ketone, in 82% yield and >\u200999.99% ee under a 50 mW\u00b7cm\u2212\u20092 IR illumination. This work opens new avenues to create artificial photoenzymes that simplify in vitro biocatalysis and enable the coupling of renewable solar energy and sustainable chemical production. Since infrared light accounts for about half of the energy in the entire solar spectrum its efficient utilization is crucial for sustainable applications of sunlight. We have demonstrated that our hybrid photobiocatalyst can effectively utilize infrared light to catalyze the enzymatic reduction of a prochiral ketone. Since these hybrid photobiocatalysts are insoluble solids they can be readily recovered, recycled and potentially employed in continuous flow operation.Biological sciences/Biotechnology/Molecular engineering/Protein designBiological sciences/Biotechnology", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "RASBiohydrogenationSupportingInformation.pdfCofactor-independent, photoenzymatic, reductions with water mediated by rGQDs.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Enzymatic reductions catalyzed by reductases generally depend on reduced nicotinamide cofactors as a hydride source. However, for industrial viability, it is more cost-effective to use water as the hydrogen source, bypassing the requirement for the cofactor. Here we report a hybrid photo-biocatalyst system based on infrared (IR) light and responsive reductive graphene quantum dots (rGQDs), for performing the direct transfer of hydrogen from water to prochiral substrates. The photo-biocatalyst, assembled from rGQDs and cross-linked aldo-keto reductase (AKR), mediates the synthesis of the pharmaceutical intermediate, (R)\u22121-[3,5-bis(trifluoromethyl)-phenyl] ethanol ((R)\u22123,5-BTPE), in 82% yield and >99.99% ee under IR illumination. Our photo-enzymatic systems can also be effectively used to drive the enzymatic reduction of imines and alkenes. Since the hybrid photo-biocatalysts are insoluble, they can be readily recovered and recycled. This work opens new avenues to create artificial photo-biocatalyst systems, enabling the facile coupling of renewable solar energy and sustainable chemical production.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Biocatalysis is widely applied in the pharmaceutical and fine chemical industries in the enantioselective production of valuable chiral chemicals under mild, aqueous conditions1,2,3. Prochiral ketone reductions catalyzed by ketoreductases, for example, are exquisitely enantioselective and cost-effective methods for the industrial production of the corresponding chiral alcohols2,4. Chiral amines have also served as a focus of attention because of the prevalence of nitrogen atoms in drugs, and imine reductases are used for their preparation5,6. Similarly, ene reductases, typically from the old yellow enzyme (OYE) family, reduce conjugated C\u2009=\u2009C double bonds7. All of these enzymes require efficient regeneration of NAD(P)H cofactors for their cost-effective use and this typically involves using a dehydrogenase in combination with a sacrificial cosubstrate, e.g. alcohol/alcohol dehydrogenase (ADH), glucose/glucose dehydrogenase (GDH) or formate/formate dehydrogenase (FDH), or a hydrogenase with atom-economic cosubstrate H2 (Fig.\u00a01a)8,9,10,11,12. Indeed, enzyme-mediated cofactor regeneration is the cornerstone of bioreduction but often suffers from unfavorable kinetics13.\n\na Strategies for the bioreduction of unsaturated compounds and the catalytic mechanism of AKR-catalyzed 3,5-BTPE reduction. During the enzymatic catalysis, ternary AKR-NADPH-substrate complexes form and the NADPH binding induces protonation of Tyr to form the catalytically active Tyr-OH\u2082\u207a species. Then, the common coenzyme binding domain of AKR permits pro-R-hydride from NADPH transfer to the carbonyl through protonation of the carbonyl by the TyrOH2+. Finally, the oxidized coenzymes NADP+ and chiral product leave. b NIR-driven photo-enzyme-coupled catalysis for 3,5-BTAP reduction. c This work, strategies for the light-driven photo-enzymatic reduction of unsaturated compounds by rGQDs/reductase photo-biocatalyst system.\n\nThe ideal sacrificial cosubstrate is water, which can be used in conjunction with electricity (electrochemical)14,15, or light (photochemical)16, as the source of hydrogen atoms without the need for a second enzyme. The use of water as a hydrogen source not only provides an economical alternative but also represents a greener and more sustainable option. This approach aligns with the principles of green chemistry by minimizing environmental impact and enhancing enzyme compatibility. Much effort has been expended, therefore, regarding the mediation of electron transfer from water to NAD(P)+ by light-driven photocatalysts. This generally involves the use of semi-conductors such as Au/TiO217 or expensive precious metal complexes such as [Cp*Rh(bpy)(H2O)]2+ (M, Cp* = pentamethylcyclopentadienyl, bpy = 2,2\u2019-bipyridyl) as electron mediators in order to avoid undesirable side reactions stemming from the radical nature of two non-catalyzed single electron transfer (SET) steps and convert these two SETs (and a protonation step) into a single step17,18,19,20,21,22,23,24,25,26,27,28,29,30,31. The electrochemical method suffers from the same issues, providing the enzymatic reductase system with complexity and reducing its economic viability14,32,33. In contrast, we envisaged a simplified biotransformation that would bypass the traditional cofactor-dependent catalytic pathway altogether.\n\nAnother problem associated with photocatalysis is the weak penetration of ultraviolet or visible light in various reaction media because biological tissues, including proteins and nucleic acids, absorb visible light much more strongly than chemical reaction media34. Moreover, although IR light is responsible for half of the energy of sunlight and has considerable penetration depth in biological tissues, its photon energy is relatively low and usually insufficient to directly stimulate photocatalysis35,36. This hurdle can be overcome by introducing an infrared light-responsive component35, such as rGQDs in which the p electrons are excited to a high-energy state (e.g. the lowest unoccupied molecular orbital, LUMO) and then transition back to the s orbital37,38.\n\nrGQDs were recently developed as near-infrared (NIR) emissive nanomaterials39,40. We previously demonstrated41 that loading rGQDs onto TiO2 nanotubes photocatalyst enabled a greatly improved NADPH photo-regeneration efficiency in the presence of M without extra sacrificial reductant due to abundant Ti-O-Ti bonds formed between the interfaces, which promoted multi-electron transfer under NIR excitation. Indeed the new-to-nature reactivity of enzymes has been greatly extended via light promotion42,43. Hyster et al. demonstrated that cofactor or a charge-transfer complex, formed by cofactor and substrate in the active site, could harvest the incident photons to induce electron transfer from the cofactor to substrate44,45. Based on a NADPH recycling system, these photo-enzymatic modes work well on asymmetric dehalogenation46, C-C bond47,48 and C-N bond formations49. In contrast, upon visible light irradiation, ene-reductases (ER) could initiate single electron oxidation, which has been successfully applied in hydrosulfonylation of olefins50,51 and lactone synthesis, without the involving of cofactor recycling52.\n\nWe envisaged that an accessible upconversion material such as rGQDs, comprising abundant conjugate structures with dangling carbon bonds, would form stable assemblies with enzymes through multiple forces (e.g. cation\u2212\u03c0, anion\u2212\u03c0, hydrophobic and \u03c0\u2212\u03c0 interactions). This would allow the short-range transfer of active hydrogen, generated by water splitting under IR illumination on GQDs, to the nearby enzyme-bound substrate without the intervention of a cofactor (Fig.\u00a01b). In this work, in order to validate our envisaged cofactor-free, infrared light driven photoenzymatic catalysis, we choose the synthesis of (R)\u22123,5-BTPE, a chiral intermediate for the drug Aprepitant used in the clinical treatment of adverse reactions caused by chemotherapy, by reduction of the corresponding prochiral ketone39,53,54,55,56. Subsequently, other photoenzymatic reductions using an imine reductase, ene reductase, and carbonyl reductase is conducted and shown to be efficient for the reduction of an imine, cinnamaldehyde, and ketone, respectively.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61908-6/MediaObjects/41467_2025_61908_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "As shown in Fig.\u00a02a, a microwave-assisted bio-orthogonal click reaction was utilized to crosslink prefunctionalized AKR protein (Gene ID: 897867), which typically binds cofactor NADPH in an extended anti-conformation to catalyze 4-pro-R hydride transfer57,58,59. The rGQDs/AKR hybrid catalyst was constructed by grafting rGQDs on crosslinked AKR (AKR-CLEs) through a simple self-assembly. The aggregation makes them convenient for further characterization. The hybrid material displayed stability comparable with that of rGQDs or AKR-CLEs which was confirmed by their Zeta potential (Fig.\u00a0S9A). Corresponding XRD and FT-IR measurements (Fig.\u00a0S9B and S10) indicated no recrystallization of rGQD and no new chemical bond formation. The morphology of rGQDs/AKR was studied by CLSM as presented in Fig.\u00a02b, SEM (Fig.\u00a0S11a) and TEM (Fig.\u00a0S11b). The rGQDs/AKR photo-biocatalyst exhibited a regular coral-like structure, consistent with that of AKR-CLEs (Fig.\u00a0S3a and S4). Subsequently, atomic force microscopy (AFM) examination revealed numerous tiny particles (rGQDs) scattered on the surface of AKR-CLEs (Fig.\u00a02c). This characteristic was confirmed by the energy dispersive X-ray spectroscopy (EDS) diagram, in which a rough hybrid surface with nanosized carbon clusters was exhibited (Fig.\u00a02d). Compared with the rGQDs precursor, there is no obvious change in carbon\u2019s chemical states present in X-ray photoelectron spectroscopy (XPS) results (Fig.\u00a02e). The resulting hybrid material retained the infrared light responsive ability. Its optical upconversion emissions at 525\u2009nm, 545\u2009nm and 661\u2009nm are similar to rGQDs under 980\u2009nm IR light excitation (Fig.\u00a02f)60, are consistent with the rGQDs being anchored on the AKR.\n\na Schematic illustration for the synthesis of rGQDs/AKR, (b) CLSM image of rGQDs/AKR stained with FITC (\u03bbEX\u2009=\u2009488\u2009nm), (c) AFM image and (d) EDS element mappings of rGQDs/AKR, (e) XPS spectra: C1s spectra of rGQDs, rGQDs/AKR. (f) PL spectra of rGQDs, rGQDs/AKR with 980\u2009nm excitation, (g) UV-Vis-NIR spectra of rGQDs, rGQDs/AKR, (h) Band structures of rGQDs, rGQDs/AKR, (i) EPR spectra of hydroxyl radicals.\n\nThe bandgaps were analyzed before and after the self-assembly of rGQDs and AKR-CLEs. As shown in Fig.\u00a02g, the light absorption of the rGQDs and rGQDs/AKR ranged from the UV to the infrared region, consistent with potentially high sunlight utilization efficiencies. Assisted by the transformed Kubelka-Munk function versus the energy (Fig.\u00a0S12) and the XPS VB spectra (Fig.\u00a0S13), the bandgap structures of rGQDs and rGQDs/AKR were depicted, obviously both of them had suitable photoredox potentials for water splitting in theory (Fig.\u00a02h). Therefore, we evaluated the photocatalytic water-splitting performance of rGQDs. As shown in Fig. S14, rGQDs exhibit photocatalytic activity for hydrogen production from water splitting under infrared light irradiation, with a H2 generation rate of 3.04\u2009\u03bcmol\u00b7gcat\u22121\u00b7h\u22121. As shown in Fig.\u00a02i, the presence of hydroxyl radicals from photocatalytic water splitting under illumination was successfully detected by ESR.\n\nThe molecular features of assembled rGQDs/AKR were characterized by molecular dynamics (MD) simulations with and without bound NADPH. Owing to the variety of conformations, rGQDs can bind to various sites on the enzyme. The two-layered rGQDs that was initially located at different positions relative to AKR-NADPH/AKR with a distance of 30 \u00c5 (Fig. S18 and S19) eventually formed stable binding complexes of rGQDs/AKR-NADPH and rGQDs/AKR within 400 ns MD simulations, as indicated by the converged RMSD (root mean squared deviation) and distance profiles (Fig. S20\u201323). The converged RMSD profiles confirmed the formation of stable binding complexes of rGQDs\u2013AKR both with and without NADPH cofactor (Fig. S20 and S21). The equilibrated binding conformations showed extensive cation\u2212\u03c0 and anion\u2212\u03c0 interactions between the surface residues of AKR and rGQD, which have been reported to have an interaction strength of ~14\u201315\u2009kcal\u00b7mol\u20131 (close to the magnitude of a hydrogen bond)61,62. Hydrophobic and \u03c0\u2212\u03c0 interactions also contributed to the stable binding. The difference in locations of rGQDs on the AKR surfaces can be ascribed to varied charge distribution due to the binding of NADPH. Notably, structure comparison of the equilibrated rGQDs/AKR-NADPH and rGQDs/AKR with the corresponding crystal structures of AKR-NADPH (PDB: 6KIY) and AKR (PDB: 6KIK) demonstrated that binding of rGQDs would not cause dramatic conformational changes to the overall structure and catalytic pocket domain of AKR, and the surface bound rGQDs would hardly hinder the binding and release of NADPH and substrate molecules (Fig. S24 and Fig.\u00a03).\n\na\u2013d The equilibrated overall binding complexes of rGQD-1/rGQD-2/rGQD-3/rGQD-4\u2013AKR, respectively, with the intermolecular interactions shown in the enlarged illustrations. The cation\u2212\u03c0 and anion\u2212\u03c0 interactions between rGQDs and the surrounding residues are indicated by pink dotted lines. The NADPH binding domains are highlighted with brown and purple colors, with the purple regions indicating the locations of the nicotinamide group.\n\n3,5-BTAP was selected as the substrate to explore the photo-enzymatic properties of rGQDs/AKR in molecular docking and MD simulations model. It is usually synthesized by enantioselective hydrogenation of 3,5-BTAP. As binding of rGQDs would not hinder the binding of substrate or change the conformation of AKR catalytic domain, simplified models were used without rGQDs included. Expectations for the rGQDs/AKR photo-biocatalyst system require that the enantioselective reduction of 3,5-BTAP can be initiated by the active hydrogen generated from water molecules by rGQDs through infrared light irradiation (Fig.\u00a04). On the basis of stably anchored 3,5-BTAP in the catalytic pocket of AKR (Fig.\u00a04a\u2013c), we anticipated that the distribution of water molecules around the binding 3,5-BTAP, representing diverse access paths of active hydrogen to the carbon atom of 3,5-BTAP carbonyl group approximately, would play a critical role in producing the chiral 3,5-BTPE. More specifically, the position of the hydrogen atom of water that is located nearest to the carbon atom (C9) of 3,5-BTAP carbonyl group relative to the plane defined by the 3,5-BTAP atoms C6, C9, and C10 basically determines the chirality of 3,5-BTPE (Fig.\u00a04). Hence, the improper dihedral of \u2220C6-C10-C9-H was monitored throughout the 20\u2009ns MD simulations, in which H means the hydrogen atom belonging to the water molecule nearest to 3,5-BTAP C7 atom. The positive and negative torsion angles correspond to the pro-(R) and pro-(S) binding forms of 3,5-BTAP, leading to the generation of (R)- and (S)-3,5-BTPE respectively. Distributions of the improper dihedral along MD simulations showed a clear preference for the pro-(R) binding forms in both AKR\u2013NADPH\u20133,5-BTAP and AKR\u20133,5-BTAP systems (Fig.\u00a0S25d and Fig.\u00a04d), resulting in an excellent enantioselective hydrogenation result (e.e. (R)\u2009>\u200999%).\n\na Equilibrated binding conformation of AKR\u20133,5-BTAP, (b) RMSD profiles of the binding components, (c) Profile of the distance between the geometric centers of W21 sidechain and aromatic ring of 3,5-BTAP, (d) and (e) Value and distribution of the key improper dihedral \u2220C6-C10-C9-H across the MD simulation. The access path of hydrogen atom (H) to the carbonyl carbon atom (C9) is indicated by a magenta dotted line. The \u03c0\u2212\u03c0 stacking and hyrogen bond interactions are indicated by cyan and pale green dotted lines.\n\nMolecular docking calculations indeed found two potential binding conformations of 3,5-BTAP, i.e. the carbonyl-out and the carbonyl-in ones (Fig.\u00a0S26). In both binding forms, 3,5-BTAP interacts with the catalytic pocket residues through hydrogen bonds. However, due to the electrostatic repulsive interactions between the carbonyl groups of 3,5-BTAP and AKR Q169 in the carbonyl-in binding form (Fig.\u00a0S26b), it is energetically unfavorable relative to the carbonyl-out binding form. Interestingly, driven by the induced-fit effect, a fast conformational switch (within 500\u2009ps) from the carbonyl-out to the carbonyl-in form of 3,5-BTAP was discovered in the MD simulation, corresponding well with the sudden decrease of the distance between the carbonyl oxygen atom of 3,5-BTAP and the hydroxyl oxygen atom of the AKR Y58 sidechain at the starting stage of MD simulation (Fig.\u00a0S27a). A representative carbonyl-out form snapshot of the AKR\u20133,5-BTAP binding complex was retrieved from the MD trajectory (Fig.\u00a0S27c). It shows that, though the aromatic ring of 3,5-BTAP tends to form \u03c0\u2009\u2212\u2009\u03c0 stacking interactions with the sidechains of W21 and Y198, their distances (4.56\u2009\u00c5) are obviously longer than a common \u03c0\u2009\u2212\u2009\u03c0 stacking interaction distance of 3.50\u2009\u00c5, indicating a transient unstable interaction. In addition, the inner trifluoromethyl group of 3,5-BTAP is located near a negatively charged carboxyl group of D53 and the hydrophobic sidechain of I242, which is energetically unfavorable (Fig.\u00a0S27c). These factors are the driving force of the conformational change of the binding 3,5-BTAP.\n\nData in Fig.\u00a0S27 demonstrates that the carbonyl-in binding complex equilibrated to a similar conformation to that in Fig.\u00a04a within 20\u2009ns. The hydrogen bond interaction with Y58 and the \u03c0\u2212\u03c0 stacking to W21 are stable, as indicated by their distance profiles (Fig.\u00a0S27b and S28c). The value and distribution of the improper dihedral \u2220C6-C10-C9-H across the MD simulation (Fig. S28d, e) also showed a clear preference for the pro-(R) binding form of 3,5-BTAP, agreeing well with our experimental result and validating the results shown in Fig.\u00a04. Therefore, it is feasible and convenient to apply rGQDs/AKR as the photocatalyst for water splitting and enantioselective hydrogenation.\n\nOur molecular simulation data provided pivotal information for the enantioselective process catalyzed by the rGQDs/AKR photo-biocatalyst system at the atomic level. These properties were evaluated by performing the photoenzymatic synthesis of (R)-3,5-BTPE in the absence of both NAD(P)H or NAD(P)+ and a noble metal complex (Fig.\u00a05a). Figure\u00a05b shows the yields of (R)-3,5-BTPE with different catalyst systems, namely (rGQDs, AKR, rGQDs/AKR, rGQDs/AKR and NADP+, rGQDs/AKR and NADP+ and M) under 18\u2009h infrared light irradiation, using isopropanol as cosolvent (50\u2009mW\u00b7cm\u22122). It clearly shows that no (R)-3,5-BTPE was observed with only AKR or rGQDs addition, but once the combination of rGQDs/AKR was provided, (R)-3,5-BTPE was produced in up to 82% yield with 99.99% ee, thus demonstrating the success of our method. We also observed that the yields (88%, 96%) were elevated when M and/or NADP+ were added to the rGQDs/AKR photo-biocatalyst system, but these were not significant compared to those in the absence of these two coenzymes. We conclude that, considering the high costs of both NADP+ and M, the photocatalytic synthesis of (R)-3,5-BTPE assisted by only rGQDs/AKR is an important step in the right direction. We also studied the effect of the wavelength of the light source. As shown in Fig.\u00a05c, the yield of (R)-3,5-BTPE was 0 under dark conditions, showing that light illumination was the requirement for this photochemical reaction. The reaction system also worked well under the irradiation of visible light or simulated sunlight (AM1.5\u2009G), with a yield of 63% and 86%, respectively. The lower yield under visible light compared with under IR light showed that our photo-biocatalyst had better responsiveness to IR light. To establish the optimum wavelength for illumination, the synthesis of (R)-3,5-BTPE was conducted using different monochromatic light sources (365\u2009nm, 750\u2009nm and 980\u2009nm). As shown in Fig.\u00a05d, it is apparent that the NIR 750\u2009nm is the best, i.e. close to the optimum upconversion excitation wavelength of rGQDs (780\u2009nm). The most suitable IR light intensity was 50\u2009mW\u00b7cm\u22122 (Fig.\u00a05e). When the light intensity is elevated to 100 or 200\u2009mW\u00b7cm\u22122, the yields are all greatly decreased to 2.9% and 1.7%, respectively. The lower yields are a result of reductions in photo-biocatalyst activity caused by the excessive local heating through the photothermal effect of the rGQDs upon strong IR irradiation. This explanation is in agreement with the results of comparison experiments (Fig.\u00a05f).\n\na HPLC spectrum of the product. (R)\u22123,5-BTPE yields of (b) using different catalyst systems including AKR, rGQDs, rGQDs/AKR, rGQDs/AKR\u2009+\u2009NADP+, and rGQDs/AKR\u2009+\u2009NADP+\u2009+\u2009M (M denotes [Cp*Rh(bpy)(H2O)]2+) with IR light (\u2009>\u2009800\u2009nm, 50\u2009mW\u00b7cm-2) irradiation, (c) using different light sources, (d) using different wavelength LED lamps as light sources, (e) using infrared light (\u03bb\u2009>\u2009800\u2009nm) as light sources with different light intensities, (f) using different rGQDs loading amounts at IR light irradiation. g Photo-enzymatic reduction of 1-methyl-3,4-dihydroisoquinolin and cinnamaldehyde with imine reductase AoIRED and ene reductase OYE1 as the corresponding biocatalyst, respectively. h Reduction of 3-chloro-1-phenylpropan-1-one using different catalyst systems, including rGQDs/carbonyl reductase (NaCBR), TiO2/NaCBR and TiO2-rGQDs/NaCBR, under corresponding photo-enzymatic reaction conditions. (i) Reuse of TiO2-rGQDs/NaCBR in cyclic catalysis. The error bars represent the standard deviations of three parallel measurements.\n\nTo assess the possible effect of rGQDs on enantioselectivity, an imine reductase AoIRED, displaying poor enantioselectivity in (1-methyl-3,4-dihydroisoquinolin) reduction, was examined63. First, distributions of the key improper dihedral \u2220N1-C10-C7-H in the AoIRED\u2013NADPH\u2013DHIQ and AoIRED\u2013DHIQ models were analyzed based on 20\u2009ns MD simulations, which showed dramatically decreased enantioselectivity in both systems. The theoretical product 1-methyl-1,2,3,4-tetrahydroisoquinoline contained almost 60% S-configuration and 40% R-configuration, i.e. 20% ee (Fig. S29-30). The result was confirmed in the photo-enzymatic reduction using rGQDs/AoIRED as the photo-biocatalyst system and DMF as the cosolvent under IR irradiation. The corresponding product was obtained in 65% yield with 22% ee, predominantly in the S- configuration, consistent with the selected enzyme\u2019s catalytic preference (Fig.\u00a05g and S31). Additionally, in the presence of isopropanol cosolvent, cinnamaldehyde was reduced to form 3-phenylpropanol (65%) through the synergistic catalysis of ene reductase OYE1 with rGQDs under IR irradiation (Fig.\u00a05g and S32-34)64. On the other hand, an electron mediator may negatively affect the conversion system due to possible ligand exchange with the active residue of the enzyme, leading to a significant drop in yield (Fig.\u00a05g and S34b)65. These results strongly suggest that the enantioselectivity and hydrogenation activity of the photoenzymatic system are controlled separately by the enzyme and photocatalyst, respectively.\n\nTo further illustrate the function of rGQDs in promoting solar light energy conversion, we integrated TiO2 with strong UV and low visible-light absorbing properties into the above photoenzymatic system. Under simulated sunlight, (S)-3-chloro-1-phenyl-1-propanol ((S)-CPPO), a key chiral intermediate for the synthesis of the chiral side chain of Fluoxetine and Atomoxetine, obtained in 72.3% yield, which is 2.7 times and 2.6 times of that without TiO2 and rGQDs, respectively (Fig.\u00a05h and S35)66,67,68,69. What\u2019s more, corresponding electrostatic assembled hybrid catalysts (TiO2-rGQDs/NaCBR) can be recycled with excellent chemical stability, maintaining 90% of its original catalytic efficiency and consistent enantioselectivity (99.9% ee) after 6-cycles (Fig.\u00a05i).\n\nAn isotope-tracer experiment was carried out to confirm the hydrogen donor in the cofactor-independent photo-enzymatic reduction system mediated by rGQDs/AKR to synthesize (S)-1-(2-chlorophenyl) ethanol. The high-resolution mass spectrum (HRMS) analysis of the product obtained using D2O reveals that it was completely labelled with two deuteriums, confirming that water was the hydrogen resource (Fig.\u00a0S36-37). In 3,5-BTAP and cinnamaldehyde reductions isopropanol, a more energetically favorable source of the hydrogen atoms than water, was used as a cosolvent. However, it is also feasible to use DMF as the sole co-solvent for 1-methyl-3,4-dihydroisoquinoline reduction or synthesize (S)-CPPO in the absence of a cosolvent. Hence, we conclude that with this hybrid photo-biocatalyst, water can be used as the sole source of hydrogen.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61908-6/MediaObjects/41467_2025_61908_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61908-6/MediaObjects/41467_2025_61908_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61908-6/MediaObjects/41467_2025_61908_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61908-6/MediaObjects/41467_2025_61908_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In conclusion, we have successfully constructed a infrared light responsive hybrid rGQDs/AKR photo-reductase system that requires neither an expensive noble metal complex nor an expensive cofactor for its activity. Moreover, the two hydrogen atoms required for the reduction are provided by a molecule of water. The catalyst was essentially enantiospecific (\u2009>\u200999.99%) in the synthesis of the chiral alcohol, (R)-3,5-BTPE. This custom-designed hybrid system provides active hydrogen atoms through photo-catalytic water decomposition and directly delivers them to the immobilized AKR for transfer to the ketone substrate. The rGQDs was also successfully combined with an imine reductase and an ene reductase to afford 1-methyl-1,2,3,4-tetrahydroisoquinoline and 3-phenylpropanol, respectively, via IR-driven photo-enzymatic reduction.\n\nThis methodology represents a paradigm shift in the development of enantioselective reductions of prochiral ketones by involving cofactor-independent photo-biocatalysts in combination with water as the sole source of hydrogen atoms and NIR light as the source of energy. We anticipate that this methodology can be used to design efficient hybrid-reductase systems that are sensitive to infrared light, and could also be used to study and control protein activity in cells and organisms. Since our hybrid system is a heterogeneous immobilized biocatalyst, it can also be readily recycled and/or used in continuous flow operation. In short, we believe that this unprecedented example of a hybrid photo-biocatalyst that requires only NIR radiation and water to perform sustainable, highly (enantio)selective reductions could represent the tip of an iceberg. It could form a basis for the development of various cofactor independent biotransformations using only water and light as the source of, respectively, reducing equivalents and energy.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The photo-biocomposite rGQDs/AKR was obtained by electrostatic self-assembly under mechanical oscillation. Typically, aldo-keto reductase aggregates were dispersed in a 5\u2009mL rGQDs (5\u2009mg\u00b7mL\u22121) suspension with a final concentration of 5\u2009mg\u00b7mL\u22121. Then the mixture was shaken continuously at 10\u2009\u00b0C for 4\u2009h to obtain hybrid material rGQDs/AKR, which were collected by freeze-drying after centrifugation.\n\nTo prepare the bio-inorganic hybrid TiO2-rGQDs/NaCBR, the assembly of TiO2 and rGQDs was first performed by shaking the mixture of 200\u2009mg of TiO2 and 40\u2009mL of rGQDs suspension (5\u2009mg\u00b7mL\u22121) at 60\u2009\u00b0C overnight. After centrifugal separation and washing with 10\u2009mL of deionized water three times, 50\u2009mg of TiO2/rGQDs composite was obtained and then assembled with carbonyl reductase (5\u2009mg\u00b7mL\u22121) in 5\u2009mL PBS buffer, pH 7.0, at 10\u2009\u00b0C for 4\u2009h to give TiO2-rGQDs/NaCBR after collection by freeze-drying and centrifugation.\n\nAll photo-enzymatic catalysis was carried out in a 45\u2009mL custom-made quartz reactor (Fig.\u00a0S15) at 20\u2009\u00b0C. For (R)-3,5-BTPE synthesis, 10\u2009mg of rGQDs/AKR were dispersed in 5\u2009mL of phosphate-buffered saline (PBS buffer) (pH\u2009=\u20097.0, 100\u2009mM), then 3,5-BTAP (32.3\u2009mg, 0.125\u2009mmol) and 3\u2009\u03bcL of isopropanol cosolvent was added. After that, the reactor was irradiated at AM1.5\u2009G with an optical filter (CEL-HXF300) for 18\u2009h. When the photoenzymatic assays were performed in the presence of NADP+ and (or) electron mediator, 0.1\u2009mM of NADP+, and (or) 0.25\u2009mM of M were added to the reaction system in sequence. After the reactions, the target product was extracted from the supernatant in the reaction solution by 3\u2009mL of anhydrous n-hexane. The yield and ee values were determined by high-performance liquid chromatography.\n\nFor 1-methyl-3,4-dihydroisoquinolin reduction, 10\u2009mg of AoIRED and 5\u2009mg of rGQDs were dispersed in 5\u2009mL of PBS buffer (pH\u2009=\u20097.0, 100\u2009mM), then 1-methyl-3,4-dihydroisoquinolin (18.2\u2009mg, 0.125\u2009mmol) and 100\u2009\u03bcL of DMF were added. For cinnamaldehyde reduction, 10\u2009mg of OYE1 and 5\u2009mg of rGQDs were dispersed in 5\u2009mL of PBS buffer (pH\u2009=\u20097.0, 100\u2009mM), then cinnamaldehyde (16.5\u2009mg, 0.125\u2009mmol) and 3\u2009\u03bcL of isopropanol were added. For 3-chloro-1-phenylpropan-1-one reduction, 10\u2009mg of NaCBR and 5\u2009mg of rGQDs were dispersed in 5\u2009mL of PBS buffer (pH\u2009=\u20097.0, 100\u2009mM), then 3-chloro-1-phenylpropan-1-one (21.0\u2009mg, 0.125\u2009mmol) was added. The above reaction mixtures were irradiated at IR irradiation (CEL-HXF300, >800\u2009nm, 50\u2009mW\u00b7cm\u22122) for 18\u2009h. After the reactions, the target products were extracted from the supernatant in the reaction solutions by 3\u2009mL of anhydrous n-hexane. The yield and ee values were determined by high-performance liquid chromatography.\n\nTo detect the recycling efficiency of TiO2-rGQDs/NaCBR, 15\u2009mg of TiO2-rGQDs/NaCBR was dispersed in 5\u2009mL of PBS buffer (pH\u2009=\u20097.0, 100\u2009mM) at a quartz reactor. Then, 3-chloro-1-phenylpropan-1-one (21.0\u2009mg, 0.125\u2009mmol) was added. The reaction mixture was stirred and irradiated at AM1.5\u2009G (CEL-HXF300, 50\u2009mW\u00b7cm\u22122) for 18\u2009h at 20\u2009\u00b0C. After that, the TiO2-rGQDs/NaCBR was separated from the reaction mixture by centrifugation (10000\u2009x\u2009g, 15\u2009min) and reused for the next cycle of reactions under standard reaction conditions. The liquid supernatant was extracted by anhydrous n-hexane and the yield was determined by HPLC.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data supporting the present study are available in the manuscript, source data file, and supplementary information. \u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Ghosh, S. et al. Exploring emergent properties in enzymatic reaction networks: design and control of dynamic functional systems. Chem. Rev. 124, 2553\u20132582 (2024).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nBell, E. L. et al. Biocatalysis. Nat. Rev. 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R. China\n\nAnming Wang,\u00a0Xiaoyu Li,\u00a0Li Qiao,\u00a0Xiaoting Pan,\u00a0Yongjian Jiang,\u00a0Wei Ye\u00a0&\u00a0Peng Gao\n\nZhejiang Key Laboratory of Medical Epigenetics, Institute of Aging Research, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, 311121, P. R. China\n\nZhiguo Wang\n\nYangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology, Huzhou, 313001, P. R. China\n\nPeng Gao\n\nMolecular Sciences Institute, School of Chemistry, University of the Witwatersrand, PO Wits., 2050, Johannesburg, South Africa\n\nRoger A. Sheldon\n\nDepartment of Biotechnology, Section BOC, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, the Netherlands\n\nRoger A. Sheldon\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nZ.G.W. contributed to the theoretical calculation. W.Y., P.G., A.M.W. and R.A.S. conceived and designed the overall study and provided comments and feedback on the discussion. L.Q. and X.Y.L. performed the analysis and drafted the manuscript with input from all co-authors. Y.J.J. and X.T.P. contributed to the data generation.\n\nCorrespondence to\n Anming Wang, Li Qiao, Zhiguo Wang, Wei Ye, Peng Gao or Roger A. 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Humping in High-Speed Laser Welding", + "journal": "Nature Communications", + "published": "05 November 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_MOESM3_ESM.avi" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_MOESM4_ESM.avi" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_MOESM5_ESM.avi" + }, + { + "label": "Supplementary Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_MOESM6_ESM.avi" + }, + { + "label": "Supplementary Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_MOESM7_ESM.avi" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_MOESM8_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_MOESM9_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-53888-w#Sec13" + ], + "code": [], + "subject": [ + "Fluid dynamics", + "Materials for devices", + "Mechanical engineering", + "Metals and alloys" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4087583/v1.pdf?c=1730898330000", + "research_square_link": "https://www.researchsquare.com//article/rs-4087583/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53888-w.pdf", + "preprint_posted": "26 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The fabrication of fuel cells relies on a rapid laser welding process. However, challenges arise with the occurrence of humping when the welding speed surpasses a critical threshold, which poses difficulties in achieving a smooth surface finish and a consistent weld strength. This study aims to elucidate the mechanisms behind humping by analyzing the morphology of molten pool and the characteristics of melt flow at varying welding speeds via in situ synchrotron high-speed X-ray imaging and computational fluid dynamics simulations. Our findings indicate that the short keyhole rear wall, the high backward melt velocity, and the prolonged tail of molten pool are the primary factors contributing to the onset of humping. Furthermore, a dimensionless humping index (\u03c0_h) was introduced, which successfully captured the onset threshold of humping across different literatures. This index not only provides a quantitative description of the humping formation tendency but also serves as a valuable tool for optimizing the laser welding process.Physical sciences/Engineering/Mechanical engineeringPhysical sciences/Energy science and technology/Fuel cellsPhysical sciences/Physics/Fluid dynamicsLaser weldinghumpingsynchrotron X-ray imagingCFD simulationscaling analyses", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "20mfirst50.aviSupplementary Video 140mfirst50.aviSupplementary Video 260mfirst50.aviSupplementary Video 375mfirst50.aviSupplementary Video 485mfirst50.aviSupplementary Video 5", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The fabrication of fuel cells relies on a rapid laser welding process. However, challenges arise with the occurrence of humping when the welding speed surpasses a critical threshold, which poses difficulties in achieving a smooth surface finish and a consistent weld strength. This study aims to elucidate the humping mechanisms by analyzing the morphology of molten pool and the characteristics of melt flow at varying welding speeds via in situ synchrotron high-speed X-ray imaging and computational fluid dynamics simulations. Our findings indicate that the short keyhole rear wall, the high backward melt velocity, and the prolonged tail of molten pool are the primary factors contributing to the onset of humping. Furthermore, a dimensionless humping index (\u03c0h) was introduced, which successfully captured the onset threshold of humping across different literatures. This index not only provides a quantitative description of the humping formation tendency but also serves as a valuable tool for optimizing the laser welding process.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The laser welding process offers several advantages, including a small heat-affected zone, fast welding speed, and high flexibility in welding path design1. These benefits make it well-suited for fuel cell fabrication, which requires long and narrow welding paths between bipolar plates2,3. Thin foils are preferred for these plates as they can reduce weight4 and enable more complex channel designs5. Welding metal foils is also applied in other applications such as motor rotors and electronic device connectors6. While increasing the laser welding speed enhances productivity, the formation of humps is particularly a major issue that limits the maximum welding speed7,8,9. Humps manifest as severe periodic undulations along the top surface of the weld seam. They not only pose difficulties in achieving a smooth surface finish but also significantly deteriorate the weld strength by reducing the effective bonded region. In laser welding, the occurrence of humping is influenced by laser parameters such as laser welding speed, power, and spot diameter10. Interestingly, the issue of humping is not unique to laser welding. It is also encountered in other arc-based and laser-based processes, such as arc welding11,12,13,14, wire arc additive manufacturing15,16,17, and powder bed fusion18,19, as the moving speed of the heat source increases. It indicates that the mechanisms governing humping are associated with the heat and mass transfer in the molten pool (MP), regardless of its origin.\n\nThe current understanding of humping can be summarized as follows. First, it always occurs at the end of MP once a critical welding speed is exceeded. Second, the formation of humps is primarily linked to the high backward melt velocity created at a high welding speed, so lowering the heat input proves effective in eliminating humping17. The shallower inclination angle of the MP boundary also reduces the deceleration rate of the backward melt velocity7,9. Third, a high welding speed results in a long and narrow MP, which is more susceptible to Rayleigh instability20,21, where a cylinder liquid phase tends to fragment into multiple droplets as the length-to-width ratio increases. However, the above understandings primarily relied on simulations and assumptions, lacking real-time experimental validation. In addition, the existing understanding is predominantly qualitative rather than quantitative, posing challenges in predicting humping under diverse process conditions.\n\nThe analyses of keyhole and fluid dynamics during the laser welding process require in situ characterization techniques7,22,23,24,25 or numerical simulations9,13,20,26. Previous studies implemented a high-speed optical camera positioned from the top to capture optical images7,22 or vapor plume23 for studying keyhole dynamics. However, this method is limited in providing information solely on the tilting angle of the keyhole front wall and cannot capture the shape of the keyhole rear wall or trailing MP. Another approach involved using transparent glass on one side of the weld to observe the keyhole from a side view24, which, however, does not fully replicate real welding conditions. In this research, in situ high-speed synchrotron X-ray imaging was adopted to resolve the limitations mentioned above. This technique has been successfully employed to analyze keyhole instability27, morphology28, and pore formation mechanisms29,30 in additive manufacturing.\n\nIn this study, in situ high-speed synchrotron X-ray imaging was employed to investigate the humping phenomenon in high-speed laser welding. The geometries of the keyhole and MP offered a crucial understanding into the mechanisms of humping formation. Then, computational fluid dynamics (CFD) simulations were conducted to analyze the characteristics of melt flow within MP, where the streamlines and volumetric flow rates provide further insights into the effects of the MP tail on humping. Lastly, a dimensionless humping index for the laser welding process was developed. This index not only identified the onset threshold of humping across different references but also described the humping tendency. It can serve as a valuable tool to predict humping in laser welding and to optimize the laser welding process.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The in situ high-speed synchrotron X-ray observation of laser welding was carried out at beamline 32-ID-B at Argonne National Laboratory (ANL), using a ytterbium single-mode continuous wave fiber laser (YLR-500-AC) with a spot diameter of 43\u2009\u00b5m. The tested material was an 85\u2009\u00b5m-thick 439 stainless steel foil. Two foils were overlap-welded from the top while in situ X-ray images were acquired from the side. More details on the experimental setup can be found in the \u201cMethods\u201d section and Supplementary Fig.\u00a01a. The corresponding power for each condition was determined beforehand at Edison Welding Institute (EWI) using a single-mode continuous wave laser (nLIGHT AFX-1000) with the same spot diameter (43\u2009\u00b5m). The laser power was iteratively increased until full penetration was achieved, as shown in Fig.\u00a01a. Full penetration is crucial in the fabrication of fuel cells to prevent leakage between channels. It is worth noting that due to the equipment setup at ANL, the fastest laser welding speed adopted was 1.42\u2009m/s, slightly lower than the fastest speed used at EWI (1.50\u2009m/s). The process parameters and the corresponding heat input were summarized in Supplementary Table\u00a01. The appearances of the weld seams are presented in Fig.\u00a01b, showing that the critical welding speed for the onset of humping is 1.00\u2009m/s. While the number of humps slightly decreased as the welding speed increased beyond this threshold, the surface topography of the weld seams (Fig.\u00a01c) revealed that the average height of humps increased from 40.73\u2009\u00b1\u20098.63\u2009\u00b5m at 1.00\u2009m/s to 58.70\u2009\u00b1\u200914.82\u2009\u00b5m at 1.42\u2009m/s. This observation suggests that the momentum of the backward melt flow continued to intensify with the welding speed, exacerbating the humping phenomenon. On the other hand, the T-peel strengths of the welds (defined as the load per unit length of weld) were tested following the configuration in refs. 31,32. Laser welds made at EWI were used because the sample size for in situ X-ray imaging was too small for mechanical testing. The results, presented in Supplementary Fig.\u00a02, indicate that the T-peel strength decreased when the welding speed exceeded 1.00\u2009m/s due to the occurrence of humping.\n\na Corresponding laser powers at each welding speed determined at Edison Welding Institute (EWI) by incrementally increasing the laser power until full penetration was achieved. b Top surfaces of weld seams at the welding speeds of 0.33, 1.00, and 1.42\u2009m/s obtained by optical microscopy. Note that the fastest speed was adopted as 1.42\u2009m/s instead of 1.50\u2009m/s due to the equipment setup at Argonne National Lab (ANL). c Surface topography of weld seams at 1.00 and 1.42\u2009m/s obtained by optical profilometry, and the average hump height relative to the weld top surface.\n\nThe results of in situ high-speed synchrotron X-ray observations are presented in Fig.\u00a02a\u2013c, where the keyhole and humping phenomena during laser welding were successfully captured. For a comprehensive view of all in situ observations for five different welding speeds, refer to Supplementary Movies\u00a01\u20135. The moving speed of the keyhole recorded during the in situ observations was consistent with the set welding speed. A keyhole is a less dense space filled with dilute metal vapor compared to liquid MP, therefore providing sufficient contrast for X-ray observation. However, because of the small difference in the attenuation rates between the liquid and solid phases of stainless steel, the MP dimensions can only be measured from the fluctuating wavy patterns on the top surface. It is worth noting that the use of thin foils in this study prevented the formation of deep keyholes and deep-keyhole-induced porosity observed in other literatures29,30,33.\n\na\u2013c In situ high-speed synchrotron X-ray observations of laser welding at the welding speeds of 0.33, 1.00, and 1.42\u2009m/s at times t0, t0+0.3, and t0+0.6 ms, where t0 was arbitrarily selected. See Supplementary Movies\u00a01\u20135 for in situ observations at five different welding speeds. d Depth of keyhole rear wall. e Molten pool (MP) length. f Keyhole width (measured) and maximum melt velocity (umax, calculated by Eq.\u00a01). The error bars in (d\u2013f) represent the standard deviation, calculated from at least 3 measurements for each sample from the in situ observations.\n\nSeveral key characteristics of the keyhole and MP geometries were quantified from the in situ X-ray observations to comprehend the humping mechanisms. First, the depth of the keyhole rear wall indicates the extent of the geometrical barrier to the backward melt flow, whereas a shorter keyhole rear wall suggests a reduced barrier to the backward melt flow. As the laser welding speed increased from 0.33 to 1.42\u2009m/s (Fig.\u00a02d), the depth of the keyhole rear wall gradually declined from 12.25\u2009\u00b1\u20094.50\u2009\u00b5m to \u2212100.82\u2009\u00b1\u200917.72\u2009\u00b5m, which was significantly beneath the top surface.\n\nSecond, a longer MP length reduces the effectiveness of the MP tail to decelerate the melt flow. The MP length was determined as the average of the minimum instantaneous MP length observed in each humping cycle under conditions where humping occurred, which will be discussed in the following section and Fig.\u00a03a. It increased from approximately 350\u2009\u00b5m at laser welding speed of 0.33\u2009m/s to the highest value of 770\u2009\u00b5m when the laser welding speed reached 1.25\u2009m/s, as shown in Fig.\u00a02e. A prolonged MP not only suggests an increased propensity for humping based on the Rayleigh instability criterion20,21 but also indicates that it is ineffective in decelerating the backward volumetric flow. This phenomenon will be discussed in the section on CFD simulations.\n\na Instantaneous molten pool (MP) length on top surface and waviness of the front part of MP, with the starting periods of humping marked in gray. b Examples of MP waviness changes during humping. At the start of humping (t0+0.38 ms), the average MP waviness was lower. Near an end (t0+0.54 ms), the MP surface showed higher waviness because of the steeper gradient of the backward melt flow\u2019s volumetric rate.\n\nThird, a greater melt velocity increases the volumetric flow rate of the backward melt flow. In terms of melt flow velocity measurement, literatures reported adding W or Ta tracer particles during in situ X-ray experiments34,35,36. However, this approach is limited by the particle size relative to the MP dimensions and the feasibility of materials premixing. This approach has been applied in arc welding34 for its larger MP size and powder bed fusion35,36 where tracer particles were premixed with the powder bed. These experiments provided direct melt flow observations, and it should be noted that quantitative velocity measurements using a single X-ray source from a side view, producing 2D images, may be inaccurate as they may not fully capture the complexities of 3D flow dynamics.\n\nBecause of the limitation of using tracer particles in this research, the maximum melt velocity (umax) was calculated by an analytical approach proposed by Beck et al.37 based on the continuity equation as follow:\n\nwhere uw is the laser welding speed, cp is the specific heat, \u03c1 is the density, r is the spot radius, k is the thermal conductivity, and Lm is the latent heat of fusion. Tb, Tm, T0 are the boiling, melting, and room temperatures, respectively. Because umax occurs on the keyhole side wall, as described by Beck et al.37, the values of cp, \u03c1, and k are obtained at Tb from the thermophysical databases11,38,39, which is listed in Supplementary Table\u00a02. The umax was consistently simulated using CFD models, which will be discussed in Fig.\u00a04a, b in the CFD simulation section. It is noted that among laser welding parameters, umax is related to the spot diameter and welding speed, however, not affected by the laser power based on this equation. The calculated result is shown in Fig.\u00a02f, where umax significantly increases with the welding speed. Besides, the width of the keyhole, measured at the depth between two welded materials, was found to be correlated with the maximum melt velocity (Fig.\u00a02f). This correlation is consistent with the assumption of this model, where umax occurs on the keyhole side wall and is predominantly horizontal. Therefore, a greater melt velocity leads to a more elongated keyhole.\n\na, b Top and side views of streamlines extracted from CFD simulation results at the welding speeds of 0.33 and 1.42\u2009m/s, respectively. c Net cross-sectional volumetric flow rate extracted from different distances of y-z cross-sections behind the keyhole at the welding speeds of 0.33\u2009m/s, 1.42\u2009m/s (start of humping), and 1.42\u2009m/s (near the end of humping), respectively. An enlarged view highlights the curves around the molten pool (MP) tail at 1.42\u2009m/s. d Schematic diagram showing the formation mechanisms of humping.\n\nThe occurrence of humps is periodic. Berger et al. first introduced the concept of conservation of volume flow to explain this phenomenon8, where the melt is incompressible, and the excessive melt is deflected upward at the end of MP to form a hump. Xue et al. conducted simulation9, proving that the melt accumulation and the Rayleigh instability collectively triggered the onset of humping. However, the above analyses were based on a high-speed optical camera from the top view8 or simulation9. Therefore, the in situ X-ray observation in this study provided clearer real-time observation of these phenomena.\n\nThe humping cycle was analyzed at the welding speed of 1.42\u2009m/s with the instantaneous MP length on the top surface and the waviness of the front part of MP, as illustrated in Fig.\u00a03a. The waviness is an indication of the gradient of volumetric flow rate, as a steeper gradient leads to a more excessive volume of backward melt and therefore a higher waviness. More details of their measurements can be found in the \u201cMethods\u201d section. Once the instantaneous MP length reached a minimum, a new hump was initiated because the MP tail cannot accommodate all backward melt flow at this moment. The humping served as an approach to release the excessive melt, slightly extending the instantaneous MP length on the surface. This period was defined as the start of humping, marked in gray in Fig.\u00a03a. During this period, the front part of the MP showed minimal waviness as the release of melt reduced the gradient of volumetric melt flow. After humping started, the growth of the hump gradually slowed down. As a result, the release of melt became less effective despite the increasing MP length, leading to a higher MP waviness near the end of humping. Finally, when the instantaneous MP length reached another minimum, a new humping cycle was triggered, repeating the process and forming periodic humps. The current observation offers direct evidence supporting the humping theory based on the conservation of volume8.\n\nIn situ high-speed synchrotron X-ray imaging provided crucial insights into the morphologies of keyhole and MP and their effects on humping formation in the laser welding process. Then, CFD simulation was conducted to quantitatively analyze the melt flow within MP. The 3D CFD model accounted for flow dynamics, eliminating the challenges of using 2D projected synchrotron X-ray images for velocity measurements. The details on the setup of CFD simulation can be found in the \u201cMethods\u201d section and Supplementary Fig.\u00a03, and the material\u2019s thermophysical properties can be found in Supplementary Fig.\u00a04. The simulation was validated with the MP dimensions (length, width, depth) and the humping characteristics (linear number density and average height of humps), as summarized in Supplementary Table\u00a03. The simulation successfully captured the shorter keyhole rear wall, shallower inclination angle of the MP boundary, and the more elongated keyhole along the welding direction at the higher welding speed, aligning well with the experimental findings from in situ high-speed synchrotron X-ray imaging.\n\nStreamlines were then extracted from the simulation models using the Runge\u2013Kutta integration method40 to visualize melt flow (Fig.\u00a04, b). umax occurred on the keyhole side wall and gradually decelerated as the melt entered the trailing MP. At the welding speed of 0.33\u2009m/s (Fig.\u00a04a), the melt flow gradually diminished to zero at roughly the midpoint of MP and then re-entered the MP before the solidification front reached it. In contrast, at the welding speed of 1.42\u2009m/s (Fig.\u00a04b), the melt flow remained directed backward and could not return to the MP, so it eventually traversed the entire MP length and be deflected upward to form a hump (as highlighted in the blue streamlines).\n\nThe net cross-sectional volumetric flow rate (Fig.\u00a04c), defined as the flow rates across the y-z cross-sections (\u2009+x is the welding direction), accounts for the effects of melt velocity and melt volume. It provides a quantitative assessment of backward melt accumulation toward the MP tail. A negative value indicates that the net volumetric flow rate is in the -x direction (backward). At the welding speed of 0.33\u2009m/s, the flow rate was relatively low (~\u22125.5\u00d710\u22127m3s\u22121)\u00a0at the front of the MP and nearly zero at the MP tail. At the welding speed of 1.42\u2009m/s, the flow rate became more negative (~\u22122.4\u00d710\u22126m3s\u22121) at the front of the MP and remained negative across the entire MP, indicating greater melt accumulation toward the MP tail and leading to humping. In addition, the gradient of the volumetric flow rate decreased from the front to the rear of the MP, suggesting that the elongated MP tail could not effectively slow down the backward melt flow due to the shallow inclination angle of the MP boundary (Fig.\u00a04b). The flow rates were then extracted at the start and near the end of humping to understand its periodic nature. At the start (blue curve in Fig.\u00a04c), the entire MP exhibited a significant net backward volumetric all the way to the MP tail, leading to humping. Conversely, near the end of humping (orange curve in Fig.\u00a04c), the MP length extended by approximately 200\u2009\u00b5m and possessed a near-zero net volumetric flow rate (enlarged view in Fig.\u00a04c). It created additional volume in the MP tail to accommodate the melt, resulting in a gradual stop of humping. This periodic humping, characterized by alternating melt accumulation and MP length extension, is consistent with the conservation of volume8 and the experimental observations in this study.\n\nBased on the analyses of in situ high-speed synchrotron X-ray imaging and CFD simulation, the formation mechanisms of humping are\u00a0 illustrated in Fig.\u00a04d. First, as the laser welding speed increases, the depth of the keyhole rear wall becomes deeper beneath the surface of the base material, reducing the barrier to backward melt flow. Second, the MP length extends with the laser welding speed, failing to decelerate the melt flow effectively\u00a0because of the shallower inclination angle and becoming more prone to Rayleigh instability. Third, the maximum backward melt velocity also rises significantly, increasing the volumetric flow toward the MP tail. These factors enhance backward melt velocity and reduce the flow barrier, leading to greater melt accumulation at the MP tail and triggering humping. When humping occurs, excess melt is released by slightly extending the MP length, which causes the humping to cease temporarily. Therefore, the phenomenon occurs periodically rather than continuously.\n\nThe prediction of humping in laser welding usually relies on simulation. Alternatively, a dimensionless index can be developed to calculate the humping tendency based solely on process parameters and material properties. This approach is simpler and more efficient since it eliminates the need for complex fluid dynamics calculations. Meng et al.41 have introduced the dimensionless humping index for the arc welding process, but its application to laser welding is limited due to inherent disparities between the two processes. In addition, laser welding lacks certain MP characteristics present in arc welding, such as temperature increase and gouging length.\n\nThe Buckingham \u03c0 theorem was first employed to determine the number of dimensionless groups, where a system with m variables and n fundamental units can be described by (m\u2212n) dimensionless groups. In this study, there are 6 variables, i.e., maximum melt velocity (umax,m\u22c5s\u22121), MP length (l,m), density (\u03c1,kg\u22c5m\u22123), specific heat (cp,m2\u22c5s\u22122\u22c5K\u22121), thermal conductivity (k,kg\u22c5m\u22c5s\u22123\u22c5K\u22121), and surface tension coefficient (\u03b3,kg\u22c5s\u22122). It is noted that the depth of the keyhole rear wall was not included as a variable, because it is correlated with the MP length (Fig.\u00a02d, e). The above 6 variables involve 4 fundamental units, which are mass (kg), length (m), time (s), and temperature (K). Therefore, the theorem suggests two dimensionless groups representing the system as follows.\n\n\u03c01 is the Peclet number (Pe)42, which indicates the relative importance of convection and conduction in heat transfer within the melt pool. \u03c02 is the Weber number, representing the free surface deformation tendency41. Note that all materials properties were extracted at the melting (liquidus) temperature from the thermophysical databases11,38,39 and were listed in Supplementary Table\u00a02, given that the temperature at the end of MP is close to the liquidus temperature.\n\numax and l can be calculated with only the process parameters (r: spot radius, uw: laser welding speed, P: power) and material properties (\u03c1, cp, k, and \u03b3). umax was calculated by Eq.\u00a01, with the calculation results shown in Fig.\u00a02f. For the MP dimensions (depth and length), the scaling law was adopted. Studies have reported that the MP depth can be scaled with P/uwr22,43 for the same material. The scaling of MP length (l) has not been previously reported due to its requirement of in situ observation. In this work, linear fitting was performed with experimental data and literature data8,44, as shown in Fig.\u00a05a, following the equation:\n\na Scaling law between the MP length and P/uw14r34 (R2\u2009=\u20090.9856) performed using the experimental results from the current study and other literatures8,44. b Calculated dimensionless humping index \u03c0h. The onset threshold occurs at approximately 10,000 across different published data8,9,22. c, d Process windows with (light yellow) and without humping (light gray) separated by the 10,000-isoline of \u03c0h and penetration depth (\u03bcm) calculated by scaling law22,43 at the spot radii (r) of 50 and 25\u2009\u00b5m, respectively.\n\nThis relationship has the highest R2 value (0.9856) among all exponent combinations for each process parameter. The MP length was determined as the average minimum instantaneous MP length in each humping cycle (Fig.\u00a03a). It is noted that the scaling law for the MP length is slightly different from the case for the MP depth (scaled with P/uwr).\n\nMultiple combinations of \u03c01 and \u03c02 can be formed; however, a simple product of them can form the dimensionless humping index (\u03c0h).\n\nThe values of \u03c1, cp, k, and \u03b3 were obtained at Tm from the thermophysical databases11,38,39, as listed in Supplementary Table\u00a02. Both umax and l were obtained from Eqs.\u00a01 and 4, respectively, rather than from the simulation results. The calculated results using the data from the current study and other references8,9,22 are shown in Fig.\u00a05b. This dimensionless humping index captures the onset threshold at approximately 10,000 of \u03c0h and describes the humping tendency. It is also in good agreement with the study from Kawahito et al.10, where among the process parameters (P,uw, and r), the humping tendency increases with a higher P, a greater uw, and a finer r when the other two parameters are fixed. It also aligns well with the conclusions from the in situ high-speed synchrotron X-ray imaging and CFD simulation, where a longer l or a greater umax increases the humping tendency.\n\nIn addition, the model allows for the prediction of the critical laser welding speed and the corresponding power where humping begins to occur. Figure\u00a05c, d shows the isolines of several penetration depths and the 10,000-isoline of \u03c0h calculated at r\u2009=\u200950 and 25\u2009\u00b5m, respectively. The intersection points between each penetration depth and the 10,000-isoline of \u03c0h represent the critical laser welding speeds and power. The model suggests several methods to enhance the critical laser welding speed for the same material system by reducing \u03c0h. The first approach is to employ a finer r, which requires a lower P for the same penetration depth. As a result, both umax and l decrease, as indicated by Eqs.\u00a01 and 4, leading to a reduced \u03c0h and an increased critical laser welding speed. By comparing Fig.\u00a05c with Fig.\u00a05d, it is evident that the critical welding speed increases from 0.72 to 0.87\u2009m/s at a penetration depth of 150\u2009\u00b5m when r is reduced from 50 to 25\u2009\u00b5m. In this study, the experimental results for spot radii of 21.5 and 13\u2009\u00b5m are presented in Supplementary Fig.\u00a05. The second approach is to reduce the thickness of the base material, which lowers the required P for full penetration and consequently decreases \u03c0h because of the reduced umax and l (Eqs.\u00a01 and 4). As shown in Fig.\u00a05d, the critical welding speed increases from 0.68 to 0.87\u2009m/s at r=25\u2009\u00b5m when the thickness is reduced from 300 to 150\u2009\u00b5m. The third approach is to employ an adjustable ring mode laser45,46, where a portion of the power is distributed to the outer ring. It allows the power of the central beam to be decreased while maintaining the same penetration depth, leading to a shorter l (Eq.\u00a04), a reduced \u03c0h, and therefore a greater critical laser welding speed.\n\nHumping is an issue that occurs not only in laser welding but also in arc welding and additive manufacturing at a high-moving speed of heat source. This study identified the formation mechanisms of humping by analyzing keyhole and MP morphologies and melt flow characteristics at various welding speeds via in situ high-speed synchrotron X-ray imaging and CFD simulation. The mechanisms are concluded as follows. First, a high welding speed leads to a shorter keyhole rear wall, therefore reducing the barrier to the backward melt flow. Second, a prolonged MP tail at a high welding speed is not only ineffective in decelerating the backward melt flow owing to the shallower inclination angle but also susceptible to Rayleigh\u2019s instability. Third, the maximum backward melt velocity increases substantially with the welding speed, leading to a significant increase in the backward volumetric flow rate of melt. These factors collectively enhance the melt accumulation towards the MP tail, ultimately causing humping. In addition, humping occurs periodically rather than continuously because of the alternating accumulation of excessive melt at the start of humping and the extension of the MP length when humping is near its end.\n\nLastly, a dimensionless humping index (\u03c0h) was developed for the laser welding process using Buckingham\u2019s \u03c0 theorem. This index, based solely on process parameters and material properties, quantitatively depicts humping formation tendency and serves as a crucial tool for optimizing laser welding by predicting critical laser welding speed and power. It also suggests several approaches to enhance the critical welding speed through reducing \u03c0h, including reducing the spot radius, decreasing the thickness of base material, and utilizing an adjustable ring mode laser.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53888-w/MediaObjects/41467_2024_53888_Fig5_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "In situ high-speed synchrotron X-ray imaging of laser welding was conducted at beamline 32-ID-B at ANL using a ytterbium single-mode continuous wave fiber laser source (YLR-500-AC) with a spot size of 43\u2009\u00b5m, a wavelength of 1070\u2009\u00b1\u200910\u2009nm, a maximum power of 520\u2009W, and an X\u2013Y galvo-scanner (intelliSCANde 30). The experimental setup is presented in Supplementary Fig.\u00a01a, b. The laser welding configuration involved overlapping two vertically stacked stainless steel foils welded from the top. An Al alloy fixture was designed to clamp the foils and prevent movement, featuring a 300\u2009\u00b5m-wide opening for the laser (Supplementary Fig.\u00a01b). The Al alloy was chosen for its lower X-ray attenuation compared to steel47. A pseudo pink X-ray beam, with the 1st harmonic energy at 24.7\u2009keV, was generated using an 18\u2009mm undulator and directed through the sample from the side during the laser welding process. The propagated X-ray signal was recorded with a high-speed camera (Photron FastCam SA\u2013Z) at a frame rate of 50,000 (temporal resolution\u2009=\u200920\u2009\u00b5s). The tested material was an 85\u2009\u00b5m-thick 439 stainless steel (Cleveland Steel, USA). The specimens were cut by electrical discharge machining into the dimension in Supplementary Fig.\u00a01c, with a gauge width of only 500\u2009\u00b5m due to the high extent of attenuation of X-ray on stainless steels.\n\nLaser welding at EWI was performed using a single-mode continuous wave fiber laser (nLIGHT AFX-1000) with the same spot diameter (43\u2009\u00b5m) and wavelength (1070\u2009\u00b1\u200910\u2009nm) as that used at ANL. The laser has a maximum power of 550\u2009W and is equipped with an X\u2013Y galvo-scanner (Scanlab HurryScan20).\n\nThe in situ X-ray images were processed by using ImageJ. Two different approaches to image processing were employed for the following purposes. In the first approach, each image was subtracted by the average of the initial 50 images to reveal the change of contrast during the welding process compared to the initial condition. For the second approach, each image was successively subtracted by the preceding time frame, revealing the contrast variations between two consecutive time frames, and providing a clearer visualization of the MP lengths on the top surfaces. Following both methods, the brightness and contrast of images were modified manually to enhance the image contrast.\n\nThe boundaries of the keyhole and MP were first manually delineated, followed by interpolation using MATLAB to achieve smooth boundaries. The dividing point between the keyhole and MP was defined as the initial point on the keyhole rear wall with a slope of \u22121. The z coordinate at this point was defined as the depth of the keyhole rear wall. Then, the region between this point and the one 350\u2009\u00b5m behind the keyhole front wall was defined as the front part of MP. This curve was then linearly fitted, with its root mean square error defined as the MP waviness.\n\nThe top surfaces of laser weld seams were characterized by optical micrography and profilometry using Keyence VX\u2013X3100. The laser confocal method was employed to measure the surface topography of the weld seams. To calibrate the weld seam into a horizontal surface, the measurements were adjusted using quadratic correction based on the positional data from the base materials adjacent to the weld seam.\n\nThe simulation of the laser welding process was performed using CFD modeling with Flow-3D under the following assumptions: (1) The melt flow was laminar, incompressible, and Newtonian. (2) The plasma inside the keyhole and the spattering were not considered. (3) No heat or mass transfer occurred at any faces of the simulation domain. The dimensions of the simulation domain are depicted in Supplementary Fig.\u00a03a, consisting of uniformly divided cubic grids with an edge length of 10\u2009\u00b5m. The domain lengths were set to 3 and 8\u2009mm for welding speeds of 0.33 and 1.42\u2009m/s, resulting in a total number of 384,000 and 1,536,000 cells, respectively. Laser welding was conducted in the +x direction, with lengths of 2 and 7\u2009mm corresponding to the respective welding speeds. The laser beam, with a spot diameter of 43\u2009\u00b5m, was considered a surface heat source, which is a method commonly used in other studies9,18,38,48,49,50 to simulate laser-material interaction. In this heat source, the heat flux was divided into each mesh, following the Gaussian distribution shown in Supplementary Fig.\u00a03b. The laser beam was reflected and absorbed based on the material absorptivity48.\n\nThe coupled governing equations in the CFD modeling included conservation of momentum, energy, and continuity48. Laser absorption, thermal conduction, surface radiation, and convection were incorporated for energy calculation. The temperature-dependent laser absorptivity data were acquired from the reference51, and the temperature-dependent physical properties were obtained from references11,38,39, as shown in Supplementary Fig.\u00a04. The recoil pressure was temperature-dependent, following Precoil=aexp[b(1\u2212TbT)] with a\u2009=\u20096000\u2009Pa and b\u2009=\u2009652. The primary forces considered in the model included recoil pressure, surface tension, viscosity, buoyancy, and gravity. Additional details regarding the force setup can be found in ref. 52.\n\nThe validation of the CFD simulation models was performed based on the MP dimensions (length, width, depth) and the humping characteristics (linear number density and average height of humps), as listed in Supplementary Table\u00a03. The differences of each key metric between experiments and simulations were all below 10%. Additionally, the umax showed a good agreement between the analytical calculation (Eq.\u00a01) and the CFD simulations, as listed in Supplementary Table\u00a04.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Change history", + "section_text": "A Correction to this paper has been published: https://doi.org/10.1038/s41467-025-57927-y", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Katayama, S. Handbook of Laser Welding Technologies (Elsevier, 2013).\n\nHaddad, E. et al. Laser micro welding with fiber lasers for battery and fuel cell based electromobility. J. Adv. Join. 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We also acknowledge Zixuan Wan\u2019s support for the discussions on CFD simulation. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, USA\n\nZen-Hao Lai\u00a0&\u00a0Jingjing Li\n\nGeneral Motors LLC, Warren, MI, USA\n\nSiguang Xu\n\nX-ray Science Division, Argonne National Laboratory, Lemont, IL, USA\n\nSamuel J. Clark\u00a0&\u00a0Kamel Fezzaa\n\nDepartment of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, USA\n\nJingjing Li\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.X. and J.L. conceived and supervised the project. S.J.C., K.F., and Z.L. led and conducted the experiments. Z.L. performed image analyses (with the help from S.J.C., K.F., J.L., and S.X.) and CFD simulation. Z.L. and J.L. wrote the manuscript.\n\nCorrespondence to\n Jingjing Li.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Van Anh Nguyen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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"Rapid sintering of high-efficiency phosphor-in-glass films for laser-driven light source", + "pre_title": "Rapid Sintering of High-efficiency Phosphor-in-Glass Films for Laser-driven Light Source", + "journal": "Nature Communications", + "published": "21 March 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_MOESM3_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_MOESM4_ESM.mp4" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_MOESM5_ESM.mp4" + }, + { + "label": "Supplementary Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_MOESM6_ESM.mp4" + }, + { + "label": "Supplementary Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_MOESM7_ESM.mp4" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_MOESM8_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_MOESM9_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_MOESM10_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-58099-5#Sec19" + ], + "code": [], + "subject": [ + "Lasers, LEDs and light sources", + "Materials chemistry", + "Optical materials and structures" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5413090/v1.pdf?c=1742641739000", + "research_square_link": "https://www.researchsquare.com//article/rs-5413090/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58099-5.pdf", + "preprint_posted": "25 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The advanced high-power-density laser-driven light source calls for all-inorganic phosphor-in-glass film (PiGF) composite as color converter to deal with the issues of durability and color tunability. One challenge remains for the PiGF is the thermal erosion and degradation of phosphor, as harsh condition or long duration time is required to densify the film for conventional sintering. With this regard, herein we develop a rapid thermal annealing (RTA) technique to prepare PiGF, attaining a high densification extent (porosity\u2009<\u20093%) by infrared annealing within seconds. A trivial interfacial reaction is detected, leading to almost intact phosphor particles and thus restrained luminous loss. Exemplified by the red-emitting Sr0.8Ca0.2AlSiN3:Eu2+ (SCASN:Eu), a record 91.2% internal quantum efficiency (IQE) in the RTA-processed PiGF and a remarkable luminous flux (LF) of 2379 lm and luminous efficacy (LE) of 140 lm/W can be achieved in the PiGF-based phosphor wheel. RTA reduces energy consumption and enables high-throughput sample screening. This preparation strategy also features material universality and design flexibility, hopefully exploring new opto-functional materials and applications.Physical sciences/Materials science/Materials for optics/Lasers, LEDs and light sourcesPhysical sciences/Optics and photonics/Optical materials and structures", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupportingInformation202411052.pdfsupporting informationSupplementaryMovie1.mp4Movie 1SupplementaryMovie2.mp4Movie 2SupplementaryMovie3.mp4Movie 3SupplementaryMovie4.mp4Movie 4", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The development of advanced high-power-density laser-driven light source requires durable and color-tunable inorganic phosphor-in-glass film composites as color converter. One challenge remains for the phosphor-in-glass film is the thermal erosion and degradation of phosphor, as harsh condition or long duration time is required to densify the film for conventional sintering. Here we develop a rapid thermal annealing technique that achieves high film densification (porosity\u2009<\u20093%) within seconds utilizing high-power (>10\u2009kW) infrared irradiation. As demonstrated by high-resolution electron microscopy observation, a trivial interfacial reaction occurs, leading to almost intact phosphor particles and thus restrained luminous loss. For instance, the red-emitting Sr0.8Ca0.2AlSiN3:Eu2+ exhibits a record internal quantum efficiency of 91.2% in the processed film and achieves a luminous flux of 2379\u2009lm and efficacy of 140\u2009lm\u2009W\u22121 after fabricating a phosphor wheel. This method reduces energy consumption, enables high-throughput screening, and offers material universality and design flexibility, paving the way for new opto-functional materials and applications.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "As the domain of lighting technology strides towards high brightness and environmental consciousness, laser-driven light source has garnered significant attention for its high-power density and wall-plug efficiency1. In the construction of laser lighting systems, nitride and oxynitride phosphors play a pivotal role as color converters, with importance equally standing with the classical garnet phosphors2. The dense crystal structure confers enhanced structural rigidity and thus thermal stability upon these compounds, while the rich and diverse local coordination environment affords greater control over the energy level positions of the doped rare earth luminescence centers. These merits are essential for the overall performance of the laser lighting systems, beneficial to attaining not only high lumen output, but also high color rendering index (CRI), optimal correlated color temperature (CCT) and wide color gamut3.\n\nLaser lighting calls for new phosphor bulk material form with high performance. Traditional phosphor encapsulation, such as those utilizing organic silicone resins as the encapsulation medium to produce phosphor-in-silicone (PiS) composite, has encountered a performance bottleneck. PiS materials are prone to aging at high temperatures, so their thermal stability and durability are often challenged4,5. To address this issue, researchers have begun to explore all-inorganic color converters, such as fluorescent single crystals6,7, fluorescent ceramics8,9,10,11, phosphor-in-ceramic (PiC)12,13, phosphor-in-glass (PiG)4,14,15,16,17, and phosphor-in-glass films (PiGF)18,19,20,21,22,23,24,25,26. Unlike garnet oxides, nitrides and oxynitrides are unable to grow into single crystals due to their anisotropy; moreover, their low diffusion coefficients make it challenging to fully densify them into ceramics with acceptable optical properties using traditional methods3. Compounding and encapsulating them with amorphous glass, which is inherently flowable when softened or melted, would be an advisable choice. The PiG composite displays unparalleled benefits in terms of cost-effectiveness, easy fabrication, and design versatility, when compared to other potential alternatives. Particularly, the sintering of PiG in the form of thick film on a substrate with high thermal conductivity will significantly alleviate luminescence saturation phenomenon under high power densities of lasers. To date, there have been great endeavors paid with the aim of developing high-performance PiG and PiGF. Nevertheless, the conventional preparation process always requires high-temperature and long-duration sintering, which consumes a considerable amount of energy and inevitably brings about phosphor degradation, chemical erosion, and especially oxidation in the case of nitride and oxynitride phosphors15,27. In this regard, the development of a novel sintering technology to fabricate PiG/PiGF in rapid manner is a pressing necessity (Supplementary Note\u00a01), yet it remains a formidable challenge due to the difficulty of material densification. In addition, the up-to-date fast sintering technologies are usually hard to popularize, since the cost and effectiveness are compromised8,28,29,30,31,32,33. Recently, Xia et al. smartly develop a rapid synthesis technique to fabricate phosphor-glass composites in seconds based on particle self-stabilization, unfortunately just like many other fast sintering strategies, the material system is limited to garnet oxides34.\n\nIn this work, we innovatively introduced rapid thermal annealing (RTA), a fast sintering technique previously used in semiconductor manufacturing processes, to prepare PiGF. We demonstrated that RTA technology, which is characterized by extremely high heating rate of up to 55\u2009\u00b0C\u2009s\u22121, is able to achieve densification of PiGF within seconds, significantly enhancing production efficiency and reducing energy consumption. Taken the red-emitting Sr0.8Ca0.2AlSiN3:Eu2+ (SCASN:Eu) nitride phosphor as an example, the fabricated SCASN:Eu PiGF via RTA exhibits restrained interfacial reaction between phosphor particles and glass matrix, resulting in an impressive internal quantum efficiency (IQE) of up to 91.2% under 455\u2009nm blue light excitation. This value exceeds those reported for all-inorganic red phosphor bulk materials in the literature. Such a high IQE in red-emissive color converter endows lighting sources based on PiGF with enhanced brightness and comfortable visual experience. RTA technology can be extended to various nitride and oxynitride PiGFs, including, but not limited to, La3Si6N11:Ce (LSN:Ce), BaSi2O2N2:Eu2+ (BaSiON:Eu), CaAlSiN3:Eu2+ (CASN:Eu), \u03b2-SiAlON:Eu2+ (\u03b2-SiAlON:Eu), and \u03b1-SiAlON:Eu2+ (\u03b1-SiAlON:Eu), as well as other substrate materials and opto-functional polycrystalline. Such scalability endows the RTA with potential application value across different phosphor systems and various application scenarios, demonstrating the great design flexibility and universality of this technology. More importantly, the uniform temperature field of the RTA technology enables high-throughput screening of materials that can rapidly validate theoretical predictions, hopefully accelerating the discovery of a variety of new PiGF materials.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Figure\u00a01a illustrates the PiGF fabrication process via the RTA method. Initially, nitride phosphors, glass powder, and an organic colloid composed of terpineol and ethyl cellulose are homogeneously mixed in a predetermined ratio (Mixing). Subsequently, the uniformly mixed slurry is applied evenly onto a substrate with high thermal conductivity using an automatic coating machine (Coating). Thereafter, the coated precursor film is subjected to a drying process in an oven at 150\u2009\u00b0C to remove the organic carrier (Drying). Ultimately, the dried precursor film is sintered within the RTA device to obtain the PiGF. Figure\u00a01b and the Supplementary Fig.\u00a01 provide an overview of how the RTA works and a digital photograph. As depicted in Fig.\u00a01b, the chamber of the RTA unit contains 13 kilowatt-grade tungsten halogen lamps, thermocouple, quartz holder, and other components. The heating lamps are symmetrically distributed across the upper and lower surfaces of the chamber, radiating energy onto the surface of PiGF sample and monocrystalline silicon wafer positioned on the quartz holder. The silicon wafer, with a bandgap energy of 1.12\u2009eV, strongly absorbs the radiation energy (\u22651.12\u2009eV) with a black-body radiation spectrum from lamps mainly distributed in the infrared region, and then conducts it to the supported substrate with high thermal conductivity. The PiGF material completes the sintering process in approximately 10\u2009s under the synergistic effect of the halogen lamps\u2019 thermal radiation and the silicon wafer\u2019s thermal conduction. The metal chamber is cooled by a circulating water system, while the lamps and quartz chamber are cooled by compressed air.\n\na Schematic illustration of the synthesis process of phosphor-in-glass film (PiGF) via the rapid thermal sintering (RTA) technology. b Internal structure of the RTA device. c 3D model for finite element simulation; 5\u2009\u00d7\u20095\u2009samples are placed on the monocrystalline Si wafer and subjected to irradiation from two planar radiation sources. d Experimental and simulated heating curves for the RTA sintering process; the experimental data are collected in every 0.5\u2009s. e Temperature distribution on the wafer surface simulated using finite element analysis at the target temperature of 550\u2009\u00b0C. Source data are provided as a Source Data file.\n\nTo delineate the distinctions between RTA technology and traditional muffle furnace sintering, the heating rate plots were recorded as shown in Supplementary Fig.\u00a02. In contrast to traditional sintering methods, RTA sintering can rapidly achieve the target temperature from room temperature at a heating rate of ~55\u2009\u00b0C\u2009s\u22121 (Supplementary Movie\u00a01). Such a short processing duration not only signifies an efficient synthesis methodology but also suggests a reduced energy consumption, amounting to merely 4.3% of that required by conventional sintering techniques (the inset of Supplementary Fig.\u00a02 and Supplementary Note 2). The finite element method (please find the details in the section of \u201cMethods\u201d) was used to simulate the heating curve and the temperature distribution on the surface of samples within the RTA equipment as it was heated to 550\u2009\u00b0C (Fig.\u00a01c). The simulation results demonstrate the temperature rise proceeds in fast speed (Fig.\u00a01d and Supplementary Movie\u00a02), thanking to the ultra-high temperature of lighting source up to >3000\u2009K. Unlike the real situation, the simulation does not take into account the electrical feedback, which should be responsible for the differences during the heating and soaking stages. A large discrepancy is observed in the cooling stage, majorly attributed to the exclusion of the metal enclosure and the water- and air-cooling systems in the simulation domain. Importantly, the temperature distribution across the silicon wafer is uniform, with a temperature difference of no more than 3\u2009\u00b0C between the center and the edge of wafer, and a difference of no more than 1\u2009\u00b0C within the area of wafer where samples are placed (Fig.\u00a01e). This confirms that multiple samples experience almost identical sintering conditions in the RTA process, thereby rendering this process suitable for large-scale manufacturing and high-throughput screening.\n\nRTA technology enables the swift and reliable synthesis of PiGF, facilitating the rapid validation of the compatibility between various glass matrices and a range of phosphors, thereby accelerating the screening process for high-performance PiGF materials (Fig.\u00a02a). As a proof-of-concept experiment, we utilized a model system comprising five common commercial nitride and oxynitride phosphors (SCASN:Eu, CASN:Eu, \u03b2-SiAlON:Eu, BaSi2O2N2:Eu, and LSN:Ce) and five commercial glass powders (GC825, GF45A, FD233, FD238, and NL-4) to demonstrate the rapid screening capability afforded by the RTA technology. The comprehensive details of the commercial glasses we used, including their composition, glass transition temperature (Tg), softening temperature (Ts), melting temperature (Tm), packed density, average particle size (D50) and refractive index, are summarized in Supplementary Table\u00a01. The related thermophysical parameters are derived from differential thermal analysis (DTA), as presented in Supplementary Fig.\u00a03. As depicted in Fig.\u00a02b, we employed a 5\u2009\u00d7\u20095 matrix setup to rapidly sinter 25 PiGFs on sapphire substrates at 550\u2009\u00b0C for 10\u2009s in one time. The quantum yield test results indicate that different phosphors have distinct optimal glass powders for compatibility under this sintering condition. The external quantum efficiency (EQE) reveals that the most compatible glass matrix for SCASN:Eu and CASN:Eu is NL-4, while for LSN:Ce and BaSi2O2N2:Eu, it is FD238, and for \u03b2-SiAlON:Eu, it is GF45A. The optimal combination of phosphor and glass will get varied under different sintering temperatures and durations (Supplementary Figs.\u00a04, 5). To swiftly ascertain the optimal sintering temperature for various glass matrices, we selected SCASN:Eu as the subject of our study and prepared a series of SCASN:Eu-PiGFs sintered for 10\u2009s at different temperatures in combination with five distinct glass matrices. This experiment was repeated four times and the total time spent was less than 10\u2009min. The EQEs of these samples are summarized in Fig.\u00a02c, showing that the combination of SCASN:Eu with NL-4 exhibits the highest EQE when sintered at 550 \u00b0C. One can also find that the optimal sintering temperature varies for different glass matrices.\n\na Schematic diagram of the process of high-throughput synthesis and screening of phosphor-in-glass films (PiGFs). Ai: phosphor type (i\u2009=\u20091\u2009~\u2009m); Bj: glass type (j\u2009=\u20091\u2009~\u2009n). b The table shows the external quantum efficiency (EQE) values of PiGFs prepared by rapid thermal annealing (RTA) with different combinations of phosphors and glasses. The insets show digital photographs of a 5\u2009\u00d7\u20095 matrix of 25 PiGFs before (top) and after (bottom) RTA sintering in daylight. c The table shows the EQE values of PiGFs prepared by RTA at different temperatures with different combinations of SCASN:Eu phosphors and glasses. d Digital photograph of the sintered glass films prepared by RTA in daylight. Digital photographs of SCASN:Eu PiGFs prepared by RTA (e) in daylight and (f) under UV light. Scale bar\u2009=\u20091\u2009cm. g Transmittance at 800\u2009nm for the glass films prepared by RTA. h Internal quantum efficiency (IQE) and (i) EQE values of SCASN:Eu PiGFs. Source data are provided as a Source Data file.\n\nTo achieve the red SCASN:Eu PiGF with the utmost quantum efficiency, a meticulous optimization of the RTA process was conducted by using the screened NL-4 glass as matrix. The precursor film samples were sintered under different temperatures of 500\u2009\u00b0C, 550\u2009\u00b0C, 600\u2009\u00b0C, 650\u2009\u00b0C, and 700\u2009\u00b0C, each with a fixed heating duration of 10\u2009s and a varied holding time of 1\u2009s, 5\u2009s, 10\u2009s, 20\u2009s, and 30\u2009s, respectively. In Fig.\u00a02d\u2013f, we show the collection of glass films and SCASN:Eu PiGF combined with sapphire substrate. The corresponding heating curves of RTA process are presented in Supplementary Fig.\u00a06. The transmittance of the glass film, the internal quantum efficiency (IQE) and the EQE of the PiGF were employed as criteria for evaluation. The optimal RTA sintering process at 550\u2009\u00b0C for 10\u2009s yields the highest transmittance for the glass film (Fig.\u00a02g), and simultaneously achieving the highest IQE and EQE for the PiGF (Fig.\u00a02h, i). Too lower the temperature or too shorter the holding time cannot densify the glass film; while in the opposite, the thermal erosion effect takes place that degrades the luminescent performance. To be mentioned, a decrease in the transmittance of the glass film is found when overheating. This phenomenon can be attributed to the volatile constituents within the glass film, which are prone to evaporate during the sintering process and then to leave behind voids (Supplementary Fig.\u00a07). The weight loss of glass supports the hypothesis of substance volatilization (Supplementary Table\u00a02). As a reference, employing the same analytical approach, we have determined that the most efficacious conditions for traditional sintering to be at 650\u2009\u00b0C for 15\u2009min, as shown in Supplementary Fig.\u00a08. It is evident that the traditional sintering necessitates higher furnace temperatures and prolonged sintering times (typically tens of minutes, or even hours18,35,36) to achieve a densification level comparable to the RTA-processed samples. RTA technique in the presence of the intense infrared radiation provided by high-power halogen tungsten lamps facilitates the rapid densification of glass films at lower temperatures.\n\nFurthermore, RTA technology shows great universality and design flexibility in the synthesis of all types of PiGFs with diverse luminescence properties combined with different substrates. The above has shown the nitride and oxynitride PiGFs sintered on sapphire plate with multi-color emissive properties. Instead of sapphire, SCASN-PiGFs can also be sintered on substrates of SiC, K9 glass, and AlN ceramic with high luminescent performance (Supplementary Fig.\u00a09). Moreover, the phosphor system can be extended to oxides, sulphides and even halides (Supplementary Fig.\u00a010). As for the luminescent performance, we took the RTA processed Y3Al5O12:Ce3+ (YAG:Ce) PiGF as an example, which yields IQE reaching as high as 95.5% and high brightness much superior to the commercial YAG:Ce3+ PiG products (Supplementary Fig.\u00a011). Moreover, the RTA-processed K2SiF6:Mn4+ PiGF and Gd2O2S:Tb3+ PiGF, with the embedded phosphors normally susceptible to thermal degradation, have an acceptable IQE of 72.6% and 58.2%, respectively (Supplementary Figs.\u00a012, 13). Beyond the fluorescent PiGFs, the persistent luminescence PiGFs can be also fabricated via RTA (Supplementary Fig.\u00a014), indicating that RTA-processed materials have broad application scenarios in the other photonic fields such as night vision, anti-counterfeiting, optical storage, etc.\n\nWe first studied the microstructural evolution of glass precursor during the RTA process. Scanning electron microscopy (SEM) characterization was performed on the glass film samples treated at 550\u2009\u00b0C for 1\u2009s, 3\u2009s, 5\u2009s, 7\u2009s, and 10\u2009s, respectively (Fig.\u00a03a). The silicate glass powders used have irregular shape with a D50 particle size of 6 \u03bcm after grinding and sieving (Supplementary Fig.\u00a015). At the onset of 1\u2009s RTA process, the glass powder particles are distinctly separated without apparent adhesion (Fig.\u00a03a, i). As the sintering time progresses, the glass frit particles begin to adhere and coalesce with adjacent particles, and simultaneously some large pores are formed by consuming small pores (Fig.\u00a03a, ii\u2013iv). In the final stage of the sintering process, one can see the large pores are expelled out and a uniformly dense glass film devoid of obvious pores or cracks is obtained (Fig.\u00a03a, v). Micro computed tomography (Micro-CT) test images of the glass film sintered for 10\u2009s via RTA are shown in Supplementary Fig.\u00a016. It can be observed that the pore size of glass film follows a Gamma distribution with an average value of 4.58\u2009\u03bcm, and the porosity is determined to be only 0.52% based on three-dimensional reconstruction analysis (inset of Supplementary Fig.\u00a016), confirming the rapid densification capability of the RTA process for the glass films. X-ray diffraction (XRD) patterns indicate that the sintered glass film possesses an amorphous structure (Supplementary Fig.\u00a017).\n\na SEM images of glass films at different sintering times of (i) 1\u2009s, (ii) 3\u2009s, (iii) 5\u2009s, (iv) 7\u2009s and (v) 10\u2009s, respectively. Scale bar\u2009=\u20095\u2009\u03bcm. b Micro-CT 3D images of the phosphor-in-glass film (PiGF) sintered for 10\u2009s; the inset shows void size distribution of glass film and the quantitative analysis of porosity (observation volume: 1000\u2009\u00d7\u20091000\u2009\u00d7\u200986\u2009\u03bcm3; analysis limit: 500\u2009nm). Scale bar\u2009=\u2009200\u2009\u03bcm. c SEM image on the surface of SCASN:Eu phosphor-in-glass film (PiGF) sintered at 10\u2009s. SCASN:Eu and glass are easily distinguished. Scale bar\u2009=\u200950\u2009\u03bcm. d SEM image on the cross section of SCASN:Eu PiGF sintered at 10\u2009s. The interface between PiGF and sapphire is clear. Thicknesses of film: 86\u2009\u03bcm. Scale bar\u2009=\u200950\u2009\u03bcm. e XRD patterns of the phosphor powders and the corresponding rapid sintering annealing (RTA) processed PiGF (SCASN-PiGF-RTA). f TEM image of one micro-plane of PiGF sectioned by FIB. Further magnification of the part of the red circle is observed. Scale bar\u2009=\u20092\u2009\u03bcm. g Elemental distribution of phosphor particles and glass matrix near the interface. Scale bar\u2009=\u2009100\u2009nm. h HRTEM image taken near the interface, right panels show FFT images of the corresponding regions. Scale bar\u2009=\u200910\u2009nm. i Magnified HRTEM image at the interface. A vague interdiffusion region (white dashed line) with a thickness of 1\u20132\u2009nm is observed, in which some high-density ionic vacancies (red dashed circle) can be clearly observed. Scale bar\u2009=\u20092\u2009nm. Source data are provided as a Source Data file.\n\nSCASN:Eu phosphor powders exhibit a rod-like shape with a D50 of 11\u2009\u03bcm (Supplementary Fig.\u00a018). SEM images of the PiGF surface reveal that the dispersed SCASN:Eu2+ phosphor particles have no obvious change in morphology and size (Fig.\u00a03c and Supplementary Fig.\u00a019), suggesting insignificant interfacial reactions. The optical microscope examination, X-ray fluorescence (XRF) testing and confocal laser scanning microscope (CLSM) observation on SCASN:Eu PiGF demonstrate the uniform distribution of phosphor particles throughout the glass matrix (Supplementary Movie\u00a03, Supplementary Figs.\u00a020, 21). To analyze porosity of the PiGF, the micro-CT test is also conducted (Fig.\u00a03b). The results indicate a porosity of 2.9%, which is higher than that of glass film sample, but still remarkably dense. It is inferred that the densification of PiGF undergoes glass softening, formation of sintering necks at contact points, wetting, and gradual expulsion of pores, as schematically illustrated in Supplementary Fig.\u00a022. For PiGF, heterogeneous infiltration and wrapping are more difficult and therefore pore expulsion is not as efficient as the case of glass film. In a typical experiment, the measured thickness of the PiGF is 86\u2009\u03bcm (Fig.\u00a03d). XRD patterns of the RTA-processed PiGF samples demonstrate that the crystalline diffraction peaks, consistent with the standard data (PDF#97-016-3204), originate from the embedded SCASN:Eu (Fig.\u00a03e).\n\nFurthermore, a micro-plane containing SCASN:Eu grain and glass matrix was sectioned using focused ion beam (FIB) for transmission electron microscope (TEM) analysis (Fig.\u00a03f). The high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) image in Fig.\u00a03g shows a clear boundary representing the interface between SCASN:Eu particle and glass matrix. The SCASN:Eu particle with higher Z-contrast, scattering more electrons, presents bright region. The corresponding energy-dispersive X-ray spectroscopy (EDS) shows the distinct elemental distribution, i.e., Sr, Al, N concentrate in phosphor particle, O enriches in glass matrix, and Si distributes over both, as expected. In comparison, a blurred interface of approximately 20\u2009nm with apparent elemental interdiffusion can be observed for the sample processed by traditional sintering (Supplementary Figs.\u00a023, 24). High-resolution TEM (HRTEM) observation in Fig.\u00a03h demonstrates that the phase transition layer (associated with thermal erosion) usually observed in the traditional sintering process is almost absent in the RTA-processed PiGF. Fast Fourier Transform (FFT) images taken on three representative regions show the typical amorphous halo and single-crystalline diffraction pattern on glass and crystal grain respectively, and both features at the interface. The observed 2-dimensional interplanar spacings are calculated as 1.84 and 2.54\u2009\u00c5, indexing to (\\(2\\bar{2}2\\)) and \\((2\\bar{2}0)\\) facets of SCASN, respectively. The measured angle between the (\\(2\\bar{2}2\\)) and \\((2\\bar{2}0)\\) facets is 44.1\u00b0, getting close to the theoretical one of 43.8\u00b0. In the enlarged HRTEM observation at the interface (Fig.\u00a03i), one can still identify a vague interdiffusion region with a thickness of 1\u20132\u2009nm, where the crystalline lattice becomes blurred and some high-density ionic vacancies are clearly observed. Thereupon, it is reasonable to infer that ionic migration occurs at the interface. These results collectively demonstrate the integrity of the phosphor particles in the PiGFs and the capability of the RTA process to significantly suppress interfacial reactions.\n\nDuring the traditional sintering of PiG (S-PiGF-TS), the nitride phosphors are invariably oxidized15. Fortunately, due to the fast RTA sintering process, which requires only 10\u2009s, the oxidation is successfully mitigated in the SCASN:Eu-PiGF-RTA (S-PiGF-RTA). As illustrated in Fig.\u00a04a, the oxidation-induced shift in the emission center is minimal (a mere 3\u2009nm), in contrast to the reference sample which exhibits a more pronounced shift of up to 7\u2009nm. Benefiting from the suppressed interfacial erosion and the relief of oxidation, the internal quantum efficiency (IQE) of S-PiGF-RTA remains as high as 91.2%, which is only 5.3% lower than that of the original powder and much better than that of the S-PiGF-TS (Fig.\u00a04b and Supplementary Table\u00a03). Compared to other red fluorescent materials (ceramics, PiG, PiGF), the present S-PiGF-RTA composite scores the highest IQE (Fig.\u00a04c and Supplementary Table\u00a04). Photoluminescence (PL) decay curves were also examined, where the decay profile of S-PiGF-RTA almost overlaps with that of phosphor powders, showing a luminescence lifetime decrease of only 7%, whereas the S-PiGF-TS exhibits a 13% reduction (Supplementary Fig.\u00a025). Additionally, we examined the thermal stability performance which is crucial in laser lighting applications. The integrated emission intensity of S-PiGF-RTA at 200\u2009\u00b0C still retains 90.4% of that at 25\u2009\u00b0C (Supplementary Fig.\u00a026), comparable to the SCASN:Eu powder and also superior to the S-PiGF-TS. Comparison of steady-state and transient-state PL characteristics corroborates the superiority of the RTA process over the traditional methods.\n\nComparison on a PL spectra, b Internal/external quantum efficiency(I/EQE) and absorption efficiency (Abs) of the SCASN:Eu phosphor-in-glass film (PiGF) fabricated by traditional sintering (TS) and rapid thermal annealing (RTA); the data of original phosphor powders are also presented for references. c IQE of SCASN:Eu phosphor-in-glass film prepared by RTA (S-PiGF-RTA) as compared with those reported red all-inorganic material (see details in Supplementary Table\u00a04). d Digital photograph of the phosphor wheels. e Luminous flux and f luminous efficacy of the phosphor wheels as a function of the incident laser power and power density. g IQEs of the fabricated nitride and oxynitride PiGFs via the RTA and TS methods and the original phosphor powders. h Corresponding Luminous flux values of nitride and oxynitride phosphor wheels prepared by RTA technique as a function of incident laser power and power density. i Comparison of EL spectra of SCASN:Eu PiGF phosphor wheels laminated with and without silver-plated aluminum plates under 27\u2009W\u2009mm\u22122 blue laser excitation. In e, f, and h, the upper x-axis represents the incident laser power, while the lower x-axis represents the incident laser power density; The incident laser power density is calculated by dividing the incident laser power by the spot area (spot area: 0.628\u2009mm\u00b2). Source data are provided as a Source Data file.\n\nTo assess the luminescence properties of the fabricated PiGFs under blue laser excitation, a series of RTA processed S-PiGF phosphor wheels were fabricated with optimization of the phosphor-to-glass (PtoG) weight ratio and film thickness (Supplementary Fig.\u00a027). The corresponding EL spectra are presented in Supplementary Figs.\u00a028, 29. The adoption of phosphor wheel configuration is based on the consideration of ease of thermal management under dynamic excitation due to more efficient heat convection to air and pulsed irradiation36. It can be observed that the sample with a PtoG weight ratio of 1:1 and a film thickness of 86 \u03bcm achieves the maximum luminous flux (LF) of 1004\u2009lm under 455\u2009nm blue laser irradiation at a power density of 27\u2009W\u2009mm\u22122. Too heavy the phosphor load or too thick the film leads to the severe heat accumulation and then the thermal runaway effect21. A S-PiGF-TS reference phosphor wheel with the same PtoG weight ratio and thickness is also prepared. There is a large difference in the body color of the samples produced by these two techniques (Fig.\u00a04d), due to the aforementioned difference in the extent of thermal erosion and oxidation In Fig.\u00a04e, f, the LF and luminous efficacy (LE) are evaluated at different power densities of blue laser. For these two samples, no luminescence saturation occurs up to the power density limit of the present measurement system. At each power density, the S-PiGF-RTA outperforms the S-PiGF-TS, with the LF and LE approximately 1.7 to 2 times higher (Supplementary Table\u00a05), confirming superiority of the RTA-processed PiGF. Of particular note are the thousands of lumens output and 60\u2009~\u2009100\u2009lm\u2009W\u22121 LE for the blue laser converted red light, which can hardly be achieved with other material forms or preparation techniques.\n\nWe employed the same PtoG weight ratio and thickness, and combined them with NL-4 glass to fabricate a series of PiGFs with varying emissive colors, including BaSi2O2N2:Eu, CASN:Eu, \u03b2-SiAlON:Eu, and \u03b1-SiAlON:Eu, utilizing the RTA technique. Their PL and PLE spectra, along with digital photographs of the samples, are presented in Supplementary Fig.\u00a030. Concurrently, we synthesized comparative samples with the same PtoG weight ratio and thickness using the traditional sintering method. The IQE test results indicate that for the various nitride/oxynitride PiGFs, those prepared using the RTA technique exhibit higher efficiency, as depicted in Fig.\u00a04g and Supplementary Table\u00a06. Brightness tests under blue laser irradiation at different power densities further substantiate this observation (Fig.\u00a04h and Supplementary Figs.\u00a031\u201335). Furthermore, we have endeavored to enhance the performance of the S-PiGF-RTA phosphor wheel by laminating a silver-coated aluminum sheet on its rear side which helps to collect more back-scattered light37. As shown in Fig.\u00a04i, under the irradiation of a blue laser with a power density of 27\u2009W\u2009mm\u22122, the output LF of the converted red light has reached a remarkable 2379\u2009lm, with a LE of 140\u2009lm\u2009W\u22121, recording the highest values among the red-emissive color converters we have examined to date (Supplementary Table\u00a07).\n\nTo assess the practicality of RTA-processed PiGF for laser-driven light sources, we constructed a display system incorporating a patterned phosphor wheel (Fig.\u00a05a). The physical prototype of the display system is depicted in Fig.\u00a05b. A portion of the blue laser beam sequentially traverses a collimating lens, a dichroic mirror, and a convex lens to irradiate the patterned phosphor wheel integrated with a silver-coated aluminum plate. The other portion of the blue beam is directed towards the primary optical path with the aid of a reflector. Subsequently, the emitted red light from SCASN:Eu, the narrow-band green light from \u03b2-SiAlON:Eu, and the blue light along the primary optical path combine to produce white light output. The composed white light is then projected onto an LCD screen, creating the display effect. The spectral distribution, which is a function of the area ratio and thickness of the red-emitting SCASN:Eu PiGF and the green-emitting \u03b2-SiAlON:Eu PiGF, has been fine-tuned for optimal display performance. As demonstrated in Fig.\u00a05c, with laser excitation at 27\u2009W\u2009mm\u22122, the patterned phosphor wheel achieves a maximum luminous flux of 3502\u2009lm, with a luminous efficacy of 206\u2009lm\u2009W\u22121. Comparative analysis with commercial YAG:Ce-based laser light sources reveals that the laser-driven phosphor wheels, utilizing SCASN:Eu PiGF and \u03b2-SiAlON:Eu PiGF as backlight sources, yield more vibrant and visually pleasing effects on the LCD screen, as evidenced in Fig.\u00a05d\u2013g and the Supplementary Movie\u00a04. Furthermore, we delved into the application of laser display technology in holographic projection. By employing a blue laser light source, YAG:Ce3+ PiGF prepared via the RTA technology, an LCD screen, and a holographic film, we assembled a rudimentary holographic display cabinet (Fig.\u00a05h). The related working mechanism is presented in Supplementary Note 3. As depicted in Fig.\u00a05i and Supplementary Movie\u00a05, it is evident within the holographic display cabinet that, the virtual three-dimensional (3D) flame is superimposed on the real figurine or hand, achieving an interaction between the virtual and real elements. This showcases the potential of RTA technology in the domain of virtual reality applications.\n\na Schematic diagram and b digital photograph of the constructed laser-driven display system based on the SCASN:Eu PiGF+\u03b2-SiAlON:Eu PiGF phosphor wheel. c Corresponding EL spectra of the phosphor wheel converted light source. Laser display visual effect on LCD screen based on (d, e) commercial YAG-converted LD light source and (f, g) the present PiGF-converted LD light source. h Schematic diagram and (i) digital photograph of the constructed holographic display system based on PiGF; inset of (i) shows superimposition of virtual flame on a real hand. Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58099-5/MediaObjects/41467_2025_58099_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussions", + "section_text": "Throughout the research history of PiG/PiGF, a key challenge is how to mitigate interfacial reactions between phosphor particles and glass components during co-sintering to preserve the exceptional luminescent properties of the phosphors. Strategies such as phosphor encapsulation, selecting glass systems with low aggressivity, and employing rapid sintering techniques have been explored4,5,16,30,31,33,34,38,39. Rapid sintering, in particular, shows promise in reducing the time for ionic migration at interfaces. The present RTA approach utilizes high-power tungsten halogen lamps to quickly elevate the temperature to the desired sintering point within seconds, effectively addressing this challenge. In addition, the RTA approach requires a lower sintering temperature compared to traditional methods due to its unique heating process. The lowered temperature prevents sufficient thermal energy from overcoming the energy barrier for ionic migration, preserving the integrity of the embedded phosphor and maintaining high QE.\n\nFrom a thermodynamic perspective, an increase in sintering temperature results in a decrease in the viscosity of glass system, leading to enhanced ionic migration between phosphor and glass. While using glass powders with a lower Ts as raw material may seem preferable, the relationship between Ts and QE is not always straightforward, as demonstrated by the RTA screening results (Fig.\u00a02c). Moreover, for the PiGF using glass powders with high Ts, the QE result is not as unfavorable as initially expected (Supplementary Figs.\u00a036, 37). In fact, the high-Ts PiGF sintered at 750\u2009\u00b0C for 10\u2009s even shows a higher IQE/EQE (i.e., 63.9%/39.1%) than that (i.e., 16.7%/12.5%) of the low-Ts NL-4 glass sintered at 700\u2009\u00b0C for 10\u2009s. Apart from the viscosity of the glass system, the influence of thermally-aggressive glass components should not be underestimated. For high-Ts glass, the presence of thermally-aggressive alkali metal ions is typically lower, thereby impeding ionic migration. It is noteworthy that the absence of alkali metal ions significantly suppresses the interfacial reactions observed in the silica-glass-based YAG:Ce PiG sintered at temperatures up to 1250\u2009\u00b0C17.\n\nFor PiGF, the densification occurs through the viscous flow of glass to fill voids among particles or cavities resulting from the evaporation of organic paste during preheating. When utilizing RTA sintering, controlling the film thickness is crucial. In Supplementary Fig.\u00a038, the film processed at 550\u2009\u00b0C for 10\u2009s shows large pores and detachment from the sapphire substrate when the film is too thick. This is attributed to the reduced thermal conduction efficiency from the film surface or the supported silicon wafer to the inner part. As a result, the glass flow becomes overly viscous to effectively expel the pores. We have determined that the maximum film thickness compatible with the RTA approach, sintered within seconds, should be kept below ~130\u2009\u03bcm. In addition, surface roughness significantly influences the luminescence performance of the PiG product. To assess this, we utilized a 3D white light interferometer to analyze the 3D surface topography of the PiGF (Supplementary Fig.\u00a039). The calculated average surface roughness is 1.096 \u03bcm. Considering the satisfactory luminous performance of the manufactured PiGF phosphor wheel, the surface roughness achieved through RTA processing is suitable for practical applications. While a smoother surface could potentially enhance luminous performance, it would entail a notable increase in costs.\n\nIn summary, we report a rapid and facile RTA approach to fabricate PiGF composite for the advanced laser-driven light source. High-power infrared light strikes the PiGF and Si substrate, producing massive heat within seconds. Finite element analysis demonstrates the extremely high heating rate and homogeneous heat distribution in the quartz cavity. These properties enable high-throughput screening of PiGF, either to determine phosphor-glass compatibility or to optimize sintering parameters. Microstructural and spectroscopic studies reveal that the RTA technology achieves rapid densification of the PiGF (with porosity\u2009<\u20093%) at lower temperatures compared to traditional sintering methods, beneficial to mitigating the effects of erosion and oxidation, thus preserving the luminescent efficiency of phosphors. We first observe high-density ionic vacancies at the phosphor-glass interface (1\u20132\u2009nm interdiffusion region) with vague crystal lattice due to ionic migration. RTA technology is especially suitable for those phosphors that are unstable when co-sintered with glass, such as the rare earth doped (oxy)nitrides of great importance for solid-state lighting, but is also suitable for common garnet oxides and other opto-functional polycrystalline phosphor and persistent phosphor systems. As an example, the RTA-processed SCASN:Eu PiGF scores the highest IQE of 91.2% among the red-emissive inorganic bulk color converters ever reported. Upon driven by 27\u2009W\u2009mm\u22122 blue laser, the output luminous flux of the converted red light for the corresponding phosphor wheel reaches a remarkable 2379\u2009lm with a LE of 140\u2009lm\u2009W\u22121. Finally, we present demo experiments of the laser-driven light sources designed to create more vivid and visually appealing effects in LCD displays, and to construct a fascinating holographic display with an interaction between the virtual and real elements. The development of RTA technology brings new opportunities to explore more efficient PiGF systems, heralding a bright future for next-generation high-power laser lighting and displays, as well as other emerging photonic applications.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The glass powders including GC825 (Okamoto Glass Co., Ltd), GF45A, FD233, FD238, and NL-4 (Anywhere New Material Co., Ltd.); phosphor powders including SCASN:Eu, \u03b2-SiAlON:Eu, LuAG:Ce, K2SiF6:Mn, and YAG:Ce (Grirem Advanced Materials Co., Ltd), CASN:Eu and BaSi2O2N2:Eu (Shandong Yingguang Advanced Materials Co., Ltd), LSN:Ce (Mitsubishi Chemical Holdings), SrAl2O4:Eu,Dy (Shenzhen looking long technology Co., Ltd.), Ca-\u03b1-SiAlON:Eu (Denka Co., Ltd.), Gd2O2S:Tb (Shanghai Keyan Phosphor Technology Co.,Ltd); and YAG:Ce-PiG plate (Doctor Optotech Co., Ltd.) are all commercially available. To fabricate PiGF, we purchased terpineol and ethyl cellulose from Aladdin China; sapphire wafers (10\u2009\u00d7\u200910\u2009\u00d7\u20090.3\u2009mm and \u03c645\u2009mm) from Xinxiang City Baihe O.E. Co., Ltd.; SiC single crystal wafers (10\u2009\u00d7\u200910\u2009\u00d7\u20090.5\u2009mm) from Beijing Tanke Blue Semiconductor Co., Ltd; K9 glass wafers (10\u2009\u00d7\u200910\u2009\u00d7\u20090.3\u2009mm) from Schott Glaswerke AG; and AlN ceramic wafers (10\u2009\u00d7\u200910\u2009\u00d7\u20090.3\u2009mm) from Guangzhou Baile New Materials Technology Co., Ltd., respectively.\n\nThe PiGF samples were prepared by co-sintering phosphors and glass powders on a high thermal conductivity substrate. The process started with the preparation of an organic solvent by dissolving 3\u2009wt% ethyl cellulose in 97\u2009wt% terpineol by stirring at 80\u2009\u00b0C for 8\u2009h. Subsequently, phosphors and glass powders were measured in a specific mass ratio. These components were then thoroughly mixed with the organic solvent in an agate mortar to achieve a powder-to-gel mass ratio of 2:1. The resulting slurry was then applied to the high thermal conductivity substrate using an automatic coating machine, with the thickness of film meticulously controlled by the height of blade, which was accurate to within 10\u2009\u03bcm. The samples were then subjected to a heat treatment in a muffle furnace at 300\u2009\u00b0C for 3.5\u2009h to ensure the complete volatilization of the organics, yielding a precursor PiGF. To complete the process, the precursor film was sintered either in an RTA device (RTP-500, estarlabs.com) or in a muffle furnace. The RTA process involved rapid heating to the set temperature \u2014 450\u2009\u00b0C, 500\u2009\u00b0C, 550\u2009\u00b0C, 600\u2009\u00b0C, 650\u2009\u00b0C, and 700\u2009\u00b0C within 10\u2009s, followed by soaking times ranging from 1\u2009s to 30\u2009s. In contrast, the muffle furnace sintering process was perfromed at a slower pace, with a heating rate of 5\u2009\u00b0C min\u22121 to the set temperature \u2014 500\u2009\u00b0C, 550\u2009\u00b0C, 600\u2009\u00b0C, 650\u2009\u00b0C, and 700\u2009\u00b0C \u2014 followed by soaking times ranging from 5 to 20\u2009min. A similar method was used for the preparation of monochrome phosphor wheels, but with different sample quantities. The preparation steps for the patterned phosphor wheel are similar, except that a screen-printing machine (Kaivo Optoelectronic Technology Co., Ltd.) with a sector-shaped stencil is used for coating in the third step.\n\nPhase determination of the prepared samples was conducted using a MiniFlex 600 powder X-ray diffractometer (Rigaku) with Cu K\u03b1 radiation (\u03bb\u2009=\u20091.540593\u2009\u00c5) over a data collection range of 10\u00b0\u201370\u00b0 at a scanning speed of 5\u00b0 min\u22121. Microstructural analysis was performed using SEM (JSM-6700F, JEOL, Japan and SU8010, HITACHI, Japan). TEM and HADDF-STEM images were acquired at room temperature using a spherical aberration-corrected STEM (JEM-ARM 200\u2009F; JEOL, Japan). The electron-transparent cross-sectional STEM specimen was prepared by focused ion beam milling (Helios G4; FEI/Thermo Fisher Scientific, USA) and further thinned by focused Ar-ion milling (NanoMill 1040; Fischione Instruments, Export, PA, USA). The elemental distribution within the PiGFs was analyzed using XRF spectrometer (M4 Tornado, Bruker, Germany). The distribution of phosphor particles on the surface of PiGF was examined using an optical microscope (HD, LUO SI, China). The 3D distribution of phosphor particles within the PiGF was investigated using CLSM with an excitation wavelength of 405\u2009nm (A1 MP, Nikon, Japan). The surface roughness of the PiGF was analyzed using a white light interferometer (LAMBDA, RTEC, USA). Sample porosity distribution and porosity analysis were carried out by using micro-CT (Nanovoxel-3432E, Sanying Precision Instruments, China). To be mentioned, the pores smaller than 500\u2009nm cannot be detected due to the resolution limit of CT scanner. The thermodynamic parameters were determined using a DTA instrument (STA449 F5, Netzsch, Germany). The refractive index of glass sample was measured by a digital refractometer (RDB, GEM Instruments). The full transmission spectra were recorded by using a UV-visible-NIR spectrophotometer (Lambda 950, Perkin Elmer). The IQE of luminescence was measured based on a 455\u2009nm semiconductor laser (3\u2009W), an integrating sphere, a cooling-heating stage, and a fiber optic spectrometer. The integrating sphere was assembled onto a cooling-heating stage, and the luminescence signal was transmitted to the spectrometer (Ocean Optics, QE pro) via a fiber. IQE was calculated by the ratio of emitted to absorbed photons. The PL and PLE spectra, as well as fluorescence decay curves, were recorded using a fluorescence spectrometer (FLS920, Edinburgh Instruments) equipped with a 400\u2009W xenon lamp and a 470\u2009nm pulsed laser source. Samples were placed on a cooling-heating stage (Linkam Scientific Instruments, THMS600E) and held for 2\u2009min at different temperatures before temperature-dependent PL spectrum measurements were taken. The photometric parameters of the samples under blue laser excitation were collected using a homemade laser illumination system, which consisted of a custom-made integrating sphere with a diameter of 10\u2009cm, a 22\u2009W blue laser (Ningbo Yuanming Photoelectric, LSR455CP), and a fiber optic spectrometer (Ocean Optics, QEpro). It should be noted that the incident 22\u2009W blue laser inevitably experiences losses after passing through the lens, resulting in a maximum light power of 17\u2009W actually reaching the sample. The testing optical path was set in a reflective mode, with the incident laser spot having an approximately circular shape and an area of about 0.628\u2009mm\u00b2. The sample was adhered to a rotating aluminum wheel that was driven by a high-speed rotating motor with a rotation speed of 3000\u2009rpm. The optical power of the blue laser was measured using a laser power meter equipped with a thermopile power probe (model 30A-BB-18, Ophir).\n\nTo explore the feasibility of bulk sample preparation through the RTA process, finite element modeling was conducted using Ansys software to investigate the temperature distribution across various regions of the silicon wafer during the process. The three-dimensional model and computational domain are depicted in Fig.\u00a01c. The upper and lower arrays of halogen tungsten lamps within the quartz chamber of the furnace were simplified as two planar radiation sources with a diameter of 170\u2009mm, with their temperatures set at 3225\u2009K; the dimensions of the quartz chamber are 170\u2009mm in length, 200\u2009mm in width, and 20\u2009mm in height, with a wall thickness of 2\u2009mm; a silicon wafer with a diameter of 101.6\u2009mm (4 inches) and a thickness of 1\u2009mm is positioned at the center of the quartz chamber. The wafer is uniformly covered with a 5\u2009\u00d7\u20095 array of 25 glass film-sapphire precursor composite materials. The quartz chamber is sealed to allow infrared radiation emitted by the radiation source to pass through, thereby heating the silicon wafer and the samples. In this case, the silicon wafer is placed in an atmospheric environment.\n\nIt should be noted that in our scenario, the metal casing and water or air cooling systems outside the computational domain were not considered, which may deviate from the actual conditions. However, we believe that during the heating and temperature stabilization phases, the impact of these factors is minimal and does not affect our discussion on the uniformity of temperature distribution across the silicon wafer and samples during the heating process. These elements can be disregarded in the simulation.\n\nThe radiation transfer process was investigated using the Discrete Ordinates (DO) radiation model. Additionally, the simulation process was coupled with the energy equation, taking into account the heat conduction within the solid materials and the heat convection process at the material-air boundary.\n\nThe radiative heat transfer process is governed by the Radiative transport equation (RTE):\n\nthe term \u2207 \u22c5 (I(r, s)s) represents the rate of change of the radiative intensity with respect to the spatial coordinate r and direction s, essentially describing the propagation rate of light rays. This term accounts for the variation in radiative intensity. The term (a\u2009+\u2009\u03c3s)I(r, s)(a\u2009+\u2009\u03c3s)I(r, s) denotes the absorption term, where a is the absorption coefficient and \u03c3s is the scattering coefficient. I(r, s) represents the radiative intensity at a given position r and direction s. This term describes the reduction in radiative intensity due to absorption and scattering processes. The term \\(\\frac{a{n}^{2}\\sigma {T}^{4}}{\\pi }\\) is the emission term, where n is the refractive index, \u03c3 is the Stefan-Boltzmann constant, and T is the temperature. It signifies the increase in radiative intensity due to the emission from the object. Lastly, the term \\(\\frac{\\sigma }{4\\pi }{\\int }_{0}^{4\\pi }I(r,{{{\\bf{s}}}}^{{\\prime} })\\Phi ({{\\bf{s}}},{{{\\bf{s}}}}^{{\\prime} })d{\\Omega }^{{\\prime} }\\) represents the scattering term, which accounts for the changes in radiation due to scattering. \u03a6(s, s\u2032) is the phase function, characterizing the probability of radiation being scattered from direction s\u2032 to direction s. The integral is taken over 4\u03c0, indicating the integration over all possible incident directions s\u2032.\n\nIn the simulation process, the DO radiation model is coupled with the energy equation. The energy equation typically takes into account the conservation of energy and can be expressed as follows:\n\nThe term \\(\\frac{\\partial (\\rho E)}{\\partial t}\\) is the unsteady term, which signifies the rate of change of the total energy per unit volume, \u03c1E, with respect to time t. Here, \u03c1 denotes the fluid density, and E represents the total energy per unit mass, encompassing internal energy, kinetic energy, and energy attributable to pressure. The term \u2207 \u22c5 [V(\u03c1E\u2009+\u2009p)] is referred to as the convective term, which describes the energy transported by the motion of the fluid. Here, V is the velocity vector, and p is the pressure. This term employs the divergence \u2207\u22c5 to depict how energy is conveyed through space via the fluid\u2019s motion. The term \u2207 \u22c5 [keff \u2207T] represents the heat conduction term, which describes the energy transfer due to thermal conduction. Here, keff is the effective thermal conductivity, T denotes the temperature, and \u2207T signifies the temperature gradient. The term \u2211hjJj is known as the diffusive term, which accounts for the energy changes due to the diffusion of multiple species. In this context, hj represents the specific enthalpy of the j-th species, and Jj denotes the diffusion flux of that species. The term \u03c4eff \u22c5 V represents the viscous dissipation term, which signifies the mechanical energy dissipation due to the viscous effects of the fluid. Here, \u03c4eff is the effective stress tensor, which characterizes the viscous forces within the fluid, and V is the fluid velocity vector. The term Sh is referred to as the enthalpy source term, which signifies the presence of external or internal energy sources.\n\nEquation (1) describes the process of radiative transfer, while Eq. (2) considers the conservation of energy. The connection between the two lies in the fact that radiation is a form of energy transfer, and in complex systems, radiation interacts with other mechanisms of energy transfer, such as heat conduction and convection, affecting the temperature field and energy balance. Consequently, in systems where radiative heat transfer is involved, these two equations must be solved in conjunction.\n\nUtilizing the Ansys software suite, a numerical simulation of the rapid thermal annealing (RTA) system was conducted. The model encompassed a substantial number of elements, totalling 1,644,875, and nodes, amounting to 1,868,928. The temporal discretization was set at intervals of 0.5\u2009s, with radiative iterations performed every 0.5\u2009s, each comprising ten energy iterations (Supplementary Movie\u00a02). This interval was deemed sufficiently small to resolve the rapid thermal transport processes. The simulated heating segment was calibrated to align with the optimal process time identified in the experimental phase, specifically targeting a temperature of 550\u2009\u00b0C for a duration of 10\u2009s.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data that support the findings of this study are available from the corresponding author upon request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Wierer, J. J., Tsao, J. Y. & Sizov, D. S. Comparison between blue lasers and light-emitting diodes for future solid-state lighting. Laser Photonics Rev. 7, 963\u2013993 (2013).\n\nADS\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nWang, L., Xie, R. J., Suehiro, T., Takeda, T. & Hirosaki, N. 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Mater. 7, 1900702 (2019).\n\nCAS\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by National Natural Science Foundation of China (No.52372161 to L.H., No.U2005213 to W.Y.S. and No.12274408 to W.Y.S.), Science Fund for Distinguished Young Scholars of Fujian Province (No.2022J06030 to L.H.), Science Fund of Fujian Province (No.2023J01217 to C.Y.), Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China (No.2021ZR134 to L.H.), and Self-deployment Project Research Program of Haixi Institutes, Chinese Academy of Sciences (No.CXZX-2022-GH11 to L.H.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, China\n\nPengfei Wang,\u00a0Hang Lin\u00a0&\u00a0Yao Cheng\n\nFujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian, China\n\nPengfei Wang\u00a0&\u00a0Hang Lin\n\nFujian Key Laboratory of Nanomaterials, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, China\n\nPengfei Wang,\u00a0Hang Lin,\u00a0Weitong Weng,\u00a0Yue Xu,\u00a0Yi Lin,\u00a0Ju Xu,\u00a0Yao Cheng\u00a0&\u00a0Yuansheng Wang\n\nUniversity of Chinese Academy of Sciences, Beijing, China\n\nPengfei Wang\u00a0&\u00a0Yue Xu\n\nPublic Technology Center, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang, China\n\nGuoxin Chen\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nL.H. conceived the initial concept. W.P.F. synthesized the materials and drafted the initial manuscript. L.H. assisted W.P.F. in analyzing the experimental results and in completing the final draft. C.G.X. contributed to the characterization of the microstructure. W.W.T. documented the sintering curves. X.Y. provided assistance in the demonstration of applications. C.Y. offered constructive suggestions on data analysis. L.Y. and X.J. aided in conducting spectral measurements. W.Y.S. oversaw the project.\n\nCorrespondence to\n Hang Lin or Yuansheng Wang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Tomokatsu Hayakawa, Zhiguo Xia, Dae-Ho Yoon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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and cellular senescence profiling of aging human skeletal muscle uncovers Maraviroc as a senotherapeutic approach for sarcopenia", + "pre_title": "Multiomics mapping and characterization of cellular senescence in aging human skeletal muscle uncovers a novel senotherapeutic for sarcopenia", + "journal": "Nature Communications", + "published": "05 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Datasets 1-9", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_MOESM3_ESM.zip" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_MOESM4_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_MOESM5_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_MOESM6_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE268953", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE268407", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE268952", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE268433", + "/articles/s41467-025-61403-y#Sec35" + ], + "code": [ + "https://github.com/Hannah-bioinfo/Scripts_Aging_SnC_MS/" + ], + "subject": [ + "Ageing", + "Muscle stem cells", + "Senescence" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5399514/v1.pdf?c=1751799950000", + "research_square_link": "https://www.researchsquare.com//article/rs-5399514/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61403-y.pdf", + "preprint_posted": "18 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Cellular senescence is recognized as a hallmark of organismal aging but how it drives aging particularly in human tissues is not fully understood, partly due to the complex heterogeneous nature of senescence. Here in this study, we leverage single-nucleus multiomics to profile senescence in mononucleated cells of human skeletal muscle and provide the first senescence atlas. We demonstrate the intra- and inter-populational transcriptomic and epigenomic heterogeneity and dynamics of senescence in the cells. We also identify commonalities and variations in senescence-associated secretory phenotypes (SASPs) among the cells and elucidate the function of SASPs in mediating cellular interactions and niche deregulation. Furthermore, we identify targetable SASP factors and demonstrate the possibility of using Maraviroc as a pharmacological senotherapeutic for treating age-associated sarcopenia in muscle. Lastly, we define transcription factors that govern senescence state and SASP induction in aging muscle and elucidate the key function and the underlying mechanism of JUNB in regulating SASP activation in senescent cells. Altogether, our findings demonstrate the prevalence and function of cellular senescence in skeletal muscle and identify a novel pharmacological intervention for sarcopenia.Biological sciences/Cell biology/SenescenceBiological sciences/Stem cells/Muscle stem cellsBiological sciences/Stem cells/AgeingHealth sciences/Health care/Therapeutics/Drug therapy/Molecularly targeted therapySenescenceskeletal muscleagingMuSCSASPsenotherapeuticsMaravirocJunB", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-5399514/v1/27c6e103cef0604c124e49af.png", + "https://assets-eu.researchsquare.com/files/rs-5399514/v1/b0b7aec21a4531d7fbcc5152.png", + "https://assets-eu.researchsquare.com/files/rs-5399514/v1/b0eecc18e8bfa7814bd919d8.png", + "https://assets-eu.researchsquare.com/files/rs-5399514/v1/4e750702f0c88372c90ee2c2.png", + "https://assets-eu.researchsquare.com/files/rs-5399514/v1/97f2a01076ff0ae3e0177e3b.png", + "https://assets-eu.researchsquare.com/files/rs-5399514/v1/bd4c5920a25e28f78ddbfeb3.png", + "https://assets-eu.researchsquare.com/files/rs-5399514/v1/8add6e5761181783299da5f0.png", + "https://assets-eu.researchsquare.com/files/rs-5399514/v1/8d06d0f77cf4c4cf695d57d0.png" + ] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplFigures.pdfSupplInfo.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Cellular senescence is a hallmark of organismal aging but how it drives aging in human tissues is not fully understood. Here we leverage single nucleus multiomics to profile senescence in mononucleated cells of human skeletal muscle and provide the first senescence atlas. We demonstrate the intra- and inter-populational transcriptomic and epigenomic heterogeneity and dynamics of cellular senescence. We also identify commonalities and variations in senescence-associated secretory phenotypes (SASPs) among the cells and elucidate SASP mediated cellular interactions and niche deregulation. Furthermore, we identify targetable SASPs and demonstrate the possibility of using Maraviroc as a pharmacological senotherapeutic for treating age-associated sarcopenia. Lastly, we define transcription factors that govern senescence state and SASP induction in aging muscle and elucidate the key function and mechanism of JUNB in SASP activation. Altogether, our findings demonstrate the prevalence and function of cellular senescence in skeletal muscle and identify a novel pharmacological intervention for sarcopenia.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The organismal aging process is both characterized and driven by cellular and molecular mechanisms termed hallmarks of aging. Cellular senescence, an adaptive response induced by multiple physiological and pathological stresses that entails irreversible cell cycle arrest and resistance to apoptosis is emerging as a dominant mechanism orchestrating various facets of aging physiology and development of many age-dependent diseases1,2,3. Senescent cells (SnCs) are associated with multiple phenotypic and molecular changes that help distinguish them from quiescent or terminally differentiated cells, for example increased lysosomal \u03b2-galactosidase activity (SA-\u03b2-GAL), upregulated expression of cell cycle inhibitors, P21CIP1/WAF1 (CDKN1A) and P16INK4a (CDKN2A)4. More importantly, SnCs are characterized by the senescence-associated secretory phenotype (SASP), which encompasses a variety of secreted bioactive factors that can include pro-inflammatory cytokines, chemokines, growth factors and matrix-remolding enzymes2,5. These SASPs are the major mediators of the paracrine effects of SnCs in their tissue microenvironment and can disrupt tissue homeostasis by promoting chronic inflammation, fibrosis and progenitor cell dysfunction. For example, SASP factors contribute to the chronic inflammation in aging tissue (so-called inflammaging) by directly initiating inflammation or indirectly inducing secondary inflammation through chronic activation of immune cells and spread of senescence locally and systemically.\n\nIt is becoming clear that the heterogeneity of SnCs and SASPs is vast, yet ill-characterized5,6; for example, the extent of the expression of different SASP elements is highly cell-type and senescence-stage dependent. Even less is known about the regulation of senescence state and SASPs particularly the upstream transcriptional regulators/events that activate the SASPs. NF-\u03baB signaling serves as a pivotal regulator of SASP interleukins and cytokines, chemokines, growth factors and other factors6,7,8; CCAAT/enhancer-binding protein-beta (C/EBP\u03b2) functions as a TF that cooperates with NF-\u03baB to modulate the expression of some SASPs9,10,11, but there remains an imperative need to define additional orchestrators and elucidate their regulatory mechanisms in governing SASP expression. Emerging single-cell technologies have enormously facilitated the profiling and characterization of senescence/SASPs heterogeneity, dynamics and regulation spatiotemporally. However, most studies so far are from model animals and there is sparse information about the prevalence and spectrum of SnCs/SASPs in human tissues such as skeletal muscle.\n\nSkeletal muscle, as a key organ of body homeostasis and mobility, suffers from age-associated sarcopenia, which involves a deterioration in muscle quantity and quality, muscle strength and muscle function12. It is believed that the condition can partly be attributed to the decreased number and function of adult muscle stem cells (MuSCs)13,14. MuSCs are indispensable for maintaining muscle homeostasis and injury-induced muscle regeneration. In aged mice, muscle repair is blunted in a large part due to the numeric and functional decline of MuSCs. Accumulated works demonstrate both intrinsic alterations in MuSCs and extrinsic deregulations in the niche microenvironment contribute to the age-related decline. The intricate interplay between the niche and MuSCs is emerging as a key question in understanding molecular mechanisms underlying muscle aging. Moreover, burgeoning evidence suggestes signs of senescence in skeletal muscle but conflicting observations are being reported12,15. For example, earlier studies reported signs of senescence in isolated MuSCs from aged mice (elevated expression of SA-\u03b2-GAL and p16, p21, and Igfbp5 mRNAs)16, but more recent studies revealed a lack of conclusive evidence for senescence-associated phenotypes such as SA-\u03b2-GAL staining on aged muscle sections17,18. Nevertheless, several studies showed that genetic elimination of SnCs restored muscle loss and inflammation in aged mice19,20. Therefore, the prevalence of the senescence in MuSCs and also in\u00a0other resident mononuclear cells that populate the interstitial microenvironment of skeletal muscle remains elusive, let alone the possible contribution of senescent MuSCs in niche deregulation of aging muscle.\n\nIt is now well believed that SnCs are therapeutically targetable; a relatively new class of drugs termed senotherapeutic approaches that selectively kill senescent cells (senolytics) or to suppress SASPs (senomorphics) are attracting unprecedented attention as a means to enable healthy aging6,21. Indeed, in preclinical models, pharmacological elimination of senescent cells restores health and youthful properties in multiple tissues. Of note, senolytic treatment with dasatinib/quercetin also increased muscle strength and function in aged mice22. Other studies, however, suggested that pro-senescence therapy can promote muscle regeneration23. Overall, we must admit that our knowledge of senescence in aging muscle is largely incomplete; we believe it is imperative to start from the ab initio defining of the unique features of SnC atlas in skeletal muscle and provide a full spectrum of senescence/SASP heterogeneity, dynamics, and regulation, which will enable the exciting opportunity for identification of novel senotherapeutic targets and drug discovery/development for sarcopenia.\n\nHere in this study, we conducted simultaneous single-nucleus RNA-seq\u2009+\u2009ATAC-seq mapping in mononucleated cells freshly harvested from young and aged human biopsies. Integrated analysis yields the first senescent cell atlas in aging human muscle and demonstrates the SASP heterogeneity and dynamics. We further elucidate the function of senescent cells, in particular the contribution of senescent MuSCs in niche modulation via SASP-mediated cell-cell interactions. Moreover, we test the possibility of senotherapeutic targeting in aging muscle and identify Maraviroc as an effective senomorphic approach for ameliorating muscle aging in mice. Furthermore, we elucidate upstream TFs governing senescence and define JUNB as a direct transcriptional activator of SASP induction via enhancer regulation in senescent MuSCs. The finding demonstrates the key role of JUNB in SASP regulation and highlights it as a potential senotherapeutic target.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To gain the first senescence blueprint of mononucleated cells in aging human muscle, we obtained hamstring muscles from biopsies of 10 male donors: five young (19\u201327\u2009years-old males who underwent anterior cruciate ligament reconstruction) and five aged (60\u201377\u2009years-old males who underwent knee replacement surgery) (Fig.\u00a01 and Suppl. Dataset\u00a01). Structurally intact and histologically healthy hamstring muscles were harvested from anatomically equivalent regions across cohorts and processed under standardized protocols. Mononuclear cells were FACS isolated and single nuclei were prepared for single-nucleus (sn) multiomics (simultaneous RNA-seq and ATAC-seq in one cell) analysis using a 10x Genomics Chromium. After stringent quality control to remove low-quality nuclei and batch effect, a total of 52,934 (30,390 from five young and 22,544 from five aged donors) qualified nuclei were obtained for downstream analysis (Suppl. Fig.\u00a01A). The snRNA-seq data quality, indicated by the number of read counts (UMI) and genes with at least one read count was highly correlated across all five donors (Suppl. Fig.\u00a01B); similar observation was made on the ATAC-seq data quality as indicated by ATAC read counts and ATAC peaks for each sample (Suppl. Fig.\u00a01C). Using unbiased clustering and uniform manifold approximation and projection (UMAP) analysis, 12 clusters of muscle resident mononuclear cells were identified with distinct transcriptomic and epigenomic signatures, including MuSCs, FAP (Fibro-adipogenic progenitors) 1, FAP 2, EC (endothelial cells) 1, EC 2, EC 3, Pericytes, SMC (smooth muscle cells) 1, SMC 2, MPs (macrophages), and B/T/NK (B-cells, T-cells and Natural Killer cells), along with a small number of nuclei from mature skeletal muscles (MSMs) despite terminally differentiated and post-mitotic myofibers were excluded from the procedure (Fig.\u00a02A, Suppl. Fig.\u00a01D and Suppl. Dataset\u00a02). The marker genes from the literature were used to annotate these clusters, for example, MYF5 and PAX7 for the MuSCs, DCN, FBN1 for the FAPs, ACTA1 for the ECs, and C1QA for the MPs (Suppl. Fig.\u00a01E-F and Suppl. Dataset\u00a02). Strong correlations in global gene expression were observed across all 10 donors within each cell type (Suppl. Fig.\u00a01G), suggesting that the within-group samples were highly comparable. Consistently, all the expected cell types were identified among donors in each age group with comparable relative abundance (Suppl. Fig.\u00a01H-I). Decreased numbers of MuSCs, ECs, SMCs, and Pericytes were observed while the numbers of MSCs, FAPs, MPs, and B/T/NK cells increased in the aged compared to the young muscles (Fig.\u00a02B), confirming the altered cellular composition and niche microenvironment during muscle aging. For the four major non-immune cell types, MuSCs, ECs, FAPs and SMCs, correlation analysis demonstrated that the cell proportions of MuSCs, ECs, and SMCs decreased, and FAPs increased with aging (Fig.\u00a02C), which was consistent with previous findings24,25. The cell type prioritization score analysis using Augur pinpointed B/T/NK cells (0.90), SMCs (0.86), MuSCs (0.84), and MSMs (0.82) as the cells most responsive to aging (Fig.\u00a02D). Furthermore, by quantifying transcriptional noise, we found most cells showed an elevated transcriptional heterogeneity among individual nuclei (Fig.\u00a02E), which is in line with the known feature of aging26. This finding was further validated by calculating the average transcriptional noise scores per donor and indeed an elevated ratio of transcriptional noise was observed in the aged group (Suppl. Fig.\u00a01J).\n\nCreated in BioRender. Li, Y. (2025) https://BioRender.com/xsxka6w.\n\nA Uniform manifold approximation and projection (UMAP) plot showing the 12 (sub)types of muscle resident mononucleated cell populations. B Sankey plots showing the distribution and relative composition of young/aged cells across 8 main cell types. C Scatter plots of MuSC, FAP, EC, and MP proportions in young/aged donors, with Pearson correlation coefficients (two-sided, unadjusted). The red regression lines represent the linear model fits, with the shaded areas indicating the 95% confidence intervals (CIs). D Arc plot showing the cell type aging responsiveness with augur scores from random forest (one-sided, unadjusted). E Boxplots of transcriptional noise in young (Y) vs. aged (A), ordered by A/Y ratio using Wilcoxon tests (two-sided, unadjusted). F\u2013I Senescence atlas: UMAPs colored by SenMayo ss-GSVA scores; senescent (Sn) vs. non-senescent (nSn) cells in MuSCs, FAPs, ECs, SMCs. Bar plots: Sn/nSn ratios in young/aged. J Dot plot of representative upregulated Sn DEG GO terms (hypergeometric test, one-sided; p\u2009<0.05, Benjamini-Hochberg-adjusted). K Schematic of the experimental design for senescence detection in human muscle or freshly isolated MuSCs. Created in BioRender. Li, Y. (2025) https://BioRender.com/xsxka6wL H&E staining and quantification of cross-section areas (CSAs) of aged vs. young human muscle. Scale bar: 50\u2009\u03bcm, n\u2009=\u20094. p\u2009=\u20090.021. M IF staining and quantification of P16 and P21 (red) on the above human muscle sections. Scale bar: 50\u2009\u03bcm, n\u2009=\u20093. p\u2009=\u20090.035 (P16), 0.035 (P21). N SA-\u03b2-GAL staining and quantification of young/aged MuSCs. Scale bar: 50\u2009\u03bcm, n\u2009=\u20093. p\u2009=\u20090.0018. O IF staining and quantification of P16 and PAX7 on the above MuSCs. Scale bar: 50\u2009\u03bcm, n\u2009=\u20093. p\u2009=\u20090.011. P RT-qPCR detection of P14, P16, P19 and P21 genes expressions in the above MuSCs. n\u2009=\u20095. p\u2009=\u20090.00020 (P14), 0.037 (P16), 0.0070 (P21). All the bar graphs are presented as mean\u2009+\u2009SD, paired two-sided Student\u2019s t-test (P) and unpaired two-sided Student\u2019s t-test (D, L-O) were used to calculate the statistical significance: *p\u2009<0.05, **p\u2009<0.01, **p\u2009<0.001, n.s.\u2009=\u2009no significance. Source data are provided as a Source Data file.\n\nNext, to define the senescence atlas, we calculated a unified senescence score (USS) by integrating four senescent gene sets (SenMayo, CellAge, GenAge, and Senescence Eigengene, Suppl. Dataset\u00a02) through single-sample Gene Set Enrichment Analysis (GSEA)27,28,29,30,31. As a result, increased percentages of senescent cells were detected in all four cell types in aged vs. young muscle: MuSCs (12.2% vs. 7.9%, Fig.\u00a02F), FAPs (26.9% vs. 3.3%, Fig.\u00a02G), ECs (13.3% vs. 9.9%, Fig.\u00a02H) and SMCs (40.3% vs. 4.7%, Fig.\u00a02I). Gene Ontology (GO) enrichment analysis revealed that compared to the non-senescent (nSn) cells, the senescent (Sn) cells were enriched for pathways such as \u201cwound healing\u201d, \u201cresponse to oxidative stress\u201d, \u201ccell adhesion\u201d and \u201ccell migration\u201d etc. (Fig.\u00a02J and Suppl. Dataset\u00a03), all of which are characteristic features of senescent cells32. The above findings from analyzing the snRNA-seq data were validated by additional experiments (Fig.\u00a02K). H&E staining of the muscle sections from additional pairs of donors revealed decreased fiber size and increased inflammation in aged vs. young muscles (Fig.\u00a02L); elevated staining of P16 and P21 proteins was also detected (Fig.\u00a02M). Moreover, total MuSCs were freshly isolated by FACS and a significant increase of SA-\u03b2-GAL+ cells (Fig.\u00a02N) and P16+ cells (Fig.\u00a02O) were detected in aged muscles, accompanied by higher mRNA levels of senescence markers, P14, P16, P19 and P21 (Fig.\u00a02P), altogether supporting the increased senescence in aged MuSCs.\n\nTo further characterize MuSC senescence, pseudotime trajectory was utilized to reveal the MuSC fate which diverged into two late paths (Fig.\u00a03A). The Early-Late 1 fate accurately captured the transition from young to aged nuclei, showing a significant increase in the proportion of aged nuclei along the pseudotime (Fig.\u00a03B and Suppl. Fig.\u00a02A); while in the Early-Late 2 path, young and aged nuclei randomly distributed (Fig.\u00a03B and Suppl. Fig.\u00a02B). Nevertheless, senescent MuSCs accumulated in both late branches (Fig.\u00a03C) with CDKN1A gene highly expressed at the ends (Fig.\u00a03D). We then aggregated the expression of published cell cycle gene set of Reactome collection (Suppl. Dataset\u00a03) along the pseudotime trajectory and calculated a module score by ss-GSVA method33; a decreased ss-GSVA score was observed over the trajectory (Fig.\u00a03E), in agreement with the known feature of cell cycle arrest in senescent cells34,35. Differentially expressed gene (DEG) analysis uncovered various senescence-related GO terms such as \u201cExtracellular space\u201d (such as FGF2, IGFBP6, IGFBP7, CXCL12, TGFB1, BMP6), \u201cCytokine/chemokine activity\u201d (such as CCL2, CXCL12, CXCL8, CXCL2), \u201cGrowth factor\u201d (such as FGF2, IGFBP6, IGFBP7) etc. enriched in the late-phase MuSCs (Fig.\u00a03F, Suppl. Fig.\u00a02C). Moreover, the DEGs in the Late 1 and Late 2 branches were enriched for distinct GO terms with Late 1 being highly pro-fibrotic compared to Late 2 (Fig.\u00a03G and Suppl. Dataset\u00a03). Altogether, the above findings demonstrate the high level of heterogeneity in senescent MuSCs.\n\nA\u2013D Discriminative dimensionality reduction (DDR) tree visualization of MuSC trajectory with mapping of pseudotime (A), age group (B), senescence annotation (C), and CDKN1A expression level (D). E Plot showing the ss-GSVA score of cell cycle activity signature genes along MuSC aging trajectory psuedotime. The solid yellow line is the local regression result for individual pseudotime bins (55 total, sized 0.10 per bin), with the gray shadow depicting the 95% CIs. F Heat map visualization of expression levels of genes (right) with correlated expression profiles to MuSC aging pseudotime ordered from Early to Late stage. Left: row annotation showing the functions of the genes. G Heat map visualization of expression levels of DEGs between Late 1 and Late 2 fates ordered by pseudotime. H, I DDR tree visualization of FAP trajectory with mapping of pseudotime (H) and senescence annotation (I). J Plot showing the ss-GSVA score of cell cycle activity signature genes along FAP aging trajectory psuedotime with 95% CI. K Heat map visualization of expression levels of genes with correlated expression profiles to FAP aging pseudotime from Early to Late stage. L, M DDR tree visualization of EC trajectory with mapping of pseudotime (L) and senescence annotation (M). N Plot showing the ss-GSVA score of cell cycle activity signature genes along EC aging trajectory with 95% CI. O Heat map visualization of expression levels of genes with correlated expression profiles to EC aging pseudotime. P, Q DDR tree visualization of SMC trajectory with mapping of pseudotime (P) and senescence annotation (Q). R Plot showing the ss-GSVA score of cell cycle activity signature genes along SMC aging trajectory with 95% CIs. S Heat map visualization of expression levels of genes with correlated expression profiles to SMC aging pseudotime. Source data are provided as a Source Data file.\n\nThe pseudotime trajectories for other cell types were also examined. FAPs displayed a more continuous and smooth cell fate trajectory (Fig.\u00a03H), along which the ratio of aged nuclei significantly increased (Suppl. Fig.\u00a02D, E) and senescent FAPs accumulated near the end (Fig.\u00a03I), accompanied by a decreased cell cycle ss-GSVA score (Fig.\u00a03J). Inflammatory response genes such as CDKN2B, CD56, CXCL8 and CXCL1 were among the most enriched pseudotime-correlated DEGs in the late stage (Fig.\u00a03K and Suppl. Fig.\u00a02F, G). Similar to FAPs, ECs exhibited a relatively simple trajectory pattern, with cells starting from early-end, going through a middle branch, then falling into the late-end (Fig.\u00a03L). Mapping of ECs along the trajectory also uncovered a significant increase of aged nuclei (Suppl. Fig.\u00a02H, I), senescent ECs (Fig.\u00a03M) and reduced cell cycle ss-GSVA score (Fig.\u00a03N). Aging ECs were enriched for genes associated with \u201cExtracellular space\u201d (such as COL1A2, CCDC80, PPKG1) and \u201cTranscription factor\u201d (such as ZEB2, MAP1B) (Fig.\u00a03O and Suppl. Fig.\u00a02J, K). In the trajectory for SMCs, we observed a mixture of young and aged nuclei in the early-end and middle phases, whereas the late end was predominantly occupied with aged nuclei (Suppl. Fig.\u00a02L, M). Most aged SMCs from the late end were detected as senescent and functionally distinct from the rest (Fig.\u00a03P, Q). We also observed a consistent decrease in cell cycle ss-GSVA score along the SMC pseudotime axis (Fig.\u00a03R); and pseudotime-associated genes were enriched for both \u201cInflammatory response\u201d (such as NF\u03baB1, TIMP1, TNXB, TNFA1P6, RUNX1) and \u201cExtracellular space\u201d (such as THBS4, NOTCH2, ITGBL1) (Fig.\u00a03S; Suppl. Fig.\u00a02N, O). Collectively, the findings reveal the temporal heterogeneity/dynamics of cellular senescence both inter-and intra-populationally among four major types of mononuclear cells in aged human muscle.\n\nKnowing the importance of SASPs in determining the senescence heterogeneity and function, we next mapped the SASP dynamics in aging muscle. We first identified senescence-associated DEGs (Sn-DEG) comparing senescent (Sn) and non-senescent (nSn) cells across the four mononuclear cell populations. A total of 1514 up- and 1576 down-regulated Sn-DEGs were identified in at least one cell type and the majority were cell type specific (Fig.\u00a04A and Suppl. Dataset\u00a03), again reinforcing the inter-cellular heterogeneity. By intersecting with the SASP set, 243 of the SASPs were found up-regulated in senescent cells and 78 were commonly shared in at least two cell populations (Fig.\u00a04B, Suppl. Dataset\u00a04); in particular, 16 were commonly shared in all four cell types including CXCL8, CXCL2, VCAN, COL12A1, MFAP5, PLAU, CXCL3, FBN1, CD44, CXCL1, SOD2, SLC39A14, MMP3, SERPINE2, ALCAM, and FSTL1 (Fig.\u00a04C). Nevertheless, >30% of the SASPs were cell type specific (25, 36, 32, 48 in MuSC, FAP, EC and SMC) (Fig.\u00a04B, Suppl. Fig.\u00a03A); for example, IGFBP7, NAMPT, CCL2, TGFB, IL7, and ANGPT2 were uniquely up-regulated in senescent MuSCs and THBS1, MAT2A, GALNT2, DPP4, ITGB3, LRPPRC in FAPs (Fig.\u00a04D). The above findings demonstrate both the commonality and the inter-populational variation in SASP constitutions. When taking a close examination of the MuSC SASPs, expectedly, we found the SASP ssGSVA score was much higher in senescent vs. non-senescent MuSCs (Fig.\u00a04E) and also in aged vs. young MuSCs (Fig.\u00a04F). DCN, VCAN, CXCL2, CCL2, CXCL1 were among the top-ranked SASPs in both senescent and aged MuSCs (Fig.\u00a04G, H and Suppl. Dataset\u00a04). The induction of DCN, CXCL1, APOD, CCL2, CXCL2, CXCL8, IGFBP6, and EGFR was further validated by RT-qPCR in freshly isolated MuSCs (Fig.\u00a04I). Altogether, the above results solidify the induction of SASPs and profile their dynamics in senescent mononuclear cells in aged human muscle.\n\nA Circos plots showing up/downregulated DEGs in senescent MuSCs, FAPs, ECs, SMCs (total DEGs in brackets). Curves showing shared Sn-DEGs between two cell types. Inner arcs (grey) showing unique/shared Sn-DEGs. B Upset plot showing the number of unique/shared upregulated SASP genes (pairwise cell comparisons). C Plot showing the up-regulated SASPs shared by at least two cell types. D Scatter Plot showing the cell type-specific upregulated SASPs. E, F Ridge map showing ss-GSVA score density for classical SASP genes in Sn vs. nSn (E) and aged vs. young (F). The dashed line corresponds to peak positions. p\u2009=\u20092.2e-16 (E), 0.0025 (F) (Wilcoxon, two-sided). G, H Heatmap of op upregulated SASPs in Sn vs. nSn (G) and aged vs. young (H). I RT-qPCR detection of top-ranked up-regulated SASP gene expressions in aged/young human MuSCs, n\u2009=\u20095. p\u2009=\u20090.0043 (DCN), 0.032 (CXCL1), 0.041 (APOD), 0.022 (CCL2), 0.0093 (CXCL2). J Bar plot comparing SASP-mediated intercellular interaction strength (aged vs. young). K Heatmap showing differential SASP interactions (aged vs. young). Red/blue represents increased/decreased signaling. Top/right bars showing incoming/outgoing signals for each cell type. L Relative flows of differentially active signaling pathways during muscle aging. M Plot showing the signal strength change by aggregating all L-R pairs within CXCL pathway. The edge color corresponds to the sender cell type, and the edge weight is proportional to the interaction strength. N, O Chord diagram visualizing MuSC-centered SASP communication as receptor (N) or sender (O). P Dot plot showing the increased SASP L-R pairs from MuSC (sender cell) to other cells (target cell) in aged vs. young group. The dot color and size represent the computed communication probability and p-values. All the bar graphs are presented as mean\u2009+\u2009SD, paired two-sided Student\u2019s t-test (I) and unpaired two-sided Student\u2019s t-test (E, F) were used to calculate the statistical significance: *p\u2009<0.05, **p\u2009<0.01, ***p\u2009<0.001, ****p\u2009<0.0001, n.s.\u2009=\u2009no significance. Source data are provided as a Source Data file.\n\nNext, to elucidate the function of the SASPs, we mapped SASP-mediated intercellular communication, a total of 810 SASP-mediated ligand (L)-receptor (R) pairs were defined among all cell types by CellChat (Suppl. Dataset\u00a04). Expectedly, SASP-mediated interaction strength was enhanced in the aged vs. young muscle (51.8 vs. 47.2, Fig.\u00a04J) along with the overall L-R mediated cell-cell interaction strength (56.8 vs. 52.0, Suppl. Fig.\u00a03B), suggesting SASP function in augmenting cellular communication and altering niche microenvironment in aging muscle. Consistently, further examination of the receiver-sender cell interactions revealed a global strengthening of SASP (Fig.\u00a04K) or all L-R (Suppl. Fig.\u00a03C) mediated interaction signals between two cell types; for example, the signals to SMCs or ECs from all other types of cells showed a remarkable increase in aged muscle. Interestingly, MuSCs received decreased signals from ECs, FAPs and Pericytes while sending higher levels of signals to BT/NK cells, ECs, MPs and MSMs (Fig.\u00a04K). Additionally, the network analysis revealed significantly altered SASP-mediated pathways in scaled information flow (the total sum of communication probability from the inferred SASP network); and a gain of inflammatory SASP-mediated pathways (for example by VISFATIN, CD226, ALCAM, and MHC-I signals) was observed in the aged muscle (Fig.\u00a04L); ECM-SASP mediated pathways were also altered, for example, increased ITGB2 pathway and decreased PTN pathway were observed (Fig.\u00a04L). Additionally, growth factors, such as FGF and IGF-mediated pathways were also changed in aged muscle (Fig.\u00a04L). Of note, we found that CXCL family (CXCL12, 2, 8, 3 and 1) may act as key SASPs mediating cellular interactions and CXCL-mediated interaction strength and frequency showed significant elevation in the aged muscle (Fig.\u00a04M, Suppl. Dataset\u00a04). By examining enhanced signaling pairs in the aged group, we identified 54 SASP-mediated L-R pairs with MuSC as either a sender (43), receptor (9) or involved in autocrine interactions (2) (Fig.\u00a04N, O, Suppl. Dataset\u00a04). Among the most age-related increased signals emanating from or received in MuSCs, MPs, SMCs, and FAPs exhibited the highest interaction frequency with MuSCs; and CD44 represented a key receptor mediating the interactions (Fig.\u00a04N, O). Next, to further examine the up-regulated SASP-mediated cell interactions in aged MuSCs, analysis of differential communication probability was performed to identify top-ranked L-R pairs emanating from MuSCs. For example, MIF-(CD74\u2009+\u2009CXCR4) signaling from MuSCs to B/T/NK cells was drastically activated in aged muscle, and MIF-ACKR3 signaling from MuSCs to FAPs, FN1-CD44 signaling from MuSCs to SMCs were also increased (Fig.\u00a04P). Altogether, the above findings define SASP-mediated cellular interactions and demonstrate the key functions of SASPs in modulating muscle microenvironment during muscle aging.\n\nAmong all the above-identified SASPs in senescent cells, we observed a robust induction of CCL3, CCL4 and CCL5 along with their receptor CCR5 in aged vs. young MuSCs (Fig.\u00a05A) and whole muscle (Suppl. Fig.\u00a04A, B); and the high induction was also confirmed by RT-qPCR in isolated MuSCs from additional human muscle donors (Fig.\u00a05B). Moreover, their induction was also found in aged mouse muscles and MuSCs by analyzing publicly available single-cell RNA-seq data (Suppl. Fig.\u00a04C, D) and RT-qPCR (Suppl. Fig.\u00a04E)36. Interestingly, in our recent study37, we have demonstrated that Maraviroc(MVC), a Ccr5 antagonist is very effective in targeting the Ccl5-Ccr5 axis thus mitigating inflammation in dystrophic mouse muscles. We thus tested if MVC can be a potential senomorphic for treating muscle aging by blocking the function of Ccl3, 4 and 5 in mice. In the first high dose short term (HDST) treatment regime, a high dose (10\u2009mg/kg) of MVC was administrated on 18\u2009months old mice intraperitoneally for 3\u2009months (Fig.\u00a05C). The MVC treatment led to an evident increase in muscle mass (28.01% increase of TA/body weight) (Fig.\u00a05D) and muscle morphology (Fig.\u00a05E) compared to the DMSO-treated mice; by H&E staining, the inflammation was also attenuated, and the muscle fiber size was increased (15.50%) (Fig.\u00a05E). As a result, the muscle function was significantly enhanced which was evidenced by a notable increase in the grip strength (15.79%) (Fig.\u00a05F) and a tapered grip strength reduction (\u0394grip) (19.75%) (Fig.\u00a05G). Consistently, when subjected to a treadmill running test, in which the mice were adapted to a treadmill followed by a stepwise increase of running speed until their exhaustion, the MVC-treated mice demonstrated higher running speed (26.42%) (Fig.\u00a05H) and longer running distance (50.00%) (Fig.\u00a05I). Overall, we observed the mice were rejuvenated to a much healthier and active state. Furthermore, the number of MuSCs was elevated (3.91% vs. 3.39%) accompanied by decreased macrophages (0.89% vs. 1.09%) but no significant change of FAPs (Fig.\u00a05J), indicating the treatment blocked the action of SASPs and improved the aging muscle niche.\n\nA snRNA-seq analysis of CCR5 axis gene expressions in human MuSCs. B RT-qPCR detection of CCR5 axis gene expressions in human MuSCs, n\u2009=\u20095. p\u2009=\u20090.012 (CCL3), 0.039 (CCL4), 0.026 (CCR5). C Schematic of high dose short term DMSO/MVC treatment/assessment regime in aging mice, n\u2009=\u20098. Created in BioRender. Li, Y. (2025) https://BioRender.com/xsxka6wD The ratio of TA muscle/body weight of the above-treated mice, n\u2009=\u20098. p\u2009=\u20090.00040. E H&E staining and quantification of CSAs of TA muscles collected from the above-treated mice. Scale bar: 50\u2009\u03bcm, n\u2009=\u20096. p\u2009=\u20090.034. F, G Grip strength and strength changes of the above-treated mice, n\u2009=\u20098. p\u2009=\u20090.014 (F), 0.017 (G). H, I Maximal running speed and distance of the above-treated mice, n\u2009=\u20098. p\u2009=\u20090.031 (H), 0.043 (I). J Flow cytometry detection of MuSC, MP, and FAP populations in the above-treated mice, n\u2009=\u20098. p\u2009=\u20090.045 (MuSC), 0.042 (MP). K scRNA-seq of mononucleated muscle cells isolated from the treated mice. Unsupervised clustering resolved 13 cell types. L Sankey plots showing the distribution and relative composition across cell types. M\u2013P Top: Bar plot of USS-defined Sn/nSn cells. Bottom: Violin plot of p21 expression. p\u2009=\u20098.07e-05 (MuSC), 7.43e-24 (FAP), p\u2009=\u20091.31e-67 (EC), p\u2009=\u20094.99e-06 (SMC). Wilcoxon test (two-sided, unadjusted). Q Bar plot showing SASP-mediated interaction strength in DMSO and MVC. R Heatmap showing the altered SASP-mediated cell-cell interaction of MVC vs. DMSO. S Interaction frequency of Ccl3-Ccr5/Ccl4-Ccr5 pairs in MVC vs. DMSO. T RT-qPCR detection of the Ccr5 axis gene expressions in whole muscles of MVC vs. DMSO, n\u2009=\u20095. p\u2009=\u20090.0024 (Ccl3), 0.00048 (Ccl4), 0.0084 (Ccl5), 0.0047 (Ccr5). U Volcano plot displaying DEGs from bulk RNA-seq performed in MuSCs from DMSO/MVC-treated mice. Log2FC\u2009>1, adjusted-p\u2009<0.05 (two-sided Wald test, Benjamini-Hochberg adjusted). V GO analysis of the above-identified 231 down-regulated DEGs. W GSEA analysis of the repressed SASPs in the above MVC vs. DMSO-treated MuSCs. X RT-qPCR detection of the Ccr5 axis gene expressions in the above MuSCs, n\u2009=\u20095. p\u2009=\u20090.017 (Ccl3), 0.0074 (Ccl4), 0.0058 (Ccr5). All the bar graphs are presented as mean\u2009+\u2009SD, paired two-sided Student\u2019s t-test (T, X) and unpaired two-sided Student\u2019s t-test (B, D\u2013J) were used to calculate the statistical significance: *p\u2009<0.05, **p\u2009<0.01, ***p\u2009<0.001, n.s.\u2009=\u2009no significance. Source data are provided as a Source Data file.\n\nThe above-uncovered niche impact was further elucidated by single-cell RNA-seq analysis of the 5437 and 5454 mononuclear cells collected from DMSO or MVC-treated mouse muscles. Among the total of 12 identified cell populations including Pro-inflammatory MPs (PI-MPs), Anti-inflammatory MPs (AI-MPs), FAPs, EC1, EC2, Tenocytes, B/T/NK cells, SMCs, MuSCs, Neutrophils, MSMs, Pericytes and Schwann cells (Fig.\u00a05K) based on normalized gene expression levels and canonical cell type specific markers (Suppl. Fig.\u00a04F, and Suppl. Dataset\u00a05), we detected a remarkably increased population of MuSCs in MVC vs. DMSO group (4.68% vs. 2.65%) accompanied by a reduced population of PI-MPs (1.91% vs. 3.79%) but interestingly not FAPs (27.72% vs. 23.28%) (Fig.\u00a05L, Suppl. Dataset\u00a05). Furthermore, we detected significantly decreased levels of cellular senescence in MuSCs (17.6% vs. 20.1%, Fig.\u00a05M), FAPs (15.8% vs. 19.3%, Fig.\u00a05N), ECs (16.7% vs. 21.9%, Fig.\u00a05O) and SMCs (20.1% vs. 31.1%, Fig.\u00a05P), which was further supported by the decreased p21 mRNA expression (Fig.\u00a05M\u2013P). Consistently, reduced p16 and p21 expressions were detected in the whole muscles (Suppl. Fig.\u00a04G, H) and also the isolated MuSCs after MVC treatment (Suppl. Fig.\u00a04I). In addition, pseudotime analysis on the MuSCs uncovered that the cells progressed into one late fate after the MVC treatment but a middle branch emerged; indeed a lower portion of senescent cells distributed in the late fate confirming the decreased senescence after the MVC treatment (Suppl. Fig.\u00a04J). Additionally, cellular crosstalk analysis revealed a global decline of cellular interactions in the MVC-treated muscle (Suppl. Fig.\u00a04K, L). A close examination of SASP-mediated cellular interaction strength showed a decreased interaction strength (Fig.\u00a05Q); further examination of the receiver-sender cell interactions also revealed a global decrease of SASP-mediated interaction signals between two cell types except for B/T/NK cell interactions (Fig.\u00a05R). For instance, MVC treatment led to reduced interactions mediated by Cxcl pathway among MuSCs, SMCs, neutrophils, and MPs (Suppl. Fig.\u00a04M). Expectedly, Ccr5 interactions with its ligands, Ccl3, Ccl4 and Ccl5 were repressed by the MVC treatment (Fig.\u00a05S, Suppl. Fig.\u00a04N, O), which was also accompanied by their reduced expression levels in the whole muscle (Fig.\u00a05T). To further examine the impact of MVC treatment on MuSCs, bulk RNA-seq was performed on freshly isolated MuSCs from the treated mice. We found that the 231 down-regulated genes (Fig.\u00a05U, Suppl. Dataset\u00a06) upon MVC treatment were enriched for SASPs-related terms such as \u201cextracellular space\u201d, \u201cinflammation response\u201d etc. (Fig.\u00a05V and Suppl. Dataset\u00a06), suggesting repressed SASP expression; this was also confirmed by the Gene Set Enrichment Analysis (GSEA) (Fig.\u00a05W and Suppl. Dataset\u00a06). Expectedly, Ccl3, Ccl4, Ccl5 and Ccr5 were among the down-regulated SASPs (Suppl. Dataset\u00a06), which was further confirmed by RT-qPCR (Fig.\u00a05X). Altogether, the above results demonstrate the potential use of MVC as a senomorphic for alleviating cellular senescence, improving muscle niche and rejuvenating aging muscle.\n\nTo further demonstrate the efficacy of MVC treatment, we next tested a low dose (2\u2009mg/kg) long-term (6\u2009months) (LDLT) regime (Suppl. Fig.\u00a05A) and found the treatment also led to a pronounced restoring effect of muscle morphology, function and niche integrity in aged mice (Suppl. Fig.\u00a05B\u2013G); we also tested a low dose (2\u2009mg/kg) and short term (3\u2009months) (LDST) regime (Suppl. Fig.\u00a05H), and the treatment did not appear to have evident restoring effect: no significant changes in muscle morphology and muscle niche were observed despite increased TA weight and muscle performance (Suppl. Fig.\u00a05I\u2013N). Moreover, when the HDST regime was applied on 2\u2009month-old young mice, no significant treatment effects were detected (Suppl. Fig.\u00a05O\u2013T), indicating the specificity of MVC efficacy on aged mice.\n\nNext to gain a holistic understanding of the senescence state/SASP regulation and identify potential upstream TF regulators for senotherapeutic targeting, we analyzed the paired snATAC-seq data which permits identification of TF binding via mapping chromatin accessibility. As a result, key TF regulators of senescent cell state were defined with enriched motifs predicted in the ATAC measurements. 234 TFs were shared in at least two cell types, and 26 were commonly found in all four cell types, including NF-\u03baB family (NF-\u03baB1, REL and RELB), several AP-1 family TFs (ATF2, 3, 4, 6, 7 and BATF3), C/EBP family (C/EBPD, B, G, A) and CREB family (CREB3, 5 and CREB3L3, 4) (Fig.\u00a06A). Both NF-\u03baB1 and C/EBPB are known key players in the regulation of senescence and SASPs7,8,9,10,11. We thus took a close examination of the previously unappreciated ATF3 factor. Of note, the accessibility of ATF3 binding motifs was increasingly enriched in Sn vs. nSn cells (Fig.\u00a06B) and also in aged vs. young cells (Fig.\u00a06C) in all four cell types, demonstrating its potential role in regulating senescence and aging. By integrating the paired snRNA-seq and snATAC-seq data, Functional Inference of Gene Regulation (FigR) analysis38 was performed to identify target genes that were activated or repressed by ATF3 binding (Suppl. Dataset\u00a07). As a result, transcriptional scores of the ATF3-activated genes were notably elevated in Sn cells (Fig.\u00a06D), while the scores of the repressed genes were lower compared to the nSn group (Fig.\u00a06E), supporting the notion that ATF3 plays a positive regulatory function in senescent cells. However, this difference was not pronounced in the comparison between the young and aged groups (Fig.\u00a06D, E). Furthermore, GO functional analysis revealed that ATF3 target genes were enriched for senescence-related terms such as cell proliferation, cell migration, regulation of chemotaxis, regulation of cell adhesion, and ECM organization, etc. (Fig.\u00a06F and Suppl. Dataset\u00a07). While displaying similar enrichment terms, differential enrichment patterns were observed in the four cell types. For instance, ATF3 target genes identified in MuSCs were highly enriched for the regulation of the MAPK cascade; target genes in FAPs exhibited a low association with cell morphogenesis while the targets in SMCs showed limited relevance to extracellular matrix organization. To construct the ATF3 regulatory network, ATF3-regulated senescent DEGs (ATF3-SnTargets) were predicted and many were shared in multiple cell types (Fig.\u00a06G and Suppl. Dataset\u00a07). Interestingly, the shared target genes (such as genes CXCL8, CXCL2, and CXCL1 shared in all four cell types) were predominantly upregulated in Sn vs. nSn cells (Fig.\u00a06G), suggesting ATF3 mainly functions to promote gene expression in senescent cells. Altogether, the above findings define potential TF regulators of cellular senescence in aging muscle and highlight ATF3 as a previously unknown key regulator of senescence in multiple mononuclear cell populations in human muscle.\n\nA Plots showing the predicted TFs governing senescence state shared by at least two cell types. B, C Box plots showing predicted ATF3 ATAC accessibility level in Sn vs. nSn cells (B) or aged vs. young groups (C). Exact p-value and other statistical details are provided in Source Data file. D, E Box plots showing the ss-GSVA gene set scores of target genes activated (D) or repressed (E) by ATF3. Full statistical details are provided in the Source Data file. F Network visualization of representative GO terms and pathways of ATF3-modulated DEGs in each cell type of aged vs. young muscle. The nodes represent GO terms or pathways, and the pie plots display the proportion of genes corresponding to a specific GO term or pathway in each cell type. G Network visualization of ATF3 targeted up- or down-regulated senescent genes in each cell type. Node size positively correlates with the number of cell types with its embedded pie chart indicating the number of up- and down-regulated DEGs. Each connecting line represents Sn-DEGs in the corresponding cell type with its color indicating log2fold change (FC) values. H, I Network visualization of core activators (H) or repressors (I) TFs in each cell type between old and young groups. Outer nodes display different cell types and the node color represents the regulation score of all TF-SASP associations averaged on each cell type. Inner nodes positively correlate with the number of all TF-SASP pairs for each cell type. Each connecting line represents the number of SASP factors regulated by certain TF for each cell type. J Heatmap showing JUNB-SASP regulation score for each cell type. K Heatmaps highlighting smoothed normalized JUNB DORC accessibility, SCT-normalized RNA expression, and DORC-RNA difference for JUNB target SASP genes in MuSCs (n\u2009=\u200932). Two-sided unpaired Student\u2019s t-test was used to calculate the statistical significance (B\u2013E): *p\u2009<0.05, **p\u2009<0.01, ***p\u2009<0.001, ****p\u2009<0.0001, n.s.\u2009=\u2009no significance. Source data are provided as a Source Data file.\n\nNext, we further elucidated the core TFs governing SASP induction, similarly we constructed TF-SASP regulatory networks and defined potential activating (Fig.\u00a06H) and suppressing (Fig.\u00a06I) TFs (Suppl. Dataset\u00a07). Again, NF-\u03baB (NF-\u03baB1, REL), and AP-1 family (JUNB, FOSL1, ATF6, FOSB, JUND) TFs were the predominant activators in all four cell types (Fig.\u00a06H); AP-1 family, such as JUNB, and FOSL1 were among the most probable TF activators (Fig.\u00a06H). Interestingly, two other AP-1 family TFs, FOS and JUN, were identified as potential suppressors (Fig.\u00a06I). To further explore the previously unappreciated JUNB-SASP regulation, we assessed JUNB-SASP association using regulation scores and defined 119 common or cell type specific JUNB-activated SASP targets (Suppl. Dataset\u00a07); Among the top 20 targets, PLAU was commonly shared in all four cell types while CXCL1, MMP2, CCL17, and BTD were unique in MuSCs, FAPs, ECs and SMCs (Fig.\u00a06J). To substantiate the potential role of JUNB in activating SASPs, DORC (domains of regulatory chromatin) analysis was performed following previous method39,40 to correlate the accessibility of JUNB motif-containing peaks near each SASP target gene with its expression. The difference between the JUNB DORC chromatin accessibility and gene expression along the pseudotime axis was calculated; we found that for most of the JUNB-SASP targets, the chromatin accessibility gain preceded that of the expression change in MuSCs (Fig.\u00a06K) as well as the other three cell types (Suppl. Fig.\u00a06A\u2013C). These observations were exemplified on the FBN1 and TNFRSF10D genes during MuSC aging trajectory (Fig.\u00a0S6D, E); for example, JUNB-related chromatin change of FBN1 was identified as an early event, occurring within the first 47 pseudotime bins, preceding the RNA expression change. Altogether the above findings suggest JUNB could be a key upstream TF inducer of SASP production, warranting further investigation.\n\nTo further elucidate JUNB regulation of SASP induction and senescence in MuSCs, we quantified the TF-SASP association by regulation scores and confirmed that JUNB was among the most prominent candidate regulators for up-regulated SASPs (Fig.\u00a07A). A closer examination revealed that the chromatin openness level of the snATAC-seq predicted JUNB binding sites was significantly increased in aged vs. young MuSCs (Fig.\u00a07B), supporting the high probability for JUNB to be a key regulatory TF in aged MuSCs. Expectedly, JUNB expression itself was also significantly elevated in aged MuSCs according to the accompanied scRNA-seq data (Fig.\u00a07C). We also examined its levels in the isolated human MuSCs; despite AP-1 TFs can be quickly activated by the isolation procedure41,42,43 in both young and aged MuSCs, a much higher level of JUNB was detected in aged MuSCs (133.35%, Fig.\u00a07D). 54 SASPs were predicted to be bound by JUNB including the top-ranked IL1R1, TGFB3, TNFRSF10D, TNFRSF1A, GDF15, PLAUR, CXCL1, PDGFB, CSF3, TIMP2 (Fig.\u00a07A and Suppl. Dataset\u00a07); which was also confirmed by RT-qPCR in aged human MuSCs (Fig.\u00a07D). NF-\u03baB family, on the other hand, regulated a very different set of SASPs (Fig.\u00a07A). Interestingly, analysis of published scRNA-seq and scATAC-seq data from mice44 also predicted mouse JunB (mJunB) as an upstream TF regulator of SASP induction in aged mouse MuSCs (Suppl. Fig.\u00a07A); consistently, mJunB upregulation was also detected in the MuSCs freshly isolated from aged vs. young mice (58.10%); moreover, the above predicted human SASP targets of human JUNB (hJUNB) were also highly expressed in aged mouse MuSCs (Fig.\u00a07E). These results suggested a conserved role of JUNB in regulating SASP induction in both human and mouse MuSCs. We thus leveraged an inducible MuSC-specific JunB knockout mouse (JunB-iKO) that was generated by crossing the JunB flox mouse with a Pax7CreER; R26Yfp mouse (Suppl. Fig.\u00a07B\u2013D) to further elucidate the regulatory mechanism. MuSCs were isolated from the Ctrl and iKO mice; expectedly we found the expression levels of the above-defined mJunB SASP targets were all significantly down-regulated (Fig.\u00a07F). Moreover, over-expression of mJunB in the MuSCs isolated from young mice induced the expression of some SASP targets including Il1r1, Plaur, Cxcl1 and Timp2 (Fig.\u00a07G). Interestingly, the above loss or gain of mJunB did not appear to affect the levels of SA-\u03b2-GAL and several marker genes such as p16, p19, p21 and p53 (Suppl. Fig.\u00a07E\u2013K), suggesting mJunB can induce SASP target activation but may not be sufficient to trigger full senescence program in mouse MuSCs.\n\nA Heatmap showing snATAC-seq DORC regulation scores for top TF-SASP associations in human MuSCs. B, C Violin plot showing chromatin accessibility (B) and expression (C) of human (h)JUNB in young vs. aged MuSCs. p\u2009=\u20090.00032 (B), 0.038 (C). Wilcoxon tests, two-sided. D RT-qPCR detection of the hJUNB and SASP target expressions in young/aged MuSCs, n\u2009=\u20094. p\u2009=\u20090.0067 (JUNB), 0.044 (TGFB3), 0.033 (GDF15), 0.0018 (PLAUR), 0.039 (CXCL1), 0.011 (PDGFB). E RT-qPCR detection of the mouse (m)JunB and SASP target expressions in young (2\u2009m)/aged(24\u2009m) MuSCs, n\u2009=\u20093. p\u2009=\u20090.016 (JunB), 0.0087 (Tgfb3), 0.034 (Gdf15), 0.0048 (Cxcl1), 0.0044 (Pdgfb), 0.0029 (Csf3). F RT-qPCR detection of the mJunB and SASP targets expressions in Ctrl/JunB-iKO MuSCs, n\u2009=\u20095. p\u2009=\u20090.023 (Il1r1), 0.0072 (Plaur), 0.0017 (Cxcl1), 0.0020 (Pdgfb), 0.0055 (Csf3). G RT-qPCR detection of the mJunB and SASP target expressions in Ctrl/JunB-overexpressing MuSCs, n\u2009=\u20093. p\u2009=\u20090.037 (Il1r1), 0.041 (Plaur), 0.0012 (Cxcl1), 0.041 (Timp2). H Pie chart showing the distribution of snATAC-seq predicted hJUNB binding sites in young/aged human MuSCs. I Pie chart showing the distribution of mJunB binding in young/aged MuSCs, detected by CUT&RUN-seq. J Average H3K27ac signal profile (\u2009\u00b1\u20091\u2009kb around mJunB sites). K, L Pie chart showing the overlapping of mJunB target genes (K) and SASP genes (L) (promoter and enhancer bound) in young/aged MuSCs. The young/aged unique SASP targets are listed. M Genomic snapshots on Cxcl1 locus showing the binding peaks of mJunB and H3K27ac in young/aged MuSCs. N, O Genomic coverage plot on CXCL1 locus showing the predicted hJUNB binding peaks in Sn vs. nSn (N) and young vs. aged (O) human MuSCs. P Chromatin openness level (DORC) versus normalized gene expression (SCT) dynamics of CXCL1 gene along MuSC aging pseudotime. Dotted line represents LOESS fit to the values obtained from sliding window bin averaged from DORC accessibility or SCT expression levels (n\u2009=\u2009100 cells per bin). All the bar graphs are presented as mean\u2009+\u2009SD, two-sided paired Student\u2019s t-test was used to calculate the statistical significance (B\u2013G): *p\u2009<0.05, **p\u2009<0.01, ***p\u2009<0.001, n.s.\u2009=\u2009no significance. Source data are provided as a Source Data file.\n\nTo further explore how hJUNB or mJunB activates SASP genes, we took a close examination of the snATAC-seq data and found a large percentage of the predicted hJUNB binding was mapped to intergenic and intron regions (56.31% in young and 35.59% in aged) in MuSCs (Fig.\u00a07H), indicating hJUNB may regulate gene expression mainly through enhancer binding. To further dissect the regulatory mechanism, CUT&RUN-seq was conducted to map mJunB binding in MuSCs from young and aged mice to define a total of 13,437 and 14,123 mJunB binding events (Suppl. Fig.\u00a07L and Suppl. Dataset\u00a08). Consistent with the above prediction in human MuSCs, a large percentage of mJunB binding peaks were mapped to intergenic and intron regions (63.50% in young and 58.99% in aged) in MuSCs (Fig.\u00a07I), and >60% of the binding sites were >3\u2009kb distal to the TSSs while a small portion of promoter binding (\u22643\u2009kb) was observed (36.66% in aged and 31.92% in young) (Suppl. Fig.\u00a07M). By intersecting with the H3K27ac CUT&RUN-seq data performed in mouse MuSCs, we found indeed most mJunB-binding sites were in active enhancer regions (Fig.\u00a07J), and its enhancer binding evidently increased in aged vs. young MuSCs (6,648 vs. 6,042, Suppl. Fig.\u00a07N). Based on promoter and enhancer binding, we identified a total of 7,361 mJunB target genes in young and 10,653 in aged MuSCs (Fig.\u00a07K and Suppl. Dataset\u00a08), which were enriched for GO functions related to \u201cprotein modification\u201d and \u201ccatabolic process\u201d (Suppl. Fig.\u00a07O and Suppl. Dataset\u00a08). To elucidate how mJunB activates SASP transcription, we defined a number of the SASP targets that were directly bound by mJunB (Suppl. Dataset\u00a08), and the number indeed increased in aged (40) vs. young (27) MuSCs (Fig.\u00a07L). Expectedly, mJunB binding mostly resided in the enhancer regions of these SASP targets. Notably, Cxcl1 was defined as a prominent target in both human and mouse (Fig.\u00a07D, E) and mJunB bound to both the promoter and an enhancer region of Cxcl1 in aged mouse MuSCs (Fig.\u00a07M). In human MuSCs, the predicted hJUNB binding on CXCL1 resided in a downstream region and showed a significantly elevated chromatin openness level in senescent (Fig.\u00a07N) and aged human MuSCs (Fig.\u00a07O). Furthermore, DORC analysis on CXCL1 locus illustrated the chromatin accessibility gain preceded the transcriptional induction, indicating the activating role of hJUNB in CXCL1 transcription (Fig.\u00a07P). Altogether, the above results solidify the key role of hJUNB/mJunB in governing SASP induction in aged MuSCs.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61403-y/MediaObjects/41467_2025_61403_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we conducted multiomics mapping of cellular senescence atlas in aging human muscle. Using a USS scoring system, we mapped the cellular senescence in the mononucleated cells in aging muscle and uncovered commonality and heterogeneity of senescence state among different cells. Knowing SASPs are the main determinant of senescence state and function, we further defined SASP composition and revealed the heterogeneity and dynamics in SASP constitution and expression. Moreover, we dissected key TFs governing cellular senescence and SASP production and defined the key role of AP-1 family TF such as ATF3 and JUNB in senescence/SASP regulation. Our study solidifies the prevalence of senescence in multiple mononuclear cells and pinpoints the presence of MuSC senescence and its potential function in altering niche microenvironment. More importantly, the above mapping led to the\u00a0identification of MVC as a potential senotherapeutic approach to delay sarcopenia progression and rejuvenate aging mice.\n\nDespite the renewed interest in studying cellular senescence and its implication in organismal aging, there is still a scarcity of knowledge in our understanding of cellular senescence in aging skeletal muscle. Our study provides the first comprehensive mapping and characterization of cellular senescence in aging human muscle. First, this is the first multiomics mapping harnessing simultaneous single-nucleus RNA-seq/ATAC-seq to enable dissection of transcriptomic and epigenomic features of senescence in the same cells. By removing the terminally differentiated, post-mitotic myofibers from the analysis, it allowed us to focus on the mononucleated cells in the niche that are prone to senesce\u00a0thus permitting an in-depth examination of senescence in MuSCs for the first time. Moreover, our study yields the first senescence blueprint on skeletal muscles from human. Although two recently emerged studies18,36 provide the first cell atlas in the aging human skeletal muscles, none focus on cellular senescence. Lastly, our mapping is largely facilitated by creating a USS scoring method that combines almost all the known senescent gene sets, which permits a useful tool for defining the wide spectrum of senescence. The heterogeneous nature of senescence across cells and tissues is a well-recognized feature that hurdles the overall advancement of the field4. A holistic understanding of the transcriptomic and epigenomic heterogeneity of senescent cells in aging human muscles will contribute to a better grasp of the biological functions of these cells, and also facilitate the identification of new markers and therapeutic strategies for alleviating sarcopenia and promoting healthy aging. Our mapping indeed uncovered wide variations of senescence at both inter- and intra-populational levels in aging muscle. Pseudotime analysis uncovered a temporal dynamic of senescence within each of the four examined cell types; senescent MuSCs clearly diverged into two different fates at the intra-populational level and senescence also showed marked different temporal patterns within FAPs, ECs and SMCs. The heterogeneity is also largely reflected by the SASP constitutions; unique compositions of SASP factors were defined in each cell population. Still, we were able to uncover shared features of senescence among the cells and common SASPs were found (Fig.\u00a04); these SASPs constitute promising clinical biomarkers for assessing the burden of senescence in aging muscle tissue and also can serve as senomorphic targets for simultaneously blocking SASP action in multiple cells.\n\nFunctionally SASP factors are the major mediators of the non-cell-autonomous effects of senescent cells through their paracrine effect in dispersing senescence and altering the niche microenvironment5,6. Indeed, the cell-cell interaction analysis sheds light on the complex SASP-mediated cellular interactions. Of note, inflammatory SASP-mediated pathways were largely enhanced in aged muscle (Fig.\u00a04L), supporting the key role of SASPs in inflammaging occurring in the aged skeletal muscle tissue. These defined interactions such as CXCL-mediated interactions occurring among multiple cell types thus represent potential targets for senomorphic design. Notably, the in-depth analysis of MuSC-centered cellular interactions reinforced our belief that MuSCs are not merely passive recipient of niche signals, they can actively modulate the niche through their secretory function37; here in aged human muscle, our finding defined many signaling pairs through which MuSCs communicate with other cell populations via secreting SASPs (Fig.\u00a04N\u2013P).\n\nLastly, the heterogeneous nature of SASPs/senescence can also arise from the inducers and regulators of SASPs which are relatively less characterized5,6. By harnessing the snATAC-seq dataset we gained the first comprehensive mapping of the upstream TF regulators of both senescence state and SASPs in aging human muscle. Along with NF-\u03baB, the well-characterized master regulator of SASPs, AP-1 family appeared as a dominant transcriptional activator of senescence/SASPs in multiple cell populations. AP-1 family TFs are known to mediate early stress responses45,46,47 and we recently elucidated the important role of ATF3 in regulating MuSC regenerative activity48. The findings from the current study illuminated a key role of ATF3 in governing senescent state in aging muscle via its extensive regulatory targets/networks, which warrants further dissection in the future. In terms of SASP regulation, we identified JUNB, also an AP-1 factor, as a prominent activator of SASP transcription in multiple cells and conducted in-depth mechanistic dissection on how JUNB activates SASPs in MuSCs. Our findings demonstrate that JUNB activates SASP activation in both human and mouse MuSCs. The direct JUNB SASP targets were identified and CXCL1 appeared to be a prominent one shared in both human and mouse. Considering AP-1 TFs have been noticed as a possible regulator of senescence state in multiple cells/tissues49,50,51,52,53,54, we thus reason targeting JUNB or other AP-1 TFs could be a powerful senomorphic approach that is worth further investigation in the future.\n\nCollectively, we believe senescence heterogeneity encompasses multiple layers, by harnessing snRNA-seq/snATAC-seq our findings highlight the transcriptomic and epigenomic variations at intra- and inter-populational levels in aging human muscle. In the future, further advancement in single-cell technology will permit multi-modal characterization of both common and heterogeneous features of SnCs in aging muscle such as secretome, proteome, metabolism, etc. In addition, it will also be necessary to include immune cells in future endeavors which were excluded from the current study due to the challenge in discerning between their non-senescent and senescent states using the inflammatory SASPs.\n\nUltimately, it is our goal to identify druggable targets for senotherapeutics to ameliorate sarcopenia and enhance healthy muscle aging. Our attempt to use MVC in aging mice yielded very positive results. Systemic delivery of MVC that blocked CCL3, 4, 5 action significantly enhanced muscle performance, delayed muscle aging and led to remarkable rejuvenating effects; the effect was pronounced under the HDST, LDLT treatment schemes and milder under LDLT. The treatment diminished senescence in multiple cells and reversed the deregulated aging muscle niche, altogether demonstrating the feasibility of pharmacological SASP inhibition as a potential senomorphic therapy for sarcopenia and potentially other conditions involving CCLs producing senescent cells. Currently, there is very limited research regarding senotherapeutics and their impact on skeletal muscle in the context of chronological aging12, our\u00a0findings thus demonstrate the utility of MVC for ameliorating muscle aging. Recently, we also demonstrated the anti-inflammatory effect of MVC treatment on DMD muscle37, therefore it could represent a potential pharmacologic intervention that concurrently targets both cellular senescence and inflammation, the two intricately associated hallmarks of organismal aging1. In the near future, it will also be possible to conduct a focused screen for senolytic or senomorphic compounds that target the unique features of senescent cells in skeletal muscle based on the foundational knowledge provided in our current study.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Hamstring muscle samples were collected during orthopaedic surgery and informed consents were obtained in written form from the patients in the Hong Kong cohort, with ages between 19 and 27\u2009years old (young group undergoing anterior cruciate ligament reconstruction) and 60\u201377\u2009years old (aged group undergoing knee replacement). Structurally intact and histologically healthy hamstring muscles were collected from anatomically equivalent regions across cohorts. The informed consent was obtained from the legally acceptable representative. Ethical approval was granted by the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (2021.255-T). Exclusion criteria were myopathy, hemiplegia or hemiparesis, rheumatoid arthritis or other autoimmune connective tissue disorders, cancer, coronary heart disease, inability to consent, or major surgery in the previous 3\u2009months. Full metadata information for the organ donors is provided in Supplementary Dataset 1.\n\nAll animal handling procedures, protocols and experiments ethics approval were granted by the CUHK AEEC (Animal Experimentation Ethics Committee) under Ref No. 22-292-HMF. The mice were maintained in an animal room with 12\u2009h light/12\u2009h dark cycles, temperature (22\u201324\u2009\u00b0C), and humidity (40\u201360%) at the animal facility in CUHK, fed with PicoLab\u00ae Select Mouse Diet 50 IF/9\u2009F Diet and provided with plenty of fresh clean water at all times. For all animal-based experiments, at least three pairs of littermates or age-matched mice were used. All animals were euthanized by asphyxiation before experiments.\n\nC57BL/6 aged mice were purchased from Gempharmatech (Nanjing, Jiangsu, China). JunBf/f mouse strains were purchased from The Jackson Laboratory (Bar Harbor, ME, USA). The JunB-inducible conditional KO (iKO, Pax7CreERT2/R26Yfp; JunBf/f) strain and Ctrl (Pax7CreERT2/R26Yfp; JunB+/+) mice were generated by crossing Pax7CreERT2/R26Yfp mice with Junbf/f mice. Primers used for genotyping are shown in Suppl. Dataset\u00a09.\n\nInducible deletion of JunB was administrated by intraperitoneal (IP) injection of tamoxifen (TMX) (Sigma-Aldrich, T5648) at 100\u2009mg/kg (body weight). Maraviroc (Sigma-Aldrich, PZ0002-25MG) treatment in aged C57BL/6 mice was administrated by IP injection at 2\u2009mg/kg (low dosage) or 10\u2009mg/kg (high dosage) every 2\u2009days for 3\u2009months (short term) or 6\u2009months (long term). For grip strength test, limb muscle grip strength of mice was measured by a grip strength meter (Kewbasis, KW-ZL-1) 3 times, the average values were calculated. For treadmill test, mice were adapted to a treadmill (Panlab, Harvard Apparatus, 76-0895) with a 5\u00b0 incline at an initial speed of 10\u2009cm/s, followed by a stepwise increase of 5\u2009cm/s every two min until their exhaustion.\n\nMuscle stem cells, fibro-adipogenic progenitors and macrophages were sorted based on established method41,42,43,55,56,57. Briefly, hindlimb muscles from mice and hamstring muscles from humans were digested with collagenase II (LS004177, Worthington, 1000 units per 1\u2009ml) for 90\u2009min at 37\u2009\u00b0C, the digested muscles were then washed in washing medium (Ham\u2019s F-10 medium (N6635, Sigma) containing 10% horse serum, heat-inactivated (HIHS, 26050088, Gibco, 1% P/S) before cells were liberated by treating with Collagenase II (100 units per 1\u2009ml) and Dispase (17105-041, Gibco, 1.1 unit per 1\u2009ml) for 30\u2009min. The suspensions were passed through a 20\u2009G needle to release cells. Mononuclear cells were filtered with a 40 \u03bcm cell strainer and sorted by BD FACSAria IV with the selection of the GFP+ (MuSCs of Ctrl and Junb iKO mice); FITC-(CD45-, CD31-) APC-(SCA1-) PE+ (VCAM\u2009+\u2009) (MuSCs of young and aged mice); FITC-(CD45-, CD31-, CD34-) APC\u2009+\u2009(CD29\u2009+\u2009) PE-CY7+ (CD56\u2009+\u2009) (MuSCs of human); FITC-(CD45-, CD31-, ITGA7-) APC\u2009+\u2009(SCA1\u2009+\u2009) (FAPs); FITC-(Cd45-) APC-(Ly6G-) eFluor450\u2009+\u2009(CD11b\u2009+\u2009) (MPs). Flowjo V10.8.1 was used for analysis of flow cytometry data. MuSCs were cultured in Ham\u2019s F10 medium with 20% FBS, 5\u2009ng/ml \u03b2-FGF (PHG0026, Thermo Fisher Scientific) and 1% P/S, on coverslips and culture wells which were coated with poly-D-lysine solution (p0899, Sigma) at 37\u2009\u00b0C overnight and then coated with extracellular matrix (ECM) (E-1270, Sigma) at 4\u2009\u00b0C for at least 6\u2009h.\n\npcDNA3.1-mouse-JunB plasmids were purchased from Youbio (http://www.youbio.cn/).\n\nTotal RNAs were extracted using TRIzol reagent (Invitrogen) following the manufacturer\u2019s protocol. For quantitative RT-PCR, cDNAs were reverse transcribed using HiScript III First-Strand cDNA Synthesis Kit (Vazyme, R312-01). Real-time PCR reactions were performed on a LightCycler 480 Instrument II (Roche Life Science) using Luna Universal qPCR Master Mix (NEB, M3003L). Sequences of all primers used can be found in Suppl. Dataset\u00a09.\n\nFor Western blot assays, according to our prior publication58,59,60, cultured cells were washed with ice-cold PBS and lysed in cell lysis buffer. Whole cell lysates were subjected to SDS\u2013PAGE and protein expression was visualized using an enhanced chemiluminescence detection system (GE Healthcare, Little Chalfont, UK) as described before55. The following dilutions were used for each antibody: JUNB (Cell Signaling Technology, #3753; 1:1000), Histone 3 (Santa Cruz, sc-56616; 1:5000). For SA-\u03b2-GAL staining, the \u03b2-galactosidase Senescence Kit (Cell Signaling Technology, #9860) was used. Briefly, cells were fixed for 15\u2009min followed by washing in PBS twice. Fixed cells were then incubated with \u03b2-galactosidase staining solution at 37\u2009\u00b0C overnight in a dry incubator (no CO2). The cells were then observed and counted for the SA-\u03b2-GAL positive cells. For immunofluorescence staining, cultured cells were fixed in 4% PFA for 15\u2009min and blocked with 3% BSA within 1\u2009h. Primary antibodies were applied to samples with indicated dilution below and the samples were kept at 4\u2009\u00b0C overnight. For immunofluorescence staining57,61, cultured cells or myofibers were fixed in 4% PFA for 15\u2009min and permeabilized with 0.5% NP-40 for 10\u2009min. Then cells were blocked in 3% BSA for 1\u2009h followed by incubating with primary antibodies overnight at 4\u2009\u00b0C and secondary antibodies for 1\u2009h at RT. Finally, the cells were mounted with DAPI to stain the cell nucleus and images were captured by a Leica fluorescence microscope. Primary antibodies and dilutions were used as following: PAX7 (Developmental Studies Hybridoma Bank; 1:50), P16 (Abcam, ab211542, 1:200). For immunohistochemistry48,57,61, in brief, slides were fixed with 4% PFA for 15\u2009min at room temperature and permeabilized in ice-cold methanol for 6\u2009min at \u221220\u2009\u00b0C. Heat-mediated antigen retrieval with 0.01\u2009M citric acid (pH 6.0) was performed for 5\u2009min in a microwave. After 4% BSA (4% IgG-free BSA in PBS; Jackson, 001-000-162) blocking, the sections were further blocked with unconjugated AffiniPure Fab Fragment (1:100 in PBS; Jackson, 115-007-003) for 30\u2009min. The biotin-conjugated anti-mouse IgG (1:500 in 4% BBBSA, Jackson, 115-065-205) and Cy3-Streptavidin (1:1250 in 4% BBBSA, Jackson, 016-160-084) were used as secondary antibodies. Primary antibodies and dilutions were used as follows: Laminin (Sigma-Aldrich L9393-100UL, 1:800), P16 (Abcam, ab270058, 1:200), P21 (Santa Cruz Biotechnology, sc-6246, 1:200) PAX7 (Developmental Studies Hybridoma Bank; 1:50) for staining of muscle cryosections. Images were slightly modified with ImageJ in which background was reduced using background subtraction and brightness and contrast were adjusted. H&E (Hematoxylin and eosin), was performed as previously described55,61,62.\n\nsnRNA-seq/ATAC-seq and scRNAseq were performed on 10x genomics platform. Briefly, mononucleated resident cells were isolated from human muscle as described in \u201cFluorescence-activated MuSC sorting and culturing\u201d part with 7-AAD (Thermo Scientific, 00-6993-50) staining for viability selection. Red blood cells were eliminated by ACK buffers (150\u2009M NH4Cl, 100\u2009mM KHCO3, 10\u2009mM EDTA-2Na) before sorting. After sorting, live cells were washed with 0.04% BSA in PBS twice and resuspended in the BSA solution. For snRNA-seq/ATAC-seq, nuclei were isolated from the suspended cells according to the manufacturer\u2019s instruction CG000366 \u2022 Rev D. Isolated nuclei were counted under a microscope and Typan blue was used to examine the number and integrity. The isolated nuclei were then resuspended at an appropriate concentration (5000\u221210000 nuclei/\u03bcl); Library construction was performed following the manufacturer\u2019s instructions for generation of Gel Bead-In Emulsions (GEMs) using the 10x Chromium system.\n\nRaw sequencing reads of human skeletal muscle were aligned to the pre-built reference on GRCh38 and counted using Cell Ranger ARC (version 2.0.1) with the default parameters. High-quality nuclei were kept based on gene expression data (>1000, and <40,000 UMI, and mitochondrial percent\u2009<20) and chromatin accessibility data (>1000, and <100,000 ATAC read counts). Seurat (version 5.0.1) object of each sample was constructed from clean nuclei and merged using.\n\nAs the standard pipeline of Seurat and Signac (version 1.12.0) package recommended, we next performed pre-processing and dimensional reduction on both assays independently. First, the RNA count matrix of each sample was normalized using the SCTransform function with the mitochondrial percent variable regressed out. To match shared cell types across samples, features and anchors for downstream integration were selected with the FindIntegrationAnchors and IntegrateData functions, ensuring accurate comparative analysis. After data integration and scaling, principal component analysis (PCA) was conducted on a new integrated assay with the RunPCA function, and clustering and dimensionality reduction analysis was performed with the FindNeighbors, FindClusters, and RunUMAP functions. Cell types were identified and annotated according to the expression levels of the classic marker genes. The marker genes of each cell type were calculated using the FindAllMarkers function with the cutoff of LogFC\u2009>1 and adjusted P-values\u2009<0.05 using t-test.\n\nSecond, the ATAC counts of each sample were normalized by RunTFIDF function. LSI coordinates at the sample level were computed on the normalized ATAC matrix using RunSVD. To identify integration anchors, different samples were projected into a shared space by FindIntegrationAnchors function. and the low-dimensional cell embeddings (the LSI coordinates) across the datasets were integrated using the IntegrateEmbeddings function. UMAP clustering was created using the integrated embeddings by RunUMAP function.\n\nA weighted nearest neighbor (WNN) graph was constructed by FindMultiModalNeighbors from a list of two-dimensional reductions: PCA from RNA assay and integrated LSI from ATAC assay. WNN graph was used for following UMAP visualization and clustering.\n\nTranscriptional noise/heterogeneity analysis was conducted following previous work63,64. To account for differences originating from UMI counts and cell-type composition in different age groups, all cells were down-sampled to a specified number of UMIs, cell numbers were then down-sampled so that equal numbers of young and aged cells were used. A list of representative invariant genes was selected as the prior method suggested to calculate the Euclidean distance from each cell to the corresponding cell-type mean vector within each age group63. Additionally, the Euclidean distances were averaged for each donor from two aged groups to further remove technical confounding. The Euclidean distance was used to measure the transcriptional noise at both single-cell and cell-type levels.\n\nThe prioritization of cell types in the response to human muscle aging was calculated and named as Augur Score using calculate_auc function from Augur package (version 1.0.3) by inputting the genes-by-cells scRNA-seq matrix and a data frame containing cell type and aged group columns.\n\nTo define senescent cells, we developed a unified senescence scoring (USS) algorithm based on five established senescence gene databases (SM: SenMayo30, CA: CellAge29, GA: GenAge28, and SE: Senescence Eigengene approach65). (Suppl. Dataset\u00a02). All cells were first divided based on cell types. Within the same cell type, ss-GSVA score was calculated for each cell with GSVA package (version 1.42.0) using the four different gene sets. Note that we intentionally excluded known senescent markers (P16, P15, P19, P21, P27, and PAI-1) from all four gene lists, since these genes will serve as additional validation of senescence signatures in the detected cell subset. ss-GSVA score SM, CA, GA, and SE were each split into two halves by the median value and senescent cells were defined as those possessing ss-GSVA scores in the upper half level.\n\nDifferentially expressed genes (DEGs) were determined in senescent vs. non-senescent for each cell type. DEGs were analyzed by FindMarkers function in Seurat using Wilcoxon Rank Sum test, and were detected with the cutoff of LogFC\u2009>0.5 and adjusted P-value\u2009<0.05. Sn-DEG lists for each cell type are shown in Suppl. Dataset\u00a03. Gene Ontology (GO) enrichment analysis for Sn-DEG sets was performed with the enrichGO function in the clusterProfiler (version 4.2.0) package. GO biological terms with Benjamini-Hochberg adjusted P-value (FDR)\u2009\u2009<0.05 were considered significantly enriched.\n\nTo infer the aging pace for selected cell types, Monocle (version 2.14.0) package was used for cell trajectory and pseudotime analysis66. Briefly, for each cell cluster from two age groups, the Sn-DEGs in the cell type were used as ordering genes for DDRTree analysis by reduceDimension function and pseudotime ordering by orderCells function. To identify genes with expression patterns positively or negatively linked to pseudotime scale, Spearman correlations between the pseudotime value and gene expression levels were calculated among cells clustered along the pseudotime trajectory. Genes with high correlation with pseudotime scale were visualized with smooth expression curves by plot_pseudotime_heatmap function in Monocle package.\n\nFor MuSCs, the cells falling into late 1 and late 2 branches were extracted, and DEGs between the two late fates were detected (late branch-DEGs). Furthermore, late branch-specific DEGs significantly linked to pseudotime at late branch stage were identified and visualized in a similar manner.\n\nTo further examine the senescence characteristics along the pseudotime trajectory, cell cycle activity signature was defined as ss-GSVA score for cell cycle gene list from REACTOME knowledgebase39.\n\nCell-cell interactions were inferred by CellChat (version 1.6.1) based on the expression of known ligand-receptor pairs in various cell types67. Cells from young and aged groups were applied to CellChat separately and merged into one CellChat object. Dysregulated signaling during aging is identified by identifyOverExpressedGenes and identifyOverExpressedInteractions functions. Age group-specific pathways and ligand-receptor pairs were also detected and visualized using functions wrapped in CellChat.\n\nATAC peaks were called for each cell type using Signac and used in subsequent analyses retaining the cell type annotations68. To search and compute enriched motifs, the DNA sequence of each peak was scanned, and a motif object was created and added to the Seurat object. Per-cell accessibility scores for known motifs were calculated and stored as a new assay (chromvar) by RunChromVAR function wrapped in chromVAR package (version 1.16.0). The chromvar assay contained chromVAR motif accessibilities and facilitated the identification of regulators of senescent cell state. Putative TF regulators were defined as those with significantly higher accessibility scores in senescent compared to non-senescent cells by wilcoxauc function from presto package (version 1.0.0).\n\nTo infer putative peak-gene regulatory interactions from paired snATAC-seq and snRNA-seq data, the distal cis-regulatory elements significantly associated with genes were computed by FigR38. DORC (domains of regulatory chromatin) analysis was conducted to assess the accessibility of peaks within a fixed window (100\u2009kb) centered around the transcription start site (TSS) of specific target genes and correlated with their expression levels. By combining the significance estimates of relative motif enrichment and RNA expression correlation for a given DORC, a signed regulation score (RS) was calculated, with the sign indicating whether the TF acts as an activator or repressor. TF-gene networks were then inferred to pinpoint candidate TF regulators. Specifically, JUNB DORC accessibility was calculated as the accessibility of JUNB motif-containing peaks in each SASP target gene, to show that JUNB accessibility can predict SASP gene expression along senescence trajectories. To visualize dynamics of DORC accessibility and gene expression of JUNB-target SASP genes along the pseudotime axis, we used the genSmoothCurves function to fit smooth spline curves for JUNB DORC accessibility and gene expression matrix dynamics along aging pseudotime on a gene-wise basis. Subsequently, these matrices were normalized to the 1-99 percentile values respectively, to the relative difference between DORC and RNA. Furthermore, we applied a loess smoothing function to the normalized DORC/RNA values in relation to the smoothed aging pseudotime, which was then overlaid and visually represented.\n\nFor single-cell RNAseq profiling in DMSO and MVC-treated mice, mononucleated cells were sorted as described in \u201cFluorescence-activated MuSC sorting and culturing\u201d part with 7-AAD (Thermo Scientific, 00-6993-50) staining for viability selection. After sorting, cells were resuspended in the BSA solution at an appropriate concentration (800\u20131200 cells/\u03bcl). Suspended cells were counted under a microscope and Typan blue was used to examine the cell viability. Library construction was performed following the manufacturer\u2019s instructions for generation of Gel Bead-In Emulsions (GEMs) using the 10x Chromium system.\n\nTo analyze the above generated scRNA-seq data, cells with low gene number (fewer than 500) and high ratio of mitochondrial genes (more than 10%) were first removed. SCTransform normalization, clustering, and cell-cell interaction analysis were conducted in a similar manner as the snRNA-seq data analysis described above.\n\nFor conducting RNA-seq (polyA\u2009+\u2009mRNA) in MuSCs from DMSO and MVC-treated mice, following our prior protocol43,48 total RNAs were subjected to polyA selection (Ambion, 61006) followed by library preparation using NEBNext Ultra II RNA Library Preparation Kit (NEB, E7770S). Libraries were paired-end sequenced with read lengths of 150\u2009bp on Illumina Nova-seq S4 instruments. The raw reads of RNA-seq were processed following the procedures described in our previous publication56. Briefly, the adapter and low-quality sequences were trimmed from 3\u2019 to 5\u2019 ends for each read, and the reads shorter than 36\u2009bp were discarded. The clean reads were aligned to mouse (mm10) reference genome with STAR. Next, Cufflinks was used to quantify the gene expression. Genes with expression level change >1.5-fold and adjusted p-value\u2009<0.1 were identified as DEGs between two conditions. GO enrichment analysis was performed using R package clusterProfiler.\n\nCUT&RUN assay was conducted following our prior protocol48 using 200,000 MuSCs cells with the CUT&RUN assay kit (Cell Signaling Technology, 86652). In brief, FISCs were harvested and washed by cell wash buffer, then bound to concanavalin A-coated magnetic beads. Digitonin Wash Buffer was used for permeabilization. After that, cells were incubated with 2\u2009\u03bcg of JUNB antibody (Cell Signaling Technology #3753) or H3K27ac (Cell Signaling Technology #8173) overnight at 4\u2009\u00b0C with shaking. Then, cell bead slurry was washed with Digitonin Wash Buffer and incubated with Protein A-MNase for 1\u2009hr at 4\u2009\u00b0C with shaking. After washing with Digitonin Wash Buffer, CaCl2 was added into the cell-bead slurry to initiate Protein A-MNase digestion, which was then incubated at 4\u2009\u00b0C for half an hour. Then 2x Stop Buffer was added to the reaction to stop the digestion. CUT&RUN fragments were released by incubation for 30\u2009min at 37\u2009\u00b0C followed by centrifugation. After centrifugation, the supernatant was recovered, and DNA purification was performed by using Phenol/Chloroform. For DNA library construction, a NEBNext\u00ae Ultra\u2122 II DNA Library Prep Kit for Illumina\u00ae (NEB, E7645S) was used according to the manufacturer\u2019s instructions. Bioanalyzer analysis and qPCR were used to measure the quality of DNA libraries including the DNA size and purity.\n\nThe obtained sequencing raw reads were first pre-processed by quality assessment, adapters trimming, and low-quality filtering, and then were aligned to the mouse reference genome (mm10) using Bowtie2, and only non-redundant reads were kept. JUNB binding sites (JUNB peaks) were identified with p-value cutoff as 0.001 by MACS269 and genome browser visualization files were generated by Homer70. To further investigate the JUNB binding sites, we used H3K27ac signal to indicate enhancer regions using our H3K27ac CUT&RUN-seq data in mouse MuSC from UCSD Human Reference Epigenome Mapping Project71.\n\nData represent the average of at least three independent experiments, humans or mice\u2009+\u2009s.d. unless indicated. The statistical significance of experimental data was calculated by the Student\u2019s t-test (two-sided). *p\u2009<0.05, **p\u2009<0.01, ***p\u2009<0.001, ****p\u2009<0.0001 and n.s.: no significance (p\u2009\u2265\u20090.05). The statistical significance for the assays conducted with MuSCs from the same human or mouse with different treatments was calculated by the student\u2019s t-test (paired). *p\u2009<0.05, **p\u2009<0.01, ***p\u2009<0.001, n.s. no significance (p\u2009\u2265\u20090.05). Specifically, a single zero-truncated negative binomial distribution was fit to the input data and each region was assigned a P-value based on the fitted distribution. Representative images of at least three independent experiments are shown in Figs.\u00a02L\u2013O; 5E; and Supplementary Figs.\u00a04H; 5C, J, Q; 7F, J.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The human skeletal muscle single-nucleus multiome within this study have been deposited in the Gene Expression Omnibus database under the accessions GSE268953. The mouse single-cell RNA-seq, bulk RNA-seq, and CUT&RUN datasets have been deposited under the accessions GSE268407, GSE268952, and GSE268433, respectively. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code used in this study is available at the GitHub repository https://github.com/Hannah-bioinfo/Scripts_Aging_SnC_MS/.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Sharma, R. Exploring the emerging bidirectional association between inflamm-aging and cellular senescence in organismal aging and disease. Cell Biochem. Funct. 42, e3970 (2024).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nZhu, Y. et al. Past and future directions for research on cellular senescence. Cold Spring Harb. Perspect. Med. 14, a041205 (2024).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nRing, N. A. R. et al. 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(project code: 2024ZD0530400); National Key R&D Program of China to H.W. (project code: 2022YFA0806003); The\u00a0InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government\u00a0 to H.W.\u00a0and T.X. (project code:INNOHK22SC01); Health and Medical Research Fund (HMRF) from Health Bureau of HK to H.W. (project codes: 10210906 and 08190626); Theme-based Research Scheme (TRS) from RGC\u00a0to H.W. (project code:T13-602/21-N); General Research Fund (GRF) from Research Grants Council (RGC) of the HongKong Special Administrative Region, China to H.W. (project codes: 14108225,\u00a014106521, 14105123,\u00a014103522,\u00a0 and\u00a014105823 to H.W.); the National Natural Science Foundation of China (NSFC) to H.W. (project codes: 82172436); Area of Excellence Scheme (AoE) from RGC to H.W. (project code: AoE/M-402/20). The\u00a0Chinese University of Hong Kong (CUHK) Strategic Seed Funding for Collaborative Research Scheme (SSFCRS) to H.W.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Yang Li, Chuhan Li.\n\nThese authors jointly supervised this work: Hao Sun, Michael Tim-Yun Ong, Huating Wang.\n\nDepartment of Orthopaedics and Traumatology, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China\n\nYang Li,\u00a0Chuhan Li,\u00a0Qin Zhou\u00a0&\u00a0Huating Wang\n\nInnoHK Center for Neuromusculoskeletal Restorative Medicine, Hong Kong Science Park, Hong Kong SAR, China\n\nYang Li,\u00a0Michael Tim-Yun Ong\u00a0&\u00a0Huating Wang\n\nDepartment of Chemical Pathology, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China\n\nXingyuan Liu,\u00a0Yulong Qiao\u00a0&\u00a0Hao Sun\n\nCenter for Tissue Regeneration and Engineering, Division of Life Science, Hong Kong University of Science and Technology, Hong Kong SAR, China\n\nTing Xie\n\nWarshel Institute for Computational Biology, Faculty of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, China\n\nHao Sun\n\nDepartment of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong SAR, China\n\nMichael Tim-Yun Ong\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nYang Li performed most of the wet-lab experiments; Chuhan Li analyzed all the high-throughput sequencing data; Xingyuan Liu, Qin Zhou and Yulong Qiao performed and helped with animal experiments, human muscle section staining, single-cell RNA-seq and JunB CUT&RUN-seq; Ting Xie provided aged mice; Michael Tim-Yun Ong provided the human muscle specimens; Hao Sun supervised computational analyses; Huating Wang supervised experiments; Yang Li, Chuhan Li and Huating Wang conceived the project and wrote the manuscript, with inputs from all authors.\n\nCorrespondence to\n Huating Wang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Multiomics and cellular senescence profiling of aging human skeletal muscle uncovers Maraviroc as a senotherapeutic approach for sarcopenia.\n Nat Commun 16, 6207 (2025). https://doi.org/10.1038/s41467-025-61403-y\n\nDownload citation\n\nReceived: 10 December 2024\n\nAccepted: 20 June 2025\n\nPublished: 05 July 2025\n\nVersion of record: 05 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61403-y\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"title": "DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads", + "pre_title": "DOLPHIN Advances Cell Representation Beyond Gene-Level by Integrating Exon-Level Quantification and Junction Reads with Deep Neural Networks", + "journal": "Nature Communications", + "published": "04 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61580-w/MediaObjects/41467_2025_61580_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61580-w/MediaObjects/41467_2025_61580_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61580-w/MediaObjects/41467_2025_61580_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61580-w/MediaObjects/41467_2025_61580_MOESM4_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-61580-w#ref-CR29", + "https://www.ncbi.nlm.nih.gov/sra/PRJNA816486", + "/articles/s41467-025-61580-w#ref-CR30", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE125970", + "/articles/s41467-025-61580-w#ref-CR31", + "https://ngdc.cncb.ac.cn/gsa/search?searchTerm=PRJCA001063", + "/articles/s41467-025-61580-w#ref-CR62", + "https://xenabrowser.net/datapages/", + "/articles/s41467-025-61580-w#ref-CR114", + "https://doi.org/10.5281/zenodo.15611935", + "/articles/s41467-025-61580-w#ref-CR126", + "/articles/s41467-025-61580-w#Sec24" + ], + "code": [ + "https://github.com/mcgilldinglab/DOLPHIN", + "https://doi.org/10.5281/zenodo.15602232", + "/articles/s41467-025-61580-w#ref-CR127" + ], + "subject": [ + "Computational models", + "Data processing", + "Machine learning", + "Software" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5474597/v1.pdf?c=1751713732000", + "research_square_link": "https://www.researchsquare.com//article/rs-5474597/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61580-w.pdf", + "preprint_posted": "05 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets.Biological sciences/Computational biology and bioinformatics/Machine learningBiological sciences/Computational biology and bioinformatics/Computational modelsGraph Neural NetworkSingle-cell SequencingExon-Level QuantificationJunction ReadsAlternative Splicing", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "reportingsummary.pdfreporting summarynreditorialpolicychecklist.pdfeditorial policy checklistnrsoftwarepolicy.pdfsoftware policyDOLPHINSupplementary.pdfSupplementary figures and tables", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Single-cell RNA sequencing (scRNA-seq) has transformed transcriptomics by enabling the profiling of gene expression at the level of individual cells, a major advance in studying cellular diversity within complex tissues1. This technology has driven significant progress across fields such as developmental biology2,3, immunology4,5, and cancer research6,7, revealing intricate cellular landscapes, elucidating developmental pathways, and identifying previously uncharacterized cell types linked to disease states8,9. By enabling high-resolution dissection of cellular states and dynamics, scRNA-seq provides insights that bridge basic biological understanding with therapeutic applications, reshaping both basic and translational research.\n\nDespite these advancements, conventional scRNA-seq analyses are predominantly gene-level, relying on gene count tables for cell representation learning and downstream tasks such as cell clustering, differential gene expression, and pseudotime trajectory inference10. Numerous computational tools, including SCANPY11, seurat12, scVI13, scGPT14, geneFormer15, scBERT16, scSemiProfiler17, and Cellar18 are designed to analyze this gene-level data. However, aggregating data at the gene level oversimplifies the transcriptomic landscape, as critical biological information encoded in exon-level reads and junction reads\u2014reads spanning exon boundaries and capturing exon connectivity\u2014is often lost19,20. This simplification masks essential details, including exon-specific expression and splicing patterns, which are crucial for accurately representing cellular states. Consequently, gene-level aggregation may lead to an oversimplified view of cellular characteristics, limiting insights into cellular function and regulation and underscoring the need for approaches that preserve this fine-grained information21.\n\nIn addition to cell representation learning, another critical task in scRNA-seq analysis is the detection and quantification of alternative splicing (AS) events. AS analysis at the gene level poses substantial challenges, as gene-level quantification obscures isoform-specific and exon-specific variations that are critical for capturing splicing dynamics. To address this, various computational tools have been developed for AS analysis in scRNA-seq data. Among junction read-based approaches, Outrigger22 constructs a de novo splicing event index by pooling junction-spanning reads across all cells and building a splice graph to identify and quantify AS events. scQuint23 adopts a different strategy by quantifying intron usage based on junction reads. To improve splicing quantification under sparse conditions, imputation-based methods such as BRIE224 and SCASL25 have been developed. BRIE2 employs a Bayesian hierarchical model to borrow information across similar cells and infer more robust Percent Spliced-In (PSI) estimates, whereas SCASL uses an iterative weighted k-nearest neighbors (KNN) strategy to impute missing PSI values. Despite these methodological advances, major gaps remain. Most existing tools were developed and benchmarked primarily on full-length scRNA-seq datasets, and their performance degrades substantially when applied to droplet-based platforms such as 10X Genomics, where coverage is sparse and biased toward transcript ends. Furthermore, nearly all methods predominantly rely on junction-spanning reads for splicing quantification. This reliance can limit sensitivity and robustness, especially in the context of scRNA-seq, where sparse coverage and frequent dropout render junction reads insufficient for capturing the full extent of splicing variability. Additionally, the exclusion of exon body reads, which represent a more abundant yet underutilized source of information, can reduce the sensitivity of existing methods in detecting subtle or complex splicing events that may be missed due to the sparsity of junction reads in scRNA-seq data.\n\nTo address these foundational limitations, we introduce DOLPHIN (\\(\\underline{{{{\\bf{D}}}}}{{{\\rm{eep}}}}\\) \\({{{\\rm{Ex}}}}\\underline{{{{\\bf{o}}}}}{{{\\rm{n}}}}\\)-\\(\\underline{{{{\\bf{l}}}}}{{{\\rm{evel}}}}\\) \\({{{\\rm{Gra}}}}\\underline{{{{\\bf{ph}}}}}\\) Neural Network for \\({{{\\rm{S}}}}\\underline{{{{\\bf{i}}}}}{{{\\rm{ngle}}}}\\)-cell \\({{{\\rm{Representatio}}}}\\underline{{{{\\bf{n}}}}}\\) Learning and Alternative Splicing), a deep learning framework that advances scRNA-seq analysis beyond conventional gene-level quantification. DOLPHIN constructs a graph for each gene, representing exons as nodes and splice junctions as edges, to model gene architecture at single-cell resolution. By integrating exon-level reads and junction reads, DOLPHIN captures a richer and more detailed transcriptional landscape compared to traditional approaches that rely solely on gene-level counts. Built on a variational graph autoencoder (VGAE) framework26,27, DOLPHIN learns cell embeddings that preserve fine-grained exon usage patterns and splicing information, enabling more accurate and informative representations of cellular states. These enhanced embeddings not only improve downstream analyses such as cell clustering and differential gene analysis but also support more sensitive AS detection. Specifically, DOLPHIN uses the learned embeddings to identify neighboring cells with similar exon and splicing profiles, aggregates junction reads across neighbors to amplify splicing signals, and substantially enhances AS detection under the sparse sequencing conditions typical of scRNA-seq. Following aggregation, PSI values are calculated using the Outrigger function from Expedition28, providing accurate and robust quantification of splicing events across diverse cell populations.\n\nWe demonstrate DOLPHIN\u2019s general applicability by validating its performance on a diverse set of scRNA-seq datasets29,30,31 that encompass distinct sequencing technologies, including full-length and droplet-based approaches, as well as a broad spectrum of tissue types and biological conditions. These datasets span healthy tissues, normal tissues from patients with cancer, and malignant tissues, thereby representing a wide range of physiological and pathological contexts. This systematic validation highlights DOLPHIN\u2019s robustness and adaptability, demonstrating its effectiveness in accurately capturing cell heterogeneity and refining complex downstream analyses across diverse experimental contexts. Across diverse scRNA-seq datasets and simulated data, our model consistently outperforms traditional gene count-based methods. By integrating exon-level and junction read information with advanced deep learning techniques, DOLPHIN enhances the resolution of single-cell transcriptomic analysis, improving cell embedding quality and enabling more detailed analyses of AS and differential gene expression. Ultimately, DOLPHIN provides an analytical framework that addresses the limitations of gene-count-based methods, enabling more precise insights into complex cellular processes and facilitating the study of disease mechanisms and therapeutic targets.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "DOLPHIN is a deep learning framework for exon-level analysis of scRNA-seq data, offering higher transcriptomic resolution than traditional gene-count methods (Fig.\u00a01). Each gene is modeled as an exon graph, where nodes represent exons and edges represent their connections via junction reads. By integrating exon and junction data, DOLPHIN generates integrative cell representations that support applications like cell clustering, differential exon analysis, and AS detection19,32,33.\n\na Preprocessing of single-cell RNA-seq data, including quantification of exon-mapped reads and exon-exon junction reads. b Construction of gene-specific exon graphs, where nodes represent exons and edges represent junctions, aggregated to form an exon graph for each cell. c Learning cell embeddings from exon-level quantification and junction reads through a Variational Graph Autoencoder (VGAE). Each exon graph is converted into feature matrices (Xi) and normalized adjacency matrices (ANi), which are processed by a Graph Attention Network (GAT) layer to capture exon dependencies. The output (Hi)\u00a0from the GAT layer is then passed to a Variational Autoencoder (VAE) that projects graph representations into a latent space (Z), defined by mean (\u03bc) and standard deviation (\u03c3) parameters, with a KL divergence term weighted by a hyperparameter (\u03b2) to regularize the latent space. The decoders reconstruct both the feature matrix (\\({{\\bf{X}}}_{i}^{{\\prime} }\\)) and raw adjacency matrix (\\({{\\bf{A}}}_{i}^{{\\prime} }\\)), with losses weighted by a hyperparameter (\u03bb) to minimize feature and adjacency reconstruction errors, thereby learning cell-specific embeddings. d Construction of a K-nearest neighbor (KNN) graph in the latent space for refining and aggregating junction reads from neighboring cells based on consensus (majority voting), which enhances junction coverage for downstream splicing analysis. e Calculation of percent-splice-in (PSI) values from aggregated junction reads, enabling accurate alternative splicing inference at the single-cell level. f High-resolution cell embeddings generated by DOLPHIN improve the characterization of cellular heterogeneity compared to conventional gene count-based methods. g Detection of exon-specific markers and identification of biological pathways that are often missed in gene-level analyses. Exon-level biomarkers were identified through differential expression analysis using MAST. h Extensive alternative splicing analysis enabled by DOLPHIN across diverse cellular populations. By default, PSI values and splicing modalities were quantified using Expedition. However, DOLPHIN can be adapted to work with other alternative splicing quantification tools.\n\nThe method operates in three main steps. First, DOLPHIN constructs an exon graph for each gene by capturing gene-specific exon connectivity from junction reads (Fig.\u00a01a, b)19,33. Raw scRNA-seq reads are aligned to a reference genome to identify exon reads and junction reads, which are then used to build exon graphs. Each exon graph has nodes representing exons annotated with their read counts and directional edges weighted by normalized junction read counts. This setup forms a cell-level structure comprising exon graphs for each gene. Second, these cell-level exon graphs are processed through a VGAE26 to produce informative cell embeddings (Fig.\u00a01c). Each cell-level exon graph is converted into adjacency and feature matrices, which are processed by a graph attention (GAT) layer34. The GAT layer dynamically assigns weights to neighboring exons, emphasizing biologically relevant exon connections informed by junction reads. The variational autoencoder (VAE) encoder then learns a latent representation Z that captures critical exon-junction relationships, optimized through a composite loss function that balances reconstruction of both exon-level features and adjacency matrices35,36. Third, DOLPHIN addresses the limited detection of junction reads in scRNA-seq by aggregating junction reads from similar cells in the junction-aware latent space (Fig.\u00a01d)37. Using a KNN approach, cells with similar exon and junction patterns are identified, and junction reads from those neighboring cells are aggregated based on majority voting. This aggregation step, schematically illustrated in Fig.\u00a01e, enriches each cell\u2019s profile with junction reads from consistent neighboring cells, enhancing detection sensitivity without introducing noise.\n\nWith these enhanced cell embeddings, DOLPHIN supports exon-level analyses such as refined cell clustering (Fig.\u00a01f), differential gene analysis at the exon level (Fig.\u00a01g), and AS detection (Fig.\u00a01h). By integrating these embeddings with splicing detection tools like Outrigger from the Expedition suite, DOLPHIN can compute PSI values, providing detailed insights into exon usage and cell-specific splicing patterns22,28.\n\nDOLPHIN enhances cell embeddings through the graph-based integration of exon-level and junction read quantification, leveraging both read types to improve the quality of cell embeddings and the accuracy of scRNA-seq clustering compared to traditional gene count methods. To demonstrate the general applicability of DOLPHIN, we validated its performance across diverse scRNA-seq datasets spanning different platforms, tissue types, and biological conditions. These included a full-length dataset from human peripheral blood mononuclear cells (PBMCs)29 and two 10X Genomics Chromium Single Cell 3\u2032 v2 datasets from normal epithelial colon and rectum tissues from gastrointestinal cancer patients30.\n\nFor each dataset, we processed four inputs through the VAE framework\u2013an exon feature matrix, a junction-based adjacency matrix, a gene count table, and the integrated feature and adjacency matrices from DOLPHIN. These components were assessed individually to evaluate their contributions and the enhancement achieved through integration. Clustering outcomes were compared to ground truth labels using Uniform Manifold Approximation and Projection (UMAP) visualizations38, with the ground truth annotations taken from the original publications, as shown in Fig.\u00a02a\u2013d and Supplementary Fig.\u00a0S1. Additionally, Adjusted Rand Index (ARI)39 and Normalized Mutual Information (NMI)40 scores were used for quantitative evaluation, as presented in Fig.\u00a02e\u2013g. DOLPHIN\u2019s integrated embeddings consistently outperformed individual matrices and gene count tables, capturing cell type-specific information at finer resolution and achieving higher ARI and NMI scores, as demonstrated in Fig.\u00a02.\n\na\u2013d UMAP plots comparing the quality of cell embeddings generated by DOLPHIN, which integrates both exon and junction read counts, against conventional gene count-based methods across multiple single-cell RNA-seq datasets. Improved clustering and separation of distinct cell populations define higher-quality embeddings. Top panels: Human peripheral blood mononuclear cells (PBMCs) analyzed using full-length single-cell RNA-seq. Middle panels: Human colon cells analyzed using 10X Genomics. Bottom panels: Human rectum cells analyzed using 10X Genomics. For each dataset, the following inputs are compared: a DOLPHIN integrating both exon and junction read counts, producing the most integrative and biologically informative embeddings. b DOLPHIN framework using gene count tables, reflecting a conventional gene-level analysis. c DOLPHIN using only exon read counts (feature matrix). d DOLPHIN using only junction read counts (adjacency matrix). e\u2013g Box plots of Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) scores comparing embedding quality across three different datasets. DOLPHIN, through the integration of exon and junction read counts, achieves significantly higher scores than approaches using exon or junction data alone or conventional gene-count methods. These metrics confirm DOLPHIN's superior clustering accuracy and alignment with known biological cell types, highlighting its performance advantage. Each score is based on N\u2009=\u200950 bootstrapping replicates using different random seeds (technical replicates). Boxes indicate the interquartile range (IQR, 25th to 75th percentile), with the line inside each box representing the median. Whiskers extend to the most extreme data points within 1.5 times the IQR from the quartiles. P values from one-sided Student\u2019s t-tests: *P\u2009<\u20090.05, **P\u2009<\u20090.01, ***P\u2009<\u20090.001, ****P\u2009<\u20090.0001; n.s. not significant. Exact P values are provided in the source data. Source data are provided as a Source Data file.\n\nIn the PBMC dataset, UMAP visualizations illustrate that DOLPHIN distinctly delineates cell clusters closely matching ground truth cell types (Fig.\u00a02a, top panel). In contrast, gene count tables yield denser clusters, obscuring subpopulations, particularly within T cell subsets (Fig.\u00a02b, top panel). The feature matrix and adjacency matrix are each able to resolve specific cell types, including monocytes (Mono), B cells, and natural killer cells, into distinct and well-defined clusters (Fig.\u00a02c, d, top panels). This suggests that both matrices effectively capture biologically relevant variations, facilitating the accurate identification of cell populations. Furthermore, their integration in DOLPHIN provides the most refined results. Supplementary Fig.\u00a0S2a highlights the abundance of exon and junction reads in full-length data, sufficient for constructing robust exon-level graphs for cell representation learning. Quantitative analysis with ARI and NMI metrics (Fig.\u00a02e) shows that DOLPHIN achieves median ARI scores 0.11 higher than gene count methods, with statistical significance (P\u2009=\u20091.98\u2009\u00d7\u200910\u22124).\n\nFor UMI-based platforms with limited gene coverage, DOLPHIN was applied to two 10X Genomics datasets. In the human colon dataset, UMAP plots show that DOLPHIN mitigates batch effects and produces well-defined clusters for Paneth-like, Goblet, and transient amplifying (TA) cells (Fig.\u00a02a, middle panel). By contrast, the gene count table exhibits batch effects, blurring cell type boundaries (Fig.\u00a02b, middle panel). The batch effect was evaluated in Supplementary Fig.\u00a0S3a, b, where we show that the DOLPHIN method exhibits significantly less batch effect compared to the gene count table approach. Notably, the integration LISI (iLISI) score showed the most substantial improvement, increasing from 0.01 with the gene count table to 0.82 with DOLPHIN, with P\u2009=\u20091.38\u2009\u00d7\u200910\u221223. Across multiple evaluation metrics, DOLPHIN demonstrated superior performance in reducing batch effects relative to the gene count table method. The feature matrix delineates Goblet and Paneth-like cells, while the adjacency matrix captures broader cell-type patterns with slightly diffuse boundaries (Fig.\u00a02c, d, middle panel). DOLPHIN\u2019s integrated embeddings achieve the best clustering accuracy, with ARI and NMI improvements of 0.10 and 0.08, respectively, over gene count tables (Fig.\u00a02f). These results were statistically significant (P\u2009=\u20094.56\u2009\u00d7\u200910\u221225 and P\u2009=\u20094.85\u2009\u00d7\u200910\u221242, respectively), highlighting DOLPHIN\u2019s robustness for low-coverage datasets.\n\nSimilarly, in the 10X rectum dataset, DOLPHIN improved clustering performance, effectively resolving Enterocyte and Goblet cell populations, as seen in UMAP plots (Fig.\u00a02a\u2013d, bottom panel). ARI and NMI metrics further confirmed its advantage, with improvements of 0.11 (P\u2009=\u20091.98\u2009\u00d7\u200910\u221244) and 0.09 (P\u2009=\u20091.05\u2009\u00d7\u200910\u221233), respectively, compared to gene count tables (Fig.\u00a02g). We also compared the batch effect between the gene count table and DOLPHIN, as shown in Supplementary Fig.\u00a0S3c, d, where the iLISI score increased from 0.06 with the gene count table to 0.40 with DOLPHIN with P\u2009=\u20094.91\u2009\u00d7\u200910\u221213. These findings demonstrate DOLPHIN\u2019s adaptability to diverse datasets and its ability to detect biologically meaningful patterns even under the 10X tag-based platforms, where exon and junction reads are much less abundant, as shown by their distribution in Supplementary Fig.\u00a0S2b, c.\n\nRobustness of cell embeddings against batch effects is critical for accurately capturing biological variation in scRNA-seq data. We evaluated the robustness of DOLPHIN embeddings by conducting two complementary analyses. First, we assessed DOLPHIN\u2019s default embeddings without applying any external batch correction to the input features. As shown in Supplementary Fig.\u00a0S3, DOLPHIN\u2019s exon-level modeling inherently mitigates batch-driven separation, resulting in robust cell embeddings even under uncorrected conditions. To further strengthen this evaluation, we compared DOLPHIN embeddings against standard batch correction methods. Specifically, we applied Harmony41 and scVI to perform batch correction on the gene count matrix, and separately applied scVI to correct batch effects in the exon-level feature matrix prior to DOLPHIN embedding. In contrast, Harmony operates only on low-dimensional embeddings and is not compatible with exon-level feature correction before DOLPHIN. As shown in Supplementary Fig.\u00a0S4a\u2013c, while all approaches reduced batch-driven separation, DOLPHIN embeddings derived from scVI-corrected exon inputs achieved the best batch mixing. This observation is further supported by quantitative metrics in Supplementary Fig.\u00a0S4d, which assess both biological conservation (ARI, NMI) and batch correction performance (batch average silhouette width (ASW), graph connectivity)42,43. Notably, applying Harmony to gene-level embeddings improved the median ARI from 0.26 to 0.41 (P\u2009=\u20091.71\u2009\u00d7\u200910\u22127), whereas DOLPHIN with batch-corrected exon inputs achieved a higher ARI of 0.49 compared to the Harmony-corrected gene count matrix\u00a0(P\u2009=\u20092.09\u2009\u00d7\u200910\u22124), indicating superior preservation of biological structure. DOLPHIN embeddings also exhibited the highest median Batch ASW and comparable graph connectivity to Harmony, reflecting strong batch mixing while maintaining biological relevance. Together, these results demonstrate that DOLPHIN\u2019s exon-level embeddings are inherently robust against batch effects and can achieve even greater performance when built upon batch-corrected exon-level inputs.\n\nBeyond clustering, DOLPHIN\u2019s exon-level embeddings enable de novo cell type annotation by capturing transcriptomic differences often missed at the gene level. To systematically assess this, we compared gene-, isoform-, and exon-level expression across annotated cell types in three datasets. For each dataset, one well-established marker gene per cell type was selected30,44,45,46, and UMAP expression patterns were visualized for their corresponding isoforms (Supplementary Figs.\u00a0S5\u2013S7). While isoform expression generally resembled gene-level patterns, several isoforms revealed finer subcluster structures. For example, in the 10X colon dataset (Supplementary Fig.\u00a0S6a), among five isoforms of the enterocyte marker SLC26A347, ENST00000453332 exhibited strong, localized expression, distinguishing subpopulations within enterocytes. Building on these observations, we emphasized exon-level features underlying DOLPHIN\u2019s embeddings (Supplementary Figs.\u00a0S8\u2013S10). Exon-level expression further refined cell type-specific patterns beyond both gene- and isoform-level analyses. In the PBMC dataset, while CUX1 gene and isoform expressions broadly marked monocytes (Supplementary Fig.\u00a0S11), specific exons (e.g., exons 19 and 20) localized to the CD16 monocyte subcluster48 (Supplementary Fig.\u00a0S8b). These results demonstrate that DOLPHIN\u2019s exon-level embeddings facilitate precise de novo annotation of cell types and subtypes, capturing biologically meaningful heterogeneity overlooked by conventional approaches.\n\nTo explore the broader applicability of DOLPHIN for cell representation learning beyond short-read scRNA-seq data, we further applied it to single-cell long-read RNA-seq datasets49. In this analysis, we generated isoform-level counts and subsequently analyzed them with SCANPY and scVI to establish isoform-based baselines. In parallel, DOLPHIN was applied directly to exon-informed features to learn cell embeddings. As shown in Supplementary Fig.\u00a0S12, DOLPHIN consistently outperformed isoform-based approaches, achieving ARI improvements of 0.27 over SCANPY (P\u2009=\u20094.04\u2009\u00d7\u200910\u221218) and 0.31 over scVI (P\u2009=\u20094.17\u2009\u00d7\u200910\u22127). These results demonstrate that DOLPHIN can deliver enhanced clustering resolution even when applied to long-read datasets.\n\nThe DOLPHIN framework leverages exon-level quantification in scRNA-seq to capture finer-grained transcriptomic details that conventional gene-level count methods often overlook. This approach enhances cell clustering accuracy and enables more insightful downstream analyses. We applied DOLPHIN to identify exon-level differentially expressed genes (EDEGs) in a pancreatic ductal adenocarcinoma (PDAC) dataset generated using the 10X Genomics Chromium Single Cell 3\u2032 v2 chemistry31 and compared these findings to those obtained with conventional gene count tables, where differential genes are identified as differentially expressed genes (DEGs). Our analysis reveals significant improvements in sensitivity and biological relevance with DOLPHIN.\n\nUsing a 10X PDAC dataset with cells from cancer and control conditions31, we first leveraged the latent cell embeddings from DOLPHIN, which integrate exon-level quantification and junction reads, for cell clustering. As shown in Fig.\u00a03a, the clustering results closely aligned with cell-type annotations from the original study, reflecting DOLPHIN\u2019s ability to capture distinct cellular identities. Focusing on cells within Leiden cluster 2, we performed differential gene expression analysis between cancer and control groups. For comparability, we applied the same cluster selection to the conventional gene count table approach to identify DEGs, ensuring that observed differences could be attributed to the method rather than clustering inconsistencies.\n\na Clustering of the PDAC dataset using DOLPHIN, with clusters labeled by subject condition, Leiden clusters, and cell type. Leiden cluster 2, highlighted, is used as an example for subsequent analyses comparing cancer and control groups. b Enrichment analysis reveals that exon-level differentially expressed genes (EDEGs) identified by DOLPHIN are significantly enriched in pancreatic cancer-related terms with lower adjusted\u00a0P-values compared to differentially expressed genes (DEGs) identified by conventional gene count-based methods. This indicates deeper biological insights. Term marked as \u201cn.s.\u201d indicate no significant enrichment. The P values comparing DOLPHIN and conventional methods were calculated using a one-sided Wilcoxon test. c A Venn diagram shows that DOLPHIN identifies significantly more EDEGs than DEGs detected by conventional gene-level methods, highlighting its enhanced sensitivity in detecting biologically meaningful changes. d Heatmap of differentially expressed exons uniquely identified by DOLPHIN across cancer and control groups, alongside corresponding gene expression levels. The heatmaps illustrate that DOLPHIN captures subtle transcriptomic changes that remain undetectable at the gene level. P-values for cancer versus control comparisons were calculated using a two-sided Wilcoxon test. e Enrichment analysis of the 896 DOLPHIN-only EDEGs shows significant associations with pancreatic cancer-related terms. In contrast, 483 DEGs identified by conventional gene count-only methods, but not at the exon level, showed no significant enrichment in these terms. Adjusted P values for each enrichment term were calculated using one-sided hypergeometric tests, followed by multiple testing correction using the Benjamini\u2013Hochberg method. f Volcano plot highlighting pancreatic cancer-related EDEGs identified by DOLPHIN, specifically from the disease term highlighted in part e. These EDEGs are not detected as DEGs by conventional gene count methods, demonstrating DOLPHIN's ability to uncover biologically important exon-level differential genes missed by traditional approaches. Non-significant differences are shaded in gray. P values were derived using MAST, which fits a hurdle model accounting for both detection rate and expression level, and were adjusted for multiple testing using the Benjamini\u2013Hochberg method. See the \u201cMethods\u201d section for details. Source data are provided as a Source Data file.\n\nIn Fig.\u00a03b, we present the results of disease and pathway enrichment analysis50 on EDEGs identified by DOLPHIN compared to DEGs identified using the gene count table. Here, pancreatic cancer-related terms show strong enrichment and lower adjusted\u00a0P values when using EDEGs detected by DOLPHIN, underscoring the method\u2019s sensitivity to relevant pathways and diseases; terms labeled \u201cn.s.\u201d (not significant) in the DEG analysis highlight the limited detection capacity of the conventional approach51,52.\n\nA Venn diagram in Fig.\u00a03c illustrates the overlap between EDEGs identified by DOLPHIN and DEGs detected using the conventional gene count table, revealing 896 unique EDEGs exclusively identified by DOLPHIN. These EDEGs correspond to genes that exhibit significant exon-level differential expression, which remain undetected when analyzed solely at the gene level using conventional methods. This highlights DOLPHIN\u2019s enhanced sensitivity in capturing subtle, exon-specific variations that are otherwise masked in gene-level analyses. To further explore the biological significance of these uniquely identified EDEGs, we specifically examined the exons that contributed to their detection. From the 896 EDEGs, we selected exons that displayed differential expression, while their corresponding genes showed no significant differential expression at the gene level. The heatmap in Fig.\u00a03d visualizes this subset, demonstrating that these exons exhibit robust differential expression when analyzed with DOLPHIN, yet are overlooked by the conventional gene count table approach. This underscores DOLPHIN\u2019s ability to uncover exon-level regulatory changes that are critical but often missed by traditional gene-centric analyses.\n\nFurther exploration of EDEGs unique to DOLPHIN is shown in Fig.\u00a03e, where disease and pathway enrichment analysis reveals significant enrichment of pancreatic cancer-related terms. To illustrate the specific gene-level differences, a volcano plot in Fig.\u00a03f shows log2 fold changes and adjusted P values for key PDAC-associated genes identified as EDEGs by DOLPHIN but missed as DEGs by the gene count table. The selection of these genes was guided by the top highlighted pancreatic cancer term in Fig.\u00a03e. Several of these genes have well-established roles in PDAC progression and therapy response, including SMAD4, a canonical tumor suppressor gene frequently mutated or lost in PDAC and associated with poor prognosis and treatment resistance53,54,55; ERCC1, a marker implicated in chemotherapy response and DNA repair deficiency in PDAC56,57; TGFBR2, a key component of TGF-beta signaling, which plays a dual role in tumor suppression and progression in pancreatic cancer58,59; and ATM, a DNA damage response kinase frequently mutated in PDAC, where its loss impairs double-strand break repair and confers increased sensitivity to DNA-damaging agents and PARP inhibitors60,61. The identification of these genes through exon- and junction-level resolution suggests that DOLPHIN can recover biologically and clinically meaningful signals that remain undetected by conventional pipelines, with potential implications for both diagnostic biomarker discovery and therapeutic targeting. The distribution of these genes underscores DOLPHIN\u2019s enhanced sensitivity, with many exhibiting exon-level differential expression that does not translate to gene-level differences, making them undetectable by conventional methods.\n\nTo assess the clinical relevance of the 896 DOLPHIN-unique EDEGs identified in this PDAC dataset, we conducted a Kaplan-Meier survival analysis using real patient survival data from The Cancer Genome Atlas (TCGA) PDAC cohort62, stratifying patients based on the expression of DOLPHIN-unique EDEGs. Given that pseudo-bulk expression profiles derived from single-cell data may introduce biases into downstream analyses, particularly due to dropout events and limited coverage of lowly expressed genes63, we instead validated the clinical relevance of our findings using matched bulk RNA-seq data to ensure more reliable interpretation of survival associations. This strategy moves beyond pseudo-bulk approximations and leverages orthogonal, external bulk datasets to provide a more robust assessment of the prognostic value of the identified genes. As shown in Supplementary Fig.\u00a0S13a, we stratified patients into high-risk and low-risk groups based on the expression of the top 100 and all 896 EDEGs, where the genes were ranked by increasing adjusted P from our DOLPHIN-based differential analysis. Across all subsets, the separation between risk groups was statistically significant, with the strongest prognostic signal observed when using the full set of 896 EDEGs (P\u2009=\u20092.22\u2009\u00d7\u200910\u221239, log-rank-sum test64). To characterize how the prognostic signal accumulates with increasing numbers of EDEGs, we plotted the association P values across ranked gene sets (Supplementary Fig.\u00a0S13b). The resulting curve demonstrates a consistent and monotonic strengthening of survival association as more top-ranked EDEGs are included. These analyses collectively demonstrate that the EDEGs uniquely identified by DOLPHIN not only capture biologically relevant information missed by gene-level approaches but also exhibit strong clinical relevance when validated against independent datasets. Additionally, we conducted a similar analysis using the junction count table to identify junction-level differentially expressed genes (JDEGs), as shown in Supplementary Fig.\u00a0S14b\u2013d. This analysis further reinforces DOLPHIN\u2019s capability in capturing transcriptomic variations beyond gene-level limitations, particularly in exon and junction reads\u00a0usage.\n\nIn addition to the results observed in Cluster 2, which contains a balanced number of cells between disease and control groups, we further examined other disease-relevant clusters to assess the robustness and generalizability of DOLPHIN under realistic group size imbalances. Given the biological relevance of ductal cells to PDAC, which originates from the epithelial lining of the pancreatic ducts, we additionally included Ductal Type 1 and Type 2 cell clusters in the EDEG and DEG comparison. Unlike Cluster 2, the ductal clusters exhibit pronounced imbalance in group sizes, reflecting a common feature of real-world single-cell datasets where cell-type abundance may vary across conditions. Specifically, this ductal cluster contains 1067 cells, including 891 from cancer samples and 176 from healthy controls, providing a challenging and biologically meaningful setting to evaluate the robustness of differential analysis. Although downsampling has been proposed as a strategy to address group imbalance65, we did not employ it in this study, as doing so would further reduce the already limited number of cells in biologically relevant populations and diminish statistical power. Results are shown in Supplementary Fig.\u00a0S15. DOLPHIN identified 445 more significant genes than the conventional gene count-based method, as shown in Supplementary Fig.\u00a0S15b. Enrichment analysis Supplementary Fig.\u00a0S15c further demonstrates the biological relevance of these additional genes: the EDEGs identified by DOLPHIN yielded stronger enrichment for pancreatic-related terms compared to DEGs. Notably, the 1491 EDEGs uniquely identified by DOLPHIN were significantly enriched in the pancreatic cancer-related term, whereas the 1046 DEGs identified only by gene count-based analysis did not yield any enrichment for such term (Supplementary Fig.\u00a0S15d). These results highlight the added biological signal gained through exon-level analysis. We also analyzed JDEGs based on DOLPHIN\u2019s junction reads. As shown in Supplementary Fig.\u00a0S15f, the 2867 JDEGs were significantly enriched for pancreatic disease-related terms. Furthermore, even when considering only the 1583 JDEGs that did not overlap with DEGs, enrichment analysis still revealed pancreatic cancer-related terms Supplementary Fig.\u00a0S15g. These findings emphasize the additional biological resolution provided by junction-level modeling and demonstrate that DOLPHIN\u2019s splicing-aware framework captures disease-relevant signals that are often missed by conventional gene expression analyses.\n\nDOLPHIN integrates exon reads and junction reads to aggregate cells based on exon-junction read patterns, making it well-suited for AS analysis at the single-cell level. To evaluate its performance, we selected Outrigger as a baseline, as it is one of the most widely used tools for AS event detection in transcriptomics65,66,67. This comparison underscores the advantages of DOLPHIN\u2019s junction-read-aware aggregation, a key feature that enhances sensitivity and accuracy in detecting AS events at the single-cell level. Notably, DOLPHIN\u2019s aggregation approach can be adapted to work with other AS tools (see benchmarking sections), showcasing its versatility.\n\nIn the full-length PBMC dataset, DOLPHIN shows marked improvements over Outrigger in detecting AS events. The top panel of Fig.\u00a04a illustrates the number of Exon Skipping (ES) and Mutually Exclusive Exon (MXE) events detected per cell using Outrigger with single-cell input versus aggregated cell input generated by DOLPHIN. In this context, \u201csingle-cell input\u201d refers to the original, unaggregated scRNA-seq reads, which were supplied directly to Outrigger without any aggregation. This configuration reflects the baseline setting used to evaluate the impact of DOLPHIN\u2019s read aggregation strategy. The results demonstrate a substantial increase in the number of detected events using DOLPHIN, with the median count for ES rising from 183 to 1215, and for MXE increasing from 4 to 22, indicating a marked enhancement in sensitivity. We next assessed whether DOLPHIN effectively enhances single-cell splicing detection by examining AS events jointly detected by both approaches (Fig.\u00a04a). While Fig.\u00a04a summarizes the total number of events per cell, it does not capture how consistently each shared event is detected across cells by the two methods. To address this, we analyzed the cell-level detection patterns of overlapping AS events (Supplementary Fig.\u00a0S16). On one hand, DOLPHIN robustly preserves the detection of AS events originally identified by the single-cell input. In Supplementary Fig.\u00a0S16a,\u00a0we present paired heatmaps for the full-length PBMC dataset, showing the detection patterns for each event across cells. We found that 97.8% of AS events detected by the single-cell input were also detected by DOLPHIN, demonstrating strong consistency. On the other hand, DOLPHIN identifies substantially more AS events beyond those captured by the single-cell input.\u00a0In Supplementary Fig. S16b,\u00a0we quantify this relationship by plotting the distribution of Pearson correlation coefficients between the detection patterns of the two methods for each AS event. Across cells, DOLPHIN detected ~4.8 times more events than the single-cell method alone. Together, these results demonstrate that DOLPHIN not only preserves the fidelity of single-cell AS detection but also enhances sensitivity by recovering a more complete landscape of splicing events across cells.\n\na\u2013c Detection of alternative splicing (AS) events across three datasets: Top: full-length PBMC, Middle: 10X colon, and Bottom: 10X rectum. a DOLPHIN identifies significantly more AS events, including exon skipping (ES) and mutually exclusive exons (MXE), compared to the baseline Outrigger tool, demonstrating superior sensitivity in detecting splicing variations. b Scatter plots of Percent Spliced-In (PSI) values show that DOLPHIN achieves higher correlation with pseudo-bulk data (used as a proxy ground truth), indicating more accurate AS quantification than conventional approaches. c UMAP plots based on PSI values reveal that DOLPHIN captures distinct cell-type-specific splicing patterns with greater clarity and biological relevance, improving resolution of splicing events missed by baseline methods. d Sashimi plots for the AS event HsaEX0051104 in the full-length PBMC dataset show stronger junction read signals after DOLPHIN aggregation, enabling detection of splicing events overlooked by conventional methods. e Similarly, for the AS event HsaEX0013878 in the 10X colon dataset, DOLPHIN enhances junction read signals, uncovering AS events missed by the baseline approaches. P values from one-sided Student\u2019s t-tests: *P\u2009<\u20090.05, **P\u2009<\u20090.01, ***P\u2009<\u20090.001, ****P\u2009<\u20090.0001. Exact P values are provided in the source data. Source data are provided as a Source Data file.\n\nTo further demonstrate DOLPHIN\u2019s capability, we compared PSI values between pseudo-bulk and single-cell samples (top panel of Fig.\u00a04b), using pseudo-bulk PSI values as a proxy ground truth, a strategy commonly employed for AS validation65,67,68. Each point represents the PSI value for a specific AS event, with a higher density of points along the diagonal in DOLPHIN indicating stronger concordance with pseudo-bulk data. The Pearson correlation increases by 0.06 (P\u2009=\u20096.37\u2009\u00d7\u200910\u2212242), indicating that the additional AS events detected by DOLPHIN exhibit comparable, if not stronger, correlation with pseudo-bulk results. This improvement reflects DOLPHIN\u2019s enhanced detection capabilities and greater precision in capturing splicing patterns. In the scatter plot, we observed a higher density of AS events along the diagonal, reflecting a broader improvement across the entire PSI spectrum. AS in most cell populations predominantly yields near-complete exon inclusion or exclusion, with intermediate splicing states being relatively rare and technically challenging to detect22,65. Building on this observation, we analyzed the distribution of detected AS events across different PSI ranges and assessed the corresponding junction read support to characterize DOLPHIN\u2019s aggregation-enhanced detectability. As shown in Supplementary Fig.\u00a0S17a (upper panel), DOLPHIN-enhanced input increased the total number of detected exon-skipping events across all three PSI categories (PSI\u2009=\u20090, 0\u2009<\u2009PSI\u2009<\u20091, and PSI\u2009=\u20091) in the full-length PBMC dataset. The most pronounced gain was observed for PSI\u2009=\u20091, with 431,406 additional events detected, although noticeable improvements were also seen in the other PSI ranges. We further examined the junction read support across the full PSI spectrum (Supplementary Fig.\u00a0S17b, upper panel). In the single-cell input, events with intermediate PSI values (e.g., between 0.4 and 0.6) exhibited substantially lower read counts, with a mean of 66 reads. After DOLPHIN enhancement, the mean read count increased to 168, a statistically significant difference (one-sided Mann\u2013Whitney U test, P\u2009<\u200910\u2212300). These results demonstrate that DOLPHIN improves the detection of AS events across the PSI spectrum, including low-coverage events with intermediate splicing levels.\n\nTo evaluate whether the PSI values reflected biologically meaningful splicing regulation, we assessed their ability to capture cell-type-specific splicing patterns. Specifically, we used PSI values as input features for cell representation and clustering analyses. The UMAP plots in the top panel of Fig.\u00a04c show that DOLPHIN-inferred PSIs yield sharper boundaries between cell types compared to single-cell PSI values alone. This improvement is quantitatively supported by a 0.38 increase in ARI (P\u2009=\u20092.70\u2009\u00d7\u200910\u2212121). These results indicate that DOLPHIN more effectively captures splicing signals that distinguish cell types, suggesting higher biological relevance and improved splicing quantification accuracy.\n\nBeyond full-length single-cell data, we extended our evaluation to the common tag-based 10X Genomics scRNA-seq data from human colon samples to demonstrate its general applicability, where DOLPHIN showed robust performance even with limited transcriptome coverage. The middle panel of Fig.\u00a04a shows that the distribution of detected events by DOLPHIN is shifted towards higher counts compared to single-cell data alone (without aggregation), with the median number of detected ES increasing from 58 to 224, and the maximum number of MXE detected per cell rising from 2 to 8. This underscores DOLPHIN\u2019s sensitivity to data with partial coverage. The concordance heatmap shown in the middle part of Supplementary Fig.\u00a0S16a further illustrates that DOLPHIN consistently preserves the original single-cell detection signals while detecting additional AS events across cells. The scatter plot between pseudo-bulk and single-cell PSI values (middle panel of Fig.\u00a04b) demonstrates an improvement in Pearson correlation by 0.02 (P\u2009=\u20091.19\u2009\u00d7\u200910\u2212208) with DOLPHIN, further validating its accuracy. In addition to the correlation improvement, we observed a higher density of AS events along the diagonal in the scatter plot, reflecting DOLPHIN\u2019s broader enhancement across the entire PSI spectrum. Specifically, in the lower panel of Supplementary Fig.\u00a0S17b, AS events with PSI\u2009=\u20091 showed the greatest increase, with an additional 480,987 events detected compared to the original single-cell input. The mean junction read count supporting AS events with intermediate PSI values (i.e., between 0.4 and 0.6) increased from 65 to 116 (P\u2009=\u20099.16\u2009\u00d7\u200910\u22123). These results confirm that DOLPHIN enhances the detection of low-coverage AS events with intermediate PSI values even in the sparse 10X dataset. The UMAP plots (middle panel of Fig.\u00a04c) demonstrate that DOLPHIN achieves clear separation of specific cell types, such as TA and enterocyte cells, with an increase in the ARI score by 0.06 (P\u2009=\u20093.70\u2009\u00d7\u200910\u221231) compared to single-cell data, highlighting its broad applicability across various datasets. We observed similar improvements with the tag-based 10X rectum data (the bottom panels of Fig.\u00a04a\u2013c and Supplementary Fig.\u00a0S16a, b). Specifically, the bottom panel of Fig.\u00a04a reveals an increase in the number of detected ES, with median values rising from 62 to 200, and for MXE events, from 1 to 2. Additionally, the bottom panel of Fig.\u00a04b shows an improved correlation with pseudo-bulk PSI values, increasing by 0.01 (P\u2009=\u20091.22\u2009\u00d7\u200910\u2212187). Notably, the bottom panel of Fig.\u00a04c shows that the UMAP plot achieves clearer separation of Enterocyte cells using DOLPHIN, further validating its robustness.\n\nTo illustrate the detailed insights DOLPHIN provides, we present examples of exon and junction read coverage for specific AS events. Fig.\u00a04d showcases the full-length PBMC splicing event HsaEX0051104 in the na\u00efve T cell sample \u201cSRR18385965,\u201d comparing single-cell data with DOLPHIN-aggregated data. HsaEX0051104, an exon-skipping event in the PTPRC gene that generates the CD45RA isoform, critical for T cell function69,70. HsaEX0051104 encompasses three exons (exon 4, exon 5, and exon 6), with junction read counts of 13 between exons 4 and 5, 31 between exons 5 and 6, and 16 between exons 4 and 6. However, in single-cell data, this splicing event is not detectable due to the absence of junction reads spanning exons 4 and 6, which are critical for validating the exon-skipping event. We applied an in silico pseudo-bulk validation strategy using CD4 T cells to independently confirm the biological existence of the AS event identified by DOLPHIN. Specifically, we generated 20 pseudo-bulk BAM files by randomly sampling 80% of CD4 T cells per replicate, simulating replicate-level coverage. VALERIE71 was then applied to profile AS events based on junction read and coverage signals across these samples. As shown in Supplementary Fig.\u00a0S18a, VALERIE consistently detected the same exon-skipping event in PTPRC (HsaEX0051104) identified by DOLPHIN, with stable PSI distributions across replicates. We further confirmed this event by applying VALERIE to DOLPHIN\u2019s single-cell BAM files for CD4 T cells (Supplementary Fig.\u00a0S18b), providing orthogonal evidence of its reproducibility and biological relevance. To further support this AS event, we visualized the pseudo-bulk read coverage using ggsashimi72. As shown in Supplementary Fig.\u00a0S18c, the sashimi plot based on full-length PBMC pseudo-bulk alignments clearly demonstrates the exon-skipping pattern corresponding to HsaEX0051104. In addition, DOLPHIN uncovers another splicing event (HsaEX0051102) involving exons 1, 3, and 4. This event is supported by 25 junction reads between exons 1 and 3, 11 reads between exons 3 and 4, and 22 reads connecting exons 1 and 4. Conversely, in this specific single cell, this event is not detected due to the lack of junction reads bridging exons 1 and 4, which are crucial for identifying this splicing pattern. In Fig.\u00a04e, we investigated the splicing event HsaEX0013878 within the CD47 gene in progenitor cell \u201cAAGCCGCCACTACAGT-1\u201d from the 10X colon dataset. CD47 has been implicated in colorectal cancer progression73,74. This event involves exons 1, 2, and 3, with 52 junction reads supporting the connection between exons 1 and 2, 30 reads between exons 2 and 3, and 70 reads between exons 1 and 3. However, this specific cell lacks the crucial junction reads linking exons 1 and 3, thereby precluding the detection of this event in this cell. The presence of this splicing event was further supported by pseudo-bulk alignments of progenitor cells (Supplementary Fig.\u00a0S18d). These examples underscore DOLPHIN\u2019s capacity to uncover complex AS patterns and demonstrate its effectiveness in enhancing single-cell AS analyses through junction-read-informed cell aggregation, revealing biologically significant insights otherwise missed by standard methods.\n\nWe assessed DOLPHIN\u2019s capability to detect cell-type-specific AS events by calculating PSI values for each event per cell type, enabling differential AS analysis. Genes associated with significantly differentially spliced events were identified as differentially spliced genes. Fig.\u00a05a, b and Supplementary Fig.\u00a0S19 highlight the biological relevance of these cell-type-specific events identified by DOLPHIN, underscoring its ability to detect distinct splicing patterns not captured by the raw single-cell data without DOLPHIN aggregation enhancement. Specifically, Fig.\u00a05a displays dot plots of the top differentially spliced events across cell types in the full-length PBMC and tag-based 10X colon datasets, respectively. The labels in the plot correspond to differentially spliced genes and event identifiers, which provide detailed information for each splicing event provided in Supplementary Table\u00a0S1 and Supplementary Table\u00a0S2. In contrast, Supplementary Fig.\u00a0S19 and Supplementary Table\u00a0S3 display the top differentially spliced events identified using raw single-cell data without aggregation. Without DOLPHIN\u2019s aggregation, the analysis based on raw data alone fails to capture the distinct splicing patterns, as evidenced by the reduced separation of PSI values across cell types. Dot colors in the plots represent the average PSI values of an event for cells from each specific cell type, further highlighting DOLPHIN\u2019s capability to detect differential splicing events that were previously missed. For example, in the full-length PBMC dataset, unique splicing events specific to B cells were challenging to distinguish from dendritic cells (DCs) and Other cells using the raw single-cell data, but are now clearly identifiable after DOLPHIN aggregation. Similarly, in the 10X colon dataset, top differentially spliced events appear more prominently in paneth-like cells compared to the single-cell method, illustrating DOLPHIN\u2019s enhanced sensitivity to cell-type-specific splicing.\n\na Dot plots showing the PSI values of the top differentially spliced events identified by DOLPHIN. b GO biological process (GOBP) enrichment analysis of biologically significant differentially spliced genes identified by DOLPHIN, with alternative splicing-related terms highlighted in red. Adjusted P-values for each enrichment term were calculated using one-sided hypergeometric tests, followed by multiple testing correction using the Benjamini\u2013Hochberg method. c Schematic illustration explaining PSI distribution splicing modality categorization. d PSI distribution for a single alternative splicing event, categorized by splicing modality across cell types, demonstrating that DOLPHIN provides clearer distinctions of splicing differences that align with cell type identities. e Splicing modality composition across single cells shows that DOLPHIN captures more distinct and biologically relevant splicing patterns by reducing the proportion of multimodal (null) categories, which represent PSI distributions without clear features. This demonstrates that DOLPHIN reduces ambiguity in alternative splicing event detection, enabling more precise analysis. f UMAP plots of cell clusters using PSI modality one-hot encoding demonstrate that the PSI splicing modalities identified by DOLPHIN retain strong cell-type-specific signals. DOLPHIN enhances the resolution of these cell-type-specific splicing patterns, providing clearer separation and biologically meaningful clustering compared to single-cell data alone. These biologically relevant alternative splicing events can contribute to more accurate cell type classification and offer insights into cellular diversity and potential disease mechanisms. P values from one-sided Student\u2019s t-tests: *P\u2009<\u20090.05, **P\u2009<\u20090.01, ***P\u2009<\u20090.001, ****P\u2009<\u20090.0001; n.s. not significant. Exact P values are provided in the source data. Source data are provided as a Source Data file.\n\nSupplementary Fig.\u00a0S20a highlights the top differentially spliced genes that could not be identified using traditional gene count-based differential expression methods, alongside their expression values in the PBMC and colon datasets. Unlike conventional approaches that primarily focus on gene expression differences, DOLPHIN leverages PSI-based differences to uncover differentially spliced genes that remain undetectable with single-cell gene count data alone. This capability is particularly evident in the 10X colon dataset, where unique splicing patterns are revealed across cell types, even in the absence of significant gene expression changes, underscoring DOLPHIN\u2019s distinct advantage in detecting splicing-driven heterogeneity. To confirm the biological significance of these findings, we performed gene ontology biological process (GOBP) enrichment analysis using differentially spliced genes. In the upper panel of Fig.\u00a05b, GOBP terms enriched in B cells from the PBMC dataset include B cell activation and B cell receptor signaling, reinforcing the biological relevance of these identified splicing events75. Additionally, GOBP terms associated with AS confirm DOLPHIN\u2019s accuracy in detecting spliced genes involved in splicing regulation. In the 10X colon dataset, GOBP enrichment analysis (lower panel of Fig.\u00a05b) revealed terms critical to enterocyte function, such as metabolic processes, aerobic respiration, and mitochondrial electron transport\u2014biological processes that are essential for maintaining gut health76,77. The identification of AS-related GOBP terms reflects the adaptive role of enterocytes in modulating gene expression in response to environmental and cellular stressors78,79. GOBP enrichment analysis for other cell types is presented in Supplementary Fig.\u00a0S21, further underscoring the functional relevance of splicing events detected by DOLPHIN. Supplementary Fig.\u00a0S20b illustrates the distinction between differentially spliced genes identified by DOLPHIN and DEGs detected using conventional gene count methods. Supplementary Fig.\u00a0S20c presents the GOBP enrichment analysis for differentially spliced genes uniquely detected by DOLPHIN, after excluding those already identified as DEGs by conventional gene count-based approaches. The GOBP enrichment analysis of these remaining genes reveals critical biological processes encoded within PSI values that cannot be detected using gene count data alone, highlighting DOLPHIN\u2019s unique ability to uncover splicing-specific regulatory mechanisms. To provide a more granular view of splicing distributions, we applied the Anchor tool from Expedition22, categorizing PSI distributions into five splicing modalities: excluded, bimodal, included, middle, and multimodal (null) (Fig.\u00a05c). This categorization reveals variations in PSI distributions across cell types, facilitating detection of cell type-specific splicing patterns. In scRNA-seq data, splicing events often exhibit varying degrees of PSI consistency within the same cell type22,65. Some events show concentrated PSI distributions corresponding to clear splicing modes, such as inclusion or exclusion, whereas others display dispersed or heterogeneous PSI patterns, classified as multimodal or null modalities. Multimodal splicing patterns can arise from genuine biological heterogeneity, including the co-expression of multiple isoforms and dynamic splicing regulation across cell types22,80. However, in sparse single-cell datasets, multimodal and null modalities can also result from technical factors such as incomplete read coverage, dropout, and measurement noise, making the interpretation of such events more challenging. Null modalities, in particular, indicate splicing signals lacking sufficient consistency across cells, thereby complicating the identification of robust, biologically meaningful splicing patterns. DOLPHIN improves signal clarity by enhancing read coverage and exon-level resolution, which increases the proportion of splicing events that can be classified into more interpretable modalities.\n\nIn the upper panel of Fig.\u00a05d, we examine the splicing event HsaEX0051104 in the PBMC dataset, comparing PSI distributions from single-cell data with DOLPHIN results. DOLPHIN identifies four distinct splicing modes across eight cell types, whereas single-cell data alone captures only three modes. Notably, DOLPHIN enhances the detection of splicing variations in CD8 T cells, shifting the distribution from a null mode to a middle mode, thereby providing a clearer and more accurate representation of these events. We performed in silico validation of this splicing event using a bootstrapped pseudo-bulk strategy. Specifically, we randomly sampled 80% of CD8 T cells multiple times to construct pseudo-bulk profiles and applied VALERIE to visualize splicing signals at the event locus. As shown in Supplementary Fig.\u00a0S22a, the consistent detection of junction usage and read coverage patterns across replicates confirms the presence of this ES event. In addition, we assigned splicing modality based on the PSI values derived from these pseudo-bulk replicates. The resulting modality, shown in Supplementary Fig.\u00a0S22b, consistently falls within the middle modality, validating the splicing distribution identified by DOLPHIN in CD8 T cells.\n\nIn the lower panel of Fig.\u00a05d, the 10X colon dataset displays the PSI distribution for the splicing event HsaEX0013878 within the CD47 gene across different cell types. The CD47 gene, known for its involvement in tumor progression and immune evasion, is typically upregulated in colorectal cancer tissues81. Due to its relevance in colorectal cancer, it is anticipated that multiple transcripts of CD47 would be detected in the colon dataset. Of the six annotated transcripts, three contain the splicing event HsaEX0013878: transcripts ENST00000398258 and ENST00000361309 include all three exons, while ENST00000517766 exhibits ES. To better contextualize the PSI distributions of this event, we incorporated pseudo-bulk transcript quantification using kallisto82 to estimate the exon inclusion probability across cell types for exon chr3:108049619-108049651 (HsaEX0013878). These estimates, shown as red dashed lines in the Supplementary Fig.\u00a0S23a, serve as in silico reference values for comparing single-cell and DOLPHIN-aggregated results. Notably, we applied bootstrapping to quantify the absolute differences between method-specific median PSI values and pseudo-bulk estimates across cell types shown in Supplementary Fig.\u00a0S23b. The DOLPHIN-aggregated PSI values showed smaller deviations from the pseudo-bulk references (P\u2009<\u200910\u2212300) compared to single-cell PSI values, suggesting that DOLPHIN provides a more accurate quantification of this exon inclusion event. This improved accuracy is particularly important because traditional single-cell approaches often produce a bimodal PSI distribution due to limited junction read coverage, complicating the assessment of splicing patterns. In contrast, DOLPHIN achieves a more stable PSI distribution with reduced variability, thereby enhancing the resolution of splicing dynamics linked to CD47\u2019s functional role in cancer.\n\nFigure 5e presents pie charts showing the splicing modality distributions for all detected splicing events across cell types. By substantially reducing the proportion of events classified as \u201cnull\u201d mode, DOLPHIN enhances the clarity and robustness of splicing signals, allowing for more accurate detection of AS events per cell type. This reduction in the null mode reflects the tool\u2019s superior sensitivity and capacity to capture richer splicing distribution information.\n\nTo validate the cell type-specific relevance of splicing modality assignments and thus the quality of AS event detection, we transformed the splicing modality data into a one-hot encoded vector for each cell, reduced the dimensionality using principal component analysis (PCA), and subsequently visualized the results using UMAP, as shown in Fig.\u00a05f. In the PBMC dataset, DOLPHIN achieves clearer separation of cell type clusters, with the ARI increasing from 0.31 to 0.57 (P\u2009=\u20096.62\u2009\u00d7\u200910\u2212121), indicating enhanced retention of cell-specific splicing information. For the 10X colon dataset, DOLPHIN produces improved segregation of different cell types, leading to a 0.08 (P\u2009=\u20092.21\u2009\u00d7\u200910\u2212111) increase in ARI, further validating its consistency in identifying cell-type-specific splicing modalities.\n\nIn this study, we demonstrate that DOLPHIN, which integrates both exon-level and junction reads, significantly outperforms methods that rely solely on gene count tables. To evaluate DOLPHIN\u2019s performance in cell representation learning, we benchmarked it against several state-of-the-art gene-level methods, including SCANPY, the deep generative model scVI, scGMAAE83, and scDeepCluster84. To broaden the methodological scope of our benchmarking and ensure a more rigorous comparison, we additionally incorporated scQuint and SCASL, two recently developed splicing-aware clustering tools for scRNA-seq data. scQuint employs a VAE to derive cell embeddings from intron usage profiles constructed using junction reads. Although it shares the same VAE-based architecture as DOLPHIN, scQuint relies exclusively on intron-level input and does not leverage exon read counts. As such, it serves as a conceptually aligned baseline for evaluating how AS feature representations influence the quality of learned cell embeddings. SCASL, on the other hand, performs clustering based on junction-derived AS probabilities, which are iteratively imputed using a KNN-based strategy and clustered using spectral clustering. Given its objective of cell-type inference from splicing features, SCASL also provides a relevant point of comparison for DOLPHIN\u2019s exon graph-based framework. Since scQuint is not recommended for use with 10X Genomics Chromium data due to its strong \\(3{\\prime}\\) bias and limited junction detection, and SCASL performs suboptimally on droplet-based \\(3{\\prime}\\) RNA-seq with low sequencing depth, we employed the full-length RNA-seq dataset for benchmarking to ensure a fair and meaningful comparison for these two splicing-aware clustering methods. We assessed cell representation quality via cell clustering performance using five metrics: ARI, NMI, Completeness Score (CS)85, Adjusted Mutual Information (AMI)86, and Fowlkes\u2013Mallows Index (FMI)85, which together provide a holistic view of clustering effectiveness based on learned cell embeddings.\n\nTo showcase the general applicability, benchmarking was conducted on three distinct datasets across different platforms, tissues, and biological conditions: full-length PBMC (Fig.\u00a06a), tag-based 10X colon tissues (Fig.\u00a06b), and tag-based 10X rectum tissues (Fig.\u00a06c). DOLPHIN consistently achieved top performance on all datasets and across multiple evaluation metrics. In the full-length PBMC dataset, it achieved the highest median scores for ARI, NMI, CS, AMI, and FMI, with statistically significant improvements over other methods based on one-sided Student\u2019s t-tests. Specifically, DOLPHIN\u2019s ARI median score of 0.64 represented a 27.86% improvement over SCANPY (P\u2009=\u20093.45\u2009\u00d7\u200910\u221275) and a 26.34% improvement over scVI (P\u2009=\u20092.86\u2009\u00d7\u200910\u221249). Its NMI score further exceeded those of SCANPY and scVI by 0.03 (P\u2009=\u20091.73\u2009\u00d7\u200910\u221229) and 0.04 (P\u2009=\u20099.58\u2009\u00d7\u200910\u221221), respectively. Consistent patterns were observed in the tag-based 10X colon and rectum datasets, where DOLPHIN achieved the highest median scores across all clustering metrics, surpassing all other gene count-based clustering approaches. In the 10X colon dataset, DOLPHIN achieved an ARI of 0.38. Among all comparisons, the smallest relative improvement was observed over scDeepCluster (6.46%, P\u2009=\u20091.08\u2009\u00d7\u200910\u22124), while the largest was over SCANPY, with a 39.34% gain (P\u2009=\u20092.30\u2009\u00d7\u200910\u221256). In the 10X rectum dataset, DOLPHIN similarly outperformed all methods, with ARI improvements of 15.76% over SCANPY (P\u2009=\u20091.46\u2009\u00d7\u200910\u221225) and 53.75% over scDeepCluster (P\u2009=\u20094.23\u2009\u00d7\u200910\u221288). In addition to outperforming gene count-based methods, DOLPHIN also demonstrated superior performance compared to splicing feature-based clustering approaches. DOLPHIN consistently outperformed both SCASL and scQuint across all metrics. scQuint showed limited clustering performance in the full-length PBMC dataset, with a median ARI of only 0.15, which is substantially lower than DOLPHIN (P\u2009=\u20099.16\u2009\u00d7\u200910\u2212186) and all other methods. SCASL achieved moderate clustering performance, with a median ARI of 0.44, which is significantly lower than that of the DOLPHIN method (P\u2009=\u20094.61\u2009\u00d7\u200910\u2212117). These findings confirm DOLPHIN\u2019s capability to generate higher-quality cell embeddings and capture distinct cell populations with superior sensitivity and precision, even across diverse datasets. This robust performance across metrics underscores the advantage of integrating exon- and junction-level data in enhancing cell representation learning in scRNA-seq analysis.\n\nThis benchmarking analysis highlights the superior cell embeddings generated by DOLPHIN, achieved through the integration of exon-level quantification and junction reads. Better cell clustering accuracy demonstrates the improved quality of these embeddings, evaluated using Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Completeness Score (CS), Adjusted Mutual Information (AMI), and Fowlkes\u2013Mallows Index (FMI) across full-length PBMC (a), 10X colon (b), and 10X rectum datasets (c). Each score is based on N\u2009=\u2009100 bootstrapping replicates using different random seeds (technical replicates). Boxes indicate the interquartile range (IQR, 25th to 75th percentile), with the line inside each box representing the median. Whiskers extend to the most extreme data points within 1.5 times the IQR from the quartiles. P values from one-sided Student\u2019s t-tests: *P\u2009<\u20090.05, **P\u2009<\u20090.01, ***P\u2009<\u20090.001, ****P\u2009<\u20090.0001; n.s. not significant. Exact P values are provided in the source data. Source data are provided as a Source Data file.\n\nTo evaluate the effectiveness of the DOLPHIN aggregation enhancement in detecting AS events, we conducted an extensive benchmarking analysis. We used conventional single-cell analysis tools, including SCANPY and scVI, to identify cell neighborhoods via low-dimensional embeddings at the gene level, and then applied an aggregation enhancement approach similar to DOLPHIN by randomly combining reads from neighboring cells. Single-cell data without any aggregation served as an additional baseline comparison to assess the impact of aggregation enhancement of different methods for detecting AS events. Aggregated cells were analyzed for AS events using six specialized tools, Expedition, MARVEL65, BRIE2, scASfind87, scQuint, and SCASL, focusing specifically on ES and MXE. ES is the most prevalent form of AS in mammalian cells, while together, ES and MXE account for over 50% of all AS events88,89,90. Among these, tools such as scASfind, scQuint, and SCASL are, according to their original publications, not recommended for use with 10X Genomics datasets characterized by low sequencing depth. These methods were primarily designed or validated for full-length scRNA-seq datasets, which offer more extensive transcript coverage and greater sensitivity for splice junction detection. Given the strong \\(3^{\\prime}\\) coverage bias and limited junction representation characteristic of 10X Genomics data, applying these tools outside their intended context may introduce confounding factors and compromise the interpretability of the results. To ensure a fair and context-appropriate comparison, we conducted benchmarking across all tools primarily on full-length RNA-seq datasets. However, to showcase the general applicability of our method, we applied DOLPHIN to 10X Genomics data and conducted a comparative analysis with AS tools compatible with the 10X dataset, including Outrigger, MARVEL, and BRIE2. This benchmarking evaluated several key metrics: the number of detected AS events, correlation with pseudo-bulk profiles, cell embedding and clustering quality based on PSI values, the number of differentially spliced genes, and clustering accuracy based on splicing modality. Since real datasets lack definitive ground truth for AS events, we also generated simulated datasets specifically for ES events, providing a controlled evaluation dataset with ground truth.\n\nOne critical metric for evaluating the effectiveness and sensitivity of AS event detection is the total number of events identified by different methods. In the full-length PBMC dataset, as illustrated in Fig.\u00a07a, DOLPHIN achieved a marked improvement in detecting ES events. When using Outrigger as the AS detection tool, DOLPHIN aggregation identified a total of 975,215 ES events across 795 cells, averaging 1227 ES events per cell. In contrast, the single-cell method without aggregation detected only 204,344 events in total, representing ~20% of the AS events identified by DOLPHIN. Aggregation via SCANPY and scVI produced similar results to the single-cell method without aggregation, identifying 293,926 and 294,658 ES events, respectively. When utilizing MARVEL as the AS detection tool, DOLPHIN further amplified its detection capabilities, identifying 1,709,186 ES events, significantly surpassing SCANPY\u2019s 828,984 and scVI\u2019s 827,115 events. These results highlight DOLPHIN\u2019s enhanced sensitivity in detecting ES events. Even for MXE events, which are less frequent, DOLPHIN showed a marked improvement. Under the Outrigger tool, DOLPHIN identified 17,906 MXE events, while SCANPY and scVI detected only 4537 and 4612 events, respectively, with the single-cell method detecting the fewest at 3626 events. Using MARVEL as the AS detection method, DOLPHIN aggregation detected 18,104 MXE events, greatly surpassing SCANPY (8880) and scVI (8960). While the magnitude of improvement varied across tools, DOLPHIN-based aggregation consistently led to an increased number of detected AS events compared to using single-cell input alone for each respective tool. Notably, SCASL exhibited the smallest gain, yet still detected 1834 additional events when using DOLPHIN-enhanced inputs. It is important to note that each method quantifies distinct types of splicing events: BRIE2, MARVEL, and Outrigger focus on exon inclusion/exclusion; SCASL estimates junction-level PSI values; scQuint quantifies intron usage; and scASfind derives node-level information from splicing graphs. Therefore, comparisons were made within each tool to assess the impact of DOLPHIN inputs, rather than across tools. In the tag-based 10X colon dataset, DOLPHIN continued to demonstrate its advantage in ES event detection as shown in Supplementary Fig.\u00a0S24a. Using Outrigger as the AS detection tool, DOLPHIN aggregation detected 950,816 ES events, which is ~3.8 times more than the 248,027 events detected by the single-cell method alone. Utilizing MARVEL as the AS detection tool, DOLPHIN extended its lead, detecting 1,596,731 ES events compared to SCANPY\u2019s 1,205,204 and scVI\u2019s 1,204,753. This represents more than a 2.3-fold increase over the single-cell method, which identified 690,410 events. Similar improvements were observed with BRIE2, where the number of detected events rose from 103,013 (single cell) to 299,625 with DOLPHIN input, again achieving the highest detection rate among all aggregation strategies.\n\nThe performance of DOLPHIN in detecting alternative splicing (AS) events was evaluated using real and simulated datasets. a\u2013e Results from single-cell RNA-seq datasets, comparing DOLPHIN to SCANPY, scVI, Outrigger, MARVEL, BRIE2, SCASL, scASfind, and scQuint. DOLPHIN consistently outperformed these methods by leveraging exon-level information. a DOLPHIN identifies more AS events, including exon skipping (ES) and mutually exclusive exons (MXE), than all other methods. b Higher Pearson correlations between single-cell and pseudo-bulk datasets indicate DOLPHIN's improved splicing quantification, based on N\u2009=\u200980 bootstrapping replicates (10 per cell type across 8 cell types, using different random seeds; technical replicates). c Higher Adjusted Rand Index (ARI) scores for cell clustering based on Percent Spliced-In (PSI) values reflect DOLPHIN's ability to capture biologically meaningful splicing patterns, based on N\u2009=\u2009100 bootstrapping replicates using different random seeds (technical replicates). d DOLPHIN detects more differentially spliced events per cell type based on Outrigger analysis, demonstrating its sensitivity to cell-type-specific splicing variations. e Higher ARI and NMI scores for splicing modality profiles, derived from Anchor analysis, further confirm DOLPHIN's ability to resolve splicing-driven cellular heterogeneity, based on N\u2009=\u2009100 bootstrapping replicates using different random seeds (technical replicates). Boxes indicate the interquartile range (IQR, 25th to 75th percentile), with the line inside each box representing the median. Whiskers extend to the most extreme data points within 1.5 times the IQR from the quartiles. P values from one-sided Student\u2019s t-tests: *P\u2009<\u20090.05, **P\u2009<\u20090.01, ***P\u2009<\u20090.001, ****P\u2009<\u20090.0001; n.s. not significant. Exact P values are provided in the source data. f, g DOLPHIN using simulated datasets with known ground truth. f Receiver Operating Characteristic (ROC) curves show higher area under the curve (AUC) values for DOLPHIN, confirming its superior accuracy. g Precision-Recall (PR) curves demonstrate DOLPHIN's higher area under the precision-recall curve (AUPRC), indicating better precision and recall for splicing event detection. Source data are provided as a Source Data file.\n\nWe next assessed the Pearson correlation91 between the cell and its corresponding cell-type-level pseudo-bulk profile. In the full-length PBMC dataset, as shown in Fig.\u00a07b, the pseudo-bulk profiles were generated using the same splicing-aware tool as the corresponding single-cell analysis, serving as a within-method proxy ground truth for evaluating PSI consistency. For all splicing-aware tools, DOLPHIN-based aggregation consistently yielded the highest median PSI correlation within each tool, with statistically significant improvements over both single-cell inputs and alternative aggregation strategies, including SCANPY and scVI. Among the AS detection tools, Outrigger demonstrated the largest gain in correlation between single-cell and pseudo-bulk PSI estimates. The median Pearson correlation increased significantly from 0.22 with single-cell input to 0.96 when using DOLPHIN-based aggregation, P\u2009=\u20094.04\u2009\u00d7\u200910\u2212179. DOLPHIN-aggregated input also outperformed aggregation using SCANPY and scVI, with improvements in median Pearson correlation of 0.65 and 0.66, respectively, both statistically significant (P\u2009=\u20096.20\u2009\u00d7\u200910\u2212164 for SCANPY; P\u2009=\u20093.05\u2009\u00d7\u200910\u2212163 for scVI). In contrast, SCASL showed the smallest improvement among all tested methods, with the median correlation increasing only marginally from 0.90 to 0.91 under the DOLPHIN setting, and the improvement remained statistically significant, P\u2009=\u20091.65\u2009\u00d7\u200910\u22129. Notably, for SCASL, both SCANPY- and scVI-based aggregations resulted in lower correlation than the original single-cell input, highlighting the robustness of DOLPHIN\u2019s aggregation approach in preserving splicing signal fidelity. In essence, DOLPHIN detects, on average, over 2.36 times more AS events than all other methods, while maintaining superior correlation with proxy ground-truth AS events derived from pseudo-bulk data. This underscores the quality of the additional AS events uniquely identified by DOLPHIN, which are missed by other methods. In the 10X colon dataset shown in Supplementary Fig.\u00a0S24b, DOLPHIN achieved the highest correlation between single-cell and pseudo-bulk PSI estimates compared to other methods. Among them, the largest improvement was observed for Outrigger, where the median correlation increased by 0.67 when using DOLPHIN-aggregated input compared to single-cell input (P\u2009=\u20095.53\u2009\u00d7\u200910\u2212166). Compared to DOLPHIN, the median correlation values obtained using SCANPY and scVI were substantially lower, with differences of 0.52 (P\u2009=\u20096.04\u2009\u00d7\u200910\u2212117) and 0.51 (P\u2009=\u20091.24\u2009\u00d7\u200910\u2212111), respectively.\n\nTo assess the consistency of AS patterns within the same cell type, we used PSI values for clustering and calculated the ARI against known cell type labels. A higher ARI indicates that PSI-based clustering better aligns with true cell type distinctions, suggesting that consistent PSI values within a cell type result in clearer clustering. As shown in Fig.\u00a07c, DOLPHIN-based aggregation consistently yielded the highest ARI scores across all evaluated tools in the full-length PBMC dataset. Notably, BRIE2 exhibited the most substantial improvement, with the median ARI increasing from 0.03 (single-cell input) to 0.40 under DOLPHIN aggregation (P\u2009=\u20092.16\u2009\u00d7\u200910\u2212119). These findings illustrate that, in the absence of effective aggregation, PSI-based clustering may fail to reveal biologically meaningful structure. In the 10X colon dataset shown in Supplementary Fig.\u00a0S24c, BRIE2 showed a relatively smaller improvement with DOLPHIN input, but its performance remained significantly higher than that of SCANPY (P\u2009=\u20099.32\u2009\u00d7\u200910\u22127) and scVI-based aggregation methods (P\u2009=\u20092.24\u2009\u00d7\u200910\u22122). The improved performance achieved with DOLPHIN highlights the ability of our aggregation strategy to enhance the resolution of cell-type-specific splicing patterns.\n\nWe further benchmarked cell-type-level differential AS analysis using PSI quantification results from Outrigger. As shown in Fig.\u00a07d, the number of differentially spliced events detected per cell type is reported. The number of differential AS events varied from hundreds92 to thousands65, depending on the dataset, with DOLPHIN exhibiting superior detection across cell types. In the full-length PBMC dataset, DOLPHIN detected up to 23.3 times more differential events in monocytes compared to the single-cell non-aggregation method. Notably, in DC cells, the aggregation approach of SCANPY detected even fewer differential AS events than the single-cell non-aggregation method, indicating that random or inappropriate aggregation not only fails to highlight cell-type-specific AS events but can even diminish existing signals. In the 10X colon dataset, DOLPHIN maintained strong performance across most cell types, highlighting its ability to capture cell-type-specific splicing differences more effectively than other approaches.\n\nWe then compared the splicing modality patterns at the cell type level using the Anchor function of Expedition to categorize each AS event. The results, shown in Supplementary Fig.\u00a0S25, demonstrate that DOLPHIN achieved a lower percentage of multimodal events, indicating stronger PSI signals for alternative events. Multimodal events are characterized by PSI distributions lacking clear, cell-type-specific patterns and instead displaying a broad, undifferentiated range across PSI values, which points to an absence of structured inclusion or exclusion indicative of specific cell states. In the Full-length PBMC dataset, DOLPHIN reduced the overall percentage of multimodal events across all cell types, with the most pronounced decrease observed in monocytes, where the multimodal percentage dropped by 18%. A similar pattern was observed in the 10X colon dataset. This reduction in multimodal events implies that DOLPHIN enhances the detection of cell-type-specific AS patterns, providing a more robust modality signal. These modality patterns were further validated by converting the modality composition to one-hot encoding for cell clustering (Fig.\u00a07e). Higher ARI and NMI values for DOLPHIN indicate that the identified modalities align more closely with cell-type-level patterns, supporting the effectiveness of DOLPHIN in distinguishing biologically relevant AS events.\n\nTo further validate and benchmark DOLPHIN\u2019s performance in detecting AS events, we developed a series of simulated datasets with known ground truth specifically designed to assess ES, the most prevalent AS event in single-cell data93,94. We generated 200 scRNA-seq datasets, each containing a total of 4,\u00a0103 simulated ES events, which served as ground truth for evaluating AS detection accuracy. Using these simulated datasets, we compared twenty method combinations by pairing five single-cell AS analysis tools (Outrigger, MARVEL, BRIE2, SCASL, and scASfind) with four aggregation strategies (no aggregation, DOLPHIN, SCANPY, and scVI). Performance was evaluated by constructing receiver operating characteristic (ROC) curves95 and precision-recall (PR) curves96 for each method, as shown in Fig.\u00a07f, g, and Supplementary Fig.\u00a0S26. In these figures, methods labeled solely by the names of AS tools indicate that the original single-cell datasets were directly analyzed without any aggregation strategy. Fig.\u00a07f, g demonstrates that DOLPHIN-based aggregation consistently enhanced the performance of all AS detection tools, as measured by improvements in the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC). Among the evaluated combinations, SCASL with DOLPHIN and MARVEL with DOLPHIN achieved the highest AUC values of 0.79, while MARVEL with DOLPHIN attained the highest AUPRC of 0.82. In addition, Supplementary Fig.\u00a0S26 presents results from SCANPY- and scVI-based aggregations. Across all tested conditions, DOLPHIN consistently outperformed not only direct single-cell analyses without aggregation but also alternative aggregation strategies, achieving superior sensitivity and precision in splicing event detection under controlled conditions.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61580-w/MediaObjects/41467_2025_61580_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61580-w/MediaObjects/41467_2025_61580_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61580-w/MediaObjects/41467_2025_61580_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61580-w/MediaObjects/41467_2025_61580_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61580-w/MediaObjects/41467_2025_61580_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61580-w/MediaObjects/41467_2025_61580_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61580-w/MediaObjects/41467_2025_61580_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we present DOLPHIN, a deep learning framework designed to enhance scRNA-seq analysis at exon-level resolution. DOLPHIN combines exon reads and junction reads into graph-structured representations of genes, which are processed with a VGAE and graph attention (GAT) layers to generate informative cell embeddings. These embeddings enable DOLPHIN to support a range of downstream analyses, including cell clustering, differential exon analysis, and AS detection, surpassing conventional gene count methods in both depth and accuracy. Compared to existing graph-based approaches for splicing detection, such as DiffSplice97 and Outrigger, DOLPHIN introduces several important differences that enhance its performance in single-cell contexts. DiffSplice constructs an expression-weighted splice graph from bulk RNA-seq data and identifies alternative splicing modules, but it does not model cell-level splicing variability and is not designed for single-cell resolution analyses. Outrigger, as part of the Expedition framework, builds a genome-wide splice graph based on pseudo-bulk junction reads aggregated across cells and subsequently quantifies PSI values at the single-cell level. However, it neither constructs per-cell graphs to retain cell-specific resolution nor makes use of exon-level reads in its graph construction. In contrast, DOLPHIN generates a gene-specific exon graph independently for each cell using both exon- and junction-reads. These per-cell graphs are integrated through VGAEs and attention-based learning to produce cell embeddings that encode cell-specific splicing patterns. By aggregating information based on these embeddings, which reflect both exon and junction read signals, DOLPHIN groups together cells with similar splicing profiles, thereby enhancing sensitivity for detecting AS events while fully preserving single-cell resolution.\n\nDOLPHIN introduces several key advancements in scRNA-seq analysis: First, DOLPHIN creates a high-resolution exon-level cellular representation by integrating exon-level quantification and junction reads, addressing the limitations of conventional gene-level analysis. Traditional approaches, which simplify each gene to a single scalar value, often lose crucial transcriptomic information. DOLPHIN\u2019s graph-based representation of exon and junction data provides a more refined and integrative view of transcriptomic architecture, significantly enhancing cell clustering accuracy. By optimizing clustering performance with the GAT layer, which emphasizes biologically relevant exon interactions, DOLPHIN enables high-resolution insights into cell type and state that extend beyond conventional methods. Second, DOLPHIN ensures broad applicability across diverse scRNA-seq platforms and biological systems, including full-length and tag-based technologies, by leveraging universally available exon and junction reads. Its modular framework is compatible with existing splicing detection tools and adaptable to datasets of varying coverage and complexity. This generalizability allows researchers to apply DOLPHIN across a range of biological systems and experimental designs, demonstrating its versatility. Furthermore, its computationally efficient architecture, which scales linearly with the number of detected genes per cell, makes DOLPHIN particularly suitable for large-scale single-cell analyses, addressing a key challenge in the field. These features position DOLPHIN as a flexible, broadly applicable framework for scRNA-seq analysis. Third, DOLPHIN enables the identification of EDEGs and cell type-specific markers, capturing subtle transcriptomic differences that remain undetectable with gene-level aggregation. This exon-level sensitivity allows DOLPHIN to uncover cell-type-specific markers and condition-specific signatures otherwise masked by gene-level data. By identifying EDEGs, DOLPHIN reveals nuanced transcriptomic profiles that distinguish subtle functional and condition-specific variations, expanding scRNA-seq analysis to capture cell-specific transcriptomic features with a precision that can benefit complex cellular analyses. DOLPHIN also enhances AS detection, a challenging area in single-cell data due to typically low junction read coverage. By leveraging VGAE-derived embeddings to aggregate junction reads from similar cells through a KNN approach and majority voting, DOLPHIN effectively increases the coverage for AS analysis. This aggregation allows for the detection of cell-type-specific splicing variations that are often missed in single-cell datasets, broadening the scope of AS analysis and advancing the identification of biologically relevant splicing patterns. Finally, DOLPHIN introduces the ability to identify differentially spliced marker genes, providing critical insights into how condition-specific splicing variations contribute to cellular diversity and functional differentiation in contexts such as diseased versus healthy cells. This analysis facilitates the discovery of novel biological mechanisms by revealing condition-specific AS patterns, enhancing the understanding of disease-associated transcriptomic changes, and supporting the identification of therapeutic targets and biomarkers. Together, these contributions position DOLPHIN as an integrative framework that extends scRNA-seq analysis beyond gene-level resolution. By incorporating exon- and junction-level data within a deep learning framework, DOLPHIN enables researchers to access previously undetectable biological insights in complex cellular systems, making it a valuable tool for studying cellular heterogeneity and gene regulation.\n\nOne current limitation of DOLPHIN lies in its lack of compatibility with many existing single-cell analysis frameworks, which predominantly operate at the gene level. In this work, we have developed and demonstrated DOLPHIN\u2019s capability to perform exon-level analyses for several foundational single-cell tasks, including cell type marker discovery, cell representation learning, clustering, and de novo cell type annotation. These applications highlight the utility of exon-level quantification and representation in uncovering insights beyond the resolution of conventional gene-level approaches. However, certain downstream tasks, such as trajectory inference and cell-cell interaction analysis, remain incompatible with exon-level data due to the reliance of these frameworks on gene-level inputs. While indirect conversion to gene-level data is possible through well-annotated exon-gene relationships, such conversions degrade the resolution and nuanced insights that exon-level analysis provides. Despite this, the adoption of tools that natively accommodate exon-level data in these tasks could enable transformative analyses, leveraging DOLPHIN\u2019s granularity to reveal finer-scale transcriptomic and cellular dynamics. Extending DOLPHIN to support these downstream tasks is beyond the scope of this work but represents a key direction for future development. In addition to expanding compatibility with downstream frameworks, another future direction involves broadening the range of AS events analyzed by DOLPHIN. This study focused on ES and MXE, which together account for over 50% of all AS events88,89,90. Although DOLPHIN can model other AS types, including intron retention, alternative \\(5^{\\prime}\\) and \\(3^{\\prime}\\) splice sites, and alternative first and last exons, these were not included in the current benchmarking. Expanding DOLPHIN\u2019s scope to cover these events represents an important avenue for future development. Building on these directions, ongoing efforts are focused on integrating exon-level data into broader single-cell workflows, aiming to provide a robust and high-resolution framework for a wider range of applications.\n\nDOLPHIN provides a high-resolution approach to scRNA-seq analysis by moving beyond gene-level quantification to integrate exon-level and junction read data, offering a more informative representation of cellular states. This framework enhances the detection of exon-level markers and AS events, which are essential for understanding cellular identity and functional diversity. With applications across fields such as immunology, developmental biology, and oncology, DOLPHIN supports researchers in gaining deeper insights into gene regulation and cellular dynamics in complex tissues. By introducing a flexible, high-resolution framework, DOLPHIN broadens the tools available for studying cellular heterogeneity and disease mechanisms, advancing the precision of single-cell transcriptomic analysis to support studies of disease processes and therapeutic discovery.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "To create an exon-level reference genome, we modified the GRCh38 annotated GTF file from Ensembl release 10798. This modification involved merging overlapping exons from multiple transcripts of a single gene, retaining the largest range of the overlapped exons as a single representative exon, as illustrated in Supplementary Fig.\u00a0S27. This approach prevents repetitive counting of overlapping exon regions across different transcripts, helping to ensure that the sum of all exon reads closely aligns with the gene-level read count. This alignment is critical for maintaining accurate quantification and avoiding distortions in downstream analyses.\n\nThe preprocessing steps differ for full-length RNA-seq and tag-based 10X Genomics data. For full-length RNA-seq data, reads were trimmed to remove adapters using Trimmomatic (v0.39)99, following the method in ref. 29. Trimmed sequences were then aligned to the modified exon-level reference genome using the splice-aware aligner STAR (v2.7.3a)100 with parameters from the original publication. BAM files generated from this alignment were processed with featureCounts (v2.0.3)101 to obtain exon and junction read counts. Gene-level counts were also extracted to support cell quality control and identify highly variable genes (HVGs). For the tag-based 10X Genomics dataset, we aligned raw RNA-seq reads to the default GRCh38 (GRCh38-2020-A) reference genome using Cell Ranger (v7.0.1)102 to generate single-cell barcodes and BAM files. BAM files were filtered to retain reads with valid barcodes using the subset-bam tool from 10X Genomics, and bamtools (v2.5.2)103 was then used to split the filtered BAM file into single-cell BAM files. Each single-cell BAM file was subsequently re-aligned to the modified exon-level reference genome using STAR under default settings. Exon and junction read counts were then obtained from these files using featureCounts with the same parameters as the full-length RNA-seq data. These exon and junction counts serve as the input for DOLPHIN. A visual overview of the preprocessing workflow for both full-length RNA-seq and 10X Genomics data is provided in Supplementary Fig.\u00a0S28.\n\nData preprocessing for conventional scRNA-seq analysis: For analyses using conventional methods such as SCANPY, all input matrices were normalized in accordance with standard requirements. For gene-level analysis, raw scRNA-seq reads were aligned to the reference genome to generate BAM files, which were processed using featureCounts to obtain raw gene count tables. Genes detected in fewer than three cells were filtered out, followed by library size normalization and log1p transformation. HVGs (top 5000 HVGs) were then selected and used as input for SCANPY analysis. Differential expression analyses were performed using the MAST framework within Seurat, employing the same normalization procedure as above. For EDEG analysis, exons detected in fewer than three cells were removed before applying library size normalization and log1p transformation. The same normalization procedures were applied to the raw adjacency matrices prior to JDEG analysis.\n\nData preprocessing for deep neural networks: deep neural networks commonly used in scRNA-seq analysis, such as scVI, typically assume that the input data follow a zero-inflated negative binomial (ZINB) distribution13 and therefore require raw count data as input. In this study, DOLPHIN follows a similar strategy: we use raw exon and junction counts as input and model them under a ZINB framework. However, compared to gene-level raw counts, exon-level counts are substantially sparser and more variable across cells, presenting additional challenges for representation learning. To address these issues, we applied library size normalization to the raw exon counts to account for differences in sequencing depth across cells, followed by rounding to the nearest integer. This preprocessing strategy preserved the ZINB distribution assumption for exon-level counts after library size normalization and rounding, while also substantially improving model performance compared to using unnormalized raw counts (Supplementary Fig.\u00a0S29). For feature and adjacency matrix construction, only the exons and junctions corresponding to the HVGs identified by SCANPY, as described above, were retained to ensure consistency across methods. To construct the adjacency matrix, raw junction read counts within each gene were normalized such that the total edge weight summed to one, facilitating comparability across genes. These normalized adjacency matrices were used as edge weights for message passing in the graph attention layers. Meanwhile, the original (unnormalized) raw junction matrices were retained as reconstruction targets in the decoder, enabling the model to jointly learn from both the exon graph structure (via message passing) and the raw junction counts (via reconstruction).\n\nTo construct the graph data, we begin by converting exon and junction read counts into feature and adjacency matrices for each gene, processed one gene at a time. These matrices are built using a reference genome GTF file to ensure that all cells have a consistent number and ordering of exons.\n\nIn this graph representation, each exon is treated as a node with a single feature: the exon read count. Exon read counts are derived from the raw exon count table produced by featureCounts using a gene annotation GTF file, ensuring an identical set of exons across all cells. For each gene Gj, containing nj exons, the feature vector \\({{{{\\bf{X}}}}}_{i}^{j}\\) for gene j in cell i is a one-dimensional vector of size nj, representing exon counts ordered from the 5\u2032 to 3\u2032 end according to the reference genome. The feature matrix for cell i is then formed by concatenating these feature vectors across all genes, resulting in a vector of size \\({\\sum }_{j=1}^{N}{n}_{j}\\), where N is the number of genes. The feature vector for gene j in cell i is denoted as \\({{{{\\bf{X}}}}}_{i}^{j}=[{E}_{i}^{j,1},{E}_{i}^{j,2},\\ldots,{E}_{i}^{j,{n}_{j}}]\\), where \\({X}_{i}^{j,k}={E}_{i}^{j,k}\\) represents the read count for exon k in gene j from cell i. Consequently, the feature matrix for each cell i is a concatenation of all its gene feature vectors: \\({{{{\\bf{X}}}}}_{i}={{\\mathrm{diag}}}\\,\\{{{{{\\bf{X}}}}}_{i}^{1},{{{{\\bf{X}}}}}_{i}^{2},\\ldots,{{{{\\bf{X}}}}}_{i}^{N}\\}\\).\n\nAfter constructing the feature matrices, we generate adjacency matrices using junction count files produced by featureCounts. These files contain junction reads associated with valid primary genes, including splice site locations, which are compared with exon locations from the featureCounts exon count table to build the adjacency matrix. Specifically, each junction\u2019s splice sites are matched to exon locations to determine the connected exons, with exons serving as nodes and junctions as directed edges from the 5\u2032 to the 3\u2032 end. Edges are defined as follows: for each junction, the left (5\u2032) splice site identifies the starting exon, and the right (3\u2032) splice site identifies the ending exon. If a splice site is within an exon region, the edge originates or terminates at that exon; if located between two exons, the edge begins or ends at the exon nearest the 5\u2032 or 3\u2032 end. Edges are created only when both connected exons have non-zero counts. The raw adjacency matrix \\({{{{\\bf{A}}}}}_{i}^{j}\\) for all exons of gene j in cell i is represented as follows:\n\nwhere \\({A}_{i}^{j,(m,n)}\\) is the matrix element at row m and column n, em,n denotes the raw junction read count between mth and nth exons, and the matrix size is nj\u2009\u00d7\u2009nj. The cell-level raw adjacency matrix Ai is constructed by arranging the gene-level raw adjacency matrices \\({{{{\\bf{A}}}}}_{i}^{j}\\) into a block diagonal matrix, such that \\({{{{\\bf{A}}}}}_{i}={{\\mathrm{diag}}}\\,\\{{{{{\\bf{A}}}}}_{i}^{1},{{{{\\bf{A}}}}}_{i}^{2},\\ldots,{{{{\\bf{A}}}}}_{i}^{N}\\}\\). The raw adjacency matrix is used to define the edge weights in the exon graph, and normalization yields the final adjacency matrix. The normalized adjacency matrix \\({{{{\\bf{AN}}}}}_{i}^{j}\\) for all exons of gene j in cell i is represented as follows:\n\nEdge weights are normalized per gene to ensure that the sum of weights equals one, enabling comparability across genes. Cell-level adjacency matrices ANi are then constructed by stacking gene-level adjacency matrices in a block diagonal format, resulting in \\({{{{\\bf{AN}}}}}_{i}={{\\mathrm{diag}}}\\,\\{{{{{\\bf{AN}}}}}_{i}^{1},{{{{\\bf{AN}}}}}_{i}^{2},\\ldots,{{{{\\bf{AN}}}}}_{i}^{N}\\}\\). This approach ensures uniform adjacency matrix dimensions across cells, with rows and columns corresponding to the same exons across all cells.\n\nDOLPHIN employs a VGAE to integrate exon and junction counts, deriving biologically informative cell embeddings. To effectively aggregate neighborhood information and capture complex relationships within the data, a Graph Attention Layer (GAT) is incorporated into the encoder of the VAE model.\n\nThe Graph Attention Layer is specifically designed for graph-structured data and leverages an attention mechanism to dynamically weight neighboring nodes based on their relevance, enhancing the model\u2019s flexibility and expressiveness. Unlike standard GAT layers that primarily focus on node features, our model includes edge features in the GAT layer, improving the ability to capture nuanced relationships between nodes. The attention scores in our model are computed using both node and edge features. The GAT layer is defined as fGAT(Xi,\u00a0Ai)\u2009=\u2009Hi, where Hi represents the attended feature vectors (or embeddings) for cell i. These attended feature vectors are obtained by averaging the outputs from multiple attention heads, denoted by M, as follows:\n\nIn this equation, \u03c3 represents the activation function applied to the layer output, such as ReLU, while M is the total number of attention heads, with each head processing the graph structure independently. The term \\({\\alpha }_{uv}^{(m)}\\) denotes the attention coefficient between node u and node v for the mth attention head, indicating the weighted importance of node v in relation to node u. \u0398(m) is the learnable weight matrix for the mth attention head, used to transform the node features, and Xv represents the feature vector for node v. The term \\({{{\\mathcal{N}}}}(u)\\cup \\{u\\}\\) denotes the set of nodes consisting of u\u2019s neighbors \\({{{\\mathcal{N}}}}(u)\\) and the node u itself. Including u in this set allows node u to incorporate information from its own features in addition to those of its neighbors during the aggregation process. The attention coefficient \\({\\alpha }_{uv}^{(m)}\\) between node u and node v for each head m is calculated using both node and edge features as follows:\n\nIn this equation, LeakyReLU is the activation function applied to the transformed features, providing non-linearity to the attention calculation. The parameters \\({{{{\\bf{a}}}}}_{s}^{m}\\), \\({{{{\\bf{a}}}}}_{t}^{m}\\), and \\({{{{\\bf{a}}}}}_{e}^{m}\\) are learnable weight vectors for the mth head, used in the attention mechanism to determine the importance of the source node, target node, and edge features, respectively. The matrices \\({{\\bf{\\Theta }}}_{s}^{(m)}\\), \\({{\\bf{\\Theta }}}_{t}^{(m)}\\), and \\({{\\bf{\\Theta }}}_{e}^{(m)}\\) are learnable weight matrices for the mth head that transform the source node, target node, and edge features into a latent space, facilitating more complex representations of relationships in the graph. The feature vectors Xu and Xv represent the source and target nodes, u and v, respectively, while euv denotes the edge feature between nodes u and v. The denominator in the expression normalizes the attention scores across all neighbors of u, including u itself, ensuring that the sum of the attention scores across all connected nodes equals one. This normalization produces a weighted importance value for each connection, allowing the model to aggregate node features in a way that reflects the relevance of each node\u2019s neighbors based on both node and edge characteristics. By incorporating edge features, the model gains additional context, allowing it to weigh the importance of neighboring nodes more effectively based on edge characteristics. This results in a richer representation and more informative aggregation of node features, enhancing model performance.\n\nThe encoder of the DOLPHIN model consists of a GAT layer followed by multiple Multi-Layer Perceptrons (MLPs). The GAT layer processes the input features from the node and adjacency matrices, producing an attention-weighted node feature representation Hi. This output is then fed into two separate MLPs, which map the node features into a latent space. Specifically, for the VAE, these MLPs compute the mean vector \u03bc\u2009=\u2009f\u03bc(Hi) and the standard deviation vector \u03c3\u2009=\u2009f\u03c3(Hi) for a multivariate Gaussian distribution. The latent representation Z is then sampled from this Gaussian distribution, \\({{{\\mathcal{N}}}}(\\mu,{\\sigma }^{2})\\), capturing the probabilistic nature of the encoded data. In this way, the encoder defines the approximate posterior distribution q\u03b8(Z\u2223Xi,\u00a0ANi), where Z denotes the learned latent representation for cell i.\n\nTo reconstruct the original data, DOLPHIN utilizes two decoders: one for the adjacency matrix \\({{{{\\bf{A}}}}}_{i}^{{\\prime} }\\) and another for the feature matrix \\({{{{\\bf{X}}}}}_{i}^{{\\prime} }\\). This dual reconstruction strategy allows the model to learn and preserve both structural and attribute information of the graph. By reconstructing both aspects of the input data, the model enhances the richness and interpretability of the latent space representation, capturing detailed properties of the graph structure. Moreover, reconstructing multiple data components increases the model\u2019s robustness and generalization ability, improving its performance on unseen data.\n\nIn the VAE model, the distributions of both the feature matrix Xi and the adjacency matrix Ai are assumed to follow a ZINB distribution, which effectively models the overdispersed and zero-inflated nature of single-cell data. The likelihood for cell i in the decoder is defined as:\n\nwhere \\({p}_{{\\varphi }_{X}}({{{{\\bf{X}}}}}_{i}| {{{\\bf{Z}}}})\\) and \\({p}_{{\\varphi }_{A}}({{{{\\bf{A}}}}}_{i}| {{{\\bf{Z}}}})\\) represent the likelihoods of reconstructing the feature matrix Xi and the adjacency matrix Ai given the latent variable Z. Specifically, these likelihoods are detailed as follows:\n\nand\n\nIn these expressions, \\({\\mu }_{{X}_{i}^{j,k}},{\\theta }_{{X}_{i}^{j,k}},{\\pi }_{{X}_{i}^{j,k}}\\) are the parameters of the ZINB distribution for the kth exon of the jth gene of cell i, and \\({\\mu }_{{A}_{i}^{j,(m,n)}},{\\theta }_{{A}_{i}^{j,(m,n)}},{\\pi }_{{A}_{i}^{j,(m,n)}}\\) are the ZINB parameters for the junction between mth exon and nth exon of the jth gene of cell i. These parameters allow the decoder to capture the complex distributional properties of both gene features and their interactions within each cell, making it more suited to the inherent variability of single-cell data. The likelihood function, therefore, models the probability of observing the reconstructed data Xi and Ai given the latent variables Z.\n\nThe VAE model\u2019s objective is to minimize the Kullback\u2013Leibler (KL) divergence between the approximate posterior q\u03b8(Z\u2223Xi,\u00a0ANi) and the prior p(Z) distributions while simultaneously minimizing the reconstruction loss for both the feature matrix Xi and the adjacency matrix Ai. The VAE loss function is formulated as:\n\nIn this formulation, the KL divergence term, scaled by the hyperparameter \u03b2, regularizes the latent space by encouraging the learned latent distribution to approximate the prior Gaussian distribution. The hyperparameter \u03b2 thereby controls the trade-off between enforcing a smooth latent space and preserving reconstruction accuracy. The reconstruction loss terms for the feature matrix and adjacency matrix measure how accurately the decoder reconstructs the original data from the latent variables. The hyperparameter \u03bb controls the balance between the reconstruction losses for the feature matrix and adjacency matrix, allowing the model to adjust to the underlying data distribution. This multi-objective loss function, with \u03b2 and \u03bb as balancing factors, enables the model to learn a meaningful latent representation while effectively reconstructing both the structural and feature information from the input data. The training time and memory usage of the DOLPHIN model are presented in the Supplementary Fig.\u00a0S30.\n\nThe latent representation Z obtained from the model is used for cell clustering. We first compute a neighborhood graph of cells and identify clusters using the Leiden algorithm104. The clustering results are visualized through UMAP38 to reveal the relationships among cells. Once cell embeddings are established, the next step is cell aggregation, which serves as the basis for analyzing AS across these aggregated cells. Aggregation is performed at the BAM file level, where each single-cell BAM file, generated during data preprocessing, is aggregated individually for each cell. Here, cell i is treated as the target cell in the aggregation process, though the procedure is repeated for all cells.\n\nThe aggregation process consists of three main steps, beginning with identifying neighboring cells for the target cell i. Using the latent representation Z, we employ a KNN approach to identify neighboring cells, with K\u2009=\u200910 as the selected default in this study. This choice of K was determined by benchmarking different values against clustering accuracy, as shown in Supplementary Fig.\u00a0S31. The AS detection performance is comparable between K\u2009=\u200910 and K\u2009=\u200915, with an average difference of only 1.4%. However, increasing K to 15 raises the computational complexity for AS detection by ~40%. Therefore, we set K\u2009=\u200910 as the default.\n\nThe second step addresses library size normalization at the BAM file level to correct for variations in sequencing depth across cells, ensuring balanced read counts within the neighborhood and reducing potential bias. For each cell i, the total read count is calculated, and all neighborhood reads are adjusted to match the read count of cell i. This normalization preserves the original sequencing data for the target cell. If a neighboring cell has fewer reads than the target, its reads are duplicated to match the target\u2019s count. Conversely, if a neighboring cell has more reads, excess reads are randomly removed. The read count for cell i remains unchanged, providing effective library size normalization. For any neighboring cell k, the library size normalization is given by:\n\nwhere mk represents the original set of reads from neighboring cell k, \u2223mk\u2223 is the total read count for k, and Sample(mk,\u00a0Q) denotes the sampling function that randomly selects Q reads from mk when \u2223mk\u2223\u2009>\u2009\u2223mi\u2223.\n\nThe final step in aggregation consolidates junction reads from neighboring cells into the target cell using a majority voting approach. Only junction reads are aggregated; thus, BAM files of neighboring cells, \\({m}^{\\prime}_{k}\\), are first filtered to retain only junction read sequences. Junctions are identified as spliced alignments, and start and end positions are provided by the STAR output file SJ.out.tab. A combined set of unique junction reads is defined as \\({\\bigcup }_{k\\in {{{\\mathcal{N}}}}(i)\\cup \\{i\\}}{\\, j}_{k}\\), where jk represents the junction reads in each neighboring cell k, and \\({{{\\mathcal{N}}}}(i)\\) denotes the set of neighbors for cell i as identified by the KNN algorithm. This combined set captures all distinct junctions across the neighborhood of cell i, including cell i itself. Each junction jt is considered for inclusion in cell i\u2019s BAM file based on its prevalence across the neighborhood. Specifically, if a junction read jt appears in over half of the neighboring cells, it is added to cell i\u2019s BAM file; otherwise, it is excluded. This condition is represented as:\n\nwhere \\({\\mathbb{I}}(\\cdot )\\) denotes the indicator function, which outputs 1 if the condition is met and 0 otherwise. The specific indicator function \\({{\\mathbb{I}}}_{{m}_{k}\\, {{{\\rm{if}}}}\\, k=i,\\, {m}^{\\prime}_{k} \\, {{{\\rm{if}}}}\\, k\\ne i}({\\,j}^{t})\\) is defined as:\n\nThis condition checks whether the junction jt is present in more than half of the K cells in the neighborhood (including cell i). The aggregated junction reads for cell i are then represented as:\n\nwhere \\({j}_{i}^{{\\prime} }\\) denotes the aggregated junction reads for cell i, and ji is the original junction read set of cell i. After aggregation, exon read sequences remain unchanged, resulting in a final set of reads for cell i represented as the combination of exon read sequences and \\({j}_{i}^{{\\prime} }\\). All retained junction locations are saved in a BED file, and neighborhood BAM files are filtered by these locations. The filtered BAM files of neighboring junctions and the BAM file of cell i are then merged. This aggregation process is repeated for each cell, with each cell sequentially serving as the target. Aggregating BAM files, rather than directly modifying exon or junction count tables, ensures compatibility with downstream analyses that rely on BAM file inputs. This approach also allows for the generation of count tables from BAM files, enhancing the flexibility of DOLPHIN for various analytical applications.\n\nTo identify EDEGs, we analyzed scRNA-seq data from four subjects in ref. 31: three patients with PDAC as the cancer group and one subject with normal pancreas tissue and duodenal intraepithelial neoplasia as the control group. Data preprocessing adhered to the 10X Genomics protocol specified in the \u201cData preprocessing\u201d section, including only cells annotated in the original study and retained through our processing pipeline. The resulting feature matrix was used as input for differential exon analysis. Cell embeddings were generated using the DOLPHIN model and subsequently clustered using SCANPY with the Leiden algorithm.\n\nTo identify EDEGs, the exon count table obtained from the \u201cData preprocessing\u201d section was used as input to the MAST framework105, to compute P values and log2 fold changes (log2FC) for each exon. Gene-level P values were then aggregated using the Stouffer method106,107, weighted by exon length. This weighting strategy reflects the rationale that longer exons, by receiving more consistent read coverage, provide more reliable statistical estimates and thus serve as a biologically grounded proxy for measurement stability in sparse scRNA-seq data. To evaluate the impact of this weighting scheme, we compared it to two alternative strategies: weighting by the mean exon read count across cells and applying uniform weights across all exons. As shown in Supplementary Fig.\u00a0S32, weighting by exon length resulted in the most biologically relevant enrichment analysis outcomes among all strategies. Similarly, the \u2223log2FC\u2223 values were averaged at the gene level, weighted by exon length. Bonferroni correction108 was applied to account for multiple testing across all genes, producing adjusted gene-level P-values and log2FC values derived from exon-level data. Genes with adjusted P\u00a0<\u00a00.05 and log2FC\u2009>\u20091 were classified as EDEGs. Additionally, we applied FDR correction using the Benjamini\u2013Hochberg procedure to DEG and EDEG analyses across multiple thresholds. As shown in Supplementary Fig.\u00a0S33, BH correction increased the number of detected genes compared to Bonferroni correction, with EDEGs consistently outnumbering DEGs, indicating that their greater sensitivity is not solely due to the choice of correction method.\n\nTo assess the distinct advantages of the EDEGs analysis in comparison to the conventional differential gene analysis, we also conducted a conventional DEGs analysis on normalized gene counts for cells in Cluster 2, using MAST to calculate adjusted P-values and log2FC for each gene. Using DOLPHIN, we identified 896 genes classified as EDEGs that were not detected as DEGs. To further refine this list, we filtered out genes that were not significantly differentially expressed in the gene count table based on the Wilcoxon rank-sum test (adjusted P\u2009>\u20090.05) using SCANPY. The remaining genes were selected for visualization in the heatmap shown in Fig.\u00a03d. The P values between the two conditions were computed using a two-sided Wilcoxon test based on the average expression values of each gene (or exon) within each group. This heatmap demonstrates that, regardless of whether MAST or the Wilcoxon test is used, genes that do not show differential expression at the gene level can still exhibit significant differential expression at the exon level when analyzed with DOLPHIN. The same analysis pipeline was applied to the comparison of ductal cells. Supplementary Fig.\u00a0S14a schematically illustrates the identification of differential exons and junctions, even when no differential expression is observed at the gene level. Toppgene50 was used to identify the enrichment of EDEGs, DEGs, and JDEGs in disease and pathways. For pathway enrichment, we utilized the WikiPathways109 dataset, while for disease enrichment, we used data from AllianceGenome110, Clinical Variation111, DisGeNET Curated112, and OMIM MedGen113 databases. For survival analysis, we first obtained bulk RNA-seq gene expression data and corresponding clinical survival information for 181 PDAC patients from TCGA62 via the UCSC Xena platform114. Genes used for survival evaluation were selected from the 896 EDEG-unique genes identified by DOLPHIN. These genes were ranked by adjusted P values in ascending order, and subsets containing the top 100,200,300 genes, and so forth up to all 896 EDEGs were sequentially selected for downstream analysis. We then followed the Scissor framework115 to assess the prognostic relevance of these EDEGs in stratifying patient risk. Specifically, patients were grouped into high-risk and low-risk categories based on the aggregate expression levels of the selected top-ranked EDEGs. Statistical significance of survival differences between these groups was evaluated using the log-rank-sum test.\n\nTo complement exon-level findings, we further investigated differential expression at the junction level by considering each exon-exon junction as an independent unit. Normalized junction counts derived from the \u201cData preprocessing\u201d section were used as input to the MAST framework105 to estimate P values and log2FC changes for each junction. These junction-level statistics were subsequently aggregated to the gene level using an unweighted Stouffer\u2019s method106,107, followed by Bonferroni correction. Similarly, gene-level log2FC were computed by averaging the absolute log2FC values across all junctions belonging to the same gene. Genes with adjusted P\u2009<\u20090.05 and absolute log2FC\u2009>\u20091 were designated as JDEGs.\n\nAggregated BAM files from DOLPHIN were aligned to the reference genome with STAR to generate junction files for input to Outrigger in Expedition: the outrigger index function identifies all AS events by pooling junction reads across cells and traversing the resulting splice graph, the outrigger validate function filters events to retain only those with canonical splice sites (commonly conserved splice junctions in transcriptomes)116, and the outrigger psi function calculates PSI values for events supported by at least 10 valid junction reads. PSI values range from 0 (complete ES) to 1 (full exon inclusion). Outrigger detects ES and MXE events directly based on observed junction reads, without any imputation. As a result, splicing events with insufficient read support yield missing (NaN) PSI values. The number of detected events is determined by counting only those events with valid (non-NaN) PSI values. Given the central role of PSI matrices in multiple downstream analyses, we adopted distinct strategies for handling missing PSI values depending on the specific analytical context. Below, we detail the origin of missing values and the imputation approaches applied in different parts of the study to ensure accurate and biologically meaningful interpretation of AS patterns.\n\nFor PSI-based cell clustering, random-value imputation was performed to address missing PSI entries and facilitate dimensionality reduction and clustering analyses. This imputation step was necessary because the PSI matrices generated during quantification contained NaN values arising from insufficient junction read coverage, leading to each cell being characterized by PSI values for a different subset of splicing events. Standard dimensionality reduction algorithms cannot accommodate such missing entries. To overcome this limitation, we adapted an imputation strategy from the MARVEL framework, whereby missing values were randomly sampled from a uniform distribution between 0 and 1. Although this approach introduces some smoothing, it preserves the underlying cell-type-associated splicing structures that would otherwise be obscured by data sparsity.\n\nFor differential splicing analysis, a different imputation strategy was employed to account for the high sparsity of PSI values, which is particularly pronounced in droplet-based scRNA-seq datasets. Specifically, missing PSI values for each splicing event were imputed using the mean PSI across cells of the same cell type, following the strategy adopted in the BRIE2 framework. Without imputation, many events would exhibit insufficient read coverage across conditions, hindering robust statistical testing. This cell-type-specific mean imputation provides a conservative approximation that facilitates differential analysis while preserving the observed variation among cells with valid PSI values. Following imputation, differential splicing was analyzed using the Wilcoxon rank-sum test, and P values were adjusted for multiple testing using the Benjamini\u2013Hochberg method, with significance determined at an adjusted P value cutoff of 0.05. Genes associated with significant AS events were designated as differentially spliced genes, and GOBP enrichment analysis was performed using ToppGene50.\n\nSplicing modalities for each event were identified using the Anchor function in Expedition, which employs a Bayesian framework to fit PSI distributions to Beta distributions. Bayes Factors were used to assign splicing modalities, such as \u201cincluded,\u201d \u201cexcluded,\u201d \u201cbimodal,\u201d or \u201cnull.\u201d To preserve the natural shape of PSI distributions, PSI values obtained from Outrigger were directly used for modality classification without applying any imputation. Cells with missing PSI values were excluded from the analysis, thereby avoiding artificial centering effects that could arise from imputing missing values with cell-type means. These modalities were numerically encoded using one-hot encoding to create a cell-by-modality matrix (Figs.\u00a05f and 7e), which was used for dimensionality reduction with PCA, followed by Leiden clustering. This approach facilitated the identification of splicing-driven subpopulations and patterns of cellular diversity.\n\nThis pipeline integrates PSI-based clustering and splicing modality analysis, enabling the discovery of biologically meaningful splicing patterns while addressing the inherent sparsity of scRNA-seq data. By linking computational insights to functional pathways, these analyses advance our understanding of cell-type-specific splicing and transcriptomic diversity.\n\nTo evaluate the necessity of DOLPHIN\u2019s core components, we conducted an ablation study to examine the performance of different versions of the method in cell representation learning, as shown in Fig.\u00a02. We also benchmarked these variations against other single-cell clustering methods for comparison, as illustrated in Fig.\u00a06. The cell type annotations were directly taken from the original publications, which generated them based on gene count tables, and were used as ground truth for evaluating cell embedding performance. All benchmarking results were obtained using a bootstrapping strategy. Specifically, in each iteration, 80% of the data were randomly subsampled, and the process was repeated 100 times to ensure robustness. Statistical significance was assessed using a one-sided Student\u2019s t-test. As shown in Supplementary Fig.\u00a0S34, the resulting score distributions closely approximated a Gaussian distribution, thereby justifying the use of the t-test in this context.\n\nFor the ablation analysis in Fig.\u00a02, we used the DOLPHIN VAE model, selectively removing components to assess their impact. Specifically, we removed the GAT layer and retained only a single decoder to create baseline versions. We tested these simplified VAE models with three distinct input configurations: the gene count table, the feature matrix, and the adjacency matrix. All inputs were preprocessed following the procedure described in the \u201cData preprocessing\u201d section for deep neural networks. This design isolates the effect of input data type on embedding quality, independent of model architecture. The latent space extracted from each configuration served as the cell representation for subsequent Leiden clustering using SCANPY. Clustering performance was evaluated against ground truth cell types using ARI and NMI as part of the ablation study. Using these metrics, our ablation study demonstrated the importance of each component in DOLPHIN, underscoring the effectiveness of its full architecture in optimizing cell representation learning. For the single-cell long-read RNA-seq dataset, raw reads were first processed using the scNanoGPS pipeline, generating high-confidence BAM files and isoform-level quantifications. Exon- and junction-level counts were then extracted from the BAM files using featureCounts and subsequently imported into DOLPHIN to obtain embeddings for comparative analysis.\n\nIn the batch effect analysis, we assessed batch effects using 10 distinct random seeds and quantitatively evaluated them with the batch-adjusted ASW117, the integrated local inverse Simpson\u2019s index (iLISI)41, and the KNN batch effect test118. For the batch correction analysis, we applied Harmony and scVI. Harmony was performed on the normalized gene count table (the same input as used in SCANPY), with PCA applied first, followed by Harmony batch correction in the PCA space. The adjusted principal components were then used for clustering. For scVI correction, the raw gene count table was used as input with the batch key specified. Batch-aware embeddings were generated and used for clustering. For exon-level batch correction, scVI was applied to the raw feature matrix to obtain a batch-corrected feature matrix, which was subsequently used by DOLPHIN for embedding generation, while the adjacency matrix remained unchanged. Batch correction performance was evaluated using ARI, NMI, batch-adjusted ASW, and graph connectivity scores computed with OmicVerse43.\n\nIn the benchmarking analysis shown in Fig.\u00a06, we compared DOLPHIN with gene-level clustering methods. The gene expression inputs for SCANPY and scVI were processed according to the normalization steps described in the \u201cData preprocessing\u201d section, while inputs for scGMMAE and scDeepCluster were processed following their respective requirements. Subsequently, we applied Leiden clustering to each latent space to standardize comparisons. For scQuint, junction reads obtained with STARsolo119 were used to compute intron usage profiles, which were input to its VAE for cell embedding. The resulting low-dimensional representations were clustered using the Leiden algorithm to identify cell populations. For SCASL, junction reads extracted from STAR were used to calculate splicing probabilities, followed by KNN-based imputation and spectral clustering on the resulting splicing feature matrix.\n\nWe evaluated clustering performance using five metrics: ARI, NMI, CS, AMI, and FMI. These metrics assess various aspects of clustering quality: ARI and NMI measure alignment with ground truth cell types, CS assesses cluster homogeneity, and AMI and FMI account for overlap between predicted and ground truth cell types.\n\nTo benchmark DOLPHIN\u2019s performance in detecting AS events, we compared four approaches that differed only in the input scRNA-seq reads, while keeping the splicing detection tools fixed. Specifically, all four conditions used the same AS detection tools but applied them to different versions of the raw scRNA-seq reads. The first approach, referred to as \u201csingle-cell AS analysis,\u201d used the original unaggregated single-cell reads as input and served as the baseline. The remaining three approaches employed read aggregation strategies based on SCANPY, scVI, and DOLPHIN, respectively. This setup allowed us to isolate and evaluate the effect of DOLPHIN\u2019s aggregation strategy on improving AS event detection.\n\nFor single-cell AS analysis, the original scRNA-seq data were aligned to the reference genome using STAR, generating both junction and BAM files. The resulting single-cell BAM files served as input for aggregation with SCANPY, scVI, and DOLPHIN. Aggregation methods employed low-dimensional embeddings of single cells (SCANPY and scVI) or DOLPHIN\u2019s graph-based representation to identify neighborhoods via a KNN algorithm (N\u2009=\u200910). Aggregated BAM files from all methods were re-aligned to the reference genome using STAR, producing junction files for junction read analysis with Outrigger or any other AS detection methods.\n\nTo ensure a fair comparison, each of the four input types was analyzed using an identical pipeline across all AS detection tools. For Outrigger, we followed the procedure described in the \u201cDOLPHIN alternative splicing analysis\u201d section. For MARVEL, STAR generated BAM and junction files; rMATS120 identified splicing events from BAMs, and PSI values were computed from junction files at single-cell resolution, retaining only events supported by 10 reads. For BRIE2, BAM files were used to quantify read counts per splicing event per cell. PSI values were then estimated using the mode1 option, which models cell-specific variation by incorporating an individual offset term for each cell. For SCASL, junction files from STAR alignment were used as input. SCASL computed splicing probabilities from junction reads and applied iterative imputation to recover missing values, enabling robust estimation of single-cell splicing profiles. For scQuint, junction reads obtained with STARsolo119 were used to compute intron counts and calculate PSI values. For scASfind, PSI values were quantified using Whippet121 and subsequently processed to generate an index for rapid querying and downstream analysis across single cells.\n\nThe number of AS events was quantified by counting valid PSI values. For Outrigger, MARVEL, scQuint, scASfind, and SCASL, detected events were defined as those with non-NaN PSI values. For BRIE2, events were considered detected if the 95% confidence interval of the imputed PSI value was less than 0.1. To evaluate cell-type-specific pseudo-bulk correlations (Fig.\u00a07b), pseudo-bulk BAM files were generated by merging the BAM files of cells belonging to the same cell type. For each splicing-aware tool, the corresponding pseudo-bulk BAM files were independently processed using the same pipeline as applied to its single-cell input, serving as a tool-specific ground truth for PSI correlation evaluation. PSI correlations were calculated based on the set of splicing events detected by at least one of the input methods. For the PSI-based clustering benchmark (Fig.\u00a07c), each tool\u2019s PSI matrix was processed using a common pipeline with PCA for dimensionality reduction and Leiden clustering. Clusters were evaluated against ground-truth cell type labels. All tools followed this procedure except SCASL, which used its own clustering method.\n\nWe used Polyester122 to simulate raw RNA-seq datasets for benchmarking AS detection, specifically targeting ES events with planted ground truth. Polyester requires two inputs: a reference sequence FASTA file and a transcript count table. RNA-seq fragments are simulated based on sequence abundance, guided by the count table. Transcript counts were derived from the full-length PBMC dataset using kallisto82. These counts were modeled with a negative binomial distribution to generate count tables for each cell, using 200 different seeds to ensure variability. For the reference sequences, exon sequences from the Ensembl GRCh38 genome were extracted, and one exon was randomly removed to create isoform2 (exon-skipping). Isoform1 (exon inclusion) retained the skipped exon and adjacent exons. Together, these isoforms formed the reference sequence for simulating ES events, serving as ground truth for evaluating AS detection methods.\n\nWe evaluated the robustness of DOLPHIN across varying sequencing depths and protocols by simulating scRNA-seq datasets from three common platforms: full-length, 10X Genomics \\(3{\\prime}\\), and 10X Genomics \\(5{\\prime}\\) protocols. Precision, recall, and F1 score were used as evaluation metrics. Two splicing detection tools (Outrigger and MARVEL) were benchmarked across four input strategies\u00a0(Supplementary Fig.\u00a0S35). Simulations used 100 bp reads for 200 cells, with total read counts ranging from 50,000 to 500,000 per cell123. Full-length simulations used complete isoform sequences, while droplet-based simulations extracted 300 bp fragments from the \\(3^{\\prime}\\) or \\(5^{\\prime}\\) ends to mimic coverage biases. Across all conditions, DOLPHIN-enhanced inputs substantially improved recall and F1 scores while maintaining high precision, particularly under low-depth and biased-coverage scenarios. Fig.\u00a07f, g, and Supplementary Fig.\u00a0S26 \u00a0show the performance under the full-length protocol with 500,000 reads per cell. For differential splicing benchmarking (Fig.\u00a07d), Outrigger was compared using either original single-cell inputs or DOLPHIN-aggregated profiles, with PSI analysis following the \u201cDOLPHIN alternative splicing analysis\u201d procedure.\n\nWe next evaluated DOLPHIN\u2019s sensitivity to ES under varying \\(3{\\prime}\\) and \\(5{\\prime}\\) tag biases (Supplementary Fig.\u00a0S36). We simulated tag-based directional coverage by extracting 100\u2013500\u2009bp from the isoform \\(3{\\prime}\\) or \\(5{\\prime}\\) ends. When the captured region was \u2264100\u2009bp, splicing detection was poor across all methods. However, above 300\u2009bp, performance substantially improved, with DOLPHIN consistently outperforming alternatives in both recall and F1. Importantly, the 300\u2013400\u2009bp capture length threshold aligns with the empirical properties of 10X Genomics 3\u2032 scRNA-seq data, where cDNA molecules are biased toward fragments of this size124,125. Analysis of our 10X colon dataset (Supplementary Fig.\u00a0S37) confirmed that 96.9% of transcripts had \\(3{\\prime}\\) capture lengths greater than 200\u2009bp, consistent with our simulation assumptions. These findings demonstrate that our simulations reflect realistic droplet-based sequencing conditions and highlight DOLPHIN\u2019s effectiveness in improving splicing detection sensitivity and accuracy in 10X-based platforms.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "We evaluated DOLPHIN using four publicly available single-cell RNA-seq datasets. The full-length human PBMC dataset from Hahaut et al.29 is accessible via the Sequence Read Archive (SRA) under project number PRJNA816486. The 10X Genomics colon and rectum single-cell RNA-seq datasets from Wang et al.30 are available in the Gene Expression Omnibus (GEO) with accession number GSE125970. Additionally, the PDAC cancer single-cell RNA-seq dataset from Peng et al.31 is hosted in the Genome Sequence Archive under project number PRJCA001063. The PDAC bulk RNA-seq data and corresponding survival information were obtained from TCGA62 and downloaded via the UCSC Xena platform114. Each single-cell RNA-seq dataset includes raw RNA-seq FASTQ files and cell type annotations, supporting replication, benchmarking, and analyses such as AS detection, clustering, and cell type classification. The example datasets associated with this study are available on Zenodo at https://doi.org/10.5281/zenodo.15611935126.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code used to develop the model, perform the analyses, and generate results in this study is publicly available and has been deposited in GitHub at https://github.com/mcgilldinglab/DOLPHIN, under the MIT license. The specific version of the code associated with this publication is archived in Zenodo and is accessible via https://doi.org/10.5281/zenodo.15602232127.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Wang, S. et al. The evolution of single-cell RNA sequencing technology and application: progress and perspectives. Int. J. Mol. Sci. 24, 2943 (2023).\n\nChoe, K., Pak, U., Pang, Y., Hao, W. & Yang, X. Advances and challenges in spatial transcriptomics for developmental biology. 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Dolphin advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads. https://doi.org/10.5281/zenodo.15602232 (2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work is supported by grants from the Canadian Institutes of Health Research (CIHR) [PJT-180505 to J.D.]; the Funds de recherche du Qu\u00e9bec\u2014Sant\u00e9 (FRQS) [295298 to J.D., 295299 to J.D., 366764 to J.D.]; the Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN2022-04399 to J.D.]; and the Meakins-Christie Chair in Respiratory Research [to J.D.].", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Quantitative Life Sciences, McGill University, Montreal, QC, Canada\n\nKailu Song,\u00a0Yumin Zheng\u00a0&\u00a0Jun Ding\n\nMeakins-Christie Laboratories, Research Institute of the McGill University Health Centre, Montreal, QC, Canada\n\nKailu Song,\u00a0Yumin Zheng,\u00a0Bowen Zhao,\u00a0David H. Eidelman\u00a0&\u00a0Jun Ding\n\nDivision of Experimental Medicine, Department of Medicine, McGill University, Montreal, QC, Canada\n\nBowen Zhao,\u00a0David H. Eidelman\u00a0&\u00a0Jun Ding\n\nHEC Montr\u00e9al, Montreal, QC, Canada\n\nJian Tang\n\nMila - Quebec AI Institute, Montreal, QC, Canada\n\nJian Tang\u00a0&\u00a0Jun Ding\n\nSchool of Computer Science, McGill University, Montreal, QC, Canada\n\nJun Ding\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.D. conceived and designed the study, developed the methodology, and planned the experiments. K.S. contributed to the design, implementation of the methodology and conducted the experiments. J.D. and K.S. participated in data collection and the analysis of computational experiments. All authors (J.D., K.S., Y.Z., B.Z., D.E., J.T.) contributed to writing the manuscript. Each author has read and approved the final manuscript for publication.\n\nCorrespondence to\n Jun Ding.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Sean Wen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads.\n Nat Commun 16, 6202 (2025). https://doi.org/10.1038/s41467-025-61580-w\n\nDownload citation\n\nReceived: 17 December 2024\n\nAccepted: 24 June 2025\n\nPublished: 04 July 2025\n\nVersion of record: 04 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61580-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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highly porous materials", + "journal": "Nature Communications", + "published": "02 September 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51491-7/MediaObjects/41467_2024_51491_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51491-7/MediaObjects/41467_2024_51491_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51491-7/MediaObjects/41467_2024_51491_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.11443644", + "/articles/s41467-024-51491-7#ref-CR71", + "/articles/s41467-024-51491-7#Sec15" + ], + "code": [ + "https://github.com/2phi/weac", + "https://pypi.org/project/weac", + "/articles/s41467-024-51491-7#ref-CR72" + ], + "subject": [ + "Characterization and analytical techniques", + "Natural hazards" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3965175/v1.pdf?c=1725361536000", + "research_square_link": "https://www.researchsquare.com//article/rs-3965175/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-51491-7.pdf", + "preprint_posted": "24 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "When porous materials are subjected to compressive loads, localized failure chains, commonly termed as anticracks, can occur and cause large-scale structural failure. Similar to tensile and shear cracks, the resistance to anticrack growth is governed by fracture toughness. Yet, nothing is known about the mixed-mode fracture toughness for highly porous materials subjected to shear and compression. We present novel fracture mechanical field experiments tailored for weak layers in a natural snowpack. Using a mechanical model for interpretation, we calculate the fracture toughness for anticrack growth for the full range of mode interactions, from pure shear to pure collapse. The measurements show that fracture toughness values are significantly larger in shear than in collapse, and suggest a power-law interaction between the anticrack propagation\r\nmodes. Our results reveal new insights into the fracture characteristics of anticracks in highly porous materials and provide important benchmarks for computational modeling.Earth and environmental sciences/Natural hazardsPhysical sciences/Materials science/Techniques and instrumentation/Characterization and analytical techniquesAnticrack propagationporous materialsmixed-mode loadingfracture toughnessweak snow layers", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "code.zipSupplementary Dataset 1supplementforreview.pdfsupplementtypeset.pdfSupplementaryInformation.pdfSupplementary Information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "When porous materials are subjected to compressive loads, localized failure chains, commonly termed anticracks, can occur and cause large-scale structural failure. Similar to tensile and shear cracks, the resistance to anticrack growth is governed by fracture toughness. Yet, nothing is known about the mixed-mode fracture toughness for highly porous materials subjected to shear and compression. We present fracture mechanical field experiments tailored for weak layers in a natural snowpack. Using a mechanical model for interpretation, we calculate the fracture toughness for anticrack growth for the full range of mode interactions, from pure shear to pure collapse. The measurements show that fracture toughness values are significantly larger in shear than in collapse, and suggest a power-law interaction between the anticrack propagation modes. Our results offer insights into the fracture characteristics of anticracks in highly porous materials and provide important benchmarks for computational modeling.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Over the past century, fracture mechanics has profoundly impacted material science and engineering, providing a crucial framework to understand and predict material failure. The complexities of fracture mechanics become particularly intriguing when applied to porous materials, where unique challenges arise due to the occurrence of compressive fractures, so-called anticracks, alongside traditional tensile and shear fractures described in classical fracture mechanics. Despite the widespread existence of porous materials in both natural environments and engineering applications, their fracture mechanics, notably the formation of anticracks, has thus far received limited attention.\n\nPorous materials, widely employed in engineering applications from fluid machinery to aerospace1, reveal a notable vulnerability to compression, resulting in the formation of anticracks. This susceptibility extends beyond foams2,3 and honeycombs4 to encompass various materials, including cellular structures5. Similar observations can be made in compacting pharmaceutical pills6, crushing cereal packs7, the failure mechanisms of bones8,9, and even in geotechnical materials like rocks10,11,12. Moreover, this phenomenon offers a comprehensive explanation for natural hazards, including deep-focus earthquakes13, landslides14, failure of embankments15, ground settlement16, and firn quakes on large glacier sheets17. In these instances, specific regions or planes with more fragile properties than the surrounding material, commonly referred to as weak layers, are prone to collapse. A striking example of anticrack propagation in weak layers is the occurrence of slab avalanches18,19, which result from the collapse of porous weak layers in a stratified snowpack, and constitute the focus of this study.\n\nThe key material property for predicting crack extension is fracture toughness, which represents the critical energy release rate necessary for a crack to propagate20. Cracks may be driven by opening or closing (mode I), shearing (mode II), or tearing (mode III) deformations, each associated with a certain fracture toughness21. In the case of combined normal, in-plane shear, or out-of-plane shear loading (mixed mode), the interaction law between the pure modes and their respective fracture toughnesses must be identified22. These interaction laws are available for many natural and engineering materials such as metals23, rock24, fiber-reinforced polymers25, bones26, or meta materials27, offering insights into the intrinsic properties and structural integrity. However, for anticracks with closing mode I, no interaction law exists for any material.\n\nExperimental measurements of fracture toughness for anticracks in brittle materials are challenging. Aside from notched samples under compressive loading3,5, the propagation saw test (PST) is employed, in particular for weak layers in snow28,29,30. In the PST, an artificial crack is cut into the weak layer of an isolated snow block until the overhanging cantilever releases the critical energy required for crack growth. However, as anticrack propagation inherently involves mode II, both in flat terrain with a horizontal bending component31 and more prominently on inclined slopes18,32, it should be recognized as a mixed-mode process. A comprehensive understanding of anticrack fracture behavior across the entire interaction regime, from pure collapse (mode I) to pure shear (mode II or III), is currently lacking due to the absence of a suitable experimental setup.\n\nWhile previous studies have focused only on experimental procedures for mode I loading3, we adopt a multidisciplinary strategy, combining fracture experiments with a closed-form model for the calculation of individual fracture modes. Among other anticrack phenomena, the collapse of weak snow layers is a present and tangible example. To enhance our understanding of the fracture behavior of porous materials under mixed-mode loading involving both closing mode I and mode II, we introduce a modification of the conventional fracture mechanical experiment PST. This methodology provides insight into previously unexplored fracture regimes. The design has the advantage that the anticrack is confined to the weak layer. As a result, the mode mixity of the crack-tip loading remains fairly constant during crack growth because the anticrack cannot kink. A similar geometry was used for some of the first measurements of supersonic shear cracks33, a phenomenon recently observed for anticracks confined in weak snow layers19,34,35. Because of the confinement of the anticrack, we measure fracture toughness in terms of critical energy release rates \\({{{{\\mathcal{G}}}}}_{{{{\\rm{c}}}}}\\) rather than stress intensity factors Ki, which take on complex values for interfacial cracks.\n\nSince fracture processes are driven by the global energy balance of structures, structural models are needed to interpret the experimental results. To analyze PST data, we consider a closed-form analytical solution for a first-order, shear-deformable, layered plate under cylindrical bending (slab) that is supported by an elastic foundation (weak layer)36. The anisotropy induced by the layered nature of snow slabs is accounted for by a stiffness matrix that distinguishes extension, bending, shear, and bending\u2013extension coupled deformations. Here, we extend the model to consider additional surface loads as used in the present experimental setup (cf. \u201cMethods\u201d). Mode I and II energy release rates are calculated from the stresses and displacements in the weak interface. The model was validated with comprehensive numerical studies18,36, full-field displacement measurements31, and dynamic energy release during anticrack growth37. Crucial inputs are the elastic material properties of snow slab and weak layer. For this purpose, we parameterized the elastic modulus of slab layers using density measurements and tested for the sensitivity of the model with respect to parameter assumptions (cf. \u201cMethods\u201d).\n\nOur tailored mixed-mode fracture test (MMFT) is based on the classical PST. Specific modifications allow us to measure the critical energy release rate over the entire range of mode interactions and, thus, allow for in situ fracture toughness measurements. The method consists of extracting a 100 cm long and 30 cm wide rectangular block from the snowpack containing an 11.5 cm thick slab layer, a weak layer, and a 6 cm thick base layer. This snow block is then mounted on a tilting device to perform MMFTs at different inclinations (Fig.\u00a01a, b). Extracting a slab of defined thickness allows for controlling its stiffness. By adding variable surface dead loads in the form of steel bars, we can then control the cut length a priori to increase the mode II contribution. Artificial anticracks are introduced by pushing the back of a snow saw into the weak layer, and upon reaching the critical cut length, the anticrack propagates causing the entire weak layer to collapse. This instability point defines the critical energy release rate derived from the model described above. Cutting can be performed in upslope or downslope direction of the tilted MMFT. Note that in most PST experiments thus far, cutting was in the upslope direction (Fig.\u00a01c, e). Additional details on the experimental procedures are given in the\u00a0Supplementary Material.\n\na Illustration of the experimental setup. b Extracted slab\u2013weak-layer assembly with added weights at 60\u00b0 prior to cutting the weak layer. c Critical cut lengths values, the measured length at which the artificially introduced crack becomes unstable, for the present study (green, \u2223S\u2223 = 88, where S is the set of experimental samples) and from the literature44 (orange, \u2223S\u2223 = 183). The literature dataset contains only upslope cuts (\u03c6 \u2264 0\u00b0) but several different weak layers and slab assemblies such that recorded cut lengths scatter widely. Tests on the same slab\u2013weak-layer assembly with constant added weights (site A), 1.59\u2009kN/m2, show a trend of increasing critical cut lengths ac with inclination for downslope cuts (\u03c6\u00a0>\u00a00\u00b0). Increasing surface dead loads (site B) with extra load, 2.53\u2009kN/m2, vs. site B, 1.48\u2009kN/m2, breaks the observed trend. Critical cut lengths were measured with \u00a0\u00b11\u2009cm uncertainty and slope angles with \u00a0\u00b12\u00b0 uncertainty. d Mode II energy release rate at the onset of unstable crack propagation as a fraction of the total energy release rate. The mode II fraction increases rapidly with inclination for downslope cuts (\u03c6\u00a0>\u00a00\u00b0) but only moderately for upslope cuts (\u03c6\u00a0<\u00a00\u00b0). All data points (site A, site B, site B with extra load, literature data) follow the same trend with little scatter. The literature dataset comprises almost no mode II contribution, even at inclinations as high as 36\u00b0. Mode-ratio uncertainties were calculated from error propagation of uncertainties in cut length (\u00b11\u2009cm), inclination (\u00b12\u00b0), and weak-layer thickness (\u00b11\u2009mm). e\u2013g Stacked histograms truncated at 20 counts per bin. The distribution of tested inclinations (e) shows that, historically, propagation saw tests were predominantly performed in flat terrain and cut upslope (orange) while the present study (green) focuses on steep downslope cuts. Critical cut lengths (f) are distributed uniformly with mean and standard deviation of ac\u00a0=\u00a031.0\u00a0\u00b1\u00a014.4\u2009cm indicating equal likelihood for a wide range of cut lengths. The distribution of mode II fractions of the total energy release rate (g) shows that the literature data (orange) contains no information on mode II energy release rates while the present study (green) covers the full range between pure mode I and pure mode II fracture toughness of weak layers.\n\nThe proposed modifications have the following advantages. A thinner slab (1) reduces the effect of slab layering, (2) decreases the bending stiffness that can cause strong stress concentrations in the weak layer, (3) more closely resembles the slender beam geometry of our theory, (4) minimizes impeding bending at the slab normal faces, and (5) allows for transporting and tilting the fragile samples as most of the slab load is removed. Tilting the snow block up to 65\u00b0 allowed us to cover the entire range of mixed-mode loading scenarios. Overall, our MMFT experiments enabled us to perform 88 experiments on two weak layers (cf. \u201cMethods\u201d). The reproducibility of the results permits generalized statements of the mixed-mode fracture properties of weak snow layers.\n\nHere, we show that fracture toughness values are significantly larger in shear than in collapse, and suggest a power-law interaction between the anticrack propagation modes.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51491-7/MediaObjects/41467_2024_51491_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "Introducing an artificial crack into the weak layer modifies the global energy balance of the system. Changes in the total potential energy per unit crack advance define the energy release rate (ERR). At the critical cut length ac (Fig.\u00a01a), the critical energy release rate is reached, marking the onset of unstable anticrack growth. Hence, ac is closely related to fracture toughness. Yet, it must not be misinterpreted as a material property, as it is influenced by many system variables including cutting direction, slope angle, slab layering, and load38. Changes in such system variables are reflected in the scatter of recorded ac values (Fig.\u00a01c). The data show that increasing the surface loads at site B from 1.48\u2009kN/m2 to 2.53\u2009kN/m2 (extra load) reduces measured critical cut lengths considerably although the same slab and weak layer were tested. Additional details are given in the \u201cMethods\u201d section.\n\nIn our data (Fig.\u00a01c, green), it is evident that ac increases with slope angle \u03c6. Previous experiments were inconclusive, suggesting either a decrease39, an increase40,41, or no discernible trend42 for ac as function of slope angle. The contradictory results can be attributed to PST recording standards43 that recommend upslope cuts. In addition, most weak layers were tested on slope angles below 36\u00b041. In this regime (\u221245\u00b0 to 0\u00b0 in Fig.\u00a01c), our results (green) are also inconclusive and mostly influenced by changes in system variables (e.g., loading and layering). The compilation of historic PST data44 (Fig.\u00a01c, orange) shows no trend in slope angle because many different weak layers and slab assemblies were tested with upslope cuts below \u00a0\u221236\u00b0. The influence of the slope angle on ac is much more pronounced when cutting in downslope direction (>0\u00b0 in Fig.\u00a01c), confirming recent theoretical considerations of the differences in shear loading of upslope and downslope cuts (see \u201cMethods\u201d section)36.\n\nThe energy balance of a weak-layer anticrack at the critical cut length, i.e., the energy release rate (ERR) at which it becomes unstable, is a fundamental material property and known as its fracture toughness. In the present setup, the total energy release rate \\({{{\\mathcal{G}}}}={{{{\\mathcal{G}}}}}_{{{{\\rm{I}}}}}+{{{{\\mathcal{G}}}}}_{{{{\\rm{II}}}}}\\) comprises closing mode (I) and shearing mode (II) contributions. Using the virtual crack closure technique allows for the identification of the ERR of a given crack length and the separation of both contributions36.\n\nFor the ac values recorded in our experiments, Fig.\u00a01d shows computed critical shearing mode ERRs (\\({{{{\\mathcal{G}}}}}_{{{{\\rm{II}}}}}\\)) as a fraction of the corresponding total ERRs (\\({{{\\mathcal{G}}}}={{{{\\mathcal{G}}}}}_{{{{\\rm{I}}}}}+{{{{\\mathcal{G}}}}}_{{{{\\rm{II}}}}}\\)). Because the weak-layer fracture toughness is a material property, all measurements collapse onto one curve despite varying structural conditions. In particular, the data recorded at site B, with extra load (2.53\u2009kN/m2) and considerably shorter cut lengths (Fig.\u00a01c, light green), collapse onto the same curve (Fig.\u00a01d). Changes in the slab\u2019s structural parameters (e.g., extra loading), that generally introduce considerable scatter in recorded critical cut lengths (Fig.\u00a01c), have much less impact on the ratio of computed critical ERRs. This highlights that fracture toughness is the crucial descriptor of weak-layer anticrack propagation and an essential input for predictions about fracture processes that precede slab avalanche release.\n\nOur data show a pronounced asymmetry between upslope (\u03c6\u00a0<\u00a00\u00b0) and downslope (\u03c6\u00a0>\u00a00\u00b0) cuts (Fig.\u00a01d)45. While downslope cuts permitted measurements of pure mode II crack propagation (at \u03c6\u00a0\u2248\u00a065\u00b0), PSTs with upslope cuts at the same inclination (\u03c6\u00a0=\u00a0\u221265\u00b0) are still dominated by mode I (more than 70% of the total ERR). For this reason, none of the historical PST experiments measured mode II contributions (Fig.\u00a01g). Note that pure mode II crack propagation required steep inclinations (up to \u03c6\u00a0=\u00a065\u00b0), downslope cuts, and in particular significant surface loads\u00a0(2.53\u2009kN/m2). Counterintuitively, pure mode I anticrack propagation is not observed in flat-field tests (\u03c6\u00a0=\u00a00\u00b0) but for upslope cuts between \u03c6\u00a0=\u00a0\u22125\u00b0 and \u221215\u00b0. Both the slope-angle asymmetry and the offset of pure mode I anticrack propagation have their origin in the combined compression and shear loading of the weak layer.\n\nThe weak layer is subjected to different sources of normal and shear deformations arising from the gravitational forces exerted by the slab and supplementary weights. These forces precipitate (i) the settlement of the slab, (ii) the generation of moments due to the eccentricities in their lines of action, and (iii) the creation of moments during the cutting process. Both categories of moments induce bending at the slab ends, yet they differ significantly in their characteristics and magnitudes. The superposition of these effects, incorporating both normal and tangential components, is responsible for the asymmetric behavior of mode II contributions to the total energy release rate (ERR), particularly with respect to variations in slope inclination and cutting direction (Fig.\u00a01d). Additional details are given in the \u201cMethods\u201d section.\n\nThe predominance of mode I in upslope cuts is due to the synergistic effects of eccentricity-induced bending (ii) and cut-induced bending (iii), which increase compression in the weak layer at the lower end of the slab, even at steep inclinations where normal settlement (i) becomes negligible. Conversely, eccentricity-induced bending (ii) causes an uplift at the upper end of the slab, stretching the weak layer as the inclination increases, while the effect of cut-induced bending (iii) reduces. Consequently, mode I vanishes at high inclinations, and a mode II dominated regime forms below \u03c6\u00a0=\u00a090\u00b0. At low angles, a pure mode I state is not observed at \u03c6\u00a0=\u00a00\u00b0, since the shear component due to bending dominates at small inclinations. This component enhances crack-tip shear deformations in downslope cuts and reduces these deformations in upslope cuts36,46.\n\nWe observe that our MMFTs, together with the model, allow us to derive energy release rates of weak-layer anticracks in pure mode I, pure mode II, and all mode interactions in between.\n\nIn most real-world cases, the total energy release rate is composed of contributions from different modes, i.e., mixed-mode loading, and rarely from one pure fracture mode. In these cases, the resistance of a material against crack growth is captured by so-called mixed-mode interaction laws, describing the limit of stable crack propagation for all load states between the pure fracture modes. Using an orthogonal distance regression47, we determined a mixed-mode interaction law for weak-layer anticracks, in the form of a power law22\n\nfrom our field data on two weak layers consisting of buried surface hoar (Fig.\u00a02a, Table\u00a01). The power-law exponents n,\u00a0m \u2208 [0, 1] are metrics for the interaction of both fracture modes where n,\u00a0m\u00a0\\(\\longrightarrow\\) 0 corresponds to independent failure modes and n,\u00a0m\u00a0=\u00a01 to a very strong interaction (see\u00a0Supplementary Material for explanations of data fitting procedures). The total fracture toughness \\({{{{\\mathcal{G}}}}}_{{{{\\rm{c}}}}}={{{{\\mathcal{G}}}}}_{{{{\\rm{I}}}}{{{\\rm{c}}}}}+{{{{\\mathcal{G}}}}}_{{{{\\rm{II}}}}{{{\\rm{c}}}}}\\) is comparable to other field data on layers of surface hoar44,48 (0.33 \u00a0\u00b1 0.17 J/m2 and 0.1 to 1.5 J/m2, respectively), and ice\u2013aluminum49 (1 J/m2) or ice\u2013steel49 (5 J/m2) interfaces. However, note that in the present case, fracture is not controlled by total fracture toughness \\({{{{\\mathcal{G}}}}}_{{{{\\rm{c}}}}}={{{{\\mathcal{G}}}}}_{{{{\\rm{I}}}}{{{\\rm{c}}}}}+{{{{\\mathcal{G}}}}}_{{{{\\rm{II}}}}{{{\\rm{c}}}}}\\), but by the interaction law given in Eq. (1). This is evident in the distinct maximum of the total energy release rate at \u03c8\u00a0\u2248\u00a00.6 when plotted against the mode ratio \u03c8 (Fig.\u00a02b).\n\na Mode I/II composition of critical energy release rates at the onset of unstable crack propagation in the surface-hoar weak layer at field site A (Fig.\u00a03) with 1.59\u2009kN/m2 added surface load (\u2223S\u2223 = 65 samples, Feb 18 to Mar 3, 2022), field site B (Fig. 3) with 1.48\u2009kN/m2 (\u2223S\u2223 = 17, Mar 7 to 9, 2022), and field side B with 2.53\u2009kN/m2 (\u2223S\u2223 = 6, Mar 10, 2022). The mixed-mode interaction law is determined from an orthogonal distance regression (p\u00a0<\u00a00.001) and shown with 95% confidence bands. b Total energy release rate \\({{{\\mathcal{G}}}}={{{{\\mathcal{G}}}}}_{{{{\\rm{I}}}}}+{{{{\\mathcal{G}}}}}_{{{{\\rm{II}}}}}\\) as a function of mode ratio \u03c8 (mode II fraction), observed in our data (\u2223S\u2223 = 88, green) and the literature dataset44 (\u2223S\u2223 = 183, orange). Because of diverse snowpack conditions, the literature dataset (orange) scatters widely. Notably, it cannot provide information on mode II failure. In the present dataset (green), we observe a maximum of the total energy release rate at \u03c8\u00a0\u2248\u00a00.6, although data for \u03c8\u00a0>\u00a00.6 is scarce. It is evident that it is not the total energy release rate that governs crack propagation, but the interaction law given in Eq. (1). c Histogram of recorded total energy release rates in literature44 with mean total critical energy release rate \\({\\bar{{{{\\mathcal{G}}}}}}_{{{{\\rm{c}}}}}=0.98\\pm 0.02\\,{{{\\rm{J}}}}/{{{{\\rm{m}}}}}^{2}\\) and median \\({\\hat{{{{\\mathcal{G}}}}}}_{{{{\\rm{c}}}}}=0.53\\pm 0.06\\,{{{\\rm{J}}}}/{{{{\\rm{m}}}}}^{2}\\). d Histogram of recorded total energy release rates in the present study with mean \\({\\bar{{{{\\mathcal{G}}}}}}_{{{{\\rm{c}}}}}=0.90\\pm 0.01\\,{{{\\rm{J}}}}/{{{{\\rm{m}}}}}^{2}\\) and median \\({\\hat{{{{\\mathcal{G}}}}}}_{{{{\\rm{c}}}}}=0.90\\pm 0.10\\,{{{\\rm{J}}}}/{{{{\\rm{m}}}}}^{2}\\).\n\nThe observed ratio of \\({{{{\\mathcal{G}}}}}_{{{{\\rm{II}}}}{{{\\rm{c}}}}}\\) to \\({{{{\\mathcal{G}}}}}_{{{{\\rm{I}}}}{{{\\rm{c}}}}}\\) is consistent with many other materials whose mode II fracture toughnesses are larger than their mode I counterparts22. This result is remarkable because macroscopically, mode I in our experiments corresponds to collapse rather than tensile failure. In solid materials, mode I cracks can only grow under tensile loads. Certainly, in highly porous materials, microscopic failure is dominated by tensile mixed-mode I/II fracture of the solid matrix, e.g., owing to the bending of surface-hoar ice crystals50. Here, we expect the dominance of mode II over mode I, i.e., \\({{{{\\mathcal{G}}}}}_{{{{\\rm{II}}}}{{{\\rm{c}}}}} \\, > \\, {{{{\\mathcal{G}}}}}_{{{{\\rm{I}}}}{{{\\rm{c}}}}}\\)22. Our data suggest that the dominance of mode II translates to the macroscopic scale of highly porous materials under compressive loads. Equally noteworthy is the fact that our data show that superimposing compression and shear weakens the material in terms of fracture (crack propagation). This is again consistent with most other materials but remarkable because superimposing compression and shear were shown to reinforce weak layers with respect to their apparent strength (initial failure)51. We attribute this to the porous and low-density microstructure of weak layers. On intact weak layers, compressive stresses can cause bond breaking at the microscale that is invisible at the macroscale if the applied stresses are smaller than the macroscopic compressive strength32. The micro defects compact and initially strengthen the weak layer with respect to shear loading. As a consequence, higher superimposed compressive loads cause a more abrupt and violent subsequent shear failure32. In the presence of a crack, the effect is not beneficial because both compression and shear loading increase the stored energy and, hence, the energy release rate. That is, both facilitate crack growth. It is important to note that the fracture toughness measured here is a macroscopic property. It encompasses all micromechanical effects and does not distinguish individual microscopic effects such as bond breaking or friction.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51491-7/MediaObjects/41467_2024_51491_Fig2_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Using a nonlocal mechanical model for the global energy balance at the onset of anticrack growth, we estimated fracture toughness values for weak snow layers under a wide range of mixed-mode loading conditions, from pure shear (mode II) to pure compression (mode I). Results from in situ experiments showed that modeled fracture toughness values followed a power-law interaction, with estimated critical energy release rates in mode I lower than in mode II. While our results provide the first mixed-mode interaction law for the fracture toughness of highly porous weak snow layers, our conclusions are limited as experiments were performed solely on buried surface hoar. For a complete picture of snow weak-layer fracture, data for other types of weak layers, such as faceted crystals or depth hoar, are needed. Furthermore, since the size and shape of the interaction law are influenced by the choice of elastic-modulus parametrization, future experiments should include video recordings to better estimate the elastic properties from measured displacement fields31. Although we performed 88 experiments, the number of measurements at high inclinations is still limited. This is mostly because these measurements are more challenging, resulting in larger measurement uncertainties (error bars) and broader confidence intervals in the pure mode II regime.\n\nOther porous materials are often described using similar power-law type interaction laws, with either equal or unequal exponents to capture the relationships between stress intensity factors under mixed loading conditions52,53. Common predictors for fracture toughness across highly porous materials include density54 and microstructure55,56, where higher density generally correlates with increased fracture toughness57. Despite the extensive literature on the tensile and bending properties of porous materials, studies focusing on compressive fracture properties remain scarce, highlighting a pervasive challenge in understanding fracture behaviors under compressive loads53,56. The concept of anticracks under compressive mode I loading has been explored in man-made materials like glass foams3 and 3D-printed brittle open-cell structures5, and studies indicate that the morphology of anticracks under compression resembles tensile mode I cracks3. Nevertheless, there is a notable absence of experiments measuring mixed compression and shear fracture toughness across various materials, especially natural porous materials such as snow.\n\nThe shape of the interaction law is different from the stress-based failure envelope where shear strength is generally lower than compressive strength51, yet \\({{{{\\mathcal{G}}}}}_{{{{\\rm{I}}}}{{{\\rm{c}}}}}\\) is smaller than \\({{{{\\mathcal{G}}}}}_{{{{\\rm{II}}}}{{{\\rm{c}}}}}\\). The common assumption that avalanches release more easily on steeper terrain58 is primarily based on avalanche observations and our understanding of snow strength. Certainly, the higher likelihood for avalanches on steeper terrain can be justified by factors such as snow friction angle. However, our results indicate that not all factors that govern the avalanche release process increase avalanche likelihood with slope angle. The relevant observation would be whether it is easier to trigger a so-called whumpf, the large-scale collapse of a weak layer, in a flat field or an avalanche in a steep slope, albeit the entire snow cover is the same. Yet, no such field study is available. Indeed, our results show that \\({{{{\\mathcal{G}}}}}_{{{{\\rm{I}}}}{{{\\rm{c}}}}}\\) is smaller than \\({{{{\\mathcal{G}}}}}_{{{{\\rm{II}}}}{{{\\rm{c}}}}}\\), suggesting that anticrack propagation may encounter less resistance in low-angle terrain. This may partly explain the enormous distances sometimes observed in the remote triggering of slab avalanches59.\n\nCrack propagation is accompanied by singular crack-tip stress concentrations and governed by energy alone60. Crack nucleation, however, originating from weakly singular and nonsingular stress concentrations, is governed by both stress and energy simultaneously. This is reflected in virtually all modern fracture mechanics methods, such as finite fracture mechanics61, or phase-field models for fracture62. Because both strength and toughness are involved, the weak layer\u2019s low shear strengths can cause early initial failure on steep slopes. This concerns not only the slope inclination but also to the angle at which skiers load the snowpack, e.g., combined compression and shear in turns. It is to be noted that weak layer strengths have only been examined in one study and significantly more data is needed51. Ideally, strength and fracture tests of the same weak layer should be performed simultaneously for a conclusive picture.\n\nHolistic computational models that use, e.g., the material-point method (MPM) or the discrete-element method (DEM), are increasingly used to investigate the dynamics of anticrack propagation in snow and avalanche release19,63,64. While such models can shed light on internal processes that cannot readily be measured, they are almost exclusively validated against the stress-based failure envelope32,63. The present data provide an additional benchmark that can be used to verify that both strength and toughness are represented correctly and in accordance with the physics of weak layer failure.\n\nThe present work represents the first measurement of a compression\u2013shear fracture-toughness interaction law derived from modeling and experiments of anticracks in highly porous materials and for weak snow layers of surface hoar. Its relevance extends beyond snow and concerns, e.g., porous rocks that may form compaction bands12, porous seams in sedimentary rocks that develop pressure dissolutions65, or brittle foams used as sandwich core materials in composite materials that dominantly transfer shear loads3. For snow avalanches, the interaction law observed in our data is very relevant as it provides fracture toughness values that could serve as crucial ingredients for predictive analytical tools18 and to assess avalanche hazards based on modeled snow cover parameters.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "All experiments were performed between February 18 and March 10, 2022, on a flat and uniform site near Davos, Switzerland (Fig.\u00a03), located on the roof of two buildings in a forest opening protected from wind. Most experiments were performed on the roof of building A, and after it was cleared of snow, we also carried out experiments on building B. The presence of a nearby creek, the absence of direct sunlight in winter, and the cold concrete roof (typically below 0 \u00b0C) create favorable conditions for the formation and preservation of surface hoar. Both weak layers tested consisted of surface hoar, buried by a snowfall at the beginning of January 2022, with a mean weak layer thickness of 9.02 mm. We characterized the snowpack with a manual snow profile66 (Fig.\u00a04), and measured snow density with a 50 cm3 cylindrical density cutter (23 mm inner diameter). Additional details on the field site and snowpack are given in the\u00a0Supplementary Material.\n\nBird\u2019s eye view of the geographic location of field sites A and B with a closer look at the roof of the building at site A.\n\na Isolated column of the snowpack at field site A on March 4, 2022, with the extracted section highlighted in green and the weak layer in orange. b Corresponding manual snow profile with hand hardness index (bar length), snow temperature (orange line), distance from ground (H), grain type (F, see legend), grain size (E), hand hardness (R). Grain types indicated in the figure legend are precipitation particles (PP), decomposing and fragmented precipitation particles (DF), rounded grains (RG), faceted crystals (FC), depth hoar (DH), surface hoar (SH), melt forms (MF), and ice formations (IF)66.\n\nTo measure fracture toughness over the entire range of mode interactions, from pure mode I to pure mode II, we performed mixed-mode fracture tests (MMFTs), modified propagation saw tests (PST). Our experiments consisted of extracting snow blocks (1000 mm long and 300 mm wide) containing a weak layer from the snowpack. This was done using an aluminum sled that we pushed into the snowpack below the weak layer of interest and isolating the snow block with snow saws. We then reduced the slab above the weak layer to a thickness of 150 mm before making serrated cuts, slanted at an angle of 65\u00b0, on the surface of the slab using a sharp device. The mean slab thickness from the weak layer to the bottom of the serrated cuts was then 115 mm. The snow block and sled were then mounted on a tilting device, consisting of a wooden base plate with guiding rails on each sidewall and two rows of 40 mm long screws that penetrated the substratum below the weak layer through holes in the sled. The guiding rails and the screws ensured that the snow block would not slide off when tilted at steep angles. On one end, the base plate was fixed to the ground on a pivot point. On the opposite end, the base plate was attached with a steel cable to a tower made of scaffolding poles, allowing us to tilt the base plate to any desired angle.\n\nMMFTs were then performed at different angles after 12 weights were placed on the slab (Fig.\u00a01a, b). Each weight consisted of a rectangular hollow steel profile with up to three metal rods. Each component (profile and rod) weighed up to 1 kg, allowing for different load levels. An artificial cut was then introduced in the weak layer by pushing the unserrated back of a snow saw (2 mm thickness) into the weak layer. At the time the crack propagated and the weak layer collapsed, the critical cut length from saw tip to slab face was measured on both sidewalls and averaged when the cutting was not perfectly perpendicular. Additional details on the experimental procedures are given in the\u00a0Supplementary Material.\n\nIn our experimental configuration, there is an asymmetry in the mode II contributions to the overall energy release rate (ERR) with slope inclination and cutting direction. This asymmetry arises from the sample geometry, the loading configuration, and the material compliance, which affect the translation of the slab as well as the rotational and bending moments in the slab (Fig.\u00a05).\n\na Acting forces, represented by line loads of the slab density q and the added dead weights p, and shown with slope-normal (x,\u00a0n) and slope-parallel tangential (z,\u00a0t) components. The effective lines of action (dashed) have slope-normal distances of cq and cp, respectively, from the weak layer. b The gravitational pull G on the total mass (q\u00a0+\u00a0p) causes a slope-normal and slope-parallel settlement of the slab. c For a rigid slab, the moment Me induced by the eccentricities cq and cp compresses the weak layer at the lower end and expands it at the upper end. d With increasing slab compliance, slab deformations concentrate toward the slab ends. e Upslope (us) cuts induce additional bending moments Mus due to the load Gus of unsupported slab segments. f Downslope (ds) cuts generate smaller bending moments (Mds) as the load (Gds) is smaller. g Slab bending induces upward shear deformations (u) at the lower slab end. h At the upper slab end, these deformations point downward.\n\nIn our experiments, a rectangular snow beam under its own weight q and additional surface loads p is inclined at an angle \u03c6 (Fig.\u00a05a). The gravitational pull on the snow beam (G in Fig.\u00a05b) induces both slope-parallel shearing and slope-normal settlement in the weak layer. Slope-parallel displacement increases from zero on flat terrain (\u03c6\u00a0=\u00a00\u00b0) to a maximum as \u03c6 approaches 90\u00b0, while slope-normal displacement decreases from its peak at \u03c6\u00a0=\u00a00\u00b0 to zero at \u03c6\u00a0=\u00a090\u00b0.\n\nEven without a cut in the weak layer, there is a rotational moment Me in the slab, compressing the weak layer at the lower end of the beam and stretching it at the upper end (Fig.\u00a05c). This is due to the eccentricities of the lines of action of the slab weight q and the added surface weights p relative to the weak layer (cq and cp in Fig.\u00a05a). This effect intensifies with increasing \u03c6 and is concentrated toward the slab ends for compliant slabs (Fig.\u00a05d).\n\nWhen a cut is introduced in the weak layer, the system configuration changes as a part of the beam becomes unsupported (Fig.\u00a05e, f). An additional bending moment is then introduced in the slab, which is larger for an upslope cut (Mus, Fig.\u00a05e) than for a downslope cut (Mds, Fig.\u00a05f). This is because the unsupported slab segment, and thus the corresponding gravitational load (G), is larger for upslope cuts than for downslope cuts. Slab bending introduces both slope-parallel shearing and slope-normal settlement in the weak layer. While there is weak layer compression for both cut directions, it is larger for upslope cuts due to the greater load. The sign of the slope-parallel shearing, on the other hand, depends on the cutting direction. For upslope cuts, slab bending counteracts gravitational shearing (Fig.\u00a05g), while for downslope cuts, it enhances gravitational shear deformations (Fig.\u00a05h).\n\nThe superposition of these three effects\u2014translation, rotation, and bending\u2014leads to the asymmetry in mode II contributions to the total ERR observed in our experiments (Fig.\u00a05d). For \u201cupslope cuts,\u201d all three effects compress the weak layer, and slab bending counteracts gravitational shearing. As a result, the ERR for upslope cuts is predominantly driven by mode I, with only a marginal increase in the mode II ratio as slope inclination rises, thus preventing a pure mode II condition even at \u03c6\u00a0=\u00a0\u221290\u00b0. In fact, slab bending is the reason why pure mode I anticrack propagation is not observed in flat-field tests (\u03c6\u00a0=\u00a00\u00b0) but occurs for upslope cuts between \u03c6\u00a0=\u00a0\u22125\u00b0 and \u221215\u00b0. For \u201cdownslope cuts,\u201d translation and slab bending compress the weak layer while eccentricity-induced bending moments lift the slab off the weak layer, an effect that increases with \u03c6. At the same time, slab bending enhances gravitational shearing. A localized absence of compressive normal contributions therefore occurs when compression and lifting effects offset each other, leading to a state of pure mode II ERR below \u03c6\u00a0=\u00a090\u00b0 for downslope cuts.\n\nWe extend the closed-form analytical model for the mechanical response stratified snowpacks of Wei\u00dfgraeber and Rosendahl36 by adding surface loads. For this purpose, we modify the equilibrium conditions, Eqs. (6a)\u2013(6c),\n\nby adding normal and tangential surface loads pn and pt, where x is the axial coordinate, N and V are normal and transverse section forces, M is the bending moment, \u03c3 and \u03c4 are the weak layer\u2019s compressive and shear stresses, qn and qt are the normal and tangential components of the slab\u2019s weight load, h and t are the thicknesses of slab and weak layer, and zs the z-coordinate of the center of the slab\u2019s gravity. This changes Eq. (A10), the vector\n\nof supported slab segments and as a consequence Eq. (13), the particular integral\n\nbut leaves stiffness matrices and the general solution unchanged. Similarly, Eq. (B14), the vector\n\nof unsupported segments is adjusted where A11,\u00a0B11,\u00a0D11, and \u03baA55 are the slabs laminate stiffness and \\({K}_{0}={B}_{11}^{2}-{A}_{11}{D}_{11}\\). This allows for the consideration of added weights, in the present case in the form of steel rods, at the slab\u2019s surface while leaving solution procedure described by Wei\u00dfgraeber and Rosendahl36 unchanged. Added weights can be represented by distributed surface loads because, owing to St. Venant\u2019s principle, their effect on weak-layer stresses and deformations is equivalent to concentrated loads in sufficient distance from the load application point. Additional details on the derivation of the governing equations are given in the\u00a0Supplementary Material.\n\nThe elastic properties of the slab and especially the weak layer are crucial input parameters for the mechanical model. As we do not have direct measurements, we used parametrizations and literature values and examined the sensitivity of the model with regard to assumptions made.\n\nThe elastic modulus of slab layers, consisting of rounded grains, was calculated from\n\nwhere \u03c10\u00a0=\u00a0917\u2009kg/m3 is the density of ice and E0\u00a0=\u00a06.5\u00a0\u00d7\u00a0103\u2009MPa is the elastic modulus of ice67,68,69. The exponent \u03b3\u00a0=\u00a04.4 was determined from the elastic response observed in flat-field experiments48. For the weak layer, consisting of buried surface hoar, we assumed a density of \u03c1wl\u00a0=\u00a0100\u2009kg/m3, Young\u2019s modulus of Ewl\u00a0=\u00a00.2\u2009MPa, and Poisson\u2019s ratio of \u03bd\u00a0=\u00a00.2570, corresponding to a ratio between elastic modulus and shear modulus of Ewl/Gwl\u00a0=\u00a02.5.\n\nTo evaluate the sensitivity of the model with regard to the above assumptions, we investigated the impacts of the exponent \u03b3 of the density parametrization, the weak-layer elastic modulus Ewl, and the ratio between elastic and shear modulus Ewl/Gwl of the weak layer (Fig.\u00a06). Physically meaningful parameter ranges are \u03b3 \u2208 [3.5, 5.0], Ewl \u2208 [0.1, 1.0]\u2009MPa, and Ewl/Gwl \u2208 [2.0, 3.0]. This yields elastic moduli of slab layers between 1 and 400\u2009MPa for densities between 150 and 400\u2009kg/m3. While all parameters have some influence on the magnitude of the energy release rates, results remain in the same order of magnitude and the principal shape of the interaction law is mostly unaffected (Fig.\u00a06). The weak-layer elastic modulus has the largest influence (Fig.\u00a06a, b). Changes in elastic properties (Ewl,\u00a0\u03b3) affect the magnitude of the total energy release rate without affecting the mode I/II ratio \\({{{{\\mathcal{G}}}}}_{{{{\\rm{I}}}}{{{\\rm{c}}}}}/{{{{\\mathcal{G}}}}}_{{{{\\rm{II}}}}{{{\\rm{c}}}}}\\) (Fig.\u00a06b, c). Even the ratio of weak-layer elastic modulus to shear modulus has only a small impact on the mode I to mode II ratio \\({{{{\\mathcal{G}}}}}_{{{{\\rm{I}}}}{{{\\rm{c}}}}}/{{{{\\mathcal{G}}}}}_{{{{\\rm{II}}}}{{{\\rm{c}}}}}\\) (Fig.\u00a06d). Overall, uncertainties from the elastic parameters used as input for the model are on the same order of magnitude as measurement uncertainties from the cut length \u0394a\u2009=\u2009\u00b110\u2009mm, weak-layer thickness \u0394t\u2009=\u2009\u00b11\u2009mm, and slope angle \u0394\u03c6\u00a0=\u00a0\u00b12\u00b0.\n\na Changes in the fracture toughness with different elastic properties. b Sensitivity to the elastic modulus of the weak layer Ewl \u2208 [0.1, 1.0]\u2009MPa48. c Sensitivity to the exponent \u03b3 \u2208 [3.5, 5.0]48 of the density parametrization of the elastic modulus of slab layers (6). d Sensitivity to the ratio of elastic and shear modulus of the weak layer Ewl/Gwl \u2208 [2.0, 3.0] representing Poisson ratios \u03bd \u2208 [0.0, 0.5] between no lateral expansion and incompressibility, respectively.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51491-7/MediaObjects/41467_2024_51491_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51491-7/MediaObjects/41467_2024_51491_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51491-7/MediaObjects/41467_2024_51491_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51491-7/MediaObjects/41467_2024_51491_Fig6_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "The dataset including data processing routines is available under Creative Commons Attribution 4.0 International license from https://doi.org/10.5281/zenodo.1144364471.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "A Python implementation of the mechanical model is publicly available from the code repository https://github.com/2phi/weac or for direct installation from https://pypi.org/project/weac (last accessed January 31, 2024)72. 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Zenodo https://doi.org/10.5281/zenodo.11121172 (2024).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We thank Henning L\u00f6we for his computer-tomography analyses of our weak-layer samples. We are grateful for the support of Moritz Altenbach in collecting the field data. This work was in part supported by grants from the Swiss National Science Foundation (200021_169424 and 200021L_201071) and funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant no. 460195514.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Institute of Structural Mechanics and Design, Department of Civil and Environmental Engineering, Technical University of Darmstadt, Franziska-Braun-Str. 3, 64285, Darmstadt, Germany\n\nValentin Adam\u00a0&\u00a0Philipp L. Rosendahl\n\nWSL Institute for Snow and Avalanche Research SLF, Fl\u00fcelastr. 11, 7260, Davos, Switzerland\n\nValentin Adam,\u00a0Bastian Bergfeld\u00a0&\u00a0Alec van Herwijnen\n\nChair of Lightweight Design, Faculty of Mechanical Engineering and Marine Technology, University of Rostock, Albert-Einstein-Stra\u00dfe 2, 18059, Rostock, Germany\n\nPhilipp Wei\u00dfgraeber\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nP.W., A.H., and P.L.R. conceived the study. V.A., B.B., and A.H. developed the experimental method. P.W. and P.L.R. developed the theoretical framework. V.A., B.B., and A.H. collected the field data. All authors contributed to the interpretation of the results and writing of the manuscript.\n\nCorrespondence to\n Philipp L. 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b/528f1402da5b90d0d0bf8f6d055639a4daa2190e989f953c476d20512ef60e81/metadata.json @@ -0,0 +1,136 @@ +{ + "title": "Quantum simulation of spin-boson models with structured bath", + "pre_title": "Quantum Simulation of Spin-Boson Models with Structured Bath", + "journal": "Nature Communications", + "published": "30 April 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59296-y/MediaObjects/41467_2025_59296_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59296-y/MediaObjects/41467_2025_59296_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-59296-y#ref-CR60" + ], + "code": [], + "subject": [ + "Quantum information", + "Quantum simulation" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5340471/v1.pdf?c=1746011213000", + "research_square_link": "https://www.researchsquare.com//article/rs-5340471/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-59296-y.pdf", + "preprint_posted": "18 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The spin-boson model, involving spins interacting with a bath of quantum harmonic oscillators, is a widely used representation of open quantum systems that describe many dissipative processes in physical, chemical and biological systems. Trapped ions present an ideal platform for simulating the quantum dynamics of such models, by accessing both the high-quality internal qubit states and the motional modes of the ions for spins and bosons, respectively. We demonstrate a fully programmable method to simulate dissipative dynamics of spin-boson models using a chain of trapped ions, where the initial temperature and the spectral densities of the boson bath are engineered by controlling the state of the motional modes and their coupling with qubit states. Our method provides a versatile and\r\nprecise experimental tool for studying open quantum systems.Physical sciences/Physics/Quantum physics/Quantum informationPhysical sciences/Physics/Quantum physics/Quantum simulation", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nJungsang Kim and Kenneth R. Brown are scientific advisors to IonQ, Inc.\r\nJungsang Kim is a shareholder of IonQ, Inc.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "StructuredbathnatureSupplementary20241027.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The spin-boson model, involving spins interacting with a bath of quantum harmonic oscillators, is a widely used representation of open quantum systems that describe many dissipative processes in physical, chemical and biological systems. Trapped ions present an ideal platform for simulating the quantum dynamics of such models, by accessing both the high-quality internal qubit states and the motional modes of the ions for spins and bosons, respectively. We demonstrate a fully programmable method to simulate dissipative dynamics of spin-boson models using a chain of trapped ions, where the initial temperature and the spectral densities of the boson bath are engineered by controlling the state of the motional modes and their coupling with qubit states. Our method provides a versatile and precise experimental tool for studying open quantum systems.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Interaction of a quantum system with its environment in an open dissipative setting can significantly influence its dynamics. A paradigmatic model of non-Markovian open quantum systems that captures the quantum nature of both the system and the environment is the spin-boson model1, which is ubiquitous in the study of condensed-matter physics2,3, chemical reactions4,5, collective modes in atomic-photonic systems6,7, and biological light-harvesting complexes8,9. Experimentally realizing the dynamics of spin-boson models in a programmable fashion is an exciting challenge10,11,12,13, which can lead to new quantum simulation approaches that reach beyond classically tractable limit.\n\nTrapped ions provide a highly controllable system of spins and bosonic motional modes, and thus are a natural platform for simulating spin-boson models. The bosonic bath can be simulated using either a large number (\u227350) of motional modes14 or a handful of dissipative motional modes15,16,17,18. While significant experimental progress has been made in controlling the qubits and motional modes together19,20,21, past works in this direction were limited to simulating a spin coherently coupled to up to two bosonic modes22,23,24,25. These studies could not consider the effects of dissipation often described by coupling of the system to a continuum of bath modes, which determines the system\u2019s long-term behavior such as reaching a steady state. A\u00a0simulation study of a spin dissipatively coupled to several modes was reported recently, albeit with limited tunability26.\n\nDissipative couplings come in two flavors: damping is when the quantum system exchanges energy with the bath (inelastic coupling) and eventually reaches thermal equilibrium with the bath, while dephasing is when the phase of the quantum system is randomized by interaction with the bath without energy exchange (elastic coupling). In this work, we perform quantum simulations of spin-boson models with programmable spectral densities using a 7-ion chain and up to 3 of its motional modes. The target spectral densities of the bath are decomposed into several Lorentzian peaks, which is characteristic of baths in real-world systems such as light-harvesting protein complexes27,28. Our approach can readily simulate the dephasing model of dissipation, by adding randomness to the control parameters of the laser that drives the spin-boson interaction, such as frequency and phase. Using this technique, we study the dynamics of typical spin-boson models1 and the vibration-assisted energy transfer (VAET) process24.\n\nOur demonstrated ability to engineer spectral density of the bath in analog quantum simulations is an important ingredient for achieving quantum advantage in studying open quantum system models that are intractable using classical methods17,18.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The spin-boson model describes a spin coupled to a continuous bath of quantum harmonic oscillators. The Hamiltonian is given by\n\nwhere J(\u03c9) is the spectral density of the bath, \\(\\hat{a}(\\omega )\\) is the annihilation operator of the bath oscillator at frequency \u03c9, and \\({\\hat{H}}_{S}=\\frac{\\epsilon }{2}{\\hat{\\sigma }}_{Z}+\\frac{\\Delta }{2}{\\hat{\\sigma }}_{X}\\) and \\({\\hat{H}}_{B}=\\int_{0}^{\\infty }d\\omega \\omega {\\hat{a}}^{{{\\dagger}} }(\\omega )\\hat{a}(\\omega )\\) are the Hamiltonian of the spin and the bath, respectively. Here, \u03f5 and \u0394 are the detuning and coupling strength between the spin states, respectively, and \u210f is set to 1 for simplicity. We assume that the spin is initially in the \\(| 0\\rangle\\) state (or the \u201cdonor\u201d state in the energy transfer model) and the time evolution of the Hamiltonian induces population transfer to the \\(| 1 \\rangle\\) state (or the \u201cacceptor\u201d state). Each bath oscillator of frequency \u03c9 is in the thermal state of average phonon number \\(\\bar{n}(\\omega )=1/({e}^{\\beta \\omega }-1)\\), where \u03b2 is the inverse of the temperature (kB\u00a0=\u00a01 for simplicity).\n\nWe decompose the spectral density J(\u03c9) of a structured bath into a sum of multiple Lorentzian peaks, each of which can be represented by a dissipative harmonic oscillator15. In this work, we consider a spin coupled to several oscillators subject to constant dephasing (\"dephased\u201d spin-oscillator model). This discrete oscillator model is described by the Hamiltonian\n\nand corresponding Lindblad operators \\({\\hat{L}}_{l}={\\sqrt{\\Gamma }}_{l}{\\hat{b}}_{l}^{{{\\dagger}} }{\\hat{b}}_{l}\\), where \u03bal represents the coupling strength between the spin and l-th oscillator, \u03bdl and \u0393l denote the frequency and the dephasing rate of the l-th oscillator, respectively, and \\({\\hat{b}}_{l}\\) is the annihilation operator of the l-th oscillator. The composite state \u03c1 of spin and oscillators follows the Lindblad master equation \\(\\dot{\\rho }=-i[{\\hat{H}}_{D},\\rho ]+{\\sum }_{l}({\\hat{L}}_{l}\\rho {\\hat{L}}_{l}^{{{\\dagger}} }-\\frac{1}{2}\\{{\\hat{L}}_{l}^{{{\\dagger}} }{\\hat{L}}_{l},\\rho \\})\\). In a reasonable regime of parameters (\u0393l\u00a0<\u00a0\u03bdl /2, \\(\\beta \\ll 2\\pi {({\\Gamma }_{l}/2)}^{-1}\\))15, this bath of dephased oscillators is assigned a spectral density composed of Lorentzian peaks\n\nwhere we assume that each oscillator is initially in the thermal state with average phonon number \\(\\bar{n}({\\nu }_{l})\\).\n\nThe spectral density of the bath determines the dynamics of the spin to leading order in \\(\\bar{\\kappa }T\\), where \\(\\bar{\\kappa }\\) is the average coupling strength and T is the evolution time (see Section\u00a0III of the\u00a0Supplementary Material). Therefore, the spin dynamics of the dephased spin-oscillator model approximately match those of the spin-boson model with J(\u03c9)\u00a0=\u00a0JLo(\u03c9) in the weak-\\(\\bar{\\kappa }\\) regime. In the unbiased (\u03f5\u00a0=\u00a00) spin-boson model1,29,30,31, the equilibrium donor-state population is 1/2 for both models, and we expect the population dynamics of the two models show an excellent match at all times for weak spin-bath coupling (Supplementary Fig.\u00a04). For the case of biased spin-boson models in the stronger coupling regime (\u03f5\u00a0\u2260\u00a00, such as discussed in the VAET case), the dynamics of the dephased spin-oscillator model we simulate can deviate significantly from the traditional spin-boson model.\n\nA more common model for dissipation is a spin coupled to oscillators subject to constant damping (\"damped\u201d spin-oscillator model) described by a pair of Lindblad operators \\({\\hat{L}}_{l1}=\\sqrt{{\\Gamma }_{l}(\\bar{n}({\\nu }_{l})+1)}{\\hat{b}}_{l}\\) and \\({\\hat{L}}_{l2}=\\sqrt{{\\Gamma }_{l}\\bar{n}({\\nu }_{l})}{\\hat{b}}_{l}^{{{\\dagger}} }\\)15,16, where \u0393l here is the damping rate of the l-th oscillator. A stronger result holds for this model: the spin dynamics are non-perturbatively equivalent to those of the spin-boson model with the same spectral density, up to all orders in \\(\\bar{\\kappa }T\\)32. Damped oscillators can be realized in trapped ions using sympathetic cooling on a chain of ions with multiple atomic species or isotopes15,16, or using qubits encoded in different internal-state manifolds of the same type of ions33. In fact, a recent work reported simulation of electron-transfer model using sympathetic cooling on the motional mode of a trapped ion34. The dephased oscillator model developed in this work expands the ability to simulate a broader set of bath models not experimentally accessible before.\n\nThe simulations of spin-boson models are performed using a linear chain of seven 171Yb+ ions (Fig.\u00a01). Two hyperfine internal states (\\(| 0 \\rangle\\) and \\(| 1 \\rangle\\)) are used to represent the spin (or qubit). The ions sit 68 \u03bcm above the surface of the trap35, and the heating rate and decoherence time of the zig-zag motional mode are 3.6(3) quanta/s and 5.2(7) ms, respectively. The other motional modes used have similar magnitudes. Details of our experimental setup can be found in Ref. 36. Using Doppler, electromagnetically-induced transparency (EIT), and sideband cooling techniques, the zig-zag motional mode can be cooled to near ground state with \\(\\bar{n}=0.036(16)\\). Standard qubit manipulation techniques can be used to initialize and measure the qubits, and single-qubit operations are driven by stimulated Raman transitions. The spin-oscillator coupling is simulated using the spin-dependent kick (SDK) operation induced by simultaneous application of blue- and red-sideband transitions (see Methods).\n\nThe ions are confined in a micro-fabricated surface trap. The spin states are encoded as the hyperfine clock states (qubit states) and the bosonic bath modes are encoded as the collective radial motional modes of the ion chain. We strategically use the center ion and the three symmetric modes (except for the center-of-mass mode, as shown in the upper left corner) in the experiment to minimize cross-mode couplings (see Methods). The laser frequencies for the SDK operations are determined by the frequency difference between the two Raman beams, and are depicted by the red and blue lines on the frequency axis detuned by \u03b4m from the mode frequency \u03c9m, with the color gradients indicating the random frequencies used to implement the decoherence simulation.\n\nThe evolution of \\({\\hat{H}}_{D}\\) in Eq. (2) is mapped to a sequence of single-qubit rotations and SDK operations via Trotterization. We apply time evolution operators over a short time interval corresponding to each term in the Hamiltonian in the interaction picture, and repeat them over many time intervals to simulate the evolution dynamics (see Methods). This approach can be readily extended to simulating more complicated molecular models consisting of many electronic states and bath modes18,37.\n\nThe dephased spin-oscillator model can be simulated using trapped ions by applying the SDK operations, which induce the coupling between the qubit and the motional mode. By adding randomness to the control parameters of the SDK operations and averaging the results over many random trials, we can implement dissipative processes such as preparing the thermal state and simulating the dephasing of the motional mode. The key idea is that certain dissipative evolutions described by the Lindblad master equation are equivalent to an average of many coherent evolutions, each subject to a stochastic Hamiltonian38. Details of the procedure is described in Methods.\n\nWe first consider a single-oscillator case (l\u00a0=\u00a01), and demonstrate the programmability of the bath\u2019s initial temperature quantified by the oscillator\u2019s average phonon number \\(\\bar{n}\\). Controlled amounts of resonant SDK operations with stochastically varying phase are applied to the ion prepared near the motional ground state, which prepares the thermal state equivalent to an ensemble of randomly displaced coherent states (see Methods). Figure\u00a02a illustrates the simulated time evolution of the donor state population when it is coupled to such initial bath state, for various values of \\(\\bar{n}\\). The dynamics exhibit coherent oscillations with an envelope of slow collapse and revival (beat-note) that signifies resonant (\u03bd1\u00a0=\u00a0\u0394) energy exchange between the system and the bath oscillator39. A thermal state of larger \\(\\bar{n}\\) is a mixture of wider range of coherent states, leading to reduced beat-note amplitude at t\u00a0\u2248\u00a02\u03c0/\u0394\u00a0\u00d7\u00a010.5.\n\na Dynamics of the coherent spin-single oscillator model \\({\\hat{H}}_{D}\\) with various values of average phonon number \\(\\bar{n}\\) for the oscillator\u2019s initial state. b Dynamics of the model with \\(\\bar{n}=0\\), J(\u03c9)\u00a0=\u00a0JLo(\u03c9) (single peak) and various values of \u03931\u00a0\u2261\u00a0\u0393. For both (a) and (b), \u03f5\u00a0=\u00a00, \u03bd1\u00a0=\u00a0\u0394, and \u03ba1\u00a0=\u00a00.1\u0394. Lines and dots represent theoretical predictions and experimental data, respectively. The error bars (size comparable to the symbols in these two plots) denote measured standard deviation over 20 trials, each trial performed with randomly drawn set of parameters and repeated 100 times. c Revival amplitude versus \\(\\bar{n}\\). Predictions of the original (solid line) model and the noise-aware (dashed line) model are compared with the experimental results (circles). d Measured values of \\(\\bar{n}\\) fitted to the predictions of the noise-aware model. The solid line depicts \\(y=x+{\\bar{n}}_{0}\\), where \\({\\bar{n}}_{0}=0.036\\) is the expected offset in the phonon number due to imperfect cooling. e Measured values of \u0393 fitted to the theoretical predictions with \\(\\bar{n}={\\bar{n}}_{0}\\). The solid line depicts y\u00a0=\u00a0x. In (d) and (e), the blue and brown shaded regions represent the expected uncertainty of the value when the populations are averaged over 20 and 200 random trials, respectively. f Lorentzian spectral densities for various target (solid) and fitted (dashed) values of \u0393. The shaded region represents the expected uncertainty of \u0393 when averaged over 20 random trials.\n\nWe compare our experimental results with the predictions of \u201cnoise-aware\u201d models, reflecting inherent noise processes in the experimental setup. We fit the experimental data using an oscillator model with initial phonon number \\(\\bar{n}+{\\bar{n}}_{0}\\) and Lorentzian spectral density with full width at half maximum (FWHM) \u03931\u00a0\u2261\u00a0\u0393 [Eq. (3)], where the values \\({\\bar{n}}_{0}=0.036\\) and \u0393\u00a0=\u00a00.0022\u0394 are extracted from independent measurements characterizing imperfect cooling and finite motional coherence time, respectively. The experimentally measured revival amplitude (Fig.\u00a02c) and fitted \\(\\bar{n}\\) (Fig.\u00a02d) match well with the predictions of noise-aware models. This shows that well-characterized experimental noise can serve as the baseline values for the thermal excitation and dephasing rate in the open quantum system model, and thus is not always a bottleneck for the accuracy of our simulations.\n\nNext, we show the tunability of the dephasing rate \u0393, which corresponds to the linewidth of the spin-boson model\u2019s spectral density. Dephasing is implemented by adding random frequency offsets to the SDK operations that simulate the time evolution (see Methods). This mimics the random fluctuations of the motional-mode frequency, following the stochastic model of motional dephasing noise in trapped-ion systems36.\n\nFigure\u00a02 b illustrates the dynamics for various values of \u0393 between 0 and 0.4\u0394, which exhibit a crossover from underdamped to overdamped dynamics as \u0393 is increased (critical damping condition at \u0393\u00a0\u2248\u00a00.18\u0394). As the damping is increased beyond the critical point, the beat-note that signifies the excitation transfer from the spin to boson back to spin is suppressed, and there is a monotonic decrease in the oscillation of the donor population in the overdamped regime. We notice a \u03c0 phase flip in the oscillation of the donor population in the beat-note in the underdamped case, signified by the phase difference between the underdamped and overdamped cases at t\u00a0\u2248\u00a02\u03c0/\u0394\u00a0\u00d7\u00a010.\n\nThe uncertainty of \\(\\bar{n}\\) and \u0393 realized in the experiment can be reduced by performing a larger number of random trials, each trial using a different set of random parameters. The shaded areas in Fig.\u00a02d and e show the standard deviation of fitted values for \\(\\bar{n}\\) and \u0393, obtained by numerical simulation of results averaged over 20 (pink) or 200 (blue) random trials. Our experiments are limited to 20 random trials due to the compiling delay required for each set of control parameters, and each trial is repeated 100 times to obtain the expectation value for the donor population. The experimental data is consistent with the standard deviation derived from 20 numerical trials. We expect to readily increase the number of random trials with fewer repetitions each in future experiments with faster compilation tools40.\n\nWe can use multiple oscillator modes to engineer a target spectral density structure for the oscillator bath. We simulate a bath of spectral density composed of up to 3 Lorentzian peaks, each represented by a motional mode of the ion chain. The bath spectral density J(\u03c9) in Fig.\u00a03a is simulated using the SDK operations over two or three motional modes. Figure\u00a03b shows the frequencies of the laser pulses used to drive the SDK, defined as the detuning of laser beams from the carrier transition frequency. The 1st, 3rd, and 5th motional modes are used, and the detuning of the laser frequency from each motional mode determines the frequency at which the bath spectral density is considered in Fig.\u00a03a. We set the electronic coupling strength \u0394\u00a0=\u00a0500 cm\u2212141, such that at temperature 77 K, \\(\\bar{n}\\approx 0.1\\) for the near-resonance vibrational modes. Figure\u00a03c shows the results for simulating the dynamics of spin-boson models with spectral densities composed of 2 and 3 Lorentzian peaks, respectively. The theoretical predictions are obtained using the time-dependent density-matrix renormalization group algorithm in the interaction picture42. The donor population features underdamped coherent oscillations, with contributions from all three modes. The measured population closely follows the theoretical predictions.\n\na Simulated spectral density of the spin-boson model\u2019s bath, where the electronic coupling strength \u0394\u00a0=\u00a0500cm\u22121. The orange and brown solid curves illustrate the spectral densities summed over two and three peaks, respectively. b Dashed lines represent the radial motional modes that are coupled by the Raman beam. Black or grey dashed lines represent modes with non-zero or zero coupling strength to the target ion, respectively. The three solid lines and the shaded regions depict the mean value and the standard deviation of the randomized pulse frequencies, and the color corresponds to the Lorentzian peak generating the final spectral density in (a). c Time evolution of the spin-boson models with J(\u03c9) in (a). The x-axis represents the simulated time of the chemical model, determined by the chosen model parameters (see Section\u00a0I of the\u00a0Supplementary Material). The bath temperature is 77 K such that \\(\\bar{n}\\approx 0.1\\), which is set for trapped ions' motional modes using imperfect sideband cooling. Curves and dots represent theoretical predictions and experimental data, respectively. Error bars are derived as in Fig.\u00a02.\n\nThe coherence time of the motional modes in our setup of \u00a0~5.2(7) ms is the leading source of noise36,43, and is comparable to the experimental time for simulating the time evolution under spectral densities generated from 2 mode (1.63 ms) and 3-mode (2.56 ms) cases. For these simulations, the contribution of motional decoherence in the experiment is identical to the random kicks that we apply in the spin-oscillator model to simulate the bath, and can be incorporated as the source of dephasing in our simulation with proper calibration43.\n\nWe further simulate the spin-boson models inspired by Leggett et al.1. The spectral density of the bath is given by\n\nwhere \u03c9c is the cutoff frequency, and s\u00a0<\u00a01, s\u00a0=\u00a01, and s\u00a0>\u00a01 represent the sub-Ohmic, Ohmic, and super-Ohmic baths, respectively. This model is widely used to characterize the noise in nanomechanical devices44, superconducting circuits12,45, and proton-transfer reactions46,47. The spin dynamics exhibit various behaviors, such as coherent oscillations, incoherent decay, and localization, depending on the values of s and A, which has been a subject of longstanding research using analytical tools1,48 and classical simulations29,30,31,49.\n\nRecognizing that the spin exchanges energy with the bath only near its resonance, we use a sum of Lorentzian lines from motional modes to approximate the spectral density JLegg(\u03c9) near the resonance frequency \u0394, with the cutoff frequency set to be much higher (\u03c9c\u00a0=\u00a010\u0394). We utilize well-established global optimization algorithms to find the optimal set of parameters (\u03bal, \u0393l, and \u03bdl in Eq. (3)) that best represent JLegg(\u03c9) (see Section\u00a0V of the\u00a0Supplementary Material). Using this approach, we consider weak spin-bath coupling (A\u00a0=\u00a00.1) and match the spectral density within the target bandwidth \u03c9 \u2208 [0.9\u0394, 1.1\u0394] with 2 Lorentzian peaks. Figure 4a shows the obtained spectra from the modes (solid lines) that approximate the spectral densities (dot-dashed lines) with s\u00a0=\u00a00.5,\u00a01.0, and 2.0, respectively. The results for the donor population dynamics interacting with a bath featuring these approximated spectral densities are shown in Fig.\u00a04b\u2013d. We observe a crossover from near-coherent (s\u00a0=\u00a02) to fully damped (s\u00a0=\u00a00.5) oscillations in the simulated time scale, owing to the different levels of spectral density J(\u03c9\u00a0=\u00a0\u0394) near resonance. The experimental data match well with theoretical predictions, where the deviations in Fig.\u00a04d result from an insufficient number of Trotterization steps (Supplementary Fig.\u00a05). Simulations of baths with fixed J(\u03c9\u00a0=\u00a0\u0394) value over varying levels of the s parameter (ranging from s\u00a0=\u00a00.3 to s\u00a0=\u00a03) are shown in Section\u00a0VI of the\u00a0Supplementary Material (Supplementary Fig.\u00a06).\n\na Spectral densities of the Leggett\u2019s model. Dot-dashed curves are the sub-Ohmic (s\u00a0=\u00a00.5), Ohmic (s\u00a0=\u00a01.0), and super-Ohmic (s\u00a0=\u00a02.0) spectral densities described by Eq. (4) (A\u00a0=\u00a00.1, \u03c9c\u00a0=\u00a010\u0394). Solid curves are the spectral densities simulated in our experiments using two motional modes. These spectral densities are designed to match the dot-dashed curves near \u03c9\u00a0=\u00a0\u0394. Insets compare the solid and dot-dashed curves within the interval \u03c9 \u2208 [0.85\u0394, 1.15\u0394]. (b)(c)(d) Dynamics of the spin-boson model for s\u00a0=\u00a00.5, 1.0, and 2.0, respectively. The solid curves represent the theoretical predictions of the spin-boson models with spectral densities given by the solid curves in (a). The red dashed curves are expected results, derived from numerical simulations of the density matrix of the qubit and motional modes subject to control operations and hardware noise. Circles denote experimental data and error bars indicate the standard deviation over 60, 20, and 20 trials for (b), (c), and (d), respectively (More trials were needed to overcome the shot noise in (b) when the donor population is near 0.5).\n\nFinally, we simulate the VAET model24 with nonzero energy detuning \u03f5 between the spin states. Here we set the spin parameters as \u0394\u00a0=\u00a030cm\u22121 and \u03f5\u00a0=\u00a0100cm\u22121. In the absence of coupling to a vibrational mode (\u03ba\u00a0=\u00a00cm\u22121), energy transfer between the detuned spin states is suppressed; however, for nonzero \u03ba, the energy transfer can be activated if the mode frequency \u03bd meets the resonance condition (single mode\u2019s index l\u00a0=\u00a01 omitted).\n\nFigure\u00a05a shows the experimental data for simulating the coherent spin-single oscillator model \\({\\hat{H}}_{D}\\) with a relatively large coupling strength (\u03ba\u00a0=\u00a030cm\u22121). When \u03bd satisfies the resonance condition of VAET (\\(\\nu=\\sqrt{{\\Delta }^{2}+{\\epsilon }^{2}}/k\\approx 104 {{\\mbox{cm}}}^{-1}/k\\) where k is an integer), energy transfer occurs24. Otherwise, energy remains localized at the donor state. Figure\u00a05b shows the impact of initial temperature of the oscillator on the resonant VAET (\u03bd\u00a0=\u00a0104cm\u22121). We create baths of two temperatures 0 K and 300 K corresponding to initial average phonon number \\(\\bar{n}=0\\) and 2.0, respectively. The energy transfer is faster for higher \\(\\bar{n}\\) in the beginning as more vibrational quanta assist the energy transfer, but it also leads to a rapid decay of oscillations over time as the coherence is lost.\n\nWe set \u03f5\u00a0=\u00a0100 cm\u22121 and \u0394\u00a0=\u00a030 cm\u22121. The x-axis represents the simulated time of the chemical model, determined by the chosen model parameters (see Section\u00a0I of the\u00a0Supplementary Material). a, b Time evolution of the donor-state population for coherent VAET models with \u03ba\u00a0=\u00a030 cm\u22121. Solid and dashed curves depict theoretical predictions of the ideal target and the noise-aware model, respectively. Circles represent experimental data and error bars are the standard deviation over 100 repetitions. In (a), bath temperature is fixed at 0 K (\\(\\bar{n}=0\\)) and \u03bd is varied. In (b), \u03bd is fixed to the resonant value 104 cm\u22121 and two temperatures 0 K and 300 K, which correspond to \\(\\bar{n}=0\\) and 2.0, respectively, are simulated. c Time evolution of the donor-state population for dissipative VAET models with \u03bd\u00a0=\u00a0104 cm\u22121 and \u0393\u00a0=\u00a010cm\u22121 at 0 K. Solid and dot-dashed curves depict theoretical predictions of the dephased spin-oscillator model and the damped spin-oscillator model, respectively. Circles denote experimental data. For \u03ba\u00a0=\u00a00cm\u22121, error bars indicate the standard deviation over 100 repetitions. For \u03ba\u00a0=\u00a010 and 20 cm\u22121, error bars indicate the standard deviation over 20 trials, each trial performed with a randomly drawn set of parameters and repeated 100 times.\n\nIn Fig.\u00a05c, we simulate the dephased spin-oscillator model with \u0393\u00a0=\u00a010cm\u22121 and various values of \u03ba, to study the impact of dissipative coupling. We note that in the weak-\u03ba regime (or short evolution time), the results agree with the theoretical predictions of the damped spin-oscillator model, which is equivalent to those of the spin-boson model with J(\u03c9)\u00a0=\u00a0JLo(\u03c9)15,32; however, as \u03ba is increased, the dephased and damped models\u2019 dynamics deviate. This is expected because, unlike the damped model, the dephased model does not extract energy from the spin-oscillator system, so the donor population will not decay towards zero even after a long time evolution when coupled to a single-oscillator bath. This example demonstrates that our method provides the ability to simulate a wider range of dissipative channels in the study of open system dynamics.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59296-y/MediaObjects/41467_2025_59296_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59296-y/MediaObjects/41467_2025_59296_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59296-y/MediaObjects/41467_2025_59296_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59296-y/MediaObjects/41467_2025_59296_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59296-y/MediaObjects/41467_2025_59296_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "While classical simulation approaches to open dissipative systems continue to improve with advancements in numerical methods, they encounter fundamental challenges when dealing with large quantum systems that span exponentially large Hilbert space17,18. Take, for example, the Dissipation-Assisted Matrix Product Factorization (DAMPF) method, a state-of-the-art method for simulating open quantum systems50. For simulating a one-dimensional chain of Ns electronic states, each coupled to a bath of Nb damped bosonic modes, the computational complexity of DAMPF scales as \\({{{\\mathcal{O}}}}({N}_{s}^{5}{N}_{b}{D}_{b}^{2}{\\chi }^{3})\\), where Db is the number of energy levels that represent each bosonic mode and \u03c7 is the bond dimension in the matrix product operator representation of the system-bath density matrix. A critical issue is the rapid, potentially exponential increase in \u03c7 with respect to the system-bath coupling strength, for a fixed target accuracy in the population dynamics. For these types of problems, trapped-ion simulators may offer a more scalable alternative, as the number of operations scales only as \\({{{\\mathcal{O}}}}({N}_{s}{N}_{b})\\) and the total duration of SDK operations grows linearly with the system-bath coupling strength18. The current state-of-the-art trapped-ion systems offer several dozens of highly coherent spins and bosonic modes that are very densely coupled with each other, the complete quantum description of which is totally intractable with classical computers of today51,52,53. Developing various control methods over these quantum systems to study important physical, chemical, biological and material systems of critical interest will open up a new computational framework in science.\n\nThe methods developed in this work utilize fully programmable control methods to simulate dissipative processes. The dephased oscillator method can be used to realize non-Gaussian bath models54, relevant for studying physical processes such as localization transitions in strongly-coupled spin-boson models1,29,30,31,49 and role of bosonic dephasing in molecular energy transfer55,56. The capability to measure the probability distribution of the motional modes20,21,22,23 may further reveal the effects of intricate vibronic interactions in biological and chemical reaction dynamics. High accuracy simulations will require development of efficient characterization methods57 and novel control protocols in the trapped-ion system58 to implement various interaction terms in the target Hamiltonian. Combined with damped spin-boson models that can be implemented using sympathetic cooling34, the long-chain trapped-ion systems can provide a versatile platform for simulating dynamics of complex open quantum systems.\n\nThe number of readily usable transverse boson modes scales as 2(N\u00a0\u2212\u00a01) (two transversal directions, less the center-of-mass mode subject to external technical noise), and the coupling strength of each ion to each mode is given by the mode participation factor (relative displacement of each ion when the mode is excited). In future experiments on longer ion chains involving operations on a larger number of modes, the mode spacing must be maintained to reduce cross-mode coupling which can be achieved by tightening the longitudinal confinement at the expense of smaller ion spacing. Further reduction of the cross-mode coupling can be achieved by reducing the laser power, at the expense of slowing down the simulation dynamics. Alternatively, the SDK control pulses can be designed to actively suppress cross-mode coupling18 (see Section\u00a0VII of the\u00a0Supplementary Material for details). The properties of the phonon states cannot be measured directly, and characterization of the boson modes requires mapping of boson properties to ion qubits that require many repetitions19,20,21. Novel and efficient methods to characterize the boson modes or spin-boson correlations are highly desired.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The experiments are performed using a linear chain of 171Yb+ ions. We use the hyperfine energy levels \\(| 0 \\rangle \\equiv | F=1;{m}_{F}=0 \\rangle\\) and \\(| 1 \\rangle \\equiv | F=0;{m}_{F}=0 \\rangle\\) in the 2S1/2 manifold as the qubit states (spin states). The collective radial motional modes of the ion chain are used to represent the bosonic bath modes (see Fig.\u00a06a).\n\na Simplified energy levels of the 171Yb+ ion and the laser pulses used for implementing the on-resonance spin-dependent kick (SDK) operation. The global beam consists of two tones driving the blue sideband (BSB) and red sideband (RSB) transitions, accompanied by the individual beam (green arrow), respectively. b Visualization of the motional-state evolution in phase space when a SDK operation of duration \u03c4 is applied, where the qubit is in the eigenstate of the operator \\({\\hat{\\sigma }}_{{\\phi }_{s}}\\equiv {\\hat{\\sigma }}_{+}{e}^{i{\\phi }_{s}}+{\\hat{\\sigma }}_{-}{e}^{-i{\\phi }_{s}}\\). Here, \\({\\hat{\\sigma }}_{+}\\) (\\({\\hat{\\sigma }}_{-}\\)) is the raising (lowering) operator of the qubit, \u03d5s (\u03d5m) is the spin (motion) phase of the laser beams, \\(\\tilde{\\Omega }\\) is the Rabi frequency of the sideband transition, and \u03b4m is the motion detuning. c Normalized eigenvector components of each motional mode, which represent the relative ion-mode coupling strengths. Motional-mode frequencies are shown in the lower right corner of each panel.\n\nThrough the sequential application of Doppler cooling, EIT cooling, and sideband cooling, the zig-zag motional mode of the 7-ion chain can be cooled to average phonon number \\(\\bar{n}=0.036(16)\\). Following this, a 370 nm laser is utilized to pump the ions to their ground state \\(| 1 \\rangle\\). Transitions between the qubit states are performed using stimulated Raman transitions driven by a pair of laser beams36. Acousto-optic modulators are employed to manipulate the frequency and phase of each laser beam, thereby enabling single-qubit gates; we apply a bit flip (X) gate to initialize the ion to the \\(| 0 \\rangle\\) state. The spin-oscillator coupling is simulated using the spin-dependent kick (SDK) operation induced by simultaneous blue- and red-sideband transitions as shown in Fig.\u00a01. Figure\u00a06b visualizes how the motional state evolves in phase space when a SDK operation of duration \u03c4, spin (motion) phase \u03d5s (\u03d5m), motion detuning \u03b4m, and sideband Rabi frequency \\(\\tilde{\\Omega }\\) is applied59. High fidelity SDK operation requires careful calibration of the light shift (Supplementary Fig.\u00a01) and the motional frequency (Supplementary Fig.\u00a02). Subsequent to the final operation, the qubit-state population is accessed via the state-dependent detection technique.\n\nThe Lamb-Dicke parameters of the 7 motional modes range from 0.073 to 0.079 due to their different frequencies. The relative ion-mode coupling strengths (normalized eigenvector components) are shown in Fig.\u00a06c. We strategically use the center ion of a 7-ion chain, as it exhibits negligible coupling to the 2nd, 4th, and 6th modes. This arrangement maximizes the effective spacing between the motional-mode frequencies and therefore suppresses cross-mode coupling (driving modes that are not targeted by the SDK operation).\n\nThe evolution of \\({\\hat{H}}_{D}\\) in Eq. (2) is mapped to a sequence of single-qubit rotations and SDK operations via Trotterization. After conjugating \\({\\hat{H}}_{D}\\) with a Hadamard gate on the qubit, the Hamiltonian can be described in the interaction picture as\n\nwhere \\({\\hat{\\sigma }}_{\\Delta }(t)\\equiv {\\hat{\\sigma }}_{+}{e}^{i\\Delta t}+{\\hat{\\sigma }}_{-}{e}^{-i\\Delta t}\\) and \\({\\hat{\\sigma }}_{+}\\) (\\({\\hat{\\sigma }}_{-}\\)) is the raising (lowering) operator of the qubit. The evolution with respect to \\({\\hat{H}}_{I}(t)\\) is discretized into N time steps, each consisting of a single-qubit rotation and an SDK operation that simulates the first and second term, respectively. Specifically, for the SDK operation at the j-th time step, the spin (motion) phase of the laser beams59 is set as \u0394tj (\u03bdtj), where \\({t}_{j}\\equiv ( \\, \\, j-\\frac{1}{2})T/N\\tau\\) and \u03c4 is the duration of the operation for each time step. This is equivalent to setting the symmetric (anti-symmetric) detuning from the sideband transitions, referred to as the spin (motion) detuning, as \u0394T/N\u03c4 (\u03bdT/N\u03c4) at all time steps. This method can be straightforwardly extended to simulating models with multiple electronic states and vibrational modes18,37; however, the laser phase needs to be tuned to track the phase of each motional mode correctly (Supplementary Fig.\u00a03 in Section\u00a0II of the\u00a0Supplementary Material).\n\nRandomizing the control parameters is a key ingredient in our approach to simulating dissipation mechanisms in spin-boson models. By taking an average of many coherent evolutions, each involving a stochastically varying parameter, we realize both heating and dephasing as described by the Lindblad master equation38. Heating and dephasing are used to tune the initial temperature and spectral linewidth of the bath, respectively.\n\nFirst, the bosonic mode at finite temperature is created by modeling the mode\u2019s heating process as interacting with an infinite-temperature external bath for a finite duration. This heating is described by a pair of Lindblad operators \\({\\hat{L}}_{1}=\\sqrt{\\Gamma {\\prime} }\\hat{b}\\) and \\({\\hat{L}}_{2}=\\sqrt{\\Gamma {\\prime} }{\\hat{b}}^{{{\\dagger}} }\\), where \\(\\Gamma {\\prime}\\) is the heating rate. We first prepare the motional mode at the ground state, and then apply \\(N{\\prime}\\) resonant (\u03b4m\u00a0=\u00a00) SDK operations of duration \\(\\tau {\\prime}=4\\Gamma {\\prime} /{\\tilde{\\Omega }}^{2}\\), spin phase \u03d5s\u00a0=\u00a00, and motion phase \u03d5m randomly drawn from [0,\u00a02\u03c0) for each operation. These stochastic operations are applied on the initial qubit-mode composite state \\(|+,n=0 \\rangle\\); note that \\(|+ \\rangle\\) is an eigenstate of the spin operator with \u03d5s\u00a0=\u00a00, and thus the qubit is decoupled from these operations. When averaged over many trials of coherent evolutions, each executed with a distinct set of \\(N{\\prime}\\) random \u03d5m values, this procedure prepares the thermal state with an average phonon number \\(\\bar{n}=N{\\prime} \\Gamma {\\prime} \\tau {\\prime}\\) (derivation provided in Section\u00a0IV of the\u00a0Supplementary Material). This can be intuitively understood as the thermal state represented as an ensemble of randomly displaced coherent states. For the experimental data in Fig.\u00a02a and Fig.\u00a05b, the average phonon number \\(\\bar{n}\\) of the initial thermal state is tuned by varying the duration \\(\\tau {\\prime}\\) of SDK operations, where the number of steps \\(N{\\prime}\\) is fixed to 5. The errors in \\(\\bar{n}\\) due to finite number of steps are discussed in Section\u00a0IV of the\u00a0Supplementary Material.\n\nNext, dephasing during the time evolution, which is described by a single Lindblad operator \\({\\hat{L}}_{1}=\\sqrt{\\Gamma }{\\hat{b}}^{{{\\dagger}} }\\hat{b}\\), is induced by giving random offsets to the motion detuning \u03b4m of the N Trotterized SDK operations. Specifically, an offset to \u03b4m is assigned for each of the N steps, with values drawn from a normal distribution characterized by a mean of zero and a standard deviation of \\(\\sqrt{\\Gamma T/N}/\\tau\\) (see Section\u00a0IV of the\u00a0Supplementary Material). The qubit population dynamics averaged over many trials of coherent evolutions, each performed with a set of N random \u03b4m values, give the dynamics with respect to the dephased spin-oscillator model. The entire instruction of operations is shown in Fig.\u00a07.\n\n\u2018H\u2019 represents the Hadamard gate on the qubit. \u2018Kick (heat)\u2019 represents the SDK operations with random \u03d5m that prepare the thermal state on average. \u2018SQ\u2019 and \u2018Kick (coh/dep)\u2019 represent the single-qubit rotations and SDK operations that simulate the first and second terms in Eq. (5), respectively. Coherent or dephased spin-oscillator model is simulated by using a fixed or randomly drawn \u03b4m value at each step, respectively.\n\nIn practice, the dephasing strength \u0393 is varied by tuning the standard deviation of the random offsets to \u03b4m at each Trotterization step. This enables tuning the FWHM of each Lorentzian peak in the bath\u2019s spectral density. The errors in the simulated dephasing strength due to a finite number of Trotterization steps are discussed in Section\u00a0IV of the\u00a0Supplementary Material (Supplementary Fig.\u00a05).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59296-y/MediaObjects/41467_2025_59296_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59296-y/MediaObjects/41467_2025_59296_Fig7_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "Source data for all main text figures are available in ref. 60. Any other data supporting the findings of this study are available upon request.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code used for the simulation and analysis of the data is available upon request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Leggett, A. 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This research is funded by the Office of the Director of National Intelligence - Intelligence Advanced Research Projects Activity, through the ARO contract W911NF-16-1-0082 (provided the experimental apparatus used in the demonstration, K.R.B and J.K.), the DOE BES award DE-SC0019400 (theoretical modeling, for K.R.B. and J.K.), and the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Grant No. OMA-2120757 (experimental concepts and validation, for K.S., M.K. and H.N.). Support is also acknowledged from the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Quantum Systems Accelerator (data analysis and validation, for K.R.B. and J.K.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Ke Sun, Mingyu Kang.\n\nDuke Quantum Center, Duke University, Durham, NC, USA\n\nKe Sun,\u00a0Mingyu Kang,\u00a0George Schwartz,\u00a0Kenneth R. Brown\u00a0&\u00a0Jungsang Kim\n\nDepartment of Physics, Duke University, Durham, NC, USA\n\nKe Sun,\u00a0Mingyu Kang,\u00a0George Schwartz,\u00a0David N. Beratan,\u00a0Kenneth R. Brown\u00a0&\u00a0Jungsang Kim\n\nDepartment of Chemistry, Duke University, Durham, NC, USA\n\nHanggai Nuomin,\u00a0David N. Beratan\u00a0&\u00a0Kenneth R. Brown\n\nDepartment of Biochemistry, Duke University, Durham, NC, USA\n\nDavid N. Beratan\n\nDepartment of Electrical and Computer Engineering, Duke University, Durham, NC, USA\n\nKenneth R. Brown\u00a0&\u00a0Jungsang Kim\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nK.S., M.K., K.R.B., and J.K. conceived the idea and developed a detailed methodology for experiments and modeling. K.S. and G.S. performed the experiments, M.K., H.N., and D.N.B. performed modeling and simulations, K.S., M.K., K.R.B., and J.K. analyzed and validated the data. K.R.B. and J.K. supervised the project, K.S., M.K., and H.N. wrote the original draft, and K.S., M.K., and J.K. revised the manuscript. All authors discussed the results and reviewed the final manuscript.\n\nCorrespondence to\n Jungsang Kim.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "J.K. and K.R.B. are shareholders of IonQ, Inc. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Patrick Becker and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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"journal": "Nature Communications", + "published": "04 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61282-3/MediaObjects/41467_2025_61282_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61282-3/MediaObjects/41467_2025_61282_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61282-3/MediaObjects/41467_2025_61282_MOESM3_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61282-3/MediaObjects/41467_2025_61282_MOESM4_ESM.mp4" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61282-3/MediaObjects/41467_2025_61282_MOESM5_ESM.mp4" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61282-3/MediaObjects/41467_2025_61282_MOESM6_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.6084/m9.figshare.29053901" + ], + "code": [], + "subject": [ + "Electrical and electronic engineering", + "Electronics, photonics and device physics" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5267717/v1.pdf?c=1751713753000", + "research_square_link": "https://www.researchsquare.com//article/rs-5267717/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61282-3.pdf", + "preprint_posted": "03 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Coherent perfect absorption (CPA), or anti-lasing, has been so far inherently restricted to continuous wave scenarios, drastically restricting its applications to standard linear steady-state systems. However, future technologies based on enhanced light-matter interactions typically require the non-linear emission and absorption of pulses, as in ultrafast optics, frequency-comb technologies, or spiking neuromorphic networks. Here, we propose to extend the reach of anti-lasing to pulsed operation. We unveil the phenomenon of ultrafast anti-lasing, in which perfect absorption of photons occurs transiently over ultrashort time scales, creating fast absorption pulses associated with broadband absorption frequency combs. This is obtained by leveraging robust topological transitions occurring in a hysteretic scattering system, which is temporally modulated to loop near a CPA singularity. Our work evidences the interplay between intrinsic memory and topology in wave scattering, unveiling the rich physics of ultrafast Floquet engineering through topological switching. We envision applications in spiking photonic networks with robust emission, routing and detection of spikes, which may form the basis for future analog neuromorphic hardware.Physical sciences/Optics and photonics/Other photonicsPhysical sciences/Physics/Electronics, photonics and device physics/Photonic devices", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "TopologicalhystereticwindingforultrafastantilasingSM.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Coherent perfect absorption (CPA), or anti-lasing, has been so far inherently restricted to continuous wave scenarios, drastically restricting its applications to standard linear steady-state systems. However, future technologies based on enhanced light-matter interactions typically require the dynamic emission and absorption of pulses, as in ultrafast optics, frequency-comb technologies, or spiking neuromorphic networks. Here, we propose to extend the reach of anti-lasing to pulsed operation. We unveil the phenomenon of fast temporal anti-lasing, in which perfect absorption of photons occurs transiently over ultrashort time scales, creating fast absorption pulses associated with broadband absorption frequency combs. This is obtained by leveraging robust topological transitions occurring in a hysteretic scattering system, which is temporally modulated to loop near a CPA singularity. Our work evidences the interplay between intrinsic memory and topology in wave scattering, unveiling the rich physics of Floquet engineering through topological switching. We envision applications in spiking photonic networks with robust emission, routing and detection of spikes, which may form the basis for future analog neuromorphic hardware.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "In physics, winding or encircling is the fundamental concept allowing the distinction between topology and triviality1,2,3. For topological systems, winding is a property defined from a continuous mapping, from the circle to a parameter space in which the physical system undergoes a closed winding loop. When going through the loop, topological systems accumulate a quantized phase, allowing their topological classification based on an integer invariant or charge2,3,4,5,6,7,8,9,10,11. Conventionally, such loops have been generated using adiabatic linear mechanisms with no memory of their history, being mostly based on the quasistatic response of the system to an external control parameter, completing the loop at an arbitrary speed.\n\nHowever, hysteretic systems can exhibit a different form of loop, whose shape depends strongly on the current and past driving amplitude and frequency. Such hysteretic behavior has recently gained significant attention due to its non-volatile character, which is crucial to neuromorphic systems, due to its dependence on previous time stages12,13,14,15. These methods have found applications in various fields, such as the development of memristors with pinched loops16,17,18,19,20 or photonics and nanoscale synapses for emulating learning and forgetting processes21,22,23,24. Although these hysteresis loops exist naturally, they have so far been restricted to real-valued responses, e.g. current-voltage characteristics, and therefore remained irrelevant to topological physics. On the other hand, considering the singular topology of scattering matrices5 may provide a way for hysteretic loops to be leveraged for controlling topological transitions and winding directions. Indeed, scattering matrix topology is based on encircling a singular point where the matrix has a zero determinant. Since this singularity is the well-known coherent perfect absorption (CPA) condition4,25,26,27,28, we surmise that introducing hysteresis in the realm of topological scattering may unveil fundamentally new effects such as fast non-volatile topological transitions accompanied by spiking-like CPA, that are only controlled by hysteretic dynamics.\n\nThis paper unveils the potential of topological hysteretic winding, demonstrating how magnetic hysteresis can be leveraged to control robust topological transitions that deeply influence the transient scattering of microwave photons, leading to fast temporal anti-lasing processes. We induce hysteretic winding loops in Floquet scattering systems made of circulators subject to time-modulated external magnetic fields, driving periodic topological transitions associated with transient singular scattering behavior. By tuning the modulation parameters, we are able to accumulate hysteretic winding charge over long times, and release it at a later stage depending on the temporal dynamics of the winding patterns. Very fast temporal coherent perfect absorption and wideband topological absorption frequency combs are observed experimentally. Our findings enrich the physics of CPA and other topological singularities, by extending them to the realm of temporal processes. We envision applications in devices leveraging the memory and robust spiking behavior of the topological scattering pulses that are emitted and absorbed in such systems.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We begin by describing our experimental setup, depicted in Fig.\u00a01a. A ferrite circulator, a fundamental nonreciprocal microwave device that transmits waves in a uni-rotational manner29, is subjected to periodic modulation using an external magnetic field whose voltage signal is generated by a function generator (see Supplementary Section\u00a0I for the justification of using circulator as the basic scattering node). The circulator is made of a ferrite dielectric cavity that is biased by an internal permanent magnet (see Methods). As a consequence, when modulating sufficiently fast the external field, magnetic hysteresis will come into play and influence the scattering response through a modulation of the ferrite\u2019s magnetic permeability30,31. The resultant hysteresis in the microwave scattering parameters is measured using a vector network analyzer, which is capable of recording in real time the evolution of the four scattering parameters between two circulator ports. These four parameters form the scattering matrix, which becomes singular when its determinant vanishes, a condition equivalent to CPA or anti-lasing. Figure\u00a01b illustrates the concept of topological hysteretic winding around the CPA singularity. Generally, a complex-valued field can support topological defects in the form of zeros, around which the phase can wind3,28,32,33, corresponding here to the singularity condition \\(\\det (S)=0\\)5, where we use the convention of scattering matrix with diagonal and off-diagonal elements being the reflection and transmission coefficients, respectively. For the time-modulated circulator, the topology is revealed in a 2D parameter space composed of the frequency detuning of the ferrite cavity \u0394f0 and the Zeeman splitting \u0394Zeeman, which are both controllable through the external magnetic modulation (see Supplementary Section\u00a0I and II). Over one period Tm, a linear system would yield a non-hysteretic response (green path), for which the trajectory is the same when increasing and decreasing the control signal, with no possibility of winding. In contrast, in the case of hysteretic scattering, winding loops are formed due to magnetic hysteresis, as the scattering matrix undergoes a different path when increasing and decreasing the control signal. This effect, widely employed in magnetic memories, is used here in the context of topological scattering. In particular, when the hysteresis loop winds about the singularity \\(\\det (S)=0\\), a phase accumulation of \u00a0\u00b12\u03c0 occurs within one driving period, resulting in a quantized accumulation of topological charge Q\u00a0=\u00a0\u00b11. We stress that this loop is made possible by temporal hysteresis, in stark contrast from conventional quasistatic winding mechanisms that do not feature any memory. This unique behavior offers exciting possibilities for the manipulation and control of transient wave scattering and topological properties. In Fig.\u00a01c, we present the experimentally measured hysteretic winding behavior at 5\u2009GHz. The complex value of \\(\\det (S)\\) is mapped onto polar coordinate to conveniently visualize the winding loop and distinguish topological (and trivial) hysteretic winding based on encircling (or not) the origin of the plot. The driving period is Tm\u00a0=\u00a010\u2009ms, its voltage modulation amplitude is 6\u2009V, and a nontrivial winding is obtained by adding an offset voltage V0\u00a0=\u00a03.5\u2009V. Figure\u00a01d highlights the accumulation of topological winding charge over a duration of 2 s, the accumulation rate being controlled by the modulation period. This shows that topological winding is very stable, and can be used as a basis for controlling the scattering, as we show in the following.\n\na Schematic of experimental setup. A ferrite-based nonreciprocal circulator is periodically modulated by an external magnetic field using a low-frequency function generator (sine wave with period Tm, modulation amplitude Vm, and DC bias V0). The magnetization hysteresis in the circulator ferrite translates to a hysteresis winding loop of the microwave scattering parameters, that can be measured with a vector network analyser. The third port of the circulator is matched. b Illustration of topological hysteretic winding. The scattering matrix topology is characterized by its determinant \\(\\det (S)\\), whose evolution is plotted with phase (color) and magnitude (height) in the 2D parameter space of frequency detuning \u0394f0 and Zeeman splitting \u0394Zeeman. The singularity \\(\\det (S)=0\\) corresponds to nonreciprocal coherent perfect absorption (CPA, marked by a star). Depending on the modulation speed and amplitude, one obtains either a non-hysteretic path, or hysteretic loops. Such loops can wind about the CPA point or not, with the winding number being defined from the total phase accumulation of \\(\\det (S)\\) over a modulation period. c Experimentally measured trivial (V0\u00a0=\u00a00\u2009V) and topological (V0\u00a0=\u00a03.5\u2009V) hysteretic winding for microwave photon scattering at 5\u2009GHz with Tm\u00a0=\u00a010\u2009ms and Vm\u00a0=\u00a06\u2009V. The complex value of \\(\\det (S)\\) is mapped onto polar coordinates. d Experimental data showing the accumulation of the topological winding charge Q over 2 s at different modulation periods of Tm\u00a0=\u00a010,\u00a02,\u00a01\u2009ms.\n\nWe now investigate ways to dynamically control this hysteretic behavior, the winding direction, and the associated topological transitions. We consider a setup where two circulators are cascaded, and subjected to close-by modulation periods and amplitudes denoted as Tm1,m2 and Vm1,m2, respectively (illustrated in Fig.\u00a02a). The system still has only two scattering ports, the other ones being terminated by matched loads. This asymmetric dual modulation causes the hysteresis loop to move and deform across different temporal periods. Looking at the charge accumulation curves (Fig.\u00a02b, c), we observe a key difference with the single circulator case, namely that topological transitions occur, due to the fact that the loop changes over a slow time scale. We observe two ways by which the topological charge of a loop can change, which we call path switch (Fig.\u00a02b) and loop switch (Fig.\u00a02c). In the path switch scenario, a single loop is involved, and its winding changes when one of its sides crosses the origin, collapsing the entire loop on the opposite side, before the other side crosses zero. This entire process results in a flipping of winding direction. The figure depicts snapshots of the winding loops at specific instants marked by arrowheads. As for the loop switch scenario, two counter-winding loops form an \u201c8\u201d shape, and these loops successively encircle the singularity, reversing the winding direction. Dynamic animations are provided in Supplementary Movies\u00a01 and 2. We also note the possibility of loop switching with two nested loops, where one loop winds inside another one with opposite winding (see Supplementary Fig.\u00a0S6 and Supplementary Movie\u00a03). It\u2019s noteworthy that under the adiabaticity assumption, the topological transition guarantees a singularity must occur somewhere within the interface, which is applicable in our temporal system: the modulation period is much smaller than the transition time. The rich dynamics of these loops presents unique opportunities for exploring transient topological scattering and switching, as we now demonstrate.\n\na Experimental setup for dynamic topological transitions. Two circulators interact through a bidirectional transmission line and are modulated at different periods Tm1,m2 or amplitudes Vm1,m2. Two of the ports are used for scattering measurements, while all other ports are left unexcited and matched. Multiple periods of winding loops are recorded over time, leading to the accumulated charge Q over time, and we observe two types of topological transitions. b Path switch topological transition, obtained for (1/Tm1,\u00a01/Tm2)\u00a0=\u00a0(100,\u00a0100.1) Hz and Vm1\u00a0=\u00a0Vm2\u00a0=\u00a04\u2009V. The loop crosses zero and collapse, before crossing zero again, flipping the winding direction. c, Loop switch topological transition, obtained for (1/Tm1,\u00a01/Tm2)\u00a0=\u00a0(100,\u00a0100.1) Hz and (Vm1,\u00a0Vm2)\u00a0=\u00a0(4,\u00a02) V. Two counter-winding loops form an \u201c8\u201d-shaped path, also allowing for a topological transition. Insets show the corresponding winding loop at the arrowhead instant, and the stars mark the singularity of \\(\\det (S)=0\\) in polar coordinates. Topological transitions occur when the color of the line changes.\n\nWe can now harness these periodic topological transitions to enable very fast temporal anti-lasing, which must occur transiently at topological transitions. Indeed, the topological transition is equivalent to \\(\\det (S)=0\\), which implies directly that a nonreciprocal CPA can be encountered periodically and transiently in situations like the one of Fig.\u00a03a (see Supplementary Fig.\u00a0S3 for the illustration of winding loops just before and after topological transitions). Since the eigenstate for a two-port non-reciprocal CPA is special and only involves excitation from one of the ports, we do not have to synchronize excitations at multiple ports to engage it, but simply send a single input wave (see Supplementary Section\u00a0IV). We note this condition is not hard to realize since we have the nonreciprocal circulator at hand. It initially has a very large nonreciprocal ratio of around 20 dB, meaning the value of backscattering is considerably small compared to forward transmission. By introducing additional magnetic bias, the value of backscattering can be further tuned to closer to zero in experiments, corresponding to the condition of nearly perfect nonreciprocity. Under this condition, the measured total outgoing intensity is shown in Fig.\u00a03b. We observe very deep and sharp absorption pulses (marked by stars), which precisely coincide with the topological transitions (Fig.\u00a03b). Remarkably, the duration of these pulses is two orders of magnitude smaller than the modulation period (the minimum\u00a0linewidth reaching \u00a0~\u00a00.003Tm), demonstrating the rapid character of the anti-lasing effect. The zoomed-in view of Fig.\u00a03c compares, in log scale, the fast CPA pulses (starred) to conventional pulses obtained at instants when the loop approaches the CPA point without crossing it. The transition-enabled pulses are significantly faster and deeper, underscoring the pivotal role of topological transitions in achieving fast and efficient anti-lasing effects. To solidify our findings, we have monitored the outgoing power at port 2 and the power leakage at the matched ports. This measurement demonstrates that when the singularity is crossed, the signal incident at port 1 is indeed not transmitted to port 2, but is transiently absorbed by the matched ports. This means that the absorption of the anti-lasing pulses is not only due to losses in the ferrite cavity, but also to absorption in the terminations at the matched ports. It shows that both anti-lasing and lasing spikes coexist in the system, and could be leveraged in future applications. Digging deeper, power spectral density (PSD) spectrums of the outgoing intensity of Fig.\u00a03b reveal intriguing frequency combs as a consequence of intrinsic hysteresis (Fig.\u00a03e, f). For the anti-lasing pulses, the comb starts at the modulation frequency of 100\u2009Hz and extends to multiple high harmonics beyond 2 kHz (Fig.\u00a03e). This is in stark contrast with the case of trivial pulses, for which the PSD spectrum has only a few significant harmonic components (Fig.\u00a03f). This implies that interplay between topological singularity and hysteretic transition strongly enhances harmonic generation, which is the mechanism underlying the generation of fast temporal pulses. This nonlinear behavior due to history-dependent scattering not only adds richness to the system\u2019s response but also unlocks the potential for a diverse range of frequency components to participate in the transient pulse generation processes.\n\na Accumulated charge over time for the hysteretic winding of \\(\\det (S)\\), obtained at the dual modulation condition around (1/Tm1,\u00a01/Tm2)\u00a0=\u00a0(100,\u00a0100.4) Hz. At periodic time instants, the topology of the winding changes forcing the CPA condition to be encountered transiently. b Outgoing intensity when injecting the CPA eigenstate. The topological transitions manifest themselves as fast temporal anti-lasing pulses (marked by blue stars). c Zoomed-in view of one of the CPA peaks (curve marked by the blue star), which is compared to multiple no-transition trivial pulses observed over one driving period in the shaded region of b. d With excitation at port 1, plot of the measured outgoing intensity at port 2 and power leakage to the matched port. Power spectral density (PSD) of time-domain outgoing intensity (e) with topological transitions and f without transition.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61282-3/MediaObjects/41467_2025_61282_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61282-3/MediaObjects/41467_2025_61282_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61282-3/MediaObjects/41467_2025_61282_Fig3_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Besides typical modulation signals, it\u2019s possible to construct more complex topological winding by controlling the modulation period and amplitude simultaneously. This provides more degrees of freedom of the hysteresis loops in the complex plane for topological winding and phase transition. Some examples are demonstrated in Supplementary Fig.\u00a0S7. The hysteretic winding enriches conventional real-valued hysteresis effects, which are widely employed for in-memory computing and neuromorphic devices, with complex-valued scattering phenomena and nontrivial topology. This enhances the routing and detection of spikes with phase-transition assisted sensitivity and noise resilience with quantized topological charge. Also, recent research is conducted for edge-state computing by employing memorized quantized Hall conductance34, and our concept can be readily extended to networks with anomalous or Chern edge state in photonic regime showing topological protection in both spatial and time domains.\n\nThe demonstrated hysteretic topological winding presents nontriviality in both topology and encircling dynamics, enabling the accumulation of in-memory quantized charges. This feature holds great promise for the implementation of neuromorphic perception35,36 and computing37 within a topologically robust network capable of robust emission, routing and detection of spikes. It is important to note that topology is not limited to a CPA singularity; it also encompasses non-Hermitian braiding and other singularities such as exceptional points32,38 or scattering zeros, unveiling a wide range of topological winding charges and transition phenomena. The utilization of topological transitions now presents an opportunity to achieve fast and high-contrast switching in dynamic systems, that can actually be regarded as a previously unrecognized temporal topological interface39. This can provide a powerful tool for realizing efficient topological switches40. Furthermore, these concepts can be readily extended to acoustic, optical, thermal emission, and quantum systems using topological Floquet configurations. This work therefore opens up possibilities for exploring topological physics, in-memory computing, spiking photonic networks, and dynamic edge states in very diverse fields.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The circulator is a surface-mounted microwave circulator, designed from a Y-shaped strip line on a printed circuit board. The three ports are placed 120\u2218 apart from each other such that they are iso-spaced. The printed circuit board is sandwiched between two pieces of ferrite. Without magnetic fields, the Y-junction strip line supports two degenerate modes at f0: right and left-handed. To bias it, two internal magnets are fixed outside, providing the required magnetic field of 50\u2009kA/m = 628 Oe, normal to the printed circuit board and polarizing the ferrite, therefore lifting the initial degeneracy, with chiral modes at f+ and f\u2212. The electromagnet is placed above the circulator and a periodic modulation of the voltage signal is applied to the electromagnet (\\(V(t)={V}_{0}+{V}_{m}\\sin (2\\pi t/{T}_{m})\\)), generating a periodically modulated magnetic field normal to the circulator\u2019s top surface. Two ports are connected to the vector network analyser (Rohde & Schwarz\u2019s ZNA67) and all other ports are connected with match loads for measuring scattering parameter over time at 5 GHz. Power leakage is measured by replacing one matched port with a spectrum analyzer (tinySA Ultra). The accumulated charge is obtained through the phase of scattering-matrix determinant over time. Then it is unwrapped and divided by 2\u03c0.\n\nThe numerical simulations of singular topology in circulators, topological hysteresis winding loop using magnetic hysteresis, and theory on nonreciprocal CPA eigenstate are provided in Supplementary Sections\u00a0I\u2013IV. They provide a quantitative validation of the observed experimental results presented in the main text.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data that support the findings of this study are available at https://doi.org/10.6084/m9.figshare.29053901.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "This study did not involve the development of a custom computer code.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Roe, J. Winding around: the winding number in topology, geometry, and analysis (American Mathematical Society Mathematics, 2016).\n\nKim, D., Baucour, A., Choi, Y.-S., Shin, J. & Seo, M.-K. Spontaneous generation and active manipulation of real-space optical vortices. 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Science 385, 657\u2013661 (2024).\n\nArticle\u00a0\n MathSciNet\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "R.F. acknowledged the funding support from the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number MB22.00028.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Laboratory of Wave Engineering, School of Electrical Engineering, EPFL, Lausanne, Switzerland\n\nHaoye Qin,\u00a0Zhe Zhang,\u00a0Junda Wang\u00a0&\u00a0Romain Fleury\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.Q., Z.Z., and R.F. conceived this study. H.Q. and Z.Z. built the theoretical model, designed the experimental setup, performed the measurements and data analysis. J. W. provided technical support. All authors contributed to the writing of the paper. R.F. supervised this work.\n\nCorrespondence to\n Romain Fleury.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Denis Baranov, Jan Wiersig, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Topological hysteretic winding for temporal anti-lasing.\n Nat Commun 16, 6189 (2025). https://doi.org/10.1038/s41467-025-61282-3\n\nDownload citation\n\nReceived: 06 November 2024\n\nAccepted: 17 June 2025\n\nPublished: 04 July 2025\n\nVersion of record: 04 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61282-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 41.25c-9.8 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LaNiO3 Sublayer Achieved by Modulating Oxygen Octahedron Rotation in LaNiO3/CaTiO3 Superlattices", + "journal": "Nature Communications", + "published": "15 November 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54311-0/MediaObjects/41467_2024_54311_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54311-0/MediaObjects/41467_2024_54311_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://zenodo.org/records/13957770" + ], + "code": [], + "subject": [ + "Electronic properties and materials", + "Surfaces, interfaces and thin films" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3995586/v1.pdf?c=1731762375000", + "research_square_link": "https://www.researchsquare.com//article/rs-3995586/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54311-0.pdf", + "preprint_posted": "14 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Artificial oxide heterostructures have provided promising platforms for the exploration of emergent quantum phases with extraordinary properties. Here, we demonstrate an effective approach to stabilize a distinct oxygen octahedron rotation (OOR) characterized by a-b-c+ in the ultrathin LaNiO3 sublayers of the LaNiO3/CaTiO3 superlattices. Unlike the a-b-c- OOR in the LaNiO3 bare film, the a-b-c+ OOR favors high conductivity, driving the LaNiO3 sublayer to metallic state even when the layer thickness is as thin as 2 unit cells (u.c.). Simultaneously, strongly preferred occupation of dx2-y2 orbital is achieved in LaNiO3 sublayers. The largest change of occupancy is as high as 35%, observed in the 2 u.c.-thick LaNiO3 sublayers sandwiched between 4 u.c.-thick CaTiO3 sublayers. X-ray absorption spectra indicate that the a-b-c+ OOR pattern of LaNiO3 achieved in the LaNiO3/CaTiO3 heterostructures has significantly enhanced the Ni-3d/O-2p hybridization, stabilizing the metallic phase in ultrathin LaNiO3 sublayers. The present work demonstrates that modulating the mode of OOR through heteroepitaxial synthesis can modify the orbital-lattice correlations in correlated perovskite oxides, revealing hidden properties of the materials.Physical sciences/Materials science/Condensed-matter physics/Surfaces, interfaces and thin filmsPhysical sciences/Physics/Condensed-matter physics/Electronic properties and materials", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Supplementaryinformation.docxSupplementary information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Artificial oxide heterostructures have provided promising platforms for the exploration of emergent quantum phases with extraordinary properties. Here, we demonstrate an approach to stabilize a distinct oxygen octahedron rotation (OOR) characterized by a\u2212a\u2212c+ in the ultrathin LaNiO3 sublayers of the LaNiO3/CaTiO3 superlattices. Unlike the a\u2212a\u2212c\u2212 OOR in the LaNiO3 bare film, the a\u2212a\u2212c+ OOR favors high conductivity, driving the LaNiO3 sublayer to a metallic state of ~100\u2009K even when the layer thickness is as thin as 2\u2009unit cells (u.c.). Simultaneously, strongly preferred occupation of dx2\u2212y2 orbital is achieved in LaNiO3 sublayers. The largest change of occupancy is as high as 35%, observed in the 2 u.c.-thick LaNiO3 sublayers sandwiched between 4\u2009u.c.-thick CaTiO3 sublayers. X-ray absorption spectra indicate that the a\u2212a\u2212c+ OOR pattern of LaNiO3 achieved in the LaNiO3/CaTiO3 heterostructures has significantly enhanced the Ni-3d/O-2p hybridization, stabilizing the metallic phase in ultrathin LaNiO3 sublayers. The present work demonstrates that modulating the mode of OOR through heteroepitaxial synthesis can modify the orbital-lattice correlations in correlated perovskite oxides, revealing hidden properties of the materials.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Perovskite-structured rare-earth nickelates (ReNiO3) have attracted great research interest due to their electronic transport and magnetic properties arising from the strong correlation of the Ni 3d orbitals1,2. Among the ReNiO3 family, LaNiO3 (LNO) is particularly interesting since it is the only member that maintains paramagnetic and metallic at all temperatures while other rare-earth nickelates undergo a metal-insulator transition (MIT) as temperature decreases3,4. Previous studies proved that the different transport properties largely originated from the different degrees or patterns of the NiO6 oxygen octahedron rotation (OOR)5,6,7. LNO possesses a rhombohedral structure with the space group R3\u00afC, in which the NiO6 octahedra tilt and rotate in the manner of a\u2212a\u2212a\u2212 in the Glazer notation8,9,10. In contrast, other ReNiO3 compounds usually have an orthorhombic structure (space group Pbnm) with the a\u2212a\u2212c+-typed OOR5,6,7,11. The different OOR patterns will result in different Ni-O-Ni bond angles in the nickelates, affecting the hybridization between Ni-3d and O-2p orbitals thus the transport behavior of the nickelates12,13,14.\n\nAn active topic on LNO is the control of orbital polarization. As well established, the basic band structure of the high Tc superconducting cuprates is that of a two-dimensionality (2D) single-band of Cu dx2\u2212y2 with spin one half and strong antiferromagnetic correlations15,16,17. Ni3+ ions in LNO possess a 3d7 electron configuration, with a fully occupied t2g shell and one electron occupied the eg state. However, the eg orbitals of bulk LNO are doubly degenerated18,19,20. Previous theoretical studies have predicted that the ultrathin LNO layer sandwiched between two insulating layers could present the analogous electronic structure to cuprates, i.e. an ordering of the planar dx2\u2212y2 orbitals that confine electronic transport to two dimensions21,22. Due to this similarity, LNO-based oxide heterostructures are believed to be candidates of high-temperature superconductors. However, this expectation has not been fulfilled till now for the following reasons. Firstly, the measured orbital polarization in LNO-based heterostructures is much smaller than that of theoretical prediction18,23,24,25,26. Possibly, the strong hybridization between Ni-3d and O-2p bands has resulted in a 3d8L state in LNO (L denotes a ligand hole on the oxygen ion), which is not susceptible to orbital polarization. Secondly, the LNO layer, either in the form of a bare film or a sublayer of superlattice (SL), becomes insulating when it is reduced to a few unit cells (u.c.) in thickness. For example, the critical thickness for the metallic LNO layer is ~3\u2009u.c. under the compressive strain imposed by LAO substrate or ~5\u2009u.c. under the tensile strain of SrTiO3 (STO) substrate19,27,28,29,30. Anderson localization induced by strain effect and reduced dimensionality is believed to be the mechanism for the strongly insulating behavior in ultrathin LNO layers31,32. Obviously, the simultaneous achievement of a metallic behavior and a larger orbital polarization in ultrathin LNO layers is the prerequisite for the realization of LNO-based superconductivity.\n\nPerovskite titanates CaTiO3 (CTO) is an insulating oxide that has very similar pseudocubic lattice parameter with LNO (3.81\u2009\u00c5 vs 3.83\u2009\u00c5). The TiO6 octahedra in the CTO films are usually orthorhombically distorted, with the a\u2212b+a\u2212-typed OOR33,34. As mentioned above, the OOR has played an important role in determining the transport behavior of nickelates. As schematically shown in Fig.\u00a01a, when grouping LNO (a\u2212a\u2212a\u2212) and CTO (a\u2212b+a\u2212) layers together to form a heterostructure, the strong OOR mismatch between LNO and CTO, i.e., the in-phase (b+) or out-of-phase (a-) rotation along the b axis, may produce distinct proximity effect, modulating the transport behavior of the ultrathin LNO.\n\na Schematic views of lattice structures of CTO with an a\u2212b+a\u2212 OOR pattern (up panel) and LNO with an a\u2212a\u2212a\u2212 OOR pattern (bottom panel). Blue and orange octahedra represent TiO6 and NiO6, respectively. The right panel shows the in-phase rotation (b+) and out-of-phase rotation (a-) along the b-axis for the CTO and LNO, respectively. b Out-of-plane \u03b8\u20132\u03b8 scans for the Lm/Cn SLs on (001)-oriented STO substrate. SL0 indicates the (001) main peak and SL-1, SL+1 indicate the satellite peaks. c RSM spectra around (103) reflection measured for the L4/C1, L4/C2 and L4/C4 SLs. d High-angle annular dark-field (HAADF) image of the cross-section of the L4/C4 SLs, recorded along [100] zone axis. The LNO and CTO sublayers are marked by orange and blue colors, respectively. e The elemental EDS maps extracted from spectral images with a selected EDS energy window for each element: La, Ca, Ni, Ti.\n\nIn this work, we demonstrate an approach to stabilize a nonequilibrium OOR in the form of a\u2212a\u2212c+ in the LNO sublayers of the LNO/CTO SLs. In sharp contrast to the a\u2212a\u2212c\u2212 pattern, the a\u2212a\u2212c+ OOR is found to favor the metallic state of the LNO layer with a thickness down to 2\u2009u.c.. More than that, a strong dx2\u2212y2 orbital polarization is achieved in the LNO sublayers. The largest change of occupancy is as high as 35%, observed in the SL with 2\u2009u.c.-thick LNO and 4\u2009u.c.-thick CTO sublayers. X-ray absorption spectra indicate that the modulated OOR pattern of LNO in the LNO/CTO SLs has significantly enhanced the Ni-3d/O-2p hybridization, stabilizing the metallic state in LNO ultrathin films. Our work suggests that engineering the nonequilibrium OOR pattern by heteroepitaxial synthesis is a feasible avenue to modify the orbital-lattice correlation in correlated systems, unveiling hidden aspects of oxide materials.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54311-0/MediaObjects/41467_2024_54311_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "[LNOm/CTOn]8 (Lm/Cn) SLs formed by alternately stacking LNO and CTO layers were epitaxially grown on (001)-oriented STO or LSAT substrates by the technique of pulsed laser deposition. Each period of the SL is composed of m unit cells of LNO and n unit cells of CTO, where m ranges from 2, 3 to 4, and n takes 1, 2, or 4. The out-of-plane lattice structure of the SLs was analyzed by x-ray diffraction (XRD) spectra as shown in Fig.\u00a01b and Supplementary Fig.\u00a01. The clear (001) Bragg peaks with thickness fringes indicates the good crystallinity and flat surface of the samples, which is also confirmed by the atomic force microscopy image in Supplementary Fig.\u00a01a. Moreover, distinct satellite peaks are detected, in agreement with the designed structure period. The satellite peaks shift towards the (001) main peak as m and n increases, which is a general feature of the XRD spectra for SL structures. To further determine the in-plane strain state of the SLs, the reciprocal space mapping (RSM) of the (103) reflection is measured. Taking L4/Cn series SLs as an example, the diffraction spots of the SL (marked by the red arrow in Fig.\u00a01c) are located just above that of the STO, i.e., all the SLs are coherently strained to the substrate without lattice relaxation. This conclusion is also applicable to other samples.\n\nTo get information on atomic arrangements, the lattice structure of the SL is further investigated by the high-resolution scanning transmission electron microscope (STEM). Figure\u00a01(d) presents the high-angle annular dark-field (HAADF) lattice image of the cross-section of the L4/C4 SL, recorded along the [100] zone axis. Due to the strong brightness contrast between La and Ca atoms, the alternate stacking of LNO and CTO layers along the [001] direction is clearly seen, confirming the coherent and epitaxial growth of the periodic structure with atomically flat interfaces (detailed line profile analysis see Supplementary Fig.\u00a02). Figure\u00a01e provides layer-resolved energy-dispersive x-ray spectroscopy (EDS) mappings from a local area (approximately two SL periods) of the HAADF image. The sub-lattices of the A-site (La, Ca) and B-site (Ni, Ti) ions show sharp interfaces between LNO and CTO layers, without signatures of cation intermixing and layer dislocations. All these results indicate the high quality of SLs with well-ordered target structures.\n\nTo evaluate the influence of interface coupling, the transport properties of SLs are investigated. Figure\u00a02a\u2013c shows the longitudinal resistivity as a function of temperature (\u03c1xx-T) for the Lm/Cn SLs on STO, where m and n range from 2 to 4 and from 1 to 4, respectively. The L2/C1 SL with the thinnest LNO and CTO sublayers shows a strongly localized behavior, exhibiting a resistivity that quickly increases upon cooling. When fixing the layer thickness of LNO to 2\u2009u.c. whereas increasing the layer thickness of CTO, a significant decrease in resistivity appears. For example, the resistivity at 300\u2009K (\u03c1300) is 76.6 m\u03a9\u2219cm for the L2/C1 SL, 23.9 m\u03a9\u2219cm for the L2/C2 SL, and 1.7 m\u03a9\u2219cm for the L2/C4 SL. More importantly, the metallic behavior is clearly identified for the L2/C4 SL in the temperature range from 300\u2009K to 130\u2009K, though a slight resistive upturn appears at low temperatures. Similar phenomena are also observed in the L3/Cn and L4/Cn series of SLs. These results are unexpected, being in stark contrast to the insulating behavior of the 4\u2009u.c.-thick LNO bare films (see Supplementary Fig.\u00a03). They suggest an increase, rather than decrease, in the conductivity of LNO as the thickness of the CTO layer increases, i.e., the LNO layers become more conductive when they are separated by thicker CTO layers.\n\na \u03c1xx-T curves for the L2/Cn, b L3/Cn, and c L4/Cn SLs on STO substrate. d \u03c1xx-T curves for the L2/Cn, e L3/Cn and f L4/Cn SLs on LSAT substrate. n represents 1, 2, and 4. The blue dashed lines represent \u03c1xx\u2009=\u20091 m\u03a9\u2219cm. g Conductivity (\u03c3300) and h carrier density (n) of the Lm/Cn SLs as functions of tLNO (m u.c.) and tCTO (n u.c.), measured at 300\u2009K. The data with red or blue color indicates the results of SLs on STO or LSAT, respectively. i The first derivative of resistivity with respect to temperature (d\u03c1 \u2044 dT) for typical SL samples. The Tcr label indicates where the hysteresis begins to appear in the cooling process.\n\nThe x-ray absorption spectra (XAS) around the Ni L3 edge and Ti L2,3 edge of the SLs are given in Supplementary Fig.\u00a04. The good vertical alignment of the Ni L3 edge and Ti L2,3 edge peaks suggests that there is no charge transfer at the LNO/CTO interface, being consistent with the results reported for the NdNiO3/SrTiO3 SL35. It means that the CTO layers remain highly insulating in the SLs since Ti ions maintain a +4-oxidation state18,36, regardless of the thickness of either the LNO or CTO sublayer. Thus, it is unusual that the resistivity of the Lm/Cn SLs is lowered by increasing the layer thickness of the CTO. To further check the strain effect on such an anomaly, the \u03c1xx\u2212T curves for the Lm/Cn SLs on LSAT are presented in Fig.\u00a02d\u2013f, demonstrating a similar change trend as the SLs on STO. Compared to the insulating phase of the L2/C1 and L3/C1 SLs, a metallic phase that maintains the lowest temperature of ~100\u2009K is obtained in the L2/C4 and L3/C4 SLs (see Supplementary Fig.\u00a05).\n\nTo better appreciate the thickness-dependent effect, Fig.\u00a02g compares the conductivity at 300\u2009K (\u03c3300) for all samples. Obviously, as the CTO inserting layer increases from 1\u2009u.c. to 4\u2009u.c., the conductance of the LNO layers is enhanced by a factor of ~45 for the 2\u2009u.c.-thick LNO layers, ~12 for the 3\u2009u.c.-thick LNO layers and ~2 for the 4\u2009u.c.-thick LNO layers. It strongly suggests that the CTO layer has an capability to improve the conduction of the neighboring LNO layer. To reveal what causes the changes in transport properties, the Hall measurements were further performed for the SLs. The Hall curves at 300\u2009K are given in Supplementary Fig.\u00a06, indicating the hole-type conduction for the Lm/Cn SLs. Thus deduced carrier density and mobility at 300\u2009K is summarized in Fig.\u00a02h and Supplementary Table\u00a01, as a function of tLNO and tCTO. We find that, at room temperature, the conduction enhancement with the increase of CTO layer thickness is primarily due to the rising of carrier density, while the carrier mobility is nearly unchanged. Further Hall analysis indicates that the CTO layer insertion would increase the carrier mobility of the LNO layer at low temperatures. For example, the carrier mobility of the L4/C4 at 5\u2009K is enhanced by 5 times as compared to that of L4/C1 (see Supplementary Table\u00a02). Thus, at low temperatures, the conduction enhancement is caused by the combined effects of a higher carrier density and the improved carrier mobility.\n\nIt should be further noticed that though the metallic state is achieved in ultrathin LNO layers at high temperatures, a resistance upturn is always preferred at low temperatures, accompanied by the hysteresis behavior while cooling and warming for some samples. This is a clear indication of first-order phase transition. Notably, such a thermal hysteresis loop in \u03c1xx\u2212T curves have never been observed before in LNO films, though a highly insulating state has been reported for ultrathin LNO films. This phenomenon reminds us of other nickelates such as NdNiO3 and PrNiO3, which have orthorhombic symmetry with a\u2212a\u2212c+ OOR pattern at room temperature. As temperature decreases, NdNiO3 and PrNiO3 undergo the first-order phase transition from the orthorhombic phase to the lower symmetry P21/n monoclinic phase37,38, accompanied by the MIT with obvious thermal hysteresis. Thus, the hysteresis behavior observed here implies that the LNO layers sandwiched between CTO layers may have a similar OOR pattern. To examine the onset temperature of the thermal hysteresis (Tcr), Fig.\u00a02(i) plots the first derivative of resistivity with respect to temperature (d\u03c1/dT) for several typical samples, where the onset temperature of the thermal hysteresis (Tcr) can be clearly identified. The deduced Tcr of the SLs are summarized in Supplementary Table\u00a03. The Tcr is lowered by the increase of CTO layer thickness, suggesting the stabilization of the metallic phase in the SLs with thick CTO layers. As for the SLs on different substrates, the Tcr of SLs on LSAT is always lower than that of SLs on STO (see Supplementary Table\u00a03). This is similar to the results of the orthorhombic-structured NdNiO3 films deposited on LSAT or STO substrate39, where the reduced tensile strain of LSAT substrate was believed to be the reason. In Supplementary Fig.\u00a07, we further compare the \u03c1xx\u2212T curves of the [L2/Cn]2 SLs on STO, LSAT, and NdGaO3 (110) substrates. The changing trend of resistivity with the increase of CTO layer thickness is nearly the same for the SLs on all substrates, independent of substrate strain. Moreover, the [L2/C4]2 SL on NdGaO3 (110) shows the lowest resistivity, which may be due to the combined effects of the smallest tensile strain and the enhanced orthorhombic tilting.\n\nAs mentioned in the introduction section, the degree or pattern of NiO6 OOR is intimately linked to the electronic structure of nickelates, offering a promising strategy for tailoring the transport behavior of the nickelates. To determine the OOR in LNO/CTO SLs, we measured the Bragg peaks with half-integer indices, which appear when the pseudocubic unit cell is doubled due to octahedral rotations8,9,10,11. The presence or absence of a distinctive set of half-order peaks reveals the rotational pattern of oxygen octahedra, while the peak intensities provide information about the degrees of the octahedron rotations40,41,42,43. In Fig.\u00a03a\u2013c, we depict the half-order peaks of the L3/Cn series of SLs. For comparison, the results of the LNO and CTO bare films are also given in Supplementary Fig.\u00a08. Firstly, the L3/C1 SL exhibits Bragg peaks with half-integer index h, k, and l (n/2, where n is an odd integer, and h = k \u2260 l, k = l \u2260 h, and h = l \u2260 k). The typical peak indices are (1/2 3/2 1/2), (3/2 1/2 1/2), (1/2 1/2 3/2). This set of half-order peaks indicates the presence of an a\u2212a\u2212c\u2212 OOR pattern in the L3/C1 SL, consistent with the LNO thick films (see Supplementary Fig.\u00a08). An interesting thing is that the half-order peak patterns unveil a striking behavior when the CTO layer thickness exceeds 1\u2009u.c. In addition to the (1/2 3/2 1/2), (3/2 1/2 1/2), (1/2 1/2 3/2) peaks, a (1/2 3/2 1) peak is also observed in the L3/C2 and L3/C4 SLs. According to the Glazer rules42,43, the appearance of (h k l) reflection with h\u2260k=n/2 and an integer l signifies the in-phase c+ rotation and rules out the out-of-phase c- rotation, i.e., the OOR pattern turns to a\u2212a\u2212c+ for the L3/C2 and L3/C4 SLs. As mentioned by previous works44,45, the orthorhombic phase of ABO3 oxides would demonstrate the antipolar-motion of A-cations, which could be used to determine the long axis of the orthorhombic structure. Detailed analysis on the antipolar-motion of A-cations in the L4/C4 SL is given in Supplementary Fig.\u00a09. It confirms that both the CTO and the LNO sublayers have turned to the a\u2212a\u2212c+ OOR pattern in the SL. This is interesting since it is distinct from the original ones (a- a- c- or a- b+ a-) of the LNO and CTO bare films. Moreover, the intensities of (1/2 3/2 1/2), (3/2 1/2 1/2), and (1/2 1/2 3/2) reflections decrease significantly from L3/C2 to L3/C4 as show in Supplementary Fig.\u00a010a, though they have the same a\u2212a\u2212c+ OOR pattern. According to Supplementary Fig.\u00a010c, the (1/2 1/2 3/2) reflection could be generated by the a\u2212 and b\u2212 rotations, while the (1/2 3/2 1/2) (or (3/2 1/2 1/2)) reflections could be generated by the a- and c- (or b- and c-) rotations, respectively. As the c- rotation mode has been ruled out by the appearance of the (1/2 3/2 1) reflection, the intensities of the (1/2 3/2 1/2), (3/2 1/2 1/2), and (1/2 1/2 3/2) reflections are mainly determined by the in-plane a- and b- rotations. Thus, their reduced intensities, from the L3/C2 SL to the L3/C4 SL, strongly suggest that the octahedral rotations along the in-plane a- and b-axes, referred to the OOR angle \u03b1 and \u03b2, are significantly suppressed by the increase of CTO layer thickness.\n\na Half-order peaks obtained from the L3/C1, b L3/C2 and c L3/C4 SLs on STO substrates. d iDPC-STEM image of a cross-section of L4/C4 SL. To clearly show octahedral tilting, the networks of NiO6 and TiO6 octahedra are superimposed on the enlarged image. The red arrows indicate the A-site antipolar displacements. e Layer-dependent tilting angle \u03b8B-O-B, obtained by averaging 20 unit cells along [100] direction, where B represents Ni or Ti for different layers. The error bars denote the standard deviation of multiple measurements. The green dashed line indicates the tilting angle of the LNO bulk. The \u03b8B-O-B bond angle is sketched in the inset plots.\n\nTo gain a deep insight into the OOR at the LNO/CTO heterointerface, integrated differential phase contrast (iDPC) STEM imaging was employed to quantify the layer-resolved octahedron tilting/rotation34. The L4/C4 SL was used as an example for this analysis (Fig.\u00a03d). The in-plane tilting angle of Ni-O-Ni or Ti-O-Ti bond (\u03b8Ni-O-Ni or \u03b8Ti-O-Ti) can be directly determined from the splitting of oxygen sites, as overlaid in the enlarged iDPC image. We can see that the oxygen site splitting is more significant in the CTO layers as compared to the LNO layers. To quantify the changes in the tilting angle, Fig.\u00a03e presents the layer-by-layer \u03b8Ni-O-Ni and \u03b8Ti-O-Ti bond angles, obtained by averaging 20 unit cells per layer. As expected, the OOR is coupled at LNO/CTO interface, resulting in the very close bond angle for the nearest LNO and CTO layer (\u03b8Ni-O-Ni~168.5\u00b0 and \u03b8Ti-O-Ti~167.5\u00b0). A larger Ni-O-Ni bond angle (or lower Ti-O-Ti bond angle) is obtained for the second LNO (or CTO) layer from the interface. It should be pointed out that, the Ni-O-Ni angles of the LNO layers in the L4/C4 SL are comparable to or even greater than that obtained in bulk LNO or thick LNO films (168.5\u00b0)8,9,10. This is quite different from the ultrathin LNO bare films with insulating behavior, where the NiO6 octahedra were highly distorted with a Ni-O-Ni bond angle of 153\u00b0~157\u00b029,46.\n\nPrevious work reported that the CTO engineered with proper OOR would become polar and even ferroelectric33, which could also affect the conductivity of the nearby LNO layers47,48. Considering this possible mechanism, we conducted the optical SHG measurements on the L4/C4 SL, and no reliable signals were obtained (see Supplementary Fig.\u00a011). It indicates that the centro-symmetricity in the SL system is not broken, making it impossible to generate ferroelectricity. This is reasonable since the CTO layer in the Lm/Cn SLs has the Pbnm symmetry that is non-polar according to the previous DFT calculation33. The above XAS results also rule out the interfacial charge transfer as the reason for the conductivity enhancement of LNO layers in the SLs. Thus, we consider that the OOR modulation of LNO by interface coupling is the most likely mechanism.\n\nIt is well acknowledged that the energy levels of the O-2p band and the Ni-3d band in nickelates are in close proximity, exhibiting a pronounced hybridization between them and thus forming the conduction band in LNO (Fig.\u00a04a). The separation between the O-2p and Ni-3d orbitals is known as the charge transfer energy \u0394, which is primarily controlled by the Ni-O-Ni bond length and bond angle12,13,14. Thus, it is reasonable that a large Ni-O-Ni bond angle of the LNO layers in the LNO/CTO heterostructure will favor an enhanced orbital overlap, supporting the metallic behavior. To gain an insight into the electronic structure and the orbital hybridization, soft XAS measurements were performed for the LNO/CTO SLs. As reported by Van Veenendaal et al. 49, changes in the multiplet splitting of the Ni L3 absorption edge could be used to estimate the degree of covalence between Ni 3d and O 2p bands. This splitting corresponds to the energy separation \u0394 between the t2g and eg levels of Ni, which is determined by the interplay between orbital hybridization and Coulomb repulsion. Previous studies on ultrathin LNO films have shown that the large splitting of the Ni L3 peak is responsible for the occurrence of electronic dead layer35,50. In the present work, we analyzed the XAS spectra around the Ni L3 edge for the Lm/Cn SLs, along with two reference data obtained from a thick (~25\u2009u.c.) and a thin (~2\u2009u.c.) LNO bare films. Figure\u00a04b and Supplementary Fig.\u00a012 present the normalized spectra of the Ni L3 edge for the Lm/Cn SLs on STO substrates. Since the La M4 line partially overlaps the Ni L3 contribution, corrections have been made to the Ni L3 edge signals (see Supplementary Fig.\u00a013). As expected, the Ni L3 edge splits into two primary peaks, and the splitting enhances as n decreases. To quantify the observed multiplet splitting energy, we fitted the Ni L3 spectra with the sum of two peaks for all samples. The resulting energy splitting is shown in Fig.\u00a04c, as a function of CTO layer thickness. For the L2/C1 SL, the Ni L3 splitting energy is ~1.48\u2009eV, close to that of the 2\u2009u.c.-thick LNO bare film (1.53\u2009eV). Subsequently, the splitting energy decreases to 1.45\u2009eV and to 1.35\u2009eV when the CTO inserting layer increases from 1\u2009u.c. to 2\u2009u.c. and to 4\u2009u.c. This trend is also observed in the series of L3/Cn and L4/Cn SLs. Especially, the splitting energy of the L4/C4 SL (1.24\u2009eV) is already comparable to that of the 25\u2009u.c.-thick LNO bare film (1.26\u2009eV). Obviously, it is the enhancement of Ni 3d-O 2p hybridization with increased CTO layer thickness (reflected by the decrease in charge-transfer energy \u0394) that stabilizes the metallic phase in ultrathin LNO layers.\n\na Band scheme proposed to explain the transport behavior of LaNiO3, where the overlapped Ni3+ and O2- bands give the metallic behavior. The shadow region shows the occupied band states. b X-ray absorption spectroscopy (XAS) of the Ni L3 edge and the fitting of Ni L3 spectra with the sum of two peaks for selected SLs. The arrows indicate the position of the second peak. Vertical dashed lines are guides for eyes to view the shift of peak position. c Splitting energy as a function of n, extracted from the curve fitting of the Ni L3 edge of SLs. The error bars represent the standard error obtained from the fitting process. d X-ray linear dichroism (XLD) data, obtained around the Ni L2-edge of the L2/C1, L2/C2, and L2/C4 SLs. Blue and red curves represent the intensities of Ic and Iab, respectively. The black curve is the XLD spectrum deduced from (Iab\u2009\u2212\u2009Ic).\n\nNow we turn our attention to the reconstruction of the Ni eg orbital in the LNO/CTO heterostructure. Figure\u00a04d displays the x-ray linear dichroism (XLD) studies conducted on the Ni L2-edge of the L2/C1, L2/C2, and L2/C4 SLs. The orbital configuration of Ni is probed by the difference in intensity between Iab (x-ray polarization parallel to the in-plane [100]) and Ic (x-ray polarization parallel to the out-of-plane [001]). Here, the Ic was corrected by considering the incident angle of 30\u00b0 between the x-ray and sample plane (see details in the experimental section). A negative XLD signal (Iab \u2013 Ic) is observed for all L2/Cn SLs, indicating the preferred occupation of the dx2\u2212y2 orbital. By integrating the intensities of Iab and Ic in the 867\u2013875\u2009eV range (after subtracting background), the ratio of holes in the eg orbitals can be quantified by the following equation20,23,24,25:\n\nwhere r is the hole ratio, h3z2\u2212r2 and hx2\u2212y2 are the number of holes in the orbital d3z2\u2212r2 and dx2\u2212y2, respectively. For bulk LNO, r = 1 was reported, indicating the fully degenerated eg orbitals18,19,20. The r values for the L2/C1, L2/C2, and L2/C4 SLs are 1.22, 1.29, and 1.35, respectively. This suggests that the relative change of occupancy r-1 is as high as 35% for the LNO/CTO SLs, significantly larger than the values of ~19% and ~3% induced by lattice strains and spatial confinement, respectively23,24,25,26. The orbital polarization can be also estimated by another quantity P, defined as:\n\nwhere n3z2\u2212r2 and nx2\u2212y2 are the number of electrons in the orbital d3z2\u2212r2 and dx2\u2212y2, respectively. Unlike r, P is sensitive to the choice of local atomic basis, i.e. the total occupancy of the eg manifold (neg), which shows fairly large variations from 1.5 to 2.1 reported in different literature51,52. Using an average value of neg = 1.8 for estimation, we obtained the positive values of P up to 18% for the LNO/CTO SLs, much higher than that observed in previous works (5\u20139%)18,25,26. To the best of our knowledge, this is the largest in-plane orbital polarization in ultrathin LNO layers, illustrating the strong impact of the OOR on orbital reconstruction.\n\nTo understand the relationship between electronic structure and macroscopic properties, the band structure and density of states (DOS) of the L3/C1, L3/C2, and L3/C4 SLs are further investigated by the density functional theory (DFT) calculations. The basic parameters of the initial structure for structural optimization are obtained from the experimental results. The results of structure optimization show that the stable structure assumes a\u2212a\u2212c\u2212 OOR pattern for L3/C1 and a\u2212a\u2212c+ OOR pattern for both L3/C2 and L3/C4, which is consistent with the experimental observations (see Supplementary Fig.\u00a014). The calculated band structures and DOS are shown in Fig.\u00a05. The band structure and DOS are spin-polarized and there is one spin channel (spin up) in the energy range \u22120.4\u2009eV to 0.4\u2009eV. The atom-projected band structures indicate that the low-energy bands (near the Fermi level) originate mainly from O atoms. About 70% of the DOS are associated with O atoms and 30% are associated with Ni atoms. Therefore, the conductivity originates from the electronic states contributed by Ni and O atoms. The weights of O and Ni atoms, matching the calculated projected band structure, indicate the orbital hybridization between O atoms and Ni atoms. The trend of conductivity for the L3/C1, L3/C2, and L3/C4 SLs can be explained in terms of the band structures and the corresponding DOS values at the Fermi level. The band structures of L3/C1 and L3/C2 are similar on a large scale, but flat bands are observed near the Fermi level in L3/C1, which contributes little to the conductivity. In addition, L3/C2 has a larger DOS at the Fermi level than L3/C1 does. For the comparison of L3/C1 and L3/C2, we need to subtract the sharp DOS peak at the Fermi level because it results from flat bands and contributes little to conductivity. These features explain the phenomenon why L3/C2 has a lower resistance than L3/C1. As for the comparison of L3/C2 and L3/C4, the main point is that L3/C4 has more bands and larger DOS at the Fermi level than L3/C2 does after subtracting the sharp peak, which indicates that L3/C4 has more effective carriers than L3/C2 does. In addition, the slope of the band curves of L3/C4 is larger than that of L3/C2, which may reduce the effective mass of carriers, leading to higher mobility. Therefore, L3/C4 has higher conductivity than L3/C2.\n\na O atom projected band structure and b spin up the density of states for the L3/C1, L3/C2, and L3/C4 SLs. The color bar describes the proportion of O atoms. The DOS at the Fermi level is 10.01, 13.23, and 15.08 states/eV corresponding to the L3/C1, L3/C2, and L3/C4 SLs (after subtracting the sharp DOS peak at the Fermi level).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54311-0/MediaObjects/41467_2024_54311_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54311-0/MediaObjects/41467_2024_54311_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54311-0/MediaObjects/41467_2024_54311_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54311-0/MediaObjects/41467_2024_54311_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In summary, we demonstrate an approach to stabilize a nonequilibrium OOR pattern of a\u2212a\u2212c+ in the LNO sublayers of the LNO/CTO SLs. Unlike the a\u2212a\u2212c\u2212 pattern in LNO bare films, the metastable a\u2212a\u2212c+ OOR pattern is found to favor the metallic state in the LNO ultrathin layers with a thickness down to 2\u2009u.c. More importantly, a strong dx2\u2212y2 orbital polarization is simultaneously achieved in the 2\u2009u.c.-thick LaNiO3 layers, with the highest change of occupancy of 35% or orbital polarization of 18%. These values are significantly larger than those achieved from other approaches, such as strain effect, spatial confinement or interfacial charge transfer. XAS results indicate that the modulated OOR pattern of LNO in the LNO/CTO SLs has significantly enhanced the Ni-3d/O-2p hybridization, stabilizing the metallic state in LNO ultrathin films. The simultaneous realization of the metallic conduction and larger dx2\u2212y2 orbital polarization in LNO ultrathin layers fulfills the basic properties of carriers in high Tc superconducting cuprates (no orbital degeneracy, spin one half, quasi-2D confinement, and antiferromagnetic correlations), showing a feasible way for searching superconductivity in LNO-based heterostructures.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "High-quality [LNOm/CTOn]8 SLs were epitaxially grown on (001)-oriented STO and LSAT substrates by the technique of pulsed laser deposition (KrF, \u03bb\u2009=\u2009248\u2009nm). During film growth, the substrate temperature was kept at 620\u2009\u00b0C and the oxygen pressure was set to 20\u2009Pa. The adopted fluence of laser pulse was 1.2\u2009J/cm2 and the repetition rate was 2\u2009Hz. The deposition rate for the LNO and CTO layers has been carefully calibrated by the technique of small angle x-ray reflectivity (XRR, see Supplementary Fig.\u00a01). The surface morphology of as-prepared films was measured by atomic force microscopy (AFM, SPI 3800\u2009N, Seiko). The crystal structure was determined by a high-resolution X-ray diffractometer (D8 Discover, Bruker) with the Cu-K\u03b1 radiation. The transport measurements were performed in Quantum Designed physical property measurement system (PPMS) with standard Hall bar geometry.\n\nAtomically resolved HAADF-STEM and iDPC-STEM experiments were carried out on a FEI Titan Cubed Themis 60-300 (operating at 300\u2009kV), which was capable of recording high-resolution STEM images with a spatial resolution of \u22480.06\u2009nm. The microscopy equipment included a high-brightness electron gun (X-FEG with a monochromator), a CS probe corrector, a CS image corrector, and a postcolumn imaging energy filter (Gatan Quantum 965 Spectrometer). The collection angle of the HAADF detector was 64\u2013200 mrad, and the iDPC image was acquired by a segmented DF4 detector with 4 quadrants. For this experiment, a convergence angle of 21 mrad was used, and the sample was kept at room temperature.\n\nThe oxygen positions are determined by the measurement and analysis of half-order diffraction peaks arising from the doubling of the unit cell due to octahedral rotations. The presence and absence of specific half-order peaks reveal the rotational pattern, while the magnitudes of the octahedral rotations are determined from the peak intensities. The half-order Bragg peak of the samples was systematically investigated at room temperature on Huber5020 six-circle diffractometer at the beamline BL02U2 in the Shanghai Synchrotron Radiation Facility.\n\nThe X-ray absorption spectroscopy (XAS) measurements are performed at the beamline BL08U1A in the Shanghai Synchrotron Radiation Facility at room temperature in a total electron yield mode. The spectra of Ni L edge are measured by changing the incident angle of the linearly polarized x-ray beam. The sample\u2019s scattering plane was rotated by 30\u00b0 and 90\u00b0 with respect to the incoming photons. When the X-ray beam is perpendicular to the surface plane, the XAS signal directly reflects the 3dx2\u2212y2 orbital occupancy. While the angle between the x-ray beam and surface plane is 30\u00b0, the XAS signal contains orbital information from both 3dx2\u2212y2 and 3d3z2\u2212r2 orbitals. Therefore, for simplifying the results, the unoccupied in-plane orbital states are proportional to Iab\u2009=\u2009I90\u00b0, while the unoccupied out-of-plane orbital states can be calculated by Ic\u2009=\u2009(I90\u00b0\u2009\u2212\u2009I30\u00b0\u00b7sin230\u00b0)/cos230\u00b0. XLD is calculated by Iab \u2013 Ic.\n\nAll first-principles calculations are performed with the projector-augmented wave method within the density functional theory53, as implemented in the Vienna ab initio simulation package software54. The generalized gradient approximation by Perdew, Burke, and Ernzerhof is used as the exchange-correlation functional55. The self-consistent calculations are carried out with a \u0393-centered (6 \u00d7 6 \u00d7 1) Monkhorst\u2013Packgrid56. The kinetic energy cutoff of the plane wave is set to 450\u2009eV. The convergence criteria of the total energy and force are set to 10\u22126 eV and 0.05\u2009eV\u2009\u00c5\u22121. 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B.G L. is thankful for the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant no. XDB33020100) and the Nature Science Foundation of China (Grant no.11974393). J. Z is thankful for the Guangdong Basic and Applied Basic Research Foundation (Grant nos. 2022A1515110648, 2023A1515010953). We acknowledge Beamline BL08U1A and BL02U2 in the Shanghai Synchrotron Radiation Facility (SSRF). This work was supported by the Synergetic Extreme Condition User Facility (SECUF).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Wenxiao Shi, Jing Zhang, Bowen Yu.\n\nBeijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing, China\n\nWenxiao Shi,\u00a0Bowen Yu,\u00a0Jie Zheng,\u00a0Mengqin Wang,\u00a0Zhe Li,\u00a0Banggui Liu,\u00a0Yunzhong Chen,\u00a0Fengxia Hu,\u00a0Baogen Shen,\u00a0Yuansha Chen\u00a0&\u00a0Jirong Sun\n\nSchool of Physical Sciences, University of Chinese Academy of Sciences, Beijing, China\n\nWenxiao Shi,\u00a0Bowen Yu,\u00a0Jie Zheng,\u00a0Mengqin Wang,\u00a0Zhe Li,\u00a0Banggui Liu,\u00a0Yunzhong Chen,\u00a0Fengxia Hu,\u00a0Baogen Shen,\u00a0Yuansha Chen\u00a0&\u00a0Jirong Sun\n\nSongshan Lake Materials Laboratory, Dongguan, Guangdong, China\n\nJing Zhang\n\nCollege of Materials Science & Engineering, Fuzhou University, Fuzhou, China\n\nJingying Zheng\n\nNingbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang, China\n\nBaogen Shen\n\nSchool of Materials Science & Engineering, Taiyuan University of Science and Technology, Taiyuan, China\n\nJirong Sun\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nW.X.S. carried out the sample fabrication and performed the transport, half-order diffraction peaks, and X-ray spectroscopic measurements. J.Z. (Jing Zhang) performed integrated differential phase contrast (iDPC) STEM imaging and analysis. B.W.Y. performed the theoretical calculation. Y.S.C. designed experiments, analyzed data and wrote the manuscript. J.Z. (Jie Zheng), M.Q.W. and Z.L. participated in sample fabrication and characterization. J.Y.Z. performed the SHG measurements. B.G.L., Y.Z.C., F.X.H. and B.G.S. discussed on physical mechanisms and DFT calculations. J.R.S. designed experiments and revised the manuscript. All authors discussed the progress of the research and reviewed the manuscript.\n\nCorrespondence to\n Banggui Liu, Yuansha Chen or Jirong Sun.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Hanjong Paik and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Improved conduction and orbital polarization in ultrathin LaNiO3 sublayer by modulating octahedron rotation in LaNiO3/CaTiO3 superlattices.\n Nat Commun 15, 9931 (2024). https://doi.org/10.1038/s41467-024-54311-0\n\nDownload citation\n\nReceived: 27 February 2024\n\nAccepted: 04 November 2024\n\nPublished: 15 November 2024\n\nVersion of record: 15 November 2024\n\nDOI: https://doi.org/10.1038/s41467-024-54311-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 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+ "journal": "Nature Communications", + "published": "23 August 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62383-9/MediaObjects/41467_2025_62383_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62383-9/MediaObjects/41467_2025_62383_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62383-9/MediaObjects/41467_2025_62383_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-62383-9#Sec13" + ], + "code": [], + "subject": [ + "Optical properties and devices", + "Photonic devices" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5347189/v1.pdf?c=1755947185000", + "research_square_link": "https://www.researchsquare.com//article/rs-5347189/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-62383-9.pdf", + "preprint_posted": "15 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Avalanche photodiodes (APDs) are crucial in emerging weak light signal detection fields. The major challenge of high-performance APDs is to achieve both an ultrahigh gain and ultralow breakdown voltage. The key to efficient carrier multiplication is searching for a high-mobility semiconductor and constructing a novel device structure with an alternative mechanism. Herein, we demonstrate the bilateral Geiger mode avalanche in two-dimensional (2D) Graphene/InSe/Cr asymmetrical Schottky junction (SJ) APDs. An ultrahigh gain of 6.3 \u00d7 107 is yielded at an extremely low breakdown voltage down to 1.4 V approaching its threshold limit of bandgap of InSe. A positive temperature coefficient of the ionization rate and an ultralow critical electric field (11.5 kV/cm) are present in the Graphene/InSe/Cr SJ APDs (GISC-SJ APDs). These support the low-bias triggering impact ionization and low-loss carrier multiplication for outstanding performance along with the unique asymmetric Schottky barrier configurations. Such device architecture enables an ultralow dark current of 100 fA, and a high sensitivity with weak light signals detection ability down to around 2900 photons at room temperature. These characteristics show the prospects of the asymmetrical InSe SJs for developing energy-efficient and high-gain APDs.Physical sciences/Optics and photonics/Optical materials and structures/Graphene/Electronic properties and devicesPhysical sciences/Optics and photonics/Optical materials and structures/Graphene/Optical properties and devicesbilateral avalancheInSeGeiger modeultrahigh gainlow breakdown voltage", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "BilateralGeigermodeavalancheinInSeSchottkyphotodiodessupplementary.pdfSupplementary informatioin", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Avalanche photodiodes are crucial in emerging weak light signal detection fields. However, most avalanche photodiodes either suffer from relatively high breakdown voltage or relatively low gain, impairing the advantages of avalanche multiplication. Herein, we report the bilateral Geiger mode avalanche in two-dimensional Graphene/InSe/Cr asymmetrical Schottky junction. A high gain of 6.3\u2009\u00d7\u2009107 is yielded at low breakdown voltage down to 1.4\u2009V approaching InSe\u2019s threshold limit of bandgap. In addition to the separated carrier injection region and avalanche multiplication region, a positive temperature coefficient of the ionization rate and a very low critical electric field (11.5\u2009kV\u2009cm\u20131) are demonstrated, leading to the nice performance. Such device architecture also enables low dark current and noise equivalent power, showing weak light signals detection ability down to around 35 photons at room temperature. This study provides alternative strategies for developing energy-efficient and high-gain avalanche photodiodes.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Avalanche photodiodes (APDs) that leverage carrier multiplication to detect weak light signals have widespread application scenarios such as optical communication, quantum cryptography, and light detection and ranging (LiDAR)1,2,3. In conventional APDs, an intense electron-phonon (e-ph) interaction results in a huge waste of energy during the charge-carrier acceleration process and inefficient carrier multiplication4,5. The multiplication region is generally in the order of micrometers, and carrier mean free path is to tens or hundreds of nanometers, which indicates that the carriers\u00a0will take 10 more chances of impact. The charge multiplication mechanism involves one type of carrier requiring a large breakdown voltage (Vbd) to achieve pronounced gain6. Typically, the threshold voltage for Si APDs7 is up to 50\u2009V, and the InGaAs APDs8 need to be biased over 60\u2009V to achieve a multiplication factor over 105. Moreover, defect states from the heteroepitaxial growth9 and the device fabrication process10,11 usually accompany a high dark current, which\u00a0will would result in stringent operational conditions and weaken the advantages of APDs.\n\nTwo-dimensional (2D) layered materials showing easy processing, strong light interaction, and quantum confinement effect have inspired investigations for highly efficient optoelectronic devices5,12,13,14,15,16. It is demonstrated that weak e-ph coupling and strong electron-hole interactions in 2D heterostructure result in efficient carrier multiplication14. Besides, the carrier multiplication phenomenon is observed by applying the small avalanche threshold energy, as low as twice the bandgap (Eg) of the 2D material5. The unique transport characteristics and structural features of 2D materials have been revealed, giving insight into the possible physical mechanisms17. These studies indicate that it is feasible to fabricate APDs with 2D materials, showing low avalanche threshold energy and efficient carrier multiplication.\n\nBy constructing stepwise WSe2 homojunction APD, a very low avalanche threshold energy approaching the fundamental limit of the Eg is reported18, but the multiplication gain of the device is only 470. The gain is improved to 5\u2009\u00d7\u2009105 by adopting the Schottky junction (SJ) structure. However, the Vbd is limited to 24\u2009V far from the fundamental threshold limit of WSe219. The performance of APD is strongly related to the materials and structures selected, both affecting the impact ionization process. InSe is physically superior to WSe2 for larger thermal velocity20, which ensures a rapid acceleration of carriers to facilitate the impact ionization process. Gao at al. realize a large multiplication factor of up to about 3\u2009\u00d7\u2009104 at the voltage of 4.3\u2009V in nanoscale vertical InSe/BP heterostructures15. Zhao et al.6 achieved a gain of 3\u2009\u00d7\u2009105 at 5.5\u2009V in a Schottky junction built from the Graphite and n-InSe, while the Graphite/InSe APDs are limited to a low temperature because of the thermally assisted carrier transport of the SJ devices. Hence, it is a very promising route that one searches a high saturation veloextemecity 2D material and constructs a novel device structure with alternative mechanisms to realize a high gain at a very low Vbd.\n\nIn this paper, we design Graphene/InSe/Cr asymmetric Schottky junction (GISC-SJ) APD devices, where p-type InSe conducting behavior from intrinsic bipolar InSe is achieved along with the different work functions of the Graphene, InSe and Cr. The asymmetrical SJs significantly avail initiating impact ionization and multiplication for the injected majority carriers and the bilateral asymmetric Geiger mode avalanche phenomenon with a steep breakdown property is observed. A high multiplication factor of up to 6.3\u2009\u00d7\u2009107 is achieved in the GISC-SJ APDs at low Vbd. The Vbd is yielded to be 1.4\u2009V approximately, which is close to the threshold energy limit of the Eg of the InSe. A positive temperature coefficient of the ionization rate and a small critical electric field (11.5\u2009kV\u2009cm\u20131) are found in the InSe APDs. The GISC-SJ APDs can operate at room temperature showing low dark current below 620 fA, and weak light signals detection ability (around 35 photons). These characteristics indicate that taking asymmetrical InSe SJs is a promising approach for developing next-generation low-power consumption and high-gain APDs.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "APDs are generally based on the PN or SJ junction, taking the junction\u2019s strong electric field (\u03b5field) under a reverse bias to trigger carrier impact ionization and multiplication. Traditional Si, Ge, InGaAs, and HgCdTe APDs are based on the PN junction (Fig.\u00a01a), where the charge multiplication process involves one-carrier cascade impact ionization (Fig.\u00a01b)21. These traditional APDs usually need a high Vbd due to the low density of final states, limitation of the momentum conservation rule, and rapid carrier cooling by phonon scattering5. It is essential to provide sufficient energy for each injected carrier to initiate impact ionization and the inefficient multiplication process4,5. In addition, linear avalanche is often accompanied by the Geiger mode one. Hence, the PN APDs display a larger dark current (Fig.\u00a01c).\n\na Diagram device structure of a PN junction. P and N represent p-type and n-type semiconductor, while PN represent the depletion region of the PN junction. b Band diagram of a PN junction, showing the one-carrier multiplication process and dark current mechanisms. The shaded area represents the multiplication region. TAT and BBT denote trap-assisted tunneling and band-to-band tunneling, respectively. c The I-V curve for the PN APD. IGeiger, ILinear, and IR are the current of the Geiger mode, linear mode, and reversed saturation, respectively. d, e Diagram device structure and electron band structure of a 2D Schottky junction (SJ), showing the carrier multiplication process at the reverse and forward cases. The shaded areas represent the multiplication regions (depletion regions). f The I-V curve of a 2D SJ APD with bilateral avalanche breakdown. \u00a0IDRI\u00a0denotes the drift current.\n\nRecently, great advances in 2D layered materials and their heterostructures have re-spurred investigations into the impact ionization for high-performance APDs5. Two distinct approaches are identified, namely field-effect transistor structure based on the SJ22,23 and heterostructure of the PN junction15,24. Thanks to the excellent switching characteristics and simple processes, the SJs well replace the PN ones in microwave devices and high electron mobility transistors25. For the 2D materials, Schottky barriers are readily formed by transferring Graphene, 2D semimetal or evaporating metal as electrodes (Fig.\u00a01d, e). In addition, 2D SJs are free from extrinsic doping and the resulting defects, and thus a perfect interface between 2D materials and electrodes is built, which enables very low leakage current26,27. The reverse leakage current can also be further restrained by constructing a higher and broader Schottky barrier26,28.\n\nIn the APDs built from two SJs sitting back-to-back architecture, carriers conquer the first forward-biased barrier and arrive at the reverse-biased SJ (Fig.\u00a01e). To realize a higher gain at a lower Vbd, the forward-biased SJ is designed with a lower barrier height for carriers to be easily injected. While the reverse-biased SJ has a higher barrier to provide a strong built-in electrical field and a longer multiplication region for carriers to proceed multiple times impact ionization. The Schottky barrier width W, i.e., multiplication region length, is described by the formula26, W\u2009=\u2009(2\u03b5sVbi/eNA)1/2. Here \u03b5s is the static dielectric constant of a semiconductor, Vbi\u2009=\u2009\u0424low (or \u0424high)/e is the built-in potential, e is the elementary charge, NA\u2009=\u2009N2D/d is the body carrier concentration of the multiplication region, and \u0424high and \u0424low denotes the high and low Schottky barriers, respectively. As depicted in Fig.\u00a01d, more multiple times impact ionization and multiplication proceed where a wider W appears at the SJ due to the \u0424high. In this case, a higher multiplication factor (M) and a low breakdown voltage are present under forward bias (Fig.\u00a01f). In contrast, when the lower Schottky barrier is reverse-biased, the majority carriers have to conquer the \u0424high to arrive at the multiplication region with a narrower W (Fig.\u00a01e), which bring about a lower M and a larger breakdown voltage (Fig.\u00a01f). Therefore, the bilateral avalanche in the asymmetrical SJs with different barrier is demonstrated showing a different multiplication behavior because of the diverse carrier injection barriers, multiplication regions and collection processes (Fig.\u00a01f).\n\nThe schematic illustration of the GISC-SJ APDs structure with asymmetrical Graphene/InSe and Cr/InSe junction is plotted in the top panel in Fig.\u00a02a. The InSe nanosheets are exfoliated from a bulk crystal and transferred onto Graphene flakes, and then Cr/Au electrodes are deposited as contacts (detailed information is present in the Methods). During the measurement, the bias on the Graphene and Cr electrode is defined as the forward and reverse bias, respectively. The thickness of InSe and Graphene are around 25\u2009nm and 5\u2009nm. The InSe and Graphene are well assembled, as confirmed by Raman spectra. The seven Raman peaks of the overlapping area of vertical InSe/Graphene (Supplementary Fig.\u00a01) are well indexed by the phonon vibration modes of the \u03b3-InSe and 2D Graphene28,29. Because of the vdW integration of InSe and Graphene, a well-defined and damage-free interface without the presence of any contamination or amorphous phases is also confirmed by the cross-sectional High-Resolution Transmission Electron Microscope (HRTEM) (Fig.\u00a02b and Supplementary Fig.\u00a02). The lattice spacing of 8.52\u2009\u00c5 and vdW gap of 1.81\u2009\u00c5 is in agreement with those of the \u03b3-InSe along the [010] zone axis12,30, and atomic stack between the Graphene and InSe further indicates a nearly ideal interface (bottom panel in Fig.\u00a02a). Such splendid contact behaviors will support excellent electrical performance and dozens of avalanche breakdown operations. At the InSe/Cr interface, however, a thin hybridized interlayer of the InSe and Cr is present (Fig.\u00a02c). This non-ideal interfacial layer is possibly from the thermal deposition process, which is a universal behavior in 2D material devices31.\n\na 3D schematic illustration of the GISC-SJ APDs (top panel), and cross-sectional HRTEM image of an InSe/Graphene interface (bottom panel). The scale bar is 1\u2009nm. b, c Cross-sectional HRTEM images of the InSe/Graphene and InSe/Cr interfaces. The scale bar is 1\u2009nm. d Linear plots (red) and logarithmic plots (blue) I-V curves under a low bias. Inset is the optical image of the GISC-SJ APD and the scale bar is 5\u2009\u03bcm. e The I-V characteristics curves of the GISC-SJ APD under the reverse and forward biases at 100\u2009K. Inset is the I-V curve at 300\u2009K. f The M dependence of the Vbd for a comparison of the GISC-SJ APDs, traditional semiconductors APDs, and other 2D materials APDs. Detailed values and references to the selected work can be found in Supplementary Table\u00a01. Error bars are standard deviations of M and Vbd.\n\nTo ensure that an asymmetric Schottky barrier is formed, the InSe devices with symmetric electrodes are fabricated. Note that the I-V curves of the Cr/InSe/Cr and Graphene/InSe/Graphene devices show a nonlinear transport behavior, indicating a Schottky contact at both Cr/InSe and Graphene/InSe interfaces (Supplementary Fig.\u00a03). More crucially, Cr and Graphene with different work functions are chosen to regulate the typical n-type to the weak p-type of the 2D InSe, which optimizes the multiplication ambiance, widens the multiplication region length, and thus facilitates the impact ionization6,15,32. The Schottky barrier is around 0.54\u2009eV at the Cr/InSe side and 0.18\u2009eV at the Graphene/InSe one (Supplementary Fig.\u00a04), respectively, which confirms the formation of asymmetric back-to-back Schottky barriers in the GISC-SJ APDs. Figure\u00a02d demonstrates obvious rectification behavior with a rectification ratio beyond 103 at |Vd\u2009|\u2009= 3\u2009V. The GISC-SJ APD shows low dark current below 100 fA. These behaviors are also demonstrated in the other APD devices, reflecting the performance is closely related to the device structure and the high reliability of the GISC-SJ APDs with Cr and Graphene electrodes (Supplementary Fig.\u00a05).\n\nNext, the avalanche characteristics of the GISC-SJ APDs are examined. To study the effect of barrier height on breakdown characteristics, we first test the electrical performance of the device at low temperatures since thermionic exaction and thermal field emission induced carrier injection is greatly suppressed at this time. When the voltage sweeps from 0\u2009V to \u201316\u2009V, the dark current maintains at 100\u2009fA and abruptly increases at a certain voltage (\u201312.9\u2009V) at 100\u2009K (Fig.\u00a02e). The steep variation in current signals a typical avalanche breakdown. The same abrupt avalanche breakdown phenomenon is also observed when the voltage sweeps from 0\u2009V to 10\u2009V (Fig.\u00a02e), implying a bilateral Geiger mode avalanche related to the bilateral Schottky barrier configuration design. More importantly, the GISC-SJ APD device exhibits a bilateral Geiger mode avalanche breakdown even in the ambient atmosphere (inset in Fig.\u00a02e).\n\nThe multiplication factor M, a crucial parameter for an APD, is defined as33,34,35, M\u2009=\u2009I/Isat, where Isat is current at Vbd and I is the current above Vbd. A high M of\u2009\u2248\u20094.6\u2009\u00d7\u2009105 is obtained comparable to that of the commercial Si and InGaAs APDs while the Vbd is only \u201313.1\u2009V, far lower than 40\u2009V of Si and 60\u2009V of InGaAs APDs8,35. Remarkably, the high M beyond 1.7\u2009\u00d7\u2009107 is achieved but with a low Vbd down to 5.1\u2009V at the forward case, and this Vbd is further optimized to be lowered to \u22481.4\u2009V, which is close to the theoretical threshold limit of Eg/e. To our best knowledge, this high M is larger than the value of 6\u2009\u00d7\u2009106 for the commercial Si APDs35. Note that the different M and Vbd at the forward and reverse cases evidence the device structure design of the asymmetric SJ with various barriers. The carriers can be readily injected at the Graphene/InSe side with a lower barrier under a smaller voltage, and thus effectively trigger impact ionization at the Cr/InSe interface where a strong built-in potential and a longer multiplication region are built. These behaviors are also demonstrated in the other APD devices, reflecting the design concept and operation principle of the GISC-SJ APDs (Supplementary Fig.\u00a05). Additionally, negligible variation of the I-V curves at Geiger mode after dozens of cycling scans further implies the high reliability and robustness of the GISC-SJ APDs (Fig.\u00a02e).\n\nTo evaluate these parameters, the M dependence of the Vbd for the commercial semiconductors Si35, Ge1, InGaAs/InP8, InAlAsSb36 and AlInAsSb/GaSb37 APDs, 2D materials APDs6,18,19,24,38, and the GISC\u2013SJ APDs are summarized in Fig.\u00a02f and Supplementary Table\u00a01. For the typical commercial semiconductor APDs, a large threshold voltage is needed to initiate the carrier impact ionization to achieve the Geiger mode avalanche, such as M of 6\u2009\u00d7\u2009106 at 40\u2009V for the Si APDs35, and M of 105 at a higher voltage of 60\u2009V for the InGaAs/InP APDs8, which poses the severe requirements on material quality, operation conditions and signal processing for these semiconductor APDs. Note that the avalanche breakdown voltage is significantly reduced and can arrive at around 5\u2009V for the new-type 2D materials APDs6. However, their multiplication factor M is below 3\u2009\u00d7\u2009105 much lower than that of the Si APDs. In contrast, for the GISC-SJ APDs, the M is yielded to be 1.7\u2009\u00d7\u2009107 and further optimized to be 6.3 \u00d7\u2009107 while the Vbd ranges from 1.4\u2009V to 5.1\u2009V, which exhibits a significant advantage in terms of high multiplication gains and low breakdown voltages.\n\nTo investigate the avalanche nature of the GISC-SJ APDs with high M and low Vbd, variable temperature experiments are conducted and the I-V curves at temperatures from 100\u2009K to 200\u2009K are given in Fig.\u00a03a and Supplementary Fig.\u00a06. This can reveal the competition of the thermal carrier transport, lattice vibration scattering and e-ph coupling, and thus well sheds light on the physics of the carrier transport and multiplication6,16. The avalanche Vbd at either a forward or reverse bias decreases with increasing the temperature, i.e. a negative temperature coefficient of the Vbd, which is in contrast to those of the traditional PN APDs where a positive temperature coefficient of the Vbd is present because of the enhanced phonon scattering with the increase of the temperature39. The threshold voltage (Vth) decreases with the temperature at either a forward or a reverse bias case (Fig.\u00a03b and Supplementary Fig.\u00a08), which well follows the transport behaviors of SJ diodes with temperature due to the narrowing of bandgap with increasing temperature40. Note that the Vbd shows the same evolution as that of the Vth with the temperature. Crucially, notice that the M does not decay with increasing the temperature at both the reverse and forward biases (Fig.\u00a03b and Supplementary Fig.\u00a08). These data indicate that the majority carrier injection can avail avalanche thanks to the drop of the Schottky barrier resulting from the Fermi-Dirac distribution expanding to a higher level with the temperature. This is also reflected by the asymmetrical Schottky barriers related different Vbd at Cr/InSe (Fig.\u00a03a Inset II) and Graphene/InSe (Fig.\u00a03a Inset III) sides, respectively.\n\na I-V curves at different temperatures from 100\u2009K\u2212200\u2009K under the reverse and forward biases. The inset II and III\u00a0at the bottom shows that the high Schottky barrier at the Cr/InSe interface prevents the injection of holes at a low bias, while the low Schottky barrier at the Cr/InSe one allows the injection of holes at a low bias. The inset I at the top gives the impact of the reduced dimensionality of e-ph coupling on the carrier acceleration and multiplication process. b The dependence of the Vth, Vbd, and M on the temperature at the forward bias case. c \u03b1 as a function of the inverse electric field at the selected temperatures of 100\u2009K, 140\u2009K and 200\u2009K, respectively. The inset is the dependence of electric field strength on the temperature when \u03b1 is fixed at 103\u2009cm\u22121. The dotted lines are a guide for the eyes. d The I-V curves of Cr/InSe/Cr devices with different channel lengths, from right to left, correspond to channel lengths of 4.58, 3.65, 2.47, 1.62, and 0.71 \u03bcm respectively. e The Vbd versus the channel length, and the dotted plot is a linear fit. The error bar shows the error in determining the breakdown voltage by identifying the current breakdown point from the repeated I-V curves, which is extracted from Supplementary Fig.\u00a09e\u2013i. The inset is the optical image of the Cr/InSe/Cr APDs with different channel lengths. The scale bar is 5\u2009\u03bcm. f The dependence of the ECR on the Eg for the InSe APDs, conventional semiconductors APDs, and 2D MoS2, WSe2, BP APDs. Detailed values and references to the selected work can be found in Supplementary Table\u00a02.\n\nIn these cases, the injected holes pass difficultly through the Cr/InSe interface at a low reverse bias, and easily through the lower barrier Graphene/InSe one at a low forward bias as shown in the inset in Fig.\u00a03a (Inset II). Hence, a higher M at a forward bias is demonstrated because of the different carrier injection efficiencies as given in Fig.\u00a02e, Fig.\u00a03b and Supplementary Fig.\u00a08. Moreover, multiplication processes are enhanced by constructing the efficient multiplication region through tuning the typical n-type InSe to a weak p-type. Nearly perfect interface configurations of the Graphene/InSe also avail the carrier collection. Consequently, a lower Vbd and a higher M are present at the forward bias in the GISC-SJ APDs. Notably, the carrier multiplication in our asymmetrical SJ APDs is triggered by the majority carriers. This differs from the multiplication mechanism of the traditional PN APDs, where the multiplication is involved by the minority carriers at a large reverse bias. These also imply that it is an efficient route to optimize the Vbd by construction of an asymmetrical Schottky junction with a higher built-in barrier difference. To confirm the drop of the Vbd is closely correlated to the asymmetric Schottky barriers, Cr/InSe/Cr devices with a similar InSe thickness of 27.4\u2009nm are also fabricated, and the Vbd is 10.6\u2009V, twice as large as that of the GISC devices as shown in the Supplementary Fig.\u00a09.\n\nGenerally, a high temperature lowers the impact ionization process since the enhanced lattice vibrations significantly reduce the carrier mean-free path and induce severe e-ph coupling in traditional semiconductor materials and devices41. In layered 2D materials, however, a large vdW gap (EvdWg) is present between the interlayers and the EvdWg of InSe is 1.85\u2009eV (Fig.\u00a03a Inset I), which can well undertake a large potential barrier, and thus avail the in-plane carrier movement and suppresses the out-of-plane charge transport17,42. Compared to the multiplication processes of traditional materials APDs, hence, 2D materials lose less energy related to the quantum confinement effect with the aid of the EvdWg, and the Vbd dramatically drops in 2D APDs. To confirm this result, a crucial characteristic parameter, ionization rate \u03b1 reflecting the multiplication ability for an APD device, is calculated. It is defined as the number of generated electron-hole pairs by an initial charge carrier per unit distance traveled and described by the formula43,44, \u03b1\u2009=\u20091/L(1\u2009\u2212\u20091/M) (detailed discussion is provided in Supplementary note\u00a01). Here M is the multiplication factor, \u03b1 is the ionization rate (here \u03b1 of hole and electron are assigned to be equal) and L is the multiplication region length. The detailed \u03b1 under the forward and reverse biases is given in Supplementary Fig.\u00a07. As a typical case, Fig.\u00a03c shows the dependence of the \u03b1 on the inverse electrical field (\u03b5field) at the selected temperatures of 100\u2009K, 140\u2009K, and 200\u2009K, and the inset gives the temperature dependence of the \u03b5field at the fixed \u03b1p of 103\u2009cm\u20131. Note that, at a lower temperature, a larger \u03b5field is applied to initiate the same impact ionization, while the avalanche readily occurs at a higher temperature, i.e. a larger ionization rate at a lower \u03b5field. This is in contrast to those of the traditional PN APDs where the \u03b1 is lowered with increasing temperature due to the enhanced lattice vibration and e-ph scattering39.\n\nThe large \u03b5field for initiating the ionization breakdown and strong correlation of the \u03b1 with the \u03b5field indicate the lower ionization possibility led by the lower thermal carrier saturation velocity (Vsat) and longer mean free path despite the weak lattice vibration scattering at a lower temperature (Fig.\u00a03c). When the temperature increases from 100\u2009K\u2212200\u2009K, however, the \u03b5field needed to achieve the same impact ionization level (103 cm\u20131) decreases significantly around three times (inset in Fig.\u00a03c). This implies that the higher thermal carriers Vsat and shorter mean free path defeat overwhelmingly the phonon scattering, and thus enhance impact possibility and dominate the avalanche processes at a higher temperature. This can be attributed to the unambiguously proofed weak e-ph coupling and large EvdWg barrier in 2D InSe6. Hence, a weak correlation of the \u03b1 to the \u03b5field, and easy ionizing at a higher temperature are demonstrated in the GISC-SJ APDs (Fig.\u00a03c), which is further supported by the M increment with increasing the temperature at both the forward and reverse biases (Fig.\u00a03b and Supplementary Fig.\u00a08). These results clearly point to the low Vbd in the asymmetric GISC-SJ APDs.\n\nThe critical electric field (ECR) is an important characteristic parameter, reflecting the intrinsic properties of a semiconductor and its device, and closely correlated to the Eg and the depletion region width W, namely multiplication region length. The W depends on the applied voltage. To obtain ECR, the InSe SJ APDs with various channel lengths L from 0.76\u2009\u03bcm to 4.58\u2009\u03bcm are fabricated (Supplementary Fig.\u00a010). Their I-V characteristic curves are given in Fig.\u00a03d and the optical image is plotted in the inset in Fig.\u00a03e. Note that the Vbd drops from 2.34\u2009V to 0.57\u2009V as the L decreases from 4.58\u2009\u03bcm to 0.76\u2009\u03bcm (Fig.\u00a03d). Crucially, the Vbd shows a linear decrease trend when the L is above 1.6\u2009\u03bcm, implying that the channel is not completely depleted and thus the effective W is lower than the L. Therefore, the effective W should be below 1.6\u2009\u03bcm. However, with further shrinking the L to 0.76\u2009\u03bcm, the I-V curve deviates from the linear dependence, and a platform appears as indicated by the arrow in Fig.\u00a03d, i.e. a negative differential resistance behavior, which is the typical characteristic for a tunneling effect transistor. Obviously, when the L is 0.76\u2009\u03bcm, the tunneling effect is present in this InSe SJ device. This also reflects the intrinsic avalanche effect rather than the tunneling in the long L InSe SJ APDs. The effective W is thus in the range from 0.76\u2009\u03bcm to 1.6\u2009\u03bcm, which is in agreement with that of the photocurrent mapping (Supplementary Fig.\u00a011). In addition, the ECR is yielded to be 11.5\u2009kV\u2009cm\u20131 defined by the slope of the relation of the Vd to the L (Fig.\u00a03e). Figure\u00a03f shows a comparison of the ECR for the GISC-SJ APDs, other 2D materials16,23,44 and bulk semiconductors APDs45. The ECR is closely dependent on the Eg for the bulk semiconductors, and the lowest ECR of Ge APDs only approaches to 100\u2009kV\u2009cm\u20131. In contrast, the ECR is nearly independent on the Eg for the 2D BP23, WSe216, MoS244 and our InSe SJ APDs, and their ECR is much lower than those of the bulk material APDs44,45. A low ECR is demonstrated in the InSe APDs, which is another crucial factor for the high M and low Vbd in the asymmetric GICS-SJ APDs.\n\nBased on the excellent electrical performance of the device, we characterize the detection performance of the device at room temperature. As presented in Fig.\u00a04a, the dark I-V curve showcases a low dark current of 6.2\u2009\u00d7\u200910\u201313 A at bias levels below \u20132\u2009V and low breakdown voltage of 2.9\u2009V. As the bandgap III\u2009\u2212\u2009VI compound semiconductor showing direct bandgap structure in multilayer regime, InSe has always been an excellent optoelectronic material for visible light detection46,47. To study the optical performance of InSe based photodetectors, we first measure the photoresponse at 520\u2009nm. Laser illumination precipitates earlier photocurrent avalanche onset and the avalanche breakdown voltage decreases from 2.6\u22121.8\u2009V with the increase of the P from 70.7\u2212418.7\u2009fW due to increased light power (Fig.\u00a04a). The avalanche gain (G) is calculated by the formula48,49, G\u2009=\u2009(Iph \u2013 Idark)/(Iph0 \u2013 Idark0). Here Iph and Idark denote the photocurrent and dark current, and Iph0 and Idark0 are the photocurrent and dark currents with G\u2009=\u20091. The G of the GISC-SJ APD reaches a high level of 3.5\u2009\u00d7\u2009106 (Fig.\u00a04b), which enables the device to have a very high responsivity of 4.8\u2009\u00d7\u2009106 A W\u20131 after avalanche multiplication (Fig.\u00a04b inset).\n\na I-V curves at darkness and laser powers from 70.7\u2009fW to 418.7\u2009fW with a laser wavelength of 520\u2009nm at room temperature. b Gain as a function of the operation voltage at the power of 70.7\u2009fW. Inset is the responsivity as a function of the operation voltage. c Normalized photoresponsivity as a function of the wavelength obtained in the GICS-SJ device (red dot line) and the increased ratio of device responsivity in APD mode compared with in photovoltaic (PV) mode (blue dot). d Normalized frequency response at different reverse biases when the incident light wavelength is 520\u2009nm. e Noise current of the GICS-SJ APD with different biases as a function of frequency at 300\u2009K. f Comparison of responsivity and detectivity for the GICS-SJ APDs, and other 2D materials APDs, PV and PG detectors. Detailed values and references to the selected work can be found in Supplementary Table\u00a04.\n\nThe red dot line in Fig.4c displays the normalized photoresponsivity as a function of the wavelength obtained in the GISC-SJ APD device. It can be seen that the device shows the strongest response to light in the 520\u2009nm band and achieves a wide spectral response ranging from 520\u2009nm to 1550\u2009nm (Supplementary Fig.\u00a012), which exceeds the cut-off wavelength of InSe due to internal photoelectron emission (IPE) effect50,51,52,53. In the PV mode, the device shows the maximum responsivity of 4.5\u2009\u00d7\u200910\u20133\u2009A\u2009W\u20131 at 520\u2009nm (Supplementary Table\u00a03), and the responsivity gradually decreases as the wavelength increases. When the detection wavelength exceeds the cut-off wavelength of InSe, due to the low quantum efficiency of the IPE effect54, the responsivity at 1270, 1450, and 1550\u2009nm are also very low, which are 3.8\u2009\u00d7\u200910\u20138, 9.9\u2009\u00d7\u200910\u20139 and 3.7\u2009\u00d7\u200910\u20139\u2009A\u2009W\u20131, respectively (Supplementary Table\u00a03). In APD mode, the responsivity of the device is greatly improved due to avalanche gain (blue dot in Fig.\u00a04c extracted from Supplementary Fig.\u00a012 and 13a). The responsivity at 520\u2009nm has been enhanced by five orders of magnitude to 158.8\u2009A\u2009W\u20131. This value is smaller than that of 4.8\u2009\u00d7\u2009106 \u2009A\u2009W\u20131, which is due to the relatively large test light power for spectrum test. In the near-infrared band, the photoresponse has been increased by about 7 orders of magnitude and the responsivity at 1550\u2009nm reaches 6.7\u2009\u00d7\u200910\u20132\u2009A\u2009W\u20131 (Supplementary Table\u00a03). Supplementary Fig.\u00a013b shows the variable power response of the device at 1550\u2009nm, demonstrating that the device can sense light signals with its intensity down to 96.2\u2009nW.\n\nTo further demonstrate the advantage in time response and weak light detection characteristic of the GISC-SJ APD, we assessed the 3\u2009dB bandwidth and noise equivalent power (NEP) at different biases. Figure\u00a04d displays normalized frequency response at different reverse biases when the incident light wavelength is 520\u2009nm. It can be seen that the 3\u2009dB bandwidth increase abruptly after breakdown, which is due to rf enhancement effect49. The 3\u2009dB bandwidth obtained is 121\u2009kHz when biased at \u20133\u2009V. Figure\u00a04e shows the noise power spectral density of the GISC-SJ APD with different biases as a function of frequency at 300\u2009K. It can be seen that the noise shapes of the device before and after avalanche are significantly different. The noise after the avalanche is mainly 1/f noise, which is similar to the noise currents of previously reported InSe/BP15 and Ni/WSe2/Pt APD19. The measured noise current (Inoise) at 1\u2009Hz and 50\u2009kHz are 1.9\u2009\u00d7\u200910\u20137 and 1.9\u2009\u00d7\u200910\u201310 A Hz\u20131/2, respectively, under the bias voltage of \u20133\u2009V. The NEP is calculated using: NEP\u2009=\u2009Inoise/R, where R is the responsivity of the photodetector55. The GISC-SJ APD achieves a very low NEP of 39.6\u2009fW\u2009Hz\u20131/2 at 300\u2009K, which is comparable to the NEP of the reported single-photon detectors1. Such a low NEP and low light power of 70.7 fW (Fig.\u00a04a) measured at 520\u2009nm indicates that the device can sense light signals with its intensity down to 35 photons in the count (the detailed calculation is given in Supplementary Fig.\u00a014). To further verify the excellent photoresponse performance of the GISC-SJ APD, we make a comparison of our GISC-SJ APD and other 2D materials APDs, photovoltaic (PV) and photogating (PG) detectors. Figure\u00a04f and Supplementary Table\u00a03 show the comparison of responsivity and detectivity at visible light. It can be seen that the GISC-SJ APDs display the highest responsivity and comparable detectivity.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62383-9/MediaObjects/41467_2025_62383_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62383-9/MediaObjects/41467_2025_62383_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62383-9/MediaObjects/41467_2025_62383_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62383-9/MediaObjects/41467_2025_62383_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In summary, asymmetrical Graphene/InSe/Cr SJ APD configuration is designed and fabricated. The device demonstrates bilateral Geiger\u00a0mode avalanche breakdown with different forward and reverse Vbd and M. The GISC-SJ APDs show high gain from 6.5\u2009\u00d7\u2009106 to 6.3\u2009\u00d7\u2009107 at low Vbd from 1.4\u2009V to 6.3\u2009V. The very low Vbd of 1.4\u2009V approaches its threshold limit of Eg of InSe. A positive temperature coefficient of the \u03b1, and very low ECR of 11.5\u2009kV\u2009cm-1 are found in the GISC-SJ APDs. Besides the designed asymmetric Schottky barrier configurations, these characters allow low bias trigging impact ionization and low-loss carrier multiplication. The GISC-SJ APD achieves a low dark current of 620 fA, and low NEP of 39.6 fW Hz\u20131/2, showing few photons detection ability of around 35 photons at room temperature. The design concept and operation principle of the InSe asymmetrical Schottky junctions provide a promising approach for developing weak light detectors with high gain and low loss.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The Graphene used here is mechanically exfoliated from the bulk material provided by HQ Graphene and then transferred to a highly p-doped silicon substrate with 285\u2009nm SiO2. While 2D InSe is mechanically exfoliated from bulk materials supplied by 6Carbon Technology. By the fixed-point transfer method on a self-constructed transfer platform, the InSe nanosheets are stacked on the Graphene. Electron-beam photolithography is used to depict the electrode patterns, and then the metal films Cr/Au (15/45\u2009nm) are deposited by thermal evaporation. After the lift-off process, the Cr/InSe/Graphene devices are successfully fabricated. Notably, Cr/InSe/Cr devices with various channels are prepared by transferring InSe on hBN through the same process.\n\nOptical and dark field images of the devices are characterized on an Olympus BX51 microscope. Lab Ram HR800 from HORIBA with a 532\u2009nm excitation laser and Olympus\u2009\u00d7\u2009100 objective lens performs the Raman spectrum. A cross-sectional sample containing the Graphene/InSe and Cr/InSe interfaces is prepared by a focused ion beam (FEI Helios G4 UX), and HRTEM is conducted using JEOL JEM-ARM300 to obtain the interface information. The thickness, channel lengths, and surface potential differences are measured by using AFM and SKPM on an Oxford Cypher S microscope.\n\nThe temperature-dependent measurements are conducted with a commercial Keysight B1500A connected to a Lake Shore probe station. For temperature-dependent measurements, the sample is first cooled to 100\u2009K, and the I-V curves at different temperatures were recorded after the temperature is reached and stabilized for 10\u2009min. Alternating forward and reverse biases are applied to the sample to character bilateral avalanche breakdown. An external laser source (Thorlabs LP520-SF15) is introduced to excite the devices to perform a broad spectral response (520, 830, 1310, 1550\u2009nm). The room temperature I-V curves and I-Vg curves are measured by an MS200 optoelectronic measurement system from Maita Optoelectronic Technology Co., LTD. A 520\u2009nm laser is incident on the sample area to perform variable power response. The transient response and \u22123\u2009dB bandwidth are recorded by an MS200 system using a PicoScope 4262 oscilloscope and a low-noise current preamplifier (SR570, Stanford Research Systems). The noise spectra of the APD are measured up to 50\u2009kHz using a signal analyzer (Keysight 35670\u2009A) combined with a low noise current preamplifier powered by electric batteries (SR570, Stanford Research Systems).", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The Source Data underlying the figures of this study are available with the paper. All raw data generated during the current study are available from the corresponding authors upon request. Source data are provided in this paper.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Na, N. et al. Room temperature operation of germanium-silicon single-photon avalanche diode. Nature 627, 295\u2013300 (2024).\n\nArticle\u00a0\n ADS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nChen, Y. A. et al. An integrated space-to-ground quantum communication network over 4,600\u2009kilometres. 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China\n\nDongyang Zhao,\u00a0Yan Chen,\u00a0Wei Bai,\u00a0Jianlu Wang\u00a0&\u00a0Junhao Chu\n\nKey Laboratory of Polar Materials and Devices (MOE) and Department of Electronics, East China Normal University, Shanghai, 200241, P. R. China\n\nDongyang Zhao,\u00a0Tao Hu,\u00a0Hechun Cao,\u00a0Xuefeng Zhao,\u00a0Yu Jia,\u00a0Jing Yang,\u00a0Yuanyuan Zhang\u00a0&\u00a0Xiaodong Tang\n\nState Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, No.500 Yutian Road, Shanghai, 200083, P. R. China\n\nDongyang Zhao,\u00a0Yan Chen,\u00a0Tao Hu,\u00a0Hechun Cao,\u00a0Xudong Wang,\u00a0Hong Shen,\u00a0Jianlu Wang\u00a0&\u00a0Junhao Chu\n\nShanghai Jian Qiao University, No.1111, Hucheng Ring Road, Shanghai, 201306, P. R. China\n\nWei Bai\n\nFrontier Institute of Chip and System, Fudan University, Shanghai, 200433, P. R. China\n\nJianlu Wang\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nD.Z. and Y.C.\u00a0contributed equally to the work. Y.C., X.W. and\u00a0W.B. conceived and supervised the research. D.Z., T.H., H.C., X.Z., and Y.J. performed the device characterizations. X.W., H.S., J.Y., Y.Z., X.T., J.W. and J.C. advised on the experiments and data analysis. X.W., Y.C., and J.W. provided experimental testing support. W.B. was responsible for project planning. D.Z. and Y.C. analyzed the data and drafted the manuscript. X.W., Y.C., and W.B. revised the manuscript. All authors discussed the results.\n\nCorrespondence to\n Yan Chen, Xudong Wang or Wei Bai.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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Bilateral Geiger mode avalanche in InSe Schottky photodiodes.\n Nat Commun 16, 7859 (2025). https://doi.org/10.1038/s41467-025-62383-9\n\nDownload citation\n\nReceived: 25 November 2024\n\nAccepted: 18 July 2025\n\nPublished: 23 August 2025\n\nVersion of record: 23 August 2025\n\nDOI: https://doi.org/10.1038/s41467-025-62383-9\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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the variability of thermal runaway behavior of Li-ion cells with little to no calorimetry data", + "journal": "Nature Communications", + "published": "27 September 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52653-3/MediaObjects/41467_2024_52653_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52653-3/MediaObjects/41467_2024_52653_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52653-3/MediaObjects/41467_2024_52653_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.nrel.gov/transportation/battery-failure.html", + "/articles/s41467-024-52653-3#ref-CR31", + "/articles/s41467-024-52653-3#Sec12" + ], + "code": [ + "https://github.com/NREL/battery-heat-output", + "/articles/s41467-024-52653-3#ref-CR31" + ], + "subject": [ + "Batteries", + "Chemical engineering", + "Scientific data" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3937313/v1.pdf?c=1727521748000", + "research_square_link": "https://www.researchsquare.com//article/rs-3937313/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-52653-3.pdf", + "preprint_posted": "11 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Accurate measurement of the variability of thermal runaway behavior of lithium-ion cells is critical for designing safe battery systems. However, experimentally determining such variability is challenging, expensive, and time-consuming. Here, we utilize a transfer learning approach to accurately estimate the variability of heat output during thermal runaway using only ejected mass measurements and cell metadata, leveraging 139 calorimetry measurements on commercial lithium-ion cells available from the open-access Battery Failure Databank. We show that the distribution of heat output, including outliers, can be predicted accurately and with high confidence for new cell types using just 0 to 5 calorimetry measurements by leveraging behaviors learned from the Battery Failure Databank. Fractional heat ejection from the positive vent, cell body, and negative vent are also accurately predicted. We demonstrate that by using low cost and fast measurements, we can predict the variability in thermal behaviors of cells, thus accelerating critical safety characterization efforts.Physical sciences/Engineering/Chemical engineeringPhysical sciences/Energy science and technology/Energy storage/BatteriesPhysical sciences/Engineering/Mechanical engineering", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.\nSupplementary Data is not available with this version.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Accurate measurement of the variability of thermal runaway behavior of lithium-ion cells is critical for designing safe battery systems. However, experimentally determining such variability is challenging, expensive, and time-consuming. Here, we utilize a transfer learning approach to accurately estimate the variability of heat output during thermal runaway using only ejected mass measurements and cell metadata, leveraging 139 calorimetry measurements on commercial lithium-ion cells available from the open-access Battery Failure Databank. We show that the distribution of heat output, including outliers, can be predicted accurately and with high confidence for new cell types using just 0 to 5 calorimetry measurements by leveraging behaviors learned from the Battery Failure Databank. Fractional heat ejection from the positive vent, cell body, and negative vent are also accurately predicted. We demonstrate that by using low cost and fast measurements, we can predict the variability in thermal behaviors of cells, thus accelerating critical safety characterization efforts.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Lithium ion (Li-ion) batteries have helped make many modern inventions practical, from electric vehicles, portable electronics, to reliably powered spacesuits1. It is vital that Li-ion batteries and the devices that use them are safe. However, there is always some risk that a cell will undergo thermal runaway (TR) due to challenging operating or environmental conditions, or defects that cause short circuits2. Additionally, if one cell in a pack undergoes TR, there is also the risk of TR propagation to neighboring cells that can result in disastrous outcomes1,3. Propagation occurs when heat generated by one cell undergoing TR heats neighboring cells, causing TR to spread. Thus it is critical to understand the heat output of Li-ion cells during TR to enable design of safe battery energy storage systems.\n\nThe heat output by a Li-ion cell greatly varies between tests and can be influenced by factors such as cell properties, cycle histories, and abuse test conditions1,2,4,5,6. However, even when identical cells are evaluated under identical abuse test conditions, considerable variability in the thermal runaway behaviors is still observed7,8. When considering cylindrical Li-ion cells, the heat ejected from the positive end, the negative end, and the cell body are each important to quantify to design safe, thermal runaway propagation resistant battery systems9,10,11. The fractional breakdown of heat ejected and not ejected (remains in cell casing) is referred to as the fractional heat output, i.e., the fraction of the total heat output that is attributed to ejected material from either end of the cell or emitted from the cell casing itself. These measurements can be recorded using a fractional thermal runaway calorimeter (FTRC), but this equipment is not widely available, and each experiment is time-consuming5. Additionally, heat output is variable such that two identical cells undergoing TR in identical environments will have varying heat outputs and behaviors depending on what happens inside the cell, resulting in a distribution of heat outputs from a cell type and possibly outliers that may present rare but hazardous failure modes4. To account for this variance, numerous abuse tests and thermal runaway measurements are necessary to understand the range of failure scenarios and thermal behaviors that occur, which is an expensive and time-consuming process12. The expense and challenge of characterizing the distribution of behaviors has been highlighted by researchers at Volkswagen and Ford12,13, and considerably delays adoption of new cell types for use in electric vehicles. Therefore, a method to predict the distribution of heat output during TR that is time- and cost-effective would be tremendously useful to accelerate safety evaluations of new cell types.\n\nWhile there are computational models simulating TR14, physics-based models require detailed knowledge of the cell chemistry and other cell properties that may be proprietary. They are also deterministic and output a single result rather than conveying the real-world complex distribution of occurrences. A data driven approach such as machine learning (ML) may be used to predict the stochastic thermal response of cells but requires empirical data to train. ML models have been commonly utilized in the battery field to shortcut the need for a physical model when making complex predictions, such as predicting internal short circuits15, state of charge monitoring16,17, health diagnosis18,19, future health prediction20,21,22. Several works have also utilized ML to improve battery design23,24. However, ML methods need plentiful, high quality, and robust data for training. Such data on thermal behaviors of Li-ion cells during thermal runaway has not been openly available until the Battery Failure Databank25 was released by the National Renewable Energy Laboratory (NREL) and National Aeronautics and Space Administration (NASA), which presents data from hundreds of FTRC tests providing information on total heat output, the fraction of heat ejected from cells, the mass ejected from cells, all of which will be used in this work. The Battery Failure Databank also hosts many high-speed synchrotron radiography videos of the thermal runaway processes which will not be used in this work. The Battery Failure Databank25 is the largest public database containing information about batteries undergoing TR; it contains test results on batteries from various manufacturers and twenty-two battery types, totaling to over 350 trials as of November 2023, and continues to expand.\n\nHere, we focus on the use of experimental data that are simple to measure such as mass ejection, and show that these data can be used to predict the complex thermal behaviors that are measured using sophisticated calorimetry techniques like FTRC. Measuring the ejected mass of cells is simple; complex equipment is not required and the throughput of testing can be high. We develop an ML model for predicting the variable fractional heat output of cells undergoing TR using only ejected mass data and the specifications of the cell provided by the manufacturer, avoiding the need for any detailed physical or electrochemical properties, or sophisticated calorimetry techniques. The predictive models were developed using a subset of data found in the Battery Failure Databank26, which for the first time facilitated a robust experimental dataset for training ML models on thermal runaway behavior. The performance of ML models is studied by cross-validation, training on data from all cell types but one, and testing on data from unseen cell types. Zero-shot predictions are made assuming that no heat output measurements have been recorded, only ejected mass measurements, and allow for qualitative estimation of the heat output from any given cell type without any calorimetry data. One-, two-, \u2026, i-shot predictions, where i FTRC measurements have been performed, enable quantitatively accurate predictions of the mean and variance of both total and fractional heat output with just 0-5 FTRC measurements for most cell types, overcoming previous limitations of models and simulations in predicting single outcomes without capturing the real-world distribution of behaviors. The ability to predict the distribution of the heat output for any given cell type from multiple ejected mass tests and only a small number of heat output measurements is expected to empower energy storage system designers to accurately and rapidly estimate the safety risks of new cylindrical cell Li-ion batteries with minimal expense and effort.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Data collection occurred over multiple years and is reported in the Battery Failure Databank from NASA and NREL25,26, provided in the\u00a0supplementary data. General trends from the\u00a0data set have been previously reported1,2,4. Since absolute values of total heat output are related to charge capacity, mass, and cell size, analysis of heat output and ejected mass is simplified by normalizing heat output by charge capacity and ejected mass by total mass5. Figure\u00a01 shows the distributions of ejected mass fraction, normalized total heat output, and fractional normalized heat outputs (positive, cell body, and negative terminal heat outputs) during TR for each of 8 cell types used for model training in this work. Only commercially-produced 18650 or 21700 format cells tested at 100% state-of-charge with more than 10 samples were considered in this work. Additional data from cells at 100% SOC but with fewer than 10 measurements, at lower SOCs, or after modifying the cell are used as a secondary test set for testing model performance. For both ejected mass fraction and total heat output, the median and variance of values across the cell types varies quite a bit, with a general trend of low ejected mass correlating to low total heat output, however, large variance of the ejected mass fraction does not necessarily correspond to large variance of the heat output (KULR 18650-K330). Of the fractional heat output measurements, the cell body heat output is the most consistent across the cell types, while both the mean and variance of the positive and negative heat outputs varies widely. Note that two cell types (LG 18650-MJ1 and Sanyo 18650-A) were observed to have zero or near-zero negative heat output, while other cell types (KULR 18650-K330) have up to 50% of their total heat output measured on the negative side.\n\nDistributions of (a) total ejected mass fraction, (b) total normalized heat output, and fractional normalized heat outputs from (c) positive, (d) cell body, and (e) negative cell faces during TR for 8 of the 22 cell types from the Battery Failure Databank26. The number above the distributions in (a) indicates the number of samples available in the data set. All violin plots have an associated box-and-whisker diagram in their centers.\n\nAs noted, there is a general correlation observed between the total ejected mass fraction and total normalized heat output, prompting this investigation to study whether heat output may be predicted simply through measurements of ejected mass, given the existing FTRC data reported in the Battery Failure Databank. Figure\u00a02a shows a linear regression of total normalized heat output from ejected mass fraction, indicating substantial noise but an overall strong correlation. However, when observing the trend for each cell type independently, as in Fig.\u00a02b, the data set as a whole has a stronger relationship between ejected mass and heat output than for any cell independently, and substantial variance in the strength of the correlation begins to appear. For example, LG 21700-M50 cells have a positive slope and relative narrow confidence intervals, indicating a strong correlation between ejected mass and heat output, while the LG 18650-MJ1 cells show zero slope with wide confidence intervals, indicating a near zero correlation. This dependency of the FTRC results on the cell type thus requires treating cell types as independent from one another, however, the global trend suggests that it should be feasible to make reasonable heat output predictions for new cell types with a very small number of FTRC measurements.\n\na Linear regression of total normalized heat output from total ejected mass fraction with 95% confidence interval of the entire data set conveyed as a semi-transparent shading. b Local linear regressions for each cell type.\n\nTo train a ML model to predict the normalized fractional heat output of batteries undergoing TR, The data shown in Fig.\u00a01 was carried through the processing steps shown in Fig.\u00a03. No filtering to remove outliers was performed, as high heat outputs or high ejected mass fractions are critical to keep in the data set, as they are representative of worst-case failure events. Predicted values were normalized cell body, negative, positive, and total heat output using a chain regression, as the total heat output is a combination of all the fractional heat outputs. The chain regressor was also found to result in lower error than treating each heat output independently. Features used to make predictions included cell metadata, FTRC experiment notes, and mass data after TR, in both absolute and normalized values. For evaluating the performance of models on predicting data from new cell types, data was split into training and test sets where all data from one cell type was held out for testing, and all other cells used for training; predictions for the test cell use zero measured heat output values, i.e., \u2018zero-shot\u2019 models. The impact of conducting additional FTRC measurements on new cell types for calibrating heat output predictions was investigated using \u2018i-shot\u2019 models, where i samples from the test cell type were added to the training set. However, this introduces a sampling bias into the model training process. For example, there are 22 possible 1-shot models for the KULR 18650-K330 cell type which can have varying effects on the model, depending on which specific FTRC trial is added to the training set. Three model architectures were considered: a baseline model, a support vector machine (SVM) model, and a gradient-boosted tree model, XGBoost27. The baseline model is a linear regression model using only ejected/retained mass features, using a global regression on all training cells to make zero-shot predictions and a local linear regression on the i samples for each i-shot prediction, exactly as shown in Fig.\u00a02. Comparison of the baseline, SVM, and XGBoost models thus demonstrates the benefit of using prior FTRC measurements as well as cell metadata for making zero-shot and i-shot predictions of heat output during TR events, as opposed to treating each cell type as an independent experiment.\n\nThe data treatment process including pre-processing, model development, and model evaluation.\n\nFigure\u00a04a\u2013h compares the RMSE between baseline, SVM, and XGBoost models for each cell. Zero-shot SVM models perform better than baseline for 4 of 8 cell types, comparably to baseline for 2 of 8 cell types, and worse than baseline for only 2 of the 8 cell types. At i\u00a0=\u00a01 to i\u00a0=\u00a05, the benefit of the SVM model becomes clear: with only a few FTRC measurements, the SVM model estimates are near converged with those made using all of the data. Cells with the worst i\u00a0=\u00a00 predictions show the largest improvements at i\u00a0=\u00a01, demonstrating that heat output predictions for \u2018outlier\u2019 cell types can be calibrated using a single FTRC measurement. SVM confidence intervals rapidly narrow, demonstrating that reasonable heat output predictions can be made for a population of cells while being tolerant to whichever subset of that population is used for measuring fractional heat output in the FTRC. In comparison, the baseline model error is dramatically higher, with large confidence intervals, because treating the TR heat output from each cell type independently means that making any estimate of TR heat output requires a significant number of samples, and the estimated heat outputs for the entire population of that cell type vary wildly depending on which samples are characterized using the FTRC. Performance of the SVM model does vary across cell types, with some cells showing an RMSE of less than 1kJ/Ah at i\u00a0=\u00a00 (KULR 18650-K330), or 5\u0303% error compared to the average total normalized heat output for the data set of about 20kJ/Ah, while others barely converge below an RMSE of 2kJ/Ah even when trained on all samples (LG 18650-MJ1, Sanyo 18650-A, Sony 18650-VC7). The convergence of baseline and SVM models to similar error at high numbers of training samples suggest that the SVM model is well fit, i.e., not overfit, as an overfit model would begin to report high error on test data as more samples are added to the training data. Comparing the two machine-learning models, SVM and XGBoost, SVM appears to be more accurate and have lower uncertainty across most cell types, and all further results will report predictions from or analysis of SVM models. Similar performance for the SVM model trained on all data is seen on a secondary test set, which is comprised of cells with fewer than 10 measurements or cells that were modified prior to the FTRC measurement, demonstrating that the set of cells used for training extrapolates usefully to new data (Supplementary Fig.\u00a01).\n\na\u2013h RMSE for the total predicted heat outputs for i\u00a0=\u00a00 to i\u00a0=\u00a0n for each cell: (a) KULR 18650-K330, (b) KULR 21700-K500, (c) LG 18650-MJ1, (d) LG 18650 (BV-220) (e) LG 18650 (BV-250), (f) LG 21700-M50, (g) Sanyo 18650-A, and (h) Sony 18650-VC7. For baseline (black), SVM (blue), and XGBoost (green) models, median RMSE for each i is denoted using a line, while 1\u03c3 and 2\u03c3 confidence intervals are shown using the dark and light shaded regions. i\u2013l RMSE of SVM models for i\u00a0=\u00a00 to i\u00a0=\u00a0n for each cell with 2\u03c3 confidence intervals: (i) normalized total heat output, (j) normalized positive heat output, (k) normalized cell body heat output, (l) normalized negative heat output.\n\nFigure\u00a04i\u2013l compares SVM model RMSEs between normalized total, positive, cell body, and negative heat outputs for each cell type. As noted previously, the worst total heat output RMSE at i\u00a0=\u00a0n (LG 18650-MJ1, 2.2kJ/Ah) is about twice that of the best cell (KULR 18650-K330, 1kJ/Ah). This trend is shared for the positive (1\u22122kJ/Ah) and cell body (0.5\u22121kJ/Ah) heat outputs, however, the RMSEs for the negative heat outputs vary by more than 10 times (about 0.2\u22122kJ/Ah), suggesting that negative heat output does not have a consistent relationship to ejected mass during TR for most cell types. Cell body heat output is predicted with the highest accuracy and with high confidence even at i\u00a0=\u00a01, as was expected from analysis of Fig.\u00a01d because cell body heat output distributions show the least variability across the cell types, that is, it is the easiest heat output to learn accurately from FTRC measurements conducted on other cells.\n\nThe predicted versus actual distributions of the normalized total heat output are shown in Fig.\u00a05 for i\u00a0=\u00a0[0,\u00a01,\u00a03,\u00a05]. As with the RMSE results in Fig.\u00a04, the distributions of half the cell types are accurately predicted at i\u00a0=\u00a00. Of those distributions that are not being well predicted, the zero-shot model predictions show two consistent traits: being biased towards the global mean of the data set, and having much lower predicted variance than the actual variance. At i\u00a0=\u00a01, the impact of the selected sample for training can be clearly seen as the means of the predicted distributions vary substantially, especially for cell types with higher or lower than average total heat outputs. As i increases, approaching the total number of samples n, the means of the predicted distributions converge towards that of the actual distribution, However, for those cells that are \u2018harder\u2019 to predict (LG 18650-MJ1, LG 18650 (BV-250), Sanyo 18650-A), the variability of the heat output shown by the actual distribution is never learned. As noted when describing Fig.\u00a02b, certain cell types have no statistically significant relationship between total ejected mass and total heat output during TR (LG 18650-MJ1), so accurately predicting the actual heat output distribution from ejected mass data is impossible, even with a better performing model.\n\nActual and SVM predicted distributions for (a) i\u00a0=\u00a00, (b) i\u00a0=\u00a01, (c) i\u00a0=\u00a03, and (d) i\u00a0=\u00a05 models. For i\u00a0\u2009>\u20090, a maximum of 20 random results from the possible \\((\\begin{array}{c}n\\\\ i\\end{array})\\) training sets are shown for visual clarity.\n\nPredicting heat output from cells sent into thermal runaway at varying SOCs was tested using available data. Both heat and mass output are strongly correlated with SOC, with higher SOCs resulting in more extreme thermal runaway events, as would be expected. While a model trained on only 100% SOC does not extrapolate well to low SOCs, including even a small amount of low SOC data in the model training results in accurate extrapolations to new cell types. See the\u00a0Supplementary Information and Supplementary Fig.\u00a04 for more details.\n\nOverall, it has been demonstrated that ejected mass values and cell metadata can be used by ML models to accurately predict the average total and fractional heat output. However, while the mean of the distributions was predicted quite well, it is important to also get an initial estimate of outlier scenarios, i.e., the infrequent cells that undergo thermal runaway with anomalously high heat output.\n\nOutliers of most interest are the infrequent cells that produce anomalously high heat during thermal runaway. The high heat outliers are important to capture to guide design of battery systems that are resistant to thermal runaway propagation. As found in previous work by the authors, the thermal behavior of cells is strongly influenced by not only how much mass is ejected but when that mass is ejected during the thermal runaway process4, i.e., how hot the mass is during thermal runaway. This complex dependence can lead to some failure scenarios being considerably more hazardous than others, and predicting the distribution of responses needs to include infrequent outlier behaviors. It has already been observed that some cells have higher than average error (LG 18650-MJ1, Fig.\u00a04i) or have predicted distributions with obviously lower variance than the actual distribution (Sanyo 18650-A, Fig.\u00a05). Here, we use Kullback-Leibler (KL) divergence to quantify the similarity between the predicted and actual heat output distributions, and utilize Mahalanobis plots to determine some of the sources of error.\n\nFigure\u00a06 shows the KL divergence for the normalized total heat output predictions for each cell. As opposed to RMSE, the KL divergence more clearly shows which cell types have accurately predicted distributions, rather than just accurately predicted means, as the best and worst KL divergence differ by 4 times versus the 2 times of RMSEs, additionally, the worst KL divergence and worst RMSE are from different cells (Sanyo 18650-A and LG 18650-MJ1, respectively), prompting further investigation of both. The KL divergence also shows continual learning for all cells with more data, obviously decreasing in value and confidence interval for most cell types even up to i\u00a0=\u00a025, while the RMSE values plateau at i\u00a0=\u00a05 to i\u00a0=\u00a010. Similar behavior is seen on cells from the secondary test set (Supplementary Fig.\u00a02).\n\nKL divergence for i\u00a0=\u00a00 to i\u00a0=\u00a0n for each cell with 2\u03c3 confidence intervals conveyed as shaded regions.\n\nThe Mahalanobis plots shown in Fig.\u00a07 demonstrate model behavior on two example cells, one where increasing i values results in continual improvement (LG 21700-M50, Fig.\u00a07a\u2013c), and one where increasing i values shows little change after i\u00a0=\u00a01 (Sanyo 18650-A, Fig.\u00a07d\u2013f). When examining Mahalanobis plots, consider that a narrow ellipsoid corresponds to a strong correlation between the two variables, while a more circular ellipse corresponds to no correlation, i.e., two independent random distributions. For the LG 21700-M50 cell, it is clear that the actual distribution shows a correlation between total heat output and total ejected mass, though the zero-shot case is not able to perfectly capture this correlation, with both the mean and variance of the heat output predicted poorly. At i\u00a0=\u00a03, the mean heat output is predicted correctly with slightly improved variability, and by i\u00a0=\u00a010 both the mean and variance of the heat output are near correct; as expected, since 10 of the 18 samples shown were used for model training. In comparison, the actual Sanyo 18650-A cell data shows little correlation between total heat output and total ejected mass, so the model can only learn the mean heat output, and has no input features that enable it to accurately predict the variability of the observed heat output even when training on 10 of the 12 samples. The SVM model, which is forced to learn a relationship between ejected mass and heat output, always predicts a distribution with a strong correlation between ejected mass and heat output, especially for cells like the Sanyo 18650-A, which has a small range of ejected mass values relative to the rest of the data set.\n\nMahalanobis plots for the (a\u2013c) LG 21700-M50 and (d\u2013f) Sanyo 18650-A cells for (a, d) i\u00a0=\u00a00, (b, e) i\u00a0=\u00a03, and (c, f) i\u00a0=\u00a010. Blue shading marks the area of actual points with the highest mahalanobis depth. Red shading denotes the same for the predicted points. Blue and red ellipses, for actual and predicted distributions respectively, cover 66% of the distribution. For i\u00a0=\u00a03 and i\u00a0=\u00a010 plots, only one random set from the \\((\\begin{array}{c}n\\\\ i\\end{array})\\) possible sample sets is shown for clarity.\n\nDespite some cells showing near zero correlation between total ejected mass and total heat output, the fractional ejected mass and heat output values may still show strong correlations, enabling mass ejected data to be used to predict the variance of the fractional heat outputs accurately, even if the variance of the total heat outputs unlearnable. Figure\u00a08 shows Mahalanobis plots for total and fractional mass/heat for the LG 18650-MJ1 cell at i\u00a0=\u00a00 and i\u00a0=\u00a03. The ellipse for the actual distribution of total ejected mass and total heat output (Fig.\u00a08a,e) is nearly a perfect circle, showing near zero correlation. However, the fractional ejected mass and heat output values are all either partially or strongly correlated, with very narrow ellipses for the cell body and negative fractional values. Thus, as i increases, even as the total heat output prediction does not substantially improve, the fraction heat output predictions are able to learn both the mean and variance of the actual distribution.\n\nPredicated and actual heat outputs and masses ejected for (a\u2013d) i\u00a0=\u00a00 and (e\u2013h) i\u00a0=\u00a03: (a, e) total, (b, f) positive, (c, g) cell body, and (d, h) negative. For i\u00a0=\u00a03 plots only one random set from the \\((\\begin{array}{c}n\\\\ i\\end{array})\\) possible sample sets is shown for clarity.Blue shading marks the area of actual points with the highest mahalanobis depth. Red shading denotes the same for the predicted points.\n\nThe relative impact of each feature on model predictions is quantified using Shapley additive explanations (SHAP)28. Figure\u00a09 reports the mean absolute SHAP value for each input feature and each regression model. Cell type has a modest impact on regression outputs, with the exception of the LG 18650-MJ1 type, which has a strong impact on the cell body and total heat output predictions. This cell type is the only cell type in this data set that contains a graphite-silicon composite electrode, which has higher specific energy density than graphite, thus resulting in a higher heat output. None of the cell types have much influence at all on the negative heat output, suggesting that the negative ejected mass fraction and heat output are not strongly related to how manufacturers design the cell casings. Of the cell design details and trigger mechanisms, the presence of a top vent (positive side) and the cell capacity have the largest impacts, with the presence of a top vent most strongly impacting positive heat output and total heat output. The presence of a bottom vent has almost zero impact on model predictions; this may be simply because this feature is redundant with the \u2019Bottom Vent (BV) actuated\u2019 feature, as the bottom vent can only actuate if it isn\u2019t there, and if it doesn\u2019t actuate, it\u2019s as if the bottom vent wasn\u2019t actually there. The experimentally measured values, that is, the ejected mass quantities, are the most important features for model prediction. Both absolute (raw) and relative (calculated) values are important, with the post-test cell-body mass and the negative ejected mass fraction having the highest impacts. The impact of these features on model predictions is intuitive, with the post-test cell-body mass being the most important feature for the total heat output, while negative ejected mass fraction is the most important for the negative heat output. Force plots for each feature and regression model, shown in Supplementary Fig.\u00a03, give more insight into the relationship between features and model outputs. For instance, low post-test cell-body mass is associated with high total heat output, as high amounts of ejected mass is correlated with both low post-test cell-body mass as well as heat generation. Overall, the SHAP analysis demonstrates that the regression models are influenced sensibly by the features, giving confidence that heat output can be predicted using ejected mass and cell details.\n\nTotal heat output (blue), positive heat output (pink), cell body heat output (purple), and negative heat output (grey). A high SHAP value corresponds to a large impact on the model output. Features ordered into categories to ease interpretation.\n\nLike most machine learning approaches on predicting battery behaviors in literature to-date, the technique presented here is limited to the specific set of training data available. The tests used for training mostly contained similar cathodes (varying stoichiometries of LiNiMnCoO2 and LiNiCoAlO2) and similar anodes (graphite with some cells expected to include a small mass fraction of SiOx). Therefore, this training set and model, in its current form, should only be applied to similar cell types and test conditions, for example, see results on the secondary test set cells in Figures\u00a0S1 and S2. Diverging from these conditions, like to cells with LiFePO4 cathodes or pure Si/SiOx anodes, cells at the end of cycle lives with complex histories, cells at lower states of charge, and cells undergoing different abuse conditions such as overcharge or nail penetration, is not recommended. As the Battery Failure Databank continues to expand over the coming years, additional chemistries, states of charge, and cycle histories may be added, which may give further confidence for predicting behaviors in new realms. Other research groups are encouraged to make their thermal runaway test data open-access, in the hope that collectively, a robust global resource of high-quality data becomes available to strengthen this predictive approach.\n\nIn its current form, this approach is anticipated to find many applications. There is an abundance of 18650 and 21700 cell models to choose from on the market, many of which have similar chemistries to the cells evaluated here but with varying power- and energy-densities due to differences in engineering and minor changes in composition. The approach provided here can be used to accurately estimate the distributions of total and fractional heat outputs of such cells by leveraging a large number of cheap and rapid measurements of ejected mass; for example, 5 FTRC measurements could be used to establish the relationship between ejected mass and heat output for a given cell type, and then tens to hundreds of ejected mass measurements could be used to with the machine-learned model to estimate the variability of heat output from thermal runaway events. This can give a guideline for benchmarking cells with regard to their thermal behaviors and therefore quickly assess the suitability of cells for specific applications or for investigating the impact of design changes on cell safety. For example, the secondary test set contains measurements on LG 18650 M36 cells with modified cell casing thickness and with or without a bottom vent, and the SVM model is able to capture the variance and magnitude of heat output from each cell type with low uncertainty with 5 or fewer FTRC measurements. However, while quick and easy predicted estimations are valuable, this approach should not replace conducting actual experiments and measuring the real distributions of behaviors. The results shown here demonstrate that about 5 FTRC measurements are necessary to be confident that the ejected mass data will lead to accurate estimations of the distribution of heat outputs. For cell types where there is no statistical relationship between ejected mass and heat output after 5 FTRC measurements, the most effective method to determine heat output variability is simply to perform as many more FTRC measurements as possible. For example, several of the cells in the secondary test set show no improvement in KL-divergence when adding FTRC measurements to the model training (i\u00a0=\u00a0n models), suggesting the heat output from these cells cannot be estimated using ejected mass, and instead would require further FTRC measurements to confidence predict heat output variability.\n\nFuture work in battery safety will aim to extend the FTRC technique to other cell formats, like prismatic or pouch cells and, potentially, the approach shown here to estimate heat output from ejected mass could be extended to those new formats as well; given the safety risk and costs associated with destructive safety testing of larger cells, this would dramatically reduce costs of safety testing for large-format lithium-ion batteries. This approach should be combined with other types of battery safety tests, such as utilizing accelerating rate calorimetry to determine thermal runaway onset temperatures, to make quantitative safety maps of commercial lithium-ion batteries so that system engineers may better design zero-propagation battery packs for a variety of applications. The usefulness of the model demonstrated in this manuscript for estimating heat output of 18650 or 21700 format lithium-ion batteries should only improve as more FTRC data is collected and added to the Battery Failure Databank.\n\nIn summary, this work demonstrates that the distribution of fractional heat output during thermal runaway can be accurately predicted for many commercial 18650 and 21700 format lithium-ion batteries by leveraging a small number of fractional thermal runaway calorimetry (FTRC) measurements, supplemented by a large number of cheap and rapid measurements of ejected mass. Analysis of the performance of an SVM model, tested on 139 FTRC measurements from 8 different cell types, demonstrates that the average heat output of most cell types can be predicted with only a single FTRC measurement with an error of less than 2kJ/Ah (about 10% error for the cells investigated here), and that only 5 FTRC measurements are required to confidently estimate the distribution of heat outputs including the occurrence of anomalously high heat outputs. This is achieved by using ML to estimate heat output from 10-30 ejected mass measurements. Adding further ejected mass measurements than available here would simply increase confidence that outlier values of the heat output distribution for any cell are adequately predicted. Additionally, the ML approach is able to distinguish cells with highly variable heat output with only 5 FTRC measurements, flagging out cells with highly variable failure from those with more predictable failures and prompting investment in further calorimetry measurements for \u2018unruly\u2019 cell types. Further testing on a secondary test set of cells with fewer than 10 measurements or with structural modifications demonstrates good performance of the trained SVM model when applied to new data, as well as demonstrating how the machine-learned model can be used to identify cells where ejected mass and heat output are not correlated, and thus additional FTRC measurements would be required to improve estimates of the heat output variability.\n\nImplementing data driven methods could vastly reduce the amount of resources required for battery safety testing by predicting an initial distribution of heat output using few experimental calorimetry trials. However, the prediction approached used here is not recommended to replace experiments and instead only used as a preliminary estimating tool to evaluate the suitability of cells for specific applications. The current training data only covers pristine cells at full state of charge with a limited range of electrode and electrolyte chemistries; while the Battery Failure Databank continues to expand to include new cell chemistries, variable states of charge, and variable cycle histories, this approach can help improve confidence in the safety and reliability of lithium-ion batteries, by providing a method to accelerate the acquisition of critical data for designing safe lithium-ion battery systems.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52653-3/MediaObjects/41467_2024_52653_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52653-3/MediaObjects/41467_2024_52653_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52653-3/MediaObjects/41467_2024_52653_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52653-3/MediaObjects/41467_2024_52653_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52653-3/MediaObjects/41467_2024_52653_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52653-3/MediaObjects/41467_2024_52653_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52653-3/MediaObjects/41467_2024_52653_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52653-3/MediaObjects/41467_2024_52653_Fig8_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52653-3/MediaObjects/41467_2024_52653_Fig9_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Experiments measuring the thermal runaway outcomes using a Fractional Thermal Runaway Calorimeter (FTRC)5,8 were taken over many years and collected into the Battery Failure Databank25. Hundreds of thermal runaway events from various cell designs were measured by recording the total heat output, heat ejected from the positive end of the cell, heat ejected from the negative end, heat from the cell casing, as well as mass ejection from the cells. Most FTRC measurements were conducted at synchrotron facilities that facilitated simultaneous high-speed X-ray radiography to visualize internal occurrences during thermal runaway, but these data are not used in this work. The version of the Battery Failure Databank used here was V2 that was available open-access on November 2023. Additions to the Databank are expected to occur periodically over the following years.\n\nA subset of the databank was used to maintain a balanced dataset for training models. Custom cells in the Battery Failure Databank, such as those with Soteria materials or test-cells with embedded Internal Short Circuiting Devices (ISCDs)1, were excluded from the training set. Only cells with more than 10 samples were used. Only trials with commercial cells at 100% state of charge were included.\n\nThe total and fractional heat outputs were normalized by dividing by the discharge capacity of the cell (kJ/Ah). Prior exploration of trends in the Battery Failure Databank1,2,4,5 revealed that normalizing heat output by capacity resulted in clear relationships between the heat output and other measured quantities such as the ejected mass fractions.\n\nRegression models predicted normalized total, positive, cell body, and negative heat outputs using a chain regression approach (predicted values from prior regressions are used as features for further regressions), on the basis that the total heat output is the sum of the fractional heat outputs. The chain regression was done in the order (cell body, negative, positive, total), as the cell body heat output was expected to be the easiest to predict, avoiding the accumulation of errors. It was found that model performance was not very sensitive to chain order. A risk of the chain regression approach is that errors will accumulate when making test predictions, but separate models treating each heat output as independent resulted in marginally worse performance across all cells. Regression targets were all z-score normalized prior on the training set.\n\nFeatures for making predictions included cell metadata (cell type, manufacturer, format, charge capacity, TR trigger mechanism), FTRC experiment notes (cell failure mechanism, bottom vent actuation), and mass data after TR, in both absolute and normalized values (total ejected mass, positive ejected mass, negative ejected mass, cell body remaining mass, unaccounted mass, i.e., initial mass minus all measured masses after TR). The numerical features were all z-score normalized on the training set. Categorical features such as cell manufacturer were one-hot-encoded, i.e. converted to a form that can be interpreted by ML models by treating each category option as an independent variable with 1 or 0 binary values.\n\n\u2018Zero-shot\u2019 models performed predictions on test data from one held-out cell type at a time, making heat output predictions with no measured heat outputs from that cell. \u2018i-shot\u2019 models made predictions for held-out cell types after copying i samples from the test data to the training data. But, this introduces model bias due to the selected samples, for instance, a cell type with 30 FTRC measurements will have 30 separate \u20181-shot\u2019 models, each with their own predictions and error statistics. However, it is not feasible to sample every possible combination of samples as there are \\(\\left(\\begin{array}{c}n\\\\ i\\end{array}\\right)\\) possible combinations of i cells from n total samples. So, i-shot models were either trained on 300 randomly sampled sets of sample combinations for \\(\\left(\\begin{array}{c}n\\\\ i\\end{array}\\right)\\) greater than 300, or all combinations for \\(\\left(\\begin{array}{c}n\\\\ i\\end{array}\\right)\\) less than or equal to 300, and error metrics like root-mean-square-error (RMSE) or Kullback-Leibler (KL) divergence are reported as distributions for each i. Figure\u00a03 shows our process and evaluation.\n\nTwo model architectures were used, a baseline model and a Support Vector Machine (SVM) model with a linear kernel, squared L2 norm weight of 1, and an epsilon of 0.1. The baseline model used just the mass features, performing a global linear regression for zero-shot predictions and a local linear regression on just the i samples copied from the held-out cell type for i-shot predictions. A SVM was chosen as the ML architecture, as it works well for small data sets and optimizes for the a hyperplane where the margin between the points closest to the decision boundary are maximized29, which was hoped to result in accurate predictions of the heat output distribution. An XGBoost architecture was also tried, and resulted in slightly worse performance across all cell-types.\n\nThe model predictions were further evaluated by calculating the Mahalanobis depth for the predicted and actual values. The predicted and actual heat outputs were plotted against the ejected masses. The Mahalanobis depth is a statistical depth function that accounts for the distance one point is from all other points in a distribution considering the covariance of the data. The covariance matrix \\(\\widehat{\\mathop{\\sum }_{x}^{-1}}\\) describes how close a point is from every other point in the data set.\n\nA point with higher depth is more similar to other points in the distribution. The various depths may then be plotted using ellipses. The ellipse borders are cutoffs, where an ellipse contains some density of points.\n\nThe ML models used in this work were developed in Python using the sklearn library30.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data used in this study is available open-access in the Battery Failure Databank: https://www.nrel.gov/transportation/battery-failure.html. Given that there may be future updates to the Battery Failure Databank, the version used in this work is also available from Zenodo31.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code used in this work is available open-access at https://github.com/NREL/battery-heat-outputand Zenodo31.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Finegan, D. P. et al. 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We would like to express our appreciation to Eric Darcy, Jacob Darst, David Petrushenko, and Will Walker at NASA Johnson Space Center and Paul Shearing at University College London for helping build the Battery Failure Databank that facilitated this work.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Materials Science and Engineering, Stanford University, Stanford, CA, 94305, USA\n\nKarina Masalkovait\u0117\n\nNational Renewable Energy Laboratory (NREL), 15014 Denver W Pkwy, Golden, CO, 80401, USA\n\nKarina Masalkovait\u0117,\u00a0Paul Gasper\u00a0&\u00a0Donal P. Finegan\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nK.M. performed the data analysis, developed the method and code, and wrote the manuscript. P.G. contributed to data analysis, preparing the code for open-access sharing on a GitHub repository, provided guidance on the technique, and contributed to writing and review of the manuscript. D.P.F. facilitated the open-access Battery Failure Databank, provided insight and direction to the project, and contributed to writing the manuscript.\n\nCorrespondence to\n Paul Gasper or Donal P. Finegan.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Zhongbao Wei, and the other, anonymous, reviewers for their contribution to the peer review of this work. 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Predicting the heat release variability of Li-ion cells under thermal runaway with few or no calorimetry data.\n Nat Commun 15, 8399 (2024). https://doi.org/10.1038/s41467-024-52653-3\n\nDownload citation\n\nReceived: 28 February 2024\n\nAccepted: 13 September 2024\n\nPublished: 27 September 2024\n\nVersion of record: 27 September 2024\n\nDOI: https://doi.org/10.1038/s41467-024-52653-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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inference of multimodal velocities and molecular mechanisms for single cells", + "pre_title": "GraphVelo allows for accurate inference of multimodal omics velocities and molecular mechanisms for single cells", + "journal": "Nature Communications", + "published": "22 August 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62784-w/MediaObjects/41467_2025_62784_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62784-w/MediaObjects/41467_2025_62784_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62784-w/MediaObjects/41467_2025_62784_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62784-w/MediaObjects/41467_2025_62784_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://figshare.com/ndownloader/files/53666738", + "https://figshare.com/ndownloader/files/53705057", + "https://figshare.com/ndownloader/files/53667548", + "https://data.humancellatlas.org/explore/projects/091cf39b-01bc-42e5-9437-f419a66c8a45", + "https://www.dropbox.com/s/c5tu4drxda01m0u/mousebrain_bin60.h5ad?dl=0", + "https://zenodo.org/records/10404879", + "https://figshare.com/ndownloader/files/53666756", + "https://figshare.com/ndownloader/files/53666588", + "https://figshare.com/articles/dataset/Mouse_Hair_Follicle_RNA_Data/22575307", + "https://figshare.com/articles/dataset/Mouse_hair_follicle_ATAC_data/22575313", + "https://figshare.com/articles/dataset/Developing_Human_Cortex_RNA_Data/22575376", + "https://figshare.com/articles/dataset/Developing_Human_Cortex_ATAC_Data/22575370", + "/articles/s41467-025-62784-w#Sec44" + ], + "code": [ + "/articles/s41467-025-62784-w#ref-CR78", + "https://github.com/xing-lab-pitt/GraphVelo", + "https://github.com/xing-lab-pitt/GraphVelo/tree/main/notebook", + "https://graphvelo.readthedocs.io/en/latest/", + "https://doi.org/10.5281/zenodo.15852884" + ], + "subject": [ + "Computational biophysics", + "Data processing", + "Machine learning" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5613372/v1.pdf?c=1755947292000", + "research_square_link": "https://www.researchsquare.com//article/rs-5613372/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-62784-w.pdf", + "preprint_posted": "14 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "RNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA velocity inference due to complex transcriptional dynamics, low expression, or lacking splicing dynamics, or data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses as input the RNA velocities inferred from existing methods and infers velocity vectors lying in the tangent space of the low-dimensional manifold formed by the single cell data. GraphVelo preserves vector magnitude and direction information during transformations across different data representations. Tests on multiple synthetic and experimental scRNA-seq data including viral-host interactome and multi-omics datasets demonstrate that GraphVelo, together with downstream generalized dynamo analyses, extends RNA velocities to multi-modal data and reveals quantitative nonlinear regulation relations between genes, virus and host cells, and different layers of gene regulation.Biological sciences/Computational biology and bioinformatics/SoftwareBiological sciences/Biophysics/Computational biophysics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "supplemental.pdfSupplementary Text", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "RNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA velocity inference due to complex transcriptional dynamics, low expression, or lacking splicing dynamics, or data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses as input the RNA velocities inferred from existing methods and infers velocity vectors lying in the tangent space of the low-dimensional manifold formed by the single cell data. GraphVelo preserves vector magnitude and direction information during transformations across different data representations. Tests on synthetic and experimental single-cell data, including viral-host interactome, multi-omics, and spatial genomics datasets demonstrate that GraphVelo, together with downstream generalized dynamo analyses, extends RNA velocities to multi-modal data and reveals quantitative nonlinear regulation relations between genes, virus, and host cells, and different layers of gene regulation.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Cells need to constantly detect and adapt to changes in extracellular and intracellular environments. Regulation of their gene transcription is a common mechanism of response. Multiple factors affect the transcriptional activity of eukaryotic genes, including cis and trans regulatory elements and chromatin structure. High throughput single-cell sequencing data provide the landscape of cell genotype. These data lack, however, information on how the cell state changes over time. Continuous efforts have been made to extract information about gene regulation and develop methods for connecting the cell states to temporal sequences of events captured by single-cell snapshot data. One group of methods that has received extensive attention is based on RNA velocity1 for predicting the changes in RNA expression states in the cell. The original RNA velocity method leverages the ratio between nascent and mature transcripts to estimate the rate of change in gene expression. This seminal study has inspired numerous methods for improved RNA velocity estimation based on information from splicing2,3,4,5,6, metabolic labeling7,8, lineage tracing9, and transcriptional factor binding10, etc.\n\nThe RNA velocity framework has, however, its inherent limitations. First, none of the RNA velocity estimation methods could be applied to any single cell transcriptomic data without restrictions. For example, the splicing-based method is not applicable to prokaryotes or viruses, or organisms without introns. Erroneous inferences of RNA velocities have also been noticed for genes having complex splicing dynamics11. Furthermore, it is difficult to estimate the RNA velocities of genes with low expression, which excludes most transcription factors. Second, multi-omics sequencing technologies provide multifaceted information on cellular states alongside the transcriptome modality, and there exist limited systematic methods to extend such velocity estimations to other modalities12,13.\n\nThe single-cell transcriptome state is usually defined by the instantaneous distribution of RNA levels, represented by a multidimensional vector of RNA levels for all (measurable) genes. The usual practice is to map such single-cell data onto a reduced space, e.g., transform from a principal component (PC) space to a UMAP representation to facilitate the visualization of the time-evolution of cell state, with each cell state being represented by a point in that space. Numerous dimension reduction and manifold learning algorithms have been developed for representation transformation. In comparison, transforming a velocity vector between representations is a nontrivial task not rigorously addressed in the single-cell field. Even worse, a visually correct vector field does not necessarily imply accurate high-dimensional velocity estimation14. La Manno et al. proposed a cosine kernel method to address this challenge, which has been adopted since then in most subsequent studies1. Li et al. mathematically proved that the cosine kernel asymptotically gives the correct direction of a velocity vector in the large sampling limit, but the magnitude information is completely lost due to a normalization procedure15. This loss of information casts concerns when such quantitative information is needed.\n\nIn this study, we tackle the above challenges through a graph-theoretical representation of RNA velocities, called GraphVelo, with dynamical systems underpinnings. GraphVelo takes an ansatz that the measured single-cell expression profiles and inferred RNA velocities collectively reflect a dynamical process and are connected through a set of dynamical equations. It exploits such additional constraints that couple a high dimensional velocity field and a corresponding single-cell state manifold, and enables the generalization of the approach in the context of multi-modal single-cell data. While the combined expression and velocity information has been widely used to infer cell state transition trajectories2,16, GraphVelo presents the advantage of enabling downstream analyses such as that performed by dynamo7 to extract quantitative information on causal gene-gene relations that dictate the cell state transitions. Benchmarking of the proposed graph framework against simulated and experimental single-cell data lends support to its broad utility.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Consider that the internal state of a cell can be specified by an N-dimensional state vector x, with N \u226b 1 generally. Assume that the temporal evolution of the cell state follows a continuous and smooth curve x(t) (see Methods for more general mathematical formulation). The instant velocity vector v(x, t) = dx(t)/dt is always tangent to the curve of x(t) (as a function of t) at x. One can generalize to the situation that the trajectories of a swarm of cells form a M-dimensional manifold \\({{{\\mathcal{M}}}}({{{\\bf{x}}}})\\) embedded in the N-dimensional state space with M \u226a N typically, as revealed by high-throughput single cell omics data. Then under the ansatz that a velocity vector v(x, t) dictates the evolution of a state vector x(t), v must lie in the tangent space of \\({{{\\mathcal{M}}}}({{{\\bf{x}}}})\\), denoted as \\({T}_{p}{{{\\mathcal{M}}}}\\). In practice, the RNA velocity vectors inferred from existing methods do not automatically satisfy this tangent space requirement (but see4).\n\nTaking various inferred single-cell RNA velocity vectors, e.g., splicing-based, metabolic labeling-based, or lineage tracing-based, as input, GraphVelo takes advantage of the nature of the low-dimensional cell state manifold to: (1) refine the estimated RNA velocity to satisfy the tangent space requirement; (2) infer the velocities of non-transcriptomic modalities using RNA velocities. GraphVelo thus serves as a plugin that can be seamlessly integrated into existing RNA velocity analysis pipelines, and help process single-cell data for downstream cellular dynamics analyses using methods such as dynamo (Fig.\u00a01a).\n\na Workflow of RNA velocity-based analyses incoporating GraphVelo. Note GraphVelo takes any form of RNA velocity (i.e., not just splicing-based velocity) as input, and the kNN neighborhood is defined in the full state space (e.g., by both scRNAseq and scATACseq in multi-omics data). b Schematic of tangent space projection and velocity transformation between homeomophic manifolds. Left: RNA velocity vectors are projected onto the tangent space defined by the discretized local manifold of neighborhood cell samples. Right: GraphVelo allows for transformation of velocity vectors from a manifold embeded in a higher dimensional space \\(({{{\\mathcal{M}}}})\\) to that in a lower-dimensional space \\((\\aleph )\\), and vice versa. c The process of minimizing the loss function of tangent space projection. Noisy velocity vectors (left) generated by adding random components orthogonal to those sampled from an analytical 2D manifold were projected back onto the 2D manifold, resulting in smooth velocity vectors that lie in the tangent space (right). d GraphVelo allows whole genome velocity inference based on the robustly estimated MacK genes (see also Fig.\u00a03). Velocities of genes undergoing variable kinetic rates, such as rapid degradation or transcription burst, are difficult to be correctly inferred by other methods, but can be inferred robustly with GraphVelo. e Virus infection dynamics and underlying host-virus interaction mechanisms uncovered by GraphVelo (see also Fig.\u00a04). Upper: pathways involved in host-virus interactions were identified using GraphVelo. Lower: GraphVelo predicted reversed trajectory of viral infection in response to in silico perturbations of viral factors. f GraphVelo provides a consistent view of epigenetic and transcription dynamics (see also Fig.\u00a05). Upper: GraphVelo analyses on multi-omics data revealed that most cell-cycle dependent genes showed decoupling between transcription dynamics and chromatin accessibility change dynamics. Lower: Effective dose-response curves reconstructed from multi-omics data revealed pioneer transcription factors increased chromatin accessibility then transcription of targe genes.\n\nPractically, GraphVelo approximates the tangent space at a cell state x by a k-nearest neighbor (kNN) graph following the local linear embedding algorithm17, and uses the more reliable data manifold \\({{{\\mathcal{M}}}}\\) to refine the velocity vectors by imposing the constraint that the N-dimensional velocity vector \\({{{\\bf{v}}}}\\) should lie in the tangent space (Fig.\u00a01b, c). Consider a given point \\({{{{\\bf{x}}}}}_{i}\\) on a manifold corresponding to the expression state i of the single cell. Its infinitesimal neighborhood forms an Euclidean space that approximates the tangent space \\({T}_{p}{{{\\mathcal{M}}}}\\). With a sufficient sampling of the neighboring cell states j in the state space, the incremental displacement vectors between cell state i and its neighboring cell states, \\({{{{\\boldsymbol{\\delta }}}}}_{{ij}}={{{{\\bf{x}}}}}_{j}-{{{{\\bf{x}}}}}_{i}\\), form a set of complete albeit possibly redundant and nonorthogonal/non-normalized basis vectors of the Euclidean space in the local region. Then the projection of the measured velocity vector onto \\({T}_{p}{{{\\mathcal{M}}}}\\) can be expressed as a linear combination (see also\u00a0Supplementary Notes\u00a01.1),\n\nwhere \\({{{{\\mathcal{N}}}}}_{i}\\) is the neighborhood of cell state i, defined by its k nearest neighbors in the feature space determined by sequencing profiles. Direct application of Eq.\u00a01 to determine the coefficients \\({\\phi }_{{ij}}\\) is numerically unstable in real data (see\u00a0Supplementary Notes\u00a01.2 for detailed discussion). Instead, we performed the projection by optimizing the following tangent space projection (TSP) loss function (Fig.\u00a01c),\n\nwhere \\(\\Vert \\cdot \\Vert\\) refers to vector modulus. \\({{{{\\mathbf{\\phi }}}}}_{i}^{{{{\\rm{corr}}}}}\\) is a heuristic \u201ccosine kernel\u201d widely used in the RNA velocity analyses for projecting velocity vectors onto a reduced space, the elements of which are \\({\\phi }_{{ij}}\\left({withj}\\in {{{{\\mathcal{N}}}}}_{i}\\right)\\) (see also\u00a0Supplementary Notes\u00a01.3); the second term \\(\\cos \\left(\\cdot,\\cdot \\right)\\) denotes the cosine similarity. The first term in the loss function learns the correct velocity magnitudes, and the second term retains the reliable direction information based on previous mathematical analyses showing that \\({{{{\\mathbf{\\phi }}}}}_{i}^{{{{\\rm{corr}}}}}\\) asymptotically gives correct direction of the velocity vector15. The L2 regularization is used to bound parameters \\({{{{\\mathbf{\\phi }}}}}_{i}\\). Hyperparameters a, \\(b\\) and \\(\\lambda\\) are for retaining the projection strength, direction, and for regularization, respectively.\n\nWith local linear embedding, it is straightforward to transform velocity between different representations. Assuming a mapping function f exists connecting manifold \\({{{\\mathcal{M}}}}\\) and \\(\\aleph\\) such that for cell i with state vector \\({{{{\\bf{x}}}}}_{i}\\) in \\({{{\\mathcal{M}}}}\\), the coordinate of the same cell in \\(\\aleph\\) is given as \\({{{{\\bf{y}}}}}_{i}=f\\left({{{{\\bf{x}}}}}_{i}\\right)\\). Since a given local patch of a continuous manifold is approximated by an Euclidean space, a locally linear transformation connects the patch in the two representations. Consequently, for a vector described by Eq.\u00a01 in \\({{{\\mathcal{M}}}}\\), the velocity vector in \\(\\aleph\\) is,\n\nwhere \\({{{{\\boldsymbol{\\delta }}}}}_{{ij}}^{{\\prime} }={{{{\\bf{y}}}}}_{j}-{{{{\\bf{y}}}}}_{i}\\). That is, one only needs to change the basis vectors.\n\nTherefore, Eqs.\u00a01\u20133 form the mathematical and computational foundation of GraphVelo. With Eq.\u00a03 one can extend velocity inference to datasets that velocity inference is not traditionally applicable such as host-virus interactome and multi-omics datasets based on the Whitney embedding theorem18. Details of the mathematical foundation were given in Methods. With velocity vectors refined with GraphVelo, one can readily perform downstream analyses, as exemplified in Fig.1d\u2013f, which will be further elaborated below in the context of specific applications.\n\nTo demonstrate the effectiveness of the geometry-constrained projection, we first benchmarked our method on a 3D bifurcation system constrained on a 2D manifold (Methods). We added random components vertical to the tangent plane to mimic the noise. The resulting velocity vectors inferred by GraphVelo through minimizing the TSP loss were consistent with the ground truth vectors (Fig.\u00a02a). Both GraphVelo and cosine kernel successfully removed the normal components (Fig.\u00a02b i) and maintained the directional information (Fig.\u00a02b ii), but only GraphVelo kept the velocity magnitude information (Fig.\u00a02b iii).\n\na Velocity vectors of an analytical three variables bifurcating vector field constrained to a spherical surface. The data points were colored by simulation time. b Violinplots of: (i) normal component of velocity vectors, (ii) cosine similarity and (iii) root mean square error (RMSE) between ground truth and velocity vectors projected by GraphVelo and cosine kernel, respectively. The number of simulated cells is 2000 for statistical test. c\u2013e Simulation of scRNA-seq data using dyngen under linear, cycling, and bifurcating differentiation models (left), and velocity fields projected on multidimensional scaling (MDS) coodinates (right) using GraphVelo-corrected velocities, respectively. Each simulation consists of 1000 cell states and 100 genes. The cells in different states were colored by their simulation time along trajectory. f\u2013h Comparisons of cosine similarity, and accuracy between the ground truth velocity vectors and dyngen simulated velocities after projection using GraphVelo TSP loss without cosine regularization, GraphVelo TSP loss with cosine regularization, cosine kernel, and random predictor, respectively. The number of dyngen-simulated cells is 1000 for statistical test. In b and f\u2013h, *** indicates Welch\u2019s independent two-sided t-test at p\u2009<\u20090.05. Violinplot in panel b shows the distribution of data points after grouping by projection methods. Boxplots in f\u2013h indicate median (middle line), first and third quartiles (box), and the upper whisker extends from the edges to the largest value no further than 1.5\u2009\u00d7\u2009IQR (interquartile range) from the quartiles, and the lower whisker extends from the edge to the smallest value at most 1.5\u2009\u00d7\u2009IQR of the edge. Source data are provided as a Source Data file.\n\nNext, we performed multifaceted evaluations of the ability of GraphVelo to robustly recover the transcriptional dynamics across a range of simulated datasets with different underlining phenotypic structures. We used dyngen19, a multi-modal scRNA-seq simulation engine, to generate gene-wise dynamics defined by gold-standard transcriptional regulatory networks (Methods). We generated simulated scRNA-seq data for networks with a variety of underlying linear, cyclic, and bifurcating topological structures, and recovered the corresponding vector field using GraphVelo-corrected velocity vectors (Fig.\u00a02c\u2013e). To comprehensively assess the outcome, we used three diverse metrics, cosine similarity, root-mean-square error (RMSE), and accuracy, which evaluate the correctness of velocity direction, magnitude, and sign, respectively. We presented (Fig.\u00a02c\u2013e) the comparative results obtained with the cosine kernel and with GraphVelo (TSP with (i.e., Eq. 2 with b \u2260 0) and without (Eq. 2 with b = 0) the cosine regularization term). By minimizing TSP loss, GraphVelo preserved both the direction and magnitude of the vector field (Fig.\u00a02f\u2013h). With an increase of noise level by adding Gaussian noise to the ground truth vectors, GraphVelo refined the distorted velocity and outperformed the cosine kernel projection consistently (Supplementary Fig.\u00a01a-c).\n\nThen, we tested whether manifold constraints could preserve the speed of the cell progression across different representations. GraphVelo was able to scale velocity vectors between the original space and the PCA space, showing a high correlation with the ground truth, even as noise levels increased, whereas the cosine kernel failed (Supplementary Fig.\u00a01d). The results on UMAP showed less agreement, which is not surprising. UMAP is a convenient representation for visualizing single cell data but is not designed for representing quantitative cell state transition dynamics. That is, UMAP is not a continuous transformation from the original gene space and cannot preserve local distances after projection.\n\nTo further explore whether GraphVelo could correct the RNA velocity estimated by the splicing kinetics, we took the velocity inferred using different packages (scVelo2, dynamo7 and VeloVI5) as input. The output from GraphVelo agreed significantly better with the ground truth compared to the raw input (Supplementary Fig.\u00a01e), highlighting the significant improvement achieved by GraphVelo in evaluating both the direction and magnitude of the velocity vector fields across all datasets.\n\nAlthough GraphVelo is designed as a velocity-correction and prediction model complementing existing velocity estimation tools, we benchmarked the performance of scVelo-based GraphVelo outputs on five independent datasets (FUCCI20, pancreatic endocrinogenesis21, dentate gyrus1, intestinal organoid22 and hematopoiesis7) against five RNA velocity estimation methods (scVelo2, VeloVI5, UniTVelo23, DeepVelo24, and CellDancer3) (Supplementary Fig.\u00a02\u20134). GraphVelo achieved noticeably improved cross-boundary correctness (CBC) score25 against input velocity and other advanced methods (see Methods for calculation details). We evaluated velocity consistency2 across two datasets whose trajectories were estimated using different tools (Supplementary Fig.\u00a04). While GraphVelo shows slightly lower overall vector smoothness scores compared to others, we observed that these models tend to produce overly smooth and homogenized velocity fields, which may obscure biologically meaningful heterogeneity. In contrast, GraphVelo preserves fine-grained local transitions and reveals subtle divergence in the vector field, particularly around fate bifurcations in endocrinogenesis data.\n\nWe examined the cell cycle datasets annotated by the dynamo package, which features a relatively simple geometry, to quantitatively evaluate our method. First, we focused on the CBC score between cell cycle states. The velocity vectors processed by TSP showed greater consistency with the ground truth compared to the unprocessed inputs (Supplementary Fig.\u00a05a). To demonstrate the effectiveness of GraphVelo in scaling velocities based on the data manifold, we used the L2 norm of velocity vectors to quantify the cell cycle speed. This analysis revealed a peak in velocity within the M and G1 phases, which was also reflected in the distribution of total UMI counts (Supplementary Fig.\u00a05b). We further validated these findings using the cycling A549 cell line sequenced by sci-fate26 (Supplementary Fig.\u00a05c) and through the temporal variation of stratified cell cycle speed based on velocities inferred from metabolic-labeling data (Supplementary Fig.\u00a05d). Leveraging the quantitative velocity vectors generated by GraphVelo, we classified genes by both the phase and peak magnitude of their velocities (Supplementary Fig.\u00a05e). Analysis of phase-magnitude relationships uncovered the sequential activation cascade of marker genes throughout the cell cycle.\n\nMost RNA velocity methods are based on biophysical models of mRNA turnover dynamics with specific assumptions that may break down in certain cases11. These methods typically provide velocities of only a subset of (~500 or less) genes, termed velocity genes in the subsequent discussions, out of a larger list of (~2-4 k) highly variable genes in a dataset, and some of the velocities are questionable. For example, the splicing-based RNA velocity may have an erroneous sign for processes under active regulation on mRNA degradation or promotors switching between states with different transcription efficiency (Fig.\u00a03a, Supplementary Fig.\u00a06).\n\na Schematitc of transcritional events mislead RNA velocity estimation in the phase portrait by standard approaches. Left: for genes exhibiting rapid degradation, the cells appear above the steady state line on the phase portrait, whereas the true velocity is negative. Right: For genes exhibiting transcription burst, the transcription rate abruptly increases at intermediate states, leading to a steady state line whose slope is overestimated. b Schematic of manifold-consistent score calculation for robustly estimated velocity genes. c The projected velocity field from GraphVelo are consistent with the erythroid differentiation by using all highly variable genes. d The correlation between GraphVelo vector field-based pseudotime and embryo time for erythroid lineage cells. Spearman correlation coefficients are shown. e GO enrichment analyses of top ranked MacK genes. f The phase portrait of two transcription burst genes (Smim1, Hba-x). g Scatter plots of: i) velocities estimated by scVelo, ii) refined velocities by GraphVelo, and iii) mature mRNA expression of transcription burst genes (Smim1, Hba-x). Cells were colored by corresponding velocity, and mature mRNA abundance, respectively, and visualized on the UMAP representation. h Gene regulatory cascade unraveled by GraphVelo-based vector field analyses that drives cell lineage commitment. Gene set enrichment was performed using one-sided Fisher\u2019s exact test, Benjamini\u2013Hochberg correction. Adjusted p-values represent FDR-corrected significance of gene set enrichment. i Activation of Gata1 inhibitor TF Spi1 lead to reversed velocity flows in gastrulation erythroid maturation investigated through in silico perturbation analyses on GraphVelo-based vector field. j Velocities derived from GraphVelo for the branching lineage in the hematopoiesis development and projected onto a pre-defined TSNE embedding. Directions of the projected cell velocities on TSNE are in agreement with the reported differentiation directions. k Terminal states identified by CellRank based on Markov chain formulation derived from GraphVelo velocities. l Phase portrait, velocity estimated by scVelo, refined velocity by GraphVelo, and gene expression of mature mRNA of identified rapid degradation genes (NPR3, ANGPT1). The cells were colored by the palantir pseudotime31 in the phase portrait. The box plots showed cell-specific \\({{{\\boldsymbol{\\gamma }}}}\\) for cells divided into bins according to pseudotime ordering in the phase protrait. The number of cells within each time bins from early to late is 653, 650, 654, 657, 657, respectively. Boxplots indicate median (middle line), first and third quartiles (box), and the upper whisker extends from the edges to the largest value no further than 1.5\u2009\u00d7\u2009IQR (interquartile range) from the quartiles and the lower whisker extends from the edge to the smallest value at most 1.5\u2009\u00d7\u2009IQR of the edge, while data beyond the end of the whiskers are outlying points that are plotted individually. Source data are provided as a Source Data file.\n\nGraphVelo uses the velocities of high-confidence genes obtained from any method as input to infer velocities of other genes. One can use several existing approaches to evaluate the confidence scores of inferred RNA velocity values of genes7. Alternatively, we identified a subset of Manifold-consistent Kinetics (MacK) Genes based on their agreement with prior knowledge or additional information acquired from other methods such as lineage tracing (Fig.\u00a03b), We first applied GraphVelo to a mouse erythroid maturation dataset27. This study provided a transcriptional landscape of the erythroid lineage with well-documented differentiation trajectory during mouse gastrulation. Previous analyses have shown that the dataset contains genes with multiple rate kinetics, leading to erroneous prediction of the cell state transition direction27,28. We selected the top 200 out of 450 velocity genes as MacK genes, representing those with robustly estimated velocities (see Methods for details). The projected vector field in UMAP showed consistency with prior knowledge in developmental biology (Fig.\u00a03c). We then used the corrected RNA velocities for dynamo velocity field analyses. The vector field-based pseudotime accurately predicted the lineage with scRNA-seq data of temporal mouse embryos (Fig.\u00a03d).\n\nPrevious studies identified multiple rate kinetics (MURK) genes showing transcription bursts in the middle of erythroid differentiation28. For example, two MURK genes (Smim1 and Hba-x) showed complex patterns of phase portrait (Fig.\u00a03f). Consequently, the RNA velocity of Simi1 inferred with scVelo was negative along a major part of the developmental axis (Fig.\u00a03g i), contradicting the trend of increasing Simi1 mRNA levels (Fig.\u00a03g iii). For Hba-x, scVelo even failed to infer its RNA velocity. On the other hand, GraphVelo inferred velocities and predicted correct kinetic patterns of these genes (Fig.\u00a03g ii). Similar performances have been observed in other MURK genes (Supplementary Fig.\u00a07a). To examine the overall prediction of cell state transitions from transcription burst genes, we projected the MURK genes velocity inferred from GraphVelo and scVelo to the predefined UMAP. The velocities from GraphVelo but not scVelo correctly captured the directional flow of differentiation using only MURK genes (Supplementary Fig.\u00a07b). We further recapitulated gene succession and oscillation magnitudes along the erythroid trajectory and evaluated the phase-magnitude relationships of all highly variable genes (Supplementary Fig.\u00a08a). Compared to non-MURK genes, MURK genes exhibited larger average velocity magnitudes and were predominantly enriched in the late stages of lineage progression. Analysis of genes with larger peak velocity amplitudes identified Fth1, Car2, and Hbb-bs as candidates with altered kinetic parameters. These dynamics patterns were evident in their phase portraits and gene expression trends (Supplementary Fig.\u00a08b, c), consistent with previous reports that Car2 transcription in erythroid cells is regulated by both the promoter activity and long-range enhancer interactions29\u2014such complex regulations lead to its transcription dynamics not well-described by the simple transcription model used in the original splicing -based RNA velocity inference.\n\nNext, we evaluated the quantitative performance of GraphVelo in estimating cell-wise transition speed. Specifically, we estimated the speed of cell state transition using the norm of velocity vector in high-dimensional space and identified the transcriptional surge stage (Supplementary Fig.\u00a07c). We hypothesized that the MURK genes, which exhibited a sudden increase in transcription rate during this stage, were responsible for the sharp acceleration in cell state transition speed. Using dynamo, we estimated the acceleration derived from the GraphVelo vector field and found that the acceleration value, as the derivative of the velocity vector, demonstrated its potential as a predictor for transcription burst genes (Supplementary Fig.\u00a07d).\n\nWith the velocity estimation extended to the whole gene space, we were able to perform comprehensive mechanistic analyses on the entire genome spectrum. First, we calculated the MacK score for each gene using the corrected RNA velocities. We hypothesized that a gene with a higher MacK score indicated a better agreement between its RNA velocity vector and the developmental axis, suggesting that the gene served as a potential lineage-driver gene. We ranked genes based on their scores and performed GO biological process enrichment analyses for the top genes. Indeed, the enriched processes were associated with erythropoiesis, including the heme biosynthetic process and interleukin-12-mediated signaling pathway30 (Fig.\u00a03e).\n\nNext, we applied dynamo to perform differential geometry analyses of the vector field and mechanistically dissected the activation cascade of erythroid marker gene Klf1 (Supplementary Fig.\u00a09a, b). Jacobian analyses based on GraphVelo vector field revealed sequential activation of driver transcription factors (TFs) Gata2, Gata1, and Klf1 during erythroid lineage differentiation, with Gata1 subsequently repressing the expression of Gata2 (Fig.\u00a03h, Supplementary Fig.\u00a09c)7. To further demonstrate the crucial role of transcriptional factor Gata1 during erythropoiesis, we performed in silico genetic perturbation across all cells. Results showed that both inhibiting Gata1 and upregulating the Gata1 repressor Spi1 lead to a reversal of normal developmental flow (Fig.\u00a03i, Supplementary Fig.\u00a09d). The above analyses collectively suggest that activation of Gata1 in the blood progenitors biased its differentiation to erythropoiesis, agreeing with experimental reports28.\n\nTo further evaluate GraphVelo, we tested the method on another dataset of human bone marrow development31. This developmental process has complex progressions from hematopoietic stem cells (HSCs) to three distinct branches: erythroid, monocyte, and common lymphoid progenitor (CLP). Again, we used the top 100 out of 454 velocity genes as MacK genes to predict the RNA velocities of 2000 highly variable genes. The GraphVelo velocity field accurately recovered the fate of cells on the sophisticated transcriptional landscape in contrast to scVelo (Fig.\u00a03j, k, Supplementary Fig.\u00a010a). By combining the likelihood estimated by scVelo with the MacK score, we identified rapid degradation and transcription burst genes whose dynamics deviated from the RNA velocity assumptions (Supplementary Fig.\u00a010b, c). ANGPT1 and RBPMS are two examples which were overall highly expressed in the progenitors and decreased quickly along the trajectories (Fig.\u00a03l), reminiscent of what was shown in Fig.\u00a03a. These genes misled RNA velocity inference with scVelo assuming a constant degradation rate constant. GraphVelo revealed a cell context-specific transcription rate \\(\\alpha=u+\\frac{{du}}{{dt}}\\) and degradation constant \\(\\gamma=(u-\\frac{{ds}}{{dt}})/s\\), thus a degradation wave along the differentiation path (Fig.\u00a03l, Supplementary Fig.\u00a010d), consistent with simulation result and reports on regulation of ANGPT1 mRNA by microRNAs such as miRNA-153-3p32 (Supplementary Fig.\u00a06d).\n\nIt is straightforward to apply Graphvelo to spatial transcriptomics datasets that permit RNA velocity inference. Note that the transcriptional dynamics of a gene can be affected by both the intracellular expression state and extracellular environmental factors. A data manifold containing additional spatial information allows distinction of cell states with similar expression profiles but distinct extracellular environments. Such a refined manifold leads to more accurate inference of the RNA velocities, which are typically performed over averaging the neighborhood of a cell state1 (see Methods) and is also used in GraphVelo for tangent space projection. We applied Graphvelo to the mouse coronal hemibrain dataset33 processed with a bin size 60, which includes spliced and unspliced transcript information at spatial context (Supplementary Fig.\u00a011a). GraphVelo inferred coherent velocity fields across brain regions, with streamlines on UMAP reflecting anatomically structured transitions that align with the spatial annotation (Supplementary Fig.\u00a011b). Compared to the uncorrected RNA velocities using the dynamo build-in module, GraphVelo captured sharper transcription speed patterns, particularly in the dentate gyrus (DG), a neurogenic region where cell proliferation and neuronal differentiation persist into adulthood34 (Supplementary Fig.\u00a011c, d). Spatial mapping of representative genes revealed that GraphVelo velocities were more spatially confined and aligned with known expression domains, whereas the uncorrected velocities were noisier and less localized (Supplementary Fig.\u00a011e). These results highlight the ability of GraphVelo to generate interpretable, spatially structured transcription dynamics in spatial transcriptomic data when splicing information is available.\n\nThe continuous battle between human immune surveillance and viral immune evasion takes place in the host cell system after viral entry. scRNA-seq data provide a massive and parallel way of assessing the time evolution of both host and viral transcripts, unraveling the delicate inherent dynamics of a virus-host system35,36. For splicing-based RNA velocity methods, genes lacking sufficient unspliced transcript counts are typically filtered out\u2014which is inherently the case for viral genes due to their absence of introns. However, several studies37,38,39 have applied tools such as scVelo to host-virus systems by focusing exclusively on host velocity genes that accurately reflect the lineage dynamics, rather than attempting to derive velocities directly from viral transcripts. GraphVelo enables inference of the velocities of virus RNA abundance based on the kinetics of host transcripts velocities, as illustrated next.\n\nWe analyzed a human cytomegalovirus (HCMV) viral infection dataset to learn viral transcriptomic kinetics in monocyte-derived dendritic cells (moDCs)39. The result from GraphVelo unraveled how viral infection progressed along the transcriptional space (Fig.\u00a04a). The velocity vectors pointed to directions consistent with an increasing trend of the percentage of viral RNAs in individual cells, which inherently served as an indicator of the infection time course40. Compared to the trend obtained with the raw RNA velocities from scVelo, the vector field-based pseudotime calculated using GraphVelo-corrected RNA velocities consistently showed higher correlation with the (pseudo)temporal progression of viral infection as reflected by viral RNA percentage (Fig.\u00a04b).\n\na Viral infection captured by the GraphVelo velocity field. Cells were colored by the percentage of viral RNA within a single cell. b Correlation between viral RNA percentage and pseudotime inferred by scVelo or GraphVelo. Correlation was calculated using a two-sided Spearman rank correlation test. c Viral RNA velocities infered by GraphVelo along the viral RNA percentage axis. The black dot line highlights the zero velocity. The solid line denotes the mean trend, dashed lines denote 1\u2009s.d. d Boxplot summarizing the MacK scores of all viral genes calculated by GraphVelo, CellRank pseudotime kernel and random predictor. The number of viral genes is 67 for the statistical test. *** indicates Welch\u2019s independent two-sided t-test at p\u2009<\u20090.05. Boxplots indicate median (middle line), first and third quartiles (box), and the upper whisker extends from the edges to the largest value no further than 1.5\u2009\u00d7\u2009IQR (interquartile range) from the quartiles and the lower whisker extends from the edge to the smallest value at most 1.5\u2009\u00d7\u2009IQR of the edge, while data beyond the end of the whiskers are outlying points that are plotted individually. e Correlation between viral infection speed and RNA abundance. Genes were ranked by Spearman correlatioin coefficients. Host and viral genes that contribute to viral DNA synthesis were marked in the left side and those contribute to viral defense response were marked in the right side. Viral genes were highlighted in red. f UMAP representation of host and viral genes with distances defined by their dynamic expression patterns along the viral RNA percentage axis. g Example dynamic expression patterns within specific clusters (Leiden4, 5, 3, 6 from top to bottom) along the viral RNA percentage axis. Zero velocity was highlighted by black dot line. The solid line denotes mean trend, Shaded region represents 1\u2009s.d. h GO enrichment of each cluster in (g). Gene set enrichment was performed using one-sided Fisher\u2019s exact test, Benjamini\u2013Hochberg correction. Adjusted p-values represent FDR-corrected significance of gene set enrichment. i Top host genes inhibited by each viral factor based on dynamo Jacobian analyses. Host effectors were organized by their involved pathways. j Dynamo prediction of total viral RNA change in response to in silico viral factor knockout. Viral factors were ranked by the mean of total viral RNA changes. The number of samples is 1454 for virtual perturbation screen. Boxplots indicate median (middle line), first and third quartiles (box), and the upper whisker extends from the edges to the largest value no further than 1.5\u2009\u00d7\u2009IQR (interquartile range) from the quartiles and the lower whisker extends from the edge to the smallest value at most 1.5\u2009\u00d7\u2009IQR of the edge. k Vector field change resultant from infinitesmal inhibition of UL123 during the viral infection process. Source data are provided as a Source Data file.\n\nFurthermore, the examination of individual genes revealed that the RNA velocities consistently predicted the trend of the mRNA expression level change with increasing virus load (Fig.\u00a04c, Supplementary Fig.\u00a012a, b). Most viral genes started with a fast-increase phase, and the expressions of some genes (e.g., UL22A) gradually saturated at high virus load, together with the corresponding RNA velocities approaching zero. One exception is UL122, whose expression profile increased first then decreased to a steady state level lower than the peak value. This overshooting is characteristic of a negative feedback network structure41. Indeed, a recent study reported that UL122 negatively regulates its own promotor42. Furthermore, comparison of MacK scores across GraphVelo, the CellRank pseudotime kernel, and randomized prediction showed that GraphVelo-computed viral RNA velocities aligned best with the transcriptome gradient of viral load (Fig.\u00a04d). Note that the MacK score also served as a reliable predictor for dynamics-driving factors, specifically viral genes in this case (Supplementary Fig.\u00a012d).\n\nWe further quantified the velocity norm of all viral factors as infection speed, and observed that the transcription of viral factors was significantly restricted initially, then gradually increased along the trajectory (Supplementary Fig.\u00a012e). Interestingly, most of the genes that exhibited positive correlation with the infection speed were related to viral DNA synthesis, while those negatively correlated to the infection speed were engaged in host viral defense response43(Fig.\u00a04e, Supplementary Fig.\u00a012f).\n\nWith GraphVelo-inferred RNA velocities, we probed the time evolution of lytic infection and the complex interplay between host and viral functional genomes. By fitting the GraphVelo velocity trends along the viral load axis, we identified genes with similar kinetic patterns (Fig.\u00a04c). Using the smoothened velocity trends to calculate the distances, we clustered the genes into seven major modules and visualized them on the UMAP space (Fig.\u00a04f). Not surprisingly, viral genes were concentrated in several enclosed regions, indicating that they formed distinct functional genomic modules during the lytic cycle40. The genes showed two major dynamical features: acceleration and deceleration along the viral load axis (Fig.\u00a04g).\n\nTo systematically investigate whether the dichotomy between host kinetics genes and viral genes share similar dynamics, we performed gene functional enrichment analyses of host genes residing close to the viral gene clusters. Genes located in the deceleration part were associated with repressing viral genome replication, which includes negative regulation of viral life cycle and known restriction factors in antiviral responses activated in DCs such as the induction of cytokine and chemokine responses as well as interactions with neutrophils44,45. In parallel, the toll-like receptor signaling pathway, required for antiviral defense of the host, was arrested46. Neutrophil related processes, which typically cooperate closely with DCs to modulate adaptive immune responses47, were suppressed. Therefore, the deceleration part also showed how critical set of host factors were silenced by viral entry to achieve immune evasion.\n\nThe acceleration groups, on the other hand, demonstrated how viruses hijacked the host cell endogenous cellular programs for virus replication. Notably, the pathways related to viral genome replication were triggered, promoting DNA replication and transcription, such as negative regulation of G1/S transition of mitotic cell cycle48, cellular response to DNA damage stimulus49 and regulation of transcription from RNA polymerase II promoter50. Cells showed a shift towards a transcriptional signature resembling the G1 phase (Supplementary Fig.\u00a012g), agreeing with previous report on HCMV infection51. Along the infection process, antiviral interferon (IFN)-\u03b3 response of moDC cells was first activated then suppressed. These results highlighted the organized and antagonistic strategies adopted by both host cells and viruses during their tug-of-war for survival and proliferation.\n\nTo investigate the crosstalk between host and viral factors systematically in depth, we performed dynamo Jacobian analyses. We scanned the entire spectrum of viral genomes and delineated how the HCMV factors silence IFN and NF\u03baB signaling (Fig.\u00a04i). A large proportion of the identified viral factors functioned in evading host cell immune responses, a finding supported by several recent studies52,53,54. In silico virus-directed knock out experiments revealed altered accumulation patterns of viral transcripts (Fig.\u00a04j). Notably, inhibition of UL123, which ranked first with the total viral RNA inhibition in our analyses, led to a qualitatively distinct trajectory. These results highlight the multifunctional UL123 locus in the viral genome as a potential target for antiviral intervention40,55. The analyses demonstrated potential usage of GraphVelo-inferred velocities for understanding the interactions between viral and host factors, assessing the effects of perturbations on infection, and designing potential antiviral interventions40.\n\nAlthough HCMV is renowned for its elaborate transcriptional landscape\u2014characterized by extensive alternative splicing, overlapping transcripts, and diverse isoform expression\u2014the RNA virus SARS-CoV-2 transcriptome reveals an even greater level of complexity within its relatively compact ~30\u2009kb genome56. To characterize the molecular mechanisms of host response which protect cells from productive trajectory, we applied GraphVelo to a SARS-CoV-2 infected Calu-3 cells dataset57. To focus on the host-virus interactions and their corresponding fate outcomes, we subset the infected cell population for downstream analyses. The infected cluster M, characterized by interferon production genes, was hypothesized to represent a subpopulation of abortively infected cells57, similar to those described in herpesviruses HSV-158 and HCMV40. We subsampled the infected cell population for downstream analyses and visualization, where cluster M exhibited distinct connectivity properties compared to the main infected groups (Supplementary Fig.\u00a013a). To confirm that M is one of the terminal states of infection outcomes with such low cell abundance (Supplementary Fig.\u00a013b), we applied GraphVelo to gain the quantitative velocity with dynamo outcomes as input (Supplementary Fig.\u00a013c). Using vector field topology analysis, we classified an initial state with relatively low viral load, two terminal states associated with high apoptosis activity, and a saddle point characterized by high viral load (Supplementary Fig.\u00a013d). Notably, the region corresponding to cluster M was identified as an attractor, confirming it represents an abortively infected cell state with a high death rate57.\n\nWe further validated the complex lineage commitments of SARS-CoV-2\u2013infected cells using the CellRank framework16, which successfully identified three potential terminal states as outcomes of host-virus competition (Supplementary Fig.\u00a013e). Interestingly, we characterized the saddle stage in dynamo as a productive terminal state governed by viral genes (Supplementary Fig.\u00a013f), exhibiting high viral transcription speed (Supplementary Fig.\u00a013g). The pathogen-triggered cell death can be driven for protecting the host or for pathogen dissemination purpose59. To investigate the underlying causes for host cell death, we performed gene functional enrichment analyses on the top correlated genes along distinct lineages (Supplementary Fig.\u00a013h). Active programs in the abortive infection lineage were highly enriched for host defense mechanisms against viral invasion, consistent with previous studies40,58. In contrast, the drivers of the apoptosis-associated lineage revealed a distinct profile. Notably, we observed enrichment for cellular responses to unfolded proteins, which have been implicated in facilitating pathogen-mediated dissemination of infected cells60,61. Additionally, ERBB2 inhibition has been shown to suppress SARS-CoV-2 replication62. These findings support the hypothesis that similar cell death outcomes may arise from fundamentally different host responses. Abortively infected cells appear to promote efficient pathogen clearance, likely through cytokine-mediated immune activation that eliminates both the infected cell and the virus. Conversely, cells undergoing virus-induced apoptosis fail to clear the virus, with cell death instead serving as a mechanism for viral escape, immune evasion, and potential dissemination to deeper tissue layers or the bloodstream.\n\nThe molecular anatomy during cell development entails multiple layers, and how different layers coordinate to regulate gene expression is a fundamental problem. For example, the anagen hair follicle features distinct lineages branching from a central population of progenitor cells. Ma et al.63. used SHARE-seq to capture both the transcriptome and the epigenome data simultaneously for the lineage commitment process from transit-amplifying cells (TACs) to the inner root sheath (IRS), cuticle layer, and medulla. Upon robust selection of estimated genes following dynamo criteria (Methods), we further refined the RNA velocities of these genes through tangent space projection and obtained the chromatin open/close dynamics from the corresponding scATAC data using GraphVelo. The resultant vector field in the combined transcriptome-epigenome space proved to reconstruct the correct multilineages differentiation paths during the anagen phase (Fig.\u00a05a).\n\na GraphVelo velocity fields of mouse hair follicle development. Cells were colored by cell macrostates. b Number of terminal states predicted by CellRank using velocities inferred with different methods. c Driver genes along multiple lineages identified through CellRank. d Topological analyses of GraphVelo vector field identified novel root cells and attractors residing in three terminal states(IRS, hair shaft-cuticle cortex, and medulla). e Expression levels of marker genes in novel root cells and expected root cells. Markers identified by Ma et al.63 were highlighted with stars, and newly identified markers were highlighted in bold. f Regression results of MSD values along the transition path from the expected root or novel root to IRS. Two genes Runx1 and Shh genes with large MSD originating from the novel root were highlighted. The solid line denotes mean of regression result, Shaded region represents 1\u2009s.d. g DTW distance between RNA velocity and chromatin velocity of individual genes. CCD genes were colored in red. The dotted line indicates the elbow point separating the decoupled genes from the rest. h GO enrichment of decoupled genes in (g). i Line plot of nomarlized RNA and chromatin velocity along pseudotime for genes predicted by GraphVelo to have notable decoupling patterns. Chromatin velocity trends were colored as brown and RNA velocity trends were colored as green. j Heatmaps of Jacobian element distribution along the axis of regulator RNA abundance of four regulator effector circuits: i) Lef1 versus Wnt3 chromatin accessibilities. ii) Hoxc13 versus Wnt3 chromatin accessibilities. iii) Lef1 versus Wnt3 transcription. iv) Hoxc13 versus Wnt3 transcription. k Effective dose-response curves obtained from integrating the averaged Jacobian elements over the corresponding normalized regulator mature mRNA regulator level in (j). Source data are provided as a Source Data file.\n\nTo test the consistency of dynamics across different modalities, we performed CellRank terminate stage analyses16 from the refined velocity vectors. Using GraphVelo velocities of either the RNA modality or the ATAC modality, we accurately estimated three diverse terminal stages (Fig.\u00a05b). For comparison, we also performed similar analyses using MultiVelo, scVelo with all velocity genes or robustly estimated genes in above GraphVelo studies and pseudotime-based vector field inferred by CellRank. The 2D projection of these vector field functions also exhibited seemingly correct velocity flow direction (Supplementary Fig.\u00a014a). However, none of them captured the cell fate commitment based on coarse-grained transition matrix (Fig.\u00a05b, Supplementary Fig.\u00a014b). Notably, the results from the RNA modality and the ATAC modality of MultiVelo gave inconsistent results. GraphVelo-corrected velocities, on the other hand, helped identify the top-correlating genes towards individual terminal populations which showed agreement with previous study64 (Fig.\u00a05c, Supplementary Fig.\u00a015).\n\nNext, we conducted differential geometry analyses based on the composite GraphVelo vector field. We identified novel root cells, which were also characterized by chromatin potential (Fig.\u00a05d)63. These novel root cells expressed distinct marker genes compared to the expected root cells using the Wilcoxon test (Fig.\u00a05e). Moreover, we unraveled differentially expressed markers identified by the original study63, as well as new differentiation-potent genes and validated their initiation properties in another transcriptome dataset (Supplementary Fig.\u00a016a)64. To further investigate how these two distinct groups of root cells convert to other cell types, we performed a least action path (LAP) analysis between different cell phenotypes. The expected and novel root cells converted to the IRS terminal state following two distinct least action paths in the vector field (Supplementary Fig.\u00a016b, c). The two paths revealed different temporal change patterns of transcription factor expression profiles (Supplementary Fig.\u00a016d). We calculated the mean-squared displacement (MSD) for every transcription factor to explore the dynamics of TFs along the path from novel root to IRS. The result demonstrated that the fate conversion by novel root was mediated by the Shh-Runx1 signaling axis (Fig.\u00a05f, Supplementary Fig.\u00a016d), which has been demonstrated in human embryonic stem cells65 and is crucial for hair development66. In summary, GraphVelo unraveled the multiple molecular mechanisms that orchestrated hair follicle morphogenesis.\n\nWith available chromatin velocity and RNA velocity, we set to quantify the coupling/decoupling relationships between chromatin structure and gene expression for each gene (see Methods). Here chromatin structure refers to the extent of exposure/accessibility of the gene locus to the environment as indicated by scATACseq data, shortly called open or closed state; and chromatin velocities refer to changes between these states as inferred from scATACseq counts at the examined gene locus. We used dynamic time warping (DTW) distances between the velocities from different omics layers to quantify the similarity between temporal patterns of these two modalities for each gene. A higher DTW value indicates higher similarity. Using the elbow of the ranked distance curve as a cutoff we identified genes that showed decoupled transcription and chromatin structure dynamics. These decoupled genes had an accumulation of cell cycle-dependent (CCD) genes found in previous study20 (Fig.\u00a05g). This group of genes showed strong involvement in cell cycle-related processes, as indicated by GO enrichment analyses (Fig.\u00a05h). Close examinations indicated that the transcription of cell cycle related genes decreased along the differentiation path, while the chromatin structure at the corresponding loci remained open (Fig.\u00a05i, Supplementary Fig.\u00a017). To validate this hypothesis, we further applied GraphVelo to a recently published 10x Multiome dataset from developing human cortex67 (Supplementary Fig.\u00a018a). Following the same analyses, we identified decoupled genes and found out that most of these genes were related to cell cycle (Supplementary Fig.\u00a018b\u2013f). which has also been reported in a previous MultiVelo study13.\n\nWe further performed dynamo differential geometry analyses on the composite transcriptome-chromatin vector field. One intriguing phenomenon observed in lineage dynamics is that Lef1 and Hoxc13 are the driver TFs correlated with domains of regulatory chromatin (DORCs) of Wnt363. Differential geometry analyses on the composite vector field can go beyond correlation analyses and provide an underlying casual mechanism. As a prerequisite for such analyses, GraphVelo-inferred RNA and chromatin velocities of the three genes correctly predicted the trend of change of mRNA and ATAC-seq counts (Supplementary Fig.\u00a019a, b), in contrast to the performance of MultiVelo (Supplementary Fig.\u00a019c). Then, Jacobian analyses on the GraphVelo vector field confirmed that priming activation of Lef1 subsequently activated the Hoxc13 TF68 (Supplementary Fig.\u00a019d, e). Both Lef1 and Hoxc13 were found to activate the Wnt3 target gene, initiating lineage commitment (Supplementary Fig.\u00a019f, g). To quantitatively understand how the two TFs affect Wnt3 chromatin structure and transcription, we plotted the response heatmap to reflect the distributions of Jacobian elements versus the abundance of mature mRNA for each TF (Fig.\u00a05j). The two terms \\(\\partial {f}_{{Wnt}3-{{{\\rm{chrom}}}}}/\\partial {x}_{{Lef}1}\\) and \\(\\partial {f}_{{Wnt}3-{{{\\rm{chrom}}}}}/\\partial {x}_{{Hoxc}13}\\) started with positive values at low concentrations of TF mRNA copy numbers then decreased to zero, indicating that increasing the level of either TF lead to further opening of the Wnt3 chromatin region, and the effect saturated at high TF expression. The other two term \\(\\partial {f}_{{Wnt}3}/\\partial {x}_{{Lef}1}\\) and \\(\\partial {f}_{{Wnt}3}/\\partial {x}_{{Hoxc}13}\\) increased with the TF levels, indicating that these two TFs also activated Wnt3 transcription. Upon integration of the Jacobian elements over regulator expression changes, we obtained the effective dose-response curves obtained (see Methods), which revealed more transparently the TF dose lag between the opening of the target chromatin region and the initialization of transcription (Fig.\u00a05k).\n\nOur results therefore illustrated the sequential events that these two driver TFs, Lef1 and Hoxc13, drove as pioneer transcription factors (PTFs) to initiate local chromatin opening and then activated the transcription of Wnt3. Notably, computational methods and experimental research confirm that Lef1 acts as a nucleosome binder and exhibits diverse binding patterns across various cell lines69. The Hox family of TFs has also been shown to have the capacity to bind their targets in an inaccessible chromatin context and trigger the switch to an accessible state70, consistent with our analyses that Hoxc13 revealing that it shared a regulation mechanism similar to that of PTF Lef1.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62784-w/MediaObjects/41467_2025_62784_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62784-w/MediaObjects/41467_2025_62784_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62784-w/MediaObjects/41467_2025_62784_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62784-w/MediaObjects/41467_2025_62784_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62784-w/MediaObjects/41467_2025_62784_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this work, we provided a general framework that extends the framework developed for RNA velocity and related approaches to various data modalities such as proteomics, spatial genomics, 3\u2009d genome organization, and imaging data, which were originally beyond the reach of this framework. We validated GraphVelo using various in vivo cellular kinetics models, confirmed its efficacy and robustness in handling complex and noisy multimodal data. Upon application to various datasets, we unraveled gene regulation relations of an extended list of genes, host-virus gene regulations, and coupling between transcription and local chromatin structures. GraphVelo can be seamlessly integrated with broad downstream analyses, such as dynamo continuous vector field analyses, as well as Markovian analyses using graph dynamo or CellRank.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Assume that a cell state can be represented by the cell volume V (or cellular compartment size) and the copy number of L \u226b 1 pairs of gene products (nm, np), where m and p designate mRNA and protein, and the bold fonts indicate vectors. For simplicity here we only consider m and p. It is straightforward to generalize to finer cell state specifications, for example, with distinction of nuclear and cytosol localizations, posttranslational states of proteins, other species such as microRNAs, epigenetic states, etc.\n\nThe temporal evolution of the cell state is described by a set of chemical master equations. When the copy numbers of molecular species are not too small, and the chemical reactions are not strongly coupled, Gillespie showed that the chemical master equations can be approximated by a set of chemical Langevin equations71. With extrinsic noises also included, we assume the ansatz that the dynamics of cell state is described by a set of generic stochastic differential equations,\n\nwhere the L-dimensional vectors \\({{{{\\bf{x}}}}}_{m}\\) and \\({{{{\\bf{x}}}}}_{p}\\) are cellular concentrations of m and p, and the \\(\\zeta\\) and \\(\\eta\\) are taken as white noises with zero mean.\n\nThe low-dimensional manifold assumption is central to machine learning approaches on data analyses. From a dynamical systems theory perspective, after a transient time, a multi-dimensional dynamical system often converges to a low-dimensional slow manifold. In practice, such property has been exploited with techniques such as quasi-steady-state approximation, quasi-equilibrium approximation. For a rigorous formulation, assume that one can identify a set of variables \\(\\left({{{\\bf{z}}}},{{{\\bf{Z}}}}\\right)\\) with (2L-M) dimensional fast variables \\({{{\\bf{z}}}}={{{\\bf{z}}}}({{{{\\bf{x}}}}}_{m},{{{{\\bf{x}}}}}_{p})\\) and M-dimensional slower variables \\({{{\\bf{Z}}}}={{{\\bf{Z}}}}({{{{\\bf{x}}}}}_{m},{{{{\\bf{x}}}}}_{p})\\). Xing and Kim extended the celebrated Zwanzig-Mori projection72,73 to a general dynamical system described by Eq.\u00a0474. The projection procedure results in a set of stochastic integral-differential equations of \\({{{\\bf{Z}}}}\\) with colored noises, which are formally equivalent to Eq.\u00a04. Then if one assumes clear time scale separation between \\({{{\\bf{z}}}}\\) and \\({{{\\bf{Z}}}}\\), the equations reduce to a set of Langevin equations with white noises,\\(\\frac{d{{{\\bf{Z}}}}}{{dt}}={{{\\bf{A}}}}({{{\\bf{Z}}}}({{{{\\bf{x}}}}}_{m},{{{{\\bf{x}}}}}_{p}))+\\eta \\left({{{\\bf{Z}}}},t\\right)\\), where \\(\\left({{{\\bf{Z}}}},t\\right)\\) are white noises with zero mean. Through ensemble averaging over the vicinity of a given point Z, one has\n\nThe equations define a M-dimensional manifold embedded in the \\(({{{{\\bf{x}}}}}_{m},{{{{\\bf{x}}}}}_{p})\\) space. A scRNA-seq data then measures the corresponding manifold projected to the transcriptomic subspace.\n\nOne should note that in practice the reported RNA velocity vector of a cell state i is typically obtained through averaging the raw velocity vectors of cell states within its neighborhood \\({{{{\\mathcal{N}}}}}_{i}\\) on the manifold as a numerical approximation of the ensemble average, \\({{{{\\bf{v}}}}}_{{{{\\bf{x}}}}i}= < \\frac{d{{{{\\bf{x}}}}}_{{{{\\bf{m}}}}}}{{dt}} > \\approx {\\sum }_{j\\in {{{{\\mathcal{N}}}}}_{i}}{{{{\\bf{v}}}}}_{{{{\\bf{x}}}}j}\\). In this work, we used the k-nearest neighbor (kNN) algorithm to define the neighborhood in single-modality datasets, including spatial-transcriptomics datasets. For multi-omics data, the neighborhood of a cell state was defined using weighted nearest neighbors (WNN)75 in the composite cell state space. The procedure of applying GraphVelo is the same for different data types, except using the neighborhoods defined on their corresponding data (cell state) manifold.\n\nEquation\u00a03 in the main text applies to the transformation between a manifold embedded in a state space and in a subspace. According to the Whitney Embedding theorem18, any smooth real M-dimensional manifold can be embedded in a 2M-dimensional real space provided that M\u2009>\u20090. Consider a full set of genes versus a subset in a scRNAseq dataset, or a combined scRNAseq/scATACseq multi-omics dataset versus the scRNAseq subset. Assume that the full cell state space has a dimensionality N, while a single cell data manifold is typically low-dimensional with M \u226a N. Then the Whitney Embedding theorem18 suggests that with proper choice of the subset the manifolds in the full space and the subspace are homeomorphic or at least piece-wise homeomorphic (Fig.\u00a01b, Supplementary Notes\u00a01.4), i.e., a one-to-one mapping exists between the two. Then applying Eq.\u00a03 allows one to infer the velocity vectors for the full-space representation from those of the subspace.\n\nLearning coefficients \\({{{{\\mathbf{\\phi }}}}}_{i}\\) in the gene space directly often fails due to thousands of gene profiles. To avoid the curse of high dimensionality and learn parameters in a compact manifold, we designed a procedure to denoise the velocities in a reduced PCA space. Specifically, we extrapolated the cell state i in the original space using the infinitesimal propagation operator to extrapolate the future state:\n\nMoreover, we estimated an optimal step size dt based on the local density to guarantee the cell states are bound to the manifold:\n\nAfter utilizing the cell-dependent \\({t}_{i}\\) to forcing the predictions inhabiting regions of the phenotypical manifold, we applied dimension reduction to project both current and future status from the gene space to the PCA space through linear transformation. Then we obtained the projected velocity vectors as:\n\nwhere Q is the PC loading matrix estimated using x that serves as the coordinate transformation matrix.\n\nThe kinetic assumptions between nascent and mature RNA fail when the underlying parameters shift along the developmental trajectory11, which leads to transcription burst and rapid degradation in the phase portraits (Fig.\u00a03a). An internal clock exists during cell proliferation and differentiation. Current methods rely on different criteria to select confident estimated velocity genes (see\u00a0Supplementary Notes\u00a01.5 for detailed discussion). Here, we presume that the velocity of robustly estimated genes should be consistent with the (pseudo)time derivative estimated under the manifold assumption. We can utilize any available ts inferred from data manifold by either pseudotime, velocity latent time, or lineage tracing to approximate the temporal information and use k nearest neighbors to define the locally linear plane. After ordering cells within the local Euclidean space, we calculate the MacK score for any gene g as an indicator of whether the sign of estimated velocity agrees with the dynamic cascades within manifold,\n\nWhere \\({{{{\\mathcal{N}}}}}_{i}\\) indicates the neighbor points of cell i, \\({\\mathbb{l}}\\) represents the indicator function and sgn returns the sign of the values. \\(\\Delta {x}_{{ij}}\\left(g\\right),{v}_{i}(g)\\) are the difference in abundance of gene g between cell i and j, and the velocity of gene g in cell i, respectively. We parallelize the calculation to scale efficiently with the number of genes, which is important due to the number of highly variable genes. We want to point out that one can use methods other than the MacK score to identify genes with reliable velocity estimations.\n\nDynamo7 offers a correction strategy by removing genes with low gene-wise confidence in the phase plane. This allows us to identify genes that appear in incorrect phase portrait positions and contribute to erroneous flow directions (illustrated in Fig3. a). To filter out genes with misleading dynamic patterns, one can supply the established lineage hierarchy information to the dyn.tl.confident_cell_velocities function in dynamo. This function scores each gene based on the agreement of its behavior in the splicing phase diagram with the input lineage hierarchy priors.\n\nDynamo is a general framework of reconstructing dynamical models from scRNAseq data, and it is straightforward to generalize to multi-modal data. The framework is based on specific realizations of Eq.\u00a05, \\({{{\\bf{v}}}}\\equiv < \\frac{d{{{\\bf{x}}}}}{{dt}}{ > }_{{{{\\bf{x}}}}}={{{\\bf{A}}}}\\left({{{\\boldsymbol{x}}}}\\right)\\), with state vector x being the transcript concentrations for scRNAseq data, and combined transcript concentrations and continuous quantification of locus-specific chromatin open-close state for multi-omics scRNAseq/scATACseq data. The variables x can be defined in various representations, e.g., the original gene space, principal component subspace, latent space defined by a variational autoencoder, etc. With GraphVelo it is straightforward to transform \\({{{{\\bf{v}}}}}_{{{{\\bf{x}}}}}\\) between different representations.\n\nThe continuous vector field functions \\({{{\\bf{A}}}}\\left({{{\\bf{x}}}}\\right)\\) contain quantitative regulation relations between genes that are learned from single cell data points of (x, vx). Various algorithms can be used to learn the analytical forms of \\({{{\\bf{A}}}}\\left({{{\\bf{x}}}}\\right)\\). The original dynamo paper illustrated a Reproducing Kernel Hilbert Space (RKHS) representation method. The method expresses \\({{{\\bf{A}}}}\\left({{{\\bf{x}}}}\\right)\\) as a linear combination of pre-selected basis functions, \\({{{\\bf{v}}}}={{{\\bf{A}}}}\\left({{{\\bf{x}}}}\\right)={\\sum }_{\\alpha }{{{{\\bf{c}}}}}_{{{{\\boldsymbol{\\alpha }}}}}{{{{\\mathbf{\\Gamma }}}}}_{{{{\\rm{\\alpha }}}}}({{{\\bf{x}}}})\\), similar to the more familiar Taylor expansion that uses a linear combination of polynomial functions to represent a continuous analytical function. It should be noted that the basis functions and so \\({{{\\bf{A}}}}\\left({{{\\bf{x}}}}\\right)\\) are generally nonlinear functions of x. Following Qiu et al.7., we chose Gaussian functions centered at selected reference points \\({\\widetilde{{{{\\bf{x}}}}}}_{{{{\\rm{\\alpha }}}}},{{{{{\\mathbf{\\Gamma }}}}}_{a}\\left({{{\\bf{x}}}}{,}{\\widetilde{{{{\\bf{x}}}}}}_{{{{\\rm{\\alpha }}}}}\\right)={{{\\rm{e}}}}}^{-2w{\\|{{{\\bf{x}}}}{-}{\\widetilde{{{{\\bf{x}}}}}}_{\\alpha }\\|}^{2}}\\), with default parameter value of w in the package dynamo. Then we determined the coefficient vectors c\u03b1 through minimizing the loss function \\(\\Phi ({{{{\\bf{c}}}}}_{1},{{{{\\bf{c}}}}}_{2},\\ldots,{{{{\\bf{c}}}}}_{{{{\\rm{m}}}}})={\\sum }_{i=1}^{n}{\\|{{{{\\bf{v}}}}}_{i}-{\\sum }_{\\alpha }\\Gamma ({{{\\bf{x}}}},{\\widetilde{{{{\\bf{x}}}}}}_{\\alpha }{)}{{{{\\bf{c}}}}}_{\\alpha }\\|}^{2}+\\frac{\\lambda }{2}{\\sum }_{\\alpha=1}^{m}{\\sum }_{\\beta=1}^{m}{{{{\\bf{c}}}}}_{\\alpha }^{\\top }\\varGamma ({\\widetilde{{{{\\bf{x}}}}}}_{\\alpha },{\\widetilde{{{{\\bf{x}}}}}}_{\\beta }{)}{{{{\\bf{c}}}}}_{\\beta },\\) where the first sum was over all the data points, and the second term was Tikhonov regularization weighted by \u03bb. The superscript T means matrix transpose. One can also use neural networks, e.g., variational autoencoders, to learn an optimal set of basis functions, and other algorithms such as neural ODE to learn \\({{{\\bf{B}}}}\\left({{{\\bf{x}}}}\\right)\\). The difference is merely algorithmic under the same framework of dynamical systems theories.\n\nThe extended dynamo vector field generally contains a nonlinear relationship about regulation between genes, and between genes and other modalities (e.g., chromatin open/close conformations). Several posterior interpretation methods exist to analyze the vector field. Below, we will describe two of them.\n\nWith the analytical form of F(x), one can calculate efficiently the Jacobian field J at any cell state x. Each element of J, \\(({J}_{{ij}}=\\partial {F}_{i}/\\partial {x}_{j})\\), can be understood as an in-silico perturbation experiment on how upregulating gene j affects the transcription rate of gene i, with all other gene expression levels kept constant at state x. For example, a positive value of \\({J}_{{ij}}\\) indicates that at x further increasing the expression of gene j causes increase of the transcription rate of gene i. Note that the sign and value of a Jacobian element alone does not unambiguously reflect the nature of the regulation. A close-to-zero Jacobian element can be associated with either no regulation or the regulator is at a saturating concentration of regulation. The regulation relation can be direct, or indirect through intermediate molecular species not implicitly treated as variables of the vector field function.\n\nComplementary to the local Jacobian analysis is to reconstruct effective dose-response (D-R) curves of a regulator-target gene pair. The curve reveals the rate of change of one quantity, e.g., the transcription rate or the chromatin open/close dynamics of the target gene, as a function of the value of a regulator, e.g., the mRNA level of a regulator or the chromatin open/close status of a specific genomic region. Note that the D-R curve is generally a multi-variate function, we designed a procedure to reconstruct an effective one-variate function7. One can genetically write the regulation on quantity \\({x}_{i}\\) as two terms with and without dependence on variable \\({x}_{j}\\),\n\nNotice that \\({F}_{i}^{2}\\) is not a function of gene j. First, we calculated the Jacobian element \\(\\frac{\\partial {F}_{i}}{\\partial {x}_{j}}\\) for each measured cell state. Note \\(\\frac{\\partial {F}_{i}}{\\partial {x}_{j}}=\\frac{\\partial {F}_{i}^{1}}{\\partial {x}_{j}}\\), so the background variation from \\({F}_{i}^{2}\\) due to effects of other genes has been numerically removed. Then from the histogram of \\(\\frac{\\partial {F}_{i}}{\\partial {x}_{j}}\\) versus \\({x}_{j}\\), we binned \\(\\frac{\\partial {F}_{i}}{\\partial {x}_{j}}\\) over \\({x}_{j}\\) and calculated \\( < \\frac{\\partial {F}_{i}}{\\partial {x}_{j}}{ > }_{\\alpha }\\), which was averaged over all data points of \\(\\frac{\\partial {F}_{i}}{\\partial {x}_{j}}\\) within bin \\(\\alpha\\). Next, we performed numerical integration to obtain \\(\\bar{{F}_{i}^{1}}({x}_{j})={F}_{i0}^{1}+{\\int }_{0}^{{x}_{j}} < \\frac{\\partial {F}_{i}}{\\partial {x}_{j}}{ > }_{\\alpha }d{x}_{j}\\approx {F}_{i0}^{1}+{\\sum }_{\\alpha } < \\frac{\\partial {F}_{i}}{\\partial {x}_{j}}{ > }_{\\alpha }\\Delta {x}_{j\\alpha }\\). In practice, if \\(\\frac{\\partial {F}_{i}}{\\partial {x}_{j}}\\) shows large variance within each bin of \\({x}_{j}\\), it may imply that other factors affect the D-R curve. For example, the regulation of \\({x}_{j}\\) on \\({x}_{i}\\) may even be opposite at the presence or absence of a specific cofactor. In this case, one should first cluster cells, e.g., grouping cells based on whether an identified cofactor reaches a threshold value, then perform the D-R curve reconstruction on individual clusters.\n\nMarius et al.16 have developed a framework named CellRank to study cellular dynamics based on a Markov chain formulation. We use CellRank to identify cell state transitions using a velocity kernel and identify terminal states within datasets by the GPCCA function module. In addition, CellRank pseudotime kernel8 is used for methods comparison in real datasets.\n\nDynamo learns a nonlinear function form of RNA velocity vector field, providing a physics-informed framework that integrates mechanism modeling and single-cell data analyses. We use dynamo to learn continuous vector field functions and perform differential geometry analyses such as gene acceleration, vector field-based pseudotime, least action path (LAP), Jacobian analyses and in silico perturbation.\n\nGraphVelo itself does not compute an ordering index of cells as we are seeking for a more quantitative method to infer RNA velocity. With an accurate RNA velocity as input, we can approximate the vector field precisely. Thus, we use the scalar potential estimated from the functional form vector field with Hodge decomposition as a proxy of time, which is implemented by dynamo package.\n\nWhile the GraphVelo framework is designed to quantify velocity vectors across different representations, known transitions between coarse cell states\u2014such as cell types or cell cycle phases\u2014can be used to evaluate the correctness of velocity directions. Suppose there are two cell populations A and B, with A a progenitor state of B. One can define the set of boundary cells between A and B as\n\nwhere \\({{{{\\mathcal{C}}}}}_{{{{\\rm{A}}}}}\\) or \\({{{{\\mathcal{C}}}}}_{{{{\\rm{B}}}}}\\) denote the sets of cells in state A or state B, \\({{{{\\mathcal{N}}}}}_{c}\\) indicates the kNN of cell c. The CBC score is then defined as\n\nwhere \\({\\#c}^{\\prime} \\in {{{{\\mathcal{C}}}}}_{{{{\\rm{B}}}}}\\cap {{{{\\mathcal{N}}}}}_{c}\\) is the number of cells in state B which is also the kNN of cell c. While the \\(({{{{\\boldsymbol{x}}}}}_{c},{{{{\\boldsymbol{v}}}}}_{c})\\) can be represented in different basis (raw count, PCA, or UMAP), we computed the CBC score in the original count space to ensure that all genes contribute to the velocity estimation. We deliberately avoided using 2D embeddings like UMAP for this purpose, as such visualizations may distort the true geometric relationships in the high-dimensional space and could lead to misleading interpretations.\n\nFor most of the cases, we expect the inferred RNA velocity vectors to be coherent in a uni-directional vector field. To quantify the local consistency of the velocity flow for each cell, we calculate the velocity consistency score2 for each cell i as the mean correlation of its velocity \\({v}_{i}\\) with velocities from neighboring cells,\n\nwhere cell j is the neighbor of cell i and cos indicates the cosine similarity. One thing should be clarified is that overly smooth and homogenized velocity fields may obscure biologically meaningful heterogeneity.\n\nThe variation of transcription rates contains the high-order dynamic information of the cell system. To model the dynamic patterns of RNA velocity along the transition path from noisy data, we refine the velocity vectors by local geometry via TSP and further fit GAM to velocity value of each gene that has been refined by GraphVelo. For any gene g, we model the velocity trend for the temporal variable t via\n\nWhere \\({v}_{{gi}}\\) indicates the velocity of gene g in cell i, f is built using penalized B-splines which allow us to automatically model non-linear mapping while maintaining additivity76. To visualize the velocity trends, we select 100 equally spaced testing points along the transition path and predict gene expression at each of them using the fitted model. The estimated velocity trends can be treated as smoothed time series for further analyses.\n\nWith manifold-constrained velocity estimated by GraphVelo, we are able to cluster genes into different functional modules that are involved in the same regulatory circuit. We recover transcription variation of both host and virus factors along the infection trajectory by fitting GAMs in the temporal indicator, the percentage of viral RNA. Next, we select 100 equally spaced time points and generate the GAM-smoothed velocity trends. We compute a kNN graph and cluster the kNN graph using the Leiden algorithm. We used k\u2009=\u200915 for the velocity-trend kNN graph and the Leiden algorithm with a resolution parameter set to 0.3 to avoid over-clustering the trends. For each recovered cluster, we compute its mean and standard deviation (pointwise, for all generated points that were used for smoothing) and visualize the smoothed trends per cluster.\n\nWe perform the DTW distance calculation by dtaidistance package. To eliminate the influence of scale in different modality, we maximum-normalize the chromatin/RNA velocity to the same range of [0, 1]. Then we fit the velocity trends of both modalities along vector field-based pseudotime to yield the smoothed velocity trends. We calculate the DTW distance between velocity trends per gene. To distinguish the decoupling genes based on their multi-modality velocities, we rank the genes based on the DTW distance and identify the elbow point as a cutoff. Genes with a distance metric larger than the cutoff are characterized as decoupling genes and used for visualization and functional analysis.\n\nThe bifurcation data (n\u2009=\u20092000 cells) for the toggle-switch system is simulated using the Gillespie algorithm. We use activation and inhibition Hill functions to model the induction and suppression effects between the two genes:\n\nWe use the simulation backend implemented by dynamo with default parameters except the timescale (reset \u03c4\u2009=\u20091) to generate the bifurcating process. We then map the synthetic dataset onto a sphere (radius r\u2009=\u200970) and yield the variable z as:\n\nThen we are able to calculate the correctly-scaled 3d vectors by infinitesimal propagation operator with sufficient small step size (dt\u2009=\u20091 in our case):\n\nTo generate high-dimensional single-cell transcriptomic data in silico, we use a multi-modal simulation engine, dyngen, to account for different developmental topologies. We constructe module networks to represent regulatory cascades and feedback loops driving progressive changes in gene expression and influencing cell fate decisions. We generate three datasets with 1000 cells and 100 genes using the linear, cyclic, and bifurcating loop backbones provided by dyngen, with all other parameters set to default values. These datasets include simulated nascent and mature mRNA counts along with ground-truth RNA velocities and known manifold structure.\n\nAs for genes with variable degradation rate, we present a minimal regulatory network with linear model in which an external signal both inhibits transcription and promotes microRNA (miRNA). The miRNA exerts a linear influence on the degradation rate of mRNA. We have miRNA\u2019s velocity as\n\nThe nascent gene transcription rate and mature mRNA\u2019s degradation rate would change to\n\nwhere \\({{{{\\rm{\\alpha }}}}}_{0}\\) and \\({{{{\\rm{\\gamma }}}}}_{0}\\) represent the constant transcription rate and degradation rate without the effect of miRNA, \\({k}_{{{{\\rm{\\alpha }}}}}\\) and \\({k}_{{{{\\rm{\\gamma }}}}}\\) represent the magnitude of influence from miRNA, t indicates the simulation time to mimic the cell-context change along trajectory.\n\nWe set the initial condition to a steady state with \\({u}_{0}={\\alpha }_{0}/\\beta\\) and \\({s}_{0}={\\alpha }_{0}/{\\gamma }_{0}\\), while the miRNA abundance \\({m}_{0}=0\\). We simulate \\({u}_{t}\\) and \\({s}_{t}\\) as the microRNA signal \\({m}_{t}\\) gradually increases. The aim is to evaluate whether the estimated RNA velocity consistently aligns in sign with the ground truth RNA velocity.\n\nTo generate genes with transcription burst phase portrait, we set the initial condition to \\({u}_{0}=0,{s}_{0}=0\\), together with \\({{{\\rm{\\gamma }}}}\\) as constant and \\(\\alpha\\) promotes to \\({\\alpha }^{{\\prime} }=3\\alpha\\) when the simulation reaches specific time.\n\nAll sequencing data in this study are downloaded publicly (see details in the \u2018Data availability\u2019 section). Though the number of confident-estimated gene sets differed case by case, GraphVelo predicted the velocity of all highly variable genes and used them for downstream calculations such as CBC score, CellRank velocity kernel, and dynamo vector field learning. We parallelized the TSP optimization to scale efficiently and set the hyperparameters in loss with a\u2009=\u20091, b\u2009=\u200910 and \u03bb\u2009=\u20091 as default in all studies (Supplementary Fig.\u00a020).\n\nFor the erythroid lineage of the mouse gastrulation, we follow the standard data pre-processing procedures implemented by scVelo and select 9815 cells and 2000 highly variable genes to construct the kNN graph using 30 nearest neighbors for downstream calculation.\n\nFor the human bone marrow dataset, we follow the standard data pre-processing procedures implemented by scVelo and selected 5780 cells and 2000 highly variable genes to construct the k-nearest neighbor graph using 30 nearest neighbors for downstream calculation. To estimate the variation of degradation rate \\(\\gamma\\) along the differentiation lineages, we divide the cells into five discrete time bins based on precomputed Palantir pseudotime. We then estimate cell-specific degradation rates and visualized their distribution shifts along the hematopoiesis trajectory.\n\nFor the mouse coronal hemibrain spatial dataset, we follow the monocle preprocessing pipeline implemented in dynamo and select 7,765 cells and 2000 highly variable genes. To include spatial information during manifold, we build a spatial kNN graph with k\u2009=\u20098 and then take the union with the kNN graph based on transcriptomic data. The combined graph is used for downstream analyses.\n\nWe sample the cells from donor 1 to eliminate sample-specific variation and further filter out cells lacking immediate early UL123 gene expression to focus on the viral infection trajectory. We adapt the monocle preprocessing recipe implemented by dynamo and yield 1454 cells and 2000 highly variable genes for further analyses. The number of nearest neighbors were set to 30 as default. Then 1022 velocity genes are used for downstream analyses.\n\nWe collect the pathway-related genes from MSigDB and perform the Jacobian analyses implemented by dynamo, using viral genes as regulators and host genes as effectors. We rank the regulation relationships based on the collections of Jacobian elements. We pick the top 50 inhibited effectors of viral genome and select the common set between pathway genes and all the effectors for visualization.\n\nIn silico knock-out experiments are performed via Dynamo. We suppressed every single virus factors per a time using dynamo.pd.perturbation function and calculated the change of total viral gene expression after perturbation.\n\nWe subsample the cells based on their infection status. We adapt the monocle preprocessing pipeline implemented in dynamo and exclude the UMI reads from lenti-virus. We obtain 5001 infected cells and 1000 highly variable genes for downstream analyses. We use both highly variable genes in host and virus genes to perform PCA and calculate the nearest neighbors with k\u2009=\u200930. Then 269 velocity genes are used for downstream analyses. We use the apoptosis-related genes collected by KEGG to calculate the apoptosis activity score of each cell using dynamo.tl.score_cells function.\n\nIn traditional scRNA-seq datasets, RNA velocity methods use smoothed spliced and unspliced RNA counts through nearest-neighbor pooling, based on the PCA space computed from transcripts alone. However, this approach is not suitable for multimodal scenarios, as it overlooks hidden variables by relying on a single modality. To construct a consistent manifold combining information from multi-modal genomics data, we utilized WNN as implemented in MultiVelo13. The WNN algorithm combines low-dimensional representations from RNA and ATAC omics data. Specifically, we use PCA results from scRNA-seq data and latent semantic indexing (LSI) from scATAC-seq as inputs. The nearest neighbors identified by WNN were then used to calculate the first moment, reducing noise in separate modalities and approximating a unified manifold for GraphVelo.\n\nThe preprocessed SHARE-seq mouse skin dataset63 is adopted directly from MultiVelo data resources. All the procedures are consistent with MultiVelo, except we get the LSI representation processed by SCARlink77. We construct the WNN graph using 50 nearest neighbors for downstream calculation. We run scVelo with \u2018stochastic\u2019 mode to estimate the RNA velocity based on the WNN graph as we discussed above.\n\nThe preprocessed human cerebral cortex data is adopted directly from the MultiVelo data resources. All the procedures are consistent with MultiVelo, except we get the LSI representation processed by SCARlink. We construct the WNN graph using 50 nearest neighbors for downstream calculation. We run scVelo with \u2018stochastic\u2019 mode to estimate the RNA velocity based on the WNN graph.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All the sequencing raw data are publicly accessible. The A549 dataset can be accessed via https://figshare.com/ndownloader/files/53666738. The FUCCI cell cycle data can be downloaded from https://figshare.com/ndownloader/files/53705057. The processed data used for benchmark are available at https://figshare.com/ndownloader/files/53667548. The mouse gastrulation subset to erythroid lineage can be extracted using scVelo\u2019s CLI: scvelo.datasets.gastrulation_erythroid() or from the original work under accession number E-MTAB-6967 of ArrayExpress. The human bone marrow can be extracted using scVelo\u2019s CLI: scvelo.datasets.bonemarrow() or through the Human Cell Atlas data portal at https://data.humancellatlas.org/explore/projects/091cf39b-01bc-42e5-9437-f419a66c8a45. The mouse coronal hemibrain spatial transcriptomic data can be downloaded from (https://www.dropbox.com/s/c5tu4drxda01m0u/mousebrain_bin60.h5ad?dl=0). The original HCMV infected moDC data can be accessed via Zenodo (https://zenodo.org/records/10404879) and the processed data can be downloaded from https://figshare.com/ndownloader/files/53666756. The SARS-CoV-2 data can be accessed via https://figshare.com/ndownloader/files/53666588. The preprocessed mouse skin development dataset can be accessed via https://figshare.com/articles/dataset/Mouse_Hair_Follicle_RNA_Data/22575307 and https://figshare.com/articles/dataset/Mouse_hair_follicle_ATAC_data/22575313. The preprocessed human cortex dataset can be downloaded from https://figshare.com/articles/dataset/Developing_Human_Cortex_RNA_Data/22575376 and https://figshare.com/articles/dataset/Developing_Human_Cortex_ATAC_Data/22575370.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The source code of python package GraphVelo78 can be downloaded from https://github.com/xing-lab-pitt/GraphVelo and is released under the BSD 3-Clause License. Reproducibility and tutorials can be found in https://github.com/xing-lab-pitt/GraphVelo/tree/main/notebook and https://graphvelo.readthedocs.io/en/latest/. The specific version of the code associated with this publication is archived in Zendo and is accessible via https://doi.org/10.5281/zenodo.15852884.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "La Manno, G. et al. RNA velocity of single cells. 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This work was supported by the National Key Research and Development Program of China (2023YFE0112300 to M.C.); National Natural Sciences Foundation of China (32261133526; 32270709; 32070677 to M.C.); the 151 talent project of Zhejiang Province (first level to M.C.), the Science and Technology Innovation Leading Scientist (2022R52035 to M.C.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Yuhao Chen, Yan Zhang, Jiaqi Gan.\n\nDepartment of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China\n\nYuhao Chen\u00a0&\u00a0Ming Chen\n\nDepartment of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA\n\nYuhao Chen,\u00a0Yan Zhang,\u00a0Ke Ni\u00a0&\u00a0Jianhua Xing\n\nDepartment of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA\n\nJiaqi Gan\u00a0&\u00a0Jianhua Xing\n\nZhejiang Key Laboratory of Multi-omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, 310016, Hangzhou, China\n\nMing Chen\n\nLaufer Center for Physical and Quantitative Biology, and Department of Biochemistry and Cell Biology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA\n\nIvet Bahar\n\nUniversity of Pittsburgh Hillman Cancer Center, Pittsburgh, PA, USA\n\nJianhua Xing\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.X. conceived and formulated the theoretical framework, Y.C. and J.G. performed tests on simulated data, Y.C. and J.X. analyzed scRNA-seq datasets, Y.C., Y.Z., J.G., and K.N. developed the package graphvelo, Y.C. and J.X. wrote the draft, M.C. and I.B. revised the manuscript.\n\nCorrespondence to\n Ming Chen, Ivet Bahar or Jianhua Xing.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Zheng Hu, Shihua Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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GraphVelo allows for accurate inference of multimodal velocities and molecular mechanisms for single cells.\n Nat Commun 16, 7831 (2025). https://doi.org/10.1038/s41467-025-62784-w\n\nDownload citation\n\nReceived: 27 December 2024\n\nAccepted: 30 July 2025\n\nPublished: 22 August 2025\n\nVersion of record: 22 August 2025\n\nDOI: https://doi.org/10.1038/s41467-025-62784-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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loss causes cancer vulnerability to immune checkpoint blockade in triple-negative breast cancer", + "journal": "Nature Communications", + "published": "27 April 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_MOESM6_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_MOESM7_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_MOESM8_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-47987-x#ref-CR60", + "/articles/s41467-024-47987-x#MOESM5", + "/articles/s41467-024-47987-x#MOESM6", + "/articles/s41467-024-47987-x#MOESM4", + "https://depmap.org/portal", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194040", + "/articles/s41467-024-47987-x#Sec35" + ], + "code": [], + "subject": [ + "Breast cancer", + "Neddylation", + "Tumour immunology" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-2687913/v1.pdf?c=1714302356000", + "research_square_link": "https://www.researchsquare.com//article/rs-2687913/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-47987-x.pdf", + "preprint_posted": "24 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Immune checkpoint blockade therapy aims to activate the immune system to eliminate cancer cells. However, clinical benefits are only recorded in a subset of patients. Here, we leveraged genome-wide CRISPR/Cas9 screens in a Tumor-Immune co-Culture System focusing on triple-negative breast cancer. We revealed that NEDD8 loss caused a vulnerability to nivolumab. Genetic deletion of NEDD8 only delayed cell division initially but cell proliferation was unaffected after recovery. Since the NEDD8 gene is commonly essential, we validated this paradigm shift with additional CRISPR screens and uncovered significantly enhanced immunogenicity in NEDD8 deficient cells using proteomics. In immunocompetent mice, PD-1 blockade lacked efficacy against established EO771 breast cancer tumors. In contrast, we observed curative effects mediated by CD8+ T cells against NEDD8 deficient EO771 tumors after PD-1 blockade. In essence, we provide evidence that NEDD8 is conditionally essential in triple-negative breast cancer and presents a synergistic drug target for PD-1/L1 blockade therapy.Biological sciences/Cancer/Tumour immunologyBiological sciences/Cell biology/Post-translational modifications/Neddylation", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nY.M. and M.P.M. were former employees of AstraZeneca and hold company shares. Y.M. received funding from Bayer Pharmaceuticals and Novo Nordisk Foundation for unrelated projects. M.P.M. receives funding from Roche and GSK for other projects. Other authors declare no conflict of interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "DataS1gRNAcounts.xlsxData S1 gRNA counts from CRISPR screensDataS2NEDD8KOproteomics.xlsxData S2 Proteomics analysis of control or NEDD8 KO cellsDataS3normaliseddatananostring.xlsxData S3 Normalized RNA counts for nanostring analysis20240226reagenttableR2.pdfTable S1-S4nrreportingsummaryPapakyriacouetalprint.pdfReporting summary", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Immune checkpoint blockade therapy aims to activate the immune system to eliminate cancer cells. However, clinical benefits are only recorded in a subset of patients. Here, we leverage genome-wide CRISPR/Cas9 screens in a Tumor-Immune co-Culture System focusing on triple-negative breast cancer (TNBC). We reveal that NEDD8 loss in cancer cells causes a vulnerability to nivolumab (anti-PD-1). Genetic deletion of NEDD8 only delays cell division initially but cell proliferation is unaffected after recovery. Since the NEDD8 gene is commonly essential, we validate this observation with additional CRISPR screens and uncover enhanced immunogenicity in NEDD8 deficient cells using proteomics. In female immunocompetent mice, PD-1 blockade lacks efficacy against established EO771 breast cancer tumors. In contrast, we observe tumor regression mediated by CD8+ T cells against Nedd8 deficient EO771 tumors after PD-1 blockade. In essence, we provide evidence that NEDD8 is conditionally essential in TNBC and presents as a synergistic drug target for PD-1/L1 blockade therapy.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Triple-negative breast cancer (TNBC) accounts for 10\u201315% of all breast cancer cases and it is the most aggressive and invasive breast cancer type with limited treatment options1. TNBC is characterized by the lack of estrogen and progesterone receptors, and shows no over-expression or amplification of human epidermal growth factor receptor 21. Chemotherapy is the standard-of-care therapy for TNBC but patients with advanced disease often develop resistance and show poor clinical outcome2. Therefore, TNBC represents a significant unmet clinical need requiring new treatment options to bring benefits to the patients.\n\nReinvigoration of anti-tumor immunity through immune checkpoint blockade (ICB) therapy against the PD-1/L1 axis has generated unprecedented clinical responses in several cancer types and is currently one of the most extensively evaluated research areas in oncology3. The therapeutic potential of ICB therapy has been tested in multiple randomized, placebo-controlled phase 3 clinical trials in TNBC patients with advanced disease. For example, pembrolizumab as a monotherapy did not outperform chemotherapy in a phase 3 clinical trial (KEYNOTE-119)4. In addition, combination of atezolizumab and nab-paclitaxel chemotherapy significantly prolonged the progression-free survival (PFS) in advanced TNBC patients (IMpassion130)5. However, benefits on overall survival (OS) did not reach statistical significance6, nor was validated in a confirmatory trial, i.e., IMpassion1317. Combining pembrolizumab with chemotherapy significantly improved PFS and OS of advanced TNBC patients and has been approved by the FDA, if stratified for PD-L1 positive tumors8. These results demonstrate the potential of immunochemotherapy but also highlight the clinical challenges in TNBC, including disease heterogeneity, choice of chemotherapy, genetic background of cancer cells, as well as the lack of validated biomarkers for patient stratification9.\n\nIn order to map the immune-regulatory landscape in cancer cells, genome-wide CRISPR/Cas9 screens have been employed in co-cultures of genetically engineered human cytotoxic T cells and human cancer cells. Essential genes for efficient killing of human melanoma cells by T cell receptor (TCR)-transduced T cells have been identified and validated10. When co-cultured with chimeric antigen receptor (CAR)-modified T cells, defects in the death receptor pathways enabled leukemic cell survival and escape of T cell-mediated killing11. A recent study also employed genome-wide CRISPR activation screens to identify melanoma cancer intrinsic resistance to genetically modified human T cells12. These previous studies reveal deep mechanistic insight on the recognition of human cancer cells by T cells and could have a significant impact on the clinical implementation of adoptive cell therapy.\n\nIn this study, we aim to reveal and validate cancer vulnerabilities to ICB drugs in human TNBC cells. This is achieved by performing genome-wide CRISPR/Cas9 screens in a Tumor-Immune co-Culture System (TICS) that has been designed to investigate clinically approved ICB antibodies13. We identify that gRNAs targeting the NEDD8 gene are significantly depleted from TNBC cells in the presence of nivolumab, suggesting its role as a TNBC vulnerability to ICB treatment. Further mechanistic investigations using advanced human cell assays and syngeneic mouse models confirm the strong immunogenic effects and anti-tumor efficacy as a result of Nedd8 deletion in ICB-treated TNBC cells. In addition, our data reveal that essentiality of some \u201ccommon essential\u201d genes, such as NEDD8, can be compensated during cell reprogramming. We propose that targeting protein neddylation could enhance response to ICB drugs in TNBC patients. However, current pharmacological inhibitors against protein neddylation should be optimized due to the inhibitory effects on immune cells and potential off-target liabilities.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To perform mechanistic investigation of clinically approved ICB drugs, i.e., nivolumab and durvalumab, we optimized a human Tumor-Immune co-Culture System (TICS), where primary human lymphocytes from healthy blood donors were co-cultured with human cancer cells. As shown in Fig.\u00a01a, a human TNBC cell line, MDA-MB-231, significantly enhanced the release of granzyme B and interferon \u03b3 (IFN\u03b3) in the presence of nivolumab or durvalumab in a ratio-dependent manner. To prove that the activation of primary human lymphocytes in TICS was dependent on antigens presented by TNBC cancer cells, we interrupted antigen presentation to CD8+ T cells by either genetic deletion of the B2M gene in cancer cells or by using a blocking antibody against HLA-ABC (Supplementary Fig.\u00a01a). This abolished the proliferation of CD8+ T cells, but had no effects on the proliferation of CD4+ T cells and natural killer (NK) cells primed by cancer cells in the same experiment (Supplementary Fig.\u00a01b).\n\na Primary human lymphocytes (300,000 per well) were co-cultured with MDA-MB-231 cells in a 96-well flat bottom plate \u00b110\u2009\u03bcg/ml nivolumab (red) or durvalumab (blue). Levels of soluble granzyme B or interferon \u03b3 (IFN\u03b3) in culture supernatants were measured on day 5 by ELISA, mean\u2009\u00b1\u2009SD, unpaired two-tailed T-test. Each symbol represents an individual lymphocyte donor (n\u2009=\u20094). b Schematic illustration and (c) the 9 overlapping hits from the genome-wide CRISPR screens when comparing co-cultures \u00b110\u2009\u03bcg/ml nivolumab. d Demonstration of the 9 commonly depleted genes according to individual gRNAs performance (depleted gRNAs in blue and enriched gRNAs in red). e Analysis of the clinical relevance of NEDD8 mRNA expression in breast cancer patients receiving paclitaxel in combination with pembrolizumab (n\u2009=\u200969) as part of the I-SPY2 neoadjuvant platform trial. Source data are provided as a source data file.\n\nAfter several optimization steps, TICS was adapted to enable genome-wide CRISPR screens to reveal genes that conferred vulnerability to ICB drugs in MDA-MB-231 cells (Fig.\u00a01b). In brief, Cas9+ human TNBC cell line MDA-MB-231 (Supplementary Fig.\u00a01c, d) was transduced with the Brunello gRNA library at an optimized MOI, according to an established protocol14. Library-transduced cells were cultured for 10 days to allow gene deletion and then co-cultured with freshly isolated primary lymphocytes from a healthy blood donor, \u00b110\u2009\u03bcg/ml nivolumab. At the end of the co-culture on day 6, lymphocytes were gently washed away and cancer cells were harvested. Of note, we observed clear differences in medium consumption and total number of alive cancer cells when nivolumab was added, due to enhanced lymphocyte activation (Supplementary Fig.\u00a01e). Frequencies of gRNAs were quantified in cancer cells using next generation sequencing and ranked according to the essentiality scores using the MAGeCK pipeline15. To increase the robustness of results, two independent screens were performed using lymphocytes from different donors at low or high lymphocyte-to-cancer (L2C) ratio (Fig.\u00a01b and Supplementary Data\u00a01).\n\nTo reveal genes controlling immune-mediated TNBC killing without nivolumab, we compared enriched and depleted gRNAs between co-culture and cancer cells cultured alone. Among the enriched genes, we identified known hits that are important for immune-mediated cancer killing, e.g., STAT1 and IFNGR2 (Supplementary Fig.\u00a02a, b). In contrast, immune inhibitory genes in cancer cells, e.g., ENPP1, CTNNB1, PRMT5, were depleted after lymphocyte-cancer co-culture (Supplementary Fig.\u00a02a, b).\n\nNext, we sought to identify candidate genes that represent cancer vulnerability to PD-1 blockade therapy. Top depleted gRNAs in both screens were selected according to the distribution of essentiality scores using a cut-off of mean minus 2 standard deviations (SD) (Supplementary Fig.\u00a02c). This resulted in 9 commonly depleted genes (NEDD8, EIF2S2, NPIPB9, FAM86B2, PTDSS1, CRNKL1, SLC38A6, FOXG1 and ZC3HAV1), when comparing nivolumab-treated co-culture and co-culture alone (Fig.\u00a01c). Because the NEDD8 gene was strongly depleted (Supplementary Fig.\u00a02d, e) and all 4 gRNAs targeting the NEDD8 gene showed robust performance (Fig.\u00a01d), we propose that it confers resistance to ICB therapy.\n\nIn order to explore the association between NEDD8 mRNA expression and response to ICB therapy in breast cancer patients, we explored published RNA sequencing results from the I-SPY2 neoadjuvant platform trial (NCT01042379)16, where patients received paclitaxel or paclitaxel in combination with pembrolizumab. In the chemo-immunotherapy arm, 44.9% of patients (n\u2009=\u200969) experienced a pathologic complete response (pCR). When stratified by NEDD8 mRNA expression, we identified worse response in NEDD8 high patients (22.2%), as compared to NEDD8 medium (51.9%) or low (45.5%) subgroups (Fig.\u00a01e).\n\nNEDD8 protein is required for post-translational modification through protein neddylation17. To study its function in human TNBC cells, we deleted the NEDD8 gene in three human TNBC cell lines, i.e., MDA-MB-231, HCC1937 and BT549, by transfecting RNP complexes containing a NEDD8 targeting gRNA, i.e., crRNA+tracrRNA. Control cells were generated at the same time by transfecting RNP complexes without the NEDD8-targeting crRNA (Fig.\u00a02a). Consistent with the public knowledge18,19,20,21,22,23,24,25 of NEDD8 being a common essential gene in >1000 human cancer cell lines (https://depmap.org/portal/achilles/), we observed a substantial decrease of cell viability after transfection of the NEDD8-targeting gRNA. To our surprise, NEDD8 deficient cells recovered with time and proliferated at the same rate as the control cells (Fig.\u00a02b).\n\nThe NEDD8 gene was deleted using CRISPR/Cas9 in three human triple-negative breast cancer (TNBC) cell lines, i.e., MDA-MB-231, HCC1937 and BT549. a Expression of the NEDD8 protein was measured using Western Blotting\u00a0(representative blot of three independent experiments was shown) and (b) cell proliferation of the wild-type control (WT ctrl) and NEDD8 knock-out (KO) cells was quantified in a live-cell imaging system. Representative experiment of three independent experiments were shown. c Genome-wide CRISPR screens were performed in MDA-MB-231 WT or NEDD8 KO cells and the gRNA frequencies were compared between day 21 and day 4 (depleted genes in blue and enriched genes in purple). Data were processed in the MAGeCK pipeline and p values were calculated from the negative binomial model. Log2 fold changes were plotted against the Log10 p values in volcano plots with highlighted gene hits. One genome-wide CRISPR screen was performed. d MDA-MB-231 WT or NEDD8 KO cells were treated with the glutathione peroxidase 4 (GPX4) inhibitor (ML210), and the dose-dependent effects on cell proliferation were quantified using a live-cell imaging system. Representative experiment of three independent replicates. e Potency of a (NEDD8-activating enzyme) NAE inhibitor, pevonedistat, on the WT or NEDD8 KO TNBC cell lines was shown at 84\u2009h. Representative experiment of three independent replicates. f Number of uniquely or commonly depleted genes in the WT MDA-MB-231 or NEDD8 KO cells was shown in a Venn diagram. g Pathway analysis on uniquely depleted genes in MDA-MB-231 WT or NEDD8 KO cells in the genome-wide CRISPR screens. Enrichment analysis was conducted using hypergeometric test and Benjamini\u2013Hochberg adjusted p values are reported. h Illustration of the conditional essentiality model of the NEDD8 gene in TNBC cells. Source data are provided as a source data file.\n\nBecause it was important to confirm that the loss of NEDD8 protein expression translated to gene essentiality, we performed genome-wide loss-of-function CRISPR screens in the MDA-MB-231 wild-type (WT) and NEDD8 knock-out (KO) cell line pair. Counts of gRNAs were compared between day 21 and day 4 after library introduction (Supplementary Data\u00a01). Our results in MDA-MB-231 WT cells showed a strong agreement to a gene essentiality screen obtained from DepMap (Supplementary Fig.\u00a03a) and the NEDD8 gene was among the top-ranked essential genes in both screens (Fig.\u00a02c and Supplementary Fig.\u00a03b). In contrast, gRNAs targeting the NEDD8 gene were not significantly changed in the KO cells between the 2 time points (Fig.\u00a02c), confirming cell line recovery after NEDD8 loss.\n\nWe identified uniquely essential genes in the WT cells, e.g., GPX4, or in the KO cells, e.g., UBE2M, MTOR, RPTOR, RHEB (Fig.\u00a02c). In accordance, a pharmacological inhibitor against GPX4, i.e., ML210, preferentially inhibited proliferation of WT cells but was not effective on NEDD8 KO cells (Fig.\u00a02d), despite comparable GPX4 protein expression levels in the cell line pair (Supplementary Fig.\u00a03c). Moreover, we observed that gRNAs targeting the NAE1 gene were depleted in both the WT and KO cells in the genome-wide screens (Fig.\u00a02c). NAE1 encodes the NEDD8-activating enzyme E1 subunit 1 (NAE1), which is a key subunit of the first heterodimer enzyme of the neddylation pathway17. Pharmacological inhibitors against NAE, i.e., pevonedistat26 and TAS446427, have been developed and tested in patients as potential anti-cancer therapies26,28. Cell proliferation assays showed that the WT and KO cells were equally sensitive to pevonedistat (Fig.\u00a02e) and TAS4464 (Supplementary Fig.\u00a03d). Deletion of NEDD8 did not influence the expression of NAE1 protein (Supplementary Fig.\u00a03e).\n\nIn order to map pathway changes in the WT/KO cell line pair, we selected strongly depleted gRNAs according to the distribution of essentiality scores using a cut-off of mean minus 3 SD (Supplementary Fig.\u00a03f). This resulted in depleted genes unique to the WT cells (n\u2009=\u2009260) and the KO cells (n\u2009=\u2009144), as well as 121 genes that were depleted in both cell lines (Fig.\u00a02f). Using the over-representation analysis, we revealed biological processes that became important upon NEDD8 deletion, e.g., DNA replication (Fig.\u00a02g). However, NEDD8 deficient cells did not show enhanced sensitivity to chemotherapeutic drugs, e.g., paclitaxel, doxorubicin or fludarabin (Supplementary Fig.\u00a04a). In contrast to the WT cells, NEDD8 KO cells appeared to rely on distinct genes to sustain key cellular processes including translation and rRNA processing (Fig.\u00a02g). This led us to a model, where the essentiality of certain \u201ccommon essential\u201d genes, e.g., NEDD8, is conditional due to system redundancy and cell proliferation can be rescued by alternative mechanisms, e.g., ubiquitination (Fig.\u00a02h).\n\nBecause protein neddylation is a key post-translational modification mechanism, we hypothesized that NEDD8 deficiency can modulate global protein expression in TNBC cells. To test this hypothesis, we performed label-free protein quantification using mass spectrometry (Supplementary Data\u00a02). Importantly, NEDD8 protein was detected only in the WT cells but not in the KO cells, validating the robustness of protein deletion as well as our previous results. With a cut-off threshold of FDR\u2009<\u20090.2 and an absolute Log2FC\u2009>\u20090.4, we identified 57 upregulated and 64 downregulated proteins in NEDD8 deficient MDA-MB-231 cells, as compared to the WT controls (Fig.\u00a03a).\n\nLabel-free protein quantification was performed using mass spectrometry in the MDA-MB-231 wild-type (WT)/NEDD8 knock-out (KO) cell line pair using 4 replicate samples of each line. a Up-regulated (red) and down-regulated (blue) proteins upon NEDD8 deletion were shown in a volcano plot. A Welch\u2019s unequal variances T-test was applied to determine differences in protein expression between control and KO cells. The False Discovery Rate was calculated to adjust the p values. b Differentially expressed proteins and unique proteins were divided into either upregulated in NEDD8 KO or upregulated in control cells for pathway analysis. A hypergeometric test was conducted to determine enriched pathways from the Reactome and Gene Ontology Biological Process collections. p values were adjusted with Benjamini\u2013Hochberg correction. c Interaction of changed proteins in the WT/KO cell line pair was grouped based on biological processes. Purple: unique in WT, Brown: unique in KO. Color is based on Log2 fold changes between KO and WT cells. d Expression of UBE2T was measured by Western Blotting. Representative image of 3 independent repeats was shown. e Control or KO MDA-MB-231 cells were treated with a UBA1 inhibitor, TAK-243, at 1000, 400, 100, 10\u2009nM or 0.1% DMSO. Cell proliferation was measured by live-cell imaging. Representative experiment of 2 independent repeats. Control or KO MDA-MB-231 cells were treated with (f) pevonedistat or (g) TAK-243 at 1000, 400, 100, 10\u2009nM or 0.1% DMSO. Cells were harvested at 24\u2009h and the expression of CDT1 was measured using western blotting. Representative western blot of 2 independent repeats. Source data are provided as a source data file.\n\nPathway analysis demonstrated that NEDD8 deletion led to upregulated proteins in several pathways, including DNA replication and metabolic process (Fig.\u00a03b). An in-depth analysis of the protein interaction network demonstrated that proteins for cell cycle and DNA replication, as well as compound metabolism, were upregulated in NEDD8 KO cells. In contrast, NEDD8 deficient cells showed attenuated protein expression for epidermal cell differentiation, cytoskeleton and chromatin organization (Fig.\u00a03c). These findings were in line with data from the genome-wide CRISPR screens (Fig.\u00a02c, g), where genes regulating DNA replication and mTOR/metabolic pathway became more essential in the KO cells.\n\nIn particular, our analysis revealed reprogramming of the post-translational modification in the absence of NEDD8 (Fig.\u00a03c). Multiple regulatory enzymes, e.g., UBE2T, UBE3C, UBE4A and SMURF2, increased in expression in NEDD8 KO cells, which could serve as compensatory mechanisms to sustain protein homeostasis and global ubiquitination (Supplementary Fig.\u00a04b). Although UBE2T was not detected in wild type cells using proteomics, i.e., \u201cunique in KO\u201d, we demonstrated a low expression using western blotting and confirmed its upregulation upon NEDD8 deletion (Fig.\u00a03d).\n\nTo functionally validate whether protein ubiquitination became indispensable in KO cells, we tested the effects of an ubiquitin E1 enzyme (UBA1) inhibitor, i.e., TAK-243, on the proliferation of control or NEDD8 KO MDA-MB-231 cells. Indeed, TAK-243 more potently inhibited the proliferation of NEDD8 KO cells, as compared to the control cells (Fig.\u00a03e). Of note, both pevonedistat and TAK-243 induced the stabilization of CDT1 in a dose-dependent manner (Fig.\u00a03f, g), which is a known cytotoxic mechanism in pevonedistat-treated cells29. While stabilization of CDT1 was comparable between KO and control cells treated with pevonedistat (Fig.\u00a03f), TAK-243 induced a stronger effect in KO cells at low concentrations (Fig.\u00a03g). Pevonedistat, but not TAK-243, strongly inhibited the modification of cullin-1 in control and KO MDA-MB-231 cells (Supplementary Fig.\u00a04c).\n\nUpon NEDD8 deletion, we identified enhanced protein expression for antigen presentation (HLA-DRA, -DRB and CD74) among immune regulatory proteins (Figs.\u00a03a, c and\u00a04a). Subsequent experiments performed in flow cytometry confirmed that NEDD8 deletion led to enhanced expression of HLA-DR on MDA-MB-231 and HCC1937 cell lines (Fig.\u00a04b). Treatment of TNBC cells with IFN\u03b3 induced surface expression of HLA-DR, which was further enhanced in the absence of NEDD8 (Fig.\u00a04b). Of note, NEDD8 deletion in TNBC cells demonstrated similar effects on the expression of HLA-DR as compared to treatment with IFN\u03b3 (Fig.\u00a04b), indicating strongly enhanced immunogenicity in KO cells. The down-regulation of surface CD55 on NEDD8 deficient human TNBC cells was also validated by flow cytometry (Supplementary Fig.\u00a04d). However, NEDD8 deficiency did not modulate surface expression of HLA-ABC, PD-L1, IFN\u03b3R\u03b1 on human TNBC cell lines at the baseline or after IFN\u03b3 treatment (Supplementary Fig.\u00a04e).\n\na Label-free quantification of peptides derived from HLA-DRA and HLA-DRB in MDA-MB-231 wild-type (WT) and NEDD8 knock-out (KO) cell lines in proteomics, 4 technical replicates. b WT or NEDD8 KO human TNBC cell lines, i.e., MDA-MB-231 and HCC1937, were treated with PBS (5 independent replicates) or 50\u2009ng/ml rhIFN\u03b3 (4 independent replicates) for 24\u2009h. Surface expression of HLA-DR was quantified using flow cytometry. WT or NEDD8 KO MDA-MB-231 cells were co-cultured with CTV-pulsed primary human lymphocytes \u00b110\u2009\u03bcg/ml nivolumab (red) or durvalumab (blue). c Release of soluble IFN\u03b3 was tested by ELISA (4 independent donors) or (d) proliferation of T cells was quantified by flow cytometry on day 5 (6 independent donors). e Ctrl or NEDD8 KO HCC1937 cells were co-cultured with primary human lymphocytes \u00b110\u2009\u03bcg/ml nivolumab or durvalumab and release of soluble granzyme B was tested by ELISA on day 5 (4 independent donors). f A truncated NEDD8 protein lacking the C-terminus diglycine residues was re-expressed in NEDD8 KO MDA-MB-231 cells (NEDD8-T) and protein neddylation was measured using Western Blotting. Representative image of 2 independent repeats. g Control (dark gray), NEDD8 KO (open) or NEDD8-T (light gray) cells (2500 cells per well) were co-cultured with primary human lymphocytes \u00b110\u2009\u03bcg/ml nivolumab and release of soluble IFN\u03b3 was tested using ELISA on day 5 (4 independent donors). All data in this figure were shown as mean\u2009\u00b1\u2009SD and unpaired two-tailed T-test was used for statistical analysis. Source data are provided as a source data file.\n\nTo test whether NEDD8 KO TNBC cells can induce stronger immune cell activation, control or NEDD8 deficient MDA-MB-231 cells were co-cultured with primary human lymphocytes in TICS\u2009\u00b1\u2009ICB drugs. Induction of soluble IFN\u03b3 and granzyme B by cancer cells were observed in culture supernatants and KO cells induced a marginal enhancement, as compared to the control cells (Fig.\u00a04c). In accordance with previous results (Fig.\u00a01a), we observed significantly increased production of these immune-activating cytokines in the presence of nivolumab or durvalumab, which was further enhanced by NEDD8-deficient cells (Fig.\u00a04c and Supplementary Fig.\u00a05a). Similar results were observed when assessing the proliferation of CD8+ and CD4+ T cells in TICS in response to either nivolumab or durvalumab (Fig.\u00a04d and Supplementary Fig.\u00a05b). The activation of NK cells by NEDD8 KO TNBC cancer cells showed only a trend of increase (Supplementary Fig.\u00a05c). The increased release of soluble granzyme B in response to nivolumab or durvalumab was confirmed using an additional control/KO cell line pair derived from HCC1937 cells (Fig.\u00a04e).\n\nTo examine the mechanistic insights of NEDD8 in cancer immunogenicity, we re-expressed a truncated form of the NEDD8 protein in MDA-MB-231 KO cells, i.e., NEDD8-T. NEDD8-T lacked the C-terminus diglycine residues30,31,32 and therefore failed to conjugate to enzymes or substrates (Fig.\u00a04f). In TICS, NEDD8-T cells demonstrated equally potent induction of immune-activating cytokines in response to ICB drugs, as compared to NEDD8 KO cells (Fig.\u00a04g). Of note, NEDD8-T cells remained sensitive to pevonedistat (Supplementary Fig.\u00a05d).\n\nNext, we sought to test whether inhibition of protein neddylation by NAE inhibitors can potentiate TNBC cancer-driven immune activation. As shown in Fig.\u00a05a, pevonedistat led to a dose-dependent inhibition of protein neddylation in MDA-MB-231 cells without clear effects on the expression of free NEDD8 protein (~9\u2009kDa). Monitored in real-time by Incucyte, pevonedistat inhibited the proliferation of 3 human TNBC cell lines in vitro with IC50 values between 180\u2009nM and 600\u2009nM (Supplementary Fig.\u00a05e). When tested on primary human lymphocytes activated with \u03b1CD3/28 beads \u00b1 rhIL2, pevonedistat demonstrated a negative impact on the proliferation of CD4+ T cells, CD8+ T cells and NK cells with comparable potencies as observed in cancer cells, i.e., between 100\u2009nM and 600\u2009nM (Fig.\u00a05b).\n\na MDA-MB-231 cells were treated with pevonedistat for 24\u2009h\u00a0and the NEDD8 expression was assessed in Western Blotting. Representative image of 3 independent repeats was shown. b CellTrace Violet (CTV)-pulsed primary human lymphocytes were incubated with pevonedistat in the presence of \u03b1CD3/28 activation beads \u00b1rhIL2\u00a0(100 ng/ml). The resulting cell proliferation was quantified by flow cytometry. Representative experiment from 3 independent donors was shown. c Primary human lymphocytes were co-cultured with MDA-MB-231 cells in the presence of pevonedistat\u2009\u00b1\u2009nivolumab or durvalumab (10\u2009\u03bcg/ml). Soluble granzyme B and IFN\u03b3 were quantified by ELISA on day 5. Six independent blood donors were included and data was shown at mean\u2009\u00b1\u2009SD, unpaired two-tailed T-test. d Effect of pevonedistat on parental or pevonedistat resistant MDA-MB-231 cells was tested in a live-cell imaging system. Representative experiment of 3 independent repeats was shown. e Primary human lymphocytes were co-cultured with parental or pevonedistat-resistant MDA-MB-231 cells \u00b1 nivolumab or durvalumab (10\u2009\u03bcg/ml). Levels of soluble granzyme B and IFN\u03b3 were measured by ELISA on day 5. Five independent donors were included and data were shown with mean\u2009\u00b1\u2009SD, unpaired two-tailed T-test. f NEDD8-activating enzyme 1 (NAE1) protein expression in control MDA-MB-231 cells or knock-out (KO) clones was measured using Western Blotting. Representative image of 2 independent repeats was shown. g The NAE1 KO MDA-MB-231 clone was treated with pevonedistat and cell proliferation was measured using live-cell imaging. Representative graph of 2 independent repeats was shown. h MDA-MB-231 cells were treated with 1000, 100\u2009nM pevonedistat or 0.1% DMSO for 24\u2009h and the total ubiquitin was tested using Western Blotting. Representative image of 2 independent repeats was shown. Control MDA-MB-231 cells were treated with 1000, 400, 100 or 10\u2009nM of TAK-243 (i) or 1000, 100, 10\u2009nM of pevonedistat (j) or 0.1% DMSO for 24\u2009h, and NAE1 protein expression was measured using Western Blotting. Representative image of 2 independent repeats was shown. Source data are provided as a source data file.\n\nTo rule out that the immune inhibitory property was specific to pevonedistat, we tested a more potent NAE inhibitor, TAS446427. Similar to the data from pevonedistat, TAS4464 potently inhibited the proliferation of TNBC cells (Supplementary Fig.\u00a05f) as well as primary human T cells (Supplementary Fig.\u00a06a). This suggested that current NAE inhibitors under clinical testing carry negative effects on primary human lymphocytes. In TICS, pevonedistat at 100\u2009nM significantly enhanced the release of granzyme B and IFN\u03b3 in combination with nivolumab or durvalumab (Fig.\u00a05c). However, the synergistic effects diminished at 1000\u2009nM, possibly due to its direct inhibition on immune cells in the co-culture (Fig.\u00a05c).\n\nTo assess the long-term effects of NAE inhibition on protein neddylation and TNBC immunogenicity, we generated treatment resistant cell lines by chronic exposure of MDA-MB-231 cells to pevonedistat in vitro (Fig.\u00a05d). Of note, compound resistant MDA-MB-231 cells demonstrated elevated protein neddylation levels (Supplementary Fig.\u00a06b), which remained sensitive to pevonedistat (Supplementary Fig.\u00a06c), ruling out treatment-driven pathway mutations33,34. Of note, resistant cells triggered significantly weaker release of IFN\u03b3 and granzyme B in response to ICB antibodies in TICS, as compared to the parental cell line (Fig.\u00a05e).\n\nTo investigate whether pevonedistat-induced protein neddylation conferred immune resistance, we deleted the NEDP1 gene using CRISPR/Cas9 in MDA-MBA-231 cells (Supplementary Fig.\u00a07a). NEDP1 removes NEDD8 from protein substrates30 and as expected, NEDP1 KO cells demonstrated substantial accumulation of neddylated enzymes and substrates (Supplementary Fig.\u00a07b). However, its deletion did not result in reduced immune activation in TICS (Supplementary Fig.\u00a07c).\n\nBecause pevonedistat was able to inhibit cells lacking NEDD8 protein (Fig.\u00a02e) or functional protein neddylation (Supplementary Fig.\u00a05d), we speculated that off-target mechanisms may contribute to the phenotype observed in drug-resistant cells. Using CRISPR/Cas9, we silenced the NAE1 gene in MDA-MB-231 cells (Fig.\u00a05f), which is the putative target for neddylation inhibitors. Pevonedistat efficiently inhibited the proliferation of NAE1-deficient cells (Fig.\u00a05g), demonstrating compound mode-of-action that are unspecific to neddylation.\n\nBecause protein ubiquitination and neddylation are closely related, we sought to investigate whether neddylation inhibitors could affect ubiquitination. As shown in Fig.\u00a05h, pevonedistat at 1000\u2009nM clearly reduced the total ubiquitin levels in MDA-MB-231 cells, which coincided with the negative effects on immune activation in TICS at this concentration (Fig.\u00a05c).\n\nWhen measuring the expression of NAE1 protein (62.7\u2009kDa) in human TNBC cells, we observed a second band at ~70\u2009kDa, which did not differ between control or NEDD8 KO cells (Supplementary Fig.\u00a03e) but was not detectable in NAE1 KO cells (Fig.\u00a05f). Treatment with UBA1 inhibitors TAK-243 (Fig.\u00a05i) or PYR41 (Supplementary Fig.\u00a07d), as well as neddylation inhibitor pevonedistat (Fig.\u00a05j) diminished the expression of this band.\n\nGiven the clear negative impact of NAE inhibitors on immune cells and cancer immunogenicity due to off-target effects, we decided to employ CRISPR/Cas9 to specifically target the Nedd8 gene in murine cancer cells for in vivo studies.\n\nBecause immune activation relied on allogeneic antigens presented by cancer cells in TICS, we decided to validate the cancer intrinsic role of the Nedd8 gene using syngeneic mouse models. Expression of the Nedd8 gene was disabled using CRISPR/Cas9 in a murine breast cancer cell line, EO771 (Fig.\u00a06a). Similar to human TNBC cells, the proliferation of EO771 murine breast cancer cells in vitro was comparable in the control/KO cell line pair after recovery (Fig.\u00a06a). Next, we implanted the control or Nedd8 KO EO771 cells subcutaneously (s.c.) on female C57BL/6 mice. When tumors were palpable, mice were treated with an \u03b1PD-1 mAb or a Rat IgG2a isotype control intraperitoneally (i.p.) on day 5, 8 and 11 (Fig.\u00a06b). In mice bearing WT tumors, we observed a moderate response to PD-1 blockade. In contrast, PD-1 blockade resulted in highly significant tumor growth delay (p\u2009<\u20090.0001) in all mice bearing Nedd8 KO EO771 tumors (Fig.\u00a06b).\n\nThe Nedd8 gene was deleted using CRISPR/Cas9 in EO771 cells. a NEDD8 protein expression was tested at different passages by Western Blotting and cell proliferation was monitored using a live-cell imaging system. Representative experiment of 3 independent repeats was shown. b Four hundred thousand control (Ctrl) or Nedd8 knock-out (KO) EO771 cells were injected subcutaneously (s.c.) in 100\u2009\u03bcl medium in 6\u201310 weeks old female C57BL/6NTac mice. When tumors were palpable, 50\u2009\u03bcg of an \u03b1PD-1 antibody (RMP1-14) or the Rat IgG2a isotype control (2A3) were injected intraperitoneally (i.p.) in 100\u2009\u03bcl PBS on day 5, 8 and 11 (8 mice per group). Tumor volumes were compared on day 24. Representative experiment of 3 repeats was shown. c Ctrl or Nedd8 KO EO771 cells were injected s.c. as above and treatment began when average tumor volume reached 50\u2009mm3 on day 14, 17 and 20 (at least 5 mice per group). Tumor growth was followed in all mice until the study endpoint. Survival of the mice was demonstrated in a Kaplan\u2013Meier curve. Representative experiment of 2 independent repeats was shown. d Six hundred thousand ctrl or Nedd8 KO EO771 cells were injected s.c. as above. A depletion antibody against CD8+ T cells (2.43) or the Rat IgG2b isotype control (LTF-2) was injected i.p. in 100\u2009\u03bcl PBS every 3 days from day 4 (200\u2009\u03bcg per mouse, 7 mice per group). Tumor growth was compared on day 17 and survival of the mice was demonstrated in a Kaplan\u2013Meier curve. Representative experiment of 2 independent repeats was shown. Data were shown as mean\u2009\u00b1\u2009SEM. Statistical differences on the tumor volumes were determined using unpaired two-tailed T-test and survival differences were calculated using Kaplan\u2013Meier curves and a log-rank test (Mantel\u2013Cox). *p\u2009<\u20090.05; **p\u2009<\u20090.01; ****p\u2009<\u20090.0001. Source data are provided as a source data file.\n\nTo assess the anti-tumor efficacy of PD-1 blockade in large tumors, we initiated the treatment when average tumor volumes reached ~50\u2009mm3. None of the mice bearing control EO771 tumors responded to PD-1 blockade (Fig.\u00a06c) and Nedd8 deficiency did not delay tumor growth when treated with the isotype control antibody, as compared to mice bearing control tumors (Supplementary Fig.\u00a07e). Strikingly, Nedd8 deletion in EO771 cells significantly delayed the progression of established tumors in response to anti-PD-1 treatment (p\u2009<\u20090.0001), resulting in a 40% complete response (Fig.\u00a06c). When treated with PD-1 blockade, mice bearing Nedd8 deficient tumors showed significantly prolonged survival, as compared to mice treated with the IgG control (p\u2009<\u20090.01, Fig.\u00a06c).\n\nBecause established EO771 control tumors are unresponsive to PD-1 blockade, we sought to prove that the potent anti-tumor efficacy in Nedd8 KO tumors after PD-1 blockade was a result of immune-mediated cytotoxicity. Mice bearing Nedd8 KO EO771 tumors were treated with a CD8 depleting antibody or a Rat IgG2b isotype control, 2 days before \u03b1PD-1 therapy with a 3-day interval (Fig.\u00a06d and Supplementary Fig.\u00a07f). Consistent with earlier results, Nedd8 deficiency significantly improved response to PD-1 blockade and survival of tumor-bearing mice, which was abrogated with the depletion of CD8+ T cells (Fig.\u00a06d).\n\nIn order to dissect immunological changes in Nedd8-deficient breast tumors, we analyzed intra-tumoral immune cell population and mRNA gene signatures using flow cytometry and the Nanostring technology, respectively. Because PD-1 blockade induced tumor regression in mice bearing Nedd8-deficient tumors, we harvested tumor tissues 2 days after the last antibody infusion before complete regressions occurred (Fig.\u00a07a). At this study endpoint, PD-1 blockade was insufficient in controlling the growth of EO771 tumors but resulted in a non-significant delay in the growth of KO tumors (Fig.\u00a07b).\n\na Six hundred thousand control (ctrl) or Nedd8 knock-out (KO) EO771 cells were injected subcutaneously (s.c.) in 100\u2009\u03bcl medium in 6\u201310 weeks old female C57BL/6NTac mice. On day 7, 10 or 13, 50\u2009\u03bcg of an \u03b1PD-1 antibody (RMP1-14) or the Rat IgG2a isotype control (2A3) were injected intraperitoneally (i.p.) in 100\u2009\u03bcl PBS (7 or 8 mice per group). b Tumor volumes were recorded until day 15, when cells were harvested for analysis using flow cytometry (6 tumors per group). Data were shown as mean\u2009\u00b1\u2009SEM and tested using unpaired two-tailed T-tests. Percentages of (c) CD25+ T cells, (d) TNF\u03b1+ T cells, (e) CD11b+ myeloid cells or (f) macrophages were compared among groups. Each dot represented an individual tumor and the average values were shown, unpaired two-tailed T-test. Tumors were harvested from an independent in vivo study with the same design and mRNA samples were isolated from tumors and quantified using a Nanostring immuno-oncology panel. Differentially expressed genes were shown when comparing (g) Nedd8 KO and control tumors treated with the isotype control antibody, or (h) treated with the PD-1 blockade using a cut-off of Log2 fold changes\u2009>\u20090.5 and p values\u2009<\u20090.05, unpaired two-tailed T-test. Source data are provided as a source data file.\n\nFlow cytometric analysis using the gating strategy in Supplementary Fig.\u00a08a revealed that frequencies of CD8+ T cells (Supplementary Fig.\u00a08b) and regulatory T cells (Supplementary Fig.\u00a08c), or the ratio between CD8+ and CD4+ T cells (Supplementary Fig.\u00a08d) were comparable among treatment groups. Notably, T cells in Nedd8-deficient tumors receiving ICB demonstrated a more functional phenotype with increased expression of surface CD25 (Fig.\u00a07c) and intracellular tumor necrosis factor \u03b1 (TNF\u03b1) (Fig.\u00a07d), while surface expression of PD-1 remained comparable on T cells among groups (Supplementary Fig.\u00a08e). Moreover, CD11b+ myeloid cells were reduced in KO tumors treated with PD-1 blockade, as compared to the IgG-treated control tumors (Fig.\u00a07e). Among myeloid cells, we observed elevated number of activated macrophages in KO tumors treated with PD-1 blockade as compared to the KO tumors treated with the isotype control antibody (Fig.\u00a07f), while Ly6G+ neutrophils showed a trend of reduction (Supplementary Fig.\u00a08f).\n\nTo gain a broader view of the intra-tumoral immunological changes, we quantified the expression of immune-related genes using a Nanostring panel (Supplementary Data\u00a03). Because PD-1 blockade was inefficient in EO771 tumors, only few genes changed upon therapy (Supplementary Fig.\u00a08g). In contrast, Nedd8 deficiency alone led to significant changes in immune-related pathways. Expression of genes associated with interferon response (Mx1, Stat1, Ifitm1, Cxcl10) and immune cell effector function (Il2, Gzma, Tnfrsf8) were significantly increased in KO tumors, as compared to control EO771 tumors (Fig.\u00a07g). In line with the results from flow cytometry, Nedd8-deficient tumors presented less abundant mRNA transcripts, e.g., Sirpa, S100a8, Csf1, Mmp9, Mmp12, Tgfbr1, for myeloid cells with a suppressive phenotype (Fig.\u00a07g). Addition of PD-1 blockade to Nedd8-deficient tumors sustained these immunological changes and potentiated antigen presentation, e.g., Cd80, and T cell activation, e.g., Il2ra/Cd25 (Fig.\u00a07h), which confirmed our earlier results using flow cytometry (Fig.\u00a07c).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-47987-x/MediaObjects/41467_2024_47987_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Triple-negative breast cancer (TNBC) is a heterogeneous disease and presents an immunosuppressive intra-tumoral landscape. Although TNBC cells show PD-L1 positivity35 and the infiltration of T cells correlates to patient survival36,37, PD-1 blockade therapy alone is yet to show clinical benefits in patients with advanced disease4. Moreover, clinical responses to immunotherapy in TNBC patients may be limited by additional factors, e.g., the immunosuppressive micro-environment in TNBC tumors, as well as the aggressive growth behavior and intrinsic resistant mechanisms of cancer cells38,39. Therefore, we hypothesize that key cancer vulnerability genes can be targeted to improve response to immunotherapy in TNBC patients.\n\nGenome-wide loss-of-function or activation screening using CRISPR/Cas9 offers a powerful tool to uncover genes that are essential for cancer cell survival and response to therapy. Several studies have been performed in human cancer cells to reveal genes controlling cytotoxicity mediated by genetically engineered T cells10,11,12. Previously, identification of cancer vulnerability to ICB antibodies using CRISPR/Cas9 loss-of-function screens has been conducted in immunocompetent mouse models bearing syngeneic tumors40,41,42. In particular, the discovery of Ptpn2 as a resistance gene to immunotherapy40 has led to the development of a small molecule compound suitable for testing in patients43. While these studies are highly relevant, murine cancer cells resembling human TNBC have not been included.\n\nInspired by a study where healthy donor-derived T cells contain clones that recognize mutated cancer neoantigens44, we have optimized a human Tumor-Immune co-Culture System (TICS) to investigate cancer-driven immune activation in response to ICB drugs13. Instead of using isolated CD8+ T cells, TICS utilizes unsorted human lymphocytes in order to identify effective orthogonal cancer killing mechanisms mediated by HLA class II epitopes or NK cells45.\n\nOur genome-wide screens in TICS reveal that the NEDD8 gene plays a crucial role in TNBC vulnerability against nivolumab. NEDD8 is a ubiquitin-like protein that governs protein neddylation, which is an important post-translational machinery17. Multiple earlier genetic screens unanimously demonstrated the essentiality of NEDD8 in cell survival18,19,20,21,22,23,24 and therefore NEDD8 is regarded as one of the \u2018common essential\u2019 genes (or \u2018pan-essential\u2019 genes20).\n\nParadoxically, we observe and validate that TNBC cells recover from genetic targeting of NEDD8 and proliferate at a comparable rate as the NEDD8-competent control cells. Combining proteomics and genome-wide CRISPR screens, we delineate the compensatory roadmap in TNBC cells upon NEDD8 loss. Ubiquitination enzymes, DNA replication machinery and the mTOR pathway become important in maintaining cell proliferation in NEDD8 deficient cells. It has been reported that atypical neddylation occurs through ubiquitin enzymes as a result of an increased NEDD8/ubiquitin ratio under stress condition30,31,32,46. However, it remains to be tested whether the absence of NEDD8 could lead to compensatory effects by the ubiquitin system. We show that global ubiquitination is not impaired in NEDD8 deficient cells and these cells become more sensitive to UBA1 inhibition. Therefore, it can be speculated that the loss of NEDD8 triggers cellular reprogramming and the ubiquitination system becomes indispensable in cancer cells.\n\nAlthough exemplified with one gene in human cancer cells, our data highlight an opportunity to refine the common essentiality theory. Whilst the essentiality of many pan-essential genes is \u201cabsolute\u201d to cancer cells, we propose that a subset of genes is \u201cconditionally\u201d essential. Loss of such genes triggers cellular reprogramming in cancer cells, which rescues cell survival through compensatory mechanisms. Further work is warranted to assess the validity of this concept in non-malignant cells.\n\nProtein neddylation is frequently amplified in cancer cells to sustain cell proliferation and has been regarded as a promising target for anti-cancer therapy. Pharmacological inhibitors, i.e., pevonedistat and TAS4464, are designed to induce cancer cell death through disruption of the enzymatic function of NAE26,27,28,29. Motivated by the strong anti-proliferative effects on cancer cells, NAE inhibitors have been evaluated in patients26,27,28. However, pevonedistat failed to deliver clinical efficacy in patients with myeloid cell malignancies in the PANTHER phase 3 clinical trial47. In a phase 1 clinical study in patients with multiple myeloma, severe liver injury led to trial termination for TAS4464 (NCT02978235). Similar dose-limiting toxicity was observed for TAS4464 in patients with solid cancers in another clinical trial48.\n\nOur results confirm that human and murine breast cancer cells present active protein neddylation, which is abolished with NAE inhibitors in a dose-dependent manner. However, the strong anti-proliferative capacity of this class of chemical compounds is not exclusive to the inhibition of protein neddylation. This is because isogenic human TNBC cell lines lacking NEDD8/NAE1 protein or expressing a non-functional NEDD8 protein, remain sensitive to NAE inhibitors. We further demonstrate that pevonedistat could dampen global protein ubiquitination at above IC50 concentrations, potentially due to the inhibition of cullin-RING ligases (CRLs)29. Because the modification of cullin-1 is detectable in KO cells, the identity of this modification remains to be tested.\n\nIn addition to the CRL-dependent mechanisms, neddylation of substrates could be conducted in a CRL-independent manner17. For example, mouse double minute 2 (MDM2) mediates neddylation of p53 and reduces its transcriptional activity through NAE149. In accordance, CRL-independent neddylation blocks substrate ubiquitination, which can be reverted by pevonedistat treatment50,51. These findings are in line with our observation, where NEDD8 KO human TNBC cells show greater sensitivity to UBA1 inhibition, possibly due to increased dependency on the ubiquitination system.\n\nThe role of protein neddylation on cancer immunogenicity has been investigated using NAE inhibitors. Pevonedistat treatment causes proteome instability and strongly potentiates response to ICB antibodies in mismatch repair-deficient (dMMR) colon cancer cells52. In glioblastoma models, pevonedistat up-regulates PD-L1 expression on cancer cells and synergizes with ICB antibodies in mice53. In our experimental models, genetic deletion of NEDD8 in human TNBC cells does not alter the expression of HLA-ABC nor PD-L1 but enhances the expression of HLA-DR. In TICS, NEDD8 KO cells strongly enhance immune activation and result in anti-tumor effects after PD-1 blockade in tumor-bearing mice. Interestingly, blocking the conjugation of NEDD8 protein to substrates by deleting the C-terminus diglycine residues achieves similar immune activation in TICS, but the therapeutic potential of this mechanism remains to be validated in mouse models. Despite the low patient number, NEDD8 mRNA expression show association to pathologic complete response rates in breast cancer patients receiving chemo-immunotherapy16. Because NEDD8 is widely expressed by many cell types in the tumor micro-environment, single-cell RNA sequencing datasets in a large cohort of TNBC patients are needed to validate the clinical relevance of our findings.\n\nWhen exposed to primary human T or NK cells in vitro, NAE inhibitors strongly dampen cell proliferation activated through the CD3/28 pathway at comparable potencies to human TNBC cancer cells. Pevonedistat at intermediate concentrations enhance immune activation primed by nivolumab, but the effect diminishes at high compound concentrations in TICS. These observations are in line with published results, where neddylation inhibitors block TCR signaling54,55 and anti-bacterial T cell immunity56. Because these compounds do not directly target NEDD8 and exert inhibitory functions on protein ubiquitination, the precise mechanistic insights of NEDD8 in immune cell activation and homeostasis should be further dissected using genome editing tools.\n\nNotably, a phase 1 clinical trial combining pevonedistat and pembrolizumab has been performed in mismatch repair deficient colon cancer patients (NCT04800627). It is reasonable to hypothesize that metronomic or intermittent dose-scheduling, as well as targeted delivery57 of these compounds to tumor lesions could improve the therapeutic index in combination with immunotherapy20. In light of the unique functions of NEDD8, modalities that directly limit NEDD8 expression, e.g., RNAi or CRISPR-based therapeutics, could mitigate negative effects of current pharmacological inhibitors on the immune system.\n\nIn summary, we have demonstrated that the NEDD8 gene is a vulnerability to ICB drugs in TNBC. Deficiency of NEDD8 protein in TNBC cancer cells alters immunogenicity that leads to potent immune response after ICB therapy. However, the detailed molecular mechanisms linking NEDD8 loss and enhanced immunotherapy response remain to be investigated. Given that NEDD8 is a key regulator for the post-translational network, modification of immune checkpoint receptors or ligands should be characterized. Further, our data uncover mechanistic insights of protein neddylation and gene essentiality. A direct and optimized targeting approach against the NEDD8 protein could pave the way to the development of next generation immunotherapy strategies in TNBC and beyond.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Details of all antibodies, reagents and oligonucleotide sequences can be found in Supplementary Tables\u00a01\u20133. The number of detected proteins in proteomics analysis was shown as Supplementary Table\u00a04.\n\nAll animals were housed at the animal facility at the Department of Immunology, Genetics and Pathology in the Rudbeck laboratory at Uppsala University, and all studies were approved by the Swedish Board of Agriculture at J\u00f6nk\u00f6ping, Sweden (Dnr: 5.8.18-06394/2020).\n\nBuffy coats from healthy donors were obtained from the Uppsala University Hospital. Because donors were fully anonymous, no ethical permission was required.\n\nIn order to study the biological effects and therapeutic potential of NEDD8 on tumor growth, NEDD8 KO or control murine breast cancer cells were injected into syngeneic mouse models. Six to ten weeks old female C57BL/6NTac or C57BL/6J mice were purchased from Taconic. All mice were housed in a barrier facility at the Rudbeck Laboratory (Uppsala University) with a humidity between 45 and 65% and an average temperature of 23 degrees. The dark/light cycle was fixed to 12\u2009h. For EO771 studies, 4\u20136\u2009\u00d7\u2009105 cells were injected subcutaneously (s.c.) in 100\u2009ul serum free Iscove\u2019s Modified Dulbecco\u2019s medium (IMDM, Thermo Fisher Scientific). Mice were palpated regularly for tumor detection. Tumor volumes were calculated using the formula V\u2009=\u2009(length*width^2)/2 and mouse body weights were monitored over the course of the study. The maximal tumor volumes were 1500\u2009mm3. When tumors were palpable or established EO771 tumor-bearing mice were injected intraperitoneally (i.p.) with an anti-PD-1 antibody (clone RMP1-14, BioXcell), or a Rat isotype IgG2a control (clone 2A3, BioXCell) every 3 days (50\u2009\u03bcg per mouse). To deplete CD8+ T cells, an anti-CD8a depleting antibody (200\u2009\u03bcg, clone 2.43, BioXCell) or an IgG2b isotype control (clone LTF-2, BioXCell) were infused i.p. 4 days after tumor inoculation every 3 days, followed by treatment with 100\u2009\u03bcg anti-PD-1 or Rat IgG2a isotype antibody on days 7, 10, 13.\n\nHuman breast cancer cell line, MDA-MB-231 (92020424, Sigma Aldrich), and HEK293T cells (CRL-3216, American Type Culture Collection, ATCC) were purchased. HCC1937 and BT549 cell lines were a gift from Dr. \u00d3scar Fern\u00e1ndez-Capetillo (Karolinska Institutet, Sweden). Mouse breast cancer cell line EO771 was kindly provided by Dr. Maria Ulvmar (Uppsala University, Sweden). Unless otherwise stated, all cell lines were maintained in IMDM medium (Thermo Fisher Scientific) containing 10% heat-inactivated Fetal Bovin Serum (FBS) and 1% penicillin-streptomycin solution (Thermo Fisher Scientific) at 37\u2009\u00b0C with 5% carbon dioxide. Cell lines were authenticated using DNA fingerprinting (Eurofins) and checked for mycoplasma infection routinely (MycoAlert, Lonza).\n\nBuffy coats from healthy blood donors were received from the blood center at the Uppsala University Hospital, Sweden. Peripheral blood mononuclear cells (PBMC) were isolated using SepMate tubes-50 (Stem Cell Technologies) by density gradient centrifugation. Briefly, 10\u2009ml Lymphoprep reagent (Stem Cell Technologies) was added to the tubes followed by addition of blood on top of Lymphoprep. The tubes were then centrifuged at 1200\u00d7g for 10\u2009min. Next, cell suspension above the Lymphoprep was collected and PBMCs were washed twice with phosphate-buffered saline (PBS, Thermo Fisher Scientific). For optimal lysis of red blood cells, 5\u2009ml ACK lysis buffer (Thermo Fisher Scientific) was added to the cells and incubated in the dark for 10\u2009min at room temperature followed by centrifugation at 500\u2009\u00d7\u2009g for 5\u2009min. After that, primary monocytes were removed by an EasySep CD14+ selection kit II (Stem Cell Technology) according to the manufacturer\u2019s instructions. Primary human lymphocytes were stored in \u2212150\u2009\u00b0C until use.\n\nTo delete genes of interest, ribonucleoprotein (RNP) complexes containing gRNAs, i.e., crRNA\u2009+\u2009tracrRNA, targeting human or mouse genes (Supplementary Table\u00a03) were introduced into cancer cells using the Neon transfection system (Thermo Scientific). Briefly, 1\u2009\u03bcl crRNAs (100\u2009\u03bcM), 1\u2009\u03bcl trancrRNA (100\u2009\u03bcM) and 1.7\u2009\u03bcl nuclease free duplex buffer (IDT) were added to a PCR tube to form the RNP complexes. A negative control reaction was set up without the crRNA sequence (referred as ctrl cells). PCR tubes were then boiled at 95\u2009\u00b0C for 5\u2009min and cooled down at 4\u2009\u00b0C. Then, Cas9 endonuclease (10\u2009mg/ml, IDT) was added to the reaction, followed by incubation at room temperature for 15\u2009min. A carrier DNA sequence (100\u2009\u03bcM) was then added to the tubes at a final volume of 0.3\u2009\u03bcl. Subsequently, the Neon transfection system was prepared according to the manufacturer\u2019s instructions, cell pellets (5\u2009\u00d7\u2009105 cells) were resuspended in 5\u2009\u03bcl resuspension buffer R or buffer T and mixed with the same volume of the RNP complex. Immediately after, the cell mixture was loaded into the neon pipette tips and the electroporation process was then run using specific programs on a Neon transfection system. Transfected cells were cultured and incubated at 37\u2009\u00b0C with 5% carbon dioxide until use. To achieve complete gene deletion, gene-targeting or control RNP complexes were repeatedly transfected to cells.\n\nThe plasmid pHAGE-EF1-dCas9-KRAB (Addgene, a kind gift of Scot Wolfe) was digested with BsrGI (New England BioLabs) and the backbone was gel purified. Gibson assembly was used to insert a gBlock (IDT) containing Gibson arms, a Kozak sequence and coding for a truncated version of the NEDD8 protein (NEDD8-T) that lacked the C-terminus diglycine residues (Supplementary Table\u00a03). The resulting plasmid was sequence verified by Sanger sequencing. For lentivirus production, 5\u2009\u00d7\u2009106 HEK293T cells were seeded in a T175 tissue culture flask and transfected with the cargo plasmid as well as packaging plasmids psPAX2 (Addgene) and pCMV-VSVG (Addgene) using serum free medium Opti-MEM and transfection reagent Fugene 6 (Promega). Virus containing medium was collected after 48\u2009h, filtered and 40-fold concentrated using the lenti X concentrator (Takara bio). Virus was pelleted by centrifuging at 1500\u2009\u00d7\u2009g for 45\u2009min at 4\u2009\u00b0C and resuspended in sterile DMEM\u2009+\u20091% BSA. The functional titer of the library virus was estimated from the fraction of puromycin resistant cells after transduction with different amounts of virus using serial dilution method. A low MOI of 0.2 was selected for the transduction of NEDD8 KO MDA-MB-231 cells followed by puromycin selection at 2\u2009\u03bcg/ml. The expression of NEDD8 was analyzed by western blotting.\n\nCell lysates were prepared for western blot analysis using antibodies against NEDD8, UBE2T, NAE1 and GPX4 (Supplementary Table\u00a01). In brief, cell pellets were lysed in RIPA buffer without additional reducing reagents (1\u2009mM EGTA, 20\u2009mM Tris, 150\u2009mM NaCl, 1\u2009mM EDTA, 1% NP-40, 1\u2009mM NaF, 1\u2009mM NaVO3, 1\u2009mM sodium phosphate) with protease inhibitor cocktail (Thermo Scientific) on ice for 15\u2009min, followed by centrifugation at 17,000\u2009\u00d7\u2009g for 12\u2009min/4\u2009\u00b0C to remove debris. According to the manufacturer\u2019s instructions, protein concentrations were determined by the Bicinchoninic Acid (BCA) Assay (Thermo Scientific). After that, the SDS loading dye-treated proteins were boiled at 70\u2009\u00b0C for 10\u2009min and separated by 4\u201312% SDS-PAGE gel (Invitrogen), transferred to nitrocellulose membrane (Invitrogen). The membranes were blocked with 5% nonfat SKIM milk powder (OXOID), followed by the addition of primary antibodies and incubation at 4\u2009\u00b0C overnight. On the following day, either anti-mouse or anti-rabbit IgG HRP-linked secondary antibody (Cell Signaling Technology) was added to the membranes at room temperature for 1\u2009h. Bands were visualized using super signal west pico plus or west femto chemiluminescent substrate (Thermo Scientific) and Amersham Imager 680 machine (GE Healthcare). After each step the membranes were washed with TBST (1X TBS, 0.05% Tween 20, dH2O). Vinculin, \u03b2-Actin or GAPDH were used as a loading control.\n\nIn order to analyze the effects of different inhibitors on breast cancer cell proliferation in real time, the incucyte zoom live imaging system was used. Triple-negative breast cancer cells were seeded at 5\u2009\u00d7\u2009103 cells in a 96-well flat bottom plate and incubated overnight to allow tumor adherence. Tumors cells were then treated with inhibitors at indicated doses or 0.1% DMSO (control) in 100\u2009\u03bcl of growth medium. The plate was then incubated into the incucyte image system at indicated time points to evaluate cancer cell proliferation. The cell confluence proportion of inhibitor-treated or DMSO-treated cells was plotted against the time. Inhibitor concentrations were log2 \u2013transformed and the half-maximal inhibitory concentration (IC50) value was calculated for each cell line using GraphPad software.\n\nPevonedistat-resistant MDA-MB-231 cells were derived from original parental cell line by continuous exposure of pevonedistat in vitro (MedChemExpress). Briefly, MDA-MB-231 WT cells (5\u2009\u00d7\u2009105) were seeded in a 6 well plate and allowed to adhere overnight at 37\u2009\u00b0C. Then, the cells were treated with 500 or 1000\u2009nM of pevonedistat and subcultured upon reaching 65\u201370% confluency. At this time point the media was removed and the above process was repeated. This development period carried out for ~3 months. The sensitivity of resistant cells to pevonedistat was determined using the incucyte zoom live imaging system, as described above. This resistant subline was stored in \u221280\u2009\u00b0C until use.\n\nPrimary human lymphocytes were isolated from healthy donors and labeled with the CTV dye as mentioned above. Lymphocytes (1 million cells/ml) were seeded in a 96-well flat bottom plate and activated with cult anti-CD3/CD28 beads (0.4\u2009\u03bcl/well, Stemcell)\u2009\u00b1\u2009rhIL2 (100\u2009ng/ml, Peprotech). Inhibitors of the neddylation pathway, i.e., pevonedistat or TAS4464 were added in 0.1% DMSO at different concentrations and incubated for 4 days. Effects of inhibitors on lymphocyte proliferation and surface markers (Supplementary Table\u00a01) were analyzed by flow cytometry on the CytoFlex instrument.\n\nTo set up the TICS assay, triple-negative breast cancer cells were harvested following the standard protocol for passaging adherent cells. Next, up to 10,000 cancer cells per well were seeded in a 96-well flat bottom plate in 100\u2009\u03bcl cell culture medium. The plate was incubated overnight to allow cell adherence. On the next day, healthy donor-derived primary human lymphocytes were incubated in PBS containing 1.42\u2009nM CellTrace Violet dye (CTV, Thermo Fisher Scientific) and incubated in the dark for 10\u2009min. After washing twice with PBS, lymphocytes (3 million cells/ml) were added to the tumor-loaded plate in 100\u2009\u03bcl culture medium. FDA-approved checkpoint inhibitors, nivolumab (Bristol-Myers Squibb) or durvalumab (AstraZeneca) were added to the TICS plate at a final concentration of 10\u2009\u03bcg/ml in order to inhibit the PD-1/L1 pathway.\n\nFor the inhibitor treatment studies, pevonedistat was added to the tumor-immune co-culture plate at indicated doses or 0.1% DMSO (control) on day 3 after co-culture. After 5 days incubation, release of IFN-\u03b3 and granzyme B were quantified by ELISA in culture supernatants. In some experiments proliferation and surface protein expression of different immune cell subsets were analyzed by flow cytometry using a CytoFlex S or LX instrument.\n\nFor in vitro assays, CTV-treated lymphocytes were harvested from TICS assay and transferred to a 96-well V bottom plate. The cells were centrifuged at 700\u2009\u00d7\u2009g for 4\u2009min, followed by washing them twice with PBS. After that, cell pellets were resuspended in 20\u2009\u03bcl PBS containing aqua fixable live/dead marker (Thermo Fisher Scientific) and then incubated at room temperature for 15\u2009min. The cells were then washed twice with PBS and resuspended in 20\u2009\u03bcl master mix containing detection antibodies for surface markers. After 20\u2009min incubation at 4\u2009\u00b0C, the cells were washed and resuspended in 150\u2009\u03bcl PBS for analysis. To determine the expression of immune related surface markers on NEDD8 KO and control cells, a multi-color flow cytometer was used. In brief, triple-negative control or NEDD8 KO breast cancer cells (5\u2009\u00d7\u2009105) were cultured in 6 well flat bottom plate in culture medium and incubated overnight to allow cells to attach. Following treatment with \u00b1 rhIFN\u03b3 (50\u2009ng/ml) for 24\u2009h, cells were harvested, and centrifuged at 350\u2009\u00d7\u2009g for 4\u2009min. Then, the cells were resuspended in 900\u2009\u03bcl PBS and distributed in a 96-well V bottom plate in triplicates (200\u2009\u03bcl/well). Subsequently, the plate was centrifuged at 700\u2009\u00d7\u2009g for 4\u2009min and resuspended in 20\u2009\u03bcl PBS containing blue-fluorescent reactive dye (Thermo Fisher Scientific), detection antibodies for surface proteins (1:100) or the matching isotype control IgG for 25\u2009min at 4\u2009\u00b0C. After being washed with PBS, the cells were resuspended in 150\u2009\u03bcl PBS and transferred into FACS tubes for analysis.\n\nFor in vivo studies, single cells from tumor tissues were generated using a Tumor Dissociation Kit (Miltenyi Biotech) using the GentleMacs instrument according to the manufacturer\u2019s instructions. Subsequently, cells were loaded in a 96-well V bottom plate, and stained with 20\u2009\u03bcl PBS containing an Aqua fixable live/dead marker (1:200) and a Fc receptor blocking antibody (1:100, Thermo Fisher Scientific). Cells were then washed with PBS and stained with 20\u2009\u03bcl PBS containing antibodies for surface proteins (1:100) for 30\u2009min at 4 degrees. To detect intracellular proteins including FoxP3 and TNFa, cells were fixed and permeabilized using a FoxP3/transcription factor staining buffer set (eBioscience) and incubated with fluorochrome-conjugated antibodies (1:50) for 45\u2009min at 4 degrees. The rest of the cells were frozen and stored in \u2212150\u2009\u00b0C until use.\n\nAll samples were read on Cytoflex S or LX (Beckman coulter), as well as a LSR Fortessa (BD Biosciences) instruments and the data were then analyzed with FlowJo software V10.\n\nIn order to quantify the mRNA expression of a panel of genes in mouse WT or Nedd8 KO tumors after anti-PD-1 treatment, mRNA molecules were isolated from single cells using the RNeasy Mini Kit (Qiagen) according to the manufacturer\u2019s instructions. Then, the purity of mRNA molecules was determine by the ratio of absorbance at 260\u2009nm and 280\u2009nm. Subsequently, mRNA samples were prepared for nanostring analysis using nCounter immuno-oncology panel.\n\nHuman IFN-\u03b3 ELISA kit (Biolegend or MabTech) and granzyme B ELISA kit (MabTech) were used to measure cytokines secretion. Supernatants were collected from TICS, followed by centrifugation at 700\u2009\u00d7\u2009g for 4\u2009min to remove cell debris. After preparation of samples, ELISAs were conducted according to the manufacturers\u2019 protocols. After measuring the absorbance at a wavelength of 450\u2009nm and 570\u2009nm, subtraction of 570\u2009nm readings from those at 450\u2009nm was performed on a CLARIOstar Plus instrument (BMG Labtech), followed by subtraction of an averaged background signal. IFN-\u03b3 and granzyme B concentrations were then calculated and plotted against different number of cancer cells using GraphPad software.\n\nControl or NEDD8 CRISPR KO MDA-MB-231 human TNBC cells were lentivirally transduced with pLenti-Cas9-T2A-Blast-BFP to express a codon optimized, WT SpCas9 flanked by two nuclear localization signals linked to a blasticidin-S-deaminase\u2014mTagBFP fusion protein via a self-cleaving peptide (derived from lenti-dCAS9-VP64_Blast, a gift from Feng Zhang, Addgene #61425). Following blasticidin selection, a stable BFP-expressing population was isolated by repeated FACS sorting (Sony SH800).\n\nThe genome-wide Brunello sgRNA library58 was synthesized as 79\u2009bp long oligos (indicated in bold in the sequence below, CustomArray, Genscript). The oligo pool was doublestranded by PCR to include an A-U flip in the tracrRNA59, 10 nucleotide long random Unique Molecular Identifiers, and an i7 sequencing primer binding site14.\n\nggctttatatatcttgtggaaaggacgaaacaccgnnnnnnnnnnnnnnnnnnnngtttaagagctagaaatagcaagtttaaataaggctagtccgttatcaacttgaaaaagtggcaccgagtcggtgcttttttGATCGGAAGAGCACACGTCTGAACTCCAGTCACNNNNNNNNNNaagcttggcgtaactagatcttgagacaaa\n\nThe resulting PCR product with the sequence was cloned by Gibson assembly into pLenti-Puro-AU-flip-3xBsmBI14. The plasmid library was input sequenced to confirm representation and packaged into lentivirus. The functional titer of the library virus was estimated from the fraction of puromycin resistant cells after transduction with different amounts of virus. For the screen, Cas9-expressing target cells were transduced with the library virus in duplicate at an approximate MOI of 0.3 and a coverage of 1000 cells per guide in the presence of 2\u2009\u00b5g/ml polybrene. Transduced cells were selected with 2\u2009\u00b5g/ml puromycin from day 2 to day 10 post transduction. For the gene-essentiality screen in WT and NEDD8 KO MDA-MB-231 cells were transduced with Brunello library virus and were propagated for 21 days. Cell numbers per replicate were kept at >80 million/replicate throughout to ensure full library coverage.\n\nAt the end of cell culture, floating cells were gently washed away and genomic DNA was isolated from cancer cells using the QIAmp DNA Blood Maxi kit (Qiagen). Guide cassettes were amplified by PCR as described14, using modified primers PCR2_fw acactctttccctacacgacgctcttccgatctcttgtggaaaggacgaaacac and PCR3_fw aatgatacggcgaccaccgagatctacac [i5] acactctttccctacacgacgctct, respectively. The amplicons were sequenced on Illumina NovaSeq, reading 20 cycles Read 1 with custom primer CGATCTCTTGTGGAAAGGACGAAACACCG; 10 cycles index read i7 to read the UMI, and six cycles index read i5 for the sample barcode.\n\nNGS data was analyzed with the MAGeCK software15 and by UMI lineage dropout analysis14. To reduce gene search space, gRNAs targeting mitochondrial and ribosomal genes (n\u2009=\u2009638) retrieved using the R package biomaRt (MT, rRNA, rRNA_pseudogene and ribozyme biotypes) were excluded. Gene essentiality scores were calculated for each gRNA using MAGeCK for each comparison.\n\nIn order to perform enrichment analysis, depleted gRNAs were selected according to the distribution of essentiality scores using a predefined cut-off, i.e., mean-2SD for TICS screens (\u22120.16 in screen 1 and \u22120.48 for screen 2) and mean-3SD for the NEDD8 synthetic lethality screen (\u22121.9 for WT cells and \u22121.77 for NEDD8 KO). Next, over-represented pathways were revealed using EnrichAnalyzer function from the MAGeCKFlute R package using the hypergeometric test method.\n\nTo compare our NEDD8 synthetic lethality screen to publicly available large scale CRISPR KO screen, we downloaded data of the 2022 Q4 release from the DepMap project [https://depmap.org/portal/download/all/], i.e., gene effects (CRISPRGeneEffect.csv) and cell line metadata (model.csv).\n\nCell pellets from MDA-MB-231 control and NEDD8 KO cells (4 pellets of each line) were lysed in 100\u2009\u00b5l of 1% \u03b2-octyl glucopyranoside and 6\u2009M urea containing lysis buffer using a sonication probe for 30\u2009s (3\u2009mm probe, pulse 1\u2009s, amplitude 40%) according to the standard operating procedure. After homogenization, the samples were incubated for 60\u2009min at 4\u2009\u00b0C during mild agitation. The lysates were clarified by centrifugation for 10\u2009min (14,000\u2009\u00d7\u2009g). Precipitate from all samples was pressed to get more liquid. The supernatant containing extracted proteins was collected and further processed. The total protein concentration in the samples was measured using the DC Protein Assay with bovine serum albumin (BSA) as a standard. Next, aliquots corresponding to 35\u2009\u00b5g of proteins were taken out for digestion. The proteins were reduced, alkylated, on-filter digested by trypsin using 3\u2009kDa centrifugal spin filter (Millipore). The collected peptide filtrate was vacuum centrifuged to dryness using a SpeedVac system. The samples were dissolved in 100\u2009\u00b5l 0.1% formic acid and further diluted 4 times prior to LC-MS/MS analysis. The peptides were separated in reversed-phase on a C18-column with 150\u2009min gradient and electrosprayed on-line to a Q-Exactive Plus mass spectrometer (Thermo Finnigan). Tandem mass spectrometry was performed applying HCD.\n\nThe RAW-data file was quantitatively analyzed by the quantification software MaxQuant 1.5.1.2. Proteins were identified by searching for proteins from Homo Sapiens proteome extracted from Uniprot in February 2020. The search parameters were set to Taxonomy: Homo Sapience, Enzyme: Trypsin. Fixed modification: Carbamidomethyl (C) and variable modifications were Oxidation (M), Deamidated (NQ). 3278 proteins (protein groups) were identified in total in all 8 samples.\n\nDifferential protein expression was calculated with R version 4.0.5. Only proteins that were expressed in at least 2 out of 4 replicates of each cell line were considered for statistical analysis. Proteins expressed at least in 3 out of 4 technical replicates of one group and not detected in all 4 technical replicates of the other cell line were considered as uniquely expressed.\n\nA Welch\u2019s unequal variances t-test was applied to determine differences in expression between proteins expressed in both control and KO. The False Discovery Rate was calculated to adjust the p value. An absolute Log2FC above 4 and an FDR below 0.2 were set as thresholds for differentially expressed proteins (DEPs). Subsequently, DEPs and unique proteins were divided into either upregulated in NEDD8 KO or upregulated in control cells for pathway analysis. Proteins were queried for over representation analysis against the Reactome and Gene Ontology Biological Process collections from the Molecular Signature Database using clusterProfiler. Protein interactions were visualized using STRING and Cytoscape V.3.9.1.\n\nPublicly available sequencing data from breast cancer patients treated with paclitaxel (n\u2009=\u2009179) or paclitaxel in combination with pembrolizumab (n\u2009=\u200969) were retrieved (GSE194040)16, which was part of the I-SPY2 neoadjuvant platform trial (NCT01042379). Patients in these two arms (n\u2009=\u2009248) were stratified according to NEDD8 mRNA expression, using quartiles as cut-off points. Patient subgroups were then annotated by their response to the treatment for further comparisons.\n\nFlowJo V10 software was used to analyze data from flow cytometry analysis. All results were summarized and analyzed using a GraphPad Prism 9 or 10 software. Appropriate statistical analyses were performed using unpaired two-tailed T-tests with significance determined at 0.05. Two-Way ANOVA test was used for comparing parameters between multiple experimental groups, as indicated in the figure legends.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The mass spectrometry proteomics data generated in this study have been deposited in the ProteomeXchange Consortium via the PRIDE60 partner repository under the identifier PXD051061. The processed proteomics results are included as Supplementary Data\u00a02. Raw data from the Nanostring analysis is included as Supplementary Data\u00a03. The processed gRNA and gene level data are included as Supplementary Data\u00a01. The publicly available large scale cell line CRISPR KO screen data (2022Q4 release) used in this study are available in the Cancer Dependency Map portal (DepMap) [https://depmap.org/portal]. 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We acknowledge support from the National Genomics Infrastructure, SNIC (project 2017-7-265), and the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX). The BioVis platform of Uppsala University was used to conduct experiments using flow cytometry, supported by Dirk Pacholsky and staff. The proteomics quantifications were performed by the Mass Spectrometry Based Proteomics Facility (Uppsala University, Sweden), by Dr. Ganna Shevchenko and Prof. Jonas Bergquist. Nanostring analysis was performed at the KIGene core facility at the Karolinska Institute in Sweden. The authors also appreciate the experimental contributions by Ms. Myra Alm\u00e9n and Dr. Yuezhi Chen and assistance on the Incucyte live-cell imaging by Prof. Tobias Sj\u00f6blom, Prof. Sven Nelander and Dr. Cecilia Krona. We thank the scientific comments from Dr. Nina Eissler on the manuscript. Y.M.\u2019s research group and the research in this study are generously supported by grants from the SciLifeLab Fellows Program (SLL2019/9), Swedish Cancer Society (220474JIA), Swedish Childhood Cancer Foundation (TJ2019-0057), Swedish Foundation for Strategic Research (FFL21-0043) and Swedish Research Council (2022-01461). M.P.M. and G.K. are supported by the European Union\u2019s Horizon 2020 Research and Innovation Programme (Grant agreement No. 950293 - COMBAT-RES).", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open access funding provided by Uppsala University.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden\n\nIrineos Papakyriacou,\u00a0Marta R\u00fabies Bed\u00f3s,\u00a0Divya Nagarajan,\u00a0Liam P. Alford\u00a0&\u00a0Yumeng Mao\n\nComputational Health Center, Helmholtz Munich, Neuherberg, Germany\n\nGinte Kutkaite\u00a0&\u00a0Michael P. Menden\n\nDepartment of Biology, Ludwig-Maximilians University Munich, Martinsried, Germany\n\nGinte Kutkaite\n\nDepartment of Biochemistry and Pharmacology, University of Melbourne, Parkville, VIC, Australia\n\nMichael P. Menden\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY.M. and I.P. initiated the project and formulated the research hypothesis. I.P., D.N. and Y.M. performed genome-wide CRISPR screens and contributed to data interpretation and analysis. I.P. performed the majority of in vitro experiments and all in vivo experiments. I.P. and D.N. performed the analysis of cells from tumor-bearing mice using flow cytometry and I.P. analyzed flow cytometry results with the support of Y.M. L.P.A. performed in vitro experiments during the revision. M.R.B. performed the analysis of proteomics results. G.K. analyzed and interpreted data from genome-wide CRISPR screens and the public patient dataset. M.P.M. supervised the data analysis and provided guidance for the project development. All authors contributed to the writing and revising of the manuscript.\n\nCorrespondence to\n Yumeng Mao.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "Y.M. and M.P.M. were former employees of AstraZeneca and hold company shares. Y.M. received funding from Bayer Pharmaceuticals and Novo Nordisk Foundation for unrelated projects. M.P.M. receives funding from Roche and GSK for other projects. Other authors declare no conflict of interest.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Shiaw-Yih Lin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Papakyriacou, I., Kutkaite, G., R\u00fabies Bed\u00f3s, M. et al. Loss of NEDD8 in cancer cells causes vulnerability to immune checkpoint blockade in triple-negative breast cancer.\n Nat Commun 15, 3581 (2024). https://doi.org/10.1038/s41467-024-47987-x\n\nDownload citation\n\nReceived: 22 March 2023\n\nAccepted: 17 April 2024\n\nPublished: 27 April 2024\n\nVersion of record: 27 April 2024\n\nDOI: https://doi.org/10.1038/s41467-024-47987-x\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"CD103\u2013CD8+ T cells promote neurotoxic inflammation in Alzheimer\u2019s disease via granzyme K\u2013PAR-1 signaling", + "pre_title": "CD103\u2013CD8+ T cells promote neurotoxic inflammation in Alzheimer\u2019s disease via granzyme K\u2013PAR-1 signaling", + "journal": "Nature Communications", + "published": "24 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1.", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_MOESM3_ESM.mp4" + }, + { + "label": "Supplementary Movie 2.", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_MOESM4_ESM.mp4" + }, + { + "label": "Supplementary Movie 3.", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_MOESM5_ESM.mp4" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_MOESM7_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_MOESM8_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_MOESM9_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_MOESM10_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_MOESM11_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data.", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_MOESM12_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE180188", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE180184", + "/articles/s41467-025-62405-6#ref-CR45", + "/articles/s41467-025-62405-6#ref-CR9", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181279", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134578", + "https://www.ebi.ac.uk/pride/archive/projects/PXD064640", + "/articles/s41467-025-62405-6#Sec65" + ], + "code": [], + "subject": [ + "Alzheimer's disease", + "Neuroimmunology" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5203345/v1.pdf?c=1758798386000", + "research_square_link": "https://www.researchsquare.com//article/rs-5203345/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-62405-6.pdf", + "preprint_posted": "22 Oct, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Immune mechanisms contribute to the neuropathology of Alzheimer\u2019s disease (AD) but the role of adaptive immune cells is unclear. Here we show that the brain CD8+ T cell compartment is dysregulated in AD patients and in a mouse model with the hallmarks of AD, accumulating activated CD103\u2013 tissue-resident memory T cells that produce large amounts of granzyme K (GrK). These CD103\u2013CD8+ T cells originate from the circulation and migrate into the brain using LFA-1 integrin. Ablation of brain CD103\u2013CD8+ T cells in AD mice ameliorated cognitive decline and reduced neuropathology. GrK induced neuronal dysfunction and tau hyperphosphorylation in human and mouse cells via protease-activated receptor-1 (PAR-1), which is expressed at higher levels in the AD brain, revealing a novel immune-mediated neurotoxic axis. We conclude that communication between CD8+ T cells and the nervous system is altered in AD, paving the way for therapies targeting T cell-dependent neurotoxic inflammation.Biological sciences/Neuroscience/NeuroimmunologyHealth sciences/Diseases/Neurological disorders/Neurodegenerative diseases/Alzheimer's diseaseCD8+ T cellsTissue resident memory cellsAlzheimer\u2019s diseaseGranzyme KLFA-1 integrinPAR-1immune-mediated neurotoxicity", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformation1scRNAseqmousebrainandmeninges.xlsxSupplementaryInformation2scRNAseqHumanBlood.xlsxSupplementaryInformation3scRNAseqHumanCSF.xlsxTerrabuioetal.SupportingInformationDocument.pdfSupplementaryVideo1Ctrl.mp4Supplementary video 1.SupplementaryVideo2GrK150nM.mp4Supplementary video 2.SupplementaryVideo3GrK150nMSCH79797100nM.mp4Supplementary video 3.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Immune mechanisms contribute to the neuropathology of Alzheimer\u2019s disease (AD) but the role of adaptive immune cells is unclear. Here we show that the brain CD8+ T cell compartment is dysregulated in AD patients and in the 3xTg-AD mouse model, accumulating activated CD103\u2013 tissue-resident memory T cells that produce large amounts of granzyme K (GrK). These CD103\u2013CD8+ T cells originate from the circulation and migrate into the brain using LFA-1 integrin. Ablation of brain CD103\u2013CD8+ T cells in 3xTg-AD mice ameliorates cognitive decline and reduces neuropathology. GrK induces neuronal dysfunction and tau hyperphosphorylation in human and mouse cells via protease-activated receptor-1 (PAR-1), which is expressed at higher levels in the AD brain, revealing a key immune-mediated neurotoxic axis. We conclude that communication between CD8+ T cells and the nervous system is altered in AD, paving the way for therapies targeting T cell-dependent neurotoxic inflammation.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The dysregulation of innate and adaptive immunity is a driving force in the development of Alzheimer\u2019s disease (AD), the most common form of dementia, affecting more than 32 million people worldwide1. Classical AD neuropathology is characterized by \u03b2-amyloid (A\u03b2) deposition, tau hyperphosphorylation, and the loss of neurons and synapses, but more recent evidence shows that chronic inflammation promoted by local and peripheral immune cells is also a hallmark of AD2,3.\n\nCD8+ T cells are one of the most intriguing components of the AD immunological landscape. During immune responses, these cytotoxic cells produce classical granzymes (such as GrA and GrB), perforin, and a plethora of cytokines4. Most CD8+ T cells in healthy non-lymphoid organs have a tissue-resident memory (Trm) phenotype, fulfill a local protective role, and can rapidly mount immune responses5. However, CD8+ T cells also infiltrate the brains of AD patients and equivalent animal models6,7,8,9,10,11,12,13. Aging is the main risk factor for AD and recent studies have shown that CD8+ T cells also accumulate as the brain ages, promoting axonal degeneration by releasing GrB14. But although CD8+ T cells are found in close proximity to neuronal structures in AD, it is unclear whether this communication between adaptive immune cells and neural cells promotes disease development9. Furthermore, recent studies suggest that the meningeal compartment, which is highly enriched in immune cell populations, may also contribute to AD, but the role of meningeal CD8+ T cells in disease development is unclear15.\n\nIn most previous studies of AD brains, CD8+ T cells have been considered as a single homogeneous population6,7,8,10. However, there is clear evidence in other tissues to support the presence of CD8+ T cell subsets with district roles during homeostasis and immune responses4. The population of CD8+CD45RA+CCR7- T effector memory (TEMRA) cells expands in the peripheral blood of individuals with mild cognitive impairment (MCI) and AD, and in the cerebrospinal fluid (CSF) following infection with Epstein\u2013Barr virus (EBV), suggesting particular CD8+ T cell phenotypes may also play a role in AD9. Furthermore, CD69+CD103+ and CD69+CD103\u2013CD8+ Trm cells have been observed in the human brain16, but the findings involved a heterogeneous group of subjects with multiple sclerosis (MS), various types of dementia and bipolar disorders, as well as controls with no brain disease. AD patients formed a minority in this mixed group, so it was not possible to determine how CD8+ Trm cell populations are represented in the AD brain.\n\nThe presence of CD8+ T cell subsets has also been proposed in transgenic animal models of AD. However, these studies involved mice with separate A\u03b2 or tau pathology and showed that CD8+ T cells may have protective or deleterious effects depending on the disease stage and type of pathology7,10,11,12,13,17. In APP/PS1 mice with late-stage A\u03b2 pathology, the abundance of CD103+CD8+ T cells increased, and their transcriptomic profile was similar to wild-type (WT) mice, potentially explaining why the depletion of CD8+ T cells does not affect amyloid pathology10,11. The Trm marker CD103 (\u03b1E integrin) is also more abundant in the 5xFAD mouse model, which develops an early and aggressive amyloid pathology, together with higher levels of CXCR6 and PD-1 compared to WT controls, and this CD8+ T cell subset has a protective anti-A\u03b2 role suppressing the activation of microglia13. Although CD8+ T cells generally seem to be deleterious in a model of pure tauopathy17, the role of specific subsets is less clear. P301S mice expressing APOE4 accumulate activated CD11c+, KLRE1+ and ISG15+ CD8+ T cells while the pool of TOX+PDCD1+ exhausted CD8+ T cells is depleted. However, P301S mice lacking APOE show no increase in the abundance of brain CD8+ T cells despite tauopathy, suggesting additional signals are needed for CD8+ T cell responses in AD12.\n\nA\u03b2 and tau pathologies have a synergistic effect in AD and diagnosis requires the presence of both hallmarks18,19,20. Here we performed single-cell RNA sequencing (scRNAseq) in the 3xTg-AD mouse model, which develops both amyloid and tau pathologies, to investigate how brain and meningeal CD8+ T cell subsets may shape the neuropathology of AD. We found that CD8+ Trm cells are strongly dysregulated in 3xTg-AD mice, with the number of activated CD103\u2013 cells increasing as the CD103 expression level declines in the CD8+ T cell population. We showed that the brain is the main site of CD8+ T cell dysregulation and found that CD103\u2013CD8+ T cells originate from the circulation and invade the brain using a mechanism dependent on integrin LFA-1. We also observed higher levels of granzyme K (GrK) in the CD103\u2013CD8+ T cells of 3xTg-AD mice and patients with AD compared to controls and discovered a role for GrK in the induction of neuronal dysfunction through the activation of protease-activated receptor 1 (PAR-1). Finally, we demonstrated that GrK\u2013PAR-1 interaction induces tau hyperphosphorylation, revealing a critical immune-mediated neurotoxic axis. Together, our data show that dysfunctional communication between the immune system and central nervous system (CNS) mediated by CD103\u2013CD8+ T cells and GrK\u2013PAR-1 signaling contributes to the development of AD, identifying key molecular mechanisms that can be targeted to prevent immune-mediated neurotoxic inflammation.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Previous studies suggested that CD8+ T cells play a detrimental role in AD, but the subsets that drive the disease and the underlying mechanisms that contribute to disease development are still unclear6,7,8,9,10,11,12. We therefore compared the phenotypes and functions of CD8+ T cells in WT and 3xTg-AD transgenic mice, the latter developing amyloid and tau pathologies representing neuropathological characteristics of human AD patients. Single-cell RNA sequencing (scRNAseq) of CD45HIGH leukocytes isolated from the meninges and brains of 3xTg-AD (n\u2009=\u20098) mice as well as sex and age-matched WT controls (n\u2009=\u20098) at the onset of cognitive deficit21,22 (Fig.\u00a01a) revealed 13 cell types, including CD8+ and CD4+ T cells, neutrophils, B cells, natural killer (NK) cells, innate lymphoid cells (ILCs), macrophages, and microglia (Fig.\u00a01b; Supp. Fig.\u00a01a-c). Clustering analysis applied to the whole CD8+ T cell population (3,098 cells representing 19.06% of all CD45HIGH leukocytes; Supp. Fig.\u00a01c) revealed the presence of five subsets (Fig.\u00a01c), each characterized by the strong expression of known marker genes (Fig.\u00a01d-f; Supp. Data\u00a01): (i) T central memory (Tcm) cells (4.52%) expressing Sell, Ccr7, Nsg2, Dapl1 and Cmah23,24; (ii) T effector cells (11.43%) expressing Gzma, S1pr5, Cx3cr1, Zeb2, Klrg1 and Klf224,25; (iii) CD103\u2013 Trm cells (64.59%) expressing Gzmk, Cxcr6, Cxcr3, Ltb, Xcl1, Tnf and Eomes26,27; (iv) proliferating Trm (TrmPROL) cells (4.29%) expressing Chek1, Stmn1, Cdk1, Mki67, Mcm2, Mcm5, Mcm7, Tfdp1 and Pola228,29 and (v) CD103+ Trm cells (15.24%) expressing Gstp3, Foxo1, Il10, Il17a and Il2ra, as well as Itgae, encoding CD103, which is related to the tissue residence phenotype30,31,32. Cd69, previously associated with the tissue residence phenotype but also with the activation of T cells5, was expressed by most of the CD8+ T cells in both 3xTg-AD and WT mice (Supp. Fig.\u00a01d). Flow cytometry experiments confirmed this phenotypic characterization of CD8+ T cells showing that: (i) Tcm highly expressed CD62L, encoded by Sell gene, and were negative for CD69; (ii) T effector highly expressed CX3CR1, granzyme A (GrA) and granzyme B (GrB), and were negative for CD69; (iii) CD103\u2013 Trm cells were positive for CD69+ and expressed high levels of Eomes; and (iv) CD103+ Trm cells were positive for CD69+ and expressed low levels of Eomes (Fig.\u00a02a, b; Supp. Fig.\u00a05f\u2013h). Very few TrmPROL cells were found in the scRNAseq dataset and these cells were not characterized by flow cytometry.\n\na Graphical overview of the scRNAseq experimental design created in BioRender. Terrabuio, E. (2025) https://BioRender.com/f3lmkb2. b UMAP plot showing CD45HIGH leukocytes detected in the brains and meninges of wild-type (WT; n\u2009=\u20098) and 3xTg-AD (n\u2009=\u20098) mice. CD8+ T cells (n\u2009=\u20093,098) are shown in red. c UMAP plots showing the five subsets of CD8+ T cells detected in the brains and meninges of WT (n\u2009=\u20098) and 3xTg-AD (n\u2009=\u20098) mice. d Bubble plot reporting the phenotypic marker genes for each CD8+ T cell subset. Transcript levels are color-coded. e Normalized expression of known marker genes on UMAP plot. Transcript levels are color-coded. f Violin plots showing the expression of marker genes for each CD8+ T cell subset. White dashed line indicates the median expression level.\n\na, b Violin plots (g) and bubble plot (h) showing the expression of phenotypic markers detected by flow cytometry in the brains and meninges of WT (n\u2009=\u20093 pools of two organs each) and 3xTg-AD (n\u2009=\u20093 pools of two organs each) mice. White dashed line indicates the median expression level in violin plots (g). MFI expressions in the bubble plot (h) are scaled and color-coded. P-values are based on two-way ANOVA - multiple comparisons. Source data are provided as a Source Data file. c Donut plots indicating the distribution of CD8+ T cell subsets in the brains of WT (left) and 3xTg-AD (right) mice d Heat map showing the expression of residency genes in the brains and meninges of WT and 3xTg-AD mice. Transcript levels are color-coded. e Violin plots showing gene expression in the brains of WT (n\u2009=\u20098) and 3xTg-AD (n\u2009=\u20098) mice. f Heat map showing the expression in CD45HIGH leukocytes of genes in brains of WT (n\u2009=\u20098) and 3xTg- AD (n\u2009=\u20098) mice. Transcript levels are color-coded.\n\nTo better understand differences in the CD8+ population between 3xTg-AD and WT mice, we separately analyzed the phenotypic changes in the brain and meningeal compartments. Notably, scRNAseq analysis revealed an almost three-fold reduction in the abundance of CD103+CD8+ Trm cells in the brains of 3xTg-AD mice (WT\u2009=\u200927.44%, 3xTg-AD\u2009=\u20099.8%) with a parallel increase in the abundance of CD103\u2013CD8+ Trm cells (WT\u2009=\u200945.9%, 3xTg-AD\u2009=\u200973.69%) (Fig.\u00a02c). Notably, the probability to observe CD103\u2013CD8+ Trm cells was significantly higher (OR\u2009=\u20093.30; P-value\u2009=\u20090) in the brain of 3xTg-AD mice compared to those of WT controls (Data source Fig.\u00a02). In contrast, the probability to observe CD103+CD8+ Trm cells was significantly lower (OR\u2009=\u20090.29; P-value\u2009=\u20090.0038) in the brain of 3xTg-AD mice compared to those of WT controls (Data source Fig.\u00a02). However, these changes were not evident in the meninges, where there was no significant accumulation of CD103\u2013 cells (OR\u2009=\u20090.72; P-value\u2009=\u20090.09) and the proportion of CD103+ cells (WT\u2009=\u200911.97%, 3xTg- AD\u2009=\u200919.01%; OR\u2009=\u20091.72; P-value\u2009=\u20090) increased only slightly (Supp. Fig.\u00a01e; Data source Supp. Fig.1). These data suggest that the major alterations in the CD8+ Trm cell compartment occur in the brain, as corroborated by data showing the downregulation of several transcription factor (TF) genes related to the CD103+ tissue residence phenotype (Fabp5, Stat1, Stat3, Stat4, Stat5a, Stat5b, Smad4, Smad6, Smad7 and Smad9) and the suppression of genes encoding key cytokines involved in the differentiation and maintenance of CD103+ Trm cells (Il15, Il7, Tgfb3, Tgfb2, Tgfb1, Il21, Tnf and Il33; Fig.\u00a02d, f). Moreover, the expression level of Itgae was reduced, whereas the expression of Eomes and S1pr1, which inhibits the CD103+CD8+ T cell phenotype5,31, was upregulated in CD8+ T cells from the brains of 3xTg-AD mice compared to WT controls (Fig.\u00a02e). These data highlight the dysregulation of brain CD8+ T cells and the depletion of the CD103+ Trm population, presumably affecting CNS immunity and contributing to disease development.\n\nThe results above were supported by unbiased cell fate trajectory analysis showing that CD8+ T cell differentiation followed a tightly organized trajectory along with the pseudo-time progression (Fig.\u00a03a), starting from a common root and ending with two differentiation states distinguished by Itgae (Arm A) and Eomes (Arm B) expression (Fig.\u00a03b). Only slight differences were observed in the meninges whereas there were clear differences in the brain (Fig.\u00a03c, Supp. Fig.\u00a01f\u2013l), where Arm A of the trajectory plot was populated by a lower proportion of CD103+CD8+ Trm cells in 3xTg-AD mice compared to WT controls (WT\u2009=\u200926.1%, 3xTg-AD\u2009=\u200914.9%) accompanied by a higher percentage of CD103\u2013CD8+ Trm cells in Arm B (WT\u2009=\u200954.5%, 3xTg-AD\u2009=\u200975.8%; Fig.\u00a03c; Supp. Fig.\u00a01h-l). This supports the hypothesis that the brain is a fundamental point of CD8+ T cell dysregulation in 3xTg-AD mice.\n\na Pseudo-time ordered trajectory plot of CD8+ T cells in the brains and meninges of WT and 3xTg- AD mice. b Expression of genes in the trajectory plots. Transcript levels are color-coded. c Trajectory plot indicating the distribution of brain CD103\u2013 and CD103+CD8+ Trm cells in WT and 3xTg-AD mice. d, e Abundances of brain CD103\u2013 (d) and CD103+ (e) CD8+ Trm cells in WT (n\u2009=\u20094) and 3xTg-AD (n\u2009=\u20094) mice. Data are means\u2009\u00b1\u2009SD. P-values based on two-tailed Student\u2019s t-test. f Scatter plot showing KEGG pathway enrichment analysis for CD103\u2013 and CD103+ CD8+ Trm cells in the brains of 3xTg-AD mice (PD = \u201cParkinson disease\u201d, ALS = \u201cAmyotrophic lateral sclerosis\u201d). g Bubble plot showing BP-GO GSEA analysis for brain CD103\u2013CD8+ Trm cells in 3xTg-AD mice. h Normalized gene expression on UMAP plot of brain and meningeal CD8+ T cells. Transcript levels are color-coded. i Violin plots showing gene expression in CD103\u2013 and CD103+CD8+ Trm cells in the brain and meninges. j Violin plot showing gene expression in CD8+ T cells in the brains of WT and 3xTg-AD mice. k Representative flow cytometry plot showing GrK expression in brain CD8+ T cells in WT and 3xTg-AD mice. l Bar plots showing the percentage of GrK\u2013, GrKMED, and GrKHIGH CD103\u2013 CD8+ Trm cells in the brains of WT (n\u2009=\u20095) and 3xTg-AD (n\u2009=\u20095) mice. Data are means\u2009\u00b1\u2009SD. P-values based on two-tailed Student\u2019s t-test. m Scatter plot showing MFIs of CD103 and GrK in CD8+ T cells in the brains of WT (n\u2009=\u20095) and 3xTg-AD (n\u2009=\u20095) mice. n, o Representative histograms (n) and violin plots (o) showing GrK MFI for brain CD103\u2013 (red) and CD103+ (blue) CD8+ Trm cells in 3xTg-AD mice. White dashed lines indicate median expressions. P-values based on two-tailed Student\u2019s t-test. p CD8+ T cells (squares) in the brains of WT and 3xTg-AD mice detected by immunofluorescence microscopy. Scale bar = 5\u2009\u00b5m or 2\u2009\u00b5m for zoomed images.\n\nFlow cytometry showed that most brain CD8+ T cells were CD69+ (Supp. Fig.\u00a01m) and clearly validated the presence of immune dysregulation in the brain CD8+ Trm cell compartment. We observed a dramatic increase in both the proportion and absolute numbers of CD69+CD103\u2013 Trm cells in the brains of 6-month-old 3xTg-AD mice compared to sex/age-matched WT controls (WT proportion = 48.025%, 3xTg-AD\u2009=\u200988.08%; WT number = 6,224.72, 3xTg-AD\u2009=\u200912,760.5), with CD69+CD103+ Trm cells showing the opposite profile (Fig.\u00a03d, e). However, we detected no significant differences between genotypes in the numbers of CD103\u2013CD8+ Trm cells populating the meninges (WT number = 9,018.66, 3xTg-AD\u2009=\u20098,895.18), highlighting the brain as a key site for pathological changes in 3xTg-AD mice (Supp. Fig.\u00a01n, o).\n\nGene set enrichment analysis (GSEA) using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database reported \u201cpathway of neurodegeneration \u2013 multiple diseases\u201d, \u201cAlzheimer\u2019s disease\u201d (AD), and \u201cHuntington disease\u201d (HD) as the three most enriched terms in the brains of 3xTg-AD mice (Supp. Fig.\u00a01p), whereas these pathways were not enriched in the meninges (Supp. Fig.\u00a01q). Importantly, whereas CD103+CD8+ Trm cells were negatively associated with the AD pathway suggesting a protective role, CD103\u2013CD8+ Trm cells infiltrating the brains of 3xTg-AD mice showed a strong positive association with this pathway (Supp. Data\u00a01), further suggesting these cells may be specifically involved in AD (Fig.\u00a03f). Moreover, GSEA applied to the biological process Gene Ontology (BP-GO) database revealed an activated phenotype for CD103\u2013CD8+ Trm cells populating the brains of 3xTg-AD mice compared to WT controls. Indeed, the five most enriched biological processes included \u201cImmune system process\u201d (GO:0002376) and \u201cRegulation of immune system process\u201d (GO:0002682), indicating pathways involved in the development of immune responses, \u201cNitrogen compound transport\u201d (GO:0071705), which is related to T cell activation33, and \u201cHydrolase activity\u201d (GO:0016787), which mediates the secretion of cytotoxic granules, suggesting a potential pro-inflammatory and cytotoxic role for CD103\u2013CD8+ T cells in AD34 (Fig.\u00a03g). Collectively, our data show a profound dysregulation of the CD8+ Trm cell compartment in the brains of 3xTg-AD mice, with a loss of CD103+ cells and the accumulation of activated CD103\u2013 cells potentially contributing to neurotoxicity and AD development.\n\nWe next sought to identify the pathological mechanisms mediated by activated CD103\u2013 cells under AD-like conditions and found that Gzmk, encoding GrK, was one of the most strongly expressed marker genes in the global CD103\u2013CD8+ Trm cell population (brain and meninges; Fig.\u00a03h, i; Supp. Fig.\u00a02a; Supp. Data\u00a01). Interestingly, the genes encoding other granzymes (Gzma and Gzmb) and perforin-1 (Prf1) were not found in the list of marker genes in CD103\u2013CD8+ Trm cells compared to other subsets (Supp. Data\u00a01). Flow cytometry experiments validated these data confirming the low expression of GrA and GrB but high expression of GrK in the CD103\u2013 CD8+ Trm population (Fig.\u00a02a, b), suggesting GrK may have a selective functional role in the CD103\u2013 CD8+ Trm cell subpopulation in AD.\n\nImportantly, Gzmk expression was upregulated in the brains but not in the meninges of 3xTg-AD mice compared to WT controls (Fig.\u00a03j; Supp. Fig.\u00a02b). Flow cytometry confirmed the scRNAseq data, showing a dramatic increase in the percentage of GrK-producing CD103\u2013CD8+ Trm cells (both GrKMED and GrKHIGH) infiltrating the brain, but not the meninges, of 3xTg-AD mice compared to WT controls, whereas no differences were observed for brain-infiltrating CD103\u2013GrK\u2013 CD8+ T cells (Fig.\u00a03k\u2013m; Supp. Fig.\u00a02c). Importantly, we also found that the strong increase in intracellular GrK expression was selective for the CD103\u2013CD8+ Trm cell population (86% of which produced GrK) when compared to the CD103+ population (Fig.\u00a03n, o), suggesting the production of GrK is a key pathological mechanism of CD103\u2013CD8+ Trm cells in the brains of 3xTg-AD mice.\n\nGrK is associated with pro-inflammatory and cytotoxic activities35, suggesting that GrK produced by CD103\u2013CD8+ T cells may induce neuronal dysfunction in AD. Immunofluorescence staining revealed the presence of GrK+CD8+ T cells in close proximity to hippocampal neurons in the brains of 3xTg- AD mice, whereas CD8+ T cells with lower GrK expression were predominantly detected in WT brains (Fig.\u00a03p; Supp. Fig.\u00a02j). These results indicate that GrK+CD103\u2013CD8+ Trm cells may contribute to neuronal alterations in 3xTg-AD mice.\n\nTo confirm that GrK+CD103\u2013CD8+ Trm cells directly induce neuronal dysfunction in AD, we used wide-field high-resolution microscopy for the live imaging of primary hippocampal neurons isolated from 3xTg-AD mice co-cultured with CD103\u2013 or CD103+ CD8+ Trm cells obtained from the livers of 3xTg-AD mice, an abundant source of CD8+ Trm cells36 (Fig.\u00a04a). Immunofluorescence staining confirmed the high density of GrK granules inside CD103\u2013CD8+ Trm cells compared to CD103+ counterparts (Fig.\u00a04b). Time-lapse fluorescence microscopy revealed that primary neurons in contact with GrK+CD103\u2013CD8+ Trm cells, but not those in contact with CD103+CD8+ Trm cells, showed significantly higher cytoplasmic calcium (Ca2+) levels compared to the negative control, clearly indicating that GrK+CD103\u2013CD8+ Trm cells induce intracellular Ca2+ dysregulation (Fig.\u00a04c), which is associated with neuronal alterations37. Importantly, purified active GrK directly induced intracellular Ca2+ release in a dose-dependent manner, unequivocally demonstrating the detrimental role of this enzyme in the context of neuronal dysfunction (Fig.\u00a04d).\n\na Graphical overview of the experimental design. Created in BioRender. Terrabuio, E. (2025) https://BioRender.com/ir5h5rf. b Immunofluorescence staining showing GrK granules inside CD103+ and CD103\u2013 CD8+ Trm cells. Scale bar = 3\u2009\u00b5m. c Intracellular Ca2+ release in neurons co-cultured with CD103+ or CD103\u2013 CD8+ Trm cells. The last time point is shown in the right panel. Data are means\u2009\u00b1\u2009SD from four independent experiments. P-values based on two-way ANOVA-multiple comparisons. Ctrl\u2013 = neurons alone. Ctrl+ = ionomycin-stimulated neurons (10\u2009\u00b5M). Source data are provided as a Source Data file. d Intracellular Ca2+ release in neurons treated with purified active GrK or vehicle. The last time point is shown in the right panel. Data are means\u2009\u00b1\u2009SD from three independent experiments. P-values based on two-way ANOVA-multiple comparisons. Source data are provided as a Source Data file. e Intracellular Ca2+ release in neurons co-cultured for 5\u2009h with CD103\u2013CD8+ Trm cells in the presence/absence of the PAR-1 inhibitor SCH79797 (100\u2009nM). The last time point (300\u2009min) is shown in the right panel. Data are means\u2009\u00b1\u2009SD from four independent experiments. P-values based on two-way ANOVA-multiple comparisons. Ctrl\u2013 = neurons alone. Ctrl+ = ionomycin-stimulated neurons (10\u2009\u00b5M). Source data are provided as a Source Data file. f, g Intracellular Ca2+ release in neurons cultured for 5\u2009h with purified active GrK alone (150\u2009nM) or with SCH79797 (50\u2009nM, or 100\u2009nM). Ctrl\u2013 = neurons alone. The last time point is shown in the right panel. Data are means\u2009\u00b1\u2009SD from three independent experiments. P-values based on two-way ANOVA-multiple comparisons. Representative images are shown in (g). Red = intracellular Ca2+ release. Scale bar = 20\u2009\u00b5m. Source data are provided as a Source Data file. h Immunofluorescence microscopy showing GrK+CD103\u2013CD8+ Trm cells near the soma (cell I) and dendrites (cell II) of a PAR-1+ neuron. Cell morphology was visualized by wide-field imaging using a DIC filter. Scale bar = 5\u2009\u00b5m (or 2\u2009\u00b5m for zoomed images I, and II).\n\nGrK can bind the thrombin receptor PAR-138, which is implicated in synaptic plasticity and memory formation in healthy mice, but also plays a negative role in some brain pathologies39,40,41. Importantly, immunofluorescence staining revealed a significant increase in both the number and area of PAR-1+ neurons in the hippocampus of 3xTg-AD mice compared to WT controls, suggesting that signaling via PAR-1, a G-protein coupled receptor (GPCR), is dysregulated in AD (Supp. Fig.\u00a02g, h). Importantly, wide-field time-lapse microscopy showed that the PAR-1 inhibitor SCH79797 strongly reduced intra-neuronal Ca2+ release in the presence of GrK+CD103\u2013CD8+ Trm cells (Fig.\u00a04e). Moreover, our data clearly showed the strong, dose-dependent inhibition of intracellular Ca2+ release in neurons cultured with 150\u2009nM purified active GrK in the presence of SCH79797 (Fig.\u00a04f, g; Supplementary Movies\u00a01-3). Immunofluorescence staining confirmed the presence of GrK+CD103\u2013CD8+ Trm cells in close contact with PAR-1+ neurons in in vitro cultures (Fig.\u00a04h), and of GrK+ CD8+ T cells in the hippocampus of 3xTg-AD mice (Supp. Fig.\u00a02i), further supporting a role for GrK\u2013PAR-1 binding in the neuronal dysfunction underlying AD. These data demonstrate that the GrK\u2013PAR-1 axis is a key immune-mediated pathological mechanism promoting neuronal dysfunction in AD.\n\nThe intraperitoneal (ip) treatment of mice with an anti-CD8 antibody depletes circulating CD8+ T cells leaving the brain Trm compartment unaltered42,43. To determine the origin of CD103\u2013 T cells, we therefore used this method to deplete circulating CD8+ T cells in 3xTg-AD mice and sex-matched WT controls (Supp. Fig.\u00a03a-h). The anti-CD8 treatment significantly reduced the abundance of brain CD103\u2013CD8+ Trm cells compared to mice treated with an isotype control antibody, reaching the levels of the control groups (Fig.\u00a05a), suggesting this cell subset is replenished from the blood. However, we detected no differences in the abundance of CD103+CD8+ Trm cells after CD8+ T cell depletion, in 3xTg-AD mice and WT controls (Fig.\u00a05a), in line with previous data showing these cells are spared by systemic depletion43. Importantly, the ablation of CD103\u2013CD8+ Trm cells in the brains of 3xTg-AD mice was paralleled by an amelioration of cognitive functions in behavioral tests, confirming that CD103\u2013CD8+ Trm cells contribute to memory decline in 3xTg-AD mice. Particularly, the Morris water maze (MWM) test showed improved hippocampal-dependent learning after CD8+ T cell depletion in 3xTg-AD mice compared to controls, as indicated by the learning curve recorded during the training days of the test (Fig.\u00a05b). In addition, the probe test showed that 3xTg-AD mice devoid of circulating CD8+ T cells actively searched the platform, maintaining the memory of the original platform position and achieving better escape performance, as also shown by the greater path efficiency and significant reduction in body rotations (Fig.\u00a05c\u2013e). The contextual fear conditioning (CFC) associative learning task revealed an amelioration of associative memory following the depletion of circulating CD8+ T cells in 3xTg-AD mice, as shown by the significant increase in the percentage of freezing responses compared to control animals treated with an isotype control (Fig.\u00a05f).\n\na Flow cytometry showing the percentage of brain CD103\u2013 and CD103+CD8+ Trm cells after anti-CD8 antibody treatment. Anti-CD8 (WT, n\u2009=\u20096; 3xTg-AD, n\u2009=\u20099), isotype-control (WT, n\u2009=\u20096; 3xTg-AD, n\u2009=\u20099). Data are means\u2009\u00b1\u2009SD from two independent experiments and P-values are based on one-way ANOVA-multiple comparisons was used. b Time spent during training (MWM test). Anti-CD8 (WT, n\u2009=\u200910; 3xTg-AD, n\u2009=\u200921), isotype-control (WT, n\u2009=\u200922; 3xTg-AD, n\u2009=\u200918). Data are means\u2009\u00b1\u2009SD from two-independent experiments (two-way ANOVA-multiple comparisons). c Representative tracking of three mice/group (MWM test). d, e Violin plots showing path efficiency (d) and number of body rotations (e) (MWM test). Data are from two-independent experiments one-way ANOVA-multiple comparisons). f Bar plot showing the percentage of freezing during the CFC test after anti-CD8 treatment. Anti-CD8 (WT, n\u2009=\u20099; 3xTg-AD, n\u2009=\u200916), isotype-control (WT, n\u2009=\u200921; 3xTg-AD, n\u2009=\u200913). Data are means\u2009\u00b1\u2009SD from two-independent experiments. One-way ANOVA-multiple comparisons was used. g\u2013i Immunohistochemical staining of the hippocampus after CD8 T cell depletion showing A\u03b2-load (g), the levels of hyperphosphorylated (h) and total (i) tau protein. Anti-CD8 (n\u2009=\u20093), isotype-control (n\u2009=\u20093). Scale bar = 20\u2009\u00b5m. Data are means\u2009\u00b1\u2009SEM. Two-tailed Student\u2019s t-test was used. ROI\u2009=\u2009624.7\u2009\u00b5m\u00d7501.22\u2009\u00b5m. j\u2013l ELISA showing A\u03b2 1\u221240 and A\u03b2 1\u221242 (j) levels of tau hyperphosphorylation (k) and total tau (l) in soluble and insoluble fractions of brain homogenates after anti-CD8 treatment. Anti-CD8 (n\u2009=\u20094), isotype control (n\u2009=\u20094). Data from three-independent experiments are means\u2009\u00b1\u2009SEM (two-tailed Student\u2019s t-test). m Dot blot showing insoluble oligomeric and fibrillar forms of A\u03b2 in brain homogenates after anti-CD8 treatment. Anti-CD8 (n\u2009=\u20094), isotype-control (n\u2009=\u20094). Data from three-independent experiments are shown as means\u2009\u00b1\u2009SEM. P-values based on two-tailed Student\u2019s t-test. n Immunofluorescence staining showing hippocampal oligomeric and fibrillar A\u03b2. Scale bar = 10\u2009\u00b5m.\n\nTo confirm whether the amelioration of cognitive deficits by anti-CD8a antibody treatment correlates with the loss of neuropathological hallmarks of AD, we stained coronal murine brain sections with anti-A\u03b2 (6E10), anti-phospho-tau (AT180), and anti-total-tau (HT7) antibodies. We observed a significant decrease in both the A\u03b2 load and levels of tau hyperphosphorylation in the hippocampus of 3xTg-AD mice following the depletion of circulating CD8+ T cells compared to animals treated with an isotype control (Fig.\u00a05g, h), whereas the levels of total tau were unchanged (Fig.\u00a05i). These data were confirmed by ELISA experiments, showing significantly less A\u03b21-40 and A\u03b21-42 deposition, and tau hyperphosphorylation (pT231, AT180) in the soluble and insoluble fractions of brain homogenates from 3xTg-AD mice depleted of CD8\u2009+\u2009T cells compared to isotype controls (Fig.\u00a05j, k), while no significant differences were observed in the levels of total tau (Fig.\u00a05l). Similarly, we observed significantly lower levels of insoluble oligomeric (A11 antibody) and fibrillar (OC antibody) A\u03b2 forms in the brain of 3xTg-AD mice depleted of CD8\u2009+\u2009T cells compared to isotype controls (Fig.\u00a05m, Supp. Fig.\u00a03j, Supp. Data 5-8). Importantly, our immunofluorescence staining showed that the oligomeric and fibrillar forms of A\u03b2 were located at the intraneuronal level in the hippocampus of 9 9-month-old 3xTg-AD mice, and both co-localized with the 6E10 signal (Fig.\u00a05n).\n\nCollectively, these results demonstrate that CD103\u2013CD8+ Trm cells originate from the circulation, infiltrate the brain, and promote memory decline and neuropathological changes that characterize AD, strongly supporting their role in AD pathogenesis.\n\nNext, we investigated the molecular mechanisms that allow circulating CD8+ T cells to infiltrate the brains of 3xTg-AD mice. Our recent work suggests that \u03b14-integrins are not involved in CD8+ T cell extravasation in 3xTg-AD mice44. However, our flow cytometry data showed a significant increase in both the percentage and mean fluorescence intensity (MFI) of LFA-1+ circulating CD8+ T cells in the blood of 3xTg-AD mice compared to age-matched WT controls (Fig.\u00a06a). These results, together with our earlier data showing the upregulation of brain endothelial intracellular adhesion molecule 1 (ICAM-1), the counterligand of LFA-122, suggest a role for LFA-1 integrin in the trafficking of CD8+ T cells into the brain. To understand the role of LFA-1 in more detail, we crossed 3xTg-AD mice with Itgal\u2013/\u2013 mice lacking functional LFA-1 integrin and observed a significant decrease in the percentage of CD103\u2013CD8+ Trm cells in the brains of 3xTg-AD/Itgal\u2013/\u2013 mice compared to 3xTg-AD animals (Fig.\u00a06b, c). Neuropathological studies showed a significantly lower A\u03b2 load and significantly less tau hyperphosphorylation (AT180) in the brains of 3xTg-AD/Itgal\u2013/\u2013 mice compared to sex- and age-matched 3xTg-AD controls, but no significant difference in the levels of total tau (Fig.\u00a06d\u2013f). Overall, these data suggest a pivotal role for LFA-1 in the accumulation of CD103\u2013CD8+ Trm cells in the brain, and further supports the origin of detrimental CD103\u2013CD8+ Trm cells in the circulation.\n\na Flow cytometry showing the percentage of LFA-1+ CD8+ T cells (left) and corresponding MFIs (right) in the blood of WT (n\u2009=\u20096) and 3xTg-AD (n\u2009=\u20096) mice. Data are means\u2009\u00b1\u2009SD, with P-values based on two-tailed Student\u2019s t-test. b Flow cytometry showing the percentage of CD45+ leukocytes among live cells. Data are means\u2009\u00b1\u2009SEM, with P-values based on two-tailed Student\u2019s t-test. c Flow cytometry showing the percentage of CD103\u2013 (left) and CD103+ (right) CD8+ Trm cells in the brains of 3xTg-AD (n\u2009=\u20096) and 3xTg-AD/Itgal\u2013/\u2013 (n\u2009=\u20096) mice. Data are means\u2009\u00b1\u2009SEM, with P-values based on two-tailed Student\u2019s t-test. d\u2013f Representative immunohistochemical staining of the hippocampus in 3xTg-AD (n\u2009=\u20093) and 3xTg-AD/Itgal\u2013/\u2013 (n\u2009=\u20093) mice, showing the A\u03b2 load (c), and the levels of hyperphosphorylated (d) and total (e) tau protein. Scale bar = 20\u2009\u00b5m. Data are means\u2009\u00b1\u2009SEM, with P-values based on two-tailed Student\u2019s t-test. g UMAP plot showing immune cell populations in human blood (n\u2009=\u20093 AD patients and n\u2009=\u20092 negative controls, NCs) by scRNAseq. h Normalized expression of CD3G, CD3D and CD3E on a UMAP plot. Transcript levels are color- coded i CD3+ T cell cluster subsets: UMAP plot showing CD8+ and CD4+ T cell subpopulations in human blood (n\u2009=\u20093 AD patients and n\u2009=\u20092 NCs). j Normalized expression of CD8A, CD8B and CD4 on a UMAP plot. Transcript levels are color- coded. k CD8+ T cell cluster subsets: UMAP plot showing early active, Tem, and na\u00efve-like CD8+ T cells in human blood (n\u2009=\u20093 AD patients and n\u2009=\u20092 NCs). l Radar plot showing AUCell score using known genes. m Heat map reporting the expression values (mean calculated using the Hurdle model) of ITGAL in AD patients (n\u2009=\u20093) and NCs (n\u2009=\u20092). Transcript levels are color-coded.\n\nTo understand the translational potential of our data, we also investigated the role of LFA-1 in the trafficking of circulating CD8+ T cells in AD patients. We analyzed a published scRNAseq dataset (accession GSE181279) of CD45+ leukocytes isolated from the blood of three AD patients and two negative controls (NCs)45 (Fig.\u00a06g\u2013m). We used known gene signatures to characterize CD8+ T cells (n\u2009=\u200910,162) in the dataset (Fig.\u00a06i, j), identifying: (i) na\u00efve; (ii) early activated; and (iii) CD8+ Tem cells46 (Fig.\u00a06k, l; Supp. Data\u00a02). Interestingly, these results showed that ITGAL, encoding the CD11a subunit of LFA-1 integrin, was upregulated in the whole population of circulating CD8+ T cells in AD patients 1 and 3, compared to controls (Fig.\u00a06m). Particularly, ITGAL expression was upregulated in the Tem subset of circulating CD8+ T cells in AD patients compared to controls (Fig.\u00a06m) but was not differentially expressed in the early activated or na\u00efve CD8+ T cell populations (Fig.\u00a06m). These results agree with our data showing a higher percentage of circulating LFA- 1+CD44+KLRG1+CD8+ Tem cells and the stronger expression of LFA-1 on Tem cells in 3xTg-AD mice compared to WT controls (Supp. Fig.\u00a04a), whereas no significant differences were observed in the KLRG1\u2013CD44+ and KLRG1\u2013CD44\u2013 subpopulations of circulating CD8+ T cells (Supp. Fig.\u00a04b, c). Collectively, these data confirm that circulating activated CD8+ T cells, particularly the Tem subset, are more activated and have a greater LFA-1-dependent migration capacity in AD patients, as well as 3xTg-AD mice.\n\nWe also analyzed a published scRNAseq dataset of CSF immune cells (accession GSE134578)9 (Fig.\u00a07a, b), revealing two subsets of CD8+ T cells (Fig.\u00a07c, d): (i) CD69+ and PRDM1+ Trm cells24,47, and (ii) KLF2-expressing CD8+ T cells (Supp. Data\u00a03). The CD8+ Trm population could be divided into two further subpopulations (Fig.\u00a07e; Supp. Data\u00a03): (i) CD103\u2013CD8+ Trm cells characterized by the expression of GZMK, EOMES, and S1PR1, and (ii) ITGAE-expressing CD103+ CD8+ Trm cells characterized by the expression of CD7, ITGA1, KLRB1, and CCR632. We observed an increase in the abundance of CD103\u2013CD8+ Trm cells during disease progression, whereas the number of CD103+CD8+ Trm cells declined, supporting our data from 3xTg-AD mice (Fig.\u00a07f). Importantly, we also observed a significant increase in GZMK expression in CD103\u2013CD8+ Trm cells in AD patients compared to healthy controls and patients with MCI (Fig.\u00a07 g, h), strongly suggesting that GrK-producing CD103\u2013CD8+ Trm cells also play a detrimental role in the development of AD in humans.\n\na UMAP plot showing leukocyte populations detected in human CSF (n\u2009=\u20094 AD, n\u2009=\u20095 MCI, and n\u2009=\u20099 healthy controls, HCs) by scRNAseq. b Normalized expression of CD3E, CD8B and CD4 in the UMAP plot. Transcript levels are color-coded. c CD8+ T cell cluster subsets: UMAP plot showing KLF2+ and Trm cell subpopulations in human CSF (n\u2009=\u20094 AD, n\u2009=\u20095 MCI, and n\u2009=\u20099 HCs). d Normalized expression of KLF2, CD69 and PRDM1 in a UMAP plot. Transcript levels are color-coded. e Trm cell cluster subsets: UMAP plot showing CD103\u2013 and CD103+ Trm cells in human CSF (n\u2009=\u20094 AD, n\u2009=\u20095 MCI, and n\u2009=\u20099 HCs). f Percentage of CD103\u2013 and CD103+ CD8+ cells in human CSF (n\u2009=\u20094 AD, n\u2009=\u20095 MCI, and n\u2009=\u20099 HCs). Data are means\u2009\u00b1\u2009SD. Source data are provided as a Source Data file. g Violin plot reporting GZMK expression in Trm cells of n\u2009=\u20094 AD patients, n\u2009=\u20095 MCI subjects, and n\u2009=\u20099 HCs. The white dot represents median expression. h Normalized expression of GZMK in a UMAP plot of all Trm cells (panel 1) and Trm cells from HCs (panel 2), MCI subjects (panel 3), and AD patients (panel 4).\n\nWe used immunofluorescence staining to determine whether GrK-producing CD103\u2013CD8+ Trm cells also accumulate in the human brain during AD (Supp. Table\u00a01). Importantly, whereas GrK\u2013CD103+ CD8+ T cells were predominantly detected in the hippocampus of controls, we observed the presence of both intraparenchymal and intravascular GrK+CD103\u2013CD8+ T cells near the hippocampal neurons of AD patients (Fig.\u00a08a). These observations match our 3xTg-AD mice, strongly supporting a role for GrK-producing cells in AD. In further agreement with our mouse data, we observed a significant increase in the abundance of intraparenchymal CD103\u2013CD8+ T cells in the hippocampus of AD patients but a lower percentage of CD103+CD8+ T cells (Fig.\u00a08b). Although not statistically significant, we observed a slight increase in the percentage of both CD103\u2013CD8+ and GrK+CD103\u2013 CD8+ T cells at the intravascular level in the hippocampus of AD patients compared to controls (Fig.\u00a08c). Importantly, our quantifications showed a significant increase in the number of intraparenchymal GrK+CD103\u2013CD8+ T cells in the hippocampus of AD patients compared to age-matched controls, suggesting a role for GrK in the neuronal dysfunction associated with AD in humans (Fig.\u00a08b).\n\na Immunofluorescence staining of control (CTRL) and AD human brain tissues. Scale bar = 40\u2009\u00b5m, or 15\u2009\u00b5m in the zoomed images. b Bar plots showing the percentage of intraparenchymal CD103\u2013, CD103+, and GrK+CD103\u2013 CD8+ T cells in the hippocampus of HCs (n\u2009=\u20093) and AD patients (n\u2009=\u20093). Data are means\u2009\u00b1\u2009SD (two-tailed Student\u2019s t-test). Source data are provided as a Source Data file. c Bar plots showing the percentage of intravascular CD103\u2013and GrK+CD103\u2013 CD8+ T cells in the hippocampus of HCs (n\u2009=\u20093) and AD patients (n\u2009=\u20093). Data are means\u2009\u00b1\u2009SD (two-tailed Student\u2019s t-test). Source data are provided as a Source Data file.\n\nFinally, we assessed whether GrK can induce neuronal alterations via PAR-1 in human cells by measuring intracellular Ca2+ released by differentiated SH-SY5Y human neuroblastoma cells treated with purified GrK in the presence or absence of the PAR-1 inhibitor SCH79797 (Fig.\u00a09a\u2013c). We observed a significant increase in the release of intracellular Ca2+ by GrK alone, whereas cells cultured with GrK in the presence of SCH79797 showed intracellular Ca2+ levels comparable to the negative control (Fig.\u00a09b, c). Next, we showed that recombinant GrK alone significantly increased the hyperphosphorylation of tau on serine residues (pS199 and pS396), but not threonine (pT231), in differentiated SH-SY5Y cells, whereas tau hyperphosphorylation on serine residues was prevented in the presence of SCH7979748 (Fig.\u00a010a\u2013d; Supp. Fig.\u00a04f\u2013h;). No differences were observed in total tau protein levels, suggesting GrK has a specific effect on the signaling machinery leading to tau hyperphosphorylation (Supp. Fig.\u00a04e, i).\n\na Immunofluorescence staining for \u03b2III-tubulin, MAP-2, pNF-H, Nestin and PAR-1 in undifferentiated and differentiated SH-SY5Y human neuroblastoma cells. b Intracellular Ca2+ release in differentiated SH-SY5Y cells cultured in the presence of 150\u2009nM GrK, or 150\u2009nM GrK and 100\u2009nM SCH79797. The last time point is shown in the bottom panel. Ctrl\u2013 = neurons alone. Ctrl+ = ionomycin-stimulated neurons (10\u2009\u00b5M). Data are means\u2009\u00b1\u2009SEM from three independent experiments. P-values based on two-way ANOVA-multiple comparisons. c Representative images of differentiated SH-SY5Y cells at different time points cultured without GrK (Ctrl\u2013), in the presence of 150\u2009nM active GrK, or in the presence of 150\u2009nM active GrK plus 100\u2009nM SCH79797. The red signal shows intracellular Ca2+ release. Scale bar = 20\u2009\u00b5m.\n\na\u2013c Bar plots and representative images showing tau phosphorylation levels on serine and threonine (AT8, AT100) (d, e) and only threonine (AT180) (f) residues in differentiated SH-SY5Y cells cultured alone (Ctrl\u2013), in the presence of 100\u2009nM SCH79797, in the presence of 150\u2009nM active GrK alone, and the presence of both. Data are means\u2009\u00b1\u2009SD of three independent experiments (two-way ANOVA-multiple comparisons). Scale bar = 5\u2009\u00b5m. d ELISA showing the levels of phosphorylation on the pS199, pS396, and pT231 residues of tau protein in differentiated SH-SY5Y cells cultured alone (Ctrl\u2013), in the presence of 100\u2009nM SCH79797, in the presence of 150\u2009nM active GrK alone, and in the presence of both. Data are means\u2009\u00b1\u2009SD of three independent experiments. P-values are based on two-way ANOVA multiple comparisons. e, f Protein enrichment analysis from three independent proteomic experiments showing the best enriched clusters of terms (h) and terms belonging to the \u201cAlzheimer\u2019s/neurodegeneration\u201d and \u201ckinase\u201d pathways (i) in SH-SY5Y human neuroblastoma cells cultured in the absence of GrK and SCH79797 (Ctrl\u2013), in the presence of 100\u2009nM SCH79797 alone, in the presence of 150\u2009nM active GrK alone, and in the presence of both. Source data are provided as a Source Data file.\n\nTo understand downstream signaling pathways induced by GrK, we analyzed the proteome of differentiated human neuroblastoma SH-SY5Y cells (treated with recombinant GrK or untreated) in the presence or absence of the PAR-1 inhibitor SCH79797 (Fig.\u00a010e). We found that GrK significantly increased the abundance of 84 proteins, including proteins involved in the development of AD (Supporting Data\u00a04). Pathway enrichment analysis applied to upregulated proteins revealed that the \u201cAlzheimer\u2019s/Neurodegeneration\u201d and \u201cKinase pathway\u201d clusters were the most enriched (Fig.\u00a010e). In the \u201cAlzheimer\u2019s/Neurodegeneration\u201d cluster, the most significantly enriched pathways included \u201camyloid fiber formation\u201d, \u201cneurodegenerative diseases\u201d, and \u201cderegulated CDK5 triggers multiple neurodegeneration\u201d (Fig.\u00a010f, Supporting Data\u00a04). The \u201cKinase pathway\u201d cluster of terms was characterized by the upregulation of pathways related to the activation of MAP kinases49, which mediate tau hyperphosphorylation, and the \u201cPost-translational protein phosphorylation pathway\u201d (Fig.\u00a010f). We also found that GrK induced the upregulation of \u201cNF-\u03baB signaling\u201d, which in neurons is associated with the modulation of ion channel protein expression, supporting Ca2+ increases at intraneuronal levels, and with a higher A\u03b2 load50. Notably, the induction of all these pathways was prevented when GrK treatment took place in the presence of SCH79797, leading to the significant upregulation of only 12 proteins (Supporting Data\u00a04), whereas enrichment analysis detected no pathways significantly related to these proteins. Together, these data clearly support our results in 3xTg-AD mice, suggesting that CD103\u2013CD8+ T cells play a negative role in AD and that blocking GrK activity and its interaction with PAR-1 may have therapeutic value to reduce neuronal dysfunction and cognitive decline in AD.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_Fig8_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_Fig9_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62405-6/MediaObjects/41467_2025_62405_Fig10_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "CD8+ Trm cells form a defensive line against infections and cancer but may also play a role in the development of chronic inflammatory conditions5. Human and mouse brains contain CD103+CD8+ and CD103\u2013CD8+ Trm cells11,13,16,42,51, but their role in AD pathogenesis is unclear. Our scRNAseq analysis revealed an altered brain CD8+ T cell compartment with more abundant CD103\u2013 Trm cells and the inhibition of signals promoting CD103 expression in the 3xTg-AD mouse model, which develops the diagnostic neuropathological features of human AD (amyloid and tau pathologies). We showed that CD103\u2013CD8+ T cells originate from the circulation, use LFA-1 integrin to extravasate into the brain and selectively produce larger quantities of GrK in 3xTg-AD mice and human AD patients compared to controls. GrK induced neuronal dysfunction and tau hyperphosphorylation in mouse and human cells via PAR-1, revealing a key immune mechanism that promotes neurotoxic inflammation and brain damage in AD (Supp. Fig.\u00a06).\n\nPrevious studies in transgenic mice with separate A\u03b2 or tau pathology showed the existence of CD8+ T cell subsets with protective or deleterious effects depending on the disease stage and pathological hallmarks7,10,11,12,13,17. Particularly, CD8+ T cells in APP/PS1 mice with advanced \u03b2-amyloid pathology show a transcriptomic profile similar to old WT animals and higher levels of CD103 were detected by flow cytometry11. Brain CD8+ T cells of 5xFAD mice with aggressive late-stage A\u03b2 pathology had higher CD103 abundance together with higher expression of CXCR6 and PD-1 (compared to WT mice), inhibiting A\u03b2 accumulation by suppressing the activation of microglia13. Previous studies using models of tau pathology suggested brain-infiltrating CD8+ T cells promote disease development, although additional signals appear to be essential for CD8+ T cell accumulation in the CNS of such mice12,17. Particularly, old P301S mice lacking APOE do not accumulate more brain CD8+ T cells despite tau pathology, suggesting that CD8+ T cell infiltration into the CNS requires APOE412. In this context, our data show that the co-occurrence of A\u03b2 and tau pathologies significantly reduces the abundance of intraparenchymal CD103+CD8+ Trm cells in the brains of 3xTg-AD mice compared to WT animals due to the downregulation of genes encoding transcription factors and cytokines involved in the differentiation and maintenance of CD103+ Trm cells31. Particularly, our results indicate strong suppression of the homeostatic and anti-inflammatory cytokine IL-33, which is required for the generation and survival of brain CD103+CD8+ Trm cells52, and are in line with previous studies showing the loss of IL-33 in AD patients53.\n\nOur results also show that CD103\u2013CD8+ Trm cells are more abundant in the brains of 3xTg-AD mice than WT controls. The accumulation of these cells (and the loss of CD103+ cells) was also observed in the brains of MS patients54 and in primary Sj\u00f6gren\u2019s syndrome55 suggesting common dysregulation of the CD8+ T cell compartment in these diseases. Similarly, CD103\u2013CD8+ Trm cells accumulated in the guts of patients with inflammatory bowel disease (IBD), and the rebalancing of CD103\u2013CD8+ and CD103+CD8+ Trm cells was associated with disease remission56, suggesting CD103\u2013 cells promote disease pathogenesis. Indeed, our data demonstrate that CD103\u2013CD8+ T cells are detrimental in 3xTg-AD mice and their depletion in the brain ameliorates neuropathology and cognitive deficits. Importantly, our data from AD patients show an increase in the abundance of intraparenchymal CD103\u2013CD8+ T cells in the hippocampus while the percentage of CD103+CD8+ T cells declines, suggesting CD103\u2013 cells may also promote disease development in human AD patients.\n\nWe also found that brain CD103\u2013CD8+ T cells originate from the circulation in 3xTg-AD mice. Previous studies have shown that intratissutal cytotoxic CD103\u2013CD8+ Trm cells originate from circulating CXCR3+ \u201cexKLRG1\u201d effector cells26 and our single-cell data showed that Cxcr3 was one of the best marker genes for the CD103\u2013CD8+ Trm cell population, providing additional evidence that these cells migrate from the blood into the brain. CXCR3 was recently shown to regulate CD8+ T cell infiltration and neuronal damage in a human 3D model of neuro-immune interactions, and CXCL10 (the chemokine ligand of CXCR3) accumulates in AD, suggesting that the trafficking of detrimental CD8+ T cells in the AD brain is mediated by CXCR3\u2013CXCL10 interaction57. Our data also show the massive depletion of CD103\u2013CD8+ T cells in the brains of 3xTg-AD/Itgal\u2013/\u2013 mice lacking LFA-1, suggesting a role for this integrin in the migration of detrimental CD8+ T cells into the brain during AD. Interestingly, an LFA-1 blockade in 3xTg-AD mice reduces neutrophil migration into the CNS and mitigates disease symptoms, suggesting a key role for this integrin in the migration of circulating leukocytes into the AD brain22. Our results also showed that the percentage of circulating LFA-1+CD44+KLRG1+CD8+ Tem cells increased (along with higher LFA-1 levels) in 3xTg-AD mice compared to WT controls, suggesting that circulating CD8+ T cells are stickier and more prone to migrate into the AD brain. This is also supported by the higher expression of ICAM- 1, the vascular ligand of LFA-1, on the brain endothelial cells of AD mouse models and human AD patients, indicating that vascular inflammation may promote the recruitment of circulating activated CD8+ T cells into the brain. Finally, our analysis of a published dataset showed that ITGAL (encoding the CD11a subunit of LFA-1) was upregulated in the whole population of circulating CD8+ T cells in AD, suggesting that the LFA-1\u2013ICAM-1 and CXCR3\u2013CXCL10 axes may act in concert to promote the invasion of the brain by peripheral CD8+ T cells in AD.\n\nWe found that most CD103+CD8+ and CD103\u2013CD8+ T cells expressed CD69, a marker of both \u201cresidency\u201d and T cell activation, suggesting CNS Trm cells may have effector activities5. However, CD103+CD8+ and CD103\u2013CD8+ T cells have distinct transcriptional phenotypes and functions in the normal intestine or during pathological conditions, including a model of brain viral infection11,26,32,51,56. The analysis of our datasets from 3xTg-AD and WT mice are consistent with these data and unbiased cell fate trajectory analysis clearly showed that brain CD8+ T cell differentiation occurred on a tightly organized trajectory starting from a common root and ending with two differentiation states distinguished by the expression of Itgae and Eomes. However, only CD103\u2013 (not CD103+) CD8+ Trm cells infiltrating the brains of 3xTg-AD mice were strongly associated with the \u201cAlzheimer\u2019s disease\u201d pathway, suggesting these cells have distinct pathological functions specifically promoting AD. Importantly, CD103\u2013CD8+ Trm cells accumulating in the brains of AD patients and 3xTg-AD mice were characterized by no significant increase in Gzmb and Prf1 expression combined with the strong expression of Gzmk and high levels of GrK protein (but low levels of GrA and GrB proteins, a distinct phenotype for CD8+ Trm cells in AD. Indeed, recent studies performed in other contexts have shown that GrK is an atypical cytotoxic molecule that can be produced and released independently of GrA and GrB and can act in the absence of perforin58,59, thus supporting the phenotype of the CD103\u2013GrK+CD8+ T cells we found in AD. GrK+ T cells may respond to cytokine stimuli alone, given that TCR stimulation can downregulate Gzmk expression, suggesting that GrK+ T cells, despite their activated phenotype, are not antigen-stimulated T cells in AD59.\n\nHigher numbers of GrK+CD8+ T cells have been reported in several pathologies, including immune aging, cancer and autoimmune diseases55,58,59,60,61, suggesting AD pathogenesis may share common immune mechanisms with other inflammatory conditions. In a heterogeneous group of subjects, GrK was more abundant in brain CD103\u2013CD69+CD8+ T cells than CD103+CD69+CD8+ cells populations, but most brain donors had no neurological disease or were patients with MS or Parkinson\u2019s diseases and no data could be correlated with AD16. A recent study found an increased percentage of CD69+CD103+ cells in the CSF of a small group of AD patients, relative to the total population of memory CD45RA\u2013CD8+ T cells62. However, these authors did not apply their CSF analysis to Trm cells (the majority of T cells in the brain parenchyma) and did not consider that CSF cells contain a significant population of KLF2+ cells with a circulation origin, as shown by our data62. CD8+ T cells have also been found in the leptomeninges of AD patients9, and despite recent studies showing the abundance of meningeal immune cells and the role of CNS borders during neurodegenerative and neuroinflammatory diseases15, our results reveal that the number of CD103\u2013CD8+ Trm cells does not change significantly and GrK secretion does not increase in the meninges, suggesting the brain is the main site of CD8+ Trm cell dysregulation.\n\nHow CD8+ T cells communicate with neuronal cells and potentially induce deleterious effects in AD is unclear. The accumulation of CD8+ T cells in the CNS of aged mice was recently found to promote axonal degeneration and age-related cognitive and motor decline through the release of GrB14, which we did not find in our studies of 3xTg-AD mice. In addition, age-related alteration of brain functions has also been associated with clonally expanded CD8+ T cells producing INF-\u03b3, which infiltrate old neurogenic niches, inhibiting the proliferation of neural stem cells63. The ablation of circulating CD8+ T cells in APP/PS1 mice does not reduce the A\u03b2 load or cognitive decline but increases the expression of neuronal genes such as Arc and Npas4, suggesting that brain CD8+ T cells may modulate synaptic plasticity10. Nevertheless, CD8+ T cells were found close to neurons in AD patients, but their ability to exert direct neurotoxic functions has not been shown9. Our data fill this knowledge gap and demonstrate that CD103\u2013CD8+ Trm cells induce neuronal dysfunction by releasing GrK. Indeed, our high-resolution live imaging studies showed that neurons from 3xTg-AD mice cultured in the presence of GrK+CD103\u2013CD8+ Trm cells undergo profound functional changes. Notably, purified active GrK also induced neuronal dysfunction, confirming the neurotoxic role of this molecule. Our proteomic data on human neuronal cells further supported these observations, showing that GrK-PAR-1 interactions activate several intracellular pathways involved in neurodegeneration and AD. Moreover, we show that GrK+CD8+ T cells accumulated in the parenchyma near hippocampal neurons in AD patients and 3xTg-AD mice, whereas GrK\u2013CD8+ T cells were preferentially located in the healthy hippocampus, further suggesting a neurotoxic role for GrK. Our data thus suggest that GrK is the basis of a key immune mechanism that induces brain neurotoxicity with a selective role in the mediation of CD103\u2013CD8+ Trm cell-dependent neuronal changes in AD.\n\nWe found that GrK released by CD103\u2013CD8+ Trm cells activates PAR-1 (the classical thrombin receptor) on primary AD neurons, leading to functional alterations and potentially affecting neuronal networks64. GrK may also activate PAR-1-expressing glial cells, such as astrocytes, which release glutamate following PAR-1 engagement, further contributing to neuronal alterations65. Previous studies have shown that GrK cleavage and PAR-1 activation also induce the phosphorylation of ERK1/2 and MAPK, triggering the secretion of pro-inflammatory cytokines such as IL-1\u03b2, MCP-1, IL-6, and IL-8, as well as upregulating the expression of adhesion molecules on monocytes, fibroblasts and cells of epithelial origin35. Considering these pro-inflammatory activities of GrK, and our data showing that the ablation of brain CD103\u2013CD8+ T cells (the majority expressing GrK) mitigates AD in mouse models, we propose that GrK\u2013PAR-1 interactions promote chronic neuroinflammation and neurodegeneration in AD.\n\nPAR-1 has a negative role in several neuropathological conditions and its inhibition reduces brain damage and inflammation in animal models of brain ischemia66. Although not related to CD8+ T cells, PAR-1 inhibition also ameliorates cognitive performance in rats injected intracerebrally with A\u03b2 peptide 1\u20134240. However, PAR-1 engagement can also promote learning and synaptic plasticity under physiological conditions39. Notably, our data show an increase in neuronal PAR-1 expression in 3xTg-AD mice, suggesting that chronic alterations in PAR-1 signaling promote immune-driven neuropathology in AD.\n\nImportantly, our data demonstrate that neuronal PAR-1 activation by GrK in mice and humans induces tau hyperphosphorylation in vitro48 and these results are in line with previous studies showing that the number of CD8+ T cells in the brains of AD patients correlates with tau pathology and Braak staging10,17. Also, the percentage of T cells in the brains of mice with tau pathology was higher than that detected in animals with amyloidosis12. Systemic CD8+ T cell ablation has no effect on behavioral changes or neuropathology in APP-PS1 mice10, further highlighting the pathological link between tau and CD8+ T cells. Along with our work, this evidence may explain why an increase in the abundance of CD103\u2013CD8+ Trm cells was not observed in mouse models of amyloidosis11,13. However, we found that the ablation of brain CD103\u2013CD8+ T cells (86% of which produce GrK) in 3xTg-AD mice reduced both amyloid and tau pathologies. The effect of CD8+ T cell inhibition on amyloidosis is supported by the fact that A\u03b2 and tau pathologies have a synergistic effect in AD and tau pathology itself may favor A\u03b2 deposition19. Together, these data indicate that CD8+ T cells can directly sustain tau pathology during AD, mechanistically linking the invasion of the brain by CD8+ T cells and neuroinflammation with the development of AD neuropathology.\n\nIn conclusion, we show that dysregulated CD103\u2013CD8+ T cells and GrK\u2013PAR-1 signaling contribute to changes in communication between the immune system and CNS, leading to neuroinflammation and neuronal dysfunction in AD. GrK inhibitors have already been developed to treat inflammatory diseases, including myocardial infarction35, whereas drugs targeting PAR-1 are already used to block platelet aggregation67, suggesting that the manipulation of GrK\u2013PAR-1 interactions may also benefit AD patients.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "All chemicals, kits, and antibodies are listed in Supp. Table\u00a02.\n\nFormalin-fixed paraffin-embedded (FFPE) hippocampal sections of controls and Alzheimer\u2019s disease (AD) cases were obtained from the Medical Research Council (MRC) London Neurodegenerative Disease Brain Bank. All material was collected from donors (females aged 51-69 at time of death as listed in Supp. Table\u00a01) from whom written informed consent for brain autopsy and the use of the material and clinical information for research purposes had been obtained by MRC London Neurodegenerative Disease Brain Bank, approved by the NRES Committee London \u2013 City & East (REC references: 08/H0704/128\u2009+\u20095). Our reference number approved by the MRC London Neurodegenerative Disease Brain Bank is BDR TRID_240. The neuropathology studies on human brain samples were approved by the Ethical Committee from the University of Verona and Azienda Ospedaliera Universitaria Integrata (AOUI), in Verona (protocol nr. 20794).\n\nThe research conducted in this study complies with all relevant ethical guidelines. Research involving animals was authorized by the Ethical Committee from the University of Verona and by the Italian Ministry of Health, Department of Veterinary Public Health, Nutrition and Food Safety, Directorate General of Animal Health and Veterinary Medicine (authorization no. 164/2016-PR and 876/2021- PR), as required by Italian legislation (D. Lgs 26/2014) as per the application of European Directive (2010/63/UE). All efforts were made to minimize the number of animals used and their suffering during the experimental procedures. 3xTg-AD (MMRRC stock no. 34830-JAX), Itgal-/- (stock no. 005257), and WT B6129SF2/J (stock no. 101045) mice were purchased from the Jackson Laboratory. 3xTg-AD mice harbor human mutations for APP, PSEN,1 and TAU proteins, developing both amyloid and tau pathologies. We backcrossed 3xTg-AD and Itgal-/- mice to obtain a transgenic line with all transgenes from the 3xTg-AD and Itgal-/- models (APPSwe, tauP301L, PS1M146V knock-in and LFA-1 knockout). All mice were housed in pathogen-free climate-controlled facilities with 12\u2009h dark/light cycle, and were provided with food and water ad libitum. For flow cytometry experiments, mice were anesthetized and perfused with Ca2+Mg2+ 1\u2009mM in PBS 1X. For immunohistochemical staining, mice were perfused with cold PBS 1X\u2009+\u2009Ca2+Mg2+ 1\u2009mM and then fixed in 4% paraformaldehyde (PFA). For ablation experiments, depleting or isotype control antibodies were administered through an intraperitoneal injection every other day for 4 weeks. All mice were randomly assigned to the experimental groups. For each experiment, we used 6-month-old mice, both male and female.\n\nBefore tissue collection, all mice were anesthetized by i.p. injection of ketamine (100\u2009mg/kg body weight) and xylazine (15\u2009mg/kg body weight) solution and perfused with Ca2+Mg2+ 1\u2009mM in PBS 1X.\n\nDura mater and leptomeninges were carefully removed from the interior aspect of the skull and surfaces of brain with fine surgical curved scissors and forceps, and enzymatically digested with a collagenase/DNase I solution (collagenase crude type IA, Merck Millipore; Deoxiribonuclease I crude lyophilized, Merck Millipore) at 37\u2009\u00b0C for 15\u2009min. Cells were washed with cold PBS 1X.\n\nBrains were collected in cold PBS1X and separated from leptomeninges. After choroid plexus removal, brains were homogenized using a gentleMACS Dissociator (Miltenyi Biotec), and enzymatically digested with a collagenase/DNase I solution (collagenase crude type IA, Merck Millipore; Deoxiribonuclease I crude lyophilized, Merck Millipore) at 37\u2009\u00b0C for 45\u2009min. Cells were passed through a 70-\u03bcm cell strainer into a new tube for Percoll (Merck Millipore) gradient centrifugation, and cells recovered from the interphase were washed with cold PBS 1X.\n\nBrains were collected without peeling the meninges in cold PBS1X. After choroid plexus removal, brains were homogenized using a gentleMACS Dissociator (Miltenyi Biotec) and enzymatically digested with a collagenase/DNase I solution (collagenase crude type IA, Merck Millipore; Deoxiribonuclease I crude lyophilized, Merck Millipore) at 37\u2009\u00b0C for 45\u2009min. Cells were passed through a 70-\u03bcm cell strainer into a new tube for Percoll (Merck Millipore) gradient centrifugation, and cells recovered from the interphase were washed with cold PBS 1X.\n\nBlood samples were collected from the retro-orbital plexus of anesthetized mice using sodium heparinized capillaries and were mixed with an equal volume of 1% dextran from Leuconostoc spp (Sigma-Aldrich) plus 10 U/ml heparin. After pelleting the erythrocytes, the overlying supernatant plasma/dextran suspension of leukocytes was washed in cold PBS 1X.\n\nSpleens were mechanically disrupted, and single-cell suspensions were obtained by passing the cells through a 70-\u03bcm strainer. After erythrocyte lysis by NaCl 0.2% and 1.2%, cells were washed with cold PBS 1X.\n\nLivers were collected in cold RPMI culture medium (Corning). They were mechanically homogenized firstly using a scalpel and then using a gentleMACS Dissociator (Miltenyi Biotec). Then, they were enzymatically digested with a collagenase/DNase I solution at 37\u2009\u00b0C for 30\u2009min. Cells were passed through a 70-\u03bcm cell strainer into a new tube for Percoll (Merk Millipore) gradient centrifugation, and cells recovered from the interphase were washed with cold PBS 1X.\n\nMeninges and brain were collected and homogenized as described above. Pools of eight female mice for 3xTg-AD and eight female mice for WT were used. The isolated leukocytes were washed with PBS 1X and labeled with an anti-CD45 BV480 antibody (1:20, BD Biosciences). After cell resuspension in PBS 1X with FBS10%, CD45+ and CD45HIGH cells were sorted from meninges and brain, respectively. We used FACSAria Fusion device (BD) with BD FACSDiva software (8.0.1 BD). Both the 3xTg-AD and WT samples consisted of > 98% viable cells.\n\nSorted cells were resuspended to a final concentration of 700 cells/\u00b5l and cDNA sequencing libraries were prepared using the 10\u00d7 Genomics Chromium Controller and the Chromium Single Cell 3\u2032 GEM, Library and Gel Bead kit v3 (Pleasanton) following the manufacturer\u2019s instructions. Briefly, 10,000 live cells were loaded onto the Chromium Controller to recover 4000 single-cell gel-bead emulsions (GEMs) per inlet, uniquely barcoded. After cDNA synthesis, sequencing libraries were generated and final 10\u00d7 library quality was assessed using the Fragment Analyzer High Sensitivity NGS kit (Agilent Technologies) before sequencing on the Illumina NextSeq500 platform, generating 75-bp paired-end reads (28\u2009bp read 1, 91\u2009bp read 2) at a depth of 50,000 reads per cell, yielding a median per-library depth of 72,783 reads per cell.\n\nRaw base call (BCL) files were processed using Cell Ranger v 6.0.1 (10\u00d7 Genomics) to obtain a unique molecular identifier (UMI) count table for each sample. Briefly, two pipelines were used: cellranger mkfastq, which converts BCL files into FASTQ files, and cellranger counts, which takes FASTQ files from cellranger mkfastq and performs alignment, filtering, barcode counting, and UMI counting. To perform these steps, Mus musculus reference data (version mm10 2020-A) were downloaded from the 10\u00d7 official website.\n\nThe SingleCellExperiment object was uploaded and analyzed using PartekFlow analysis software. First, we filtered out low-quality cells (doublets, damaged cells, or those with too few reads), evaluating the number of read counts per cell, the number of detected genes per cell, and the percentage of mitochondrial reads per cell. After the recommended normalization, through which counts were normalized and presented in logarithmic scale in CPM (count per million) approach, features were filtered, excluding genes that are not expressed by any cells in the dataset. Batch correction was provided using the Harmony task, available in the PartekFlow software. Principal component analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) dimensional reduction were performed before cell clustering based on AUCell task, which is a tool that permits to identify cells that are actively expressing genes within a gene list. We used published and available gene lists to annotate cells14,46. CD8+ T cells were then extracted and subclustered. Cluster biomarker genes were automatically computed by performing a Student\u2019s t-test on the selected attribute. Next, gene-specific analysis (GSA), pathway analysis, and gene set enrichment analysis (GSEA) were performed on the cell population of interest using PartekFlow plugins. We used PartekFlow software to run, after the scaling expression, the trajectory analysis, which is based on Monocle3 R package. GraphPad Prism 9.0 (v 9.0.0) was used to prepare donut, bubble, and bar plots. The following R-packages were used to prepare other graphs: Seurat (version 5.0.1), ggplot2 (version 3.4.4), hrbrthemes (version 0.8.0), viridis (version 0.6.5), monocle3 (version 1.3.1), ggpubr (version 0.6.0), ComplexHeatmap (version 2.15.4), Scillus (version 0.5.0).\n\nWe analyzed two publicly available datasets of Single-cell RNAseq data from AD patients. Those from Gate et al. harboring CSF leukocytes9 (accessible under the accession code GSE134578), and the other harboring circulating leukocytes by Xu et al.45 (accessible under the accession code GSE181279). We analyzed each of these datasets with PartekFlow software as previously described for our dataset, but using human references.\n\nAfter cell blocking with an anti-CD16/32 Fc-Block (1:100, BioLegend) for 10\u2009min at room temperature (RT), cells were stained for 25\u2009min in staining buffer (PBS1X with 10% Fetal Bovine Serum - FBS) at 4\u2009\u00b0C in the dark. The following antibodies were used to stain: 1) Brain and meninges samples: CD11a/CD18 FITC (1:50, Miltenyi Biotech), CD103 BV421 (1:50, BD Biosciences), CD8 APC-H7 (1:50, BD Biosciences), CD69 BV650 (1:50, BD Biosciences), TCR\u03b3\u03b4 PE-CF594 (1:50, BD Biosciences), CD62L PE (1:50, BD Biosciences), CD45 BV605 (1:50, BD Biosciences), CD27 APC (1:50, BD Biosciences), CD11b APC-R700 (1:50, BD Biosciences), Ly6G BV510 (1:50, BD Biosciences), CD4 PE-Cy7 (1:50, BD Biosciences), CD197 BV786 (1:50, BD Biosciences), and CD44 BV711 (1:50, BD Biosciences); 2) Blood samples: CD11a/CD18 FITC (1:50, Miltenyi Biotech), CD62L PE (1:50, BD Biosciences), TCR\u03b3\u03b4 PE-CF594 (1:50, BD Biosciences), VLA-4 PeCy7 (1:50, Miltenyi Biotech), Ly6G BV421 (1:50, BD Biosciences), CD44 BV510 (1:50, BD Biosciences), CD45 BV786 (1:50, BD Biosciences), CD4 APC (1:50, BD Biosciences), CD11b APC- R700 (1:50, BD Biosciences), and CD8 APC-H7 (1:50, BD Biosciences); 3) Spleen samples: CD11a/CD18 FITC (1:50, Miltenyi Biotech), CD103 BV421 (1:50, BD Biosciences), CD8 APC-H7 (1:50, BD Biosciences), CD69 BV650 (1:50, BD Biosciences), TCR\u03b3\u03b4 PE-CF594 (1:50, BD Biosciences), CD62L PE (1:50, BD Biosciences), CD45 BV605 (1:50, BD Biosciences), CD27 APC (1:50, BD Biosciences), CD11b APC-R700 (1:50, BD Biosciences), Ly6G BV510 (1:50, BD Biosciences), CD4 PE-Cy7 (1:50, BD Biosciences), CD197 BV786 (1:50, BD Biosciences), and CD44 BV711 (1:50, BD Biosciences); 4) Brain plus meninges: CD69 SB436 (1.5:50, ThermoFisherScientific), CD44 BV510 (1:50, BD Biosciences), CXCR3 (2:50, ThermoFisherScientific), TCR\u03b3\u03b4 BV650 (0.75:50, ThermoFisherScientific), CD45 BV711 (1:50, ThermoFisherScientific), CX3CR1 FITC (1.5:50, ThermoFisherScientific), CD103 PE (1.5:50, ThermoFisherScientific), CD62L (2.5:50, ThermoFisherScientific), CD4 PE-Cy7 (1.5:50, ThermoFisherScientific), CXCR6 APC (2.5:50, ThermoFisherScientific), CD8 APC-AF750 (4:50, ThermoFisherScientific). Antibodies against chemokine and cytokine receptors were added to the sample in a separate mix, and incubated 5\u2009min 37\u2009\u00b0C, and 10\u2009min RT before to add other antibodies as previously described. Cells were then stained with 7AAD viability dye (BioLegend) for 5\u2009min at RT before flow cytometry analysis.\n\nTo evaluate GrK expression, cells were stained with ViobilityTM Fixable Dye (1:100, Miltenyi Biotec) for 15\u2009min RT in the dark. After washing, cells were stained with CD45 APC-Vio770 (1:100, Miltenyi Biotec), CD8 PE-Cy7 (1:100, BioLegend), CD103 BV421 (1:100, BD Biosciences), CD3 BV650 (1:100, BD Biosciences) surface markers for 25\u2009min at 4\u2009\u00b0C in the dark. Next, cells were washed and resuspended firstly in Fixation buffer (BD Biosciences) and next in Intracellular Staining Perm Wash Buffer 1X (BD Biosciences). In the end, samples were stained with the primary anti-GrK antibody (1:100, PA550980, ThermoFisher Scientific) for 25\u2009min at 4\u2009\u00b0C in the dark, and then with the goat anti-rabbit AF488 secondary antibody (1:100, ThermoFisher Scientific) for 25\u2009min at 4\u2009\u00b0C in the dark. To evaluate expression of phenotypical markers, cells were stained with FVS700 (1:1000, BD Bioscience) in PBS 1X, and incubated for 15\u2009min at 4\u2009\u00b0C in the dark. After washing, and after cell blocking with an anti-CD16/32 Fc-Block (1:100, BioLegend) for 10\u2009min at room temperature (RT), cells were stained with CD45 BV786 (2.5:50, ThermoFisher Scientific), TCR\u03b3\u03b4 BV650 (0.75:50, ThermoFisherScientific), CD4 PE-Cy7 (1.5:50, ThermoFisherScientific), CD8 APC-AF750 (4:50, ThermoFisherScientific), CD69 BV605 (1:50, BD Bioscience), CD103 BV711 (2.5:50, ThermoFisherScientific), CD44 BV510 (1:50, BD Biosciences), for 25\u2009min at 4\u2009\u00b0C in the dark. After washing, cells were fixed and permeabilized with Transcriptional Factor Buffer set (BD Pharmigen) following manufacturer's instructions. Then, samples were stained with the primary anti-GrK antibody (1:100, PA550980, ThermoFisher Scientific) for 25\u2009min at 4\u2009\u00b0C in the dark, and then with the goat anti-rabbit AF488 secondary antibody (1:100, ThermoFisher Scientific) for 25\u2009min at 4\u2009\u00b0C in the dark. After washing, cells were also stained with GrA eFluor 450 (1:50, ThermoFisher Scientific), GrB PE (1.75:50, SONY), and EOMES eFluor 660 (2.5:50, ThermoFisher Scientific) for 25\u2009min at 4\u2009\u00b0C in the dark.\n\nFlow cytometry analysis was performed using an LSR Fortessa X-20 flow cytometer (BD) with BD FACSDiva software (8.0.1 BD). Data were analyzed with FlowJo software (v 10). As gating strategy, we take cell of interest discriminating doublets. We counted as living cells those negative for 7AAD viability dye. Cells were gated for CD45 expression, identifying leukocytes. Next, we removed all the other cell subsets (Neutrophils Ly6g+CD11b+; Myeloid cells CD11b+; \u03b3\u03b4 T cells TCR \u03b3\u03b4\u2009+\u2009; CD4+ T cells) before detecting CD8+ T cells and analyzing the subset of interest (CD103- CD69+ and CD103+ CD69+ cells). Particularly, we calculated the percentage of CD103- CD69+ and CD103+ CD69+ cells on the total population of CD8+ T cells. Of note, we tested the anti-GrK antibody before to proceed with the experiments. A GrK+ population was correctly detected in the stained sample compared to unstained and AF488 Fluorescence Minus One (FMO) control (Supp. Fig.\u00a02d\u2013f). Gating strategies from each experiment are available in Supp. Fig.\u00a05a\u2013h.\n\nLivers were collected from 3xTg-AD mice between 6 and 8 months of age. Leukocytes were isolated from the liver as described above. Then, CD8+ T cells were enriched by negative selection using a CD8\u03b1\u2009+\u2009T cell isolation kit (Miltenyi Biotech) following the manufacturer\u2019s instructions. After cell blocking by an anti-CD16/32 Fc-Block (1:100, BD Biosciences) for 10\u2009min at RT, sample was stained 25\u2009min in staining buffer (PBS1X with 10% FBS) at 4\u2009\u00b0C with the following primary antibodies: CD103 BV421 (1:20, BD Biosciences), and CD8 APC-H7 (1:520, BD Biosciences), CD69 BV605 (1:20, BD Biosciences). Cells were then stained with 7AAD viability dye (BioLegend) for 5\u2009min at RT before cell sorting. Hepatic CD69+CD103+CD8+ and CD69+CD103-CD8+ T cells were sorted using FACSAria Fusion device (BD) with BD FACSDiva software (8.0.1 BD) from the viable population (Supp. Fig.\u00a05e). Next, freshly sorted CD8\u2009+\u2009T cells were washed and seeded in RPMI medium (Corning) supplemented with 2.5% penicillin/streptomycin (Sigma- Aldrich), 1% Glutagro (Corning), 10% FBS, IL-2 (5\u2009ng/ml, R&D systems) and IL-7 (5\u2009ng/ml, R&D systems) overnight before in vitro co-cultures.\n\nHippocampal brain region was dissected from the brains of new-born 3xTg-AD mice (3 to 7 days old) and dissociated using the Adult Brain Dissociation kit (Miltenyi Biotec) and the gentleMACS Dissociator (Miltenyi Biotec) according to manufacturer\u2019s instructions. After removing debris and red blood cells, the sample was labeled with a non-neuronal biotin antibody cocktail (Miltenyi Biotec), and neurons were enriched by negative selection. Neurons were resuspended in Neurobasal medium (Gibco) with 2% B-27 supplement (Gibco), 1% Glutagro (Corning) and 1% penicillin/streptomycin (Sigma-Aldrich). We seeded 200.000 neurons/well in 48-well plates pre-coated with poly-D-lysine and laminin (Merk Millipore). Neurons were cultured for two weeks, and then half of the culture medium was replaced with fresh medium every other day. Purity of neurons was >95%.\n\nHuman neuroblastoma SH-SY5Y cells (94030304-CDNA-20UL, Sigma-Merck) were cultured in DMEM/F-12 (Biowest) complete (supplemented with 10% FBS, 1% Glutagro (Corning), Aldrich). For the differentiation cells were seeded for immunofluorescence at a density of 8 \u00d7 103 cells/well in 24 well plate on coverslips previously coated with ECMax gel (Sigma-Aldrich) and for the calcium imaging assay at a density of 4 \u00d7 103 cells/well in 48 well plate. Cells were exposed to differentiation medium 24\u2009h after seeding. For neuronal differentiation, cells were cultured in DMEM/F-12 complete supplemented with 10\u2009\u03bcM retinoic acid (Sigma-Aldrich) for 6 days followed by a 3-day differentiation step in neurobasal medium (Gicbo) enriched with 50\u2009ng/ml recombinant human BDNF (rhBDNF, Peprotech), 2\u2009mM db-cAMP (Sigma-Aldrich), 20\u2009mM KCl, 2% B27 supplement (Gibco), and 1% Glutagro (Corning). The expression of neuronal markers was assessed by immunofluorescence as shown in Fig.\u00a07a.\n\nFor all experiments, neurons cultured in the absence of CD8\u2009+\u2009T cells were used as negative control (Ctrl-), whereas neurons treated with 10 \u03bcM ionomycin (Sigma-Aldrich) were used as positive control (Ctrl\u2009+\u2009) during the time-lapse live imaging experiment.\n\nPrimary murine neurons were labeled with Biotracker 609 Red Ca2+ AM Dye (Merck Millipore) according to the manufacturer\u2019s instructions. In parallel, CD69+CD103+ and CD69+CD103- Trm CD8+ T cells were labelled with CellTrackerTM Blue CMAC Dye (1:250, ThermoFisher Scientific). Then, CD69+CD103+CD8+ and CD69+CD103-CD8+ T cells were seeded separately on neurons at a 1:4 ratio.\n\nPrimary murine neurons were labeled with Biotracker 609 Red Ca2+ AM Dye (Merck Millipore) according to the manufacturer\u2019s instructions and resuspended in phenol red-free medium in the presence or absence of SCH79797 PAR-1 inhibitor (DBA). Then, we add mouse recombinant GrK (Cusabio) or sorted CD69+CD103- Trm CD8+ T cells stained with CellTrackerTM Blue CMAC Dye (1:250, ThermoFisher Scientific) at a 1:4 ratio.\n\nDifferentiated human SH-SY5Y neuroblastoma cells were labeled with Biotracker 609 Red Ca2+ AM Dye (Merck Millipore) according to the manufacturer\u2019s instructions and resuspended in phenol red- free medium in the presence or absence of SCH79797 PAR-1 inhibitor (DBA). Then, human recombinant GrK (Cusabio) was added.\n\nCa2+-dependent changes of intracellular fluorescence were acquired with a LD Plan-Neofluar 20\u00d7/0.4 Corr M27 objective mounted on AxioObserver 7 microscope (Zeiss), equipped with a thermostatic chamber and a Hamamatsu camera. Exposure time was manually set and left unmodified through time-lapse experiments. Images were acquired every 10\u2009min for 5\u2009h under controlled conditions. For each scene, time-lapse videos were split into individual frames and analyzed using ZEN v2.6 software (Zeiss). Data were expressed as positive area for intracellular Ca2+ staining overtime.\n\nFemale and male 3xTg-AD and WT mice of 6 months of age were treated using an anti-CD8\u03b1 depleting antibody (0.22\u2009mg/mouse, BioXCell) for a total of four weeks. Treatment was performed every other day through an intraperitoneal injection. Control mice were treated with an isotype antibody (produced in-house, Clone Y13-259). Blood was drawn as previously described to confirm ablation of circulating CD8+ T cells (Supp. Fig.\u00a03a, b). After the treatment, mice were left untouched for other 4 weeks, to minimize the stress induced by the manipulation, before to proceed with behavioral tests.\n\nMorris water maze (MWM) test and the Contextual fear conditioning (CFC) task were performed as previously described. Before MWM and CFC, we evaluate the basic abilities and anxiety of the mice through ledge, hindlimb clasping, and open field tests. Ledge consists in placing mice on cage\u2019s ledge, where they typically walk along, attempting to descend back to the cage. A scoring system is adopted to detect and evaluate coordination deficits, and the test was conducted in triplicate68. During the hindlimb clasping test, mice lifted, and the hindlimb position was observed for 10\u2009seconds. Depending on hindlimb retraction, a score is given68. Open field test evaluates locomotion, exploration, and anxiety of mice (Supp. Fig.\u00a03f). Mice were placed into the open field square cage (50x50x30 cm) and were left free to explore the environment for 20\u2009min: 10 minutes of acclimatization and 10 minutes of test. Videos were analyzed by AnyMaze software.\n\nAfter behavioral tests mice were anesthetized and perfused as previously described. Brains were collected and placed in 4% PFA overnight. Next, brains were transferred in 30% sucrose for cryoprotection. When fully soaked with sucrose, brains were frozen, protected by tissue-tek optimal cutting temperature (OCT, DDK Italia) embedded compound. Then, they were cut in 30\u2009\u03bcm slices using a cryostat (CM1520 Leica). Coronal sections, after incubation with blocking buffer (2% Normal Goat Serum, plus 0.4% Triton X-100 in PBS 1X) for 1\u2009hour, were stained with anti-A\u03b2 (6e10, 1:1000 BioLegend) antibody, and with anti-pTau (AT180, 1:200 ThermoFisher Scientific), and anti-total tau (HT7, 1:200 ThermoFisher Scientific) antibodies. Particularly, for tau staining the sections were treated with citrate buffer pH = 6 (BioOptica) at 85\u2009\u00b0C for 30\u2009min for antigen retrieval. After washing with PBS 1X plus 0.05% Tween-20, sections were added in 3% H2O2 for 10\u2009min at RT, followed by the incubation with the biotinylated goat anti-mouse secondary antibodies for 2\u2009h at RT. The immunoreactivity was visualized using DAB reagent. Images were acquired with Plan-Apochromat 20\u00d7/0.8 M27 objective on Axio Imager.Z2 microscope equipped with Axiocam 506 Color (Zeiss). Images were analyzed with ZEN blue software. Brightness, contrast, and color balance were adjusted over the whole image without eliminating any information present in the original. Investigators were blinded with respect to the genotype of the mice and the treatment. We analyzed a minimum of five sections per mouse.\n\nMice were anesthetized and perfused as previously described. After overnight fixation with 4% PFA, brains were washed and, after following treatments with ethanol 70%, ethanol 90%, ethanol 100%, and xylene, they underwent paraffin-embedding. 5\u2009\u03bcm thick coronal brain slices were obtained using a microtome (DiaPath). Following treatments with xylene, ethanol 100%, ethanol 90%, and ethanol 70%, sodium citrate was used for antigen retrieval. Sections were incubated for 2\u2009h with blocking buffer (2% Normal Goat Serum, plus 0.5% Triton X-100 and 2,5% Fetal Bovine Serum in PBS 1X) before being stained with rabbit anti-mouse PAR-1 antibody PAR-1 (1:100, Bioss). After washing with PBS 1X plus 0.05% Tween-20, sections were added in 3% H2O2 for 10\u2009min at RT, followed by incubation with the biotinylated goat anti-mouse secondary antibodies for 2\u2009h at RT. The immunoreactivity was visualized using DAB reagent. Images were acquired with Plan- Apochromat 20\u00d7/0.8 M27 objective on Axio Imager.Z2 microscope equipped with Axiocam 506 Color (Zeiss). Images were analyzed with ZEN blue software. Brightness, contrast, and color balance were adjusted over the whole image without eliminating any information present in the original. Investigators were blinded with respect to the genotype of the mice. We analyzed a minimum of nine sections per mouse.\n\nBrain homogenates were prepared as described by Illouz T. et al.69. Briefly, mice were perfused as previously described, and half brains were snap frozen and stored at \u221280\u2009\u00b0C. Then, half brains were weighted, cut in small pieces, pottered, and sonicated with a volume of RIPA lysis extraction buffer (Thermo Fisher Scientific) dependent on their weight. Then, samples were centrifugated at 17,000\u00d7\u2009g for 90\u2009min. Supernatants were stored as soluble fraction, while pellet was resuspended in tri-fluoro- acetic acid (TFA) and then dry under N2 gas stream. After resuspension in PBS 1X and neutralization with NaOH, samples were centrifugated at 17,000\u2009g for other 90\u2009min. Supernatants were stored as insoluble fraction. Soluble and insoluble fractions were snap frozen and stored at \u221280\u2009\u00b0C.\n\nSH-SY5Y cells were growth, differentiated, and stimulated with recombinant GrK in the presence or absence of SCH79797 PAR-1 inhibitor. Unstimulated cells were used as negative control. Then, cells were washed with PBS 1X and then lysed using 200 \u03bcl of RIPA lysis extraction buffer (Thermo Fisher Scientific). Samples were incubated for 10\u2009min and then centrifugated at 17,000\u00d7\u2009g for 10\u2009min. Supernatants were stored at \u221280\u2009\u00b0C.\n\nProteins present in brain homogenates (soluble and insoluble fraction) or cell lysates prepared as described above were measured using Bradford assay (SERVA) following the manufacturer\u2019s instructions. ELISA experiments were performed loading 2 \u03bcg/\u03bcl of proteins for brain homogenates soluble fractions, 0.2 \u03bcg/\u03bcl of proteins for brain homogenates insoluble fractions, and 0.05 \u03bcg/\u03bcl of proteins for cell lysates. 1-40\u2009A\u03b2 and 1-42\u2009A\u03b2, or tau pT231, pS199, pS396 or total tau were measured by ELISA following manufacturer\u2019s instructions.\n\nProteins from brain homogenates (soluble and insoluble fraction) prepared as described above were measured using Bradford assay (SERVA) following the manufacturer\u2019s instructions. Dot blot experiments were performed, loading 200 \u03bcg/\u03bcl of proteins for the soluble fractions and 40 \u03bcg/\u03bcl of proteins for the insoluble fractions. After sample adsorption on the blotting membrane (Amersham), we performed blocking with 5% milk in TBS 1X. Next, membranes were stained with anti-oligomers (A11, ThermoFisher Scientific) or anti-fibrils (OC, ThermoFisher Scientific) antibodies for 1\u2009h RT. After washing, membranes were stained with HRP secondary antibody for 1\u2009h RT. After washing, membranes were developed using ECL (MerckMillipore).\n\nFor immunohistochemical staining mice were anesthetized by i.p. injection of ketamine (100\u2009mg/kg body weight) and xylazine (15\u2009mg/kg body weight) solution and perfused with cold PBS 1X\u2009+\u2009Ca2+Mg2+ 1\u2009mM and then fixed in 4% paraformaldehyde (PFA).\n\nFree-floating PFA-fixed coronal mouse brains sections were incubated in blocking buffer (2% Normal Goat Serum, plus 0.4% Triton X-100 in PBS 1X) for 2\u2009h at room temperature and then treated with rabbit anti-mouse CD8 (1:400, CellSignaling) primary antibody overnight at 4\u2009\u00b0C, and then with goat anti-rabbit AF594 secondary antibody (1:1000, Invitrogen) for 1\u2009hour at RT in the dark. After washing with PBS 1X plus 0.05% Tween-20, we added rabbit anti-mouse GrK (1:500, ThermoFisher Scientific) overnight at 4\u2009\u00b0C, and then, after washing, biotinylated goat anti-rabbit (1:500, Merck Millipore) for 1\u2009hour RT. We wash with PBS 1X plus 0.05% Tween-20 before adding anti-biotin streptavidin AF488 (1:1000, Invitrogen) and we incubate 45\u2009min at RT. After washing with PBS 1X plus 0.05% Tween-20, we added mouse anti-mouse NeuN (1:200, Sigma-Aldrich) overnight at 4\u2009\u00b0C, and then rabbit anti-mouse AF680 (1:500, Invitrogen). Nuclei were stained with DAPI (1:1000, D9542, Sigma-Aldrich). In the end, the sections were washed with PBS 1X, transferred to glass slides, and mounted with Dako medium. Images were acquired with Plan- Apochromat 40\u00d7/0.95 M27 objective on Axio Imager.Z2 microscope equipped with a Hamamatsu camera. For imaging of fibrillar/oligomeric amyloid beta forms, brain slices were incubated with a blocking solution and then treated overnight at 4\u2009\u00b0C with either rabbit polyclonal antibodies against amyloid oligomers (A11, 57006, ThermoFisher, 1:1000) or amyloid fibrils (OC, 57005, ThermoFisher, 1:1000) and anti-\u03b2-amyloid, 1-16 antibody (6E10, 80300, Biolegend, 1:1000). Then, slices were incubated with secondary antibodies: goat anti-rabbit AF488 (a11034, Invitrogen, 1:1000) and goat anti-mouse AF680 (A-21057 Invitrogen, 1:1000), for 1\u2009hour at RT in the dark. After washing with PBS 1X containing 0.05% Tween-20, the sections were incubated with a pan-neuronal marker (MAB2300, Millipore, 1:200) and then rabbit anti-mouse AF568 secondary antibody (a10037, 1:1000, Invitrogen). Nuclei were stained with DAPI (1:1000, D9542, Sigma-Aldrich). Finally, the sections were transferred to glass slides and mounted with Dako medium. Z-stack images were acquired with Plan- Apochromat 20\u00d7/0.8 M27 objective on Axio Imager.Z2 microscope equipped with Apotome.2 (Zeiss) and a Hamamatsu camera. The images were post-processed with Imaris (9.6.0 version), applying a Gaussian filter.\n\nHepatic CD69+CD103-CD8+ (200.000 cells) and CD69+CD103+CD8+ (200.000 cells) T lymphocytes, isolated as previously described, were fixed with 4% PFA for 20\u2009min at RT. Fixed cells were washed and resuspended in blocking solution (2% Normal Goat Serum, 2% Bovine Serum Albumin, 0.2% Triton X-100) for 1\u2009hour at 4\u2009\u00b0C. Cells were stained firstly with anti-mouse GrK (1:250, ThermoFisher Scientific) primary antibody for 1\u2009hour at RT, and then with goat anti-rabbit AF647 secondary antibody (1:1000, Invitrogen) for 1\u2009hour at 4\u2009\u00b0C in the dark. Nuclei were counterstained with DAPI (1:2000, Sigma-Aldrich). Cells were transferred on a poly-lysinated glass slide and left adhering for 20\u2009min. Slides were mounted and acquired under the microscope Axio Imager.Z2 microscope (Zeiss) with a Plan Apochromat 100\u00d7/1.46 oil DIC (UV) M27 objective equipped with Hamamatsu camera.\n\nPrimary murine neurons isolated as previously described were co-cultured with 5\u2009h with hepatic CD69+CD103-CD8+ T lymphocytes (1:4 ratio), isolated as previously described. Next, cells were fixed with 4% PFA for 10\u2009min at RT. Fixed cells were washed and resuspended in blocking solution (2.5% FBS, and 0.1% Triton X-100) for 30\u2009min at RT. Cells were stained firstly with an anti-mouse GrK (1:500, ThermoFisher Scientific) primary antibody for 1\u2009hour at RT, and then with goat anti-rabbit AF488 secondary antibody (1:1000, ThermoFisher Scientific) for 1\u2009hour at RT in the dark. Then, cells were stained with anti-mouse PAR-1 (1:200, Bioss) for 1\u2009hour at RT in the dark, and, after washing, with goat anti-rabbit AF546 secondary antibody (1:1000, Invitrogen) for 1\u2009hour in the dark. Nuclei were counterstained with DAPI (1:2000, Sigma-Aldrich). Slides were acquired under the microscope Axio Imager.Z2 microscope (Zeiss) with a Plan Apochromat 100\u00d7/1.46 oil DIC (UV) M27 objective equipped with Hamamatsu camera. Wild-field imaging with dichroic filter was used to visualize the morphology of neurons and of CD8+ T cells.\n\nSH-SY5Y cells (both differentiated and undifferentiated) were fixed in 4% paraformaldehyde for 10\u2009min at RT and then washed with PBS 1X. To verify the differentiation state of SH-SY5Y cells, fixed cells were permeabilized and blocked with 0.3% Triton X-100\u2009+\u200910% Normal Goat Serum (Vector Laboratories) in PBS for 1\u2009hour at RT. Then, cells were incubated overnight with the following primary antibodies at 4\u2009\u00b0C: rabbit anti-human MAP2 (1:100, ThermoFisher Scientific), mouse anti-human Nestin (1:100, Sigma-Aldrich), rabbit anti-mouse \u03b2III-tubulin (1:100, Cusabio), or rabbit anti-human NF-H (1:100, Abcam). Appropriate secondary antibodies conjugated with AF488 (1:800, Molecular Probes) or AF594 dyes (1:800, ThermoFisher Scientific) were used for 1\u2009hour at RT. Nuclei were stained with DAPI (1:2000, Sigma-Aldrich). To verify GrK-dependent tau hyperphosphorylation, we stimulated differentiated SH-SY5Y cells untreated or pre-treated for 1\u2009hour with SCH79797 PAR-1 inhibitor (DBA) with mouse recombinant GrK (Cusabio) for 24\u2009h. Not treated differentiated SH-SY5Y cells were used as a negative control. Differentiated SH-SY5Y cells pre-treated for 1\u2009hour with SCH79797 PAR-1 inhibitor (DBA) were used to ensure that SCH79797 PAR-1 inhibitor alone was unable to induce tau hyperphosphorylation in these cells. Next, cells were fixed in 4% PFA for 10\u2009min. Fixed cells were permeabilized and blocked with 0.2% Triton X-100\u2009+\u20094% BSA\u2009+\u20092% Normal Goat Serum (Vector Laboratories). Cells were stained with (i) mouse \u03b1- human HT7 primary antibody (1:1000, ThermoFisher Scientific) to visualize total tau protein, or (ii) mouse \u03b1-human AT8 primary antibody (1:1000, ThermoFisher Scientific) to visualize tau hyperphosphorylation on serine 202 and threonine 205 residues, or (iii) mouse \u03b1-human AT100 primary antibody (1:1000, ThermoFisher Scientific) to visualize tau hyperphosphorylation on serine 214 and threonine 212 residues, or (iv) mouse \u03b1-human AT180 primary antibody (1:1000, ThermoFisher Scientific) to visualize tau hyperphosphorylation on threonine 231 residues, for 1\u2009hour at RT in blocking solution (0.2% Triton X-100\u2009+\u20094% BSA\u2009+\u20092% Normal Goat Serum). Next, cells were washed before adding goat anti-mouse AF488 secondary antibody (1:1000, Invitrogen) for 1\u2009hour at RT. Nuclei were stained with DAPI (1:2000, Sigma-Aldrich). Slides were acquired under the microscope Axio Imager.Z2 microscope (Zeiss) with a Plan Apochromat 100\u00d7/1.46 oil DIC (UV) M27 objective equipped with Hamamatsu camera. Levels of total tau and hyperphosphorylated tau protein were analyzed with ZEN blue software. Brightness, contrast, and color balance were adjusted over the whole image without eliminating any information present in the original. Investigators were blinded with respect to the treatment. We analyzed a minimum of 10 regions of interest (ROIs) for each condition and we repeated the experiment three times.\n\nFFPE sections were deparaffinized and incubated with antigen retrieval solution. Sections will be treated with primary antibodies: rabbit anti-human CD8\u03b1 antibody (1:100, Abcam), rabbit anti-human CD103 antibody (1:100, Abcam), and anti-GrK antibody (1:100, ThermoFisher Scientific). The sections were incubated sequentially overnight at 4\u2009\u00b0C in blocking solution (5% BSA, 2% Normal Goat Serum, and 0.5% Triton x-100) and then were incubated with appropriate fluorophore-conjugated secondary antibodies. Nuclei were stained with DAPI (1:1000, Sigma-Aldrich). Finally, the sections were mounted with Dako medium. Images were acquired using a Zeiss Axio Imager.Z2 microscope (Zeiss) and manually quantified in blind fashion.\n\nHuman neuroblastoma SH-SY5Y cells were growth, differentiated, and stimulated with GrK in the presence and absence of SCH79797 PAR-1 inhibitor as previously described. Next, cell extracts were prepared using the EasyPep Mini MS Sample Prep Kit (Thermo Fisher Scientific) following the manufacturer\u2019s protocol. Briefly, 1 \u00d7 106 cells per well were lysed in lysis buffer with Universal Nuclease by pipetting ten times. Protein concentration was measured using a Microplate BCA Protein Assay Kit (Pierce\u2122, Thermo Fisher Scientific). 50\u2009\u03bcg of protein were reduced, alkylated (50\u2009\u03bcL solution, 95\u2009\u00b0C, 10\u2009min), then digested with 50\u2009\u03bcL trypsin/Lys-C at 37\u2009\u00b0C for 3\u2009h. Digestion was stopped with 50\u2009\u03bcL of Digestion Stop Solution. For peptide cleanup, samples were loaded onto peptide cleanup columns, centrifuged twice at 1500 \u00d7 g for 2\u2009min, washed with 300\u2009\u03bcL of Wash Solution A and B, and eluted with 300\u2009\u03bcL of Elution Solution. Peptides were dried in a vacuum centrifuge and resuspended in 0.1% TFA for LC-MS/MS analysis.\n\nLiquid chromatography-mass spectrometry (LC-MS) analyses were performed using an Ultimate 3000 nano-UHPLC system coupled to an Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Fisher Scientific). Tryptic peptides (1\u2009\u03bcg per sample) were injected onto the column (2 \u03bcm, 500 \u00d7 0.075\u2009mm) and initially washed for 5\u2009min at a flow rate of 300 nL/min with 4% acetonitrile (ACN) in 0.1% formic acid (FA). Peptide separation was achieved using a linear gradient from 4% to 50% ACN over 90\u2009min. Ionization was performed via a nanospray electrospray ionization (ESI) source in positive ion mode, with an applied voltage of 1.5\u2009kV and a capillary temperature maintained at 275\u2009\u00b0C. Mass spectrometry data acquisition was conducted in data-dependent acquisition (DDA) mode. Full MS1 spectra were collected in the Orbitrap analyzer over an m/z range of 375\u20131500 with a resolution of 120,000 (at m/z 200), standard automated gain control (AGC) and maximum injection time settings, and precursor ion charge states of 2+ to 5\u2009+\u2009. For MS2 analysis, precursor ions exceeding an intensity threshold of 3.0 \u00d7 104 and within a charge range of 2+ to 5+ were selected within a 4.0\u2009Da isolation window and fragmented using high-energy C-trap dissociation (HCD) at a normalized collision energy of 30%. The resulting fragment ions were analyzed in the Orbitrap at a resolution of 30,000 (at m/z 200). To minimize redundant fragmentation, a dynamic exclusion time of 45\u2009s was applied. Each experimental condition was analyzed in triplicate.\n\nTo ensure data quality, blank samples (30% ACN in water) and a quality control (QC) sample consisting of HeLa lysate digest were injected every four runs. Raw mass spectrometry data were processed using Proteome Discoverer (v2.5) with mass tolerances of 10 ppm (MS1) and 0.02\u2009Da (MS2). Peptide spectra were searched against the UniProt protein database with the following search parameters: trypsin cleavage with up to two missed cleavages, carbamidomethylation of cysteine as a fixed modification, and methionine oxidation and N-terminal acetylation as variable modifications. Protein and peptide identification confidence was assessed via the Percolator algorithm, applying a false discovery rate (FDR) threshold of 0.01.\n\nLabel-free quantification was performed using at least two unique peptides per protein, with abundance calculations based on pairwise ratio-based comparisons, and statistical significance was determined using a t-test background-based approach. Proteins were considered significantly modulated if they exhibited an adjusted P-value\u2009<\u20090.05 and a fold change (FC)\u2009>\u20091.3.\n\nEnrichment analysis on significantly differentially expressed proteins was performed using PathFindeR R package. Next, enriched pathways were clustered using the SimplifyEnrichment R package, and the identified clusters were manually annotated.\n\nFor statistical analysis, the GraphPad Prism 9.0 (v 9.0.0) was used. Data are depicted as mean with standard deviation (SD) or standard error of the mean (SEM) as indicated in the respective Figure legends. P-values of P\u2009<\u20090.0001 or P\u2009<\u20090.001 were considered significant, P\u2009<\u20090.01 very significant, and P\u2009<\u20090.05 significant. Data were tested with two-tailed Mann-Whitney U-test to compare unmatched groups with non-Gaussian distribution, or with two-way analysis of variance (ANOVA) followed by Turkey\u2019s multiple comparison to determine differences among multiple datasets (threshold for significance: P\u2009\u2264\u20090.05). Statistical analyses are available in the data source files. The number of animals/samples used in each experiment is specified in figure legends.\n\nTo assess differences in cell type proportions between WT and 3xTg-AD mice (only brain, only meninges, and both tissues together), we computed odds ratios (ORs) with 95% confidence intervals (CIs) for each cell type. The ORs were calculated as:\n\nwhere p3xTg-AD and pwT represent the proportions of a given cell type in 3xTg and WT mice, respectively. The standard error (SE) of the log(OR) was computed as:\n\nwhere \u03b7WT,yes, \u03b7WT,no, \u03b73xxTg-AD,yes, \u03b73xxTg-AD,no represents the numerosity of the given cell type (yes) or all the others (no) in WT and 3xTg-AD mice respectively. The 95% CI for the OR was obtained by exponentiating the bounds of the log(OR)\u2009\u00b1\u20091.96 \u00d7 SE. To evaluate the statistical significance of observed differences, we performed a permutation test (10,000 iterations). At each iteration, cell type labels were randomly reassigned between groups while maintaining the original total number of WT and 3xTg-AD cells. The empirical P-value was computed as the fraction of permuted ORs deviating from 1 as much or more than the observed OR. Multiple testing correction was applied using the False Discovery Rate (FDR) method.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Our datasets of scRNA-seq data are deposited online in the GEO and are publicly available under accession numbers GSE180188 and GSE180184. Human scRNA-seq datasets obtained from Xu et al.45 and Gate et al9. are available under GSE181279 and GSE134578 accession codes, respectively. All data associated with this study can be found in the paper or in supplementary materials. 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We thank the Center for Technological Platforms form the University of Verona for providing the genomic, proteomic and flow cytometry platforms.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Medicine, University of Verona, Strada le Grazie 8, Verona, Italy\n\nEleonora Terrabuio,\u00a0Enrica Caterina Pietronigro,\u00a0Alessandro Bani,\u00a0Vittorina Della Bianca,\u00a0Carlo Laudanna,\u00a0Barbara Rossi,\u00a0Bruno Santos-Lima,\u00a0Elena Zenaro,\u00a0Gabriele Tosadori,\u00a0Nikolaos Vareltzakis,\u00a0Fabiana Mainieri,\u00a0Antonella Calore,\u00a0Gabriele Angelini\u00a0&\u00a0Gabriela Constantin\n\nThe Center for Biomedical Computing (CBMC), University of Verona, Verona, Italy\n\nEleonora Terrabuio,\u00a0Carlo Laudanna,\u00a0Daniela Cecconi\u00a0&\u00a0Gabriela Constantin\n\nCentro Piattaforme Tecnologiche (CPT), University of Verona, Verona, Italy\n\nGiulia Finotti\u00a0&\u00a0Monica Castellucci\n\nDepartment of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy\n\nErmanna Turano\n\nDepartment of Biotechnology, University of Verona, Strada Le Grazie 15, Verona, Italy\n\nMatteo Calgaro,\u00a0Nicola Vitulo,\u00a0Daniela Cecconi\u00a0&\u00a0Jessica Brandi\n\nNeurology Unit A, Azienda Ospedaliera Universitaria Integrata of Verona, P. le Stefani, Verona, Italy\n\nBruno Bonetti\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nThe study was designed by E.T., E.C.P. and G.C. E.T., G.T., M.C., N.Vi., E.Z. and M.C. performed scRNAseq and bioinformatics analysis. E.Terrabuio, E.C.P., A.B., G.F., A.C., F.M. and B.S.L. performed flow cytometry experiments. In vivo experiments and behavioral tests were performed and analyzed by E.Terrabuio, and E.C.P. Immunohistochemistry and immunofluorescence experiments on human and murine samples were performed and analyzed by E.C.P., G.A. and C.L. B.B. provided AD samples and contributed to human data analysis. Immunofluorescence experiments on in vitro co-culture were performed by E.C.P. and E. Turano. E.Terrabuio, E.C.P, A.B, C.L. and V.D.B. conducted in vitro live-cell imaging. E.Terrabuio and B.R. analyzed in vitro live cell image data. E.Terrabuio, D.C., J.B., E.C.P. and N.Va. prepared the cells for proteomics and performed the proteomic analysis. E.T., E.C.P. and G.C. wrote the manuscript. G.C. provided the financial support.\n\nCorrespondence to\n Eleonora Terrabuio or Gabriela Constantin.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Wassim Elyaman and the other, anonymous, reviewer for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Terrabuio, E., Pietronigro, E.C., Bani, A. et al. CD103\u2013CD8+ T cells promote neurotoxic inflammation in Alzheimer\u2019s disease via granzyme K\u2013PAR-1 signaling.\n Nat Commun 16, 8372 (2025). https://doi.org/10.1038/s41467-025-62405-6\n\nDownload citation\n\nReceived: 18 October 2024\n\nAccepted: 15 July 2025\n\nPublished: 24 September 2025\n\nVersion of record: 24 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-62405-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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SLC30A10-mediated manganese transport", + "pre_title": "Mechanisms of Manganese Transport by SLC30A10 in Maintaining Mitochondrial Homeostasis", + "journal": "Nature Communications", + "published": "29 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63616-7/MediaObjects/41467_2025_63616_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63616-7/MediaObjects/41467_2025_63616_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63616-7/MediaObjects/41467_2025_63616_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63616-7/MediaObjects/41467_2025_63616_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-62603", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-62604", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-62605", + "https://doi.org/10.2210/pdb9KVX/pdb", + "https://doi.org/10.2210/pdb9KVY/pdb", + "https://doi.org/10.2210/pdb9KVZ/pdb", + "https://www.rcsb.org/structure/8J7W", + "https://www.rcsb.org/structure/8ZSZ", + "https://www.rcsb.org/structure/8ZSB", + "https://www.rcsb.org/structure/6XPE", + "https://www.rcsb.org/structure/8J7T" + ], + "code": [ + "https://github.com/Hanting-lab/MD-simulation/tree/main/SLC30A10" + ], + "subject": [ + "Cryoelectron microscopy", + "Membrane proteins", + "Mitochondria", + "Permeation and transport" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5454185/v1.pdf?c=1759231109000", + "research_square_link": "https://www.researchsquare.com//article/rs-5454185/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63616-7.pdf", + "preprint_posted": "18 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Manganese (Mn) is an essential trace element required for various physiological processes, yet excessive levels induce oxidative stress and lead to severe health issues. The transporter SLC30A10 is crucial for maintaining Mn homeostasis by exporting Mn from cells to prevent toxic accumulation. Mutations in the SLC30A10 gene disrupt this function, resulting in manganese accumulation, which leads to disorders such as hypermanganesemia with dystonia 1 (HMNDYT1). Here, we show that SLC30A10 mediates Mn efflux under elevated Mn conditions, alleviating oxidative stress and preserving mitochondrial integrity. High-resolution cryo-EM structures reveal a unique Mn-binding site in SLC30A10, setting it apart from other SLC30 family transporters. We further demonstrate that the HMNDYT1-linked D40A mutation disrupts the binding and transport of Mn, identifying D40 as a potential therapeutic target. These findings provide structural insights into Mn transport and potential avenues for targeting Mn toxicity.Biological sciences/Structural biology/Electron microscopy/Cryoelectron microscopyBiological sciences/Biochemistry/Proteins/Membrane proteins", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Shenetalsupplfile.pdfSupplemenrary files", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Manganese ion (Mn\u00b2\u207a) is crucial for various physiological processes, yet excessive levels disrupt cellular homeostasis and impair the function of multiple organelles. The transporter SLC30A10 plays a pivotal role in Mn\u00b2\u207a homeostasis by exporting Mn\u00b2\u207a from cells, preventing toxic effects. Mutations in the SLC30A10 gene result in Mn\u00b2\u207a accumulation and lead to disorders such as hypermanganesemia with dystonia 1 (HMNDYT1). Despite its physiological significance, the structural basis underlying Mn\u00b2\u207a binding and the detailed transport mechanisms of SLC30A10 remain unknown. Here, we present diverse conformations of high-resolution cryo-electron microscopy (cryo-EM) structures that reveal a Mn\u00b2\u207a-binding site in SLC30A10, setting it apart from other SLC30 family transporters. Furthermore, we show that the HMNDYT1-associated D40A mutation interrupts Mn\u00b2\u207a binding and transport, identifying D40 as a potential therapeutic target. These findings provide structural insights into Mn\u00b2\u207a transport mechanisms mediated by SLC30A10, advancing our understanding of Mn\u00b2\u207a binding and potential targets for future therapeutic exploration.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Manganese (Mn) is an essential trace element crucial for numerous physiological processes, including metabolism, regulation of oxidative stress, immune responses, and brain development1,2. However, excessive accumulation of Mn\u00b2\u207a can lead to severe health issues, particularly affecting the nervous system, blood, and liver3,4. Mn toxicity affects multiple cellular processes, including Golgi, endoplasmic reticulum (ER), and endosomal function, thereby impairing protein folding, post-translational modification, intracellular transport and degradation5,6,7,8. In addition, elevated Mn\u00b2\u207a levels can disrupt mitochondrial function by impairing oxidative phosphorylation and increasing the production of reactive oxygen species (ROS), leading to cell death9,10,11,12. Given these detrimental effects, a comprehensive understanding of the cellular mechanisms regulating Mn\u00b2\u207a homoeostasis is critically important.\n\nIn mammals, SLC30A10 (also known as ZnT10) is a distinctive Mn\u00b2\u207a transporter, specifically exporting Mn\u00b2\u207a from cells to maintain Mn\u00b2\u207a homoeostasis, whereas other family members primarily transport zinc ions (Zn\u00b2\u207a)13,14,15,16,17,18,19,20,21. Defects in the SLC30A10 gene lead to Mn\u00b2\u207a accumulation in various tissues, causing a condition known as hypermanganesemia with dystonia 1 (HMNDYT1), polycythemia, and cirrhosis22,23,24,25,26,27,28,29,30. Patients harbouring SLC30A10 mutation such as c.119\u2009A\u2009>\u2009C (p.Asp40Ala) exhibit elevated Mn\u00b2\u207a levels, movement disorder, increased red blood cell counts, and chronic liver disease30. In mouse models, deletion of SLC30A10 in the central nervous system leads to significant Mn\u00b2\u207a accumulation in the basal ganglia and thalamus, underscoring its crucial role in Mn\u00b2\u207a efflux and homoeostasis31,32,33,34,35. Although previous studies, based on predicted models and mutagenesis experiments, have proposed several residues impacting SLC30A10\u2019s Mn\u00b2\u207a transport and provided valuable insights into its functional properties14,36,37, the precise Mn\u00b2\u207a binding site and detailed transport mechanisms of SLC30A10 are yet to be fully elucidated.\n\nIn this study, we present cryo-electron microscopy (cryo-EM) structures of human SLC30A10, which reveal a Mn\u00b2\u207a-specific binding site previously unidentified within the ZnT family. This finding, along with the delineation of two distinct conformations, advances our knowledge of Mn\u00b2\u207a transport. Site-directed mutagenesis, corroborated by functional assays in mammalian cells, has clarified the molecular mechanisms by which SLC30A10-mediated Mn\u00b2\u207a efflux, which reduces Mn\u00b2\u207a overload-induced oxidative stress and sustains cell viability, primarily through interactions at the Mn\u00b2\u207a coordination site (D40, N127, D248, S252). Our findings elucidate the pathogenesis of the spontaneous D40A mutation in SLC30A10, providing a theoretical basis for targeted therapy for HMNDYT1 treatment.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Exposure to high levels of Mn\u00b2\u207a leads to toxic accumulation, particularly in the brain1. To investigate its impact, we treated nerve-derived SH-SY5Y cells with MnCl2 and measured intracellular Mn content using the cellular Fura-2 manganese extraction assay (CFMEA assay)38,39,40. The results showed a significant increase in Mn content, accompanied by cell death (Supplementary Fig.\u00a01a, b). Normalizing Mn content to double-stranded DNA (dsDNA) using PicoGreen dsDNA assay further confirmed a marked elevation in Mn levels (Fig.\u00a01a, Supplementary Fig.\u00a01c). Cell survival assay showed that 100\u2009\u03bcM MnCl2 exposure for 24\u2009h caused ~20% cell death (Fig.\u00a01b, c), highlighting the cytotoxic effects of excessive Mn\u00b2\u207a.\n\na Normalized Mn levels in SH-SY5Y cells (n\u2009=\u20095). b, c Flow cytometry detection (b) and statistical analysis (c; n\u2009=\u20093) of cell survival by Annexin V / PI. d Cell survival by Annexin V / PI in the absence or the presence of 100\u2009\u03bcM MnCl2 for 24\u2009h in Dox-inducible SLC30A10 overexpressed SH-SY5Y cells\u00a0(n\u00a0=\u00a04). -Dox, without Dox treatment. +Dox, treated with 100\u2009ng/ml Dox. e Normalized Mn levels in SLC30A10 transfected HEK293T cells by ICP-MS. Cells transfected with vector served as control. Data were normalized to control (n\u2009=\u20094). f In vitro Mn2+ transport assay of SLC30A10. Purified human SLC30A10 protein and calcein-salt were reconstituted into liposome. The fluorescence of calcein was quenched by Mn2+ that is transported into the liposome (n\u2009=\u20093). Transport was initiated by the addition of 100\u2009\u03bcM MnCl\u2082. The fluorescence of MnCl2-free proteoliposome was measured to determine the background signal (F0). The fluorescence of 0\u2009s was measured as maximum (max) fluorescence. The relative fluorescence was calculated using the equation (F/Fmax)-(1-F0/F0max). g, h Cryo-EM map of human SLC30A10 in the Mn2+-bound state in the side view. The two monomers are coloured in red and blue. i, j Cylinder representation of SLC30A10 in the Mn2+-bound state in the side view. Mn2+ is in purple spheres. The flexible region in the loops between TM4 and TM5 not observed in the structure are shown as dash line. k Cylinder representation of SLC30A10 in the Mn2+-bound state in the extracellular view. l Topology of SLC30A10 monomer. All bar graphs above represent mean\u2009\u00b1\u2009SEM. Statistical analysis was performed using two-tailed unpaired t-test (a, c, e), one-way ANOVA with Dunnett\u2019s multiple comparisons test (d), or two-way ANOVA with Sidak\u2019s multiple comparisons test (f) (****p\u2009<\u20090.0001; ***p\u2009<\u20090.001; **p\u2009<\u20090.01; *p\u2009<\u20090.05; ns not significant). The experiments were independently repeated 3\u2009times.\n\nInvestigating the broader cellular effects, we observed that Mn\u00b2\u207a can induce oxidative stress, elevate ROS, and disrupt mitochondrial function, leading to cell death (Supplementary Fig.\u00a01d\u2013f). To pinpoint mechanisms that could counteract these effects, we screened several manganese transporters using quantitative real-time PCR (qRT-PCR). SLC30A10, localized on the plasma membrane, was the only efflux transporter that showed upregulated expression (Supplementary Fig.\u00a01g), consistent with its established role in Mn\u00b2\u207a overload management41,42. Knockdown of SLC30A10 led to higher Mn\u00b2\u207a levels, elevated ROS content, decreased mitochondrial membrane potential (MMP), and reduced ATP levels, resulting in increased cell death (Supplementary Fig.\u00a01h\u2013m). Conversely, in SLC30A10-overexpressing cells, both the total Mn content in cells and the Mn levels within mitochondria were significantly reduced (Supplementary Fig.\u00a01n\u2013p), accompanied by decreased ROS levels, preserved mitochondrial function, and improved cell survival (Fig.\u00a01d, Supplementary Fig.\u00a01q\u2013t). These knockdown and overexpression results align with previous findings that SLC30A10 plays a key role in regulating manganese homoeostasis13,31. Collectively, our findings underscore the critical function of SLC30A10 in maintaining mitochondrial integrity and protecting against Mn\u00b2\u207a-induced cytotoxicity.\n\nTo validate the specificity of SLC30A10 for Mn2+ transport, we overexpressed SLC30A10 in HEK293T cells and observed that it specifically reduced Mn content but had no effect on Zn levels (Fig.\u00a01e, Supplementary Fig.\u00a02a, 3a\u2013d). We then purified full-length human SLC30A10 protein and reconstituted it into liposomes for ion transport assays (Supplementary Fig.\u00a02b, c). These liposomes containing SLC30A10 exhibited significant Mn\u00b2\u207a transport activity, with the Km for Mn\u00b2\u207a determined to be 100.06\u2009\u00b1\u200924.53\u2009\u03bcM (Fig.\u00a01f and Supplementary Fig.\u00a02d\u2013f). Further tests confirmed no significant transport of other divalent ions such as Zn\u00b2\u207a, Ca\u00b2\u207a, or Mg\u00b2\u207a, reinforcing SLC30A10\u2019s specificity for Mn\u00b2\u207a (Supplementary Fig.\u00a03e\u2013g).\n\nTo gain deeper insight into the mechanism of Mn2+ transport by SLC30A10, we prepared full-length human SLC30A10 samples in the presence or absence of Mn2+ for cryo-EM and single-particle analysis (Supplementary Fig.\u00a04, 5 and Supplementary Table\u00a01). We obtained cryo-EM maps of SLC30A10 at 2.79\u2009\u00c5 resolution in the presence of Mn2+ and 2.94\u2009\u00c5 in its absence. The high-resolution maps enabled unambiguous assignment of most side chains. Under both conditions, SLC30A10 forms a homodimer consistent with other family members15,16,17,18,19,20,21 (Fig.\u00a01g, h). Each monomer comprises a transmembrane domain (TMD) and a cytoplasmic C-terminal domain (CTD) (Fig.\u00a01i\u2013l). The CTD consists of three \u03b1-helices (\u03b11, \u03b12, \u03b13), and four \u03b2-sheets (\u03b21a, \u03b21b, \u03b22, \u03b23). The TMD contains six transmembrane helices (TM1-TM6), connected by loops of varying lengths. Except for the excessively long and flexible loop between TM4 and TM5, all loops exhibit stable electron densities. The helix bundle near the extracellular region is tightly packed, while the cytoplasmic region is more loosely arranged, indicating that we captured the homodimers in the inward-facing (IF) conformation. We further explored structural heterogeneity in Mn2+ transport using 3D classification and refinement. This yielded an asymmetric dimer structure with a resolution of 3.34\u2009\u00c5 in the presence of Mn2+, revealing heterogeneous conformations of the two subunits (Supplementary Fig.\u00a04, 6). These conformations appear to represent a transition from the IF to outward-facing (OF) state.\n\nIn the Mn\u00b2\u207a-bound IF structure, we observed a Mn\u00b2\u207a ion nestled within the cytosolic cavity of SLC30A10 (Fig.\u00a02a). The Mn\u00b2\u207a is coordinated by an atypical binding site formed by transmembrane helices TM2, TM4, and TM5, which has not been previously observed in this transporter family. Key residues, including D40 (TM2), N127 (TM4), D248 (TM5), and S252 (TM5), create an octahedral coordination around the Mn2+ (Fig.\u00a02b and Supplementary Fig.\u00a07a\u2013c). Additional interactions involving E25 and M44 appear to further stabilize D40\u2019s coordination with the Mn2+ through hydrogen bond, electrostatic forces, and hydrophobic effect (Supplementary Fig.\u00a07d). In the Mn2+-free structure, the electron density at this binding site diminishes (Fig.\u00a02c and Supplementary Fig.\u00a07e). Molecular Dynamics (MD) simulations corroborate the coordination environment, confirming its critical role in Mn\u00b2\u207a binding and suggesting mechanisms for ion specificity (Fig.\u00a02d, Supplementary Fig.8 and Supplementary Table\u00a02).\n\na Electrostatic surface potential of the SLC30A10 inward-facing (IF). Mn2+ is in purple spheres. Negatively to positively charged regions are coloured from red to blue. The black box indicates the focused area shown in (b, c). b Zoomed-in view of the Mn2+-binding site in the side view. The density is contoured at 1.7\u03c3. c The density map of the Mn2+-binding site. The density is contoured at 2\u03c3. Blue, Mn2+ bound; orange, Mn2+ free. d Time-dependent variation of the distance between Mn2+ and the coordinating residue atoms in MD simulation. e Transport ability of SLC30A10 WT and mutations in HEK293T cells (n\u2009=\u20093). Statistical analysis was performed using one-way ANOVA with Dunnett\u2019s multiple comparisons test. f Structural comparison of Mn2+-bound SLC30A10 (IF) and Zn2+-bound ZnT1 (IF, PDB code:8XMF). The SLC30A10 is shown in blue, and ZnT1 in grey. The boxes indicate the focused areas shown in (g, h). g Zoomed-in view of the Mn2+-binding site from the extracellular view. Blue, Mn2+-bound SLC30A10 (IF); grey, Zn2+-bound ZnT1 (IF). h Comparison of the ZnT1 ion-binding site with the corresponding residues in SLC30A10. Blue, Mn2+-bound SLC30A10 (IF); grey, Zn2+-bound ZnT1 (IF); Mn2+ is in purple sphere; Zn2+ is in pink sphere. i Sequence alignment of human SLC30A10, ZnT1, ZnT3, ZnT4, ZnT7 and ZnT8 across a portion of the TM2 and TM5 region. j Transport ability of SLC30A10 WT and mutations in HEK293T cells (n\u2009=\u20093). Statistical analysis was performed using one-tailed one-sample t-test.\u00a0All \u03c3 values are derived from locally extracted and resampled maps surrounding the Mn\u00b2\u207a-binding site. All bar graphs above represent mean\u2009\u00b1\u2009SEM from three independent experiments (****p\u2009<\u20090.0001; ***p\u2009<\u20090.001; **p\u2009<\u20090.01; *p\u2009<\u20090.05; ns not significant).\n\nTo evaluate the functional importance of these Mn2+ binding residues, we performed site-directed mutagenesis and assessed Mn2+ transport in HEK293T cells. Cells expressing the D40A, N127A, D248A, S252A, E25A, and M44A mutants exhibited significantly higher intracellular Mn levels compared to those expressing wild-type (WT) SLC30A10. The D40A, N127A, D248A, and S252A mutants completely lost transport function, while the E25A and M44A mutants showed reduced Mn2+ transport capability (Fig.\u00a02e, Supplementary Fig.\u00a09). These results confirm the essential roles of these residues in Mn2+ transport by SLC30A10. Notably, a single-point mutation in SLC30A10 at position D40 (D40A) leads to Mn2+ accumulation in humans, causing HMNDYT1 disease (Supplementary Fig.\u00a07f and Supplementary Table\u00a03). The patient with HMNDYT1 is characterised by neurological impairment, including gait deviation, cognitive decline and language deficits30. This highlights how a loss-of-function mutation at a critical Mn2+ binding site directly contributes to the onset of HMNDYT1.\n\nStructural comparison between ZnT1 and SLC30A10 reveals conserved transmembrane helices but a distinct Mn\u00b2\u207a-binding site in SLC30A10 (Fig.\u00a02f, g). While ZnT1\u2019s canonical \u201cHDHD\u201d motif (two His, two Asp) forms a tight Zn\u00b2\u207a binding site, most of SLC30A10\u2019s corresponding residues (N43, D47, H244, D248) do not directly coordinate Mn\u00b2\u207a (Fig.\u00a02h, i). The only non-conserved residue N43 had no effect on Mn\u00b2\u207a transport when mutated to N43A or N43H, and neither did H244A. However, the H244N mutation moderately reduced activity, likely due to changes in electrostatic interactions. In contrast, D47A abolished transport and D47N/D47E reduced activity, suggesting a role in Mn\u00b2\u207a recruitment at the cytoplasmic face. D248N also abolished transport, consistent with its role in Mn\u00b2\u207a coordination. Multi-site mutations uniformly abolished Mn\u00b2\u207a transport, further underscoring the importance of these residues for transport function (Fig.\u00a02j and Supplementary Fig.\u00a011a, b). Although N43 mutations had little effect in SLC30A10, the H43N mutation in ZnT1 reduced Zn\u00b2\u207a transport and enabled Mn\u00b2\u207a transport (Supplementary Fig.\u00a011c, d), confirming its importance in ZnT1. Previous studies investigating mutations at N43 have reported conflicting conclusions regarding its role in Mn\u00b2\u207a transport14,36,37. While some findings suggest N43 is critical for Mn\u00b2\u207a selectivity14, others report that N43A and N43H mutants retain transport activity comparable to WT36,37. Our results align with the latter and support the view that Mn\u00b2\u207a transport selectivity of SLC30A10 is not governed by a single residue, but rather involves more complex mechanisms. This contrast highlights a more complex selectivity mechanism in SLC30A10.\n\nWe further assessed non-conserved residues near the binding site with side chains projecting into the transport cavity S39, C93, G251, V254, and I291 (Supplementary Fig.\u00a011e). Mutations in these residues, individually and in combination, impaired Mn\u00b2\u207a transport (Supplementary Fig.\u00a011f), suggesting they may contribute to specificity by shaping the cavity or modulating ion accessibility.\n\nWe obtained both symmetric inward-inward facing (IF-IF) and asymmetric inward-outward facing (IF-OF) conformations of SLC30A10 in the presence of Mn2+. Significant conformational changes occur in the transmembrane domain (Fig.\u00a03a\u2013d and Supplementary Fig.\u00a012a, b). In the OF protomer, TM5 rotates inward by 22.4\u00b0, closing the cavity that was previously open to the cytoplasm (Figs.\u00a02a, 3c). Conversely, transmembrane helices TM1 and TM2 near the extracellular side rotate outward by 37.1\u00b0 and 20.6\u00b0, respectively, creating an open cavity that extends into the extracellular space (Fig.\u00a03d, e). This spatial separation prevents these residues from forming a stable coordination complex in the OF conformation. Structural comparisons between the OF protomers of SLC30A10 and related transporters, such as ZnT1, ZnT7, and ZnT8, showed similar backbone arrangements (Fig.\u00a03f, g and Supplementary Fig.\u00a012c\u2013h). However, unlike in these counterparts, no ion-like density was observed near the ion-binding residues of SLC30A10, which were identified based on homology and structural alignment. This absence of density could suggest a lower ion-binding affinity at this site within the SLC30A10 compared to other members of ZnT family.\n\na Structural overlay of SLC30A10 inward-facing (IF) and outward-facing (OF). The IF conformation is coloured in blue, and OF conformation in green, respectively. The box indicates the focused area shown in panels (b\u2212d). b\u2212d Zoomed-in view of the overlay of IF and OF in sideview (b), intracellular view (c) and extracellular view (d). e Electrostatic surface potential of the transmembrane domain of SLC30A10 (OF). Negatively to positively charged regions are coloured from red to blue. f, g The OF conformation of SLC30A10 structure aligned with the ZnT1 (PDB code:8ZSZ), ZnT7 (PDB code:8J7W) and ZnT8 (PDB code:6XPE). SLC30A10, ZnT1, ZnT7 and ZnT8 are coloured in green, grey, yellow and purple, respectively. Zn2+ is in pink spheres.\n\na Proposed model for the Mn\u00b2\u207a transport mechanism of SLC30A10: The orientation and relative positions of helices and key residues are shown based on the structural data. Purple spheres represent Mn\u00b2\u207a ions. For clarity, only the transmembrane domain (TMD) of one protomer is shown. Mn\u00b2\u207a is attracted to a negatively charged cytoplasmic cavity (Red) and coordinated by D248, D40, N127, and S252. The ions are ultimately exported through an extracellular cavity, exiting the cell. b\u2013e Statistical analysis of normalized Mn levels (b; n\u2009=\u20095), ROS levels (c; n\u2009=\u20093), mitochondrial membrane potential (d; n\u2009=\u20093) and ATP levels (e; n\u2009=\u20093) in the presence of 100\u2009\u03bcM MnCl2 for 24\u2009h in SH-SY5Y cells. f, g Flow cytometry detection (f) and statistical analysis (g; n\u2009=\u20093) of cell survival by Annexin V / PI staining in SH-SY5Y cells. All bar graphs above represent mean\u2009\u00b1\u2009SEM from three independent experiments. Statistical analysis was performed using one-way ANOVA with Dunnett\u2019s multiple comparisons test (****p\u2009<\u20090.0001; ***p\u2009<\u20090.001; **p\u2009<\u20090.01; *p\u2009<\u20090.05; ns not significant).\n\nOur structural analysis proposes a working model for the Mn\u00b2\u207a transport mechanism of SLC30A10 (Fig.\u00a04a). Mn\u00b2\u207a is initially attracted into a negatively charged cavity near the cytoplasmic side, then transferred to residue D248, where it forms a specific coordination binding site with D40, N127, and S252. Once Mn\u00b2\u207a is securely bound at this central site, the resulting coordinated interaction triggers conformational changes in TM5, TM1 and TM2. This transition shifts SLC30A10 from the IF to the OF conformation, allowing the release of Mn\u00b2\u207a into the extracellular space.\n\nTo validate this proposed model and investigate the role of SLC30A10 in protecting neuronal cells from Mn\u00b2\u207a-induced cytotoxicity, we performed mutagenesis targeting key residues involved in Mn\u00b2\u207a transport. SH-SY5Y cells transfected with SLC30A10 mutants D40A, N127A, D248A, and S252A were exposed to MnCl\u2082 for 24\u2009h. These mutants failed to reduce intracellular Mn levels (Fig.\u00a04b and Supplementary Fig.\u00a013a, b). Compared to cells expressing the WT SLC30A10, cells with mutant transporters showed decreased mitochondrial membrane potential, reduced ATP levels, and increased ROS accumulation, indicating impaired mitochondrial function (Fig.\u00a04c\u2013e and Supplementary Fig.\u00a013c). Consequently, the mutants were unable to protect the cells from Mn2+-induced cytotoxicity (Fig.\u00a04f, g). These results substantiate that the Mn\u00b2\u207a transport process, mediated by the specific Mn\u00b2\u207a binding site and adjacent negatively charged residues, is crucial for the protective function of SLC30A10 in neuronal cells. Our results elucidate SLC30A10\u2019s critical role in reducing cellular Mn\u00b2\u207a levels, thereby preserving mitochondrial health and preventing the cytotoxic effects of Mn\u00b2\u207a overload.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63616-7/MediaObjects/41467_2025_63616_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63616-7/MediaObjects/41467_2025_63616_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63616-7/MediaObjects/41467_2025_63616_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63616-7/MediaObjects/41467_2025_63616_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "As a Mn\u00b2\u207a efflux transporter, SLC30A10 is upregulated in response to Mn\u00b2\u207a overload, playing a vital role in mitigating toxicity caused by excessive Mn\u00b2\u207a accumulation41,42. Our study confirms that elevated Mn\u00b2\u207a levels induce SLC30A10 expression, which in turn facilitates Mn\u00b2\u207a efflux, reduces oxidative stress, and maintains mitochondrial function. Conversely, a loss of SLC30A10 function correlates with increased cell death, while its overexpression not only decreases intracellular Mn\u00b2\u207a levels but also preserves mitochondrial integrity and enhances cell survival. These findings underscore the indispensable role of SLC30A10 in protecting cells from Mn\u00b2\u207a toxicity through its contributions to efficient Mn\u00b2\u207a efflux.\n\nAlthough the role of SLC30A10 in Mn\u00b2\u207a transport is well recognised, the structural basis underlying its transport remains elusive. Previous studies based on homology modelling and sequence analysis proposed residues such as D40, N127, D248, and E25 as potential contributors to Mn\u00b2\u207a export14,36,37. These predictions are now experimentally validated by our structural analysis, which confirms that these residues form key components of the Mn\u00b2\u207a-binding site. However, computational models alone often struggle to resolve the structural details of metal-binding environments, where precise side-chain positioning is essential for accurately identifying binding pockets and capturing conformational dynamics. By determining high-resolution cryo-EM structures of full-length human SLC30A10 in both Mn\u00b2\u207a-bound and Mn\u00b2\u207a-free states, we were able to overcome these challenges and gain mechanistic insights that were inaccessible through modelling approaches. In addition to validating the previously proposed residues, our structures identify S252 as a critical Mn\u00b2\u207a-coordinating residue and implicate M44 in stabilising the binding environment. These findings refine the composition of the Mn\u00b2\u207a-binding site and advance our understanding of how SLC30A10 mediates metal transport. Furthermore, we capture an outward-facing conformation of the transporter and report a cluster of structured water molecules adjacent to the ion-binding site, features that may play a role in Mn\u00b2\u207a coordination, and which have not been observed in other SLC30 family structures.\n\nFunctional validation supports the importance of these structural insights. Mutations in the coordinating residues, particularly D40A, abolish transport activity. Clinically, the D40A mutation corresponds to a spontaneous variant (c.119\u2009A\u2009>\u2009C, p.Asp40Ala) found in patients with HMNDYT1, who exhibit neurological symptoms and liver damage due to the disrupted manganese homoeostasis30. Mouse models similarly show hepatic Mn\u00b2\u207a accumulation and metabolic alterations upon SLC30A10 deficiency34, though how SLC30A10 regulates liver metabolism requires further investigation.\n\nBy linking high-resolution structural insights with functional outcomes, our study provides a comprehensive mechanistic framework for understanding how SLC30A10 facilitates Mn\u00b2\u207a efflux and highlights the far-reaching consequences of mutations like D40A. These findings advance our understanding of the molecular mechanism of Mn\u00b2\u207a transport and offer potential therapeutic strategies to restore SLC30A10 function and address both neurological and hepatic manifestations of manganese toxicity.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "HEK293T (SCSP-502) and SH-SY5Y (SCSP-5014) were purchased from the Cell Bank of the Chinese Academy of Sciences and were authenticated by the supplier using STR profiling. HEK293F (R790-07) was purchased from Invitrogen and have undergone supplier-performed quality control testing. HEK293T and SH-SY5Y cells were cultured in dulbecco\u2019s modified eagle medium (DMEM, Thermo Fisher Scientific) supplemented with 10% (v/v) fetal bovine serum (FBS, Moregate), 100 units/mL penicillin and 0.1\u2009mg/mL streptomycin (MeilunBio) at 37\u2009\u00b0C under 5% CO2. HEK293F cells were cultured in FreeStyle293 medium (Thermo Fisher Scientific) at 37\u2009\u00b0C, under 8% CO2 on an orbital shaker platform rotating at 120\u2009rpm. SLC30A10 over-expressed and knockdown SH-SY5Y cells were established by our lab and has not been authenticated. 1\u2009\u03bcg/ml puromycin was added to the medium for SLC30A10-overexpressed and knockdown stable SH-SY5Y cells. The human SLC30A10 gene was cloned into the pLVX-Tet3G vector (Takara, 631847), and a doxycycline (Dox)-induced SLC30A10 overexpressing stable cell line was established by infecting SH-SY5Y cells with lentiviruses. The SLC30A10 was overexpressed when 100\u2009ng/ml doxycycline (Dox) was added to the medium for 48\u2009h. The SLC30A10 knockdown SH-SY5Y cells were generated using the pLKO.1 vector (Addgene, 8453), via infecting with viruses. The shRNA sequences for human SLC30A10 were GCCATCATATTCTATGTGCTT (shRNA1) and CCACGGACAAAGTCTTAACAA (shRNA2). PEI (Yeasen) was used for transient transfection of HEK293F cells, and Liposomal transfection reagent (MeilunBio) was used for HEK293T and SH-SY5Y transient transfection according to the manufacturer\u2019s instructions.\n\nSite-directed mutagenesis of SLC30A10 was performed using homologous recombination PCR, and the sequences of all constructs were verified by DNA sequencing in BioSune. After cells were transfected with SLC30A10 wild-type or mutant plasmids, the CFMEA assay was performed to measure cellular Mn concentrations following a previously published protocol33. Briefly, cells were seeded in poly-D-lysine-coated 96-well plates with black edges and clear bottoms (Cellvis). After 24\u2009h, cells were transfected with 200\u2009ng of SLC30A10 plasmids per well, with vector-transfected cells serving as controls. For HEK293T cells, 300\u2009\u03bcM MnCl2 was added. After 4\u2009h of MnCl2 exposure, the medium was aspirated, and cells were washed twice with pre-warmed PBS. Then, 100\u2009\u03bcL of assay buffer (PBS with 0.1% Triton X-100) containing 500\u2009nM Fura-2 salt (Sigma) was added to each well. The plate was incubated at 37\u2009\u00b0C for at least 1\u2009h, protected from light. Fura-2 salt fluorescence was then measured using a BioTek Synergy2 Microplate Reader with excitation/emission wavelengths of 360\u2009nm/535\u2009nm. A standard curve for Mn concentration was generated using varying concentrations of MnCl2 and 500\u2009nM Fura-2 salt. Mn concentrations and mass of Mn (MMn) in the cell extracts were calculated based on this standard curve. For SH-SY5Y cells, 100\u2009\u03bcM MnCl2 was added, and Mn levels were measured after 24\u2009h of treatment.\n\ndsDNA from cell extracts post-CFMEA analysis was quantified using the PicoGreen dsDNA reagent (Yeasen). Briefly, 80\u2009\u03bcL of 1:400 diluted PicoGreen reagent in TE buffer (10\u2009mM Tris-HCl, 1\u2009mM EDTA, pH 7.5) was added to a 96-well plate. Then, 2\u2009\u03bcL of cell extract from the CFMEA assay was added. After incubating for 5\u2009min, protected from light, fluorescence was measured (excitation/emission wavelength: 480\u2009nm/520\u2009nm). A dsDNA standard curve was generated using varying concentrations of \u03bbDNA, and mass of dsDNA (MDNA) in each well was calculated using the linear equation derived from the standard curve.\n\nMn content relative to DNA (AMn) was calculated using the equation (MMn/MDNA), and data were represented as pg Mn/ng dsDNA. Finally, the data were normalised to the control group using the equation (AMn/AMncontrol). Mn2+ transport ability was calculated using the equation ((AMncontrol-AMn)/relative protein expression level). Relative protein expression levels were quantified by calculating the grayscale intensity from immunoblotting analysis using Fiji software. All statistical analyses, comparing wild-type and mutant cells, were performed using GraphPad Prism software.\n\nThe procedure of zinc transport assay is similar to the manganese transport ability analysis above. Briefly, 24\u2009h after transfection, HEK293T cells were treated with ZnCl2 for 4\u2009h. Then cells were washed twice with PBS and loaded with 100\u2009\u03bcL of assay buffer (PBS with 0.1% Triton X-100) containing 1\u2009\u03bcM FluoZin-3 salt (Sigma). After dark incubation at 37\u2009\u00b0C for 2\u2009h, the 96-well plates were measured using a BioTek Synergy2 Microplate Reader with excitation/emission wavelengths of 488\u2009nm/525\u2009nm. A standard curve based on the linear relationship of F-F0 was used to calculate the content of intracellular zinc. The measurement of dsDNA is the same as the method above. Zn2+ transport ability was calculated using the MZn/MDNA.\n\nRelative Zn levels were also assessed using the membrane-permeable fluorescent dye FluoZin-3 AM, following a previously published method16. Cells were washed with PBS, treated with 60\u2009\u00b5M ZnCl2 and 1\u2009\u00b5M membrane-permeable FluoZin-3 AM (ThermoFisher Scientific) in DMEM for 50\u2009min at 37\u2009\u00b0C. After incubation, cells were washed three times with the buffer (20\u2009mM HEPES-Na pH 7.4, 125\u2009mM KCl, 5\u2009mM NaCl, 10\u2009mM glucose, and 10\u2009\u00b5M phenanthroline) for microscopy imaging. Fluorescence intensities from cells without ZnCl\u2082 treatment were used to calculate the average background signal (F0). Relative Zn levels were then calculated as the ratio of F to F0 (F/F0).\n\nDifferent cell models were analysed by ICP-MS. Transiently transfected HEK293T cells were used to assess changes in total cellular Mn levels, while stably transfected HEK293T cells were used to examine Zn levels. Stably transfected SH-SY5Y cells were utilized to quantify mitochondrial Mn content. To measure metal levels in HEK293T cells, cells were treated with 100\u2009\u03bcM MnCl\u2082 for 1\u2009h or 60\u2009\u03bcM ZnCl\u2082 for 50\u2009min. The cells were then harvested and washed with PBS containing 10\u2009mM EDTA three times, followed by ICP-MS analysis15. For mitochondrial Mn quantification, SH-SY5Y WT and stable SLC30A10-overexpressing cells were treated with 100\u2009\u03bcM MnCl\u2082 for 16\u2009h. Mitochondria were then isolated using a mitochondrial isolation kit (Beyotime) according to the manufacturer\u2019s instructions. Metal concentrations were measured by ICP-MS and normalised to total cell or mitochondrial weight.\n\nProteins were resolved in 10% SDS-PAGE, transferred to PVDF membranes (cytiva), and incubated with primary antibodies against DYKDDDDK tag (Flag tag, proteintech, cat#66008-4-Ig, lot#10027647, 1:5000, https://www.ptglab.com/products/Flag-tag-Antibody-66008-4-Ig.htm), GAPDH (proteintech, cat#60004-4-Ig, lot#10027647, 1:5000, https://www.ptglab.com/products/GAPDH-Antibody-60004-1-Ig.htm). The second antibody is Peroxidase AffiniPure Goat Anti-Mouse IgG (H\u2009+\u2009L) (YEASEN, cat#33201ES60, lot#P1203971, 1:10000). The relative protein expression levels were calculated using Fiji software.\n\nHEK293T or SH-SY5Y cells were seeded on poly-D-lysine coated glass coverslips and grown for 24\u2009h. Flag-tagged SLC30A10 and plasma-mCherry plasmids (Addgene, 55779) were co-transfected into cells for 16\u2009h. After fixed in 4% PFA and permeabilised by Triton-X, the cells were blocked in blocking buffer (2% BSA in PBST). The cells were then immunolabeled at 4\u2009\u00b0C overnight with the flag antibody\u00a0(proteintech, cat#66008-4-Ig, lot#10027647, 1:5000, https://www.ptglab.com/products/Flag-tag-Antibody-66008-4-Ig.htm). The next day, the cells were washed three times with PBS, followed by incubation with a FITC fluorescein-conjugated secondary antibody (Beyotime, cat#A0568, lot#102522221208, 1:200, https://www.beyotime.com/product/A0568.htm) for 1\u2009h. The nuclear was stained with DAPI (Yeasen) followed by three times washing with PBS. Finally, the coverslips were sealed with antifade mounting medium. Images were recorded by the Olympus FV3000 confocal system at 60\u00d7 oil objective. All images shown are representative of at least three randomly selected fields.\n\nTotal RNA was extracted from SH-SY5Y cells using RNA isolation kit (Vazyme). cDNA was prepared with the RT SuperMix Kit (Vazyme). This kit could remove genomic DNA. qRT-PCR was performed with SYBR probes by StepOnePlus Real-Time PCR System (Thermo Fisher Scientific). The primer sequences used in this study are described in Supplementary Table\u00a04.\n\nAfter being treated with MnCl2, cells were digested, washed with PBS and immediately stained with propidium iodide (PI, Beyotime) and Annexin-V (AV, Beyotime). Then cells were analysed by flow cytometry (FACS Fortessa, BD) at once. Data were analysed by Flowjo (v10.8.1) software. Gating strategy was shown in Supplementary Fig.14a.\n\nThe JC-1 probe was used to detect the mitochondrial membrane potential. After being treated with 100\u2009\u03bcM MnCl2 for 24\u2009h, SH-SY5Y cells were collected and incubated with JC-1 probe at 37\u2009\u00b0C for 30\u2009min. Then the cells were washed twice with cold JC-1 buffer. Then, the fluorescence intensity of the probe was measured using a flow cytometer (FACS Fortessa, BD) in the FITC and PE channels. Data were analysed by FlowJo (v10.8.1) software. The gating strategy was shown in Supplementary Fig.14b.\n\nThe DCFH-DA probe was used to detect the ROS levels of cells. After being treated with 100\u2009\u03bcM MnCl2 for 24\u2009h, SH-SY5Y cells were collected and incubated with 10\u2009\u03bcM DCFH-DA probe at 37\u2009\u00b0C for 30\u2009min. Then the cells were washed twice with PBS. The cells were detected by flow cytometry (FACS Fortessa, BD). Data were analysed by FlowJo (v10.8.1) software. The gating strategy was shown in Supplementary Fig.14c.\n\nThe CellTiter-Meiluncell luminescent reagent (MeilunBio) was used to detect the relative content of ATP in cells. SH-SY5Y cells were cultured in 96-well plates. After being treated with 100\u2009\u03bcM MnCl2 for 24\u2009h, cells were equilibrated at room temperature for 10\u2009min. Then the cells were treated with CellTiter-Meiluncell luminescent reagent at room temperature for 10\u2009min. The relative ATP levels were measured with luminescence using a microplate reader (Spark, TECAN). Data were analysed by GraphPad Prism software.\n\nGene encoding the full-length human SLC30A10 (Uniprot: Q6XR72) was cloned into a modified pEG vector, incorporating\u00a0a 3\u2009\u00d7\u2009Flag tag, a Twin-Strep tag and an HRV 3\u2009C protease site at the N terminus. For protein expression, 1\u2009L HEK293F cells were transiently transfected with 2\u2009mg SLC30A10 plasmid and 4\u2009mg PEI at a cell density of 2.5\u2009\u00d7\u2009106 cells/ml. Cells were cultured at 37\u2009\u00b0C with 8% CO2 for 24\u2009h and harvested by centrifugation at 1500\u2009\u00d7\u2009g for 10\u2009min. The cell pellets were resuspended in buffer A (20\u2009mM HEPES, 150\u2009mM NaCl, pH 7.4) containing 1% DDM, 0.1% CHS, 1\u2009mM PMSF, 6\u2009\u03bcg/mL protease inhibitor cocktail (Aprotinin:Pepstatin:Leupeptin (w/w)\u2009\u2009=\u2009\u20091:1:1). The resuspended cell pellets were dounced using a glass homogenizer, and agitated gently at 4\u2009\u00b0C for 2\u2009h. The lysate was centrifuged at 202,000\u2009x\u2009g for 1\u2009h to remove cell debris. The supernatant was incubated with flag resin (Genscript, L00432) at 4\u2009\u00b0C for 1\u2009h. Then the resin was collected in a gravity column and washed with 20 column volumes of buffer A containing 0.05% GDN. The protein was eluted using 0.4\u2009mg/ml 3\u2009\u00d7\u2009flag peptide (Everylab) with 3 column volumes. The eluted protein was concentrated with a 50\u2009kDa cut-off Amicon Ultra spin (Millipore) and subjected to size-exclusion chromatography (SEC) using a Superose 6 Increase 10/300 GL column (Cytiva) equilibrated in buffer A containing 0.01% GDN. The peak fractions corresponding to purified SLC30A10 were collected and concentrated to 4\u2009~\u20098\u2009mg/mL for cryo-EM studies.\n\nFor the purification of SLC30A10 in the presence of manganese, the protein was purified following a similar protocol. Specifically, 1\u2009mM MnCl2 was added, and the pH was adjusted to 6.0 using MES. In brief, Buffer B (20\u2009mM MES, 150\u2009mM NaCl, 1\u2009mM MnCl2, pH 6.0) was used throughout the purification process to replace buffer A.\n\nPOPC, POPE, and POPG (Avanti Polar Lipids) were mixed with a ratio of 10:7:3 (wt:wt:wt) and dried in a vacuum centrifugal concentrator for 3\u2009h to remove chloroform. The mixed lipid was resuspended in a reconstitution buffer (20\u2009mM MES, 150\u2009mM KCl, and 0.1 % GDN, pH 6.0) to a final concentration of 10\u2009mg/ml. The purified SLC30A10 was added at 1:100 (wt / wt, protein: lipid), and dialysed at 4\u2009\u00b0C in dialysis cassettes (thermo) against dialysis buffer (20\u2009mM MES and 150\u2009mM KCl, pH 6.0) for 6\u2009days to remove the detergent. The reconstituted liposome was sonicated for 20\u2009s and frozen-thawed for three cycles to incorporate Fura-2 salt (50\u2009\u03bcM) or calcein-salt (250\u2009\u03bcM). Then, the Fura-2/calcein-containing liposomes were separated from the bulk dye on a PD-10 column (cytiva). Liposomes were pelleted using ultracentrifugation at 100,000\u2009g for 30\u2009min and resuspended with reconstitution buffer (20\u2009mM MES and 14.5\u2009mM KCl, pH 6.0). The lower concentrations of KCl in the assay buffer established the desired membrane potential, and liposomes were added to 96-well black edge and clear-bottom plates (Cellvis).\n\nTo assess the transport specificity of SLC30A10 for different metals, 1\u2009mM of MnCl\u2082, CaCl\u2082, ZnCl\u2082, or MgCl\u2082 was added to initiate transport, and Fura-2 was used to monitor metal uptake. Fluorescence of Fura-2 was monitored in a Spark multimode microplate reader (Tecan) with 360\u2009nm / 535\u2009nm (excitation/emission) for the Mn2+ transport assay. For groups of Ca2+, Zn2+, and Mg2+ transport assays, fluorescence of Fura-2 was measured at 340\u2009nm/ 510\u2009nm, 380\u2009nm / 510\u2009nm, respectively. The fluorescence ratio was normalised by the first value after the addition of ions.\n\nCalcein was used to analyse the kinetics of SLC30A10. The quenching of calcein by Mn was measured with 492\u2009nm/ 518\u2009nm (excitation/emission) at 10\u2009s intervals. The fluorescence of the MnCl2-free proteoliposome was measured to determine the background signal (F0). The fluorescence of 0\u2009s was measured as maximum (max) fluorescence. Relative fluorescence was calculated using the equation (F/Fmax)-(1-F0/F0max). Initial transport rates were determined from the change in relative fluorescence within the first minute following the addition of MnCl\u2082 at the start of the assay. Kinetic analysis of SLC30A10 was performed by fitting the Michaelis-Menten equation to the initial transport rates43.\n\nAliquots of 3.0\u2009\u03bcl\u20094-8\u2009mg/mL purified SLC30A10\u2009samples in different conditions were applied to glow-discharged holey carbon grids (Quantifoil, R1.2/1.3, Au, 300 mesh). The grids were blotted for 3.5\u20134.0 s using Leica EM GP2 at 4\u2009\u00b0C with 75% humidity, and plunge-frozen into the liquid ethane.\n\nCryo-EM datasets were collected on Titan Krios G4 cryo-electron microscope operated at 300\u2009kV, equipped with a Falcon 4i Direct Electron Detector and a Selectris X energy filter (Thermo Fisher Scientific). Movie stacks were automatically collected using EPU at a magnification of 130,000\u00d7 with a pixel size of 0.932\u2009\u00c5 for a total dose per EER (electron event representation) movie of \u223c50\u2009e\u2013/\u00c52. The defocus range was set between -0.8 to -1.8\u2009\u00b5m.\n\nAll datasets were processed in CyroSPARC (v4.5.3)44. For SLC30A10 in the presence of Mn2+, a total of 25,390 cryo-EM movies were collected. Motion correction was performed on all movies using MotionCor245 with a 5\u2009\u00d7\u20095 patch alignment. The contrast transfer function (CTF) parameters for each micrograph were estimated using Gctf_v1.1846. Particle picking was conducted with Gautomatch_v0.56 (https://github.com/JackZhang-Lab/Gautmatch), and the motion-corrected micrographs with particle coordinates were imported into CryoSPARC for further analysis47. Particles were extracted and subjected to one round of two-dimensional (2D) classification to remove junk particles, resulting in 4,091,140 good particles for further 2D classification. Good classes were then used for ab initio reconstruction. Particles from classes displaying membrane protein characteristics and their corresponding cryo-EM maps were refined using non-uniform refinement under C2 symmetry, resulting in a consensus map. Multiple rounds of heterogeneous refinement and three-dimensional (3D) classification were performed on the original dataset to enrich particles in the symmetric IF-IF conformation. Selected particles were subjected to non-uniform (NU) refinement and local refinement with C2 symmetry imposed with a mask covering the transmembrane domain yielding an improved map at 2.79\u2009\u00c5 resolution47. The remaining particles were subjected to additional rounds of heterogeneous refinement and focused 3D classification using a monomer mask. A small subset of classes was identified to adopt the asymmetric IF-OF conformation. A final map was produced by performing NU refinement and local refinement at 3.34\u2009\u00c5.\n\nFor SLC30A10 in the absence of Mn2+, data was processed similarly. In brief, a total of 6879 EER movie stacks were imported, and the selected particles were subjected to several rounds of 2D classification and a round of Ab initio reconstruction to generate the initial models. The 3D classification was performed with multi-round heterogeneous refinement in CryoSPARC. Particles belonging to a class with well-defined features were further refined using NU refinement and local refinement with C2 symmetry and yielded final maps at 2.94\u2009\u00c5.\n\nThe predicted model of SLC30A10 was generated by AlphaFold248 and was fitted to the cryo-EM map density in UCSF ChimeraX49. The initial model was manually refined and adjusted using the phenix.real_space_refine in PHENIX50 and Coot51. Manganese ions and water molecules were manually added to the model to fit the corresponding densities observed in the map. The complete model was re-imported into PHENIX for further refinement and optimization. The statistics of cryo-EM data collection and model refinement are summarized in Supplementary Table\u00a01. All figures were prepared with ChimeraX.\n\nMolecular dynamics (MD) simulation was conducted for SLC30A10-Mn2+ system. The structure and force fields for the MD system were generated utilizing the CHARMM-GUI (v.3.7)52. The CHARMM36 force field for protein, POPC lipids and TIP3P water molecules model53,54. The force field of Mn was generated using the CHARMM General Force Field (CGenFF) programme55,56. Neutralization of the initial system was attained through the strategic addition of Na+ and Cl- ions, culminating in a final NaCl concentration of 0.15\u2009M. The system comprised SLC30A10 (IF-IF homo) protein, Mn, POPC lipids, TIP3P water, and NaCl.\n\nSubsequently, the energy minimization, equilibration and real MD production were performed by GROMACS (v.2023.3)57. The energy minimization process employed the steepest descent algorithms until the maximum force <1000\u2009kJ\u2009mol-1\u2009nm-1 to obviate any steric clashes or inappropriate geometry of the system. Throughout the ~2\u2009ns equilibration process, the temperature of system was adeptly regulated at 303.15\u2009K via berendsen thermostat for 500\u2009ps (NVT ensemble) and pressure was maintained at 1\u2009bar via berendsen thermostat for 1\u2009ns (NPT ensemble)58. The hydrogen bonds were judiciously constrained using LINCS throughout the equilibration process59.\n\nAfter pre-equilibration, MD simulation was carried out over 1000\u2009ns, saving coordinates at 10\u2009ps intervals. The temperature of the system was steadfastly held at 303.15\u2009K through the Nose-Hoover thermostat and the pressure of the system was maintained at 1\u2009bar using the Parrinello-Rahman barostat60. Long-range electrostatics interactions were computed using the particle mesh Ewald (PME)61 method with a cutoff of 12\u2009\u00c5. Short-range van der Waals interactions were smoothly switched to zero within the range of 10\u2009\u00c5 to 12\u2009\u00c5. Periodic boundary conditions (PBC) were implemented in the MD systems. The SLC30A10-Mn system was run for three replicas with different initial velocities.\n\nAll data were analysed using GraphPad Prism 8 software (GraphPad Inc.). The unpaired Student\u2019s t-test, one-sample t-test, and ANOVA were used to analysis the results as indicated in figure legends. Data in bar graphs are presented as mean\u2009\u00b1\u2009SEM. Levels of statistical significance are indicated as follows: *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, and ****p\u2009<\u20090.0001. Other descriptive statistics and tests are provided in the Figure legends.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Source data are provided with this paper. The UniProt accession code of human SLC30A10 is Q6XR72. The 3D cryo-EM density maps of human SLC30A10IF-IF, SLC30A10IF-OF and SLC30A10Apo have been deposited to the Electron Microscopy Data Bank under the accession numbers EMD-62603 (Cryo-EM density map of SLC30A10 in Mn2+-bound state, determined in inward-facing conformation), EMD-62604 (Cryo-EM density map of SLC30A10, determined in asymmetric conformations-one subunit in an inward-facing Mn2+-bound and the other in an outward-facing Mn2+-unbound conformation) and EMD-62605 (Cryo-EM density map of SLC30A10 in the absence of Mn2+, determined in inward-facing conformation), respectively. The coordinates of human SLC30A10IF-IF, SLC30A10IF-OF and SLC30A10Apo have been deposited to the Protein Data Bank under the accession codes 9KVX (Cryo-EM structure of SLC30A10 in Mn2+-bound state, determined in inward-facing conformation), 9KVY (Cryo-EM structure of SLC30A10, determined in asymmetric conformations-one subunit in an inward-facing Mn2+-bound and the other in an outward-facing Mn2+-unbound conformation) and 9KVZ (Cryo-EM structure of SLC30A10 in the absence of Mn2+, determined in inward-facing conformation), respectively. The Protein Data Bank coordinates used for structural alignments are 8J7W (human ZnT7 of Zn2+-bound state in heterogeneous conformations one in IF and the other in OF conformation), 8ZSZ (human ZnT1 of Zn2+-bound state in OF conformation), 8ZSB (human ZnT1 of Zn2+-unbound state in OF conformation), 6XPE (human ZnT8 of Zn2+-bound state in OF conformation), and 8J7T (human ZnT7 of Zn2+-unbound state in OF conformation).", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code and associated files for the MD simulations are available on GitHub at [https://github.com/Hanting-lab/MD-simulation/tree/main/SLC30A10].", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Budinger, D., Barral, S., Soo, A. K. S. & Kurian, M. A. The role of manganese dysregulation in neurological disease: emerging evidence. 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This work was supported by the grants from the National Science and Technology Innovation 2030 Major Projects of China (STI2030-Major Projects 2022ZD0212600 to H.Y.), the National Natural Science Foundation of China (32171216 to H.Y.), China Postdoctoral Science Foundation (2023M730689 to X.S.), and Fudan Undergraduate Research Opportunities Program (23216 to R.H.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Xurui Shen, Jinlun Kylian Zhang.\n\nZhongshan Hospital, Institute for Translational Brain Research, State Key Laboratory of Brain Function and Disorders, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200032, China\n\nXurui Shen\u00a0\n (\u7533\u8bb8\u745e),\u00a0Jinlun Kylian Zhang\u00a0\n (\u5f20\u664b\u7eb6),\u00a0Peixin Sun\u00a0\n (\u5b59\u57f9\u6b23),\u00a0Huiwen Zhong\u00a0\n (\u949f\u5f57\u6587),\u00a0Rui He\u00a0\n (\u4f55\u745e),\u00a0Shiliang Wang\u00a0\n (\u738b\u4e16\u6881)\u00a0&\u00a0Hanting Yang\u00a0\n (\u6768\u6db5\u5a77)\n\nDepartment of Liver Surgery and Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China\n\nXiaojun Guo\u00a0\n (\u90ed\u6653\u519b)\u00a0&\u00a0Hanting Yang\u00a0\n (\u6768\u6db5\u5a77)\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.Y. and X.S. conceived and designed the experiments. X.S. performed flow cytometry experiments, ICP-MS, proteoliposome assay, purified and prepared the samples for cryo-EM study, and collected cryo-EM data; J.K.Z. processed cryo-EM data, built models, and analysed the structures under supervision of H.Y.; P.S. performed MD simulations; X.S. and H.Z. performed cell-based ion export assay with help from R.H.; X.S. and S.W. performed the confocal imaging with assistance from X.G.; X.S., J.K.Z., P.S., and H.Y. analysed and interpreted the results. X.S., J.K.Z. and H.Y. wrote the manuscript with input from all authors.\n\nCorrespondence to\n Hanting Yang\u00a0\n (\u6768\u6db5\u5a77).", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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"Predicting transcriptional responses to novel chemical perturbations using deep generative model", + "journal": "Nature Communications", + "published": "26 October 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_MOESM6_ESM.csv" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_MOESM7_ESM.csv" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_MOESM8_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_MOESM9_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-53457-1#ref-CR1", + "/articles/s41467-024-53457-1#ref-CR2", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE92742", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4150378", + "https://sites.broadinstitute.org/ccle/", + "https://maayanlab.cloud/CREEDS/", + "https://ngdc.cncb.ac.cn/omix/release/OMIX005223", + "http://prnet.drai.cn", + "/articles/s41467-024-53457-1#ref-CR65", + "/articles/s41467-024-53457-1#ref-CR66", + "https://ngdc.cncb.ac.cn/omix", + "https://ngdc.cncb.ac.cn/omix/release/OMIX006910", + "/articles/s41467-024-53457-1#Sec34" + ], + "code": [ + "/articles/s41467-024-53457-1#ref-CR67", + "https://github.com/Perturbation-Response-Prediction/PRnet" + ], + "subject": [ + "Computational models", + "High-throughput screening", + "Virtual drug screening", + "Virtual screening" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3917469/v1.pdf?c=1730027235000", + "research_square_link": "https://www.researchsquare.com//article/rs-3917469/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53457-1.pdf", + "preprint_posted": "19 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Understanding transcriptional responses to chemical perturbations is central for drug discovery, but exhaustive experimental high-throughput screening of disease and compound combinations is unfeasible. To overcome this limitation, here we present a perturbation-conditioned deep generative model named PRnet for predicting transcriptional responses to novel chemical perturbations that were never experimentally perturbed at bulk and single-cell levels. Evaluation indicated that PRnet outperformed alternative methods in predicting responses across novel compounds, pathways, and cell lines. PRnet enables gene-level response interpretation and novel compounds screening for diseases based on gene signatures. PRnet further identified and experimentally tested novel compounds candidates against small cell lung cancer and colorectal cancer. Lastly, PRnet generated a large-scale integration atlas of perturbation profiles, covering 88 cell lines and 52 tissues perturbed by various screening compound libraries. PRnet provided a robust and scalable candidate recommendation workflow and has successfully recommended drug candidates for 233 different diseases based on the atlas. Overall, PRnet is an effective and valuable tool for cell- and gene-based therapeutics screening.Biological sciences/Drug discoveryBiological sciences/Computational biology and bioinformatics/High-throughput screeningBiological sciences/Computational biology and bioinformatics/Computational models", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryTable1druglikecandidatesforSCLC.xlsxTable 1SupplementaryTable2activecandidatesforSCLC.xlsxTable 2SupplementaryTable3cellinfo.csvTable 3SupplementaryTable4drugesalldiseases.csvTable 4ExtendedData.pdfExtended Data figure and table", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Understanding transcriptional responses to chemical perturbations is central to drug discovery, but exhaustive experimental screening of disease-compound combinations is unfeasible. To overcome this limitation, here we introduce PRnet, a perturbation-conditioned deep generative model that predicts transcriptional responses to novel chemical perturbations that have never experimentally perturbed at bulk and single-cell levels. Evaluations indicate that PRnet outperforms alternative methods in predicting responses across novel compounds, pathways, and cell lines. PRnet enables gene-level response interpretation and in-silico drug screening for diseases based on gene signatures. PRnet further identifies and experimentally validates novel compound candidates against small cell lung cancer and colorectal cancer. Lastly, PRnet generates a large-scale integration atlas of perturbation profiles, covering 88 cell lines, 52 tissues, and various compound libraries. PRnet provides a robust and scalable candidate recommendation workflow and successfully recommends drug candidates for 233 diseases. Overall, PRnet is an effective and valuable tool for gene-based therapeutics screening.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Transcriptional responses to chemical perturbations reveal fundamental insights into biological functioning and play an integral role in both disease understanding and drug discovery. Bulk and single-cell RNA-sequencing (scRNA-seq) experiments facilitate the high-throughput screen (HTS) of chemical perturbations at the omics level. Recent HTS studies1,2 have experimental profiled thousands of independent perturbations exposing cells or cell lines to compounds. These transcriptional responses to chemical perturbations revealed coherent interpretable gene-level programs representing individual and cellular processes and quantified them in response to chemical perturbation. Although encouraging progress has been made, experimentally screening chemical perturbations remains a time-consuming and expensive process with a low discovery rate of new therapies3. It is unfeasible to conduct an exhaustive exploration of the vast, novel chemical perturbation space by experimentally screening disease and compound combinations.\n\nIn the past decades, deep learning-based methods have emerged as important tools for modeling transcriptional responses to perturbations. Many approaches have been proposed recently for modeling HTS perturbation responses. CPA4 utilized an auto-encoder-based model to map chemical induce transcriptomic effect into a latent space to reconstruct perturbation response. Biolord5, scGen6, and scVIDR7 performed counterfactual prediction via deep encoder-decoder/generator-based generative frameworks, which take a control cell and unseen labels as input to predict the gene expression of the unseen cellular states. These recently published cell perturbation modeling tools precisely simulated chemical perturbations and predicted chemical perturbations in gene expression of unseen cell types, but few predict responses to novel chemicals. Following CPA4, chemCPA8 introduced a new encoder-decoder architecture that incorporated the compounds\u2019 structure to predict the perturbational effects of unseen drugs. Deep generative frameworks with encoder-decoder and its variants effectively predicted single-cell gene expression perturbations. CellOT and CINEMA-OT9,10 leveraged optimal transport to match paired unperturbed-perturbed observations. Optimal-transport-based matched the observations experimentally perturbed but were incapable of modeling novel perturbations, such as novel compounds or novel cell types. Linear regression-based methods11,12 estimate the impact of perturbations on gene expression by linearly combining the effects of genetic perturbation. However, this linear combination approach leads to limitations in accurately modeling the nonlinear chemical perturbations across diverse cell types and compound combinations. GEARS and CellOracle13,14 leveraged a knowledge graph of gene-gene relationships to predict genetic perturbation outcomes. However, graph-based models relied on accurate prior knowledge leading to the lack of scalability. Given that most diseases are associated with characteristic gene expression profiles, Connectivity Map (CMap)15 proposed a concept that connected genes, drugs, and diseases by virtue of common gene-expression signatures, leading to projects such as CMap15, L10001, etc. Inspired by this concept, DLEPS16, OCTAD17 and other studies (such as18,19,20, etc.) utilized gene signature matching methods to screen candidates by finding drugs that reverse the disease signature. DLEPS16 predicted chemically induced changes in transcriptional profiles directly from molecular structure, and OCTAD17 virtually screened compounds by matching the cancer-specific expression signature to compound-induced gene expression profiles. Although DLEPS and OCTAD screened candidates effectively based on bulk HTSs, they failed to predict cell-type-specific transcriptional response to novel perturbations and model cellular heterogeneity, which is highly relevant to treatments. Consequently, perturbations response models are required to address the limited exploration power to novel perturbations of existing experimental and computational methods, which are also needed for predicting the response to unseen perturbations and discovering promising therapeutic drug candidates. Deep generative models, including Generative Adversarial Network (GAN21), Variational Auto-Encoder (VAE22), Denoising Diffusion Probabilistic Model (Diffusion23), Normalization Flow (NF24), Generative Pre-Trained Transformer (GPT25), and so on, learn the probability density of observable samples and generate new samples. Deep generative models have greatly improved diverse areas (for example, natural language processing25,26, computer vision27,28, chemicals29, and so on), suggesting the potential for applications in drug discovery.\n\nHere we present PRnet, which is a flexible and scalable perturbation-conditioned deep generative model for predicting transcriptional responses to novel chemical perturbations that were never experimentally perturbed at bulk and single-cell levels. PRnet is a new encoder-decoder architecture based generative model which comprises three components, including the Perturb-adapter, the Perturb-encoder, and the Perturb-decoder. PRnet adapts novel compounds and diseases in various perturbation scenarios by taking compound structures and unperturbed transcriptional profiles as input to predict transcriptional responses. The Perturb-adapter uses simplified molecular-input line-entry system30 (SMILES) chemical encoding as input, enabling generalization to unseen compounds without prior knowledge and annotation. The learnable latent space of PRnet facilitates gene-level response interpretation and capturing heterogeneity. PRnet was trained with close to one hundred million bulk HTS observations perturbed by 175,549 compounds and tens of millions single cell HTS observations perturbed by 188 compounds. Crucially, the model operates as a data-driven model, allowing for effective generalization to novel perturbations. The evaluation indicated that PRnet outperformed alternative approaches in predicting changes and expression in transcription response to novel compounds, pathways, and cell lines in bulk and single-cell HTS data. To further validate the effectiveness, PRnet has been utilized to identify novel bioactive compounds against small cell lung cancer (SCLC) and seek novel natural compounds against colorectal cancer (CRC). Experimental validation demonstrated the activity of novel candidate compounds against SCLC and CRC cell lines within the appropriate predicted ranges of concentration.\n\nThis model\u2019s flexibility and scalability make it a valuable tool to screen candidates for various diseases. We, therefore, leveraged PRnet to in silico screen various compound libraries and generated a virtual large integration atlas of perturbation profiles, covering 88 cell lines and 52 tissues, as well as compound libraries comprising 935 FDA-approved drugs, 4158 active compounds, 30,456 natural compounds, and 29,670 drug-like compounds. PRnet also provided a robust and scalable candidate recommendation workflow for diseases according to the reference changes in gene sets. Given disease-specific or sensitive compound gene signatures, gene set enrichment analysis (GSEA) is employed to assess the potential efficacy of compounds against these diseases. PRnet successfully recommended 577 drug candidate lists from 577 studies for 233 different diseases based on the profiles atlas. In three cases of metabolic disorders, including non-alcoholic steatohepatitis (NASH), polycystic ovary syndrome (PCOS) patients, and inflammatory bowel disease (IBD), drugs recommended by PRnet have been supported by previous literature with human or animal studies. PRnet enables effective prediction of transcriptional responses to novel complex chemical perturbations and screening large-scale compound libraries for specific diseases, thus being a valuable tool for gene-based therapeutics screening.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "In this paper, we formulate the transcriptional response prediction as a distribution generation problem conditioned on perturbations. Cells inherently recognize and respond to chemical stimuli, with their responses influenced by both external stimuli and their intracellular state. In the single cell or bulk HTS, transcriptional responses to chemical perturbations are affected by multiple conditions, such as compound structures, compound dosages, and covariates, such as cell types, and cell lines. Given an n-dimensional unperturbed transcriptional profile (a single cell or bulk RNA-seq observation) and a chemical perturbation imposed by a compound with a specific structure and dosage, PRnet aims to predict the distribution of the perturbed transcriptional profile. Modeling perturbation patterns enables capturing the novel chemical perturbation effect on gene-level programs and quantifying them in various perturbation scenarios (Fig.\u00a01a).\n\na Problem formulation: Given unperturbed transcriptional profiles (single cell or bulk) and applied perturbations (structures and the dosages of the compounds), predict transcriptional responses. Red arrows indicate changes in transcriptional profiles. b Model architecture: PRnet is a perturbation-conditioned deep generative model for transcriptional response prediction with three components, including the Perturb-adapter, the Perturb-encoder, and the Perturb-decoder. Crucially, the model operates as a data-driven model, allowing for effective generalization to novel perturbations. c Screening candidates with PRnet: The training compound library comprises a bulk high-throughput screening library (175,549 bioactive compounds), and a single-cell high-throughput screening library (188 active compounds). The screening library comprises four compound libraries, including 935 FDA-approved drugs, 4158 active compounds, 30,455 natural compounds, and 29,670 in-house druglike compounds, respectively. For in-silico screening, PRnet initially predicts the average transcriptional profile, fold-change in the gene, and gene rank after perturbing the specific cell line with screening compounds. Given the gene signature for a particular disease or sensitive drug, the enrichment scores of screening compounds were computed for compound ranking. In vitro validation experiments were conducted using MTT assays on colorectal cancer and small cell lung cancer cell lines to validate the activity of compound candidates. After in vitro validation, PRnet is utilized to recommend drug candidates for 233 different diseases. d The large-scale integration atlas of perturbation profiles, including the L1000 dataset, the Sci-plex3 dataset, the FDA-approved drugs dataset, the anti-cancer compounds dataset, the natural compounds dataset, the drug-like compounds dataset, and the Gtex dataset. Some icons were created in BioRender. Qi, X. (2024) biorender.com/l45e810.\n\nPRnet (as detailed in \u201cMethods\u201d) is a flexible and scalable perturbation-conditioned deep generative model designed to predict transcriptional responses to novel complex chemical perturbations at bulk and single-cell levels. The design of PRnet comprises three components, including the Perturb-adapter, the Perturb-encoder, and the Perturb-decoder (Fig.\u00a01b). In preprocessing, each perturbed profile is assigned an unperturbed transcriptional profile of the same cell line. Initially, given a chemical perturbation imposed by the structure of compounds represented by Simplified Molecular Input Line Entry System30 (SMILES) strings and the dosages of the corresponding compounds, PRnet leverages RDKit31 to capture the functional topology information of the structures, generate the Functional-Class Fingerprints (FCFP) of compounds. These FCFPs were scaled by their dosages and then summed to generate rescaled Functional-Class Fingerprints (rFCFP) embedding. Without loss of generality, for the i-th compound perturbation, the Perturb-adapter encodes the fingerprint rFCFPi to an additive latent embedding \\({z}_{i}^{p}\\) which allows generalization to novel compounds and compound combinations. Then, the Perturb-encoder maps the chemical perturbation effect on heterogeneous unperturbed states \\({x}_{i}^{u}\\) into the interpretable latent space \\({z}_{i}^{l}\\). At last, the Perturb-decoder estimates the distribution of the transcriptional response \\({{{\\mathcal{N}}}}({x}_{i}| {\\mu }_{i},{\\sigma }_{i}^{2})\\) within the context of the chemical perturbation effect on the unperturbed state \\({z}_{i}^{l}\\), the applied perturbation \\({z}_{i}^{p}\\) and a noise \\({z}_{i}^{n}\\). PRnet encodes the chemical effect on the unperturbed state to a learnable latent space, estimates the distribution, and performs conditioned sampling to generate a transcriptional response with biological and chemical contexts. Sampling generates a specific transcriptional profile \\({\\widehat{x}}_{i}\\) that provides gene-level up- and down-regulation information. For bulk HTS data, the predicted transcriptional responses of 978 landmark genes \\({\\widehat{x}}_{i}\\) are transformed into 12,328 genes by linear transformation. For single-cell HTS data, 5000 highly variable genes (HVGs) of transcriptional profile are selected. SMILES30 is widely used for representing chemical structures due to its simplicity and efficiency in encoding complex molecules as strings. By taking SMILES of the compound as the input, the Perturb-adapter has sufficient flexibility to screen large-scale compound libraries without any prior knowledge. Driven by data, PRnet automatically identifies the heterogeneity in latent space corresponding to compound, dosage contexts, and cell-type specific contexts. This allows the model to directly generalize to novel perturbation scenarios involving novel compounds, pathways, cell types, and cell lines that have not been previously perturbed.\n\nWith the ability to predict transcriptional responses to novel perturbations, PRnet enables efficient screening of candidates for complex diseases (Fig.\u00a01c). Inspired by the assumption embodied in the CMap15 concept that gene signatures are used as indicators reflecting the underlying mechanisms of diseases, PRnet predicted new therapeutic candidates by finding drugs that reverse the disease signature. There are two steps to applying PRnet to downstream tasks. In step 1, for in silico screening, PRnet predicts the transcriptional profile of specific cell lines perturbed by a user-defined compound library with multiple gradient concentrations. In step 2, we calculate the average transcriptional profile and fold-change in gene expression of each compound, and rank genes according to their fold-change values. Then, given the query gene signature for a particular disease or known sensitive compounds, gene set enrichment analysis (GSEA32) was employed to evaluate compound efficacy with their enrichment scores. Finally, compounds are ranked based on these enrichment scores. Large-scale high-throughput screening data are initially fitted to the model, facilitating its adaptability to diverse compound libraries and diseases.\n\nPRnet was trained on two compound libraries and screened four compound libraries (Fig.\u00a01c). The training compound libraries contain a bulk high-throughput screening library consisting of over 883,269 transcriptional profiles of 175,549 bioactive compounds1, and a single-cell high-throughput screening library consisting of 290,888 transcriptional profiles of 188 active compounds2. Being well trained in HTS observations, PRnet enables in silico high-throughput screening of novel compound libraries for various cell lines. PRnet has further been applied to screen active and drug-like compounds for SCLC and natural compounds for CRC. In vitro validation experiments with MTT assays confirmed the efficacy of the candidate compounds against SCLC and CRC cell lines. Lastly, PRnet screened four compound libraries and generated a large-scale integration atlas of perturbation profiles (Fig.\u00a01d), including (1) 82 cell lines perturbed by 935 FDA-approved drugs, (2) 88 cell lines perturbed by 4158 active compounds, (3) 14 CRC cell lines perturbed by 30,456 natural compounds, (4) 6 SCLC cell lines perturbed by 29,670 drug-like compounds and (5) 54 tissues perturbed by 935 FDA-approved drugs. Based on the large-scale integration atlas of perturbation profiles, PRnet is capable of a variety of downstream applications. PRnet has been utilized to recommend drugs for 233 different diseases. PRnet successfully predicted drug candidates for these diseases and demonstrates the potential in drug discovery.\n\nTo evaluate the performance of PRnet for predicting responses to unseen perturbations, all datasets were strictly split by perturbation attributes (compound_split, cell_line_split, and pathway_split) into 3 subsets: train, validation, and test (Supplementary Fig.\u00a01a). The held-out test sets were used to simulate datasets of novel perturbations. Three train-test data split strategies were employed to assess the performance of out-of-distribution perturbation scenarios, including (1) Random Split: randomly divides compounds and cell lines, (2) Unseen Compounds: testing compounds not seen perturbed during training, and (3) Unseen cell lines: testing cell lines not seen perturbed during training. Five-fold cross-validation was applied in each split strategy, and the average performance over five folds was computed as the overall metric for comparison. Two high-throughput screening data of different resolutions were used to test model performance, consisting of a bulk HTS dataset (from the L1000 project1) and a single cell HTS dataset (from the sci-Plex3 assay2). All models were trained and compared separately on two HTS datasets.\n\nWe employed bulk high-throughput screening data from the L1000 project1 to fit the model, in which 978 genes (hereinafter called landmark genes) were selected to represent the diversity of biological pathways and processes in human cells. We first preprocessed these data (as detailed in \u201cMethods\u201d) and obtained 836,352 paired bulk RNA-seq observations (represented by the expression levels of 978 landmark genes), covering 82 cell lines and their perturbation data perturbated by 175,549 compounds. To quantitatively evaluate the compound-induced gene expression changes, we compared the Pearson correlation between the true and predicted post-perturbation of the average logarithm of fold-change in gene expression (log(FC)) for the hold-out test set with alternative approaches. The \u201cPearson of log(FC) in compounds\u201d metric evaluated the Pearson correlation between the true and predicted mean log(FC) perturbed by the same compound in the test set. We demonstrated the performance of the PRnet on the bulk HTS data in Fig.\u00a02a, where a higher value indicates a better performance. PRnet consistently demonstrated the best performance across all three split strategies, particularly well-fitted in unseen compound prediction scenarios with an average Pearson Correlation (PCC) of 0.8. PRnet significantly outperformed in predicting unseen cell line log(FC) with an increase in PCC over 0.3 compared to other approaches. Some hold-out predicted cases in predicting transcriptional responses of unseen compounds and cell lines were illustrated in Supplementary Fig.\u00a01b\u2013d. In more challenging scenarios, we evaluated the performances of predicting cell-line-specific compound-induced changes in genes by the \u201cPearson of log(FC) in cov_compounds\u201d metric. The \u201cPearson of log(FC) in cov_compounds\u201d\ufeff metric is the Pearson correlation between the true and predicted mean log(FC) perturbed by the same compound within the same cell line in the test set. PRnet achieved the best performance in the \u201cPearson of log(FC) in cov_compounds\u201d metric across three scenarios. In particular, PRnet exhibited more than two times better performance in unseen cell line predictions compared to other methods and demonstrated improvements of 0.16 in unseen compound predictions, demonstrating the generalization of PRnet to novel perturbations (Fig.\u00a02b).\n\na Performance of out-of-distribution perturbation scenarios in three train-test data split strategies (1) Random split, (2) Unseen compounds split, and (3) Unseen cell lines split. The \u201cPearson of log(FC) in compounds\u201d metric evaluates the mean log(FC) perturbed by the same compounds in the test set, which are presented as mean values \u2009\u00b1\u2009SD. Error bars indicate the standard error (SD) for each method, and the points (n\u2009=\u20095) refer to the 5-fold used for validation. b The \u201cPearson of log(FC) in cov_compounds\u201d metric evaluated mean the (log(FC)) of compounds within the same cell line in the test set. Results are presented as mean values \u2009\u00b1\u2009SD (n\u2009=\u20095). c T-SNE representations of latent embeddings learned by PRnet. Post-perturbation transcriptional profiles are from 82 cell lines from 20 different organs/tissues. d The \u201cR2 in compounds\" metric evaluates the R2 score of the average gene expression perturbed by the same compounds in the test set. Results are presented as mean values\u2009\u00b1\u2009SD (n\u2009=\u20095). e The \u201cR2 in cov_compounds\u201d metric evaluates the R2 score of the average gene expression perturbed by the same compounds within the same cell line. Results are presented as mean values \u2009\u00b1\u2009SD (n\u2009=\u20095). f T-SNE representation of latent embeddings from massive single-cell screens of 188 drugs across 3 cancer cell lines. g T-SNE representation of latent embedding of transcriptional profiles from MCF7 cell line perturbed with AG-14361. The pseudo-dose trajectory is displayed with a black line. Source data are provided as a Source Data file.\n\nTo better characterize the heterogeneous gene-level change under certain perturbations, it is desirable to identify the set of cells or cell lines and isolate the precise variations enriched in the data from the corresponding cell lines or cells. After training, PRnet learned interpretable embeddings in the latent space in the context of the base unperturbed state and the applied perturbation. The low-dimensional (t-SNE33) representation (Fig.\u00a02c) illustrates the latent embeddings of post-perturbation transcriptional profiles learned by PRnet. In the latent space, embeddings from the same cell line tend to form a cluster together. Each cancer cell lines form specific gene-level responses for corresponding perturbations. In a way, PRnet captures strong cell line-specific transcriptional profile variations under different conditions. Interestingly, we observed that the embedding learned by PRnet also represents cell line similarity in response to various perturbations. Figure\u00a02c illustrates the t-SNE representation of the latent embeddings for all cell lines, where cell lines originating from the same organ exhibit a similarity preference in the latent space, resulting in close spatial locations, such as cell lines from the colon, breast, and lung. Supplementary Fig.\u00a06a\u2013c demonstrates in detail the low-dimensional (t-SNE) representation of the latent embeddings for cell lines from colorectal cancer, breast cancer, and lung cancer, respectively. To quantitatively evaluate the similarity of perturbations among cell types, we computed the normalized cosine similarity among the mean embeddings in the latent space for all cell lines. The resulting cosine similarity heatmap (Supplementary Fig.\u00a06g) shows that most cell lines from the same tissue exhibit higher similarity in the latent space compared to those from different tissues.\n\nHuman cancer cell lines have facilitated drug discoveries in cancer biology, but they are neither clonal nor genetically stable. This instability can generate variability in drug sensitivity, as shown by Ben-David et al.34. The genomic evolution of cancer cell lines leads to a high degree of variation across cell line strains. To explore the impact of inter- and intra-heterogeneity of cell lines on drug responses, we conducted additional experiments focusing on A549 and MCF7 cell strains, as described by Ben-David et al.34 in Supplementary Note\u00a02. These experiments were designed to explore inter- and intra-cellular heterogeneity, similar patterns of the same MOA drug across cell strains, and screening candidate compounds for heterogeneous cells. These results indicated that drug response was highly similar to genetic or gene expression which aligned with findings in the original study. These findings underscore the utility of our model in capturing both inter- and intra-heterogeneity, enhancing its application in screening for specific cancer strains.\n\nPRnet adapted to model profiles of HTS at different resolutions. The sci-Plex assay2 screened 188 compounds in 3 cancer cell lines at single-cell resolution, measuring millions of cells. The screened cell lines A549 (lung adenocarcinoma), K562 (chronic myeloid leukemia), and MCF7 (breast adenocarcinoma) were treated to each of these 188 compounds at four dosages (10\u2009nM, 100\u2009nM, 1\u2009\u03bcM, 10\u2009\u03bcM). To quantitatively evaluate the performance of PRnet in single-cell HTS data, we followed commonly used metric4,5,6 to compare the R2 score between the true and predicted post-perturbation gene expression for the hold-out test set with alternative approaches (Fig.\u00a02d, e). The \u201cR2 in compound\u201d\ufeff metric evaluated the R2 score of the average post-perturbation gene expression of the same compound in the test set. We also compared the cell-type-specific performance \u201cR2 in cov_compound\u201d\ufeff metric, which evaluated the R2 score of the average post-perturbation of the same compound within each specific cell line. PRnet outperformed alternative models in the \u201cR2 in the compound\u201d\ufeff and \u201cR2 in cov_compound\u201d\ufeff metrics in unseen compounds (R2 in the compound: 0.969) and unseen pathways scenarios (R2 in the compound: 0.97) than the other methods. The low-dimensional (t-SNE) representation illustrates latent embeddings learned by PRnet from massive single-cell screens of 188 compounds across 3 cancer cell lines (Fig.\u00a02f). The latent embeddings automatically cluster the concordance of cells to their cell types. The results demonstrated that PRnet not only captures the heterogeneous responses of large-scale HTS but also resolves similar responses of homogeneous cells to various perturbations. The t-SNE embedding (Fig.\u00a02g) in the latent space of MCF7 cells treated with AG-14361 produces a pseudo-dose trajectory, indicating that AG-14361 induced heterogeneous responses. Several other pseudo-dose trajectory examples were illustrated in Supplementary Fig.\u00a06d\u2013f.\n\nTo validate the clinical relevance of PRnet, we incorporated scRNA-seq data from a cohort of pediatric acute myeloid leukemia (AML) patients35. We trained PRnet to predict transcriptional responses to chemotherapy treatments and validated its potential for clinical application. PRnet showed robustness in predicting transcriptional responses for pediatric AML patients post-chemotherapy, demonstrating PRnet\u2019s potential to assist in clinical applications. For detailed experimental results, please see Supplementary Note\u00a01 and Supplementary Fig.\u00a08.\n\nWe reason that PRnet can be a valuable tool to analyze and capture functional transcriptional experiments that aim to uncover gene programs or effects on various conditions by introducing perturbation to the system. To investigate this in greater detail, we first collected and tested the hold-out perturbation data of compound vorinostat. Vorinostat (suberoylanilide hydroxamic acid) is the first FDA-approved HDAC inhibitor for the treatment of cutaneous manifestations of cutaneous T-cell lymphoma (CTCL) and is also currently being studied as monotherapy and in combination therapy for other types of cancers36,37. Results demonstrated a comprehensive ability of PRnet to capture gene-level fold changes in all of the 71 cell lines (Fig.\u00a03a). By comparing the transcriptional responses of cell lines from different organs, we observed that PRnet captured cell-type-specific responses. For instance, cell lines from muscle exhibited relatively weaker responses and smaller fold changes compared to those from the lung. Figure\u00a03a shows that PRnet correctly captures both the right trend and the magnitude of perturbation of the top up- and down-regulation genes across 71 cell lines from 16 tissues/organs. Taking gene FAM57A and TP53 as examples, PRnet made accurate predictions in cases of both up- and down-regulation in all cell lines after perturbation. In addition, PRnet even correctly predicted the fold change values in the expression of FAM57A across all cell lines. Figure\u00a03b shows a detailed comparison between the predicted and actual distributions of fold changes in gene expression for three representative cell lines from different organs (HT29: colorectal adenocarcinoma, A549: lung adenocarcinoma and MCF7: breast adenocarcinoma,) treated with vorinostat. It can be observed that PRnet aligns consistently with the distribution of predicted and true observations and accurately predicts the up- and down-regulation trends of the top 5 genes with high log(FC) values. We employed the KEGG pathway Gene Set Enrichment Analysis (GSEA) on the average predicted post-perturbations gene rank of Vorinostat across all cell lines. The GSEA results (Fig.\u00a03c, d and Supplementary Fig.\u00a03a) reveal that Vorinostat is enriched in pathways related to fundamental cellular processes related to tumor suppressor mechanisms. The GSEA results indicated that Vorinostat suppresses pathways such as Cell cycle, DNA replication, and Spliceosome, and activates pathways including Autophagy - animal, Lysosome, Phagosome, and so on, which are all associated with autophagy and apoptosis in tumor cells38.\n\na The heatmap illustrates the average log(FC) of the gene expression profiles predicted by PRnet and the ground truth for 71 cell lines under the drug Vorinostat. The horizontal axis represents genes, while the vertical axis represents cell lines, with the color orange indicating upregulation and the color green indicating downregulation. b The violin plots illustrate the distribution of log(FC) for the top 5 up-and down-regulated genes in multiple perturbations of cell lines, including A549, HT29, and MCF7, from three cancer types treated with the drug Vorinostat. c The GSEA results of average post-perturbation changes in expression of 71 cell lines perturbed by Vorinostat. GSEA was performed for KEGG pathway enrichment. Enriched terms were identified by adjusted p-values (p.adjust) \u00a0<\u20090.05 (Benjamini-Hochberg method). d The category net plot visualizes the functional enrichment result of Vorinostat with suppressed pathways colored in blue and activated pathways colored in red. e The box plots illustrate the log(FC) of the top 20 up-and down-regulated genes in cell lines HT29 with the drug bortezomib (n\u2009=\u2009865 observations), MG-132 (n\u2009=\u2009892 observations), and wortmannin (n\u2009=\u2009207 observations), respectively. The middle line in the box plot, median; box boundary, IQR; whiskers, 1.5\u2009\u00d7\u2009IQR; minimum and maximum, not indicated in the box plot; gray dots, points beyond the minimum or maximum whisker. f The box plots illustrate the expression of the 10 different expression genes of cell lines A549 (n\u2009=\u2009450 cells), K562 (n\u2009=\u2009430 cells), and MCF7 (n\u2009=\u2009988 cells) perturbed by the drug GSK-LSD1, respectively. The middle line in the box plot, median; box boundary, IQR; whiskers, 1.5\u2009\u00d7\u2009IQR; minimum and maximum, not indicated in the box plot; gray dots, points beyond the minimum or maximum whisker.\u00a0Source data are provided as a Source Data file.\n\nTo demonstrate the generalization of PRnet, we also analyzed perturbation observations of other hold-out compounds on a gene-by-gene basis. We collected and tested some case perturbation data of HT29 with the most observations. Figure\u00a03e illustrates the log(FC) of the top 20 up- and down-regulated genes in multiple perturbations of HT29 cell lines treated with the hold-out compounds bortezomib, MG-132, and wortmannin, respectively. These results suggested that PRnet is able to capture regulated gene level information that is consistent with evidence from corresponding compounds in inferring different cancer transcriptional profile conditions that can be missed by perturbations analysis. The predicted distribution of fold changes in gene expression after perturbation closely aligned with the actual observed distribution, indicating the accuracy of PRnet in capturing the perturbation effects. More predicted gene-level perturbation responses of cell lines of breast cancer exhibited similar performance (Supplementary Fig.\u00a02a). Besides, Fig.\u00a03f demonstrates the ability of PRnet to predict cell-type-specific gene-level perturbation transitions in single-cell HTS observations. In the case of predicting the response of perturbing A549, K562, and MCF7 cell lines with GSK-LSD1, PRnet correctly captured both the right trend in gene expression and the magnitude of response across all 10 differentially expressed genes (Fig.\u00a03f). Similar performance was observed for several other examples across perturbation conditions (Supplementary Fig.\u00a04a\u2013c). The ability to capture changes in gene-level programs under different compound conditions and resolutions indicates the robustness, generalization, and precise performance of PRnet in predicting perturbation responses.\n\nHaving been trained to simulate experimental measurements of high-throughput screening, PRnet was applied to identify potential novel compound candidates for the treatment of small cell lung cancer (SCLC). SCLC is an extremely aggressive lung cancer characterized by small cells with limited cytoplasm forming clusters or spheroids39. Despite an initial positive response to conventional chemotherapy and radiation, SCLC often recurs rapidly, with less than 5% of patients surviving five years. Currently, highly effective treatment options for this disease remain unresolved, making drug development efforts for this cancer a high priority.\n\nGiven several novel cell lines of SCLC, namely NCI-H69, NCI-H526, NCI-H446, NCI-H209, and NCI-H196, and DMS114, we first employed PRnet to predict the transcriptional response of SCLC cell lines to sensitive compounds. Then, we in silico screened two user-defined compound libraries to identify potential compound candidates against SCLC (Fig.\u00a04a), namely an active compound library (4158 compounds) from Selleckchem, and an in-house druglike compound library (29,670 compounds). Through in silico screening, PRnet predicted the transcriptional responses of each compound across eight concentration gradients perturbing 6 cell lines, with each scenario repeated three times for computational robustness and calculated gene rank of changes in the average post-perturbation expression of each compound. After that, the predicted up/downregulated genes of sensitive compounds on their cell lines were used as the GSEA gene signature input to calculate the enrichment scores. We then performed GSEA to calculate the enrichment scores of compounds in libraries and ranked them according to their scores (Supplementary Datas\u00a01, 2). Ultimately, three compounds ((+)-Fangchinoline, (+)-JQ-1, and SEL120-34A HCl) at the top rank (rank \u2264\u20093 for enrichment score of sensitive compounds) were chosen as the candidate set (Fig.\u00a04b). Among them, it has been proved that small cell lung cancer (SCLC) cells are exquisitely sensitive to growth inhibition by the BET inhibitor (+)-JQ-1 (CAS No. : 1268524-70-4)40, and (+)-Fangchinoline and SEL120-34A HCl were assessed experimentally. We also explored the suitable activity concentrations of candidate compounds by calculating the enrichment scores of perturbed transcriptional profiles at concentration gradients. As shown in Fig.\u00a04c, d, concentrations in the range of 1\u201310\u2009\u03bcmol/L might be the proper inhibitory concentration for these candidate compounds.\n\na PRnet predicted the perturbed transcriptional profiles of 6 SCLC cell lines and 14 CRC cell lines. For in silico screening, PRnet first predicted the transcriptional profiles of SCLC cell lines perturbed by a multi-concentration gradient of 4158 active compounds, as well as 29,670 in-house druglike compounds. PRnet also predicted the transcriptional profiles of 14 CRC cell lines perturbed by 30,456 natural compounds. Then, post-perturbation fold-changes and average fold-changes of compounds across cell lines are computed. The\u00a0gene ranking is performed based on the fold-change values. Given the predicted gene signature of sensitive compounds, the model computes the enrichment scores for up- and down-regulated gene sets of screening compounds. Finally, compounds are ranked based on the enrichment scores. For in vitro testing, MTT assays were performed for the evaluation of cell viability. b The heatmap illustrates the enrichment score of candidates for sensitive compounds. c, d The line charts plot the enrichment score of SCLC cells exposed to (+)-Fangchinoline and SEL120-34A HCl. e, f The cell survival curve of SCLC cells exposed to (+)-Fangchinoline and SEL120-34A HCl (n = 3 replicates). IC50 (half-maximal inhibitory concentration) are presented as mean values \u2009\u00b1\u2009SD. g, h The cell survival curve of CRC cells exposed to 7-Methoxyrosmanol and Mulberrofuran Q (n\u2009=\u20093 replicates). IC50 are presented as mean values \u00a0\u00b1 SD.\u00a0Source data are provided as a Source Data file. Some icons were created in BioRender. Qi, X. (2024) biorender.com/c85y849.\n\nWe utilized MTT assays to examine the activities of compound candidates against SCLC cells. Six human SCLC cell lines (NCI-H69, NCI-H526, NCI-H446, NCI-H209, and NCI-H196, and DMS114) were chosen for experiments. Results revealed the activity of SEL120-34A HCl (CAS No. : 1609452-30-3) and (+)-Fangchinoline (CAS No. : 436-77-1) against small cell lung cancer (SCLC) cell lines. SEL120-34A HCl and (+)-Fangchinoline showed significant inhibitory effects on the proliferation of SCLC cells, and exhibited an IC50 (half-maximal inhibitory concentration) of less than 10 \u03bcmol/L, indicating their inhibitory effect on SCLC cell viability (Fig.\u00a04e, f). (+)-Fangchinoline and SEL120-34A HCl moderately inhibited the viability of SCLC cell lines. Detailed antiviability activities of two candidates are shown in Supplementary Table\u00a03. These findings suggested the potential therapeutic efficacy of SEL120-34A HCl and (+)-Fangchinoline in the context of SCLC treatment, highlighting their promising role as active compounds against this aggressive lung cancer subtype.\n\nColorectal cancer (CRC) ranks as the third most common cancer and the second leading cause of cancer death worldwide41. Advances in molecularly targeted therapy and immunotherapy over the past decades have significantly improved patient survival41. However, some CRC patients, initially responsive to these treatments, quickly develop insensitivity. The emergence of drug resistance in cancer treatment significantly reduces patient outcomes. We aim to explore more potential novel treatment options for colorectal cancer to mitigate the impact of drug resistance through in silico screening of a large-scale library of natural compounds (30,456 compounds42).\n\nWe extended the application of PRnet to screening novel natural compounds for the treatment of CRC. We first leveraged PRnet to predict the post-perturbation transcriptional profiles of sensitive compounds in 14 CRC cell lines (HT29, LOVO, MDST8, RKO, HT115, SW948, SNU1040, SNUC4, SNUC5, SW480, SW620, HCT116, CL34, and NCIH508). Then, we used the predicted up/downregulated genes of sensitive compounds on their cell lines as the GSEA gene signature. After that, we also in silico screened a large-scale natural compounds library containing 30,456 natural ingredients. PRnet predicted the transcriptional responses of each compound across eight concentration gradients perturbing 14 cell lines with three repeats and calculated the post-perturbation average gene rank of compounds. Two natural compounds (7-Methoxyrosmanol and Mulberrofuran Q) at the top rank (rank \u2264\u20095 for enrichment score of sensitive compounds) were chosen as the candidate set (see Supplementary Tables.\u00a01, 2).\n\nWe utilized MTT assays to examine the activities of candidate compounds against CRC cells. Six human CRC cell lines, namely HT29, SW480, Caco-2, SW620, HCT116, and Colo205, were chosen for the experiments. The results revealed that 7-Methoxyrosmanol (CAS No. : 113085-62-4) and Mulberrofuran Q (CAS No. : 101383-35-1) inhibited the viability of CRC cell lines (Fig.\u00a04e, f). 7-Methoxyrosmanol and Mulberrofuran Q moderately inhibited the viability of CRC cell lines. Detailed antiviability activities of two candidates are shown in Supplementary Table\u00a03. These findings showed the activity of 7-Methoxyrosmanol and Mulberrofuran Q against CRC cell lines, suggesting their potential efficacy in CRC treatment.\n\nWith the ability to characterize specific gene-level perturbation responses and identify anti-cancer compounds, PRnet was applied to in silico screen novel compound libraries and cell lines and generate a large-scale integration atlas of perturbation profiles across various scenarios (Fig.\u00a05a). PRnet was trained with two datasets: (1) the L1000 dataset, a bulk high-throughput screening library consisting of 883,269 transcriptional profiles from 82 cell lines perturbed by 175,549 biologically active compounds, and (2) the Sci-plex3 dataset, a single-cell high-throughput screening library consisting of 290,888 transcriptional profiles from 3 cell lines perturbed by 188 active compounds. The L1000 dataset1 screened cell lines derived from over 20 diverse tissues and exposed to compounds targeting multiple genes and pathways (Supplementary Fig.\u00a05a\u2013c). The Sci-plex3 dataset screened three cancer cell lines treated by 188 compounds targeting a diverse range of targets and molecular pathways, covering various mechanisms of action (Supplementary Fig.\u00a05d\u2013f). After training, PRnet was applied to screen various perturbation scenarios to generate a large-scale integration atlas of perturbation profiles. Through virtual screening, PRnet has predicted over 25 million post-perturbation expression profile atlas of perturbation profiles which consists of five parts: (1) the FDA-approved drugs dataset: a bulk virtual high-throughput screening library containing 1,891,330 transcriptional profiles from 82 cell lines perturbed by 935 FDA-approved drugs, (2) the anti-cancer compounds dataset: a bulk virtual high-throughput screening library containing 8,781,784 transcriptional profiles from 88 cell lines perturbed by 4158 active compounds, (3) the natural compounds dataset: a bulk virtual high-throughput screening library containing 10,233,230 transcriptional profiles from 14 colorectal cancer cell lines perturbed by 30,456 natural compounds, (4) the bioactive compounds dataset: a bulk virtual high-throughput screening library containing 4,272,486 transcriptional profiles from 6 small cell lung cancer cell lines perturbed by 29,670 druglike compounds, and (5) the Gtex dataset: a bulk virtual high-throughput screening library containing 1,245,510 transcriptional profiles from 54 tissues perturbed by 935 FDA-approved drugs. Details of all cell lines are provided in Supplementary Data\u00a03. PRnet offered a broad perspective by providing insights into how perturbations impact the transcriptional landscape on a large scale and extended its utility to diverse screening contexts. The large-scale integrated atlas of perturbation profiles can be applied to a variety of downstream application scenarios. For example, the FDA-approved drug dataset can be used for drug repositioning to recommend drugs for specific diseases based on gene signatures (see PRnet provided a robust and scalable drug 568 recommendation workflow based on the profiles atlas). The anti-cancer compounds dataset, the natural compounds dataset, and the bioactive compounds dataset are valuable for screening new anti-cancer compounds (see PRnet identified active compounds against small cell 430 lung cancer and PRnet found natural compounds against colorectal 482 cancer). In addition, the Gtex dataset can be useful to analyze the toxicity of compounds in different tissues. PRnet imports gene-level functionality in perturbations of different compounds and empowers flexibility by utilizing user-defined compounds\u2019 structures and transcription profiles to estimate the gene expression matrix. These profiles can be compared against various perturbation conditions(dosages, structures of compounds) using PRnet, to evaluate the impact of using different compounds on specific gene-expression profiles from single cell and bulk data. These diverse profiles provided potential solutions for drug discovery, disease treatment, and toxicity analysis.\n\na The large-scale integration atlas of perturbation profiles. b PRnet provided a recommended workflow for specific diseases. In step 1, given the structures of the screening library, PRnet predicts the post-perturbed transcriptional profiles of all compounds across multiple concentration gradients in 82 cell lines. In step 2, the transcriptional profiles of 978 landmark genes are transformed into 12,328 genes through linear transformation. Subsequently, the perturbation fold-change and the average fold-change across cell lines for the transformed expression profile are computed. Gene ranking is then performed based on the fold-change values. In step 3, given the gene signature for a particular disease, the model computes the enrichment scores for up- and down-regulated gene sets of screening compounds. Finally, compounds are ranked based on the enrichment scores. c\u2013e Scatter plots of the enrichment score of compounds against NASH, Crohn\u2019s disease, and PCOS. The computationally predicted candidates are highlighted with their names in the upper right corner. The color gradient shows the density of the dots. Source data are provided as a Source Data file. Some icons were created in BioRender. Qi, X. (2024) biorender.com/z56i777.\n\nPRnet provides a comprehensive drug recommendation workflow based on the large-scale integration atlas of perturbation profiles (Fig.\u00a05b). In step 1, given the compounds\u2019 structure of the screening library, PRnet predicts the post-perturbed transcriptional profiles of all compounds across multiple concentration gradients across cell lines. In step 2, the transcriptional profiles of 978 landmark genes are transformed into 12,328 genes through linear transformation. Subsequently, the perturbation fold-change and the average fold-change across cell lines for the transformed expression profile are computed. The\u00a0gene ranking is then performed based on the fold-change values. In step 3, given the gene signature for a specific disease, PRnet computes the enrichment scores for up- and down-regulated gene sets of screening compounds. Finally, compounds in the screening library are ranked based on these enrichment scores. These downstream applications demonstrated the versatility and utility of PRnet in addressing diverse challenges in the field of drug discovery and perturbation analysis.\n\nWe leveraged the drug recommendation workflow to identify drug candidates for the treatment of 233 different diseases. For the user-defined compound library, we here chose the FDA-approved drug dataset, which comprises 935 FDA-approved drugs. All gene signatures of 233 diseases versus normal were collected from the CREEDS project (CRowd Extracted Expression of Differential Signatures43). The up/downregulated genes of a specific disease were used as the GSEA gene signature input to calculate enrichment scores (see \u201cMethods\u201d) of drugs. Finally, we obtained 577 candidate lists from 577 studies for 233 unique diseases (Supplementary Data\u00a04). Taking three metabolic disorder diseases, including nonalcoholic steatohepatitis (NASH), inflammatory bowel disease (IBD), and polycystic ovary syndrome patients (PCOS) as examples, we provided enrichment scores of each drug for the three diseases and the recommended drug candidates for them. The enrichment scores are plotted in Fig.\u00a05c\u2013e, drugs positioned at the upper right corner (rank\u2264\u200910 for enrichment score) were chosen as the candidate set for literature verification. Supplementary Table\u00a04 lists the Top 10 enrichment scores for compounds against NASH, Crohn\u2019s disease, and PCOS.\n\nNonalcoholic steatohepatitis (NASH) is liver inflammation and damage caused by a buildup of fat in the liver without standard treatment and any well-established drug targets. After calculation, we ultimately selected a set of candidate drugs from the upper right corner (rank\u2264\u20096 for enrichment score) as candidate treatments for NASH (Fig.\u00a05c). Literature verification revealed that Mirabegron (rank 1), Vidofludimus (rank 5), and Rifaximin (rank 6) have literature support for use in the treatment of NASH. A study on high-fat diet rats44 suggested that mirabegron may have a protective effect against NASH as it improves liver enzymes, lipids, serum HbA1c, fasting insulin and glucose, insulin resistance index, and serum adiponectin levels, as well as ameliorates hepatic histopathologic changes in NASH-induced rats. A study on obesity mice45 suggested vidofludimus reduced hepatic steatosis and inflammation in obesity mice and has been repurposed to a therapeutic potential in the treatment of NASH by targeting FXR based on the newly established relationships among drugs, targets, and diseases. Rifaximin therapy appeared to be effective and safe in modifying NASH through reduction of serum endotoxin and improvement of insulin resistance, proinflammatory cytokines, CK-18, and NAFLD-liver fat score. Patients with biopsy-proven NASH with Rifaximin therapy showed that Rifaximin appeared to be effective and safe in modifying NASH46,47. We also performed KEGG pathway enrichment analysis of predicted up/down-regulated genes of Mirabegron, Vidofludimus, and Rifaximin (Supplementary Fig.\u00a07). The KEGG pathway enrichment analysis of downregulated genes for both Mirabegron and Rifaximin suggested they may downregulate pathways associated with NASH.\n\nCrohn\u2019s disease and ulcerative colitis are idiopathic inflammatory bowel disorders (IBD). Crohn\u2019s disease is a relapsing systemic inflammatory disease that primarily affects the gastrointestinal tract with extraintestinal manifestations and associated immune disorders48. After ranking the drugs, two drugs positioned at the upper right corner (rank\u2264\u20099 for enrichment score) were chosen as the candidate set (Fig.\u00a05d). The literature verification revealed that Escin (rank 3) and Ozanimod (rank 9) have literature support for use in the treatment of Crohn\u2019s disease. Escin is the main bioactive ingredient of Semen aesculi, which improves intestinal barrier dysfunction of IBD via Akt/NF-\u03baB signaling pathway49. Ozanimod is a once-daily sphingosine 1-phosphate receptor modulator for the treatment of inflammatory bowel disease and was approved by the FDA. Phase 2 clinical trial had proved that Ozanimod can be a novel oral small molecule therapy for the treatment of Crohn\u2019s disease50,51. The KEGG pathway enrichment analysis results of predicted up/down-regulated genes of Escin and Ozanimod are illustrated in Supplementary Fig.\u00a07. The predicted upregulated pathways (Supplementary Fig.\u00a07) suggested Escin may upregulate the PI3K/Akt signaling pathway which regulates a broad cascade of target proteins including nuclear factor kappa B (NF-\u03baB) and glycogen synthase kinase-3\u03b2 (GSK-3\u03b2)52.\n\nPolycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder in which the major endocrine disruption is excessive androgen secretion or activity, and a large proportion of women also have abnormal insulin activity. We also chose positively scored drugs as candidates for PCOS (rank\u2264\u20099 for enrichment score, Fig.\u00a05e). The literature verification revealed that Enzalutamide (rank 3), Linagliptin (rank 8), and Topiramate (rank 9) have literature support for use in the treatment of PCOS. Enzalutamide has been suggested for use as an antiandrogen to treat hirsutism and hyperandrogenism in women with polycystic ovary syndrome53,54 investigated the effect of linagliptin and/or I3C on experimentally-induced PCOS in female rats, and the results suggested that linagliptin/I3C combination might represent a beneficial therapeutic modality for amelioration of PCOS. PCOS has completed phase 3 trials for Topiramate, and phentermine-topiramate extended-release (PHEN/TPM) resulted in the most loss of weight and total body fat55. The predicted regulated pathways (Supplementary Fig.\u00a07) of Enzalutamide showed that Enzalutamide may upregulate the PI3K/Akt signaling pathway and MAPK signaling pathway, which are related to Androgen signaling56. The predicted downregulated pathways suggested Topiramate may downregulate the Lipid and atherosclerosis and the Fat digestion and absorption pathway, thereby contributing to weight loss. All three literature verification results illustrated that PRnet recommended reliable and effective drugs for different diseases, and hence is a valuable tool for drug discovery.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53457-1/MediaObjects/41467_2024_53457_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Recent advancements in high-throughput screening have profiled thousands of independent chemical perturbations, providing crucial insights into the fundamental responses of biological systems to perturbations. However, screening all possible disease and compound combinations is experimentally unfeasible. To address the limited exploration power of existing experimental methods, we developed PRnet, which supports the prediction of transcriptional responses to novel chemical perturbations that were never experimentally studied at bulk and single-cell levels. PRnet serves as a valuable tool that facilitates gene-level response interpretation in various novel perturbation scenarios and effectively recommends candidates for various diseases.\n\nPRnet has the unique ability to screen various novel compound libraries for specific diseases and infer their post-perturbed transcriptional response. Given the structures of compounds in libraries and unperturbed transcriptional profile, PRnet encodes the perturbation and unperturbed state to an interpretable latent space as condition contexts to generate transcriptional responses. Trained on extensive data, PRnet is able to adapt to complex chemical perturbations of generalization to novel compounds and cell lines. To further validate the effectiveness of PRnet, we identified novel compounds candidates against SCLC and natural compounds against CRC. Experimental validations confirmed the activity of candidate compounds, showcased the capabilities of PRnet to guide the design of new screens, and reduced the time and costs of experiments. Lastly, PRnet generated a large-scale integration atlas of perturbation profiles and demonstrates its capability to recommend drug candidates for 233 complex diseases based on reference changes in gene sets. The flexibility and scalability of PRnet make it a valuable tool for guiding the design of screening strategies for gene-based therapeutics.\n\nAlthough PRnet is effective in predicting drug candidates, there is still room for improvement. Due to the scalability of the SMILES format30 and the RDKit31, PRnet is able to encode unseen complex compounds as embeddings. SMILES is widely used for representing chemical structures due to its simplicity and efficiency in encoding complex molecules as strings. However, SMILES may not fully capture complex molecular features such as 3D geometry, conformational flexibility, or dynamic behavior, which can be important for understanding molecular interactions. Alternative encoding methods such as MOL/SDF files and graph-based representations are more flexible representations in capturing the 3D geometry structure of compounds. MOL/SDF formats represent chemical structures with explicit atom coordinates and bond connectivity. Graph-based representations use graph theory to represent molecules where atoms are nodes and bonds are edges. These methods can provide a more intuitive and flexible representation. However, they are more complex or require pre-training to obtain better representations. In the scenario of large-scale in silico screening, we chose SMILES to encode chemical structures for its simplicity and scalability. Alternative encoding methods, such as MOL/SDF files and graph-based representations, will be considered in\u00a0the future work.\n\nThe reverse signature paradigm to connect disease and drug established by Lamb et al in CMap15 has demonstrated its effectiveness in previous studies16,17,18,19,20. However, this may not hold true for all diseases, and there are instances where transcriptional changes do not correlate directly with drug sensitivity. Jie Cheng et al.57 found the reverse signature paradigm may perform poor accuracy when some disease signatures are of low quality. Rasool Bhat58 discusses how cancer cells exhibit phenotypic plasticity under drug treatment, leading to drug resistance which often involves complex regulatory networks that are not fully captured by changes in gene expression alone. The reverse signature paradigm is an effective matching algorithm in many diseases. But there are also cases that not fit this algorithm, more comprehensive omics data, such as genomics, epigenetics, and proteomics should be considered in the future work to better characterize disease states and facilitate drug discovery.\n\nIn addition, in the process of compound screening, we mainly focus on the impact at the gene level, lacking consideration for the effects on phenotypes, such as the Area Under the Curve (AUC) or half-maximal inhibitory concentration (IC50) derived from the experimental dose-response curve. To comprehensively assess compound impacts, future research directions could involve incorporating more phenotype data for a holistic assessment of the relationship between genes and phenotypes. Moreover, expanding the scope of perturbation scenarios and incorporating extensive biological knowledge will be considered to enhance the model\u2019s predictive capabilities in the future. Furthermore, we expect PRnet to extend its utility beyond the prediction of chemical perturbations to encompass various perturbation experiments, including genetic perturbations and other forms, thereby contributing to the advancement of drug discovery.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "In this work, we formulate transcriptional response prediction as a distribution generation problem conditioned on perturbations. Given a dataset \\(D={({x}_{i},{P}_{i})}_{i=1}^{N}\\), where \\({x}_{i}\\in {{\\mathbb{R}}}^{n}\\) denotes as the n-dimensional gene expression and Pi is the attribute set of perturbation, PRnet was trained to learn a function f that predicts transcriptional responses \\({\\widehat{x}}_{i}=f({x}_{i}^{u},{P}_{i})\\) to novel perturbations. Here, \\({x}_{i}^{u}\\) represents the unperturbed gene expression, \\(\\widehat{{x}_{i}}\\) is the predicted perturbed gene expression, and Pi\u2009=\u2009(si,\u00a0di) is the attribute set of perturbation, which includes the chemical structures si and dosages of the compounds di. As a generation model, the Gaussian negative log-likelihood loss is chosen as the loss function. For HTS RNA-seq datasets \\(D={({x}_{i},{P}_{i})}_{i=1}^{N}={({x}_{i},({s}_{i},{d}_{i}))}_{i=1}^{N}\\), the gene expression xi, compounds si, and dosages di attributes are usually considered, in which si\u2009=\u2009(si,1,\u00a0si,2,\u00a0.\u00a0.\u00a0.\u00a0,\u00a0si,M) describes the Canonical SMILES format of M compounds of perturbation i and di\u2009=\u2009(di,1,\u00a0di,2,\u00a0.\u00a0.\u00a0.\u00a0,\u00a0di,M) are the dosages of compounds. When di,j\u2009=\u20090, the compound j was not applied in perturbation i. If M\u2009=\u20090, there is no perturbation performed, and in this case, perturbation i is an unperturbed state \\({x}_{i}^{u}\\), otherwise is in a perturbed state. In addition, the covariates vector ci contains discrete covariates such as cell types, cell lines, or species, depending on the available data. While ci does not directly serve as input to the function f, it relates to the intermediate variable vector \\({x}_{i}^{u}\\) of the function f. Each perturbed profile xi is assigned an unperturbed transcriptional profile of the same cell line \\({x}_{i}^{u}\\) by random selection in the dataset. The goal of PRnet is to learn a function f that predicts transcriptional responses \\({\\widehat{x}}_{i}=f({x}_{i}^{u},{P}_{i})\\) to novel perturbations. Given an unperturbed gene expression \\({x}_{i}^{u}\\), PRnet maps the unperturbed state to a distribution \\({{{\\mathcal{N}}}}({x}_{i}\\,| \\,{\\mu }_{i},{\\sigma }_{i}^{2})\\), and the sample to a perturbed state \\({\\widehat{x}}_{i}\\).\n\nPRnet is a perturbation-conditioned generative model aimed at predicting gene expression profiles under different perturbations. The design of PRnet consists of three components: (1) the Perturb-adapter, a scalable adapter \\({f}_{pert}:{\\mathbb{Z}}\\to {{\\mathbb{R}}}^{k}\\) encodes complex perturbations (si,\u00a0di) into a k-dimensional perturbation embedding \\({z}_{i}^{p}\\), (2) the Perturb-encoder \\({f}_{enc}:{{\\mathbb{R}}}^{k+n}\\to {{\\mathbb{R}}}^{e}\\) encodes the combined learnable perturbation embedding \\({z}_{i}^{p}\\) and unperturbed gene expression \\({x}_{i}^{u}\\) to a latent space to get the embedding \\({z}_{i}^{l}\\), and (3) the Perturb-decoder \\({f}_{dec}:{{\\mathbb{R}}}^{e+k+m}\\to {{\\mathbb{R}}}^{2n}\\) decodes combines perturbation embedding \\({z}_{i}^{p}\\), latent state \\({z}_{i}^{l}\\) and noise \\({z}_{i}^{n}\\) into Gaussian likelihood distribution \\({{{\\mathcal{N}}}}({x}_{i}\\,| \\,{\\mu }_{i},{\\sigma }_{i}^{2})\\) of perturbed state and generates perturbed gene expression \\({\\widehat{x}}_{i}\\) by sampling from \\({{{\\mathcal{N}}}}({x}_{i}\\,| \\,{\\mu }_{i},{\\sigma }_{i}^{2})\\). We elaborate on the three components in the following.\n\nThe Perturb-adapter encodes the i-th perturbation Pi as a fixed-size embedding \\({z}_{i}^{p}\\in {R}^{k}\\) (k\u2009=\u200964). Given the i-th perturbation Pi, the j-th compound of Pi was first represented as the canonical SMILES format si,j, and was converted to a fixed size fingerprint embedding FCFPi,j (FCFP4 fingerprints: Functional-Class Fingerprints with radius\u2009=\u20092 which focus on the functional topological pharmacophore) by RDKit31 encoder \\(H:{\\mathbb{Z}}\\to {{\\mathbb{R}}}^{h}\\) (h\u2009=\u20091024). Then, the rescaled Functional-Class Fingerprints rFCFPi embedding of Pi was calculated by the weighted sum of all fingerprint embeddings of all compounds applied by Pi:\n\nwhere \u03d5 is a log scale function using the dosage of j-th compound. At last, the final perturbation embedding \\({z}_{i}^{p}\\) was generated by the Perturb-adapter \\({E}_{{\\theta }_{p}}\\):\n\nwhere \u03b8p are the parameters of the Perturb-adapter, which is a 2-layer feedforward neural network.\n\nThanks to the scalability of the SMILES30 format and the RDKit31, which can encode any compound to a latent embedding, the design of the Perturb-adapter is scalable to in silico screen novel compounds and cell line perturbations.\n\nInspired by the Variational Auto-Encoder (VAE22) framework, PRnet uses the encoder and decoder framework to estimate the Gaussian distribution of each gene parameterized with the mean (\u03bci) and the variance (\\({\\sigma }_{i}^{2}\\)). Due to the technical limitation, only unpaired perturbed and unperturbed transcriptional profiles were observed. To match the expression profiles before and after perturbation, for scRNA-seq datasets, each perturbed cell was assigned with an unperturbed cell from the same cell type by random selection in the dataset, and for bulk RNA-seq datasets, the same cell line observation was assigned for the perturbed state. Given the perturbation embedding \\({z}_{i}^{p}\\) generated by the Perturb-apaptor, the Perturb-encoder \\({E}_{{\\theta }_{e}}\\) mapped the \\({z}_{i}^{p}\\) and the unperturbed gene expression \\({x}_{i}^{u}\\) to a latent embedding \\({z}_{i}^{l}\\in {R}^{e}\\) (e\u2009=\u200964). Then, the perturb-decoder \\({E}_{{\\theta }_{d}}\\) accepted \\({z}_{i}^{p}\\), \\({z}_{i}^{l}\\), and the noise \\({z}_{i}^{n}\\) to estimate the Gaussian likelihood distribution \\({{{\\mathcal{N}}}}({x}_{i}\\,| \\,{\\mu }_{i},{\\sigma }_{i}^{2})\\) of the perturbed profile:\n\nwhere both \\({E}_{{\\theta }_{e}}\\) and \\({E}_{{\\theta }_{d}}\\) are 2-layer feedforward neural networks, and \u03b8eand \u03b8d are the parameters of \\({E}_{{\\theta }_{e}}\\) and \\({E}_{{\\theta }_{d}}\\), respectively. In the design of PRnet, the perturbation embedding \\({z}_{i}^{p}\\) was used as the input for both the perturb-encoder and the perturb-decoder. Multiplexing \\({z}_{i}^{p}\\) in the perturb-decoder combined the chemical and biology context helped assemble a more precise output. And the noise \\({z}_{i}^{n}\\) increases the robustness of the model. Once the estimated Gaussian likelihood distribution \\({{{\\mathcal{N}}}}({x}_{i}\\,| \\,{\\mu }_{i},{\\sigma }_{i}^{2})\\) was generated, PRnet sampled the estimated perturbed gene expression \\({\\widehat{x}}_{i}\\) from it:\n\nwhere \u03c6(.) denotes the non-linear softplus function. \u03bci and \\({\\sigma }_{i}^{2}\\) are the mean and the variance for each gene, which are n-dimensional vectors. And \\({\\widehat{x}}_{i}\\) is the estimated n-dimensional transcriptional responses to perturbations.\n\nThe training objective of PRnet is to minimize the Gaussian negative log-likelihood loss defined as:\n\nWhere eps is used to clamp \\({\\sigma }_{i}^{2}\\) for stability, and is set to 1e-6 by default. By minimizing the loss of all cells/samples, PRnet is able to learn the most proper parameters.\n\nThe training datasets (a bulk HTS dataset from the L1000 project1 and a single cell HTS dataset from the sci-Plex3 assay2) were split by dividing the attribute set of perturb Pi = (si,\u00a0di,\u00a0ci).\n\nTo train and test PRnet, all datasets were split into three subsets: train, valid, and test in a ratio of 6:2:2, according to different split strategies. A total of four split strategies were designed:\n\nrandom_split: randomly divides compounds and cell lines\n\ncompound_split: groups the datasets according to si for each profile, and then splits groups of the dataset\n\ncell_line_split: groups the datasets according to ci for each profile, and then splits groups of the dataset\n\npathway_split: groups the datasets according to ci from the same pathway for each profile, and then splits groups of the dataset\n\nFor bulk RNA-seq datasets, random_split, compounds_split, and cell_line_split were applied, and for scRNA-seq datasets, random_split, compound_split, and pathway_split were applied. Strict data split can prevent data leakage and bring better generalization performance. PRnet is trained using 5-fold cross-validation for each split category.\n\nThe table below (see Table\u00a01) outlines the values for the hyperparameters involved in PRnet training. The same set of hyperparameters is used across all splits and datasets.\n\nFour metrics were used to quantitatively evaluate the performance of PRnet:\n\nfold\u00a0\u2212\u00a0change: describes the ratio changes between the gene expression of unperturbed and perturbed state:\n\nWhere fci and \\({\\widehat{fc}}_{i}\\) are the ground truth and predicted perturbed fold change in gene expression xi, respectively. A higher value of fold\u00a0\u2212\u00a0change indicates a better performance.\n\nR2: is the mean coefficient of determination score between the predicted and true perturbed gene expression of all cells:\n\nwhere O denotes the number of cells in test datasets, \\({\\widehat{x}}_{i}\\), xi and \\({\\bar{x}}_{i}\\) are the predicted, true perturbed gene expression and mean of the ground truth perturbed gene expression, respectively. A higher value of R2 indicates a better performance.\n\nPearson of log(FC) in compounds: evaluates the Pearson correlation between the true and predicted post-perturbation of the average logarithm of the fold-change in gene expression (log(FC)) perturbed by the same compound:\n\nwhere fci,j and \\({\\widehat{fc}}_{i,j}\\) denote as the ground truth and predicted post-perturbation fold change in gene expression xi perturbed by compound j. The set \\({(f{c}_{i,j})}_{i=1}^{n}\\) represents all perturbed fold-changes for compound j. \\(\\overline{\\log (f{c}_{j})}\\) and \\(\\overline{\\log ({\\widehat{fc}}_{j})}\\) are the ground truth and predicted post-perturbation average logarithm of logarithm of the fold-change in gene expression for compound j, respectively. PCCj is the Pearson correlation coefficient of the mean log(FC) perturbed by compound j. The average of the Pearson correlations for all m compounds in the test set is referred to as the \u201cPearson of log(FC) in compounds\u201d. A higher value of \u201cPearson of log(FC) in compounds\u201d indicates a better performance.\n\nPearson of log(FC) in cov_compounds: evaluates the Pearson correlation between the true and predicted post-perturbation of the average logarithm of the fold-change in gene expression (log(FC)) perturbed by the same compound within the same cell line:\n\nwhere \\(f{c}_{i,j}^{c}\\) and \\(\\widehat{f{c}_{i,j}^{c}}\\) denote the ground truth and predicted post-perturbation fold change in gene expression xi perturbed by compound j in covariate c, respectively. The covariate c represents cell line or cell type of xi. \\({(f{c}_{i,j}^{c})}_{i=1}^{n}\\) represents all perturbed fold-changes for compound j in covariate c. \\(\\overline{\\log (F{C}_{j}^{c})}\\) and \\(\\overline{\\log (\\widehat{F{C}_{j}^{c}})}\\) are the ground truth and predicted post-perturbation average logarithm of logarithm of the fold-change in gene expression for compound j in covariate c, respectively. \\(PC{C}_{j}^{c}\\) is the Pearson correlation coefficient of the mean log(FC) perturbed by compound j in covariate c. The average of the Pearson correlations for all z \u201ccov_compounds\u201d conditions in the test set is referred to as the \u201cPearson of log(FC) in cov_compounds\u201d. A higher value of \u201cPearson of log(FC) in cov_compounds\u201d indicates a better performance.\n\nR2 in compounds: is the mean coefficient of determination score between the predicted and true post-perturbation gene expression of the same compound:\n\nwhere xi,t and \\(\\widehat{{x}_{i,t}}\\) denote as the ground truth and predicted post-perturbation gene expression xi perturbed by compound t. The set \\({({x}_{i,t})}_{i=1}^{p}\\) represents all post-perturbation gene expression for compound t. \\(\\overline{{x}_{t}}\\) and \\(\\overline{\\widehat{{x}_{t}}}\\) are the ground truth and predicted post-perturbation average gene expression for compound t, respectively. \\({R}_{t}^{2}\\) is the coefficient of determination score of the mean post-perturbation gene expression of compounds t. The average of the coefficient of determination score for all T compounds in the test set is referred to as the \u201cR2 in compounds\u201d. A higher value of \u201cR2 in compounds\u201d indicates a better performance.\n\nR2 in cov_compounds: is the mean coefficient of determination score between the predicted and true post-perturbation gene expression of the same compound within each specific cell line:\n\nwhere \\({x}_{i,t}^{c}\\) and \\(\\widehat{{x}_{i,t}^{c}}\\) denote as the ground truth and predicted post-perturbation gene expression xi perturbed by compound t in covariate c, respectively. The covariate c represents the cell line or cell type of xi. The set \\({({x}_{i,t}^{c})}_{i=1}^{p}\\) represents all post-perturbation gene expression for compound t in covariate c. \\(\\overline{{x}_{t}^{c}}\\) and \\(\\overline{\\widehat{{x}_{t}^{c}}}\\) are the ground truth and predicted post-perturbation average gene expression for compound t in covariate c, respectively. \\({{R}^{2}}_{t}^{c}\\) is the coefficient of determination score of the mean post-perturbation gene expression of compounds t in covariate c. The average of the coefficient of determination score for all Q \u201ccov_compounds\u201d conditions in the test set is referred to as the \u201cR2 in cov_compounds\u201d. A higher value of \u201cR2 in cov_compounds\u201d indicates a better performance.\n\nWe use the following baseline models to compare model performance:\n\nLinear model: This model uses a linear regression model to learn weights between all genes and perturbations. The linear model uses MSELoss to learn perturbation effects applied to the control sample/cell. Let \u03b8l represent the weight matrix of the linear model, the perturbed gene expression would be:\n\nMLP model: MLP model utilizes Multilayer Perceptron (MLP) to fit the effect of perturbation to genes. The input is passed to an MLP model(Input Layer, Linear with output dimension 128, BatchNorm1d, LeakyReLU, Linear with output dimension as input size, and ReLU). Let \u03b8MLP represent the parameters of the linear model, the perturbed gene expression would be:\n\nTo calculate the pseudo-dose trajectory, we take the mean t-SNE embedding of cells with the same dosage as a point on the pseudo-dose trajectory:\n\nwhere zi,d is the t-SNE latent embedding of cell i at dose d, nd is the number of cells at dose d, and \\(\\overline{{z}_{d}}\\) is the mean t-SNE embedding for dose d, representing a point on the pseudo-dose trajectory. The sequence of these mean latent embeddings \\(\\overline{{z}_{d}}\\) forms the pseudo-dose trajectory.\n\nInspired by the CMap connectivity score15 and L1000 project1, PRnet employed a similar reverse signature paradigm to screen candidates based on gene signatures. PRnet provided a workflow for screening candidates for complex diseases according to the reference changes in gene sets.\n\nStep 1: Given the SMILES of the screening compounds library and unperturbed transcriptional profiles of specific cell lines, PRnet predicts the perturbed transcriptional profiles of all compounds across multiple concentration gradients. This prediction is performed with three repeats to ensure computational robustness.\n\nStep 2: The transcriptional profiles of 978 landmark genes are transformed into profiles of 12,328 genes using linear transformation vectors derived from the L1000 project1. The fold-change of transcriptional profiles is then calculated, which includes the average fold-change of each compound across cell lines. Genes are ranked based on their fold-change values. Gene expression signatures representing the disease states or sensitive compound responses are generated, known as query signatures. Query signatures include an up-regulated gene set and a down-regulated gene set which are the reversed signatures of the disease. Gene expression signatures of screening compounds are also generated, which are the ranked gene lists.\n\nStep 3: The Kolmogorov-Smirnov test is employed to calculate enrichment scores, which measure the connectivity of screening compound profiles with the query signatures. The score to reverse disease up- and down-regulated features are calculated separately and then summed together:\n\nwhere p is the number of genes in the upregulated gene set of query signature, q is the number of genes in the downregulated gene set of query signature, n is the number of genes in the computed transcriptional profile, and R(m) is the rank of a specific gene in the rank list. The enrichment score is commonly used in the field to evaluate the distribution of the predicted gene rank in the reference gene signature. The gene signature is an up- and down-regulated gene set. The up- and down-regulated gene set of the specific disease is identified from the reference study. The up- and down-regulated gene sets of the sensitive compounds to cell lines are computed by PRnet. Note that the up- and down-regulated gene sets represent the reverse gene signatures of a specific disease, indicating the up- and down-regulate gene sets that the compounds are expected to enrich. Finally, compounds are ranked based on these enrichment scores, and the top-ranked compounds are recommended for diseases.\n\nMorgan fingerprints were calculated by the RDKit31 in Python (2023.3.2). The atlas of perturbation profiles was organized into anndata format through the Scanpy package59 in Python (1.9.1). T-SNE33 clustering was performed by the Scanpy package in Python (1.9.1). Figures related to computation were plotted by matplotlib60 (3.5.2), seaborn61 in Python (0.12.0), and the ggplot62 package in R (3.5.1). The GSEA and KEGG pathway enrichment analysis were performed by the Clusterprofiler package63 in R (4.6.2), and plot by the gseaplot, enrichplot and cnetplot. A P-value \u00a0<\u20090.05 was defined as the cutoff criterion. Experimental figures were plotted by GraphPad Prism. Some figures were plotted by Chiplot (https://www.chiplot.online/). And some icons in figures are created in BioRender (https://www.biorender.com/). The deep learning model was constructed by Pytorch64 (1.12.1).\n\nAll the cell lines used in this work were purchased from the American Type Culture Collection (ATCC). HT29, SW480, SW620, and HCT116 cells were cultured in DMEM (Gibco) medium supplemented with 10% fetal bovine serum, 100 U/mL penicillin, and 100 U/mL streptomycin. Colo205, NCI-H196, NCI-H209, NCI-H446, NCI-H526, and NCI-H69 cells were cultured in RPMI-1640 (Gibco) medium supplemented with 10% fetal bovine serum, 100 U/mL penicillin, and 100\u2009\u00b5/mL streptomycin. DMS114 cells were cultured in Waymouth\u2019s MB 752/1 (Gibco) medium supplemented with 10% fetal bovine serum, 100\u2009\u00b5/mLpenicillin, and 100\u2009\u00b5/mL streptomycin. All the cell lines were incubated at 37\u2009\u2218C in a humidified 5% CO2 atmosphere. All cells were negative for mycoplasma, and these cell lines are not among those commonly misidentified by the International Cell Line Authentication Committee (ICLAC).\n\nMTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay was performed for the evaluation of cell viability. The cells were seeded into 96-well plates at a density of 2000\u201320000 cells per well in full medium. Overnight, test compounds with indicated concentrations were added for 72\u2009h. 20\u2009\u03bcL of MTT (5\u2009mg \u22c5 mL\u22121 in saline, Sigma) was then added per well for 2\u2009h, and 50\u2009\u03bcL SDS (20%, dissolved in H2O containing 1% HCl) was added per well, followed by incubating overnight. The absorbance at 570 nm was measured using a multiscan spectrum reader (BMG lab tech). The cell survival rate was calculated subsequently.\n\nThe HTS RNA-seq datasets used for this study all underwent the same preprocessing. For single-cell RNA-seq data, the gene expression of each cell was normalized by total counts over all genes with log-transformation, and 5000 highly variable genes (HVGs) were selected using Scanpy. For bulk RNA-seq data, 978 different genes of level 3 were normalized with log transformation for training and evaluation. These data were split into training, validation, and test sets by dividing various perturbation conditions (such as compounds, cell type, and pathway) with a ratio of 6:2:2. And 5-fold cross-validation was applied for training.\n\nThe L1000 project2 contains more than 1 million bulk RNA-seq observations with 978 landmark genes of level 3. We performed data cleaning using the following criteria: deleted insufficient compound conditions(observations \u00a0<\u20095); removed invalid compound SMILES which were not successfully parsed by RDKit; assigned each perturbed observation an unperturbed observation and removed the unpaired observations. Finally, we obtained 175,549 cov_compounds_dose_name condition (unperturbed-perturbed pair), which contains 82 cell lines and 17,202 compounds.\n\nThe sci-Plex3 study2 was subseted to 290,888 cells. We performed data cleaning using the following criteria: randomly subsampled the dataset to half size; selected 5000 highly variable genes (HVGs) using Scanpy; removed invalid molecule SMILES which could not successfully be parsed using RDKit31; assigned each perturbed observation an unperturbed observation and remove the unpaired observations. Finally, we obtained 2244 cov_drug_dose_name condition (unperturbed-perturbed pair), which contains three cancer cell lines (A549, MCF7, K562), which were treated with 188 different compounds in 4 dosages (10, 100, 1000, and 10000\u2009nM) and vehicle for unperturbed cells.\n\nThe unperturbed profiles of small cell lung cancer collected from CCLE contain 5 cell lines, including NCI-H69, NCI-H526, NCI-H446, NCI-H209, and NCI-H196. We performed data preprocessing using the following criteria: selected 978 landmark genes of unperturbed profiles; and filled in the valid gene expression with the mean expression value of all genes in the current cell line. After the virtual screening, we obtained 4272486 transcriptional profiles.\n\nSix compound libraries were used in this study. The training compound libraries are collected from the L10001 and the sci-Plex3 datasets2. in silico screening, an FDA-approved library (TargetMol, n\u2009=\u2009935), an active compound library (Selleckchem, n\u2009=\u20094158), a natural compound library (Herb, n\u2009=\u200930,456), and an in-house drug-like compound library(n = 29,670) were used to screen the positive chemicals. The sensitive compounds of cell lines are collected from Genomics of Drug Sensitivity in Cancer (GDSC) (https://www.cancerrxgene.org/) with z-score\u2265\u20092.0.\n\nThe gene signature of diseases was collected from CRowd Extracted Expression of Differential Signatures (CREEDS)43. The CREEDS project annotated 839 diseases versus normal signatures from Gene Expression Omnibus (GEO). We collected the gene signature of the disease by removing the study with the intersected set of up- and down-regulated genes and 12,328 genes and set less than 1 gene and finally obtained 577 studies for 233 unique diseases. The gene signatures of diseases were downloaded from CREEDS.\n\nWe collected scRNA-seq data of paired pre- and post-chemotherapy whole bone marrow samples from 13 pediatric AML patients who achieved disease remission following chemotherapy, as described by Zhang et al.35. These patients were treated with either a low-dose chemotherapy (LDC) regimen or a standard-dose chemotherapy (SDC) regimen. The LDC regimen consisted of cytarabine (10\u2009mg/m2) and mitoxantrone or Idarubicin, administered concurrently with G-CSF (5\u2009\u03bcg/kg). The SDC regimen consisted of cytarabine (100\u2009mg/m2), daunorubicin, and etoposide. We compiled the expression profiles of all 224,217 cells from the 13 patients and performed normalization and log transformation. Each cell was annotated with patient information, cell type, and chemotherapy regimen based on the information provided in the study. The LDC regimen was annotated with cytarabine and mitoxantrone, while the SDC regimen was annotated with cytarabine, daunorubicin, and etoposide. Through highly variable gene analysis, we retained 33,538 highly variable genes for training. The scRNA-seq data of patients were downloaded from the Genome Sequence Archive for Humans at the BIG data center, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation under accession number (OMIX005223).\n\nWe collected expression profiles for 27 MCF7 strains and 23 A549 strains from Ben-David et al.34. Expression profiles for 978 L1000 landmark genes were used for each strain. We included 35 compounds from the chemical screen in the study34, retaining 33 after removing those that RDKit could not generate to FCFP fingerprints. We also collected the drug response to 35 active compounds of MCF7 strains in the chemical screen, including decreasing (active), decreasing (weakly active), and inactive.\n\nAll samples discussed in this manuscript (cell viability assays and perturbation profile atlas generation) were measured and analyzed as technical triplicates. No data were excluded from the analyses.\n\nFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "For the bulk and single-cell datasets, we used expression profiles from the L10001 and the sci-Plex32 datasets. The L1000 and the sci-Plex3 datasets were downloaded from the Gene Expression Omnibus with the accession number (GSE92742) and (GSM4150378), respectively. Small cell lung cancer is available from The Cancer Cell Line Encyclopedia Project (CCLE) (https://sites.broadinstitute.org/ccle/). The gene signatures of diseases were downloaded from CREEDS. The scRNA-seq data of patients were downloaded from the Genome Sequence Archive for Humans at the BIG data center, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation under accession number (OMIX005223). We provided a website (prnet.drai.cn) to browse and download compound libraries and predicted results. The predicted signature results data in this paper have been deposited in the OMIX65,66, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (https://ngdc.cncb.ac.cn/omixdatabase under accession code OMIX006910). Source data are provided in this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code for PRnet67 is available at (https://github.com/Perturbation-Response-Prediction/PRnet).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437\u20131452 (2017).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nSrivatsan, S. R. et al. Massively multiplex chemical transcriptomics at single-cell resolution. Science 367, 45\u201351 (2020).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nDrews, J. Drug discovery: a historical perspective. Science 287, 1960\u20131964 (2000).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nLotfollahi, M. et al. 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Perturbation-Response-Prediction/PRnet: PRnet, https://doi.org/10.5281/zenodo.13751384 (2024).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by The National Key R&D Program of China (2021YFC2500203 to Y.Z.), The National Natural Science Foundation of China (32341019 to Y.Z., 32070670 to Y.Z.), Ningbo major project for high-level medical and healthcare teams (2023030615 to Y.Z.), Beijing Natural Science Foundation Haidian Origination and Innovation Joint Fund (L222007 to Y.Z.), Ningbo Science and Technology Innovation Yongjiang 2035 Project(2024Z229 to Y.Z.), Major Project of Guangzhou National Laboratory (GZNL2023A03001 to Y.Z.), Open Project of National Key Laboratory of Oncology Systems Medicine (KF2422-93 to Y.Z.), The National Key R&D Program of China (2022YFF1203303 to Y.Z.). The authors would like to acknowledge the Nanjing Institute of InforSuperBahn MLOps for providing the training and evaluation platform.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Xiaoning Qi, Lianhe Zhao, Chenyu Tian.\n\nResearch Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China\n\nXiaoning Qi,\u00a0Lianhe Zhao,\u00a0Baoping Wan\u00a0&\u00a0Yi Zhao\n\nUniversity of Chinese Academy of Sciences, Beijing, China\n\nXiaoning Qi,\u00a0Lianhe Zhao,\u00a0Zhen-Lin Chen\u00a0&\u00a0Yi Zhao\n\nDepartment of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China\n\nChenyu Tian,\u00a0Yueyue Li\u00a0&\u00a0Shengyong Yang\n\nKey Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China\n\nZhen-Lin Chen\n\nLuoyang Institute of Information Technology Industries, Luoyang, Henan, China\n\nPeipei Huo\n\nWest China Hospital, Sichuan University, Chengdu, Sichuan, China\n\nRunsheng Chen\n\nUniversity of Chinese Academy Sciences, Nanjing, Jiangsu, China\n\nXiaodong Liu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nX.Q. developed the kernel algorithms of PRnet, implemented the models, analyzed data, and wrote the manuscript. L.Z. contributed to data analysis, as well as revising and editing the manuscript. C.T. and Y.L. validated compound candidates through MTT assays and contributed to the writing. Z.-L.C. supported data analysis and participated in writing, reviewing, and editing. P.H. developed the PRnet website. R.C. facilitated the collaboration and supervised the research. X.L. and B.W. provided support for academic survey and platform development. S.Y. and Y.Z. directed the study and manuscript writing.\n\nCorrespondence to\n Shengyong Yang or Yi Zhao.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Sudin Bhattacharya, Xiling Shen, and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery.\n Nat Commun 15, 9256 (2024). https://doi.org/10.1038/s41467-024-53457-1\n\nDownload citation\n\nReceived: 11 March 2024\n\nAccepted: 11 October 2024\n\nPublished: 26 October 2024\n\nVersion of record: 26 October 2024\n\nDOI: https://doi.org/10.1038/s41467-024-53457-1\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53420-0/MediaObjects/41467_2024_53420_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53420-0/MediaObjects/41467_2024_53420_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-53420-0#Fig2", + "/articles/s41467-024-53420-0#Fig5", + "/articles/s41467-024-53420-0#MOESM1", + "/articles/s41467-024-53420-0#MOESM1", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-43405", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-43404", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-43406", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-43400", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-43401", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-43402", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-43403", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-43394", + 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"/articles/s41467-024-53420-0#Sec32" + ], + "code": [], + "subject": [ + "Biochemistry", + "Biophysics", + "Structural biology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4087134/v1.pdf?c=1729508744000", + "research_square_link": "https://www.researchsquare.com//article/rs-4087134/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53420-0.pdf", + "preprint_posted": "23 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Protease-containing ABC transporters (PCATs) couple the energy of ATP hydrolysis to the processing and export of diverse cargo proteins across cell membranes to mediate antimicrobial resistance and quorum sensing. Here, we combine biochemical analysis, single particle cryoEM, and DEER spectroscopy in lipid bilayers along with computational analysis to illuminate the structural and energetic underpinnings of coupled cargo protein export. Our integrated investigation uncovers competitive interplay between nucleotides and cargo protein binding that ensures the latter\u2019s orderly processing and subsequent transport. The energetics of cryoEM structures in lipid bilayers are congruent with the inferred mechanism from ATP turnover analysis and reveal a snapshot of a high-energy outward-facing conformation that provides an exit pathway into the lipid bilayer and/or the extracellular side. DEER investigation of the core ABC transporter suggests evolutionary tuning of the energetic landscape to fulfill the function of substrate processing prior to export.Biological sciences/Structural biologyBiological sciences/Biophysics/Molecular biophysics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "supplementaryfiguressubmission.pdfSUPPLEMENTAL MATERIAL", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Protease-containing ABC transporters (PCATs) couple the energy of ATP hydrolysis to the processing and export of diverse cargo proteins across cell membranes to mediate antimicrobial resistance and quorum sensing. Here, we combine biochemical analysis, single particle cryoEM, and DEER spectroscopy in lipid bilayers along with computational analysis to illuminate the structural and energetic underpinnings of coupled cargo protein export. Our integrated investigation uncovers competitive interplay between nucleotides and cargo protein binding that ensures the latter\u2019s orderly processing and subsequent transport. The energetics of cryoEM structures in lipid bilayers are congruent with the inferred mechanism from ATP turnover analysis and reveal a snapshot of a high-energy outward-facing conformation that provides an exit pathway into the lipid bilayer and/or the extracellular side. DEER investigation of the core ABC transporter suggests evolutionary tuning of the energetic landscape to fulfill the function of substrate processing prior to export.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "ABC transporters are exquisite molecular machines that traffic solutes across lipid bilayers in both directions powered by the energy of ATP hydrolysis1,2. Broadly classified into importers and exporters, functional specialization among ABC transporters led to a fascinating spectrum of molecular architectures3. A subfamily of ABC exporters specializes in the processing and translocation of entire protein domains across cell membranes. Referred to as Peptidase-Containing ABC transporters (PCATs), their architecture consists of a core homodimeric ABC exporter covalently linked to a cysteine protease domain (C39 or PEP domain)4,5,6,7. Cargos of PCATs includes bacteriocins and quorum-sensing polypeptides, fundamental bacterial defense strategies4,5,8,9.\n\nOne of the most structurally characterized PCATs is PCAT1 from the thermophilic organism C. thermocellum10,11,12. The homodimeric core ABC transporter of PCAT1 has the canonical structure of ABC Type IV exporters with two cytoplasmic nucleotide-binding domains (NBDs) and two transmembrane domains (TMDs). PCAT1 cargo protein, hereafter referred to as Sub, was identified as a 90-residue polypeptide containing an N-terminal leader sequence, which is cleaved by the PEP domain of PCAT111. Initial crystal structures of PCAT1 in two conformations11 were followed by several cryoEM snapshots under different biochemical conditions10,12. Collectively the structural record revealed the molecular architecture of PCAT1, identified elements of its ATP-dependent conformational changes, and elucidated a partial view of its interactions with Sub.\n\nPersistently enigmatic, not only for PCAT1 but other PCATs as well13, has been the coupling mechanism of cargo protein export to the cycle of ATP turnover. CryoEM structures of Sub-bound PCAT1 identified density corresponding to Sub located in the cytoplasmic-facing chamber and tethered to the signal sequence which is bound to the PEP domain12. In contrast to these inward-facing (IF) conformations, an occluded conformation (OC) bound to ATP-\u03b3S displayed an inaccessible chamber and assembly of the NBD catalytic dimer11. Although a recent ATP-bound cryoEM structure in detergent micelles was interpreted as an outward-facing (OF) conformation10, it was not clear that the incremental opening of the extracellular side is sufficient to accommodate the export of a polypeptide the size of Sub.\n\nUnlike solute and drug ABC exporters, PCAT1 modifies its cargo protein prior to export. Thus, alternating access of the transporter core must be coupled to the processing of the signal peptide in the PEP domain. To uncover the steps involved in this coupling, we carry out a systematic study that integrates mechanistic elements deduced from extensive ATP turnover studies, structural insight gleaned from single particle cryoEM analysis in lipid nanodiscs, and conformational dynamics from molecular dynamics (MD) simulations and double electron electron resonance (DEER)14,15 spectroscopy. CryoEM structures determined in the presence of nucleotides and/or Sub were correlated with findings from biochemical analysis of ATP hydrolysis. Furthermore, we investigate the intrinsic properties of the core ABC transporter lacking the PEP domain through a combination of cryoEM and DEER. Together, our results illuminate the structural mechanism by which the cargo protein couples to the ATP turnover cycle, an insight only accessible in the context of lipid bilayers. Along with a cryoEM structure of a bone fide OF conformation of the core transporter, our results stimulate a model of transport that departs from the canonical model of ABC transporters and contextualizes previous studies of PCAT1 in detergent micelles10.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The widely accepted canonical model of ABC exporters1, which has guided much of the structural investigations in the field, posits the population of at least two intermediates. Typically, an IF state, featuring a large chamber open to the intracellular side, isomerizes to an OF state which features a splaying of the extracellular side of these transporters. Occluded conformations have been reported where the substrate binding chamber is closed on both sides of the transporter. Alternating access is powered by ATP binding and hydrolysis although most models1,2, with few exceptions16,17,18,19, assume that the former drives transition to OF whereas hydrolysis catalyzes the return to the IF. Thus, the current structural record emphasizes states stabilized by ATP, often in a background of catalytically impaired NBDs, or by trapping with vanadate following ATP hydrolysis (ADP-Vi). The latter condition has been shown to trap a high energy state populated subsequent to hydrolysis of ATP. Following this paradigm, previous investigations of PCAT1 determined Sub and ATP-bound structures, although studies into the ADP-Vi state were conspicuously absent10,11.\n\nBecause PCAT1 modifies its cargo prior to export, we hypothesized that this requirement imposes mechanistic constraints on its ATP hydrolysis cycle. Therefore, we examined the roles of nucleotides, Sub and Mg2+, in ATP turnover in lipid nanodiscs. Under conditions of equimolar ATP and Mg2+, we observed the expected hyperbolic isotherm with Michaelis-Menten parameters that were similar to those reported previously by us20 and others11 (Fig.\u00a01A). However, in contrast to Lin et al. 11, we find that addition of Sub reproducibly increased Vmax with negligible effects on Km (Supplementary Table\u00a01). Furthermore, vanadate did not inhibit ATP turnover as would be expected for a typical ABC exporter. This suggests either quick dissociation of Mg2+ thereby shortcutting formation of the vanadyl/ADP complex, or rather limited stability of the latter in the confines of the nucleotide binding sites (NBSs) that form at the interface of the Walker A motif from one NBD and the signature motif of the other NBD.\n\nA Sub stimulates ATP hydrolysis whereas ADP competitively inhibits it (see Supplementary Table\u00a01). Sub partly reverses the inhibition of ADP. PCAT1 turnover data is the average of 15 repeats from 8 biological repeats. PCAT1 inhibition with 2\u2009mM ADP with 5\u2009mM Vi is the average of 4 technical from 2 biological repeats. Turnover data with 5\u00d7 Sub is derived from 5 biological repeats. B Biphasic shape of ATP turnover determined under 10\u2009mM Mg2+ curve suggests partial inhibition by free Mg2+. The data is the average from at least 2 biological repeats. C Multiphasic dependence of Vmax on the Mg2+ to ATP ratio. The data is the average of at least two biological repeats. All data are presented as mean values\u2009\u00b1\u2009standard deviation.\n\nATP turnover by PCAT1 shows an unusual dependence on free Mg2+. This was initially gleaned from ATP turnover curves obtained at constant concentration of Mg2+ (10\u2009mM, Fig.\u00a01B). A biphasic dependence indicated that, in the presence of free Mg2+ (below 10\u2009mM ATP), the ATPase activity of PCAT1 is partially inhibited. To dissect the origin of this effect, we carried out Mg2+ titration at constant ATP concentrations. The resulting curves reveal distinct multiphasic behavior (Fig.\u00a01C). As expected, low concentration of Mg2+ stimulates ATP hydrolysis reflecting the integral role of Mg2+ in the chemical reaction. However, a change in slope is observed as Mg2+ concentration increases followed by the appearance of an inhibitory effect under conditions of excess free Mg2+. As discussed below, these distinct phases may partly reflect shifts in conformational states of the transporter along with changes in nucleotide affinity.\n\nImportantly, we find that Sub attenuates the inhibitory effects of free Mg2+. The peak Vmax observed in the presence of Sub is almost 50% higher than the one observed in the ATP turnover assay (Fig.\u00a01C). Comparing Vmax from Fig.\u00a01A, C indicates that partial inhibition of ATP turnover occurs even under the conditions of equimolar ATP and Mg2+ concentrations of Fig.\u00a01A. The multiphasic characteristic of the titration curve persists in the presence of Sub including the onset of Mg2+ inhibition at high free Mg2+ concentration although the peak shifts to higher Mg2+/ATP ratios (Fig.\u00a01C).\n\nTo further decipher the origin of the Mg2+ dependence, we investigated the effects of ADP on ATP turnover. We observed a steep competitive inhibition (Fig.\u00a01A) that is reversed by addition of Sub (Fig.\u00a01A), suggesting that either ADP and Sub binding is mutually exclusive, or that Sub reduces the affinity of ADP relative to ATP. Titration with ADP highlights that stimulation of PCAT1 by substrate requires ADP concentration in a physiological range (Supplementary Fig.\u00a01) Together, the ADP and Mg2+ inhibitions (albeit at high concentration of free Mg2+) and their abrogation by Sub suggest that ADP is stabilized in the NBSs following ATP hydrolysis. This mechanism explains the lack of stimulation by Sub reported by Lin et al.11 since, in that work, they used a continuous ATP regeneration system that depleted free ADP.\n\nIn support of these conclusions, we have performed calculations using the free energy perturbation (FEP) method to estimate the relative binding free energy of ATP and ADP (see methods). The simulations were performed on the ATP-bound, putatively OF structure of PCAT1 in the absence of the substrate reported by Kieuvongngam and Chen10 (PDB:7T54). Interestingly, ATP has a lower affinity as compared to ADP with a more significant difference in the presence of Mg2+ (Table\u00a01; compare \\(\\Delta \\Delta {G}_{{Mg}}=\\Delta {G}_{{MgATP}}-\\Delta {G}_{{MgADP}}\\) to \\(\\Delta \\Delta {G}_{{noMg}}=\\Delta {G}_{{ATP}}-\\Delta {G}_{{ADP}}\\)). The results indicate that the PCAT1 favors binding ADP over ATP at least in this structure. This is in contrast with other bacterial ABC transporters such as MsbA21 (PDB:3B60) and Sav186622 (PDB: 2HYD) for which similar simulations on the OF conformation yielded negative binding free energies in all cases (Table\u00a01), indicating that ATP is favored for binding over ADP in the absence and presence of Mg2+ with a more significant difference in the absence of Mg2+.\n\nThe inhibitory effect of Mg2+ was not observed in detergent micelles implying that ADP competitive inhibition of ATP turnover was also substantially blunted (Supplementary Fig.\u00a02). Therefore, we investigated the conformations of PCAT1 in lipid nanodiscs focusing primarily on previously unexplored biochemical conditions that represent intermediates in the putative transport cycle such as in the presence of ADP, ADP/Mg2+ and ADP/Sub. Regardless of biochemical conditions, cryoEM conformations of PCAT1 in lipid nanodiscs segregated into two classes that we label as IF and OC (Fig.\u00a02, Supplementary Fig.\u00a03, Supplementary Fig.\u00a04, Supplementary Fig.\u00a05, Supplementary Table\u00a02, Supplementary Table\u00a03). The IF conformations, which appeared very similar in the presence or absence of Sub, feature a chamber open to the cytoplasm and partially to the inner leaflet of the membrane. In contrast, the chamber was occluded to the cytoplasm in OC conformations. Moreover, the OC conformations were devoid of identifiable Sub density even when a large molar excess was added to the sample. The PEP domains in the OC conformations were disordered precluding their modeling. Finally, the conformational changes from IF to OC lead to the assembly of what is considered the pre-catalytic NBSs. Supplementary Table\u00a04 compares our IF and OC structures to previous crystal and cryoEM structures of PCAT1 in detergent micelles10,11.\n\nBoth ADP (panel B) and ATP (panel E) stabilize an occluded (OC) conformation in the absence of Mg2+. Sub (panel C) and Mg2+ (panel A) shift the equilibrium toward the inward-facing (IF) conformation in the presence of ADP, whereas ATP/Sub bound PCAT1 remains in an OC conformation. PCAT1* denotes the cysteine-free PCAT1 (panel D).\n\nWe determined two IF structures under conditions of excess Sub: one of WT-PCAT1 and one in a protease-deficient construct where all the cysteines (PCAT1*), including at the active site of the PEP domain were replaced (Fig.\u00a02C, D). The two structures are almost identical with a root mean square deviation (RMSD) of 1.388\u2009\u00c5. The substitution of the active site cysteine with serine essentially anchors Sub to the C39 domain. While we could model the leader peptide in both structures, only in PCAT1* did we observe strong Sub density in the TMD (Supplementary Fig.\u00a06). Despite multiple attempts at local refinement we were unable to obtain structural details of the bound Sub (Supplementary Fig.\u00a06) suggesting that Sub is disordered when bound to PCAT1 TMD as previously noted12.\n\nWhile the trapped PCAT1 conformations were either IF or OC, the ligand dependence and the implied energetics were unexpected and inconsistent with a previous10 model of PCAT1. Of central importance is the observation of an OC conformation in the presence of ADP, a finding that structurally manifests the implied high affinity of this nucleotide to PCAT1, as deduced from ATP hydrolysis studies above (Fig.\u00a01 and Supplementary Fig.\u00a01). Similarly, in lipid nanodiscs, an OC conformation is stabilized by the binding of ATP in the absence of Mg2+. This contrasts with the previous report where a more outward-open conformation was reported for Mg2+-free PCAT1 bound to ATP but in detergent micelles10. Supplementary Fig.\u00a07 shows a residue by residue RMSD between the structures of ATP-bound PCAT1 in nanodiscs and detergent micelles.\n\nThe structure of ADP-bound PCAT1 in the presence of Sub provides a basis for their apparent competitive binding deduced from biochemical analysis. An IF conformation is obtained when an excess of Sub is included in the sample both in the presence or absence of Mg2+ (Fig.\u00a02A, C). This contrasts with the persistence of the OC conformation when ATP is bound in the presence of Sub but absence of Mg2+ (Fig.\u00a02E), suggesting that ATP binding excludes the cargo protein from the binding chamber whereas Sub stabilizes the IF even in the presence of ADP (Fig.\u00a02A, C, D). Furthermore, previous cryoEM studies found that binding of ATP/Mg2+ and Sub resets the PCAT1 conformation to predominantly IF conformations10.\n\nIf Sub and ADP can concomitantly bind as evidence by the structures, then the apparent competition must arise from a change in relative affinity of ATP and ADP induced by Sub. We noticed that the cryoEM data set from the PCAT1/ADP/Mg2+ sample consisted of two classes of particles that differ in the extent of opening of the NBDs (Supplementary Fig.\u00a08). In contrast, Sub-bound structure in the presence of ADP/Mg2+ consisted of one class of particles. In these structures, the leader peptide makes direct contact with residues near the A-loop (Fig.\u00a03) suggesting a structural basis for the reversal of ADP competitive inhibition (Fig.\u00a01A). The A-loop plays a central role in stabilizing bound nucleotides through a highly conserved Tyr, here Tyr 495, which interacts with the adenosine moiety of ATP and ADP. Comparing PCAT1 structures in the presence and absence of Sub (class 2) (Fig.\u00a03) reveals changes in the conformation of the A-loop and the signature sequence motif (residues 623\u2013627), which could potentially underlie the coupling between Sub and ATP/ADP binding. The disorder of the signature motif in the absence of Sub may reduce the affinity of ATP binding by destabilizing the formation of the pre-catalytic NBS.\n\nComparison of PCAT1_IF_ADP/Mg2+_class2 structure(light color) and PCAT1_ IF_ADP/Mg2+/Sub structure(dark color) shows that the leader peptide interacts with the A-loop altering its conformation. In addition, its binding stabilizes the signature motif as evidenced by the stronger density in that region in the IF structure determined in the presence of Sub (dark green). The cryoEM densities are shown at 4.6\u2009\u03c3 for PCAT1_ IF_ADP/Mg2+/Sub and 7.7\u2009\u03c3 for PCAT1_IF_ADP/Mg2+_class2.\n\nTo quantitatively assess the effect of Sub binding, we calculated the relative affinity of PACT1 to ATP and ADP in the IF conformation stabilized by Sub (see methods). We included MsbA as a reference since the IF structure of Sav1866 has not been determined. The results show that the binding preference of ADP over ATP calculated from the putative OF conformation is not observed in the IF state of the transporter (Supplementary Table\u00a05). Instead, there is a preference for binding ATP both in the absence and presence of Mg2+. The preference for ATP over ADP is somewhat similar in PCAT1 and MsbA; however, the presence of substrate in PCAT1 increases this preference significantly with around 10\u201313\u2009kcal/mol difference in binding affinity of ATP relative to ADP. Another interesting observation is that the presence of Mg2+ only slightly increases the affinity of ADP relative to ATP by around 3\u20135\u2009kcal/mol whereas the opposite effect of around 18\u2009kcal/mol is observed in the putative OF state. These findings indicate that the relative affinity of ATP to ADP depends on the conformation of PCAT1, particularly in the presence of substrate. Focusing on the relative Mg2+-ATP/Mg2+-ADP binding affinity, MsbA shows little conformational dependence and a persistent preference for Mg2+-ATP. In contrast, PCAT1 shows a preference for Mg2+-ATP in the inward-facing state and a strong preference for Mg2+-ADP in the outward-facing state.\n\nA model, suggested by the data above, wherein the leader peptide modulates the relative ADP/ATP affinity and thus the ATP turnover rate would predict abrogation of ATP hydrolysis stimulation if the Sub lacks the leader peptide. In agreement with this prediction, we find that a leaderless construct of Sub does not stimulate ATP hydrolysis (Supplementary Fig.\u00a09).\n\nIn search for insight into the complex modulation of ATP turnover by Mg2+, we compared structures obtained in the presence and absence of Mg2+. Regardless of the conformation, we found that Mg2+ binding leads to substantial conformational changes at the NBSs. Specifically, the cryoEM density corresponding to a helix consisting of residues 651\u2013665, which packs against the Walker B motif, appears weak and uninterpretable in the absence of Mg2+, suggesting that Mg2+ contributes to the ordered arrangement of this helix (Fig.\u00a04). Thus, the ordering of this helix may contribute to the energetics of IF stability in the presence of Mg2+, in addition to being required for triggering the hydrolysis of ATP. A structure of PCAT1 bound to Mg2+ did not reveal evidence of structural alterations beyond the unwinding of the 651\u2013665 helix.\n\nCryoEM densities are shown at the 0.09 contour level in Chimera. The cryo-EM maps are shown at 2.4\u2009\u03c3, 1.8\u2009\u03c3, 1.5\u2009\u03c3, 1.7\u2009\u03c3 from top to bottom.\n\nTo further confirm the role of Mg2+ in the stability of the helix, we set out to estimate the free energy associated with the alpha-helicity of the region between T650 and L668 using well-tempered metadynamics23 simulations with a collective variable defined based on the helical content of this region. We modeled PCAT1_ADP and PCAT1_ ADP/Mg2+ on the basis of the same structure (PDB:7T54) that contains a helix in the 650\u2013668 region. The free energy profiles indicate that the PCAT1 bound to only ADP does not have a stable helix in this region with its global minimum being around 20% helical content. In contrast, PCAT1 bound to Mg2+-ADP has a stable helix with a global minimum of around 85% helicity and another local minimum of around 65% helicity (Supplementary Fig.\u00a010).\n\nDetailed analysis of the NBS structures reveals hints into the origin of PCAT1 affinity to ADP in the OC conformation. PCAT1\u2019s signature motif deviates from the canonical sequence, specifically by the replacement of glycine 625 by glutamate, yielding an LSEGQ sequence. We observed that the side chain of E625 is proximal to the \u03b3 phosphate (Supplementary Fig.\u00a011). This glutamate may also be a contributing factor to the lack of stable inhibition of ATP turnover by Vanadate.\n\nThe structures presented above as well as those obtained in detergent micelles10 unequivocally reveal an energetic bias favoring the IF conformations in the presence of Mg2+/nucleotides and when bound to Sub. Therefore, we reasoned that removing the PEP domain may alter the energetic of the conformational landscape. For this purpose, we used a transporter core construct devoid of the PEP domain, hereafter referred to as PCAT1_CC. We also substituted all the native cysteines to allow for the subsequent introduction of spin labels (see below). CryoEM structures of the core transporter bound to ADP/Mg2+ were very similar to the structures in the context of the full-length transporter (Fig.\u00a05 and supplementary Fig.\u00a012, Supplementary Fig.\u00a013, supplementary Table\u00a06). However, the OC conformation of the core transporter stands in stark contrast to full length PCAT1 where the same conditions (ADP/Mg2+) stabilizes the IF conformation.\n\nUnlike the full length PCAT1, binding of Mg2+ (panel A, panel B with sub bound, panel C with ATP and Vi bound) does not stabilize the IF conformation in the cryoEM grids. ATP in the absence of Mg2+ stabilizes the OF conformation (panel D).\n\nConsistent with our prediction that the PEP domain stabilizes an IF conformation, we found that the core transporter bound to ATP in the absence of Mg2+ adopts an OF conformation. Compared to the ATP-bound structure obtained in detergent micelles of full length PCAT1 (PDBID:7T54)10, we observe large displacements of TMs 5 and 6 that further open the extracellular side of the transporter (Fig.\u00a06). The movement of TM6 is facilitated by a hinge around residue 446 whereas TM5 undergoes a tilt without large backbone distortion. A prominent side opening to the outer leaflet of the bilayer develops, between TMs1 and 6, that may enable the exit of Sub or its more hydrophobic regions. We note that the OF conformation of the core transporter could not be trapped if the sample was incubated at 50\u2009\u00b0C prior to the plunging of the grid.\n\nComparison of the putative OF conformation of PCAT1 in detergent micelles (gold) with the OF conformation of the transporter core, PCAT1_CC, in lipid nanodiscs (teal). A Side view of the two structures highlighting the opening of the latter to the outer leaflet of the membrane. B Extracellular view identifying the movement of individual helices.\n\nThe removal of the PEP domain increases the kcat of ATP turnover (Supplementary Table\u00a01) as was previously reported20. In addition, the sensitivity of ATP turnover to Sub is abrogated (Supplementary Fig.\u00a014) although we have demonstrated that Sub binds the core transporter PCAT1_CC in the TMD20. This finding reinforces our model that the leader peptide directly interacts with the A-loop to stimulate ATP hydrolysis. In nanodiscs, the multiphasic dependence of Mg2+ concentration remains intact in PCAT1_CC suggesting that it is an intrinsic property of the core transporter. The ATP turnover of the core transporter is efficiently inhibited by ADP similar to the full length PCAT1.\n\nThe mechanistic properties revealed above for the full-length transporter depart from the canonical model of ATP-powered alternating access of solute ABC exporters. To test if these are intrinsic properties of the core transporter, i.e. PCAT1 lacking the PEP domain, we investigated its ATP-dependent conformational changes by DEER (Supplementary Table\u00a07). For this purpose, we measured distances between spin labels under different biochemical conditions. Spin labels were selected to report on the intracellular and extracellular sides of the TMD as well as on the assembly of the NBDs. All spin labeled mutants showed robust ATP turnover parameters (Supplementary Table\u00a08).\n\nOverall, we observed large distance changes (Fig.\u00a07 and supplementary Fig.\u00a015) spanning the membrane region, the intracellular side of the TMDs and the NBDs, most prominently in the presence of ATP-\u03b3S. Comparison of experimental and predicted distance distributions (on the basis of the ATP-\u03b3S-bound crystal structure11) reveals conformational changes that reflect the transition from an IF to an OC conformation. Figure\u00a07 also displays predicted distance distributions on the basis of two previously10 determined IF cryoEM structures that differ by the degree of intracellular opening referred to \u201cwide\u201d and \u201cnarrow\u201d. The sign of the changes in the average distances indicates a closing motion that occludes access to the Sub binding chamber. At the extracellular side, only site 202 showed distance changes, likely reporting rearrangements of the loop linking TMs 1 and 2. Deviations from predicted distributions, calculated based on the OF structure of the core transporter, are most prominent at the extracellular side. For most sites, except 372 and 703, distance distributions under apo conditions are broad and appear to span the range of distances under other conditions indicating conformational sampling between states of similar free energies.\n\nExperimental and predicted distance distributions are shown in solid and dashed lines respectively. Binding of ATP-\u03b3S (solid green traces) induces large distance changes in the NBDs and the intracellular side of the TMD. OF_8VP1 is the OF from this paper.\n\nIn addition to revealing the structural elements involved in the IF/OC transition, the DEER data reflect the energetic dependence of the conformational changes. In contrast to the implications from cryoEM structure of the transporter core that ADP/Mg2+ binding favors an OC conformation, analysis of distance distributions in the presence of ADP/Mg2+ suggests an IF conformation with limited structural changes relative to the Apo condition (compare Figs.\u00a07, 8). The sites where changes are induced by ADP/Mg2+ map to the membrane segment of TM3 (residue 287) as well as to a site in the NBD selected to monitor the NBSs (residue 498). Remarkably, addition of Sub attenuates the distance changes at TM3, in agreement with the finding in the full-length PCAT1 that Sub binding attenuates ADP inhibition and stabilizes the IF conformation (Fig.\u00a08).\n\nDEER distance distributions of the core transporter in lipid nanodiscs under turnover conditions (solid teal traces). Experimental and predicted distance distributions are shown in solid and dashed lines respectively. Except for 307 and 498, the distributions under turnover conditions are superimposable with those bound to ADP/Mg2+ (solid purple traces).\n\nWhile most experimental and predicted distance distributions were congruent, we observed striking deviations at two sites, one in TM3 (287) and one in TM6 (442) (Fig.\u00a08, Supplementary Fig.\u00a015). Upon inspection, these sites appear to be in regions not well resolved in the crystal and cryoEM structures of the occluded and putative OF conformations10. It is also possible that the repacking of the spin label side chains due to the structural rearrangement contributes to the deviation from the predicted distances.\n\nATP binding or hydrolysis provides the energy input to drive conformational changes in most ABC exporters reported to date. Specifically, for many homodimeric transporters, the binding of ATP drives the population of an OF conformation. In contrast, for the transporter core of PCAT1, DEER data under turnover conditions, defined by equimolar high concentrations of ATP and Mg2+, reflect a mostly apo-like conformation (Fig.\u00a08). These results are consistent with the conclusions from cryoEM10 of predominantly IF conformation when PCAT1 is bound to ATP/ Mg2+ and support the conclusion that the OF conformation is a high energy state that is likely to be transiently populated.\n\nDistinct distance changes under turnover conditions observed along TM3 as well as in the NBSs mirror those observed in the presence of ADP despite a short incubation time under which ATP remains in excess. Of particular interest are the multi-population distance distributions at residue 498 where the binding of ADP/ATP elicits distinct distance changes (Fig.\u00a08 and Supplementary Fig.\u00a015). This residue is in proximity to the A-loop which contains the highly conserved Tyr495 that packs against the adenosine moiety (Fig.\u00a03). Both ADP/ Mg2+ and ATP/Mg2+ shift the equilibrium away from the Apo conformation (Fig.\u00a08). The disagreement between predicted and experimental distributions is attributed to the ambiguous cryoEM density in these regions as well as the repacking of the spin label.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53420-0/MediaObjects/41467_2024_53420_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53420-0/MediaObjects/41467_2024_53420_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53420-0/MediaObjects/41467_2024_53420_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53420-0/MediaObjects/41467_2024_53420_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53420-0/MediaObjects/41467_2024_53420_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53420-0/MediaObjects/41467_2024_53420_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53420-0/MediaObjects/41467_2024_53420_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53420-0/MediaObjects/41467_2024_53420_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our integrated biochemical, structural, spectroscopic, and computational investigation reveals previously unnoted elements of PCAT1 ATP turnover cycle, including the structural and energetic bases of cargo protein coupling, and complements previous structural studies10,11 by uncovering missing intermediates in the conformational cycle. Fundamental to our approach is the use of lipid nanodiscs which exposes mechanistic elements that are abrogated by solubilization in detergent micelles. Our results expound on the notion10 that PCAT1s evolved a distinct conformational energy landscape compared to canonical solute ABC transporters. They answer the central question of how the coupling between nucleotide turnover and cargo protein processing is achieved as we illustrate in the model below (Fig.\u00a09).\n\nThe coupled cycle is initiated by binding of Sub either to the PCAT1 bound to ATP/Mg2+ in the IF conformation (1a) or to ADP-bound PCAT1 in the OC conformation (1b). The coupled cycle proceeds through processing of Sub (2) followed by ATP hydrolysis (3) to populate an OF conformation that releases leaderless Sub to the extracellular side or the outer leaflet of the membrane. Subsequently, Mg2+ dissociation (4b) could trap the transporter in an occluded conformation to reduce futile ATP hydrolysis. Alternatively, Mg2+-ADP dissociation with Mg2+-ATP binding (4a) could facilitate an IF conformation in an uncoupled cycle without Sub binding where ATP hydrolysis (5a) followed by dissociation of Mg2+ (5b) results in the OC conformation.\n\nCentral to our model is the discovery of tenacious competitive inhibition of ATP hydrolysis by ADP (Supplementary Fig.\u00a01) and the abrogation of this inhibition by Sub. These findings uncover the missing link that relates the ATP hydrolysis cycle to the export of Sub. High affinity binding of ADP, following Mg2+ dissociation, may be a mechanism to reduce futile ATP hydrolysis by trapping the transporter in an occluded conformation. Weakening of ADP binding by Sub then triggers the initiation of the cycle, simultaneously driving the transporter to the IF conformation and signaling to the NBS the binding of the cargo protein thereby increasing the affinity to ATP/Mg2+. Our model posits that the modulation of the relative affinity of ATP and ADP is achieved structurally by the replacement of the highly conserved glycine by glutamate at position 625 in the canonical signature motif (Supplementary Fig.\u00a011), which results in an electrostatic repulsion with the \u03b3 phosphate of ATP. Finally, Mg2+ plays an important role in the ATP turnover cycle. The energetic preference of IF conformation(s) upon binding of Mg2+ further ensures the kinetic coupling of ATP hydrolysis to Sub processing by reducing the probability of transition to OF conformations.\n\nUnlike solute ABC transporters, where the cycle entails the binding and release of unmodified substrate, PCAT1 processes its cargo protein by removing an N-terminal signal sequence prior to transport. Thus, for a productive or coupled cycle to be executed, two requirements have to be satisfied. First, ATP binding and hydrolysis must occur subsequent to the cleavage of the cargo signal sequence. Second, only the processed cargo is to be extruded. This latter requirement is likely satisfied by the strong interaction between the signal sequence and the PEP domain which anchors unprocessed Sub to the transporter likely preventing its translocation prior to processing. One of the consensus findings in this paper and by Kieuvongngam and Chen10 is the stabilization of IF conformations by Sub. In contrast, evidence have been advanced that substrates drive the formation of OC conformation or at least the formation of the pre-catalytic NBSs for some solute ABC transporters1. Sub extrusion out of the chamber requires at least the binding of ATP as evidenced by multiple structures presented here and by the Chen group10,11. Not only does ATP occlude the chamber but no evidence of Sub can be found in ATP-bound structures.\n\nOur model allows for an uncoupled cycle in the absence of Sub. The high concentrations of ATP/Mg2+ in cells would imply that a\u00a0PCAT1 population is bound to this nucleotide (Fig.\u00a09, step 1a). However, unlike other ABC exporters, PCAT1 is predominantly in an IF conformation under these conditions, where its PEP domain is docked to the core transporter, enabling high affinity binding of the leader peptide and insertion of the cargo protein in the TMD. Binding of Sub stimulates ATP turnover by accelerating the ATP to ADP exchange of step 4a. Moreover, it funnels the ATP loaded IF of PACT1 through step 1a. Our data demonstrating that physiological levels of ADP are required for Sub stimulation rationalize the lack of stimulation by Sub in an ADP-depleting system11.\n\nTwo Sub molecules are expected to bind as previously demonstrated12 although we surmise that only one is transported through the TMD. The binding of Sub (Fig.\u00a09, step 2) stimulates ATP hydrolysis, kinetically accelerating transition to the OF conformation (Fig.\u00a09, step 3). Because the transition to the OF leads to disorder of the PEP domain, the binding to the now cleaved leader peptide is weakened leading to its dissociation. Although the model was formulated on the basis of binding of two Sub molecules as reported previously10, it is conceivable that an asymmetric conformation, wherein only one Sub molecule is bound, is populated. In this case, we surmise that one of the NBSs where the A-loop contact the Sub would be ATP-bound whereas the other would likely have ADP. While our model attempts to harmonize current structural and functional data, it leaves aspects of the mechanism, such as the role of Mg2+ and the ATP/ADP interplay under physiological conditions, unaccounted for.\n\nSubsequent to ATP hydrolysis, it is likely that inorganic phosphate is quickly dissociated inducing the shift to the ADP-bound OC conformation (Fig.\u00a09, step 4b), as demonstrated by the lack of trapping with vanadate. The ADP-bound OC conformation is then destabilized by Sub binding which primes the transporter for another cycle (Fig.\u00a09, step 1b). Our model reinforces the role of kinetics, noted previously10, in the transport cycle of PCATs. ATP hydrolysis and the concomitant transition to the OF is the rate limiting step ensuring prior processing of the cargo protein. Multiple investigations on type IV ABC exporters have now established that while they share a similar structural mechanism of alternating access, the tuning of energetics and kinetics, as exemplified by PCAT1, represent yet another example of divergence within a conserved fold.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53420-0/MediaObjects/41467_2024_53420_Fig9_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Codon-optimized wild-type PCAT1 derived from C. thermocellum (GenScript) was cloned into the pET19b vector encoding an N-terminal decahistidine tag under control of an inducible T7 promoter. A cysteine-deficient variant of PCAT1 (designated as PCAT1-CL or PCAT1*) was engineered, entailing the substitution of native cysteines with the following amino acids: C12S, C21S, C25S, C129F, C171A, C581A, C687A, and C713L. Additionally, we constructed a variant lacking the C39 peptidase domain (termed PCAT1_CC), featuring the following cysteine mutations: C171A, C581A, C687A, and C713L. All primers used for mutagenesis has been listed in the excel supplemental file.\n\nPCAT_CC was used as the template to introduce cysteine and background mutations via site-directed mutagenesis using complementary oligonucleotide primers. Substitution mutations were generated using a single-step PCR wherein the entire template plasmid was replicated from a single mutagenic primer. PCAT_CC mutants were sequenced using both T7 forward and reverse primers to confirm mutagenesis and the absence of aberrant changes. Mutants are designated by the native residue and primary sequence position followed by the mutant residue.\n\nThe substrate of PCAT1, Cthe_0535, herein referred to as Sub, was previously identified as a 90-residue protein containing an N-terminal signal sequence and encoded in the same operon as PCAT111, was codon-optimized and synthesized for expression in Escherichia coli by cloning into PET19b vector similarly to PCAT1 with an N-terminal decahistidine tag.\n\nE. coli C43 (DE3) cells were freshly transformed with the pET19b vector harboring the PCAT1 constructs. A single transformant colony was utilized to inoculate Luria\u2013Bertani (LB) media (LabExpress) supplemented with 0.1\u2009mg/mL ampicillin (Gold Biotechnology). This culture was grown overnight (\u223c15\u2009h) at 34\u2009\u00b0C, which was then used to inoculate 4\u2009L of minimal medium A at a 1:50 dilution. The cultures were incubated with shaking at 37\u2009\u00b0C until they achieved an absorbance at 600\u2009nm (Abs600\u2009nm) of approximately 0.8. PCAT_CC expression was induced by the addition of 1\u2009mM IPTG (Gold Biotechnology). The cultures were incubated overnight (\u223c15\u2009h) at 18\u2009\u00b0C and then harvested by centrifugation.\n\nThe resulting cell pellets were resuspended in 80\u2009mL of resuspension buffer (50\u2009mM Tris\u22c5HCl, pH 7.4, 150\u2009mM NaCl, 10% [vol/vol] glycerol) with 10\u2009mM DTT. The cells were lysed by five passes through an Avestin C3 homogenizer. Cell debris was removed by centrifugation at 9000\u2009\u00d7\u2009g for 10\u2009min. Membranes were subsequently isolated from the supernatant by centrifugation at 200,000\u2009\u00d7\u2009g for 1.5\u2009h.\n\nThe isolated membrane pellets were solubilized in the resuspension buffer containing 20\u2009mM \u03b2-DDM (Anatrace) and 0.5\u2009mM DTT and incubated on ice with stirring for 1\u2009h. Insoluble material was cleared by centrifugation at 200,000\u2009\u00d7\u2009g for 30\u2009min. The clarified extract was bound to 1.0\u2009mL (bed volume) of Ni-NTA Superflow resin (Qiagen) at 4\u2009\u00b0C for 2\u2009h. After washing with 5 bed volumes of buffer containing 50\u2009mM imidazole, PCAT1 was eluted with buffer containing 300\u2009mM imidazole.\n\nCysteine mutants of PCAT1 were labeled with two rounds of 20-fold molar excess 1-oxyl-2,2,5,5-tetramethylpyrroline-3-methyl methanethiosulfonate (Enzo Life Sciences) per cysteine at room temperature in the dark over a 4\u2009h period, after which the sample was placed on ice at 4\u2009\u00b0C overnight (\u223c15\u2009h) to yield the spin label side chain R1. Excess spin label was removed by size exclusion chromatography over a Superdex200 Increase 10/300 GL column (GE Healthcare) into 50\u2009mM Tris, pH 7.4, 50\u2009mM NaCl, 1\u2009mM \u03b2-DDM, and 10% (vol/vol) glycerol buffer. Peak fractions of purified PCAT1 were combined and concentrated using an Amicon Ultra 100,000 MWCO filter concentrator (Millipore), and the final concentration was determined by A280 measurement (\u03b5\u2009=\u2009107,500\u2009M\u2009\u2212\u20091\u22c5cm\u22121 for PCAT-CL and WT, \u03b5\u2009=\u200973,160\u2009M\u2009\u2212\u20091\u22c5cm\u22121 for PCAT-CC,) in preparation for subsequent experimental applications.\n\nE. coli polar lipids and l-\u03b1 phosphatidylcholine (PC) (Avanti Polar Lipids) were combined in a 3:2 (wt/wt) ratio, solubilized in chloroform, evaporated to dryness on a rotary evaporator, and desiccated overnight under vacuum in the dark. The lipid mixture was rehydrated in 50\u2009mM MOPS pH 7.4, 50\u2009mM NaCl buffer to a final concentration of 20\u2009mM, homogenized by freezing and thawing for 10 cycles, and stored in small aliquots at \u221280\u2009\u00b0C.\n\nMSP1D1 and MSP1D1E3 proteins were expressed and purified as described in previous studies [3,4] and dialyzed into 50\u2009mM Tris/MES pH 7.5 buffer. Purified MSP1D1/MSP1D1E3 was concentrated using a 10,000 MWCO filter concentrator, with final protein concentration determined by A280 measurement (MSP1D1, \u03b5\u2009=\u200921,430\u2009M\u22121\u2009\u22c5\u2009cm\u22121; MSP1D1E3 \u03b5\u2009=\u200929,910\u2009M\u22121\u2009\u22c5\u2009cm\u22121).\n\nFor reconstitution into nanodiscs purified PCAT1 or mutants thereof in \u03b2-DDM micelles were combined with hydrated E. coli polar lipids/PC lipid mixture, MSP, and sodium cholate at the following molar ratios: lipid:MSP, 50:1; MSP:PCAT1 at 10:1; and detergent:lipid, 3:1. The mixtures were incubated at 4\u2009\u00b0C for one hour. Detergent removal was facilitated by two additions of 0.1\u2009g/mL Biobeads (Bio-Rad), each followed by incubation at 4\u2009\u00b0C. Subsequently, 0.2\u2009mg/mL Biobeads were added and mixed overnight, with an additional hour of mixing the next day after adding another 0.2\u2009mg/mL Biobeads. Biobeads were then removed using a 0.45\u2009\u00b5m filter. Size exclusion chromatography was employed to segregate nanodiscs with PCAT1 from empty nanodisc into 50\u2009mM MOPS, pH 7.4, 50\u2009mM NaCl buffer with 10% (vol/vol) glycerol. The PCAT1-containing nanodiscs were concentrated using an Amicon ultra 100,000 MWCO filter concentrator and assessed via SDS/PAGE to verify reconstitution and estimate reconstitution efficiency. Quantification of spin-labeled mutants in nanodiscs was performed as previously described, comparing the intensity of their integrated continuous-wave electron paramagnetic resonance (CW-EPR) spectrum to that in detergent micelles24.\n\nContinuous-wave electron paramagnetic resonance (CW-EPR) spectra of spin-labeled PCAT1 samples were collected at room temperature on a Bruker EMX spectrometer (X-band, 9.5\u2009GHz) at an incident power of 10\u2009mW and 1.6 Gauss modulation amplitude. Scan width was 120\u2013150 Gauss.\n\nDouble Electron-Electron Resonance (DEER) spectroscopy was conducted using an Elexsys E580 EPR spectrometer at a Q-band frequency of 33.9\u2009GHz. This was performed employing a dead-time free four-pulse sequence at 83\u2009K, with pulse lengths of 10 to 14\u2009ns (\u03c0/2) and 20 to 28\u2009ns (\u03c0) for the probe pulses, and 40\u2009ns for the pump pulse. The frequency separation between the observe and pump pulses was 63\u2009MHz. To ascertain the role of nucleotide and Sub in the conformational cycling of the transporter, DEER samples were prepared with 10\u2009mM MgATP\u03b3S, 10\u2009mM MgATP, or 10\u2009mM MgADP with and without 5-fold molar excess of Sub. Samples for DEER were cryoprotected with 24% (vol/vol) glycerol, incubated at 50\u2009\u00b0C for 20\u2009mins (2\u2009min for MgATP) and flash-frozen in liquid nitrogen.\n\nAnalysis of primary DEER decays was performed using proprietary software developed in-house, operating within the MATLAB (MathWorks) environment as previously described25. Briefly, the software conducts a global analysis of DEER decays under varying conditions for the same spin-labeled pair. It assumes that the distance distribution comprises a sum of Gaussian functions, where the number and proportion of these functions are established based on a statistical criterion. For few sites (207, 303, 226 and 324), the DEER decay was shorter than optimal due to signal to noise consideration. However, confidence band analysis supports our interpretation.\n\nFor the prediction of distance distributions on the PCAT1 structures, in silico 1\u2009ns molecular dynamics simulations were conducted. These simulations utilized dummy spin labels and followed default parameters as outlined in the DEER Spin-Pair Distributor at the CHARMM-GUI website26,27.\n\nThe expression and purification of Sub was performed as previously outlined with slight modifications20. Briefly, sub was expressed in E. coli BL21 (DE3) Gold competent cells. Cells were grown in 1\u2009L Terrific Broth at 37\u2009\u00b0C with shaking to an OD600 of 1.8\u20132.0, induced with 1\u2009mm IPTG and allowed to grow for an additional 4\u2009h. The cultures were harvested by centrifugation at 6000\u2009\u00d7\u2009g for 15\u2009min and the cell pellet was subsequently resuspended in lysis buffer composed of 50\u2009mM Tris, pH 7.5, 150\u2009mM\u2009NaCl, 5\u2009mM EDTA, with a protease inhibitor cocktail (Roche). Cells were lysed by sonication (30 cycles of 10\u2009s on/25\u2009s off, 40% amplitude). The lysate was centrifuged at 17,000\u2009\u00d7\u2009g for 15\u2009min to sediment unbroken cells and inclusion bodies containing Sub.\n\nThe cell pellets were washed in 25\u2009ml of wash buffer containing 50\u2009mM Tris, pH 7.5, 150\u2009mM NaCl, 0.5\u2009mM EDTA, 0.5\u2009mM DTT, 1% (w/v) Triton X-100. The suspension was centrifuged at 17,000\u2009\u00d7\u2009g for 15\u2009min, and the cell pellet wash was repeated with the same buffer without Triton. Pellet containing the inclusion bodies was solubilized by resuspension in the same wash buffer (2.5\u2009mL) supplemented with 10\u2009mM DTT and 6\u2009M urea under constant stirring at room temperature for 30\u2009mins or until dissolved. After centrifugation at 17,000\u2009\u00d7\u2009g for 15\u2009min, the solution was rapidly diluted using buffer containing 50\u2009mM Tris, pH 7.5, 150\u2009mM NaCl, 0.01% \u03b2-DDM to initiate refolding. The sample was further centrifuged to remove insoluble material and nickel-affinity chromatography was performed to isolate Sub. Sub was further purified using a 5\u2009mL Hi-Trap desalting column in GF buffer to remove imidazole. Sub was then concentrated in an Amicon Ultra 3000 MWCO filter concentrator (Millipore) and stored in small aliquots at \u221280\u2009\u00b0C for future experiments. The purity of Sub was confirmed via SDS-PAGE analysis.\n\nATPase activity for PCAT1 and PCAT1 mutants was determined by a calorimetric inorganic phosphate assay as previously described20. Briefly, nanodisc reconstituted PCAT1 in 50\u2009mM MOPS, pH 7.4, 50\u2009mM NaCl buffer was incubated with increasing concentrations of ATP at 50\u2009\u00b0C for 8\u2009min. The reaction was halted upon returning to ice and the addition of an equal volume of 12% SDS. The color was subsequently developed using a 1:1 solution of ammonium molybdate (2% in 1\u2009M HCl) and ascorbic acid (12% in 1\u2009M HCl). The absorbance of samples was measured at a wavelength of 850\u2009nm on a BioTek Synergy H4 microplate reader. The amount of phosphate released was determined by comparison to a standard curve generated from inorganic phosphate. The maximal rate (Vmax) of PCAT1 and its variants was derived using the Levenberg-Marquart nonlinear least squares fitting approach in Origin (OriginLab). To determine the effect Mg2+ on ATP turnover, Mg2+ was added at equimolar concentrations of ATP or increasing Mg2+ concentration with constant ATP concentration.\n\nNanodisc reconstituted PCAT1 wild type, PCAT1-CL, and PCAT1-CC with MSP1D1 scaffold protein under different biochemical conditions were prepared at a final concentration of 0.5\u2009mg/ml. Samples were applied to glow discharged Quantifoil UltraAU Foil holey grids (1.2/1.3, 300 mesh). For both wild type and cysteine-less PCAT1 (PCAT1*) samples with ADP in a Mg2+-free environment, a final concentration of 10\u2009mM ADP, 1\u2009mM EDTA, 0.5\u2009mM EGTA, and/or Sub (at five times the protein concentration) was mixed with the protein. These mixtures were incubated at 50\u2009\u00b0C for 10\u2009min prior to application to the grids. In the case of PCAT1 with ADP/Mg2+, the samples contained a final concentration of 10\u2009mM ADP and 10\u2009mM MgCl2, along with optional Sub, and followed the same incubation procedure. For wild type PCAT1 in an Mg2+ environment, only 10\u2009mM MgCl2 was included in the sample, which was then incubated at 50\u2009\u00b0C for 10\u2009min. Similarly, for wild type PCAT1 with ATP in a Mg2+-free environment, the final concentration comprised 5\u2009mM ATP, 1\u2009mM EDTA, 0.5\u2009mM EGTA, and 5x substrate, followed by incubation at 50\u2009\u00b0C for 1\u2009min before grid application.\n\nPCAT1_CC samples in MSP1D1 nanodiscs, along with nucleotides, substrate, and additives at 1.4\u2009mg/ml, was applied to glow-discharged Quantifoil holey carbon grids (1.2/1.3, Cu, 300 mesh). For PCAT1_CC with ADP/Mg2+, the final concentration included 10\u2009mM ADP, 10\u2009mM MgCl2, and optional Sub, incubated at 50\u2009\u00b0C for 10\u2009min. For vanadate trapping PCAT1-CC sample, the final concentrations were 6\u2009mM ATP, 6\u2009mM MgCl2, and 5\u2009mM Vi, also incubated at 50\u2009\u00b0C for 10\u2009min. The OF PCAT1-CC sample in a Mg2+-free environment consisted of 10\u2009mM ATP, 1\u2009mM EDTA, and 0.5\u2009mM EGTA, incubated on ice for 30\u2009min.\n\nGrids were blotted for 3.0\u20134.5\u2009s with a blotting force between 10\u201312 and 100% relative humidity at 8\u2009\u00b0C. They were then plunge-frozen in liquid ethane cooled by liquid nitrogen using a Vitrobot System Mark IV (Gatan). CryoEM data collection occurred at liquid nitrogen temperature using a 300\u2009kV TEM with Gatan K3 BioQuantum and ThermoScientific Falcon3 direct electron detectors. All cryoEM movies were recorded with a 20\u2009eV slit width from the energy filter, at a nominal magnification of 130kx, corresponding to a calibrated pixel size of 0.647\u2009\u00c5/pix. Additional details are available in the supplementary material.\n\nImage processing for all data sets was conducted using CryoSparc v4.2.128. We initiated the process by applying Patch Motion Correction and Patch CTF estimation to the imported movies. Depending on the image quality, a manual curation of exposures was occasionally necessary to eliminate poor-quality images, thereby enhancing the resolution of the resulting maps.\n\nParticle selection was conducted using the Blob Picker, with diameters ranging from 100\u2009\u00c5 to 200\u2009\u00c5. This was followed by a 2D classification process, employing a box size of 360 pixels, to acquire templates for subsequent template picking using the Template Picker. The particles selected by this method were extracted with a box size of 360 pixels. We then conducted a 2D classification, with class numbers varying between 50 and 200.\n\nThe selected particles were subsequently utilized for Ab-Initio Reconstruction, with the number of classes set between 2 and 4. The quality of the maps and the number of particles were manually assessed, guiding the decision to apply 2 to 4 maps for Heterogeneous Refinement. Optimization of the final map was achieved through homogeneous refinement, non-uniform refinement, and local refinement, applying C2 symmetry where applicable.\n\nStructure generation commenced with Model Angelo29, followed by manual inspection and refinement using Phenix 1.20.130. PDB validation confirmed that all structures were free from major issues. For visualization, figures were generated utilizing UCSF Chimera31 and Pymol (The PyMOL Molecular Graphics System, Version 1.2r3pre, Schr\u00f6dinger, LLC.).\n\nAll-atom MD simulations were performed on PCAT1, MsbA, and Sav1866 systems. For the PCAT1 simulations in the putative OF state, we used the cryoEM structure of ATP-bound PCAT1 reported by Kieuvongngam and Chen10 (PDB:7T54). Magnesium ions were introduced into the system by aligning the nucleotides of the 7T54-based structure with the equivalent nucleotides in PDB:7T57 (which contains Mg2+), and then substituting the magnesium-complexed nucleotides into our structure. By removing the Mg2+ from these structures, we then generated the magnesium-free nucleotide structures. We therefore made 4 models of outward-facing PCAT1 including PCAT1_ATP, PCAT1_ADP, PCAT1_ ATP/Mg2+ and PCAT1_ ADP/Mg2+. Based on PDB: 2HYD22 and PDB:3B6021, we generated similar models for Sav1866 and MsbA, respectively. In addition, we used the cryoEM structure reported here (PDB: 8VP3) for the inward-facing structure of PCAT1 in the presence of ADP/Mg2+and Sub. By replacing the ADP by ATP and removing the Mg2+ in either model, we generated 4 models for PCAT1/Sub in the inward-facing state. The same 4 models were then used, after removing the Sub to generate another 4 models for substrate-free PCAT1 in the inward-facing state. In addition, an inward-facing MsbA structure based on PDB:3B5W21 was created and used within the same strategy to dock ATP, ADP, ATP/Mg2+, or ADP/Mg2+.\n\nIn all cases, once the preliminary structure of protein and nucleotide with or without Mg2+ had been obtained, CHARMM-GUI26,32 was then used to build the simulation systems. Using Membrane Builder33, structures were embedded in a membrane bilayer consisting of 45% POPE, 45% POPG, and 10% cardiolipin (TOCL2), oriented using PPM2.034, and then solvated using TIP3P35 water along with 0.15\u2009M NaCl and neutralizing counterions. The approximate final size of each transporter system was ~450,000 atoms. In addition, we simulated solvated the ATP, ADP, Mg2+-ATP, or Mg2+-ADP in water to perform simulations in the absence of transporters.\n\nAll simulations were run with NAMD2.1436 using periodic boundary conditions and the CHARMM36m forcefield37,38. The default CHARMM-GUI equilibration for membrane systems begins with a 10,000-step energy minimization using the conjugate gradient method, followed by a 6-step restraining regimen which was performed over 22.5\u2009ns in an NVT ensemble. A further 20\u2009ns of production was then performed for each system in an NPT ensemble under equilibrium conditions. All simulations up to this point were carried out at 310\u2009K using a Langevin integrator with a damping coefficient of \u03b3\u2009=\u20091.0\u2009ps-1 and a 2\u2009fs timestep. A pressure of 1\u2009atm was maintained using the Nos\u00e9\u2013Hoover Langevin piston method39,40. The cutoff distance for non-bonded interactions was set to 12\u2009\u00c5, and long-range electrostatics were computed with the Particle Mesh Ewald (PME)41 method.\n\nA free energy perturbation (FEP)42 approach was used to calculate the relative binding free energy of ADP/ATP. The FEP process consisted of gradually annihilating (forward process) or returning (backward process) the \u03bb-phosphates for the systems in question to simulate the hydrolysis of bound ATP into ADP in the presence and absence of the Mg2+ ion. Each simulation comprised 20 windows (\u0394\u03bb\u2009=\u20090.05 per window), with each window consisting of 10\u2009ps of alchemical equilibration followed by 240\u2009ps of data collection (5\u2009ns total). Every window for a given simulation was initialized with the same initial conformation coming from the previously described equilibrated trajectories, but with the phosphates already partially annihilated, allowing all windows to be run simultaneously in parallel. To circumvent the endpoint problem for van der Waals interactions43,44, a soft-core potential was used such that the electrostatic interactions were fully decoupled from the system by \u03bb\u2009=\u20090.5. Free energy differences were calculated using the Bennett Acceptance Ratio (BAR)45 method. The resulting data from FEP calculations were statistically analyzed using the VMD46 ParseFEP plugin47 to obtain the relative binding free energies.\n\nFollowing the equilibrium simulations, well-tempered metadynamics23 simulations were conducted for PCAT1_ADP and PCAT1_ADP/Mg2+ systems in the outward-facing state for 100\u2009ns with the same parameters used for the equilibrium simulations. The collective variable \u201calpha\u201d (\u03b1)48 was utilized to investigate the \u03b1-helical propensity of residues between T650 and L668.\n\nCollective variable \u201calpha\u201d (\\(\\alpha\\)) is defined as a one-dimensional scalar to quantify the \\(\\alpha\\)-helical propensity of a peptide of length N:\n\nwhere \\({\\theta }_{n}\\) is the angle formed by \\({C}_{\\alpha }^{\\left(n\\right)}-{C}_{\\alpha }^{\\left(n+1\\right)}-{C}_{\\alpha }^{\\left(n+2\\right)}\\) and \\({d}_{n}\\) is distance between \\({O}^{(n)}\\) and \\({N}^{(n+4)}.\\) \\(f(\\theta )\\) and \\(g(d)\\) are score functions (both ranging from 0 to 1) quantifying the likelihood of \\(\\theta\\) and \\({d}\\) being associated with an \\(\\alpha\\)-helix. More specifically:\n\nwhere \\({\\theta }_{0}\\) and \\(\\delta \\theta\\) are 88 and 15 degrees, respectively and \\({d}_{0}\\) is 3.3 \\(\\mathring{\\rm A}\\). For the well-tempered metadynamics, the height and width of the gaussians were 1\u2009kcal/mol and 0.05, respectively, and the pseudo temperature was 1200\u2009K.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data that support this study are available from the corresponding authors upon request. All cryoEM 3D density maps of the PCAT1 in nanodiscs in Fig.\u00a02, Fig.\u00a05, supplementary table\u00a02 and supplementary table\u00a06 have been deposited in the Electron Microscopy Data Bank under accession codes EMD-43405 (PCAT1_OC_ADP); EMD-43404 (PCAT1_IF_ADP/Sub); EMD-43406 (PCAT1_IF_ADP/Mg2+_Class2); EMD-43400 (PCAT1_IF_ADP/Sub/Mg2+); EMD-43401 (PCAT1*_IF_ADP/Sub); EMD-43402 (PCAT1_IF_Mg2+_Class2); EMD-43403 (PCAT1_OC_ATP/Sub); EMD-43394 (PCAT1_CC_ADP/Mg2+); EMD-43396 (PCAT1_CC_ADP/Mg2+/Sub); EMD-43393 (PCAT1_CC_ATP/Mg2+/Vi); EMD-43398 (PCAT1_CC_ATP); EMD-44015 (PCAT1_IF_ADP/ Mg2+_Class1); EMD-44402 (PCAT1_IF_ Mg2+_Class1)(supplementary table\u00a02 and supplementary table\u00a06). Atomic coordinates for the atomic models have been deposited in the Protein Data Bank under accession codes 8VPA (PCAT1_OC_ADP); 8VP9 (PCAT1_IF_ADP/Sub); 8VPB (PCAT1_IF_ADP/Mg2+_Class2); 8VP3 (PCAT1_IF_ADP/Sub/Mg2+); 8VP5 (PCAT1*_IF_ADP/Sub); 8VP6 (PCAT1_IF_Mg2+_Class2); 8VP8 (PCAT1_OC_ATP/Sub); 8VOX (PCAT1_CC_ADP/Mg2+); 8VOZ (PCAT1_CC_ADP/Mg2+/Sub); 8VOW (PCAT1_CC_ATP/Mg2+/Vi); 8VP1 (PCAT1_CC_ATP); 9AZL (PCAT1_IF_ADP/Mg2+_Class1); 9BAA (PCAT1_IF_ Mg2+_Class1). The MD simulation data necessary to reproduce the data including equilibration and FEP simulation input files are deposited to the Zenodo repository [https://doi.org/10.5281/zenodo.13864175]. Previous published structures referred in the manuscript is 7T54. A source data file is available.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Thomas, C. & Tampe, R. Structural and mechanistic principles of ABC transporters. Annu Rev. 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The simulations were performed on Frontera at the Texas Advanced Computing Center (TACC) through LRAC award CHE21003, made possible by the National Science Foundation award OAC-1818253.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA\n\nRuojing Zhang,\u00a0Kevin L. Jagessar,\u00a0Richard A. Stein,\u00a0Erkan Karakas\u00a0&\u00a0Hassane S. Mchaourab\n\nDepartment of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR, USA\n\nMatthew Brownd,\u00a0Adithya Polasa\u00a0&\u00a0Mahmoud Moradi\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nR.Z. performed the cryoEM experiments and the ATPase assays. K.J. performed DEER measurement in collaborations with R.S. A.P. and M.B. performed the MD simulation under the supervision of M.M. E.K. provided advice for structure determination and analysis. H.M. designed the research. R.Z., R.S. and H.M. analyzed, and interpreted the structures. H.M. wrote the manuscript with input from all authors.\n\nCorrespondence to\n Hassane S. Mchaourab.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Conformational cycle of a protease-containing ABC transporter in lipid nanodiscs reveals the mechanism of cargo-protein coupling.\n Nat Commun 15, 9055 (2024). https://doi.org/10.1038/s41467-024-53420-0\n\nDownload citation\n\nReceived: 19 March 2024\n\nAccepted: 11 October 2024\n\nPublished: 20 October 2024\n\nVersion of record: 20 October 2024\n\nDOI: https://doi.org/10.1038/s41467-024-53420-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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TPE-Embedded Butterfly Bis-Crown Ether with Controllable Conformation and Supramolecular Chiroptical Property", + "journal": "Nature Communications", + "published": "21 August 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "http://www.ccdc.cam.ac.uk/data_request/cif", + "/articles/s41467-024-51607-z#MOESM1", + "/articles/s41467-024-51607-z#Sec14" + ], + "code": [], + "subject": [ + "Molecular self-assembly", + "Self-assembly" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4143303/v2.pdf", + "research_square_link": "https://www.researchsquare.com//article/rs-4143303/v2", + "nature_pdf": "https://www.nature.com/articles/s41467-024-51607-z.pdf", + "preprint_posted": "11 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Understanding how subtle structural differences between macrocyclic conformational isomers impact their properties and separation has garnered increasing attention in the field of supramolecular synthetic chemistry. In this work, a series of tetraphenylene (TPE)-embedded butterfly bis-crown ether macrocycles (BCE[n], n = 4-7), comprising two crown ether side rings and a TPE core, were synthesized through intramolecular McMurry coupling. Unexpectedly, the presence of flexible oligoethylene chains with varying lengths were found to influence molecular conformation via intramolecular interactions, resulting in the formation of two stabilized conformers with specific semi-rigid symmetric/asymmetric structures (sym-BCE[n] and asym-BCE[n], n = 5, 6). Moreover, it is noteworthy that neither symmetric nor asymmetric conformers are present in the more rigid BCE[4] or the more flexible BCE[7]. Interestingly, these conformers display distinct fluorescence properties and host-guest binding abilities, and only sym-BCE[5] can serve as a host for chiral polymer binding, resulting in the formation of chiral supramolecular assemblies through host-guest interaction induced chirality. Moreover, both circular dichroism (CD) and circularly polarized luminescence (CPL) signals of the obtained assemblies could be switched off by the addition of sodium ion (Na+), suggesting potential applications in the field of dynamic chiral materials.Physical sciences/Chemistry/Supramolecular chemistryPhysical sciences/Chemistry/Supramolecular chemistry/Self-assembly", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "asymBCE6cifreport1.pdfDataset 3B02SupportingInformation.docasymBCE5checkcif.pdfDataset 1BasymBCE5.cifDataset 1AsymBCE5cifreport.pdfDataset 2BBCE7.pdfDataset 5BsymBCE5.cifDataset 2ABCE4cifreport.pdfDataset 4BasymBCE6Kcheckcif.pdfDataset 6BasymBCE6K.cifDataset 6AasymBCE6.cifDataset 3ABCE4.cifDataset 4ABCE7.cifDataset 5A02SupportingInformation.docSupporting Information of the Manuscript", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Understanding how subtle structural differences between macrocyclic conformational isomers impact their properties and separation has garnered increasing attention in the field of supramolecular synthetic chemistry. In this work, a series of tetraphenylene (TPE)-embedded butterfly bis-crown ether macrocycles (BCE[n], n\u2009=\u20094\u20137), comprising two crown ether side rings and a TPE core, are synthesized through intramolecular McMurry coupling. Unexpectedly, the presence of flexible oligoethylene chains with varying lengths are found to influence molecular conformation via multiple intramolecular interactions, resulting in the formation of two stabilized conformers with specific semi-rigid symmetric/asymmetric structures (sym-BCE[n] and asym-BCE[n], n\u2009=\u20095, 6). Moreover, it is noteworthy that neither symmetric nor asymmetric conformers are present in the more rigid BCE[4] or the more flexible BCE[7]. Interestingly, these conformers display distinct fluorescence properties and host-guest binding abilities, and only sym-BCE[5] can serve as a host for chiral polymer binding, resulting in the formation of chiral supramolecular assemblies through host-guest interaction induced chirality. Moreover, both circular dichroism and circularly polarized luminescence signals of the obtained assemblies can be switched off by the addition of sodium ion, suggesting potential applications in the field of dynamic chiral materials.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Tetraphenylethylene (TPE) represents one of the most prevalent aggregation-induced emission (AIE)-active moieties, making it an ideal fluorescent building block for integration into macrocycles1,2,3,4,5,6,7. It is noteworthy that a TPE derivative theoretically possesses a diverse range of conformations, which are determined by the varying dihedral angles within the TPE core. Manipulating these conformational changes can lead to compounds with distinct photophysical properties8,9,10,11,12,13. By restricting the dihedral angles in the AIE compounds, these molecules with diverse conformations hold great potential for precise modulation of material functionalities14,15,16,17. Moreover, the TPE group exhibits either clockwise or anticlockwise rotational patterns in its propeller-like P or M configurations, respectively, by restricting the intramolecular flipping of phenyl rings. For example, the P or M configurations of TPE can also be constrained through the introduction of a rigid cyclic structure, offering the possibility for construction of chiral materials18,19,20,21. So far, achieving distinct conformations for TPE compounds remains a formidable challenge.\n\nHerein, we report the synthesis of a series of TPE-based bis-crown ethers (BCE[n], n\u2009=\u20094\u20137) through intramolecular McMurry coupling interaction. In order to achieve AIE-active macrocycles with distinct conformations in both solution and solid state, chains with appropriate length were introduced to connect the phenyl-substituents, thereby restricting the dihedral angles of TPE core. The TPE unit was cyclized through flexible ethylene glycol chains, and it was observed that varying chain lengths resulted in different degrees of distortion in the TPE core due to intramolecular tension. Surprisingly, both BCE[5] and BCE[6] exhibit two conformations, namely sym-BCE[n] and asym-BCE[n] (n\u2009=\u20095, 6), respectively. Moreover, it is noteworthy that neither symmetric nor asymmetric conformer is present in the more rigid BCE[4] or the more flexible BCE[7]. Furthermore, variations in cavity size and shape result in diverse fluorescence and binding properties among different conformers. It is worth noting that only sym-BCE[5] can act as a host for chiral polymer guest binding, thereby enabling the generation of chiral materials with controllable handedness through supramolecular chiral amplification induced by host-guest interaction (Fig.\u00a01).\n\nSchematic illustration of the two conformations of bis-crown ether macrocycles and the guest-modulated chiroptical signal in the assembled system.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "TPE-cored butterfly-shaped macrocycles were synthesized by embedding TPE units into crown ether through a facile three-step process (Fig.\u00a02). Our methodology distinguishes itself from previous reports on crown ether-functionalized TPE-based macrocycles obtained through direct intermolecular cyclization22,23,24, as it is challenging to achieve high yields of smaller-sized crown ether ring-functionalized TPE-macrocycles due to the strain resulting from intramolecular cyclization.\n\nDesign strategy, synthesis routes, and different conformations of BCE[n] (n\u2009=\u20094\u20137).\n\nBCE[n] (n\u2009=\u20094\u20137) with identical skeletal structures but varying lengths of crown ether chains were synthesized, and the synthesis route as well as the detailed procedures are elaborated in Supplementary Figs.\u00a014, 27. Taking BCE[5] as a representative example, we first obtained the cyclic diketone by reacting two dihydroxybenzophenone molecules with two equivalents of tetraethylene glycol ditosylate in a highly concentrated reaction solution (Supplementary Figs.\u00a01\u201313). With the diketone in hand, the target macrocycle BCE[5] could be efficiently obtained via an intramolecular McMurry reaction in the final coupling step. As expected, three additional BCE[n] (n\u2009=\u20094, 6, 7) were also successfully prepared using this convenient methodology with moderate yields (Supplementary Figs.\u00a014, 27). The obtained BCE[n] were fully characterized by 1H NMR, 13C NMR, HR-ESI-MS, and single crystal analyses to validate their structural integrity (Supplementary Figs.\u00a015\u201326, 28\u201333).\n\nThe reaction of the diketone compound with zinc powder (5 equiv.) and TiCl4 (2.5 equiv.) resulted in the formation of BCE[5] product, which was obtained with a moderate yield (38%). This product exhibited blue color in solid state under UV-irradiation. To further optimize the yield, we explored the feasibility of increasing the quantity of zinc powder by varying its ratio from 5 to 40 equivalents. The 1H NMR spectra revealed slight variations in the products obtained under different conditions of zinc powder equivalents (Supplementary Fig.\u00a040). In Fig.\u00a03a and Supplementary Fig.\u00a015, it is evident that reducing the amount of zinc powder used in the reaction resulted in the observation of two distinct peaks at 6.68 and 6.91 ppm for the C\u2013H protons of the aromatic protons in the product, as observed in CDCl3. However, when increasing the amount of zinc powder to 20 equiv. and 30 equiv., a broadening peak resembling a mixture was observed, indicating the gradual formation of side-products (Supplementary Fig.\u00a040). By employing 40 equiv. of zinc powder, another pure product was successfully obtained as confirmed by the 1H NMR spectra (Supplementary Fig.\u00a018). Interestingly, this product exhibits a distinct green color in its solid state under UV-irradiation. Additionally, its 1H NMR spectrum shows slight differences compared to the previously mentioned product. Specifically, the resonance peaks at 6.82 and 6.88 ppm show increased proximity to each other, and its (\u2212OCH2) signal corresponding to the ether chains displays a slightly downfield chemical shift (Fig.\u00a03a). Notably, there is also noticeable disparity between the chemical shifts of these two products around 130\u2013140 ppm on the 13C NMR spectra (Supplementary Figs.\u00a016, 20). Furthermore, mass spectrometry analysis confirmed that both products possessed identical molecular weight consistent with our target molecule, suggesting the formation of two conformers (Supplementary Figs.\u00a017, 20). By varying the amount of zinc powder from 5 to 40 equiv., column chromatography demonstrated controllable production of these two products with different yields (Fig.\u00a03b). The yield of the blue conformer gradually decreased as more zinc powder is used, while conversely, the yield of the green conformer gradually increased.\n\na Comparison of the 1H NMR (400\u2009MHz, CDCl3, 298\u2009K) spectra of two products (top: 40 eq. zinc; bottom: 10 eq. zinc; inset: fluorescence images excited by UV-365 nm, scale bar: 1\u2009cm). b yields of two products (blue conformer and green conformer) under varying zinc amounts (5\u201340 eq. zinc).\n\nThe single crystals of both products were obtained through slow vapor diffusion of n-hexane into chloroform solution containing the macrocycles. The crystal structures confirm that the macrocycles consist of a TPE core and two crown ether rings positioned on either side (Supplementary Figs.\u00a044, 45). Similar to other TPE derivatives, the four phenyl rings are not coplanar with the central C\u2009=\u2009C double bond, resulting in a characteristic propeller-like structure. The solid state structure of the product obtained with 10 equiv. of zinc powder exhibits an asymmetric structure (asym-BCE[5]) with dihedral angles measuring 47.74\u00b0, 45.95\u00b0, 37.31\u00b0, and 51.68\u00b0 between the plane of phenyl ring and C\u2009=\u2009C bond, respectively. However, the crystal of the other product obtained with 40 equiv. of zinc powder adopts a highly symmetric butterfly-shaped conformation (sym-BCE[5]), with their dihedral angles measuring 54.53\u00b0, 54.53\u00b0, 44.08\u00b0, and 44.08\u00b0, respectively. Those two conformations arise from different degrees of distortion induced by flexible crown ether ring units on the phenyl rings. Both left-handed helical (M) conformation and right-handed (P) conformations can be equally observed within one unit cell, indicating that these two crystals are racemic.\n\nThe combination of NMR and single crystal data reveals that BCE[5] adopts two stable conformations in both solid state and solution. The crystal structures of these conformers suggest the presence of potential weak intramolecular interactions, which are likely crucial for maintaining the conformational state within this confined environment. The existence of multiple intramolecular interactions, such as C-H\u00b7\u00b7\u00b7O and C-H\u00b7\u00b7\u00b7\u03c0 interactions, within the BCE[5] skeleton may influence both cavity shape and TPE angle. For example, due to the spatial proximity (Fig.\u00a04a, b), asym-BCE[5] is expected to have a higher number of C-H\u00b7\u00b7\u00b7O interactions compared to sym-BCE[5]. Moreover, there are notable disparities in their respective stacking configurations. As shown in Fig.\u00a04c, the crystal packing structure of symmetrical sym-BCE[5] reveals intermolecular interactions within the three adjacent molecules through C-H\u00b7\u00b7\u00b7O bond. In contrast, asymmetrical asym-BCE[5] displays intermolecular interactions involving four neighboring molecules, leading to a more tightly packed arrangement (Fig.\u00a04d). This close packing restricts the rotation of TPE in the crystalline phase. Consequently, sym-BCE[5] exhibits a more obvious cyan color compared to its asymmetric counterpart (Supplementary Fig.\u00a049).\n\nCrystal structures and C\u2009\u2212\u2009H\u22c5\u22c5\u22c5O distances (\u00c5) of (a) sym-BCE[5] and c its packing mode. b asym-BCE[5] and d its packing mode. (Oxygen atoms, red; TPE, blue; Carbon atoms, gray; Hydrogen atoms, white; disordered solvent molecules are omitted for clarity).\n\nTo further investigate the disparities in molecular conformations and structures between sym-BCE[5] and asym-BCE[5], density functional theory (DFT) calculations were performed based on their respective crystal structures with Gaussian 09 software package (Revision D. 01) using M06-2X functional with 6\u2013311\u2009G(d, p) basis25. Computationally optimized geometries confirm that both conformations resemble their original crystal structures, indicating their thermodynamic stability. The twisted conformation of asym-BCE[5] exhibits a lower energy state compared to the sym-BCE[5] conformer (Easym\u2009=\u2009\u2212\u20091494777.3\u2009kcal\u2009mol\u22121 vs Esym\u2009=\u2009\u2212\u20091494784.7\u2009kcal\u2009mol\u22121 according to the DFT calculation), suggesting its enhanced stability (Supplementary Fig.\u00a050). Consequently, the structure of asym-BCE[5] is more thermodynamically stable than that of sym-BCE[5].\n\nBased on the aforementioned experimental data and analysis, it is evident that the yields of these two conformers are influenced by the McMurry reaction condition26,27,28,29. The varying amounts of zinc powder may induce a templated effect, leading to a preference for one conformer in the product. Additionally, the polar surface of low-valent titanium would serve as an additional template, simultaneously promoting the formation of one stable conformer structure30,31,32,33. Despite the seemingly excessive amount of 10 equiv. zinc powder, the limited solubility of zinc powder in THF results in an insufficient concentration of Zn2+. Consequently, this inadequate Zn2+ concentration within the reaction mixture leads to formation of a more stable asymmetrical conformation of asym-BCE[5] as typically observed in McMurry reactions34. To confirm the significance of Zn2+ amount on sym-BCE[5]/asym-BCE[5] selectivity, we added additional ZnCl2 to the previously mentioned reaction system with initially insufficient Zn2+. As a result, the yield of sym-BCE[5] increased to 62.5%. To confirm the binding ability of diketone 2 with Zn2+, 1H NMR titration experiments were conducted, which revealed its exceptional capability in accommodating Zn2+ ions by electron-rich cavity (Supplementary Fig.\u00a052). Additionally, Job\u2019s plot experiments demonstrated a 1:1 stoichiometry between diketone 2 and Zn2+, indicating the formation of complexes (Supplementary Fig.\u00a053). Based on these findings, we propose that during reagent preparation, a sufficient amount of pre-organized Zn2+ ions could be accommodated within the electron-rich cavity of ether rings in diketone, leading to the adoption of a specific symmetrical conformation (Supplementary Fig.\u00a054).\n\nTo further validate the role of potential weak interactions in maintaining distinct conformations, CD3OD was introduced into the CDCl3 solution of sym-BCE[5] to investigate its ability to attenuate their intramolecular interactions (Fig.\u00a05a). An increase in Ha\u2019 and Hb\u2019 protons and a gradual disappearance of Ha and Hb protons were observed, providing evidences for the successful transformation from sym-BCE[5] to asym-BCE[5]. In contrast, when CD3OD was added to the CDCl3 solution of asym-BCE[5], no significant change in chemical shift occurred (only solvent-induced chemical shift changes were observed). Furthermore, the influence of base/acid on the transformation from sym-BCE[5] to asym-BCE[5] was further investigated. In the case of triethylamine (TEA), which is unable to disrupt the multiple weak interactions within sym-BCE[5], no significant change in 1H NMR spectra was observed upon TEA addition (Supplementary Figs.\u00a055, 56). However, trifluoroacetic acid (TFA) can form hydrogen bonding with the ethylene glycol chain of sym-BCE[5], effectively disrupting weak interactions. The distinct chemical shifts observed within the aromatic region indicate successful conformational changes induced by TFA (Fig.\u00a05b and Supplementary Figs.\u00a057, 58).\n\na 1H NMR (400\u2009MHz, 298\u2009K) spectra of sym-BCE[5] (3.0\u2009mM) in the mixed solvent (500\u2009\u03bcL CDCl3 and different contents of CD3OD) and asym-BCE[5] in the mixed solvent (500\u2009\u03bcL CDCl3 and 30\u2009\u03bcL CD3OD). b comparison\u00a0of the 1H NMR (400\u2009MHz, CDCl3, 298\u2009K) spectra of sym-BCE[5] and asym-BCE[5] (3.0\u2009mM) with addition of 0.01 eq. TFA (blue peaks are asym-BCE[5], red peaks are sym-BCE[5]).\n\nIn addition, variable-temperature (VT) 1H NMR experiments were conducted to investigate the stability and dynamic transformation process of BCE[5]. To avoid the potential disruption of weak intramolecular interactions, CDCl3 was chosen as the solvent. No merging or splitting phenomena were observed in the NMR spectra for any protons, indicating that increasing the solution temperature would not disrupt the intramolecular interaction and thus the conformation of sym-BCE[5] or asym-BCE[5] could be maintained (Supplementary Figs.\u00a059, 60). Moreover, VT-NMR experiments were conducted in the solution of C2Br2D4 (1,2-dibromoethane-1,1,2,2-d4) to investigate its behavior at elevated temperatures (Supplementary Figs.\u00a061, 62). The results indicate a subtle shift in chemical resonance, suggesting a transition from sym-BCE[5] to asym-BCE[5] (Supplementary Fig.\u00a061), with the latter being thermodynamically more stable.\n\nSubsequently, two conformers of TPE-embeded bis-crown ethers BCE[6] with longer pentaethylene glycol linkers, namely sym-BCE[6] and asym-BCE[6], were successfully synthesized (Supplementary Figs.\u00a021\u201326, 41). However, it should be noted that the yield of sym-BCE[6] is significantly lower. The crystal structure of asym-BCE[6] was also obtained, as shown in Supplementary Fig.\u00a046, similar to asym-BCE[5], the dihedral angles between the plane of phenyl ring and C\u2009=\u2009C bond were measured to be 48.96\u00b0, 40.56\u00b0, 47.83\u00b0, and 46.50\u00b0, respectively. However, our attempts to obtain the crystal structure of sym-BCE[6] were unsuccessful, so we employed DFT calculation to model its structure (Supplementary Fig.\u00a051). The results showed that the dihedral angles of TPE in sym-BCE[6] were 46.52\u00b0, 46.52\u00b0, 48.05\u00b0, and 48.05\u00b0, respectively. Interestingly, the presence of multiple intramolecular interactions results in the formation of a small pocket within the side chain, leading to a distinct conformation of sym-BCE[6] compared to asym-BCE[6] (Fig.\u00a06a, b). The size of the asym-BCE[6] cavity is restricted by this pocket, potentially hindering its binding with ions. The different conformations arise from varying degrees of dihedral angles of TPE induced by flexible crown ether ring units on phenyl rings. Furthermore, we successfully obtained a cocrystal of asym-BCE[6] and K+ ion through slow vapor diffusion of n-hexane into acetone solution. Figure\u00a06c illustrates that asym-BCE[6] can bind with K+ ion in a 1:1 ratio, while another cavity is occupied by a water molecule (Supplementary Fig.\u00a048). This indicates that the binding process between a K+ ion and one cavity may result in reduced electron density within the adjacent cavity, thereby impeding its ability to bind another K+ ion.\n\na DFT-optimized structure for sym-BCE[6] was calculated with Gaussian 09 software package (Revision D. 01) using M06-2X functional with 6\u2013311\u2009G(d, p) basis. b crystal structure of asym-BCE[6]. c cocrystal of asym-BCE[6]-K+ (OH\u2212, Hydrogens are omitted for clarity) and its packing mode. (Oxygen atoms, red; TPE, blue; Carbon atoms, gray; Hydrogen atoms, white; K+ atoms, purple; disordered solvent molecules are omitted for clarity).\n\nIn order to further investigate the effect of glycol-linked chain length on the conformation of bis-crown ethers, we synthesized BCE[4] with shorter triethylene glycol side chains and BCE[7] with longer hexaethylene glycol side chains (Supplementary Figs.\u00a028\u201333). Interestingly, only one conformer was formed in each case (Supplementary Figs.\u00a039, 42). This can be attributed to the fact that the short rigid side chain fixes the conformation in a twisted structure, while the long flexible side chain fails to restrict the dihedral angles of TPE core, resulting in an inability to fix the conformation into specific structures. To confirm their conformations, single crystals of BCE[4] and BCE[7] were obtained through slow vapor diffusion of n-hexane into chloroform solution containing the macrocycles (Supplementary Figs.\u00a043, 47). The crystal data strongly indicate that BCE[4] and BCE[7] exhibit a single conformation (the dihedral angles of TPE core in BCE[4] and BCE[7] is 37.90\u00b0, 51.35\u00b0, 47.99\u00b0, 44.00\u00b0 and 42.47\u00b0, 45.84\u00b0, 50.55\u00b0, 48.46\u00b0, respectively). By integrating all experiment data, it can be deduced that the occurrence of different conformers also relies on the level of flexibility within the crown ether skeleton. This distinctive phenomenon can only be observed in the semi-rigid macrocycles BCE[5] and BCE[6].\n\nFurthermore, the photophysical properties of the macrocycle isomers were investigated. Taking BCE[5] as an example, the sym-BCE[5] and asym-BCE[5] conformers both exhibited a comparable absorption profile, characterized by two distinct bands in the range of 230\u2013430\u2009nm (Supplementary Fig.\u00a063). Given that BCE[5] possesses typical aggregation-induced emission (AIE) characteristics, we examined the AIE effect of sym-BCE[5] and asym-BCE[5] using fluorescence emission spectroscopy in chloroform-acetone mixtures with varying acetone fractions, respectively. The fluorescence intensity of sym-BCE[5] in pure chloroform initially shows a detectable response, which gradually increases upon the addition of acetone as a poor solvent, accompanied by bluish-green emission. The maximum fluorescence intensity is achieved at 455\u2009nm when the acetone fraction reaches 90%, indicating that sym-BCE[5] exhibits typical AIE characteristics (Fig.\u00a07a, c). Further increasing the volume fraction of acetone to 95% leads to a slight decrease in fluorescent intensity due to precipitate formation. For asym-BCE[5], it shows no significant fluorescence emission when the acetone content ranges from 0 to 90%. However, when the acetone fraction reaches 95%, it exhibits intense emission as well (Fig.\u00a07b). In comparison to sym-BCE[5], the fluorescence emission enhancement and intensity of asym-BCE[5] were relatively low under identical conditions. It is noteworthy that the maximum emission wavelength of asym-BCE[5] at 430\u2009nm is blue-shifted compared to that of sym-BCE[5]. This spectral shift can be attributed to their distinct packing arrangements35,36. What\u2019s more, the AIE behaviors of BCE[4] and BCE[7] were investigated in a mixed solvent of CHCl3-n-hexane, respectively (Supplementary Fig.\u00a064). Compared with BCE[7], BCE[4] exhibits higher intensity and a hypsochromic shift, indicating that the shortest linker in BCE[4] could more efficiently restrict its rotation than the longer flexible linker in BCE[7]. Additionally, we also determined the absolute fluorescence quantum yield and fluorescence emission spectra of each BCE[n] compound in the solid states (Supplementary Figs.\u00a065, 66). The fluorescence quantum yields were found to be 27.92% (sym-BCE[5]), 25.67% (asym-BCE[5]), 24.86% (sym-BCE[6]), 23.36% (asym-BCE[6]), 40.21% (BCE[4]), and 21.36% (BCE[7]) for different compounds, respectively. This could be attributed to the decrease in phenyl rotation restriction with longer flexible chains and different packing models.\n\nFluorescence spectra of a sym-BCE[5] and b asym-BCE[5] in CHCl3-acetone mixed solvent with different volume fractions of acetone (\u03bbex\u2009=\u2009350\u2009nm, c\u2009=\u200920.0\u2009\u00b5M). c emission\u00a0photos of sym-BCE[5] in CHCl3 with facetone\u2009=\u200910\u201390 vol% under 365\u2009nm UV-light.\n\nThe inherent helical chirality of TPE is widely recognized when the rotation of the phenyl rings is constrained37. Inspired by the chirality amplification phenomenon observed in supramolecular systems, we aim to induce and amplify the chirality of our macrocycles through host-guest interactions by utilizing a chiral guest molecule38,39,40,41,42. Additionally, revealing the chirality of amino acid-containing copolymers poses significant challenges due to the absence of chromophoric groups and a racemic mixture of amino acid enantiomers43. To address these issues, we propose an approach to design a predominant single-handed conformation between chiral amino acid co-polymer guest and BCE[n], with the anticipation that the intrinsic chirality of BCE[n] can be induced in the presence of an additional chiral guest.\n\nSubsequently, we constructed a supramolecular assembly through host-guest interactions between achiral BCE[n] and a chiral amino acid polymer LG (DG). The structures and detailed synthesis procedures of chiral polymers LG (DG) are provided in the\u00a0Supplementary Information (Supplementary Figs.\u00a034\u201338). Prior to investigating the chiral amplification between the host and the guest, we initially examined their host-guest interaction by employing benzyl N-benzoyl-L-alaninate (Gm) as a model guest monomer. The complexations between each BCE[n] compound and Gm were investigated using 1H NMR titration experiments. However, except for sym-BCE[5], none of the proton signals showed a significant shift when mixed with Gm for the other five\u00a0bis-crown ethers, indicating no complexation occurred (Supplementary Figs.\u00a070\u201374). As depicted in Fig.\u00a08 and Supplementary Fig.\u00a067, the sym-BCE[5] host experienced significant chemical shifts of the protons with increasing equivalents of Gm. The peak of Ha, Hb, Hc, and He,f in sym-BCE[5] exhibited splitting and upfield shifts. A slight upfield shift was also observed in the chemical shift of proton H1 in Gm. When the amount of Gm reached 1.0 equiv. or more, no significant changes were observed in NMR spectra, indicating a saturated condition. These observations suggest that the monomer guest Gm can form a stable 1:1 complex with sym-BCE[5], and the complexation between Gm and sym-BCE[5] exhibits a slow exchange process. Furthermore, Job\u2019s Plot analysis further verifies the binding ratio is 1:1, which may be attributed to steric hindrance limiting the binding ability of sym-BCE[5] towards a second guest (Supplementary Fig.\u00a068). The association constant (Ka) for the sym-BCE[5]\u2283Gm complex was determined to be 9.3\u2009\u00d7\u2009102 M\u22121 by UV-vis titration experiments (Supplementary Fig.\u00a069).\n\n1H NMR titration (400\u2009MHz, CDCl3, 298\u2009K) spectra of sym-BCE[5] (3.0\u2009mM), sym-BCE[5] + Gm (1:1, 3.0\u2009mM) and Gm (3.0\u2009mM).\n\nDue to the induced chiral amplification from the chiral polymer guest LG (DG) to the AIE-active host sym-BCE[5], we inferred that this supramolecular assembly could exhibit circular dichroism (CD) properties (Fig.\u00a09a). Therefore, CD spectra were carefully examined at varying molar ratios of sym-BCE[5]/LG ranging from 1:0 to 1:1.1. Upon addition of LG to the achiral sym-BCE[5] solution, a strong negative cotton effect at 350\u2009nm could be observed in the CD spectra. This can be attributed to chiral amplification of the macrocycle sym-BCE[5] induced by host-guest complexation. The highest CD intensity is achieved when LG is present in an equimolar ratio with sym-BCE[5]. Further increasing LG up to 1.1 equiv. did not result in significant changes in the intensity of CD signals. Therefore, our focus primarily lies on investigating the optimal stoichiometric ratio of 1:1 for achieving chiral amplification in the sym-BCE[5]\u2283LG system. Under identical conditions, DG exhibited a positive cotton effect at 350\u2009nm, representing mirror image CD signals compared to those obtained for the sym-BCE[5]\u2283LG system.\n\na CD spectra of sym-BCE[5] with increasing concentrations of LG and DG (0 to 1.1 equiv). b CPL spectra of sym-BCE[5]\u2283LG (1:1) and LG (blue and black lines, \u03bbex.\u2009=\u2009350\u2009nm), sym-BCE[5]\u2283DG (1:1) and DG (green and red lines, \u03bbex\u2009=\u2009350\u2009nm). c SEM images of sym-BCE[5]\u2283DG (up) and sym-BCE[5]\u2283LG (bottom). d CD spectra of LG, DG, sym-BCE[5]\u2283LG (1:1), sym-BCE[5]\u2283DG (1:1), sym-BCE[5]\u2283LG\u2009+\u2009Na+ (1:1:1) and sym-BCE[5]\u2283DG\u2009+\u2009Na+ (1:1:1).\n\nThe circularly polarized luminescence (CPL) properties of the host-guest assemblies between sym-BCE[5] and chiral guest were also explored. As shown in Fig.\u00a09b, both sym-BCE[5]\u2283LG and sym-BCE[5]\u2283DG complexes exhibited a pair of mirror-image CPL signals in the range of 400\u2013500\u2009nm due to the chiral amplification facilitated by strong host-guest interactions and well-order assembled nanostructures. The maximum glum values of CPL for sym-BCE[5]\u2283LG and sym-BCE[5]\u2283DG were determined to be \u20121.80\u2009\u00d7\u200910\u22122 and 1.84\u2009\u00d7\u200910\u22122 at 455\u2009nm, respectively. In contrast, the guest LG and DG alone did not exhibit any CPL signals at any wavelength, highlighting the essential role played by the AIE-active achiral host in facilitating optical performance.\n\nSubsequently, transmission electron microscopic (TEM) and scanning electron microscopy (SEM) measurements were utilized to explore the morphologies of polymeric guests and their assemblies. TEM images revealed that LG or DG self-assembled into nanowire structure with average widths of ~100\u2009nm and 200\u2009nm in chloroform, respectively (Supplementary Fig.\u00a081). When sym-BCE[5] was mixed with LG, SEM and TEM images clearly showed that the resulting supramolecular complex sym-BCE[5]\u2283LG exhibited a left-handed linear nanostructure with helixes, while DG with the opposite molecular chirality formed a right-handed nano-helical structure (Fig.\u00a09c, and Supplementary Fig.\u00a081).\n\nBased on the aforementioned investigation of host-guest interactions, sym-BCE[5] exhibited superior binding properties towards guest molecules, making it an ideal candidate for further comprehensive exploration. Considering the potential site for metal ion binding with crown ether rings, the introduction of competitive metal ions could effectively regulate the chiral assemblies. It is widely recognized that sodium cations exhibit a stronger affinity towards crown ethers compared to secondary amines44,45,46,47,48,49. Therefore, sodium tetrakis[3,5-bis(trifluoromethyl)-phenyl]borate (NaBArF) was selected as a competitive guest to modulate the CD and CPL switching behavior of the host-guest complex, presenting an innovative approach for fabricating dynamic CPL-active materials50.\n\nThe binding behavior of Na+ cation to sym-BCE[5] was investigated using 1H NMR spectroscopy in CDCl3 solution. Upon addition of Na+, significant chemical shift changes were observed, with the maximum chemical shift reached when one equivalent of Na+ was added (Supplementary Fig.\u00a075). This observation suggests the formation of a 1:1 complex between sym-BCE[5] and Na+. Furthermore, the binding behavior exhibited fast exchange kinetics on the NMR spectroscopic timescale. Job\u2019s Plot confirmed a 1:1 stoichiometry similar to BCE[6] in its binding with K+ ion (Supplementary Fig.\u00a076), and the association constant (Ka) was calculated as 2.70\u2009\u00d7\u2009103 M\u22121 by analyzing sequential changes in UV-vis absorbance of sym-BCE[5] in the presence of varying concentrations of Na+ (Supplementary Fig.\u00a077). This indicates that Na+ competes effectively in regulating the self-assembly of sym-BCE[5]-based assemblies. In contrast, minimal changes in chemical shifts were observed in the CDCl3 solution of asym-BCE[5] under identical conditions, suggesting a lack of strong binding affinity between asym-BCE[5] and Na+ cation (Supplementary Fig.\u00a078). This could be attributed to the unfavorable effect of its more twisted and smaller-sized cavities on guest binding of asym-BCE[5].\n\nTitration experiments were further conducted to investigate the CD spectra changes upon adding Na+ cations (0 to 1.0 eq.), aiming to induce the CD/CPL switching process. As a result, CD spectra exhibited a quenching effect on the CD signal with increasing amounts of Na+ (Supplementary Fig.\u00a080). Consequently, as shown in Fig.\u00a09d, the addition of Na+ resulted in a decrease in CD intensity for both sym-BCE[5]\u2283LG and sym-BCE[5]\u2283DG, indicating disassembly of assemblies. Based on these observations, it can be inferred that competitive binding of Na+ to the sym-BCE[5] host leads to a simultaneous switch-off in both CD and CPL signals for the supramolecular assemblies (Fig.\u00a010).\n\nCartoon representation of the CD and CPL signal of sym-BCE[5]\u2283LG (DG) can be modulated by Na+.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_Fig8_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_Fig9_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51607-z/MediaObjects/41467_2024_51607_Fig10_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In conclusion, a series of bis-crown ether named BCE[n] (n\u2009=\u20094\u20137) with AIE-active TPE cores were synthesized by intramolecular coupling of cyclic precursor diketone compounds. The incorporation of flexible side chains into the rigid TPE core resulted in a specific strain on the molecules, leading to the existence of two conformers in semi-rigid BCE[5] and BCE[6], depending on the chain length. These conformers exhibit varying dihedral angles within the TPE core and variations in the shapes of crown ether cavities. Additionally, they can be effectively purified using conventional column chromatography. Due to subtle differences in conformation and ring size, their unique host-guest selective binding behaviors as well as their photophysical properties were systematically investigated. By utilizing host-guest interaction and supramolecular chiral amplification, sym-BCE[5]-based chiral assemblies were constructed to demonstrate both CD and CPL signals in the presence of chiral polymeric guests. Furthermore, these assemblies also exhibit Na+-responsive switch-off for CD and CPL signals. This highlights the significance of conformational diversification in elucidating the exceptional properties of supramolecular macrocycles while providing valuable insights into their structure-property relationships.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Under an argon atmosphere, a mixture of 4,4\u2032-dihydroxybenzophenone (1.0\u2009mmol), compound glycol ditosylate (2.5\u2009mmol), KI (0.25\u2009mmol), and K2CO3 (20.0\u2009mmol) in anhydrous MeCN (10\u2009mL) was refulxed overnight. The reaction mixture was filtered and rinsed three times with DCM. Organic layer was washed with deionized water, dried over Na2SO4. After filtration, the organic layer was collected and concentrated under vacuum. The crude product was purified by column chromatography over silica gel (ethyl acetate/petroleum ether, 1:1, v/v) to afford dieketone derivatives 1\u20134 as a white soild.\n\nUnder nitrogen atmosphere, diketone derivative (1.0\u2009mmol) and zinc powder (10.0\u2009mmol) were dissolved in anhydrous THF (10\u2009mL). The mixture was cooled to \u221210 \u00b0C and TiCl4 (5.0\u2009mmol) was slowly added. After stirring for 1\u2009h, the reaction mixture was warmed to room temperature and then refluxed overnight. The reaction was quenched by the addition of NaHCO3 solution. After filtration, the organic layer was collected and concentrated. The crude product was purified by silica gel column chromatography using PE/EA\u2009=\u20092/1, v/v) as eluent to obtain a colorless soild asym-BCE[n].\n\nUnder nitrogen atmosphere, diketone derivative (1.0\u2009mmol) and zinc powder (40.0\u2009mmol) were dissolved in anhydrous THF (10\u2009mL). The mixture was cooled to \u221210 \u00b0C and TiCl4 (20.0\u2009mmol) was slowly added. After stirring for 1\u2009h, the reaction mixture was warmed to room temperature and then refluxed overnight. The reaction was quenched by the addition of NaHCO3 solution. After filtration, the organic layer was collected and concentrated. The crude product was purified by silica gel column chromatography using PE/EA\u2009=\u20092/1, v/v) as eluent to obtain a colorless soild sym-BCE[n] as a major product and asym-BCE[n] as a minor product.\n\nThe synthesis and characterization of BCE[n] (n\u2009=\u20094\u20137) presented in this work, the experimental details, and additional data of tests were listed in the\u00a0Supplementary Information.\n\nNMR spectra were recorded on a Bruker AV400 and AV600 (400\u2009MHz and 600\u2009MHz) spectrometer. High-resolution electrospray ionization mass spectra (HR-ESI-MS) were recorded on an Agilent 6540Q-TOF LCMS equipped with an electrospray ionization (ESI) probe operating in the positive-ion mode with direct infusion. UV-vis absorption spectra were taken on a SHIMADZU UV-1700 spectrometer. Fluorescence spectra of solutions and powder were recorded on an Edinburg FLS-1000 steady-state and time-resolved fluorescence spectrometer using a xenon lamp as the excitation source. The absolute fluorescence quantum yield in the solid state was measured by using a calibrated integrating sphere on the same fluorescence spectrometer. CD spectra were determined using a JASCO J-810 spectrometer. CPL spectra were recorded by using a JASCO CPL-300 spectrometer. TEM analysis was performed on a JEM-2100 instrument. SEM images were captured with a Hitachi S-4700 microscope. Single crystal X-ray diffraction data were collected on a Bruker D8 VENTURE CMOS X-ray diffractometer (Mo\u2013K\u03b1 radiation, \u03bb\u2009=\u20090.71073\u2009\u00c5).", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The X-ray crystallographic coordinates for structures reported in this study have been deposited at the Cambridge Crystallographic Data Center (CCDC), under deposition numbers 2323861 (BCE[4]); 2242720 (asym-BCE[5]); 2323862 (sym-BCE[5]); 2323863 (asym-BCE[6]); 2323865 (BCE[7]); 2323867 (asym-BCE[6]\u2009+\u2009K+ complexes). These data can be obtained free of charge from The Cambridge Crystallographic Data Center via www.ccdc.cam.ac.uk/data_request/cif. The authors declare that the data supporting the findings of this study are available within the paper and its\u00a0Supplementary Information. And the coordinates of computationally determined structures are available from source data. 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Lett. 34, 107558 (2023).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by the National Natural Science Foundation of China (Nos. 22271154 and M-0411), the Innovation Support Program of Jiangsu Province (BZ2023055), and the China Postdoctoral Science Foundation project (2022M721601). The authors thank Prof. Myongsoo Lee, Prof. Jochen Niemeyer, Prof. Leyong Wang, and Prof. Yong Liang for their valuable suggestions.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Xueqi Tian, Minzan Zuo.\n\nCollege of Materials Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China\n\nXueqi Tian,\u00a0Minzan Zuo,\u00a0Ni Mao,\u00a0Kaiya Wang,\u00a0Yanshan Sheng,\u00a0Krishnasamy Velmurugan\u00a0&\u00a0Xiao-Yu Hu\n\nCollege of Chemistry and Chemical Engineering, Jiangxi Normal University, Nanchang, China\n\nXueqi Tian,\u00a0Jianmin Jiao\u00a0&\u00a0Xiao-Yu Hu\n\nSchool of Chemistry and Chemical Engineering, Nanjing University, Nanjing, China\n\nYuhong Shen\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nX. T. and M. Z. contributed equally to this work. X. T. and M. Z. drafted the manuscript and conceived the project. X.-Y. H. supervised the project and revised the manuscript. X. T., Y. S., N. M., Y. S., and K. V. performed the experiments. K. W. and J. J. helped with X-ray crystallography characterization. All authors collectively analyzed the data, discussed the results, and provided comments on the manuscript.\n\nCorrespondence to\n Xiao-Yu Hu.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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steering chlorination reaction for water purification", + "journal": "Nature Communications", + "published": "18 March 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57841-3/MediaObjects/41467_2025_57841_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57841-3/MediaObjects/41467_2025_57841_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57841-3/MediaObjects/41467_2025_57841_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-57841-3#Sec11" + ], + "code": [], + "subject": [ + "Heterogeneous catalysis", + "Pollution remediation" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4697524/v1.pdf?c=1742382437000", + "research_square_link": "https://www.researchsquare.com//article/rs-4697524/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-57841-3.pdf", + "preprint_posted": "04 Aug, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Carbon nanotube (CT)\u2013based chemical technologies particular in heterogenous advanced oxidation processes (AOPs) used for water purification, have been exploited over the several decades. Many strategies of CT modification have been exploited to improve the catalytic performance in the remediation processes. However, the strain fields of intrinsic defect sites on CT steering AOPs (such as chlorination) have never been reported. Here we for the first time explored the strained defect sites steering chlorination process onto high\u2013efficient and green abatement of 2,4\u2013dichlorophenol. Characterizations and theory analysis unveil that the strained defect sites with the elongated sp2 hybridized C\u2013C bond displaying the larger spin\u2013lattice relaxation boost electronic reactivity with chlorine molecules via the initial Yeager\u2013type adsorption. As a result, the amounts and catalogs of reactive species in our chlorination are tunable on demand, such as the ratio of high\u2013selectivity ClO\u2022 ranging from 38.8% in pure defects\u2013based system to 87.5% in strain dominated process, which result in generating the harmless intermediates and even deep mineralization of organic. Thus, this work highlights the vital role of the strain fields on steering chlorination reaction for greener water purification even beyond.Earth and environmental sciences/Environmental sciences/Environmental chemistry/Pollution remediationHealth sciences/Health care/Drug regulation", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Supplementaryinformation.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Carbon nanotube (CNT)\u2013based heterogeneous advanced oxidation processes (AOPs) used for water purification have been exploited for several decades. Many strategies for modifying CNTs have been utilized to improve their catalytic performance in remediation processes. However, the strain fields of the intrinsic defect sites on CNT steering AOPs (such as chlorination) have not yet been reported. Here, we explored the strained defect sites for steering the chlorination process for water purification. The strained defect sites with the elongated sp2 hybridized C\u2013C bonds boost electronic reactivity with the chlorine molecules via the initial Yeager\u2013type adsorption. As a result, the reactive species in chlorination can be regulated on demand, such as the ratio of high\u2013selectivity ClO\u2022 ranging from 38.8% in conventional defect\u2013based systems to 87.5% in our strain\u2013dominated process, which results in the generation of harmless intermediates and even deep mineralization during 2,4\u2013DCP abatement. This work highlights the role that strain fields have on controlling the extent of chlorination reactions.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Recently, widely focused strain engineering of carbon nanotube (CNT) substrate has enabled the optimization of the physicochemical characteristics of molecular catalysts even at the sub\u2013nanoscale; an example includes tuning the curvature of monodispersed metal phthalocyanines1,2,3. The strain fields caused by the substrates can continuously or discretely transform the geometric and electronic layout and energy level of the metal\u2013based overlayer to increase the catalytic activity via so\u2013called essential interface reconstructions4,5,6. Interface reconstructions have been customarily attained using interlayer interactions, which involve van der Waals, \u03c0\u2013\u03c0 or donor\u2012acceptor conjugation between the substrate and molecular catalysts3,7. As such, the interface reconstructions from the substrates and catalytic center of the metal\u2013based overlayer usually consume considerable energy in the strain fields. In addition, the contributions of the strain fields on the improvement in the reaction performances always appear to be dependent on the reaction sites. Thus, limited reactivity enhancement is usually observed8\u201210; for example, the continuous tuning the strain of the IrO6 octahedron in Sr2IrO4 slightly enhances the oxygen evolution from 1.55 to 1.40\u2009V overpotential at 10\u2009mA\u2009cm\u22122\u20099. Therefore, the strain fields on CNTs need to be efficiently utilized without useless energy consumption in the foregoing removable interface reconstruction. To the best of our knowledge, this type of design in CNT\u2013based chemical remediation technologies for water purification has not yet been reported.\n\nTogether with these considerations, herein, we report a simple pyrolysis strategy for CNT modification to develop a set of catalysts with stable defect sites and tunable strain fields. These catalysts have been used in the mature chlorination reactions for more than a century for water purification11,12, where the emerging contaminant of 2,4\u2013dichlorophenol (2,4\u2013DCP) as a model pollutant was abated. A series of characterizations, such as electron microscopy, spectroscopy techniques such as X-ray absorption fine structure (XAFS) spectroscopy, X\u2013ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR) were used to identify the defect sites and strain fields on the modified catalysts. Chlorination by CNTd\u2013S2 with the optimum strain for water purification was expected to overcome the limitations of the high cost of UV irradiation and poor anti\u2012disturbance performance during the conventional homogenous UV/chlorine process. Many experimental results and theoretical analyses based on density functional theory (DFT) calculations clearly revealed the important roles of the strain fields in the high\u2013efficiency activation of chlorine and the green abatement pathway of 2,4\u2013DCP to preliminarily respond to the aforementioned attainable expectations. Lastly, possible practical applications of this technology were confirmed by evaluating its capacity for decontaminating water.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "A commercial multi\u2013walled CNT (Supplementary Fig.\u00a03) was used as a catalyst precursor considering that it had facile defect sites and tunable strain fields13. The CNTs were first modified by a widely used dicyandiamide pyrolysis strategy14,15; during this strategy, CNTd (with defects), CNTd\u2012S1 (with defects and weak strain) and CNTd\u2012S2 (with defects and strong strain) were respectively obtained under increasing pyrolysis temperatures (see the Supplementary Methods for catalyst synthesis). Characterizations of high\u2013resolution TEM (HR\u2013TEM, Fig.\u00a01a\u2013c and Supplementary Fig.\u00a04) and atomic force microscopy (AFM, Supplementary Fig.\u00a05) intuitively revealed that the above modification process worked effectively. In detail, the HR\u2013TEM image of CNTd\u2013S2 (Supplementary Fig.\u00a04c) illustrates the evident lattice distortion in CNTd\u2013S2. Integrated pixel intensities of the CNTd\u2013S2 (Fig.\u00a01c) from its lattice fringes (Fig.\u00a01a) display a more distorted intensity compared with the smooth intensity on the CNTd (Fig.\u00a01b, c), qualitatively uncovering the above lattice distortion. In addition, compared to CNTd, CNTd\u2013S2 has a larger diameter (from 15.2 to 19.4\u2009nm, Supplementary Figs.\u00a03, 5 and 6), higher porosity (Supplementary Fig.\u00a07) and greater expanded d\u2013spacing (0.34 to 0.36\u2009nm, Fig.\u00a01c). These characteristics of CNTd\u2013S2 were likely attributed to feasible thermo\u2013expansion and gas evolution (such as NH3 and NO) during the high temperature and dicyandiamide pyrolysis processes16. These factors would destabilize the sp2 hybridized C\u2013C bonds of CNTs, as demonstrated by the characterizations (Supplementary Fig.\u00a08) and molecular dynamical simulations (Supplementary Fig.\u00a09). The Raman spectra of the catalysts display three main peaks: D band, G band and 2D peak (Fig.\u00a01d). The D band of the catalysts that usually depicts the site defects on the basal plane, displays a higher intensity for CNTd than that of CNT, whereas the D band is nearly stable for these catalysts of CNTd, CNTd\u2013S1 and CNTd\u2013S2 (Supplementary Fig.\u00a010). These results indicate heavier defects on the CNTd than on the pristine CNT but a similar degree of defects for CNTd, CNTd\u2013S1 and CNTd\u2013S2. Moreover, high\u2013resolution XPS spectra of C 1\u2009s on 286.15\u2009eV binding energy assigned to the defects of catalysts (Fig.\u00a01e) and EPR technique (Supplementary Fig.\u00a011) for analysis of the unpaired electrons trapped by the defects of catalysts, both confirmed the above stable defects for the CNTd series. Combining the theoretical analyses (Supplementary Fig.\u00a012) and the conclusions from the reported works17, dual vacancy defects were potentially present in our catalysts.\n\nLattice fringes of (a) CNTd\u2013S2 and (b) CNTd. The selected area from the HR\u2013TEM images in Supplementary Fig.\u00a04a, c. c Integrated pixel intensities of CNTd\u2013S2 and CNTd obtained from the inverse fast Fourier transformation images in corresponding Figures (a, b), respectively. d Raman spectra of the catalysts. e High\u2013resolution C 1\u2009s XPS spectra of catalysts. f La analysis acquired from the XRD spectra of the catalysts in Supplementary Fig.\u00a013. The inset is a schematic diagram of La on the tube. g Strain analysis of CNTd\u2012S2 obtained by geometric phase analysis from the HR\u2013TEM images in Supplementary Fig.\u00a04c. h HAADF\u2013STEM EELS spectra and (i) NEXAFS spectra for the C K\u2013edge from the CNTd, CNTd\u2013S1 and CNTd\u2013S2 catalysts.\n\nOur catalysts\u2019 G band in the Raman spectra is associated with the planar stretching of sp2\u2013hybridized C atoms (Fig.\u00a01d)18. Interestingly, we clearly observed a bathochromic shift in the G band from 1592 to 1560\u2009cm\u20131 for CNT to CNTd\u2013S2. The findings highlight the increased tensile strain of the CNTd series because of the elongated sp2 hybridized C\u2013C bonds8,9. The 2D peak of the Raman spectra, which is often related to the stretching vibration of the stacking sheet, increases in intensity in the catalysts (Fig.\u00a01d), indicating expanded planes19. Additionally, their X\u2013ray diffraction (XRD) patterns (Supplementary Fig.\u00a013) demonstrate the shift of the (002) facet toward a smaller angle for the CNTd series, further indicating expanded planes20. We accordingly observed the enlarged average crystallite sizes (La) on the tube walls of the catalysts using the Debye\u2013Scherrer equation (Fig.\u00a01f)18. The corresponding geometric phase analysis of CNTd\u2013S2 (Fig.\u00a01g) from its HR\u2013TEM results (Supplementary Fig.\u00a04c) visually reveals the homogenous strain mapping on the wall21. Based on these observations, we conclude that the CNTd series developed with a stable degree of defects but increased tensile strain. Electron energy\u2013loss spectroscopy (EELS) analysis of the C K\u2013edge in the catalysts of the CNTd series obtained from an aberration\u2013corrected high\u2013angle annular dark field scanning transmission electron microscopy (AC\u2013HADDF\u2013STEM) displays increasingly intensity on the sharp peaks at 284.5\u2009eV (Fig.\u00a01h and Supplementary Fig.\u00a014). These results indicate more excited electron transitions from the C 1\u2009s to the \u03c0* orbital on the sp2 hybridized C atom20. Furthermore, the C K\u2013edge near\u2013edge XAFS (NEXAFS) spectra of the CNTd series in Fig.\u00a01i show the two evident peaks at 285.0\u2009eV and ~292.7\u2009eV, which are respectively assigned to the \u03c0* and \u03c3* excitations in the sp2 hybridized C\u2013C bond. The peak of \u03c0* excitation increased and correlated to the complex energy transition of C 1\u2009s to \u03c0*22. Importantly, intensive resonance at the \u03c3* sites (~292.7\u2009eV, Fig.\u00a01i) further highlights the incremental strain in our catalysts23. Notably, these changes in the C 1\u2009s chemical microenvironment potentially indicate improved chemical reactivity of the sp2 hybridized C atom after strain (Fig.\u00a02a).\n\na Schematic illustration of catalysts with the strain effect. b Time profile of the free chlorine concentration activated by our catalysts. Reaction conditions: 0.1\u2009g\u2009L\u22121 catalyst, 4.5\u2009mg\u2009L\u22121 free chlorine. c EPR technique using DMPO as a spin\u2013trapping agent for the detection of the reactive species shown in (b). d Reactive species analysis during the chlorination process induced by our catalysts. e Spin density of \u2022OH, Cl\u2022 and ClO\u2022 from our DFT calculations.\n\nWe used CNTd series activating oxidants for the reactive species evolution to evaluate the above chemical reactivity. The fastest decay of the probe molecule via chlorine activation manifested that the chlorination process of CNTd\u2013S2 enabled to produce a significant number of reactive species (Supplementary Fig.\u00a015). Thus, free chlorine utilization was further evaluated under activators of the CNTd series. As expected, CNTd\u2013S2 displayed the best utilization of free chlorine (Fig.\u00a02b and Supplementary Figs.\u00a016, 17). Sluggish rates and nearly no catalytic kinetics were observed in CNTd and CNT\u2012based chlorination processes, respectively. An inveterate opinion is that defects and/or doped heteroatoms in CNT usually serve as active sites for oxidant activation24,25. The contribution of defects during chlorination process was considered to be non\u2012negligible owing to the enhanced performance from CNT to CNTd. Considering the absence of heteroatom doping (Supplementary Figs.\u00a014, 18 and 19) and the similar degree of defects for the CNTd series (Fig.\u00a01d and Supplementary Figs.\u00a08 and 11), we attributed the above improved performance to the strain fields on our catalysts, such as the highest strain of CNTd\u2013S2.\n\nThe reactive species generated in the above chlorination reactions were further examined using the spectroscopy characterization and probe experiments. The DMPOX signal characterized by the EPR technique using 5,5\u2013dimethyl\u20131\u2013pyrroline n\u2013oxide (DMPO) as a spin\u2013trapping agent highlighted the increased exposures of the reactive species under enhanced strain fields (Fig.\u00a02c and Supplementary Fig.\u00a020)26. The results from the probe experiments indicated that \u2022OH, Cl\u2022 and ClO\u2022 were present throughout the chlorination process (Fig.\u00a02d and Supplementary Figs.\u00a021 and 22). The unpaired electrons of the three radicals delocalized on Cl or O atoms (Fig.\u00a02e), and the radicals enabled the electrophilic attack; for example, the ClO\u2022 electrophilicity index of 2.15\u2009eV was even higher than the well\u2013known \u2022OH of 1.98\u2009eV (Supplementary Table\u00a01)27,28. Other reactive species, such as singlet oxygen (1O2, Supplementary Fig.\u00a023), were not detected in the present system. Interestingly, as shown in Fig.\u00a02d, the strained catalysts could increase the exposure of reactive species by 13.2 times using the strain fields, and this value was much greater than that of the defects (only 1.7 times). Moreover, the strained catalysts could tune the distribution of reactive species, and the ratio of ClO\u2022 increased from 38.8% to 87.5%. The vital product of ClO\u2022 in chlorination was desirable for water purification because of its outstanding advantages, such as high selectivity and mild oxidation ability29. These deductions clarified the highly important role of the strain fields on CNTd than the widely reported defects alone during oxidant activation.\n\nWe analyzed the electronic structures and energy band levels of the catalysts to clarify the above diverse chlorination performances. The decreased oxidation temperature from 627.1 to 587.0\u2009\u00b0C for the strained catalysts in their differential thermogravimetry (DTG) curves (Fig.\u00a03a and Supplementary Fig.\u00a024) indicated that the strained catalysts had the ability of more thermodynamic\u2013feasible electron transfer20. This ability was further confirmed by electrochemical experiments based on Tafel polarization curves (Fig.\u00a03b) and electrochemical impedance spectroscopy of the catalysts (Supplementary Fig.\u00a025). For example, the more negative corrosion potential and larger corrosion current occurred in the Tafel polarization curves of the strained catalysts indicate more beneficial electron transfer30. This likely originated from the elongated sp2 hybridized C\u2013C bond along with the larger spin\u2013lattice relaxation (as demonstrated in Fig.\u00a01) due to the metallic or semiconducting nature of CNT31. The electronic band gap of the CNTd series was then determined via ultraviolet photoemission spectroscopy (UPS, Fig.\u00a03c and Supplementary Fig.\u00a026). The highest occupied state (HOS), which corresponds to the highest orbital energy level of the occupied electron32, displayed an increased energy level from 0.74\u2009eV in CNTd to 1.20\u2009eV in CNTd\u2013S2. This result indicated a downward p band center and increased electronic reactivity for the C atom with greater spin\u2013lattice relaxation caused by strain33. These changes in the electronic state induced the total energy improvement of the catalysts (Fig.\u00a03d). Our experimental (Fig.\u00a03c) and theoretical (Fig.\u00a03d and Supplementary Fig.\u00a027) results demonstrated the increased work function on the strained catalysts, and revealed the improved ability of the electron transition from the Fermi energy to the vacuum level. Additionally, the strained catalysts caused a shift their energy band structure toward Fermi energy (Fig.\u00a03e), indicating a growing number of gap states near the Fermi energy34. Therefore, the strain fields that modified the electronic state and improved the reactivity of C atoms to boost the electron transfer with free chlorine were clearly demonstrated.\n\na DTG curves and (b) Tafel polarization curves for the CNTd series. c Electronic band gap of the CNTd series from the experimental UPS measurement. Ev Energy level of vacuum, Ef Energy level of Fermi, HOS Highest occupied state, WF Work function. d Work function and total energy and (e) band structure of the CNTd series obtained by of the DFT calculations. The insets in (d) are schematic diagrams of the strained defect site.\n\nWe subsequently investigated the evolution mechanism of the above reactive species during chlorination of the CNTd series. The EPR test of the catalysts showed a stimulated signal intensity on the strained catalysts after they reacted with free chlorine (Fig.\u00a04a and Supplementary Fig.\u00a028). These results indicated that more unpaired electrons on the strained catalysts participated in the chlorination process35. A set of electrochemical linear sweep voltammetry (LSV) tests for the CNTd series in a rotating disk electrode apparatus under an inert gas atmosphere revealed enhanced electron transfer and selectivity (Supplementary Figs.\u00a029 and 30, Fig.\u00a04b) to quantify the above electronic conduct. For example, the electron transfer number calculated by the modified Koutecky\u2013Levich equation (Supplementary Methods for Electrochemical experiments) was 5\u00d710\u20133 \u03bcA rpm-1 for the CNTd\u2013S2 and 2.9\u2009\u00d7\u200910\u22123\u2009\u03bcA\u22121 for the CNTd catalyst36,37. DFT calculations based on charge density difference and Bader charge analysis demonstrate an evident increase of charge transfer (1.442|e\u2009|\u2009) from the CNTd\u20136% strain to hypochlorous acid (HOCl) (Fig.\u00a04c)11. The free chlorine used here was mainly HOCl based on the solution pH and its pKa=7.5 (Supplementary Figs.\u00a031 and 32)38. In the above electrochemical process, a negative increase and a positive decrease in the current response occurred for CNTd and CNTd\u2013S2, respectively, when both reacted with free chlorine (Supplementary Figs.\u00a029a, e, and 30a, e). Both the supplementary LVS tests in a common three\u2013electrode system (Fig.\u00a04d and Supplementary Fig.\u00a033) and conductivity tests of catalysts with or without free chlorine (Supplementary Fig.\u00a034) supports these results. In contrast, almost no current change in the positive potential zone was observed (Supplementary Fig.\u00a035). These observations emphasized the changeable electronic reduction of free chlorine via strain fields.\n\na EPR test of CNTd and CNTd\u2013S2 mixed with (the solid line) or without (the polygon dots) free chlorine. b Modified Koutecky\u2013Levich plot of the free chlorine reduction measured by a rotating disk electrode apparatus for CNTd and CNTd\u2013S2 to estimate the electron transfer number between the catalysts and free chlorine. The inset illustrates the reaction process of HOCl and catalyst onto the electrode. c Charge density difference and Bader charge analysis of CNTd without strain (0%) and with 6% strain reacting with HOCl, as calculated by DFT. The isosurfaces of green and yellow indicate the depletion and accumulation of electrons, respectively. d LSV curves of CNTd and CNTd\u2013S2 with (the half\u2013hollow dots) or without (the hollow dots) free chlorine. e Comparison of the adsorption energy and adsorption types between CNTd without strain (0) and with 6% strain toward free chlorine. The insets are schematic diagrams of HOCl adsorption onto the sites of the strained and unstrained. In\u2012situ Raman spectra to detect the intermediates in f CNTd\u2013S2/free chlorine system and (g) CNTd/free chlorine system. h Free energy diagram of CNTd without strain (0%) and with 6% strain used for chlorination. IS: initial state; TS: transition state; FS: final state. The insets are schematic diagrams of HOCl decomposition processes onto the sites of the strained and unstrained.\n\nA plausible explanation for the above findings is that the strained catalysts likely tuned the chemisorption types of free chlorine. HOCl adsorbed on the CNTd\u20130 strain and CNTd\u20136% strain displayed adsorption energies of \u22120.264 and \u22121.445\u2009eV, respectively (Fig.\u00a04e). This difference in the adsorption energy was likely attributed to the different chemisorption types between the HOCl and CNTd series. Our DFT calculations confirmed that the side\u2013on Yeager\u2013type adsorption between HOCl and CNTd\u20136% strain was more thermodynamically favorable, in which both Cl and O atom were simultaneously contact with two C atoms at the strained defect sites (Supplementary Figs.\u00a036\u201338, 42). However, the end\u2013on Pauling\u2013type adsorption between the HOCl and CNTd\u20130 strain was thermodynamically feasible because of the negative adsorption energy (\u20130.264\u2009eV), during which the Cl atom first contacted one C atom at a defect site (Supplementary Figs.\u00a039\u2013S42). In\u2013situ Raman spectroscopy was further used to detect bond stretching during the above chlorination reactions (Supplementary Fig.\u00a043). Two clear evolutionary peaks at approximately 660\u2009cm\u20131 and 1015\u2009cm\u22121 assigned to C\u2013Cl and C\u2013O stretching, respectively, were observed in the CNTd\u2013S2/free chlorine system (Fig.\u00a04f)39. In contrast, only one peak at approximately 675\u2009cm\u22121 was observed in the CNTd/free chlorine system (Fig.\u00a04g). Notably, the slight redshift of the C\u2013Cl stretching from 675 to 660\u2009cm\u20131 after being strained again indicated intensive electronic interactions between free chlorine molecules and CNTd\u2013S240. These findings clearly showed the regulated adsorption types for free chlorine by the strained catalysts. Importantly, this regulated chemisorption conducted via our strain fields accelerated the possible rate\u2013determining step in chlorination because the adsorption of free chlorine onto catalysts was an uphill endothermic process, according to their Gibbs free energy diagram (Fig.\u00a04h). The subsequent heterolytic cleavage of Cl\u2013O in free chlorine is a spontaneous exothermic process especially for CNTd\u20136% strain, since the energy barrier showed a decrease from \u20130.912\u2009eV for CNTd\u20130 to \u20134.291\u2009eV for CNTd\u20136% strain. These results elucidate the improved adsorption and enhanced cleavage of free chlorine by our strained catalysts.\n\nWe applied this chlorination to water purification in view of the surging demand for clear water41,42. 2,4\u2013DCP, a widespread emerging contaminant in aqueous environments43, was treated in the chlorination process. Its degradation rate after the optimized dosage of reactants (Supplementary Figs.\u00a044 and 45) was in good accordance with the free chlorine utilization induced by the CNTd series (Fig.\u00a02b). For example, a slight utilization of free chlorine (Fig.\u00a02b) along with the slow abatement of 2,4\u2013DCP (Supplementary Fig.\u00a045) occurred in the CNTd\u2013based system. In contrast, remarkable 2,4\u2013DCP abatement was achieved in the CNTd\u2013S2/free chlorine system, i.e., the strain\u2013dominated system displayed a degradation rate of 2.7 times higher than that of the defect\u2013based chlorination process (Supplementary Fig.\u00a045b). Notably, the strain\u2013dominated system significantly increased the mineralization during the 2,4\u2013DCP abatement (Fig.\u00a05a and Supplementary Figs.\u00a046\u201348). The removal of total organic carbon (TOC) was 5.0\u2013fold greater in the CNTd\u2013S2\u2013based system than that in the CNTd\u2013based system during 2,4\u2013DCP mineralization (Supplementary Fig.\u00a047). Thus, our system was superior to most reported works for 2,4\u2013DCP mineralization (Supplementary Fig.\u00a049). These results indicated that our strained catalysts for highly efficient utilization of free chlorine resulted in accelerated mineralization of 2,4\u2013DCP by the C\u2013C cleavage and did not involve the C\u2013C or C\u2013O coupling, as recently reported28,44.\n\na Time course of TOC removal in the reaction system. b Amperometric i\u2013T curves of CNTd\u2013S2 in a three\u2013electrode electrochemical system. Free chlorine and 10\u2009mg\u2009L\u22121 2,4\u2013DCP were added to the electrolyte at run times of approximately 100\u2009s and 200\u2009s, respectively. c Degradation pathway of 2,4\u2013DCP in the CNTd\u2013S2/free chlorine system and its corresponding Gibbs free energy. The inset shows the activation energy of the transition state (TS). d Schematic illustration of the contact angle between CNTd or CNTd\u2013S2 after contact with a solution of free chlorine alone, and a mixed solution of free chlorine and 2,4\u2013DCP. The dark color indicates the contact angle from the catalyst in contact with the free chlorine solution. The light color means the contact angle from the catalyst in contact with the mixed solution of free chlorine and 2,4\u2013DCP. e Schematic illustration of the practical application scenario of our continuous\u2013flow system. The SEM image of the CNTd\u2013S2 membrane as an inset shows a rough 200\u2009\u03bcM height of the membrane. f Evaluation of the long\u2013term operation of this continuous\u2013flow system for the removal of 2,4\u2013DCP, 4\u2013chlorophenol and methylene blue. The inset displays a digital photo of the continuous\u2013flow system where the polluted water body of methylene blue was treated and decolorized by this apparatus. The effluent was colorless. Reaction conditions: 45\u2009mg\u2009L\u22121 free chlorine, 10\u2009mg\u2009L\u22121 organic contaminants, and a 4\u2009mL\u2009min\u22121 flow rate.\n\nWe further explored the mechanism of 2,4\u2013DCP abatement in the present system. The results from the scavenger experiments (Supplementary Figs.\u00a050a\u2013c) reconfirmed that ClO\u2022 was responsible for 2,4\u2013DCP abatement in the strain\u2013dominated system (Fig.\u00a02d), and other degradation pathways, such as 1O2 oxidation (Supplementary Figs.\u00a023 and 50d\u2013f) or mutual activation degradation (Supplementary Fig.\u00a051)45, were not observed. Moreover, the electrochemical amperometric i\u2013T curves of CNTd\u2013S2 showed a negative and concentration\u2013dependent current jump after the addition of free chlorine. This phenomenon reasserted the reduction of free chlorine by the foreign electron from CNTd\u2013S2 (see discussion in Fig.\u00a04). Almost no fluctuation for the current after 2,4\u2013DCP addition demonstrated that no electron transfer occurred between 2,4\u2013DCP and the reactants. It was possibly caused by the frontier molecular orbitals of reactants and the lowest electrophilicity index for 2,4\u2013DCP (1.18\u2009eV, Supplementary Table\u00a01)46. For instance, a higher lowest unoccupied molecular orbital (LUMO) for 2,4\u2013DCP than HOCl (Supplementary Fig.\u00a052) indicates difficulty acquiring electrons for the former when both co\u2012existed. Moreover, the electrostatic potential (ESP) and Fukui index mapping of 2,4\u2013DCP further reveal the uneven charge distribution of its surface chemical sites (Supplementary Fig.\u00a053 and Table\u00a02). These results indicate that 2,4\u2013DCP was vulnerable to the electrophilic, nucleophilic and radical attacks of the reactive species. Closer observation of both the ESP and Fukui indices of the 2,4\u2013DCP molecule enables determination of the two C\u2013Cl bonds and the single \u2013OH group as the electrophilic sites and the three C (1\u2009C, 3\u2009C or 6\u2009C) probably as the radical or the nucleophilic sites.\n\nTo verify the previously mentioned predictions, we detected the intermediates of 2,4\u2013DCP degradation via HPLC\u2013MS. The seven possible monomers (Supplementary Fig.\u00a054) detected as the mainproducts included the ortho\u2013 or para\u2013chlorophenol (P4), ortho\u2013 or para\u2013chlorinated dihydroxybenzenes (P3), ortho\u2013 or para\u2013benzoquinone (P2) and chlorinated p\u2013benzoquinone (P1). After considering the foregoing theoretical analysis of the ESP and Fukui indices, we reasoned three possible pathways of 2,4\u2013DCP degradation, as shown in Supplementary Fig.\u00a055. Further evaluation by thermodynamics and kinetics demonstrated that pathway I was more beneficial (Fig.\u00a05c, Supplementary Figs.\u00a056 and 57). In pathway I, 2,4\u2013DCP first underwent a single electron transfer by a hydrogen abstract process on the \u2013OH group to form the crucially excited 2,4\u2013DCP\u2022 (an oxygen radical) due to the electrophilic attack of ClO\u2022, \u2022OH and Cl\u2022 (Fig.\u00a05c inset and Supplementary Fig.\u00a058). The excited 2,4\u2013DCP\u2022, which was obtained from some elemental reactions involving free\u2013radical addition and hydrogen abstract, was then desaturated to generate chlorinated p\u2013benzoquinone (P1). P1 was further dechlorinated to generate ortho\u2013 or para\u2013benzoquinone (P2) by the homolytic cleavage of 4C\u201312Cl by ClO\u2022 electrophilic oxidation, considering that there is a negative change of Gibbs free energy (\u0394G) in the reaction of P1 oxidized to P2 by ClO\u2022 (Fig.\u00a05c). Nevertheless, it is thermodynamically unfavorable for P2 production from P1 oxidation by \u2022OH or Cl\u2022 due to the positive \u0394G (Fig.\u00a05c). The minimum gap (9.15\u2009eV) between the HOMO of P1 and the LUMO of ClO\u2022 also indicated the favorable oxidation of P1 by the ClO\u2022 (Supplementary Fig.\u00a059). This process consumed OH\u2013, decreasing the pH of the solution (Supplementary Fig.\u00a031b). For 2,4\u2013DCP abatement in the CNTd/free chlorine system, six possible intermediates, including trichlorophenol and some dimers were detected (Supplementary Fig.\u00a060). Similarly, two possible pathways of 2,4\u2013DCP degradation were proposed (Supplementary Fig.\u00a061). In the two pathways, the excited 2,4\u2013DCP\u2022 (a carbon radical) was formed via single electron transfer from the \u2022OH attack at 6\u2009C or 1\u2009C because f0 on the two C was highly positive and \u0394G was negative (Supplementary Figs.\u00a062 and 63 and Supplementary Table\u00a02). Subsequently, the excited 2,4\u2013DCP\u2022 underwent either self\u2013coupling to generate P1 dimers and then generated a desaturation product of P2 via Cl\u2022 oxidation, or a Cl\u2022 addition for the production of P3. We evaluated the toxicity using the well\u2012known Ecological Structural Activity Relationships (ECOSAR) program35. A greener and simpler pathway toward 2,4\u2013DCP degradation existed in our strain\u2013dominated chlorination process, as outlined in Fig.\u00a05a and c, and Supplementary Figs.\u00a047 to 49, and 64. Unfortunately, some chlorinated products with high toxicity, such as trichlorophenol and dimers, were present in the traditional defect\u2013based chlorination systems for the 2,4\u2013DCP abatement (Supplementary Fig.\u00a064). These contrasts in the degradation pathway were mainly attributed to the high\u2013concentration and selective reactive species and the sensitized surface energy after the 2,4\u2013DCP addition in the strain\u2013dominated system (Figs.\u00a02d, 5d and Supplementary Fig.\u00a065).\n\nThe possible practical applications of our technology were evaluated by exploring the anti\u2013disturbance ability with respect to water matrix and long\u2013term water remediation. On the one hand, the experimental results showed that our system could not only resist the disturbance of some common inorganic anions (such as Cl\u2013, SO42\u2013 and HCO3\u2013, Supplementary Figs.\u00a066a\u2013c) and typical natural organic matter (humic acid, Supplementary Fig.\u00a066d), but also bear broad pH range (from pH 3.18 to 9.20, Supplementary Fig.\u00a066e) and different practical water bodies (including drinking water, tap water and surface water, Supplementary Fig.\u00a066f and Table\u00a03). These results highlight the crucial role of the high\u2013selectivity ClO\u2022 and the qualified anti\u2013disturbance ability to water matrix in future practical popularization. On the other hand, a continuous\u2013flow reactor was constructed for the long\u2013term treatment of contaminants. The core part of the reactor was the CNTd\u2013S2 membrane which was prepared from CNTd\u2013S2 supported on a mixed cellulose film (Fig.\u00a05e inset and Supplementary Fig.\u00a067a). This reactor equipped with a CNTd\u2013S2 membrane could effectively treat organics including 2,4\u2013DCP, 4\u2013chlorophenol and methylene blue, and had a stable degradation rate of more than 99% for long\u2013term operation (10\u2009hours, Fig.\u00a05f and Supplementary Fig.\u00a067b). The stable performance likely resulted from the robust physicochemical structure of CNTd\u2013S2 (Supplementary Fig.\u00a068). Hence, these admirable indices indicated that our strain\u2013dominated chlorination technique was suitable for practical applications.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57841-3/MediaObjects/41467_2025_57841_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57841-3/MediaObjects/41467_2025_57841_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57841-3/MediaObjects/41467_2025_57841_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57841-3/MediaObjects/41467_2025_57841_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57841-3/MediaObjects/41467_2025_57841_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Here, we systematically illustrated the strained catalysts for the steering of the chlorination process for water purification. Stable defects and increased strain fields were produced on easily available CNT using one\u2013step pyrolysis process. The strained catalysts have an increased energy level of HOS and an improved work function. They display the enhanced electronic activity of the C atom and then benefit the charge transport in the chlorination process. Owing to these characteristics, the strained catalysts could tune the adsorption types of free chlorine (a side\u2013on Yeager\u2013type adsorption) by increasing the adsorption energy, thus boosting the heterolytic cleavage of Cl\u2013O. As a result, the strain\u2013dominated chlorination process could result in higher exposure of reactive species and a tunable distribution of reactive species. Thus, 2,4\u2013DCP abatement by our system displayed in faster kinetics, deeper mineralization and a greener pathway. Finally, our technology was capable of preventing disturbances in water matrix and long\u2013term water remediation. Therefore, this technology nears the practice applications in water purification.\n\nDespite these encouraging lab\u2013scale results, such as the ultralow dosage of chlorine (4.5\u2009mg\u2009L\u22121) and the greener pathway of 2,4\u2013DCP abatement, concerns regarding the disinfection by\u2013products (DBPs) during the chlorination process still remain an issue for future practical applications47. Further identifying the site\u2013specific reactivity of the strain fields (e.g., imperfection types on tubes) for more effective chlorination and controllable evolution of reactive species is a promising alternative for mitigating DBP generation prior to large\u2013scale real applications6.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Chemicals, characterizations such as Cl\u2013 concentration (Supplementary Fig.\u00a01), catalysts\u2019 synthesis, chlorination process involving free chlorine measurement (Supplementary Fig.\u00a02), electrochemical test, DFT calculation method and the Supplementary Figs. and tables, etc., were recorded in the file of Supplementary Information.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data supporting the findings of the study are included in the main text and supplementary information files. Raw data can be obtained from the corresponding author upon request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Su, J. J. et al. Strain enhances the activity of molecular electrocatalysts via carbon nanotube supports. Nat. Catal. 6, 818\u2013828 (2023).\n\nArticle\u00a0\n CAS\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nFan, Y. Z. et al. Highly efficient metal-free nitrate reduction enabled by electrified membrane filtration. Nat. Water 2, 684\u2013696 (2024).\n\nArticle\u00a0\n CAS\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nZhu, S. et al. Biaxially\u2010strained phthalocyanine at polyoxometalate@carbon nanotube heterostructure boosts oxygen reduction catalysis. Angew. Chem. Int. Ed. 62, e202309545 (2023).\n\nArticle\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nWang, L. et al. Tunable intrinsic strain in two-dimensional transition metal electrocatalysts. 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The authors sincerely thank Shuhan Qin from China Pharmaceutical University for beneficial assistance on molecular dynamics simulations.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Yinqiao Zhang, Mohan Chen.\n\nSchool of Engineering, State of Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, PR China\n\nYinqiao Zhang,\u00a0Mohan Chen,\u00a0Xuanyu He,\u00a0Hao Liang,\u00a0Jingge Shang,\u00a0Jianqiu Chen\u00a0&\u00a0Sijin Zuo\n\nSchool of Environment, Tsinghua University, Beijing, PR China\n\nErzhuo Zhao\n\nSchool of Engineering, Westlake University, Hangzhou, Zhejiang, PR China\n\nKai Liu\n\nCollege of Environmental Science and Engineering, Nankai University, Tianjin, PR China\n\nMinghua Zhou\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY.Q.Z. and S.J.Z. designed the experiment. M.H.C. and Y.Q.Z. conducted the experiment and analyzed the results. X.Y.H. and H.L. contributed to conducting TEM, in\u2013situ Ramman characterization. E.Z.Z. carried out the DFT calculation. M.H.Z., J.G.S. and K.L. provided constructive suggestions for the project. J.Q.C. proposed and supervised the project. S.J.Z. wrote and revised the paper.\n\nCorrespondence to\n Jianqiu Chen or Sijin Zuo.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Charles-Fran\u00e7ois de Lannoy and the other, anonymous, reviewers for their contribution to the peer review of this work. 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Evaluation of Label-Free Quantification in Human Plasma: Benchmarking with a High Dynamic Range Multispecies Sample Set", + "journal": "Nature Communications", + "published": "02 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM7_ESM.xlsx" + }, + { + "label": "Supplementary Data 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM8_ESM.xlsx" + }, + { + "label": "Supplementary Data 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM9_ESM.xlsx" + }, + { + "label": "Supplementary Data 8", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM10_ESM.xlsx" + }, + { + "label": "Supplementary Data 9", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM11_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM12_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM13_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_MOESM14_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "http://proteomecentral.proteomexchange.org", + "/articles/s41467-025-64501-z#ref-CR60", + "https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD056598", + "https://repository.jpostdb.org/entry/JPST003358", + "/articles/s41467-025-64501-z#MOESM11", + "https://doi.org/10.5281/zenodo.17131745", + "https://doi.org/10.5281/zenodo.17131745", + "/articles/s41467-025-64501-z#Sec19" + ], + "code": [ + "https://github.com/HanYoo1402/LFQ-Bench-Scripts-for-PYE-Multicenter-Study", + "https://doi.org/10.5281/zenodo.17018339" + ], + "subject": [ + "Mass spectrometry", + "Proteome informatics", + "Proteomic analysis", + "Standardization", + "Translational research" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5618718/v1.pdf?c=1759489633000", + "research_square_link": "https://www.researchsquare.com//article/rs-5618718/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-64501-z.pdf", + "preprint_posted": "18 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Human plasma is routinely collected during clinical care and constitutes a rich source of biomarkers for diagnostics and patient stratification. Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is a key method for plasma biomarker discovery, but the high dynamic range of plasma proteins poses significant challenges for MS analysis and data processing. To benchmark the quantitative performance of neat plasma analysis, we generated a multispecies sample set based on a human tryptic plasma digest containing varying low level spike-ins of yeast and E. coli tryptic proteome digests, termed PYE. The sample set was distributed across twelve different sites and analysed on state-of-the-art LC-MS platforms in data-dependent (DDA) and data-independent acquisition (DIA) modes, resulting in a total of 1,116 individual LC-MS runs. Centralized data analysis showed that DIA methods outperform DDA-based approaches regarding identifications, data completeness, accuracy, and precision. DIA achieved excellent technical reproducibility, as demonstrated by coefficients of variation (CVs) between 1.5% and 4.6% at protein level. Comparative analysis of different setups clearly shows a high overlap in identified proteins and proves that accurate and precise quantitative measurements are feasible across multiple sites, even in a complex matrix such as plasma, using state-of-the-art instrumentation. The collected dataset, including the PYE sample set and strategy presented, serves as a valuable resource for optimizing the accuracy and reproducibility of LC-MS and bioinformatic workflows for clinical plasma proteome analysis.Biological sciences/Biological techniques/Proteomic analysisBiological sciences/Computational biology and bioinformatics/Proteome informaticsHealth sciences/Medical research/Translational researchBiological sciences/Systems biology/StandardizationBiological sciences/Biological techniques/Mass spectrometry", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplTable1.xlsxSupplementary Table 1SupplTable2.xlsxSupplementary Table 2SupplTable3.xlsxSupplementary Table 3SupplTable4.xlsxSupplementary Table 4SupplTable5.xlsxSupplementary Table 5SupplTable6.xlsxSupplementary Table 600PYEmanuscriptSupplemetaryInfo.pdfSupplementary Info File", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Human plasma is routinely collected during clinical care and constitutes a rich source of biomarkers for diagnostics and patient stratification. Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is a key method for plasma biomarker discovery, but the high dynamic range of plasma proteins poses significant challenges for MS analysis and data processing. To benchmark the quantitative performance of neat plasma analysis, we introduce a multispecies sample set based on a human tryptic plasma digest containing varying low level spike-ins of yeast and E. coli tryptic proteome digests, termed PYE. By analysing the sample set on state-of-the-art LC-MS platforms across twelve different sites in data-dependent (DDA) and data-independent acquisition (DIA) modes, we provide a data resource comprising a total of 1116 individual LC-MS runs. Centralized data analysis shows that DIA methods outperform DDA-based approaches regarding identifications, data completeness, accuracy, and precision. DIA achieves excellent technical reproducibility, as demonstrated by coefficients of variation (CVs) between 3.3% and 9.8% at protein level. Comparative analysis of different setups clearly shows a high overlap in identified proteins and proves that accurate and precise quantitative measurements are feasible across multiple sites, even in a complex matrix such as plasma, using state-of-the-art instrumentation. The collected dataset, including the PYE sample set and strategy presented, serves as a valuable resource for optimizing the accuracy and reproducibility of LC-MS and bioinformatic workflows for clinical plasma proteome analysis.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Human blood and blood-derived components (i.e., serum and plasma) reflect an individual\u00b4s health state and are routinely used for in vitro diagnostics, often referred to as a liquid biopsy, to either monitor, detect, predict, or rule out diseases. Plasma, the liquid blood component, is obtained by removing cellular material from whole blood through centrifugation in the presence of anti-coagulants such as heparin, ethylenediaminetetraacetic acid (EDTA), or sodium citrate. Plasma and serum are the most collected biofluids globally, easily accessible and routinely taken from thousands of patients daily. As such, they are valuable sources of (bio)markers reflecting the states of various disorders and illnesses and has become the focus of pharmacological, biomedical, and clinical pursuits.\n\nThe vast majority of biological processes are controlled and carried out by proteins. Liquid chromatography-mass spectrometry (LC-MS) has evolved as the leading technology for investigating proteins and analysing entire proteomes across diverse biological systems, making it a powerful tool for (protein) biomarker discovery1,2. Within their detection limits, MS-based proteomic approaches allow for the unbiased and comprehensive characterization of all proteins in a system with high analytical specificity. Most of these workflows employ a bottom-up approach, where sample proteins are first digested in vitro with sequence-specific proteases, such as trypsin, to generate peptides for analysis. Despite tremendous technological advances in the field of MS over the past two decades, plasma proteome analysis by this technology remains challenging due to the extremely high dynamic range of plasma proteins, which spans over 11 orders of magnitude3,4. Albumin, the most abundant plasma protein at a concentration of ~70\u2009mg/mL, constitutes around 55% of the total plasma protein content, while the 22 most abundant proteins collectively account for 99% of the overall plasma protein mass3,4. In MS-based bottom-up proteomic workflows, the majority of quantified peptide intensities arises from these highly abundant plasma proteins, significantly hindering the detection and quantification of peptides derived from lower-abundance proteins. As a result, in typical MS analyses of neat plasma, only a few hundred classical plasma proteins can be reliably detected and quantified across multiple studies4,5. These include proteins with a functional role in blood such as albumin, apolipoproteins, immunoglobulins, and acute phase proteins, as well as members of the coagulation cascade. Lower-abundance proteins, including those derived from tissue leakage or signaling proteins such as cytokines, often fall outside the dynamic range of detection spanning ~4\u20135 orders of magnitude on most of the current generation instrument platforms4. Even when detected, quantifying low-abundant plasma proteins remains challenging, as they are prone to lower signal-to-noise ratios, poor ion statistics, and missing (peptide intensity) values across runs, all of which contribute to higher variance and reduced quantitation precision and accuracy6,7,8.\n\nOver the past two decades, significant efforts have been made to reduce the dynamic range of plasma samples and enhance the depth of plasma proteome coverage. Strategies such as immunoaffinity-based depletion of abundant proteins9,10,11, selective precipitation12, nanoparticle-assisted enrichment13,14,15 and magnetic bead-based isolation of plasma extracellular vesicles16 enabled the identification of up to ~4500 proteins in plasma. Despite their advantages, these methods are often constrained by high costs, limited throughput, and technique-specific biases17,18. Consequently, analysis of neat plasma continues to be a commonly used approach in proteomic studies.\n\nIn clinical contexts, achieving accurate and reproducible quantification is essential. The discovery and verification of potential biomarkers depend heavily on the dynamic range, accuracy, and precision of quantitative measurements across large cohorts, multiple platforms, and study centers. Over the past years, several intra- and interlaboratory studies have addressed this issue using distinct benchmark sample sets to assess quantitative reproducibility of different (label-free) proteomic LC-MS workflows or data analysis tools7,8,19,20,21,22,23. Such benchmark samples can be generated by spiking synthetic peptides or proteins into a matrix at known amounts20,21,22,23, mixing whole proteomes at distinct ratios7,8,19,24,25,26 or a combination of both27. Common to these sample sets is that they represent a ground truth and allow either to optimize different steps of an LC-MS workflow, assess its qualitative and quantitative performance24, or conduct cross-center comparisons20,28. Hence, these samples are widely used, e.g., for comparing software tools and data analysis workflows, as they facilitate the selection of the best-performing quantitative data analysis pipeline for distinct LC-MS setups6,8,29. Moreover, they allow the evaluation of novel MS hardware30, facilitate the benchmarking of software for data analysis31,32, and help optimize (data) processing algorithms to improve quantitative precision and accuracy7. Additionally, they are a valuable tool for multilaboratory20 and cross-platform comparisons26,29,30, providing a snapshot of the technological landscape and workflow performance at the respective study timepoint. Recently, Fr\u00f6hlich et al.25 introduced a mixed proteome dataset designed to incorporate real-world inter-patient heterogeneity, enabling the benchmarking of data-independent acquisition (DIA) data analysis workflows in clinical settings, particularly for formalin-fixed paraffin-embedded tissue samples. However, a ground truth benchmark set specifically for assessing quantitative accuracy and precision in neat plasma analysis has yet to be established. Recently, the CLINSPECT-M consortium, part of the German MSCoreSys clinical proteomics initiative, initiated a round-robin study among its six proteomic laboratories assessing current best practices for sample preparation and LC-MS measurement for clinically relevant body fluids such as plasma and cerebrospinal fluid33.\n\nIn this work, we complement this effort by evaluating the quantitative performance of neat plasma analysis across twelve different partner sites of the MSCoreSys clinical proteomics research consortium (https://www.mscoresys.de/), including different state-of-the-art LC-MS instrument platforms. To this end, we introduce a benchmark set of six samples based on a human tryptic plasma digest containing varying amounts of tryptic digests of yeast and Escherichia coli proteomes (PYE). The PYE benchmark set is an evolution of the hybrid proteome sample set initially described by Kuharev et al.19 and Navarro et al.7, addressing the challenges posed by the high dynamic protein range typical for neat plasma. Each participating site received and measured the PYE sample set on their respective LC-MS platforms using data-dependent acquisition (DDA)- and/or DIA-based methods. Importantly, no particular guidelines, protocols, or restrictions were enforced. All generated raw data have been centrally analysed through a unified pipeline, using MaxQuant34,35 for DDA and DIA-NN36 for DIA data. The resulting dataset clearly demonstrates that accurate and precise protein quantification applying state-of-the-art MS-based proteomics is achievable, even within the complex plasma matrix, across different instrument platforms and multiple sites when applying DIA-based approaches.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The aim of the present study was to assess and benchmark qualitative and quantitative reproducibility as well as the accuracy and precision across multiple sites and instrument platforms using a benchmark sample set that addresses the challenges of protein dynamic range in neat plasma. To this end, we defined a multispecies sample set based on a human tryptic plasma digest, containing varying spike-in levels of tryptic-digested yeast and E. coli (PYE) proteomes. The PYE benchmark set comprises six samples in total: PYE1 A and B, PYE3 A and B, PYE9 A and B. In these samples, human plasma digest serves as a high dynamic range background, whereas low-level spike-ins of E. coli and yeast tryptic peptides mimic regulated proteins between two samples, A and B, allowing to evaluate precision and accuracy of label-free quantification. In samples PYE1 A and B, human plasma proteins account for 90% of the total protein mass, and yeast and E. coli proteins for the remaining 10% (Fig.\u00a01a). Tryptic peptides were combined in the following ratios: sample PYE A contains 90% w/w human, 2% w/w yeast, and 8% w/w E. coli proteins. Sample PYE B is composed of 90% w/w human, 6% w/w yeast, and 4% w/w E. coli proteins. To simulate the challenges of protein dynamic range in clinical plasma samples, the samples PYE1 A and B were further diluted using tryptically digested human plasma, thus additionally reducing the spike-in levels of yeast and E.coli digests (see Fig.\u00a01a). PYE3 refers to a 1:3 and PYE9 to a 1:9 dilution of the PYE1 sample set, with PYE9 containing only 1.1% of non-human proteins. The samples were centrally prepared and shipped to all participating sites on dry ice. Shipped sample amounts depended on the LC-MS setup used at the respective site. Per setup, all samples were to be analysed in six replicate injections. Additionally, two blank injections had to be performed prior to the sample runs to avoid carry-over from system quality control runs, typically conducted using HeLa or K562 tryptic digests (see also method section). MS raw data files were uploaded and analysed centrally using either MaxQuant, for DDA, or DIA-NN, for DIA data.\n\na Left panel: The PYE sample set was centrally prepared and consists of six different samples. A tryptic digest of human plasma (orange) serves as background. Varying spike-ins of tryptic E. coli (green) and yeast proteomes (violet) mimic differentially regulated proteins between samples with the denominations A and B enabling the evaluation of label-free quantification in a plasma background. Exact sample compositions are provided in the black boxes (percent of total protein mass). To resemble the challenges of protein dynamic range in clinical plasma samples, sample set PYE1 was further diluted with tryptic human plasma reducing the proportion of tryptic E. coli and yeast proteomes in the sample sets PYE3 and PYE9. Middle panel: PYE samples were shipped for LC-MS analysis to twelve different sites of the MSCoreSys network. Right panel: Subsequent raw data and statistical analyses of all acquired data sets were conducted centrally. b Overview of instrumentation and LC-MS setups used in the round robin study. Parts of a partially generated in Biorender (https://BioRender.com/9vhkffx, https://creativecommons.org/licenses/by-sa/4.0/ for R logo).\n\nIn total, twelve study centers of the MSCoreSys consortium (sites A to L; for an overview on site specific setups see Table\u00a01) took part in the round robin study, collecting 34 full PYE data sets (most of them, with a few exceptions, comprising six replicate measurements of samples PYE1 A, PYE1 B, PYE3 A, PYE3 B, PYE9 A, and PYE9 B, see Table\u00a01 and Supplementary Data\u00a01). Measurements were conducted on different instrument platforms in either DDA and/or DIA mode, encompassing 1116 individual LC-MS runs. Overall, 13 DDA and DIA data sets were acquired using the exact same LC-MS setup, allowing a direct comparison of both acquisition modes. Mass spectrometers from various manufacturers were used in the present study for data collection, including instruments from ThermoFisher (Orbitrap Eclipse, Orbitrap Exploris 480, Orbitrap Fusion Lumos, Q Exactive HF, Q Exactive HF-X), Bruker (timsTOF Pro, timsTOF Pro2) and Sciex (zenoTOF). In total, seven different LC platforms were used for peptide separation prior to MS analysis, including the following models, Ultimate 3000, Vanquish Neo and EASY-nLC 1200 from ThermoFisher, Evosep One (Evosep), nanoElute (Bruker), nanoAcquity and M-Class from Waters Corporation. Most of the LC systems were operated in the nanoflow range, four sites (sites D, E, F, and K, see Table\u00a01) included micro-flow LC-MS/MS analyses on their Vanquish Neo LC and M-Class systems. Overall, 13 different LC-MS setups were used, with the Ultimate 3000 being the predominant LC platform and the Orbitrap Exploris 480 the prevalent MS instrument in this study (see Fig.\u00a01b, Supplementary Data\u00a01).\n\nTo compare the performance of the different LC-MS setups, we first evaluated the number of proteins and peptides that were identified in each setting and sample (see Fig.\u00a02a, b, Supplementary Figs.\u00a01 and 2, Supplementary Data\u00a02). Overall, we observed a high variability in protein and peptide identifications (IDs) between the different LC-MS setups and acquisition modes as exemplarily shown for PYE1 (Fig.\u00a02a, Supplementary Fig.\u00a01a, Supplementary Data\u00a02). IDs were markedly lower for the DDA as compared to the DIA datasets: In case of DDA, IDs ranged from 919 to 2759 protein groups (1743 protein groups and 15,835 peptides on average), whereas numbers of identified protein groups varied between 1433 and 4653 (with an average of 3193 detected proteins and 29,259 peptides) in case of DIA. Moreover, DIA approaches demonstrated superior reproducibility in terms of identified proteins and peptides, as exemplarily illustrated for the PYE1 A/B set. On average, 84.2% of proteins were consistently identified across all runs within each DIA setup, while this was the case for only 51.5% of proteins (on average) within a DDA setup (Fig.\u00a02a, Supplementary Data\u00a02).\n\na Number of identified protein groups in the PYE1 sample set for the different setups. Colours indicate the number of proteins identified in all replicate runs per setup (complete, black), equal and more than 50% of runs (grey) as well as sparse (below 50%, orange) and unique identifications (red). White numbers indicate proteins identified in all replicate runs and red dots the number of identified proteins in relation to the programmed gradient length, i.e., protein IDs per min. Letters refer to the sites (Supplementary Data\u00a02). b Number of identified protein groups across the whole PYE dataset (PYE1, PYE3, PYE9) for each setup split by species (human: orange, yeast: violet, E. coli: green). Numbers refer to proteins identified in PYE1 (Supplementary Data\u00a03). Upset plots showing the overlap of identified proteins in the PYE1 sample set by c DDA- and d DIA-based approaches across multiple sites and LC-MS platforms. Proportions of human (orange), yeast (violet) and E. coli (green) proteins are indicated within the bars. Source data are provided as a Source Data file.\n\nBesides the acquisition mode, the number of identified proteins also depended on the analysis time, i.e., gradient length. For example, the DIA dataset with the lowest number of IDs (L_nAcqu_tTOF) was acquired running an 11\u2009min gradient, whereas the gradient length was 102\u2009min for the setup with the highest protein IDs (H_ulti_ex). Many sites, however, used similar gradient lengths for the LC-MS analyses ranging either between 29 and 48\u2009min or around 60 and 70\u2009min for DIA and mainly around and above 50\u2009min for DDA analyses. Interestingly, averaging the ID numbers, we did not observe marked differences between setups with a gradient length of 29\u201348\u2009min (3235 protein groups) and 60\u201370\u2009min (3039 protein groups) in DIA mode. However, for some DIA setups with similar analysis times, we observed marked differences in the protein ID rate, i.e., proteins identified in relation to gradient length (see Fig.\u00a02a). This can likely be attributed to the lab-specific differences in instrumentation and LC-MS method settings. For example, most of the TOF datasets were acquired using 29\u201348\u2009min gradients, while the 60\u201370\u2009min datasets constitute mainly Orbitrap data. Among the 60\u201370\u2009min datasets the two microflow setups (D_Vanq_ex and E_Vanq_ex) show slightly lower protein IDs (on average around 2400 proteins) as compared to the other setups with similar gradient length (averaging 3465 protein groups). In contrast to our expectations, we observed no significant systematic influence of peak capacity, cycle time, or signal response on the number of identifications. Overall, we found an overlap of 683 proteins (from a total of 3506 proteins) that were identified in all DDA datasets and 928 out of 5785 proteins that were shared across all DIA runs for PYE1. Over 1600 proteins were shared in 90% of DIA datasets, i.e., across 18 setups. Moreover, 541 proteins were consistently detected in all 34 LC-MS setups (Fig.\u00a02c, d, Supplementary Fig.\u00a03). These numbers are, of course, impacted by setups with lower proteome coverage. When comparing different instrument setups with similar coverage or those with fewer IDs to those with a deeper proteome coverage, we observed a significant overlap of identified proteins, reaching in many cases up to 80\u201390% (Supplementary Fig.\u00a03), highlighting the reproducibility of LC-MS based plasma proteomic analyses across different labs.\n\nThe choice of processing software can significantly impact the number of peptide and protein IDs, owing to differences in search and protein inference algorithms. To assess the influence of software on IDs and to process both, the DIA and DDA data, with the same tool, we additionally analysed the whole round robin dataset with the latest version of FragPipe (version 23, see Supplementary Figs.\u00a04\u20136). In case of the DDA analyses, a marked increase in proteome coverage and reproducibility was observed, as reflected by an enhanced overlap among technical replicates and across distinct LC-MS instrumentation setups compared to the MaxQuant results. In contrast, proteome coverage was markedly lower for DIA as compared to the DIA-NN analysis, which on average yielded around 25% more protein IDs compared to FragPipe. Hence, the gap between DDA and DIA is by far not as prevalent when processing the dataset in FragPipe with some matching setups showing similar numbers of IDs. Nevertheless, on average, IDs were higher in DIA mode (around 17%) comparing all matching DDA and DIA runs. Of note, IDs across the different LC-MS setups show similar patterns as compared to MaxQuant and DIA-NN, with the same setups achieving highest and lowest numbers of IDs, respectively.\n\nAcross all settings, the highest number of proteins was consistently identified in PYE1 A/B as compared to PYE3 A/B and PYE9 A/B samples, which is to be expected as the percentage of E. coli and yeast proteins is highest in the PYE1 set. Regarding species-specific IDs, the numbers of detected human plasma proteins were similar between PYE1, PYE3, and PYE9 within each setting, while we observed a marked drop in IDs for E. coli and yeast proteins from PYE1 to PYE3 and PYE9 (Fig.\u00a02b, Supplementary Data\u00a03). Independent of the LC-MS setting used, a three-fold reduction of spike-in levels of E. coli and yeast tryptic digests reduced the number of E. coli and yeast protein IDs around 1.85-fold in DDA and 1.7-fold in DIA mode between PYE1 and PYE3 and around 2.35- (DDA) as well as 2-fold (DIA) between PYE3 and PYE9, respectively.\n\nThis is also reflected when integrating the results from all DDA and DIA datasets across the different sites (Fig.\u00a03a, b). For both, DDA and DIA mode, the dynamic range of identified proteins is similar between PYE1, PYE3, and PYE9, spanning four orders of magnitude in the case of each species, except for human plasma proteins identified by DIA which cover six orders of magnitude. However, with each dilution step from PYE1 to PYE9, a distinct number of E. coli and yeast proteins falls below the detection limit, resulting in a reduced proteome coverage for both, DDA and DIA datasets. In DIA mode, we observed a 1.3- (E. coli) to 1.4-fold (yeast) decrease in protein IDs in PYE3 and a 2.0- (E. coli) to 2.5-fold (yeast) decrease in PYE9 as compared PYE1. In case of DDA, the drop was slightly higher. Here, ID numbers decreased by factors of around 1.6 in case of PYE3 and 2.6 for PYE9 as compared to PYE1 for both yeast and E. coli proteins. Overall, abundances of commonly identified proteins show a high correlation for both acquisition modes between the PYE1, PYE3 and PYE9 sample sets (Fig.\u00a03c, d). As anticipated from the serial dilution between sample sets, point clouds pertaining to E. coli and yeast proteins center around the expected ratios indicated by the dotted lines.\n\na, b Dynamic range of identified proteins in PYE1, PYE3 and PYE9 across the full dataset (i.e., summarizing normalized protein abundances from all LC-MS runs) split by species and acquisition mode. Panel (a) displays the dynamic range for the DDA and panel (b) for the DIA dataset. To generate the dynamic range plot, protein intensities were integrated across all different LC-MS setups and divided by the maximum observed intensity, set to 100%. Correlation of normalized protein abundances between PYE1, PYE3 and PYE9 for c the DDA and d DIA datasets. Protein intensities were averaged and normalized separately for each LC-MS setup to the highest LFQ intensity of each individual setup. Dotted lines indicate the expected values for the comparison between the different PYE dilutions. Coefficient of determination (R2) is displayed in the graphs for human (orange), yeast (violet), and E. coli: (green) proteins. Linearity of E. coli protein LFQ abundances analysed in DDA (e) and DIA mode (f), exemplarily depicted for two setups, A_ulti_ex and G_nLC_tTOF. The design of the PYE sample set allows to compare LFQ values of E. coli spike-ins across six dilution levels. We binned proteins according to their LFQ abundance values (averaged across six replicate injections) in sample PYE1A into 10 equal-sized bins calculating for each bin the median value (red dot). Median abundance values for these bins (i.e., associated with the proteins assigned to initial bins) were calculated and are plotted for all six PYE samples. Light grey lines represent individual protein response curves. Source data are provided as a Source Data file.\n\nNotably, the design of the PYE sample additionally allows to determine the lower limit of detection (LOD) and linearity for thousands of analytes as a function of their signal intensities by comparing label-free quantification (LFQ) values of individual proteins of E.coli spike-ins across six dilution levels, covering a 18-fold difference between PYE_1A and PYE_9B (Fig.\u00a03e, f). Overall, both DDA and DIA showed good linearity across all six samples. In addition, our analysis revealed that the 10% lowest abundant E.coli proteins (as defined by a low LFQ value in PYE1) already fall below detection limit in the PYE3_A sample in DDA, while they remain detectable in both PYE3_A and PYE3_B samples in DIA mode, indicating a lower LOD for DIA quantification.\n\nAs reproducibility is a key aspect in large-scale proteomic studies and we observed a strong influence of the acquisition mode in terms of proteome coverage, we next compared the quantitative performance between the different DIA and DDA datasets in more detail. In terms of run-to-run reproducibility, i.e., reproducibility between replicate injections, DIA-based LC-MS workflows markedly outperformed the DDA-based methods independent of the LC-MS setup used. Median coefficients of variation (CVs) of protein abundances ranged between 6.4% and 54.7% (average 15.4%) for DDA and between 3.3% and 9.8% (average 5.9%) for DIA analyses as exemplarily shown for PYE1 A in Fig.\u00a04a, b (similar numbers were observed for PYE1 B, Supplementary Fig.\u00a07a,b, Supplementary Data\u00a04). Among the DIA datasets, data derived from timsTOF instruments showed slightly higher variance (average of median CVs: 8.16%) as compared to the other DIA setups (4.87%). Similar trends were also observed for the data processed in FragPipe, where the DIA-based methods display lower CVs as compared to their DDA-based counterparts (Supplementary Fig.\u00a07c, d).\n\nCoefficients of variation (CVs) of protein abundances for replicate analyses of sample PYE1 A were calculated for each LC-MS setup revealing lower quantitative reproducibility for a DDA as compared to b DIA approaches. Boxplot center lines represent the median value, boundaries the interquartile range and whiskers the 5th/90th percentiles of the dataset. The red line marks 25% CV and green line 10% CV. For detailed information on the number of replicate injections for each setup (n\u2009=\u20096 in most cases) see Table\u00a01. c, d Evaluation of RT stability (displayed as CVs of RT, calculated for sample PYE1, n\u2009=\u200912 in most cases, six technical replicates for each, PYE1 A and PYE1 B, for details see Table\u00a01) shows reproducible elution of peptides for most of the LC setups. Center line in the boxplots represents the median value, bounds of boxes the interquartile range and whiskers the 5th/90th percentiles of the dataset. c the RT CVs for the DDA and panel (d) for the DIA datasets. Median RT CVs are plotted against the chromatographic peak capacity for each chromatographic setup for the e DDA and f DIA datasets. Dot sizes indicate gradient length. Gray: Nanoflow, red: Microflow setup, see also Supplementary Data\u00a04. Source data are provided as a Source Data file.\n\nAs very different chromatographic setups were used in the present study, including those at higher flow rates (sites D, E, F, and K), we additionally assessed chromatographic performance evaluating the retention time (RT) stability across replicate runs, an essential factor particularly for label-free quantitative workflows where features are mapped across multiple runs37. Overall, the peptide elution behavior was stable and highly reproducible for most of the LC settings, with median RT CVs below 0.35% across all 34 setups (Fig.\u00a04c, d). Only few setups (nine in total) displayed slightly higher RT variance with median values above 0.35%, including two setups (D_Vanq_ex DDA, I_nLC_ex DIA) with markedly higher RT CVs (0.99% and 1.19%) compared to the other setups. In contrast to our expectations, we observed no marked differences regarding RT CV or peak capacity (Fig.\u00a04e, f) between the micro- and nano-flow settings in the present dataset. We further noted that, independent of gradient length or flow rate, a less reproducible peptide elution, i.e., higher RT CVs, also correlated with an overall lower chromatographic peak capacity (Fig.\u00a04e, f. Supplementary Data\u00a04). This observation was slightly more prevalent for the DIA as compared to the DDA dataset. Particularly DIA methods can benefit from a high peak capacity, i.e., good chromatographic performance, as many downstream processing tools use chromatographic elution profiles for spectral deconvolution and mapping of precursor and product ions.\n\nThe present multicenter study comprises 13 matching DDA and DIA datasets, where exactly the same LC-MS setup was used for data acquisition (i.e., analysing the samples at the same site on the same LC-MS platform, with the same LC method and column setup, see Table\u00a01 and Supplementary Data\u00a01), which allows a direct back-to-back comparison of the two acquisition modes (Fig.\u00a05). The majority of these datasets were acquired on Orbitrap platforms. Summarizing the quantitative results of the PYE1 analysis across all 13 LC-MS setups, we found that DIA approaches show on average higher accuracy and precision as compared to the DDA-based methods (Fig.\u00a05a, Supplementary Data\u00a05): The interquartile range (IQR, Q75-Q25) of the global distribution of log-transformed ratios (log2(PYE1 A/PYE1 B)) of protein abundances, averaged across all 13 DIA datasets, ranged between 0.07 for plasma, 0.16 for E. coli and 0.22 for yeast proteins. The variance was higher in the case of DDA (IQRplasma\u2009=\u20090.11, IQRE. coli\u2009=\u20090.19 and IQRyeast\u2009=\u20090.27). Moreover, calculated values (averaged across all 13 datasets) were closer to the expected ratios for plasma and for E. coli proteins in the DIA runs as compared to the DDA analysis. Only in case of yeast proteins, the DDA measurements showed on average better accuracies as compared to DIA with an absolute difference from the expected ratio of 0.14 versus 0.18 in case of DIA. This effect can most likely be attributed to the higher proteome coverage in DIA, where particularly medium and low-abundant proteins, that are not detected by DDA, can be still identified and quantified (Fig.\u00a05b\u2013e). Overall, similar trends in terms of quantitative precision and accuracy can also be seen for PYE 3 and PYE 9 where in most cases, DIA methods outperform DDA-based approaches, as exemplarily shown for an Orbitrap as well as a timsTOF setup in Fig.\u00a05b, c and Table\u00a02. Interestingly, both timsTOF setups (C_nE_tTOF and G_nE_tTOF) displayed a systematic error of accuracy values in the same direction for both the DDA and DIA dataset.\n\na Violin plots of log-transformed ratios (log2(PYE1 A/PYE1 B)) of protein abundances for matching DDA and DIA LC-MS setups. Solid lines within the violin plot indicate the median log2(A/B) value for each setup and red dashed lines the expected log2(A/B) values for human (orange), yeast (violet), and E. coli (green) proteins (Supplementary Data\u00a05). Log-transformed ratios (log2(A/B)) of proteins were plotted over the log-transformed intensity of sample A for DDA and DIA data acquired with the same LC-MS setup on b an Orbitrap as well as c a timsTOF platform. d Percentage of missing values for yeast proteins in PYE1 A as compared to PYE1 B (ranked by protein abundance) for the DDA and DIA dataset. e Percentage of missing values for yeast proteins in the PYE1 B DDA dataset as compared to the PYE1 B DIA dataset dependent on protein abundance across all 13 LC-MS setups displayed in panel (a). d, e X-axis: Rank as defined by the average normalized intensity (INTProtein/INTmax) across all 13 setups. Y-axis: Missingness (1-(number of detections/number of runs)) across all 13 setups and injection replicates as percent values.\n\nAdditionally, we compared the data completeness for identified yeast proteins across all 13 DDA and DIA datasets. To this end, we mapped the yeast proteins identified in the PYE1 B sample, ranked by their abundance, to those identified in PYE1 A summarizing the results across all 13 datasets. In line with the higher proteome coverage and overlap between the technical replicates (Fig.\u00a02a), the 13 DIA datasets showed a markedly higher data completeness for the yeast spike-in as compared to their matching DDA datasets (Fig.\u00a05d, Supplementary Fig.\u00a08): While the DDA dataset displayed 50% missing values already at protein rank 828, the DIA data reached a value of 50% missingness at protein rank 1637 (Fig.\u00a05d). Additionally, we directly compared the two datasets mapping the yeast proteins identified in sample PYE1 B (Fig.\u00a05e). Here, 50% missing values occurred at protein rank 742, and around 1200 yeast proteins were uniquely detected in the DIA PYE1 B dataset, further highlighting the superior performance of DIA compared to DDA-based methods in the present study.\n\nNext, we evaluated the quantitative performance of the 20 different DIA setups. All LC-MS setups demonstrated excellent performance in terms of accuracy and precision for label-free quantification of highly abundant proteins in the PYE sample set (Fig.\u00a06, Supplementary Figs.\u00a09\u201312). However, for proteins in the low abundant range accurate quantification can still be challenging. Yeast proteins make up the smallest proportion of the PYE samples A and B by quantity. Moreover, yeast proteins are spiked in at a ratio of 1:3, while the ratio for E. coli proteins is 1:2, making it even more challenging to estimate the correct ratio between samples A and B for yeast as compared to E. coli or human proteins. This is also reflected in the results. For example, variance is markedly higher in the PYE1 set for yeast as compared to E. coli proteins (IQR of the global distribution of log2(FC) values across all 20 datasets: IQRyeast\u2009=\u20090.23 and IQRE. coli\u2009=\u20090.17, see also Fig.\u00a06a and Supplementary Data\u00a06). Upon additional dilution of the yeast and E. coli proteomes in the PYE3 and the PYE9 samples (Fig.\u00a06b, Supplementary Fig.\u00a012), variance increases for both species (to IQRyeast\u2009=\u20090.27 and IQRE. coli\u2009=\u20090.19 in the PYE9 set). Interestingly, precision slightly improves for human proteins from PYE1 to PYE9, likely due to a decrease of the yeast and E.coli proteome background. Particularly in the lowest abundance tertile accurate and precise quantification still remains challenging. This becomes evident when looking exclusively at the log2(FC) distributions of the proteins in the low abundance range (i.e., the tertile of the dataset encompassing the proteins with the lowest abundance values, Fig.\u00a06c, d). Across all dilutions, comprising sample sets PYE1 to 9, accuracy and precision are markedly lower, particularly for E.coli and human proteins, in the lowest abundance tertile as compared to the full dataset that includes also the mid and high abundant proteins (Fig.\u00a06a, b Supplementary Fig.\u00a012).\n\nViolin plots of log-transformed ratios (log2(A/B)) of protein abundances for a the full PYE1 and b PYE9 set (Supplementary Data\u00a06). Violin plots of log-transformed ratios (log2(A/B)) of protein abundances in the lowest intensity tertile for c the PYE1 and d PYE9 set. Solid black lines within the violin plot indicate the median log2(A/B) value for each setup and red dashed lines the expected log2(A/B) values for human (orange), yeast (violet), and E. coli (green) proteins. e Deviation of the median log-transformed ratio (log2(A/B)) of protein abundances from the expected value. Plots summarize data for human (orange), yeast (violet) and E. coli proteins (green) for the PYE1, PYE3 and PYE 9 datasets. f Correlation of median accuracies (calculated for yeast proteins in the PYE1 set) with other metrics such as number of identified proteins (left), median CV [%] of protein abundances (middle) and number of datapoints acquired across the chromatographic peak (right panel). Dot sizes indicate gradient length. Blue: Orbitrap, orange: TOF analyzer, see also Supplementary Data\u00a07. Source data are provided as a Source Data file.\n\nLooking at the full PYE dataset (Fig.\u00a06e), accuracy follows a similar trend as the precision. Averaging across all datasets, accuracies of calculated log2(FC) values for human proteins improved from the PYE1 to the PYE9 sample set (with an average absolute difference between median and expected values of 0.10 in PYE1 and 0.01 in PYE9; Fig.\u00a06a, b, Supplementary Data\u00a06). Comparing yeast and E. coli proteomes, deviations from the expected ratios are markedly higher for yeast as compared to E. coli proteins in all sample sets, i.e., PYE1, PYE3 and PYE9 (Fig.\u00a06e). Accuracy is similar for yeast proteins between samples PYE1, PYE3 and PYE 9, whereas there is a slightly higher deviation from the expected values in PYE9 as compared to PYE1 for E. coli proteins.\n\nInterestingly, most TOF setups show a similar trend regarding their LFQ values, which display a consistent shift from the expected values for yeast and human proteins in the same direction (Fig.\u00a06a, b, e), indicating a potential issue with background correction for the TOF data overestimating LFQ abundances for low abundant proteins7. This effect can potentially be attributed to an overall higher background in TOF mass spectra as compared to those derived from Orbitrap platforms, or alternatively to different background subtraction algorithms. For the Orbitrap LC-MS setups, we observe varying effects. For example, the two micro-flow setups (D_Vanq_ex and E_Vanq_ex), show the highest accuracy and precision for human proteins as compared to all other setups. However, deviations from the expected log2(FC) values point to an underestimation of LFQ values for low-abundant yeast and E. coli proteins. For other Orbitrap setups, e.g., D_ulti_ecl, H_ulti_ecl, H_ulti_ex, we observe a systematic error (in PYE1 and PYE3) of the calculated log2(FC) values for all species towards a higher log2(FC) than expected.\n\nTo better understand some effects, we additionally evaluated for the yeast proteins if some metrics, such as data points per peak, number of identified proteins, peak capacity, or mean CV, correlate with quantification accuracy and precision at a proteome-wide scale (exemplarily shown for PYE1, Fig.\u00a06f and Supplementary Fig.\u00a013, Supplementary Data\u00a07) and found that the median deviation from expected values slightly increased in datasets with higher ID numbers. Moreover, in datasets that display higher accuracies, more data points were recorded across a chromatographic peak. Interestingly, we observed a slightly opposing trend regarding the precision (Supplementary Fig.\u00a013), which improved when higher numbers of proteins were identified. Other factors, i.e., mean CV or data points per peak, did not correlate with improved precision, i.e., lower variance.\n\nTo leverage the advantages of multicenter studies, particularly regarding method transferability and interlaboratory reproducibility, we re-analysed the PYE1 sample set at site L using the Ultimate/Exploris DIA configurations from sites G and H (G_ulti_ex, H_ulti_ex), as well as the EASY-nLC 1200/timsTOF DIA setup from site G (G_nLC_TOF). Re-analysis of the PYE1 sample at site L, using the LC-MS configurations from the original sites, yielded highly comparable numbers of protein and peptide identifications (Fig.\u00a07a) with substantial overlap (Fig.\u00a07b), effectively demonstrating the interlaboratory transferability of the methods. Additionally, the quantitative profiles closely mirrored the distribution patterns observed in the original round robin dataset (Fig.\u00a07c). Of note, the H_ulti_ex and G_nLC_tTOF DIA setups from sites G and H yielded the highest proteome coverage in the round robin study. In line with the round robin data, remeasurements at site L also provided lower proteome coverage for the G_ulti_ex setup, which uses the same LC-MS and column setup as H_ulti_ex, but half the analysis time, i.e., 60\u2009min versus 120\u2009min and slightly different DIA method with adapted lower cycle time (see Supplementary Data\u00a01).\n\na Numbers of identified proteins and peptides in the PYE1 samples comparing data from the round robin study with remeasurement using the same setup as indicated in site L (n\u2009=\u200912, six technical replicates for each, PYE1 A and PYE1 B). b Overlap of identified protein groups between the round robin data and the remeasurements in site L. c Violin plots of log-transformed ratios (log2(A/B)) of protein abundances in PYE1 for the round robin data and the remeasurements at site L. d Numbers of identified proteins and peptides in the PYE1 samples comparing the nLC_tTOF round robin data from site G with remeasurements in site L using different LC and MS settings (as indicated in the table on the right; n\u2009=\u200912, six technical replicates for each, PYE1 A and PYE1 B). e Upset plot depicting the overlap of identified protein groups from the measurements in (c). f Numbers of identified proteins and peptides in neat plasma samples (without spike-ins, n\u2009=\u20096 replicate injections) analysed at three different sites using LC-MS setups as indicated (for more details see Supplementary Data\u00a01). LC-MS setups G_ulti_ex and H_ulti_ex were used at the respective sites as indicated (light blue) as well as at site L (darker blue). g Upset plot depicting the overlap of identified protein groups from the measurements in (g). Source data are provided as a Source Data file. In panels (a, d, f) points represent individual injections; bars and error bars show mean\u2009\u00b1\u2009sd.\n\nTo further explore how the number of IDs is influenced on a distinct platform, we additionally conducted a back-to-back comparison of the timsTOF setups from sites G and L. G_nLC_TOF, the timsTOF setup providing the highest IDs, uses an IonOpticks Aurora column (75\u2009\u00b5m ID \u00d7 25\u2009cm) for peptide separation running a 30\u2009min gradient at 300\u2009 nL/min (Fig.\u00a07d, e). We analysed the PYE1 sample using the MS method of site G but the LC setting from site L (Bruker PepSep setup, 150\u2009\u00b5m ID \u00d7 25\u2009cm, 35.5\u2009min gradient at 850 nL/min). This resulted in a marked drop in the number of identified proteins and peptides (Fig.\u00a07d, e). In contrast, we observed no marked differences in IDs between the MS methods from sites G and L (30\u2009Da versus 25\u2009Da fixed window schemes, different IMS range and cycle time). These data clearly indicate, that the LC setup used by site G (IonOpticks Aurora column 75\u2009\u00b5m ID \u00d7 25\u2009cm, 30\u2009min gradient at 300\u2009nL/min, final amount of 28% (v/v) ACN) outperforms the conditions used by site L in the round robin study (Bruker PepSep column 150\u2009\u00b5m ID \u00d7 25\u2009cm, 35.5\u2009min gradient at 850\u2009nL/min up to 38% (v/v) ACN).\n\nTo evaluate whether the findings from the PYE analyses are applicable to native plasma samples, we analysed a neat plasma sample without any spike-ins across three different sites using four different Orbitrap-based LC-MS setups from the round robin study (Fig.\u00a07f, g). Consistent with the results obtained from the PYE analyses, we observed similar trends regarding the number of IDs as compared to the round robin study with a high degree of overlap (Fig.\u00a07g). In line with the round robin study, the setup with the longest gradient and analysis time, i.e., H_ulti_ex, provided the best proteome coverage also for the neat plasma sample. This clearly demonstrates that depending on the scope of a (clinical) study one has to balance proteome depth, quantitative performance, and sample throughput when choosing an LC-MS setup for plasma analysis.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64501-z/MediaObjects/41467_2025_64501_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Over the past two decades, plasma proteomics has evolved significantly, progressing from basic protein cataloguing to sophisticated workflows that quantify thousands of proteins with high precision16,38,39. Despite these advancements, plasma remains a challenging sample matrix for LC-MS-based proteomics due to its tremendous dynamic range3,4. High-abundant proteins, such as albumin and immunoglobulins, can overshadow lower-abundance proteins, many of which hold potential as biomarkers for disease. Early plasma proteomics studies using DDA-based methods identified typically only a few hundred proteins3,40, with a bias toward high-abundant ions and inconsistent detection of low-abundance peptides across analyses. Workflows incorporating off-line fractionation and depletion strategies improved proteomic depth, extending coverage to over 1000 proteins identified per sample, albeit with significant time costs10,11. DIA-based approaches address challenges of dynamic range and reproducibility by capturing all ions in a mass-to-charge range without bias41, thereby improving consistent and reproducible detection of low-abundance proteins. Coupled with high-resolution MS, DIA enables robust, efficient identification of over 500\u20131000 proteins from neat plasma, minimizing fractionation needs and advancing biomarker discovery in large-scale studies42,43. While some studies show DIA outperforms DDA in plasma proteomics by capturing a broader ion range and enhancing low-abundance protein quantification44, systematic comparisons across various LC-MS platforms are limited. Such research is essential, as differences in LC and mass spectrometer hardware configurations affect resolution, sensitivity, and scan speed, impacting DIA and DDA performance. Additionally, variations in LC parameters, including gradient length, column and flow rate, also influence peptide separation and detection45,46. Despite the high potential of LC-MS proteomics for protein identification and quantification, its diagnostic use is limited by a lack of standardized workflows and validation processes required for accreditation4,47. Cross-platform studies would clarify how different parameters affect DDA and DIA, guiding method selection for standardization and demonstrating each method\u2019s practical benefits across diverse workflows for plasma proteomics.\n\nHere, we designed and conducted a multicenter study including twelve partner sites of the German research cores for mass spectrometry in systems medicine (MSCoreSys) to assess label-free quantification performance on a benchmark sample set, simulating the high protein dynamic range typical of neat plasma. Including multiple sites and a diverse range of LC-MS setups, with data centrally analysed using standardized software (MaxQuant for DDA and DIA-NN for DIA, FragPipe for both acquisition modes), lends robustness to our findings. We focused on critical parameters such as intra- and inter-laboratory reproducibility, highlighting proteins consistently detected across LC-MS platforms at various sites. Additionally, we evaluated the total number of quantified proteins, quantitative reproducibility, data completeness, and the precision and accuracy of quantification.\n\nUnlike previous benchmark studies that used a HeLa digest as a matrix7,19, we generated a multispecies sample set based on a human tryptic plasma digest with varying spike-in amounts of tryptic digests of yeast and E. coli proteomes. This effectively simulates the high protein dynamic range of human plasma and the low abundance of potential biomarker candidates4,22. Specifically, the initial sample set (PYE1 A/B) was diluted incrementally at a 1:3 ratio with a human tryptic plasma digest, reaching maximum dilution in PYE9 A/B, where human plasma proteins constituted 98.9% of the total protein mass, with yeast and E. coli proteins comprising the remaining 1.1%. Notably, even at these low spike-in levels, current-generation instrument platforms provided precise and accurate label-free quantification of several hundreds of yeast and E. coli proteins in the present study. Our analysis of proteome coverage across various LC-MS setups, acquisition modes, and PYE sample dilutions showed that DIA consistently outperformed DDA in protein and peptide ID numbers, with DIA workflows offering greater run-to-run reproducibility and higher consistency in protein identification. Notably, the detection of hundreds of non-human proteins across the full dynamic range indicates that current DIA based proteomic platforms are likely to cover the entire plasma proteome in the upper 3\u20134 orders of magnitude of dynamic range. Compared to DDA, DIA-based workflows achieved up to eight times higher proteome coverage, improved quantitative reproducibility, and significantly fewer missing values, consistent with previous studies24,41,48. However, identifications on the protein as well as peptide level can be significantly impacted by the software tool and settings used for data processing and database search. The gap in proteome coverage between the DDA and DIA dataset markedly decreased upon data processing in FragPipe highlighting the importance of exploring different software tools and parameters for data analysis when planning a (clinical) study. Overall, our data demonstrate that a technical reproducibility between replicates with less than 6% CV are achievable across different setups and instrument platforms using DIA-based approaches. This indicates that precise label-free quantification is feasible even in a complex matrix such as plasma using state-of-the-art workflows. This high precision and accuracy in label-free quantification underscore DIA as the preferred acquisition method for the analysis of plasma and other high-dynamic range proteomes using LC-MS. Interestingly, while DIA excelled in identification and quantification metrics, our study also revealed that longer gradient times generally led to higher ID rates. However, differences in the LC-MS setup including, for example, instrument type, column characteristics, etc., more profoundly affected detection rates, even with similar gradient durations. Notably, all participating sites used chromatographic setups that were optimized for plasma proteomics to provide optimal sensitivity, reproducibility, and data quality. Optimizing chromatography is thought to be particularly important in DIA due to its continuous, wide-window sampling, where optimal peak sharpness and separation are essential for capturing high-quality fragment ion spectra and maximizing identification rates. However, in contrast to our expectations, we did not observe a significant correlation of chromatographic parameters, i.e., peak capacity or retention time stability, with the respective proteomic coverage or quantitative metrics. This may likely be attributable to the multiparametric setup of the participating labs and the high dynamic range of the PYE sample set.\n\nAlthough challenges remain in accurately quantifying low-abundance proteins in plasma proteomics, our findings underscore the significant improvements in LC-MS-based workflows in recent years, which now offer enhanced quantitation accuracy and precision. Here, our findings align with a recent study in which a mixed proteome benchmark set based on HeLa digest was used to assess the impact of DIA-NN processing parameters on the evaluation of QE-HF data and a cross-platform comparison. In the mentioned study, a CV cut-off of 5% was suggested as a threshold for deeming workflows or datasets quantitatively reproducible29. Looking ahead, we anticipate that further developments in chromatography and mass spectrometric instrumentation will push the boundaries of both proteome depth and data quality. While reference studies from the early 2000s demonstrated state-of-the-art plasma proteomics with the identification of around 100\u2013200 proteins, it is now routinely possible to achieve a coverage of >500\u20131000 proteins42,43. Recent comparisons between instruments, like the Orbitrap Exploris 480 and Astral, demonstrate promising gains in sensitivity, highlighting the potential for even greater precision in low-abundance protein quantification30, particularly also with respect to plasma analysis49.\n\nOur dataset not only identifies areas for further improvement but also serves as a valuable resource for software development, offering a comprehensive overview of current technological capabilities in LC-MS workflows. Moreover, we could demonstrate how multicenter studies can facilitate the reproducible transfer of methods across different sites. These advancements show how LC-MS technology has evolved into a robust and reliable platform with great potential for biomarker discovery and validation. It sets the stage for a continuously increasing role of quantitative proteomics in systems medicine and clinical research.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Unless otherwise stated, all solvents (HPLC and Ultra LC-MS grade) were purchased from Roth and all chemicals were obtained from Sigma.\n\nHuman plasma was commercially obtained from BioCat GmbH (Heidelberg, Germany) and tested negative for HIV, ZIKA Virus, STS (Syphilis) and Hepatitis B/C. A pure culture of the Saccharomyces cerevisiae bayanus, strain Lalvin EC-1118 was obtained from Eaton (www.eaton.com). E. coli was purchased from Thermo Fisher Scientific.\n\nE. coli cells were lysed using a urea-based lysis buffer (7\u2009M urea, 2\u2009M thiourea, 5\u2009mM dithiothreitol (DTT), 2% (w/v) CHAPS). Lysis was further promoted by sonication at 4\u2009\u00b0C for 15\u2009min using a Bioruptor (Diagenode, Li\u00e8ge, Belgium). Yeast proteins were extracted using alkaline pre-incubation with 0.1\u2009M NaOH (VWR, USA) followed by an additional incubation step in lysis buffer containing 1% (w/v) SDS (Carl Roth, Germany) at 95\u2009\u00b0C.\n\nAfter lysis, the concentrations of E. coli and yeast proteins were determined using the Pierce 660\u2009nm protein assay (Thermo Fisher Scientific) according to the manufacturer\u00b4s protocol. Neat plasma was diluted 166-fold in urea-based buffer (7\u2009M urea, 2\u2009M thiourea, 5\u2009mM dithiothreitol (DTT), 2% (w/v) CHAPS) prior to digestion.\n\nHuman plasma, yeast and E. coli proteins were digested on an Biomek i7 robotic pipetting platform (Beckman Coulter Life Sciences, Indianapolis, USA) equipped with a positive pressure adapter (Amplius, Germany) using an adapted filter-aided sample preparation (FASP) protocol50. All digestion steps are detailed in Distler et al.51 and were implemented on the Biomek i7 liquid-handling robot. Unless stated otherwise, each step of the semi-automated FASP workflow was performed as described51 and carried out by the liquid-handling robot applying a positive pressure of 500\u2009mbar for 6\u201315\u2009min to force the liquid through the filter membranes. All volumes were adapted to 100\u2009\u00b5L/well, except for the trypsin digestion and the elution steps after overnight digestion: Sample aliquots (corresponding to 30\u2009\u00b5g of protein per well) were manually transferred onto AcroPrep Advance 96-well 350\u2009\u00b5L 30\u2009K Omega filter plates (Pall Cooperation, USA) which had been additionally preconditioned with 0.1% (v/v) formic acid (FA) and urea-based lysis buffer (7\u2009M urea, 2\u2009M thiourea, 5\u2009mM dithiothreitol (DTT), 2% (w/v) CHAPS) in case of plasma and E. coli. After sample transfer, membranes were washed once with a urea-based wash buffer (8\u2009M urea, 0.1\u2009M Tris-HCl, pH 8.5). Proteins were then reduced for 15\u2009min at 56\u2009\u00b0C using 8\u2009mM DTT dissolved in the urea-based wash buffer followed by an additional washing step. Afterwards, proteins were alkylated with 50\u2009mM iodoacetamide (IAA, in urea-based wash buffer) for 20\u2009min at room temperature. Excess IAA was removed by two washes using the urea-based wash buffer and additionally quenched with 8\u2009mM DDT for 15\u2009min at 56\u2009\u00b0C. Afterwards, the membrane washed twice with urea-based wash buffer followed by three additional washing steps with 50\u2009mM NH4HCO3. Proteins were then digested overnight at 37\u2009\u00b0C adding 40\u2009\u00b5L of trypsin (Trypsin Gold, Promega, Madison, WI) dissolved in 50\u2009mM NH4HCO3, 0.02% (w/v) DDM in water at an enzyme-to-protein ratio of 1:50 (w/w) corresponding to 0.6\u2009\u00b5g of trypsin per well. After digestion, tryptic peptides were recovered from the membrane adding 40\u2009\u00b5L 50\u2009mM NH4HCO3. Flow-throughs were acidified with FA to a final concentration of 0.1% (v/v) FA. Tryptic peptides from multiple well plates were pooled in case of all three species to obtain digest stock solutions for the generation of the PYE sample set.\n\nDigest quality of the different stocks was assessed by LC-MS (checking for impurities, peptide abundances, total ion current as well as number of peptide and protein IDs). Tryptic peptides were subsequently mixed in predefined ratios to generate hybrid proteome samples. In total, the PYE benchmark set comprises six samples, PYE1 A and B, PYE3 A and B, PYE9 A and B (at 2\u2009\u00b5g/\u00b5L protein). For the PYE1 sample set, tryptic peptides were combined in the following ratios: sample A was composed of 90% w/w human, 2% w/w yeast, and 8% w/w E. coli proteins. Sample B was composed of 90% w/w human, 6% w/w yeast, and 4% w/w E. coli proteins (Fig.\u00a01a). To generate the PYE3 sample set, samples PYE1 A and B were further mixed with tryptic human plasma peptides at a ratio of 1:3. PYE3 samples were then further diluted threefold with human plasma peptides resulting in the PYE9 sample set.\n\nAfterwards, samples were shipped to all participating sites on dry ice. Shipped sample amounts (i.e., volumes) were dependent on the LC-MS setup used at the respective site providing higher sample amounts to the sites that used a microflow LC-MS setup (see Table\u00a01 and Supplementary Data\u00a01).\n\nBlood samples were collected from five healthy volunteers from site L (see also ethics statement). EDTA plasma was prepared by centrifugation at 1780\u2009\u00d7\u2009g for 10\u2009min. The resulting plasma samples were pooled and stored at \u221280\u2009\u00b0C until further processing. Proteolytic digestion of the collected plasma pool was performed using an adapted FASP protocol50. All digestion steps are detailed in Distler et al.51 and were performed manually in a 96-well format analogue to the procedure described above (preparation of the PYE benchmark sample set). In brief, 20\u2009\u00b5g of sample material were manually transferred into each well of an AcroPrep Advance 96-well 350\u2009\u00b5L 30\u2009K Omega filter plate (Pall Cooperation, USA), which had been preconditioned with 0.1% (v/v) FA.\n\nAll volumes, except the volume of the trypsin solution and the steps on day two, corresponded to 100\u2009\u00b5L/well. After sample transfer, membranes were washed once with a urea-based wash buffer (8\u2009M urea, 0.1\u2009M Tris-HCl, pH 8.5) followed by reduction of proteins using 8\u2009mM DTT. After two washing steps with urea-based wash buffer, proteins were alkylated with 50\u2009mM IAA. Excess IAA was removed by two washes and quenched with 8\u2009mM DDT. Afterwards, the membrane was washed twice with urea-based wash buffer followed by three additional washing steps with 50\u2009mM NH4HCO3. Proteins were subsequently digested overnight at 37\u2009\u00b0C with trypsin gold (0.4\u2009\u00b5g/well, Promega, USA) in 40\u2009\u00b5L 50\u2009mM NH4HCO3. After digestion 40\u2009\u00b5L 50\u2009mM NH4HCO3 were added to the samples to recover tryptic peptides. Samples were acidified with 10\u2009\u00b5L 1 % formic acid, which was added to the wells of the 96-well collection plate containing eluted peptides (Waters, USA). Peptides were pooled into one sample pool, which was aliquoted, and lyophilized. Lyophilized sample was sent out to three different partner sites (i.e., sites G, H, and L). At the different sites samples were re-constituted in 0.1% FA (v/v) in water (final concentration of 1\u2009\u00b5g/\u00b5L) followed by a further dilution to 200\u2009ng/\u00b5L in 0.1% FA (v/v) for LC-MS measurements.\n\nAll participating sites were asked to analyse the PYE benchmark sample set using their preferred LC-MS setup for the characterization of plasma samples according to the following measurement scheme: (1) blank injection, (2) Hela QC (e.g., Pierce\u2122 HeLa, Thermo Scientific), (3) two blank injections, (4) PYE samples in the following order, PYE A9, PYE B9, PYE A3, PYE B3, PYE A1, PYE B1), (5) blank injection. All samples had to be analysed in multiple replicates (ranging from three to optimally six replicate injections). No other restrictions were imposed on the study centers regarding LC-MS setup, gradient length, on-column load, etc. Detailed description of the LC-MS settings are provided in the supplementary section (see Extended Material and Methods section of the Supplementary Info file).\n\nAll MS raw data sets of the participating partner sites were collected and centrally analysed in the Tenzer laboratory.\n\nThe analysis of DDA data sets was performed using MaxQuant (version 2.3.1.0)34,35. Data were searched against a customized database, which was generated by compiling the SwissProt database entries of the human, yeast and E. coli reference proteomes and a list of common contaminants (UniProtKB release 2020_03, total of 31,039 entries). For each LC-MS setup and PYE dilution, i.e., PYE1, PYE3 and PYE9, data processing was performed separately. Default MaxQuant parameters were applied, including label-free quantification and match between runs (MBR) enabled. The LFQ minimum ratio count was set to two peptides. Trypsin was chosen as the enzyme and up to two missed cleavages were allowed. Carbamidomethylation of cysteine was set as a fixed modification, while methionine oxidation was specified as variable modification. The FDR was set to 1% for both PSMs and protein level (for parameter file, see Supplementary Data\u00a08).\n\nThe DIA data were all processed using DIA-NN (version 1.8.1)36 applying the default parameters for library-free database search (see Supplementary Data\u00a08). For each LC-MS setup and PYE dilution, i.e., PYE1, PYE3 and PYE9, analysis was performed separately. Data were queried against the same database as the DDA datasets (see previous paragraph). For peptide identification and in-silico library generation, trypsin was set as protease allowing one missed cleavage. Carbamidomethylation was set as fixed modification and the maximum number of variable modifications was set to zero. The peptide length ranged between 7 and 30 amino acids. The precursor m/z range was set to 300\u20131800, and the product ion m/z range to 200\u20131800. As quantification strategy we applied the robust LC (high precision) mode with RT-dependent median-based cross-run normalization enabled. We used the build-in algorithm of DIA-NN to automatically optimize MS2 and MS1 mass accuracies and scan window size. Peptide precursor FDRs were controlled below 1%.\n\nPYE data were additionally processed using FragPipe52 (version 23.0), separately for each LC-MS setup and measurement mode. ZenoTOF raw files were converted to mzML beforehand using MSConvert53 (version 3.0.20280) with vendor peak picking. The data were searched against the same protein sequence database used for MaxQuant and DIA-NN analyses including the same number of reversed decoy sequences generated by FragPipe. For all DDA experiments the LFQ-MBR workflow was employed, which uses IonQuant54 for MS1-level quantification. As part of this workflow, normalization of intensities across runs was disabled as we observed some strange effects in the DDA set using cross-run normalisation. For diaPASEF data the DIA_SpecLib_Quant_diaPASEF workflow was used which applies diaTracer55 for spectrum deconvolution prior to searching. All other DIA experiments were processed using the DIA_SpecLib_Quant workflow, leveraging MSFragger-DIA31 for direct peptide identification. DIA quantification was performed using the integrated DIA-NN (version 1.8.2 beta 8) module with cross-run normalization disabled via the --no-norm command. To ensure a fair comparison across workflows, key parameters were standardized: the precursor mass tolerance was set from 20 to 20\u2009ppm, and the fragment mass tolerance was 20\u2009ppm. A maximum of one missed tryptic cleavage and one methionine oxidation was allowed. FDR filtering and report generation were conducted using the --picked and --prot 0.01 flags. Default settings were maintained for all other parameters.\n\nThe software reports of each data set (PYE dilution, site and instrument setup) were processed separately. All downstream analyses were conducted after removing reversed sequences and potential contaminants, allowing only proteins identified by 2 or more peptides. In case of the DIA data (DIA-NN), Q.Value, PG.Q.Value, Lib.Q.Value, and Lib.PG.Q.Value had to be additionally below or equal 0.01 for all plots containing quantitative information. For the generation of plots that contain statistics related to the calculated log2(FC) values between samples A and B (e.g., violin plots, log2(FC) plots, etc.), proteins had to be identified and quantified in at least three technical replicates in each condition, i.e., sample A and B (for both, DDA and DIA datasets). Of note, Peptides shared between species were excluded for log2(FC) plots (and violin plots), but taken into account to calculate numbers of identified proteins and peptides. A comprehensive overview of identified and quantified proteins and peptides across all sites for the DDA (MaxQuant) and DIA (DIA-NN) analyses can be also assessed via Zenodo at [https://doi.org/10.5281/zenodo.17131745]. Additionally, an overview of the search results from all software tools uploaded to jPOST/ProteomeXchange (JPST003358/PXD056598) is provided in Supplementary Data\u00a09.\n\nDownstream analysis of the result files from MaxQuant, DIA-NN and FragPipe was performed in R (version 4.3.2)56 using in-house scripts to calculate and report a set of metrics including the visualization of log2(FC) changes, identification rates (number of identified proteins and peptides for benchmark species), technical variance (the median CV for protein abundances and retention times), global accuracy (the median deviation of log2 ratios to the expected value), global precision of quantification (defined by the interquartile range and the standard deviation of log2 ratios). Identification completeness (bar plots) as well as RT CV plots summarizing results across multiple data sets were inspired by the mpwR (https://CRAN.R-project.org/package=mpwR)57 and the log2(FC) plots for individual setups by the LFQBench package7. ggplot2 was used to design the plots, except for the upset plots58, which were generated with ComplexUpset59.\n\nFor the analyses displayed in Fig.\u00a03 processing results were integrated across the different LC-MS setups merging the processing results (from the analyses described above for each dilution level and species). Intensities for each protein were aggregated by calculating the mean and normalized against the maximum reported protein intensity value within each LC-MS setup. These normalized values were then combined across labs for each PYE dilution to obtain a single intensity value per protein, which was then ranked (Fig.\u00a03a, b). For the scatter plot analysis, protein intensities were averaged and normalized separately for each LC-MS setup and PYE dilution level, to assess and plot the correlation between protein intensities across the different PYE dilution levels, i.e., PYE sample sets (Fig.\u00a03c, d). To this end, we divided the LFQ values for each protein by the LFQ value of the most abundant protein (highest LFQ value) for each site and setup. Ratio were then multiplied by 100 to convert into percent, with 100% corresponding to the highest LFQ value. Figure subpanels have been integrated using Adobe Illustrator (version 29.7.1). Bar plots in Figs.\u00a01 and 7 have been generated using GraphPad Prism (version 10.5.0).\n\nBlood samples were taken at the University Medical Center of the Johannes Gutenberg University Mainz from five healthy donors after obtaining informed consent. All experiments containing human blood plasma from these donors were approved by the ethics committee of the Landes\u00e4rztekammer Rheinland-Pfalz, Mainz No. 837.439.12 (8540-F) and thus performed in compliance with all relevant laws and guidelines.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The raw mass spectrometry data generated in this study along with the database search results have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the jPOST partner repository60 with the dataset identifiers PXD056598 (ProteomeXchange) [https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD056598] and JPST003358 (jPOST, https://repository.jpostdb.org/entry/JPST003358) (PYE analyses from all partner sites as well as plasma proteome experiments). An overview of deposited data files is also provided in Supplementary Data\u00a09. Source data are provided with this paper via Zenodo at [https://doi.org/10.5281/zenodo.17131745]. Additional data files providing a full summary of identified proteins and peptides across all sites for the DDA and DIA analyses can be also assessed via Zenodo at [https://doi.org/10.5281/zenodo.17131745].\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The R scripts for reproducing the figures are available via GitHub at [https://github.com/HanYoo1402/LFQ-Bench-Scripts-for-PYE-Multicenter-Study and Zenodo at [https://doi.org/10.5281/zenodo.17018339].", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Bader, J. M., Albrecht, V. & Mann, M. MS-based proteomics of body fluids: the end of the beginning. Mol. 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This work was supported by the German Ministry of Education and Research (BMBF) as part of the National Research Initiative Mass Spectrometry in Systems Medicine (MSCoreSys), under the following grant agreement numbers: CLINSPECT-M [FKZ 03LW0248 and FKZ 161L0214E to S.M.H., FKZ 161L0214A and 16LW0243K to B.K., J.T., FKZ 161L0214C to A.I., B.K., S.F.L.], SMART-CARE [FKZ 161L0213 to J.K., SMART-CARE 031L0212B, SMART-CARE2 16LW0234 to U.K.], MSTARS [01EP2201 to M.R. and 16LW0239K to M.M.], CurATime [diAMs, FKZ 03ZU1202EA to S.T.] and DIASyM [FKZ 031L0241A/B to S.T.], DIASyM2 [FKZ 03LW0241K to S.T.] as well as the BMBF LiSyM-Cancer networks SMART-NAFLD 031L0256A and C-TIP-HCC 031L0257C and the German Center for Lung Research, DZL3.0 82DZL004B4 and DZL4.0 82DZL004C4 to U.K. Additionally, we acknowledge FOR 5146, by HORIZON EUROPE of the European Research Council within the network ARTEMIS 101136299 funded to U.K. This work was further funded by the German Research Foundation as follows: DFG SFB1066 (TP-Q6 to S.T.),\u00a0SFB1292/2 (project number 318346496, TP11 to U.D., and TP-Q01 to S.T.); the DFG priority program SPP 2225 (grant number 446605368 to U.D.) and the DFG Germany\u2019s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy \u2013 project number 390857198 to S.F.L). The BayBioMS, BayBioMS@MRI and Charit\u00e9 core facility mass spectrometers were funded in part by the German Research Foundation: INST 95/1435-1 FUGG (Exploris 480) and INST 95/1436-1 FUGG (Orbitrap Fusion Lumos) to BayBioMS; INST 95/1649-1 FUGG (Exploris 480) and INST 95/1650-1 FUGG (Orbitrap Eclipse) to BayBioMS@MRI; grant number 492697668 (zenoTOF) to the Core Facility of Mass Spectrometry at the Charit\u00e9. This work was further supported by the Research Center for Immunotherapy (FZI) of the Johannes Gutenberg-University Mainz.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Institute of Immunology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany\n\nUte Distler,\u00a0Han Byul Yoo,\u00a0Dana Hein,\u00a0Malte Sielaff,\u00a0Marian Scherer,\u00a0Anna M. Jozefowicz,\u00a0Christian Leps\u00a0&\u00a0Stefan Tenzer\n\nResearch Center for Immunotherapy (FZI), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany\n\nUte Distler,\u00a0Han Byul Yoo,\u00a0Dana Hein,\u00a0Malte Sielaff,\u00a0Marian Scherer,\u00a0Anna M. Jozefowicz,\u00a0Christian Leps\u00a0&\u00a0Stefan Tenzer\n\nMetabolomics and Proteomics Core, Helmholtz Zentrum M\u00fcnchen, German Research Center for Environmental Health, Munich, Germany\n\nOliver Kardell,\u00a0Christine von Toerne,\u00a0Juliane Merl-Pham\u00a0&\u00a0Stefanie M. Hauck\n\nGerman Cancer Research Center (DKFZ), Heidelberg, Germany\n\nDavid Gomez-Zepeda\u00a0&\u00a0Stefan Tenzer\n\nImmunoproteomics Unit, Helmholtz-Institute for Translational Oncology (HI-TRON) Mainz, Mainz, Germany\n\nDavid Gomez-Zepeda\u00a0&\u00a0Stefan Tenzer\n\nClinical Protein Analysis Unit (ClinZfP), Biomedical Center, Faculty of Medicine, LMU Munich, Munich, Germany\n\nTeresa K. Barth\u00a0&\u00a0Axel Imhof\n\nChair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany\n\nJohanna T\u00fcshaus\u00a0&\u00a0Bernhard Kuster\n\nGerman Center for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich, Germany\n\nPieter Giesbertz\u00a0&\u00a0Stefan F. Lichtenthaler\n\nNeuroproteomics, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany\n\nPieter Giesbertz\u00a0&\u00a0Stefan F. Lichtenthaler\n\nDivision of Proteomics of Stem Cells and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany\n\nTorsten M\u00fcller,\u00a0Georg Kliewer,\u00a0Karim Aljakouch\u00a0&\u00a0Jeroen Krijgsveld\n\nMedical Faculty, Heidelberg University, Heidelberg, Germany\n\nTorsten M\u00fcller,\u00a0Georg Kliewer,\u00a0Karim Aljakouch\u00a0&\u00a0Jeroen Krijgsveld\n\nDivision Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Member of the German Center for Lung Research (DZL), Heidelberg, Germany\n\nBarbara Helm,\u00a0Henry Unger,\u00a0Dario L. Frey\u00a0&\u00a0Ursula Klingm\u00fcller\n\nGerman Center for Lung Research (DZL) and Translational Lung Research Center Heidelberg (TLRC), Heidelberg, Germany\n\nBarbara Helm,\u00a0Dario L. Frey\u00a0&\u00a0Ursula Klingm\u00fcller\n\nLiver Systems Medicine against Cancer (LiSyM-Krebs), Heidelberg, Germany\n\nHenry Unger,\u00a0Dominic Helm\u00a0&\u00a0Ursula Klingm\u00fcller\n\nProteomics Core Facility, German Cancer Research Center (DKFZ), Heidelberg, Germany\n\nDario L. Frey,\u00a0Dominic Helm\u00a0&\u00a0Luisa Schwarzm\u00fcller\n\nMax-Delbr\u00fcck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany\n\nOliver Popp\u00a0&\u00a0Philipp Mertins\n\nMax-Delbr\u00fcck-Center for Molecular Medicine in the Helmholtz Association (MDC), Spatial Proteomics Group, Berlin, Germany\n\nDi Qin\u00a0&\u00a0Fabian Coscia\n\nBavarian Center for Biomolecular Mass Spectrometry (BayBioMS), TUM School of Life Sciences, Technical University of Munich, Freising, Germany\n\nSusanne I. Wudy,\u00a0Christina Ludwig\u00a0&\u00a0Bernhard Kuster\n\nDepartment of Biochemistry, Charit\u00e9 Universit\u00e4tsmedizin Berlin, Berlin, Germany\n\nLudwig Roman Sinn\u00a0&\u00a0Markus Ralser\n\nCore Facility High-Throughput Mass Spectrometry, Charit\u00e9 Universit\u00e4tsmedizin, Berlin, Germany\n\nLudwig Roman Sinn\u00a0&\u00a0Michael M\u00fclleder\n\nBavarian Center for Biomolecular Mass Spectrometry at Klinikum rechts der Isar (BayBioMS@MRI), TUM School of Medicine and Health, Technical University of Munich, Munich, Germany\n\nJulia Mergner\n\nMunich Cluster for Systems Neurology (SyNergy), Munich, Germany\n\nStefan F. Lichtenthaler\n\nGerman Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany\n\nUrsula Klingm\u00fcller\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nU.D. and S.T. conceived and supervised the study. M. Scherer, C. Leps, D. Hein, U.D., prepared and distributed samples, U.D., O.K., M. Sielaff, A.M.J., D.G.Z., C.T., J.M.P., T.K.B., J.T., P.G., T.M., G.K., K.A., B.H., H.U., D.L.F., D. Helm, L.S., O.P., D.Q., S.I.W., L.R.S., J.M., C. Ludwig. conducted mass spectrometric analyses. U.D., H.B.Y., M. Sielaff, D. Hein, analysed the data, U.D. and H.B.Y. generated figures and prepared the initial draft of the manuscript. O.K., M. Sielaff, A.M.J., D.G.Z., T.K.B., J.T., P.G., K.A., B.H., H.U., D.L.F., D. Helm, L.R.S., J.M., C. Ludwig, A.I., B.K., S.F.L., J.K., U.K., P.M., F.C., M.R., M.M., S.M.H., S.T. discussed results and contributed to writing. All authors reviewed the final manuscript version.\n\nCorrespondence to\n Ute Distler or Stefan Tenzer.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "T.M. and G.K. are employees of Bruker. B.K. is a co-founder and shareholder of OmicScouts and MSAID. He has no operational role in either company. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Multicenter evaluation of label-free quantification in human plasma on a high dynamic range benchmark set.\n Nat Commun 16, 8774 (2025). https://doi.org/10.1038/s41467-025-64501-z\n\nDownload citation\n\nReceived: 10 December 2024\n\nAccepted: 18 September 2025\n\nPublished: 02 October 2025\n\nVersion of record: 02 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-64501-z\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_MOESM10_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 9", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_MOESM11_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 10", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_MOESM12_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 11", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_MOESM13_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 12", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_MOESM14_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 13", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_MOESM15_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 14", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_MOESM16_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 15", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_MOESM17_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 16", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_MOESM18_ESM.txt" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_MOESM19_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_MOESM20_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://www.ebi.ac.uk/metabolights/MTBLS8694", + "https://www.ebi.ac.uk/metabolights/MTBLS8440", + "https://doi.org/10.6084/m9.figshare.29093192.v1", + "https://metaspace2020.org/dataset/2025-02-27_13h37m58s" + ], + "code": [], + "subject": [ + "Bacterial host response", + "Innate immunity", + "Natural products", + "Symbiosis" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3853431/v1.pdf?c=1749035193000", + "research_square_link": "https://www.researchsquare.com//article/rs-3853431/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60234-1.pdf", + "preprint_posted": "05 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Protection against pathogens is a major function of the gut microbiota. Although bacterial natural products have emerged as crucial components of host-microbiota interactions, their exact role in microbiota-mediated protection is largely unexplored. We addressed this knowledge gap with the nematode Caenorhabditis elegans and its microbiota isolate Pseudomonas fluorescens MYb115 that is known to protect against Bacillus thuringiensis (Bt) infection. We find that MYb115-mediated protection depends on sphingolipids that are derived from an iterative type I polyketide synthase (PKS), thereby describing a noncanonical pathway of bacterial sphingolipid production. We provide evidence that MYb115-derived sphingolipids affect C. elegans tolerance to Bt infection by altering host sphingolipid metabolism. This work establishes sphingolipids as structural outputs of bacterial PKS and highlights the role of microbiota-derived sphingolipids in host protection against pathogens.\n\n*/** shared first or last authorshipBiological sciences/Microbiology/Bacteria/Bacterial host responseBiological sciences/Chemical biology/Natural products", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "TableS1.xlsxS1TableS2.xlsxS2TableS3.xlsxS3TableS4.xlsxS4TableS5.xlsxS5TableS6.xlsxS6TableS7.xlsxS7", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Protection against pathogens is a major function of the gut microbiota. Although bacterial natural products have emerged as crucial components of host-microbiota interactions, their exact role in microbiota-mediated protection is largely unexplored. We addressed this knowledge gap with the nematode Caenorhabditis elegans and its microbiota isolate Pseudomonas fluorescens MYb115 that is known to protect against Bacillus thuringiensis (Bt) infection. We find that MYb115-mediated protection depends on sphingolipids (SLs) that are derived from an iterative type I polyketide synthase (PKS) cluster PfSgaAB, thereby revealing a non-canonical pathway for the production of bacterial SLs as secondary metabolites. SL production is common in eukaryotes but was thought to be limited to a few bacterial phyla that encode the serine palmitoyltransferase (SPT) enzyme, which catalyses the initial step in SL synthesis. We demonstrate that PfSgaB encodes a pyridoxal 5\u2019-phosphate-dependent alpha-oxoamine synthase with SPT activity, and find homologous putative PKS clusters present across host-associated bacteria that are so far unknown SL producers. Moreover, we provide evidence that MYb115-derived SLs affect C. elegans defence against Bt infection by altering SL metabolism in the nematode host. This work establishes SLs as structural outputs of bacterial PKS and highlights the role of microbiota-derived SLs in host protection against pathogens.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "A major function of the gut microbiota is its contribution to host protection against pathogens1. The protective mechanisms conferred by the gut microbiota are complex and include direct competitive or antagonistic microbe\u2013microbe interactions and indirect microbe-host interactions, which are mediated by the stimulation of the host immune response, promotion of mucus production, and maintenance of epithelial barrier integrity2. Microbiota-derived metabolites are known to play an important role in the crosstalk between the gut microbiota and the immune system3,4,5. Of these metabolites, bacterial natural products have emerged as crucial components of host-microbiota interactions6,7,8.\n\nBacterial natural products (also called secondary or specialised metabolites) are chemically distinct, often bioactive compounds that are not required for viability, but mediate microbial and environmental interactions9. Some of the most studied natural products include polyketides, which are derived from polyketide synthase (PKS). PKS are found in many bacteria, fungi, and plants, and produce structurally diverse compounds by using an assembly line mechanism similar to fatty acid synthases10. Many PKS-derived natural products show potent antibiotic (e.g., erythromycin and tetracycline), antifungal (e.g., amphotericin and griseofulvin) or immunosuppressant (e.g., rapamycin) activities11 and have thus long played a central role in advancing therapeutic treatments for a wide range of medical conditions. The majority of characterised polyketides were isolated from free-living microbes, while only a few are known to be gut microbiota-derived8. Most well-studied examples of PKS-derived products from the microbiota are virulence factors associated with pathogenicity12. Few PKS-encoded natural products were reported to play a role in microbiota-mediated protection against pathogens both directly and indirectly. For example, the antifungal polyketide lagriamide supports direct symbiont-mediated defence of eggs against fungal infection in the beetle Lagria vilossa13. A PKS cluster of the rodent gut symbiont Limosilactobacillus reuteri is required for activating the mammalian aryl hydrocarbon receptor, which is involved in mucosal immunity14. Additionally, L. reuteri PKS was recently demonstrated to exhibit antimicrobial activity and to drive intraspecies antagonism15. Yet, the vast majority of microbiota-encoded PKS are of unknown function and mechanistic studies linking specific microbial natural products to host phenotypes are scarce.\n\nThe Pseudomonas fluorescens isolate MYb115 belongs to the natural gut microbiota of the model organism Caenorhabditis elegans16. It was previously found that MYb115 protects C. elegans against the harmful effects of infection with Bacillus thuringiensis (Bt) without directly inhibiting pathogen growth, likely through an indirect, host-dependent mechanism17,18. The nature of the microbiota-derived protective molecule and the involved host processes were unknown. Here, we identify a biosynthetic gene cluster (BGC) in MYb115 encoding an iterative type I PKS that is required for MYb115-mediated protection and produces sphingolipids (SLs). Thus, we discovered a non-canonical pathway for the production of bacterial SLs, which relies on a BGC, the P. fluorescens PKS cluster PfSgaAB. Hence SLs are produced as secondary metabolites. We additionally demonstrate that MYb115-derived SLs affect C. elegans SL metabolism and establish the importance of C. elegans SL metabolism for survival after Bt infection.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The natural microbiota isolate P. fluorescens MYb115 protects C. elegans against infection with the Gram-positive pathogenic B. thuringiensis strain Bt247 likely through a host-dependent mechanism18, but the nature of the microbiota-derived protective molecule was unknown. We performed an antiSMASH analysis19 of the MYb115 genome to identify natural product BGCs. We found three BGCs in the MYb115 genome, encoding a non-ribosomal peptide synthetase (NRPS), an iterative type I PKS cluster, and an arylpolyene pathway.\n\nWe modified the PKS and NRPS clusters of MYb115 by inserting the inducible arabinose PBAD promoter. Thus, while induction of BGC expression requires arabinose supplementation, no expression should be observed in the absence of arabinose supplementation, mimicking a deletion phenotype20. We assessed the ability of MYb115 PBADsga (MYb115 PKS cluster, Fig.\u00a01A) and MYb115 PBADnrpA (MYb115 NRPS cluster, Fig.\u00a01B) in an induced (+\u2009arabinose) and non-induced (\u2212\u2009arabinose) state to protect C. elegans against Bt247 infection. We found that infected C. elegans exposed to induced MYb115 PBADsga showed significantly increased survival when compared to infected worms on MYb115 PBADsga in a non-induced state (Fig.\u00a01A, C and Supplementary Data\u00a01). Arabinose supplementation had no effect on resistance of C. elegans to Bt infection on its standard laboratory food Escherichia coli OP50 (Supplementary Data\u00a01). While the PKS gene cluster affects MYb115-mediated protection, we did not observe significant differences in worm survival with or without arabinose supplementation on the MYb115 PBADnrpA strain (Fig.\u00a01B and Supplementary Data\u00a01). We therefore focused on the P. fluorescens MYb115 PKS gene cluster (hereafter PfSgaAB) in our subsequent analyses.\n\nA, B Survival proportion of C. elegans N2 on P. fluorescens MYb115 PBADsga (A) or MYb115 PBADnrpA (B) induced with arabinose (solid line) or in a non-induced state without arabinose supplementation (dashed line) 24\u2009h post infection with B. thuringiensis Bt247. Bt407 was used as a non-pathogenic control. The data shown is representative of three independent runs with four replicates each (see Supplementary Data\u00a01). C LC-MS chromatogram of MYb115 PBADsga extracts from cultures with (solid line) and without (dashed line) arabinose supplementation. Upon induction with arabinose, three compounds (1\u20133) are produced. D Schematic representation of the MYb115 PKS gene cluster and its modifications. Polyketide synthase (PKS) SgaA, alpha-oxoamine synthase (AOS) SgaB and inducible arabinose promoter (PBAD). E Survival proportion of N2 on E. coli OP50, MYb115, or MYb115 knockout mutants. C. elegans on both tested mutants MYb115 \u0394sgaA, and MYb115 \u0394sgaB were significantly more susceptible (p\u2009=\u20091.17E-09 or p\u2009=\u20092.00E-16, respectively) to infection with Bt247 than worms on wildtype MYb115. Means \u00b1 standard deviation (SD) of n\u2009=\u20094, are shown in survival assays (A, B, E), n\u2009=\u20093 in (F). Statistical analyses were carried out using the generalized linear model (GLM) framework with a binomial distribution. All tests were two-sided, and p-values were adjusted for multiple comparisons using the Bonferroni correction. Significance is indicated as ***p\u2009<\u20090.001. F Survival proportion of N2 on MYb115 \u0394sgaA/PvanCCsgaAB, which expresses SgaAB under the vanillic acid-inducible PvanCC promoter on the pSEVA631 plasmid. Survival was assessed 24\u2009h post-infection with Bt247, comparing vanillic acid-induced (solid line) and non-induced (dashed line) conditions (p\u2009=\u20093.53E-11). G LC-MS chromatogram of MYb115 wt, \u0394sgaA and \u0394sgaB. H Correlation of area under the C. elegans survival curve (AUC) and peak intensity, representing bacterial SL abundance. Each facet represents the correlation for a specific bacterial SL compound (1\u20136), with different bacterial treatments indicated by colour. Correlations were calculated using the two-sided Spearman method, correlation coefficients are shown with 95% confidence intervals. Source data and additional survival runs are provided in Supplementary Data\u00a01.\n\nWe then deleted either, the PKS SgaA (MYb115 \u0394sgaA), or the alpha-oxoamine synthase (AOS) SgaB (MYb115 \u0394sgaB) (Fig.\u00a01D) to confirm the requirement of PfSgaAB in MYb115-mediated protection. While MYb115 provided significant protection against infection in C. elegans compared to worms on E. coli OP50 (Fig.\u00a01E18), protection of worms on both MYb115 mutants was lost (Fig.\u00a01E). Protection in MYb115 \u0394sgaA was restored upon expression of SgaAB from the vanillic acid-inducible PvanCC promoter on a plasmid (pSEVA631) (Fig.\u00a01F). These results clearly demonstrate a role of PfSgaAB in MYb115-mediated protection.\n\nMYb115-mediated protection against Bt247 infection depends on the PKS cluster PfSgaAB. We next asked which natural product is produced by PfSgaAB. Using LC-MS, we identified three compounds that are produced in MYb115 PBADsga upon induction with arabinose (Fig.\u00a01C). We subsequently established that the compounds 1\u20133 are also produced, but less abundant, in MYb115, and that both MYb115 deletion mutants (MYb115 \u0394sgaA, and MYb115 \u0394sgaB) are not able to produce compounds 1\u20133 (Fig.\u00a01G). MS2 experiments revealed that compounds 1\u20133 show structural similarities to commercially available long chain sphinganines (Fig.\u00a0S1). We determined the molecular composition through isotopic labelling experiments and confirmed that compounds 1\u20133 are very long chain sphinganines (C24, C26, C28, Fig.\u00a0S2A\u2013C).\n\nMoreover, we performed lipidomic analysis of MYb115 using high-resolution Liquid Chromatography Tandem Mass Spectrometry (HRES-LC-MS/MS) and found that in addition to the three sphinganines 1\u20133 MYb115 produces compounds 4\u20136, each with masses 154\u2009Da heavier than those of the three sphinganine derivatives (Fig.\u00a0S2D\u2013F and Supplementary Data\u00a03). Since the masses of 4\u20136 did not match any known lipids in the MS-DIAL LipidBlast (version 68) dataset, we used the exact mass and different lipid headgroups to propose structures for compounds 4\u20136. We conclude that compounds 4\u20136 are most likely phosphoglycerol sphingolipids (PG-sphingolipids). Next, we analysed the relative abundance of sphinganines 1\u20133, and PG-sphingolipids 4\u20136 in MYb115 and MYb115 PBADsga induced by arabinose or repressed by glucose supplementation. While the sphinganines 1\u20133 were more abundant in the induced MYb115 PBADsga samples, the total abundance of PG-sphingolipids 4 and 5 did not differ compared to MYb115 supplemented with arabinose (Fig.\u00a0S2G). Thus, increase in sphinganine production does not necessarily lead to increase in PG-sphingolipid production.\n\nMany BGCs are silent under typical laboratory conditions and activation of secondary metabolic pathways can be a challenge21. Indeed, we observed substantial variations in the protective effect of MYb115 under our standard laboratory conditions over the course of this project. We hypothesized that the variations in the protective effect are related to variations in SL production. To test this hypothesis, we used MYb115 PBADsga and MYb115 \u0394sgaA/PvanCCsgaAB, in which SL production can be activated by arabinose and vanillic acid supplementation, respectively, and that produce SLs at much higher levels than wildtype MYb115 (Fig.\u00a0S3). We harvested MYb115 PBADsga and MYb115 \u0394sgaA/PvanCCsgaAB pellets of the same bacterial cultures, whose protective effect we then tested in survival analyses, and visualized SL production using MALDI mass spectrometry spot assays22. SL production indeed varied between different bacterial cultures, even under conditions of targeted induction. Most importantly, we found that abundances of sphinganines 1\u20133 and PG-sphingolipid 4 correlate significantly with worm survival following Bt247 infection (Fig.\u00a01H and Supplementary Data\u00a01), providing further evidence that host protection is dependent on these SLs. Since our results also demonstrate that SL production and the associated protective effect is variable under the given laboratory conditions, we always controlled for the protective effect in our experiments.\n\nSL synthesis in bacteria and eukaryotes involves the condensation of an amino acid (typically serine in mammals) and a fatty acid (typically palmitate in mammals) via the serine palmitoyltransferase (SPT) enzyme that uses pyridoxal phosphate (PLP) as cofactor for serine decarboxylation and coupling to palmitoyl-CoA23. In the case of MYb115, the protective SLs 1\u20133 and PG-sphingolipid 4, are produced by the two-gene cluster PfsgaAB (Sphinganine biosynthesis A and B), in which sgaA encodes a PKS and sgaB encodes an AOS with predicted structural homology to SPTs deposited on the Protein Data Bank (https://www.rcsb.org/ PDB: 2JG2; 2X8U; 3A2B; 8GUH, Fig.\u00a0S4). Like its SPT homologues, PfSgaB is expected to condense fatty acyl-thioesters with L-serine to give 3-ketodihydrosphinganine (3-KDS)-like intermediates, which is the first committed step in SL biosynthesis (Fig.\u00a02A24,25). To confirm this activity, the gene encoding PfSgaB was codon-optimised, synthesised and cloned into pET28a with an N-terminal poly-His tag for downstream purification (Figs.\u00a0S5 and S6). Recombinant expression in E. coli BL21(DE3) resulted in yellow-tinged biomass, from which PfSgaB was purified to homogeneity using tandem cobalt-IMAC and size-exclusion chromatography (SEC, Fig.\u00a0S7). SEC analysis provided an estimated molecular weight (MW) of 108 kDA, consistent with protein dimerization (Fig.\u00a0S8). We first incubated recombinant, purified PfSgaB with varying concentration of L-serine and observed PLP:L-serine external aldimine formation by UV\u2013vis spectroscopy (413\u2009nm), with and estimated Kd\u2009=\u20091.30\u2009\u00b1\u20090.0256\u2009mM (Figs.\u00a02B and S9). Following this, we probed PfSgaB condensation activity using acyl-CoAs 7\u20139 as a surrogate co-substrates (Fig.\u00a02C), capturing the CoASH by-product using 5,5\u2032-dithio-bis(2-nitrobenzoic acid) (DTNB, Fig.\u00a0S10A). Using this colorimetric assay, a clear response was obtained when PfSgaB was incubated with both L-serine and 7, in contrast to all negative controls (Fig.\u00a0S10B). Furthermore, PfSgaB shows virtually exclusive preference for L-serine over deoxysphingolipid-forming amino acids L-alanine and glycine (Fig.\u00a02D). Similar condensation activity with L-serine was also observed when 8 and 9 were used as co-substrates (Fig.\u00a0S10C). We subsequently identified all corresponding 3-KDS products 10\u201312 by LC/ESI-MS (m/z\u2009=\u2009300.29, 328.33, 356.37, see Fig.\u00a02E, F). Moreover, through isotopic labelling experiments we could show that 13C15N-labelled serine is incorporated during sphinganine biosynthesis in MYb115 in vivo (Supplementary Data\u00a02). Taken all together, this data confirms the functional assignment of PfSgaB as a SPT and the key gateway into SL biosynthesis in MYb115.\n\nA Biosynthesis scheme of MYb115-derived PG-sphingolipids 4\u20136. The production of 3-ketodihydrosphinganines (KDSs) is catalysed by the iterative PKS (iPKS) PfSgaA and PLP-dependent serine palmitoyltransferase (SPT) PfSgaB. The reduction of KDSs to dihydrosphinganines 1\u20133 is presumably catalysed by the KDS reductase homologue PfSgaC. B PLP external aldimine formation following the addition of up to 10 mM L-serine (L-ser), monitored by UV\u2013vis spectroscopy. External aldimine formation is signified by an increase in absorbance at 413\u2009nm. C Schematic representation of PfSgaB-catalysed decarboxylative condensation between acyl-CoAs 7\u20139 and L-ser to give 3-ketodihydrosphinganines 10\u201312. D Relative activity of PfSgaB in the presence of C16-CoA 7 and L-serine, L-alanine or glycine, determined using the DTNB assay (412\u2009nm). UV\u2013vis measurements were recorded after 20\u2009min of incubation. Error bars represent the standard deviation of three technical replicates. All measurements were corrected for non-specific background absorbance. E Extracted ion (EI) chromatograms of PfSgaB-derived products 10\u201312, detected by LC/ESI-MS. F [M\u2009+\u2009H]+ ions of PfSgaB-derived products 10\u201312, detected by LC/ESI-MS. The theoretical m/z is shown for each product.\n\nFurthermore, we identified a putative short chain dehydrogenase/reductase (SDR) in the MYb115 SL BGC (locus ID: KW062_RS19775), which shares homology with several eukaryotic 3-ketodihydrosphinganine reductases (KDSR, see Figs.\u00a0S11 and 12). KDSR homologues are also found in fungal BGCs that produce SL-like, PKS-derived mycotoxins such as fumonisin (FUM13, Uniprot: W7LL82)26 and sphingofungin (SphF, Uniprot: B0XZV2)27. KDSR catalyses the reduction of 3-KDS to dihydrosphinganine (DHS)28,29; whilst this step is ubiquitous in eukaryotic SL biosynthesis, it is unusual in bacterial SL pathways outside of Bacteroides and Prevotella species30,31. Taken together with gene context, we propose that this enzyme, hereafter named PfSgaC, mediates 3-KDS reduction in MYb115. The inclusion of this eukaryotic-like step further distinguishes this BGC from canonical bacterial SL biosynthesis.\n\nIterative PKS were originally found in fungi and only rarely in bacteria10. However, a large number of bacterial iterative PKS were identified more recently32. While only a few bacterial iterative PKS and their products have been studied, our work is to our knowledge the first example of a PKS cluster shown to be involved in SL biosynthesis and also the first description of a P. fluorescens isolate as SL producer. We explored the distribution of the two-gene PfsgaAB in bacteria listed in the NR NCBI database and found 6,101 homologous putative PKS clusters (Supplementary Data\u00a04). Interestingly, the homologous PKS clusters were present in bacteria that are known to be closely associated with hosts, including human pathogens and opportunistic pathogens (Fig.\u00a03A). When we analysed the distribution of the target BGC class at the genus level, we found that the putative PKS cluster is dominantly distributed in Burkholderia (Fig.\u00a03B). Interestingly, Burkholderia pseudomallei K96243 has previously been shown to produce sphingosine-1-phosphate lyases33,34, but like Pseudomonas, Burkholderia is not yet a known SL producer. The fact that we found the potential PKS cluster SgaAB in Burkholderia suggests that they may be able to produce sphingosine-1-phosphate and not just degrade it.\n\nThe monomodular PKS (KW062_RS19805) and the alpha-oxoamine synthase (KW062_RS19800) in P. fluorescens MYb115 (NZ_CP078138) were searched against the NR NCBI database (https://www.ncbi.nlm.nih.gov/) using cblaster (1.8.1)89. A Five representative PKS cluster SgaAB homologs from various bacterial genera aligned and visualised using clinker90. B Total distribution of 6101 PKS cluster SgaAB homologs across different bacterial genera. The width of each box represents the percentage of all identified PKS cluster SgaAB homologs, found in each bacterial genus are provided as source data in Supplementary Data\u00a04.\n\nIn a first step towards exploring the function of microbiota-derived SLs in mediating the interaction with the host, we tested whether MYb115-produced SLs affect the ability of MYb115 to colonise the host or modulate host feeding behaviour. We did not observe a difference in host colonisation between MYb115 and MYb115 \u0394sgaA (Fig.\u00a0S13A and Supplementary Data\u00a05), nor did we see differences in C. elegans feeding behaviour on MYb115 and MYb115 \u0394sgaA (Fig.\u00a0S13B and Supplementary Data\u00a05).\n\nMYb115 protects C. elegans against Bt infection without directly inhibiting pathogen growth, likely through an indirect, host-dependent mechanism18. When grown on MYb115 C. elegans is also protected against infection with another Bt strain, Bt679, that produces distinct pore-forming toxins (PFTs) (Fig. S1417,18) and this protection also depends on bacterial SL production (Fig.\u00a0S14 and Supplementary Data\u00a06). We thus considered that activation of general host defence mechanisms may contribute to MYb115-mediated protection and performed gene expression profiling of 1-day adult worms on either protective MYb115 or non-protective MYb115 \u0394sgaA in the absence and presence of pathogenic Bt247 (Fig.\u00a04A and Supplementary Data\u00a07). We did not observe any genes differentially regulated between worms on SL-producing MYb115 and worms on the MYb115 \u0394sgaA mutant when using an adjusted p-value cutoff of 0.05. This may indicate that MYb115-derived SLs do not strongly affect C. elegans on the transcript level, but more strongly influence the host on the proteome or metabolome level. Using a less stringent cutoff (non-adjusted p-value\u2009<\u20090.01), we nevertheless identified 122 differentially expressed (DE) genes between the two treatments in the absence of Bt247 (23 genes were down regulated and 99 genes upregulated in worms on MYb115 \u0394sgaA (Supplementary Data\u00a07) and 48 DE genes in the presence of Bt247 (22 genes were down regulated and 26 genes upregulated in worms on MYb115 \u0394sgaA (Supplementary Data\u00a07). Genes that are related to C. elegans pathogen defence were indeed enriched in both gene sets, including targets of known pathogen defence pathways, such as the p38 and JNK-like MAPK pathways (Fig.\u00a04B, C and Supplementary Data\u00a07). However, we did not find any evidence of the involvement of the p38 MAPK and the JNK MAPK KGB-1 in decreasing MYb115-mediated protection against Bt247 (Fig.\u00a04B, C and Supplementary Data\u00a08).\n\nA Transcriptional response of C. elegans to MYb115-derived SLs. Enrichment analysis of genes differentially regulated between worms exposed to SL-producing MYb115 and worms exposed to non-SL producing MYb115 \u0394sgaA in the presence of pathogenic Bt247 (Supplementary Data\u00a07). B, C Survival of p38 and JNK MAPK pathway mutants. Means\u2009\u00b1\u2009standard deviation (SD) of n\u2009=\u20094 (p38 MAPK pathway (B)); n\u2009=\u20093 (kbg-1(ums3) survival (C)) are shown in all survival assays. Statistical analyses were carried out using the generalized linear model (GLM) framework with a binomial distribution. All tests were two-sided, and p-values were adjusted for multiple comparisons using the Bonferroni correction. Significance is indicated as, ***p\u2009<\u20090.001, **p\u2009<\u20090.01, *p\u2009<\u20090.05. All p-values can be found in Supplementary Data\u00a08. nsy-1(ag3) and sek-1(km4) share the same N2 control since the experiment was conducted in parallel, with statistical analysis adjusted accordingly, as highlighted in Supplementary Data\u00a08. Source data are provided in Supplementary Data\u00a08.\n\nThe damage caused by Bt PFTs leads to loss of intestinal barrier function and we have previously shown that MYb115 limits Bt-induced damage to the intestinal epithelium18. Here, we used a C. elegans strain expressing PGP-1::GFP, a labelled ATP binding-cassette transporter, whose expression is restricted to the apical plasma membrane of the intestinal epithelium35, to test if MYb115-derived SLs are involved in mitigating Bt-induced damage. Indeed, in Bt247 infected worms on arabinose-induced MYb115 PBADsga, we found a clear reduction of the relocalisation of the PGP-1::GFP marker to intracellular vesicles (Fig.\u00a05 and Supplementary Data\u00a09), which is regarded as a response to membrane damage caused by PFTs36. In contrast, there was no difference in the numbers of intracellular vesicles between infected worms on E. coli OP50 and the MYb115 \u0394sgaA mutant (Fig.\u00a05), suggesting that the SLs produced by MYb115 PBADsga contribute to protection of the intestinal barrier following Bt infection.\n\nVisualisation and quantification of vesicular structures following Bt247 infection. Worms were raised on either E. coli OP50, P. fluorescens MYb115 \u0394sgaA or P. fluorescens MYb115 PBADsga + arabinose for 72\u2009h and then infected with Bt247. Confocal images of PGP-1::GFP were captured 4\u2009h after exposure to Bt247 mixed with either OP50, MYb115 \u0394sgaA or MYb115 PBADsga + arabinose. For each worm all PGP-1::GFP positive vesicles were scored and categorised into either of the three groups \u201c0 vesicles\u201d, \u201c\\(\\le\\) 10 vesicles\u201d or \u201c\\( > \\) 10 vesicles\u201d. Representative images of worms are shown, highlighting magnified regions of PGP-1::GFP positive vesicles (indicated by white arrows) following Bt247 infection. Scale bar: 100\u2009\u00b5m. The proportions of worms in each category are displayed as stacked bar plots for each replicate. Population size varied between 14 and 25 individuals (n\u2009=\u20093). Source data are provided in Supplementary Data\u00a09.\n\nMouse lipid metabolism was previously shown to be affected by gut microbiota-derived SLs37. Moreover, in a C. elegans Parkinson disease model, the probiotic B. subtilis strain PXN21 protects the host against protein aggregation by modulating SL metabolism38. Thus, we hypothesised that MYb115-derived SLs impact host metabolism. To test this hypothesis, we integrated the transcriptomic data into the iCEL1314 genome-scale metabolic model of C. elegans39 to create context-specific models, simulating metabolite flow through the C. elegans reactions network under specific treatment conditions (see methods). Statistical analysis of the reaction fluxes resulted in 16 (Bt247 +) and 16 (Bt247 \u2212) significant differences when comparing MYb115 \u0394sgaA and MYb115 worms (Supplementary Data\u00a010). Through a pathway enrichment analysis of the significant reactions against a background of our model pathways, we found that in the absence of Bt247, animals colonised by MYb115 or MYb115 \u0394sgaA varied in the activity of multiple pathways linked with SL precursor production, such as monomethyl branched chain fatty acid biosynthesis, as well as SL metabolism itself (Fig.\u00a06A and Supplementary Data\u00a010). In the presence of Bt247, propanoate metabolism was most strongly affected (Fig.\u00a0S15A and Supplementary Data\u00a010). Under both infection and non-infection conditions, the valine, leucine, and isoleucine degradation pathway were significantly enriched. This pathway degrades branched-chain amino acids and is directly connected with propanoate metabolism that provides components for the synthesis of the C15iso fatty acid, which is the precursor for SLs in C. elegans40. We also focused on SL metabolism directly: Flux variability analysis41 revealed a significant difference in upper bound values for the SL metabolism reactions in worms infected with Bt247 on MYb115 versus MYb115 \u0394sgaA (t-test p-value\u2009<\u20090.001). Among those reactions, six reactions that all have ceramide as a substrate or product had the strongest changes (Fig.\u00a0S15B and Supplementary Data\u00a010). Overall, these findings suggest that worms colonised by MYb115 versus MYb115 \u0394sgaA have a significantly reduced capacity to generate SLs.\n\nA Enriched metabolic pathways if the C. elegans (iCEL1314) metabolic model were identified following a comparison of worm models integrated with transcriptome data from worms treated with MYb115 with worms treated with MYb115 \u0394sgaA. Significant reactions obtained by calculating two-sided p-values from linear regression models (data ~ treatment) of FVA centres and OFD data layers were used for Flux Enrichment Analaysis (FEA) against the background of all reactions within the iCEL1314 C. elegans metabolic model. Benjamini-Hochberg was applied only for FEA output due to high pathway/reaction collinearity. Source data are provided in Supplementary Data\u00a010. B Reduced SL contents in worms exposed to MYb115 compared to worms exposed to MYb115 \u0394sgaA. The heatmap shows the differences in ratio of detected SLs between the mean of MYb115 \u0394sgaA and the mean of MYb115. The boxplot shows the difference in ratio of Sphingomyelin (t43:1) in worms exposed to MYb115 \u0394sgaA and MYb115, all remaining boxplots can be found in Fig.\u00a0S16. Boxplots display the median (line), the first and third quartiles (box edges), and whiskers extending to the smallest and largest values within 1.5\u00d7 the interquartile range. Points beyond this range are shown as outliers. Statistical analysis was done with a two-sided Welch\u2019s t- test (n\u2009=\u20095), * p-value\u2009<\u20090.05, ** p-value\u2009<\u20090.01. Dihydroceramides (DhCer), Ceramides (Cer), Sphingomyelins (SM), Hexosylceramides (HexCer), with hydroxylated fatty acyls (t) or non-hydroxylated fatty acyls (d), Hexosylceramides with phytosphingosine base and hydroxylated fatty acyls (HexCer(q)), monomethyl phosphoethanolamine glucosylceramide (mmPEGC(q)). Source data are provided in Supplementary Data\u00a011.\n\nThe metabolic network analysis revealed that SL metabolism reactions show differential activity between MYb115 and MYb115 \u0394sgaA. To confirm that MYb115-derived SLs affect C. elegans SL metabolism, we performed lipidomic profiling of C. elegans exposed to MYb115 or MYb115 \u0394sgaA. We identified C. elegans SLs by manual interpretation of MS1 and MS2 data and used SLs that have previously been described in C. elegans containing a C17iso-branched chain sphingoid base and different length of N-Acyl chains as input42 (Supplementary Data\u00a011). Since the employed analytical method cannot separate between different hexoses attached to the SL, they were annotated as hexosylceramides (HexCers), which showed the neutral loss of 162.052275\u2009Da. Monomethylated phosphoethanolamine glucosylceramides (mmPEGCs), a class of C. elegans phosphorylated glycosphingolipids, were identified based on fragments as previously described43,44.\n\nWe were not able to detect MYb115-derived sphinganine in worms on MYb115. Likewise, we did not detect any SLs based on sphinganines produced by MYb115. A possible explanation is that bacterial sphinganine concentrations in worms are below the detection limit. However, we found different complex host SLs based on the C17iso-branchend chain sphingoid base typical for C. elegans with N-acyl sides of length 16\u201326 without or with hydroxylation. In addition to previously established SLs, we identified HexCer with an additional hydroxyl group instead of the double bond in the sphingoid base. In total, we identified 40\u2009C. elegans SLs from different SL classes. We did not observe a difference in C. elegans C17iso sphinganine or C17iso sphingosine, but in certain dihydroceramide (DhCer) and ceramide (Cer) species between worms on MYb115 or MYb115 \u0394sgaA. Also, complex SLs downstream of ceramides, i.e., sphingomyelins (SMs) and HexCers were increased in worms on MYb115 \u0394sgaA, and some even significantly increased (Fig.\u00a06B). Individual SL profiles are shown in Fig.\u00a0S16. Most of the significant changes occurred at the lower or upper end of the detected N-acyl chain length. No changes occurred in SLs containing an N-acyl of 22 or 24 carbon length. However, the series of SM(d33:1, d35:1, d37:1), showed a consistent and significant increase. Additionally, SM(t37:1) and SM(t43:1) as well as the corresponding HexCer(t37:1) and SM(t37:1) increased significantly. Notably, we found the highest fold-changes between MYb115 and MYb115 \u0394sgaA-exposed worms for mmPEGC. However, changes were not significant and so far, the biosynthesis pathway of mmPEGCs is unknown.\n\nTogether, our data suggest that MYb115-derived SLs interfere with C. elegans SL metabolism mainly at the conversion of dihydroceramide and ceramide to sphingomyelins and hexosylceramides.\n\nSince MYb115 affects host SL metabolism and protects the worm against Bt infection, we next asked whether alterations in nematode SL metabolism affect C. elegans survival following Bt infection. We performed survival experiments using several C. elegans mutants of SL metabolism enzymes (Figs.\u00a07A\u2013C and S17 and Supplementary Data\u00a012). We assessed the general involvement of SL metabolism in the response to Bt infection in the presence of the non-protective lab food E. coli OP50. We found that mutants of the C. elegans serine palmitoyl transferases sptl-1(ok1693) and sptl-3(ok1927), which catalyse the de novo synthesis of the C17iso sphingoid base, and the ceramide synthase mutants hyl-1(ok976) and hyl-2(ok1766)) showed increased survival on Bt in comparison to wildtype N2 worms (Fig.\u00a07C), whereas the survival phenotype of two ceramide metabolic gene mutants, namely cgt-1(ok1045) and cerk-1(ok1252), was variable (Fig.\u00a07C). cgt-1 encodes one of three C. elegans ceramide glucosyltransferases that generate glucosylceramides (GlcCers). cerk-1 is a predicted ceramide kinase that catalyses the phosphorylation of ceramide to form ceramide-1-phosphate (C1P). In contrast, the sms-1(ok2399) mutant was clearly more susceptible to Bt247 infection than wildtype worms. sms-1 encodes a C. elegans sphingomyelin synthase that catalyses the synthesis of sphingomyelin from ceramide. Accordingly, the asm-3(ok1744) mutant, which lacks the enzyme that breaks down sphingomyelin to ceramide, showed increased resistance to Bt247 (Fig.\u00a07C). Notably, the ceramidase mutants asah-1(tm495) and asah-2(tm609) were also significantly more susceptible to Bt247 infection than the C. elegans control (Fig.\u00a07C). asah-1 encodes a C. elegans acid ceramidase that converts ceramide to C17iso-sphingosine, which is subsequently phosphorylated by the sphingosine kinase SPHK-1 to C17iso-sphingosine-1-phosphate45. Together, these results suggest that inhibition of de novo synthesis of ceramide and inhibition of the conversion of ceramide to GlcCer or C1P increases survival of C. elegans infected with Bt247, while inhibition of the conversion of ceramide to sphingomyelin or sphingosine decreases survival of Bt247-infected animals.\n\nA Overview of SL metabolism in C. elegans. C. elegans produces sphingoid bases which are derived from a C17 iso-branched fatty acid and are thus structurally distinct from those of other animals with mainly straight-chain C18 bases40. C. elegans SLs consist of a sphingoid base backbone derived from C15iso-CoA and serine, which is N-acylated with fatty acids of different lengths as well as different functional groups at the terminal hydroxyl group. Dihydroceramides (DhCers) are formed from C17iso sphinganine and fatty acids or 2-hydroxy fatty acids. Desaturation at the 4th carbon yields ceramides (Cers), which are the precursors of complex SLs such as sphingomyelin (SM) and glucosylceramide (HexCer). Mutants of SL metabolism genes in bold were tested in survival assays shown in (C). B Schematic survival comparing N2 wildtype (solid line) versus mutant strains (dashed lines), the difference of the area under the survival curve (AUC) is shaded in brown when the mutants are more susceptible to the infection than the control and in green when the mutants are more resistant to the infection. C Heatmap represents the \u0394AUC of the survival of the C. elegans SL metabolism mutants versus average of the wildtype N2 strain. Each box represents an independent experiment, consisting of three to four technical replicates (individual bars). The intensity of the bar colour reflects the overall summary across all experiments, while the statistical analysis was performed separately for each experiment. Statistical analyses were carried out using the GLM framework with a binomial distribution. All tests were two-sided, and p-values were adjusted for multiple comparisons using the Bonferroni correction. Significance is indicated as *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001. Each individual survival curve can be found in Fig.\u00a0S17A, B. Source data and exact p-values are provided in Supplementary Data\u00a012.\n\nTo elucidate the role of specific SLs in defence against Bt infection we supplemented Bt infected worms with the commercially available SLs ceramide, sphingomyelin, and sphingosine-1-phosphate. We found that supplementation with C18 and C20 ceramide significantly improved survival rates, while C22 ceramide, C16 sphingomyelin, C18 sphingomyelin, d-sphingosine, and S1P did not affect survival (Fig.\u00a0S18 and Supplementary Data\u00a013). These findings and the phenotypic differences between the ceramide metabolic gene mutants imply that the inhibition of ceramide metabolism (and the associated increase in ceramide content) is not the only factor determining susceptibility to infection.\n\nWe additionally assessed C. elegans SL metabolism mutant survival on the protective microbiota isolate MYb115. MYb115 and the inhibition of de novo synthesis of ceramide or the breakdown of sphingomyelin to ceramide protect worms against infection with Bt247. Therefore, we did not expect to see an effect of MYb115 on the increased survival phenotype of the sptl-1, -3, hyl-1, -2, and asm-3 mutants. Our results are fully consistent with these expectations, the mutants were more resistant to Bt infection also on MYb115 (Fig.\u00a07C). However, both ceramidase mutants asah-1(tm495) and asah-2(tm609), which were more susceptible to Bt247 infection on E. coli OP50, were as susceptible as and even more resistant than wildtype worms on MYb115, respectively (Fig.\u00a07C). Notably, MYb115 also ameliorated the susceptibility phenotype of the sms-1(ok2399) mutant (Figs.\u00a07C and S17). These data indicate that MYb115 interacts with host SL metabolism at least at the conversion of ceramide to sphingomyelin and C17iso-sphingosine.\n\nOur data suggest a mechanistic link between microbiota-mediated alterations in host SL metabolism and protection against Bt infection. As a step towards exploring the underlying mechanisms, we tested two potential links between SLs and C. elegans defence against Bt infection. First, we explored the possible involvement of the mitochondrial surveillance response, which requires ceramide biosynthesis46 and second we explored the role of complex glycosphingolipids that are receptors of the Bt Cry toxin Cry5B47. Bt247 infection did not induce the expression of the mitochondrial stress-induced hsp-6p::gfp reporter, neither did MYb115 (Fig.\u00a0S19A), indicating that mitochondrial surveillance is not involved in MYb115-mediated protection against Bt247 infection. Also, the Bt-toxin resistant (bre) mutants bre-2(ye31) and bre-3(ye26), which are defective in the biosynthesis of the Cry5B glycosphingolipid receptor47, are susceptible to Bt247 infection and still protected by MYb115 (Fig.\u00a0S19B and Supplementary Data\u00a014). We thus confirm previous results48 and can exclude an involvement of the bre genes in MYb115-mediated protection against Bt247.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60234-1/MediaObjects/41467_2025_60234_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Understanding microbiota-host interactions at the level of the molecular mechanism requires the identification of individual microbiota-derived molecules and their associated biological activities that mediate the interaction. In this study we demonstrate that P. fluorescens MYb115-mediated host protection18, depends on bacterial-derived SLs. We show that MYb115 produces protective SLs by a BGC encoding an iterative PKS and an AOS with SPT activity. This finding is important since eukaryotes and all currently known SL-producing bacteria depend on the serine palmitoyl transferase (SPT) enzyme, which catalyses the initial step in the de novo synthesis of ceramides, for SL production as primary metabolites. Indeed, the SPT gene is conserved between eukaryotes and prokaryotes and its presence in bacterial genomes has been used as an indication of SL production. While SL production is ubiquitous in eukaryotes, it is thought to be restricted to few bacterial phyla. Known SL-producing bacteria include the Bacteroidetes and Chlorobi phylum, and a subset of Alpha- and Delta-Proteobacteria49. More recently, two additional key enzymes required for bacterial ceramide synthesis have been identified, bacterial ceramide synthase and ceramide reductase31. Phylogenetic analysis of the three bacterial ceramide synthetic genes has identified a wider range of Gram-negative bacteria, as well as several Gram-positive Actinobacteria with the potential to produce SLs31. However, our finding that P. fluorescens MYb115 produces SLs by the BGC-encoded PKS/AOS PfSgaAB, was previously unknown and therefore indicates that there are non-canonical ways of producing SLs as secondary metabolites in bacteria. Moreover, our analysis of the distribution of PfSgaAB in bacteria revealed that homologous putative PKS clusters are present in bacteria that are so far unknown SL producers. This finding strongly suggests that PKS cluster-dependent biosynthesis of SLs is prevalent across bacteria.\n\nBy comparing the C. elegans transcriptome response to MYb115 with the response to the MYb115 PKS mutant in a metabolic network analysis, we observed an effect of MYb115-derived SLs on host fatty acid and SL metabolism. Our C. elegans lipidomic profiling corroborated the transcriptomic data, providing evidence that MYb115-derived SLs alter C. elegans SL metabolism, resulting in the reduction of certain complex SL species. A similar effect of gut microbiota-derived SLs on host lipid metabolism was previously observed in mice: Bacteroides thetaiotaomicron-derived SLs reduce de novo SL production and increase ceramide levels in the liver37. Also, B. thetaiotaomicron-derived SLs alter host fatty acid and SL metabolism and ameliorate hepatic lipid accumulation in a mouse model of hepatic steatosis50. In humans, bacterial SL production correlates with decreased host-produced SL abundance in the intestine and is critical for maintaining intestinal homeostasis51. Thus, interference with host SL metabolism may be a general effect of bacterial-derived SLs.\n\nWhat role do MYb115-derived SLs play in host protection against Bt? The current study reveals that MYb115-derived SLs protect the C. elegans intestinal barrier, affect the activation of pathogen defence genes, and affect host fatty acid and SL metabolism. Consistent with an important role of host SLs in C. elegans defence, earlier studies have provided evidence for the involvement of SL metabolism in the host response to infection with Pseudomonas aeruginosa and Enterococcus faecalis52,53. We previously described an association between modulations in fatty acid and SL metabolism and increased tolerance to Bt infection48. In line with this, we here demonstrate that modulations in SL metabolism strongly affect survival of infected animals. We do, however, not yet understand how exactly susceptibility to Bt infection is affected by SL modifications. Our functional genetic analysis of C. elegans SL metabolism enzymes shows that inhibition of de novo synthesis of ceramide and inhibition of the conversion of ceramide to glucosylceramides or ceramide-1-phosphate increases survival of C. elegans infected with Bt247, while inhibition of the conversion of ceramide to sphingomyelin or sphingosine decreases survival of Bt247-infected animals. Supplementation with C18 and C20 ceramide increased survival after Bt infection. These results and the phenotypic differences between the ceramide metabolic gene mutants imply that the enhanced susceptibility to infection is influenced yet not exclusively caused by the inhibition of ceramide metabolism (and the associated increase in ceramide content) in these mutant backgrounds. The enzymes responsible for SL production and turnover comprise a complex metabolic network that gives rise to numerous bioactive molecules, which participate in highly complex and interconnected pathways influencing a multitude of physiological processes54,55. Also, SL metabolism shares common substrates with other metabolic routes and is, for example, highly connected to other lipid metabolic networks. Consequently, imbalances in SL metabolism in a mutant may have far-reaching consequences for host physiology.\n\nMYb115 interacts with host SL metabolism at least at the conversion of ceramide to sphingomyelin and sphingosine, since the susceptibility phenotypes of the respective ceramide metabolic gene mutants are ameliorated or even abrogated in MYb115-treated animals, respectively. However, the effect of MYb115 on the phenotype of a SL metabolism mutant (increased survival after Bt infection) may not be directly linked to its effect on wildtype worms (decrease in sphingomyelin and other SLs). The conclusions we can draw from our data are that SL-producing MYb115 decreases certain host SL species, including sphingomyelin species, in comparison to non-SL-producing MYb115 and that MYb115 ameliorates the survival phenotype of C. elegans ceramide metabolic gene mutants following Bt infection. Indeed, we cannot exclude that this effect is indirect or due to other effects of MYb115 on the host.\n\nSLs are not only required for the integrity of cellular membranes, but can also act as bioactive signalling molecules involved in regulation of a myriad of cell activities, including pathogen and stress defence pathways54. For example, many bacterial pathogens, including Bt, produce virulence factors that target and damage mitochondria56,57. A C. elegans surveillance pathway, which detects mitochondrial defects and activates xenobiotic-detoxification and pathogen defence genes, requires ceramide biosynthesis46. Since we, however, did not find any evidence of mitochondrial surveillance activation by Bt247 infection or MYb115 (Fig.\u00a0S19A), it is unlikely that this defence pathway is involved in MYb115-mediated protection. Also, in C. elegans, glucosylceramide deficiency was linked to an increase in autophagy58,59, which plays an important role in cellular defence after attack by certain Bt PFTs60. Notably, glucosylceramides serve as a source for the synthesis of complex glycosphingolipids. In C. elegans, the BRE proteins BRE-2, BRE-3, BRE-4, and BRE-5 are required for further glucosylation of glucosylceramide, leading to complex glycosphingolipids that are receptors of the B. thuringiensis Cry toxin Cry5B47. However, Bt247 only expresses the unique toxin App6Ba61, which belongs to the PFT class of alpha helical pesticidal proteins62. These proteins are unrelated to Cry5B at the level of their primary sequences and structure63. Indeed, we could previously exclude an involvement of the bre genes in C. elegans defence against Bt247, given that bre mutants are susceptible to Bt247 infection (Fig. S19B48) and did not find evidence of an effect on MYb115-mediated protection (Fig.\u00a0S19B). Still, MYb115-mediated interference with SL metabolism might affect membrane organisation and dynamics, as well as vesicular transport, which in turn might affect other membrane-associated Bt toxin receptors through modifying their localisation in the plasma membrane. C. elegans is thus an ideal experimental system to study the downstream impact of microbiota-derived SLs in the context of pathogen protection, an area that is still largely unexplored64.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The wildtype C. elegans strain N2 (Bristol)65 and all SL mutant strains were purchased as indicated in Table\u00a01. Worms were grown and maintained on nematode growth medium seeded with the E. coli strain OP50 at 20\u2009\u00b0C, according to the routine maintenance protocol66. Worm populations were synchronised and incubated at 20\u2009\u00b0C. Since we did not cross out the mutant strains, we confirmed the mutations in all C. elegans sphingolipid mutant strains via PCR (Fig.\u00a0S20). The following primer were used for genotyping: asah-1: forward AGTGGGTGTTCGGATTGGAGG, reverse GGTTGGTGCGGGATGACAAG; sptl-3: forward AGCCGTGGCAAATGGAAAGTG, reverse ATGGAGTTTCTGCGCGATTGATG; cgt-1: forward ACTTCAGCTACCACTCCTTCATCAC, reverse AACTTTCCTTTCGATTCCTGGACC; asah-2: forward CGCCGAAGTGCTTGACGTAC, reverse CCAACATTGGCGCGAGTAAGC; sms-1: forward TGGTTGCGTTTCTGATGCTCG, reverse TGAGACCGAGCCCGAACATG; for sptl-1, asm-3, hyl-1, hyl-2 and cerk-1 already published primers were used46 (Supplementary Data\u00a015).\n\nThe standard laboratory food source E. coli OP50 was previously obtained from the CGC. The natural microbiota isolate P. fluorescens MYb115 (NCBI Reference Sequence: NZ_CP078138.1) isolated from the natural C. elegans strain MY379 was used16.\n\nThe promoter-exchange strain MYb115 PBADsga for targeted in-/activation of the sgaAB BGC was generated via insertion of the inducible PBAD promoter upstream of the BGC following an established protocol20. The resulting plasmid (pCEP_kan_sgaA) was transformed into the conjugation host E. coli ST18 via electroporation and introduced into MYb115 via conjugation67. The promoter was induced by adding 0.02% (w/v) arabinose (ara) to the culture medium and repressed by adding 0.05% glucose (glc) to the growth medium. Deletions of the single genes sgaA and sgaB were carried out following a previously established protocol based on conjugation and homologous recombination67,68. Briefly, fragments upstream and downstream of the target gene were amplified by PCR and assembled into a plasmid using the pEB17 vector69. The resulting plasmids (pEB17_kan_\u0394sgaA and pEB17_kan_\u0394sgaB) were subsequently transformed into the conjugation host E. coli via electroporation and the plasmid was introduced into MYb115 via conjugation67, sequences are shown in Supplementary Data\u00a016. For the complementation of MYb115 \u0394sgaA and MYb115 \u0394sgaB we conducted a series of experiments, inserting the vanillic acid-inducible PvanCC promoter upstream of sgaA or the complete sgaAB BGC on the plasmid pSEVA631, which was then introduced into the MYb115 mutants. In detail: genomic DNA (gDNA) of MYb115 was isolated via Monarch\u00ae Genomic DNA Purification Kit (NEB) and used as the template for PCR amplification. The corresponding gene fragments of sgaA and sgaAB, together with the pSEVA631 (https://seva-plasmids.com/find-your-plasmid/) plasmid backbone with overhangs suitable for Gibson cloning, were amplified using Q5\u00ae High-Fidelity DNA Polymerase (NEB) and then purified by gel extraction using the Monarch\u00ae DNA Gel Extraction Kit (NEB). The following primers are used for PCR amplification: sgaA forward CTAGAGAAAGAGGGGAAATACTAGTTGACAAAGCGTAGACAGGTAG, sgaA_reverse CAGGGTTTTCCCAGTCACGACTCACTCAATCAAACGGTTAGGTG; sgaAB_forward TTGACAAAGCGTAGACAGGTAG, sgaAB_reverse CAGGGTTTTCCCAGTCACGACTCACCCAATCTTCGCCAATTC; pSEVA631_backbone_forward GTCGTGACTGGGAAAACCCT, pSEVA631_backbone_reverse CTAGTATTTCCCCTCTTTCTCTAGT. Gibson cloning employing NEBuilder\u00ae HiFi DNA Assembly Cloning Kit (NEB) assembled the constructed plasmids, which were subsequently transformed by electroporation into electro competent E. coli DH10B. Finally, plasmids were isolated by the PureYield\u2122 Plasmid Miniprep System (Promega).\n\nPlasmids were transformed via electroporation into electro competent MYb115 mutants. At least three different colonies were selected for further small-scale production analysis. Cells were cultivated overnight in LB media with 75\u2009\u00b5g/mL gentamicin. Afterwards, 100\u2009\u00b5L overnight grown culture were inoculated in 5\u2009ml XPP medium69 containing 75\u2009\u00b5g/mL gentamicin and 100 \u00b5M\u00a0vanillic acid. The cells were cultivated for 3 days at 28\u2009\u00b0C and 200\u2009rpm.\n\nOne hundred microlitres of the cultures were taken and extracted with methanol at a 1:1 ratio by shaking 10\u2009min at room temperature. Followed by further diluting the mixtures 1:10 with methanol and centrifuged at 13,000\u2009rpm for 30\u2009min. Cleared supernatants were used for further HPLC/MS analysis. HPLC/MS analysis was conducted on an UltiMate 3000 system (Thermo Fisher) coupled to an AmaZonX mass spectrometer (Bruker) with an ACQUITY UPLC BEH C18 column (130\u2009\u00c5, 2.1\u2009mm\u2009\u00d7\u2009100\u2009mm, 1.7-\u03bcm particle size, Waters) at a flow rate of 0.4\u2009mL/mL (5\u201395% acetonitrile/water with 0.1% formic acid, vol/vol,16\u2009min, UV detection wavelength 190\u2013800\u2009nm) and an electrospray ionization (ESI) source set to positive ionization mode.\n\nOnly the complementation that included the complete sgaAB BGC restored SL production (detection of compounds 1 (m/z 414.4 [M\u2009+\u2009H]+), 2 (m/z 386.4 [M\u2009+\u2009H]+), and 3 (m/z 442.4 [M\u2009+\u2009H]+)) by HPLC/MS analysis and only in the MYb115 \u0394sgaA mutant (Fig.\u00a0S21). The resulting MYb115 \u0394sgaA /PvanCCsgaAB strain was tested in C. elegans survival assays, for which bacteria were first grown overnight at 28\u2009\u00b0C with shaking (180\u2009rpm) in 5\u2009mL of Tryptic Soy Broth (TSB) supplemented with gentamicin (75\u2009\u00b5g/mL). This was followed by a three-day cultivation in XPP medium69 containing gentamicin and 100\u2009\u00b5M vanillic acid to induce the PvanCC promoter. All other bacteria were grown on Tryptic Soy Agar (TSA) plates at 25\u2009\u00b0C and liquid bacterial cultures were grown in TSB in a shaking-incubator overnight at 28\u2009\u00b0C.\n\nOf note: We could confirm targeted activation of SL production in MYb115 PBADsga by arabinose supplementation (Fig.\u00a01C and https://metaspace2020.org/dataset/2025-02-27_13h37m58s), suggesting that the PBAD promoter is not leaky in this system. In contrast, SL production was observed in cultures of two MYb115 \u0394sgaA/PvanCCsgaAB strains even without induction by vanillic acid supplementation, indicating leakiness of the PvanCC promoter under certain conditions. However, the addition of vanillic acid usually led to a further significant increase in SL production (https://metaspace2020.org/dataset/2025-02-27_13h37m58s). All primer sequences can be found in Supplementary Data\u00a01. For survival assays with B. thuringiensis, we used the strain MYBt18247, MYBt18679 (Bt247 and Bt679, respectively, our lab strains) and Bt40770 as non-pathogenic control48,71. Spore aliquots of both strains were obtained following a previously established protocol72 with minor modifications18.\n\nRoughly 500 synchronised N2 worms were raised on PFM plates inoculated with MYb115 or MYb115 \u0394sgaA (OD600nm of 10) from L1 to L4 stage. At L4 stage worms were transferred to control plates or infection plates (microbiota mixed with Bt247 spores 1:100). Transcriptomic response was assessed 24\u2009h post-transfer, with three independent replicates. Worms were washed off the plates with M9-T (M9 buffer + 0.02% Triton X-100), followed by three gravity washing steps. The worm pellets were resuspended in 800\u2009\u00b5L TRIzol (Thermo Fisher Scientific, Waltham, MA, United States). Worms were broken up prior to RNA extraction by treating the samples with four rounds of freeze-thaw cycles using liquid nitrogen and a thermo block at 46\u2009\u00b0C. The RNA was extracted using Direct-zol\u2122 RNA MicrolPrep (Zymo Research, R2062) and stored at \u221280\u2009\u00b0C.\n\nThe RNA was processed by Lexogen (Vienna, Austria) using the 3\u2019 mRNAseq library prep kit and sequenced on an Illumina NextSeq2000 on a P3 flow cell in SR100 read mode. FASTQ files were checked for their quality with MultiQC73, filtered and trimmed with cutadapt74 and aligned to the C. elegans reference genome WBcel235 with the STAR aligner (Spliced Transcripts Alignment to a Reference75) followed by an assessment using RseQC76. Ultimately, HTseq-count v0.6.077 generated the raw gene counts. The count normalization with the median of ratios method for sequencing depth and RNA composition as well as the analysis for differential expression by a generalised linear model (GLM) was performed using DESeq278. Raw data and processed data have been deposited in NCBI\u2019s Gene Expression Omnibus79 and are accessible through GEO Series accession number GSE245296.\n\nFor LC-MS analysis, 1\u2009mL liquid culture was harvested via centrifugation (1\u2009min, 20\u2009\u00b0C, 17,000\u2009\u00d7\u2009g). The cell pellet was resuspended in 1\u2009mL MeOH and incubated at 30\u2009\u00b0C for 30\u2009min. The resulting extract was separated from the cell debris via centrifugation (30\u2009min, 20\u2009\u00b0C, 17,000\u2009\u00d7\u2009g), diluted and submitted to LC-MS measurements. LC-MS measurements were performed on a Dionex Ultimate 3000 (Thermo Fisher Scientific) coupled to an Impact II qToF mass spectrometer (Bruker Daltonics). Five microlitres sample were injected and a multistep gradient from 5 to 95% acetonitrile (ACN) with 0.1% formic acid in water with 0.1% formic acid over 16\u2009min with a flow rate of 0.4\u2009mL/min was run (0\u20132\u2009min 5% ACN; 2\u201314\u2009min 5\u201395% ACN; 14\u201315\u2009min 95% ACN; 15\u201316\u2009min 5% ACN) on a Acquity UPLC BEH C18 1.7\u2009\u00b5m column (Waters). MS data acquisition took place between minutes 1.5 and 15 of the multistep LC gradient. The mass spectrometer was set to positive polarity mode with a capillary voltage of 2.5\u2009kV and a nitrogen flow rate of 8\u2009L/min. We compared the MS2 data of compounds 1\u20133 to the MS2 data obtained from commercially available sphinganines (sphinganine (d18:0) and sphinganine (d20:0), Avanti Polar Lipids).\n\npET28a-PfsgaB (synthesised and cloned by Genscript) was used to transform chemically-competent E. coli BL21 (DE3) cells via the heat-shock method. Colonies were developed overnight on VLB-kanamycin agar plates (50\u2009\u00b5g\u2009mL\u22121). A single colony was propagated in LB-kanamycin media (50\u2009mL) and incubated overnight (37\u2009\u00b0C) with agitation. The cells were subcultured (OD600nm\u2009=\u20090.1, 37\u2009\u00b0C) in fresh VLB-kanamycin media (500\u2009mL) until mid-log phase. The cultures were cooled to room temperature and protein expression was induced by the addition of IPTG (0.1\u2009mM). Protein expression proceeded overnight at 20\u2009\u00b0C with rigorous agitation. The biomass was harvested by centrifugation using a Fiberlite F14-6\u2009\u00d7\u2009250\u2009y fixed-angle rotor (7000\u2009rpm, 5\u2009min), combined into 2\u20135\u2009g yellow-tinged pellets using a Fiberlite F15-8\u2009\u00d7\u200950cy fixed angle rotor (5000\u2009rpm, 10\u2009min) and stored at \u221220\u2009\u00b0C. When needed, cell pellets were defrosted and resuspended (10% w/v) in ice-cold Binding/Storage buffer containing HEPES (50\u2009mM, pH 7.5), NaCl (250\u2009mM), glycerol (10% v/v) and PLP (25\u2009\u00b5M). Benzamidine hydrochloride (1\u2009mM) was added to the resuspension and the cells were lysed by sonication (10\u2009s pulse/second cooldown, 15 cycles) on ice. Cell debris was pelleted by high-speed centrifugation using a Fiberlite F15-8\u2009\u00d7\u200950cy fixed angle rotor (13,000\u2009rpm, 40\u2009min, 4\u2009\u00b0C). The cell-free extract was collected and clarified by filtration (Millex-HP 0.45\u2009\u00b5m polyethersulfone, Merck). PfSgaB was purified from the cell-free extract using a HiTrap TALON Crude 1\u2009mL column. The bound PfSgaB protein was washed with copious Binding buffer (20\u2009mL) and eluted with imidazole (150\u2009mM). Yellow fractions containing PfSgaB were pooled and further purified using a HiLoad Superdex S200 16/600\u2009pg (120\u2009mL) SEC column, using Binding/Storage buffer as mobile phase. Purified fractions were concentrated by centrifugal concentration (50\u2009kDa MWCO, <4\u2009mL). For long-term storage, aliquots of PfSgaB were flash frozen in liquid nitrogen and stored at \u221280\u2009\u00b0C.\n\nA reaction mixture (1000\u2009\u00b5L) containing purified PfSgaB (10\u2009\u00b5M) and L-serine (0.3125\u201310\u2009mM) was prepared in a reaction buffer containing HEPES (50\u2009mM, pH 7.5), NaCl (250\u2009mM) and glycerol (10% v/v). The mixture was incubated at room temperature for 5\u2009min and analysed using a pre-blanked spectrophotometer (300\u2013700\u2009nm). External aldimine \u03bbmax\u2009=\u2009413\u2009nm.\n\nA reaction mixture (200\u2009\u00b5L) containing purified PfSgaB (5\u2009\u00b5M), L-serine (10\u2009mM) and DTNB (250\u2009\u00b5M) was initiated by the addition of acyl-CoAs 7\u20139 (100\u2009\u00b5M) in a reaction buffer containing HEPES (50\u2009mM, pH 7.5), NaCl (250\u2009mM) and glycerol (10% v/v). Negative controls were prepared by the replacement of the reaction component(s) with buffer. Amino acid specificity was determined by the replacement of L-serine with L-alanine (10\u2009mM) or glycine (10\u2009mM). Absorbances were measured over the course of 20\u201360\u2009min using a BioTek Synergy HXT (28\u2009\u00b0C, 412\u2009nm), configured for pathlength correction. A molar attenuation coefficient of 14150\u2009M\u22121 cm\u22121 was used to convert absorbance into concentration using Beer\u2019s law.\n\nA reaction mixture (200\u2009\u00b5L) containing purified PfSgaB (10\u2009\u00b5M), L-serine (10\u2009mM) and acyl-CoA (100\u2009\u00b5M) was prepared in a reaction buffer containing HEPES (50\u2009mM, pH 7.5), NaCl (250\u2009mM) and glycerol (10% v/v). The reactions were incubated at 28\u2009\u00b0C for 18\u2009h with rigorous shaking. The reactions were quenched by the addition of ice-cold LC-MS grade MeOH (200\u2009\u00b5L) containing formic acid (2% v/v). Precipitate was removed by microcentrifugation (13,300\u2009rpm, 5\u2009min). The supernatant was sampled for LC/ESI-MS analysis in positive ion mode using a Waters SYNAPT G2 HDMS, equipped with a Waters ACUITY Premier CSH C18 column (1.7\u2009\u00b5m particle size, 2.1\u2009mm ID, 100\u2009mm length). Analytes were resolved using a water/ACN gradient (5\u201395% ACN) over 12\u2009min. 0.1% formic acid was used as the mobile phase modifier.\n\nThe P. fluorescens MYb115 (NCBI accession: NZ_CP078138) SL BGC was identified and annotated using antiSMASH80. Sequence homologues were retrieved using BLASTp and UniProt. Multiple sequence alignments were generated using ClustalOmega81 and visualised using ESPript 3.082. AlphaFold383 was used for predictive structural modelling. Structural models were visualised and analysed using ChimeraX (v1.8)84.\n\nBacterial cultures producing the sphinganine compounds were grown in ISOGRO\u00ae-13C and ISOGRO\u00ae-15N (Sigma Aldrich) medium and subsequently analysed by LC-MS to determine the number of carbon and nitrogen atoms, respectively. To confirm the incorporation of serine into the sphinganines, MYb115 PBADsga cultures were grown in XPP medium69 with addition of all proteinogenic amino acids (Carl Roth GmbH\u2009+\u2009Co. KG, Karlsruhe) except serine. To test the incorporation, either 13C315N-labelled (Sigma Aldrich) serine or regular serine (Carl Roth GmbH\u2009+\u2009Co. KG, Karlsruhe) displaying the usual isotopic abundances were used. This should result in the production of two isotopologues of each sphinganine. With addition of 13C315N-labelled serine, the isotopologue that is mmonoisotopic\u2009+\u20093 should be labelled with two 13C isotopes and one 15N isotope, since one carbon atom is lost through the elimination of CO2 during the condensation. In the cultures with regular serine, the isotopologue that is mmonoisotopic\u2009+\u20093 should be labelled with three 13C isotopes because of the higher natural abundance of 13C compared (1.1%) to 15N (0.4%). The two isotopologues, 13C3 and 13C215N, were distinguished by their respective masses.\n\nFor the metabolic model analysis, transcriptomic data was integrated into the iCEL1314 C. elegans metabolic model using the MERGE pipeline39 in MATLAB (version: 9.11.0.1769968 (R2021b)) using the COBRA toolbox85) to create context-specific (CS) models of each sample using iMAT++27. This method not only integrates transcriptomic data into the model, but also simulates the optimal flux distribution (OFD) for the fitted transcriptomic data, as well as provides a flux variability analysis (FVA)41 output that describes the minimum (lb) and maximum (ub) flux values that each reaction can take within each CS model under the same in silico dietary conditions. Gene categorization was performed in Python86 (version 3.10.6) using 0.7816 (mu1), 4.856 (mu2) and 8.15 (mu3), as rare, low, and high expression category cutoffs, respectively. We would like to point out that the iMAT++ algorithm used to integrate the transcriptomic data into the iCEL1314 metabolic model is done on a sample basis, therefore any statistical comparisons of gene expression are not taken into account during this process. This means that comparing the differences in simulation results of reactions encoded by a certain gene and the logFC values of this gene might not directly match in direction. This is a desirable attribute of metabolic modelling since we can predict metabolic requirements of an organism that are in conflict with the gene expression differences\u2014these might be caused by post-translational modifications or other effects. Differences between generated metabolic models were assessed by fitting a linear regression model (data ~ treatment) using FVA41 centres (([ub-lb]/2)) and OFD values (equivalent to parsimonious FBA solution) from each model. We subsequently contrasted MYb115 and MYb115 \u0394sgaA, combining unique significant reaction names (alpha\u2009=\u20090.01) across the different simulation data layers (OFD and centres), and performed a Flux Enrichment Analysis (FEA)61 using these names to obtain significantly affected metabolic model pathways. For SL metabolism pathway analysis, FVA was performed on all reactions, with biomass objective minimum set to 50%. Upper bound values were grouped by pathway, then normalized against the mean on the MYb115 flux values for each reaction. Lower bound values were not analysed due to the unidirectional nature of most reactions (lb = 0).\n\nFor the bacterial lipidomics experiment, we adapted the extraction method from Brown et al. 51. 5\u2009mL liquid cultures were incubated for 24\u2009h at 30\u2009\u00b0C. The equivalent of 1\u2009mL OD600nm of 5 was harvested by centrifugation (1\u2009min, 20\u2009\u00b0C, 17,000\u2009\u00d7\u2009g). The cell pellet was resuspended in 0.4\u2009mL H2O. 1.5\u2009mL CHCL3/MeOH (1:2) were added and the extracts were mixed by vortexing. The cell mixture was incubated at 30\u2009\u00b0C with gentle shaking, after 18\u2009h 1\u2009mL CHCl3/H2O (1:1) was added. After phase separation, the organic phase was dried using a nitrogen evaporator and stored at \u221220\u2009\u00b0C.\n\nThe relative quantification and annotation of lipids was performed by using HRES-LC-MS/MS. The chromatographic separation was performed using a Acquity Premier CSH C18 column (2.1\u2009\u00d7\u2009100\u2009mm, 1.7 \u03bcm particle size, VanGuard) a constant flow rate of 0.3\u2009mL/min with mobile phase A being 10\u2009mM ammonium formate in 6:4 ACN:water and phase B being 9:1 IPA:ACN (Honeywell, Morristown, New Jersey, USA) at 40\u2009\u00b0C. For the measurement, a Thermo Scientific ID-X Orbitrap mass spectrometer was used. Ionisation was performed using a high temperature electrospray ion source at a static spray voltage of 3500\u2009V (positive) and a static spray voltage of 2800\u2009V (negative), sheath gas at 50 (Arb), auxiliary gas at 10 (Arb), and ion transfer tube and vaporiser at 325 and 300\u2009\u00b0C, respectively.\n\nData dependent MS2 measurements were conducted applying an orbitrap mass resolution of 120,000 using quadrupole isolation in a mass range of 200\u20132000 and combining it with a high energy collision dissociation (HCD). HCD was performed on the ten most abundant ions per scan with a relative collision energy of 25%. Fragments were detected using the orbitrap mass analyser at a predefined mass resolution of 15,000. Dynamic exclusion with an exclusion duration of 5\u2009s after 1 scan with a mass tolerance of 10\u2009ppm was used to increase coverage. For lipid annotation, a semi-quantitative comparison of lipid abundance and annotated peaks were integrated using Compound Discoverer 3.3 (Thermo Scientific). The data were normalised to the maximum peak area sum of all samples, the p-value per group ratio calculated by a one-way ANOVA with Tukey as post-hoc test, and the p-value adjusted using Benjamini-Hochberg correction for the false-discovery rate87. The p-values were estimated by using the log-10 areas. The normalized peaks were extracted and plotted using R (4.1.2) within RStudio using the following packages: ggplot2 (3.4.0), readxl (1.4.1), grid (4.1.2), gridExtra (2.3) and RColorBrewer (1.1-3). Metabolomics data have been deposited to the EMBL-EBI MetaboLights database88 with the identifier MTBLS8694.\n\nThe monomodular PKS (KW062_RS19805) and the AOS aminotransferase (KW062_RS19800) in P. fluorescens MYb115 (NZ_CP078138) were searched against the non-redundant (nr) National Center for Biotechnology Information (NCBI) database using cblaster (1.3.18)89. PKS clusters encoded by various bacterial genera were aligned and visualised using clinker90.\n\nFor lipidomic profiling, N2 worms exposed to MYb115 or MYb115 \u0394sgaA were used. Approximately 10,000 worms were raised on either of the bacteria for 70\u2009h until they were young adults. Excess bacteria were removed by three gravity washing steps using M9 buffer. The buffer was thoroughly removed, and the samples were snap-frozen in liquid nitrogen.\n\nExtraction and analysis of lipids were performed as described previously91. Worm pellets were suspended in MeOH and homogenised in a Precellys Bead Beating system (Bertin Technologies, Montigny-le-Bretonneux, France), followed by addition of MTBE. After incubation water was added and through centrifugation the organic phase was collected. The aqueous phase was re-extracted using MTBE/MeOH/H2O (10/3/2.5\u2009v/v/v). Organic phases were combined and evaporated to dryness using a SpeedVac Savant centrifugal evaporator (Thermo Scientific, Dreieich, Germany). Proteins were extracted from the residue debris pellets and quantified using a BCA kit (Sigma-Aldrich, Taufkirchen, Germany). Lipid profiling was performed using a Sciex ExionLC AD coupled to a Sciex ZenoTOF 7600 under control of Sciex OS 3.0 (Sciex, Darmstadt, Germany). Separation was achieved on Waters Cortecs C18 column (2.1\u2009mm\u2009\u00d7\u2009150\u2009mm, 1.6\u2009\u00b5m particle size) (Waters, Eschborn, Germany). 40% H2O/60% ACN\u2009+\u200910\u2009mM ammonium formate/0.1% formic acid and 10% ACN / 90% iPrOH\u2009+\u200910\u2009mM ammonium formate/0.1% formic acid were used as eluents A and B. Separation was carried out at 40\u2009\u00b0C at a flow rate of 0.25\u2009mL/min using a linear gradient as followed: 32/68 at 0.0\u2009min, 32/68 at 1.5\u2009min, 3/97 at 21\u2009min, 3/97 at 25\u2009min, 32/68 at 25.1\u2009min, 32/68 at 30\u2009min. Analysis was performed in positive ionisation mode.\n\nDried samples were re-dissolved in H2O/ACN/iPrOH (5/35/60, v/v/v) according to their protein content to normalise for differences in biomass. Ten microlitres of each sample were pooled into a QC sample. The remaining sample was transferred to an autosampler vial. The autosampler temperature was set to 5\u2009\u00b0C and 5\u2009\u00b5L were injected for analysis. MS1 ions in the m/z range 70\u20131500 were accumulated for 0.1\u2009s and information dependent acquisition of MS2 was used with a maximum number of 6 candidate ions and a collision energy of 35\u2009eV with a spread of 15\u2009eV. Accumulation time for MS2 was set to 0.025\u2009s yielding a total cycle time of 0.299\u2009s. ZenoTrapping was enabled with a value of 80,000. QC samples were used for conditioning of the column and were also injected every 5 samples. Automatic calibration of the MS in MS1 and MS2 mode was performed every 5 injections using the ESI positive Calibration Solution for the Sciex X500 system or the ESI negative Calibration Solution for the Sciex X500 system (Sciex, Darmstadt, Germany).\n\nData analysis was performed in a targeted fashion for SLs (Supplementary Data\u00a011). SLs were identified by manual interpretation of fragmentation spectra following established fragmentation for different SL classes: m/z 268.263491, 250.252926 and 238.252926 for C17iso sphingosine and m/z 270.279141, 252.268577 and 288.289706 for C17iso sphinganine based derived SLs. Data analysis was performed in Sciex OS 3.0.0.3339 (Sciex, Darmstadt, Germany). Peaks for all lipids indicated below were integrated with a XIC width of 0.02\u2009Da and a Gaussian smooth width of 3 points using the MQ4 peak picking algorithm. All further processing was performed in R 4.2.1 within RStudio using the following packages: tidyverse (v1.3.2), readxl (1.4.1), ggsignif (0.6.4), ggplot2 (3.3.6), scales (1.2.1). Significance was tested using a two-sided Welch-Test within ggsignif. Metabolomics data have been deposited to the EMBL-EBI MetaboLights88 database with the identifier MTBLS8440.\n\nBacterial biomass was screened for the presence of SLs via MALDI mass spectrometry spot assays. Briefly, 1\u2009\u00b5l of each cell pellet was transferred on a microscopy slide and air dried. The microscopy slide with spots of cells was covered with a matrix (sDHB) using a pneumatic sprayer (HTX Science). The spots were analysed using the AP-SMALDI5 AF source (Transmit, Giessen) connected to a QExactive HF (Thermofisher) as described previously22.\n\nA step size of 100\u2009\u00b5m in X and Y direction was used to image all spots in positive ionisation mode. The MS settings where as followed: positive ionisation mode, mass range m/z 300\u20131200\u2009Da, S-Lens 100, capillary voltage 4\u2009kV, mass resolution 240000 at m/z 200\u2009Da. The raw data was uploaded to figshare https://doi.org/10.6084/m9.figshare.29093192.v1 and transformed into imzml and deposited to metaspace2020.org for browsing of images (datasets: MPIMM_514_QE_P https://metaspace2020.org/dataset/2025-02-27_13h37m58s). For relative quantification of each compound 1\u20136, a region of interest (ROI) was drawn around each bacterial spot and the peak intensity for M\u2009+\u2009H+ (see Supplementary Data\u00a01) per pixel was averaged within this ROI.\n\nB. thuringiensis survival assays were performed as described previously with minor adjustments18,92,93. N2 wildtype worms and the SL mutants were synchronised and grown on PFM plates seeded with 1\u2009mL MYb115 or OP50 (OD600nm of 10) until they reached the L4 stage. Infection plates were inoculated with each of the bacteria adjusted to OD600nm of 10 mixed with Bt247 spores or Bt407. For the infection L4 worms were washed off the plates with M9 buffer and 30 worms were pipetted onto infection plates and incubated at 20\u2009\u00b0C. To assess survival, all worms were counted as either alive or dead 24\u2009h after infection. Worms were considered dead if they did not respond to light touch with a platinum wire picker. We plotted all survivals as survival curves (Fig.\u00a0S17) but provided a summary of the data in a heatmap (Fig.\u00a07C). The area under the survival curve (AUC) was calculated for the C. elegans mutant strains and the mean AUC of C. elegans wildtype N2. The AUC for the mutant strain was then subtracted from the mean AUC of wildtype worms (\u0394AUC). Based on the \u0394AUC values, the shading for the heatmap was determined (Fig.\u00a07B). To test the effect of SLs on the survival of C. elegans we supplemented the worms with a range of different commercially available SLs. C18, C20 and C22 ceramide, as well as C16 and C18 sphingomyelin, were prepared in ethanol at concentrations of 0.5\u2009mg/ml or 10\u2009mg/ml, respectively. Prior to inoculating the Bt assay plates with Bt-OP50 mixture, 60\u2009\u00b5g of the SLs were inoculated onto PFM plates and thoroughly dried. A stock solution of 25\u2009mM of D-Sphingosine in EtOH was prepared, for the assay either 50 or 100\u2009\u00b5M were used for each infection plate. Sphingosine-1-phosphate was diluted in MeOH (2\u2009mM) and 20\u2009\u00b5M was used for each infection plate. In all survival assay equal amounts of EtOH or MeOH was used as control treatment. All SLs were obtained from Biomol GmbH - Life Science Shop, Germany.\n\nBt survival assays were done each with three to four replicates per treatment group and around 30 worms per replicate for each independent experiment. Statistical analyses were performed with RStudio (Version 4.1.2)94. GLM analysis with Tukey multiple comparison tests95 and Bonferroni96 correction were used for all survival assays individually. Graphs were plotted using ggplot297 and were edited in Inkscape (Version 1.4).\n\nTo test for differences in colonisation of C. elegans L4 and young adults by MYb115 and MYb115 \u0394sgaA, colonisation was quantified by counting colony forming units (CFUs). Worms were exposed to MYb115 and MYb115 \u0394sgaA from L1 to L4 larval stage or additionally 24\u2009h until worms reached young adulthood. To score the CFUs, worms were washed off their plates with M9-T (M9 buffer\u2009+\u20090.025% Triton-X100) followed by five gravity washing steps with M9-T. Prior to soft bleaching, worms in M9-T were paralysed with equal amounts of M9-T and 10\u2009mM tetramisole to prevent bleach solution entering the intestine. Worms were bleached for 2\u2009min with a 2% bleach solution (12% NaClO diluted in M9 buffer). Bleaching was stopped by removing the supernatant and washing the samples with PBS-T (PBS: phosphate-buffered saline\u2009+\u20090.025% Triton-X100). A defined number of worms was transferred into a new tube with PBS-T. A subsample of this was used as a supernatant control, while the remaining sample was homogenised with sterile zirconia beads (1\u2009mm) using the BeadRuptor 96 (omni International, Kennesaw Georgia, USA) for 3\u2009min at 30\u2009Hz. Homogenised worms were diluted (1:10/1:100) and plated onto TSA plates, as well as the undiluted supernatant as control. After 48\u2009h at 25\u2009\u00b0C, colonies were counted and the CFUs per worm were calculated. To determine significant differences, we performed a t-test.\n\nTo score the pumping rate, i.e., the back and forth movement of the grinder, worms were exposed to either MYb115 or MYb115 \u0394sgaA. Pumping was scored at L4 larval stage, young adults and young adults infected with Bt247 (1:100). Only worms that were on the bacterial lawn were counted for a period of 20\u2009s. 15\u201320 worms per condition were counted. To determine significant differences, we performed pairwise Wilcoxon test.\n\nTo visualise intestinal morphology and integrity of C. elegans upon infection with Bt247 the worm strain GK70 (dkls37[Pact-5::GFP:pgp-1]) was synchronized. L1 larvae were exposed for 72\u2009h to E. coli OP50, P. fluorescens MYb115 \u0394sgaA and MYb115 PBADsga supplemented with arabinose, followed by a 4\u2009h infection with Bt247. Worms were picked into 10\u2009mM tetramisole on agar-padded object slides. The number of membrane vesicles was counted and sorted into either of the three categories: \u201c0 vesicles\u201d, \u201c\\(\\le\\) 10 vesicles\u201d or \u201c\\( > \\) 10 vesicles\u201d. For each treatment populations of 14\u201325 worms were scored, the experiment was repeated three times.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Caenorhabditis elegans RNAseq data reported in this article have been deposited at NCBI\u2019s Gene Expression Omnibus and are accessible through GEO Series accession number GSE245296. P. fluorescens MYb115 metabolomics data have been deposited to the EMBL-EBI MetaboLights database with the identifier MTBLS8694. C. elegans metabolomics data have been deposited to the EMBL-EBI Metabolights database with the identifier MTBLS8440. 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We thank the Caenorhabditis Genetics Center (University of Minnesota, Minneapolis, Minnesota, USA), funded by the NIH Office of Research Infrastructure Programs (P40OD010440) for C. elegans strains. Work in the Bode lab was partially supported by an ERC advanced grant (835108) and the Max-Planck Society. Work in the Metabolomics and Proteomics Core and Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum M\u00fcnchen was partially supported by the German Science Foundation DFG (Project number 431572533 (MetClassNet) to M.W.).", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Dominic J. Campopiano, Helge B. Bode, Katja Dierking.\n\nDepartment of Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany\n\nLena Peters,\u00a0Barbara Pees,\u00a0Johanna Jarstorff,\u00a0Anna Czerwinski,\u00a0Hinrich Schulenburg\u00a0&\u00a0Katja Dierking\n\nDepartment of Natural Products in Organismic Interactions, Max-Planck-Institute for Terrestrial Microbiology, Marburg, Germany\n\nMoritz Drechsler,\u00a0Jing Liu,\u00a0Yi-Ming Shi\u00a0&\u00a0Helge B. Bode\n\nMolecular Biotechnology, Department of Biosciences, Goethe-University Frankfurt, Frankfurt, Germany\n\nMoritz Drechsler\u00a0&\u00a0Helge B. Bode\n\nSchool of Chemistry, The University of Edinburgh, Edinburgh, UK\n\nMichael A. Herrera,\u00a0Francesca Lubbock\u00a0&\u00a0Dominic J. Campopiano\n\nCore Facility for Metabolomics and Small Molecule Mass Spectrometry, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany\n\nGeorgia Angelidou\u00a0&\u00a0Nicole Paczia\n\nResearch Unit Analytical BioGeoChemistry, Helmholtz Zentrum M\u00fcnchen, Neuherberg, Germany\n\nLiesa Salzer\n\nResearch Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, Kiel, Germany\n\nKarlis Arturs Moors\u00a0&\u00a0Christoph Kaleta\n\nCAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China\n\nYi-Ming Shi\n\nMax Planck Institute for Evolutionary Biology, Pl\u00f6n, Germany\n\nHinrich Schulenburg\n\nMetabolomics and Proteomics Core, Helmholtz Zentrum M\u00fcnchen, Neuherberg, Germany\n\nMichael Witting\n\nChair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Freising-Weihenstephan, Germany\n\nMichael Witting\n\nDepartment of Metabolomics, Institute for Human Nutrition and Food Science, Kiel University, Kiel, Germany\n\nManuel Liebeke\n\nMax Planck Institute for Marine Microbiology, Bremen, Germany\n\nManuel Liebeke\n\nCenter for Synthetic Microbiology (SYNMIKRO), Phillips University Marburg, Marburg, Germany\n\nHelge B. Bode\n\nDepartment of Chemistry, Phillips University Marburg, Marburg, Germany\n\nHelge B. Bode\n\nSenckenberg Gesellschaft f\u00fcr Naturforschung, Frankfurt, Germany\n\nHelge B. Bode\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nL.P., H.S., D.J.C., H.B.B. and K.D. conceptualized the project. L.P., M.D., M.A.H., J.L., B.P., J.J., A.C., F.L. and L.S. performed experiments. L.P., M.D., M.A.H., J.L., B.P., J.J., G.A., K.A.M., Y.M.S., M.L. and M.W. analysed data. N.P., H.S., C.K., M.W., M.L., D.J.C., H.B.B. and K.D. supervised the work. L.P. and K.D. interpreted the data and wrote the initial draft of the manuscript with support from M.D., M.A.H., K.A.M. and M.W. All authors discussed and revised the manuscript.\n\nCorrespondence to\n Dominic J. Campopiano, Helge B. Bode or Katja Dierking.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Wenjing Qi, and the other, anonymous, reviewers for their contribution to the peer review of this work. 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2023 western North American heat wave was fueled by the record-warm Atlantic Ocean", + "journal": "Nature Communications", + "published": "16 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.metoffice.gov.uk/hadobs/hadisst/", + "/articles/s41467-025-61859-y#ref-CR98", + "https://rda.ucar.edu/datasets/ds628.0/", + "/articles/s41467-025-61859-y#ref-CR97", + "https://www.ncdc.noaa.gov/cdo-web/search?datasetid=GHCND", + "/articles/s41467-025-61859-y#Sec20" + ], + "code": [ + "https://github.com/ESCOMP/CESM", + "http://cola.gmu.edu/grads/" + ], + "subject": [ + "Atmospheric dynamics", + "Natural hazards" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5522259/v1.pdf?c=1752750566000", + "research_square_link": "https://www.researchsquare.com//article/rs-5522259/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61859-y.pdf", + "preprint_posted": "08 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "According to the World Meteorological Organization (WMO), 2023 was ranked as the warmest year in the global surface temperature record since 1850, setting new warm surface temperature records over more than 20% of the global land surface (WMO, 2023). In particular, the southwestern United States (US) and Northern Mexico experienced their longest stretch of record-breaking heat wave from late-June to mid-August, affecting over 100 million people, and causing over 200 deaths. This compounded drought and heatwave caused $14.5 billion in economic loss, the costliest natural hazard of 2023. Our analysis based on observational data indicates that the 2023 heat wave event was directly linked to a strong anticyclonic blocking pattern that persisted for more than six weeks across the western US. Regression analysis and dedicated atmospheric model simulations suggest that the anticyclonic blocking pattern was ultimately forced by the extremely warm sea surface temperature in the Atlantic. An extreme value analysis shows that the warm Atlantic along with a growing El Ni\u00f1o in the Pacific were responsible for doubling the heat wave number, tripling the heat wave days, and increasing the duration of heat wave events by about 50 percent in the region.Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamicsEarth and environmental sciences/Natural hazards", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8Figure 9", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-5522259/v1/ffcc1ed1859d0f14e12c1caa.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-5522259/v1/588e0a8636766f44df9894e1.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-5522259/v1/fbe79c05990d4175c8e2a6e1.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-5522259/v1/d45d13f5713e7ae59bf25114.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-5522259/v1/7a3ed6bc935cd5e3a0498bab.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-5522259/v1/7404f0a072100c5e48c4e4d8.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-5522259/v1/b51e800159a1ae4472807f14.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-5522259/v1/7bc463e25022b4f73c61807e.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-5522259/v1/370999b7c76d49d5542bc356.jpg%3FmaxDims%3D150x150&w=256&q=75.png" + ] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Heatwave2023ManuscriptSupplementaryMaterials.pdfThe longest-lasting 2023 western North American heat wave was fueled by the record-warm Atlantic Ocean", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "According to the World Meteorological Organization, 2023 was ranked as the second warmest year in the global surface temperature record since 1850, setting warm surface temperature records over more than 20% of the global land surface. In particular, the southwestern United States (US) and Northern Mexico experienced their longest stretch of record-breaking heat wave, affecting over 100 million people, causing over 200 deaths, and $14.5 billion in economic loss. Here we show that the 2023 heat wave event was linked to a strong anticyclonic blocking pattern that persisted for more than six weeks across the western US. Regression analysis and atmospheric model simulations suggest that the anticyclonic pattern was ultimately forced by the extremely warm sea surface temperature in the Atlantic. The combination of a warm Atlantic and a developing Pacific El Ni\u00f1o significantly amplified regional heat waves, doubling their number, tripling their days, and increasing their duration by about 50%.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The year 2023 was the second warmest on records1,2, only surpassed by 2024 (Monthly Global Climate Report for Annual 2024). Of particular notoriety was the boreal summer months (i.e., June-August), which were characterized as the hottest summer with more than 20% of the land surface setting extreme warm records2 and multiple heatwave events over most continents3, with several concurrent events simultaneously affecting multiple regions (Lembo et al.4). The year 2023 also produced the highest number of heat-related deaths in the United States (U.S.) in the 21st century, with 2325 deaths in association with severe heat waves5. One of these severe heat wave events occurred over the southwestern U.S. and Mexico, which extended from mid-June to early August, affected over 100 million people, and was responsible for 303 death that occurred in a span of just two weeks in Maricopa County, Arizona (https://www.maricopa.gov/1858/Heat-Surveillance). The compounded effect of extreme heat and drought was responsible for $14.5 billion in economic loss (https://www.ncei.noaa.gov/news/national-climate-202312), making this event the costliest weather and climate disaster of 2023 in North America. This event featured prolonged extreme surface temperatures, with Phoenix, Arizona experiencing both the longest continuous stretch (31 days, from 30 June to 30 July) of daily maximum temperature exceeding 43.3\u2009\u00b0C (110\u2009\u00b0F) and the warmest nighttime minimum temperature on record (36.1\u2009\u00b0C).\n\nExcessive heat puts significant stress on human health, resulting in increased morbidity and mortality6,7, with some of the most notorious events being responsible for hundreds and even thousands of deaths in the most extreme cases; for example, the 1980 US heat wave8,9, the 1995 event in Chicago, Illinois10, the 2003 European heat wave11, the 2010 Russian event12,13, the 2011 event over the Great Plains of the US14,15,16, the 2021 northwestern North American event17,18,19,20,21,22, the 2023 South American heat wave2. In fact, extreme heat is the leading cause of weather-related mortality in the US, topping other more notorious weather hazards, like tornadoes and hurricanes (US, https://www.weather.gov/hazstat/). Heat waves are also the leading cause of natural hazards-related deaths in Australia, accounting for more than 55% of the reported fatalities23. Moreover, these extreme heat wave events have been observed more frequently in many regions24, with a significant increase in the number and severity of heat waves in recent decades as a result of rising surface temperatures25. However, the effects of increasing temperatures on heat extremes go beyond changes in the mean climate and include shifts in the extremes as well26,27, where the duration and frequency are expected to increase this century28,29. All of these effects are further exacerbated by a projected increased exposure to heat extremes due to population growth30, increased urbanization, agricultural loss, and aridification31,32,33.\n\nIn addition to the longer-term trends, understanding shorter term weather and climate variability is essential for improving heat wave predictions and future projections. Heat waves are often linked to large-amplitude atmospheric circulation patterns driven by quasi-stationary and propagating Rossby waves and their interaction with the overall synoptic flow, topography, and land-sea contrast4. These interactions result in persistent anticyclonic flow and blocking events34, leading to flow stagnation and prolonged periods of clear sky, enhanced incoming solar radiation, drought conditions, and reduced soil moisture, all of which further exacerbates surface warming35,36,37. While these blocking patterns are part of the atmospheric synoptic circulation, significant effort has been undertaken to further understand longer-term drivers of these patterns, with the aim to improve their predictions beyond the weather forecast range. Slower-acting coupled atmospheric-land-ocean processes are often attributed to heat waves. For example, sub-seasonal variations in midlatitude atmospheric circulation patterns have been shown to precede heat waves over the US by 15\u201320 days38. Enhanced convective activity from the East Asian Monsoon was found to force a mid-latitude wave train across the Pacific, leading to enhanced blocking pattern, which promotes the occurrence of US Great Plains heat waves15. Others have shown that persistent midlatitude circulation patterns forced by tropical sea surface temperature (SST) anomalies are modulators for drought and extreme heat over the western US39,40,41. A recent work found that boreal summer tropical Atlantic SST anomalies modulate heat wave occurrences over North America16. In that work, it was found that a warmer tropical Atlantic enhances atmospheric convection over the Caribbean Sea and produces a Gill-type atmospheric response42. This, in turn, produces an anticyclonic Rossby wave source over the Great Plains, thus enhancing subsidence and significant surface warming, leading to heat domes. The aforementioned works, and many others, have provided a better understanding of the coupled climate system as it pertains to heat extremes. This collectively suggests that the inherently longer timescales of oceanic and land process variabilities could aid in extending the prediction of high-impact extreme events beyond the weather timescales. Although current coupled models tend to underestimate regional terrestrial temperature variability, decreasing prediction skill at longer lead-times43.\n\nBesides the multiple land temperature records that were set in 2023, global oceans also experienced record warm SST44,45. Of special notoriety were the record SSTs over the tropical Atlantic, which were up to one degree Celsius warmer than climatology. These warm oceanic temperatures were not just confined to the surface, as oceanic heat content was at record levels in 202344. The North Atlantic has experienced a warm period since around 1995, owing to its relation to the positive phase of the Atlantic Multidecadal Variability46,47. In addition, an increase in the energy imbalance from an upsurge in greenhouse gasses44,48,49 has contributed to a steady rise in ocean surface temperatures and heat content, among other effects50,51,52. Furthermore, the 2020 emission regulations from the International Maritime Organization aimed at reducing ship sulfate aerosol emissions may have contributed to the recent warm surface temperatures over the North Atlantic53,54. Warm North Atlantic SSTs have been shown to modulate local and remote atmospheric circulation, with significant influences on precipitation47,55,56, tropical cyclone activity57, as well as extreme land surface temperatures49,58, and SSTs over the Pacific Ocean59. The tropical Pacific was also characterized by warm SSTs associated with a growing El Ni\u00f1o60 (i.e., the positive phase of the El Ni\u00f1o Southern Oscillation, ENSO). ENSO is one of the dominant modes of interannual climate variability, which has been shown to affect extreme surface temperatures through modulation of atmospheric circulation61,62,63, including the occurrence of heat waves over North America64,65.\n\nThis work suggests a physical link between the co-occurrence of the long-duration (i.e., several weeks) extreme heat over the southwest US and Mexico and the record warm North Atlantic SSTs and a growing El Ni\u00f1o in the Pacific. Thus, we hypothesize a physical connection that the extremely warm 2023 interbasin Pacific-Atlantic SSTs were responsible for the persistence of the longest-lasting heatwave in the region. For this, we use observational records and general circulation model experiments to show that the growth and persistence of this heat wave event were supported by remote forcing from the record warm SSTs in the Atlantic, a growing El Ni\u00f1o event in the Pacific, and the interbasin synergy effect of Pacific-Atlantic forcing. This interbasin synergy is in reference to the constructive interaction between the warm Atlantic and ENSO in modulating the heat wave occurrence and not to the active debate about tropical Pacific/Atlantic interaction conundrum66,67.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The maximum and minimum near-surface temperatures along with their climatological mean and 95th percentiles are shown in Fig.\u00a01a for the summer of 2023 for Phoenix, Arizona (see Methods for definition of climatology). Note that the conditions were near their climatological mean for most of June. However, starting around 1 July, both maximum and minimum temperatures were at or exceeded their 95th percentile threshold (T95) for several days. The brunt of the heatwave was experienced from 13 July to 30 July, with 18 consecutive days of maximum temperatures exceeding the T95 by as much as 4.5\u2009\u00b0C. Beyond that, four other shorter-duration extreme heat periods were experienced up to 10 September, where the temperatures reverted to their climatological mean. Besides being the longest-lasting heatwave event in the region, the 13\u201330 July 2023 event was also responsible for the warmest minimum temperature and tied for the third warmest maximum temperature on record (Fig.\u00a01b, maximum temperatures of 50\u2009\u00b0C and 49.4\u2009\u00b0C were recorder in 1990 and 1995). While the southwest US and Mexico are notorious for extreme, persistent heat, previous events were less severe, with the previous record for Phoenix of consecutive days above T95 being 8 days, less than half the duration of the 2023 event. During the peak of the event, warm temperature anomalies >5\u2009\u00b0C affected most of the region (Fig.\u00a01c). Similar conditions were observed in other metropolitan areas, such as Las Vegas, Nevada, Albuquerque, New Mexico, El Paso, Texas, and San Antonio, Texas (Supplementary Fig.\u00a01).\n\na Seasonal evolution of maximum (red) and minimum (blue) temperature for Phoenix Arizona for the year 2023 from 1 June to 20 September. The long-term daily mean is shown by the dashed line, whereas the 5th and 95th percentiles are shown by the shading region. Excess above the 95th percentile is shown by red shading and stipples for both maximum and minimum temperatures. b Observed histogram of maximum (red) and minimum (blue) temperature for Phoenix, Arizona for the period 1 June to 31 August from 1955 to 2023. The extremely warm temperatures of 19 July 2023, during the peak amplitude of the heat wave, are shown for reference. c Five-day averaged surface temperature anomaly centered on 19 July 2023. Source data are provided as a Source Data file.\n\nThe spatiotemporal evolution of the maximum surface temperature anomaly during the heatwave is shown in Supplementary Fig.\u00a02. Note that significant positive temperature anomalies were already present over most of Mexico as early as mid-June, coinciding with negative anomalies over the western US. The warm anomalies subsided by early July, but then reappeared around the second week of July, engulfing most of the western US in the second half of the month. The event then shifted back south in early August and finally progressed eastward over the Great Plains by mid-August, where several record temperatures were broken in other regions (e.g., New Orleans experienced a maximum temperature of 40.5\u2009\u00b0C on 27 August).\n\nThe physical processes responsible for the growth and long persistence of the temperature anomalies over the southwest US are investigated next. A key question here is, why did the extreme heat last over twice as long as other previous events? Fig.\u00a02 describes the temporal evolution, averaged every 10 days for easier illustration, of several relevant dynamical and thermodynamic variables from June-August and averaged from 30\u00b0\u201335\u00b0N to 115\u00b0\u2013110\u00b0W, representing a box approximately encompassing the state of Arizona. A negative 200\u2009hPa geopotential height anomaly was present during early June along with a negative 850\u2009hPa temperature and 2-m temperature anomalies, which rapidly degraded and turned positive. These anomalies reached their maximum around mid-July, coinciding with the peak of the heatwave, and remained positive for the rest of the summer. The energy budget (see Methods and Eq.\u00a04) can be used to discern the driving process that led to the rapid growth of the positive low-level temperature anomaly. For example, for most of June, there were significant positive net shortwave, longwave radiations, and sensible heat fluxes at the surface (values\u2009>\u200920\u2009Wm2). This coincided with enhanced vertically integrated (975\u2013800\u2009hPa, see Methods) heating rates (heating > 2\u2009\u00b0C day\u22121) due to vertical advection and to enhanced surface heating. By early July, most of the strong heating anomalies weakened, but remained slightly positive. However, significant surface sensible heat flux (>20\u2009Wm2) and vertical diffusive heating rates (heating > 2\u2009\u00b0C day\u22121) supported the continuation of the T850 anomalies throughout July, along with longwave radiation heating during August. Of note is that surface latent heat flux was negative throughout the summer, enhancing the surface warming through radiative and sensible heating via an increase in the Bowen ratio, which links water and energy balances of the climate system. A Bowen ratio increase, often present during mega heat waves68, suggests a reduction in the evaporation/evapotranspiration and an increase in the sensible heating and thus increase in near surface temperatures.\n\nEnergy budget averaged every 10 days over the southwest U.S. and northwest Mexico (30\u00b0\u201335\u00b0N and 115\u00b0\u2212110\u00b0W) from 6 June to 24 August 2023. Each row represents (from the top): 200 hPa geopotential height anomaly [gpm], 850\u2009hPa temperature and 2-m temperature anomalies [\u00b0C], vertically integrated anomalous heating rates from 975 to 800\u2009hPa [\u00b0C day\u22121], and surface heat fluxes (net surface shortwave and longwave radiation, sensible and latent heat fluxes) [Wm\u22122]. Daily anomalies are derived from the long-term monthly mean for the 1979\u20132022 period. See Methods for heat budget definitions.\n\nIn contrast, adiabatic heating anomaly was fairly negative throughout the summer, lessening the impact of the remaining heating terms and thus dampening the temperature tendency. In addition, heating due to horizontal advection played a relatively small role in the evolution of the temperature anomaly, this is expected due to the broad temperature anomaly (Fig.\u00a01c) and stagnant flow pattern (Fig.\u00a03) associated with this heat wave. Overall, the low-level temperatures warmed significantly during June to mid-July, and then remained elevated for most of the summer, aided by significant surface heat fluxes and vertical heating rates (i.e., vertical advective, diffusive, and longwave heating).\n\na Potential vorticity and wind at the 350\u2009K isentropic level during the maximum amplitude of the heat wave on 18 July 2023. Thick vectors depict anti-cyclonic fluid trapping, a proxy for heat dome and air flow stagnation. The thick black line indicates the location of the dynamical tropopause. The blue star on each panel represents the location of Phoenix, Arizona. b Latitude-vertical cross-section along 112\u00b0W on 18 July 2023 of anomalous potential temperature (color) and potential temperature (magenta 10\u2009K intervals). Also shown are zonal wind anomalies (light black contours at 3\u2009m/s intervals), the tropopause level as measured by the 2 PVU (thick black line), and anti-cyclonic fluid trapping (circle hatching at 10\u22125\u2009s\u22121). c Vertical atmospheric profile over Tucson, Arizona. The profile is plotted on a skewT-logP thermodynamic diagram. The vertical axis is pressure (hPa), the skewed thin axis is temperature (Celsius), and the dry and moist adiabats are also shown. The vertical profile of environmental temperature, dew point, and wind speed and direction is denoted by the thick-red line, thick-blue line, and wind barbs, respectively.\n\nWe now turn our attention to the spatial distribution of the circulation anomalies during the peak of the heatwave. The analysis uses potential vorticity (PV) and circulation on constant potential temperature (i.e., isentropic surface), which poses several advantages over pressure surfaces in that PV can be used as a parcel tracer and is conserved for frictionless adiabatic motion69. Figure\u00a03a depicts the 18 July 2023 potential vorticity and wind at the 350\u2009K isentropic level, as well as the location of the dynamical tropopause (thick black contour), defined to be the 2 PVU (2\\(\\cdot\\)10\u22126\u2009K\u2009m2\u2009kg\u22121\u2009s\u22121) iso-surface, which serves as the boundary of tropospheric and stratospheric air. Note that the flow is anticyclonic over the southwestern U.S and northern Mexico, with significant fluid trapping (thick arrows), suggesting a blocking pattern (see Methods for definition on trapped flow). In fact, the upstream and downstream troughs, along with the anticyclone, are manifestations of a classic \u201comega\u201d blocking pattern, which is well known to persist for significant periods of time and is responsible for stagnant flow patterns. Regions under anticyclonic trapping often experience significant surface shortwave heating due to clear skies (Supplementary Fig.\u00a03). These conditions contribute to extreme surface warming for prolonged periods, with little ventilation due the trapped air mass and flow stagnation. While atmospheric blocking is a manifestation of planetary waves70, the aforementioned anticyclonic trapping persisted for several weeks, as shown by a time-averaged flow (Supplementary Fig.\u00a04).\n\nA latitude-vertical cross-section along 112\u00b0W, which is approximately through the center of the anticyclone, shows the anomalous circulation features during the peak of the heatwave (Fig.\u00a03b). Note that there is a deep upper-level (700\u2013100 hPa) anticyclonic circulation centered around 35\u00b0N with easterlies (westerlies) around 25\u00b0N (45\u00b0N). The 2 PVU contour shows a dome-like feature, with PV decreasing northward of 25\u00b0N (i.e., a meridional PV inversion) with a subsequent poleward increase. In addition, there is a significant downward (upward) bulging of the potential temperature surfaces at lower (upper) levels, indicating low to mid-level heating. This is shown by the potential temperature anomalies (colors in Fig.\u00a03b), indicating potential temperature anomalies around 8\u2009\u00b0C near the surface and similar amplitude negative anomalies in the stratosphere. In relation to the strong mid-level anticyclone, the air mass from 25\u00b0N to 50\u00b0N is significantly trapped (hatching in Fig.\u00a03b, see Eq.\u00a05 for definition) for most of the tropospheric depth, providing little to no ventilation and thus exacerbating the lower-level warm anomalies. In the presence of a trapped flow, most of the ventilation comes from vertical advection from the boundary layer up, which is the case here as shown in the heat budget analysis (Fig.\u00a02). The vertical profile over Tucson, Arizona on 18 July 2023 (Fig.\u00a03c) shows a well-mixed lower troposphere, with a deep dry layer of constant potential temperature (i.e., red line and potential temperature lines are parallel) and mixing ratios (i.e., blue line and dashed black lines are parallel) from the surface up to about 650\u2009hPa, indicating strong vertical turbulent heat and moisture fluxes associated with enhanced surface heating from prolonged clear sky conditions. The profile also shows a marked decrease in dew point temperature above 500\u2009hPa, indicative of subsidence associated with the high-pressure dome as well as weak wind speed and weak vertical wind shear (hodograph in the top right of Fig.\u00a03c).\n\nThe southwest U.S. and northern Mexico are regions where most of the precipitation and thus soil moisture is obtained during the summer monsoon71,72,73 (i.e., North American monsoon, NAM, see Methods for definition). In addition, soil moisture and surface air temperature are strongly correlated through longwave radiation, sensible, and latent heating through land-atmospheric coupling74, thus a moisture deficit could exacerbate warm surface temperatures and heat wave occurrence36,75. Thus, it is important to assess the state of the NAM, which is the dominant source of precipitation over the region. Note that 2023 was one of the weakest NAM years on record, with precipitation anomalies of \u22121.4\u2009mm/day (Fig.\u00a04a). A correlation analysis between the NAM index and maximum July temperatures over the study region for the 1979\u20132022 period reveals a negative correlation r\u2009=\u2009\u22120.58 (p\u2009<\u20090.01) and a spatial pattern similar to the 2023 temperature anomalies over the southwest U.S. and Mexico (Supplementary Fig.\u00a05).\n\na Time series of the North American monsoon (NAM) index for June\u2013August. Years of significant NAM index anomalies are highlighted by color-filled bars based on the 99-percentile significance level based on the student-T test. b Climatological vertically integrated moisture transport (vector, Kg\u2009m\u22121\u2009s\u22121) and its divergence (color, Kg\u2009m\u22122\u2009s\u22121), where negative values indicate convergence. c Same as (b) but for the 2023 anomalies computed from the departure from the 1979\u20132022 climatology. d Zonal-vertical cross-section along 28\u00b0N of the moisture transport climatology (black contours, solid lines indicate positive values, dotted lines indicate negative values) and 2023 anomaly (color). e Same as d but for the meridional cross-section along 110\u00b0W. Red hatching in b indicates the region that meets the criteria for the NAM monsoon index (see Methods).\n\nIt is worth noting that a significant portion of the moisture and thus precipitation that feeds into the NAM region originates from two sources: (1) a Pacific source, mostly through the Sea of Cortez, and (2) an Atlantic source via the Caribbean and Great Plains low-level jet system72,73. Previous works have shown that warmer tropical Atlantic SSTs weakens the western edge of the North Atlantic Subtropical High, weakening the Caribbean low-level jet76, thus modulating moisture transport and precipitation over North America77,78,79. As shown in Fig.\u00a04b, the climatological (vertically-integrated) moisture transport into the NAM region originates from the Pacific and Atlantic sectors, converging over the NAM region and thus producing precipitation there. The anomalous moisture transport shown in Fig.\u00a04c represents the sum of the flux of mean moisture by anomalous wind \\((v^{\\prime} \\bar{q})\\), flux of anomalous moisture by mean wind (\\(\\bar{v}q^{\\prime}\\)), and flux of anomalous moisture by anomalous wind (\\(v^{\\prime} q^{\\prime}\\)), where bar (primes) represents climatology (anomaly) respectively, where \\(v\\) is the horizontal wind and \\(q\\) depicts specific humidity. Note that this circulation was disrupted and reversed in 2023 (Fig.\u00a04c), with northerly moisture flux anomalies over the Sea of Cortez and a generally westerly moisture flux anomaly from the Pacific into the Atlantic, all of which resulted in moisture flux divergence over the NAM, reducing the monsoon to one of its weakest on record (Fig.\u00a04a).\n\nTo further assess the origin of the reduced NAM and drought conditions, Fig.\u00a04d shows a zonal-vertical cross-section of meridional moisture flux (\\({vq}\\)) along 28\u00b0N from 120\u00b0W to 100\u00b0W. Similarly, Fig.\u00a04e shows a meridional-vertical cross-section of the zonal moisture flux along 110\u00b0W from 15\u00b0N to 40\u00b0N. These cross-sections are specifically chosen to depict the main core region of the moisture fluxes that feed the NAM. Note that there are two main cores of climatological northward moisture transport (Fig.\u00a04d): (1) around 110\u00b0W at about 925\u2009hPa and (2) east of 105\u00b0W at higher elevations around 850\u2009hPa. The former is associated with a Pacific moisture source while the latter is linked to an Atlantic moisture source72,73. In 2023, significant negative meridional moisture flux anomalies were present over both moisture flux regions that feed the NAM (Fig.\u00a04d) with significant southward moisture transport and divergence south of 30\u00b0N (Fig.\u00a04e). Further analysis on moisture transport anomalies was performed by separating changes in circulation \\(\\left(v\\right)\\) and moisture \\(\\left(q\\right)\\). It was found that circulation anomalies were primarily responsible for the 2023 reduction in NAM moisture sources and precipitation (Supplementary Fig.\u00a06 and 7). While the reduction (expansion) of the Atlantic (Pacific) subtropical high during the summer of 2023 appeared to be responsible for the reduction in NAM precipitation, other factors like enhanced atmospheric stability from increasing temperatures could have reduced the available surface moisture, inhibiting precipitation and diminishing the NAM80. The increased atmospheric stability is exacerbated under uniform SST warming81, and could have been at play in 2023, given the warm interbasin SSTs.\n\nHere, we investigate whether any large-scale climate features were responsible for the persistent heat wave. For example, Fig.\u00a05 shows the SST, mean sea level pressure, and precipitation anomalies for June and July 2023 over the Pacific-Atlantic sector. In the tropical Pacific, the positive SST anomalies are indicative of the growing El Ni\u00f1o event, which attained a moderate amplitude by the end of 202360. Further north, the extratropical Pacific SSTs depict a negative anomaly near the west coast of Mexico and California and a positive anomaly along 40\u00b0N over the North Pacific current. Over the Atlantic sector, record-breaking warm SST anomalies were observed for the eastern portion of the basin in June, which then migrated westward, encompassing the entire basin with record SSTs in July. The North Atlantic experienced record warm SSTs in 202344, with temperatures greater than 1\u2009\u00b0C above the 1981\u20132010 climatology. Sea level pressure anomalies in June experienced a see-saw pattern with positive anomalies over the Pacific and negative anomalies over the Atlantic, indicating a strengthening of the Pacific subtropical high and a weakening of the Atlantic subtropical high (Fig.\u00a05c). In July, the mean sea level pressure anomalies relaxed over the Pacific and weakened but remained negative over the Atlantic basin. Of interest is the low-pressure anomaly over the southwestern U.S. and Mexico in July (Fig.\u00a05d), indicating the presence of a surface heat low. Precipitation anomalies (Fig.\u00a05e, f) indicate a classical El Ni\u00f1o pattern with a southward shift of the Intertropical Convergence Zone (ITCZ) in the Pacific as well as a reduction in precipitation over the North American Monsoon. Negative precipitation anomalies are also evident over the heat wave region. As depicted in the heat budget analysis (Fig.\u00a02), the surface energy balance showed significant anomalous positive surface sensible heating and negative latent heating during most of the 2023 summer.\n\na Sea surface temperature (SST) anomaly, c mean sea level pressure anomaly (color) and total sea level pressure (contour), and e precipitation anomaly for June 2023. b, d, f are the same as a, c, e but for July 2023. Black stipples in a, b, e, f indicate statistical significance at a 95% confidence level based on a student T test. Green boxes are the Ni\u00f1o3 [5\u00b0N\u20135\u00b0S and 150\u00b0W\u201390\u00b0W] and tropical North Atlantic [5\u00b0N\u201325\u00b0N and 20\u00b0W\u201380\u00b0W] regions.\n\nGiven that both the Atlantic and Pacific basins show remarkable SST anomalies (Fig.\u00a05), it is worth investigating whether these SST anomalies have any connection to the temperature in the southwest U.S. and Mexico. For this, a partial least-square regression analysis is performed using a tropical North Atlantic SST index (TNA, area average over 5\u00b0N\u201325\u00b0N and 20\u00b0W\u201380\u00b0W) and a tropical Pacific SST index (Ni\u00f1o3, area average over 5\u00b0N\u20135\u00b0S and 150\u00b0W\u201390\u00b0W), shown by green boxes in Fig.\u00a05a. Although these two SST indices have a temporal correlation of 0.16, and thus are not significantly correlated, we carry out a partial regression analysis to adequately extract their independent linear relationship with respect to 200\u2009hPa stream function, 200\u2009hPa temperature, and 2-m air temperature anomalies (Fig.\u00a06). Note that a combined effect of warm TNA and positive Ni\u00f1o3 (i.e., El Ni\u00f1o) is associated with 200\u2009hPa anticyclonic circulation anomalies throughout the tropics along with 200\u2009hPa warm temperature anomalies that extend over the southern U.S. (Fig.\u00a06c) and corresponding warm 2-m maximum air temperatures (Fig.\u00a06d) that mimic the anomalies associated with the 2023 heat wave event (Fig.\u00a06a, b). Separating the relative contribution of each basin, it is noted that the Atlantic SST anomalies are playing the dominant role given by the regression of TNA-only shown in Fig.\u00a06e, f. Note that the upper-level response is the formation of an anticyclone and warm temperature anomalies over Mexico (Fig.\u00a06e). This is associated with a Gill-type atmospheric response42 in association with the enhanced diabatic heating over the tropical Atlantic and Caribbean linked to the warm SST anomalies over the TNA47,79,82. The 2-m air temperature (Fig.\u00a06f) also shows enhanced warming over Mexico and the southern U.S., similar to the conditions associated with warm TNA and positive Ni\u00f1o3 (Fig.\u00a06d). In contrast, the role of the Pacific SSTs is small, as shown in Fig.\u00a06g, h. Thus, while there was a developing El Ni\u00f1o in the boreal summer of 2023, El Ni\u00f1o events tend to produces tropospheric warming throughout the tropics via a fast equatorial Kelvin wave and off-equatorial anticyclonic anomalies83,84,85, similar to Fig.\u00a06e. However, ENSO response to the northern hemisphere is strongest later in the seasonal cycle83. Thus, the warm Pacific SSTs appear to have played a minimal role, at least in a linear sense, since extratropical ENSO teleconnections are known to peak in the boreal winter and are relatively weaker in other seasons83,84,85. However, non-linear interactions (i.e., synergy between the warm Atlantic and a growing El Ni\u00f1o) could also be playing an important role. This synergy may not be readily extracted from the simple linear regression presented; thus, a dedicated sensitivity experiment is carried out next.\n\na Anomalous 200 hPa temperature (color) and streamfunction (black contour, 106\u2009s\u22121) for June\u2013August of 2023. b same as (a) but for 2-m maximum air temperature anomaly. c Regression coefficient of Ni\u00f1o3 plus tropical North Atlantic (TNA) SST and 200\u2009hPa temperature (color) and 200\u2009hPa streamfunction (black contour, 106\u2009s\u22121). d Same as (c) but for 2-m maximum air temperatures. The regression coefficients are computed for June\u2013August for the 1979\u20132023 period through partial regression, and the units are per standard deviation of the SST anomalies. Panels (e, f) are the same as (b, c) but for the regression coefficients with TNA SSTs only and holding Ni\u00f1o3 SSTs constant. Similarly, (g, h) show the regression coefficients with respect to Ni\u00f1o3 SSTs, holding TNA SSTs constant.\n\nThe previous sections discussed the coincident climate events (i.e., uniform SST warming from the extreme warm SSTs in the Atlantic, a growing El Ni\u00f1o in the Pacific, and a record low NAM) which could have been responsible for the occurrence and extended duration of the southwest US and Northern Mexico heat wave in 2023. While attributing a single cause to heat waves is difficult given their synoptic nature, previous works have shown that large-scale climate variations can modulate their occurrence. For instance, precipitation and thus soil moisture are strongly coupled to surface temperatures74. It is also known that remote SST anomalies can modulate US climate through circulation changes on interannual timescales16,86,87 and even on decadal timescales88. Therefore, we isolate the effect that the record warm Atlantic SSTs and the growing El Ni\u00f1o had on the longest-lasting southwest US heat wave event of 2023. For this, we perform sensitivity experiments using an atmospheric general circulation model (AGCM, see Methods) by prescribing the observed 2023 SST anomalies over the Atlantic and Pacific basins and climatology elsewhere (Supplementary Fig.\u00a08). Analysis of these AGCM experiments is presented here in terms of the differences between the prescribed global 2023 SST experiment (GBL23) minus the climatology experiment (CTL) (see Methods section). Analysis from the ensemble mean of the 100 ensemble simulations shows enhanced surface net shortwave radiation over northern Mexico and the southwestern US as well as enhanced surface net longwave radiation, enhanced sensible heat flux, and reduced latent heat flux (Supplementary Fig.\u00a09). These surface energy fluxes are consistent with enhanced warming and reduced moisture over the analysis region and consistent with the anomalies observed in 2023.\n\nThe large-scale circulation changes associated with 2023 SSTAs are shown by the difference between GBL23 minus CTL experiment for June-July-August (JJA) 200hPa temperature and 200\u2009hPa streamfunction (Fig.\u00a07a). Note the anomalous heat dome structure and associated anticyclonic circulation pattern over Mexico and the southwestern US, which is remarkably consistent with the observed partial regression pattern (Fig.\u00a06c). Here, the model responds to the 2023 SST forcing by producing an anticyclonic circulation anomaly over Mexico (positive contours), leading to enhanced near surface temperatures for most of the region (Fig.\u00a07b). While the AGCM response is very similar to that of the observed analysis (Fig.\u00a06), it is worth separating the relative contributions of Atlantic and Pacific SST anomalies. For this, we look at the SST sensitivity experiments with Atlantic-only (ATL23) and Pacific-only (PAC23) prescribed 2023 SSTs. Consistent with the regression analysis, the Atlantic SST forcing appears to play a more dominant role by creating an upper-level anticyclonic circulation anomaly (Fig.\u00a07c) and enhanced near-surface warm temperatures (Fig.\u00a07d) comparable in amplitude to that of the GBL23 experiment. In contrast, the PAC23 experiment shows relatively weak homogeneous upper tropospheric warming and anticyclonic circulation at low latitudes (Fig.\u00a07e), a feature typical of a developing El Ni\u00f1o event. Thus, the surface temperature signal over the southwestern U.S. and Mexico is very small in comparison (Fig.\u00a07f).\n\nComposite difference of simulated (a) 200\u2009hPa temperature (color) and 200\u2009hPa streamfunction (black contour, 106\u2009s-1) and b 2-m air temperature from the atmospheric general circulation model (AGCM) experiment with prescribed 2023 global SSTs (GBL23). c, d are the same as (a, b) but for the AGCM experiments with Atlantic-only 2023 SSTs (ATL23). Similarly, (e, f) show the composites from the AGCM experiment with prescribed Pacific-only SSTs (PAC23). Similarly, (g, h) show the synergy between the Atlantic and Pacific forcings (see Methods). The composite differences are with respect to the control experiment (CTL) for JJA.\n\nThe sources for these teleconnections are analyzed via Rossby wave sources (RWS, see Methods). Note that there are a few regions of anticyclonic RWS in the GBL23 experiment (Fig.\u00a08a) over the equatorial Pacific and Atlantic, near the location of the ITCZ, and also over the Greater Antilles. These are consistent with the warm 2023 SST forcing prescribed in the GBL23 experiment, which led to upper-level 200\u2009hPa divergence (Fig.\u00a08c) and 200\u2009hPa anticyclonic circulation consistent with a Gill-type response (Fig.\u00a08b). Further decomposition of the RWS into its component demonstrates that mean vortex stretching by the anomalous divergent flow (Fig.\u00a08e) was the main driver of the anticyclonic RWS and thus the anticyclonic circulation over Mexico.\n\nComposite difference of simulated (a) 200\u2009hPa Rossby wave source (RWS), b 200\u2009hPa streamfunction (contour, 106\u2009s\u22121) and anomalous vorticity advection (color), c 200\u2009hPa velocity potential (contour, 106\u2009s\u22121) and mean vorticity advection (color), d 200\u2009hPa rotational wind (vector, ms-1) and anomalous vortex stretching (color), and e 200\u2009hPa divergent wind component (vector, ms\u22121) and mean vortex stretching. The units for the RWS terms are 10\u221211\u2009s\u22122, see Methods for definition. Composites are from the atmospheric general circulation model (AGCM) experiment with prescribed 2023 global SSTs (GBL23). The composite differences are with respect to the control experiment (CTL) for June-July-August.\n\nThe individual roles of each ocean basin are investigated using the ATL23 and PAC23 sensitivity experiments. For the Atlantic-only forcing (Supplementary Fig.\u00a010), a similar pattern emerges where the upper-level divergent flow being responsible for the anticyclonic RWS over the Atlantic basin, which leads to a downstream (westward) intensification of the upper-level anticyclone over Mexico and the eastern Pacific between 20 and 40\u00b0N. In contrast, the Pacific-only SST sensitivity experiment (Supplementary Fig.\u00a011) yields anticyclonic RWS over the ITCZ, in association with the developing El Ni\u00f1o, and consistent with upper-level divergence over the heating region83. However, this signal is smaller than those from the ATL23 forcing. The larger RWS from the ATL23 compared to the PAC23 sensitivity experiment is probably due to the fact that SSTs in the Atlantic were at their peak while the El Ni\u00f1o in the Pacific was still in the growing phase (Supplementary Fig.\u00a08).\n\nAs discussed earlier, non-linear interactions (i.e., synergy between the warm Atlantic and a growing El Ni\u00f1o) could also be playing an important role in exacerbating extreme heat in the region. This interaction can be readily extracted from the AGCM experiments by isolating the interbasin synergy following Eq.\u00a07 (see Methods) and is shown in Fig.\u00a07g, h). Note that the synergy component mostly shows upper tropospheric warming and relatively warm near surface temperatures over the southwest US, while troughing and cooler temperatures upstream and downstream of the heat wave region (Fig.\u00a07h).\n\nBesides changes in large-scale circulation patterns, the occurrence of heat wave events is also investigated within the AGCM experiments. For this, daily outputs of maximum temperature from the model experiment are used to compute changes in several heat wave characteristics. Here, the daily T95 percentile maximum temperature is computed from a 300-year control simulation. Then, heat wave amplitude, number of heat wave events, number of heat wave days, and the duration of each event are computed relative to the control T95 (Fig.\u00a09). For all four indices, the difference between the GBL23 and CTL experiment is normalized by the expected occurrences in the CTL experiment, e.g., (GBL23\u2009\u2212\u2009CTL)/CTL. The relative changes between the GBL23 relative to the CTL experiment show an amplitude increase on the order of 10% (mostly over Mexico, where amplitude is measured as the maximum 2-meter temperature anomaly averaged over the duration of the event). The number of heat wave events more than doubled (e.g., greater than 100% increase), the number heat wave days tripled, and a >20% increase in the duration of heat waves occurred over the southwestern US and Mexico in the GBL23 sensitivity experiment. Thus, the sensitivity experiment further validates the role of the 2023 SSTs in modulating the occurrence and persistence of heat wave events over the study region.\n\nComposite difference of simulated 2023 minus control atmospheric general circulation model (AGCM) experiments during June-July-August for (a) average amplitude of heat waves, b number of heat wave events, c number of heat wave days, and d heat wave duration. Units are percentage changes as defined by the number of events during the simulated 2023 minus control AGCM divided by the total events in the control experiment, and multiplied by 100, such that a 100% increase translates into a doubling of the occurrence.\n\nA composite analysis for several heat wave metrics is shown in Fig.\u00a010 for the grid point closest to Phoenix, Arizona from the AGCM experiment. For instance, the CTL (GBL23) case produced 77 (184) heat wave events. This is a 138% increase over the expected climatology, of which 75% is attributed to Atlantic SSTs, 31% to the Pacific, and 32% to non-linear or synergic interbasin interactions (Fig.\u00a010a). Similarly, a total of 307 (853) heat wave days were produced by the CTL (GBL23) experiment (Fig.\u00a010b), a 177% increase above climatology of which 89%, 27%, and 61% are attributed to the Atlantic-only, Pacific-only, and interbasin SST synergy respectively. The average event duration also increased by around 18% from the climatological 3.9 days to 4.6 days (Fig.\u00a010c), of which 10% was due to the Atlantic-only and the remaining 8% from the interbasin synergy, and no contribution from the Pacific-only case. The longest-lasting heat wave in the CTL (GBL23) experiment was 9 (14) days, all while the GBL23 experiment experienced several double-digit event durations (Supplementary Table\u00a01). Besides more and longer-lasting events, the GBL23 experiment also produced larger amplitude events. Looking at the return period in years of a very high threshold (a high threshold is chosen here as a temperature similar to the 2023 heat wave event) and modeled by a Pareto distribution89, a 45\u2009\u00b0C daily maximum temperature is observed once every 45 (14) years in the CTL (GBL23) experiment (Fig.\u00a010d). This represents a 222% increase in frequency above climatology (e.g., frequency is inversely proportional to return period), where 137%, 60%, and 25% were due to the Atlantic-only, Pacific-only, and interbasin SST synergy respectively.\n\na Heat wave number, b heat wave days, c duration in days, and d frequency of exceeding a 45\u2009\u00b0C threshold in years for the grid-point closest to Phoenix, Arizona. The components are: the expected value or climatology (CTL), Atlantic-only 2023 (ATL23) sea surface temperature (SST) sensitivity, Pacific-only 2023 SST sensitivity (PAC23), and the Pacific-Atlantic Synergy, scaled by the total contribution corresponding to the GBL23 experiment (2023 SST sensitivity). The numeric values in the center are the climatology (top) and 2023 SST sensitivity experiment (bottom). The percentage values in each of the slices indicate the increase/decrease relative to climatology (CTL, or 100%). These values are from the atmospheric general circulation model AGCM experiments. See Methods.\n\nThe JJA daily maximum temperature is also significantly higher in the GBL23 experiment (ensemble mean of 40.2\u2009\u00b1\u20090.31\u2009\u00b0C) compared to the CTL experiment (ensemble mean of 39.1\u2009\u00b1\u20090.30\u2009\u00b0C), see Supplementary Table\u00a01. This difference of over 1\u2009\u00b0C is significant against the background weather noise, which is taken from the ensemble spreads of the 100 realizations from each experiment. Similarly, the minimum temperature is also higher in the GBL23 experiment (26.8\u2009\u00b1\u20090.34\u2009\u00b0C) compared to the CTL case (25.8\u2009\u00b1\u20090.31\u2009\u00b0C). A daily minimum temperature excess over a very high threshold (here 31\u2009\u00b0C) is observed every 30 (11) years in the CTL (GBL23) experiments (Supplementary Table\u00a01).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_Fig8_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_Fig9_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61859-y/MediaObjects/41467_2025_61859_Fig10_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "This work investigated the record-breaking, large amplitude, broad spatial scale, and persistent heat wave event that impacted the southwestern U.S. and Mexico in 2023. This heat wave set multiple warm temperature records, both for the maximum and minimum daily temperatures in multiple locations. It was also responsible for the longest stretch of very high temperatures in multiple cities across the U.S. and Mexico, which contributed significant stress on human health, agriculture, and infrastructure. It was found that a large-scale, semi-persistent atmospheric blocking pattern was anchored over the region for most of the summer, a feature that enhanced incoming solar radiation, thermal heating, and sensible heating, deep subsidence, while reducing precipitation, thus increasing surface temperatures. The atypical features of this extreme event suggested that there were slow-varying large-scale forcings at play, potentially promoting its occurrence. It was shown that the homogeneous interbasin warm SSTs from the record warm Atlantic Ocean and a growing El Ni\u00f1o event in the Pacific modulated large-scale atmospheric circulation. These circulation changes thus promoted a semi-permanent anticyclonic blocking pattern, significantly increasing surface heating, which led to the growth and persistence of the heat wave. In addition, the NAM was one of the weakest on record, as a result of reduced moisture fluxes from the Pacific and Atlantic sectors. A partial regression analysis from observations and dedicated model experiments with prescribed SSTs confirmed that the extremely warm tropical Atlantic Ocean in 2023 was the dominant factor, which increased the likelihood of heat waves over the region. Meanwhile, the Pacific SSTs' influence was much smaller, at least when measured as a stand-alone forcing. However, the interbasin synergy effect of Pacific-Atlantic forcing proved to be central in further exacerbating the likelihood of heat waves in the study region, including extending their duration and enhancing the warm temperature anomalies.\n\nThere are many factors potentially contributing to the record warm Atlantic SSTs. On the global scale, the ocean continues to warm, not only at the surface but at depths as well44, with ocean heat content steadily increasing due to the Earth\u2019s energy imbalance48,49. However, the warming has been concentrated in the upper ocean, with the effect of increasing stratification44,90. This makes the upper ocean more stable and less prone to mixing by the winds, exacerbating the surface warming, and thus increasing the SSTs further. This is very relevant for the North Atlantic, where the current warming has been shown to be concentrated near the surface44,90. On decadal timescales, the Atlantic Multidecadal Variability46,47, also influences the enhanced ocean heat content. On shorter timescales, El Ni\u00f1o developed in 2023, which has been shown to redistribute heat from deeper layers to those near the surface, thus also yielding higher SSTs91,92. Locally in the Atlantic, the winds were weaker than normal due to a series of atmospheric features. A weaker Bermuda High, which induces a weaker near-surface wind, enhances warming through reduced evaporative cooling at the surface93,94. Other factors like enhanced shortwave solar radiation and reduced aerosols could also be at play90,95,96, including emission regulations from the International Maritime Organization53,54. However, causes for the extreme warm Atlantic SSTs are a subject of further investigation, outside the main scope of this paper.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Atmospheric variables (e.g., vertical profiles of temperature, moisture, geopotential height, wind, and heating rates) are obtained from monthly and daily means from the Japanese 55-year Reanalysis97 (JRA55) for the period of 1955\u20132023 with a 55\u2009km spatial resolution, and 60 vertical levels up to 0.1\u2009hPa. Data interpolated to 1.25\u00b0\u2009\u00d7\u20091.25\u00b0 spatial resolution and 37 vertical levels from 1000\u2009hPa to 1\u2009hPa were used. Heating rates are provided directly by the JRA55 reanalysis, which includes net atmospheric radiation (short and longwave radiation), vertical diffusion heating rate, which represents the energy transfer without phase change; latent heat from moist processes, which includes the large-scale condensation heating rate and the convective heating rate. Daily maximum and minimum temperatures are obtained from meteorological observations for several stations along the southwestern U.S. from the NOAA/National Center for Environmental Information from 1950 to 2023. Observed SSTs are obtained from the Hadley Centre HadSSTv2 product98 at a 1\u00b0 horizontal resolution for the period of 1900\u20132023. Anomalies are defined relative to the climatological period from 1979 to 2022.\n\nWe assess heat waves by the Excess Heat Factor99 (EHF) used in operational forecasts as well as in research studies15,16,100. The EHF index (Eq.\u00a01) is defined by combining two excess heat indices, namely an acclimatization index (Eq.\u00a02), which measures the current 3-day temperature anomaly relative to the previous 30-day period; and a significant exceedance index, which measures the excess 3-day temperature over a high threshold, taken here as the 95th percentile temperature for that given day (Eq.\u00a03). Here, Ti corresponds to the daily maximum surface temperature for day i and T95 is the 95th percentile temperature for that given day. A positive EHF characterizes heat wave conditions that persist for a minimum of three days.\n\nTo isolate the physical processes responsible for the development and long persistence of the heat wave, a heat budget analysis is performed (Eq.\u00a04) for the temporal evolution of a temperature anomaly T due to horizontal advection \\(\\left(\\vec{{{\\boldsymbol{u}}}}\\cdot \\nabla T\\right)\\), vertical advection \\(\\left(\\omega \\frac{\\partial T}{\\partial p}\\right)\\), adiabatic heating \\(\\left(\\omega \\frac{{RT}}{{C}_{p}P}\\right)\\), and diabatic heating (Qnet). This last term consists of shortwave, longwave, diffusive, and latent heating. In Eq.\u00a04, \\(\\vec{{{\\boldsymbol{u}}}}\\) and \\(\\omega\\) are the horizontal and pressure velocities, R\\(=287\\) J\u2009Kg\u22121\u2009K\u22121 is the ideal gas constant for dry air, and \\({C}_{p}={\\mathrm{1,004}}\\) J\u2009Kg\u22121\u2009K\u22121 is the specific heat of dry air at constant pressure. Equation\u00a04 is vertically integrated from pressure level \\(975-800\\,{\\mathrm{hPa}}\\), chosen here to be representative of low-level temperature variations101. The integral is mass-weighted and normalized by the total integration thickness \u0394P to preserve the original units of \u00b0C/day of the heating rates and for easier comparison.\n\nFlow characteristics associated with blocking pattern and heat wave dome are assessed by the instantaneous local Lyapunov exponent (Eq.\u00a05).\n\nWhere, \\({\\lambda }_{+}\\) is the positive Lyapunov exponent, which is equivalent to the dilation rate. Here, D\u2009=\u2009\u2202u/\u2202x\u2009+\u2009\u2202u/\u2202y is the horizontal divergence, \\(E=\\sqrt{{E}_{{st}}^{2}{+E}_{{sh}}^{2}}\\) is the deformation, Est\u2009=\u2009\u2202u/\u2202x\u2009\u2212\u2009\u2202v/\u2202y is the stretching deformation and Esh\u2009=\u2009\u2202u/\u2202y\u2009\u2212\u2009\u2202v/\u2202x is the shear deformation, and \\(\\zeta=\\partial {{\\rm{v}}}/\\partial x-\\partial u/\\partial y\\) is the relative vorticity. The imaginary component of \\({\\lambda }_{+}\\) (i.e., where \\(\\zeta=\\partial {{\\rm{v}}}/\\partial x-\\partial u/\\partial {{\\rm{y}}}\\)) is used to represent regions of fluid trapping102, or where the flow is dominated by vorticity. If \\(\\zeta < 0\\), then the trapped flow is anticyclonic in nature, thus leading to significant air mass modification due to reduction in ventilation from adjacent air masses, and reinforcing the heat dome and surface warming103.\n\nThe NAM index follows the concept of the global monsoon104,105, defined by area-averaging precipitation over western North America with the constraints that the June-July-August-September minus December-January-February-March precipitation range is greater than 2\u2009mm/day and the local summer precipitation exceeds 55% of the total annual precipitation.\n\nRossby wave source106 is defined by Eq.\u00a06, where the anomalous RWS is defined by the anomalous vorticity advection by the mean divergent wind, mean vorticity advection by the anomalous divergent winds, anomalous vortex stretching, and mean vortex stretching, respectively. Here, u is the divergent wind (i.e., irrotational component of the flow), \\(\\zeta\\) is the relative vorticity, and f is the Coriolis parameter. Overbar denotes the mean climatology, and primes represent the deviation from climatology.\n\nTo isolate the effect of significant tropical SST warming, several atmospheric general circulation model (AGCM) experiments are performed. We prescribe SST anomalies to the Community Atmosphere Model version 6 (CAM6) coupled to the Community Land Model version 5 (CLM5), which are part of the Community Earth System Model version 2 (CESM2). First, the control case is integrated by prescribing the SST climatology globally based on the 1979-2022 observing period, which is referred to here as the CTL case and is run for 300 years. Then, a model experiment is conducted where SST is prescribed based on the observed 2023 SST only over the Atlantic and Pacific and is held to climatology elsewhere, referred here onward as the GBL23 case (see Supplementary Fig.\u00a08 for prescribed SST anomalies). For the GBL23 case, the last 100 years of the CTL are used as initial conditions to create 100 GBL23 ensembles. That is, the experiments are integrated for one year starting on every 1 January from the CTL (e.g., branch simulation), thus creating ensembles with initial conditions that are one year apart from each other. This approach guarantees more than sufficient separation among ensembles as it pertains to weather noise (i.e., the atmospheric initial state is completely different and independent among ensembles) while each ensemble is being forced by the same underlying SST anomalies.\n\nTo isolate the relative influences of the Atlantic and Pacific SST anomalies on atmospheric circulation, two additional model experiments are performed where the Atlantic-only and Pacific-only 2023 SST anomalies are prescribed, these experiments are referred to as ATL23 and PAC23, respectively. All experiments were integrated under a year-2000 atmosphere composition (i.e., CESM2 component set F2000). The experiments are evaluated with respect to their differences relative to the CTL runs, thus, any contrast between the two experiments is attributed to SST anomalies in 2023, whereas the ensemble spread of each model run is used as uncertainty estimation (i.e., weather noise) through a bootstrapping technique. It is worth mentioning that besides the modulating effects of circulation, heat waves are also influenced by complex land-atmosphere interactions/feedbacks that may not be well resolved in CESM2. In addition, significant topographic features are present in the study area, which have been shown to play an important role in surface temperature variations, but may not be well-resolved at these horizontal resolutions. Moreover, complex air-sea interactions are also missing from these AGCM simulations as the atmospheric model is forced by SSTs with no ocean thermodynamic nor dynamical feedback.\n\nThe synergy between the Atlantic and Pacific SST sensitivity is extracted from the AGCM experiments following Eq.\u00a07. This is possible because the GBL23 experiment comprises the total linear plus non-linear SST sensitivities, whereas the targeted basin experiments (i.e., ATL23 and PAC23) isolate interbasin interactions. See Supplementary Information for more details on the definition of interbasin synergy and derivation of Eq.\u00a07.\n\nA bootstrapping method is used to determine confidence intervals by subsampling the dataset. All analyses presented are obtained by randomly selecting r samples out of n observations with replacement (Eq.\u00a08). This is done 500 times in order to build a significant distribution of composites and assign 95th percentile confidence levels.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Hadley Centre HadSSTv2 product for the period of 1900\u20132023 was obtained from https://www.metoffice.gov.uk/hadobs/hadisst/ (Rayner et al.98). The Japanese 55-year Reanalysis was obtained from https://rda.ucar.edu/datasets/ds628.0/ (JRA55, Kobayashi et al. 97) for the period of 1955 \u2013 2023. 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Hosmay Lopez and Sang-Ki Lee acknowledge support from base funds from AOML\u2019s Physical Oceanography Division.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Atlantic Oceanographic and Meteorological Laboratory, NOAA, Miami, FL, USA\n\nHosmay Lopez\u00a0&\u00a0Sang-Ki Lee\n\nCooperative Institute for Marine and Atmospheric Studies, University of Miami, Miami, FL, USA\n\nRobert West\u00a0&\u00a0Dongmin Kim\n\nGeophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, NJ, USA\n\nLiwei Jia\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.L. conceived the study and wrote the initial draft of the paper. H.L., S.K.L., R.W., D.K. and L.J. contributed to the design, the statistical analysis, and interpretation of the results as well as the writing of the final version of the paper.\n\nCorrespondence to\n Hosmay Lopez.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous, reviewer(s) for their contribution to the peer review of this work. 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1,3,4-thiadiazoles from acyl hydrazines and nitroalkanes using elemental sulfur", + "pre_title": "Chemoselective Ligation between Acyl Hydrazines and Nitroalkanes as 1,3,4-Thiadiazoles using Elemental Sulfur", + "journal": "Nature Communications", + "published": "03 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61359-z/MediaObjects/41467_2025_61359_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61359-z/MediaObjects/41467_2025_61359_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Synthetic chemistry methodology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5608572/v1.pdf?c=1751627291000", + "research_square_link": "https://www.researchsquare.com//article/rs-5608572/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61359-z.pdf", + "preprint_posted": "16 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Substituted 1,3,4-thiadiazoles find extensive use in pharmaceutical, agricultural, and materials chemistry. The incorporation of adaptable heterocyclic pharmacophores results in tunable hybrid molecules with diverse medicinal properties. In this study, the direct coupling of primary nitroalkanes and acyl hydrazines (hydrazides) was achieved simply by the mild action of S8 and Na2S. This method now delivers wide varieties of multi-functionalized 1,3,4-thiadiazoles in excellent yields. The reaction is scalable, shows a broad substrate scope, and tolerates a wide range of functional groups. The power of this method is exemplified with over twenty acyl hydrazines, spanning a diverse range of nitroalkane substrate classes, as well as the concise and scalable synthesis of 1,3,4-thiadiazole derivatives of over ten distinct types of drugs and peptides.Physical sciences/Chemistry/Organic chemistry/Synthetic chemistry methodologyPhysical sciences/Chemistry/Organic chemistry/Combinatorial libraries", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SIV10wxn.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Substituted 1,3,4-thiadiazoles find extensive use in pharmaceutical, agricultural, and materials chemistry. The incorporation of adaptable heterocyclic pharmacophores results in tunable hybrid molecules with diverse medicinal properties. In this study, the direct coupling of primary nitroalkanes and acyl hydrazines (hydrazides) is achieved simply by the mild action of S8 and Na2S. This method now delivers wide varieties of multi-functionalized 1,3,4-thiadiazoles in excellent yields. The reaction is scalable, shows a broad substrate scope, and tolerates a wide range of functional groups. The power of this method is exemplified with over twenty acyl hydrazines, spanning a diverse range of nitroalkane substrate classes, as well as the concise and scalable synthesis of 1,3,4-thiadiazole derivatives of over ten distinct types of drugs and peptides.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The 1,3,4-thiadiazole structural unit is often used as a heteroaromatic bioisostere of an amide bond. While improving hydrolytic stability, this amide replacement tends to retain the hydrogen bonding network within the acceptor site and is widely selected as a key motif in drug design targeting antibacterial agents, anticancer agents, anti-convulsants and anti-inflammatory agents1,2,3,4. Representative drug molecules are shown in Fig.\u00a01a, including: cefazedone, a cephalosporin antibiotic used to treat bacterial infections5; sulfamethizole, a sulfonamide antibiotic used to treat a wide variety of susceptible bacteria6; Glybuzole, an antihyperglycemic antidiabetic7; MK-8189, a unique candidate in Phase II clinical development for the treatment of schizophrenia8; and BI-3231, a potential targeted drug to treat for NASH and liver disease9. Heterocyclic grafting with the structural alteration of amino acids as heterocycles, such as oxazoles and thiazoles, has been shown to improve the passive membrane permeability of macrocycles10,11,12,13. In addition to pharmaceutical applications, 1,3,4-thiadiazoles have shown great promise in agriculture, including herbicides, fungicides, insecticides, and plant growth regulators14, as exemplified by the herbicide, flufenacet, which can control annual grass weeds, sedges, and small broadleaf weeds15. In the field of optoelectronic materials the 1,3,4-thiadiazole structure has also been adopted to provide excellent electron-accepting, thermal, and chemical stabilities16,17,18.\n\na Application of 1,3,4-Thiadiazoles in pharmaceuticals and pesticides. b Traditional synthetic routes and limitations; c This work: mild and modular synthesis 1,3,4-thiadiazole directly from nitroalkanes and acyl hydrazines using elemental sulfur.\n\nThe synthesis of 1,3,4-thiadiazoles is traditionally based on indirect condensation methods via sulfuration of the corresponding 1,4-dicarbonyl19 or acyl precursor20 using phosphorus sulfide reagents such as P2S5 or Lawesson-types of reagents (Fig.\u00a01b). These methods require harsh electrophilic sulfurizing reagents with low functional group compatibility and selectivity, making them inappropriate for the incorporation of 1,3,4-thiadiazoles in drug molecules and chemical biology. To fully exploit the potential of 1,3,4-thiadiazoles in medicinal chemistry research, we realized that it would be essential to design a more direct ligation strategy to access a diverse array of 1,3,4-thiadiazole structures. This strategy should: (1) utilize readily accessible substrates; (2) operate under mild conditions; (3) tolerate a wide range of functional groups and give good overall yields; (4) avoid the use of expensive noble metals; (5) be scalable; and (6) be modular to facilitate the straightforward synthesis of disubstituted 1,3,4-thiadiazoles with varied structures. Herein, we describe an efficient method for synthesizing 1,3,4-thiadiazole structural units that meet all the above criteria (Fig.\u00a01c)21.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61359-z/MediaObjects/41467_2025_61359_Fig1_HTML.png" + ] + }, + { + "section_name": "Results and discussion", + "section_text": "Primary nitroalkanes are readily available for synthesis and used as carbonyl precursors to form carboxylic22,23, amides24,25,26,27, esters28. Elemental sulfur (S8) is abundant in nature, non-toxic, non-volatile, non-hygroscopic and odorless, making it an ideal sulfur source29,30,31,32,33. Stemming from our recent thioamidation study of primary nitroalkanes with elemental sulfur in the presence of Na2S, we soon realized that nitroalkanes can be made to behave as a masked thioacylating species, which would be able to condense with acyl hydrazines to afford 1,3,4-thiadiazole products34. At the outset, we selected commercially available benzoyl hydrazine 1a and nitroethane 2a as simple starting materials. In initial experiments, when S8 and Na2S were used, we were pleased to isolate the disubstituted thiadiazole 3a in 76% yield (Table\u00a01, entry 1). Here we found the chemical yield of 3a was affected dramatically by the work up procedure (see Supplementary Table\u00a01 for details). Considering the price, Na2S\u00b75H2O and Na2S\u00b79H2O were both investigated, which gave similar results to non-hydrated Na2S (cf. entries 1-3). Encouraged by these results, further optimization was carried out using Na2S\u00b79H2O to improve the chemical yield of 3a. A survey of different solvents soon demonstrated DMF and THF to deliver better yields of 3a (cf. entries 4-7 and entry 2) and the strong base NaOtBu proceeded comparably well to Na2S\u00b79H2O (cf. entry 8-9). Notably, the reported methods to make 1,3,4-thiadiazoles are principally conducted under anhydrous conditions in non-polar solvent systems4. This greatly precludes the synthesis of polar drugs and more complex peptides. We therefore tested the polar solvent system of DMF/H2O at various ratios for 1,3,4-thiadiazole formation (entry 10\u201311). The use of Na2S\u00b79H2O in a mixture of DMF/H2O (9:1) at room temperature proceeded excellently (entry 10), although yields decreased with higher water content, for example using DMF/H2O (5:1) (entry 11).\n\nWith high yielding conditions under various solvent systems in hand with S8/Na2S\u00b79H2O, the substrate scope of various acyl hydrazines were first investigated with nitroethane in DMF (Fig.\u00a02a). Acyl hydrazines carrying aliphatic, aromatic, alcohol, CF3, amine, furan, thiophene, pyridine, pyrimidine, aromatic amine, electron-deficient amine, free acid, and OH units were all tolerated well under the reaction conditions, providing the 1,3,4-thiadiazole products in high yields (3a\u20133r). Importantly, the \u03b1-amino product 3r was produced with complete stereochemical integrity. No epimerization of potentially labile \u03b1-stereocenters was observed (see Supplementary Fig.\u00a01 for HPLC analyses). Changing the primary nitroalkane to bear valuable functionalities such as hydroxyl group, ester, amide, ketone, acetal and triazole, similarly furnished the corresponding thiadiazoles 4a\u20134j from benzoyl hydrazine 2a in good yields (Fig.\u00a02b). Notably, Cbz amino protecting groups were tolerated well under our reaction conditions (4f). When using chiral nitroalkanes, reactions proceeded in moderate yield with minimal epimerization at \u221210 \u02daC with iPrO-S-S-OiPr (e.g., 4k, 96% ee). (See Supplementary Table\u00a02 and Supplementary Fig.\u00a02) As anticipated, this method was found to be extremely useful for the modular synthesis of different thiadiazole-containing bioactive compounds, for example, the marketed drug sulfamethizole (5a) can be readily prepared in good to excellent yield, as well as various highly bioactive thiadiazole-derivatives 5b-i (Fig.\u00a02c).\n\nReactions were carried out with 0.2\u2009mmol of 1, 0.4\u2009mmol of 2, 0.4\u2009mmol of S8 and 0.36\u2009mmol of Na2S\u00b79H2O in DMF (2\u2009mL) under nitrogen with stirring at rt for 24\u2009h. a Scope of acyl hydrazines; b Scope of nitroalkanes; c Analogue functionalization of bioactive compounds.\n\nThe introduction of heterocyclic grafting such as oxazole and thiazole units, into well-established medicinally active peptides or cyclic peptides, results in an increase in structural diversity of peptides and importantly improves the passive membrane permeability of macrocycles10,11,12,13,35. We thus explored the thiadiazole-grafting of peptides from their respective nitro- and hydrazide-bearing peptide fragments in DMF. Importantly, the peptide hydrazides (6) can be conveniently prepared through Fmoc-based solid phase peptide synthesis (SPPS; Supplementary Fig.\u00a03)36 and nitromethyl peptides (7) can be easily prepared via Boc-based peptide synthesis (Supplementary Fig.\u00a06). Coupling of fragments 6 and 7 in the presence of elemental sulfur furnished the 2,5-substituted 1,3,4-thiadiazoles 8a\u20138c in good to excellent yields. (Fig.\u00a03a, Supplementary Fig.\u00a07 for HPLC and MS analysis of 8c). Under our current conditions, peptides containing unprotected cysteine and histidine units were found to be incompatible. However, fluorophores and biotin were found to be compatible under mild conditions, for example, providing 8d and 8e. We further explored our coupling strategy in the construction of cyclic peptides, as inspired by the introduction of oxazole units, which can improve the activity and stability of peptides10,11,12,13,35. (Fig.\u00a03b) Here, the nitro-hydrazine cyclization precursor 9 was readily synthesized via solid peptide synthesis (Supplementary Fig.\u00a03 and Supplementary Fig.\u00a05), then treated with our standard conditions. To our delight, a 20-membered cyclic peptide 10 was obtained in 50% yield. (Fig.\u00a03c)\n\na Late-stage C-terminal functionalization of peptide hydrazines; b Peptide ligation between nitroalkane and hydrazine fragments; c Intramolecular cycloaromative formation of cyclic peptide.\n\nAs an important structural unit widely present in various drug molecules, traditional approaches to introduce the 1,3,4-thiadiazole unit occur at an early stage of a synthesis followed by subsequent functionalization. These approaches, however, tend to show poor selectivity and low efficiency for complex drug molecules and peptides9. A representative example is BI-3231, which is a potent and selective HSD17B13 inhibitor that is under in-vivo development by Boehringer-Ingelheim (Fig.\u00a04a)9. In the previous synthetic route from thio-semi-carbazide, 7 steps were required, including excess amounts of copper bromide and a palladium catalyst. Now, the direct hetero-annulation between the aroyl hydrazine 13 and nitroalkane 12 gave BI-3231 in excellent overall yield over 3 steps, whereby the nitroalkane 12 can be readily prepared via simple bromide substitution of 11 by nitrite (Supplementary Fig.\u00a09 for the preparing 11 from 5-methyluracil).\n\na Synthetic route of BI-3231; b Synthetic route of MIK-8189; c Synthetic route of 19.\n\nAnother example is MIK-8189, which is under Phase II development by Merck for the treatment of schizophrenia and Alzheimer\u2019s disease (Fig.\u00a04b)8. In the previous synthetic route from \u03b1-amino hydrozine, 6 steps were required. Here, the direct coupling of the acyl hydrazine 14 and nitroethane in the presence of elemental sulfur gave the thiadiazole 15 which, when treated with the cyclopropyl-methyl alcohol 16 by a known procedure, afforded MIK-8189 in excellent overall yield, (Supplementary Fig.\u00a010 for the preparing 14 from 4,6-dichloro-2-methylpyrimidine). To further demonstrate the advantages of this direct 1,3,4-thiadiazole coupling method, we compared it to the O-S exchange reaction with Lawson\u2019s reagent. At Idorsia Pharmaceuticals, ethyl 5-(2,4-difluorophenyl)-1,3,4-thiadiazole-carboxylate 19 is a key intermediate to making ACKR3 modulators37. For example, the first generation of 19 involved the acylation of commercially available 2,4-difluorobenzoic acid hydrazide 17 with ethyl chloro-oxoacetate, followed by cyclization of 18 with Lawesson\u2019s reagent (Fig.\u00a04c)38. This route suffered from handling the malodorous Lawesson\u2019s reagent and removing its lipophilic byproducts, and the final product had to be purified by column chromatography. In the second-generation route, designed for large-scale synthesis, excess amounts of CuBr were required to make 21 from 20 via a Sandmeyer reaction, and Pd-coupling with boric acid 22 produced the diary ester 19. On a 20\u2009g scale, we found the direct coupling of commercially available 2,4-difluorobenzoic acid hydrazide 17 and commercially available \u03b1-nitro acetate 23 with elemental sulfur and base furnished 19 in excellent yield without the need for further purification after a simple aqueous acid work up. Collectively, these examples emphasize the current thia-annulation method as a practical tool for the mild, late-stage installation of 1,3,4-thiadiazole moieties into highly functionalized molecules.\n\nControl experiments to confirm the likely reaction mechanism are summarized in Fig.\u00a05. First, when acyl hydrazine 1a was mixed with elemental sulfur and Na2S for 24\u2009hours, the acyl hydrazine 1a was completely recovered (Fig.\u00a05a, eq. (1)). Second, when ethyl nitroacetate was mixed with elemental sulfur and Na2S in d6-DMSO, the bis-thioacid sodium salt 24 was obtained quantitively by NMR (Fig.\u00a05a, eq. (2); see Supplementary Fig.\u00a012-13 for NMR and HRMS analysis). Third, when the bis-thioacid sodium salt 24 was mixed with acylhydrazine 1a in the presence of elemental sulfur and Na2S, no reaction was observed, which indicated 24 to not be the likely intermediate (Fig.\u00a05a, eq. (3)). In addition, when nitroethane was mixed with acylhydrazine 1a in the presence of elemental sulfur and Na2S, the uncyclized thioacylated hydrazide 25 mostly formed together with trace amounts of desired thiadiazole 3a, prior to silica gel chromatographic purification (Fig.\u00a05a, eq. (4)). After testing different work up procedures, the acyclic intermediate 25 was conveniently annulated to 3a in excellent yield through mild aqueous acid work-up in DMF (Fig.\u00a05a, eq. (5)). Alternatively, intermediate 25 can be converted into the 1,3,4-oxadiazole counterpart of 3a by treatment with MeI (see Supplementary Table\u00a03 for detail). Based on the above control reactions and our previous report34, a plausible reaction pathway is proposed in Fig.\u00a05b. Thus, by the mild action of elemental sulfur and Na2S, nitroalkane 2 first converts to its thioacyl nitrate derivative 26, which then reacts with the aroyl-hydrazine 1 to afford the thioacylated hydrazide 27 that readily heteroaromatizes to the 1,3,4-thiadiazole 3 during mild aqueous work-up. (Fig.\u00a05b).\n\na Control reactions to prove the reaction mechanism; b Plausible reaction mechanism.\n\nIn summary, we have developed a direct and modular synthesis of 1,3,4-thiadiazoles with utility in complex chemical environments for hetero-annulations. The method is operationally simple, exhibits excellent chemoselectivity with diverse functional groups, maintains stereochemical integrity even with epimerizable substrates and products, and eliminates the need for extensive protection/deprotection steps. With the ready availability of nitroalkanes and hydrazides with broad structural diversity and complexity, numerous applications and uses are anticipated across the chemical and biological sciences. For now, we have demonstrated its utility in making thiadiazole chemical libraries and medicinal analogs, in scaling the synthesis of marketed drugs and key thiadiazole intermediates, in the late-stage terminus capping and grafting of peptides, and in the intramolecular annulation of a cyclic peptide precursor.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61359-z/MediaObjects/41467_2025_61359_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61359-z/MediaObjects/41467_2025_61359_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61359-z/MediaObjects/41467_2025_61359_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61359-z/MediaObjects/41467_2025_61359_Fig5_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Nitroalkane (0.4\u2009mmol) and acyl hydrazine (0.2\u2009mmol) are added to S8 (0.4\u2009mmol) and Na2S\u00b79H2O (0.36\u2009mmol, 1.8 equiv.) in DMF (2\u2009mL) under nitrogen. The reaction mixture is stirred at room temperature and monitored by TLC until the acyl hydrazine is consumed, typically after 24\u2009h, then 2\u2009N HCl solution is added and the reaction stirred for a further 2\u2009h. The crude residue is then purified by silica-gel flash-column chromatography, if needed.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All experimental procedures, characterization data, and NMR spectra are available in the supplementary materials. All data are available from the corresponding author upon request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Han, X., Yu, Y. L., Hu, Y. S. & Liu, X. H. 1,3,4-Thiadiazole: a privileged scaffold for drug design and development. Curr. Top. Med. 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We also thank the University of Lincoln for its support.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "School of Chemistry, Xi\u2019an Jiaotong University, Xi\u2019an, 710049, PR China\n\nXiaonan Wang,\u00a0Xiuwen Yu,\u00a0Ruixi Qian,\u00a0Silong Xu\u00a0&\u00a0Jing Li\n\nDepartment of Chemistry, School of Natural Sciences, University of Lincoln, Brayford Pool, Lincoln, LN6 7TS, UK\n\nMartin J. Lear\n\nThe First Affiliated Hospital of Xi\u2019an Jiao Tong University, Xi\u2019an, 710061, China\n\nWangxiao He\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.L. conceived the idea and supervised the whole project. X.N.W., X.W.Y., and R.X.Q. performed all the synthetic experiments and analyzed results. X.N.W., S.L.X., W.X.H., M.J.L., and J.L. co-wrote the manuscript. All authors approved the final version of the manuscript for submission.\n\nCorrespondence to\n Jing Li.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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Chemoselective synthesis of 1,3,4-thiadiazoles from acyl hydrazines and nitroalkanes using elemental sulfur.\n Nat Commun 16, 6127 (2025). https://doi.org/10.1038/s41467-025-61359-z\n\nDownload citation\n\nReceived: 12 December 2024\n\nAccepted: 19 June 2025\n\nPublished: 03 July 2025\n\nVersion of record: 03 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61359-z\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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GPa", + "journal": "Nature Communications", + "published": "12 May 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59385-y/MediaObjects/41467_2025_59385_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59385-y/MediaObjects/41467_2025_59385_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.6084/m9.figshare.28477868" + ], + "code": [ + "/articles/s41467-025-59385-y#MOESM1", + "https://github.com/ScottNotFound/pymeccano" + ], + "subject": [ + "Core processes", + "Geochemistry", + "Inner planets" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4542043/v1.pdf?c=1747134368000", + "research_square_link": "https://www.researchsquare.com//article/rs-4542043/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-59385-y.pdf", + "preprint_posted": "30 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Despite making up 5\u201320 wt.% of Earth's predominantly iron core, the melting properties of elemental nickel at core conditions remain poorly understood, due largely to a dearth of experimental data. We present an in situ X-ray diffraction study performed on laser shock-compressed samples of bulk nickel, reaching pressures up to ~500 GPa. Hugoniot states of nickel were targeted using a flat-top laser drive, with in situ X-ray diffraction data collected using the Linac Coherent Light Source. Rietveld methods were used to determine the densities of the shocked states from the measured diffraction data, while peak pressures were determined using a combination of measured particle velocities, shock transit times, hydrodynamic simulations, and laser intensity calibrations. We observed solid compressed face-centered cubic (fcc) Ni up to at least 332(30) GPa along the Hugoniot---significantly higher than expected from the majority of melt lines that have been proposed for nickel. We also bracket the partial melting onset to between 377(38) GPa and 486(35) GPa.Earth and environmental sciences/Planetary science/Core processesEarth and environmental sciences/Planetary science/Inner planetsShock compressionmeltnickelX-ray diffraction", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "nickelnatcommsi.pdfSupplementary Information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Despite making up 5-20 wt.% of Earth\u2019s predominantly iron core, the melting properties of elemental nickel at core conditions remain poorly understood, due largely to a dearth of experimental data. We present here an in situ X-ray diffraction study performed on laser shock-compressed samples of bulk nickel, reaching pressures up to \u00a0~\u00a0500\u2009GPa. Hugoniot states of nickel were targeted using a flat-top laser drive, with in situ X-ray diffraction data collected using the Linac Coherent Light Source. Rietveld methods were used to determine the densities of the shocked states from the measured diffraction data, while peak pressures were determined using a combination of measured particle velocities, shock transit times, hydrodynamic simulations, and laser intensity calibrations. We observed solid compressed face-centered cubic (fcc) Ni up to at least 332 \u00b1 30\u2009GPa along the Hugoniot\u2014significantly higher than expected from the majority of melt lines that have been proposed for nickel. We also bracket the partial melting onset to between 377 \u00b1 38\u2009GPa and 486 \u00b1 35\u2009GPa.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Nickel is an abundant impurity element in the Earth\u2019s iron-rich core and likely also plays a significant role in other planetary interiors containing metallic cores1,2. In the case of the Earth, estimates from cosmochemistry suggest nickel could compose between 5 and 20 w.t.% of the core3,4,5. It has long been recognized that a solid inner core is currently crystallizing out of the core liquid6. The density contrast between solid and liquid and the depth of the inner core boundary are well constrained by seismology, but the composition and melting temperature of the core material at the extreme conditions of the inner core (330\u2009GPa to 360\u2009GPa) remain unknown. The melting temperature is particularly unconstrained and could change by up to 1000\u2009K depending on the impurity elements alloyed with iron, and with the experimental platform used to infer melting.\n\nRecent interpretations of seismic data have revealed a previously unknown complexity in the structure of the inner core7,8. This signature may be crystallographic in origin, and thus explained by an \u201cinnermost core,\u201d or it could be indicative of trapped liquid along grain boundaries of the inner core solid9,10. While many studies have investigated iron and iron alloys under planetary core conditions11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29, direct observations of the structure and phase of relevant impurity end-members, such as nickel, are lacking. The stability of high-pressure phases, and in particular the melting transition and co-existing solid structure, are essential inputs for thermodynamic mixing models of core-relevant compositions30. This is especially true for nickel, which mixes on the liquidus with iron in the face-centered cubic (fcc) phase at pressures below 100\u2009GPa31,32. At the higher pressures relevant to the core, the liquidus phase of nickel has not yet been observed experimentally. There are some predictions that a body-centered cubic (bcc) polymorph of nickel may be stabilized at high pressures and temperatures, which could change the mixing properties between iron and nickel33,34,35, while recent ab initio calculations suggest that alloying of nickel affects the crystallization sequence of iron solid phases at core pressures35.\n\nAlthough extensive shock studies were performed on nickel starting in the 1950s, no information regarding the onset of melt can be extracted from those measurements36,37,38,39,40,41,42,43,44,45. Given this lack of melting data on nickel, many geophysical models46,47 have instead relied on direct measurements of iron at core conditions, and on extrapolations from lower-pressure measurements on nickel and iron-nickel alloys11,12,13,14,15,16,17,18,29. Despite the existence of a large body of theoretical work performed on nickel under extreme pressures and temperatures, to date there have been very few experimental studies into the crystal structure of nickel at core conditions48. This lack of a direct experimental examination of the crystal structure of nickel along the principal Hugoniot at core-relevant pressures means that the melt line of nickel remains weakly constrained above 100\u2009GPa49,50. A measurement of the incipient melting and melt-completion pressures\u2014along with the co-existing solid crystal structure\u2014would provide the first experimental inputs for mixing models of iron and nickel phase relations.\n\nRecent advances using in situ X-ray diffraction (XRD) methods for the study of matter under dynamically-loaded stresses provide unprecedented experimental access to atomic structure and bonding under extreme conditions51,52,53,54,55,56,57,58, and have been used to redefine the melting curves of elements such as molybdenum, tantalum, and iron by measuring the pressure at which solid and liquid co-exist on shock compression59,60,61. In these cases, the slope of the melting curve has been found to be much steeper than previously determined in the \u00a0~100\u2009GPa to \u00a0~200\u2009GPa range accessible to static compression, demonstrating the importance of collecting data in the multi-Mbar range.\n\nHere, we present an in situ XRD study on shocked bulk nickel, allowing us to measure the onset of melt. We find that solid single-phase fcc-Ni persists to higher pressures along the principal Hugoniot than would be expected from the majority of proposed melt lines, and arrive at a minimum incipient melting pressure of 332\u2009\u00b1\u200930\u2009GPa.", + "section_image": [] + }, + { + "section_name": "Results and discussion", + "section_text": "Visual inspection of the VISAR images reveals that the breakout was planar over the region probed by the X-rays in all of the runs reported here. A summary of the VISAR data is shown in Table\u00a01. We were able to determine breakout times for all runs, but particle velocities and associated pressures could only be determined for runs 097, 099, and 493. At laser intensities of 1.56\u2009\u00d7\u20091013\u2009W\u2009cm\u22122 and higher, we observed a blanking of our VISAR data that was likely due to the LiF window becoming opaque at elevated pressures\u2014a phenomenon that is known to occur at pressures above \u00a0~200\u2009GPa in LiF (corresponding to pressures above \u00a0~400\u2009GPa in Ni)62,63. For this reason, particle velocities at the Ni\u2013LiF interface could not be experimentally determined for runs 160, 151, 420, 422, and 149.\n\nTo determine the peak stress in the sample for runs 097 and 099, we impedance-matched the particle velocity at the Ni\u2013LiF interface, yielding stresses of 183\u2009\u00b1\u200923\u2009GPa and 240\u2009\u00b1\u200918\u2009GPa for runs 097 and 099, respectively64. These data are displayed in Table\u00a01. Other data from the VISAR measurements, including the time the shockwave reached the Ni\u2013LiF interface (breakout time), as well as the time the X-ray probe was triggered (probe time), are presented in Table\u00a01. From these data, we are confident that our X-ray diffraction data were obtained under compression.\n\nTo determine pressure for runs 160, 151, 420, and 422, we used the densities determined from fitting the X-ray diffraction data to calculate pressures based on a fit to the literature data36,37,38,41,42,43,44,65. To determine a pressure for Run 149, where solid fcc-Ni was not observed, the pressures determined from diffraction were used to construct a calibration curve fitted against laser intensities. To calculate laser intensity values, we used measurements of the actual laser spot profile, which gave a more accurate measure of the laser spot diameter and intensity profile than the nominal values. These measurements showed that 50% of the laser energy is contained within 139.7\u2009\u03bcm for the 150\u2009\u03bcm phase plates, and within 240.8\u2009\u03bcm for the 300\u2009\u03bcm phase plates. When calculating laser intensity, this 50% factor was included, and the measured diameters were used to calculate the intensities. Pressures derived from both methods are presented in Table\u00a02. Comparing Tables\u00a01 and 2, we see excellent agreement between the pressures derived from VISAR measurements and the pressures derived from XRD-measured densities. The\u00a0supplementary material contains a comparison table showcasing good agreeement between pressures determined from VISAR, pressures derived from XRD densities, and pressures calculated from the laser intensity calibration curve.\n\nIn the following sections, stresses are reported with experimental errors for those shots where VISAR analysis was possible (runs 097, 099, and 493); for all other shots we report pressure values determined either directly from densities matched to a fit to literature data (runs 160, 151, 420, and 422), or using our laser intensity calibration curve (run 149). For the determination of the pressures derived from the X-ray diffraction measured density, we constructed an exponential fit to literature pressure\u2013density data. The error is a combination of (1) the Rietveld refinement model of the XRD, with the error calculated out to 3\u03c3, and (2) a 99% confidence interval of the exponential fit to the literature data. For the determination of pressures from the laser intensity calibration curve, the error arises from a 99% confidence interval to Equation (3).\n\nFigure\u00a01 shows the integrated diffraction intensities plotted in q-space for the selected runs. The main panel features seven runs from the primary experiment being reported here (LV13), arranged with the incident laser energy upon the target increasing from bottom to top. A separate panel is used to plot a single run from a second experiment (L10075), which is discussed below. The reflections from uncompressed fcc-Ni are clearly observed in all runs, consistent with the X-rays having arrived prior to shock breakout at the Ni\u2013LiF interface. Reflections that can be assigned to compressed fcc-Ni are highlighted with red triangles, and their positions were fit simultaneously with whole-pattern Rietveld methods using the GSAS-II package66 to determine the density of the compressed phase. Density values and results from the GSAS-II package66 fits are shown in Table\u00a02. The d-spacing of the individual peaks of the compressed phase was also determined with single-peak fitting and compared with the d-spacing expected for fcc-Ni from the literature (Supplementary Materials)36,37,38,41,42,43,44,65. The close agreement with the expected spacing confirms a lack of significant distortion away from cubic symmetry.\n\nFitted polynomial backgrounds have been subtracted for clarity (see\u00a0Supplementary Materials for uncorrected integrations, Figs.\u00a0S3\u2013S9). Ambient fcc-Ni reflections are highlighted with green bars. Locations of Bragg reflections from the compressed fcc-Ni, where present, are highlighted with red triangles to provide a guide to the eye. Diffraction patterns are ordered in ascending pressure from bottom to top (see Table\u00a02). Shaded orange areas show a single Gaussian peak, which was fitted along with a polynomial background (subtracted here), and are taken to indicate a diffuse signal arising from the presence of melt. Pressures in blue represent pressures calculated from VISAR data, whereas pressures in black indicate pressures calculated from densities modeled by Rietveld refinements of the XRD data or pressures from the laser intensity calibration curve.\n\nFor the compressed (111) reflection, we see a clear shift to higher q with increasing shock energy. In run 097, it appears at slightly lower q than the ambient (200) reflection; in runs 099 and 160, it overlaps with the ambient (200) reflection; in runs 151, 420, and 422, it separates from the ambient (200) reflection and continues to move to higher q. We also observe the emergence of a broad diffuse signal centered on the compressed (111) peak in runs 151, 420, 422, and 149 (vida infra).\n\nIn all runs, the texture of the diffraction (azimuthal intensity around Debye-Scherrer cones, see\u00a0Supplementary Materials) from the ambient samples is similar, showing distinct intensity variations along the azimuth that are typical of rolled foils. However, during shock, the Bragg intensity of the compressed (200) reflection becomes much more textured, and in all cases, the majority of the intensity arises from a single localized Bragg spot. This is clearly seen in the dewarped images plotted in the\u00a0Supplementary Materials. This change in texture is evidence of a significant reorganization of crystalline domains, consistent with a large uniaxial compression of the sample59.\n\nWe examined the measured XRD images for signs of diffuse scattering that may provide evidence of liquid, as has been well-documented in studies on other materials14,67,68,69,70,71,72,73,74,75,76. During the ambient pressure melting of nickel, a diffuse signal arising from ambient liquid nickel appears at a q-spacing of ~3.1\u2009\u00c5\u22121, closely matching the position of the (111) Bragg peak77,78. We can thus expect the q-spacing of nickel melt signal under high pressures to be close to the (111) Bragg peak position at that density. The XRD runs can be separated into those which show no obvious diffuse scattering above the background (097 and 099), and those in which a distinct broad peak has appeared alongside the compressed fcc-Ni peaks, and whose position can be refined during Rietveld modeling using a Gaussian peak on top of the polynomial background (151, 420, 422, and 149). We extracted the fitted position of each of these peaks and found that they fall within the range 3.5\u2009\u00c5\u22121 to 3.7\u2009\u00c5\u22121, which is consistent with diffuse signal from a dense nickel liquid (i.e., melt). (For a discussion of the minimum melt detectability, see the\u00a0Supplementary Materials.)\n\nWe compared our shock compression data to literature data on nickel in order to assess the consistency between our results and the field as a whole36,37,38,41,42,43,44,65. Figure\u00a02 shows a pressure\u2013density plot with our measured density values placed on a Hugoniot calculated from the literature data36,37,38,41,42,43,44,65. For the LV13 dataset, the data points are color-coded to distinguish whether or not the pressure comes from the Rietveld refined density (green), from VISAR measurements (purple), or from the laser intensity calibration curve (red). Similarly, data from the L10075 dataset are color-coded to identify whether the pressure came from the Rietveld refined density (open purple) or from VISAR measurements (teal). We also plot literature data as well as recent computational Hugoniot data, and find our data to be consistent.\n\nThree methods to obtain the peak pressure for each run, detailed in the\u00a0Supplementary Materials, are represented for the LV13 campaign. Pressure values calculated from the measured density values obtained from the X-ray diffraction images are plotted as green points. Pressure values determined from the calibration equation described by Equation (3) are plotted as red points. Pressure values for runs 097 and 099 were found by VISAR impedance matching and are plotted as purple points (note that VISAR data for the other runs is not available due to the high opacity of the LiF window at high pressures). For the L10075 campaign, pressure from the measured density values is represented as open green points, whereas pressure from VISAR measurements is shown as open purple points. Circles represent runs where fcc-Ni was observed, whereas squares represent runs where fcc-Ni and liquid were observed. Data from the shock compression literature is shown as black crosses36,37,38,41,42,43,44,65. An exponential fit to the literature data is represented as a black line, with 95% confidence intervals shown as gray dashed lines. This exponential fit included data up to 1000\u2009GPa, which is not pictured above. Three Hugoniot models are also shown. In orange is the Ni SESAME 83103 multi-phase Hugoniot, in blue is the Ni SESAME 3100 single-plase Hugoniot, and in pink is the Ni Hugoniot by Prisbrey92,93,94.\n\nThe melt curve of nickel as a function of pressure and temperature has been experimentally studied in static compression experiments up to pressures of \u00a0~100\u2009GPa, while ab initio techniques have been used to calculate the melt curve up to \u00a0~330\u2009GPa. Perhaps somewhat surprisingly, the slope of the melt curve at higher pressures varies quite dramatically between studies (see Fig.\u00a03)33,50,67,68,70,79,80,81,82,83,84,85,86,87,88,89,90,91.\n\nThe inset shows theoretical melt lines proposed for low pressures, all of which terminate at 100\u2009GPa. All melt lines are plotted as dashed lines. Identifying codes are a combination of first-author surname initial and year of publication, and are tabulated in full in the\u00a0Supplementary Materials. Data points shown in the inset are taken from the static compression melting experiments reported in ref. 70 and are color coded to indicate whether the authors observed solid Ni, liquid Ni, or a mix of the two (see legend). The main plot shows the reported melt lines that extend beyond 100\u2009GPa. In the main plot, the single-phase SESAME 3100 Ni Hugoniot, the multi-phase SESAME 83103 Ni Hugoniot, and several other Hugoniot models from the literature are represented as solid lines with different shades of gray to distinguish them from each other33,34,92,93,94,95. In the inset, only the SESAME 83103 Ni Hugoniot is represented for clarity. Yellow star represents run 493, the highest pressure run we obtained with usable VISAR data and XRD data. For this run, note that pressure was measured with VISAR, whereas temperature was not measured. This point is the result of placing the pressure value onto the principal single-phase Hugoniot.\n\nA recent study used X-ray absorption spectroscopy (XAS) to examine statically compressed and laser-heated samples of Ni, yielding a wide range of both solid and liquid data against which to compare the various melt studies70,79. Although these data tend to support the more recent predictions of a steeper melt line, the XAS data were measured only up to \u00a0~100\u2009GPa, limiting the ability to constrain the melt line under much higher pressures using these data. Figure\u00a03 shows a comparison of all Ni melting studies along with several Hugoniot models, including multi-phase and single-phase computational studies33,34,92,93,94,95. At pressures below 100\u2009GPa, the Hugoniot lies well below the seven computational melt lines of varying slope that have been proposed in the literature. At pressures between 100\u2009GPa and 300\u2009GPa, the variation in the slope of proposed melt lines is large (Fig.\u00a03, main panel), leading to intercepts with the Hugoniot ranging from as low as \u00a0~170\u2009GPa to as high as \u00a0~290\u2009GPa.\n\nThe peak pressures reached in each of the seven runs reported here from experiment LV13 range from (183\u2009\u00b1\u200923)\u2009GPa to (527\u2009\u00b1\u200987)\u2009GPa, which covers the broad range of intercepts expected across all of the reported melt lines in the literature. For the three lowest pressure runs (183\u2009\u00b1\u200923\u2009GPa, 240\u2009\u00b1\u200918\u2009GPa, 377\u2009\u00b1\u200938\u2009GPa) we see no evidence of melt, suggesting that the onset of melt occurs at higher pressures along the Hugoniot. In the next three higher-pressure runs (486\u2009\u00b1\u200935\u2009GPa, 515\u2009\u00b1\u200967\u2009GPa, and 531\u2009\u00b1\u200952\u2009GPa), evidence of melted nickel is present, suggesting that the onset of melt occurs below 486\u2009\u00b1\u200935\u2009GPa. Thus, we are able to bracket the onset of melt between 377\u2009\u00b1\u200938\u2009GPa and 486\u2009\u00b1\u200952\u2009GPa. This range is significantly higher than the vast majority of literature melt lines. The appearance of full melt in the highest pressure run 149 (527\u2009\u00b1\u200987\u2009GPa) suggests an upper pressure for melt completion at \u00a0~500\u2009GPa.\n\nThe observation of fcc-Ni up to 531\u2009\u00b1\u200952\u2009GPa implies a coexistence of melt over a pressure range of at least \u00a0~45\u2009GPa, and perhaps even greater. This may be partly due to small differences in how much of the sample was compressed at the time of the X-ray probe, and thus could be influenced by the timescale of melting. We also note that our diagnostic has a minimum melt detectability that may influence our first observation of melt and the resulting inference of the coexistence region. Both of these points are discussed further in the\u00a0Supporting Information.\n\nIn the L10075 experiment, we were able to collect VISAR data up to 332\u2009\u00b1\u200930\u2009GPa, where we observed the solid compressed fcc phase of Ni at a density of 13.97\u2009\u00b1\u20090.07\u2009g\u2009cm\u22123. From the LV13 dataset, we can bracket the onset of melting to be in the region of 377\u2009\u00b1\u200938\u2009GPa to 486\u2009\u00b1\u200935\u2009GPa. If our higher pressure runs were also on-Hugoniot, then fcc-Ni may persist up to pressures as high as 531\u2009\u00b1\u200952\u2009GPa.\n\nWe have reported in situ X-ray diffraction data measured up to pressures of \u00a0~500\u2009GPa in shocked bulk nickel, which is the highest pressure reported to date for any structural study on this element. We observe the persistence of solid fcc-Ni on the principal Hugoniot up to at least 332\u2009\u00b1\u200930\u2009GPa. Given that this measured incipient melting pressure is significantly higher than would be expected based on the majority of theoretical work, our results provide experimental support for a steeper melt line for nickel than for iron. The appearance of diffuse scattering at q-values consistent with dense liquid nickel indicates partial melting onset between 377\u2009\u00b1\u200938\u2009GPa and 486\u2009\u00b1\u200935\u2009GPa.\n\nOur results place the nickel melt line above that of iron, which was also measured by X-ray diffraction at the National Ignition Facility and Dynamic Compression Sector11,61. Of particular note is the relative comparison between iron and nickel using a laser-shock compression platform and X-ray diffraction as the diagnostic of melting. Although temperature is not measured directly in these shock compression experiments, we can infer from the similarity between Fe and Ni thermoelasticity and Hugoniot relations that the melting of nickel occurs at significantly higher temperatures than iron at the conditions of the inner core boundary. In other words, the large difference in the onset of melting pressure can only be explained by a different shape of the melting curve, rather than by a higher temperature of Ni at the same Hugoniot state. Our melting inference for nickel is in contrast to previous work, in which static compression EXAFS data were used to place the nickel melt below that of iron49. On the other hand, the higher melting temperature of nickel is in agreement with recent computational work that found the nickel melt line to fall above the iron melt line at inner core conditions by ~700\u2009K\u2013800\u2009K35.\n\nA steeper nickel melt line has implications for the chemistry and dynamics of Earth\u2019s inner core. Nickel has generally been assumed to mix nearly ideally with iron at inner core conditions based on much lower pressure measurements31. In a qualitative sense, the significantly higher melting temperature and fcc crystal structure at the inner core pressures imply that non-ideal contributions to the free energy of mixing could be present in the Fe\u2013Ni system. This implies that the liquidus field of iron alloys that include nickel is broad and may promote an extended region of solid and liquid mixing at the inner core boundary96. Further investigation of nickel-bearing iron alloys may help inform dynamic models of inner core boundary \u201csediments\u201d and the inclusion of extensive melt along grain boundaries within the inner core9,97. More generally, our results show that further research into the interaction between nickel and iron at core pressures is needed to determine the effect of alloying on the melting temperature.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59385-y/MediaObjects/41467_2025_59385_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59385-y/MediaObjects/41467_2025_59385_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59385-y/MediaObjects/41467_2025_59385_Fig3_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Shock compression experiments were performed at the Matter in Extreme Conditions (MEC) endstation of the Linac Coherent Light Source (LCLS) at the SLAC National Accelerator Laboratory98. The data presented in this study were collected at MEC in the standard configuration during two separate campaigns (LV13 and L10075) with slightly different specifications. Experiments for the LV13 dataset were performed in the standard configuration, which is illustrated in Fig.\u00a04. An ablatively-driven shockwave was launched into target packages using a 10\u2009ns or 15\u2009ns near flat-top pulse from four arms of a \u00a0~60\u2009J laser (\u03bb\u2009=\u2009527\u2009nm). The flat-top pulse shape shown in \u00a0Supplementary Materials was selected in order to deliver a temporally steady shock to the material and yield a state upon the principal Hugoniot of nickel. The lasers had a nominal spot diameter of 150\u2009\u03bcm, with 50% of the energy delivered over 140\u2009\u03bcm. In some of the runs, 300\u2009\u03bcm phase plates were used, with 50% of the energy delivered over 241\u2009\u03bcm. The angles of incidence of the lasers were \u00b120\u00b0 to the sample normal. The X-rays were directed normal to the sample, as shown in Fig.\u00a04. Diffraction data was collected on four ePix 10k QUADS detectors (see IV B for details.)\n\na Schematic representation of the experimental setup used to collect the VISAR and in situ X-ray diffraction data at the MEC hutch. The incident X-rays are normal to the sample plane, defined as 0\u00b0. The two drive lasers are focused onto the X-ray probe region, but each offset by \u00b120\u00b0 in the horizontal plane. VISAR was performed at the Al\u2013LiF interface. b The target package layer thicknesses for LV13. c Representative X-ray diffraction image from one of the four ePix 10k detectors is shown. Signals from the face-centered cubic (fcc) phase of Ni are highlighted. Reflections annotated in white indicate those of ambient nickel, while those annotated in teal indicate those of the compressed phase.\n\nThe in-sample stress was varied through the rotation of a half-waveplate combined with a polarizer\u2014both placed within the beam path\u2014to attenuate the drive laser energy reaching the target. In all experiments except runs 420 and 422, a \u00a0~80\u2009\u03bcm polyimide (Kapton-B) layer was used as the ablator; for runs 420 and 422, which had no window (free surface), we used a 100\u2009\u03bcm polyimide layer. The polyimide layers were coated with 200\u2009nm of aluminum on the laser drive side, and bonded to the sample with a thin layer of epoxy on the other side. The sample layer was 22\u2009\u00b1\u20093\u2009\u03bcm-thick cold-rolled light-tested nickel foil purchased from Goodfellow. It was characterized as a fcc (\\(Fm\\bar{3}m\\)) with a\u2009=\u20093.5238 \u00c5 (\u03c1\u2009=\u20098.911\u2009g\u2009cm\u22123). Windows consisted of \u00a0~100\u2009\u03bcm-thick single crystals of (100)-LiF (lithium fluoride) that had been coated with 200 nm of Al. The Al-coated side of the windows was bonded to the Ni foil with a thin layer of epoxy. The Al coating provided a reflective layer for velocity interferometry system for any reflector (VISAR) measurements99.\n\nData from experiment L10075 was collected at MEC at a later date, but with a similar experimental setup. The most salient difference between L10075 and LV13 was the sample package design, and the differences are noted here. In L10075, the 50\u2009\u03bcm polyimide ablator was coated with a 0.2\u2009\u03bcm layer of platinum. The nickel foil was 12.5\u2009\u03bcm, and the window was 100\u2009\u03bcm of lithium fluoride with 0.3\u2009\u03bcm of titanium as the reflective layer for VISAR measurements. In L10075, the optical drive laser had been upgraded to deliver a maximum of \u00a0~100\u2009J of laser energy on target.\n\nParticle velocities were measured at the Ni\u2013LiF interface using VISAR, wherein an Nd:YAG 532\u2009nm laser light source was focused onto the Ni\u2013LiF interface. We used line VISAR to collect spatial information along one dimension of the sample with a total field of view of 360\u2009\u03bcm. Care was taken to ensure that this field of view overlapped with the X-ray focal spot (40\u2009\u03bcm for LV13 and 20\u2009\u03bcm for L10075) so that the measured velocities would be pertinent to the measured diffraction. Two VISAR channels were employed, with different velocity per fringe sensitivities, to resolve any potential fringe shift (velocity) ambiguity related to the UP measurement of the near-instantaneous shock front99. For each reported run, the shock arrival time at the Ni\u2013LiF interface is constant, within the temporal and spatial resolution of the VISAR streak camera, over the region probed by the X-rays.\n\nThe line-VISAR images from both VISAR streak cameras (VISAR 1 and VISAR 2) were analyzed to yield one-dimensional profiles of Ni\u2013LiF interface velocity. These profiles are shown in Fig.\u00a05. The uniformity of the velocity states after shock breakout captured by VISAR at the Ni\u2013LiF interface indicated that the sample experienced a near-temporally steady shock. The timing of the sharp rise in the particle velocity gives a measure of the shock breakout time, while the mean of the peak velocities immediately following breakout gives a measure of the stress state achieved in the sample. The sample stresses were determined using impedance matching. Specifically, we used the linear fits for the dependence of US (shock velocity) on up (particle velocity) derived from the literature data on the equation of state of LiF100 and literature data on the equation of state of Ni (Equations (1) and (2))36,37,38,41,42,43,44,65,101.\n\nCorrections were applied to account for the refractive index of LiF under shock compression102.\n\nSolid traces represent data from VISAR 1, and dashed traces represent data from VISAR 2. Shaded regions reflect the standard deviation of the calculated particle velocity. Time is normalized to arrival at the Ni/LiF interface. Here, the transmitted compression waves are described by a fast initial rise, after which there is a distribution of velocity states up to a peak value. At late times, the velocity drops off due to a stress release associated with the end of the applied laser drive. Temporal unsteadiness in the peak compression state is attributed to non-ideal laser pulse shaping, reverberations within the epoxy glue layers of the sample, and, at late times, a reverberation within the Ni foil itself (this is visible 1.5\u2009ns after breakout in run 493). Note that expected transit times of the shock through the Ni foil for each run are presented in Table\u00a01, and range from 2.90\u2009ns for run 097 to 1.16\u2009ns for run 493. Determination of the average shock stress and distribution of stress states includes the range of velocity states above the initial shock. Error is shown as the shaded region surrounding each trace. Arrows indicate the region over which the range of velocities was considered, taking into account the transit times listed in Table\u00a01 for each run.\n\nThe HYADES package was used to simulate the progression of the shockwave through the sample, allowing for an estimate of the pressure within each layer as well as the speed at which the shockwave traveled through the target package103. The results of these simulations are shown in the\u00a0Supplementary Materials.\n\nIn our experiments, the sample is uniaxially compressed. While the use of the term \u201cpressure\u201d throughout the paper suggests a hydrostatically compressed state, we cannot rule out the presence of deviatoric stresses, which would\u2014in the case of our measurements and indeed all previous Hugoniot measurements\u2014give rise to higher values of longitudinal stress (as determined from our VISAR measurements) and therefore the reported pressure33,34,92,93,94,95. In the analysis of Fowles104 using the L\u00e9vy\u2013von Mises yield criterion105, this stress deviation corresponds to two-thirds of the yield strength. However, while the high-pressure strength of Ni is unknown, strength measurements on other metals106 suggest that the difference between the longitudinal stress and hydrostatic pressure in our experiments is on the order of a few\u2009GPa. This represents a systematic uncertainty in our reported pressure values.\n\nIn total, eight runs were taken over a range of shock pressures. Three of these runs (097, 099, and 493) used VISAR impedance matching analysis to determine the Ni sample pressures (183(23), 240(18), and 332(30)\u2009GPa). For other runs, where VISAR was not available due to poor target reflectivity, the sample pressure was constrained by a combination of hydrodynamic simulations, and laser intensity calibrations (see\u00a0Supplementary Materials).\n\nThe laser intensity calibration curve uses the pressure values determined from a pressure\u2013density relationship that we constructed using an equation of the form proposed by Drake and Lindl107,108,109. The fit is shown in the\u00a0Supplementary Materials and the equation is given below (Equation (3)).\n\nThis calibration curve is fitted to data with a maximum laser intensity of 2.22\u2009\u00d7\u20091013 W\u2009cm\u22122 (2.22\u2009\u00d7\u200910\u22122\u2009PW\u2009cm\u22122).\n\nThe uncertainty in stress is a contribution of the following: (i) the standard distribution of velocity states above the initial shock; (ii) the accuracy which fringe shifts can be measured110 in the line-VISAR systems, taken here as 0.177\u2009km\u2009s\u22121 for VISAR 1 and 0.039\u2009km\u2009s\u22121 for VISAR 2 for the LV13 experiment (5% of a fringe shift; for the L10075 experiment, these values are 0.102\u2009km\u2009s\u22121 for VISAR 1 and 0.242\u2009km\u2009s\u22121 for VISAR 2) (iii) uncertainty in the LiF and Ni Hugoniot models; and (iv) for line-VISAR additional velocity uncertainties from spatial non-planarities in the compression drive111 and random frequency structure on fringes which can shift the central position of a fringe (due to random intensity speckle structure emerging from the VISAR input fiber)112. Other contributors to stress uncertainty which are considered small relate to uncertainties in the refractive index of LiF102, uncertainties in the timing of the X-ray probe with respect to the VISAR, uncertainties in the measurements of sample thickness, and non-uniformities in target layer thicknesses resulting in compression wave arrival at different times across the VISAR field of view.\n\nXRD data were collected in transmission geometry, with the incoming X-ray free-electron laser X-ray beam incident at 0\u00b0 degrees to the sample normal. For the LV13 campaign, the self-amplified spontaneous emission\u2013mode X-rays had a peak flux energy of 12.6\u2009keV (\u03bb\u2009=\u20090.984\u2009\u00c5). The X-rays were quasimonochromatic (0.2% \u0394E/E) and contained 1012 photons. Diffraction images were collected using four ePix 10k QUAD detectors arranged to capture diffraction over q\u2009=\u20091.5\u2009\u00c5\u22121 to 7.5\u2009\u00c5\u22121 (q\u2009=\u20094\u03c0sin(\u03b8)/\u03bb, where \u03bb is the X-ray wavelength and \u03b8 is the Bragg scattering angle). The pixel size of the ePix 10k QUADS is 100\u2009\u03bcm by 100\u2009\u03bcm. All of the XRD data reported in this work were collected with 50\u2009fs duration X-ray pulses with an on-sample 40\u2009\u03bcm spot diameter.\n\nThe data for the L10075 experiment (run 493) was collected using X-rays with a peak flux energy of 10.1\u2009keV (\u03bb\u2009=\u20091.23 \u00c5), and with an on-sample spot diameter of 20\u2009\u03bcm. This experiment also used the ePix 10k QUAD detectors, but Q2 was positioned differently to capture high-angle data. Raw X-ray diffraction images are shown in the\u00a0Supplementary Materials. Processed diffractograms are shown in Fig.\u00a01. A detailed discussion on the calibration procedure and handling of the X-ray diffraction is present in the\u00a0Supporting Materials.\n\nIn all reported data, the X-rays probed the sample before shock breakout at the Ni\u2013LiF interface, ensuring that only peak pressure states and ambient (unshocked) sample were measured. XRD data were recorded on each sample prior to laser-shock experiments, and then in situ data were collected during laser-shock compression.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59385-y/MediaObjects/41467_2025_59385_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59385-y/MediaObjects/41467_2025_59385_Fig5_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "The raw X-ray diffraction and VISAR images used in this study are available in the Figshare database using the following link [https://doi.org/10.6084/m9.figshare.28477868].", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The data analysis code implementing the methods described in the\u00a0Supplementary Materials is available at https://github.com/ScottNotFound/pymeccano.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "McDonough, W. 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Lawrence Livermore National Laboratory is operated by Lawrence Livermore National Security, LLC, for the US Department of Energy, National Nuclear Security Administration under Contract No. DE-AC52-07NA2734 and was supported by the LLNL-LDRD Program under Project Nos. 21-ERD-032. Document number: LLNL-JRNL-866398. Use of the LCLS, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. The MEC instrument is supported by the U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences under Contract No. SF00515.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Chemistry, University of Massachusetts Amherst, Amherst, MA, USA\n\nKimberly A. Pereira\u00a0&\u00a0James P. S. Walsh\n\nLawrence Livermore National Laboratory, Livermore, CA, USA\n\nSamantha M. Clarke,\u00a0Saransh Singh,\u00a0Richard Briggs,\u00a0Christopher P. McGuire,\u00a0Cara Vennari,\u00a0Amy L. Coleman,\u00a0Carol Davis,\u00a0Trevor Hutchinson,\u00a0Jon H. Eggert\u00a0&\u00a0Raymond F. Smith\n\nLinac Coherent Light Source, SLAC National Accelerator Laboratory, Menlo Park, CA, USA\n\nHae Ja Lee,\u00a0Dimitri Khaghani,\u00a0Bob Nagler,\u00a0Eric Galtier\u00a0&\u00a0Eric Cunningham\n\nDepartment of Physics, Clarendon Laboratory, University of Oxford, Oxford, UK\n\nDavid McGonegle\n\nAtomic Weapons Establishment, Aldermaston, UK\n\nDavid McGonegle\n\nEarth and Planets Laboratory, Carnegie Institution for Science, Washington, DC, USA\n\nSally J. Tracy\n\nFirst Light Fusion, Oxford, UK\n\nMartin G. Gorman\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.M.C. and R.F.S. conceived the project. K.A.P., S.M.C., S.S., R.F.S., and J.P.S.W. designed the experiments. C.D. assembled the targets. K.A.P., S.M.C., S.S., R.B., C.P.M, D.M., C.V., M.G.G., A.L.C., and J.P.S.W. performed the experiments. H.J.L., D.K., B.N., E.G., and E.C. advised on the experimental setup and assisted in data collection at SLAC. S.J.T. contributed calibration data. T.H. and J.H.E. contributed code to the data analysis. K.A.P. analyzed the data with guidance from S.M.C., S.S., R.B., R.F.S., and J.P.S.W. M.G.G., D.M., C.P.M., and R.B. contributed to the interpretation of the data. K.A.P. and J.P.S.W. wrote the manuscript.\n\nCorrespondence to\n Samantha M. Clarke or James P. S. Walsh.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Prithachakaran Renganathan, Youjun Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Stability of the fcc phase in shocked nickel up to 332\u2009GPa.\n Nat Commun 16, 4385 (2025). https://doi.org/10.1038/s41467-025-59385-y\n\nDownload citation\n\nReceived: 27 December 2024\n\nAccepted: 22 April 2025\n\nPublished: 12 May 2025\n\nVersion of record: 12 May 2025\n\nDOI: https://doi.org/10.1038/s41467-025-59385-y\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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single-atom catalysts for efficient methanol steam reforming", + "pre_title": "Phase-interface-anchored cadmium single-atom catalysts for efficient methanol steam reforming", + "journal": "Nature Communications", + "published": "19 August 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63060-7/MediaObjects/41467_2025_63060_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63060-7/MediaObjects/41467_2025_63060_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.6084/m9.figshare.27905838", + "/articles/s41467-025-63060-7#ref-CR37" + ], + "code": [], + "subject": [ + "Catalytic mechanisms", + "Energy", + "Heterogeneous catalysis", + "Structural properties" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5524438/v1.pdf?c=1755687968000", + "research_square_link": "https://www.researchsquare.com//article/rs-5524438/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63060-7.pdf", + "preprint_posted": "05 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Employing interface engineering to design innovative single-atom catalysts (SACs) for effective methanol steam reforming (MSR) presents an attractive yet formidable challenge. Herein, we report phase-interface confined Cd/P25 SACs, where Cd atoms are stably anchored at the phase interface between anatase (101) and rutile (110) facets. The formed Cd-O-Ti phase interface sites exhibit asymmetric geometric and electronic properties that achieve 100% methanol conversion, minimal CO selectivity (<0.1%), and sustained stability exceeding 150 hours. The H2 production rate at these interface sites was approximately 15-fold and 8-fold higher than that of anatase and rutile surface sites, respectively. Enhancing the phase interface density through atmosphere pretreatment can further increase the H2 production rate by an additional 11%. Furthermore, these powder SACs can be 3D printed into kilogram-scale monolithic catalysts, advancing practical in-situ hydrogen generation applications. We anticipate that the strategy presented here will be instrumental in the design of novel SACs and in advancing the application of MSR.Physical sciences/Chemistry/Catalysis/Heterogeneous catalysisPhysical sciences/Energy science and technology/Renewable energy/Hydrogen energy/Hydrogen fuelPhysical sciences/Materials science/Nanoscale materials/Structural propertiesPhysical sciences/Engineering/Chemical engineering", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "NatureCatalysisSupplementaryInformation.pdfSupplementary Information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Employing interface engineering to design innovative single-atom catalysts (SACs) for effective methanol steam reforming (MSR) presents an attractive yet formidable challenge. Here, we report phase-interface confined Cd/P25 SACs, where Cd atoms are stably anchored at the phase interface between anatase (101) and rutile (110) facets. The Cd-O-Ti phase interface sites formed exhibit asymmetric geometric and electronic properties that enable 100% methanol conversion, a low CO concentration (~0.1\u2009mol%) in the effluent gas, and sustained stability exceeding 150\u2009h. The H2 production rate at these interface sites is approximately 15-fold and 8-fold higher than that of anatase and rutile surface sites, respectively. Enhancing the phase interface density through atmosphere pretreatment can further increase the H2 production rate by an additional 11%. Furthermore, these powder SACs can be 3D printed into kilogram-scale monolithic catalysts, advancing practical in-situ hydrogen generation applications.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Methanol steam reforming (MSR), a pivotal and cost-effective technology for on-site hydrogen generation, when integrated with polymer electrolyte membrane fuel cells (PEMFCs), has markedly advanced the trajectory of hydrogen energy systems towards decentralization, miniaturization, and portability, particularly in the transportation sector1. Conventional Cu-based and noble metal-based nanoparticle catalysts are often plagued by limited stability and high CO selectivity2,3,4,5, leading to shortened operational lifespans and Pt electrode poisoning in integrated PEMFCs. This challenge arises from the ambiguity surrounding the surface atomic active sites within nanoparticle catalysts, which hampers the precise tuning of microstructural and local electronic properties.\n\nSingle-atom catalysts (SACs), with their distinct ability to finely modulate atomic-scale structures and local electronic properties beyond the reach of nanoparticle catalysts, have been the focus of intensive research aimed at the targeted enhancement of catalytic performance across a spectrum of reactions6,7. The presence of abundant defects within the support is essential for the formation of stable SACs, with the local coordination environment of these defects playing a decisive role in shaping the properties and reactivity of the single atoms8,9. Employing interface engineering in support materials is an effective approach to regulating the defect properties. This is because interfaces, such as twin boundaries, phase interfaces, and heterojunctions, which are defect-rich regions, typically exhibit enhanced polar electronic domains and unique coordination geometries, resulting in a substantial increase in catalytic activity10,11,12,13,14,15. However, modulating and utilizing interface-specific structures to precisely confine and anchor single atoms has not been previously reported and remains a formidable challenge, primarily attributed to complex interfacial structures and the energy mismatch associated with traditional metals. The influence of the interface microenvironment on the properties of single atoms and their reactivity is not yet fully understood, presenting a critical gap in the development of highly efficient SACs.\n\nReducible TiO2, widely recognized as an ideal and specific support, has garnered substantial research attention, with a particular focus on P25 and its phase interfaces16,17,18,19. Cutting-edge studies have identified that the interfaces between anatase and rutile could act as conduits for significantly enhancing electron transfer, leading to robust activation and dissociation of H2O in photocatalysis20,21,22,23. Building on this insight, seeking and anchoring single atoms as active sites for CH3OH dissociation at these phase interfaces to create cooperative catalytic sites may constitute an effective strategy for designing highly efficient MSR catalysts.\n\nHerein, we successfully fabricate the phase-interface-confined Cd/P25 SACs. The stable anchoring of Cd atoms at the anatase (101)-rutile (110) phase interface is facilitated by their energetic compatibility and the presence of abundant interfacial Ti defects. Various characterizations reveal that these unique Cd\u2013O\u2013Ti phase interface cooperative sites confer high activity and stability in the MSR reaction. The H2 production rate at these phase interface sites is approximately 15-fold and 8-fold higher than that of anatase and rutile surface sites, respectively. By tuning the phase interface density, we achieve an enhanced H2 production rate of 292.9\u2009mmol\u2009gcat\u22121\u2009h\u22121. Additionally, the reaction energy barrier for the formate decomposition pathway is significantly lowered at these phase interface sites, leading to CO selectivity (C mol%) below 0.5%. Notably, such SACs can be 3D printed into monolithic catalysts at the kilogram scale, markedly advancing their technological viability and practical application prospects.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To initially explore the effects of P25 properties on active metals in the MSR reaction, we systematically incorporated 5\u2009wt% of Cu, Pd, Pt, and Cd onto Degussa P25 using a vacuum rotary impregnation technique. The Cd-incorporated P25 catalyst (5Cd/P25) exhibited exceptional performance under reaction conditions (290\u2009\u00b0C, 0.1\u2009MPa; steam-to-carbon (S/C) ratio\u2009=\u20093/1; N2 carrier gas flow\u2009=\u200930\u2009mL\u2009min\u22121; liquid feed rate\u2009=\u20093\u2009mL\u2009g\u22121\u2009h\u22121): achieving complete methanol conversion (100%) while maintaining ultralow CO concentration (0.13\u2009mol%) in the effluent gas and CO selectivity below 0.5% (Fig.\u00a01a). This performance stood in stark contrast to the traditional MSR-active metals Cu, Pd, and Pt, which, when supported on P25, failed to achieve full conversion and exhibited a CO selectivity greater than 2%. Moreover, when Cd was deposited on alternative supports such as SiO2, ZnO, CeO2, and ZrO2, the CH3OH conversion was lower than 10%. These findings underscore the distinctive catalytic properties conferred by the interaction between Cd and the P25 support. We further evaluated the influence of the crystalline phase of TiO2 on the performance of Cd-supported catalysts. Upon examining 5Cd/A (anatase) and 5Cd/R (rutile), and their mixture (composed of 80% 5Cd/R and 20% 5Cd/A), we observed CH3OH conversions of 21.0%, 39.0%, and 18.1% at 290\u2009\u00b0C, respectively (Fig.\u00a01b and Supplementary Fig.\u00a01). These corresponded to CO selectivity of 3.6%, 2.2%, and 3.7%, respectively. In stark contrast, the H2 production rate for 5Cd/P25 soared to 97.7\u2009mmol gcat\u22121\u2009h\u22121, outperforming the rates for 5Cd/A, 5Cd/R, and their mixture by factors of 4.9, 2.6, and 5.6, respectively. Additionally, adjusting the mixture ratio still failed to achieve reaction performance comparable to that of 5Cd/P25 (Supplementary Fig.\u00a02), underscoring the unique catalytic properties of this catalyst. Notably, Cd-containing catalysts do not require H2 reduction prior to the reaction, a step traditionally necessitated by Cu-based or noble metal catalysts (Supplementary Fig.\u00a03). This eliminates the need for pre-reduction, simplifying the reforming apparatus and reducing operational costs. Moreover, it enhances the catalysts\u2019 compatibility with the dynamic start-stop cycles of PEMFCs, thereby facilitating more efficient energy management.\n\na, b Methanol conversion, product selectivity, and H2 production rate for various catalysts. c Long-term stability test of 5Cd/P25. Reaction conditions: 290\u2009\u00b0C, S/C molar ratio\u2009=\u20093/1, 0.1\u2009MPa, N2 carrier gas flow\u2009=\u200930\u2009mL\u2009min\u22121, liquid feed rate\u2009=\u20093\u2009mL\u2009g\u22121\u2009h\u22121. d Apparent activation energy evaluation (methanol conversion less than 10%). e XRD patterns of fresh catalysts. f XPS spectra of the Cd 3d orbitals for fresh catalysts. g Cd K-edge XANES spectra of fresh catalysts. h K3-weighted \u03c7 (k) function of EXAFS spectra. HAADF-STEM and EDS elemental maps for 5Cd/A (i), 5Cd/R (j), and 5Cd/P25 (k) catalysts (isolated Cd atoms highlighted by a yellow arrow).\n\nOur study revealed an initial increase followed by a decrease in both CH3OH conversion and H2 production rate as the Cd content varied from 1 to 10\u2009wt% on P25, with optimal performance at the 5\u2009wt% loading (Supplementary Fig.\u00a04). The Cd-based catalysts exhibited enhanced operational stability (Supplementary Fig.\u00a05), with 5Cd/P25 demonstrating exceptional durability by maintaining a stable H2 production rate (>97.0\u2009mmol\u2009gcat\u22121\u2009h\u22121) and CO selectivity below 0.5% throughout a rigorous 150\u2009h continuous test (Fig.\u00a01c). The 5Cd/P25 catalyst displayed the lowest apparent activation energy, measured at 107.3\u2009\u00b1\u20090.6\u2009kJ\u2009mol\u22121, significantly outperforming its counterparts: 5Cd/A with an apparent activation energy of 146.6\u2009\u00b1\u20090.5\u2009kJ\u2009mol\u22121, and 5Cd/R at 121.9\u2009\u00b1\u20090.6\u2009kJ\u2009mol\u22121 (Fig.\u00a01d and Supplementary Fig.\u00a06). This lower energy barrier was a key factor contributing to the superior catalytic performance of the 5Cd/P25 in the MSR process. Our optimization endeavors with the 5Cd/P25 catalyst yielded a H2 production rate of 158.4\u2009mmol\u2009gcat\u22121\u2009h\u22121, with complete CH3OH conversion of 100% and a minimal CO selectivity of 0.4%, realized by elevating the S/C ratio to 1/1 (Supplementary Fig.\u00a07). However, an excess S/C ratio beyond 1/1 led to a decline in CH3OH conversion and the emergence of CH4, likely due to competitive adsorption of reactants on the catalyst surface. At the optimal S/C ratio of 1/1, reducing the feed rate from 3 to 0.5\u2009mL\u2009g\u22121\u2009h\u22121 enabled complete CH3OH conversion (100%) even at a reduced temperature of 250\u2009\u00b0C (Supplementary Fig.\u00a08). The maximum H2 production rate achieved was 267.9\u2009mmol\u2009gcat\u22121\u2009h\u22121 at a rate of 6\u2009mL\u2009g\u22121\u2009h\u22121, which was 14.7-fold and 7.7-fold higher than the rates of 5Cd/A and 5Cd/R, respectively, under identical conditions (Supplementary Fig.\u00a09). The hydrogen-production activity of the system meets the state-of-the-art requirements for PEMFC vehicle applications (see \u201cMethods\u201d section).\n\nTo elucidate the disparities in catalytic performance influenced by the crystalline phase, we conducted an in-depth comparative analysis of the micro-geometric and electronic properties. The inductively coupled plasma optical emission spectrometry (ICP-OES) analysis revealed similar actual Cd loadings in the 5Cd/A (4.6\u2009wt%), 5Cd/R (4.8\u2009wt%), and 5Cd/P25 (4.9\u2009wt%) catalysts. X-ray diffraction (XRD) patterns revealed the absence of diffraction peaks for Cd species in both 5Cd/P25 and 5Cd/A, indicative of their well-dispersed state (Fig.\u00a01e). Conversely, the presence of CdTiO3 species on 5Cd/R was corroborated by XRD and Raman spectroscopy (Supplementary Fig.\u00a010). Calculations showed a significantly lower lattice mismatch of 2.4% between CdO and R, in contrast to the 24.1% mismatch with A (Supplementary Table\u00a01). This smaller mismatch likely facilitated the formation of CdTiO3 on R. X-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES) confirmed the oxidation states of Cd species as +2, with no metallic Cd detected (Fig.\u00a01f and Supplementary Fig.\u00a011). X-ray absorption near-edge spectroscopy (XANES) provided corroborating evidence that the white line features of all examined samples closely resembled those of the CdO reference (Fig.\u00a01g). Extended X-ray absorption fine structure (EXAFS) analysis revealed the presence of only Cd\u2013O scattering paths at approximately 2.24\u2009\u00c5 in all samples, with coordination numbers of 6.2, 5.4, and 5.8 for 5Cd/A, 5Cd/R, and 5Cd/P25, respectively (Fig.\u00a01h and Supplementary Table\u00a02). These findings suggest that Cd exists as isolated atoms in the three catalysts, exhibiting a coordination geometry similar to that of the surface Ti atoms, characterized by octahedral or square pyramidal structural motifs. To further reveal the atom configuration, we employed transmission electron microscopy (TEM) analysis. The average particle sizes of A, R, and P25 were 21.5, 44.0, and 29.5\u2009nm, respectively, with no evidence of Cd species particles (Supplementary Fig.\u00a012). Energy-dispersive X-ray spectroscopy (EDS) mapping images showed the high dispersion of Cd species on the surface of A and R (Fig.\u00a01i, j), while most Cd species were predominantly situated at the interface between P25 particles (Fig.\u00a01k). High-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) further revealed atomically dispersed Cd atoms (bright contrast spots) on A (101) and R (110) facets of 5Cd/A and 5Cd/R (Supplementary Figs.\u00a013 and 14). Notably, in the 5Cd/P25 catalyst, Cd single atoms were primarily anchored at the anatase (101)/rutile (110) phase interface, as evidenced by the distinct bright contrast band observed along the interfacial boundary in Fig.\u00a01k and Supplementary Fig.\u00a015. Moreover, bright contrast spots were observed along the Ti atomic rows, implying that in all three samples, Cd may be situated at surface Ti defects and bonded with lattice oxygen to form SACs characterized by a Cd\u2013O\u2013Ti configuration. Based on the above results, models of Cd1/A (101), Cd1/R (110), and Cd1/A (101)-R (110) were constructed using density functional theory (DFT) calculations, corresponding to the 5Cd/R, and 5Cd/P25 catalysts, respectively (Supplementary Fig.\u00a016). The coordination structure of the interface Cd\u2013O\u2013Ti site exhibited asymmetry, which was markedly distinct from the symmetric structure of the surface site. Therefore, it could be inferred that the variation in performance was attributable to subtle differences in the coordination environment of the single atoms, which underscored the distinctive coordination of the Cd atom at the P25 phase interface.\n\nTo identify the actual active sites and their configurations during the reaction, an in-depth analysis of the spent catalysts was conducted. Post-reaction examination revealed subtle peaks indicative of CdTiO3 in the XRD patterns of the 5Cd/P25 catalyst (Supplementary Fig.\u00a017). This observation suggests that the formation of CdTiO3 may have originated from the transformation of a limited number of isolated Cd atoms present on the R phase of P25, as no such transformation was detected in the 5Cd/A catalyst. The oxidation state of Cd in all spent SACs remained around +2, as confirmed by XPS, XANES, and EXAFS analyses, with no evidence of metallic Cd or CdO (Supplementary Figs.\u00a018 and 19). By contrast, the Cd\u2013O coordination numbers for the 5\u2009Cd/R exhibited a significant increase from 5.2 to 6.2 (Supplementary Table\u00a03), likely due to the partial transformation of Cd single atoms into CdTiO3. This finding could account for the initial drop in CH3OH conversion from ~40% to ~25% (Supplementary Fig.\u00a05b). Similarly, a decline in CH3OH conversion was noted, concurrently with the pronounced emergence of CdTiO3, when the loading of Cd surpassed 5\u2009wt% in the 5Cd/P25 catalyst (Supplementary Figs.\u00a04 and 20). Moreover, the synthesized CdTiO3 and CdTiO3/TiO2 demonstrated negligible catalytic activity under identical reaction conditions, effectively ruling out CdTiO3 as an active species (Supplementary Table\u00a04).\n\nThe structure and reaction performance of the 5\u2009Cd/P25 catalyst were unaffected by H2 reduction at 290\u2009\u00b0C (Supplementary Figs.\u00a03 and 21). However, when the reduction temperature was increased to 400\u2009\u00b0C, a sharp decrease in CH3OH conversion from 100% to 33.6% was observed (Supplementary Fig.\u00a022). HAADF-STEM revealed the formation of sub-nanometer Cd clusters, corroborated by EXAFS spectra showing prominent Cd\u2013Cd coordination at ~2.8\u2009\u00c5, suggesting that metallic Cd or clusters were detrimental to catalytic activity (Supplementary Fig.\u00a023). Additionally, the absence of CH3OH conversion over pure metallic Cd and CdO supported the conclusion that Cd single atoms were the exclusive active species (Supplementary Table\u00a04). Cd species supported on alternative oxides like ZrO2, although achieving atomic dispersion, exhibited inferior catalytic performance (Fig.\u00a01a and Supplementary Fig.\u00a024). This suggests that the Cd\u2013O\u2013Ti configuration is a more effective active site, particularly at phase interfaces. The H2 production rate was normalized against the specific surface area of TiO2 (Supplementary Table\u00a05) to assess the intrinsic catalytic activity. Despite this normalization, the 5Cd/P25 catalyst, with a smaller specific surface area, showed the highest H2 yield (2.2\u2009mmol\u2009m2\u2009h\u22121). This indicates that the superior catalytic performance is independent of the textural properties of TiO2 (Supplementary Fig.\u00a025). In situ electron paramagnetic resonance (EPR) measurements disclosed that the 5Cd/P25 catalyst displayed the weakest oxygen vacancy signal under reaction conditions (Fig.\u00a02a). Moreover, quasi-in situ XPS experiments showed that the oxygen vacancy concentration remained relatively stable (10\u201312% area ratio) with negligible impact on the oxidation state or electronic properties of Cd species (Supplementary Fig.\u00a026). Consequently, the results ruled out the hypothesis that oxygen vacancies played a dominant role in dictating the reaction activity, while highlighting that the exceptional performance originated from the Cd\u2013O\u2013Ti interfacial sites.\n\na In situ EPR spectra at a S/C molar ratio of 1/1 under ambient and 290\u2009\u00b0C conditions. b Sabatier-type correlations between Cd\u2013O coordination number, Cd oxidation state, and H2 production rate. c Atomic-resolution HAADF-STEM with Ti L-edge EELS spectra across anatase/rutile interface. d Electrochemical CV curves in 0.1\u2009M H2SO4 solution. e Charge density differences of structure models (Ti atoms: light blue; O atoms: red; Cd atoms: purple). Yellow and green indicate the gain and loss of electrons, respectively. f DFT-calculated adsorption energies for CH3OH and co-adsorbed CH3OH/H2O on Cd1/A (101), Cd1/R (110), and Cd1/A (101)-R (110).\n\nThe electronic properties of the Cd\u2013O\u2013Ti sites on three SACs were further investigated. The binding energy of the Cd 3d5/2 peak for 5Cd/P25 was measured at 405.3\u2009eV, which was intermediate between the values for 5Cd/A (405.5\u2009eV) and 5\u2009Cd/R (405.1\u2009eV) (Fig.\u00a01f). Intriguingly, the white line energies followed a similar trend, with the estimated oxidation states of Cd being +1.87, +1.75, and +1.63 for 5Cd/A, 5Cd/P25, and 5Cd/R, respectively, derived from the 0.5 position of the normalized near-edge intensity (Fig.\u00a02b). The correlation between the oxidation states and coordination numbers, similar to the Sabatier principle, elucidated the unique electronic properties conferred by the Cd\u2013O\u2013Ti interface sites. Electron energy loss spectroscopy (EELS) showed a shift of the Ti L-edge to lower energies upon anchoring Cd single atoms at the phase interface of P25, confirming electron transfer from Cd to Ti atoms and the consequent formation of a strong interaction (Fig.\u00a02c). This finding was corroborated by H2-TPR experiments, which recorded a higher reduction temperature for the 5Cd/P25 catalyst, indicative of the enhanced stability and interaction at the Cd\u2013O\u2013Ti interface sites (Supplementary Fig.\u00a027). Electrochemical cyclic voltammetry (CV) indirectly assessed the electron transfer capability, with the 5Cd/P25 catalyst exhibiting a lower oxidation peak voltage\u2014corresponding to the electron release process as depicted in Fig.\u00a02d\u2014compared to other catalysts24. This finding suggests a more rapid electron transfer rate, which is likely to enhance the adsorption and activation of reactants. Furthermore, due to its asymmetric structure, the interface showed an asymmetric charge density (polar electronic domain) in the charge density difference distribution analysis (Fig.\u00a02e), and exhibited a higher electron density of state (Supplementary Fig.\u00a028). This enhanced electron state density enabled the interfacial Cd atom to have the lowest reactant adsorption energy (Fig.\u00a02f). The stronger adsorption and activation capabilities for reactants were verified through the temperature-programed desorption (TPD) experiments of CH3OH (Supplementary Fig.\u00a029). In general, the distinctive and asymmetric geometric and electronic properties of the Cd\u2013O\u2013Ti sites at the phase interface are instrumental in elucidating the enhanced catalytic performance observed in the 5Cd/P25 catalyst.\n\nTo disclose the structural specificity underlying the anchoring of Cd single atoms at the interface between A (101) and R (110), surface defects of the supports were investigated using positron annihilation lifetime spectra (PALS, Supplementary Fig.\u00a030 and Supplementary Table\u00a06). The lifetime components \u03c41 and \u03c42 were assigned to positrons captured by monovacancies and larger defect clusters, respectively25,26. In contrast, the P25 support exhibited a shorter lifetime \u03c42 (364\u2009ps), alongside the highest relative intensity (I2), which might have been attributed to a high concentration of defect clusters. DFT calculations revealed that the formation energy of Ti vacancy defects at the phase interface was significantly lower than within the bulk phase surface, indicating a higher likelihood of their formation at these interfaces (Supplementary Fig.\u00a031). We tentatively inferred that the phase interface of A and R provided abundant defects, such as Ti vacancy clusters, that anchored the Cd single atoms. HAADF-STEM further elucidated the microstructure of the phase interface in pure P25. Lattice discontinuities and irregularities were clearly identified at the interface between the A (101) and R (110) facets, as shown by the gray line in Fig.\u00a03a, indicating the presence of interface defects. Atomic-resolution characterization further revealed that subtle disorder in the interfacial atomic arrangement was confined to a narrow region at the interface, as highlighted by the white circle in Fig.\u00a03b. These observations were likely due to surface atom rearrangements resulting from the lattice mismatch between the A (101) and R (110) facets, leading to the formation of abundant Ti defects at the incoherent phase interface (asymmetric structure). Additionally, low-temperature EPR using 2, 4, 6-trichlorophenol as a hole scavenger detected a pronounced signal of electrons trapped in distorted Ti4+ tetrahedral sites at the P25 phase interface (g\u2009=\u20091.979) (Fig.\u00a03c)27. However, the interface signal vanished upon the introduction of Cd, leaving only the signals from electrons trapped within the A lattice (g\u22a5\u2009=\u20091.982) and surface (g||\u2009=\u20091.922). This observation suggests that the presence of distorted interface Ti4+ tetrahedral sites enhances electron transfer, thereby promoting the selective formation and stabilization of Cd single atoms at the phase interface. DFT calculations showed that the A (101) and R (110) facets could form a stable phase junction (Fig.\u00a03d). The presence of a disordered interface and defects resulted in elevated surface energy (Supplementary Fig.\u00a032). Consequently, Cd atoms were more readily anchored at the interface due to their substantially lower binding energy of \u22126.34\u2009eV (Fig.\u00a03e). This value was significantly more favorable compared to the binding energies observed for Cd atoms on the individual A (101) facet at \u22124.62\u2009eV and the R (110) facet at \u22125.94\u2009eV. These findings provided a plausible explanation for the preferential anchoring of Cd atoms at the phase interface in P25.\n\na, b HAADF-STEM images of the phase interface in P25. c Low-temperature EPR spectra acquired after UV light illumination at 20\u2009\u00b0C in the presence of 2, 4, 6-trichlorophenol. d Theoretic structure models of A (101)-R (110) (upper) and Cd1/A (101)-R (110) interfaces (lower). e Binding energies of Cd atoms on the A (101), R (110), and A (101)-R (110) facets. f Correlative effects of N2 calcination temperature, maximum interface density, and H2 production rate (Inset shows the interface model). g HAADF-STEM images of 5Cd/P25 catalysts after H2 pretreatment at 500\u2009\u00b0C for 4 (left) and 8 (right)\u2009h. h Correlations among maximum interface density, H2 production rate, and Cd loss rate following H2 pretreatment.\n\nNotably, other active metals (e.g., Pt, Pd, Cu) deposited on P25 at equivalent concentrations (5\u2009wt%) exhibited nanoparticle aggregation on its surface (Supplementary Fig.\u00a033). However, single-atom formation of transition metals (e.g., Pt) on the P25 phase interface remained unattainable even at 0.2\u2009wt% loading, concomitant with a marked decline in catalytic performance (Supplementary Fig.\u00a034). These findings suggest that the formation of single atoms at the interface necessitates a favorable match between the metal\u2019s properties and the interfacial energy. The ionic radius of Cd differs more markedly from that of Ti than those of Pd, Pt, and Cu. Moreover, the electronegativity of Cd is most analogous to that of Ti (Supplementary Table\u00a07). These characteristics may endow Cd atoms with a significant difference in average internal energy between the lattice and phase interface, rendering them particularly prone to segregation and anchoring at Ti defect sites within the interface, thereby minimizing the interfacial energy. This attribute elucidates the challenge faced by other metals in achieving single-atom dispersion at the P25 interface. Additionally, Cd single atoms at the phase interface were not observed when Cd was supported on a uniform mixture of 80% anatase and 20% rutile (5Cd/80A-20R), which exhibited reduced CH3OH conversion (27.1%) and CO selectivity (3.7%) (Supplementary Fig.\u00a035). This outcome implies that the unique phase interface structure of P25, enriched with defects, likely arises from an in situ phase transformation from anatase to rutile, where rutile nucleates and epitaxially grows on the anatase surface28. To validate this hypothesis, we systematically investigated the phase transformation by calcining A under static-air conditions at varying temperatures (Supplementary Fig.\u00a036 and Supplementary Table\u00a08). At a calcination temperature of 700\u2009\u00b0C, the A/R weight fraction ratio of 80.2/19.8% and corresponding catalytic performance closely mirrored those of 5Cd/P25. HAADF-STEM images revealed the formation of Cd single atoms at the phase interface, which corroborated the formation of Ti defects through the in situ phase transformation.\n\nWe further investigated the effect of phase interface density on the catalytic performance. Employing a simplified geometric model that treated A and R particles as stacked cubes of distinct sizes, we calculated theoretical interface densities based on phase weight fractions and crystallite sizes (Supplementary Fig.\u00a037 and Supplementary Table\u00a08)18. Although calcining A in air-regulated phase weight fractions, the resulting interfacial density was limited to 4.7\u2009m2\u2009g\u22121 due to the enlarged crystallite size, which was lower than that of 5Cd/P25 (7.9\u2009m2\u2009g\u22121). Consequently, we implemented N2 pretreatment (500\u2013700\u2009\u00b0C) on P25 to enhance interface density prior to Cd loading. Increasing calcination temperature from 500 to 700\u2009\u00b0C concurrently elevated the rutile weight fraction and crystallite size. Both maximum interface density and H2 production rate followed volcano trends, peaking at 8.5\u2009m2\u2009g\u22121 and 266.0\u2009mmol gcat\u22121\u2009h\u22121, respectively, at 600\u2009\u00b0C (Fig.\u00a03f). Further interface engineering on P25 via H2 pretreatment (500\u2009\u00b0C, variable durations) adjusted phase ratios without substantially altering the crystallite size (Supplementary Table\u00a08). Prolonged pretreatment was found to increase the interface density. Notably, the phase interface region appeared brighter after a 4-h H2 pretreatment, and the number of bright interface bands significantly increased in the HAADF-STEM images (Fig.\u00a03g left and Supplementary Fig.\u00a038). Atomic-resolution analysis further revealed that the distinct bright interface bands correspond to dispersed Cd single atoms (Supplementary Fig.\u00a038). Moreover, ICP-OES results showed that the Cd loss rate of the spent catalysts substantially decreased with the increase of the interface density (Fig.\u00a03h). This trend suggests an increased number of single atoms stably anchored at the interface, thereby leading to a higher interface site density. A 4-h H2 pretreatment yielded the highest interface density (10.1\u2009m2\u2009g\u22121) and a remarkable H2 production rate of 292.9\u2009mmol gcat\u22121\u2009h\u22121 (Fig.\u00a03h), exceeding conventional MSR catalysts (Supplementary Table\u00a09). An 8-h pretreatment, however, resulted in the formation of small Cd particles (1\u20132.5\u2009nm) (Fig.\u00a03g right), which led to increased Cd loss after the reaction and compromised catalytic performance. This was likely attributed to the disruption of the phase interface structure.\n\nSince the reaction mechanism determines the product selectivity, we scrutinized various proposed reaction mechanisms for the novel catalysts, including methanol decomposition-water gas shift (MD\u2009\u2212\u2009WGS), methyl formate hydrolysis, and formate decomposition (Supplementary Fig.\u00a039). The MD reaction was initially investigated over the 5Cd/P25 catalyst at 290\u2009\u00b0C under a methanol partial pressure of 20\u2009kPa with a feed rate of 12\u2009mL\u2009g\u22121\u2009h\u22121. CH3OH conversion was limited to 1.7%, with CO2 constituting ~77% of the products, accompanied by CO (~10%) and CH4 (~13%) (Supplementary Fig.\u00a040). The formation of CO2 was proposed to originate from reactions between transient intermediates and hydroxyl groups (OH) on the catalyst support. Progressive depletion of surface OH species and accumulation of adsorbed intermediates correlated with the observed decline in methanol conversion to <1.0% over time. This finding indicates that, although thermodynamically favorable at high temperatures, MD is kinetically disfavored. Furthermore, comparative evaluation of WGS activity under equivalent conditions (290\u2009\u00b0C, CO/H2O\u2009=\u20091/1, PH2O\u2009=\u200920\u2009kPa) revealed limited catalytic performance, with CO conversion decaying from 9.0% to 4.5% over 10\u2009h and a correspondingly low H2 production rate of 1.6\u2009mmol\u2009g\u22121\u2009h\u22121. Strikingly, MSR under identical CH3OH and H2O partial pressures (20\u2009kPa each) demonstrated a two-order-of-magnitude enhancement in H2 production rates (264.7\u2009mmol\u2009g\u22121\u2009h\u22121). These findings suggest that WGS contributes negligibly to H2 generation, confirming that the MD-WGS pathway is unlikely to be viable for the Cd-based catalysts.\n\nIn situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) was conducted to elucidate the reaction mechanism. When exposed to CH3OH/H2O between 50 and 320\u2009\u00b0C, only weak methanol physisorbed peaks (\u03c5 (CO) at 1000\u20131100\u2009cm\u22121, \u03b4 (CH) at 1300\u20131500\u2009cm\u22121, and \u03c5 (CH) at 2800\u20133000\u2009cm\u22121) were observed on A, R, and P25, aside from H2O adsorption and *OH peaks (\u03b4 (H2O) at 1651\u2009cm\u22121 and \u03c5 (OH) at 3710\u2009cm\u22121) (Supplementary Fig.\u00a041 and Supplementary Table\u00a010). These findings demonstrate that the functionality of supports is limited to facilitating H2O adsorption and dissociation. In stark contrast, after loading Cd single atoms, distinct methoxy (*CH3O, \u03b4 (CH) at 1431\u2009cm\u22121) and bidentate formate (b-*HCOO\u2212, \u03c5s (OCO) at 1336\u2009cm\u22121, \u03c5as (OCO) at 1554\u2009cm\u22121) species emerged5,29, which indicated that Cd single atoms were responsible for CH3OH adsorption and dissociation (Fig.\u00a04a\u2013c). As the reaction temperature increased, the *CH3O and b-*HCOO\u2212 were rapidly consumed, coinciding with the emergence of gaseous CO2 peaks at 2308 and 2375\u2009cm\u22121, indicating that H2 generation resulted from formate transformation. Notably, on 5Cd/P25, *CH3O and b-*HCOO\u2212 were transformed more rapidly at a lower temperature (200\u2009\u00b0C) compared to 5Cd/A (260\u2009\u00b0C) and 5Cd/R (320\u2009\u00b0C) (Fig.\u00a04d). This trend aligns with the catalytic performance, implying that interfacial Cd single atoms are more conducive to intermediates transformation. Additionally, no characteristic peaks indicative of methyl formate (*HCOOCH3) were detected in the 1700\u20131800\u2009cm\u22121 range during the reaction and the Ar sweeping stage30. Similarly, temperature-programmed surface reaction (TPSR) analyses exclusively identified formate species, with no indication of HCOOCH3 (Fig.\u00a04e and Supplementary Fig.\u00a042). These findings rule out mechanism pathways involving methyl formate hydrolysis via esterification of formic acid with methanol, followed by its hydrolysis to yield CO2 and H2. Therefore, we propose that the formate decomposition pathway dominates the reaction mechanism on the SACs, where methanol initially decomposes into *CH3O, subsequently dehydrogenates to *H2CO, and reacts with *OH groups from dissociated H2O to form *HCOO\u2212. This intermediate then further dehydrogenates, yielding H2 and CO2.\n\na\u2013c In situ DRIFTS spectra of MSR on 5Cd/A, 5Cd/R, and 5Cd/P25 at 50\u2013320 \u00b0C. d Temperature-dependent changes in the normalized peak area of the *CH3O (1431 cm\u22121) and b-*HCOO\u2212 (1554 cm\u22121) species. e TPSR profiles for the 5Cd/P25 catalyst. f KIE measurements on the 5 Cd/P25 catalyst by modulating the isotopic composition of the feed gas. g Reaction order of CH3OH on the 5 Cd/P25 catalyst. h Relative energy diagram depicting the formate decomposition pathway across three structural models.\n\nThe kinetic isotope effect (KIE) experiments were conducted on the 5Cd/P25 catalyst to reveal the reaction kinetics. Switching the feedstock from CH3OH/H2O to CH3OD/H2O resulted in essentially unchanged H2 generation rates, yielding a KIE value (KH/KD) of 1.1 (Fig.\u00a04f). In contrast, substituting CH3OH/H2O with CD3OD/H2O caused a dramatic 4-fold decline in H2 production rates, corresponding to a significantly elevated KIE of 3.7. These findings establish that C\u2013H bond cleavage exhibits substantially greater kinetic resistance than O\u2013H bond scission, potentially identifying the rate-determining step (RDS) in the MSR reaction. To identify the specific RDS in elementary reactions, we derived the reaction rate equation based on the established formate mechanism. According to previous reports, assuming that methoxy dehydrogenation (*CH3O\u2192*CH2O\u2009+\u2009*H) was RDS, the derived rate equation showed a first-order dependence on CH3OH pressures (Supplementary Fig.\u00a043). Considering competitive adsorption between CH3OH and H2O on active sites, the theoretical CH3OH reaction order was predicted to be less than unity (<1). The experimentally determined CH3OH reaction order of 0.72 demonstrated excellent agreement with the kinetic model (Fig.\u00a04g), providing conclusive evidence that methoxy dehydrogenation constitutes the RDS in this pathway.\n\nDFT calculations based on the established Cd1/A (101), Cd1/R (110), and Cd1/A (101)-R (110) models provided complementary theoretical insights into the MSR reaction pathway. The computed energy landscape revealed distinct adsorption energy profiles: *CHO exhibited significantly lower adsorption energies on Cd1/A (101) (\u22121.45\u2009eV) and Cd1/R (110) (\u22121.18\u2009eV) compared to the Cd1/A (101)-R (110) model (4.1\u2009eV) (Supplementary Fig.\u00a044). This energy disparity implies preferential CO generation pathways on Cd1/A (101) and Cd1/R (110) surfaces. Conversely, critical intermediates such as *CH2O-*OH, *CHOOH, and *CHOO displayed substantially reduced adsorption energies on the Cd1/A (101)-R (110) surface. These findings demonstrate that the formate decomposition pathway is thermodynamically more favorable on the Cd1/A (101)-R (110) catalyst, attributable to its asymmetric coordination environment and enhanced charge density at the interface Cd single-atom sites.\n\nThe transition states of the formate decomposition pathway were further investigated (Fig.\u00a04h and Supplementary Figs.\u00a045\u201347). Consistent with in situ DRIFTS observations, DFT calculations revealed that both the adsorption and the stepwise dehydrogenation of CH3OH occurred at the Cd active site. The activation energy barriers for CH3OH dehydrogenation (TS1: 0.64\u2009eV) were significantly lower than those for *CH3O dehydrogenation (TS2: 0.87\u2009eV) on the Cd1/A (101)-R (110) surface. This clearly demonstrates that the C\u2013H bond cleavage encounters higher kinetic resistance than O\u2013H bond scission, aligning with the experimental KIE results. Following *CH3O dehydrogenation to *CH2O, H2O adsorption and dissociation on adjacent Ti atoms generated *H and *OH species (TS3: 0.37\u2009eV). Subsequent CH2O/OH recombination formed CH2OOH (TS4: 0.43\u2009eV), which further dehydrogenated to produce CO2 and H2. Although TS1 and TS2 barriers on Cd1/A (101)-R (110) were modestly elevated relative to Cd1/A (101) (TS3/TS4: 1.07/0.60\u2009eV) and Cd1/R (110) (TS3/TS4: 0.57/0.53\u2009eV), the dissociation of H2O (TS3) and CH2OOH formation (TS4) exhibited significantly lower energy barriers in the phase interface model. These results demonstrated that the unique structural properties of the interfacial structure enhanced the dissociation and activation of H2O, thereby facilitating the subsequent formation of formate and H2. Consequently, these observations rationalized the high activity and low CO selectivity of the 5Cd/P25 catalyst. Additionally, the highest activation energy barrier (TS2: 0.87\u2009eV) confirmed *CH3O dehydrogenation as the RDS on the Cd1/A (101)-R (110), consistent with previous kinetic analysis.\n\nMonolithic catalysts, renowned for their efficacy in enhancing heat and mass transfer and in reducing reaction pressures, were fabricated via direct-write 3D printing of 5Cd/P25 powder into structures with controlled pore dimensions (Fig.\u00a05a). This method enabled kilogram-scale production while preserving structural integrity and mechanical robustness (Supplementary Fig.\u00a048). Catalysts with reactor-optimized dimensions (\u03a610\u2009\u00d7\u20095\u2009mm, Supplementary Fig.\u00a049) showed peak performance at 0.5\u2009mm pore size, achieving 100% CO2 conversion over 100\u2009h while suppressing CO concentration (0.07\u2009mol%) and selectivity (0.3%) versus powder catalysts (Fig.\u00a05b, Supplementary Fig.\u00a050).\n\na 3D printing fabrication of monolithic 5Cd/P25 catalysts with tunable pore sizes (0.5\u20131.0\u2009mm). b Catalytic performance assessment of the monolithic 5Cd/P25 catalyst with 0.5\u2009mm pore size. c Multiphysics simulation of reaction-diffusion processes in the monolithic catalyst (0.5\u2009mm pore size): velocity, pressure, and temperature fields.\n\nPost-reaction characterization confirmed the structural stability of the catalyst, with no signs of degradation or particle agglomeration (Supplementary Fig.\u00a051). Computational Fluid Dynamics (CFD) simulations revealed that the monolithic catalyst featuring 0.5\u2009mm pores possessed more compact linear channels, enhancing mass transfer while minimizing pressure drop to 0.0075\u2009Pa (Fig.\u00a05c and Supplementary Fig.\u00a052). Additionally, smaller pores ensured a more uniform temperature profile and superior heat transfer. Consequently, these monolithic catalysts are poised to optimize industrial process performance.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63060-7/MediaObjects/41467_2025_63060_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63060-7/MediaObjects/41467_2025_63060_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63060-7/MediaObjects/41467_2025_63060_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63060-7/MediaObjects/41467_2025_63060_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63060-7/MediaObjects/41467_2025_63060_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In summary, we have successfully anchored Cd single atoms at the phase interface of P25, a process driven by the compatibility of Cd atom properties and interface energy and the presence of abundant Ti defects. The resultant unique Cd\u2013O\u2013Ti interface sites, featuring asymmetric coordination geometry and elevated charge density, enable enhanced adsorption of reactants/intermediates. These structural attributes further reduce the energy barrier for both water dissociation and its activation within the formate decomposition pathway, thereby achieving complete methanol conversion with suppressed CO selectivity. The interface site density can be flexibly modulated to further boost catalytic activity. Moreover, the catalyst powders can be upscaled to monolithic forms via 3D printing, thereby improving mass and heat transfer characteristics. This research provides insights into the design of novel SACs and the development of practical MSR catalysts.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "All chemicals were of analytical grade and were used as purchased without further purification. Cadmium nitrate tetrahydrate (Cd(NO3)2\u22c54H2O), cupric nitrate trihydrate (Cu(NO3)2\u22c53H2O), chloroplatinic acid hexahydrate (H3PtOCl6), palladium nitrate (Pd(NO3)2\u22c52H2O) were purchased from Sinopharm Chemical Reagent Co. Degussa P25 was purchased from Evonik industries. Anatase, rutile, SiO2, ZrO2, ZnO, CeO2, Al2O3, and 2, 4, 6-trichlorophenol were purchased from Aladdin Chemical Reagent Company. Bentonite was purchased from Shanghai Haohong Biopharmaceutical Technology Co. Hypromellose and polyvinyl alcohol 1799 were purchased from Shanghai Titan Scientific Co. Deionized water was used throughout this study.\n\nAll supported catalysts were synthesized via vacuum rotary impregnation. For instance, 5Cd/P25 was prepared by dispersing 1\u2009g of Degussa P25 in 80\u2009mL of deionized water, followed by the addition of 0.02\u2009mmol\u2009mL\u22121 Cd(NO3)2\u22c54H2O (the loadings are equivalent to the metal-to-support mass ratio). The mixture was stirred for 1\u2009h and then dried at 60\u2009\u00b0C for 1\u2009h using a vacuum rotary evaporator. The product was subsequently calcined at 500\u2009\u00b0C for 3\u2009h in static air. A similar method was employed for the synthesis of other supported Cd-based catalysts.\n\nA similar method was employed for the synthesis of the 5Cd/80A-20R catalyst, which involved combining 0.8\u2009g of anatase and 0.2\u2009g of rutile in 80\u2009mL of deionized water, with the subsequent addition of 0.02\u2009mmol\u2009mL\u22121 Cd(NO3)2\u22c54H2O, followed by stirring, drying, and calcination under identical conditions.\n\nCd(NO3)2\u00b74H2O (0.617\u2009g) and P25 powder (0.160\u2009g) were homogenously mixed through a rotary evaporation process at 65\u2009\u00b0C. The mixture was then dried at 80\u2009\u00b0C for 12\u2009h and ground thoroughly with an agate mortar. The resulting samples were calcined at 650\u2009\u00b0C for 8\u2009h under static air, then immersed in 200\u2009mL of 1\u2009mol\u2009L\u22121 HNO3 aqueous solution and stirred magnetically for 3\u2009h. Solid-liquid separation was achieved through six cycles of centrifugation (4447\u2009\u00d7\u2009g, 10\u2009min each) with deionized water washing. The purified precipitate was dried at 80\u2009\u00b0C for 12\u2009h to obtain phase-pure CdTiO3 powder.\n\n1\u2009g of rutile was dispersed in 80\u2009mL of deionized water, followed by the addition of 0.08\u2009mmol\u2009mL\u22121 Cd(NO3)2\u22c54H2O. The mixture was stirred for 12\u2009h and then dried at 60\u2009\u00b0C for 1\u2009h using a vacuum rotary evaporator. The product was subsequently calcined at 700\u2009\u00b0C for 3\u2009h in static air.\n\nFor air pretreatment, anatase was calcined in static air at temperatures of 600, 700, and 800\u2009\u00b0C for 2\u2009h each. For the N2 pretreatment, P25 was calcined in a N2 flow (100\u2009mL\u2009min\u22121) at temperatures of 500, 600, and 700\u2009\u00b0C for 2\u2009h each. Following this, the Cd precursor was deposited onto the pretreated P25 via vacuum rotary impregnation. For the H2 pretreatment, P25 was reduced in a H2 flow (100\u2009mL\u2009min\u22121) at 500\u2009\u00b0C for durations of 2, 4, and 8\u2009h. The Cd precursor was then similarly impregnated onto the P25. All pretreated samples were subsequently calcined at 500\u2009\u00b0C for 3\u2009h in static air.\n\nInitially, a homogeneous powder mixture was prepared by blending 90\u2009wt% of our in-house synthesized 5Cd/P25, 5\u2009wt% Bentonite, 3.5\u2009wt% Hypromellos, and 1.5\u2009wt% Poly (Vinyl Alcohol) (PVA) 1799 to a total of 100\u2009g in an agate mortar. This powder mixture was then loaded into a 250\u2009mL ball mill jar, to which a measured amount of deionized water and a small quantity of glycerin were added. Subsequently, the jar was placed into a planetary ball mill and stirred under settings of 580\u2009rpm\u2009min\u22121, with bidirectional milling for a duration of 15\u2009h. The milling process culminated in a uniformly mixed printing slurry with appropriate rheological properties.\n\nThe rheological characterization of the 3D-printable 5Cd/P25 slurry, conducted using a HAAKE MARS 40 rheometer, revealed rapid viscosity recovery at both low and high shear rates, meeting the requirements for additive manufacturing processes.\n\nThe slurry formulated for 3D printing was loaded into a 50\u2009mL syringe equipped with a 0.26\u2009mm diameter print nozzle. Utilizing a Bio-Architect\u00aeWS 3D bioprinter, the printing parameters were set as follows: layer height of 0.17\u2009mm, air pressure of 0.35\u2009MPa, and a print speed of 10\u2009mm\u2009s\u22121. The cylindrical model specifications were a diameter of 9.2\u2009mm and a height of 5\u2009mm. By adjusting the printing gap, monolithic 5Cd/P25 catalysts with distinct pore sizes of 0.5\u2009mm, 0.6\u2009mm, 0.8\u2009mm, and 1.0\u2009mm were printed. These catalysts with varying pore sizes were subjected to freeze-drying for 24\u2009h, followed by calcination in air at a heating rate of 5\u2009\u00b0C min\u22121, and maintained at 450\u2009\u00b0C for 3\u2009h to remove the organic binders. Kilogram-scale printing is achieved through continuous feeding.\n\nMetal loadings were detected by ICP-OES analysis on a Perkin-Elmer Optima 8000 instrument. XRD patterns were collected on a Rigaku Ultima IV X-ray powder diffractometer utilizing Cu K\u03b1 radiation (\u03bb\u2009=\u20091.54056\u2009\u00c5) at 40\u2009kV and 40\u2009mA. Raman spectra were recorded on a Thermo Fisher Scientific DXR 2xi equipped with an electron-multiplying charge-coupled device (EMCCD) detector and a 514\u2009nm Ar ion laser. XPS and AES analyses were conducted on a Thermo Fisher Scientific ESCALAB 250Xi. Calibration of the C1s peak for all catalysts was set at 284.8\u2009eV. XANES and EXAFS spectroscopy were collected on the beamline BL11B in SSRF. Samples were homogeneously coated onto adhesive tape and measured at the Cd K-edge under ambient conditions using a Si (111) double-crystal monochromator. Data analysis was performed using Athena software. TEM and HRTEM analyses were conducted on a JEOL-JEM 2011 microscope operating at 200\u2009kV. HAADF-STEM, EDX mapping, and EELS images were acquired on a JEM-ARM300F instrument. Scanning electron microscopy (SEM) images were acquired on a Zeiss Supra 55 at a low accelerating voltage of 5\u2009kV. Nitrogen adsorption measurements were performed on a TriStar II 3020 analyzer. 100\u2009mg of the sample was degassed at 200\u2009\u00b0C under vacuum for 10\u2009h, followed by nitrogen adsorption-desorption isotherms at 77\u2009K. The specific surface area was determined using the Brunauer\u2013Emmett\u2013Teller (BET) equation, while the total pore volume and average pore size were assessed via the Barrett\u2013Joyner\u2013Halenda (BJH) method.\n\nH2-TPR analyses were conducted on a Micromeritics Autochem II 2920 instrument, integrated with a thermal conductivity detector (TCD) and an MKS Cirrus 2 mass spectrometer. For each measurement, 100\u2009mg of the sample was initially purged with Ar at a flow rate of 30\u2009mL\u2009min\u22121 and heated to 200\u2009\u00b0C for 2\u2009h to eliminate moisture, followed by cooling to 50\u2009\u00b0C. The gas stream was then switched to H2, and the temperature was ramped to 800\u2009\u00b0C at a rate of 10\u2009\u00b0C min\u22121. CH3OH-TPD measurements were executed using the same setup. After moisture removal and cooling to 50\u2009\u00b0C, CH3OH was absorbed onto the sample by Ar bubbling for 1\u2009h. Excess surface adsorption was removed by Ar purging for 30\u2009min until a stable baseline was achieved. The desorption process was then carried out from 50 to 500\u2009\u00b0C. TPSR measurements followed a similar protocol, with a mixture of CH3OH/H2O (1/1) introduced by Ar bubbling as the temperature increased from 50 to 500\u2009\u00b0C.\n\nPALS was conducted using a high-resolution ORTEC fast-slow coincidence system, achieving a time resolution of approximately 201\u2009ps. The samples were positioned symmetrically around a 5\u2009mCi 22Na positron source, yielding a total count rate of 1 million counts.\n\nCV measurements were conducted using an Autolab Electrochemical Workstation (PGSTAT302N) configured in a standard three-electrode setup. The fluorine-doped tin oxide (FTO) glass was cleaned ultrasonically with a mixture of acetone, ethanol, and water (1:1:1\u2009v/v/v) for 30\u2009min. The sample served as the working electrode, a platinum foil acted as the counter electrode, and a saturated Ag/AgCl electrode functioned as the reference electrode. For the preparation of the working electrode, 10\u2009mg of the catalyst was mixed with 950\u2009\u03bcL of a water/ethanol solution (1:1\u2009v/v) and 50\u2009\u03bcL of Nafion solution. After 30\u2009min of sonication, 100\u2009\u03bcL of the homogeneous suspension was applied to the cleaned FTO glass electrode with an active area of 2\u2009cm\u2009\u00d7\u20091.5\u2009cm and air-dried. The electrochemical cell was purged with argon to exclude oxygen interference. A 50\u2009mL solution of 0.1\u2009M H2SO4 served as the electrolyte, and the CV scans were performed at a scan rate of 20\u2009mV\u2009s\u22121.\n\nIn situ EPR spectra were acquired using a Bruker EMX Plus instrument. The presence of oxygen vacancies was detected within the field range of 3300\u20133500\u2009G, corresponding to g-values from 1.94 to 2.04. Samples (0.1\u2009g) were suspended in 100\u2009g of a CH3OH/H2O (1/1) mixture. N2 was flushed through the in situ cell to shield the samples from atmospheric interference. EPR signals were captured at both ambient and elevated temperatures (290\u2009\u00b0C). For low-temperature EPR, spectra were recorded over the field range of 3200\u20133550\u2009G with g-values spanning from 1.90 to 2.02 at a temperature of \u2212213\u2009\u00b0C. Powdered samples (0.2\u2009g) were sonicated in 5\u2009mL of ultrapure water, to which 5\u2009mL of a 0.8\u2009mM 2,4,6-trichlorophenol solution was added. The mixture was then deoxygenated by purging with N2 for 1\u2009h. A defined volume of this solution was cooled to \u2212213\u2009\u00b0C in the EPR in situ cell, where it was subjected to irradiation from a 300\u2009W xenon lamp for 30\u2009min. Following this, the EPR spectrum was recorded under constant light exposure and compared with a standard reference (g\u2009=\u20092.00285\u2009\u00b1\u20090.00005) to ensure the uniformity and precision of the g-tensor values.\n\nQuasi-in situ XPS was performed on a Thermo Fisher Scientific ESCALAB 250Xi. Initially, samples were placed into the reaction chamber under a continuous flow of Ar (50\u2009mL\u2009min\u22121) and heated to 290\u2009\u00b0C. Subsequently, the samples were transferred directly to the analysis chamber under vacuum to prevent air exposure for XPS analysis. Following analysis, the samples were returned to the reaction chamber, where they were exposed to a mixture of CH3OH/H2O, introduced via Ar bubbling for 2\u2009h, prior to reanalysis in the XPS chamber. This procedure was also applied to samples that were subjected to a 4-h reaction, ensuring consistent methodology.\n\nIn situ DRIFTS measurements were performed on a Thermo FTIR spectrometer (Nicolet IS50) with a mercury-cadmium-telluride (MCT) detector. The in situ DRIFT spectra were recorded by collecting 64 scans at a resolution of 4\u2009cm\u22121. Prior to measurement, the sample was purged with Ar at 250\u2009\u00b0C for 1\u2009h to collect the background spectrum, and then cooled to 50\u2009\u00b0C. The catalysts were exposed to CH3OH and a CH3OH/H2O (1/1) mixture at a flow rate of 30\u2009mL\u2009min\u22121 under atmospheric pressure. Spectra were recorded as the temperature and reaction time increased during the catalytic process.\n\nA continuous flow fixed-bed reactor (8\u2009mm inner diameter) was used to evaluate catalytic performance. Typically, 0.2\u2009g of catalyst (40\u201360 mesh) was packed between quartz wool plugs. After the reactor was increased to the set point, CH3OH and H2O were premixed in a specific molar ratio and pumped into the vaporizer operating at 180\u2009\u00b0C with 1% N2/Ar (30\u2009mL\u2009min\u22121) as both carrier gas and internal standard. Gaseous products were analyzed online using a Shimadzu GC-2014 C gas chromatograph equipped with a thermal conductivity detector (TCD) and a TDX-01 column for the analysis of H2, N2, CH4, CO, and CO2.\n\nMethanol conversion and product selectivity were calculated as follows:\n\nwhere the n is the molar amount, m is the weight of catalysts, and t is the reaction time.\n\nThe Vienna Ab initio Simulation Package (VASP) was used based on density functional theory (DFT)31. Using the electron exchange and correlation energy was treated within the generalized gradient approximation in the Perdew-Burke-Ernzerhof functional (GGA-PBE)32. The calculations were done with a plane-wave basis set defined by a kinetic energy cutoff of 400\u2009eV. The energies were converged to 10\u22125 eV in the self-consistent field, and a conjugate-gradient algorithm was used to relax the atomic position until the forces acting on each atom were less than 0.02\u2009eV\u2009\u00c5\u22121. The DFT-D3 method developed by Grimme was applied in all calculations to describe the long-range dispersion interactions33.\n\nThe GGA\u2009+\u2009U approach was used to treat the 3d orbital electrons of Ti with the effective Hubbard on-site Coulomb interaction parameter (U\u2032\u2009=\u2009U\u2009\u2212\u2009J)34. The value of U\u2032 was set to 4, according to the proposed value from previous works35,36. Bader charge analysis and charge density difference analyses were performed using VESTA to analyze the electron properties. The transition-state (TS) structures were searched using the climbing image nudged elastic band (CI-NEB) algorithm, with four images along the reaction pathway. All the minima and TS were confirmed through vibrational frequency calculations.\n\nThe anatase (101) slab models with 1\u2009\u00d7\u20094 unit cells and rutile (110) models with 3\u2009\u00d7\u20092 unit cells were cut from the optimized structures of bulk anatase (a\u2009=\u2009b\u2009=\u20093.870\u2009\u00c5 and c\u2009=\u20099.563\u2009\u00c5) and bulk rutile (a\u2009=\u2009b\u2009=\u20094.695\u2009\u00c5 and c\u2009=\u20093.071\u2009\u00c5). A vacuum layer of 15\u2009\u00c5 was employed to prevent interactions between slabs. The lower-half layers of the slab were kept frozen, and the upper-half layers were allowed to relax. The k-point sampling was obtained from the Monkhorst-Pack scheme with a (2\u2009\u00d7\u20093\u2009\u00d7\u20091) mesh for optimization and electronic structure calculations. The heterostructures were constructed with rutile (110) and anatase (101). One of the surface Ti atoms in the unit cell was substituted by a Cd atom, which served as the model for the catalysts.\n\nThe single-atom binding energy (EB) is defined as the energy to introduce one Cd atom:\n\nThe vacancy formation energy (Ef) is defined as the defect formation energy of TiO2 from the elimination of Ti atoms:\n\nTo investigate the flow diffusion phenomena, pressure drop, and heat transfer effects of monolithic catalysts with varying porosities, CFD simulations were performed using the software OpenFOAM\u00ae\u221212 (OpenFOAM-12 owned, developed, and released by the OpenFOAM Foundation openfoam.org). A simplified geometric model of the monolithic catalyst was constructed using the software ZWSOFT\u00ae, closely resembling the actual dimensions and structure, with individual pillar diameters of 0.26\u2009mm and spacings of 0.5\u2009mm, 0.6\u2009mm, 0.8\u2009mm, and 1.0\u2009mm for the 3D Cd/TiO2 monolithic catalysts, corresponding to surface areas of 2149.7\u2009mm2, 1853.7\u2009mm2, 1489.8\u2009mm2, and 1255.9\u2009mm2, respectively. CH3OH/H2O was used as the flowing medium to simulate the flow (0.110\u2009m\u2009s\u22121, 0.085\u2009m\u2009s\u22121, 0.082\u2009m\u2009s\u22121, and 0.073\u2009m\u2009s\u22121) and temperature fields. The actual operation of structured catalysts is highly complex, involving the coupling of various physical and chemical processes, such as turbulence, heat transfer, mass transfer, and chemical reactions. Therefore, the actual process was simplified under the assumption that no chemical reactions occur as the gas flows through the structured catalyst, the gas at the entrance is in a steady flow state, and fully expands shortly after the entrance; the reactor wall temperature is kept constant. Under these assumptions, the flow and temperature fields of the structured catalyst were meticulously simulated, with intensive grid refinement at the catalyst site and repeated iterative processes after providing initial boundary conditions.\n\nThe 2023 Toyota Mirai is reported to consume 1.4\u2009kg of H2 for every 100 miles and has a range of 300\u2013400 miles (US Department of Energy. Compare Fuel Cell Vehicles http://www.fueleconomy.gov/feg/fcv_sbs.shtml). We estimate the amount of Cd/P25 catalyst required as follows: traveling at 100 miles per hour, the Mirai\u2019s hydrogen consumption rate is 1.4\u2009kg per hour. With a hydrogen production rate of 268\u2009mmol\u2009gcat\u22121\u2009h\u22121 for the Cd/P25 catalyst, approximately 2.6\u2009kg of this catalyst would be needed to meet the Mirai\u2019s hourly demand. This is only a theoretical estimate, and practical applications still need to solve engineering process problems and further optimize to meet the application.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. The DFT calculation data generated in this study have been deposited in Figshare (data https://doi.org/10.6084/m9.figshare.27905838)37.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Palo, D. R., Dagle, R. A. & Holladay, J. D. Methanol steam reforming for hydrogen production. Chem. 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Luo), 22108289 (X.L.), and 22279158 (X.L.)), and the CNOOC Institute of Chemicals & Advanced Materials (No. YJSCZX07956YJ (H.W.)). We thank the BL11B beamline at the Shanghai Synchrotron Radiation Facility (SSRF), Shanghai, PR China, for supporting the X-ray absorption measurements.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Institute of Carbon Neutrality, ShanghaiTech University, Shanghai, PR China\n\nShunan Zhang,\u00a0Haozhi Zhou,\u00a0Hao Liang,\u00a0Ruonan Zhang,\u00a0Yuhan Sun\u00a0&\u00a0Hui Wang\n\nCAS Key Laboratory of Low-Carbon Conversion Science and Engineering, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, PR China\n\nZilong Shao,\u00a0Xiaofang Liu,\u00a0Hu Luo,\u00a0Lin Xia\u00a0&\u00a0Hui Wang\n\nShanghai Institute of Clean Technology, Shanghai, PR China\n\nBaohuan Wei,\u00a0Zhen Hu\u00a0&\u00a0Yuhan Sun\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.Z. and H.W. designed the project. H.W. and Y.S. guided and supervised the research. S.Z., H.Z., and Z.S. performed the catalyst preparation and catalytic reaction test. S.Z., Z.S., H. Liang, and R.Z. conducted the characterization. B.W. contributed to DFT calculations. Z.H. performed research on 3D printing. S.Z., X.L., H. Luo, L.X., and H.W. analyzed the data and wrote the paper. All authors contributed to the discussion of the results.\n\nCorrespondence to\n Hui Wang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Navneet Kumar Gupta and the other anonymous reviewer(s) for their contribution to the peer review of this work. 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secondary structure via base pair motif energy", + "journal": "Nature Communications", + "published": "01 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60048-1/MediaObjects/41467_2025_60048_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60048-1/MediaObjects/41467_2025_60048_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60048-1/MediaObjects/41467_2025_60048_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-60048-1#ref-CR65", + "https://github.com/heqin-zhu/BPfold", + "/articles/s41467-025-60048-1#Tab1", + "/articles/s41467-025-60048-1#Tab2", + "/articles/s41467-025-60048-1#MOESM1", + "/articles/s41467-025-60048-1#MOESM1", + "/articles/s41467-025-60048-1#Fig4", + "/articles/s41467-025-60048-1#Fig5", + "/articles/s41467-025-60048-1#Fig7", + "/articles/s41467-025-60048-1#MOESM1", + "/articles/s41467-025-60048-1#MOESM1", + "/articles/s41467-025-60048-1#MOESM1", + "/articles/s41467-025-60048-1#Sec17" + ], + "code": [ + "/articles/s41467-025-60048-1#ref-CR65", + "https://github.com/heqin-zhu/BPfold" + ], + "subject": [ + "Computational models", + "Genome assembly algorithms", + "Machine learning", + "Protein structure predictions" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5321981/v1.pdf?c=1751454783000", + "research_square_link": "https://www.researchsquare.com//article/rs-5321981/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60048-1.pdf", + "preprint_posted": "31 Oct, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "RNA secondary structure plays essential roles in modeling RNA tertiary structure and further exploring the function of non-coding RNAs. Computational methods, especially deep learning methods, have demonstrated great potential and performance for RNA secondary structure prediction. However, the generalizability of deep learning models is a common unsolved issue in the situation of unseen out-of-distribution cases, which hinders the further improvement of accuracy and robustness of deep learning methods. Here we construct a base pair motif library which enumerates the complete space of locally adjacent three-neighbor base pair and records the thermodynamic energy of corresponding base pair motifs through de novo modeling of tertiary structures, and we further develop a deep learning approach for RNA secondary structure prediction, named BPfold, which employs hybrid transformer and convolutional neural network architecture and an elaborately designed base pair attention block to jointly learn representative features and relationship between RNA sequence and the energy map of base pair motif generated from the above motif library. Quantitative and qualitative experiments on sequence-wise datasets and family-wise datasets have demonstrated the great superiority of BPfold compared to other state-of-the-art approaches in both accuracy and generalizability. The significant performance of BPfold will greatly boost the development of deep learning methods for predicting RNA secondary structure and the further discovery of RNA structures and functionalities.Biological sciences/Computational biology and bioinformatics/Protein structure predictionsBiological sciences/Computational biology and bioinformatics/Machine learningBiological sciences/Computational biology and bioinformatics/Computational modelsBiological sciences/Computational biology and bioinformatics/Genome informatics/Genome assembly algorithmsRNA secondary structure predictionBase pair motifBase pair attentionDeep learning", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "NCBPfoldsupp.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Deep learning methods have demonstrated great performance for RNA secondary structure prediction. However, generalizability is a common unsolved issue on unseen out-of-distribution RNA families, which hinders further improvement of the accuracy and robustness of deep learning methods. Here we construct a base pair motif library that enumerates the complete space of the locally adjacent three-neighbor base pair and records the thermodynamic energy of corresponding base pair motifs through de novo modeling of tertiary structures, and we further develop a deep learning approach for RNA secondary structure prediction, named BPfold, which learns relationship between RNA sequence and the energy map of base pair motif. Experiments on sequence-wise and family-wise datasets have demonstrated the great superiority of BPfold compared to other state-of-the-art approaches in accuracy and generalizability. We hope this work contributes to integrating physical priors and deep learning methods for the further discovery of RNA structures and functionalities.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "RNA secondary structure plays vital roles in modeling the RNA tertiary structure through base pairing interactions1, demonstrating the remarkable versatility and functional mechanisms of RNA in biological systems and cellular processes, such as catalytic functionality2,3, regulatory functions4, and intron splicing events5. Generally, RNA secondary structure forms a sequential of stem and loop regions, with stem regions composed of consecutive paired nucleotide bases6 and loop regions composed of unpaired bases. Furthermore, loop regions exhibit various structure motifs stabilized by non-canonical base pairs and other polar interactions, such as tetra loops, kissing-loops, kink turn, and G-quadruplex7.\n\nDiscovering the secondary structure of RNA is important and necessary for modeling the tertiary structure and further exploring the potentialities of interactions between RNA structures and other biomolecules, such as proteins and ligands, which is crucial for drug design and RNA-based therapies8,9. As the field of RNA research continues to expand, so does the need for precise and reliable detection of RNA secondary structures. Chemical probing techniques, such as Selective 2\u2032-Hydroxyl Acylation analyzed by Primer Extension10, provide a way to infer the secondary structure of RNA molecules by selectively probing the reactivity of the RNA nucleotides. Advances in computational methods that predict RNA secondary structures from sequence data alone have greatly improved the effectiveness and efficiency for modeling RNA secondary structures, which enhances our fundamental knowledge of RNA biology and paves the way for innovative applications in medicine, biotechnology, and beyond11.\n\nDuring the past three decades, various computational methods have been developed to predict RNA secondary structures, such as comparative sequence analysis and thermodynamic models. Comparative sequence analysis12,13,14,15 predicts structures by searching for homologous sequences, which is effective and accurate when the target sequence is hit in the homologous sequences database. However, the number of known RNA families is a few thousand in Rfam16,17, resulting in poor generalizability and accuracy of the comparative methods for unknown sequences. Thermodynamic models18,19,20,21,22,23,24,25,26,27,28,29 aim at finding the best structure from thermodynamically stable candidates that are selected through free energy minimization. These methods assign each structure with a score, with parameters obtained from experiments, such as Vienna RNAfold22,30, RNAstructure23,31, and EternaFold24). Also, CONTRAfold, ContextFold, and EternaFold can be categorized into shallow machine learning (ML) methods. These non-ML methods and shallow learning methods are effective in predicting simple target secondary structures that only contain nested base pairs, while they have problems dealing with complex structures such as non-nested pairs (pseudo-knots), and cannot predict non-canonical base pairs compared to end-to-end deep learning (DL) methods32.\n\nIn recent years, DL methods33,34,35,36,37,38 raise researchers\u2019 significant attention, greatly boost the speed of prediction, and achieve high accuracy. As data-driven approaches, DL methods utilize the benefit of big data and learn the implicit features and intrinsic of data distribution in hidden space via deep neural networks. Once the neural network has learned to build the mapping and the relationship between input data (RNA sequence) and output data (RNA secondary structure), it can predict the secondary structure of an unknown arbitrary input RNA sequence. For instance, Singh et al.35 propose SPOT-RNA, an ensemble of two-dimensional deep neural networks equipped with transfer learning on a high-quality dataset. Fu et al.34 develop a U-shaped fully convolutional image-to-image network, named UFold, which converts an RNA sequence into an image-like representation to predict RNA secondary structure. Sato et al.38 propose MXfold2 that learns RNA folding scores by integrating Turner\u2019s nearest-neighbor free energy parameters into deep neural networks.\n\nAlthough existing DL methods behave well on currently known test datasets, their performances degrade rapidly as sequence similarity decreases in situations of unseen RNA families and data distributions compared to non-ML methods32,39,40, which indicates poor generalizability and the possibility of overfitting on training datasets. To mitigate this, researchers resort to integrating auxiliary information into DL models. For example, UFold34 uses an alternative representation of the RNA sequence derived from CDPfold41 to enhance the relationship between the input RNA sequence and thermodynamic prior of base pairs, SPOT-RNA35 takes full advantage of evolutionary information, and MXfold238 regularizes the learning of the model with a penalty loss on the folding score for deviating too far from thermodynamic estimation. With the help of auxiliary information, these methods have made some progress in improving prediction accuracy. However, a general data insufficiency and low data quality problem plaques RNA structure prediction, including secondary structure prediction. Unlike protein structure prediction, which possesses a sufficiently large number of high-quality data to represent the underlying distribution, which guarantees the effectiveness of DL methods such as AlphaFold42,43, the number, quality, and coverage of available RNA structure data are relatively very low44. Therefore, for RNA secondary structure prediction, how to develop a reliable DL model under such data insufficiency and further deal with out-of-distribution samples is an unsolved problem, hindering further improvements in the accuracy and generalizability of DL learning models.\n\nIt is known that enriching data at the secondary structure level is quite hard for both experimental and computational methods. Instead, it is more computationally efficient to predict the tertiary structure of short-sequence RNA motifs. Luckily, RNA secondary structure is mainly dependent on the structure motifs45. Motivated by these and different from previous attempts that integrate knowledge prior into data-driven models incompletely, we propose to leverage the local short-distance interactions of base pairs and enumerate the whole space of adjacent neighboring patterns of all canonical base pairs, named as base pair motif, aiming at enriching data at the base-pair level completely.\n\nIn this paper, we propose BPfold, a DL model integrated with thermodynamic energy from the complete space of the upstream and downstream of three-neighbor base pair motifs for predicting RNA secondary structures. BPfold comprises two key components:\n\nBase pair motif energy. A base pair motif is a canonical base pair (i.e., A-U, U-A, G-C, C-G, G-U, and U-G) together with its local spatially adjacent bases, which dominates the local structure of the base pair. We explore the entire space of the base pair motifs of r neighbors and compute their energy by de novo modeling the tertiary structure of the base pair motif. After storing the computed tertiary structures and corresponding energy items in the motif library, we can quickly obtain the base pair motif energy for any base pair of any arbitrary input RNA sequence. Thereby, this auxiliary input energy fully covers the data distribution at the base-pair level, mitigating the current insufficient database, and eliminating the major hurdle posed by the generalizability of de novo DL models.\n\nBase pair attention. In the BPfold neural network, we elaborately design a base pair attention block, which combines transformer46 and convolution47 layers and enables information integration between RNA sequence and base pair motif energy. The base pair attention block aggregates the attention map of the RNA sequence and the base pair motif energy to effectively learn the base pair knowledge from the RNA sequence. BPfold takes advantage of the DL approach, predicts the accurate RNA secondary structure in seconds, and has great generalizability that accounts for the learned knowledge of thermodynamic energy.\n\nWe conduct sequence-wise and family-wise cross-validation experiments on multiple benchmark datasets to evaluate the accuracy and generalizability of BPfold. ArchiveII48 (3966 RNAs) and bpRNA-TS049 (1305 RNAs) are sequence-wise datasets while Rfam12.3\u201314.1016,17 (10,791 RNAs) contains cross-family RNA sequences and PDB50 (116 RNAs) consists of high-quality experimentally validated RNA structures. Quantitative and qualitative results demonstrate the superiority of the proposed BPfold in accuracy and generalizability against other learning-based methods and non-learning methods. We expect this work will take a meaningful step toward fast and robust prediction of RNA secondary structures.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "In this work, we present BPfold (Fig.\u00a01a), a DL approach integrated with thermodynamic energy for RNA secondary structure prediction. Aiming at improving the generalizability and accuracy of the DL-based model, we compute the base pair motif energy and design a base pair attention neural network block. As demonstrated in Fig.\u00a02, a base pair motif is a canonical base pair (i.e., A-U, U-A, G-C, C-G, G-U, and U-G) together with their neighboring bases. We compute the de novo RNA tertiary structure and obtain its thermodynamic energy of the complete space of r-neighbor base pair motif to mitigate the insufficient coverage of base pair in existing datasets. Furthermore, as illustrated in Fig.\u00a01b, we equip BPfold with a custom-designed base pair attention block (described in Section \u201cDeep neural network with base pair attention\u201d), which applies an attention mechanism to the base pair motif energy (described in Section \u201cBase pair motif energy as thermodynamic prior\u201d) and RNA sequence feature to perfectly learn representative knowledge from RNA sequence and thermodynamic energy.\n\na BPfold takes RNA sequence and corresponding base pair motif energy map generated from base pair motif library as inputs, consisting of transformer blocks with designed base pair attention, and outputs contact map. After applying physical constraints to the contact map in refinement procedures, we obtain the final predicted secondary structure. b The detailed structure of the proposed base pair attention block which jointly fuses the sequence features Xi and energy matrix features Mi for enhanced learning of base pair interactions. When computing self-attention, Q,\u00a0K,\u00a0and V represent query, key, and value matrixs, respectively. c The detailed structure of convolution block in base pair attention block. d The detailed structure of squeeze & excitation (SE) block in convolution block.\n\na For any canonical base pair (i.e., A-U, U-A, G-C, C-G, G-U, and U-G) in an RNA sequence, the upstream and downstream three neighboring bases (denoted as N, an arbitrary base of A, U, G, or C) of the base pair form two base pair motifs, an inner base pair motif with neighboring bases extending to the middle of the RNA sequence, and an outer base pair motif with neighboring bases extending to both ends of the RNA sequence. Note that the inner base pair motif can be categorized into inner hairpin base pair motif and inner chain-break base pair motif (demonstrated in this figure) in accordance with the distance of the paired bases. b For any canonical base pair (i,\u00a0j) from an RNA sequence of L nucleotides, we firstly find the corresponding outer/inner base pair motifs of this base pair and then query the energy items in the base pair motif library, which forms the (i,\u00a0j) element of the outer/inner energy maps in a shape of L\u2009\u00d7\u2009L.\n\nAs Fig.\u00a02 shows, we define three categories of base pair motifs, namely (inner) hairpin base pair motif, inner chainbreak base pair motif and outer chainbreak base pair motif, which are denoted as BPMiH, BPMiCB, and BPMoCB, respectively. We build the base pair motif library by modeling the tertiary structures of all three-neighbor base pair motifs and storing corresponding energy items in the motif library. Specifically, each tertiary structure of base pair motif is computed by our previous de novo RNA structure modeling method BRIQ51, which employs Monte Carlo (MC)52 algorithm to sample candidate RNA tertiary structures and evaluates the BRIQ energy score of each sampled tertiary structure. BRIQ energy score is a combined energy score of physical energy using density functional theory and statistical energy calibrated by quantum mechanism, supplying a trade-off measurement of thermodynamic energy between computational speed and accuracy. Furthermore, each energy score of the base pair motif is normalized according to its sequence length and motif category. After obtaining all energy items, given an RNA sequence of length L, for any base i and base j, we built two energy maps in the shape of L\u2009\u00d7\u2009L. One for the outer base pair motif and the other for the inner base pair motif denoted as M\u03bc and M\u03bd, respectively, which are used as input thermodynamic information by the BPfold neural network.\n\nAfter constructing the base pair motif library, we make a detailed analysis of this library, which is displayed in Fig.\u00a03. Firstly, we demonstrate the coverage of the base pair motif in current datasets. As Fig.\u00a03a shows, the RNAStrAlign dataset contains adequate chainbreak base pair motifs with only 249 chainbreak motifs missing. However, as for the hairpin motifs, RNAStrAlign misses 24,576 motifs, weighting 32.3% of total 75,990 motifs, covering a small data distribution, which hinders the pattern recognition of hairpin base pair motifs for DL models (See the data coverage of the other four datasets and the intersection of base pair motifs from these datasets in Supplementary Figs.\u00a01 and 2, respectively). Furthermore, we store the intermediate results of the base pair motif energy map when BPfold predicts secondary structures from the third convolutional layer, and then apply t-SNE53 decompression to these feature maps to visualize the latent embeddings of base pair energy maps from the six largest RNA families (accounting for approximate 90%) of ArchiveII dataset. As Fig.\u00a03b demonstrates, BPfold learns discriminative embeddings of family-wise RNAs effectively, projecting the features of energy maps to a wide-range scattered latent vectors. In addition, we visualize the heatmap of the stored intermediate feature map of the base pair energy map from the third convolutional layer in Fig.\u00a03d. Compared with the ground truth heatmap displayed in Fig.\u00a03c, the feature map correctly captures the interactions of base pairs, annotated in black circles, which indicates that base pair motif energy assists the neural network with the interpretation of base pair interactions. Although there are false positive interactions annotated in yellow circles, these weak responses will be eliminated by subsequent transformer layers and refinement procedures. In view of the above analysis of the base pair motif library, we expect that our proposed base pair motif library that takes into account tertiary structures and energy items can positively contribute to any computational method.\n\na Pie visualization of the data coverage of hairpin and chainbreak (including inner and outer) base pair motifs in current datasets, which are denoted as BPMiH, BPMiCB, and BPMoCB, respectively. The outer ring of each pie chart represents the hairpin and chainbreak distribution in the whole base pair motif library (75,990 motifs, blue for hairpin motifs, brown for chainbreak motifs). The inner ring represents the hairpin and chainbreak distribution in each dataset (light blue for hairpin motifs, light brown for chainbreak motifs, and grey for missing motifs). b t-SNE visualization of the latent feature map of base pair motif energy map at the third convolutional layer from various RNA families in ArchiveII dataset (n\u2009=\u20093966 RNAs). c Ground truth heatmap visualization of the secondary structure of an example RNA sequence. d Heatmap visualization of the extracted latent feature map of the same RNA sequence from subfig (c). The corrected responses of base pair interactions are annotated in black circles.\n\nTo investigate the effectiveness of the proposed base pair motif energy, which is the key contribution of BPfold, we conduct ablation studies to demonstrate the performances of BPfold (1) with and without base pair motif energy; (2) with one category of base pair motif energy. In this experiment, we train BPfold on training datasets of RNAStrAlign54 and bpRNA-1m49 under five different configurations of base pair motif energy: (1) with BPM (all); (2) without BPM; (3) with BPMiH; (4) with BPMiCB; (5) with BPMoCB, respectively, and evaluate them on family-wise dataset Rfam12.3\u201314.1016,17.\n\nAs shown in Fig.\u00a04a and Supplementary Table\u00a02, BPfold with all base pair motif energy achieves INF, F1, precision and recall of 0.694, 0.689, 0.660, and 0.741, respectively, behaving better than any other configurations under all metrics (\u2200 p value\u2009<\u20090.001 using one-sided t-test on F1 score, such as BPMiCB with p value\u2009=\u20096.066\u221217), indicating that each category of base pair motif energy is essential for addressing the gaps in data distribution at the base-pair level regarding thermodynamic energy, significantly enhancing performance on unseen data and providing BPfold with improved generalizability and robustness. Figure\u00a04b and Supplementary Table\u00a02 demonstrate the detailed results of BPfold with and without base pair motif energy on the Rfam12.3\u201314.10 dataset and its five specific RNA families with the most RNA sequences: Cobalamin (riboswitches that regulate adjacent genes), skipping-rope (RNA motifs likely function in translate as small RNAs), Twister-P1 (ribozymes), Cyclic di-GMP-II (riboswtiches that are common in species within the class Clostridia and the genus Deinococcus) and RAGATH-18 (self-cleaving ribozymes in bacteria). BPfold with base pair motif energy achieves much better performances than BPfold without base pair motif energy on these unseen RNA families, indicating that the thermodynamic energy greatly improves the generalizability and accuracy of DL models.\n\na Ablation study of BPfold under five configurations of BPM on family-wise dataset Rfam12.3\u201314.10 (n\u2009=\u200910,791 RNAs): (1) with BPM (all); (2) without BPM; (3) with BPMiH; (4) with BPMiCB; (5) with BPMoCB. Data are presented as mean values\u2009\u00b1\u2009SD. b Ablation study of BPfold with and without base pair motif energy on Rfam12.3\u201314.10 (n\u2009=\u200910,791 RNAs) and its five specific RNA: Skip-rope (n\u2009=\u2009240 RNAs), Twister (n\u2009=\u2009236 RNAs), C-di-G (n\u2009=\u2009197 RNAs), RAGATH (n\u2009=\u2009160 RNAs), and Cobalamin (n\u2009=\u2009286 RNAs). The 25th percentiles, median, 75th percentiles are shown as dashed lines from bottom to top, while whiskers spanning the full data range of [0,\u00a01]. With the integration of all base pair motifs, BPfold improves the prediction accuracy by a large gap on unseen RNA families.\n\nAdditionally, we make head-to-head comparisons of BPfold with BPM energy compared to the other four configurations on Rfam12.3\u201314.10. As shown in Supplementary Fig.\u00a03, head-to-head analysis demonstrates that the accuracy of BPfold with (all) base pair motif energy is above that of any other configuration in the majority of sample points, indicating the advantage of base pair motif energy is much clearer on improving accuracy and generalizability of learning-based data-driven neural networks.\n\nIn this and the following subsection, we compare proposed BPfold with other nine state-of-the-art methods, including (1) DL methods: SPOT-RNA (committed on Jun. 23, 2022)35 and MXfold2 version 0.1.238; (2) Shallow learning methods: ContextFold version 1.019, CONTRAfold version 2.0218, and EternaFold version 1.3.124; (3) Non-learning methods: Linearfold (committed on Aug. 29, 2022)21, RNAfold in ViennaRNA package version 2.6.422,30, SimFold25 in MultiRNAFold55 package version 2.0 in RNAsoft package56 and RNAstructure version 6.423,31. The training datasets are the same for trainable models (i.e., BPfold, SPOT-RNA, MXfold2, CONTRAfold), namely RNAStrAlign54 and bpRNA49, except SPOT-RNA that employs PDB dataset50 for transfer learning and can not be retrained because it does not disclose training module, while the other methods use default parameters. For a fair comparison with SPOT-RNA, we train BPfold in a manner of 5-fold cross-validation and apply early stopping to prevent over-fitting. All methods are evaluated on sequence-wise test datasets that contain distinguished sequences from training datasets and family-wise test datasets that consist of unseen RNA families as out-of-distribution validation under measurements of interaction network fidelity (INF)57, F1-score, precision, and recall (sensitivity).\n\nTo evaluate the performance of our BPfold model on sequence-wise datasets, we report the results of BPfold on ArchiveII48 test set and bpRNA-TS049 test set, compared with the above DL methods, shallow learning method, and non-learning methods. Same as previous DL methods34,36,38, for fair comparison and resource saving, BPfold is trained on RNA sequences with lengths no more than 600 nucleotides from RNAStrAlign54 and bpRNA49 datasets.\n\nAs Fig.\u00a05a and Table\u00a01 demonstrate, on bpRNA-TS0 dataset, non-ML methods achieve an average F1 score in the range of [0.507, 0.530] and shallow learning (SL) methods achieve an average F1 score in the range of [0.516, 0.547], dropping behind DL methods such as 0.575 of Mxfold2 and 0.625 of SPOT-RNA. With the base pair motif energy, BPfold further improves the performance and obtains an average F1 score of 0.658, leading ahead of other methods by a remarkable gap (\u2200 p value\u2009<\u20090.001 using one-sided t-test on F1 score, such as SPOT-RNA with p value\u2009=\u20098.988\u2009\u00d7\u200910\u22124, MXfold2 with p value\u2009=\u20094.297\u2009\u00d7\u200910\u221214, and ContextFold with p value\u2009=\u20093.718\u2009\u00d7\u200910\u221236). Compared with the previous state-of-the-art method SPOT-RNA, BPfold achieves an about 5% increase in F1 score and INF metric. As for the ArchiveII dataset, Fig.\u00a05b and Table\u00a01 demonstrate similar rankings except ContextFold obtains the second place. BPfold also reaches the highest prediction accuracy, achieving an average F1 score of 0.820 and an INF of 0.823, significantly outperforming any other methods (\u2200 p value\u2009<\u20090.001 using one-sided t-test on F1 score, such as SPOT-RNA with p value\u2009=\u20095.662\u2009\u00d7\u200910\u221276 and MXfold2 with p value\u2009=\u20091.119\u2009\u00d7\u200910\u2212121) except ContextFold (p value\u2009=\u20090.345 using one-sided t-test on F1 score). ContextFold obtains an F1 score of 0.818 and an INF of 0.820 on the ArchiveII dataset, slightly lower than that of BPfold. In general, BPfold predicts more accurate RNA secondary structures compared with other methods on these sequence-wise datasets from the same data distribution.\n\nDeep learning methods, shallow learning methods, and non-learning methods are marked as blue, brown, and green, respectively. The median is marked as white, while the 25th and 75th percentiles are indicated by the bottom and top of black band, respectively. Each whisker spans the full data range of [0,\u00a01]. a Sequence-wise dataset bpRNA-TS0 (n\u2009=\u20091305 RNAs). b Sequence-wise dataset ArchiveII (n\u2009=\u20093966 RNAs). c Family-wise dataset Rfam12.3\u201314.10 (n\u2009=\u200910,791 RNAs). d High-quality dataset PDB (n\u2009=\u2009116 RNAs).\n\nWith the same configurations and training procedures, we further evaluate our BPfold model on family-wise datasets of unseen RNA families from out-of-distribution data to verify its model generalizability. Since the training set bpRNA contains RNA sequences from Rfam version 12.2, we collect Rfam12.3\u201314.10 dataset from Rfam16,17 database by retaining the newly added unseen families of version 14.10 from version 12.3, which contains 10,791 RNA sequences from 1992 RNA families after removing similar sequences by CD-HIT-EST58 with a threshold of 80%.\n\nAs Fig.\u00a05c and Table\u00a02 demonstrate, DL methods obtain satisfying performances even though the family-wise test dataset is out of distribution, such as Mxfold2 obtains F1 score of 0.664 on Rfam12.3\u201314.10 and SPOT-RNA achieves an F1 score of 0.672. As mentioned above, Mxfold2 takes advantage of integrating free energy minimization into the DL model and SPOT-RNA utilizes evolutionary information. However, these kinds of auxiliary information are not complete and bias-free, which hinders the overall performance of unseen data. In contrast, BPfold outperforms any other DL methods and non-DL methods (\u2200 p value\u2009<\u20090.001 using one-sided t-test on F1 score, such as SPOT-RNA with p value\u2009=\u20093.403\u2009\u00d7\u200910\u22128) with the help of thermodynamic energy, exploring the complete space of 3-neighbor base pair motif and mitigating the out-of-distribution data at base-pair level, achieving the best F1 score of 0.689 and INF of 0.694. However, non-DL methods also achieve comparable performances with the help of thermodynamic parameters and physical laws, such as EternaFold and RNAstructure. We also evaluate the performances of the above methods on bpRNA-new, a subset of Rfam12.3\u201314.10, which contains 5401 RNAs with a maximum sequence length of 439 nucleotides from Rfam version 12.3 to Rfam version 14.2. As Supplementary Table\u00a03 shows, BPfold also wins first place on the bpRNA-new dataset, obtaining an F1 score of 0.647 and an INF of 0.655, indicating the great power of the proposed base pair motif for improving the generalizability in family-wise evaluation. It is worth mentioning that the non-ML method LinearFold achieves the second-best precision of 0.677 compared to the best precision of 0.678 and shallow learning method EternaFold which employs thermodynamic parameters wins the first place in recall of 0.746, which indicates the effectiveness of non-ML approaches in modeling RNAs from new families to some extent.\n\nFurthermore, we evaluate BPfold on PDB, a widely used benchmark dataset that contains high-resolution RNA X-ray tertiary structures. We divide this set into three test sets as SPOT-RNA does, namely TS1, TS2, and TS3, which contain 60, 38, and 18 sequences, respectively. As Fig.\u00a05d, Table\u00a02, and Supplementary Table\u00a03, 4 demonstrate the F1 score, precision, and recall metrics of canonical pairs (i.e., A-U, U-A, G-C, C-G, G-U, and U-G), BPfold achieves an F1 score of 0.814 and an INF of 0.817, behaves better than any other methods, demonstrating high prediction accuracy and strong generalizability in detecting dense canonical pairs of RNA secondary structures on this high-quality experimentally validated dataset. While the F1 score of BPfold is not significantly better than SPOT-RNA (p value\u2009=\u20090.407 using one-sided t-test on F1 score) and MXfold2 (p value\u2009=\u20090.086), BPfold predicts more accurate structures than other methods, such as ContextFold (p value\u2009=\u20090.005), CONTRAfold (p value\u2009=\u20090.010), EternaFold (p value\u2009=\u20090.026). SPOT-RNA utilizes the PDB dataset for transfer learning, which could explain why the difference in F1 score between BPfold and SPOT-RNA on the PDB dataset is minimal.\n\nTo demonstrate the efficiency of RNA secondary structures prediction methods and evaluate the prediction time at the inference stage, we conduct experiments on a selected dataset that contains 174 RNAs with lengths varying from 60 to 1851 nucleotides uniformly. To decrease the influence of randomness, we repeat 10 times for each RNA sequence. We select DL methods SPOT-RNA and MXfold2, shallowing learning method CONTRAfold, and non-learning method LinearFold for comparison because it is particularly hard and slow for some methods to predict long RNA sequences such as RNAstructure. The results demonstrated in Supplementary Fig.\u00a04 show that BPfold predicts RNA secondary structures within 10\u2009s for RNAs no longer than 1000 nucleotides, and within 40\u2009s for RNAs no longer than 1851 nucleotides, which is comparable with MXfold2 and CONTRAfold. SPOT-RNA takes several times longer time for predicting long sequences while LinearFold is the fastest method that predicts secondary structures within 1 second for RNAs no longer than 1851.\n\nApart from the above quantitative experimental analysis, we also provide qualitative visualization of the RNA secondary structures predicted by BPfold to verify the detailed interactions of each nucleotide. To achieve this, we utilize the RNA visualization tool VARNA59 to draw the figure, which takes various formats of RNA secondary structures, such as dot-bracket notation, bpseq, and ct format. For comparison, we also show the structures of native annotations (ground truth structures) and other two excellent methods, the deep-learning method SPOT-RNA and the traditional method CONTRAfold. As Fig.\u00a06 visualizes, each column displays the structures of one method and each row displays one example. In these samples, structures predicted by BPfold are similar to native structures, and more accurate and robust than other methods. Specifically, Fig.\u00a06a shows the superiority of BPfold in precision on an example of Brucella abortus S19 signal recognition particle RNA, with 97% precision and 94% recall, respectively. Meanwhile, Fig.\u00a06b displays an example of the delta-J-delta-K domain of EMCV IRES60 from PDB50 dataset (PDB ID\u2009=\u20092NC1) in radiate style, where lines with blue solid circle denote non-canonical pairs. As it shows, BPfold has the ability to predict non-canonical pairs, outperforming other methods, with 100% precision and 93% recall, respectively. Figure\u00a06c displays the structures of the lariat capping ribozyme61 from PDB50 dataset (PDB ID\u2009=\u20094P95) in a circular style. This RNA sequence is a long sequence with 192 nucleotides and dense interactions. BPfold successfully models the long-range connections and predicts the most accurate structure than other methods, with 93% precision and 96% recall, respectively. Furthermore, we display the effect of applying removing isolated base pairs in refinement procedures. As Supplementary Fig.\u00a05a demonstrates, compared with Supplementary Fig.\u00a05b, there are many long-distance isolated base pairs marked in red, which are irrational and unstable, distorting the local structure of the loop region. After refinement, these isolated base pairs are removed.\n\na An example of Brucella abortus S19 signal recognition particle from ArchiveII48 dataset (srp_Bruc.suis._AE014291), displayed in radiate style. From left to right, these structures are from native, BPfold, SPOT-RNA, and CONTRAfold, respectively. BPfold predicts the most accurate interactions compared with native reference structure, with 97% precision and 94% recall, respectively. b The solution structure of the delta-J-delta-K domain of EMCV IRES60 from PDB50 dataset (PDB ID\u2009=\u20092NC1), displayed in radiate style, where lines with blue solid circle denote non-canonical pairs. From left to right, these structures are from native, BPfold, SPOT-RNA, and CONTRAfold, respectively. BPfold can correctly predict canonical pairs and non-canonical pairs, with 100% precision and 93% recall, respectively. c The crystal structures of the lariat capping ribozyme61 from PDB50 dataset (PDB ID\u2009=\u20094P95), displayed in circular style. In such a dense prediction situation, BPfold predicts the most accurate base interactions than other methods, with 93% precision and 96% recall, respectively.\n\nTo measure the reliability of the predicted secondary structures, we further construct a confidence index, which supplies a quality assessment of predicted secondary structures in case the native structures are not available. In BPfold, the neural network directly generates the contact map \\(\\tilde{Y}\\in {R}^{L\\times L}\\), then we apply structural constraints on \\(\\tilde{Y}\\) in refineme procedures, obtaining the final contact map \\({\\tilde{Y}}_{{{{\\rm{refine}}}}}\\in {R}^{L\\times L}\\). Therefore, the difference between \\(\\tilde{Y}\\) and \\({\\tilde{Y}}_{{{{\\rm{refine}}}}}\\) reflects the reliability and quality of predicted structures to some extent, which means that the less difference between \\(\\tilde{Y}\\) and \\({\\tilde{Y}}_{{{{\\rm{refine}}}}}\\) indicates the more reliability and accuracy of the output contact map that meets the structural constraints. As a result, to form the confidence index, we compute the cosine similarity between \\(\\tilde{Y}\\) and \\({\\tilde{Y}}_{{{{\\rm{refine}}}}}\\) and scale it to the range of [0,\u00a01], which can be formulated as:\n\nwhere k\u2009=\u20091.522 and b\u2009=\u2009\u22120.086 are empirically selected coefficients for adjusting the range of confidence index to [0,\u00a01] according to predicted structures from all currently available test datasets. For robust output, the final confidence index will further be truncated to 0 or 1 if it exceeds the range of [0,\u00a01].\n\nTo demonstrate the validity of the proposed confidence index, we compute the two-sided Pearson\u2019s correlation coefficient between confidence indexes and F1 scores on five datasets (i.e., bpRNA-TS0, bpRNA-new, ArchiveII, Rfam12.3\u201314.10, PDB) together with mixed total datasets which consisting of 16,178 different RNAs. As Supplementary Table\u00a05 shows, we achieve high correlation coefficients of 0.641, 0.676, 0.728, 0.692, 0.675, and 0.658 with low p-values, respectively, which indicates the correlation between confidence index and F1 score and further suggests that our proposed confidence index provides us a referable and reliable insight of the predicted RNA secondary structures. Figure\u00a07 and Supplementary Fig.\u00a06 display direct views of the strong correlation between the designed confidence index and the F1 score metric on different datasets.\n\nWe compute the cosine similarity between the contact map generated from the neural network and the contact map after refinement. The Pearson correlation coefficient between the prediction accuracy (F1 score) and confidence index reaches 0.728 and 0.692, respectively. The approximate relation between F1 and CI, together with the correlation coefficients are displayed in the bottom right corner of each figure. a archiveII (n\u2009=\u20093966 RNAs), with 95% confidence interval\u2009=\u2009[0.713,\u00a00.742]. b Rfam12.3\u201314.10 (n\u2009=\u200910,791 RNAs), with 95% confidence interval\u2009=\u2009[0.682,\u00a00.702].", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60048-1/MediaObjects/41467_2025_60048_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60048-1/MediaObjects/41467_2025_60048_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60048-1/MediaObjects/41467_2025_60048_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60048-1/MediaObjects/41467_2025_60048_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60048-1/MediaObjects/41467_2025_60048_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60048-1/MediaObjects/41467_2025_60048_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60048-1/MediaObjects/41467_2025_60048_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our method, BPfold, aiming at improving the generalizability and accuracy of DL approaches, creatively proposes the complete space of a 3-neighbor base pair motif and its thermodynamic energy to tackle the problem of data insufficiency and out-of-distribution situation at the base-pair level and further bridges the knowledge prior with input RNA sequence by the elaborately designed base pair attention neural network block. The designed transformer network architecture and energy map representation facilitate the identification of long-distance interactions and the extension to unseen long RNA sequences. Experimental results on sequence-wise datasets and family-wise datasets confirm the superiority of BPfold over other methods in accuracy, generalizability, robustness, and inference speed. Additionally, we construct a confidence index for BPfold to provide a reference for the reliability of predicted RNA secondary structures. BPfold is publicly available and serves as an effective tool for RNA structure modeling.\n\nMore importantly, the proposed base pair motif and the idea of combining thermodynamic prior with input RNA sequences can be integrated into any other data-driven models. For instance, UFold34 processes an image-like representation of RNA sequences and an alternative matrix representation41 in consideration of possible hydrogen bonds of canonical base pairs, in which this matrix representation can be readily replaced by the energy map of base pair motif presented in this study. By doing so, the neural network achieves a complete perception of the interactions of a 3-neighbor base pair, much more accurate than the roughly counted number of hydrogen bonds. Besides, the computed energy scores of base pair motifs by the BRIQ51 force field supplies a reliable estimation of base pair interactions, which works effectively in other thermodynamic energy-based methods. In fact, EternaFold24 obtains thermodynamic measures via high-throughput experiments to update the parameters in CONTRAfold18. Similarly, it is possible to apply the framework of CONTRAfold with the statistical energy scores of base pair motifs. Note that this set of base pair motif energy scores can be further updated with the development of BRIQ or other tertiary structure de novo modeling methods.\n\nDespite the innovations of BPfold in RNA sequence analysis, BPfold confronts several challenges. Firstly, on the one hand, the base pair motif can be further extended and more reliable by computing more neighboring bases, such as four or more upstream and downstream bases. In this study, we only model the tertiary structures of 3-neighbor base pair motifs using BRIQ51 due to the large computation cost. On the other hand, we could introduce more knowledge prior in extra forms rather than base pair motifs, such as RNA family information and SHAPE10 data, to make a better understanding of the input RNA sequences and further enhance the generalization and accuracy of the models. Secondly, existing RNA secondary structure datasets consist of RNA sequences whose lengths are mainly less than 600 nucleotides or shorter, and existing DL methods, including BPfold, are also trained on these sequences. Therefore, to relieve the performance degradation brought by long sequences, BPfold applies a dynamic positional embedding for scalable feature embedding in the base pair attention block. However, the long sequence problem still remains unsolved; we believe that the key is to enrich the distribution of long sequences in the current training dataset. Thirdly, the prediction of non-canonical pairs is difficult for data-driven methods since there are few annotations (only in the PDB dataset) of these interactions for DL approaches to learn from. Previous DL methods34,35,36,37 did not design a specific strategy or module to predict non-canonical pairs. As a result, the best F1-score of these methods is pretty low, only reaching 0.22 as benchmarked in ref. 32. Regardless of that, BPfold has the ability to predict non-canonical pairs and pseudo knots (Exemplified in Fig.\u00a06), and the accuracy of BPfold for these interactions would be greatly improved when corresponding data annotations for training are adequate in the future. Further work may apply few-shot learning, domain-adaption, semi-supervised learning, and data augmentation to tackle this problem progressively.\n\nThe generalizability of DL models on newly discovered unseen RNA families is an inevitable issue in current research. Technically speaking, there are various common techniques to relieve the overfitting of models. As for training, we can apply early stopping to stop the learning of models before the model tends to overfitting on training data. Besides, as for the model, we also apply multiple-fold cross-validation to decrease the systematic error and select the best hyper-parameters of model architecture according to the amount of data. In this study, we innovatively propose the base pair motif and energy map from the data aspect to fully cover the data distribution at the base-pair level. BPfold receives the information not only from the RNA sequence but also the energy map of all paired canonical pairs in this RNA sequence, which allows the specific prediction of each input RNA sequence and hinders the overfitting of similar RNA sequences. As a result, evidenced in experiments on family-wise dataset Rfam12.3\u201314.10, BPfold achieves the best performances against other methods, revealing the great generalizability and accuracy in modeling RNA secondary structures from unseen RNA families. Apart from base pair motif, high-throughput chemical probing data24,40 such as SHAPE62 can be applied to provide information of the chemical activity of nucleotide bases. Note that because the auxiliary information involved in existing DL methods such as BPfold, MXfold2, UFold, and SPOT-RNA is implicitly learned by neural networks, further improvements in modeling RNA secondary structures can be achieved when learning approaches can explicitly integrate physical laws into neural networks.\n\nIn summary, we show a great prospect of BPfold in improving generalizability and accuracy with base pair motif for RNA secondary structure prediction, expecting that BPfold will inspire further advancements in the integration of physical priors with DL techniques, as well as enhance our understanding of RNA structures and their biological functions.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The performance and generalizability of DL models are highly dependent on training data, which currently may be hampered by the lack of structural data and the limitation of data diversity. To tackle this, we aim at improving the coverage of data at the base-pair level and bringing thermodynamic energy to RNA sequence modeling. In view of the locality of the secondary structures of RNA, we define the base pair motif, a canonical base pair (i.e., A-U, U-A, G-C, C-G, G-U, and U-G) together with the r upstream and downstream neighboring bases of each base.\n\nSpecifically, base pair motifs can be divided into three categories, for any two bases indexed as i and j(i\u2009<\u2009j) of an RNA sequence: (1) Inner hairpin base pair motif (BPMiH): While neighboring bases extend to inner sequence and j\u00a0\u2212\u00a0i\u2009\u2264\u20092r, the downstream of the base i and the upstream of base j are continuous in sequence and form a hairpin loop, with the base pair motif denoted as [i,\u00a0i\u00a0+\u00a01,\u00a0\u2026,\u00a0j]. There are \\(\\mathop{\\sum }_{i=3}^{6}{4}^{i}=5440\\) sequences for each canonical base pair of this category. (2) Inner chain-break base pair motif (BPMiCB): While neighboring bases extend to inner sequence and j\u00a0\u2212\u00a0i\u2009>\u20092r, the downstream of base i is not continuous with the upstream of base j, forming a chain-break. Therefore, the base pair motif consists of two chains, denoted as [i,\u00a0i\u00a0+\u00a01,\u00a0\u2026,\u00a0i\u00a0+\u00a0r :\u00a0j\u00a0\u2212\u00a0r,\u00a0j\u00a0\u2212\u00a0r\u00a0+\u00a01,\u00a0\u2026,\u00a0j], where : represents chain-break of RNA sequence. There are 46\u2009=\u20094096 sequences for each canonical base pair of this category. (3) Outer base pair motif (BPMoCB): While neighboring bases extend to outer both ends of the sequence, the base pair motif is denoted as [j,\u00a0j\u00a0+\u00a01,\u00a0\u2026,\u00a0j\u00a0+\u00a0r :\u00a0i\u00a0\u2212\u00a0r,\u00a0i\u00a0\u2212\u00a0r\u00a0+\u00a01,\u00a0\u2026,\u00a0i]. Besides that, we also deal with special corner cases while base i or base j has no sufficient r neighboring bases upstream or downstream. There are \\(\\mathop{\\sum }_{i,j=1,(i,j)\\ne (3,3)}^{3}{4}^{i}\\times {4}^{j}=3129\\) sequences for each canonical base pair of this category. In total, there are 6\u2009\u00d7\u2009(5440\u00a0+\u00a04096\u00a0+\u00a03129)\u2009=\u200975990 base pair motifs for all six kinds of canonical base pairs (i.e., A-U, U-A, G-C, C-G, G-U, and U-G). In the formation of these, the paired bases are always located at the beginning and the end of each category of base pair motif.\n\nAfter enumerating all possible sequences of r-neighbor base pair motifs, we use our previous de novo RNA tertiary structures modeling method BRIQ51 to model the tertiary structure of each base pair motif and extract energy score Ebpm(Lbpm,\u00a0Ibreak) estimated by BRIQ force field of whole motif sequence, where Lbpm is the length of base pair motif and Ibreak represents whether there is a chain-break in this motif. Then we compute the energy score Ebpm of each motif by eliminating the influence of a single strand and normalizing the value with the maximum energy of the motif with the same length in the same category, which can be formulated as:\n\nFinally, we establish a base pair motif library with these energy items and tertiary structures. As Fig.\u00a02 demonstrates, with this energy table, for any two bases of RNA sequence with index being i and j(i\u2009<\u2009j), we query the outer/inner base pair motif energy items of corresponding outer/inner base pair motif for any canonical base pair (i.e., A-U, U-A, G-C, C-G, G-U, and U-G) and we set the energy of any other non-canonical pair to zero. Therefore, for an RNA sequence with L nucleotides, we can obtain the outer/inner energy maps M\u03bc and M\u03bd in the shape of L\u2009\u00d7\u2009L.\n\nAs shown in Fig.\u00a01, BPfold is a deep neural network consisting of consecutive N modified transformer blocks. In each transformer block, there is an elaborately designed base pair attention block (Fig.\u00a01b), composed of hybrid convolutional block (Fig.\u00a01c) which applies the squeeze-and-excitation (SE) block63 (Fig.\u00a01d) to adaptively re-calibrate the channel-wise feature response and explicitly build the relationship of energy maps, along with an enhanced self-attention mechanism that aggregates attention map from RNA sequence and base pair motif energy, learning thermodynamic knowledge in the complete space of r-neighbor base pair motif to improve the generalizability of model in situation of unseen RNA sequence and families.\n\nThe original transformer block46 is composed of a multi-head self-attention module (MSA), followed by a feed-forward network (FFN) which consists of a two-layer multi-layer perceptron with a GELU activation. A LayerNorm layer is adopted before each MSA and each FFN, and a residual shortcut is adopted after each module.\n\nTo integrate the thermodynamic priors in the form of energy maps into this attention mechanism, we design the base pair attention block. As illustrated in Fig.\u00a01b, when processing self-attention, a base pair attention block applies several 3\u2009\u00d7\u20093 convolutional layers (denoted as CONV) to the energy map to establish the relationship among base pairs. Furthermore, This attention block adds the thermodynamic feature map to the attention map of sequence features, imposing the thermodynamic relationship of base pairs on RNA sequences. More specifically, the input of the neural network is an RNA sequence of length L, containing four bases, i.e., A, C, G, U (other unknown bases will be converted to the above four bases). The input RNA sequences are firstly padded with \u201cSTART\u201d, \u201cEND\u201d, and \u201cEMPTY\u201d tokens to a uniform length \\({L}_{\\max }\\) to deal with variable lengths, which are then encoded into a D-dimensional embedding using trainable parameters, forming the input feature X in a shape of \\({L}_{\\max }\\times D\\). Meanwhile, the energy maps M\u03bc and M\u03bd of the outer base pair motif and inner base pair motif are prepared according to the input RNA sequence and the stored energy table. Similar to the input feature of RNA sequences, both M\u03bc and M\u03bd energy maps are padded with zero to the shape of \\({L}_{\\max }\\times {L}_{\\max }\\). The base pair attention block processes can be formulated as follows: (1) Obtaining base pair attention maps:\n\nwhere Mi is the i-th base pair attention block, [M\u03bc :\u00a0M\u03bd] denotes the concatenation of base pair energy maps M\u03bc and M\u03bd, and \u03b8 represents learnable parameters. (2) Integrating base pair attention maps with sequence feature maps into the transformer block:\n\nwhere i\u2009=\u20091,\u00a02,\u00a0\u2026,\u00a0N with N being the number of transformer blocks. After N base pair attention blocks, we obtain the output orthogonal matrix \\(\\tilde{Y}\\) in shape of \\({L}_{\\max }\\times {L}_{\\max }\\) by applying matrix multiplication between XN and its transpose \\({X}_{N}^{T}\\), namely \\(\\tilde{Y}={X}_{N}\\times {X}_{N}^{T}\\), which represents the possibility score of each nucleotide being paired with other nucleotides.\n\nTo relieve the impact of efficiency brought by padding, we apply a length-matching strategy for sampling a mini-batch at the training stage. Specifically, we set a series of buckets {B0,\u00a0B1,\u00a0B2,\u00a0\u2026} and assign each input RNA sequence to a bucket \\({B}_{i},i=\\lfloor \\frac{L}{{L}_{p}}\\rfloor\\) where L is the length of RNA sequence and Lp is the predefined interval of buckets. Mini-batches are sampled from the same bucket, which leads to a controllable padding size Lp. This length-matching strategy is especially effective in dealing with the varying lengths of input RNA sequences from tens for short sequences to thousands for large sequences.\n\nBPfold is implemented in PyTorch framework64 and trained by minimizing the binary cross-entropy between the predicted contact matrix \\(\\tilde{Y}\\) and the true contact matrix Y using the ADAM optimization algorithm. The number of parameters is listed in Supplementary Table\u00a06. To leverage the imbalanced distribution of paired bases and unpaired bases, we adopt a positive weight \u03c9\u2009=\u2009300 to derive the loss function as below:\n\nwhere \u03b8 denotes all learnable parameters of the neural network, i \u2208 {1, 2, \u2026, L} and j \u2208 {1, 2, \u2026, L} denote the row and column, respectively, index of matrices.\n\nWhen predicting an RNA sequence, we apply refinement procedures to the output contact map \\(\\tilde{Y}\\) generated by the BPfold neural network to impose physical constraints on RNA secondary structures and rule out invalid base pairs. Specifically, the following rules of constraints are considered in which the first three rules are inspired from previous study34,37: (1) Only Waston-Crick pairs (i.e., A-U, U-A, G-C, C-G) and Wobble pair (G-U, U-G) are allowed for canonical pairs while others are allowed for non-canonical base pairs; (2) A loop region has at least two bases for ruling out sharp loops; (3) Overlapping pairs are discarded. We encode these constraints as matrix transformations and apply them to the output contact matrix. Since the output contact matrix is already a symmetric matrix (\\(\\tilde{Y}={X}_{N}\\times {X}_{N}^{T}\\)), we do not explicitly declare symmetric processing. (4) Isolated base pairs are removed. An isolated base pair has no consecutive neighboring helix and is not stable enough to form a base pair in most situations. We verify all paired bases and remove isolated base pairs to rule out long-distance unstable interactions.\n\nWe utilize several widely used open-source benchmark datasets for evaluating the performances of our proposed BPfold and comparing it with state-of-the-art RNA secondary structure prediction methods. Specifically, these benchmark datasets are as follows:\n\nRNAStralign54: This dataset contains 37,149 RNA sequences from eight RNA families, with sequence lengths ranging from approximately 30 to 3000 nucleotides (nt). Similar to previous work34,37,38, we remove redundant sequences and invalid secondary structures, and obtain a total of 29,647 unique sequences. Furthermore, we filter sequences with lengths no more than 600 nt, forming a training dataset that consists of 19,313 sequences.\n\nbpRNA-1m49: This dataset contains 102,318 RNA sequences from 2588 RNA families. Following MXfold238, we use CD-HIT program58 to remove similar sequences with a cut-off of 80% and split the processed dataset into two sub-dataset for training and testing, named TR0 and TS0, which contain 12,114 and 1305 sequences, respectively, with sequence lengths ranging from 22 to 499 nt.\n\nArchiveII48: This dataset is the most widely used benchmark dataset for evaluation of RNA secondary structures, consisting of 3966 RNA sequences from ten RNA families, i.e., 5s rRNA 16s rRNA, 23s rRNA, tRNA, tmRNA, telomerase RNA RNase P, SRP, group I Intron, and group II Intron. Among them, 3911 sequences have a length below 600 nt while the other 55 RNA sequences are all from group II Intron which have a max length of 1800 nt.\n\nRfam12.3\u201314.10: We construct this dataset by initially collecting 50,779 RNA sequences that are newly added to the latest version of Rfam16,17, namely from Rfam version 12.3 to Rfam version 14.10, which includes newly added cross-family sequences that are not present in the bpRNA training dataset. After using CD-HIT-EST58 to remove redundant sequences with sequence similarity of more than 80%, this dataset contains 10,791 unique sequences from 1992 RNA families, with lengths of 68 sequences ranging from 600 nt to 951 nt and the other ranging from 26 nt to 600 nt. We employ this dataset for family-wise evaluation. We also evaluate models on bpRNA-new dataset derived from Rfam version 12.3 to Rfam version 14.2 by MXfold238 which contains 5401 sequences, with sequence length ranging from 33 nt to 489 nt.\n\nPDB50: This dataset is a benchmark dataset, consisting of 116 RNA sequences with high-resolution (<\u20093.5\u00c5) RNA X-ray structures, with sequence lengths ranging from 32 to 355 nt. According to previous study34,35,36, the PDB dataset is divided into three sub datasets, i.e., TS1, TS2, and TS3, with 60, 38, and 18 sequences, respectively.\n\nFor performance evaluation of predicted RNA secondary structures, we use precision (P), recall (R, a.k.a. sensitivity), F1 score, and interaction network fidelity (INF) to assess the quality of base pair prediction, which is a binary classification problem. We calculate the macro-averages of these metrics of canonical base pairs. Specifically, these metrics are defined as below:\n\nwhere TP, FP, and FN denote true positive (the number of correctly predicted base pairs), false positive (the number of incorrectly predicted base pairs), and false negative (the number of base pairs whose reference structures are not predicted), respectively.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data used in this study are available at Zenodo65 and GitHub (https://github.com/heqin-zhu/BPfold). These data include datasets, base pair motif energy scores, model parameters and source data. The source data underlying Tables\u00a01, 2, S2\u2013S5, and Figs.\u00a04, 5, 7, S3, S4, S6 are provided in the Source Data file.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The source code and program tool of BPfold method is publicly available at Zenodo65 and GitHub (https://github.com/heqin-zhu/BPfold).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Butcher, S. E. & Pyle, A. M. The molecular interactions that stabilize rna tertiary structure: Rna motifs, patterns, and networks. Acc. Chem. Res. 44, 1302\u20131311 (2011).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nDoudna, J. A. & Cech, T. R. The chemical repertoire of natural ribozymes. Nature 418, 222\u2013228 (2002).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nStrulson, C. A., Molden, R. C., Keating, C. D. & Bevilacqua, P. C. 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Kevin Zhou\n\nSuzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, 215123, China\n\nHeqin Zhu,\u00a0Fenghe Tang,\u00a0Ke Chen,\u00a0Peng Xiong\u00a0&\u00a0S. Kevin Zhou\n\nCenter for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advance Research, USTC, Suzhou, Jiangsu, 215123, China\n\nHeqin Zhu,\u00a0Fenghe Tang\u00a0&\u00a0S. Kevin Zhou\n\nKey Laboratory of Intelligent Information Processing of Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China\n\nQuan Quan\n\nJiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology, Suzhou, Jiangsu, 215123, China\n\nS. Kevin Zhou\n\nState Key Laboratory of Precision and Intelligent Chemistry, USTC, Hefei, Anhui, 230026, China\n\nS. Kevin Zhou\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.Z. constructed the motif library, designed the network architectures, conducted the experiments, analyzed the results, and wrote the paper. P.X. and S.K.Z. conceived the idea, supervised the study, and designed the experiments. P.X. also participated in the design of the core components. F.T. carried out part of the experiments and participated in the design of networks. Q.Q. and K.C. helped in the analysis of data results. All authors read, contributed to the discussion, and approved the final paper.\n\nCorrespondence to\n Peng Xiong or S. Kevin Zhou.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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Deep generalizable prediction of RNA secondary structure via base pair motif energy.\n Nat Commun 16, 5856 (2025). https://doi.org/10.1038/s41467-025-60048-1\n\nDownload citation\n\nReceived: 23 October 2024\n\nAccepted: 12 May 2025\n\nPublished: 01 July 2025\n\nVersion of record: 01 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60048-1\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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dynamics of a superconducting quantum processor", + "pre_title": "Robustly learning the Hamiltonian dynamics of a superconducting quantum processor", + "journal": "Nature Communications", + "published": "06 November 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52629-3/MediaObjects/41467_2024_52629_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52629-3/MediaObjects/41467_2024_52629_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Quantum information", + "Quantum simulation" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3813225/v1.pdf?c=1730984842000", + "research_square_link": "https://www.researchsquare.com//article/rs-3813225/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-52629-3.pdf", + "preprint_posted": "15 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The required precision to perform quantum simulations beyond the capabilities of classical computers imposes major experimental and theoretical challenges. The key to solving these issues are highly precise ways of characterizing analog quantum simulators. Here, we robustly estimate the free Hamiltonian parameters of bosonic excitations in a superconducting-qubit analog quantum simulator from measured time-series of single-mode canonical coordinates. We achieve the required levels of precision in estimating the Hamiltonian parameters by maximally exploiting the model structure, making it robust against noise and state-preparation and measurement (SPAM) errors. Importantly, we are also able to obtain tomographic information about those SPAM errors from the same data, crucial for the experimental applicability of Hamiltonian learning in dynamical quantum-quench experiments. Our learning algorithm is highly scalable both in terms of the required amounts of data and post-processing. To achieve this, we develop a new super-resolution technique coined tensorESPRIT for frequency extraction from matrix time-series. The algorithm then combines tensorESPRIT with constrained manifold optimization for the eigenspace reconstruction with pre- and post-processing stages. For up to 14 coupled superconducting qubits on two Sycamore processors, we identify the Hamiltonian parameters---verifying the implementation on one of them up to sub-MHz precision---and construct a spatial implementation error map for a grid of 27 qubits. Our results constitute a fully characterized, highly accurate implementation of an analog dynamical quantum simulation and introduce a diagnostic toolkit for understanding, calibrating, and improving analog quantum processors.Physical sciences/Physics/Quantum physics/Quantum simulationPhysical sciences/Physics/Quantum physics/Quantum information", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Hlearningsupplemental.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Precise means of characterizing analog quantum simulators are key to developing quantum simulators capable of beyond-classical computations. Here, we precisely estimate the free Hamiltonian parameters of a superconducting-qubit analog quantum simulator from measured time-series data on up to 14 qubits. To achieve this, we develop a scalable Hamiltonian learning algorithm that is robust against state-preparation and measurement (SPAM) errors and yields tomographic information about those SPAM errors. The key subroutines are a novel super-resolution technique for frequency extraction from matrix time-series, tensorESPRIT, and constrained manifold optimization. Our learning results verify the Hamiltonian dynamics on a Sycamore processor up to sub-MHz accuracy, and allow us to construct a spatial implementation error map for a grid of 27 qubits. Our results constitute an accurate implementation of a dynamical quantum simulation that is precisely characterized using a new diagnostic toolkit for understanding, calibrating, and improving analog quantum processors.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Analog quantum simulators promise to shed light on fundamental questions of physics that have remained elusive to the standard methods of inference1,2. Recently, enormous progress in controlling individual quantum degrees of freedom has been made towards making this vision a reality3,4,5,6. While in digital quantum computers small errors can be corrected7, it is intrinsically difficult to error-correct analog devices. Yet, the usefulness of analog quantum simulators as computational tools depends on the error of the implemented dynamics. Meeting this requirement hinges on devising characterization methods that not only yield a benchmark of the overall functioning of the device [e.g.,8,9,10], but more importantly provide diagnostic information about the sources of errors.\n\nDeveloping characterization tools for analog quantum simulators requires hardware developments as well as theoretical analysis and method development. With the advent of highly controlled quantum systems, efficient methods for identifying certain Hamiltonian parameters from dynamical data have been devised for specific classes of Hamiltonians. Key ideas are the use of Fourier analysis11,12,13,14,15,16,17 and tracking the dynamics of single excitations18,19,20,21,22,23. For general Hamiltonian models, specific algebraic structures of the Hamiltonian terms can be exploited24,25. Generalizing these ideas, a local Hamiltonian can be learned from a single eigenstate or its steady state26,27,28,29,30,31 or using quantum-quenches32,33, an approach dubbed \u201ccorrelation matrix method\u201d34. Alternatively, one can apply general-purpose machine-learning methods35,36,37,38,39. More recently, optimal theoretical guarantees have been derived for Hamiltonian learning schemes40,41,42 based on Pauli noise tomography43,44. Crucially, these protocols assume perfect mid-circuit quenches, which\u2014as we find here\u2014can be a limiting assumption in practice.\n\nThis recent rapid theoretical development is not quite matched by concomitant experimental efforts. The effectiveness of some of these methods has been demonstrated for the estimation of a small number of coupling parameters of fixed two- and three-qubit Hamiltonians in nuclear magnetic resonance (NMR) experiments45,46,47,48. While in NMR, the dominant noise process is decoherence, in tunable quantum simulators such as superconducting qubits, trapped ions or cold atoms in optical lattices, state preparation and measurement (SPAM) errors, as we also demonstrate here, play a central role. Initial steps at characterizing such errors as well as the dissipative Lindblad dynamics for up to two qubits in a superconducting qubit platform have been taken recently49,50. Hamiltonian learning of thermal states has recently also been applied in many-body experiments as a means to characterize the entanglement of up to 20-qubit subsystems whose reduced states are parameterized by the so-called entanglement Hamiltonian51,52,53. The challenge remains to develop and experimentally demonstrate the feasibility of scalable methods for a robust and precise identification of Hamiltonian dynamics of intermediate-size systems subject to both incoherent noise and systematic SPAM errors.\n\nIn this work, we develop bespoke protocols to robustly and precisely identify the full Hamiltonian of a large-scale bosonic system and implement those protocols on superconducting quantum processors. Given the complexity of the learning task, we focus on identifying the non-interacting part of a potentially interacting system. We are able to estimate the corresponding Hamiltonian parameters as well as SPAM errors pertaining to all individual components of the superconducting chip for up to 14-mode Hamiltonians tuned across a broad parameter regime, in contrast to previous experiments. Given the identified Hamiltonians, we quantify their implementation error. We demonstrate and verify that a targeted intermediate-size Hamiltonian is implemented on a large region of the superconducting processor with sub-MHz accuracy in a broad parameter range.\n\nTo this end, building on previous ideas for Hamiltonian identification19,24, we devise a simple and robust algorithm that exploits the structure of the system at hand. For the identification we make use of quadratically many experimental time-series tracking excitations via expectation values of canonical coordinates. Our structure-enforcing algorithm isolates two core tasks that need to be solved in Hamiltonian identification after suitable pre-processing of the data: frequency extraction and eigenspace reconstruction.\n\nTo solve the first task in a robust and structure-specific way, we develop a novel algorithm coined tensorESPRIT, which utilizes ideas from super-resolving, denoised Fourier analysis54,55,56 and tensor networks to extract frequencies from a matrix time-series. For the second task we use constrained manifold optimization over the orthogonal group57. Crucially, by explicitly exploiting all structure constraints of the identification problem, our method allows us to distinguish and obtain tomographic information about state-preparation and measurement errors. In the quench-based experiment this information renders identification and verification of the dynamics experimentally feasible in the first place. We further support our method development with numerical simulations of different noise effects and benchmark against more direct algorithmic approaches. We find that in contrast to other approaches our method is scalable to larger system sizes out of the reach of our current experimental efforts.\n\nOur work constitutes a detailed case study that lays bare and provides solutions for the difficulties of practical Hamiltonian learning in a seemingly simple system. It thus provides a blueprint and paves the way for devising practical model-specific identification algorithms both for the interaction parameters of bosonic or fermionic systems and more complex settings.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We characterize the Hamiltonian governing analog dynamics of Google Sycamore chips which consist of a two-dimensional array of nearest-neighbor coupled superconducting qubits. Each physical qubit is a non-linear oscillator with bosonic excitations (microwave photons)58. Using the rotating-wave approximation the dynamics governing the excitations of the qubits in the rotating frame can be well described by the Bose-Hubbard Hamiltonian59\n\nwhere \\({a}_{i}^{{\\dagger} }\\) and ai denote bosonic creation and annihilation operators at site i, respectively, \\(\\mu \\in {{\\mathbb{R}}}^{N}\\) are the on-site potentials, \\(J\\in {{\\mathbb{R}}}^{N\\times N}\\) are the hopping rates between nearest neighbor qubits, and \\(\\eta \\in {{\\mathbb{R}}}^{N}\\) are the strength of on-site interactions. The qubit frequency, the nearest-neighbor coupling between them, and the non-linearity (anharmonicity) set \u03bc,\u00a0J, and \u03b7. We are able to tune \u03bc and J on nanosecond timescales, while \u03b7 is fixed for a given setting of \u03bc and J. Hence, the Sycamore chip can be used to implement time evolution under Hamiltonians of the form (1) at various parameter settings and is therefore an analog simulator. In a practical application such as in Ref. 60, it is crucial to benchmark how accurately the implemented dynamics is described by a targeted Hamiltonian.\n\nHere, we focus on the specific task of identifying the values of \u03bci and Ji,j. The corresponding non-interacting part of the Hamiltonian acting on N modes can be conveniently parametrized as\n\nwith an N\u2009\u00d7\u2009N real symmetric parameter matrix h with entries hi,j, which is composed of the on-site chemical potentials \u03bci on its diagonal and the hopping energies Ji,j for i\u2009\u2260\u2009j. The identification of the non-interacting part H(h) of HBH can be viewed as a first step in a hierarchical procedure for characterizing dynamical quantum simulations with tunable interactions and numbers of particles.\n\nThe non-interacting part H(h) of the Hamiltonian HBH can be inferred when initially preparing a state where only a single qubit is excited with a single photon. For initial states with a single excitation, the interaction term vanishes, hence effectively \u03b7\u2009=\u20090. Consequently, only the two lowest energy levels of the non-linear oscillators enter the dynamics. Therefore, referring to them as qubits (two-level systems) is precise. Specifically, we identify the parameters hi,j from dynamical data of the following form. We initialize the system in \\(\\vert {\\psi }_{n}\\rangle : \\!\\!\\!=({\\mathbb{1}}+{a}_{n}^{{\\dagger} }){\\vert 0 \\rangle }^{\\otimes N}/\\sqrt{2}\\) and measure the canonical coordinates \\({x}_{m}=({a}_{m}+{a}_{m}^{{\\dagger} })/2\\) and \\({p}_{m}=({a}_{m}-{a}_{m}^{{\\dagger} })/(2{{\\rm{i}}})\\) for all combinations of m,\u00a0n\u2009=\u20091,\u00a0\u2026,\u00a0N. In terms of the qubit architecture, this amounts to local Pauli-X and Pauli-Y basis measurements, respectively. We combine the statistical averages over multiple measurements to obtain an empirical estimator for \\({\\langle {a}_{m}(t)\\rangle }_{{\\psi }_{n}}={\\langle {x}_{m}(t)\\rangle }_{{\\psi }_{n}}+{{\\rm{i}}}\\,{\\langle {p}_{m}(t)\\rangle }_{{\\psi }_{n}}\\). For particle-number preserving dynamics, this data is of the form\n\nIt therefore directly provides estimates of the entries of the time-evolution unitary at time t in the single-particle sector of the bosonic Fock space.\n\nIn Fig.\u00a01, we show an overview of the experimental procedure, and the different steps of the Hamiltonian identification algorithm. Every experiment uses a few coupled qubits, from the larger array of qubits on the device (Fig.\u00a01a). On those qubits, the goal is to implement the time-evolution with targeted Hamiltonian parameters h0, which are subject to connectivity constraints imposed by the couplings of the qubits. To achieve this, we perform the following pulse sequence to collect dynamical data of the form (3). Before the start of the sequence, the qubits are at frequencies (of the \\(\\left\\vert 0\\right\\rangle\\) to \\(\\left\\vert 1\\right\\rangle\\) transition) that could be a few hundred MHz apart from each other. In the beginning, all qubits are in their ground state \\(\\left\\vert 0\\right\\rangle\\). To prepare the initial state, a \u03c0/2-pulse is applied to one of the qubits, resulting in its Bloch vector moving to the equator. Then ramping pulses are applied to all qubits to bring them to the desired detuning around a common rendezvous frequency (6500\u2009MHz in this work). At the same time, pulses are applied to the couplers to set the nearest-neighbor hopping to the desired value (20\u2009MHz in this work). The pulses are held at the target values for time t, corresponding to the evolution time of the experiment. Subsequently, the couplers are ramped back to zero coupling and the qubits back to their initial frequency, where \u3008xm(t)\u3009 and \u3008pm(t)\u3009 on the desired qubit m is measured. The initial and final pulse ramping take place over a finite time of 2\u20133\u2009ns, and therefore give rise to a non-trivial effect on the dynamics, which we take into account in the identification procedure. In fact, we find that the effects of the ramping phase are the domininant source of SPAM errors in the quench-based analog simulation. The experimental data (Fig.\u00a01b) on N qubits are N\u2009\u00d7\u2009N time-series estimates of \\({\\langle {a}_{m}(t)\\rangle }_{{\\psi }_{n}}\\) for t\u2009=\u20090,\u00a01,\u00a0\u2026,\u00a0T ns and all pairs n,\u00a0m\u2009=\u20091,\u00a0\u2026,\u00a0N. Given those data, the identification task amounts to identifying the \u201cbest\u201d coefficient matrix h, describing the time-sequence of snapshots of the single-particle unitary matrix \\(\\frac{1}{2}\\exp (-{{\\rm{i}}}th)\\).\n\na The time evolution under a target Hamiltonian h0 is implemented on an part of the Google Sycamore chip (gray) using the pulse sequence depicted in the middle. b The expected value of canonical coordinates xm and pm for each qubit m over time is estimated from measurements using different \u03c8n as input states. c The data shown in (b) for each time t0 can be interpreted as a (complex-valued) matrix with entries indexed by measured and initial excited qubit, m and n. The identification algorithm proceeds in two steps: 1. From the matrix time-series, the Hamiltonian eigenfrequencies are extracted using our newly introduced algorithm coined tensorESPRIT, introduced in the Supplemental Material, or an adapted version of the ESPRIT algorithm. The blue line indicates the denoised, high-resolution signal as \u201cseen\u201d by the algorithm. 2. After removing the initial ramp using the data at some fixed time, the Hamiltonian eigenspaces are reconstructed using a non-convex optimization algorithm over the orthogonal group. We obtain a diagonal orthogonal estimate of the final ramp. From the extracted frequencies and reconstructed eigenspaces, we can calculate the identified Hamiltonian \\(\\hat{h}\\) that describes the measured time evolution and a tomographic estimate of the initial ramp.\n\nWe can identify the generator h of the unitary in two steps (Fig.\u00a01c), making use of the eigendecomposition of the Hamiltonian (see \u201cMethods\u201d). In the first step, the time-dependent part of the identification problem is solved, namely, identifying the Hamiltonian eigenvalues (eigenfrequencies). In the second step, given the eigenvalues, the eigenbasis for the Hamiltonian of h is determined. In order to make the identification method noise-robust, we furthermore exploit structural constraints of the model. First, the Hamiltonian has a spectrum such that the time-series data has a time-independent, sparse frequency spectrum with exactly N contributions. Second, the Fourier coefficients of the data have an explicit form as the outer product of the orthogonal eigenvectors of the Hamiltonian. Third, the Hamiltonian parameter matrix is real and has an a priori known sparse support due to the experimental connectivity constraints. These structural constraints are not respected by various sources of incoherent noise, including particle loss and finite shot noise, and coherent noise, in particular the SPAM error. Thereby, an identification protocol that takes these constraints into account is intrinsically robust against various imperfections. Importantly, we do not assume that the dynamics of the device is completely governed by a non-interacting, particle-number preserving Hamiltonian of the form (2). We rather impose this as a constraint on the reconstructed Hamiltonian and, as such, identify the best-fit Hamiltonian satisfying the constraint. Our approach thus robustly identifies the non-interacting part of a potentially interacting system.\n\nTo robustly identify the sparse frequencies from the experimental data, we develop a new super-resolution and denoising algorithm tensorESPRIT that is applicable to matrix-valued time series and uses tensor network techniques in conjunction with super-resolution techniques for scalar data55. Achieving high precision in this step is crucial for identifying the eigenvectors in the presence of noise. To robustly identify the eigenbasis, in the second step, we perform least-square optimization of the time-series data under the orthonormality constraint with a gradient descent algorithm on the manifold structure of the orthogonal group57. Additionally, we can incorporate the connectivity constraint on the coefficient matrix h by making use of regularization techniques61.\n\nThe initial and final ramping pulses result in a time-independent, linear transformation at the beginning and end of the time series. It is important to stress that such ramping pulses are expected to be generic in a wide range of experimental implementations of dynamical analog quantum simulations. Robustness of a Hamiltonian identification method against these imperfections is essential for accurate estimates in practice. We can model the effect of such state preparation and measurement (SPAM) errors via linear maps S and M, which alters our model of the ideal data (3) to\n\nThese linear maps capture the effect of particle-number preserving quenches, as well as the projection of more general channels to the single-particle subspace. Any deviation of the observed experimental dynamics from our model of the data (4) will be visible in the quality of fit.\n\nWhile for the frequency identification such time-independent errors \u201conly\u201d deteriorate the signal-to-noise ratio, for the identification of the eigenvectors of h it is crucial to take the effects of non-trivial S and M into account. Given the details of the ramping procedure, we expect that the deviation of the initial map S from the identity will be significantly larger than that of the final map M and provide evidence for this in the Methods. In particular, the final map will be dominated by phase accumulation on the diagonal.\n\nBy pre-processing the data, we can robustly remove an arbitrary initial map S. By post-processing, we can obtain an orthogonal diagonal estimate \\({\\hat{D}}_{M}\\) of the final map M. We give numerical evidence that the estimate \\({\\hat{D}}_{M}\\) gives good results in the particular experimental setting. From the identified Hamiltonian and an orthogonal diagonal estimate \\({\\hat{D}}_{M}\\) of M, we get an estimate \\(\\hat{S}\\) of S.\n\nThere are two main remaining sources of error that affect the Hamiltonian identification. First, the estimate \\(\\hat{h}\\) has a statistical error due to the finite number of measurements used to estimate the expectation values. Second, any non-trivial final map M will produce a systematic error in the eigenbasis reconstruction and the tomographic estimate \\(\\hat{S}\\). We partially remedy this effect with an orthogonal diagonal estimate \\({\\hat{D}}_{M}\\) of M.\n\nIf the dynamics of the device is indeed coherent and particle-number preserving, the learned model will allow us to accurately predict the dynamics of the device in the single-particle subspace. If, additionally, interactions are negligible, the predictive power of our model extends to dynamics of more particles. This allows us to benchmark the Sycamore chip as a programmable quantum simulator of the non-interacting part of a Bose-Hubbard model. Accurately predicting the dynamics of many particles requires a generalization of our method to at least the two-particle sector.\n\nWe implement and characterize different Hamiltonians from time-series data on two distinct quantum Sycamore processors\u2014Sycamore #1 and #2. The Hamiltonians we implement have a fixed overall hopping strength Ji,j\u2009=\u200920\u2009MHz and site-dependent local potentials \u03bci on subsets of qubits. Specifically, we choose the local potentials quasi-randomly \\({\\mu }_{q}=20\\cos (2\\pi qb)\\,\\,{\\mbox{MHz}}\\,\\), for q\u2009=\u20091,\u00a0\u2026,\u00a0N, where b is a number between zero and one. In one dimension, this choice corresponds to implementing the Harper Hamiltonian, which exhibits characteristic \u201cHofstadter butterfly\u201d frequency spectra as a function of the dimensionless magnetic flux b62.\n\nWe measure deviations in the identification in terms of the analog implementation error of the identified Hamiltonian \\(\\hat{h}\\) with respect to the targeted Hamiltonian h0 as\n\ndefined in terms of the \u21132-norm, which for a matrix A is given by \\({\\left\\Vert A\\right\\Vert }_{{\\ell }_{2}}={({\\sum }_{i,j}| {A}_{i,j}{| }^{2})}^{1/2}\\). We also use the analog implementation error to quantify the implementation error of the initial map \\(\\hat{S}\\) as \\({{{\\mathcal{E}}}}_{{{\\rm{analog}}}}(\\hat{S},{\\mathbb{1}})\\), and of the eigenfrequencies \\({{\\rm{eig}}}(\\hat{h})\\) as \\({{{\\mathcal{E}}}}_{{{\\rm{analog}}}}({{\\rm{eig}}}(\\hat{h}),{{\\rm{eig}}}({h}_{0}))\\). Notice that the analog implementation error of the frequencies in the data from the targeted Hamiltonian eigenfrequencies give a lower bound to the overall implementation error of the identified Hamiltonian. This is because the \u21132-norm used in the definition (5) of \\({{{\\mathcal{E}}}}_{{{\\rm{analog}}}}\\) is unitarily invariant and any deviation in the eigenbasis, which we identify in the second step of our algorithm, will tend to add up with the frequency deviation.\n\nIn Fig.\u00a02, we illustrate the properties of a single Hamiltonian identification instance in terms of both how well the simulated time evolution fits the experimental data (a,d,e) and how it compares to the targeted Hamiltonian (b) and SPAM (c). We find that most entries of the identified Hamiltonian deviate from the target Hamiltonian by less than 0.5\u2009MHz with a few entries deviating by around 1\u20132\u2009MHz. The overall implementation error is around 1\u2009MHz. The error of the identification method is dominated by the systematic error due to the final ramping phase that is around 1\u2009MHz for the individual entries, see the Supplemental Material for details. Small long-range couplings exceeding the statistical error are necessary to fit the data well even when penalizing those entries via regularization. These entries are rooted in the effective rotation by the final ramping before the measurement and within the estimated systematic error.\n\na The full experimental time-series data \\({\\langle {x}_{m}(t)\\rangle }_{{\\psi }_{n}}\\) for m,\u00a0n\u2009=\u20091,\u00a0\u2026,\u00a05 and the best fit of those data in terms of our model \\(\\frac{1}{2}{(M\\exp (-{{\\rm{i}}}th)S)}_{m,n}\\) for a diagonal and orthogonal M and linear map S (solid lines). b The target Hamiltonian matrix h0, the identified Hamiltonian \\(\\hat{h}\\), and the deviation between them. The error of each diagonal entry is \u00a0\u00b1(0.16\u2009+\u20090.99)\u2009MHz and of each off-diagonal entry \u00a0\u00b1(0.12\u2009+\u20090.50)\u2009MHz and comprises of the statistical and the systematic error, respectively. The analog implementation error \\({{{\\mathcal{E}}}}_{{{\\rm{analog}}}}(\\hat{h},{h}_{0})\\) is 0.73\u2009\u00b1\u2009(0.07\u2009+\u20090.62)\u2009MHz, and 0.32\u2009\u00b1\u20090.00\u2009MHz for the eigenfrequencies. The analog implementation error \\({{{\\mathcal{E}}}}_{{{\\rm{analog}}}}(\\hat{S},{\\mathbb{1}})\\) of the identified initial map is 0.61\u2009\u00b1\u2009(0.00\u2009+\u20090.12). c The real part of the initial map \\(\\hat{S}\\) and the diagonal orthogonal estimate \\({\\hat{D}}_{M}\\) of the final map M, inferred from the data using the identified Hamiltonian \\(\\hat{h}\\). d Absolute value of the time-domain deviation of the fit from the full experimental data for each time series, given by deviation\\([\\hat{h},\\hat{S},{\\hat{D}}_{M}]{(t)}_{m,n}: \\!\\!\\!={\\langle {a}_{m}(t)\\rangle }_{{\\psi }_{n}}-\\frac{1}{2}{\\hat{D}}_{M}\\exp (-{{\\rm{i}}}t\\hat{h})\\hat{S}\\). The insets represent the root-mean-square deviation of the Hamiltonian fit from the experimental data per time series, averaged over the evolution time for each matrix entry (m,\u00a0n), resulting in an entry-wise summarized quality of fit. We find a total root-mean-square deviation of the fit of 0.14. e Instantaneous root-mean-square deviation of the identified Hamiltonian \\(\\hat{h}\\), initial map \\(\\hat{S}\\) and final map \\({\\hat{D}}_{M}\\) and of the target Hamiltonian h0 with initial map fit S0 from the experimental data averaged over the distinct time series.\n\nThe fit deviation from the data (Fig.\u00a02e) exhibits a prominent decrease within the first few nanoseconds of the time evolution. This indicates that the time evolution differs during the initial phase of the experiment as compared to the main phase of the experiment, which we can attribute to the initial pulse ramping of the experiment. The identified initial map describing this ramping (Fig.\u00a02c) is approximately band-diagonal and deviates from being unitary, indicating fluctuations of the effective ramps between different experiments.\n\nWe find a larger time-averaged real-time error (Fig.\u00a02d) in all data series \\({\\langle {a}_{m}\\rangle }_{{\\psi }_{n}}\\) in which Q4 was measured, indicating a measurement error on Q4. We also observe a deviation between the parameters of the target and identified Hamiltonian in qubits Q3 and Q4 and the coupler between them. Since the deviation of the eigenfrequencies is much smaller than of the full Hamiltonian, we attribute those errors also to a non-trivial final ramping phase at those qubits that leads to a rotated eigenbasis.\n\nIn Fig.\u00a03, we summarize multiple identification data of this type to benchmark the overall performance of a fixed set of qubits. In panel (a), we show the measured Fourier domain data for 51 different values of the magnetic flux b \u2208 [0, 1]. In panel (b), we plot the deviation of the frequencies identified from the data. Most implemented frequencies deviate by less than 1\u2009MHz from their targets. Importantly, the frequency identification is robust against systematic measurement errors. When comparing the analog implementation errors of the full Hamiltonian (Fig. 3c) to the corresponding frequency errors, we find an up to fourfold increase in implementation error. The Hamiltonian implementation error is affected by a systematic error due to the non-trivial final ramp. We estimate this error using a linear ramping model; see the Supplemental Material for details. Since the deviation lies outside of the combined systematic and statistical error bars, our results indicate that the targeted Hamiltonian has not been implemented exactly.\n\na In an N\u2009=\u20096 subset of connected qubits, by varying b from 0 to 1, we implement 51 different Hamiltonians. The plot shows the Fourier transform of the time domain data. b The extracted eigenfrequencies (denoised peaks in (a)) are shown as colored dots, where the assigned color is indicative of the deviation between targeted eigenfrequencies (gray lines) and the identified ones from position of the peaks. c Analog implementation error \\({{{\\mathcal{E}}}}_{{{\\rm{analog}}}}(\\hat{h},{h}_{0})\\) of the identified Hamiltonian (dark red) compared to the implementation error \\({{{\\mathcal{E}}}}_{{{\\rm{analog}}}}({{\\rm{eig}}}(\\hat{h}),{{\\rm{eig}}}({h}_{0}))\\) of the identified frequencies (golden). Colored (gray) error bars quantify the statistical (systematic) error. d Layout of the six qubits on the Sycamore processor and median of the entry-wise absolute-value deviation of the Hamiltonian matrix entries from their targeted values across the ensemble of 51 different values of b\u2009\u2208\u2009[0, 1].\n\nIn Fig.\u00a03d, we show the median of the entry-wise deviation of the identified Hamiltonian from its target over all magnetic flux values. Thereby, the ensemble of Hamiltonians defines an overall error benchmark. This benchmark can be associated to the individual constituents of the quantum processor, namely, the qubits, corresponding to diagonal entries of the Hamiltonian deviation, and the couplers, corresponding to the first off-diagonal matrix entries of the deviation.\n\nWe use this benchmark over an ensemble of two flux values to assess a 27-qubit array of superconducting qubits. To do so, we repeat the analysis reported in Fig.\u00a03 for 5-qubit dynamics on different subsets of qubits and extract average errors of the individual qubits and couplers involved in the dynamics, both in terms of the identified Hamiltonian and the initial and final maps. Summarized in Fig.\u00a04, we find significant variation in the implementation error of different couplers and qubits. While for some qubits the effects of the initial and final maps are negligible, for others they indicate the potential of a significant implementation error. From a practical point of view, such diagnostic data allows to maximally exploit the chip\u2019s error for small-scale analog simulation experiments. Let us note that within the error of our method the overall benchmark for the qubits and couplers for 5-qubit dynamics agrees with that of 3- and 4-qubit dynamics.\n\nOver the grid of 27 qubits, we randomly choose subsets of connected qubits and couplers of size N\u2009=\u20095. On each subset we implement two Hamiltonians with b\u2009=\u20090,\u00a00.5 and run the identification algorithm. Two instances are shown in (a). For each subset, we compute the deviation of the identified Hamiltonian and initial map from their respective target and assign it to each qubit or coupler involved. Due to overlap of subsets, each qubit or coupler has been involved in at least five different choices of subsets. b, c Show the median deviation for the Hamiltonian and initial map implementations, respectively. d Shows the mean of the sign flips in the identified (diagonal\u2009\u00b1\u20091) final map for each qubit.\n\nAll of the Hamiltonian identification experiments discussed so far (Figs.\u00a02, 4) were implemented on the Sycamore #1 chip. In order to compare these results to implementations on a physically distinct chip with different calibration, and to demonstrate the scalability of our method, we implement Hamiltonian identification experiments for an increasing number of qubits on the Sycamore #2 chip. More precisely, for a given number of qubits N, we implement many different Hamiltonians with quasi-random local potentials, as shown in Fig.\u00a03c for N\u2009=\u20096. We then average the analog implementation errors of the Hamiltonians and frequencies for several system sizes. The results are shown in Fig.\u00a05. Notably, comparing the two different processors, the overall quality of fit does not depend significantly on either the number of qubits or the processor used. This indicates, first, that our reconstruction method works equally well in all scenarios and, second, that both quantum processors implement Hamiltonian time evolution that closely fits our model assumption. We also notice that the overall analog implementation error does not significantly depend on the system size. This signifies that no additional non-local errors are introduced into the system as the size is increased. At the same time, the overall error of Hamiltonian implementations on Sycamore #2 is much worse compared to those on Sycamore #1, indicating that Sycamore #2 was not as well calibrated. Hamiltonian identification thus allows us to meaningfully compare Hamiltonian implementations across different physical systems and system sizes.\n\nWe measure the analog implementation error of the implemented Hamiltonians (dark red) and their eigenfrequencies (golden) as well as the deviation \\({({\\sum }_{l=0}^{L}\\parallel {\\mathsf{deviation}}[\\hat{h},\\hat{S},{\\hat{D}}_{M}]({t}_{l}){\\parallel }_{{\\ell }_{2}}^{2}/({N}^{2}(L+1)))}^{1/2}\\) of the fit from the experimental data (dark blue) all averaged over implementations of Hamiltonians with quasi-random local potential on an increasing number of qubits on two different quantum processors\u2014Sycamore #1 (circles) and #2 (diamonds). Each point is the mean of the respective quantity over 51 Hamiltonian implementations (21 for N\u2009=\u20095 and 20 for N\u2009=\u200914 on Sycamore #2). The data points at N\u2009=\u20096 on Sycamore #1 summarizes Fig.\u00a03c. The error bars represent one standard deviation.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52629-3/MediaObjects/41467_2024_52629_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52629-3/MediaObjects/41467_2024_52629_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52629-3/MediaObjects/41467_2024_52629_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52629-3/MediaObjects/41467_2024_52629_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52629-3/MediaObjects/41467_2024_52629_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We have implemented analog simulation of the time-evolution of non-interacting bosonic Hamiltonians with tunable parameters for up to 14 qubit lattice sites. A structure-exploiting learning method allows us to robustly identify the implemented Hamiltonian that governs the time-evolution. To achieve this, we have introduced a new super-resolution algorithm, referred to as tensorESPRIT, for precise robust identification of eigenfrequencies of a Hermitian matrix from noisy snapshots of the one parameter unitary subgroup it generates. Thereby, we diagnose the deviation from the target Hamiltonian and assess the accuracy of the implementation. We achieve sub-MHz error of the Hamiltonian parameters compared to their targeted values in most implementations. Combining the average performance measures over ensembles of Hamiltonians we associate benchmarks to the components of the superconducting qubit chips that quantify the performance of the hardware on the time evolution and provide specific diagnostic information. Within our Hamiltonian identification framework, we are able to identify SPAM errors due to parameter ramp phases as a severe limitation of the architecture. Importantly, such ramp phases are present in any analog quantum simulation of quenched dynamics. Our results show that minimizing those is crucial for accurately implementing a Hamiltonian.\n\nThe experimental and computational effort of the identification method scales efficiently in the number of modes of the Hamiltonian. We have also numerically identified the limitations of more direct algorithmic approaches and demonstrated the scalability of our method under empirically derived noise and error models.\n\nWe have demonstrated and custom-tailored our approach here to a superconducting analog quantum simulation platform. It can be applied directly to any bosonic and fermionic analog simulation platform which allows for accurate preparation and measurement of single particle excitations at specific lattice sites. Generalizing our two-step approach developed here, we expect a polynomial scaling with the dimension of the diagnosed particle sector and therefore remain efficient for diagnosing two-, three- and four-body interactions, thus allowing to build trust in the correct implementation of interacting Hamiltonian dynamics as a whole. Furthermore, it is in some cases possible to adapt the method to Hamiltonians with general non-particle number preserving free part. From a broader perspective, with this work, we hope to contribute to the development of a machinery for precisely characterizing and thereby improving analog quantum devices.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "We use the Sycamore quantum processor composed of quantum systems arranged in a two-dimensional array. This processor consists of gmon qubits (transmons with tunable coupling) with frequencies ranging from 5 to 7\u2009GHz. These frequencies are chosen to mitigate a variety of error mechanisms such as two-level defects. Our coupler design allows us to quickly tune the qubit-qubit coupling from 0 to 40+ MHz. The chip is connected to a superconducting circuit board and cooled down to below 20\u2009mK in a dilution refrigerator. The median values of the T1 and T2 times of the qubits are T1\u2009=\u200916.1\u2009\u03bcs, T2\u2009=\u20095.3\u2009\u03bcs (Ramsey interferometry) and T2\u2009=\u200917.8\u2009\u03bcs (after CPMG dynamical decoupling). Each qubit has a microwave control line used to drive an excitation and a flux control line to tune the frequency. The processor is connected through filters to room-temperature electronics that synthesize the control signals. We execute single-qubit gates by driving 25\u2009ns microwave pulses resonant with the qubit transition frequency, resulting in single-qubit gate fieldity of 99.8% as measured via randomized benchmarking.\n\nThe pulses used in the experiment are pre-distorted in order to compensate for filters on the control lines. In order to calibrate this distortion, we send rectangular pulses to the qubits and monitor the frequency change of the qubits. This allows us to know the response of the microwave lines at the qubits (i.e., the deviation from a rectangle) and compensate for distortions. The ramp time can be as fast as 2\u20133\u2009ns and the distortions take the form of overshoot and undershoots with a long response time of 100ns. After compensating for the distortions, the qubit frequency remains fixed.\n\nThe qubits are connected to a resonator that is used to read out the state of the qubit. The state of all qubits can be read simultaneously by using a frequency-multiplexing. Initial device calibration is performed using \u201cOptimus\u201d63 where calibration experiments are represented as nodes in a graph.\n\nSuccinctly written, our data model is given by\n\nwhere m,\u00a0n\u2009=\u20091,\u00a0\u2026,\u00a0N label the distinct time series, l\u2009=\u20090,\u00a0\u2026,\u00a0L labels the time stamps of the L\u2009+\u20091 data points per time series. The matrices S and M are arbitrary invertible linear maps that capture the state preparation and measurement stage, as affected by the ramping of the eigenfrequencies of the qubits and couplers to their target value and back (see Fig.\u00a01). In the experiment, we empirically estimate each such expectation value with 1000 single shots.\n\nOur mindset for solving the identification problem is based on the eigendecomposition \\(h=\\mathop{\\sum }_{k=1}^{N}{\\lambda }_{k}\\left\\vert {v}_{k}\\right\\rangle \\left\\langle {v}_{k}\\right\\vert\\) of the coefficient matrix h in terms of eigenvectors \\(\\left\\vert {v}_{k}\\right\\rangle\\) and eigenvalues \u03bbk. We can write the data (6) in matrix form as\n\nwhere we have dropped S and M for the time being. This decomposition suggests a simple procedure to identify the Hamiltonian using Fourier data analysis. From the matrix-valued time series data y[l]\u00a0(7), we identify the Hamiltonian coefficient matrix h in two steps. First, we determine the eigenfrequencies of h. Second, we identify the eigenbasis of h. To achieve those identification tasks with the largest possible robustness to error, it is key to exploit all available structure at hand.\n\nIn order to robustly estimate the spectrum, we exploit that the signal is sparse in Fourier space. This structure allows us to substantially denoise the signal and achieve super-resolution beyond the Nyquist limit64,65. A candidate algorithm for this task, suitable for scalar time-series, is the ESPRIT algorithm, which comes with rigorous recovery guarantees55,56. To extract the Hamiltonian spectrum from the matrix time-series y[l], we apply ESPRIT to the trace of the data series (for \\(S=M={\\mathbb{1}}\\))\n\nThe drawback of this approach is that if the spectrum of the Hamiltonian is sufficiently crowded, which will happen for large N, the Fourier modes in F[l] become indistinguishable and ESPRIT fails to identify the frequencies. In particular, ESPRIT is not able to identify degeneracies in the spectrum.\n\nTo overcome this issue and obtain a truly scalable learning procedure applicable to degenerate spectra, we develop a new algorithm coined tensorESPRIT. TensorESPRIT extends the ideas of ESPRIT to the case of matrix-valued time series using tensor network techniques. Using tensorESPRIT also improves the robustness of frequency estimation to SPAM errors. For practical Hamiltonians, tensorESPRIT becomes necessary for systems with N\u2009\u2273\u200912; as we find in numerical simulations summarized in Section C and detail in Section IV.B of the Supplemental Material.\n\nTensorESPRIT (ESPRIT) comprises of a denoising step, in which the rank of the Hankel tensor (matrix) of the data is limited to its theoretical value. Subsequently, rotational invariance of the data is used to compute a matrix from the denoised Hankel tensor (matrix), the spectrum of which has a simple relation to the spectrum of h. In the case of ESPRIT, this amounts to a multiplication of the denoised Hankel matrix by a pseudoinverse of its shifted version. Contrastingly, tensorESPRIT uses a sampling procedure to contract certain sub-matrices of the denoised Hankel tensor with the pseudoinverse of other sub-matrices. Details on both algorithms can be found in the Supplemental Material.\n\nTo identify the eigenspaces of the Hamiltonian, we use the eigenfrequencies found in Step 1 to fix the oscillating part of the dynamics in Eq. (7). What remains is the problem of finding the eigenspaces \\(\\vert {v}_{k}\\rangle \\langle {v}_{k}\\vert\\) from the data. This problem is a non-convex inverse quadratic problem, subject to orthogonality of the eigenspaces, as well as the constraint that the resulting Hamiltonian matrix respects the connectivity of the superconducting architecture. Formally, we denote the a priori known support set of the Hamiltonian matrix as \u03a9, so that we can write the support constraint as \\({h}_{\\bar{\\Omega }}=0\\), where \\(\\bar{\\Omega }\\) denotes the complement of \u03a9 and subscripting a matrix with a support set restricts the matrix to this set. We can cast this problem into the form of a least-squares optimization problem\n\nequipped with non-convex constraints enforcing orthogonality, and the quadratic constraint restricting the support. In order to approximately enforce the support constraint, we make use of regularization61. It turns out that this can be best achieved by adding a term [66, App.\u00a0A]\n\nto the objective function (9), where \u03bc\u2009>\u20090 is a parameter weighting the violation of the support constraint. We then solve the resulting minimization problem by using a conjugate gradient descent on the manifold of the orthogonal group57,67, see also the recent work68,69,70 for the use of geometric optimization for quantum characterization.\n\nWithout the support constraint this gives rise to an optimization algorithm that converges well, as shown in the Supplemental Material. However, the regularization term makes the optimization landscape rugged as it introduces an entry-wise constraint that is skew to the orthogonal manifold. To deal with this, we consecutively ramp up \u03bc until the algorithm does not converge anymore in order to find the Hamiltonian that best approximates the support constraint while being a proper solution of the optimization problem. For example, for the data in Fig.\u00a02 the value of \u03bc is 121. In order to avoid that we identify a Hamiltonian from a local minimum of the rugged landscape, we only accept Hamiltonians that achieve a total fit of the experimental data within a 5% margin of the fit quality of the unregularized recovery problem, and use the Hamiltonian recovered without the regularization otherwise.\n\nThe experimental design requires a ramping phase of the qubit and coupler frequencies from their idle location to the desired target Hamiltonian and back for the measurement. In effect, the data model (6) includes time-independent linear maps M and S that are applied at the beginning and end of the Hamiltonian time-evolution. The maps affect both the frequency extraction and the eigenspace reconstruction.\n\nFor the frequency extraction using ESPRIT, the Fourier coefficients of the trace signal F[l] become \u3008vk\u2223SM\u2223vk\u3009. While the frequencies remain unchanged the Fourier coefficients now deviate from unity, significantly impairing the noise-robustness of the frequency identification. This effect is still present, albeit weaker, in tensorESPRIT, in the case of non-unitary SPAM errors. The eigenspace reconstruction is affected much more severely and requires careful consideration, as detailed below and in the Supplemental Material.\n\nWe can remove either the initial map S or the final map M from the data. To remove S, we apply the pseudoinverse (\u22c5)+ of the data y[l0] at a fixed time \\({t}_{{l}_{0}}\\) to the entire (time-dependent) data series in matrix form. For invertible S and M this gives rise to\n\nThe caveat of this approach is that the shot noise that affected the single time point y[l0] can lead to correlated errors in every entry of the new data series \\({y}^{({l}_{0})}\\).\n\nWe can reduce the error induced by these correlations by effectively averaging over \u201ccorrected\u201d data series \\({y}^{({l}_{0})}\\) with different l0. To this end, we compute the concatenation of data series for different choices of l0, e.g., for every s data points 0,\u00a0s,\u00a02s, \u2026, \u230aL/s\u230bs giving rise to new data \\({y}_{{{\\rm{total}}},{{\\rm{s}}}}=({y}^{(0)},{y}^{(s)},{y}^{(2s)},\\ldots,{y}^{(\\lfloor L/s\\rfloor s)})\\in {{\\mathbb{C}}}^{\\lfloor L/s\\rfloor L}\\). If the data suffers from drift errors, it is also beneficial to restrict each data series \\({y}^{({l}_{0})}\\) to entries \\({y}^{({l}_{0})}[\\kappa ]\\) with \u03ba \u2208 [l0\u2009\u2212\u2009w,\u00a0l0\u2009+\u2009w], i.e., the entries in a window of size w around l0. In practice, we use s\u2009=\u20091 and w\u2009=\u200950 for the reconstructions on Sycamore #1, and s\u2009=\u20091,\u00a0w\u2009=\u2009L for those on Sycamore #2.\n\nAs we argue below, the final map M is nearly diagonal here. Hence, we can use ytotal,s from Eq. (9) and it is justified to apply the support constraint in the eigenspace reconstruction step. However, the eigenspace reconstruction will suffer from systematic errors due to the final map, even in the case when it is nearly diagonal. Below, we explain a method to partially remove this error.\n\nThe systematic error in the reconstructed Hamiltonian eigenbasis can be expressed as an orthogonal rotation DM from the eigenbasis that is actually implemented. Due to the gauge freedom in the model (6), we cannot hope to identify DM fully without additional assumptions. However, as elaborated on in the Supplemental Material, we can find a diagonal orthogonal estimate \\({\\hat{D}}_{M}\\) of the true correction DM and hence remove a sign of the systematic error. To this end, we assume that the experimental implementation of the target Hamiltonian does not flip the sign in the hopping terms and remedy the sign of systematic error due to the final map by fitting a diagonal orthogonal rotation of the Hamiltonian eigenbasis \\({\\hat{D}}_{M}\\) that minimizes the implementation error. We update the reconstructed Hamiltonian to\n\nwhere \\(\\tilde{h}={\\sum }_{k}{\\lambda }_{k}\\vert {v}_{k}\\rangle \\langle {v}_{k}\\vert\\) and \\(\\{\\left\\vert {v}_{k}\\right\\rangle \\}\\) is the eigenbasis obtained by solving the problem (9), and use \\({\\hat{D}}_{M}\\) as an estimate of M. We can now obtain a tomographic estimate of the initial map through\n\nAs explained above, the pre-processing step allow us to remove either the initial map S or final map M from the data, while we can only find a diagonal orthogonal estimate of the remaining map. A priori it is unclear which one of the two maps should be removed in order to reduce the systematic error more.\n\nWe have already treated the initial and final ramping phases on a different footing, however. The reason for this is rooted in the specifics of the ramping of the couplers compared to the qubits. The couplers need to be ramped from their idle frequencies to provide the desired target frequencies of 20 MHz. This is why we expect the time scale of the initial ramping to be mainly determined by the couplers, namely the delay until they arrive around the target frequency and the time it takes to stabilize at the target frequency. In contrast, the final ramping map becomes effectively diagonal as soon as the couplers are again out of the MHz regime. We therefore expect that the initial map has a sizeable non-diagonal orthogonal component, whereas the final map is approximately diagonal.\n\nWe build trust in this assumption using experimental data in Fig.\u00a06. We observe that the deviation of the orthogonal part \\({\\hat{O}}_{S}\\) of the identified initial map \\(\\hat{S}\\) from its projection \\({\\hat{D}}_{S}\\) to diagonal orthogonal matrices is much larger than the corresponding deviation for the final map (Fig.\u00a06a). Moreover, both the root-mean-square fit of the data (Fig.\u00a06c) and the analog implementation error of the identified Hamiltonian with its target (Fig.\u00a06b) are significantly improved when removing the initial ramp, as compared to removing the final ramp. This indicates that S induces a larger systematic error than M. Correspondingly, it is indeed more advantageous to remove the initial map in the pre-processing and fit the final map with a diagonal orthogonal matrix, validating the approach taken here.\n\nWe identify Hamiltonians of a set of 5-qubit Hamiltonians with Hofstadter butterfly potentials \\({\\mu }_{q}=20\\cos (2\\pi qb)\\) MHz for qubits q\u2009=\u20091,\u00a0\u2026,\u00a05 and flux value b in without regularization. a Deviation of the orthogonal part \\({\\hat{O}}_{S}\\) (\\({\\hat{O}}_{M}\\)) of the identified initial map \\(\\hat{S}\\) (final map \\(\\hat{M}\\)) from the closest diagonal orthogonal matrix \\({\\hat{D}}_{S}\\) (\\({\\hat{D}}_{M}\\)). b Analog implementation error of the corresponding identified Hamiltonians \\({\\hat{h}}_{S}\\) (\\({\\hat{h}}_{M}\\)). c Total root-mean-square deviation of the time series data from the Hamiltonian fit.\n\nOverall, the recovered model \\((\\hat{h},\\hat{S},{\\hat{D}}_{M})\\) fits the experimental data well, as demonstrated in Figs.\u00a02, 5, 6, and gives good prediction accuracy on simulated data, as demonstrated in Fig.\u00a07 in the next section as well as the Supplemental Material. In the Supplemental Material, we provide further numerical evidence that this approach leads to small systematic errors and recovers a model with good predictive power.\n\nRecovery error of frequencies (golden) and Hamiltonians (red) from simulated time series averaged over 20 instances of Harper Hamiltonians for different system sizes. The error bars represent one standard deviation. The evolution is simulated for up to 0.6\u2009\u03bcs and sampled at a rate of 250\u2009MHz. Statistical noise is simulated using 103 shots per expected value and SPAM is modeled by using randomly chosen idle qubit and coupler frequencies, linear ramping of 1.5\u2009GHz/s padded by 0.05\u2009ns. The fitting error of the time series is depicted in blue, right y-axis. We refer to the Supplemental Material, Sec. VII A for details.\n\nWe benchmark our identification algorithm against more direct approaches in numerical simulations including models for statistical and systematic errors in the Supplemental Material VI. We find that, indeed, already for small system sizes, the regularized manifold optimization algorithm developed here features an improved robustness against state preparation and measurement errors compared to (post-projected) linear inversion. For intermediate system sizes (N\u2009>\u200910), exploiting structure in the recovery algorithm then becomes an imperative. In particular, for larger system sizes the eigenspectrum of the Hamiltonian becomes unavoidably narrower spaced, leading to (near-)degeneracies. We find that on instances of the Harper Hamiltonian studied here linear inversion approaches cannot be applied at all for N\u2009>\u200920. Regularized conjugate gradient decent in contrast yields good recovery performance even for larger systems. The same limitations apply to a direct Fourier analysis of the cumulative time series data using ESPRIT, as described above. For different families of Hamiltonians, we find that above a system size of N\u2009\u2248\u200920 tensorESPRIT still consistently recovers the frequency spectrum, while the ESPRIT algorithm fails to do so.\n\nUsing structure not only allows our algorithm to denoise the data and achieve error robustness, it also makes precise Hamiltonian identification possible even with the number of measurements dramatically reduced in the spirit of compressed sensing. As described above, the number of measurements scales quadratically with the system size. We find that using the conjugate gradient algorithm the identification procedure reliably recovers Hamiltonians even when it has access to only about 3% of the measurements. In this regime, the linear inverse problem of finding the eigenvectors is underdetermined. Thus, the required experimental resources can be significantly reduced for large system sizes.\n\nTo demonstrate our method\u2019s scalability, Fig.\u00a07 shows the recovery performance of the structure-exploiting algorithm on simulated data under realistic models for SPAM errors and with finite measurement statistics in the regime where the baseline approaches could not be applied anymore.\n\nAs detailed in the Supplemental Material, tensorESPRIT has computational complexity in \\({{\\mathcal{O}}}({L}^{2}{N}^{3})\\). It is not straight-forward to bound the computational complexity of the conjugate gradient descent, as it depends on the required precision of the matrix exponential and the number of descent steps until convergence. The entire identification algorithm consumes \\({{\\mathcal{O}}}(L{N}^{2})\\) memory. In practice, we find that the algorithm can be easily deployed on a consumer-grade laptop computer, e.g., reconstructing Hamiltonians of size N\u2009=\u200950 in around 5\u2009min.\n\nWe here discuss how we estimate the systematic and statistical contributions to the error on the identified Hamiltonian \\(\\hat{h}\\) and initial map \\(\\hat{S}\\). Note that the impact of the systematic error on predicting results of experiments with the same initial and final ramps is reduced due to the gauge invariance of the model (6). Due to this freedom, some of the error in identifying \\({\\hat{D}}_{M} \\sim M\\) gets accounted for by a corresponding error in the identification \\(\\hat{h} \\sim h\\) and \\(\\hat{S} \\sim S\\) in expressions of the type \\(\\hat{M}{e}^{-it\\hat{h}}\\hat{S}\\). This prediction error can be further decreased by running the algorithm twice\u2014removing the initial map in the first run and the final map in the second run, using the first ramp estimates to partially remove the ramps from the data before running the second iteration of the identification. This procedure is detailed and supported by numerical evidence in the Supplemental Material.\n\nIn order to estimate the magnitude of the systematic error that is induced by the non-trivial final map, we use a linear model of the final ramping phase with a constant ramping speed and constant wait time between the coupler and qubit ramping. We detail and present validation of this ramping model with a separate experiment in the Supplemental Material, where we also provide empirical estimates of the model parameters.\n\nGiven a Hamiltonian matrix \\(\\hat{h}\\) and the initial ramp \\(\\hat{S}\\) obtained from experimental data, we recover the Hamiltonian matrix \\({\\hat{h}}^{{\\prime} }\\) from data simulated using the model \\((\\hat{h},\\hat{S},M)\\), where M is the final ramp given by our ramping model. We use \\(| f(\\hat{h})-f({\\hat{h}}^{{\\prime} })|\\) as an estimate of the systematic error on quantities of the form \\(f(\\hat{h})\\in {\\mathbb{R}}\\),\n\nWe estimate the effect of finite measurement statistics on the Hamiltonian estimate that is returned by the identification method via parametric bootstrapping. To this end, we simulate time series data with statistical noise using Haar-random unitaries S as initial ramps, the identified Hamiltonian \\(\\hat{h}\\) and final ramp \\(M={\\mathbb{1}}\\), as detailed in the Supplemental Material.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52629-3/MediaObjects/41467_2024_52629_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52629-3/MediaObjects/41467_2024_52629_Fig7_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "The experimental data is available from the authors upon request.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code is available from the authors upon request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Feynman, R. P. Simulating physics with computers. Int J. Theor. 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Quantum 7, 1053 (2023).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We acknowledge contributions from Charles Neill, Kostyantyn Kechedzhi, and Alexander Korotkov to the calibration procedure used in this analog approach. We would like to thank Christian Krumnow, Benjamin Chiaro, Alireza Seif, Markus Heinrich, and Juani Bermejo-Vega for fruitful discussions in early stages of the project. The hardware used for this experiment was developed by the Google Quantum AI hardware team, under the direction of Anthony Megrant, Julian Kelly, and Yu Chen. D.H. acknowledges funding from the U.S. Department of Defense through a QuICS Hartree fellowship. This work has been supported by the BMBF (DAQC, MUNIQC-Atoms), for which it provides benchmarking tools for analog-digital superconducting quantum devices, as well as by the DFG (specifically EI 519 20-1 on notions of Hamiltonian learning, but also CRC 183 and GRK 2433 Deadalus). We have also received funding from the European Union\u2019s Horizon2020 research and innovation program (PASQuanS2) on programmable quantum simulators, the Munich Quantum Valley (K-8), the Einstein Foundation, Berlin Quantum, and the ERC (DebuQC).", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Dominik Hangleiter, Ingo Roth.\n\nJoint Center for Quantum Information and Computer Science (QuICS), University of Maryland and NIST, College Park, MD, USA\n\nDominik Hangleiter\n\nJoint Quantum Institute (JQI), University of Maryland and NIST, College Park, MD, USA\n\nDominik Hangleiter\n\nDahlem Center for Complex Quantum Systems, Freie Universit\u00e4t Berlin, Berlin, Germany\n\nDominik Hangleiter,\u00a0Ingo Roth,\u00a0Jon\u00e1\u0161 Fuksa\u00a0&\u00a0Jens Eisert\n\nQuantum Research Center, Technology Innovation Institute (TII), Abu Dhabi, United Arab Emirates\n\nIngo Roth\n\nHelmholtz-Zentrum Berlin f\u00fcr Materialien und Energie, Berlin, Germany\n\nJens Eisert\n\nGoogle Quantum AI, Mountain View, CA, USA\n\nPedram Roushan\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nD.H. and I.R. conceived of the Hamiltonian identification algorithm. J.F. conceived of the tensorESPRIT algorithm. D.H., I.R., and J.F. analyzed the experimental data and benchmarked the identification algorithm. P.R. took the experimental data. D.H. and I.R. wrote the initial manuscript. D.H., I.R., J.F., J.E., and P.R. contributed to discussions and writing the final manuscript.\n\nCorrespondence to\n Dominik Hangleiter or Ingo Roth.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Liubov Markovich, Jinzhao Sun, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Robustly learning the Hamiltonian dynamics of a superconducting quantum processor.\n Nat Commun 15, 9595 (2024). https://doi.org/10.1038/s41467-024-52629-3\n\nDownload citation\n\nReceived: 01 February 2024\n\nAccepted: 17 September 2024\n\nPublished: 06 November 2024\n\nVersion of record: 06 November 2024\n\nDOI: https://doi.org/10.1038/s41467-024-52629-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Proteins for Cryo-Electron Tomography", + "journal": "Nature Communications", + "published": "08 January 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55767-w/MediaObjects/41467_2024_55767_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55767-w/MediaObjects/41467_2024_55767_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55767-w/MediaObjects/41467_2024_55767_MOESM3_ESM.mp4" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55767-w/MediaObjects/41467_2024_55767_MOESM4_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55767-w/MediaObjects/41467_2024_55767_MOESM5_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-55767-w#Fig5", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-61019", + "https://www.ebi.ac.uk/empiar/EMPIAR-12469/" + ], + "code": [ + "https://thuem.net", + "/articles/s41467-024-55767-w#ref-CR34", + "https://github.com/thuem/MPicker", + "https://doi.org/10.5281/zenodo.14264179" + ], + "subject": [ + "Biophysics", + "Cellular imaging", + "Cryoelectron tomography", + "Transmission electron microscopy" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4404303/v1.pdf?c=1736341638000", + "research_square_link": "https://www.researchsquare.com//article/rs-4404303/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-55767-w.pdf", + "preprint_posted": "29 May, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Advancements in cryo-electron tomography (cryoET) allow the structure of macromolecules to be determined in situ, which is crucial for studying membrane protein structures and their interactions in the cellular environment. However, membranes are often highly curved and have a strong contrast in cryoET tomograms, which masks the signals from membrane proteins. These factors pose difficulties in observing and revealing the structures of membrane proteins in situ. Here, we report a membrane-flattening method and the corresponding software, MPicker, designed for the visualization, localization, and orientation determination of membrane proteins in cryoET tomograms. This method improves the visualization of proteins on and around membranes by generating a flattened tomogram that eliminates membrane curvature and reduces the spatial complexity of membrane protein analysis. In MPicker, we integrated approaches for automated particle picking and coarse alignment of membrane proteins for sub-tomogram averaging. MPicker was tested on tomograms of various cells to evaluate the method for visualizing, picking, and analyzing membrane proteins.Biological sciences/Biophysics/Membrane biophysicsBiological sciences/Cell biology/Cellular imagingBiological sciences/Biological techniques/Structure determination/Electron microscopy/Cryoelectron tomography", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Advancements in cryo-electron tomography (cryoET) allow the structure of macromolecules to be determined in situ, which is crucial for studying membrane protein structures and their interactions in the cellular environment. However, membranes are often highly curved and have a strong contrast in cryoET tomograms, which masks the signals from membrane proteins. These factors pose difficulties in observing and revealing the structures of membrane proteins in situ. Here, we report a membrane-flattening method and the corresponding software, MPicker, designed for the visualization, localization, and orientation determination of membrane proteins in cryoET tomograms. This method improves the visualization of proteins on and around membranes by generating a flattened tomogram that eliminates membrane curvature and reduces the spatial complexity of membrane protein analysis. In MPicker, we integrated approaches for automated particle picking and coarse alignment of membrane proteins for sub-tomogram averaging. MPicker was tested on tomograms of various cells to evaluate the method for visualizing, picking, and analyzing membrane proteins.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Cryo-electron tomography (cryoET) is a powerful method for directly studying three-dimensional (3D) structures of organisms in their native state. Membranes are abundant in cells and are key platforms for various biological processes via embedded or associated proteins. Therefore, understanding the in situ structure and spatial distribution of membrane proteins is important1,2,3. However, recognizing and analyzing membrane proteins in cryoET tomograms is often challenging owing to the crowded environment of cells, low signal-to-noise ratio (SNR), and the missing wedge effect. Tomograms are usually viewed as a series of two-dimensional (2D) slices parallel to the lamellar sample, referred to as x\u2013y slices, which are generally perpendicular to the direction of the incident electron beam. The membranes are usually randomly oriented in a tomogram and hence do not match the structured voxel arrangement of tomogram data; thus, direct observation of membrane proteins, such as through x\u2013y slice images, is difficult. Furthermore, since the contrast of membranes in tomograms is substantially stronger than that of the embedded membrane proteins, it often masks the visible signals of membrane proteins. These factors limit the in situ structural analysis of membrane proteins for not only spatial organization but also high-resolution structure determination by sub-tomogram averaging (STA).\n\nVisualization is often a prerequisite in cryoET for identifying and locating proteins, including proteins embedded in or associated with membranes. Some software packages, such as IMOD4, have integrated tools to display a planar slice in a tomogram in an arbitrary direction, facilitating the visualization of membrane proteins distributed on a plane5. However, the curved nature of cell membranes makes visualization under a simple plane difficult. Membranorama2 is a software specialized for membrane protein visualization, that offers an alternative approach for visualizing membrane proteins by mapping the molecular landscapes of membranes on a 3D membrane surface extracted from membrane segmentation, allowing the manual selection and orientation determination of membrane proteins.\n\nIn addition to visualization methods that assist manual particle picking, other image-feature-analysis-based methods, including template matching and deep-learning-based target detection that serve as tools for automated particle picking have also been developed. Some intrinsic properties of membrane proteins, such as unique distribution patterns different from those of soluble proteins, can be used for recognition. PySeg6 utilizes membrane segmentation and automatically localizes membrane proteins by tracking a density network near the membrane. MemBrain7 is a deep-learning-based method that utilizes the geometry of segmented membranes to extract and pre-align candidate subvolumes, thereby reducing the influence of membrane signals.\n\nIn this study, we develop a membrane-flattening-based method using the software MPicker for visualizing and localizing membrane proteins in a tomogram. MPicker can flatten a selected membrane in a tomogram by transforming the curved membrane and its adjacent density into a reformatted tomogram, termed the flattened tomogram. The fundamental concept of flattening is to reduce the dimensionality of the curved membrane and associated objects from 3D to 2D. This transformation allows for the convenient visualization and analysis of their spatial arrangements and enables the adoption of many conventional tools previously developed for 2D analysis. As a result, automated particle picking, 2D classification, and 3D particle alignment can be integrated into the MPicker workflow with minimal adaptation. In summary, a variety of analyses are supported on the flattened tomogram, including visualization, particle picking, and preliminary estimation of particle orientation. The key functional features of MPicker were demonstrated using diverse cell samples.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Membrane flattening is the core process of MPicker, which supports the visualization and localization of proteins on the membrane surface. The thylakoid membrane in the Chlamydomonas tomogram2 (Fig.\u00a01a) was used to illustrate this procedure. The raw tomogram intended for flattening may first undergo denoising and missing wedge correction to enhance visualization8, as well as membrane segmentation to generate a membrane mask. Membrane flattening comprises three steps: extracting the surface of a selected membrane, flattening the membrane surface to a plane, and extending the flattened region around the surface to produce a flattened tomogram.\n\na Input tomogram (EMD-10780) and membrane mask. A seed point (red arrow) is added to specify the membrane to be flattened. b Workflow to extract (left panel) and flatten (right panel) the surface. Several key elements are the starting points (red and green) on a membrane mask (white), the extracted membrane surface (gray), a cylindrical surface (cyan), and the final plane (yellow) for projection. c Membranes before and after flattening. Different colors are used to distinguish the position of the flattened surface in the raw tomogram, and the position of the x\u2013y slices in the flattened tomogram. d Representative x\u2013y, x\u2013z, and y\u2013z slices of the flattened tomogram. Picked particles of ATP synthase (green) and ribosome (orange) are labeled by circles. e 3D view of the cyan slice in (c). X\u2013Y\u2013Z indicates the coordinate system of the raw tomogram.\n\nIn the first step, MPicker needs a set of starting points (red curve and green surface in Fig.\u00a01b left panel) on the membrane surface to generate a curved surface expression by thin-plate spline (TPS) interpolation or polynomial fitting. If a membrane mask (Fig.\u00a01a right panel) is provided, generated by either MPicker\u2019s build-in subroutine (Supplementary Fig.\u00a01) or a third-party approach, the user is required to label one or multiple seed points on the mask to specify a membrane piece of interest. Subsequently, MPicker automatically extracts the points near the seed points on the mask to serve as starting points (see \u201cMethods\u201d). In the case of missing a membrane mask, for example, failing to perform segmentation on some low-quality membranes, manual labeling of the starting points on a membrane surface is an alternative way in MPicker. After obtaining a curved surface expression using the starting points, MPicker calculates and stores the normal vectors of the surface at each point for the third step and future orientation estimation.\n\nIn the second step, MPicker projects the extracted membrane surface on a best-fit cylindrical surface (cyan surface in Fig.\u00a01b right panel) and further unrolls the cylindrical surface to a plane (yellow plane in Fig.\u00a01b right panel). To accommodate various membrane shapes, MPicker supports two types of cylindrical surfaces: the open type described by a polynomial, and the closed type described by an elliptic formula (see \u201cMethods\u201d). For example, the cyan surface in Fig.\u00a01b is an open cylindrical surface described by an eighth-order polynomial. At the end of this step, pixel-level coordinate mapping between the membrane surface and the plane can be established. By plotting the membrane surface density on the plane, a flattened membrane surface image, referred to as the central slice, is generated.\n\nIn the third step, MPicker extends the central slice along both directions of the local normal vectors, generating a series of parallel slices spaced equally with a single pixel interval (Fig.\u00a01c). All these slices constitute a new tomogram, termed the flattened tomogram. The z-axis of the flattened tomogram is perpendicular to the membrane, and each x\u2013y slice corresponds to an isometric surface parallel to the membrane surface. In the flattened tomogram, all local cellular structures are preserved relative to the adjacent membrane. For example, the membrane retains its thickness, and proteins locally retain their orientation relative to the membrane. At the end of this step, the voxel-level coordinate mapping between the flattened and raw tomograms is calculated and stored.\n\nThe spatial organization of proteins relative to the original membrane is simplified with the flattened tomogram, supporting further analysis of protein distributions on the membrane, analysis of protein-membrane interactions, and particle picking (Fig.\u00a01d). MPicker can also render a flattened tomogram slice as a curved surface, and thus depict the spatial relationships of objects in the original 3D space (Fig.\u00a01e).\n\nMembrane flattening involves a transformation from a curved surface to a plane, or say, mapping of an arbitrary surface to a plane, which is also termed surface parameterization in other fields9. Flattening can cause geometric distortions. For example, the geodesic distance between two points or the shape of an object on a curved surface may change after being mapped to a plane. The transformation from a cylindrical surface to a plane is a distortion-free intermediate flattening process. Distortion may occur only during the transformation from the original curved surface to a cylindrical surface. In practice, few surfaces can be transformed into planes without distortion. Therefore, MPicker provides a quantitative evaluation of distortion to avoid the possible misinterpretation of structural features.\n\nGenerally, the distortions from surface parameterization (or flattening in this work) can be described by area and shape distortions. Intuitively, a unit circle may become an ellipse with a semi-major axis \u03c31 and a semi-minor axis \u03c32 after flattening (Fig.\u00a02a). MPicker models flattening-induced distortion in a local region using a linear transformation (see \u201cMethods\u201d). The area distortion is quantified by the change in the area of the ellipse, \u03c31 \u03c32 (if larger than 1, records as expansion, else, takes the reciprocal and records as contraction). The shape distortion is quantified by the ratio between two axes, \\(\\frac{{\\sigma }_{1}}{{\\sigma }_{2}}\\).\n\na Distortions induced by linear transformations. b A representative membrane region (green) to be flattened in a Cyanobacterium thylakoid tomogram (EMD-13771). The surfaces corresponding to the central slice and a parallel slice in (c) are indicated by an orange dotted curve and a red curve, respectively. c Distortions presented by color maps. The membrane region and the slice in a flattened tomogram are labeled with the same color as the corresponding elements in (b). d 3D view of the slice indicated by the red in both (b) and (c). The cylindrical surface (cyan) and the final plane (yellow) for projection are shown. The corresponding locations in different views are connected by red dashed lines.\n\nWe used a cyanobacterial thylakoid membrane piece10 (green box in Fig.\u00a02b) to demonstrate the flattening-induced distortion. The selected membrane surface (dotted orange line in Fig.\u00a02b) was flattened and extended to generate a flattened tomogram (Fig.\u00a02c). The selected membrane surface corresponded to the central slice in the flattened tomogram (orange arrows and a short line in Fig.\u00a02c). We chose a slice parallel to the central slice (red short line in Fig.\u00a02c) and calculated its distortion. The area distortion predominantly manifests as an expansion (blue color, Fig.\u00a02c left) because the corresponding surface in the raw tomogram (red line in Fig.\u00a02b) is located on the inner side of the curved central membrane surface (dotted orange line in Fig.\u00a02b), and hence slightly stretched during flattening. MPicker also provides a visualization tool that maps a slice in the flattened tomogram back to the 3D space (Fig.\u00a02d, Supplementary Fig.\u00a02), which serves as a distortion-free reference for comparison.\n\nHere, most regions on the selected slice show less than two-fold distortions for both area (Fig.\u00a02c, left) and shape (Fig.\u00a02c, right). Projecting the membrane surface on a cylindrical surface (Fig.\u00a02d) is essential for minimizing distortion. Direct projection on a plane without cylindrical intermedium would cause significant distortion on both ends of the flattened membrane (Supplementary Fig.\u00a02). However, for a complicated surface that deviates significantly from a cylindrical shape, using cylindrical intermedium might be insufficient for flattening. An elegant approach is to use a triangle mesh as an intermedium, the parameterization of which has been extensively studied9,11. However, the generation of a triangle mesh usually requires a membrane mask and is highly dependent on membrane segmentation quality. Noises and segmentation errors affect the degree of matching between the triangle mesh and the original membrane and thus may cause failure in further flattening. Nevertheless, MPicker supports membrane flattening based on a given triangle mesh (see \u201cMethods\u201d). In a test using the Chlamydomonas endoplasmic reticulum (ER)1, we generated a triangle mesh for a membrane with a complex shape and successfully flattened the membrane (Supplementary Fig.\u00a03). The flattened tomogram reveals a clear distribution of ribosomes on the membrane surface.\n\nTo validate membrane flattening, MPicker was tested on the membranes of various cell samples, including Chlamydomonas chloroplasts, cyanobacteria, and HeLa cell nuclei.\n\nSeveral thylakoid membranes were observed initially. In a tomogram of Chlamydomonas chloroplasts (EMD-10780, Fig.\u00a01a), MPicker exhibits various proteins, such as photosystem II (PSII) complexes randomly distributed on the thylakoid membrane (cyan and blue slices in Fig.\u00a01c). In another tomogram of cyanobacteria (EMD-13771, Fig.\u00a02b), several phycobilisome (PBS) complex strings attached to the thylakoid membrane are visualized in a flattened tomogram (Fig.\u00a02c).\n\nIn another Chlamydomonas chloroplast tomogram, the inner chloroplast membrane with two budding vesicles (labeled as 1 and 2 in Fig.\u00a03a) was flattened. Budding vesicles were ignored by excluding their starting points, so two holes appear at the budding sites in the flattened tomogram (Fig.\u00a03b). Intriguingly, many protein particles were observed surrounding the budding sites on the intermembrane side of the inner membrane (Fig.\u00a03c).\n\na Representative view of a Chlamydomonas chloroplast tomogram. Two budding vesicles are labeled 1 and 2 (cyan). b x\u2013y, x\u2013z, and y\u2013z section views of the budding vesicles in a flattened tomogram. c x\u2013y slice in the flattened tomogram, corresponding to the intermembrane side of the inner membrane (yellow curve in a). In the subgraphs of two localized regions, the holes corresponding to budding regions are indicated by cyan circles, and the proteins near the holes are labeled in yellow. (b) and (c) share the same scale bar. d Representative view of a HeLa cell nuclear membrane tomogram (EMD-11992). e x\u2013y slices and a side section view of a flattened tomogram. Positions of x\u2013y slices are marked by lines with the same color as in (d) and (e). Nuclear pore complexes (NPCs; yellow circles), polyribosomes (green arrows), and putative lamina filaments (blue arrows) are pointed out in these x\u2013y slices. To enhance the contrast of the membrane and NPC (yellow), 61 y\u2013z slices (about 103\u2009nm) of the flattened tomogram were summed to get the side section view.\n\nIn the tomogram of HeLa cells12 (Fig.\u00a03d), the nuclear membrane nearly perpendicular to the z-axis was invisible owing to the missing wedge effect and could only be recognized indirectly by the fuzzy gap between the cytoplasm and the nucleoplasm. Generating a membrane mask is often difficult in such cases. Alternatively, we manually labeled 22 starting points along the center of the gap (cyan dashed line) and carried out flattening. In the flattened tomogram (Fig.\u00a03e, Supplementary Movie\u00a01), the nuclear membrane exhibited a weak signal and was flat. Nuclear pore complexes (NPCs, yellow circles) were observed in the membrane, and many polyribosomes on the cytoplasmic side (green arrows) and filaments on the nuclear side (blue arrows) were visible in slices of the flattened tomogram.\n\nMPicker was also tested on flattening the cylindrical and spherical surfaces (Supplementary Fig.\u00a04). For cylindrical surfaces, a cylindrical influenza virion13 was projected onto an elliptic cylindrical surface and flattened (Supplementary Fig.\u00a04a). For spherical surfaces, MPicker provides two methods to flatten them. One treats spherical membranes as cylindrical surfaces and hence sacrifices their bottom and top parts that are largely smeared out by the missing-wedge effect, as demonstrated by the human immunodeficiency virus14 (HIV) and Tick-borne encephalitis virus15 (TBEV) (Supplementary Fig.\u00a04b, c). The second method is to represent a surface in a triangle mesh (as in Supplementary Fig.\u00a03) including the bottom and top parts, and flatten the whole sphere, as shown in Supplementary Fig.\u00a04d.\n\nThe flattened tomogram generated in MPicker provides enriched insight into not only protein organization on the membrane but also the relationship among macromolecules near the membrane. Several examples are presented below to demonstrate the advantages of flattened tomograms.\n\nIn red algae, the PBS and PSII extra-membrane domains are located on opposite sides of the thylakoid membrane. The PBS is on the stromal side, and the PSII extra-membrane domain is on the luminal side. By generating a flattened tomogram of a thylakoid membrane piece3 (Fig.\u00a04a), we examined the relationship between PBS and PSII. Previous studies have shown that PBSs are organized along parallel strings formed by PSIIs3. This feature was clearly visualized in the two slices of the flattened tomogram containing PBS and PSII (Fig.\u00a04b). Furthermore, the PSII lines were disrupted in some regions (indicated by yellow arrows), which is consistent with the results of a previous report16.\n\na Representative view of a red alga chloroplast tomogram (EMD-31243). b Flattened tomogram of the thylakoid membrane (red region in a). Two x\u2013y slices (orange, cyan) on different sides of a membrane correspond to the vertical lines in the flattened tomogram. The positions and orientations of phycobilisome (PBS) particles are labeled by red arrows and copied to the slice showing PSII. The breaking points in PSII strings are marked by yellow arrows. (a) and (b) share the same scale bar. c Representative view of Chlamydomonas tomogram data as shown in Fig.\u00a03a. d Flattened tomogram of a thylakoid membrane pair (red region in c). Two x\u2013y slices (green, and magenta) show the lumen side density of two nearby membranes, respectively. Particles from the two slices were drawn with different colors and shown on a merged image. (c) and (d) share the same scale bar.\n\nThylakoid membranes are often stacked in a pairing manner, and PSII may play a role in this pairing17. We selected a pair of thylakoid membranes from Chlamydomonas (Fig.\u00a04c) and generated a flattened tomogram (Fig.\u00a04d). The two adjacent membranes were tightly appressed in a nearly parallel manner so that they could be flattened together. Two slices with PSII were extracted from the two membranes and were marked in green and magenta, respectively. Their spatial relationships on the membrane surface were visualized by merging the two slices. No significant overlap was observed between the PSII from adjacent membranes, which is consistent with the results of a previous study2.\n\nBy flattening the membrane, particle picking in a 3D density map is simplified and becomes a task for 2D images. To facilitate particle picking, MPicker calculates and stores the coordinate mapping between the flattened and raw tomograms in order to convert the particle coordinates to each other.\n\nParticle identification and selection depended mainly on the density of the extramembrane region in the tests. This is because the transmembrane region is often obscured by strong signals from the phospholipid bilayers. The structural features of a membrane protein, such as shape and contrast, often vary across slices in a flattened tomogram. Therefore, slices with obvious particle features in a flattened tomogram are a good choice for particle picking.\n\nParticle picking in a flattened tomogram is similar to that used in single particle analysis (SPA). In addition to manual picking, MPicker wraps EPicker18, a deep-learning-based software for automated particle picking on SPA micrographs. EPicker picks and assigns scores to particles in all (or user-specified) slices of a flattened tomogram. Since a particle in a flattened tomogram usually appears in multiple slices, MPicker removes duplicate picking based on scores, ensuring that the particle is picked only once.\n\nParticle picking was evaluated on paired thylakoid membranes from four Chlamydomonas tomograms (from the same dataset shown in Fig.\u00a04c). First, 559 particles with distinct features were manually picked from 20 flattened tomograms and used to train a deep-learning model of EPicker. EPicker was then used for automated particle picking (Supplementary Fig.\u00a05). To minimize false positives, we limited the particle search range to slices proximal to the thylakoid membrane surface, which roughly covered the extramembrane regions of membrane proteins. Initially, 5798 particles were picked from 192 flattened tomograms. After manual corrections, 1616 wrong picks were deleted and 1144 missed picks were added; thus, 5326 particles were finally used for subsequent analysis (see the following section). In practice, when combined with automated picking, the labor required for particle picking can be significantly reduced.\n\nDuring membrane flattening, MPicker records the orientation (normal vector) of each membrane region. Membrane protein orientation is highly constrained by their interaction with the membrane and often limited to a 2D in-plane rotation on the membrane surface. Once the 2D orientation (in-plane rotation angle) is determined and combined with the membrane orientation, the 3D orientation of membrane protein particles can be deduced, allowing for direct 3D reconstruction. Considering the flexibility of proteins and possible flattening errors, further local alignment is often required to obtain the final reconstruction. This means that we can skip the global alignment in the STA and proceed directly to the local alignment, which helps reduce computational complexity. To determine the 2D orientation of a protein on a membrane, we propose two approaches. One is to manually label particle orientation on the flattened tomogram slices based on their shape characteristics, and the other is to utilize 2D classification. Two examples were used to demonstrate the two approaches.\n\nFor manual labeling, we chose red algae PBSs as an example, which showed a clear orientation signature in the flattened tomogram slice. A total of 268 PBS particles were picked from three red algae tomograms. Their 2D orientations were manually labeled in MPicker (like red arrows in Fig.\u00a04b) and used to calculate the 3D orientation parameters and to directly reconstruct a density map using RELION219 (Fig.\u00a05a). The reconstruction was subsequently improved by further local alignment and achieved a resolution of 38.6\u2009\u00c5 (Fig.\u00a05b). The PBS rods are clearly shown in the map.\n\na Representative section views of the red alga PBS density map reconstructed directly using the orientations manually labeled in MPicker. b Improved density map after local alignment. c 2D class averages of particles picked on Chlamydomonas thylakoid membranes. Two classes (green) with clear PSII features were selected for STA. d Section views of the PSII density map reconstructed directly using the orientations converted from 2D classification. Two x\u2013y slices (green, and magenta) are shown. Their positions in the x\u2013z slice (black) are marked by horizontal lines. e Section views of the improved density map after local alignment. f The model of PSII\u2013LHCII supercomplex C2S2M2L2 (PDB: 6KAD) fitted to the map in (e). The components C, S, M, and L are drawn in different colors. g Density map after applying \u201cTomo CTF refinement\u201d and \u201cTomo frame alignment\u201d in RELION4. The map was shown at a low contour level of 0.3 and a high contour level of 1.95 for the extramembrane region and transmembrane region, respectively.\n\nFor the 2D classification approach, the particle projections along the normal vector of the membrane are subjected to alignment in 2D classification to derive the in-plane rotation angle on the membrane surface for each particle. Using the projection of an entire particle is not necessary, and projecting parts of the particle, such as the extramembrane region, is often a good choice. MPicker generates projections directly from raw tomograms to avoid the possible distortion and interpolation errors induced by flattening. Furthermore, because the cryoET data suffer from the missing wedge issue, the particle projection along the membrane normal vector may also be affected by the missing wedge, so the missing wedge must be considered during the 2D classification (Supplementary Fig.\u00a06). We refer to RELION\u2019s 3DCTF model19, which is used to process the contrast transfer function (CTF) and missing wedge in the STA. For each particle, we generated a 2D weighting image based on the 3DCTF model and the particle projection direction (see \u201cMethods\u201d), referred to as 2DCTF (Supplementary Fig.\u00a06c). The 2DCTF integrates the information of both the missing wedge and the CTF. By combining the projection and corresponding 2DCTF of all the particles, we performed a 2D classification and determined the 2D orientations of the particles on the membrane.\n\nWe tested the above approach using 5326 particles picked in the previous section from Chlamydomonas thylakoids. MPicker was used to extract particle projections from the raw tomograms and compute the corresponding 2DCTF images. The 2D classification was performed using the THUNDER220 software (Fig.\u00a05c). A total of 1846 particles from two classes (green boxes in Fig.\u00a05c) with clear PSII structural features were selected, and the corresponding sub-tomograms were extracted from the raw tomograms for subsequent STA. The 3D orientation parameters of these PSII particles were calculated based on the 2D alignment results and the normal vectors of the corresponding membrane region, and then were used for direct 3D reconstruction (Fig.\u00a05d). After further local refinement, the reconstruction was improved, resulting in a map at a resolution of 25.4\u2009\u00c5 (Fig.\u00a05e, f). Then the \u201cTomo CTF refinement\u201d and \u201cTomo frame alignment\u201d in RELION421 were applied and finally improved the resolution to 24.2\u2009\u00c5 (Fig.\u00a05g, Supplementary Fig.\u00a07a, b). The density map shows fine structural details matching previously reported in situ structures resolved by STA2,22. Interestingly, although the transmembrane region was omitted during the 2D classification, STA for the entire protein complex revealed a transmembrane region with distinct features. Upon fitting a SPA-derived Chlamydomonas PSII\u2013LHCII supercomplex C2S2M2L2 model23 (PDB entry code: 6KAD) to the resulting density map, the transmembrane region density showed high agreement with the model (Fig.\u00a05f, Supplementary Fig.\u00a07a). Some densities were also observed on the stromal side, which may be a region of a previously reported unidentified stromal protein (USP)23 (red arrow in Supplementary Fig.\u00a07b).\n\nFor the same PSII dataset, when calculating the particle orientations from scratch, we failed to obtain a reasonable result during the global alignment phase of STA. We then tried different alignment settings in RELION2 and finally achieved a more reasonable outcome by reducing the initial angular sampling step to 1.8\u00b0, as opposed to the previously used default value of 7.5\u00b0. Further analysis (see \u201cMethods\u201d) indicated that the reduction in step size improved the in-plane angular alignment on the membrane surface (Supplementary Fig.\u00a08a, b). Although the STA from scratch eventually got success, the quality of the reconstructed density maps did not match those obtained from the coarse alignment provided by MPicker, indicating that the alignment from scratch is not as good as that from MPicker (Supplementary Fig.\u00a08c). We speculate that the membrane signal was excluded in the 2D classification of particles with only the extramembrane region, and hence a better estimation of in-plane rotation angles could be achieved. In the conventional 3D method, the membrane signal may inevitably influence the alignment, especially for the protein with a small and thin extramembrane region such as PSII.\n\nIn conclusion, the prior knowledge of in-plane rotation angle is useful to improve the robustness of the STA, as well as its computational efficiency, by skipping the challenging global alignment in the STA. Even if the 5798 particles selected by EPicker were used directly without manual correction, a similar reconstruction result could also be obtained following the same procedure (Supplementary Fig.\u00a07c, d).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55767-w/MediaObjects/41467_2024_55767_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55767-w/MediaObjects/41467_2024_55767_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55767-w/MediaObjects/41467_2024_55767_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55767-w/MediaObjects/41467_2024_55767_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55767-w/MediaObjects/41467_2024_55767_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Identifying and analyzing membrane proteins from cryoET data is often challenging. We developed MPicker that facilitates membrane protein visualization and analysis by flattening membrane structures within the tomogram. The flattened tomogram provides a planar view that displays comprehensive protein information on the membrane in the form of slices, thereby simplifying the observation of protein distribution on the membrane surface. The flattened tomogram includes not only the membrane but also the adjacent densities on both sides of the membrane, which simplifies the analysis of the spatial relationship among macromolecules on both sides of the membrane. The flattened tomogram also simplifies protein localization on the membrane and orientation determination. The coordinates and orientations can be converted between flattened and raw tomograms, which can benefit STA calculation.\n\nMPicker provides additional tools to handle complex membrane structures and low-quality data. First, MPicker supports the manual labeling of membrane surfaces when a membrane mask is missing. Second, MPicker supports a sophisticated method that uses a triangle mesh to define the shapes of complex membrane surfaces. Third, MPicker provides tools to avoid the misinterpretation of flattened membrane structures, including a tool to quantify the flattening-induced distortion and a 3D rendering tool to show undistorted membrane surfaces in 3D space.\n\nMembrane flattening reduces the spatial freedom of membrane proteins from the 3D space to the 2D plane, which is beneficial for STA in at least two steps. First, flattening simplifies 3D particle picking in a tomogram to a 2D process similar to particle picking in SPA. Therefore, particle-picking tools designed for SPA, such as EPicker, can be applied to the flattened tomograms. Theoretically, flattened tomograms can also be processed using other particle-picking software designed for SPA. Second, MPicker assists global orientation estimation through manual labeling or 2D classification, making STA more computationally efficient than that directly from scratch. These features make MPicker serve as a more complete tool with more flexibility for analyzing membrane proteins than other tools (Supplementary Table\u00a01).\n\nCurrently, membrane extraction requires manual interaction and is the most time-consuming part of MPicker. Automation is still needed, so that users can integrate the membrane flattening to their workflows without much manual intervention. Moreover, improved particle-picking methods that can take full advantage of flattened tomograms have yet to be developed. In addition, improving the 2D classification of protein projections on the membrane surface may be useful, which should be more computationally efficient than 3D classification in isolating small or unknown proteins from a mess of mixed molecules on the membrane. On the other hand, MPicker has been specifically optimized for particles on cellular membranes. In cases where membrane proteins are symmetrically or densely packed on viruses and tubular structures, particle-picking methods that rely on symmetry or oversampling are frequently employed. These methods may provide higher efficiency in particle picking compared to MPicker. However, MPicker\u2019s capability to flatten vesicles and tubes is a distinct advantage that sets it apart.\n\nIn summary, MPicker is a powerful tool to address the challenges of visualizing and analyzing membranes and their associated proteins for cryoET. MPicker was developed in Python, has an interactive graphical user interface (GUI) (Supplementary Fig.\u00a09), and can be downloaded from https://thuem.net.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The first step of membrane flattening in MPicker is to acquire a set of points on the membrane surface as starting points for further surface fitting. These starting points can be acquired semi-automatically based on a given membrane mask. To accomplish this, MPicker requires the user to first specify a few starting points on the membrane surface as seeds, termed seed points, and then extend these seed points to extract additional starting points. MPicker requires at least one seed point; more seed points can provide a more detailed selection of the membrane surface. When multiple seed points are specified, MPicker combines the extraction results of each seed point. For each seed point, the user needs to specify which one of the three axes, X-, Y-, or Z-axis, is the most perpendicular to the membrane surface. In the following, we describe how to extract the starting points from a mask based on one seed point in the case that the membrane surface is roughly perpendicular to the X-axis. For cases perpendicular to the other two axes, the algorithm can be achieved by switching the order of axes. The case perpendicular to the Y-axis is achieved by replacing X, Y, and Z with Y, X, and Z, respectively. The case perpendicular to the Z-axis is achieved by replacing X, Y, and Z with Z, Y, and X, respectively.\n\nThe process of extracting starting points from the mask can be divided into three steps. In the first step, MPicker extracts a boundary with a single voxel thickness from the mask (Supplementary Fig.\u00a010a, b). MPicker provides two methods for boundary extraction: binary erosion on the mask (set the eroded points as the boundary), and layer-wise 2D Canny edge detection24. In the second step, MPicker extends the starting points from the seed point along the boundary. The extension is carried out within the x\u2013z plane by pixel-wisely increasing and decreasing Z coordinates (blue curve in Supplementary Fig.\u00a010c). During the extension process, the Z coordinates of the newly added points should change monotonically, otherwise, the extension is terminated. Users can also specify the maximum extension length to terminate the extension. In the third step, based on the points obtained from the previous step, MPicker further extended from each point (blue point in Supplementary Fig.\u00a010c) along the boundary in a similar manner as the previous step but within the x\u2013y plane (magenta curve in Supplementary Fig.\u00a010c). During the extension process, the Y coordinates should change monotonically; otherwise, the extension is terminated. Finally, all the extracted points were combined to form the final starting points. MPicker separately applied one-dimensional extensions to the x\u2013z and x\u2013y slices to ensure that the surface formed by the extracted starting points was not too complex.\n\nIn practice, MPicker also removes outliers from the starting points. To determine the outliers, MPicker first calculates the average distance between each starting point and its 50 nearest neighbors and then calculates the mean and standard deviation of these average distances. Finally, the starting points whose average distance is greater than the mean plus two standard deviations are considered outliers. This method is commonly used in point-cloud processing25.\n\nDuring the flattening, we defined three Cartesian coordinate systems for different usage. The Cartesian coordinate system consisting of the raw tomogram axes is denoted as X\u2013Y\u2013Z. For each membrane to be flattened, MPicker performs a translational and rotational transformation on the X\u2013Y\u2013Z coordinate system to obtain a transitional Cartesian coordinate system, denoted as x\u2013y\u2013z. In the x\u2013y\u2013z coordinate system, the membrane surface will be described in the form of a single-valued function z = f(x,y) to simplify subsequent processing. The Cartesian coordinate system consisting of the flattened tomogram axes is denoted as u\u2013v\u2013w. For consistency with the terminology used in tomogram processing, the X\u2013Y slice of the raw tomogram and the u\u2013v slice of the flattened tomogram are referred to as the x\u2013y slice in this study. The \u201cx\u2013y\u201d is unrelated to the x\u2013y\u2013z coordinate system described here.\n\nTo obtain the coordinate system x\u2013y\u2013z and describe the membrane surface in the form z = f(x,y), MPicker needs to find a viewing direction as the z-axis, from which the membrane appears flat. To determine this direction, MPicker implements two methods. The first method involves fitting a plane to the starting points and using the direction perpendicular to the fitted plane as the z-axis. The second method is to find a projection direction that maximizes the projected area of the starting points and use it as the z-axis.\n\nOnce the x\u2013y\u2013z coordinate system is determined, MPicker uses surface fitting to find the f(x,y) that best matches the starting points. MPicker provides two surface fitting methods. The first method is polynomial fitting, which approximates the starting points with a N-th degree bivariate polynomial:\n\nwhere N is specified by the user. The second method uses TPS interpolation (implemented using scipy.interpolate.RBFInterpolator). Compared to polynomial fitting, this method can better describe surfaces with complex shapes but requires more computations. Therefore, MPicker does not directly use all starting points (Supplementary Fig.\u00a010d), but first downsamples these points (Supplementary Fig.\u00a010e) to reduce the computation. MPicker then used these downsampled points as interpolation points and generated a continuous surface, z = f(x,y), using TPS interpolation (Supplementary Fig.\u00a010f). The users can specify the smoothness of the fitted surface: a higher smoothness may lead to a higher error between the surface and interpolation points. However, this also means better resistance to noise in the data. In practice, MPicker also removes interpolation points with large errors as outliers and then performs another round of TPS interpolation to further increase its robustness to noise.\n\nAfter generating the surface represented by z = f(x,y), the next step is to flatten the regions on both sides of the generated surface. To obtain the density of each voxel in the flattened tomogram, the mapping between the u\u2013v\u2013w and X\u2013Y\u2013Z coordinate systems must be determined (Supplementary Fig.\u00a010g) in order to get the density at the corresponding voxel position in the raw tomogram. Once the density of each point in the u\u2013v\u2013w coordinate system was obtained, the final flattened tomogram was formed (Supplementary Fig.\u00a010h).\n\nTo establish the mapping between the u\u2013v\u2013w and X\u2013Y\u2013Z coordinate systems from the surface \\(z=f\\left(x,y\\right)\\), MPicker first attempted to map the surface on a plane, which was the central slice of the flattened tomogram. Specifically, MPicker described the surface \\(z=f\\left(x,y\\right)\\) using the following parametric equations:\n\nThe relationship between the x\u2013y\u2013z and X\u2013Y\u2013Z coordinate systems can be calculated as follows:\n\nwhere R represents the rotation matrix, T is the transpose operation, t is the translation vector, and \u00d7 is the matrix multiplication.\n\nFor a clear description of the flattening process, we assume that Eq.\u00a02 was already obtained for now (details are described below), which means that the corresponding raw tomogram coordinates (X(u,v), Y(u,v), Z(u,v)) are known for each point (u,v) in the central slice of the flattened tomogram. To further compute the corresponding raw tomogram coordinates for each point on either side of the central slice, MPicker extends each point in the central slice along the normal vector (and the reverse direction) of the surface. The number of extended pixels was specified by the user. Assuming that the number of extended pixels is \\({w}_{t}\\), the total thickness of the flattened tomogram is \\({2w}_{t}+1\\) pixels. Further assuming that the central slice is the wc-th slice in the flattened tomogram (wc = wt + 1), and the (normalized) normal vector of each point on the surface is represented by (NX(u,v), NY(u,v), NZ(u,v)), the mapping between the u\u2013v\u2013w and X\u2013Y\u2013Z coordinate systems can be obtained as follows:\n\nAt this point, the corresponding raw tomogram coordinates can be calculated for each voxel in the flattened tomogram. By performing trilinear interpolation on the raw tomogram, the density of each voxel in the flattened tomogram can be obtained.\n\nTo obtain normal vectors (NX(u,v), NY(u,v), NZ(u,v)) from Eq.\u00a02, MPicker calculates the (unnormalized) normal vector using the following equation:\n\nThis is also equivalent to first computing the (unnormalized) normal vector represented in the x\u2013y\u2013z coordinate system by \\(\\left(-\\frac{\\partial f}{\\partial x},-\\frac{\\partial f}{\\partial y},1\\right)\\) and then computing its representation in the X\u2013Y\u2013Z coordinate system by rotation. In practice, MPicker uses numerical differentiation methods to compute partial derivatives.\n\nNow we explain how to get Eq.\u00a02, in other words, how the surface is mapped to the central slice of the flattened tomogram. Since the surface can be described by \\(z=f\\left(x,y\\right)\\), and the relationship between the x\u2013y\u2013z and X\u2013Y\u2013Z coordinate systems is already known (Eq.\u00a03). If the relationship between \\(\\left(u,v\\right)\\) and \\(\\left(x,y\\right)\\) can be expressed as\n\nthen Eq.\u00a02 can be obtained by combining Eq.\u00a03, and 6, and \\(z=f\\left(x,y\\right)\\).\n\nThe simplest method of generating Eq. 6 is to directly set \\(u\\left(x,y\\right)=x\\) and \\(v\\left(x,y\\right)=y\\). This is equivalent to projecting the surface directly on a plane, along the z-axis. In practice, MPicker projects the surface on the best-fit cylindrical surface and then unfolds it to minimize distortion. We assume that this best-fit cylindrical surface can be described by an N-th degree polynomial \\(z=h\\left(y\\right)\\), as follows:\n\nwhere N is specified by the user. This means that the cylindrical surface is only bent on the y-axis. In this case, MPicker establishes the relationship between \\(\\left(u,v\\right)\\) and \\(\\left(x,y\\right)\\) as follows:\n\nwhere \\({y}_{\\min }\\) is the bound of the tomogram. In this way, the equidistant sampling of v corresponds to equal arc-length sampling on the cylindrical surface, effectively creating a 2D coordinate system u\u2013v on the cylindrical surface. In practice, MPicker uses numerical methods to compute the integral in Eq.\u00a08 and the inverse function of \\(v\\left(y\\right)\\), i.e., \\(y\\left(v\\right)\\). To achieve a better fit for the cylinder, MPicker simultaneously optimized Eq.\u00a03 and Eq. 7 to find the cylinder (implemented via scipy.optimize.least_squares), instead of performing fitting in two separate steps. Furthermore, to store the membrane in a flattened tomogram with a small size, MPicker might set the cylindrical surface bending direction to a suitable direction, not necessarily to the y-axis as described above.\n\nIn addition to projecting on a polynomial cylindrical surface, MPicker supports projection on an elliptic cylindrical surface. Since an elliptical cylindrical surface cannot be described by a single-valued function like z = f(x,y) in the Cartesian coordinate system, the projection process differs slightly. In the first step, MPicker fits an elliptical cylinder based on the starting points. In the second step, a coordinate system is created on the elliptical cylinder surface. In the third step, the starting points were transformed into this new coordinate system for surface fitting. In short, the x\u2013y\u2013z Cartesian coordinate system is replaced by a new coordinate system based on an elliptical cylinder, and its relationship with the X\u2013Y\u2013Z coordinate system no longer satisfies Eq.\u00a03. The following steps are the same as before, and here, MPicker directly sets \\(u\\left(x,y\\right)=x\\) and v(x,y) = y, because MPicker creates the x\u2013y\u2013z coordinate system directly on the cylindrical surface so that the equal arc-length sampling on the cylindrical surface already corresponds to the equidistant sampling on the x\u2013y plane.\n\nThe distortion caused by flattening can be described locally by a linear transformation, that is, circles are deformed into ellipses. This is a conclusion drawn from computer graphics9, and we only briefly explain it here and describe the specific method used by MPicker to calculate the degree of distortion.\n\nEach flattened tomogram slice corresponds to a surface in 3D space. Suppose that the mapping between the slice and the surface can be described by Eq.\u00a02, where \\(\\left(u,v\\right)\\) are the 2D coordinates of a point on the flattened tomogram slice, and \\(\\left(X,{Y},Z\\right)\\) are the coordinates of the corresponding point in the original 3D space. If a point on this slice undergoes a small displacement \\(\\left({du},{dv}\\right)\\), and its corresponding displacement in 3D space is \\(\\left({dX},{dY},{dZ}\\right)\\). The relationship between \\(\\left({du},{dv}\\right)\\) and \\(\\left({dX},{dY},{dZ}\\right)\\) can be described as a linear transformation as follows:\n\nThis means a small circle on the slice will be approximately mapped to a small ellipse on the surface. We assume that the circle is formed by a set of points \\(\\left({u}_{0}+{du},{v}_{0}+{dv}\\right)\\) that satisfy \\({(du)}^{2}+{(dv)}^{2}={\\left(1 \\, {pixel}\\right)}^{2}\\), and the corresponding ellipse is formed by points \\(\\left({X}_{0}+{dX},\\,{Y}_{0}+{dY},{Z}_{0}+{dZ}\\right)\\). To obtain the semi-major axis and semi-minor axis of the ellipse, we calculate the maximum length and minimum length of the vector \\(\\left({dX},{dY},{dZ}\\right)\\). According to Eq.\u00a09, the square of the length can be calculated as follows:\n\nHere, \\({\\stackrel{\\rightharpoonup}{r_{u}}}=\\left(\\frac{\\partial X}{\\partial u},\\frac{\\partial Y}{\\partial u},\\frac{\\partial Z}{\\partial u}\\right)\\) and \\({\\stackrel{\\rightharpoonup}{r_{v}}}=\\left(\\frac{\\partial X}{\\partial v},\\frac{\\partial Y}{\\partial v},\\frac{\\partial Z}{\\partial v}\\right)\\) are the tangent vectors of the surface, and \\(\\cdot\\) represent the vector dot products. In Eq.\u00a010, \\({\\sigma }_{1}^{2}\\) and \\({\\sigma }_{2}^{2}\\,\\left({\\sigma }_{1}\\ge {\\sigma }_{2} > 0\\right)\\) are the two eigenvalues of the matrix \\(\\left[\\begin{array}{cc}E & F\\\\ F & G\\end{array}\\right]\\). The two eigenvalues are just the squares of the maximum and minimum lengths of the vector \\(\\left({dX},{dY},{dZ}\\right)\\), because \\((d{u^{\\prime} })^{2}+(d{v^{\\prime} })^{2}=(d{u})^{2}+(d{v})^{2}={\\left(1{pixel}\\right)}^{2}\\). Therefore, \\({\\sigma }_{1}\\) and \\({\\sigma }_{2}\\) in Eq.\u00a010 are just the semi-major axis and semi-minor axis of the ellipse.\n\nIn practice, MPicker uses numerical differentiation to compute the tangent vectors \\({\\stackrel{\\rightharpoonup}{r_{u}}}\\) and \\({\\stackrel{\\rightharpoonup}{r_{v}}}\\). In short, for each point (u0,v0) on the flattened tomogram slice, MPicker only needs the corresponding raw tomogram coordinates of four nearby points (u0 \u00b1 1,v0 \u00b1 1) to calculate the distortion caused by flattening at that point.\n\nMPicker can also flatten the membrane surfaces represented by triangle meshes. To achieve this, the triangle mesh must have an appropriate parameterization9. Specifically, MPicker requires each vertex \\(\\left(X,{Y},Z\\right)\\) of the triangle mesh to be associated with a corresponding 2D coordinate \\(\\left(u,v\\right)\\), similar to Eq.\u00a02. Here \\(\\left(u,v\\right)\\) are also referred to as the texture coordinates. Here, we illustrate the flattening process using the Chlamydomonas ER membrane as an example (Supplementary Fig.\u00a03).\n\nFor user convenience, MPicker provides scripts to generate triangle meshes with texture coordinates. Users can use the starting points extracted from the mask using MPicker (or other forms of point clouds) as inputs (orange curve in Supplementary Fig.\u00a03a). The mesh-generation process can be divided into three steps (Supplementary Fig.\u00a03b). First, MPicker downsamples the starting points and removes outliers to obtain a point cloud with appropriate spacing. Second, MPicker reconstructs a triangle mesh from the point cloud using the Poisson surface reconstruction method26 (provided in the library Open3D25). Third, MPicker uses the OptCuts11 software to compute the triangle mesh parameterization. In practice, the triangle mesh generated by surface reconstruction can be simplified by reducing the number of triangles to improve computational efficiency.\n\nAfter obtaining a triangle mesh with appropriate texture coordinates, the flattening process was similar to that described in the previous section. Assuming the parameterization is appropriate, MPicker directly uses the texture coordinates \\(\\left(u,v\\right)\\) as the \\(u\\) and \\(v\\) in Eq.\u00a02. Since the vertex data are discrete, MPicker first uses the coordinates \\(\\left(X,{Y},Z\\right)\\) and the corresponding texture coordinates \\(\\left(u,v\\right)\\) at each vertex to separately fit three continuous functions, \\(X\\left(u,v\\right)\\), \\(Y\\left(u,v\\right)\\), and \\(Z\\left(u,v\\right)\\), using TPS interpolation. This gives Eq.\u00a02. The remaining steps are the same as before: MPicker calculates the normal vector for each point using Eq.\u00a05, and then determines the mapping between the u\u2013v\u2013w coordinate system and the X\u2013Y\u2013Z coordinate system using Eq.\u00a04 and finally generates a flattened tomogram (Supplementary Fig.\u00a03c). As before, MPicker could map the flattened tomogram slices back into 3D space (Supplementary Fig.\u00a03d).\n\nHere, MPicker uses OptCuts software and provides a wrapper for convenience. OptCuts is a software that can automatically calculate parameterization with small distortions for triangle meshes. The user simply specifies a threshold for the degree of distortion, and OptCuts minimizes the lengths of the cuts made on the meshes while ensuring that the average distortion remains below the specified threshold. The advantage of this method is that the surface can exhibit a small overall distortion after flattening, without being cut into fragments. OptCuts measures the distortion using \\({\\sigma }_{1}^{2}{+\\sigma }_{2}^{2}+{\\sigma }_{1}^{-2}{+\\sigma }_{2}^{-2}\\), where \\({\\sigma }_{1}\\) and \\({\\sigma }_{2}\\) have similar meanings as in Eq.\u00a09.\n\nMost of the tomogram data used in this study were obtained from the Electron Microscopy Data Bank (EMDB), except for the Chlamydomonas tomogram data shown in Figs.\u00a03a and 4c. In addition, we acquired and used the raw data of EMD-31243, EMD-31244, and EMD-31247 to calculate the STA of the red algae PBS3.\n\nThe Chlamydomonas tomograms were prepared as follows. Chlamydomonas reinhardtii wild-type strain (mt-; CC-1691) used in this study is available from the Chlamydomonas Resource Center (University of Minnesota, St. Paul, MN, USA). Strains were cultured in Tris\u2013acetate\u2013phosphate (TAP) plates or liquid medium with aeration at 23\u2009\u00b1\u20090.5\u2009\u00b0C with a light/dark cycle of 14/10\u2009h at a light intensity of 8000\u2009lx, as described previously27. When the cells reached an OD600 of approximately 2, they were centrifuged at 751\u00d7g for 2\u2009min 2\u20138 times to concentrate the cells. Then, 3\u2009\u03bcL cell suspension was pipetted on glow-discharged grids (200 mesh Cu lacey carbon; glow-discharged using a PELCO easiGlow Glow Discharger, Ted Pella Inc), and 3\u2009\u03bcL suspension buffer was added to the back of the grid. The grids were then blotted from the back and plunge-frozen using Leica EM GP (Leica Microsystems) set to 75% humidity, 25 \u00b0C temperature, and blotting time 6\u20138\u2009s. The lamellae were thinned to approximately 150\u2009nm using a dual-beam FIB-SEM system (Helios NanoLab DualBeam G3 UC, Thermo Fisher Scientific) with a cold stage (PP3010T, Quorum). CryoET data were collected using a 300\u2009kV Titan Krios electron microscope (Thermo Fisher Scientific) equipped with a Cs corrector, BioQuantum energy filter, and K3 direct electron detector (Gatan). Micrographs were acquired in the super-resolution mode with 19,500\u00d7 nominal magnification and 3.63\u2009\u00c5 calibrated pixel size. Tilt series were acquired using SerialEM28 with a bidirectional tilt scheme, ranging from 11\u00b0 to 67\u00b0 and then from 9\u00b0 to \u221255\u00b0, with an angular increment of 2\u00b0 and a defocus range of \u22124 to \u22126\u2009\u00b5m. At each tilt angle, a micrograph consisting of 8 frames (0.25\u2009s/frame) was collected, with a total dose of 124 e\u2212/\u00c52 for the entire tilt series. The beam-induced motion was corrected using MotionCor229. The defocus of the micrographs was determined using CTFFIND430. Alignment and reconstruction of the tilt series were performed using the IMOD4 software. Four tomograms abundant in thylakoid membrane pairs were selected for further processing. Tomograms binned by three folds were used for membrane segmentation, membrane flattening, and particle picking. Tomograms binned by two folds were used for 2D classification and STA.\n\nThe contrast and SNR of a flattened tomogram depend directly on the contrast and SNR of the raw tomogram. Therefore, all tomograms used for flattening were preprocessed for better visualization using IsoNet8 to denoise the tomograms and reduce the effect of missing wedge.\n\nFor the membrane mask, MPicker provides a built-in membrane segmentation functionality with a GUI. Membrane segmentation is based on a small pretrained neural network model of approximately 70 MB in size. The training data for this model were derived from five labeled public FIB-SEM datasets31. The data were converted into 72 sets of MRC files, each with a size of 200 \u00d7 200 \u00d7 200 voxels, along with the corresponding labels for training. The training procedure is based on DeepTomo. The segmentation model used was based on the architecture of the 3D U-Net32. The feature extraction network consisted of a downsampling path and an upsampling path, each containing five blocks. Each block consisted of multiple convolutional layers, upsampling layers, instance normalization layers, and Leaky ReLU activation functions. For the training settings, we used the Adam optimizer with a gradually decaying learning rate in the order of 3 \u00d7 10\u22124, 10\u22124, 10\u22125, and 10\u22126. During training, we randomly cropped regions of 128 \u00d7 128 \u00d7 32 voxels from individual tomogram data as input data using a batch size of eight and trained for 200 epochs. The training process was run on a single NVIDIA A100 GPU and took approximately 4\u2009h.\n\nGeneration of a membrane mask using MPicker consists of two steps (Supplementary Fig.\u00a01a). In the first step, a pretrained neural network is used to process the tomogram and generate continuous membrane scores. In the second step, the scored tomogram is post-processed to obtain a final binary membrane mask. Post-processing includes Gaussian filtering of the scores, binarization based on a specified threshold, and noise removal using connected component analysis. Connected component analysis involves removing 2D small connected components in each x\u2013y slice and then removing 3D small connected components. For the tomogram in Fig.\u00a02b (960 \u00d7 928 \u00d7 178 voxels), the segmentation took about 12\u2009min on a workstation with two RTX 2080Ti GPUs.\n\nMPicker wraps EPicker for particle picking in flattened tomograms and provides a GUI. Since EPicker is designed for 2D images, MPicker pre- and post-process the tomogram data. While generating the training dataset (Supplementary Fig.\u00a05a), MPicker divides the flattened tomogram into several x\u2013y slices and converts the labeled 3D coordinates into the corresponding 2D coordinates on the slices. For particle picking, MPicker divides the flattened tomogram into several x\u2013y slices, uses EPicker to pick particles on each slice, and converts the 2D coordinates into 3D coordinates (Supplementary Fig.\u00a05b). EPicker assigns a score to each candidate particle, and MPicker allows the user to specify the minimum score and maximum number of particles to be picked. Additionally, MPicker removes duplicates based on the spacing and scores of the particles to prevent the same particle from being picked multiple times on different slices. Since EPicker can only recognize 2D features, it is recommended to pick particles only in x\u2013y slices where the structural feature is clear.\n\nTo select membrane proteins on the flattened tomogram of the Chlamydomonas thylakoid membranes (Fig.\u00a04d), 559 particles from 34 x\u2013y slices of 20 flattened tomograms were manually labeled for training. The trained model identified 5798 particles in 192 flattened tomograms. Since the extramembrane region of the protein is small, we usually picked particles on slices near the central slice, typically, in a range of three slices. Although the direct use of these particles could yield the correct structure (Supplementary Fig.\u00a07c, d), we manually corrected the picking results for better structural quality (Supplementary Fig.\u00a05b) and obtained 5326 particles.\n\nEPicker is a 2D image processing software, and its fast computational speed is one of its advantages. During training, we used a batch size of four and trained for 120 epochs, which took approximately 20\u2009min. The particle-picking speed was approximately five slices per second. The calculations were performed using a single RTX 2080Ti GPU.\n\nTo automatically determine the 2D orientation of membrane proteins and screen high-quality particles, we performed 2D classification of the proteins selected from Chlamydomonas thylakoid membranes (Fig.\u00a05c, Supplementary Fig.\u00a07c). Since the normal vectors of the membrane at each point were obtained during flattening, MPicker projected each protein along its normal vector to obtain a 2D image. To preserve as much structural information as possible, MPicker extracts the projection images directly from the raw tomogram, which is binned by two folds with a voxel size of 7.26\u2009\u00c5. The resulting projection images were 70 \u00d7 70 pixels in size, with a projection depth of seven pixels.\n\nFor these projections, conventional 2D classification yielded poor results with noticeable stripe-like artifacts in the averaged 2D images (Supplementary Fig.\u00a06a, b) possibly due to a misalignment caused by the missing wedge. According to the central slice theorem, a projection in real space corresponds to a slice in Fourier space. As 3D particles have a missing wedge in the frequency domain, the 2D images generated by projecting the particles also have a missing wedge in the frequency domain, the specific direction and size of which depend on the projection direction.\n\nTo reduce the effects of the missing wedge, weighting images describing both the CTF and missing wedge were used in the 2D classification (Supplementary Fig.\u00a06c), with a function similar to that of the 3DCTF used in STA by RELION. To generate the weighting images, RELION was first used to generate a 3DCTF for each particle. MPicker then extracts a slice from the 3DCTF along the projection direction of each particle, according to the central slice theorem, to create a 2DCTF that serves as the weighting image of the particle. To use customized weights in 2D classification, THUNDER2, an upgrade of THUNDER20 software, was used for classification. Without considering the missing wedge, the results obtained by THUNDER2 were similar to those obtained by RELION. However, when the effect of the missing wedge was considered, the performance of the 2D classification improved (Supplementary Fig.\u00a06d), with clearer contours and the elimination of stripe-like artifacts. Regrettably, only PSII (1846/5326) was identified in the 2D averages. Improving the performance of 2D classification is a topic for future research. In practice, MPicker can generate a simple 3DCTF for each tomogram, allowing all the particles within the same tomogram to share a single 3DCTF, thereby saving time in generating the 3DCTF.\n\nCompared with 3D classification, 2D classification offers advantages such as faster computation, higher stability, and independence from the initial models. In Chlamydomonas thylakoid membranes, the orientations of membrane proteins are generally consistent with the normal vectors, and the extramembrane regions are relatively flat, resulting in large projection areas that contain the major structural features of the proteins. During projection, we used only the density of the extramembrane region of the protein, because interference from membrane signals is often strong. Projections were used instead of slices to minimize the effect of coordinate errors in picking and allow for modeling the missing wedge using 2DCTF. Projecting subvolumes along the z-axis, followed by conventional 2D classification, is a common practice. This is a special case because projections along the z-axis do not have a missing wedge unless the tilt series does not contain the image at a 0\u00b0 tilt angle.\n\nUsing the normal vectors of the membrane to constrain the angle search for membrane proteins in STA is a common practice; however, normal vectors can only provide partial constraints (two Euler angles out of three). Knowing the 2D orientation of the protein in the plane perpendicular to the normal vector is necessary to obtain complete protein orientation information and constrain the angle search. MPicker provides a function to manually label this orientation. In the flattened tomogram, after labeling the particle center, the user only needs to label another point to specify the direction (arrows in Fig.\u00a04b). The line connecting these two points corresponds to the direction vector in the coordinate system of the raw tomogram. If this direction is taken as the x-axis of the local coordinate system of the particle, and the direction of the membrane normal vector is taken as the z-axis of the local coordinate system, then the complete orientation information can be calculated. Notably, the direction vector is usually not strictly perpendicular to the normal vector; therefore, in practice, MPicker uses its components perpendicular to the membrane normal vector.\n\nSpecifically, assuming that the normalized user-labeled direction vectors are (vxx,vxy,vxz) and the normalized normal vectors are (nx,ny,nz), the rotation matrix describing the particle orientation can be calculated directly as \\(\\left[\\begin{array}{ccc}v{x}_{x} & v{y}_{x} & {n}_{x}\\\\ v{x}_{y} & v{y}_{y} & {n}_{y}\\\\ v{x}_{z} & v{y}_{z} & {n}_{z}\\end{array}\\right]\\), where (vyx,vyy,vyz) = (nx,ny,nz) \u00d7 (vxx,vxy,vxz), and \u00d7 represents the vector cross products. Orientation information in other forms, such as Euler angles, can also be converted directly from this rotation matrix.\n\nObtaining orientation information from 2D classification is similar to the process described above because the in-plane rotation angles obtained from 2D classification provide the same information as the direction vectors described above. MPicker provides scripts to convert the 2D classification results. In addition, MPicker can convert the 2D displacements calculated in the 2D classification into 3D displacements (in the plane perpendicular to the normal vector). MPicker also allows the user to specify additional translation and rotation angle for each class obtained from the 2D classification. This allows the merging of different classes in 2D classification, considering that the same type of protein may be classified into more than one class. The two selected classes in Fig.\u00a05c were merged in this way before applying the STA.\n\nTo calculate the STA of the red algae PBS, 268 PBS particles were manually selected from the three tomograms. The PBS particle orientations within the same tomogram were similar; therefore, three tomograms were used. Based on the manually labeled orientations in MPicker, RELION2 was then used for the direct reconstruction and local alignment. The tomograms used in the STA were binned by two folds to a voxel size of 6.86\u2009\u00c5. For direct reconstruction, RELION was set to perform 3D classification without alignment, and the number of classes was set to one. For local alignment, the result of the direct reconstruction was used as a reference, and \u201cinitial angular sampling\u201d and \u201clocal searches from autosampling\u201d in RELION were set to 7.5\u00b0. The C2 symmetry was also imposed, yielding a final resolution of 38.6\u2009\u00c5.\n\nThe STA workflow for Chlamydomonas PSII was similar to that for PBS. The tomograms used for the STA were binned by two folds to a voxel size of 7.26\u2009\u00c5. After 2D classification, the coordinates and orientations of 1846 particles from the four tomograms were used for direct reconstruction. For local alignment, the result of the direct reconstruction was used as a reference, and both \u201cinitial angular sampling\u201d and \u201clocal searches from autosampling\u201d in RELION were set to 3.7\u00b0. The C2 symmetry was imposed, yielding a map at 25.4\u2009\u00c5 resolution. After alignment, \u201cTomo CTF refinement\u201d and \u201cTomo frame alignment\u201d in RELION4 were applied and improved the resolution to 24.2\u2009\u00c5. RELION4 was also used to reconstruct the final density map on unbinned data with a voxel size of 3.63\u2009\u00c5.\n\nThe final Fourier shell correlation (FSC) curves of the two STA result maps are shown in Supplementary Fig.\u00a011.\n\nTo compare the result of STA starting without initial particle orientation (obtained from 2D classification and membrane normal vectors by MPicker), we performed STA for PSII using RELION2 with the conventional workflow. The global alignment from scratch with the default initial angular sampling step of 7.5\u00b0 failed. Then we reduced the step size to 3.7\u00b0 and 1.8\u00b0, respectively, and performed STA with C2 symmetry imposed again. The results are shown in Supplementary Fig.\u00a08.\n\nThe final orientations were compared to the results of our workflow (using the orientations estimated by MPicker as the initial orientations). We separated the orientations (three Euler angles) into two parts, normal vectors and in-plane angles. For the initial angular sampling step of 3.7\u00b0 (Supplementary Fig.\u00a08a), the differences in normal vectors were small (the median value was 0.6\u00b0), while the differences in in-plane angles were large (the median value was 33.8\u00b0). To investigate the difference, all particles were divided into two equally sized groups with a large (purple) and a small (orange) difference in the in-plane angles. The particles in the two groups were then 3D reconstructed, separately. For each group, the reconstruction was performed twice, one using the orientations obtained from the conventional workflow (3.7\u00b0), and the other using those obtained from our workflow (MPicker). Both the two reconstructions based on the orientations estimated by MPicker show clear structural features. However, the reconstructions based on the orientations obtained from the conventional refinement (3.7\u00b0) show a huge difference between the two groups, and the group with the large angular difference shows the wrong structural features of the complex, which indicates the misalignment for the in-plane rotation angle. For the initial angular sampling step of 1.8\u00b0 (Supplementary Fig.\u00a08b), similar analyses were carried out. The differences in normal vectors were still small (the median value was 0.5\u00b0), while the differences in in-plane angles were still large (the median value was 13.6\u00b0) but smaller than that of 3.7\u00b0. The reconstructions based on the orientations obtained from the conventional refinement (1.8\u00b0) also show a larger difference between the two groups, compared to that of our workflow (MPicker). This implied that the in-plane rotation angles obtained from our workflow were still more accurate because we assumed that the reconstructions of the two halves of particles should be similar if the orientation estimation was accurate. As for the final density maps obtained from different workflows (Supplementary Fig.\u00a08c), our workflow gave the best result, and for the other two workflows starting from global alignments, a finer initial sampling step (1.8\u00b0) gave a better result.\n\nMembrane flattening is not a computationally intensive task, and hence MPicker requires just minimal computer performance. All tasks using MPicker in this work can be run on a typical laptop, except for the part of membrane segmentation and EPicker which require a general-purpose GPU. The time required for membrane flattening is typically several seconds, depending on the area of the membrane, the spacing of the interpolation points, and the surface projecting method (onto a plane or a cylindrical surface).\n\nAs an example, to generate a flattened tomogram in Fig.\u00a04d, MPicker took \u223c3\u2009s on surface extraction (5 seed points), \u223c8\u2009s to generate a flattened tomogram (172 \u00d7 833 \u00d7 31 voxels, and flattening through a cylindrical surface and using 414 interpolation points with 12-pixels spacing). For another smaller surface (144 \u00d7 393 \u00d7 31 voxels for the flattened tomogram), MPicker took \u223c2\u2009s on surface extraction (2 seed points) and \u223c3\u2009s on flattening through a cylindrical surface (using 218 interpolation points with 12-pixel spacing). All these tests were run with an Intel Xeon Gold 6132 CPU.\n\nMPicker is written in Python and uses several essential libraries, such as the NumPy, SciPy, Pillow, OpenCV, and Scikit-Image libraries for data processing; the mrcfile library for reading and writing MRC files; and the PyQt5 library for GUI development. The deep-learning framework used for membrane segmentation was based on the PyTorch library. The Open3D library was used for 3D rendering and geometry processing in MPicker. TPS interpolation in MPicker was implemented using the scipy.interpolate.RBFInterpolator. The cylindrical surface fitting uses scipy.optimize.least_squares as a nonlinear least-squares solver. Users will need to install EPicker, THUNDER2, and OptCuts individually if required, and MPicker provides wrappers for EPicker and OptCuts. RELION versions 2.1 and 4.0 were used for STA. We used the Open3D library and Chimera33 for 3D visualization.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The entry codes of the tomograms from the Electron Microscopy Data Bank (EMDB) are EMD-10780, EMD-13771, EMD-11992, EMD-31243, EMD-31244, EMD-31247, EMD-10409, EMD-11075 and EMD-19003. The tomogram TS_43 in EMPIAR-10164 was also used. The entry codes of the structure models from the Protein Data Bank are 6KAD and 6KAC. The final density map of PSII\u2013LHCII shown in Fig.\u00a05g was deposited in the EMDB under accession number EMD-61019. The corresponding raw data was deposited in the EMPIAR under accession number EMPIAR-12469.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The program code is available for downloading from our project website [https://thuem.net] along with detailed information about software installation and usage. The code34 is also available on GitHub [https://github.com/thuem/MPicker] and Zenodo [https://doi.org/10.5281/zenodo.14264179].", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Albert, S. et al. Direct visualization of degradation microcompartments at the ER membrane. Proc. Natl Acad. Sci. USA 117, 1069\u20131080 (2020).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nWietrzynski, W. et al. Charting the native architecture of Chlamydomonas thylakoid membranes with single-molecule precision. 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Chem. 25, 1605\u20131612 (2004).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nYan, X. et al. MPicker: visualizing and picking membrane proteins for cryo-electron tomography, thuem/MPicker. Zenodo https://doi.org/10.5281/zenodo.14264179 (2024).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by funds from the National Natural Science Foundation of China (32241023 and 92254306 to X.L.), the Tsinghua-Peking Joint Center for Life Sciences, and the Beijing Frontier Research Center for Biological Structure. We acknowledge Zhengmao Wang of Junmin Pan lab for providing Chlamydomonas cells. We thank all the users for testing the software. We acknowledge the Tsinghua University Branch of China National Center for Protein Sciences Beijing for providing facility support in computing and cryoEM instruments.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Key Laboratory for Protein Sciences of Ministry of Education, School of Life Sciences, Tsinghua University, Beijing, China\n\nXiaofeng Yan,\u00a0Weilin Huang,\u00a0Hao Wang,\u00a0Tianfang Zhao,\u00a0Mingtao Huang\u00a0&\u00a0Xueming Li\n\nState Key Laboratory of Membrane Biology, School of Life Sciences, Tsinghua University, Beijing, China\n\nXiaofeng Yan,\u00a0Weilin Huang,\u00a0Hao Wang,\u00a0Tianfang Zhao,\u00a0Mingtao Huang\u00a0&\u00a0Xueming Li\n\nTsinghua-Peking Joint Center for Life Sciences, Beijing, China\n\nXiaofeng Yan,\u00a0Weilin Huang,\u00a0Hao Wang,\u00a0Tianfang Zhao,\u00a0Mingtao Huang\u00a0&\u00a0Xueming Li\n\nBeijing Frontier Research Center for Biological Structure, Beijing, China\n\nXiaofeng Yan,\u00a0Weilin Huang,\u00a0Hao Wang,\u00a0Tianfang Zhao,\u00a0Mingtao Huang\u00a0&\u00a0Xueming Li\n\nSchool of Life Sciences, Tsinghua University, Beijing, China\n\nXiaofeng Yan,\u00a0Shudong Li,\u00a0Weilin Huang,\u00a0Hao Wang,\u00a0Tianfang Zhao,\u00a0Mingtao Huang,\u00a0Niyun Zhou\u00a0&\u00a0Xueming Li\n\nDepartment of Electronic Engineering, Tsinghua University, Beijing, China\n\nShudong Li,\u00a0Weilin Huang,\u00a0Tianfang Zhao,\u00a0Mingtao Huang\u00a0&\u00a0Yuan Shen\n\nBeijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China\n\nWeilin Huang,\u00a0Tianfang Zhao,\u00a0Mingtao Huang\u00a0&\u00a0Yuan Shen\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nX.L. conceived the project; X.Y. designed and implemented the algorithm of membrane flattening; X.Y. wrote the major part of the program; S.L. developed the initial frame of graphic user interface; W.H. and N.Z. developed the membrane segmentation; H.W. prepared the cryoET data of Chlamydomonas; T.Z. supported the integration of EPicker; M.H. provided the support about THUNDER2; N.Z. and Y.S. offered useful suggestions; X.Y. performed the tests; X.L. and X.Y. wrote the manuscript; and all authors revised the manuscript.\n\nCorrespondence to\n Xueming Li.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Yan, X., Li, S., Huang, W. et al. MPicker: visualizing and picking membrane proteins for cryo-electron tomography.\n Nat Commun 16, 472 (2025). https://doi.org/10.1038/s41467-024-55767-w\n\nDownload citation\n\nReceived: 19 May 2024\n\nAccepted: 24 December 2024\n\nPublished: 08 January 2025\n\nVersion of record: 08 January 2025\n\nDOI: https://doi.org/10.1038/s41467-024-55767-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"Uncovering causal gene-tissue pairs and variants: A multivariable TWAS method controlling for infinitesimal effects", + "journal": "Nature Communications", + "published": "02 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1-22", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_MOESM3_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_MOESM4_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_MOESM5_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_MOESM6_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-61423-8#MOESM3", + "https://gtexportal.org/home/downloads/adult-gtex/qtl", + "/articles/s41467-025-61423-8#Sec35" + ], + "code": [ + "/articles/s41467-025-61423-8#MOESM1", + "https://github.com/harryyiheyang/TGVIS", + "/articles/s41467-025-61423-8#ref-CR66", + "https://ovlwff-yihe-yang.shinyapps.io/tgvis_shiny/", + "/articles/s41467-025-61423-8#ref-CR67" + ], + "subject": [ + "Genome-wide association studies", + "Software", + "Statistical methods" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5285011/v1.pdf?c=1751540901000", + "research_square_link": "https://www.researchsquare.com//article/rs-5285011/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61423-8.pdf", + "preprint_posted": "09 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Transcriptome-wide association studies (TWAS) are commonly used to prioritize causal genes underlying associations found in genome-wide association studies (GWAS) and have been extended to identify causal genes through multivariable TWAS methods. However, recent studies have shown that widespread infinitesimal effects due to polygenicity can impair the performance of these methods. In this report, we introduce a multivariable TWAS method named Tissue-Gene pairs, direct causal Variants, and Infinitesimal effects selector (TGVIS) to identify tissue-specific causal genes and direct causal variants while accounting for infinitesimal effects. In simulations, TGVIS maintains an accurate prioritization of causal gene-tissue pairs and variants and demonstrates comparable or superior power to existing approaches, regardless of the presence of infinitesimal effects. In the real data analysis of GWAS summary data of 45 cardiometabolic traits and expression/splicing quantitative trait loci (eQTL/sQTL) from 31 tissues, TGVIS improves causal gene prioritization and enhances the biological interpretability over existing methods.\u2003Biological sciences/Genetics/Genetic association study/Genome-wide association studiesBiological sciences/Computational biology and bioinformatics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "DataanalysisusingTGVIS.pdfSupplemetal materialsSupplementalTables.xlsxSupplemetal tables", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Transcriptome-wide association studies (TWAS) are commonly used to prioritize causal genes underlying associations found in genome-wide association studies (GWAS) and have been extended to identify causal genes through multivariate TWAS methods. However, recent studies have shown that widespread infinitesimal effects due to polygenicity can impair the performance of these methods. In this report, we introduce a multivariate TWAS method named tissue-gene pairs, direct causal variants, and infinitesimal effects selector (TGVIS) to identify tissue-specific causal genes and direct causal variants while accounting for infinitesimal effects. In simulations, TGVIS maintains an accurate prioritization of causal gene-tissue pairs and variants and demonstrates comparable or superior power to existing approaches, regardless of the presence of infinitesimal effects. In the real data analysis of GWAS summary data of 45 cardiometabolic traits and expression/splicing quantitative trait loci from 31 tissues, TGVIS is able to improve causal gene prioritization and identifies novel genes that were missed by conventional TWAS.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Over the past two decades, genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits1,2,3. However, most GWAS signals are detected in non-coding regions and have been shown to have complex regulatory landscapes across different tissues and cell types4, making it challenging to pinpoint causal variants and genes driving these GWAS signals. Joint GWAS and expression quantitative trait loci (eQTL) data analysis methods, such as colocalization5, transcriptome-wide association studies (TWAS)6, and cis-Mendelian randomization (cis-MR)7,8, have been developed to prioritize causal genes at GWAS loci9. Colocalization simultaneously examines the expression of a gene and a trait to determine whether they share common causal genetic variants at a locus5. Both TWAS and cis-MR assume a causal diagram where eQTLs regulate tissue-specific gene expression that subsequently affects a trait, and they identify these tissue-specific causal genes by testing the significance of the causal effect estimates6,7,8. Furthermore, these methods have been extended to a broader range of molecular phenotypes, such as splicing events10 and protein abundance11, with regulatory QTLs being splicing QTLs (sQTLs) and protein QTLs (pQTLs), which we call xQTLs in general.\n\nNevertheless, colocalization, TWAS, and cis-MR are all univariable methods that statistically measure the marginal correlations of genetic effect sizes between a trait and a tissue-specific expression of a gene. Non-causal gene-tissue pairs may be falsely detected by these univariable methods due to the cis-gene-tissue co-regulations with causal gene-tissue pairs9,12,13. The underlying mechanism may come in the following respects: the tissue-specific eQTLs of a causal gene are in linkage disequilibrium (LD) with (1) the eQTLs of nearby non-causal genes14 and (2) the eQTLs of causal genes expressed in non-causal tissues15. In addition, some variants can influence a trait independently of causal gene-tissue pairs, which are frequently denoted as direct causal variants14 and horizontal pleiotropy16. The non-causal gene-tissue pairs may be incorrectly detected when their eQTLs are in LD with direct causal variants.\n\nMultivariate TWAS methods, such as causal TWAS (cTWAS)14, gene-based integrative fine-mapping through conditional TWAS (GIFT)17, and tissue-gene fine-mapping (TGFM)15, have been proposed to address these issues. Specifically, cTWAS is a Bayesian multivariate TWAS method, which identifies causal genes and direct causal variants among multiple candidates using the sum of single effects (SuSiE)14,18 by examining tissues separately. TGFM extends cTWAS to allow multiple tissues to be analyzed simultaneously and can identify the trait-relevant tissues beyond the causal variants and genes. Furthermore, GIFT is a frequentist multivariate TWAS method, which explicitly models both expression correlation and LD of eQTLs across multiple genes through a likelihood framework.\n\nHowever, Cui et al. 19 recently reported that current Bayesian fine-mapping methods, including SuSiE18,20 and FINEMAP21, have a high replication failure rate (RFR) in practice. Cui et al. 19 discovered that the widespread infinitesimal effects are the sources of the high RFR, and accounting for the infinitesimal effects can reduce the RFR and improve statistical power. In general, the infinitesimal effect model is equivalent to a polygenic architecture in which all genetic variants contribute to phenotypic variation, each with small effects22. Cui et al.19 extended this model to a cis-region, which assumes that a subset of variants has relatively large effect sizes besides the infinitesimal effects. Notably, the impact of infinitesimal effects is not limited to fine-mapping, as it has been observed to inflate the test statistics in standard TWAS23 and traditional linkage studies24. Thus, due to the lack of modeling infinitesimal effects, it is expected that cTWAS and TGFM can be vulnerable to spurious prioritization and reduced statistical power.\n\nWe present the tissue-gene pair, direct causal variants, and infinitesimal effect selector (TGVIS), a multivariate TWAS method to identify causal gene-tissue pairs and direct causal variants while incorporating infinitesimal effects. TGVIS employs SuSiE14,18 for fine-mapping causal gene-tissue pairs and direct causal variants, and uses restricted maximum likelihood (REML)25 to estimate the infinitesimal effects. In addition, we introduce the Pratt index26 to rank the importance for improving the prioritization of causal genes and variants. We applied TGVIS to identify causal cis-gene-tissue pairs and direct causal variants for 45 cardiometabolic traits using GWAS datasets with the largest sample sizes to date3,27,28,29,30,31,32,33,34,35, by incorporating the eQTL and sQTL summary statistics from 28 tissues from genotype-tissue expression (GTEx)13, and the eQTL summary statistics of kidney tubulointerstitial36, kidney glomerular36, and pancreatic islets37 tissues. We summarized the causal gene-tissue pairs and direct causal variants, highlighted the pleiotropic effects at the gene-tissue level, and demonstrated the different functional activity38 of eQTLs/sQTLs mediated through gene-tissues and the direct causal variants. Moreover, we mapped the trait-relevant major tissues and demonstrated the enrichments of genes identified by TGVIS in terms of colocalization5, on the silver standard of lipid genes14, FDA-approved drug-target genes39, and genes detected through pQTL summary data11. Our study reveals a broader picture of gene and tissue co-regulations, which can provide novel biological insights into complex traits.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Figure\u00a01A illustrates the causal diagram assumed in this report. Specifically, we hypothesize that a set of xQTLs influences the products of genes (e.g., expressions and splicing events) at a locus. Gene co-regulation9,13, i.e., the correlation of xQTL effects among multiple gene products, can emerge due to shared xQTLs or being in LD among them. Meanwhile, tissue co-regulation12,40,41, defined as the correlation of gene expression across multiple tissues, can arise because of the same mechanism. In the gene and tissue co-regulation network, certain gene-tissue pairs directly influence a trait without mediation by other gene-tissue pairs, which are referred to as causal gene-tissue pairs. In addition, some genetic variants may directly influence the trait, which we consider as direct causal variants. Besides these direct causal variants, which have relatively large effects, we assume there are polygenic or infinitesimal effects that can be modeled through a normal distribution with mean zero and small variance19. In addition to the biological basis of polygenic trait architecture, there are non-biological factors that can produce similar effects to infinitesimal models. These include population structure, errors in estimating the LD matrix, imputation errors in GWAS effect sizes, and the possibility that a true causal variant was either not genotyped or was removed during LD clumping (Methods).\n\nA A hypothetical causal diagram illustrating the relationships between variants (including xQTLs, direct causal variants, and non-causal variants), tissue-specific gene expressions, and an outcome in a cis-region, where the arrows indicate the flow of causal effects in the causal diagram. Variants may be in LD, with only a subset having cis-regulatory effects. Gene expressions or splicing events are tissue-specific and form a complex co-regulation network. Only molecular phenotypes directly connected to the outcome are considered causal. B Locus-zoom plot of the LDL-C GWAS in the PCSK9 locus. The bottom panel displays the coding regions of genes located within this locus, including PCSK9, UPS24, BSND, etc. P values were calculated by \\({\\chi }^{2}\\)-test with 1 degree of freedom. C Workflow of TGVIS, consisting of three main steps. (I) Input, including GWAS summary data, eQTL summary data from multiple tissues, and LD matrix. (II) Preprocessing, including eQTL selection and pre-screening. We applied S-Predixcan to pre-screen some noise pairs, aiming to reduce the dimension of the multivariable TWAS model to a reasonable scale. (III) Estimation, where TGVIS first selects the causal gene-tissue pairs and direct causal variants via SuSiE and then estimates the infinitesimal effect via REML. (IV) Output, including the causal effect estimate, direct causal effect estimate, and infinitesimal effect estimates. We output plots demonstrating the causal gene-tissue pairs, direct causal variants and predicted infinitesimal effects: (1) the Pratt indices and other statistics such as PIPs, estimates, SEs of causal gene-tissue pairs in the 95% credible sets, (2) the Pratt indices of the direct causal variants in the 95% credible sets, and (3) the best linear unbiased predictors of infinitesimal effects. The non-zero variance in output III in this figure suggests the non-zero contribution of infinitesimal effects. The figure was created in BioRender. Yang, Y. (2025) https://BioRender.com/tpngnr4.\n\nThe curse of dimensionality poses a substantial challenge in the multivariate TWAS model. Figure\u00a01B illustrates this challenge by an example of the association evidence with low-density lipoprotein cholesterol (LDL-C)1 at the PCSK9 locus, where dozens of coding genes and long non-coding ribonucleic acids (lncRNAs) are located, along with multiple potential direct causal variants. Conventional statistical methods cannot precisely identify causal gene-tissue pairs and variants because there are many correlated candidates that frequently range from hundreds to thousands18. The proposed TGVIS overcomes the curse of dimensionality. Figure\u00a01C describes the workflow of TGVIS, where the inputs are the GWAS summary statistics of a trait, xQTL summary statistics of gene-tissue pairs, and a reference LD matrix of the variants at the locus. TGVIS is a two-stage method. In the first stage, TGVIS employs SuSiE to identify a small set of xQTLs that best predict the genetic effects of gene-tissue pairs. These xQTLs are treated as informative variants instead of biologically causal variants. In the second stage, TGVIS utilizes a profile-likelihood approach to estimate the causal effects of gene-tissue pairs and directly causal variant effects with SuSiE18,20, and model the infinitesimal effects via REML25. This profile-likelihood iterates until all estimates are converge. The details are described in the \u201cMethods\u201d and the Supplementary Materials.\n\nIn practice, another challenge arises when selecting a causal gene-tissue pair based solely on its posterior inclusion probability (PIP) because many gene-tissue pairs share the same sets of xQTLs at a locus, making them statistically indistinguishable. SuSiE groups these pairs into a credible set during fine-mapping and introduces a single effect to describe the contributions of the variables in the same credible set. Therefore, all inferences should be made based on the single effects defined by SuSiE\u2019s credible sets. In TGVIS, we introduce the Pratt index42,43 as a metric parallel to PIP, to quantify the contribution of a credible set of gene-tissue pairs and direct causal variants. While PIP measures the significance of variables from a Bayesian viewpoint, the Pratt index quantifies their predictive importance. In the application, we calculated the cumulative Pratt index of variables in a 95% credible set (CS-Pratt) and filtered out the credible sets with low CS-Pratt values (Methods and Supplementary Materials). We observed that this procedure improved the precision of causal gene and variant identification.\n\nWe compared the TGVIS with 4 multivariate MR and TWAS methods: cisIVW44, Grant202245, cTWAS14, and TGFM15. We applied the following criteria for determining the causality: the 95% credible set for TGVIS, TGFM, and cTWAS; P\u2009<\u20090.05 for cisIVW; and selection by lasso in the Grant2022. We did not consider univariable methods because of their high type-I error rates when the goal is to identify causal genes, given that xQTL effect sizes for multiple genes are often correlated14. Detailed information on the settings and more simulation results beyond the specific case presented below can be found in Methods and Supplementary Materials.\n\nWe first assessed the accuracy of causal effect estimation for gene-tissue pairs. When infinitesimal effects were absent, TGVIS showed a mean square error (MSE) for causal effect estimates similar to that of cTWAS, and TGFM, while both cisIVW and Grant2022 exhibited substantially larger MSE (as shown in the left two panels in Fig.\u00a02A). However, when infinitesimal effects were present, TGVIS demonstrated a visibly lower MSE compared to the other methods, with cTWAS and TGFM showing ~32% higher MSE than TGVIS (as shown in the right two panels in Fig.\u00a02A). These results indicate that TGVIS generally outperforms its competitors by accounting for infinitesimal effects.\n\nA The MSE of causal effect estimates under no pleiotropy, in the presence of direct causal variants, infinitesimal effects, and both. B The true negative rate of identifying all 98 non-causal gene-tissue pairs under different scenarios i.e., no pleiotropy, in the presence of direct causal variants, infinitesimal effects, and both. This is equivalent to that if a method incorrectly identifies any non-causal pairs as causal, it will not be counted as a true negative event. C Bar plots display the true positive rates of identifying all 2 causal gene-tissue pairs under different scenarios. D The averaged number of identified direct causal variants by the different methods. The number of true causal variants were set to 0, 2, 0, and 2 for no-pleiotropy, direct-causal-variant, infinitesimal-effects, and direct-causal-variant and infinitesimal-effects, respectively. E The averaged correlation of the true and estimated direct causal effects across simulations. F The averaged correlation of the true and predicted infinitesimal effects across simulations.\n\nWe then compared the true negative rate (TNR) and true positive rate (TPR) of these five methods. A true negative is defined as a method that correctly identifies all 98 non-causal gene-tissue pairs. Similarly, a true positive is defined as a method that correctly identifies the 2 causal gene-tissue pairs. Across all the scenarios (Fig.\u00a02B), TGVIS achieved the highest TNR, with an average of 0.614, followed by TGFM and cTWAS, with average TNRs of 0.513 and 0.499, respectively. CisIVW and Grant2022 performed worst, with average TNR of 0.064 and 0.013, respectively, indicating that these two methods are prone to identifying a substantial number of false-positive gene-tissue pairs. On the other hand, TGVIS exhibited a similar TPR (average TPR\u2009=\u20090.667) as TGFM, cTWAS, and cisIVW (average TPRs of 0.649, 0.667, and 0.661, respectively), while Grant2022 had the highest TPR (average TPR\u2009=\u20090.831) (Fig.\u00a02C), which is not surprising given that Grant2022 also has lowest TNR.\n\nWe further assessed the performance in detecting direct causal variants. In scenarios where no direct causal variants were present, among the 400 variants, the TGVIS identified fewer direct causal variants, with an average number of 0.92, compared to 2.39 for TGFM and 2.38 for cTWAS (Fig.\u00a02D). Due to the LD among the 400 variants, we estimated that they correspond to ~77 independent variants (number of eigenvalues\u2009>\u20091). Under the null hypothesis of no direct causal variants, we would expect to detect at most 4 false-positive variants. Thus, all three methods are relatively conservative when no direct causal variants are present. When there were two direct causal variants present, TGVIS identified an average of 2.86 direct causal variants, compared to 3.58 for both cTWAS and TGFM. The averaged correlations between the estimated and true direct causal effects across simulations were high for all three methods (Fig.\u00a02E). However, predicting infinitesimal effects remains challenging, as evidenced by an average correlation of 0.663 between the predicted and true infinitesimal effects in TGVIS (Fig.\u00a02F). Additionally, direct causal effect estimates were consistent in terms of mean square error (MSE), whereas the variance of infinitesimal effects was inflated due to absorbing estimation errors from direct causal effects and gene-tissue pair effects carrying genetic information (Supplementary Materials).\n\nWe systematically analyzed 45 cardiometabolic traits and eQTL/sQTL summary statistics (Supplementary Data\u00a01\u20132) to identify potential causal gene-tissue pairs and direct causal variants. For the TVGIS, we considered whether a gene-tissue pair or direct causal variant was causal if (1) it was within a 95% credible set and (2) had a CS-Pratt\u2009>\u2009 0.15. The criteria of CS-Pratt >0.15 was established based on empirical evidence by summarizing the CS-Pratt scores from the all the loci and traits we analyzed (Methods). For TGFM, we followed the authors\u2019 recommendation of considering individual PIP\u2009>\u20090.5 as indicative of causality. We did not compare with cTWAS because it analyzes tissues separately14.\n\nTGVIS and TGFM identified a median of 119.5 and 227.5 causal gene-tissue pairs, and 42 and 183 causal variants per trait (Fig.\u00a03A and Supplementary Data\u00a03\u20136), respectively. Additionally, TGVIS detected a median of 0.313 causal gene-tissue pairs and 0.115 direct causal variants per locus, while TGFM identified a median of 0.469 causal gene-tissue pairs and 0.466 direct causal variants per locus (Fig.\u00a03C). Overall, TGVIS reduced the number of causal gene-tissue pairs by a median of 55.7% and the number of direct causal variants by 24.5% per trait compared to TGFM. Along with our simulations showing that TGVIS and TGFM have comparable power, with TGVIS exhibiting a lower false positive rate, our real data results are likely to support the improved resolution of TGVIS over TGFM; see, e.g., the four examples shown in Fig.\u00a07.\n\nA, B The number and proportion of causal and likely novel causal gene-tissue pairs identified by TGVIS and TGFM, respectively. Likely novel gene-tissue pairs are defined as those do not present in the list of significant gene-tissue pairs identified by univariable S-PrediXcan (P\u2009<\u20090.05/20000). The proportion refers to the average number of causal and likely novel causal gene-tissue pairs per locus. C The number and proportion of direct causal variants identified by TGVIS and TGFM. D The distribution of the number of traits affected by causal gene-tissue pairs. E, F The distributions of scores for FathmmXF and Encode H3K9me3Sum annotations. Raincloud plots illustrate four classes: direct causal variants and xQTLs of causal gene-tissue pairs identified by TGVIS and TGFM. Pairwise Wilcoxon signed-rank test P values (two-side) are displayed at the top, while medians of annotation scores are shown at the bottom. The median was shown as a black bar. The lower and upper hinges corresponded to the 25th and 75th percentiles. The \u201csample sizes\u201d in the test are the numbers of variants, which are 1256, 4787, 9552, 19057 for TGVIS (direct causal variant), TGVIS (xQTL of gene-tissue pairs), TGFM (direct causal variant), TGFM (xQTL of gene-tissue pairs), respectively. Source data are provided as a Source Data file. The figure was created in BioRender. Yang, Y. (2025) https://BioRender.com/b65f9a0.\n\nWe expected that general causal gene-tissue pairs detected by TGVIS and TGFM would likely be included among those identified by univariable TWAS methods such as S-PrediXcan46. Surprisingly, among the causal pairs identified by TGVIS, a median of 34.3% were undetected by S-PrediXcan, and this proportion was 60.1% for TGFM (Fig.\u00a03A and Supplementary Data\u00a022). For example, TGVIS identified SCN2A-Nerve_Tibial as a novel causal gene-tissue pair for 17 traits (Supplementary Fig.\u00a037) but was missed by S-PrediXcan. In fact, among the 17 traits, S-PrediXcan only\u00a0identified SCN2A-Nerve_Tibial for type 2 diabetes. Our findings suggest SCN2A may regulate a wide range of metabolic traits. These results indicate that TGVIS not only fine-maps causal genes but also uncovers novel genes by modeling multiple tissue-gene pairs simultaneously.\n\nWe investigated how many traits can be influenced by a causal gene-tissue pair, reflecting the pleiotropic effect at the gene-tissue level. Among the causal gene-tissue pairs falling in credible sets of sizes \u2264\u00a02, 22.4% identified by TGVIS and 16.7% by TGFM exhibit pleiotropic effect (Fig.\u00a03D and Supplementary Data\u00a07\u20138), indicating that many of these causal genes contribute to shared biological mechanisms across multiple traits.\n\nWe further examined whether the direct causal variants and xQTLs mediated by causal gene-tissue pairs differ in functionality using functional annotations38 (Methods). Significant differences were observed between these two types of variants identified by either TGVIS or TGFM across multiple annotations (Supplementary Data\u00a011). As shown in Fig.\u00a03E, F, the direct causal variants generally have higher FathmmXF and h3k9me3 scores than the xQTLs mediated by causal gene-tissue pairs (Wilcoxon signed-rank test, P\u2009<\u20092.2E-16), suggesting distinct biological mechanisms for many of these variants.\n\nWe observed that multiple eGenes and sGenes often shared the same set of variants as their xQTL, highlighting the importance of making inferences based on credible sets rather than individual variables. Most credible sets consisted of 2 to 4 gene-tissue pairs (60.5%), although some credible sets included more than 10 (11.5%) for TGVIS (Fig.\u00a04A and Supplementary Data\u00a012). In comparison, TGFM resulted in predominantly featured single gene-tissue pairs (56.0%) and 2 to 4 pairs (41.7%) per credible set (Supplementary Fig.\u00a018 and Supplementary Data\u00a012). On the other hand, most of the credible sets only had one xQTL (66.6%), followed by two xQTLs (12.6%) for TGVIS (Fig.\u00a04B and Supplementary Data\u00a013). As for TGFM, these percentages were 26.9% for one xQTL and 24.4% for two xQTLs (Supplementary Fig.\u00a020 and Supplementary Data\u00a013). These differences arise because TGFM resampled all xQTLs in the 95% credible sets, typically incorporating more variants, whereas TGVIS applied a stricter criterion for selecting xQTLs (Methods, Supplementary Fig.\u00a018\u201319).\n\nA The ratio of identified causal gene-tissue pairs per credible set by TVGIS. Different gene-tissue pairs may share the same set of xQTLs, and end in the same credible set. B The ratio of the number of causal eQTLs over the number of sQTLs per causal gene-tissue pair, indicating the distribution of eQTLs and sQTLs contributing to the gene-tissue pairs. C The distribution of eGene and sGene in credible sets identified by TGVIS and TGFM. When a credible set contains multiple gene-tissue pairs, we calculate the proportion of eGenes and sGenes. D The distribution of Pratt Index estimates for different traits, with a comparison between TGVIS and TGFM. In the boxplot, each point represents the Pratt Index of various molecular phenotypes within a single locus. The median was shown as a black bar. The lower and upper hinges corresponded to the 25th and 75th percentiles. Source data are provided as a Source Data file. The figure was created in BioRender. Yang, Y. (2025) https://BioRender.com/ch89ux4.\n\nWe investigated the proportions of identified causal eGenes and sGenes for the 45 cardiometabolic traits (Methods). TGVIS showed eGenes and sGenes proportions of 58.1% and 41.9%, respectively, while TGFM resulted in 63.5% for eGenes and 36.5% for sGenes (Fig.\u00a04C and Supplementary Fig.\u00a021). These results align with the proportions observed in the GTEx Consortium (63% cis-eQTL vs. 37% cis-sQTL)13, with TGFM\u2019s proportions being slightly closer. A potential explanation is that TGVIS\u2019 eGenes and sGenes were more likely enriched for causal genes specific to cardiometabolic traits, leading to a slight difference, though this difference is not substantial.\n\nWe calculated the Pratt index of gene-tissue pairs, direct causal variants, and infinitesimal effects based on their additive property (Fig.\u00a04D and Supplementary Data\u00a015), which helps measure the contributions of these three potentially correlated components (Methods). For TGVIS, the median of the Pratt index was 0.161, 0.059, and 0.182 for gene-tissue pairs, direct causal variants, and infinitesimal effects, respectively, with a median sum of the Pratt index of 0.403. In comparison, for TGFM, the median of the Pratt index was 0.145 for gene-tissue pairs and 0.114 for direct causal variants, with a median sum of the Pratt index of 0.262. These results support the existence of widespread infinitesimal effects.\n\nWe searched for the major relevant tissues by counting their numbers to the causal gene-tissue pairs in credible sets identified by TGVIS and TGFM (Methods). We ranked the top relevant tissues according to their contributions and clustered similar traits and tissues based on the similarity of the identified causal gene-tissue pairs (Fig.\u00a05A, B and Supplementary Figs.\u00a022 and 23). Overall, we observed similar major relevant tissues and clustering patterns using both methods, although there were some notable differences. TGVIS tended to cluster similar traits more closely together than TGFM. For instance, TGVIS grouped all blood pressure traits into close clusters, placing them near coronary artery disease (CAD), whereas TGFM positioned systolic and diastolic blood pressures (SBP and DBP) farther from pulse pressure (PP) and CAD. Similarly, serum lipid traits were clustered together by TGVIS, but not by TGFM. On the other hand, arterial tissues consistently emerged as the major tissue for blood pressure traits and CAD, while heart tissues were the major tissue for the QRS complex, atrial fibrillation, QT interval, and JT interval. Fibroblasts were highlighted as an important tissue for many traits, aligning with recent findings about their role in tissue integrity and chronic inflammation, alongside other tissues such as adipose tissue and liver47.\n\nA Heatmaps display the major tissues associated with each trait, identified by TGVIS. B Heatmaps display the major tissues associated with each trait, identified by TGFM. The major gene-tissue pairs are cataloged based on stringent criteria (CS-Pratt\u2009>\u20090.15 for TGVIS and PIP\u2009>\u20090.5 for TGFM), and the proportions of major tissues derived from significant gene-tissue pairs for each trait are quantified. Hierarchical clustering is applied to arrange the heatmaps, utilizing the Ward2 method and Euclidean distance. C Major tissues of lipid traits identified by TGVIS and TGFM. This panel shows bar plots detailing the number of causal gene-tissue pairs for various lipid traits, including HDL-C, LDL-C, TC, triglycerides, APOA1, and APOB, as identified by both TGVIS (top) and TGFM (bottom). Source data are provided as a Source Data file. The figure was created in BioRender. Yang, Y. (2025) https://BioRender.com/1s8s2iy.\n\nIt is possible that the major tissue rank may be affected by the number of background genes expressed in tissue and the eQTL sample size, although most of the tissue data in this study was from the GTEx data, with the sample size being generally comparable for different tissues. We first examined the correlation between the number of background genes expressed and the count of causal genes relevant to a tissue across the traits, and the median correlation is \u22120.16 (SE\u2009=\u20090.1410), suggesting that background expressed genes do not affect the rank of a tissue. We next calculated the correlation between the eQTL sample size and the count of causal genes relevant to a tissue. We observed the median correlation of 0.6214 (SE\u2009=\u20090.1178), which is not entirely surprising. We then regressed the count of causal genes on the sample size and calculated the corresponding residuals. The residuals\u00a0were highly correlated with the count of causal genes (median rank correlation\u2009=\u20090.7443, SE\u2009=\u20090.1382), suggesting the tissue rank cannot be fully explained by eQTL sample size. Thus, our major tissue map may be partially affected by the eQTL sample size, warranting future replication using large eQTL data.\n\nWe considered several lipid traits, including LDL-C, HDL-C, TC, triglyceride, apolipoprotein A1 (APOA1), and apolipoprotein B (APOB), as examples to illustrate the proportional counts of each tissue identified in the credible sets. For HDL-C and triglycerides, the most relevant tissue was subcutaneous adipose (Fig.\u00a05C). In contrast, liver tissue was consistently the most relevant tissue for LDL-C, APOB, and TC, despite the small sample size for the liver tissue gene expression data13. For APOA1, the two most relevant tissues were the liver and subcutaneous adipose tissue. Supplementary Figs.\u00a024\u201332 display the plots of major tissues for the rest of the traits. Overall, TGVIS and TGFM produced consistently the most relevant tissues.\n\nTo evaluate the accuracy of the prioritization of causal gene-tissue pairs, we first compared the colocalization evidence of the causal credible sets identified by TGVIS and TGFM through Coloc-SuSiE5. Since a credible set could include multiple tissue-gene pairs, we defined a colocalization of a credible set in two criteria: (1) the credible set contained at least one gene-tissue pair that is colocalized with the trait; (2) more than 50% of the gene-tissue pairs in the credible set were colocalized with the trait (Methods). TGVIS had much higher proportions of colocalized credible sets (the median proportions across traits were 93.1% and 77.8% for the two criteria, respectively) than TGFM (the median proportions across traits were both 40.9% for two criteria) (Fig.\u00a06A and Supplementary Data\u00a016\u201317), suggesting a substantial number of causal tissue-gene pairs identified by TGFM do not have colocalization evidence.\n\nA The colocalized proportions of causal credible sets (under two criteria) yielded by TGVIS and TGFM, respectively. B The numbers and proportions of causal cis-genes in the list of FDA-approved drug-target genes provided by Trajanoska et al., identified by TGVIS (left) and TGFM (right), respectively. C The number of significant pGenes in univariable MR analysis and the ratio of significant pGene in univariable MR analysis divided by significant eGenes/sGenes in eQTL/sQTL analysis. Source data are provided as a Source Data file. The figure was created in BioRender. Yang, Y. (2025) https://BioRender.com/ouhjfzd.\n\nWe next followed the previous analysis strategy14 to assess the causal genes for LDL-C identified by TGVIS and TGFM. Precision was evaluated using the 69 known lipid-related genes as the silver standard positive gene set, and nearby genes within a 1MB-radius region as the negative set, as studied by Zhao et al. 14. We disregarded the tissue part of the identified causal gene-tissue pairs and then calculated how many causal genes were within the lists of sliver and nearby genes. TGVIS demonstrated a precision of 60.0% (9 out of 15), outperforming TGFM, which had a precision of 37.5% (10 out of 28) (Supplementary Data\u00a018 and Supplementary Fig.\u00a033).\n\nIt is reasonable to assume that causal genes are more likely to be druggable targets. We utilized the published list of 6,690 FDA/EMA-approved non-cancer drugs (Supplementary Data\u00a01 provided by Trajanoska et al. 39) to calculate the enrichment of the identified causal genes in the drug list (Fig.\u00a06B and Supplementary Data\u00a019\u201320). Although the number of causal genes identified by TGVIS in the drug-targeted gene list was only 74.3% of that identified by TGFM, the enrichment identified by TGVIS was 1.43 times more than that by TGFM (P\u2009=\u20091.56E-3).\n\nWe hypothesized that causal genes detected through eQTLs/sQTLs may be more likely to demonstrate association evidence in protein data. To test this, we conducted univariable MR analysis of protein abundances (pGenes) in blood tissue for genes identified by TGVIS and TGFM, using both trans- and cis-pQTLs as instrument variables (Fig.\u00a06C and Supplementary Data\u00a021). On average, 18.1% of pGenes identified by TGVIS showed significant causal evidence, compared to 13.7% of pGenes for TGFM (P\u2009=\u20093.1E-3). However, this proportion is lower than the estimated true positive association rate of 27.8% between predicted cis-regulated gene expression and plasma protein abundances48. The discrepancy may arise from the fact that pGenes are influenced by widespread trans-pQTLs11, whereas predicted gene expression is predominantly contributed by cis-eQTLs, and their trans-regulated effects are much more difficult to detect. This result suggests that eGenes/sGenes and pGenes may represent distinct biological processes related to complex traits48.\n\nWe exemplified four loci associated with LDL-C, CAD, and BMI. The first locus contains the PCSK9 gene for LDL-C (Fig.\u00a07A). TGVIS identified three 95% credible sets, including PCSK9-Whole_Blood and two direct causal variants rs11591147 and rs11206517 (Fig.\u00a07B). After applying the threshold of CS-Pratt\u2009>\u20090.15, PCSK9-Whole_Blood (CS-Pratt\u2009=\u20090.17) and rs11591147 (CS-Pratt\u2009=\u20090.492) remained. In contrast, TGFM identified nine gene-tissue pairs and direct causal variants with PIPs\u2009>\u20090.5 (Fig.\u00a07C), including the MROH7-Esophageal_Mucosa, which has no clear connection to the biology of LDL-C. Applying the CS-Pratt threshold, PCSK9-Whole_Blood (CS-Pratt\u2009=\u20090.204) and rs11591147 (CS-Pratt\u2009=\u20090.524) remained consistent with the results yielded by TGVIS. This example demonstrates how TGVIS reduces false positives by modeling infinitesimal effects and applies the Pratt Index as an additional criterion.\n\nA PCSK9 locus-zoom plot for LDL-C GWAS. B PCSK9 locus results for TGVIS. C PCSK9 locus results for TGFM. D HMGCR locus-zoom plot for LDL-C GWAS. E HMGCR locus results for TGVIS. F HMGCR locus results for TGFM. G PHACTR1 locus-zoom plot for CAD GWAS. H PHACTR1 locus results for TGVIS. I PHACTR1 locus results for TGFM. J FTO locus-zoom plot for BMI GWAS. K FTO locus results for TGVIS. L FTO locus results for TGFM. In each panel of fine-mapping results, the upper portion displays individual PIPs of identified gene-tissue pairs and direct variants, while the lower portion shows Pratt indices of identified credible sets. For TGVIS, causality is determined by (1) the variables are in a 95% credible set and (2) the Pratt index of this credible set is larger than 0.15. For TGFM, the causality is determined by (1) the individual PIP is larger than 0.5. The red diamond in a locus-zoom plot indicates the most significant SNP at the locus. PIPs were calculated by SuSiE. Source data are provided as a Source Data file. The figure was created in BioRender. Yang, Y. (2025) https://BioRender.com/jrzcdig.\n\nThe second locus contains the HMGCR gene causal49 to LDL-C (Fig.\u00a07D). TGVIS identified five 95% credible sets (Fig.\u00a07E). The first credible set (the darkest green) includes 9 gene-tissue pairs, such as HMGCR-Muscle_Skeletal and five of its sGenes in esophagus mucosa, nerve tibial, fibroblasts, and adipose visceral, all sharing the same xQTL rs2112653. When we applied the threshold of individual PIP\u2009>\u20090.5, none of the pairs in this credible set were selected, although they were all in a 95% credible set. However, this set had the highest CS-Pratt of 0.322 among the five 95% credible sets. Conversely, TGFM identified POLK-Lung (CS-Pratt\u2009=\u20090.684) but missed the crucial HMGCR gene (Fig.\u00a07F). This is likely a false discovery, as HMGCR inhibitor is a key component of statins, which works by inhibiting HMG-CoA reductase and thus reduces LDL-C in the blood49.\n\nIn the third example, we focused on the PHACTR1 locus related to CAD (Fig.\u00a07G). Both TGVIS and TGFM identified a major credible set at this locus, including PHACTR1-Artery_Coronary and PHACTR1-Artery_Aorta, with CS-Pratt values of 0.632 and 0.612, respectively (Fig.\u00a07H, I). In TGVIS, the individual PIPs of them were both 0.5, and the cumulative PIP for this credible set was 1. In contrast, TGFM resampled both the eQTL effect estimates and the individual PIPs (Methods), resulting in a higher individual PIP and individual Pratt index for PHACTR1-Artery_Aorta (PIP\u2009=\u20090.597, Pratt\u2009=\u20090.472) than PHACTR1-Artery_Coronary (PIP\u2009=\u20090.222, Pratt\u2009=\u20090.053). However, as noted by Strober et al. 15, this resampling process tends to favor gene-tissue pairs with larger sample sizes, which may explain the exclusion of PHACTR1-Artery_Coronary. TGVIS adheres to the original interpretation of SuSiE that the variables within a credible set cannot be distinguished from the available data.\n\nThe final exemplary locus is the FTO locus associated with BMI (Fig.\u00a07J). TGVIS identified only two direct causal variants, rs7206790 and rs3751813, and did not find any gene-tissue pairs at this locus (Fig.\u00a06K). In contrast, TGFM identified four gene-tissue pairs: FTO_Kidney_Glomerulus, FTO_Thyroid, FTO_Artery Tibial, and IRX3-Adipose_Subcutaneous, and five direct causal variants (Fig.\u00a07L). However, the associations between obesity and the expression of the FTO gene in the kidney glomerulus, thyroid, and tibial artery are not well-established in the literature. After applying the Pratt index threshold, only two direct causal variants, rs7206790 and rs3751813, remained, which is consistent with the result from the TGVIS. When we reduced the locus radius from 1MB to 500KB and re-ran the analysis, both TGVIS and TGFM identified the sGene of FTO-Pancreas as causal, with CS-Pratt values of 0.345 and 0.407, respectively (Supplementary Fig.\u00a035). The sQTLs of this FTO sGene are rs7206790 and rs11642841, which have been reported by Xu et al.50. This example suggests that when applying multivariate TWAS methods, the size of a cis-region can be sensitive and needs to be calibrated.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61423-8/MediaObjects/41467_2025_61423_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this report, we developed TGVIS to identify causal gene-tissue pairs and direct causal variants in loci identified through GWAS by integrating xQTL summary statistics. Compared to cTWAS14 and TGFM15, TGVIS not only analyzes multiple tissue-specific xQTL summary data simultaneously to pinpoint causal gene-tissue pairs and direct causal variants, but also models the widespread presence of infinitesimal effects underlying polygenic traits to reduce false discovery rates in detecting causal molecular phenotypes19. In addition, TGVIS quantifies the importance of a causal variable by the Pratt index, which has been well established in statistics42,43 and has recently been applied to estimate the gene-by-environment contribution26. Through simulations, we demonstrated that under the presence of infinitesimal effects, TGVIS has lower MSE and higher TPR and TNR compared to both cTWAS and TGFM (Fig.\u00a02). In real data analysis, TGVIS outperformed TGFM in the following four aspects: (1) identifying more interpretable major trait-relevant tissues (Fig.\u00a05); (2) resulting in a higher proportion of colocalized causal credible sets (93.1% vs 40.9%, Fig.\u00a06A); (3) achieving notably higher precision in the \u201csilver standard\u201d sets of lipids (60.0% vs 37.5%, Supplementary Data\u00a015); and (4) demonstrating significantly greater enrichment evidence based on druggable genes (1.43 times, Fig.\u00a06B) and causal proteins (1.31 times, Fig.\u00a06C). We also observed that the default PIP\u2009>\u20090.5 for TGFM may be a little liberal but a threshold of PIP\u2009>\u20090.9 may be too conservative, and incorporating Pratt index\u2009>\u20090.15 will lead to much consistent causal gene-tissue pairs and variants with TGVIS (Supplementary Materials).\n\nOur analysis of 45 cardiometabolic traits provides several key insights. First, we identified a median of 34.3% causal gene-tissue pairs that were missed in univariable TWAS analysis, suggesting that TGVIS is able to identify novel genes besides fine-mapping the genes detected by conventional TWAS (Fig.\u00a03A), representing a significant advance in TWAS. Second, we observed that infinitesimal effects can make a substantial contribution to local genetic variation of traits besides the gene-tissue pairs and direct variant (Fig.\u00a04D), which is consistent with recent studies19,24,51. Beyond underlying biological mechanisms such as the polygenicity of human complex traits, the emergence of infinitesimal effects may also be attributed to non-biological factors, particularly population structure, estimation errors in the LD matrix, xQTL effect sizes, and trait GWAS imputation (Methods). Both empirical observations and theoretical investigation underscore the importance of including infinitesimal effects in future genetic research and methodological development. Third, our study indicates that a significant proportion of causal gene-tissue pairs (22.4%) exhibit pleiotropic effects at the gene-tissue level, suggesting shared biological mechanisms across multiple traits (Fig.\u00a03D and Supplementary Data\u00a07\u20138). Fourth, our findings suggest that for most traits, only a limited number of relevant major tissues are involved (Fig.\u00a04A), implying that concentrating multi-omics data analyses on these relevant major tissues can be more powerful and efficient, as well as it can make the findings more biologically interpretable. For example, when the analysis is focused on the four major blood-pressure-relevant tissues, i.e., adrenal gland, artery, heart, and kidney, it leads to the identification of more causal gene-tissue pairs, with an increased Pratt index for blood pressure traits\u00a0(Yang, Y. et al. Personal communication 2025). Fifth, our results indicate that only 18.1% of causal genes from eQTL/sQTL analyses also show causal evidence in univariable MR using pQTL summary data (Fig.\u00a06C), suggesting that gene expressions and protein abundance represent distinct biological processes in complex traits48. Finally, we identified an average of 0.304 causal gene-tissue pairs per locus and failed to identify any causal gene-tissue pairs in many GWAS loci (Fig.\u00a03A), which is consistent with the recent study showing that the GWAS and eQTL studies are systematically biased toward different types of variants4. Interestingly, the eQTLs/sQTLs of causal gene-tissue pairs and direct causal variants have substantially different functional annotations (Fig.\u00a03E, F and Supplementary Data\u00a011), warranting further investigation.\n\nOur study has some limitations. First, due to the data and computational constraints, we only analyzed genes using cis-eQTL/sQTL summary statistics, limiting our ability to distinguish between genes that share cis-eQTLs/sQTLs, which may lead to false discoveries. This issue could potentially be addressed by incorporating trans-eQTLs/sQTLs, although this would require much larger sample sizes. In addition, we observed that a credible set often contains 2-4 gene-tissue pairs (Fig.\u00a03C), likely due to the small sample size in the GTEx data, which results in only 1 or 2 eQTL/sQTL for most gene-tissue pairs (Fig.\u00a04B). In other words, while TGVIS was able to narrow down to a range of causal gene-tissue pairs, it could not always pinpoint the exact causal pair(s) in some loci. Incorporating external information, such as colocalization evidence with TGVIS, may aid in distinguishing these pairs52. Second, our eQTL/sQTL analysis relies on bulk tissue expression data, which may limit our ability to identify cell-type-specific causal genes53. For example, recent studies increasingly suggest that FTO may not be the causal gene for BMI; instead, experimental evidence indicates that IRX3 and IRX5 are the causal genes54. However, the causality of IRX3 and IRX5 was observed in experiments using preadipocytes, rather than bulk subcutaneous adipose tissue, which may explain why TGVIS failed to identify these genes (Supplementary Fig.\u00a036). Third, we used the Pratt index26 to rank the importance of variables, but it has inherent statistical limitations26. In simulations, the Pratt index slightly underestimates the true contribution, although this underestimation becomes negligible as the sample size increases (Supplementary Figs.\u00a01\u20138). In real data analysis, we used an empirical cutoff learned by K-means (CS-Pratt\u2009=\u20090.15) to extract important causal variables, which gives us higher precision but may have potentially hindered the discovery of causal gene-tissue pairs with small to moderate causal effects. Fourth, the window size of cis-region can have an impact on the result, and the current TGVIS only applies the convention method of \u00b11\u2009Mb from the transcription start site (TSS). Applying automatically selecting the window sizes may improve statistical power and accuracy to identify causal genes and warrants for additional investigation55. Last, as suggested in previous studies15, the inference of causality based on statistical methods comes with a caveat, assuming no model misspecification and no potential causal elements are missing from the model.\n\nIn summary, our developed TGVIS and accompany software pipeline provide a valuable tool in fine-mapping and interpreting GWAS findings.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The causal diagram shown in Fig.\u00a01A can be described by the following multivariate TWAS model:\n\nwhere \\({y}_{i}\\) is a trait; \\({X}_{{ijt}}\\) is the levels (e.g., expressions and splicing events) of the \\({j}^{{{\\mbox{th}}}}\\) gene and \\({t}^{{{\\mbox{th}}}}\\) tissue pair; \\({{{{\\bf{G}}}}}_{i}={\\left({G}_{i1},\\ldots,{G}_{{iM}}\\right)}^{{{\\!\\!\\top }}}\\) is an \\((M\\times 1)\\) vector of genetic variants in the cis-region; \\({{{\\boldsymbol{\\theta }}}}={\\left({\\theta }_{11},\\ldots,{\\theta }_{{JT}}\\right)}^{{{\\!\\!\\top }}}\\) is an \\((\\,{JT}\\times 1)\\) vector of causal effects with \\({\\theta }_{{jt}}\\) being the causal effect of the \\(\\left(j,t\\right)\\) th tissue-gene pair; \\({{{\\boldsymbol{\\gamma }}}}={\\left({\\gamma }_{1},\\ldots,{\\gamma }_{M}\\right)}^{{{\\top }}}\\) is an \\((M\\times 1)\\) vector of direct causal effects; \\({{{\\boldsymbol{\\upsilon }}}}={\\left({\\upsilon }_{1},\\ldots,{\\upsilon }_{M}\\right)}^{{{\\top }}}\\) is an \\((M\\times 1)\\) vector of infinitesimal effects; and \\({\\epsilon }_{i}\\) is the random error. Let \\({{{{\\boldsymbol{\\beta }}}}}_{{jt}}={({\\beta }_{{jt}1},\\ldots,{\\beta }_{{jtM}})}^{{{\\top }}}\\) is an \\((M\\times 1)\\) vector of the cis-eQTL effects of \\({JT}\\) tissue-gene pairs. Then we have\n\nwhere \\({\\epsilon }_{{ijt}}\\) is the noise of the \\(j{t}^{{{\\mbox{th}}}}\\) gene-tissue pair. The reduced form of (1) is then given by:\n\nwhere mathematically \\({\\epsilon }_{i}={\\varepsilon }_{i}+{\\sum }_{j=1}^{J}{\\sum }_{t=1}^{T}{\\epsilon }_{{ijt}}{\\theta }_{{jt}}\\).\n\nAn alternative version of (1) based on summarized statistics56 is:\n\nwhere \\(\\hat{{{{\\bf{a}}}}}={\\left({\\hat{a}}_{1},\\ldots,{\\hat{a}}_{M}\\right)}^{{{\\!\\!\\top }}}\\) represents the GWAS effects of the outcome, \\({{{\\bf{R}}}}\\) is an \\(\\left(M\\times M\\right)\\) LD matrix of the \\(M\\) variants, and \\({\\sigma }_{\\alpha }^{2}\\) is the variance of this model. The eQTL effect vector \\({{{{\\boldsymbol{\\beta }}}}}_{{jt}}\\) follows the model based on summarized statistics below:\n\nwhere \\({\\hat{{{{\\bf{b}}}}}}_{{jt}}={({\\hat{b}}_{{jt}1},\\ldots,{\\hat{b}}_{{jtM}})}^{{{\\top }}}\\) represents the marginal cis-eQTL effect estimates for the \\(j{t}^{{{\\mbox{th}}}}\\) tissue-gene pair, and \\({\\sigma }_{{\\beta }_{{jt}}}^{2}\\) denotes the variance of this model.\n\nTo resolve this curse of dimensionality, we utilized the three sparsity conditions that are commonly assumed in current fine-mapping methods18,21: (SP1) one or small number of variants causally contribute to tissue or cell-type specific gene expression13; (SP2) one or small number of gene-tissue pairs causally contribute to the trait14,15; (SP3) one or small number of direct causal variants exist with relatively large effect sizes14,15. In terms of statistical model: SP1 corresponds to \\({{{{\\boldsymbol{\\beta }}}}}_{{jt}}\\) being sparse for all \\(j\\) \\({{\\mbox{and}}}\\) \\(t\\); SP2 corresponds to \\({{{\\boldsymbol{\\theta }}}}\\) being sparse; SP3 corresponds to \\({{{\\boldsymbol{\\gamma }}}}\\) being sparse. In addition, we incorporated that variants can have infinitesimal effects: \\({{{\\boldsymbol{\\upsilon }}}}\\) is normally distributed with a mean 0 and a small, unknown variance19. To our best knowledge, infinitesimal effects have not been modeled in current multivariate TWAS methods.\n\nTGVIS first applies SuSiE20 to estimate the non-zero eQTL effect for each gene-tissue pair, based on the fine-mapping model (Eq.\u00a05). Specifically, we set \\(L=3\\) for each pair and determined the non-zero \\({cis}\\)-regulatory effects based on two criteria: (1) if they are within any 95% credible set and their PIPs exceeds 0.25, and (2) if their individual PIPs are >0.5. The rationale behind this approach is that SuSiE\u2019s 95% credible set can sometimes include too many weakly correlated variants (even after removing highly correlated ones using LD clumping), leading to low PIPs for each variant. Therefore, we used a moderate threshold to filter out credible sets with too many variants. Additionally, due to the low power of detection, the maximum PIP of credible sets might fall below 0.95, so we retained variants with individual PIPs >0.5. Since a locus often contains over 10,000 gene-tissue pairs (mostly sGenes), dynamically selecting using BIC would be computationally burdensome. Additionally, with GTEx sample sizes under 200, only 1\u20132 gene-tissue pairs can be identified for most gene-tissue pairs. Therefore, we choose to fix \\(L=3\\).\n\nInfinitesimal effects may also influence the prediction of gene expression. We did not consider this issue in xQTL selection, because the variance of estimation errors of GWAS effect sizes is much larger than the variance of the infinitesimal effects when the sample size is small.\n\nTGVIS estimates \\({{{\\boldsymbol{\\theta }}}}\\), \\({{{\\boldsymbol{\\gamma }}}}\\), and \\({{{\\boldsymbol{\\upsilon }}}}\\) using a profile likelihood approach. Given the estimate \\({{{{\\boldsymbol{\\upsilon }}}}}^{\\left(s\\right)}\\) from the sth iteration, we considered the following fine-mapping model:\n\nwhere \\(\\hat{{{{\\bf{B}}}}}=({\\hat{{{{\\boldsymbol{\\beta }}}}}}_{11},\\ldots,{\\hat{{{{\\boldsymbol{\\beta }}}}}}_{{jt}},\\ldots,{\\hat{{{{\\boldsymbol{\\beta }}}}}}_{{JT}})\\) is an \\(M\\times {JT}\\) matrix consisting of estimated \\({cis}\\)-regulatory effects. To update \\({{{\\boldsymbol{\\gamma }}}}\\) and \\({{{\\boldsymbol{\\theta }}}}\\) simultaneously, we applied the same scheme as cTWAS and TGFM, using the function susie_rss(\\(\\cdot\\)). The input z-score vector is computed as:\n\nand the other elements of input correlation matrix are computed as:\n\nThe outputs are denoted as \\({{{{\\boldsymbol{\\gamma }}}}}^{\\left({{s}}+1\\right)}\\) and \\({{{{\\boldsymbol{\\theta }}}}}^{\\left({{{\\boldsymbol{s}}}}+1\\right)}\\).\n\nNext, we consider the following model:\n\nwhere \\({{{{\\boldsymbol{\\eta }}}}}^{\\left(s+1\\right)}={{{\\bf{R}}}}(\\hat{{{{\\bf{B}}}}}{{{{\\boldsymbol{\\theta }}}}}^{\\left(s+1\\right)}+{{{{\\boldsymbol{\\gamma }}}}}^{\\left(s+1\\right)}).\\) The penalized quasi-likelihood (PQL) of \\({{{\\boldsymbol{\\upsilon }}}}\\) is\n\nwhich results in\n\nwhere \\({\\sigma }_{\\alpha }^{\\left(s\\right)2}\\) is the current variance estimate. The variance \\({\\sigma }_{\\upsilon }^{\\left(s\\right)2}\\) is updated by REML:\n\nwhich simplifies to\n\nwhere \\(M\\) is the number of variants. We replace \\({\\sigma }_{\\upsilon }^{2}\\) in the last term by its current estimate \\({\\sigma }_{\\upsilon }^{\\left(s\\right)2}\\) to obtain a closed-form expression. Note that in Eq. (13), \\(\\frac{{\\sigma }_{\\alpha }^{\\left(s\\right)2}}{{\\sigma }_{\\upsilon }^{\\left(s\\right)2}}\\) is usually replaced by \\(\\frac{1}{{\\sigma }_{\\upsilon }^{\\left(s\\right)2}}\\) to avoid non-identifiability issues25.\n\nWhen the profile likelihood converges, TGVIS estimates \\({\\sigma }_{\\alpha }^{2}\\) as follows:\n\nIn the software of TGVIS, we applies the convergence criterion: the convergence tolerance is smaller than a threshold (e.g., max(\\({||}{{{{\\boldsymbol{\\theta }}}}}^{\\left({{{\\boldsymbol{s}}}}+1\\right)}-{{{{\\boldsymbol{\\theta }}}}}^{\\left({{{\\boldsymbol{s}}}}\\right)}|{|}_{2},{||}{{{{\\boldsymbol{\\gamma }}}}}^{\\left(s+1\\right)}-{{{{\\boldsymbol{\\gamma }}}}}^{\\left(s\\right)}|{|}_{2}\\))\u2009<\u20090.001), and the number of iterations is larger than 50 (e.g., \\(s > 50\\)).\n\nBased on Eq. (3), we define the BIC for summary data:\n\nwhere \\(M\\) is the number of IVs and \\({\\mbox{df}}\\) is the degree of freedom of the model57. In practice, \\({\\sigma }_{\\alpha }^{2}\\) is replaced by its empirical estimate \\({\\hat{\\sigma }}_{\\alpha }^{2}\\), and \\({\\mbox{df}}\\) is the sum of non-zero causal effect estimates and non-zero direct causal variant estimates. Our default setting assumes \\(L\\) can be 2,3,4,5,6,7, or 8 and uses BIC to select the optimal \\(L\\) among them. We found that when considering the infinitesimal effect, it tends to capture variants with very small effects that SuSiE does not identify, making it rare for \\(L\\) to exceed 8 in practice.\n\nWe use the Pratt index to assess the contribution of a gene-tissue pair. For a general linear model: \\({y}_{i}={\\sum }_{j=1}^{p}{X}_{j}{\\beta }_{j}+{\\epsilon }_{i}\\), the Pratt index of \\({x}_{{ij}}\\) is defined as \\({V}_{j}={\\beta }_{j}\\times {b}_{j},\\) where \\({b}_{j}={{\\mathrm{cov}}}(y,{X}_{j})\\). This definition assumes standardization where \\({\\mbox{E}}\\left(y\\right)={\\mbox{E}}({X}_{j})=0\\) and \\({{\\mathrm{var}}}\\left(y\\right)={{\\mathrm{var}}}({X}_{j})=1\\), \\(1\\le j\\le p\\). The Pratt index measures the contribution of a variable in a linear model because \\({R}^{2}={\\sum }_{j=1}^{p}{V}_{j}\\) where \\({R}^{2}={{\\mathrm{var}}}({\\sum }_{j=1}^{p}{X}_{j}{\\beta }_{j})/{{\\mathrm{var}}}(y)\\). In practice, the Pratt index can be estimated by \\({\\hat{V}}_{j}={\\hat{\\beta }}_{j}\\times {\\hat{b}}_{j}\\), where \\({\\hat{b}}_{j}\\) is the sample correlation between \\({X}_{j}\\) and \\(y\\).\n\nThe proportion of variance explained (PVE) is defined as \\({PV}{E}_{j}={\\beta }_{j}^{2}\\), assuming that all variables are standardized. The Pratt index has two key advantages over PVE: (1) Pratt indices are additive across variables, and (2) the sum of Pratt indices is the total trait variance explained by covariates. In contrast, PVE lacks these advantages.\n\nPratt index serves as an additional important matric for evaluating a gene or causal variant besides PIP. While the PIP reflects the statistical significance of a variable from a Bayesian perspective, the Pratt index quantifies its predictive contribution to the outcome when multiple predictors are correlated. PIP behaves similarly to a frequentist p-value and is influenced by sample size, in contrast that Pratt index is less affected by sample size.\n\nWe show how to yield the Pratt index \\({V}_{{jt}}\\) in practice. We first estimate the marginal correlation:\n\nAs for the causal effect estimate \\({\\hat{\\theta }}_{{jt}}\\), we apply the transformation\n\nsince the Pratt index requires the covariates and trait are all standardized. Thus, the Pratt index of the \\(\\left(j,t\\right)\\) th gene-tissue pair is\n\nSince Pratt indices are additive, the Pratt index of a credible set is simply calculated as\n\nNote that the Pratt index is only comparable within the same locus, as it represents the ratio of the variance explained by the variable to the total variance of the trait.\n\nIt is worth comparing the gene-tissue pair, direct causal variant, and infinitesimal effect contributions at a locus. To simplify the estimation, we consider the linear predictors of all gene-tissue pairs and pleiotropy:\n\nand \\(\\widetilde{{{{\\bf{a}}}}}={{{{\\bf{R}}}}}^{-\\frac{1}{2}}\\hat{{{{\\bf{a}}}}}\\), where \\({{{{\\bf{R}}}}}^{-\\frac{1}{2}}\\) is specified to remove the correlations of \\(\\hat{{{{\\bf{B}}}}}\\) and \\(\\hat{{{{\\bf{a}}}}}\\). Then, the Pratt indices for the gene-tissue pairs, direct causal variants, and infinitesimal effects are\n\nWe used empirical data to determine the threshold for Pratt index to enhance the precision of causal selection. Specifically, we employed K-means clustering with clusters to group the CS-Pratt indices of all gene-tissue pairs and direct variants identified by TGVIS within the 95% credible sets. We hypothesize that one cluster contains credible sets with smaller CS-Pratt values, which are more likely to include falsely causal variables. Interestingly, regardless of whether we focus on gene-tissue pairs, direct causal variants, or both, the minimum value in the cluster with the larger centroid consistently remains at 0.15 (Supplementary Fig.\u00a034). Consequently, we set the threshold at CS-Pratt\u2009=\u20090.15 to prioritize the gene-tissue pairs and direct causal variants identified by TGVIS, considering variables with CS-Pratt\u2009>\u20090.15 to have a higher likelihood of being true causal.\n\nHere we list four possible reasons that can lead to an infinitesimal effect. First, it has been gradually understood that even within the same ethnic group, such as the European population, different subgroups may have different genetic architectures, leading to different LD structures. Therefore, it is natural to suspect that the LD structures of populations in the GTEx consortium and those in traits GWAS differ, which results in\n\nWhen we try to estimate \\({{{{\\boldsymbol{\\beta }}}}}_{{jt}}\\) using \\({{{{\\bf{R}}}}}_{{\\mbox{Meta}}}\\), then \\({\\hat{{{{\\boldsymbol{\\beta }}}}}}_{{jt}}\\) is biased to \\({{{{\\boldsymbol{\\beta }}}}}_{{jt}}\\), which generates infinitesimal effect \\({{{\\boldsymbol{\\upsilon }}}}={\\sum}_{{jt}}({{{{\\boldsymbol{\\beta }}}}}_{{jt}}-{\\hat{{{{\\boldsymbol{\\beta }}}}}}_{{jt}}){\\theta }_{{jt}}\\). It should be noted that the small sample size in the GTEx consortium can also cause biased eQTL effect estimates, resulting in the appearance of infinitesimal effects. There are other possible sources which may lead to infinitesimal effects, such as (2) the estimation errors of LD matrix, (3) the imputation errors of outcome GWAS effect sizes, and (4) a true causal variant is either not genotyped or is filtered out during the LD clumping, which are shown in Supplementary Materials. It should be noted that all four sources are not biologically relevant, although we can model them through the infinitesimal effect model.\n\nIn implementation, dynamically determining whether to consider the infinitesimal effect is a clever empirical measure. Therefore, we apply the score test of the variance of the random effect in the linear mixed model to test whether the variance of the infinitesimal effect is zero. Specifically, we consider the following hypothesis testing problem:\n\nThe testing statistics of this hypothesis test is constructed according to Zhang and Lin58. Let \\({{{\\bf{A}}}}=({{{\\bf{R}}}}{\\hat{{{{\\bf{B}}}}}}_{{{{{\\mathscr{M}}}}}_{\\theta }},{{{{\\bf{R}}}}}_{{{{{\\mathscr{M}}}}}_{\\gamma }})\\) and \\({{{\\boldsymbol{\\vartheta }}}}={({{{{\\boldsymbol{\\theta }}}}}_{{{{{\\mathscr{M}}}}}_{\\theta }}^{{{\\top }}},{{{{\\boldsymbol{\\gamma }}}}}_{{{{{\\mathscr{M}}}}}_{\\gamma }}^{{{\\top }}})}^{{{\\top }}},{\\mbox{where }} \\, {{\\mathscr{M}}}_{\\theta}\\,{\\mbox{ and }}\\,{{\\mathscr{M}}}_{\\gamma} \\) refer to the index sets of non-zero elements in \\({\\boldsymbol{\\theta }}\\) and \\({{\\boldsymbol{\\gamma }}}\\), respectively. When \\({\\sigma }_{\\upsilon }^{2}=0\\) and \\({\\sigma }_{\\upsilon }^{2} > 0\\), the covariance matrix of \\(\\hat{{{{\\boldsymbol{\\alpha }}}}}-{{{\\bf{A}}}}{{{\\boldsymbol{\\vartheta }}}}\\) are\n\nrespectively. Similar to estimating \\({\\sigma }_{\\upsilon }\\), we replace \\({\\sigma }_{\\alpha }^{2}\\) by 1 to avoid non-identifiability. The score described in Zhang and Lin58 defined the following three statistics:\n\nwhere \\({{{\\bf{P}}}}={{{{\\bf{R}}}}}^{-1}-{{{{\\bf{R}}}}}^{-1}{{{\\bf{A}}}}{({{{{\\bf{A}}}}}^{{{\\top }}}{{{{\\bf{R}}}}}^{-1}{{{\\bf{A}}}})}^{-1}{{{{\\bf{A}}}}}^{{{\\top }}}{{{{\\bf{R}}}}}^{-1}\\). Under the null hypothesis, \\(u\\sim \\kappa {\\chi }_{v}^{2}\\) where \\(\\kappa=h/\\left(2e\\right)\\) and \\(v=2{e}^{2}/h\\). If the null hypothesis is accepted, we enforce \\({{{{\\boldsymbol{\\upsilon }}}}}^{\\left(s+1\\right)}=0\\).\n\nIn addition to true biological polygenicity, the infinitesimal component may also absorb residual genetic signals not adequately modeled due to estimation errors. For this reason, we do not claim the score test is to test whether there is a biological polygenicity. Instead, the purpose of modeling infinitesimal effects, similar to the rationale proposed by Cui et al. 19, is to improve the precision of identifying both gene\u2014tissue pairs and direct causal variants.\n\nFor cTWAS, since its software is designed to be user-friendly to practical projects, it involves complex settings that are not ideal for simulations, such as requiring a reference panel in BED format and a.db file of eQTL fine-mapping data. Therefore, we directly utilize the principles of cTWAS to develop an R function that employs SuSiE for the first-stage selection of \\({cis}\\)-regulatory effects and the second-stage selection of causal and horizontally pleiotropic effects. Therefore, we did not consider the first step of cTWAS to estimate two universal prior parameters using the EM algorithm across all loci in the genome. Instead, we restrict cTWAS simulations to a single locus. In addition, we applied the following settings for cTWAS, TGFM, and TGVIS: for the prior weight \\(\\pi\\) in SuSiE, we applied \\(\\pi={p}^{-1}\\) for gene-tissue pairs and \\(\\pi={M}^{-1}\\) for variants, where \\(p\\) represents the number of gene-tissue pairs and \\(M\\) the number of variants.\n\nAt the time of writing this paper, the TGFM software has not yet been released, and hence, we also developed the TGFM software on our side. TGFM\u2019s two-stage resampling scheme can make it significantly slower than cTWAS and TGVIS, even with a modest number of resampling iterations (e.g., 100) in each stage. To improve computational efficiency, we applied a slightly different resampling scheme compared with the original TGFM. Specifically, we first resampled the eQTL effects from the posterior for \\(25\\) times, calculated their mean as \\({\\hat{\\beta }}_{{jt}}^{{t}_{i}}\\), and used these means to estimate \\({\\hat{{{{\\boldsymbol{\\theta }}}}}}^{{t}_{i}}\\) and \\({\\hat{{{{\\boldsymbol{\\gamma }}}}}}^{{t}_{i}}\\). This procedure was repeated \\(100\\) times, recording the estimates and PIP for each iteration. We then compute the mean of \\({t}_{1}\\times {t}_{2}\\) resampled eQTL effects, \\({\\hat{\\beta }}_{{jt}}^{{t}_{i}},\\) and estimate the empirical \\({\\hat{{{{\\boldsymbol{\\theta }}}}}}^{TGFM}\\) and \\({\\hat{{{{\\boldsymbol{\\gamma }}}}}}^{TGFM}\\). The PIPs of \\({\\hat{{{{\\boldsymbol{\\theta }}}}}}^{TGFM}\\) and \\({\\hat{{{{\\boldsymbol{\\gamma }}}}}}^{TGFM}\\) were taken as the empirical PIPs given by SuSiE in each resampling iteration. Finally, we recorded the credible sets of variables from the final step and calculated the PIPs and Pratt indices of credible sets by summing the individual PIPs and Pratt indices of variables within each credible set.\n\nAnother key difference between TGVIS and methods such as cTWAS and TGFM is that the latter require in-sample LD matrices, which are often unavailable for many GWAS datasets. In contrast, TGVIS uses LD matrices estimated from an external reference panel.\n\nWe simulated 20 genes across 5 tissues, resulting in \\(p=\\) 100 gene-tissue pairs. Correlations were simulated both within and between genes across tissues. The first and last gene-tissue pairs were designated as causal, with effect sizes of \\({\\theta }_{1}=1\\) and \\({\\theta }_{100}=-1\\), respectively. The total number of variants was \\(M=\\) 400, with only 1,2,3, or 4 of them being eQTLs with non-zero effects for each gene-tissue pair, while the remaining variants were associated with the trait due to LD. We set 4 different sample sizes for the eQTL data (\\({n}_{{eQTL}}\\)\u2009=\u2009100, 200, 400, 800) and a fixed trait GWAS sample size \\({n}_{{trait}}=\\) 0.5M. Infinitesimal effect were generated from a normal distribution, and gene-tissue pairs, direct causal variants, and infinitesimal effects together were set to explain the trait heritability. For example, when only gene-tissue pairs and infinitesimal effects are present, they each explain 50% of the local heritability for the outcome. When all three are present, each explains 33% of the local heritability for the outcome. The detailed settings, along with corresponding R codes, are provided in Section 2 of the Supplementary Materials.\n\nWe conducted a meta-analysis on a subset of the 45 metabolic and cardiovascular traits. The publicly available data for these traits are listed in Supplementary Data\u00a01, while the MVP GWAS summary statistics can be accessed through dbGAP under accession number phs001672.v7.p1. For the pleiotropy traits of SBP and DBP, we applied the approach developed in Zhu et al. 32 using the most recent GWAS summary statistics of SBP and DBP. To perform the meta-analysis, we used METAL59. We performed the meta-analysis on the Z-scores, weighting by the sample sizes of the meta-analysis datasets. For binary trait, we always used the effective sample size \\({n}_{eff}\\). We used CHR:BP (in GRCH37) as the identifier.\n\nWe utilized bulk eQTL and sQTL summary statistics from 28 tissues provided by GTEx13 (with sample size N ranging from 113 (Lymphocytes) to 588 (Muscle Skeletal)), as well as additional eQTL summary statistics from tubulointerstitial36 (N\u2009=\u2009311), kidney glomerular36 (N\u2009=\u2009240), and islet37 (N\u2009=\u2009420) tissues (Supplementary Data\u00a02).\n\nOur study used variants from the UKBB project conducted by Neale\u2019s lab, which initially includes 13 million SNPs. We selected ~9.3 million SNPs with a minor allele frequency >0.01 for our analysis. We also identified the top 9620 unrelated individuals from ~500,000 individuals in the UKBB (Field ID: 22828), consisting of 5205 females and 4475 males. Data from these 9.3 million SNPs were extracted for these individuals to construct our LD reference panel.\n\nWe restricted the studied regions to those within 1MB of the genome-wide significant loci for these traits. These loci were identified using the clumping and thresholding (C\u2009+\u2009T) method in PLINK60: --clump-kb 1000, --clump-p1 5E-8, --clump-p2 5E-8, and --clump-r2 0.01.\n\nWe recommend using C\u2009+\u2009T to filter out variants in high LD, which prevents the inclusion of numerous highly correlated or redundant variants in the analysis, which can unnecessarily complicate the model and result in multiple credible sets consisting of these variants. We evaluated the minimum P-value of each variant across gene-tissue pairs in eQTL/sQTL data. In PLINK, we applied the C\u2009+\u2009T with the following parameters: --clump-kb 1000, --clump-p1 1E-5, --clump-p2 1E-5, and --clump-r2 0.5. Given that the true causal variant for a trait might not be included in the eQTL/sQTL variants, we combined these variants from outcome GWAS satisfying P\u2009<\u20095E-8 and r2\u2009<\u20090.5.\n\nNote that this preprocessing step removes SNPs in moderate to high LD by clumping (r2\u2009<\u20090.5), as the goal of the first stage is to build an accurate prediction of exposures. Our analyses show that using variant sets clumped at different LD thresholds yields comparable prediction accuracy, and also suggest that both LD clumping and biological infinitesimal effects contribute to the detection of infinitesimal effects (Supplementary Materials).\n\nWe used the minimum P-value from S-Predixcan and a modifier accounting for direct causal variants (Supplementary Materials) to exclude eGenes/sGene with P\u2009>\u20090.05. These weak filters will eliminate redundant gene-tissue pairs, thereby reducing the model\u2019s dimensionality. Since our goal is fine-mapping the causal gene-tissue pairs and identifying direct causal variants on the GWAS loci with significant signals, it will not induce a winner\u2019s curse.\n\nWe compared the causal gene-tissue pairs identified by TGVIS and TGFM with the significant gene-tissue pairs identified by S-PrediXcan. We considered genes with P\u2009<\u20090.05/20,000 as significant gene-tissue pairs in tissue specific S-PrediXcan analysis. We did not adjust for number of tissues. We then searched the gene-tissue pairs identified by TGVIS or TGFM but missed by S-PrediXcan.\n\nWe combined the direct causal variants and xQTLs of causal gene-tissue pairs identified by TGVIS or TGFM across all 45 traits into two separate files and uploaded them to the FAVOR online platform38 to obtain annotation scores for these variants. We performed Wilcoxon signed-rank test with both \u201cless\u201d and \u201cgreater\u201d as alternative hypotheses for determining the direction of shift location and calculated corresponding P values. We used the R package FDREstimation to convert the P values to FDR Q-values using the Benjamini\u2013Yekutieli (BY) procedure. Annotations with Q-values <\u00a00.05 were considered to have significantly different scores.\n\nFor TGVIS, a 95% credible set often includes multiple gene-tissue pairs. In such cases, we calculated the proportion of each tissue appearing among these pairs, allowing the number of tissues in a causal credible set of gene-tissue pairs to be fractional. For TGFM, we first removed the gene-tissue pairs with PIPs < 0.5, and then applied the same procedure to map the dominant tissues.\n\nWe applied the following strategy to map silver genes. First, we checked each credible set to see if any genes are part of the silver gene list; if so, we counted 1. If no silver gene was present, we then checked if any genes in the credible set were among the nearby genes; if so, we also counted 1. In other words, each credible set of gene-tissue pairs was counted only once. For TGFM, we first removed the gene-tissue pairs with PIPs <0.5, and then applied the same procedure as TGVIS to count the silver and nearby genes. Similar to the mapping procedure for silver genes, we examined each credible set identified as causal to see if it contained any druggable genes. If a druggable gene is present, we count it once.\n\nWe used the following statistics to compare the enrichments of TGVIS and TGFM. For example, regarding TGVIS and a given trait, we consider three metrics: the number of causal genes identified by TGVIS, the overlap between genes identified by TGVIS and those in the drug-target list, and the ratio of these two metrics (referred to as Ratio hereafter). To compare whether TGVIS or TGFM had a higher enrichment across traits, we performed a paired t-test using two vectors of Ratio.\n\nWe use colocalization to evaluate the causal credible sets identified by TGFM and TGVIS. Within each region, we select variants from the outcome GWAS with P-values less than 5E-5 and \\({r}^{2}\\)\u2009<\u20090.81 for colocalization analysis. We perform fine-mapping on both the outcome and the gene-tissue pairs within credible sets using SuSiE, then calculate the posterior probability for hypothesis \\({H}_{4}\\), i.e., both traits are associated and share the same single causal variant, between each outcome and exposure pair using Coloc-SuSiE. We use a posterior probability of \\({H}_{4}\\) >\u00a00.5 as the threshold to determine colocalization evidence between gene-tissue pairs and the outcome; notably, as long as at least one variant meets this criterion, it suffices.\n\nWe performed both univariable and multivariable MR using pQTLs of protein abundance as IVs to evaluate the identified causal tissue-gene pairs. Because to the lack of tissue-specific protein data, we focused on a subset of pGenes identified in blood tissues provided by Sun et al. 11. We selected independent, genome-wide significant pQTLs for each protein as IVs. The selection method for independent IVs was C\u2009+\u2009T (--clump-kb 1000 --clump-p1 5e-8 --clump-p2 5e-8 --clump-r2 0.01 using PLINK), with LD reference panels consisting of the 9680 individuals and 9.3\u2009M variants from UKBB. We applied five univariable MR methods: MRMedian61, IMRP62, MRCML63, MRCUE64, and MRBEE65. Both MRCUE and MRBEE account for sample overlap, with sample overlap correlations estimated using insignificant variants. We used the R package FDREstimation to convert the P-values obtained by these methods to FDR Q-values, using \u201cBY\u201d as the adjustment method. A pGene was considered significant if it was identified as such by at least four methods. We also conducted an analysis comparing the enrichments of TGVIS and TGFM, where the three corresponding metrics are: the overlap between causal genes identified by TGVIS or TGFM and the pGenes reported in Sun et al. 11, the number of significant pGenes identified in univariable MR analysis, and the ratio of these two metrics.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The GWAS summary data, eQTL summary data, and pQTL summary data used in this study can be downloaded from the \u201cData available\u201d section of the literature listed in Supplementary Data\u00a01\u20132. The GTEx summary data can be obtained from https://gtexportal.org/home/downloads/adult-gtex/qtl. The GWAS data in the Million Veteran Program (MVP) are available through database of genotypes and phenotypes (dbGAP) under accession number phs001672.v7.p1. The individual-level data from the UKBB used for estimating the LD matrix was accessed through Application ID: 81097.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code used to perform the analyses and generate results in this study is available in the Supplementary Material. The TGVIS R package can be downloaded from https://github.com/harryyiheyang/TGVIS/66. The interactive Shiny web application was developed to facilitate exploration and visualization of TGVIS results but with the latest GTEx v10 data at https://ovlwff-yihe-yang.shinyapps.io/tgvis_shiny/67.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Graham, S. E. et al. 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Zenodo https://doi.org/10.5281/ZENODO.15620332 (2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by grant HG011052 and HG011052-03S1 (to X.Z.) from the National Human Genome Research Institute (NHGRI), and HL086694 from National Institute of Heart, Lung, and Blood, USA", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA\n\nYihe Yang,\u00a0Noah Lorincz-Comi\u00a0&\u00a0Xiaofeng Zhu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY.Y. and X.Z. conceived and designed the study. Y.Y. performed all analysis. Y.Y. and X.Z. drafted the manuscript. N.L. edited the manuscript. X.Z. supervised this project.\n\nCorrespondence to\n Xiaofeng Zhu.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.\n\nThe study was approved by the institutional review board (IRB number: STUDY20180592) at Case Western Reserve University.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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Uncovering causal gene-tissue pairs and variants through a multivariate TWAS controlling for infinitesimal effects.\n Nat Commun 16, 6098 (2025). https://doi.org/10.1038/s41467-025-61423-8\n\nDownload citation\n\nReceived: 02 December 2024\n\nAccepted: 23 June 2025\n\nPublished: 02 July 2025\n\nVersion of record: 02 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61423-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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VIP Neurons", + "journal": "Nature Communications", + "published": "01 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60679-4/MediaObjects/41467_2025_60679_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60679-4/MediaObjects/41467_2025_60679_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60679-4/MediaObjects/41467_2025_60679_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [ + "https://zenodo.org/records/11179846", + "https://zenodo.org/records/11179853" + ], + "subject": [ + "Cellular neuroscience", + "Neural circuits" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4320313/v1.pdf?c=1751457346000", + "research_square_link": "https://www.researchsquare.com//article/rs-4320313/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60679-4.pdf", + "preprint_posted": "08 May, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Gain modulation allows neurons to dynamically adjust their responsiveness to sensory inputs without changing selectivity. While this process is well-characterized in sensory areas, its role in higher-order brain regions, like those governing spatial navigation and memory, is unclear. Here, we used all-optical methods in mice performing a spatial task to demonstrate that vasoactive-intestinal peptide (VIP)-expressing neurons selectively control the gain of place fields in the retrosplenial cortex (RSC) through disinhibition. Optogenetic manipulation revealed that this disinhibition selectively amplifies in-field activity, improving spatial coding accuracy. In contrast, VIP neurons in the hippocampus have minimal impact on place field gain. Notably, simulations indicate that the benefit of gain modulation for RSC place cells is exceptionally large compared to hippocampal place cells due to their much higher out-of-field activity and, therefore, lower signal-to-noise ratio. These findings reveal an area-specific specialization of VIP-mediated gain control, enhancing spatial coding and, potentially, the formation of new spatial memories.Biological sciences/Neuroscience/Neural circuitsBiological sciences/Neuroscience/Cellular neuroscienceGain modulationretrosplenial cortexhippocampusinhibitory interneuronsVIP neuronsplace cellsdisinhibitiontwo-photon microscopyoptogeneticsArchTChrimsonRGCaMP6.", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-4320313/v1/fd338fc01cc6e052dce898f0.png", + "https://assets-eu.researchsquare.com/files/rs-4320313/v1/911289fb865df43a60fad442.png", + "https://assets-eu.researchsquare.com/files/rs-4320313/v1/a3dd381d065b78ec59ea2ccf.png", + "https://assets-eu.researchsquare.com/files/rs-4320313/v1/a03df1dd05b872604a0ee112.png", + "https://assets-eu.researchsquare.com/files/rs-4320313/v1/5821b9d2d3b203e459819ff1.png" + ] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "FigureS1.pdfSupp. Figure S1. In vitro validation of ArchT effect on VIP cells in RSC L2/3 and CA1. A-I) Effect of ArchT stimulation of VIP cells in RSC. Opsin labelled VIP cells were patch-clamped in RSC brain slices and optogenetic stimulation was performed using a 638 nm diode laser (0.15-3.3 mW/mm2, 12 Cells, 3 animals). A) Schematic of the experiment. B) Coronal section (40 \u03bcm thick) showing ArchT-TdTomato labeling in RSC VIP cells. C) Example VIP cell firing with the fluorescent image of the cell (ArchT-TdTomato, overlaid with the oblique illumination image). D) Example of light stimulation on the membrane potential of VIP cells. Red trace shows the optical stimulation pattern (2 mW/mm2 for 15 s). E) Mean change in the membrane potential during ArchT stimulation. Paired t-test; membrane potential Before- vs. During stimulation p = 0.0013, During vs. After stimulation p = 0.00004, Before vs After stimulation p = 0.24. F) The hyperpolarizing effect was stable over 15 seconds. Membrane potential was sampled for 1 second at the beginning (#1), the middle (#2), and the end (#3) during the long stimulation (see example in panel D). Paired t-test; membrane potential First vs. Middle p = 0.07, Middle vs. Last p = 0.52, First vs. Last p = 0.17. G) ArchT stimulation decreases the firing frequency in VIP cells. Firing is induced by injection of a current pulse. H) Mean change in action potential (AP) frequency of VIP cells during opto stimulation. Paired t-test; firing frequency Before vs. During p = 0.015, During vs. After p = 0.014, Before vs. After p = 0.48. I) The effect on the firing frequency was stable over 15 seconds. Paired t-test; firing frequency during the stimulation First vs. Middle p = 0.6, Middle vs. Last p = 0.56, First vs. Last p = 0.45. J-M) CA1 data: same as on panel E-F and H-I (8 Cells, 2 animals). Statistics for panel: K) paired t-test; membrane potential Before vs. During p = 0.0012, During vs. After p = 0.0011, Before vs. After p = 0.0081. L) paired t-test; membrane potential during the stimulation First vs. Middle p = 0.00014, Middle vs. Last p = 0.000042, First vs. Last p = 0.32. M) paired t-test, firing frequency Before vs. During p = 0.0012, During vs. After p = 0.0011, Before vs. After p = 0.0081. N) paired t-test; firing frequency during the stimulation First vs. Middle p = 0.00993, Middle vs Last p = 0.7, First vs Last p = 0.0033. For statistical analysis on panels E, F, H, I, and J-M, data with different light intensities were pooled together.FigureS2.pdfSupp. Figure S2. In vitro validation of ChrimsonR effect on RSC L2/3 and CA1 VIP cells. A-F) Effect of ChrimsonR stimulation in RSC VIP cells. Opsin-labeled VIP cells were patch-clamped in vitro brain slices; optogenetic stimulation was carried out with a 625 nm LED (4-22 mW/mm2, 15 cells, 3 animals). A) Schematic of the experiment. B) Coronal section (40 \u03bcm thick) showing ChrimsonR-TdTomato labeling in RSC VIP cells. C) Example VIP cell firing with the fluorescent image of the patched cell (ChrimsonRTdTomato). D) Example of the ChrimsonR effect on VIP cell firing in the RSC. The top row, red, shows the optical stimulation pattern (red, 20 Hz sinusoid stimulation for 9 s). Bottom: the VIP cell's membrane potential during stimulation (black). The cell fires at least one action potential during each sinus cycle. Doublets are marked with red stars. E) The recording on panel D) is enlarged at three time points: at the beginning, middle, and end of Opto-stimulation. Note that at least one action potential was evoked by each stimulation, even at the end of the stimulus. F) Mean change in the number of action potentials (AP) during a sinus cycle in VIP cells during opto stimulation. The mean AP number per cycle was calculated by taking 1 second at the beginning, the middle, and the end of the 9-second opto stimulation. (Paired t-test: firing frequency during the stimulation 9 mW/mm2 First vs. Middle p = 0.18, Middle vs. Last p = 0.7, First vs. Last p = 0.29; firing frequency during the stimulation 18-22 mW/mm2 First vs. Middle p = 0.1, Middle vs. Last p = 0.89, First vs. Last p = 0.11). G-I) Same experiments were done in area CA1 (9 cells, 3 animals). Paired t-test for panel I: firing frequency during the stimulation 9 mW/mm2; First vs. Middle p = 0.25, Middle vs. Last p = 0.21, First vs. Last p = 0.07; firing frequency during the stimulation 18 mW/mm2 First vs. Middle p = 0.01, Middle vs. Last p = 0.09, First vs. Last p = 0.007.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Gain modulation allows neurons to dynamically adjust their responsiveness to inputs without changing selectivity. While well-characterized in sensory areas, its role in higher-order brain regions governing spatial navigation and memory is unclear. Here, we used all-optical methods in mice performing a spatial task to demonstrate that vasoactive-intestinal peptide (VIP)-expressing neurons selectively control the gain of place cells and other cell types in the retrosplenial cortex (RSC) through disinhibition. Optogenetic manipulation revealed that this disinhibition, while broadly affecting network activity, selectively amplifies in-field place cell activity, improving spatial coding accuracy. In contrast, VIP neurons in the hippocampus have minimal impact on place field gain. Notably, simulations indicate that the benefit of gain modulation for RSC place cells is large compared to hippocampal place cells due to their much higher out-of-field activity and, therefore, lower signal-to-noise ratio. Here, we show an area-specific VIP-mediated gain control, enhancing spatial coding and, potentially, memory formation.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "A neuron\u2019s input-output relationship reveals a fundamental aspect of neural processing. The slope of this relationship, known as gain, reflects the neuron\u2019s sensitivity to synaptic input changes1,2,3. Gain modulation allows neurons to dynamically adjust their responsiveness while maintaining their receptive field selectivity1,3,4. This ubiquitous mechanism is essential in various sensory processes, such as attentional modulation5,6, contrast-invariance of orientation tuning in the visual cortex7,8, and adjusting the dynamic range of auditory responses to different sound levels9. Gain modulation is therefore hypothesized to fine-tune signal amplification, enhancing the saliency of important information3. While studied in sensory regions, particularly the visual cortex, its impact on non-sensory information processing is less understood.\n\nAmong the myriad of non-sensory tuning properties discovered in the brain, place cells stand out as a quintessential example10. Yet, the gain modulation mechanisms controlling these spatially tuned cells in vivo remain unexplored. After place cells were discovered in the hippocampus10, place-like cells were also reported in several neocortical areas11,12,13,14,15,16,17. Both hippocampal and neocortical place cells exhibit similar tuning properties along a linear path11,18. However, neocortical cells typically show lower spatial selectivity, attributed to higher out-of-field activity compared to hippocampal neurons18,19. This lower selectivity raises the question of whether mechanisms exist to regulate the signal-to-noise ratio of spatially tuned cells in the neocortex, potentially through gain modulation.\n\nHere, we propose that cortical inhibitory interneurons play a pivotal role in regulating the gain of place-tuned neurons1,3. Specifically, we tested the contribution of vasoactive intestinal peptide (VIP)-expressing inhibitory interneurons. This circuit motif, where VIP neurons predominantly inhibit other inhibitory neurons and thus excite principal cells through disinhibition20,21,22,23, is a promising mechanism for modulating place cells. VIP neurons are ubiquitous in cortical circuits21,24,25,26,27,28,29,30,31,32,33,34, and their functions align closely with conditions associated with place cell activation: they promote learning and plasticity35,36,37,38,39,40,41,42,43,44, contribute to the overrepresentation of rewards45 and objects46, and are excited by locomotion42,45,47,48,49,50,51,52,53,54, active sensing55,56,57, and attention58,59. However, whether VIP cells modulate place fields remains unexplored.\n\nIn this work, we investigated the influence of VIP neurons on spatially tuned cells in the retrosplenial cortex (RSC)11, as well as their impact on place cells in the hippocampus. Using head-fixed mice trained to find hidden rewards and optogenetics to achieve gain or loss of function, we found that VIP neurons in RSC amplify place field responses without altering place selectivity. In contrast, the same manipulations have little effect on CA1 pyramidal neurons in the hippocampus. Simulations reveal that selective place field amplification in RSC improves spatial coding because of its high out-of-field activity. However, because out-of-field activity is low in the hippocampus, place-field amplification would have little effect on spatial coding. These results highlight the adaptive nature of disinhibition, demonstrating its ability to tailor circuit function to specific demands.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "First, we determined if VIP neurons in RSC are engaged during spatial navigation. We trained head-fixed mice to navigate a circular treadmill with tactile cues to locate a hidden reward in darkness15 (Fig.\u00a01a, 4 mice, 18 sessions, 58 \u00b1 0.9 laps per session, mean\u2009\u00b1\u2009SEM). Well-trained mice slowed before the reward site, indicating they learned the rewarded location (Fig.\u00a01b). To measure the neuronal activity of VIP neurons using two-photon microscopy, we injected the Cre recombinase-dependent Ca2+ indicator GCaMP6s in the agranular RSC of VIP-Cre mice60,61. Given the anterior-posterior heterogeneity of RSC function62,63, we focused on the medial-anterior part of RSC where place-tuned cells were previously found11,15,64,65,66 (Fig.\u00a01c).\n\na Head-fixed mouse running laps in darkness on a wheel with tactile cues (2 sandpaper strips, 2 rows of hot glue spikes) searching for a hidden water reward. b Mean running speed (top) and lick rate (bottom) of well-trained mice (mean\u2009\u00b1\u2009SEM across 4 mice, 18 sessions). c Top: Cartoon of coronal brain section and location of the window implant. Bottom: Example window above RSC (window diameter: 2.5\u2009mm centered \u22122.2 posterior from bregma). Black square shows a typical field of view (FOV) next to the central sinus. d Two-photon fluorescence image showing a typical FOV of VIP cells expressing GCaMP6s in L2/3 of RSC (147\u2009\u00b5m below pia). VIP-Cre mice were injected with an AAV to deliver Cre-dependent GCaMP6s. e Example fluorescence traces (fractional change in fluorescence, DF/F) of 5 VIP cells during 8 laps. Bottom traces show the mouse\u2019s position on the track, speed, and lick rate. Gray-shaded areas indicate periods where velocity falls <10\u2009cm/s (horizontal blue line). f Distribution of Pearson correlation coefficients between VIP neuronal activity and running speed (837 VIP cells in total, 4 mice, 18 sessions). Cells were categorized as negatively modulated (<5th percentile of shuffled distribution, blue), positively modulated (>95th percentile, red), or non-modulated (gray). mean\u2009\u00b1\u2009SEM is indicated above the distribution. g Average activity profile of neurons classified in (f) (mean\u2009\u00b1\u2009SEM). h Percentage of VIP cells classified in (f). The pie chart shows average across sessions. i Top: Average running speed (mean\u2009\u00b1\u2009SEM, 4 mice/18 sessions) aligned to start/stop events, excluding reward zone transitions. Bottom: Activity of positively correlated VIP neurons from (f) aligned similarly. Note: Fewer cells contribute if start/stop events were absent in some sessions; neural activity lags speed due to slow Ca2+ indicator kinetics. j Activity of VIP neurons that were negatively correlated with locomotion aligned to running start and stop events (mean\u2009\u00b1\u2009SEM, 4 mice, 18 sessions). Source data is provided as a Source Data file.\n\nIn vivo imaging enabled to visualize VIP neurons in the upper layers 2/3 of agranular RSC (Fig.\u00a01d, depth: 108 \u00b1 8.9\u2009\u00b5m below the pia, 4 mice, 18 fields of view (FOV), 837 VIP cells, we always imaged one FOV per session). VIP neurons exhibited two distinct response profiles: the majority were positively correlated with running velocity (45.2 \u00b1 4.6 %, mean \u00b1 SEM across sessions, see Methods), while a smaller population showed negative velocity correlations (17.4 \u00b1 2.6 %, mean \u00b1 SEM across sessions) (Fig.\u00a01e\u2013h). These correlations remained relatively stable throughout a recording session (Supplementary Fig.\u00a0S1). To disentangle locomotion from reward-related activity, we analyzed neuronal activity relative to start/stop events, excluding those near the reward zone (see Methods). This confirmed that the observed modulation was indeed locomotion-related (Fig.\u00a01i, j). We also investigated whether VIP cells exhibited spatially selective firing, similar to place cells (Supplementary Fig.\u00a0S2a). However, spatially selective VIP cells (23.3\u2009\u00b1\u20094.9 %, mean\u2009\u00b1\u2009SEM across sessions) appeared to cluster in two populations that were preferentially active in either the first or second half of the track, corresponding to periods of acceleration and deceleration, respectively (Supplementary Fig.\u00a0S2b). Furthermore, this VIP population had much broader place fields compared to principal neurons in the RSC11,15,64 (Supplementary Fig.\u00a0S2c), and most of them (16.9\u2009\u00b1\u20093.6 % of the total VIP population, mean\u2009\u00b1\u2009SEM across sessions) were also significantly correlated with running speed (Supplementary Fig.\u00a0S2d). This suggests spatially selective VIP cells encode locomotion variables (speed/acceleration), not location. In summary, our findings demonstrate that most VIP interneurons in the agranular RSC are primarily modulated by locomotion, with distinct subpopulations exhibiting positive and negative correlations.\n\nMeasuring excitatory neurons (using Thy1-GCaMP6s mice67) in layer 2/3 revealed that 20.5% were place cells tiling the track during navigation (Fig.\u00a02a\u2013d, 665 out of 3244 cells measured in total, 5 mice, 12 sessions). The prevalence of place-tuned neurons prompted us to investigate the role of VIP neurons in shaping this spatial representation.\n\na Example two-photon FOV showing GCaMP6s-expressing principal cells in L2/3 RSC (143\u2009\u00b5m depth, Thy1-GCaMP6 GP4.3 mouse line). b Example DF/F traces from 5 place cells across laps, shown with mouse position, speed, and lick rate. c Examples of 8 place-tuned neurons during a session. d Session-averaged place cell tuning curves, sorted by peak response location (5 mice, 12 sessions). e Left: Schematic of simultaneous two-photon imaging of principal neurons and optogenetic inhibition of VIP cells using Cre-dependent ArchT (AAV injection, 5 mice). Bottom left: FOV showing ArchT-expressing VIP cells (120\u2009\u00b5m depth). Middle: Example cell responses during trials with (red arrow) and without random red light stimulation (25% random trials, 638\u2009nm, 3\u2009mW/mm\u00b2, 10\u2013157\u2009cm on track). Right: Trials sorted into \u201copto-off\u201d, \u201copto-on\u201d, and \u201cafter-opto\u201d trials (trials following an opto-on trial). f Average tuning curves for example place cells (top: cell from e) during opto-off, opto-on, and after-opto trials. g Peak place field DF/F amplitude: opto-off vs. opto-on trials (dots: cells; red line: linear regression, R\u00b2\u2009=\u20090.51). Pie chart: percentage of significantly modulated place cells. h Left: VIP inhibition significantly reduced peak place field DF/F (mean\u2009\u00b1\u2009SEM across sessions; dots: session medians, n.s.: not significant). Right: Normalized percentage change in peak DF/F. (Data for g, h: 985 cells, 15 sessions, 5 mice; two-sided Wilcoxon signed-rank test: p\u2009=\u20090.00012 off/on, p\u2009=\u20090.00043 on/after, p\u2009=\u20090.104 off/after; *: p\u2009\u2264\u20090.05). i\u2013l Same as (e-h) but using VIP-Cre mice expressing the excitatory opsin ChrimsonR, activated with 20\u2009Hz sinusoidal 638\u2009nm light (peak 10\u2009mW/mm\u00b2). (Data: 394 cells, 11 sessions, 6 mice). k Regression R\u00b2\u2009=\u20090.68. l Left: VIP activation significantly increased peak place field DF/F (mean\u2009\u00b1\u2009SEM across sessions; dots: session medians). Two-sided Wilcoxon signed-rank test: p\u2009=\u20090.0039 off/on, p\u2009=\u20090.00098 on/after, p\u2009=\u20090.0068 off/after; *: p\u2009\u2264\u20090.05. Source data is provided as a Source Data file.\n\nWe tested the role of VIP neurons using optogenetics. We crossed Thy1-GCaMP6s mice with VIP-Cre61 mice and injected adeno-associated virus (AAV) in the RSC to deliver the Cre-dependent inhibitory opsin ArchT68 or the excitatory opsin ChrimsonR69, both red-shifted opsins (Supplementary Fig.\u00a0S3). Patch clamp recordings confirmed robust, constant VIP cells modulation by light under our stimulation parameters (trial duration in vivo: 6.2\u2009\u00b1\u20090.25\u2009s, mean\u2009\u00b1\u2009SEM across in vivo sessions, 21 mice, 49 sessions) (Supplementary Fig.\u00a0S4a\u2013i and Supplementary Fig.\u00a0S5a\u2013f, 12 cells expressing ArchT, 15 cells expressing ChrimsonR). To quantify the effects of optogenetic activation in vivo, we co-expressed ChrimsonR and GCaMP6s in VIP neurons and measured their responses to 10-second light stimuli at two intensities (3 and 10\u2009mW/mm\u00b2, Supplementary Fig.\u00a0S6a\u2013c). Increasing the light intensity from 3 to 10\u2009mW/mm\u00b2 resulted in an increase in both the response amplitude (median and interquartile range: 0.27 (0.084 \u2212 0.71) vs. 0.39 (0.07 \u2212 1.3) DF/F; 147 VIP cells, 1 mouse, 7 sessions) and the fraction of activated VIP neurons (56% versus 69%), indicating that 3-10\u2009mW/mm\u00b2 is sufficient to achieve robust activation.\n\nNext, we modulated VIP neuronal activity in vivo while mice performed the task. We recorded excitatory neurons and inhibited ArchT-expressing VIP neurons in randomly chosen 25 or 33 % of the trials for the entire length of a trial (Fig.\u00a02e, f). When we organized the \u201copto-off\u201d and \u201copto-on\u201d trials, we discovered that the place field amplitude was reduced in the opto-on condition. To verify that the effect was temporally precise and reversible, we also investigated place cell responses in the first trial following opto-on trials (\u201cafter-opto\u201d). These data showed that place cell responses were rapidly restored within seconds after the red light was turned off, indicating that this modulation does not cause long-lasting effects (Fig.\u00a02e, f). Altogether, the place field amplitude of 47 % of cells was significantly reduced in opto-on trials (Fig.\u00a02g, 5 animals, 15 sessions, 985 place cells in total, power density 3.0\u2009\u00b1\u20090.5\u2009mW/mm2), while it was significantly enhanced in only 4.1 % of place cells (Fig.\u00a02g). The peak DF/F amplitudes of the place fields decreased on average by 28.12\u2009\u00b1\u20095.33 % (Fig.\u00a02h, mean\u2009\u00b1\u2009SEM, opto-off: 0.23\u2009\u00b1\u20090.02 across sessions; opto-on: 0.16\u2009\u00b1\u20090.01; after-opto: 0.22\u2009\u00b1\u20090.02; Wilcoxon signed-rank test, p\u2009=\u20090.00012 for opto-off vs. opto-on, p\u2009=\u20090.00043 for opto-on vs. after-opto, p\u2009=\u20090.104 for opto-off vs. after-opto). Importantly, light stimulation in opsin-free Thy1-GCaMP6s mice did not affect place cell amplitude (9 animals, 12 sessions, 581 place cells in total; opto-off DF/F amplitude: 0.21\u2009\u00b1\u20090.02; opto-on amplitude: 0.20\u2009\u00b1\u20090.02; after-opto amplitude: 0.20\u2009\u00b1\u20090.02, Wilcoxon signed-rank test, p\u2009=\u20090.21 for opto-off vs. opto-on, p\u2009=\u20090.85 for opto-on vs. after-opto, p\u2009=\u20090.25 for opto-off vs. after-opto). In summary, inhibiting VIP cells reduces the amplitude of place cell responses in RSC, compatible with a disinhibition mechanism.\n\nConversely, stimulating ChrimsonR-expressing VIP cells selectively enhances place cell activity in the RSC (Fig.\u00a02i, j). The peak amplitude increased significantly in 19.1% of place cells, while only 3.6% showed a decrease (Fig.\u00a02k, 6 mice, 11 sessions, 394 place cells in total, power density 3.5\u2009\u00b1\u20091.1\u2009mW/mm2). The place field amplitude increased on average by 30.01\u2009\u00b1\u20093.03 %, and this effect rapidly reversed in the first trial after light stimulation (Fig.\u00a02l, left panel, opto-off DF/F amplitude: 0.19\u2009\u00b1\u20090.01; opto-on amplitude: 0.24\u2009\u00b1\u20090.012; after-opto amplitude: 0.16\u2009\u00b1\u20090.01; Wilcoxon signed-rank test, p\u2009=\u20090.0039 for opto-off vs. opto-on, p\u2009=\u20090.00098 for opto-on vs. after-opto, p\u2009=\u20090.0068 for opto-off vs. after-opto). Light stimulation in opsin-free Thy1-GCaMP6s mice did not affect place cell amplitude (10 animals, 11 sessions, 618 place cells in total; opto-off DF/F amplitude: 0.21\u2009\u00b1\u20090.01; opto-on amplitude: 0.22\u2009\u00b1\u20090.02; after-opto amplitude: 0.20\u2009\u00b1\u20090.01; Wilcoxon signed-rank test, p\u2009=\u20090.207 for opto-off vs. opto-on, p\u2009=\u20090.031 for opto-on vs. after-opto, p\u2009=\u20090.094 for opto-off vs. after-opto).\n\nTo determine if the effects of VIP modulation vary along the track, we analyzed how optogenetic manipulation impacted principal neuron activity at different locations. Silencing VIP interneurons resulted in consistent changes in activity across the entire track (Supplementary Fig.\u00a0S7a, c). However, activating VIP neurons produced a more pronounced effect in the first half of the track compared to the second (Supplementary Fig.\u00a0S7b, c). This modulation influenced both the strength and reliability of principal neuron responses (Supplementary Fig.\u00a0S8a, b). We also examined whether the magnitude of these effects varied with the cortical depth of the recorded neurons, but found no significant relationship (Supplementary Fig.\u00a0S9a, b).\n\nTo further explore the specificity of VIP modulation, we categorized principal neurons based on their functional properties: place cells, pre-reward cells (active near the reward location), tactile cue cells, speed cells, and uncategorized cells (see Methods). Our analysis revealed that VIP modulation impacted most of these cell types similar to place cells, but with some exceptions (Supplementary Fig.\u00a0S10a\u2013d, i\u2013k), indicating a broad influence on RSC circuit activity that extends beyond place cells. Overall, these results highlight the enhancement of place cell activity in the RSC by VIP neurons, a disinhibitory effect that also extends to other principal cell types, supporting their role in spatial navigation.\n\nTo elucidate the mathematical nature of VIP neuron modulation on place cells, we investigated whether disinhibition acted through an additive or multiplicative mechanism. An additive effect would increase overall firing rates (both in-field and out-of-field), resembling a leftward offset in the input-output (I-O) curve. Conversely, a multiplicative effect would selectively amplify place field responses, akin to an increase in the I-O curve\u2019s slope (gain).\n\nWe employed a linear transformation analysis on place cell responses70 during control conditions and optogenetic manipulations (Fig.\u00a03a). This shows that inhibiting VIP neurons with ArchT resulted in a decrease in the gain of the linear transformation without a significant offset, suggesting a gain modulation mechanism (Fig.\u00a03b, left). Conversely, ChrimsonR activation increased the gain without an offset (Fig.\u00a03b, right). Consistently across sessions, inhibiting VIP neurons led to a decrease in the average gain of all place cells (Fig.\u00a03c, upper panel, 5 mice, 15 sessions, 1167place cells), an increased gain when enhancing VIP cells (Fig.\u00a03f, upper panel, 6 mice, 11 sessions, 467 place cells), and little effect in mice without opsin (Fig.\u00a03c, lower panel, 9 mice, 12 sessions, 653 place cells; and Fig.\u00a03f, lower panel, 10 mice, 11 sessions, 690 place cells).\n\na Schematic illustrating arithmetic transformations of place cell tuning curves. Left: Example tuning curve showing in-field (gray) and out-of-field firing. Middle: Linear transformation plotting DF/FOPTO-ON vs DF/FOPTO-OFF per spatial bin, quantifying changes in gain (m) and offset (b) between modulated and control. Right Panels: Examples of pure gain (multiplicative/divisive) and pure offset (additive/subtractive) modulation. b Examples of linearly transformed tuning curves when inhibiting (ArchT) or enhancing (ChrimsonR) VIP neuron activity. Each plot shows 80 data points of a single place cell (80 spatial bins of ~2\u2009cm representing the entire track). Insets show place-tuning curves (blue: control, red: modulated). The linear fit (y = m*x + b) is indicated (red). c Linear fits are based on the average gain (m) and offset (b) of all place cells in a session (15 sessions, 5 ArchT-expressing mice) or opsin-free control mice (12 sessions, 9 mice). d Distributions of gain (left) and offset (right) from linear transforms for all place cells in ArchT mice (1167 cells) versus opsin-free controls (653 cells). Gain significantly decreased during VIP inhibition, while the offset did not change (Kolmogorov-Smirnov test; gain: p\u2009=\u20091.24 \u00d7 10-116, offset: p\u2009=\u20090.43.) Insets: 90% confidence intervals were calculated from the control (No-opsin) distributions. Pie charts show fraction of cells with significantly different gain (20.8 % decreased, 2.8 % increased) or offset (6.6 % decreased, 5.7 % increased) compared to control distribution. e Contribution of gain and offset changes to modulation of place field peak DF/F for significantly modulated cells (from (d), 548 cells). Box plots show median (line), 25th\u201375th percentiles (box), and min/max range (whiskers). f\u2013h Same as c\u2013e but when enhancing VIP cells with ChrimsonR (467 cells, 11 sessions, 6 mice; opsin-free control mice: 690 cells, 11 sessions, 10 mice, Kolmogorov-Smirnov test; gain: p\u2009=\u20092.5 \u00d7 10-6, offset: p\u2009=\u20091.61 \u00d7 10-5). Pie chart gain: 4.7 % decreased, 25.3 % increased. Pie chart offset: 8.8 % decreased, 20.1 % increased. Box plots (89 cells) show median (line), 25th\u201375th percentiles (box), and min/max range (whiskers). Source data is provided as a Source Data file.\n\nTo quantify changes in gain and offset, we fitted a linear model to place field responses (Fig.\u00a03d, g). Compared to controls lacking opsin expression, inhibiting VIP neurons (ArchT) decreased the gain without significantly altering the offset (Kolmogorov-Smirnov test; gain: p\u2009=\u20091.24 \u00d7 10-116, offset: p\u2009=\u20090.43). This strongly indicates that VIP inhibition primarily modulates place cell response gain. Conversely, activating VIP neurons (ChrimsonR) increased the gain and, to a lesser degree, also the offset (Kolmogorov-Smirnov test; gain: p\u2009=\u20092.5 \u00d7 10-6, offset: p\u2009=\u20091.61 \u00d7 10-5). Further analysis showed that ArchT-mediated changes in place field amplitude were almost entirely due to gain modulation (Fig.\u00a03e). ChrimsonR also predominantly induced amplitude changes through gain modulation, with a smaller offset contribution (Fig.\u00a03h).\n\nThe canonical model of neocortical circuits proposes that VIP interneurons mediate disinhibition primarily by regulating somatostatin-expressing (SST) inhibitory neurons28,56,71,72. To investigate this model in the RSC, we conducted experiments using SST-Cre x Thy1-GCaMP6s mice. We found that inhibiting SST neurons with ArchT increased the activity of place cells, specifically through a gain modulation mechanism (Supplementary Fig.\u00a0S11, 5 mice, 8 sessions, 511 place cells). While this result supports the canonical model, suggesting that VIP neurons in the RSC may regulate the gain of principal cells by suppressing SST interneurons, our evidence is indirect. Moreover, our findings do not exclude the possibility of alternative disinhibitory pathways in which VIP cells inhibit other types of inhibitory interneurons. Fully understanding these complex interactions would require a detailed connectome analysis of the RSC, but such a resource is not yet available.\n\nPrevious studies have highlighted the limited linear range of fluorescent GCaMP6 indicators, exhibiting supra-linear responses at low neuronal activity levels and saturation at high activity levels60,73. To ensure this did not confound our findings, we assessed the linearity of light-mediated fluorescence changes in place cells. Using a goodness-of-fit analysis (see Methods), we found that only 4.4% of the modulated place cells did not fit a linear model. This suggests that the observed fluorescence changes remained largely within the GCaMP6 indicator\u2019s linear range. In summary, our results demonstrate that inhibiting or enhancing VIP neuronal activity modulates place cell responses in the neocortex primarily through a gain modulation mechanism, analogous to a divisive and multiplicative mathematical operation, respectively.\n\nGiven our findings in the RSC, we next investigated whether VIP neurons exert similar effects on place cell activity in the hippocampus. We focused on area CA1, expressing GCaMP6s selectively in VIP neurons and recording their activity in well-trained mice (5 mice, 8 sessions, 164 VIP cells). Similar to the RSC, we observed both positively and negatively modulated VIP cells during locomotion (Supplementary Fig.\u00a0S12a, b). However, the proportion of these two cell types was more balanced in CA1, unlike in the RSC where positively modulated cells were more prevalent (Supplementary Fig.\u00a0S12c\u2013e; positively modulated: 26.8\u2009\u00b1\u20094.9 %; negatively modulated: 24\u2009\u00b1\u20094.7 %, mean\u2009\u00b1\u2009SEM across sessions).\n\nWe also examined whether CA1 VIP neurons exhibited spatial tuning (Supplementary Fig.\u00a0S13a). As in the RSC, VIP cells meeting our place field criteria (36\u2009\u00b1\u20097.9 %) tended to cluster into two populations, preferentially active in either the first or second half of the track, coinciding with periods of acceleration and deceleration, respectively (Supplementary Fig.\u00a0S13b). Moreover, these cells had much broader place fields than CA1 principal neurons (Supplementary Fig.\u00a0S13c), and most (20.0\u2009\u00b1\u20095.6 %) were also classified as speed-tuned (Supplementary Fig.\u00a0S13d). These findings suggest that, similar to their counterparts in the RSC, VIP neurons in CA1 may not primarily encode spatial location. Instead, their activity appears to be modulated by locomotion-related variables such as speed and/or acceleration.\n\nNext, we tested whether VIP cells have a similar effect on place cells in area CA1 as we observed in RSC. We used Thy1-GCaMP6s mice crossed with VIP-Cre mice and implanted a chronic imaging window over the dorsal hippocampus (area CA1, Fig.\u00a04a). Analysis of well-trained mice revealed that 17.4% of recorded neurons exhibited place fields tiling the track (Fig.\u00a04b\u2013d, 7 mice, 9 sessions, 6196 recorded neurons in total). Hippocampal place fields exhibited comparable widths to those in the RSC (median and interquartile range, CA1: 37.31\u2009cm (25.53\u201349.09\u2009cm); RSC: 37.31\u2009cm (27.49\u201349.09\u2009cm), Mann-Whitney test, p\u2009=\u20090.145). However, CA1 place fields showed a slightly greater amplitude (median and interquartile range, DF/F for CA1: 0.23 (0.15\u20130.45); RSC: 0.22 (0.16\u20130.33) Mann\u2013Whitney test, p\u2009=\u20090.032).\n\na Typical two-photon field of view (FOV) showing GCaMP6s-expressing principal cells in hippocampal CA1 stratum pyramidale (Thy1-GCaMP6s mouse). b Example fluorescence traces from 5 place-tuned cells over 5 laps. Bottom: Corresponding mouse position, speed, and lick rate. c Examples of 8 place-tuned neurons during a session for 25 trials. d Session-averaged activity of place-tuned neurons (1079 cells, 9 sessions, 7 mice), sorted by peak response location. e Left: Diagram of simultaneous CA1 principal cell imaging and optogenetic inhibition of VIP neurons expressing Cre-dependent ArchT (AAV injection, 7 mice). Bottom Left: Example image of ArchT-expressing VIP neurons (\u2009~\u2009100\u2009\u00b5m depth). Middle: Example place cell activity over 48 laps; red light (638\u2009nm, 6\u2009mW/mm\u00b2, track pos. 10-157\u2009cm) applied in random 33% of trials (red arrows). Right: Same cell\u2019s trials sorted by condition: opto-off, opto-on, after-opto. f Average responses of example place cells during opto-off, opto-on, and after-opto trials. Top left cell is shown in (e). g Scatter plot comparing place field peak DF/F during opto-off versus opto-on trials (each dot = 1 cell). Pie chart: percentage of significantly modulated place cells (1262 cells, 17 sessions, 7 mice). h Left: Effect of VIP inhibition (ArchT) on place field peak DF/F across sessions (mean\u2009\u00b1\u2009SEM; 17 sessions, 7 mice). Each point is the median peak amplitude per session. Statistics: Two-sided Wilcoxon signed-rank test (p\u2009=\u20090.38 off vs on; p\u2009=\u20090.72 on vs after; p\u2009=\u20090.72 off vs after; *: p\u2009\u2264\u20090.05, n.s.: not significant. Right: Relative change in peak DF/F: (DF/FON - DF/FOFF) / (DF/FOFF). i\u2013l Same analyses as (e\u2013h) for VIP activation using ChrimsonR (AAV delivery). Stimulation: 20\u2009Hz sinusoidal 638\u2009nm light (peak 10\u2009mW/mm\u00b2). Data from 1180 cells, 12 sessions, 6 mice. l Left: Summary effect (ChrimsonR) on peak DF/F across sessions (mean\u2009\u00b1\u2009SEM; 12 sessions, 6 mice), two-sided Wilcoxon signed-rank test, p\u2009=\u20090.002 off vs on, p\u2009=\u20090.00049 on vs after, p\u2009=\u20090.027 off vs after). *: p\u2009\u2264\u20090.05. Right: Relative change in peak DF/F: (DF/FON - DF/FOFF) / (DF/FOFF). Source data is provided as a Source Data file.\n\nPatch clamp recordings using hippocampal brain slices confirmed robust modulation of VIP cells by ArchT or ChrimsonR expression under our light stimulation parameters, and we verified that the modulation was constant throughout a trial (in vivo stimulation duration was typically 5.5\u2009\u00b1\u20090.36\u2009s across sessions (19 mice, 36 sessions), and in-vitro test duration was 15\u2009s for ArchT experiments, Supplementary Fig.\u00a0S4j\u2013n, 8 cells; and 9\u2009s for ChrimsonR experiments, Supplementary Fig.\u00a0S5g\u2013i, 9 cells). To quantify the effectiveness of optogenetic stimulation in CA1, we co-expressed ChrimsonR and GCaMP6s in VIP interneurons (Supplementary Fig.\u00a0S6d\u2013f) and measured their responses to 10-second light pulses at two intensities (3\u2009mW/mm\u00b2 and 10\u2009mW/mm\u00b2). Increasing the light intensity from 3 to 10\u2009mW/mm\u00b2 caused only a small increase in the response amplitude (median and interquartile range: 0.26 (0.085\u20130.49) vs. 0.31 (0.1\u20130.53) DF/F; Wilcoxon sign rank test, p\u2009=\u20096.28 \u00d7 10-4; 44 VIP cells, 2 mice, 5 sessions) and no change in the fraction of activated neurons (77% for both intensities, Wilcoxon sign rank test, p\u2009=\u20090.3). This indicates that a light intensity of 3-10\u2009mW/mm\u00b2 is sufficient to achieve near-saturating activation of CA1 VIP neurons.\n\nWe next investigated whether silencing VIP neurons in the hippocampus using ArchT in vivo (Supplementary Fig.\u00a0S14) would replicate the disinhibitory effects observed in the RSC (Fig.\u00a04e\u2013h, 7 mice, 17 sessions, 1262 place cells recorded in total, power density 6.4\u2009\u00b1\u20090.6\u2009mW/mm2). Notably, unlike the RSC, inhibiting CA1 VIP neurons did not produce a significant change in the average place field amplitude (Fig.\u00a04e, f). Furthermore, only a small proportion of place cells exhibited significant modulation of their responses (Fig.\u00a04g, 5.7 % decreased, 1.8 % increased). Overall, the average response of all place cells within a FOV remained largely unaffected (Fig.\u00a04h, left panel, opto-off DF/F amplitude: 0.25\u2009\u00b1\u20090.03; opto-on DF/F amplitude, 0.24\u2009\u00b1\u20090.03; after-opto DF/F amplitude, 0.25\u2009\u00b1\u20090.03; Wilcoxon signed-rank test, p\u2009=\u20090.38 for opto-off vs. opto-on, p\u2009=\u20090.72 for opto-on vs. after-opto, p\u2009=\u20090.72 for opto-off vs. after-opto). In contrast to the lack of suppression observed during VIP inhibition, artificially boosting the activity of VIP neurons with ChrimsonR (Supplementary Fig.\u00a0S15) produced a subtle but significant increase in the average place cell amplitude (Fig.\u00a04i-l, 6 mice, 12 sessions, 1180 place cells, power density 9.0\u2009\u00b1\u20090.5\u2009mW/mm2). While a limited proportion of cells exhibited significant individual changes (Fig.\u00a04k, right: 4.91% decreased, 6.55% increased), the overall population response within a field of view was significantly enhanced (Fig.\u00a04l, left panel, opto-off DF/F amplitude: 0.22\u2009\u00b1\u20090.01; opto-on DF/F amplitude, 0.25\u2009\u00b1\u20090.01; after-opto DF/F amplitude, 0.20\u2009\u00b1\u20090.01; Wilcoxon signed-rank test, p\u2009=\u20090.002 for opto-off vs. opto-on, p\u2009=\u20090.00049 for opto-on vs. after-opto, p\u2009=\u20090.00273 for opto-off vs. after-opto).\n\nAs in the RSC, we also examined whether the effects of VIP modulation in CA1 varied along the track. Silencing VIP interneurons in CA1 did not produce any significant changes in principal neuron responses across the entire track (Supplementary Fig.\u00a0S7d, f). Activating VIP neurons, however, resulted in a small effect that was slightly larger in the first half compared to the second half of the track (Supplementary Fig.\u00a0S7e, f). We also investigated whether the magnitude of these effects varied with the cortical depth of the recorded neurons but found no significant relationship (Supplementary Fig.\u00a0S9c, d).\n\nTo explore the potential specificity of VIP modulation, we checked the different functional cell categories in hippocampus similarly to RSC. Our analysis revealed that silencing VIP cells did not affect the activity of place cells, pre-reward cells, tactile cue cells, speed cells, or uncategorized cells. The small effect observed when activating VIP cells was significant only for place cells and uncategorized cells (Supplementary Fig.\u00a0S10e\u2013k). Altogether, in contrast to the suppressive effect observed in the RSC, inhibiting VIP neurons did not significantly alter hippocampal place field activity. However, artificially enhancing VIP neuron activity produced a modest but significant increase in place cell responses.\n\nTo understand why VIP modulation differentially affects hippocampal and RSC activity, we performed computational simulations examining the spatial distribution of synaptic inputs on pyramidal cell dendrites. In the hippocampus, VIP interneurons disinhibit CA1 pyramidal cells by primarily inhibiting SST-expressing OLM cells, which specifically target distal tuft dendrites74. While entorhinal cortical input to these tufts may be less critical for established place fields75,76, CA3 input to the basal and oblique dendrites appears crucial for place cell activity76,77, though this remains debated78,79. This anatomical segregation suggests that VIP modulation, by disinhibiting distal tufts, may not strongly influence CA3-driven place tuning. Indeed, simulations using a reconstructed CA1 neuron with realistic place-modulated excitation of basal and oblique dendrites, coupled with varying levels of tonic distal inhibition, showed negligible effects on place field amplitude (Fig.\u00a05a\u2013c). In contrast, RSC place cells receive place-tuned inputs to both layer 1 (targeting tuft dendrites) and layer 2/3 (targeting oblique dendrites)15. Assuming VIP neurons similarly disinhibit SST interneurons in RSC, which project to both layers, excitatory and VIP-modulated inhibitory inputs would converge onto the same dendritic compartments. Consistent with this, our simulations demonstrate that in such RSC-like scenario inhibition powerfully controls place field gain (Fig.\u00a05d\u2013f).\n\na Reconstructed morphology of a CA1 pyramidal neuron used for simulations. Colors indicate a hypothetical distribution of synaptic input during spatial navigation in familiar environments: red, place-modulated excitatory input from CA3 to basal, apical, and oblique dendrites; blue, tonic inhibitory input from OLM cells to stratum lacunosum-moleculare dendrites. b Example of simulated CA1 pyramidal cell membrane potential as the animal traverses the cell\u2019s place field. c Input-output relationship of place field response under three levels of OLM inhibition to tuft dendrites in CA1 (mean\u2009\u00b1\u2009SEM, 5 model instantiations with randomized synapse distributions and activation times). d Hypothetical \u2018RSC-like\u2019 distribution of excitatory and inhibitory synaptic input. e Place field firing rates during place field traversal under three levels of OLM input, assuming an RSC-like synaptic input distribution. f Input-output relationship of place field responses under three levels of OLM inhibition to tuft dendrites, assuming an RSC-like synaptic input distribution (mean\u2009\u00b1\u2009SEM, 5 model instantiations with randomized synapse distributions and activation times). Source data is provided as a Source Data file.\n\nAdditional factors may also contribute to the more pronounced gain modulation via VIP cells in the RSC. We found that the density of VIP neurons in the RSC is 3.8 times higher than in area CA1 (RSC: 2144\u2009\u00b1\u2009152 cells/mm\u00b3; CA1: 568\u2009\u00b1\u200932 cells/mm\u00b3; 4 VIP mice; p\u2009=\u20090.001; see Methods). A higher density of VIP neurons suggests a greater capacity for disinhibition within RSC circuits, as a higher density would lead to more synaptic contacts onto other inhibitory neurons and possibly enhance gain modulation of principal cells. Moreover, a larger fraction of VIP cells in the RSC were positively modulated during locomotion compared to CA1 (Fig.\u00a01h versus Supplementary Fig.\u00a0S12c). Taken together, the lower density of VIP cells in area CA1, coupled with a smaller proportion of these cells being engaged during locomotion, could also contribute to the weaker influence of VIP-mediated disinhibition in the hippocampus.\n\nWe next investigated how VIP function influences spatial information encoded by principal neurons in the RSC (Fig.\u00a06a\u2013f, 11 mice, 19 sessions) and area CA1 (Fig.\u00a06g\u2013l, 13 mice, 27 sessions). Employing Bayesian decoding of neuronal activity, we quantified the difference between the predicted and actual position of the mouse along the track (example in Fig.\u00a06b). In the RSC, inhibiting VIP neurons with ArchT significantly increased the decoding error (Fig.\u00a06c). The decoding error was lowest around the reward location and higher in the center of the track (Fig.\u00a06c, right panel). This data suggest that VIP function is critical for maintaining accurate spatial coding within the RSC (opto-off error: 15\u2009\u00b1\u20091.4\u2009cm; opto-on error, 20.5\u2009\u00b1\u20091.6\u2009cm; after-opto error 17.2\u2009\u00b1\u20091.4\u2009cm; Wilcoxon signed rank test, opto-on vs. opto-off p\u2009=\u20090.001, opto-on vs. after-opto = 0.0093, after-opto vs. opto-off p\u2009=\u20090.0186). To further investigate this, we also calculated the spatial information content using the Skaggs method, a well-established metric for quantifying the amount of spatial information encoded by individual neurons80,81 (Fig.\u00a06d). The spatial information content significantly decreased when inhibiting VIP cells (3675 cells, 11 sessions, Wilcoxon signed rank test, opto-on vs. opto-off p\u2009=\u20090.0337, opto-on vs. after-opto p\u2009=\u20090.5845, after-opto vs. opto-off p\u2009=\u20090.2061). Conversely, when we increased VIP activity in RSC with ChrimsonR, the spatial decoding error decreased (Fig.\u00a06e, opto-off error: 22.4\u2009\u00b1\u20091.9\u2009cm; opto-on error, 18.3\u2009\u00b1\u20092\u2009cm; after-opto error 22.7\u2009\u00b1\u20091.4\u2009cm, Wilcoxon signed rank test, opto-on vs. opto-off p\u2009=\u20090.0078, opto-on vs. after-opto = 0.0195, after-opto vs. opto-off p\u2009=\u20090.6406), and the spatial information content increased (Fig.\u00a06f, 2438 cells, 8 sessions, Wilcoxon signed rank test, opto-on vs. opto-off p\u2009=\u20090.0117, opto-on vs. after-opto p\u2009=\u20090.4219, after-opto vs. opto-off p\u2009=\u20090.6406). These data indicate that VIP neurons in the RSC enhance the accuracy of spatial information encoded by both neuronal populations and individual neurons.\n\na Window location for RSC recordings. b Example Bayesian decoding of position using all RSC cells in a FOV. c Left: Experimental design. Middle: Decoding error across sessions (mean\u2009\u00b1\u2009SEM; 5 mice, 11 sessions). Statistics: Two-sided Wilcoxon signed-rank test, opto-on vs off p\u2009=\u20090.001, on vs after p\u2009=\u20090.0093, after vs off p\u2009=\u20090.0186. Right: Decoding error vs track position (mean\u2009\u00b1\u2009SEM). d Spatial information in RSC during VIP inhibition (mean\u2009\u00b1\u2009SEM; 11 sessions). Points are session medians. Statistics: Two-sided Wilcoxon signed-rank test, opto-on vs off p\u2009=\u20090.0337, on vs after p\u2009=\u20090.5845, after vs off p\u2009=\u20090.2061; *: p\u2009\u2264\u20090.05, n.s.: not significant. e, f Same as (c, d) for VIP activation (6 mice, 7 sessions). Statistics: Two-sided Wilcoxon, *: p\u2009\u2264\u20090.05). e Decoding error: opto-on vs off p\u2009=\u20090.0078, on vs after p\u2009=\u20090.0195, after vs off p\u2009=\u20090.6406. f Spatial information: opto-on vs off p\u2009=\u20090.0117, on vs after p\u2009=\u20090.4219, after vs off p\u2009=\u20090.6406. g\u2013l Decoding error and spatial information for CA1 recordings during VIP modulation (mean\u2009\u00b1\u2009SEM). ArchT (7 mice, 15 sessions), decoding error: two-sided Wilcoxon: on vs off p\u2009=\u20090.8616, on vs after p\u2009=\u20090.1262, after vs off p\u2009=\u20090.0946. Spatial info: two-sided Wilcoxon: on vs off p\u2009=\u20090.1947, on vs after p\u2009=\u20090.6606, after vs off p\u2009=\u20090.0946. ChrimsonR (6 mice, 12 sessions): decoding error: two-sided Wilcoxon: on vs off p\u2009=\u20090.311, on vs after p\u2009=\u20090.6045, after vs off p\u2009=\u20090.2661. Spatial info: two-sided Wilcoxon, on vs off p\u2009=\u20090.0061, on vs after p\u2009=\u20090.0461, after vs off p\u2009=\u20090.0024; *: p\u2009\u2264\u20090.05. m Comparison of place cell in/out-field spike rates in RSC (299 cells) and CA1 (141 cells), using extracellular data from Alexander et al.18. n Simulations: Example rates of 5 simulated place cells. Ensembles of 30 cells (Poisson firing and empirical field widths tiling the track). Bottom: Position decoding error while varying in/out-field rates. o Simulations. Top: Simulated relationship between decoding error, in-field rate, and out-of-field rate. Points show experimental spike rates from RSC and CA1 (data from (m)). Bottom: Example RSC and CA1 tuning highlighting differences in out-of-field firing. Source data is provided as a Source Data file.\n\nIn contrast to RSC, optogenetic inhibition of VIP neurons in the hippocampus did not influence the decoding error (Fig.\u00a06g\u2013i, opto-off error: 19.6\u2009\u00b1\u20092.1\u2009cm; opto-on error, 19.1\u2009\u00b1\u20092\u2009cm; after-opto error 18.5\u2009\u00b1\u20092\u2009cm, Wilcoxon signed rank test, opto-on vs. opto-off p\u2009=\u20090.8616 opto-on vs. after-opto = 0.1262, after-opto vs. opto-off p\u2009=\u20090.0946) or the spatial information content (Fig.\u00a06j, 8909 cells, 15 sessions, Wilcoxon signed rank test, opto-on vs. opto-off p\u2009=\u20090.1947, opto-on vs. after-opto p\u2009=\u20090.6606, after-opto vs. opto-off p\u2009=\u20090.0946). Also, increasing VIP cell activity with ChrimsonR did not affect the decoding error (Fig.\u00a06k, opto-off error: 15.5\u2009\u00b1\u20092.8\u2009cm; opto-on error, 14.8\u2009\u00b1\u20092.5\u2009cm; after-opto error 14.9\u2009\u00b1\u20092.8\u2009cm, Wilcoxon signed rank test, opto-on vs. opto-off p\u2009=\u20090.311 opto-on vs. after-opto = 0.6045, after-opto vs. opto-off p\u2009=\u20090.2661) but increased the spatial information content (Fig.\u00a06l, 6299 cells, 11 sessions, Wilcoxon signed rank test, opto-on vs. opto-off p\u2009=\u20090.0061, opto-on vs. after-opto p\u2009=\u20090.0461, after-opto vs. opto-off p\u2009=\u20090.0024). Our data indicate that, under normal conditions, VIP neurons in CA1 contribute minimally to spatial representations. However, artificially enhancing VIP activity significantly increases the spatial information encoded by individual neurons, revealing a latent capacity for enhancing spatial coding within this cell population.\n\nThe spatial information encoded by a neural circuit depends on the peak firing rate of place cells within their place field compared to their background (out-of-field) firing rate. This signal-to-noise ratio (SNR) critically influences how much spatial information a place cell conveys. To investigate how VIP neurons might influence SNR, we analyzed in-field and out-of-field firing rates in both the RSC and hippocampus. While fluorescence-based calcium imaging doesn\u2019t provide absolute firing rates, we analyzed extracellular recording data from a recent study comparing RSC and hippocampal activity during behavior18 (Fig.\u00a06m). This analysis revealed that in-field firing rates are comparable between RSC and CA1 (RSC: 17.3\u2009\u00b1\u20090.95\u2009Hz, CA1: 14.3\u2009\u00b1\u20090.73\u2009Hz, mean\u2009\u00b1\u2009SEM, 229 RSC cells, and 141 CA1 cells). However, the RSC exhibits much higher out-of-field background firing rates (RSC 3.6\u2009\u00b1\u20090.27\u2009Hz, CA1, 0.0\u2009\u00b1\u20090.1 \u00d7 10-4\u2009Hz, mean\u2009\u00b1\u2009SEM).\n\nGiven the poorer signal-to-noise ratio (SNR) observed in RSC place cells, we hypothesized that these cells would benefit significantly from the VIP-mediated modulation. To test this computationally, we simulated neuronal activity to investigate how the spatial decoding error depends on in-field and out-of-field firing rates (Fig.\u00a06n, o, top panel, see Methods). Our simulations, using a population of 30 place cells with Poisson-distributed firing and realistic place field widths, revealed a key finding. The decoding error is insensitive to in-field firing rates at low out-of-field firing rates (Fig.\u00a06o, blue arrow). In contrast, for higher out-of-field firing rates, the decoded position error is more steeply dependent on the in-field firing rate (Fig.\u00a06o, red arrow). Mapping the experimentally measured firing rates from RSC and CA1 (Fig.\u00a06m) onto this model data (Fig.\u00a06o) highlights a crucial distinction: RSC spatial decoding is very sensitive to peak firing rates, while CA1 is not. This suggests that VIP modulation of place fields in the RSC significantly improves spatial coding while offering minimal benefit in CA1. Our findings highlight how disinhibition mechanisms, like VIP neuron activity, can be precisely tailored to the specific needs of a circuit.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60679-4/MediaObjects/41467_2025_60679_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60679-4/MediaObjects/41467_2025_60679_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60679-4/MediaObjects/41467_2025_60679_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60679-4/MediaObjects/41467_2025_60679_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60679-4/MediaObjects/41467_2025_60679_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60679-4/MediaObjects/41467_2025_60679_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our study reveals distinct VIP neuron influences on spatial coding in RSC and hippocampus, two brain regions critical for navigation82,83. In RSC, we discovered robust VIP-mediated place cell gain modulation. This enhances spatial information representations, likely compensating for RSC\u2019s lower signal-to-noise ratio18.\n\nOur findings demonstrate that, similar to the hippocampus45,48, RSC VIP neurons show locomotion-modulated activity biased towards positive modulation (Fig.\u00a01h). In the hippocampus, VIP subtypes have been classified into four categories; Three disinhibitory types (expressing: VIP+/Calretinin-, VIP+/Calretinin+, VIP+/muscarinic R2+) and one basket-type cell (VIP+/CCK+) that inhibits pyramidal cells84. Recent work shows that VIP+/CCK+ basket cells45,54,85 and VIp\u2009+\u2009/muscarinic R2+ cells48,54 are negatively modulated by locomotion but turn on when the animals stop. Thus, only disinhibitory types are active during locomotion, targeting OLM-cells that control pyramidal tuft excitability. ArchT inhibition during locomotion likely affects only active disinhibitory types since the VIP+/CCK+ basket cells are already silent. However, ChrimsonR activation likely engages all VIP neurons, including the basket cells (\u223c18% of all VIP cells26,45). The net effect of ChrimsonR excitation of VIP is therefore harder to predict, as it involves both disinhibition and basket cell inhibition. However, our findings indicate that the disinhibitory effect dominates. Neocortical VIP neurons have not yet undergone the same detailed classification as hippocampal VIP neurons. However, both populations share the common characteristic of preferentially targeting SST and, to a lesser extent, PV interneurons, resulting in pyramidal cell disinhibition33.\n\nOur study supports the canonical model whereby VIP neurons target SST inhibitory neurons, which, in turn, inhibit pyramidal neuron dendrites22,23,28,56,86,87. The mathematical operation of VIP cells on neuronal tuning varies depending on the brain area. In the auditory cortex, VIP-mediated disinhibition functions as an additive operation23,88, while in the primary visual cortex, it mainly acts as a gain modulation for visual processing47,59,89,90 (but see ref. 91). Increased VIP cell activity during locomotion likely reflects their sensitivity to acetylcholine and potentially other neuromodulators47. Interestingly, locomotion also enhances the activity of SST neurons92. This suggests a dynamic interplay where VIP neurons, activated by locomotion, suppress SST neuron activity. Given that SST neurons inhibit pyramidal cell dendrites, effectively regulating their response gain, we propose that VIP neurons modulate gain through this disinhibitory circuit involving SST neurons. Since VIP neurons are typically activated during locomotion, which is also a requirement for the emergence of place cells, these neurons seem ideally suited to control place cell gain. However, place-cell-like sequences can also emerge during attentive immobility. Whether VIP neurons also amplify such responses during immobility remains to be determined.\n\nThe mechanisms by which dendrite-targeting inhibitory neurons modulate the gain of pyramidal cells have been investigated experimentally and through computational modeling. Two mechanisms have been identified1,3: (1) divisive gain control through shunting inhibition and (2) suppression of dendritic excitation, which is inherently nonlinear due to the activation of NMDA receptors or dendritic Na+ or Ca2+ spikes. Note that for a gain modulation, the inhibition does not need to be place tuned and matched to the place fields. Previous work has shown that a tonic shunting inhibition suffices for gain modulation (reviewed in ref. 1). Our simulations also demonstrate this using a place tuned excitatory synaptic input and a constant, tonic level of shunting inhibition (Fig.\u00a05). Altogether, this dynamic interplay between VIP and SST neurons offers a flexible mechanism for modulating pyramidal cell activity and adapting cortical processing to changing behavioral states.\n\nA key finding is VIP neurons\u2019 contrasting influence in RSC versus hippocampus. Despite documented activity during locomotion and spatial navigation42,45,48,54 (Supplementary Fig.\u00a0S12), silencing hippocampal VIP neurons does not significantly modulate place cell firing (Fig.\u00a04e\u2013h). Here, we explore potential explanations for this disparity: One factor could be that the density of VIP neurons in the hippocampus is lower compared to the neocortex (by a factor 3.8), and the proportion of positively modulated VIP cells is lower in area CA1 compared to RSC (Supplementary Fig.\u00a0S12). However, the influence of VIP cells likely depends on connectivity parameters between VIP cells, SST cells, and potentially other interneuron types (such as parvalbumin-expressing interneurons) and pyramidal neurons. The short-term plasticity properties of these connections will also play a crucial role. However, all these parameters remain unexplored in RSC. Another possibility could be that hippocampal VIP neurons are insufficiently activated during our task. We show, indeed, that artificially boosting VIP cell activity with optogenetics can increase place cell responses (Fig.\u00a04i-l). Such stronger activation could occur during reward consumption23,57 or fear conditioning27,93. A third possible explanation is that disinhibitory VIP cells and inhibitory VIP basket cells22,33,84 exert an opposing but balanced influence over place cell responses in area CA1. However, VIP basket cells form only a small fraction (18 %) of VIP neurons45. Moreover, by boosting VIP cell activity with optogenetics, we show that place cell responses can increase (Fig.\u00a04i-l), indicating that this hypothesis is unlikely. Finally, the specific location of the SST inhibitory synapses may play a crucial role (Fig.\u00a05). Hippocampal VIP disinhibitory neurons primarily target a specific SST subtype in the Stratum Oriens26, which inhibits the distal tuft dendrites of pyramidal neurons in the Stratum Lacunosum Moleculare22,33. The distal tuft dendrites of CA1 pyramidal neurons receive input from the entorhinal cortex, while the proximal oblique and basal dendrites receive input from area CA3. Previous work indicates that CA1 place cell responses are mainly driven by input from area CA376,77,94 (but see ref. 78). Therefore, SST inhibition and CA3 excitation would be confined to two different dendritic compartments that are electrically separated, preventing VIP modulation from having a significant impact. Notable, because entorhinal cortex input to the tuft dendrites appears to be key for new place cell formation95, hippocampal VIP neurons might be more important for creating new place cells in novel environments96 rather than influencing existing ones during familiar environment exploration40,95. Indeed, we found using the same optogenetic approach as described here that inhibiting VIP neurons slowed, and exciting them accelerated, the rapid formation of place cells as mice entered a novel environment96.\n\nThe recent discovery of place-tuned neurons in numerous neocortical areas suggests that VIP-mediated disinhibition of place-tuning may represent a canonical circuit motif across the neocortex29,33. This possibility is intriguing, especially considering the reported expansion of putative VIP neurons as a key feature of the human cortex compared to mice97. VIP neurons are strongly driven by neuromodulators such as acetylcholine and serotonin32,44,47,98, which are associated with heightened attention and arousal. This connection raises the exciting possibility that VIP neuron activity is dynamically sculpted by these neuromodulators to selectively enhance spatial representations and memory formation during behaviorally relevant situations.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "We used VIP-IRES-Cre mice (JAX #010908) crossed with Thy1-GCaMP6s mice (GP 4.3 line #024275 from JAX) for calcium imaging and optogenetic experiments. SST-IRES-Cre mice (JAX #013044) were crossed with Thy1-GCaMP6s mice for ArchT experiments. All mice were 3.0\u2009\u00b1\u20090.2 months old (mean\u2009\u00b1\u2009SEM) at the time of surgery. Both male and female mice were used in the study. For calcium imaging of VIP interneurons, we expressed GCaMP6s in VIP-Cre mice using AAV (4 mice for RSC and 5 mice for area CA1). For ArchT experiments in VIP interneurons, we used VIP-Cre x Thy1-GCaMP6s mice (5 mice for RSC and 7 for CA1). For ChrimsonR experiments in VIP interneurons, we used VIP-Cre x Thy1-GCaMP6s mice (6 mice for RSC and 6 mice for CA1). For ArchT experiments in SST interneurons, we used SST-Cre x Thy1-GCaMP6s mice (5 mice). We used 16 mice for control experiments. Mice were group-housed (1-4 littermates per cage) on a reversed 12-hour light/dark cycle, with experiments conducted during their dark phase. They were maintained in a temperature (21.5\u2009\u00b0C) and humidity (55%)-controlled environment with ad libitum access to food and water-restricted access to water as described previously99, receiving water drop rewards during the task. All procedures were approved by the Norwegian Food Safety Authority (FOTS 6590, 7480, 19129, 30014) and conducted in accordance with the Norwegian Animal Welfare Act.\n\nWe used a Styrofoam running wheel with a circumference of 157\u2009cm (50\u2009cm diameter, 10\u2009cm wide; Bakedeco, Brooklyn, New York), with two types of tactile cues glued to the wheel: black sandpaper strips (10\u2009cm wide, 2\u2009cm long, locations: 26-28\u2009cm, 86-88\u2009cm) and 2 sets of hot-glue spikes (locations: 46-48\u2009cm, 66-68\u2009cm, 1.5-2\u2009cm high). The absolute position of the mouse on the running wheel was calculated using a rotary encoder signal. Additionally, the starting location of the track was calibrated in every lap by using an IR emitter-receiver pair (transceiver) facing the running wheel and having a small black cue glued on the side of the wheel at the start location. We controlled water reward delivery with a solenoid valve and a custom-made lick port99 to detect licks.\n\nThe behavior was automated using custom-written LabVIEW routines (National Instruments, 2017) to control all actuators, sensors, and data acquisition, record behavioral parameters (3\u2009kHz), and trigger the microscope to start and stop recording. The lick-port signals, rotary encoder signals, and two-photon frame clock were acquired using a DAQ (X-Series, PCIe 6321, National Instruments). We used the two-photon frame clock signal to synchronize the two-photon images with the recorded signals via the DAQ. The behavior setup was positioned under a custom-built two-photon microscope in a light-tight enclosure built from Thorlabs parts (25\u2009mm optical rails, Thorlabs, XE25L48), blackout hardboard (Thorlabs, TB4) and blackout fabric (Thorlabs, BK5).\n\nSurgery was carried out under isoflurane anesthesia (3% induction, 1-1.5% maintenance) while maintaining body temperature at 37 degrees Celsius with a heating pad (Harvard Apparatus). We delivered a subcutaneous injection of 0.1\u2009mL Marcaine (bupivacaine 0.25% m/V in sterile water) at the scalp incision site. For RSC surgeries, after removing the scalp, we implanted a titanium head bar and made a circular cranial window with a 2.5\u2009mm diameter using a dental drill. The center of the window was AP \u22122.2\u2009mm, ML 0\u2009mm from bregma. After virus injections, we implanted a glass window consisting of a circular outer window (3.5\u2009mm diameter, #1.5 coverslip glass) glued to a half-moon-shaped inner window (2.5\u2009mm diameter, #2 coverslip glass) with optical adhesive (Norland Optical Adhesive; Thorlabs NOA61). We positioned the inner glass over the left RSC, and while applying gentle pressure, we affixed the window to the skull with cyanoacrylate glue. Finally, we covered the exposed skull with dental cement100. For hippocampal surgeries, the center of the window was AP \u22122.0\u2009mm, ML 1.5\u2009mm from bregma. Virus injections into CA1 were performed at least one week before the craniotomy. After drilling a 2.5\u2009mm craniotomy, we removed cortical tissue and most of the corpus callosum above dorsal CA1 using a suction device (\u223c40\u2009bar). We implanted a metal ring (diameter 2.5\u2009mm, height 1.5\u2009mm) glued to a thin glass window at the bottom (#1.5 coverslip glass) with optical adhesive (Norland Optical Adhesive; Thorlabs NOA61). We affixed the metal ring to the skull with cyanoacrylate glue, and we covered the exposed skull with dental cement outside the metal ring101. We administered postoperative analgesia (Temgesic, 0.1\u2009mg/kg) subcutaneously, and animals were monitored for 2-3 days after surgery for any sign of distress or pain, and additional analgesia was administered if needed.\n\nWe used AAV1-syn-flex-GCaMP6s (Addgene #100845) for imaging VIP cells, and AAV5-Syn-flex-rc[ChrimsonR-tdTomato] (Addgene, #62723) or AAV5-CAG-flex-ArchT-tdTomato (Addgene, #28305) for optogenetic modulation of VIP or SST cells. In some cases, the virus was diluted with sterile PBS to get a virus titer of 1012. In RSC, we injected the virus in 5-6 equally distributed sites (500\u2013600\u2009\u00b5m between injection sites, between \u22121.0 to \u2212 3.7\u2009mm AP, and 0 to 1\u2009mm\u2009ML) at 250\u2009\u00b5m depth. For calcium imaging of VIP cells, we injected 20\u2013100\u2009nl/site of AAV1-syn-flex-GCaMP6s; for optogenetic activation of inhibitory neurons, we injected 150\u2009nl/site of AAV5-Syn-flex-rc[ChrimsonR-tdTomato]; and for optogenetic inactivation of VIP cells we injected 30\u2009nl/site of AAV5-CAG-flex-ArchT-tdTomato. Virus injections in area CA1 were done at least one week before the window implantation. We injected the virus to one location in the dorsal area CA1: AP \u2212 2.0, ML\u2009+\u20091.2, at 1200 and 1050\u2009\u00b5m from the brain surface. For optogenetic activation of VIP cells, we injected 2\u2009\u00d7\u2009100\u2009nl of AAV5-Syn-flex-rc[ChrimsonR-tdTomato], and for optogenetic inactivation of VIP cells, we injected 2 \u00d7100\u2009nl of AAV5-CAG-flex-ArchT-tdTomato. Animals were used for experiments between 1-4 months after virus injections. The GCaMP6s, as well as the opsin expression, were always checked with a 2\u2009P microscope before we started experiments.\n\nMice were water-restricted after a 10-day post-operative recovery period99. Animals received 1-1.5\u2009ml water per day while we monitored their body weight to ensure they maintained at least 80% of their initial body weight. During the first week of water restriction, we handled mice daily in dim light conditions to gradually habituate them to the experimenter and to the head fixation. During the training process, mice first learned to run head-fixed on a blank polystyrene wheel, receiving randomly delivered water rewards (2\u2009\u00b5l/drop). After animals learned to run in the random foraging task, we introduced the tactile cues, and the animal had to learn the fixed location of the hidden water reward. The entire training procedure typically lasted 2 weeks. If the animals performed at least 100 laps in 20\u2009minutes, we moved the setup under the microscope and did 3-5 days of additional training before we started data acquisition.\n\nWe used a custom-built two-photon microscope (INSS) designed to provide enough space under the objective to accommodate the large running wheel. We acquired images at 31\u2009Hz (512 \u00d7 512 pixels) using the open-source acquisition software SciScan (LabVIEW, National Instruments). The excitation wavelength was 930 or 950\u2009nm using a MaiTai DeepSee ePH DS laser (SpectraPhysics). The average power measured under the objective (N16XLWD-PF, Nikon) was 50\u2013120\u2009mW. Photons were detected using GaAsP photomultiplier tubes (PMT2101/M, Thorlabs). The primary dichroic mirror was a 700\u2009nm LP (Chroma), and the photon detection path consisted of a 680\u2009nm SP filter (Chroma), a 565\u2009nm LP dichroic mirror (Chroma), and a 510/80 BP filter (Chroma). The typical FOV size was 500\u2009\u00d7\u2009500\u2009\u00b5m for VIP cell imaging and between 625-833 \u00d7 625-833\u2009\u00b5m for pyramidal cell imaging. We recorded VIP cells or pyramidal cells in L2/3 of agranular RSC at 85\u2013170\u2009\u00b5m depth from the pial surface and recorded CA1 pyramidal cells at 80\u2013150\u2009\u00b5m depth from the top of CA1 (alveus).\n\nIn either 25% or 33% of the trials (1 out of 4, or 1 out of 3 trials, randomly selected), we applied optogenetic stimulation after the animal consumed a reward (typically wheel position 4-15\u2009cm) and continued stimulation until the mouse reached the next reward (157\u2009cm). We excluded data from the first 20\u2009cm of the track due to the variability of the position where the animal resumes to run. The light stimulation lasted typically for 5\u20138\u2009seconds, with a cut-off time of 15\u2009seconds. For ChrimsonR stimulation, we used a 20\u2009Hz sinusoid stimulus pulse to avoid depolarization block, whereas for ArchT, we used a square pulse. To achieve intermittent stimulation with ChrimsonR, the troughs of the sinusoidal command voltage were replaced with 0\u2009V, effectively creating a 25\u2009ms unstimulated interval between each 25\u2009ms long half-sinusoid. To implement simultaneous two-photon Ca2+ imaging and optogenetic modulation, we mounted a red laser diode (Oclaro, 638\u2009nm 700\u2009mW) in the position where normally the red channel photomultiplier would be located. To make the beam shape more circular, we focused the light into an optical fiber (Thorlabs, #M15L01) using a fiber coupler with a collimation lens (Thorlabs, #PAF2S-11B)102. The beam was then focused on the objective back aperture such that it diverged when entering the brain. The diameter of the beam was approximately 700 \u03bcm (close to the size of the imaging FOV). The laser power of this beam was measured prior to each experiment and was kept in the range of 1\u201310\u2009mW/mm2. To prevent the intense red light from entering the photomultiplier of the green channel, we mounted two optical density 6 filters (#ET510/80, Chroma) in series after the secondary dichroic, slightly angled in relation to each other to achieve a higher optical density attenuation (#T565lpxr, Chroma). Since the red light used for optogenetics could spread through the brain and stimulate the back of the retina, we used a similar-wavelength masking light positioned in front of the eyes (Thorlabs, LED630E). This masking light (20\u2009Hz) was on during the entire session.\n\nImages were registered using a combination of custom and published code. Images were first de-stretched to correct for distortions resulting from the sinusoidal speed profile of the resonance scan mirror of the microscope. Next, rigid and non-rigid motion correction was performed using custom-written scripts using NoRMCorre103. For image segmentation, somatic regions of interest (ROIs) were detected using a custom auto-segmentation method followed by manual curation after visual inspection of each ROI. To correct neuropil contributions, a doughnut-shaped ROI of the surrounding neuropil was automatically created for each ROI by dilating the neuropil ROI four times larger than the original ROI. If the soma ROI or the neuropil ROI overlapped with another soma ROI, that area was excluded from the ROI. Then, pixel values were averaged for each ROI per imaging frame. ROI fluorescence changes were defined as a fractional change DF/F according to DF/F(t) = (F(t)-F0)/F0, where F0 is the baseline activity defined as the 20th percentile of the ROI fluorescence signal. We calculated DF/F(t) values for all ROIs and related neuropil areas, which were subtracted from the ROI DF/F(t) values, and a correction factor was added to ensure that the soma DF/F remained positive. DF/F(t) signal was used for further analysis. Importantly, F0 was a single value applied to the entire session such that it would not correct for baseline fluorescence changes during optogenetic manipulations.\n\nAfter experiments were finished, we routinely checked the expression of the injected opsin or GCaMP in RSC or CA1. We deeply anesthetized mice with pentobarbital sodium (90\u2009mg/kg), and once the reflexes were absent, they were transcardially perfused with 4% (w/v) paraformaldehyde in phosphate-buffered saline (PBS). We kept the brains for at least 24\u2009h in 4% paraformaldehyde, then transferred them to a cryoprotective solution (2% DMSO in 0.125\u2009M phosphate buffer) the next day and stored them overnight at 4 degrees Celsius. We embedded the brains, froze them at \u221240 degrees Celsius, and cut them into 40\u2009\u00b5m coronal sections with a freezing microtome. Brain sections were either mounted directly or underwent immunohistochemistry to enhance the tdTomato signal. For direct mounting, sections were placed on Superfrost Plus slides (Gerhard Menzel GmbH, Braunschweig, Germany), dried overnight, cleared with xylene, and coverslipped with Eukitt (Sigma-Aldrich). To amplify the tdTomato signal in cells expressing opsins, sections underwent anti-RFP immunohistochemistry. Briefly, sections were rinsed in PBS (3\u2009\u00d7\u200910\u2009minutes), blocked in PBS with 10% goat serum and 0.1% Triton (2\u2009h), and incubated with rabbit anti-RFP primary antibody (1:500, Rockland, ThermoFisher Scientific cat. no. 600-401-379) in blocking buffer for 48\u2009h at room temperature. Following PBS washes (3\u2009\u00d7\u200910\u2009minutes), sections were incubated with AlexaFluor 555-tagged goat anti-rabbit secondary antibody (1:500, ThermoFisher Scientific, A-11008) in PBS with 2% goat serum for 2\u2009h at room temperature. After final PBS washes (3\u2009\u00d7\u200910\u2009minutes), sections were mounted and coverslipped. All sections were imaged using a Zeiss LSM700 confocal microscope.\n\nFor testing ChrimsonR and ArchT in VIP cells in vitro, we performed in patch-clamp experiments in brain slices. VIP-Cre animals were injected with either a Cre-dependent ChrimsonR or ArchT (see details above). We cut brain slices 1.5-2.5 months after viral injections to match the expression duration of in vivo recordings. We used a recent protocol that improves neuronal viability, particularly when using older mice104. Mice were anesthetized with isoflurane, then transcardially perfused with ice-cold NMDG-HEPES ACSF solution, containing (in mM): 92 NMDG, 2.5 KCl, 1.25 NaH2PO4, 30 NaHCO3, 20 HEPES, 25 glucose, 2 thiourea, 5 Na-ascorbate, 3 Na-pyruvate, 0.5 CaCl2\u00b72H2O, and 10 MgSO4\u00b77H2O. The brains were quickly removed, and acute 250 \u03bcm thick coronal slices were cut with a vibratome (Leica VT1200; Leica Microsystems) containing either the dorsal hippocampal region or RSC. Slices were incubated for 25\u2009minutes in a holding chamber filled with 35\u2009\u00b0C NMDG-HEPES ACSF, gradually increasing the NaCl concentration. Then slices were transferred to another holding chamber containing HEPES-HOLDING ACSF (in mM): 92 NaCl, 2.5 KCl, 1.25 NaH2PO4, 30 NaHCO3, 20 HEPES, 25 glucose, 2 thiourea, 5 Na-ascorbate, 3 Na-pyruvate, 2 CaCl2\u00b72H2O, and 2 MgSO4\u00b77H2O. Recordings were carried out at 34\u2009\u00b0C using RECORDING ACSF solution, containing (in mM): 124 NaCl, 2.5 KCl, 1.25 NaH2PO4, 24 NaHCO3, 12.5 glucose, 5 HEPES, 2 CaCl2\u00b72H2O, and 2 MgSO4\u00b77H2O. The pH of all ACSF solutions was set to 7.3-7.4, osmolality 300-310 mOsmol/kg, and solutions were saturated with carbogen (95% O2 / 5% CO2) prior to use. Slices were kept up to 6\u2009h in the holding chamber prior to use. Cells were visualized using oblique infrared illumination and a water immersion lens (60x, Olympus). VIP cells expressing the opsin were identified based on their red fluorescence (tdTomato) using a 594\u2009nm LED illumination (M595L4, Thorlabs). Whole-cell recordings were conducted in current-clamp mode using a MultiClamp 700\u2009A amplifier (Molecular Devices). Electrophysiological traces were filtered at 3\u2009kHz and digitized online at 20\u2009kHz. Patch pipettes were pulled (PC-100, Narishige) from thin-walled borosilicate glass capillaries (1.5\u2009mm outer diameter, 1.17\u2009mm inner diameter; Harvard Apparatus). Pipette resistance was 6\u20138\u2009M\u03a9. The intracellular solution contained (in mM): 130 K-gluconate, 5 KCl, 2 MgCl2; 10 creatine phosphate, 10 HEPES, 2 Na2ATP, 1 Na2GTP; pH was adjusted to 7.3; the osmolality was 290\u2013295\u2009mOsmol/kg. VIP cells were held at their resting membrane potential during most of the recordings. In a few cases, a maximum of \u2212100\u2009pA direct current injection was necessary to keep the cell between \u221265 and \u221270\u2009mV. In some experiments, we evoked action potentials by injecting a long (8-21\u2009s) depolarizing (50\u2013100\u2009pA) current pulse. Optogenetic stimulation was evoked using a 625\u2009nm LED light source (ThorLabs, #M625L4) or a 638\u2009nm diode laser (Oclaro, 700\u2009mW). Data were acquired and analyzed with Wavesurfer (MATLAB) and custom code. All drugs were purchased from Sigma unless indicated otherwise.\n\nThe Allen Brain Connectivity Atlas in situ hybridization experiments105 were used to quantify the density of VIP expressing cells in anterior agranular RSC and dorsal CA1 in 4 Vip-IRES-Cre x Ai14 (RCL-tdT) transgenic mice (3 males and 1 female). The age of animals was 48.3\u2009\u00b1\u20095.9 days. We counted labeled cells in 25-micrometer-thick coronal sections (that included both CA1 and RSC) in a 0.5 mm2 area (5 slices per mouse between AP \u22121.2 and \u22122.2\u2009mm). We calculated the mean of the number of VIP cells per slice for each mouse, comparing RSC and CA1 using a paired two-tailed t-test.\n\nSimulations were performed in NEURON/7.4106 via the pyNeuroML-interface107 in NeuroML/v2beta4108. Data handling and analysis were done in Python/2.7.15, using the NumPy/1.11.9, SciPy/0.17.0, and Matplotlib/ 1.5.1 libraries. The Migliore model of a CA1 pyramidal cell was implemented without parameter modifications109. To simulate place cell activity in familiar environments, we randomly distributed 100 excitatory AMPA-like synapses (maximum conductance: 0.5 nS; rise time constant: 1\u2009ms; decay time constant: 11\u2009ms; reversal potential: 0\u2009mV) across the basal, apical, and oblique dendrites. Additionally, 50 tonically active GABA-like inhibitory synapses (maximum conductance: 0.5 nS; rise time constant: 0.13\u2009ms; decay time constant: 11\u2009ms; reversal potential: \u221270 mV) were randomly placed on stratum lacunosum-moleculare dendrites, with Poisson-distributed activation rates as shown in Fig.\u00a05. Place field input was simulated by activating excitatory synapses with random onsets and offsets (Gaussian distributed: mean onset 5000\u2009ms, mean offset 6000\u2009ms, standard deviation 500\u2009ms). For simulations resembling RSC activity, excitatory inputs were distributed on the oblique, apical, and tuft dendrites, while inhibitory synapses were placed on the tuft dendrites. Simulations ran for 10\u2009seconds with a 0.1\u2009ms time step. Each data point represents the average of five model instances with randomized synapse distributions and activation times.\n\nWe analyzed DF/F and behavioral time series with MATLAB. The behavioral time series were first binned to match the two-photon microscope frame rate (31\u2009Hz) by finding the behavioral data sample closest in time to the imaging frame or, for some signals, taking the average of some samples around that sample.\n\nTo quantify the changes in the position tuning curve during optical stimulation (opto-off, opto-on, and after-opto cases), we first divided the 157\u2009cm long linear track into 80 bins (bin size of 1.9635\u2009cm). We excluded the frames from the analysis where the animal\u2019s speed was less than 1\u2009cm/s (when the animal stopped). For each cell, the DF/F at each bin was summed and divided by the occupancy count (number of image frames the animal spent within that bin), then we averaged the identical bins\u2019 activity separately for trials belonging to either opto-off, opto-on or after-opto trials conditions, creating the final average tuning curves. Since we applied the optical stimulation between 20-157\u2009cm, we discarded all data recorded in the first 20\u2009cm of the trials (first 10 bins).\n\nWe detected position-tuned cells based on criteria adapted from Mao et al.11. Potential place cells were identified by finding significant peaks in the position tuning curves (using opto-off trials), with a place field width of 2-100\u2009cm where the DF/F activity exceeded 50% of the difference between the maximum and minimum DF/F values of the position tuning curve. The mean DF/F within the potential place field must be two times larger than the mean DF/F outside the potential place field., and the peak activity had to occur within the field in at least 30% of trials. Cells with multiple peaks or tactile cue preferences were excluded. Place cell subcategories: Place cells were divided into two categories: \u2018on the track\u2019 place cells (peak firing between 20-127\u2009cm) and \u2018pre-reward\u2019 place cells (peak firing between 127-157\u2009cm). Tactile cue cells: Cells with double peaks in their activity around the tactile cue pairs (sandpaper or hot glue), with a specific distance between peaks (60 or 20\u2009cm; 2\u2009cm jitter allowed), were classified as tactile cue cells. Speed-modulated cells: Cells with activity significantly correlated with speed but not position were classified as speed-modulated cells. Uncategorized cells: Cells not exhibiting significant place, speed, or tactile responses were categorized as uncategorized.\n\nIf the dataset did not pass the Kolmogorov-Smirnov normality test, non-parametric statistical tests were used (two-sided Wilcoxon signed rank test or Mann-Whitney test) with a significance of p\u2009=\u20090.05. For normal distributed data we used parametric statistical tests, such as the Student t-test (two-sided, paired or non-paired). The figures show mean\u2009\u00b1\u2009SEM unless noted otherwise.\n\nTo analyze the response profiles of modulated cells, we compared their mean activity (averaged over trials) with and without light stimulation, plotting these values for each position bin. We fitted a linear function to this data. Cells with a goodness of fit of R2\u2009>\u2009=\u20090.7 we considered to have a linear response. For cells with R2\u2009<\u20090.7, we used the Wald\u2013Wolfowitz runs test. If the null hypothesis of randomness was rejected (p\u2009<\u20090.05), we assumed nonlinearity.\n\nWe smoothed VIP cell DF/F traces using a Gaussian kernel with a standard deviation of 10. Similarly, the running speed signal was smoothed to reduce noise. To investigate the temporal relationship between DF/F and running speed, we calculated the Pearson correlation between the smoothed DF/F trace and the speed signal, with the speed signal lagged backwards by up to 0.32\u2009s (10 time bins). For each DF/F trace, the Pearson correlation was calculated at each time lag, and the lag corresponding to the peak absolute correlation was identified. The value of this peak correlation was selected as the measure of the strongest temporal relationship between the DF/F trace and running speed.\n\nTo assess the significance of this correlation for each individual cell, the temporal alignment of the DF/F trace was disrupted by circularly shuffling the trace randomly for 1000 iterations. In each iteration, the Pearson correlation was recalculated between the shuffled DF/F trace and the lagged speed signal at all backward lags, producing a distribution of chance-level peak correlations for that specific cell. From this distribution of shuffled peak correlations, thresholds for positive and negative modulation were defined. The mean plus or minus two standard deviations of the chance-level peak correlations defined the threshold for positive and negative modulation, respectively. Cells were classified as positively speed-modulated if their observed peak correlation exceeded the positive threshold and as negatively speed-modulated if their observed peak correlation fell below the negative threshold.\n\nThe final value that was used for plotting the distribution of correlations was the peak correlation between the DF/F trace and the lagged running speed signal. This peak correlation was selected based on the Pearson correlation coefficient calculated at each backward lag.\n\nTo investigate the changes in VIP activity in response to running-start and running-stop events, we first identified the resting intervals by determining when the animals\u2019 speed falls below 10\u2009cm/s for a minimum of 2\u2009s. A stop event was defined as a 4\u2009s time window starting 2\u2009s before the initiation of the resting interval and ending 2\u2009s after. Conversely, a start event was a time window starting 2\u2009s before the conclusion of the resting interval until 2\u2009s after. Events containing reward delivery were excluded from the analysis. The VIP activity response for each event was computed as the mean across the identified events.\n\nWe separated the positions on the wheel into 15 equal bins. The first 20\u2009cm of the track were excluded. Next, we assumed that the probability of an animal\u2019s position is given by:\n\nAssuming statistical independence between neuronal signals \\(P({{{{\\rm{k}}}}}_{{All}}{{{\\rm{|pos}}}})\\) can be written as:\n\nwhere k indexes across all neuronal signals and the firing rates \\({\\lambda }_{t}\\). We assumed that the probability of recording the activity \\({{{{\\rm{k}}}}}_{{{{\\rm{t}}}}}\\) in time bin t of length dt (dt = 32,3\u2009ms) follows a Poisson distribution:\n\nThe variable parameters \\(w\\) convert the state vector \\(X\\) into an expected firing rate \\({\\lambda }_{t}\\) :\n\nwhere i indexes across the position bins. For every observed activity train \\({{{{\\rm{k}}}}}_{t}\\), we learned the parameters \\(w\\) by optimizing the cost function (using MATLAB\u2019s fminunc function):\n\nWe used a flat prior \\(P({{{\\rm{pos}}}})\\), with which we assumed the animals do not have a certain expectance about the occupancy of the positions on the wheel, and the probability for each position at time point t is given by:\n\nWith Const. being a normalization factor so that \\(P\\left({{{\\rm{pos}}}} | {{{{\\rm{k}}}}}_{{All}}\\right)\\) sums to 1. We defined the position with the highest (max) probability as the decoded position for time point t.\n\nWe quantified the decoding error as the mean distance between the predicted and actual position on the wheel. We trained the model in a 5-fold cross-validation procedure and defined the fold that led to the median decoding error as the resulting model.\n\nWe assessed the in-field and out-of-field firing rates of place cells in the retrosplenial cortex (RSC) and the hippocampal area CA1 using extracellular recording data from Alexander et al.18. The peak neuronal activity was classified as the in-field firing rate, while the 5th percentile was identified as the out-of-field firing rate.\n\nWe generated spike trains \\({{{{\\rm{k}}}}}_{t}\\) assuming a Poisson process. In this process, the firing rates \\({\\lambda }_{t}\\) were made position-dependent, imitating the firing patterns of position-modulated cells through a Gaussian distribution. To ensure even coverage of all positions, we selected the means of the Gaussian distributions of firing rates to be approximately 5\u2009cm apart, with a standard deviation set to 4\u2009cm. Depending on the specific inquiry, we manipulated either the baseline of the firing rate or the peak activity. Subsequently, we modeled spike trains based on the position profile of a mouse running on a wheel.\n\nWe divided the track into 31 equally spaced spatial bins. For each cell we calculated an activity map, for which we determined the mean activity divided by the time spent within the position bin for each trial. We calculated the spatial information score:\n\nWith N the total number of position bins, pi the probability that the animal is in the i-th position bin, fi the averaged DF/F activity in the i-th bin, and f the averaged DF/F activity over all bins. Since this spatial information metric was developed for firing rates, we followed Climer et al.81. and scaled the resulting SI by the factor of c\u2009=\u20090.14 (DF/F/Hz) (scaling factor c for GCaMP6s). With this scaling, we aimed to normalize the relationship between the actual firing rate and DF/F.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The datasets generated during and/or analysed during the current study are available from the corresponding author.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Code for analysis, test data and simulations can be found on GitHub: https://zenodo.org/records/11179846, https://zenodo.org/records/11179853.", + "section_image": [] + }, + { + "section_name": "Change history", + "section_text": "In the version of this article initially published, the Acknowledgements did not include thanks for a Norwegian Research Council grant (#333868) to K.V., as is now amended in the HTML and PDF versions of the article.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Silver, R. 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(2025) https://BioRender.com/g0yf5dv). Finally, we thank members of the Vervaeke lab and Hua Hu for providing critical comments that significantly improved the draft of the manuscript.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Institute of Basic Medical Sciences, Section of Physiology, University of Oslo, Oslo, Norway\n\nNora Lenkey,\u00a0Anna Christina Garvert,\u00a0M\u00e1t\u00e9 Neubrandt,\u00a0Birgit Kriener\u00a0&\u00a0Koen Vervaeke\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization, N.L. and K.V.; methodology, N.L., A.C.G., B.K, M.N. and K.V.; investigation, N.L., A.C.G. and B.K.; software, N.L., A.C.G., B.K., M.N.; formal analysis, N.L., A.C.G., B.K., M.N.; supervision, K.V.; writing \u2013 original draft, N.L. and K.V.; writing \u2013 review & editing, N.L., A.C.G., M.N. and K.V.; funding acquisition, N.L. and K.V.\n\nCorrespondence to\n Nora Lenkey or Koen Vervaeke.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Chenglin Miao and the other, anonymous reviewer(s) for their contribution to the peer review of this work. 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Brain region-specific gain modulation of place cells by VIP neurons.\n Nat Commun 16, 5863 (2025). https://doi.org/10.1038/s41467-025-60679-4\n\nDownload citation\n\nReceived: 24 April 2024\n\nAccepted: 29 May 2025\n\nPublished: 01 July 2025\n\nVersion of record: 01 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60679-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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with high-temperature oxidation resistance", + "journal": "Nature Communications", + "published": "08 February 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56709-w/MediaObjects/41467_2025_56709_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56709-w/MediaObjects/41467_2025_56709_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56709-w/MediaObjects/41467_2025_56709_MOESM3_ESM.zip" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56709-w/MediaObjects/41467_2025_56709_MOESM4_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56709-w/MediaObjects/41467_2025_56709_MOESM5_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.6084/m9.figshare.25237933", + "/articles/s41467-025-56709-w#Sec15" + ], + "code": [], + "subject": [ + "Surfaces, interfaces and thin films" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3964420/v1.pdf?c=1739020033000", + "research_square_link": "https://www.researchsquare.com//article/rs-3964420/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-56709-w.pdf", + "preprint_posted": "05 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Despite numerous efforts involving surface coating, doping, and alloying, maintaining surface stability at high temperatures without compromising intrinsic properties has remained challenging. Here we present a groundbreaking method to address the accelerated oxidation of metals like Cu, Ni, and Fe at temperatures exceeding 200 \u00b0C. Inspired by the concept that oxygen (O) itself could effectively obstruct the pathway of O infiltration, the study achieved a significant breakthrough by immobilizing the blocking O. Through extensive calculations considering various elements (C, Al, Si, Ge, Ga, In, and Sn) to anchor O on Cu surfaces, Si emerged as the optimal element. The theoretical findings were validated through systematic sputtering deposition experiments. The introduction of anchoring elements to reinforce Cu\u2013O bonds enabled the formation of an atomically thin barrier on the Cu surface, rendering it impermeable to O even at high temperatures, while preserving its intrinsic conductivity. This remarkable oxidation resistance, facilitated by the impermeable atomic monolayer, opens exciting opportunities for researchers and industries to overcome limitations associated with the use of oxidizable metal films.Physical sciences/Physics/Condensed-matter physics/Surfaces, interfaces and thin filmsPhysical sciences/Materials science/Condensed-matter physics/Surfaces, interfaces and thin filmsImpermeable monolayeranchoring elementshigh-temperature oxidation resistanceatomic sputtering epitaxydensity functional theory", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "HTORSupplementaryInformation.pdfSupplementary information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Despite numerous efforts involving surface coating, doping, and alloying, maintaining surface stability of metal at high temperatures without compromising intrinsic properties has remained challenging. Here, we present a pragmatic method to address the accelerated oxidation of Cu, Ni, and Fe at temperatures exceeding 200\u2009\u00b0C. Inspired by the concept that oxygen (O) itself can effectively obstruct the pathway of O infiltration, this study proposes the immobilization of O on the metal surface. Through extensive calculations considering various elements (C, Al, Si, Ge, Ga, In, and Sn) to anchor O on Cu surfaces, Si emerges as the optimal element. The theoretical findings are validated through systematic sputtering deposition experiments. The introduction of anchoring elements to reinforce Cu\u2013O bonds enables the formation of an atomically thin barrier on the Cu surface, rendering it impermeable to O even at high temperatures (400\u2009\u00b0C) while preserving its intrinsic conductivity. This oxidation resistance, facilitated by the impermeable atomic monolayer, opens promising opportunities for researchers and industries to overcome limitations associated with the use of oxidizable metal films.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "While metals like Cu, Ni, and Fe are extensively used in various applications, their susceptibility to oxidation, especially in harsh environments, limits their broader utility. The degradation process is aggravated in harsh environments. The quest for metals that can maintain purity and properties under extreme thermal and chemical conditions fuels the search for alternatives to noble metals. While alloying and electroplating are common methods to combat oxidation1,2,3,4, attempts to develop surface passivation alternatives using organic molecules, inorganic compounds, or C-based materials have been numerous5,6,7,8,9,10,11,12.\n\nRecently, a solvothermal treatment of Cu using sodium formate introduced crystallographic reconstruction of the Cu surface13. However, achieving passivated Cu capable of withstanding harsh high-temperature environments while retaining intrinsic physical properties has remained elusive within existing paradigms. In this study, we present a cost-effective, scalable, and easily adaptable methodology for achieving the high-temperature oxidation resistance (HTOR) of metal films. The approach involves forming an atomically thin skin layer, where oxygen (O) itself acts as a barrier to block O passage, while specific elements serve as anchors to immobilize the O.\n\nTo identify an appropriate anchoring element, we employed a materials design approach using density functional theory (DFT). Calculations with elements exhibiting strong O bonding potential, including C, Al, Si, Ge, Ga, In, and Sn, showed that Si emerged as the most suitable anchoring element. Subsequent experimental trials involved uniformly sputtering Si atoms onto oxidizable metals, revealing Si\u2019s robust anchoring capability in preventing oxidation even at temperatures exceeding 400 \u00b0C, irrespective of the surface state or crystallinity of the oxidizable metal. Metals treated with Si showed no structural degradation or noticeable loss of electrical properties. Microscopy and theoretical modeling unveiled that HTOR originates from the formation of an atomically thin Si\u2013Cu\u2013O layer, named SiCuOx, on the surface.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Preventing high-temperature oxidation of oxidizable metals is challenging. Despite surface coatings or alloying attempts, O can still infiltrate a metal due to its relatively small size compared to metal elements, allowing it to move within the metal. However, the mere entry of O does not immediately trigger oxidation. When the O-to-Cu atomic ratio exceeds a certain threshold14 or ambient temperature increases, oxygen readily penetrates the copper lattice, leading to oxidation. To address this, we devised a unique strategy using O itself to prevent high-temperature oxidation. Even under a vacuum, some O becomes physically adsorbed, enveloping the Cu surface. The adsorbed O ions then form Cu\u2013O bonds at the surface, suggesting they might be able to act as a thinnest sealant agent blocking the passage of further O infiltration if they could be held in place by an anchoring element (Fig.\u00a01a).\n\na Schematic model of the surface bonding of Si-deposited SCCF. b The energy profiles of O penetration into pristine SCCF and the Si-, Al-, Ge- and Ga-deposited SCCF. An activation energy higher than that of the pristine SCCF indicates the enhanced oxidation resistance of the anchoring element. Note that the energy calculations were performed using the atomistic models containing a triple layer of anchoring element\u2013oxygen\u2013Cu(111) surface, which are detailed in Supplementary Fig.\u00a01. Source data are provided as a Source Data file. c Oxidation-driven color changes of SCCFs with thermal treatment at different temperatures in air. The A1-A6 represent a pristine SCCF (A1) heated at 200\u2009\u00b0C for 5\u2009min (A2), 200 \u00b0C for 10\u2009min (A3), 260\u2009\u00b0C for 1.5\u2009min (A4), 300\u2009\u00b0C for 3\u2009min (A5) and 300\u2009\u00b0C for 30\u2009min (A6). d Invariant color of Si-deposited SCCFs under harsher conditions, indicating exceptional HTOR. The B1-B6 represent a 5\u2009nm Si-deposited SCCF (B1) heated at 200\u2009\u00b0C and 300\u2009\u00b0C for 30\u2009min (B2, B3), and a 10\u2009nm Si-deposited SCCF (B4) heated at 200\u2009\u00b0C and 400\u2009\u00b0C for 30\u2009min (B5, B6). For each designated temperature point, more than 10 experiments were conducted on fresh samples. Visual inspection indicated that the ten samples at each individual temperature were virtually identical.\n\nTo seek potential O-anchoring elements, a model of an anchoring system was developed in which the sealant O and the anchor layer were adsorbed onto a Cu(111) thin film. The binding energy of the anchor element was calculated using DFT for elements including C, Al, Si, Ge, Ga, and In (Supplementary Fig.\u00a01). Of these, C and In had positive binding energies with Cu\u2013O, indicating weak binding, whereas Ge, Ga, Si, and Al had negative binding energies, indicating strong binding with Cu\u2013O (Supplementary Fig.\u00a01). For the anchoring elements with negative binding energies, we constructed a model with a more realistic anchoring layer and calculated its oxidation resistance by calculating the activation energy of O penetration (Fig.\u00a01b). Although the DFT calculations showed all four candidate elements had enhanced activation energies, the Si and Al anchoring systems had the highest oxidation resistance. Considering the necessity of a single-crystal target for atomic sputtering epitaxy (ASE)14 to achieve uniform atomic dimension surface deposition, Si, Ge, and Al were candidates for experiments. Ultimately, Si was selected as the primary anchoring material due to its availability as commercial wafers, while Al, despite being a single crystal, showed considerable surface oxidation, making uniform Cu surface coating unfeasible. The unavailability of Ge as a single-crystal target necessary for sputtering deposition supported Si as the best choice. The experimental findings are presented in Fig.\u00a01c, d.\n\nTo elucidate the origin of the high-temperature oxidation resistance (HTOR), we first used single-crystal Cu thin films (SCCFs, A1 in Fig.\u00a01c) and Si-deposited SCCFs prepared by ASE (Methods section). Typically, SCCFs exhibit a full-color spectrum by precisely controlling their oxide layer thickness with heat treatment between 240 and 400 \u00b0C15, which reflects the coherent propagation of the oxide front on the SCCF surface. After heat treatment of pristine SCCFs (A1) at 200\u2009\u00b0C for 5\u2009min (A2), 200\u2009\u00b0C for 10\u2009min (A3), 260\u2009\u00b0C for 1.5\u2009min (A4), and 300\u2009\u00b0C for 3\u2009min (A5), the surfaces appeared vivid orange, plum, red, and yellow, respectively. When the heat treatment was overly high and long, such as 300\u2009\u00b0C for 30\u2009min (A6), the Cu film changed completely to a black CuO phase and became brittle16 (Fig.\u00a01c). By contrast, the Si-deposited Cu surface exhibited HTOR. A 5\u2009nm Si-deposited SCCF (B1 in Fig.\u00a01d) retained its original color after heat treatment up to 300 \u00b0C for 30\u2009min (B2, B3 in Fig.\u00a01d). A 10\u2009nm Si-deposited SCCF (B4 in Fig.\u00a01d) was also unchanged after treatment up to 400 \u00b0C for 30\u2009min (B5, B6 in Fig.\u00a01d), which was the maximum temperature that the Si-deposited SCCF could withstand. More detailed structural information of the area enclosed in the dashed box in B4 and B6 of Fig.\u00a01d is presented in Supplementary Fig.\u00a02, which shows that the treated SCCF surface remains clean without oxidation-induced undulations or grain boundaries. The temperature triggering oxidation increased with the Si thickness to an extent but considering the invariance of physical properties such as conductivity, 10\u2009nm Si was the optimal thickness to achieve HTOR.\n\nX-ray diffraction (XRD) and grazing incidence XRD (GIXRD) analyses were performed to prove whether the structural integrity is sustained under high-temperature heating. Log-scale \u03b8-2\u03b8 XRD results (Fig.\u00a02a, b) show that the copper surface remains stable under heating up to 300 \u00b0C after Si deposition. When the incidence angle was set to 1\u00b0 and 0.5\u00b0 in GIXRD (Fig.\u00a02d, e, g, h), no peak was detected, indicating that the Cu(111) orientation was well maintained up to 300 \u00b0C. Even after being heated at 450 \u00b0C, there was no significant sign of oxidation in the \u03b8-2\u03b8 measurements (Fig.\u00a02c). However, very weak peaks corresponding to (11\\(\\bar{1}\\)) and (111) of CuO phase were detected in GIXRD (Fig.\u00a02f, i), indicating the evidence of oxidation. Nanoscale analyses further confirmed that the Si-deposited SCCF heated at 300 \u00b0C showed no noticeable structural and chemical changes compared to the non-heated counterpart (Supplementary Fig.\u00a03a, b). In contrast, when heated to 450 \u00b0C, it is revealed that the Si-deposited SCCF suffered from an oxidation-induced structural degradation (Supplementary Fig.\u00a03c).\n\na\u2013c Log-scale \u03b8\u20132\u03b8 XRD, d-f GIXRD at an incidence angle of 1\u00b0 and g\u2013i GIXRD at an incidence angle of 0.5\u00b0 for Si-deposited single-crystal Cu thin film (SCCF) and those heated at 300\u2009\u00b0C for 30\u2009min and 450 \u00b0C for 30\u2009min, respectively. Source data are provided as a Source Data file.\n\nTo understand the surface chemical states of the Si-deposited SCCF with HTOR, depth-profiling X-ray photoelectron spectroscopy (XPS) measurements were performed (Fig.\u00a03). The depth-profiling XPS was carried out at a low etching rate of 0.1\u2009nm/s to minimize damage, and the total thickness of the Si layers was estimated to be approximately 10\u2009nm. The results confirmed that Si exists in a tetravalent state (Si4+, 103.3\u2009eV) mixed with a substantial amount of trivalent state (Si3+, 102.5\u2009eV) and elemental Si0 (99.5\u2009eV) within a thin thickness range on the outermost part (0\u2009s etching) of the deposited layer on the SCCF film (Fig.\u00a03a). This is because the surface exposed to air is prone to undergo oxidation. After etching the initial part of the deposited layer, we discovered that the intermediate Si was mainly reduced to a trivalent state (Si3+, 102.5\u2009eV)17,18. This suggests that the middle section (20 to 80\u2009s etching, corresponding to 2\u20138\u2009nm in depth) of the deposited Si layer underwent incomplete oxidation to form an amorphous silicon suboxide (SiOx, 0\u2009<\u2009x\u2009<\u20092) before reaching the Cu film. More interestingly, we found that the relative portion of Si0 state turned out to be increased at the bottom part of the deposited layer proximate to the SCCF surface. This indicates that non-reacted elemental Si0 is highly concentrated just above the SCCF film. Taking the XPS depth-profiling results into account, three distinctive parts are formed in the deposited layer. Considering that the binding energy of Cu 2p3/2, indicating Cu0 state (932.5\u2009eV)19, remained unchanged across the deposited layer, it is confirmed that the surface of SCCF was not oxidized (Fig.\u00a03b). In samples heated at 300 \u00b0C (Fig.\u00a03c), the change in the valence state of Si atoms exhibited similar to the Si-deposited SCCF sample. The outermost layer (0\u2009s etching) predominantly consisted of a\u00a0tetravalent state (Si4+, 103.3\u2009eV), with a substantial portion of unreacted Si0 (99.5\u2009eV). In contrast, the intermediate layer (2 to 8\u2009nm in depth) displayed the characteristic reduced state (Si3+, 102.5\u2009eV). Interestingly, the elemental Si0 peak present in the Si-deposited sample exhibited a subtle shift toward higher energy following heating. This shift is attributed to the moderate annealing temperature, which results in the minimal oxidation and the presence of lower oxidation states due to adsorbed oxygen. Given that the binding energy of Cu 2p3/2 slightly shifted to higher energy at the upper part of the deposited layer20, some diffused Cu atoms were oxidized. However, the surface of the SCCF remained unaltered (Fig.\u00a03d). In contrast, the sample heated at 450 \u00b0C (Fig.\u00a03e) shows a different chemical structure of the deposited layer. The oxidation state of all Si atoms was identified as Si4+, with a tiny portion of Si0, indicating that the whole deposited film at such a high temperature was almost oxidized to form a SiO2 phase. Consistently, the binding energy of Cu 2p3/2 showed a notable shift by ~0.4\u2009eV compared to that of the SCCF film, indicating\u00a0that all Cu atoms were also oxidized (Fig.\u00a03f). The XPS measurements confirmed that the copper surface can remain stable at 300 \u00b0C and underwent a\u00a0transition to a copper oxide at such a high temperature of 450 \u00b0C, consistent with the XRD results. The comprehensive XRD and XPS analyses confirmed that the HTOR effect, which keeps the Cu surface intact, remains stable at least up to 300 \u00b0C. Next, to gain insights into atomic-level surface characteristics associated with the origin of HTOR, we utilized advanced microscopy and spectroscopy techniques.\n\nProfiles of Si 2p and Cu 2p peaks for a, b Si-deposited single-crystal Cu thin film (SCCF), c, d post-heated sample at 300\u2009\u00b0C for 30\u2009min, and e, f post-heated sample at 450 \u00b0C for 30\u2009min, respectively. Note that the depth profiling was conducted at a low etching rate of 0.1\u2009nm/sec to reduce a sputter-damage effect on the sample. The solid lines in Fig.\u00a03a, c, e represented different oxidation states of silicon: black (4+), magenta (3+), green (2+), pink (1+), red (0) and navy (reproduced spectrum). Detailed information regarding the fitting parameters is provided in Supplementary Note 1 in supplementary materials, and source data are provided as a Source Data file.\n\nThe characteristics of a Si-deposited SCCF were investigated using annular dark-field scanning transmission electron microscopy (ADF-STEM) combined with electron energy loss spectroscopy (EELS) (Fig.\u00a04)21. The ADF-STEM image (top panel in Fig.\u00a04a) showed the uniform surface of the Si-deposited SCCF. EELS elemental mapping of Si (green), O (red), and Cu (blue) in the film revealed an overlayer consisting of three distinct layers on the SCCF surface (Fig.\u00a04a). The EELS intensity profiles of each element (Fig.\u00a04b) indicated that the overlayer consisted of a SiOx\u2013Si\u2013SiOx triple structure. Low-loss EELS analysis revealed that the three layers were electronically independent and existed as amorphous layers. The spectra of the top and bottom SiOx layers showed a different feature compared to that of the Si layer or Cu film for the low loss energy range (5\u201335\u2009eV), which is dominated by plasmon generation reflecting information on the local dielectric properties and electron density22. The core-loss EELS analysis for Si L edge further confirmed that the probable formation of either a Cu alloy or a solid solution was ruled out because it revealed the absence of Si diffusion into the Cu film (Supplementary Fig.\u00a04). The ADF-STEM image of the Si-deposited SCCF revealed a flat surface with occasional monoatomic surface steps (Fig.\u00a04c)14. The Cu L2,3 edges after ~930\u2009eV originally exhibit a step-like edge structure because of the almost filled d-band. By contrast, a sharp L3 peak due to the transition of the 2p3/2 to d states is observed in the Cu2O (Fig.\u00a04d). Hence, the change in the Cu bonding state could be detected by tracking the change in the Cu L3 intensity. Figure\u00a04e shows a series of Cu L2,3 edges obtained for each Cu(111) layer from the inside to the surface. Moving to the Cu surface, we observed a slight increase in the L3 peak (see the red profiles). By measuring the intensity of the L3 peak (A) relative to that of the flat level (B) and comparing it with the pristine SCCF (Fig.\u00a04f), we observed that the A/B ratio (red) increased gradually to a high value across the Cu surface compared to that of the untreated SCCF (blue). This implies that Cu atoms at the surface bonded with O atoms chemically.\n\na (top) Low-magnification annular dark-field (ADF) image and (bottom) electron energy loss spectroscopy elemental maps of Si (green), O (cyan), and Cu (blue) of the Si-deposited SCCF. b Normalized elemental profiles of the three elements. The gray profile of the signal contrast in the ADF image is given in reference to the Cu film. c Atomic resolution ADF image of the Si-treated SCCF showing the atomically clean surface. d Energy loss near edge structures (ELNES) of Cu L2,3 for Cu2O and the untreated SCCF. e Series of Cu L2,3 ELNES obtained across the Si-treated SCCF surface, as indicated by the vertical arrow in (c). f Profiles showing the change in the intensity ratio, A/B, of the Cu L3 (peak A at ~934\u2009eV) to the flat level (peak B at ~940\u2009eV) for the pristine SCCF (violet) and Si-treated SCCF (orange). Note that the A/B ratio of fully oxidized Cu2O is ~1.98 (orange spectrum in d). The solid line denotes the reference of pure Cu. Source data are provided as a Source Data file.\n\nThe elemental profiles across the interface between the bottom SiOx and the SCCF (Fig.\u00a04b and Supplementary Fig.\u00a05) show that the O K (red) profile is much delayed toward the SCCF surface than the Si L (green) profile, indicating the bond order of Si\u2013O\u2013Cu linkage formed at the SCCF surface. The corresponding Cu L, O K, and Si L spectra (Supplementary Fig.\u00a05) extracted from the interface region support the formation of the atomic-level triple layer, which might be a core driver of the oxidation resistance. We designate the characteristic surface triple layer with Cu\u2013O\u2013Si linkage as the \u201cSiCuOx\u201d layer and will hereafter refer to these samples with a modified surface as \u201csioxed\u201d SCCF instead of Si-deposited SCCF. To check whether the atomic-level triple layer contributes to the high-temperature oxidation resistance (HTOR) effect, the sioxed SCCF was heated at 300 \u00b0C for 30\u2009min after peeling off the nanoscale SiOx\u2013Si\u2013SiOx triple overlayer (Supplementary Figs.\u00a06 and 7). The result shows that the SCCF surface was maintained without forming oxide, the same as the one with the nanoscale triple overlayer, proving the significant role of the atomic-scale surface-modified layer in realizing the HTOR property. It is worth noting that the formation mechanism of the nanoscale triple overlayer remains elusive and seems inevitable empirically. However, we consider that the weakly bonded bottom SiOx layer has contributed to stably forming the anchor Si-sealant O on the Cu surface, i.e., the SiCuOx layer, for blocking posterior oxidation under high-temperature ambiance.\n\nIncreasing the ionic bond character at the Cu surface induces lattice compression due to strong lattice-charge coupling23. To find structural evidence, we performed geometric phase analysis (GPA)24,25 to map the shear strain field distribution (Exy) for the sioxed SCCF (Fig.\u00a05a). The results indicated that the sioxed SCCF had the same structural quality as the untreated SCCF without defects14. To assess the probable change in the Cu bond length at the surface, we measured the projected atomic distance (PAD) for the STEM image26,27,28. The out-of-plane (H) PAD difference map for the sioxed SCCF is shown on the right side of Fig.\u00a05b. Histograms for the three regions (I\u2013III) along the respective in-plane [\\(11\\bar{2}\\)] and out-of-plane [111] directions are given in Fig.\u00a05c, d, which show how the lattice changes towards the surface for in-plane (A) and out-of-plane (H) directions, respectively. For the out-of-plane direction (H), a subtle lattice contraction near the surface was observed (Fig.\u00a05d), whereas no difference was observed for the in-plane direction (A) (Fig.\u00a05c). The estimated magnitude of the out-of-plane contraction was ~6\u2009pm (translating to approximately \u22122.8% as compressive strain), and the range of the compression extended to three\u00a0atomic layers deep from the surface, which differed from the pristine SCCF showing no picoscale PAD displacement in any direction (Supplementary Fig.\u00a08a\u2013c). Additional measurements of a Si-treated sample prepared from a different batch showed the same feature (Supplementary Fig.\u00a08d\u2013f). The impact of the compressive strain on the oxygen infiltration was explicitly addressed by the theoretical calculation. (Supplementary Fig.\u00a09) The microscopic and spectroscopic investigations revealed three features of the sioxed SCCF: first, the SCCF surface had HTOR induced by the characteristic surface structure with Si\u2013O\u2013Cu bonds (the SiCuOx skin layer); second, the Cu lattice structure was preserved without the formation of a Cu\u2013Si alloy; and third, characteristic lattice contraction (\u22122.8%) for the out-of-plane direction was detected at the surface.\n\na Paired annular dark-field (ADF) images and the corresponding Exy maps for the surface part of the sioxed SCCF. The atomic resolution cross-sectional ADF-STEM images were obtained for the [\\(1\\bar{1}\\)0] orientation, and the strain maps were obtained by geometric phase analysis (GPA). The reference area used to calculate the relative strain is marked by the white box in the map. The irrelevant complex patterns of the carbon film are shaded in gray for clarity. b Out-of-plane projected atomic distance (PAD) map of the sioxed SCCF. The schematic at the top left represents the PAD (A and H\u2009=\u2009B\u00b7cos\u03b8) measurement. Note that the magnitude of the PAD map is expressed by false colors corresponding to the bottom color scale bar for visualization, representing the difference between the measured PADs and the values (A\u2009=\u20092.23\u2009\u00c5 and H\u2009=\u20092.11\u2009\u00c5) of bulk Cu. c, d Histograms showing the PAD distribution of A (in-plane distance) and H (out-of-plane distance), obtained for the three regions marked I\u2013III. Gaussian fits are represented by solid lines, while dashed lines denote the mean values of each region. Reproducibility was confirmed through additional measurements conducted on the Si-treated sample prepared from a different batch. e Schematic diagram of the structure of the sioxed Cu(111) thin film. f Structural model of the SiCuOx skin layer showing the side (top) and top (bottom) views. For clarity, the top view was generated after making a crosscut along the dotted line. Brown, red, and violet spheres represent Cu, O, and Si atoms, respectively. Small white spheres represent O atoms in the amorphous SiO2 layer. g Energy profiles of O atoms penetrating the pristine and sioxed SCCF surfaces. Path 1\u20134 represent slightly different paths the O atoms can take penetrating the sioxed SCCF surfaces. The solid black line denotes the SCCF surface, delineating the boundary between the external and internal structures. The dashed line indicates the region where SiCuOx has formed. Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56709-w/MediaObjects/41467_2025_56709_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56709-w/MediaObjects/41467_2025_56709_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56709-w/MediaObjects/41467_2025_56709_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56709-w/MediaObjects/41467_2025_56709_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56709-w/MediaObjects/41467_2025_56709_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "To elucidate the high-temperature oxidation resistance (HTOR) of sioxed Cu, we used first-principles DFT29,30,31 to construct a microscopic model of the SiCuOx layer based on our experimental observations involving energy minimization of an extensive list of candidates. As noted in our STEM analysis, the SiCuOx layer (the boxed regions in Fig.\u00a05e, f) showed no Si diffusion into the Cu thin film, and the face-centered cubic Cu structure was preserved up to the outermost Cu layer. This also indicated that the SiCuOx layer in direct contact with the Cu thin film was a layer of O atoms, consistent with the STEM-EELS profile analysis (Fig.\u00a04b). The energetically favorable model of SiCuOx also exhibited lattice contraction for the out-of-plane direction across the surface (Supplementary Fig.\u00a09), with a compressive strain of ~5.7%, consistent with the characteristic layer compression phenomenon observed in the sioxed Cu thin film (Fig.\u00a05b\u2013d). Our DFT calculation showed that the optimized structure of SiCuOx had an energetically stable interface with an interface energy of \\(-2.72\\,{\\mbox{eV}}/{\\mathring{\\rm A} }^{2}\\). The energy profile in Fig.\u00a05g shows that the penetration of an O atom through a pristine Cu(111) surface requires an activation energy of 2.71 eV14. For the sioxed Cu thin film, the surface was fully covered with densely populated sealant O atoms blocking all adsorption sites and Si atoms anchoring O atoms in place (Fig.\u00a05f). Due to the self-regulating behavior, diffusion through the SiO2 layer and penetration through the dense SiCuOx layer of external O atoms would be unlikely. Rather, the O atoms in contact with the Cu thin film at the interface would be more likely to infiltrate. Figure\u00a05g shows that there are several different paths for O infiltration with high activation energies. Even along the easiest path, the O atom at the interface requires an activation energy of 5.41\u2009eV to infiltrate into the second subsurface interlayer space (Supplementary Fig.\u00a010). This activation energy exceeds that of the untreated Cu and dramatically reduces the oxidation reaction rate. Note that the activation energy (EA) for O-atom infiltration from the first to second subsurface interlayer space of the sioxed SCCF (2.89\u2009eV) is much higher than that of the untreated SCCF (1.28\u2009eV) (Fig.\u00a05g). The diffusion of O atoms in the interior is hindered because the first few Cu(111) layers near the interface are compressed in the out-of-plane direction and form a hardened crust, as evidenced by the compressive strain observed in our STEM (Supplementary Fig.\u00a08) and theoretical (Supplementary Figs.\u00a09 and 11) data. Our analysis reveals that the HTOR provided by SiCuOx originates from the following three factors, as schematically illustrated in Fig.\u00a05e: The sealant O atoms in the sioxed interface (red O atoms in Fig.\u00a05f) block all entry of additional O atoms into the Cu film; the sealant O atoms in the interface are in turn anchored by Si atoms and require a much higher activation energy to break the bond with Si atoms and infiltrate the Cu film; and SiCuOx compresses the top layers of the Cu film (region I in Fig.\u00a05b). To further elucidate the influence of Si\u2013O-Cu triple bonds on the HTOR, additional DFT calculations for oxygen infiltration were performed when the SiCuOx layer had atomic defects such as Si point and Si\u2013O pair defects. The results revealed that the activation energy for oxygen infiltration into Cu substantially decreases more than 3.6\u2009eV compared to the case of the SiCuOx layer without defects, implying that the formation of atomically uniform SiCuOx layer is crucial for achieving effective HTOR (Supplementary Fig.\u00a012). The bond character analysis shows that the O-atom in the SiCuOx layer is bonded with the anchoring elements much more strongly than the one near Si\u2013 or Si\u2013O defects leading to the strong oxidation resistance of the SiCuOx layer (Supplementary Fig.\u00a013).\n\nFigure\u00a06a shows the resistivity of reference samples and sioxed SCCFs after heat treatment. The heat treatments lasted 30\u2009min, and the Si deposited for the measurements was 10\u2009nm thick. The first and second panels show the reference resistivity of three representative materials and the invariant resistivity after SiCuOx treatment, respectively. The last two panels show the critical temperature at or above which pristine and sioxed SCCFs can no longer exhibit high-temperature oxidation resistance (HTOR). Remarkably, the resistivity of the six films was maintained at almost the same value as that of pristine Cu, even after heat treatment up to 420 \u00b0C. Moreover, all sioxed samples remained unchanged for up to 60\u2009h at 200\u2009\u00b0C (Supplementary Fig.\u00a014). The resistivity value close to that of an insulator after heat treatment above 470 \u00b0C indicated that the sioxed film was mostly oxidized above that temperature.\n\na Resistivity measurement of sioxed SCCFs as a function of temperature and comparison with reference materials and non-treated SCCF. Error bars represented the standard deviation of resistivity measurements obtained from five randomly selected samples. The dashed lines represent the reference resistivity values for bulk Au (\\({\\rho }_{{Au}}\\)) and Cu (\\({\\rho }_{{Cu}}\\)), respectively. Upward arrows indicate divergence of the resistance beyond the conductive regime with increasing heating temperature. Source data are provided as a Source Data file. b HTOR of sioxed Cu foils. c HTOR of a sioxed polycrystalline Cu film deposited and patterned on a polymer substrate for flexible devices. Note that the temperature of 150 \u00b0C is the maximum tolerable value for the polymer substrate. d Pictures of Fe foil (upper row) and sioxed Fe foil (bottom row) before and after heat treatment at 300\u2009\u00b0C for 30\u2009min. e Pictures of Ni foil (upper row) and sioxed Ni foil (bottom row) before and after heat treatment at 400\u2009\u00b0C for 30\u2009min. Scale bars are 5\u2009mm. To ensure statistical validity, each experimental condition was conducted with a minimum of ten samples at each designated temperature. A visual assessment of the samples indicated a consistency in coloration across all replicates.\n\nThe HTOR effect in various forms of Cu was again demonstrated to be feasible. When the process was applied to commercial Cu foils (Fig.\u00a06b), polycrystalline Cu grown on a polymer substrate of polyester (Fig.\u00a06c), and polycrystalline Cu film on glass (Supplementary Fig.\u00a015), a useful HTOR effect was observed. The commercial Cu foils generally oxidized when heated at 150 \u00b0C for 30\u2009min (Fig.\u00a06b, upper panel, middle) and then completely transformed into CuO, which is black, when heated at 300\u2009\u00b0C for 30\u2009min (upper panel, rightmost). In striking contrast, the sioxed foils did not show any color change up to 300 \u00b0C after 30\u2009min and only showed a slight discoloration and oxidized phase above 400 \u00b0C after 30\u2009min (Fig.\u00a06 and Supplementary Fig.\u00a016). At a moderately high temperature of 200\u2009\u00b0C, the sioxed Cu foils withstood oxidation for 60\u2009h or longer. The sioxed polycrystalline Cu on the polyester substrate is stable on heating for several hours at temperatures up to 150 \u00b0C, which is the maximum temperature the polyethersulfone (PES) substrate can withstand. Whereas the patterned circuitry using polycrystalline Cu/PES corroded completely after thermal treatment at 150 \u00b0C for 30\u2009min (upper panel in Fig.\u00a06), the sioxed Cu/PES showed no corrosion even after thermal treatment at 150 \u00b0C for 10\u2009h and maintained the same conductivity after 1,000 bending cycles (lower panel in Fig.\u00a06c). The SiCuOx process notably enhanced oxidation resistance even to Cu-specific etchants (Supplementary Fig.\u00a017). Our extra DFT calculations further show that the sioxing process would give HTOR to Cu thin films with surfaces other than (111) orientation (Supplementary Fig.\u00a018). The above sioxing effects were not limited to Cu but also worked for Fe (Fig.\u00a06d) and Ni (Fig.\u00a06e); thus, the sioxing effect is universally applicable to oxidizable metals. The features of the sioxed Fe and sioxed Ni forming the HTOR were almost the same as those of the sioxed Cu (Supplementary Fig.\u00a019).\n\nIn summary, we achieved notable HTOR for Cu adaptable to high-temperature environments. This approach, realized through sputtering deposition of Si under optimized ASE conditions, is reliably applicable to some oxidizable metals (Ni and Fe in this case), ranging from single-crystalline thin films to patterned polycrystalline films and foils with rough surfaces. This versatility ensures a broad spectrum of applications. The HTOR is a result of effectively blocking O pathways by O atoms themselves, anchored in place by Si atoms. The formation of an atomically thin SiMOx layer, stemming from Si\u2013O\u2013M (M = Cu, Ni, or Fe) bonding at the surface, along with a few layers of metal crust hardened during SiMOx formation, further impedes O infiltration. Additionally, sioxed Cu retains the electrical properties and geometric uniformity of pristine Cu. This superior property is sustained up to 400\u2009\u00b0C, representing near-permanent corrosion resistance at room temperature. Considering the operating temperature range of 70\u2013125 \u00b0C required for all-round harsh-environment electronics32, our approach opens promising avenues for future technologies based on oxidizable metals.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56709-w/MediaObjects/41467_2025_56709_Fig6_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "SCCFs were prepared using the ASE technique14,15. The base pressure of the sputtering chamber was maintained at <1.7 \u00d7 10\u22126 Torr and the working pressure at 5.4 \u00d7 10\u22123 Torr. The radio frequency (RF) (13.56\u2009MHz) sputtering power was set to 25\u2009W with an Ar gas flow of 50 sccm. The Ar gas purity was 99.9999% (6\u2009N). A 430-\u03bcm-thick double-sided-polished 2-inch (001) Al2O3 wafer was used as the substrate for SCCF growth. The SCCFs grew to thicknesses between 100 and 200\u2009nm at a substrate temperature of 170 \u00b0C and a growth rate of ~3\u2009nm/min, which ensured an ultraflat Cu (111) surface with only occasional monoatomic steps14,15. Polycrystalline Cu thin films (PCCFs) were grown on glass substrates under the same sputtering conditions as SCCFs. For the formation of the SiMOx skin layer, Si was deposited on SCCFs, PCCFs, and commercial Cu, Fe, and Ni foils using an RF sputter system with a base pressure of <1.5 \u00d7 10\u22125 Torr and a working pressure of 5.4 \u00d7 10\u22123 Torr. The RF (13.56\u2009MHz) sputtering power was set to 40\u2009W with an Ar gas flow of 20 sccm. Under these conditions, the Si layer was deposited at an average growth rate of ~4\u2009nm/min. For the Cu foil, the Si layer was deposited on both sides of the foil to prevent diffusive propagation of backside oxidation. The substrate temperature in the Si sputtering process was varied from room temperature to 200\u2009\u00b0C. To test the effect of organic matter as a substrate, a Si layer was deposited at room temperature on a 188-\u03bcm-thick polyester substrate (V7610; SKC, Seoul, South Korea). The etching process for patterning was conducted using an etchant with a mixing ratio of CH3COOH:H2O2:H2O\u2009=\u20091:1:2. We conducted 10 independent repetitions of thermal treatment at each temperature point. Samples were not intentionally excluded, except in cases where they were damaged, such as being scratched on the surface or dropped in the thermal treatment furnace during handling. The sample dimensions were 10 \u00d710\u2009mm, as a 2-inch wafer could yield 12 samples, and we assessed reproducibility by testing 10 samples from the same batch.\n\nX-ray diffraction (XRD) measurements were performed using a PANalytical Empyrean Series 2 instrument with a Cu\u2013K\u03b1 source (40\u2009kV, 30\u2009mA) and a linear detector (Malvern PANalytical, Malvern, UK). Data were collected within the range of 20\u00b0 <2\u03b8\u2009<\u200980\u00b0, with a step size of 0.0167\u00b0 and a dwell time of 0.5\u2009s per point. For GIXRD, the X\u2019pert PRO MPD (PANalytical, England) instrument of the KBSI Daegu Center was used. Cu K beams served as the X-ray sources (40\u2009kV, 30\u2009mA). Data were collected within the range of 20\u00b0 <2\u03b8\u2009<\u200970\u00b0, with a step size of 0.04\u00b0, omega angle of 1\u00b0 and a dwell time of 2\u2009s per point. Scanning electron microscopy (SEM), EBSD, PF, and IPF measurements were made with a Zeiss SUPRA40 VP with a scanning electron microprobe (Zeiss, Oberkochen, Germany). X-ray photoelectron spectroscopy (XPS; ESCALAB250, Thermo Scientific, Waltham, MA, USA) was used for elemental depth profiling with a multichannel detector covering the energy range 0\u20131200\u2009eV. The depth profiles were acquired with an energy step size of 0.1\u2009eV and two different acquisition times of 5 and 51.3\u2009s. To check whether the chemical state change was influenced during the sputtering process in XPS analysis, depth profiling was performed using two different ion beams: a finely focused 1\u2009keV Ar+ and an unfocused 5\u2009keV Ar+ broad beam. No noticeable variations in binding energy were observed throughout the depth profiling. The resistivities of the samples were measured using a Hall effect measurement system (Ecopia HMS-3000; Bridge Technologies, Oslo, Norway). For atomic-scale characterization, cross-sectional TEM thin samples were prepared using a focused ion beam system (Helios NanoLab 450 \u2013 FEI; Nanolab Technologies, Milpitas, CA, USA), and possibly damaged surface layers were removed from the samples by subsequent low-energy Ar ion beam surface milling at 700\u2009eV for 15\u2009min (Model 1040 NanoMill; Fischione, Export, PA, USA). The atomic structures of the samples were captured in ADF imaging mode using double Cs-corrected STEM (JEM-ARM200CF; JEOL, Tokyo, Japan) operating at 200\u2009kV, equipped with EELS (Quantum ER965; Gatan, Pleasanton, CA, USA) and energy dispersive x-ray spectroscopy (EDX; JED-2300T, JEOL), which were used for chemical analysis of the samples. The angle ranges of the ADF detector and probe convergence semiangle were 45\u2013180 and ~24 mrad, respectively. For quantitative analysis of local strain components in the pristine and sioxed SCCFs, the GPA technique was used, which allows the mapping of two-dimensional local displacement fields by analyzing the phase shift between noncollinear Fourier components of lattice vectors g1 and g2. This GPA mapping with atomic resolution structure images provides information on the relative lattice displacement field at subnanometer resolution. The atomic positions and projected bond lengths were measured from the ADF-STEM images using home-built software with a 1024 \u00d7 1024-pixel resolution. The shape of the energy loss near-edge structure (ELNES) of an element sensitively varies in response to the chemical and electronic changes around it14,23. Therefore, to assess the chemical nature of the SCCF surface, we examined the change in the fine structures of the Cu L edge across the surface from the inside of the film using monochromatic STEM (JEM-MonoARM200F; JEOL) at the Korea Basic Science Institute (KBSI). For EELS measurements of the samples, the core-loss EELS spectrum imaging (SI) datasets of the Cu L edge were obtained as 46 \u00d7 93 pixels, translated as 15.5 \u00d7 31.4 nm2, with a scan step of 0.3\u2009nm from the inside to the surface of the samples. The vertical line scan data across the surface were extracted by horizontally averaging the SI data over a 0.3\u2009nm length. The energy dispersion was 0.25\u2009eV/ch and the dwell time 0.5\u2009s/pix. The selected range of energy loss was set to 475\u2013986\u2009eV, including the O K and Cu L2,3 edges. The random noise in the core-loss spectra was reduced by principal component analysis, which was implemented in a commercial software package (MSA; HREM Research, Tokyo, Japan), and the power-law dependency of the background intensity was removed before signal extraction of the core-loss O K and Cu L2,3 edges. Min\u2013max normalization between 0 and 1 was applied to the core-loss Cu L-edge for the energy loss range from 920\u2009eV to 991\u2009eV. To suppress the exaggerating effect of unrelated noise levels in evaluating the A/B peak ratio, the location-wise Cu L-edge profiles were obtained by averaging the energy width of 0.3\u2009eV. Error bars for the A/B ratio reflect the local statistical variation, captured by the standard deviation of three spectral measurements acquired each single atomic row within the same sample. The low-loss EELS SI dataset for the sioxed SCCF sample was obtained with an energy dispersion of 0.1\u2009eV/ch and a dwell time of 0.005\u2009s/pix at 46 \u00d7 93-pixel points with a spatial dimension of 0.3\u2009nm/pix and an energy range of 102\u2009eV, including a zero-loss peak. Nanoscale STEM-EDX maps of the constituent elements of the samples were obtained at a 256 \u00d7 256-pixel resolution with a high-efficiency dual Si drift detector x-ray detector system with a 100 mm2 collection window for each detector, and the sample drift during acquisition was corrected by tracking the reference area assigned at the acquisition setup.\n\nAll total energy calculations and geometry optimizations were performed with DFT in the generalized gradient approximation using the Perdew\u2013Burke\u2013Ernzerhof exchange-correlation functional29 with the projected augmented plane-wave method30, as implemented in VASP31. The electron wave functions were expanded in a plane-wave basis set with a cutoff energy of 400\u2009eV, and the spin polarization effect was considered in all calculations. The structure of sioxed Cu was constructed as a supercell containing Cu substrate, an interface, a SiO2 layer, and a vacuum. The Cu substrate was represented by a slab of seven layers of a (2 \u00d7 2) lateral supercell of Cu(111). The SiO2 layer was represented by a slab of (1 \u00d7 1) lateral supercells of SiO2 (001) \u03b1-cristobalite (space group p41212). A 12\u2009\u00c5 vacuum layer was used to eliminate the interaction between periodic images of the slabs. The exposed O atoms of the SiO2 layer were passivated by H atoms. The bottom three layers of the Cu substrate were fixed in their bulk positions, and the remaining atoms were fully relaxed until the force on each atom was less than 0.001\u2009eV/\u00c5 and the change in total energy was less than 10\u20135 eV\\(\\,{{\\rm{eV}}}\\). The Brillouin zone was sampled using a gamma centered 5 \u00d7 5 \u00d7 1 k-point mesh. The activation energy for O infiltration was calculated using the nudged elastic band method33. The interface energy used to compare the stabilities of different models of the SiCuOx structure was calculated as \\({E}_{{\\mathrm{int}}}=\\left[{E}_{{\\mbox{tot}}}-{\\sum}_{i}{N}_{i}{\\mu }_{i}\\right]/A\\), where Etot is the total energy of the combined system, A is the lateral area of the supercell, and Ni and \u03bci are the number and chemical potential of the \\(i\\)-th species, respectively. The atomic coordinates of the optimized computational model were provided as the Supplementary Data\u00a01 in VASP POSCAR file format.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The authors declare that the main data supporting the findings of this study are available within the article and its Supplementary Information files. 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Computer time allocation was provided by the High-Performance Computing Center (HPCC) for S.-G.K. at Mississippi State University. M.C. acknowledges support from the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Division of Materials Sciences and Engineering.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Su Jae Kim, Young-Hoon Kim, Bipin Lamichhane.\n\nCrystal Bank Research Institute, Pusan National University, Busan, Republic of Korea\n\nSu Jae Kim\n\nDepartment of Energy Science, Sungkyunkwan University, Suwon, Republic of Korea\n\nYoung-Hoon Kim,\u00a0Sang-Hyeok Yang,\u00a0Seon Je Kim,\u00a0Min-Hyoung Jung\u00a0&\u00a0Young-Min Kim\n\nCenter for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA\n\nYoung-Hoon Kim\u00a0&\u00a0Miaofang Chi\n\nDepartment of Physics and Astronomy, Mississippi State University, Mississippi State, MS, USA\n\nBipin Lamichhane,\u00a0Binod Regmi\u00a0&\u00a0Seong-Gon Kim\n\nCenter for Computational Sciences, Mississippi State University, Mississippi State, MS, USA\n\nBipin Lamichhane,\u00a0Binod Regmi\u00a0&\u00a0Seong-Gon Kim\n\nCopper Innovative Technology (CIT) Co., Busan, Republic of Korea\n\nYousil Lee\n\nElectron Microscopy Research Group, Korea Basic Science Institute (KBSI), Daejeon, Republic of Korea\n\nJae Hyuck Jang\n\nGraduate School of Semiconductor Materials and Devices Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea\n\nHu Young Jeong\n\nDepartment of Physics and Center for Berry Curvature-based New Phenomena, Chung-Ang University, Seoul, Republic of Korea\n\nMaeng-Je Seong\n\nGordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA\n\nHak Soo Choi\u00a0&\u00a0Se-Young Jeong\n\nCenter for 2D Quantum Heterostructures, Institute for Basic Science (IBS), Suwon, Republic of Korea\n\nYoung-Min Kim\n\nDepartment of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea\n\nSe-Young Jeong\n\nDepartment of Optics and Mechatronics Engineering, Engineering Research Center for Color-Modulated Extra-Sensory Perception Technology, Pusan National University, Busan, Republic of Korea\n\nSe-Young Jeong\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.-Y.J. and S.J.K. conceived this work. S.-Y.J., Y.-M.K., and S.-G.K. supervised the work and wrote the manuscript. S.J.K. and Y.L. performed thin film growths and prepared sioxed samples. Y.-H.K., J.H.J., and Y.-M.K. performed STEM, EDX, and EELS analyses. Y.-H.K., and S.-H.Y. developed Python-based home-built code. M.-H.J., Seon J.K. and H.Y.J. conducted TEM sample preparation. S.J.K. and Y.L. performed EBSD and XPS experiments and Y.-H.K. assisted with the data analyses. M.C., M.-J.S. and H.S.C. performed data interpretation. S.-Y.J. and S.-G.K. established the theoretical model, and S.-G.K., B.L. and B.R. carried out first-principles calculations. All authors participated in the manuscript review.\n\nCorrespondence to\n Seong-Gon Kim, Young-Min Kim or Se-Young Jeong.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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An impermeable copper surface monolayer with high-temperature oxidation resistance.\n Nat Commun 16, 1462 (2025). https://doi.org/10.1038/s41467-025-56709-w\n\nDownload citation\n\nReceived: 17 February 2024\n\nAccepted: 27 January 2025\n\nPublished: 08 February 2025\n\nVersion of record: 08 February 2025\n\nDOI: https://doi.org/10.1038/s41467-025-56709-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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production, enabled by electrolytic extraction of calcium from waste cement", + "pre_title": "Recycled cement production, with zero emissions", + "journal": "Nature Communications", + "published": "21 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64339-5/MediaObjects/41467_2025_64339_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64339-5/MediaObjects/41467_2025_64339_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64339-5/MediaObjects/41467_2025_64339_MOESM3_ESM.mov" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64339-5/MediaObjects/41467_2025_64339_MOESM4_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64339-5/MediaObjects/41467_2025_64339_MOESM5_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-64339-5#Sec16" + ], + "code": [], + "subject": [ + "Chemical engineering", + "Electrochemistry" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5398260/v1.pdf?c=1761131317000", + "research_square_link": "https://www.researchsquare.com//article/rs-5398260/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-64339-5.pdf", + "preprint_posted": "08 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "We report here the electrochemical production of clinker clinker precursor, containing calcium derived from the electrolytic decomposition of waste cement. Our \u201ccement recycler\u201d contains a cement electrolyzer, coupled to a cement digestion vessel, and a Ca(OH)2 isolation vessel. The cement electrolyzer creates acid and base equivalents and converts waste cement into calcium hydroxide and silica. The electrolyzer is demonstrated at current densities up to 300 mA cm\u20132 to produce yields of >80% for Ca(OH)2. We experimentally verified a 99.8% reduction in CO2 emissions when using fresh waste cement, and an 80% reduction when using aged cement (enriched with absorbed CO2) from a demolition site. Our cement recycler bypasses the need for virgin limestone and could cut global CO2 emissions by nearly 1 Gt annually, while also helping divert substantial waste cement from landfills.Physical sciences/Engineering/Chemical engineeringPhysical sciences/Chemistry/Electrochemistry/Electrocatalysis", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nT.J., S.R., and C.P.B. have filed a US provisional patent application on the work presented in this manuscript. The remaining authors declare no competing interests. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "12.2024Supplementaryvideo1AvisualdemonstrationoftheIntegratedRecycleroperatedat200mAcm2.ThegenerationofHandOHXXXionsintheclosedloopelectrolytewithanadditionofpHindicatoreffectivepHrange4XXX10duringe.mp4Supplementary video 12.2024SupportinginformationRecycledcement.pdfSupplementary information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "We report here the electrochemical production of cement clinker precursor, containing calcium derived from the electrolytic decomposition of waste cement. Our \u201ccement recycler\u201d contains a cement electrolyzer, coupled to a cement digestion vessel, and a Ca(OH)2 isolation vessel. The cement electrolyzer creates acid and base equivalents, and converts waste cement into Ca(OH)2 and SiO2. The electrolyzer was demonstrated at current densities up to 300\u2009mA\u2009cm\u20132 to produce yields higher than 80% for Ca(OH)2. We experimentally verified a 99.8% reduction in CO2 emissions when using fresh waste cement, and an 80% reduction when using aged cement (enriched in CO2 due to atmospheric carbonation) from a demolition site. Our cement recycler bypasses the need for virgin limestone and could cut global CO2 emissions by nearly 1 Gt annually, while also helping divert substantial waste cement from landfills.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The cement industry contributes 8% of global CO2 emissions, largely due to the carbon-intensive process of producing cement clinker, the main component of Portland cement (Fig.\u00a01). These emissions are due to the decomposition of limestone (CaCO3) releasing stoichiometric amounts of CO2 (Eq. (1)), and kilns, heated by fossil fuels, operating at temperatures exceeding 900\u2009\u00b0C and 1450\u2009\u00b0C to decompose limestone and form clinker (mainly 2CaO\u00b7SiO2 and 3CaO\u00b7SiO2; Eqs. (2) and (3), respectively)1,2,3.\n\nIn industrial cement production, limestone (CaCO3) is ground and thermally decomposed in a calciner at 900\u2009\u00b0C to produce lime (CaO) and CO2 (steps 1\u20135, blue). CaO is then heated with silica (SiO2) in a kiln at >1450\u2009\u00b0C to form cement clinker (2CaO\u00b7SiO2 and 3CaO\u00b7SiO2; steps 6\u20137, blue)4,5,10. The cement clinker is further processed into Portland cement and used in buildings (steps 8\u201310, blue). Our alternative electrochemical pathway (orange) uses a cement recycler to convert pre-screened waste cement into hydrated lime (Ca(OH)2) with minimal CO2 emissions (\u22640.2%). The Ca(OH)2 is then combined with SiO2 in the kiln to form the same cement clinker.\n\nWe took the perspective that calcium is the active, valuable ingredient of limestone, and that the process of extracting calcium from limestone is carbon intensive4,5. This insight led us to consider if there are other sources of calcium that could be used to form the same clinker used in the cement industry today. Our line of questioning pointed us toward cementitious waste. Despite the high carbon intensity of clinker production, it is estimated that 5% of cement (120\u2013200 million tons per year) actually goes unused. This excess cement is discarded each year due to ineffective unloading and surplus preparation6,7, much of which is either sent to landfills8 or downcycled for lower-grade applications like backfill, road sub-base, or shoreline protection9. In addition to this pure cement waste, there is also 3\u201310 Gt of concrete-derived construction and demolition waste generated every year.\n\nWe previously developed a continuous-flow cement electrolyzer that converts CaCO3 into Ca(OH)210,11. This electrolyzer was designed to electrolytically generate H\u207a ions for reaction with CaCO3 to form Ca2+ ions, which then, in turn, react with OH\u207b generated at the cathode to form Ca(OH)2 (Eq. (4) and Supplementary Fig.\u00a01). This Ca(OH)2, or \u201chydrated lime\u201d, can then be mixed with silica (SiO2) calcined to form clinker.\n\nWaste cement accounts for roughly 10\u201315\u2009wt% of cementitious waste12,13,14,15, which can be decomposed under acidic conditions to extract Ca2+ ions without releasing CO216. We therefore set out to use waste cement as a feedstock instead of limestone. Given the abundance of waste cement17, recycling these materials in our reactor would offer a practical method for decarbonizing cement production18,19.\n\nHere, we introduce a reactor system, which we denote as the \u201ccement recycler\u201d, capable of electrochemically generating Ca(OH)2 from waste cementitious feedstock, with effectively no CO2 emissions. We achieved this goal by integrating our previously reported \u201ccement electrolyzer\u201d, a waste cement digestion vessel (\u201ccalcium extractor\u201d), and a Ca(OH)2 isolation unit (\u201clime extractor\u201d). These three units work collaboratively to directly extract Ca2+ ions from waste cement in high yield (80%) to form high-purity Ca(OH)2 (90% m/m). This cement recycler forms Ca(OH)2 with a 99.8% reduction in CO2 emissions relative to conventional cement production when using fresh cement waste. The CO2 emissions are reduced by 80% when extracting calcium from aged waste cement (derived from concrete in demolished building materials, which contains CO2 that is absorbed over the life span of the material). We see opportunities to use the cement recycler to reduce our reliance on virgin limestone, to reduce cementitious waste, and to eliminate the need for thermal recycling20,21. The cement electrolyzer has the potential to cut annual global CO2 emissions by nearly 1 Gt by repurposing the large amounts of cementitious waste generated worldwide22.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64339-5/MediaObjects/41467_2025_64339_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "The waste cementitious materials used in this study include both fresh waste cement and aged waste cement6,7,17. Fresh waste cement refers to freshly lab-hydrated cement that has not been cured into concrete, while aged waste cement is derived from concrete in demolished building materials. For this work, we utilized different types of commercial cement as sources of fresh waste cement: (i) White Portland Cement (White PC), (ii) St. Marys\u00ae Portland Cement (St. Marys), and (iii) Rapid Set\u00ae CEMENT ALL Multi-purpose Concrete (CementAll). These cement powders were each hydrated, set for 28 days, and then crushed into chips. Aged waste cement was sourced from a local demolition site (Riverside Recycling LTD., Canada).\n\nThe chemical compositions of the waste cement were analyzed using powder X-ray diffraction (Supplementary Fig.\u00a02, Supplementary Table\u00a01). Fresh waste cement showed the presence of Ca(OH)2, likely formed during clinker hydration. In contrast, aged waste cement contained negligible amounts of Ca(OH)2, with CaCO3 being one of the dominant species (Supplementary Table\u00a01). The higher concentration of CaCO3 in aged cement is expected due to weathering carbonation (where Ca(OH)2 is converted into CaCO3 over time) over the lifespan of the material. While these differences are relevant for evaluating the carbon footprint of cement recycling, calcium silicate hydrate remains the predominant Ca-bearing phase and the principal hydration product in both fresh waste cement and aged waste cement23,24. In acid-mediated calcium extraction, calcium silicate hydrate serves as the primary source of leachable calcium, and the chemical reactivity determines the efficiency of the extraction25.\n\nOur cement recycler integrates: a cement electrolyzer, a waste cement digestion vessel (calcium extractor), and a Ca(OH)2 isolation unit (lime extractor).\n\nThe cement electrolyzer (Fig.\u00a02 and Supplementary Fig.\u00a03) follows a similar design to our previous work (Supplementary Fig.\u00a01)10,11. The anode and cathode (active area\u2009=\u20095\u2009cm2) are made of nickel foam, a widely studied and commonly used electrode material for alkaline water electrolysis26. The anode chamber is separated from the central chemical chamber by a bipolar membrane (BPM), while a cation exchange membrane (CEM, Nafion\u2122) separates the chemical chamber from the cathode chamber10. The oxygen evolution reaction (OER) occurs at the anode (Eq. (5))10 using a circulated 3\u2009M KOH anolyte (Fig.\u00a02, shown in black). The BPM produces OH\u207b ions through water dissociation to maintain a stable anolyte concentration (Eq. (6)). The chemical and cathode chambers share a continuously circulated closed-loop electrolyte (0.2\u2009M CaCl2 and 1\u2009M KCl, 100\u2009mL, shown in pink and blue) at a flow rate of 1\u201320\u2009mL\u2009min\u20131. This closed-loop electrolyte facilitates the transfer of H+ ions from the BPM into the chemical chamber, and is diverted to the calcium extractor where acid digestion drives waste cement decomposition (Eq. (7)). The closed-loop electrolyte also supports the hydrogen evolution reaction (HER) in the cathode chamber where OH\u207b ions are generated and reacted to precipitate Ca(OH)2 (Eq. (8)). The overall balanced reaction is presented in Eq. (9).\n\nThe anolyte (3\u2009M KOH) circulates exclusively within the anode chamber, while the closed-loop electrolyte (0.2\u2009M CaCl2 and 1\u2009M KCl) flows through both the chemical and cathode chambers. H\u207a ions generated by the bipolar membrane (BPM) are directed to the calcium extractor, where they react with waste cement to extract Ca2+ ions. These Ca2+ ions are carried by the closed-loop electrolyte to the cathode chamber, where they react with OH\u2013 ions produced at the cathode, forming Ca(OH)2 precipitate. The Ca(OH)2 is then separated in the lime extractor, and the closed-loop electrolyte is recirculated for continued operation.\n\nTo both decompose waste cement and isolate Ca(OH)2, the calcium extractor and lime extractor were integrated with the cement electrolyzer (Fig.\u00a02 and Supplementary Fig.\u00a03). Both extractors were adapted from Hempel distillation columns, and were equipped with cotton filters to prevent precipitate from entering the electrolyzer. For experimental campaigns, the calcium extractor was loaded with fresh/aged waste cement chips (detailed preparation procedure described in Methods), and received electrolyte from the chemical chamber. The acidic electrolyte (measured to have a pH of 0.5\u20131.0) flowing into the calcium extractor dissolved most of the reactive calcium from the waste cement, and the Ca2+-rich eluent from the column was recirculated to the cathode chamber. Unreacted cement and solid byproducts, such as SiO2 verified by X-ray diffraction, were captured on filters. The Ca2+-rich solution was reacted with the caustic (OH\u2013 ions) produced at the cathode to form Ca(OH)2 precipitate. The precipitate was directed into the lime extractor for separation and isolation of Ca(OH)2. The eluent electrolyte from the lime extractor was then recirculated into the chemical chamber to close the loop for the electrolyte solution.\n\nThe experimental campaigns were operated in galvanostatic mode at current densities of 100\u2009mA\u2009cm\u20132, 200\u2009mA\u2009cm\u20132, and 300\u2009mA\u2009cm\u20132. We demonstrated operation at 200\u2009mA\u2009cm\u20132 in Supplementary Movie\u00a01, where we also tracked the generation of H+ and OH\u2013 ions in the closed-loop electrolyte using a pH indicator with an effective range of pH 4\u201310 (pH 4\u2009=\u2009red; pH 10\u2009=\u2009purple). We then performed experimental campaigns to monitor experimental parameters such as electrolyte pH, cell voltage, and voltage stability. Unless otherwise specified, all performance measurements of the cement recycler reported below were conducted using aged waste cement as the feedstock.\n\nAfter electrolysis, the cotton filter from the lime extractor was immersed in 100\u2009mL of deionized water and subjected to ultrasonication to release the electrochemically produced Ca(OH)2 into suspension. The resulting product was recovered by filtration and dried at 60\u2009\u00b0C. The SiO2-rich byproduct produced from waste cement decomposition was collected from the calcium extractor, dried, and sieved through a No. 60 mesh. The retained fraction (>250\u2009\u03bcm) and the passed fraction (<250\u2009\u03bcm) were separately analyzed by X-ray diffraction to identify the crystalline compositions.\n\nDuring electrolysis, the chemical chamber receives an alkaline electrolyte saturated with Ca(OH)2 from the lime extractor (Fig.\u00a03a). Based on the Ksp of Ca(OH)2 and the Ca2+ concentration at room temperature (Ksp, Ca(OH)2\u2009=\u20096.9\u2009\u00d7\u200910\u22126 at 20\u2009\u00b0C, [Ca2+]\u2009=\u20090.2\u2009M), the pHtheoretical is calculated to be 11.8 when entering the chemical chamber of the cement electrolyzer. This strongly alkaline electrolyte needs to become acidic before it exits the chemical chamber and enters the calcium extractor. A lower electrolyte flow rate enables a higher pH swing by providing a longer residence time for the electrolyte in the electrolyzer to receive more H+ ions from the BPM.\n\na Schematic of pH sampling points: pHin, calcium, pHout, calcium, and pHin, lime. b Top row: Theoretical PE (calculated from Ksp and [Ca2+]) and actual PE (calculated from pH values) as a function of electrolyte flow rate. Bottom row: The pH dependence on the flow rate of the closed-loop electrolyte within the cement recycler. pH values and the derived PEs were obtained from single measurements.\n\nTo evaluate the impact of flow rate on waste cement decomposition, we calculated the theoretical proton efficiency (PE) in the chemical chamber as a function of flow rate (Eq. (10), which can be substituted to yield Eq. (11)):\n\nwhere I is the total current (A), t is a time conversion factor (60\u2009s\u2009min\u20131), F is the Faraday constant (96485.3321\u2009s\u2009A mol\u20131), [OH\u2013] is the concentration of OH\u2013 ions in the electrolyte (6.28\u2009\u00d7\u200910\u20133 M, calculated from pHtheoretical), Q is the flow rate of the closed-loop electrolyte (mL min\u20131). Importantly, a higher PE generates a more acidic electrolyte, increasing the rate of waste cement decomposition in the calcium extractor. Figure\u00a03b shows theoretical PE values as a function of flow rate.\n\nWe measured the actual PE by analyzing the pH of the closed-loop electrolyte at different flow rates. We measured the pH of the closed-loop electrolyte within the reactor at the inlet (pHin, calcium) and outlet (pHout, calcium) of the calcium extractor, and the inlet (pHin, lime) of the lime extractor (Fig.\u00a03a). There is no measurable change in pH between pHin, lime and pHout, lime at the outlet of the lime extractor, and so we do not include pHout, lime. At 200\u2009mA\u2009cm\u20132, we observed a sharp increase in pHin, calcium from 0.61 to 11.28 when the flow rate was increased from 2.5\u2009mL\u2009min\u20131 to 20\u2009mL\u2009min\u20131 (Fig.\u00a03b). Higher pHin, calcium values indicated less effective Ca2+ extraction from waste cement at higher flow rates. The pHout, calcium and pHin, lime remained relatively stable at around 10 and 12, respectively. Figure\u00a03b (pink line) compares the actual PE with the theoretical values, emphasizing the importance of maintaining a low flow rate. The discrepancy between theoretical and actual PEs was due to the incomplete consumption of electrochemically generated H+ in the calcium extractor, as reported by the acidic pHout, calcium at the outlet of the calcium extractor (Fig.\u00a03b).\n\nWe extended our investigation to other current densities (100\u2009mA\u2009cm\u20132 and 300\u2009mA\u2009cm\u20132), determining that flow rates of 1.5\u2009mL\u2009min\u20131, 5.0\u2009mL\u2009min\u20131, and 10.0\u2009mL\u2009min\u20131 achieve a PE of 85\u201390% at current densities of 100\u2009mA\u2009cm\u20132, 200\u2009mA\u2009cm\u20132, and 300\u2009mA\u2009cm\u20132, respectively (Fig.\u00a03b).\n\nWe evaluated the key electrolyzer performance parameters for our cement recycler under different conditions. Voltages of 4.6\u2009V, 6.3\u2009V, and 7.7\u2009V were required to drive electrolysis at current densities of 100\u2009mA\u2009cm\u20132, 200\u2009mA\u2009cm\u20132, and 300\u2009mA\u2009cm\u20132, respectively (Fig.\u00a04a, Supplementary Table\u00a02). These voltages were measured at the idealized flow rates determined in the previous section (1.5\u2009mL\u2009min\u20131, 5.0\u2009mL\u2009min\u20131, and 10.0\u2009mL\u2009min\u20131 at 100\u2009mA\u2009cm\u20132, 200\u2009mA\u2009cm\u20132, and 300\u2009mA\u2009cm\u20132, respectively.) and were consistent with our previously reported values. As anticipated, the voltage increased with higher current densities10,27.\n\na Voltage measured as a function of the current density of the cement recycler. Flow rates of 1.5\u2009mL\u2009min\u20131, 5.0\u2009mL\u2009min\u20131, and 10.0\u2009mL\u2009min\u20131 were set for current densities of 100\u2009mA\u2009cm\u20132, 200\u2009mA\u2009cm\u20132, and 300\u2009mA\u2009cm\u20132, respectively. Error bars represent the standard deviation of four independent measurements. b The concentration of Ca2+ ions in the closed-loop electrolyte, exiting the chemical chamber and before entering the calcium extractor, was measured using inductively coupled plasma optical emission spectroscopy during electrolysis at 200\u2009mA\u2009cm\u20132. Error bars represent the standard deviation of three independent experiments. c Cell voltages (pink, corresponding to the left y-axis) and Ca2+ concentration of the closed-loop electrolyte within the reactor at the outlet of the calcium extractor ([Ca2+]out, calcium, blue, corresponding to the right y-axis) from the cement recycler overtime at 100\u2009mA\u2009cm\u20132. All voltages are reported as the full electrolyzer voltage measured in a two-electrode configuration. No reference electrode was employed, and no iR correction was applied.\n\nWe then evaluated the concentration of Ca2+ ions in the closed-loop electrolyte over time to ensure that steady-state conditions were maintained during electrolysis. Since an initial addition of 0.2\u2009M Ca2+ ions was made to the electrolyte to promote Ca(OH)2 precipitation, which is slightly soluble in water, it was essential that the Ca2+ ions were not depleted during the process. To maintain a steady electrolyte composition, the Ca2+ ions consumed at the cathode must be replenished by the calcium extractor. To confirm the effective extraction of Ca2+ ions from waste cement, we collected aliquots of the closed-loop electrolyte at the outlet of the calcium extractor at regular intervals ([Ca2+]out, calcium, Fig.\u00a04b). The aliquots were analyzed by inductively coupled plasma optical emission spectroscopy to monitor Ca2+ concentrations of the closed-loop electrolyte over time. During a 2-h electrolysis experiment at 200\u2009mA\u2009cm\u20132, we observed constant [Ca2+]out, calcium with a variance of less than 2.5% (Fig.\u00a04b). Experiments at 100\u2009mA\u2009cm\u20132 and 300\u2009mA\u2009cm\u20132 also showed constant [Ca2+]out, calcium readings (Supplementary Figs.\u00a04 and 5, respectively). We also performed a control experiment using an electrolyte without added Ca2+ ions at 200\u2009mA\u2009cm\u20132 for 2\u2009hours (Fig.\u00a04b). In this case, the [Ca2+]out, calcium stabilized at 670 ppm (0.02\u2009M) after 30\u2009minutes, with only trace amounts of Ca(OH)2 (yield of 3.6%, Table\u00a02), likely due to the ion product being below the Ksp, Ca(OH)2.\n\nFinally, we investigated the durability of both the cell voltage and the calcium concentration [Ca2+]out, calcium in the closed-loop electrolyte. We observed a gradual increase in voltage during continuous electrolysis, primarily due to the deposition of Ca(OH)2 on the CEM and the cathode (Supplementary Fig.\u00a06, blue). To mitigate this issue, we initially adopted a strategy of cleaning the cathode chamber with 0.5\u2009M HCl for 5\u2009min after each hour of electrolysis at 100\u2009mA\u2009cm\u20132 over a 15-h period. However, this frequent cleaning protocol is impractical for large-scale deployment and would hinder broader industrial adoption of the cement recycler.\n\nTo address this limitation, we implemented anti-scaling modifications in the cathode chamber. Specifically, we incorporated a polyaniline coating on the NafionTM membrane, inspired by our recent work11, which effectively suppresses Ca(OH)2 formation at the CEM. We also applied a hydrophobic PTFE coating to the Ni cathode to reduce adhesion of Ca(OH)2 precipitates28. These combined modifications extended the acid-cleaning interval from 1\u2009h to 10\u2009h (Fig.\u00a04c, pink, Supplementary Fig.\u00a07, and Supplementary Table\u00a03). Only a small voltage increase of 30% was observed during the 100+ hours of operation. Notably, the [Ca2+]out, calcium in the closed-loop electrolyte remained constant under these conditions (Fig.\u00a04c, blue), and no evidence of Ni dissolution was observed for either the anode or cathode (Supplementary Fig.\u00a08). Overall, these results demonstrate that the cement recycler can sustain the production of Ca(OH)2 over prolonged periods of time without electrode degradation or significant decline in voltage stability or [Ca2+]out, calcium.\n\nWe sought to measure the concentrations and purities of the gaseous (CO2 from the calcium extractor) and solid products (Ca(OH)2 in the lime extractor, SiO2 in the calcium extractor) in the cement recycler.\n\nWe used gas chromatography to quantify CO2 emissions ([CO2]calcium extractor). We first conducted control experiments using natural limestone in the calcium extractor, with N2 gas purging through the calcium extractor at a flow rate of 200 sccm during 10-min electrolysis at 100, 200, and 300\u2009mA\u2009cm\u20132. We measured high [CO2]calcium extractor values of 7600, 14,000, and 21,000 ppm, respectively (Fig.\u00a05a). Under identical conditions, experiments with aged waste cement showed 75\u201380% lower [CO2]calcium extractor values. In contrast, fresh waste cement produced [CO2]calcium extractor below 280 ppm across 100\u2013300\u2009mA\u2009cm\u20132 (Fig.\u00a05a, blue line and Supplementary Fig.\u00a09). These lower [CO2]calcium extractor values are consistent with a lower carbonate content of the waste cement relative to natural limestone. Given that the atmospheric CO2 concentration is 400 ppm, we conclude that our cement recycler operates with effectively zero CO2 emissions when processing fresh waste cement.\n\na CO2 concentrations ([CO2]calcium extractor) evolved from the calcium extractor during the electrolysis of fresh waste cement (St. Marys, blue), aged waste cement (pink), and natural limestone (gray) at current densities of 100\u2009mA\u2009cm\u20132, 200\u2009mA\u2009cm\u20132, and 300\u2009mA\u2009cm\u20132 with N2 as the carrier gas (flow rate\u2009=\u2009200 sccm). b X-ray diffraction patterns of the solid Ca(OH)2 product isolated from the lime extractor after electrolyzing various fresh waste cement (blue), aged waste cement (pink), and natural limestone (gray) at 200\u2009mA\u2009cm\u20132 for 2\u2009h. Minor peaks corresponding to impurities are indicated by dashed lines: KHCO3 (red), CaCO3 (black), CaSO4 (green), and KAlO2 (gray).\n\nAfter 2-h electrolysis experiments at 200\u2009mA\u2009cm\u20132, we collected the Ca(OH)2 from the lime extractor and the SiO2 from the calcium extractor. Fourier-transform infrared spectroscopy confirmed the presence of Ca(OH)2 in the product collected from the lime extractor (Supplementary Fig.\u00a010). We used powder X-ray diffraction and reference intensity ratios (Fig.\u00a05b) to identify and quantify the relative amounts of Ca(OH)2 and byproducts (e.g., CaCO3, CaSO4, and KHCO3; Table\u00a01). The electrolysis products using both fresh and aged waste cement contained 79\u201392% Ca(OH)2 by mass. We also confirmed the formation of amorphous SiO2 in the calcium extractor (Supplementary Fig.\u00a011).\n\nTo calculate the yield of the desired Ca(OH)2 product, we used the following equation:\n\nRearrangement of Eq. (12) with theoretical and actual PE values (Fig.\u00a03) gives:\n\nwhere MCa(OH)2 is the molar mass of Ca(OH)2 in g mol\u20131. Our results showed that the yield of Ca(OH)2 was 55% from aged waste cement, and 66\u201380% from fresh waste cement after 2\u2009h of electrolysis at 200\u2009mA\u2009cm\u20132 (Table\u00a02).\n\nWe analyzed energy efficiency (EE) in accordance with Eq. (14):\n\nwhere Ecell, theoretical is the potential needed to drive the water splitting reaction at the electrodes and the BPM, Ecell, actual is the cell-measured voltage, pHanolyte is the pH of the 3\u2009M KOH anolyte, and pHtheoretical is 11.8. The constants EOER, EBPM, EHER are 1.23\u2009V, 0.83\u2009V, and 0\u2009V, respectively29,30. The EEs for the electrolysis of aged waste cement at 100\u2009mA\u2009cm\u20132, 200\u2009mA\u2009cm\u20132, and 300\u2009mA\u2009cm\u20132 were calculated to be 40.4%, 28.8%, and 22.7%, respectively.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64339-5/MediaObjects/41467_2025_64339_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64339-5/MediaObjects/41467_2025_64339_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64339-5/MediaObjects/41467_2025_64339_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64339-5/MediaObjects/41467_2025_64339_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we demonstrate the electrochemical conversion of fresh and aged waste cement into Ca(OH)2 and SiO2, key precursors for the production of cement clinker. We successfully recycled various cementitious feedstocks, including three commercial cement products (termed fresh waste cement), and aged waste cement from a local building demolition. We produced high yields of up to 80% of Ca(OH)2 with purities ranging from 79% to 92% (Tables\u00a01 and 2).\n\nThe purity of the electrochemically produced Ca(OH)2 meets the requirements for direct use in kiln-based clinker production, as stipulated by European Standard EN 197-1 (required purity of CaCO3 feedstock: >75%). The impurities present in Ca(OH)2, such as CaCO3, CaSO4, and KHCO3, are thermally decomposed into CaO and K2O during clinker production at temperatures exceeding 1450\u2009\u00b0C2,31,32. These impurities are expected to have limited influence on clinker quality. However, the contribution of K2O should be carefully monitored, because the total alkali (i.e., Na2O and K2O) content in clinker is typically limited to below 1\u2009wt%33.\n\nBoth fresh and aged waste cement produce Ca(OH)2 of equally high purity. However, the yield from aged cement was lower, likely due to the presence of inactive aggregates. When using aged cement as the feedstock, the highest yield (55%) was achieved at a current density of 200\u2009mA\u2009cm\u20132 (Table\u00a02). The lower yield (38%) at low current density (100\u2009mA\u2009cm\u20132) suggests insufficient OH\u207b was generated at the cathode to efficiently form Ca(OH)2. At a higher current density of 300\u2009mA\u2009cm\u20132, we conjecture that the lower yield (38%) is attributed to a pH imbalance. This imbalance could be caused by parasitic H\u207a ion migration into the cathode chamber. Possible causes for this H\u207a ion migration include: (i) incomplete H\u207a consumption in the calcium extractor, as indicated by the acidic pHout, calcium (Fig.\u00a03b); and (ii) H\u207a crossover into the cathode chamber across the CEM at higher current densities.\n\nWe observed voltage creep during extended operation of 100+ hours, likely due to Ca(OH)2 deposition on both the CEM and the cathode. At an industrial scale, we acknowledge that the reactor configuration will need to evolve significantly, as is common in electrochemical scale-up34. While we cannot yet prescribe the exact design, we demonstrate a potential anti-scaling strategy to reduce Ca(OH)2 accumulation in the cathode chamber (Fig.\u00a04c and Supplementary Fig.\u00a07). This strategy, along with flow dynamics optimization and mechanical design improvements, form a promising basis for continuous or low-maintenance operation, which will be developed in future work35,36.\n\nThe cement recycler operation depends on the continuous supply of Ca2+ ions in the closed-loop electrolyte, which precipitate as Ca(OH)2 product during electrolysis. We found that we needed to add Ca2+ ions (through 0.2\u2009M CaCl2) at the beginning of the experiment to drive effective Ca(OH)2 production. In a control experiment without this additional CaCl2, we observed significantly reduced yields (3.6%). This result indicates that a sufficient [Ca2+]out, calcium is needed to drive the precipitation reaction at the cathode. If the [Ca2+]out, calcium is too low, OH\u207b ions will react with H\u207a ions from the chemical chamber and drive a rapid pH increase in the calcium extractor (Supplementary Fig.\u00a012).\n\nWe also note that our cement recycler system efficiently separates gaseous (O2, CO2, and H2) and solid products (Ca(OH)2 and SiO2) (Fig.\u00a02 and Supplementary Fig.\u00a03). Based on our estimates, the combustion of H2 and O2 produced by the electrolyzer at 88% efficiency is sufficient to fuel a kiln for clinker production, and could therefore completely eliminate the need for fossil fuels10. This estimation, combined with the near-zero CO2 emissions during electrolysis of fresh waste cement (<280 ppm at 100\u2013300\u2009mA\u2009cm\u20132), provides a clear opportunity for genuinely zero-emission cement. Alternatively, electric arc furnaces have also been shown to achieve clinker production, eliminating emissions when powered by renewable electricity21. While aged waste cement will produce higher levels of CO2, the levels of 1500\u20134200 ppm are significantly lower than those emitted from natural limestone (7000\u201321,000 ppm). The results indicate that the electrochemical process requires the generation of 44.4\u2009mol of acid to decompose 1 liter of aged waste cement, and 45.4\u2009mol of acid to decompose 1 liter of fresh waste cement.\n\nRecent studies highlight electrochemical methods for recycling cementitious materials, further validating the potential of such approaches for reducing carbon emissions18,19. Our system uniquely advances this field by achieving a continuous reactor capable of handling both fresh and aged waste cement, encompassing the full spectrum of cementitious resources. We acknowledge that processing and isolating waste cement from demolition waste increases the operating expenses of the recycling pathway. However, the byproducts of cementitious waste recycling, including rebars, aggregates, sand, and SiO2 generated from waste cement decomposition, span a range of scales from meter to nanometer and can be readily sorted and reused in further concrete production (Supplementary Fig.\u00a013). Moreover, a tipping fee of $50\u2013200 ton\u20131 helps manage these costs37,38,39. Beyond these practical benefits, this technology offers a transformative shift for the cement industry in addressing both waste and emissions. By eliminating high-temperature calcination and directly recovering calcium through a low-emission process, it decouples cement production from limestone consumption and fossil fuel combustion. This technology initiates a circular, electrified cement economy based on demolition waste. Therefore, our system represents a scalable and economically viable pathway toward a carbon-neutral construction industry.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The materials used for the cement recycler experiments are presented below. The cement electrolyzer was purchased from Dioxide Materials. The bipolar membrane (BPM, Fumasep FBM, 20\u2009cm\u2009\u00d7\u200930\u2009cm\u2009\u00d7\u2009160\u2009\u03bcm) was purchased from the Fuel Cell Store. The cation exchange membrane (CEM, Nafion\u2122 N117, 30\u2009cm\u2009\u00d7\u200930\u2009cm\u2009\u00d7\u2009183\u2009\u03bcm) was purchased from Ion Power Inc. Nickel foam (99.9%) was purchased from MTI Corporation and roll-compressed to 400\u2009\u03bcm. Previous reports confirm that all membrane materials we used in the study (i.e., BPM, CEM, and PTFE gaskets) have high resistance against mechanical vibration and concentrated acid and/or base1,2,3,10. White Portland Cement and Natural limestone were purchased from Amazon.ca. St. Marys\u00ae Portland Cement and Rapid Set\u00ae CEMENT ALL Multi-purpose Concrete were purchased from the Home Depot. Aged waste concrete from building demolition was collected from Riverside Recycling LTD. We primarily removed rebars and large-grained aggregates through grinding and only selected waste cement chips with sizes of 1\u20135\u2009mm. Pure gasses: N2 (99.999%), H2 (99.999%), and Ar (99.999%) gasses were obtained from Linde Canada Inc. KOH (\u226585%), KCl (99.0\u2013100.5%), CaCl2\u00b72H2O (\u226599%), and HCl (37%) were purchased from Sigma Aldrich. All materials were used without any further purification. Fumasep FBM was soaked in 1\u2009M KCl for 24\u2009h before use. Nafion\u2122 117 was soaked in 1\u2009M CaCl2 for 24\u2009h before use. The anolyte (3\u2009M KOH) and the closed-loop electrolyte (0.2\u2009M CaCl\u2082 and 1\u2009M KCl) were prepared by dissolving the respective salts in deionized water using volumetric flasks, and the solutions were stored in polypropylene bottles one day before use.\n\nAll experiments were conducted at room temperature, unless otherwise stated. The X-ray diffraction patterns of waste cement and Ca(OH)2 product were obtained on an Empyrean system using Cu K\u03b1 radiation (\u03bb\u2009=\u20091.5405\u2009\u00c5) as the X-ray source with a \u03b8\u2009\u2212\u20092\u03b8 mode. Fourier-transform infrared spectra were measured in a Bruker Invenio-R FTIR Spectrometer with RT-DLaTGS detector. The concentration of Ca2+ cations in the anolyte and catholyte was measured by inductively coupled plasma optical emission spectroscopy.\n\nThe voltage responses of the electrolyzer using current densities ranging from 100 to 300\u2009mA\u2009cm\u20132 were measured using a Keithley 2280S-32-6 DC Power Supply. For stability tests, the recording of electrolyzer voltages started after 10\u2009min of stabilization. The voltage stability of the electrolyzer was tracked at a current density of 100\u2009mA\u2009cm\u20132. For the cement recycler, we washed the cathode chamber with 0.5\u2009M HCl for 5\u2009min after every 1\u201310\u2009h of electrolysis. Unless otherwise noted, all voltages are reported as the full electrolyzer voltage measured in a two-electrode configuration. No reference electrode was employed, and no iR correction was applied.\n\nBased on experimental gas chromatography results (Fig.\u00a05a), we achieved approximately a 75% reduction in CO2 emissions when converting aged waste cement into Ca(OH)2. We therefore assume that aged waste cement is composed of 75\u2009mol% 3CaO\u00b72SiO2\u00b73H2O (Mw\u2009=\u2009342.46\u2009g\u2009mol\u20131) and 25\u2009mol% of CaCO3 (Mw\u2009=\u2009100.09\u2009g\u2009mol\u20131). The density of hydrated cement is around 2.5\u2009g\u2009cm\u20133\u20064. With this information, we calculated that 1000\u2009cm3 (1 liter) of aged waste cement contains 6.66\u2009mol of 3CaO\u00b72SiO2\u00b73H2O and 2.22\u2009mol of CaCO3. Accordingly, 44.4\u2009mol of HCl is required to fully decompose aged waste cement based on Eq. (7).\n\nFor fresh waste cement, we achieved nearly 100% reduction in CO2 emissions during its conversion into Ca(OH)2. Therefore, we assumed fresh waste cement contains 75\u2009mol% of 3CaO\u00b72SiO2\u00b73H2O and 25\u2009mol% of Ca(OH)2 (Mw\u2009=\u200974.09\u2009g\u2009mol\u20131). Calculations showed that 1 liter of fresh waste cement contains 6.81\u2009mol of 3CaO\u00b72SiO2\u00b73H2O and 2.27\u2009mol of Ca(OH)2. The estimated amount of HCl required for complete decomposition is 45.4\u2009mol.\n\nThe standard CO2 emissions from cement production using the traditional thermal method is 688\u2009kg CO2 tcement\u20131\u20065. According to the experimental gas chromatography results, we achieved a reduction of CO2 emissions by 75% when converting aged waste cement into Ca(OH)2, and a near 100% reduction in CO2 emissions when converting fresh waste cement into Ca(OH)2.\n\nWe then estimated the CO2 emissions from the life cycle of converting aged waste cement into Ca(OH)2 using the cement recycler, and subsequently converting it into cement clinker using a kiln. In our calculations, we assume that the electrolyzer is powered by renewable electricity, and the kiln for clinker production is powered by hydrogen combustion (sourcing from H2 and O2 produced from the electrolyzer). A combustion efficiency of 88% was determined to be powerful enough to cover the heat requirement for the kiln (Supplementary Fig.\u00a013)5. Therefore, we conclude that there is no new CO2 emitted from electricity production and the clinker production process. Rather, it releases the original CO2 captured by the aged waste cement due to the weathering carbonation in its lifespan. Therefore, this pathway achieves carbon neutrality for cement production. With the above parameters and assumptions, we estimated that by using the electrochemical pathway developed in this work, we can reduce CO2 emissions by 0.3\u20130.9 Gt when compared to the traditional thermal method (Fig.\u00a01).\n\nOnly 100\u2009mL of closed-loop electrolyte was circulated between the chemical and cathode chambers. After 2\u2009h electrolysis at 200\u2009mA\u2009cm\u20132, we recovered up to 1.79\u2009g (fresh waste cement) of Ca(OH)2, indicating that at least 0.31\u2009g of product had calcium sourced from the waste cement feedstock. Additionally, the Ca2+ concentration remained stable in the electrolyte (Fig.4b). This indicated that Ca2+ was being consumed to form Ca(OH)2 as quickly as it was being replenished by acid extraction of Ca2+ from waste cement. Based on the 0.02\u2009mol of Ca2+ available in the electrolyte, the 10-h and 100-h electrolysis experiments resulted in products that had <10% and <1%, respectively, of calcium theoretically sourced from the electrolyte.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The source data supporting the figures and tables are available in the source file. Additional data supporting the findings in this study are available either within the paper or the Supplementary Information, or from the corresponding author on request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Mohamad, N., Muthusamy, K., Embong, R., Kusbiantoro, A. & Hashim, M. H. Environmental impact of cement production and solutions: a review. Mater. Today. Proc. 48, 741\u2013746 (2022).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nEllis, L. D., Badel, A. F., Chiang, M. L., Park, R. 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Sustainability 14, 10034 (2022).\n\nArticle\u00a0\n ADS\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "The authors are grateful to the New Frontiers in Research Fund (NFRFT-2022-00197), the Natural Sciences and Engineering Research Council of Canada (RGPIN-2024-06486), Canada Foundation for Innovation (229288), Canadian Institute for Advanced Research (BSE-BERL-162173), and Canada Research Chairs for financial support. This research was undertaken thanks in part to funding from the Canada First Research Excellence Fund, Quantum Materials and Future Technologies Program. Scanning electron microscopy imaging was performed in the Centre for High-Throughput Phenogenomics at the University of British Columbia. The authors thank Dr. Maureen Soon for performing the Inductively-coupled Plasma Optical Emission spectroscopy measurements and Dr. Anita Lam for help in the XRD measurements.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Tengxiao Ji, Shaoxuan Ren.\n\nDepartment of Chemistry, The University of British Columbia, Vancouver, BC, Canada\n\nTengxiao Ji,\u00a0Shaoxuan Ren,\u00a0Gaopeng Jiang,\u00a0Giuseppe V. Crescenzo,\u00a0Sabrina S. Scott,\u00a0Yongwook Kim,\u00a0Aubry S. R. Williams,\u00a0Jordan Rumscheidt,\u00a0Monika Stolar\u00a0&\u00a0Curtis P. Berlinguette\n\nDepartment of Chemical and Biological Engineering, The University of British Columbia, Vancouver, BC, Canada\n\nYumeng Yang\u00a0&\u00a0Curtis P. Berlinguette\n\nStewart Blusson Quantum Matter Institute, The University of British Columbia, Vancouver, BC, Canada\n\nCurtis P. Berlinguette\n\nCanadian Institute for Advanced Research (CIFAR), 661 University Avenue, Toronto, ON, Canada\n\nCurtis P. Berlinguette\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nT.J., S.R. and C.P.B. devised the concept. T.J., S.R., Y.Y., and J.R. conducted the electrolysis experiments. Y.K. and A.S.R.W. performed gas chromatography setup and calibration. G.J. assisted with the design of electrolyzer durability experiments. T.J. wrote the initial manuscript draft, and T.J., S.R., G.V.C., and S.S.S. contributed to subsequent revisions. M.S. and C.P.B. supervised the project.\n\nCorrespondence to\n Curtis P. Berlinguette.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "T.J., S.R., and C.P.B. have filed a patent application on the work presented in this manuscript. C.P.B. is a founder of a company commercializing the technology. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Low-emission cement clinker precursor production, enabled by electrolytic extraction of calcium from waste cement.\n Nat Commun 16, 9302 (2025). https://doi.org/10.1038/s41467-025-64339-5\n\nDownload citation\n\nReceived: 19 November 2024\n\nAccepted: 16 September 2025\n\nPublished: 21 October 2025\n\nVersion of record: 21 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-64339-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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perovskite/silicon tandem solar cells", + "journal": "Nature Communications", + "published": "30 September 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52309-2/MediaObjects/41467_2024_52309_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52309-2/MediaObjects/41467_2024_52309_MOESM2_ESM.pdf" + }, + { + "label": "Solar Cells Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52309-2/MediaObjects/41467_2024_52309_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52309-2/MediaObjects/41467_2024_52309_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-52309-2#Sec13" + ], + "code": [], + "subject": [ + "Photovoltaics", + "Solar cells", + "Synthesis and processing" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3991063/v1.pdf?c=1727780762000", + "research_square_link": "https://www.researchsquare.com//article/rs-3991063/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-52309-2.pdf", + "preprint_posted": "12 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Tunnel oxide passivating contact (TOPCon) silicon solar cells are rising as a competitive photovoltaic technology, seamlessly blending high efficiency with cost-effectiveness and mass production capabilities. However, the numerous defects from the fragile silicon oxide/c-Si interface and the low field-effect passivation due to the inadequate boron in-diffusion in p-type TOPCon (p-TOPCon) reduce their open-circuit voltages (VOCs), impeding their widespread application in the promising perovskite/silicon tandem solar cells (TSCs) that hold a potential to break 30% module efficiency. To address this, we develop highly passivated p-TOPCon structure by optimizing the oxidation conditions, boron in-diffusion and aluminium oxide hydrogenation, thus pronouncedly improving the implied VOC (iVOC) of p-TOPCon to 715 mV and the VOC of double-sided TOPCon bottom cells to 710 mV. Consequently, integrating with perovskite top cells, our proof of concept 1 cm2 n-i-p perovskite/silicon TSCs exhibit VOCs exceeding 1.9 V and a highest reported efficiency of 28.20%, which paves a way for TOPCon cells in the commercialization of future tandems.Physical sciences/Energy science and technology/Renewable energy/Solar energy/PhotovoltaicsPhysical sciences/Nanoscience and technology/Nanoscale materials/Synthesis and processingPhysical sciences/Materials science/Materials for energy and catalysis/Solar cells", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SurpportinginformationNatCommun20240225.pdfSupplementary Materials for Highly passivated TOPCon bottom cells for perovskite/silicon tandem solar cells", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Tunnel oxide passivated contact (TOPCon) silicon solar cells are rising as a competitive photovoltaic technology, seamlessly blending high efficiency with cost-effectiveness and mass production capabilities. However, the numerous defects from the fragile silicon oxide/c-Si interface and the low field-effect passivation due to the inadequate boron in-diffusion in p-type polycrystalline silicon (poly-Si) passivated contact reduce their open-circuit voltages (VOCs), impeding their widespread application in the promising perovskite/silicon tandem solar cells (TSCs) that hold a potential to break 30% module efficiency. To address this, we have developed a highly passivated p-type TOPCon structure by optimizing the oxidation conditions, boron in-diffusion, and aluminium oxide hydrogenation, thus pronouncedly improving the implied VOC (iVOC) of symmetric samples with p-type TOPCon structures on both sides to 715\u2009mV and the VOC of completed double-sided TOPCon bottom cells to 710\u2009mV. Consequently, integrating with perovskite top cells, our proof of concept of 1\u2009cm2 n-i-p perovskite/silicon TSCs exhibit VOCs exceeding 1.9\u2009V and a high efficiency of 28.20% (certified 27.3%), which paves a way for TOPCon cells in the commercialization of future tandems.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Perovskite/silicon tandem solar cells (TSCs) have attracted considerable attention due to their advantages in efficiency and potential fabrication cost. Recent advancements have propelled this type of TSCs to achieve a record efficiency of 33.9%1. Additionally, the shared module structures with single-junction solar cells facilitate the integration of TSCs into module production without the need for additional scaffolds and wires. Silicon solar cells with the Tunnel Oxide Passivated Contact (TOPCon) structures are rising with the advantages of high power conversion efficiency (>26%)1 because it has already proven to be an upgrade from existing Passivated Emitter and Rear Contact cell production lines. These advantages make TOPCon cells, even with TOPCon structures on both sides, become a promising candidate as bottom sub-cells for perovskite/silicon TSCs.\n\nIn the pursuit of high-efficiency TSCs, texturing the front side of the silicon cell is indispensable as it could significantly reduce the refection of incident photons and extend the propagation length of photons in the absorbers2,3,4. TOPCon bottom cells contribute lower voltage in TSCs compared to mainly researched silicon heterojunction (SHJ) bottom cells, which could be ascribed to the weak passivation of the textured p-type side. The saturated current density (J0,s)\u00a0of the p-type sides in TOPCon cell and SHJ cell is reported as 15.2\u2009fA\u00b7cm\u22122 and 5.1\u2009fA\u00b7cm\u22122, respectively5,6, which may support the correlation of lower p-type side passivation with lower voltage. Although p-type TOPCon structures on planar substrates have exhibited high passivation with implied open-circuit voltage (iVOC) over 730\u2009mV7,8,9, the structures on textured counterparts still suffer from a lower passivation quality compared to p-type a-Si:H passivated textured sides in SHJ cells. This is primary due to the higher defect density in and/or near the ultrathin silicon oxide (SiOx) interlayer, resulting from a higher defect density on textured surface before oxidation, and the formation of degraded SiOx and boron-based clusters formed during boron in-diffusion10. The rounding etching process on the textured surface indeed reduces surface defects but simultaneously enhances reflectance9,11. Therefore, the textured surface is kept intact but an additional Si/SiOx stack was inserted to reduce boron corrosion on the SiOx/c-Si interface12. With a common alumina oxide/silicon nitride (AlOx/SiNx)\u00a0capping layer, the sample showed an iVOC of 710\u2013720\u2009mV. Further enhancement was achieved with a multilayer hydrogenation stack, effectively suppressing defect density and acquiring an iVOC above 720\u2009mV. However, the additional insertion and multilayer structure increase complication of production line, leading to higher costs.\n\nIn this work, industry-compatible fabrication methods, such as ambient-pressure thermal oxidation and in situ plasma-enhanced chemical vapor deposition (PECVD), are employed to prepare highly passivated p-type TOPCon structures and double-sided TOPCon bottom cells on industrial textured wafers. The use of thermal oxidation avoids damages from ion-bombardment during the high-power plasma oxidation13,14. Different from successive thermal oxidation and poly-Si deposition in a single tool, ex situ thermal oxidation yields ultrathin SiOx layers on both wafer sides in an oxidation facility, such as a tube furnace, after which the wafers are transferred to PECVD facility to deposit intrinsic and doped silicon. The oxidation and silicon deposition processes are performed in different facilities. The mass production process technology of TOPCon structures via in situ doped amorphous silicon (a-Si)\u00a0has been achieved by Feldmann et al. in 202015 and Ma et al. in 202316. Compared to widely used low pressure\u00a0CVD, this in situ PECVD technique provides high versatility not only in dopant species and doses, but also in multilayer film structure. Additionally, it boasts a high SiH4 utilization rate and allows for single-side oxidation or deposition. Furthermore, this technique may avoid the damage of quartz furnaces and carriers caused by wrap-round effect17. The combination of ex-situ thermal oxidation and in situ doped a-Si is able to achieve single-side TOPCon structures. Chemical and field-effect passivation are significantly promoted through mitigating ultrathin SiOx distortion by stronger oxidation, enhancing boron in-diffusion by employing a higher thermal budget, and suppressing a higher residual defect density by strengthened hydrogenation. Consequently, the iVOC of the sample with p-type TOPCon structures on both sides exceeds 715\u2009mV with single-side J0,s below 13\u2009fA\u00b7cm\u22122, and the VOC of completed bottom cells approaches 710\u2009mV. Employing the optimized TOPCon bottom cells, 1\u2009cm2 n-i-p monolithic perovskite/silicon TSCs achieve high VOCs of 1.9\u2009V and a high efficiency of 28.2% (certified 27.3%). We choose n-i-p configuration to align with single-junction silicon bottom cells featuring p\u2013n junctions at the front side. This configuration holds promise for achieving higher efficiency and prioritizing compatibility.", + "section_image": [] + }, + { + "section_name": "Results and discussion", + "section_text": "The inserted sketch in Fig.\u00a01a illustrates the structure of the passivation sample. The wafers were textured to form sub-micrometer-sized random pyramids on both sides, which were capped by p-type poly-Si films to form p-type TOPCon structures. Firstly, ultrathin SiOx layers on both wafer sides were formed in an oxidation tool using varying process conditions, and then the wafers are transferred to a PECVD tool to deposit intrinsic and doped silicon. After the high temperature annealing under different thermal budgets, the TOPCon structures were hydrogenated by AlOx:H with increased thickness. This specific pyramid size was employed to meet the requirements of perovskite top cells.\n\na Effective carrier lifetime curves and (b) illumination intensity-dependent iVOC curves of control (in black) and target (in red) samples. The insert in (a) is a structure sketch of double-sided p-type TOPCon structure on textured wafers. The promotion of textured p-type TOPCon structure passivation by optimizing processes step by step: (c) extending oxidation time, (d) elevating 30\u2009min annealing temperature, (e) extending 920\u2009\u00b0C annealing dwell time, and (f) increasing hydrogenated AlOx thickness. The solid curves with closed symbols are for iVOC and hollow columns are for J0,s. The added values are iVOCs and corresponding J0,ss. The \u201cN2\u2009+\u2009O2\u201d refers to 500 sccm N2\u2009+\u2009500 sccm O2.\n\nFigure\u00a01a, b displays the effective carrier lifetime (\u03c4eff) curves over minority carrier density and iVOC curves under different illumination intensities, respectively. The fabrication processes of the control samples include a 650\u2009\u00b0C/5\u2009min oxidation, 920\u2009\u00b0C/30\u2009min annealing, as well as a 15\u2009nm AlOx:H hydrogenation, according to refs. 9,10,18. The control sample exhibits a \u03c4eff of 273\u2009\u03bcs and a J0,s of 34.2\u2009fA\u00b7cm\u22122 at minority carrier densities of 2\u2009\u00d7\u20091015 and 5\u2009\u00d7\u20091015\u2009cm\u22123, respectively, together with an iVOC of 689\u2009mV under 1 Sun illumination. This passivation is lower than that of reference published by Larionova. et al. in 201710. To provide a clear understanding of the challenges in passivating textured c-Si wafers, these processes were also performed on a double-sided planar wafer, demonstrating a significantly higher passivation with an iVOC of 716\u2009mV, a J0,s of 5\u2009fA\u00b7cm\u22122, and a \u03c4eff of 1161\u2009\u03bcs. These results seem to be state of the art, as shown in Fig.\u00a0S1 and Table\u00a0S1.\n\nThe optimization of the textured p-type TOPCon structure sample can be summarized in three key aspects: varying thermal oxidation conditions for different ultrathin SiOx layers, enhancing the thermal budget in high-temperature annealing for deeper boron in-diffusion, and capping thicker AlOx:H layers for stronger hydrogenation. As depicted in Figs.\u00a01c and S2, a strong oxidation condition results in a relatively high iVOC of 690\u2013700\u2009mV. On the base of enhanced oxidation condition, the thermal budget of high-temperature annealing was increased. Figures\u00a01d, e and S3 demonstrate that a 9\u2009min oxidation followed by a 940\u2009\u00b0C annealing yields a high iVOC of 706\u2009mV. Further extension of oxidation time had an inferior effect. Afterwards, in the aluminum oxide hydrogenation process, the AlOx:H layer thickness was increased up to 35\u2009nm to facilitate strong hydrogen injection. We initially experimented with a bilayer hydrogenation stack of AlOx/SiNx but found that it had a negligible effect on improving the passivation quality. Therefore, we used a thicker AlOx:H layer to enhance passivation. As shown in Fig.\u00a01a, b, f, after optimization the passivation level increased apparently, with an iVOC of 715\u2009mV at 1 Sun illumination and a J0,s of 12.9\u2009fA\u00b7cm\u22122 at the minority carrier density of 5\u2009\u00d7\u20091015\u2009cm\u22123. The \u03c4eff of 788\u2009\u03bcs at the minority carrier density of 2\u2009\u00d7\u20091015\u2009cm\u22123 is also largely higher than the control one. After capping with SiNx, the iVOC further increased to 716\u2009mV, which is tagged as the pink circle in Fig.\u00a0S1. This is the state-of-art passivation level of the p-type TOPCon structure on a textured wafer based on ex situ oxidation and in situ doped a-Si. This data is higher than that from Larionova et al.10, Kale et al.19, and Mack et al9., but lower than the contact with iVOC 724\u2009mV and J0,s 8\u2009fA\u00b7cm\u22122 reported by Stodolny et al12. These textured p-type TOPCon structures were all based on thermal oxide. The possible reasons for the lower passivation of the former three contacts may be the single-layer SiNx:H hydrogenation, as well as the lack of full process optimization. In our p-type passivated contacts the p-type poly-Si layers are 30\u2009nm thick, which are hydrogenated by single-layer AlOx:H. The higher passivation level of the later one may be ascribed to in situ low pressure thermal oxidation, poly-Si/SiOx/poly-Si/SiOx/c-Si multilayer structure and multilayer AlOx/SiNx/AlOx for hydrogenation. The insertion of additional SiOx/poly-Si leads to lower boron concentration and reduced recombination centers in c-Si, and multilayer hydrogenation injects more H into contact structure which passivates more defects at SiOx/c-Si interface.\n\nFirstly, we conduct a comparison of the thicknesses and integration of ultrathin SiOx layers on planar and textured wafers. For spectroscopic ellipsometry (SE) analysis, polished wafers with (111)-orientation were used to redraw the oxide formation on the inclined plane of the textured surface. Here, we just wanted to compare the SiOx thicknesses on planar wafers and on inclined facets of textured wafers. Table\u00a0S2 displays the calculated thickness of SiOx under diverse oxidation conditions, revealing a gradual increase in SiOx thickness on the (111) wafer with stronger oxidation. Though the calculated thickness may not be highly accurate, it is enough to show the trend in SiOx thickness. Furthermore, the transmission electron microscope (TEM) images in Fig.\u00a0S4b\u2013d depict that for a 5\u2009min oxidation, the SiOx on textured surface is thinner, less uniform in thickness, and exposes silicon to metal directly at some positions. In contrast, the SiOx on the planar surface is more uniform in thickness and covers the silicon surface more completely, as shown in Fig.\u00a0S4a. This observation is in accordance with previous studies20,21, suggesting that a strong oxidation process may be necessary to form a thicker SiOx layer with higher uniformity in thickness, providing enhanced protection to the textured silicon surface. The TEM image in Fig.\u00a0S4f indicates that a 9\u2009min oxidation results in a thicker SiOx layer than the 5\u2009min oxidated case in Fig.\u00a0S4c, and it more conformally segregates the wafer surface from the metal Pd. Additional morphology of the 9\u2009min SiOx on valleys and tips can be found in Fig.\u00a0S4e\u2013g. The advantages of 9\u2009min SiOx remain obvious in poly-Si/ SiOx/Si structures. After high-temperature annealing, the 9\u2009min SiOx remains more uniform and consecutive than the 5\u2009min one, as shown in Fig.\u00a02a, b. The corresponding energy disperse spectroscopy (EDS) mappings of oxygen indicate that during annealing, oxygen diffuses into the adjacent silicon to passivate dangling bonds, and the 9\u2009min SiOx results in a more uniform diffusion (Fig.\u00a02c). Briefly, a 5\u2009min SiOx interlayer becomes thinner and more discontinued and the diffused O eliminates less defects after annealing, which leaves more defects near the silicon base surface and leads to a lower passivation level with iVOC of 689\u2009mV and J0,s of 34.2\u2009fA\u00b7cm\u22122. Meanwhile, a 9\u2009min SiOx interlayer protects the silicon base from contacting poly-Si directly after annealing, accompanied by more defects passivated by diffused O, yielding a higher iVOC of 706\u2009mV and a lower J0,s of 18.6\u2009fA\u00b7cm\u22122.\n\nTEM images of Pd/poly-Si/SiOx/c-Si structures on incline facets of textured wafers for (a) control and (b) target samples, and (c) their local EDS images of O distribution.\n\nNext, we focus on the chemical state of Si in ultrathin SiOx, which was checked via X-ray photoelectronic spectroscopy (XPS) on SiOx formed directly on textured wafers. The deconvoluted Si spectra for various oxidation conditions are exhibited in Fig.\u00a03a\u2013c, which shows the oxidation conditions and the corresponding calculated Si4+ peak area ratios. Obviously, a higher oxidation temperature, a longer oxidation time, or a higher oxygen ratio leads to a higher Si4+ peak proportion, such as 22.7%, 32.1%, and 35.4% for 1, 5, and 9\u2009min oxidation, respectively, meaning an increased Si4+ concentration in SiOx. Additionally, a stronger oxidation causes the Si4+ peak to shift to a higher binding energy (BE), as indicated by the arrows. This suggests that the SiOx layer is more robust after a strong oxidation. Although the SiOx interlayers will all suffer from distortion and form defects after high temperature annealing due to the stress from silicon to SiOx22,23, it can still be deduced that SiOx generated from strong oxidation exhibits superior homogeneity and a higher Si4+ content and BE. Consequently, this lends it a heightened resistance against stress, thereby reducing the likelihood of distortion and minimizing defects within the SiOx layer. This leaves fewer carrier recombination centers near the SiOx/c-Si interface and thus a higher passivation.\n\nThe influence of oxidation temperature (left column), oxidation time (middle column), and oxidation ambient (right column) on (a\u2013c) Si chemical state in SiOx, (d\u2013f) interface state density at SiOx/c-Si, and (g\u2013i) boron in-diffusion profile. The \u201cN2\u2009+\u2009O2\u201d in (c) refers to 500 sccm N2\u2009+\u2009500 sccm O2. The inserted sketch in (f) shows sample structure for C\u2013V test. The \u201cJ0,Auger_calculated\u201d in (g) represents Auger recombination rate calculated using in-diffusion profiles in c-Si.\n\nSubsequently, the fixed charge density (Qf) and interface state density (Dit) of the SiOx/c-Si interface was measured and evaluated using 1\u2009MHz capacitance-voltage (C\u2013V) and conductance-voltage (G\u2013V) curves for various oxidation conditions on textured wafers. The Qf and Dit were calculated using the data obtained from C\u2013V to G\u2013V curves with Eqs.\u00a01 and 224,25, respectively:\n\nwhere Cox is the high capacitance plateau of the oxide interlayer, \u03a6ms is the difference in metal and semiconductor working functions, Vfb is the flat band voltage, A is the area of the metal disk, q is the electron element, Gm,max is the measured highest conductance, \u03c9 is the angular frequency, and Cm is the capacitance corresponding to the measured highest conductance. The C\u2013V curves and corresponding Dit values in terms of oxidation conditions are plotted in Fig.\u00a03d\u2013f. With similar Cox, the C\u2013V curve shifts to a lower voltage position for stronger oxidation, leading to a lower Vfb and thus lower Qf of the ultrathin SiOx/AlOx stack. This suggests that stronger oxidation results in fewer defects at the SiOx/c-Si interface. The evaluated Dit values may show this trend more directly, with 3.04\u2009\u00d7\u20091012\u2009cm\u22122\u00b7eV\u22121 for 5\u2009min oxidation (in red line) verses 2.78\u2009\u00d7\u20091012\u2009cm\u22122\u00b7eV\u22121 for 9\u2009min oxidation (in yellow line) for example. A higher oxidation temperature or O2 ratio also yields a remarkable decrease in Dit. After high-temperature annealing, as plotted in Fig.\u00a04a, Dit decreases significantly by an order of magnitude, and Dit of the target sample is lower than that of the control one, showing 3\u2009\u00d7\u20091011\u2009cm\u22122\u00b7eV\u22121 vs. 5.95\u2009\u00d7\u20091011\u2009cm\u22122\u00b7eV\u22121. This suggests that 9\u2009min SiOx plus 940\u2009\u00b0C annealing is effective in eliminating more defects on the textured c-Si surface, yielding a higher chemical passivation effect.\n\nThe comparisons of (a) C\u2013V curves, (b) boron in-diffusion profiles, and (c) O-H peaks in AlOx:H films of control and target samples.\n\nIn addition to the analysis of the SiOx layer or SiOx/c-Si interface, one key property of the TOPCon structure is the dopant in-diffusion profile obtained by electrochemical capacitance-voltage (ECV) measurement. It shows the active dopant concentration at a specific depth, including poly-Si, SiOx interlayer, and c-Si beneath SiOx, which helps to study of the field-effect and chemical passivation. Figure\u00a03g\u2013i displays the doping profiles of textured samples with enhanced oxidation strength after 920\u2009\u00b0C annealing. All of them exhibit the trend that stronger oxidation results in a lower depth of boron in-diffusion in c-Si, suggesting fewer boron dopants penetrate ultrathin SiOx. This means less damage to SiOx, benefiting chemical passivation. Moreover, the corresponding Auger recombination rates (J0,Auger_calculated) can be calculated based on profiles in c-Si below the SiOx interlayer using the EDNA2 program in PV Lighthouse26,27. The Auger recombination rate is positively related to the net dopant concentration (Ndop) and excessive carrier density (\u0394n)28,29. For the p-type TOPCon structure, the boron diffusion profile means active boron concentration at an increased depth in c-Si beneath the SiOx interlayer, and Auger recombination involving one electron and two holes. Therefore, the Auger rate at low injection condition in c-Si below SiOx can be worked out based on the active boron concentration at a specific depth. At high injection condition, the Auger rate in c-Si below SiOx is dominated by excessive hole density. As a result, an enhanced boron in-diffusion leads to higher boron concentration in c-Si below SiOx, and thus higher Auger rate after illumination. A shallow dopant in-diffusion may simultaneously reduce J0,Auger_calculated, improving the \u03c4eff\u00a027. However, the low active boron concentration in c-Si beneath SiOx may allow more photo-generated electrons to reach the defect-rich SiOx/c-Si interface, accelerating carrier recombination30,31,32. At the same time, a lower boron in-diffusion depth may produce a higher contact resistivity, inhibiting the extraction of generated carriers. Therefore, annealing temperature and dwell time were increased based on strengthened oxidation to explore a balance of chemical passivation, field-effect passivation, and contact resistivity. The comparison of boron in-diffusion profiles between control and target samples can be found in Fig.\u00a04b. With low Dit from 9\u2009min SiOx, 940\u2009\u00b0C annealing leads to deeper boron in-diffusion, which may provide high passivation in both chemical and field-effect aspects. Besides, the calculated J0,Auger_calculated increases only slightly by less than 0.1\u2009fA\u00b7cm\u22122, having a negligible impact on passivation. Briefly, the lack of boron in-diffusion depth after 920\u2009\u00b0C annealing forms a weaker field effect passivation, contributing to lower passivation with iVOC of 689\u2009mV and J0,s of 34.2\u2009fA\u00b7cm\u22122 for the control sample. Meanwhile, 940\u2009\u00b0C annealing results in deeper boron in-diffusion thus a stronger field effect passivation, which may be a reason for the higher iVOC of 706\u2009mV and the lower J0,s of 18.6\u2009fA\u00b7cm\u22122 for the target sample.\n\nIt could be deduced from the evolution of p-type TOPCon structures that hydrogenation after high temperature annealing also plays a crucial role in achieving high-level passivation. Therefore, the thickness of the AlOx:H layer was increased by performing more cycle numbers during atomic layer deposition (ALD) to provide sufficient H for passivating p-type TOPCon structures on the textured surface. The cross-sectional scanning electron microscopy\u00a0(SEM) images in Fig.\u00a0S5 of the c-Si/SiOx/poly-Si/AlOx:H stacks confirm that a higher ALD cycle number indeed improves AlOx:H thickness to ~30\u2009nm on the inclined facet of a pyramid. The AlOx:H layer on the valley or tip also becomes thicker. This guarantees sufficient passivation effect for different parts of the textured surface. Furthermore, as the Fourier Transform infrared spectroscopy (FTIR) data shown in Fig.\u00a04c, the as-deposited 30 nm-thick AlOx:H layer possesses a larger O-H bond peak than the 15\u2009nm one, and this peak collapses drastically after annealing, meaning that a large number of hydrogens are released to passivate defects33,34. Consequently, the 30\u2009nm-thick AlOx:H layer attributes to achieve the summit passivation.\n\nThe VOC of a c-Si solar cell can be expressed by a single diode equation35,36,37,38:\n\nwhere JL is the illuminated current density, J0,emitter is the saturated current density of the emitter, J0,bulk is the saturated current density in the wafer bulk, and J0,front or J0,rear is the saturated current density on the front or rear surface. It can be deduced that for a certain temperature, light absorption, c-Si substrate, and p\u2013n junction, a high surface saturated current density, i.e., a high surface carrier recombination rate, will lead to a low VOC for a TOPCon cell. Referring to Eq.\u00a01, we can evaluate or predict the VOC of a bottom cell to some extent. Under the assumptions that the incident light intensity, the carrier recombination rate in the wafer bulk, and the n-type TOPCon structure passivation are fixed and independent with p-type side settings, the VOC, or the whole passivation, of a bottom cell can be mainly determined by the p-type TOPCon structure passivation quality. The parameters are defined as JL\u2009=\u200935\u2009mA\u00b7cm\u22122, J0,bulk\u2009=\u200910\u2009fA\u00b7cm\u22122, J0,n-type contact\u2009=\u20095\u2009fA\u00b7cm\u22122, and the test temperature is 300\u2009K. For a p-type TOPCon structure with low passivation, the J0,p-type contact may be 40\u2009fA\u00b7cm\u22122, which leads to an VOC of ~703\u2009mV. When J0,p-type contact decreases to 15\u2009fA\u00b7cm\u22122, the calculated VOC will reach ~718\u2009mV.\n\nAn n-type TOPCon structure with high passivation and low contact resistivity on textured wafer is indispensable for a high-performance bottom cell. The structure sketch of a double-sided n-type TOPCon structure on textured wafer is inserted in Fig.\u00a05a. To identify a suitable n-type contact, the deposition time of n-type a-Si was tuned from 80 to 400\u2009s, followed by a 940\u2009\u00b0C annealing and a 15\u2009nm-thick AlOx:H hydrogenation. As displayed in Fig.\u00a05a, b, sample with a deposition time of 320\u2009s yields an iVOC of 737\u2009mV and a J0,s of 5.7\u2009fA\u00b7cm\u22122. With the optimized 30\u2009nm-thick AlOx:H hydrogenation, the iVOC and J0,s of the n-type TOPCon structure passivation sample reach 746\u2009mV and 3.4\u2009fA\u00b7cm\u22122, respectively.\n\na The iVOC and J0,s of n-type TOPCon structures with different a-Si deposition times on industrially textured wafers. The insert is the structure sketch of double-sided n-type TOPCon structure on textured wafers. b Illumination intensity-dependent iVOC curves of 320\u2009s n-type TOPCon structures hydrogenated by 15\u2009nm (in black) and 30\u2009nm (in red) AlOx:H. The corresponding passivation parameters are inserted. c The I\u2013V curves for the target p-type (in green) and n-type (in orange) TOPCon structures for the complete bottom cell. The insert is the schematic structure of a complete n-i-p type double-sided TOPCon bottom cell with sub-micrometer-sized pyramids on the front side and industrial micrometer pyramids on the rear side, respectively. d The J\u2013V curves and corresponding data of control and target complete bottom cells.\n\nThe low J0,s of an n-type TOPCon structure together with a highly passivated p-type TOPCon structure contribute to the high iVOC of 728\u2009mV of the hydrogenated bottom cell. The insert in Fig.\u00a05c depicts the structure sketch of a complete n-i-p type double-sided TOPCon bottom cell. The front side features a control or target p-type TOPCon structure on a sub-micrometer-sized pyramid textured surface, capped by a thick indium tin oxide (ITO) layer and a grid metal electrode for carrier lateral collection. The rear side incorporates an n-type TOPCon structure on a micrometer-sized pyramid textured surface, capped by a full area metal electrode. Figure\u00a05c plots the current-voltage (I\u2013V) curves of the optimized p- and n-type TOPCon structures measured using Cox-Strack method39. The straight curves mean that both TOPCon structures show ohmic contacts, and the p- and n-type TOPCon structures offer contact resistivities of 46 and 40\u2009m\u03a9\u00b7cm2, respectively. For comparison, the contact resistivities of p- and n-type TOPCon structures for control bottom cells were also measured using the same method, which are 127 and 332\u2009m\u03a9\u00b7cm2, respectively. As a result, the complete bottom cells with control and target TOPCon structures exhibit current density\u2013voltage (J\u2013V)\u00a0curves with obvious difference, especially in VOC and fill factor\u00a0(FF), as shown in Fig.\u00a05d. The VOC of the target bottom cell reaches 706\u2009mV and is 25\u2009mV higher than the control one, which may result from a 25\u2009mV difference in iVOC between the control and target p-type TOPCon structure as discussed in Fig.\u00a01, as well as a 9\u2009mV increase in iVOC of the n-type TOPCon structure. It should be noted that the passivation difference between hydrogenated bottom cells and complete bottom cells may be explained by the unavoidable defects induced by plasma luminescence and electron or particle bombardment during ITO sputtering40,41, increased recombination rate from the poly-Si metallization with metal42,43,44, and H escape during ITO layer and metal electrode preparation45,46,47. Additionally, the higher FF of the target bottom cell may also play a key role for increased efficiency. The lower contact resistivities of p- and n-type TOPCon structures in the target bottom cell lead to a lower series resistance of 0.6\u2009\u03a9 for the target bottom cell than 1\u2009\u03a9 for the control bottom cell, which may be the main reason for the higher FF. In summary, the optimized TOPCon structures yield both a higher VOC and a higher FF, which endows the target bottom cell with a marginally increased efficiency. The statistical data of performance parameters of complete bottom cells can be found in Fig.\u00a0S6.\n\nAfter demonstrating the effectiveness of the improvements in p-type TOPCon structures and complete Si bottom cells, we fabricated n-i-p type monolithic perovskite/silicon TSCs using the target bottom cells (See Experimental Section for more details). The schematic view and cross-sectional SEM images of the TSCs are shown in Fig.\u00a06a. Consequently, a champion efficiency of 28.20% with an VOC of 1.90\u2009V, a FF of 78.91% and a short-circuit current density (JSC) of 18.82\u2009mA\u00b7cm\u22122 in 0.9226\u2009cm2 aperture area was obtained for the target device in our laboratory tests (Fig.\u00a06b) which is the top-ranking level for n-i-p type monolithic perovskite/silicon TSCs at present. For n-i-p type monolithic perovskite/silicon tandem cells, Zheng et al. and Aydin et al. reported efficiencies of 27.2% and 27.1% for ~1\u2009cm2 tandem cells, respectively, which are the highest efficiencies for published n-i-p type monolithic perovskite/silicon tandem cells48,49. The dominating reason for our higher efficiency is the higher VOC, 1.9\u2009V, compared to these two cells, 1.82\u20131.83\u2009V. Another 1\u2009cm2 tandem cell reported by Shen et al. shows 24.5% efficiency, which is caused by a lower VOC of 1.76\u2009V and a JSC of 17.8\u2009mA\u00b7cm\u22122\u200950. Therefore, our cell shows top-ranking efficiency for 1 cm2 n-i-p type monolithic perovskite/silicon TSCs. Statistics of the photovoltaic parameters imply the improved performance and reproducibility with the target bottom cells (Fig.\u00a0S7). Another tandem cell was sent to Shanghai Institute of Microsystem and Information Technology (SIMIT) for authoritative certification and achieved 27.31% (25.69%) efficiencies with over 1.91\u2009V VOCs in reverse (forward) voltage scanning directions (Fig.\u00a0S8 and Table\u00a0S3). The champion device from our lab measurements was not dispatched to SIMIT for calibration due to unavailability of testing schedules during that period. Regarding the comparison of results, the disparities observed in photovoltaic parameters such as VOC, JSC, FF, and PCE between our champion device in lab measurement and another device in SIMIT calibration are within the acceptable margin of <5%, which is commonly deemed satisfactory for devices sourced from different production batches. The notable variance between the two sets of testing results primarily stems from hysteresis, a phenomenon intricately linked to factors such as disparate bias scanning rates, pre-light soaking conditions, spectral distribution variations, ambient environmental disparities between the testing sites, and inherent differences in the devices themselves.\n\na Sketch structure (left) and cross-sectional SEM images (right) of the target tandem device. The scale bars are 500\u2009nm. b J\u2013V curves and (c) EQE spectra of the two related TSCs. d MPP tracking stability of TSCs with the control and target TOPCon bottom cells.\n\nFigure\u00a06c exhibits the external quantum efficiencies (EQEs) and the integrated currents of the champion tandem devices. 0.26 and 0.66\u2009mA\u00b7cm\u22122 integrated current increases are unveiled for perovskite top cell and optimized silicon bottom cell, respectively, which is consistent with the JSC from the J\u2013V measurements. The increased current density in the target tandem cell, as observed from the EQE results, can be primarily attributed to the highly passivated p-type TOPCon structure achieved through our optimized process. Additionally, our optimized process results in an approximately 10\u2009mV increase in the iVOC of the rear-side n-type TOPCon structure, contributing to a slight improvement in the ~1000\u2009nm wavelength zone of the EQE data. The current density of our tandem cell is limited by perovskite top cell, and the enhanced JSC of J\u2013V measurements for the target tandem device is consistent with the EQE results that exhibit ~0.2\u2009mA\u2009cm\u22122 improved integrated current density in the top cell. It should be noted that, the observed lower FFs in the complete bottom cell testing (73\u201375% for control and target cells) primarily stem from issues with the front metal gate-line. This results in lateral carrier transport loss within the ITO and p-type TOPCon structure during measurement, rather than inherent issues with the crystalline silicon bottom cell itself. However, this issue is alleviated in tandem devices, as carriers only need to recombine in the middle layer without encountering the challenges of lateral carrier transport and collection. In addition, the mismatch of current densities of top and bottom cells, which is 18.1\u201318.4 and 18.8\u201319.5\u2009mA\u00b7cm\u22122 respectively according to Fig.\u00a06c, will also lead to higher FFs51. Consequently, while the complete bottom cell may exhibit relatively poor FFs, the corresponding tandem devices demonstrate higher FFs (ranging from 76% to 79%). Furthermore, maximum power point (MPP) tracking tests were conducted to monitor the operation stability of the un-encapsulated tandem devices under ~1 sun LED illumination (Fig.\u00a0S9) in a N2 flow. As shown in Fig.\u00a06d, both types of the tandems demonstrate impressive longevities over 500\u2009h, which indicates the commercialized potential of our tandem devices. The initial declines may originate from a \u201cburn-in\u201d decay often occurring in single-junction perovskite solar cells, which is related to ion migration, type of charge selective contact, and defect accumulation at the transport layer/perovskite interfaces52,53,54,55.\n\nIn this work, a highly passivated p-type TOPCon structure on randomly textured industrial wafers was achieved using industry-compatible techniques, which leads to a high efficiency of an n-i-p type monolithic perovskite/Si TSCs. The enhanced thermal oxidation condition forms more uniform ultrathin SiOx on textured silicon surface with higher Si4+ concentration and blue shift in BE, as well as lower interface state density at SiOx/c-Si. This endows SiOx with weaker distortion and lower recombination centers after high-temperature annealing, thus resulting in higher chemical passivation. The increased thermal budget of high-temperature annealing deepens boron in-diffusion, which promotes field-effect passivation soundly but Auger recombination slightly. Furthermore, a strengthened hydrogenation process was performed via increasing AlOx:H thickness, passivating more defects and leaving fewer recombination centers a step further after hydrogen injection. As the consequence, the iVOC of the p-type TOPCon structure increased to 715\u2009mV, which is the summit passivation for the textured p-type TOPCon structure using ex-situ oxidation. Based on the optimized double-sided TOPCon bottom cell, the perovskite/Si TSCs exhibit a high VOC of 1.9\u2009V and a remarkable efficiency of 28.2%, which is the top-ranking level for n-i-p type monolithic perovskite/silicon TSCs currently.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52309-2/MediaObjects/41467_2024_52309_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52309-2/MediaObjects/41467_2024_52309_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52309-2/MediaObjects/41467_2024_52309_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52309-2/MediaObjects/41467_2024_52309_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52309-2/MediaObjects/41467_2024_52309_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52309-2/MediaObjects/41467_2024_52309_Fig6_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "The crystalline silicon wafers used in this study were industrial n-type Czochralski (Cz) wafers with (100) oriental. The double-side textured wafers with ~155\u2009\u03bcm thickness, ~0.8\u2009\u03a9\u00b7cm resistivity, and pyramid size below 1\u2009\u03bcm (sub-micrometer-size) (Fig.\u00a0S10a) were prepared via an industrial texturing technique in the lab. The texturing process is performed in ~70\u2009\u00b0C potassium hydroxide-contained aqua solution for ~15\u2009min with 8 vol.% commercial sub-micrometer pyramid texturing additives under 800\u2009rpm stirring. Such pyramid size was chosen for the convenience of the fabrication of perovskite top cell. After standard RCA cleaning, ultrathin silicon oxide layers were prepared via thermal oxidation on both sides of the wafers. The oxidation conditions were tuned in three dimensions, i.e., temperature (550\u2013750\u2009\u00b0C), duration time (1\u20139\u2009min), and oxygen proportion (0\u2013100%). Then, ~40\u2009nm-thick boron-doped a-Si\u00a0(p+ a-Si) was deposited on both sides of wafers using PECVD. The chamber temperature was 220\u2009\u00b0C; SiH4:H2:B2H6 flow ratio was 5:20:3 sccm; pressure was 0.5\u2009Torr; plasma power and deposition time was 5\u2009W and 160\u2009s, respectively. Afterwards, high temperature annealing was performed to facilitate silicon crystallization as well as boron diffusion and activation, which was followed by in situ wet nitrogen hydrogenation56. The borosilicate glass (BSG) films above poly-Si were removed by dipping in diluted HF solution. Additional hydrogenation process was performed via AlOx:H deposition in an ALD system, followed by vacuum annealing in a tube furnace. The ALD camber temperature was 200\u2009\u00b0C; the pressure was 0.03\u2009Torr; the pulse time for Trimethylaluminium/N2/H2O/N2 is 0.15/10/0.2/10\u2009s; the cycle number for 15 and 30\u2009nm AlOx:H was 115 and 230, respectively. At last, the chamber was purged with N2 for 3 times. Finally, SiNx coatings were deposited on both sides of the wafer by PECVD.\n\nThe passivation properties of planar and textured samples were characterized by the iVOC, the single-side J0,s and the \u03c4eff, which were obtained by Sinton WCT-120 lifetime tester57,58,59. The former one was measured using quasi steady state (QSS) mode, and the latter two used transient mode. The contact resistivity of the optimized sample with a single-sided p-type TOPCon structure on a textured surface was measured referring the Cox-Strack method39. Active boron concentration profiles in poly-Si and c-Si were measured based on the ECV method by WEP Wafer Profiler CVP2160,61. Nano- or micro-structures of samples were observed by SEM using Hitachi Regulus 8230.\n\nThe characterizations of ultrathin SiOx layer and SiOx/c-Si interface include the thickness, integrity, and chemical state of SiOx layer, and the residual Dit of SiOx/c-Si interface. The SiOx was prepared on polished (111)-oriented wafers for SE on Woollam Complete EASE equipment, and on textured wafers for TEM observation on Talos F200X. The measurements of chemical state of SiOx and Dit of SiOx/c-Si were performed toward the structure of SiOx above textured wafers, via XPS on Axis Ultra DLD and C\u2013V method25 on Keysight B1500A, respectively. The H content in AlOx:H film was represented by the integrated area of O-H bond peak, which was measured via FTIR on THermo NICOLET 6700.\n\nThe c-Si bottom cells with double-sided TOPCon structures were prepared based on double-side textured n-type Cz (100)-oriental wafers with low bulk resistivity. These wafers were the same with those for passivation property. The bottom cell for tandem cell fabrication has a 2.5\u2009\u00d7\u20092.5\u2009cm2 wafer size and 1\u2009\u00d7\u20091\u2009cm2 work area, while the one for checking bottom cell performance has a 4\u2009\u00d7\u20094\u2009cm2 wafer size and 2.2\u2009\u00d7\u20092.2\u2009cm2 work area. Initially, SiNx protection layer was deposited on the textured surface of a single-sided industrially textured wafer (Fig.\u00a0S10b), which was followed by the texturing process to form a textured surface with pyramid size below 1 \u03bcm. Then, the SiNx protection layer was removed by dipping in diluted HF solution. For the convenience of top cell fabrication, the side with a pyramid size below 1 \u03bcm was defined as the front side, while another side with industrial large pyramids was the rear side. After RCA cleaning, ultrathin silicon oxide layers were formed via thermal oxidation under optimized condition on both sides of wafers. Then, p+ a-Si and phosphorus-doped a-Si (n+ a-Si) were deposited on the front and rear side, respectively, by in situ PECVD. The deposition detail of p-type a-SiH4 was the same with the passivation samples above. For n-type a-Si:H, the chamber temperature was 220\u2009\u00b0C; SiH4:H2:PH3 flow ratio was 5:20:6 sccm; pressure was 0.5\u2009Torr; plasma power and deposition time was 6\u2009W and 320\u2009s, respectively. The following processes were high-temperature annealing, wet nitrogen hydrogenation, AlOx hydrogenation, and SiNx deposition, which were similar to symmetric passivation samples. The iVOC and \u03c4eff were measured by Sinton WCT-120 lifetime tester57,58,59. Then, SiNx/AlOx stack above the work area was removed by HF dipping. After that, ~10\u2009nm-thick ITO was deposited on the front side as intermediate layer for the 2.5\u2009cm wafer, but ~100\u2009nm-thick ITO as a carrier collection layer for 4\u2009cm wafer. The ITO film was prepared by physical vapor deposition. The passivation dropped drastically after ITO preparation, but it was recovered by forming gas annealing at 250\u2009\u00b0C for 10\u2009min. Afterwards, the work area on the rear side was fully covered by a\u2009~\u2009100 nm-thick Ag metal electrode prepared by thermal evaporation. Here, an additional process was applied on the 4\u2009cm wafer to form a stack of Al/Ag metal electrode with a grid and fingers above the ITO film using evaporation through a shadow mask. The passivation of the cell sample was monitored by photo-luminescence after AlOx hydrogenation and subsequent steps. The performance of the bottom cells, including VOC, JSC, FF and efficiency, was tested by a 4-wire solar cell tester consisting of a Keithley 2400 source meter and a Class AAA solar simulator produced by EnliTech Co., Ltd, which provided a light intensity of 100\u2009mW\u00b7cm\u22122.\n\nTo fabricate perovskite/silicon TSCs, a 12\u2009nm-thick tin dioxide (SnO2)\u00a0layer were deposited on the top of silicon bottom cells by a 150-cycle thermal ALD (KE-MICRO, PE ALD-F50R) with the chamber at 120\u2009\u00b0C and Tetrakis(dimethylamino)tin(IV) (TDMASn) source at 80\u2009\u00b0C. TDMASn/purge1/H2O/purge2 times were 0.4/5/1.5/15\u2009s with a constant 90-sccm nitrogen flow. After UV-ozone treatment of the ALD-SnO2 layer, a SnO2 nanocrystal solution (Alfa Aesar, 15%) diluted ten times with a mixed deionized water and ethanol (1:1, vol:vol) was dynamically spin-coated on the first SnO2 layer at 5000\u2009rpm for 35\u2009s. Then, 150\u2009\u00b0C-30 min post-annealing and 15\u2009min UV-ozone treatment were conducted. A 1.7\u2009M Cs0.05FA0.8MA0.15Pb(I0.75Br0.25)3 perovskite precursor solution, fully dissolved into a mixed solvent system comprising anhydrous dimethylformamide/dimethyl sulfoxide (4:1, vol:vol), was then spin-coated at 600/2000/8000\u2009rpm for 6/54/15\u2009s, and 300\u2009\u03bcL chlorobenzene as an anti-solvent was dropped onto the center of the substrate 10\u2009s before the end of the rotation procedure. The perovskite absorber layer was annealed at 105\u2009\u00b0C for 15\u2009min. A 5\u2009mg\u00b7mL\u22121 phenethylammonium iodide (PEAI) in isopropanol was dynamically spin-coated on the perovskite upper surface at 5000\u2009rpm for 35\u2009s. 2,2\u2032,7,7\u2032-tetra(N,N-di-tolyl)amino-9,9-spiro-bifluorene (Spiro-TTB) doped with 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4-TCNQ) as the hole transport layer was thermally evaporated to a 20\u2009nm thickness with a\u2009~\u200914.3 wt.% doping ratio. For the front transparent electrode, 15\u2009nm-thick molybdenum oxide\u00a0(MoOx)\u00a0and 100\u2009nm-thick indium zinc oxide\u00a0(IZO) were deposited by thermal evaporation at a 0.2\u2009\u00c5\u00b7s\u22121 rate and radio frequency sputtering under a 45\u2009W power, respectively. Silver was evaporated through a shadow mask to a 300\u2009nm thickness with a 1\u2009\u00d7\u20091\u2009cm2 active area. Finally, a 100\u2009nm-thick magnesium fluoride (MgFx)\u00a0was thermally evaporated as the antireflection layer.\n\nThe cross-sectional SEM images of TSCs were collected using a field-emission SEM (S-4800, Hitachi). The J\u2013V measurements were performed through a digital source meter (Keithley 2400) and a solar simulator (94022\u2009A, Newport) with illumination calibrated by a standard silicon cell (Bunkoukeiki, BS-520BK). The curves were achieved both in reverse (2.0 to \u22120.1\u2009V) and forward (\u22120.1 to 2.0\u2009V) voltage scanning modes with 200 data points. The mask for J\u2013V measurements has a 0.9226\u2009cm2 aperture area calibrated by SIMIT. EQE measurements were carried out through a QE system (QEX10, PV measurement, Inc). For perovskite top cell measurement, an infrared light-bias LED with 850\u2009nm peak emission was used to saturate the silicon bottom cells, and a 0.6\u2009V bias voltage was used to build almost short-circuit conditions. For silicon bottom cell measurement, a blue light-bias LED with a 455\u2009nm peak emission was used to saturate the perovskite top cells and a 1\u2009V bias voltage was applied to build an almost short circuit condition. For MPP tracking of tandems, the un-encapsulated devices operated under ~1 Sun LED illumination in a N2 flow at room temperature. The illumination intensity was calibrated to 100\u2009mW\u00b7cm-2 with the standard silicon cell from J\u2013V measurements.\n\nFurther information on research design is available in the\u00a0Nature Research Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All the main data are available in the main text, the Supplementary Information, and the Source Data file. 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(Y.Z., J.Y. and X.Y.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Zetao Ding, Chenxia Kan.\n\nNingbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang, China\n\nZetao Ding,\u00a0Shengguo Jiang,\u00a0Meili Zhang,\u00a0Hongyu Zhang,\u00a0Wei Liu,\u00a0Mingdun Liao,\u00a0Zhenhai Yang,\u00a0Yuheng Zeng\u00a0&\u00a0Jichun Ye\n\nUniversity of Chinese Academy of Sciences, Beijing, China\n\nZetao Ding,\u00a0Shengguo Jiang,\u00a0Meili Zhang,\u00a0Hongyu Zhang,\u00a0Yuheng Zeng\u00a0&\u00a0Jichun Ye\n\nLaboratory of Optoelectronic and Information Materials and Devices, Zhejiang Provincial Engineering Research Center of Optoelectronic Materials and Devices, Ningbo, Zhejiang, China\n\nZetao Ding,\u00a0Shengguo Jiang,\u00a0Meili Zhang,\u00a0Hongyu Zhang,\u00a0Wei Liu,\u00a0Mingdun Liao,\u00a0Yuheng Zeng\u00a0&\u00a0Jichun Ye\n\nState Key Laboratory of Silicon and Advanced Semiconductor Materials and School of Materials Science & Engineering, Zhejiang University, Hangzhou, Zhejiang, China\n\nChenxia Kan,\u00a0Pengjie Hang\u00a0&\u00a0Xuegong Yu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nZ.D., C.K., X.Y., Y.Z. and J.Y. designed the experiments and supervised the project. Z.D., C.K., S.J., M.Z. and H.Z. performed the material and film preparation and characterization. Z.D., C.K., S.J., M.L. and W.L. fabricated and characterized the devices. Z.D. and Y.Z. did the numerical simulations. Z.D., C.K., Z.Y., P.H., Y.Z. and J.Y. contributed to data analysis. Z.D., C.K., P.H., Z.Y., Y.Z., X.Y. and J.Y. wrote the manuscript. All authors reviewed and contributed to the final version of the manuscript.\n\nCorrespondence to\n Yuheng Zeng, Xuegong Yu or Jichun Ye.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Guang Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Highly passivated TOPCon bottom cells for perovskite/silicon tandem solar cells.\n Nat Commun 15, 8453 (2024). https://doi.org/10.1038/s41467-024-52309-2\n\nDownload citation\n\nReceived: 26 February 2024\n\nAccepted: 29 August 2024\n\nPublished: 30 September 2024\n\nVersion of record: 30 September 2024\n\nDOI: https://doi.org/10.1038/s41467-024-52309-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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b/8e64a7220292bf33c8ff951537b8c1ae4c1fa16fd7c66359f4c75a271cb78f46/metadata.json @@ -0,0 +1,202 @@ +{ + "title": "Precision targeting of \u03b2-catenin induces tumor reprogramming and immunity in hepatocellular cancers", + "pre_title": "Precision targeting of \u03b2-catenin induces tumor reprogramming and immunity in hepatocellular cancers", + "journal": "Nature Communications", + "published": "30 May 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60457-2/MediaObjects/41467_2025_60457_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60457-2/MediaObjects/41467_2025_60457_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60457-2/MediaObjects/41467_2025_60457_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60457-2/MediaObjects/41467_2025_60457_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE270977", + "https://vivli.org/", + "/articles/s41467-025-60457-2#MOESM1", + "/articles/s41467-025-60457-2#Sec29" + ], + "code": [], + "subject": [ + "Cancer microenvironment", + "Hepatocellular carcinoma", + "Targeted therapies" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5494074/v1.pdf?c=1748603161000", + "research_square_link": "https://www.researchsquare.com//article/rs-5494074/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60457-2.pdf", + "preprint_posted": "11 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "First-line immune checkpoint inhibitor (ICI) combinations show responses in subsets of hepatocellular carcinoma (HCC) patients. Nearly half of HCCs are Wnt-active with mutations in CTNNB1 (encoding for \u03b2-catenin), AXIN1/2, or APC, and demonstrate limited benefit to ICI due to an immune excluded tumor microenvironment. We show significant tumor responses in multiple \u03b2-catenin-mutated immunocompetent HCC models to a novel siRNA encapsulated in lipid nanoparticle targeting CTNNB1 (LNP-CTNNB1). Both single-cell and spatial transcriptomics revealed cellular and zonal reprogramming of CTNNB1-mutated tumors, along with activation of immune regulatory transcription factors IRF2 and POU2F1, re-engaged type I/II interferon signaling, and alterations in both innate and adaptive immune responses upon \u03b2-catenin suppression with LNP-CTNNB1. Moreover, LNP-CTNNB1 synergized with ICI in advanced-stage disease through orchestrating enhanced recruitment of cytotoxic T cell aggregates. Lastly, CTNNB1-mutated patients treated with atezolizumab plus bevacizumab combination had decreased presence of lymphoid aggregates, which were prognostic for response and survival. In conclusion, LNP-CTNNB1 is efficacious as monotherapy and in combination with ICI in CTNNB1-mutated HCCs through impacting tumor cell intrinsic signaling and remodeling global immune surveillance, providing rationale for clinical investigations.Biological sciences/Cancer/Cancer microenvironmentBiological sciences/Cancer/Gastrointestinal cancer/Liver cancer/Hepatocellular carcinomaBiological sciences/Cancer/Cancer therapy/Targeted therapieshepatocellular carcinomaWnt\u03b2-cateninimmunotherapymolecular therapysingle cellspatial transcriptomicsprecision medicine", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5Figure 7Figure 8", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-5494074/v1/e124cbd7574f17bae1230de2.png", + "https://assets-eu.researchsquare.com/files/rs-5494074/v1/7d1561ca19c6891e0111f866.png", + "https://assets-eu.researchsquare.com/files/rs-5494074/v1/8a44aa7777b787c60c3b6fe0.png", + "https://assets-eu.researchsquare.com/files/rs-5494074/v1/e2d528ab42654faf0bece4b6.png", + "https://assets-eu.researchsquare.com/files/rs-5494074/v1/e19636d2ea0715d8713ba47e.png", + "https://assets-eu.researchsquare.com/files/rs-5494074/v1/a60dbff3363e3e4ba355f54b.png", + "https://assets-eu.researchsquare.com/files/rs-5494074/v1/9e14205fc12160756ae041fa.png" + ] + }, + { + "section_name": "SIGNIFICANCE", + "section_text": "b-catenin is currently an \u201cundruggable\u201d target. Thus, utilizing a novel LNP-encapsulated siRNA targeting b-catenin, we demonstrate its efficacy for precision therapy in aggressive preclinical models, mechanisms underlying b-catenin-mediated immune escape, and synergy with ICI, paving a way forward for clinical trials.\n", + "section_image": [] + }, + { + "section_name": "INTRODUCTION", + "section_text": "Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death globally.1 Despite the shift in therapeutic management of advanced disease over the last five years from multi-tyrosine kinase inhibitors (TKIs) (e.g., sorafenib) to immunotherapy with immune-checkpoint inhibitor (ICI) combinations (e.g., atezolizumab plus bevacizumab), objective response rates (ORRs) remain low at ~\u200930% with overall survival\u2009<\u20092 years.2\u20135 Preclinical and clinical studies investigating molecular correlates of ICI response have yielded novel insights into potential mechanisms of resistance, including but not limited to immune exclusion, with Wnt/\u03b2-catenin activation contributing to this phenotype.6\u20138 Wnt/\u03b2-catenin pathway activity is observed in up to 50% of tumors from patients with HCC, with mutations mostly occurring in CTNNB1 (26\u201337%), AXIN1/2 (8\u201310%), and APC (3\u20135%).9\u201312 Gain-of-function (GOF) mutations in CTNNB1 (encoding for \u03b2-catenin) are one of the major trunk mutational events in HCC and occur mostly as missense mutations in exon 3 at serine and threonine residues or the ubiquitination destruction motif, which interfere with its degradation, leading to constitutive \u03b2-catenin activation and target gene transcription.13,14 Patients with CTNNB1-mutated HCC have upregulation of known Wnt/\u03b2-catenin target genes, including GLUL, AXIN2, LGR5, and TBX3.11 In fact, glutamine synthetase (GS; encoded by GLUL) immunohistochemistry is used as a biomarker for patients with CTNNB1-mutated HCC.15 However, targeting these downstream Wnt target genes has revealed novel negative feedback loops in the Wnt/\u03b2-catenin oncogenic circuit,16,17 necessitating the need to focus on targeting \u03b2-catenin directly for precision therapy. Despite improved molecular stratification of HCC over the last decade, with recognition of Wnt/\u03b2-catenin driven tumors overlapping with Hoshida S318 or Boyault G5/G6 subclasses19, these different molecular stratifications have not yielded prognostic implications due to a lack of clinically approved targeted or biomarker-driven precision therapeutics. \u03b2-catenin has traditionally been an \u201cundruggable\u201d target, despite preclinical studies elucidating the molecular and metabolic addiction to \u03b2-catenin oncogenic signaling in CTNNB1-mutated HCC.20\u201323 Thus, \u03b2-catenin is a prime target for precision therapy. Advances in RNAi technology over the last two decades have resulted in multiple approved RNAi therapies,24 and RNAi-mediated gene silencing has proven to be an excellent tool for targeting the traditionally \u201cundruggable\u201d, especially in hepatic tissue. In the current study, we investigate the relevance of RNAi-mediated \u03b2-catenin inhibition in patient-derived CTNNB1-mutated HCC organoids and multiple humanized mouse models of CTNNB1-mutated HCC at different treatment windows and elucidate the underlying mechanisms of response in both hepatic and immune compartments through both single-cell and spatial approaches. Our findings provide the mechanistic basis for clinical investigations of this RNAi therapeutic targeting \u03b2-catenin for HCC treatment as a novel treatment paradigm in the form of monotherapy and/or in combination with immunotherapy in human subjects belonging to the Wnt-\u03b2-catenin active HCC subclass.", + "section_image": [] + }, + { + "section_name": "RESULTS", + "section_text": " RNAi-mediated \u03b2-catenin Inhibition Results in Potent CTNNB1 Knockdown in vitro and in vivo To study the effects of RNAi-mediated inhibition in \u03b2-catenin-mutated HCC, we utilized a novel siRNA that targets the CTNNB1 gene, with both mouse and human specificity, encapsulated in a lipid nanoparticle (referred hereafter as LNP-CTNNB1). We first assessed whether LNP-CTNNB1 affected growth in a patient-derived HCC organoid (23277) with known mutation in CTNNB1.25 72-hour treatment with LNP-CTNNB1 at 20nm concentration led to a notable decrease in both the number and size of the organoid compared to treatment with a LNP-CTRL (Fig.\u00a01a-b). Thus, LNP-CTNNB1 demonstrates efficacy in mutant-CTNNB1 human HCC organoid cultures. Next, to assess its pharmacodynamic effects, we first delivered LNP-CTNNB1 via tail vein intravenous (I.V.) injection to mouse livers which were transfected with human S45Y-mutant-CTNNB1 gene (S45Y-hCTNNB1 mice) via sleeping beauty hydrodynamic tail vein injection (SB-HDTVi) system. We have previously reported that mouse hepatocytes overexpressing mutant-\u03b2-catenin alone via SB-HDTVi method do not develop HCC,26 but require a secondary driver, such as hMet, Kras, or mutant-Nrf2 to induce HCC.20,26,27 After 4 treatments at 3mg/kg dosing in S45Y-hCTNNB1 mice (Figure S1a), we observed an appreciable decrease in liver weight to body weight ratio (LW/BW ratio), which is consistent with the role of \u03b2-catenin in regulating liver growth and size (Figure S1b-d).28,29 Additionally, expression of two well-known \u03b2-catenin target genes via immunohistochemistry (IHC), GS and Cyclin D1 (CCND1), was absent throughout the liver lobule, indicating high mCTNNB1 gene knockdown (Figure S1e-f). Moreover, Myc-tag (present on the S45Y-hCTNNB1 plasmid) positive cells were absent throughout the liver parenchyma in LNP-CTNNB1 treated mice compared to islands of Myc-tag positive cells in LNP-CTRL mice, indicating high hCTNNB1 gene knockdown (Figure S1e-f). Thus, LNP-CTNNB1 targets both endogenous mouse and mutant human CTNNB1 with high potency and specificity in vivo. Before testing efficacy of siRNA-mediated CTNNB1 knockdown, we assessed whether there were any adjuvant effects of the LNP itself on the tumor immune microenvironment (TIME). We treated mice with either PBS, LNP-CTRL, or LNP-CTNNB1 utilizing a similar LNP frequency and dosage scheme as in Figure S1a, yet applied this to our T41A-mutant-\u03b2-catenin-Nrf2 (\u03b2-N) model (Figure S1g), which we have previously shown to represent 9\u201312% of all human HCC.27 Following treatment, we observed a decrease in liver weights and LW/BW ratio in LNP-CTNNB1 treated mice (Figure S1h-i), yet no appreciable difference in liver serum biochemistries (Figure S1j). Next, we performed bulk RNA-sequencing on all 3 treatment groups, and observed that PBS and LNP-CTRL treated animals are transcriptionally very similar, yet unique to the LNP-CTNNB1 treated animals (Figure S1k). Additionally, gene set enrichment analysis using gene ontology pathways demonstrated that the immune phenotype is similar between PBS and LNP-CTRL treated mice, suggesting the LNPs do not alter the immune excluded phenotype observed in CTNNB1-mutated HCC (Figure S1l). RNAi-mediated \u03b2-catenin Inhibition Impairs Tumor Growth in Multiple Immunocompetent CTNNB1-mutated and non-CTNNB1-mutated HCC Mouse Models with Durable Response in Early-stage Disease Setting We next assessed the in vivo efficacy of LNP-CTNNB1 in CTNNB1-mutated and non-mutated HCC models. We first performed a dose titration study to determine lowest dose efficacy in our \u03b2-N model. We administered once weekly I.V. injections over 6 weeks of LNP-CTNNB1 starting at 5-weeks post-HDTVi, when microscopic tumor foci are established, at 3mg/kg, 1mg/kg, 0.3mg/kg, 0.1mg/kg, and 0.03mg/kg dosages (Figure S2a). There were significant tumor burden reductions across a wide LNP-CTNNB1 dose range (3mg/kg, 1mg/kg, 0.3mg/kg, and 0.1mg/kg), as evident by gross visualization and reduced LW/BW ratio (Figure S2b-f, Fig.\u00a01c-g). However, at 3mg/kg dosage, following the 4th dose, we observed mortality in one of four mice, which was likely due to the high LNP dose and frequency. Additionally, the 0.3mg/kg, 0.1mg/kg, and 0.03mg/kg LNP-CTNNB1 dosages resulted in partial responses, with remnant microscopic tumor foci observed in 0.3mg/kg and 0.1mg/kg treated animals (Figure S2e) and macroscopic tumor nodules present in animals treated with 0.03mg/kg (Figure S2b, e). However, significant tumor responses were observed at the 1mg/kg dosage in LNP-CTNNB1 treated mice as noted via H&E, IHC for Myc-tag and GS/Ki67, and magnetic resonance imaging (MRI) (Fig.\u00a01g; Figure S3a-d). Thus, following this dose titration study in the \u03b2-N model, we determined that the 1mg/kg LNP-CTNNB1 dosage had profound in vivo efficacy for treatment of \u03b2-catenin-mutated HCC preclinical models without observable adverse effects. To extrapolate our findings to additional \u03b2-catenin-mutated HCC preclinical models that we have previously reported, we next tested LNP-CTNNB1 in the more aggressive S45Y-mutant-\u03b2-catenin-Met (\u03b2-M) model, which represents 11% of human HCC.26 Here, we started treatment at 3-weeks post-HDTVi, a timepoint when microscopic tumor foci are established. Remarkably, following continued once weekly I.V. administration at 1mg/kg dosage over 6 weeks, there was a decrease in gross tumor burden (Fig.\u00a01h-l), and also a significant tumor response observed via H&E, Myc-tag, and GS/Ki67 IHC following LNP-CTNNB1 treatment (Fig.\u00a01l; Figure S3e-f). Moreover, starting at 3-weeks post-HDTVi, we tested LNP-CTNNB1 at the 1mg/kg dosage in a third CTNNB1-mutated model, the S45Y-mutant-\u03b2-catenin-Nrf2-Met (\u03b2-N-M) model, which represents\u2009~\u20095% of human HCC, independent of \u03b2-N and \u03b2-M models.30 Following a similar treatment protocol to the \u03b2-M model, we again observed significant tumor responses (Fig.\u00a01m-q; Figure S3g-h ), similar to the results obtained in the \u201ctwo-hit\u201d models (\u03b2-N and \u03b2-M). Lastly, we wanted to assess response to LNP-CTNNB1 in models that were not CTNNB1-mutated due to the general mitogenic function of Wnt-\u03b2-catenin signaling pathway in the liver.31 \u03b2-Catenin suppression by LNP-CTNNB1 in the Nrf2-hMet (N-M) model led to a decrease in LW/BW and in macroscopic disease (Figure S4a-d), yet there was persistence of microscopic nodules, which depicted inferiority in response when compared to mutant-\u03b2-catenin-driven tumors (Figure S4e). This decrease in tumor burden was observed despite HCC nodules in this model not homogenously positive for the bonafide Wnt target GS. We have also previously reported that c-Met/sgAxin1 tumors require intact \u03b2-catenin to initiate tumorigenesis.32 We also tested dependence on \u03b2-catenin in another independent non-CTNNB1-mutated HCC model using genetic approach (Figure S4h). \u03b2-Catenin deletion in SB-HDTVi induced Akt-NRas HCC in \u03b2-catenin floxed mice through simultaneous administration of pCMV-cre or control led to a significant improvement in overall survival and less tumor burden in pCMV-Cre compared to control, although tumors still persisted (Figure S4i). Thus, overall, we observed that \u03b2-catenin inhibition alone for CTNNB1-mutated HCC is most effective in early-stage disease setting as evident through significant tumor responses in multiple models of CTNNB1-mutated HCC, and as partial responses in \u03b2-catenin non-mutated HCC models. Next, we assessed the long-term durability of the significant tumor responses observed in both the \u03b2-N and \u03b2-M models with LNP-CTNNB1 treatment at 1mg/kg dosage initiated at an early-stage disease treatment setting. Following the same treatment protocol in \u03b2-N (Fig.\u00a01c) and \u03b2-M (Fig.\u00a01h) models, we then withdrew LNP-CTNNB1. In the \u03b2-N model, treatment was ceased at 10 weeks, yet by ~\u200922.5 weeks post-LNP-CTNNB1 treatment, gross tumor burden became equivalent to the tumor burden observed in mice with LNP-CTRL treatment at ~\u200910.5 weeks which had been lethal in \u03b2-N mice (Figure S5a-b). Thus, with LNP-CTNNB1 treatment in \u03b2-N model, overall survival was significantly extended by ~\u200912 weeks (p\u2009<\u20090.001) (Figure S5c). The nodules that re-appeared at the ~\u200922.5-week timepoint were positive for both GS and Nqo1 (Nrf2-target) (Figure S5d). In the \u03b2-M model, treatment was ceased at 8 weeks, yet by ~\u200916.5 weeks post-LNP-CTNNB1 treatment, gross tumor burden was equivalent to that observed with LNP-CTRL treatment at the ~\u20097.5 weeks which had been lethal in \u03b2-M mice (Figure S5e-f). Thus, LNP-CTNNB1 treatment in the \u03b2-M model extended overall survival by ~\u20099 weeks (p\u2009<\u20090.001) (Figure S5g). The nodules that reappeared at ~\u200916.5-week timepoint in \u03b2-M model were positive for GS and V5-tag (present on hMet plasmid) (Figure S5h). Overall, LNP-CTNNB1 treatment as monotherapy more than doubled the survival of mice in both HCC models although tumors recurred after treatment cessation. These recurring tumors appear to be mutant-\u03b2-catenin-driven and not due to appearance of de novo resistant clones. \n\nEarliest Biological Response to RNAi-mediated \u03b2-catenin Inhibition Observed at 3-days Following Initial LNP-CTNNB1 Treatment Given the robust tumor responses following LNP-CTNNB1 treatment, we proceeded to investigate the earliest biological response observed following \u03b2-catenin knockdown within the tumor cells. In the \u03b2-N model, we followed mice over a 3-week treatment course (LNP-CTNNB1 injected weekly x 3) and sacrificed mice at 1-, 3-, 5-, 7-, 14-, and 21-days post the first treatment (Fig.\u00a02a). Over this 21-day treatment time course, the visible tumor foci or LW/BW ratio progressively trended lower in the LNP-CTNNB1 group although differences were insignificant (except day 5) when compared to time-matched LNP-CTRL group (Figure S6a; Fig.\u00a02b). However, at 3-days post a single LNP-CTNNB1 dose, RNA expression of Ctnnb1, along with Wnt target genes, Glul, Ccnd1, Lect2, and Rgn were significantly decreased in LNP-CTNNB1 mice compared to LNP-CTRL mice (Fig.\u00a02c). Additionally, GS protein expression visualized via IHC was decreased within tumor nodules, but retained in hepatocytes around central veins, at this 3-day timepoint, and by 14-days GS expression was absent in central vein hepatocytes in the LNP-CTNNB1 treated animals (Fig.\u00a02d; Figure S6b). Ki67 and TUNEL IHC also demonstrated significantly decreased tumor cell proliferation and increased cell death, respectively, at the 3-day timepoint, which was not observed at the 1-day timepoint (Fig.\u00a02e-f; Figure S6c-d). Given these results, we also administered a single treatment to \u03b2-M animals and sacrificed mice at 3-days post-treatment (Figure S7a). While there was no significant difference in gross tumor burden (Figure S7b), a single dose of LNP-CTNNB1 significantly decreased LW/BW ratio (Figure S7c-e), decreased intra-tumoral GS expression but retained V5-tag expression (Figure S7f-g). Also, there were significantly less intra-tumoral Ki67-positive cells and significantly more TUNEL-positive cells (p\u2009<\u20090.01) (Figure S7h-i). Thus, the earliest evident biological response following RNAi-mediated \u03b2-catenin inhibition in both models occured at 3-days post-LNP treatment. To understand the transcriptional consequences of \u03b2-catenin knockdown in HCC, we performed RNA-sequencing (RNA-seq) on both the \u03b2-N and \u03b2-M models treated with either LNP-CTRL or LNP-CTNNB1 at the 3-day timepoint. Each model clustered distinctly with LNP-CTNNB1 groups for each model clustering independently from the LNP-CTRL groups as shown via PCA analysis (Fig.\u00a02g). Differential gene expression analysis comparing LNP-CTRL vs LNP-CTNNB1 demonstrated 455 upregulated and 628 downregulated genes in the \u03b2-N model, and 608 upregulated and 634 downregulated genes in the \u03b2-M model, with 230 common downregulated and 73 common upregulated genes (Fig.\u00a02h-i). Common downregulated genes included Wnt/\u03b2-catenin target genes and pericentral marker genes (e.g., Glul, Axin2, Lgr5, Notum, Lect2, Ccnd1, Cyp2e1, Cyp1a2, and Oat), and common upregulated genes were midzonal and periportal marker genes (e.g., Hamp2, Cyp8b1, and Cyp2f2) (Fig.\u00a02j). From both models, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis demonstrated positive enrichment of metabolic pathways, cell death pathways, immune activation pathways, NF\u03baB signaling, and extracellular matrix signaling, along with negative enrichment of cell cycle, Wnt signaling pathways, fatty acid metabolism, retinol metabolism, and cytochrome P450 metabolic pathways (Fig.\u00a02k-l). Thus, we inferred \u03b2-catenin mutations in HCC confer most profound effects on tumor cell growth/proliferation, metabolism, and the tumor microenvironment.\nIntegrated Single-Cell Analyses Reveal De Novo Formation of Reprogrammed Hepatocytes Within Remnant Tumor Nodules\nTo further interrogate tumor cell intrinsic biological effects that occurred at the 3-day timepoint, we administered LNP-CTRL or LNP-CTNNB1 at 5-weeks post-HDTVi to \u03b2-N model mice and performed single-cell RNA-sequencing (scRNA-seq) analysis on a hepatocyte-enriched single-cell population following whole liver perfusion. In total, 94,650 single cells were sequenced with 26,851 in the LNP-CTRL group and 67,799 in the LNP-CTNNB1 group. Unbiased clustering on the integrated dataset resulted in 10 unique cell populations (Figure S8a), annotated as a) Dying/injured hepatocytes, b), Hepatic stellate cells, c) Kupffer cells, d) Erythroid cells, e) Endothelial cells, f) Low-quality hepatocytes, g) Reprogrammed hepatocytes (expressing both zone 1 & 2 markers Arg1, Ass1, Pck1, Hal, Hamp2, with Nrf2 tumor targets Prdx2, Prdx5, Gstm1, Gpx1), h) Zone 1 CTNNB1 WT (GS-negative) hepatocytes, i) Zone 1/2 CTNNB1 MUT (GS+) hepatocytes, and j) Zone 3 CTNNB1 WT & MUT (GS+) hepatocytes based on differential gene expression analysis per cluster (Figure S8b-c). KEGG pathway enrichment analysis comparing each hepatocyte cluster to all other clusters revealed that top pathways for Zone 3 CTNNB1 WT & MUT (GS+) hepatocytes were bile acid secretion, drug metabolism \u2013 cytochrome P450, and fatty acid metabolism, which are all known hallmarks of CTNNB1-mutated HCCs (Figure S8d).33 Zone 1/2 CTNNB1 MUT (GS+) hepatocytes and Zone 1 CTNNB1 WT (GS-negative) hepatocytes were interestingly enriched for arginine biosynthesis and amino acid biosynthesis KEGG pathways (Figure S8e-f), which are known metabolic hallmarks of zone 1 metabolism. This pathway analysis reveals the metabolic heterogeneity of tumor cells along the portal-central axis. Cell-type proportion analysis comparing LNP-CTRL and LNP-CTNNB1 groups demonstrated less Zone 3 CTNNB1 WT & MUT (GS+) hepatocytes along with de novo appearance of reprogrammed hepatocytes following LNP-CTNNB1 treatment (Fig.\u00a03a-b). KEGG pathway analysis and gene set enrichment analysis on the reprogrammed hepatocytes demonstrated enrichment of pathways across all three liver lobule zones, including biosynthesis of cofactors (Zone 1), arginine biosynthesis (Zone 1), peroxisome (Zone 1), glutamate metabolism (Zone 3), glycolysis/TCA cycle (Zone 3), along with fatty acid metabolism, a pathway hallmark of CTNNB1-mutated hepatocellular cancers (Figure S9a-d). Cell cycle phase-specific gene expression analysis on hepatocyte clusters importantly demonstrated that tumor cells (both Zone 3 CTNNB1 WT & MUT [GS+] and Zone 1/2 CTNNB1 MUT [GS+] hepatocytes) were the most proliferative, while reprogrammed hepatocytes and Zone 1 CTNNB1 WT (GS-negative) hepatocytes were the least proliferative with proportionally fewer cells in G2M phase of the cell cycle (Fig.\u00a03c). In fact, reprogrammed hepatocytes and Zone 1 CTNNB1 WT were the two enriched hepatocyte populations following LNP-CTNNB1 treatment. Interestingly, Zone 1/2 CTNNB1 MUT [GS+] hepatocytes were the most proliferative tumor cell population, with the most cells in G2/M cell cycle phase (Figure S3c). We next performed pseudotime analysis on all the hepatocyte populations in this dataset to define cell states which demonstrated the intermediate cell state of these reprogrammed hepatocytes which occurred along the trajectory of Zone 3 CTNNB1 WT & MUT (GS+) hepatocytes to Zone 1 CTNNB1 WT (GS-negative) hepatocytes (Fig.\u00a03d). Thus, these reprogrammed hepatocytes are an intermediate cell phenotype, likely reflecting cancer cell differentiation to normal hepatocyte-like cells and contributing to the rapid cell turnover observed following LNP-CTNNB1 treatment. Next, to confirm the spatial identity of these reprogrammed hepatocytes, we performed single-cell spatial transcriptomics using Molecular Cartography\u2122 platform on tissue sections from the 3-day timepoint with LNP-CTRL or LNP-CTNNB1 treatment in the \u03b2-N model. The 100-gene panel consisted of markers specific for Wnt/\u03b2-catenin targets, metabolic zonation, and non-parenchymal cell types (see Methods). Following data pre-processing and automatic cell segmentation, in total, 19,301 single cells were sequenced from multiple regions of interest (ROIs) with 10,227 cells across 6 ROIs in LNP-CTRL group and 9,074 cells across 5 ROIs in LNP-CTNNB1 group. Unbiased clustering resulted in 9 unique cell populations, annotated as a) H1: Zone 3 CTNNB1 MUT (GS+), b) H2: Zone 3 Central Vein (CV) CTNNB1 WT (GS+), c) H3: Zone 3 CTNNB1 WT (GS-negative), d) H4: Zone 2\u20133 CTNNB1 WT (GS-negative), e) H5: Zone 1 CTNNB1 WT (GS-negative), f) H6: Reprogrammed hepatocytes, g) Hepatic stellate cells, h) Immune cells, and i) Endothelial cells, based on marker gene expression per cluster (Figure S10a-d). Clustering by treatment condition demonstrated similar enrichment of reprogrammed hepatocytes and loss of H1: Zone 3 CTNNB1 MUT (GS+) hepatocytes in LNP-CTNNB1 group (Fig.\u00a03e-f), similar to the scRNA-seq analysis (Fig.\u00a03a-b). Cluster Mapping to tissue Section (CMapS) confirmed the tumoral origin of the H6 cluster representing the reprogrammed hepatocytes (Fig.\u00a03g-h). In fact, spatial visualization and quantification of Wnt target genes revealed that \u03b2-catenin-mutated tumor cells are defined by expression of Glul, Tbx3, Axin2, Lgr5, Lect2, and Ccnd1 (Figure S11a-b), along with their identity intimately linked to zone 3 metabolic genes (and processes), including Cyp2e1, Cyp1a2, and Oat, with exclusion of zone 1 metabolic genes (and processes), including Cyp2f2, Ass1, and Arg1 (Figure S12a-b). However, with LNP-CTNNB1 treatment, tumor cells begin to express zone 1 markers, including Cyp2f2, Arg1, and Ass1 (Figure S12a-b), while decreasing expression of zone 3 genes (e.g., Cyp2e1, Cyp1a2, and Oat). IHC validated these sc-Spatial transcriptomic findings and confirmed decreases in CYP2E1 and OAT, with increased expression of zone 1 markers ARG1 and CYP2F2, and zone 2 marker HAMP1/2 (Figure S12c). Additionally, pseudotime analysis on the sc-Spatial transcriptomic data confirmed the intermediary phenotype of the H6: reprogrammed hepatocytes (Fig.\u00a03i), as observed in the scRNA-seq data (Fig.\u00a03d). Lastly, for verification, cell cluster quantification was performed across each ROI within tumoral and non-tumoral regions (using Glul as tumoral landmark) (Figure S13a-b), which revealed a significant decrease in cell density of clusters with active \u03b2-catenin signaling, and significant increase in cell density of the H6: reprogrammed hepaotcytes cluster, which occurred mostly in tumoral regions (Figure S13c). Overall, this integrated single-cell analysis revealed that \u03b2-catenin-mutated tumor cells are exclusively zone 3 metabolic and respond to \u03b2-catenin suppression by turning off expression of these genes while differentiating towards zone 1/2 hepatocyte-like cells, thus reprogramming their metabolic machinery.\nEarly \u03b2-catenin Suppression Induces an Innate Immune Response Characterized by Type I/II Interferon Network Signaling\nCMapS also revealed more immune cells in the LNP-CTNNB1 group compared to the LNP-CTRL group (Fig.\u00a03g-h), which was also quantified (Figure S13c). To further investigate alterations in the immune landscape following LNP-CTNNB1 treatment in an unbiased manner, scRNA-seq was performed on an immune-enriched single-cell suspension from \u03b2-N treated animals. In total, 20,235 single cells were sequenced with 8,499 cells across 3 individual biological replicates in the LNP-CTRL group and 11,736 cells across 3 individual biological replicates in the LNP-CTNNB1 group. Unbiased clustering on the integrated dataset resulted in initially 21 unique clusters across the three biological replicates in the two treatment conditions (Figure S14a-b). To gain insights into the global immune cell changes, we combined and annotated the clusters as: a) T cells, b) B cells, c) NK cells, d) Hepatocytes, e) Myeloid, f) Proliferative, g) Dendritic cells, h) Endothelial cells, and i) Hepatic stellate cells, based on known marker gene expression for each of these cell types (Figure S14c-d). The majority cell populations that were ultimately sequenced were T cells, B cells, and Myeloid cells. We further subclustered and annotated these populations to better understand the T cell and myeloid cell functional states using marker genes previously described34 (Figure S15a-b; Fig.\u00a04a-c). The major differences observed following treatment were a 3-fold enrichment of \u201cM1-like\u201d pro-inflammatory macrophages in the LNP-CTNNB1 group (12.4%) compared to LNP-CTRL group (4.1%) (Fig.\u00a04b, d). At the 3-day time point following LNP-CTNNB1 treatment, we did not observe any significant differences in CD4 T cell populations in the \u03b2-N model from the immune-enriched scRNA-seq analysis (Figure S15c), or the sc-Spatial Transcriptomic analysis (Figure S15d-e). Additionally, in the the \u03b2-M model, IHC for CD4 did not reveal differences at the 3-day timepoint following LNP-CTNNB1 treatment (Figure S15e). Thus, innate immunity via myeloid cells, appear to be the predominant cell population which shifts 3-days post treatment (Fig.\u00a04d). To investigate functional changes within the \u201cM1-like\u201d macrophage population, we performed differential gene expression comparing the \u201cM1-like\u201d macrophages from LNP-CTRL and LNP-CTNNB1 treatment. Gene ontology (GO) pathway analysis demonstrated enrichment of both response to type I/II interferon and interferon alpha/beta pathways following LNP-CTNNB1 treatment (Fig.\u00a04e). CellChat analysis, which determines pathway level changes based on gene expression of ligand-receptor interactions35, showed enrichment of IFN-II and TNF signaling in the \u201cM1-like\u201d macrophage population in the LNP-CTNNB1 treatment group (Fig.\u00a04f). Specifically, this analysis shows high probability of cell communication via Ifng from proliferative T cells with Ifngr1 and Ifngr2 on \u201cM1-like\u201d macrophages, and other macrophage cell populations solely in the LNP-CTNNB1 group (Fig.\u00a04g). Thus, increased type I/II interferons released from the immune compartment (likely from T cells and macrophages) following LNP-CTNNB1 treatment are engaging with macrophages in the TIME milieu, and in part contributing towards polarizing them towards a pro-inflammatory anti-tumor phenotype. To validate our findings that IFN\u03b3 is mediating an anti-tumor immune response following LNP-CTNNB1 treatment (Fig.\u00a04f-g), we treated \u03b2-M mice with IFN\u03b3 3x weekly for 5 weeks, which led to a significant decrease in tumor burden compared to vehicle controls (Fig.\u00a04h-j). Thus, early \u03b2-catenin suppression induces recruitment of innate effector cells which mediate response to enhanced interferon network signaling driving an anti-tumor immune response.\nMutated-\u03b2-catenin Represses a Module of Transcription Factors which Drives Immune Exclusion in CTNNB1-mutated HCC\nGiven the general amplified immune response early after LNP-CTNNB1 treatment, we next investigated potential tumor cell-intrinsic molecular mechanisms driving the immune excluded phenotype in \u03b2-catenin-mutated HCCs. We utilized bulk RNA-seq datasets which contained the transcriptome of multiple \u03b2-catenin-mutated HCC mouse models (GSE125336) and \u03b2-catenin knockout mouse livers (GSE68779) and performed transcription factor enrichment analysis on the 162 common genes which were downregulated in \u03b2-catenin-mutated HCC and upregulated in \u03b2-catenin knockout livers. We identified multiple transcription factors, including Irf2 (p\u2009=\u20090.0052) and Pou2f1 (p\u2009=\u20090.0023), as candidate transcription factors with known binding to the upregulated genes in \u03b2-catenin knockout livers (Fig.\u00a05a). To prioritize targets for potential therapeutic relevance, we further analyzed the scRNA-seq dataset (Fig.\u00a03a) and performed differential gene expression analysis on the Zone 3 CTNNB1 WT & MUT (GS+) hepatocyte cell population, and observed Irf2 and Pou2f1 target genes upregulated following LNP-CTNNB1 treatment (Fig.\u00a05b). To confirm whether tumor hepatocytes could be mediating IRF2 and POU2F1 downstream signaling to influence immune response, we investigated Irf2 and Pou2f1 expression in both human and mouse liver scRNA-seq datasets36 (GSE192742). We observed Irf2/IRF2 and Pou2f1/POU2F1 expression in hepatocyte cell populations in both mouse and human livers (Fig.\u00a05c; Figure S16a), suggesting that \u03b2-catenin-mediated IRF2 suppression may be a hepatocyte cell intrinsic process. Interestingly, expression of IRF2 and POU2F1 target genes in TCGA-LIHC cohort were notably downregulated in HCC patients with either CTNNB1, AXIN1, or APC mutations compared to those that did not have mutations known to confer \u03b2-catenin activation (Fig.\u00a05d). Thus, we hypothesized that mutated-\u03b2-catenin is a repressing a module of transcription factors (TFs) driving immune exclusion and limiting an anti-tumor immune response. To validate that repression of IRF2, POU2F1, and other TFs are driving immune exclusion in \u03b2-catenin-mutated HCC, we first overexpressed either pT3 (empty vector) or Irf2 (\u03b2-M-IRF2) in the \u03b2-M model (Fig.\u00a05e). We observed a significant decrease in overall tumor burden grossly at 7.5-weeks post-HDTVi and via decreased LW/BW ratio in Irf2-overexpression \u03b2-M model (Fig.\u00a05f-h). RNA-seq confirmed the overexpression of Irf2 in the \u03b2-M-IRF2 mice at the 7.5-week timepoint where less tumor burden was evident (Figure S16b). Expectedly, given the known immunomodulatory roles of IRF2 and its involvement in type I/II interferon signaling37, we observed an increased presence of immune aggregates as evident by CD45 IHC (Figure S16c). This was validated with fluorescence-activated cell sorting (FACS) on isolated immune cells from \u03b2-M-pT3/\u03b2-M-IRF2 mouse HCC which demonstrated significant increases in total CD4\u2009+\u2009cells with decreases in T regulatory populations in the \u03b2-M-IRF2 group (Figure S16d, S17a). Next, we overexpressed either pT3 (empty vector) or Pou2f1 (\u03b2-N-POU2F1) in the \u03b2-N model (Fig.\u00a05i). We also observed here a significant decrease in overall gross tumor burden at 10.7-weeks post-HDTVi in Pou2f1-overexpression \u03b2-N model (Fig.\u00a05j-l) and via histology (Figure S18a). These findings were also validated in the \u03b2-M model where significant reductions in tumor burden were observed at 7.7-weeks post-HDTVi in \u03b2-M-POU2F1 group (Figure S18b-e). Interestingly, IHC for CD4, CD8, and CD20 revelaed increased recruitment of T and B cells aggregating in the TIME in the \u03b2-N-POU2F1 group (Fig.\u00a05m). RNA-seq confirmed the overexpression of Pou2f1 in the \u03b2-M-POU2F1 mice at the 7.7-week timepoint, along with decreased enrichment of our previously reported mutated-\u03b2-catenin gene signature (Figure S18f-h).30 Additionally, GO pathway analysis demonstrated enrichment of T and B cell activation and proliferation (Fig.\u00a05n). Lastly, given the less well characterized role of POU2F1 mediating an immune response, as compared to known functions of IRF2,37,38 we administered \u03b1CD3 to deplete CD3\u2009+\u2009immune cells from \u03b2-M-POU2F1 mice (Figure S19a). Interestingly, at 8.3-weeks post-HDTVi, there was a significant increase in tumor burden in \u03b2-M-POU2F1\u2009+\u2009\u03b1CD3 versus \u03b2-M-POU2F1\u2009+\u2009IgG animals (Figure S19b-c), suggesting an immune-dependent role for POU2F1-mediated tumor regression in CTNNB1-mutated HCC. Overall, mutated-\u03b2-catenin represses IRF2, POU2F1, and likely other TFs, which limits transcription of key chemokines and cytokines important for priming recruitment of lymphocytes needed for an effective anti-tumor immunity and ICI response. RNAi-mediated \u03b2-catenin Inhibition Impairs Tumor Growth in Multiple Immunocompetent CTNNB1-mutated HCC Mouse Models in Late-stage Disease Setting with Response Associated with Restored Adaptive Immune Surveillance To assess the translatability of our findings to clinically relevant advanced-stage HCC, we next assessed the in vivo activity of LNP-CTNNB1 in late-stage disease CTNNB1-mutated HCC models, including both the \u03b2-N and \u03b2-M models. First, we assessed response to late-stage intervention in the \u03b2-N model where we administered once weekly I.V. LNP treatments starting at 8-weeks post-HDTVi to mimic clinically relevant advanced-stage disease (Fig.\u00a06a). Interestingly, after 6 cycles we observed a heterogenous response to LNP-CTNNB1 with 5/8 animals responding and 3/8 animals demonstrating poor response at 13.5-weeks post-HDTVi (Fig.\u00a06b-c; Figure S20a). Unsurprisingly, tumor foci in responder animals were less proliferative (evident via Ki67 IHC) and showed decreased expression of \u03b2-catenin (Myc-tag) and \u03b2-catenin targets, such as GS, via IHC (Figure S20b-c). Next, we studied response to LNP-CTNNB1 in the more aggressive \u03b2-M model with once weekly I.V. treatments starting at 6-weeks post-HDTVi to mimic clinically relevant advanced-stage disease (Fig.\u00a06d). Like the \u03b2-N model, we observed a heterogeneous response to LNP-CTNNB1 with 5/8 animals responding and 3/8 animals demonstrating no response at 10.5-weeks post-HDTVi (Fig.\u00a06e-f; Figure S21a). Similarly to the \u03b2-N model, we observed fewer tumor foci that were Myc-tag, GS/Ki67, and cyclin D1 positive in the responder animals (Figure S21b-d). To investigate the mechanistic basis of the observed heterogeneous response, especially in the more aggressive \u03b2-M model, we employed the 10X Visium platform to perform unbiased spatial transcriptomics on an LNP-CTRL treated \u03b2-M HCC (\u201c\u03b2-M Control\u201d), 2 LNP-CTNNB1 treated \u03b2-M HCC showing minimal/no response (\u201c\u03b2-M NR-1\u201d; \u201c\u03b2-M NR-2\u201d), and an LNP-CTNNB1 treated \u03b2-M HCC showing response (\u201c\u03b2-M R-1\u201d). In total, we sequenced 17,685 spots across the 4 slides, with 4,461 spots in \u03b2-M Control, 4,331 in \u03b2-M NR-1, 4,842 in \u03b2-M NR-2, and 4,051 in \u03b2-M R-1. After integrating data from all slides, unbiased clustering revealed 17 clusters conserved across the different treatments (Fig.\u00a06g; Figure S22a-b). CMapS and cluster proportion analysis revealed increases in cluster 3 within tumor nodules in \u03b2-M NR animals, and increases in clusters 2, 13, and 14 in the \u03b2-M R animal (Fig.\u00a06h-j; Figure S22a-b). Given the lack of single cell specificity with the 10X Visium platform, we wanted to address pseudocell composition of these clusters, and performed differential gene expression per cluster compared to all other clusters (Figure S23a-q). To address mechanistic basis of response, we characterized clusters 2, 13, and 14 which were expanded in the \u03b2-M R animal. Cluster 2 expressed zone 1 and 2 metabolic genes, including Cyp2f2, Pck1, Cps1, and Hamp analogous to the reprogrammed tumor cell population observed in the early-stage LNP-CTNNB1 treatment setting (Figure S23c). Clusters 13 and 14 were enriched in lymphocyte markers (Figure S23n-o). Visualization of lymphocyte marker gene expression by cluster demonstrated enrichment of T and B cell genes in clusters 13 and 14 (Fig.\u00a06k; Figure S24a-b), with these 2 clusters enriched in the \u03b2-M R animal. Given the role of T cells in promoting anti-tumor immunity, we examined expression of T cell marker genes Cd2, Cd3d, Cd3e, Cd3g, and Cd4 by cluster and treatment response group, which revealed enrichment of Cd3e, Cd3g, and Cd4 within \u03b2-M R animals in clusters 9 and 12 (Fig.\u00a07a), respectively, in which these tumor cell specific clusters decreased, compared to \u03b2-M Control and \u03b2-M NR animals (Fig.\u00a06h). This was also confirmed via IHC which demonstrated increased CD3\u2009+\u2009cells throughout tumors and organized into lymphoid aggregates in \u03b2-M R animals (Fig.\u00a07b). GO GSEA demonstrated significant enrichment of response to IFN\u03b3 in cluster 9 and positive regulation of T cell proliferation in cluster 12 (Fig.\u00a07c-d; Figure S24c-d). To further discern the enhanced adaptive anti-tumor immune surveillance in \u03b2-M R animals, we performed spatially enhanced CellChat35 analysis to investigate ligand-receptor interactions between different clusters and within different treatment response groups. This analysis revealed enrichment of MHC-II signaling with antigen communication from most clusters to CD4\u2009+\u2009cells in cluster 12 (tumor cluster) only in \u03b2-M R animals compared to both \u03b2-M Control and \u03b2-M NR animals (Figure S25a-d). Overall, \u03b2-M R animals demonstrate reinvigorated and persistent adaptive immune surveillance with active T and B cell infiltration, T cell proliferation, and engaged IFN\u03b3 signaling in intra-tumoral compartments, which likely was not sustained long-term in the NR phenotype in advanced disease setting. RNAi-mediated \u03b2-catenin Inhibition Synergizes with Immunotherapy in Advanced Disease Setting in CTNNB1-mutated HCC Mouse Model We next investigated if administration of both LNP-CTNNB1 and ICI in late-stage HCC would synergize and promote long-term anti-tumor immunity. We posit that the NR phenotype during late-stage HCC LNP-CTNNB1 treatment reflected a lack of sustained active lymphocyte proliferation, infiltration, and response to IFN\u03b3 signaling in the intra-tumoral compartment. Following a similar scheme for advanced-stage disease LNP treatment in the \u03b2-M model, we added IgG or \u03b1-PD1 to the regimen 3-days after LNP dose, which was determined based on enhanced IFN signaling at this timepoint, and harvested mice by 10.5-week timepoint or when moribund to assess and compare response, and also performed a survival study to determine long-term anti-tumor immunity (Fig.\u00a07e). By the 10.5-week timepoint, LNP-CTRL mice were all moribund with \u03b1-PD1 alone not impacting tumor burden, yet the combination of LNP-CTNNB1\u2009+\u2009\u03b1-PD1 resulted in enhanced efficacy with absence of any non-responders compared to LNP-CTNNB1\u2009+\u2009IgG treated animals (Fig.\u00a07f-g). Additionally, MRI demonstrated less hyperintense foci in LNP-CTNNB1 treated mice receiving \u03b1-PD1 compared to IgG treatment (Fig.\u00a07h). Interestingly, hCTNNB1 knockdown was enhanced in the LNP-CTNNB1 treated mice receiving \u03b1-PD1 compared to IgG treatment (p\u2009=\u20090.02) suggesting an augmented response with \u03b1-PD1 (Fig.\u00a07i). To investigate potential mechanisms of LNP-CTNNB1\u2009+\u2009\u03b1-PD1 synergy we performed IHC for granzyme B (GZMB) to address cytotoxic T cell activity and observed an overall increase in GZMB\u2009+\u2009lymphoid aggregates within and surrounding remnant tumor nodules in LNP-CTNNB1 treated mice receiving \u03b1-PD1 compared to IgG treatment (p\u2009=\u20090.08) (Fig.\u00a07j-k), suggesting improved anti-tumor immunity in mice receiving combination therapy. Concomitantly, mice receiving LNP-CTNNB1\u2009+\u2009\u03b1-PD1 survived significantly longer than those receiving LNP-CTNNB1\u2009+\u2009IgG (p\u2009=\u20090.02) or either of the LNP-CTRL treatment groups (Fig.\u00a07l), suggesting synergy of \u03b2-catenin suppression with immunotherapy. TLS/LA are Enriched in Atezolizumab plus Bevacizumab Responders and CTNNB1-wild-type Patients in IMbrave150 Trial and Associated with Survival Given the restored adaptive immune surveillance and lymphoid aggregate (LA) presence upon \u03b2-catenin knockdown, we were interested whether there was a relationship between tertiary lymphoid structure (TLS)/LA, CTNNB1 mutation, and ICI response from the IMbrave150 phase III clinical trial. In this trial of 178 HCC patients in the biomarker-evaluable population (BEP), 175 were scored by a clinical pathologist for presence of immune infiltration (TLS, LA, diffuse infiltrate [DI], and none) from hematoxylin & eosin (H&E) slides. Overall, majority of patients, irrespective of treatment arm, had LA (n\u2009=\u200971/175), while fewer had TLS (n\u2009=\u20098/175) or DI (n\u2009=\u20098/175) (Fig.\u00a08a). Interestingly, among responders, those in the atezolizumab plus bevacizumab arm tended to be enriched for presence of TLS/LA, which was not observed in the sorafenib arm (Fig.\u00a08b). Additionally, patients with TLS/LA correlated with improved progression-free (PFS) and overall survival (OS), which was more pronounced in the atezolizumab plus bevacizumab arm (Fig.\u00a08c). Moreover, patients with TLS/LA had significantly increased expression of a previously reported B cell signature (Bsig), which was found to be correlated with TLS/LA presence in head and neck cancer,39 compared to patients with DI/None (Fig.\u00a08d-e). Increased Bsig expression was also observed in atezolizumab plus bevacizumab arm in patients with CR/PR and SD, while decreased Bsig expression was observed in those with PD (Fig.\u00a08f). Interestingly, Bsig was not associated with response in the sorafenib arm, indicating that TLS/LA recruitment may be primed with atezolizumab plus bevacizumab combination (Fig.\u00a08f). Lastly, we observed that CTNNB1-mutated patients had significantly lower Bsig expression compared to CTNNB-wild-type patients (Fig.\u00a08g). Thus, formation of TLS/LA may be restricted by mutated-\u03b2-catenin due to repression of various TFs in HCC affecting overall response to combination ICI. ", + "section_image": [] + }, + { + "section_name": "DISCUSSION", + "section_text": "We report strong in vitro and vivo efficacy of a novel LNP-formulated siRNA targeting CTNNB1 mRNA transcript for treatment of \u03b2-catenin-mutated HCC as monotherapy in early-stage disease or in combination with ICI at late-stage disease. We identified through unbiased scRNA-seq and spatial transcriptomic approaches a novel tumor-cell intrinsic role of \u03b2-catenin-mediated IRF2 and POU2F1 repression driving an immune excluded TIME and inert type I/II interferon responses in \u03b2-catenin-mutated HCC with in vivo validation. Additionally, we demonstrate upon \u03b2-catenin suppression, \u03b2-catenin-mutated tumor cells reprogram towards zone 1/2 hepatocyte-like cells, revealing the novel role of mutated-\u03b2-catenin in driving zone 3 (pericentral) tumor metabolism. Our work demonstrates that \u03b2-catenin is now targetable in murine HCC to overcome ICI resistance and supports the high impact development of clinical investigations utilizing LNP-CTNNB1 as a monotherapy or in combination with ICI to achieve therapeutic benefit in HCC patients with Wnt/\u03b2-catenin activation. \u03b2-catenin is most active in the pericentral (zone 3) region in the hepatic lobule with hepatocytes in each of the three zones of the hepatic lobule expressing genes important for different metabolic functions, known as liver metabolic zonation.33 Given the localization of \u03b2-catenin to zone 3, it is no surprise that \u03b2-catenin-mutated tumors preferentially originate and clonally expand from hepatocytes residing within zone 3, and these tumors share unique metabolic addictions to processes canonically identified in zone 3. In fact, we have previously shown that CTNNB1-mutated HCC is addicted to glutamine synthesis,40 as part of \u03b2-catenin-GS-mTOR axis.21 Additionally, CTNNB1-mutated HCCs demonstrate addiction to xenobiotic metabolism through GSTM3.41 However, surprisingly, tumors with \u03b2-catenin oncogenic activation are not glycolytic (zone 3 metabolism), but are fatty acid oxidative (zone 1 metabolism) addicted.42 Here, we show that \u03b2-catenin-mutated tumors residing specifically in zone 3 are metabolically wired to perform canonical zone 3 metabolic processes with a focus on fatty acids as substrates, while \u03b2-catenin-mutated tumor cells in zone 1 are metabolically wired to perform canonical zone 1 metabolic processes with a focus on arginine metabolism and amino acid biosynthesis. We have also uniquely demonstrated that \u03b2-catenin-mutated tumor cells in zone 1 possess the highest proliferative capacity compared to those in zone 3, suggesting that despite \u03b2-catenin-mutated HCCs being well-differentiated, less proliferative tumors, in ectopic regions of absent Wnt signals or in presence of normal zone 1 signals, proliferation may be favored over metabolic homeostasis. Whether zone 1 \u03b2-catenin-mutated HCCs in current model are due to clonal expansion, evolution, or budding from zone 3 tumors to eventually establish in zone 1, or an artifact of plasmid transfection in rare hepatocytes in zone 1 requires further investigation. However, despite these tumor intrinsic pathways, the overall tumor biology and metabolism may also be regulated by local zonal environment and signals. Overall, we demonstrate that suppressing \u03b2-catenin in CTNNB1-mutated tumors reprograms zone 3 tumors towards a zone 1/2 metabolic phenotype as early as 3-days post LNP treatment, which contributes to the phenotypic differentiation and metabolic re-wiring, loss of tumor nodules, and normalization of hepatic parenchyma and liver mass. Such reprogramming may yield novel metabolic vulnerabilities to be exploited for additional therapies in the future. Cancers with Wnt/\u03b2-catenin activation are considered non-T cell-inflamed across a variety of tumor types, including HCC, melanoma, esophageal, and others.6,7,43,44 This has been associated with resistance to ICIs, specifically of the anti-PD-1/anti-PD-L1 class of agents.43 Preclinical studies with genetic mouse models have revealed tumor-intrinsic roles of \u03b2-catenin regulating expression of transcription factor (TF) repressors (e.g., ATF3), which in turn modulate expression of crucial chemokine genes, including CCL4 and CCL5, involved in T cell priming and recruitment to the TIME.7,44 In HCC, many key chemokines are lowly expressed in CTNNB1-mutated patients, suggesting that potentially alternative mechanisms other than direct transcriptional repression may explain this phenomenon, given that \u03b2-catenin-TCF/LEF complex does not have binding sites at promoter regions for all these chemokines.7 In KRAS-mutated colorectal cancer, where ICI is also ineffective, expression of chemokines involved in IFN network signaling, such as CXCL3, were found to be mediated through KRAS-mediated interactions with IRF2.37 Here, we identified a novel tumor cell-intrinsic interaction of \u03b2-catenin/IRF2 where IRF2 (and IFN network signaling) is suppressed in \u03b2-catenin-mutated HCC. We demonstrate that \u03b2-catenin suppression directly increases IRF2 expression in \u03b2-catenin-mutated HCC models, with subsequent increases in interferon signaling molecules and antigen presentation machinery components. Additionally, we demonstrate that forced expression of IRF2 in \u03b2-catenin-mutated HCC model is sufficient to convert a non-T cell-inflamed to T cell-inflamed tumor. Given that our unbiased bioinformatic analysis identified other putative TFs, including POU2F1, whose function may be modulated in the context of \u03b2-catenin-mutated livers, we posit there exist an immune-regulatory module of TFs suppressed by mutated-\u03b2-catenin which modulates expression of key cytokines and chemokines involved in immune response, possibly in other tumor types as well. In fact, we and others have previously described the role of \u03b2-catenin in sequestering NF-\u03baB, resulting in immune exclusion.45\u201347 Thus, pharmacologic targeting of \u03b2-catenin likely has clinical implications across a broad spectrum of tumor types to improve ICI clinical efficacy in part through modulation of key TFs involved in priming immune recruitment and engaging in global adaptive immune surveillance. We have shown here that targeting \u03b2-catenin directly impacts both tumor cell intrinsic biology and simultaneously reprograms the TIME from non-T cell-inflamed to T cell-inflamed, with innate immune remodeling occurring as early as 3-days post LNP treatment. This innate immune remodeling coincided with first observed biological effect of \u03b2-catenin knockdown at 3-days. Biological effects due to siRNA knockdown are usually observed within hours in vitro,48 yet we observed a protracted time course in vivo, likely due to the systemic delivery method. Additionally, prior work has illustrated that adaptive immune surveillance begins to remodel at least 7\u201310 days following oncogene withdrawal, which explains the significant adaptive immune effects we observed studying late-stage response after 6 weeks of LNP treatment.49 However, the profound anti-tumor effects we observed here likely would not be so pronounced through targeting downstream effector molecules of the Wnt/\u03b2-catenin signaling pathway. Specifically, we and others have previously shown that genetic deletion or pharmacologic inhibition of downstream effectors of \u03b2-catenin-TCF/LEF interactions, such as cyclin D1 (encoded by CCND1),50 GS,16 mTORC1,21 TBX3,17 AXIN251, or TNFRSF1952 either result in partial tumor responses or compensatory negative feedback loops leading to enhanced tumorigenesis. For example, it has been shown that hepatocarcinogenesis is not dependent on cyclin D1 as \u03b2-catenin-mutated tumors induced in Ccnd1-null background mice still develop through compensatory cyclin D2 expression.50 Additionally, conditionally deleting TBX3 or GS in mice with \u03b2-catenin-mutated HCC exacerbates tumorigenesis through YAP/TAZ inhibition or nitrogen metabolic rewiring, respectively.16,17 Moreover, our group has previously identified metabolic addiction to \u03b2-catenin-GS-mTOR axis in \u03b2-catenin-mutated HCC and evaluated mTOR inhibitor (e.g., rapamycin, everolimus) response in multiple preclinical models of \u03b2-catenin-mutated HCC. However, response to LNP-CTNNB1 results in more consistent, robust, and durable responses in preclinical models.21,27 Lastly, targeting solely TNFRSF19 will likely impact expression of chemokines involved in immune recruitment, yet there would be minimal impact on intrinsic tumor cell biology.52 Thus, targeting \u03b2-catenin directly is a holistic and rational strategy leading to durable anti-tumor immune responses through inhibiting multiple mechanisms hitting a truncal event, and impacting not only tumor-cell intrinsic biology, but also simultaneously remodeling the TIME architecture to promote long-lasting anti-tumor immunity. Therapeutic targeting of Wnt/\u03b2-catenin oncogenic signaling has been pursued over the last two decades with no therapeutic agent ultimately resulting in translation to the clinic. First, given the ubiquitous role of \u03b2-catenin in many cell types, translation of many agents has been limited due to on-target, off-tumor effects.43,53 Small-molecule inhibitors which limit interactions between \u03b2-catenin and TCF/LEF or \u03b2-catenin and cAMP response element\u2013binding protein (CREB)\u2013binding protein (CBP), or repurposed drugs against Wnt activity have shown in vitro inhibitory effects, yet lack strong in vivo efficacy, likely due to alternative escape mechanisms.9 Alternative methods of Wnt/\u03b2-catenin inactivation have investigated porcupine (PORCN), tankyrase (TNKS), or Frizzled (FZD) receptor inhibitors, however, these are ineffective and far too upstream in the pathway for treating tumors with GOF CTNNB1 mutations due to subsequent independence of Wnt/FZD receptor binding.9 Thus, RNAi- or antisense-mediatred gene silencing approaches have proven to be an effective therapeutic approach to reduce CTNNB1 mRNA levels in tumors. Efficacy has previously been shown by our group and others across a variety of different tumor types.20,22,23,54 Our work here builds upon these previous findings and demonstrates that RNAi-mediated \u03b2-catenin inhibition via LNP for HCC results in minimal off-target effects with strong and durable on-target effects. In summary, we propose a synergistic two-part working mechanism of response to RNAi-mediated \u03b2-catenin inhibition in preclinical CTNNB1-mutated HCC models (Fig.\u00a07m). First, early response to LNP-CTNNB1 treatment includes cessation of tumor cell proliferation and concomitant metabolic zonal reprogramming with zone 3 tumor cells converting to zone 1/2 hepatocytes. Second, cancer cell reprogramming simultaneously occurs with conversion of an immunologically cold to hot TIME in which macrophages repolarize from a M2-like to M1-like-phenotype in the tumor immune compartment and mediate potent anti-tumor immune responses. Simultaneously, IRF2 and POU2F1 re-engagement in the tumoral compartment, when \u03b2-catenin is suppressed, acts as a mediator of enhanced interferon network signaling and primes lymphocyte recruitment and infiltration, with all these tumor-intrinsic and TIME remodeling mechanisms ultimately driving synergy with \u03b1-PD1 in the advanced-stage disease setting. Based on our findings, RNAi-mediated inhibition of \u03b2-catenin may have the potential to provide anti-tumor effects as a monotherapy in early stage disease or in neoadjuvant setting in patients with Wnt-\u03b2-catenin active liver tumors. These proof-of-concept studies also support the clinical investigation of RNAi therapeutic approaches targeting \u03b2-catenin in combination with ICI in advanced-stage Wnt-\u03b2-catenin active-HCC patients.", + "section_image": [] + }, + { + "section_name": "Declarations", + "section_text": "Acknowledgments\u00a0\nThis work was supported by NIH grants R01CA251155, R01CA250227, and Endowed Chair for Experimental Pathology to SPM. This work was also supported in part by Sponsored Research Agreement to SPM by Alnylam Pharmaceuticals. This work was also funded in part by T32EB001026 to BML and TY. This work was also funded in part by F30CA284540 to BML. This work was also supported in part by the University of Pittsburgh Center for Research Computing through the resources provided and by NIH grant P30DK120531 to Pittsburgh Liver Research Center (PLRC) for services provided by the Genomics and Systems Biology Core. This work was also supported in part by UPMC Hillman Cancer Center Core grants P30CA047904 and UM1CA186690 to JJL.\u00a0\nAuthor Contributions\u00a0\nConceptualization: B.M.L. and S.P.M.; Methodology: B.M.L., E.R.D., T.M.Y., S.L., M.T., M.M., M.R.E., Y.W., W.B., J.T., and S.P.M.; Software: B.M.L., T.Y.M., S.L., and J-J.L.; Formal Analysis: B.M.L., E.R.D., T.M.Y., S.L., X.G., H.K., T.D., Y.W., W.B., J.T., and S.P.M.; Investigation: B.M.L., E.R.D., T.M.Y., S.L., M.T., C.C., Y.L., S.S., X.G., H.K., J-J.L., A.S-V., Y.K., M.P., T.K.H., L.M.F., B.L., A.R., R.P.R., P.P., M.R., A.B., R.R., T.D., J.T. and S.P.M.; Resources: T.D., E.G., X.C., M.M., Y.W., W.B., J.T., and S.P.M.; Writing\u2014Original Draft: B.M.L.; Writing\u2014Review and Editing: T.D., J.J.L., A.L., X.C., M.M., Y.W., W.B., and S.P.M.; Visualization: B.M.L., E.R.D., T.M.Y., S.L., and M.T.; Supervision: J.T. and S.P.M.; Project Administration: S.P.M.; Funding Acquisition: B.M.L. and S.P.M.\nDeclaration of interests\u00a0\nDr. Satdarshan P. Monga has received research grants from Alnylam Pharmaceuticals. He also received funding from Fog Pharmaceuticals and is a consultant on Advisory Boards for Surrozen, AntlerA, Alnylam, Mermaid Bio, Vicero Inc, and UbiquiTx, and there is no pertinent conflict of interest of these entities as relevant to the current manuscript. Drs. Tulin Dadali, Martin Maier, and Wendy Broom are employed by Alnylam Pharmaceuticals, Cambridge, MA. Drs. Xiangnan Guan, Hartmut Koeppen, and Yulei Wang are employed by Genentech Inc., San Francisco, CA. No other authors have any relevant conflicts of interests to declare regarding the current study.\nDeclaration of generative AI and AI-assisted technologies\nDuring the preparation of this work, the author(s) used ChatGPT-3.5 in order to assist with debugging of some R packages that had lack of in-depth user documentation in their vignettes. After using this tool or service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209\u2013249 Cheng AL, Qin S, Ikeda M et al (2022) Updated efficacy and safety data from IMbrave150: Atezolizumab plus bevacizumab vs. sorafenib for unresectable hepatocellular carcinoma. J Hepatol 76(4):862\u2013873 Sangro B, Chan SL, Kelley RK et al (2024) Four-year overall survival update from the phase III HIMALAYA study of tremelimumab plus durvalumab in unresectable hepatocellular carcinoma. Ann Oncol Rimassa L, Finn RS, Sangro B (2023) Combination immunotherapy for hepatocellular carcinoma. 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Cancer Cell 18(5):485\u2013498 Patil MA, Lee SA, Macias E et al (2009) Role of cyclin D1 as a mediator of c-Met- and beta-catenin-induced hepatocarcinogenesis. Cancer Res 69(1):253\u2013261 Lustig B, Jerchow B, Sachs M et al (2002) Negative feedback loop of Wnt signaling through upregulation of conductin/axin2 in colorectal and liver tumors. Mol Cell Biol 22(4):1184\u20131193 Wong AM, Ding X, Wong AM et al (2022) Unique molecular characteristics of NAFLD-associated liver cancer accentuate beta-catenin/TNFRSF19-mediated immune evasion. J Hepatol 77(2):410\u2013423 Jung YS, Park JI (2020) Wnt signaling in cancer: therapeutic targeting of Wnt signaling beyond beta-catenin and the destruction complex. Exp Mol Med 52(2):183\u2013191 Ganesh S, Shui X, Craig KP et al (2018) RNAi-Mediated beta-Catenin Inhibition Promotes T Cell Infiltration and Antitumor Activity in Combination with Immune Checkpoint Blockade. Mol Ther 26(11):2567\u20132579", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nSatdarshan Monga received grant funding from Alnylam. He is consultant for Fog pharmaceuticals and Alnylam. Wendy Broom, Tulin Dadali and Martin Maier are employees of Alnylam. Yulei Wang, Xiangnan Guan and Hartmut Koeppen are employees of Genetech.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "MergedOnlineSupplementFiguresLegendsMethods.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "First-line immune checkpoint inhibitor (ICI) combinations show responses in subsets of hepatocellular carcinoma (HCC) patients. Nearly half of HCCs are Wnt-active with mutations in CTNNB1 (encoding for \u03b2-catenin), AXIN1/2, or APC, and demonstrate heterogeneous and limited benefit to ICI due to an immune excluded tumor microenvironment. We show significant tumor responses in multiple \u03b2-catenin-mutated immunocompetent HCC models to a novel siRNA encapsulated in lipid nanoparticle targeting CTNNB1 (LNP-CTNNB1). Both single-cell and spatial transcriptomics reveal cellular and zonal reprogramming, along with activation of immune regulatory transcription factors IRF2 and POU2F1, re-engaged type I/II interferon signaling, and alterations in both innate and adaptive immunity upon \u03b2-catenin suppression with LNP-CTNNB1 at early- and advanced-stage disease. Moreover, ICI enhances response to LNP-CTNNB1 in advanced-stage disease by preventing T cell exhaustion and through formation of lymphoid aggregates (LA). In fact, expression of an LA-like gene signature prognosticates survival for patients receiving atezolizumab plus bevacizumab in the IMbrave150 phase III trial and inversely correlates with CTNNB1-mutatational status in this patient cohort. In conclusion, LNP-CTNNB1 is efficacious as monotherapy and in combination with ICI in CTNNB1-mutated HCCs through impacting tumor cell-intrinsic signaling and remodeling global immune surveillance, providing rationale for clinical investigations.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death globally1. Despite the shift in therapeutic management of advanced disease over the last five years from multi-tyrosine kinase inhibitors (TKIs) (e.g., sorafenib) to immunotherapy with immune-checkpoint inhibitor (ICI) combinations (e.g., atezolizumab plus bevacizumab), objective response rates (ORRs) remain low at ~30% with overall survival <2 years2,3,4,5. Preclinical and clinical studies investigating molecular correlates of ICI response have yielded novel insights into potential mechanisms of resistance, including but not limited to immune exclusion, with Wnt/\u03b2-catenin activation contributing to this phenotype6,7,8. Wnt/\u03b2-catenin pathway activity is observed in up to 50% of tumors from patients with HCC, with mutations mostly occurring in CTNNB1 (26\u201337%), AXIN1/2 (8\u201310%), and APC (3\u20135%)9,10,11,12. Gain-of-function (GOF) mutations in CTNNB1 (encoding for \u03b2-catenin) are one of the major trunk mutational events in HCC and occur mostly as missense mutations in exon 3 at serine and threonine residues or the ubiquitination destruction motif, which interfere with its degradation, leading to constitutive \u03b2-catenin activation and target gene transcription13,14. Patients with CTNNB1-mutated HCC have upregulation of known Wnt/\u03b2-catenin target genes, including GLUL, AXIN2, LGR5, and TBX311. In fact, glutamine synthetase (GS; encoded by GLUL) immunohistochemistry is used as a biomarker for patients with CTNNB1-mutated HCC15. However, targeting these downstream Wnt target genes has revealed novel negative feedback loops in the Wnt/\u03b2-catenin oncogenic circuit16,17, necessitating the need to focus on targeting \u03b2-catenin directly for precision therapy.\n\nDespite improved molecular stratification of HCC over the last decade, with recognition of Wnt/\u03b2-catenin-driven tumors overlapping with Hoshida S318 or Boyault G5/G6 subclasses19, these different molecular stratifications have not yielded prognostic implications due to a lack of clinically approved targeted or biomarker-driven precision therapeutics. \u03b2-catenin has traditionally been an undruggable target, despite preclinical studies elucidating the molecular and metabolic addiction to \u03b2-catenin oncogenic signaling in CTNNB1-mutated HCC20,21,22,23. Thus, \u03b2-catenin is a prime target for precision therapy. Advances in RNAi technology over the last two decades have resulted in multiple approved RNAi therapies24, and RNAi-mediated gene silencing has proven to be an excellent tool for targeting the traditionally undruggable, especially in hepatic tissue25.\n\nIn this work, we investigate the relevance of RNAi-mediated \u03b2-catenin inhibition in patient-derived CTNNB1-mutated HCC organoids and multiple humanized mouse models of CTNNB1-mutated HCC at different treatment windows and elucidate the underlying mechanisms of response in both hepatic and immune compartments through both single-cell and spatial transcriptomic approaches. Our findings provide a mechanistic basis for clinical investigations of this RNAi therapeutic targeting \u03b2-catenin for HCC treatment as an innovative treatment paradigm in the form of monotherapy and/or in combination with ICI in human subjects belonging to the Wnt-\u03b2-catenin active-HCC subclass.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To study the effects of RNAi-mediated inhibition in \u03b2-catenin-mutated HCC, we utilized a novel siRNA that targets the CTNNB1 gene, with both mouse and human specificity, encapsulated in a lipid nanoparticle (referred hereafter as LNP-CTNNB1). We first assessed whether LNP-CTNNB1 affected growth in a patient-derived HCC organoid (23277) with known mutation in CTNNB126. After 48- and 72-h treatment with LNP-CTNNB1 at 20\u2009nM concentration, we observed a significant decrease in both the numbers and the size of the organoids compared to treatment with the LNP-CTRL (Fig.\u00a01a, b). Thus, LNP-CTNNB1 demonstrates efficacy in mutant-CTNNB1 human HCC organoid cultures.\n\na Schematic of CTNNB1-mutated patient-derived HCC organoid LNP treatment (20\u2009nM). b (Left) Brightfield images at baseline, 48-, and 72-h. Scale bar represents 200\u2009\u03bcm. (Right) Quantification of number of multi-cellular organoids per high powered field (HPF) and organoid diameter (n\u2009=\u20093 biological replicates per treatment and time point). c LNP treatment scheme in \u03b2-catenin-Nrf2 (\u03b2-N) model. Mice received once weekly intravenous (I.V.) injections at 1\u2009mg/kg dosage starting at 5-weeks post-hydrodynamic tail vein injection (HDTVi). d Representative gross liver images of LNP-CTRL and LNP-CTNNB1 (1\u2009mg/kg) treated \u03b2-N animals at 10.5-week timepoint. e, f Liver weights (p\u2009=\u20090.0008) and liver weight/body weight (LW/BW) (p\u2009=\u20090.0002) comparing LNP-CTRL (n\u2009=\u20096) and LNP-CTNNB1 (n\u2009=\u20094; 1\u2009mg/kg) treated \u03b2-N animals. g (Left) Representative images of immunohistochemistry (IHC) for glutamine synthetase (GS)/Ki67 co-stain comparing LNP-CTRL and LNP-CTNNB1 (1\u2009mg/kg) treated \u03b2-N animals. (Right) Quantification of %GS+ area comparing LNP-CTRL (n\u2009=\u20095) and LNP-CTNNB1 (n\u2009=\u20094). h LNP treatment scheme in \u03b2-catenin-hMet (\u03b2-M) model. Mice received once weekly I.V. injections at 1\u2009mg/kg dosage starting at 3-weeks post-HDTVi. i Representative gross liver images of LNP-CTRL and LNP-CTNNB1 (1\u2009mg/kg) treated \u03b2-M animals at 8.5-week timepoint. j, k Liver weights and LW/BW comparing LNP-CTRL (n\u2009=\u20093) and LNP-CTNNB1 (n\u2009=\u20097; 1\u2009mg/kg) treated \u03b2-M animals. l (Left) Representative images of IHC for GS/Ki67 co-stain comparing LNP-CTRL and LNP-CTNNB1 (1\u2009mg/kg) treated \u03b2-M animals. (Right) Quantification of %GS+ area comparing LNP-CTRL (n\u2009=\u20093) and LNP-CTNNB1 (n\u2009=\u20096). m LNP treatment scheme in \u03b2-catenin-Nrf2-hMet (\u03b2-N-M) model. Mice received once weekly I.V. injections at 1\u2009mg/kg dosage starting at 3-weeks post-HDTVi. n Representative gross liver images of LNP-CTRL and LNP-CTNNB1 (1\u2009mg/kg) treated \u03b2-N-M animals at 7.5-week timepoint. o, p Liver weights (p\u2009=\u20090.01) and LW/BW (p\u2009=\u20090.0098) comparing LNP-CTRL (n\u2009=\u20094) and LNP-CTNNB1 (n\u2009=\u20093; 1\u2009mg/kg) treated \u03b2-N-M animals. q (Left) Representative images of IHC for GS/Ki67 co-stain comparing LNP-CTRL and LNP-CTNNB1 (1\u2009mg/kg) treated \u03b2-N-M animals. (Right) Quantification of %GS+ area comparing LNP-CTRL (n\u2009=\u20093) and LNP-CTNNB1 (n\u2009=\u20093) (p\u2009=\u20090.0003). Created in BioRender. Lehrich (2025) https://BioRender.com/smaki7g. Lehrich (2025) https://BioRender.com/858w237. Lehrich (2025) https://BioRender.com/6ce7tob. Lehrich (2025) https://BioRender.com/9127h6y. For (b), (e\u2013g), (j\u2013l), and (o\u2013q), data presented as mean values\u2009\u00b1\u2009standard deviation (SD) and P-values calculated by unpaired two-tailed Student\u2019s t-test. Source data are provided as a Source Data File. For (g), (l), (q), scale bar indicates magnification. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, ****p\u2009<\u20090.0001.\n\nNext, to assess its pharmacodynamic effects, we first delivered LNP-CTNNB1 via tail vein intravenous (I.V.) injection to mouse livers which were transfected with human S45Y-mutant-CTNNB1 gene (S45Y-hCTNNB1 mice) via sleeping beauty-hydrodynamic tail vein injection (SB-HDTVi) system. We have previously reported that mouse hepatocytes overexpressing mutant-\u03b2-catenin alone via SB-HDTVi do not develop HCC27, but require a secondary driver like hMet, mutant-Kras, or mutant-Nrf2, to induce HCC20,27,28. After 4 treatments at 3\u2009mg/kg dosing in S45Y-hCTNNB1 mice (Supplementary Fig.\u00a01a), we observed an appreciable decrease in liver weight and liver weight to body weight ratio (LW/BW), which is consistent with the role of \u03b2-catenin in regulating liver growth and size (Supplementary Fig.\u00a01b\u2013d)29,30. Additionally, expression of two well-known \u03b2-catenin target genes GS and Cyclin D1 (CCND1) via immunohistochemistry (IHC) were absent throughout the liver lobule indicating high mCTNNB1 gene knockdown (Supplementary Fig.\u00a01e, f). Concomitantly, Myc-tag (present on the S45Y-hCTNNB1 plasmid) positive cells were absent in the LNP-CTNNB1 treated mice compared to islands of Myc-tag positive cells in the LNP-CTRL mice (Supplementary Fig.\u00a01e, f). Thus, LNP-CTNNB1 targets both endogenous mouse and mutant human CTNNB1 with high potency and specificity in vivo.\n\nPrior to testing efficacy of siRNA-mediated CTNNB1 knockdown in HCC, we assessed whether there were any effects of the LNP itself on the tumor immune microenvironment (TIME). We treated mice injected with T41A-\u03b2-catenin-G31A-Nrf2 (\u03b2-N model) with either PBS, LNP-CTRL, or LNP-CTNNB1 (Supplementary Fig.\u00a01g) utilizing a similar frequency and dosage scheme as in Supplementary Fig.\u00a01a. \u03b2-N model has been previously shown to represent 9\u201312% of all human HCC28. Following treatment, we observed a decrease in liver weight and LW/BW in LNP-CTNNB1 treated mice (Supplementary Fig.\u00a01h, i) with no differences in liver serum biochemistries (Supplementary Fig.\u00a01j). Next, we performed bulk RNA-sequencing across all 3 treatment groups, and observed that PBS and LNP-CTRL treated animals are transcriptionally very similar, and distinct from the LNP-CTNNB1 treated group (Supplementary Fig.\u00a01k). Additionally, gene set variation analysis using gene ontology (GO) pathways demonstrated that the immune phenotype is similar between PBS and LNP-CTRL treated mice, suggesting that the LNPs themselves do not influence the TIME in the CTNNB1-mutated HCC (Supplementary Fig.\u00a01l).\n\nWe next evaluated the in vivo efficacy of LNP-CTNNB1 in CTNNB1-mutated and non-mutated HCC models. We first performed a dose titration study to determine the lowest efficacious dose in the \u03b2-N model. We administered once weekly I.V. injections at 3, 1, 0.3, 0.1, and 0.03\u2009mg/kg dosages over 6 weeks of LNP-CTNNB1 starting at 5-weeks post-HDTVi, which we previously determined as a timepoint when microscopic tumor foci are already established28 (Supplementary Fig.\u00a02a). There were significant reducations in the tumor burden across a wide dose range of the LNP-CTNNB1 (3, 1, 0.3, and 0.1\u2009mg/kg), as evident grossly, by liver weight, and by LW/BW (Supplementary Fig.\u00a02b\u2013d, Fig.\u00a01c\u2013f). However, at 3\u2009mg/kg dosage, following the 4th dose, we observed mortality in one of four mice, which was likely due to the high LNP dose and frequency. Additionally, the 0.3, 0.1, and 0.03\u2009mg/kg LNP-CTNNB1 dosages resulted in partial responses, with remnant microscopic tumor foci observed in 0.3 and 0.1\u2009mg/kg treated animals (Supplementary Fig.\u00a02e) and macroscopic tumor nodules present in animals treated with 0.03\u2009mg/kg (Supplementary Fig.\u00a02b, e). At the 1\u2009mg/kg LNP-CTNNB1 dosage there was no morbidity or mortality observed in these mice. Additionally, there were no gross phenotypic changes to other organs including lungs, spleen, intestine, and heart. H&E of the spleens across a wide dose range did not demonstrate any microscopic changes (Supplementary Fig.\u00a02f). We did not observe any gross neurological, gastrointestinal, genitourinary, cardiovascular, or respiratory deficits and/or distress following the once weekly treatments over 6 weeks, similar to what has been noted in rodents previously across a broad dose range, along with absence of any adverse signs or toxicity including any alterations in body weight or liver function tests31. Significant tumor responses were observed at 1\u2009mg/kg LNP-CTNNB1 dosage as noted via H&E, IHC for Myc-tag and GS/Ki67, and magnetic resonance imaging (MRI) (Fig.\u00a01g; Supplementary Fig.\u00a03a\u2013d). As a result, we utilized the 1\u2009mg/kg LNP-CTNNB1 dose for treatment of \u03b2-catenin-mutated HCC preclinical models.\n\nTo extrapolate our findings to additional \u03b2-catenin-mutated HCC preclinical models that we have previously reported, we next tested LNP-CTNNB1 in the S45Y-mutant-\u03b2-catenin-Met (\u03b2-M) model, which represents 11% of human HCC27. Treatment was initiated at 3-weeks post-HDTVi when microscopic tumor foci are already established and based on the more aggressive tumor phenotype in this model, as determined by us previously27. Once weekly I.V. administration at 1\u2009mg/kg LNP-CTNNB1 dosage over 6 weeks led to a significant decrease in tumor burden grossly (Fig.\u00a01h\u2013k), and histologically as observed via H&E, Myc-tag, and GS/Ki67 IHC (Fig.\u00a01l; Supplementary Fig.\u00a03e, f). Lastly, starting at 3-weeks post-HDTVi, a timepoint with known microscopic tumor burden32, we tested 1\u2009mg/kg LNP-CTNNB1 dosage in a third CTNNB1-mutated model, the S45Y-mutant-\u03b2-catenin-Nrf2-Met (\u03b2-N-M) model, which represents another unique ~5% subset of human HCC32. Following a similar treatment protocol, we again observed significant tumor responses grossly and microscopically (Fig.\u00a01m\u2013q; Supplementary Fig.\u00a03g, h).\n\nNext, we wanted to assess response to LNP-CTNNB1 in models that were not CTNNB1-mutated, due to the general mitogenic function of Wnt/\u03b2-catenin signaling pathway in the liver33. \u03b2-Catenin suppression by LNP-CTNNB1 in the Nrf2-hMet (N-M) model at 8-weeks post-HDTVi, a timepoint with known microscopic tumor burden32, led to a decrease in liver weight and LW/BW and in macroscopic disease (Supplementary Fig.\u00a04a\u2013d), although there was persistence of some microscopic nodules, which depicted inferiority in response when compared to mutant-\u03b2-catenin-driven tumors (Supplementary Fig.\u00a04e\u2013g). This decrease in tumor burden was observed despite HCC nodules in this model not being homogenously positive for the bonafide Wnt target GS (Supplementary Fig.\u00a04e). We have also previously reported that c-Met/sgAxin1 tumors require intact \u03b2-catenin to initiate tumorigenesis34. We also tested dependence on \u03b2-catenin in another independent non-CTNNB1-mutated HCC model using a genetic approach (Supplementary Fig.\u00a04h). \u03b2-Catenin deletion in SB-HDTVi induced Akt-NRas HCC in \u03b2-catenin floxed mice through simultaneous administration of pCMV-cre or control led to a significant improvement in overall survival and less tumor burden in pCMV-Cre compared to control, although tumors still persisted (Supplementary Fig.\u00a04i). Thus, overall, we observed that \u03b2-catenin inhibition alone for CTNNB1-mutated HCC is most effective in early-stage disease setting as evident through significant tumor responses in multiple models of CTNNB1-mutated HCC, and as partial responses in \u03b2-catenin non-mutated HCC models.\n\nLastly, we assessed the long-term durability of the significant tumor responses and overall survival (OS) in both the \u03b2-N and \u03b2-M models following LNP-CTNNB1 treatment at 1\u2009mg/kg dosage. Following the same treatment protocol in \u03b2-N (Fig.\u00a01c) and \u03b2-M (Fig.\u00a01h) models, we then withdrew LNP-CTNNB1. In the \u03b2-N model, following treatment cessation, mice were moribund by ~22.5-weeks post-LNP-CTNNB1 treatment, with gross tumor burden becoming equivalent to the tumor burden observed in LNP-CTRL treated mice at ~10.5 weeks, which is a lethal timepoint in \u03b2-N model (Supplementary Fig.\u00a05a, b). Thus, with LNP-CTNNB1 treatment in \u03b2-N model, OS was significantly extended by ~12 weeks (p\u2009<\u20090.01) (Supplementary Fig.\u00a05c). The nodules that reappeared at the ~22.5-week timepoint were positive for both GS and Nqo1 (Nrf2-target) (Supplementary Fig.\u00a05d). Similarly, in the \u03b2-M model, following treatment cessation, mice were moribund by ~16.5-weeks post-LNP-CTNNB1 treatment, with gross tumor burden becoming equivalent to the tumor burden observed in LNP-CTRL treated mice at ~7.5 weeks, which is a lethal timepoint in \u03b2-M model (Supplementary Fig.\u00a05e, f). Thus, LNP-CTNNB1 treatment in the \u03b2-M model extended OS by ~9 weeks (p\u2009<\u20090.001) (Supplementary Fig.\u00a05g). The nodules that reappeared at ~16.5-week timepoint in \u03b2-M model were also positive for GS and V5-tag (present on hMet plasmid) (Supplementary Fig.\u00a05h). Overall, LNP-CTNNB1 treatment as monotherapy more than doubled the OS of mice in both HCC models although tumors recurred after treatment cessation. These recurring tumors appear to be mutant-\u03b2-catenin-driven and not due to appearance of any de novo resistant clones.\n\nGiven the robust tumor responses following LNP-CTNNB1 treatment, we investigated the earliest biological response observed following \u03b2-catenin knockdown within the tumor cells. In the \u03b2-N model, we followed mice over a 3-week treatment course (LNP-CTNNB1 injected weekly \u00d73) and sacrificed mice at 1-, 3-, 5-, 7-, 14-, and 21-days post the first LNP treatment (Fig.\u00a02a). Over this 21-day treatment time course, the visible tumor foci or LW/BW progressively trended lower in the LNP-CTNNB1 group although differences were insignificant (except day 5) when compared to time-matched LNP-CTRL group (Supplementary Fig.\u00a06a; Fig.\u00a02b). However, at 3-days after a single LNP-CTNNB1 dose, RNA expression of Ctnnb1, along with Wnt target genes, Glul, Ccnd1, Lect2, and Rgn were significantly decreased in LNP-CTNNB1 compared to LNP-CTRL mice (Fig.\u00a02c). Additionally, GS protein visualized via IHC was decreased in tumor nodules but retained in pericentral hepatocytes at the 3-day timepoint, while it was absent also in the pericentral hepatocytes by 14-days in the LNP-CTNNB1 group (Fig.\u00a02d; Supplementary Fig.\u00a06b). Ki67 and TUNEL IHC also demonstrated significantly decreased tumor cell proliferation and trend toward increased cell death, respectively, at the 3-day timepoint, which was not observed at the 1-day timepoint (Fig.\u00a02e, f; Supplementary Fig.\u00a06c, d). Given these results, we administered a single LNP treatment to \u03b2-M animals and sacrificed mice at 3-days post-treatment (Supplementary Fig.\u00a07a). While there was no significant difference in gross tumor burden (Supplementary Fig.\u00a07b), a single dose of LNP-CTNNB1 significantly decreased liver weight and LW/BW (Supplementary Fig.\u00a07c\u2013e), decreased intra-tumoral GS expression but retained V5-tag expression (Supplementary Fig.\u00a07f, g). Also, there were significantly less intra-tumoral Ki67-positive cells and significantly more TUNEL-positive cells (Supplementary Fig.\u00a07h, i). Thus, the earliest evident biological response following RNAi-mediated \u03b2-catenin inhibition in both models occurred at 3-days post-LNP treatment.\n\na LNP treatment scheme in \u03b2-catenin-Nrf2 (\u03b2-N) model. b (Left) Representative gross liver images and (Right) liver weight/body weight (LW/BW) comparing LNP-CTRL (n\u2009=\u20094) and LNP-CTNNB1 (n\u2009=\u20094; 1\u2009mg/kg) \u03b2-N treated animals 3-days post 1st LNP treatment. c qPCR RNA expression levels of Ctnnb1 and \u03b2-catenin target genes (Glul, Ccnd1, Lect2, Rgn) between LNP-CTRL (n\u2009=\u20093) and LNP-CTNNB1 (n\u2009=\u20093) \u03b2-N treated animals. d, e Representative immunohistochemistry (IHC) images for glutamine synthetase (GS), Ki67, and TUNEL comparing LNP-CTRL and LNP-CTNNB1 treated \u03b2-N animals 3-days post 1st LNP treatment. Scale bar represents 100\u2009\u03bcm. f Quantification of number of Ki67- and TUNEL-positive cells across multiple high-power fields (HPF) between LNP-CTRL (n\u2009=\u20093) and LNP-CTNNB1 (n\u2009=\u20093) treated \u03b2-N animals 3-days post 1st LNP treatment (p\u2009=\u20090.0006). g Principal component analysis of bulk RNA-sequencing transcriptomic profiles of \u03b2-N and \u03b2-M model treated with LNP-CTRL or LNP-CTNNB1 and harvested 3-days post-LNP treatment, using all genes (n\u2009=\u20093\u20134 per condition and model). h Venn diagram highlighting number of common downregulated differentially expressed genes (DEGs) (n\u2009=\u2009230) between \u03b2-N and \u03b2-M models treated with LNP-CTRL or LNP-CTNNB1 3-days post-LNP treatment. i Venn diagram highlighting number of common upregulated DEGs (n\u2009=\u200973) between \u03b2-N and \u03b2-M models treated with LNP-CTRL or LNP-CTNNB1 3-days post-LNP treatment. DEGs defined by FDR\u2009=\u20090.05 and fold change >1.5. j Heatmap of selected common downregulated and upregulated genes demonstrating normalized z-score expression value in each model with each LNP treatment condition from (h) and (i). k Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway gene set enrichment analysis (GSEA) in \u03b2-N model comparing LNP-CTRL and LNP-CTNNB1 treated animals. l KEGG pathway GSEA in \u03b2-M model comparing LNP-CTRL and LNP-CTNNB1 treated animals. For (k, l), normalized enrichment score (NES) was normalized from ES, which was calculated by Kolmogorov\u2013Smirnov-like statistic and the p-value was calculated using the one-tailed empirical permutation test procedure. Created in BioRender. Lehrich (2025) https://BioRender.com/kymtir0. For (b), (c), and (f), data are presented as mean values\u2009\u00b1\u2009standard deviation (SD) and P-values calculated by unpaired two-tailed Student\u2019s t-test. Source data are provided as a Source Data File. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, ****p\u2009<\u20090.0001.\n\nTo understand the transcriptional consequences of \u03b2-catenin knockdown in HCC, we performed bulk RNA-sequencing (RNA-seq) on both the \u03b2-N and \u03b2-M models treated with either LNP-CTRL or LNP-CTNNB1 at the 3-day timepoint. Each model clustered distinctly with LNP-CTNNB1 groups for each model clustering independently from the LNP-CTRL groups as shown via PCA analysis (Fig.\u00a02g). Differential gene expression analysis comparing LNP-CTRL vs LNP-CTNNB1 demonstrated 455 upregulated and 628 downregulated genes in the \u03b2-N model, and 608 upregulated and 634 downregulated genes in the \u03b2-M model, with 230 common downregulated and 73 common upregulated genes (Fig.\u00a02h, i). Common downregulated genes included Wnt/\u03b2-catenin target genes and pericentral hepatocyte markers (e.g., Glul, Axin2, Lgr5, Notum, Lect2, Ccnd1, Cyp2e1, Cyp1a2, and Oat), and common upregulated genes were midzonal and periportal hepatocyte markers (e.g., Hamp2, Cyp8b1, and Cyp2f2) (Fig.\u00a02j). From both models, gene set enrichment analysis (GSEA) using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways demonstrated relative positive enrichment of metabolic and tumor microenvironment pathways, along with relative negative enrichment of cell cycle, Wnt signaling, and xenobiotic metabolism pathways (Fig.\u00a02k, l). Thus, we inferred \u03b2-catenin mutations in HCC confer most profound effects on tumor cell growth/proliferation, tumor metabolism, and tumor microenvironment.\n\nTo further interrogate tumor cell-intrinsic biological effects that occurred at the 3-day timepoint, we administered LNP-CTRL or LNP-CTNNB1 at 5-weeks post-HDTVi to \u03b2-N model mice and performed single-cell RNA-sequencing (scRNA-seq) analysis on a hepatocyte-enriched single-cell population following whole liver perfusion. In total, 94,650 single cells were sequenced with 26,851 in the LNP-CTRL group and 67,799 in the LNP-CTNNB1 group. Unbiased clustering on the integrated dataset resulted in 10 unique cell populations (Supplementary Fig.\u00a08a), annotated as (a) Dying/injured hepatocytes, (b), Hepatic stellate cells, (c) Kupffer cells, (d) Erythroid cells, (e) Endothelial cells, (f) Low-quality hepatocytes, (g) Reprogrammed hepatocytes (expressing both zone 1 & 2 markers Arg1, Ass1, Pck1, Hal, Hamp2, with Nrf2 tumor targets Prdx2, Prdx5, Gstm1, Gpx1), (h) Zone 1 CTNNB1 WT (GS\u2212) hepatocytes, (i) Zone 1/2 CTNNB1 MUT (GS+) hepatocytes, and (j) Zone 3 CTNNB1 WT & MUT (GS+) hepatocytes based on differential gene expression analysis per cluster (Supplementary Fig.\u00a08b, c). KEGG pathway enrichment analysis comparing each cluster to all other clusters revealed that top pathways for Zone 3 CTNNB1 WT & MUT (GS+) hepatocytes were bile acid secretion, drug metabolism \u2013 cytochrome P450, and fatty acid metabolism, which are all known hallmarks of CTNNB1-mutated HCC (Supplementary Fig.\u00a08d)35. Zone 1/2 CTNNB1 MUT (GS+) hepatocytes and Zone 1 CTNNB1 WT (GS\u2212) hepatocytes were interestingly enriched for arginine biosynthesis and amino acid biosynthesis (Supplementary Fig.\u00a08e, f), which are known metabolic hallmarks of zone 1 metabolism35. This KEGG pathway enrichment analysis reveals the metabolic heterogeneity of tumor cells along the portal-central axis.\n\nCell-type proportion analysis comparing LNP-CTRL and LNP-CTNNB1 demonstrated less Zone 3 CTNNB1 WT & MUT (GS+) hepatocytes along with de novo appearance of reprogrammed hepatocytes following LNP-CTNNB1 treatment (Fig.\u00a03a, b). KEGG and GO pathway enrichment analysis on the reprogrammed hepatocytes demonstrated enrichment of pathways across all zones, including biosynthesis of cofactors (Zone 1), amino acid catabolism (Zone 1), arginine biosynthesis (Zone 1), glutamate metabolism (Zone 3), glycolysis/TCA cycle (Zone 3), along with fatty acid metabolism, a hallmark of CTNNB1-mutated hepatocellular cancers (Supplementary Fig.\u00a09a\u2013d). Cell cycle phase-specific gene expression analysis on hepatocyte clusters importantly demonstrated that tumor cells (both Zone 3 CTNNB1 WT & MUT [GS+] and Zone 1/2 CTNNB1 MUT [GS+] hepatocytes) were the most proliferative, while reprogrammed hepatocytes and Zone 1 CTNNB1 WT (GS\u2212) hepatocytes were the least proliferative with proportionally fewer cells in S and G2M phases of the cell cycle (Fig.\u00a03c). In fact, reprogrammed hepatocytes and Zone 1 CTNNB1 WT (GS\u2212) were the two enriched hepatocyte populations following LNP-CTNNB1 treatment. Interestingly, Zone 1/2 CTNNB1 MUT (GS+) hepatocytes were the most proliferative tumor cell population, with the most cells in S and G2M cell cycle phases (Fig.\u00a03c). We next performed pseudotime analysis on all the hepatocyte populations in the dataset which demonstrated the intermediate cell state of the reprogrammed hepatocytes occurring along the trajectory of Zone 3 CTNNB1 WT & MUT (GS+) hepatocytes to Zone 1 CTNNB1 WT (GS\u2212) hepatocytes (Fig.\u00a03d). Thus, reprogrammed hepatocytes are an intermediate cell phenotype, reflecting tumor cell differentiation to normal hepatocyte-like cells and contributing to the rapid cell turnover observed following LNP-CTNNB1 treatment.\n\na Uniform manifold approximation and projection (UMAP) visualization of single-cell RNA-seq data following liver perfusion and enrichment of hepatocyte cell populations from LNP-CTRL and LNP-CTNNB1 treated \u03b2-catenin-Nrf2 (\u03b2-N) animals 3-days post-LNP treatment. UMAP split by treatment condition with 94,650 cells total across both treatment conditions. LNP-CTRL (n\u2009=\u20092) has 26,851 cells in the library; LNP-CTNNB1 (n\u2009=\u20093) has 67,799 cells in the library after data integration. b Pie chart of cell-type proportions between LNP-CTRL and LNP-CTNNB1 treatment conditions from (a). c Cell cycle regression scoring visualized via pie charts depicting cell cycle phase proportions in each of the indicated hepatocyte cell clusters. Each pie slice represents a group of cells colored by whether the RNA expression fits single cells belonging to G1 (red), S (green), or G2M (blue) phases of the cell cycle. d Pseudotime trajectory analysis on UMAP plot subset to only hepatocyte specific cell populations using the Zone 3 CTNNB1 WT and MUT (GS+) cell cluster as the root. e UMAP visualization of single-cell spatial transcriptomic data via Molecular CartogrpahyTM platform taken from frozen liver tissue sections of LNP-CTRL (n\u2009=\u20091) and LNP-CTNNB1 (n\u2009=\u20091) treated \u03b2-N animals 3-days post-treatment. UMAP generated based on expression of 100 genes. UMAP split by treatment condition with 19,301 cells total across both treatment conditions (LNP-CTRL library has n\u2009=\u20096 regions of interest (ROIs) with 10,227 cells total; LNP-CTNNB1 library has n\u2009=\u20095 ROIs with 9074 cells total). Labeled cell populations indicated by color. f Pie chart of cell-type proportions between LNP-CTRL and LNP-CTNNB1 treatment conditions from (e). g Spatial plots of LNP-CTRL and LNP-CTNNB1 regions of interest demonstrating visualization of certain cell populations by color from (e, f) on a virtual tissue section. h Dot plot visualization of various zonated marker gene expression (for all zones 1\u20133) for each hepatocyte cluster from (e, f). i Pseudotime trajectory analysis on Uniform manifold approximation and projection (UMAP) plot using the H1: Zone 3 CTNNB1 MUT (GS+) cluster as the root/origin. For (b), (c), and (f) labeled cell populations indicated by color and Source data are provided as a Source Data File.\n\nNext, to confirm the spatial identity of the reprogrammed hepatocyte population, we performed single-cell spatial transcriptomics using Molecular CartographyTM platform on tissue sections from the 3-day timepoint with LNP-CTRL or LNP-CTNNB1 treatment in the \u03b2-N model. The 100-gene panel consisted of markers specific for Wnt/\u03b2-catenin targets, metabolic zonation, and nonparenchymal cell types (Supplementary Table\u00a07). Following data pre-processing and automatic cell segmentation, in total, 19,301 single cells were sequenced from multiple regions of interest (ROIs) with 10,227 cells across 6 ROIs in LNP-CTRL group and 9074 cells across 5 ROIs in LNP-CTNNB1 group. Unbiased clustering on all 100 genes resulted in 9 unique cell populations, annotated as (a) H1: Zone 3 CTNNB1 MUT (GS+), (b) H2: Zone 3 Central Vein (CV) CTNNB1 WT (GS+), (c) H3: Zone 3 CTNNB1 WT (GS-negative), (d) H4: Zone 2\u20133 CTNNB1 WT (GS-negative), (e) H5: Zone 1 CTNNB1 WT (GS-negative), (f) H6: Reprogrammed hepatocytes, (g) Hepatic stellate cells (HSCs), (h) Immune cells, and (i) Endothelial cells (ECs), based on marker gene expression per cluster (Supplementary Fig.\u00a010a\u2013d). Clustering by treatment condition demonstrated enrichment of reprogrammed hepatocytes (46.3% vs 2.7%) with loss of H1: Zone 3 CTNNB1 MUT (GS\u2009+\u2009) hepatocytes (1.2% vs 29.0%) in LNP-CTNNB1 group (Fig.\u00a03e, f), similar to the scRNA-seq analysis (Fig.\u00a03a, b). Spatial plots confirmed the tumoral origin of the H6 cluster representing the reprogrammed hepatocytes (Fig.\u00a03g, h). In fact, spatial visualization and quantification of Wnt target genes in tumoral and non-tumoral regions, including both hepatocyte and nonparenchymal cell populatons, revealed that \u03b2-catenin-mutated tumor cells are defined by expression of bonafide Wnt targets Glul, Tbx3, Axin2, Lgr5, Lect2, and Ccnd1 (Supplementary Fig.\u00a011a\u2013c) with their identity intimately linked to zone 3 metabolic genes (and processes), including Cyp2e1, Cyp1a2, and Oat, with exclusion of zone 1 metabolic genes (and processes), including Cyp2f2, Ass1, and Arg1 (Supplementary Fig.\u00a012a, b). However, with LNP-CTNNB1 treatment, tumor cells begin to express Cyp2f2, Arg1, and Ass1 along with diminished expression of Cyp2e1, Cyp1a2, Oat and others (Supplementary Fig.\u00a012a, b). IHC validated the sc-Spatial transcriptomic findings and confirmed decreased expression of zone 3 markers CYP2E1 and OAT, with increased expression of zone 1 markers ARG1 and CYP2F2 (Supplementary Fig.\u00a012c). Additionally, pseudotime analysis on the sc-Spatial transcriptomic data confirmed the intermediary phenotype of the H6: reprogrammed hepatocytes (Fig.\u00a03i), similar to the scRNA-seq data (Fig.\u00a03d). Lastly, for validation, cell cluster quantification was performed within tumoral and non-tumoral regions (using Glul as tumoral landmark) (Supplementary Fig.\u00a013a, b), which revealed a significant decrease in cell density of clusters with active \u03b2-catenin signaling, and significant increase in cell density of the reprogrammed hepaotcytes, which occurred mostly in tumoral regions following LNP-CTNNB1 treatment (Supplementary Fig.\u00a013c). Overall, this integrated single-cell analysis revealed that \u03b2-catenin-mutated tumors are exclusively zone 3 and respond to \u03b2-catenin suppression by turning off expression of these genes while differentiating towards zone 1/2 hepatocyte-like cells, thus reprogramming their overall metabolic machinery.\n\nSpatial plots also revealed a significant increase in the immune cell cluster intratumorally in the LNP-CTNNB1 group compared to the LNP-CTRL group (Fig.\u00a03g), which was also quantified (Supplementary Fig.\u00a013c). To further investigate alterations in the immune landscape in an unbiased manner at the 3-day time point following LNP-CTNNB1 treatment, scRNA-seq was performed on an immune-enriched single-cell suspension from \u03b2-N treated animals. In total, 20,235 single cells were sequenced with 8499 cells across 3 individual biological replicates in the LNP-CTRL group and 11,736 cells across 3 individual biological replicates in the LNP-CTNNB1 group. Unbiased clustering on the integrated dataset across all cells resulted in 9 cell populations, which were annotated as: (a) T cells, (b) NK cells, (c) Myeloid cells, (d) B cells, (e) Dendritic cells, (f) Stellate cells, (g) Endothelial cells, (h) Hepatocytes, and (i) Proliferative T cells, based on known marker gene expression for each of these cell types (Supplementary Fig.\u00a014a, b). Across the 3 biological replicates for each treatment condition, the majority cell populations that were detected were T cells, B cells, NK cells, and myeloid cells (Supplementary Fig.\u00a014c\u2013e). We further subclustered and annotated these populations to better understand the T cell and myeloid cell functional states using marker genes previously described36 (Supplementary Fig.\u00a015a, b; Fig.\u00a04a\u2013c). The major difference observed following treatment was a 3-fold enrichment of M1-like pro-inflammatory macrophages in the LNP-CTNNB1 group (12.4%) compared to LNP-CTRL group (4.1%), which was trending toward significant enrichment when averaged across the 3 samples per LNP treatment groups (p\u2009=\u20090.0653) (Fig.\u00a04b, d). However, at this 3-day time point following LNP-CTNNB1 treatment, we did not observe any significant differences in CD4+ T cell subpopulations in the \u03b2-N model from the scRNA-seq analysis (Supplementary Fig.\u00a015c), or the sc-spatial transcriptomic analysis (Supplementary Fig.\u00a015d, e). Additionally, in the the \u03b2-M model, IHC for CD4 did not reveal differences at the 3-day timepoint following LNP-CTNNB1 treatment (Supplementary Fig.\u00a015f). CD8+ T cell subpopulations were not significantly altered following LNP-CTNNB1 treatment from this early scRNA-seq analysis (Supplementary Fig.\u00a015c), although we did detect increased CD8a expression in tumoral compartment from the sc-Spatial Transcriptomic analysis (Supplementary Fig.\u00a015d, e). Thus, innate immunity via myeloid cells, appears to be the predominant cell population which shifts at 3-days post-treatment (Fig.\u00a04d).\n\na UMAP visualization of integrated single-cell RNA-seq data following liver perfusion and enrichment of immune populations from LNP-CTRL (n\u2009=\u20093) and LNP-CTNNB1 (n\u2009=\u20093; 1\u2009mg/kg) treated \u03b2-catenin-Nrf2 (\u03b2-N) animals 3-days post-LNP treatment. UMAP split by LNP treatment condition. b Stacked bar plot of cell-type proportions between LNP-CTRL (n\u2009=\u20098499 cells) and LNP-CTNNB1 (n\u2009=\u200911,736 cells) from (a). Labeled cell populations indicated by color for (a, b). c Dot plot visualization of expression of M1-like and M2-like macrophage phenotype markers. d Bar plot comparing percentage of M1-like macrophages amongst the total cell population in the LNP-CTRL (n\u2009=\u20093) and LNP-CTNNB1 (n\u2009=\u20093; 1\u2009mg/kg) groups (p\u2009=\u20090.0653). e Gene Ontology (GO) pathway gene set enrichment analysis (GSEA) in the M1-like macrophage population comparing LNP-CTRL and LNP-CTNNB1 groups. f Stacked horizontal bar plot comparing relative information flow from CellChat between LNP-CTRL and LNP-CTNNB1 (1\u2009mg/kg) groups. Boxed pathways show 100% information flow in LNP-CTNNB1 (1\u2009mg/kg) group. IFN-II signaling highlighted in light blue show 100% enriched in LNP-CTNNB1 (1\u2009mg/kg) group. g Chord diagram for IFN-II pathway in LNP-CTNNB1 (1\u2009mg/kg) group demonstrating information flow from proliferative T cells to macrophage populations. No information flow in LNP-CTRL treated animals. h IFN\u03b3 treatment schematic in \u03b2-catenin-hMet (\u03b2-M) model. Mice received multi-weekly intra-peritoneal (I.P.) injections of IFN\u03b3 at 1\u2009\u00d7\u2009106 IU/ml dosage or vehicle control starting at 3-weeks post-hydrodynamic tail vein injection (HDTVi). Mice were sacrificed at 7.5-weeks post-HDTVi. i, j Representative gross liver images and liver weight/body weight (LW/BW) comparing \u03b2-M animals treated with either vehicle control (n\u2009=\u20093) or IFN\u03b3 (n\u2009=\u20098) at 7.5-week timepoint (p\u2009=\u20090.0086). k Representative immunohistochemistry (IHC) images for S100A8/9 comparing Vehicle and IFN\u03b3 treated \u03b2-M animals. 10\u00d7 objective lens, scale bar is 100\u2009\u03bcm. IHC was repeated at least twice on tissue sections from multiple animals. Created in BioRender. Lehrich (2025) https://BioRender.com/9a8wkfh. For (d, j), data presented as mean values \u00b1 standard deviation (SD) and P-values calculated by unpaired two-tailed Student\u2019s t-test. For (e), normalized enrichment score (NES) was normalized from ES, which was calculated by Kolmogorov\u2013Smirnov-like statistic and the adjusted p-value was calculated using the one-tailed empirical permutation test procedure. Source data are provided as a Source Data File. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, ****p\u2009<\u20090.0001.\n\nTo investigate functional changes within the M1-like macrophage population, we performed differential gene expression comparing the M1-like macrophages from LNP-CTRL and LNP-CTNNB1 treatment. GO pathway GSEA demonstrated enrichment of both response to type I/II interferon (IFN) and IFN alpha/beta pathways following LNP-CTNNB1 treatment (Fig.\u00a04e). CellChat analysis, which suggests cell signaling pathway level changes based on gene expression of ligands and cognate receptors37, showed enrichment of IFN-II and TNF signaling in the M1-like macrophage population following LNP-CTNNB1 treatment (Fig.\u00a04f). Specifically, this analysis shows increased probability of cell communication via Ifng from proliferative T cells to Ifngr1 and Ifngr2 on M1-like macrophages, and other macrophage cell populations solely in the LNP-CTNNB1 group (Fig.\u00a04g). Thus, increased type I/II IFNs released from the immune compartment (likely from T cells, macrophages, and dying tumor cells) following LNP-CTNNB1 treatment, engage with macrophages in the TIME milieu, and in part contribute towards polarizing them towards a pro-inflammatory anti-tumor phenotype. To validate our findings that a type I/II IFN response is, in part, a driver of the anti-tumor immune response following LNP-CTNNB1 treatment (Fig.\u00a04f, g), we treated \u03b2-M mice with IFN\u03b3 3\u00d7 weekly for 5 weeks, which led to a significant decrease in tumor burden (Fig.\u00a04h\u2013j) accompanied by increases in S100A8/9-positive cells, a marker for M1-like macrophages, when compared to vehicle control group (Fig.\u00a04k). Thus, early \u03b2-catenin suppression in \u03b2-catenin-mutated tumors induces local IFN release which is likely recruiting and reprogramming intra-tumoral myeloid cells to drive an anti-tumor immune response.\n\nGiven the amplified IFN response early after LNP-CTNNB1 treatment, we next investigated potential tumor cell-intrinsic molecular mechanisms driving this phentoype following \u03b2-catenin suppression. Also, \u03b2-catenin-mutated HCCs are well known for an immune cell excluded phenotype7. Analysis of TCGA-LIHC revealed CTNNB1-mutated HCCs downregulate pathways involved in type I/II IFN signaling, T cell activation, and chemokine signaling (Supplementary Fig.\u00a016a, b). To identify potential mechanisms, we utilized bulk RNA-seq datasets which contained the transcriptome of multiple \u03b2-catenin-mutated HCC mouse models (GSE125336) and \u03b2-catenin knockout mouse livers (GSE68779) and performed transcription factor enrichment analysis on the 162 common genes downregulated in \u03b2-catenin-mutated HCC and upregulated in \u03b2-catenin knockout livers. We identified multiple transcription factors (TFs), including Irf2 (p\u2009=\u20090.0052) and Pou2f1 (p\u2009=\u20090.0023), as candidate TFs with known binding to the upregulated genes in \u03b2-catenin knockout livers (Fig.\u00a05a). Interestingly, further analysis of Zone 3 CTNNB1 WT & MUT (GS+) hepatocytes from the scRNA-seq dataset (Fig.\u00a03a) revealed upregulation of Irf2 and Pou2f1 target genes, inferred from an unbiased analysis (Fig.\u00a05a), following LNP-CTNNB1 treatment (Fig.\u00a05b). To further confirm hepatocytes as the cell source of Irf2 and Pou2f1, which would potentially drive an immune response upon \u03b2-catenin knockdown or loss, we investigated IRF2/Irf2 and POU2F1/Pou2f1 expression in both human and mouse liver scRNA-seq datasets38 (GSE192742). We observed IRF2/Irf2 and POU2F1/Pou2f1 expression in hepatocyte cell populations in both human and mouse livers (Supplementary Fig.\u00a016c, d), suggesting \u03b2-catenin-mediated IRF2/POU2F1 suppression is hepatocyte-intrinsic. Likewise, analysis of the TCGA-LIHC cohort revealed IRF2 and POU2F1 expression was not significantly different between patients with or without Wnt/\u03b2-catenin activity, rather, the target genes of IRF2/POU2F1 were significantly downregulated (p\u2009=\u20090.009) in patients with either CTNNB1, AXIN1, or APC mutations compared to other patients (Fig.\u00a05c, d). High expression of IRF2/POU2F1 target genes was associated with improved disease-free survival (p\u2009=\u20090.01) in all TCGA-LIHC patients and in those with Wnt/\u03b2-catenin activating mutations (p\u2009=\u20090.065) (Supplementary Fig.\u00a016e, f). Thus, we hypothesized that mutated-\u03b2-catenin is repressing a module of TFs driving immune exclusion and limiting an anti-tumor immune response.\n\na Schematic of pipeline comparing whole transcriptomes of \u03b2-catenin-mutated HCC to \u03b2-catenin knockout livers. b Dot plot highlighting Irf2 and Pou2f1 target genes within the Zone 3 CTNNB1 WT and MUT (GS+) cell population between LNP-CTRL and LNP-CTNNB1 from Fig.\u00a03a, b. c Heatmap of z-scored expression values of IRF2/POU2F1 target genes in TCGA-LIHC patients (n\u2009=\u2009374) and adjacent normal (n\u2009=\u200950). Data stratified by CTNNB1- (n\u2009=\u200998), AXIN1- (n\u2009=\u200918), and APC-mutated patients (n\u2009=\u20093). d Boxplots of normalized expression of IRF2, POU2F1, and IRF2/POU2F1 target genes stratified by adjacent normal (n\u2009=\u200950), Wnt/\u03b2-catenin-mutant (n\u2009=\u2009119), and -wild-type (n\u2009=\u2009255). e Schematic of \u03b2-catenin-hMet (\u03b2-M) animals co-injected with pT3 or IRF2 at time of hydrodynamic tail vein injection (HDTVi) and sacrificed at 7.5-weeks post-HDTVi. f Representative gross liver images from \u03b2-M-pT3 and \u03b2-M-IRF2 animals. Scale bar indicates 1 centimeter (cm). g, h Liver weights (p\u2009=\u20090.0008) and liver weight/body weight (LW/BW) (p\u2009=\u20090.0003) comparing \u03b2-M-pT3 (n\u2009=\u20097) and \u03b2-M-IRF2 (n\u2009=\u200912) animals at 7.5-week timepoint. i Schematic of \u03b2-catenin-Nrf2 (\u03b2-N) animals co-injected with pT3 or POU2F1 at time of HDTVi and sacrificed at 10.7-weeks post-HDTVi. j Representative gross liver images from \u03b2-N-pT3 and \u03b2-N-POU2F1 animals. k, l Liver weights (p\u2009=\u20090.0013) and LW/BW (p\u2009=\u20090.0005) comparing \u03b2-N-pT3 (n\u2009=\u20094) and \u03b2-N-POU2F1 (n\u2009=\u20094) animals at 10.7-week timepoint. m Representative IHC images for CD4, CD8, and CD20 comparing \u03b2-N-pT3 and \u03b2-N-POU2F1 animals at 10.7-week timepoint. n GO pathway gene set enrichment analysis comparing \u03b2-M-POU2F1 to \u03b2-M-pT3. Created in BioRender. Lehrich (2025) https://BioRender.com/25qvbwj. Lehrich (2025) https://BioRender.com/qqvvb4x. Lehrich (2025) https://BioRender.com/ecs8omd. For (g, h), (k, l), data presented as mean values\u2009\u00b1\u2009standard deviation (SD) and P-values calculated by unpaired two-tailed Student\u2019s t-test. For (d), the center line shows the median, the box limits show the interquartile range (IQR; the range between the 25th and 75th percentile) and the whiskers show 1.5\u00d7 IQR. For (d), One-way ANOVA with Tukey-HSD post-hoc adjusted p-values comparing Wnt/\u03b2-catenin-mutant vs wild-type are: p\u2009=\u20090.80, p\u2009=\u20090.54, and p\u2009=\u20090.009 for IRF2, POU2F1, and IRF2/POU2F1 target gene expression, repsectively. For (n), NES was normalized from ES, which was calculated by Kolmogorov\u2013Smirnov-like statistic and the adjusted p-value was calculated using the one-tailed empirical permutation test procedure. Source data are provided as a Source Data File. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, ****p\u2009<\u20090.0001.\n\nTo validate that repression of IRF2 and POU2F1 are driving immune exclusion in \u03b2-catenin-mutated HCC, we first overexpressed either pT3 (empty vector) or Irf2 (\u03b2-M-IRF2) in the \u03b2-M model (Fig.\u00a05e). We observed a decrease in overall gross tumor burden and significant decrease in LW/BW in \u03b2-M-IRF2 mice at 7.5-weeks post-HDTVi (Fig.\u00a05f\u2013h). RNA-seq confirmed the overexpression of Irf2 in the \u03b2-M-IRF2 mice at the 7.5-week timepoint where less tumor burden was evident (Supplementary Fig.\u00a017a). Expectedly, given the known immunomodulatory roles of IRF2 and its involvement in type I/II IFN signaling39, we observed increased immune aggregates as evident by IHC for CD45 (Supplementary Fig.\u00a017b). The composition of the immune infiltrate was examined with fluorescence-activated cell sorting (FACS) on isolated immune cells from \u03b2-M-pT3 and \u03b2-M-IRF2 HCC, which demonstrated significant increases in total CD4+ T cells with decreases in T regulatory cell populations in the \u03b2-M-IRF2 mice (Supplementary Fig.\u00a017c; Supplementary Fig.\u00a018a). Next, we overexpressed either pT3 (empty vector) or POU2F1 (\u03b2-N-POU2F1) in the \u03b2-N model (Fig.\u00a05i). We also observed a significant decrease in overall gross tumor burden at 10.7-weeks post-HDTVi in POU2F1-overexpression \u03b2-N model (Fig.\u00a05j\u2013l) and via histology (Supplementary Fig.\u00a019a). These findings were also validated in the \u03b2-M model where significant reductions in tumor burden were observed at 7.7-weeks post-HDTVi (Supplementary Fig.\u00a019b\u2013f). IHC for CD4, CD8, and CD20 revelaed T and B cells in aggregates in the TIME in the \u03b2-N-POU2F1 mice (Fig.\u00a05m). RNA-seq confirmed the overexpression of POU2F1 in the \u03b2-M-POU2F1 mice at the 7.7-week timepoint, along with decreased enrichment for published mutated-\u03b2-catenin gene signature (MBGS) (Supplementary Fig.\u00a019g\u2013i)32. Additionally, GSEA with GO pathways demonstrated enrichment of both T and B cell activation and proliferation (Fig.\u00a05n). Lastly, given the less well characterized role of POU2F1 mediating an immune response, as compared to IRF239,40, we administered IgG/\u03b1CD3 to deplete CD3+ immune cells from \u03b2-M-POU2F1 mice (Supplementary Fig.\u00a020a), which was confirmed via IHC for CD3 on spleens (Supplementary Fig.\u00a020b). At 8.3-weeks post-HDTVi, there was an increase in gross tumor burden and significant increase in liver weight, LW/BW, and spleen weight in \u03b2-M-POU2F1 + \u03b1CD3 versus \u03b2-M-POU2F1 + IgG animals (Supplementary Fig.\u00a020c\u2013f), suggesting an immune-dependent role for POU2F1-mediated tumor regression in CTNNB1-mutated HCC. Overall, mutated-\u03b2-catenin represses IRF2, POU2F1, and likely other TFs, which limits transcription of key signaling molecules (i.e., cytokines and chemokines) important for priming lymphocyte recruitment needed for effective anti-tumor immunity.\n\nTo further assess translatability, we evaluated the in vivo activity of LNP-CTNNB1 in advanced-stage disease in \u03b2-M and \u03b2-N HCC models. First, we assessed response to LNP-CTNNB1 in the \u03b2-M model with once weekly I.V. treatments starting at 6-weeks post-HDTVi (Fig.\u00a06a). We observed a heterogeneous response with 5/8 animals responding and 3/8 animals demonstrating minimal/poor response to LNP-CTNNB1 at 10.5-weeks post-HDTVi (Fig.\u00a06b, c; Supplementary Fig.\u00a021a). Next, we assessed response in the \u03b2-N model where we administered once weekly I.V. LNP treatments starting at 8-weeks post-HDTVi (Fig.\u00a06d). Similar to the \u03b2-M model, after 6 cycles we observed a heterogeneous response with 5/8 animals responding and 3/8 animals demonstrating minimal/poor response to LNP-CTNNB1 at 13.5-weeks post-HDTVi (Fig.\u00a06e, f; Supplementary Fig.\u00a021b). Thus, LNP-CTNNB1 appears to be efficacious in advanced-stage disease setting, with a subset of animals responding less optimally.\n\na LNP treatment schematic in \u03b2-catenin-hMet (\u03b2-M) model for advanced-stage disease. Mice received once weekly intravenous (I.V.) injections at 1\u2009mg/kg dosage starting at 6-weeks post-hydrodynamic tail vein injection (HDTVi) and sacrificed at 10.5-weeks post-HDTVi. b Representative gross liver images of LNP-CTNNB1 treated \u03b2-M animals at 10.5-week timepoint demonstrating non-responders (NR) and responders (R) compared to LNP-CTRL \u03b2-M animals when moribund at ~8.5-weeks. c Liver weight/body weight (LW/BW) comparing LNP-CTRL (n\u2009=\u20094) and LNP-CTNNB1 (n\u2009=\u20098) treated \u03b2-M animals at 8.5-week and 10.5-week timepoint, respectively (p\u2009=\u20090.0373). d LNP treatment schematic in \u03b2-catenin-Nrf2 (\u03b2-N) model for advanced-stage disease. Mice received once weekly intravenous (I.V.) injections at 1\u2009mg/kg dosage starting at 8-weeks post-HDTVi. e Representative gross liver images of LNP-CTNNB1 treated \u03b2-N animals at 13.5-week timepoint demonstrating NR and R compared to LNP-CTRL \u03b2-N animals when moribund at ~10.5-weeks. f LW/BW comparing LNP-CTRL (n\u2009=\u20096) and LNP-CTNNB1 (n\u2009=\u20098) treated \u03b2-N animals at 10.5-week and 13.5-week timepoint, respectively (p\u2009=\u20090.0005). g (Top) Spatial plot of all 17 clusters from the uniform manifold approximation and projection (UMAP) visualization showing the spatial localization on the H&E tissue section. (Bottom) Spatial plot of cluster 3 ident on the H&E tissue section. h Stacked bar chart of cluster proportions for the 3 LNP response (Control, NR, and R) groups. Asterisk denotes cluster 3. i (Top) Spatial plot of all 13 spot clusters from the UMAP visualization showing the spatial localization on the H&E tissue section. (Bottom) Spatial plot of cluster 11 ident on the H&E tissue section. j Stacked bar chart of cluster proportions for the 3 LNP response (Control, NR, and R) groups. Asterisk denotes cluster 11. k Waterfall plot of predicted kinases upstream of the differentially expressed genes (DEGs) in cluster 3 from the \u03b2-M model. l Waterfall plot of predicted transcriptional regulators upstream of the DEGs in cluster 11 from the \u03b2-N model. Created in BioRender. Lehrich (2025) https://BioRender.com/63585xs. Lehrich (2025) https://BioRender.com/6yyrkql. For (c, f), data presented as mean values\u2009\u00b1\u2009standard deviation (SD) and P-values calculated by unpaired two-tailed Student\u2019s t-test. Source data are provided as a Source Data File. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, ****p\u2009<\u20090.0001.\n\nTo investigate the basis of the observed heterogeneous response, we first utilized the 10\u00d7 Visium platform to perform unbiased spatial transcriptomics on an LNP-CTRL treated \u03b2-M HCC (\u03b2-M Control), 2 LNP-CTNNB1 treated \u03b2-M HCC showing minimal/poor response (\u03b2-M NR-1; \u03b2-M NR-2), and an LNP-CTNNB1 treated \u03b2-M HCC showing response (\u03b2-M R-1). In total, we sequenced 17,685 spots across the 4 slides, with 4461 spots in \u03b2-M Control, 4331 spots in \u03b2-M NR-1, 4842 in \u03b2-M NR-2, and 4051 spots in \u03b2-M R-1. After integrating the transcriptomes from all slides, unbiased clustering revealed 17 clusters conserved across the different conditions with changes in cluster proportions and spatial patterns of such changes (Supplementary Fig.\u00a022a, b). Spatial plots and cluster proportion analyses revealed \u03b2-M R animals had increases in clusters 1, 2, 13, and 14, while \u03b2-M NR animals had increases in cluster 3 (Fig.\u00a06g, h). We also performed 10\u00d7 Visium spatial transcriptomics on an LNP-CTRL treated \u03b2-N HCC (\u03b2-N Control), 2 LNP-CTNNB1 treated \u03b2-N HCC showing minimal/poor response (\u03b2-N NR-1; \u03b2-N NR-2), and two LNP-CTNNB1 treated \u03b2-N HCC showing response (\u03b2-N R-1; \u03b2-N R-2) to validate findings from the \u03b2-M model. Here, we sequenced 17,130 spots across the 5 slides, with 3884 spots in \u03b2-N Control, 3624 spots in \u03b2-N NR-1, 4037 spots in \u03b2-N NR-2, 3390 spots in \u03b2-N R-1, and 2195 spots in \u03b2-N R-2. Integration and unbiased clustering across the \u03b2-N transcriptomes revealed 13 conserved clusters with changes in cluster proportions and spatial patterns of such changes (Fig.\u00a06i; Supplementary Fig.\u00a022c, d). \u03b2-N R animals showed increases in clusters 3, 4, and 5, while \u03b2-N NR animals showed increases in cluster 11 (Fig.\u00a06j).\n\nTo ascertain mechanisms of response to LNP-CTNNB1, we first assessed whether there were cell cycle differences between NR and R animals in the \u03b2-M model. Utilizing gene signatures for S and G2M phases, spots belonging to specific clusters were categorized as belonging to G1, S, or G2M phases of the cell cycle (Supplementary Fig.\u00a023a). There was a graded decrease in expression of S and G2M signature genes from control to NR to R animals (Supplementary Fig.\u00a023b). Spatial mapping and quantification of the spots belonging to G1, S, or G2M phases revealed majority of spots corresponding to tumoral regions had increased expression of S and G2M signature genes in both \u03b2-M Control and NR animals, while \u03b2-M R animals showed majority of cell clusters to be in G1 phase (Supplementary Fig.\u00a023c\u2013e). Furthermore, RNA in situ expression of Mki67 and validation with IHC for Ki67 expressed throughout the cell cycle, confirmed active proliferation in tumor nodules of \u03b2-M NR animals (Supplementary Fig.\u00a023f, g). These results were also observed in the \u03b2-N model (Supplementary Fig.\u00a024a\u2013g). Thus the NR phenotype in both models demonstrates active cellular proliferation likely due to insufficient \u03b2-catenin knockdown.\n\nWe next assessed whether the residual proliferating tumor nodules in the NR mice in both models were Wnt/\u03b2-catenin active. We evaluated expression of our previously reported mutated-\u03b2-catenin gene signature (MBGS)32, which detects Wnt activation in whole and spatial transcriptomic datasets with high sensitivity and specificity. MBGS had graded decrease in expression from control to NR to R phenotype in both models (Supplementary Fig.\u00a025a, b) and highest expression in clusters 4, 9, and 12 in \u03b2-M model and in cluster 7 in \u03b2-N model (Supplementary Fig.\u00a025c, d). As expected, we observed that the majority of tumor nodules which were proliferating in the NR phenotype were MBGS-high (Supplementary Fig.\u00a025e, f). However, a subset of tumor nodules in the NR were MBGS-low and proliferating, implying active tumor growth despite sufficient \u03b2-catenin knockdown. These were cluster 3 in the \u03b2-M model and cluster 11 in the \u03b2-N model (Fig.\u00a06g, i). Spatial pseudotime analysis demonstrated these MBGS-low/Mki67-high nodules were insufficiently reprogrammed from zone 3 tumor to zone 1/2 hepatocytes (Supplementary Fig.\u00a026a\u2013d), and transcriptional trajectory indicating tumoral origin. Thus, a subset of tumor nodules in the NR phenotype may have persisted due to insufficient dosing (i.e., incomplete \u03b2-catenin suppression), yet some tumor nodules that expanded appear to be biologically distinct.\n\nTo identify biological origin of these persistent tumor nodules, we performed differential gene expression analysis across each cluster in the \u03b2-M (Supplementary Fig.\u00a027a) and \u03b2-N models (Supplementary Fig.\u00a028a). Cluster 3 in \u03b2-M model revealed upregulation of KEGG and GO pathways involved in PI3K-Akt signaling and actin cytoskeleton remodeling (Supplementary Fig.\u00a029a). We then performed promoter enrichment analysis on the top DEGs from cluster 3 and identified MET as one of the top kinases predicted to be enriched (Fig.\u00a06k). This was validated with V5-tag IHC where we observed persistence of V5-tag+ clones in NR phenotype (Supplementary Fig.\u00a029b). Additionally, cluster 11 in the \u03b2-N model revealed upregulation of KEGG and GO glutathione metabolism and reactive oxygen species metabolic pathways (Supplementary Fig.\u00a030a). Promoter enrichment analysis on the top DEGs from cluster 11 identified NFE2L2 as one of the top transcription factors predicted to be enriched (Fig.\u00a06l). We also validated this finding with NQO1 IHC where we observed persistence of NQO1+ clones in NR phenotype (Supplementary Fig.\u00a030b). Thus, MBGS-low nodules, which expanded in the NR phenotype, were likely derived from clonal expansion of tumor cells which were predominant upregulators of pathways specific to the secondary driver as a means to escape \u03b2-catenin suppression. Overall, heterogeneity in advanced-stage disease response appears to be due in part to insufficient \u03b2-catenin suppression and in part due to an escape mechanism.\n\nGiven the known role of \u03b2-catenin in promoting a non-T cell-inflamed TIME7, we examined whether LNP-CTNNB1 sufficiently reprogrammed the adaptive immune response in advanced-stage HCC. First, we observed increased expression of T cell markers, including Cd3d, Cd3e, Cd3g, Cd4, and Cd8a in cluster 14 in the \u03b2-M model, (Supplementary Fig.\u00a031a), which had expanded in the R phenotype. Additionally, cluster 14 in the \u03b2-M model showed increased expression of a T cell module score, which was also enriched in \u03b2-M R animals (Supplementary Fig.\u00a031b, c). Interestingly, we also observed enrichment of these T cell markers in MBGS-high tumor-specific clusters (clusters 9 and 12 in \u03b2-M model) within R animals, demonstrating active T cell infiltration within residual tumor nodules, which was likely driving anti-tumor immunity (Fig.\u00a07a; Supplementary Fig.\u00a031d). A similar amplified T cell response was observed in the \u03b2-N model with increased T cell marker expression within the MBGS-high cluster 7 in R animals (Supplementary Fig.\u00a032a\u2013d), although not as pronounced as the \u03b2-M model. In the \u03b2-M model, GSEA on clusters 9 and 12 comparing R to control demonstrated enrichment of GO pathways involved in leukocyte-mediated immunity, response to IFN\u03b3, leukocyte cell adhesion, cell killing, and T cell proliferation (Supplementary Fig.\u00a033a, b). Increased IFN\u03b3 expression was observed at the RNA and protein level following LNP-CTNNB1 treatment (Supplementary Fig.\u00a033c, d). We validated the increased T cell infiltrate via IHC in the \u03b2-M model demonstrating increased CD3+ and CD4+ cells within tumor nodules with organization into lymphoid aggregates (LA) in the \u03b2-M R animals (Fig.\u00a07b, c). We observed the LAs to be also composed of B cells, as noted by increased B cell markers gene expression in clusters 13 and 14, the MBGS-high tumor-specific clusters (clusters 9 and 12), specifically enriched in \u03b2-M R animals, along with increased CD20+ cells via IHC (Supplementary Fig.\u00a034a\u2013e). To further characterize the enhanced adaptive immune response in \u03b2-M R animals, we performed spatially enhanced CellChat37 analysis to investigate ligand-receptor interactions between different clusters across the responders. This analysis revealed enrichment of MHC-II signaling with antigen communication from most clusters to CD4+ cells in cluster 12 specifically in \u03b2-M R animals compared to both \u03b2-M Control and \u03b2-M NR animals (Supplementary Fig.\u00a035a\u2013d). Morevoer, an IRF2/POU2F1 target gene module score showed increased expression in cluster 12 of \u03b2-M R compared to NR and control animals (Supplementary Fig.\u00a035e), further suggesting an IRF2-mediated IFN response upon \u03b2-catenin suppression. Overall, the R phenotype demonstrated reinvigorated and persistent adaptive immune surveillance with active T and B cell infiltration, T cell proliferation, and engaged IFN signaling in the intra-tumoral compartment, which was not observed in the NR phenotype in the advanced-stage disease setting.\n\na Dot plot of T cell markers (Cd3d, CD3e, Cd3g, Cd4, Cd8a) in clusters 9 and 12 stratified by LNP treatment response (LNP-CTRL [blue]; non-responder [NR; red], responder [R; green]) in \u03b2-catein-hMet (\u03b2-M) from Fig.\u00a06g, h. b, c Representative immunohistochemistry (IHC) images for CD3 and CD4 in \u03b2-M animals treated with LNP-CTRL or LNP-CTNNB1. 5\u00d7 objective magnification. Scale bar indicates 200\u2009\u03bcm. d Dot plot of Cd274 expression by LNP treatment response in \u03b2-M. e Schematic of LNP + IgG/\u03b1-PD1 in \u03b2-M model. Mice received weekly LNP injections at 1\u2009mg/kg dosage starting at 6-weeks post-hydrodynamic tail vein injection (HDTVi) with twice weekly injections of IgG/\u03b1-PD1 (200\u2009\u03bcg) for two weeks starting 3-days after first LNP treatment. f Representative gross liver images of LNP-CTNNB1 \u00b1 IgG/\u03b1-PD1 treated \u03b2-M animals at 10.5-week timepoint compared to LNP-CTRL \u00b1 IgG/\u03b1-PD1 when moribund. g Liver weight/body weight (LW/BW) comparing LNP-CTNNB1 \u00b1 IgG/\u03b1-PD1 (n\u2009=\u20098/n\u2009=\u20098) treated \u03b2-M animals at 10.5-week timepoint to LNP-CTRL \u00b1 IgG/\u03b1-PD1 (n\u2009=\u20094/n\u2009=\u20094) when moribund. h Magnetic resonance images (MRI) of LNP-CTNNB1 \u00b1 IgG/\u03b1-PD1 treated \u03b2-M animals at 10.5-week timepoint. i RNA expression levels by qPCR of mCTNNB1 and hCTNNB1 in \u03b2-M animals treated with LNP-CTNNB1 \u00b1 IgG/\u03b1-PD1 (n\u2009=\u20098). j Representative tiled IHC images for granzyme B (GZMB) from LNP-CTNNB1 \u00b1 IgG/\u03b1-PD1 treated \u03b2-M animals at 10.5-week timepoint. Scale bar indicates magnification. k Violin plot showing quantification of number of GZMB+ lymphoid aggregates per tissue section in tumoral regions from LNP-CTNNB1 \u00b1 IgG/\u03b1-PD1 (n\u2009=\u20098) treated \u03b2-M animals at 10.5-week timepoint. l Kaplan\u2013Meier overall survival (OS) curve comparing \u03b2-M animals treated with LNP-CTRL \u00b1 IgG/\u03b1-PD1 (n\u2009=\u20093/n\u2009=\u20094) or LNP-CTNNB1 \u00b1 IgG/\u03b1-PD1 (n\u2009=\u200910/n\u2009=\u20098) (p\u2009=\u20090.0188). m Schematic of two-part mechanistic working model for LNP-CTNNB1 response in \u03b2-catenin-mutated HCC. Created in BioRender. Lehrich (2025) https://BioRender.com/z4h1j67. Lehrich (2025) https://BioRender.com/hjjd4zn. For (g, i), data presented as mean values\u2009\u00b1\u2009standard deviation (SD). For (g), P-values calculated by one-way ANOVA with Tukey-HSD post-hoc correction. For (i, k), P-values calculated by unpaired two-tailed Student\u2019s t-test. For (l), P-value calculated by log-rank test to compare difference in mean OS time between LNP-CTNNB1 + IgG and LNP-CTNNB1 + \u03b1-PD1. Source data are provided as a Source Data File. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, ****p\u2009<\u20090.0001.\n\nDue to the known role of \u03b2-catenin promoting and IRF2 inhibiting transcription of Cd274 (encoding PD-L1 on tumor cells)41,42,43, a classical immune checkpoint mediating T cell exhaustion, we hypothesized that the NR phenotype may be driven by T cell exhaustion through increased expression of Cd274. Indeed, we observed increased Cd274 and Pdcd1 (encoding PD1 on T cells) expression in \u03b2-M NR and control compared to R animals (Fig.\u00a07d; Supplementary Fig.\u00a031e), implying lack of sustained and active lymphocyte effector function with increased exhaustion in NR, likely due to insufficient \u03b2-catenin suppression. This led us to hypothesize that administration of ICI would enhance LNP-CTNNB1 response in advanced-stage disease through limiting T cell exhaustion via PD1/PD-L1 axis, thus promoting sustained anti-tumor immunity. IgG or \u03b1-PD1 was added 3-days after the first LNP dose with continued LNP treatment scheme as for the advanced-stage disease in the more aggressive \u03b2-M model. We harvested mice by 10.5-week timepoint or when moribund to assess response and performed a survival study to determine long-term anti-tumor immunity (Fig.\u00a07e). By 10.5-weeks, LNP-CTRL mice were all moribund with \u03b1-PD1 alone suggesting no impact on tumor burden, whereas the combination of LNP-CTNNB1 + \u03b1-PD1 resulted in enhanced efficacy with absence of any NR compared to LNP-CTNNB1 + IgG treated animals (Fig.\u00a07f, g). Additionally, MRI demonstrated less hyperintense foci in LNP-CTNNB1 + \u03b1-PD1 compared to LNP-CTNNB1 + IgG treated mice (Fig.\u00a07h). Decrease in hCTNNB1, indicative of tumor burden driven by mutant-CTNNB1, was enhanced in the LNP-CTNNB1 + \u03b1-PD1 compared to LNP-CTNNB1 + IgG treated mice (p\u2009=\u20090.0395) suggesting an augmented response to LNP-CTNNB1 with \u03b1-PD1 (Fig.\u00a07i). To determine whether there was enhanced effector T cell function in animals treated with LNP-CTNNB1 + \u03b1-PD1, we performed IHC for granzyme B (GZMB), a marker for cytotoxic T cells, which is known to be expressed at higher levels in tertiary lymphoid structures (TLS)/LA44. We observed an increased trend in the quantity of GZMB+LA within remnant tumor nodules in LNP-CTNNB1 + \u03b1-PD1 compared to LNP-CTNNB1 + IgG treated mice (p\u2009=\u20090.0989) (Fig.\u00a07j, k). Concomitantly, mice receiving LNP-CTNNB1 + \u03b1-PD1 survived significantly longer than those receiving LNP-CTNNB1 + IgG (p\u2009=\u20090.019) (Fig.\u00a07l), supporting an enhanced response to LNP-CTNNB1 with \u03b1-PD1. Overall, administration of \u03b1-PD1 augmented LNP-CTNNB1 response and restored adaptive immunity.\n\nGiven the enhanced recruitment of both T and B cells into organized LA following LNP-CTNNB1 treatment in mice, we were interested in examining such a relationship between TLS/LA, CTNNB1 mutation, and ICI response in patients. In the IMbrave150 phase III clinical trial, unresectable HCC patients received atezolizumab and bevacizumab in one arm versus sorafenib in another6. Atezolizumab and bevacizumab arm showed improved response rates, overall survival (OS) and progression-free survival (PFS). Accessing this information, 174 HCC patients were scored by a clinical pathologist for the presence of immune infiltration ranging from TLS, LA, diffuse infiltrate [DI], or none from the hematoxylin & eosin (H&E) slides. Overall, majority of the patients, irrespective of treatment arm, had LA (n\u2009=\u200971/174), while fewer had TLS (n\u2009=\u20098/174) or DI (n\u2009=\u20098/174) (Fig.\u00a08a). Interestingly, among the responders, those in the atezolizumab plus bevacizumab arm tended to be enriched for presence of TLS/LA, which was not observed in the sorafenib arm (Fig.\u00a08b). Additionally, patients with TLS/LA correlated with improved PFS and OS, which was more pronounced in the atezolizumab plus bevacizumab arm (Fig.\u00a08c). Moreover, patients with TLS/LA had significantly increased expression of a LA-like gene signature, which consisted of previously reported marker genes for B cells, CD4 Tconv cells, and CD4 Tfh cells45 compared to the patients with DI/None (Fig.\u00a08d, e). Interestingly, high expression of the LA-like gene signature was associated with improved PFS (p\u2009=\u20090.0639) and OS (p\u2009=\u20090.000697) in the atezolizumab plus bevacizumab arm (Fig.\u00a08f). Importantly, we observed that CTNNB1-mutated patients had significantly lower expression of the LA-like gene signature compared to CTNNB1-wild-type patients (Fig.\u00a08g). This was also confirmed in the TCGA-LIHC cohort (Supplementary Fig.\u00a036a\u2013c). Thus, CTNNB1-mutated patients express fewer genes important for TLS/LA formation compared to CTNNB1-wild-type patients, ultimately influencing response to combination ICIs.\n\na Stacked bar plot of number of patients in IMbrave150 phase III trial having either tertiary lymphoid structure (TLS), lymphoid aggregate (LA), diffuse infiltrate (DI), or none (n\u2009=\u2009174). b Stacked bar plot of number of patients in IMbrave150 phase III trial having either TLS, LA, DI, or None stratified by treatment group and clinical response: atezolizumab + bevacizumab (AtezoBev; n\u2009=\u2009116) versus sorafenib (Sor; n\u2009=\u200958). c (Top) Kaplan\u2013Meier progression-free survival (PFS) and overall survival (OS) curves comparing patients with TLS/LA in atezolizumab + bevacizumab versus sorafenib arms. (Bottom) Kaplan\u2013Meier PFS and OS curves comparing patients with DI/None in atezolizumab + bevacizumab versus sorafenib arms (n\u2009=\u2009174; AtezoBev, n\u2009=\u2009116; Sor, n\u2009=\u200958). d Expression of a \u201cLA-like\u201d gene signature stratified by whether patients in IMbrave150 phase III trial had TLS/LA or DI/None (n\u2009=\u2009174; p\u2009=\u20090.025). e Expression of a \u201cLA-like\u201d gene signature stratified by whether patients in IMbrave150 phase III trial had TLS, LA, DI, or None (n\u2009=\u2009174; p\u2009=\u20090.0095). f (Left) Kaplan\u2013Meier PFS curve comparing patients with high LA-like gene signature in atezolizumab + bevacizumab versus sorafenib arms (n\u2009=\u200988; AtezoBev, n\u2009=\u200959; Sor, n\u2009=\u200929). (Right) Kaplan\u2013Meier OS curve comparing patients with high LA-like gene signature in atezolizumab + bevacizumab versus sorafenib arms (AtezoBev, n\u2009=\u200959; Sor, n\u2009=\u200929). g Box plot depicting \u201cLA-like\u201d gene signature stratified by CTNNB1 mutational status in all patients and within each of the two treatment arms from IMbrave150 phase III trial (n\u2009=\u2009129; p\u2009=\u20090.002). For (c, f), hazard ratios (HRs) and 95% confidence intervals (CIs) were determined using a univariate Cox model. Kaplan\u2013Meier log-rank test was used to compare differences in PFS/OS outcomes. For the boxplots in (d, e, g), the center line shows the median, the box limits show the interquartile range (IQR; the range between the 25th and 75th percentile) and the whiskers show 1.58\u00d7 IQR. For (d, g), P-values were calculated using two-tailed unpaired t-test. For (e), P-value was calculated using one-way analysis of variance (ANOVA).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60457-2/MediaObjects/41467_2025_60457_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60457-2/MediaObjects/41467_2025_60457_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60457-2/MediaObjects/41467_2025_60457_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60457-2/MediaObjects/41467_2025_60457_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60457-2/MediaObjects/41467_2025_60457_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60457-2/MediaObjects/41467_2025_60457_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60457-2/MediaObjects/41467_2025_60457_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60457-2/MediaObjects/41467_2025_60457_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We report strong in vitro and vivo efficacy of a novel LNP-formulated siRNA targeting CTNNB1 mRNA transcript for treatment of \u03b2-catenin-mutated HCC as monotherapy in early-stage disease or in combination with ICI in advanced-stage disease. We identified through unbiased bulk RNA-seq, scRNA-seq, and spatial transcriptomic approaches tumor-cell-intrinsic roles of \u03b2-catenin-mediated IRF2 and POU2F1 repression driving an immune-excluded TIME and inert type I/II IFN responses in \u03b2-catenin-mutated HCC with in vivo validation. Additionally, we demonstrate upon \u03b2-catenin suppression, \u03b2-catenin-mutated tumor cells reprogram towards zone 1/2 hepatocyte-like cells, revealing the unique role of mutated-\u03b2-catenin in driving zone 3 (pericentral) tumor metabolism. Our work demonstrates that \u03b2-catenin is now directly targetable in murine HCC and supports the high impact development of clinical investigations utilizing LNP-CTNNB1 as monotherapy or in combination with ICI to achieve therapeutic benefit in HCC patients with Wnt/\u03b2-catenin activation.\n\n\u03b2-Catenin is most active in the pericentral (zone 3) region in the hepatic lobule with hepatocytes in each of the three zones of the hepatic lobule expressing genes important for different metabolic functions, termed liver metabolic zonation35. Given the localization of \u03b2-catenin to zone 3, it is no surprise that \u03b2-catenin-mutated tumors preferentially originate and clonally expand from hepatocytes residing within zone 3, and these tumors share unique metabolic addictions to processes canonically identified in zone 3. In fact, we have previously shown that CTNNB1-mutated HCC is addicted to glutamine synthesis46, as part of \u03b2-catenin-GS-mTOR axis21. Additionally, CTNNB1-mutated HCCs demonstrate addiction to xenobiotic metabolism through GSTM347. However, surprisingly, tumors with \u03b2-catenin oncogenic activation are not glycolytic (zone 3 metabolism), but are fatty acid oxidative (zone 1 metabolism) addicted48. Here, we show that \u03b2-catenin-mutated tumors residing specifically in zone 3 are metabolically wired to perform canonical zone 3 metabolic processes with a focus on fatty acids as substrates, while \u03b2-catenin-mutated tumor cells in zone 1 are metabolically wired to perform canonical zone 1 metabolic processes with a focus on arginine metabolism and amino acid biosynthesis. We have also uniquely demonstrated that \u03b2-catenin-mutated tumor cells in zone 1 possess the highest proliferative capacity compared to those in zone 3, suggesting that despite \u03b2-catenin-mutated HCCs being well-differentiated, less proliferative tumors, in ectopic regions of absent Wnt signals or in presence of normal zone 1 signals, proliferation may be favored over metabolic homeostasis. Whether zone 1 \u03b2-catenin-mutated HCCs in current model are due to clonal expansion, evolution, or budding from zone 3 tumors to eventually establish in zone 1, or an artifact of plasmid transfection in rare hepatocytes in zone 1 requires further investigation. However, despite these tumor cell-intrinsic pathways, the overall tumor biology and metabolism may also be regulated by local zonal environment and signals. Overall, we demonstrate that suppressing \u03b2-catenin in CTNNB1-mutated tumors reprograms zone 3 tumors towards a zone 1/2 metabolic phenotype as early as 3-days post-LNP treatment, which contributes to the phenotypic differentiation and metabolic rewiring, loss of tumor nodules, and normalization of hepatic parenchyma and liver mass. Such reprogramming may yield unique metabolic vulnerabilities to be exploited for additional therapies in the future.\n\nCancers with Wnt/\u03b2-catenin activation are considered non-T cell-inflamed across a variety of tumors, including HCC, melanoma, esophageal, and others6,7,49,50. This has been associated with mixed ICI responses, specifically of the anti-PD1/anti-PD-L1 agent class49. In HCC, many chemokines are lowly expressed in CTNNB1-mutated patients, which directly influence immune cell recruitment, infiltration, and function7. For example, upon re-expression of CCL5, survival of \u03b2-catenin-mutated tumors was prolonged, along with increased antigen-specific CD8+ T cells7. Analogously, in KRAS-mutated colorectal cancer, where ICI is ineffective, expression of key chemokines involved in IFN network signaling, such as CXCL3, are downregulated when KRAS is mutated due to direct interaction with IRF239. In the context of \u03b2-catenin-mutated tumors, previous work has described that \u03b2-catenin sequesters and inhibits p65 (a subunit of NF-\u03baB) leading to reduced p65 binding, target gene expression, and inflammation51,52,53. Here, our unbiased bioinformatic analysis identified both IRF2 and POU2F1 as candidate TFs whose target genes are downregulated when \u03b2-catenin is active. We demonstrated that \u03b2-catenin suppression directly increases IFN signaling molecules and antigen presentation machinery components in \u03b2-catenin-mutated HCC murine models, with forced expression of either IRF2 or POU2F1 in two \u03b2-catenin-mutated HCC models sufficient to delay tumor development and convert a non-T cell-inflamed to a T cell-inflamed tumor. Thus, in \u03b2-catenin-mutated HCC, we posit that mutated-\u03b2-catenin is directly interacting with an immune-regulatory module of key TFs, which are normally active when \u03b2-catenin is absent from the nucleus, influencing expression of essential factors necessary for immune cell recruitment and function. Additionally, \u03b2-catenin has been shown to promote expression of PD-L1 on tumor cells by regulating expression of Cd27441,42,54. On the contrary, IRF2 has been shown to inhibit transcription of Cd274 on tumor cells43. Adequate \u03b2-catenin loss thus prevents expression of this classical immune checkpoint mediating T cell exhaustion dually, to thus synergize with ICIs and promote anti-HCC effect which can have wider therapeutic implications. Overall, pharmacological targeting of \u03b2-catenin likely has clinical implications across a broad spectrum of tumor types to enhance ICI clinical efficacy in part through modulation of key TFs involved in priming immune cell recruitment and re-engaging global immune surveillance.\n\nWe have shown here that targeting \u03b2-catenin directly impacts both tumor cell-intrinsic biology and simultaneously reprograms the TIME from non-T cell-inflamed to T cell-inflamed, with innate immune remodeling occurring as early as 3-days post-LNP treatment. This innate immune remodeling coincided with first observed biological effect of \u03b2-catenin knockdown at 3-days. Biological effects due to siRNA knockdown are usually observed within hours in vitro55, yet we observed a protracted time course in vivo, likely due to the systemic delivery method. Additionally, prior work has illustrated that adaptive immune surveillance begins to remodel at least 7\u201310 days following oncogene withdrawal, which explains the significant adaptive immune effects we observed studying advanced-stage response after 6 weeks of LNP treatment56. However, the profound anti-tumor effects we observed here likely would not be so pronounced through targeting downstream effector molecules of the Wnt/\u03b2-catenin signaling pathway. Specifically, we and others have previously shown that genetic deletion or pharmacologic inhibition of downstream effectors of \u03b2-catenin-TCF/LEF interactions, such as cyclin D1 (encoded by CCND1)57, GS16, mTORC121, TBX317, AXIN258, or TNFRSF1959 either result in partial tumor responses or compensatory negative feedback loops leading to enhanced tumorigenesis. For example, it has been shown that hepatocarcinogenesis is not dependent on cyclin D1 as \u03b2-catenin-mutated tumors induced in Ccnd1-null background mice still develop through compensatory cyclin D2 expression57. Additionally, conditionally deleting TBX3 or GS in mice with \u03b2-catenin-mutated HCC exacerbates tumorigenesis through YAP/TAZ inhibition or nitrogen metabolic rewiring, respectively16,17. Moreover, our group has previously identified metabolic addiction to \u03b2-catenin-GS-mTOR axis in \u03b2-catenin-mutated HCC and evaluated mTOR inhibitor (e.g., rapamycin, everolimus) response in multiple preclinical models of \u03b2-catenin-mutated HCC. However, response to LNP-CTNNB1 results in more consistent, robust, and durable responses in preclinical models21,28. Lastly, targeting solely TNFRSF19 will likely impact expression of chemokines involved in immune cell recruitment, yet there would be minimal impact on tumor cell-intrinsic biology59. Thus, targeting \u03b2-catenin directly is a holistic and rational strategy leading to durable anti-tumor immune responses through inhibiting multiple mechanisms hitting a truncal event, and impacting not only tumor cell-intrinsic biology, but also simultaneously remodeling the TIME architecture to promote long-lasting anti-tumor immunity.\n\nTherapeutic targeting of Wnt/\u03b2-catenin oncogenic signaling has been pursued over the last two decades with no therapeutic agent ultimately resulting in translation to the clinic. First, given the ubiquitous role of \u03b2-catenin in many cell types, translation of many agents has been limited due to on-target, off-tumor effects49,60. Small-molecule inhibitors which limit interactions between \u03b2-catenin and TCF/LEF or \u03b2-catenin and cAMP response element\u2013binding protein (CREB)\u2013binding protein (CBP), or repurposed drugs against Wnt activity have shown in vitro inhibitory effects, yet lack strong in vivo efficacy, likely due to alternative escape mechanisms9. Alternative methods of Wnt/\u03b2-catenin inactivation have investigated porcupine (PORCN), tankyrase (TNKS), or Frizzled (FZD) receptor inhibitors, however, these are ineffective and far too upstream in the pathway for treating tumors with GOF CTNNB1 mutations due to subsequent independence of Wnt/FZD receptor binding9. Thus, RNAi- or antisense-mediated gene silencing approaches have proven to be an effective therapeutic approach to reduce CTNNB1 mRNA levels in tumors. Efficacy has previously been shown by our group and others across a variety of different tumor types20,22,23,61. Our work here builds upon these previous findings and demonstrates that RNAi-mediated \u03b2-catenin inhibition via LNP for HCC results in minimal off-target effects with strong and durable on-target effects.\n\nIn summary, we propose a synergistic two-part working mechanism of response to RNAi-mediated \u03b2-catenin inhibition in preclinical CTNNB1-mutated HCC models (Fig.\u00a07m). First, response to LNP-CTNNB1 treatment includes cessation of tumor cell proliferation and concomitant metabolic zonal reprogramming with zone 3 tumor cells converting to zone 1/2 hepatocyte-like cells. Second, there is remodeling of the TIME with macrophages repolarizing towards a M1-like-phenotype, along with restored adaptive immune surveillance. This is driven by IRF2/POU2F1 re-engagement upon \u03b2-catenin knockdown, which act as mediators of enhanced IFN network signaling, suppression of PD-L1 expression, and priming of lymphocyte (T and B cell) recruitment and infiltration. All these tumor cell-intrinsic and TIME remodeling mechanisms ultimately drive enhanced efficacy of LNP-CTNNB1 to \u03b1-PD1 in advanced-stage disease. Based on our findings, RNAi-mediated inhibition of \u03b2-catenin may have the potential to provide anti-tumor effects as a monotherapy in early-stage disease or in neoadjuvant setting in patients with Wnt-\u03b2-catenin active liver tumors. These proof-of-concept studies also support the clinical investigation of RNAi therapeutic approaches targeting \u03b2-catenin in combination with ICI in advanced-stage Wnt-\u03b2-catenin active-HCC patients.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Approval for all animal husbandry and experimental procedures, including animal housing and diet, complies with all relevant ethical regulations, and performed under the guidelines and approval of the National Institutes of Health and the University of Pittsburgh School of Medicine Institutional Animal Use and Care Committee (IACUC). All FVB/NJ male mice were purchased from the Jackson Laboratory (Bar Harbor, ME). Ctnnb1f/f (on a C57Bl6 background) male and female mice were used for this study as well and purchased form the Jackson Laboratory (Bar Harbor, ME). For \u03b2-catenin-mutated HCC studies, we used 6\u20138-week-old FVB/NJ male mice. Male mice were utilized given the increased prevalence of liver cancer in males. Mice were fed a standard chow diet ad libitum, with access to water, enrichment, and exposed to 12\u2009h light/dark cycles in ventilated cages. Mice were housed to individual cages with each having 3\u20134 mice. Body weights were monitored once weekly. As tumors developed in mice over time, they were monitored for signs of morbidity, including increased abdominal distension/girth, grimace, and body weights not to exceed 20\u201325% of aged-matched controls, and were appropriately euthanized and considered moribund. Mice were fasted 4\u20136\u2009h prior to sacrifice. At time of sacrifice, gross images of the liver were recorded with a handheld camera, along with documenting the body and liver weights, and gross morphology of the mouse livers with a ruler measurement. At time of sacrifice, whole blood was drawn from inferior vena cava via insulin syringe and expunged into microcentrifuge tubes to be spun at 2500\u2009\u00d7\u2009g for 10\u201315\u2009min at room temperature to collect serum. Serum was then collected, processed, and sent to be analyzed for liver function tests, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase via UPMC Clinical Pathology Laboratories.\n\nFor mutant-\u03b2-catenin constructs, we utilized the S45Y-hCTNNB1-Myc-tag and T41A-hCTNNB1-Myc-tag plasmids, which are human constructs of the gene27,28. Additionally, we utilized the hMet-V5-tag and G31A-hNFE2L2 plasmids, which are also human constructs of the genes27,28. Both the T41A-hCTNNB1-Myc-tag and G31A-hNFE2L2 plasmids were subcloned into the pT3-EF1\u03b1h destination vector, while the S45Y-hCTNNB1-Myc-tag and hMet-V5-tag plasmids were subcloned into the pT3-EF5\u03b1h destination vector27,28. We also utilized the myr-AKT and N-RasV12 plasmids known to induce Akt/NRas HCC62. We purchased the mouse pFUW-tetO-Irf2 plasmid from Addgene (plasmid #139814) and pENTR223-POU2F1 plasmid from DNASU (plasmid # HsCD00505520). We then subcloned these plasmids into the pT3-EF1\u03b1h plasmid using Gateway PCR cloning technology (Invitrogen, Carlsbad, CA) to make a pT3-EF1\u03b1h-Irf2 and pT3-EF1\u03b1h-POU2F1 plasmids. The pCMV-pT3, pCMV-SB transposase, and pCMV-Cre plasmids have been described previously27. The plasmid constructs were purified using Endotoxin-Free Maxiprep kit (NA 0410, Sigma-Aldrich, St. Louis, MO) for eventual hydrodynamic delivery. For hydrodynamic delivery, plasmids were diluted into 0.9% normal saline (NaCl) purchased from TEKNOVA (#S5815).\n\nWe utilized the sleeping beauty-hydrodynamic tail vein injection (SB-HDTVi) model, which has been extensively described by our group20,27,28. We utilize 5 HCC mouse models in this study: (1) T41A-hCTNNB1/G31A-hNFE2L2 (\u03b2-N), (2) S45Y-hCTNNB1/hMet (\u03b2-M), (3) S45Y-hCTNNB1/G31A-hNFE2L2/hMet (\u03b2-N-M), (4) G31A-hNFE2L2/hMet (N-M), and (5) Akt/NRas models. For the \u03b2-N model, 20\u2009\u03bcg of pT3-EF1\u03b1h-T41A-hCTNNB1-Myc-tag and 20\u2009\u03bcg of NFE2L2-plasmid (pT3-EF1\u03b1h-G31A-hNFE2L2) were mixed. For the \u03b2-M model, 10\u2009\u03bcg of pT3-EF5\u03b1h-S45Y-hCTNNB1-Myc-tag and 10\u2009\u03bcg of hMET-plasmid (pT3-EF5\u03b1h-hMet-V5-tag) were mixed. For the \u03b2-N-M model, 10\u2009\u03bcg of pT3-EF5\u03b1h-S45Y-hCTNNB1-Myc-tag, 10\u2009\u03bcg of NFE2L2-plasmid (pT3-EF1\u03b1h-G31A-hNFE2L2), and 10\u2009\u03bcg of hMET-plasmid (pT3-EF5\u03b1h-hMet-V5-tag) were mixed. For the N-M model, 20\u2009\u03bcg of NFE2L2-plasmid (pT3-EF1\u03b1h-G31A-hNFE2L2) and 20\u2009\u03bcg of hMET-plasmid (pT3-EF5\u03b1h-hMet-V5-tag) were mixed. For the Akt/NRas model, 10\u2009\u03bcg pT3-EF1\u03b1-HA-myr-AKT and 10\u2009\u03bcg of pT2-Caggs-N-RasV12 were mixed with pCMV-pT3 (60\u2009\u03bcg; empty vector; solely the backbone plasmid) or pCMV-Cre plasmids (60\u2009\u03bcg) to delete endogenous genes in Ctnnb1 floxed mice background. For \u03b2-M-pT3 and \u03b2-M-IRF2 models, we mixed 20\u2009\u03bcg of pT3-EF5\u03b1h-S45Y-hCTNNB1-Myc-tag, 20\u2009\u03bcg of hMET-plasmid (pT3-EF5\u03b1h-hMet-V5-tag), and either 20\u2009\u03bcg of pCMV-pT3 or Irf2 plasmid (pT3-EF1\u03b1h-Irf2). For the \u03b2-M-pT3 and \u03b2-M-POU2F1 models, we mixed 20\u2009\u03bcg of pT3-EF5\u03b1h-S45Y-hCTNNB1-Myc-tag, 20\u2009\u03bcg of hMET-plasmid (pT3-EF5\u03b1h-hMet-V5-tag), and either 60\u2009\u03bcg of pCMV-pT3 or POU2F1 plasmid (pT3-EF1\u03b1h-POU2F1). For the \u03b2-N-pT3 and \u03b2-N-POU2F1 models, we mixed 20\u2009\u03bcg of pT3-EF1\u03b1h-T41A-hCTNNB1-Myc-tag, 20\u2009\u03bcg of Nrf2-plasmid (pT3-EF1\u03b1h-G31A-hNFE2L2), and either 60\u2009\u03bcg of pCMV-pT3 or POU2F1 plasmid (pT3-EF1\u03b1h-POU2F1). pCMV-Sleeping Beauty transposase (pCMV/SB) plasmid is mixed with each of the oncogenic plasmids at a concentration of 25:1 in 2\u2009mL normal saline (0.9% NaCl) and filtered through 0.22\u2009\u03bcm filter (Millipore) for hydrodynamic injection. For hydrodynamic injection, 2\u2009mL volume is injected in the lateral tail vein in 5\u20137\u2009s.\n\nChemically modified and functionalized siRNAs for this study were synthesized and characterized by Alnylam Pharmaceuticals63,64. The lipid nanoparticles (LNPs) containing encapsulated siRNAs were formulated and characterized using validated techniques65,66 with the biodegradable ionizable lipid bis(3-pentyloctyl) 9-((4-(dimethylamino)butanoyl)oxy)heptadecanedioate. The LNPs used in this study size range from 60 to 80\u2009nm with siRNA encapsulation efficiency of ~95% (PDI\u2009=\u20090.045) with quality control performed by Alnylam Pharmaceuticals31. LNP formulations from Alnylam Pharmaceuticals have demonstrated a rapid accumulation of the parent lipids in the liver and plasma of mice with elimination from plasma by 8-h, liver by 48-h, and spleen by 96-h post systemic administration via tail vein injection31. Biodistribution of the LNPs in mice have also been reported to accumulate primarily in the liver and spleen31. A control siRNA was synthesized which is not predicted to bind to any location in the transcriptome and the lack of activity was confirmed by RNA-sequencing (RNA-seq) analysis performed by Alnylam Pharmaceuticals as part of quality control. The siRNA targeting CTNNB1 has demonstrated ability to reduce levels of both mouse and human CTNNB1 transcripts. In this study we refer to the LNP encapsulating control siRNA as LNP-CTRL and LNP encapsulating siRNA targeting CTNNB1 transcript as LNP-CTNNB1.\n\nsiCTNNB1: sense \u2013 UACUGUUGGAUUGAUUCGAAA,\n\nantisense - UUUCGAAUCAAUCCAACAGUAGC\n\nsiControl: sense ACCCAGAUAUUUUUAUCGCGU,\n\nantisense \u2013 ACGCGAUAAAAAUAUCUGGGUCG\n\nCTNNB1-mutated patient-derived HCC organoid 23277, which has a S33F mutation in exon 3 of CTNNB1, was kindly provided by Ernesto Guccione26. These organoids were cultured and expanded in 24-well plates with tumor organoid medium26. Organoids were treated with LNP-CTRL or LNP-CTNNB1 at 20\u2009nM concentration and imaged at 48-h and 72-h post-LNP treatment. Experiments were performed in triplicate.\n\nThe therapeutic formulations of LNP-CTRL and LNP-CTNNB1 were diluted from their stock formulations into phosphate buffered saline (PBS) to reach final concentrations of either 3, 1, 0.3, 0.1, and 0.03\u2009mg/kg dosages. The PBS/LNP-CTRL/LNP-CTNNB1 formulations were administered once weekly at the indicated time points below via lateral tail vein (intravenous; I.V.) injection using insulin syringe as the delivery method. For early-stage intervention in the \u03b2-N model, dosing started at 5-weeks post-HDTVi. For early-stage intervention in the \u03b2-M model, dosing started at 3-weeks post-HDTVi. For early-stage intervention in the \u03b2-N-M model, dosing started at 3-weeks post-HDTVi. For early-stage intervention in the N-M model, dosing started at 8-weeks post-HDTVi. For late-stage intervention in the \u03b2-N model, dosing started at 8-weeks post-HDTVi. For late-stage intervention in the \u03b2-M model, dosing started at 6-weeks post-HDTVi. For combination therapy study of LNP-CTNNB1 with \u03b1-PD1 in \u03b2-M model at late-stage, mice were randomized to receive either 200\u2009\u03bcg of either IgG control antibody (BioXcell; InVivoMAb rat IgG2a isotype control; Catalog #BE0089) or \u03b1-PD1 antibody (BioXcell; InVivoMAb anti-mouse PD1 (CD279); Catalog #BE0146) starting 3-days after the 6-weeks post-HDTVi timepoint when given LNP-CTRL/LNP-CTNNB1 treatments. The animals received IgG/\u03b1-PD1 2\u00d7/week over 2-weeks. The IgG/\u03b1-PD-1treatments were given via intra-peritoneal (I.P.) injection. For IFN\u03b3 (Miltenyi; #130-105-774) treatments, mice were treated at the indicated time points at a concentration of 1\u2009\u00d7\u2009104 IU starting 3-weeks post-HDTVi. For \u03b1-CD3 immune depletion studies, \u03b2-M-pT3 or \u03b2-M-POU2F1 mice were treated with either IgG isotype control (BioXcell; InVivoMAb rat IgG2a isotype control; Catalog #BE0089) or InVivoMAb \u03b1-mouse CD3 (Catalog #BE0002, clone 17A2) at 200\u2009\u03bcg concentration at the indicated time points.\n\nAt the time of animal sacrifice, whole blood was collected via either drainage from IVC or retroorbital blood draw. Serum was processed through centrifugation at 3000\u2009\u00d7\u2009g for 10\u2009min at cold 4\u2009\u00b0C temperature (supernatant contained the serum). Liver tissue from all samples were either fixed in 10% neutralized formalin (Fisher Chemicals) at room temperature for 48\u201372\u2009h, followed by transfer into 70% ethanol, dehydration, and paraffin embedded for histology analysis, or flash frozen in liquid nitrogen and stored long-term at \u221280\u2009\u00b0C.\n\nFormalin, fixed paraffin embedded (FFPE) liver tissue was sectioned at 4\u2009\u03bcm thick slices using a standard microtome. Liver tissue sections were deparaffinized in xylene and graded ethanol followed by Hematoxylin (Fisher Chemical Harris Modified Method Hematoxylin Stains #SH26-500D) and Eosin (Eosin Y, #23-314-63) and dehydrated, mounted, and cover-slipped. Slides were imaged on Zeiss Axioskop microscope.\n\nFFPE sections were deparaffinized in xylene and graded ethanol (10%, 95%, 90%) and rinsed with PBS or water. For antigen retrieval, samples were placed in either Citrate Buffer (0.01\u2009M, pH 6.0), Tris-EDTA (0.01\u2009M, pH 9.0) or DAKO (Agilent Technologies; S236784-2) and heated in a steamer or a pressure cooker for 20\u2009min. Next, samples were placed in 3% H2O2 solution (Fisher Chemicals) for 10\u2009min to quench endogenous peroxide activity. After appropriates washing in saline, slides were blocked with Super Block (ScyTek Laboratories) for 10\u2009min to prevent non-specific antibody binding. Next, the sections were incubated with the primary antibodies diluted in PBS at 4\u2009\u00b0C overnight or at room temperature. A list of primary antibodies and the specific antigen retrieval conditions for each is provided in Supplementary Table\u00a01. This was followed by incubation with species-specific biotinylated secondary antibodies for 15\u201330\u2009min at room temperature. Next, the sections were washed with 1\u00d7 PBS and incubated with ABC reagent (Vectastain ABC Elite kit, Vector Laboratories) for 5\u201310\u2009min followed by washing with 1\u00d7 PBS. The signal was detected using DAB Peroxidase Substrate Kit (Vector Laboratories) using the positive control to determine timing of signal. Appropriate negative controls were used. Finally, slides were counterstained with hematoxylin (Thermo Fisher Scientific), dehydrated, mounted, and cover-slipped. 5\u00d7 and 10\u00d7 views were imaged on Zeiss Axioskop microscope. Whole-slide tiled images were taken with a Fritz Precipoint microscope (Nikon).\n\nFor co-staining of glutamine synthetase (GS)/Ki67 via IHC, slides were initially incubated with GS (and subsequently with species-specific biotinylated secondary antibody) and developed using ABC-AP reagent (Vectastain ABC Elite kit, Vector Laboratories) for 30\u2009min and incubated with Vector Red reagent (Vector Laboratories) and developed in the dark for 15\u2009min. On the second day, slides were incubated with Ki67 (and subsequently with biotinylated secondary antibody) and developed using ABC reagent (Vectastain ABC Elite kit, Vector Laboratories) for 10\u2009min and incubated with DAB reagent (Vector Laboratories) and developed in the light for 1\u20132\u2009min. Finally, slides were counterstained with hematoxylin (Thermo Fisher Scientific), dehydrated, mounted, and cover-slipped. 5\u00d7 and 10\u00d7 views were imaged on Zeiss Axioskop microscope (EMD Millipore). Whole-slide tiled images were taken with a Fritz Precipoint microscope (Nikon).\n\nIHC Images stained with indicated markers and quantified with %Area of indicated marker were exported as.TIFF files from the Zeiss Axioskop microscope and loaded into ImageJ. A color deconvolution plugin was downloaded (https://github.com/landinig/IJ-Colour_Deconvolution2/blob/main/colour_deconvolution2.jar) and used to deconvolute the different color channels. The brown stain channel was used to color threshold based on the intensity of the DAB staining on IHC. The same threshold parameters were used for each indicated stain comparing LNP-CTRL and LNP-CTNNB1. Raw data was exported to an Excel file and analyzed in GraphPad Prism. Technical tissue replicates were averaged across to determine the %Area for each biological replicate value. Thus, individual data points are reported as an average of the technical replicates.\n\nWhole liver tissue chunks were homogenized in TRIzol (Thermo Scientific) and RNA was extracted with RNeasy Micro Kit (Qiagen) following the manufacturer instructions. RNA was reverse transcribed into cDNA using SuperScript III (Invitrogen). cDNA was diluted in nuclease free water and stored long-term at \u221220\u2009\u00b0C.\n\nReal-time PCR was performed in technical duplicates on a StepOnePlus Real-Time PCR System (Applied Biosystems) using the Power SYBR Green PCR Master Mix (Applied Biosystems). Target gene expression was normalized to the housekeeping genes Rn18s, and fold change was calculated utilizing the \u0394\u0394-Ct method. Primers used are listed in Supplementary Table\u00a02.\n\nThis analysis was performed by Alnylam Pharmaceuticals. Whole liver was homogenized with a tissue grinder and approximately 10\u2009mg of tissue was dissolved in QIAzol (Qiagen; 217061). RNA was extracted with miRNeasy 96 kit (Qiagen; 74004) following manufacturer instructions. RNA was reverse transcribed into complementary DNA (cDNA) using Taqman advanced fast polymerase chain reaction (PCR) mix (Thermo Scientific; 4374967). Real-time PCR was performed in technical duplicate on a Quantstudio quantitative PCR (qPCR) System (Thermo Scientific; A28135) using the Taqman advanced fast PCR mix (Thermo Fisher; 4444557). Target gene expression was normalized to housekeeping gene GAPDH, and fold change was calculated utilizing the \u0394\u0394-Ct method. Primers used are listed in Supplementary Table\u00a03.\n\nLiver tissue was weighed and homogenized in ice cold, sterile filtered 1\u00d7 phosphate buffered saline (PBS), pH 7.4 (10\u2009\u00b5L per mg tissue). Lysate was incubated on ice for 4\u2009h and centrifuged 2500\u2009\u00d7\u2009g for 5\u2009min at 4\u2009\u00b0C. Supernatant was collected and used directly. IFN-\u03b3 was measured in liver lysate following manufacturers instructions (DY485, R&D Systems, Minneapolis, MN). Briefly, Microplates (96 wells) were coated with working dilution of capture antibody overnight and blocked with 1% bovine serum albumin (BSA) diluted in sterile filtered 1\u00d7 PBS pH 7.4. Samples and detection antibody were incubated for 2\u2009h at room temperature with slight agitation. Working concentration of streptavidin-horse radish peroxidase (HRP) and color substrate mix (DY999, R&D Systems, Minneapolis, MN) were incubated at room temperature for 20\u2009min with slight agitation. Plate was washed with 1\u00d7 PBS pH 7. 4 with 0.05% Tween-20 after all steps except color substrate incubation. Product development was stopped with 1\u2009N HCl and absorbance was measured at 450 and 540\u2009nm on BioTek Synergy XTS Microplate Reader (Agilent Technologies, Santa Clara, CA). IFN-\u03b3 levels were calculated following manufacturer recommendation and converted into pg IFN-\u03b3 per mg of liver tissue.\n\nIn vivo MRI was performed using a Bruker AV3HD 9.4\u2009T/30\u2009cm scanner, equipped with a BGA-12S HP gradient insert and a 40\u2009mm transmit/receive coil, running ParaVision 6.0.1 (Bruker BioSpin, Billerica MA). Mice were anesthetized using 1\u20131.5% Isoflurane via a nose cone, with respiration continuously monitored and temperature maintained at 37\u2009\u00b0C with warm air (SA Instruments, Stoney Brook, NY) during image acquisition. Following positioning and pilot scans, T2-weighted axial images of the liver were acquired using a Rapid Acquisition with Relaxation Enhancement (RARE) sequence, with the following parameters: Echo Time/Repetition Time (TE/TR)\u2009=\u200920/4000\u2009ms, averages\u2009=\u200912, a field of view (FOV) of 36\u2009\u00d7\u200924\u2009mm, 192\u2009\u00d7\u2009128 matrix, 59 slices with a 0.5\u2009mm slice thickness and a RARE factor\u2009=\u20098. The T2-weighted images were visualized using DSIstudio (https://dsi-studio.labsolver.org).\n\nStandard pipelines for transcriptome sequencing, quality control, data pre-processing, and mapping to mouse reference transcriptome were performed28. There were 5 different mouse whole transcriptome datasets analyzed in this study. First, RNA-seq analysis was performed on n\u2009=\u20093 PBS vehicle, n\u2009=\u20093 LNP-CTRL, and n\u2009=\u20093 LNP-CTNNB1 at 3\u2009mg/kg dosage treated animals from the \u03b2-N model after 4 LNP dosages. The RNA-seq data for this analysis is deposited to Gene Expression Omnibus (GEO) under accession number: GSE290449. Second, RNA-seq analysis was performed on n\u2009=\u20093 LNP-CTRL and n\u2009=\u20093\u20134 LNP-CTNNB1 at 1\u2009mg/kg dosage treated animals from each \u03b2-catenin-mutated HCC model (\u03b2-N & \u03b2-M models) after 3-days post-treatment. The RNA-seq data for this analysis is deposited to GEO under accession number: GSE270414. Third, RNA-seq analysis was performed on publicly available transcriptomic data from \u03b2-catenin-mutated HCC mouse models (GSE125336) and microarray gene expression data \u03b2-catenin knockout mouse livers (GSE68779). Fourth, RNA-seq was performed on liver tumors from \u03b2-M-pT3 (n\u2009=\u20092) and \u03b2-M-IRF2 (n\u2009=\u20094) animals. The RNA-seq data for this analysis is deposited to GEO under accession number: GSE270415. Fifth, RNA-seq was performed on liver tumors from \u03b2-M-pT3 (n\u2009=\u20093) and \u03b2-M-POU2F1 (n\u2009=\u20093) animals. The RNA-seq data for this analysis is deposited to GEO under accession number: GSE290444. For each of these different transcriptomic datasets, raw FASTQ files were first preprocessed by FastQC for quality control, Trimmomatic for filtering out low-quality reads67, and STAR for reads alignment and gene count quantification68. After pre-processing data normalization was performed followed by principal component analysis (PCA). Then, based on the gene count data, differential gene expression analysis was performed using the R package DEseq269 to identify differentially expressed genes (DEGs). DEGs (upregulated and downregulated) were selected based on filtering criteria of absolute fold change \u22651.5 and FDR\u2009=\u20090.05. Volcano plots were used to depict upregulated and downregulated genes using EnhancedVolcano package in R. Gene set enrichment analysis (GSEA) with clusterProfiler R package using ranked genes was performed using Gene Ontology: Biological Processes (GO:BP) or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways from Molecular Signatures Database (MsigDB). To discover regulatory transcription factors (TFs) of common differentially expressed genes, an enrichment test was performed to select TFs with known downstream regulatory genes which overlapped with the common differentially expressed genes using the JASPAR database70,71. Heatmap of normalized expression values for genes was used to depict expression of individual genes between relative conditions. Waterfall plot was used to demonstrate positive or negative enrichment of certain pathways colored by p-value for each pathway.\n\nCells from the liver were isolated using a two-step collagenase perfusion72. Under anesthesia, a 27.5\u201d catheter was used to circulate 0.3\u2009mg/mL of collagenase II (Worthington, Lakewood, NJ) through the liver via the inferior vena cava. Livers were then collected in Dulbecco\u2019s modified Eagle\u2019s medium/F12 supplemented with 15\u2009mM of HEPES (Corning, Corning, NY)\u2009+\u20095% fetal bovine serum (FBS; Biowest, Bradenton, FL). After manual digestion and agitation, cell suspensions were passed through a 100- then 70-\u03bcm cell strainer and washed twice using low-speed centrifugation (50\u2009\u00d7\u2009g for 5\u2009min) to separate out nonparenchymal cells. Hepatocyte viability, determined by trypan blue staining, was typically >80%.\n\nHepatic lymphocytes and monocytes were enriched from the nonparenchymal fraction using a Percoll gradient combined with centrifugation73. Briefly, the nonparenchymal fraction from liver perfusions were centrifuged (805\u2009\u00d7\u2009g for 10\u2009min) followed by 36% Percoll separation in Roswell Park Memorial Institute 1640 medium supplemented with +5% FBS. Erythrocytes were removed using Ammonium-Chloride-Potassium Lysing Buffer (Thermo Fisher Scientific). Isolated cells were counted for viability, which was typically >90%, using trypan blue.\n\nEnriched lymphocytes from liver perfusions were stained with fluorescent-conjugated antibodies (Supplementary Table\u00a04) and a Fixable Dead Cell Stain (Thermo Fisher Scientific). Data was acquired using Cytek Aurora (Cytek Biosciences, Bethesda, MD) equipped with five lasers and analyzed using FlowJo software v10.1 (Treestar, Ashland, OR).\n\nHepatocytes isolated and purified from whole liver perfusions were sequenced along with the enriched nonparenchymal cell fraction (mostly lymphocytes/monocytes) were used for tandem scRNA-sequencing coupled with immune profiling separately. For the hepatocyte-enriched cell population sequenced (GEX; GSE270714), cells isolated from 2\u20133 individual animals were pooled together in each treatment condition and sequenced in separate wells on the flow cell. For the immune-enriched population (scRNA-seq with immune profiling; GSE270974), cells isolated from 6 individual animals (3 LNP-CTRL and 3 LNP-CTNNB1) were feature barcoded, hashed, and then pooled to be sequenced in one well on the flow cell. Specifically, the nonparenchymal cell fraction was feature barcoded and marked using TotalSeq\u2122-C Mouse Universal Cocktail (Biolegend, San Diego, CA) following manufacturer\u2019s recommendations, along with BCR and TCR library preparation. At minimum 10,000 hepatocytes were sequenced (non-multiplexed wells) and 30,000 immune cells (multiplexed wells) were sequenced with at least 20,000 reads per cell in the non-multiplexed wells and 5000 reads per cell in the multiplexed wells. Multi-plexing was performed according to standard CellPlex protocols. Single-cell sequencing was performed on NovaSeq 2000 instrument and library prep with 5PrimeV2 was performed according to standard protocols. The raw FASTQ files for the pooled library were first processed using CellRanger for alignment against mm10 mouse genome using standard CellRanger multi pipeline with the 5p hashtag demultiplexing configuration file. Then, the demultiplexed aligned BAM files were re-formatted into FASTQ files per sample. Lastly, the CellRanger multi was applied to individual samples to align the data to mouse genome by integrating multi-omics data: gene expression, antibody capture, VDJ-T and VDJ-B. Ultimately, we generated the gene count matrices on a per-sample and per-cell basis for downstream analysis.\n\nWe used standard Seurat74 pipelines for quality control of the data at cell and gene level. We made sure to not include empty cells, doublets/multiplets, or dead/dying cells. This was determined by using nFeature_RNA\u2009>\u2009500 & nFeature_RNA\u2009<\u20092000, nCount_RNA\u2009\u2265\u20090 & nCount_RNA\u2009<\u200910,000, and percent.mt\u2009\u2265\u20090 & percent.mt <10 for analysis of the GEX dataset (GSE270714); and, nFeature_RNA\u2009>\u2009200 & nFeature_RNA\u2009<\u20095000, nCount_RNA\u2009\u2265\u20090 & nCount_RNA\u2009<\u200912,000, and percent.mt\u2009\u2265\u20090 & percent.mt <10 for analysis of the scRNA-seq with immune profiling dataset (GSE270974). Quality control was visualized with violin plots of each of these parameters. After this step, we resulted in 26,851 high-quality cells from LNP-CTRL and 67,799 high-quality cells from LNP-CTNNB1 treatment for GEX dataset (GSE270714). Additionally, we resulted in 8499 high-quality cells from LNP-CTRL and 11,736 high-quality cells from LNP-CTNNB1 treatment for scRNA-seq with immune profiling dataset (GSE270974).\n\nFor each of the matrices, we integrated the LNP-CTRL and LNP-CTNNB1 datasets together using standard Seurat v4 pipeline of SelectIntegrationFeatures, PrepSCTIntegration, FindIntegrationAnchors, and IntegrateData using n\u2009=\u20093000 features. Then, dimensionality reduction method of principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) was performed75. Initial cluster calls were generated using FindNeighbors and FindClusters function with dims\u2009=\u20091:30 and resolution\u2009=\u20090.2. Then, the dataset was swapped to RNA assay for visuals and variable features were identified, along with normalization and scaling of the data using FindVariableFeatures, NormalizeData, and ScaleData functions. Red blood cell contamination was inspected via Feature plots and Violin plots of Hba-a1 and Hbb-bs genes. To identify specific cell types, present in each cluster, the FindAllMarkers function was used to determine differentially expressed genes per cluster compared to all other clusters. This identified 16 clusters in total for the hepatocyte-enriched dataset and 22 clusters in total for the immune cell-enriched dataset. We utilized the marker genes for different cell types present in Supplementary Table\u00a05 and Supplementary Table\u00a06, which is based on the literature and manual curation36. Clusters were then annotated and cell-type proportions per treatment condition were determined for each of the two datasets and visualized with either pie chart or stacked bar graphs.\n\nDifferential gene expression analysis, comparing each cluster to all other clusters, was performed on the integrated datasets for each cell type/cluster using the Seurat FindMarkers function to identify genes differentially expressed for each cell type irrespective of treatment condition. Differential gene expression within each cluster between treatment conditions was performed using the Seurat FindMarkers function and renaming the idents such that cells from each treatment condition were a different ident in the comparison. Differentially expressed genes (DEGs) were identified based on cut-off thresholds of adjusted p-value of <0.05 and absolute log2 Fold Change >0.25, or more stringent cutoff used in case of many DEGs. We utilized volcano plots to illustrate specific DEGs using the EnhancedVolcano R package with p-value cutoff of p\u2009=\u20090.05. Pathway enrichment analysis was performed using clusterProlifer R package using the DEGs identified and referencing GO:BP or KEGG pathways from MSigDB. Top pathways were visualized using dot plot function in clusterProlifer R package.\n\nCell cycle scoring was performed on the hepatocyte-enriched dataset using Seurat CellCycleScoring function. The expression matrix was retrieved from the source vignette and the S phase and G2M phase genes were extracted and converted to mouse orthologs from human genes. Cells that are not in S or G2M phase are classified as G1 phase. Pie charts were used to visualize distribution of percentage of cells in each phase of the cell cycle for each cluster.\n\nPseudotime trajectory analysis was performed on the integrated hepatocyte-enriched dataset following subsetting for only the normal hepatocyte or tumor hepatocyte clusters (Zone 3 CTNNB1 WT and MUT (GS+), Zone 1/2 CTNNB1 MUT (GS+), Zone 1 CTNNB1 WT (GS\u2212), Reprogrammed Hepatocytes) using the monocle3 R package and standard pipelines76. First, a Cell Data Set (CDS) object was created, which transferred the UMAP cluster labels and cell metadata. Second, pseudotime analysis was performed and included calculations of potential cell trajectories using the published algorithm. The root node was chosen as the Zone 3 CTNNB1 WT and MUT (GS+) cell cluster. The results were visualized using the plot_cells function.\n\nCell-to-cell interaction analysis on the immune cell-enriched population was performed using the CellChat V1 R package37. This package allows for analysis and inference of cell-to-cell communication using relative ligand-receptor expression. For visualization of specific communication pathways (i.e., ligand-receptor pairs), we used all the standard pipelines built in the CellChat R package37.\n\nMouse liver tissue was preserved frozen using OCT media following submersion in cold 2-methyl-butane. Frozen mouse liver tissue was sectioned with a cryostat (Leica, CM 1850-3-1) at a temperature of \u221217\u2009\u00b0C. Tissue sections were 10\u03bcm thick and placed on the Resolve Biosciences slide also cold at \u221217\u2009\u00b0C. Following sectioning and placement, the slide was stored at \u221280\u2009\u00b0C overnight, then shipped to Resolve BioSciences on dry ice for analysis. Probes were devised using Resolve BioSciences\u2019 proprietary algorithm77. The 100-gene panel used in this study is described in Supplementary Table\u00a07. Resolve BioSciences performed the tissue fixation, 100-plex priming, hybridization, slide imaging, and fluorescent-tagging using their standard pipelines77. Then, regions of interest were imaged with fluorescent signals removed during a decolorization step to achieve a unique combinatorial code for each target gene in the panel77. The final images of the Molecular CartographyTM signals could then be viewed on Resolve BioSciences website which has a plugin tool for viewing the signals of each of the 100 genes at different magnification scales. We used tissue from one animal per LNP treatment group. Ultimately, 5-6 regions of interest (ROIs) were selected per tissue section for analysis. Single-cell spatial transcriptomic analysis was performed by quantifying gene counts per cell using automatic cell segmentation via QuPath software78. The libraries from each treatment condition (LNP-CTRL and LNP-CTNNB1) were then integrated together using the R package Seurat74,79. Analyzed cells included those filtered for greater than or equal to 10 gene counts per cell. Following these quality control steps, we performed unbiased clustering on all 100 genes using the dimensionality reduction method of principal component analysis (PCA) and uniform manifold approximation and projection (UMAP)75. Clusters corresponding to different cell types, including stellate, endothelial, and immune cells, and zones were identified and annotated from the UMAP based on expression of different landmark genes described in Supplementary Table\u00a0877. The cluster proportions per treatment condition were depicted as pie charts or stacked bar plots.\n\nNext, using Seurat, feature plots and violin plots were used to visualize gene expression across different clusters and between treatment conditions. Additionally, dot plots were used to visualize expression of specific genes within each cluster in terms of number of cells expressing that gene in each cluster and the expression level. Normalized expression values of each of the 100 genes were also visualized with heatmap across each of the different clusters. Lastly, once clusters were annotated using Supplementary Table\u00a08 landmark genes, cells corresponding to these specific clusters were mapped back onto the virtual slide using SpatialPlot function to visualize spatial location of specific clusters using a different color for each cluster, which corresponded to the same cluster color on the UMAP plot. Moreover, pseudotime trajectory analysis to define cell trajectories and cell states was performed on the integrated dataset using all clusters with the monocle3 R package76. The same pipelines for this were performed as described above in the single-cell RNA-sequencing dataset. The root node was chosen as the H1: Zone 3 CTNNB1 MUT (GS+) cell cluster. Lastly, within each ROI, tumor nodules were outlined based on the expression of Glul as a landmark gene for \u03b2-catenin-mutated tumor nodules. Then, gene counts and expression density (gene counts per area) were quantified within each tumor boundary and averaged across the defined regions (tumor or non-tumor). This was also performed on a cell basis to determine cell density per region (tumor or non-tumor). The single-cell spatial transcriptomic data from this analysis is deposited to Gene Expression Omnibus (GEO) under accession number: GSE270708.\n\nTissue sections of 10\u2009\u03bcm thickness were cut from mouse liver tumor FFPE blocks and were deparaffinized, H&E stained, then decrosslinked using standard 10\u00d7 genomics protocols. Regions of interest (ROIs) were selected by outlining ~6.5\u2009mm2 areas. Standard Visium CytAssist protocols were used for FFPE tissue for generation of sequence ready libraries. Quality control parameters were a DV200% \u226530%. For 10\u00d7 Visium spatial transcriptomics bioinformatic analysis, sequencing reads from 55\u2009\u00b5m tissue regions (spots) were first preprocessed by 10\u00d7 Space Ranger software for alignment and spatial gene count quantification. The processed data were then imported into R using the package Seurat74. For the \u03b2-M model, spots across 4 samples (\u03b2-M LNP-CTRL [BL-131], 2 \u03b2-M LNP-CTNNB1 NR [BL-146, BL-147], and 1 \u03b2-M LNP-CTNNB1 R [BL-152]) were normalized and integrated. For the \u03b2-N model, spots across 5 samples (\u03b2-N LNP-CTRL [BL-551], 2 \u03b2-N LNP-CTNNB1 NR [BL-584, BL-587], and 2 \u03b2-N LNP-CTNNB1 R [BL-585, BL-586]) were normalized and integrated. For both datasets, following normalization and integration, dimensionality reduction via principal component analysis and uniform manifold approximation was performed. K-means clustering identified 17 clusters in the \u03b2-M model and 13 clusters in the \u03b2-N model, which were overlaid on hematoxylin and eosin sections using the SpatialPlot function in Seurat. Differential gene expression was used to identify up- and downregulated genes in each cluster using the FindAllMarkers function in Seurat with return.thresh\u2009=\u20090.05. We utilized volcano plots to illustrate specific DEGs (defined by adjusted p-value\u2009<\u200910e-32 and absolute log2FC\u2009>\u20090.5) using the EnhancedVolcano R package. Cell-type composition in each cluster was inferred based on the expression of marker genes in Supplementary Table\u00a09 from literature and manual curation36. Marker genes were visualized in dot plot format across clusters and by treatment condition. Gene set enrichment analysis was performed with the clusterProfiler R package80 using ordered ranked gene lists with gene sets from GO:BP from MSigDB collection81, with a p-value threshold of 0.05. Pathway enrichment analysis was performed with the clusterProfiler R package80 using DEGs (defined by adjusted p-value\u2009<\u20090.05 and absolute log2FC\u2009>\u20090.25) and gene sets from GO:BP and KEGG from MSigDB. Upstream regulator analysis was performed using Qiagen software with the DEGs from each cluster. Cell cycle regression and pseudotime analysis were performed as described in the \u201cSingle-cell RNA-sequencing and analysis\u201d section. Version 2 of the CellChat R package82 was used to determine probabilities of spatial cell-cell communication between clusters, based on expression of ligands and receptors in spatially proximal spots. The 10\u00d7 Visium transcriptomic data from this analysis is deposited to Gene Expression Omnibus (GEO) under accession number GSE270997 (subseries: GSE270975 and GSE290445).\n\nThe Cancer Genome Atlas (TCGA) RNA-seq whole exome sequencing and transcriptomic data were downloaded from Genomic Data Commons (GDC) through the R Bioconductor package GenomicDataCommons. We selected patients from TCGA with Liver Hepatocellular Carcinoma (TCGA-LIHC) for analysis. This dataset contains 424 cases with 50 of these being adjacent normal liver tissue. Raw gene counts were normalized and used for downstream analyses. We identified that 98 out of the 374 TCGA-LIHC patients had CTNNB1 mutations, 18 had AXIN1 mutations, and 3 had APC mutations. Differential gene expression was performed to identify pathways differentially regulated between CTNNB1-mutated and normal patients and CTNNB1-mutated and CTNNB1-wild-type patients using gene set enrichment analysis with GO_BP pathways from MsigDB. IRF2 and POU2F1 target genes plotted in the heatmap are provided in Supplementary Table\u00a010. The 33 IRF2 and 10 POU2F1 target genes which were intersected for common overlap and plotted (39 common genes). The 39 genes identified were inferred IRF2/POU2F1 target genes retrieved from https://maayanlab.cloud/chea3/ and which overlapped with the 162 common genes which were upregulated in \u03b2-catenin knockout livers and downregulated in \u03b2-catenin-mutated HCC mouse livers. A heatmap of z-scored values was generated to overlap patients with and without tumor, and with and without CTNNB1, AXIN1, or APC mutations, and the relative normalized expression scores of IRF2/POU2F1 target genes. Boxplots were used to visualize expression of IRF2, POU2F1, or a module score of the IRF2/POU2F1 target genes. Module score was calculated based on normalized mean expression of the common 39 target genes. Survival data was downloaded directly from cBioPortal website and integrated with the expression data.\n\nWe retrospectively analyzed genomic and transcriptomic data (Whole Exome Sequencing and RNA-seq data) from the IMbrave150 trial6 and associated this data with CTNNB1 mutational status, tertiary lymphoid structure/lymphoid aggregate (TLS/LA)-like signature expression score, TLS/LA/diffuse infiltrate (DI) presence, and clinical parameters (overall and progression-free survival [OS/PFS] and clinical response using mRECIST criteria). First, TLS/LA/DI/ presence was assessed by Genentech Pathologist (author H.K.) and associated with clinical response based on mRECIST criteria (CR/PR, SD/PD, or NA)6. Second, TLS/LA/DI presence was associated with PFS and OS in each treatment arm (atezolizumab plus bevacizumab or sorafenib). Third, a previously described B and CD4 Tconv and CD4 Tfh cell signature expression score was merged and overlapped with genes sequenced in TCGA-LIHC, resulting in 140 genes, which was associated with TLS/LA/DI presence, clinical response based on mRECIST criteria, and CTNNB1 mutational status45. The source data has been previously deposited in the European Genome-Phenome Archive under accession no. EGAS00001005503.\n\nSingle-cell data from both human and mouse livers were accessed from the LiverCellAtlas website directly (https://www.livercellatlas.org/). The human data was accessed here and images of the UMAP plots were taken (https://www.livercellatlas.org/umap-humanAll.php). The mouse data was accessed here and images of the UMAP plots were taken (https://www.livercellatlas.org/umap-ststmouseAll.php). Expression of IRF2/Irf2 and POU2F1/Pou2f1 were queried in these datasets to investigate cell-type specific expression of these transcription factors in normal human/mouse liver.\n\nThe data presented throughout the manuscript is represented as mean\u2009\u00b1\u2009standard deviation (SD) for each bar plot. The indicated statistical tests were performed in either R or Prism 10.3.1 software (GraphPad Software Inc., Boston, Massachusetts, USA, www.graphpad.com). For our study, P\u2009<\u20090.05 was considered statistically significant (*p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, ****p\u2009<\u20090.0001).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "For high-throughput multi-omics data, all the raw and processed files are uploaded to Gene Expression Omnibus (GEO) with accession ID GSE270977. The bulk RNA-seq CTNNB1/NFE2L2 data, CTNNB1/hMet/NFE2L2 data, CTNNB1/MET/IRF2 data, and CTNNB1/NRF2/POU2F1 data can be downloaded by GSE290449, GSE270414, GSE270415, and GSE290444, respectively; the single-cell spatial transcriptomics data by Resolve Biosciences Molecular Cartography can be accessed by GSE270708; the single-cell RNA-seq GEX and the single-cell coupled with hashtag immune profiling data can be downloaded by GSE270714 and GSE270974, respectively; and, the spatial transcriptomics data by 10\u00d7 Visium platform can accessed by GSE270975 (\u03b2-M model) and GSE290445 (\u03b2-N model). All clinical, raw RNA-seq and WES data for the IMbrave150 trial are deposited in the European Genome-Phenome Archive under accession no. EGAS00001005503. Qualified researchers may request access to individual patient-level data through the clinical study data request platform (https://vivli.org/). The remaining data are available within the Source Data file. All key resources used are provided in Supplementary Table\u00a011. Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "No custom code was generated for this manuscript.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 71, 209\u2013249 (2021).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nCheng, A. L. et al. Updated efficacy and safety data from IMbrave150: Atezolizumab plus bevacizumab vs. sorafenib for unresectable hepatocellular carcinoma. J. Hepatol. 76, 862\u2013873 (2022).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nSangro, B. et al. 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This work was also funded in part by T32EB001026 to B.M.L. and T.Y. This work was also funded in part by F30CA284540 to B.M.L. This work was also supported in part by the University of Pittsburgh Center for Research Computing through the resources provided and by NIH grant P30DK120531 to Pittsburgh Liver Research Center (PLRC) for services provided by the Genomics and Systems Biology Core. This work was also supported in part by UPMC Hillman Cancer Center Core grants P30CA047904 and UM1CA186690 to J.J.L.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Junyan Tao, Satdarshan P. Monga.\n\nOrgan Pathobiology and Therapeutics Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA\n\nBrandon M. Lehrich,\u00a0Tyler M. Yasaka,\u00a0Silvia Liu,\u00a0Catherine Cao,\u00a0Yuqing Liu,\u00a0Sucha Singh,\u00a0Vik Meadows,\u00a0Jia-Jun Liu,\u00a0Anya Singh-Varma,\u00a0Yekaterina Krutsenko,\u00a0Minakshi Poddar,\u00a0Ravi P. Rai,\u00a0Panari Patel,\u00a0Madeline Riley,\u00a0Aaron Bell,\u00a0Reben Raeman,\u00a0Junyan Tao\u00a0&\u00a0Satdarshan P. Monga\n\nDepartment of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA\n\nBrandon M. Lehrich,\u00a0Tyler M. Yasaka,\u00a0Silvia Liu,\u00a0Sucha Singh,\u00a0Vik Meadows,\u00a0Jia-Jun Liu,\u00a0Minakshi Poddar,\u00a0Ravi P. Rai,\u00a0Panari Patel,\u00a0Madeline Riley,\u00a0Aaron Bell,\u00a0Reben Raeman,\u00a0Junyan Tao\u00a0&\u00a0Satdarshan P. Monga\n\nPittsburgh Liver Research Center, University of Pittsburgh and University of Pittsburgh Medical Center, Pittsburgh, PA, USA\n\nBrandon M. Lehrich,\u00a0Evan R. Delgado,\u00a0Tyler M. Yasaka,\u00a0Silvia Liu,\u00a0Mohammad N. Taheri,\u00a0Vik Meadows,\u00a0Jia-Jun Liu,\u00a0Ravi P. Rai,\u00a0Aaron Bell,\u00a0Reben Raeman,\u00a0Mo R. Ebrahimkhani,\u00a0Junyan Tao\u00a0&\u00a0Satdarshan P. Monga\n\nMedical Scientist Training Program, University of Pittsburgh, Pittsburgh, PA, USA\n\nBrandon M. Lehrich\u00a0&\u00a0Tyler M. Yasaka\n\nDepartment of Pathology, Division of Experimental Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA\n\nEvan R. Delgado,\u00a0Mohammad N. Taheri\u00a0&\u00a0Mo R. Ebrahimkhani\n\nDepartment of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA\n\nMohammad N. Taheri\u00a0&\u00a0Mo R. Ebrahimkhani\n\nTranslational Medicine, Genentech Inc., San Francisco, CA, USA\n\nXiangnan Guan,\u00a0Hartmut Koeppen\u00a0&\u00a0Yulei Wang\n\nUniversity of Pittsburgh School of Medicine, Pittsburgh, PA, USA\n\nT. Kevin Hitchens\u00a0&\u00a0Lesley M. Foley\n\nHepatic Surgery Center, Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China\n\nBinyong Liang\n\nDepartment of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA\n\nAlex Rialdi,\u00a0Ernesto Guccione\u00a0&\u00a0Amaia Lujambio\n\nAlnylam Pharmaceuticals, Boston, MA, USA\n\nTulin Dadali,\u00a0Martin Maier\u00a0&\u00a0Wendy Broom\n\nUPMC Hillman Cancer Center and University of Pittsburgh, Pittsburgh, PA, USA\n\nJason J. Luke\n\nCancer Biology Program, University of Hawaii Cancer Center, Honolulu, HI, USA\n\nXin Chen\n\nDivision of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA\n\nSatdarshan P. Monga\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: B.M.L. and S.P.M.; Methodology: B.M.L., E.R.D., T.M.Y., S.L., M.T., M.M., M.R.E., Y.W., W.B., J.T. and S.P.M.; Software: B.M.L., T.M.Y., S.L. and J.-J.L.; Formal Analysis: B.M.L., E.R.D., T.M.Y., S.L., X.G., H.K., T.D., Y.W., W.B., J.T. and S.P.M.; Investigation: B.M.L., E.R.D., T.M.Y., S.L., C.C., Y.L., M.T., X.G., H.K., S.S., V.M., J.-J.L., A.S-V., Y.K., M.P., T.K.H., L.M.F., B.L., A.R., R.P.R., P.P., M.R., A.B., R.R., T.D., M.R.E., J.T. and S.P.M.; Resources: T.D., E.G., M.R.E., X.C., M.M., Y.W., W.B., J.T. and S.P.M.; Writing\u2014Original Draft: B.M.L.; Writing\u2014Review and Editing: T.D., J.J.L., A.L., X.C., M.M., Y.W., W.B. and S.P.M.; Visualization: B.M.L., E.R.D., T.M.Y., S.L. and M.T.; Supervision: J.T. and S.P.M.; Project Administration: S.P.M.; Funding Acquisition: B.M.L. and S.P.M.\n\nCorrespondence to\n Junyan Tao or Satdarshan P. Monga.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "S.P.M. has received research grants from Alnylam Pharmaceuticals. He also received funding from Fog Pharmaceuticals and is a consultant on Advisory Boards for Surrozen, AntlerA, Alnylam, Mermaid Bio, Vicero Inc., and UbiquiTx, and there is no pertinent conflict of interest of these entities as relevant to the current manuscript. T.D., M.M., and W.B. are employed by Alnylam Pharmaceuticals, Cambridge, MA. X.G., H.K., and Y.G. are employed by Genentech Inc., San Francisco, CA. No other authors have any relevant conflicts of interests to declare regarding the current study.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Dan Duda and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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Precision targeting of \u03b2-catenin induces tumor reprogramming and immunity in hepatocellular cancers.\n Nat Commun 16, 5009 (2025). https://doi.org/10.1038/s41467-025-60457-2\n\nDownload citation\n\nReceived: 09 December 2024\n\nAccepted: 21 May 2025\n\nPublished: 30 May 2025\n\nVersion of record: 30 May 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60457-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"title": "VolcanoSV enables accurate and robust structural variant calling in diploid genomes from single-molecule long read sequencing", + "pre_title": "VolcanoSV enables accurate and robust structural variant calling in diploid genomes from single-molecule long read sequencing", + "journal": "Nature Communications", + "published": "13 August 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51282-0/MediaObjects/41467_2024_51282_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51282-0/MediaObjects/41467_2024_51282_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51282-0/MediaObjects/41467_2024_51282_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51282-0/MediaObjects/41467_2024_51282_MOESM4_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-51282-0#Tab1", + "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/NIST_SVs_Integration_v0.6/", + "https://github.com/marbl/CHM13", + "https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/v3.4/", + "https://doi.org/10.5281/zenodo.10456757", + "/articles/s41467-024-51282-0#Sec33" + ], + "code": [ + "https://github.com/maiziezhoulab/VolcanoSV", + "/articles/s41467-024-51282-0#ref-CR56" + ], + "subject": [ + "Genome informatics", + "Software" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3843810/v1.pdf?c=1723633664000", + "research_square_link": "https://www.researchsquare.com//article/rs-3843810/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-51282-0.pdf", + "preprint_posted": "12 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Structural variants (SVs) significantly contribute to human genome diversity and play a crucial role in precision medicine. Although advancements in single-molecule long-read sequencing offer a groundbreaking resource for SV detection, identifying SV breakpoints and sequences accurately and robustly remains challenging. We introduce VolcanoSV, an innovative hybrid SV detection pipeline that utilizes both a reference genome and local de novo assembly to generate a phased diploid assembly. VolcanoSV uses phased SNPs and unique k-mer similarity analysis, enabling precise haplotype-resolved SV discovery. VolcanoSV is adept at constructing comprehensive genetic maps, encompassing SNPs, small indels, and all types of SVs, making it well-suited for human genomics studies. Our extensive experiments demonstrate that VolcanoSV surpasses state-of-the-art assembly-based tools in the detection of insertion and deletion SVs, exhibiting superior recall, precision, F1 scores, and genotype accuracy across a diverse range of datasets, including low-coverage (10x) datasets. Additionally, VolcanoSV outperforms assembly-based tools in the identification of complex SVs, including translocations, duplications, and inversions, in both simulated and real cancer data. In comparison to existing alignment-based tools, VolcanoSV is robust to evaluation parameters and accurately identifies breakpoints and SV sequences.Biological sciences/Computational biology and bioinformatics/Genome informaticsBiological sciences/Computational biology and bioinformatics/Softwarelong-read sequencingdiploid assemblystructural variantsSNPssmall indelsPacBioNanopore", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "VolcanoSV20240107SI.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Structural variants (SVs) significantly contribute to human genome diversity and play a crucial role in precision medicine. Although advancements in single-molecule long-read sequencing offer a groundbreaking resource for SV detection, identifying SV breakpoints and sequences accurately and robustly remains challenging. We introduce VolcanoSV, an innovative hybrid SV detection pipeline that utilizes both a reference genome and local de novo assembly to generate a phased diploid assembly. VolcanoSV uses phased SNPs and unique k-mer similarity analysis, enabling precise haplotype-resolved SV discovery. VolcanoSV is adept at constructing comprehensive genetic maps encompassing SNPs, small indels, and all types of SVs, making it well-suited for human genomics studies. Our extensive experiments demonstrate that VolcanoSV surpasses state-of-the-art assembly-based tools in the detection of insertion and deletion SVs, exhibiting superior recall, precision, F1 scores, and genotype accuracy across a diverse range of datasets, including low-coverage (10x) datasets. VolcanoSV outperforms assembly-based tools in the identification of complex SVs, including translocations, duplications, and inversions, in both simulated and real cancer data. Moreover, VolcanoSV is robust to various evaluation parameters and accurately identifies breakpoints and SV sequences.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Structural variants (SVs) refer to alterations in the genomic DNA that are greater than 50 base pairs (bp), encompassing insertions, deletions, translocations, duplications, and inversions1. Even though the absolute number of SVs in a typical human genome is less than small variants like single nucleotide polymorphism (SNPs), due to their large size, SVs impact more base pairs than all single-nucleotide differences in the human genome2. Consequently, SVs have a significant impact on individual phenotypes and diseases3,4,5, such as Parkinson\u2019s diseases6. Moreover, a lot of cancers are proven to be related to somatic SVs7,8,9,10,11,12. Therefore, the accurate and comprehensive characterization of SVs is of great clinical significance. However, SV characterization still remains one of the least resolved problems in genomics13.\n\nBefore the advent of long-read sequencing technologies, SV detection methods primarily relied on short-read sequencing (next-generation sequencing). These methods often depended on discordant read pairs, read depth analysis, split-read alignments, and de novo assembly14. However, due to the limited length of short reads and the absence of extensive genomic context, SV detection using short reads typically struggled to achieve high recall or precision, particularly for large insertions.\n\nThe introduction of single-molecule sequencing, exemplified by PacBio and Nanopore, has been an important development for improving SV detection15. Long reads, with average lengths ranging from 10 to 20 kilobases (kb), offer the capability to readily span medium to large SVs without the need for complex assembly processes. This enables the detection of SVs by analyzing mapping information alone. The drawback of traditional long reads is their error-prone sequencing, i.e., the error rate of traditional long reads ranges from 10% to 20%. Hence, error correction and false discovery control are necessary for tools using traditional long reads. A new technology, PacBio Hifi reads, can achieve high accuracy (comparable to short reads accuracy, \u00a0<1% error rate) while keeping the advantage of expanding large genomic regions. Hifi reads achieve high accuracy by using multiple subreads and sequencing by consensus result. Using Hifi reads, SV detection tools can avoid the need for error correction, enhance efficiency, and gain a more reliable result at the same time.\n\nThe standard approach for analyzing whole-genome long-read data from an individual involves aligning it to a reference genome (read alignment-based) to detect variants. Read alignment-based methods are attractive because they do not require extensive computational resources or high sequencing coverage. Numerous read alignment-based SV callers have emerged recently, including NanoVar16, cuteSV17, pbsv18, Sniffles219, MAMnet20, SVDSS21, and DeBreak22. Classifying\u00a0a tool as\u00a0alignment-based is not absolute, even though it utilizes\u00a0read alignment. For instance, DeBreak can be considered a hybrid method as it combines read alignment with local assembly to detect SVs. Similarly, MAMnet can be categorized as a deep learning-based approach that relies on read alignment information. Hybrid and deep learning-based methods are promising in detecting SVs compared to traditional alignment-based methods in most aspects. Nevertheless, methods relying even partially on reads alignment often have limitations in the accurate representation of a complete genome, an SV\u2019s initial and ending position in the genome (its \u201cbreakpoints\u201d), and in identifying the full SV sequence22. Alignment-based, and these hybrid or deep learning-based methods concentrate solely on target and local signals, making it challenging to generate a comprehensive map for genome-wide variants.\n\nAn alternative strategy is to assemble the entire genome of an individual solely based on their reads (de novo assembly) and then compare the assembly with a reference genome, a process that demands more computational resources. Currently, only a handful of assembly-based SV callers have been introduced, such as Dipcall23, SVIM-asm24, and PAV25. Assembly-based methods surpass alignment-based methods in achieving accurate SV detection with respect to breakpoints and SV sequences. Nevertheless, whole-genome assembly often produces a complete map for genome-wide variants by compromising precision and incurring a significant number of false positives, particularly in the case of error-prone long reads.\n\nHere, to address existing limitations in alignment-based and assembly-based methods, we present VolcanoSV, an innovative hybrid SV detection pipeline that leverages phased SNPs to generate phased diploid assembly for precise haplotype-resolved SV analysis. VolcanoSV offers several advantageous features. (1) It outperforms state-of-the-art assembly-based SV callers, demonstrating higher recall, precision, F1 scores, and genotype accuracy across a diverse range of datasets, including low-coverage (10x) datasets, without compromising accuracy. (2) VolcanoSV is compatible with all mainstream long-read sequencing platforms, which vary considerably in sequencing error rates, and can discover various types of SVs including deletions, insertions, duplications, inversions, and translocations. Moreover, VolcanoSV excels in detecting and phasing SNPs and small indels along with SVs. (3) It exhibits more robust performance by accurately identifying breakpoints and SV sequences. (4) VolcanoSV-vc, the assembly-based SV calling component, has a low false discovery rate. With these features, VolcanoSV is well-suited for generating comprehensive genetic maps for human genomics studies.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We first investigated SV detection using 4 assembly-based methods (VolcanoSV (v1.0.0), PAV (freeze2), SVIM-asm (v1.0.2), and Dipcall) in 14 PacBio Hifi, CLR, and ONT datasets, 9 simulated long reads datasets, and two paired tumor-normal CLR and ONT datasets. For Hifi data, three assembly-based SV callers (PAV, SVIM-asm, and Dipcall) could use as input the diploid assembly result of hifiasm (v0.16)26. For CLR and ONT data, Flye (v2.9-b1768)27 plus HapDup (v0.5-iss10)28 were used to generate a dual assembly for the three assembly-based tools. We selected hifiasm and Flye plus HapDup to generate assembly since they provided the best assembly results for SV calling29. VolcanoSV employed its own haplotype-aware assembly component (VolcanoSV-asm) to produce a diploid assembly. To further demonstrate VolcanoSV\u2019s robust performance across different SV evaluation thresholds, we compared SV calls from four assembly-based methods in terms of breakpoint identification and SV sequence accuracy. Among the 14 long-read sequencing datasets (Table\u00a01), five PacBio HiFi datasets were referred to as Hifi_L1, Hifi_L2, Hifi_L3, Hifi_L4, and Hifi_L5. They had approximately 56\u00d7, 30\u00d7, 34\u00d7, 28\u00d7, and 41\u00d7 coverage, respectively. Three PacBio CLR datasets were referred to as CLR_L1, CLR_L2, and CLR_L3 and their coverage was 89x, 65x, and 29x, respectively. We also used six ONT datasets referred to as ONT_L1, ONT_L2, ONT_L3, ONT_L4, ONT_L5, and ONT_L6. Their coverage was approximately 48\u00d7, 46\u00d7, 57\u00d7, 36\u00d7, 47\u00d7, and 51\u00d7. More information for each SV caller and dataset is provided in Table\u00a01. VolcanoSV utilizes a reference genome and long-read data to generate a high-quality haplotype-resolved diploid assembly. Using this assembly, all types of variants are comprehensively detected. The VolcanoSV pipeline is illustrated in Figs.\u00a01 and 2. A detailed description is provided in the Methods section.\n\nThe main workflow of VolcanoSV consists of two key components, VolcanoSV-asm and VolcanoSV-vc. VolcanoSV-asm (left, blue square) comprises three conceptual modules to perform diploid assembly (partitioning reads into corresponding haplotypes, assigning unphased reads, and performing a haplotype-aware local assembly). The output of this component is processed by the VolcanoSV-vc component (center, red rectangle) to perform variant detection. Further details are provided in the Methods section.\n\nVolcanoSV-vc includes three main modules: a large indel SV detection, b complex SV detection, and c small indel detection. The output of this component is a phased VCF file. Further details are provided in the Methods section.\n\nTo assess the performance of insertion and deletion SV detection, we applied four assembly-based tools, VolcanoSV, PAV, SVIM-asm, and Dipcall, across 14 long-read libraries of HG002. We evaluated their results against the Genome in a Bottle (GIAB) SV gold standard23. An SV benchmarking tool, Truvari (v4.0.0)30, was employed to compare the SV calls of each tool with the GIAB SV gold standard. Truvari evaluates SVs within Variant Call Format files (VCF) by analyzing four essential similarity metrics (reference distance, reciprocal overlap, size similarity, sequence similarity) across all SV pairs within a designated region, while also ensuring SV type and genotype match between compared SV pairs. If any of these metrics exceed user-defined thresholds, the SV pair fails to be a candidate match. The following metric/parameter setting was used in Truvari: p\u2009=\u20090.5, P\u2009=\u20090.5, r\u2009=\u2009500, O\u2009=\u20090.01, representing a moderate-tolerance metric/parameter set to evaluate SV calls. Specifically, parameter p, or pctstim, ranging from 0 to 1.0, controls the minimum allele sequence similarity used to identify the SV calls being compared\u00a0as the same. Parameter P, also known as pctsize, ranges from 0 to 1.0, defining the minimum allele size similarity required between the two SVs. Parameter O, also referred to as pctovl, ranges from 0 to 1.0 and determines the minimum threshold of reciprocal overlap ratio between the base and comparison call. It is only applicable on deletions that can be used to evaluate their breakpoint shift. Parameter r, or refdist, ranging from 0 to 1000\u2009bp, limits the threshold for maximum reference location difference of the SVs\u00a0being compared, which can be used to evaluate the breakpoint shift of insertions.\n\nWe first determined the average performance across different PacBio Hifi, CLR, and ONT datasets for the four assembly-based tools (Table\u00a02). Across Hifi datasets, VolcanoSV achieved the best average F1 (91.03% and 94.19%) and genotyping accuracy (98.32% and 99.01%) for insertions and deletions. Across CLR datasets, VolcanoSV achieved the best average F1 (89.72% and 93.70%) and genotyping accuracy (97.07% and 98.58%) for insertions and deletions. In ONT datasets, VolcanoSV also attained the best average F1 (90.10% and 93.13%), and genotyping accuracy (98.00% and 99.06%) for insertions and deletions.\n\nWhen we examined each dataset (Fig.\u00a03, Table\u00a02, and Supplementary Tables\u00a01\u20133), VolcanoSV consistently outperformed all other tools, achieving the highest F1 scores for both insertions and deletions for all 14 libraries. In the context of the five Hifi datasets (Fig.\u00a03, Table\u00a02, and Supplementary Table\u00a01), VolcanoSV achieved the highest ranking in terms of all performance metrics. Specifically, for insertions, VolcanoSV consistently surpassed all other tools across all metrics, with F1 score, recall, precision, and GT concordance outperforming the second-ranked tool by an average of 1.29%, 0.67%, 1.92%, and 0.59%, respectively. With respect to deletions, VolcanoSV sustained its advantage, demonstrating an average superiority of 1.07% in F1 score, 0.48% in recall, 1.52% in precision, and 0.53% in GT concordance over the second-ranked tool.\n\na, b F1 heatmap for deletions (DEL) and insertions (INS) by four assembly-based tools. c, d Recall bar plots for insertions deletion (DEL) and insertions (INS) by four assembly-based tools. e, f Precision bar plots for insertions deletion (DEL) and insertions (INS) by four assembly-based tools. g, h Genotype accuracy (represented by GT_concordance) bar plots for insertions deletion (DEL) and insertions (INS) by four assembly-based tools. Source data are provided as a Source Data file.\n\nIn the three CLR datasets (Fig.\u00a03, Table\u00a02, and Supplementary Table\u00a02), VolcanoSV stood out as the top performer across all metrics and libraries, with distinct advantages. For insertions, VolcanoSV\u2019s performance metrics, including F1 score, recall, precision, and GT concordance, were 3.30%, 0.87%, 4.61%, and 4.20% higher than the second-ranked tool. Likewise, for deletions, VolcanoSV outperformed the second-ranked tool by 4.87%, 6.19%, 3.19%, and 1.71% higher scores on average for F1, recall, precision, and GT concordance. It is noteworthy that CLR data exhibited a significantly higher error rate, varying between 10% to 20%. PAV, SVIM-asm, and Dipcall exhibited significantly inferior performance in PacBio CLR when contrasted with Hifi datasets. Effectively eliminating false positive calls is a crucial step following the SV detection process. VolcanoSV incorporates a precise SV filtering procedure and an advanced GT prediction model within its workflow, resulting in a notable enhancement in performance compared to all other tools.\n\nFor the six ONT datasets (Fig.\u00a03, Table\u00a02, and Supplementary Table\u00a03), VolcanoSV still maintained a substantial lead. In terms of insertions, VolcanoSV outperformed the second-ranked tool, with an average F1 score and precision that were 1.5% and 2.68% higher, respectively. Regarding the insertion recall, on ONT_L3-5, VolcanoSV\u2019s recall was 0.38% higher on average than the second-ranked tool. On ONT_L1 and L6, VolcanoSV exhibited the second-highest recall, with just an average of 0.14% less compared to the top recall. However, on ONT_L2, VolcanoSV only demonstrated the third-highest recall, with 1.03% less compared to the top recall. For the insertion GT concordance, VolcanoSV achieved a 0.29% higher GT concordance than the second-ranked tool, except for ONT_L2, where SVIM-asm reached the highest GT concordance with a margin of 0.16% over VolcanoSV. In terms of deletions, VolcanoSV outperformed the competition with an average F1 score, precision, and GT concordance that were 0.89%, 1.87%, and 0.62% higher, respectively, than the second-ranked tool. In terms of deletion recall, on ONT_L1, L3, L4, and L6, VolcanoSV\u2019s recall was 0.13% higher on average than the second-ranked tool. However, on ONT_L2 and L5, PAV and SVIM-asm attained the best recall, with an increase of 0.18% on average, in comparison to VolcanoSV.\n\nIn summary, VolcanoSV emerged as the top-tier choice for assembly-based SV detection across different long-read datasets, exhibiting superior performance and consistency, particularly for PacBio HiFi and CLR datasets in terms of F1 score, recall, precision, and GT concordance. VolcanoSV still demonstrated its superiority for ONT datasets in terms of F1 score, precision, and GT concordance. With respect to the recall in insertions and deletions, VolcanoSV achieved the best recall in 3\u20134 out of 6 datasets.\n\nAlthough VolcanoSV further improved F1 scores and genotyping accuracy compared to the second-ranked tool in each dataset, the\u00a0potential benefits impact of this improvement on downstream analyses remained unclear. To delve deeper into these unique true positive (TP) SVs with correct genotypes (GT) identified by VolcanoSV compared to the second-ranked tool, we annotated the SVs with their predicted effects on other genomic features using the Ensembl Variant Effect Predictor (VEP)31.\n\nFor example, we extracted and annotated 300 unique TP SVs with correct genotypes identified by VolcanoSV compared to the second-ranked tool, SVIM-asm, in Hifi_L1. These SVs overlapped with 258 genes, 939 transcripts, and 120 regulatory features, including 266 novel SVs and 34 existing ones. Among these, 125 SVs were coding sequence variants and 16 were in-frame deletions. More information on the consequences of these SVs is demonstrated in Supplementary Fig.\u00a01. Additionally, we performed an exact match comparison of these SVs in terms of sequence and breakpoints with those in the Genome Aggregation Database (gnomAD)32. We identified 29 matching SVs, 12 of which are rare variants with an allele frequency (AF) of less than 1%. Furthermore, we found that these SVs allowed an additional 85 genes to be phased by VolcanoSV. Genes are considered phased if all heterozygous variants within it are phased.\n\nWe performed these analyses for all 14 long-read datasets (Supplementary Table\u00a04 and Supplementary Fig.\u00a01), and the results suggested that the improvement by VolcanoSV over the second-ranked tool in each dataset provided a substantial number of SVs with potential phenotypic effects.\n\nAs we observed in the previous section, VolcanoSV achieved the highest recall in all PacBio Hifi and CLR datasets, and half of the ONT datasets. To further assess the overlapping calls among different tools and understand why VolcanoSV generated many more true positive calls, we employed an UpSet plot to visualize the total number of shared true positive (TP) calls and unique TP calls for all four assembly-based tools (Fig.\u00a04a\u2013c). Overall, the four tools we examined exhibited a substantial overlap in TP calls, as indicated by the rightmost bar in the plot. Specifically, across Hifi_L2, CLR_L1, and ONT_L1 data, these four tools shared 8457, 7834, and 8151 TP calls, respectively. Notably, VolcanoSV contributed the largest number of unique TP calls. For Hifi_L2, VolcanoSV, PAV, SVIM-asm, and Dipcall had 43, 35, 4, and 1 unique TP calls, respectively. In the case of CLR_L1, these tools had 174, 28, 11, and 4 unique TP calls, while for ONT_L1, they had 56, 19, 5, and 2 unique TP calls. The SV annotation analysis for the unique SVs by VolcanoSV was illustrated in Supplementary Table\u00a05 and Supplementary Fig.\u00a02.\n\na\u2013c UpSet plot for analysis of shared and unique true positive (TP) calls between different assembly-based tools. d\u2013f F1 accuracy of SV detection at different size ranges. The negative size range represents deletions and the positive size range represents insertions. The bar plot shows benchmark SV distribution at different size ranges. The line plot shows the F1 score of four different methods. g F1 and GT_F1 heatmap for complex SV detection on simulated data. h The recall heatmap for complex somatic SV detection on real data. Source data are provided as a Source Data file.\n\nTo assess the influence of structural variant (SV) size on the accuracy of SV detection, we generated F1 scores for SV detection across different SV size ranges (Fig.\u00a04d\u2013f). In general, VolcanoSV demonstrated top-tier accuracy across most size ranges, except for insertions within the 10\u201350\u2009kb range. Specifically, in Hifi_L2, VolcanoSV consistently excelled in medium and large-size deletions (50\u2009bp\u20136\u2009kb) and insertions (50\u2009bp\u20134\u2009kb). When it came to very large insertions (6\u201350\u2009kb), VolcanoSV\u2019s performance exhibited some variability. However, in the case of very large deletions (6\u201350\u2009kb), VolcanoSV consistently maintained top performance, with the exception of deletions within the 6\u20137\u2009kb range. Notably, in the case of extremely large deletions (10\u201350\u2009kb), VolcanoSV outperformed other tools significantly in terms of F1 scores.\n\nThe performance of VolcanoSV was even more remarkable for CLR and ONT data. On CLR_L1 and ONT_L1, VolcanoSV achieved the highest F1 scores for deletions across all size ranges and insertions within the 50\u2009bp\u20135\u2009kb range. However, for very large insertions (5\u201350\u2009kb), VolcanoSV\u2019s performance showed some variability. It is worth highlighting that in CLR data, VolcanoSV displayed notably superior performance in detecting very large deletions (7\u201350\u2009kb) compared to other tools. Upon further analysis of the remaining 11 datasets, VolcanoSV demonstrated consistent and superior performance across different size ranges compared to the other three assembly-based tools (Supplementary Fig.\u00a03).\n\nUp to this point, we have assessed deletion and insertion SVs, which account for most SVs, but other SVs such as translocations (TRA), inversions (INV), and duplications (DUP) also describe different combinations of DNA rearrangements. The lack of benchmarking data for such complex SVs makes it difficult to evaluate tools. To extend the evaluation to complex SV detection, we first applied VolcanoSV and other tools to simulated data. Dipcall was not involved in this analysis since it was not designed to detect complex SVs.\n\nWe used VISOR (v1.1.2)33 to insert SNPs and complex SVs into the hg19 human reference, and then used PBSIM (v3.0.0)34 to simulate reads for different libraries (Hifi, CLR, and ONT). We compared the SV call results with the initial inserted SVs to calculate F1 and F1 for genotyping accuracy (GT_F1) scores. Detailed simulation and evaluation details are described in the Methods section. Since every reciprocal TRA has four breakends, it is hard to decide whether the genotype is correct or not. We thus only calculated F1 and GT_F1 for INVs and DUPs. In Fig.\u00a04g, VolcanoSV demonstrated the highest F1 performance across all simulated libraries and complex SV types, except for TRAs in Hifi data (Hifi_TRA). Specifically, on CLR and ONT data, VolcanoSV surpassed SVIM-asm, achieving F1 scores that were on average 18%, 23%, and 63% higher for TRAs, INVs, and DUPs, respectively. Moreover, VolcanoSV\u2019s GT_F1 scores were on average 33% and 57% higher than SVIM-asm\u2019s GT_F1 for INV and DUP, respectively. For TRAs in Hifi data, VolcanoSV\u2019s F1 was only 1% lower than the best F1 by SVIM-asm. PAV was not designed to detect TRAs and DUPs but also had the worst performance on INVs. Noticeably, VolcanoSV achieved much higher F1 and GT_F1 scores for DUPs in all simulated data compared to SVIM-asm, primarily attributed to the effective duplication recovery process implemented in VolcanoSV. With respect to GT_F1, VolcanoSV achieved the best performance on all simulated libraries and complex SV types, except for INVs in Hifi data (Hifi_INV).\n\nWe next applied VolcanoSV and SVIM-asm on two publicly available sets of tumor-normal paired libraries (Pacbio CLR and ONT) provided by Talsania et al.35 and the high confidence HCC1395 somatic SV callset they provided as the benchmark, to further evaluate three classes of somatic complex SVs. To detect somatic SVs, we first applied each assembly-based tool on every library to generate VCF files independently, then used SURVIVOR (v1.0.6)36 to call somatic variants based on the paired normal-tumor VCFs, and finally compared the somatic SV result to the provided benchmark callset. The high-confidence benchmark callset has a total of 1777 SVs, including 551 insertions, 717 deletions, 146 translocations, 133 inversions, and 230 duplications. Since this high-confidence callset is incomplete, we only plotted recall. VolcanoSV outperformed SVIM-asm in terms of recall across all different libraries and all complex SV types, especially for DUPs (Fig.\u00a04h). Specifically, on PacBio CLR data, VolcanoSV\u2019s recall was 23%, 18%, and 12% higher than SVIM-asm for TRAs, INVs, and DUPs respectively. On ONT data, VolcanoSV\u2019s recall was 13%, 9%, and 17% higher than SVIM-asm for TRAs, INVs, and DUPs respectively. Although VolcanoSV outperformed SVIM-asm in detecting complex SVs, its recall was relatively lower compared to its performance for simulated data or indel SVs. To understand the reason behind this low recall and whether VolcanoSV predominantly detects common complex SVs identifiable by other long-read datasets, we analyzed the overlapping VolcanoSV calls between paired normal and tumor HCC1395 samples, and HG002. Detailed results and discussion are provided in Supplementary Fig.\u00a04 and Supplementary Notes\u00a01.1. Our analysis revealed that 1) VolcanoSV failed to detect a sufficient number of unique somatic complex SVs in the tumor sample, in addition to the germline SVs; 2) The high-confidence benchmark callset included SV calls only from alignment-based tools, and therefore using it to evaluate VolcanoSV might introduce bias. Overall, it is still challenging to solely use assembled contigs to detect complex SVs due to the limitations of assembly algorithms and the complexity of graph construction. Many genome assembly algorithms build contigs by following the simplest paths through overlapping reads, which may miss complex SVs. These variants create irregular patterns that do not fit into the straightforward paths the algorithms usually prefer or disrupt the continuity of the graph.\n\nTo further benchmark VolcanoSV\u2019s robustness to different sequencing coverages against the three assembly-based SV callers (PAV, SVIM-asm, and Dipcall), we evaluated SV calling results on subsampled Hifi_L1, CLR_L1, and ONT_L1. All three datasets were subsampled to 40x, 30x, 20x, 10x, and 5x coverage using rasusa (v0.6.0)37. Hifi_L1 and CLR_L1 were additionally subsampled to 50x coverage due to their higher original coverage.\n\nThe four tools exhibited similar subsampling effects on Hifi_L1 (Fig.\u00a05a and Supplementary Table\u00a06). Noticeable changes in terms of recall, precision, and F1 were only\u00a0observed after the coverage dropped to 5x. For both insertions and deletions, VolcanoSV showed greater robustness to subsampling effects on Hifi_L1 compared to the other three tools, since it could still preserve high F1 scores at 10x coverage. At 5x coverage, SVIM-asm kept the highest F1, followed by VolcanoSV.\n\na Recall-precision-F1 curves show the subsampling effect on deletion and insertion by different tools on Hifi_L1. b Recall-precision-F1 curves show the subsampling effect on deletion and insertion by different tools on CLR_L1. c Recall-precision-F1 curves show the subsampling effect on deletion and insertion by different tools on ONT_L1. The coverage depth varies from 5\u00d7, 10\u00d7, 20\u00d7, 30\u00d7, 40\u00d7 to 50\u00d7. Solid lines with markers are for different coverage depths, and corresponding dashed lines are for genotyping (gt) accuracy. For both insertions and deletions, we zoom in on the top right part of the plot to demonstrate the curves more clearly. Source data are provided as a Source Data file.\n\nTwo main patterns were observed in the subsampling effects on CLR_L1 and ONT_L1 (Fig.\u00a05b, c, Supplementary Table\u00a07, and Supplementary Table\u00a08). VolcanoSV and Dipcall showed relatively stable precision across different coverages, but lower recall at lower coverages (5\u201310\u00d7). On the other hand, PAV and SVIM-asm were able to maintain relatively better recall as coverage decreased, however, their precision declined quickly as coverage dropped to lower coverages. Considering F1 as the metric, VolcanoSV still demonstrated greater robustness against subsampling on CLR_L1 and ONT_L1, as it exhibited better F1 scores compared to the other three tools when the coverage dropped to 10\u00d7 and 5\u00d7.\n\nWe also examined subsampling effects on genotyping accuracy. The genotyping performance shared a similar trend with the overall accuracy for the subsampling effects. Across all coverages (5\u201350\u00d7), VolcanoSV maintained the best genotype accuracy compared to the other three tools.\n\nDue to the complex nature of structural variants, SV benchmark tools such as Truvari usually do not require a detected SV to have exactly the same breakpoints and sequence as the true SV to be considered correct, but instead use a set of evaluation parameters to control the matching tolerance. While the matching tolerance can facilitate fair SV comparisons, the choice of the evaluation parameters is usually empirical and subjective, which increases the uncertainty of the comparison outcomes and could limit our understanding of the SV calling performance. A lenient threshold might underestimate distinctions among SV callers, while a stringent threshold could exaggerate the superiority of a particular SV caller. Therefore, we used a grid search experiment to explore Truvari\u2019s parameters to thoroughly investigate the robustness of VolcanoSV and other assembly-based tools38. As described before,\u00a0evaluation parameters include pctstim (p), pctsize (P), pctovl (O), and refdist (r). In general, higher values of p, P, and O, along with lower values of r, establish more stringent comparison criteria. This means that the SVs being compared\u00a0will need to exhibit greater sequence similarity, allele size similarity, reciprocal overlap ratio, or closer proximity to the reference sequence in order to be classified as the same SV. More descriptions of Truvari and its parameters are provided in the Methods section. Specifically, in our grid search SV evaluation experiments, we varied p, P, O from 0 to 1 in increments of 0.1, and r from 0 to 1\u2009kb in increments of 100\u2009bp.\n\nWe first evaluated deletions on Hifi_L1. Parameters p and O had the most significant effects. As illustrated in Fig.\u00a06a, when p and O increased (i.e. a more stringent correspondence was required between the call and the gold standard to be accepted as a true positive), all four assembly-based tools, Dipcall, SVIM-asm, PAV, and VolcanoSV demonstrated stable and high performance across the grid search and could still maintain reasonable F1 scores under the most stringent parameter settings. Among them, VolcanoSV showed the most robust performance with the changes of evaluation parameters and maintained the highest F1 score across all entries. Parameters P and r had little effect on deletion evaluation for all methods unless they were set to 1.0 or 0\u2009bp, respectively.\n\na Grid search heatmap of F1 values for deletions by different assembly-based tools. b Distribution of breakpoint shift for deletions by assembly-based tools. c Distribution of alternate sequence similarity for deletions by assembly-based tools. d\u2013f Equivalent visual representations as shown in a\u2013c for insertions. Source data are provided as a Source Data file.\n\nIn terms of evaluating insertions on Hifi_L1, parameters p and r were chosen as the representative pair to compare performance across methods. Compared to deletions, insertions called by assembly-based tools were slightly more robust to the changes of evaluation parameters (Fig.\u00a06d), except for the most stringent conditions (p\u2009=\u20091.0 or r\u2009=\u20090). SVIM-asm, PAV, and VolcanoSV, maintained relatively higher performance across grid searches than Dipcall. Overall, VolcanoSV achieved higher F1 than SVIM-asm and PAV for each grid. The parameter O was not used since it is not applicable to insertion evaluation. The parameter P only had a significant effect on insertion evaluation for SV callers when it was set to 1.0. This result was consistent with its effect on deletion evaluation. We have also observed similar patterns on grid searches for all methods on CLR_L1 and ONT_L1 (Supplementary Figs.\u00a05 and 6).\n\nTo reveal why assembly-based methods like VolcanoSV were robust to changes of evaluation parameters, we further analyzed the distribution of SV breakpoint and alternate allele sequence similarity for several representative tools. The breakpoint shift was determined by calculating the maximum difference in reference locations between the two compared SV calls. The similarity in SV sequences was computed based on the edit distance between the two compared SV calls. The results showed that all four tools had a near zero breakpoint shift and near 100% SV sequence similarity with the benchmark callset (Fig.\u00a06b, c, e, f). This result underscores that the ability to accurately capture SV breakpoints and alternative allele sequences contributes to the tool\u2019s resilience in rigorous SV evaluation scenarios, a capability possessed by all assembly-based methods.\n\nWe employed the complete VolcanoSV pipeline to produce diploid assembly, ultimately facilitating the detection of all types of structural variants. VolcanoSV-vc, serving as the assembly-based SV calling component of VolcanoSV, is versatile enough to function as a standalone tool, accepting assembly inputs from other assemblers. We thus investigated the SV calling performance of VolcanoSV-vc by taking hifiasm\u2019s assembly as input for Hifi data, and Flye\u2009+\u2009HapDup\u2019s assembly as input for CLR and ONT data. In comparison to the three other assembly-based methods (PAV, SVIM-asm, and Dipcall), VolcanoSV-vc achieved the best F1 across all 14 PacBio Hifi, CLR, and ONT datasets (Supplementary Tables\u00a09\u201311 and Supplementary Fig.\u00a07).\n\nAlthough benchmarking against the GIAB SV gold standard is an efficient and precise procedure to evaluate and compare the SV calling performance of different tools, the GIAB gold standard SV callset is not a complete set and could also contain false positives. We thus used T2T-CHM13 (v2.0) as an additional reference genome and the long reads from the CHM13 sample to call SVs and evaluate the false discovery rate. Specifically, we used hifiasm (v0.16) to assemble long reads into dual contigs, and applied different assembly-based SV callers to the contigs against both T2T-CHM13 and GRCh38 reference genomes (v2.1.0). We did not use the VolcanoSV-asm component for the diploid assembly since the CHM13 sample derives from a single homozygous complete hydatidiform mole and is not suitable for the haplotype-phasing module to achieve diploid assembly. The false discovery rate was computed as the ratio of the number of detected SVs against CHM13 to the number of SVs against GRCh38.\n\nGiven the remarkably precise T2T CHM13 assembly generated with multiple complementary technologies, ideally, no SV should be discovered when the contigs assembled from CHM13 long reads are aligned to the T2T-CHM13 reference. However, we did observe a few SV calls when performing the variant calling due to the imperfection of assembly and bias introduced by the aligner and variant caller. Therefore, the number of false discoveries can serve as an objective measure of how robust a variant caller is. We performed this experiment on two Hifi datasets from CHM13 (denoted as Hifi_L6 and Hifi_L7) for different assembly-based SV callers, and the results were collected in Table\u00a03. We observed that overall, VolcanoSV achieved the lowest false discovery rate (FDR) compared with the other three tools. Specifically, on Hifi_L6, VolcanoSV achieved the lowest baseline FDR at 3.03%, followed by Dipcall (3.05%), SVIM-asm (4.33%), and PAV (9.21%).\n\nIn terms of total calls against the T2T-CHM13 reference (relative to which the false discovery rate is calculated), VolcanoSV ranked second with 902 calls, following behind Dipcall, which had 737 calls. In terms of total calls against the GRCh38 reference, VolcanoSV had the highest number (29748), followed by SVIM-asm (26384), Dipcall (24187), and PAV (17540). On Hifi_L7, VolcanoSV maintained its lead with the lowest baseline FDR at 2.22%, followed by Dipcall (2.94%), SVIM-asm (3.36%), and PAV (4.52%). For total calls against the T2T-CHM13 reference, VolcanoSV had the lowest false calls (593), followed by Dipcall (705), PAV (794), and SVIM-asm (857). In terms of total calls against the GRCh38 reference, VolcanoSV had the highest number (26737), followed by SVIM-asm (25480), Dipcall (23976), and PAV (17563).\n\nWe conducted additional analysis on the falsely discovered SVs based on the T2T-CHM13 reference. The results, illustrated in the UCSC Genome Browser (Supplementary Figs.\u00a08 and 9) and Table\u00a03, showed that 92.6%, 96.4%, 94.2%, and 92.7% of FD for Hifi_L6 by VolcanoSV, PAV, SVIM-asm, and Dipcall, respectively, were located in heterochromatin regions like centromeres and telomeres. For Hifi_L7, the percentages were lower, with 79.9%, 85.8%, 81.8%, and 82.4% of SVs identified by VolcanoSV, PAV, SVIM-asm, and Dipcall, respectively, also located in these complex regions. Notably, the majority of FDs were concentrated in chromosomes 1, 7, 9, 15, and 16. Heterochromatin regions pose challenges for assembly, often requiring specialized graph-based methods for accurate resolution.\n\nWhile VolcanoSV was originally designed with a focus on detecting structural variants (SVs), the diploid assembly also provides the potential for uncovering small variants (\u226450\u2009bp) across the entire genome. We benchmarked SNPs and small indels by VolcanoSV with PAV and Dipcall since they both detect small variants through the assembly. In Hifi data (Supplementary Table\u00a012), VolcanoSV demonstrated the highest F1 scores in SNPs for four out of five datasets and excelled in small indels for all five datasets, achieving peak F1 scores of 99.42% for SNPs and 98.34% for small indels. When applied to CLR data (Supplementary Table\u00a013), VolcanoSV outperformed PAV and Dipcall, achieving the best F1 scores in both SNPs and small indels across all three datasets. Similarly, for ONT data (Supplementary Table\u00a014), VolcanoSV exhibited superior performance in SNPs and small indels for all three datasets, although all three tools displayed low precision in small indel calls. It is noteworthy that detecting small indels based on assembly proved to be ineffective for ONT data.\n\nWe note that VolcanoSV includes a haplotype phasing module, enabling not only detection but also phasing of all types of variants, including SNPs, small indels, and SVs. The end result is the production of a phased VCF file.\n\nTo assess the runtime and memory usage of all tools, we used three different long-read datasets as representatives. All tools were tested on AMD EPYC 7452 Processor CPUs with 32 cores and 1TB memory. We first investigated the computation cost for the assembly phase. For Hifi_L1 (Supplementary Table\u00a015), VolcanoSV-asm and hifiasm finished within 1474 and 440 CPU hours with peak memory usage reaching around 168GB and 214GB, respectively. In the CLR_L1 library, VolcanoSV-asm and Fly\u2009+\u2009HapDup finished within 2582 and 10673 CPU hours with peak memory usage of 259GB and 691GB, respectively; Finally, for ONT_L1, VolcanoSV-asm and Fly\u2009+\u2009HapDup finished in 4397 and 7886 CPU hours with peak memory usage of 85GB and 337GB, respectively. In summary, owing to the phasing and local assembly modules, VolcanoSV-asm demonstrated lower memory consumption and runtime than Fly\u2009+\u2009HapDup; it required more runtime and less memory compared to hifiasm. However, assembly procedures for assembly-based tools are often more computationally expensive than alignment procedures for alignment-based tools. For instance, when considering the same three libraries, alignment procedures utilizing minimap2 (v2.24-r1122)39 only consume 189, 118, and 116 CPU hours, with peak memory usage reaching approximately 31GB, 29GB, and 31GB, respectively.\n\nSecondly, we benchmarked the computational costs for the assembly-based variant calling phase. For Hifi_L1 (Supplementary Table\u00a016), VolcanoSV-vc, PAV, SVIM-asm, and Dipcall finished within 21, 97, 17, and 11 CPU hours with peak memory of 21GB, 101GB, 37GB, and 48GB, respectively. For CLR_L1, VolcanoSV-vc, PAV, SVIM-asm, and Dipcall finished within 123, 1098, 7, and 10 CPU hours with peak memory of 215GB, 67GB, 26GB, and 42GB, respectively, For ONT_L1, VolcanoSV-vc, PAV, SVIM-asm, and Dipcall finished within 32, 133, 8, and 8 CPU hours with peak memory of 34GB, 53GB, 34GB, and 47GB, respectively. In summary, due to the variant filtering and refinement module, VolcanoSV-vc consumed less memory but more runtime than SVIM-asm and Dipcall on Hifi and ONT data. However, on CLR data, VolcanoSV-vc exhibited a substantial memory consumption. PAV consistently required significantly more runtime than other assembly-based tools. Compared to state-of-the-art alignment-based tools17,18,19,38, VolcanoSV-vc shows no significant difference in computation cost.\n\nWe selected hifiasm and Flye as the default assemblers for various long-read datasets after evaluating several upstream long-read assemblers, including hifiasm26, Flye27, Peregrine40, wtdbg241, IPA42, HiCanu43, and Shasta44. Our assessment considered their impact on VolcanoSV\u2019s downstream large indel SV calling performance. These two assemblers consistently demonstrated superior performance on PacBio Hifi (hifiasm), and for CLR and ONT datasets (Flye), respectively. However, users also have the flexibility within the assembly module in VolcanoSV to choose the most appropriate or new assemblers to fulfill their specific requirements. For example, when assembling regions enriched in segmental duplications (SDs), these two assemblers may not be the most suitable choice. To investigate the performance of different assemblers in SD-enriched regions, we employed Flagger (v0.3.3)45 to detect misassemblies enriched in SDs in three representative libraries. Flagger is a read-based pipeline that maps long reads to the phased diploid assembly in a haplotype-aware manner. It identifies coverage inconsistencies within these read mappings that are likely due to assembly errors. Details are provided in the Methods section.\n\nFor ONT_L1, we additionally employed wtdbg2 (v2.2.5), Shasta (v0.10.0), and NextDenovo (v2.5.2)46 in VolcanoSV to perform local assembly for all phase blocks. Using Flagger, we first identified collapsed components in diploid assembly by VolcanoSV using different assemblers. These collapsed components represent regions with additional copies in the underlying genome that have been collapsed into a single copy, indicating potential misassemblies for SDs. We then further assessed the SD reliability of the Flagger annotation for collapsed components. To achieve this, we aligned the collapsed components from all diploid assemblies to the GRCh38 reference genome and intersected them with the SD annotations for HG002 based on GRCh38. We then calculated and compared the total length of SD annotation regions that overlap with collapsed regions by Flagger across all assemblies. The results demonstrated that VolcanoSV assembly using Flye, wtdbg2, Shasta, and NextDenovo generated 34.9\u2009MB, 47.6\u2009MB, 34.2\u2009MB, and 22.7\u2009MB of collapsed components, respectively, which suggested collapsed SDs.\n\nFor CLR_L1, we additionally employed wtdbg2, and NextDenovo in VolcanoSV to perform local assembly for all phase blocks. The results demonstrated that VolcanoSV assembly using Flye, wtdbg2, and NextDenovo generated 28.8\u2009MB, 32\u2009MB, and 30.2\u2009MB of collapsed components, respectively, which suggested collapsed SDs. For Hifi_L1, we additionally employed Hicanu (v2.1.1) in VolcanoSV to perform local assembly for all phase blocks. The results demonstrated that VolcanoSV assembly using hifiasm and Hicanu generated 17.4\u2009MB and 15.7\u2009MB of collapsed components, respectively, which suggested collapsed SDs.\n\nThese results suggested that NextDenovo in VolcanoSV assembled fewer collapsed regions than Flye in ONT_L1, indicating its potential superiority for regions enriched in SDs. Similarly, Hicanu in VolcanoSV outperformed hifiasm in Hifi_L1. Notably, Flye in VolcanoSV assembled the fewest collapsed regions in CLR_L1 compared to other assemblers.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51282-0/MediaObjects/41467_2024_51282_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51282-0/MediaObjects/41467_2024_51282_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51282-0/MediaObjects/41467_2024_51282_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51282-0/MediaObjects/41467_2024_51282_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51282-0/MediaObjects/41467_2024_51282_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51282-0/MediaObjects/41467_2024_51282_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this work, we introduce VolcanoSV, a reference-assisted, haplotype-resolved, assembly-based, structural variant detection method. It relies on a sophisticated k-mer-based reads partitioning method, and performs contig-based reads signature collection and rigorous FP filtering, followed by genotype correction. The combination of these steps enables VolcanoSV to attain remarkable performance in structural variation discovery. Our benchmarking analysis, evaluated against the ground-truth HG002 SV callset from genome in a bottle (GIAB), demonstrated superior levels of recall, precision, F1 score, and genotype accuracy compared to existing state-of-the-art assembly-based tools across a diverse range of datasets, including low-coverage (10\u00d7) ones. Moreover, VolcanoSV was robust across SV evaluation parameter settings and achieved accurate breakpoints and novel sequence identification. It consistently achieved the highest F1 score across all combinations of evaluation parameters compared to other assembly-based tools. Due to the limited availability of ground-truth SV calls other than for large insertions and deletions, VolcanoSV was benchmarked for duplications, inversions, and translocations in both simulated and real cancer datasets. This analysis demonstrated a high level of performance in complex SV detection. Furthermore, because the ground-truth SV callset is incomplete, VolcanoSV-vc was further benchmarked to detect SVs from the CHM13 sample by employing the new reference genome T2T-CHM13. VolcanoSV-vc showed the lowest false discovery rate of any available tool. However, because the diploid assembly key component \u201cVolcanoSV-asm\u201d of VolcanoSV aims to achieve haplotype-resolved diploid assembly via heterozygous SNPs, it is not suitable for samples like CHM13, which\u00a0 is derived from a single homozygous complete hydatidiform mole to perform diploid assembly. In addition to SV detection, VolcanoSV also demonstrated superior performance in small variants detection and assembled a phased VCF file as the final product.\n\nIn comparison to existing SV callers, VolcanoSV is a comprehensive workflow, and we recommend users to use it directly to generate diploid assembly and detect variants. However, the VolcanoSV-asm component can be used independently to generate diploid assembly inputs for other assembly-based tools. Additionally, the VolcanoSV-vc component can be independently used to detect variants by taking any diploid assembly as input from other assembly tools. We highlight two noteworthy and advantageous features of VolcanoSV: (1) It combines both the contig-based signatures and read-based signatures to jointly infer and filter structural variants, resulting in a superior level of sensitivity and precision. Alignment-based methods heavily rely on the reads\u2019 quality and the aligner\u2019s capabilities. In regions with high repetition or other complexities, most aligners struggle to confidently map reads, leading to reduced sensitivity in SV detection. Our reference-assisted de novo assembly-based approach generates longer contigs, effectively resolving the issue of alignment ambiguity. However, genome-wide assembly may introduce artifacts and produce false calls. Therefore, employing contig-based signatures to generate a draft SV map and combining it with read-based signatures for false positive filtering represents a judicious hybrid approach, harmonizing the strengths of both approaches and compensating for their respective limitations. (2) VolcanoSV implements a k-mer-based reads partitioning method, significantly improving the ratio of reads that are suitable for diploid assembly, therefore boosting the variant calling performance. We consider this k-mer-based method as an innovative and complementary strategy for phasing. Current phasing software can only make use of the heterozygous SNP information, while the heterozygosity introduced by indels or SVs is ignored. This k-mer-based method takes into consideration the heterozygosity caused by indel and any other variants, therefore facilitating the generation of a more comprehensive haplotype-resolved assembly. Although assembly-based variant detection approaches are often much more demanding in computational resources than alignment-based approaches, assembly-based approaches are more likely to generate comprehensive SV calls with precise breakpoints and alternate sequences across the whole genome. VolcanoSV incorporates hifiasm and Flye as default assemblers for local assembly, however, users have the flexibility to utilize an appropriate or new assembler based on their needs.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "VolcanoSV uses a reference sequence and long reads data to generate a high-quality haplotype-resolved diploid assembly, from which it then comprehensively detects SVs and removes false positives. Not only limited to SVs, VolcanoSV also integrates two modules to collect SNPs and small indels, and further refine these small variants. For the final product, VolcanoSV provides users with a complete map of all types of phased variants. The main workflow of VolcanoSV consists of two key components (VolcanoSV-asm and VolcanoSV-vc) interconnected through six conceptual modules: (a) Partitioning reads relying on haplotype phasing (VolcanoSV-asm, Fig.\u00a01); (b) Unphased reads assignment through unique k-mer similarity analysis (VolcanoSV-asm, Fig.\u00a01); (c) Haplotype-aware local assembly via augmented phase blocks (VolcanoSV-asm, Fig.\u00a01); (d) Contig alignment-based large indel SV detection and refinement (VolcanoSV-vc, Fig.\u00a02); (e) Complex SV collection, recovery and filtering (VolcanoSV-vc, Fig.\u00a02); (f) Small indel collection and refinement (VolcanoSV-vc, Fig.\u00a02). The final output is a phased Variant Call Format (VCF) file. Details for each module are described in the following sections.\n\nVolcanoSV is a reference-assisted haplotype-resolved assembly approach using as input a phased alignment BAM file and a phased VCF file including all heterozygous SNVs. For haplotype phasing, VolcanoSV integrates Longshot (v0.4.1), a recently developed tool for SNV calling and phasing47. VolcanoSV then partitions reads into the corresponding haplotype within each phase block. Leveraging a Pair-Hidden Markov Model (pair-HMM) and the HapCUT2 algorithm48 for read-based haplotype estimation, Longshot has demonstrated exceptional utility in mitigating the challenges posed by the high error rates inherent to long sequencing reads47. In the total 14 long reads datasets we investigated, approximately 75% of the reads are assigned to a certain phase block and haplotype, while a substantial proportion of the reads are still not assigned to any phase block (Fig.\u00a01). We referred to these intractable reads from Longshot as \u201cunphased reads\u201d. These reads often do not cover enough or any heterozygous SNVs for a haplotype phasing algorithm to resolve them. We thus designed a new algorithm to partition them into two haplotypes within a certain phase block.\n\nTo accurately assign the unphased reads to the corresponding haplotype, a unique k-mer similarity-based cost-efficient approach is designed (Fig.\u00a01). The underlying mechanism relies on the fact that each haplotype (within each phase block) is composed of distinct sets of mutation events (SNPs, small indels, and SVs) and these events allow us to extract a haplotype-specific unique k-mer set to represent it. VolcanoSV can then assign unphased reads to the specific haplotype by comparing the correspondence between each unphased read and nearby haplotypes relying on unique k-mer sets similarity. iVolcanoSV utilizes every two adjacent phase blocks of phased reads to define unique k-mers and extract haplotype-specific unique k-mer sets for all four haplotypes. Unique k-mers are defined as ones only appearing in one of four haplotypes. For each unphased read, VolcanoSV also extracts k-mers and then quantifies the percentage of its unique k-mers which are assigned to each haplotype of two adjacent phase blocks. If an unphased read is originally drawn from one specific haplotype, it is expected to see a high correspondence between the unphased read and the haplotype. VolcanoSV uses an empirical distribution quantile-based significance test to quantify the correspondence based on four calculated percentages and then assigns each unphased read. If an unphased read cannot be assigned when the significance test does not pass the criterion, VolcanoSV assumes this read to be drawn from both haplotypes and assigns it to both haplotypes of its nearest phase block. At the end of this module, unphased reads are partitioned to the corresponding haplotype for each phase block. The detailed methods are described as follows:\n\nFirstly, VolcanoSV assigns each unphased read to its candidate phase blocks. The criteria for determining the candidate phase block are as follows:\n\nIf the unphased read overlaps with the phase block(s) according to the coordinates, the overlapping phase block(s) will be assigned as its candidate phase block(s).\n\nIf the unphased read does not overlap with any phase block, but is in the gap between two consecutive phase blocks, these two phase blocks will be assigned as candidates.\n\nIf the above two conditions do not apply, i.e., the unphased read is aligned to the start or end segment of a chromosome and does not overlap with any phase block, the nearest phase block will be assigned as the candidate.\n\nAs a result, every unphased read is assigned to at least one phase block and two PS_HPs. \u201cPS_HP\u201d refers to one haplotype of a phase block in the following context.\n\nSecondly, to determine which PS_HP (haplotype of a phase block) the unphased read is drawn from, a unique k-mer similarity-based analysis is performed. VolcanoSV first collects a raw k-mer set for every candidate PS_HP. The raw k-mer set for a PS_HP is the union of k-mers from all phased reads belonging to this PS_HP. When collecting k-mers, the length of kmer is set to 12, and step size is set to 1 by default. Next, VolcanoSV creates \u201cfingerprint\u201d (unique) k-mer sets that are exclusive to every PS_HP. The fingerprint k-mer set of a PS_HP is defined as the intersection between the raw k-mer set of this PS_HP and the symmetric difference among all candidates PS_HPs. For example, if an unphased read has 4 candidate PS_HPs, of which the raw k-mer sets are R1, R2, R3, R4, then the symmetric difference among them is\n\nThe fingerprint (unique) k-mer set of each PS_HP is defined as follows\n\nDenoting the k-mer set of the unphased read as S, the unique k-mer similarity metrics between the unphased read and the four candidates PS_HPs are then defined as the size of their k-mer sets intersections.\n\nThe normalized similarity metrics for the four candidates PS_HPs are calculated as follows\n\nVolcanoSV repeats this procedure for all unphased reads and their candidate PS_HPs. VolcanoSV thus collects all normalized similarity metrics, forming a normalized similarity vector \u03c7. To finally determine which PS_HP the unphased read is drawn from, VolcanoSV utilizes an empirical distribution quantile-based significance test to evaluate the normalized similarity metrics between unphased reads and candidate PS_HPs. A level r (10% by default) is used, and the cut-off threshold for significance is the (1\u00a0\u2212\u00a0r) quantile of the normalized similarity vector \u03c7. Metrics exceeding this threshold are considered significant, and reads are assigned accordingly. The null hypothesis (H0) posits that the normalized similarity metric between the unphased read and the candidate PS_HP is not significantly different from what would be expected by random chance, i.e., \\({{{{\\rm{NormSim}}}}}_{i}\\le {Q}_{1-r}({{{\\boldsymbol{\\chi }}}})\\). For each unphased read, we compare its normalized similarity metric to the cut-off Q1\u2212r(\u03c7). If the metric is higher than the cut-off, it is considered significant, suggesting a potential association with the corresponding PS_HP. Conversely, if a normalized similarity metric for an unphased read does not exceed the cut-off, we fail to reject the null hypothesis for that specific read and candidate PS_HP combination, implying that there is no significant association and the observed similarity might be due to random chance. If an unphased read can not be assigned to any candidate PS_HP based on the significance test, it will be partitioned to both haplotypes of its nearest phase block.\n\nOnce all unphased reads are partitioned to the corresponding haplotype of a certain phase block, VolcanoSV performs haplotype-aware local assembly on all partitioned reads for each haplotype of all augmented phase blocks. For PacBio Hifi data, VolcanoSV employs hifiasm (v0.14)26 to perform the local assembly. For PacBio CLR or ONT data, Flye27 is utilized for assembly in this study. Additionally, users have the flexibility within this module to choose the most optimal or new assemblers to fulfill their specific requirements. We also added functionality allowing users to select specific assemblers for target regions defined by a BED file. At the end of this module, haplotype-resolved contiguous sequences (contigs) are generated for each phase block.\n\nTo detect SVs based on haplotype-resolved contigs, VolcanoSV performs a contig-to-reference alignment using minimap2 (v2.24-r1122)39. VolcanoSV then collects large insertion (INS), deletion (DEL), inversion (INV), translocation (TRA), and duplication (DUP) signatures relying on the contigs alignment BAM file. For small insertions and deletions (indels), signatures are usually inferred through the CIGAR field of the BAM file (intra-alignment), while the signatures of large INS, DEL, INV, TRA, and DUP can only be inferred through split alignment (inter alignment). Specifically, for large indel SVs, VolcanoSV adapts and optimizes the reads signature methods from traditional reads alignment-based tools17,49 to haplotype-resolved contigs alignment to collect signatures.\n\nLarge INSs are detected when two disjoint segments on a contig are aligned to two adjacent segments on the reference genome in the same orientation. Likewise, large deletions are inferred when two adjacent segments on a contig are aligned to two disjoint segments on the reference genome with a gap in between. We denote the aligned segment\u2019s start and end coordinates relative to a contig as Contig_s(start) and Contig_e(end), and relative to the reference as Ref_s(start) and Ref_e(end). These notations are also illustrated in the pipeline Fig.\u00a02. Two metrics can be defined using the start and end coordinates of a pair of split-alignments from a single contig (two segments from the split-alignment are referred to as indices 1 and 2 in the metrics below).\n\nDiff_dis is defined as the difference between the distance of two segments on the contig and their distance on the reference. Specifically, in the presence of an INS, the distance between the two segments on the contig will be greater than their distance on the reference because a subset of the contig sequence can not be continuously aligned to the reference. In the case of a DEL, the situation is reversed due to the loss of sequence from the contig compared to the reference. In practice, Diff_dis should be approximately equal to the size of SVs. Therefore, we defined an empirical threshold parameter for Diff_dis, denoted as THolp, which is set to 30\u2009bp by default. This ensures the signature VolcanoSV generates will adequately cover any SV larger than or equal to 50\u2009bp.\n\nDiff_olp represents the overlapping portion of the alignment between two segments. For INSs, this alignment occurs on the reference sequence, whereas for DELs, it occurs on the contig. The value of Diff_olp should not be excessively large, even in the presence of a large INS or DEL. In the case of INSs, if two non-contiguous segments flanking the inserted sequence are accurately aligned to the reference, their alignment records should be contiguous on the reference. A significant overlap on the reference, despite a large Diff_dis, still suggests a false positive. Similarly, for deletions, if the two segments on the contig exhibit a substantial overlap, this likely indicates a false positive. To mitigate false positives, a threshold parameter for Diff_olp, denoted as TH_olp, has been introduced and is set to 3000\u2009bp by default.\n\nSo, VolcanoSV decides a large INS exits if the following conditions apply\n\nor a large DEL exits if the following conditions apply\n\nIf the rule applies, the INS signature is collected by VolcanoSV as follows\n\nThe DEL signature is collected by VolcanoSV as follows\n\nLarge indel SV signatures are collected by VolcanoSV on contigs from each haplotype separately. Ideally, the contig depth for one haplotype should be equal to 1 for any position and every signature should be disjoint perfectly. However, in practice, due to assembly artifacts and contig alignment issues, there may be overlapping contigs aggregated at certain positions, which could cause the signatures to have redundancy. Therefore, VolcanoSV employs a clustering algorithm to refine signatures collected from each haplotype. The clustering procedure is illustrated in the pipeline Fig.\u00a02. In the first step, signatures are divided into two categories, INS and DEL, and are sorted by breakpoint positions respectively. Next, VolcanoSV uses a nearest-neighbor chain algorithm to cluster (merge) the signatures of each category. To achieve this, each signature is considered as a node, if the similarity between two adjacent nodes passes a certain similarity threshold, an edge is added between them. VolcanoSV calculates the similarity metrics from the first to the last pair of signature nodes in the sorted list. As a result, a substantial number of disjoint subclusters are generated and each subcluster represents an SV call. In most cases, the subcluster contains only one signature, which is directly selected as the actual SV call. In the few cases where a subcluster contains more than one signature, VolcanoSV selects the signature with the largest length as the actual SV call. In more detail, the similarity metric between each pair of INS signatures is computed as follows (Each pair of signatures is referred to as indices 1 and 2 in the metrics below):\n\nThe condition for two INS signature nodes to be clustered together is\n\nwhere THshift_intrahap is a distance threshold parameter used to merge any two INS signatures from the same haplotype. VolcanoSV sets this threshold to 100\u2009bp by default to ensure a stringent merging of signatures within the same haplotype. \\({{{{\\rm{TH}}}}}_{{{{\\rm{sim}}}}}\\) is another threshold for SV similarity, specifically SV length, used to merge any two INS signatures from the same haplotype. Once a stringent THshift_intrahap is applied, VolcanoSV sets a moderately tolerant threshold for \\({{{{\\rm{TH}}}}}_{{{{\\rm{sim}}}}}\\), which is 0.5 by default.\n\nThe similarity between each pair of DEL signatures is measured as follows:\n\nThe condition for two DEL signature nodes to be clustered together is\n\nThe default threshold values of THshift_intrahap and \\({{{{\\rm{TH}}}}}_{{{{\\rm{sim}}}}}\\) for DEL are the same as those for INS. For any two DEL signatures, VolcanoSV also evaluates their overlapping ratio and uses the same similarity threshold parameter, \\({{{{\\rm{TH}}}}}_{{{{\\rm{sim}}}}}\\).\n\nAfter collecting and clustering signatures from each haplotype, a pairing algorithm is applied to merge large indel SV calls and determine the genotype. SV calls from each haplotype are first merged and sorted by position, for INS and DEL SVs respectively. Next, a chaining procedure similar to the previous within-haplotype signature clustering is performed on the sorted SV list under a different threshold setting. Specifically, an edge will be added between two INS SVs if the following condition applies:\n\nAn edge will be added between two DEL SVs if the following condition applies:\n\nCompared to intra-haplotype clustering, the distance threshold for inter-haplotype clustering is relatively more relaxed. THshift_interhap is set to 200\u2009bp by default because more influencing factors, such as assembly artifact and alignment ambiguity, are expected to affect the signatures between two haplotypes. The SV similarity threshold, specifically for SV length and DEL overlapping ratio, remains the same as in intra-haplotype clustering, setting to 0.5 by default.\n\nAs a result, a substantial number of subclusters is generated to represent INS and DEL SVs, respectively. Similar to the procedure in within-haplotype signature clustering, the largest SV call in each subcluster is kept. In addition, if a subcluster contains SVs from both haplotypes, the genotype is labeled as \u201c1\u22231\u201d. If a subcluster contains SVs from only one haplotype, the genotype is labeled as \u201c0\u22231\u201d or \u201c1\u22230\u201d depending on which haplotype the SVs are drawn from with a specific phase block.\n\nInferred from the contig-to-reference alignment file, the raw SV calls might contain some noise or artifacts introduced by the assembly and alignment procedure. Therefore, a filtering algorithm based on read alignment information is designed to remove false calls from raw SV calls.\n\nFor large indel SVs, similar to the procedure of collecting signatures from the contig-to-reference alignment file, reads-based SV signatures are first collected by scanning the read-to-reference file. Next, for each raw SV call, VolcanoSV scans through all read-based signatures of the same type (either INS or DEL) within the 1\u2009kb region centered around the inferred breakpoint of the raw SV and calculates the SV length similarity. Denoting the size of raw SV as svlenraw and the size of read-based signature as svlenread, the SV length similarity between them is calculated as follows\n\nIf the SV length similarity is greater than the minimum SV length similarity threshold (0.5 by default), this read-based signature will be regarded as supporting the raw SV. A raw SV call needs at least one supporting signature, otherwise it will be filtered out as a false call. Since the read-based alignment information is more reliable for small-size SVs, we only apply this filtering procedure for indel SV signatures within the size range of 50\u2013250\u2009bp.\n\nCompared to INS calls, whole-genome assembly-based methods like VolcanoSV often generate more false positive DEL calls. To further remove false positives in DEL calls, a more stringent filtering method is required. VolcanoSV first scans through read-based signatures and collects all DEL signatures that are within the 1\u2009kb flanking region around the breakpoint of each candidate DEL call. Next, VolcanoSV calculates the supporting signature depth for each candidate DEL call in the following fashion:\n\nwhere svlen_sigi is SV length of the ith supporting signature within the flanking region, and N is the number of supporting signatures in the flanking region. By collecting the supporting signature depth metrics for all candidate DELs, VolcanoSV obtains a vector R. A DEL SV is considered true if the below condition applies:\n\nwhere \\(\\widetilde{{{{\\bf{R}}}}}\\) is the median value of R, and lb and rb are the left and right boundary ratios, which are dependent on the long reads data type. For example, for Hifi data, lb = 0.2 and rb = 2.6; For CLR data, lb = 0.19 and rb = 3.0; for ONT data, lb = 0.24 and rb = 2.8. Those parameters are drawn empirically.\n\nTo further remove the redundancy in the filtered SV call set, we designed a rigorous one-to-K clustering algorithm to identify duplicate SV calls in which SV calls are first separated by category and sorted by position. VolcanoSV then compares each SV and its K neighboring SVs (within the flanking region of 500\u2009bp) and calculates similarity metrics. VolcanoSV adds an edge between this SV and any neighboring SV that passes the similarity threshold. The similarity metric includes breakpoint distance, SV length similarity, and SV sequence similarity (edit distance).\n\nFor INS SVs, the threshold for adding an edge between two calls is\n\nFor DEL SVs, the threshold for adding an edge between two calls is\n\nwhere THshift_redun_INS and THshift_redun_DEL are set to 500\u2009bp and 300\u2009bp, respectively. The SV similarity threshold parameter, \\({{{{\\rm{TH}}}}}_{{{{\\rm{sim}}}}}\\), retains the default setting of 0.5. For the DEL length similarity threshold, \\({{{{\\rm{TH}}}}}_{{{{\\rm{sim}}}}\\_{{{\\rm{relax}}}}}\\) is set to 0.1 by default. The distance threshold is more relaxed compared to previous intra- and inter-haplotype clustering to further remove redundancy that was overlooked in the earlier clustering steps. THshift_redun_DEL is more stringent than THshift_redun_INS, while the \\({{{\\rm{svlen}}}}\\_{{{\\rm{sim}}}}\\) threshold for DEL is more relaxed than for INS, reflecting observed redundancy distribution patterns: redundant INSs tend to be further apart with highly consistent sizes, whereas redundant DELs are typically closer together with more variable sizes. These empirically derived thresholds ensure that VolcanoSV achieves both sensitive and highly accurate clustering.\n\nAfter all edges are added, VolcanoSV selects the SV call of the largest SV length as the final prediction for each subcluster. This one-to-K clustering method is more extensive than previous intra and inter-haplotype clustering because it is more tolerant to noise or false calls when chaining consecutive SVs. For example, let\u2019s consider three consecutive SV calls: a 1\u2009kb INS, 60\u2009bp INS (very likely a false call), and 1.01\u2009kb INS. In the previous chaining algorithm, since the second call is not similar to either the one before or the one after, the chain would be broken and the two otherwise very similar INS calls can not be clustered together. However, they will be clustered together in the one-to-K cluster algorithm since any SV pairs within a certain size range will be compared. In addition, this clustering method is also rigorous by taking sequence similarity into consideration to avoid false clustering.\n\nSince every contig produced by VolcanoSV\u2019s pipeline is associated with a certain phase block and a haplotype, it is straightforward to pair SV calls from contigs of both haplotypes within a phase block to determine the phased genotype of SV calls. VolcanoSV applies a heuristic decision tree model to further refine the genotype for large indel SVs.\n\nSpecifically, VolcanoSV takes five parameters as input: genotype inferred by contigs (0\u22231 or 1\u22231), SV size (binary variable, equal to 1 if the SV size is greater than 1\u2009kb, otherwise equal to 0), SV type (DEL or INS), long reads sequencing technology (Hifi, CLR or ONT), and the relative supporting read-based signature ratio (number of the supporting read-based signatures/local read depth). VolcanoSV then uses an empirical threshold to predict the genotype. In more detail, the first four parameters are categorical and they define a tree structure with 24 leaf nodes, while the last parameter, the relative supporting signature ratio, is a continuous variable ranging from 0 to 1. Ideally, the relative supporting signature ratio should be tightly associated with the actual genotype, i.e., the relative ratio should be close to 0.5 for heterozygous SVs, and 1 for homozygous SVs. However, in practice, this relative ratio varies due to complex confounding factors (SV type, SV size, sequencing technology, etc). Therefore, VolcanoSV applies 24 different thresholds with respect to the relative supporting signature ratio, with the 24 leaf nodes. For any large indel SV, given the first four parameters, VolcanoSV will first decide the leaf node and the corresponding threshold for the relative supporting ratio, then use the threshold and the observed relative supporting ratio to predict the genotype.\n\nWith respect to complex SVs including inversions (INVs), translocation (TRAs), and duplications (DUPs), VolcanoSV integrates the signature collection pipeline from SVIM-asm24 and implements a specific duplication recovery and complex SV filtering pipeline to generate the final complex SV calls.\n\nINVs are inferred when two adjacent segments on the contig are aligned to the reference genome in different orientations. To filter false calls, VolcanoSV first adds a 1\u2009kb flanking region around each INV, and then investigates aligned reads information around both the start and end position of the INV. In either breakpoint position, if there exists at least one read that is aligned to the reference genome in two distinct orientations, this inversion signature will be then kept, otherwise, it will be removed as a false call.\n\nTRAs are inferred when two segments from a contig are aligned to two different chromosomes. To filter false calls, VolcanoSV first adds a 1\u2009kb flanking region around each TRA. VolcanoSV then investigates the aligned reads information around both breakends (BND) of the TRA. If a certain amount of reads (\u22650.25*read depth) is aligned to both breakends, the TRA call will be kept, otherwise it will be filtered out as a false call.\n\nDUPs are inferred when two adjacent segments on the contig are aligned to identical or overlapping segments on the reference genome. For DUPs, the scenario is very different from the other SV types. Due to the limitation of the current alignment method, a significant amount of DUPs in the contigs are in fact treated as INSs in the alignment practice. As a result, DUP events are most often represented either by an insertion cigar or a split-alignment that leads to INS prediction. Adapted to this scenario, VolcanoSV utilizes a specific pipeline to recover the missed DUP calls from INS calls. Specifically, VolcanoSV extracts the alternate allele sequences of all INS calls and aligns them back to the reference genome. If an inserted sequence is aligned back to a position that is close to the insertion breakpoint, then this insertion call is recovered as a DUP call. Through this recovery procedure, VolcanoSV achieves a comprehensive and reliable duplication discovery.\n\nFor small (2\u201349\u2009bp) indel calling, VolcanoSV integrates a signature collection pipeline from Dipcall. This pipeline scans through the contig-to-reference BAM file to gather the indel signatures. VolcanoSV then utilizes a customized k-mer content analysis to filter and refine the indel calls. In more detail, after collecting the indel signatures from the contig-to-reference BAM file, VolcanoSV first extracts the indel context sequence (with an additional 20\u2009bp flanking region around each indel) directly from the contig. Subsequently, for each indel, VolcanoSV assesses the k-mer content from the reads-aligned BAM file within the 100\u2009bp region centered around the indel breakpoints. For a candidate indel, if more than 70% of the kmers in its contig context are also present in the k-mer content of the reads from the corresponding region, this indel will be counted as a high confidence call; otherwise, it will be regarded as an assembly or alignment artifact and will be filtered out. For SNP calling, VolcanoSV by default provides the phased SNP call results inferred by Longshot.\n\nThe GIAB community provides a gold standard SV set for the HG002 sample50, which includes 4117 deletions and 5281 insertions in defined \u201chigh-confidence\u201d regions characterized by multiple sequencing platforms. SV calls (deletions and insertions) from all tools were evaluated against this benchmark using Truvari30, which is a commonly used open-source toolkit for the comparison, annotation, and analysis of structural variation.\n\nTruvari provides metrics/parameters including pctstim (p), pctsize (P), pctovl (O), and refdist (r) to set different criteria for SV evaluation depending on the needs of the specific analysis. The parameter p controls the minimum allele sequence similarity used to identify two SV calls as identical. The similarity is calculated from the edit distance ratio between the reference and alternate haplotype sequences of the base and comparison call. Setting p to zero can disable this comparison. The parameter P corresponds to the minimum allele SV length similarity between the compared SVs, which is calculated by dividing the length of the shorter SV with the longer one. The parameter O determines the minimum threshold of the reciprocal overlap ratio between the base and comparison call, and it is only applied to deletions for evaluating the effect of breakpoint shift on deletion accuracy. The parameter r represents the threshold for maximum reference location difference of the compared SVs, which can be used to evaluate the effect of breakpoint shift on insertion accuracy. In general, higher values of p, P and O, and lower values of r set more stringent comparison criteria, as they will require the compared SVs to have higher sequence and SV length similarity, larger spatial overlapping ratio, or closer location to the reference sequence to be considered as the same SV.\n\nBreakpoint shift is calculated from the reference genome location difference between the true positive SVs called by the tools and the corresponding benchmark SVs. Called SVs and benchmark SVs are paired up by the \u201cMatchID\u201d provided by Truvari. For each deletion, the start and end coordinate differences between the called SV and benchmark SV are calculated, and the maximum value of these two is chosen as the value for breakpoint shift. For insertions, the breakpoint shift is defined as the start coordinate difference. Breakpoint shift values larger than 200\u2009bp are merged to the 200+bp bin in the distribution plot.\n\nWe used the UCSC Genome Browser51,52 to load various tracks for annotating and visualizing false discoveries identified by four assembly-based tools. To annotate heterochromatin regions53,54, such as centromeres and telomeres, in the T2T-CHM13 reference, we downloaded two BED files from https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/annotation/chm13v2.0_censat_v2.0.bedand https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/annotation/chm13v2.0_telomere.bed. These BED files were merged and intersected with the false discoveries using bedtools55.\n\nbedtools intersect -a ${FD_VCF} -b ${Telo_CenSat_BED} -u > ${out_VCF}\n\nTo assess the quality of our assemblies, we employed a read-based pipeline, Flagger45, to annotate the contig regions. Flagger identifies different types of misassemblies within a phased diploid assembly. The pipeline performs haplotype-aware mapping of long reads to the combined maternal and paternal assembly. It identifies potential assembly errors by pinpointing coverage inconsistencies in these mappings.\n\nIn this study, we utilized the basic version of Flagger (flagger_end_to_end_no_variant_calling_no_ref_no_secphase.wdl, which can be found at\u00a0https://github.com/mobinasri/flagger/releases/tag/v0.3.3). The input files included the read-to-contig BAM file and the contig FASTA file. The primary input parameters were maxReadDivergence and window size. For maxReadDivergence, we set the value to 0.02 for HiFi and 0.09 for ONT and CLR data, as recommended by the Flagger GitHub manual (https://github.com/mobinasri/flagger). The default window size used by Flagger is 5\u2009MB, which is suitable for most whole-genome scale assemblies. However, since the VolcanoSV-asm pipeline is based on a local assembly strategy, we adjusted the window size to 150\u2009kb to accommodate all contigs, including those shorter than 5\u2009MB. With these input files and parameter settings, Flagger fits the Gaussian mixture model and generates a BED file with contig regions annotated by four labels: haploid, error, (falsely) duplicated, and collapsed. Regions labeled as haploid are expected to have error-free assemblies. The erroneous component, modeled by a Poisson distribution, represents the regions with very low read support. The (falsely) duplicated component is characterized by regions with only half of the haploid component\u2019s mean coverage. The collapsed component\u2019s mean coverage is constrained to be multiples of the haploid component\u2019s mean. We utilized collapsed components to annotate potential misassemblies enriched in segmental duplications (SDs).\n\nTo assess the SD reliability of the Flagger annotation, we aligned the regions of collapsed components from diploid assemblies by VolcanoSV to the GRCh38 reference genome and intersected them with the SD annotations for HG002 based on GRCh38. The HG002 SD annotation file was downloaded from https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/v3.4/. We calculated and compared the total length of SD annotation regions that overlap with collapsed regions by Flagger across all VolcanoSV assemblies, which were generated using different assemblers including wtdbg2, Shasta, NextDenovo, and Hicanu.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "PacBio CLR, Hifi, and ONT sequencing reads for HG002 are available at GIAB and NCBI. The high-confidence HCC1395 somatic SV callset and the Pacbio and ONT Tumor-Normal paired libraries of HCC1395 are publicly accessible at NCBI. PacBio Hifi sequencing reads for CHM13 are available at NCBI. Table\u00a01 lists hyperlinks for all 20 previously mentioned real datasets. The Tier1 benchmark SV callset and high-confidence HG002 region were obtained from https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/NIST_SVs_Integration_v0.6/. T2T assembly is publicly available at https://github.com/marbl/CHM13. The HG002 SD annotation file was downloaded from https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/genome-stratifications/v3.4/. All assembled contig files and VCF files that support the findings of this study are available from https://doi.org/10.5281/zenodo.10456757. Data and code required to reproduce the results presented in this study are available in the Source Data file.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All code is available at https://github.com/maiziezhoulab/VolcanoSV under the MIT License56.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Sudmant, P. H. et al. 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VolcanoSV-v1.0. https://doi.org/10.5281/zenodo.12671886 (2024).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by the NIH NIGMS Maximizing Investigators\u2019 Research Award (MIRA) R35 GM146960 to X.M.Z.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Can Luo, Yichen Henry Liu.\n\nDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA\n\nCan Luo\u00a0&\u00a0Xin Maizie Zhou\n\nDepartment of Computer Science, Vanderbilt University, Nashville, TN, USA\n\nYichen Henry Liu\u00a0&\u00a0Xin Maizie Zhou\n\nData Science Institute, Vanderbilt University, Nashville, TN, USA\n\nXin Maizie Zhou\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nX.M.Z. conceived and led this work. C.L and X.M.Z. designed the framework. C.L. implemented the framework, and C.L. and Y.H.L. performed data analysis. C.L, Y.H.L, and X.M.Z. wrote the manuscript.\n\nCorrespondence to\n Xin Maizie Zhou.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Derek Bickhart and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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phonon-mediated dephasing of color centers in hexagonal boron nitride probed by electron beams", + "journal": "Nature Communications", + "published": "08 March 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57584-1/MediaObjects/41467_2025_57584_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57584-1/MediaObjects/41467_2025_57584_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57584-1/MediaObjects/41467_2025_57584_MOESM3_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-57584-1#Fig1", + "/articles/s41467-025-57584-1#Fig2", + "/articles/s41467-025-57584-1#Fig3", + "/articles/s41467-025-57584-1#Fig4", + "https://doi.org/10.5281/zenodo.14832120", + "/articles/s41467-025-57584-1#ref-CR61", + "/articles/s41467-025-57584-1#Fig1", + "/articles/s41467-025-57584-1#Fig2", + "/articles/s41467-025-57584-1#Fig3", + "/articles/s41467-025-57584-1#Fig4", + "/articles/s41467-025-57584-1#Sec13" + ], + "code": [], + "subject": [ + "Optical materials and structures", + "Optical spectroscopy" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3857268/v1.pdf?c=1741604794000", + "research_square_link": "https://www.researchsquare.com//article/rs-3857268/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-57584-1.pdf", + "preprint_posted": "28 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Defect centers in hexagonal boron nitride (hBN) have been extensively studied as room-temperature single-photon sources. The electronic structure of these defects exhibits strong coupling to phonons, as evidenced by the observation of phonon sidebands in both photoluminescence and cathodoluminescence spectra. However, the dynamics of the electron-phonon coupling as well as phonon-mediated dephasing of the color centers in hBN remain unexplored. Here, we apply a novel time-resolved CL spectroscopy technique (Nature Physics\u00a019, 869\u2013876 (2023)) to explore the population decay to phonon states and the dephasing time T2 with sub-femtosecond time resolution. We demonstrate an ultrafast dephasing time of only 200 fs and a radiative decay of about 585 fs at room temperature, in contrast with all-optical time-resolved photoluminescence techniques that report a decay of a few nanoseconds. This behavior is attributed to efficient electron-beam excitation of coherent phonon-polaritons in hBN, resulting in faster dephasing of electronic transitions. Our results demonstrate the capability of our sequential cathodoluminescence spectroscopy technique to probe the ultrafast dephasing time of single emitters in quantum materials with sub-femtosecond time resolution, heralding access to quantum-path interferences in single emitters coupled to their complex environment.Physical sciences/Optics and photonics/Optical materials and structures/NanoparticlesPhysical sciences/Optics and photonics/Optical physics/Polaritons", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "hBNEDPHSTalebiSupplementary.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Defect centers in hexagonal boron nitride (hBN) have been extensively studied as room-temperature single-photon sources. The electronic structure of these defects exhibits strong coupling to phonons, as evidenced by the observation of phonon sidebands in both photoluminescence and cathodoluminescence spectra. However, the dynamics of the electron-phonon coupling as well as phonon-mediated dephasing of the color centers in hBN remain unexplored. Here, we apply a novel time-resolved CL spectroscopy technique to explore the population decay to phonon states and the dephasing time T2 with sub-femtosecond time resolution. We demonstrate an ultrafast dephasing time of only 200\u2009fs and a radiative decay of about 585\u2009fs at room temperature, in contrast with all-optical time-resolved photoluminescence techniques that report a decay of a few nanoseconds. This behavior is attributed to efficient electron-beam excitation of coherent phonon-polaritons in hBN, resulting in faster dephasing of electronic transitions. Our results demonstrate the capability of our sequential cathodoluminescence spectroscopy technique to probe the ultrafast dephasing time of single emitters in quantum materials with 1.5\u2009fs time resolution, heralding access to quantum-path interferences in single emitters coupled to their complex environment.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Van der Waals materials have been extensively studied due to their fascinating multi-physics functionalities. They provide a platform for strongly correlated materials1,2 and a landscape for polariton physics3. In particular, hexagonal boron nitride (hBN) appears to be a strong candidate for the various forms of light-matter interactions. In the\u00a0infrared, hBN hosts phonon polaritons4,5,6, while in the visible and ultraviolet, defect centers in hBN appear as room-temperature single-photon emitters7,8,9. Several forms of defect centers, emitting number-state photons with their wavelengths spanning the entire visible to ultraviolet range have been reported and studied using photoluminescence (PL)10,11 and cathodoluminescence (CL)9,12 spectroscopy. The emitters in hBN also exhibit exceptional spin13,14 and electro-optical15,16 properties.\n\nRoom-temperature photon emission from defect centers in other materials, such as diamond17,18 and GaN19, has been extensively researched in parallel and has established itself as a paradigm for quantum-optics-based technologies20. However, equivalent emitters in a thin van der Waals material with a refractive index lower than that of diamond allow for an efficient implementation of the emitters in solid-state quantum networks and devices, enabling a broad range of applications for future quantum technologies21,22,23. Defect centers in hBN, therefore, manifest themselves as such a candidate.\n\nThe photophysics of the defect centers in hBN is characterized by strong coupling of the emitters to phonons24,25,26. The excitation of phonons reduces the dephasing time of electronic transitions and represents a limit for the realization of Fourier-transform-limited emitters. Therefore, the decoupling of quantum emitters trapped between hBN layers from in-plane phonon excitations has been discussed as a mechanism to achieve Fourier-transform-limited transitions27,28. Despite all these efforts, a direct probing of the phonon-mediated decoherence mechanisms and dephasing of single hBN emitters has remained unexplored, partly due to the limitations of all-optical techniques to probe femtosecond dynamics at deep subwavelength spatial dimensions.\n\nIn contrast to light, electron beams can be focused to sub-nanometer spot sizes and excite single defect centers in solid-state materials, such as hBN29 and diamond30. In particular, several forms of defect centers have been studied using CL spectroscopy12,31,32,33 that emit in the entire visible range. However, the photophysics dynamics of the emitters, such as the phonon-mediated dephasing of single emitters, is still unexplored, even with electron beams.\n\nHere, we use a recently developed phase-locked photon\u2013electron spectroscopy technique based on sequential CL spectroscopy to unravel the phonon-mediated dephasing time of quantum emitters34,35. Using a broadband metamaterial-based electron-driven photon source36,37 (EDPHS), which emits sub-cycle photons with a collimated spatial profile and temporal distribution of 1.4\u2009fs, we generate a coherent superposition of phonon states. Therefore, the CL emission from quantum emitters after the interaction with the EDPHS radiation exhibits coherent and incoherent contributions. Moreover, distinguishing the dynamics of coherent and incoherent CL emission from the delay between the electron and EDPHS photons exciting the sample leads to the determination of both the population relaxation (\\({T}_{1}\\)) and dephasing (\\({T}_{2}\\)) time scales of the emitters interacting with electron beams, which are of the order of \\({T}_{1}=200\\,{{\\rm{fs}}}\\) and \\({T}_{2}=580\\,{{\\rm{fs}}}\\), which is significantly smaller than the reports based on all-optical characterization techniques. We provide a theoretical model based on a master equation for single emitters coupled to both EDPHS light and electron beams, which agrees well with our experimental results, and demonstrates the important aspect of incoherent electron excitation in understanding the physics of the interactions and the increase in the decay time.\n\nOur work not only provides valuable insights into the photophysics of hBN emitters coupled to phonons, but also enables a deep understanding of the mechanisms of the radiation from emitters excited with electron beams. Moreover, it demonstrates the unique ability of our CL technique to couple to deep subwavelength emitters, paving the way for future applications in probing the dynamics of quantum emitters implemented in integrated photonic networks and solid-state quantum devices.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The hBN defects employed in our experiments are formed by liquid exfoliation of pristine hBN flakes from high-quality crystals onto holey carbon transmission electron microscopy grids. We have analyzed various liquids to identify quantum emitters that remain stable under intense electron beam illumination, as detailed in the Methods section. In particular, we found that the exploitation of isopropanol leads to scarcely-positioned stable emitters in thin hBN flakes. The emission wavelength of most stable emitters is centered at \\({\\lambda }_{1}=880\\,{{\\rm{nm}}}\\), that is followed by two phonon sidebands at the wavelengths of \\({\\lambda }_{2}=797{{\\rm{nm}}}\\) and \\({\\lambda }_{3}=670\\,{{\\rm{nm}}}\\) (Fig.\u00a01a, b). The electronic transitions in hBN defect centers are strongly coupled to phonon excitations, which form a quantum ladder in the potential landscape of the atomic defect26 and lead to sequential relaxation of the population generated by electron beams, and subsequent emission of photons in an incoherent manner (Fig.\u00a01a). This fact is confirmed by the CL spectra obtained from the defect centers at room temperature (Fig.\u00a01b).\n\nExperiment 1: a (Left) Schematic of an atomic defect in hBN and its energy diagram (right), representing a two-level electronic system coupled to higher energy phonon states. A swift electron interacting with the defect incoherently populates the defect to its higher energy states. Sequential transitions to lower energy phonon states, followed by an electronic transition to the ground state, emit photons of different wavelengths, resulting in the observed distinct peaks shown in the CL spectrum (b). Experiment 2: c An ultrabroadband and coherent chirped optical pulse, emitted from the EDPHS, generates a coherent superposition of the phonon states. After the electron interaction, the CL emission from the initially superpositioned system has two contributions: a coherent and incoherent CL radiation. The coherent CL emission is prominently recovered by acquiring the momentum-resolved CL radiation along specific angular ranges (angle-resolved CL mapping). d Spectral interferences in the acquired CL intensity for a defect center already prepared in a superposition, at the depicted polar angles \\(\\theta\\). The EDPHS spectrum is indicated by the green-shaded area. Source data are provided as a Source Data file.\n\nA moving electron exciting the defect statistically populates the quantum system into higher order states, mainly enabled by bulk plasmons38, phonons39,40, or cascaded interaction of secondary electrons with defects41, without generating a coherent superposition of the states. Incoherent light generation from single defects is associated with the generation of light in number states, where the expectation value of the field operator ideally vanishes, similar to PL experiments with defect centers42,43,44. This is fundamentally different from the interaction of quantum systems with coherent light pulses, which generate a coherent superposition of the states depending on the frequency and duration of the pulses45. The timescale within which the generated coherence remains in the system, i.e., the dephasing time, is crucial for the realization of Fourier-transform-limited single-photon sources, and interferometry techniques based on single-photon emitters46.\n\nIn order to better comprehend the dynamics of the quantum system interacting with the electron beams, we use here a quantum master equation that is particularly suitable for modeling the incoherent generation of photons, recast as42\n\nwhere \\(\\hat{\\rho }\\) is the density matrix and \\(\\hat{H}\\) is the system Hamiltonian. \\({\\hat{D}}_{{{\\rm{rad}}}}\\) and \\({\\hat{D}}_{{{\\rm{ex}}}}\\) are the Lindblad operators associated with the spontaneous emission and electron beam excitations, respectively (see Supplementary Note\u00a01). First, without interaction with external light, within which the Hamiltonian remains as \\({\\sum }_{n=1}^{N}\\hslash {\\omega }_{n}|n\\rangle \\langle n|\\), the dynamics of the diagonal terms of the density matrix are decoupled from the off-diagonal terms, representing the generation of an incoherent population due to the interaction with electron beams. Second, the generated CL emission from the system is modeled with the spectral density function, linked with the expectation value of the photon number operator (\\({\\sigma }^{+}{\\sigma }^{-}\\)) in the frequency domain and is derived as\n\nwhere \\({\\hat{\\sigma }}_{mn}^{+}\\) and \\({\\hat{\\sigma }}_{mn}^{-}\\) are the creation and annihilation operators associated with the transitions from the mth to the nth state. Within the weak interaction regime, \\(S(\\omega )\\) is related to the Fourier transform of the diagonal terms of the density matrix (\\({\\rho }_{nn}\\) with n\u2009>\u20091), thus relating the incoherent CL emission to the population relaxation. Particularly, we notice, that in order to model the CL spectra obtained experimentally here, the consideration of eight quantum states is required (see Supplementary Note\u00a01), mainly due to the anharmonic nature of the potential landscape, which leads to the free induction decay47 and an additional broadening of the CL phonon peaks. The phonon quantum states initiate from a molecular-like system with a densely packed and unequally spaced quantum ladder for phonons with their transition wavelengths to the ground state positioned between 550\u2009nm and 797\u2009nm (Supplementary Figs.\u00a01\u20133).\n\nTo probe the dephasing time of phonon states, we use an EDPHS as an internal radiation source inside a scanning electron microscope (SEM) to generate optical pulses that are phase-locked to the near-field of the moving electron36. Our EDPHS is fabricated using focused ion milling to create a pattern of distributed nanopinholes in a gold thin film, positioned on top of a Si3N4 membrane (see Supplementary Note\u00a02 and Supplementary Figs.\u00a04 and 5). The position of the nanopinholes is pre-designed to enable a collimated beam profile34, for an electron interacting with the EDPHS in the central region of the structure. The emission from the EDPHS is ultrabroadband, covering the spectral range from 560\u2009nm to 940\u2009nm (Fig.\u00a01d). This ultrabroadband and coherent emission, generates a coherent superposition of phonon states. The delay between the EDPHS radiation and the swift electron interacting with the sample is controlled via a piezo stage by changing the distance between the sample and the EDPHS as \\(\\tau=L({v}_{{{\\rm{el}}}}^{-1}-{c}^{-1})\\). Here, \\(L\\) is the distance between the sample and the EDPHS, \\({v}_{{{\\rm{el}}}}\\) is the group velocity of the electron in the vacuum, and \\(c\\) is the speed of light.\n\nTherefore, the CL emission from the incident electron beam interacting with the coherently superposed quantum states now has two counterparts: a coherent part and an incoherent part. The coherent radiation from the emission centers is distinguished from the incoherent emission by performing interferometry with the generated CL light from the sample and the EDPHS (See Supplementary Fig.\u00a06). The CL emission naturally interferes with the coherent EDPHS radiation, forming spectral interference fringes,\u00a0that is revealed by decomposing the total emission into its different angular components (Fig.\u00a01d). Moreover, as we will show below, the visibility of the interference fringes changes by changing the delay between the incoming electron and the EDPHS radiation, which allows us to retrieve the dephasing time of the generated phonon superposition. In this case, the Hamiltonian of the system interacting with the EDPHS radiation changes as \\(\\mathop{\\sum }\\limits_{n=1}^{N}\\hslash {\\omega }_{n}|n\\rangle \\langle n|-\\hat{\\mu }\\cdot \\overrightarrow{E}(t)\\), where \\(\\overrightarrow{E}(t)\\) is the electric field associated with the EDPHS radiation and \\(\\hat{\\mu }\\) is the dipole transfer matrix of the system. The coherent CL radiation arises from the induced coherent polarization in the system, modeled as \\(P(t,\\tau )=\\bf {tr}\\{\\hat{\\mu }\\hat{\\rho }(t,\\tau )\\}\\), where \\(t\\) is the elapsed time and \\(\\tau \\,\\) is the delay between the EDPHS and electron excitations. The polarization, unlike the expectation value of the photon number operator (Eq. (2)), is related to the off-diagonal elements of the density matrix, which is now nonzero due to the interaction with the EDPHS light. Therefore, its dynamic is related to the dephasing time of the system, as will be shown below.\n\nThe nature of the defect centers in hBN is widely debated. While almost all experiments clearly demonstrate the strong interaction between phonons and electrons, the nature of the atomic structure of the defect is still not fully understood. Here, we perform CL spectroscopy, high-resolution transmission electron microscopy (HRTEM), and low-energy electron energy-loss spectroscopy (EELS) to shed light on the nature of the defects and phonon excitations.\n\nOur CL spectroscopy measurements clearly show the excitation of two types of defects, whose emission wavelengths are strongly thickness dependent. In the region of interest where we perform our time-resolved CL spectroscopy measurements, we denote the excitation of electronic transitions, with the emission wavelength at \\({\\lambda }_{1}=880\\,{{\\rm{nm}}}\\), followed by two phonon sidebands (Fig.\u00a02a). The defect distribution is resolved by performing spectral imaging, where we plot the CL intensity corresponding to the emissions at \\({\\lambda }_{1}=880\\,{{\\rm{nm}}}\\), \\({\\lambda }_{2}=797{{\\rm{nm}}}\\), and \\({\\lambda }_{3}=670\\,{{\\rm{nm}}}\\) versus the scan position (Fig.\u00a02b). The spectral image associated with the electronic transitions at \\({\\lambda }_{1}=880\\,{{\\rm{nm}}}\\), shows a scattered distribution of the emitters. However, the first phonon peak is homogeneously distributed within the thicker region of the flake at some distance from the edge, and the second phonon transition is more localized at the edge.\n\na CL spectrum of the hBN flake integrated over the red box, featuring three different emission energies, marked by \\({\\lambda }_{1}\\), \\(\\,{\\lambda }_{2}\\), and \\({\\lambda }_{3}\\). The inset depicts an SEM image of the measured hBN flake, which is placed on a holey carbon film by liquid exfoliation. The red area marks the measurement area. b Hyperspectral CL images of the marked area for the three spectral peaks indicated. The purple box shows the position at which the results in Figs.\u00a03 and 4 are acquired. c Low-loss electron energy loss spectra of the flake showing three distinguished phonon peaks. The inset shows the transmission electron microscopy image of the flake. d Scanning EELS images of the inset figure in (c), integrated along the highlighted energy regions E1, E2, and E3, showing the spatial distribution of the phonon resonances. e High-energy EELS measurement of the hBN flake showing peaks at the energies associated with the boron and nitrogen K-edge transitions, but no peak for carbon. The results are obtained from the region shown by a black box in (f) right bottom panel. f (Left) HRTEM image of an hBN flake. The larger image shows the atomic structure of the flake, indicating the presence of some defects in the atomic lattice. The inset shows the Fourier transform of the image. (Right) The transmission electron microscopy image of the measured area and the resulting color-coded image representing the atomic composition of hBN flake on holey carbon. Here, boron and nitride are yellow and green, respectively, while carbon is blue and oxygen is red.\n\nIn order to explore the phonon excitations in our hBN flakes in more detail, we perform low-energy EELS (LE-EELS) with a Nion electron microscope48,49 (Fig.\u00a02c, d). Our LE-EELS measurements show the excitation of three distinct and closely spaced phonon peaks, at the energies of \\({E}_{1}=157 {{\\rm{meV}}}\\), \\({E}_{2}=169 {{\\rm{meV}}}\\), and \\({E}_{3}=186 {{\\rm{meV}}}\\) (Fig.\u00a02c). The difference between the CL peaks at \\({\\lambda }_{1}\\) and\u00a0\\({\\lambda }_{2}\\), when translated to the energy scale, agrees well with the phonon resonances revealed by the LE-EELS measurements. Moreover, the distribution of the phonon resonances at the resonant peak \\({E}_{2}=169 {{\\rm{meV}}}\\) is more localized along the edges, while the other resonances are more homogeneously distributed inside the bulk, which agrees well with the distribution of the phonon sidebands revealed by CL spectral imaging. The highly non-localized behavior of the phonon resonances as well as their strong thickness dependence suggest the excitation of phonon polaritons. hBN flakes are extensively studied within the Reststrahlen lower and upper bands, and electron beams couple particularly strongly to coherent hyperbolic phonon polaritons in hBN50. The energy range of the phonons in our flakes is within the upper Reststrahlen band of hBN (169\u2013200\u2009meV), which is sandwiched between the transverse-optical and longitudinal-optical phonon energies, and is expected to couple effectively to polaritons as well.\n\nIn addition, to better explore the nature of the electronic transitions, and in particular to understand whether the defects are due to external atomic impurities such as carbon51,52, we performed analytical high-energy EELS (HE-EELS) (Fig.\u00a02e). First, the HRTEM image of the flake shows the high-quality single-crystal nature of the flakes (Fig.\u00a02f). The Fourier-transformed image as displayed in the inset, better represents the crystallinity of the flakes. Moreover, the high-energy electron energy-loss spectrum does not show any K-edge transition associated with the carbon (\\({E}_{{{\\rm{C}}}}\\,=\\,290\\,{{\\rm{eV}}}\\)) within the acquisition window considered here (Fig.\u00a02e). Therefore, we rule out the excitation by an external carbon defect, especially since the density of the defects associated with the transitions is quite high in the flakes.\n\nIn addition to the defects studied above, some of our liquid-exfoliated flakes exhibit another class of defects, with the emission centered at the wavelength of \\(\\lambda=570\\,{{\\rm{nm}}}\\) (Supplementary Note\u00a04 and Supplementary Figs.\u00a07 and 8), indicating a double-peak nature, which is further revealed by the PL spectroscopy measurements (Supplementary Fig.\u00a09). However, the peak centered at the energy of \\(\\lambda=880\\,{{\\rm{nm}}}\\), is not visible in the PL spectra. By performing both CL and PL measurements on different flakes and at different positions, all of which show similar results, we conclude that the transition resonance at \\(\\lambda=880\\,{{\\rm{nm}}}\\) is dark in the PL measurements and carries a dipole oriented perpendicular to the plane of the flake, generated by boron vacancies53, and thus perfectly couples to the electron-beam excitations. This claim is particularly supported by the better coupling of the radially polarized light generated by the EDPHS to the phonon excitations (Fig.\u00a01d and Fig.\u00a03). The emission wavelength does not change when the kinetic energy of the electron beam and its current are varied (Supplementary Fig.\u00a010), allowing us to rule out the generation of electron-beam-induced defects, strain, or the existence of charge defects.\n\na Measured momentum-resolved CL intensity maps at depicted delays. Here, \\({k}_{\\parallel }={k}_{0}\\sin \\theta=\\,\\sqrt{{k}_{x}^{2}+{k}_{y}^{2}}\\) is the parallel wave number and \\(\\theta\\) is the polar emission angle with respect to the normal to the sample plane. b Measured CL intensity spectra at different delays for \\(\\theta=10^\\circ \\pm 2^\\circ\\). c Calculated polarization at the corresponding delays, indicating the vanishing of coherence at delays significantly longer than the dephasing time. d Plot of the fringe visibility versus delay. An exponential fit (red line) to the data reveals the dephasing time \\({T}_{2}=220\\)\u00a0fs. The CL signals are taken from the positions marked in Fig.\u00a02b by the purple box. Source data are provided as a Source Data file.\n\nThe EDPHS radiation interacting with the flake induces a coherent polarization in the flake, due to the generation of a coherent superposition of quantum states. This aspect is similar to a \\(\\pi /2\\) pulse used in spin-echo experiments54, which creates a coherent superposition between ground and excited states. The moving electron further interacts with the flake with a given time delay with respect to the EDPHS pulse, where the induced polarization stimulates the electron to produce coherent CL radiation. Thus, in contrast to spin-echo measurements and multi-dimensional spectroscopy schemes55, which are based on multiple excitation schemes and highly nonlinear processes (four-wave mixing), our technique here relies on the different mechanisms of radiation from electron beams interacting with the sample to generate coherent and incoherent CL. In this way, the already generated coherent CL further interferes with the EDPHS polarization in the sample, resulting in prominent interference fringes within the energy-momentum CL map (Fig.\u00a03a).\n\nThe coherent superposition generated by the EDPHS radiation decays over time within the dephasing time scale of the induced phonon polarization. Therefore, the generated CL signal has only a coherent nature within the time scale in which the EDPHS-induced-polarization maintains its coherence. Thanks to the remarkable mutual coherence between the EDPHS light and the near-field distribution of the moving electron, a high visibility of the order of \\(F(\\tau=0)=0.57\\) for the interference fringes is observed, where \\(\\tau=0\\) is associated with the time within which both electron beam and the peak of the EDPHS radiation reach the sample at the same time. This is possible due to the retardation effect in the EDPHS structure and the time frame in which the induced polarization in the EDPHS contributes to the radiation34.\n\nThe visibility of the interference fringes, measured as\n\ngradually decreases with the time delay \\(\\tau\\) between the electron beam and the EDPHS radiation. Here, \\({I}_{\\max }\\) and \\({I}_{\\min }\\) are the maximum and minimum intensities of the CL signal at a given time delay. The observed interference fringes are most prominent at the wavelength associated with the phonon sidebands. We control the delay with the piezo stage in steps of 12\u2009fs, which allows us to examine the interference fringes with sufficient time resolution. The line profiles of the spectral interferences at specific delays and within the angular range of \\(10^\\circ \\pm 2^\\circ\\) better indicate the fading of the visibility of the interference fringes over time (Fig.\u00a03b), which also agrees well with the theoretical model based on the generation of a coherent CL signal due to the interaction with the EDPHS-induced coherent phonon polarization (Fig.\u00a03c). Furthermore, the visibility of the interference fringes versus the delay \\(\\tau\\) shows an exponential decay, allowing us to measure the dephasing time of \\({T}_{2}=200\\,{{\\rm{fs}}}\\) for the phonon polarizations (Fig.\u00a03d). It is important to notice that the spectral interference fringes do not change their spectral period upon different delay times. This rules out that our observed fringes stem simply from quantum beats, which would result in their spectral frequency being inversely proportional to \\(\\tau\\). To better estimate the consistency in determining the coherent and incoherent CL signals, an inverse Fourier transform along the photon energy axis was used and the ratio of the broadening of the AC term to the peak time was calculated to estimate the error bar as shown in Fig.\u00a03d.\n\nRemarkably, angle-resolved spectral maps (see Fig.\u00a03a) show the angular ranges into which different excitations in the sample emit photons. Phonon transitions in the wavelength range of 550\u2009nm to 680\u2009nm emit in the angular range of \\(\\theta=8^\\circ\\) to \\(\\theta=16^\\circ\\), while the electronic transition peaking at the wavelength of 880\u2009nm emits most significantly in higher angular ranges \\(\\theta > 60^\\circ\\) (outer edges of the cone), further confirming the excitation of an electric dipole moment perpendicular to the surface of the flake.\n\nIn contrast to the dephasing dynamics of the coherent CL signal, the decay of the incoherent CL signal is related to the decay of the population \\({T}_{1}\\). This is due to the fact that the intensity of the incoherent CL is directly related to the generated population in the system, which further decays and releases CL signal. EDPHS radiation interacting with the sample increases the carrier density in the excited states, leading to an increase in the intensity of the CL signal compared to pure electron beam or EDPHS excitation. As the generated EDPHS-induced population decays over time, the CL intensity drops to an incoherent summation of the EDPHS spectrum and the CL spectrum from the sample after a long delay between the EDPHS and sample excitation.\n\nTo uncover the population decay \\({T}_{1}\\), the delay between the EDPHS and the electron beam arriving at the sample was varied at the steps of only 120\u2009as, by measuring the integrated CL spectrum over the entire angular range of the emission above the sample, with a collection efficiency of \\(1.46\\,\\pi \\,{{\\rm{sr}}}\\) (Fig.\u00a04a, top). The CL intensity shows an exponential decay, in good agreement with theoretical calculations based on the expectation value of the number operator (Fig.\u00a04a, bottom), which corresponds to the CL intensity when the EDPHS radiation is included in the interaction Hamiltonian.\n\na (Top) Experimental and (Bottom) theoretical CL intensity spectra versus the delay \\(\\tau\\) between the EDPHS radiation and the electron arriving at the sample. b Measured CL intensity integrated over the entire angular range at depicted delays. The EDPHS radiation is indicated by the green line. The CL signals are taken from the positions marked in the inset of Fig.\u00a02a. c Logarithm of the (Left) measured and (Right) calculated CL intensity (\\({\\Gamma }^{{{\\rm{CL}}}}\\)) of three main emission wavelengths versus the delay \\(\\tau\\). An exponential function (solid lines) is fitted to the data to obtain the corresponding damping times \\({{{\\rm{\\tau }}}}_{{{\\rm{d}}}}\\) for each wavelength. Source data are provided as a Source Data file.\n\nThe population relaxations for the different transitions observed are slightly different. While the\u00a0first phonon state peaking at 805\u2009nm shows an ultrafast population decay of only 289\u2009fs, the decay corresponding to the second phonon state is significantly longer (Fig.\u00a04b). Theoretically, the contribution to dephasing from population relaxation is \\({T}_{1}/2\\), which is 292\u2009fs for the phonon transitions. This is slightly larger than the value of 200\u2009fs for the measured dephasing time, due to the phonon-phonon coupling and rephasing processes occurring in the ensemble of phonon states.\n\nSince the phonon states are energetically tightly packed in the wavelength range from 520\u2009nm to 700\u2009nm, a significant broadening of the CL signal is observed, due\u00a0to free-induction decay and weak coupling between the phonon energy states in this range. In addition, the quantum-path interferences in the EDPHS-induced and electron-induced excitation and decay paths lead to significant spectral fluctuations in this region (Fig.\u00a04c).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57584-1/MediaObjects/41467_2025_57584_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57584-1/MediaObjects/41467_2025_57584_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57584-1/MediaObjects/41467_2025_57584_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57584-1/MediaObjects/41467_2025_57584_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussions", + "section_text": "Exploring and controlling the dephasing time of single quantum emitters, implemented in solid-state systems and coupled to integrated photonic devices, is a key aspect in the further development of quantum technology and computation. Generally, revealing the dephasing dynamics in optical systems requires highly nonlinear processes, including four-wave mixing and techniques such as multidimensional electronic spectroscopy56.\n\nThe decoherence process manifests itself in the interferometry techniques as a degradation of the visibility of the interference fringes57. This provides a powerful technique, for example using two-photon interference58, to map the dephasing time of quantum systems.\n\nHowever, in all these techniques, coupling to single defects has been proven to be challenging. Our method here, which is based on the interference effect in the sequential interaction of photons generated by the EDPHS and the sample, provides a significant improvement in addressing individual quantum systems and defects with high spatial and temporal resolution, as demonstrated here by applying it to defects in a thin hBN flake.\n\nThe dephasing and population decays allocated to our defects in the hBN flakes are significantly faster than the decay time reported by all-optical techniques. The population decay for the hBN defects reported so far was in the range of a few nanoseconds. This is mainly due to the fact that PL experiments are performed using either cw light or light pulses with a much narrower bandwidth compared to the EDPHS radiation, which precludes coupling to higher energy phonon states and superposition generation, as well as coupling to coherent phonons (See Supplementary Note\u00a05 and Figs.\u00a012, 13). Particularly, a major factor underlying the ultrafast dephasing time of the emitters observed here is due to the excitation of coherent phonon polaritons and their coupling to defects, with their propagation mechanisms and radiative nature leading to a faster decoherence mechanism for the emitter, as well as an enhanced population decay. In particular, electron beams, due to their ultrabroadband excitation mechanisms, provide an efficient way to simultaneously couple to both phonon polaritons in the far-infrared and electronic transitions in localized defects in the visible. It should also be noted that the emitters studied here show a strong coupling to coherent phonon polaritons, in contrast to the emitter centers emitting at shorter wavelengths (see Supplementary Note\u00a04 and Supplementary Fig.\u00a011). In particular, for the latter class of defects, the emission does not show a coherent nature, mainly due to their weak coupling to coherent phonon excitations, which precludes the possibility of studying their dephasing dynamics with the multi-sequential CL technique proposed here.\n\nOur results here have been based on the excitation of single emitters with electron beams, agreeing well with our theoretical framework that emphasizes coupling to single emitters. The focus remained on the exploration of the relaxation dynamics of single emitters. However, the system and methods developed can be applied to exploration of the dynamics of multiple emitters, when the density of the emitters increases (See Supplementary Note\u00a06 and Supplementary Figs.\u00a014 and 15). While ensuring a long dephasing time for single emitters is important for quantum technologies based on interferometry techniques, coherent phonon polarization in the hBN offers a wealth of possibilities, to enable quantum-sensitive measurements based on novel types of correlations in matter. Coherent phonons lead to an enhanced coupling between emitters, enabling emergent synchronization phenomena. For this to happen, one could consider coupling the emitters to photonic cavities with their resonant modes taking place within the upper Reststrahlen band (See Supplementary Note\u00a05). They lead to novel types of superradiance in hBN flakes with a high density of emitters but need to be further investigated.\n\nOur method thus allows the exploration of a rich set of physical phenomena, from single-emitter dephasing of quantum emitters in general to phonon- and photon-mediated correlations, including polaritons in different van der Waals materials, correlations in hybrid two-dimensional materials, and Moir\u00e9-induced polaritons and nonlinearities. This could pave the way to a better understanding of the emerging phenomena and localization effects in deep-subwavelength systems, but also in systems at mesoscopic scales. The experiments conducted here provide the first proof-of-concept demonstrating the applicability of our sequential CL technique for investigating the detailed dynamics of a single quantum emitter. However, further improvements in the technical design could enhance accessibility to various emitters, such as by incorporating a scanning piezo stage for the sample holder. In other words, the scanning functionality of the SEM cannot be utilized in our current setup, as it would simultaneously scan the EDPHS structure.\n\nOur technique transcends the methods available to explore dephasing dynamics by incorporating both luminescence spectroscopy and interferometry in a single scheme. The all-optical analog of this method, which includes photoluminescence (PL) spectroscopy and Mach-Zehnder interferometry, also allows for the exploration of ultrafast dephasing dynamics59. However, the sequential CL spectroscopy reported here offers greater flexibility in scanning materials at 1\u2009nm spatial resolution and accessing randomly positioned defects in two-dimensional materials. Moreover, leveraging both coherent and incoherent interactions of electron beams with defects, we are able to recover not only dephasing time, but also population decay in a single experiment.\n\nThe interaction of the EDPHS radiation with the sample is similar to the incorporation of coherent radiation in Ramsey-type interferometry schemes preparing the sample in a coherent superposition60. The temporal duration within which the system freely evolves allows for altering the relative phase between the components of the superposition. In contrast with Ramsey-type interferometry though, our second pulse incorporates an electron beam, allowing for both coherent and incoherent interactions, where both the dephasing time and population decay are retrieved.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Hexagonal boron nitride (hBN) crystals were purchased from the HQ Graphene Company. To produce thin nanosheets, a liquid phase exfoliation process was applied to bulk hBN in isopropanol (Merck, \u226599.8%). The exfoliation process used an ultrasonicator (320W, Bandelin Sonorex, RK100H) with a timer and heat controller to prevent solvent evaporation. Sonication was performed in an ice bath using a cycle program of 5\u2009min on followed by 1\u2009min off, for a total of 180\u2009min. The resulting suspension was drop-casted onto a holey carbon mesh grid for subsequent characterization.\n\nAll measurements detailed in this investigation involving cathodoluminescence (CL) spectroscopy, angle-resolved, and energy-momentum techniques were conducted utilizing the ZEISS Sigma field-emission scanning electron microscope with an attached Delmic SPARC CL system.\n\nThroughout the entire experimental process, the electron microscope was consistently operated at an acceleration voltage of 30\u2009kV unless otherwise specified. Two specimens were utilized simultaneously for phase-locked photon-electron spectroscopy. The upper one was an EDPHS-producing collimated light maintained by a nano-positioning system from SmarAct GmbH with three axial degrees of freedom, 1\u2009nm step size accuracy, and a dynamic range of 12\u2009mm. The lateral and vertical positions were precisely controlled with respect to the sample (See Supplementary Note\u00a02 and Supplementary Fig.\u00a04).\n\nThe second sample was a thin layer of hBN crystal that was placed on a carbon TEM grid held by the SEM stage. The beam current was set to 11\u2009nA during the measurements. The CL detector was an aluminum parabolic mirror positioned below both samples. This mirror efficiently collects the generated CL radiation and projects it onto a CCD camera. Its specifications include an acceptance angle of 1.46\u03c0 sr and a focal length of 0.5\u2009mm. For spectral selection of the CL light, bandpass filters can be inserted into the optical path. During the measurements, the acquisition time for each pixel was set to 250\u2009ms for hyperspectral imaging and 30\u2009s for angle-resolved imaging.\n\nHRTEM and EELS measurements were performed in a JEOL ARM200CF transmission electron microscope equipped with a Cs corrector in the imaging system. The TEM was operated at 200\u2009kV. EELS spectra were recorded with a CCD camera attached to a Gatan Imaging filter (GIF Quantum ERS). Spectral imaging was performed in the scanning mode with an electron-probe size smaller than 0.5\u2009nm. Spectral imaging is achieved by acquiring EELS data from each pixel within a 2D area and then extracting element-specific absorption edges. HRTEM images were acquired in parallel beam mode using a Gatan OneView camera. All data displayed are raw data.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The experimental raw data substantiating Fig.\u00a01, Fig.\u00a02b, Fig.\u00a03, and Fig.\u00a04 that support the findings of this study are available in \u201cZonodo\u201d with the identifier https://doi.org/10.5281/zenodo.1483212061. Source Data for Figs.\u00a01a, b, 2b, 3a, c, 4a are provided with this paper.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The numerical code used to simulate the data is available from the corresponding author on request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Raja, A. et al. Dielectric disorder in two-dimensional materials. Nat. Nanotechnol. 14, 832\u2013837 (2019).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nSpivak, B., Kravchenko, S. 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Wrachtrup (Stuttgart University). N.T. acknowledges as well fruitful discussions with M. Kociak (CNRS, France). This project has received funding from the European Research Council (ERC) under the European Union\u2019s Horizon 2020 research and innovation program under grant agreement no. 802130 (Kiel, NanoBeam) and grant agreement no. 101017720 (EBEAM), and from Deutsche Forschungsgemeinschaft under Grant agreement nos. 525347396 and 447330010, and from Volkswagen Stiftung (Momentum Grant). M.H. and H.G. thank DFG, BMBF, and ERC grant (COMPLEXPLAS) for funding.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Institute of Experimental and Applied Physics, Kiel University, Kiel, Germany\n\nM. Taleb,\u00a0P. H. Bittorf,\u00a0M. Black\u00a0&\u00a0N. Talebi\n\nKiel Nano, Surface and Interface Science KiNSIS, Kiel University, Kiel, Germany\n\nM. Taleb\u00a0&\u00a0N. Talebi\n\n4th Physics Institute and Research Center SCoPE, University of Stuttgart, Stuttgart, Germany\n\nM. Hentschel\u00a0&\u00a0H. Giessen\n\nStuttgart Center for Electron Microscopy, Max Planck Institute for Solid State Research, Stuttgart, Germany\n\nW. Sigle\u00a0&\u00a0P. A. van Aken\n\nDepartment of Physics & Center for the Science of Materials Berlin (CSMB), Humboldt-Universit\u00e4t zu Berlin, Berlin, Germany\n\nB. Haas\u00a0&\u00a0C. Koch\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nM.T., P. B., and M.B. Performed the experiments and analyzed the data together with N.T. M.H. fabricated the EDPHS structure. N.T. conceived the data, performed the simulations, and wrote the manuscript with contributions from all coauthors. H.G., P.v.A., and C.K. contributed to the discussions. W.S. and B.H. performed the transmission electron microscopy imaging and electron energy-loss spectroscopy.\n\nCorrespondence to\n N. 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Ultrafast phonon-mediated dephasing of color centers in hexagonal boron nitride probed by electron beams.\n Nat Commun 16, 2326 (2025). https://doi.org/10.1038/s41467-025-57584-1\n\nDownload citation\n\nReceived: 05 April 2024\n\nAccepted: 24 February 2025\n\nPublished: 08 March 2025\n\nVersion of record: 08 March 2025\n\nDOI: https://doi.org/10.1038/s41467-025-57584-1\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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seismic swarms leading up to the 2024 M7.3 Hualien earthquake", + "pre_title": "Aseismic slip and seismic swarms leading up to the 2024 M7.3 Hualien earthquake", + "journal": "Nature Communications", + "published": "13 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64117-3/MediaObjects/41467_2025_64117_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64117-3/MediaObjects/41467_2025_64117_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64117-3/MediaObjects/41467_2025_64117_MOESM3_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64117-3/MediaObjects/41467_2025_64117_MOESM4_ESM.mp4" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64117-3/MediaObjects/41467_2025_64117_MOESM5_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "http://www.obspy.org", + "https://doi.org/10.5281/zenodo.14064472", + "https://gdmsn.cwb.gov.tw/", + "https://bats.earth.sinica.edu.tw/" + ], + "code": [], + "subject": [ + "Natural hazards", + "Seismology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5449840/v1.pdf?c=1760440029000", + "research_square_link": "https://www.researchsquare.com//article/rs-5449840/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-64117-3.pdf", + "preprint_posted": "25 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Recognizing the locations and timing of aseismic fault slip during the earthquake cycle may be critical for short-term earthquake forecasting and hazard assessment. Abundant M \u2265 6 earthquakes and widespread aseismic slip in a double-vergence suture in eastern Taiwan provide a rare opportunity to better understand the role of aseismic slip in earthquake cycle deformation and triggering relationships. By tracking aseismic slip captured by repeating earthquake sequences and earthquake swarms in eastern Taiwan from 2000 to mid 2024, we observed an acceleration of aseismic slip rate that occurred approximately three years preceding the April 3, 2024 Mw7.3 Hualien earthquake. The accelerated aseismic slip revealed by the repeating earthquake data was accompanied by a four-month-long active seismic swarm starting on April 18, 2021, in the 2024 Mw7.3 epicentral area. The swarm occurred on the west-dipping Central Range fault exhibited a northward and upward migration pattern with a diffusion rate of ~ 6 m2/s. After 2021 swarm sequence, the regional aseismic slip rate initially decreased until an Mw 6.1 event in June 2022, followed by a gradual increase in the aseismic slip rate leading up to the mainshock over approximately 1.5 years. Concurrent with the accelerated aseismic slip, the Central Range Fault was characterized by a heightened seismicity rate until the 2024 Mw7.3 mainshock. The resulting static Coulomb stress change reveals that accumulated aseismic and seismic slips from swarms, RESs, and M6+ events on the CRF from mid-2021 to mid-2022 generally produce positive stress changes (up to 20 kPa) on the same fault. The sequential seismic-aseismic interplay of faulting in the epicentral area of the 2024 Hualien event provides a rare example of aseismic-slip-induced stress triggering leading up to the 2024 Mw7.3 earthquake.Earth and environmental sciences/Solid Earth sciences/SeismologyEarth and environmental sciences/Natural hazards", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "movieS1.mp4Movie S1movieS2.mp4Movie S2Supplfinal.pdfSupporting information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Understanding the role of aseismic slip in earthquake cycles is essential for assessing seismic hazards and short-term forecasting. Eastern Taiwan\u2019s double-vergence suture zone, where the Philippine Sea Plate subducts beneath the Eurasian Plate, experiences frequent M\u2009\u2265\u20096 earthquakes and widespread aseismic slip, making it an ideal natural setting to study earthquake triggering processes. Here we demonstrate how aseismic deformation contributed to the April 3, 2024 Mw7.3 Hualien earthquake by analyzing a 24-year catalog of repeating earthquake sequences (RESs) and earthquake swarms. We find that six out of nine swarms in the epicentral area, northern Longitudinal Valley, were accompanied by increasing aseismic slip rates, as revealed by RESs on the west-dipping Central Range Fault (CRF). A notable aseismic slip episode in 2021 indicated by GNSS signals, the accelerated RESs-derived slip rate, and a four-month-long swarm sequence with high diffusivity (~5.2\u2009m\u00b2/s), suggests joint contributions from over-pressured fluids and deep fault creep. Following this episode, a sequence of M6+ events occurred in 2022, and both seismicity and aseismic slip gradually increased again starting in 2023. Coulomb stress modeling indicates that cumulative aseismic and seismic slips since 2021 generated up to ~30\u2009kPa positive stress on the eventual 2024 rupture, promoting fault weakening and shallower seismicity. This study provides compelling evidence for aseismic-slip-induced stress triggering of a major earthquake and highlights the importance of integrating aseismic processes into earthquake hazard models for collisional fault systems.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Understanding the interaction between slow slip and regular earthquakes is key to advancing our knowledge of earthquake nucleation processes e.g., refs.\u20091,2. While some studies suggest that slow slip events (SSEs) may trigger large earthquakes by modifying local stress conditions e.g., refs.\u20093,4,5,6, others propose that SSEs relieve accumulated stress and reduce the likelihood of large events7,8. Simulations of SSEs9 indicate that recurrence intervals of long- and short-term SSEs may shorten prior to a major earthquake. However, a global catalog comparison of SSEs and nearby seismicity does not reveal a consistent correlation between slow slip and subsequent earthquake activity10. These contrasting interpretations may arise from limited observation period, low detection resolution, or the inherently variable behavior of slow slip phenomena. To date, compelling evidence of ubiquitous precursory slow slip remains limited.\n\nIn this study, we examine aseismic slip behavior in eastern Taiwan using two complementary indicators: repeating earthquake sequences (RESs) and earthquake swarms. Repeating earthquakes, which reflect localized fault creep, are sensitive to variations in aseismic loading e.g., ref.\u20096. Earthquake swarms, meanwhile, often indicate transient aseismic slip and/or fluid overpressure, and are highly sensitive to subtle changes in stress and pore pressure e.g., refs.\u200911,12,13. Laboratory and theoretical studies show that changes in pore pressure can reduce effective normal stress, promoting a spectrum of slip modes including slow and fast earthquakes14,15,16. Taiwan provides an exceptional natural laboratory to investigate these processes due to its rapid tectonic deformation, frequent large earthquakes, and dense seismic instrumentation. Here, we explore the interaction between aseismic and seismic slip preceding the April 3, 2024 Mw7.3 Hualien earthquake.\n\nTaiwan is one of the most seismically active regions globally, especially along the active Longitudinal Valley (LV) suture zone in eastern Taiwan. This zone, resulting from the collision between the Luzon Arc on the Philippine Sea Plate and the continental crust of the Eurasian Plate, hosts two parallel, head-to-head fault structures with opposing dips: the east-dipping Longitudinal Valley Fault (LVF) to the east and the west-dipping Central Range Fault (CRF) to the west e.g., refs.\u200917,18,19. Since 2000, these fault systems have produced 13 M\u2009\u2265\u20096 events (Table\u00a0S1), revealing complex interaction patterns, including (1) simultaneous ruptures or delayed triggering during sequences of M7 events e.g., refs.\u200920,21 and (2) out-of-phase occurrences of M\u2009\u2265\u20096 events due to the stress shadow effect22. Among these, two events exceeded magnitude 7.0, as shown by numbers 10 and 11 in Fig.\u00a01 and Table\u00a0S1 of the supplementary material.\n\na Simplified tectonic setting of Taiwan. Tectonic units from west to east are the Coastal Plain (CP), the Western Foothills (WF), the Hsuehshan Range (HR), the Central Range (CeR) and the Coastal Range (CoR). The black rectangle marks the area shown in (b). b Distribution of Mw \u2265 6 mainshocks in eastern Taiwan since 2000, with their focal mechanisms and aftershocks within 1 month shown by correspondingly colored circles. Number on the top of the focal mechanisms corresponds to the ID number with mainshock information listed in Table\u00a0S1. The red thick line indicates the surface trace of the Longitudinal Valley fault (LVF) with a strike of\u00a0approximately N30\u00b0E. Repeating earthquake sequences (RESs) updated to 2024 are denoted by brown diamonds, highlighting the creeping LVF in the south from near the surface to a depth of 25\u2009km and the creeping Central Range fault (CRF) in the north at depths of 10-25\u2009km. c, d Cross-sections A-A\u2019 and B-B\u2019 within a zone of 25\u2009km width (gray dashed bracket in b) showing the approximate fault geometry of the east-dipping LVF and west-dipping CRF. The background color represents the Vp/Vs ratio27. Blue-shaded bars indicate the first-order fault geometry of the CRF and LVF.\n\nThe April 3, 2024 Mw7.3 Hualien earthquake likely nucleated on the LVF23, as inferred from the alignment of\u00a0the earlier stage of aftershocks (see Movie\u00a0S1 of the supplementary material). This event was followed by two M6-7 and 25 M5-6 aftershocks within a month, which reveal conjugate ruptures on both the northern LVF and CRF24(purple circles in Fig.\u00a01b). In contrast, the September 18, 2022 Mw7.0 Guanshan-Chihshang earthquake sequence initiated on the southern CRF and triggered aftershocks on both the CRF and the creeping segment of LVF, the Chihshang Fault (marked by light purple circles in Fig.\u00a01b, d)21,22,25. The seismicity, aftershocks, and slow slips along both creeping and locked segments of the LVF and CRF present a unique opportunity to improve understanding of fault interactions via aseismic slip and the role of aseismic slip in earthquake cycles.\n\nIn this study, we compile a 24-yr catalog of RESs and earthquake swarms to assess the spatiotemporal association between slow slip indicators and regional seismicity, and further, investigate aseismic slip evolution leading up to the 2024 Mw7.3 Hualien mainshock. In the following sections, we (1) quantify the aseismic slip rate variations using RESs data, (2) analyze swarm migration and dynamics, (3) identify GNSS-observed slow slip episodes, and (4) evaluate seismicity rate changes preceding the mainshock. Finally, we use Coulomb stress modeling to explore how these aseismic processes may have contributed to triggering the 2024 Mw7.3 event.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64117-3/MediaObjects/41467_2025_64117_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "RESs involve groups of events characterized by nearly identical waveforms, magnitudes, and locations, representing repeated ruptures of the same fault patches. Their short recurrence intervals imply rapid loading from surrounding aseismic slip, making them effective indicators of fault creep and interseismic deformation rates26,27,28,29. In eastern Taiwan, RESs have been documented along the creeping segments of the LVF and CRF30,31,32,33,34. We updated the previously established RESs catalog30 from 2000\u20132011 to 2012\u20132024, to include events through May 2024. The RES detection approach is described in Methods.\n\nThe updated RESs catalog contains 649 events across 148 sequences from January 1, 2012\u2013April 18, 2024, with magnitudes ranging from 2.0 to 5.1. Merging this with the January 1, 2000 through December 31, 2011 RES catalog30, the integrated dataset comprises 1499 RES events in the 148 sequences. Each sequence includes 3\u201334 events. As shown by diamonds in Fig.\u00a01, these events cluster into two spatial groups: in the northern LV (north of 23.6\u00b0N), RESs occur on the west-dipping CRF, while in the southern LV (south of 23.6\u00b0N), they cluster along the east-dipping LVF.\n\nUsing the scaling relationships developed for creeping sections in eastern Taiwan30, the slip for each repeating event can be obtained using the scaling relationship between the slip estimate (d) and seismic moment (Mo) as logd\u2009=\u2009\u2212 1.21\u2009+\u20090.11 logMo and logd\u2009=\u2009\u2212 1.96\u2009+\u20090.14 logMo for the LVF and CRF, respectively (see Methods for how these scaling laws were inferred). Slip histories from multiple RESs were then combined to estimate the spatiotemporal variations in aseismic slip driving the repeating events. The regional cumulative slip was calculated in one-day increments by measuring the change in slip between consecutive days. To ensure that the calculation window exceeded the length of any data gaps but remained shorter than the targeted recurrence pattern, we applied a 180-day averaging window. This smoothing reduces short-term variability and enables a more robust determination of the slip-rate time series30.\n\nFigure\u00a02 presents the temporal distribution of RESs events and the corresponding aseismic slip rate, while Fig.\u00a0S1 in the supplementary material visualizes the along-strike spatiotemporal evolution of M\u2009\u2265\u20092 seismicity density and RESs-derived aseismic slip. We systematically tested the sensitivity of the computed slip rate to different averaging windows (Fig.\u00a0S1b\u2013c). Shorter averaging windows yield higher temporal resolution but are prone to noise when below the RESs inter-event time (mean: 15 days; maximum: 127 days). Longer windows increase reliability but may introduce time lags30. Near-zero slip rates using 1-month time window (Fig.\u00a0S1b\u2013c\u00a0) typically reflect gaps in RESs detection.\n\nMagnitude distribution (top), occurrence rate, and slip rate (bottom) of RESs since 2000 for (a) northern Longitudinal Valley (LV) on the Central Range fault (CRF) and (b) southern LV on the Longitudinal Valley fault (LVF). Cumulative RESs events and the derived slip rates are denoted by black and dark red lines, respectively. Using a moving window analysis, the RESs slip rate is calculated as the average value within a 3-month window before and after each plotted point. The plotted value is updated daily, advancing forward in time. Vertical red dashed lines indicate the occurrence time of Mw \u2265 6 events with the ID number corresponding to Table\u00a0S1. Black arrows and black dashed lines mark the significant change of the RESs occurrence rate. The occurrence rate averaged over each sub-period is indicated by the number.\n\nIn the southern LV area where RESs are\u00a0widely distributed along the east-dipping LVF, from near surface to 30\u2009km (Fig.\u00a01d), the highest aseismic slip rate peak occurred shortly after the 2003 Mw6.8 Chihshang earthquake, coinciding with a sharp increase in RES activity. Following the Mw6.8 mainshock, the RESs occurrence rate tripled from 13.7 to 41.6 events per year, which persisted for 6 years. In the northern LV where the RESs mainly occurred on the west-dipping CRF below the depth of 10\u2009km, the RESs occurrence rate is generally higher than in\u00a0the southern LV; The small fluctuations in aseismic slip rate tend to show annual variation30. The most pronounced CRF aseismic slip acceleration followed the April 18, 2019 Mw6.1 event, with RESs activity increasing from 33.3 to 45.8 events/year.\n\nNot all large earthquakes in eastern Taiwan influence aseismic slip as captured by RESs. Along the northern CRF, where more than five M\u2009\u2265\u20096 earthquakes occurred during the study period, the RESs-inferred aseismic slip rate remains relatively stable. This may reflect the fact that RESs tend to occur at depths of 10\u201325\u2009km, whereas most large earthquakes ruptured shallower parts of the fault (<10\u2009km). Only RESs located in close proximity to mainshock hypocenters (e.g., Number 6 in Fig.\u00a01) show evidence of stress-driven acceleration in aseismic slip.\n\nAseismic slip can also manifest through earthquake swarms, clusters of earthquakes lacking a clear mainshock and a typical aftershock decay pattern. These swarms are generally associated with fluid intrusions and elevated pore pressure e.g., refs.\u200935,36,37, which can induce fault unclamping and promote slow slip38,39. A previous study in Taiwan26 analyzed 153\u2009M\u2009\u2265\u20093 swarm sequences (4726 events) and their relationship with 59\u2009M\u2009\u2265\u20096 earthquakes, finding limited evidence of precursory swarms. Instead, swarms more often followed large earthquakes. As demonstrated by the\u00a0previous study26, a significantly higher proportion of swarm events followed M\u2009\u2265\u20096 earthquakes when the separation distance was less than 10\u2009km, highlighting a spatial dependence in post-mainshock swarm triggering.\n\nTo build a more comprehensive swarm catalog, we applied a composite clustering method26 that integrates multiple declustering algorithms. Swarm sequences were identified from events with M\u2009\u2265\u20092.0, consistent with the estimated magnitude of completeness (Mc) for this region (see Methods for details). From January 1, 2000 to April 30, 2024, we identified 15 swarm sequences comprising 4109 events with magnitude ranging from M2.0 to 5.9 (Table\u00a0S2 and Fig.\u00a0S2). Of these, 82% of the swarm events (in 10 sequences) occurred in the northern LV (Fig.\u00a0S2a), coinciding with the aftershock zone of the 2024 Mw7.3 Hualien earthquake and along the west-dipping CRF (Fig.\u00a01b, c, Fig.\u00a0S2c). These northern swarms are spatially associated with high Vp/Vs zones (cross-section in Fig.\u00a0S2b\u2013c), suggesting a fluid-saturated environment. In addition, the composite method yields a declustered earthquake catalog that excludes mainshock-aftershock sequences and swarm-like clusters, which is used for background seismicity analyses.\n\nThe longest swarm sequence (Sequence 9) occurred from April 18 to August 18, 2021, comprising 796 events, including nine 6\u2009>\u2009M\u2009\u2265\u20095 earthquakes (Fig.\u00a03). Approximately 2 months after the first event, 86% of\u00a0swarm events occurred at\u00a0depths shallower than 15\u2009km, ultimately reaching regions of high Vp/Vs ratio and low Vp and Vs27. The swarms mainly spanned depths of 5\u201322\u2009km, partially overlapping with RESs depths (13\u201322\u2009km, green squares in Fig.\u00a0S11). This swarm sequence demonstrated bilateral migration, with 69% of events propagating upward and 31% downward. Compared to the vertical direction, northward migration was less pronounced (Fig.\u00a03a, b and Movie\u00a0S2).\n\na Spatial distribution of the swarm sequence from April 18 to August 18, 2021. Circles represent swarm events, with color indicating the time elapsed since the beginning of the sequence and size proportional to magnitude. Stars indicate events with magnitude greater than 5.0. The\u00a0first event of the swarm sequence is denoted by white circle. Green squares denote repeating earthquake events that occurred within a\u2009\u00b1\u20090.5-year window relative to the swarm onset (total time window: 1 year). b Along-strike distribution of swarm and repeating earthquake events. The along-strike distance is defined between points p and p\u2019, corresponding to the start and end of the pink line in Fig. 1b. c Across-strike distribution of swarm and repeating earthquake events. d Magnitude-time\u00a0plot of the swarm events, showing the temporal occurrence and relative size of earthquakes within the sequence. e Time-space evolution of the swarm sequence. (see Supplemental Movie\u00a0S2 for animated view of event sequence). The black solid line indicates the best-fit synthetic diffusion curve (D\u2009=\u20095.4\u2009\u00b1\u20091.1\u00a0m2/s) determined by the majority of the 90th percentile distance points (pink squares) obtained each day, following \\(r=\\sqrt{4\\pi {Dt}}\\), where r is the distance (km) from the first event, t is the elapsed time (days), and D is the diffusivity (m2/s) with uncertainty denoted by one standard deviation (dashed lines).\n\nTo quantify the swarm\u2019s migration, we applied a diffusion model to the swarm triggering front. Assuming constant permeability and pore pressure conditions, the hydraulic diffusivity (D) was estimated by fitting the diffusion equation \\(r=\\sqrt{4{{{\\rm{\\pi }}}}{Dt}}\\), where r is the distance from the first event in a swarm sequence to the triggering front, t is the elapsed time from the start of diffusion, and D is the hydraulic diffusivity28. The best-fit curve was selected to span the majority of 90th percentile distance points (pink squares in Fig.\u00a03e) obtained every 20 earthquakes29. The minimum RMS misfit between the observed and predicted triggering front is obtained using moving time bins containing 20 events, with 10 events overlapping between consecutive bins.\n\nA sensitivity test was performed by sequentially removing early data points (the first 1 to 20 pink squares in Fig.\u00a03e). In other words, the first time we removed the first point (90th percentile obtained using the first 20 events) to measure D1, the second time we removed the initial two points for D2, and the third time D3 for removing three points, and finally, D20 for removing the first 20 points at the earlier stage of the sequence. This yielded a distribution of diffusivity estimates (D1 to D20), from which we computed the mean and standard deviation. For Seq. 9, the mean hydraulic diffusivity was approximately 5.4\u2009m\u00b2/s, with standard deviation of 1.1\u2009m\u00b2/s, indicating rapid fluid migration from the mid-crust.\n\nKey parameters for each swarm sequence in northern LV area are listed in Table\u00a0S2, including onset time, duration, sequence duration, number of M\u2009\u2265\u20095 events, seismic moment (maximum and cumulative), b-value, mean diffusivity (D), and standard deviation (SD). Notably, a new swarm initiated 19 days after the 2024 Mw7.3 mainshock on April 22, was excluded from this study due to the catalog cutoff at April 30, 2024. We found that across all swarm sequences, the cumulative moment scales positively with both maximum event size and swarm duration, but inversely with b-value. All sequences except Seq. 1 had b-values\u2009<\u20090.91. This pattern can be explained by the tendency for sequences with a larger maximum event to also include a higher proportion of relatively large events, resulting in lower b-values. Such sequences, often led by relatively large events (e.g., M\u2009>\u20095), tend to persist for longer durations and release greater cumulative seismic moment.\n\nMigration was observed in all swarm sequences but showed no consistent directivity (Figs.\u00a0S3\u2013S11). High D values were generally associated with greater uncertainty, suggesting that diffusivity estimates exceeding ~10\u2009m\u00b2/s should be treated with caution. In contrast, focused swarms (e.g., Seqs. 8 and 9) featured more events and yielded lower diffusivity standard deviations, providing more reliable estimates.\n\nWe examined the temporal relationship between swarm sequences and deeper aseismic slip using the RESs time series. Figure\u00a04c compares individual swarm timelines with the evolving aseismic slip rate. To assess potential triggering or causal relationship, we aligned all swarms by their onset (time\u2009=\u20090) and stacked the surrounding aseismic slip rate histories across a\u2009\u00b1\u2009180-day window (Fig.\u00a04b). We found that 6 out of 9 swarm sequences were preceded by an increase in aseismic slip rate, suggesting a possible causal link between deep aseismic slip and the initiation of swarm activity. Exceptions include Seqs. 4-6, where no clear precursory acceleration was observed.\n\na Distribution of RESs and earthquake swarms in the northern LV area. The ten different colors represent the ten sequences. Brown diamonds indicate RESs, while the circles represent the earthquake swarms and\u00a0are color coded by the index of earthquake swarm sequence. Red stars indicate the M6+ event since 2000. b Aseismic slip rate changes 180 days before and during the swarm period. Color corresponds to different swarm sequences in (a). Gray vertical line marks the timing of the first event of each\u00a0earthquake swarm. Horizontal dashed lines indicate the mean value of slip rate 180 days before and during the swarm onset. c Time evolution of earthquake swarms (colored circles) and the RESs derived average aseismic slip rate. Here, the aseismic slip rate from the RESs in the close-up area (gray box in (a)) is\u00a0slightly smaller than the one for RESs slip rate in Fig.\u00a02a. The number next to the circles in (b) and (c) denotes the swarm sequence identification. Black stars and vertical dashed lines denote the times of the M6+ events in (a). Start and end of the along-strike distance is denoted by p and p\u2032, corresponding to the start and end of the pink line in Fig.\u00a01b.\n\nTo test the robustness of the temporal relationship between the changes in aseismic slip rate and swarm activity, we performed a sensitivity analysis by calculating the average aseismic slip rate before (dpre) and after (dpost) each swarm onset across sliding time windows of 1 to 12 months. The difference (df = dpost - dpre) quantifies whether slip rate accelerated before or after the swarm onset. A negative df indicates precursory acceleration, while a positive df suggests slip increase following the swarm. Despite some variability across time windows, Seqs. 3, 4, 5, and 6 consistently exhibited negative df values, while Seqs. 2, 7, and 9 showed positive df (Fig.\u00a0S12). Most consistent results were obtained using 3- to 6-month averaging windows, which revealed precursory acceleration in 6 of 9 cases, supporting the hypothesis that increased aseismic slip often precedes swarm activity. Seqs. 2 and 9 show the strongest temporal and spatial alignment between swarms and RESs (i.e., relatively large number of repeating events are activated in the close neighborhood of swarm events, indicated by green squares vs. colored circles in Figs.\u00a0S4 and S11), with significant slip rate changes near swarm onsets (Fig.\u00a0S12).\n\nTo further examine\u00a0the spatiotemporal relationship between RESs accelerations, swarms, and M\\(\\ge\\)6 events, we plotted separation distance against time difference between each pair of phenomena in Fig.\u00a0S13. Here, the aseismic slip acceleration is defined by the peak in RESs-derived slip rate history in Fig.\u00a04c. (1) Swarm vs. RESs acceleration (Fig.\u00a0S13a): Spatial separations range from 10 to 50\u2009km, with time differences generally <0.4\u2009yr. However, smaller spatial separation does not consistently correspond to shorter interaction times, suggesting different underlying mechanisms. (2) Swarm vs. M\\(\\ge\\)6 events (Fig.\u00a0S13b): A positive correlation is observed, as that closer swarm-mainshock pairs tend to occur with shorter separation times. The closest pairing (27\u2009km) corresponds to a long-duration swarm and short time lag. (3) RESs acceleration vs. M\u2009\u2265\u20096 events (Fig.\u00a0S13c): No clear trend is observed, despite spatial separations of 5\u201340\u2009km and time lags <0.35\u2009yr. These results indicate that while swarms and M\u2009\u2265\u20096 events exhibit possible triggering relationships, RESs accelerations appear more loosely coupled with subsequent large earthquakes. Further statistical examination is needed to confirm and strengthen these correlations and interpretations.\n\nTo quantify crustal deformation associated with the swarm sequences, particularly after 2019 where RESs-derived slip rate began to accelerate (Fig.\u00a02a), we analyzed GNSS data pre-processed by the Taiwan geodetic model at the Institute of Earth Science, Academia Sinica, Taiwan (https://tgm.earth.sinica.edu.tw/). To detect the transient deformation, we selected 11 GNSS stations (Fig.\u00a05 and Fig.\u00a0S14) and processed\u00a0the time series data by correcting long-term linear trends, seasonal periodic motions, local Mw \u2265 6 coseismic offsets and related post-seismic deformation (the GNSS processing methodology is detailed in the\u00a0Methods section).\n\na Distribution of the selected 11 GNSS stations (triangles) and the swarm sequences in 2019 (red dots) and 2021 (black dots). Stars indicate the Mw \u2265 5.5 events since 2019. b, c (Top) N and E components of the processed GNSS signals, color coded by different stations shown in (a). Black curve represents the moving median of 1-month data. Vertical lines denote the occurrence time of the Mw \u2265 5.5 events since 2019. (Bottom) Magnitude-time plot with cumulative number of swarm events. The red, black, and grey dots in the bottom panel indicate the 2019 swarm sequence, 2021 swarm sequence, and background seismicity, respectively. Blue lines indicate the cumulative number of swarm events. Orange-shaded bars indicate the swarm period.\n\nThe corrected time series reveals transient deformation patterns coinciding with swarm activity (Fig.\u00a05b, c). The 2019 swarm (denoted by red dots in Fig.\u00a05a and lower panel of Fig.\u00a05b, c), occurred approximately four months after the Mw 6.1 earthquake and ~30\u2009km to its south, is found to produce ~10\u2009mm of eastward and southward displacement at stations FLNM and DNFU. During the 2021 swarm sequence (denoted by black dots in Fig.\u00a05a and lower panel of Fig.\u00a05b, c) particularly between Mw 5.9 and Mw 5.5 events, gradual eastward displacements of ~15\u2009mm were observed at stations YENL, SOFN, <10\u2009mm at TUNM and HUAL, and up to ~20\u2009mm at NDHU. We interpret these displacements as primarily reflecting a combination of post-seismic and aseismic deformation, rather than coseismic slip from the Mw 5.9 event at the depth of 15\u2009km and its afterslip. This interpretation is supported by the fact that most seismicity between the Mw5.9 and Mw5.5 events was concentrated around 10\u2009km depth (Fig.\u00a03b, c), a depth unlikely to generate measurable surface displacements. Moreover, the GNSS displacement time series during this interval can be well modeled by a post-seismic exponential decay function without requiring a co-seismic step (Fig.\u00a0S15).\n\nTo interpret the observed transient deformation, we performed a forward modeling assuming a west-dipping fault plane as the primary rupture surface. Due to the limited number of GNSS stations (only two or three) showing significant displacements near the swarm epicenters, the fault geometry was constrained simply using the locations and focal mechanisms of M\u2009\u2265\u20095 swarm earthquakes. The dip and rake angles were fine-tuned to better fit\u00a0the observations (see Table\u00a0S3). Assuming that the observed transient deformation is solely tied to slip associated with the swarm events, we approximated the imposed fault slip based on the total seismic moment released during each sequence. For the 2019 swarm, this corresponds to ~2\u2009cm of slip across a 15\u2009\u00d7\u200920\u2009km area. For the 2021 swarm (time period between Mw 5.9 and Mw 5.5 event), we consider two fault segments based on the depth distribution of swarm events: ~5\u2009cm of slip on the shallow segment (40\u2009\u00d7\u200910\u2009km area) and ~4\u2009cm on the deeper segment (40\u2009\u00d7\u200918\u2009km area), as illustrated by the blue vectors in Fig.\u00a0S16.\n\nThe forward modeling results show that for the 2019 swarm, the predicted displacements account for only about half of the observed surface motion (red and black arrows in Fig.\u00a0S16), suggesting a significant contribution from additional aseismic slip. In contrast, the predicted displacements for the 2021 swarm are slightly larger than the observations. These discrepancies may arise from simplifications in fault geometry and limited GNSS station coverage. A denser GNSS network and more detailed fault modeling will be necessary to better constrain the aseismic slip contributions.\n\nIn the region surrounding the active swarms (grey box in Fig.\u00a04a), intense aseismic slip episodes were observed following the April 18, 2019, M6.1 earthquake. This is indicated by a sharp increase in the occurrence rate of RESs at the time of this M6.1 event (Fig.\u00a02a), followed four months later by the onset of the 2019 swarm on August 2, 2019 (Seq. 8). Figure\u00a06 summarizes the temporal evolution from 2019 to 2024, including swarm activity, RESs-derived aseismic slip rate, GNSS displacements corrected using monthly averages, and seismicity parameters.\n\na Temporal distribution of earthquake swarm events. b Repeating earthquake sequences (RESs) derived seismic slip rate color coded with various averaging window. c Cumulative number of RESs events. Colored dashed lines show the estimated occurrence rate, with numbers representing the computed rate values. d Cumulative moment of RESs events. Colored dashed lines show the estimated moment rate, with numbers representing the computed moment rate values. e The corrected 1-month moving average (removing co-seismic effect and linear trend) GNSS time series of YENL, TUNM, and SOFN stations (E component) in the north and DNFY and FLUM (N component) in the south of the study area. The time series are from events in the close-up area northern LV area (gray box in Fig.\u00a04a) since 2019 when the RESs derived aseismic slip accelerated. Vertical red dashed lines indicate the times of five M6+ events in the study\u00a0area, while grey lines indicate two M6+ events occurred in the south LV area. f\u2013h Temporal distribution of occurrence rate of seismicity, a- and b- values using original earthquake catalog. Shallow seismicity (\u2264 15\u2009km) and deep seismicity (\\( > \\) 15\u2009km) are indicated by dark blue and light blue, respectively. i Occurrence rate of declustered seismicity. The shaded orange area marks the time span of the 2019 and 2021 swarms. Numbered arrows denote different stages of seismic and aseismic slip behavior. The time history of seismic and aseismic behavior was determined using a centered 30-day averaging window, in steps of one day, consistent with the daily resolution of GNSS data. Seismicity and swarm events are counted as the number per day. Daily seismicity rate and a- and b-values are calculated using a moving window approach, in which a\u2009\u00b1\u200915-day window is centered on each day. Estimates are only shown for windows containing more than 100 events to ensure statistical robustness.\n\nIn April 2019, an M6.1 earthquake occurred at the depth of 20\u2009km in the northern part of the study area, spatially close to a cluster of deep RESs. These RESs responded with a notable acceleration in slip rate (first red dashed line in Fig.\u00a06). Approximately 3.5 months later, a separate swarm sequence occurred to the south (first orange peak in Fig.\u00a06a), accompanied by a discernible increase in the RESs activity, which is visible in the monthly average slip rate (green curve in Fig.\u00a06b), along with ~10\u2009mm of gradual displacement recorded at nearby GNSS stations (blue circles in Fig.\u00a06e). Given the considerable spatial separation between the swarms, RESs, and the 2019 M6.1 event, we infer the presence of two distinct aseismic slip episodes: (1) a deep slip event at depths greater than 15\u2009km in the north, where deep, widely distributed RESs aligned along the mountain strike responded to the nearby 2019 M6.1 event, leading to significant acceleration of aseismic slip rate; and (2) shallower slip episodes in the south, where aseismic slip was detected by nearby GNSS stations during the shallow swarm sequence.\n\nIn contrast, the prolonged 2021 swarm sequence from April 17 to August 29, spanned a broad depth range from ~24\u2009km to near the surface and overlapped substantially with RESs (Fig.\u00a0S11), yet occurred independently of any major earthquake. This episode marked the onset of the most consequential aseismic\u2013seismic coupling observed in the 24-year record that may have initiated a sequence of fault processes that ultimately led to the 2024 Mw7.3 Hualien earthquake. This swarm-RESs coupling, and its potential connection to the 2024 Mw7.3 earthquake, requires detailed examination. The spatiotemporal evolution of this activity can be divided into four stages:\n\nStage 1 (April-August, 2021): Acceleration of aseismic slip and swarm activity. The intense earthquake swarm lasted from April 17 to August 29, initiated near 14\u2009km depth and exhibited multiple waves of activity. During the first two months (first spike for 2021, Fig.\u00a06a), swarm events clustered at depths of 15\u201320\u2009km and migrated upward and northward. At the same time, the RESs-derived aseismic slip rate at 15\u201325\u2009km depth began to accelerate, a trend most evident in the monthly average (green line in Fig.\u00a06b). Increases in RESs\u2019 moment rate and occurrence rate are also observed during this interval (Fig.\u00a06b\u2013d). In the later half of the sequence (second spike for 2021 in Fig.\u00a06a), the swarm activity intensified at shallower depths, accompanied by a renewed rise in the RESs-derived slip rate. This indicates that deep aseismic deformation persisted while shallow brittle failure became more pronounced. Concurrently, GNSS stations near swarm zone recorded transient horizontal displacements (Fig.\u00a06e).\n\nNotably, the swarm period is marked by a sharp increase in shallow seismicity (\u226415\u2009km), reflected in both\u00a0a greater number of events and elevated a- and b-values (Fig.\u00a06f\u2013h, dark blue). In contrast, the declustered background seismicity (Fig.\u00a06i) shows no comparable change, indicating that the observed acceleration in shallow seismicity was driven primarily by the\u00a0swarm itself rather than by background seismic processes. Over the full four-month period, the shallow seismicity rate increased from 4.39 to 10.75 events/day, while the deep seismicity rate remained steady at ~4 events/day (Fig.\u00a0S17). The simultaneous acceleration of swarm activity, enhanced RESs-derived slip rate, and transient surface deformation suggest an aseismic slip episode that likely accompanied by fluid migration and activated both brittle and aseismic slip along multiple depth levels of CRF.\n\nStage 2 (September, 2021 to late-2022): Cascade-like M6+ events. After the 2021 swarm, both the RESs-derived slip rate and overall seismicity declined until late 2022. During this period, however, a sequence of large earthquakes occurred, including the March 22 M6.7 event in southern LV (number 7 in Fig.\u00a01), the June 20 M6.1 event in northern LV (event 8 in Fig.\u00a01), and September 17 -18 M6.5 and M7.0 events in southern LV (events 9 and 10 in Fig.\u00a01). During this period, earthquake occurrence in southern LV increased significantly. In contrast, the northern LV experienced a pronounced decline in seismicity, with the shallow rate dropping from 10.75 to 2.51 events/day and the deep seismicity rate also\u00a0dropped from 3.79 to 2.46 events/day (Fig.\u00a0S17). Stage 2 is therefore characterized by a cascade of seismic slip events occurring primarily outside the swarm zone.\n\nStage 3 (2023 to 2024): Gradually accelerated seismic and aseismic slip rates. Beginning in October 2022, the RESs-derived slip rate slightly increased, as shown in the long-period trends (6-month averaging windows in Fig.\u00a06b), and continued until a few months prior to the April M7.3 event. While the annual number of repeating earthquakes remained nearly constant (~45 events/year, Fig.\u00a06c), their seismic moment rate increased from ~1.3\u20131.4\u2009\u00d7\u200910\u00b9\u2075 to 2.2 \u2009\u00d7\u200910\u00b9\u2075 N-m/day (Fig.\u00a06d). This pattern indicates a larger slip per event, consistent with progressive deep fault creep and mechanical weakening at depths greater than 15\u2009km.\n\nMeanwhile, background seismicity from both original and declustered earthquake activities intensified, particularly at shallow depths when compared to earlier time periods. As shown by Fig.\u00a06f\u2013h, seismicity rate, a- and b-values increased notably in the shallow crust following the cascade of M6+ events. The rising a-value reflects an overall increase in seismic activity, while the increasing b-value suggests a greater proportion of smaller events. This pattern is consistent with Fig.\u00a0S17, which shows a rise in the occurrence rate of shallow earthquakes from 2.51 to 8.37 events/day across 2023, while seismicity at greater depths shows\u00a0a relatively minor increase, from 2.46 to 5.05 events/day. The declustered catalog further reveals that both shallow and deep seismicity began a gradual, slight rise roughly one year before the mainshock.\n\nTo reconcile (1) the observed increase in deep aseismic slip and RESs moment rate, (2) the rise in seismicity rate in both declustered and original earthquake catalogs, and (3) the elevated a- and b-values in the shallow crust one year before the mainshock, we propose a depth-dependent fault evolution model. At greater depths (>15\u2009km), the CRF appears to undergo progressive aseismic slip, likely accompanied by upward fluid migration. This deep process may have weakened the overlying crust and promote more small events on both the LVF and CRF. The concurrent rise in shallow seismicity and elevated a- and b-values suggests a brittle response within a more heterogeneous and a\u00a0possibly fluid-rich environment. These patterns suggest that deep slow slip and shallow brittle failure\u00a0have operated as coupled processes along the CRF, which may represent an intermediate stage in the preparatory phase leading to large earthquake nucleation. However, given that aseismic slip signals are resolved only at monthly timescales, it remains difficult to establish a direct temporal link between aseismic and seismic slip in the final month preceding the mainshock.\n\nStage 4 (2024 April to May): M7.3 event and renewed shallow swarm activity. The April 3, 2024 M7.3 mainshock, with a hypocenter depth of 21\u2009km, was followed by a sequence of aftershocks aligned along the steeply east-dipping LVF until April 22. At that time, a new shallow swarm was initiated at shallow depths above 11\u2009km on the gently west-dipping CRF (Fig.\u00a0S18). This post-mainshock sequence included 5 M6+ events while two of them overlapped spatially with the 2021 swarm, likely representing a recurrence of swarm activity on the same structural segment. Because the RESs catalog ends shortly after the mainshock, the evolution of deeper aseismic slip following the mainshock remains unconstrained and requires further monitoring.\n\nIn summary, we propose that since 2019, the CRF has been progressively activated through both RESs and swarm activity. As illustrated by the conceptual model in Fig.\u00a07, the mid-2021 swarm sequence initiated at ~14\u2009km depth, partially overlapping with the creeping segment of the CRF characterized by abundant RESs, and subsequently migrated upward to ~5\u2009km and downward to ~ 20\u2009km. During this period, deep aseismic slip remained elevated, suggesting that a propagating fluid pressure perturbation may have driven both the swarm and RESs activity observed in Stage 1. Significant aseismic slip likely contributed to the triggering of fluid-assisted earthquake swarms, with upward fluid migration potentially weakening the fault at shallow depths. However, the 2022 M6.1 event in Stage 2, located ~30\u2009km south of the swarm zone and opposite the migration direction, implies that fluid migration alone cannot fully explain its occurrence. In Stage 3, seismicity intensified along a broader extent of the fault, especially at shallow depths, and was followed by the 2024 M7.3 mainshock on the east-dipping LVF and renewed swarms on the west-dipping CRF. The recurrence of CRF swarm activity after the mainshock highlights the need to evaluate how prior aseismic slip and earlier seismic events may have jointly loaded this segment. The following Coulomb stress models assess whether the observed sequence of seismic and aseismic slip episodes collectively promoted failure on the M7.3 rupture plane.\n\nRed and blue lines indicate different fault behaviors of Central Range fault. Purple, orange, and yellow dashed lines outline the distribution of the 2019, 2021, and 2024 swarms, respectively, while the black dashed line outlines the distribution of the RESs. The bilateral fluid migration pattern during the 2021 swarm is denoted by arrows. The background color indicates the Vp/Vs ratio28. The east-dipping black line next to the 2024 M7.3 mainshock represents the assumed rupture plane approximated by the aftershock evolution revealed in Movie\u00a0S1.\n\nTo establish whether the sequential seismic and aseismic slip episodes can be explained by static stress triggering, we applied a series of Coulomb stress models and focused on the role of aseismic slip on the M6+ events at Stages 2\u20134. The fault models used for individual aseismic and seismic slip events are described in Methods \u201cFault models for static stress computation\u201d and listed in Table\u00a0S4. The stress evolution at successive rupture sites is illustrated by sequential plots of the Coulomb stress change in Figs.\u00a0S19\u2013S22.\n\nAs shown in Fig.\u00a0S19, cumulative slip from the majority of 2021 swarm sequence at depths shallower than 11\u2009km (shallow aseismic slip episodes) resulted in a 10\u2009kPa increase in Coulomb stress on the 2022 M6.1 rupture, while the aseismic slip from RESs at deeper portion of the fault contributed an additional 6\u2009kPa stress change. This suggests that the M6.1 event may have been triggered by aseismic slip episodes in 2021. Both the shallow and deeper aseismic slip however, induced a negative stress change on the fault segment that later hosted the April 22, 2022 M6.7 event.\n\nThe stress changes associated with major seismic events (the 2022 March 22 M6.7, 2022 June 20 M6.1, 2022 September 18 M7.0, and 2024 April 3 M7.3 events) were also examined by evaluating the stress changes induced by preceding ruptures (Figs.\u00a0S20\u2013S22). The results indicate that the major earthquakes in 2022 can be largely explained by stress change from previous M6+ events. Specifically, the 2022 March 22 M6.7 event produced a 10\u2009kPa stress increase on the fault segment that later hosted the 2022 June 20 M6.1 event. This M6.1 event later contributed a 2\u2009kPa stress increase on the fault of the subsequent M7.0 event. However, the September 18 M7.0 event resulted in a \u22123\u2009kPa stress change on the fault segment hosting the 2024 M7.3 event. This suggests that the M7.0 event alone does not fully account for the occurrence of the M7.3 mainshock.\n\nInstead, the combined effect of both aseismic and seismic events likely played a crucial role in the stress triggering of the 2024 M7.3 event. This interpretation is supported by the cumulative Coulomb stress increase of up to 30\u2009kPa at the eventual rupture location, resulting from deep aseismic episodes in 2021 and three major M6+ ruptures in 2022 (Fig.\u00a0S23). Notably, a stress change on the order of 10\u2009kPa is commonly considered sufficient to influence fault failure timing in tectonically loaded systems40,41,42. Therefore, a cumulative 30\u2009kPa increase represents a substantially elevated stress state, capable of significantly advancing the timing of a large earthquake. In addition to fault weakening due to upward fluid migration, this level of stress accumulation from both slow and fast slip along the CRF likely contributed to the enhanced seismicity and RESs activity observed during Stage 3, ultimately facilitating nucleation of the April 3, 2024 Mw7.3 mainshock.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64117-3/MediaObjects/41467_2025_64117_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64117-3/MediaObjects/41467_2025_64117_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64117-3/MediaObjects/41467_2025_64117_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64117-3/MediaObjects/41467_2025_64117_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64117-3/MediaObjects/41467_2025_64117_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64117-3/MediaObjects/41467_2025_64117_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "During the 2021 swarm, rapid pore-pressure diffusion from approximately 22\u2009km to the near-surface is required to explain the inferred diffusivity of 5.4\u2009m2/s. In addition to the stress increments induced by the aseismic slips that can trigger earthquakes, the increase in pore pressure can also destabilize pre-existing fractures within the fluid-saturated zone, thereby generating earthquakes. The presence of a high pore pressure environment in the shallow CRF may play an important role in modulating earthquake cycles in eastern Taiwan.\n\nAs summarized in Table\u00a0S2, the estimated diffusivities for Seqs. 1 to 9 range from 2.1 m2/s to 31.0\u2009m\u00b2/s. Higher diffusivities (D\u2009>\u200910\u2009m\u00b2/s) in Seqs. 1, 6, and 7 are associated with large uncertainties, as indicated by coefficient of variation between 41\u201357%. Earthquake locations may initially be\u00a0scattered during the first several days before becoming concentrated along clustered strands or confined within the triggering front, which reveals a clear migration pattern. This dispersion in early stages contributes to relatively large uncertainty in the D estimates. However, the D uncertainty may also come from the earthquake location (4.6\u2009\u00b1\u20093.9\u2009km at depth43). Despite the simplified approximation of the diffusion rates, which captures the majority of triggering fronts (represented by the 90th percentile distance points in one-day bins), the space-time evolution of swarm events (Figs.\u00a0S3\u2013S11) still exhibits a visible migration trend with D higher than a few m2/s.\n\nFigure\u00a0S24 summarizes the scaling of swarm diffusivity and duration using swarm data from northeast Japan and this study. The swarms in northeast Japan and the northeast LV area in Taiwan follow a negative correlation described in a power law of duration \\(\\propto {D}^{-0.5}\\). This suggests that swarms lasted longer when they migrated slowly with limited spatial extension, a trend commonly observed in various tectonic settings e.g., ref.\u200929. The diffusion rates measured in this study are relatively high compared with those observed in other natural swarms, such as 0.008\u20131.5\u2009m\u00b2/s from multiple swarms triggered by the M9.0 Tohoku earthquake in northeast Japan29, 0.09\u20130.12\u2009m\u00b2/s from the long-lived earthquake swarm preceding the M7.7 Noto earthquake44,45, and ~1\u2009m\u00b2/s from natural swarms in the Long Valley caldera in California. Before\u00a0the M7.7 Noto earthquake, several intermittent seismic activities were observed, with rapid hypocenter migration with high diffusivity (101\u2013102 m2/s)44. This migration can be explained by a combined effect from aseismic slip induced stresses and rapid fluid flow in a highly permeable environment.\n\nThe diffusion rate inferred in this study is higher than most swarm observations in various tectonic settings (references in the previous paragraph) but lower than the rates of 3\u201390\u2009m2/s from injection-induced swarms in Soultzsous-Forets, France, and 15\u20132300\u2009m2/s from volcanic activity in Fagradalsfjall46. The upper limit for pore pressure diffusion in seismogenic fractures is estimated at 10 m2/s47. Several mechanisms have been proposed to explain fast diffusivity, including sudden increases in permeability, low viscosity of pore fluids, highly pressurized pore fluids, stress transfer, and aseismic slip e.g., refs.\u200928,29,38,46,48,49,50,51,52,53. Although crustal permeability, fluid viscosity, and pore pressure are difficult to estimate in this study, the concurrent acceleration of aseismic slip rate from RESs and geodetic signal of slow slip suggests that the fast migration requires not only fluid movement but also the involvement of aseismic slip that triggers seismicity.\n\nAnother key parameter for distinguishing fluid-assisted from slip driven swarms is migration velocity11. By analyzing global migrating swarm sequences, two different behavioral regimes were previously proposed: (1) slow slip driven sequences - migration velocities up to several tens\u00a0of kilometers per day (2) fluid-induced sequences \u2013 migration velocities of hundreds of meters per day. The northern LV swarms were plotted against global data11 (Fig.\u00a0S25). While two downward-migrated sequences fall within the slow-slip regime, most upward-migrated sequences lie near the boundary between the two regimes, suggesting contribution from both mechanisms. A significant portion of slow slip, supported by deep-seated repeating earthquakes, must be considered in facilitating fluid-induced processes, which likely contributed to the fast propagation of swarm events in this study.\n\nThe northern LV area is a collision-subduction transition zone where the westernmost Philippine Sea Plate subducts beneath the Eurasian Plate. Numerous earthquakes occur along a west-dipping seismogenic zone (CRF), extending to a depth of ~25\u2009km. The CRF serves as a lithological boundary, separating the metamorphic belt to the west from the deformed accretionary wedge to the east. It delineates the exhumed metamorphic\u00a0rocks beneath the Central Range from the sedimentary formations and volcaniclastic rocks beneath the Coastal Range e.g., ref.\u200954. Most repeating events and earthquake swarms are\u00a0aligned along the west-dipping CRF, suggesting that the two aseismic phenomena are spatially connected and temporally correlated. The swarm-prone area within the shallow CRF is believed to be fluid-saturated, as indicated by high Vp/Vs shown in Fig.\u00a07.\n\nSome events within the swarm sequences exhibit long duration, low-frequency energy (<5\u2009Hz), referred to as low-frequency earthquake swarms, which are believed to result from fluid movement through the damage zone55,56,57. During the active swarm sequences in mid-2021, a significant number of low frequency earthquake swarms were identified58, suggesting the presence of a fluid-rich body at depths of less than 11\u2009km beneath the Longitudinal Valley. This interpretation is supported by observations of high Vp/Vs, low Vp and Vs, low resistivity59,60, and low Qp and low Qs61. The presence of fluid around the shallow CRF coincides with the location of swarm events in the study area and may be linked to the accreted forearc materials of the PSP and/or the fractured sediments along the LV suture62. Given that the high Vp/Vs body is adjacent to the exhumed Yuli Belt (i.e., high-pressure metamorphic rocks), these fluids may have also been released during the exhumation, leading to a dynamic, tectonic-hydrothermal fluid system located at shallow depths that could facilitate aseismic slip63,64,65,66,67,68,69,70,71. In addition to fluid-induced aseismic slip episodes, it is also possible that deeper aseismic slip pulses modify pore fluid pressure, thereby promoting fluid movement.\n\nIn summary, the suture zone in eastern Taiwan, formed by arc\u2013continent collision between the Eurasian and Philippine Sea plates, generates frequent large earthquakes and aseismic slip, posing a major seismic hazard. From 2000 to 2024, this region hosted 13 M6+ events, including the April 3, 2024 Mw7.3 Hualien earthquake. Using repeating earthquake sequences (RESs) and swarm catalogs, we investigated aseismic slip preceding these events. Most swarms occurred in the northern Longitudinal Valley, within the 2024 epicentral area, and six of\u00a0the nine sequences coincided with accelerating aseismic slip detected by deeper RESs on the west-dipping Central Range fault (CRF).\n\nA notable mid-2021 episode featured a four-month swarm that began at ~14\u2009km depth, migrated upward to ~5\u2009km and downward to ~20\u2009km, and was accompanied by 10\u201320\u2009mm GNSS displacements. The swarm displayed bilateral migration with rapid diffusivity (~5.2\u2009m\u00b2/s), consistent with combined effects of overpressured fluids and deep aseismic slip. This episode was followed by a cascade of M6+ earthquakes in 2022 and renewed increases in slip and seismicity in 2023. The acceleration cannot be explained solely by afterslip of the nearby M6.1 event, suggesting progressive CRF activation through coupled swarm and RES activity.\n\nStress modeling shows that aseismic and seismic slips during 2021\u20132022 added up to ~30\u2009kPa positive Coulomb stress on the eventual rupture. Together with fluid-driven weakening of the CRF, these processes likely promoted shallow seismicity and facilitated nucleation of the 2024 mainshock. These results highlight the role of aseismic slip and fluids in earthquake triggering and hazard assessment in Taiwan.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "We analyzed seismicity in the area bounded by latitudes 22.6\u00b0\u201323.4\u00b0 and longitudes 120.8\u00b0\u2013122\u00b0 from January 2000 to April 2024 using data from the Central Weather Administration (CWA) in Taiwan. Crustal earthquakes with depths shallower than 50\u2009km were selected, corresponding to the average Moho depth in eastern Taiwan (Wang et al., 2010). To ensure statistical robustness in the temporal distribution of earthquakes, we applied the maximum likelihood estimation method72 to determine the minimum magnitude of completeness (Mc), which was estimated to be 2.0. Consequently, a minimum magnitude threshold of 2.0 was adopted for the seismicity analyses. The final catalog comprises 133,627 events with magnitudes ranging from 2.0 to 7.3.\n\nUsing the published repeating earthquake waveform procedure30 for events from 2000 to the end of 2011, the repeating earthquake catalog was updated from 2012 to the end of April 2024. The same procedure30,32 was followed to identify repeating earthquakes using two criteria: (1) a cross-correlation coefficient (CCC) higher than 0.9 at more than three stations and (2) a small differential S-P time (dSmP) that ensures more than 50% source overlap. By assuming 3\u2009MPa stress drop, Vp\u2009=\u20094.0\u2009km/s, and Vp/Vs\u2009=\u20091.78, the threshold of dSmP \u2264 0.02\u2009s is used for ML\u2009\u2265\u20092.5 events, while dSmP \u2264 0.01\u2009s is used for ML\u2009<\u20092.5 events.\n\nThe seismogram records used in this study were selected from the seismic stations operated by the Taiwan Seismological and Geophysical Data Management System (GDMS). GDMS was established through a collaboration between the Central Weather Administration (CWA) and the Institute of Earth Sciences, Academia Sinica (IESAS). Vertical component seismograms from 50,934\u2009M\u2009\u2265\u20092 earthquakes in the CWA hypocenter catalog for the period January 1, 2012 to April 30, 2024 were used. All earthquake pairs with separation distances\u00a0of less than 20\u2009km were initially selected. The time window for each event included 3\u2009s before the P-wave arrival and 57\u2009s afterwards.\n\nAssuming that the cumulative slip of repeating earthquakes reflects tectonic loading over a single seismic cycle and can be approximated using GPS-derived interseismic slip rates73, as previously adopted in studies30,32,34. We combined these geodetically constrained slip rates with the cumulative seismic moment release of each sequence to estimate the average rupture area for individual repeating events, as defined in Eqs. 3 and 4. The seismic moment (Mo) was calculated using catalog magnitude (ML) data converted to moment magnitude (Mw)74, and then to Mo using the empirical formulation75:\n\nThe rupture area A and seismic moment release rate (\\(\\widetilde{{M}_{0}}\\)) for each repeating sequence can be computed using Eq.\u00a03, and slip for each event in the same sequence is inferred using Eq.\u00a04.\n\n\\({T}_{i}\\) is the total duration of the sequence, \\(\\mu\\) is the shear modulus, and \\(\\dot{\\widetilde{d}}\\) is the interseismic slip rate. By applying this framework, a\u00a0previous study established empirical scaling relationships between slip and seismic moment for different regions30. In the southern Longitudinal Valley (LV), the repeating earthquake sequences (RESs) yielded a best-fit relation of log d\u2009=\u2009\u20131.21\u2009+\u20090.11\u2009log Mo, while in the northern LV, the corresponding scaling was log d\u2009=\u2009\u20131.96\u2009+\u20090.14\u2009log Mo. These regional differences reflect variations in fault structure, interseismic loading, and RES depth, and highlight the importance of local calibration in slip moment scaling analyses.\n\nTo distinguish earthquake swarms from mainshock-aftershock sequences, the swarm sequence is expected to exhibit high seismicity density without a dominant mainshock. In more swarm-like clusters, a higher proportion of background seismicity is typically observed. A\u00a0previous study26 proposed a composite declustering method that mitigates uncertainties associated with arbitrary parameter choices in individual models. Following their approach, we applied three declustering methods: the Epidemic-Type Aftershock Sequence (ETAS) model76, the nearest-neighbor method (NN13)77, and the interaction-zone method (Re85)78, which links earthquakes based on spatial and temporal proximity.\n\nThe identification of earthquake swarms requires the determination of density rate anomalies (\u03b4\u03bc0). To do so, the density rate (\u03bc0) of the declustered seismicity is first estimated to represent the background rate as\n\nwhere \u03c9 denotes the earthquake probability of the background event, l denotes a spatial smoothing parameter, and T indicates the temporal smoothing parameter. \\({{t}}_{i}\\) represents the occurrence time of an earthquake in the catalog. The scaling parameter \u03b1i is defined as\n\nwhere ts and te are the starting and ending times of the input catalog. The procedure was followed for estimating the stacked density rate (\u03bc0stack) using three different declustering algorithms26. The same parameter settings for the declustering algorithms were maintained, but the smoothing parameters for the density rate (Eq.\u00a01) were modified from T\u2009=\u200915 days and l\u2009=\u200930\u2009km to T\u2009=\u20095 days and l\u2009=\u20093\u2009km, in consideration of the different magnitude cutoffs. The chosen cutoff magnitude was 3.0 for all of Taiwan26, but here M2.0 is used to obtain a more complete swarm catalog in eastern Taiwan.\n\nTo identify density rate anomalies (\u03b4\u03bc0), the local rate change of \u03bc0stack in each swarm was estimated as \u03b4(t) = {(\u03bc0stack(t) - \u03bc0average(t))/\u03bc0average(t)}x100, where \u03bc0average(t) is the averaged \u03bc0(x,y,t) within a 3\u2009km radius of each event, with the average \u03bc0average being computed over a two-year window centered on time t (Fig.\u00a0S26a). As shown by Fig.\u00a0S26b, the resulting number of clusters is found to decrease smoothly with increasing threshold on \u03b4. To discriminate between swarm-like and aftershock-like clusters, the latter type is expected to have greater magnitudes. Thus, in Fig.\u00a0S26c, the number of clusters with a maximum magnitude greater than 6 is plotted against \u03b4 threshold. The number of M\\(\\ge \\,\\)6 clusters decreases from 13 to 0 when \u03b4 changes from \u221220 to 15, while the corresponding number of total clusters decreases from 140 to 27. To retain more swarm-like clusters while excluding all the aftershock-like clusters, the optimal \u03b4 was chosen as 15 in this study.\n\nThe Taiwan Continuous GNSS Array is operated by the Institute of Earth Sciences, Academia Sinica (IESAS), the Central Weather Administration (CWA), the Geological Survey and Mining Management Agency (GSMMA), and the Ministry of the Interior (MOI). GNSS position time series for stations across Taiwan are available for download from the Taiwan Geodetic Model (TGM) website (https://tgm.earth.sinica.edu.tw/TseriesList). GNSS data are processed using GipsyX/RTGx software79, incorporating final orbit and clock parameters from Caltech Jet Propulsion Laboratory (JPL) for precise satellite positioning. To mitigate atmospheric errors, we apply an ionosphere-free linear combination technique80, which integrates multiple frequency observations to reduce ionospheric effects. Tropospheric delays are corrected using the Vienna Mapping Function81. Ocean tide loading effects are removed using the FES2004 model82, which accounts for deformations caused by Earth-ocean gravitational interactions. The daily coordinate time series are processed in the International Terrestrial Reference Frame 2014 (ITRF14) and then transformed into a local NEU (north, east, up) coordinate system.\n\nTo analyze these GNSS time series, we first apply least-squares regression to model and remove long-term linear trends, annual and semi-annual seasonal signals, instrumental offsets, and coseismic displacements. For earthquakes showing clear co-seismic offsets, we further correct for postseismic deformation by fitting an exponential decay function of the form:\n\nwhere \\(\\Delta\\)(t) presents the time-dependent postseismic displacement, A is the initial postseismic amplitude, t is time since the mainshock, and \u03c4 is the characteristic decay timescale. Only magnitude greater than 6.0 events in this study area (listed in Tables\u00a0S1 and S5) are assumed to generate postseismic deformation considered in this correction. For smaller events, we evaluate the significance of potential postseismic effects by comparing the root-mean-square (RMS) misfits between models with and without exponential decay term. If the difference is less than 10% of the mean observational uncertainty, we consider the postseismic effect negligible and did not apply postseismic correction.\n\nThe modeling is guided by visual inspection to ensure that the fitting exponential decay captures the observed relaxation at each station. The estimated postseismic decay constants (\u03c4) and their uncertainties are summarized in Table\u00a0S5. Residual time series are computed by subtracting the full regression model (including postseismic terms) from the raw observations. The original and corrected GNSS time series are shown in Fig.\u00a0S14 and Fig.\u00a05, respectively.\n\nTo better understand whether the sequential seismic and aseismic slip episodes can also be explained by static stress triggering, a series of computations of Coulomb stress change was conducted. The source model and receiver fault assumptions are listed in Table\u00a0S4. The apparent 2021 slow slip episodes comprised two segments: one illuminated by the shallow swarm activity (5\u201315\u2009km) and the other by the deeper RESs (15\u201325\u2009km). The fault geometry along the northern CRF was approximated by the averaged strike, dip, and rake from the M5+ events, while the fault length and width were approximated by the spatial distribution of both the swarm and RES activity. Similarly, the aftershock distribution and published source model were adopted for parameterizing the three mainshocks in 2022. The fault areas of the M6.1 June 20, 2022, M6.7 March 22, 2022, and M7.0 September 18, 2022 events are represented by the spatial distribution of the aftershocks that occurred in the month following each mainshock, while their fault orientations were derived from the focal mechanisms of the mainshocks.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data are available in the main text or the supplementary materials. The Python software package Obspy (www.obspy.org) was used for seismic data processing and waveform filtering. The stastic Coulomb stress change is calculated using Coulomb 3.4. Figures were produced using Generic Mapping Tools (GMT), and Matlab. The catalogs of repeating earthquake sequences, earthquake swarms are available at https://doi.org/10.5281/zenodo.14064472. The earthquake catalog of Taiwan can be retrieved from https://gdmsn.cwb.gov.tw/. The focal mechanism of Taiwan can be retrieved from https://bats.earth.sinica.edu.tw/.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All software used in this work is open source. The code used to generate each figure and result is available through the contact information from the original publications. Requests for further materials should be directed to Wei Peng (weipeng@ntnu.edu.tw).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Obara, K. & Kato, A. Connecting slow earthquakes to huge earthquakes. Science 353, 253\u2013257 (2016).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nRadiguet, M. et al. Triggering of the 2014 Mw7.3 Papanoa earthquake by a slow slip event in Guerrero, Mexico. Nat. Geosci. 9, 829\u2013833 (2016).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nKato, A. et al. 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The TEC contribution number for this article is 00204.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Earth Sciences, National Taiwan Normal University, Taipei, Taiwan\n\nWei Peng\u00a0&\u00a0Kate Huihsuan Chen\n\nDepartment of Earth and Planetary Science, University of California, Berkeley, CA, USA\n\nRoland B\u00fcrgmann\n\nInstitute of Earth Sciences, Academia Sinica, Taipei, Taiwan\n\nYa-Ju Hsu\u00a0&\u00a0Yan-Hong Chen\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: Wei Peng, Kate Huihsuan Chen, Roland B\u00fcrgmann, Ya-Ju Hsu Data curation: Wei Peng, Ya-Ju Hsu, Yan-Hong Chen Investigation: Wei Peng Methodology: Wei Peng, Kate Huihsuan Chen, Ya-Ju Hsu Supervision: Kate Huihsuan Chen Validation: Wei Peng, Ya-Ju Hsu, Yan-Hong Chen Writing \u2013 original draft: Kate Huihsuan Chen, Wei Peng Writing \u2013 review & editing: Kate Huihsuan Chen, Roland B\u00fcrgmann, Ya-Ju Hsu.\n\nCorrespondence to\n Kate Huihsuan Chen.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks R. 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MoS2-based Memtransistors", + "journal": "Nature Communications", + "published": "28 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64536-2/MediaObjects/41467_2025_64536_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64536-2/MediaObjects/41467_2025_64536_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.26207/2hsj-0n18" + ], + "code": [], + "subject": [ + "Electronic devices", + "Two-dimensional materials" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5845160/v1.pdf?c=1761735942000", + "research_square_link": "https://www.researchsquare.com//article/rs-5845160/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-64536-2.pdf", + "preprint_posted": "21 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Memristive crossbar architectures are promising as efficient, low-power inference engines for edge AI applications. However, inputs with minor differences often yield similar outputs, requiring additional processing methods such as confidence scoring, feedback mechanisms, crossbar redundancy, or hybrid analog-digital approaches to resolve. These methods can be impractical for resource-limited edge devices. In contrast, three-terminal memtransistors can dynamically tune conductance via gate control, effectively resolving similar outputs and enhancing separability without retraining. Here, we present dense, large-scale crossbar array architectures incorporating up to 2048 MoS2 memtransistors per array, achieving >92% yield across multiple arrays. These architectures demonstrate the ability to resolve inference ambiguities through gate modulation without the need for costly retraining or reprogramming. We also validated their performance by successfully classifying handwritten digits from the MNIST database. Additionally, these memtransistors exhibit write energies as low as ~0.2 fJ, maintain read margins up to 10\u2075, and offer long-term non-volatile retention exceeding three years. To the best of our knowledge this is the largest array-level demonstration based on 2D materials reported thus far in literature. Additionally, we benchmark the performance of MoS2 memtransistors against other 2D material-based architectures and project their potential compared to state-of-the-art AI accelerators.Physical sciences/Materials science/Materials for devices/Electronic devicesPhysical sciences/Materials science/Nanoscale materials/Two-dimensional materials", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformation.pdfSupplementary Information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Memristive crossbar architectures are promising as efficient, low-power inference engines for edge AI applications. However, inputs with minor differences often yield similar outputs, requiring additional processing methods such as confidence scoring, feedback mechanisms, crossbar redundancy, or hybrid analog-digital approaches to resolve. These methods can be impractical for resource-limited edge devices. In contrast, three-terminal memtransistors can dynamically tune conductance via gate control, effectively resolving similar outputs and enhancing separability without retraining. Here, we present dense, large-scale crossbar array architectures incorporating up to 2048 MoS2 memtransistors per array, achieving >92% yield across multiple arrays while individual memtransistors exhibit write energies as low as ~0.2 fJ, maintain read margins up to 10\u2075, and offer a projected retention exceeding three years. These architectures demonstrate the ability to resolve inference ambiguities through gate modulation without the need for costly retraining or reprogramming. We also validate their performance by successfully classifying handwritten digits from the MNIST database. Finally, we benchmark the performance of MoS2 memtransistors against other 2D material-based architectures and project their potential compared to state-of-the-art AI accelerators. We believe that this work furthers the ongoing development of in-memory processors for decentralized edge applications and that future studies aimed at reducing device-to-device variation and improving long-term non-volatile memory would only enhance inference capabilities.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "With the growing demand for the implementation of artificial intelligence (AI) in decentralized edge applications, proactive co-design of software and hardware has become critical1,2. This has led to a distinct paradigm shift towards in-memory computing architectures inspired by biological neural networks (BNNs) for the development of next-generation artificial neural network (ANN) accelerators and inference engines1,3,4,5,6,7,8,9,10. In this regard, memristive crossbar array architectures modeled on biological synapses have already attracted significant attention for their ability to parallelly implement rapid, energy-efficient vector-matrix multiplication (VMM) operations through simple employment of Ohm\u2019s and Kirchoff\u2019s laws across their constituent non-volatile memory (NVM) cells. Crossbar array architectures therefore offer significant advantages for accelerating ANNs in hardware, most notably for tasks that require extensive matrix computations, such as image or speech classification, with extant experimental demonstrations including image classification9,11,12, image/signal processing13,14, video classification15, etc. While such real-time, real-world data processing applications are of great interest for implementation at the network edge, e.g., in self-driving cars or automated factories, numerous challenges at the software and hardware levels still need to be addressed before data-center-independent, latency-free inference can be realized. Of these, perhaps the most persistent is the tendency for inputs (e.g., images) with minor differences to result in outputs with high degrees of classification confusion between them. This problem is only exacerbated as datasets become more complex, necessitating additional processing through methods such as confidence scoring16,17,18 and recursive feedback mechanisms19,20,21 at the software level or crossbar redundancy22,23 and hybrid analog-digital approaches24,25,26 at the hardware level. While each of these approaches possess their own benefits, they often add significant energy-/time-/area-overhead to inference engine operation, making them impractical for resource-constrained edge applications. Instead, an alternative path to efficient edge computation may lie in emulation of high-level neural/synaptic mechanisms at the base computation unit (i.e., NVM cell)27,28.\n\nWhile resistive memories9,29,30, charge-trapping (FLASH) memories31,32,33, and ferroelectric memories34,35,36, among others, have already seen serious investigation as NVM cell technologies, one group of emerging alternatives that currently shows promise for in-memory/sensor computing through back-end-of-line (BEOL) integration with standard complementary metal-oxide-semiconductor (CMOS) technology is two-dimensional (2D) materials. Monolayer MoS2, a semiconductor from the transitionmetal dichalcogenide (TMDC) family, has attracted particular attention for use in three-terminal memtransistor devices for NVM applications. These devices exploit monolayer MoS2\u2019s sensitivity to charge variation stemming from its high surface-area-to-volume ratio to realize distinct, non-volatile conductance levels (memory states) using charge-trapping gate stacks37,38,39 or metallic floating gate architectures14,40,41 akin to traditional Si-based FLASH memories. Notably, the presence of a third (gate) terminal in the memtransistor architecture sets it apart from the two-terminal NVMs commonly used in crossbar architectures by allowing for the application of a separate gate bias during crossbar operation, screening the electric field across the device channel and effectively tuning the conductance state (weight) of the device. Extant studies of three-terminal MoS2-based memtransistors primarily employ the gate terminal as a programming input during weight assignment14,40,41,42, shifting the threshold and enabling fixed excitatory or inhibitory behavior for later inference operations. However, from a biological perspective, this capability is highly reminiscent of the activities performed by modulatory neurons (interneurons) in heterosynaptic plasticity28,43,44. In this process, the stimulation of one neuron causes a change in the strength (weight) of the connections between other, inactivated, neurons. A study of repeated heterosynaptic modulation events in biological neural networks has found that this can lead to persistent changes in synaptic connections45, thus contributing to the formation of long-term memories46. Developing methods of implementing heterosynaptic plasticity in hardware has therefore been deemed an important goal for realizing next-generation neuromorphic systems with greater energy efficiency28. For crossbar architectures, this offers a method of tuning prewritten conductance states by applying different gate biases and could potentially be used to exaggerate differences between otherwise similar classes, enhancing the separability of highly confused outputs without the need for resource-intensive data processing.\n\nIn this article, we present crossbar array architectures utilizing up to 2048 memtransistors based on monolayer MoS2 integrated with a charge-trapping memory stack as NVM cells. We achieve a high yield of >92% across multiple crossbar array demonstrations, with constituent devices displaying write energies as low as ~\u20090.2 fJ while retaining read margins (ratios between programmed and erased states) as high as 105 and projected retentions exceeding three years. We also investigate the influence of cell area on device- and array-level operations, ultimately demonstrating equivalent performance at information densities of up to 1.94\u2009Mb/cm2 (assuming 1-bit operation). We then demonstrate how our architecture can be partitioned to accurately achieve simple inference operations (e.g., shape detection) in hardware. Uniquely, we also demonstrate how the three-terminal nature of MoS2-based memtransistors can be exploited to dynamically tune conductance states during inference to modulate weights, and therefore outputs, without changing the underlying conductance state in a manner similar to modulatory synapses; notably, this process allows for weight tuning and task-specific separation of high confusion objects without requiring retraining of the network or reprogramming of individual weights, energy-/time-intensive operations that are difficult to realize in edge applications with strict energy/latency constraints. Finally, we experimentally demonstrate image classification of handwritten digits from the Modified National Institute of Standards and Technology (MNIST) database47 using a 64\u2009\u00d7\u200932 (2\u2009kb) MoS2 memtransistor crossbar array.", + "section_image": [] + }, + { + "section_name": "Results & discussion", + "section_text": "A schematic representation of the base computational unit (NVM cell) utilized for our primary crossbar array architecture is shown in Fig.\u00a01a. Control of individual NVM cells within the arrays is achieved through dedicated 5/20\u2009nm Ti/Pt gate lines (word lines), which are deposited first onto a commercial p++-Si/SiO2 substrate using electron-beam (e-beam) evaporation. Atomic layer deposition (ALD) is then used to grow a charge-trapping gate stack consisting of a 15\u2009nm Al2O3 blocking layer, a 3\u2009nm HfO2 trapping layer, and a 7\u2009nm Al2O3 tunneling layer; the efficacy and operational principles of this gate stack for memory applications have already been reported on previously37,38,39, with a summarized description being provided below. The dielectric stack is then etched near the gate contact pads to provide electrical access for later operation, and large-area, monolayer MoS2 grown via metal-organic chemical vapor deposition (MOCVD) is transferred onto the substrate using a wet transfer process before being etched into the discrete channels for each constituent device. Details regarding the quality/characterization of the as-grown MoS2 film are provided in Supplementary Note\u00a01. Source and drain (bit) lines are then formed by evaporating 40/30\u2009nm Ni/Au, with subsequent evaporation of 90\u2009nm Al2O3 and 40/70\u2009nm Ti/Au to form insulating crosspoints and conductive bridges, respectively, for the source/drain overlaps. Note that all access lines were purposefully kept narrow (1.5\u2009\u00b5m wide) compared to prior array-level demonstrations to avoid large parasitic capacitances and increase the crosspoint breakdown voltage. Further details regarding the fabrication process can be found in the Methods section.\n\na Schematic of the basic non-volatile memory (NVM) cell design containing a single memtransistor. Terminals are split into row (drain and gate) and column (source) accesses, with each cell occupying an area of 676\u2009\u00b5m2. b Optical image of a representative 16\u2009\u00d7\u200910 crossbar array based on the design shown in (a). Zoomed-in image shows constituent memtransistors with the drain, source, and gate lines labeled. Scale bar denotes 100\u2009\u00b5m (10\u2009\u00b5m for zoomed-in). c Overlapped transfer characteristics, i.e., drain-to-source current (IDS) versus back-gate voltage (VBG), taken at a drain-to-source voltage (VDS) of 1\u2009V from constituent memtransistors. d Hysteresis loops for a representative device from (b, c) taken at VDS\u2009=\u20091\u2009V. Multiple loops were taken by sweeping VBG over the noted ranges to determine the presence/size of the memory window at different gate voltages; a sizable memory window of ~\u20097\u2009V can be noted for the\u2009+/\u2212\u200910\u2009V sweep. e Three-dimensional scatter plot showing the distribution of ON-state and OFF-state currents taken at VDS\u2009=\u20091\u2009V, denoted as ION (pink) and IOFF (cyan), respectively; devices/cells marked in gray registered as an open circuit (OC) when measured. Notably, 158/160 devices were found to work (98.8% yield). f, g Maps of (f) threshold voltage (Vth) and (g) subthreshold slope (SS) across the array. OC devices are marked. h, i Histograms of (h) Vth and (i) SS. The means (\u00b5) and standard deviations (\u03c3) are noted for each.\n\nAn optical image of a representative 16\u2009\u00d7\u200910 array is shown in Fig.\u00a01b along with a zoomed-in image showcasing several constituent NVM cells, each possessing a cell area of only 676 \u00b5m2. Here, cell area is defined as the area (Length\u2009\u00d7\u2009Width) of the repeating unit for a given crossbar design. This allows us to achieve an information density, defined as the amount of storable information (i.e., weights) in bits per cm2, of ~\u20090.15\u2009Mb/cm2. Note that, for the purposes of this work, 1-bit (two-state) operation is assumed in all cases for the purposes of calculating information density. To further investigate the scalability of our MoS2 memtransistor technology for achieving higher information density targets, more compact crossbar array architectures were also designed and tested; the most compact design featured cell areas of 51.5\u2009\u00b5m2, providing an ultimate information density of ~\u20091.94\u2009Mb/cm2 while retaining comparable performance to our primary design. Further details regarding this investigation and an overview of the results for each design are provided in Supplementary Note\u00a02, 3. While the achieved integration/information densities reported here still lie far short of the >\u20091 Tb/cm2 necessary for full-scale emulation of BNNs in hardware26, they nonetheless compare favorably to other extant 2D-material-based array-level demonstrations; while some reports have demonstrated cell areas down to ~\u20095\u2009\u00b5m2 per cell, the ultimate array size of those works was significantly less than that shown here. A comprehensive comparison of this work with existing 2D literature14,30,48,49,50,51,52,53,54 is provided in Supplementary Note\u00a04.\n\nThe memory capabilities/operations of the Al2O3/HfO2/Al2O3 gate dielectric stack incorporated into each MoS2 memtransistor (NVM cell) can be easily understood through the band diagrams shown in Supplementary Note\u00a05. As has been studied elsewhere37,38,39, the NVM capabilities of each device/cell are governed by the trapping/detrapping of charge carriers in the middle HfO2 charge-trapping layer when bias pulses of sufficient magnitude are applied to the back-gate in program/erase (write) operations. These trapped charges screen the electric field across the MoS2 channel, shifting the threshold voltage (Vth) of the device and allowing for the realization of distinct conductance (memory) states at a given read voltage (Vread). The polarity of the applied pulse determines which charge carriers are trapped/detrapped, with holes (electrons) being trapped when negative (positive) pulses are applied and vice versa, while the pulse time and magnitude determine the change in carrier concentration in the charge-trapping layer (i.e., the size of the Vth shift, or memory window). For the purposes of this work, the memory window (\u0394Vth) is defined as the difference in Vth, taken at a constant current level, between programmed/erased states. Note that many extant 2D-material-based crossbar demonstrations48,55,56,57,58 program (write) devices through application of a drain bias pulse to prompt defect migration across the semiconducting channel, thereby changing the threshold (conductance) of the device; depending on the biases required, with lower biases always preferable for greater energy efficiency, this can lead to destructive read operations similar to those that affect two-terminal memristor technologies55,56. In addition, defect engineering of the channel, e.g., through low-energy Ar plasma treatment59, may be required to provide a sufficiently high concentration of defects for appreciable memory effects to be seen at low voltages, thus complicating the fabrication process. Conversely, for our demonstration, programming is performed through the application of bias pulses to the gate terminal to promote charge-trapping in the dielectric stack, thereby shifting the threshold by screening the electric field across the channel in a manner analogous to traditional FLASH memories37,38,39. As the drain terminal is decoupled from the programming mechanism, this minimizes the risk of destructive read events. Furthermore, unlike defect-migration-based memories, charge-trapping memories do not require any preprocessing (i.e., defect engineering) of the channel material, relying solely on the as-fabricated dielectric stack for memory operation.\n\nThe transfer characteristics, i.e., drain-to-source current (IDS) versus VBG, taken at a drain-to-source voltage (VDS) of 1\u2009V are shown in Fig.\u00a01c for the representative array shown in Fig.\u00a01b; the hysteresis characteristics of a representative device when swept between varying VBG ranges are shown in Fig.\u00a01d, demonstrating how the memory window, and thus the achievable conductance states, of a single device evolve as increasing VBG is applied. A histogram of the memory window extracted across 32 memtransistors is shown in Supplementary Note\u00a06. The mean and standard deviation were found to be 7.30\u2009V and 0.40\u2009V, respectively. The read margins from these devices under the same conditions were also extracted, with the results also being shown in Supplementary Note\u00a06. As can be seen, most devices achieved read margins >\u2009104, with all achieving read margins >103, which is more than sufficient for the implementation of inference operations in hardware9,14. Furthermore, as shown in Fig.\u00a01e\u2013h for the large-scale (16\u2009\u00d7\u200910) array presented in Fig.\u00a01b, the crossbar array architectures discussed here display impressively high yields of >95% with reasonably good device-to-device uniformity in ON/OFF-state performance, Vth, and subthreshold slope. As discussed in Supplementary Note\u00a02, 3, and 6, these metrics were found to be similar across each design tested, indicating minimal influence from design on \u0394Vth, read margin, energy consumption, or yield. For comparison, please note that previous large-scale demonstrations only reached reported yields of ~\u200983% or lower for single arrays14,30,48,49,50,51,52,53,54 (see Supplementary Note\u00a04 for comparative analysis of yield with previous 2D-based array-level demonstrations). As defective NVM cells can have a significant negative impact on crossbar operations, and therefore on ANN tasks such as image classification, signal processing, etc., the high yield and reliability of the MoS2-based memtransistors demonstrated in this work therefore represent a notable step forward for the eventual adoption of 2D-material-based NVMs in large-scale logic accelerators. The array architectures demonstrated in this work (both here and in the Supplemental) also compare favorably in terms of write energy, ranging from tens of pJ (base) to below 1 fJ (peak); all write energies were estimated using the equation Energy\u2009=\u2009Time\u2009\u00d7\u2009Current\u2009\u00d7\u2009Voltage, which is commonly used to estimate switching energy for NVMs60. The terms \u201cbase\u201d and \u201cpeak\u201d included in the assessment of our work refer to the pulse time (write time), with base referring to our typical pulse time of 100\u2009ms (used for all demonstrations unless otherwise states) and peak referring to our minimum confirmed pulse time of 1\u2009\u00b5s (see Supplementary Note\u00a07). While several other array-level works have also utilized ultrafast write times, our NVM capabilities being controlled by the gate means that the write current of our devices is limited to the gate leakage current, which remained in the region of several tens of pA even at the largest gate biases applied (~\u200910\u2009V), thus allowing for severely reduced energy expenditure. Note that, due to experimental setup limitations, the minimum reliable read time was constrained to 4\u2009\u00b5s.\n\nOne significant advantage of utilizing memtransistors as NVMs as opposed to more mature resistive memory technologies is their multi-terminal nature. This allows for devices to be modulated through both the non-volatile program/erase operations mentioned previously and through dynamic applications of VBG during read operations. By varying VBG during reads, the conductance state (weight) programmed into a device can be selectively tuned either higher (through application of a positive gate bias) or lower (through application of a negative gate bias) as needed without expending the energy/time required for a switching event; as read margin will vary depending on where it is assessed in the memory window, this approach may also be used to increase or decrease the ratio between states as needed for different applications. Notably, no charge-trapping is required for modulating weights in this manner, meaning that this can be performed at the default read speed (clock speed) of the system being used to conduct logic operations in the array.\n\nThe ability of our memtransistor architecture to modulate preprogrammed conductance states through the application of a modulatory back-gate bias (Vmod) is shown in Supplementary Note\u00a08. It can be seen that >6 distinct output levels can be produced from a single binary state, thus offering a mechanism to emulate intermediate synaptic strengths without the time/energy costs and variability and drift issues commonly associated with analog memory writes. As we will discuss in further detail below, this ability to control conductance states (weights) independently from the application of program/erase operations provides an extra degree of freedom when performing inference operations, allowing us to dynamically potentiate or depress weights to more clearly differentiate between outputs in high confusion. This capability stands to improve system-level performance in multiple ways. First, it increases output separability, which is especially valuable for tasks where resolution must be preserved. Second, gate tuning serves as a runtime calibration mechanism that compensates for device non-idealities or variation without requiring reprogramming or retraining. Together, these elements indicate that, unlike multilevel resistive memories, which require iterative tuning and exhibit degraded endurance at intermediate states, our method retains the simplicity and stability of binary memory while possessing some of the flexibility of analog systems. While not intended to replace multilevel storage where precision is essential, dynamic gate modulation provides an efficient alternative for inference tasks where robustness, low power, and tolerance to device variation are more critical than static precision.\n\nTo implement this approach in hardware, inference ambiguities would first need to be identified via a readout layer, which monitors the difference between dedicated output nodes (\u0394Iout). If this value falls below a predefined threshold (e.g., 5% of the maximum difference) for a given input, the output (inferred digit) would be flagged as uncertain. This function could be achieved using a comparator circuit. Following inference, flagged outputs would be subjected to dynamic modulation. A control unit would then trigger a lookup into an external memory table storing modulatory gate biases (Vmod) for the relevant output nodes. These Vmod values would be preassigned during network training based on the object classes being analyzed and reflect coarse bias adjustments tailored to enhance the separability of commonly confused class pairs (e.g., within\u2009+/\u2212\u20092\u2009V for the demonstrations discussed in this work). This allows the system to resolve ambiguous cases through a single re-read, rather than reprogramming device states or performing retraining. The peripheral overhead for this process would be modest, consisting of the comparator circuit for \u0394Iout evaluation and flagging, the external lookup table (with the size depending on the number of high confusion classes and desired Vmod resolution), and a shared DAC or multiplexer to apply Vmod to the array. Of course, it is important to acknowledge that DAC/ADC operation significantly contributes to the energy consumption of neuromorphic accelerator chips25; however, as modulation would only be triggered for a subset of inference cases, often defined by task-specific importance (e.g., hazard detection in surveillance), the power draw for this use case should not meaningfully contribute to the overall power budget.\n\nWhile the crossbar architectures developed as part of this work show high yield, uniformity, and information density, thus demonstrating improvements in several areas critical for the realization of 2D-based logic accelerators, assessing their potential for in-memory computing applications requires a stringent investigation of the programming behavior of individual NVMs and the accuracy of logic operations performed across the arrays. For each array design, individual devices may be independently accessed by selecting the terminals of the corresponding row (drain and gate terminals) and column (source terminal). Once a device is accessed, operations are split into two categories: programming/erasing and reading. For program/erase operations, two distinct schemes were investigated as part of this work, the schematics of which are shown in Supplementary Note\u00a09. In the first, a bias pulse is simply applied to the corresponding gate access line with a time 100\u2009ms and a magnitude 10\u2009V, adjusting the carrier concentration in the charge-trapping layer as outlined in the NVM discussion above. While straightforward and effective, this scheme was found to affect all devices in a given row, making it ill-suited to programming distinct conductance states (weights) in individual memtransistors for logic operations; however, it may still be utilized effectively for setting each NVM in an array to a low conductance state (LCS) ahead of weight assignment through the application of large positive gate biases (Verase) in an initialization step. This primarily serves to standardize conductance states between different logic operations performed during array testing. This effectively suppresses persistent low-resistance paths across the array, limiting the number of conductive routes through which sneak currents can propagate61. Please note that this approach does not eliminate sneak paths entirely. Even devices in the OFF-state possess finite resistance, and under large array sizes, half-selected paths can contribute measurable leakage. However, with OFF-state conductances only reaching a few pS and read voltages maintained at 1\u2009V, the resulting leakage currents are typically below 10 pA, well within the tolerances of common sensing circuits. For applications wherein the weights of a given array are held constant, this step may not be required. Furthermore, as shown in Supplementary Note\u00a010, experimental testing has verified that each gate access line is sufficiently electrostatically isolated to prevent programming of devices in adjacent rows, thus allowing for subsets of devices to be initialized without affecting the entire array.\n\nAn overview of the second program/erase scheme, which may be used to assign/adjust individual weights, is also provided in Supplementary Note\u00a09. Here, a half-biasing scheme is utilized to maximize the gate-to-source voltage (VGS) across the targeted cell while minimizing VGS across all other cells on the same gate access line, thus allowing for single device programming. The gate access line corresponding to the targeted cell is subjected to a given programming bias of half its typical value (Vprogram/2) while its corresponding source line is held to the same bias at opposite polarity (-Vprogram/2), thus providing a VGS across the cell equal to Vprogram. All other source lines are held at Vprogram/2, ensuring that VGS\u2009=\u20090\u2009V for all other cells on the same gate access line, thus preventing programming of unwanted cells. As negative gate biases are applied for programming events, the targeted cell and all others in the same row are held in the OFF-state throughout, leading to low programming currents and ensuring a low programming energy expenditure. While the application of large source biases could lead to sneak path current through cells in other rows, this is partially suppressed through the initialization step placing all devices into the OFF-state; sneak path current can be further suppressed by applying a small (<\u20095\u2009V) negative bias to every other gate access line to ensure that devices in those rows are turned completely OFF without programming them. After programming, read operations may be performed to probe the conductance state of a device by applying Vread to the respective gate access line while also applying a given VDS (VDS,read) to its respective drain terminal. For the purposes of this work, Vread\u2009=\u20090\u2009V and VDS,read\u2009=\u20091\u2009V unless otherwise stated.\n\nTo verify the ability of our MoS2-memtransistor-based crossbar array architecture to function as an inference engine, a proof-of-concept inference demonstration aiming to classify simple shapes in hardware was first performed. It should be noted that for this demonstration, and all others discussed in this work, the analog weights achievable using our MoS2 memtransistor architecture37,38,39 are deliberately binarized between logic \u201c0\u201d and logic \u201c1\u201d. Binary neural networks (BNNs) have been recognized as well-suited choices for edge platforms where compute energy and memory footprint must be tightly controlled62,63. For such use cases, binarization serves three purposes: (i) it minimizes the need for high-resolution analog memory states, which require costly write-verify schemes to program64 and are prone to error due to the low read margin between different states20,63; (ii) it simplifies on-chip multiply\u2013accumulate operations to XNOR and bitwise logic62,65, reducing active energy costs, while also terminating sneak paths through unwanted cells61, reducing passive energy costs; and (iii) it provides a robust pair of initial states\u00a0that can be dynamically modulated through an applied gate bias to increase inference accuracy, as will be discussed later. An overview of this demonstration is shown in Fig.\u00a02. A set of five 5\u2009\u00d7\u20095 binary (black-and-white) shapes was chosen, and software-based training was used to prepare a weight map corresponding to the conductance states of a 25\u2009\u00d7\u20095 sub-array of a 100\u2009\u00d7\u200910 (1\u2009kb) memtransistor crossbar array, with a schematic representation of this process and optical images of the array being shown in Fig.\u00a02a. See Supplementary Note\u00a011 for details on the characterization of the sub-array used in this demonstration. As with the smaller arrays discussed above, we noted near 100% yield, read margins >\u2009103, and no overt spatial trends for common device performance metrics across both the 25\u2009\u00d7\u20095 sub-array and the full 100\u2009\u00d7\u200910 array. The memory performance of devices in this array was also assessed separately from the smaller arrays detailed above to accurately determine what effects, if any, scaling has on our program/erase capabilities, with the results being presented in Supplementary Note\u00a012, 13. From these results, we note minimal spatial influence on hysteresis (i.e., read margin and memory window) or retention. Furthermore, a fit of ~\u20091\u2009h retention measurements indicates that the projected long-term retention exceeds three years, in part due to the intrinsically-OFF nature of the devices meaning that change in read margin over time will predominantly depend on the change in ON-state conductance. A corresponding investigation of minimum write/read time stability for the same array is presented in Supplementary Note\u00a014. The endurance of the MoS2 memtransistor architecture utilized in this work was also assessed. Supplementary Note\u00a015 shows the results for a representative NVM cell over 500 program/erase cycles (Vprogram\u2009=\u2009\u2212\u200910 V, Verase\u2009=\u2009+\u200910\u2009V, tpulse\u2009=\u2009100\u2009ms). Some degradation in the memory ratio between the ON-state and the OFF-state was seen during cycling; however, the read margin remains >\u2009102, indicating suitable endurance for NVM cell application. The retention of the same device over a period of ~\u20091\u2009h, read at a constant VDS of 1\u2009V, is shown in Supplementary Note\u00a016 before and after endurance testing. While the read margin shows similar degradation to the cycling results, little-to-no change in long-term retention can be seen, indicating minimal write disturbance from repeated program/erase cycles.\n\na Schematic overview of shape identification. First, five 5\u2009\u00d7\u20095 binary (logic \u201c0\u201d or \u201c1\u201d) shapes were chosen. A 25\u2009\u00d7\u20095 array of binary weights was generated and mapped to a 25\u2009\u00d7\u20095 sub-array of a 100\u2009\u00d7\u200910 hardware array (pictured), with logic \u201c0\u201d represented by a low conductance state (OFF-state) and logic \u201c1\u201d represented by a high conductance state (ON-state). Scale bar denotes 100\u2009\u00b5m (20\u2009\u00b5m for zoomed-in). b Expected and (c) experimental results when the binary shapes in (a) were fed to the sub-array. d Demonstration of heterosynaptic weight modulation, wherein varying the gate bias inside of a conductance state (ON-state, green curve) allows for the potentiation (increase, green dotted line) or depression (decrease, red dotted line) of the effective conductance state (weight) seen by the array. e Schematic for heterosynaptic weight modulation for shapes \u201c4\u201d and \u201c5\u201d from (a), wherein weights corresponding to areas of high similarity/difference between the shapes are dynamically depressed/potentiated through the application of negative/positive gate biases (Vmod). f\u2013h Effects of dynamic depression and potentiation on identification between \u201c4\u201d and \u201c5\u201d. Uniform application of (f) a depressive Vmod (Vmod\u2009=\u2009\u2212\u20092 V) and (g) a potentiating Vmod (Vmod\u2009=\u2009+\u20092\u2009V) were found to have a minimal effect on distinctiveness. h When depressive and potentiating Vmod were selectively applied, a high distinctiveness between outputs was achieved. i Bar plots showing the output ratios between nodes for each shape applied (top) and input shapes for each node tested (bottom) for each case, i.e., standard (blue), uniform depression (orange), uniform potentiation (yellow), and combined depression and potentiation (purple).\n\nAn image classification test was then conducted, with each of the 5\u2009\u00d7\u20095 shapes transformed into 25\u2009\u00d7\u20091 input vectors before being sequentially fed to the hardware array as voltage pulse trains through the drain terminal; for each input shape/vector, white pixels were expressed as a 0\u2009V input while blue pixels were expressed as a 1\u2009V input voltage. As inputs were applied, multiply-accumulate (MAC) operations were conducted along each column (node) of the array, each of which were indexed to a particular shape during training. The output currents from each node were taken and compared to determine which shape corresponded to the maximum current value, as this represents the result for the inference operation following the max-current sensing rule9. The results in Fig.\u00a02b, c show the expected (simulated) and experimental results, respectively, for the shape identification task outlined above. From the simulation, the maximum output for each node should correlate with its respective shape, which was indeed the case for the experimental results; each shape was correctly identified at its respective node and was clearly differentiable from all other inputs. These results thus verified the array\u2019s ability to perform hardware-based image classification tasks using our MoS2-memtransistor-based crossbar array architecture.\n\nWhile the results are accurate in this case, it can be clearly seen that certain input/node combinations result in a higher degree of uncertainty, which could potentially lead to misclassification in more complex datasets or noisy real-world scenarios. While weights could be optimized for these scenarios on-site through retraining/reprogramming, these are resource-intensive operations that are more-than-likely infeasible in most emerging edge applications for hardware-based logic accelerators. Here, the ability to dynamically modulate the weights of memtransistors through the application of Vmod to the gate terminal, as discussed above and further illustrated through the demonstration shown in Fig.\u00a02d, provides an additional method of verifying inference results not achievable in traditional two-terminal resistive memories. An overview of this proposed verification scheme is presented in Fig.\u00a02e\u2013i. For a given set of possible classes, in this case the five binary shapes shown in Fig.\u00a02a, the areas of greatest difference/similarity for each possible combination are identified and corresponding mask matrices generated, with the example schematic in Fig.\u00a02e showing how heterosynaptic weight modulation could be used in the case of shapes \u201c4\u201d and \u201c5\u201d. Image classification is then performed as detailed above, with the caveat that the ratio between two outputs must be over a given threshold value. If the results lie below that threshold, the mask matrix corresponding to that shape combination is recalled as an input vector, and a second MAC operation is performed as it is fed to the gate terminals as voltage pulses aiming to either potentiate (positive) or depress (negative) the weights representing areas of high difference or similarity, respectively. The output currents from the relevant nodes are then assessed to determine the true input following the max-current sensing rule.\n\nOutputs for this process when conducted on the 25\u2009\u00d7\u20095 sub-array discussed/detailed above for differentiation of shapes \u201c4\u201d and \u201c5\u201d are shown in Fig.\u00a02f\u2013h. Uniform application of a depressive Vmod (Vmod\u2009=\u2009\u22122\u2009V) and a potentiating Vmod (Vmod\u2009=\u2009+2\u2009V) were both found to have a minimal effect on distinctiveness, though the output current registered at each node for each input shape did vary as expected (from tens of pAs for uniform depression to hundreds of nAs for uniform potentiation). However, when depressive and potentiating Vmod were selectively applied as outlined in Fig.\u00a02e, high distinctiveness between outputs was achieved, thus reducing confusion. The output ratios between nodes for each shape applied and input shapes for each node tested are shown in Fig.\u00a02i for each case presented, i.e., the standard inference case (Fig.\u00a02c), uniform depression (Fig.\u00a02f), uniform potentiation (Fig.\u00a02g), and combined depression and potentiation (Fig.\u00a02h). It can be clearly seen that application of Vmod to selectively depress or potentiate areas of high or low similarity, respectively, results in a significant increase in the ratio between outputs for each combination tested, confirming the ability of heterosynaptic weight modulation to enhance accuracy and reduce confusion. This\u00a0indicates that our memtransistor-based crossbar architectures have potential for implementing non-traditional logic operations in hardware by leveraging their multi-terminal nature.\n\nIt should also be noted that this dynamic modulation approach has significant implications for energy-efficient computing. Per standard FET operation, this dynamic modulation requires charging the oxide (back-gate) capacitance (Cox), which is ~\u20093.8 fF per device. The energy cost per modulation can then be approximated using the equation Emod\u2009=\u2009\u00bd\u00b7Cox\u00b7Vmod\u00b2. For the demonstrations used in this work, modulatory voltages between +/\u2212\u20092\u2009V are used, resulting in an additional energy expenditure of <\u20098 fJ per device. In contrast, standard weight retraining approaches (digital or analog) require either reprogramming memory cells or propagating weight updates through the network. For resistive memories, iterative write-verify schemes consume the majority of a crossbar array\u2019s energy budget64; this\u00a0can consume upwards of 100 pJ per device66 depending on the type of memory, number of bits, etc. In addition, retraining often increases latency and may not be feasible in constrained edge environments. This disparity supports the use of three-terminal modulation as an efficient alternative for fine-tuning network behavior with minimal overhead.\n\nExpanding our efforts to more closely investigate real-world ANN applications, we then performed single-layer neural network inference operations aiming to classify handwritten digits from the MNIST datasets. An overview of this demonstration is shown in Fig.\u00a03. For this demonstration, a 64\u2009\u00d7\u200932 (2048 devices, 2\u2009kb) crossbar array was prepared, with an optical image of the final chip containing the array being shown in Fig.\u00a03a\u2013c along with zoomed-in optical images of the 2\u2009kb array and constituent NVM cells. Raman spectroscopy was used to assess the quality and uniformity of the MoS2 film used for the fabrication of the large-scale array. These results are presented in Supplementary Note\u00a017. The Raman spectra was taken across nine points corresponding to the corners, sides, and center of the array with a 532\u2009nm laser. The mean \\({E}_{2{{{\\rm{g}}}}}^{1}\\) (in-plane) and \\({A}_{1{{{\\rm{g}}}}}\\) (out-of-plane) peak locations were found to be 385.26\u2009cm\u22121 and 403.15\u2009cm\u22121, respectively, with a mean peak separation of 17.89\u2009cm\u22121. The standard deviations for each of these parameters was comparatively low (0.30\u2009cm\u22121, 0.30\u2009cm\u22121, and 0.42\u2009cm\u22121, respectively), indicating good spatial uniformity of the MoS2 film.\n\nOptical images of (a) a 1.5\u2009\u00d7\u20091.5\u2009cm chip containing a 64\u2009\u00d7\u200932 crossbar array comprising 2048 MoS2 memtransistors, including\u00a0(b) a zoomed-in image showing the full 2\u2009kb array (scale bar of 100\u2009\u00b5m) and (c) a further zoomed-in image showing nine constituent memtransistors (scale bar of 10\u2009\u00b5m). d Schematic showing preprocessing performed on training and test (inference) images taken from the Modified National Institute of Standards and Technology (MNIST) database. The original images (28\u2009\u00d7\u200928 pixels) were downscaled to 13\u2009\u00d7\u200913 pixels and binarized to fit into a 64\u2009\u00d7\u200930 sub-array for this demonstration. Downscaled images were then converted to 169\u2009\u00d7\u20091 input vectors and split into three sub-vectors for input to the array. A dataset comprising 10,000 resized/reshaped images were then used for training and weight assignment; for this demonstration, simulated weights were split between logic \u201c0\u201d and \u201c1\u201d and later converted to targeted conductance states for hardware implementation. e Heatmap showing the distribution of simulated weights following training. A test dataset of 1000 resized/reshaped MNIST images was fed to the simulated array for verification of network inference/classification. f Confusion matrix showing the classification results for the simulated\u00a0inference check described in (e). An overall accuracy of 88.1% was achieved. g Heatmap showing the distribution of conductance states assigned to the hardware array in respect to the simulated weight distribution shown in (e), with weights of \u201c1\u201d mapped to a conductance state of ~\u200950\u2009nS and weights of \u201c0\u201d mapped to the OFF-state conductance (a few pS). Cells marked NaN either display an open circuit or high gate leakage; the overall yield of devices remained high at ~\u200992.2%. h Hysteresis characteristics of the 1770 working devices in the 64\u2009\u00d7\u200930 sub-array extracted at VDS\u2009=\u20091\u2009V, which show low intrinsic device-to-device variation, memory windows >\u20095\u2009V, and read margins >\u2009104. i Histogram showing the distribution of the final conductance states (weights) represented in (g). j Confusion matrix showing the classification results for hardware-based inference performed on the memtransistor array shown in (a\u2013c) as per the conductance state (weight) distribution shown in (g). A test dataset comprising 1000 resized/reshaped MNIST images was applied to the drain terminals of the array as voltage inputs (either 0\u2009V or 1\u2009V, depending on the corresponding pixel value); output currents along corresponding columns/nodes were then individually registered and compared to all other outputs to determine the inferred digit for each case. An overall accuracy of 85.6% was registered.\n\nA simple binary classification scheme was then tested with the goal of classifying all ten digits (0-9) comprising the MNIST dataset. As schematically represented in Fig.\u00a03d, MNIST images (28\u2009\u00d7\u200928 pixels) were first downscaled to 13\u2009\u00d7\u200913 pixels using bicubic interpolation and transformed into 169\u2009\u00d7\u20091 vectors to serve as inputs. To simplify the experimental demonstration, the greyscale pixels of the MNIST dataset were also binarized to either 0 or 1 during this process, making each image black and white; further discussion on this preprocessing, and on its effects on inference accuracy, can be found in the Methods section. Software-based training was then used to prepare a binary weight map from a training dataset consisting of 10,000 MNIST images, as shown in Fig.\u00a03e. Testing of the simulated single-layer network was then performed using a dataset consisting of 1000 randomly selected binarized 13\u2009\u00d7\u200913 images, curated such that each digit (0\u20139) was represented exactly 100 times to ensure fair assessment of the network\u2019s ability to infer different digits. The results from this simulation-based MNIST inference test are presented by the confusion matrix in Fig.\u00a03f. An overall accuracy of 88.1% was achieved; while digits such as \u201c0\u201d, \u201c1\u201d, and \u201c6\u201d demonstrated high classification accuracy, the overall accuracy was constrained by the networks limited ability to classify digits such as \u201c5\u201d, which demonstrated a high degree of confusion with \u201c8\u201d and \u201c3\u201d. While non-ideal, this relatively low overall accuracy is to be expected due to the nature of the problem presented, i.e., single-layer inference across a large dataset consisting of ten highly compressed and binarized handwritten digits. Following the simulation, the binary weights of the single-layer network were mapped to corresponding conductance states in a 64\u2009\u00d7\u200930 sub-array of the 2\u2009kb array shown in Fig.\u00a03a\u2013c, with weights of \u201c1\u201d being mapped to a conductance state of ~\u200950\u2009nS and weights of \u201c0\u201d being mapped to the OFF-state conductance (a few pS). A map of the final conductance states across the sub-array is shown in Fig.\u00a03g; here, NVM cells marked NaN either display an open circuit or high gate leakage, both of which are believed to stem from damage to the corresponding horizontal drain line. Note that, despite the loss of five complete rows to these non-idealities, the overall yield of devices across the sub-array remained high at ~92.2%. Hysteresis characteristics and final conductance states (weights) for the working cells are also shown in Fig.\u00a03h, i, respectively, demonstrating good uniformity across the breadth of the array as well as a significant read margin of >104 between differently weighted cells. For the sake of clarity, devices demonstrating a measured conductance below the noise floor conductance of the characterization system (~\u200910\u221212\u00a0S) are marked as being at the noise floor. For this hardware-based inference demonstration, the outputs from three columns of the sub-array correspond to a single digit utilized in the MNIST dataset, e.g., the first three columns in the sub-array correspond to the digit \u201c0\u201d, the next three columns correspond to \u201c1\u201d, etc. When an image (binary 169\u2009\u00d7\u20091 vector split into three sub-vectors) is applied as voltage inputs (either 0\u2009V or 1\u2009V depending on the corresponding pixel value) to the drain terminal, the output currents from each column set (node) are individually registered. The summed output currents from each node are then compared to determine the inferred digit. Please note that, due to limitations with the experimental setup, summation of output currents was performed externally for the sake of this demonstration. The results for this classification scheme are shown in Fig.\u00a03j. An overall accuracy of 85.6% was registered for the hardware demonstration; at only 2.5% lower than simulated, a similar simulation-to-hardware gap to previous MNIST classification explorations11, this indicates that the MoS2-based crossbar arrays developed and demonstrated in this work can successfully implement inference operations with a high degree of similarity to simulations, thus indicating potential as neuromorphic accelerators. Deviations from the ideal case demonstrated in Fig.\u00a03f can be attributed to device-to-device variation, such as that shown in Fig.\u00a01,\u00a0and the non-ideal yield noted in Fig.\u00a03g; these factors can be optimized in future work through array-level compensation schemes67,68,69 and optimization of the fabrication and MoS2 growth processes. A comparison of the current and predicted status of our work with other emerging logic accelerators for neural networks5,6,7,8,10,25,26,32,70,71,72,73,74,75 is presented in Supplementary Note\u00a018.\n\nWhile the above MNIST inference demonstration shows the ability of our MoS2-based crossbar arrays to implement complex classification operations in hardware with a high degree of similarity to simulations, thus acting as neuromorphic accelerators, the results still leave much to be desired in terms of overall accuracy. This is fairly in line with expectations; from simulation, we can see that even the ideal case accuracy is limited to 88.1% due to limitations of performing inference across highly compressed and binarized handwritten digits, which is in turn further exacerbated by device-to-device variations and non-ideal device yield once implemented in hardware. Together, these issues contribute to the high degree of confusion shown between certain digits in Fig.\u00a03j, such as \u201c5\u201d and \u201c8\u201d, dragging down the overall accuracy. While certainly non-ideal, these complications effectively mirror the complexities of various real-world scenarios, where noisy, low-resolution datasets and uncertainty between classifications are the norm rather than the exception. While many common real-world scenarios can be addressed at the training stage depending on the application, such as rain or snow in surveillance footage, addressing unexpected events that may impact collected data, such as out-of-season weather at odd times of day, may require supervised data labeling and\u00a0subsequent retraining of the network to obtain classification at reasonable accuracies, tasks that\u00a0are\u00a0completely untenable for isolated edge computing applications. Here, our ability to dynamically modulate pretrained weights, as discussed above and in reference to Fig.\u00a02, provides a distinct advantage for differentiating classes with otherwise high degrees of confusion, opening avenues for automatic verification schemes in decentralized edge applications. An example of such a verification scheme performed on a 64\u2009\u00d7\u200910 sub-section of the array presented in Fig.\u00a03 is presented for the cases of two sets of highly-compressed (8\u2009\u00d7\u20098-pixel) MNIST digits, \u201c5\u201d and \u201c8\u201d, and \u201c8\u201d and \u201c9\u201d, in Fig.\u00a04. Simulation and experimental results for the 8\u2009\u00d7\u20098-pixel MNIST digit classification across all ten digits are presented in Supplementary Note\u00a019. As shown in Fig.\u00a04a, by cumulatively considering pixel intensities across a wide body of images for each digit, features of generally higher or lower importance for each digit can be identified. Taking the difference in cumulative pixel intensity (\u0394 pixel intensity) between different digits then allows for the features of highest and lowest contrast between them to be identified, as shown in Fig.\u00a04b for digits \u201c5\u201d and \u201c8\u201d and in Fig.\u00a04c for digits \u201c8\u201d and \u201c9\u201d. By dynamically potentiating the pre-programmed weights pertaining to features of high contrast and depressing weights for features of low contrast, as enabled through the application of positive and negative Vmod to their appropriate gate lines, respectively, the accuracy of inference operations between two specific digits of the MNIST dataset can be enhanced; as this does not require time/energy intensive retraining or reprogramming of weights, this procedure can serve as a rapid automatic verification scheme for differentiating otherwise highly confused classes in resource-limited hardware applications. The results for implementing such a scheme on the hardware-based inference results displayed in Fig.\u00a03 are shown in Fig.\u00a04d\u2013g. Figure\u00a04d, e show confusion matrices between \u201c5\u201d and \u201c8\u201d and \u201c8\u201d and \u201c9\u201d, respectively, for the standard hardware-based inference demonstration originally shown in Fig.\u00a03; alternatively, Fig.\u00a04f, g show confusion matrices for both cases when heterosynaptic modulation is dynamically applied to areas of high and low contrast between digits, as identified in Fig.\u00a04b, c, respectively. As can be seen, considering \u201c5\u201d and \u201c8\u201d without weight modulation returns a classification accuracy of only 65%, which increases to 79.5%, a 14.5% increase, with potentiation/depression of respective weights. Similarly, consideration of digits \u201c8\u201d and \u201c9\u201d shows 77% classification accuracy without modulation and 81% classification accuracy with modulation. Together, these results indicate that dynamic modulation of weights, when properly applied through the exploitation of the gate terminal, allows for enhanced classification accuracy among highly confused classes in MoS2-memtransistor-based crossbar arrays. As this does not require time/energy-intensive retraining or reprogramming of weights, this procedure may serve as a rapid automatic verification scheme for differentiating classes in resource-limited hardware applications.\n\na Schematic representation showing how consideration of MNIST digit datasets, in this case downscaled (8\u2009\u00d7\u20098-pixel) and binarized \u201c5\u2019s\u201d and \u201c8\u2019s\u201d, can lead to identification of areas of greater and lower importance to digit recognition, showcased here as areas of greater and lower cumulative pixel intensity (\u0394 pixel intensity), respectively, when images are overlapped. b, c By taking the difference in \u0394 pixel intensity between two digits of interest, (b) \u201c5\u201d and \u201c8\u201d and (c) \u201c8\u201d and \u201c9\u201d, the features of highest and lowest contrast may be identified. d, e Confusion matrices between (d) \u201c5\u201d and \u201c8\u201d and (e) \u201c8\u201d and \u201c9\u201d for the hardware-based inference performed on a 64\u2009\u00d7\u200910 sub-section of the array in Fig.\u00a03. f, g Confusion matrices between (f) \u201c5\u201d and \u201c8\u201d and (g) \u201c8\u201d and \u201c9\u201d for cases wherein heterosynaptic modulation is dynamically applied to areas of high and low contrast between digits, as identified in (b) and (c), respectively. Increases in overall classification accuracy of 14.5% and 4% can be seen for \u201c5\u201d and \u201c8\u201d and for \u201c8\u201d and \u201c9\u201d, respectively, compared to the standard case.\n\nIn summary, we have demonstrated crossbar array architectures utilizing monolayer-MoS2-based memtransistors as their base computational unit with information densities and sizes up to 1.94\u2009Mb/cm2 (1-bit operation) and 2\u2009kb, respectively. A high yield of >92% with low device-to-device variation was confirmed for each array investigated as part of this work, with constituent devices displaying switching energies as low as ~\u20090.2 fJ, read margins as high as 105, and projected retentions exceeding three\u00a0years. Simple shape identification was\u00a0performed accurately in hardware, while investigation of MNIST handwritten digit inference found only a 4.5% difference between simulation and experiment, establishing that our crossbar array architectures may function as neuromorphic accelerators for the implementation of neural network operations. Finally, we also show how the three-terminal nature of MoS2-based memtransistors as NVM cells may be exploited to reduce confusion between similar outputs in inference tasks (e.g., shape identification and MNIST classification) in a method analogous to modulatory synapses in BNNs, thus opening avenues for automatic verification schemes without costly retraining/reprogramming of weights. We believe that this work furthers the ongoing development of in-memory processors for decentralized edge applications by demonstrating large-scale, hardware-based inference on MoS2-based NVMs and investigating their potential for implementing non-traditional logic operations through the exploitation of their multi-terminal nature. Future material and fabrication optimization would serve to tighten variation, improve yield, and enhance the long-term non-volatile memory characteristics of individual devices, while inference accuracy could be enhanced at the array-level through dedicated compensation schemes.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64536-2/MediaObjects/41467_2025_64536_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64536-2/MediaObjects/41467_2025_64536_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64536-2/MediaObjects/41467_2025_64536_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64536-2/MediaObjects/41467_2025_64536_Fig4_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "The growth of MoS2 monolayer film on 2\u201d diameter c-plane sapphire substrates was carried out in a metal-organic chemical vapor deposition (MOCVD) system (https://doi.org/10.60551/znh3-mj13) equipped with a cold-wall horizontal reactor with an inductively heated graphite susceptor with gas-foil wafer rotation76. The molybdenum hexacarbonyl (Mo(CO)6) (99.99%, Sigma-Aldrich) was used as the metal precursor, while hydrogen sulfide (H2S) was the chalcogen source with H2 as the carrier gas. The Mo(CO)6 powder was maintained inside a stainless-steel bubbler where the temperature and pressure of the bubbler were held at 20\u2009\u00b0C and 625\u2009Torr, respectively. The MoS2 monolayer was grown in a single-step process77. Before the growth, the sapphire was ramped up under H2 to the growth temperature at 975\u2009oC and pre-annealed for 10\u2009min. During the growth, H2S and Mo(CO)6 were introduced to the reactor for a designated time to complete MoS2 monolayer growth in a single step. The molybdenum flow rate was set as 3.4\u2009\u00d7\u200910\u20133 sccm and the chalcogen (H2S) flow rate was set as 400\u2009sccm while the reactor pressure was maintained at 50\u2009Torr. Then, the MoS2 monolayer was annealed under H2 and H2S ambient for 10\u2009min at 975\u2009oC before cooling down to inhibit the decomposition of the obtained MoS2 film. Using this condition, the growth of a fully coalesced monolayer MoS2 was achieved in 10\u201330\u2009min. across the 2\u201d sapphire substrate.\n\nAtomic force microscopy (AFM) images of the as-grown MoS2 film were taken using a Bruker Dimension Icon system; Scanasyst air probe AFM tips with a nominal tip radius of about 2\u2009nm and spring constant of 0.4\u2009N\u2009m\u2009\u2212\u20091 were used for the measurements, and the images were collected using peak-force tapping mode with a peak force of 500\u2009pN and a scan speed of 2\u2009Hz. The Raman and photoluminescence (PL) spectra of the as-grown film were taken using a Witec Alpha-300 Apyron system with a 100\u2009\u00d7\u2009objective at a 4\u2009mW laser power; the system was enclosed within an N2-ambient glovebox with ~\u20095\u2009ppm of O2 and H2O.\n\nTo define the back-gate access lines (word lines), a commercially-purchased substrate (thermally-grown 285\u2009nm SiO2 on p++-Si) was spin-coated at 4000 RPM for 45\u2009s with a bilayer electron beam (e-beam) photoresist consisting of MMA EL6 and PMMA A3; following application, these resists were baked at 150\u2009\u00b0C for 90\u2009s and 180\u2009\u00b0C for 90\u2009s, respectively. The bilayer photoresist was then patterned using a Raith EBPG5200 e-beam lithography tool and developed using a 1:1 mixture of 4-methyl-2-pentanone (MIBK) and IPA (60\u2009s) and then rinsed using IPA (45\u2009s). The back-gate electrodes of 3/20\u2009nm Ti/Pt were then deposited using e-beam evaporation in a Temescal FC-2000 Bell Jar Deposition System. Liftoff of the remaining photoresist and excess metal was achieved using acetone; the substrate was then cleaned using 2-propanol (IPA). Two subsequent e-beam lithography and evaporation processes were then conducted to deposit 60\u2009nm Al2O3 and 30/60\u2009nm Ti/Au to form insulating crosspoints and conductive bridges, respectively, for the overlapping back-gate access lines in the 1S1T design. An atomic layer deposition (ALD) process was then implemented to grow the back-gate dielectric stack consisting of 15\u2009nm Al2O3 (\u03b5ox\u2009\u2248\u200910), 3\u2009nm HfO2 (\u03b5ox\u2009\u2248\u200925), and 7\u2009nm Al2O3 across the entire substrate, which includes all four designs; all ALD processes were conducted at 200\u2009\u25e6C and without breaking vacuum. Access to the individual Pt back-gate contact pads was achieved via a reactive ion etch (RIE) process conducted in a Plasma-Therm Versalock 700. First, etch patterns were defined using e-beam lithography with ZEP 1:1 photoresist. The dielectric stack was then dry etched using a BCl3 RIE chemistry at 5\u2009\u00b0C for 40\u2009s; this process was split into two 18\u2009s etch steps separated by 30\u2009s stabilization steps to minimize heating in the substrate. The photoresist was then removed using Photo Resist Stripper (PRS 3000) heated at 50\u2009\u00b0C for one hour and the substrate was cleaned using IPA.\n\nMoS2 film transfer from the growth substrate to the application substrate was performed using a PMMA-assisted wet transfer process. First, as-grown MoS2 on a sapphire substrate was spin-coated with PMMA A6 twice and subsequently baked at 150\u2009\u00b0C for 120\u2009s to ensure good PMMA/MoS2 adhesion. The corners of the spin-coated film were then scratched using a razor blade and immersed inside a de-ionized (DI) water bath for 1\u2009hr. Capillary action caused the water to be preferentially drawn into the substrate/MoS2 interface, owing to the hydrophilic nature of sapphire and the hydrophobic nature of MoS2 and PMMA, separating the PMMA/MoS2 stack from the sapphire substrate. The separated film was then fished from the water bath solution using the application substrate. Subsequently, the substrate was baked at 50\u2009\u00b0C and 70\u2009\u00b0C for 1\u2009hr and 15\u2009min, respectively, to remove moisture and promote film adhesion, thus ensuring a pristine interface, before the PMMA was removed by immersing the sample in acetone overnight, and the substrate was cleaned with a subsequent 30\u2009min IPA bath.\n\nTo define the channel regions of the MoS2 memtransistors found in each NVM cell, the application substrate, with MoS2 transferred on top, was spin-coated with PMMA A6 (4000 RPM for 45\u2009s) and baked at 180\u2009\u00b0C for 90\u2009s. The resist was then exposed using a Raith EBPG5200 e-beam lithography tool and developed using a 1:1 mixture of 4-methyl-2-pentanone (MIBK) and IPA (60\u2009s) and then rinsed using IPA (45\u2009s). The exposed monolayer MoS2 film was subsequently etched using a sulfur hexafluoride (SF6) RIE process at 5\u2009\u00b0C for 15\u2009s. Next, the sample was rinsed in acetone and IPA to remove the e-beam resist. A subsequent lithography step was conducted to form source/drain electrodes. The substrate was spin-coated at 4000 RPM for 45\u2009s with methyl methacrylate (MMA) EL6 and PMMA A3; following application, these resists were baked at 150\u2009\u00b0C for 90\u2009s and 180\u2009\u00b0C for 90\u2009s, respectively. E-beam lithography was again used to pattern the source and drain, and development was again performed using a 1:1 mixture of MIBK/IPA and an IPA rinse for the same times as previously. 40\u2009nm of Ni and 30\u2009nm of Gold (Au) were deposited using e-beam evaporation to form the electrodes. Finally, a lift-off process was performed to remove the excess Ni/Au by immersing the sample in acetone for 1\u2009hr, followed by IPA for another 30\u2009mins to clean the substrate. Two subsequent e-beam lithography, evaporation, and lift-off processes were then conducted to deposit 90\u2009nm Al2O3 and 40/70\u2009nm Ti/Au to form insulating crosspoints and conductive bridges, respectively, for the overlapping source/drain access lines found in every design. Please note that all source/drain access pads were deposited concurrently with the final conductive bridge step. For samples containing 64\u00d732 (2\u2009kb) arrays, 50/150\u2009nm Ti/Au was instead deposited in the final step to allow for the formation of thick dedicated wire bonding pads on the periphery of the substrates. For all array designs/sizes discussed, the individual memtransistors have channel lengths/widths of 1/1\u2009\u00b5m.\n\nElectrical characterization of individual memtransistors was performed in a Lake Shore CRX-VF probestation under atmospheric conditions using a Keysight B1500A parameter analyzer. Ultrafast program/erase and read operations were confirmed using a B1525 SPGU fast pulsing module and a Keysight PZ2100A mainframe equipped with a PZ2120A module, respectively.\n\nGrayscale MNIST digit images of dimension 28\u2009\u00d7\u200928 were resized to 8\u2009\u00d7\u20098-pixel images using bicubic interpolation. Next, the 8\u2009\u00d7\u20098 resized images were binarized using a single threshold value. The first 1000 images corresponding to each class in the 60,000-image training set were extracted to create a 10,000-image training set. Furthermore, 100 images corresponding to each class were randomly selected from the 10,000-image testing set to create a 1000-image testing set. No images were repeated in the training or testing set. For the purposes of the proof-of-concept demo, a single-layer neural network was trained on the preprocessed training set using a simulated annealing algorithm. The network structure included 64 inputs (flattened binary 8\u2009\u00d7\u20098 MNIST digit images) fully connected to 10 output nodes that represent each digit from 0 to 9. For this work, the output layer employed a linear activation, and the network prediction was determined by the output node that generated a maximum value during any inference operations. A preliminary weight map was generated prior to the training process by binarizing 50,000 images from the grayscale 8\u2009\u00d7\u20098 training set. Next, the simulated annealing algorithm ran for 20,000 iterations at 7 descending temperatures. Within each iteration, a single weight in the binarized weight map was chosen randomly to flip into the opposite state, either from weight 0 to 1 or vice versa. This new solution was then evaluated based on an objective function defined as the training set inference accuracy minus a penalty for the number of weights used. This penalty aims to remove unnecessary weights of \u20181\u2019 and reward sparsity in weight representation that may reduce crossbar energy consumption and minimize hardware-related non-idealities such as sneak path. If the proposed new solution reduced the value of the objective function compared to the prior iteration, then the new solution was always accepted. If the proposed new solution increased the value of the objective function compared to the prior iteration, then a probabilistic process was used to determine if the new solution would be accepted. The difference between the output of the objective function evaluated for the new and prior solutions was used as input to an exponential function that depends on the predefined temperature. The exponential function dictates the probability of accepting the new solution. After training, the simulated annealing algorithm reached an accuracy of 88.1% on the 1000-digit testing set.\n\nWeights obtained through the software-based approach discussed above were mapped to a 64\u2009\u00d7\u200932 crossbar array through program/erase operations. First, the array was initialized by setting each memtransistor to a low conductance state (LCS) through the application of a large negative gate bias to each gate access line. The trained weights were then converted to real-world conductance values, with 0 being converted to LCSs and 1 being converted to high conductance states (HCSs), by programming each device as dictated by the trained weight matrix. As discussed in the main text above, similar read margins between individual devices allowed for the implementation of an open-loop programming scheme, thus eliminating the time/energy overhead required for a write/verify scheme. For digit classification, 64\u2009\u00d7\u20091 input vectors were fed to the array, with MAC operations being conducted along each column (node); the output for each node was then assessed as the probability of its respective digit being the input. For cases wherein multiple nodes displayed similar outputs (probabilities), a predetermined verification scheme was implemented based on the nodes (digits) in question. Weights were either potentiated (strengthened) or depressed (weakened) depending on whether they corresponded to areas of greater difference or similarity, respectively, between the images through the application of a modulatory gate bias (positive for potentiation, negative for depression); reconducting MAC operations across the modulated weights thus allowed for greater certainty in classification to be obtained for a given input.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Relevant data supporting the key findings of this study are available within the article and the Supplementary Information file. Data on MoS2\u00a0samples produced in the 2DCC-MIP facility, including growth recipes and characterization data, are available at https://doi.org/10.26207/2hsj-0n18.\u00a0All raw data generated during the current study are available from the corresponding authors upon request.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code used for the neural network models and data processing discussed in this work are available from the corresponding author upon request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Andri, R., Cavigelli, L., Rossi, D. & Benini, L. YodaNN: An ultra-low power convolutional neural network accelerator based on binary weights. In IEEE Computer Society Annual Symposium on VLSI. 236-241 (IEEE, 2016).\n\nZhou, H., Alvarez, J. M. & Porikli, F. 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Kumar,\u00a0Dev Krishna Thiyyadi Baiju\u00a0&\u00a0Saptarshi Das\n\n2D Crystal Consortium Materials Innovation Platform, Materials Research Institute, Penn State University, University Park, PA, USA\n\nChen Chen,\u00a0Joan M. Redwing\u00a0&\u00a0Saptarshi Das\n\nMaterials Science and Engineering, Penn State University, University Park, PA, USA\n\nThomas McKnight\u00a0&\u00a0Joan M. Redwing\n\nMaterials Research Institute, Penn State University, University Park, PA, USA\n\nThomas McKnight,\u00a0Joan M. Redwing\u00a0&\u00a0Saptarshi Das\n\nNaval Information Warfare Center Pacific, Pearl City, HI, USA\n\nSean Tadekawa,\u00a0Evan Haines,\u00a0Richard Ordonez\u00a0&\u00a0Cody Hayashi\n\nElectrical Engineering and Computer Science, Penn State University, University Park, PA, USA\n\nSaptarshi Das\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nT.F.S. and S.D. conceived the idea and designed the experiments. T.F.S. designed the crossbar architectures, developed the fabrication process flow, fabricated the devices, performed the device characterization and inference experiments, and wrote the manuscript. A.P. developed the network models and simulations. J.M.K. and D.K.T.B. assisted with large-scale inference experiments. T.F.S., A.P., S.T., E.H., R.O., C.H., and S.D. analyzed the data, discussed the results, and agreed on their implications. C.C. and T.M. grew MOCVD-grown MoS2 films under the supervision of J.M.R. All authors contributed to the preparation of the final manuscript.\n\nCorrespondence to\n Saptarshi Das.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Xuewei Feng and the other anonymous reviewers for their contribution to the peer review of this work. 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Gram-positive bacteria", + "journal": "Nature Communications", + "published": "16 August 2024", + "supplementary_0": [ + { + "label": "Supplementary information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_MOESM2_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_MOESM3_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_MOESM4_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_MOESM5_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_MOESM6_ESM.mp4" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_MOESM7_ESM.mp4" + }, + { + "label": "Supplementary Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_MOESM8_ESM.mp4" + }, + { + "label": "Supplementary Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_MOESM9_ESM.mp4" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_MOESM10_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "http://doi.org/10.2210/pdb8V3T/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-42953", + "http://doi.org/10.2210/pdb8V3Z/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-42961", + "http://doi.org/10.2210/pdb8V40/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-42962", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-42957", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-42958", + "http://doi.org/10.2210/pdb8V3W/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-42956", + "http://doi.org/10.2210/pdb8V41/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-42963", + "http://doi.org/10.2210/pdb8V43/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-42964", + "http://doi.org/10.2210/pdb8V3X/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-42959", + "http://doi.org/10.2210/pdb8V3Y/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-42960", + "/articles/s41467-024-51038-w#Sec19" + ], + "code": [], + "subject": [ + "Bacteriology", + "Cryoelectron microscopy", + "Microbiology" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4007122/v1.pdf?c=1723892858000", + "research_square_link": "https://www.researchsquare.com//article/rs-4007122/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-51038-w.pdf", + "preprint_posted": "26 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Due to envelope differences between Gram-positive and Gram-negative bacteria1, engineering precision bactericidal contractile nanomachines2 requires atomic-level understanding of their structures; however, only those killing a Gram-negative bacterium are currently known3,4. Here, we report the atomic structures of an engineered diffocin, a contractile syringe-like molecular machine that kills the Gram-positive bacterium Clostridioides difficile. Captured in one pre-contraction and two post-contraction states, each structure fashions six proteins in the bacteria-targeting baseplate, two proteins in the energy-storing trunk, and a collar protein linking the sheath with the membrane-penetrating tube. Compared to contractile machines targeting Gram-negative bacteria, major differences reside in the baseplate and contraction magnitude, consistent with differences between their targeted envelopes. The multifunctional hub-hydrolase protein connects the tube and baseplate and is positioned to degrade peptidoglycan during penetration. The full-length tape measure protein forms a coiled-coil helix bundle homotrimer spanning the entire length of the diffocin. Our study offers mechanical insights and principles for designing potent protein-based precision antibiotics.Biological sciences/Microbiology/Bacteria/Bacterial structural biologyBiological sciences/Structural biology/Electron microscopy/Cryoelectron microscopy", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nCo-author Jeff F. Miller is a cofounder, equity holder and chair of the scientific advisory board of Pylum Biosciences, Inc., a biotherapeutics company in San Francisco, CA, USA; co-author Dean Scholl is an employee and equity holder of the same company.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Supplementarymovie1Precontractionoverall.mp4Movie 1: Overall structure of the pre-contraction diffocinSupplementarymovie2Precontractionmodels.mp4Movie 2: Computer constellation of atomic models into a pre-contraction diffocinSupplementarymovie3Postcontractionoverall.mp4Movie 3: Overall structure of the post-contraction diffocinSupplementarymovie4Conformationalheterogeneityofposttrunk.mp4Movie 4: 3DVA analysis of the trunk of post-contraction diffocin revealing conformational heterogeneitySupplementarymovie5Tapemeasureprotein.mp4Movie 5: Tape measure protein density of the pre-contraction diffocinSupplementarymovielegends.pdfSupplementary movie legends", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Due to envelope differences between Gram-positive and Gram-negative bacteria, engineering precision bactericidal contractile nanomachines requires atomic-level understanding of their structures; however, only those killing Gram-negative bacteria are currently known. Here, we report the atomic structures of an engineered diffocin, a contractile syringe-like molecular machine that kills the Gram-positive bacterium Clostridioides difficile. Captured in one pre-contraction and two post-contraction states, each structure fashions six proteins in the bacteria-targeting baseplate, two proteins in the energy-storing trunk, and a collar linking the sheath with the membrane-penetrating tube. Compared to contractile machines targeting Gram-negative bacteria, major differences reside in the baseplate and contraction magnitude, consistent with target envelope differences. The multifunctional hub-hydrolase protein connects the tube and baseplate and is positioned to degrade peptidoglycan during penetration. The full-length tape measure protein forms a coiled-coil helix bundle homotrimer spanning the entire diffocin. Our study offers mechanical insights and principles for designing potent protein-based precision antibiotics.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Clostridioides difficile (C. difficile) is a Gram-positive pathobiont and one of the most prominent sources of nosocomial infection, responsible for almost a quarter million hospitalizations and thirteen thousand deaths per year in the US alone1. Dysbiosis of the gut microbiota following antibiotic exposure can disrupt colonization resistance, leading to C. difficile infection (CDI), which can manifest as life-threatening colitis. Treatment of CDI with antibiotics can result in further disruption of the gut microbiota and, in some cases, antibiotic-refractory recurrent disease2. Engineered contractile nanomachines based on R-type bacteriocins, such as the R-type diffocins of C. difficile3,4,5 and R-type pyocins of Pseudomonas aeruginosa6, as well as non-contractile nanomachines based on F-type bacteriocins, such as the F-type pyocins of Pseudomonas aeruginosa7,8 and F-type monocins from Listeria monocytogenes7, hold promise for developing precision medicines that kill antibiotic-resistant pathogens without harming beneficial microbes and without selecting for horizontal transfer of resistance determinants. These bacteriocins kill by dissipating transmembrane ion gradients needed to sustain metabolic activity of their target bacteria7.\n\nR-type pyocins, contractile bacteriocins produced from Gram-negative bacteria, have been well studied and the structure of an R-type pyocin is described at the atomic level9. They resemble Type VI secretion systems (T6SS)10, virulence cassettes from Photorhabdus asymbiotica (PVCs)11, and contractile phage tails12, as they convert chemical energy stored in the double-layered trunk region to mechanical force required to pierce target cell envelopes. Phages with a contractile tail have long been a model for studying extracellular contractile injection systems (CISs)13. Their effective means of binding to host bacteria and establishing a genome translocation channel through the bacterial envelope have allowed phages to successfully infect and replicate in host bacteria. As extracellular contractile machineries released by some bacteria to kill competing strains, bacteriocins have similar but simplified biological constructs compared to phages7,14,15: they lack the DNA-containing head but still possess a needle-like central spike, a baseplate with fibers, a sheath-tube trunk, and a collar at the end of the trunk. The cylindrical trunk, comprised of a hollow tube in the center and a sheath enclosing the tube, is assembled by multiple copies of the tube and sheath proteins. Specific attachment of baseplate tail fibers to receptors on the surface of a target bacterial cell triggers conformational changes of the neighboring baseplate, which leads to reorientation of sheath proteins and contraction of the entire sheath assembly. Since the sheath and the tube are anchored together at the opposite end by the collar, collapsing the sheath pushes the tube through the baseplate, driving it to puncture the cell wall and underlying cytoplasmic membrane. As a result, the ion gradient across the membrane is breached, killing the bacterium. This highly specific (ligand-receptor driven) and efficient (mechanical penetration-based) mechanism presents great potential for applications that require precise ablation of bacterial species or strains7.\n\nContractile systems that target Gram-positive bacteria, with cell walls ranging 30\u2013100\u2009nm in thickness, confront the challenge of penetrating a significantly thicker cell wall compared to those targeting Gram-negative organisms, which typically have cell walls only a few nanometers thick16. Differences in penetration mechanisms required to puncture these two types of bacteria have been recognized17. For example, a peptidoglycan hydrolase/lysin is often present in Gram-positive systems (in contrast to its absence in Gram-negative-targeting systems)18. Though this hydrolase is conserved among many phages and its structure has been partially resolved, there is limited knowledge regarding its precise position in the contractile apparatus and its method of infiltrating the cell wall.\n\nHere, we present the atomic structures of a diffocin, thus filling the knowledge gap regarding structures of Gram-positive-targeting contractile injection systems. The structures of the entire assembly in the pre- and post-contraction states reach 2.2 and 3.6\u2009\u00c5 resolution, respectively; all structures were resolved using cryogenic electron microscopy (cryoEM). Comparisons between our models and other existing contractile nanomachines unveil penetration mechanisms specific to Gram-positive bacteria, as well as those shared with phages and phage tail-like nanomachines targeting Gram-negative bacteria.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The diffocin gene cluster (Fig.\u00a01a) engineered to eradicate epidemic C. difficile BI/NAP1/027 strains5 was expressed in Bacillus subtilis, and the diffocin sample used for cryoEM imaging was purified by density gradient centrifugation. Both pre- and post-contraction diffocin particles were present in the purified sample (Fig.\u00a01b). The shape of the diffocin in the pre-contraction state mirrors a syringe, and its typical length is about 146\u2009nm (Fig.\u00a01b\u2013d). During contraction, the sheath layers of the trunk are compressed by about 47\u2009nm (Fig.\u00a01c, d, f).\n\na Organization of engineered diffocin genes. Gene accession numbers of wild-type diffocin are shown below the corresponding genes. A portion of the gene segment encoding the tail fiber and the genes encoding the receptor-binding protein and two tail fiber chaperones were replaced with those of the phi027 prophage, which targets the epidemic C. difficile BI/NAP1/027 strain5. Genes framed in black encode proteins that are resolved in the cryoEM reconstructions. b A representative cryoEM image showing diffocin particles in the pre- and post-contraction states. Scale bar, 100\u2009nm. c Length of diffocin particles in pre-contraction, post-contraction transitional, and post-contraction final states measured from collar to baseplate (detailed in \u201cMethods\u201d). The sample sizes are 1088, 742, and 872 for the particles in the pre-contraction, post-contraction transitional, and post-contraction final states, respectively. Medians shown as black lines. Statistics performed by two-tailed unpaired t-test; P value is 1.4 \u00d7 10\u221215 (****P\u2009<\u20090.0001). Source data of the length of diffocin particles are provided as a Source Data file. d\u2013f Composite cryoEM density maps of the diffocin in the pre-contraction (d), post-contraction transitional (e), and post-contraction final (f) states. A sectional view of the pre-contraction state is presented in (d). Structural subunits are colored as in (a). L1-32 denotes layers of the sheath.\n\nThere is a symmetry mismatch among the three major parts of the diffocin: the collar (C6 symmetry), the trunk (C6+helical symmetry), and the baseplate (C6\u2009+\u2009C3 symmetry). By applying the corresponding symmetry during single-particle reconstructions, we determined cryoEM structures of the collar, trunk, and baseplate regions of pre-contraction diffocin separately at resolutions of 2.7\u2009\u00c5, 2.2\u2009\u00c5, and 2.6\u2009\u00c5, respectively (Supplementary Figs.\u00a01 and 2 and Supplementary Table\u00a01). A montaged map containing 32 layers of sheath-tube (L1-32) was made by computationally stitching the three parts together (Fig.\u00a01d and Supplementary Movie\u00a01). Asymmetric densities inside the tube were further resolved by segmented symmetry relaxation. With these maps, we were able to assign 10 of the 15 diffocin gene products (Fig.\u00a01a), including the full-length tape measure protein, and built the atomic model of the diffocin in the pre-contraction state (Fig.\u00a02, Supplementary Fig.\u00a03 and Supplementary Movie\u00a02).\n\nCryoEM densities encasing the corresponding atomic models of individual diffocin proteins in the pre-contraction state are shown on both sides of diffocin complex. Regions of the cryo-EM density map (semi-transparent densities) superimposed with atomic models (sticks) are shown in boxes, demonstrating the agreement between the observed and modeled amino acid side chains. Numbers denote chain termini.\n\nCIS contraction initiates from a conformational change of the baseplate, then propagates through the length of the sheath to the collar in a proposed wave-like fashion19,20,21. CryoEM data processing of the post-contraction diffocin particles resolved two different substates (Supplementary Fig.\u00a01a, d, e and Supplementary Movie\u00a03), an apparent transitional state and a final state, which is 2\u2009nm shorter in length (Fig.\u00a01c, e, f). The primary structural distinction between the two states lies in the last four sheath layers (L29-32) adjacent to the collar region. In the transitional state these layers are only partially contracted, while they are fully contracted in the final state (Fig.\u00a01e, f) (see \u201cPartially contracted collar-proximal sheath layers\u201d section below for details). The rest of the sheath layers (L1-28) are all fully contracted in both the transitional and final states. Using the same strategy, we determined the cryoEM structures of the collar, trunk, and baseplate in the transitional and final states at 3.6\u20136.1\u2009\u00c5 resolution (Supplementary Figs.\u00a01 and 2, and Supplementary Table\u00a01). The observation that sheath layers on the baseplate side are fully contracted, while those on the collar side are partially contracted, supports the wave-like propagation model from the baseplate to the collar, and our structure of the diffocin in the transitional state provides atomic resolution details of a intermediate of potential relevance to other Gram-positive CISs.\n\nThe diffocin baseplate has an inner section that connects to the tube and an outer section that connects to the sheath. The inner section comprises three proteins: tube tail (CD1367), hub-hydrolase (CD1368), and spike (CD1369) (Fig.\u00a03a). Six copies of the tube tail form a hexameric ring immediately below the first ring of the tube. Three copies of the hub-hydrolase sit below the tube tail, enclosing the N-terminal \u03b1-helices of the spike trimer (Fig.\u00a03a, b). The outer section of the baseplate consists of sheath initiator (CD1370) and triplex proteins (CD1371 and CD1372). Two conformers of CD1371, Tri2A and Tri2B, and one of CD1372, Tri1, form the triplex, and six copies of the triplex form the baseplate wedge, surrounding the inner baseplate (Fig.\u00a03a and Supplementary Fig.\u00a04a, d). The sheath initiator acts as an intermediate between the triplex and the first layer of the sheath, and between the tube tail and the hub-hydrolase (Figs.\u00a03a and\u00a04). While sharing a similar overall architecture, the baseplate of the diffocin has one less protein than that of the R-type pyocin9. Furthermore, the diffocin baseplate has a specialized central spike and a multifunctional hub-hydrolase protein for penetrating the envelope of Gram-positive bacteria.\n\na Longitudinal (left) and transversal (right) cut views of the cryoEM map of the diffocin baseplate in the pre-contraction state. b Ribbon diagram of the spike trimer. A monomer is colored according to the linear diagram. At the tip of the spike, His72 and His74 from three monomers collectively chelate with an iron ion. Comparison of the central parts of the diffocin (c) and R-type pyocin (d) baseplates. e Linear schematic and ribbon diagram of the hub-hydrolase. The hub (blue), lytic transglycosylase (yellow) and endopeptidase (red) are connected sequentially through linkers (gray). The catalytic centers of two hydrolases are labeled with pentagrams. f Zoom-in views of the catalytic triad of hydrolases. The lytic transglycosylase and endopeptidase cleave glycosidic bonds and peptide bonds of the peptidoglycan mesh, respectively. NAG N-acetylglucosamine, NAM N-acetylmuramic acid.\n\na Interactions between the sheath initiator and the baseplate shown in perpendicular views. Two conformers of the sheath initiator (shown as pink and blue surfaces) bind to the hub-hydrolase alternatively. b Ribbon diagrams of the two conformers of the sheath initiator. The structures are rainbow colored from N-terminus (blue) to C-terminus (red). Differences between the two conformers are present at the N-terminal loops; C-terminal globular domains are the same. c Two interfaces on the C-terminal domain of the sheath initiator (gray surface) with the sheath and the triplex core bundle. d Close-up view of the handshake \u03b2-sheet formed by the C-terminal domain of the sheath initiator and two neighboring sheath subunits. e Close-up view of the interface between the C-terminal domain of the sheath initiator and the triplex core bundle. f Close-up view of the interfaces between sheath initiators and the hub-hydrolase. The N-terminal loops of adjacent sheath initiators adopt different conformations to bind at the junction of hub domains.\n\nThe central spike of the diffocin baseplate consists of three copies of the spike protein (CD1369) arranged at the end of the tube with C3 symmetry (Fig.\u00a03b). Each spike protein has three domains: an N-terminal \u03b1-helix (residues 1\u201323), a conserved \u03b2-barrel with oligonucleotide/oligosaccharide-binding (OB) fold (residues 24\u201360 and 81\u2013108), and a \u03b2-hairpin (residues 61\u201380) that is integrated into the \u03b2-barrel (Fig.\u00a03b). Three \u03b2-hairpins collectively form the tip of the central spike. This is a distinctive feature of the diffocin as compared to other structurally characterized CISs9,11,12,22,23,24, which mainly use a C-terminal \u03b2-helix as their tips (Fig.\u00a03c, d). Interestingly, even though the diffocin spike has somewhat different architecture compared to R-type pyocin9, it still carries a density corresponding to the ferric ion position at the tip. This putative ferric ion is coordinated by three sets of histidine doublets, His72 and His74, from the \u03b2-hairpins (Fig.\u00a03b). Such interactions stabilize the tip for efficient puncturing through the Gram-positive bacteria envelope while allowing the hub-hydrolase protein to degrade the peptidoglycan layer.\n\nThe 581 residue-long multifunctional hub-hydrolase protein (CD1368) is organized into two domains, a conserved unifunctional hub domain (residues 1\u2013314) paralleling gp27 of phage T4 and a bifunctional hydrolase domain (residues 338\u2013581), both of which are connected by a flexible loop (residues 315-337) (Fig.\u00a03e). Three copies of the hub domain form a barrel with C3 symmetry, which functions as a 6-to-3-fold symmetry adapter between the tube tail and the central spike (Fig.\u00a03c). The hydrolase domain has a dumbbell shape, with its two lobes binding to the peripheral region of the baseplate wedge (Fig.\u00a03a and Supplementary Fig.\u00a04h). The overall organization of the hub-hydrolase trimer is similar to the ripcord protein of the R-type pyocin (Fig.\u00a03c, d), suggesting a similar role in lowering the activation energy required to trigger contraction9. Given that the local resolution of the peripheral region of the baseplate is only sufficient to assign secondary structural elements (Supplementary Fig.\u00a01c and Supplementary Fig.\u00a05a, b, g), the model of the hydrolase domain and surrounding triplex motifs were built with the assistance of AlphaFold225. The two lobes of the hydrolase domain are characterized as a lytic transglycosylase (residues 338-458) and an endopeptidase (residues 465-581), respectively, which cleave two types of covalent bonds in the peptidoglycan mesh of Gram-positive bacteria (Fig.\u00a03e, f). The lytic transglycosylase contains a conserved catalytic triad of Tyr-Asp-Asp, responsible for cleaving the \u03b2-1,4 glycosidic linkages between N-acetylmuramic acid (NAM) and N-acetylglucosamine (NAG) of the glycan strands (Fig.\u00a03f and Supplementary Fig.\u00a05c, d). The endopeptidase domain belongs to the NlpC/P60 family and its conserved catalytic triad of Cys-His-His cleaves the amide bonds between amino acids within the oligopeptide chain (Fig.\u00a03f and Supplementary Fig.\u00a05e,f). We did not observe the density of the hydrolase domains at the spike-proximal tube region in the post-contraction state (Supplementary Fig.\u00a05h\u2013j) due to the limited number of particles. However, given the conserved catalytic triads of these two hydrolase domains, we speculate that the tandem hydrolases act as a pair of scissors, efficiently degrading the thick peptidoglycan layer of C. difficile during the contraction-initiated drilling process (see the \u201cDiscussion\u201d section below).\n\nThe baseplate wedge of the diffocin features a hexagonal iris-like architecture (Fig.\u00a03a), which is formed by heterotrimeric complexes called the triplex (Tri2A-Tri2B-Tri1) (Supplementary Fig.\u00a04a, d), following the nomenclature of a similar architecture in R-type pyocins9. Within each triplex, the N-terminal regions of the three subunits form a core bundle and a trifurcation unit, from which the C-terminal dimerization domains of Tri2A and Tri2B extend in opposite directions to interact with neighboring triplexes (Supplementary Fig.\u00a04a, d, e). The C-terminal region of Tri1, along with the tail fiber linked to it, was not resolved in the cryoEM maps. Notably, both Tri2A and Tri2B include a wing domain inserted between the core bundle and the trifurcation unit (Supplementary Fig.\u00a04a), reminiscent of gp6 in phage T412. These wing domains contribute to the interface with the hydrolase domain of the hub-hydrolase (Supplementary Fig.\u00a04h).\n\nThe baseplate wedges in the transitional and final states are almost identical, with both presenting a post-contraction conformation. By comparing them to the pre-contraction baseplate wedge, we observed repositioning of the dimerization domains of Tri2A and Tri2B, along with a twist of the trifurcation unit relative to the core bundle (Supplementary Fig.\u00a04b, c); the dimer interface between neighboring triplexes is, however, preserved (Supplementary Fig.\u00a04f, g). These movements result in an expansion of the iris ring from 24 to 26\u2009nm, with the ring structure remaining intact (Supplementary Fig.\u00a04d, f). Although the conformational change in the diffocin baseplate wedge is less dramatic than in R-type pyocin, where the iris ring breaks apart after contraction9, it is still sufficient to initiate the contraction and release the bound hydrolase domain of the hub-hydrolase.\n\nThe contraction signal, triggered by the tail fibers attaching to receptors on the cell envelope, is transmitted from Tri1 to the baseplate wedge and then relayed to the sheath through the sheath initiator (CD1370). The N-terminal domain (residues 49\u2013105) of the sheath initiator binds to the core bundle of the triplex, while the C-terminal \u03b2-hairpin (residues 106\u2013135) interacts with two sheath proteins on the first sheath layer by forming a conserved handshake interface (Fig.\u00a04c, d, e). Additionally, two conformers of the N-terminal loop (residues 1-48) from adjacent sheath initiators bind to one hub-hydrolase, intermediating the 6-to-3-fold symmetry mismatch between the baseplate wedge and hub-hydrolase (Fig.\u00a04a, b, f).\n\nThe sheath protein (CD1363) has four conformations in the pre-contraction state, all sharing the same structural domains but differing in the tilt angles of the N- and C-terminal extensions (Supplementary Fig.\u00a06a). We define these conformations as conformers A, B, C, and D. As the trunk assembles from the baseplate to the collar, conformer A forms the first layer, followed by alternating contributions from conformers B and C, and finally, conformer D terminates the sheath by interacting with the collar (Fig.\u00a01d). Each sheath subunit engages with its neighboring sheath subunits via the conserved handshake interface, progressively assembling into the complete sheath in the form of stacked hexameric rings with helical symmetry (rise = 79.5\u2009\u00c5 and twist = 35\u00b0) (Fig.\u00a05a, c). The central tube inside the sheath is formed by stacked hexameric rings of tube protein (CD1364), and the interactions between the tube and different sheath conformers are the same (Supplementary Fig.\u00a06b). The inner surface of the tube has a net neutral charge stratified between alternating positive and negative charges, similar to R-type pyocin and T6SS, but different from phages T4 and 80\u03b1 (Supplementary Fig.\u00a07).\n\nTop and side views of diffocin trunks in the pre- (a) and post-contraction states (b). Only four layers, L(n-1)/(n)/(n+1)/(n+2), are shown for simplicity. Sheath proteins are presented as ribbon diagrams and tube proteins are presented as gray surfaces. The inner and outer diameters of sheath rings are labeled on the top views. Ribbon diagrams depicting interactions between neighboring sheaths in the pre- (c) and post-contraction states (d). Four \u03b2-strands from three neighboring sheath subunits jointly form the conserved handshake \u03b2-sheet. Schematic diagrams for diffocin sheath topology of the extended mesh created by the handshake interaction of augmented \u03b2-sheet in the pre- (e) and post-contraction states (f).\n\nDuring contraction, the diffocin sheath undergoes a transition from the alternating conformers B and C to a singular post-contraction conformer (Fig.\u00a05e, f). The external diameter of the sheath increases from 19\u2009nm to 22\u2009nm, and the inner diameter increases from 8\u2009nm to 10\u2009nm, which enables the detachment of the sheath from the tube (Fig.\u00a05a, b). The distance between adjacent sheath layers is decreased from 4\u2009nm to 2.6\u2009nm (Fig.\u00a05c, d), resulting in a total contraction of 34%, much less than the 70% contraction of R-type pyocin9,14. This is evidently due to the insertion of an \u03b1-\u03b1 corner motif on the tip of the C-terminal \u03b2-hairpin of the sheath protein, which acts as a spacer between adjacent sheath layers during contraction (Fig.\u00a05c, d and Supplementary Fig.\u00a06c). In contrast to the pre-contraction sheath, the post-contraction sheath exhibits evidently subtle motion, as revealed by the three-dimensional variability analysis of post-contraction sheath segments (Supplementary Movie\u00a04). The helical twist and rise of these segments vary within the ranges of 27.61\u201328.97\u00b0 and 25.36\u201326.72\u2009\u00c5, respectively (Supplementary Fig.\u00a02c, d). This is in line with the increase of entropy from the metastable, higher energy pre-contraction state to the lowest energy post-contraction state.\n\nThe inner tube and outer sheath are tethered together by a hexameric ring of collar protein (CD1362) at the collar-proximal end of the diffocin (Fig.\u00a06a). The N-terminal domain of the collar subunit binds to the tube, while its C-terminal \u03b2-strand extends towards the C-terminal \u03b2-hairpin of last sheath, forming a three-\u03b2-strand handshake interaction (Fig.\u00a06e). These interfaces are maintained during contraction (Fig.\u00a06e\u2013g), enabling the collar to function as a force transducer between sheath contraction and tube ejection.\n\nSurface representations of the diffocin collars in the pre-contraction (a), post-contraction transitional (b), and post-contraction final states (c). The three structures are aligned using their collars. Five collar-proximal layers of the sheath (L32\u201328) are shown for each structure. Sheath color is coded by different conformations. L29\u201328 of sheath in the transitional state and L30\u201328 of sheath in the final state are trunk sheathes with the post conformer. d, Plot of outer diameters of sheath L32\u201328 in three states. e\u2013g Views showing the interface between the collar and sheath layer 32 (L32) in three states. h Additional interactions between collar and sheath L32 that only appear in the final state, labeled as hexagram in right panel of (g).\n\nWe captured two states of the diffocin in solution after contraction (Fig.\u00a06b, c), which show different structures in the last four sheath layers (L29-32) adjacent to the collar region (Fig.\u00a06a\u2013d) and in the interfaces between the collar and last sheath layer (Fig.\u00a06e\u2013h). In the transitional state, these sheath layers are partially contracted, as indicated by the gradually decreasing diameter of the hexameric sheath rings from L28 to L32 (Fig.\u00a06d). Specifically, the diameter of L28 in the transitional state is similar to that in the final state, while the diameter of L32 in the transitional state is almost identical to that in the pre-contraction state (Fig.\u00a06d). Consequently, the last few sheath layers still interact with the central tube in the transitional state. These features closely resemble the computationally simulated transition state of the R-type pyocin sheath-tube complex19. The conformational change from the transitional state to the final state results in a 37\u00b0 clockwise rotation and additional 2\u2009nm injection of the central tube. In addition to the interface between the C-terminal \u03b2-strand of the collar and the C-terminal \u03b2-hairpin of the last sheath in the transitional state (Fig.\u00a06f) a second interface between the collar and the last sheath is observed in the final state (Fig.\u00a06g, h). Lys92/97 on the N-terminal domain of the collar form salt bridges with Glu323 on the \u03b1-\u03b1 corner motif of the last sheath (Fig.\u00a06h). These interactions might provide extra mechanical stability to the junction after contraction. The insufficient contraction of the sheath and the absence of additional collar-sheath interactions indicate that the transitional state is metastable relative to the final state and has potential for further contraction.\n\nThe length of phage tails and other phage tail-like injection systems is determined by a tape measure protein (TMP), which is located in the lumen of central tube24,26,27,28,29. To date, no structure has been reported for any full-length TMP, likely due to its intrinsic flexibility and mismatch of symmetry with the rest of the respective bacteriocin or phage tail. It has been demonstrated that TMPs assemble either as trimers or hexamers; twenty and thirty-five C-terminal residues of the TMPs of phages 80\u03b1 and T5, respectively, were previously resolved as trimers29,30, while sixfold helical features were suggested for the TMP from phage Pam324.\n\nTo resolve the asymmetric structure of the full-length diffocin TMP (CD1366), we divided the pre-contraction diffocin particles into six segments, spanning from the collar to the baseplate, and gradually relaxed their symmetry from C6 to C3 to C1 during cryoEM data processing using featureless spherical masks (Supplementary Fig.\u00a08). A composite map of the full-length TMP was generated by montaging the asymmetric densities of the TMP from each segment (Fig.\u00a07a, Supplementary Fig.\u00a08d and Supplementary Movie\u00a05). In the map, TMP densities run along the entire tube lumen from the collar to the baseplate, with a length of ~1388\u2009\u00c5 and a diameter of ~25\u2009\u00c5 (Fig.\u00a07a, h). About 75% of the TMP densities exhibit features of three distinct rod-shaped densities around the central axis with quasi-C3 symmetry, reminiscent of a coiled-coil composed of three helixes (Fig.\u00a07a\u2013e). TMP densities in the baseplate region reveal high-resolution features with C3 symmetry, and the C-terminal \u03b1-helices (residues 795-817) of three TMPs were de novo built into the densities (Fig.\u00a07e\u2013g). These \u03b1-helices interact with the N-terminal \u03b1-helices of the spike trimer by forming an intertwined helix bundle (Fig.\u00a07f, g). The rest of the TMP (residues 1-794) was modeled with the assistance of AlphaFold225, which predicted most of the secondary structures as \u03b1-helices. The density of the TMP ends at the collar region and it displays higher flexibility (Fig.\u00a07a, b). In summary, the diffocin TMP assembles as a trimer with a coiled-coil pattern (Fig.\u00a07h), with the N-termini interacting with the collar and the C-termini anchored on the spike.\n\na Sectional view of composite cryoEM density map of the diffocin in the pre-contraction state. TMPs are colored in magenta and the other components are in semitransparent colors. b\u2013e Zoom-in views of diffocin TMP densities as indicated in (a). Corresponding top views are shown on the right. TMP models are fitted into the cryoEM densities in side views. f Interface between the TMP trimer and spike trimer. g Interactions between the C-terminal helix of the TMP and the N-terminal helix of the spike. Residues on the interface are labeled and shown in stick representation. h Model of the full-length diffocin TMP. One TMP subunit is rainbow colored from its N-terminus (blue) to C-terminus (red). i Illustration of predicted secondary structures of TMPs from phages and phage tail-like nanomachines. Alpha-helices, transmembrane (TM) domains and disordered regions were predicted by Phyre231. Globular domains were predicted by AlphaFold225. Globular domains in the same color indicate their structural similarity (see details in Supplementary Fig.\u00a09).\n\nIn addition to determining the length of the diffocin, other potential functions of TMP were predicted by AlphaFold225 and Phyre231 structure prediction tools. AlphaFold2 predicted a tertiary structure of residues 289-425 of CD1366 with relatively high pLDDT confidence score, depicting it as a globular domain (Fig.\u00a07h, i and Supplementary Fig.\u00a09a). This predicted globular domain (PGD) is comprised of a series of short (<15 amino acids) \u03b1-helices connected by turns/loops (Supplementary Fig.\u00a09a). Its corresponding cryoEM density is relatively flexible and appears as multiple short rods connected linearly (Fig.\u00a07a), indicating that these series of short \u03b1-helices are linearly arranged in the lumen of central tube (Fig.\u00a07a, h). Additionally, Phyre231 predicts that residues 540\u2013684 of CD1366 form five transmembrane (TM) helices (Fig.\u00a07h, i).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51038-w/MediaObjects/41467_2024_51038_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "By providing an atomic description of a bactericidal contractile system against Gram-positive bacteria, the atomic structures of the diffocin complex in its pre- and post-contraction states reported here offer a number of important insights into the mechanism of how this system works as compared to CISs targeting Gram-negative bacteria.\n\nCIS contraction initiates at the baseplate. Comparison between the baseplate components of the diffocin and R-type pyocin complexes highlights the unique challenge that this Gram-positive-targeting CIS faces regarding thick cell wall degradation. The hydrolase domain of the hub-hydrolase of the diffocin functions like a pair of scissors made up of two blades: the lytic transglycosylase and the endopeptidase, which have conserved catalytic triads of Tyr-Asp-Asp and Cys-His-His, respectively (Fig.\u00a03e, f and Supplementary Fig.\u00a05a\u2013f). These scissors are predicted to catalytically cut the glycosidic linkages of glycan strands and the peptide bonds of oligopeptide chains in the peptidoglycan mesh with the aforementioned conserved catalytic triads (Supplementary Fig.\u00a05c\u2013f), thereby destroying the thick peptidoglycan layer of C. difficile. This bifunctional hydrolase domain not only works as a hydrolase but also acts like the ripcord protein of R-type pyocin9. It binds tightly to the dimerization ring of the baseplate in the pre-contraction state. When the dimerization ring receives a triggering signal from the tail fiber, the conformational change of the triplex will cause the release of the hydrolase domain from the dimerization ring. As the spike drills into the Gram-positive cell, the released hydrolase domain would commence its catalytic function in a rotational motion to degrade the cell wall of the Gram-positive bacteria.\n\nContraction of known CISs propagates continuously through the sheath, eventually ending at the collar. However, contraction of the diffocin pauses at collar-proximal sheath layers prior to completion, giving rise to two post-contraction states. The four-layer sheath near the collar only contracts partially in the transitional state, allowing for the tube to be extended an additional 2\u2009nm in the final state (Fig.\u00a01e,f and Fig.\u00a06b,c). After contraction to the transitional state, the tube protrudes 45\u2009nm away from the baseplate (Supplementary Fig.\u00a010b). This protruding tube may encounter difficulty in penetrating the entire cell envelope of C. difficile, which is composed of an outermost S-layer, followed by a thick peptidoglycan layer and a lipid bilayer inner membrane32 with an average thickness of approximately 58 nm33. Rather, it may be necessary to pause briefly, allowing the hydrolase domain of the hub-hydrolase to continue its catalytic action, thoroughly degrading the thick peptidoglycan layer of C. difficile. The stored chemical energy remaining in the transitional state would be used in the final contraction to eject the TMP from the tail tube lumen. In the absence of spatial constraints, the coiled-coil TMP of the diffocin will likely refold to adopt a globular domain with five TM helices once detached from the collar and spike and released from the tube lumen during contraction. As a result, fifteen TM helices can be inserted and form a sizeable pore on the inner membrane of the envelope, dissipating the cellular electrochemical gradient and killing the bacterium (Supplementary Fig.\u00a010c). This step-wise contraction mechanism may be used to accommodate the characteristics of the envelope structure of Gram-positive bacteria.\n\nIn addition to their primary function of determining the length of the phage tail, phage TMPs are involved in a myriad of other secondary, host-related functions after ejection from the tail tube lumen: particular amino acid sequences were identified as peptidases responsible for degrading the host cell wall30,34,35, RNA polymerases used for transcribing DNA36, and TM domains facilitating the formation of a channel on the inner membrane for DNA translocation into the host cell37. Like the diffocin TMP, the TMPs of Gram-positive-targeting phages phiCD119, TP901-1 and P2 also have a predicted TM region (Fig.\u00a07i), with five, three, and four TM helices, respectively, potentially forming a sizeable channel that assists genomic transduction37,38. R-type pyocin contracts 70\u2009nm upon injection into the cell9,19, greater than the approximately 29\u2009nm cell envelope of Pseudomonas aeruginosa39, a Gram-negative bacteria composed of a periplasm containing a thin peptidoglycan layer sandwiched between the lipid bilayer outer and inner membranes16,39; therefore, its protruding tube may completely penetrate the thin peptidoglycan layer after contraction with relative ease and subsequently puncture into the inner membrane of the target cell (Supplementary Fig.\u00a010a). Gram-negative-targeting, such as phages T4 and T5, present little to no predicted TM region within their TMPs (Fig.\u00a07i). Cyanophage Pam3 is a notable exception, containing a 217 residue-long TM region (Fig.\u00a07i) that can form seven TM helices. Pam3 targets the Gram-negative cyanobacteria Pseudanabaena mucicola that also has a thick peptidoglycan and S-layer similar to those in Gram-positive bacteria40,41. Its TMP may employ its TM features to form a genomic transduction channel across the inner membrane resembling Gram-positive-targeting phage TMPs. Thus, we propose that phages and phage tail-like bacteriocins targeting cells with a thick peptidoglycan layer have a prominent TM region, possibly forming a sizeable conduit on the cell inner membrane to assist with genetic transduction and cell killing, respectively.\n\nAlphaFold225 predicts a globular domain within the diffocin TMP, consistent with similar PGDs present in other phages and phage tail-like nanomachines (Fig.\u00a07i). The PGDs of diffocin CD1366, P2 ORF14 and Pam3 gp16 share a conserved tertiary structure; the PGDs of R-type pyocin PA0625, phiCD119 gp17 and TP901-1 ORF45 share a conserved tertiary structure; the PGDs of Pvc14 and Afp14 share a conserved tertiary structure (Fig.\u00a07i and Supplementary Fig.\u00a09b). Searching for homologous proteins with NCBI BLAST42 or similar structures in Dali server43 yielded no matches for any of these PGDs, suggesting a lack of known conserved function. The PGD of the Gram-positive-targeting diffocin shares structural similarities with that of phage counterparts P2 (targeting Gram-positive bacteria) and Pam3 (targeting Gram-negative bacteria), suggesting a lack of specificity based on cellular envelope character (Fig.\u00a07i and Supplementary Fig.\u00a09b). PGDs of the Gram-negative-targeting R-type pyocin and the PGDs of phages phiCD119 and TP901-1, both of which target Gram-positive bacteria, have similarly predicted structures, further highlighting the non-discriminatory nature of PGDs (Fig.\u00a07i and Supplementary Fig.\u00a09b). Notably, all aforementioned phages and phage tail-like bacteriocins contain a PGD within their TMP that indicates potential non-selective supplementary features of TMPs upon ejection from their quasi-linear conformations in the tube.\n\nBacteriocins hold great promise as precision antibiotics to alleviate the widespread bacterial resistance problem facing humanity arising from decades of overusing wide-spectrum antibiotics. Looking forward, the structure of a contractile bacteriocin targeting a Gram-positive bacterium presented here opens the doors for engineering potent, nucleotide-free contractile nanomachines for applications in biomedicine and food industry. The diffocin construct we imaged has a modified receptor-binding domain that specifically targets the C. difficile S-layer protein so that it kills epidemic lineages of the bacteria in the gastrointestinal tract4,5. To target other Gram-positive bacterial pathogens, the TMP and the receptor-binding domain should be changed to match the characteristics of the specific pathogens according to the thickness of their cell envelope and surface receptor. Future computational and cellular cryogenic electron tomography studies similar to those applied to R-type pyocin19 and phage infection44 should shed light on other transient structural states and mechanism of action of the diffocin-mediated killing of C. difficile cells, allowing for engineered optimization.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The diffocin construct used in this study was Av-CD291.2/construct KX592438, which was engineered to eradicate C. difficile, especially the BI/NAP1/027 strain type5. This construct has a modified receptor-binding domain that specifically targets the C. difficile S-layer protein. For production, the diffocin gene cluster was integrated into B. subtilis genome and was induced as previously described5. Diffocins were purified from crude lysates by polyethylene glycol precipitation followed by differential centrifugation with a final ultracentrifuge step to pellet diffocin particles. This sample was further purified using a 10% to 50% sucrose (w/v) gradient at 77,000 \u00d7 g for 1.5\u2009h at 4\u2009\u00b0C. After centrifugation, one band was visible at about 25% position and was extracted gently by fractionation with a 100\u2009\u00b5L pipette from the top of the centrifuge tube along its side. The extracted sample was then diluted to a final volume of 4\u2009mL with Tris buffer (10\u2009mM Tris, 130\u2009mM NaCl, pH 7.4). The diluted sample was concentrated using a 100\u2009kDa Amicon molecular filter to approximately 50\u2009\u00b5L. This dilution-concentration step was repeated 3 more times in the same filter to remove the gradient material, resulting in a final sample volume of 50\u2009\u00b5L in Tris buffer for cryoEM imaging.\n\nFor cryoEM data collection, a 2.5\u2009\u03bcL aliquot of the purified diffocin sample was loaded onto a Quantifoil\u00ae 1.2/1.3, 200 mesh grid, blotted for 5\u2009seconds at force 2, and then flash-frozen in liquid ethane with a Vitrobot Mark IV (FEI/Thermo-Fisher). CryoEM grids were loaded into a Thermo Fisher Titan Krios electron microscope operated at 300\u2009kV for automated data collection using SerialEM45. Movies of dose-fractionated frames were acquired with a Gatan K3 direct electron detector in super-resolution mode at a pixel size of 0.55\u2009\u00c5 on the sample level. The total dose rate on the sample was set to ~50 electrons per \u00c52, which was fractionated into 50 frames with an exposure time of 0.06\u2009s for each frame. A total of 29,750 movies were acquired in two imaging sessions. Frames within each movie were 2\u00d7 binned (pixel size of 1.1\u2009\u00c5), aligned to correct beam-induced drift, and dose weighted using UCSF MotionCor246. Dose-weighted micrographs were used for the following CTF determination, particle picking and final reconstruction. Contrast transfer function (CTF) parameters of each micrograph were determined by CTFFIND447.\n\nCryoEM data processing workflows are outlined in Supplementary Fig.\u00a01 and 2. All steps described below were performed with RELION 4.048 unless otherwise indicated. Resolutions of the cryoEM maps were estimated on the basis of the gold standard49 Fourier shell correlation (FSC)\u2009=\u20090.143 criterion. Local resolution evaluations were determined by RELION with two independently refined half-maps. Data collection and processing statistics are given in Supplementary Table\u00a01.\n\nDiffocin particles in both the pre- and post-contraction states were present in cryoEM micrographs and can be readily distinguished by eye (Fig.\u00a01b). The two ends (collar and baseplate) of each diffocin were manually picked from 2000 representative micrographs and screened by 2D classification. Particles from the best classes were selected to train a particle detection model in Topaz50 for subsequent neural network-based particle picking from all micrographs. The picked particles were extracted in dimensions of 300 \u00d7 300 square pixels for both the pre- and post-contraction states. After several rounds of 2D classification and 3D classification with C6 symmetry, 202,971 collar particles in the pre-contraction state (pre-collar), 414,022 baseplate particles in the pre-contraction state (pre-baseplate), 117,846 collar particles in the post-contraction state (post-collar), and 30,712 baseplate particles in the post-contraction state (post-baseplate) were selected and processed separately in the following steps (Supplementary Fig.\u00a01a).\n\nFor the pre-collar, the particles were shifted along the C6 axis if necessary to ensure that their collars were at the same height. After an additional round of 3D classification, 144,368 pre-collar particles from the best class were selected and refined to 2.7\u2009\u00c5 resolution with C6 symmetry.\n\nFor the pre-baseplate, 353,620 particles were selected after 3D classification and refined to 2.6\u2009\u00c5 resolution with C6 symmetry. However, densities at the spike region were much worse than those at the trunk region, suggesting there was a symmetry mismatch between the spike and the trunk as observed in other CISs. After relaxing symmetry from C6 to C3, we obtained a 2.7\u2009\u00c5 resolution reconstruction of the pre-baseplate with reasonable densities at the spike region. To improve the density of the baseplate triplex, 116,539 particles were selected after an alignment-free 3D classification focused on the triplex region and refined to 2.9\u2009\u00c5 resolution with C3 symmetry imposed.\n\nFor the post-collar, the initially selected 117,846 particles were a mixture of post-collar and post-trunk particles. To separate them, we conducted an alignment-free 3D classification using a spherical mask covering the collar region. Additionally, two types of post-collars were separated: one is more compressed (shorter) than the other one (Supplementary Fig.\u00a01a). After removing bad particles, we obtained C6-symmetrized reconstructions of the shorter collar (post-collar short) at 3.9\u2009\u00c5 resolution and the longer collar (post-collar long) at 3.6\u2009\u00c5 resolution (Supplementary Fig.\u00a01a).\n\nFor the post-baseplate, 24,004 particles were selected after 3D classification and refined to 5\u2009\u00c5 resolution with C6 symmetry. These particles were further classified into two groups based on the relative location between the inner tube and the baseplate. The two groups of particles were refined separately with C6 symmetry to 5.6\u2009\u00c5 resolution (post-baseplate state1) and 6.1\u2009\u00c5 resolution (post-baseplate state2) (Supplementary Fig.\u00a01a). We believe that post-baseplate state1 and post-baseplate state2 correspond to post-collar long and post-collar short, respectively; this is because the offset of the tube between post-baseplate state1 and post-baseplate state2 matches with the offset of collar between post-collar long and post-collar short sets. This is confirmed by manual inspection of these particles on cryoEM images (see \u201cLength statistics of diffocin in different contraction states\u201d section for details). Post-collar long is the collar of diffocin in the transitional state and post-collar short is the collar of diffocin in the final state, as illustrated in Fig.\u00a06. Thus, post-collar long, post-collar short, post-baseplate state1 and post-baseplate state2 were defined as post-collar transitional, post-collar final, post-baseplate transitional and post-baseplate final, respectively (Supplementary Fig.\u00a01d,e).\n\nFor the trunk regions, the helical rise of the pre- and post-contraction states were initially determined to be 40\u2009\u00c5 and 25\u2009\u00c5, respectively, based on the reconstructions of the collar and baseplate (Supplementary Fig.\u00a01). Trunk segments separated during the initial 3D classification step (Supplementary Fig.\u00a01a) were used as \u201cseeds\u201d for particle picking. These trunk segments (G0 segment) were refined with C6 symmetry. The resulting center of each segment was shifted along the helical axis (Z-axis) in both directions by one layer to generate new segments. These segments were combined with G0 segments and re-extracted after removing duplicates (inter-segment distance less than 1.5 layers). These newly extracted segments were refined with C6 symmetry, and then subjected to alignment-free 3D classification to remove poor particles. The resulting good segments (G1 segments) were shifted by another layer to generate G2 segments following the same procedures. After 3\u20134 rounds of shifting and selection, 2.3 million pre-trunk segments and 1.1 million post-trunk segments were extracted in dimensions of 300 \u00d7 300 square pixels (Supplementary Fig.\u00a02a), with each segment having at least one unique asymmetric unit. The extracted segments were subjected first to 2D classification, followed by a 3D classification to eliminate poor particles. The selected pre-trunk segments were 3D refined with C6 symmetry and helical symmetry (helical rise of 40.0\u2009\u00c5 and helical twist of 17.5\u00b0). The following 3D classifications yielded two distinct classes that differed only by a shift of ~40\u2009\u00c5 along the helical axis and a rotation of ~17.5\u00b0. We shifted the segments in one class by 40\u2009\u00c5 and then combined them with the segments in the other class. Duplicated segments were removed (inter-segment distance less than 100\u2009\u00c5). Finally, 450,732 pre-trunk segments were refined to 2.2\u2009\u00c5 resolution with C6 and helical symmetry imposed. The refined helical rise is 79.5\u2009\u00c5 and the refined helical twist is 35.0\u00b0 (Supplementary Fig.\u00a02a). For the post-trunk, continuous heterogeneity of sheath layer was observed and analyzed by cryoSPARC 3D Variability Analysis (3DVA)51 (see \u201cQuantitative analysis of conformational heterogeneity of post-trunk\u201d section for details). Finally, 65,376 post-trunk segments selected from alignment-free 3D classification were refined to 3.6\u2009\u00c5 resolution with C6 and helical symmetry imposed, and the refined helical rise and twist of this reconstruction were 25.6\u2009\u00c5 and 27.8\u00b0, respectively.\n\nFor the tape measure protein, each pre-contraction diffocin particle was boxed into six segments (one collar segment, four trunk segments, and one baseplate segment) for processing (Supplementary Fig.\u00a08a, b). The box size of these segments was 300 pixels, and the center-to-center distance between neighboring segments was 250 pixels. The pre-collar, pre-trunk 1, and pre trunk 2 segments were initially refined with C6 symmetry and the pre-trunk 3, pre-trunk 4, and pre-baseplate segments were refined with C3 symmetry (Supplementary Fig.\u00a08a). The C6 symmetry segments were subsequently relaxed from C6 to C3 by alignment-free 3D classifications using spherical masks covering the tape measure protein regions. For each type of segment, three 3D classifications were conducted in parallel with three spherical masks (diameter of 110 pixels) distributing along the Z-axis. The resulting classes with good tape measure protein features were selected, and then relaxed symmetry from C3 to C1 by another round of alignment-free 3D classifications using the same masks. The classes with good features of the tape measure protein were selected (Supplementary Fig.\u00a08c). The C3 symmetry segments were subsequently relaxed from C3 to C1 symmetry via a near identical process, with the only difference being the use of a 60-pixel diameter mask for the pre-baseplate segment, as opposed to the aforementioned 110-pixel mask used in all other segments (Supplementary Fig.\u00a08c). Finally, a composite map of entire tape measure protein was generated by montaging 20 pieces of small maps from six segments (Supplementary Fig.\u00a08d).\n\nBoth the pre-contraction and post-contraction atomic models were built in Coot52. CD1362, CD1363, CD1364, CD1366 (residues 795-817), CD1367, CD1368 (residues 1\u2013314), CD1369, CD1370, CD1371 (residues 1\u201376) and CD1372 (residues 1\u201380) were modeled de novo. With cryoEM density maps of the pre-contraction state at 2.2\u20132.9\u2009\u00c5 resolution, we were able to clearly and confidently assign not only the backbone \u03b1-carbon positions, but also side chain identities. For each protein chain, we identified regions with strong secondary structures, such as \u03b1-helices, allowing us to determine the peptide direction during initial modeling stages. We placed batons to locate the position of \u03b1-carbons within the density map and converted the batons into poly-alanine chains with the mainchain function. The poly-alanine chains were then mutated into its proper alignment sequence. Large protruding side chains in the density map belonging to phenylalanine, tyrosine, and tryptophan residues were used as markers to align the sequence to the map. De novo modeling was not applied to proteins CD1366 (residues 1\u2013794), CD1368 (residues 315\u2013581), CD1371 (residues 77\u2013346) and CD1372 (residues 81\u2013148) due to their weak densities in the map. Predicted tertiary structures for these proteins were generated by AlphaFold225. We fitted the predicted structures into the density and refined manually in Coot52. For cryoEM density maps of the post-contraction state at 3.6\u20136.1\u2009\u00c5 resolution, we used the pre-construction models as initial models. These initial models were docked into the density maps of post-contraction diffocin by rigid-body fitting, followed by manually rebuilding in Coot52.\n\nAtomic models were refined through an iterative process between automatic refinement with Phenix53 and manual corrections in Coot52. For the automatic refinement step, the atomic models were refined using the phenix.real_space_refine command of the Phenix package53 with default refinement parameters. First, we refined individual proteins with the whole cryoEM maps to improve their secondary structure, Ramachandran, rotamer restraints, and intramolecular clashing score. Then, we combined all proteins to generate the full models of the baseplate, collar and trunk in Coot52 and refined the full models in Phenix in order to separate clashing atoms between adjacent monomers. At each of the refinement steps, we manually inspected the models to assess quality of the refinement, made manual adjustments and repeated the refinement steps until a final structure was reached.\n\nThe final refined models were validated with EMRINGER Score54, Ramachandran Plot, C-beta, and map CC as well as MolProbity55 and the results are summarized in Supplementary Table\u00a01. Figures and movies were generated with UCSF ChimeraX56.\n\nThe collar and baseplate particles used for the final cryoEM reconstructions were mapped back to raw cryoEM images, and then manually inspected to match up the collar and baseplate from the same diffocin particle. Diffocin particles presenting only the collar or baseplate or those with ambiguous features were excluded from consideration. Through the manual inspection, we also confirmed that post-baseplate state1 and post-baseplate state2 correspond to post-collar long and post-collar short, respectively (Supplementary Fig.\u00a01a). Finally, 1088 collar-baseplate pairs from pre-contraction diffocin, 742 pairs from post-contraction transitional state diffocin, and 872 pairs from post-contraction final state diffocin were selected from 1000 cryoEM images for analysis. For each collar-baseplate pair, the distance from the center of the collar to the center of the baseplate was calculated using their coordinates. The length of each diffocin particle was calculated by adding up the distance from the center of the collar to the center of the baseplate, the distance from the center of the collar to the top of the collar (measured from the cryoEM map of collar), and the distance from the center of the baseplate to the bottom of the baseplate (measured from the cryoEM map of baseplate).\n\n528,409 post-trunk segments initially refined with C6 and helical symmetry were subjected to 3DVA51 in cryoSPARC and separated into 20 clusters based on the 3DVA result (Supplementary Fig.\u00a02c). Segments in each cluster were refined individually with C6 and helical symmetry imposed (Supplementary Fig.\u00a02d). Helical rise and twist for each cluster were refined as well.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The cryoEM density maps and corresponding atomic models have been deposited in the EMDB and PDB, respectively. The accession numbers are listed as follows: pre-contraction collar (PDB: 8V3T and EMD-42953); post-contraction collar in transitional state (PDB: 8V3Z and EMD-42961); post-contraction collar in final state (PDB: 8V40 and EMD-42962); pre-contraction baseplate reconstructed in C6 symmetry (EMD-42957); pre-contraction baseplate reconstructed in C3 symmetry (EMD-42958); pre-contraction baseplate focused refinement on triplex region (PDB: 8V3W and EMD-42956); post-contraction baseplate in transitional state (PDB: 8V41 and EMD-42963); post-contraction baseplate in final state (PDB: 8V43 and EMD-42964); pre-contraction trunk (PDB: 8V3X and EMD-42959); post-contraction trunk (PDB: 8V3Y and EMD-42960). Source data of the length of diffocin particles are provided with this paper.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "CDC. 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We acknowledge the use of resources at the Electron Imaging Center for Nanomachines [supported by UCLA and by instrumentation grants from the NIH (1S10OD018111) and NSF (DBI-1338135 and DMR-1548924)].", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA, USA\n\nXiaoying Cai,\u00a0Yao He,\u00a0Iris Yu,\u00a0Anthony Imani,\u00a0Jeff F. Miller\u00a0&\u00a0Z. Hong Zhou\n\nThe California NanoSystems Institute (CNSI), University of California, Los Angeles (UCLA), Los Angeles, CA, USA\n\nXiaoying Cai,\u00a0Yao He,\u00a0Iris Yu,\u00a0Anthony Imani,\u00a0Jeff F. Miller\u00a0&\u00a0Z. Hong Zhou\n\nPylum Biosciences, San Francisco, CA, 94080, USA\n\nDean Scholl\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nZ.H.Z., J.F.M., and D.S. conceived the project; D.S. prepared the samples; X.C. and Y.H. recorded cryoEM images; X.C. and Y.H. determined the cryoEM structures; X.C., I.Y., Y.H., and A.I. built the atomic models; Z.H.Z., X.C., Y.H., I.Y., D.S. and J.F.M. interpreted the models; X.C., Y.H., A.I. and I.Y. made figures. Z.H.Z., J.F.M., X.C., Y.H., A.I., and I.Y. wrote the paper; and all authors contributed to the editing of the manuscript.\n\nCorrespondence to\n Jeff F. Miller or Z. Hong Zhou.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "J.F.M. is a cofounder, equity holder and chair of the scientific advisory board of Pylum Biosciences, Inc., a biotherapeutics company in San Francisco, CA, USA. D.S. is an employee and equity holder of the same company. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Marco van Belkum, Han Remaut and Cong-Zhao Zhou for their contribution to the peer review of this work. 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0000000000000000000000000000000000000000..fed645a56413c480d5e4d2278db217d095db6966 --- /dev/null +++ b/923daaf6d6c84e51a943b4920e071ca6840a3ba818659bd25b99c75911688fb3/metadata.json @@ -0,0 +1,136 @@ +{ + "title": "Intensification of extreme cold events in East Asia in response to global mean sea-level rise", + "pre_title": "Intensification of extreme cold events in East Asia in response to global mean sea-level rise", + "journal": "Nature Communications", + "published": "30 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63727-1/MediaObjects/41467_2025_63727_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63727-1/MediaObjects/41467_2025_63727_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://zenodo.org/records/16909285" + ], + "code": [ + "https://github.com/NorESMhub/NorESM" + ], + "subject": [ + "Atmospheric science", + "Climate change" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5500958/v1.pdf?c=1759317305000", + "research_square_link": "https://www.researchsquare.com//article/rs-5500958/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63727-1.pdf", + "preprint_posted": "09 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Today, the global mean sea level(GMSL) stands ~20cm higher than at the beginning of the last century, and the rate of sea-level rise has been accelerating in recent decades. Even a slight, globally uniform sea-level rise can notably impact atmospheric and oceanic circulations at climatic and potentially synoptic scales. However, the extent to which sea-level rise will influence extreme weather remains largely unknown. Here, we specifically focused on East Asia and conducted experiments to investigate the effects of GMSL rise. Our experiments demonstrate that GMSL rise, even in tens of centimeters, promotes stronger and more frequent extreme cold events. This effect is attributed to weakened mid-latitude westerly winds and increased occurrence of blocking events over Eurasia. Our study presents the first evidence that GMSL rise will modify synoptic systems and intensify extreme events. Both coastal and inland countries are exposed to threats arising from GMSL rise.Earth and environmental sciences/Climate sciences/Atmospheric scienceEarth and environmental sciences/Climate sciences/Climate change", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformation.docxFigure S1-9", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Today, the global mean sea level (GMSL) stands\u2009~\u200920\u2009cm higher than at the beginning of the last century, and the rate of sea-level rise has been accelerating in recent decades. Even a slight, globally uniform sea-level rise can notably impact atmospheric and oceanic circulations at climatic and potentially synoptic scales. However, the extent to which sea-level rise will influence extreme weather remains largely unknown. Here, we focus on East Asia and conduct climate model experiments to investigate the effects of GMSL rise on winter cold extremes. Our experiments demonstrate that GMSL rise promotes stronger and more frequent extreme cold events, and this influence is expected to strengthen significantly in the coming century. This effect is attributed to weakened mid-high latitude westerly winds and increased occurrence of blocking events over Eurasia. Our study presents evidence that GMSL rise can modify synoptic systems and intensify extreme events, suggesting that both coastal and inland countries are exposed to threats arising from GMSL rise.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Global mean sea level (GMSL) rise is a non-negligible factor in present and future climate systems. GMSL has risen by ~0.2\u2009m over the past century and is projected to rise further by 0.38\u2009m (0.77\u2009m) by 2100 under the SSP1\u20131.9 (Shared Socio-economic Pathways; SSP5\u20138.5) scenarios1, reflecting sea-level commitment from past, present and future emissions2. Observations reveal that the rise of GMSL over the past century has magnified flooding in coastal regions3. Furthermore, recent research indicates that this slight (in tens of centimeters) globally uniform sea-level rise is strong enough to alter large-scale atmospheric and oceanic circulations4, particularly at mid-high latitudes. As a result, GMSL rise can modulate global climate and potentially impact regional weather systems. However, how sea-level rise influences synoptic systems or extreme weather/climate events is largely unknown.\n\nHere, we use winter extreme cold events in East Asia as an example to address the impact of sea-level rise on synoptic systems. In recent years, East Asia has experienced unprecedented cold winters and increased extreme weather events5,6,7. For example, in 2020/2021, an extreme cold event occurred, resulting in record-breaking low temperatures at over 60 meteorological stations in China and causing local transportation and electric systems to break down8,9,10,11. In 2022/2023, an extreme cold event killed at least four persons in Japan and the Korean Peninsula12,13. Moreover, in December 2023, numerous regions in China witnessed the largest recorded temperature drop. Recent studies suggest that these extreme cold events were linked to large-scale atmospheric circulation anomalies\u2014such as Ural blocking\u2014which are modulated by Arctic Sea ice14,15,16,17,18,19,20,21 and oceanic variability in the Atlantic and Pacific22,23,24,25.\n\nConsidering this context, we conducted eight sea-level sensitivity experiments with different levels of GMSL rise, encompassing both recent historical conditions (SL0.15-0.3\u2009m) and projected future scenarios (SL0.625\u2009m or more, Methods). Here, GMSL rise is represented by a globally uniform uplift of the ocean reference surface\u2014an idealized but scientifically justified simplification4. In all SL experiments, GMSL rise was imposed at the start of the simulation and remained fixed throughout the integration. We also performed one pre-industrial (PI) control run and one experiment without sea-level rise (SL0m). All experiments were run for 2200 model years, with analyses focusing on the last 200 years of the model output.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Our sensitivity experiments have revealed that as GMSL rises, East Asia experiences a greater intensity and higher frequency of extreme cold days (ECDs) (Fig.\u00a01a, b). ECDs are defined as days with mean temperatures below the 10th percentile (Methods). When GMSL rise is below 0.3\u2009m, the intensification of ECDs remains limited (Fig.\u00a01a, b large dots). A GMSL rise of 0.625\u2009m marks the onset of significant increases in both cumulative intensity and frequency (Fig.\u00a01a, b small dots). When GMSL rise exceeds 1.25\u2009m, the increases in both metrics remain consistently significant (Fig.\u00a01c, d). However, it is important to note that the relationship between the response of ECDs and sea-level rise is nonlinear (Supplementary Fig.\u00a01-3).\n\na The number of sea-level experiments that generate increased cumulative intensity of winter (DJF) extreme cold days at each land model grid. All results are based on the 200-year mean of each experiment. b As in (a), but for frequency. The large dots indicate that in the SL0.15\u2009m/0.3\u2009m, significant changes are simulated in each land model with a confidence level greater than 90% (t-test). The small dots indicate additional regions where significant changes emerge in the SL0.625\u2009m. The stippling indicates that at least one (small dots) and two (large dots) sets of low sea level rise experiments (SL0.15-0.625\u2009m) simulate a significant change with a confidence level greater than 90% (t-test) at each land model. c, d display the cumulative intensity and frequency, regionally averaged in the deep blue regions within the box surrounded by black dash lines in (a) and (b). The solid (hollow) dots indicate the mean change is significant (insignificant) at a 90% confidence level (t-test) compared to the SL0m experiment. Note the cumulative intensity and frequency from our pre-industrial experiment are 17.6\u2009\u00b0C and 9 days; the cumulative strength results are taken as absolute values for better demonstration.\n\nExtreme cold events in mid-high latitudes are often associated with the generation and maintenance of blocking anticyclones characterized by a weakening of mid-latitude westerly winds on sub-seasonal timescales (10\u201320 days). Here, our experiments reveal that the sea level rise can cause prolonged persistence of blocking circulation and associated cold extremes in East Asia. Analyses utilizing Self-Organizing Maps26 (SOM, Methods) show that a specific synoptic pattern (SOM1, Fig.\u00a02a) in winter favors the ECDs occurrence in East Asia (Fig.\u00a02b). The SOM1 field exhibits a north-positive/south-negative dipole at 500 hPa geopotential height, similar to a blocking circulation (compare Fig.\u00a02a and Supplementary Fig.\u00a04). Compared to the SL0m, the frequency and max persistence of SOM1 increases in almost all sea-level experiments (Fig.\u00a02c). The increase in max persistence can be significant (90% confidence level) even when the GMSL uplift is only tens of centimeters (Fig.\u00a02c asterisk). Although the behavior in SOM1 is non-linear, the consistent increase in frequency and max persistence provides robust evidence showing the influence of GMSL rise on the synoptic scale. Meanwhile, the other two patterns (SOM2 and SOM3), which reduce East Asian winter ECDs, have their frequency and max persistence declining in most sea-level experiments (Supplementary Fig.\u00a05).\n\nThe Eurasian winter circulation patterns in the sea-level experiments are divided into three clusters (SOM1, SOM2, SOM3) at a synoptic scale. a The winter (DJF) anomalies in geopotential height (shade, gpm) and wind (arrows) fields at 500 hPa in SOM1. b The changes in frequency of extreme cold days (shading, %) and surface air temperature (contours of \u22122 and \u22124\u2009\u00b0C) under SOM1. c The variations in the frequency and max persistence (days) of SOM1 in the sea-level rise experiments relative to SL0m. The asterisk indicates a significant change (90% confidence level with t-test) in the mean value compared to SL0m. All results are based on the 200-year mean of each experiment.\n\nFurther SOM analyses demonstrate the SOM1 field consists of three clusters related to blocking, with a positive geopotential height anomaly appearing over Northern Europe in SOM1.1 (Supplementary Fig.\u00a06d), over the Ural Mountains in SOM1.2 (Supplementary Fig.\u00a06e), and over Eastern Russia in SOM1.3 (Supplementary Fig.\u00a06f). They correspond to three important typical blockings: high-latitude European blocking27, Ural blocking28, and Okhotsk blocking28. These blockings reinforce the trough-ridge structure over Eurasia for an extended period\u2014a week or even longer\u2014thus allowing more transport of cold air masses from the Arctic into East Asia28,29,30,31,32,33,34. Compared to the SL0m, the frequency of SOM1.1 and SOM1.3 enhances in most sea-level experiments, whereas the appearance of SOM1.2 is more dominant in the SL0.3\u2009m and SL2.5\u2009m experiments (Supplementary Fig.\u00a06g).\n\nThe increased occurrence of blocking events (BE) over Eurasian mid-high latitudes explains much of the intensification in East Asian ECDs. As sea-level rise, winter background westerly winds weaken (Fig.\u00a03a), allowing larger-scale eddies to become stationary35, which favors the development of BE (Fig.\u00a03b and Supplementary Fig.\u00a07). Meanwhile, the mid-latitude potential vorticity gradient weakens with rising sea-level, suggesting a more non-linear response of BE36. The heightened occurrence of BE in these regions can explain up to ~45% of enhanced ECDs cumulative intensity (R2\u2009=\u20090.45, Fig.\u00a03c), as well as ~49% of the heightened ECDs frequency (R2\u2009=\u20090.49, Supplementary Fig.\u00a08). Notably, even when excluding the effect of high sea-level rise experiments (0\u20132.5\u2009m), our conclusion is still robust (light blue lines in Fig.\u00a03c).\n\na The anomalies in geopotential height (shading, gpm) and wind (arrows) fields at 500 hPa, composited in the sea-level experiments in winter, relative to SL0m. The stippling on shading and black arrow indicate that all sea-level experiments agree with the sign of the ensemble mean anomalies. The results of each experiment are shown in Supplementary Fig.\u00a09. b The number of sea-level experiments that generate an increased blocking frequency at each land model grid in winter (DJF). The increase in blocking frequency in Northern Europe and Eastern Siberia consistently corresponded with the increase in SOM1.1 and SOM1.3 across all sea-level experiments. c The linear regression between the frequency of blocking events in 0\u2013150\u00b0E, 55\u201375\u00b0N, and cumulative intensity of extreme cold days in East Asia. The light/deep blue lines show the linear fits by the SL0-2.5/0\u201320\u2009m experiments, and the shadows show a 90% confidence interval. All results are based on the 200-year mean of each experiment.\n\nThe weakening of westerly winds in Eurasian mid-latitudes is closely connected to the significant warming in the North Pacific (Supplementary Fig.\u00a010), driven by rising sea-level. This surface warming can generate significant positive geopotential height anomalies through both thermal and eddy forcing (Supplementary Fig.\u00a011), thereby triggering an eastward-propagating Rossby waves\u2014a mechanism supported by prior studies37,38. As a result, wave-train-like anomalies appear at the Northern Hemisphere mid-high latitudes (Fig.\u00a04a). A positive geopotential height anomaly extends from Northern Europe to Eastern Russia, while a negative anomaly develops over East Asia, indicating a weakening of both the westerly winds and the meridional potential vorticity gradient in the mid-high latitudes. These anomalies exhibit an equivalent barotropic structure (compare Fig.\u00a03a and Fig.\u00a04a), consistent with previous results due to the North Pacific surface warming39.\n\na The anomalies in the 200 hPa geopotential height (shading, gpm) and horizontal wave activity flux (arrows, using the climatological wind of SL0m as the background wind), composited in the sea-level experiments in winters, relative to SL0m. b As in (a), but for the average temperature (shading, \u00b0C) and atmosphere energy transport (ET, arrows) between 850\u00a0hPa and 500 hPa. c, As in (a), but for the geopotential height at 53\u00a0hPa. The stippling on shading and black arrow indicate that all sea-level experiments agree with the sign of the ensemble mean anomalies. The results of each experiment are shown in Supplementary Figs.\u00a012\u201314. All results are based on the 200-year mean of each experiment.\n\nMeanwhile, such responses in large-scale atmospheric circulations strengthen the poleward atmospheric energy transport, contributing to the regional warming of the Arctic. When sea-level rises, the anticyclonic anomaly over Eastern Siberia transports more energy into the Arctic near 100\u00b0E (Fig.\u00a04b black arrows). This enhanced poleward energy transport can warm the Arctic troposphere while cooling the East Asian troposphere40 (Fig.\u00a04b shading), which reduces poleward temperature gradient, thus favoring the weakened westerly wind as well as the enhanced occurrence of BE.\n\nIn addition, another response due to the North Pacific warming appears in the polar vortex (Fig.\u00a04c). Due to the weakened zonal wavenumber-1 waves in response to North Pacific warming (Supplementary Fig.\u00a015), the polar vortex shifts towards North America and away from the Eurasian continent41,42,43, further promoting blocking events and enhancing cold air intrusion in East Asia36,42,44.\n\nOur atmosphere-only experiments affirm the significant influence of North Pacific warming on ECDs in East Asia (Methods, Supplementary Table\u00a01). When winter sea surface temperature (SST) anomalies from the North Pacific are introduced without additional modifications in these atmosphere-only experiments, a notable intensification of ECDs emerges (Fig.\u00a05). Remarkably, these responses remain significant even when utilizing winter SST anomalies from coupled experiments featuring small GMSL rise in centimeters (Fig.\u00a05 white dot).\n\na, b Similar to Fig.\u00a01a, b. Here, we conducted nine atmosphere-only experiments (corresponding to sea-level rise 0\u221220\u2009m, Methods). By applying the winter monthly average sea surface temperature anomaly to CAM4, we obtained the consistent intensification of extreme cold over East Asia. The results of each experiment are shown in Supplementary Figs.\u00a016\u201317. These experiments run for 45 years, with analysis focusing on the final 40 years.\n\nFurthermore, our atmosphere-only experiments confirm the key mechanisms driving the intensification of ECDs. The experiments reveal a decrease in westerly winds and an increase in BE in the mid-high latitudes of Eurasia (Fig.\u00a06), resulting in intensification of ECDs. These changes are closely linked to tropospheric wave-train-like anomalies, Arctic warming, and polar vortex shift, driven by North Pacific warming (Supplementary Fig.\u00a018).\n\na, b Similar to Fig.\u00a03a, b, but for the result of atmosphere-only experiments. We observe that North Pacific warming leads to a uniform increase in the blocking frequency across Eurasian mid-high latitudes. This suggests that the reduced blocking frequency in the Ural region under sea-level rise (Fig.\u00a03b)\u2014less favorable for extreme cold events in East Asia\u2014may be influenced by other factors, such as the cooling in North Atlantic and Barents-Kara Seas9. The results of each experiment are shown in Supplementary Fig.\u00a019. All results are based on the 40-year mean of each experiment.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63727-1/MediaObjects/41467_2025_63727_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63727-1/MediaObjects/41467_2025_63727_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63727-1/MediaObjects/41467_2025_63727_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63727-1/MediaObjects/41467_2025_63727_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63727-1/MediaObjects/41467_2025_63727_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63727-1/MediaObjects/41467_2025_63727_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our study highlights the effect of GMSL rise on synoptic systems. Even a small uniform sea-level uplift \u2013 one aspect of GMSL rise \u2013 can promote stronger and more frequent winter extreme cold events in East Asia. Furthermore, this study suggests that the responses of winter extreme cold events are probably nonlinear within the magnitude of current and projected GMSL change by the end of this century. The non-linear relationship is related to the variable circulation pattern, partly stemming from the inherent complexity of the climate system, such as the nonlinear changes in SST4,45 and blocking events46.\n\nConsidering the GMSL rise, the potential significance of North Pacific warming in influencing winter extreme cold events in East Asia in the future becomes evident. In our coupled sea-level experiments, the simulated North Pacific warming correlates with enhanced northward oceanic heat transport and a linear increase in water flow through the Bering Strait4 (Supplementary Fig.\u00a020). These simulated changes appear robust and are further supported by recent data indicating a rising net water flux through the Bering Strait in ~0.010\u2009Sv/year47.\n\nAt the same time, several limitations of our simulations should be acknowledged. First, our coupled sea-level experiments have been conducted over a span of 2200 years, a duration sufficient to induce substantial warming in the North Pacific. In the upcoming century, the warming in this region due to sea-level rise may exhibit a slower rate and a smaller magnitude. Second, while our simulations effectively capture the long-term responses to GMSL rise, they do not account for transient responses. Third, we used a uniform sea-level rise and did not account for regional differences (Supplementary Fig.\u00a021). Results from our additional atmosphere-only experiment suggest that regional sea-level variations may also influence winter extreme cold events in East Asia, although the effects appear minor and less significant (Supplementary Fig.\u00a022). Fourth, in scenarios with much higher GMSL rise (e.g., greater than 2.5\u2009m, which is unlikely to occur within the next century), atmospheric CO2 concentrations are expected to far exceed 400 ppm. The warming caused by high CO2 levels and the associated increase in climate system variability could potentially offset the effects of sea-level rise. Nevertheless, ongoing concerns regarding the influence of sea-level rise cannot be dismissed. As time progresses, the enduring impacts of GMSL rise are likely to become increasingly prominent.\n\nOur study underscores that the threats arising from rising sea levels are not limited to coastal regions alone but extend to inland areas. In addition to extreme cold events, adjustments to ocean circulation prompted by sea-level rise may affect natural variability (such as Pacific Decadal Oscillation) associated with other extreme weather events. Meanwhile, sea-level rise contributing to warming at high latitudes, particularly over Greenland, could expedite the onset of climate tipping points. Thus, the risks associated with sea-level rise are global in scope. Moreover, the processes through which sea-level rise influences the global climate are more complicated than simply lifting the sea-level datum. Further studies on sea-level rise require the development of a new generation of climate models. Given that sea-level rise will continue to rise throughout this century, an urgent assessment of the global disaster risk stemming from sea-level rise is imperative.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "NorESM1-F is a computationally efficient version of the Norwegian Earth System Model family48. It was built upon the Community Climate System Model, version 4, and developed based on the version for the fifth phase of the Coupled Model Intercomparison Project (CMIP5), NorESM1-M. Like NorESM1-M, NorESM1-F uses the same atmosphere-land grid with a horizontal resolution of 2\u00b0 but has a new tripolar grid with a nominal 1\u00b0 horizontal resolution for the ocean-sea-ice components. The tripolar grid provides a higher horizontal resolution (~40\u2009km) and is more isotropic at high northern latitudes. There are 26 vertical levels in the atmosphere and 53 vertical layers in the ocean component, respectively.\n\nWe designed ten simulations, PiControl, CO2400, CO2400sl0.15m, CO2400sl0.3m, CO2400sl0.625m, CO2400sl1.25m, CO2400sl2.5m, CO2400sl5m, CO2400sl10m and CO2400sl20m (Supplementary Table\u00a01, use abbreviations in the main text). Sea-level rise is implemented by lowering topography and increasing bathymetry (Supplementary Table\u00a01). In all sea-level experiments, CO2 concentration and sea-level rises are imposed at the start of the simulation and remain fixed throughout the integration. In all SL experiments, atmospheric CO2 concentration was fixed at 400 ppm (close to current levels) to isolate the impact of GMSL rise. All sea-level experiments run for 2200 model years, and we analyze the model output for the last 200 years. Here, we only introduce the three newly added experiments: CO2400, CO2400sl0.15m, and CO2400sl0.3m. For more other experiments, please see Zhang et al.4.\n\nIn CO2400, the atmospheric CO2 level is 400 ppm, and all other boundary conditions (including orbital parameters, CH4 level, bathymetry, and topography) are identical to the PiControl experiment. CO2400 is initialized from the Levitus49 temperature and salinity and run for 2200 model years, which is different from the original 1700-year-long CO2400 experiment3 initialized from the PiControl experiment. Due to different initialization and integration lengths, CO2400 and CO2400original exhibit differences in the simulated SST. But no matter which CO2400 experiment is used, the key mechanism that intensifies extreme cold events over East Asia\u2014North Pacific warming\u2014remains robust (Supplementary Fig.\u00a023).\n\nIn CO2400sl0.15\u2009m and CO2400sl0.3m, we consider a GMSL rise of 0.15,0.30\u2009m (close to present) in sea level experiments. Except for the ocean bathymetry and topography height, all other boundary conditions are identical to the previous sea level experiments4.\n\nWe designed nine atmosphere-only simulations to verify the effect of warming North Pacific. The control run is forced by climatological SST annual cycle of the CO2400 experimental. In the sensitive run, monthly North Pacific SST anomalies obtained from the 200-year mean (Supplementary Fig.\u00a010 red box) of SL0.15-20\u2009m are added onto the climatological SST from December to February. In addition, we conducted an atmosphere-only experiment to assess the influence of regional sea-level (rsl) anomalies. These anomalies were derived from satellite altimetry-based sea surface height data over 1993\u20132023, with the global mean removed to isolate spatial patterns, and imposed as changes in surface geopotential height (PHIS). All experiments are integrated for 45 model years. We analyze the model outputs in the last 40 model year. For the threshold of extreme cold events, we use the same values as the coupled experiments analysis.\n\nWe define the temperature threshold by the 10th percentile of the winter daily surface air temperature (SAT) distribution of the PiControl experiment (following Kolstad et al.)50. An extreme cold day is identified for each grid point when the SAT is below the threshold.\n\nFor the intensity of extreme cold events, we use the cumulative intensity, which refers to the integration of SAT anomaly at each grid point:\n\nThe cumulative intensity is helpful as it integrates intensity and persistence in a single number. Note that we count extreme cold events lasting 3/5 days or longer, the results are taken as absolute values for better demonstration. In the main text, we show the results of the 3 days, and the results of the 5 days are shown in the Supplementary Fig.\u00a03.\n\nFollowing Scherrer et al.51 and Davini et al.52, we adopt gradient-reversal (REV) indices to identify blocking events. A 2D extension of the blocking index is defined by:\n\nwhere \u03bb (\\(\\phi\\)) is the longitude (latitude) at a given grid point. \u03bb (\\(\\phi\\)) ranges in longitudes from 0\u00b0-360\u00b0 (latitudes 45\u00b0-70\u00b0N); and Z(\u03bb, \u03d5) is the daily 500 hPa geopotential height at the grid point (\u03bb, \u03d5). It is considered as an instantaneous blocking when the grid point (\u03bb, \u03d5) satisfies the following formula:\n\nThe blocking frequency at a given grid is defined as the percentage of the days of instantaneous blocking divided by the total number of days for the winter.\n\nFor the blocking events, we define the area of the instantaneous blocking in the region (0\u2013150\u00b0E, 55\u201375\u00b0N) that exceeds at least 5.0\u2009\u00d7\u2009105\u2009km2 within a 10\u00b0\u2009\u00d7\u200910\u00b0 sliding window and lasts at least 5 days (similar to Woollings et al.)34.\n\nSelf-Organizing Maps26 (SOM) projects a non-linear mapping of a high-dimensional input vector onto a regularly arranged topological low-dimensional array. This method, extensively used in atmospheric sciences in recent decades, is effective in characterizing large-scale circulation patterns and identifying their possible impacts on weather and climate extremes53,54. The SOM program used here is the Python miniSOM library.\n\nWe carry out SOM clustering in two rounds. For the first round, we select winter (DJF) daily 500 hPa geopotential height from CO2400 and eight sea-level experiments over the domain 0\u2013150\u00b0E, 35\u201385\u00b0N in the data pool to carry out SOM analyses, obtaining three clusters (cold East Asia SOM1, warm East Asia SOM2, and general East Asia SOM3). Then, we only select the 500 hPa geopotential height chosen into the SOM1 cluster in the data pool to conduct SOM clustering for the second round (Supplementary Fig.\u00a05). We have tested several SOM arrays, including 1 \u00d7 2, 1 \u00d7 3, and 2 \u00d7 2. The 1 \u00d7 3 node is sufficient to represent the range of large-scale circulation patterns and is easily distinguishable.\n\nDue to the inherent stochasticity of the SOM, multiple random runs are recommended. Here, we use 10 random runs to select the SOM array that better represents the input data. The similarity of the SOM clusters to the contained data vectors is expressed using the quantization error55:\n\nwhere N is the number of data-vectors and \\({{\\mbox{m}}}_{{{\\mbox{x}}}_{{\\mbox{i}}}}\\) is the best matching prototype of the corresponding \\({{\\mbox{x}}}_{{\\mbox{i}}}\\) data-vector.\n\nThe total energy transport in a unit air column from 1000 hPa to 500 hPa follows the equation given by Graversen et al40.\n\nThe atmospheric energy includes the internal energy (CpT), the latent energy (Lq), the potential energy (gz), and the kinetic energy (k). Here, v is wind velocity vector, Cp is specific heat capacity for constant volume, T is absolute temperature, L is the specific heat of condensation of sublimation, q is specific humidity, g is gravity, and z is height; while ps and p0 represent surface pressure and upper-level pressure.\n\nPotential vorticity gradient (PVy) is a key influence on blocking activity46. Under the lower-PVy background condition, the blocking event has weaker energy dispersion and stronger nonlinearity so that it exhibits longer persistence, slower decay and weaker eastward movement. The PVy in the barotropic atmosphere is expressed as:\n\nwhere \\(\\beta\\) is the meridional gradient of planetary vorticity, \\(\\bar{u}\\) is the basic zonal wind, and \\(F\\) is the barotropic Froude number.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The winter mean data including frequency and cumulative intensity of extreme cold events, blocking frequency, air temperature, wind, geopotential height, and sea surface temperature from the sea-level experiments are publicly available on Zenodo (https://zenodo.org/records/16909285). More model output can be provided, upon request, from the author C.D. 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Res. 152, 123\u2013137 (2013).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This study was jointly supported by the National Natural Science Foundation of China (grant no. 42125502), the SapienCE (project no. 262618), and other projects (projects nos. 314371, 229819 and 221712) from the Norwegian Research Council, as well as the computing resources from Notur/Norstore projects NN9133/NS9133, NN9486/NS9486, and NN9874/NS9874, and the Department of Atmospheric Science, China University of Geosciences (CUG).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Atmospheric Science, School of Environmental Studies, China University of Geoscience, Wuhan, China\n\nCaoyi Dong,\u00a0Yong Liu\u00a0&\u00a0Mingna Wu\n\nCentre for Severe Weather and Climate and Hydro-geological Hazards, Wuhan, China\n\nCaoyi Dong,\u00a0Yong Liu\u00a0&\u00a0Mingna Wu\n\nDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China\n\nZhongshi Zhang\n\nSchool of Geographic Science, Nantong University, Nantong, China\n\nZhongshi Zhang\n\nState Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, P.R. China\n\nZhongshi Zhang\n\nGeophysical Institute, University of Bergen, Bergen, Norway\n\nNoel Keenlyside\n\nNansen Environmental and Remote Sensing Centre, Bjerknes Centre for Climate Research, Bergen, Norway\n\nNoel Keenlyside,\u00a0Antonio Bonaduce,\u00a0Jiping Xie\u00a0&\u00a0Roshin P. Raj\n\nNORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway\n\nStefan Pieter Sobolowski\u00a0&\u00a0Odd Helge Otter\u00e5\n\nCentre for Early Sapiens Behaviour, University of Bergen, Bergen, Norway\n\nStefan Pieter Sobolowski\u00a0&\u00a0Odd Helge Otter\u00e5\n\nPlateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, University of Information Technology, Chengdu, China\n\nBo Liu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nZ.Z. designed and performed the simulations. C.D. performed the data analysis and wrote the draft of the paper. N.K., S.P.S., and O.H.O. contributed to the analyses of atmospheric dynamics. A.B., J.X., R.P.R., Y.L., B.L., and M.W. contributed to interpreting the results. All authors contributed to reviewing and editing the paper.\n\nCorrespondence to\n Zhongshi Zhang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Dehai Luo and the other anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Dong, C., Zhang, Z., Keenlyside, N. et al. Intensification of extreme cold events in East Asia in response to global mean sea-level rise.\n Nat Commun 16, 8700 (2025). https://doi.org/10.1038/s41467-025-63727-1\n\nDownload citation\n\nReceived: 21 November 2024\n\nAccepted: 28 August 2025\n\nPublished: 30 September 2025\n\nVersion of record: 30 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63727-1\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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enrich rhizosphere Pseudomonas to enhance nitrogen utilization and lateral root growth in Populus", + "journal": "Nature Communications", + "published": "07 February 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM7_ESM.xlsx" + }, + { + "label": "Supplementary Data 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM8_ESM.xlsx" + }, + { + "label": "Supplementary Data 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM9_ESM.xlsx" + }, + { + "label": "Supplementary Data 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM10_ESM.xlsx" + }, + { + "label": "Supplementary Data 8", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM11_ESM.xlsx" + }, + { + "label": "Supplementary Data 9", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM12_ESM.xlsx" + }, + { + "label": "Supplementary Data 10", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM13_ESM.xlsx" + }, + { + "label": "Supplementary Data 11", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM14_ESM.xlsx" + }, + { + "label": "Supplementary Data 12", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM15_ESM.xlsx" + }, + { + "label": "Supplementary Data 13", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM16_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM17_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_MOESM18_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://ngdc.cncb.ac.cn/gsa/browse/CRA015093", + "https://ngdc.cncb.ac.cn/gsa/browse/CRA015096", + "https://ngdc.cncb.ac.cn/gsa/browse/CRA015469", + "https://ngdc.cncb.ac.cn/gsa/browse/CRA015475", + "https://doi.org/10.6084/m9.figshare.26426578", + "/articles/s41467-025-56226-w#Sec31" + ], + "code": [], + "subject": [ + "Microbial communities", + "Plant molecular biology", + "Transcriptomics" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4090444/v1.pdf?c=1739020041000", + "research_square_link": "https://www.researchsquare.com//article/rs-4090444/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-56226-w.pdf", + "preprint_posted": "21 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Plant growth behavior is a function of genetic network architecture. The importance of root microbiome variation driving plant functional traits is increasingly recognized, but the genetic mechanisms governing this variation are less studied. Here, we collected roots and rhizosphere soils from nine Populus species belonging to four sections, generated metabolite and transcription data for roots and microbiota data for rhizospheres, and conducted comprehensive multi-omics analyses. We demonstrated that the roots of robust Leuce poplar enriched more plant growth-promoting rhizobacteria, which compared with the poorly performing poplar, agreeing with the \u2018Matthew effect\u2019 on poplar-microbe interaction. Moreover, we confirmed that Pseudomonas was strongly associated with tricin and apigenin biosynthesis and identified that gene GL3 was critical for tricin secretion. The elevated tricin secretion via constitutive transcription of PopGL3 and PopCHS4 could drive Pseudomonas colonization in the rhizosphere and further enhance poplar growth, nitrogen acquisition, and lateral root development in nitrogen-poor soil. This study reveals plant-metabolite-microbe regulation patterns contribute to the poplar fitness and thoroughly decoded the key regulatory mechanisms of tricin, and provided new insights into the interactions of the plant\u2019s key metabolites with its transcriptome, rhizosphere microbes.Biological sciences/Plant sciences/Plant molecular biologyBiological sciences/Molecular biology/TranscriptomicsBiological sciences/Microbiology/Microbial communities", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Wuet.al.SupportingInformation20240313.pdfSupplementaryData.xlsData S1, Data S2, Data S3, Data S4, Data S5, Data S6, Data S7, Data S8, Data S9 and Data S10", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Plant growth behavior is a function of genetic network architecture. The importance of root microbiome variation driving plant functional traits is increasingly recognized, but the genetic mechanisms governing this variation are less studied. Here, we collect roots and rhizosphere soils from nine Populus species belonging to four sections (Leuce, Aigeiros, Tacamahaca, and Turanga), generate metabolite and transcription data for roots and microbiota data for rhizospheres, and conduct comprehensive multi-omics analyses. We demonstrate that the roots of vigorous Leuce poplar enrich more Pseudomonas, compared with the poorly performing poplar. Moreover, we confirm that Pseudomonas is strongly associated with tricin and apigenin biosynthesis and identify that gene GLABRA3 (GL3) is critical for tricin secretion. The elevated tricin secretion via constitutive transcription of PopGL3 and Chalcone synthase (PopCHS4) can drive Pseudomonas colonization in the rhizosphere and further enhance poplar growth, nitrogen acquisition, and secondary root development in nitrogen-poor soil. This study reveals that plant-metabolite-microbe regulation patterns contribute to the poplar fitness and thoroughly decodes the key regulatory mechanisms of tricin, and provides insights into the interactions of the plant\u2019s key metabolites with its transcriptome and rhizosphere microbes.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Rhizosphere microbial community structure is highly dynamic in part due to the changes in root exudation over the course of plant development1,2,3. Although some chemical signals released by plants facilitate specific interactions, many have been recognized by previous studies. For example, flavonoids (luteolin, apigenin, etc.) could interact with rhizobial nodulation (NodD) proteins activating the transcription of nodulation genes responsible for the deformation of plant root hairs and assisting rhizobial entry via infection threads4,5; coumarins selectively affect the assembly of rhizosphere microbial communities, inducing the colonization of Pseudomonas simiae WCS417, thereby improving the niche establishment of microbial partners6. Under the infection of pathogens, plants employ a \u201ccry for help\u201d strategy, and signaling chemicals (L-malic acid, salicylic acid, etc.) activated by the immune response change the composition of the rhizosphere microbiome, recruiting beneficial microorganisms to help them resist these stresses7,8. Therefore, it is crucial and challenging to elucidate the causal relationship between plant metabolites and beneficial microbes. Flavonoids are one of the most studied classes of such metabolites, regulating both plant development and the interaction with commensal microbes2. Root secretion of flavonoids occurs frequently under biotic stress and is involved in promoting microbial colonization during stress generation9,10. Infection of part of the tomato (Solanum lycopersicum) root system with Ralstonia solanacearum changes numerous root exudates and involves disease suppression via the recruitment of disease-suppressing Streptomyces for colonization, which was associated with increased exudation of 3-hydroxyflavone9. Under abiotic stress, flavonoid production is often elevated in plants11. However, our knowledge of how flavonoid-mediated plant-microbe interactions may improve plant resistance to abiotic stresses remains elusive.\n\nThe process of microbial recruitment by plants is essentially a \u201ctop-down\u201d regulatory process, in which functional genes alter rhizosphere microbial community composition based on the regulation of metabolites or other signaling molecules6,12,13. Previous studies have effectively integrated plant transcriptomics or genomics with microbiome community data using methods such as Weighted Gene Co-Expression Network Analysis (WGCNA)14 and Microbiome-Wide Association Studies (MWAS) analyses15,16, demonstrating the significance of host gene expression (CYP72A154 and Nucleotide-Binding-Leucine-Rich-Repeat) in shaping the composition of microbial communities. However, analyzing the correlation between host genes and the microbial community cannot elucidate the role of metabolites in plant-regulated microbial structure. The establishment of a comprehensive network involving genes, metabolites, and rhizosphere microbes becomes crucial for a thorough understanding of plant-microorganism interactions.\n\nPoplar (Populus L.), a globally cultivated fast-growing and high-yielding timber tree species, comprises five sections: Leuce, Aigeiros, Tacamahaca, Turanga, and Leucoides17. Distinct poplar genotypes exhibit various growth characteristics18,19, and these differences profoundly influence the productivity and adaptability of the poplars20,21, because the enhancement of certain growth traits may be closely linked to a plant\u2019s resistance to environmental stressors22,23. The fast-growing P. euramericana Dode manifests a more developed root system than the slow-growing P. simonii Carr, concurrently displaying heightened capabilities for nitrogen uptake and assimilation, promoting growth in nitrogen-deficient environments20. Additionally, diverse poplar genotypes (or sexes) shape rhizosphere communities by recruiting specific microbial taxa24,25,26, under which microbes may alter host performance and fitness directly or via ecosystem services such as nutrient accessibility27,28,29. However, the genetic mechanism by which poplar genotypes regulate the overall assembly and functional changes of microbial communities in nitrogen-poor conditions remains largely unclear, as do the effects of host genotype-selected rhizosphere microbiomes on poplar growth and fitness.\n\nIn this study, we hypothesize that (1) the comprehensive gene-metabolite-microbe co-expression network provides an effective research tool to elucidate the genetic mechanisms by which poplar regulates metabolite-mediated specific microbial recruitment; (2) poplar regulates the secretion of flavonoids by functional genes to reshape the rhizosphere microbial community, and attract specific microbes that, through the \u201ccry for help\u201d, improve plant adaptability to low-nitrogen stress. We combined transcriptome, metabolome, and microbiome datasets across various genotypes of poplar roots to establish a comprehensive gene-metabolite-microbe network. By using this multidimensional dataset, the mechanisms of poplar recruits target beneficial microbes and how these microbes affect host fitness were revealed. The function of the key genes was further investigated by using molecular experiments. We highlight how host genes, root exudates, and the rhizobiome have mutual interactions and how these explicit processes affect plant fitness.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To assess the importance of the root microbiome in plant fitness, we performed a pot experiment on nine representative poplar species derived from four sections (Leuce, Aigeiros, Tacamahaca, and Turanga; grown in low nitrogen, unsterilized natural mixed soil; total nitrogen: 0.089%). After three months of growth, eleven phenotypes (plant height, ground diameter, shoot biomass, root biomass, root length, leaf length, leaf width, leaf area, leaf number, chlorophyll content, and leaf nitrogen content) of poplar were detected (Supplementary Fig.\u00a01; Supplementary Data\u00a01). Results indicated that seedlings from Leuce demonstrated the most superior biomass, followed by Aigeiros and Tacamahaca, while Turanga exhibited the lowest (ANOVA, P values\u2009<\u20090.01; Supplementary Fig.\u00a01C, D). The growth parameters varied among species. For example, mean root biomass ranged from 0.56 to 15.76\u2009g, plant height ranged from 27.02 to 129.91\u2009cm, and leaf area ranged from 1.06 to 76.30\u2009cm2 (Supplementary Data\u00a01). Notably, the shoot biomass of the fast-growing P. tomentosa Lumao 50 (LM50) was 14.02 times greater than the slowest-growing P. euphratica H (Peu-H).\n\nTo investigate whether genotype-mediated soil microbiota was involved in shaping disparities in poplar growth, a follow-up soil transplant experiment was conducted on the vigorous LM50 and the poorly performing Peu-H. When Peu-H was transplanted into the soil in which LM50 had previously been grown (LM50-grown soil), Peu-H significantly increased shoot biomass compared with soil in which Peu-H had previously been grown (Peu-H-grown soil; an increase of 27.22%; ANOVA, P values\u2009<\u20090.01; Fig.\u00a01A, B). In contrast, LM50 showed significant growth inhibition when transplanted into Peu-H-grown soil compared with LM50-grown soil (19.58% decrease in shoot biomass; ANOVA, P values\u2009<\u20090.01; Fig.\u00a01A, B). It is noticeable that LM50 produced 7.37\u2009g more shoot biomass than Peu-H in sterilized soil, nevertheless, this discrepancy widened to 8.29\u2009g (Peu-H-grown soil) and 10.26\u2009g (LM50-grown soil), respectively (Fig.\u00a01A, B). A similar trend was also observed in P. alba\u2009\u00d7\u2009P. glandulosa 84K (84K; P values\u2009<\u20090.05; Supplementary Fig.\u00a02A\u2013C). Notably, there were no significant differences in nutrients in soils (bulk soils or rhizosphere soils) previously grown with LM50 and Peu-H (ANOVA; Supplementary Table\u00a01), and root exudates from all genotypes had no significant effect on poplar (84K and Peu-H) biomass in sterilized soil (Supplementary Fig.\u00a02D\u2013I). These results demonstrate that plant-associated microbiota positively influences poplar growth, but the extent of this effect varies depending on plant genotype. Specifically, the soil microbial community shaped by the vigorous genotype was more conducive to plant growth, whereas the promoting effect of the soil microbial community recruited by the less robust genotype was weaker.\n\nA Morphological differences of LM50 and Peu-H transplants in different soils (LM50-grown soil or Peu-H-grown soil). B Plant height and fresh shoot biomass of LM50 and Peu-H transplants in different soils (LM50-grown soil or Peu-H-grown soil). n\u2009=\u2009\u20093 biologically independent samples. Each bar represents the mean\u2009\u00b1\u2009SEM. C Linear discriminant analysis effect size (LEfSe) was performed to identify the rhizosphere bacteria that are differentially represented between the different poplar sections. From the inside to the outside, the sequence is boundary\u2014phylum\u2014class\u2014order\u2014family\u2014genus. Each node represents a species, and the larger the node, the higher the relative abundance. The letters represent different phyla, and the colors indicate that the species is significantly different in the corresponding section (LDA score\u2009>\u20092, two-sided Kruskal\u2013Wallis test, FDR adjusted P values\u2009<\u20090.05). Principal Component Analysis (PCA; P values were calculated by one-way PERMANOVA) and Hierarchical Clustering Analysis (HCA) of the microbiome (D, G; ASV\u2009>\u20092), phenotype (E, H), and transcriptome (F, I; TPM\u2009>\u20090) datasets from the nine poplar species. Different letters indicate significantly different groups (One-way ANOVA, P values\u2009<\u20090.05; P values are shown in the Source Data file). FW, fresh weight. Scale bars: (A) 10\u2009cm. Source data are provided as a Source Data file.\n\nTo evaluate the impact of the different poplar genotypes on the microbiome composition and functional potential, samples of bulk soil and rhizosphere soil were collected. Bacterial community composition across the nine poplar species was investigated for each sample type (bulk soil and rhizosphere soil) using Illumina MiSeq sequencing of the V3\u2013V4 region of the 16S rRNA gene. The annotated taxonomic levels were (Domain, Phylum, Class, Order, Family, and Genus)30. Across sections, Turanga showed the highest Shannon diversity in rhizosphere microbiota, followed by Aigeiros, Tacamahaca, and Leuce (ANOVA, P values\u2009<\u20090.05; Supplementary Fig.\u00a03A). By contrast, there was no significant difference among bulk soil samples.\n\nThe compositional variation of the rhizosphere microbiome among the four sections is driven by significant shifts in the relative abundance of 22 specific bacterial phyla (Linear discriminate analysis effect size \u201cLEfSe\u201d, LDA score\u2009>\u20092, Kruskal\u2013Wallis test, FDR adjusted P values\u2009<\u20090.05; Fig.\u00a01C, Supplementary Fig.\u00a03B, and Supplementary Data\u00a02). At the genera level, we identified 109 specific markers in Turanga, 90 in Aigeiros, 45 in Tacamahaca, and 37 in Leuce (LDA score\u2009>\u20092, Kruskal\u2013Wallis test, FDR adjusted P values\u2009<\u20090.05; Fig.\u00a01C), respectively. We noticed that Nitrosospira (1.07%), Actinomadura (0.03%), and Tumebacillus (0.23%) were highly abundant in the Turanga (Supplementary Data\u00a03). Bacillus (1.61%) and Enterobacter (2.80%) were found to be enriched in Aigeiros. Notably, the 37 marker genera detected in Leuce accounted for 41.15% of the relative abundance, with Pseudomonas having the highest abundance at 13.77%. Together, these results indicate that genotype properties establish root-inhabiting bacterial communities by selecting specific microbial taxa.\n\nTo explore the correlation between poplar gene expression and rhizosphere microbial recruitment, we generated 73.7\u2009Gb of root transcriptomic data across nine poplar species (grown in low nitrogen, unsterilized natural mixed soil). This identified 38,739 expressed genes (TPM\u2009>\u20090). The Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) based on all microbial (amplicon sequence variant, ASV\u2009>\u20092; PERMANOVA, R2\u2009\u2009=\u2009\u20090.52, P values\u2009<\u20090.01; Fig.\u00a01D, G and Supplementary Data\u00a04), phenotypic (PERMANOVA, R2\u2009\u2009=\u2009\u20090.92, P values\u2009<\u20090.01; Fig.\u00a01E, H and Supplementary Data\u00a04), and transcriptomic (PERMANOVA, R2\u2009\u2009=\u2009\u20090.69, P values\u2009<\u20090.01; Fig.\u00a01F, I and Supplementary Data\u00a04) data clearly classified the nine poplar species into four distinct subgroups, each associated with a specific section. This indicates that microbial composition, growth characteristics, and gene expression were significantly affected by genotype. Moreover, functional enrichment analyses revealed that differentially expressed genes (DEGs; |log2FC|\u2009\u2265\u20091, FDR adjusted P values\u2009<\u20090.05; Supplementary Data\u00a05) among the four sections were significantly enriched in functions related to flavonoid metabolism (P values\u2009<\u20090.05; Supplementary Fig.\u00a04 and Supplementary Data\u00a06). Flavonoids play vital roles in the assembly of plant root microbiome communities, such as the roots of Arabidopsis (Arabidopsis thaliana L.) and maize (Zea mays L.)31,32. Thus, we hypothesized that poplar functional genes regulate flavonoid synthesis to mediate changes in rhizosphere microbiome composition and diversity.\n\nNext, we quantified 129 flavonoids from the root samples of nine poplar species, of which 110 (85.27%) were differentially accumulated across at least two species (fold change\u2009\u2265\u20093 or \u2264\u20090.333, P values\u2009<\u20090.05; Supplementary Data\u00a07). To gain further insights into the gene-metabolites-microbiome regulatory network, differential flavonoids were initially classified into six clusters based on their accumulation patterns using the k-means clustering algorithm (Supplementary Data\u00a08). Subsequently, a rigorous correction (Pearson; r\u2009\u2265\u20090.7, P values\u2009<\u20090.01) was employed to screen for DEGs and ASVs that were significantly associated with the flavonoids in each cluster (Fig.\u00a02, Supplementary Fig.\u00a05, and Supplementary Data\u00a08). Across all clusters, a total of 17,698 DEGs and 2579 ASVs were co-expressed with at least one flavonoid. The genes, flavonoids, and microbes within these clusters demonstrated a distinct abundance pattern related to specific sections, such as Turanga (Cluster I), Leuce (Cluster IV), and Aigeiros (Cluster V). Altogether, these results indicate that the trends of gene expression, flavonoid accumulation, and rhizosphere microbial enrichment show significant section specificity.\n\nThe k-means clustering algorithm and Pearson\u2019s correlation analysis (two-sided; r\u2009\u2265\u20090.7, P values\u2009<\u20090.01) divided poplar gene expression profiles (red), flavonoid metabolome expression profiles (blue), and microbiome (ASVs; orange) into six clusters. The X-axis depicts 27 samples from nine poplar species, and the Y-axis depicts the Z-scores standardized for each gene, flavonoid, and ASV. The bold line is the mean. The numbers shown in each box (for example, 3672 genes, 11 flavonoids, and 865 ASVs for Cluster I) come from the number of genes, flavonoids, and ASVs for all 27 samples in each cluster. The numbers on the X-axis represent the samples: Leuce (1\u20133, Pto-M; 4\u20136, 84K; 7\u20139, Pal-Y; 10\u201312, LM50); Aigeiros (13\u201315, H3-1; 16\u201318, 107); Tacamahaca (19\u201321, Pot-M; 22\u201324, Psz-Z); Turanga (25-27, Peu-H). The genes of Cluster I were enriched in Turanga (higher expression than at least one section; |log2FC|\u2009>\u20091, two-sided, FDR adjusted P values\u2009<\u20090.05) and the genes of Cluster IV were enriched in Leuce (higher expression than at least one section; |log2FC|\u2009>\u20091, two-sided, FDR adjusted P values\u2009<\u20090.05). Source data are provided as a Source Data file.\n\nTo investigate whether the co-expression network could provide insights into the gene-flavonoid-microbial regulatory network in poplar, we identified 147 enzyme genes within the network that encode enzymes catalyzing the twelve enzymatic reaction steps of the flavonoid biosynthesis pathway (phenylalanine metabolism, phenylpropanoid biosynthesis, and flavonoid biosynthesis; Supplementary Data\u00a09). Four out of thirteen CHS genes and all three flavonoid 3\u2032-hydroxylase (F3\u2032H) genes were present in Cluster IV, where F3\u2032H plays a pivotal role in catalyzing the conversion of naringenin to eriodictyol and dihydrokaempferol to dihydroquercetin, crucial precursors in the biosynthesis of flavones and flavanols33,34. Correspondingly, eleven (11/21) flavones were observed in Cluster IV. Notably, our findings revealed the presence of 26 basic helix-loop-helix (bHLH) and 38 MYB transcription factors in Cluster IV. Members of the two gene families often synergistically regulate flavonoid biosynthesis35,36. Moreover, two flavonol synthase (FLS)\u00a0genes and seven (7/16) flavonols were highly correlated in Cluster V. In summary, our network data contains a substantial number of genes related to flavonoid synthesis, indicating that the co-expression network facilitates elucidating the genetic mechanisms of microbial recruitment and identifying candidate genes.\n\nTo further enrich putative regulating networks, we specifically focused on Cluster IV and Cluster I, which peaked at the Leuce and Turanga, respectively (Supplementary Fig.\u00a05A\u2013C), as the two sections demonstrated the most contrasting growth performances (Supplementary Fig.\u00a01C, D). Functional enrichment analyses showed genes in Cluster I (enriched in Turanga; |log2FC|\u2009\u2265\u20091, P values\u2009<\u20090.05; Supplementary Data\u00a05) were mainly associated with housekeeping functions such as genetic information processing, ribosome biogenesis, and mismatch repair (P values\u2009<\u20090.05; Supplementary Fig.\u00a06A; Supplementary Data\u00a06). By contrast, genes in Cluster IV (enriched in Leuce; |log2FC|\u2009\u2265\u20091, P values\u2009<\u20090.05; Supplementary Data\u00a05) are involved in energy and matter cycles (carbon fixation in photosynthetic organisms, nitrogen metabolism, and energy metabolism), as well as flavonoid metabolism (phenylalanine metabolism, phenylpropanoid biosynthesis, and flavonoid biosynthesis; P values\u2009<\u20090.05; Supplementary Fig.\u00a06B; Supplementary Data\u00a06). Therefore, we subsequently selected Cluster IV for further analysis.\n\nWe found that the ASVs in Cluster IV predominantly belong to Proteobacteria (234/433, 54.04%) and Bacteroidetes (81/433, 18.71%), with Pseudomonadaceae (62), Chitinophagaceae (22), Xanthomonadaceae (19), and Burkholderiaceae (16) being the most numerous, and these taxa were particularly enriched in the Leuce (LDA score\u2009>\u20092, FDR adjusted P values\u2009<\u20090.05; Supplementary Data\u00a02). Within the metabolic cluster, the flavones, tricin, and apigenin (with their derivatives), were uncovered as the most enriched metabolites in Leuce (ANOVA, P values\u2009<\u20090.01; Fig.\u00a03A). Apigenin could recruit beneficial bacteria such as Rhizobium, Oxalobacteraceae, and Pseudomonas, enhancing the plant\u2019s nitrogen uptake capacity31,37,38. Tricin is structurally similar to apigenin and shares the same KEGG pathway as apigenin (ko00944), suggesting that tricin may have a similar biological function to apigenin (Fig.\u00a03B).\n\nA Apigenin and tricin (including derivatives) heat map in the root systems of different poplar sections. 27 samples correspond to different colors (each color corresponds to one section). Asterisks denote the flavones that were enriched in Leuce (One-way ANOVA, P values\u2009<\u20090.05; P values are shown in the Source Data file). B Chemical structure formulas for apigenin and tricin. C Correlation network of flavonoid-related genes, flavones, and ASVs in Cluster IV. Red color indicates genes detected in the network. Highly correlated associations (two-sided; Pearson; r\u2009\u2265\u20090.7, P values\u2009<\u20090.01) were present. D Correlation network of the top 20 microbial families with flavone modules and gene modules of Cluster IV (two-sided; Mantel test; P values\u2009<\u20090.05). The gene modules (00910 nitrogen metabolism, 00710 Carbon fixation by Calvin cycle, 00400 Phenylalanine, tyrosine, and tryptophan biosynthesis, 00360 phenylalanine metabolism, 00940 phenylpropanoid biosynthesis, and 00941 flavonoid biosynthesis) are modules of KEGG enrichment analysis in Cluster IV. The flavone modules are apigenin (with derivatives) and tricin (with derivatives) of Cluster IV. The node size represents the number of elements included (for example, the flavonoid biosynthesis module has 21 genes). Solid edges indicate positive relationships. Edge thickness denotes the strength of correlations. E Pearson\u2019s correlation between Pseudomonadaceae and the shoot and root biomass of poplars (two-sided). The gray shading around the line represents a confidence interval of 0.95. F Pearson\u2019s correlation (two-sided) between dominant genera and eleven characteristics of poplars. The color of the heat map represents the size of the correlation coefficient. Source data are provided as a Source Data file.\n\nTo determine whether poplar rhizosphere microbiome composition is linked with its transcriptome signature, flavonoid metabolism, and growth performance, we performed detailed analyses of the subnetwork of genes, flavones, microbes, and growth traits. Consistent with our expectations, the expression of genes in the flavonoid metabolism (phenylalanine metabolism, phenylpropanoid biosynthesis, and flavonoid biosynthesis) showed a significant positive correlation with the accumulation of flavones (P values\u2009<\u20090.01; Fig.\u00a03C). The accumulation of flavones was associated with ASVs from Pseudomonadaceae, Burkholderiaceae, Cellvibrionaceae, and Xanthomonadaceae (P values\u2009<\u20090.01), among which Pseudomonadaceae had the highest number of ASVs (58; all ASVs belong to the Pseudomonas). Notably, Pseudomonadaceae was among the top families showing the highest correlation with flavone modules and flavonoid-related gene modules (P values\u2009<\u20090.01; Fig.\u00a03D). Pseudomonadaceae, taxa enriched from 0.86% in the bulk soil to the highest abundance in the Leuce rhizosphere (13.78%), was specifically enriched in Leuce and correlated with poplar growth (ANOVA, P values\u2009<\u20090.01; Fig.\u00a03E and Supplementary Fig.\u00a07A, B). In particular, at the genus level, Pseudomonas, which had been demonstrated to have beneficial potentials in nitrogen fixation, phosphorus solubilization, secretion of growth hormones, and antimicrobial activities39,40, was strongly correlated with plant growth characteristics (P values\u2009<\u20090.01; Fig.\u00a03F and Supplementary Fig.\u00a07C). Overall, these results showed that specific bacteria taxa become enriched as a consequence of Leuce-specific properties and are associated with gene expression, flavonoid accumulation, and plant growth.\n\nWe further isolated eleven Pseudomonas strains from rhizosphere soil samples of Leuce (Pto-M, 84K, Pal-Y, and LM50), and the 16S rRNA genes of ten isolates exhibited highly homologous (>\u00a099%) to ASVs of Cluster IV (Supplementary Data\u00a010). Further characterization showed that seven isolates possessed the capacity for nitrogen fixation and carried the nitrogen fixation (nifH) gene, and eight isolates demonstrated the secretion of indole-3-acetic acid (IAA; Supplementary Fig.\u00a08A\u2013D). Flagellated bacteria, such as pseudomonads, could achieve movement towards plant roots through swarming motility, with the success of this process determining the efficiency of root colonization41. Notably, Pto1, Pto5, and Pto10 enhanced swarming motility in the presence of 5\u2009\u03bcM tricin and 100\u2009\u03bcM apigenin (Fig.\u00a04A). qRT-PCR analysis suggests that flagellar-related genes (motA, fliG, and bifA)37 and biofilm formation-related gene algU were activated42, which may be critical for successful bacterial root colonization (ANOVA, P values\u2009<\u20090.01; Fig.\u00a04B).\n\nA Swarming motility of Pseudomonas strains Pto1, Pto5, and Pto10 on 0.3% agar medium in the presence of either 5\u2009\u03bcM tricin or 100\u2009\u03bcM apigenin. B qRT-PCR assays revealed that tricin and apigenin induce the expression of flagellar-related genes (motA, fliG, flhA, and bifA) and biofilm formation-related genes (algU and rmlD) in pseudomonad (Pto1). The DMSO-treated strain was used as a negative control. n\u2009=\u20093 biologically independent samples. C Pot experiment of inoculating poplar (84K) with pseudomonads in nitrogen-poor soil. D Dry shoot biomass, fresh root biomass, plant height, and leaf nitrogen concentration of poplar (84K) inoculated with pseudomonads in nitrogen-poor soil. n\u2009=\u20093 biologically independent samples. E Growth differences of poplars (84K) inoculated with Pto1 in sterile nitrogen-poor culture medium. Whole plant (left), root (middle), leaf (right). F The secondary root number, secondary root length, and total fresh biomass of poplars (84K) inoculated with Pto1 in sterile nitrogen-poor medium and sterile nitrogen-rich medium. n\u2009=\u20093 biologically independent samples. 10\u2009mM MgSO4 solution was used as a negative control (two-sided Student\u2019s t-test). H Growth differences of wild-type (WT), plt3plt5plt7, and wox11wox12 Arabidopsis seedlings growing on 1/2 MS agar plates with Pto1, IAA, TIBA, Pto1\u2009+\u2009TIBA, or mock. I Quantification of secondary root (SR) number and primary root length in WT, plt3plt5plt7, and wox11wox12 Arabidopsis seedlings under mock, IAA, TIBA, Pto1\u2009+\u2009TIBA, and Pto1 inoculated conditions. n\u2009=\u2009\u2009 5 biologically independent samples. Different letters indicate significantly different groups (One-way ANOVA, P values\u2009<\u20090.05; P values are shown in the Source Data file). Each bar represents the mean\u2009\u00b1\u2009SEM. DW, dry weight; FW, fresh weight. Scale bars: (C) 10\u2009cm; (E) 1\u2009cm; (H) 1\u2009cm. Source data are provided as a Source Data file.\n\nTo investigate the potential of Pseudomonas isolates on poplar fitness, we inoculated three individual strains and constructed synthetic communities (SynComs: Pto1, Pto5, and Pto10). Using 15N isotope labeling, we traced the nitrogen absorbed by poplar from the soil, while nitrogen fixed by microbes from the air remained unlabeled. Inoculated isolates significantly increased the shoot biomass (26.04%\u201348.03%), root biomass (57.51%\u201381.46%), and leaf nitrogen content (7.98%\u201310.15%) of poplar (84K; ANOVA, P values\u2009<\u20090.01; Fig.\u00a04C, D and Supplementary Fig.\u00a09A). Similar trends were also observed in other plant species (P values\u2009<\u20090.01; Supplementary Fig.\u00a010). After inoculation, the 15N ratio of leaves of inoculated isolates decreased, indicating that pseudomonads promoted the nitrogen absorption of poplar through biological nitrogen fixation (BNF; Supplementary Table\u00a02). Consistently, in sterile nitrogen-poor even medium, the number and length of poplar secondary roots (SRs) increased by 9.92 and 2.88 times after inoculation with Pto1, respectively (P values\u2009<\u20090.01; Fig.\u00a04E, F). However, the promoting effects of Pto1 on poplar growth and SR induction were diminished in a medium with sufficient nitrogen supply (Fig.\u00a04F and Supplementary Fig.\u00a09B). These results suggest that the functions of pseudomonads may rely on the cross-talk between specific nitrogen starvation signaling and plant responses.\n\nThe intricate architecture of the root system in dicotyledons has multiple types of SRs and encompassing lateral roots (LRs), adventitious lateral roots (adLRs), which are regulated by distinct genetic pathways43,44,45,46. To elucidate the nature of the induced SRs following inoculation with Pto1, we conducted a structural analysis of the root systems of the plethora 3,5,7 (plt3plt5plt7) Arabidopsis triple mutant, which exhibits compromised LR formation, and the wuschel-related homeobox 11,12 (wox11wox12) Arabidopsis double mutant, displaying deficiencies in adLR and adventitious roots (AR) formation47. In the plt3plt5plt7 mutants inoculated with Pto1, no visible SR was observed at seven days (Fig.\u00a04H, I). Conversely, the wox11wox12 mutants exhibited a significant increase in the number of SRs, comparable to the increase observed in wild-type roots following Pto1 inoculation. When IAA was added to the medium, the development pattern of Arabidopsis roots was similar to that resulting from Pto1 inoculation. However, the auxin inhibitor 2,3,5-triiodobenzoic acid (TIBA) hindered SR growth in all Arabidopsis lines, regardless of the presence of Pto1. These results suggest that Pto1-secreted IAA plays a critical role in inducing PLT3PLT5PLT7-mediated LR pathways in Arabidopsis.\n\nWe conducted an analysis of the co-expression network to identify the regulators associated with flavone biosynthesis, given its significance in microbial recruitment. In Cluster IV, which enriched apigenin and tricin, a member of the bHLH transcription factor family, bHLH1 (i.e., GL3), was identified (enriched in Leuce; ANOVA, P values\u2009<\u20090.01; Supplementary Fig.\u00a011A). It exhibited strong co-expression with flavones and genes related to flavonoid biosynthesis (Supplementary Fig.\u00a011A). DAP-seq experiment revealed that PopGL3 could regulate the transcription of peroxidase 2 (PopPA2), PopF3\u2032H, Phospho-2-oxo-3-deoxyheptonate aldolase (PopDAHP), cinnamoyl coa reductase 1 (PopCCR1), and tetrahydroberberine oxidase (PopTHB), which are involved in flavonoid synthesis (Fig.\u00a05A\u2013C). The constitutive expression of PopGL3 or PopCHS4 could activate the transcription of PopF3\u2032H and flavone synthase (PopFNS) and release more tricin in the rhizosphere of the PopCHS4-OE (chalcone synthase catalyzes the first committed step of the multi-branched flavonoid pathway) and PopGL3-OE lines (P values\u2009<\u20090.01; Fig.\u00a05 E, F, Supplementary Fig.\u00a012, and Supplementary Fig.\u00a013).\n\nA KEGG enrichment analyses of DAP experimental analysis results for PopGL3 (One-sided Fisher exact-test, P values are not adjusted). B A flavonoid-related gene network was established based on the DAP assay results of PopGL3. Pearson correlation coefficient values were calculated for each pair of genes (two-sided). Solid edges indicate positive relationships. Edge thickness denotes the strength of correlations. Asterisks denote the genes that were the result of two repeats of the DAP experiment, and the other genes were the result of one repeat. C Schematic representation of flavonoid biosynthesis and regulation in poplar. The red font indicates genes regulated by PopGL3 based on the DAP assay (results of at least one repeat). D Growth differences between WT, PopCHS4-OE, and PopGL3-OE poplar lines in sterilized or unsterilized nitrogen-poor soil. Gene relative expression level (E), root interior and root exudate flavone concentration (F) of WT, PopCHS4-OE, and PopGL3-OE poplar lines in unsterilized nitrogen-poor soil. n\u2009=\u20093 biologically independent samples. G Dry shoot biomass and leaf nitrogen concentration of WT, PopCHS4-OE, and PopGL3-OE poplar lines in sterilized or unsterilized nitrogen-poor soil. n\u2009=\u2009 3 biologically independent samples. Abundance differences between PopCHS4-OE (H) and PopGL3-OE (I) poplar lines and WT rhizosphere microbiomes at the genus level (two-sided Welch\u2019s t-test, by STAMP). nWT\u2009=\u20093, nPopCHS4-OE\u2009=\u20096, nPopGL3-OE\u2009=\u20096; biologically independent samples. Asterisks indicate significant differences between different groups (two-sided Student\u2019s t-test, ***P values\u2009<\u20090.001, **P values\u2009<\u20090.01, *P values\u2009<\u20090.05, ns: not significant; P values are shown in Source Data\u00a0file). Different letters indicate significantly different groups (One-way ANOVA, P values\u2009<\u20090.05; P values are shown in the Source Data file). Each bar represents the mean\u2009\u00b1\u2009SEM. DW, dry weight; FW, fresh weight. Scale bar: (D) 15\u2009cm. Source data are provided as a Source Data file.\n\nThe contribution of BNF to nitrogen nutrition in different poplar genotypes was determined by the 15N isotope dilution method. Following two months of growth in an unsterilized natural soil mixture (low nitrogen; with a small amount of 15N-labeled ammonium nitrate), PopGL3-OE and PopCHS4-OE plants displayed increased biomass and leaf nitrogen accumulation (P values\u2009<\u20090.01; Fig.\u00a05D, G and Supplementary Fig.\u00a011B, C). Compared with the wild-type, the contribution of BNF by transgenic plant root microorganisms increased (Supplementary Table\u00a03). Conversely, all genotypes grew weakly in sterilized soil, with no difference in biomass production. Amplicon sequencing was used to elucidate the reasons for the differences in BNF among different poplar genotypes. The results indicated that transgenic (PopGL3-OE and PopCHS4-OE) plants reshaped the rhizosphere microbial composition and significantly enriched Pseudomonas (P values\u2009<\u20090.01; Fig.\u00a05H, I, Supplementary Fig.\u00a011D, E, by STAMP; Supplementary Fig.\u00a014, Supplementary Data\u00a011, by ANCOM-BC2).\n\nEvidence from our experiment suggests that the increased abundance of Pseudomonas in the transgenic plants is like due, in part, to the greater absolute depletion of most other bacterial lineages, but does not rule out the positive selection by the transgenic plants through the tricin pathway. To confirm the increased root colonization of Pseudomonas in the transgenic plants, we tagged Pto1 with the red fluorescent protein (RFP) gene and used confocal microscopy to image the colonization of root tissue across various genotypes. We observed significantly enhanced colonization and increased fluorescence density in the roots of PopGL3-OE and PopCHS4-OE plants using confocal microscopy (P values\u2009<\u20090.01; Fig.\u00a06A, B). Additionally, the colony forming units (CFUs) statistics further confirm this conclusion (P values\u2009<\u20090.01; Fig.\u00a06C). In conclusion, PopGL3, a regulator of flavone biosynthesis, recruits Pseudomonas by secreting tricin to promote the growth and nitrogen uptake of poplar. Taken together, these data suggest that in a controlled laboratory setting and in the absence of other microbes, the observed increase in Pseudomonas abundance in the PopGL3-OE plants is accompanied by increased colonization and that this increase is potentially beneficial to poplar fitness.\n\nA Confocal fluorescence imaging of RFP-tagged Pto1 colonizing poplar roots of different genotypes. B The fluorescence intensity of RFP in poplar roots of different genotypes. The fluorescence intensity of the samples was measured by the ImageJ. n\u2009=\u20095 biologically independent samples (two-sided Student\u2019s t-test). C Colonization amount of different genotypes of poplar roots with Pto1. Colony forming units (CFUs) were quantified per fresh weight of roots. n\u2009=\u20095 biologically independent samples (two-sided Student\u2019s t-test). Each bar represents the mean\u2009\u00b1\u2009SEM. Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Given the importance of the rhizomicrobiome in plant development, nutrition acquisition, and stress tolerance, deciphering the molecular regulatory network of plant-microbe interactions could substantially contribute to improving plant yield and quality. Current multi-omics studies of plant-microbial interactions have mostly relied on methods such as WGCNA and MWAS, which are confined to the analysis of these binary transcriptome-microbiome datasets, often failing to effectively find metabolites (or other signaling molecules) that directly shape the structure of plant-microbial communities14,15,16. In this study, a dataset comprising gene expression, metabolic profiling, and microbial community derived from four sections of poplar was generated, constructing a comprehensive gene-flavonoid-microbe co-expression network (Supplementary Fig.\u00a015). The 110 differential flavonoids, 17,698 DEGs, and 2579 ASVs were classified into six co-expression clusters. Among them, DEGs and ASVs, which are closely related to flavonoids, accounted for 45.69% of the total expressed genes and 9.30% of the total rhizosphere ASVs, respectively.\n\nWithin this network, we identified 147 enzyme genes that encode enzymes catalyzing the twelve enzymatic reaction steps of the flavonoid biosynthesis pathway. The MBW ternary complexes containing R2R3-MYB and bHLH transcription factors along with WD-repeat proteins have been reported to regulate the biosynthesis of flavonoids48,49. A total of 97 MYB and 72 bHLH transcription factors were identified, including MYC2 (bHLH), which shifted root microbiota composition to enhance Arabidopsis growth and immunity under shade50. Moreover, we\u2019ve unveiled the pivotal role of flavonoids in shaping the composition of the poplar root-associated microbial community, particularly in their intimate associations with beneficial microbes like Pseudomonas, Bacillus, and Actinobacteriota, known to confer advantages to plant fitness9,41,51. The investigation within the network not only unveils intricate linkages between plant genetic regulation and metabolite synthesis but also elucidates the direct influence of these metabolites on the structure of microbial communities, offering valuable guidance for future experimental designs.\n\nAlthough the clustering patterns of DEGs and ASVs strongly associated with flavonoids are consistent with global genes and microbes, some compounds, such as hormones and terpenoids, were not quantified in our samples due to the limited scope of detection in this study. Additionally, the root endosphere microbiome or fungi were not also tested. When we obtain this information, the number of genes and microbes co-expressed with metabolites is likely to increase further, providing a richer resource for in-depth investigations of the plant genetic networks that regulate the recruitment of microbes by metabolic pathways.\n\nPrevious studies have shown that stressed plants recruit beneficial bacteria to colonize their roots by secreting metabolites, promoting the opposite effects on plant growth and health induced by stress, known as a \u201ccry for help\u201d strategy9,41. Flavonoids, a major category of specialized metabolites in plants, significantly influence plant growth and development and play a critical role in mediating several plant-microbe interactions1,3. For instance, maize FNSI2-mediated apigenin and luteolin have been shown to enhance the abundance of Oxalobacteraceae in the plant rhizosphere, improving host performance under nutrient-limiting conditions31.\n\nUsing the gene-flavonoid-microbe co-expression network, we investigated Cluster IV, which peaked at the fastest-growing Leuce. We found that genes in Cluster IV were significantly enriched in flavonoid metabolism-related pathways, including phenylalanine metabolism, phenylpropanoid biosynthesis, and flavonoid biosynthesis. Notably, Pseudomonadaceae (62) in Cluster IV is the taxa with the most numerous ASVs. The Pseudomonadaceae have been demonstrated to enhance plant growth through the processes of BNF or phosphorus solubilization39. Within the metabolic cluster, flavones such as tricin and apigenin (with their derivatives) were the most abundant and were significantly enriched in Leuce. Correlation analysis revealed that genes related to flavonoid biosynthesis and flavones exhibited the strongest association with Pseudomonadaceae and Pseudomonas, while the increased abundance of Pseudomonadaceae and Pseudomonas was highly correlated with poplar\u2019s growth characteristics.\n\nExperiments have shown that apigenin and tricin (in Cluster IV) enhance the swarming motility and biofilm synthesis of pseudomonad isolates, and this flavone-mediated mechanism significantly promotes the mobility of the pseudomonads at the soil/root interface, favoring the successful colonization of the plant root surface37,41. In Cluster IV, the transcription factor GL3 had strong co-expression with flavones and genes related to flavonoid biosynthesis. GL3 was reported to interact with MYB transcription factors and WD40 repeat proteins to form the MYB-bHLH-WD40 (MBW) transcriptional complex, regulating anthocyanin synthesis48,52. However, the potential roles of GL3 in flavone synthesis and interactions with the rhizosphere microbiome remain unclear. Rhizosphere microbiome analyses of PopGL3-OE and PopCHS4-OE plants, integrating the metabolite profiles of root extracts and secretions, demonstrate the causal role of PopGL3 in tricin secretion and recruitment of Pseudomonas. A series of inoculation experiments with tricin-mediated isolates confirmed their beneficial effects on poplar growth, nitrogen accumulation, and SR growth. In summary, our findings suggest that the poplar GL3 gene regulates tricin synthesis and secretion to call for pseudomonad colonization to help it grow and nitrogen absorption under nutrition-deficient conditions (Fig.\u00a07).\n\nIn nitrogen-poor soil, poplar roots secreted flavone and recruited Pseudomonas to colonize the rhizosphere, thus changing the composition of the rhizosphere microbial community. By secreting auxin IAA, Pseudomonas can induce secondary root formation to promote plant growth and nitrogen absorption indirectly and promote plant growth and nitrogen absorption directly through biological nitrogen fixation.\n\nWe found that LM50 (Leuce) produced 7.37\u2009g more stem biomass than Peu-H (Turanga) in sterile and nitrogen-poor soil. In the unsterilized, nitrogen-deficient soil, this difference widened to 8.29\u2009g (Peu-H-grown soil) and 10.26\u2009g (LM50-grown soil), respectively. Notably, there were no significant differences in nutrients between soils (bulk soils or rhizosphere soils) previously grown with LM50 and Peu-H; root exudates had no significant effect on poplar growth; and bulk soil microbial diversity showed no significant variation. These results indicate that genotype-specific microbiota exerts varying degrees of positive feedback on poplar growth. Each section recruits specific taxa to shape its own rhizosphere microbial community. For instance, the Leuce enriches Pseudomonas to aid in nitrogen uptake and SR growth. The abundance of Pseudomonas in the Leuce, Aigeiros, Tacamahaca, and Turanga is 13.77%, 3.47%, 2.78%, and 2.76%, respectively. Therefore, differences in the host\u2019s ability to \u201ccry for help\u201d to beneficial microorganisms lead to different degrees of feedback from recruited microorganisms on their own fitness. This disparity, possibly regulated by plant genes, signifies that vigorous Leuce elevated the tricin secretion via heightened GL3 expression, driving pseudomonad colonization in the rhizosphere and enhancing growth, nitrogen acquisition, and SR development in nitrogen-poor soil (Fig.\u00a07). Consistent with our finding, plant resistance genes GsMYB10 transgenic soybean recruited Bacillus and Aspergillus, which further enhanced plant resistance to stresses under aluminum (Al) toxicity53. In social psychology, the \u201cMatthew effect\u201d describes the phenomenon that the strong become stronger and the weak become weaker54. We introduce the concept of the \u201cMatthew effect\u201d in plant-microbial interactions. That is, vigorous or resistant plant genotypes can recruit specific microbes to give them more growth advantages or better resistance. Parallelly, this effect may also be reflected in the interaction between microbes and roots. Root caps and root hairs serve as crucial determinants for the assembly process of the rhizosphere microbiome55,56. As the quantity and length of SR increase, the spatial distribution of plant-secreted nutrients and metabolites also expands, enhancing the plant\u2019s regulatory influence over the rhizosphere microbial community. Conversely, the rhizosphere\u2019s available area for microbial colonization expands with the development of SRs, prompting enhanced beneficial activities by microorganisms toward the plant. This is consistent with previous studies showing that the assembly process of plant rhizosphere microorganisms is closely related to plant root structure57,58,59.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56226-w/MediaObjects/41467_2025_56226_Fig7_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Nine species of poplars from four sections (Supplementary Table\u00a04), Leuce (Populus tomentosa\u2009\u00d7\u2009P. bolleana M \u201cPto-M\u201d, P. alba\u2009\u00d7\u2009P. glandulosa 84K \u201c84K\u201d, P. alba\u2009\u00d7\u2009P. glandulosa Y \u201cPal-Y\u201d, P. tomentosa Lumao 50 \u201cLM50\u201d), Aigeiros (P. euramericana 74/76 \u201c107\u201d, P. euramericana H3-1 \u201cH3-1\u201d), Tacamahaca (P. trichocarpa M \u201cPot-M\u201d, P. szechuanica Z \u201cPsz-Z\u201d), and Turanga (P. euphratica H \u201cPeu-H\u201d), were examined in this study. Tissue culture plantlets of poplar clones (84K, Psz-Z, Pot-M, and Peu-H) were maintained in our laboratory, while the remaining five species were collected from the GuanXian state-owned P. tomentosa forest farm in Shandong Province, China (E: 115\u00b022\u20328\u2032\u2032, N: 36\u00b030\u203254\u2032\u2032) to acquire sterile monoclonal tissue culture seedlings.\n\nThe soils of Leuce, Aigeiros, and Tacamahaca were collected from the plantations of P. tomentosa, P. euramericana, and P. simonii in the GuanXian state-owned forest farm, respectively, while the soil of Turanga was collected from the natural forest of P. euphratica in Danglang tribe, Aksu, Xinjiang (E: 80\u00b015'18\u201d, N: 40\u00b045'39\u201d; Supplementary Table\u00a05). Notably, the poplars in these forests were more than 15 years old and had never been fertilized. At a distance of 2\u20133\u2009m around the poplars, we collected 10\u201340\u2009cm of soil after removing the surface 10\u2009cm of soil. Each forest soil is taken from at least five poplars, and all soil samples collected from each site are mixed.\n\nTo provide more abundant microorganisms for different genotypes of poplar, four parts of soil were mixed in equal volumes and thoroughly stirred. Subsequently, tissue culture seedlings of the nine poplar species were simultaneously transplanted into the mixed soil for pot experiments, ensuring at least five biological replications per species while randomly situating all poplar samples. They were grown in the same environment (phytochamber conditions: 25\u2009\u00b0C; 16\u2009h day/8\u2009h night light cycle) for 3 months, and water poured every 2 days. In order to ensure the normal growth of the seedlings, we used 1\u2009g fertilizer (Huaduo 1, China; N: 20%, P: 20%, and K: 20%) in 1\u2009L sterile water to fertilize after transplanting and watered 300\u2009ml per pot of poplar, not again in the later period.\n\nOn the day of destructive sampling, we examined eleven representative characteristics of nine poplar species, encompassing plant height, ground diameter, shoot biomass, root biomass, root length, leaf length, leaf width, leaf area, leaf number, chlorophyll content, and leaf nitrogen content (Supplementary Data\u00a01). Since the root and rhizosphere soil of one Peu-H plant were insufficient for subsequent sequencing experiments, the two individual plants were treated as one biological replicate, and the one biological replicate of all phenotypic data for Peu-H was the average of the two biological plants. For other poplar species, three plants of comparable growth were selected as biological replicates. Leaf length and width of the third, fourth, and fifth completely expanded leaves at the top were measured, and the leaf area was calculated by ImageJ (v.1.53q)60 analysis. Leaves were defined as fully expanded if the leaf length was more than 4.0\u2009cm. Chlorophyll content was determined as the average of 20 measurements with a chlorophyll meter (SPAD-502 Plus, Konica Minolta, Japan) in the middle third of the leaf in the longitudinal direction. The complete above-ground part of the plant was harvested, and fresh biomass was determined.\n\nEach plant\u2019s rooting system was subsampled for the assessment of multiple response variables: root metabolomics for flavonoids metabolite analysis, root transcriptome analysis, and rhizosphere soils for 16S rRNA amplicon-based sequencing. In order to focus analyses on the most active roots61, only fine roots (<\u00a02\u2009mm in diameter) were utilized for these analyses. For metabolomics and transcriptomics, roots were quickly rinsed in deionized water and frozen in liquid nitrogen immediately. For rhizosphere soil, the qualified roots were collected in a 50-ml centrifuge tube containing 30\u2009ml of sterile Phosphate Buffer Saline (PBS) buffer (pH 7.0, per liter: 6.33\u2009g NaH2PO4\u00b7H2O, 16.5\u2009g Na2HPO4\u00b77H2O, and 200\u2009\u03bcl Silwet L-77) and stored on ice for further processing in the laboratory. For each plant, bulk soil samples were collected from around the root system and frozen in liquid nitrogen immediately. Rhizosphere samples were extracted from the corresponding root segments. Centrifuge tubes with samples were shaken for 30\u2009min at 50\u2009rpm in a constant-temperature shaker incubator, and the shaking step was repeated twice. Afterwards, rhizosphere samples were centrifuged for 10\u2009min at 4,500\u2009g at 4\u2009\u00b0C. The supernatant was then removed, and sterile water was added to resuspend the soil. Finally, the samples were frozen in liquid nitrogen and stored at \u221280\u2009\u00b0C. All samples were stored at \u221280\u2009\u00b0C until processed.\n\nTo investigate the effect of poplar genotypes on the nutrient acquisition capacity of root microbiota, we examined the growth status of LM50 and Peu-H under nitrogen-poor conditions when subjected to reciprocal root microbiota inoculation. The soil transplantation experiment was carried out as previously described with minor modifications31. Firstly, sterile-cultured seedlings of LM50 and Peu-H were transplanted into mixed natural soil for pot cultivation, with a normal growth of 8 weeks. During transplantation, rhizosphere soils from poplar (LM50 and Peu-H) were collected according to the described method, and the rhizosphere soil suspension was reintroduced into the original pots from which the plants were uprooted. Subsequently, LM50, Peu-H, and 84K sterile-cultured seedlings were individually transplanted into pots containing soil (bulk soil and rhizosphere soil) corresponding to either the LM50 or Peu-H genotype, with three replicates for each treatment. As a control, sterile PBS buffer was used to reintroduce sterilized soil. For soil sterilization, a uniform mixture of soil that was used for Peu-H and LM50 cultivation was subjected to high-pressure sterilization at 121\u2009\u00b0C for 60\u2009min, followed by cooling at room temperature for at least 24\u2009h. Plant growth was monitored after transplantation, and after a continuous growth period of 8 weeks, both the shoot and root biomass of the poplar were measured.\n\nThe basic nutrients in rhizosphere soils and bulk soils of LM50 and Peu-H were measured. The soil chemical traits, including total nitrogen, available phosphorus, and soil organic matter, were measured according to standard protocols62. Available potassium was analyzed by the flame photometer method63.\n\nTo demonstrate how different types of poplar root exudates affect poplar growth, we collected LM50 and Peu-H exudates using sterile hydroponics and applied to soil pots. Initially, both LM50 and Peu-H were rooted in solid rooting media, growing roots of 1\u20132\u2009cm after 10 days. The root systems were carefully removed from the solid medium and placed at the center of a custom-made sterile hydroponic setup (Supplementary Fig.\u00a016) containing the same volume of liquid medium (1/2 MS, no sucrose), with 10 replicates for each genotype. After 20 days, poplar developed hydroponic root systems, root exudates were collected and passed through a 0.2-\u00b5m filter. All root exudates of each genotype were mixed separately and stored at \u221280\u2009\u00b0C. Subsequently, sterile-cultured seedlings of 84K and LM50 were transplanted into sterilized soil for pot cultivation, and 20\u2009ml of various types of root secretions were applied to the soil pots every 2 weeks. 1/2 MS sterile (no sucrose) solution of uncultured plants was used as a control. After growing continuously for 8 weeks (LM50 grows for 6 weeks), the shoot and root biomass of the poplar were measured.\n\nTo unveil the intricate genetic regulatory network of poplar mediating microbial composition, the R package cluster (v.2.1.4) with the k-means method was used to analyze the co-expression/co-regulation of flavonoids in root samples of nine poplars. Rigorous Pearson\u2019s correlation analysis was performed to identify the DEGs and ASVs (r\u2009\u2265\u20090.7, P values\u2009<\u20090.01) that were significantly associated with each flavonoid using the WGCNA (v.1.71) package.\n\nCorrelation analysis of Mantel tests was performed using the vegan (v.2.6-2) package between modules and microbiological families. The networks were visualized using Cytoscape (v.3.9.1)64.\n\nThe swarming motility experiment followed previously described with minor modifications37. Briefly, Pseudomonas strains were cultured in liquid King\u2019s B (KB) medium for 12\u2009h until reaching a turbidity of 1.0 at 600\u2009nm. Tricin and apigenin were separately prepared as 1\u2009mM and 10\u2009mM stock solutions in DMSO. Flavones at final concentrations of 1, 3, 5, 10, 20, 30, 50, and 100\u2009\u03bcM were added to semi-solid Luria-Bertani (LB) medium containing 0.3% (w/v) agar in proportion to the volume. DMSO was added in equal volume to the negative control and then used after condensation. Data were collected at 12\u2009h after inoculation. Each experiment was performed using three independent agar plates.\n\nThe cultured Pto1 cells were harvested by centrifugation at 4\u2009\u00b0C. Total RNA was extracted with Trizol and chloroform. The first-strand cDNAs were synthesized using a GoScript\u2122 Reverse Transcription System (Promega, USA). For real-time PCR analysis, gene-specific primers listed in Supplemental Data\u00a012 were used. Each PCR reaction (20\u2009\u03bcl) contained 10\u2009\u03bcl of SYBR Premix ExTaq (TaKaRa, Japan), 1\u2009\u03bcl of cDNA samples, and 200\u2009nM primers. The reactions were performed on a 7500 Fast Real-Time PCR System (Applied Biosystems, USA). The thermocycling conditions were 95\u2009\u00b0C for 30\u2009s and 40 cycles of 95\u2009\u00b0C for 5\u2009s, 60\u2009\u00b0C for 30\u2009s. Amplification specificity was assessed using a melting curve analysis. The cDNA of the DMSO-treated strain was used as a template for the negative control. The DAPDH gene was used to normalize the data, and at least three biological replicates were performed.\n\nTo explore the potential of pseudomonads in enhancing plant growth and nitrogen uptake under nitrogen-deficient conditions, we inoculated Pseudomonas isolates into soil pots and sterile tissue culture plant roots, respectively. For soil potting, natural mixed soil was subjected to high-pressure sterilization at 121\u2009\u00b0C for 60\u2009min. The soil was left to cool at room temperature for at least 24\u2009h before sterile-cultured poplar seedlings were transplanted into the sterilized soil for pot cultivation. Wheat (Triticum aestivum L.) and radish (Raphanus sativus L.) seeds were surface-sterilized with a 4% NaClO solution (v/v) for 15\u2009min, washed in sterile water three times for 15\u2009min, and seeded into the sterilized natural soil for germination. Pseudomonas strains (Pto1, Pto5, and Pto10) were cultured in KB liquid medium overnight at 28\u2009\u00b0C, then centrifuged and resuspended in 1\u2009\u00d7\u2009PBS buffer to an OD600 of 0.2. Each strain, or the simple SynComs (Pto1, Pto5, and Pto10), was inoculated onto three seedlings every 2 weeks, with an inactivated heavy suspension as a control. After 8 weeks of growth (wheat and radish were 6 weeks), the shoot and root biomass of plants were measured.\n\nFor aseptic tissue culture, the low-nitrogen medium consisted of 1/2 Murashige & Skoog (MS; 300\u2009mg/L NH4NO3), while the normal nitrogen medium contained 1/2 MS (1650\u2009mg/L NH4NO3). Bacterial cultures (Pto1) were centrifuged, washed, and resuspended in 10\u2009mM MgSO4, adjusted to OD600\u2009=\u20091.0. Seedlings of 84K poplar were grown for 10 days until the root length reached 1\u20132\u2009cm, and the resuspended bacterial solution was dropped on the root. 10\u2009mM MgSO4 solution was used as a negative control. Shoot and root biomass was determined after 20 days of growth, while SR density was calculated manually by the number of SRs of the main root.\n\nArabidopsis thaliana L. cv Columbia-0 (Col0) was employed as a wild-type (WT). The utilized Arabidopsis mutants included plt3plt5plt7 and wox11wox1247. Arabidopsis seeds underwent surface sterilization using 3% (v/v) sodium hypochlorite and were planted on plates containing 1/2 MS medium supplemented with 5\u2009g/L sucrose. After 1 week of growth, transfer to the new medium (CK or 10\u2009\u03bcM 2,3,5-Triiodobenzoic acid \u201cTIBA\u201d, or 0.1\u2009mg/L indole-3-acetic acid \u201cIAA\u201d) and place the above 10\u2009mM MgSO4 suspensions on the roots of Arabidopsis. The SR numbers were counted after 7\u201310 days of co-culture.\n\nTotal RNA was extracted from the root tissues of 84K. The frozen roots were fully pulverized using liquid nitrogen, and the RNA molecules were then meticulously extracted via the utilization of the RNeasy Mini Kit (Qiagen, Germany) following the instructions provided by the manufacturer. The first-strand cDNA synthesis is described for bacterium samples. The full-length coding sequences (CDS; Supplementary Data\u00a013) of the target genes were cloned from the 84K cDNA library template using the specific primer (Supplementary Data\u00a012), and the CDSs were linked to the pBI121-eGFP overexpression vector by One-step cloning technology. Plasmids containing the correct insertion were introduced into Agrobacterium tumefaciens strain GV3101. The transformation of 84K was carried out following the previously described transformation method for calli65. After obtaining antibiotic-resistant seedlings, PCR identification was performed using two primers to determine whether the target genes were inserted into the 84K genome. DNA was extracted using the Plant Genomic DNA Kit (TIANGEN, China) following the instructions provided by the manufacturer. The first set of primers were the sequences at both ends of the polyclonal site inserted by the target gene. To eliminate GV3101 contamination, a second set of primers was designed based on the GV3101 virulence gene VirD2 sequence (Supplementary Data\u00a012). The first group of primers has DNA bands, and the second group of primers without bands are transgenic lines. At least 10 independent lines were obtained for each gene, and the optimal lines were selected for further investigation based on target gene expression levels and target traits.\n\nFresh roots of transgenic lines and WT were harvested and rapidly ground into a fine powder using liquid nitrogen. Total RNA was extracted as described above. The RNA concentration and the 260/280\u2009nm ratio were determined using NanoDrop 2000. The first-strand cDNA synthesis and real-time PCR analysis as described for bacterium samples. The 18S gene (Primer sequences in Supplemental Data\u00a012) was used to normalize the data, and at least three biological replicates were performed.\n\nThe pre-cultured Pto1 was transferred to a new liquid LB medium at a volume ratio of 1:1000. The culture was incubated at 28\u2009\u00b0C for 20\u201324\u2009h to a turbidity of 1.8\u20132.0 at 600\u2009nm. Then, 1\u2009ml of bacterial solution was collected in a sterile centrifuge tube and centrifuged at 4\u2009\u00b0C and 13,500\u2009g for 1\u2009min. After decanting the supernatant, 1\u2009ml of pre-chilled sterile ddH2O was added and gently suspended to wash the cells. Centrifuge for 1\u2009min and then wash again. Centrifuge for 2\u2009min and wash for the last time. Centrifuge for 3\u2009min and pour out the supernatant. Add 100\u2009\u03bcl of pre-cooled sterile ddH2O and gently suspend the cells for future use.\n\nThen, 5\u2009\u03bcl of the plasmid (carried RFP tag) was added to the competent cells and gently mixed. The mixture was subsequently added to a pre-chilled electroporation cuvette (inner groove width 2\u2009mm) and incubated on ice for 10\u2009min. The MicroPulser electroporator (BIO-RAD, USA) was used for the conversion, with a voltage of 2.5\u2009KV and a time of 5.0\u2009ms. The mixture was rinsed with 500\u2009\u03bcl of LB culture solution and transferred to a new centrifuge tube. Water bathed at 28\u2009\u00b0C for 1\u2009h, the mixture was evenly spread onto solid LB medium containing antibiotics and incubated at 28\u2009\u00b0C. Positive monoclonal clones were identified using PCR and selected for subsequent experiments.\n\nTo confirm the colonization behavior of Pto1 in the poplar rhizosphere, it was tagged with an RFP tag. Following the hydroponic cultivation method described above, both transgenic and wild-type poplars were grown in a sterile, equal-volume 1/2 MS solution (10\u2009g/L sucrose), with five replicates for each genotype. After 20 days, 200\u2009\u03bcl of Pto1-RFP suspensions (resuspended in 10\u2009mM MgSO4; OD600\u2009=\u20090.5\u20130.6) was added for co-cultivation. After 2 days, the RFP fluorescence signal was observed using a laser-scanning confocal microscope (Leica TCS SP8, Germany). The fluorescence intensity of the samples was measured by the ImageJ (v.1.53q).\n\nFollowing the hydroponic cultivation method described above, both transgenic and wild-type poplars were grown in a sterile, equal-volume 1/2 MS solution (10\u2009g/L sucrose), with five replicates for each genotype. After 20 days, 200\u2009\u03bcl of Pto1 suspensions (resuspended in 10\u2009mM MgSO4; OD600\u2009=\u20090.5\u20130.6) were added for co-cultivation. After 2 days, the roots soaked in the culture solution were collected, and the surface moisture was drained with sterile paper. The roots were collected in pre-weighed tubes and fresh weight was recorded. Samples were machine-homogenized by TissueLyser (Retsch) at a frequency of 25\u2009Hz. A continuous diluent was prepared with a PBS solution and spread on KB agar plates. After overnight culturing at 28\u2009\u00b0C, bacteria colonies were counted and expressed as CFU/mg root fresh weight.\n\nThe observed significant differences in the variance of parameters were evaluated using two-sided Student\u2019s t-tests (for two groups) or one-way ANOVA (for more than two groups). We performed PCA for rhizosphere bacterial community composition, phenotype, and root transcriptome data using the prcomp package in R software (v.4.1.3), and used the hclust package for HCA. Statistical significance of differences between poplar genotypes was assessed by PERMANOVA using the vegan (v.2.6-2) package. No specific statistical methods were used to predetermine the sample size. No data were excluded from analysis in any of the experiments. In the same experiment, all plant materials were exposed to the same growth conditions, and plants of different genotypes or treatments were randomly placed. The investigators were blinded to allocation during all experiments and outcome assessments.\n\nAll other methods used in this study are described in the Supporting Information (Supplementary Methods 1\u20137).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The raw amplicon data of four poplar sections are publicly accessible in the Genome Sequence Archive of the Beijing Institute of Genomics BIG Data Center, Chinese Academy of Sciences, under CRA015093. The raw root transcriptome data of four poplar sections are publicly available under CRA015096. The raw amplicon data for transgenic poplars and wild types are publicly available under CRA015469. The raw DAP-seq data for the PopGL3 gene are publicly available under CRA015475. 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We thank Professors Viola Willemsen and Huchen Li for sharing the plt3plt5plt7 and wox11wox12 mutants.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Jiadong Wu, Sijia Liu.\n\nState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, PR China\n\nJiadong Wu,\u00a0Sijia Liu,\u00a0Haoyu Zhang,\u00a0Sisi Chen,\u00a0Jingna Si,\u00a0Lin Liu,\u00a0Yue Wang,\u00a0Shuxian Tan,\u00a0Yuxin Du,\u00a0Zhelun Jin,\u00a0Jianbo Xie\u00a0&\u00a0Deqiang Zhang\n\nNational Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, PR China\n\nJiadong Wu,\u00a0Sijia Liu,\u00a0Haoyu Zhang,\u00a0Sisi Chen,\u00a0Jingna Si,\u00a0Lin Liu,\u00a0Yue Wang,\u00a0Shuxian Tan,\u00a0Yuxin Du,\u00a0Zhelun Jin,\u00a0Jianbo Xie\u00a0&\u00a0Deqiang Zhang\n\nKey Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, PR China\n\nJiadong Wu,\u00a0Sijia Liu,\u00a0Haoyu Zhang,\u00a0Sisi Chen,\u00a0Jingna Si,\u00a0Lin Liu,\u00a0Yue Wang,\u00a0Shuxian Tan,\u00a0Yuxin Du,\u00a0Zhelun Jin,\u00a0Jianbo Xie\u00a0&\u00a0Deqiang Zhang\n\nThe Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, Beijing Forestry University, Beijing, PR China\n\nJiadong Wu,\u00a0Sijia Liu,\u00a0Haoyu Zhang,\u00a0Sisi Chen,\u00a0Jingna Si,\u00a0Lin Liu,\u00a0Yue Wang,\u00a0Shuxian Tan,\u00a0Yuxin Du,\u00a0Zhelun Jin,\u00a0Jianbo Xie\u00a0&\u00a0Deqiang Zhang\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nD.Z. and J.X. designed the experiments. J.W., S.L., H.Z., J.S., and Y.W. collected and analyzed the data. J.W., S.L., H.Z., and Y.W. performed the experiments. J.W. and J.X. wrote the manuscript. H.Z., S.C., S.T., Y.D., L.L., and Z.J. revised the manuscript. D.Z. and J.X. obtained funding and are responsible for this article. All authors read and approved the manuscript. J.W. and S.L. contributed equally to this work.\n\nCorrespondence to\n Jianbo Xie or Deqiang Zhang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous, reviewer(s) for their contribution to the peer review of this work. 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Flavones enrich rhizosphere Pseudomonas to enhance nitrogen utilization and secondary root growth in Populus.\n Nat Commun 16, 1461 (2025). https://doi.org/10.1038/s41467-025-56226-w\n\nDownload citation\n\nReceived: 13 March 2024\n\nAccepted: 13 January 2025\n\nPublished: 07 February 2025\n\nVersion of record: 07 February 2025\n\nDOI: https://doi.org/10.1038/s41467-025-56226-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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actin filaments contribute to peripheral widening in developing stereocilia", + "journal": "Nature Communications", + "published": "01 July 2025", + "supplementary_0": [ + { + "label": "Supplementary information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60976-y/MediaObjects/41467_2025_60976_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60976-y/MediaObjects/41467_2025_60976_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60976-y/MediaObjects/41467_2025_60976_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60976-y/MediaObjects/41467_2025_60976_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.6084/m9.figshare.28680503", + "/articles/s41467-025-60976-y#Sec40" + ], + "code": [], + "subject": [ + "Actin", + "Developmental biology", + "Hair cell" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5448262/v1.pdf?c=1751454557000", + "research_square_link": "https://www.researchsquare.com//article/rs-5448262/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60976-y.pdf", + "preprint_posted": "04 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "In the auditory and vestibular systems, stereocilia are actin-based protrusions that convert mechanical stimuli into electrical signals. During development, stereocilia elongate and widen by adding filamentous actin (F-actin), attaining their mature shape necessary for mechanosensitive function. Myosin motors, including MYO3A/B and MYO15A, are required for normal stereocilia growth, but the regulation of actin and the impact of myosins on actin assembly remain unclear. We focused on stereocilia widening, which requires the addition of new filaments to the bundle of linear F-actin comprising the initial stereocilia core. Our findings revealed that newly expressed actin incorporates at the stereocilia tip first, then along the shaft to promote stereocilia widening. The newly incorporated F-actin surrounded the existing F-actin core, suggesting that the core is stable once formed, with additional actin adding only to the periphery. To better understand the nature of incorporating actin, we used several probes to detect globular (G-) actin, F-actin barbed ends, and F-actin pointed ends. While F-actin core filaments are parallel and thought to present only barbed ends at stereocilia tips, we also detected F-actin pointed ends, indicating a previously undetected population of short actin filaments. Overexpression of actin resulted in abundant F-actin pointed and barbed ends along the periphery of the stereocilia shaft, suggesting that short actin filaments contribute to stereocilia widening. Short actin filament levels correlated with the levels of MYO3A/B and MYO15A at stereocilia tips, suggesting these myosins generate or stabilize short actin filaments essential for stereocilia widening and elongation.Biological sciences/Cell biology/Cytoskeleton/ActinBiological sciences/Neuroscience/Auditory system/Hair cell", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Stereocilia, the actin-based mechanosensory protrusions of inner ear sensory hair cells, require precise dimensional control for proper mechanotransduction, yet the mechanisms governing actin assembly during development remain unclear. Their size and shape are determined by a stable core of long, parallel, unbranched filamentous (F-) actin. We find that during stereocilia widening, which is a key process for function and stability, newly expressed actin first integrates at the tip, then along the periphery of the core. To understand how actin assembles, we probe for globular (G-) actin, F-actin barbed ends, and pointed ends, and identify a tip-enriched population of short actin filaments. Overexpressing actin increases these filaments at the stereocilia periphery, suggesting a role in widening. In addition, the tip-localized myosins MYO3A/B and MYO15A, essential for normal growth, generate or stabilize short filaments. We propose that these short filaments are intermediates that mature into the long F-actin known to comprise the stereocilia core.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Stereocilia are giant microvilli-like protrusions on sensory hair cells that are specialized to detect sound as well as linear and angular acceleration in the inner ear. The mechanosensitive function of hair cells relies on stereocilia that are organized into rows of decreasing heights, which in the case of cochlear hair cells can detect nanometer-scale deflections induced by sound. Similar to microvilli, stereocilia are built around a filamentous actin (F-actin) core where hundreds of filaments are packed together and uniformly oriented with their fast-growing barbed ends towards the protrusion tip1,2. Regulation of the growth and stability of these F-actin bundles is crucial for stereocilia development and maintenance.\n\nEarly in development, stereocilia resemble microvilli before undergoing stages of growth where they lengthen or widen. These growth stages have been best described in mouse auditory inner hair cells (IHCs)3,4, which are the cochlear hair cell subtype that transmits sound information via afferent innervation to the central nervous system. In stage I, microvilli emerge on the IHC apical surface, then a subset of those microvilli elongate in stage II to form stereocilia that are arranged in rows of different lengths. Stereocilia then widen during stage III, which corresponds to postnatal days 0-8 for IHCs in the apical turn of the mouse cochlea. Finally, in stage IV, stereocilia in the tallest row, referred to as row 1, elongate to their final length4 to produce the mature stereocilia bundle with a staircase-like morphology (Fig.\u00a01a). These observations suggest actin behaves differently in each growth stage to either lengthen or thicken the F-actin bundle, which dictates stereocilia shape and ultimately hair cell mechanosensitivity.\n\na Diagrams of inner hair cell (IHC) stereocilia, oriented en face in a 3D view and as a side view. b A single P6 IHC imaged at timepoints (from 2 to 18\u2009h) after transfection with EGFP-actin. Left panels are 3D reconstructions oriented en face except for the final image, which is a top-down view (scale bar represents 5\u2009\u03bcm). Right panels are side views of stereocilia made from x-z reslices (scale bar represents 1\u2009\u03bcm). The outline of a row 1 stereocilium is traced by yellow dashed lines and the cuticular plate is denoted by blue dashed lines. The lowest panel is an x-y slice showing stereocilia in cross-section mid-way down row 1 stereocilia. The inset (1 \u00d71\u2009\u03bcm), a magnified region marked by a red box, demonstrates peripheral EGFP-actin localization around row 1 stereocilia. c P5 IHC 18\u2009h post EGFP-actin transfection imaged by expansion microscopy, stained with antibodies against \u03b2-actin (ACTB) (magenta) and EGFP (green). Panels show a 3D reconstruction (Scale bar represents 10\u2009\u03bcm), a x-z reslice through the center of stereocilia, and a top-down view of x-y slice (Scale bars represent 2 \u03bcm). Yellow and blue arrows indicate row 1 and row 2, respectively. d Lattice SIM images of EGFP-actin or EGFP-FSCN2 (green and grey) from P5 IHCs 18\u2009h post transfection with phalloidin-stained F-actin (magenta). Scale bar represents 5\u2009\u03bcm. b\u2013d The experiments were repeated three times with similar results.\n\nSeveral studies have documented actin behavior in developing and mature stereocilia2,5,6,7,8,9, but have not converged on a clear understanding of how actin assembles to produce the correct stereocilia core dimensions. Early studies2,5 assessed EGFP-actin localization in fixed cells at timepoints after transfection and observed that EGFP-actin incorporated at stereocilia tips first, and then seemed to progress down the stereocilia shaft with time. These observations were interpreted as evidence of actin treadmilling, such that the stereocilia actin core underwent continuous renewal where actin monomers added at the tips and were released at the bases. Subsequent studies6,7,8,9 using a variety of approaches confirmed that stereocilia actin incorporation is most evident at stereocilia tips, and further demonstrated that actin addition at tips can drive elongation. However, these studies also demonstrated that the stereocilia actin core was highly stable once formed, thus contradicting the treadmilling model. Absent treadmilling, it is unclear how actin assembly at stereocilia tips contributes to stereocilia widening.\n\nStereocilia lengthening and widening is known to depend on different tip-localized unconventional myosin complexes. MYO15A is a core component of the elongation complex, so named because IHCs lacking any member of the complex have exceptionally short stereocilia10,11,12,13,14. The elongation complex localizes to row 1 and row 2 tips in early postnatal development, and more exclusively to row 1 tips during stage IV growth. The paralogs MYO3A and MYO3B also localize to both row 1 and row 2 stereocilia tips15,16,17; however, their loss results in stereocilia that are longer and thinner than normal stereocilia16, suggesting the myosin-III proteins promote widening at the expense of elongation. While MYO15A, MYO3A, and MYO3B play pivotal roles in stereocilia growth, it is unclear how they regulate actin behavior to promote elongation or widening. Stereocilia widening is particularly puzzling because there is not a known mechanism by which actin incorporation at stereocilia tips can lead to the addition of new F-actin to the seemingly stable stereocilia actin core.\n\nWe investigated how stereocilia widen, first by using high-resolution live-cell imaging of transfected IHCs to visualize actin incorporation in widening stereocilia. Consistent with prior work, fluorescent actin first incorporated at stereocilia tips before extending to the stereocilia shaft. Rather than a treadmilling mechanism, the new actin surrounded the existing, stable actin core, suggesting that new filaments incorporate first at stereocilia tips and then along the stereocilia shaft to produce new, peripheral core filaments. We then used actin binding proteins as probes to reveal that G-actin, as well as F-actin barbed and pointed ends, are all detectable at stereocilia tips. Discovering F-actin pointed ends at stereocilia tips was unexpected because F-actin in the stereocilia core is thought to be uniformly oriented with only barbed ends at tips1. This discrepancy suggests that there is a population of short actin filaments at stereocilia tips. The level of this actin species depends on MYO15A and MYO3A/B, suggesting that short actin filaments serve as intermediates in myosin-dependent stereocilia actin assembly.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60976-y/MediaObjects/41467_2025_60976_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "To understand how widening occurs within stereocilia, we revisited the question of EGFP-actin incorporation by imaging individual P6 IHCs at intervals after transfection using high-resolution Airyscan microscopy. In these experiments, EGFP-actin invariably incorporated at the tips of stereocilia first, and then subsequently appeared along the stereocilia shaft (Fig.\u00a01b). In contrast to the treadmilling model2,5, we observed that EGFP-actin added to the periphery of the stereocilia shaft, but not to the preexisting F-actin core. This pattern of addition was particularly evident 18\u2009h after transfection in longitudinal slices showing the entire stereocilia length, and in cross sections in the middle of the stereocilia where EGFP-actin appeared as a ring (Fig.\u00a01b, top-down view). To further define the spatial localization of transfected EGFP-actin, we also imaged samples that were fixed 18\u2009h after transfection at higher resolution using both expansion microscopy\u00a0(U-ExM) (Fig.\u00a01c) and lattice structured illumination microscopy (lattice-SIM) (Fig.\u00a01d). As with live cells, the most typical incorporation pattern was a bright cap of EGFP-actin at the stereocilia tip and a thin ring of EGFP-actin around the stereocilia shaft (Fig.\u00a01c, d). Culturing P5 IHCs for 48\u2009h after EGFP-actin transfection produced a similar, peripheral incorporation pattern, consistent with the existing core being stable once formed (Supplementary Figs.\u00a01a, b). We also transfected P3 IHCs, and in this case EGFP-actin incorporation was also peripheral, but less evident, suggesting the stable F-actin component of the core is thinner earlier in development (Supplementary Fig.\u00a01c, d). In contrast to EGFP-actin, transiently expressed fascin-2 actin crosslinker (EGFP-FSCN2), imaged with lattice-SIM, infiltrated into the existing core of P6 IHC stereocilia (Fig.\u00a01d, right panel), which is consistent with a previously documented EGFP-FSCN2 turnover pattern18 and shows that the core is accessible to proteins similar in size to actin. Together, these findings demonstrate new EGFP-actin incorporates first at the tip, then surrounds the existing stereocilia F-actin core.\n\nActin incorporation at stereocilia tips is thought to result from polymerization at the barbed ends of the actin filaments that are part of the stereocilia core2,5,6,7. However, it is unclear how actin filaments are added to the periphery of the core to cause widening. We hypothesized that widening depends on an intermediate arrangement of F-actin during stereocilia development, which we sought to characterize using probes to detect G-actin, as well as the barbed or pointed ends of F-actin. We analyzed intact cells as well as cells extracted with saponin, which depletes the soluble G-actin pool from cells19. To characterize the distribution of G-actin in IHC stereocilia, we first expressed RPEL1-EGFP, a G-actin binding domain from the protein MAL fused to EGFP20, in IHCs. This G-actin reporter was detected throughout the stereocilia, but was noticeably enriched at tips (Fig.\u00a02a, b). When cells were extracted with saponin, RPEL1-EGFP signal was drastically reduced to nearly undetectable levels (Fig.\u00a02a, right panels). We also probed for G-actin in freshly dissected, saponin-permeabilized cochlear tissue from P5 mice with the JLA20 antibody, which selectively binds to G-actin but not F-actin19. Under these conditions JLA20 staining was enriched at row 2 tips but was not evident at row 1 tips (Fig.\u00a02c\u2013e). In contrast, the AC-15 anti-\u03b2-actin antibody19 stained the entire length of stereocilia (Fig.\u00a02c, left panels). Thus, JLA20 staining likely marks a population of G-actin at row 2 stereocilia tips that is anchored to prevent extraction by saponin treatment. Interestingly, the JLA20 signal was absent by P9 (Fig.\u00a02c, e), suggesting that G-actin at row 2 tips is developmentally regulated.\n\na IHCs 18\u2009h post transfection with RPEL1-EGFP, unextracted or extracted by saponin before fixation. F-actin was stained with phalloidin (magenta). The experiments were repeated three times with similar results. b Representative line scans drawn down the center of stereocilia in RPEL1-EGFP transfected, unextracted IHCs at P5. In (b) and (d), line scans were drawn from tip towards the base down the center of stereocilia, the peak fluorescent signal was set as 0 on x axis, and the fluorescent intensity was normalized to the row 1 maximum. c Images of IHCs from saponin-permeabilized P5 or P9 mouse organ of Corti that was probed before fixation with antibodies against \u03b2-actin (AC15) or G-actin (JLA20) (green and grey); F-actin was counterstained post-fixation with phalloidin (magenta). d Representative line scans of JLA20 stained P5 IHC stereocilia. e Quantification of JLA20 level at stereocilia tips normalized to the average intensity of cell junctions. Sample size (cells, cochleae): P5 (24, 9), P9 (33, 11). Smaller circles represent the average value of stereocilia tips from individual cells. Larger open circles represent the average value of cells from one individual cochlea (N). P values from two-tailed paired t tests based on N are indicated. P\u2009<\u20090.05 is considered statistically significant. Scale bars represent 5\u2009\u03bcm. Source data are provided as a Source Data file.\n\nTo further define the actin network in stereocilia, we transfected cells with constructs encoding the F-actin binding proteins EGFP-TMOD1 and EGFP-SH3BGRL2, which each recognizes F-actin pointed ends21,22. Both pointed-end binding proteins preferentially bound to stereocilia tips compared to the stereocilia shaft (Fig.\u00a03a, b, Supplementary Fig.\u00a02a, b). Following saponin extraction, the tip enrichment of TMOD1 signals became more evident, with a greater reduction in shaft signal (Fig.\u00a03a, c), indicating that a subpopulation of TMOD1-bound F-actin is anchored at tips. To further assay for F-actin pointed ends, we probed saponin permeabilized cochlear tissue with purified actin pointed-end binding proteins DNaseI23 and His-TMOD1. Both proteins bound robustly at row 1 and row 2 stereocilia tips (Fig.\u00a03d). We detected F-actin barbed ends with a similar strategy, this time employing purified His-CAPZ protein24. As expected, based on the known arrangement of F-actin core filaments, His-CAPZ preferentially labeled the tips of stereocilia (Fig.\u00a03d). While F-actin barbed ends are expected at stereocilia tips because actin in stereocilia is comprised of parallel filaments with barbed ends terminating at tips, detecting pointed ends at tips was unexpected. The pointed end signal suggests that there is a population of short actin filaments at stereocilia tips (Fig.\u00a03e). We will refer to these putative actin filaments as tip filaments to distinguish them from the bundled core filaments, which comprise the main structure of stereocilia.\n\na EGFP-TMOD1 transfected P5 IHCs, either unextracted or extracted by saponin before fixation, that were also stained with phalloidin for F-actin (magenta). b Representative line scans drawn down the center of stereocilia of EGFP-TMOD1 transfected, unextracted P5 IHCs. The peak EGFP level was set as 0 on x axis and the fluorescence intensity was normalized to the maximal fluorescence intensity of row 1. c Line scans quantifying EGFP-TMOD1 levels from stereocilia tips toward shafts in saponin-extracted or unextracted P5 IHCs. Sample size (stereocilia, cells, cochleae): unextracted (18, 6, 2), extracted (33, 11, 3). The shadow lines represent individual stereocilia; the thick solid lines are the average level of all stereocilia. The data were plotted as mean \u00b1 SD and analyzed by two-way ANOVA (P\u2009<\u20090.0001 for the effects of extraction, distance from stereocilia tips, and interactions between these parameters, based on stereocilia). d His-TMOD1, DNaseI, or His-CAPZ (green, grey) localization after probing permeabilized IHCs at P5-6. F-actin was stained with phalloidin (magenta). The experiments were repeated three times with similar results. e Diagrams of potential actin structures in stereocilia with F-actin barbed and pointed ends bound by His-CAPZ and His-TMOD1, respectively. f Lattice SIM images of His-TMOD1, DNaseI, and His-CAPZ (cyan, thermal lookup table) at row 1 stereocilia tips with phalloidin-stained F-actin (magenta), which were quantified in (g, h). Magenta arrowheads denote the position of peak fluorescence intensity. g Representative line scans drawn down the center of stereocilia showing the intensity of His-TMOD1 (blue), DNaseI (purple), His-CAPZ (red), and F-actin actin (black). The stereocilia tip is indicated by black dashed arrow. Peak intensities for His-TMOD1, DNaseI, and His-CAPZ are indicated by colored dashed arrows. The offsets of the probe centers from the stereocilia tips were determined and plotted in (h). h A frequency histogram showing the pixel offsets of His-TMOD1 (blue), DNaseI (purple), and His-CAPZ (red) from the stereocilia tip. Sample size (stereocilia, cells, cochleae) of each probe (75, 15, 3). The histogram of each probe is fitted with a Gaussian curve. Mean offsets for peak of the Gaussian curves based on the pixel size (31\u2009nm x 31\u2009nm): His-TMOD1, 16\u2009nm; DNaseI, 24\u2009nm; His-CAPZ, 51\u2009nm. R-squared value of the fit: His-TMOD1, 0.996; DNaseI, 0.976; His-CAPZ, 0.982. Scale bars represent 5\u2009\u03bcm. Source data are provided as a Source Data file.\n\nTo better understand the relationship between barbed and pointed end probes at stereocilia tips, we compared the relative localization or offset of maximal value of the His-CAPZ, His-TMOD1, or DNaseI spot from the tip of the phalloidin-stained F-actin in stereocilia in lattice-SIM images (Fig.\u00a03f, g). The peak intensity of these probes from a histogram (Fig.\u00a03h) had similar displacement for the pointed-end probes DNaseI and His-TMOD1. The barbed end probe, His-CAPZ, was more distal, indicating that the barbed and pointed ends are spatially separated. These results are consistent with a model where short tip filaments run parallel with core filaments such as that presented in Fig.\u00a03e.\n\nIf tip filaments are separate from stereocilia core filaments, then they should have different solubility and behavior. To test this idea, we extracted freshly dissected cochlear tissue with a high-salt buffer to disrupt protein binding interactions that depend on electrostatic charge, and then labeled His-TMOD1 in standard buffer. The phalloidin-stained stereocilia core appeared unchanged by high-salt extraction but pointed end labeling at stereocilia tips was reduced by around 70% (Supplementary Fig.\u00a03a, b), which is consistent with extraction of tip filaments. Barbed end levels probed by His-CAPZ were also reduced (Supplementary Fig.\u00a03c, d), though to a lesser extent, likely reflecting labeling of the remaining core filament barbed ends. To assess tip filament behavior, we incubated postnatal cochlear explants in media containing latrunculin A (LatA), a drug that sequesters G-actin and depolymerizes dynamic F-actin structures25. In P5 explants, LatA treatment for 1\u2009h reduced His-TMOD1 labeling by 50% at row 1 tips and 65% at row 2 tips. Pointed end levels largely recovered 4\u2009h after the drug was washed out (Fig.\u00a04a, b). The loss of tip filaments with LatA treatment, and their regeneration following washout, show that they require ongoing polymerization to stay intact, which is behavior typical of dynamic actin networks.\n\na His-TMOD1 staining (green, grey) of P5 IHCs after 1-hour latrunculin A (LatA) treatment and 4-hour recovery following washout; F-actin stained with phalloidin (magenta). b Quantification of His-TMOD1 level from row 1 and row 2 stereocilia tips. Sample size (stereocilia, cells, cochleae): DMSO (100, 20, 4), LatA or washout (125, 25, 5). The fluorescence intensity was normalized to the average fluorescence intensity of row 1 stereocilia from DMSO-treated samples and plotted as mean \u00b1 SD based on cochleae. Scale bar represents 5\u2009\u03bcm. c Left panels are His-TMOD1 labeling (green, grey) of apical IHCs at P3, 7, 8, and 9. F-actin in stereocilia was stained with phalloidin (magenta). Scale bar represents 5\u2009\u03bcm. Regions marked by yellow boxes are magnified to the side (Scale bar represents 2\u2009\u03bcm). The timeline on the right is of IHC bundle development in mouse cochlea showing the growth details of specific rows during stage III and IV4. d, e Quantification of His-TMOD1 level from stereocilia tips at P3, 7, 8, and 9. Sample size (stereocilia, cells, cochleae): P3 (119, 17, 3), P7 (112, 16, 4), P8 (189, 27, 7), P9 (126, 18, 5). Row 1 and 2 are separately plotted. f Quantification of His-TMOD1 level from row 1 stereocilia shafts at P3, 7, 8, and 9. Sample size (stereocilia, cells, cochleae): P3 (57, 19, 3), P7 (48, 16, 4), P8 (81, 27, 7), P9 (54, 18, 5). Fluorescence quantifications from (d) to (f) were normalized to the average fluorescence intensity of the row 1 level from P3 samples and plotted as mean \u00b1 SD based on cochleae. In (b) and (d\u2013f), Normalized His-TMOD1 level from individual stereocilia were plotted as small dots with the color corresponding to their cochleae, represented as larger open circles. P values from two-tailed unpaired t tests comparing cochlea averages are indicated. P\u2009<\u20090.05 is considered statistically significant. Source data are provided as a Source Data file.\n\nIf tip filaments contribute to stereocilia growth, their levels would likely correlate with when stereocilia are widening during stage III growth (P0-P8) or elongating during stage IV (P8-P15)4. By probing permeabilized cochlea dissected from mice of postnatal ages, we found that His-TMOD1 staining at tips was highest at P3, and at that age the staining was similar at row 1 and row 2 tips. As development progressed, staining decreased at the tips of both rows, but more rapidly in row 2 so that by P9 signal was faint at row 1 tips and lost at row 2 tips (Fig.\u00a04c\u2013e). His-TMOD1 labeling of stereocilia shafts, though faint compared to staining at tips, followed the same trend, decreasing between P7 and P8 as widening slowed (Fig.\u00a04c, f). Thus, pointed end levels at stereocilia tips and along the shaft correlated well with widening, but tip filaments were still present at row 1 stereocilia tips when they switched to elongation.\n\nTo assess the actin states during widening, we overexpressed EGFP-actin and measured the level of barbed and pointed ends in the shaft as stereocilia widen (Fig.\u00a05). Actin overexpression increased stereocilia width compared to untransfected cells as assessed by measuring the full width at half maximum intensity of phalloidin staining (Fig.\u00a05b, Supplementary Fig.\u00a04a). His-TMOD1 and His-CAPZ staining of saponin-permeabilized tissue was increased in stereocilia shafts of EGFP-actin expressing cells (Fig.\u00a05c, d). The staining was more enriched at the periphery of the shafts (Supplementary Fig.\u00a04b\u2013d), corresponding to the addition of EGFP-actin added to the sides of the existing F-actin core. As both barbed and pointed ends were detected uniformly along the stereocilia length, the newly added actin is likely composed of short filaments, rather than long filaments that grow continuously from either the stereocilia tip or base.\n\na His-TMOD1 or His-CAPZ (magenta, grey) labeling in permeabilized IHCs 18\u2009h after transfection with EGFP-actin (green). F-actin is stained with phalloidin (blue) to show stereocilia. Regions of interest are denoted by light blue dashed boxes and magnified to the right. For comparison, stereocilia shaft labeling of His-TMOD1 or His-CAPZ is indicated by blue arrows in untransfected cells and magenta arrows in EGFP-actin transfected cells. b\u2013d Graphs showing the linear correlation of the EGFP-actin level in the stereocilia shaft with stereocilia width (b), His-TMOD1 shaft staining (c), and His-CAPZ shaft staining (d). Stereocilia are plotted as individual symbols and those from the same cell are represented by identical color and shape. Simple linear regression analysis was applied to the data. R-squared values for linear regressions from (b) to (d) are 0.66, 0.52, 0.55, respectively. Sample size (stereocilia, cells, cochleae): Width (592, 119, 22), His-TMOD1 (420, 84, 13), His-CAPZ (172, 35, 9). Scale bar represents 5\u2009\u03bcm. Source data are provided as a Source Data file.\n\nTo assess the requirement of barbed or pointed end polymerization for actin incorporation at the tip or shaft, we expressed red fluorescent protein (RFP)-tagged polymerization-incompetent mutants DVD-actin (D286A, V287A, D288A) and AP-actin (A204E, P243K). Existing actin filaments can incorporate DVD-actin at their barbed ends and AP-actin at their pointed ends, but the mutant actin then blocks subsequent monomer addition26,27,28. Both mutant actins localized to stereocilia tips, but did not appreciably incorporate along the stereocilia shaft as compared to neighboring cells that expressed normal EGFP-actin (Fig.\u00a06a). Interestingly, in co-expression experiments, AP-actin, and to a lesser extent DVD-actin, decreased co-expressed EGFP-actin incorporation in the tip region (Fig.\u00a06a, b). In contrast, EGFP-actin still incorporated similarly along the shaft whether or not it was co-expressed with mutant actin, which is evident in longitudinal line scans along stereocilia (Fig.\u00a06c). These observations suggest that actin incorporation is regulated differently at the stereocilia tip than along the shaft.\n\na IHCs 18\u2009h after transfection with EGFP-actin (green) and mutant actins (magenta). Left panels: IHCs transfected with EGFP-actin alone or in combination with RFP-DVD-actin; Right panels: IHC transfected with EGFP-actin alone or in combination with RFP-AP-actin, adjacent to an untransfected IHC. White arrows denote the transfected constructs; selected regions are magnified in (b). Scale bar represents 5\u2009\u03bcm. b Magnified insets from left to right: expression of EGFP-actin only, co-expression of EGFP-actin with RFP-DVD-actin or RFP-AP-actin. Scale bar represents 1\u2009\u03bcm. c Line scans quantifying the fluorescence distribution of RFP-mutant actin relative to EGFP-actin from the co-transfected IHCs. The peak RFP level was set as 0 on x axis and the fluorescence intensity was normalized to the maximal fluorescence intensity of row 1. The line scan results were collected from \u22120.4\u2009\u03bcm (above tips) to 0.8\u2009\u03bcm (below tips) on x axis as described. The shadow lines represent individual stereocilia; the thick solid lines with error bars are the average level of all stereocilia with SD. Sample size (stereocilia, cells): DVD-actin (57, 19), AP-actin (48, 16). Source data are provided as a Source Data file.\n\nOur data suggests that short actin filaments at stereocilia tips and along the shaft are key intermediates in forming the stereocilia core. We next wanted to understand how tip filaments relate to tip-localized myosins, which regulate stereocilia elongation and widening. Two different classes of myosins, including MYO3A and MYO3B, encoded by the Myo3a and Myo3b genes, as well as MYO15A, localize to stereocilia tips10,15. MYO15A is essential for normal elongation and for establishing the unique protein complements that define row 1 and row 2 tips10,11,29. MYO3A/B, in contrast, is required to establish normal stereocilia dimensions, which are abnormally tall and thin in the mouse double knockout16,17. As an initial test whether myosins interact with tip filaments, we incubated saponin-permeabilized cochlear tissue with 4\u2009mM sodium orthovanadate. This inhibitor stabilizes ADP-Pi bound myosin, which is a conformation that binds F-actin weakly30,31. Compared to the control condition, vanadate treatment decreased the level of His-TMOD1 staining at stereocilia tips and along the stereocilia shaft (Supplementary Fig.\u00a05a, b). This reduction is consistent with the hypothesis that myosins normally bind tip filaments and prevent their loss following cell permeabilization.\n\nWe next sought to localize tip filaments relative to MYO15A and MYO3A which are known to occupy different zones at stereocilia tips. Using U-ExM and lattice SIM, we found tip filaments overlapped both the broader MYO3A zone and the more distal MYO15A zone (Fig.\u00a07a, Supplementary Fig.\u00a05c, d). The specific patterns differed according to stereocilia row. In row 1, MYO15A localized as a cap at stereocilia tips; by contrast, MYO3A localized just below the MYO15A zone. Tip filaments, labeled with His-TMOD1 detected by an anti-TMOD1 antibody, were detected in both the MYO15A and MYO3A zones (Fig.\u00a07a). In row 2 stereocilia, MYO15A antibody staining was reduced compared to row 1 and was restricted to a small patch at the distal tip (Fig.\u00a07a, right panels). MYO3A staining was distributed more broadly at the tip (Fig.\u00a07a, middle panels). As at row 1 tips, His-TMOD1 labeled tip filaments overlapped both myosins, but most tip filaments in row 2 coincided with the more prevalent MYO3A staining.\n\na Representative images of His-TMOD1 stained row 1 stereocilia and row 2 stereocilia, co-labeled after expansion microscopy with antibodies to endogenous MYO3A or MYO15A (green) from P5 mice. NHS-ester (grey) stained total protein. The experiments were repeated three times with similar results. b His-TMOD1 (green, grey) labeling in Myo3 double mutant (Myo3a\u039414/\u039414 Myo3b\u039412/\u039412, noted in Supplementary Fig.\u00a06a) and littermate control IHCs at P4. c Quantification of His-TMOD1 level from the tips of row 1 stereocilia in Myo3a;Myo3b G0 mutants generated via i-GONAD (Supplementary Fig.\u00a06a) and in littermate control IHCs, each at P4. Sample size (stereocilia, cells, cochleae/mice): control (60, 12, 4), mutant (60, 12, 4). d HA-TMOD1 (green, grey) labeling in Myo15a mutant (Myo15a\u039425/\u039425) and littermate control (Myo15a\u039425/+) IHCs at P4. e Quantification of HA-TMOD1 level from the tips of row 1 stereocilia in Myo15a mutant and littermate control IHCs (P4). Sample size (stereocilia, cells, cochleae): control (125. 25, 6), mutant (85, 17, 5). In (c) and (e), normalized TMOD1 level from individual stereocilia were plotted as small dots with the color corresponding to their cochleae, represented as larger open circles. Results were plotted with mean \u00b1 SD based on cochleae. P values for two-tailed unpaired t tests are indicated. P\u2009<\u20090.05 is considered statistically significant. f His-TMOD1 (magenta, grey) labeling of an IHC 18\u2009h after transfection with EGFP-K50R-MYO3A (green) and a neighboring untransfected cell (P5). F-actin was stained by phalloidin (blue). g Graphs of His-TMOD1 and EGFP-K50R-MYO3A level at row 1 or row 2 stereocilia tips. Simple linear regression analysis was applied and R-squared values were 0.52 (row 1) and 0.51 (row 2). h His-TMOD1 (magenta, grey) labeling of an IHC 18\u2009h after transfection with EGFP-MYO15A-2 (green) and a neighboring untransfected cell (P5). i Graph of His-TMOD1 and EGFP-MYO15A-2 level at row 1 stereocilia tips. Simple linear regression analysis was applied and R-squared value was 0.88. Sample size (stereocilia, cochleae) in (g) and (i): MYO3A (245, 10), MYO15A (238, 9). Scale bars represent 5\u2009\u03bcm. Source data are provided as a Source Data file.\n\nTo assess the consequence of myosin loss on tip filaments in IHC stereocilia, Myo3a and Myo3b were mutated with CRISPR/Cas9 that was delivered to E0.7 embryos in situ by the i-GONAD technique32, which generated a variety of small deletions in early exons of each gene that encode N-terminal kinase domains (Supplementary Fig.\u00a06a). Myo3a\u039414/\u039414 Myo3b\u039412/\u039412 IHCs in mice bred from a G0 founder (mutant 2 alleles, Supplementary Fig.\u00a06a) had long and thin IHC stereocilia at P4 that resembled those from previously described Myo3a/b double knockouts16 (Fig.\u00a07b). The mutant IHC stereocilia had markedly reduced His-TMOD1 staining at row 1 and row 2 stereocilia tips compared to cells from wild-type tissue, or from littermate mice heterozygous at both loci (Fig.\u00a07b, c). Using a similar approach, we also generated a Myo15a mutant mouse with a 25-base pair deletion in exon 19, which encodes the motor domain. Homozygous Myo15a\u039425/\u039425 IHCs had reduced pointed-end labeling as compared to heterozygous littermates (Fig.\u00a07d-e), though the decrease was less than for the Myo3a/b mutants. A similar reduction in His-TMOD1 staining was observed in mice homozygous for the well-characterized Myo15a shaker allele (Supplementary Fig.\u00a06d), which has a mutation in the motor domain thought to abolish the function of all isoforms11,13,29,33. In addition, one G0 CRISPR/Cas9 Myo15a mutant had a mosaic phenotype where hair cells with normal morphology and His-TMOD1 staining were adjacent to cells with short stereocilia and reduced His-TMOD1 levels (Supplementary Fig.\u00a06c). Finally, MYO3A localized normally in the Myo15a\u039425/\u039425 mutant (Supplementary Fig.\u00a06e). Similarly, MYO15A localized to stereocilia tips at normal levels in the Myo3a/b double mutant (Supplementary Fig.\u00a06f), suggesting that each myosin independently contributes to tip filament levels.\n\nTo determine if increasing MYO15A or MYO3A protein levels increased tip filament levels, we transfected IHCs with EGFP-MYO3A (Supplementary Fig.\u00a07a); EGFP-K50R-MYO3A, which lacks kinase activity that autoinhibits motor function34 (Fig.\u00a07f); or EGFP-MYO15A isoform 2 (Fig.\u00a07h). His-TMOD1 staining at both row 1 and row 2 tips increased with EGFP-MYO3A expression (Supplementary Fig.\u00a07a) or with EGFP-K50R-MYO3A levels (Fig.\u00a07f, g). EGFP-MYO15A-2 expression also increased His-TMOD1 staining, albeit primarily at row 1 tips, reflecting the localization pattern of this myosin (Fig.\u00a07h, i). Of note, stereocilia were slightly wider with overexpression of MYO3A (Supplementary Fig.\u00a07b, c), which was not observed with MYO15A transfection, suggesting that tip filaments connected to MYO3A are more likely to enter the widening pathway. Together, these data show that the level of short actin filaments at stereocilia tips is influenced by both MYO3A and MYO15A, suggesting that these myosins may regulate stereocilia growth in part through the use of short actin filaments.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60976-y/MediaObjects/41467_2025_60976_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60976-y/MediaObjects/41467_2025_60976_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60976-y/MediaObjects/41467_2025_60976_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60976-y/MediaObjects/41467_2025_60976_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60976-y/MediaObjects/41467_2025_60976_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60976-y/MediaObjects/41467_2025_60976_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "This study offers insights into how actin behaves in developing stereocilia that are widening, which is a key developmental step in stereocilia maturation. Super-resolution imaging revealed that newly expressed EGFP-actin first accumulates at stereocilia tips before incorporating along the stereocilia shaft. Critically, the new actin does not replace the existing stereocilia F-actin core, but rather it surrounds it, suggesting that a stereocilia shaft widens by adding new actin filaments to its periphery. We found that the arrangement of actin at the stereocilia tip is more complicated than is accounted for by existing models, all of which describe only termination of the long F-actin filaments that form the stereocilia core. Besides these core filaments, we also characterized a dynamic population of short actin filaments at stereocilia tips using probes detecting F-actin barbed and pointed ends. Short actin filaments seem to also be involved in stereocilia widening because both barbed and pointed ends of F-actin increase along the stereocilia shaft as EGFP-actin incorporates. Finally, tip filament levels are regulated by MYO3A and MYO15A proteins, which are critical determinants of stereocilia width and length. Based on the relationship between tip filaments, myosins, and stereocilia growth, we propose that myosins use, and perhaps generate, tip filaments as intermediates in the assembly of new stereocilia core filaments (Fig.\u00a08).\n\nThe drawing depicts a longitudinal section of a widening stereocilium and a hypothetical widening mechanism consistent with the data in this study. The grey filaments are long, stable core filaments bundled by F-actin crosslinks and new actin filaments are added to widen and stabilize the tip or the shaft. At the first timepoint, there are short actin filaments, shown in magenta, at the tip and along the periphery of the core. Tip widening occurs as these filaments form at the tip and are captured and stabilized by MYO3A. The source of tip filaments is unknown, but the MYO15A-dependent elongation complex is a favored candidate. Short filaments along the shaft, nucleated by an unknown mechanism, are extended by new actin to produce stable core filaments. Additional short filaments form along the periphery as widening continues.\n\nThe source of short actin filaments is one of the most interesting unsolved mysteries. One possibility is that there is an as-yet unidentified nucleator, such as the ARP2/3 complex or formins, that resides at stereocilia tips and generates short filaments. Alternatively, existing core filaments could be severed by ADF or cofilin, both of which localize to row 2 stereocilia tips and regulate stereocilia growth. In addition, as levels of MYO3 and MYO15A proteins correlate with tip filaments abundance, perhaps myosins themselves directly nucleate F-actin. There is experimental support for this idea, as non-muscle myosin II has long been known to nucleate actin in vitro35,36,37. More recently, purified, recombinant MYO15A was shown to have a similar nucleation activity, both in bulk assembly assays and more directly by watching filaments form in TIRF assays38,39. In addition, a novel Myo15a point mutation in the motor domain both decreased stereocilia growth and the nucleation ability of the purified protein in vitro39. Although a direct nucleation mechanism is intriguing because it would neatly couple the myosins that are critical for stereocilia growth with F-actin assembly, other mechanisms are conceivable. For example, myosin motors can exert enough force to break actin filaments40 and could perhaps generate tip filaments from core filaments in this fashion. Yet another possibility is that myosins deliver a nucleation factor, although this seems less likely considering that MYO15A and MYO3A both increase tip filament levels even though their tails bind different cargos.\n\nWe propose that during stereocilia widening, short actin filaments exist along the stereocilia shaft, which subsequently mature into long actin filaments, which are well-known to characterize the actin core. This proposal is supported by His-TMOD1 staining observed along the stereocilia shaft in stage III inner hair cells, suggesting that pointed ends are distributed throughout the length of the developing stereocilia core. Consistent with short filaments maturing into long filaments as widening concludes, pointed-end staining decreased as the cells entered stage IV. In addition, overexpressing EGFP-actin increased both barbed and pointed end labeling along stereocilia shafts, indicating that short actin filaments were formed as stereocilia widened. Short actin filaments that contribute to widening could originate at the stereocilium tip and then move down in some fashion to populate the shaft. Such a tip origination model is consistent with data showing that actin assembly is most evident at stereocilia tips, and that MYO3A/B localizes to stereocilia tips, regulates tip filament levels, and is required for normal widening.\n\nWhile the tip origination model seems feasible, it is also possible that the stereocilia shaft and tip both use short actin filaments, but that those filaments are nucleated independently by distinct mechanisms. In keeping with this idea, expressing mutant, non-polymerizable actin that blocked either the barbed or pointed ends of existing filaments perturbed the addition of EGFP-actin to the tip but not to the shaft. Regardless of their origin, both models posit that short actin filaments, seeded along the periphery of an existing filament bundle, grow by monomer addition or annealing to form the long, unbranched actin filaments characteristic of stereocilia.\n\nIdentifying actin states within stereocilia is complicated by the exceptionally high density of F-actin, as well as the high concentration of other proteins at stereocilia tips, which together interfere with direct imaging approaches. We instead deployed a repertoire of G- and F-actin binding proteins as selective probes to detect either monomeric actin or the barbed or pointed ends of F-actin. Abundant data on actin structure as monomers, filaments, and filaments bound to CAPZ and TMOD1 are available and are useful for interpreting the results of our labeling experiments. At the barbed end of actin filaments, monomers present subdomains 1 and 3 and recent cryo-EM data show that these domains undergo a conformational change to flatten relative to each other as monomers polymerize onto the filament end41. Thus, a barbed end binding protein like CAPZ or an antibody like JLA20 can readily distinguish F-actin from G-actin. The pointed ends of the filament do not change as dramatically compared to the monomeric state41. Consequently, proteins like DNaseI bind to both monomers and pointed ends with high affinity. Nevertheless, some members of the tropomodulin protein family, including TMOD1, can block pointed end polymerization without binding G-actin42. The specificity of TMOD1 most likely arises from multivalent interaction of TMOD1 with both actin subunits that are found at the pointed end. Similarly, SH3BGRL2, a more recently identified pointed end binding protein, binds between the interface of two actin units, with no evidence that it binds to G-actin22. In addition to structural considerations, G-actin and F-actin are also extracted from cells differently, with G-actin largely removed from cells after treatment with low concentrations of saponin19. In contrast, F-actin is mostly unchanged after extraction, presumably because the polymerized actin population is large and interconnected. In the current study, we noted clear differences between probes for G-actin and for pointed ends in intact cells, which demonstrated that the pointed-end probes are not just detecting G-actin. In addition, saponin extraction nearly eliminated signal from the RPEL1-EGFP G-actin probe, while EGFP-TMOD1 was depleted from the cell body and stereocilia shaft, but not from the tip. Thus, the idea of pointed ends, and thus short actin filaments, being at stereocilia tips and along the stereocilia shaft relies on the combined results of multiple probes in intact and extracted cells.\n\nActin polymerization is most evident at stereocilia tips and, correspondingly, we found that G-actin detected by the RPEL1-EGFP probe is also enriched at stereocilia tips. It is unclear if G-actin is trafficked to tips or if it is enriched by trapping mechanism after diffusing to tips, but the RPEL1-EGFP signal is almost always absent after a short extraction with saponin. The RPEL1 protein has a relatively low affinity for G-actin, so some of the signal decrease could be dissociation of the probe. However, G-actin is well-known to be diffusible so it is more likely that the pool of G-actin at stereocilia tips is not tightly associated with existing F-actin structures or other bound proteins. This view is supported by JLA20 staining, which is not evident at row 1 tips after saponin extraction suggesting that the pool of actin marked by RPEL1-EGFP was lost.\n\nInterestingly, JLA20 did stain the tips of row 2 stereocilia from P5 mice, but not P9 mice, after saponin treatment, revealing that some G-actin is resistant to extraction. JLA20 also stained the tips of microvilli on the apical surface of hair cells (Fig.\u00a02c). These microvilli are shrinking and will be lost as the stereocilia bundle develops. Similarly, row 2 stereocilia lengths are also decreasing from P0-P9 as bundle architecture is refined4, while row 1 stereocilia length remains constant. Stereocilia shortening and microvilli disassembly likely involve the actin severing proteins ADF and cofilin, which localize to the tips of row 2 stereocilia and apical surface microvilli. In addition, row 2 stereocilia and microvilli are both longer on hair cells lacking ADF and cofilin activity9. These observations suggest that the anchored G-actin detected by JLA20 could be a transient by-product of ADF/cofilin mediated actin disassembly in the tips of protrusions. It is unclear whether the liberation of G-actin during shortening contributes to concurrent tip filament formation or row 2 stereocilia widening.\n\nStereocilia morphogenesis is required for normal hearing and balance, and it is also a fascinating case study in how cells generate complex shapes. Here, we provided evidence of short actin filaments in developing stereocilia, both at the tips and along the widening shaft. We propose that these short filaments are intermediates that mature into the long actin filaments that have long been known to comprise the stereocilia actin core.\n\nThe primary limitation of this study is that the proposed widening mechanism partially relies on expression of exogenous actin and has not been validated through observation of endogenous actin incorporation. While our findings reveal a strong correlation of tip filaments to the occurrence of widening, the precise process by which actin adds to the periphery of core filaments and extends to the full length of stereocilia remains unclear.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60976-y/MediaObjects/41467_2025_60976_Fig8_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Inbred C57BL/6 mice were used for all experiments. All animal procedures were approved by the Institutional Animal Care and Use Committee of Indiana University \u2013 Indianapolis (School of Science). Mice were housed in individually ventilated cages with free access to food and water. Pregnant females were provided with nesting materials and received breeder diet (#5015, Purina Lab Diet) instead of the standard diet (#5001, Purina Lab Diet). The animal facility operated on a 12:12-hour light/dark cycle. Ambient temperature and relative humidity were maintained at 22\u2009\u00b1\u20092\u2009\u00b0C and 40\u201360%, respectively. The day of birth is referred to as postnatal day 0 (P0). Both male and female mice were used for all experiments.\n\nThe i-GONAD method was performed by following the published method32,43. Guide RNA (gRNA) targeting either Myo15a or Myo3a and Myo3b were made by mixing crRNA (listed below under materials) and tracrRNA (Integrated DNA Technologies, IDT) at 100 \u03bcM in duplex buffer (IDT), heating to 94\u2009\u00b0C for 5\u2009min, and cooling to room temperature. To assemble the ribonucleoprotein (RNP) complex, 2.25 \u03bcl gRNA and 0.75 \u03bcl recombinant S. pyogenes Cas9 endonuclease (IDT) were mixed in 4.5 \u03bcl OPTI-MEM medium (Gibco) and incubated at room temperature for 20\u2009min. Fast Green (1 \u03bcl of a 1% filtered solution, Thermo Fisher Scientific) was added to aid visualization. Female mice with copulation plugs, estimated to be 14-16\u2009h post mating, were anesthetized with isoflurane. The oviducts were exposed by an incision on the back of the mice. Each side of oviduct was injected with the RNP complex solution via a glass micropipette glass needle. The oviducts were electroporated with square wave pulses (8 pulses, duration 5\u2009ms, interval 1\u2009s, field intensity 50\u2009V) delivered using tweezertrodes (BTX) connected to an ECM830 electroporator (BTX). The oviducts were repositioned, and the incision was closed with surgical staples. The resulting pups were genotyped by amplifying the targeted regions by polymerase chain reaction (PCR) using primers (Myo15a_sq_F/R, Myo3a_sq_F/R or Myo3b_sq_F/R) from genomic DNA isolated from tail snips. PCR products were purified using ReliaPrep DNA Clean-up and Concentration System (Promega, Cat. # A2892) and sequenced by amplicon sequencing service from Plasmidsaurus Inc. The mutations on each pup were identified by aligning the raw sequence reads.\n\nPrimers and guide RNAs were customized and purchased from IDT.\n\ncrRNA targeting Myo15a: 5\u2019-/AltR1/rCrUrArCrArArGrGrCrUrCrArCrArCrGrGrUrGrG\n\nrGrUrUrUrUrArGrArGrCrUrArUrGrCrU/AltR2/\u22123\u2019\n\ncrRNA targeting Myo3a: 5\u2019-/AltR1/rCrArCrCrGrCrCrUrGrArArCrArCrCrUrCuCrGrU rGrUrUrUrUrArGrArGrCrUrArUrGrCrU/AltR2/\u22123\u2019\n\ncrRNA targeting Myo3b: 5\u2019-/AltR1/rCrArUrCrUrCrGrGrUrGrGrArUrGrArUrUrCrGrG\n\nrGrUrUrUrUrArGrArGrCrUrArUrGrCrU/AltR2/\u22123\u2019\n\nMyo15a_sq_F/R: 5\u2019-AGCAGGGGACCTATGACA-3\u2019 / 5\u2019-GAACCCCTGAATAGCGTAACT-3\u2019\n\nMyo3a_sq_F/R: 5\u2019-CAGGGCAAAGAAAGAGAATAAC-3\u2019 / 5\u2019-CATCCAGACTACAGATACATGC-3\u2019\n\nMyo3b_sq_F/R: 5\u2019-GTCAAGGGCCTTCTGAGG-3\u2019 / 5\u2019-CCTCACGTGTTGAAGCAATAG-3\u2019\n\nPrimary antibodies for immunostaining were 1:200 rabbit anti-MYO15A-Pan (PB48), 1:100 rabbit anti-MYO3A (custom antibody from Genemed Synthesis Inc., raised against the C-terminal sequence, NPYDYRRLLRKTSQRQR, of mouse MYO3A), 1:100 mouse anti-TMOD1 (Thermo Fisher, Cat. # MA5-25612), 1:500 rabbit anti-GFP (Torrey Pines Biolabs, Cat. # TP401), 1:100 Alexa Fluor 488 mouse anti-\u03b2-actin (AC-15, 1.45\u2009mg/ml, Novus Biologicals, Cat. # NB600-501), 1:50 monoclonal mouse anti-\u03b3-actin antibody clone 1\u201337 IgG purified from ascites44 and dye-conjugated (Thermo Fisher Antibody Labeling Kit Cat. #A20183), 1:200 Alexa Fluor 488/555 mouse anti-His (Genscript, Cat. # A01800 or # A01801) or 488 mouse anti-HA (Genscript, Cat. # A01806). Primary antibodies for immunoprobing unfixed, saponin permeabilized tissue were 100\u2009nM mouse JLA20 from concentrated supernatant (198 \u03bcg/ml, Developmental Studies Hybridoma Bank, University of Iowa) and 100\u2009nM Alexa Fluor 488 mouse anti-\u03b2-actin (AC-15). The secondary antibodies were 1:200 488/568/647 Alexa Fluor goat anti-rabbit or anti mouse IgG (Thermo Fisher).\n\nEGFP-\u03b2-actin was a gift from Michael Davidson (Addgene plasmid # 56421)45. pEGFP-FSCN2 was cloned by shuttling a mouse Flag-Fscn2 cDNA46 to the pDest40 vector (Thermo Fisher). The pEGFP-C1-RPEL1-EGFP-3xNLS (Addgene plasmid #58469)47 and then a stop codon was inserted after EGFP by PCR mutagenesis using the Q5 Site-Directed Mutagenesis Kit (NEB, E0554) to eliminate expression of the nuclear localization signal (NLS). The pEGFP-C1-mTMOD1 construct48 was donated by Dr. Velia M. Fowler (University of Delaware). The pEGFP-SH3BGRL2 was synthesized from GenScript based on the DNA sequence of SH3BGRL2 (SH3 domain binding glutamate rich protein like 2) from NCBI reference sequence: NM_172507.5. The expression of untagged actin was accomplished using pActin-IRES-EGFP, a dual expression construct with a single promotor that uses an internal ribosomal entry site (IRES) to drive EGFP expression. DNA encoding \u03b2-actin followed by the IRES was synthesized by GenScript and cloned into the N terminal of EGFP in the vector pcDNA3.1. Mutant \u03b2-actin plasmids, RFP-DVD-actin (DVD286,287,288AAA) and RFP-AP-actin (AP204,243EK), were previously made27 and were gifted by Dr. Dmitri S. Kudryashov (The Ohio State University). EGFP-MYO3A, EGFP-K50R-MYO3A (kinase dead)34,49, and EGFP-MYO15A-2 (short isoform)10,29 were previously described.\n\nAuditory hair cells were transfected with plasmid DNA by the injectoporation technique50. Briefly, the sensory epithelium was dissected from C57BL/6 mice at postnatal day 3,\u00a05,\u00a0or 6 in Hank\u2019s Balanced Salt Solution (HBSS, Life Technologies, Cat. # 14025092), and the cochlear duct was opened by making an incision between Reissner\u2019s membrane and the stria vascularis. The tissue was explanted by adhering it to a plastic, tissue-culture treated dish (USA Scientific, Cat. # CC7672-3359) containing DMEM/F12 (Thermo Fisher Scientific, Cat. # 11039047) with 0.1\u2009mg/ml penicillin. The culture was incubated at 37\u2009\u00b0C with 5% CO2 for 2\u2009h before injectoporation was performed. For the injection step, a glass micropipette with a 2\u2009\u03bcm tip diameter loaded with plasmid DNA (1-2\u2009mg/ml in water) was oriented perpendicular to the IHC row. The tip of the micropipette was inserted into the space between two IHCs and pressure was supplied by a microinjector to inject plasmid into the tissue. An ECM 830 electroporator was used to deliver a series of three 15\u2009ms 60\u2009V square-wave electrical pulses at 1\u2009s intervals to platinum wire electrodes that were 2\u2009mm apart and positioned directly over the injection site. After the electroporation, the culture media was exchanged with Neurobasal-A medium (Thermo Fisher Scientific, Cat. # 12349015) supplemented with 2 mM L-glutamine (Thermo Fisher Scientific, Cat. # 25030081), 1x N-2 supplement (Thermo Fisher Scientific, Cat. # 17502048), 1.5\u2009\u03bcg/ml D-glucose (Thermo Fisher Scientific, Cat. # 410955000), and 0.1\u2009mg/ml penicillin.\n\nCultured explants were transfected with EGFP-actin (2\u2009mg/ml in water) by injectoporation. Cultures were incubated at 37\u2009\u00b0C with 5% CO2, then moved to a 37\u2009\u00b0C microscope incubator for live-cell acquisition. Transfected cells were imaged at 2, 4, 18\u2009h post injectoporation. Image stacks (0.13\u2009\u03bcm intervals) were acquired in Airyscan mode with a Zeiss Apochromat 40x/1.0 NA water immersion objective on a Zeiss LSM 900 microscope. To avoid contamination, the cultured dishes were exchanged with fresh culture media after each live cell acquisition. Laser power for EGFP capture was 1% for time point 2 and 4\u2009h and 0.1% for 18\u2009h as EGFP-actin levels increased with time. Raw Airyscan images processed using Huygens Array Detector Deconvolution software within the Essential software package using automatic settings. Image stacks were processed in Imaris 10.0.1 for 3D construction and in Fiji for reslicing.\n\nCultured explants were transfected with EGFP-actin or EGFP-FSCN2 (2\u2009mg/ml in water) by injectoporation and fixed at 18\u2009h post-transfection with 4% formaldehyde (Electron Microscopy Sciences, Cat. # 15710) in HBSS for 2\u2009h and stained with Alexa Fluor 568 phalloidin (0.5 U/ml, Invitrogen, Cat. # A12380) in PBS with 0.1% Triton X-100 (Sigma, Cat. # X-100-100ML) at room temperature for 1\u2009h. The tectorial membrane was removed and the tissue was mounted in Prolong Diamond (Thermo Fisher Scientific, Cat. # P36961). Calibration slides for channel alignment were prepared using tissue stained with Alexa Fluor 488 and 568 phalloidin (0.5 U/ml, Invitrogen) following the same procedures from above.\n\nUltrastructural expansion microscopy (U-ExM) was performed based on protocols adapted from previously described methods51,52. Cochlear tissue was fixed at room temperature for 30\u2009min. After fixation, tissue was washed with PBS and transferred to 1.4% formaldehyde/2% acrylamide (FA/AA) in PBS at 37\u2009\u00b0C for overnight incubation. The FA/AA solution was removed on the second day followed by 3 times wash of PBS. The tissue was then incubated in monomer solution (19% w/w sodium acrylate (Pfaltz & Bauer, Cat. # S03880), 10% v/v acrylamide (Sigma, Cat. # A4058), 0.1% v/v N, N\u2019-methylenebisacrylamide (Sigma, Cat. # M1533) in PBS) for 90\u2009min at room temperature. After incubation, the cochlear tissue was transferred to a petri dish lid. Excess monomer solution was removed from the surrounding area of the tissue. Subsequently, 2.5 \u03bcl of 10% N,N,N\u2032,N\u2032-Tetramethylethylenediamine (TEMED) and 10% ammonium persulphate solution were added to 45 \u03bcl monomer solution, which was mixed thoroughly, and 30 \u03bcl was quickly pipetted onto the dish before being mounted under a 12\u2009mm round coverslip. After polymerization, the resulting hydrogel was then incubated at 37\u2009\u00b0C humidified chamber for 1\u2009h before adding room temperature denaturation buffer (200\u2009mM sodium dodecyl sulfate (SDS), 200\u2009mM NaCl, 50\u2009mM Tris, pH 9) for 15\u2009min. The gel was then detached from the coverslip, transferred to a new denaturation buffer, and denatured at 95\u2009\u00b0C for 1\u2009h. The denatured gel was transferred in a 150\u2009mm petri dish filled with 20\u2009ml MilliQ water and placed on a 50-rpm shaker for 30\u2009min. The gel was further expanded by replacing water two more times every 30\u2009min. The expanded gel was shrunk by washing with PBS for 15\u2009min. The shrunken gel was then incubated with 0.02\u2009mg/ml Alexa Fluor 546 succinimidyl ester (NHS-ester, Thermo Fisher, Cat. # A20102) for 1\u2009h before washing 3 times with PBS. The gel was incubated with blocking buffer (2.5% BSA in PBS, 0.5% Triton-X-100) for 1\u2009h blocking at room temperature on the shaker and washed with PBS 3 times before antibody staining. Primary antibodies were prepared in the staining buffer (1% BSA in PBS, 0.2% Triton-X-100) and incubated with the gel overnight at room temperature on the shaker. The gel was washed with PBS 3 times before being incubated with secondary antibodies in PBS for 3\u2009h at room temperature on the shaker. Following the incubation, the stained gel was washed with PBS and then transferred to the 10\u2009cm petri dish filled with water, undergoing a second round of expansion during 3 subsequent water washes. The gels were trimmed, and mounted in a glass-bottom dish under a glass coverslip. To estimate sample expansion parameters, tissue and gels were measured before and after expansion. Tissue expanded by a factor of 3.8x compared to 3.9x for gels without tissue.\n\nEGFP was detected by rabbit-anti-GFP primary antibody followed by the incubation of the secondary antibody goat anti-rabbit Alexa Fluor 488 or 568. His-TMOD1 was incubated with unfixed tissue at P5. After probing, the tissue was fixed and processed as described above. Anti-TMOD1, anti-MYO15A-Pan, or anti-MYO3A antibodies were incubated with gels for 24\u2009h at room temperature on the shaker, washed, and incubated with the secondary goat anti-rabbit or anti-mouse Alexa Fluor 488 antibody for 3\u2009h. The whole expanded tissue was stained by Alexa Fluor 546 conjugated NHS-ester to label total protein.\n\nThe expression construct pReceiver-B01-mTmod1 and purified His-TMOD1 were gifted by Dr. Alla Kostyukova (Washington State). HA-Tmod1 has an N-terminal 6xHis tag followed by a fusion of the \u201cspaghetti monster (sm)\u201d HA tag53 with mTmod1. This construct was generated by amplifying the smHA sequence from a plasmid pCAG-smFP-HA (Addgene #59759) with forward primer (5\u2019-ccatcaccatcattcgaaggaaggtACCATGTACCCTTATGATGTGC-3\u2019) and reverse primer (5\u2019- gtctgtaggacatggtaccgcctgccccAGCGTAGTCCGGGACATC-3\u2019). The amplified product was then assembled with EcoRI-digested pReceiver-B01-mTmod1 using the NEBuilder HiFi DNA Assembly Kit (New England Biolabs, Cat. # E5520S). The entire plasmid was sequenced using nanopore sequencing (sequencing performed by Plasmidsaurus). Competent E. coli Rosetta (DE3) pLysS cells were transformed with the His-Tmod1 or HA-Tmod1 plasmid. Successful transformants were selected by incubating bacteria at 37\u2009\u00b0C on LB agar plates containing ampicillin. A single colony was used to grow a 1-liter culture in LB-ampicillin medium, which at OD620nm 0.6 was induced to express protein with 0.4\u2009mM IPTG and further incubated for 5\u2009h at 37\u2009\u00b0C. The cells were harvested by centrifugation and resuspended in 20\u2009ml lysis buffer (50\u2009mM Na-phosphate (pH 6.8), 300\u2009mM NaCl, 5\u2009mM 2-mercaptoethanol (BME), 1\u2009mM phenylmethylsulfonyl fluoride (PMSF), 1 tablet cOmplete protease inhibitor, 20\u2009mM imidazole, 10% glycerol). The resuspended cells were sonicated and then centrifuged to remove cellular debris. The supernatant was loaded onto a Ni-NTA column which was pre-equilibrated with the wash buffer (50\u2009mM Na-phosphate (pH 6.8), 300\u2009mM NaCl, 5\u2009mM BME, 1\u2009mM PMSF, 20\u2009mM imidazole, 10% glycerol). After washing the column with 50\u2009ml of wash buffer, the His-TMOD1 protein was then eluted with 70\u2009ml elution buffer with a gradient of 20-250\u2009mM imidazole (50\u2009mM Na-phosphate (pH 6.8), 300\u2009mM NaCl, 1\u2009mM BME, 1\u2009mM PMSF, 10% glycerol, 250\u2009mM imidazole for the maximum concentration). The elution was collected in 90 fractions. The purity of the His-TMOD1 or HA-TMOD1 fractions was assessed by SDS-PAGE. Fractions with over 95% purity were pooled together and dialyzed overnight in the storage buffer (20\u2009mM Tris HCl (pH 8.0), 1\u2009mM EDTA, 1\u2009mM dithiothreitol (DTT), 10% glycerol). The concentration of the dialyzed His-TMOD1 or HA-TMOD1 was measured, and the aliquots were stored at \u221280\u2009\u00b0C. The His-CAPZ was previously made9 and aliquots used in this study were thawed from stocks stored at \u221280\u2009\u00b0C.\n\nCochleae were dissected in HBSS and the lateral wall was removed prior to permeabilization in cytoskeleton buffer (20\u2009mM HEPES (pH 7.5), 138\u2009mM KCl, 4\u2009mM MgCl2, 3\u2009mM EGTA, 1% bovine serum albumin) with 0.05% saponin. Purified proteins were included at the follow concentrations: 714\u2009nM His-TMOD1 (6XHis-tagged mouse tropomodulin-1) to label F-actin pointed-ends, 3.6 \u03bcM Alexa Fluor 488 conjugated DNase I (deoxyribonuclease I, Thermo Fisher, Cat. # D12371), or 400\u2009nM His-CAPZ (mouse CAPZB and 6XHis-tagged CAPZA1) to label barbed ends of actin filaments. Samples were incubated in this solution for 5\u2009min at room temperature before being washed by cytoskeleton buffer without saponin. Samples were then fixed with 4% formaldehyde in HBSS for 2\u2009h at room temperature. Samples were rinsed with PBS and the tectorial membrane and Reissner\u2019s membrane were removed from the fixed tissues. Samples then were incubated overnight at 4\u2009\u00b0C with 5\u2009ng/ml Alexa Fluor 488 conjugated anti-His antibody and 1 U/ml Alexa Fluor 568 phalloidin in PBS with 0.01% Triton X-100. Tissues were rinsed by PBS 3 times and mounted in Prolong Diamond for imaging. To visualize His-TMOD1 and His-CAPZ in EGFP-Myosin transfected cells, 5\u2009ng/ml Alexa Fluor 647 conjugated anti-His antibody was used instead. Samples labeled with Alexa Fluor 488 conjugated DNaseI were incubated with 1 U/ml Alexa Fluor 568 phalloidin in PBS with 0.01% Triton X-100 at room temperature for 1\u2009h before mounting. Apical IHCs were defined as being at a cochlear location approximately 30% of the distance from the apical end.\n\nFreshly dissected sensory epithelia were permeabilized in cytoskeleton buffer with 0.05% saponin, subsequently mixed with 100\u2009nM JLA20 or FITC-AC15. Samples were incubated in this solution for 5\u2009min at room temperature before being washed by cytoskeleton buffer without saponin. Samples were then fixed with 4% formaldehyde in HBSS for 2\u2009h at room temperature. Samples were rinsed with PBS and the tectorial membrane and Reissner\u2019s membrane were removed from the fixed tissues. Samples were then incubated overnight at 4\u2009\u00b0C with 1:200 secondary antibody goat anti-mouse Alexa Fluor 488 (to detect JLA20) and 1 U/ml Alexa Fluor 568 phalloidin in PBS with 0.01% Triton X-100. Tissues were rinsed with PBS 3 times and mounted in Prolong Diamond for imaging.\n\nExplants were transferred from culture dishes to HBSS 18\u2009h post-transfection with RPEL1-EGFP or EGFP-TMOD1, then rinsed in cytoskeleton buffer with 0.05% saponin and immediately fixed with 4% formaldehyde in HBSS for 2\u2009h at room temperature.\n\nThe cultured explants were categorized into 3 groups: DMSO, LatA (Latrunculin A, Sigma, Cat. # 428026), and washout. After 1-hour pre-culture, solutions from the DMSO group were exchanged with Neurobasal-A medium containing DMSO (1:1000) at a final concentration of 0.1%. The solutions from both LatA and washout group were exchanged with Neurobasal-A medium containing LatA (1:1000) with a final concentration of 1 \u03bcM. After a 1-hour incubation at 37\u2009\u00b0C and 5% CO2, the DMSO and LatA groups were then harvested and transferred to the cytoskeleton buffer for His-TMOD1 probing assay described above. The explants from the washout group were washed with warm Neurobasal-A medium 6 times to decrease the LatA concentration to be less than 0.01 \u03bcM. The dishes were further cultured for 4\u2009h before performing His-TMOD1 probing assay.\n\nCochleae were dissected from C57BL/6 mice at postnatal day 5 and lateral walls were removed to expose the sensory epithelia. One cochlea was transferred to a high-salt buffer (20\u2009mM HEPES (pH 7.5), 138\u2009mM KCl, 4\u2009mM MgCl2, 3\u2009mM EGTA, 1% bovine serum albumin, 500\u2009mM NaCl and 0.2% saponin for 2\u2009min incubation at room temperature, while the other cochlea was transferred to the same buffer without NaCl and 0.05% saponin for 2-minute incubation as a control. After high-salt extraction, tissues were washed twice with cytoskeleton buffer containing 0.05% saponin before being probed for 5\u2009min at room temperature with His-TMOD1 or His-CAPZ. Samples were then washed with cytoskeleton buffer without saponin and fixed with 4% formaldehyde at room temperature for 2\u2009h. The subsequent steps after fixation were as described above in the purified protein probing assay. In the high salt experiment, the anti-His antibody was Alexa Fluor 555 conjugated and the phalloidin was Alexa Fluor 488.\n\nDissected P5 organs of Corti were transferred to cytoskeleton buffer (20\u2009mM HEPES (pH 7.5), 138\u2009mM KCl, 4\u2009mM MgCl2, 3\u2009mM EGTA, 1% bovine serum albumin) and 0.05% saponin with or without 4\u2009mM sodium orthovanadate54 (New England Biolabs, Cat. # P0758S). After a 1-minute incubation, His-TMOD1 (714\u2009nM) was added to the solution and incubated at room temperature for 5\u2009min. Samples were then washed with cytoskeleton buffer without saponin and fixed with 4% formaldehyde at room temperature for 2\u2009h. The subsequent steps after fixation were as described above in the purified protein probing assay.\n\nSlides in Figs.\u00a03d, 4, 5, 7f, h, Supplementary Figs.\u00a02, 3, 4a, 6c, 6f, 7a were imaged with a Leica Plan Apo 63x/1.40 NA oil immersion objective on Leica SP8 inverted confocal microscope operating in resonant scanning mode (Leica Microsystems, RRID:SCR_018169). Images were captured using Leica Application Suite X (Leica Microsystem, RRID:SCR_013673) and deconvolved using Leica LIGHTNING deconvolution with the default settings. Slides in Figs.\u00a02, 3a, 6, 7b, d, Supplementary Figs.\u00a01, 4b, 4c5a, and 6e were imaged with a Zeiss Plan-Apochromat 63x/1.4 NA oil immersion objective on a Zeiss LSM 900 microscope with an Airyscan detector. Raw Airyscan images were processed in Zen software using default settings. U-ExM samples were imaged with a Leica HC FLUOTAR L 25x/0.95 NA water immersion objective on the Leica SP8. Slides in Supplementary Fig.\u00a06d were imaged using a Nikon Apochromat TIRF 100x/1.49 NA oil immersion objective on a Nikon Ti2-E with a spinning disk confocal scan head (CSU-X1, Yokogawa) and captured on a sCMOS camera (Prime95B, Teledyne Photometrics). Structured illumination (SIM) images were acquired at 26 to 30 \u00b0C with a 63x/1.4 NA oil immersion lens on a Zeiss (Oberkochen, Germany) lattice-based Elyra 7 microscope with dual PCO.edge 4.2 sCMOS cameras for detection. Illumination grid selection and z-spacing was guided by the software and kept consistent across images. Post-acquisition processing was performed with software-recommended standard filtering for the 488-nm channel, without baseline subtraction and with \u201cscale to raw\u201d checked. Contrast was manually adjusted to retain both dim and bright structures due to the high dynamic range of the phalloidin signal. Verification of channel alignment was carried out as previously described4. ImageJ (NIH, RRID:SCR_002285) was used to adjust colors and display values for the images presented in figures.\n\n3D reconstructions in Fig.\u00a01 were made with Imaris software. All images for fluorescent quantification were analyzed using ImageJ.\n\nLine scans were drawn from the top of stereocilia down the center of stereocilia. In Figs.\u00a02b, d, 3b, c, the peak level in green channel was set as 0 on x axis and the fluorescence intensity was normalized to the maximal fluorescence intensity of row 1. In Fig.\u00a03g, the stereocilia tip was defined as being the point where phalloidin intensity reached the mean value of fluorescence in the tip region. The fluorescence intensity was normalized to the average fluorescence intensity of line scan values in each channel. In Fig.\u00a06c, the peak level in RFP channel was set as 0 on axis and the fluorescence intensity was normalized to the maximal intensity in each channel.\n\nThe signals at stereocilia tips were collected by drawing a 10\u00d710 pixels circle (0.4\u2009\u03bcm in diameter). The shaft signals in stereocilia during postnatal development were collected by drawing a line (around 1\u2009\u03bcm) along the shaft. Mean value of either circle or line was measured on the maximum intensity projection. In Fig.\u00a02e, the fluorescence intensity of JLA20 was normalized to the average intensity of the cell border by cochlea. In Fig.\u00a04b, His-TMOD1 signals were normalized to the average fluorescence intensity of row 1 stereocilia from DMSO-treated samples. In Fig.\u00a04d\u2013f, His-TMOD1 signals were normalized to the average fluorescence intensity of row 1 stereocilia at P3. In Fig.\u00a05, the shaft signals were collected by drawing a 10\u00d710 pixel circle (0.45\u2009\u03bcm in diameter) at a point of 0.45\u2009\u03bcm below stereocilia tip. The average shaft intensity of His or EGFP signals was measured and normalized to the average fluorescence intensity of the corresponding His labeling in all cells or EGFP labeling in transfected cells, respectively. The shaft level of EGFP or His labeling in each stereocilium was determined by multiplying the average shaft intensity by the width of the stereocilium. In Fig.\u00a07c, His-TMOD1 signals were normalized to the average fluorescence intensity of row 1 stereocilia in control. In Fig.\u00a07e, HA-TMOD1 levels were normalized to the average fluorescence intensity of cell junctions in each image acquisition. In Fig.\u00a07g, i, the circle size for collecting tip signals was increased to 14\u00d714 pixels (0.6\u2009\u03bcm in diameter). EGFP signals were normalized to the average EGFP levels at stereocilia tips of the same row in all transfected cells. His-TMOD1 signals were normalized to the average fluorescence intensity of stereocilia tips in control cells.\n\nThe stereocilia width was defined by measuring full width at half maximum (FWHM) at a position 0.45\u2009\u03bcm below stereocilia tip. The FWHM was measured using a nonlinear curve fitting with the Gaussian function in OriginLab 2022. The measurements for samples imaged by expansion microscopy in Supplementary Fig.\u00a07 were performed as follows: The \u201cFire\u201d LUT function was applied to the maximum intensity projection. The image display range was set from 0 to 40% of the maximum displayed value. A line was drawn perpendicular to the stereocilia, across pixels with saturated intensity (at least 40% of the maximum value). The line length was measured at a position 1.41\u2009\u03bcm below stereocilia tip.\n\nAnalysis was determined by the availability of quantifiable cochleae; no statistical method was used to predetermine sample size. Only cochlear samples in which stereocilia were orientated parallel to the coverslip such that the full length was captured within a z-stack that was less than 0.5 microns thick were chosen for analysis. Maximum intensity projections of selected stacks covering the entire stereocilia length were created for analysis on individual cell. Each experiment was conducted at least two times to validate replicability. The data were collected from at least 3 wildtype cochleae and 3 mutant littermates per measurement, at least 3 cells per cochlea, and 3-7 stereocilia per cell. Statistical analysis was performed with GraphPad Prism 10. SuperPlots55 were applied to present the data with column table format from GraphPad. Smaller circles represent stereocilia or cells and larger circles represent mice or cochleae in all graphs. Each color corresponds to a replicate unit (mouse or cochlea as indicated). For plots in Figs.\u00a05b\u2013d, 7g, and 7I, each symbol corresponds to a stereocilium. Stereocilia from the same cell are represented by identical color and shape. A two-tailed Student\u2019s t test was used for pairwise comparisons. A two-way two-sided ANOVA was used to determine significant differences in Fig.\u00a03c. Data are presented as mean\u2009\u00b1\u2009standard deviation (SD). P\u2009<\u20090.05 is considered statistically significant.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Custom antibodies, DNA constructs, and mouse mutants that support this study are available upon request to the corresponding author. 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Cell Biol. 219, e202001064 (2020).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by Pennsylvania Lions Hearing Research Grant (C.M.Y), R01DC011034 (P.G.B.G), R01DC002368 (P.G.B.G), R01DC018827 (J.E.B), and R01DC015495 (B.J.P).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Biology, Indiana University, Indianapolis, IN, USA\n\nXiayi Liao,\u00a0Chun-Yu Tung\u00a0&\u00a0Benjamin J. Perrin\n\nOregon Hearing Research Center, Oregon Health & Science University, Portland, OR, USA\n\nJocelyn F. Krey\u00a0&\u00a0Peter G. Barr-Gillespie\n\nVollum Institute, Oregon Health & Science University, Portland, OR, USA\n\nJocelyn F. Krey\u00a0&\u00a0Peter G. Barr-Gillespie\n\nDepartment of Pharmacology and Therapeutics, University of Florida, Gainesville, FL, USA\n\nGhazaleh Behnammanesh\u00a0&\u00a0Jonathan E. Bird\n\nDepartment of Cellular and Molecular Physiology, Penn State College of Medicine, Hershey, PA, USA\n\nJoseph A. Cirilo Jr.\u00a0&\u00a0Christopher M. Yengo\n\nDepartment of Cellular and Molecular Medicine, The University of Arizona, Tucson, AZ, USA\n\nMert Colpan\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nX.L. and B.J.P. developed the concept, designed the study, and wrote the paper, which all authors edited. X.L. and B.J.P. produced the figures. J.A.C., M.C., C.M.Y., J.F.K., P.G.B.-G., J.E.B. and B.J.P. contributed reagents. C.-Y.T. performed the i-GONAD procedure. X.L., C.-Y.T., J.F.K., and G.B., collected data, and all authors contributed to data analysis.\n\nCorrespondence to\n Benjamin J. Perrin.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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Myosin-dependent short actin filaments contribute to peripheral widening in developing stereocilia.\n Nat Commun 16, 5835 (2025). https://doi.org/10.1038/s41467-025-60976-y\n\nDownload citation\n\nReceived: 13 November 2024\n\nAccepted: 06 June 2025\n\nPublished: 01 July 2025\n\nVersion of record: 01 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60976-y\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Healable and Scalable\nEngineered Living Materials", + "journal": "Nature Communications", + "published": "12 November 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53052-4/MediaObjects/41467_2024_53052_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53052-4/MediaObjects/41467_2024_53052_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53052-4/MediaObjects/41467_2024_53052_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53052-4/MediaObjects/41467_2024_53052_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-53052-4#Sec27" + ], + "code": [], + "subject": [ + "Biomaterials \u2013 proteins", + "Environmental biotechnology", + "Polymers" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3898282/v1.pdf?c=1731503181000", + "research_square_link": "https://www.researchsquare.com//article/rs-3898282/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53052-4.pdf", + "preprint_posted": "21 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Novel design strategies are essential to realize the full potential of Engineered Living Materials (ELMs), including their biodegradability, manufacturability, sustainability, and ability to tailor functional properties. Toward these goals, we present Mechanically Engineered Living Material with Compostability, Healability, and Scalability (MECHS) \u2013 a material that integrates these features in the form of a stretchable plastic that is simultaneously flushable, compostable, and exhibits the characteristics of paper. This plastic/paper-like material is produced directly from cultured bacterial biomass (40%) producing engineered curli protein nanofibers in scalable quantities (0.5-1 g L-1). The elongation at break (1-160%) and Young\u2019s modulus (6-450 MPa) of MECHS was tuned to more than two orders of magnitude. By genetically encoded covalent crosslinking of curli nanofibers, we increase the Young\u2019s modulus by two times. MECHS biodegrades completely in 15-75 days, while its mechanical properties are comparable to petrochemical plastics and thus may find use as compostable materials for primary packaging.Biological sciences/Biotechnology/Biomaterials/Biomaterials – proteinsBiological sciences/Biotechnology/Nanobiotechnology/NanostructuresPhysical sciences/Materials science/Structural materials/Mechanical propertiesPhysical sciences/Engineering/Chemical engineeringPhysical sciences/Nanoscience and technology/Nanoscale materials/Synthesis and processing", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nAvinash Manjula-Basavanna, Anna M. Duraj-Thatte and Neel S. Joshi are inventors on a U.S. Provisional Patent Application (63/604,497) submitted by Northeastern University.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "MECHSSupplementaryInformation.pdfMechanically Tunable, Compostable, Healable and Scalable Engineered Living Materials", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Advanced design strategies are essential to realize the full potential of engineered living materials, including their biodegradability, manufacturability, sustainability, and ability to tailor functional properties. Toward these goals, we present mechanically engineered living material with compostability, healability, and scalability \u2013 a material that integrates these features in the form of a stretchable plastic that is simultaneously flushable, compostable, and exhibits the characteristics of paper. This plastic/paper-like material is produced in scalable quantities (0.5\u20131\u2009g\u2009L\u22121), directly from cultured bacterial biomass (40%) containing engineered curli protein nanofibers. The elongation at break (1\u2013160%) and Young\u2019s modulus (6-450\u2009MPa) is tuned to more than two orders of magnitude. By genetically encoded covalent crosslinking of curli nanofibers, we increase the Young\u2019s modulus by two times. The designed engineered living materials biodegrade completely in 15\u201375 days, while its mechanical properties are comparable to petrochemical plastics and thus may find use as compostable materials for primary packaging.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The emerging field of Engineered Living Materials (ELMs) employs synthetic biology design principles to harness the programmability and the manufacturing capabilities of living cells to produce functional materials1,2,3,4. ELMs research not only provides avenues to integrate life-like properties into materials but also aims to realize de novo functionalities that are not found in natural or synthetic materials5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21. In recent years, several ELMs have been developed to demonstrate various functionalities such as adhesion, catalysis, mineralization, remediation, wound healing, and therapeutics etc22,23,24,25,26,27,28,29,30,31. ELMs that are mechanically stiff or soft have also been reported, but the rational modulation of mechanical properties to a wide range through genetic programming remains elusive5,6,9,10,11,25,32. In this regard, we present an ELM called MECHS, which stands for Mechanically Engineered Living Material with Compostability, Healability, and Scalability (Fig.\u00a01).\n\na Native and (b, c) functional curli nanofibers were separately produced from engineered Escherichia coli. d The treated biomass of engineered E. coli was dried ambiently to biofabricate MECHS films in a scalable manner. MECHS films exhibit plastic-like stretchability, mechanical tunability, and skin-like healability. Parts of the schematics were created in BioRender. Duraj-thatte, A. (2024), BioRender.com/x16s696, BioRender.com/u23i785 and BioRender.com/p06e527.\n\nAdvances in biomanufacturing are important at a time when human-made materials have been estimated to outweigh all the living biomass of planet Earth33. The existing paradigm of a linear materials economy (make-use-dispose) for synthetic materials is causing potentially irreversible damage to our ecosystem in the form of pollution and global warming. While many strategies will need to be employed to address these challenges, it is clear that bio-based manufacturing will need to be part of the solution34. Inspired by natural systems that utilize sustainable feedstocks and energy-efficient processes, coupled with their biodegradation to initiate a new cycle, biomanufacturing should strive to create materials that have similar recyclability or potential for conversion to benign components to create a circular material economy35,36,37. Such nature-inspired sustainable solutions enabled by biomanufacturing will also make inroads toward practical implementation through a combination of appropriate government policies, public interest, and investment38.\n\nPreviously, we had reported a bioplastic known as AquaPlastic composed of recombinant protein nanofibers produced by E. coli9. It exhibited Young\u2019s modulus of ~\u20091\u2009GPa and ultimate tensile strength of ~\u200925\u2009MPa, comparable to petrochemical plastics and other bioplastics9. AquaPlastic was also resistant to various chemicals (e.g., acid, base, and organic solvents), and could adhere to and coat a wide range of surfaces, protecting them from wear and environmental conditions9. However, the broad utility of AquaPlastic was limited due to its brittleness and lack of scalability. In addition, we had earlier shown that whole microbial biomass could be dried to form cohesive and glassy stiff materials with a streamlined fabrication and higher yields compared to AquaPlastic, at the expense of tunability12.\n\nIn this work, we report a fabrication strategy to combine whole cellular biomass and engineered extracellular matrix protein nanofibers that facilitate tuning of their mechanical properties. Our material, MECHS, exhibits properties similar to both plastic and paper, showcasing: (1) a fabrication strategy that enables large-scale production of flexible films at ambient conditions, analogous to paper manufacturing; (2) genetic engineering to tailor their tensile stiffness and strength; (3) compositional and morphological analysis; (4) compostability, (5) a landscape of achievable mechanical properties comparable to conventional petrochemical plastics, bioplastics and other relevant bio- and synthetic materials; and, (6) prototypes for disposable packaging applications, contributing to the creation of a sustainable circular material economy.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53052-4/MediaObjects/41467_2024_53052_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "MECHS is fabricated from a combination of whole E. coli cells and engineered recombinant curli nanofibers. Curli are an extracellular matrix component of microbial biofilms and are composed of nanofibers self-assembled from a protein building block, CsgA (Fig.\u00a01a\u2013d, Table\u00a01 and Supplementary Table\u00a01)39. Curli nanofibers are resistant to heat, solvents, pH, detergents, and denaturants, and thus serve as a good biopolymeric scaffold for robust materials40. To express the recombinant curli nanofibers, we used an E. coli strain that we previously developed (PQN4), in which the chromosomal curli genes (csgBAC, csgDEFG) have been deleted41. PQN4 was transformed with a pET21d plasmid vector encoding a synthetic curli operon, csgBACEFG, containing all the genes necessary for CsgA production, secretion, and extracellular assembly. In a typical biofabrication of MECHS, the curli-containing E. coli biomass was treated with 1\u20135% (w v\u22121) of sodium dodecyl sulfate (SDS) to obtain a gelatinous substance, which enables facile casting in a silicone mold. Ambient drying in the mold resulted in films that were brittle and, in some cases, (1% and 2% SDS) convoluted (Supplementary Figs.\u00a01, 2). To achieve flexible MECHS films, we added glycerol (1\u20135% w v\u22121), a plasticizer commonly used with bioplastics, to the gelatinous curli biomass prior to casting (Supplementary Figs.\u00a03, 4)42.\n\nMECHS films that had been pre-treated only with SDS (i.e., gelator) and no glycerol (i.e., plasticizer), were brittle as measured by tensile mechanical tests, with elongation at break values of 0.6\u2009\u00b1\u20090.4% (Fig.\u00a02a\u2013e and Supplementary Figs.\u00a05a and 6). With 1% plasticizer, the elongation at break was found to increase considerably to 10.2\u2009\u00b1\u20096.9% (Fig.\u00a02b\u2013f and Supplementary Fig.\u00a05b). Similarly, as the plasticizer content increased to 2%, 3%, 4%, and 5%, the elongation at break increased significantly to 35.5\u2009\u00b1\u20097.7%, 70.1\u2009\u00b1\u200916.3%, 101.9\u2009\u00b1\u200928.8%, and 159.3\u2009\u00b1\u200925% respectively (Fig.\u00a02b\u2013e, g\u2013j and Supplementary Fig.\u00a05c\u2013f). On the other hand, the corresponding Young\u2019s modulus decreased from 450\u2009\u00b1\u2009206.4\u2009MPa to 6.6\u2009\u00b1\u20091.7\u2009MPa as the plasticizer amount increased (Fig.\u00a02d). Ultimate tensile strength values of MECHS films also decreased with increasing plasticizer (Fig.\u00a02e). Overall, our method further streamlines the fabrication of flexible MECHS films from our previous demonstrations by casting directly from whole microbial biomass, without the need for filtration and extensive washing9. However, it also provides an opportunity to tailor their mechanical properties by two orders of magnitude by the inclusion of the engineered curli nanofibers and a plasticizer.\n\na Genetic design of E. coli to produce curli nanofibers. b Representative stress-strain curves of MECHS treated with 0 to 5% plasticizer. c Elongation at break, (d) Young\u2019s modulus, and (e) Ultimate tensile strength of MECHS treated with 0 to 5% plasticizer. Biological replicates n\u2009=\u200910. Data represented as mean\u2009\u00b1\u2009standard deviation. c *p\u2009=\u20090.0132, ****p\u2009<\u20090.0001. d *p\u2009=\u20090.029, **p\u2009=\u20090.0021, **p\u2009=\u20090.0011, ***p\u2009=\u20090.0009, ***p\u2009=\u20090.0007. e *p\u2009=\u20090.1203, ***p\u2009=\u20090.0009, ****p\u2009<\u20090.0001. One-way ANOVA followed by Tukey\u2019s multiple comparisons test. f\u2013j Representative photographs of tensile tests of MECHS films with a lateral dimension of 0.5\u2009cm by 4\u2009cm. f 1%, (g) 2%, (h) 3%, (i) 4%, and (j) 5% of plasticizer. Left image: initial. Right image: before the break.\n\nMotivated by the above results, we genetically engineered the curli nanofibers to further modulate the mechanical properties of MECHS. We previously developed a Biofilm Integrated Nanofiber Display (BIND), wherein genetic fusions to CsgA are used to modulate material properties of assembled curli nanofibers41. During extracellular self-assembly, the robust \u03b2-helical blocks of CsgA fusions, stack on top of each other to form functional curli nanofibers with the desired peptide/protein fusions displayed on their surface. We used the genetic programmability of BIND to increase the stiffness of MECHS through covalent crosslinking. To achieve this, we utilized the third generation of split proteins derived from the adhesion domain, CnaB2 of Streptococcus pyogenes (SpyTag/SpyCatcher), wherein a spontaneous reaction between the side chains of lysine and aspartic acid residues results in the formation of an isopeptide bond43. This amide bond formation was reported to have high reactivity with >\u200990% completion in 15\u2009min at 10\u2009nM concentration, and for 10\u2009\u03bcM, the half-time was less than 30\u2009s43. Moreover, the reaction does not require any activating groups and is highly specific even in various complex biological media. SpyTag and SpyCatcher were each genetically grafted to CsgA via a linker to obtain CsgA-SpyTag and CsgA-SpyCatcher (Fig.\u00a03a)43. These two CsgA constructs were expressed from separate plasmids in a co-culture, and the resulting curli biomass was used to fabricate MECHS films (denoted as CL1, Fig.\u00a01b). The tensile tests of CL1 showed that their Young\u2019s modulus (51.6\u2009\u00b1\u200918.4\u2009MPa) and ultimate tensile strengths (1.6\u2009\u00b1\u20090.4\u2009MPa) were twice that of CsgA only (i.e., not crosslinked) based MECHS films, (Fig.\u00a03c, d, f and Supplementary Figs.\u00a07a, 8a). However, the elongation at break of CL1 was found to reduce to 29.8\u2009\u00b1\u20098.6% (Fig.\u00a03e). We also tried analogous experiments with a large spacer (disordered protein domain of 225 amino acids) in between CsgA and the SpyTag/SpyCatcher domains (Figs.\u00a03b,\u00a01c)44. We introduced the large spacer for two reasons. 1) To verify if the observed increase in stiffness of CL1 was due to the covalent crosslinking of SpyTag and SpyCatcher. 2) To test if an intrinsically disordered large protein can modulate the mechanical properties such as stiffness, toughness, and elongation at break. MECHS films with this composition (i.e., CL2) were also found to have Young\u2019s modulus (46.6\u2009\u00b1\u200927.9\u2009MPa), ultimate tensile strength (1.4\u2009\u00b1\u20090.7\u2009MPa), and elongation at break (21.9\u2009\u00b1\u20096%), in the same range as that of CL1 (Fig.\u00a03c\u2013e and Supplementary Figs.\u00a07b, 8a, b). The inter-fibrillar interactions of curli nanofibers in CsgA are that of relatively weaker supramolecular interactions, whereas, for CL1 and CL2, the inter-fibrillar covalent crosslinking of curli nanofibers is expected to resist the deformation of MECHS films leading to increased Young\u2019s modulus and ultimate tensile strength. However, this was achieved at the expense of elongation at break for CL1 and CL2 films. Although the covalent crosslinks enhance the stiffness of CL1 and CL2, we speculate that the softer biomass in the interstices between curli aggregates provides alternate pathways for crack propagation. Moreover, the slight decrease in Young\u2019s modulus and the ultimate tensile strength of CL2 in comparison to CL1 might be attributed to the effect of the disordered spacer domain. We reason that an even bigger spacer domain might lead to significant reductions in stiffness and enhanced extensibility.\n\nGenetic design of E. coli to produce the functional curli nanofibers to covalently crosslink (a) CL1: SpyTag and SpyCatcher (SpyCat) domains fused to CsgA, (b) CL2: SpyTag and SpyCat domains fused to CsgA via the Spacer. c Representative stress-strain curves of MECHS films consisting of CsgA, CL1, and CL2 with 3% plasticizer. d Young\u2019s modulus, and (e) Elongation at the break for CsgA, CL1, and CL2 with 3% plasticizer. Biological replicates n\u2009=\u200910 for CsgA, n\u2009=\u200915 for CL1, and n\u2009=\u200920 for CL2. Data represented as mean\u2009\u00b1\u2009standard deviation. d *p\u2009=\u20090.01, *p\u2009=\u20090.0295. e ****p\u2009<\u20090.0001. One-way ANOVA followed by Tukey\u2019s multiple comparisons test. f Representative photographs showing a tensile test of CL1 film with the lateral dimension of 0.5\u2009cm by 4\u2009cm. Left image: initial. Right image: before the break. g Plot of normalized Congo Red absorbance and the weights of wet cell pellets. For Congo Red absorbance, biological replicates n\u2009=\u20093 for Sham and n\u2009=\u20096 for CsgA, CL1 and CL2. For weights of wet cell pellets, biological replicates n\u2009=\u20093 for Sham and n\u2009=\u200910 for CsgA, CL1 and CL2. h Plot of the estimated wet weight of curli nanofibers and the wet weight percentage of estimated curli nanofibers to the cell pellet. Biological replicates n\u2009=\u20096 for the wet weight of curli nanofibers and n\u2009=\u20093 for the percentage weight. Data represented as mean\u2009\u00b1\u2009standard deviation. i Field Emission Scanning Electron Microscopy (FESEM) images of CsgA, CL1 and CL2. Top row: cell cultures. Scale bar 1\u2009\u03bcm. Middle row: Top view of MECHS. Scale bar 10\u2009\u03bcm. Bottom row: Side view of MECHS. Scale bar 10\u2009\u03bcm.\n\nGiven the highly heterogeneous nature of the whole biomass that forms MECHS, we wanted to perform a detailed compositional analysis to understand the effects of various components therein. We focused on determining the amounts of curli biomass, gelator, and plasticizer in the final product, which may not be obvious from the fabrication protocol of MECHS. For example, treatment of the wet biomass with 1\u20135% gelator and/or plasticizer does not mean that the final MECHS film contains 1\u20135% gelator and/or plasticizer by mass since only a portion of the original SDS and glycerol will associate with the cell pellet and the rest will be discarded with the supernatant, prior to film casting.\n\nWe first focused on estimating the amount of curli nanofibers present in the films on a per-weight basis using a standard Congo Red pull-down assay for curli quantification (Fig.\u00a03g and Supplementary Fig.\u00a09a). These relative amounts of curli were converted to absolute mass estimates with a calibration curve generated using purified curli nanofibers (wet weights of CsgA fused with His-tag i.e., CsgA-His). We estimated that 500\u2009ml cultures of CsgA, CL1, and CL2 produced 530\u2009\u00b1\u2009188\u2009mg, 431\u2009\u00b1\u2009159\u2009mg, and 399\u2009\u00b1\u2009154\u2009mg of curli nanofibers, respectively (Fig.\u00a03h and Supplementary Fig.\u00a09b). The wet weights of whole cell pellets obtained from 500\u2009ml cultures of CsgA, CL1 and CL2 were found to be 2647\u2009\u00b1\u2009130\u2009mg, 2483\u2009\u00b1\u2009157\u2009mg, 2490\u2009\u00b1\u2009118\u2009mg, respectively (Fig.\u00a03g). Thus, we could estimate the percent of wet weight contributed by curli nanofibers for each construct (Fig.\u00a03h). Notably, it is possible that the fused SpyTag/SpyCatcher domains may interfere with Congo Red binding, leading to an underestimation of curli nanofiber yields. On the other hand, 500\u2009ml cultures of PQN4 with a sham plasmid (no curli operon) were found to have a wet cell pellet weight of 1936\u2009\u00b1\u2009123\u2009mg (Fig.\u00a03g). It is interesting to note that the differences in wet pellet mass between curli-producing and sham plasmids roughly corresponds to the mass of curli nanofibers in each culture, calculated from the calibrated Congo Red binding assay (Fig.\u00a03g, h).\n\nWe then set out for an extensive weight analysis to better understand the composition and the effect of various steps involved in the fabrication of MECHS. First, we determined that the ambient drying of the wet pellet of curli biofilm (without the treatment of gelator and plasticizer) results in a dry pellet with a weight percentage (dry to wet pellet) of 20.3\u2009\u00b1\u20091.8% (Supplementary Fig.\u00a010a, b). The dry weight of MECHS films obtained after treatment of 1\u20135% of gelator was found to be about 100\u2009mg, while the dry weight of the supernatant (collected from all the SDS treatment and water washings of cell pellets) was found to increase linearly (Supplementary Fig.\u00a011a\u2013d). It is to be noted that the experimentally obtained sum of weights of MECHS and the corresponding dry supernatant were consistent with their theoretically calculated weights (Supplementary Fig.\u00a012a\u2013e). Further, we estimated that the weights of the gelator-treated MECHS films were nearly half of the estimated dry weight (20.3% of wet pellet weight) of curli biomass (Supplementary Fig.\u00a011c). Similarly, the weights of MECHS films obtained from 1% and 2% gelator were nearly 45% and 30%, respectively, of the estimated total weight of all precursors, whereas that for 3\u20135% gelator was about 25% (Supplementary Fig.\u00a011d). These results also suggest that the convoluted MECHS films obtained from 1% and 2% gelator upon drying could be attributed to the incorporation of more cellular biomass into the films, while the 3\u20135% gelator might extract more cellular components like lipids into the supernatant (Supplementary Fig.\u00a02b, c). Moreover, it is to be noted that unlike 1% and 2% of gelator concentrations, the 3\u20135% of gelator leads to better gelatinous curli biomass (Supplementary Fig.\u00a01). As the percentage weight of MECHS with respect to the dry weight of curli biofilm remains at around 45%, it suggests that the higher gelator (3\u20135%) content might not lead to additional loss of biomass into supernatant (Supplementary Fig.\u00a011c). This latter inference is also supported by the fact that weight of dried supernatant increases in steps of ~\u200950\u2009mg, which is consistent with the expected increase in the theoretical weights of gelator (e.g., 5\u2009ml of 1% accounts for 50\u2009mg) (Supplementary Fig.\u00a011b).\n\nAs noted above, 3\u20135% gelator-treated MECHS comprises nearly 45% dry weight of the whole cell pellet, then we reasoned that by determining the amount of SDS, we could estimate the total (cellular and curli) biomass in the MECHS (Supplementary Fig.\u00a011c). By using Energy Dispersive X-ray Analysis (EDAX) we found out that for 3% gelator-treated MECHS, the weight percentage of Sodium and Sulfur elements were 2.2\u2009\u00b1\u20090.2% and 4.5\u2009\u00b1\u20090.3%, respectively, whereas the same elements for the curli biofilm cell pellet (without SDS treatment) were 0.6\u2009\u00b1\u20090.1% and 1.2\u2009\u00b1\u20090.5%, respectively (Supplementary Fig.\u00a013). Using this data, we estimate that for 3% gelator-treated films, roughly 5% (~\u20091.6% Sodium and ~\u20093.3% Sulfur) of MECHS weights could comprise of SDS (Supplementary Fig.\u00a011a, c). Therefore, we can estimate that about 40% of the total cellular and curli biomass might be utilized to form the gelator-treated MECHS.\n\nOn the other hand, based on the weights of plasticizer-treated MECHS films and their corresponding dry supernatant weights, we could estimate that 15\u201320% of the total plasticizer utilized might get incorporated into MECHS, assuming that no additional biomass was lost to the supernatant during this phase of fabrication (Supplementary Figs.\u00a011, 14 and 15). In addition, the weights of MECHS films of CsgA, CL1, and CL2 and their dried supernatants were in the same range, which further validates that the covalent crosslinking in CL1 and CL2, leads to increased stiffness and not due to any variations in the plasticizer amounts (Supplementary Figs.\u00a016 and 17).\n\nField Emission Scanning Electron Microscopy (FESEM) images from cultures of CL1, and CL2 showed aggregated mats of material, presumably due to nanofiber bundling promoted by the SpyTag/SpyCatcher covalent crosslinking. Images obtained from CsgA cultures did not show such aggregation (Fig.\u00a03i). FESEM images of MECHS (top and side view) further showed that the curli biomass is densely packed to form continuous films (Fig.\u00a03i and Supplementary Figs.\u00a018, 19).\n\nTo test the relative compostability of MECHS films compared to other conventional plastics and bioplastics, we buried samples of each in a commercially available compost called fishnure, derived from fish manure. Experiments were performed in a mini greenhouse setup with samples of uniform size and shape (Supplementary Figs.\u00a020, 21). Under these conditions, MECHS films biodegraded completely in 15 days, while all the other samples did not (Fig.\u00a04a, c and Supplementary Figs.\u00a021\u201323). Toilet paper and kimwipes biodegraded to 70% and 40%, respectively (Fig.\u00a04a, c and Supplementary Fig.\u00a021). The bioplastics, cellulose acetate (CA), and poly-L-lactic acid (PLLA) were biodegraded by 13% and 1% respectively, whereas the petrochemical plastics polyethylene terephthalate (PET) and low-density polyethylene (LDPE) did not show any biodegradation (Supplementary Fig.\u00a022). On the other hand, two different commercial polyvinyl alcohol (PVA) formulations, PVA-Mc and PVA-Sp, lost 17% weight and completely disappeared in 5 days, respectively (Supplementary Fig.\u00a023).\n\nRepresentative photographs showing the biodegradation of MECHS and toilet paper in (a) a fresh fishnure (b) a dry fishnure. The lateral dimensions of the MECHS film and the toilet paper were 5\u2009cm by 5\u2009cm. c Plot shows the normalized biodegradation weight loss of MECHS (CsgA, CL1, and CL2), toilet paper, kimwipe (KW), polyvinyl alcohol - Mckesson (PVA-Mc), cellulose acetate (CA), poly-L-lactic acid (PLLA), polyethylene terephthalate (PET) and low-density polyethylene (LDPE). Biological replicates n\u2009=\u20093 for CsgA, CL1 and CL2. Technical replicates n\u2009=\u20093 for paper, KW, PVA-Mc, CA, PLLA, PET, and LDPE. Data represented as mean\u2009\u00b1\u2009standard deviation. Photographs show the dissolution of d MECHS e toilet paper, f PVA-Mc, and g polyvinyl alcohol - Superpunch (PVA-Sp). d\u2013g Lateral dimension of the films was 1\u2009cm by 5\u2009cm. h Photograph of a black bean seedling grown in the soil mixed with fishnure (comprising biodegraded MECHS) in a 9:1 ratio. i FESEM image of MECHS film healed by placing microliters of water at the site of abrasion (black arrows). Scale bar 200 \u03bcm. j Photograph shows the MECHS films welded (black arrows) by using water. Scale bar 0.5\u2009cm.\n\nSome of the mass loss in the experiments above may have been attributable to dissolution in the moist fresh fishnure, rather than biodegradation, especially for MECHS and PVA. Therefore, we performed additional compostability tests in fishnure that was dried (i.e., placed in the greenhouse for 50 days). Under these conditions, MECHS films were able to biodegrade completely in 75 days (Fig.\u00a04b, c and Supplementary Fig.\u00a024). The toilet paper, kimwipe, and CA were found to degrade by about 60, 16, and 13%, respectively, whereas PLLA, PET, and LDPE did not show any biodegradation in dry fishnure (Fig.\u00a04b, c and Supplementary Figs.\u00a024, 25). However, PVA-Mc had nearly 10% weight loss, whereas PVA-Sp was found to be intact even after 75 days in dry fishnure. We could not determine the weight loss of PVA-Sp as the film was firmly sticking to the fishnure granules. These experiments show that the MECHS films are completely compostable and that their biodegradation compares favorably to many plastics, bioplastics, and even toilet paper.\n\nFor the potential use of MECHS as flushable packaging materials, we tested its ability to dissolve in water (Fig.\u00a04d, e). The MECHS films did not dissolve completely, likely due to the network of hydrophobic curli nanofibers. We speculate that the more water-soluble components like glycerol, SDS, and the other cellular biomass leach into water more readily. PVA-Sp dissolves completely in water, whereas PVA-Mc dissolves only partially, leaving behind water-insoluble strips possibly compromising its biodegradation (Fig.\u00a04f, g). Furthermore, except for MECHS, all the other plastics compared here are composed only of carbon, hydrogen, and oxygen. Therefore, their biodegradation is often considered, in terms of breakdown completely, to lead to carbon dioxide. However, MECHS is largely composed of protein, making it the only plastic amongst those compared here with any significant nitrogen content. Therefore, it may be reasonable to consider its potential as a biofertilizer to support plant growth (Fig.\u00a04h). Further, MECHS films could also be healed and welded by using microliters of water at the site of abrasion or attachment and subsequently ambient dried (Fig.\u00a04i, j).\n\nThe fabrication method presented in this work yielded 500\u20131000\u2009mg of MECHS films per liter of culture, which is nearly 10 times higher than the 50\u2013100\u2009mg obtained from our previously reported AquaPlastic protocol9. In addition, the MECHS biofabrication method speeds up the process in comparison to the tedious and slow filtration process utilized for AquaPlastic. We achieved these yields even with a standard shake-flask format that is routinely used in laboratory settings for recombinant protein production. Therefore, tens of liters of bacterial culture could be used to fabricate large MECHS prototypes, such as thin films, tens of centimeters in one dimension (Fig.\u00a05a, b and Supplementary Fig.\u00a026). We also created a detergent pod as an example of the flushable and biodegradable primary package (Fig.\u00a05c).\n\na Photograph shows a refined prototype of MECHS film with a lateral dimension of 5\u2009cm by 50\u2009cm. b Photograph shows the optical transparency of MECHS film with a lateral dimension of 10\u2009cm by 15\u2009cm. c Photograph of a detergent pod (lateral dimension of 4\u2009cm by 3\u2009cm) wrapped with a MECHS film. d Ashby plot shows Young\u2019s modulus and elongation at break for MECHS and various synthetic materials and biomaterials. Low-density polyethylene (LDPE), Polytetrafluoroethylene (PTFE), Poly-L-lactic acid (PLLA), Polyethylene terephthalate (PET), Cellulose acetate (CA), Polypropylene (PP), Polyvinyl chloride (PVC), Polyvinyl alcohol - Superpunch (PVA-Sp), Polyvinyl alcohol - Mckesson (PVA-Mc), Aluminum foil (Al Foil), Parafilm, Kimwipes (KW) and Toilet paper.\n\nTo better visualize and compare the mechanical properties of MECHS, we present an Ashby plot of Young\u2019s modulus and elongation at break for various plastics, bioplastics, biomaterials, and synthetic materials (Fig.\u00a05d and Supplementary Figs.\u00a027\u201335). It is thus evident that Young\u2019s modulus of MECHS is in the same range of LDPE, PTFE (polytetrafluoroethylene), PVA, and paper, while its elongation at break matches that of CA, PVC (polyvinyl chloride), PP (polypropylene), PET, PLLA, and parafilm.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53052-4/MediaObjects/41467_2024_53052_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53052-4/MediaObjects/41467_2024_53052_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53052-4/MediaObjects/41467_2024_53052_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53052-4/MediaObjects/41467_2024_53052_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We previously reported the curli nanofiber-based bioplastic fabrication protocol (i.e., AquaPlastic), which involved the filtration of bacterial culture to concentrate curli nanofibers and form gels9. Using that protocol, concerns about clogging necessitated the use of filters with 10\u2009\u03bcm pores, leading to the loss of significant amounts of curli nanofibers. The MECHS fabrication protocol described in this paper increased the yield of bioplastic by a factor of ten by utilizing not only all the curli nanofibers in the pelletized biomass but also the other water-insoluble cellular biomass. We also found that the SDS gelator could be supplemented with a plasticizer like glycerol to obtain flexible films of MECHS, as compared to the significantly more brittle AquaPlastic. Glycerol, being a byproduct of the biodiesel industry, offers several advantages viz., nontoxic, low-cost, and renewable45. Unlike the conventional petrochemical plastics and other bioplastics that are processed by thermal molding, MECHS was molded into films by ambient drying of gelatinous biomass, which we have termed aqua molding. The healing and welding of MECHS films by using tiny droplets of water are termed aqua healing and aqua wedding, respectively.\n\nThe tunability of MECHS, with its range of mechanical properties (e.g., elongation at break 1\u2013160%; Young\u2019s modulus 6\u2013450\u2009MPa) and transparency, provides a promising platform to access biodegradable alternatives to synthetic materials like petrochemical plastics. We were also able to use our streamlined protocol to achieve high yields of 0.5\u20131\u2009g\u2009L\u22121 and generate large, refined prototypes. Further, we could obtain ~\u200940\u2009cm2 of MECHS film per liter of culture. Therefore, to biofabricate a roll of MECHS film with the lateral dimensions 2\u2009m\u2009\u00d7\u20095\u2009cm, we would require ~\u200925\u2009L of culture. Another notable feature of this work is that 40% of the total cellular biomass gets incorporated into the plastic/paper-like MECHS, which could also be instrumental in attracting further research to utilize cellular biomass for the development of various sustainable functional materials.\n\nDuring the MECHS biofabrication, most of the SDS ends up in the supernatant, which, when dried, resulted in a brown-yellowish color pellet. Thus, we believe that SDS, being a surfactant removes the brown-yellowish color of the cell pellet, which makes MECHS film transparent. Curli nanofibers are assembled from CsgA protein building blocks that comprise a rigid beta-helical structure, which, in simple terms, can be regarded as a quasi-crystalline ordering. So, when these curli nanofibers (without plasticizer) based rigid materials are subjected to tensile stress beyond its yield point, the strain-induced crack propagates, and it quickly breaks the material. However, by incorporating a plasticizer like glycerol, the amorphous nature of the plasticizer that surrounds the rigid curli nanofibers inhibits crack propagation by subjecting the material to undergo plastic deformation. Thus, with increasing plasticizer amounts from 1\u20135%, the elongation at break was found to increase from 1 to 160%.\n\nPlastics are one of the most abundant human-made materials, with over 8.3 billion tons produced cumulatively, 79% of which are estimated to have accumulated in landfills and oceans46. In addition, the contamination of microplastics in almost all parts of the globe further enhances their threat to our health and the environment47,48. Biodegradable bioplastics account for less than 1% of the global plastic market, and their limited properties warrant the development of alternatives35. Given that the typical lifetime of packaging material is 1-2 years, and the packaging industry accounts for nearly one-third of the plastic market, there exists a tremendous scope and opportunity for biodegradable packaging, though success will likely need to be achieved through the commercialization of drop-in replacements for existing materials. Notably, water-soluble polymers like PVA (commonly found in detergent pods) have limited biodegradation under diverse settings of land and water49. In many cases, dissolvable polymers like PVA are blended with petrochemical plastics to enhance certain material properties, but this limits their water dispersibility and biodegradability (as observed in our biodegradation tests with the commercially available PVA-Mc)50.\n\nAlthough we were able to develop refined prototypes of MECHS thin films, additional work will be needed to improve the mechanical properties (e.g., ultimate tensile strength, tear strength) and resistance to water. Furthermore, the circular materials economy loop will have to be closed by employing a feedstock for bacterial culture derived closely from CO2 fixation, such as cellulose hydrolysate obtained from agricultural waste. There are also several opportunities to utilize synthetic biology tools to tailor the material properties of curli nanofibers, which need to be explored. The concept of using biodegraded MECHS as a biofertilizer for plant growth warrants further investigation. All in all, in this work, we have demonstrated that the manufacturing capabilities of living cells can be employed to produce mechanically tunable, scalable, and compostable ELMs as a potential alternative to synthetic materials like plastics. Finally, we believe that innovative approaches involving synthetic biology and materials engineering could lead to greater advancements in creating energy-efficient and sustainable solutions to a greener ecosystem.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Low-density polyethylene LDPE (ET31-FM-000151, 50\u2009\u03bcm thick), Polytetrafluoroethylene PTFE (FP30-FM-000250, 50\u2009\u03bcm thick), Poly-L-lactic acid PLLA (ME33-FM-000150, 50\u2009\u03bcm thick), Polyethylene terephthalate PET (ES30-FM-000150, 50\u2009\u03bcm thick), Cellulose acetate CA (AC31-FM-000151, 50\u2009\u03bcm thick) and Polypropylene PP (PP30-FM-000250, 50\u2009\u03bcm thick) were obtained from Goodfellow Corporation. Polyvinyl chloride PVC (S-16280, 15\u2009\u03bcm thick) was obtained from Uline. Polyvinyl alcohol - Superpunch PVA - Sp (ASIN: B01M11T6U5; a water-soluble stabilizer for embroidery and it is claimed to be made from 100% PVA and thus it dissolves completely in water), Polyvinyl alcohol - Mckesson PVA - Mc (ASIN: B01ETFMUH2; a hot water soluble bag, which is also claimed to be made from PVA, but it does not dissolve completely in water at room temperature, probably because PVA might be blended with other components.) and Silicone mats (ASIN: B09SPB72TT) were obtained from Amazon. Aluminum foil, Parafilm, Toilet paper, and Kimwipes were obtained from Reynolds Consumer Products, Bemis Company Inc., Signature Select, and Kimberly-Clark Corporation, respectively. Glycerol (G9012) and Sodium dodecyl sulfate SDS (S0295) were obtained from Sigma-Aldrich and Teknova, respectively.\n\npET21d plasmid was cloned with the curli operon genes csgA, csgB, csgC, csgE, csgF, and csgG that encodes the proteins necessary for the biosynthesis of curli nanofibers and it is labeled as pET21d-CsgA. The genes encoding the SpyTag peptide and SpyCatcher protein derived from an earlier report43 were fused to the C-terminus of CsgA with an intervening 36 amino acid flexible linker to obtain the plasmids pET21d-CsgA-SpyTag and pET21d-CsgA-SpyCatcher, respectively (Supplementary Table\u00a01). The gene encoding the Spacer, an intrinsically disordered protein44, was inserted between the linker and the SpyTag or the SpyCatcher to obtain pET21d-CsgA-Spacer-SpyTag and pET21d-CsgA-Spacer-SpyCatcher, respectively. The genes were synthesized (Integrated DNA Technologies) and cloned into pET21d vector using isothermal Gibson assembly (New England Biolabs).\n\nThe plasmids pET21d-CsgA, pET21d-CsgA-SpyTag, pET21d-CsgA-SpyCatcher, pET21d-CsgA-Spacer-SpyTag, and pET21d-CsgA-Spacer-SpyCatcher were separately transformed into PQN4, an E. coli cell strain derived from LSR10 (MC4100, \u0394csgA, \u03bb(DE3), CamR) with the deletion of the curli operon (\u2206csgBACEFG) to produce the corresponding MECHS41.\n\npET21d-CsgA plasmid was transformed into PQN4 and streaked onto a lysogeny broth (LB) agar plate containing 100\u2009\u00b5g\u2009ml\u22121 carbenicillin and 0.5% glucose (m v\u22121) for catabolite repression of T7RNAP and incubated overnight at 37\u2009\u00b0C. A single colony of PQN4-pET21d-CsgA was picked from the agar plate and cultured at 37\u2009\u00b0C in 5\u2009ml LB media, 100\u2009\u00b5g\u2009ml\u22121 carbenicillin, and 2% glucose (m v\u22121). The overnight culture was transferred to a fresh 500\u2009ml LB media containing 100\u2009\u00b5g\u2009ml\u22121 carbenicillin and cultured for 48\u2009h in incubator shakers (225\u2009rpm, 37\u2009\u00b0C) to express the CsgA curli protein nanofibers.\n\nFor Covalently Crosslinked-1 (CL1), the plasmids pET21d-CsgA-SpyTag and pET21d-CsgA-SpyCatcher were separately transformed into PQN4 and streaked onto lysogeny broth (LB) agar plates containing 100\u2009\u00b5g\u2009ml\u22121 carbenicillin and 0.5% glucose (m v\u22121) for catabolite repression of T7RNAP and incubated overnight at 37\u2009\u00b0C. A single colony was picked from the agar plates of PQN4-pET21d-CsgA-SpyTag and PQN4-pET21d-CsgA-SpyCatcher and cultured separately at 37\u2009\u00b0C in 5\u2009ml LB media, 100\u2009\u00b5g\u2009ml\u22121 carbenicillin and 2% glucose (m v\u22121). The overnight cultures of PQN4-pET21d-CsgA-SpyTag and PQN4-pET21d-CsgA-SpyCatcher were transferred to a fresh 500\u2009ml LB media containing 100\u2009\u00b5g\u2009ml\u22121 carbenicillin and co-cultured for 48\u2009h in incubator shakers (225\u2009rpm, 37\u2009\u00b0C) to express and covalently crosslink the engineered curli protein nanofibers. Similarly, the plasmids pET21d-CsgA-Spacer-SpyTag and pET21d-CsgA-Spacer-SpyCatcher were utilized for Covalently Crosslinked-2 (CL2) MECHS.\n\nThe 48\u2009h cell culture (500\u2009ml) of PQN4-pET21d-CsgA (CsgA) was centrifuged (5000\u2009\u00d7\u2009g, 10\u2009min) to pelletize the curli biomass, which was then washed with 250\u2009ml of deionized water by centrifuging (5000\u2009\u00d7\u2009g, 10\u2009min) to remove the residual quantities of culture media. 1\u2009\u00d7\u2009g (wet pellet) of curli biofilm biomass was first dispersed in 5\u2009ml of deionized water and subsequently added with 5\u2009ml of 1, 2, 3, 4, or 5% (w v\u22121) of sodium dodecyl sulfate (SDS, serves as a gelator and also helps to obtain the transparent MECHS films by taking away the brown-yellowish color of cell pellet into the supernatant), which was then mixed on a shaker for 2\u2009h at room temperature. The resulting gelatinous biomass was washed with 10\u2009ml of deionized water twice by centrifuging (5000\u2009\u00d7\u2009g, 10\u2009min) to remove the soluble biomolecules and the excess SDS. This SDS treated gelatinous biomass was casted and ambient dried on a silicone mold to obtain the MECHS films that were brittle.\n\nTo realize the flexible films of MECHS, the 3% SDS-treated gelatinous biomass of PQN4-pET21d-CsgA (CsgA) was added with 5\u2009ml of 1, 2, 3, 4, or 5% (w v\u22121) of glycerol (serves as a plasticizer) and mixed on a shaker for 1\u2009h at room temperature. The glycerol treated and centrifuged (5000\u2009\u00d7\u2009g, 10\u2009min) biomass was casted on a silicone mold and ambient dried to obtain the flexible MECHS films.\n\nSimilarly, to realize the Covalently Crosslinked (CL1 and CL2) films of MECHS, 5\u2009ml of 3% SDS and 5\u2009ml of 3% glycerol treated curli biomass was utilized. For all constructs, a minimum of ten replicates were tested.\n\n100\u2009\u03bcL of cell culture was vacuum filtered on a membrane (0.22\u2009\u03bcm pore size, Millipore GTTP02500) and washed with 100\u2009\u03bcL of deionized water thrice. The samples were fixed by immersing in 2\u2009ml 1:1 mixture of 4% (w v\u22121) glutaraldehyde and 4% (w v\u22121) paraformaldehyde at room temperature, overnight. The samples were gently washed with water, and the solvent was gradually exchanged to ethanol (200 proof) with an increasing ethanol 15-minute incubation step gradient [25, 50, 75, and 100% (v v\u22121) ethanol]. The samples were then dried in a critical point dryer, placed onto SEM sample holders using silver adhesive (Electron Microscopy Sciences) and sputtered until they were coated in a 10\u201320\u2009nm layer of Pt/Pd. Whereas the films of MECHS were directly sputter coated with a 10\u201320\u2009nm layer of Pt/Pd without critical point drying. Images were acquired using a Zeiss Gemini 360 FESEM equipped with a field emission gun operating at 5\u201310\u2009kV. Representative images from three independent samples were reported.\n\nOxford Instruments Ultim Max EDS equipped with AZtecLive software attached to Zeiss Gemini 360 FESEM was utilized to detect the elements as well as determine their composition using factory standards. EDS spectra were recorded on sample\u2019s surface with the lateral dimensions of 225\u2009\u03bcm by 170\u2009\u03bcm. Data from three independent samples were reported.\n\nOptical images were acquired using a Canon EOS Rebel SL3 Digital SLR Camera equipped with XIT 58\u2009mm 0.43 Wide Angle Lens and XIT 58\u2009mm 2.2x Telephoto Lens. Representative images from three independent samples were reported.\n\nTensile measurements of MECHS, commercially available plastics, bioplastics, and all other materials mentioned in this report were performed using a DHR-3 rheometer (TA Instruments) under ambient laboratory conditions. Films with lateral dimensions of 4\u2009cm by 0.5\u2009cm under a constant linear deformation of 1\u2009\u03bcm s\u22121 were utilized for tensile tests. A minimum of five samples were tested for each type.\n\nThe thickness of the films was measured using a contact profilometer, Dektak 3ST equipped with a 2.5\u2009\u03bcm stylus having a vertical resolution of 1\u2009\u00c5. A minimum of three tests were performed for each sample.\n\nThe MECHS prototype of 50\u2009cm\u2009\u00d7\u20095\u2009cm lateral dimension was fabricated from 6\u2009L cultures of PQN4-pET21d-CsgA (obtained by using 3% SDS and 3% glycerol treatment), whereas the 15\u2009cm\u2009\u00d7\u200910\u2009cm and the detergent pod prototypes were obtained from that of 4 and 3\u2009L cultures, respectively.\n\nThe films of MECHS (PQN4-pET21d-CsgA, obtained by using 3% SDS and 3% glycerol) were cut using scissors, and ~\u200910\u2009\u03bcL of deionized water was added at the cut site and subsequently dried at ambient laboratory conditions to heal the cut. A minimum of three samples were tested. Similarly, MECHS films of 0.5\u2009cm by 5\u2009cm were welded by using ~\u200910\u2009\u03bcL of deionized water and subsequently dried at ambient laboratory conditions.\n\nA commercially available odorless organic humus compost named Fishnure (Amazon, ASIN: B086KXT5TQ), which is made from fish manure, was utilized for the biodegradation test. Samples with lateral dimensions of 5\u2009cm\u2009\u00d7\u20095\u2009cm were buried in a tray containing 3.5\u2009kg of Fishnure. The biodegradation experiment was conducted in a mini greenhouse (Amazon, ASIN: B01D7GHEES) setup (exposed to direct/indirect sunlight through the large windows of the laboratory), wherein a temperature of 20\u2009\u00b0C and a relative humidity of 80% was maintained. The films of MECHS degraded completely in 15 days in a freshly opened bag of Fishnure. In another biodegradation experiment, a dry (by placing in the mini greenhouse setup for 50 days) Fishnure was utilized and under these conditions, films of MECHS degraded completely in 75 days. A minimum of three samples were tested for each type.\n\n1\u2009ml of cell culture (as described above: 48\u2009h, 500\u2009ml at 37\u2009\u00b0C) was pelleted by centrifuging (6000\u2009\u00d7\u2009g, 10\u2009min), and the resulting cell pellet was incubated with 1\u2009ml of 0.004% (w v\u22121) Congo Red dye for 10\u2009min. The dye-treated cell culture was pelletized by centrifuging (6000\u2009\u00d7\u2009g, 10 min), and the resulting supernatant (200 \u03bcL) was utilized to measure the absorbance at 480 nm in a plate reader. The net Congo Red absorbance of curli in CsgA, CL1 and CL2 were determined by subtracting the absorbance values of cell pellet having a sham plasmid (without curli operon), to account for the non-specific binding to other biomolecules.\n\nTo estimate the curli nanofibers produced, we utilized 0.004% (w v\u22121) Congo Red dye to prepare a standard curve for various concentrations of purified CsgA. Herein, C-terminal His-tagged CsgA (CsgA-His) was expressed and purified using Ni-NTA (Nickel-nitrilotriacetic acid resin) column51. However, after eluting the CsgA-His with the elution buffer, the buffer was exchanged with water using a 10\u2009kDa Amicon centrifugal filter. This buffer exchange facilitates fibrillation of CsgA-His and the resulting pellet (wet weight) was utilized for the CsgA (CsgA-His) Congo Red standard curve. A minimum of three samples were tested for each type.\n\nAll experiments presented in this article were repeated at least three times (n\u2009\u2265\u20093) on distinct samples or biological replicates, as clearly specified in the figure legends or the relevant Methods sections. In all cases, data are presented as the mean and standard deviation. GraphPad PRISM 8, OriginPro 2024, Oxford Instruments Ultim Max EDS AZtecLive software, TRIOS software V5.2, Adobe Photoshop 2024 and Adobe Illustrator 2024 were utilized for plotting and analyzing data. For micrographs and optical images, we present representative images.\n\nFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All relevant data supporting the findings of this study are available within the Article and its Supplementary Information.\u00a0Source data are provided in this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Nguyen, P. Q., Courchesne, N.-M. D., Duraj-Thatte, A., Praveschotinunt, P. & Joshi, N. S. Engineered living materials: Prospects and challenges for using biological systems to direct the assembly of smart materials. Adv. Mater. 30, 1704847 (2018).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nLiu, A. P. et al. The living interface between synthetic biology and biomaterial design. Nat. 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We thank Arjun Rajesh and Bismay Hota for their assistance with capturing photographs of MECHS biodegradation and prototypes. Parts of the schematics were adapted from BioRender.com.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts, USA\n\nAvinash Manjula-Basavanna\u00a0&\u00a0Neel S. Joshi\n\nDepartment of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA\n\nAvinash Manjula-Basavanna\u00a0&\u00a0Anna M. Duraj-Thatte\n\nDepartment of Bioengineering, Northeastern University, Boston, Massachusetts, USA\n\nAvinash Manjula-Basavanna\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nA.M.-B. conceived the project and performed all the experiments and analyses. A.M.-B. and A.M.D.-T. cloned all the curli variants. A.M.-B. and N.S.J. wrote and edited the manuscript. All authors discussed and commented on the manuscript.\n\nCorrespondence to\n Avinash Manjula-Basavanna or Neel S. Joshi.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "A.M.-B., A.M.D.-T., and N.S.J. are inventors on a U.S. Provisional Patent Application (63/604,497) submitted by Northeastern University. All authors declare no other competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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0000000000000000000000000000000000000000..683b3e858202eed84a968b29be88c95a9439fe07 --- /dev/null +++ b/93c6389d707e757a854e2c6d188faec8ea35e79587b4f8627a42a4333811228d/metadata.json @@ -0,0 +1,131 @@ +{ + "title": "Exponentially-enhanced quantum sensing with many-body phase transitions", + "pre_title": "Exponentially-enhanced quantum sensing with many-body phase transitions", + "journal": "Nature Communications", + "published": "03 June 2025", + "supplementary_0": [ + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60291-6/MediaObjects/41467_2025_60291_MOESM1_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://github.com/SaubhikSarkar/QFI_First_Order_Phase_Transition" + ], + "code": [ + "https://github.com/SaubhikSarkar/QFI_First_Order_Phase_Transition" + ], + "subject": [ + "Phase transitions and critical phenomena", + "Quantum metrology" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5331341/v1.pdf?c=1749035285000", + "research_square_link": "https://www.researchsquare.com//article/rs-5331341/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60291-6.pdf", + "preprint_posted": "12 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Quantum sensors based on critical many-body systems are known to exhibit enhanced sensing capability. Such enhancements typically scale algebraically with the probe size. Going beyond algebraic advantage and reaching exponential scaling has remained elusive when all the resources, such as the preparation time, are taken into account. In this work, we show that many-body systems featuring first order quantum phase transitions can indeed achieve exponential scaling of sensitivity, thanks to their exponential energy gap closing. Remarkably, even after considering the preparation time using local adiabatic driving, the exponential scaling is sustained. Our results are demonstrated through comprehensive analysis of three paradigmatic models exhibiting first order phase transitions, namely Grover, $p$-spin, and biclique models. We show that this scaling survives moderate decoherence during state preparation and also can be optimally measured in experimentally available basis.Physical sciences/Physics/Quantum physics/Quantum metrologyPhysical sciences/Physics/Condensed-matter physics/Phase transitions and critical phenomena", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Quantum sensors based on critical many-body systems are known to exhibit enhanced sensing capability. Such enhancements typically scale algebraically with the probe size. Going beyond algebraic advantage and reaching exponential scaling has remained elusive when all the resources, such as the preparation time, are taken into account. In this work, we show that many-body systems featuring first order quantum phase transitions can indeed achieve exponential scaling of sensitivity, thanks to their exponential energy gap closing. Remarkably, even after considering the preparation time using local adiabatic driving, the exponential scaling is sustained. Our results are demonstrated through comprehensive analysis of three paradigmatic models exhibiting first order phase transitions, namely Grover, p-spin, and biclique models. We show that this scaling survives moderate decoherence during state preparation and also can be optimally measured in experimentally available basis. Our findings comply with the fundamental bounds and we show that one can harness the exponential advantage through an adaptive strategy even away from the phase transition point.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Quantum sensing is an important component of quantum technologies due to its potential for developing a new generation of probes, capable of environmental monitoring with unprecedented precision beyond classical sensors1. In this context, the sensitivity of a probe can be quantified by Fisher information, inverse of which puts a bound on the uncertainty of the estimation protocol2,3. In classical sensors, Fisher information, at best, scales linearly with resources, such as the system size L (standard limit). Quantum features may result in super-linear scaling of Fisher information, known as quantum enhanced sensitivity. This has been discovered in a series of seminal works by Giovannetti et al., where they showed that a special form of entangled states, known as the Greenberger-Horne-Zeilinger (GHZ) states, can be used to estimate the phase imprinted by a unitary operation with Fisher information scaling as L2 (Heisenberg limit)4. In the presence of k-body interactions in the generator of the unitary operation, the sensitivity can be further enhanced to5L2k. In a fundamentally different approach, quantum enhanced sensitivity has also been identified in many-body systems6 when they go through a quantum phase transition. This includes, first-order7,8,9, second-order10,11,12,13,14,15,16, Floquet17, time crystal18,19,20, Stark21 and quasi-periodic22 localization, and topological23,24 phase transitions. In all these critical systems, where Fisher information scales algebraically as L\u03b2 (with \u03b2\u00a0>\u00a01), the many-body system goes through an algebraic energy gap closing in its spectrum. This gives rise to the conjecture that energy gap closing might be the reason behind quantum enhanced sensitivity25, which is supported by a recent seminal work26 on metrological limits. Non-equilibrium quench dynamics in many-body systems have also been explored for achieving quantum-enhanced sensitivity27 in which Fisher information also depends on evolution-time t and typically scales as t2L\u03b2, following the scope of the generalized Heisenberg limit5,28. While in all these cases, Fisher information, and thus the precision, scales algebraically, one may wonder whether quantum features can result in the possibility of even a better quantum advantage, namely exponentially enhanced quantum sensing.\n\nExponential enhancement has in fact been reported in ref. 29, for the GHZ-based sensing protocols where the required entanglement in the initial state demands exponentially large number of unitary gates, making its implementation very challenging. In non-Hermitian systems exponential sensitivity can be achieved in the eigenenergy spectrum at exceptional points (parameter value where multiple eigenvalues and eigenstates coalesce)30,31,32,33,34. However, it is debated whether the quantum advantage would survive the quantum noise arising from the non-orthogonality of the eigenstates35,36. Proposals based on tight-binding non-Hermitian topological systems have also reported exponential sensitivity37,38,39 for inferring the value of a perturbative boundary coupling in the steady state. While these works show great potential for quantum enhancement, the schemes are restrictive for several reasons: (i) the preparation time for the steady state is typically long whose consideration in resource analysis may destroy quantum advantage; (ii) the schemes are limited to driven coupled resonators as non-Hermitian Hamiltonians cannot faithfully describe an open system evolution beyond a short time; and (iii) the necessity for measuring a perturbatively small coupling exclusively at the boundary is also a big constraint. In fact, a fundamental constraint derived in ref. 40 show that non-Hermitian sensors cannot perform better than Hermitian counterparts. Therefore, finding a concrete protocol with Hermitian systems showing exponential scaling advantage even when the resources are taken into account is highly desirable.\n\nIn this work, we show that it is indeed possible to achieve the exponential scaling for sensitivity by leveraging the first-order phase transitions where the energy gap also closes exponentially in system size. We then show that even if the preparation time of the critical state is taken into account, the exponential sensitivity still prevails. This can be intuitively understood from the aforementioned bound5,28 bearing the quadratic scaling in time which itself grows exponentially with system size. Our results are shown analytically for a paradigmatic model, namely Grover model, and numerically for p-spin and a biclique spin model that are prototypical systems from a quantum annealing perspective. The results satisfy the fundamental bounds of quantum sensing schemes, and the estimation process can be performed in experimentally available measurement basis. We consider the issue of decoherence during state preparation and show that the exponential scaling is sustained up to certain dephasing strength. The local nature of criticality-based sensors is also addressed and an adaptive estimation strategy is sketched out to harness the full advantage of the exponential scaling for arbitrary value of the parameter to be estimated.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "In this work, we will be considering single parameter estimation, where the value of an unknown parameter \u03b8 is estimated by performing measurements on a quantum state \u03c1(\u03b8) that encodes the parameter. The quantum state is known as the probe state and the measurement outcomes are fed into an estimator function to infer the value of the parameter. In general, the measurement can be described by a complete set of Positive Operator Valued Measurement (POVM) {\u03a0n} where the nth outcome occurs with probability \\({p}_{n}(\\theta )=\\,{\\mbox{Tr}}\\,\\left[\\rho (\\theta ){\\Pi }_{n}\\right]\\). The uncertainty of estimating the unknown parameter \u03b8, quantified by standard deviation \u03b4\u03b8, is bounded through Cram\u00e9r-Rao inequality \\(\\delta \\theta \\ge 1/\\sqrt{M\\,{F}^{C}}\\). Here, M is the total number of measurements and the basis-dependent classical Fisher information (CFI) is3\\({F}^{C}={\\sum }_{n}{p}_{n}{({\\partial }_{\\theta }\\log {p}_{n})}^{2}\\). In order to have a measurement-independent quantity, one can maximize the CFI with respect to all possible measurements to obtain Quantum Fisher Information (QFI) FQ, namely \\({F}^{Q}={\\max }_{\\{{\\Pi }_{n}\\}}\\;{F}^{C}\\). As a result, the Cram\u00e9r-Rao inequality becomes\n\nwhere QFI gives the ultimate precision limit of the estimation. Interestingly, for evaluating the QFI one can avoid the notorious optimization over all possible measurement basis and instead consider the symmetric logarithmic derivative (SLD) operator \\({{{\\mathcal{L}}}}\\), implicitly defined as\n\nThe QFI is then expressed as \\({F}^{Q}=\\,{\\mbox{Tr}}\\,\\left[\\rho (\\theta ){{{{\\mathcal{L}}}}}_{\\theta }^{2}\\right]\\). For pure states \\(\\rho (\\theta )=\\left\\vert \\psi (\\theta )\\right\\rangle \\left\\langle \\psi (\\theta )\\right\\vert\\), the expressions are simplified to \\({{{{\\mathcal{L}}}}}_{\\theta }=2{\\partial }_{\\theta }\\rho (\\theta )\\), and consequently3\n\nAs QFI quantifies the rate of change of the probe state, it is also equivalent to the fidelity susceptibility. In the context of the ground state of a Hamiltonian H(\u03b8), this leads to another expression for QFI41\n\nHere \\(\\left\\vert {\\psi }_{n}\\right\\rangle\\) and En are the n-th eigenvector and eigenvalue of H(\u03b8). It is worth emphasizing that to achieve the ultimate precision limit, given by the QFI, one has to perform measurement in the optimal basis. The optimal measurement basis is not unique, although one choice is always given by the projectors formed from the eigenvectors of the SLD operator \\({{{{\\mathcal{L}}}}}_{\\theta }\\).\n\nWhile in the Cram\u00e9r-Rao inequality (Eq. (1)) the estimation precision, quantified by standard deviation, is bounded by \\(1/\\sqrt{{F}^{Q}}\\), there has been an interest to find analytical bounds on the QFI. Such bounds are quite insightful to give us a hint for the best possible scaling of the QFI. These bounds have been established for various scenarios, including non-equilibrium dynamics5, ground state of many-body Hamiltonians26, and steady state sensing28. In particular, we are concerned with the ground state probe, for which the upper bound of QFI has been derived recently26 to concretely prove the connection with both the energy gap and the spectral properties of the Hamiltonian. For Hamiltonians in the form H(\u03b8)\u00a0=\u00a0HC\u00a0+\u00a0H\u03b8, with a control term HC and the parameter dependent term H\u03b8, the upper bound of QFI of the ground state is given by26,\n\nwhere the operator seminorm is the difference between the maximum and minimum eigenvalues, \\(| | {\\partial }_{\\theta }{H}_{\\theta }| |={\\lambda }_{\\max }-{\\lambda }_{\\min }\\) and \u0394 is the energy gap between the ground state and the first excited state. Both these terms typically display scaling behaviour in system size near critical points, which controls the scaling of the upper bound. Thus, the ultimate scaling of the QFI may be determined by the individual scaling behaviour of these two terms.\n\nOn the other hand, when the probe state is prepared dynamically by evolving a suitably chosen initial state with a Hamiltonian consisting of a time-dependent control term in the form H(\u03b8,\u00a0t)\u00a0=\u00a0HC(t)\u00a0+\u00a0H\u03b8, the upper bound of QFI is given by the generalized Heisenberg limit5,28\n\nNote that Eq. (6) is valid for any dynamical scenario, including the adiabatic state preparation. In such schemes, the time needed for adiabatic preparation of the probe in the ground state of a complex Hamiltonian can be made inversely proportional to the minimum energy gap, namely \u00a0~\u00a01/\u039442. Hence, by replacing time t with 1/\u0394 in Eq. (6) one can effectively see the connection with the bound given in Eq. (5).\n\nIt is worth emphasising that both the Eqs. (5) and (6) only impose an upper bound on the QFI. In fact, while these bounds are very insightful for capturing the scaling in many-body probes, they usually overestimate the value of the QFI, which is the relevant quantity for determining the achievable precision. Indeed, for the particular systems considered in this work, the QFI near criticality expectedly follows the bound but does not saturate it in general.\n\nQuantum many-body systems have been proven to be very useful to serve as quantum sensors achieving quantum enhanced sensitivity in both equilibrium and non-equilibrium scenarios25. In particular, the ground state of many-body systems across various types of phase transitions have been identified as effective quantum sensors. In such systems the Hamiltonian, in general, has the form\n\nwhere H1 and H2 are two competing terms and \u03b8 is the unknown parameter to be estimated. The H2 component therefore serves as the derivative terms in Eqs. (4), (5), and (6). When the role of competing terms become comparable, say at \u03b8\u00a0=\u00a0\u03b8c, the system may go through a phase transition where the ground state \\(\\left\\vert {{{\\rm{GS}}}}(\\theta )\\right\\rangle\\) changes dramatically. From the spectral perspective, the ground state and the first excited state go through an anti-crossing at \u03b8\u00a0=\u00a0\u03b8c where the energy gap vanishes in the thermodynamic limit. If the energy gap closes exponentially with the system size, then the system goes through a first order phase transition in which the order parameter discontinuously jumps across the transition point. On the other hand, if the energy gap closes algebraically, then the order parameter changes continuously and it is the first derivative that becomes non-analytic at the phase transition. While the capability of utilizing second order phase transitions as effective quantum sensors has been fully characterized12, the first order phase transitions have not been completely explored. As we shall see in the following sections, first order phase transitions indeed allow for estimating \u03b8 with exponential sensitivity, quantified by exponential scaling of QFI with the system size. In the following we introduce three paradigmatic models with first order phase transitions, namely Grover, p-spin, and biclique spin systems.\n\nWe first consider a system consisting of L qubits which span a Hilbert space of dimension N\u00a0=\u00a02L. Every qubit configuration can coherently tunnel to another with equal probability, though one specific qubit configuration \\(\\left\\vert m\\right\\rangle\\) has a different energy from the rest. In this situation one can write the Hamiltonian,\n\nwhere\n\nwith\n\nOne can easily show that the Hamiltonian in Eq. (8) can be effectively be written as a two level system spanned by \\(\\left\\vert m\\right\\rangle\\) and \\(\\left\\vert {m}^{\\perp }\\right\\rangle\\) as,\n\nThis model is analytically tractable and will serve as a robust theoretical foundation for our conclusions. In this representation, the first-order phase can be analytically shown to be occurring at \u03b8c\u00a0=\u00a0142.\n\nThe second model we consider is based on p-spin model43,44, in a system of L qubits, represented by,\n\nwhere, p and k are integer numbers and 0\u2009\u2264\u2009\u03bb\u2009\u2264\u20091 is an external parameter that tune the system to feature either first or second order phase transition. For \u03bb\u00a0=\u00a01, one gets back the traditional p\u00a0\u2212\u00a0spin model, in which one has a first order phase transition for p\u2009\u2265\u20093. By choosing increasing values of p \u2265 3 for \u03bb\u00a0=\u00a01, it is possible to shift the critical point from \u03b8c\u00a0=\u00a01.3 for p\u00a0=\u00a03 towards \u03b8c\u00a0=\u00a01 for p\u00a0\u2192\u00a0\u221e which corresponds to the Grover model45. For \u03bb\u00a0\u2260\u00a01, we have an additional antiferromagnetic fluctuation term46, i.e. the middle term in Eq. (12), which can change the first order phase transition to a second order one. For instance by choosing \u03bb\u00a0=\u00a00.1,\u00a0p\u00a0=\u00a05,\u00a0k\u00a0=\u00a02 one observes a second order quantum phase transition at \u03b8c\u00a0=\u00a01.847. Due to degeneracy issues with even p, we shall only consider the odd cases in this work.\n\nFinally, we consider a biclique graph that can be easily implemented on existing quantum hardware and has been utilized in studies of maximum weighted independent set (MWIS) problems48,49. In such graphs, the system is partitioned into two subsystems A and B with LA and LB spins, respectively. We consider LA\u00a0=\u00a0LB\u00a0+\u00a01 which means the total system size will be L\u00a0=\u00a02LA\u00a0+\u00a01. Every spin in the subsystem A interacts with every spin in subsystem B with antiferromagnetic Ising interaction with strength J. In addition, the two subsystems are affected by two different uniform magnetic fields hA and hB. To induce a competing term the whole system is subjected to a uniform transverse magnetic field. The Hamiltonian can be expressed as49\n\nBy tuning the longitudinal magnetic fields hA and hB one can engineer the emergence of a first order phase transition at different values of \u03b8c.\n\nNow we discuss the sensing capabilities of the three models introduced in the previous section to estimate \u03b8 in the ground state due to phase transition. We focus on the scaling of two quantities with respect to the system size. First, we consider the scaling of the energy gap which is necessary to characterize the type of the phase transition. Second, we analyze the scaling of the QFI as a figure of merit for the sensing capability of our models.\n\nFor the Grover model, one can obtain the eigenspectrum analytically to compute the energy gap,\n\nNote that N\u00a0=\u00a02L is the Hilbert space size. The energy gap \u0394 has a minimum at \u03b8\u00a0=\u00a0\u03b8c\u00a0=\u00a01 with \\({\\Delta }_{c}=\\Delta ({\\theta }_{c},N)=2/\\sqrt{N}={2}^{1-\\frac{L}{2}}\\). For the ground state of the system one can compute the QFI with respect to \u03b8 which takes the form\n\nThe peak structure of QFI around the critical point is shown in Fig.\u00a01a. As the system approaches its critical point, the QFI becomes \\({F}_{c}^{Q}={F}^{Q}({\\theta }_{c})=(N-1)/4\\approx {2}^{L-2}\\) in large L limit. This exponential scaling of \\({F}_{c}^{Q}\\) is numerically verified in Fig.\u00a01b which shows that the asymptotic behavior is captured by finite number of qubits as well. We also observe the critical exponent for the QFI growth is twice of that for the gap decrease.\n\na QFI around the critical point for different system sizes. b QFI scaling at criticality (\u03b8c\u00a0=\u00a01). The dotted line shows the asymptotic QFI value.\n\nIt is also informative to verify the bound on the QFI given by Eq. (5). Interestingly, the QFI bound is almost saturated at the critical point for large system sizes. Here, \u2223\u2223H2\u2223\u2223 = 1, and the QFI can be shown analytically to obey the bound (see the Methods section). In this model the scaling of both the QFI and the bound is merely determined by the scaling of the energy gap.\n\nThe second model that we consider for sensing is the p\u00a0\u2212\u00a0spin model, introduced in Eq. (12). In this model, not only the critical point \u03b8c can be tuned by controlling p, k, and \u03bb, but also the nature of phase transition can be controlled. For example, for p\u00a0=\u00a05, k\u00a0=\u00a02 and \u03bb\u00a0=\u00a00.1, the phase transition is of second order type and happens at47\u03b8c\u00a0=\u00a01.8. To show this, in Fig.\u00a02a, we plot the energy gap as function of system size at criticality. As the figure shows, the energy gap closes algebraically, i.e. \u0394c \u221d L\u03b1 with \u03b1\u00a0\u2248\u00a01.46, signaling the second order nature of the phase transition. The corresponding QFI at the critical point is also plotted as a function of system size L in Fig.\u00a02b. Clearly, the QFI shows an algebraic scaling i.e. \\({F}_{c}^{Q}\\propto {L}^{\\beta }\\) with \u03b2\u00a0\u2248\u00a02.87 which is the conventional behavior at the second order quantum phase transitions. Note that we again observe that \u03b2\u00a0~\u00a02\u03b1.\n\na Energy gap scaling for p-spin model (Eq. (12)) for \u03bb\u00a0=\u00a00.1,\u00a0p\u00a0=\u00a05,\u00a0k\u00a0=\u00a02 at criticality occurring near \u03b8\u00a0=\u00a01.8. b Algebraic QFI scaling at criticality. c Energy gap scaling for \u03bb\u00a0=\u00a01,\u00a0p\u00a0=\u00a03 at criticality occurring near \u03b8\u00a0=\u00a01.3. d Exponential QFI scaling at criticality for this case.\n\nBy tuning \u03bb\u00a0=\u00a01 and p\u00a0=\u00a03 one can observe a first order phase transition at45\u03b8c\u00a0=\u00a01.3. The energy gap in this case is known to close exponentially with a multiplicative correction term, so that45\u0394c\u00a0~\u00a0Le\u2212\u03b1L. As shown by the numerical fit in Fig.\u00a02c, \u03b1\u00a0~\u00a00.09. The corresponding ground state QFI at the critical point exponentially grows with L, i.e. \\({F}_{c}^{Q} \\sim {e}^{\\beta L}\\) with \u03b2\u00a0\u2248\u00a00.18, as shown in Fig.\u00a02d.\n\nThe relation \u03b2\u00a0~\u00a02\u03b1 can be explained by the equivalence between QFI and fidelity susceptibility in Eq. (4). At criticality, the dominant contribution in the sum on the right hand side of the Eq. (4) comes from the first term (with the first excited state) and the overlap in the numerator were found to be linearly scaling with system size. This cancels the linear multiplicative scaling factor of the gap in the denominator and consequently \u03b2\u00a0=\u00a02\u03b1.\n\nOne can also verify this by considering the scaling of the bound in Eq. (5). In this model, one can show that \u2223\u2223H2\u2223\u2223 = 2L, which implies that this term also contributes to the scaling of the bound and cancels the linear scaling factor that appears in the energy gap as well at the critical point. Consequently, both the bound and the QFI scales purely exponentially with respect to the system size. Unlike the Grover model, the bound is not saturated near criticality in p-spin model, despite having the same scaling behaviour (see the Methods section). This arises due to different prefactors for the bound and the computed QFI.\n\nNow we focus on the sensing capacity of the biclique spin model described in Eq. (13). Following the recipe of refs. 48,49, we take \\({h}_{A[B]}=\\left({L}_{B[A]}J-2\\frac{{W}_{A[B]}}{{L}_{A[B]}}\\right)\\), with J\u00a0=\u00a01, WA\u00a0=\u00a00.49J and WB\u00a0=\u00a00.5J. For these choices of parameters, the first order quantum phase transition takes place at \u03b8c\u00a0\u2248\u00a00.05. In Fig.\u00a03a we plot the scaling of energy gap at the critical point, namely \u0394c, with respect to system size L. We observe an exponential falling off \u0394c\u00a0~\u00a0e\u2212\u03b1L with exponent \u03b1\u00a0\u2248\u00a01.43. Consequently, the corresponding ground state QFI at the critical point exponentially grows with systems size as \\({F}_{c}^{Q} \\sim {e}^{\\beta L}\\) with exponent \u03b2\u00a0\u2248\u00a02.94, as displayed in Fig.\u00a03b. The observation of \u03b2\u00a0~\u00a02\u03b1 applies here also.\n\na Energy gap scaling for the biclique spin system (Eq. (13)) at criticality occurring near \u03b8c\u00a0=\u00a00.05 with \\({h}_{A[B]}=\\left({L}_{B[A]}J-2\\frac{{W}_{A[B]}}{{L}_{A[B]}}\\right)\\) with J\u00a0=\u00a01, WA\u00a0=\u00a00.49J and WB\u00a0=\u00a00.5J. b QFI scaling at criticality for this system.\n\nIn biclique spin model, the scaling analysis is limited to small system sizes as large number of spins cannot be handled by exact diagonlization method. Regarding the bound in Eq. (5), one can see that \u2223\u2223H2\u2223\u2223 = 2L. The scaling of the upper bound therefore consists of an extra linear factor, along with the exponential size dependence coming from the energy gap, which is obtained through finite-size numerics. Thus, the QFI is shown numerically to obey the bound predicted by Eq. (5) (see the Methods section), although the bound is never saturated.\n\nSo far, we have considered system size as the only resource for sensing. However, since we focus on the ground state QFI, we need to first prepare the ground state of the corresponding Hamiltonians. Typically, there are two ways to prepare a many-body system in its ground state: (i) cooling to ground state; and (ii) adiabatic state preparation. Since the energy gap closes exponentially, both of these methods face severe challenges as cooling will be affected by critical slowing down and adiabatic state preparation requires extremely long preparation times. One may also consider the preparation time as a resource for accomplishing the sensing task. In order to incorporate time into resource analysis, one may consider the total time Ttot the is used for collecting the data through probe preparation and measurement. If the preparation of the probe takes time T, within the available total time one can get M\u00a0=\u00a0Ttot/T number of measurement. By inserting this into Eq. (1) one gets \\(\\delta \\theta \\ge 1/\\sqrt{{T}_{{{{\\rm{tot}}}}}{F}^{Q}/T}\\). This immediately suggests that for incorporating the total time as a resource, one has to consider the rescaled QFI, i.e. FQ/T, as the new figure of merit. The rescaled QFI has long been used for resource analysis in various works50,51,52,53,54.\n\nWhile both cooling and adiabatic state preparation are affected by closing of the energy gap, for sake of simplicity we shall only focus on adiabatic state preparation in this work. The adiabatic theorem states that to prepare the ground state of a many-body system one can start with an easily preparable ground state of a simple Hamiltonian and slowly change the Hamiltonian into the desired one. If the evolution is slow enough, taking place over a long time T, then quantum state of the system follows the ground state of the instantaneous Hamiltonian and thus reach the desired ground state at the end of the evolution. The original formulation of the adiabatic theorem requires that \\(T \\sim 1/{\\Delta }_{\\min }^{2}\\) where \\({\\Delta }_{\\min }\\) is the minimum energy gap of the Hamiltonian throughout the evolution55. However, there has been a lot of effort to speed up the state preparation42,56,57,58. In fact, it has been demonstrated that one can reach the ground state with high fidelity even if the evolution time only scales as \\(T \\sim 1/{\\Delta }_{\\min }\\)42.\n\nIn order to analyze preparation time in our schemes, we re-parameterize the Hamiltonian in Eq. (7) into the following time-dependent form\n\nwhere the parameter \u03b8 is now equivalent to (1\u00a0\u2212\u00a0s(t))/s(t). The parameter s evolves from 0, where the probe is initialized in the ground state of H2, to a value corresponding to the desired \u03b8. The minimum energy gap happens at \u03b8\u00a0=\u00a0\u03b8c. Therefore, it is plausible to make the preparation time scale as T\u00a0~\u00a01/\u0394c. As we have shown already, the QFI typically scales as \\({F}_{c}^{Q} \\sim {e}^{\\beta L}\\) and the energy gap closes as \u0394c\u00a0~\u00a0e\u2212\u03b1L. Consequently, our new figure of merit \\({F}_{c}^{Q}/T \\sim {e}^{(\\beta -\\alpha )L}\\). Remarkably, as demonstrated in all examples, we universally observe \u03b2\u00a0~\u00a02\u03b1 which results in \\({F}_{c}^{Q}/T \\sim {e}^{\\beta L/2}\\), signaling exponential advantage even when the preparation time is included in our resource analysis.\n\nTo verify the above statement, we numerically prepare the ground state of each of the three models described before using local adiabatic driving42, which results in T\u00a0~\u00a01/\u0394c. We start with s\u00a0=\u00a00, i.e. the ground state of H2, and then evolve s with time over a long time interval T using a particular schedules(t). This choice of time-dependent s(t) for local adiabatic driving needs to be fast when the system is far from criticality and slow near the critical point. To get the quantum state at each time one has to solve the Schrodinger equation\n\nwith the initial state \\(\\left\\vert \\psi (0)\\right\\rangle\\) being the ground state of H2.\n\nFor the Grover model, it can be analytically shown that42\\(s(t)=N\\,({\\tan }^{-1}\\sqrt{N-1}(2s-1)+{\\tan }^{-1}\\sqrt{N-1})\\,/\\,(2\\epsilon \\sqrt{N-1})\\). This results in T\u00a0=\u00a0\u03c0/2\u03f5\u0394c where the fidelity between the state of the probe and the instantaneous ground state, namely \\({{{\\mathcal{F}}}}(t)=| \\langle {{{\\rm{GS}}}}(t)| \\psi (t)\\rangle {| }^{2}\\), is lower bounded as \\({{{\\mathcal{F}}}}(t)\\ge (1-{\\epsilon }^{2})\\). We have numerically verified this in Fig.\u00a04a, where we plot the fidelity \\({{{\\mathcal{F}}}}\\) versus \u03b8 for a system of size L\u00a0=\u00a020. As the figure shows, one can achieve a fidelity of 0.99 at the critical point. Furthermore, we compare the variation of FQ across \u03b8c for the exact ground state \\(\\left\\vert {{{\\rm{GS}}}}(s(t))\\right\\rangle\\) in Fig.\u00a04b and the prepared state \\(\\left\\vert \\psi (s(t))\\right\\rangle\\) in Fig.\u00a04c. We observe that for that the small loss of fidelity has very little effect on QFI, which indicates that the exponentially effective quantum sensing at the first order critical point in the Grover model survives under local adiabatic state preparation. For the other two models the schedule s(t) was derived numerically using local adiabatic driving and the total preparation time was expectantly found to be bounded by 1/\u0394c (see the Methods section). The corresponding results for the p-spin model are shown in Fig.\u00a04d\u2013f, where we observe results similar to the previous case. For the biclique model, as shown in Fig.\u00a04g, the local adiabatic evolution results in the fidelity going below 0.98 near the critical point \u03b8c out of the three systems. Correspondingly, we observe that there is an increase in FQ for the ground state prepared by local adiabatic evolution compared to the exact ground state. It turns out that for small system sizes, the minuscule excitations above the true instantaneous ground state caused by the time evolution results favourably for the QFI.\n\n(Top row) Grover model. a Fidelity \\({{{\\mathcal{F}}}}\\) of the adiabatically evolved state with the instantaneous ground state. b QFI and CFI of the instantaneous ground state. c QFI and CFI of the adiabatically evolved state. (Middle row) p-spin model. d Fidelity \\({{{\\mathcal{F}}}}\\) of the adiabatically evolved state with the instantaneous ground state. e QFI and CFI of the instantaneous ground state. f QFI and CFI of the adiabatically evolved state. (Bottom row) biclique spin system. g Fidelity \\({{{\\mathcal{F}}}}\\) of the adiabatically evolved state with the instantaneous ground state. h QFI and CFI of the instantaneous ground state. i QFI and CFI of the adiabatically evolved state. 20-qubit system was used for the Grover model, 30 qubits for the p-spin model, and a 5-qubit system with J\u00a0=\u00a01, WA\u00a0=\u00a04J and WB\u00a0=\u00a03.5J was used for the biclique system.\n\nHaving established the fact that the critical QFI scales exponentially even after taking the adiabatic preparation time into account, we now give a concrete framework to create the probe state of for an unknown \u03b8, which is the realistic sensing scenario. Without loss of generality, we assume that the sensing apparatus is designed to detect a non-negative \u03b8, and consequently, its dynamics is governed by Eq. (7). As we know H1 and H2, we can determine the critical parameter \u03b8c, while \u03b8 still remains unknown. We then apply a time-dependent control field s(t)/(1\u00a0\u2212\u00a0s(t)) to the H1 component and a critical field \u03b8c to the H2 component, so that the total Hamiltonian becomes\n\nComparing with Eq. (7), the gap closing for this Hamiltonian occurs at sc\u00a0=\u00a0(\u03b8\u00a0+\u00a0\u03b8c)/(\u03b8\u00a0+\u00a02\u03b8c), which is \u22651/2. At t\u00a0=\u00a00, s is taken to be 0 as before and the initial state is the ground state of H2, which can be easily prepared. Using the same adiabatic evolution as before to keep the system in the instantaneous ground state, s is then increased until the value s\u00a0=\u00a01/2 is reached. As the gap closing point is not explicitly crossed, the system always stays on one side of the criticality. This prevents the unwanted creation of excitations that would result in fidelity reduction. At s\u00a0=\u00a01/2, the Hamiltonian given by Eq. (18) takes the form of Eq. (7), and the created probe state is used to estimate (\u03b8\u00a0+\u00a0\u03b8c). To obtain the unknown parameter \u03b8, one needs to subtract \u03b8c from the estimated value. Numerical confirmation of this procedure is displayed in Fig.\u00a05 with the Grover model on a 20-qubit system near the critical point \u03b8c\u00a0=\u00a01. As Fig.\u00a05a shows, the fidelity of the prepared state with the actual ground state stays very close to unity. Consequently the QFI and the CFI calculated with the true ground state and the prepared state also match, as shown in Fig.\u00a05b and c, respectively.\n\na Fidelity of the adiabatically prepared state with actual ground state. QFI and CFI of (b) the ground state and (c) the adiabatically prepared state. The results are shown for the Grover model with 20-qubits.\n\nWe also note that such a time-dependent preparation scheme follows the QFI bound in Eq. (6) (see the Methods section). Additionally, using Eq. (5) we can now find a bound for the rescaled QFI as a new figure of merit. Since the time needed to prepare the ground state is T\u00a0~\u00a01/\u0394c \u2265 1/\u0394, one can easily show that the rescaled QFI is bounded as \\({F}^{Q}/T\\le \\frac{| | {\\partial }_{\\theta }{H}_{\\theta }| {| }^{2}}{\\Delta }\\). This indicates that even when time is incorporated in our resource analysis, the rescaled QFI still benefits from the scaling of both the energy gap as well as the \u2223\u2223\u2202\u03b8H\u03b8\u2223\u22232. In all the above examples, the exponential advantage comes from the energy gap. Indeed, the dependence of the bound of the rescaled QFI on the energy gap indicates the exponential advantage even after considering time as a resource.\n\nAs shown in Fig.\u00a04, it is possible to determine a set of measurement basis relevant for experimental realization, that seem to be optimal. For the Grover model, \\(\\{\\left\\vert m\\right\\rangle,\\left\\vert {m}^{\\perp }\\right\\rangle \\}\\) is an optimal basis. For the p-spin model, the total magnetization is one optimal basis. For the biclique spin system, the imbalance between the total magnetization in the two subsystems is given by the operator \\({{{\\mathcal{I}}}}={\\sum }_{{j}_{A}=1}^{{L}_{A}}{\\sigma }_{{j}_{A}}^{z}-\\mathop{\\sum }_{{j}_{B}=1}^{{L}_{B}}{\\sigma }_{{j}_{B}}^{z}\\). The eigenbasis of this operator serves as an optimal basis.\n\nDephasing is a common source of decoherence in spin system dynamics. To quantify the robustness against dephasing during adiabatic evolution, we employ the master equation formalism for the system density operator \u03c1,\n\nwhere \u03b3 is the effective rate of decoherence and cn is the Lindblad operator. For the Grover model, H\u00a0=\u00a0HGrover and there is only one Lindblad operator \u03c3z between the states \\(\\left\\vert m\\right\\rangle\\) and \\(\\left\\vert {m}^{\\perp }\\right\\rangle\\). Our calculations show that even up to a strong decoherence strength \u03b3\u00a0=\u00a00.1, the signatures of first order phase transition remain intact along with the exponential growth of critical QFI (see Fig.\u00a06a). Moreover, \\({F}_{c}^{Q}\\) shows an algebraic decay with increasing decoherence strength (see Fig.\u00a06b for 30 qubits with exponent \u00a0\u2248\u00a00.93).\n\n(Top row) Grover model with: (a) scaling of critical QFI \\({F}_{c}^{Q}\\) for various decoherence strength \u03b3; and (b) \\({F}_{c}^{Q}\\) as a function of \u03b3 at a fixed system size L\u00a0=\u00a030. (Middle row) p-spin model with: (c) scaling of \\({F}_{c}^{Q}\\) for various \u03b3; and (d) \\({F}_{c}^{Q}\\) as a function of \u03b3 at L\u00a0=\u00a010. (Bottom row) biclique spin system (J\u00a0=\u00a01, WA\u00a0=\u00a04J, and WB\u00a0=\u00a03.5J) with: (e) scaling of \\({F}_{c}^{Q}\\) for various \u03b3; and (f) \\({F}_{c}^{Q}\\) as a function of \u03b3 at L\u00a0=\u00a011.\n\nFor p-spin model, H\u00a0=\u00a0Hp\u2212spin and the Lindblad operators are \\({\\sigma }_{j}^{z}\\). Our calculations show that the exponential growth of \\({F}_{c}^{Q}\\) is retained in this case as well, although up to a lower decoherence strength \u03b3\u00a0=\u00a00.01J (see Fig.\u00a06c). For the algebraic decay of \\({F}_{c}^{Q}\\) with increasing decoherence strength for 10 qubits, the exponent was \u00a0\u2248\u00a00.58 (see Fig.\u00a06d).\n\nFor the biclique system with same local Lindblad operators \\({\\sigma }_{j}^{z}\\), we also found that the exponential growth of \\({F}_{c}^{Q}\\) is retained up to a lower decoherence strength \u03b3\u00a0=\u00a00.01J (see Fig.\u00a06e). Up to this strength we see the effect of decoherence is quite weak on the critical QFI values. For the algebraic decay of \\({F}_{c}^{Q}\\) with increasing decoherence strength for 11 qubits, the exponent was \u00a0\u2248\u00a00.15 (see Fig.\u00a06f).\n\nRealizing the Grover model requires all-to-all connectivity that can be provided by strongly coupled cavity modes59,60,61. Such connectivity would also be useful for p-spin models. However, another connection between p-spin model and ultra-cold bosons bouncing on an oscillating atom mirror was established in ref. 62. The dynamics can be described effectively by a two-mode Bose-Hubbard model when the driving frequency of the mirror is twice of the natural frequency of the bosons falling onto the mirror under gravity63. Mapping between the bosonic operators and spin operators leads to the realization of p-spin models with p\u00a0=\u00a02 for two-body contact interaction. Higher order interactions are speculated to give rise to higher p-spin models that are considered in this work.\n\nThe biclique system can in principle be implemented in the D-Wave Pegasus or Zephyr architecture. Although, due to limited coherence time, schedule control, and constrained measurement processes, the merit of the near-term experiments might be limited. Specifically, the Pegasus graph of the D-Wave Advantage system5.4 device hosts 5614-qubits among which one can find the correct embedding of the biclique graph in the setup by using the D-Wave Ocean python package. The architecture already contains 8-qubit Chimera cells with complete bipartite connectivity64, that can be further coupled by external couplers to achieve a maximum connectivity of 1 qubit to 15 qubits. Thus, the maximum system size of the biclique model that can be simulated in D-Wave architecture is L\u00a0=\u00a029. One has to then initialize the system by setting up the local fields hA and hB in the positions of the real qubits and the couplings J. Finally, using the standard quantum annealing protocols to tune \u03b8, one may observe the exponentially enhanced sensitivity near the critical points described in this work.\n\nIn criticality-based sensing strategies, the quantum advantage is dominantly available in the vicinity of the critical point. Therefore, one needs to tune the probe, e.g. by applying an external control field, to operate near criticality and achieve the best performance. Away from criticality, the scaling advantage is typically available up to a finite system size. In Fig.\u00a07a, the QFI FQ(\u03b8) for the Grover model is plotted against system size L for different distances \u03b4 from criticality. This shows how the optimal length increases with decreasing \u03b4 and the maximum QFI value achievable increases exponentially. The results are qualitatively same for the p-spin model, as shown in Fig.\u00a07b. Although the biclique system shows similar trends, due to the limitation of small system sizes we do not include it in this report. Based on the behaviour of the optimal lengths, an adaptive strategy is needed to obtain and update prior information iteratively about the unknown parameter65,66,67,68.\n\na Grover model. b p-spin model. The maximum size L to sustain exponential sensitivity increases with decreasing distance from criticality. Our adaptive sensing strategy utilizes this feature.\n\nWe now exemplify this adaptive strategy with the Grover model for which analytical results are available. From the expression of QFI in Eq. (15), it is easy to see that for any \u03b4 departure from criticality, i.e. \u03b8\u00a0=\u00a0\u03b8c\u00a0\u00b1\u00a0\u03b4, QFI maximizes for a system size L\u03b4, see Fig.\u00a07a. Using a probe with size L\u03b4 one can reach a precision which, at the worst case, is determined by the minimum QFI attained in the range [\u03b8c\u00a0\u2212\u00a0\u03b4,\u00a0\u03b8c\u00a0+\u00a0\u03b4]. This helps us to track the maximum uncertainty. It is easy to see that this quantity is \\({F}_{\\min }^{Q}={F}^{Q}({L}_{\\delta },{\\theta }_{c}+\\delta )=\\frac{1}{{\\delta }^{2}{(2+\\delta )}^{2}}\\), with the corresponding optimal system size\n\nThe adaptive strategy can now be summarized in terms of a two-step process within each iteration:\n\nAt the n-th step, we assume that we have a prior knowledge about the unknown parameter as \\({\\theta }_{{{{\\rm{est}}}}}^{(n-1)}\\pm {\\delta }^{(n-1)}\\), where \u03b4(n\u22121) is the uncertainty of our knowledge. Then, based on this prior knowledge, a control field \\({\\theta }_{{{{\\rm{ctl}}}}}^{(n)}\\) is applied such that the total effective parameter is \\({\\theta }_{{{{\\rm{est}}}}}^{(n-1)}+{\\theta }_{{{{\\rm{ctl}}}}}^{(n)}={\\theta }_{c}\\). For the given uncertainty \u03b4(n\u22121), one can select a probe for this step with optimal size \\({L}^{(n)}={L}_{{\\delta }^{(n-1)}}\\), see Eq. (20). For this probe size, a single use of the probe takes time T(n)\u00a0~\u00a01/\u0394(n), where \u0394(n) is the energy gap of the probe of size L(n).\n\nWith this probe we perform M measurements, which requires the time resource of MT(n) at this iteration, to update the estimation of the effective parameter to \\({\\theta }_{{{{\\rm{eff}}}}}^{(n)}\\) with a better precision \u03b4(n). By deducting the control field, the new prior information for the next step is obtained as \\({\\theta }_{{{{\\rm{est}}}}}^{(n)}\\pm {\\delta }^{(n)}\\). It is worth emphasizing that the uncertainty \u03b4(n) will be used for choosing the probe size in the next iteration. Note that as the precision is improved, the optimal probe size gets larger which in turn further improves the precision.\n\nThese steps are repeated until the desired precision is achieved.\n\nNow we show explicitly how the uncertainty \u03b4(n) is improved iteratively. Assuming that our sample size M is large enough and the estimator is optimal, one can saturate the Cram\u00e9r-Rao bound. As we want to ensure that even the maximum possible error is improved iteratively, we consider the worst scenario at each step where the true parameter value is the farthest from the critical point. Here, the uncertainty becomes \\({\\delta }^{(n)}\\simeq 1/\\sqrt{M{F}_{\\min }^{Q\\,(n)}}=1/\\sqrt{M{F}^{Q}({L}^{(n)},{\\theta }_{c}+{\\delta }^{(n-1)})}\\). To evaluate the performance of the probe after n iterations, while incorporating the total time as the resource, one has to consider the figure of merit as \\({F}_{\\min }^{Q\\,(n)}/{T}_{{{{\\rm{tot}}}}}^{(n)}\\), where \\({T}_{{{{\\rm{tot}}}}}^{(n)}=M{\\sum }_{k\\le n}{T}^{(k)}\\) is the total time spent to reach this stage. Now, by inverting Eq. (20) to get \u03b4(n) in terms of L(n) and incorporating it in Eq. (15) we get, \\({F}_{\\min }^{Q\\,(n)} \\sim {2}^{{L}^{(n)}}\\) and \\({T}_{{{{\\rm{tot}}}}}^{(n)}\\le Mn{T}^{(n)} \\sim Mn/{\\Delta }^{(n)}\\). As \u0394(n) is lower bounded by its critical value \\({\\Delta }_{c}^{(n)}\\), we recall the scaling relations for the Grover model from Eqs. (14)\u2013(15), and write\n\nThis clearly shows that the adaptive rescaled QFI scales exponentially with the probe size L(n). Nonetheless, we still numerically investigate the scaling of \\({F}_{\\min }^{Q\\,(n)}/{T}_{{{{\\rm{tot}}}}}^{(n)}\\) with respect to both n and L(n). In Fig.\u00a08a, we see that \\({F}_{\\min }^{Q\\,(n)}/{T}_{{{{\\rm{tot}}}}}^{(n)}\\) falls off exponentially with step number n, which signals that the adaptive strategy is very efficient even with few iterative steps and very modest number of measurements M. In Fig.\u00a08b and c, we show that the exponential scaling of \\({F}_{\\min }^{Q\\,(n)}/{T}_{{{{\\rm{tot}}}}}^{(n)}\\) is indeed retained as was predicted above. Therefore we conclude that even with the consideration of the largest uncertainty at each step with finite M measurements, while accounting for all the resources, the exponential scaling advantage is retained in this adaptive strategy.\n\n(Top row) Maximum uncertainty with optimal estimator with initial uncertainty 0.1. The ratio of QFI and cumulative preparation time at each iteration vs. (a) iteration step, and vs. system size at each step with measurement numbers (b) M\u00a0=\u00a050, (c) M\u00a0=\u00a0100. (Bottom row) Uncertainty with Bayesian estimator with true parameter 0.9 and initial range [0.8,\u00a01.1]. d Iterative uncertainty at each step. Iterative figure of merit vs. system size at each step with (e) M\u00a0=\u00a050, (f) M\u00a0=\u00a0100.\n\nThe above analysis assumes that the Cram\u00e9r-Rao bound is achievable at all the iterations using M measurements. Now, we show that this is indeed possible by performing a Bayesian estimation69,70, while keeping M to be a modest value. As discussed before, at the n-th step of the iterative procedure, we apply a control field \\({\\theta }_{{{{\\rm{ctl}}}}}^{(n)}\\), based on our previous estimation \\({\\theta }_{{{{\\rm{est}}}}}^{(n-1)}\\pm {\\delta }^{(n-1)}\\), to make sure that the probe operates around the critical point. We prepare the probe in its ground state \\(\\left\\vert GS\\right\\rangle\\) corresponding to the total effective parameter. We choose the measurement basis {\u03a0k} as the one specified in the \u2018Optimal basis\u2019 section. We then simulate generating M number of experimental data by randomly sampling from the probability distribution of the ground state in this basis. If the k-th outcome is obtained nk times, then \\(\\mathop{\\sum }_{k=1}^{d}{n}_{k}=M\\) and d is the total number of possible outcomes. For the Grover model that we consider here, d\u00a0=\u00a02. This measured probability distribution {nk/M} is then compared with the model probability distribution \\(\\{{p}_{k}={\\left\\langle {{{\\rm{GS}}}}| {\\Pi }_{k}| {{{\\rm{GS}}}}\\right\\rangle }_{{\\theta }^{(n)}}\\}\\). This is done with the aid of the \u2018likelihood\u2019 function, given by the multinomial distribution \\(P(\\{{n}_{k}\\}| \\theta )=\\frac{M!}{{\\prod }_{k}{n}_{k}!}{\\prod }_{k}{p}_{k}^{{n}_{k}}\\). If no information is available other than the range of \u03b8(n) between \\({\\theta }_{\\min }\\) and \\({\\theta }_{\\max }\\), then the initial \u2018prior\u2019 is the uniform distribution \\(P(\\theta )=\\frac{1}{{\\theta }_{\\max }-{\\theta }_{\\min }}\\). Using Bayes\u2019 theorem, we now write the \u2018posterior\u2019 distribution P(\u03b8\u2223{ni})\u00a0=\u00a0P({ni}\u2223\u03b8)\u2009P(\u03b8), and normalize it. In this work, at each iteration n, we take the prior to be a flat distribution in the range \\([{\\theta }_{{{{\\rm{est}}}}}^{(n-1)}-5{\\delta }^{(n-1)},{\\theta }_{{{{\\rm{est}}}}}^{(n-1)}+5{\\delta }^{(n-1)}]\\). Note that one can also use the posterior of the (n\u00a0\u2212\u00a01)-th iteration as the prior, however, this can cause large fluctuations that would demand large M to converge. For large enough M, the final posterior distribution is Gaussian, the mean and standard deviation of which serve as the \\({\\theta }_{{{{\\rm{est}}}}}^{(n)}\\) and \u03b4(n), respectively. Although M is typically a few thousands in experiments, here M\u00a0=\u00a050 or M\u00a0=\u00a0100 was sufficient. As shown in Fig.\u00a08d, the uncertainty at each step \u03b4(n) falls off exponentially with n and the exponents grow in magnitude as M is increased. For the purpose of resource analysis, we then look at \\(1/M{\\delta }^{(n)2}{T}_{{{{\\rm{tot}}}}}^{(n)}\\), which is an analogue of the ratio of QFI and total preparation time that was considered before. As shown in Fig.\u00a08e and f, not only does this quantity show the desired exponential scaling, it is also quantitatively similar to the case of optimal estimators shown in Fig.\u00a08b and c. This empirical analysis based on Bayesian estimation clearly demonstrates that the adaptive strategy is very effective to harness the exponential advantage even if the unknown parameter of interest is away from the critical point.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60291-6/MediaObjects/41467_2025_60291_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60291-6/MediaObjects/41467_2025_60291_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60291-6/MediaObjects/41467_2025_60291_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60291-6/MediaObjects/41467_2025_60291_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60291-6/MediaObjects/41467_2025_60291_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60291-6/MediaObjects/41467_2025_60291_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60291-6/MediaObjects/41467_2025_60291_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60291-6/MediaObjects/41467_2025_60291_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "To utilize quantum features for enhancing sensing precision several strategies have been put forward which resulted in sensors based on GHZ-like entangled states, criticality and non-equilibrium dynamics. In most of these methods, the QFI scales algebraically with respect to system size, i.e. FQ\u00a0~\u00a0L\u03b2. Surpassing algebraic advantage and reaching exponential scaling has remained elusive when all the resources, such as the preparation time, are taken into account. Here, we have shown that a class of systems with first order quantum phase transitions with exponential energy gap closing can indeed achieve exponential scaling for the QFI. Remarkably, the exponential scaling nature is preserved even if the state preparation time, through local adiabatic driving, is accounted for. We have illustrated our results by considering three distinct models, namely Grover, p-spin, and biclique spin systems, featuring first order phase transition. The results comply with the fundamental bounds that have been established for quantum probes. In addition, they are robust against moderate decoherence and the optimal bases are also experimentally realizable. While criticality-based sensing is inherently local in nature, we have shown, with an adaptive estimation strategy, that it is always possible to harness the exponential scaling for sensing arbitrary parameters to unprecedented precision. Our results can in principle be verified with D-Wave quantum devices in which the biclique spin system may be implemented. This work paves the way for a concrete precision sensing strategy with applications in estimating fundamental physical constants, which require ultra-accurate local probes.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "We first show that the ground state QFI for the Grover model in Eq. (15) is upper bounded according to Eq. (5) by \u2223\u2223H2\u2223\u22232/\u03942\u00a0=\u00a0N2/(N2(1\u2212\u03b8)2\u00a0+\u00a04N\u03b8). To see this, we start with\n\nwhich proves the desired relation as the LHS is the upper bound and the RHS is the QFI. We also notice that at criticality (\u03b8\u00a0=\u00a01), the QFI almost saturates the upper bound for large system sizes. Additionally, for the ground state preparation scheme presented in the main text according to the evolution under the Hamiltonian in Eq. (18), the relevant bound for the time-dependent QFI is given by Eq. (6). In Fig.\u00a09a we show that this bound is satisfied during state preparation both at criticality and away from it. The upper bounds and the QFI of the ground states for the p-spin and biclique models near criticality are shown in Fig.\u00a09b and c, respectively.\n\na QFI evolution within the bound during ground state preparation of the Grover model with 20 qubits. Upper bounds and QFI of the ground states in (b) p-spin model with 30 spins, and (c) the biclique model with 9 spins.\n\nFor the adiabatic state preparation based on the Eq. (17), the condition for the fidelity of the evolved state with the instantaneous ground state to be large, namely, \\({{{\\mathcal{F}}}}(t)=| \\langle {{{\\rm{GS}}}}(t)| \\psi (t)\\rangle {| }^{2}\\ge 1-{\\epsilon }^{2}\\), is\n\nwith \\(\\left\\vert {\\psi }_{1}(t)\\right\\rangle\\) as the instantaneous first excited state. Transferring the time-dependence on s(t), we can write\n\nFor the p-spin model, we numerically observe that \\(| \\left\\langle {\\psi }_{1}(s)| \\frac{d}{ds}H(s)| \\,{\\mbox{GS}}\\,(s)\\right\\rangle | < L\\). Therefore we take the preparation time for the ground state at s,\n\nThe resulting time was found to scale as \u00a0~e0.055L for p\u00a0=\u00a03 and \u03bb\u00a0=\u00a01, which is advantageous as this exponent as even smaller than that of 1/\u0394c. Similar results were found for the biclique system as well.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60291-6/MediaObjects/41467_2025_60291_Fig9_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "The datasets generated during and/or analysed during the current study are available in the Github repository https://github.com/SaubhikSarkar/QFI_First_Order_Phase_Transition.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code used in this study is available at the Github repository https://github.com/SaubhikSarkar/QFI_First_Order_Phase_Transition.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Degen, C. L., Reinhard, F. & Cappellaro, P. Quantum sensing. Rev. Mod. Phys. 89, 035002 (2017).\n\nArticle\u00a0\n ADS\u00a0\n MathSciNet\u00a0\n \n Google Scholar\u00a0\n \n\nBraunstein, S. L. & Caves, C. M. 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S.S. acknowledges support from National Natural Science Foundation of China (Grant No. W2433012). R.G. and S.B. acknowledge EPSRC grants EP/Y004590/1 MACON-QC and EP/R029075/1 Non-Ergodic Quantum Manipulation for support.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China\n\nSaubhik Sarkar\u00a0&\u00a0Abolfazl Bayat\n\nKey Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 611731, China\n\nSaubhik Sarkar\u00a0&\u00a0Abolfazl Bayat\n\nShimmer Center, Tianfu Jiangxi Laboratory, Chengdu, 641419, China\n\nAbolfazl Bayat\n\nDepartment of Physics and Astronomy, University College London, Gower Street, WC1E 6BT, London, UK\n\nSougato Bose\u00a0&\u00a0Roopayan Ghosh\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nA.B. and S.S. proposed the resource for quantum-enhanced sensitivity while R.G. and S.B. proposed the concrete systems for implementation. R.G. performed the analytical calculations and S.S. performed the numerical simulations. All the authors contributed in writing the manuscript.\n\nCorrespondence to\n Abolfazl Bayat, Sougato Bose or Roopayan Ghosh.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Exponentially-enhanced quantum sensing with many-body phase transitions.\n Nat Commun 16, 5159 (2025). https://doi.org/10.1038/s41467-025-60291-6\n\nDownload citation\n\nReceived: 25 October 2024\n\nAccepted: 19 May 2025\n\nPublished: 03 June 2025\n\nVersion of record: 03 June 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60291-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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Lewy bodies", + "pre_title": "Synaptic vesicle endocytosis deficits underlie GBA-linked cognitive dysfunction in Parkinson\u2019s disease and Dementia with Lewy bodies", + "journal": "Nature Communications", + "published": "26 September 2025", + "supplementary_0": [ + { + "label": "Supplemental Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_MOESM2_ESM.docx" + }, + { + "label": "Supplementary Dataset 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_MOESM6_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_MOESM7_ESM.pdf" + }, + { + "label": "Transparent Peer review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_MOESM8_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_MOESM9_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE283187", + "/articles/s41467-025-63444-9#Sec26" + ], + "code": [], + "subject": [ + "Dementia", + "Parkinson's disease" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5649173/v1.pdf?c=1758971201000", + "research_square_link": "https://www.researchsquare.com//article/rs-5649173/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63444-9.pdf", + "preprint_posted": "26 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "GBA is the major risk gene for Parkinson\u2019s disease (PD) and Dementia with Lewy Bodies (DLB), two common \u03b1-synucleinopathies with cognitive deficits. We investigated the role of mutant GBA in cognitive decline by utilizing Gba (L444P) mutant, SNCA transgenic (tg), and Gba-SNCA double mutant mice. Notably, Gba mutant mice showed early cognitive deficits but lacked PD-like motor deficits or \u03b1-synuclein pathology. Conversely, SNCA tg mice displayed age-related motor deficits, without cognitive abnormalities. Gba-SNCA mice exhibited both cognitive decline and exacerbated motor deficits, accompanied by greater cortical phospho-\u03b1-synuclein pathology, especially in layer 5 neurons. Single-nucleus RNA sequencing of the cortex uncovered synaptic vesicle (SV) endocytosis defects in excitatory neurons of Gba mutant and Gba-SNCA mice, via robust downregulation of genes regulating SV cycle and synapse assembly. Immunohistochemistry and electron microscopy validated these findings. Our results indicate that Gba mutations, while exacerbating pre-existing \u03b1-synuclein aggregation and PD-like motor deficits, contribute to cognitive deficits through \u03b1-synuclein-independent mechanisms, involving dysfunction in SV endocytosis.Biological sciences/Neuroscience/Diseases of the nervous system/Parkinson's diseaseBiological sciences/Neuroscience/Diseases of the nervous system/DementiaGBASNCACognitive dysfunctionParkinson\u2019s diseaseParkinson\u2019s disease with dementiaDementia with Lewy bodiesLewy body dementiasnRNA-seqsynaptic vesicle endocytosissynaptic plasticity\u03b1-synuclein\u03b1-synucleinopathies", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.\n\"All experiments in this study were done with approval and oversight of the Yale IACUC.\"", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplTable1DEGlistGbavsWTVidyadharaetal.2024.xlsxSupplementary Table 1SupplTable2DEGlistGbaSNCAvsWTVidyadharaetal.2024.xlsxSupplementary Table 2SupplTable3DEGlistSNCAvsWTVidyadharaetal.2024.xlsxSupplementary Table 3SupplTable4DEGsdrivingGOpathwaysVidyadharaetal.2024.xlsxSupplementary Table 4SupplementalinformationVidyadharaetal.2024.pdfSupplementary figures with figure legends", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "GBA is the major risk gene for Parkinson\u2019s disease (PD) and dementia with Lewy bodies (DLB), two common \u03b1-synucleinopathies with cognitive deficits. Here we investigate the role of mutant GBA in cognitive decline by utilizing Gba (L444P) mutant, SNCA transgenic (tg), and Gba-SNCA double mutant mice. Notably, Gba mutant mice show cognitive decline but lack PD-like motor deficits or \u03b1-synuclein pathology. Conversely, SNCA tg mice display age-related motor deficits, without cognitive abnormalities. Gba-SNCA mice exhibit both cognitive decline and exacerbated motor deficits, accompanied by greater cortical phospho-\u03b1-synuclein pathology, especially in layer 5 neurons. Single-nucleus RNA sequencing of the cortex uncovered synaptic vesicle (SV) endocytosis pathway defects in excitatory neurons of Gba mutant and Gba-SNCA mice, via downregulation of genes regulating SV cycle and synapse assembly. Immunohistochemistry and electron microscopy validate these findings. Our results indicate that Gba mutations, while exacerbating pre-existing \u03b1-synuclein aggregation and PD-like motor deficits, contribute to cognitive deficits through \u03b1-synuclein-independent mechanisms, involving dysfunction in SV endocytosis.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "GBA is the major risk gene for Parkinson\u2019s disease (PD) and dementia with Lewy bodies (DLB)1,2,3,4,5,6, two late-onset neurodegenerative diseases, characterized by the neuronal accumulation of Lewy bodies composed of \u03b1-synuclein7. PD is classified as a movement disorder, although dementia affects around 50% of PD patients within 10 years after symptom onset7. DLB is a dementia, in which cognitive decline is generally the first and most predominant symptom7. Significantly, PD patients with GBA mutations exhibit greater and faster cognitive decline than idiopathic PD1,7,8,9. Cognitive dysfunction in both PD and DLB, which entails visuospatial, memory, and executive dysfunction, is strongly correlated with neocortical Lewy pathology7. However, the mechanisms through which GBA predisposes to cognitive dysfunction, as well as to developing \u03b1-synucleinopathies in general, are not well understood.\n\nHomozygous or biallelic mutations in GBA cause the lysosomal storage disorder, Gaucher disease (GD)10,11. Two prevalent mutations, due to founder effects, are N370S (p.N409S) and L444P (p.L483P) (without and with the 39AA leader sequence)12,13. GD patients have a 20-fold increased risk of developing PD accompanied by exacerbated cognitive decline. Heterozygous carriers of GBA mutation are at 5-fold increased risk for developing both PD and cognitive dysfunction1,2,8,9,14,15,16,17,18. In the case of DLB, GBA and SNCA, the gene for \u03b1-synuclein, are top GWAS hits. Interestingly, GBA mutations confer an even higher risk of developing DLB4,5,6. GBA encodes glucocerebrosidase 1 (GCase1), a lysosomal hydrolase responsible for breaking down the bioactive lipid glucosylceramide (GlcCer) to glucose and ceramide. In the absence of GCase1, GlcCer and other glycosphingolipids accumulate. Interestingly, GCase1 deficiency and glycosphingolipid accumulation are also observed in post-mortem brains of patients with sporadic PD and in aging brains19,20,21,22. Glycosphingolipid accumulation correlates with a higher burden of \u03b1-synuclein or Lewy pathology in several brain areas20,22,23. These genetic, clinical, and epidemiological studies emphasize the importance of understanding mechanisms of GBA-linked cognitive dysfunction.\n\nThe prevailing hypothesis in the field is that GBA mutations lead to GCase1 deficiency, which, through a combination of lysosomal dysfunction and glycosphingolipid accumulation, trigger \u03b1-synuclein aggregation, resulting in Lewy body formation and consequently, disease associated phenotypes. We and others have shown that glycosphingolipids can directly interact with \u03b1-synuclein and promote aggregation in vitro24,25. Our long-lived mouse models of GD carrying the Gba N370S and L444P mutations exhibited reduced GCase1 activity and accumulation of glycosphingolipids in the liver, spleen, and brain26. As GD patients with the L444P mutation have pronounced cognitive deficits27, in this study, we conducted a thorough examination of Gba L444P mice, in conjunction with the well-established SNCA tg PD mice that overexpress mutant human \u03b1-synuclein, and their crossbreeds, i.e. Gba-SNCA mice. Based on our findings, we suggest that synaptic endocytosis dysfunction plays a role in cognitive deficits of GBA-linked PD and DLB.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To determine the relative contributions of GBA and SNCA\u00a0in motor and cognitive domains, we performed behavioral analyses of wild-type (WT), Gba, SNCA tg, and Gba-SNCA mouse sex-balanced cohorts. We conducted longitudinal evaluations of motor behavior every 3 months to establish the age of onset and progression of PD-like motor deficits compared to cognitive behavior deficits.\n\nMotor deficits were phenotyped using four complementary assays: balance beam, grip strength, hind limb clasping, and open-field locomotion tests. In the balance beam test, number of runs completed in a minute and the average time per run were used to assess motor performance28 (Fig.\u00a01A and Supplementary Fig.\u00a01A, D, E). Gba mice consistently performed well on this task, comparable to WT mice, up to 12 months. In contrast, SNCA tg mice could perform this task at 3 months but began showing deficits at 6 months, which worsened by 12 months. Notably, Gba-SNCA mice demonstrated exacerbated balance beam\u00a0deficits compared to SNCA tg mice, with significant deficits appearing as early as 6 months. By 9-12 months, Gba-SNCA mice were severely affected and unable to navigate the balance beam (Fig.\u00a01A and Supplementary Fig.\u00a01A, D, E). We noted similar deficits in grip strength. WT and Gba mice did not show deficits, while SNCA tg mice developed age-related decline in grip strength, which was exacerbated in Gba-SNCA mice (Fig.\u00a01B and Supplementary Fig.\u00a01B, F, G). In hind limb clasping test, SNCA tg mice did not show clasping, similar to WT (Fig.\u00a01C and Supplementary Fig.\u00a01H). Gba mice showed a trend towards increased clasping, whereas Gba-SNCA mice showed a significant increase across all ages, indicating a synthetic motor phenotype (Fig.\u00a01C and Supplementary Fig.\u00a01H). In the open field, distance traveled was comparable among all groups across ages, except for Gba-SNCA mice (Fig.\u00a01D). At 9 months, a significant loss in exploration/locomotion was noted in Gba-SNCA mice, which worsened at 12 months (Fig.\u00a01D and Supplementary Fig.\u00a01I). No genotypes showed anxiety-like behavior, as evaluated by the time spent in the inner/outer circles of the open field (Supplementary Fig.\u00a01C). There was no difference across the mice strains for body weight, however, Gba-SNCA mice stopped gaining weight after 6 months (Supplementary Fig.\u00a01J). In summary, motor assessments demonstrate that Gba mutants do not exhibit any appreciable motor deficits. Nonetheless, Gba mutation significantly exacerbates existing age-related motor deficits in SNCA tg mice.\n\nWT, Gba, SNCA tg and Gba-SNCA mice cohorts were evaluated in four motor and two cognitive behavior tests in a longitudinal manner. A Balance beam behavior. B Grip strength of all four limbs. C Hind limb clasping behavior. D Open field locomotory behavior. E Fear conditioning test. F Novel object recognition. n\u2009=\u20099-12 mice for motor behavior and 6-19 for cognitive behavior, both sexes were used. For A\u2013F, two-way repeated measure ANOVA followed by Sidak\u2019s multiple comparison test was used. Student\u2019s t-test with Welch\u2019s correction was used for (E) and one-way ANOVA followed by Tukey\u2019s multiple comparison test was used for (F). \u2018*\u2019 indicates p-value. Data are presented as mean\u2009\u00b1\u2009SEM. ns not significant; \u2217p\u2009<\u20090.05, \u2217\u2217p\u2009<\u20090.01, \u2217\u2217\u2217p\u2009<\u20090.001, \u2217\u2217\u2217\u2217p\u2009<\u20090.0001.\n\nNext, we evaluated the impact of Gba and SNCA mutations on cognition by employing fear conditioning and novel object recognition (NOR) tests. To avoid confounds due to learning, we performed these on two separate sets of mice at 3 months and 12 months, prior to and after the onset of motor deficits in SNCA tg mice (Fig.\u00a01A\u2013D). For fear conditioning, mice were habituated to operant boxes and exposed to paired tone and shock stimuli on the training day, with freezing responses to the tone alone measured 24\u2009hours later. We counted the number of freeze episodes after start of the tone and observed a conditioned fear response in WT and SNCA tg mice at both 3 and 12 months (Fig.\u00a01E). However, Gba and Gba-SNCA mice did not show a significant response on the testing day, especially at 12 months. While this is suggestive of cognitive impairment, the results were confounded by the heightened freezing response shown by Gba and Gba-SNCA mice for aversive stimulus on the training day (Fig.\u00a01E).\n\nTo substantiate the cognitive findings from fear conditioning, we performed a NOR test to assess recognition memory. Mice spend more time with the novel object when cognitively normal (Fig.\u00a01F). Gba mice spent significantly less time with the novel object at 3 months and maintained this behavior at 12 months, suggesting an early cognitive impairment (Fig.\u00a01F). As Gba mice do not have motor problems, these results reflect memory impairments. Interestingly, SNCA tg mice did not show deficits in the NOR test (Fig.\u00a01F), whereas Gba-SNCA do, which was comparable to Gba mice (Fig.\u00a01F). Thus, our cognitive behavior assays suggest that the Gba mutation alone can cause cognitive impairment, with the poor cognition in Gba-SNCA mice likely driven by Gba mutation.\n\nIn Lewy body pathology, \u03b1-synuclein is phosphorylated at Ser129, forms aggregates, and accumulates in the soma, whereas under normal conditions, it is primarily localized to presynaptic termini. To investigate whether Gba-mediated cognitive deficits and accelerated motor deficits are associated with \u03b1-synuclein pathology, we performed immunohistochemistry on 3- and 12-month-old mice brains, staining for \u03b1-synuclein, pSer129\u03b1-syn, and the neuronal marker NeuN. We found that Gba mice did not exhibit increased \u03b1-synuclein levels in the soma, or accumulation of pathological pSer129\u03b1-syn in the cortex both at 3 and 12 months (Fig.\u00a02A\u2013C). Similar observations were made in the CA1 hippocampus and by Western blotting of whole brain homogenates (Supplementary Fig.\u00a02C\u2013J). These findings suggest that the Gba mutation alone is insufficient to cause widespread \u03b1-synuclein pathology\u00a0at these ages.\n\nA Representative images of cortices of WT, Gba, SNCA tg, and Gba-SNCA mice (12 months) immunostained for NeuN (red), \u03b1-synuclein (magenta) and pSer129-\u03b1-syn (green). Note increased \u03b1-synuclein levels (arrow) and pSer129-\u03b1-syn expression in the neuronal soma of Gba-SNCA double mutant when compared to SNCA tg mice. Scale\u2009=\u2009150\u2009\u00b5m. Inset scale\u2009=\u200915\u2009\u00b5m. B Cortical \u03b1-synuclein expression at 3 and 12 months, normalized to respective WT average at each time point. C Cortical pSer129-\u03b1-syn expression at 3 and 12 months, normalized to respective WT average at each time point. D Percentage of cortical neurons positive for pSer129-\u03b1-syn at 3 and 12 months. E NeuN positive cortical neuronal number at 3 and 12 months. F Distribution of pSer129-\u03b1-syn expression intensity (Range of 0-255) in the cortical neurons at 3 months of age (WT: Mean\u2009=\u20090.35, 25% Percentile\u2009=\u20090.0024, 75% Percentile\u2009=\u20090.1252; Gba: Mean\u2009=\u20090.04, 25% Percentile\u2009=\u20090, 75% Percentile\u2009=\u20090.0058; SNCA tg: Mean\u2009=\u200913.05, 25% Percentile\u2009=\u20095.211, 75% Percentile\u2009=\u200918.534; Gba-SNCA: Mean\u2009=\u200923.781, 25% Percentile\u2009=\u20099.221, 75% Percentile\u2009=\u200933.972) and, G 12 months of age (WT: Mean\u2009=\u20091.28, 25% Percentile\u2009=\u20090.439, 75% Percentile\u2009=\u20091.731; Gba: Mean\u2009=\u20091.482, 25% Percentile\u2009=\u20090, 75% Percentile\u2009=\u20092.124; SNCA tg: Mean\u2009=\u200926.282, 25% Percentile\u2009=\u200912.357, 75% Percentile\u2009=\u200932.834; Gba-SNCA: Mean\u2009=\u200953.741, 25% Percentile\u2009=\u200932.233, 75% Percentile\u2009=\u200966.691). H Cortical layer specific expression of \u03b1-synuclein at 3 months of age, and I 12 months of age. J Cortical layer specific expression of pSer129 \u03b1-syn at 3 months of age, and K 12 months of age. n\u2009=\u20093-5, sex balanced. One-way ANOVA followed by Tukey\u2019s multiple comparison test was used for statistics. \u2018*\u2019 indicates p-value. Data are presented as mean\u2009\u00b1\u2009SEM. ns not significant; \u2217p\u2009<\u20090.05, \u2217\u2217p\u2009<\u20090.01, \u2217\u2217\u2217p\u2009<\u20090.001, \u2217\u2217\u2217\u2217p\u2009<\u20090.0001.\n\nIn Gba-SNCA mice, where GCase1 deficiency coexists with a pre-existing \u03b1-synuclein pathology26, there is significantly increased cortical \u03b1-synuclein and pSer129\u03b1-syn levels, especially at 12 months of age when compared to SNCA tg (Fig.\u00a02A\u2013C). We also noted increased \u03b1-synuclein in the neuronal soma of Gba-SNCA mice as an independent measure of \u03b1-synuclein pathology (Fig.\u00a02A, arrows, enlarged inserts). Interestingly, in CA1 hippocampus, the expression of \u03b1-synuclein and pSer129\u03b1-syn, and the somal increase of \u03b1-synuclein in Gba-SNCA mice were comparable to SNCA tg mice (Supplementary Fig.\u00a02C\u2013G). Thus, in contrast to the cortex, Gba mutation only nominally exacerbates pSer129\u03b1-syn pathology, mostly in synaptic layer of CA1 at 12 months (Supplementary Fig.\u00a02G). This might be in part due to high expression of the human SNCA tg in the hippocampus26,29.\n\nNext, we examined the intensity distribution of pSer129\u03b1-syn in cortical neurons as well as cortical layer specific \u03b1-synuclein and pSer129\u03b1-syn expression, at 3 and 12 months of age (Fig.\u00a02A, F\u2013I). Gba mice did not show pSer129\u03b1-syn pathology. Gba-SNCA mice had a higher intensity of pSer129\u03b1-syn pathology in cortical neurons at 3 months (median value of 20 vs 0 in WT and Gba, and 11 in SNCA tg mice), which further worsened at 12 months (Fig.\u00a02F, G) (median value of 47 vs 1 in WT, 0.2 in Gba, and 20 in SNCA tg mice). As the percentage of neurons expressing pSer129\u03b1-syn in Gba-SNCA was comparable to SNCA tg mice (Fig.\u00a02D), these data suggest that the burden of pSer129\u03b1-syn per neuron in Gba-SNCA mice was greater. We did not see cortical neuronal loss in any of the mice (Fig.\u00a02A, E), indicating that the observed behavioral deficits are not due to gross neurodegeneration.\n\nLayer-specific analysis of \u03b1-synuclein expression revealed that cortical layer 1, which is heavily innervated by neurites, showed higher expression of \u03b1-synuclein compared to other layers (Fig.\u00a02H, I, 3 and 12 months). Conversely, cortical layers 5 and 6a, which predominantly consist of excitatory neurons, showed higher pathological pSer129\u03b1-syn expression in SNCA tg and Gba-SNCA mice (Fig.\u00a02J, K, 3 and 12 months), which is more evident when pSer129\u03b1-syn expression was normalized to \u03b1-synuclein (Supplementary Fig.\u00a02A, B, 3 and 12 months). Together, these results suggest that the Gba mutation did not independently cause \u03b1-synuclein pathology but worsened pre-existing \u03b1-synuclein pathology in the cortex of SNCA tg mice, preferentially in layers 5 and 6a. With our behavior experiments (Fig.\u00a01), these observations suggest that the cognitive deficits in Gba mutants emerge independently of pSer129\u03b1-syn pathology.\n\nUniform Manifold Approximation and Projection (UMAP) dimension reduction for A Gba (Amber), B Gba-SNCA (Green) and C SNCA tg (Red) overlayed over WT (Black) mouse cortical snRNAseq expression. D UMAP showing clusters of cortical cell types identified by expression signatures. In A\u2013D, UMAP 1 is shown on the x-axis and UMAP 2 on the y-axis. E Proportions of the cell types in the cortices of wild type and transgenic mice. F The number of differentially expressed genes (DEGs) per cell type in Gba, Gba-SNCA, and SNCA mutant mice after correction for genome-wide comparisons and filtering out of genes with log2FC\u2009<\u20090.2. n\u2009=\u20094 for WT and Gba mice, and n\u2009=\u20093 for Gba-SNCA and SNCA tg mice.\n\nTo understand cellular diversity and mechanisms for GBA-linked cognitive dysfunction, we performed single nucleus RNA sequencing (snRNA-seq) on cortical tissue from mice of all four genotypes (n\u2009=\u200914; 3-4 mice/genotype). We chose to perform this analysis on 12-month-old mice, as Gba-SNCA mice show enhanced behavioral deficits and \u03b1-synuclein pathology, while lacking gross neurodegeneration in the cortex, allowing us to investigate disease-relevant mechanisms.\n\nWe dissected cortices and utilized our previous mouse brain nuclei isolation protocol30, followed by snRNA-seq on the 10X Chromium platform. We isolated 104,750 nuclei, that after cross-sample alignment and clustering exhibited a spatial UMAP grouping uncorrelated with individual samples or genotype (Fig.\u00a03A\u2013E and Supplementary Fig.\u00a03A, B).\n\nThe transcriptional signatures from 104,750 nuclei, segregated into 13 broad cortical cell type clusters (Fig.\u00a03D, E), exhibiting specific expressions of established cell-type markers (Supplementary Fig.\u00a02C). We identified three types of excitatory neurons (ExN: ExN1, ExN2, ExN3), four types of inhibitory neurons (InN: InN1, InN2, InN3, InN4), two types of oligodendrocytes (Oligo and Oligo2), oligodendrocyte precursor cells (OPC), astrocytes (Astro), and microglia (MG) (Fig.\u00a03D). Vascular endothelial cells (Vasc) were also identified but were not further studied. The characteristic marker gene expression for each cell cluster is shown in Supplementary Fig.\u00a03C\u2013G. Expression in all ExNs is consistent with pyramidal neurons. In ExN1, differential expression was consistent with large layer 5 pyramidal neurons shown e.g. by Fezf2 expression. In the largest ExN subcluster, ExN2, differential expression was consistent with pyramidal neurons from several neocortical layers (Fig.\u00a03D and Supplementary Fig.\u00a03D\u2013G). The InN subclusters collectively express several classical InN markers, such as Vip, Sst, Erbb4, while the subclusters InN1 and InN3 specifically contain layer 2/3 interneurons (Supplementary Fig.\u00a03C). Typical marker signatures used for Oligo and Oligo2 (Mbp, Ptgds, Mal), suggests that Oligo2 also contains minor neuronal populations in addition to oligodendrocytes (Supplementary Fig.\u00a03C). The OPC (Vcan, Epn2, Tnr), Astro (Aqp4, Prex2, Luzp2) and MG (Cd74, C1qa, Csf1r) markers are consistent with prior literature30,31. The relative proportions of major cell types were roughly similar between the four genotypes (Fig.\u00a03E).\n\nNext, we examined the expression of endogenous Gba, mouse Snca in WT brains and the expression of human transgenic SNCA (hSNCA) using Thy-1 in SNCA tg brains (Supplementary Fig.\u00a03H\u2013J). Snca is enriched in excitatory neuronal clusters, including ExN1, consistent with published literature (Supplementary Fig.\u00a03H)32,33. This pattern was also true for hSNCA33,34,35, although hSNCA is also expressed in glial cell types in SNCA tg mice (Supplementary Fig.\u00a03I). The high hSNCA expression in the predominantly layer 5-populated ExN1 cluster (Supplementary Fig.\u00a03I) is consistent with the layer 5 specific increases of \u03b1-synuclein pathology demonstrated by immunohistochemistry (Fig.\u00a02H and Supplementary Fig.\u00a02A). In contrast, Gba is generally found at low levels in brain cells36,37,38 (Supplementary Fig.\u00a03J).\n\nAfter correcting for genome-wide comparisons, we identified up- and down-regulated differentially expressed genes (DEGs) in all cell types in Gba, Gba-SNCA and SNCA tg mice cortices compared with WT (Fig.\u00a03F and Supplementary Dataset\u00a01\u20133). To gain insights into the cognitive deficits seen in Gba mice, we focused on DEGs in neuronal clusters, comparing Gba with WT (Supplementary Dataset\u00a01). Strikingly, Gba mutant mice showed a general downregulation of many genes functioning at the synapse (Arc, Syp, Actb, Nrg1, Nlgn1, Grm7, Grip1, Ptprd, Il1rapl2, Gabra1, Cntn5, Lingo2, Erbb4, Nptn, Lrrtm4, Cntnap2, Lrfn5), suggestive of a synaptic dysregulation signature related to Gba. Ahi1, a gene important for cortical development and vesicle trafficking was upregulated in all neuronal classes in Gba mice.\n\nTo define the major pathways impacted, we performed unbiased gene ontology (GO) enrichment analysis comparing Gba to WT (Fig.\u00a04A, C). As shown by heatmaps depicting the top biological pathway changes, we found a consistent decrease in synaptic pathways in cortical ExNs of Gba mice driven by reduced expression of Syp, Actg1, Actb, Nlgn1, Grm8, Nrg1, Arc (Fig.\u00a04A). ExN1 and ExN2 shared robust downregulation of genes involved in synaptic vesicle\u00a0endocytosis (SVE), presynaptic endocytosis, vesicle-mediated transport in synapse, and postsynapse organization in Gba mice (Fig.\u00a04A, highlighted, Supplementary Dataset\u00a04). \u03b2-actin and \u03b3-actin (Actb, Actg1) are both crucial for SVE by aiding formation of endocytic pits, and their downregulation therefore points to reduction in early steps of SVE. Additionally, in Gba mice, ExN1 and ExN2 showed downregulation of cellular pathways and genes involved in both pre- and postsynapse organization, and synaptic protein-containing complex localization (Fig.\u00a04A, highlighted, Supplementary Dataset\u00a04). In the smaller ExN3 cluster, DEGs were fewer and involved in lysosomal lumen acidification (Supplementary Dataset\u00a04). In contrast, the significant upregulated pathways in ExN1 and ExN2 of Gba involve RNA splicing (Fig.\u00a04C and Supplementary Dataset\u00a04). Inhibitory neurons (InN1-4) in Gba mutant mice showed downregulation of multiple synapse-associated pathways, including genes involved in synapse organization, synapse membrane adhesion in InN1, synapse assembly in InN4, Wnt signaling in InN2, and axonogenesis (Fig.\u00a04A and Supplementary Dataset\u00a04). The upregulated pathways in InN1-4, similar to ExNs, are related to RNA splicing (Fig.\u00a04C and Supplementary Dataset\u00a04).\n\nHeatmap with the significantly down- and up-regulated gene ontology (GO) biological pathway alterations in 12 month old Gba- (A, C) and Gba-SNCA mice (B, D), for each neuronal cluster type, revealed by unbiased analysis of enrichment of genome-wide corrected DEGs. Analysis of significant DEGs that participate in synapse function, as annotated by SynGO, after correction for multiple comparisons and filtering out of genes with log2FC\u2009<\u20090.2 in excitatory (ExN1-3, E, G) and inhibitory (InN1-4, F, H) neurons. Cnet plots of differentially expressed synapse associated genes as annotated by SynGO, in I Gba-, and J Gba-SNCA mice cortices, after -correction for multiple comparisons. n\u2009=\u20094 for WT and Gba mice, and n\u2009=\u20093 for Gba-SNCA and SNCA tg mice.\n\nNext, we compared DEGs in neuronal clusters in Gba-SNCA with WT (Supplementary Dataset\u00a02). Consistent with our finding of a Gba-driven synapse effect, ExN clusters in Gba-SNCA mice show robust downregulation of synapse related genes (Supplementary Dataset\u00a04). GO enrichment analysis comparing Gba-SNCA to WT revealed the top downregulated pathways in cortical ExN1 and ExN2 were SV cycle, vesicle-mediated transport in synapse, and SVE (driven by Actb, Actg1, Unc13a, Cacna1a, Calm1, Btbd9, Prkcg, Pacsin1 reductions Fig.\u00a04B, highlighted). Although the individual DEGs between Gba and Gba-SNCA are not identical, the synaptic pathways being impacted are highly similar, suggestive of a common synaptic dysregulation signature related to Gba (Compare Fig.\u00a04A with 4B). In Gba-SNCA InNs we see distinct pathways such as ubiquitin-protein transferase activator activity in InN1 and tRNA aminoacylation in InN2-4 being downregulated (Fig. 4B). The upregulated pathways in Gba-SNCA in ExNs are related to focal adhesion assembly and in InNs are diverse and include amyloid binding (Fig.\u00a04D).\n\nNext, we analyzed all neuronal DEGs through SynGO to define synaptic DEGs in Gba and Gba-SNCA mice. The SynGO analysis revealed more significant suppression of synaptic genes in ExN classes compared to InN classes in both genotypes (Fig.\u00a04E\u2013H). Salient synaptic genes downregulated in Gba ExN are Rab26, and Hspa8 (Fig.\u00a04E). Rab26 regulates SVE and autophagy, while Hspa8 encodes Hsc70 which functions with auxilin to regulate both the initial and late-stages of clathrin mediated endocytosis, further indicating reduced SVE. Cnet plots revealed enrichment of converging and predominantly down-regulated synaptic genes and pathways in both Gba and Gba-SNCA mouse cortices (Fig.\u00a04I, J), consistent with\u00a0GO analyses (Compare Fig.\u00a04A, B, with Fig.\u00a04I, J).\n\nTo evaluate if the downregulation of synapse organization pathways leads to a decrease in excitatory synapse number, we performed electron microscopy on 12-month old cortex samples. Electron microscopy was chosen as it allows for accurate quantification of synapse numbers while avoiding problems of individual synaptic protein marker differences across genotypes. As seen in Fig.\u00a05F, G, number of excitatory synapses in deep layers of cortex is indeed reduced in Gba mutant and Gba-SNCA mice compared to SNCA and WT mice.\n\nA Representative images showing cortical and CA1 hippocampal expression of endophilin-A1 (Endo-A1) and phosphatidylinositol 4,5-bisphosphate (PIP2), two markers of synaptic vesicle endocytosis, in WT, Gba, SNCA tg, and Gba-SNCA mice at 12 months of age. B Cortical Endo-A1 expression at 12 months, normalized to WT average. C Cortical PIP2 expression at 12 months, normalized to WT average. D Endo-A1 expression in the CA1 Hippocampal synaptic layer, normalized to WT average. E PIP2 expression in the CA1 hippocampal synaptic layer, normalized to WT average. Data are presented as mean\u2009\u00b1\u2009SEM for (B\u2013D). Scale\u2009=\u200950\u2009\u00b5m. *p\u2009<\u20090.05. n\u2009=\u20094-5 brains/genotype. F Electron micrographs of cortical layer 5/6 number for excitatory synapses. G Quantitation of excitatory synapses in the cortical layer 5/6. Data are presented as mean\u2009\u00b1\u2009SEM, Scale\u2009=\u2009500\u2009nm. *p\u2009<\u20090.05, ***p\u2009<\u20090.001. H Electron micrographs of excitatory synapses in cortical layer 5/6 showing synaptic vesicles (SVs, arrows) and clathrin-coated vesicles (CCVs, arrowheads) in WT, Gba mutant, SNCA tg, and Gba-SNCA mice. Note SVs with variable shapes and sizes in Gba-SNCA synapse (v). Quantitation of SVs (I) and CCVs (J) in the excitatory synapses of the cortical layer 5/6. Data are presented as mean\u2009\u00b1\u2009SEM, Scale\u2009=\u2009250\u2009nm, Scale for inset\u2009=\u200950\u2009nm. *p\u2009<\u20090.05, **p\u2009<\u20090.01, One-way ANOVA followed by Tukey\u2019s multiple comparison test was used for statistics. \u2018*\u2019 indicates p-value. ****p\u2009<\u20090.0001, N\u2009=\u20093 brains in WT and Gba mutant mice, and N\u2009=\u20092 brains in SNCA tg and Gba-SNCA mice, 23-25 micrographs, 150-300 synapses, per genotype.\n\nTo assess SVE changes at the protein level, we immunostained cortex and hippocampus sections for the SVE protein endophilin A1 (a risk allele for PD) and the endocytic lipid phosphatidylinositol-4,5-bisphosphate (PIP2). Conforming with the snRNA expression data, endophilin A1 and PIP2, showed a decreased trend in the cortex (Fig.\u00a05A, Cortex, B, C). Interestingly, in the synaptic layer in CA1 of the hippocampus where endophilin A1 and PIP2 are enriched, we noted significantly reduced endophilin A1 and PIP2 expression in Gba and Gba-SNCA mice (Fig.\u00a05A, CA1 hippocampus, D, E). Together, these observations corroborate our findings from snRNA-seq analysis of Gba-driven suppression of SVE genes and show these deficits are not limited to the cortex.\n\nTo determine whether these transcriptional and protein expression changes affect the SV cycle, we examined electron micrographs of excitatory synapses in the cortical layer 5/6 and quantified SVs and clathrin coated vesicles (CCVs), which serve as proxies for SV cycling and SVE (Fig.\u00a05H\u2013J). In the Gba mutant mice, the number of SVs was comparable to that in WT mice, however, a significant loss of CCVs was observed, potentially indicating a slowdown in clathrin-mediated SVE (Fig.\u00a05H\u2013J). In Gba-SNCA mice, there was a marked reduction in both SVs and CCVs (Fig.\u00a05H\u2013J), with several synapses showing SVs with variable shapes and sizes (Fig.\u00a05H), indicating a severe disruption of SVE and SV recycling. Interestingly, the SNCA tg mice displayed a significant increase in CCVs (Fig.\u00a05H\u2013J), consistent with findings in other \u03b1-synuclein models39,40. Together, these findings reveal a distinct pattern of SVE disruption in Gba mutant mice, which is exacerbated in Gba-SNCA mice, with altered SV recycling potentially leading to cognitive dysfunction.\n\nAs cortical layer 5 neurons exhibited the highest vulnerability in terms of \u03b1-synuclein pathology (Fig.\u00a02A, H and Supplementary Fig.\u00a02A\u2013B), and transcriptional changes associated with SVE (Fig.\u00a04A, B), we further investigated the ExN1 cluster which has high hSNCA transgene and Gba expression (Supplementary Fig.\u00a03I, J). Upon subclustering, ExN1 was divided into six subclusters (Supplementary Fig.\u00a04A\u2013D), all of which contained cells expressing the layer 5 marker Fezf2 (Supplementary Fig.\u00a04D, E). One subcluster, ExN1.1, was characterized by high expression of Arc (Supplementary Fig.\u00a04D), which is downregulated in Gba mutant neurons (Supplementary Fig.\u00a04F). Our analysis confirmed the greatest downregulation of synapse-associated genes in the ExN1 subclusters in both Gba and Gba-SNCA mice (Supplementary Fig.\u00a04D, F\u2013I). In contrast, non-synaptic DEGs were evenly up- and downregulated (Supplementary Fig.\u00a04F, G). SV cycle pathways were consistently downregulated throughout ExN1, largely driven by the same genes identified in our non-targeted analysis (Fig.\u00a04A, B). Additionally, Rab26, a key regulator of SVE and autophagy41, was downregulated in both Gba genotypes (Supplementary Fig.\u00a04H, I). These findings suggest that layer 5 excitatory neurons are selectively vulnerable because of both high hSNCA and Gba expression and SV cycling deficits driven by Gba mutations. These mechanisms appear to act synergistically to exacerbate \u03b1-synuclein pathology in Gba-SNCA mice.\n\nCompared to neuronal clusters, glial clusters in Gba and Gba-SNCA cortices exhibited fewer DEGs (Fig.\u00a03F and Supplementary Dataset\u00a01\u20133). In Gba cortex, MG showed altered gene expression patterns indicative of reduced synaptic remodeling. Notably, synapse pruning and regulation of SV clustering pathways were downregulated (Fig.\u00a04A and Supplementary Dataset\u00a04). Postsynaptic neurotransmitter receptor diffusion trapping was upregulated (Fig.\u00a04C and Supplementary Dataset\u00a04). These changes reinforce synapse dysfunction as a central pathological mechanism in Gba mutant cortex. The down and upregulated pathways in astrocytes in Gba are related to cellular extravasation and morphology (Fig.\u00a04A, C and Supplementary Dataset\u00a04). There were fewer DEGs in OPCs and Oligodendrocytes (Fig.\u00a04A, C); therefore, clear pathway differences are harder to discern (Fig.\u00a04A, C).\n\nIn Gba-SNCA, MG exhibit decreased endoplasmic reticulum stress response, while SNARE binding and aspects of phosphatidylinositol binding were upregulated (Fig.\u00a04B, D and Supplementary Dataset\u00a04). Gba-SNCA Astro exhibit decreased tRNA aminocylation and increased amyloid-beta binding (Fig.\u00a04B, D). In Gba-SNCA OPCs, phosphatidylinositol binding was downregulated, while Oligodendrocytes showed modest pathway changes (Fig.\u00a04B, D). Overall, in Gba mice, all glial cell types have muted responses, while Gba-SNCA mice had slight astrocytic activation. To confirm our analysis, we immunostained with the microglial marker Iba1, CD68 for activated microglia, and GFAP for astrocytes (Supplementary Fig.\u00a05). We did not observe any significant increase in Iba1 or CD68+ve microglial number, suggesting negligible microglial activation in Gba and Gba-SNCA cortex. We observed a trend towards increased GFAP levels in Gba-SNCA mice compared to WT (Supplementary Fig.\u00a05A\u2013D). Together, these data suggest that glial responses are modest, consistent with snRNAseq data.\n\nSNCA tg mice showed the greatest number of DEGs compared to WT relative to Gba and Gba-SNCA mice (Fig.\u00a03F and Supplementary Dataset\u00a03). The neuronal clusters showed broad alterations of synapse related gene expression. In contrast to the Gba mouse lines, both up and down-regulated DEGs are involved in synapse assembly, regulation of synapse structure or activity, and postsynapse organization (Supplementary Fig.\u00a06A, B and Supplementary Dataset\u00a03). SynGO analysis of these DEGs revealed regulation of synapse structure and function as the main pathway impacted in SNCA tg mice (Supplementary Fig.\u00a06C\u2013E), particularly in glutaminergic synapses (Supplementary Fig.\u00a06A, B). Several of the synaptic genes downregulated in SNCA ExN, particularly in ExN1, take part in synaptic membrane adhesion (Ptprd, Lrfn5, Lrrc4c, Nlgn1, Nrg1). In contrast to downregulation in Gba mouse lines, SNCA mice have upregulation of genes that participate in the postsynaptic density (Dgkz. Hspa8, Dbn1) and modulate phosphatidylinositol activity (Dgkz, Dgkb) including PIP5KIA which synthesizes PIP2. The latter upregulation can influence SV cycling, possibly as a compensatory effect. As excitatory synapse number was not changed significantly in cortical regions of SNCA tg mice (Fig.\u00a05F, G) compared to WT, these transcriptional changes only result in functional deficits. Consistent with the observed cortical \u03b1-synuclein pathology at this age (Fig.\u00a02A, D), unfolded protein handling was upregulated, as were pathways involved in protein folding and refolding specifically in ExN1 and ExN2 clusters (Supplementary Fig.\u00a06B and Supplementary Dataset\u00a03). Additionally, regulation of protein ubiquitination was upregulated in all ExNs (Supplementary Fig.\u00a06B and Supplementary Dataset\u00a03). OPCs and oligodendrocytes show changes related to oligodendrocyte differentiation. MG showed down-regulation in immune receptor binding and up-regulation in cation channel activity (Supplementary Fig.\u00a06A, B and Supplementary Dataset\u00a03). In Astros, cell junction assembly was decreased, and ion channel activity was increased (Supplementary Fig.\u00a06A, B). However, we did not observe significant microgliosis or astrogliosis in SNCA tg mice cortices by immunohistochemistry (Supplementary Fig.\u00a05). Despite SNCA transcriptional changes, Gba signatures are predominant in Gba-SNCA cortices.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63444-9/MediaObjects/41467_2025_63444_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Surveys of PD\u00a0and DLB patients and their caregivers highlight that maintaining cognitive abilities is a major unmet need42. The GBA gene is an ideal choice to investigate this non-motor symptom, because it is the most common risk gene for PD\u00a0and DLB1,2,3,4,5,6,7,8, and GBA mutations are linked to cognitive deficits in both diseases8,18. Traditionally, GBA-associated cognitive deficits have been attributed to cortical Lewy pathology, particularly the aggregation of \u03b1-synuclein in cortical regions. However, our study challenges this long-held view by providing compelling evidence that cognitive dysfunction can occur independently of cortical \u03b1-synuclein pathology\u2014marking a significant conceptual shift in our understanding of GBA-linked PD and DLB. This conclusion stems from a comprehensive, longitudinal behavioral and pathological analyses using a Gba-SNCA mouse model, alongside Gba mutant, SNCA tg, and WT controls. Mechanistic insights were further revealed through snRNA-seq of the cerebral cortex of all four genotypes, which uncovered the synaptic dysfunction in Gba mutations\u2014most notably deficits in SVE. This dataset, a large-scale transcriptomic resource focused on Gba-mediated changes in brain, is publicly available via the NCBI (GEO, accession number: GSE283187) and offers a valuable platform for further exploration. Collectively, our findings not only redefine the role of cortical \u03b1-synuclein pathology in cognitive impairment of PD and DLB but also provide mechanistic insights with potential for therapeutic developments focused on synaptic mechanisms.\n\nCognitive dysfunction has been noted in several existing mice models of PD designed to study motor deficits, including those focused on \u03b1-synuclein pathology43,44,45,46. These models involve overexpression of hSNCA mutations or the use of pre-formed fibrils to induce \u03b1-synuclein aggregation47 and have been instrumental in elucidating the mechanisms of \u03b1-synuclein pathology and its impact on neurodegeneration and cognitive decline. However, they do not fully replicate the complex genetic and pathological features of human PD and DLB, importantly, the contribution of GBA. Additionally, most biallelic Gba mouse models are hampered by early lethality, precluding age-related studies, and hence investigated as heterozygotes47,48,49. Here, we build on our previous analyses of long-lived biallelic Gba mutant mice and Gba-SNCA26 and show that Gba-SNCA mice are an excellent model of GBA-linked PD and DLB. Gba-SNCA mice offer significant advancements as they exhibit worsened motor deficits compared to SNCA tg in an age-dependent manner as well as clear cognitive deficits, closely mirroring the human condition. This is further evidenced by exacerbation of cortical \u03b1-synuclein pathology in Gba-SNCA mice. Significantly, by comparing Gba, SNCA tg, and Gba-SNCA mice, we were able to demonstrate that the Gba mutation alone can drive cognitive dysfunction. These data are congruent with recent studies using heterozygous L444P Gba mutant mice47,49. Notably, Gba-SNCA mice exhibited enhanced motor deficits, but cognitive deficits were on par with Gba mice, matching the similar synaptic pathway deficits in their neuronal populations as assessed by snRNAseq. In sum, Gba-SNCA mice capture the complexities of GBA-linked PD and DLB and serve as a good mouse model for these synucleinopathies.\n\nIt is important to note that most patients with GBA-associated PD and DLB carry only one allele with a GBA variant. However, our choice of the biallelic Gba mutant mouse model is motivated by the fact that biallelic GBA mutations gives an even higher risk of PD and DLB3,14,50. Foundational evidence for the association of GBA with PD arose from clinical observations of PD prevalence in patients with biallelic GBA mutations3,50. Individuals with biallelic mutations, i.e. GD patients, have a significantly higher risk of developing PD (4.7% by age 60) compared to heterozygous carriers (1.5%)1,14. Moreover, biallelic GBA mutations, particularly severe variants like L444P, are associated with an earlier age at onset and a greater burden of cognitive dysfunction14,51,52. While patients with GBA-associated PD do not develop severe cognitive deficits prior to motor symptoms, in GBA-associated DLB, cognitive impairment often precedes or coincides with motor manifestations4,5,6, as observed in our mouse models. This model thus provides a valuable platform to investigate shared and distinct pathogenic mechanisms between PD and DLB, including the relationship between GBA mutations and synaptic deficits.\n\nA striking finding is that cognitive dysfunction occurs independent of or precede \u03b1-synuclein pathology in Gba mutants. pSer129\u03b1-syn is the gold standard to define Lewy bodies in both PD and DLB53,54,55,56,57,58,59,60,61. Yet, we did not detect any pSer129\u03b1-syn in Gba mutant brain nor did we observe increased \u03b1-synuclein levels in the soma, an independent measure of pathology62. This was most evident in the hippocampus, where the synaptic and cell body layers are demarcated. While \u03b1-synuclein pathology in cortical areas does lead to cognitive dysfunction in mice overexpressing mutant \u03b1-synuclein or those injected with PFFs46, our study specifically challenges the necessity of \u03b1-synuclein pathology in the development of cognitive deficits in GBA mutations. Although we observed modest (not-significant) increases in physiological \u03b1-synuclein within the synaptic layers of the cortex (18.2% at 3 months, 21.4% at 12 months, vs. WT in layer 1)\u2014findings further supported by another recent study focused on the hippocampus49\u2014this likely represents a compensatory effect in the absence of true phospho-\u03b1-synuclein pathology. Clinical support for this comes from children with neuronopathic forms of Gaucher disease, who show cognitive deficits but do not have neocortical \u03b1-synuclein pathology63,64. It would be interesting to explore whether aging Gba mice could initiate \u03b1-synucleinopathy or if other pathological forms of \u03b1-synuclein are involved. Moreover, our findings reveal a more pronounced excitatory synapse loss in cortical layer 5 of Gba mutant mice compared to SNCA transgenic mice, despite more synaptic transcriptomic alterations in the latter. This suggests that GBA mutations can drive synaptic dysfunction through mechanisms independent of \u03b1-synuclein\u2014and potentially in a more severe manner. Such insights may help explain the higher incidence of cognitive impairment in PD patients with GBA mutations compared to sporadic cases.\n\nIn contrast to cognitive deficits, motor deficits are strongly related to \u03b1-synuclein pathology in Gba-SNCA mice. Notably, we observed significant \u03b1-synuclein pathology in cortical layers 5, similar to that seen in other PD mice models and human PD and DLB patients33,65. Our snRNA-seq data suggest that layer 5 ExNs are also vulnerable to Gba mediated synaptic dysfunction, which could contribute to an increased accumulation of \u03b1-synuclein pathology in cortical layer 5, and in turn, the severity of the motor deficits. Thus, Gba-SNCA mice also highlight specific cortical neuronal vulnerabilities, allowing for further investigations into cortical mechanisms of PD and DLB.\n\nOur snRNAseq analysis showed clear evidence of specific synaptic changes. Many synaptic genes that function in SV cycle, SVE, synapse organization, synapse membrane adhesion, and synapse assembly were downregulated in neuronal clusters in the cortex of Gba as well as in Gba-SNCA mice, suggesting common synaptic dysfunction mechanisms linked to Gba. Because Gba mutant mice do not show \u03b1-synuclein pathology, this implies that synaptic dysfunction directly contributes to the observed cognitive deficits, rather than a consequence of disease pathology or neurodegeneration. In support of this tenant, we observed excitatory neuronal synapse loss in the cortex of Gba mutant and Gba-SNCA mice. In other dementias such as Alzheimer\u2019s disease, synapse loss correlates tightly with cognitive decline66. Our findings suggest that this is likely true for GBA-linked PD and DLB, in line with available clinical studies67,68,69.\n\nSVE was the major pathway downregulated in neurons in both Gba mutant and Gba-SNCA mice, supported by our immunohistochemistry and electron microscopy experiments. Three key genes driving this pathway are Hspa8, Actb, and Arc. Hspa8 (encoding Hsc70) contributes to synapse vesicle uncoating and functions with Dnajc6/PARK19, a familial PD gene70,71. Actb regulates SVE and the cytoskeleton. Arc is an activity regulated gene that regulates transcription of many synaptic and SVE genes72. Interestingly, both clinical and experimental data link SVE deficits to cognitive deficits. The levels of dynamin1, the endocytic GTPase, correlate with Lewy body dementia73. Patients with mutations in DNAJC6/PARK19 and Synj1/PARK20 which encode two key SVE proteins--auxilin and synaptojanin1\u2014have cognitive deficits. Endocytic mutant mice show deficits in NOR and fear conditioning74,75, supporting the tenet that SVE deficits lead to cognitive dysfunction. We suggest that Arc could serve as an upstream regulator of the synaptic transcriptional changes seen in Gba and Gba-SNCA cortices.\n\nAnother contributor to the Gba-driven alteration of SV cycling is plasma membrane lipid composition changes. Importantly, we found CCVs to be reduced in synapses in deep cortical layers in Gba mutant neurons together with transcriptomic signatures of reduced SV generation and recycling. Our data also indicate that this reduction possibly occurs due to lipid composition changes caused by GCase deficiency. Emerging evidence indicates altered sphingolipid composition, such as in Gba mutants, may interfere with phosphoinositide biology at membranes76,77. This Gba-driven dysfunction could cause reduced SVE by disrupting early stages of SVE. In contrast, in SNCA tg CCVs were increased in our EM analysis. The transcriptomic data indicate that this may be related to inefficient uncoating of CCVs during later phases of SV recycling, making them accumulate. This would represent two related but different mechanisms of SV recycling dysfunction in Gba and SNCA mutations. Future research should address the topic of co-regulated lipids, and SVE in Gba mutants.\n\nRegarding limitations of the study, while the Gba-SNCA mouse model effectively replicates characteristics of GBA-linked PD and DLB, its application in large-scale preclinical studies may be limited by the resource-intensive nature of the model, which requires multiple genetic lines, complex breeding strategies, and extensive genotyping to obtain viable Gba-SNCA double mutants. Our snRNAseq analysis highlights the critical role of synaptic dysfunction in GBA-linked cognitive decline, occurring independent of \u03b1-synuclein pathology. While the DEGs were highly significant and consistently changed, many genes in the Gba mutants did not exhibit large fold changes versus controls. This was expected, as studied genotypes model age-related risk, but individual DEGs will benefit from being validated. Furthermore, while transcriptional analysis and histology confirmed significant disruptions in SVE and synapse assembly in cortical excitatory neurons of both Gba and Gba-SNCA mice, the upstream regulators of these changes remain to be identified. Our comprehensive transcriptomic dataset provides a rich resource for further exploration, and we encourage the scientific community to leverage this data to elucidate GBA-linked disease mechanisms.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "All animal experiments were executed in accordance with the National Institutes of Health guidelines for the Care and Use of Laboratory Animals and with the approval of the Yale University Institutional Animal Care and Use Committee (Protocol number 11117). Mice were maintained in temperature and humidity controlled room on a 12-hour light-dark cycle with access to standard chow ad libitum. Gba mutant mice have been previously described in Mistry et al. (2010)78 and Taguchi et al. (2017)26. These mice have a copy of the Gba L444P mutant allele and a Gba KO allele, with Gba expression rescued in skin to prevent early lethality. SNCA tg mice overexpress the human \u03b1-synuclein A30P transgene (heterozygous), and have also been previously described29. Gba mutant mice were crossed to SNCA tg to obtain Gba-SNCA double mutant mice. Age and sex matched WT mice were used controls.\n\nWT, Gba, SNCA tg, and Gba-SNCA mice were examined for motor behavior longitudinally at 3, 6, 9, and 12 months of age as described previously (n\u2009=\u20099-12 mice/genotype, sex-matched)28. The balance beam test assesses the ability to walk straight on a narrow beam from a brightly lit end towards a dark and safe box. Number of times a mouse could perform this behavior in a minute and the average time taken for each run were evaluated. The grip strength of all the limbs and the forelimbs was assessed by measuring the maximum force (g) exerted by the mouse in grasping specially designed pull bar assembly in tension mode, attached to a grip strength meter (Columbus Instruments, Ohio, USA). Mice, when picked up by the base of the tail and lowered to a surface, extend their limbs reflexively in anticipation of contact. Mice with certain neurological conditions display hind limb clasping instead of extension. Mice were tested on this maneuver for 30 secs and the hindlimb, forelimb, and trunk clasps were scored (0: no clasp; 1: one hind limb clasp; 2: both the hind limbs clasp; 3: Both the hind limbs clasping with at least one forelimb clasp; 4; Both the hind limbs clasp with trunk clasping). For evaluation of overall locomotory capabilities, mice were allowed to explore an open field arena for 5\u2009minutes, which was videotaped to assess the distance travelled using Noldus Ethovision CT software.\n\nTo evaluate cognition, we employed fear conditioning and novel object recognition (NOR) tests. To avoid learning-induced confounding factors, we performed these tests on two separate sets of mice at 3 and 12 months. For fear conditioning test, we initially habituated mice in standard operant boxes for 2\u2009minutes, followed by exposure to a 30-second neutral stimulus (a 80\u2009dB tone), which ended with 2\u2009seconds of an aversive stimulus (a 0.1 to 1.0\u2009mA electric shock). This pairing associates the neutral stimulus with fear, leading the mice to exhibit fear responses, such as freezing, when exposed to the tone alone. We tested for freezing 24\u2009hours later on the testing day. Cognitively normal mice will form a conditioned fear response, exhibiting increased freezing behavior on the testing day compared to the training day after exposure to the tone alone, indicating their ability to associate tone with the electric shock they received on the training day. We measure this conditioned fear response as the number of freeze counts after starting the tone for a total of 3\u2009minutes. The NOR test was used to assess the recognition memory. First, mice were acclimatized to the novel object arena without any objects in it. After 24\u2009hours, familiarization session was performed where mice were presented with two similar objects for 8\u2009minutes. After 18-20\u2009hours, one of the two objects was replaced by a novel object and mice were allowed to explore for 8\u2009minutes. Mice being exploratory animals, spend more time with novel object when their cognition is normal, which we used as a measure of NOR test.\n\nEqual number of male and female mice at 3 and 12 months of age (n\u2009=\u20093-6/genotype) were used for immunohistochemistry. Mice were anaesthetized using isoflurane inhalation and perfused intracardially with chilled 0.9% heparinized saline followed by chilled 4% paraformaldehyde (PFA) in 0.1\u2009M phosphate buffer (PB). The brains were post-fixed in the same buffer for ~48\u2009hours and cryoprotected in increasing grades of buffered sucrose (15 and 30%, prepared in 0.1\u2009M PB), at 4\u2009\u00b0C, and stored at \u221280\u2009\u00b0C until sectioning. Sagittal brain sectioning (30 \u03bcm thick) was performed using a cryostat (Leica CM1850, Germany), and the sections were collected on gelatinized slides, and stored at -20\u2009\u00b0C until further use. For immunofluorescence staining, sections were incubated in 0.5% triton-X 100 (Tx) (15\u2009mins), followed by incubation in 0.3\u2009M glycine (20\u2009mins). Blocking was performed using 3% goat serum (90\u2009mins), followed by overnight incubation (4\u2009\u00b0C) in the primary antibodies. Following day, sections were incubated in Alexa-conjugated secondaries for 3-4\u2009hours, followed by coverslip mounting using an antifade mounting medium with (H-1000, Vectashield) or without (H-1200, Vectashield) DAPI. Coverslips were sealed using nail polish. 1X PBS with 0.1 % Tx was used as both washing and dilution buffer. Below is the list of antibodies used and their dilutions (Table\u00a01).\n\nFluorescent images were acquired using a fluorescence slide scanner (VS200, Olympus) or confocal microscope (LSM 800, Zeiss) with a 40X objective using appropriate Z-depth. Images were then analyzed using FIJI software from National Institute of Health (NIH), blinded for genotype. Whole cortex was demarcated as per Paxinos and Franklin, 2008. After performing sum intensity projection, the expression intensity was measured on an 8-bit image as the mean gray value on a scale of 0\u2013255, where \u20180\u2019 refers to minimum fluorescence and \u2018255\u2019 refers to maximum fluorescence. Normalization was performed by dividing all expression values in each group by the average value of the WT group, allowing for direct comparison between groups while accounting for variability in baseline expression levels. For counting NeuN+ neurons, images were thresholded using the \u2018otsu\u2019 algorithm and the cells larger than 25 mm2 were counted using the \u2018analyze particles\u201d function28. A similar method was used to count Iba1+ve microglial cells, and CD68 +ve cells. GFAP+ astroglial cells were counted manually using the \u2018cell counter\u2019 function. Regions of interest (ROIs) obtained for individual NeuN+ were overlayed on pSer129\u03b1-syn staining to obtain numbers of neurons that were positive for pSer129\u03b1-syn. To analyze the cortical layer-specific expression of \u03b1-synuclein and pSer129\u03b1-syn, we initially determined the proportions of cortical layers (1 to 6b) in sagittal brain slice images obtained from the Allen Brain Atlas (https://mouse.brain-map.org/). This involved drawing perpendicular lines across cortical layers using FIJI software. Proportions were calculated across three different sample areas of the entire cortex. Intensity profiles for \u03b1-synuclein and pSer129\u03b1-syn expression across cortical layers were then generated by drawing perpendicular lines and utilizing the \u201cRGB Profile Plot\u201d function on FIJI for the corresponding sample areas. The resulting expression values, scaled from 0 to 255, were subsequently assigned to the proportions of layers 1 to 6b obtained from the Allen Brain Atlas.\n\nFresh brains were dissected from the mice after euthanasia by isoflurane inhalation and cervical dislocation and quickly homogenized to prepare protein samples. These samples were separated by electrophoresis on 12% SDS-polyacrylamide gels to resolve proteins by molecular weight. Following electrophoresis, proteins were transferred onto polyvinylidene difluoride membranes and then blocked for 1\u2009hour at room temperature using 5% goat serum dissolved in TBST (Tris-buffered saline with 0.1% Tween-20). Blocked membranes were subsequently incubated overnight at 4\u2009\u00b0C with primary antibodies diluted in the same blocking buffer. The following primary antibodies were used: mouse anti-\u03b1-synuclein (BD Biosciences-610786, 1:1000 dilution), rabbit anti-pSer129-\u03b1-synuclein (Abcam- ab51253, 1:1000 dilution), and mouse anti-\u03b2-actin (GeneTex-GTX629630, 1:1000 dilution, used as a loading control). After incubation, membranes were thoroughly washed in TBST and incubated for 1\u2009hour at room temperature with species-specific secondary antibodies conjugated to infrared dyes (LI-COR IRDye Goat secondary antibodies), diluted 1:5000 in blocking buffer. Membranes were then washed using TBST and imaged using a LI-COR Odyssey imaging system. Densitometry values were measured using ImageJ, and the signal intensities of \u03b1-synuclein and pSer129-\u03b1-synuclein were normalized to \u03b2-actin intensity, which served as a loading control.\n\nFollowing isoflurane inhalation anesthesia, brains of 12-month-old mice (n\u2009=\u20092-3 per genotype) were fixed via intracardial perfusion with a solution of 2% PFA and 2% glutaraldehyde in 0.1\u2009M PB. This was followed by an overnight immersion in 0.1\u2009M cacodylate buffer containing 2.5% glutaraldehyde and 2% PFA28. The cortical layers 5 and 6 were then dissected and processed at the Yale Center for Cellular and Molecular Imaging\u2019s Electron Microscopy Facility. Electron microscopy imaging was conducted using an FEI Tecnai G2 Spirit BioTwin Electron Microscope, and the resulting micrographs were analyzed for excitatory asymmetric synapses, their synaptic vesicles and clathrin coated vesicles using FIJI software, with the analysis performed blind to genotype.\n\nFresh cortical tissue were dissected from left hemisphere of 12 month old WT, Gba, SNCA tg and Gba-SNCA mice after euthanasia by isoflurane inhalation and cervical dislocation. Single nuclei were isolated as previously described with modifications30. All procedures were carried out on ice or at 4 oC. Briefly, fresh cortical tissue was homogenized in 8.4\u2009ml of ice-cold nuclei homogenization buffer [2\u2009M sucrose, 10\u2009mM Hepes (pH 7.5), 25\u2009mM KCl, 10% glycerol, 1\u2009mM EDTA (pH 8.0), and ribonuclease (RNase) inhibitors freshly added (40U/ml)] using a Wheaton Dounce tissue grinder (10 strokes with the loose pestle and 10 strokes with the tight pestle). The homogenate was carefully transferred into a 15\u2009ml ultracentrifuge tube on top of 5.6\u2009ml of fresh nuclei homogenization buffer cushion and centrifuged at 25,000\u2009rpm for 60\u2009min at 4\u2009\u00b0C in an ultracentrifuge. The supernatant was removed, and the pellet was resuspended in 1\u2009ml of nuclei resuspension buffer [15\u2009mM Hepes (pH 7.5), 15\u2009mM NaCl, 60\u2009mM KCl, 2\u2009mM MgCl2, 3\u2009mM CaCl2, and RNase inhibitors freshly added (40U/ml)] and counted on a hemocytometer with Trypan Blue staining. The nuclei were centrifuged at 500\u2009g for 10\u2009min at 4\u2009\u00b0C with a swing bucket adaptor. They were subsequently resuspended at a concentration of 700 to 1200 nuclei/\u03bcl in the nuclei resuspension buffer for the next step of 10x Genomics Chromium loading and sequencing.\n\nAfter quality control, we recovered a total of 104,750 nuclei, including 31,906 nuclei from WT, 28,568 from Gba mutant, 26,579 from SNCA tg, and 17,697 from Gba-SNCA cortices. The mean reads per nuclei was 34,312 and the median number of identified genes per nuclei was 2410 in all samples. The snRNA-seq libraries were prepared by the Chromium Single Cell 3\u2032 Reagent Kit v3.1 chemistry according to the manufacturer\u2019s instructions (10x Genomics). The generated snRNA-seq libraries were sequenced using Illumina NovaSeq6000 S4 at a sequencing depth of 300 million reads per sample. For snRNA-seq of brain tissues, a custom pre-mRNA genome reference was generated with mouse genome reference (available from 10x Genomics) that included pre-mRNA sequences, and snRNA-seq data were aligned to this pre-mRNA reference to map both unspliced pre-mRNA and mature mRNA using CellRanger version 3.1.0. The raw data are available on NCBI GEO GSE283187.\n\nAfter quality control filtering by eliminating nuclei with less than 200 genes or more than 5% mitochondrial gene expression (poor quality nuclei) or more than 6,000 genes (potential doublets) per nucleus, we profiled 104,750 brain nuclei. Seurat (version 4.2.0) single cell analysis R package was used for processing the snRNA-seq data. The top 2000 most variable genes across all nuclei in each sample were identified, followed by the integration and expression scaling of all samples and dimensionality reduction using principal components analysis (PCA). Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) was then applied to visualize all cell clusters, and the classification and annotation of distinct cell types were based on known marker genes of each major brain cell type and the entire single nucleus gene expression matrix were investigated but were not used in downstream analyses.\n\nDifferential expression analysis for snRNA-seq data was performed using MAST79 (i.e. the default generalized linear model test) in the function FindMarkers of the Seurat package (4.1.0) in R to identify differentially expressed genes (DEGs), adjusted for sex, batch, and read depth. MAST is a generalized linear model that treats cellular detection rate as a covariate. For all cells, the threshold for DEGs was set as the expression log2 fold change of Mutant/WT mice being greater than 0.2 and significantly changed (p\u2009<\u20090.05) after Benjamini-Hochberg (BH) -correction for multiple (genome-wide) comparisons, using default parameters, and adjusted for confounders. Adjustment for confounders was made for differences in sex, batch and read depth with MAST79 before inclusion of DEGs in downstream analyses.\n\nGene-set and protein enrichment analysis was performed using the function enrichGO from the R package clusterProfiler in Bioconductor (3.14)80, with the DEGs that were significant after correction (see above) as input. Cnet plots were produced using the same package. The top three GO terms from biological process (BP) based on the lowest p-value were identified and plotted in a heatmap, without selection. Molecular function (MF) subontologies were shown instead of BP in a few cases (\u2009<\u20098) where BP contained duplicates or triplicates of the same pathway, for illustrative purposes, with the same DEGs and ranking used for BP and MF subontologies. The background genes were set to be all the protein-coding genes for the mouse reference genome. Default values were used for all parameters. In GO pathway analysis DEGs were entered only if significant after genome-wide BH correction (for the total number of expressed genes), using Seurat default settings. In targeted analyses of genes of interest \u2013 i.e., genes known to function in the synapse as defined by the SynGO consortium (https://syngoportal.org), correction for multiple comparisons was done for the number of investigated genes. 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We thank Bet\u00fcl Y\u00fccel for contributing to genotyping of SNCA tg mice.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: D. J. Vidyadhara, David B\u00e4ckstr\u00f6m.\n\nDepartment of Neurology, Yale University, New Haven, CT, USA\n\nD. J. Vidyadhara,\u00a0David B\u00e4ckstr\u00f6m,\u00a0Risha Chakraborty,\u00a0Jae-Min Park\u00a0&\u00a0Sreeganga. S. Chandra\n\nDepartment of Neuroscience, Yale University, New Haven, CT, USA\n\nD. J. Vidyadhara,\u00a0David B\u00e4ckstr\u00f6m,\u00a0Risha Chakraborty,\u00a0Jae-Min Park\u00a0&\u00a0Sreeganga. S. Chandra\n\nDiscipline of Neuroscience, Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA\n\nD. J. Vidyadhara\n\nCenter for Neurodegenerative Disease and Therapeutics, Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA\n\nD. J. Vidyadhara\n\nDepartment of Clinical Science, Neurosciences, Ume\u00e5 University, Ume\u00e5, Sweden\n\nDavid B\u00e4ckstr\u00f6m\n\nDepartment of Internal Medicine, Yale University, New Haven, CT, USA\n\nJiapeng Ruan\u00a0&\u00a0Pramod K. Mistry\n\nVan Andel Institute, Grand Rapids, MI, USA\n\nJae-Min Park\n\nProgram in Cellular Neuroscience, Neurodegeneration and Repair, Yale University, New Haven, CT, USA\n\nSreeganga. S. Chandra\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nD.J.V., D.B., and S.S.C. conceptualized the study. D.J.V. conducted behavioral experiments and analyzed the data, prepared samples for histology and Western blotting, and performed immunohistochemistry, imaging, and image analyses. D.B. prepared samples for snRNA-seq and analyzed the snRNA-seq data. R.C. analyzed behavioral data and performed immunohistochemistry, Western blotting, imaging, and image analyses. J.R. set up founder breeding colonies and conducted genotyping for Gba L444P/KO and Gba-SNCA mice. J.P. assisted with snRNA-seq data analysis. P.M. contributed to the initial conceptualization of the study and provided founder colonies of Gba L444P/KO mice. D.J.V., D.B., and S.S.C. wrote the manuscript. All authors have read and contributed to the manuscript.\n\nCorrespondence to\n Sreeganga. S. Chandra.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Vidyadhara, D.J., B\u00e4ckstr\u00f6m, D., Chakraborty, R. et al. Synaptic vesicle endocytosis deficits underlie cognitive dysfunction in mouse models of GBA-linked Parkinson\u2019s disease and dementia with Lewy bodies.\n Nat Commun 16, 8484 (2025). https://doi.org/10.1038/s41467-025-63444-9\n\nDownload citation\n\nReceived: 15 December 2024\n\nAccepted: 12 August 2025\n\nPublished: 26 September 2025\n\nVersion of record: 26 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63444-9\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 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multi-particle systems through parametric pumping", + "journal": "Nature Communications", + "published": "19 May 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59631-3/MediaObjects/41467_2025_59631_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59631-3/MediaObjects/41467_2025_59631_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59631-3/MediaObjects/41467_2025_59631_MOESM3_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59631-3/MediaObjects/41467_2025_59631_MOESM4_ESM.mp4" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59631-3/MediaObjects/41467_2025_59631_MOESM5_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-59631-3#ref-CR52" + ], + "code": [ + "/articles/s41467-025-59631-3#ref-CR52" + ], + "subject": [ + "Acoustics", + "Condensed-matter physics", + "Statistical physics, thermodynamics and nonlinear dynamics" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5027451/v1.pdf?c=1747739232000", + "research_square_link": "https://www.researchsquare.com//article/rs-5027451/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-59631-3.pdf", + "preprint_posted": "16 Oct, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Processes from crystallization to protein folding to robot self-assembly rely on achieving specific configurations of interacting particles. However, precise manipulation of even a small number of particles into more than one configuration without the use of feedback control is still challenging when controlled externally without feedback loops, especially if the interactions are non-conservative. Here we show how one can select, and switch between, specific structures without relying on tuning the energetic landscape of the system. Exploiting that different particle configurations have different mechanical resonances, we use parametric pumping to selectively excite and destroy undesired structures, making the targeted one an absorbing state. We demonstrate this approach with an acoustically levitated five-particle system in the Rayleigh limit, where the interactions are not only non-reciprocal and thus non-conservative, but also non-pairwise. With results from experiments and simulations on three additional systems ranging up to hundreds of particles, we demonstrate the generality of this method, providing a new path for non-invasive structure control of multi-particle systems.Physical sciences/Physics/Condensed-matter physicsPhysical sciences/Physics/Statistical physics, thermodynamics and nonlinear dynamics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "ExtendedDataMovie1.mp4Extended Data Movie 1: Parametric pumping and structure control of levitated particlesExtendedDataMovie2.mp4Extended Data Movie 2: Parametric pumping and shape control of a levitated granular raft", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Processes from crystallization to protein folding to micro-robot self-assembly rely on achieving specific configurations of microscopic objects with short-ranged interactions. However, the small scales and large configuration spaces of such multi-body systems render targeted control challenging. Inspired by optical pumping manipulation of quantum states, we develop a method using parametric pumping to selectively excite and destroy undesired structures to populate the targeted one. This method does not rely on free energy considerations and therefore works for systems with non-conservative and even non-reciprocal interactions, which we demonstrate with an acoustically levitated five-particle system in the Rayleigh limit. With results from experiments and simulations on three additional systems ranging up to hundreds of particles, we show the generality of this method, offering a new path for non-invasive manipulation of strongly interacting multi-particle systems.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Precise control of multi-particle systems is challenging due to the multiplicity of stable structures within a high-dimensional configuration space. To guide such systems towards the targeted structure, techniques such as acoustic1,2,3,4 or optical trapping5,6,7,8 create localized field gradients that apply forces on individual particles. This has enjoyed success for facile particle manipulation with wide-ranging applications, including reconfigurable displays and phononic crystals9,10,11. However, these methods isolate particles into potential wells that are separated on the scale of the incident wavelength, preventing their applicability to structures with closely spaced particles where particle interactions become important. Another approach, therefore, focuses on utilizing the particle interactions themselves to tailor the free-energy landscape. This creates an effective guiding force favoring the desired configuration, employing methods based on, e.g., electromagnetic interactions12,13,14,15,16, shaped and patchy particles17,18,19, or DNA bonding20,21,22. These methods are typically optimized to produce a single targeted configuration and cannot be extended easily to allow for reconfigurability. Out-of-equilibrium self-assembly23,24,25 further complicates this picture by invalidating the basic notion of a uniquely defined free-energy landscape, and consequentially the precise control of such systems remains difficult. These shortcomings can be somewhat alleviated using active feedback control to apply corrective forces14,25,26, at the cost of requiring real-time sensing, actuation, and substantial computation. Still, structural control remains a non-trivial task, even for clusters containing only a small number of particles27,28,29,30,31. This can be yet more complicated in non-conservative systems with non-reciprocal interactions32,33,34.\n\nTo address this problem, we borrow an idea from optical pumping35. Consider a two-state system (Fig.\u00a01a), where incident light pumps state A into an unstable excited state before it randomly decays into either state A or B. Due to the mismatch of gap energy, B is an absorbing state that cannot be activated, so the concentration of state B grows as the excitation and decay of A continues. Thus, precise state control is achieved without a feedback loop by selectively activating and depopulating the undesired states. We can adapt this strategy for precise assembly of particles in many systems with tunable interactions4,7,36,37,38,39, including those without energy conservation, provided that the states to be manipulated are not isospectral40,41, i.e., that these states have distinguishable vibrational bands. Specifically, we use parametric pumping42,43,44 to excite and destabilize undesired structures, combined with quenching to favor the targeted one, as sketched in Fig.\u00a01b. By cycling between pumping and quenching, we effectively create a diagram similar to Fig.\u00a01a, where state B is \u2018quiet\u2019 and absorbing. This method reduces the high-dimensional control problem to a 1D frequency domain and, simultaneously, circumvents the need to fine-tune energy landscapes for state selection, enabling us to control systems with non-reciprocal interactions as well.\n\na Selective excitation of state A in a system of trapped atoms, creating a dark state B. The vertical axis indicates the potential energy of the system, Epot. When state A is excited by light with appropriate energy, it will be pumped into an excited state (orange arrow), from which it can randomly decay into one of the base states (black arrows). State B cannot be activated by incident light; thus, pumping concentrates atoms in state B. b Cycling between pumping and quenching to create an absorbing 'quiet' state, which does not rely on the presence of well-defined potential energy levels and therefore is not limited to conservative systems. The vertical axis indicates the kinetic energy, Ekin, where Ekin\u2009=\u20090 refers to a base state of the system. By activating a multi-particle system\u2019s vibration mode with parametric pumping, the system will oscillate to the point of transitioning into an unstable state (red arrow). When the pumping is turned off, the system in the unstable state is quenched by damping and will randomly decay back into one of the base states. By cycling between pumping and quenching, the time-averaged outcome is the creation of an absorbing state B, similar to the diagram in (a). c\u2013f Application of pump-quench cycling to control the configuration of, c an acoustically levitated cluster of five particles, d a simulated cluster of a rod and two spheres, e a simulated cluster of 13 spheres, and f the size of an acoustically levitated granular raft.\n\nIn the following, we demonstrate this approach with experiments and simulations that focus on acoustically levitated particle systems. We first discuss a five-particle system with non-reciprocal interactions, Fig.\u00a01c, showing reversible control between two configurations. We then extend the method to systems with a larger number of excitable configurations from which we can select targeted outcomes: a cluster of levitated particles with anisotropic shape, Fig.\u00a01d; a 3D Lennard-Jones cluster of 13 particles, Fig.\u00a01e; and a levitated granular raft comprising hundreds of particles, Fig.\u00a01f.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59631-3/MediaObjects/41467_2025_59631_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "We use a single-axis Langevin-type horn to generate intense ultrasound (frequency f\u2009\u2248\u200934,800\u2009Hz) capable of lifting small particles against the force of gravity, as shown in Fig.\u00a02a. A piezoelectric transducer driven by a function generator is affixed to an aluminum horn, which acts as a resonant amplifier. The horn is positioned one half wavelength (h\u2009=\u2009\u03bb/2) above a transparent reflector plate, creating a standing sound wave. Particles levitate just below the pressure nodal plane, midway in the gap. The bottom surface of the resonant horn is slightly curved, which focuses the sound and also produces lateral confinement of particles near the center of the nodal plane. We use polystyrene spheres with diameter D\u2009=\u200941\u2009\u03bcm. Residual charge on particle surfaces is eliminated by a photo-ionizer, and the reflector plate is ITO-coated and grounded. Two high-speed cameras capture simultaneously the side and the bottom view of levitated particles. A pressure sensor is positioned near the acoustic cavity to measure and control the acoustic pressure. Details about the acoustic levitation setup are discussed in Methods.\n\na Diagram of experimental setup. b Tunable acoustic interactions between particles. The right plot has a higher acoustic power than the left. The thickness of the red lines connecting the particle centers indicates the strength of the effective spring constant and the background shows the streaming flow velocity field magnitude vst, from finite-element method simulation. c Sound-induced net force between two spheres, for acoustic energy densities E1\u2009<\u2009E2\u2009<\u2009E3. d Piezo input voltage with modulating frequency fam and modulation depth \u03b5am. e Pumping state diagram for the in-plane vibration mode of the 2-sphere `molecule', showing the mode\u2019s growth rate \\({\\gamma }_{2}^{*}\\) as function of fam and \u03b5am. Gray isolines indicate \\({\\gamma }_{2}^{*}=1\\), where pumping balances dissipation. Maximum modulation depth in the experiments at two sound frequencies is shown by red and pink lines. f Video snapshots of a 2-sphere 'molecule' pumped with parameters marked by the pink circle in (e). g Center-to-center distance r between two spheres when pumped. Dashed vertical lines refer to the snapshots in (f).\n\nWe first demonstrate acoustic parametric pumping of a \u2018molecule\u2019 consisting of two spheres mutually bound by sound-induced forces. In this simplest case, the interaction is conservative and (to linear order) spring-like. For two solid, levitating spheres with diameters and center-to-center distance r much smaller than the wavelength of incident sound (the near-field Rayleigh limit, \u03bb\u2009>>\u2009r\u2009\u2265\u2009D), the net force at close approach arises from the competition between attractive acoustic scattering force and repulsive force from viscous streaming flow near the particle boundaries34.\n\nAs sketched in Fig.\u00a02b, the balance between the two forces results in a stable, steady-state separation rss of the two particles in the \u2018molecule\u2019. Importantly, both forces are proportional to the sound energy density E in the acoustic cavity. Therefore, rss does not change when E is tuned by a changing sound pressure. However, the effective spring constant for small displacements around rss does scale with E, as shown in Fig.\u00a02c. This enables us to selectively excite internal degrees of freedom of interacting particles with parametric pumping, which is fundamentally different from modulating a background field to move particles collectively1,3,44. More detailed discussion of the scattering and streaming interactions can be found in refs. 4,34.\n\nTo conduct parametric pumping, we modulate transducer input voltage by varying the amplitude according to \\((1+{\\varepsilon }_{{{{\\rm{am}}}}}\\sin (2\\pi {f}_{{{{\\rm{am}}}}}t))\\), where fam is the modulation frequency and \u03b5am the modulation depth, Fig.\u00a02d. Since energy density E depends quadratically on transducer voltage34, it becomes\n\nWhen levitated particles are subject to such a modulated acoustic field, tuning fam and \u03b5am can activate specific vibrational modes (VMs), growing their vibration amplitude to destabilize the structure. As an example, the pumping state diagram in Fig.\u00a02e shows how the effective growth rate of the in-plane \u2018breathing\u2019 mode of the two-particle system, \\({\\gamma }_{2}^{*}=\\frac{{\\gamma }_{2}}{{\\gamma }_{d}}\\), depends on both fam and \u03b5am. Here \u03b32 is the growth rate without damping, and \u03b3d is the damping rate caused by air viscosity, see Methods. See\u00a0Supplementary Information for out-of-plane VMs. Similar to solutions of Mathieu\u2019s equation45, we find that the VM can be activated within Arnold tongues, i.e., with sufficiently large \u03b5am when fam is close to a factor 2/n times the VM\u2019s resonance frequency (n\u2009\u2208\u2009Z+). The isolines \\({\\gamma }_{2}^{*}=1\\), where growth rate equals damping rate, are shown as gray lines in Fig.\u00a02e. Only regions that have \\({\\gamma }_{2}^{*} > 1\\) can be used to pump a VM.\n\nBecause the acoustic cavity has a finite response time and also a characteristic response function that depends on f, fam and \u03b5am, the experimentally measured, steady-state modulation depth in the acoustic trap, \\({\\varepsilon }_{{{{\\rm{am}}}}}^{{{{\\rm{eff}}}}}\\), is usually smaller than \u03b5am. Therefore, when analyzing VMs and comparing with simulations, we need to consider \\({\\varepsilon }_{{{{\\rm{am}}}}}^{{{{\\rm{eff}}}}}\\). The largest \\({\\varepsilon }_{{{{\\rm{am}}}}}^{{{{\\rm{eff}}}}}\\) achievable in our setup, i.e., using 100% piezo voltage modulation at different frequencies, is shown in Fig.\u00a02e. If the system is driven with f\u2009=\u200934,650\u2009Hz (pink), away from the cavity resonance \\({f}_{{{{\\rm{res}}}}}=34800\\)Hz (red), a higher \\({\\varepsilon }_{{{{\\rm{am}}}}}^{{{{\\rm{eff}}}}}\\) can be achieved(see\u00a0Supplementary Information), crossing the \\({\\gamma }_{2}^{*}=1\\) isolines and allowing for VM pumping.\n\nIn Fig.\u00a02f, we show two snapshots of the pumped 2-sphere system, and in Fig.\u00a02g, we plot their center-to-center distance r(t) as a function of time, demonstrating how parametric pumping activates this in-plane VM (also see Supplementary Movie\u00a01). Vibration amplitude saturates at larger t because the pumping rate \u03b32 drops and \u03b3d grows as the system departs the linear region, yielding \\({\\gamma }_{2}^{*}=1\\).\n\nAdding particles increases the variety of possible stable configurations in the levitation plane. For five levitating spheres, the interactions due to attractive sound scattering and repulsive microstreaming allow for two such configurations: either a pentagon or a cross structure, as shown in Fig.\u00a03a. Furthermore, while the interactions between a pair of levitated spheres can be well-approximated with a pair potential34,46, this is not the case for multiple interacting particles. To demonstrate this, Fig.\u00a03b, c show results from a finite-element method (FEM) simulation of the pentagon state.\n\na Snapshot of five spheres in the unstable state (left) and in their two stable states, pentagon and cross (right). b, c Simulation of steady streaming flow velocity field magnitude vst around the five spheres when one sphere is displaced by 0.3D (original position indicated by gray dashed circle). Red arrows show the net force felt by the sphere at the other end of the dashed pink lines. d Overlay of pumping state diagrams for the cross state, \\({\\gamma }_{c}^{*}\\) (blue) and the pentagon state, \\({\\gamma }_{p}^{*}\\) (red). The maximum modulation depth in the experiment is indicated by the black line. Pumping parameter combinations marked by the blue cross and the red pentagon are chosen to activate one or the other state. e, f Transitioning between states. Plots show total kinetic energy when the pentagon, or the cross state, is repeatedly pumped (time intervals shaded light red or blue) and quenched (white), with snapshots of the evolving configurations. g Reversible switching between cross and pentagon states by altering the pumping parameters on the fly. h Cumulative probability of successful transitioning as a function of cycles needed. Solid lines: experiments; dashed lines: Markov model. Error bars: standard deviation.\n\nWe now focus on a pair of particles (indicated by the pink dashed line that links their centers), where one particle is displaced 0.3D to the left or right of its stable position. In both cases, resulting force felt by the other one (red arrow) are not pointing along the pink dashed line, nor pointing in the same direction. This is not possible for pairwise interactions, rather signaling multibody interactions that arise from background flow field generated by all particles collectively.\n\nThe sound-induced interactions also violate Newton\u2019s third law, i.e., the force on particle A due to the presence of particle B is not equal and opposite to the force on B due to A. This non-reciprocity can be detected as an asymmetry in the linear stiffness matrix describing the five-particle system (Supplementary Fig.\u00a02). The multibody and non-reciprocal nature of the levitated 5-particle system implies that we cannot define a unique energy landscape connecting both structures, rendering it infeasible for a landscape tuning method to drive this system between configurations. However, this is possible with parametric pumping, as discussed next.\n\nIrrespective of the complexity of the interactions among particles, to linear order, we can obtain the VMs and their associated eigenfrequencies from the stiffness matrix for each stable configuration. By diagonalizing the stiffness matrices calculated by FEM simulation, we find 8 VMs for the cross state and 6 for the pentagon state (Supplementary Fig.\u00a03). We then calculate the effective growth rates \\({\\gamma }_{[c,p],i}^{*}({f}_{{{{\\rm{am}}}}},{\\varepsilon }_{{{{\\rm{am}}}}})\\) for all i modes of the cross (c), and pentagon (p) states in the same way as for the two-particle system. The maximum of these growth rates for a given state,\\({\\gamma }_{[c,p]}^{*}({f}_{{{{\\rm{am}}}}},{\\varepsilon }_{{{{\\rm{am}}}}})={\\max }_{i}({\\gamma }_{[c,p],i}^{*}({f}_{{{{\\rm{am}}}}},{\\varepsilon }_{{{{\\rm{am}}}}}))\\) is shown in blue and red for the cross and the pentagon, respectively, in the pumping state diagram in Fig.\u00a03d. We can find combinations of (fam,\u00a0\u03b5am) that activate one (blue or red), both (purple) or none (white) of the states. Note that only the region on and below \\({\\varepsilon }_{{{{\\rm{am}}}}}^{{{{\\rm{eff}}}}}({f}_{{{{\\rm{am}}}}})\\), shown by the black line, is accessible to us experimentally with f\u2009=\u200934,750\u2009Hz. On that line, two locations suitable for parametric pumping are marked by the blue cross and red pentagon symbol.\n\nFigure\u00a03e and Supplementary Movie\u00a01 give an example of guiding the five-particle cluster to form a cross. The sound field is driven with fam\u2009=\u2009290\u2009Hz, \\({\\varepsilon }_{{{{\\rm{am}}}}}^{{{{\\rm{eff}}}}}=0.40\\) (red pentagon symbol in Fig.\u00a03d) to destabilize the pentagon without affecting the cross. As a result, during the first pumping cycle the cluster is excited into the unstable state and during quenching (e.g., when the sound field modulation is turned off) it relaxes back into the pentagon state. The second pumping cycle drives the cluster unstable again, but this time it relaxes into the cross state during quenching, where it remains in subsequent cycles. The opposite transition can also be achieved, as shown in Fig.\u00a03f and Supplementary Movie\u00a01, where the sound field is modulated using fam\u2009=\u2009170\u2009Hz, \\({\\varepsilon }_{{{{\\rm{am}}}}}^{{{{\\rm{eff}}}}}=0.77\\) (blue cross symbol in Fig.\u00a03d).\n\nThe pumping parameters fam and \u03b5am can be adjusted on the fly, giving the flexibility of switching rapidly back and forth between the two configurations of the 5-particle cluster. This is demonstrated in Fig.\u00a03g and Supplementary Movie\u00a01. For both directions, only a few pump-quench cycles are required for a high probability of configuration switching (Fig.\u00a03h). This agrees qualitatively with a Markov model of the pump-quench process (dashed lines, see Methods).\n\nTo show how the pump-quench approach performs in scenarios where the configuration space is more complicated than for the 5-sphere system, we next discuss simulations of a particle cluster with three states and a cluster with over 70 states. The first example consists of two spheres interacting with a rigid rod comprised of three spheres, as shown in the inset of Fig.\u00a04a. Forces between particles are designed to mimic the conservative-part of interactions in the experiment(see Methods) and this system has three stable configurations(inset: red, green and blue) with similar energies.\n\na Pumping state diagram for a simulated 3-state system comprising a rod and two spheres. Inset: color code for the particle configurations corresponding to the three states and their activation. At certain spots in the state diagram, only one state is activated (red, blue, and green) or one state is stable (pink, cyan, and gold), or all three states are activated (black). b Probability of not reaching the targeted state decreases exponentially. Protocol I (square symbols) applies pump-quench cycles alternating between two sets of parameters marked by crosses with different colors in a. Protocol II (circle symbols) applies a single-step process using parameters marked by the circles in a. Open symbols: simulation data. Color of the circle symbol denotes the targeted structure. Lines: Markov model. c Pumping state diagram for a simulated cluster of 13 spheres with Lennard-Jones interactions at T\u2009=\u20090. In this three-dimensional cluster all spheres are in direct contact with neighbors. Red line shows the boundary above which pumping is possible (\u03b3*\u2009=\u20091) for the icosahedral sphere arrangement; gray lines are the boundaries for other structures. Red cross: the specific pumping parameter combination used. d Probability of not reaching the targeted icosahedral configuration (sketched in red) decreases exponentially. Examples of other, non-targeted configurations are sketched in gray.\n\nIn the simulation, we can modulate the interaction strength between particles directly (rather than indirectly via the energy density, Eq. (1)). Thus, the effective spring constant is modulated according to \\(k={k}_{0}(1+{\\varepsilon }_{{{{\\rm{am}}}}}\\sin (2\\pi {f}_{{{{\\rm{am}}}}}t))\\). The pumping state diagram of this system is shown in Fig.\u00a04a. Again, we can find pumping parameter combinations where only one state is activated (red, green, and blue), two states are activated simultaneously (pink, cyan, and gold), or all three states are activated (black). With these parameter combinations, we can design pump-quench sequences that guide the system into different targeted configurations.\n\nFor example, if the blue configuration is targeted as the desired end state, the red and the green ones have to be activated and destabilized. To achieve this, we can first pump with parameters marked by the red cross and activate the red structure, then quench and next use the parameters marked by the green cross to activate the green structure, then quench again. Repeating this particular sequence makes the blue state absorbing. Starting from 1000 random initial conditions, the blue squares in Fig.\u00a04b show that the probability of not attaining the blue structure decreases exponentially with the number of quenching steps. This matches the prediction from a Markov model (the dashed line, see Methods). The same applies when instead the red or the green structure is targeted.\n\nBecause there are regions in the pumping state diagram where multiple states can be activated simultaneously, we can implement a second method where we destabilize two structures at the same time using parameters marked by the circles in Fig.\u00a04a. This is significantly more efficient (Fig.\u00a04b circles), as confirmed by the shorter absorbing time calculated from the Markov models (Supplementary Fig.\u00a04).\n\nThe second example is a three-dimensional cluster of 13 particles with Lennard-Jones interactions. Simulating this system at zero temperature, we find >70 distinct stable structures. This leads to a pumping state diagram densely populated with overlapping resonant regions (Fig.\u00a04c), each corresponding to combinations of parameters \u03b5am and fam that activate a particular configuration, some of which are shown in gray in Fig.\u00a04d. However, there still are non-overlapping regions that allow for certain configurations to be selected. Here we show this for the icosahedral configuration (red in Fig.\u00a04d), whose activation boundary in the pumping state diagram in Fig.\u00a04c is indicated by the red line. Due to the high symmetry of the icosahedron, its activation region exhibits a characteristic gap as a function of fam, which allows us to destabilize all other structures by pumping with parameters marked by the red (+) sign. Similar to the previous example, when starting from 104 random initial states, the probability of not reaching the targeted icosahedron decays exponentially with the number of pump-quench cycles (Fig.\u00a04d). While the clear gap in the spectrum of vibration frequencies makes the icosahedral configuration particularly selectable, configurations with more overlap in the state diagram can be targeted similarly. We show this in the\u00a0Supplementary Information, where we discuss how structural symmetry, damping coefficient, particle number, and number of states affect state selectability.\n\nAs the particle number increases, the pumping state diagram becomes more crowded, and it becomes more difficult to selectively activate individual configurations or find absorbing states. Nevertheless, parametric pumping can still be utilized to activate collective, bulk vibration modes. Here we show how this can be employed to control the size of a levitated granular raft containing more than 300 particles. Rafts of this size can be driven to exhibit bulk oscillations in their overall shape, similar to levitated liquid drops42. With our experimental setup, the frequencies most conveniently accessible to destabilize the raft belong to the shape oscillation mode with threefold symmetry. As shown in Fig.\u00a05a and Supplementary Movie\u00a02, by pumping this mode for 0.3\u2009s, the raft as a whole is excited so strongly that particles no longer stay bound acoustically and escape. When subsequently quenched for 1s, the raft stabilizes, but at a reduced size due to loss of particles. Rafts of different sizes and therefore different masses exhibit shape oscillation resonances centered around different frequencies, similar to liquid droplets where these resonances are given by Rayleigh\u2019s equation42. Therefore, when the pump-quench cycling is continued with fixed fam, the raft\u2019s size will eventually become too small to be activated and will no longer lose particles. Thus, by starting with large rafts and applying different pumping frequencies fam, different terminal raft sizes can be targeted (Supplementary Fig.\u00a08). Figure\u00a05b shows how this self-limiting process shrinks large rafts. The raft size saturates automatically over 20 pump-quench cycles as the targeted diameter is approached. On the right, initial and final images of the rafts are compared, with color corresponding to the data for different fam.\n\na A levitated raft being pumped to undergo shape oscillations to shed particles, before being quenched and stabilized with fewer particles. The yellow dashed circle has the same size in each image and serves as a visual guide. b Evolution under repeated pump-quench cycling of the number of particles in a raft for different pump frequencies. Images to the right of the plot show the initial and final raft configuration, demonstrating the ability to control the raft size.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59631-3/MediaObjects/41467_2025_59631_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59631-3/MediaObjects/41467_2025_59631_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59631-3/MediaObjects/41467_2025_59631_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59631-3/MediaObjects/41467_2025_59631_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our results show that cyclic switching between parametric pumping and quenching can control the structural configuration of strongly interacting particle systems even when the interactions are multibody and non-reciprocal in nature. Instead of engineering highly localized potential gradients, this approach excites vibrational modes to drive the (re-) configuration process. As such, our method does not rely on an underlying free-energy landscape and thus can work with non-conservative systems. It does not depend on specific particle properties or geometries, and it can be generalized to control structures when multiple stable configurations are available, as long as the resonances for activated states and targeted absorbing states are sufficiently well separated in the pumping state diagram.\n\nAs our method only depends on a suitable spectrum of excitable vibration modes, we expect it can be applied to a wide range of systems with tunable interactions beyond acoustically levitated ones, both reciprocal and non-reciprocal, including dielectric and magnetic particles36,37, as well as optically trapped particles7,38,39. For example, used for directed self-assembly, it might offer a versatile way to selectively eliminate undesired sub-structures early on, before they seed the subsequent assembly into larger dysfunctional configurations. Since our approach focuses on manipulating the interactions of particles in close proximity, we expect that it can be integrated with far-field methods, which can generate reconfigurable or steerable potential gradients over larger length scales. For example, using far-field-based methods9,11 to generate an array of acoustic potential wells, and seeding each with multiple particles, parametric pumping of these wells could then be applied to generate many copies of the targeted particle configuration simultaneously. Finally, extensions might adapt parametric pumping for controlling the assembly of micro-robots16, non-contact cell counting47, and manipulation of defects in colloidal crystals and liquid crystals48,49.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "We use a single-axis ultrasound transducer to levitate particles, as shown in Fig.\u00a02a. An aluminum horn is attached to a stack of piezo elements, which is driven by a signal generator (Keysight, EDU33212A) and an amplifier (AA Lab Systems, A-301 HS). The bottom of the horn is positioned half a sound wavelength above a transparent reflector plate to generate a standing wave. Both the horn and the reflector are grounded to minimize particle charging.\n\nTwo high-speed cameras capture the bottom and side views of the levitated particles. To improve image quality, we used backlighting: Light from one source, angled upward, is reflected from the white-painted bottom surface of the horn, enabling us to capture high-resolution bottom-view images with camera 1 (Phantom T1340) through a mirror angled at 45 degrees. Camera 1 is equipped with a Nikon ED AF Micro Nikkor 200\u2009mm lens to capture the dynamics of the levitated 5-particle system, and with a Navitar Resolv4K lens (working distance 72\u2009mm) for imaging the levitated 2-particle system and the larger rafts. A second light source illuminates from the left, enabling us to simultaneously capture side-view images with camera 2 (Phantom, VEO1010S, equipped with a Nikon AF Micro Nikkor 70\u2013180\u2009mm lens).\n\nA temperature controller (Conductus, LTC-20) stabilizes the horn temperature within 50\u2009mK and minimizes any temperature-dependent drift in acoustic power. An acrylic enclosure thermally isolates the setup from the ambient conditions in the lab, further improving temperature control. An interferometric pressure sensor (XARION Laser Acoustics, Eta100 Ultra) is positioned next to the acoustic cavity to measure the real-time sound pressure. Before each experiment, an X-ray de-ionizer (Hamamatsu, L9491) is turned on for 10 seconds to neutralize the electric charge on the levitated particles.\n\nWe use polystyrene spheres with diameter D\u2009=\u2009(41.1\u2009\u00b1\u20090.5)\u2009\u03bcm (microParticles GmbH). Before being placed into the acoustic trap, these particles are coated with a layer of nanoparticles (Evonik Aeroxide AluC) to minimize sticking due to van der Waals interaction.\n\nWe use the FEM solver COMSOL Multiphysics to carry out simulations of vibrational modes, taking into account particle interactions due to scattered sound as well as sound-induced microstreaming. The simulation domain is a cylinder with diameter 10\u2009mm and height 4.94\u2009mm, corresponding to half of the ultrasound wavelength \u03bb. The particles are placed on the nodal plane in the center of this cylinder. The simulation proceeds in two steps. The first step is a thermoviscous acoustics calculation in the frequency domain, where the top of the cylinder is set to oscillate in the vertical, z-direction, and the bottom is fixed. To mimic the open side of the acoustic cavity, the side wall of the cylinder is set to have a slip boundary condition. The surfaces of levitated particles are set to no-slip boundary conditions. A second step is a laminar flow calculation, carried out to obtain the time-averaged streaming flow between particles. Forces acting on each particle are calculated by integrating the viscous stress and pressure on the particle surface.\n\nThe stiffness matrix, K, for each configuration can then be constructed by simulations in which each individual particle is slightly displaced from its stable, steady-state position. Kij represents the linear response of the system in direction of j-th degree of freedom (DoF) when a displacement occurs in the direction of the i-th DoF, with 1\u2009\u2264\u2009i,\u00a0j\u2009\u2264\u200915. To calculate this response, we displace the particle along the direction of the i-th DoF across a distance from +0.5\u2009D to \u22120.5\u2009D using a total of 11 simulations with the smallest displacement of 0.01\u2009D near the stable position and conduct a polynomial fit of the resulting force acting in the direction of the j-th DoF as a function of the displacement (to third order). Kij is then given by the linear term of this fit. Matlab is used to vary the displacement of particles in all simulations. This method does not assume pairwise or reciprocal interactions and, therefore, is suitable for analyzing the linear response of the system regardless of the nature of the interactions. A reduced-order model could be used to reduce the complexity of the stiffness matrix calculation. Order reduction can be done by considering the symmetry of the system (such as permutation symmetry in the pentagon state, reported in the main text), or by focusing only on the relevant vibrational modes achievable in the experiments (such as adapting a continuum model that represents the granular rafts reported in the main text as homogeneous materials with effective material properties). Another useful way of locating the vibrational frequencies in our system, as well as in other possible systems, is conducting a frequency sweep experimentally. Specifically, by applying parametric pumping to the system with a small modulating depth and a small modulating time while sweeping through different frequencies, one can observe the growth rate of vibrational modes for different structures without destroying them, and then one can use this information to determine the best combination of parameters to select structures of interest.\n\nFor an ensemble of particles with generalized coordinates x, inertial matrix M and stiffness matrix K, its equation of motion is\n\nwhen subject to linear damping with damping coefficient \u03b3d. Under acoustic parametric pumping, Eq. (1), the equation of motion becomes\n\nBy simultaneously diagonalizing M and K we can separate different VMs. For each VM with eigenfrequency \u03c9i and eigenvector xi, we assume a solution of the form x\u2009=\u2009\u03b7i(t)xi. The equation then becomes\n\nwhich is integrated numerically (scipy.integrate.odeint). The function \u03b7i(t) oscillates as a function of time t, and its amplitude will exponentially increase or decrease. Thus, an exponential fit can be made using the peaks of \u03b7i(t), giving the mode\u2019s growth rate \u03b3i.\n\nIn the two simulated systems, the rod-plus-spheres cluster and the 3D LJ sphere cluster, the interaction strength can be modulated directly, so the equation is\n\nand we can extract \u03b3i in the same manner as described above. Finally, to obtain the normalized growth rates plotted in the parametric pumping state diagrams, we divide the damping-free growth rate \u03b3i which is found by solving the equation of motion for the case of \u03b3d\u2009=\u20090, by the damping rate \u03b3d.\n\nIn this particular example, we do not consider the multibody and non-reciprocal nature of acoustic interactions and instead focus on particle shape. Therefore, for the interactions between all spheres in this system, including the three spheres making up the rod, we take a generalized Lennard-Jones potential50,\n\nWe choose m\u2009=\u20093 to mimic the attractive acoustic scattering forces in the experiment that scale with r\u22124, and choose n\u2009=\u20096 to provide a short-range repulsive interaction that mimics microstreaming. E0 is set to 10\u2009nJ so that the interaction strength is similar to that in the experiment. The three stable states have potential energies \u221279.4\u2009nJ, \u221273.2\u2009nJ, and \u221272.1\u2009nJ. A weak confining force is applied to all particles to bring them back together during the quenching step of a cycle. The molecular dynamics simulation is carried out using LAMMPS51 with damping coefficient 6.28\u2009\u00d7\u200910\u22126\u2009kg\u2009s\u22121 and timestep 5\u2009\u00d7\u200910\u22128\u2009s.\n\nThe Markov model for the levitated five-particle experiment is built with transition matrices shown in Supplementary Fig.\u00a04a, where the numbers are determined from experimental observations after the quenching step, and are based on the assumption that pumping will always bring a base state into the unstable state. This model qualitatively captures the observed rapid absorption. However, in the experiments, there can be a weak history dependence because sometimes the particles retain oscillations due to non-conservative interactions for a longer time than the quenching period. This is not captured by the Markov model and is likely to contribute to the quantitative difference we see in Fig.\u00a03h between measured data and prediction.\n\nAbsorbing Markov models for the rod-plus-spheres cluster are built with transition matrices shown in Supplementary Fig.\u00a04b, c. These matrices refer to the cases when the system is pumped with different parameters marked in Fig.\u00a04a. For example, the first matrix with the red cross sign on the top shows the transition rate between three states, red (r), green (g), and blue (b), for the case that the system is pumped using the parameters marked by the red cross. Under this procedure, the green and blue states are stable, but the red state has a non-zero possibility of transition. The numbers in the matrices are obtained from simulating one pump-quench cycle with 10,000 random initial conditions for each set of parameters. Supplementary Fig.\u00a04d shows the transition matrix for the case that the system is successively pumped with two sets of parameters, i.e., the first method mentioned in the main text. As an example, the first transition matrix corresponds to the system being first pumped using blue cross parameters, quenched, pumped using the green cross parameters, and then quenched again. The result is identical to that obtained by multiplying the two matrices corresponding to the two crosses. Supplementary Fig.\u00a04e, f gives the expected quenching steps needed by absorption into the desired state, calculated from the transition matrices in Supplementary Fig.\u00a04c, d.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data that support the findings of this study have been deposited in the Materials Data Facility database52.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code that supports the findings of this study has been deposited in the Materials Data Facility database52.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Melde, K., Mark, A. G., Qiu, T. & Fischer, P. Holograms for acoustics. Nature 537, 518\u2013522 (2016).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nOzcelik, A. et al. Acoustic tweezers for the life sciences. Nat. 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(2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We thank Nina Brown, Tali Khain, and Tom Witten for insightful discussions. This research was supported by the National Science Foundation through award number DMR-2104733. The work utilized the shared experimental facilities at the University of Chicago MRSEC, which is funded by the National Science Foundation under award number DMR-2011854. The research also benefited from computational resources and services supported by the Research Computing Center at the University of Chicago.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Physics, University of Chicago, Chicago, IL, USA\n\nQinghao Mao,\u00a0Brady Wu\u00a0&\u00a0Heinrich M. Jaeger\n\nJames Franck Institute, University of Chicago, Chicago, IL, USA\n\nQinghao Mao,\u00a0Brady Wu,\u00a0Bryan VanSaders\u00a0&\u00a0Heinrich M. Jaeger\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nQ.M., B.W., B.V. and H.M.J. conceived the study. Q.M. performed the experiments and simulations. All authors contributed to the analysis of the results and to the writing of the manuscript.\n\nCorrespondence to\n Qinghao Mao.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Structural reconfiguration of interacting multi-particle systems through parametric pumping.\n Nat Commun 16, 4637 (2025). https://doi.org/10.1038/s41467-025-59631-3\n\nDownload citation\n\nReceived: 09 October 2024\n\nAccepted: 25 April 2025\n\nPublished: 19 May 2025\n\nVersion of record: 19 May 2025\n\nDOI: https://doi.org/10.1038/s41467-025-59631-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Kinetics in Thiol-Ene-Systems through\nAntagonistic Photoreactions", + "journal": "Nature Communications", + "published": "26 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63407-0/MediaObjects/41467_2025_63407_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63407-0/MediaObjects/41467_2025_63407_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.16752061" + ], + "code": [], + "subject": [ + "Design, synthesis and processing", + "Polymerization mechanisms", + "Polymers" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5521813/v1.pdf?c=1758971359000", + "research_square_link": "https://www.researchsquare.com//article/rs-5521813/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63407-0.pdf", + "preprint_posted": "19 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The use of two wavelengths to activate different photoreactions in a resin system has recently attracted much attention in the scientific community. Here, wavelength orthogonal photochemistry was used to spatially control the curing kinetics of the thiol-ene photopolymerization reaction. In the investigated resin system, radical curing is activated by a type II photoinitiator at 450 nm, while light at 365 nm is used to photorelease a base, resulting in an inhibition of the curing reaction. The antagonistic nature of these photoreactions is demonstrated in laser writing and grey scale patterning experiments. The controlled inhibition and retardation of the thiol-ene curing reaction in a spatial manner paves the way for numerous applications in 3D printing or sub-\u00b5m photolithography.Physical sciences/Materials science/Soft materials/PolymersPhysical sciences/Chemistry/Photochemistryantagonistic photoreactionsthiol-enedual-wavelengthphotochemistry", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-5521813/v1/bdc84d26c6bead25b905424a.png", + "https://assets-eu.researchsquare.com/files/rs-5521813/v1/164344148fb6d3492e69ca3e.png", + "https://assets-eu.researchsquare.com/files/rs-5521813/v1/797b83bd5cd3abc0187a0f15.png", + "https://assets-eu.researchsquare.com/files/rs-5521813/v1/c08cce4e08811c00b3806bb8.png" + ] + }, + { + "section_name": "Full Text", + "section_text": "Light as a trigger for network formation has been widely used over the past decades due to its powerful advantages over other stimuli.[1] Using photochemical initiation, spatial and temporal control is readily achievable, explaining the increasing interest in photosensitive reactive systems.[2,3] Additional control over light-triggered reactions is provided through the concept of wavelength-selective activation, in which the responses exhibited by different chromophores can be influenced by the wavelength applied. As highlighted by various authors,[4,5] the integration of multiple wavelength-responsive reactions within a single material is key to unlocking the full potential of light-triggered reactions in photochemistry. In a recent article, I. Irshadeen and coworkers defined three types of two-color interactions \u2013 namely, synergistic, orthogonal, and antagonistic systems. Synergistic means that simultaneous illumination with at least two different wavelengths is required to activate the photoreaction. When photochemical chromophores behave orthogonally, they lead to different reactions that do not compete with each other. In antagonistic interactions, one wavelength initiates a reaction while the other wavelength quenches the reaction initiated by the first wavelength.[6,7]\u00a0\nThe principle of antagonistic photochemistry has already been applied to Stimulated Emission Depletion (STED)-inspired lithography, allowing photopolymerization to be performed with single-digit nanometer spatial resolution.[8,9] In addition, the group of Scott et al. reported on the use of antagonistic photochemistry to spatially suppress (meth)acrylate polymerization, enabling volumetric stereolithography.[10]\nHowever, to the best of our knowledge, this type of strategy has not been applied to precisely control reaction kinetics of radical mediated thiol-ene photopolymerization.\nThiol-ene chemistry has been the subject of extensive research for several years, which can be explained by the excellent biocompatibility and toughness of these networks compared to cured (meth)acrylates.[11] Whether Scott's concept of selective termination of photopolymerization reactions by radical recombination can be applied to thiol-based chemistry is more than questionable due to the complex chain transfer reactions in such systems.[10]\u00a0\nIn previous work, Bowman et al. showed that amines exert a retarding effect on the radical-mediated thiol-ene reaction.[12] Generally, thiol-ene reactions are deemed click-reactions, with high yields among other qualities.[13] In order to reach sufficient inhibition of the thiol-ene curing reaction, basic amines need to be introduced to the resin system. Those amines lead to deprotonation of the thiol, resulting in thiolate anions. Subsequently, a metastable disulfide anion species is formed as a result of interaction between thiyl radicals and thiolate anions. Consequently, the thiol-ene coupling reaction is inhibited due to the removal of the reacting thiyl radicals from the reaction. This effect occurs provided that the pKa of the thiol is lower than the pKa of the conjugated acid of the amine.[12]\nInspired by this study, in the current work the concept was advanced by using a photobase generator to enable the spatial release of an amine base. To realize the antagonistic control of the reaction kinetics in the used thiol-ene resin, wavelength orthogonality of the initiation and quenching reaction is required (Figure 1).\nCamphorquinone (CQ) was chosen as the radical photoinitiator (PI) because of its particularly low absorption at 365\u00a0nm and its ability to initiate polymerization in the 450\u00a0nm region. Due to its unique UV-Vis absorption characteristics (Figure 2b), CQ is often used in dual wavelength reactive systems.[14] Recently, Hu et al. demonstrated rapid continuous 3D printing of acrylates using CQ as a PI with blue light while UV-activated butyl nitrite inhibits the radical chain growth locally.[15] CQ is a type II initiator that abstracts hydrogen from donor molecules (e.g., amines or thiols) during irradiation via a bimolecular mechanism.[16]\nThiol monomers based on esters of mercaptopropionic acid, as the monomer used in the system at hand, show a pKa value in the range of 10.[17] Considering the pKa of aliphatic thiols as well as the UV absorption characteristics of CQ, 2-(2-nitrophenyl)-propyloxycarbonyl-1,1,3,3-tetramethylguanidine (NPPOC-TMG) was chosen as a strong latent amine base. It was synthesized according to a literature protocol and provides a pKa of 13.6 in its unprotected state.[18,19] The wavelength dependent response of the CQ and NPPOC-TMG was investigated in a stochiometric mixture of the monomers pentaerythritol-tetrakis(3-mercaptopropionat) PETMP and triallyl-triazine-2,4,6(1H,3H,5H)-trione (TATO, see Figure 2a). Importantly, TATO shows hardly any homopolymerization and, due to the high electron density of the carbon double bond, is not susceptible to thiol anions making it inert to base catalyzed thiol-Michael reactions.[20\u201322]\nFigure 3a depicts the wavelength dependent curing kinetics of the resin as obtained by FTIR spectroscopy. In the first step, visible light irradiation at 450 nm initiates the radical curing process, which can be followed by the decrease of the C=C double bond (3080 cm-1) and thiol (2570 cm-1) absorption bands (the related FTIR spectra are provided in Figure S3 and Figure\u00a0S4). Under these conditions, the photobase generator remains unaffected. As soon as the formulation is additionally irradiated with UV light at 365 nm (to unblock NPPOC-TMG and to release TMG as strongly basic amine), the curing speed of the thiol-ene reaction decreases significantly to almost zero. This effect is due to the aforementioned consumption of the reacting thiyl radicals through the formation of a metastable disulfide radical anion species.[12] The formation of TMG can be monitored by the decrease of the absorption band of the C-N bond at 1530 cm-1, which is broken during the photocleavage.[23,24]\nThe data clearly demonstrate both the wavelength orthogonal reaction response of the CQ and NPPOC-TMG as well as their antagonistic behavior in the PETMP-TATO system.\nFurthermore, Figure 3b shows that base-induced retardation of the reaction can be triggered at any stage of the curing process. It is worth noting that the introduction of these amines via cleavage of the photobase generator inevitably initiates the inhibition process, whether before or during activation of the radical photoinitiator.\nIn addition to curing kinetics, the influence of the photogenerated base on network formation was investigated by photorheology. Figure 2c shows the loss and storage moduli over time, with the gel point defined as the intersection of the two moduli. While the gel point is reached after an illumination dose of 140,4 Jcm-2 at 450 nm, the resins pre-exposed to light of 365 nm show no gelation, confirming the inhibitory effect of the formed amine on network formation. The major advantage of this antagonistic approach is that the reactivity of the thiol-ene formulation can be tuned in a spatially resolved manner. To demonstrate this, a thin resin layer on a glass substrate was illuminated with 365 nm (7.84 Jcm-2) through\u00a0a cherry-shaped photomask. After removing the photomask, the irradiation was switched to 450\u00a0nm, illuminating the entire layer with 146.40\u00a0Jcm-2. The solvent treatment showed that the area not previously covered by the photomask was completely soluble, indicating that no polymer network had formed in this area.\u00a0Figure 3c shows the cured layer after washing with isopropanol.Going a step beyond low-resolution mask lithography, the antagonistic nature of the two photoreactions has also been demonstrated in laser writing experiments. In this approach, two lasers were used \u2013 a curing laser with an emission maximum at 488 nm (Figure S5) and a monochromatic 355 nm laser for inhibition and. Both lasers were focused on the same spot but activated independently. Figure 4a shows successful writing with a minimum feature size of 1.4 \u00b5m (line width) using only the curing laser. As a control, a similar writing experiment was performed with 355 nm, but as expected, no gelation occurred. Figure 4b shows the result of laser writing with the curing laser (488 nm) always on (displayed as blue lines in figure) and the inhibition laser (355 nm, pink line in figure) turned on for small and arbitrary time periods. Simultaneous illumination with both lasers stopped curing completely, in a manner similar to STED lithography[25], resulting in gaps in the written lines, confirming the antagonistic behavior of both photoreactions even at the microscale.\nIn addition to spatial control, this approach also enables precise adjustment of the reaction kinetics by the amount of photogenerated base. Since the introduction of the base into the resin is triggered photochemically, the intensity of the inhibition can be varied by changing the illumination dose. This concept was applied to gray scale experiments as shown in Figure 4c.\nIn this approach, 3 rectangles (4 mm x 10 mm) were inscribed with 365 nm with increasing dose (288 mJcm-2 to 1440 mJcm-2) within defined resin layer and then exposed to 460 nm with a dose of 1050 Jcm-2 over the complete area. Due to the differences in the inhibition, i.e. reaction kinetics, a structure with different heights was obtained.\nIn summary, an antagonistically behaving photoactivated system within a thiol-ene network was successfully demonstrated. A dual wavelength approach was realized to locally inhibit the thiol-ene curing reaction. While curing was initiated at 450 nm, the reaction can be stopped at 365 nm. The photochemically induced inhibition on the macroscopic scale was shown by contact lithography, while laser writing experiments demonstrated the antagonistic character of the resin on the micrometer scale. In addition to spatial control, this approach also offers the possibility of tuning the kinetics of the thiol-ene photoreaction, which was demonstrated in gray-scale experiments. This approach holds great potential for advanced nanolithography using laser setups similar to those used in STED-inspired lithography or for volumetric stereolithography approaches for thiol-ene based resins.", + "section_image": [] + }, + { + "section_name": "Declarations", + "section_text": "Acknowledgements\nPart of the research was carried out within the COMET-Module project\u201d Repairtecture\u201d (project-no.: 904927) at the Polymer Competence Center Leoben GmbH (PCCL, Austria) within the framework of the COMET-program of the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology and the Federal Ministry of Labour and Economy. Funding was provided by the Austrian Government and the State Governments of Styria and Upper Austria.We would like to thank the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology and the Austrian Research Promotion Agency (FFG) for funding the \u201c3DFit4Wear\u201d project as part of the Production of the Future program line (project no. 891254).\nSupporting Information\nThe authors have cited additional references within the Supporting Information.[26]", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "\nY. Yagci, S. Jockusch, N. J. Turro, Macromolecules 2010, 43, 6245\u20136260.\nE. Blasco, M. Wegener, C. Barner-Kowollik, Adv. Mater. 2017, 29, DOI 10.1002/adma.201604005.\nS. Aubert, M. Bezagu, A. C. Spivey, S. Arseniyadis, Nat. Rev. Chem. 2019, 3, 706\u2013722.\nP. Lu, D. Ahn, R. Yunis, L. Delafresnaye, N. Corrigan, C. Boyer, C. Barner-Kowollik, Z. A. Page, Matter 2021, 4, 2172\u20132229.\nX. Zhang, W. Xi, S. Huang, K. Long, C. N. Bowman, Macromolecules 2017, 50, 5652\u20135660.\nI. M. Irshadeen, S. L. Walden, M. Wegener, V. X. Truong, H. Frisch, J. P. Blinco, C. Barner-Kowollik, J. Am. Chem. Soc. 2021, 143, 21113\u201321126.\nJ. Hobich, E. Blasco, M. Wegener, H. Mutlu, C. Barner-Kowollik, Macromol. Chem. Phys. 2023, 224, 1\u201310.\nJ. Fischer, G. Von Freymann, M. Wegener, Adv. Mater. 2010, 22, 3578\u20133582.\nP. M\u00fcller, R. M\u00fcller, L. Hammer, C. Barner-Kowollik, M. Wegener, E. Blasco, Chem. Mater. 2019, 1966\u20131972.\nM. P. De Beer, H. L. Van Der Laan, M. A. Cole, R. J. Whelan, M. A. Burns, T. F. Scott, Sci. Adv. 2019, 5, 1\u20138.\nC. E. Hoyle, T. Y. Lee, T. Roper, J. Polym. Sci. Part A Polym. Chem. 2004, 42, 5301\u20135338.\nD. M. Love, K. Kim, J. T. Goodrich, B. D. Fairbanks, B. T. Worrell, M. P. Stoykovich, C. B. Musgrave, C. N. Bowman, J. Org. Chem. 2018, 83, 2912\u20132919.\nC. E. Hoyle, C. N. Bowman, Angew. Chemie - Int. Ed. 2010, 49, 1540\u20131573.\nT. F. Scott, B. A. Kowalski, A. C. Sullivan, C. N. Bowman, R. R. McLeod, Science (80-. ). 2009, 324, 913\u2013917.\nM. Hu, H. Cheng, Y. Feng, 3D Print. Addit. Manuf. 2024, 11, 476\u2013484.\nA. A. P\u00e9rez-Mondrag\u00f3n, C. E. Cuevas-Su\u00e1rez, J. A. Gonz\u00e1lez-L\u00f3pez, N. Trejo-Carbajal, A. M. Herrera-Gonz\u00e1lez, J. Photochem. Photobiol. A Chem. 2020, 403, DOI 10.1016/j.jphotochem.2020.112844.\nC. F. Bernasconi, R. B. Killion, J. Am. Chem. Soc. 1988, 110, 7506\u20137512.\nS. J. Angyal, W. K. Warburton, J. Chem. Soc. 1951, 2492\u20132494.\nX. Zhang, W. Xi, G. Gao, X. Wang, J. W. Stansbury, C. N. Bowman, ACS Macro Lett. 2018, 7, 852\u2013857.\nP. Shen, S. Z. Moghaddam, Q. Huang, A. E. Daugaard, Mater. Today Commun. 2019, 21, 100657.\nH. Lu, J. A. Carioscia, J. W. Stansbury, C. N. Bowman, Dent. Mater. 2005, 21, 1129\u20131136.\nK. Ganabady, N. C. Negrini, J. C. Scherba, B. M. Nitschke, M. R. Alexander, K. H. Vining, M. A. Grunlan, D. J. Mooney, A. D. Celiz, ACS Appl. Mater. Interfaces 2023, 15, 50908\u201350915.\nG. Socrates, Infrared and Raman Characteristic Group Frequencies, John Wiley & Sons, Ltd, Chichester, 2001.\nF. S. Parker, Applications of Infrared Spectroscopy in Biochemistry, Biology, and Medicine, Springer New York, NY, New York, 1971.\nR. Wollhofen, J. Katzmann, C. Hrelescu, J. Jacak, T. A. Klar, Opt. Express 2013, 21, 10831.\nX. Zhang, W. Xi, G. Gao, X. Wang, J. W. Stansbury, C. N. Bowman, ACS Macro Lett. 2018, 7, 852\u2013857.\n", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupportingInformationManuscript.docxSupporting Information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The use of two wavelengths to activate different photoreactions in a resin system has recently attracted much attention in the scientific community. Here, wavelength orthogonal photochemistry was used to spatially control the curing kinetics of the thiol-ene photopolymerization reaction. Antagonistic photochemical control is successfully applied to thiol-ene polymerization. In the investigated resin (pentaerythritol-tetrakis(3-mercaptopropionat); PETMP and triallyl-triazine-2,4,6(1H,3H,5H)-trione; TATO) system, radical curing is activated by a type II photoinitiator at 450\u2009nm, while light at 365\u2009nm is used to photorelease a base, resulting in an inhibition of the curing reaction. The antagonistic nature of these photoreactions is demonstrated in laser writing with minimum feature sizes below 0.5\u2009\u00b5m as well as in grey scale patterning experiments. Spatially controlled inhibition and retardation of the thiol-ene curing reaction on a sub-micron scale have potential applications in advanced large area lithography, e.g. interference lithography.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Light as a trigger for network formation has been widely used over the past decades due to its powerful advantages over other stimuli1. Using photochemical initiation, spatial and temporal control is readily achievable, explaining the increasing interest in photosensitive reactive systems2,3. Additional control over light-triggered reactions is provided through the concept of wavelength-selective activation, in which the responses exhibited by different chromophores can be influenced by the wavelength applied. As highlighted by various authors4,5, the integration of multiple wavelength-responsive reactions within a single material is key to unlocking the full potential of light-triggered reactions in photochemistry. In a recent article, Irshadeen et al. defined three types of two-color interactions\u2014namely, synergistic, orthogonal, and antagonistic systems. Synergistic means that simultaneous illumination with at least two different wavelengths is required to activate the photoreaction. When photochemical chromophores behave orthogonally, they lead to different reactions that do not compete with each other. In antagonistic interactions, one wavelength initiates a reaction while the other wavelength quenches the reaction initiated by the first wavelength6,7.\n\nThe principle of antagonistic photochemistry has already been applied to Stimulated Emission Depletion (STED)-inspired lithography, allowing photopolymerization to be performed with feature sizes below 50\u2009nm8,9. In addition, the group of Scott et al. reported on the use of antagonistic photochemistry to spatially suppress (meth)acrylate polymerization, enabling volumetric stereolithography10. More recently, Marco et al. introduced an elegant antagonistic system employing a monomer with photoswitchable reactivity. One color of light (625\u2009nm) is used to deactivate said dienophile monomer\u2014effectively suppressing polymerization\u2014while another wavelength (365\u2009nm) is able to switch the dienophile back to its active state. The authors demonstrated the\n\napplicability of their system in spatially patterned films. However, the resolution reached did not exceed the millimeter-range, limiting the range of potential application at this state11.\n\nDespite these examples of existing systems, to the best of our knowledge, antagonistic control of network formation has not been applied to precisely control reaction kinetics of radical mediated thiol-ene photopolymerization.\n\nThiol-ene chemistry has been the subject of extensive research for several years, which can be explained by the excellent biocompatibility and toughness of these networks compared to cured (meth)acrylates12. Whether Scott\u2019s concept of selective termination of photopolymerization reactions by radical recombination can be applied to thiol-based chemistry is more than questionable due to the complex radical transfer reactions in such systems10. Recently, Thijssen et al. introduced radical inhibition of thiol-ene systems in volumetric 3D printing. Their approach uses a radical inhibitor to delay or hinder polymerization as long as unconsumed inhibitor molecules are present in the system, aiming at optimizing control over the required dose in volumetric printing13. However, since there is no trigger for the inhibiton (e.g., light or temperature), control over the system is limited.\n\nIn previous work, Bowman et al. showed that amines exert a retarding effect on the radical-mediated thiol-ene reaction14. Generally, thiol-ene reactions are deemed click-reactions, with high yields among other qualities15. In order to reach sufficient inhibition of the thiol-ene curing reaction, basic amines need to be introduced to the resin system. Those amines lead to deprotonation of the thiol, resulting in thiolate anions. Subsequently, a metastable disulfide anion species is formed as a result of interaction between thiyl radicals and thiolate anions. Consequently, the thiol-ene coupling reaction is inhibited due to the removal of the reacting thiyl radicals from the reaction. This effect occurs provided that the pKa of the thiol is lower than the pKa of the conjugated acid of the amine14.\n\nInspired by this study, in the current work the concept was advanced by using a photobase (PB) generator to enable the spatial release of an amine base. To realize the antagonistic control of the reaction kinetics in the used thiol-ene resin, wavelength orthogonality of the initiation and quenching reaction is required (Fig.\u00a01).\n\nSchematic illustration of the curing and inhibition mechanism in the presented thiol-ene resin system.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63407-0/MediaObjects/41467_2025_63407_Fig1_HTML.png" + ] + }, + { + "section_name": "Results and discussion", + "section_text": "Camphorquinone (CQ) (see Fig.\u00a02a) was chosen as the radical photoinitiator (PI) because of its particularly low absorption at 365\u2009nm and its ability to initiate polymerization in the 450\u2009nm region. Due to its unique UV\u2013Vis absorption characteristics (Fig.\u00a02b), CQ is often used in dual wavelength reactive systems16. Recently, Hu et al. demonstrated rapid continuous 3D printing of acrylates using CQ as a PI with blue light while UV-activated butyl nitrite inhibits the radical chain growth locally17. CQ is a type II initiator that abstracts hydrogen from donor molecules (e.g., amines or thiols) during irradiation via a bimolecular mechanism18.\n\na Resin formulation with photoinitiator and photobase generator. b Absorption spectra of CQ and NPPOC-TMG versus the emission spectra of the light sources used. c Comparison of the photorheological behavior of the resin with and without prior photobase release. The gelpoint is marked through a dashed vertical line as the crossover of storage modulus (G\u2019) and loss modulus (G\u2019\u2019).\n\nThiol monomers based on esters of mercaptopropionic acid, as the monomer used in the system at hand, show a pKa value in the range of 1019. Considering the pKa of aliphatic thiols as well as the UV absorption characteristics of CQ, 2-(2-nitrophenyl)-propyloxycarbonyl-1,1,3,3-tetramethylguanidine (NPPOC-TMG) was chosen as a strong latent amine base. It was synthesized according to a literature protocol and provides a pKa of 13.6 in its unprotected state20,21. The wavelength dependent response of the CQ and NPPOC-TMG was investigated in a stochiometric mixture of the monomers pentaerythritol-tetrakis(3-mercaptopropionat) PETMP and triallyl-triazine-2,4,6(1H,3H,5H)-trione (TATO, see Fig.\u00a02a). Importantly, TATO shows hardly any homopolymerization and, due to the high electron density of the carbon double bond, is not susceptible to thiol anions making it inert to base catalyzed thiol-Michael reactions22,23,24.\n\nFigure\u00a03a shows the wavelength-dependent curing kinetics of the resin as determined by FTIR spectroscopy. Upon irradiation with visible light at 450\u2009nm, the radical curing process is initiated, which is evidenced by the decreasing absorption of the C=C double bond (3080\u2009cm\u207b\u00b9) and thiol groups (2570\u2009cm\u207b\u00b9). The corresponding FTIR spectra are provided in Figures\u00a0S3 and S4. Under these conditions, the photobase generator remains unaffected. When the formulation is subsequently exposed to UV light at 365\u2009nm\u2014triggering the deprotection of NPPOC-TMG and the release of TMG as a strongly basic amine\u2014the thiol-ene curing rate drops drastically, approaching zero. This inhibition arises from the consumption of reactive thiyl radicals through the formation of a metastable disulfide radical anion species14. The release of TMG can be followed by the decrease of the C\u2013N absorption band at 1530\u2009cm\u207b\u00b9, which is formed during the photocleavage process25,26.\n\na Wavelength dependent behavior of the different resin components, the color of the background corresponds to the wavelength used (blue \u2212 450\u2009nm, pink \u2212 365\u2009nm). b Stopping curing at different times. c Schematic masking experiment setup and cured masked layer after washing with solvent.\n\nThe data demonstrates both the wavelength orthogonal reaction response of the CQ and NPPOC-TMG as well as their antagonistic behavior in the PETMP-TATO system.\n\nIn order to evaluate the effect of CQ content on the curing rate of the system, resins were formulated with 2, 3, and 4\u2009wt% of CQ. Curing was monitored through FTIR spectroscopy while illuminating with 450\u2009nm. Figure\u00a0S5a shows the thiol and ene conversion over time. As expected, conversion and reaction rate increase with higher CQ content. The resin\u2019s maximum reactivity was achieved with 4\u2009wt% CQ, therefore this concentration was selected for subsequent experiments. Additionally, the optimal concentration of photolatent base required for efficient inhibition was investigated by varying the NPPOC-TMG to thiol group ratio between 0.1, 0.2, and 0.4. All three resins underwent the same illumination regime: 450\u2009nm for 10\u2009min, followed by switching to 365\u2009nm for one minute, then back to 450\u2009nm. Figure\u00a0S5b displays the thiol and ene conversion over time. As can be seen from the graph, the photobase content also influences the curing speed\u2014the conversions in the system with a PB:SH ratio of 0.1 are much faster than in the other two systems. This effect also occurs before photobase activation, which is thought to be due to the latent basicity of the uncleaved photobase. It has been reported in literature that covalently bond photolatent tertiary amine bases exhibit a pKa above 727. Upon cleavage of NPPOC-TMG a drastic slowing down of the reaction speed is clearly seen even in the resin with the lowest photobase content. However, the slopes of the corresponding curves are still visibly higher than in the systems with 0.2 or 0.4 ratios of PB:SH. In both of these resins, the system\u2019s response upon photobase activation is of similar effectiveness.\n\nInhibition is not only dependent on the amount of photobase but also the illumination dose of the inhibition light source as shown in Fig.\u00a0S5c. In this experiment illumination was first performed with 450\u2009nm for 10\u2009min before it was switched to 365\u2009nm to activate the photo base. The 365\u2009nm illumination dose was varied between 110\u2009mJ\u2009cm\u22122, 440 mJ\u2009cm\u22122, and 1320\u2009mJ\u2009cm\u22122. As the energy input for photobase generator cleavage is reduced, less base is released into the system, resulting in less efficient inhibition during subsequent illumination with 450\u2009nm. This effect is most evident in the curves corresponding to the lowest inhibiting light dose of 110\u2009mJ\u2009cm\u2212\u00b2, which shows a noticeably steeper slope in the normalized conversion profile. In contrast, the difference between the slopes for 440\u2009mJ\u2009cm\u2212\u00b2 and 1320\u2009mJ\u2009cm\u2212\u00b2 is significantly less pronounced, suggesting that even a dose of 440 mJ\u2009cm\u2212\u00b2 is sufficient to effectively suppress thiol\u2013ene polymerization in very thin layers.\n\nFurthermore, Fig.\u00a03b shows that base-induced retardation of the reaction can be triggered at any stage of the curing process. It is worth noting that the introduction of these amines via cleavage of the photobase generator inevitably initiates the inhibition process, whether before or during activation of the radical photoinitiator.\n\nThe broad applicability of the inhibition strategy across various ene-monomer systems is demonstrated in Figure\u00a0S6. Thiol and ene conversions were measured both without photobase activation and after activation of NPPOC-TMG by 365\u2009nm light for 1\u2009min, following an initial 10-min curing period under 450\u2009nm illumination. After photobase cleavage, the illumination was switched back to 450\u2009nm.\n\nFigure\u00a0S6a shows the inhibition behavior in a resin system containing a vinyl ester monomer. While overall conversions are higher compared to the allyl ether system shown in Fig.\u00a0S6b, photobase activation significantly slows the polymerization in both cases\u2014highlighting the method\u2019s compatibility with different ene-types. Figure\u00a0S6c presents results from a vinyl ether-based resin, where curing behavior was again responsive to photobase activation. Interestingly, this system exhibited a more pronounced increase in conversion after base release than the other monomers studied, suggesting less efficient inhibition control.\n\nIt should be noted that optimization of photobase and initiator concentrations, as well as modifications to the illumination protocol, are expected to further enhance inhibition performance across all systems.\n\nTo extend the scope, additional tests were conducted with thiol\u2013acrylate and thiol\u2013methacrylate resin formulations (Fig.\u00a0S7). In these systems, however, photobase activation did not inhibit polymerization. In the methacrylate-based resin (Fig.\u00a0S7a), the radical homopolymerization of methacrylate groups proceeded largely unaffected by the presence of the base. A slight increase in thiol conversion is likely due to a concurrent thiol\u2013Michael addition, although radical polymerization appears to dominate. In contrast, the acrylate-based resin (Fig.\u00a0S7b) showed the expected thiol\u2013Michael reaction upon base release20, resulting in a rapid increase in both thiol and ene conversions.\n\nIn addition to curing kinetics, the influence of the photogenerated base on network formation was investigated by photorheology. Figure\u00a02c shows the loss and storage moduli over time, with the gel point defined as the intersection of the two moduli (vertical dashed line). While the gel point is reached after an illumination dose of 140.4\u2009J\u2009cm\u22122 at 450\u2009nm, the resins pre-exposed to light of 365\u2009nm show no gelation, confirming the inhibitory effect of the formed amine on network formation. The major advantage of this antagonistic approach is that the reactivity of the thiol-ene formulation can be tuned in a spatially resolved manner. To demonstrate this, a thin resin layer on a glass substrate was illuminated with 365\u2009nm (7.84\u2009J\u2009cm\u22122) through a cherry-shaped photomask. After removing the photomask, the irradiation was switched to 450\u2009nm, illuminating the entire layer with 146.40\u2009J\u2009cm\u22122. The solvent treatment showed that the area not previously covered by the photomask was completely soluble, indicating that no polymer network had formed in this area. Figure\u00a03c shows the cured layer after washing with isopropanol.\n\nGoing a step beyond low-resolution mask lithography, the antagonistic nature of the two photoreactions has also been demonstrated in direct laser writing experiments. In this approach, two lasers were used \u2013 a curing continuous wave laser (\u03bb\u2009=\u2009435\u2009nm) and a pulsed inhibition laser (\u03bb\u2009=\u2009355\u2009nm, see Figure\u00a0S8) Both lasers were laterally and axially aligned and focused on the photoresist. The writing speed was fixed to 20\u2009\u00b5m/s; the power of curing the curing laser was chosen to be 20\u2009\u00b5W and the power of inhibition laser was set to 0.42\u2009\u00b5W. Figure\u00a04a presents a SEM micrograph illustrating successful laser writing and inhibition. A structure consisting of 5 lines with 100\u2009\u00b5m length is shown. In this experiment, the curing laser was continuously running, while the inhibition laser was switched on and off in a precisely controlled manner. Activating the inhibition laser while writing with 435\u2009nm stopped curing completely, in a manner similar to STED lithography28, resulting in gaps in the written lines, confirming the antagonistic behavior of both photoreactions even at the microscale. Additionally, lateral feature sizes as a function of curing laser power and writing speed were analyzed to get a more thorough picture of ideal writing conditions. Figure\u00a0S9 shows a SEM image of lines written with different writing speeds: 10, 20, 30, 40, and 50\u2009\u00b5m/s. (columns from right to left) and power ranging from 8 to 40\u2009\u00b5W (from top to bottom). Analyzed average lateral feature sizes and writing thresholds are presented in Fig.\u00a0S10a and b respectively. In all cases, the line width increases linearly with rising laser power. Writing at 40\u2009\u00b5m/s was possible above 30\u2009\u00b5W, but at 50\u2009\u00b5m/s, lines thinned and turned irregular below 39\u2009\u00b5W. At 10\u2009\u00b5m/s and higher powers writing produced irregular, oversized features due to excessive illumination doses. Linear writing thresholds were observed, indicating the expected one photon absorption process29.\n\na SEM image of a laser-written structure consisting of five lines, illustrating controlled inhibition of polymerization. The curing laser was on continuously for all five lines whereas the inhibition laser was off while writing the first line (topmost), turned on one, two, and three times while writing the second, third, and fourth lines, respectively, and was continuously on during writing of the fifth line. Colored lines correspond to the laser paths (blue for the curing laser \u03bb\u2009=\u2009435\u2009nm, pink for the inhibition laser \u03bb\u2009=\u2009355\u2009nm). The dots visible on the left side of the fifth line correspond to a short deactivation of the inhibition laser in order to mark the position of the laser path. b Greyscale experiment\u2014height profile of the obtained structure. c Dose-dependent variation in the depth of the formed valleys.\n\nLaser writing was used to systematically investigate photobase diffusion and the resulting spatial and temporal limits on the applicability of the system. The experimental setup involved a three-step writing sequence: first, a reference line was written using a 435\u2009nm laser. At a defined lateral distance\u2014varied throughout the experiments\u2014a second line was exposed to 355\u2009nm light to release the photobase. Subsequently, a third line was inscribed with the 435\u2009nm laser.\n\nUsing SEM imaging (see Fig.\u00a0S11 for an example), all line sets were analyzed by measuring the width of each line and noting whether the third line was still visible. The difference in line width between the first and the second line served as a measure for inhibition: higher photobase presence in the exposed area reduces polymerization efficiency, resulting in narrower lines. The absence of the third line suggested significant photobase diffusion\u2014here polymerization was inhibited completely.\n\nTo further evaluate diffusion behavior, both the lateral distance between the second and the third line, and the delay times between these exposure steps (ranging from 1 to 11\u2009s), were varied. The results, presented in Figure\u00a0S12, plot the relative change in line width against time delay and spatial separation (6\u201317\u2009\u00b5m). These changes directly reflect inhibition efficiency and, by extension, the diffusion behavior of the photobase. As shown in the heat map, the effective diffusion radius at a delay time of one second is approximately 9\u2009\u00b5m, with longer delay times leading to increased diffusion.\n\nIn this context, Forman et al. deemed the diffusivity of the inhibitor as the limiting factor in terms of resolution30. The degree of photobase diffusion observed limits the suitability of the investigated resin system for high-resolution techniques such as STED lithography. However, it is well suited for single-step illumination lithography techniques, such as interference lithography31, which are less affected by the diffusion properties of photoinitiators or inhibitors. Increasing resin viscosity up to solid state, e.g., in photo-resists, potentially improves the obtainable resolutions by limiting base diffusion.\n\nIn addition to spatial control, this approach also enables precise adjustment of the reaction kinetics by the amount of photogenerated base. Since the introduction of the base into the resin is triggered photochemically, the intensity of the inhibition can be varied by changing the illumination dose. This concept was applied to gray scale experiments as shown in Fig.\u00a04b, c.\n\nIn this approach, 3 rectangles (4\u2009mm\u2009\u00d7\u200910\u2009mm) were inscribed with 365\u2009nm with increasing dose (288\u2009mJ\u2009cm-2 to 1440\u2009mJ\u2009cm\u22122) within defined resin layer and then exposed to 460\u2009nm with a dose of 1050\u2009J\u2009cm-2 over the complete area. Due to the differences in the inhibition, i.e., reaction kinetics, a structure with different heights was obtained.\n\nThe storage stability of the resin was assessed by tracking its viscosity over time. As illustrated in Figure\u00a0S13, the viscosity increased from 505.9\u2009mPa\u2009s to 1058.9\u2009m\u2009Pa\u2009s within 10 days\u2014an effect attributed to dark reactions, a well-documented phenomenon in thiol\u2013ene systems32. Adding a phosphoric acid stabilizer\u2014as introduced by P. Esfandiari et al.\u2014has been shown to effectively alleviate this issue and increase storage stability33.\n\nTo further investigate the impact of non-activated photobase on resin reactivity, curing kinetics were measured for formulations with and without a photobase generator (see Fig.\u00a0S14a, b). The results clearly show that the mere presence of the photobase reduces both the polymerization rate and the maximum conversion, likely due to the inherent basicity of the protected base26.\n\nWhile the reactivity of the photobase-free resin remained stable over time, the formulation containing the photobase exhibited a gradual decline in curing rate during storage. This suggests that slow, unintended cleavage of the photobase may occur over time, adversely affecting the polymerization behavior. Although this cleavage does not pose an issue for the applications presented therein, a strategy to mitigate the cleavage over time could be the addition of small amounts of acid\u2014analogous to cationic polymerization systems, were small amounts of base are added for stabilization reasons34.\n\nIn summary, an antagonistically behaving photoactivated system to control thiol-ene reaction\u00a0kinetics was successfully demonstrated. A dual wavelength approach was realized to locally inhibit the thiol-ene curing reaction. While curing was initiated at 450\u2009nm, the reaction can be stopped at 365\u2009nm. Several types of ene monomers were shown to successfully undergo photobase induced inhibition. The photochemically induced inhibition on the macroscopic scale was shown by contact lithography, while laser writing experiments demonstrated the antagonistic character of the resin on the micrometer scale. In addition to spatial control, this approach also offers the possibility of tuning the kinetics of the thiol-ene photoreaction, which was demonstrated in gray-scale experiments. This approach holds great potential for large area patterning such as interference lithography31, where illumination is done in a singular step as opposed to laser scanning lithography. Additionally, the authors are working on utilizing the inhibition strategy for thiol-ene resins in continuous 3D-printing. Inhibiting curing locally with a second wavelength (365\u2009nm) enables the introduction of a so-called \u201cdead zone\u201d\u2014similar to continuous printing approaches for acrylate-based resins35. Specifically, coupling the inhibition wavelength into a waveguide at the bottom of a vat photopolymerization 3D-printer could reduce adhesion to the surface through the formation of an inhibition layer.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63407-0/MediaObjects/41467_2025_63407_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63407-0/MediaObjects/41467_2025_63407_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63407-0/MediaObjects/41467_2025_63407_Fig4_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "All chemicals were used as received without further purification. Pentaerythritol tetrakis(3-mercaptopropionate) (PETMP) was donated from Bruno Bock GmbH (Marschacht, Germany). All other chemicals were purchased from Sigma-Aldrich (St. Louis, USA).\n\nFor kinetics measurements (FTIR and photo-rheology) and masking experiments a LED Control 5S from Opsytec Dr. Gr\u00f6bel (Germany) was used with different UV-LEDs (365\u2009nm and 450\u2009nm). The emission spectra and intensities of the used light sources were determined with an Ocean Optics (USA) Ocean Insight STS-UV Miniature Spectrometer. For data processing, Spectragryph optical spectroscopy software (version v1.2.16.1.) was used.\n\nPETMP (37.67\u2009mol%) and 1,3,5-triallyl-1,3,5-triazin-2,4,6-(1H,3H,5H)-trione (TATO, 50.23\u2009mol%) were added into a brown glass vial in stoichiometric amounts. Pyrogallol (0.1\u2009wt%), camphorquinone (CQ, 4\u2009wt%) and 2-(2-nitrophenyl)-propyloxycarbonyl-1,1,3,3-tetramethylguanidine (NPPOC-TMG, 3.6\u2009wt%) were added to the liquid resin and stirred on a magnetic stirrer at room temperature until all solid components were dissolved. When investigating the influence of the content of CQ and NPPOC-TMG, the respective ratios were varied\u20142\u20134\u2009wt% of CQ and 0.1\u20130.4 ratios of PB:SH groups. To study different ene monomers, divinyl adipate, diethylene glycol divinyl ether, 1,4-butanediol diacrylate\u00a0and 1,4-butanediol dimethacrylate were used in resins at a stoichiometric ratio to the thiol groups. For masking and grayscale experiments 0.05\u2009wt% avobenzone was added to improve the resolution.\n\nThe synthesis of NPPOC-TMG was done according to literature protocol1. 2-(2-Nitrophenyl)propyl chloroformate (NPPOC, 3.71\u2009mmol) was dissolved in 20\u2009ml of DCM, while TMG (6.31\u2009mmol) was dissolved in 50\u2009ml of DCM. The solution of NPPOC was added to the TMG solution dropwise over the course of 10\u2009min. The reaction mixture was stirred at room temperature for 8\u2009h. Subsequently, the mixture was washed with brine three times and then once with DCM. After drying over sodium sulfate, solvent was removed on a rotary evaporator. Figure\u00a0S1 and Fig.\u00a0S2 show the corresponding NMR spectra of the obtained compound.\n\n1H-NMR (400\u2009MHz, chloroform-d): \u03b4 7.75\u20137.68 (m, 1H), 7.61\u20137.45 (m, 2H), 7.39\u20137.22 (m, 1H), 4.26 (d, J\u2009=\u20096.8\u2009Hz, 2H), 3.83\u20133.60 (m, 1H), 2.80 (s, 12H), 1.34 (d, J\u2009=\u20097.2\u2009Hz, 3H)\n\n13\u2009C NMR (101\u2009MHz, chloroform-d): \u03b4 165.95, 159.93, 159.93, 150.29, 138.26, 132.49, 128.47, 126.94, 124.04, 68.42, 67.71, 50.74, 39.73, 36.34, 33.84, 18.52, 17.56.\n\nFor characterization of the synthesized compound 1H-NMR and 13C-NMR spectra were recorded with a Varian 400-NMR spectrometer. Data analysis was done with MestReNova version 14.2.0.\n\nUV\u2013Vis spectra of CQ (concentration\u2009=\u20093\u2009mmol/l) and NPPOC-TMG (concentration\u2009=\u20090.05\u2009mmol/l) were recorded in acetonitrile with a Varian Cary 50 UV\u2013Vis spectrophotometer (Agilent Technologies Inc, Santa Clara, USA; software: CaryWinUV Scan version 3.00(182)).\n\nCuring kinetics of the resin were measured between two CaF2 platelets on a VERTEX 70 FTIR spectrometer from Bruker (USA) in transmission mode. Spectra were recorded from 4000\u2009cm\u22121 to 800\u2009cm\u22121 with a resolution of 4\u2009cm\u22121 and a total of 16 scans. Measurements were taken of the unilluminated sample and after stepwise illumination with 450\u2009nm (4\u2009cm, 22\u2009mW\u2009cm\u22122) and 365\u2009nm (3\u2009cm, 43.5\u2009mW\u2009cm\u22122). SpectraGryph optical spectroscopy software (version v1.2.16.1.) was used for baseline correction of the spectra as well as normalizing (area of CO-peak, 1655\u20131790\u2009cm\u22121) them. The CO2 absorption bands were removed manually to improve clarity of the shown spectra. Integration of the absorption bands of the C=C double bond (3055\u20133110\u2009cm\u22121), the thiol (2530\u20132605\u2009cm\u22121) and the bond between 1513 and 1550\u2009cm\u22121, relating to the cleavage of the photobase, was used to follow the respective curing reactions.\n\nPhoto-rheology measurements were performed on an Anton Paar (Austria) Modular Compact Rheometer 102 with a parallel-plate measuring system (diameter\u2009=\u200925\u2009mm) with a constant gap of 0.1\u2009mm. The resin was illuminated from below through a quartz plate with LED light sources (450\u2009nm and 365\u2009nm, LED Control 5S, see Light sources) at 25\u2009\u00b0C.\n\nStability of the formulation was investigated through viscosity measurements over the course of several days using a Physica MCR\u00a0501 rheometer (Anton Paar, Austria) with plate-plate geometry. The plate diameter was 25\u2009mm with a gap distance of 0.1\u2009mm at 25\u2009\u00b0C. The viscosity was measured during a frequency sweep from 1 to 300\u2009s\u22121 with intervals of 3\u2009s\u22121. The measurement of each point was conducted within a minute, and the average value was taken as a viscosity value at the exact shear rate.\n\nMasking experiments were performed between a fluorinated ethylene propylene film and a glass slide. During illumination with 365\u2009nm (7.84\u2009J\u2009cm\u22122) the mask was placed on top of the film and removed before curing the whole film with 450\u2009nm (146.40\u2009J\u2009cm\u22122). After curing, the film on the glass slide was washed with isopropanol.\n\nFor laser writing experiments, a custom-built optical setup incorporating two laser sources was used. The curing laser was a multimode continuous-wave (CW) laser diode, (\u03bb\u2009=\u2009435\u2009nm, Laser Tree, China). To expand the beam diameter, the laser output was coupled into a telescopic system consisting of an entrance achromatic lens (AC254-050-A-ML, f\u2009=\u200950.0\u2009mm; Thorlabs, USA) and a collimating bi-convex lens (LB1676-A, f\u2009=\u2009100.0\u2009mm; Thorlabs, USA), resulting in a beam expansion factor of 2. To extract the fundamental transverse electromagnetic mode (TEM\u2080\u2080), a 15\u2009\u00b5m pinhole (900PH-15, Newport, USA) was placed at the focal plane of the entrance lens. The inhibition laser was a pulsed, diode-pumped, passively Q-switched solid-state laser (1Q 355-2, CryLaS GmbH, Germany; \u03bb\u2009=\u2009355\u2009nm, pulse width\u2009=\u20091\u2009ns, repetition rate\u2009=\u200910\u2009kHz). This beam was also expanded using a telescopic system composed of a plano-convex entrance lens (LA1986-A, f\u2009=\u2009125.0\u2009mm; Thorlabs, USA) and a collimating lens (LA1509-A, f\u2009=\u2009150.0\u2009mm; Thorlabs, USA), resulting in a moderate increase in beam diameter. No pinhole was used. The expansion was configured such that the beam slightly underfilled the back aperture of the objective lens, thereby increasing the effective voxel size in the focal plane. Figure\u00a0S5 shows the full emission spectra of both lasers. Both beams were focused and co-aligned onto the sample plane using a 100\u00d7 oil immersion objective lens (NA\u2009=\u20091.46, alpha-Plan Apochromat; Zeiss, Oberkochen, Germany). Laser powers were measured at the back aperture of the objective using a LaserPoint Plus2power meter with an PD-50-D9-UV-photodiode sensor (Vimodrone, Italy). Sample positioning and scanning were performed using a three-axis piezo stage (P-562.3CD, Physik Instrumente PI, Germany) offering bidirectional positioning accuracy of 2\u2009nm in x/y and 4\u2009nm in z, with a travel range of 200\u2009\u00b5m along each axis. Unless stated otherwise, the sample scan velocity was set to 20\u2009\u00b5m/s. After writing, samples were washed with ethanol and acetone to remove any unpolymerized resin.\n\nScanning electron microscopy (SEM) analysis was carried out using a Tescan Clara system (Brno, Czech Republic). To improve electrical conductivity and image resolution, samples were coated with a thin layer of gold using a Cressington 108 sputter coater (Cressington Scientific Instruments, Watford, UK) prior examination. The SEM was utilized in Ultra High Resolution and Depth mode, operating either the Everhart Thornley Detector or the Axial Beam Detector, depending on the imaging requirements. Parameters such as working distance, electron beam current, and accelerating voltage were carefully optimized for each specimen to obtain high resolution images at print magnifications of 400\u00d7 and 1000\u00d7, corresponding to fields of view of 318\u2009\u00b5m and 127\u2009\u00b5m respectively.\n\nFor evaluation of lateral line width (for writing speed and diffusion experiments), line width was measured in 5 different places over the length of the line and averaged using ImageJ (open source) software.\n\nA custom-built printer (Luxinergy GmbH, Austria) with two different light sources (365\u2009nm and 460\u2009nm) was used to create the structured layer that was analyzed in the gray scale experiments. First, three stripes (4\u2009mm\u2009\u00d7\u200910\u2009mm) were illuminated with 365\u2009nm with varying intensity (288\u2009mJ\u2009cm\u22122, 864\u2009mJ\u2009cm\u22122, 1440\u2009mJ\u2009cm\u22122). After that, the whole sample (20\u2009mm\u2009\u00d7\u200910\u2009mm) was illuminated with 450\u2009nm to cure the layer (1050\u2009J\u2009cm\u22122). For measuring the height profile of the sample, a Keyence (Japan) laser confocal microscope VKX 1100 was used with 20\u00d7 magnification. 30 pictures were stitched in X direction to create a picture over the length of the sample.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The source data generated in this study have been deposited in the Zenodo database https://doi.org/10.5281/zenodo.16752061. All other data are available from the corresponding author upon request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Yagci, Y., Jockusch, S. & Turro, N. J. 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We would like to thank the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology and the Austrian Research Promotion Agency (FFG) for funding the \u201c3DFit4Wear\u201d project as part of the Production of the Future program line (project no. 891254) and the \u201cFast3DCast 2.0\u201d project as part of the BRIDGE program line (project no. 891107). Part of the research was carried out within the Austrian Science Fund (FWF) program line Principal Investigator Projects within the project PAT3523723.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Institute of Chemistry of Polymeric Materials, Technical University of Leoben, Leoben, Austria\n\nRita Johanna H\u00f6ller,\u00a0Stefanie Monika M\u00fcller\u00a0&\u00a0Thomas Griesser\n\nDepartment of Medical Engineering, University of Applied Sciences Upper Austria, Linz, Austria\n\nDmitry Sivun\u00a0&\u00a0Jaroslaw Jacak\n\nInstitute of Applied Physics, Johannes Kepler University Linz, Linz, Austria\n\nGeorgii Gvindzhiliia\u00a0&\u00a0Thomas A. Klar\n\nInstitute of Materials Science and Testing of Polymers, Technical University of Leoben, Leoben, Austria\n\nLukas Haiden\n\nPolymer Competence Center Leoben GmbH, Leoben, Austria\n\nSandra Schl\u00f6gl\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nR.J.H., D.S., G.G., S.M.M., and L.H. performed experimental work, Data analysis was done by R.J.H. and D.S.; T.G.: and S.S. conceptualized the work. R.J.H. wrote the original draft. T.A.K. and J.J. contributed to the discussion of the study. T.G.: supervised the work. All authors critically reviewed and edited the manuscript and were involved in scientific discussions.\n\nCorrespondence to\n Thomas Griesser.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Kailong Jin, Hatice Mutlu, and the other, anonymous, reviewer for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "H\u00f6ller, R.J., Sivun, D., Gvindzhiliia, G. et al. Spatial control of curing kinetics in thiol-ene-systems through antagonistic photoreactions.\n Nat Commun 16, 8487 (2025). https://doi.org/10.1038/s41467-025-63407-0\n\nDownload citation\n\nReceived: 18 December 2024\n\nAccepted: 19 August 2025\n\nPublished: 26 September 2025\n\nVersion of record: 26 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63407-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Protein Language Model for Structure-Based Discovery of Highly Efficient and Robust PET Hydrolases", + "journal": "Nature Communications", + "published": "05 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61599-z/MediaObjects/41467_2025_61599_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61599-z/MediaObjects/41467_2025_61599_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61599-z/MediaObjects/41467_2025_61599_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61599-z/MediaObjects/41467_2025_61599_MOESM4_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61599-z/MediaObjects/41467_2025_61599_MOESM5_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61599-z/MediaObjects/41467_2025_61599_MOESM6_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61599-z/MediaObjects/41467_2025_61599_MOESM7_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-61599-z#Sec28" + ], + "code": [ + "https://github.com/ai4protein/VenusMine", + "https://doi.org/10.5281/zenodo.15680583" + ], + "subject": [ + "Bioinformatics", + "Biotechnology", + "Hydrolases" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5492523/v1.pdf?c=1751800035000", + "research_square_link": "https://www.researchsquare.com//article/rs-5492523/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61599-z.pdf", + "preprint_posted": "15 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Plastic waste, particularly polyethylene terephthalate (PET), poses significant environmental challenges, prompting extensive research into enzymatic biodegradation. However, existing PET hydrolases (PETases) are constrained to a narrow sequence space and exhibited limited performance for effective biodegradation. This study introduces a protein discovery pipeline, ProMine, which integrates protein language models (PLMs) with a representation tree to identify PETase based on structural similarity using sequence information. Using the crystal structure of IsPETase as a template, we employed ProMine to search for and cluster target proteins. PETase candidates were further screened using PLM-based assessments of solubility and thermostability, leading to the selection of 34 proteins for biochemical experiments. The results showed that 14 candidates exhibited PET degradation activity across a temperature range of 30-60 \u2103. Notably, we identified a PET hydrolase from Kibdelosporangium banguiense (KbPETase), which has a melting temperature 32 \u2103 higher than that of IsPETase and exhibits the highest PET degradation activity within 30-60 \u2103 compared to other wild-type PETases. KbPETase also shows higher catalytic efficiency than FastPETase. X-ray crystallography and molecular dynamics simulations revealed that KbPETase has a conserved catalytic domain and enhanced intramolecular interactions, contributing to its improved functionality and thermostability. This work demonstrates a novel deep learning approach for discovering natural PETases with enhanced properties.Biological sciences/Computational biology and bioinformatics/Data miningBiological sciences/Biochemistry/Enzymes/HydrolasesBiological sciences/Computational biology and bioinformatics/Machine learning", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nA patent application \tCN202410267798.X relating to the PETase discovered in this study has been filed in the name of Shanghai Jiao Tong University, pending. B.W. B.Z. P.T. and L.H. are the inventors of this patent. The other authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SI.pdfSupplementary Information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Plastic waste, particularly polyethylene terephthalate (PET), presents significant environmental challenges, driving extensive research into enzymatic biodegradation. However, existing PET hydrolases (PETases) are limited by narrow sequence diversity and suboptimal performance. This study introduces VenusMine, a protein discovery pipeline that integrates protein language models (PLMs) with a representation tree to identify PETases based on structural similarity using sequence information. Using the crystal structure of IsPETase as a template, VenusMine identifies and clusters target proteins. Candidates are further screened using PLM-based assessments of solubility and thermostability, leading to the selection of 34 proteins for biochemical validation. Results reveal that 14 candidates exhibit PET degradation activity across 30\u201360 \u00b0C. Notably, a PET hydrolase from Kibdelosporangium banguiense (KbPETase) demonstrates a melting temperature (Tm) 32 \u00b0C higher than IsPETase and exhibits the highest PET degradation activity within 30 \u2013 65 \u00b0C among wild-type PETases. KbPETase also surpasses FastPETase and LCC in catalytic efficiency. X-ray crystallography and molecular dynamics simulations show that KbPETase possesses a conserved catalytic domain and enhanced intramolecular interactions, underpinning its improved functionality and thermostability. This work demonstrates a novel deep learning approach for discovering natural PETases with enhanced properties.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Plastic waste poses serious risks to human health and the environment, contributing to pollution, toxic chemical exposure, and ecosystem disruption1,2,3. Recycling plastic is therefore essential to mitigate these negative impacts, conserve natural resources, and reduce environmental contamination. Polyethylene terephthalate (PET), one of the most widely used plastics, has already been extensively utilized in packaging, textiles, and various consumer products4. Due to its durability and extensive use, PET accumulation in landfills and natural ecosystems has become a significant environmental issue1,2,5. Traditional mechanical and chemical recycling methods for PET are limited in both efficiency and environmental sustainability6,7,8. As a result, developing biological solutions for PET degradation, particularly through the use of hydrolase enzymes to product high-quality recycled PET (rPET), has obtained significant attention from both scientific research and industry9,10,11,12,13,14,15,16,17,18.\n\nMultiple enzymes capable of degrading PET have been biochemically and structurally identified and characterized19,20,21,22,23. The most effective PET-degrading enzymes to date are derived from culturable microorganisms isolated from environmental microbial communities, such as the widely used Ideonella sakaiensis PET hydrolase (IsPETase)12. However, approximately 99% of microbial species in nature are unculturable, making metagenomic data a crucial resource for discovering new enzymes24. Successes in this field include the identification of leaf and branch compost cutinase (LCC)19, PHL-7 (PES-H1)22,25, PE-H26, Bacterium HR29 (BhrPETase)21, and Thermobifida fusca cutinases (TfCut1 and TfCut2)20. Recently, a thermophilic PET-degrading enzyme, MG8, was discovered from human saliva metagenomic data23. Despite these advancements, the performance of these wild-type (WT) PETases remains insufficient for industrial PET enzymatic hydrolysis. Industrial-scale PET biodegradation requires high-temperature conditions to achieve high yields of rPET27,28, as temperatures approaching PET\u2019s glass transition release molecular energy, facilitating PETase-catalyzed hydrolysis18,29,30,31,32,33,34. However, WT PETases are limited by low catalytic activity and thermostability10,31,35. Therefore, discovering new WT PETase with high catalytic activity and thermostability are crucial for advancing PETase engineering for the PET biodegradation industry.\n\nThe discovery of functional proteins is crucial for advancing both biotechnology and the life sciences36. Sequence-based screening remains the most widely used approach for enzyme discovery36,37, leading to the identification of enzymes such as LCC19 and MG823. Although sequence-based methods-such as identifying conserved residues, sequence similarities, or hidden Markov models (HMMs)-are effective, proteins with similar functions may still go undetected when rely solely on sequence information38. Conversely, because the three-dimensional (3D) structure of a protein largely dictates its function, structure-based discovery offers a more robust approach, yielding a more diverse repertoire for enzyme mining39,40. While structural data in public databases remains limited41,42, deep learning methods, including AlphaFold43, have shown great promise in accurately predicting protein 3D structures, enabling the large-scale exploration and classification of proteins with structure queries.\n\nIn this study, we develop VenusMine, a structure-based enzyme discovery pipeline integrating sequence/structure retrieval tools and protein language models to identify thermostable PETases. Through systematic screening of 34 selected candidates using VenusMine, we identify 14 active PETases, including 8 with a melting temperature 10\u2009\u00b0C higher than IsPETase and 3 demonstrating significantly enhanced catalytic activity. Notably, PETase from Kibdelosporangium banguiense (KbPETase) exhibits exceptional properties, with a Tm 32\u2009\u00b0C higher than IsPETase and catalytic activity at 50\u2009\u00b0C that outperforms IsPETase and LCC under their optimal reaction conditions. KbPETase also displays enhanced catalytic efficiency (kcat/KM) compared to FastPETase and LCC. Overall, we establish a structure-based enzyme mining approach combined with protein language models, which is both reliable and sensitive for identifying and screening candidate PETase, thereby accelerating the discovery of high-performance enzymes.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Our structure-based enzyme discovery pipeline for PETase consists of two primary stages (Fig.\u00a01a). First, we aimed to compile a comprehensive collection of potential PETases sharing structural similarity with known enzymes. Starting with IsPETase as the query structure (PDB: 5XFY44), we utilized the structure search tool FoldSeek45 to perform structure similarity searches (see details in Methods). Given that structure databases are still limited (e.g., AFDB and ESMAtlas), we used the identified proteins as queries to conduct an extensive sequence search through the NCBI NR sequence database using MMseqs2 (see details in Methods). This approach resulted in a vast repertoire of 33,247,501 candidates, which could cover almost all potential PETases that share a similar structure to our query protein, IsPETase (Fig.\u00a01b). This approach resulted in a vast repertoire of 33,247,501 candidates and kept\u00a0436,488 after 50% sequence identity clustering, which could cover almost all potential PETases that share a similar structure to our query protein,\u00a0IsPETase (Fig. 1b).\n\na The pipeline for PETase discovery combines structure retrieval with FoldSeek, sequence retrieval with MMSeqs2, protein embeddings computation via ProstT5, and representation tree construction to identify functional and robust PETase. b The UMAP visualization of the PETase candidates identified by ProstT5. Green points indicate proteins with previously validated PET hydrolytic activity or annotated with corrected EC number 3.1.1.101, while red points represent those not annotated with the corresponding EC number. Pink points represent proteins identified using FoldSeek, and orange points correspond to proteins discovered through sequence-based searches. c The UMAP visualization of the PETase candidates colored by representation tree clusters. d The representation tree constructed using embeddings from ProstT5. Green bars represent proteins with previously validated PETase activity, blue bars present enzymes with the correct EC number, while red bars indicate enzymes not annotated with the correct EC number. Two clusters are selected for further screening: the first cluster (dark red) and the third cluster (orange), arranged in a counterclockwise direction.\n\nThe second stage of the pipeline clusters candidate proteins to identify those most likely to exhibit optimal functionality and desired characteristics. Here, we utilized the sequence embeddings from the protein language model ProstT546 to represent the proteins. To reduce computational cost, we used representative sequences from 50% identity sequence clusters to calculate embeddings for subsequent analysis. ProstT5 has learned a mapping from protein sequences to structures, effectively representing the proteins in the structure space. This means that proteins with closer embeddings in Euclidean space are more likely to share similar structures. Subsequently, agglomerative clustering was applied to the protein embeddings to group the candidate enzymes and build a representation tree (Fig.\u00a01c, d). To elucidate the functions and characteristics of each cluster, a list of well-studied enzymes with annotations are clustered together for comparison. The agglomerative clusters enabled us to pinpoint the most promising clade for PET degradation activity, which contains more confirmed PETase, while others contained enzymes from different EC numbers (Fig.\u00a01d).\n\nFor further screening, we selected two clusters (cluster 1 and 3), as these clusters contained all reported high-activity wild-type PETases (e.g., IsPETase, LCC, TFH). A full list of known PETases within these clusters is provided in Supplementary Data\u00a02. These clusters underwent multi-tiered screening: (1) A fine-tuned ESM247,48 model predicted Tm, with sequences demonstrating Tm larger than that of IsPETase retained; (2) ProtSolM49 eliminated candidates with low solubility predictions; (3) ESMfold-predicted structures were aligned with IsPETase (PDB:5XFY) using TM-align, excluding proteins with TM-score below 0.5 or sequences exhibiting catalytic triad mismatches. Finally, the 34 top-ranked candidates by predicted Tm were finally selected for experimental validation, balancing computational predictions with practical experimental throughput constraints (Supplementary Fig.\u00a01). This hierarchical approach ensured systematic identification of thermostable, soluble candidates while preserving structural fidelity to functional PETase architectures.\n\nWe analyzed the sequences of 34 candidates to identify signal peptides using SignalP, followed by truncation to enhance expression efficiency (Supplementary Figs.\u00a02\u20133)50,51. After codon optimization (Supplementary Data\u00a01), the modified sequences were cloned into the pET28a (+) vector and expressed in E. coli BL21(DE3) with an N-terminal His-tag for purification. Among the candidates, 26 were successfully expressed and purified. The high expression success rate can also be attributed to the effective pre-screening capabilities of the PLM in the initial selection process. The p-nitrophenyl butyrate (pNPB) molecule contains an ester bond, and PETase similarly catalyzes the cleavage of ester bonds in PET (Supplementary Fig.\u00a04). Therefore, we employed pNPB as a substrate to rapidly assess ester bond hydrolysis activity by measuring the absorbance of the reaction product, a method widely utilized in PETase discovery and related research12,18,19,33,52,53(Supplementary Figs.\u00a04 and 5). After incubating proteins with substrates at 37\u2009\u00b0C, 14 of the 26 expressible proteins exhibited ester bond-cleaving activity (Fig.\u00a02a).\n\na Enzyme activity of 14 proteins showing effective ester bond hydrolysis, as measured using p-nitrophenyl butyrate (pNPB) as a substrate at 37\u2009\u00b0C. Reactions were performed in triplicate; data are presented as mean values\u2009\u00b1\u2009SD. b The Tm of the 14 proteins exhibiting ester bond cleavage activity. Reactions were performed in triplicate; data are presented as mean values\u2009\u00b1\u2009SD. c Comparison of the degradation activity of candidate proteins on PET films. The reaction was conducted in 50\u2009mM Glycine-NaOH (pH 9.0) at various temperatures (30, 40, 50, 55, and 60\u2009\u00b0C) for 72\u2009h. Reactions were performed in triplicate; data are presented as mean values\u2009\u00b1\u2009SD.\n\nTo evaluate PET degradation performance50, we used commercially available amorphous PET film (AF-PET, Goodfellow Cambridge Ltd, Cat. No. ES301445) as the substrate, which is widely used for assessing PET degradation activity13,54. The aromatic reaction products mono(2-hydroxyethyl) terephthalate (MHET) and terephthalic acid (TPA) were quantified using ultra-high-performance liquid chromatography (UPLC)13. For comparative analysis, we used IsPETase, the reference protein in our structural searches, as a control baseline. Since the optimal catalytic temperatures of the enzymes were unknown, we tested the activity of the 14 candidates across a range of 30\u221260\u2009\u00b0C. All proteins exhibited activity within this range, with 11 of the 14 displaying degradation activity comparable to IsPETase (Fig.\u00a02c). Given that Tm is an indicator of PETase thermostability and performance11, we used differential scanning fluorimetry (DSF) to determine the Tm of the proteins. The results showed that the Tm of the 14 proteins ranged from 36.4\u2009\u00b0C to 80.1\u2009\u00b0C (Fig.\u00a02b), with eight\u00a0candidates\u00a0demonstrating superior thermostability compared to the IsPETase,\u00a0indicating the VenusMine\u2019s effectiveness in identifying thermostable and functional PETases. We also observed that the three aforementioned properties exhibited low correlations (Supplementary Fig.\u00a06), highlighting the complexity of PETase activity and suggesting that multiple factors likely contribute to overall degradation performance. However, the PLM was able to capture relevant features, enabling high-positive screening of candidate PETases.\n\nNotably, APET-14 showed the highest catalytic activity at 50\u2009\u00b0C, with a 97-fold increase compared to IsPETase at 30\u2009\u00b0C (Fig.\u00a02c). and demonstrated excellent thermostability, with a Tm increase of 32.4\u2009\u00b0C over IsPETase, as validated by nanoDSC (Supplementary Fig.\u00a07). These results indicate that APET-14 (GenBank: WP_209642273.1, PETase from Kibdelosporangium banguiense55, hereafter KbPETase) combines high enzymatic activity and thermostability, traits essential for efficient PET degradation, and was thus selected for further evaluation. In addition, the sequence similarity between KbPETase and other known PETases ranges from 30% to 50%, highlighting the capability of VenusMine to identify functional PETase in regions of low sequence similarity (Supplementary Fig.\u00a08).\n\nWe compared the activity of KbPETase to that of other characterized PET degrading enzymes, such as IsPETase12 (mesophilic), PE-H26 (mesophilic), BTA-256 (mesophilic), Cut_19053 (thermophilic), Thc_Cut157 (thermophilic), TFH58 (thermophilic), and LCC19 (thermophilic), across a temperature range of 30\u221265\u2009\u00b0C (Fig.\u00a03a). Note that Cut190, IsPETase, LCC, and PE-H are used as superior starting templates for directed evolution in industry18.\n\na PET film degradation activity of the previously characterized PETases compared to KbPETase. The reaction was conducted in 50\u2009mM Glycine-NaOH (pH 9.0) at various temperatures (30, 40, 50, 55, 60 and 65\u2009\u00b0C) for 72\u2009h. Reactions were performed in triplicate; data are presented as mean values\u2009\u00b1\u2009SD. b Comparison of the Tm of KbPETase with other reported WT PETases using DSF. Reactions were performed in triplicate; data are presented as mean values\u2009\u00b1\u2009SD. c Comparison of the enzymatic kinetics curves of KbPETase, LCC and FastPETase using pNPB as the substrate. Reactions were performed in triplicate; data are presented as mean values\u2009\u00b1\u2009SD. d The depolymerization percentages of PET by KbPETase and FastPETase at 50\u2009\u00b0C, as well as by LCC at 65\u2009\u00b0C, were determined through UPLC analysis of the released products. Reactions were conducted in triplicate, and the results are presented as mean values\u2009\u00b1\u2009SD.\n\nCompared to IsPETase, which exhibits the highest catalytic activity at room temperature moderate temperatures with its optimal catalytic activity at 30\u2009\u00b0C12, KbPETase demonstrates a 97-fold higher catalytic activity at its optimal temperature of 50\u2009\u00b0C. (Fig.\u00a03a). To assess thermostability, we analyzed the Tm of KbPETase and other enzymes, finding that its enhanced activity likely correlates with its superior thermostability (Fig.\u00a03b). Time-dependent PET degradation further revealed the stability of KbPETase, with continuous activity enhancement over time, unlike IsPETase, which plateaued after 24\u2009h at 30\u2009\u00b0C (Supplementary Fig.\u00a09). Compared to LCC, a highly active thermophilic PETase19, KbPETase exhibits 5.7-fold higher activity under the same reaction conditions at 50\u2009\u00b0C, and even outperformed LCC by 1.47-fold at its optimal temperature of 65\u2009\u00b0C (Fig.\u00a03a) (Fig.\u00a03a), despite the fact that higher temperatures closer to PET glass transition temperature can facilitate faster degradation7,18,27,32. This superiority persisted when LCC was tested in its optimal 100\u2009mM potassium phosphate buffer (pH 8.0) at 65\u2009\u00b0C, with KbPETase at 50\u2009\u00b0C maintaining 1.15-fold higher activity (Supplementary Fig.\u00a010). In addition, the degradation products of KbPETase exhibited a slightly higher proportion of TPA (Fig.\u00a03d),\u00a0a critical factor for PET recycling27,59. However, when we tested the Michaelis-Menten constants33, we found that while the Km values were similar, KbPETase exhibited a 1.5-fold higher kcat and a 1.3-fold higher catalytic efficiency (kcat/KM) compared to LCC (Fig.\u00a03c and Table\u00a01). These findings indicate that KbPETase combines the moderate-temperature catalytic efficiency typical of mesophilic enzymes with the elevated activity characteristic of thermophilic counterparts.\n\nWe also compared the performance of KbPETase with FastPETase, the most active mutant derived from IsPETase, known for its optimal catalytic performance also at 50\u2009\u00b0C54.KbPETase exhibits a Tm that is 8\u2009\u00b0C higher than that of FastPETase. Evaluation of PET degradation activity across a temperature gradient revealed that KbPETase demonstrated 1.71-fold higher activity than FastPETase at their shared optimal temperature (Supplementary Fig.\u00a011). To investigate the underlying reasons for this difference, we also determined the Michaelis-Menten kinetic constants of both enzymes using pNPB as a substrate33. KbPETase demonstrates a 1.87-fold higher catalytic efficiency compared to FastPETase, along with superior substrate affinity (Fig.\u00a03c and Table\u00a01). Besides, KbPETase produced TPA at a concentration that is 2.2-fold higher than that of FastPETase (Fig.\u00a03d). However, when compared to the engineered variant LCCICCG, KbPETase exhibited comparable activity at 50\u2009\u00b0C, achieving approximately half of the optimal catalytic activity of LCCICCG (Supplementary Fig.\u00a012)16. These results suggest that KbPETase could serve as a more promising template for enzyme engineering compared to other WT enzymes, such as IsPETase and LCC, which exhibit extreme mesophilic or thermophilic traits.\n\nTo provide structural insights into the enhanced PET-degrading activity of KbPETase, we determined its structure using X-ray crystallography at a resolution of 1.75\u2009\u00c5 (PDB: 9IW9). Overall, the structure of KbPETase exhibits a high degree of conservation compared to IsPETase (PDB: 5XH3) (Fig.\u00a04a). The catalytic triad (S128-H206-D176) and substrate pocket of KbPETase are also conserved relative to those of IsPETase (S131-H208-D177) (Fig.\u00a04b and c). This conservation suggests that VenusMine not only retains the overall structure scaffold but also preserves the active site, ensuring the enzymatic activity necessary for PET degradation.\n\na Crystal structure of KbPETase (green, PDB: 9IW9) and IsPETase (blue, PDB: 5XH3). The substrate structure (yellow) is derived from the IsPETase structure and structurally aligned with the KbPETase pocket. Pocket structure of (b) IsPETase and (c) KbPETase. d Phylogenetic tree of the 34 candidate proteins selected for experimental validation (blue and green) alongside previously reported PETases (orange). The APET candidates with confirmed activity are marked with green, otherwise in blue. (e, f) Sequence logo plots for the pocket residues. The height of each letter indicates the conservation score for each site, indexed by the residue position in KbPETase, with catalytic traid marked in red. g Structure-based alignment of pocket residues from APET (green) and known PETases (orange). Residues are annotated by their biochemical properties, with hierarchical clustering presented on the right. Residue indices correspond to their positions in KbPETase.\n\nA phylogenetic tree was constructed to include APET candidates alongside known PETases (Fig.\u00a04d). The discovered enzymes are broadly distributed throughout the tree. One major clade contains most known PETases, including IsPETase, LCC, Cut190, and Thc_cut1, from which KbPETase also originates. Furthermore, several new clades emerged, populated by APET candidates positioned closer to the root of the phylogenetic tree. This arrangement suggests that VenusMine may effectively identify ancestral enzymes based on conserved structural features.\n\nTo further investigate the substrate pocket, structure alignment was performed using TM-align. Compared with reported PETases, the newly discovered APETs exhibit greater diversity in pocket residues while retaining a conserved catalytic triad (Fig.\u00a04e\u2013g). Notably, residues at positions 58, 129, and 155 show higher variability than those of known PETases (Fig.\u00a04e, f). Hierarchical clustering based on pocket residues revealed that APETs are distributed across all major types of known PETases.\n\nTo elucidate the molecular basis of KbPETase\u2019s enhanced thermostability, we performed comparative all-atom molecular dynamics (MD) simulations with LCC and IsPETase, representing thermophilic and mesophilic PET hydrolases, respectively. Analysis of the radius of gyration (Rg) distributions (Fig.\u00a05a\u2013c) revealed that KbPETase predominantly adopts conformations with significantly reduced Rg values compared to LCC and IsPETase. This structural compactness correlates with its experimental thermostability, suggesting that reduced conformational entropy minimizes thermal unfolding propensity-a hallmark of enzymes adapted to fluctuating environmental conditions.\n\na\u2013c The distribution of Rg for KbPETase (a), LCC (b) and IsPETase (c). d\u2013f Residue-wise root mean square fluctuation (RMSF) mapped onto structures, with green (KbPETase), red (LCC) and blue (IsPETase) for low RMSF and yellow for high RMSF. Residues in catalytic pockets are presented with sticks. g\u2013i non-covalent interactions, including numbers of (g) global hydrogen bonds, (h) catalytic pocket hydrogen bonds, and (i) global salt bridges. Data are presented as mean values\u2009\u00b1\u2009SD.\n\nRoot mean square fluctuation (RMSF) profiles further highlighted critical differences in local flexibility (Fig.\u00a05d\u2013f). While LCC and IsPETase exhibited pronounced dynamics in loop regions proximal to their catalytic pockets (highlighted as yellow sticks), KbPETase demonstrated restricted flexibility in these functionally critical regions, with elevated fluctuations confined to peripheral N-terminal loops. This dichotomy suggests that KbPETase achieves a balance between global stability and local flexibility, potentially optimizing both substrate accessibility and catalytic efficiency across a broad temperature range.\n\nAnalysis of non-covalent interactions revealed hierarchical trends in intramolecular stabilization (Fig.\u00a05g\u2013i). LCC, the most thermostable enzyme, maintained the highest global counts of salt bridges and hydrogen bonds, followed by KbPETase and IsPETase. This gradient mirrors their experimental Tm, underscoring the role of cumulative non-bonded interactions in conferring thermostability. To further identify the critical hydrogen bonds in the catalytic pocket of the three proteins, we calculated the lifetime of every individual hydrogen bond within active sites (Details can be found in the section of Method). Notably, hydrogen bond lifetimes within the catalytic pocket\u2014a metric reflecting bond persistence\u2014showed KbPETase surpassing both counterparts, with the N175-D205 interaction exhibiting the longest lifetime (\u03c4\u2009=\u20095.00\u2009\u00b1\u20090.21\u2009ns, Supplementary Fig.\u00a013). Such prolonged hydrogen bonding may stabilize the catalytic triad geometry, facilitating substrate orientation and transition-state stabilization.\n\nThese findings collectively delineate the biophysical signature of KbPETase: a compact scaffold resistant to entropic unfolding, paired with dynamic yet stable active site interactions. This dual strategy\u2014global rigidity tempered by localized flexibility\u2014may explain its unique functional profile, balancing thermostability with catalytic efficiency. By circumventing the traditional trade-off between thermal resilience and ambient-temperature activity observed in extremophilic enzymes, KbPETase emerges as a versatile candidate for PET depolymerization processes requiring operational adaptability.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61599-z/MediaObjects/41467_2025_61599_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61599-z/MediaObjects/41467_2025_61599_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61599-z/MediaObjects/41467_2025_61599_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61599-z/MediaObjects/41467_2025_61599_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61599-z/MediaObjects/41467_2025_61599_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The increasing environmental impact of plastic waste, particularly PET, underscores the urgent need for innovative and effective biodegradation strategies5,8,11,28. Consequently, the search for promising PET-degrading enzymes has been widely pursued, leading to the successful characterization of several proteins to date12,18,19,20,21,23. However, current enzyme discovery methods predominantly rely on sequence-based analysis and are constrained by the high costs of experimental biochemical or biophysical protein structure characterization, as well as the limited accuracy of traditional computational folding simulations60,61. This reliance limits PETase discovery to a narrow sequence landscape50.\n\nHere, we present VenusMine, a structure-guided pipeline integrating FoldSeek-based structural searches, ProstT5-driven clustering, and deep learning-aided thermostability/solubility predictions. This methodology expands enzyme mining beyond sequence homology, enabling the discovery of KbPETase. KbPETase demonstrates remarkable catalytic performance, exhibiting superior activity at 50\u2009\u00b0C, which establishes it as a highly practical candidate for industrial PET recycling. Structural and molecular dynamics analyses reveal a conserved catalytic architecture and strengthened intramolecular interactions, underpinning its enhanced thermostability and functional resilience. In contrast to engineered variants like FastPETase, KbPETase\u2019s wild-type origin and unoptimized catalytic efficiency present a promising starting point for directed evolution, offering substantial potential for further refinement and application.\n\nVenusMine not only expands the repertoire of known PETases but also establishes a robust framework for future enzyme discovery. With advancements in deep learning and protein language models, it may soon be possible to predict protein structures, and even complexes, more accurately, enabling the preliminary selection of candidate proteins through modeling. This approach not only aids in identifying exceptional enzyme molecules akin to \u201chidden gems\u201d within databases but also enhances the accuracy and precision of the screening process. The emergence of KbPETase provides an improved template for the engineering of high-performance enzymes, expanding the molecular library and offering new tools for the bioconversion and recycling of PET.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "In the structural search step, we utilized the default setting of the FoldSeek server website (https://search.foldseek.com/search). The structure of IsPETase (PDB: 5XFY) is selected as the only query protein for the FoldSeek search. Target structure database include AlphaFold databases (UniProt50 v4, SwissProt v4, Proteome v4), MGnify-ESM30 (v1), PDB100, and GMGCL 2204.The core structure is defined by a manual structure check of IsPETase, ranging from residue 51 to 234, covering the reported catalytic triad and the scaffold of the PET hydrolase. Any searched protein align range not covering this region was excluded from the following steps. This structure search step identified 4235 structural similar protein sequences. The Code of this part is available at step 2 in https://github.com/ai4protein/VenusMine.\n\nIn the sequence search step, the sequences found in the structure search from different databases were merged and used as queries for the sequence search through the NCBI\u2019s NR database. This step is using MMseqs2 easy-search method with 2 rounds of iterations and sets max-seq to 2500, resulting in 33,247,501 sequences. After obtaining this vast repertoire, MMseqs2 easy-cluster method with 50% sequence identity is used to saving the cost for the following ProstT5 embeddings computation step. Resulting 436,488 representative proteins derived from 50% sequence identity clusters. Code for this part is available at steps 3 and 4 in https://github.com/ai4protein/VenusMine.\n\nWe systematically evaluated various computational approaches to assess their effectiveness in identifying and classifying proteins within enzyme commission (EC) groups, in order to find the optimal and most efficient method to discover novel enzymes. Utilizing a curated dataset of 265,488 protein sequences from the reviewed UniProt database (SwissProt), we focused on proteins up to 1000 amino acids in length and EC numbers with no less than 50 annotated enzymes. Our benchmarking compared traditional methods, including BLASTp and MMseqs2 for sequence similarity and FoldSeek with AlphaFold predictions for structural alignment against protein language models such as ESM-2 650\u2009M, ESM-1b, and ProstT5. For each EC number, 10 starting points were randomly selected from the candidate pool, while other enzymes with annotated EC number as positive examples, enzymes without target EC number serve as negative examples. By calculating E-value for BLASTp, MMseqs2 and FoldSeek, and representation distances for protein language models, we are able to analyze the area under the ROC curve (AUROC) and area under precision-recall curve (AUPR) scores for 745 EC groups. This comprehensive evaluation provided insights into the strengths and limitations of each approach, highlighting the potential of protein language models in enhancing protein discovery and classification tasks. Results showed that both the structure-based methods FoldSeek and ProstT5 performed better than BLASTp methods, and ProstT5 is the only unsupervised deep-learning method that performed better than BLASTp (Supplementary Figs.\u00a014\u201317). As a result, we select the structure-aware language model ProstT5 for our further studies.\n\nWe use the protein sequence-to-structure language model ProstT5 to calculate the embeddings of the protein candidates\u2019 sequences. Since ProstT5 is a bi-directional translation model between protein sequence and structure, the \u201cAA2fold\u201d mode of ProstT5 model is used to get a more structure-informed representation based on input sequences. Based on the representation of multiple sequences, we used agglomerative clustering to build a dendrogram for the sequences. The distances between representations are calculated as the Euclidean distance, and the agglomerative clustering method is using ward algorithm from scipy.cluster.hierarchy package. The code of this part is available at steps 5 and 6 in https://github.com/ai4protein/VenusMine.\n\nWithin the 2 selected cluster in representation tree, there are 6763 sequences advanced to the next screening step. We used a fine-tuned version of the ESM247 to predict the melting temperature (Tm) of the candidates using datasets from the literature48. In addition, we employed ProtSolM to predict protein solubility49. Only candidates with predicted Tm and solubility values exceeding those of the query sequence were retained, narrowing the pool to 223 sequences.\n\nFor further refinement, the structures of the 223 sequences were predicted using AlphaFold262, and any sequence with a pLDDT score below 75 was discarded. Structural alignment was then performed to ensure that the candidate proteins possessed the catalytic active site necessary for PET hydrolase activity. Finally, the candidates were ranked by their predicted Tm values, and the top 34 sequences were selected for wet-lab validation.\n\nThe genes encoding APET1-34 were synthesized and optimized for expression (Supplementary Data\u00a01) in Escherichia coli by Sangon Biotech (Shanghai, China). Signal peptides for APET1-34 were predicted using SignalP and subsequently removed from the synthetic DNA sequences. The synthesized genes for APET1-34 were then cloned into the NdeI and NotI sites of the pET-28a (+) expression vector, which features an N-terminal His-tag for protein purification. Detailed nucleotide sequences of APET1-34 are provided in Supplementary Data\u00a01.\n\nThe expression plasmid was transformed into Escherichia coli BL21(DE3) competent cells. A 30\u2009mL seed culture was grown at 37\u2009\u00b0C in LB medium containing 50\u2009\u00b5g/mL kanamycin, and then transferred to a 500\u2009mL shaker flask containing the same antibiotic concentration. The cultures were incubated at 37\u2009\u00b0C until the OD600 reached 1.0, at which point protein expression was induced by the addition of isopropyl-\u03b2-D-thiogalactopyranoside (IPTG) to a final concentration of 0.8\u2009mM. Induction was followed by incubation at 16\u2009\u00b0C for 16\u201320\u2009h. Cells were harvested by centrifugation at 3345\u2009\u00d7\u2009g for 30\u2009min, and the resulting pellets were collected for subsequent purification. The cell pellets were resuspended in lysis buffer (25\u2009mM Tris-HCl, 500\u2009mM NaCl, pH 7.4) and disrupted by ultrasonication (Scientz, China). The lysates were then centrifuged at 17418\u2009\u00d7\u2009g for 30\u2009minutes at 4\u2009\u00b0C, and the supernatants were loaded onto Ni-NTA columns (Smart Lifesciences, China, Ni NTA Beads 6FF: SA005500) pre-equilibrated with lysis buffer (25\u2009mM Tris-HCl, 500\u2009mM NaCl, pH 7.4). Following sample loading and subsequent washing steps, bound proteins were eluted using an elution buffer (25\u2009mM Tris-HCl, 500\u2009mM NaCl, 250\u2009mM imidazole, pH 7.4). The protein was concentrated and buffer-exchanged using Amicon Ultra centrifugal filters (Millipore, 10\u2009kDa MWCO) through repeated cycles of dilution with lysis buffer (25\u2009mM Tris-HCl, 500\u2009mM NaCl, pH 7.4) and centrifugation at 3345\u2009\u00d7\u2009g for 25\u2009min intervals at 4\u2009\u00b0C for ultrafiltration. Finally, the fractions containing the purified protein were flash-frozen at \u2212\u200920\u2009\u00b0C in storage buffer (25\u2009mM Tris-HCl, pH 7.4, 500\u2009mM NaCl, 10% glycerol).\n\nThe Tm values were determined using the Differential Scanning Fluorimetry (DSF) method with the Protein Thermal Shift Dye Kit (Thermo Fisher, U.S.A). To prepare the reaction mixture, 1.0\u2009\u03bcL of SYPRO Orange Dye (SUPELCO, U.S.A) was diluted in 49\u2009\u03bcL of lysis buffer (25\u2009mM Tris-HCl, 500\u2009mM NaCl, pH 7.4). Next, 1\u2009\u03bcL of the diluted dye was combined with 19\u2009\u03bcL of protein solution at a concentration of 0.1\u2009mg/mL. DSF experiments were conducted using the LightCycler 480 Instrument II (ROChe, U.S.A). The reaction mixture was first equilibrated at 25\u2009\u00b0C, then gradually heated to 99\u2009\u00b0C at a rate of 0.05\u2009\u00b0C/s, with a 2\u2009min hold at the final temperature. Data processing was performed using the Protein Thermal Shift software.\n\nNano DSC measurements were performed by using Nano DSC instruments (TA, U.S.A.). The concentration of PETases was 0.5\u2009mg/ml in a buffer containing 25\u2009mM Tris\u2013HCl (pH\u2009=\u20097.5) and 500\u2009mM NaCl. All the experiments were carried out at temperatures ranging from 10 to 110\u2009\u25e6C with a heating rate of 1\u2009\u25e6C/min and under a pressure of 3\u2009atm. The melting curves of PETases were subtracted from the buffer scans.\n\nScreening for activity on 4-Nitrophenol butyrate (pNPB). All reactions were conducted in 96-well plates with a total reaction volume of 100\u2009\u03bcL. The final concentration of pNPB was 0.8\u2009mM, and the enzyme concentration was 100\u2009\u00b5g/mL. Each reaction mixture contained 10\u2009\u03bcL of pNPB solution (dissolved in anhydrous ethanol), 80\u2009\u03bcL of 10\u2009mM potassium phosphate buffer (pH 8.0), and 10\u2009\u03bcL of enzyme solution, which were incubated at 37\u2009\u00b0C for 10\u2009min. The reaction was terminated by adding 100\u2009\u03bcL of anhydrous ethanol to quench the enzymatic activity. Each experiment was performed in triplicate. The concentration of p-nitrophenol was measured at 410\u2009nm using an MD SpectraMax iD5 microplate reader (Molecular Devices, U.S.A.). The specific activity was calculated by determining the product quantity using a standard curve and then combining it with the enzyme concentration and reaction time. (Supplementary Fig.\u00a04). All experiments were conducted in triplicate to ensure reproducibility.\n\nFor Kinetics analysis, enzymatic reactions involving 4-nitrophenyl butyrate (pNPB) were conducted in 96-well plates with a total reaction volume of 100\u2009\u03bcL. The final pNPB concentrations ranged from 0.2 to 1.8\u2009mM, while the enzyme concentration was maintained at 0.5\u2009\u00b5g/mL. Each reaction mixture contained 10\u2009\u03bcL of pNPB solution (dissolved in anhydrous ethanol), 80\u2009\u03bcL of 10\u2009mM potassium phosphate buffer (pH 8.0), and 10\u2009\u03bcL of enzyme preparation, which was incubated at 50\u2009\u00b0C for 3\u2009min. The methods for reaction termination and detection were identical to those described in the preceding enzymatic activity screening section. One unit of enzyme activity was defined as the amount of enzyme required to convert 1 \u03bcmol of pNPB per minute. Kinetic parameters were determined by nonlinear regression analysis of the Michaelis-Menten equation using GraphPad Prism software (version 8.0), with initial velocity data obtained from triplicate measurements at varying substrate concentrations.\n\nIn the PET degradation activity assays of 26 candidate proteins and the comparative analysis of KbPETase with other wild-type enzymes, the following conditions were employed: amorphous PET film (Goodfellow, England) was cut into circular discs with a diameter of 6\u2009mm for each reaction. The discs were incubated in 2900\u2009\u00b5L of glycine-NaOH buffer (pH 9.0, 50\u2009mM) containing 100\u2009\u00b5L of enzyme solution (stock concentration 0.5\u2009mg/mL) at 30, 40, 50, 55, 60 and 65\u2009\u00b0C for 72\u2009h. The reaction was terminated by heating the mixture at 90\u2009\u00b0C for 10\u2009min. Each sample was diluted to fall within the linear detection range for terephthalic acid (TPA) and mono(2-hydroxyethyl) terephthalate (MHET). After filtration through a 0.22\u2009\u00b5m filter, the assay solution was analyzed by UPLC. All experiments were performed in triplicate. For the comparison of activity with LCC, LCCICCG, and FastPETase, the reaction buffers for all three enzymes were replaced with 100\u2009mM potassium phosphate buffer, pH 8.0, while other conditions remained consistent with those described above.\n\nUPLC analysis was conducted using a Waters ACQUITY Arc system (Waters, U.S.A.) equipped with an autosampler and a UV detector set to 260\u2009nm. The separation was performed on a Kinetex XB-C18 100\u2009\u00c5, 5\u2009\u00b5m, 50\u2009\u00d7\u20092.1\u2009mm LC column (Phenomenex, U.S.A.) using a stepped, isocratic solvent gradient. Mobile phase A consisted of water with 0.1% formic acid, and mobile phase B was acetonitrile, with a fixed flow rate of 1.0\u2009mL/min. Samples were injected at either 1\u2009\u00b5L. Following injection, the mobile phase was maintained at 13% buffer B for 52\u2009s to separate mono(2-hydroxyethyl) terephthalate (MHET) and terephthalic acid (TPA), then ramped up to 95% buffer B for 33\u2009seconds to separate larger reaction products and contaminants. The buffer was then returned to 13% for column re-equilibration, with a total run time of 1.8\u2009min. Peaks were identified by comparison to chemical standards prepared from commercial TPA and in-house synthesized MHET, and the peak areas were integrated using software. Under these conditions, TPA eluted around 1.0\u2009min, MHET around 2.3\u2009min, and small amounts of bis(2-hydroxyethyl) terephthalate (BHET) and longer oligomers eluted between 2.7 and 3.2\u2009min. The concentrations of TPA and MHET were determined by constructing standard curves.\n\nCrystals of KbPETase, grown using hanging drop vapor diffusion by mixing equal volumes of protein and a buffer solution containing 1.26\u2009M Sodium phosphate monobasic monohydrate,0.14\u2009M Potassium phosphate dibasic at a temperature 291\u2009K. Crystals were rapidly soaked in the reservoir solution supplemented with 20% glycerol as cryo-protectant, mounted on loops, and flash-cooled at 100\u2009K in a nitrogen gas cryo-stream. Crystals Diffraction data was collected from a single crystal at Shanghai Synchrotron Radiation Facility (SSRF) BL18U beamline, China, with a wavelength of 1.75\u2009\u00c5 at 100\u2009K. The diffraction data were processed and scaled with HKL-3000. Relevant statistics were summarized in Supplementary Table\u00a01.\n\nThe structure was solved by the molecular replacement method with a starting model predicted by AlphaFold II. The Initial model was build using PHENIX.autobuild. Manual adjustment of the model was carried out using the program COOT, and the models were refined by PHENIX.refinement and Refmac5. Stereochemical quality of the structures was checked by using PROCHECK. All of the residues locate in the favored and allowed region and none in the disallowed region. Refinement resulted in a model with excellent refinement statistics and geometry (Supplementary Table\u00a01). The structure of KbPETase was deposited in the Protein Data Bank, with PDB code 9IW9.\n\nThe structures of proteins were obtained from the PDB database. For KbPETase, we utilized the crystal structure resolved in this study, while PDB(8CMV) was used for the simulation of LCC and PDB (6EQG) for IsPETase. All of the three PDB are apo structure, without substrate. Protein and a large number of water molecules were filled in a cubic box. Chlorine counter ions were added to keep the system neutral in charge. The CHARMM27 force field was used for the complex and the CHARMM-modified TIP3P model was chosen for water63,64,65. The simulations were carried out at 310\u2009K. After the 4000-step energy minimization procedure, the systems were heated and equilibrated for 100\u2009ps in the NVT ensemble and 500\u2009ps in the NPT ensemble. For each protein, 500\u2009ns production simulations were conducted with a trajectory saving frequency of 10\u2009ps. The final 300\u2009ns (30,000 frames) were extracted for subsequent analysis. The integration step was set to 2\u2009fs, and only the covalent bonds with hydrogen atoms were constrained by the LINCS algorithm. Lennard-Jones interactions were truncated at 12\u2009\u00c5 with a force-switching function from 10 to 12\u2009\u00c5. The electrostatic interactions were calculated using the particle mesh Ewald method with a cutoff of 12\u2009\u00c5 on an approximately 1\u2009\u00c5 grid with a fourth-order spline. The temperature and pressure of the system were controlled by the velocity rescaling thermostat and the Parrinello-Rahman algorithm, respectively. All MD simulations were performed using the GROMACS 2025.1 software package.\n\nTo systematically evaluate the stability of hydrogen bonds within the catalytic sites of the three proteins, we computed the lifetime (\u03c4) of each identified hydrogen bond using the following methodology:\n\nFirst, the autocorrelation function of each hydrogen bond was calculated by:\n\nHere, hi,j(t0) is a binary indicator (0 or 1) for hydrogen bond formation between donor i and acceptor j at time t0, and the angle brackets denote time averaging over all trajectory frames. Subsequently, the lifetime (\u03c4) of each hydrogen bond was derived by integrating the autocorrelation function over the total simulation time T:\n\nHere, dt represents the trajectory sampling interval (10\u2009ps). Larger \u03c4 values correspond to more stable hydrogen bonds, reflecting their persistence across the simulation.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The refined model of KbPETase generated in this study has been deposited in the Protein Data Bank with PDB code 9IW9. All data that support the findings of this study are presented within the article and its supplementary files. For further inquiries or requests for additional information, please contact the corresponding authors. The raw data for the figures generated in this study are provided in the Source Data file. The detailed data of all of the PETases discovered can be found in the supplementary data.\u00a0Source data are provided in this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code of the PETase discovery pipeline can be found in https://github.com/ai4protein/VenusMine. They can also be accessed via Zenodo in https://doi.org/10.5281/zenodo.15680583.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Jambeck, J. R. et al. Plastic waste inputs from land into the ocean. Science 347, 768\u2013771 (2015).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nBorrelle, S. B. et al. Predicted growth in plastic waste exceeds efforts to mitigate plastic pollution. Science 369, 1515\u20131518 (2020).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nMacLeod, M., Arp, H. P. H., Tekman, M. B. & Jahnke, A. 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Part of the computations in this paper were run on the Siyuan-1 cluster supported by the Center for High Performance Computing at Shanghai Jiao Tong University.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Lirong Zheng\n\nPresent address: Department of Cell and Developmental Biology & Michigan Neuroscience Institute, University of Michigan Medical School, Ann Arbor, Michigan, USA\n\nThese authors contributed equally: Banghao Wu, Bozitao Zhong.\n\nSchool of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China\n\nBanghao Wu,\u00a0Bozitao Zhong,\u00a0Runye Huang,\u00a0Shifeng Jiang,\u00a0Liang Hong\u00a0&\u00a0Pan Tan\n\nShanghai National Center for Applied Mathematics (SJTU Center) & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China\n\nBanghao Wu,\u00a0Bozitao Zhong,\u00a0Lirong Zheng,\u00a0Runye Huang,\u00a0Mingchen Li,\u00a0Liang Hong\u00a0&\u00a0Pan Tan\n\nZhang Jiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China\n\nBanghao Wu,\u00a0Runye Huang\u00a0&\u00a0Liang Hong\n\nShanghai Artificial Intelligence Laboratory, Shanghai, China\n\nMingchen Li,\u00a0Liang Hong\u00a0&\u00a0Pan Tan\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nL.Z., P.T., and L.H. conceptualized and supervised this research project. B.Z. and P.T. developed the methodology and designed the pipeline. B.Z., S.J., and M.L. implemented the method and conducted the in-silico screening of PETase. B.W. and R.H. conducted the wet-lab experiments. L.Z. and P.T. conducted MD simulations. B.W., L.Z., P.T., B.Z., and L.H. wrote the manuscript. All authors reviewed and accepted the manuscript.\n\nCorrespondence to\n Lirong Zheng, Liang Hong or Pan Tan.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "A patent application CN202410267798.X relating to the PETase discovered in this study has been filed in the name of Shanghai Jiao Tong University, pending. B.W. B.Z. P.T., and L.H. are the inventors of this patent. The other authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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force\u2013position multimodal perception using a monocular camera", + "pre_title": "Cost-Effective 18 DOF Dexterous Hand: Fusing Force-Position Multimodal Sensing of Whole Hand with a Monocular Camera", + "journal": "Nature Communications", + "published": "23 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62122-0/MediaObjects/41467_2025_62122_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62122-0/MediaObjects/41467_2025_62122_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62122-0/MediaObjects/41467_2025_62122_MOESM3_ESM.zip" + } + ], + "supplementary_1": [ + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62122-0/MediaObjects/41467_2025_62122_MOESM4_ESM.pdf" + }, + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62122-0/MediaObjects/41467_2025_62122_MOESM5_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-62122-0#Sec24" + ], + "code": [], + "subject": [ + "Electrical and electronic engineering", + "Mechanical engineering" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5410108/v1.pdf?c=1753355363000", + "research_square_link": "https://www.researchsquare.com//article/rs-5410108/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-62122-0.pdf", + "preprint_posted": "09 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The anthropomorphic hand plays a crucial role in human-machine interaction tasks. However, there are very few hands that realize multimodal perception with high DOF in a low-cost way. We present a dexterous hand that achieves multimodal sensing solely through a camera. The hand has 18 DOF but does not require any position or force sensors, making it low-cost and easy to manufacture. We developed an integrated base for the hand that provides both actuation and multimodal sensing information simultaneously. This includes the angles of 18 joints, 5 fingertip positions and contact forces, as well as information on object softness and texture. The core principle of sensing is that the camera can track the displacement and tension of all tendons simultaneously. By characterizing tendon properties and coupling them with the hand dynamics, we developed a multimodal sensing model. Experiments indicate that our hand has potential in multimodal sensing and dexterity.Physical sciences/Engineering/Mechanical engineeringPhysical sciences/Engineering/Electrical and electronic engineering", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nShiwei Chen, Peiji Wang, and Cheng Wei are the inventors of two patent applications, submitted to Harbin Institute of Technology. The patents involve the principles of visual perception measurement and the dexterous hand design.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryMaterials.pdfSupplementary Materialsmovie.zipVideo files of supplementary materials", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The anthropomorphic hand plays a crucial role in human-machine interaction tasks. However, there are very few hands that realize multimodal perception with high degrees of freedom (DOF) in a low-cost way. Here, we present a dexterous hand that achieves multimodal sensing solely through a camera. The hand has 18 DOF but does not require any position or force sensors, making it cost-effective and easy to manufacture. We develop an integrated forearm for the hand that provides both actuation and multimodal sensing information simultaneously. This includes the 18 joint angles, 5 fingertip positions and contact forces, and information on object softness and contour. The core principle of perception is that the camera can track the displacement and tension of all tendons simultaneously. The multimodal perception model is developed by characterizing tendon properties and coupling them with the hand dynamics. Experiments indicate that our hand has potential in multimodal sensing and dexterity.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The uncertainty of unstructured environments presents a significant challenge for robots1. To enable versatile and precise manipulation in complex environments, numerous anthropomorphic hands have been developed for applications2,3, such as human-robot interaction4,5,6,7, as well as in industrial8,9,10,11,12 and medical prosthetics13,14,15. Among these, the Shadow Hand16,17, widely used by researchers, provides unprecedented accuracy and dexterity due to its ultra-high degrees of freedom (DOF) and numerous sensors for position, pressure, torque, and temperature18.\n\nHowever, the increasing DOF in robotic systems proportionally amplify the demand for force and position sensors. This growth introduces three key challenges19: integration complexity (e.g., sensor mounting constraints, wires, and communication protocols); changes in finger dynamics; and higher costs. Therefore, achieving low-cost, human-like sensory capabilities remains a significant challenge for robotic hands. The human hand perceives information from the surrounding environment through three primary sources: sensory receptors in the skin, proprioceptive inputs from muscles and joints, and centrally-originating signals20. To reduce dependency on the number and variety of sensors, an effective approach is to use multimodal sensors that integrate proprioception and tactile sensing.\n\nWith advancements in neuroscience, information science, and new materials and sensors21, numerous sensor mechanisms have been developed to simultaneously measure proprioception (such as strain and bending) and tactile information (such as contact force). Examples include those based on conductive textile22, e-skin23,24,25, triboelectric nanogenerators (TENGs)26, liquid metal27, ionic liquid28, ionogel (printed)29, nanocomposite30,31,32, smart braid33,34,35, waveguide36,37 and heterogeneous sensing38. Among these, sensors based on optical waveguides have been integrated into soft prosthetic hands36 to perceive curvature, elongation, and tactile information. Although tactile sensing is limited to single-point pressure at the fingertips and relies on complex circuitry and wiring, it has already demonstrated the potential for multimodal perception.\n\nAnother category of multimodal sensing approaches involves vision-based tactile sensors. These sensors primarily utilize cameras to capture images of contacted objects, and subsequently leverage image recognition techniques to extract tactile information, which serves as feedback for robotic manipulation39,40,41,42,43. Representative sensors include GelForce44,45, Gelsight46, TacTip47, GelSlim48,49, which can achieve texture recognition50, grasping forces51, and temperature sensing52. However, the perceptual information from these sensors is typically limited to the fingertips and constrained by manufacturing processes and size, potentially interfering with the dynamics at the fingertips.\n\nIntegrating the drive components with multimodal sensing components could be a promising solution53,54, as this would reduce the impact (such as wiring, size, mechanism dynamics, and maintainability) of sensors on the robot\u2019s body. The drive components of DLR hand include 38 flexible antagonistic spring element (FAS) sensors used to obtain tendon tension55. Another tendon-based robotic hand56 utilizes motor rotary encoders to indirectly measure tendon length and tension. However, this approach requires the installation of sensors for each drive component, which increases the size and complexity of the drive components.\n\nThe integration of visual systems with drive components may address this challenge57,58. The vision camera offering the high resolution and low cost, can also observe all drive components in the field of view at the same time. It can therefore reduce the number of sensors on the drive components. A passive soft hand without drive components is proposed, utilizing cameras to simultaneously track markers on each tendon to obtain tendon length and tension59, which are used to estimate hand posture and external forces. In previous work60, we explored the potential of visual integration in a fully actuated finger, achieving proprioception (joint angles) and external sensing (joint torques).\n\nIn this work, we propose a low-cost, high-DOF and vision-based multimodal sensing hand (VMS Hand). It consists of an actuation-perception forearm and modular fingers that do not require any sensor installation (Fig.\u00a01a), facilitating easy manufacturing and maintenance. The actuation-perception forearm utilizes a monocular camera to achieve multimodal sensing (Fig.\u00a01b) for the dexterous hand manipulation (Fig.\u00a01c), capturing the 18 joints angles (Fig.\u00a01d), external torques (Fig.\u00a01e), positions and contact forces at 5 fingertips (Fig.\u00a01f), as well as the softness and contour of contacted objects. We conducted various experiments on position and force to evaluate its sensing capabilities and dexterity. The vision-based approach eliminates the need for traditional position/force sensors on the fingers, significantly reducing sensing complexity and cost compared to traditional robotic hands (see Supplementary Tables\u00a01, 2).\n\na The vision-based multimodal sensing (VMS) hand mechanism design. Left: Comparison between our VMS hand and the human hand (reproduced with permission from Alamy Stock Photo). The VMS hand uses a motor-tendon system to mimic the muscle (red) and tendon (white) mechanism. The VMS hand captures information with a camera and sends image to the controller, while human hand sensory signals are transmitted to the brain via nerves (yellow). Right: the VMS hand prototype, fitted with a 3D printed enclosure. The exposed fingers indicates that there are no sensors attached to the fingers. b Principle of multimodal perception. During hand movement, a camera continuously tracks the reflective markers at both ends of each tendon-connected spring, capturing changes in tendon length and tension. This data is then integrated into a multimodal model to provide information on position, force, and object properties. c Demonstration of hand grasping capabilities: precise grasp (ball) and power grasp (bottle). d Demonstration of hand perception capabilities. While performing a manipulation task (e.g., grasping a bottle), the multimodal perception model provides real-time feedback on joint angles (left), external torque (center), and fingertip contact force (right).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62122-0/MediaObjects/41467_2025_62122_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "The VMS Hand mimics the human hand\u2019s structure, comprising a forearm, palm, five fingers, and a 3D-printed enclosure, as shown in Fig.\u00a01a. The forearm integrates a motor-tendon actuation system that replicates the muscle-tendon transmission mechanism of the human hand61. Compliant force transmission is achieved via springs62, mimicking the connective tissue membranes in biological muscle-tendon systems. Inspired by neural tactile signal transmission in humans, the VMS Hand embeds a monocular camera in the forearm assembly. This camera captures real-time tendon motion images (see Fig.\u00a01b and Supplementary Movie\u00a01), which are processed by perception algorithms to extract multimodal information (e.g., contact forces, joint angles).\n\nThe VMS Hand comprises 18 DOF, with its forearm controlling hand movements through 13 active tendons (Fig.\u00a02a). The layout of the active tendons is shown in Fig.\u00a02b. The forearm integrates 13 modular actuation units and a camera to achieve actuation and perception (Fig.\u00a02c). Each actuation unit\u2019s core component is a tension spring that serves dual functions: mechanically transmitting motor power to the tendon system while simultaneously reflecting tendon tension information through its own deformation. To enable a monocular camera to observe all spring deformations, the 13 actuation units are arranged in a circular pattern. Each actuation unit is equipped with a planar mirror angled at 45 degrees relative to the spring plane (Fig.\u00a02d), allowing the camera to capture virtual images of all springs through mirror reflections.\n\na Overview of the VMS hand in which the forearm integrating 13 actuation units controls 5 fingers via 13 active tendons. The palm is connected to the forearm by four connecting rods. Motion-coupling tendons on the fingers enable synergistic movement between the proximal interphalangeal (PIP) and distal interphalangeal (DIP) joints. b Layout of the 13 active tendons in the palm. c The workflow diagram of the actuation-perception forearm mechanism. d The Schematic of an actuation unit. Motor rotation induces spring deformation \\(\\delta x\\). Since the tendon origin is fixed to Slider-A, \\(\\delta m\\) represents the tendon displacement. A brown arrow indicates the initial distance between Slider-A and slider-b. The plane mirror redirects markers into camera view (\\({{\\rm{m}}}{'}\\) and \\({{\\rm{M}}}{'}\\)). e The abduction (top) and flexion (middle) of the metacarpophalangeal (MCP) joint are controlled by 2 active tendons, and the PIP joint is controlled by 1 active tendon (bottom). f Kinematic parameters (palmar view) and reset structures (dorsal view) of the primary fingers. g The structure of the secondary fingers, which differs from the primary finger in that the MCP joint has no degrees of freedom (DOF) for abduction.\n\nTwo sliders (slider-A and slider-B) are mounted at each end of the spring, with limited movement along linear guides (see Supplementary Fig.\u00a01a). To rapidly track positional changes at the spring ends, reflective markers are installed on the slider surfaces. Due to the spring\u2019s initial length, slider-B\u2019s movement would exceed the planar mirror\u2019s effective reflection area during motion. Therefore, an additional slider-b is added to the linear guide rails and connected to slider-B via a rigid rod. Consequently, the spring\u2019s deformation \\(\\delta x=\\delta M-\\delta m\\) can be calculated as the displacement difference between slider-A and slider-b (Fig.\u00a02d). Since the tendon origin is fixed to Slider-A, the tendon length changes are represented by the displacement \\(\\delta m\\) of slider-A. Thus, during the dexterous hand\u2019s motion, a monocular camera tracks in real time the displacements of the markers \\({m}_{i},{M}_{i}(i=1,2,\\cdots 13)\\) at both ends of the springs in the 13 actuation units. These measurements are fed into the multimodal perception model, enabling real-time estimation of the hand\u2019s position and force feedback.\n\nThe five fingers are modular, each containing three joints: metacarpophalangeal (MCP), proximal interphalangeal (PIP) and distal interphalangeal (DIP). This reduces manufacturing complexity and facilitates post-maintenance. The MCP joints of the thumb, index, and middle fingers feature two DOF enabling abduction and flexion (Fig.\u00a02e), controlled by two active tendons with dual restoring springs on the dorsal palm for joint reset. The kinematic parameters of each finger are shown in Fig.\u00a02f. Although sharing the same mechanical configuration, the MCP joints of the ring and little fingers are actuated by a single tendon, retaining only flexion DOF due to their auxiliary role in grasping and spatial constraints in forearm integration10,63,64 (Fig.\u00a02g).\n\nBased on the differences in the DOF of the MCP joints, the thumb, index finger, and middle finger are described as primary fingers, while the ring finger and little finger are referred to as secondary fingers. Considering the kinematic coupling characteristics of the PIP and DIP joints of the human hand64, the DIP joint was designed as a passive joint that moves in synchronization with the PIP via a pair of parallel tendons. This pair of parallel tendons is referred to as motion-coupled tendons, similar to the function of four-bar linkage. The PIP joint is actuated by an active tendon (Fig.\u00a02f). Tension springs on the backs of the fingers provide the resetting function for the PIP and DIP joints.\n\nThere is a clear geometric relationship between the joint angles \\({{\\bf{q}}}\\) of the dexterous hand and the ideal tendon lengths \\({{\\bf{l}}}\\), as shown in Eq. (1). For a single tendon, since it undergoes elastic deformation when subjected to tension (Fig.\u00a03a), the ideal tendon length \\(l\\) can be expressed as \\(l=\\delta m-\\delta l\\). \\(\\delta m\\) represents the displacement of marker m (equivalent to the tendon input displacement), obtained by real-time tracking of the internal vision (see Supplementary Movie\u00a02). \\(\\delta l\\) denotes the elongation of the tendon itself. \\(\\delta l\\) is related to the physical properties and tension of the tendon itself, and can thus be expressed as \\(\\delta l=f(\\delta x)\\), where \\(\\delta x=\\delta M-\\delta m\\) represents the spring\u2019s deformation.\n\na The relationship between tendon input displacement \\(\\delta m\\) and output ideal length \\(l\\) during motion. b The relationship between joint angle and tendon displacement and velocity; the arrows indicate the trend of joint angle changes. c The relationship between \\(\\delta M\\) and \\(\\delta m\\) under different joint angles and contact conditions. d The results of visual recognition, where the yellow and white fonts represent the identifiers for the markers m and M on each actuation unit, respectively. The yellow line indicates the distance (\\(\\delta m\\)) between marker m and the origin. e Tension variation along the tendon transmission path; f Visual recognition results during the pressing of the force sensor by the fingertip of the ring finger. The data within the red box indicates the length change \\(\\delta m\\) of one tendon controlling the ring finger, and the deformation \\(\\delta x\\) of the series-connected spring. g Fitting results of external torque \\({{{\\boldsymbol{\\tau }}}}_{ext}\\) and increment of spring deformation \\(\\delta {x}_{c}\\) under different joint configurations.\n\nAn angle calibration platform (see Supplementary Fig.\u00a02a) has been built to evaluate the tendon elongation. However, since the spring does not exhibit a significant change in length until the initial tension is exceeded (see Supplementary Fig.\u00a02b, c), it becomes challenging to determine the elongation \\(\\delta l\\) by solving the \\(f(\\delta x)\\). Figure\u00a03a shows that the mapping relationship between \\(\\delta m\\) and \\(l\\) differs during the phases of increasing and decreasing tendon displacement. To estimate joint angle from tendon displacement \\(\\delta m\\), tendon velocity \\(\\delta \\dot{m}\\) is used to distinguish the direction of tendon movement. The responses of joint angle relative to tendon displacement and tendon velocity are shown in Fig.\u00a03b. Based on Eq. (1), the ideal tendon length \\(l\\) is geometrically related to the joint angle. Therefore, \\(l\\) can be expressed as:\n\nBy performing polynomial fitting on the ideal tendon length \\(l\\) and \\((\\delta m,\\delta \\dot{m})\\), we obtained fitting function \\(\\varGamma (\\cdot )\\). Substituting Eq. (2) into Eq. (1) yields the relationship between the finger joint angles \\({{\\bf{q}}}\\) and \\((\\delta {{\\bf{m}}},\\delta \\dot{{{\\bf{m}}}})\\), as shown in Eq. (3). The fingertip position can be obtained from the forward kinematics model of the finger.\n\nAnother additional exploration focused on the impact of finger-environment contact on tendon characteristics, as this would help the dexterous hand rely solely on internal vision to determine contact. Typically, the tendon tension will differ when the finger reaches the same joint configuration under non-contact and external force conditions, meaning that the same \\(\\delta m\\) corresponds to different spring deformations \\(\\delta x\\). However, when the deformation of the spring is within the dead zone (Supplementary Fig.\u00a02b), the system cannot accurately determine contact. Fortunately, we found that the displacement \\(\\delta M\\) of the marker at the slider-b of the spring consistently showed significant movement. Therefore, \\(\\delta x\\) can be replaced by \\(\\delta M\\) and apply the same approach to detect contact, can be expressed as:\n\nwhere \\(F(\\cdot )\\) represents the mapping function from \\(\\delta m\\) to \\(\\delta M\\) in a non-contact state. \\(\\delta \\hat{M}\\) is the predicted displacement of the marker M in the non-contact state based on \\(\\delta m\\). Figure\u00a03c illustrates the relationship between \\(\\delta M\\) and \\(\\delta m\\) at different joint configurations and contact conditions. The straight segments in the figure represent the mapping relationship between \\(\\delta M\\) and \\(\\delta m\\) in the non-contact state, while the three inflection points indicate instances of contact. Since the displacements of all markers (m and M) can be output by vision in real time (Fig.\u00a03d), the contact states of the different fingers can be obtained according to Eq. (4).\n\nIn an ideal scenario, the relationship between fingertip force, joint torque, and tendon tension in the dexterous hand can be analyzed using classical robotic dynamics. Since the tendon is in series with the stretching spring, the input tension of the tendon can be indirectly measured through the spring deformation. However, frictional losses are inevitably present in the tendon transmission path, necessitating a quantitative analysis of these losses to determine the output tension at the tendon end.\n\nFor a joint controlled by an active tendon, the analysis of the tension transmission process is shown in Fig.\u00a03e. When the fingertip makes contact with the environment, the torque \\({\\tau }_{l}^{i}\\) exerted by the i-th active tendon to resist the external torque can be expressed as Eq. (5). Detailed derivation can be found in Supplementary Method 1.\n\nWhere \\({t}_{c}\\) represents the moment at the instant of contact; \\(\\delta x(t)\\) represents the i-th spring deformation, derived indirectly via visual tracking of markers displacement(see Fig.\u00a03f and Supplementary Movie\u00a03); \\(\\delta {x}_{c}^{i}\\) represents the deformation increment of the i-th spring after contact; \\({K}_{i}=\\psi (q,\\mu )\\) is the equivalent stiffness coefficient of the i-th series tendon spring, which is related to the joint angle \\(q\\) and friction coefficient \\(\\mu\\) at the moment of contact.\n\nBased on Eq. (5) and the finger dynamics, the external torque \\({{{\\boldsymbol{\\tau }}}}_{ext}\\) at each finger joint can be obtained, as shown in Eq. (6). Figure\u00a03g illustrates the variation of external torque \\({{{\\boldsymbol{\\tau }}}}_{ext}\\) with increment of spring deformation \\(\\delta {x}_{c}\\) under varying joint angle. The fingertip contact force \\({{{\\bf{F}}}}_{ext}={({{{\\bf{J}}}}^{T})}^{{{\\boldsymbol{+}}}}{{{\\boldsymbol{\\tau }}}}_{ext}\\), where \\({({{{\\bf{J}}}}^{T})}^{{{\\boldsymbol{+}}}}\\) is the generalized inverse of the Jacobian matrix transpose.\n\nThe finger joint angles can be calculated from the marker displacement \\(\\delta m\\) measured by the forearm-mounted camera using Eq. (3). We define the joint numbering as \\({q}_{ij}\\), where \\(i=1,2,\\ldots 5\\) sequentially represents the thumb, index, middle, ring, and little finger; For primary fingers (e.g., thumb), \\(j=1,2,3,4\\) corresponds to the MCP abduction joint, flexion joint, PIP joint, and DIP joint respectively; For secondary fingers, \\(j=1,2,3\\) represents the MCP flexion joint, PIP joint, and DIP joint.\n\nThe accuracy of position perception was evaluated by 12 repetitive joint motion experiments. The camera-estimated angles \\(\\hat{{{\\bf{q}}}}\\) and encoder-measured ground truth \\({{\\bf{q}}}\\) were synchronously recorded (see Fig.\u00a04a and Supplementary Fig.\u00a03). Results showed mean absolute errors of 1.14\u00b0, 1.04\u00b0, and 0.95\u00b0 for the MCP abduction joint, flexion joint and PIP joint, respectively. Since the MCP abductor joint is actuated differentially by two tendons, small variations in the differential tendon lengths are amplified into larger angular deviations. Consequently, the prediction errors are further magnified. Variations in accuracy among the different joints may arise from factors such as mechanical dimensional tolerances incurred during manufacturing and calibration inaccuracies.\n\na Comparison of the internal perception joint angles \\(\\hat{{{\\bf{q}}}}\\) and the actual joint angles \\({{\\bf{q}}}\\) of the VMS Hand. b Joint angle tracking response under multi-step reference signal input. c Anti-disturbance experiment during finger motion, with orange arrows indicating external disturbance directions. d The visual recognition results during the tennis ball grasping. e The time-varying joint angle profiles during the grasping task. f The three-dimensional trajectories of five fingertips throughout the grasping process. g The grasp taxonomy with all 33 standard modes.\n\nThe dexterous hand achieves closed-loop position control by real-time acquisition of joint angle feedback signals integrated with position control algorithms. As the MCP abduction and flexion joints are actuated by two coordinated tendons, the desired joint angles \\({{{\\bf{q}}}}_{d}\\) must be converted into corresponding tendon length variation \\({{{\\bf{l}}}}_{d}\\) to establish a decoupled joint control model. For a given set of desired joint angles \\({{{\\bf{q}}}}_{d}(t)\\), the corresponding desired tendon length \\({{{\\bf{l}}}}_{d}(t)\\) change can be computed using Eq. (1). Subsequently, a delay-compensated control input \\({{\\bf{u}}}(t)\\) is constructed, as shown in Eq. (7).\n\nwhere \\({{{\\bf{K}}}}_{f}\\) is the feedforward position gain, \\({{{\\bf{K}}}}_{p}\\) and \\({{{\\bf{K}}}}_{d}\\) are the feedback position gain and derivative gain, respectively. \\(\\varsigma\\) represents the system time delay calibrated via frequency response analysis or step response experiments (see Supplementary Fig.\u00a04)\n\nTo validate the tracking performance and robustness of the proposed control system, we conducted stepped reference trajectory tracking experiments and external disturbance tests (see Supplementary Movie\u00a04). The reference trajectory was designed as a multi-step signal with 5\u00b0 increments at 1-second intervals. The angle tracking performance is shown in Fig.\u00a04b; with the addition of the feedforward term, the tracking error decreased by 34.3% compared to the case without feedforward control (see Supplementary Fig.\u00a05). To evaluate the anti-interference capability of system, six external force perturbations (three downward/three upward) were applied during fingertip motion (Fig.\u00a04c). The experimental results demonstrated that the perception system could detect the position changes caused by the external disturbances. The controller promptly adjusted and restored motion to the preset target position after force removal, demonstrating the robustness and stability of the adopted control scheme.\n\nTo assess the dexterous hand\u2019s ability to synergistically perceive angle and position, we conducted experiments with grasping a tennis ball. During the experiment, a camera on the forearm tracked the displacement of all markers in real time (Fig.\u00a04d), enabling simultaneous monitoring of the joint angles (Fig.\u00a04e) and the five fingertips positions (Fig.\u00a04f). The dexterity of the VMS hand was evaluated in the standardized Feix GRASP taxonomy65 test (see Supplementary Movie\u00a05). The VMS hand successfully implemented 33 grasping modes (Fig.\u00a04g), including precise grasping operations requiring fingertip coordination, such as pen holding, egg pinching, and chopstick manipulation. All grasping tasks were stably executed via feedforward-feedback closed-loop control based on predefined joint angle configurations. Experimental results demonstrating the system\u2019s adaptability in multi-scenario grasping applications.\n\nRelying only on the information provided by the internal vision of the forearm, the VMS hand can detect contact and also provide real-time feedback on the external torques at the joints and external forces at the fingertips. To evaluate the external perception capabilities of the hand, we conducted three parts of experiments: contact detection, contact force evaluation, and object rotation experiments.\n\nThe plate was positioned above a six-dimensional force sensor, enabling the sensor to reflect changes in force during fingertip contact with the plate. Simultaneously, the actuation-perception forearm transmits \\(\\delta M\\) and \\(\\delta m\\) in real time to the tendon contact model (see Eq. (4)) to detect contact occurrence (Fig.\u00a05a). This data can be compared with the force sensor measurements to validate the effectiveness of contact detection.\n\na Experimental results of contact detection for the ring finger; b Experimental results of contact detection for the four fingers and comparison with force sensor measurements; c External force test experimental platform (top), and visual recognition images during little finger pressure sensor measurement (bottom). d Comparison of the internal perception external torques \\({\\hat{{{\\boldsymbol{\\tau }}}}}_{ext}\\) and the actual external torques \\({{{\\boldsymbol{\\tau }}}}_{ext}\\) of the little finger; e Comparison of the internal perception external forces \\({\\hat{{{\\bf{F}}}}}_{ext}\\) and the actual external forces \\({{{\\bf{F}}}}_{ext}\\) of the little finger; f Visual tracking images during object manipulation. g Fingertip contact force variations during in-hand object rotation. h External torque variations across joints during object manipulation.\n\nThe fingertips of the index, middle, ring, and little fingers were sequentially controlled to press the plate and then return to their initial positions (see Supplementary Movie\u00a06). The detection results and the force sensor\u2019s response curve are shown in Fig.\u00a05b. The results indicate that the normal force from the sensor gradually increased upon fingertip contact with the tray and decreased as the fingertips returned to the initial position. During this process, our measurement system also detected fingertip contact in real time, consistent with the trends observed in the force sensor data.\n\nIn the characterization of tendon transmission, we calibrated the friction coefficient \\(\\mu\\) and \\({K}_{1}\\) for each tendon during the transmission process. In this experiment, we controlled the little finger to press the six-dimensional force sensor under different joint configurations to evaluate the measurement capabilities of external torques and contact forces (see Supplementary Movie\u00a07). The experimental setup was shown in Fig.\u00a05c. During the experiment, the six-dimensional force sensor provided real-time outputs of the external force\u00a0\\({{{\\bf{F}}}}_{ext}^{6{{\\rm{x}}}1}\\) applied by the fingertip. The \\(\\delta m\\) output from the actuation-perception forearm was used to measure the joint configuration \\({{\\bf{q}}}\\) and calculate the Jacobian matrix \\({{\\bf{J}}}\\), while \\(\\delta x\\) was substituted into Eq. (6) to measure the joint external torque \\({\\hat{{{\\boldsymbol{\\tau }}}}}_{ext}\\) and the fingertip contact force \\({\\hat{{{\\bf{F}}}}}_{ext}={({{{\\bf{J}}}}^{T})}^{-1}{\\hat{{{\\boldsymbol{\\tau }}}}}_{ext}\\). The actual external torque is given by \\({{{\\boldsymbol{\\tau }}}}_{ext}={{{\\bf{J}}}}^{T}{{{\\bf{F}}}}_{ext}\\).\n\nThe relationship between external torque and fingertip contact force with the increment of spring deformation after contact for different joint configurations is shown in Fig.\u00a05d and Fig.\u00a05e, respectively. Experimental results indicate that our measurement model effectively characterizes the transmission properties of tendons at various contact angles. The maximum error in the normal contact force during the experiment reached 0.49\u2009N. The errors in contact force may arise from angle inaccuracies, internal visual recognition errors, and calibration errors in the friction coefficient. The actuation-perception base can output not only the normal force \\({F}_{z}\\) but also the tangential force \\({F}_{x}\\), which aids in slip detection during grasping tasks.\n\nThe position sensing capability of the dexterous hand during grasping ((Fig.\u00a04e, f) and its flexibility (Fig.\u00a04g) have been demonstrated. To further showcase the hand\u2019s external force sensing ability, an in-hand object rotation experiment was designed (see Supplementary Movie\u00a08). First, a stable grasp of the object is achieved using a preset grasping configuration. Then the tension of the ring and little finger tendons were increased to enhance fingertip contact pressure and induce object rotation. Figure\u00a05f illustrates the visual-tracked spring deformation variations during object manipulation. The dynamic profiles of fingertip output forces and joint external torque for the ring and little fingers are shown in Fig.\u00a05g and Fig.\u00a05h, respectively. Experimental results demonstrate that the vision-based multimodal perception scheme can achieve real-time monitoring of joint torque and fingertip contact force changes. With this information, the hand has the potential to perform various complex tasks, including object manipulation, in-hand repositioning, multi-finger coordination, force control, haptic feedback integration, adaptive grasping, and tool use.\n\nTactile feedback allows robotic hands to assess the physical properties of objects, including softness and surface texture, facilitating the adjustment of grasping force and posture to optimize manipulation task performance. To validate the active tactile sensing capability of the VMS hand, experiments for softness detection and contour recognition were designed.\n\nThe robotic hand quantifies object softness by measuring differences in compression displacement. Its principle is that when the fingertip applies the same force to surfaces of varying softness, the surfaces produce different compression displacements due to differences in material compliance. When a fingertip is controlled by a single tendon, the time point of contact detection is defined as \\({t}_{c}\\). We define the force applied by the fingertip as positively correlated with the deformation of the spring after contact, represented as \\(\\delta {x}_{c}=\\delta x-\\delta x({t}_{c})\\). Additionally, the compression displacement of the fingertip is positively correlated with the displacement of the tendon after contact, denoted as \\(\\delta {m}_{c}=\\delta m-\\delta m({t}_{c})\\). Thus, the softness of the object can be defined as \\({K}_{obj}=\\delta {x}_{c}/\\delta {m}_{c}\\).\n\nA validation experiment was conducted using three representative materials (wood, foam, and sponge) to evaluate tactile softness perception (see Supplementary Movie\u00a09). During the experiment, pressure was applied to the materials by independently adjusting the tendon tension of the middle finger PIP joint, while maintaining the same pressing speed for all materials. The states of the middle finger pressing the surfaces of the wood block, foam block, and sponge block are shown in Fig.\u00a06a. The vision system within the actuation-perception forearm continuously provides real-time feedback on \\(\\delta x\\) and \\(\\delta m\\), with the recognition results shown in Fig.\u00a06b. Based on \\(\\delta m\\), \\(\\delta x\\) and the contact moment \\({t}_{c}\\), the relationship between \\(\\delta {x}_{c}\\) and \\(\\delta {m}_{c}\\) can be obtained, as shown in Fig.\u00a06c. The slope of this curve reflects the softness\u00a0\\({K}_{obj}\\). As expected, the experimental data indicate a decreasing order of softness: wood block, foam block, and sponge block. It is noteworthy that since the fingertip shell itself deforms under pressure, employing shell materials with higher Shore hardness may improve softness detection performance (see Supplementary Fig.\u00a06).\n\na The fingertip of the middle finger presses against the states of wood, foam, and sponge; b The visual recognition images and output displacement information (\\(\\delta x\\) and \\(\\delta m\\)), when the fingertip presses against different objects; c The curves of \\(\\delta {x}_{c}\\) and \\(\\delta {m}_{c}\\) during the pressing of different objects, where the slope reflects the softness of the target; d Detection of the contours of objects at different heights (above) and the scanning results (below); e Detection of the contour of a stapler (above) and the scanning results (below); f Detection of the contour of a mouse (above) and the scanning results (below).\n\nWhen external visual and laser radar devices reconstruct the shape of objects, they may encounter occlusions. Using tactile devices to touch occluded surfaces could address this challenge. Here, since the actuation-perception forearm can provide contact force detection and fingertip position information, we utilize the fingertips as tactile sensors to identify the object contours. The coordinate systems of the robotic arm and hand are unified into the world coordinate system (Fig.\u00a06d). This ensures that when the robotic arm moves horizontally (along the negative z-axis) to scan the object surface, the fingertip position is referenced to the world coordinate system rather than the hand base.\n\nThe object surface was positioned directly beneath the palm to ensure contact detection during finger flexion (see Supplementary Movie\u00a010). Initial contact triggers spatial registration between fingertip coordinates and surface contact points. Upon contact detection, the 3D position was recorded, followed by controlled negative z-axis arm motion for continuous contour mapping. Using this approach, we tested the fingers\u2019 ability to recognize surface contours of step heights (Fig.\u00a06d). The VMS hand could also distinguish the shapes of irregular objects, such as stapler (Fig.\u00a06e) and computer mouse (Fig.\u00a06f). While the accuracy and sensitivity of the VMS hand remain inferior to human hand capabilities, it has already demonstrated potential in shape reconstruction.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62122-0/MediaObjects/41467_2025_62122_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62122-0/MediaObjects/41467_2025_62122_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62122-0/MediaObjects/41467_2025_62122_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62122-0/MediaObjects/41467_2025_62122_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62122-0/MediaObjects/41467_2025_62122_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this paper, we propose the VMS hand that offers low cost, multimodal perception, and dexterity. The fingers are modular and do not require the installation of any position sensors or expensive force sensors. Therefore, they are easy to manufacture and maintain, making them suitable for harsh environments (such as tasks involving high electromagnetic interference or grasping sharp objects). Due to the hand\u2019s cost-effectiveness (see Supplementary Table\u00a01), we believe it will have a wide range of applications, such as in industrial humanoid robots and prosthetic hands.\n\nThe proposed vision-based multimodal sensing scheme has been validated through a series of positional, external force, and tactile experiments. It demonstrates two core advantages: First, the sensing cost is cost effective, accounting for only 6% of the total system cost (see Supplementary Table\u00a02). The cost advantage becomes more pronounced as the number of DOF increases. Second, it simplifies manufacturing and maintenance processes. Compared to flexible electronic sensing requiring sophisticated fabrication techniques, the core components of this vision-based solution are easily accessible and assembled using common materials such as springs, tendons, planar mirrors, and a camera. Moreover, the vision-based sensing scheme can be adapted to other mechanisms, including tendon-driven robotic arms and cranes.\n\nWhile the VMS hand demonstrates significant potential in terms of cost and sensing capabilities, there remain areas that require further enhancement. First, the flexibility could be enhanced by increasing the DOF of the wrist. The actuation and sensing components for the wrist could be integrated into the existing forearm, requiring only adjustments to size and camera field of view. Second, the selection of camera and actuator has a significant impact on forearm size. Parameters including the lens\u2019s minimum working distance, camera resolution, and frame rate influence forearm size and weight. Meanwhile, the current servo motors\u2019 bulky size leads to excessive actuator layout space consumption. Adopting smaller brushless DC motors could improve forearm compactness.\n\nThe spatial separation between actuators and sensors introduces non-collocation challenges. While the feedforward control law mitigates this issue for predefined trajectory tracking tasks, advanced control strategies (e.g., adaptive control66) should be explored to enable dexterous manipulation in complex scenarios. Furthermore, the current sensing accuracy remains inferior to specialized sensors. Integrating precise physical models with machine learning techniques or enhancing calibration methods (e.g., self-calibration67) could further improve perception accuracy.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Our objective is to develop a dexterous hand system that combines multimodal perception with low cost, demonstrating the feasibility of a vision-based integrated actuation-perception approach. Due to the high resolution and wide field of view of the vision camera, the advantages of the integrated actuation-perception scheme become more pronounced as the robot\u2019s DOF increase. We designed an actuation unit composed of commonly used materials such as motor, springs, reflective markers, and planar mirrors, making it easy to manufacture and maintain. Thirteen actuation units are arranged in a circular array, with a low-cost industrial camera mounted on the base of the actuation units to capture the deformation of the springs across all units. The dexterous hand is designed to be tendon-driven, allowing for high flexibility and intrinsic compliance controlled by the actuation units. Thorough exploration of the spring deformation data helps us gather valuable information about the dexterous hand, such as joint angles and fingertip forces.\n\nThe actuation unit of the finger is designed to be modular, consisting of a planar mirror bracket, planar mirror, linear guides, bearing carriage, springs, tendons, wire spools (radius\u2009=\u200910\u2009mm), servo motor, and mounting plate, as shown in Supplementary Fig.\u00a01a. To minimize the weight of the module, we adopted a hollow design for the mounting plate, retaining only the necessary components to secure the servo motor, linear guides, and planar mirror bracket. Two linear guides are fixed to the mounting plate by a guide rod support, allowing the springs to stretch axially. The servo motor drives the rotation of the wire spools, which in turn stretches the tendons and the springs. The ends of the springs are secured to two sliders, with reflective circular markers mounted on their surfaces for camera recognition. The planar mirror is mounted on a bracket that is inclined at a 45-degree angle to the mounting plate, ensuring that the virtual image of the reflective markers in the mirror forms a 90-degree angle with the mounting plate.\n\nTo reduce the overall size of the forearm part, we arranged the 14 actuation units in a circular pattern and fixed them to a circular base (Supplementary Fig.\u00a01b). The 14th actuation unit is reserved for potential future use to add DOF. It will serve as a backup for future enhancements in DOF. We designed a circular PCB that facilitates 16-channel PWM output and manages the power supply for the entire system. A mini-industrial camera (201 fps at 1280\u2009\u00d7\u20091024 Mono 8, 1.5\u2009W at 5 VDC) is positioned at the center of the circular circuit board to track the reflective markers on the actuation units. The parameters of the camera and lens are shown in Supplementary Table\u00a03. A circular LED light source is fixed to the mounting plate for illumination.\n\nThe circular PCB is secured to the mounting plate of the actuation units using screws. A cooling fan is mounted on the upper circular base to dissipate heat from the camera and circuit board. To prevent interference from external light fluctuations during recognition, a 3D-printed circular enclosure is installed on the forearm, with ventilation holes designed to ensure heat dissipation.\n\nEach finger is modular, featuring identical structural designs as illustrated in Fig.\u00a02b. After assembling the fingers, one end of the tendon is anchored to the joint, then routed through the pulley system on the palm to connect with the forearm. The material properties of the tendon are shown in Supplementary Table\u00a04. To minimize losses during the tendon transmission process, ball bearings are installed on each pulley. To avoid coupling between the MCP and PIP joints, the tendon controlling the motion of the PIP joint is routed through the axis of the MCP joint and ultimately connected to the actuation unit. Except for the restoring spring in the MCP joint of the thumb, the restoring springs for all other joints are mounted on the dorsal side of the palm and fingers. The bottom of the restoring springs is secured to the palm with a rectangular base, which has two screws to adjust the pre-tension of the return springs. The palm of the dexterous hand is fixed to the upper surface of the arm using four aluminum alloy rods. Both the palm and finger components are machined from aluminum alloy to ensure structural durability, while the enclosures of the forearm and finger adopt 3D-printed components (black resin) for cost efficiency and weight reduction.\n\nThe basic principle of camera recognition for circular reflective markers is contour detection. This is achieved by detecting changes in the gradient of image grayscale values to extract variations in the center pixel. We utilized OpenCV to implement this fundamental function. The real challenge lies in quickly tracking the pixel changes of 26 reflective markers, which is crucial for real-time control. To address this, we adopted an image segmentation and multithreaded concurrent processing approach, dividing the camera image into four rectangular regions and performing contour detection in each of the four threads. Finally, the pixel coordinates of all the markers are output in sequence to the controller.\n\nTypically, a monocular camera can only output two-dimensional pixel coordinates and pixel distances. To obtain physical distances, we place all detected targets on the same plane, allowing us to calibrate the scaling factor between pixel distance and actual distance. First, before mounting the camera to the forearm, we perform intrinsic calibration using Zhang\u2019s checkerboard method to correct image distortion.\n\nSubsequently, the camera is fixed to the mounting plate inside the forearm, ensuring its optical axis remains parallel to the spring plane. We adjust the optical path using a planar mirror to ensure that the virtual image plane of all reflective markers is perpendicular to the camera\u2019s optical axis. Since the actuation units are arranged in a circular layout and the mirror brackets are uniformly installed, the virtual images remain coplanar, establishing a fixed proportional relationship between single-pixel distance and real-world distance. Finally, since slider B and slider b are rigidly connected by a rod, the actual distance between them is known and can be used to calculate the scaling factor K (K = actual distance / pixel distance), thereby completing the calibration.\n\nWe installed angle encoders (AS5600, 12-bit) on the joints of the fingers and obtained the actual angle through PWM sampling at 100\u2009Hz (see Supplementary Fig.\u00a03a), which was then sent to the dexterous hand\u2019s controller through the Serial protocol. The actual angle can be converted to the tendon output displacement \\(l\\) by Eq. (1). Note that the angle encoders will be removed once tendon calibration is complete. The tendon input displacement \\(\\delta m\\) is provided by feedback from the actuation-perception forearm at a frequency of 150\u2009Hz. By controlling the position of the servo motor, the joint angle is rotated to its maximum angle and then returned to the starting position, with tendon displacement \\(\\delta m\\) and joint angles recorded in real-time. The tendon displacement \\(\\delta m\\) was filtered using a Butterworth low-pass filter (cut-off frequency: 50\u2009Hz), and tendon velocity \\(\\delta \\dot{m}\\) was derived through differentiation of displacement followed by moving average filtering (window length: 35). To establish a mapping model \\(\\varGamma (\\cdot )\\) between joint angles and tendon parameters, the linear polynomial regression model was employed for functional fitting.\n\nIn order to obtain the relationship between the marker \\(m\\) and \\(M\\) during free motion, we control the joint angle of the hand from 0 to the maximum angle and record the displacement \\((\\delta m,\\delta M)\\) during the motion. The mapping function \\(F(\\cdot )\\) from \\(m\\) and \\(M\\) is obtained through cubic polynomial fitting.\n\nWe developed a force measurement platform (Supplementary Fig.\u00a07a), where a six-dimensional force sensor (K6D40, 50\u2009N) is mounted on the surface of the platform. The specific parameters of the force transducer are shown in the Supplementary Table\u00a05. The height of the platform can be adjusted using a knob, allowing us to configure the fingers to apply pressure to the force sensor at different joint configurations. The external force \\({{{\\bf{F}}}}_{ext}\\) applied by the fingertip is provided by feedback from the force sensor, while the camera outputs the spring deformation \\(\\delta x\\) in real time. The signals from the six-dimensional force sensor are converted to digital values using a high-precision digital amplifier (GSV-8) and are ultimately transmitted to the dexterous hand\u2019s controller via a Serial protocol at a frequency of 500\u2009Hz. The joint angle is calculated by substituting the displacement \\(\\delta m\\) from the base into the position measurement model, as shown in (3). For each joint controlled by active tendons, control the fingertip to press the six-dimensional force sensor in five different joint configurations, and record the spring deformation \\(\\delta x\\), joint angle \\(q\\), and external torque \\({{{\\boldsymbol{\\tau }}}}_{ext}={{{\\bf{J}}}}^{T}{{{\\bf{F}}}}_{ext}\\) during the experiment (Supplementary Fig.\u00a07b). The gray wolf optimization algorithm is used to fit the multi-objective optimization function \\(\\psi (\\cdot )\\).\n\nThe controller of the dexterous hand system utilizes an NVIDIA Xavier NX board to run the visual detection and motion control programs. The only input source is a monocular camera, connected to the controller via USB 3.0 for communication and power supply. The motion control program sends commands to the circular PWM circuit board through a serial port with a baud rate of 115200, operating at a communication frequency of 100\u2009Hz. Upon receiving the commands, the PWM circuit board outputs 13 PWM signals to the servos. The camera can achieve a frame rate of 150 fps in RGB image output mode, and the optimized visual processing program allows for real-time processing of each frame.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data supporting the findings of this study are available within the article and its supplementary files.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All the relevant codes can be directed to, and will be fulfilled by, the corresponding authors.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Cui, J. & Trinkle, J. Toward next-generation learned robot manipulation. Sci. Robot. 6, eabd9461 (2021).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nBillard, A. & Kragic, D. Trends and challenges in robot manipulation. 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Zhang (HMU) for contributing expertise in human hand anatomy and providing suggestions for the paper illustrations.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "School of Aerospace, Harbin Institute of Technology, Harbin, China\n\nShiwei Chen,\u00a0Jiapeng Li,\u00a0Zhiming Deng,\u00a0Peiji Wang,\u00a0Cheng Wei\u00a0&\u00a0Xibin Cao\n\nState Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster, Harbin, China\n\nShiwei Chen,\u00a0Jiapeng Li,\u00a0Zhiming Deng,\u00a0Peiji Wang,\u00a0Cheng Wei\u00a0&\u00a0Xibin Cao\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.C. fabricated the dexterous hand, designed the experiments, conducted the experiments, analyzed the experimental data, and wrote the manuscript; J.L. conducted the experiments, processed the data, edited the manuscript; Z.D. developed the visual recognition program and conducted the experiments; P.W. participated in the design and fabrication of the dexterous hand, edited the manuscript; C.W. proposed the concept, guided the experiments, and edited the manuscript. X.C. guided the theoretical research, supported the experimental validation, and reviewed the manuscript.\n\nCorrespondence to\n Cheng Wei or Xibin Cao.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "S.C., P.W., and C.W. are inventors of an invention disclosure of the patent filed by the Harbin Institute of Technology (ZL 202311262707.5, granted on 17 January 2025) related to multimodal perception principles in this work. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Nathan Lepora, Zhuang Zhang, and Jung Kim for their contribution to the peer review of this work. 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Interaction Prediction", + "journal": "Nature Communications", + "published": "26 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62235-6/MediaObjects/41467_2025_62235_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62235-6/MediaObjects/41467_2025_62235_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62235-6/MediaObjects/41467_2025_62235_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62235-6/MediaObjects/41467_2025_62235_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://github.com/zhaoyanpeng208/EviDTI/tree/main/dataset", + "https://zenodo.org/records/14056305", + "https://github.com/zhaoyanpeng208/EviDTI/tree/main/dataset/len_v_s_dataset", + "https://zenodo.org/records/14056305", + "https://github.com/zhaoyanpeng208/EviDTI/tree/main/dataset/len_case_dataset", + "https://zenodo.org/records/14056305", + "https://doi.org/10.6084/m9.figshare.28816634", + "/articles/s41467-025-62235-6#Sec33" + ], + "code": [ + "https://github.com/zhaoyanpeng208/EviDTI", + "https://zenodo.org/records/15760471", + "/articles/s41467-025-62235-6#ref-CR83", + "https://github.com/agemagician/ProtTrans", + "https://numpy.org/", + "https://pandas.pydata.org/", + "https://seaborn.pydata.org/", + "https://matplotlib.org/", + "https://www.pymol.org/" + ], + "subject": [ + "Computational chemistry", + "Machine learning", + "Virtual drug screening" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5374284/v1.pdf?c=1753614454000", + "research_square_link": "https://www.researchsquare.com//article/rs-5374284/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-62235-6.pdf", + "preprint_posted": "12 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Drug-target interaction (DTI) prediction is a crucial component of drug discovery. Recent deep learning methods show great potential in this field but also encounter substantial challenges. These include generating reliable confidence estimates for predictions, enhancing robustness when handling novel, unseen DTIs, and mitigating the tendency toward overconfident and incorrect predictions. To solve these problems, we propose EviDTI, a novel approach utilizing evidential deep learning (EDL) for uncertainty quantification in neural network-based DTI prediction. EviDTI integrates multiple data dimensions, including drug 2D topological graphs and 3D spatial structures, and target sequence features. Through EDL, EviDTI provides uncertainty estimates for its predictions. Experimental results on three benchmark datasets demonstrated the competitiveness of EviDTI against five state-of-the-art baseline models. In addition, our study shows that EviDTI can calibrate prediction errors. More importantly, well-calibrated uncertainty information enhanced the efficiency of drug discovery by prioritizing DTIs with higher confident predictions for experimental validation. In a case study focused on tyrosine kinase inhibitors (TKIs), uncertainty-guided predictions identified novel potential inhibitors targeting FAK and FLT3. These results underscore the potential of evidential deep learning as a robust tool for uncertainty quantification in DTI prediction and its broader implications for accelerating drug discovery.Biological sciences/Computational biology and bioinformatics/Machine learningBiological sciences/Computational biology and bioinformatics/Virtual drug screening", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "EviDTISupplementalinformation.docx", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Drug-target interaction (DTI) prediction is a crucial component of drug discovery. Recent deep learning methods show great potential in this field but also encounter substantial challenges. These include generating reliable confidence estimates for predictions, enhancing robustness when handling novel, unseen DTIs, and mitigating the tendency toward overconfident and incorrect predictions. To solve these problems, we propose EviDTI, a novel approach utilizing evidential deep learning (EDL) for uncertainty quantification in neural network-based DTI prediction. EviDTI integrates multiple data dimensions, including drug 2D topological graphs and 3D spatial structures, and target sequence features. Through EDL, EviDTI provides uncertainty estimates for its predictions. Experimental results on three benchmark datasets demonstrate the competitiveness of EviDTI against 11 baseline models. In addition, our study shows that EviDTI can calibrate prediction errors. More importantly, well-calibrated uncertainty information enhances the efficiency of drug discovery by prioritizing DTIs with higher confident predictions for experimental validation. In a case study focused on tyrosine kinase modulators, uncertainty-guided predictions identify novel potential modulators targeting tyrosine kinase FAK and FLT3. These results underscore the potential of evidential deep learning as a robust tool for uncertainty quantification in DTI prediction and its broader implications for accelerating drug discovery.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Drug discovery is the process of discovering drugs that can treat diseases and improve human health. It involves multiple steps1,2, including target identification, compound screening, and lead optimization. Identifying drug-target interactions (DTI) plays a crucial role in compound screening3,4. Although traditional biomedical measurements obtained from in vitro experiments are reliable, they come with significant drawbacks, such as high costs and lengthy development cycles, which significantly limit the pace of drug development5.\n\nIn silico methods for predicting DTIs, particularly deep learning (DL) techniques, have received much attention for their potential to reduce drug development costs, shorten time and increase the success rate of new drugs6,7,8,9. These methods can be broadly classified into network-based methods and proteochemometrics (PCM)10. Network-based methods integrate drug-target, drug-drug, and protein-protein interactions, etc. multiple networks into a unified network10,11,12.\n\nRecently, PCM methods have received increasing attention. PCM employs representations of drug and protein information to improve the accuracy of DTI predictions, and its performance largely dependent on the effectiveness of molecular and protein representations13,14. Typically, amino acid sequences of protein are typically used as input for proteins, while molecular graph and SMILES strings are commonly used as input for drugs. To obtain representation of proteins and drugs, convolutional neural networks (CNNs)15, recurrent neural networks (RNNs), graph neural networks (GNNs)16,17,18 and transformer models19,20 are typically applied. A great deal of innovative work has been undertaken to predict interactions more effectively14.To improve model interpretability and capture local interactions of drug-target, the gated cross-attention mechanisms have received more attention21,22,23,24. To address the problem of small dataset sizes and incomplete representations in DTI prediction, pre-training models are emerging as a promising solution25,26,27. These models demonstrate excellent scalability and generalization capabilities across a wide range of prediction tasks28,29,30,31,32. To achieve more comprehensive and nuanced representations, many methods integrate multimodal techniques that combine different types of data33,34.\n\nDespite the significant advances achieved by DL in DTI prediction, its practical application still faces a major challenge: high probability predictions do not necessarily correspond to high confidence35,36. The root of this problem lies in the fundamental difference between DL models and human cognitive models37. Humans are able to dynamically adjust the confidence level according to the knowledge boundary, giving certain answers to familiar questions and explicitly expressing \u201cuncertainty\u201d about unknown domains. In contrast, traditional DL models generate predictions for all inputs, including out-of-distribution and noisy samples. More critically, traditional DL models lack probability calibration ability and may produce high prediction probabilities even in low confidence situations. This phenomenon of \u201coverconfidence\u201d has the tendency to introduce unreliable predictions into downstream processes, including the pushing of false positives into experimental validation, the omission of potentially active compounds in virtual screening, and even the designing of clinical trial protocols based on false predictions. These situations not only lead to inefficient use of resources, but also have the potential to delay the drug discovery process.\n\nUncertainty quantification (UQ) methods can address above challenge and thus improve the robustness of neural models in scientific applications38,39,40,41,42,43,44, especially in the field of drug discovery. The core value of UQ is to provide a reliable basis for decision-making by distinguishing between plausible predictions and high-risk predictions. Typically UQ45,46 methods include Bayesian neural networks47 and sampling-based48,49 methods. However, these methods typically rely on multiple random sampling to approximate the underlying uncertainty function50,51,52, resulting in high computational costs and extended runtimes. This poses a significant limitation for large-scale DTI prediction. Evidential deep learning (EDL) offers a promising alternative that provides a direct way to learn about uncertainty without relying on random sampling53,54. Furthermore, EDL can be integrated into existing network structures without major architectural modifications. Several approaches have demonstrated the potential of EDL in the field of drug discovery and development43,55,56,57.\n\nIn this work, we propose an EDL-based DTI prediction framework (EviDTI). The framework utilizes pre-trained knowledge as well as multi-dimensional representations to enhance the model performance. More importantly, EviDTI provides prediction confidence estimation by introducing EDL to help identify drug candidates that are most likely to be successful, thus reducing the risk and cost associated with false positives. The framework comprises three main components: a protein feature encoder, a drug feature encoder, and an evidence layer. In protein feature encoder, the protein language pre-trained model ProtTrans30 is employed to extract protein sequence features. The light attention mechanism is then employed to provide insights into local interactions at the residue level. For drug features, 2D graph representations are obtained using our previously proposed pre-trained model, MG-BERT58. Additionally, 3D features of drugs are encoded via geometric deep learning. The learned protein and drug representations are concatenated and fed into the evidential layer to obtain DTI probabilities and uncertainties. In a comprehensive evaluation across 11 DTI prediction models, EviDTI demonstrates competitive performance. Moreover, we demonstrate that evidential-based uncertainty can effectively calibrate prediction errors. This uncertainty information can accelerate drug discovery and repositioning by prioritizing DTIs with more confident predictions for experimental validation. By applying uncertainty-guided prediction in the discovery of potential tyrosine kinase modulators, EviDTI identifies potential new modulators targeting tyrosine kinase FAK and FLT3, highlighting the utility of EviDTI in drug discovery. By bridging the gap between prediction and reliability, EviDTI offers a trustworthy solution for DTI prediction. The source code is available at https://github.com/zhaoyanpeng208/EviDTI.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We developed an EDL-based DTI prediction model, EviDTI. As shown in Fig.\u00a01, the EviDTI framework consists of three main components: protein feature encoder, drug feature encoder, and evidential layer. Given a drug-target pair as input, the protein feature encoder utilizes the protein sequence pre-training model ProtTrans as the initial encoder to generate an initial target representation. This representation undergoes further feature extraction through the light attention (LA) module. In the drug feature encoder, both 2D topological information and 3D structural information of the drug are encoded. For the drug 2D topological graph, an initial representation is derived using our previously proposed molecule pre-training model MG-BERT36, which is subsequently processed by a 1DCNN. The 3D spatial structure of the drug is converted into an atom-bond graph and a bond-angle graph, with the representation obtained through the GeoGNN module. The target and drug representation are then concatenated and fed into the evidential layer. The output of the evidential layer is the parameter \u03b1, which is used to calculate the prediction probability and the corresponding uncertainty value.\n\nFor a given drug-target pair, the protein feature encoder employs the pre-trained ProtTrans for initial target representation, further refined by a light attention (LA) module. The drug feature encoder processes the 2D topology and 3D structure representations. The 2D representation is derived from the pre-trained MG-BERT model and processed by 1D CNN. The 3D structure representation is obtained via the GeoGNN. These representations are concatenated and fed into the evidence layer, which outputs parameter \u03b1 for prediction probability and uncertainty.\n\nTo evaluate the effectiveness of the EviDTI framework, we validated it on three different experimental datasets: DrugBank, Davis59, and KIBA60. These datasets were randomly divided into training, validation and test sets in a ratio of 8:1:1. We used seven evaluation metrics: accuracy (ACC), recall, precision, Matthews correlation coefficient (MCC), F1 score, area under the ROC curve (AUC), and area under the precision-recall curve (AUPR) to assess model performance. EviDTI is compared with three traditional machine learning methods, including Random Forests (RFs)61, Support Vector Machines (SVMs)62 and Naive Bayesian (NB)63. These methods have been widely used in DTI prediction and are important benchmarks for evaluating the performance of our proposed method. In addition, EviDTI is compared with eight state-of-the-art models in the field, including DeepConv-DTI19, GraphDTA64, MolTrans20, HyperAttention17, TransformerCPI65, GraphormerDTI24, AIGO-DTI66, and DLM-DTI27.\n\nThe performance results on the DrugBank dataset are shown in Table\u00a01. EviDTI shows robust overall performance on all metrics, especially in terms of precision (81.90%), and competitive values for Accuracy (82.02%), MCC (64.29%) and F1 score (82.09%). Besides, we evaluated EviDTI on the Davis and KIBA datasets, which are particularly challenging due to class imbalance. The performance results on the Davis and KIBA datasets are presented in Tables\u00a02 and 3, respectively. Specifically, on the KIBA dataset, EviDTI outperformed the best baseline model by 0.6% in accuracy, 0.4% in precision, 0.3% in MCC, 0.4% in F1 score, and 0.1% in AUC. On the Davis dataset, EviDTI exceeds 0.8% in accuracy, 0.6% in precision, 0.9% in MCC, 2% in F1 score, 0.1% in AUC, and 0.3% in AUPR. These results demonstrate the robustness and superior performance of EviDTI when dealing with complex and unbalanced datasets. Overall, EviDTI achieved robust and superior performance on three benchmark datasets, which further validates the effectiveness and competitiveness of EviDTI.\n\nIn order to evaluate the effectiveness of the EviDTI in predicting novel DTIs, we introduced a cold-start scenario following the practice established by Wang et al12. Supplementary Table\u00a02 demonstrates the performance of EviDTI under the cold-start scenario. EviDTI outperforms other models in several evaluation metrics, especially in accuracy (79.96%), recall (81.20%), F1 score (79.61%) and MCC value (59.97%). Its AUC value (86.69%) is slightly lower than TransformerCPI\u2019s 86.93%. These results demonstrate that EviDTI is a competitive model in cold start scenarios.\n\nTwo ablation studies were conducted to explored the impact of different combinations of dimensional features and the use of pre-trained model features on DTI prediction.\n\nFirstly, we compare the performance of models using single-dimensional features with models using a multidimensional feature fusion strategy. Supplementary Figs.\u00a01 and\u00a02 illustrate the various feature combination architectures used. Specifically, three multidimensional feature combinations were evaluated on three datasets:\n\nEviDTI: combines the 2D topological representation and the 3D structural representation of small molecules of small molecules, and protein sequence representation.\n\nEviDTI w/o drug 3D: utilizes 2D topological representation of small molecules and the protein sequence representation.\n\nEviDTI w/o drug 2D: utilizes 3D structural representation of small molecules and the protein sequence representation.\n\nAs shown in Fig.\u00a02a and Supplementary Table\u00a03, the EviDTI model consistently outperformed the other two models across all three datasets in most evaluation metrics. This result indicates that multidimensional feature fusion significantly enhances DTI prediction performance.\n\na Performance comparison on the DrugBank, KIBA and Davis datasets using single-dimensional features and multidimensional feature fusion strategies. Five independent replications of each method were performed (n\u2009=\u20095). Data are expressed as means\u2009\u00b1\u2009std. b Performance comparison of feature extraction with and without pre-trained models on DrugBank, KIBA, and Davis datasets. Five independent replications of each method were performed (n\u2009=\u20095). Data are expressed as means\u2009\u00b1\u2009std. Source data are provided as a Source Data file.\n\nTo explore the benefits of using pre-trained models as initial feature extractors, we compared the performance of architectures with and without pre-trained models. Supplementary Figs.\u00a03 and\u00a04 depict the different ablation architectures examined:\n\nEviDTI-Protein Integer: replaces the protein pre-training model ProtTrans with integer coding and a CNN for feature extraction.\n\nEviDTI-Drug 2D GCN: replaces the small molecule pre-training model MG-BERT with a two-layer GCN.\n\nFigure\u00a02b and Supplementary Table\u00a04 illustrate the performance of these different model frameworks across the three datasets. It is evident that the architectures utilizing pre-trained models for initial feature extraction outperformed those that did not across all metrics. This finding underscores the value of leveraging pre-trained models to enhance DTI prediction performance.\n\nFollowing the evaluation of EviDTI\u2019s prediction performance on the benchmark dataset, it becomes critical to ensure that the model can provide reliable estimates of uncertainty in the DTI prediction task. To assess whether the model can effectively assess prediction uncertainty, two hypotheses are proposed and tested:\n\nSamples with correct predictions should have low uncertainty, while samples with incorrect predictions should have high uncertainty.\n\nSamples with lower uncertainty should exhibit higher prediction accuracy.\n\nThese assumptions are based on the EDL principle, which views learning as a process of acquiring evidence. The more evidence accumulated, the greater the confidence and the higher the probability of prediction.\n\nFirst, the correlation between uncertainty and prediction results was assessed on the three benchmark datasets. Figure\u00a03a illustrates the relationship between the sample prediction results and the uncertainty values. The horizontal axis divides the sample into true positives (TP, true value 1, predicted value 1), false positives (FP, true value 0, predicted value 1), false negatives (FN, true value 1, predicted value 0) and true negatives (TN, true value 0, predicted value 0). The vertical axis represents the distribution of uncertainty values for each group.\n\na A Mann\u2013Whitney test was performed on the error distribution of uncertainty in samples classified as TP, FP, FN, TN for three datasets: DrugBank (n\u2009=\u20093312 observations), KIBA (n\u2009=\u200911,639 observations), and Davis (n\u2009=\u20092,583 observations). The central line indicates the median, the box bounds indicate the 25th and 75th percentiles, whiskers extend to the minimum and maximum values (within 1.5\u00d7 interquartile range), and outliers are shown as individual points. All tests were two-sided, with no adjustments made for multiple comparisons. Asterisks indicate statistically significant differences based on Mann\u2013Whitney U test p-values: ****p\u2009\u2264\u20090.0001. Significance is indicated as follows: For DrugBank dataset, TP vs. FN has a p-value of 1.055e-10, FP vs. TN has a p-value of 4.954e-74, TP vs. FP has a p-value of 1.546e-51, FN vs. TN has a p-value of 1.895e-26. For KIBA dataset, TP vs. FN has a p-value of 9.713e-30, FP vs. TN has a p-value of 4.954e-74, TP vs. FP has a p-value of 1.546e-51, FN vs. TN has a p-value of 1.895e-26. For Davis dataset, TP vs. FN has a p-value of 3.502e-09, FP vs. TN has a p-value of 5.662e-45, TP vs. FP has a p-value of 6.667e-21, FN vs. TN has a p-value of 7.434e-40. b Test data sorted and divided into 20 confidence intervals based on uncertainty. All tests were two-sided with no adjustments made for multiple comparisons. The ACC was calculated for samples within each confidence interval. Five independent replications (n\u2009=\u20095) were performed in each data set. Data are presented as mean\u2009\u00b1\u2009std. Source data for the figure are shown in Supplementary Data. Source data are provided as a Source Data file.\n\nIn all benchmark datasets, the incorrectly predicted samples (FP and FN) typically exhibit higher uncertainty than the correctly predicted samples. However, there were performance differences between the datasets. Fewer outliers were observed in the Davis and KIBA datasets than in the DrugBank dataset, which may be due to the fact that the DrugBank dataset contains a wider range of proteins and small molecules with complex and diverse structures. In contrast, the Davis and KIBA datasets contain a more limited diversity of proteins and small molecules of kinases.\n\nWe further analyzed whether the model provides a reliable measure of uncertainty in the cold-start dataset. Supplementary Fig.\u00a05 demonstrates the relationship between sample prediction results and uncertainty values in the cold-start dataset. The incorrectly predicted samples (FP and FN) in the cold-start dataset also typically exhibit higher uncertainty than the correctly predicted samples.\n\nNext, we examined the relationship between sample uncertainty and prediction accuracy to validate the second hypothesis. Samples were ranked according to their uncertainty values and divided into 20 confidence intervals, each containing 5% of the total samples. For example, the first interval includes the top 5% of samples with the lowest uncertainty, while the second interval comprises samples with an uncertainty ranking of 6\u201310%. The accuracy within each interval was then calculated separately, as shown in Fig.\u00a03b.\n\nFigure\u00a03b shows that within the confidence interval with the lowest uncertainty (top 5 per cent), i.e., the region with the highest confidence in the model\u2019s predictions, the accuracy exceeds 90 per cent for all three benchmark datasets, and is close to 100 per cent for the KIBA and Davis datasets. In contrast, within the confidence interval with the highest uncertainty (96\u2013100%), the accuracy ranged between 0.5 and 0.6. This suggests that the model effectively communicates uncertainty and therefore these predictions should be treated with caution. In conclusion, the predictive accuracy of the model decreases progressively with increasing uncertainty.\n\nThese findings suggest that the uncertainty estimates generated by EviDTI can be an important tool for calibrating predictions. It also implies that EviDTI can help reduce the risks and costs associated with incorrect DTI predictions, providing important support for the decision-making process in drug discovery.\n\nAfter validating that EviDTI can effectively calibrate predictions, we explored its application in real-world scenarios. In practical settings, model performance in high-confidence intervals is particularly important. Therefore, we aimed for the model to achieve good uncertainty calibration in these intervals and to guide the identification of new DTIs.\n\nFirst, we used the out-of-fold (OFR)45,67 to examine the predictive performance of the model at high confidence intervals. By setting thresholds ranging from 0.1 to 0.01, we compare the performance of the models at different confidence levels. To elucidate the role of evidential uncertainty, we compare two approaches: a probability-based approach that does not use uncertainty and an uncertainty-based approach that uses evidential uncertainty to obtain probabilities. Figure\u00a04a\u2013c shows the OFR (based on five random data splits) for the different methods on each dataset.\n\na Comparison of OFR between uncertainty-based and probability-based frameworks on DrugBank dataset at different thresholds. Five independent replications of each method were performed (n\u2009=\u20095). Data are expressed as means\u2009\u00b1\u2009std. b Comparison of OFR between uncertainty-based and probability-based frameworks on the KIBA dataset at different thresholds. Five independent replications of each method were performed (n\u2009=\u20095). Data are expressed as means\u2009\u00b1\u2009std. c Comparison of OFR between uncertainty-based and probability-based frameworks on the Davis dataset at different thresholds. The line represents the mean OFR, and the shaded area indicates the standard deviation. Five independent replications of each method were performed (n\u2009=\u20095). Data are expressed as means\u2009\u00b1\u2009std. d Hit rates of the Top20 ranked predictions determined by uncertainty ranking strategies and probability ranking strategies. Five independent replications of each method were performed (n\u2009=\u20095). Data are expressed as means\u2009\u00b1\u2009std. e Case study. Column interaction is the true label of the DTIs. Column Uncertainty-based is the predicted probability based on the uncertainty method and the predicted label, with the uncertainty given by the model in parentheses. Column Probability-based is the predicted probability of the probability-based method and the predicted labels. Source data are provided as a Source Data file.\n\nAccording to Fig.\u00a04a\u2013c and Supplementary Table\u00a05, the probability-based approach achieves higher prediction performance at lower thresholds. As the threshold increases, the OFR of both approach decreases, with the uncertainty-based approach decreasing more significantly. The uncertainty-based method has a lower OFR on all three datasets when the threshold is below 0.02. This suggests that the predictive performance of EviDTI in high confidence intervals is higher than that of the method without EDL because it provides better uncertainty calibration.\n\nNext, we conducted several case studies to illustrate how uncertainty can improve prediction reliability. As shown in Fig.\u00a04e, for some DTIs, probability-based methods yield incorrect predictions with high prediction probabilities that may mislead experimental validation. In contrast, uncertainty-based methods identify these predictions as unreliable by assigning a high uncertainty score. This ability to convey prediction uncertainty is critical to avoid misguidance.\n\nWhile our experiments were trained and validated on the same datasets (with similar sample and feature distributions), real-world challenges involve predicting unseen DTIs, where the distribution differs from the training data. EDL\u2019s ability to assess uncertainty in such cases is essential for making informed decisions about prediction reliability.\n\nTo conduct this study, we turned to external datasets to assess the potential of evidence uncertainty in predicting unseen samples. We constructed an independent test set of all new drugs and their targets approved by the US Food and Drug Administration in 202268 to ensure that these drugs did not appear in the training set, as detailed in Methods. A total of 24 pairs of drug-target interactions and 24 pairs of randomly generated negative interactions were collected for model performance testing. These newly reported drugs typically have novel backbones and targets that occupy different regions of chemical space.\n\nWe compared two ranking strategies for DTI identification: (1) ranking based on predicted probabilities (probabilities-based ranking), and (2) ranking based on uncertainty scores (uncertainty-based ranking). Hit rates are used to evaluate these strategies, which reflect the proportion of true DTIs correctly identified with minimal false positives. We first select the top-ranked prediction and calculate the hit rate under that ranking. Then, the ranking thresholds are gradually increased and the hit rates of the two ranking strategies are computed at each threshold. Figure\u00a04d shows the hit rate curves determined by these two ranking strategies. Uncertainty-based ranking achieves a 100% hit rate for Top3 predictions and a hit rate of over 80% for Top10 predictions. In contrast, the probability-based ranking achieves a 100% hit rate only for the Top1 prediction and exhibits greater volatility for the Top3 prediction, indicating a lack of robustness in its model predictions. The above results illustrate that uncertainty-based rankings reduce false positives, suggesting that evidential uncertainty can reduce false positives in top DTIs.\n\nIt is worth noting that the performance of EviDTI on external datasets (based on standard assessment metrics) is not impressive. In addition, the study investigates whether UQ enhances the overall prediction performance in comparison to non-uncertainty-based models. The performance of model with UQ integration and the model without UQ integration was compared across three benchmark datasets. The model without UQ integration has the same architecture but does not use the evidential layer. As shown in Supplementary Fig.\u00a06, the model with UQ integration performs comparably to the model without UQ integration respect to the standard evaluation metrics. However, uncertainty information remains critical. The primary objective of DTI identification is to maximize the chances of discovering a true DTI by prioritizing high-confidence predictions rather than exhaustively testing all possibilities. Incorporating uncertainty into DTI screening can improve hit rates, reduce false positives, and increase the efficiency of drug discovery by focusing experimental validation on the most reliable predictions.\n\nIn summary, these findings demonstrate that EviDTI, by quantifying uncertainty, offers a promising solution for improving the reliability of predictions and the efficiency of screening in drug-target discovery.\n\nTyrosine kinases are a critical class of enzymes that regulate cell signaling and play a central role in cancer therapy by driving proliferation and survival pathways69,70. Tyrosine kinase modulators have been widely used to regulate these kinase by binding to their phosphorylation sites. However, the efficacy of single-target tyrosine kinase modulators is frequently constrained due to the complexity. Additionally, single-target inhibitors frequently induce drug resistance, diminishing their clinical utility71. Multi-target tyrosine kinase modulators offer a promising solution by regulating multiple cancer-associated kinases simultaneously, disrupting the tumor signaling network and improving therapeutic efficacy.\n\nWe investigated the potential of EviDTI for the discovery of novel multi-targeted tyrosine kinase regulators. Initially, the efficacy of EviDTI in predicting interactions between tyrosine kinases and tyrosine kinase modulators was validated. Subsequently, EviDTI was applied to screen for new multi-targeted regulators and evaluated them by in vitro experiments.\n\nFirstly, we validated the performance of EviDTI based on patent. Specifically, the interaction data between two Lenvatinib analogs (LYD-2-45 and LYD-2-49) and 11 tyrosine kinase targets was obtained from the patent (Grant No. CN 116751161\u2009A). A total of 22 drug-target pairs were collected, including 20 positive interactions (confirmed interactions) and 2 negative interactions (non-interactions). The probabilities and uncertainties of these drug-target pairs were obtained using EviDTI, which was trained on the DrugBank database. The predicted results are shown in Supplementary Table\u00a06, EviDTI gave correct predictions for 16 drug-target pairs.\n\nSecondly, we validated the performance of EviDTI based on the literature. To perform this validation, we collected 67 known tyrosine kinase targets from literatures and 51 potential tyrosine kinase modulators from the Targetmol-Tyrosine Kinase Modulators Library72 (see METHODS for details). The uncertainty score and probability of interactions between the above kinases and regulators were obtained using the model trained on DrugBank dataset, and the predictions were ranked according to the uncertainty score. The predictive performance of the EviDTIs was validated in three aspects. The first aspect is to validate how many predicted DTIs with the lowest uncertainty score have been reported in literature. As shown in Supplementary Table\u00a07, among the top 10 predicted DTIs, two DTIs were validated in the literature (Interaction between Flumatinib mesylate and c-Kit [Rank 6, Uncertainty value\u2009=\u20090.0419]; Interaction between Flumatinib mesylate and Bcr-Abl [Rank 10, Uncertainty value\u2009=\u20090.0429]73). The second aspect is to validate how many literature-reported DTIs between tyrosine kinase modulators and tyrosine kinase targets are predicted by EviDTI. A total of 27 DTIs were identified between 67 tyrosine kinase targets and 51 tyrosine kinase modulators. The 27 DTIs have been obtained from multiple literature sources. Supplementary Table\u00a08 shows the results of the predictions made using EviDTI. Among the 27 known DTIs, 21 DTIs were predicted by EviDTI. 10 out of 21 predicted DTIs have high confidence (the uncertainty score less than 0.1). The third aspect is to validate how many DTIs between drugs in the top 10 predicted DTIs and their literature-reported targets could be predicted by EviDTI. We collected the drugs in the top 10 predicted DTIs and their all literature-reported targets, totally 10 DTIs between these drugs and their all targets. The results of these predictions are displayed in Supplementary Table\u00a09. Among these 10 DTIs, 7 DTIs were predicted by EviDTI, 4 out of 7 predicted DTIs have high confidence (the uncertainty score less than 0.1). This analysis shows that EviDTI performs well in predicting DTIs reported in the literature, and in particular shows significant potential in the discovery of tyrosine kinase modulators.\n\nThirdly, based on EviDTI\u2019s predictions for interactions between 67 tyrosine kinase targets and 51 potential modulators, the most promising interactions (lowest uncertainty scores) were selected for experimental validation. We focused on two key tyrosine kinases targets (FAK and FLT3). For each kinase, the seven interactions with the highest confidence (lowest uncertainty score) between these targets and potential modulators were prioritized for experimental validation. These interactions were validated using ADP-Glo\u2122 Kinase Assay Kit. For the FAK kinase experiment, we selected PF-562271 as the positive control. In our experimental system, the 50% effective concentration of this compound was 2.91\u2009\u00b1\u20090.47\u2009nM, which is comparable to the previously reported kinase inhibition activity (FAK: 1.5\u2009nM)74. As shown in Fig.\u00a05b, Tyrphostin 975 and Vodobatinib76 exhibited an inhibition of FAK activity with the 50% effective concentration of 35.7\u2009\u00b1\u20093.4\u2009nM and 85.7\u2009\u00b1\u20098.2\u2009nM, respectively. Also, Flumatinib mesylate73 inhibited FAK activity with a 50% effective concentration of 14.9\u2009\u00b1\u20092.1\u2009nM (Supplementary Table\u00a010a and Supplementary Fig.\u00a07a\u2013d). For the FLT3 kinase experiment, sorafenib are used as the positive control. The 50% effective concentration of sorafenib in our system was 51.7\u2009\u00b1\u20098.47\u2009nM, which is comparable to literature value (FLT3: 58\u2009\u00b1\u200920\u2009nM)77. As shown in Fig.\u00a05c, Tyrphostin 975 and Vodobatinib76 activated FLT3 activity with the 50% effective concentration 1265.9\u2009\u00b1\u2009244.6\u2009nM and 406.8\u2009\u00b1\u200974.5\u2009nM, respectively (Supplementary Table\u00a010b and Supplementary Fig.\u00a07e\u2013g).\n\na The validation framework on multi-target tyrosine kinase modulators. Initially, the validation was carried out using the data reported in the patents. Two lenvatinib analogs with 11 known targets were collected from the patents to conduct this validation. Subsequently, the validation was carried out using the data reported in the literature. The uncertainty score and probability of interactions between the 67 tyrosine kinase targets and 51 tyrosine kinase modulators were predicted via EviDTI. Finally, two targets of interest were selected from the 67 targets collected above. Based on the uncertainty for these two targets and 51 modulators, the interactions with lowest uncertainty between these two targets and the seven modulators were validated experimentally. b The 50% effective concentration of Tyrphostin 9, Vodobatinib, Flumatinib and PF-562271 in the FAK kinase ADP-Glo assays, respectively. PF-562271 as positive control. Mean\u2009\u00b1\u2009SEM of three independent experiments is shown (n\u2009=\u20093). c The 50% effective concentration of Vodobatinib, Tyrphostin 9 and sorafenib in the FLT3 kinase ADP-Glo assays, respectively. Sorafenib as positive control. Mean\u2009\u00b1\u2009SEM of three independent experiments is shown (n\u2009=\u20093). Source data are provided as a Source Data file.\n\nIn summary, EviDTI demonstrated its potential in identifying novel multi-target tyrosine kinase modulators, not only by predictively validating the targets of existing drugs, but also by experimentally validating its effectiveness in improving the efficiency of drug discovery and reducing the risk of development.\n\nEviDTI provides an additional advantage by offering residue-level insights and elucidating key factors in drug design. The light attention module facilitates this capability by enabling visualization of the contribution of each amino acid to the final predicted outcome.\n\nFirstly, a visualization case study demonstrated the relationship between attention weights and binding residues. Four DTI pairs were randomly selected from the DrugBank dataset, and their tertiary structures were downloaded from the PDB database. The PDB IDs of these complexes are 1y91, 1d6n, 1czh, and 1z83, respectively. Attention values were mapped to these structures and visualized using PyMOL (Fig.\u00a06). In Fig.\u00a06, the binding pockets of proteins and small molecules are visualized, showcasing residues with high attentional values. Across all cases, residues with high attention values coincide with the binding site, underscoring its importance in predicting and validating the attention mechanism\u2019s efficacy.\n\na\u2013d The correctly predicted amino acid residues surrounding the corresponding ligands (sticks) are highlighted. The residues around the corresponding ligands that were correctly predicted are highlighted in the figure. Their color indicates the degree of contribution of these residues to the prediction results. 3D representations of all structures were visualized using Pymol software.\n\nOverall, the case study findings demonstrate the interpretability of the model and are expected to reveal previously unexplored local interactions, providing opportunities to mine hidden knowledge. These findings could guide strategies to help develop drug targets.\n\nTo further explore the interpretability of the model, all known DTI complex structures appearing in the DrugBank dataset were collected from the PDB database. The degree of correspondence between the residues with the highest attention value and the binding site was also evaluated using the binding sites-hit ratio.\n\nSpecifically, the residues were first ranked according to their attention value, and the top N% residues were selected to calculate the binding sites hit ratio. A true binding site is defined as residue whose Euclidean distance from the drug is within 4\u2009\u00c5 of any atom.\n\nThe binding sites hit ratio at different thresholds is reported in Table\u00a04 and Supplementary Fig.\u00a08. When the number of true binding sites was used as the threshold value, the binding sites hit ratio was 0.125, and more than 70% of the binding sites ranked in the top 50% of the attention values. These results suggest that confidence in the interpretability of protein sequences may be lacking because 1D protein sequences (used as inputs for protein information in our model) do not necessarily indicate the 3D conformation and location of the binding pocket. However, the results for primary protein sequences are encouraging, and it is reasonable to assume that further incorporation of 3D protein information into the modeling framework will ultimately improve the model\u2019s interpretability of the drug-target interaction network.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62235-6/MediaObjects/41467_2025_62235_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62235-6/MediaObjects/41467_2025_62235_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62235-6/MediaObjects/41467_2025_62235_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62235-6/MediaObjects/41467_2025_62235_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62235-6/MediaObjects/41467_2025_62235_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62235-6/MediaObjects/41467_2025_62235_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Although current deep learning models have made significant progress in DTI prediction, they typically lack the ability to provide confidence estimates for predictions. This limitation seriously affects the effectiveness of these models in real-world applications, where decision making often requires a clear understanding of the reliability of the predicted results. To bridge the gap between predictive models and practical applications, we present EviDTI, a novel framework that integrates evidential-based deep learning. This method not only predicts DTI with high accuracy, but also provides reliable quantification of uncertainty, which enhances the practicality and reliability of the model. This is important for advancing the field of drug discovery as it is directly related to the success and cost-effectiveness of experiments. EviDTI leverages 2D topological graphs, 3D molecular geometries of drug molecules, and target protein sequences for DTI prediction. Additionally, EDL is employed to estimate uncertainty, providing a robust measure of prediction confidence. The performance comparison across three datasets demonstrates that EviDTI achieves competitive performance compared to 11 DTI prediction models. This study demonstrates that EDL can provide a reliable uncertainty quantification for DTIs prediction. Importantly, we showed that uncertainty-guided predictions can prioritize experiments in drug discovery and drug repositioning studies. The experimental validation of interactions between two tyrosine kinase targets and seven potential tyrosine kinase modulators (totally 14 potential DTIs) was performed. Among those predicted DTIs, 5 DTIs showed binding affinity, and 4 of them reached the nanomolar level of binding affinity. These results demonstrate that EviDTI is a powerful tool that can accelerate the translation process from theoretical models to actual drug development.\n\nHowever, the model still has some limitations. Although EviDTI was designed with the objective of identifying potential DTIs, it is currently cannot determine whether the molecule has an activating or inhibitory effect on the target. To overcome this limitation, future directions for improvement include introducing more molecular biochemical properties into the model, developing a multi-task learning framework capable of predicting activation and inhibition simultaneously.\n\nIn addition, EviDTI has difficulty in providing discriminating probabilities and uncertainties for interactions between compounds and the target and its mutants. The development of effective drugs that specifically targeting mutants is essential to improve treatment outcomes, especially in diseases driven by specific genetic alterations such as acute myeloid leukemia (AML). However, EviDTI relies on one-dimensional protein sequence information, its ability to capture the nuanced effects of mutations may be limited. We believe in incorporating more informative representations such as protein 3D structural information and pharmacologically perturbed transcriptomics data can more effectively address these limitations.\n\nIn the future, the model can be extended as follows to enhance its performance and generalizability. First, with the rapid advancements in 3D protein structure prediction methods29,78, incorporating 3D protein structure information into DTI predictions has the potential to significantly enhance prediction accuracy. However, modeling 3D protein structures remains complex and considerably increases computational time; therefore, this study did not utilize such information in the modeling process. Nevertheless, we believe that integrating 3D structural data\u2014particularly when combined with pocket information79,80\u2014can yield deeper insights into interaction mechanisms, ultimately leading to more accurate DTI predictions and improved interpretation of these interactions.\n\nFurthermore, this study uses LA module to obtain amino acid attention weights, providing molecular-level insights into DTIs. However, the EviDTI framework still faces limitations in explicitly learning the interactions between drug and protein local structures. Several approaches have been developed to visualize the contributions of protein sequences and drug substructures to the final prediction results through a cross-attention mechanism, thus enhancing the interpretability of the model. Combining this cross-attention mechanism with evidential-based deep learning can improve model accuracy and interpretability while effectively calibrating uncertainty.\n\nMoreover, while EDL can calibrate prediction uncertainty for unknown samples, it does not inherently improve prediction performance for out-of-distribution samples (i.e., cross-domain generalization). Transfer learning can address this limitation by aligning the feature distributions of the training set with new samples and learning transferable representations. Combining EDL with transfer learning could significantly enhance the efficiency of drug discovery and drug repositioning.\n\nFinally, the EviDTI framework, with its focus on generalizability, holds the potential to be extended for application to other interaction prediction problems. This includes, but is not limited, to enzymatic reaction kinetic parameters prediction and drug-drug interaction prediction. The adaptability and versatility of this framework open up new avenues for research and application in the field of drug-target interaction prediction.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Three datasets DrugBank68, Davis59, and KIBA60 were used for model evaluation. Supplementary Table\u00a01 summarizes these datasets.\n\nDrugBank: the data used in this study were published on January 3, 2020 (version 5.1.5). Drugs with inorganic compounds, very small molecule compounds [e.g., iron (DB01592), zinc (DB01593)], or SMILES strings that the RDKit Python package could not recognize were removed. Drugs with a SMILES length greater than 300 were also removed to comply with the maximum input of MG-BERT. Ultimately, 6148 drugs and 4085 targets, totaling 16,531 pairs of known drug-target interactions, were collected, and these known drug-target interactions were used as positive samples for training. We randomly disrupted these positive pairs to generate negative samples, specifically by randomly selecting a drug-target interaction pair and forming a negative drug-target interaction pair between the target in that pair and another drug with a known drug-target interaction pair. Negative samples with the same number of positive samples, totaling 16,531 negative sample pairs, were generated through the above steps. Thus, a total of 33,062 pairs of samples were collected in the DrugBank dataset, with a ratio of positive to negative samples of 1:1.\n\nDavis and KIBA: The Davis and KIBA dataset records wet-lab values for determining the binding affinity between drugs and proteins. We refer to previous work and divide positive and negative samples using 5.0 and 12.1 as thresholds to construct a binary classification dataset.\n\nIn order to assess the ability of the model to predict novel compound interactions in real-world drug discovery, we implemented a cold-start scenario following the practice established by Wang et al.12 Selected 10% of drugs from DrugBank dataset, and then all the DTI pairs associated with these drugs were used as a test set. Resulting in 3170 cold-start test samples VS. 29,894 training samples.\n\nTo perform the unseen data prediction (see \u201cUncertainty prediction accelerates drug discovery\u201d), we collected all new drugs approved by the FDA in 2022 and their targets from the DrugBank dataset. In 2022, the FDA approved 37 new drugs for marketing, including 22 new molecular entities and 15 new biologic applications. Only molecular drugs were selected among these new approvals, while drugs already appearing in the training set were removed. Ultimately, we collected 13 drugs, 22 targets, and 24 pairs of interactions. We used the same method for constructing the negative samples as for the DrugBank training set, randomly generating as many negative samples as positive samples. Thus, we collected 48 pairs of samples for out-of-domain performance testing. In these samples, the ratio of positive to negative samples was 1:1.\n\nThe protein feature encoder consists of an initial encoding module and a LA module81.\n\nIn the initial coding module, we extracted sequence information using the widely used protein language model ProtTrans30 (denoted as ProtT5), which is a self-supervised autocoder based on the transformer model. A 1024-dimensional sequence embedding for each residue was extracted using ProtT5. For each protein, we obtained a \\(L\\times 1024\\)-dimensional feature matrix, where L is the length of the protein chain. We did not train ProtT5, but used its locked parameters to obtain the representations.\n\nThe protein feature matrix is encoded using light attention. The input to light attention is a protein embedding \\(x\\) of size \\(L\\times {d}_{{in}}\\), where \\(L\\) is the length of the protein chain, i.e., the number of amino acids, and \\({d}_{{in}}\\) is the length of the initial representation of each amino acid, in this case, 1024. Two independent 1D convolutions transform the input. Convolution is used in the length dimension to generate attention coefficients and values \\(e,v\\).\n\nwhere \\(b\\) is a learned bias, \\({s}\\) is filter sizes, \\({W}^{(e)}\\) is the weights of the 1D convolutions.\n\nTo obtain the attention weights, softmax normalization is performed on \\({e}_{{ij}}\\). The attention weight \\({\\alpha }_{{ij}}\\) for the j-th residue and the i-th feature dimension is computed as:\n\nThe weight distribution of each feature dimension i is independent, and they can produce different attention patterns. The attention distribution calculates a weighted sum of the transformed residual embeddings \\({v}_{{ij}}\\). Thus, we obtain a representation \\(x{\\prime}\\) independent of protein length.\n\nThe drug feature encoder consists of a 2D topology encoding module and a 3D structure encoding module.\n\nIn the 2D topology encoding module, the small molecule pre-training model MG-BERT58 is used to extract drugs\u2019 2D topological graph information. MG-BERT is a pre-trained model based on a self-supervised learning approach for learning atomic-level representations of drug molecules. The model uses the transformer architecture and employs a mask-based self-supervised learning approach. Again, we did not train MG-BERT but used its frozen parameters to obtain an initial representation of the drug. In this work, the representation obtained by MG-BERT is extracted as an initial representation of the drug molecule, which is used as input to 1DCNN to obtain a final representation of the 2D information of the drug.\n\nIn the 3D structure encoding module, given a 3D structure graph, an atom-bond graph and a bond-angle graph are constructed, under which the representation vectors of atoms and bonds are learned iteratively82. In the atom-bond graph, the nodes of the graph are atoms, and the edges of the graph are covalent bonds of atomic bonds. In the bond-angle graph, the nodes of the graph are the bonds, and the edges are the bond angles between the two bonds. The scalar atomic distances and angles are mapped into high-dimensional vectors using the Gaussian kernel function. In a more concrete sense, the representation vectors of atom \\(u\\) and bond \\((u,v)\\) for the kth iteration are denoted as \\({h}_{u}\\) and \\({h}_{{uv}}\\), respectively. The initialization is set as \\({h}_{u}^{(0)}={x}_{u}\\) and \\({h}_{{uv}}^{(0)}={x}_{{uv}}\\).\n\nGiven the bond \\((u,v)\\), its representation vector \\({h}_{{uv}}^{(k)}\\) at the kth iteration is formulated by\n\n\\(N(u)\\) and \\(N(v)\\) represent the adjacent atoms of u and \\(\\,v\\),\\(\\{(u,w):w\\in N(u)\\}\\cup \\{(v,w):w\\in N(v)\\}\\) are the neighboring bonds of \\((u,v)\\).\n\nThe function \u201cAggregate\u201d aggregates messages, while \u201cCombine\u201d updates the bond-angle graph.\n\nThe information from adjacent bonds and corresponding bond angles is aggregated into \\({a}_{{uv}}^{\\left(k\\right)}\\), and subsequently, the bond representation \\(\\left(u,v\\right)\\) is updated based on the aggregated information.\n\nFor an atom \\(u\\), at the kth iteration, its representation vector \\({{h}}_{u}^{\\left(k\\right)}\\) can be expressed as:\n\nThe messages from the bonds are learned from the bond-angle graph. The aggregated messages then update the representation vector of atom u.\n\nThe atom\u2019s representation vectors at the final iteration are integrated to obtain the molecular representation vector \\({h}_{G}\\) through the READOUT function.\n\nThe learned drug and protein representations are concatenated and serve as inputs to the evidential layer. The evidence layer is a fully connected neural network with N layers. Unlike classical neural networks, the softplus layer is used to ascertain non-negative output. In the deep evidential classification model, the joint evidence distribution is presented as a Dirichlet distribution, which is used to represent the interaction prediction and the degree of evidence associated with that prediction. The outputs of the evidence layer are used as parameters of the predicted Dirichlet distribution. For the DTI prediction task, the network has two outputs.\n\nIn subjective logic (SL), for each class \\(k=1,\\,.\\,..,K\\), SL will provide a belief mass bk and an overall uncertainty mass u to compose a framework for evaluating the K classification. These mass values are all non-negative and sum to 1, i.e.,\n\nwhere \\({{\\rm{u}}}\\ge \\,0\\) and\\(\\,{{{\\rm{b}}}}_{k}\\,\\ge \\,0\\) for \\({k}=1,\\,.\\,..,K\\).\n\nThe bk value is computed from the amount of evidence for the kth class. Let \\({{{\\rm{e}}}}_{k}\\,\\ge \\,0\\) be the evidence for the kth class, then the belief bk and uncertainty u are calculated as\n\nwhere\\(\\,S=\\,{\\sum }_{i=1}^{K}{e}_{i}-1\\).\n\nHere, the evidence is defined as a measure of the support from the data collection to a certain class to support the sample classification. A belief mass assignment, i.e., subjective opinion, corresponds to a Dirichlet distribution with parameters \\({\\alpha }_{k}={e}_{k}+1\\).\n\nThat is:\n\nWhere \\({{\\rm{S}}}=\\,{\\sum }_{i=1}^{K}{\\alpha }_{i}\\)\n\nThe expected probability of the Kth class is the mean of the corresponding Dirichlet distribution, calculated as\n\nThe EviDTI is trained end-to-end using a multi-objective loss function \\({{\\boldsymbol{L}}}(\\varTheta )\\) to achieve the maximum fit and minimum incorrectly predicted evidence for the model.\n\nWhere \\({\\lambda }_{t}\\) is the annealing coefficient, t is the index of the current training epoch, \\({D}({p}_{i}\\left|\\left\\langle 1,...,1\\right\\rangle \\right.)\\) is the uniform Dirichlet distribution, and \\({\\widetilde{\\alpha }}_{i}={y}_{i}+(1-{y}_{i})\\odot {\\alpha }_{i}\\) is the Dirichlet parameters after removal of the non-misleading evidence from predicted parameters \\({\\alpha }_{i}\\) for sample i. More details about the Evidential Deep Learning to Quantify Classification Uncertainty.\n\nFor the DrugBank, Davis and KIBA dataset, we retrained and tuned all models based on the parameters provided in their respective GitHub repositories. All methods were conducted with five repeated experiments, and the results are reported as the mean and variance.\n\nDeepConv-DTI utilizes CNNs to extract features from proteins and fully connected networks (FCNs) to extract features from drugs, followed by making predictions.\n\nGraphDTA employs GNN and CNN to represent drugs and proteins, respectively. As Nguyen et al. reported, the GAT_GCN combination is chosen as the feature extractor in subsequent experimental comparisons.\n\nFor a fair comparison, GraphDTA were adapted according to the following steps:(1) A Sigmoid activation function was added after the last layer to convert the model output to binary probabilities. (2) A binary cross-entropy was used as the loss function instead of the original mean square error (MSE) loss. (3) A grid search was performed to optimise key hyperparameters such as learning rate and batch size, and a validation set was used to determine the best configuration.\n\nMolTrans employs the transformer model to encode protein and drug sequences into feature embeddings. These embeddings are used to construct an interaction matrix, which is subsequently processed by CNNs and FCNs to make predictions.\n\nTransformerCPI utilizes the Transformer architecture, treating drugs and proteins as distinct sequences. By employing CNN and GCN, the model generates representations for protein sequences and atom structures, respectively. Subsequently, TransformerCPI extracts interaction features through the transformer decoder and employs linear layers to produce the final output, representing the interaction probability.\n\nHyperAttention DTI is an end-to-end bio-inspired model built upon the CNN and attention mechanism. Deep CNNs are employed to learn feature matrices for both drugs and proteins adeptly. The attention mechanism was leveraged on these feature matrices to capture intricate non-covalent intermolecular interactions among atoms and amino acids, assigning an attention vector to each atom or amino acid.\n\nAIGO-DTI proposes an Adaptive Iterative Graph Optimization (AIGO)-DTI prediction framework. This framework integrates atomic cluster information and enhances molecular features through the design of functional group prompts and graph encoders, thereby optimizing the construction of DTI association networks. Furthermore, the optimization of graph structure is transformed into a node similarity learning problem, utilizing multi-head similarity metric functions to iteratively update the network structure to improve the quality of DTI information.\n\nGraphormerDTI integrates the Graph Transformer neural network with a 1D-CNN to extract the representations of drug and target, respectively. Specifically, GraphormerDTI embeds molecular graphs into vector representations through iterative Transformer-based message passing. The structural representations of the molecules are encoded through node centrality encoding, node spatial encoding and edge encoding. The attentional mechanisms are used to model interactions.\n\nDLM-DTI was comprised of three primary components: the drug encoder, the target encoder, and the interaction prediction head. The target encoder incorporates both the teacher and student models of language models for protein sequences. The teacher model employed for target sequence encoding was the ProtBERT model. The student model was designed to be consistent with the original teacher model with fewer layers to conduct knowledge distillation. The function of the drug encoder is to convert SMILES sequences to molecular representations, and this is achieved by employing the ChemBERTa encoder. The interaction prediction header predicts interaction probabilities by concatenating drug and target representations.\n\nTo verify the correlation between residues with higher attention values in LA and drug-target binding sites, we obtained the real three-dimensional structures of the DTI interaction pairs in the DrugBank dataset from the PDB database for statistical analysis of binding sites.\n\nFirst, drug-target pairs lacking the true structure of the protein were excluded. Next, for each protein, only structures containing small molecules that interact with them in the PDB file were retained. The binding sites of the drugs were then calculated, defining a binding residue as any atom of that residue within a 4\u2009\u00c5 Euclidean distance of the drug molecule. Finally, to ensure accurate mapping of attention values to residues, the amino acid sequence indexes in the PDB were aligned with those in UniProt, which were used during training.\n\nIn the Light Attention network, for a given amino acid sequence of length L, the network will output an \\(L\\times {d}_{{in}}\\) dimensional attention vector, where \\({d}_{{in}}\\) denotes the dimension of the attention vector. We get the attention value for each amino acid by taking the average of this matrix in the\\(\\,{d}_{{in}}\\) dimension. A larger attention value means that the amino acid contributes more to DTI prediction.\n\nEviDTI is implemented in Python 3.8 and PyTorch 1.12.0, as well as functions in PYG 2.2.0, Scikit-learn 1.2.2, Numpy 1.24.3, Pandas 1.4.2, and RDKit 2020.09.1. The batch size was set to 32, and the Adam optimizer was used with a learning rate of 5e-4 and a weight decay of 1e-5. We allowed the model to run for a maximum of 500 epochs for all datasets, and if the model\u2019s accuracy on the validation set did not decrease within 30, training would stop. The parameters of all neural networks are listed in the Supplementary Table\u00a011 and Supplementary Fig.\u00a09.\n\nThere were 746 potential tyrosine kinase modulators collected from the Targetmol-Tyrosine kinase modulator library (L2200). To ensure the credibility of the experiments, we excluded kinase modulators with drug similarity greater than 0.7 to the DrugBank dataset and ultimately retained 52 potential modulators. Drug similarity was calculated based on the Tanimoto coefficient.\n\nThe FAK (Cat#V1971) and FLT3 (Cat#V4064) were purchased from Promega. Compounds Tyrphostin 9 (Cat#T2479), GSK180736A(Cat#3513), Vodobatinib (Cat# T8882), BIX02188 (Cat# T1744), PKG drug G1 (Cat# T4661), ST271 (Cat# T4511), Flumatinib mesylate (Cat# T7861), Mubritinib (Cat# T6124), PF-562271 (Cat# T6124) and Sorafenib (Cat# T0093L) were purchased from Targetmol, USA. All compounds were tested from 1000\u2009nM, with 3-fold dilution for 10 points.\n\nIn vitro kinase activity assays were conducted through ADP-Glo assays (#V9101) provided by Promega. The protocol for the FAK and Flt3 assay is described as follows: Enzyme, substrate, ATP, and compounds were diluted in 1X reaction buffer composed of 40\u2009mM Tris (pH 7.5), 2\u2009mM MnCl, 100\u2009\u00b5M sodium vanadate. In a 384-well low-volume plate, l \u03bcl of the compound at indicated doses or 5% dimethyl sulfoxide (DMSO), 2\u2009\u03bcl of FAK or FLT3 enzyme (15\u2009ng/well), and 2\u2009ul of substrate/ATP mix (final concentration: 20\u2009\u03bcM ATP) were added to each well. The plate was then incubated at 25\u2009\u00b0C for 60\u2009min to allow for kinase activity. Following the enzymatic reaction, 5\u2009\u03bcl of ADP-GloT Reagent was added to each well, and the plate was incubated at 25\u2009\u00b0C for an additional 40\u2009min. Subsequently, 10\u2009\u03bcl of Kinase Detection Reagent was added to convert ADP to ATP and introduce luciferase and luciferin to detect ATP, and the plate was incubated for a final 30\u2009min at 25\u2009\u00b0C. Luminescence was recorded with an integration time of 0.5\u2009s using the SpectraMax iD3 instrument (Molecular Devices).\n\nThe 50% effective concentration was calculated using Prism 8 by fitting the following equation:\n\nwhere X is a log of concentration, Y is a response, and top and bottom are the responses of controls, each assay was repeated at least three times, and we computed the mean and standard deviation for the values.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The source data of three datasets used to train and evaluate the model is provided at https://github.com/zhaoyanpeng208/EviDTI/tree/main/dataset and https://zenodo.org/records/14056305. The source data of 67 targets with 51 drugs used for literature validation of tyrosine kinase modulators is provided in https://github.com/zhaoyanpeng208/EviDTI/tree/main/dataset/len_v_s_dataset and https://zenodo.org/records/14056305. The source data of two Lenvatinib analogs (LYD-2-45 and LYD-2-49) with 11 tyrosine kinase targets used for patent validation of tyrosine kinase modulators is provided in https://github.com/zhaoyanpeng208/EviDTI/tree/main/dataset/len_case_dataset and https://zenodo.org/records/14056305. Source data are provided with this paper through Figshare https://doi.org/10.6084/m9.figshare.28816634.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The source data and codes of EviDTI are available on GitHub at https://github.com/zhaoyanpeng208/EviDTI, which has also been deposited in the Zenodo under accession code https://zenodo.org/records/1576047183. 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All individuals have consented to the acknowledgment. This work was supported by the National Key R&D Program of China (Grant No. 2023YFC2604400, 2024YFA1307700) and Natural Science Foundation of Shanghai (Grant No. 25ZR1402171). Funding was provided to S.H. (2023YFC2604400), X.B. (2024YFA1307700), and Y. Zhao (25ZR1402171).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Yanpeng Zhao, Yuting Xing, Yixin Zhang.\n\nAcademy of Military Medical Sciences, Beijing, China\n\nYanpeng Zhao,\u00a0Yixin Zhang,\u00a0Yifei Wang,\u00a0Mengxuan Wan,\u00a0Duoyun Yi,\u00a0Shangze Li,\u00a0Huiyan Xu,\u00a0Hongyang Zhang,\u00a0Ziyi Liu,\u00a0Guowei Zhou,\u00a0Mengfan Li,\u00a0Xuanze Wang,\u00a0Zhengshan Chen,\u00a0Ruijiang Li,\u00a0Lianlian Wu,\u00a0Dongsheng Zhao,\u00a0Song He\u00a0&\u00a0Xiaochen Bo\n\nSchool of Medicine, Shanghai University, Shanghai, China\n\nYanpeng Zhao,\u00a0Mengxuan Wan\u00a0&\u00a0Peng Zan\n\nDefense Innovation Institute, Academy of Military Science, Beijing, China\n\nYuting Xing\n\nShanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China\n\nDuoyun Yi,\u00a0Huiyan Xu,\u00a0Hongyang Zhang,\u00a0Ziyi Liu\u00a0&\u00a0Peng Zan\n\nCollege of Computer Science and Technology, National University of Defense Technology, Changsha, Hunan, China\n\nChengkun Wu\n\nLaboratory of Digitizing Software for Frontier Equipment, National University of Defense Technology, Changsha, Hunan, China\n\nChengkun Wu\n\nAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China\n\nGuowei Zhou\u00a0&\u00a0Lianlian Wu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY. Zhao, Y.X., C.W., S.H., D.Z., and X.B. conceived the study; Y. Zhao, Y.X., Y. Zhang, D.Y., M.W., H.X., and S.L. performed the experiments; Y. Zhao, H.Z., Z.L., G.Z., M.L, Z.C., and X.W. conducted the surveys; Y. Zhao, Y.X., L.W., and R.L. collated the data; Y. Zhao, Y.X., Y.W. and Y. Zhang performed the writing-primer preparation; S.H., D.Z., and X.B. performed the writing-reviewing and editing, and S.H., D.Z., P.Z., and X.B. supervised the study. All authors have read and agreed to the published version of the manuscript.\n\nCorrespondence to\n Peng Zan, Song He or Xiaochen Bo.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Brad Haubrich, Guishen Wang, and the other, anonymous, reviewer for their contribution to the peer review of this work. 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Evidential deep learning-based drug-target interaction prediction.\n Nat Commun 16, 6915 (2025). https://doi.org/10.1038/s41467-025-62235-6\n\nDownload citation\n\nReceived: 01 November 2024\n\nAccepted: 14 July 2025\n\nPublished: 26 July 2025\n\nVersion of record: 26 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-62235-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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HIV-1 fusion peptide during CD4-induced envelope opening", + "journal": "Nature Communications", + "published": "17 May 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59721-2/MediaObjects/41467_2025_59721_MOESM1_ESM.pdf" + }, + { + "label": "Reporting summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59721-2/MediaObjects/41467_2025_59721_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59721-2/MediaObjects/41467_2025_59721_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59721-2/MediaObjects/41467_2025_59721_MOESM4_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.rcsb.org", + "https://www.ebi.ac.uk/emdb/", + "https://doi.org/10.2210/pdb9D90/pdb", + "https://doi.org/10.2210/pdb9D8Y/pdb", + "https://doi.org/10.2210/pdb9D98/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-46655", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-46671", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-46672", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-46653", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-46670", + "https://doi.org/10.2210/pdb5ACO/pdb", + "https://doi.org/10.2210/pdb6CM3/pdb", + "https://doi.org/10.2210/pdb5I8H/pdb", + "https://doi.org/10.2210/pdb5VN3/pdb", + "/articles/s41467-025-59721-2#Sec18" + ], + "code": [], + "subject": [ + "Cryoelectron microscopy", + "HIV infections" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5090208/v1.pdf?c=1747566413000", + "research_square_link": "https://www.researchsquare.com//article/rs-5090208/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-59721-2.pdf", + "preprint_posted": "05 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The hydrophobic fusion peptide (FP), a critical component of the HIV-1 entry machinery, is located at the N terminal stretch of the envelope (Env) gp41 subunit1-3. The receptor-binding gp120 subunit of Env forms a heterodimer with gp41 and assembles into a trimer, in which FP is accessible for antibody binding3. Env conformational changes or \u201copening\u201d that follow receptor binding result in FP relocating to a newly formed interprotomer pocket at the gp41-gp120 interface where it is sterically inaccessible to antibody4. The mechanistic steps connecting the entry-related transition of antibody accessible-to-inaccessible FP configurations remain unresolved. Here, using SOSIP-stabilized Env ectodomains5, we visualized atomic-level details of a functional entry intermediate, where partially open Env was bound to receptor CD4, co-receptor mimetic antibody 17b, and FP-targeting antibody VRC34.01, demonstrating that FP remains antibody accessible despite substantial receptor-induced Env opening. We determined a series of structures delineating stepwise opening of Env from its closed state to a newly resolved intermediate and defining downstream re-organizations of the gp120-gp41 interface that ultimately resulted in FP burial in an antibody-inaccessible configuration. Our studies improve our understanding of HIV-1 entry and provide information on entry-related conformation reorganization of a key site of HIV vulnerability to neutralizing antibody.Biological sciences/Structural biology/Electron microscopy/Cryoelectron microscopyHealth sciences/Diseases/Infectious diseases/HIV infectionsHealth sciences/Medical research/Translational research", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "TableS1.xlsxTable S1FPpaperSupplementfigures14Sept2024.pdfSupplementary Materials", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The hydrophobic fusion peptide (FP), a critical component of the HIV-1 entry machinery, is located at the N terminus of the envelope (Env) gp41 subunit. The receptor-binding gp120 subunit of Env forms a heterodimer with gp41. The gp120/gp41 heterodimer assembles into a homotrimer, in which FP is accessible for antibody binding. Env conformational changes or \u201copening\u201d that follow receptor binding result in FP relocating to a newly formed interprotomer pocket at the gp41-gp120 interface where it is sterically inaccessible to antibodies. The mechanistic steps connecting the entry-related transition of antibody accessible-to-inaccessible FP configurations remain unresolved. Here, using SOSIP-stabilized Env ectodomains, we visualize that the FP remains accessible for antibody binding despite substantial receptor-induced Env opening. We delineate stepwise Env opening from its closed state to a functional CD4-bound symmetrically open Env in which we show that FP was accessible for antibody binding. We define downstream re-organizations that lead to the formation of a gp120/gp41 cavity into which the FP buries to become inaccessible for antibody binding. These findings improve our understanding of HIV-1 entry and delineate the entry-related conformational trajectory of a key site of HIV vulnerability to neutralizing antibody.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The HIV-1 envelope glycoprotein (Env), a homotrimer of gp120-gp41 heterodimers, mediates virus entry into host cells1,2,3. The gp120 subunit engages host receptors, while the gp41 subunit contains a fusion peptide (FP) that inserts into the host membrane to effect host and virus membrane fusion1,4,5,6. Prior to its binding to host receptors, the HIV-1 Env is characterized by a closed configuration with gp120 protomers packed against each other and the gp41 subunit, while the highly conserved and immunodominant coreceptor-binding region at the Env trimer apex remains occluded by packing of the first and second (V1V2) as well as the third (V3) variable loops (Fig.\u00a01A)2,3,7,8. At the trimer base, FP comprises a hydrophobic stretch of 15\u201320 amino acids at the gp41 N terminus5,9. FP is a site of vulnerability to broadly neutralizing antibodies (bnAbs) and thus is a focus of vaccine development efforts7,10,11.\n\nA Structure of pre-fusion, pre-receptor, closed HIV-1 Env (PDB: 5I8H) bound to broadly neutralizing, fusion peptide-directed antibody VRC34.01. The Env is shown in surface representation with the gp120 subunits colored light gray, and within the gp120 subunits, the V1V2 loop colored wheat, V3 loop olive and the residues contributing to the bridging sheet in the open Env colored red. The gp41 subunits are colored black with the fusion peptide (FP) colored cyan. The antibody VRC34.01 is shown in ribbon representation bound to its FP-centered epitope. B Structure of pre-fusion, CD4-bound open HIV-1 Env bound to CD4-induced antibody 17b. The Env is colored similarly as in panel A. CD4 is shown as a yellow ribbon and 17b Fab is shown as an orange ribbon. C Surface plasmon based binding (SPR) analysis monitoring FP burial. Env was incubated at 25\u2009\u00b0C with either sCD4 alone or with CD4 and the coreceptor mimicking antibody 17b. At different time-points after incubation, binding was measured to the fusion peptide targeting antibody VRC34.01. Some elements of the SPR schematic were created in BioRender. Acharya, P. (2025) https://BioRender.com/m74zcsz. D Simultaneous Env opening and fusion peptide burial were measured by incubating Env with CD4 and at different time-points injecting over a VRC34.01 IgG or a 17b IgG surface. Source data for panels (C, D) are provided as a\u00a0Source Data file. Data shown are representative of at least two independent experiments. E Cryo-EM reconstructions of three distinct populations of CD4/17b-bound, partially open Env bound to VRC34.01. Population 1 is bound to VRC34.01 at all three sites. The blue arrows indicate sites unoccupied by VRC34.01 in Populations 2 and 3.\n\nSource Data.\n\nHIV-1 Env uses its gp120 subunits to engage the CD4 receptor on the surface of human immune cells. CD4-induced conformational changes have been structurally characterized in virus-associated Env by cryo-electron tomography (cryo-ET)6,12,13, while high-resolution structural definition of receptor-induced Env opening has been obtained by single-particle cryo-EM analysis of stabilized, soluble Env ectodomains14,15,16. Both lines of evidence have synergized to facilitate our understanding of HIV-1 entry-related intermediate states and have enabled visualization of functionally relevant Env structural changes across resolution scales.\n\nCD4-induced Env conformational changes, collectively termed as \u201cEnv opening\u201d, include rigid-body displacement and rotation of the gp120 subunits resulting in up to ~40\u2009\u00c5 shift in the positioning of the V1V2 base (Fig.\u00a01B)17. Env opening is accompanied by internal rearrangements within gp120 that involve disruption of inter-protomer interactions formed by the gp120 V1V2 and V3 regions, release of the V3 loop, and formation and exposure of the bridging sheet. The V3 loop and bridging sheet are the structural elements that form the binding site for a GPCR coreceptor, either CCR5 or CXCR418,19,20. The structural signatures of CD4-induced Env opening include the bridging sheet and the \u03b10 helix in gp120 that were first defined in crystal structures of CD4-bound monomeric gp1204. CD4-induction of Env also re-organizes the gp41 subunit17,21 resulting in burial of FP within a gp41 cavity such that it is no longer accessible for antibody binding (Fig.\u00a01A, B).\n\nWhile high-resolution structural details have been elucidated for FP in an antibody-accessible conformation (the closed configuration of Env prior to receptor engagement)7 and in an antibody-inaccessible conformation after CD4 receptor-induced opening of Env17, the mechanistic details of this FP relocation remain unclear. Here, we use conformation-sensitive antibodies as molecular probes to simultaneously track the trajectories of Env opening and of FP accessibility. For FP accessibility, we used the prototype FP-directed antibody VRC34.017, isolated from a chronically HIV-1-infected individual, which binds at an epitope comprised primarily of the gp41 FP residues 512\u2013519 (contributing ~55% of total interactive surface area) and gp120 glycan N88 (~26% of the total interactive surface area). For Env opening, we used the CD4-induced (CD4i) antibody 17b to assess the formation and exposure of the bridging sheet upon CD4-triggering of Env. As the formation of the bridging sheet requires disruption of the V1V2 cap at the trimer apex and at least partial Env opening, binding to 17b was also an indicator of Env opening4,22.\n\nHere, using Env ectodomains stabilized by an intraprotomer gp120/gp41 disulfide and an Ile to Pro change in gp41 (SOSIP)8, we perform cryogenic electron microscopy (cryo-EM) to define intermediates where FP remains accessible to antibody binding despite substantial Env opening. Among these conformations are populations with their gp120 protomers either partially rotated from the pre-receptor closed Env conformation or more substantially rotated to resemble the geometry observed in the CD4-induced fully open conformation described previously17,23. The partially rotated gp120 is associated with antibody-accessible FP, whereas further gp120 displacement along an axis orthogonal to the central trimer axis resulted in FP burial, suggesting an association of FP burial with the extent of gp120 displacement. Taken together, our data provide evidence that accessibility of FP to antibody binding persists post-receptor engagement despite substantial Env opening. Furthermore, we define the mechanistic steps that lead to FP burial and antibody inaccessibility upon further Env opening. Our results resolve several gaps in our knowledge of\u00a0HIV-1 entry and provide information relevant to the development of vaccines and therapeutics.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59721-2/MediaObjects/41467_2025_59721_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "To assess CD4-induced changes in FP accessibility, we measured binding to the FP-targeted antibody VRC34.01 at different time points following incubation of BG505.SOSIP Env with either CD4 alone, or together with the fragment antigen binding (Fab) of the coreceptor-mimicking antibody 17b that recognizes an epitope presented upon CD4-induced Env opening (Fig.\u00a01C and Supplementary Fig.\u00a0S1). VRC34.01 binding decreased after CD4-induction, and the decrease was more profound in the presence of 17b Fab. A control experiment without the addition of CD4 or 17b showed no change in VRC34.01 binding to BG505.SOSIP Env. We next assessed simultaneous changes in FP exposure measured by binding to VRC34.01, and Env opening measured by binding to 17b (Fig.\u00a01D and Supplementary Fig.\u00a0S1). 17b binding increased post CD4 addition, indicating Env opening and exposure of the bridging sheet, while VRC34.01 binding decreased.\n\nTo visualize the impact of CD4-bound Env conformations on FP positioning, we incubated BG505 SOSIP Env with CD4 and 17b Fab at 25\u2009\u00b0C, and performed single particle cryo-EM on the Env complexes at selected time-points, 1.3\u2009h (hr), 20\u2009h, and 3 days, post CD4/17b addition, with VRC34.01 Fab added 30\u2009min before the samples were vitrified for cryo-EM analysis (Figs.\u00a01E, 2, and Supplementary figs. S2\u2013S7, Table\u00a0S1). We identified three particle populations across the three cryo-EM datasets that differed in their stoichiometries of bound VRC34.01 Fab (Fig.\u00a01E, Supplementary Fig. S7, and Supplementary Table\u00a0S2). Population 1 dominated at all three time points and consisted of a partially open Env in which each of the three gp120-gp41 protomers were bound to CD4, 17b Fab and VRC34.01 Fab. Another population, named Population 2, was detected at all three time points, albeit in smaller proportions relative to Population 1 (Supplementary Fig.\u00a0S7). In Population 2, each of the three gp120 subunits were bound to CD4 and 17b Fab, while only two protomers were bound to VRC34.01 Fab. The proportion of Population 2 relative to Population 1 increased with longer incubation times. At the 3-day time point, a third population, named Population 3, was detected that resembled Populations 1 and 2 in their bound CD4 and 17b stoichiometries but only had a single VRC34.01 Fab bound, leaving two protomers not bound to VRC34.01.\n\nA Three views of Population 1 structure shown in cartoon representation with gp41 colored black, gp120 gray, CD4 yellow, VRC34.01 blue, 17b orange. Glycans are shown as sticks. FP within the gp41 subunit is colored cyan. Within gp120, bridging sheet is colored red and \u03b10 helix green. B Population 1 coordinates including Env (gp120 in gray, gp41 in black) and CD4 (yellow) fitted into the in situ cryo-ET reconstruction of a partially open CD4-bound Env (EMD-29294). C (Left to right) Vectors describing position of gp120 relative to gp41. The gp120 structure (blue), gp120 V1/V2 region (green), and gp41 (orange) in the closed state overlayed with the centroid locations depicting the dihedral, angles, and distances describing the position of gp120 relative to gp41. Dihedral, angle, and distance values for closed, intermediate, and open state structures. Data shown as scatter dot plots with horizontal lines indicating the mean and standard deviation. List of structures used for the calculations is provided in Supplementary Table\u00a03. Source data are provided as a\u00a0Source Data file. D (Left) Population 1 protomer shown in surface representation zoomed-in at the location of the FP. FP is shown in cartoon representation. The gp120 subunit is colored light gray, gp41 black, FP cyan and FPPR pale green. (Middle) One protomer of the partially open Env bound to CD4, 17b Fab and 8ANC195 Fab (PDB ID: 6CM3) shown in surface representation zoomed-in at the location of the FP (shown in cartoon representation). The gp120 subunit is colored gray, gp41 black, FP dark teal and FPPR light pink. (Right) Overlay of a Population 1 protomer with a protomer of a partially open CD4,17b,8ANC195-bound Env (PDB ID: 6CM3). The gp120 subunits were used for the superposition. Inset zooms in on the FP and FPPR. Zoomed-in panel is slightly rotated compared to zoomed-out view for better visualization. The solid lines (pale green for Population 1 and light pink for 6CM3) show the distance between FPPR residues Gln 540 and gp120 residue Phe 223.\n\nSource Data.\n\nWhile the experiments described above were performed by incubating Env with the ligands at 25\u2009\u00b0C, similar trends were observed in SPR binding assays when the incubations were performed at 37\u2009\u00b0C (Supplementary Figs.\u00a0S1 and S9). A cryo-EM dataset obtained by incubating BG505 SOSIP with CD4 and 17b Fab at 37\u2009\u00b0C for 2\u2009h, followed by a 30\u2009min incubation with VRC34.01 Fab at 37\u2009\u00b0C prior to plunge freezing yielded structures representing Populations 1, 2 and 3. The appearance of the Population 3 structure earlier than could be detected in the 25\u2009\u00b0C cryo-EM datasets suggested that the CD4-induced Env conformational changes are more rapid at 37\u2009\u00b0C than at 25\u2009\u00b0C. The recurrence of these structures across independent experiments highlights the reproducibility of these structural states.\n\nIn summary, we identified three populations of CD4-induced Env in our cryo-EM datasets with differing stoichiometries of bound VRC34.01. These results confirmed that FP remained accessible to VRC34.01 binding despite substantial Env opening and suggested FP accessibility to antibody binding is hindered at the sites in Populations 2 and 3 that were not bound by VRC34.01.\n\nTwo distinct structural configurations of the FP have thus far been defined in the literature, one that is antibody accessible in the pre-fusion closed Env7 and a second that is sequestered within a gp41-gp120 pocket in a partially or fully open CD4-induced Env17,21. In this study, we have discovered new CD4-induced Env intermediates that are open enough to bind the bridging sheet-directed antibody 17b and yet retain the ability to bind a FP-targeting antibody. To understand Env-structural changes that enable CD4-induced opening, while the FP remains in an antibody-accessible configuration, we first examined Population 1, which was the dominant population in all the cryo-EM datasets (Figs.\u00a01E, 2, and Supplementary Fig.\u00a0S7, Tables\u00a0S1 and S2). We selected the Population 1 reconstruction from the 1.3-hr time-point for our analysis as it contained the largest number of particles and the highest resolution among the Population 1 structures from the three datasets.\n\nIn Population 1, the gp120 subunits exhibited known structural markers of the CD4-induced conformation2,4,17, including the bridging sheet at the 17b-binding interface and residues 63\u201373 assembled into the \u03b10 helix (Fig.\u00a02A and Table\u00a0S2). The gp41 subunit appeared conformationally less perturbed and was bound to VRC34.01 with a similar interaction interface dominated by the FP and the gp120 N88 glycan, as previously observed in the structure of VRC34.01 in complex with the closed BG505 SOSIP (Fig.\u00a0S10)7. Although no symmetry had been applied during the cryo-EM data processing, the three protomers were highly similar in the symmetrically open Population 1 intermediate (Fig.\u00a0S10). Our Population 1 structure revealed a similar gp120 opening geometry as the cryo-ET structure of membrane-associated HIV-1ADA.CM Env bound to three membrane-associated CD4 molecules (Fig.\u00a02B)6, suggesting that Population 1 represents a physiologically relevant entry intermediate.\n\nWe next studied the Population 1 structure using a previously defined set of vectors that report on structural rearrangements associated with rigid body movements in gp120 relative to gp41 (Fig.\u00a02C and Table\u00a0S3)24. These vectors describe the orientation of gp120 relative to the gp41 three-helix bundle, capturing rotation of gp120 away from the trimer central axis and rotation orthogonal to the trimer central axis. These measures effectively capture differences between closed, open, and intermediate state Envs. All three Population 1 protomers clustered together in all measures examined and were similar to previously published structures of BG505 (PDB: 6CM3) or B41 (PDB: 6EDU) SOSIP bound to CD4, 17b and 8ANC195 Fab21. The Population 1 structures were distinct from previously published open and open occluded state structures (PDBs: 5VN3 and 5VN8, respectively) in gp120 rotations described by a dihedral angle (\u03c6) that defines orthogonal rotation and angles (\u03b81 and \u03b82) describing rotations relative to the trimer central axis (Fig.\u00a02C). However, the distance between the gp120 core and W571 was similar between the open and intermediate structures. Contrasting each with the closed state structure clusters indicates the open and open occluded structures occupy distinct angles in the dihedral and the angle between the gp41 three-helix bundle and gp120 termini, while the Population 1 and CD4, 17b, 8ANC195-bound Envs differ in the angle describing gp120 rotation away from the central trimer axis. In summary, our vector analysis indicates that the partially open Population 1 intermediate described here shifts the gp120 domains away from the central axis, while the open and open-occluded structures shift the gp120 domains orthogonal to the trimer central axis.\n\nWe compared the configuration of the FP in the previously published partially open CD4, 17b, 8ANC195-bound structure (PDB: 6CM3) and the partially open CD4,17b, VRC34.01-bound Population 1 structure resolved in this study (PDB: 9D90) (Fig.\u00a02D). In the Population 1 structure (VRC34.01-bound), the FP was extended out of the Env core to bind the VRC34.01 antibody, whereas, in the 8ANC195-bound structures, the FP was buried in an intra-protomer gp120/gp41 hydrophobic pocket. The formation of the pocket for FP sequestration in the CD4, 17b, 8ANC195-bound structure was facilitated by a shift in the position of the FP proximal region (FPPR) primarily involving straightening of the FPPR helix creating space for FP burial. The distance between the C\u03b1 atoms of FPPR residue Gln540 and the gp120 residue Phe233 measured at 13\u2009\u00c5 for the Population 1 structure and at 19\u2009\u00c5 for the CD4,17b, 8ANC195 bound BG505 SOSIP structure (Fig.\u00a02D). A similar FP configuration was observed in the B41-complex with CD4, 17b and 8ANC195, suggesting that this is an isolate-independent conformational state (Supplementary Fig.\u00a0S11). The difference in FP accessibility while maintaining overall similar protomer geometry suggested that in this intermediate state the FP can either be antibody-accessible or it can be occluded. While VRC34.01 binds FP and stabilizes its accessible configuration, 8ANC195 stabilizes the FP occluded configuration.\n\nIn summary, we identified a CD4-triggered partially open Env intermediate on the HIV-1 entry pathway, with a protomer geometry that accommodates an antibody-accessible or a buried FP, with a conformational change in the FPPR being the major facilitator for this conformational switch of FP.\n\nIn addition to the near symmetric, partially open Population 1 state where VRC34.01 was bound at each of the three FP sites, we also identified populations that were bound to either one or two VRC34.01 Fabs, leaving two and one FP sites unbound, respectively, despite saturating amounts of VRC34.01 Fab being used for sample preparation (Figs.\u00a01E, and 3A, B and Supplementary Fig.\u00a0S7 and Tables\u00a0S1, S2). As expected, based on binding to antibody 17b, the bridging sheet and the \u03b10 helix were formed in all gp120 protomers in the Population 2 and Population 3 structures (Fig.\u00a03A, B, and Supplementary Table\u00a0S2). Examining the unbound sites revealed FP sequestered within a gp120/gp41 pocket in an antibody-inaccessible configuration (Fig.\u00a03C, D), thus providing a structural explanation for the lack of antibody binding to these FP sites.\n\nA Three views of the Population 2 structure shown in cartoon representation with gp41 colored black, gp120 light gray, CD4 yellow, VRC34.01 Fab blue, 17b Fab orange. Glycans are shown in stick representation. The FP within the gp41 subunit is colored cyan. Within gp120, the bridging sheet is colored red and the \u03b10 helix green. Blue circle-headed arrows indicate the gp41 positions that are not bound to VRC4.01 Fab. B View of Population 3 coordinates from the viral membrane shown in cartoon representation with gp41 colored black, gp120 light gray, CD4 yellow, VRC34.01 Fab blue, 17b Fab orange. Glycans are shown in stick representation. The FP within the gp41 subunit is colored cyan. C Population 2 structure zoomed-in view of gp41 subunit that was not bound to VRC34.01 showing the buried FP in cyan and the FPPR in light green. The EM map (EMD-46671) contoured at a level of 0.105 in ChimeraX is shown as a transparent surface with fitted coordinates shown in cartoon representation. D Population 3 structure zoomed-in view of its two gp41 subunits that were not bound to VRC34.01, showing the buried FP in cyan and the FPPR in light green. The EM map (EMD-46672) contoured at a level of 0.123 in ChimeraX is shown as a transparent surface with fitted coordinates shown in cartoon representation. E, F Extent of Env openness measured as the distance between residue 368 (blue spheres) and residue 124 (red spheres) in (E) previously published Env conformational states and (F). Env conformational states defined in this study.\n\nUnlike the near-symmetric Population 1 structure, Populations 2 and 3 displayed marked asymmetries. To quantify Env opening, we measured interprotomer distances between the CD4-binding site gp120-residue Asp368 and gp120-residue Pro124 at the Env trimer apex (Fig.\u00a03E, F). As previously recognized, the closed and open Env structures showed substantial differences in these distances23 (Fig.\u00a03E). In the closed Env trimer (PDB: 5ACO)25, the distance between the Asp368 residues and Pro124 residues measured 14.7\u2009\u00c5 and 56\u2009\u00c5, respectively. These distances were much larger in the CD4,17b-bound open Env trimer (PDB: 5VN3) at 76.7\u2009\u00c5 and 84.3\u2009\u00c5, respectively. By contrast, in the CD4,17b,8ANC195-bound partially open Env (PDB: 6CM3), these distances were intermediate between the open and closed, at 65\u2009\u00c5 and 79.4\u2009\u00c5, respectively. Since all three structures were reconstructed by imposing C3 symmetry during cryo-EM map refinement, each of these interprotomer distances was identical within each structure. These distances measured in the Population 1 structure were similar to the distances in the CD4,17b,8ANC195-bound partially open structure (PDB: 6CM3) (Fig.\u00a03F). Since no symmetry was applied during the reconstruction of the Population 1 map, three distances were noted for each measure: 63\u2009\u00c5, 66\u2009\u00c5, and 66.9\u2009\u00c5 for the interprotomer distances between residue Asp368, and 75\u2009\u00c5, 77.6\u2009\u00c5 and 78.2\u2009\u00c5 for the interprotomer distances between residue Pro124. In Population 2, the two protomers that were bound to VRC34.01 showed similar separation as observed in Population 1,\u00a061.4\u2009\u00c5 and 74.6\u2009\u00c5 between residues Asp368 and Pro124, respectively. The protomer that was not bound to VRC34.01 showed greater gp120 displacement with these distances approaching closer to those observed in the CD4,17b-bound open Env trimer (PDB: 5VN3). In Population 3, the two protomers that were not bound to VRC34.01 had buried FPs and showed gp120 geometries closer to the fully open Env conformation.\n\nTaken together, our results demonstrate that FP burial rendering it inaccessible to an FP-directed antibody requires further Env opening and transition of Env geometry past a symmetrically open intermediate that occurs earlier during CD4-induced Env opening. This CD4,17b,VRC34.01-bound BG505 SOSIP Population 1 (PDB: 9D90, this study) intermediate resembles the cryo-ET structure of the CD4-bound HIV-1ADA.CM Env6, and the single particle cryo-EM structures of CD4,17b,8ANC195-bound BG505 SOSIP (PDB: 6CM3)23 and CD4,17b,8ANC195-bound B41 SOSIP (PDB: 6EDU)23. In this early intermediate, FP can adopt a buried (antibody-inaccessible) or an antibody-accessible conformation. This intermediate is characterized by gp120 protomers opening like the petals of a tulip where the Env trimer apex separates and gp120 is displaced from the trimer central axis, as a rigid body hinging about the gp120 N/C termini at the trimer base. For stable sequestration of FP that renders it unavailable for antibody binding, further displacement of the gp120 protomers is needed, in the form of a lateral rotation in a plane roughly parallel to the viral membrane and about an axis orthogonal to the central trimer axis.\n\nTo elucidate the gp41 structural features involved in stable FP sequestration, we examined differences in the vicinity of the FP between the partially open Population 1 conformation (PDB: 9D90; this study) and the previously described fully open Env (PDB 5VN3)17. The fully open structure showed a greater displacement of the gp120 subunits visualized by the clear separation of signature \u03b10 helix from gp41, while in the Population 1 structure this region was helical but remained associated with the gp41 subunit (Fig.\u00a04A).\n\nA HIV-1 Env structures organized by extent of opening. Left to right: closed, VRC34.01-bound Env (PDB: 5I8H) with gp41 colored olive, FPPR orange and FP cyan; partially open, CD4,17b,VRC34.01-bound Env (PDB:9D90; this study) with gp41 colored black, FPPR light green and FP cyan; partially open, CD4,17b,8ANC195-bound Env (PDB:6CM3) with gp41 colored magenta, FPPR light pink and FP teal; partially open CD4,17b,VRC34.01-bound Env (EMD-46671; this study) with gp41 colored black, FPPR light green and FP cyan; fully open, CD4,17b-bound Env (PDB:5VN3) with gp41 colored blue, FPPR light blue and FP cyan. The gp120 subunit is colored gray. Inset: Zoomed-in view of region around \u03b10 helix (green). B 180\u00b0 rotated views A with brown squares around the FP (colored cyan except in partially open CD,17b,8ANC195-bound BG505 structure, where it is colored teal). C Zoomed-in views of region around FP. Red arrows indicate direction of CD4-induced Env opening from pre-CD4, closed Env to CD4-induced fully open Env. D Comparisons of gp41 organization between fully open Env with sequestered and inaccessible FP (PDB: 5VN3), and (left) P1 (transiently exposed FP), (middle) partially open Env with transiently buried FP (PDB: 6CM3), and (right) P2 (inaccessible FP). Red arrows (left and middle panels) indicate HR2 movement that allows the FPPR to re-orient creating space for FP burial. E\u2013G Locations in Env structure (PDB 4ZMJ fitted into EMDB-21412) where two fluorophores (Cy3 and Cy5 derivatives) are attached for smFRET imaging (E), three-dimensional presentation (F) and quantification (G) of conformational distribution-indicated FRET histograms observed from the gp120-gp41 perspective. The probability of each state (G), presented as mean \u00b1 s.e.m. (uncertainty), was derived from histograms (F), each compiled from Nm\u2009>\u2009200 traces (specifically, 241, 213, 236, and 242), as detailed in Fig.\u00a0S12F. The determining parameters are listed in Table\u00a0S4. Virus EnvBG505 samples three primary conformational states (PT Pre-triggered, PC Prefusion Closed, and CO: CD4-bound open). PT predominates in the ligand-free condition, while VRC34 shifts the conformational landscape differently from that of the CD4-bound opening. Source data are provided as a\u00a0Source Data file.\n\nSource Data.\n\nAt the FP site, the most striking difference was observed in the gp120/gp41 pocket where FP was buried in the fully open structure versus this region in the partially open intermediate (Fig.\u00a04B, C). In the CD4,17b-bound fully open structure (PDB; 5VN3), this pocket was much larger and measured at ~26\u2009\u00c5 between FPPR residue Gln540 and gp120 residue Phe233, with the buried FP adopting an extended loop conformation to fill the larger space of the pocket. By contrast, in the partially open intermediate, this distance measured 13\u2009\u00c5 in Population 1 (CD4,17b, VRC34.01-bound structure) where the FP was exposed and 19\u2009\u00c5 in the CD4,17b, 8ANC195-bound structure where the FP was buried and assumed a helical conformation. Progressive straightening of FPPR along with changes in both HR1 and HR2 regions of gp41 orchestrated the enlargement of this pocket, which in the fully open structure assumes an interprotomer character with one of its walls lined with the HR1 helix of the adjacent protomer. Thus, the concerted gp120/gp41 re-organizations that resulted in the formation of a larger FP-binding pocket may be responsible for the stable sequestration of the FP in the fully open structure.\n\nWe observed that the Population 2 gp41 with buried FP had a conformation similar to that of the fully open Env (PDB: 5VN3), with HR1 helices showing close overlap and the FPPR straightened out further compared to the partially open Population 1 and the 6CM3 structures, albeit not to the extent of the fully open structure (Fig.\u00a04D). In both Population 2 and fully open 5VN3 structures, the movement of the HR2 region around residues 638\u2013662 (indicated by red arrow in Fig.\u00a04D) creates room for the FPPR unbending. The gp120/gp41 pocket in this Population 2 protomer measured 21\u2009\u00c5, with the cavity size approaching that of the cavity measured in the fully open structure.\n\nIn summary, our data show that the FP configuration undergoes stepwise changes as a consequence of CD4-induced movements in gp120 and gp41. From a closed, pre-receptor state where FP is accessible to antibodies, Env proceeds to partially open states where FP remains available to the FP-directed antibody VRC34.01. Only upon more extensive rotation of gp120 and concerted changes in gp41 does FP become fully buried within a gp120/gp41 pocket and, as a result, no longer accessible to antibody binding.\n\nsmFRET analysis of Env on the surface of intact virions has revealed conformational shifts of virus Env from a pre-triggered (PT) state through a pre-receptor closed (PC) state to a fully open CD4-bound conformational state (CO) in response to CD4 activation26,27. The pre-fusion, pre-receptor closed Env on virions resembles the FP-accessible Env structure complexed with three VRC34.01 (PDB: 5I8H), while the fully open Env was associated with the symmetric Env structure bound with three CD4 and three 17b (PDB: 5VN3), and a pre-triggered state was suggested that is undefined in currently available structures26,27. We asked whether the structural differences between partially open VRC34.01-bound Env structures characterized in this study and the fully open FP-sequestered Env would be reflected at the global population level of Env conformations presented on virions. We performed smFRET experiments at room temperature (~25\u2009\u00b0C) of two different fluorescently click-labeled EnvBG505 on intact HIV-1Q23 virions28, in which donor/acceptor fluorescent probes were placed between gp120 V1 and V4 or between gp120 V4 and gp41 \u03b16, respectively (Supplementary Fig.\u00a0S12A). Placing FRET probes at different paired structural elements of Env allowed us to visualize global conformational changes of Env from two different structural perspectives, gp120 V1-V4 and gp120-gp41 (Fig.\u00a04E\u2013G, and Supplementary Fig. S12, and Table\u00a0S4). Using these two imaging systems, we observed distinct FRET histograms of virus Env and similar trends of histogram shifts across different experimental conditions: ligand-free, in the presence of ligands VRC34.01, VRC34.01\u2009+\u2009sCD4\u2009+\u200917b, and sCD4\u2009+\u200917b (Figs.\u00a04E\u2013G and\u00a0S12, Table\u00a0S4). The distinct FRET histograms observed from the gp120 V1-V4 (Supplementary Fig.\u00a0S12B, D) and gp120-gp41 (Supplementary Figs.\u00a0S12F and 4F) imaging systems are expected, as they capture Env dynamics from two different structural perspectives (Supplementary Figs.\u00a0S12C and 4E). Due to differences in viewing angles, the FRET efficiencies associated with each primary state vary between systems. Of note, the similarity in shift directions under ligand-free and ligand-present conditions (Figs.\u00a04E\u2013G and\u00a0S12) suggests that Env undergoes global conformational changes at the population level, independent of the observation angle. In this analysis, we applied the previously well-defined three-state (PT, PC, CO) Gaussian distributions28 to describe the FRET histograms, which reflect the overall conformational landscape of Env on virions. As expected, ligand-free Env exhibited predominance of the pre-triggered conformation, and Env, in the presence of sCD4 and 17b, prevailed in the fully open CD4-bound state. In the presence of VRC34.01, a decrease was observed in the PT population with an increase in the PC population, consistent with previously published results with the JR-FL Env7. For the VRC34.01\u2009+\u2009sCD4\u2009+\u200917b sample, the smFRET histograms suggested that the Env conformational distributions resided between the PC and CO conformations (Fig.\u00a04F, and Supplementary Figs. S12B, D, F). Quantifying and comparing the propensity of each primary conformational state occupied by virus Env under ligand-free and different ligand-bound conditions, we observed distinct conformational effect on Env by VRC34.01 in the presence of sCD4\u2009+\u200917b, positioned on the Env activation pathway between the effect of VRC34.01 alone and the sCD4\u2009+\u200917b CO state (Fig.\u00a04G and Supplementary Fig.\u00a0S12E). These results were consistent between the observations from the gp120-gp41 (Fig.\u00a04E\u2013G and Supplementary Fig. S12F) and the gp120 V1-V4 structural perspectives (Supplementary Figs.\u00a0S12B\u2013E). Thus, smFRET analysis of the impact of VRC34.01 on the CD4,17b-bound Env was consistent with VRC34.01 stabilizing an intermediate state on the path of CD4-induced Env opening.\n\nWe next sought to visualize CD4-induced Env conformational transitions that occur upstream to Population 1 by performing single particle cryo-EM analysis on a sample of BG505 SOSIP that was incubated with sCD4 for 2\u2009h, followed by the addition of VRC34.01 Fab 30\u2009min before sample vitrification. As antibody 17b works synergistically with CD4 to open Env (Fig.\u00a01C), we rationalized that excluding 17b may allow us to capture earlier stages of CD4-induced Env conformational changes. Two distinct particle populations were revealed in the cryo-EM dataset, which yielded reconstructions of 4.08\u2009\u00c5 (Population 4) and 4.14\u2009\u00c5 (Population 5) global resolutions. For both populations, all three protomers were bound to one each of CD4 and VRC34.01 Fab (Fig.\u00a05A, B and Supplementary Fig.\u00a0S13, and Table\u00a0S1, S3). The two populations differed in the extent of rotation of their gp120 subunits. In Population 4, one of the three gp120 protomers was rotated roughly to the extent observed in the Population 1 structure, while the other two protomers were minimally rotated from their pre-receptor conformation. The distances between the CD4 binding site residue Asp 368 in the two minimally rotated protomers measured 60.2\u2009\u00c5 and was thus closer to the distance observed in the closed, pre-receptor Env (~56\u2009\u00c5) (Fig.\u00a03E) than to the distances measured in the partially open Population 1 intermediate (~75\u201378.2\u2009\u00c5) (Fig.\u00a03F). The third protomer that had rotated was separated in this measure from the two other protomers by 61.7\u2009\u00c5 and 66.9\u2009\u00c5. The bridging sheet and the \u03b10 helix, which are the structural components associated with CD4-induced Env opening, were only seen in the rotated gp120 protomers. Although two protomers were minimally rotated, their V1V2 regions were unstructured, thus suggesting that CD4-induced disruption of the V1V2 cap of the closed, pre-receptor Env precedes the rotation of the protomers. This is consistent with time-resolved, temperature-jump small-angle x-ray scattering studies that suggest an order-to-disorder transition in the trimer apex precedes Env transitions involving protomer rotation29. In Population 5, two gp120 protomers were rotated and showed formation of the bridging sheet and \u03b10 helix, while the third protomer remained minimally rotated. Both Populations 4 and 5 were bound to VRC34.01 Fab at all the three FP sites, as expected, since the gp120 subunits had not rotated far enough to allow the gp41 conformational changes required for FP burial and steric inaccessibility to antibodies.\n\nA CD4,VRC34.01-bound BG505 SOSIP Env with rotation, bridging sheet (red) and \u03b10 helix (green) formation observed in a single gp120. B CD4,VRC34.01-bound BG505 SOSIP Env with rotation, bridging sheet (red) and \u03b10 helix (green) formation observed in two gp120 subunits. C A structure-guided mechanism for stepwise Env opening along the HIV-1 entry pathway. Top panel structures were determined previously, bottom panel structures were determined in this study. Stepwise transitions are marked with numbers within a circle on top of each structure starting from (1) the binding of a single CD4 to a closed Env trimer (PDB: 5U1F, 8FYI). This is followed by (2) opening of the Env trimer (EMD-29292) that allows additional CD4 molecules to bind. (5) A partially open Env conformation was described bound to CD4, a coreceptor (Co-R) mimicking antibody, and the gp120/gp41 interface targeting antibody 8ANC195 (PDB: 6CM3, 6EDU) where the FP was buried within a gp41 cavity. The CD4-induced opening of the HIV-1 Env culminates in the complete rotation of all the gp120 subunits that are accompanied by gp41 conformational changes and resulting in the burial of FP. This state is numbered (8) in this schematic. This study showed that the geometry of the functional entry intermediate (5) that was also visualized on membrane-associated Env (EMD-29294), was compatible with the FP being either buried or exposed, and thus, in this conformation the FP was accessible to antibodies. Further, this study filled in mechanistic gaps between (2) and (5) by showing stepwise gp120 rotations to reach this functional entry intermediate. Finally, this study visualized a stepwise mechanism for how the functional entry intermediate (5) may transition to the fully open Env (8), yet again by stepwise opening of the each gp120 subunit from its partially rotated to the fully rotated conformation, which was accompanied by burial of the FP in the corresponding protomer. Schematics of membranes and the host receptors were created in BioRender. Acharya, P. (2025) https://BioRender.com/7tpbllk.\n\nIn summary, Population 4 and 5 structures represent conformational states preceding the partially opened Population 1 conformation. Taken together, our results demonstrate sequential CD4-induced opening of the gp120 protomers and are consistent with previous studies that show initiation of CD4 binding to the closed Env begins with a single CD4, which induces opening of gp120 protomers needed for binding of additional CD4 molecules16,30.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59721-2/MediaObjects/41467_2025_59721_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59721-2/MediaObjects/41467_2025_59721_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59721-2/MediaObjects/41467_2025_59721_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59721-2/MediaObjects/41467_2025_59721_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "HIV-1 entry involves sequential receptor mediated conformational changes. Knowledge of how each part of the Env moves, in synergy with other parts, provides clues that have enabled the design of immunogens17,31. Despite years of intense research, critical gaps remain in our knowledge of the HIV-1 entry mechanism. In this study, we have addressed one such gap related to the fate of the FP during receptor mediated Env conformational changes. The HIV-1 FP is accessible and a target for bnAbs in the closed Env but becomes buried within a gp120/gp41 cavity in the receptor-bound fully open Env7,17. Here, we determine a series of structures that model a stepwise transition of the FP between these two accessible and buried steps. In Table\u00a0S2 we summarize the structural differences between these Env states. We add new knowledge to a previously described functional, receptor-bound, partially open Env intermediate6 by showing that the FP remains accessible to FP-directed antibodies (Population 1; Step 5 of Fig.\u00a05C). Elucidating the stepwise formation of this functional intermediate (Steps 1\u20134, Fig.\u00a05C), we describe two structures, Populations 4 and 5, that represent CD4-induced events earlier than Population 1, with the bridging sheet and \u03b10 helix formed in one, two or all three gp120s, in Populations 4, 5 and 1, respectively. We describe the conformational changes downstream of Population 1, where sequential loss of FP accessibility and reduction of VRC34.01 stoichiometry occurs in one protomer in Population 2 and in two protomers in Population 3, as the gp120 subunits in these protomers undergo further lateral rotation to lead to the fully open Env17 where all gp120s are fully rotated and FP in all protomers stably sequestered and unable to bind to VRC34.01 (Steps 6\u20138, Fig.\u00a05C). From its receptor-bound, fully open conformation, further conformational changes are needed for Env to release its fusion peptide for insertion into the host membrane. Future studies will reveal how other FP-targeting antibodies interact with the receptor-bound Env intermediates and whether they recapitulate the interactions made by VRC34.01. Additional studies will also be needed to address the generality of the observed intermediates across different HIV-1 isolates.\n\nThe FP site of vulnerability on HIV-1 is one of the few sites of Env vulnerability against which vaccination has succeeded in elicited antibodies of over 50% neutralization breadth32. Like VRC34.01, these antibodies recognize the accessible conformation of FP in the prefusion-closed state, with the revealed mechanistic details indicating antibody recognition of FP extends into the early entry intermediates identified in this study. Thus, in both the prefusion-closed state as well as early entry intermediates, FP appears to have considerable conformational flexibility, which is crucial for vaccine priming with flexible peptides linked to carriers10,33.\n\nFrom being solvent exposed and accessible to antibodies to becoming transiently, then stably sequestered in an antibody inaccessible conformation, to finally being released from this sequestered state to insert into the host membrane and mediate fusion, the HIV-1 FP follows a trajectory that is unique among Type 1 fusion proteins. The typical norm for other viruses is to hold their FP in a partially or wholly occluded, sometimes metastable conformation in the pre-receptor state, which receptor binding mediated conformational changes then release for insertion into the host membrane34. For SARS-CoV-2, for example, FP-directed antibodies have been described but these antibodies cannot access their FP epitopes in the pre-receptor conformation of the spike protein35,36,37,38. Receptor binding mediated conformational changes reveal these cryptic epitopes to allow antibody binding36,39,40. In summary, the HIV-1 FP tracks a unique trajectory among Type 1\u00a0fusion proteins with its multistep receptor-induced conformational transitions. Despite these differences, the central role of receptor-induced conformational changes in controlling and maneuvering the FP through its pre-receptor conformation to its fusion competent state is a common feature amongst all Type 1 fusion proteins.\n\nOne apparent conundrum this study elucidates is the genesis of the FP as a site of vulnerability in HIV-1. Why, in a virus that has evolved exemplary defenses to shield its vulnerabilities from the immune system, would the highly conserved FP be exposed and a focus for targeting by broadly neutralizing antibodies? Why would it not be hidden from the immune system within the pre-receptor, closed Env? In this study, we have shown that at least partial opening of the HIV-1 Env trimer is required for burial of FP within a gp41 cavity that forms because of this opening. While a partially open Env can accommodate FP burial, this study demonstrated that such burial is not stable, and only upon more substantial Env opening does FP become stably sequestered. Since opening of Env, even partial, exposes epitopes that make the virus more susceptible to neutralization, on balance, it may have been advantageous for maintaining the neutralization resistant compact and closed HIV-1 Env conformation to leave the FP exposed, and shield it with a few strategically placed glycans. The exposure of the FP, and thus the creation of the site of vulnerability to antibody, may be a mitigating step towards preventing greater vulnerability due to Env opening that may accompany burial of the flexible hydrophobic FP.\n\nAnother partially open Env conformation that has been described is the open-occluded conformation that is sampled in a receptor-independent manner by both the native Env as well as Env ectodomain constructs used for vaccine applications41. A recent study combining MD simulations and smFRET analysis has proposed the open-occluded conformation as neutralization-relevant intermediate of Env on the transition trajectory42. Structures of the open-occluded Env ectodomain show FP to be buried within a gp41 cavity41. Indeed, the hydrophobic solvent-exposed FP may be a source of Env metastability and its proclivity to shield itself, even if transiently, within a hydrophobic pocket may provide the underlying rationale for the accessibility of partially open Env conformations that allow such FP occlusion.\n\nIn summary, our study provides a stepwise mechanism for receptor-induced opening of HIV-1 Env and elucidates the trajectory of the fusion peptide from its solvent-exposed configuration in the pre-receptor, closed Env to its buried, antibody-inaccessible configuration in the receptor-bound, fully open Env. The use of SOSIP-stabilized Env ectodomain constructs has enabled high resolution structures of receptor bound Env intermediate states in this and previous studies16,17,21,23,41. While incorporation of stabilizing mutations may impact Env opening, interpreting these high-resolution structures together with structural and spectroscopic studies on virion associated Env demonstrates the physiological relevance of key Env intermediates identified using SOSIP-stabilized Envs6,26. Collective evidence in our study performed using the SOSIP-stabilized Env ectodomain from the BG505 isolate suggests that the structure of Population 1 represents a general intermediate on the HIV-1 entry pathway and that this intermediate is accessible for binding to broadly neutralizing antibodies such as VRC34.01 and 8ANC195. The evidence includes the recurrence of similar geometry in structures obtained from different isolates (BG505 or B41) and in complex with different gp120/gp41 targeting antibodies (8ANC195 and VRC34.01), and their concurrence with the cryo-ET resolved structure of the CD4-bound HIV-1ADA.CM Env in the membrane context. Our work thus reveals a functional entry intermediate (Population 1) with subunit geometry compatible with both a solvent exposed, antibody accessible FP, and an occluded, antibody inaccessible FP. By elucidating the accessibility of FP during receptor-induced Env conformational changes our study reveals key insights into this critical component of the HIV-1 entry machinery, which is a major target site for vaccine development.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "HIV-1 Env ectodomain constructs used in this study were purified form HEK293S GnT1- cells (Thermo Fisher Scientific) diluted at the time of transfection to 1.25\u2009\u00d7\u2009106 cells/mL. Before transfection, cells were diluted in FreestyleTM 293 Expression Medium (Cat No. 12338018) to 1.25\u2009\u00d7\u2009106 cells/mL at a volume of 950\u2009mL. Plasmid DNA expressing the Env ectodomain and furin were co-transfected at a 4:1 ratio (650\u2009\u03bcg and 150\u2009\u03bcg per transfection liter, respectively) and incubated with 293fectinTM transfection reagent (ThermoFisher Cat No. 12347019) in Opti-MEM I Reduced Serum Medium (ThermoFisher Cat No. 31985062). The diluted mixture was added to the cell culture which was incubated at 37\u2009\u00b0C, 9% CO2 on a shaker at 120\u2009rpm for 6 days. On day 6 the cell supernatant was harvested by centrifuging the cell culture at 4000\u2009x\u2009g for 30\u2009min. The supernatant was filtered with a 0.45 \u03bcm PES filter and concentrated to approximately 100\u2009mL using a Vivaflow\u00ae 200 cross-flow cassette (Sartorius Cat No. VF20P2).\n\nThe cell culture supernatant was passed through 10\u2009mL PGT145 IgG-conjugated affinity column equilibrated in 20\u2009mM PBS, pH 7.5. Following loading and washing, Env trimers were eluted using 3\u2009M MgCl2, pH 7.2. The eluted Env were concentrated to ~1\u2009mL with a Centricon-70 100\u2009kDa filter (Millipore Sigma). After concentrating, Env were filtered through 0.22 \u03bcM filter to remove any aggregates before loading on Superose 10/300 GL column (Cytiva) size exclusion column pre-equilibrated in PBS on an AKTA Pure (Cytiva) system. The fractions corresponding to the Env trimers were pooled, concentrated, flash frozen in liquid nitrogen frozen for long-term storage at \u221280\u2009\u00b0C.\n\nAntibodies were produced in Expi293 cells and purified using a Protein A affinity column followed by size exclusion chromatography using a HiLoad Superdex 200 column equilibrated in 20\u2009mM PBS, pH 7.5, 0.002% w/v Azide.\n\n4-domain CD4 was produced in Expi293 cells and purified by Q425-affinity chromatography, followed by size exclusion chromatography using a HiLoad Superdex 200 column equilibrated in 20\u2009mM PBS, pH 7.5, 0.002% w/v Azide.\n\nSurface Plasmon Resonance binding assays were performed on a T-200 Biacore system (GE-Healthcare) operating at either 25\u2009\u00b0C or 37\u2009\u00b0C. HBS-EP+ (10\u2009mM HEPES, pH 7.4, 150\u2009mM NaCl, 3\u2009mM EDTA and 0.05% surfactant P-20) was used as running buffer. A 40\u2009nM solution of BG505.SOSIP prepared in the running buffer was incubated at 25\u2009\u00b0C or at 37\u2009\u00b0C with either 200\u2009nM of CD4, or with 200\u2009nM CD4 and 200\u2009nM 17b Fab. All samples were pre-incubated at the indicated temperature before mixing. After mixing, the samples were rapidly transferred to the Biacore T-200 temperature-controlled sample chamber which was pre-warmed to 25\u2009\u00b0C or 37\u2009\u00b0C. The samples were kept within the temperature-controlled sample chamber for the duration of the experiment. The binding surface was prepared by flowing 100\u2009nM each of, 17b IgG and VRC34.01 IgG over each flow cells 2 and 4, respectively at 10\u2009\u00b5l/min flow rate for 30\u2009s with the 1st and 3rd flow cells serving as reference for 2nd and 4th flow cells, respectively. After surface preparation, the analyte (either BG505 SOSIP alone or BG505 SOSIP with CD4 or BG505 SOSIP with CD4 and 17b Fab) was flowed at 30\u2009\u00b5l/min flow rate for 60\u2009s. The same injections were carried out using HBS-EP+ buffer to obtain a reference curve. The sensorgrams were blank corrected in the Biacore T-200 evaluation software.\n\nPurified BG505 SOSIP.664 trimer sample stocks were diluted to a concentration of 1.3\u2009mg/mL and were incubated with five molar excess of 4D CD4 and 5-molar excess of 17b Fab. After mixing, the samples were incubated at 25\u2009\u00b0C or 37\u2009\u00b0C for different incubation times. VRC34.01 Fab in 5-fold molar excess concentration was added 30\u2009min before freezing grids. To prevent interaction of the trimer complexes with the air-water interface during vitrification, the samples were incubated in 0.085\u2009mM n-dodecyl \u03b2-D-maltoside (DDM). A 3.5-\u03bcL drop of protein was deposited on a Quantifoil-1.2/1.3 grid (Electron Microscopy Sciences, PA) that had been glow discharged for 10\u2009s using a PELCO easiGlow Cleaning System (Ted Pella). After a 30\u2009s incubation in >95% humidity in a chamber that was maintained at either 25\u2009\u00b0C or 37\u2009\u00b0C, excess protein was blotted away for 2.5\u2009s before being plunge frozen into liquid ethane using a Leica EM GP2 plunge freezer (Leica Microsystems). Frozen grids were imaged in a Titan Krios microscope (Thermo Fisher) equipped with a K3 detector (Gatan), using the Latitude software. The cryoSPARC (Punjani et al.) software was used for data processing43. Raw movies were motion corrected using Patch Motion Correction and Contrast Transfer Function (CTF) were estimated. Micrographs with CTF estimates greater than 8\u2009\u00c5 were discarded. Automated blob picker software was used to assign the particle position, and the particles were extracted with the 320-pixel extraction box size Fourier cropped to 80 pixels. Following particle extraction, multiple rounds of 2D classification was performed to remove junk particles and re-extraction of clean particles with 320-pixel box size. A reference free ab-initio 3D reconstruction was used to create 3D reconstructions representing diverse conformational states of the Env. Further, multiple rounds of heterogeneous refinement was performed to get rid of the noise. Finally, non-uniform refinement was used on the \u00a0clean particle set to obtain high resolution cryo-EM map.\n\nPhenix, Coot, Pymol, Chimera, ChimeraX and Isolde were used for model building and refinement44,45,46,47,48,49.\n\nThe methods of packaging and fluorescent labeling of replication-incompetent amber-free HIV-1Q23 viral particles with incorporated EnvBG505 have been described previously28. HIV-1 virions that lack reverse transcriptase (\u0394RT) were prepared and used for imaging. Amber-free HIV-1Q23 virions incorporated with two different double-tagged Env were used in this study, including dual-amber N136TAG S401TAG and hybrid click/peptide V4-A1 R542TAG. Amber-free V1V4 N136* S401* (*, unnatural amino acid - ncAA) viruses carrying click-chemistry-reactive ncAA at 136 in V1 and 401 in V4 were produced by co-transfecting HEK293T cells with a tag-free \u0394RT plasmid, an Env-tagged variant N136TAG S401TAG (TAG, amber stop codon) \u0394RT plasmid, and an amber suppressor plasmid tRNAPyl/NESPylRSAF. The amber suppressor can express tRNA and its cognate amino acid acyl-tRNA-synthetase in HEK293T cells. ncAA TCO* (250\u2009\u03bcM) was added to the transfection system. Similarly, V4A1 R542* viruses were prepared using the Env-tagged V4A1 (peptide A1 tag: DSLDMLEM in V4 loop) R542TAG \u0394RT plasmid. The ratio of tag-free vs. tagged Env plasmids used during transfection was adjusted based on previously characterized Env expression levels28 to ensure that, statistically, on average, one tagged protomer within an Env trimer on a virion was available for fluorescent labeling (enzymatically or click)26,27,28,50. 40\u2009h post-transfection, the supernatant was harvested and filtered, then viruses were concentrated at 113,000 \u2009\u00d7\u2009g for 2\u2009h using an ultracentrifuge. Next, the virus pellet was resuspended using the labeling buffer containing 50\u2009mM HEPES, 10\u2009mM MgCl2, and 10\u2009mM CaCl2. The fluorescent labeling of prepared virus Env was similar to the previously described26,27,28,50. For the amber-free V1V4 N136* S401* viruses, two TCO* were fluorescently labeled by 0.1\u2009\u00b5M tetrazine-conjugated LD555-TTZ and LD655-TTZ by click chemistry. For the amber-free V4A1 R542* viruses, the A1 peptide in V4 was labeled by LD655-CoA, 0.65\u2009\u00b5M in the presence of enzyme AcpS (5\u2009\u00b5M), and the TCO* in gp41 R542 were click labeled by LD555-TTZ. Dyes were customized by Lumidyne Technologies. The above reaction mixture was incubated at room temperature overnight in the dark. PEG2000-biotin was then added at a final concentration of 0.1\u2009mg/ml to the labeled viruses, followed by 30\u2009min incubation at room temperature. Then, the labeled viruses were further purified using a 6%\u201318% gradient of Opti-prep (Sigma-Aldrich) and centrifuged at 197,120\u2009\u00d7\u2009g for 1\u2009h at 4\u2009\u00b0C.\n\nAll single-molecule fluorescence resonance energy transfer (smFRET) data of fluorescently labeled viruses were collected using a custom-made prism-based total internal reflection fluorescence (prism-TIRF) microscope equipped with a fluorescence signal detection system. The detailed operating manual has been described previously28. Briefly, the sample loading module, a streptavidin-coated PEG passivated biotin quartz imaging chamber, was cleaned with the imaging buffer, and the background fluorescence signal was removed using the high-intensity laser. The imaging buffer contains 50\u2009mM Tris pH 7.4, 50\u2009mM NaCl, a cocktail of triplet-state quenchers, and oxygen scavenger: 2\u2009mM protocatechuic acid and 8\u2009nM protocatechuic-3,4-dioxygenase. The labeled viruses were then loaded into the sample loading module. Un-immobilized viruses were removed using the imaging buffer, and the fluorescence signals were collected. Under the ligand-present experimental conditions, fluorescently labeled viruses were incubated with the indicated 0.1\u2009mg/ml antibody/ligand (>5x above IC95) for 30\u2009min at room temperature before imaging. All fluorescence signals were recorded simultaneously on two synchronized sCMOS cameras (Hamamatsu ORCA-Flash4.0 V3) at 25\u2009Hz for 80\u2009s. The smFRET data were viewed, processed, and analyzed using the SPARTAN software package51 shared by the Scott Blanchard lab and custom MATLAB-based scripts.\n\nRecorded 80-second movies (2000 frames/movie) were extracted as donor/acceptor fluorescence traces (time series) with background subtracted and crosstalk corrected. The energy transfer efficiency (FRET efficiency values or simplified as FRET in figures) from the donor fluorophore to the acceptor fluorophore was calculated using FRET\u2009=\u2009IA/(\u03b3ID\u2009+\u2009IA), in which ID and IA represent the fluorescence of the donor and acceptor, respectively, and \u03b3 is the correlation coefficient compensating for variations in detection efficiencies. FRET traces (FRET efficiency traces) were further derived. FRET traces reflect real-time relative distance changes between donor and acceptor, resulting from the global conformational dynamics of Env. Under each experimental condition, more than 200 individual traces were included in the final FRET histogram. These included traces meet the following filter settings: 1) a single photo bleaching point (ruling out cases of multiple labeled protomers in a trimer, multiple labeled Envs on one virion, no-labeled Env on one virion; 2) sufficient signal-to-noise ratio; 3) anti-correlated feature between donor and acceptor fluorescence (indicating active Env undergoing conformational changes, ruling out inactive Env as well as Env lacking either donor or acceptor or both). We used automatic filters in combination with manual visualization to ensure that traces of molecules with only one Cy3/Cy5-labeled protomer in a trimer on a viral particle were included for further data processing. FRET traces that meet all the above-mentioned criteria were included to compile FRET histograms/distributions. FRET histograms (conformational distributions) were presented as mean \u00b1 s.e.m. and fitted into a sum of three distinct Gaussian/Normal distributions using the least-squares fitting algorithm in MATLAB. Parameters were determined based on visual inspection of all traces that exhibit state-to-state transitions and the idealization of individual traces using three-state hidden Markov modeling. Each Gaussian represented one conformational state of virus Env. The area under each Gaussian curve was further calculated and presented as mean \u00b1 s.e.m, providing an estimation of relative state occupancy, that is, the probability of the corresponding state Env occupies with associated uncertainty.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The cryo-EM maps and atomic models generated in this study have been deposited in the wwPDB and EMBD databases (https://www.rcsb.org, https://www.ebi.ac.uk/emdb/) under accession codes: PDB IDs, 9D90, 9D8Y, 9D98 and EMD IDs, EMD-46655 [https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-46655], EMD-46671, EMD-46672, EMD-46653, EMD-46670. 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This work was supported by NIH grants R01 AI145687 (P.A.), U54 AI170752 (P.A., R.H., and M.L.), R01 AI181600 from NIH/NIAID, an R35 GM151169 from NIH/NIGMS to M.L., and the Vaccine Research Center, an Intramural Division of NIAID, NIH.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Duke Human Vaccine Institute, Duke University, Durham, NC, USA\n\nBhishem Thakur,\u00a0Katarzyna Janowska,\u00a0Salam Sammour,\u00a0Rory Henderson\u00a0&\u00a0Priyamvada Acharya\n\nDepartment of Cellular and Molecular Biology, School of Medicine, University of Texas at Tyler Health Science Center, Tyler, Texas, USA\n\nRevansiddha H. Katte,\u00a0Wang Xu\u00a0&\u00a0Maolin Lu\n\nDepartment of Medicine, Duke University, Durham, NC, USA\n\nRory Henderson\n\nAaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, and Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA\n\nPeter D. Kwong\n\nVaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA\n\nPeter D. Kwong\n\nDepartment of Surgery, Duke University, Durham, NC, USA\n\nPriyamvada Acharya\n\nDepartment of Biochemistry, Duke University, Durham, NC, USA\n\nPriyamvada Acharya\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nP.A. conceived the project and oversaw the study. B.T. and P.A. designed binding studies and cryo-EM experiments. B.T. expressed and purified proteins, performed SPR assays, optimized specimen, prepared cryo-EM grids, collected cryo-EM data, performed map and coordinate refinement, and performed structural analysis. R.K., W.X., and M.L. performed smFRET analysis of virus Env, and K.J. assisted with cryo-EM data collection. S.S. assisted with protein purification. R.H. performed vector analysis. P.D.K. assisted with initial SPR experiments. B.T. and P.A. wrote the first draft of the manuscript. All authors reviewed and commented on the manuscript.\n\nCorrespondence to\n Priyamvada Acharya.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare the following competing interests: B.T. and P.A. have applied for patents on HIV-1 Envelope modifications related to this work. The other authors declare no competing interest.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Gabriel Ozorowski, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Conformational trajectory of the HIV-1 fusion peptide during CD4-induced envelope opening.\n Nat Commun 16, 4595 (2025). https://doi.org/10.1038/s41467-025-59721-2\n\nDownload citation\n\nReceived: 15 October 2024\n\nAccepted: 30 April 2025\n\nPublished: 17 May 2025\n\nVersion of record: 17 May 2025\n\nDOI: https://doi.org/10.1038/s41467-025-59721-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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electronic correlations in compositionally complex alloys", + "journal": "Nature Communications", + "published": "12 September 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52349-8/MediaObjects/41467_2024_52349_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52349-8/MediaObjects/41467_2024_52349_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52349-8/MediaObjects/41467_2024_52349_MOESM3_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-52349-8#Sec17" + ], + "code": [ + "https://www.ebert.cup.uni-muenchen.de/index.php/en/software-en" + ], + "subject": [ + "Electronic properties and materials", + "Electronic structure", + "Metals and alloys" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3981895/v1.pdf?c=1726225725000", + "research_square_link": "https://www.researchsquare.com//article/rs-3981895/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-52349-8.pdf", + "preprint_posted": "28 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Exploring the intricate interplay between disorder and correlations in compositionally complex alloys, our study employs resonant and valence band photoemission, optical conductivity, and electrical resistivity, complemented by density functional theory-based linear response calculations. By applying dynamical mean-field theory, we identify correlation signatures and damping in spectra, emphasizing the significance of many-body effects, especially in states far from the Fermi edge. Electronic transport remains dominated by chemical and magnetic disorder. Our results advance understanding of element specific electronic correlations in CrMnFeCoNi, elucidating the complex physical nature of compositionally complex alloys.Physical sciences/Materials science/Condensed-matter physics/Electronic properties and materialsPhysical sciences/Materials science/Structural materials/Metals and alloysPhysical sciences/Materials science/Theory and computation/Electronic structure", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "20240222Supplement.pdfSupplementary Info", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Owing to their exceptional mechanical, electronic, and phononic transport properties, compositionally complex alloys, including high-entropy alloys, represent an important class of materials. However, the interplay between chemical disorder and electronic correlations, and its influence on electronic structure-derived properties, remains largely unexplored. This is addressed for the archetypal CrMnFeCoNi alloy using resonant and valence band photoemission spectroscopy, electrical resistivity, and optical conductivity measurements, complemented by linear response calculations based on density functional theory. Utilizing dynamical mean-field theory, correlation signatures and damping in the spectra are identified, highlighting the significance of many-body effects, particularly in states distant from the Fermi edge. Electronic transport remains dominated by disorder and potentially short-range order, especially at low temperatures, while visible-spectrum optical conductivity and high-temperature transport are influenced by short quasiparticle lifetimes. These findings improve our understanding of element-specific electronic correlations in compositionally complex alloys and facilitate the development of advanced materials with tailored electronic properties.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Compositionally complex alloys (CCAs), which comprise the diverse range of medium- to high-entropy alloys (HEAs), are an exciting class of materials, consisting of randomly distributed multi-principal elements on crystalline lattices1,2. Aiming on HEAs, the denomination stems from the entropy term overruling the enthalpy of formation of individual phases when mixing a large number of elements, hence escaping phase separation. They exhibit exceptional mechanical attributes including elevated toughness, minimal plastic deformation, and enhanced tensile and yield strengths3,4, along with intriguing electronic as well phononic transport properties5,6,7,8. CCAs, including HEAs, bridge the structural gap between crystalline solids and amorphous materials, exhibiting long-range periodicity but with atom variations on lattice sites inducing site disorder akin to Anderson localization9. For years, the question of electron propagation in such an environment has persisted10, lacking the translational invariance ensuring the validity of the Bloch theorem for propagating electronic waves, yielding electronic localization. Empirical observations report electric resistivity (\u03c1) approaching the Ioffe-Regel limit with a subdued d\u03c1/dT dependence11, demonstrating that the effect of increasing residual resistivity in Cantor-Wu alloys may be linked to magnetic disorder effects12. Regarding the d\u03c1/dT dependence various explanations have been suggested, including Anderson localization13, or quantum interference in accordance with Mooij correlations14. For the latter, it becomes evident that, in addition to electron-phonon and spin scattering, many-body effects arising from the electron-electron interaction may play a significant role. However, to date, this issue has not been thoroughly investigated.\n\nIn this work, we probe the role of electronic correlation next to disorder effects in CrMnFeCoNi, likely the most studied HEA and prototype CCA, by employing resonant (ResPES) and valence-band (VB) photoemission spectroscopy (PES), optical conductivity and temperature dependent electrical resistivity measurements. All these measurements are supported and explained by electronic structure calculations based on density functional theory (DFT) and dynamical mean-field theory (DMFT). The results demonstrate that chemical and magnetic disorder in CCAs predominantly influence the electronic properties in the vicinity of the Fermi level. In contrast, electronic many-body effects gain significance when probing electronic structure-derived properties involving states distant from the Fermi edge. This is exemplified by the case of electrical conductivity at high temperatures and optical properties in the visible and UV spectral range.", + "section_image": [] + }, + { + "section_name": "Results and discussion", + "section_text": "In ResPES employing X-ray absorption at the L3-edge, photon energies proximate to the edge\u00a0excite photoelectrons in the direct photoemission channel (2p63dn + \u210f\u03c9 \u2192 2p63dn\u22121 + ef) and in the channel of the dipole transition of a core electron to an unoccupied state (2p63dn + \u210f\u03c9 \u2192 2p53dn+1). At the L-edges the second channel typically dominates (at the M-edges the two channels have comparable magnitudes and interfere, giving rise to the resonant Fano profile) and the subsequent decay of this intermediate state (2p53dn+1 \u2192 2p63dn\u22121 + ef), akin to an Auger process, gives rise to a strong resonant enhancement of the ResPES signal15,16. Upon progressive increase of photon energy above the edge, ResPES from the VB typically evolves through two regimes, depending on the core-hole and conduction-band lifetimes: (1) coherent ResPES, where the core electron excited into the conduction band is coupled with VB electron ejected to vacuum17,18. In this case the spectral peaks stay at constant binding energy (EB) and their shape reflects the element-specific partial density of states (pDOS) of the VB; (2) incoherent resonance Auger regime, where the conduction-band electron is decoupled from the two VB electrons, one filling the core hole and another ejected to vacuum19. In this case the spectral peaks stay at constant kinetic energy (Ek), and their shape is related to the self-convolution of the pDOS. To summarize, our ResPES measurements reveal site- or element-specific information on the electronic structure suitable for probing complex alloys where band-overlapping or hybridization is commonplace20.\n\nElement-specific L3 X-ray absorption spectroscopy (XAS) data are depicted next to ResPES photoelectron energy distribution curves (EDCs) in Fig.\u00a01 on the binding energy (EB) scale relative to the Fermi energy EF (calibrated by the Fermi level of gold).\n\nThe figure shows XAS L3-edge spectra (right side of each panel, in white, with intensity in arbitrary units) and corresponding ResPES energy distribution curves (EDCs) for Cr, Mn, Fe, Co, and Ni. The EDCs are plotted on a binding energy (EB) scale, with the photon energy as the vertical axis. The white lines on the ResPES plots indicate the EDC intensities at the XAS L3 maxima, marked by arrows. For Cr, the EDCs exhibit a pronounced peak at a constant EB, indicating the presence of valence band features. In contrast, for Fe, Co, and Ni, the EDCs display maxima at constant kinetic energy, suggesting significant Auger contributions and highlighting the transition from radiationless ResPES (constant EB below XAS maximum) to resonant Auger regimes (constant Ek XAS L3 maximum). The shift of the 6\u2009eV satellite in pure Ni towards higher EB\u2009=\u20097.2\u2009eV in the alloy, may be attributed to the d-band filling after alloying with more electropositive elements.\n\nFor Cr, and in contrast to the other elements, the EDCs exhibit a pronounced maximum at constant EB of 1.8\u2009eV for an extended range of incident photon energies. This indicates that the coherent ResPES contribution (constant EB) dominates over the resonant Auger one (constant Ek) in the whole shown photon-energy range close to the L3 XAS maximum20,21,22,23. For pure Cr, a VB peak in the EDCs of ResPES measurements was observed at 1.2 eV23. For Mn we find an EDC maximum at the L3 edge at 3.6\u2009eV, with a faint shoulder extending up to EB\u2009=\u20097.5\u2009eV, hinting to a small resonant Auger contribution at constant Ek above the resonance. For Fe, Co, and Ni, the main features of the EDCs are clearly attributable to the Auger process22,23,24,25 (see\u00a0Supplementary Information SI for a more detailed EDC plot on the specific elements). The EDC maxima for the XAS L3 edge are 4.7\u2009eV, 4.2\u2009eV, and 7.2\u2009eV EB, respectively. In no case a dominant VB contribution at constant EB is observed. Comparing these data with those of pure elements provides information on band filling, hybridization, electronic correlations, chemical disorder and crystal field effects. Ni, sharing the fcc crystal structure and comparable lattice constant with the CrMnFeCoNi HEA6, mainly allows for a focus on the effects induced by chemical disorder. The measured EDCs display a shift of the well-known 6\u2009eV satellite for pure Ni26,27 towards 7.2\u2009eV in the CrMnFeCoNi HEA. Shifts as large as 1.4\u2009eV towards higher EB have been observed for Ni based alloys and intermetallic compounds25,28,29, and explained by d-band filling through the hybridization of wave functions located on different lattice sites after alloying with more electropositive elements28,30,31.\n\nFigure 2a depicts the calculated d-band partial density of states (pDOS) of individual elements, for LDA (yellow solid line) and LDA\u2009+\u2009DMFT (blue solid line), next to the EDCs from Fig.\u00a01 (black solid line). For calculation details see \u201cMethods\u201d and SI. Since we are interested only in the peak position, all graphs are normalized to their maximum. Marked differences emerge between LDA\u2009+\u2009DMFT and pure LDA, including strong satellites (Mn, Co, and Ni) and a generalized band-narrowing (Ni and Co). Eventually, the spectral weight (excluding satellites) shifts to lower EB by including DMFT. For Cr and Fe, d-band satellites merge with the sp-pDOS (see detailed figures on band resolved pDOS in the SI). With increasing d-band filling, there is a progressive split-off of the formed Hubbard bands towards higher EB, although this effect is mitigated between Mn and Fe by the large difference in U. In Cr and Fe, the value of U is so small with respect to the bandwidth that no detached satellite peak can form; rather, it reflects a renormalization of the DOS, altering the d-band shape from rectangular to triangular. The positions of the satellites in the pDOS are compared to the ResPES data. For Mn, the small shoulder in the ResPES data at 7.5\u2009eV coincides with the satellite at 8\u2009eV in the pDOS. A comparable analysis for Fe and Co is not possible. For Ni, the pDOS data reveal a satellite at 8.2\u2009eV EB, elevated by 1\u2009eV compared to that determined experimentally via ResPES.\n\na Calculated partial density of states (pDOS) for Cr, Mn, Fe, Co, and Ni in the CrMnFeCoNi alloy using LDA and LDA\u2009+\u2009DMFT, shown as yellow and blue solid lines, respectively. Measured energy distribution curves (EDCs) at the XAS L3 absorption maximum as black solid lines. The self-convolutions of the pDOS (Cini-Sawatzky Theory, CST) are given by dashed lines in corresponding colors. For Fe and Ni, additional pDOS calculations using LDA\u2009+\u2009DMFT with modified Hubbard U values are shown as green lines. In the pDOS the LDA\u2009+\u2009DMFT approach introduces satellite features not present in the pure LDA results for all elements. For Cr, the EDCs align well with the pDOS, being valence band like, while Mn shows good overlap between the EDCs and CST maxima. In contrast, Fe, Co, and Ni exhibit increasing distance between EDCs and self-convolution peaks. Lager U values for Fe and Ni shift the satellites towards higher binding energies without significantly altering the overall pDOS. b Comparison of experimental valence band photoemission spectroscopy (PES) data (black line) with one-step model calculations for LDA (yellow dashed line) and LDA\u2009+\u2009DMFT (blue line). For LDA\u2009+\u2009DMFT a shoulder at 8\u2009eV (Ni marker), which is absent in the LDA results is found, and the LDA\u2009+\u2009DMFT spectrum is more smeared overall. The offset between PES and calculations at high binding energies is due to the experimental background not mimicked in the calculations. d Calculated Bloch spectral function (BSF) for CrMnFeCoNi using LDA (c) and LDA\u2009+\u2009DMFT (d). The color maps represent the spectral intensity. For LDA, the states near the Fermi energy are already smeared through disorder, whereas for LDA\u2009+\u2009DMFT, bands at higher binding energies also exhibit significant smearing due to reduced quasiparticle lifetimes, to the extent that subbands may not be resolved, as seen between the \u0393 and L points.\n\nWe apply the Cini-Sawatzky Theory (CST)32,33,34 by comparing the measured EDCs (assumed as Auger spectra) with the self-convoluted single-particle pDOS. Within the CST framework, Auger signals may be categorized into distinct regimes, according to the ratio between electronic bandwidth W and on-site Coulomb interaction U. For U >> W, the spectra correspond to the quasi-atomic limit with split-off satellites at high EB, whereas being band-like for U << W. For the latter Lander35 proposes that the Auger signal equals the self-convolution of the single-particle band. At U\u2009~\u2009W, a complex interplay occurs, resulting in the superposition of both states. According to the CST, for systems with nearly filled d-bands, a discernible shift of the Auger spectra towards lower EB is found, displacing the maximum of the self-convolution of the DOS by U relative to the Auger signals28,36,37. This is explained by the energy difference of the two-hole state and the two one-hole states being equal to the Coulomb interaction on the two-particle energy scale. The ResPES maxima (black solid lines) are aligned with the self-convolution which are depicted in Fig. 2a by dashed colored lines. It is evident, that for Cr, only the ResPES contribution is measured, as the EDCs spectral shape coincides with that of the sp-pDOS. For Mn and Fe, which are approximately situated in the band-like limit with U\u2009<\u2009W, the self-convolution and ResPES data on the two-electron scale show a congruence in their peaks. For Co, where the EDC is dominated by the resonant Auger spectral weight, a minor offset of approximately 1.6\u2009eV is identified with U, whose value is slightly smaller than our suggested U of 2.5\u2009eV (see \u201cMethods\u201d). For Ni, the corresponding offset amounts to 4\u2009eV, which is slightly larger than the value of 3\u2009eV used as U in the LDA\u2009+\u2009DMFT calculation. This trend is expected, considering the different filling of the 3d-band28,38,39,40, as well as the limitations of the DMFT solver41. In order to investigate the sensitivity of the CST we calculate the CrMnFeCoNi HEA with the initially given U values, but increasing them for Fe from 1.5\u2009eV to 2\u2009eV and Ni from 3\u2009eV to 4\u2009eV. The pDOS (solid line) as well as the\u00a0self-convoluted signal (dashed line) are given in Fig.\u00a02a as green lines. For Fe there is barely a difference in the pDOS visible with a slight increase of the satellite. No shift in the self-convolution signal is found. For Ni, the d-block shifts also barely recognizably towards the Fermi edge, but the correlation satellite splits off significantly towards higher EB of almost 10\u2009eV. Consequently, the main peak of self-convolution hardly changes. The difference between EDC and self-convolution is 4\u2009eV, which corresponds exactly to the Hubbard U from the LDA\u2009+\u2009DMFT calculations. The variation of the element-specific U value of Fe and Ni does not change the pDOS of the other elements (see the more detailed plot in the SI). However, since the extreme displacement of the split-off satellite is not observed experimentally, we keep the initial chosen pure element U values in our calculations.\n\nFigure\u00a02b presents VB PES measurements for \u0127\u03c9\u2009=\u20091200\u2009eV, alongside one-step model calculations42 for the LDA as well LDA\u2009+\u2009DMFT potentials (see SI for details). Experimentally, a satellite feature is visible, attributable to Ni at approximately 7\u2009eV, as corroborated by ResPES. The LDA\u2009+\u2009DMFT calculation reveals a peak at approximately 8\u2009eV, which perfectly overlaps with the Ni satellite in the calculated pDOS. The offset between theory and experiment is attributable to the perturbative nature of the DMFT solver, and aligns well with a 1\u2009eV offset observed in pure Ni43, which confirms our choice of U a posteriori. Despite the satellite, the shoulder spanning roughly from 3\u2009eV to 4\u2009eV, paralleling the experimental observations, is also reproduced in the LDA\u2009+\u2009DMFT calculation. The plateau ranging from 5\u2009eV EB to 8\u2009eV in the LDA\u2009+\u2009DMFT calculation resonates well with the experimental data, which extends from 4\u2009eV EB to 7\u2009eV. The LDA approach fails to adequately capture any of these correlation fingerprints. Despite the strong agreement between experimental and theoretical results, we still observe a discrepancy in the bandwidth, which is slightly narrower in LDA\u2009+\u2009DMFT. This difference can however have an extrinsic origin, as e.g., arise from subtle contributions from a minor surface oxide layer (see the oxygen 1\u2009s peak in the\u00a0wide scan XAS measurement provided in the SI). Also, the experimental background (intensity at lowest EB) accounts for a systematic deviation. Besides, we are able to conclude that the assumption of Hubbard U parameters for CrMnFeCoNi, aligned with the pure metals, is justified, and electronic correlations play a site-specific role, comparable to that of pure 3d transition metals.\n\nThe influence of the broadening of states due to chemical disorder and quasiparticle lifetimes can be discerned in the computed Bloch spectral function (BSF) depicted in Fig.\u00a02c, d for LDA and LDA\u2009+\u2009DMFT, respectively. Near EF, both calculations reveal a scarcely dispersive d-band block with strongly localized electrons, while the parabolic dispersion of the sp-bands becomes apparent at higher EB. The d-bands around 2\u2009eV are for the LDA\u2009+\u2009DMFT case so extensively smeared, especially along the symmetry line X-\u0393-L, that sub-bands cannot be resolved. The strongly localized satellite states are perceptible over a constant smeared background up to 9\u2009eV. Arguing qualitatively, the spectral width of the d-states implies that the lifetimes of these electrons must be exceedingly short. Interestingly, this arises mainly from correlation effects at high EB, but from the combined action of correlations and chemical/magnetic disorder in the vicinity of EF.\n\nQuasiparticle lifetimes \u03c4 can be obtained from the self-energy function \u03a3 from DMFT via \u210f/\u03c4\u2009=\u20092Im(\u03a3). Figure\u00a03a displays element-specific lifetimes for Fe and Ni, obtained by evaluating the Greens function on the real energy axis. Data for other elements are plotted in the SI. For context, black lines show pure element calculations in their natural crystal structures, alongside corresponding experimental data from literature43,44,45,46. These calculations align well, although the Ni lifetimes for excited states above 1\u2009eV slightly exceed experimental observations. For all elements, near EF, a Fermi liquid theory-like behavior emerges46, with \\(\\tau \\propto {\\left(E-{E}_{{{{\\rm{F}}}}}\\right)}^{-2}\\). To assess the effects of altered lattice constants on the element-specific self-energy, lifetimes for Fe and Ni within the fcc lattice but the HEA\u2019s lattice constant are illustrated in yellow in Fig.\u00a03a. While Ni shows a negligible variation, \u03b3-Fe exhibits notably reduced lifetimes, despite having the same Hubbard U. This is not surprising, considering that \u03b3-Fe is known to have stronger magnetic fluctuations leading to a complex magnetic landscape47. The bottom row of Fig.\u00a03a contrasts the pure elements in the fcc HEA structure against the CPA-derived disordered paramagnetic state. Here, both elements show an increased \u03c4, akin to those in their natural crystals. For the LDA\u2009+\u2009DMFT calculation of CrMnFeCoNi with U\u2009=\u20094\u2009eV for Ni and 2\u2009eV for Fe, the green lines in Fig.\u00a03a depict the results. For Ni, lifetimes above EF demonstrate a 3/4 reduction compared to the 3\u2009eV calculation, reflecting the ratio of U values. A comparable trend is given for Fe. As observed in the pDOS data, other elements are not affected by the variation of U (see SI). Our calculations indicate a minor influence of chemical disorder on DMFT derived lifetimes, particularly when contrasted with data for the pure elements and taking the effect of changing crystal structure into account. Furthermore, \u03c4 exhibit a nearly linear dependency to variations in U.\n\na \u03c4 from LDA\u2009+\u2009DMFT calculations for Fe and Ni. Top: Black lines show calculated \u03c4 for pure elements in their natural crystal structures; yellow lines show \u03c4 for pure metals within the fcc structure and CrMnFeCoNi lattice constant (\u03b3-Fe). Experimental values are from photoemission spectroscopy (PES, below EF)43 and time-resolved two-photon photoemission (TR-2PPE, above EF)44,45,46 of pure elements. Ni shows negligible variation, while \u03b3-Fe exhibits notably reduced lifetimes. Bottom: Blue lines correspond to the CrMnFeCoNi HEA results. Green lines show results with increased U for Fe and Ni (2\u2009eV and 4\u2009eV, respectively). Increasing U reduces \u03c4, particularly for Ni at E\u2009>\u2009EF. b Real (Re(\u03c3)) and imaginary (Im(\u03c3)) parts of the complex optical conductivity (\u03c3) versus photon energy (\u210f\u03c9). LDA calculations are shown as yellow lines, and LDA\u2009+\u2009DMFT calculations as blue lines. Experimental data from reflectometry (Exp. 1) and ellipsometry (Exp. 2) are given by black lines and marked by arrows. LDA\u2009+\u2009DMFT results show better agreement with experimental data, especially in the visible and UV ranges, for both Re(\u03c3) and Im(\u03c3).\n\nThe dynamic response of electrons in the CrMnFeCoNi HEA is further probed by means of complex optical conductivity \u03c3(\u03c9) measurements. Typically, \u03c3(\u03c9) reveals for metals a Drude peak in Re(\u03c3(\u03c9)), which broadens with increasing scattering rate and eventually may merge with existing higher-energy interband transitions. Thus separation into inter- and intra-band contributions becomes challenging for correlated 3d transition metals. We therefore compute the \u03c3(\u03c9) tensor via the Kubo formalism48 by the current-current correlation function49.\n\nDue to the fully relativistic formulation of the current density operator j(r), both paramagnetic and diamagnetic terms are implicitly included, with the latter yielding a Drude-like contribution49,50. However, as in our approach electron-phonon collision driven damping mechanisms are not considered a priori, a phenomenological complex photon energy \u0393ep is introduced overruling the infinite small lifetime 0+ from adiabatic switching on of the external perturbative field. We adopt a constant \u0393ep value of 0.340\u2009eV, attributed to the substantially diminished electronic mean free path, proximate to the lattice parameter, as well the given Fermi velocity for such disordered alloys51. Our calculations utilize both LDA and LDA\u2009+\u2009DMFT, with the latter including energy dependent electron-electron scattering \u0393ee through the imaginary part of the self-energy inherently incorporated in the Greens function. Although the DMFT scheme solely considers d-band states in \u03a352, an empirical choice of \u0393ee for the sp-band is not obligatory. It has been shown analytically53 as well numerically within the GW formalism54 that d-band screening dominates the total self-energy, and thus scattering rates, when considering open d-band metals.\n\nCalculation results are depicted in Fig.\u00a03b (LDA in yellow, LDA\u2009+\u2009DMFT in blue) alongside experimental data (see SI). The left subfigure displays the real part of the optical conductivity, Re(\u03c3(\u03c9)), corresponding to the absorptive component. The results from LDA and LDA\u2009+\u2009DMFT show for low \u210f\u03c9 a constant trend with a minor offset between each other, and dispersive variations in the VIS range where both curves decline. Quasiparticle lifetimes from the DMFT scheme improve the absorptive part of \u03c3(\u03c9) drastically, especially in the visible and UV range, where a perfect agreement with experiment is found. Here the pure LDA calculations underestimate the absorption. Also for the LDA\u2009+\u2009DMFT case, at photon energies of 5\u2009eV and above, the fine structure found in the LDA calculation is blurred (see small oscillation between 6\u2009eV and 8\u2009eV for real and imaginary part). This behavior reflects the smearing of sub-bands, induced through the quasi-particle lifetimes, as seen within the BSF in Fig.\u00a03c, d. For Im(\u03c3(\u03c9)), corresponding to the dispersive or reflective part, the situation is similar. The experiments show low values for small \u210f\u03c9 (approaching zero), comparable to the Drude-behavior, with a resonance peak in the VIS region. The LDA calculation overestimates this peak threefold and is non-symmetric on a log scale, thus not purely Drude-like. The resonance position in Im(\u03c3(\u03c9)) is found at \u210f\u03c9\u2009=\u20092.3\u2009eV experimentally, and 2.7\u2009eV in the LDA and at 2.4\u2009eV in the LDA\u2009+\u2009DMFT calculations. Besides improving spectral position, LDA\u2009+\u2009DMFT calculations reduce the resonance maximum significantly, achieving an improved experimental alignment.\n\nRevisiting temperature-dependent electrical resistivity, we focus on correlation effects. Figure\u00a04a depicts our four-point resistivity measurements for different metals, from Ni to CrMnFeCoNi HEA, over a broad temperature range (4\u2009K to 800\u2009K). A dominant increase in residual resistivity is observed when transitioning from FeNi to CrFeNi, which is attributed to the coupling of ferromagnetic and antiferromagnetic elements within the alloys12. This results in smearing across both spin channels in the BSF of CrFeNi, whereas FeNi exhibits solely smearing in the minority spin channel12. Thus, Ni and NiFe show well-defined quasiparticle transport properties within the majority channel with progressive increase in upward curvature up to Curie temperature at 625\u2009K and to 835\u2009K, respectively. For the alloys exhibiting higher residual resistivity, similar trends are observed: \u03c10 ranges around 100\u2009\u00b5\u03a9cm, and d\u03c1/dT remains low (and after subtraction of \u03c10, d\u03c1/dT seems to be identical).\n\na Electrical resistivity (\u03c1) measurements vs. temperature (T) for Ni, FeNi, CrFeNi, CrFeCoNi, and CrMnFeCoNi. The ternary to quinary alloys show a sharp increase in residual resistivity, while Ni and FeNi exhibit quasiparticle temperature dependency. The LDA\u2009+\u2009DMFT calculation for CrMnFeCoNi is shown by the yellow dashed line. (b) Comparison of experimental resistivity for CrMnFeCoNi (black line) with LDA (yellow dashed line) and LDA\u2009+\u2009DMFT (blue line) calculations, normalized by their residual resistivity. The LDA\u2009+\u2009DMFT results show better agreement with experimental data, particularly at higher temperatures, where states farther from the Fermi edge contribute to transport. (c and d) Fermi surface of CrMnFeCoNi calculated using LDA (c) and LDA\u2009+\u2009DMFT (d). Color maps represent spectral intensity. Both graphs show smeared Fermi surfaces, with chemical disorder significantly affecting states near EF. Many-body effects do not introduce additional smearing.\n\nWe compare the CrMnFeCoNi measurement with linear response calculation within the Kubo-Greenwood formalism55. The alloy analogy model55 is employed to mimic temperature-dependent lattice vibrations, inherently integrating electron-phonon scattering processes56. Corroborating the BSF calculations, both LDA and LDA\u2009+\u2009DMFT results yield comparable \u03c10 of 67 \u00b5\u03a9cm. By Fermi surface analysis the resistivity may be calculated from the k-space smearing (electron mean free path), as well the surface area8. Figure\u00a04c, d show the Fermi surfaces calculated with the LDA and LDA\u2009+\u2009DMFT potentials, which exhibit remarking similarities. This supports our linear response results. However, the substantial deviation from the experimental value of 105\u2009\u00b5\u03a9cm could be influenced by potential short-range ordering57,58,59, which has already been experimentally shown to increase resistivity60. Such mechanisms are not included within the CPA framework and need to be captured with more sophisticated methodologies like non-local CPA61,62. The localization mechanism must also be considered. While Hubbard localization is addressed within the LDA\u2009+\u2009DMFT framework, the onset of Anderson localization is inherently not captured by the CPA approach63,64. The temperature dependence of \u03c1(T) is presented in a normalized format \u03c1(T)/\u03c10 in Fig.\u00a04b and demonstrates that while LDA closely aligns with experimental data, even indicating a reduced d\u03c1/dT, LDA\u2009+\u2009DMFT yields perfect agreement across all temperatures. This aligns with the BSFs, where LDA\u2009+\u2009DMFT exhibits increased smearing at higher EB, thus impacting electronic transport at elevated temperatures. For a direct comparison of the LDA\u2009+\u2009DMFT calculation with the experiments, the results are presented also in Fig.\u00a04a as dashed yellow line. A comparison clearly shows that the temperature dependence is well-represented, despite the already discussed offset in the residual resistivity.\n\nOur findings identify CrMnFeCoNi as a material characterized by pronounced electronic correlations. These correlations exist in addition to the prevailing chemical and magnetic disorder, which smear the band structure in the vicinity of EF and thus primarily cause the high residual resistivity. The extent of many body effects by means of on-site Coulomb interaction within the Hubbard model mirrors those of the containing pure elements. Correlation effects gain in significance with increasing distance from the Fermi edge, which was demonstrated both experimentally and theoretically utilizing electronic spectroscopies and temperature dependent electronic transport. Especially in the calculation of the optical response, accounting for quasiparticle lifetimes dramatically improve the absorptive as well dispersive part of the optical conductivity in the VIS-UV range, as energy dependent electron-electron scattering may overrule electron-phonon scattering rates.\n\nOur findings broaden the understanding of electronic correlations in CrMnFeCoNi HEA, offering a robust framework for exploring the complex electronic structures of various CCAs. This deeper insight enables a more accurate prediction and optimization of electronic structure-derived properties, such as thermophysical, transport, and optical properties, which is vital for the future development of advanced materials for applications in medicine, industry, and science.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52349-8/MediaObjects/41467_2024_52349_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52349-8/MediaObjects/41467_2024_52349_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52349-8/MediaObjects/41467_2024_52349_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52349-8/MediaObjects/41467_2024_52349_Fig4_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "All alloys were first synthesized by mixing and pressing powders of elemental metals (total mass 1.5\u2009g) into pellets to achieve the targeted composition. The pellets were placed in an arc-melting chamber, with glowing elemental zirconium used to remove any residual oxygen. Arc melting was performed three times, and the alloys were subsequently annealed in an evacuated quartz ampoule for one month at 1030\u2009\u00b0C to enhance sample homogeneity. The specimens were cut into approximately 0.5\u2009mm thick disks for photoemission and optical conductivity measurements and into bars of dimensions 8\u2009\u00d7\u20093\u2009\u00d7\u20090.5\u2009mm\u00b3 for four-point electrical resistivity measurement. The series of prepared samples included Ni, NiFe, NiFeCr, NiFeCrCo, and NiFeCrCoMn. Characterization of the HEA samples using X-ray diffraction confirmed a well-defined fcc crystalline structure. Scanning electron microscopy and electron-dispersive X-ray spectroscopy analysis revealed the actual composition and satisfactory dispersion of constituent elements.\n\nXAS and ResPES measurements were conducted at the soft-X-ray ARPES endstation at the ADRESS beamline65,66 of the Swiss Light Source, Paul Scherrer Institute, Switzerland. The CrMnFeCoNi alloy\u00a0was kept at a base temperature of 20\u2009K. The photon energy was scanned along each of the relevant L3 absorption edges at steps of 100\u2009meV, while at the same time recording a valence band spectrum with a hemispherical electron analyzer at a resolution better than 100\u2009meV.\n\nFor the optical conductivity measurements, the CrMnFeCoNi sample was ground with a SiC paper and subsequently polished with diamond paste with decreasing grain sizes to achieve a root mean square surface roughness of 2\u2009nm. The spectra were obtained by ellipsometry (Sentech SE 850) in the visible to infrared range and by reflectance measurements with subsequent Kramers-Kronig transformation in the far infrared part of the elecromagnetic spectrum.\n\nDFT calculations of CrMnFeCoNi were performed within the fully relativistic spin polarized multiple scattering Korringa-Kohn-Rostoker (SPR-KKR) Greens function formalism67,68. Many-body correlation effects beyond local density approximation (LDA) were added via DMFT, as implemented in SPR-KKR52. Chemical disorder was accounted for by the coherent potential approximation (CPA)69,70 and the paramagnetic state above 20 K71 was mimicked by the disordered local moment scheme72. An appealing feature of our approach is that it allows to consider local quantum and disorder fluctuations on the same footing. This has also already been successfully realized by other groups73. For LDA\u2009+\u2009DMFT calculations, the Hubbard U for 3d electrons was set to the values found for pure elements, while the Hund exchange was J\u2009=\u20090.94\u2009eV for all elements. The U values for Cr, Mn, Fe, Co, Ni were equal to 2.0\u2009eV74, 3.0\u2009eV41, 1.5\u2009eV, 2.5\u2009eV and 3.0\u2009eV43, respectively. A variation in J was not considered significant, as it has only marginal effects on the electronic structure and related spectra within the choosen calculation scheme. For further details, see SI.\n\nWe applied a self-convolution to the calculated partial density of states and compared the results within the CST32,33,34 with the experimental data obtained fromResPES. This approach aims to replicate the two-hole interactions using single-particle ground states, necessitating the presentation of the resultant self-convolution on a two-particle energy scale37. Consequently, the new energy axis is multiplied by a factor of two. Prior to convolution, the pDOS data were interpolated on a refined energy grid.\n\nIn our study, valence band photoemission42 and electronic transport calculations, including the optical conductivity tensor, were conducted using the SPR-KKR code and a Kubo-framework for linear response, respectively. These calculations incorporated electron-phonon interactions within the alloy analogy model55, considering temperature-dependent atomic displacements and a comprehensive k-point mesh for accuracy. The optical response to external electromagnetic fields was detailed through a current-current correlation function reformulated in Green\u2019s function terms, capturing the Drude contribution within a fully relativistic framework49. Our findings primarily focus on the isotropic \u03c3(\u03c9) in paramagnetic CrMnFeCoNi HEA. For an in-depth explanation of methodologies, including the computational parameters and models employed, the reader is reffered to the SI.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data generated and presented in this study are available in the main article and the Supplementary information. Source data are provided as a Source Data file.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The SPRKKR multiple scattering package is freely available (no costs apply) under the specific user license and the package can be downloaded following registration at https://www.ebert.cup.uni-muenchen.de/index.php/en/software-en. All input files, post-processing procedures and scripts used to evaluate experimental and theoretical data are available from the authors on request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "George, E. P., Raabe, D. & Ritchie, R. O. High-entropy alloys. Nat. Rev. Mater. 4, 515\u2013534 (2019).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nMiracle, D. B. & Senkov, O. N. A critical review of high entropy alloys and related concepts. Acta Mater. 122, 448\u2013511 (2017).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nGludovatz, B. et al. A fracture-resistant high-entropy alloy for cryogenic applications. Science 345, 1153\u20131158 (2014).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nLi, Z., Zhao, S., Ritchie, R. O. & Meyers, M. A. Mechanical properties of high-entropy alloys with emphasis on face-centered cubic alloys. Prog. Mater. 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This research is also part of the project No. 2022/45/P/ST3/04247 co-funded by the National Science Center and the European Union\u2019s Horizon 2020 reseach and innovation program under the Marie Skodowksa-Curie grant agreement no 945339, awarded to I.D.M. For the purpose of Open Access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Trpimir Iv\u0161i\u0107\n\nPresent address: Department of Physical Chemistry, Ru\u0111er Bo\u0161kovi\u0107 Institute, Zagreb, Croatia\n\nNew Technologies Research Center, University of West Bohemia, Plzen, Czech Republic\n\nDavid Redka,\u00a0Saleem Ayaz Khan,\u00a0Heinz P. Huber\u00a0&\u00a0J\u00e1n Min\u00e1r\n\nDepartment of Applied Sciences and Mechatronics, Munich University of Applied Sciences HM, Munich, Germany\n\nDavid Redka\u00a0&\u00a0Heinz P. Huber\n\nInstitute of Physics, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, Lausanne, Switzerland\n\nEdoardo Martino,\u00a0Xavier Mettan,\u00a0Luka Ciric,\u00a0Davor Tolj,\u00a0Trpimir Iv\u0161i\u0107,\u00a0J. Hugo Dil\u00a0&\u00a0L\u00e1szl\u00f3 Forr\u00f3\n\nDepartment of Chemistry, Ludwig-Maximilians-University Munich, Munich, Germany\n\nAndreas Held\u00a0&\u00a0Hubert Ebert\n\nPhoton Science Division, Paul Scherrer Institut, Villigen, Switzerland\n\nMarco Caputo,\u00a0Eduardo Bonini Guedes,\u00a0Vladimir N. Strocov\u00a0&\u00a0J. Hugo Dil\n\nInstitute of Physics, Nicolaus Copernicus University, Toru\u0144, Poland\n\nIgor Di Marco\n\nDepartment of Physics and Astronomy, Uppsala University, Uppsala, Sweden\n\nIgor Di Marco\n\nStavropoulos Center for Complex Quantum Matter, Department of Physics and Astronomy, University of Notre Dame, Notre Dame, IN, USA\n\nL\u00e1szl\u00f3 Forr\u00f3\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.M., L.F., and H.D. developed the concept. E.M., X.M., L.C., D.T., and T.I. prepared the samples and performed transport measurements. M.C., E.B.G., V.S., and H.D. were responsible for the photoemission experiments at the Swiss Light Source. D.R. and J.M. conducted the CST analysis. D.R. and S.K. carried out the DFT calculations. I.D.M. interpreted the DMFT results, and H.H. discussed the optics. H.E., J.M., and A.H. developed the KKR package. D.R. wrote the manuscript in close collaboration with J.M., L.F., and H.D. All authors contributed to discussions and commented on the manuscript.\n\nCorrespondence to\n Heinz P. Huber or J\u00e1n Min\u00e1r.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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"Modality-projection universal model for comprehensive full-body medical imaging segmentation", + "pre_title": "Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation", + "journal": "Nature Communications", + "published": "24 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64469-w/MediaObjects/41467_2025_64469_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64469-w/MediaObjects/41467_2025_64469_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64469-w/MediaObjects/41467_2025_64469_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64469-w/MediaObjects/41467_2025_64469_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-64469-w#MOESM1", + "/articles/s41467-025-64469-w#Sec33" + ], + "code": [ + "https://github.com/YixinChen-AI/MPUM", + "https://doi.org/10.5281/zenodo.16730886", + "/articles/s41467-025-64469-w#MOESM1" + ], + "subject": [ + "Diagnostic markers", + "Translational research" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5614211/v1.pdf?c=1761589097000", + "research_square_link": "https://www.researchsquare.com//article/rs-5614211/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-64469-w.pdf", + "preprint_posted": "14 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The integration of deep learning in medical imaging has shown great promise for enhancing diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to the inherent variability in data characteristics. This study aims to introduce and evaluate a Modality Projection Universal Model (MPUM). MPUM employs a novel modality-projection strategy, which allows the model to dynamically adjust its parameters to optimize performance across different imaging modalities. The MPUM demonstrated superior accuracy in identifying anatomical structures, enabling precise quantification for improved clinical decision-making. It also identifies metabolic associations within the brain-body axis, advancing research on brain-body physiological correlations. Furthermore, MPUM's unique controller-based convolution layer enables visualization of saliency maps across all network layers, significantly enhancing the model\u2019s interpretability.Health sciences/Medical research/Translational researchHealth sciences/Biomarkers/Diagnostic markersuniversal modelsegmentationmulti-modalityintracranial hemorrhageepilepsy", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The integration of deep learning in medical imaging has significantly advanced diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to inherent inter-modality variability. Here we present the Modality Projection Universal Model (MPUM), trained on 861 subjects, which dynamically adapts to diverse imaging modalities through a modality-projection strategy. MPUM achieves state-of-the-art, whole-body organ segmentation, providing rapid localization for computer-aided diagnosis and precise anatomical quantification to support clinical decision-making. A controller-based convolutional layer further enables saliency map visualization, enhancing model interpretability for clinical use. Beyond segmentation, MPUM reveals metabolic correlations along the brain-body axis and between distinct brain regions, providing insights into systemic and physiological interactions from a whole-body perspective. Here we show that this universal framework accelerates diagnosis, facilitates large-scale imaging analysis, and bridges anatomical and metabolic information, enabling discovery of cross-organ disease mechanisms and advancing integrative brain-body research.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Universal models, characterized by their ability to generalize across diverse tasks without fine-tuning, have emerged as a powerful framework in many fields. By leveraging shared representations1, these models offer unparalleled adaptability across a wide range of applications. In medical imaging, universal models have gained attention for their potential to generalize across various anatomical regions, imaging modalities, and clinical tasks2. However, significant challenges arise due to intrinsic modality differences and the complexity of anatomical structures involved. In response, several large-scale datasets, such as TotalSegmentator3,4 and Dense Anatomical Prediction (DAP)5, various universal challenges like BodyMaps246 and the Universal Lesion Segmentation7, as well as related models like MedSAM2, CDUM8, PCNet9, TotalSegmentator3, STUNet10, and LUCIDA11 have collectively advanced the development of universal medical imaging models. These models support efficient multi-task processing by enabling accurate organ identification, clinical diagnostics, report generation, and treatment monitoring, while also reducing the time and effort required for manual annotation. This brings substantial benefits to both clinical practice and research.\n\nSimilar to universal models, foundation models represent an alternative approach to developing generalizable AI in medical imaging. Foundation models are typically pre-trained using self-supervised learning on large volumes of unannotated or weakly annotated data12,13,14,15, whereas universal models are trained directly on annotated datasets spanning multiple tasks. Despite these differences, both approaches aim to generate high-dimensional representations that can generalize across various tasks. However, foundation models face inherent limitations, particularly in multi-category segmentation tasks, where the binary nature of contrastive learning is less effective. Additionally, foundation models typically require task-specific fine-tuning, which compromises their practicality as truly versatile tools in medical applications. In contrast, universal models demand large annotated datasets, which is a major bottleneck since high-quality labels in medical imaging are labor-intensive16,17. Despite these limitations, exploring universal models is crucial because they offer the potential for truly versatile tools in medical applications without the need for task-specific fine-tuning8,18,19, capable of handling multiple tasks simultaneously and streamlining clinical workflows.\n\nWith the efforts of the communities, annotated medical multi-task datasets have become increasingly mature. For example, the TotalSeg dataset3 includes segmentation annotations for 104 types of CT anatomical structures from 1204 unique subjects. The DAP dataset5 covers 133 types of\u00a0anatomical structures with 533 CT scans. The CDUM dataset8 combines multiple single-task CT datasets to build a multi-task model capable of recognizing 25 organs and 6 types of tumors. The TotalSegMRI dataset4 covers 59 anatomical structures from 298 MR scans. Compared to single-task segmentation tasks, multi-task training not only scales dataset size but also leverages task synergy. This is evident in how shared boundaries among adjacent tissues lead to improved6,9,10,20.\n\nCurrently, research on cross-modal foundation/universal segmentation has followed four paradigms: prompt-driven models, structure-adaptive models, native 3D models, and few-shot/zero-shot models. Prompt-driven models like MedSAM2, adapt Segment-Anything with point- or box-based cues and can tackle more than 140 tasks, but their dependence on user interaction and diminished accuracy on irregular organs limit full automation. Structure-adaptive networks (e.g., SPADNet21 and UniSeg22) mix expert kernels at inference, yet their encoders receive modality information only after feature extraction and the publicly released models recognize just seven anatomies. Few- or zero-shot approaches typified by UniverSeg18 generalize to unseen organs through a 2D support set, although whole-body scans would require thousands of slices and the accuracy still lags behind fully supervised baselines. Native 3D foundations like TotalSegmentator3 and VISTA3D23 operate directly on voxels and cover more than 100 classes, but each is trained on a single modality and therefore inherits modality-specific bias. CDUM8 integrates text embeddings into segmentation models to capture anatomical relationships. STUNet10 is a scalable and transferable UNet model series, with sizes ranging from 14 million to 1.4 billion parameters. SAT19 is designed to segment a wide array of medical images using text prompts. PCNet9 utilizes prior category knowledge to guide the universal segmentation model in capturing inter-category relationships. Among the existing research on universal medical models, only SAT is trained as a multi-task model on multi-modality data, whereas others are focused on single-modality data. Although multi-modality datasets are larger and potentially more powerful, they introduce challenges like conflicting feature distributions and increased training instability.\n\nTo address these limitations, we propose a versatile medical segmentation model based on a multi-modality projection mechanism. This mechanism allows for the extraction of modality-specific features from a shared high-dimensional space, enabling generalization across different imaging modalities without fine-tuning. Each organ has a high-dimensional latent feature, which can be projected in different directions to representations. This multi-modality projection allows for a unified understanding across different imaging techniques. Our modality projection universal model (MPUM) was trained using data from 861 unique subjects. Figure\u00a01 illustrates our key methodological innovations and experimental designs. Our proposed MPUM is designed with two key characteristics: precise brain segmentation and comprehensive whole-body segmentation. To demonstrate the clinical impact of MPUM, we focus on three case studies: identification (Case 1), diagnosis (Case 2), and analysis (Case 3).\n\nCase 1 (Fig.\u00a01c): Technical validation of segmentation performance, comparing MPUM to other advanced universal models.\n\nCase 2 (Fig.\u00a01d): MPUM\u2019s role as a computer-aided diagnosis tool for intracranial hemorrhage (ICH) localization in CT scans.\n\nCase 3 (Fig.\u00a01e): MPUM as a comprehensive analysis tool. Both epilepsy and Alzheimer\u2019s Disease (AD), which are marked by structural-metabolic inter-brain correlations, benefit from whole-body PET/CT analysis.\n\na Training process of the MPUM leveraging data from three distinct modalities. b Comparison of two common multi-modality data training strategies with our proposed modality-projection strategy. c Application of the MPUM as an aided identification tool across three modalities (over 200 categories). d The MPUM is utilized as a computer-aided diagnosis (CAD) tool for precise localization of intracranial hemorrhage with CT scans. e Application of the MPUM as an aided analysis tool in identifying altered metabolic correlations in regions affected by epilepsy and AD. f, Additional experimental results, including t-SNE feature visualizations and saliency map analysis.\n\nFor ICH, our model addresses a critical challenge in the emergency room, where rapid diagnosis is vital, but radiologists may experience delays. MPUM enhances diagnostic efficiency by accurately identifying hemorrhages in CT scans, enabling quicker decision-making and timely intervention. Additionally, our team\u2019s long-standing focus on the brain-body axis24, which regulates multiple physiological systems. MPUM supports comprehensive metabolic analysis in neurological conditions. By revealing brain-brain and brain-body metabolic associations, MPUM has the potential to uncover systemic biomarkers and deepen understanding of disease, such as epilepsy and Alzheimer\u2019s Disease (AD). Overall, the MPUM has transformative potential for clinical workflows, reducing manual annotation and improving diagnostic accuracy.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64469-w/MediaObjects/41467_2025_64469_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "We developed a deep learning-based multi-modality universal segmentation model. In the identification tasks, the model demonstrates superior performance in anatomical structure identification, outperforming existing segmentation models in terms of Dice and surface Dice metrics. In aided diagnosis, it accurately detected and quantified intracranial hemorrhages in CT scans, significantly improving diagnostic accuracy among general practitioners in a clinical setting. For aided analysis, the model facilitated the identification of significant metabolic alterations in epilepsy and AD, revealing both brain-brain and brain-body associations across the body. Furthermore, this section includes interpretable insights through visualization of feature operators and saliency maps.\n\nWe compared MPUM\u00a0with state-of-the-art universal segmentation models, including CDUM8, PCNet9, and STUNet10, as well as the UNet25 with parameters adjusted to match the complexity of the other models. PCNet9, MPUM, and STUNet10 each have approximately 60M trainable parameters, whereas the classic 3D UNet contains around 19M. We scaled the classic 3D UNet by increasing the number of feature channels at each stage by a factor of 1.75 (e.g., 32\u00a0\u2192\u00a056,\u00a064\u00a0\u2192\u00a0112,\u00a0\u2026,\u00a0512\u00a0\u2192\u00a0896). This brought its parameter count to approximately 59M. By normalizing model capacity, we ensure that the performance differences genuinely reflect innovations in architecture and design, rather than differences in model complexity. In addition, we evaluated three training strategies for multi-modality data: modality-mixed, modality-specific, and our proposed modality-projection strategy. To make a fair and comprehensive comparison, we applied a consistent training protocol across all models. Specifically, we adopted standardized preprocessing (e.g., resampling to 2\u2009mm isotropic resolution), patch-based training with 128\u2009\u00d7\u2009128\u2009\u00d7\u2009128 input size, and augmentation strategies such as random Gaussian smoothing and contrast adjustment. All networks were optimized with the Adam optimizer (initial learning rate 3e\u00a0\u2212\u00a04, weight decay 3e\u00a0\u2212\u00a05), with the learning rate reduced by a factor of 10 whenever the validation loss plateaued. We used a batch size of 4 and trained for up to 200 epochs, employing early stopping based on validation performance to avoid overfitting. The training objective was a combination of categorical cross-entropy and soft Dice loss. Each network\u2019s weights were initialized with the He-normal method and trained from scratch.\n\nAs shown in Fig.\u00a02a, for MRI body segmentation, our projection strategy outperformed all other approaches, achieving the highest Dice score of 0.7751 and the highest surface Dice score of 0.5471. In comparison, the best-performing mixed strategy model, STUNet, achieved a Dice of 0.7560 and a surface Dice of 0.5100, while the best modality-specific strategy, STUNet, reached a Dice of 0.7627 and a surface Dice of 0.5211.\n\na Dice score and surface Dice metrics for CT\u00a0(left), MR\u00a0(middle), and PET\u00a0(right) segmentation across UNet, CDUM, PCNet, and STUNet using three strategies: modality-mixed, modality-specific, and modality-projection (used by MPUM). b The impact of multi-modality training on brain and body segmentation\u00a0tasks, reported in terms of Dice performance. Source data are provided as a Source Data file.\n\nSimilarly, in the CT body segmentation, the projection strategy again demonstrated superior performance, with a Dice score of 0.8517 and a surface Dice score of 0.8506. The closest competitor, the modality-specific STUNet, achieved a Dice of 0.8462 and a surface Dice of 0.8395, whereas the mixed strategy STUNet model recorded a Dice of 0.8394 and a surface Dice of 0.8288.\n\nIn the case of CT brain segmentation, the projection strategy continued to yield the highest scores, with a Dice of 0.7419 and a surface Dice of 0.6872. The highest scores among the other strategies were from the modality-specific PCNet, which achieved a Dice of 0.6540 and a surface Dice of 0.5982, and the mixed strategy STUNet, with a Dice of 0.6318 and a surface Dice of 0.5417.\n\nIn Fig.\u00a02, we compare three multi-modality training strategies: (1) modality-mixed, where a single model is trained on all modalities with shared parameters; (2) modality-specific, where separate models are trained per modality; and (3) our proposed modality-projection strategy, where shared latent representation is dynamically projected into modality-specific convolutional kernels via a controller module. While the modality-mixed approach benefits from data scale, it suffers from conflicting optimization gradients across modalities. The modality-specific strategy avoids such conflict but misses cross-modality synergy. In contrast, our projection method resolves this tension by learning a shared latent representation and dynamically projecting it into modality-specific convolutional kernels using a controller module. As shown in Fig.\u00a02a, this approach achieves the highest Dice and surfaceDice scores across all benchmarks (e.g., MRI body: 0.7751 vs. 0.7560/0.7627; CT brain: 0.7419 vs. 0.6318/0.6540), confirming its robustness and effectiveness.\n\nWe further benchmarked MPUM against two universal segmentation frameworks (TotalSegmentator and VISTA3D) on a single organ analysis in Supplementary Table\u00a02. MPUM attains the highest Dice in the majority of the categories, with statistically significant gains (p\u00a0<\u00a00.05) in over 60 organs. It matches or slightly outperforms existing models on high-contrast targets like the liver and spleen, while showing notable advantages in challenging structures such as vessels (e.g., inferior vena cava: 0.903 vs. 0.893/0.872), musculoskeletal regions (e.g., gluteus maximus: 0.951 vs. 0.939/0.920), and small abdominal organs (e.g., colon: 0.904 vs. 0.861/0.834). For interactive segmentation frameworks like MedSAM2, manual box prompts segment some structures accurately (e.g., left\u00a0kidney, right\u00a0rib\u00a010), but its segmentation of the liver and the left autochthon was less precise than the outputs of TotalSegmentator, VISTA3D, and MPUM (see Supplementary Fig.\u00a06). For the spleen, its irregular shape forced the box prompt to include adjacent rib, causing MedSAM to mis-segment the rib and fail to isolate the spleen. For these reasons, we excluded SAM-based models from our automated benchmark. The details are shown in Supplementary Table\u00a02.\n\nIntracranial hemorrhage (ICH) is a life-threatening emergency where quick diagnosis and treatment decisions are critical for patient survival26,27,28. ICH has a high early mortality, with up to 40% of patients dying within a year of the event29,30, and a significant proportion of survivors suffering from lasting functional impairments31. Quick and accurate identification of ICH is essential for timely medical intervention, which can significantly improve patient outcomes32,33,34.\n\nRecent studies highlight that precise ICH localization significantly influences management and outcomes. Brainstem hemorrhages carry the worst prognosis, as they are significantly associated with higher mortality35. Furthermore, patients with brainstem hemorrhages experience significantly worse health-related quality of life, particularly in the pain domain, compared to those with cerebellar hemorrhages35. Intraventricular extension with resulting hydrocephalus markedly worsens outcome: a meta-analysis reveals that patients with ICH, IVH, and hydrocephalus have substantially higher 30- and 90-day mortality rates compared to those with ICH alone36. Likewise, lobar (cortical) ICH is associated with higher early mortality than deep hemorrhages (26.7% vs. 16.5% in one cohort)37. Pooled analyses indicate early surgical evacuation of lobar ICH does not significantly improve functional outcome38. In contrast, basal ganglia or thalamic hemorrhages can benefit from surgery in selected cases: a large cohort study demonstrated that patients with good preoperative motor function who underwent hematoma evacuation had favorable neurological outcomes at 3 months39. Together, these findings highlight the significant prognostic and therapeutic implications of distinguishing between lobar, deep, cerebellar, brainstem, and ventricular ICH.\n\nThe goal of Case 2 is to enable the MPUM as a clinical aid for fast and accurate ICH diagnosis. The experimental setup for Case 2 consists of two parts: firstly, we tested 100 cases on the Instance2022 dataset40,41 and conducted statistical analyses on the volume of brain regions affected by ICH. Secondly, we validated the effectiveness of the MPUM with the validation of three senior radiologists and three junior radiologists on the in-house ICH dataset from emergency department.\n\nOur MPUM framework was fine-tuned on the Instance2022 dataset to identify hemorrhagic areas from CT head scans. As depicted in Fig.\u00a03a, the original pre-trained MPUM provides brain regions maps from CT scans, while the fine-tuned MPUM yields segmentation map for hemorrhages. By integrating these two predictions, we obtained precise aided diagnosis results. For instance, in the Fig.\u00a03a, the model autonomously detected a hemorrhage involving 3868 mm3 in the left insula and 2241 mm3 in the left putamen. Precise quantification of ICH through CT scans plays a critical role in clinical decision-making and treatment planning33. Accurate volume measurements are essential for determining the extent of hemorrhage, monitoring its progression, and evaluating the risk of further complications like hematoma expansion, which is closely associated with worse outcomes32. We conducted a detailed analysis of hemorrhage volumes across various brain regions (see Supplementary Fig.\u00a01). Some regions, such as the insula, show relatively higher average volumes, which aligns with clinical observations where certain brain areas are more prone to larger hemorrhages due to their vascular structures and the prevalence of small vessel disease32,42. This detailed quantification has proven crucial for developing more targeted therapies and enhancing diagnostic accuracy43.\n\na MPUM assists in hemorrhage detection and brain region mapping from CT head scans, facilitating accurate diagnosis. The red-shaded region indicates an intracranial hemorrhage. b MPUM enhances diagnostic performance and supports less-experienced radiologists in real-world clinical settings. Source data are provided as a Source Data file.\n\nAdditionally, we collected 28 ICH CT scans, along with diagnostic reports, from the emergency department of an external medical center. The reports contain precise diagnostic results assisted by MRI imaging, which is regarded as ground truth. For the diagnostic accuracy test, three experienced radiologists with more than five years of experience and three general radiologists analyzed these cases, assessing the brain regions affected by ICH. Each radiologist should determine one or multiple hemorrhagic regions from the following categories: frontal lobe hemorrhage, temporal lobe hemorrhage, parietal lobe hemorrhage, occipital lobe hemorrhage, basal ganglia hemorrhage, cerebellar and brainstem hemorrhage, subarachnoid hemorrhage, subdural hemorrhage, and ventricular hemorrhage. We adopted a rigorous experimental design:\n\nCT only. Both junior and senior radiologists reviewed raw CT volumes without any computer assistance. These results serve as baselines, with junior performance as the reference for measuring improvement and senior performance as the expert benchmark.\n\nCT + lesion mask. Junior radiologists were provided with lesion masks generated by Viola44, the top-performing model from the 2022 ICH Segmentation Challenge. Comparing this setting with the CT-only baseline quantifies the benefit of automated hemorrhage detection.\n\nCT + lesion mask + MPUM brain atlas. Junior radiologists received both the lesion mask and the MPUM-derived brain region map. This setting evaluates whether providing anatomical localization in addition to lesion detection further improves diagnostic accuracy by assisting spatial reasoning and regional interpretation.\n\nAs summarised in Fig.\u00a03b, without any assistance, senior radiologists achieved accuracies of 100%, 92.9%, and 85.7%, whereas junior radiologists reached only 78.6%, 75.0%, and 60.7%. Most junior radiologists\u2019 errors stemmed from incomplete identification of all hemorrhage regions and confusion between adjacent brain areas. Supplying an automated lesion mask raised junior performance (e.g., Junior-1 from 78.6% to 89.3%, with similar gains for Junior-2 and Junior-3). When the MPUM brain-region atlas was also provided, their accuracies climbed further to 96.4%, 85.7%, and 89.3%, underscoring the utility of precise regional localization and the diagnostic value of the MPUM framework.\n\nThe automated lesion mask directs radiologists to voxels with high hemorrhage probability, shortening search time and minimizing the risk of missing small or low-contrast foci, thereby reducing false-negatives. The MPUM atlas provides a detailed anatomical map that clearly delineates boundaries, such as the interface between the temporal lobe and the lateral ventricle. Junior doctors often struggle to identify these boundaries accurately. By linking each highlighted lesion voxel to a specific neuroanatomical region, the atlas reduces region-of-interest confusion (e.g. mistaking parietal hemorrhage for ventricular bleeding), thereby boosting localization accuracy and specificity. In short, the lesion mask addresses the question \u201cWhere is the bleed?\u201d, while the atlas identifies its exact neuro-anatomical location. Their combination replicates the workflow of expert radiologists and explains the stepwise performance gains observed in the study.\n\nWe assessed the efficacy of our universal model as a comprehensive analysis tool across neurological disorders, examining how epilepsy and AD affect metabolic associations. PET/CT enables simultaneous, non-invasive metabolic assessment of multiple organs, facilitating the study of systemic and inter-organ interactions45,46. This capability is crucial for elucidating complex diseases through metabolic connectivity studies that span both brain networks and whole-body physiology.\n\nOur universal model facilitates rapid identification of regions of interest (ROIs) in human tissue structures, significantly reducing the manpower costs. We analyzed whole-body PET/CT data from a control group (n\u2009=\u200922) and a group of patients without active epilepsy episodes (n\u2009=\u200950). Utilizing our pre-trained universal model, we identified 215 ROIs in the CT scans (details in Supplementary Table\u00a03). From 215 ROIs, we excluded 12 ROIs due to high background radioactivity, including the bladder, inferior vena cava, aorta, and pulmonary artery, leaving 203 effective ROIs. We defined metabolic associations between two different regions as one pair, such as the left kidney and liver. In total, we analyzed 20,503 pairs for metabolic associations, which comprised 3403 brain-brain pairs, 7140 body-body pairs, and 9960 brain-body pairs. Given that epilepsy is a brain-triggered disorder, our analysis focused on brain-related pairs, excluding the 7140 body-body pairs.\n\nWe examined whether the metabolic associations in each pair were significantly altered due to epilepsy. Among the 3,403 brain-brain pairs, 228 pairs showed significant changes in correlation due to epilepsy (p\u2009<\u20090.001). Notably, 108 pairs involved the \u2018right anterior temporal lobe lateral part\u2019, and 68 pairs involved the \u2018right middle and inferior temporal gyrus\u2019 (see Supplementary Fig.\u00a02a, b). These findings suggest a strong link between the metabolic activity in these brain areas and epilepsy since epilepsy significantly alters the correlation. Figure\u00a04a illustrates the metabolic associations of the \u2018right Anterior temporal lobe lateral part\u2019 with other brain regions. For the control group, there are high correlations between the \u2018right anterior temporal lobe lateral part\u2019 and other brain regions, whereas the patient group showed weak metabolic connections. Figure\u00a04a also shows a similar phenomenon for the \u2018right middle and inferior temporal gyrus\u2019. Previous studies indicate that both regions are common sites for epileptic foci47,48,49.\n\nWe analyzed the metabolic associations between the epilepsy patient group (n\u2009=\u200950) and the control group (n\u2009=\u200922), using Fisher Z-Transformation to calculate the significance of differences in Pearson correlation coefficients. a Schematic representation of the connectivity among brain regions associated with the right anterior temporal lobe lateral part and the right middle and inferior temporal gyrus. The left diagrams illustrate the strong metabolic connection within the control group. Notably, these correlations are statistically significantly reduced in the patient group (p\u2009<\u20090.001). b Altered metabolic connectivity between the Pallidum and Vertebrae T1-T12 affected by epilepsy.\n\nTo illustrate the broader applicability of our model in neurodegenerative disease, we conducted an additional analysis using brain 18F-FDG PET data from the ADNI cohort (97 cognitively normal [CN], 328 mild cognitive impairment [MCI], and 42 Alzheimer\u2019s disease [AD] subjects). Due to the lack of publicly available whole-body PET/CT scans for AD patients, we utilized ADNI brain-only data. Following the same pipeline, we included age adjustment and FDR correction. As shown in Supplementary Fig.\u00a08a, b, in the CN vs. MCI comparison, altered associations emerged in the middle and inferior temporal gyri and posterior superior temporal gyrus. These regions are well-known to distinguish MCI or early AD converters from healthy controls50. In the MCI vs. AD comparison (Supplementary Fig.\u00a08c, d), the most notable changes were found in the inferior lateral parietal cortex and postcengbtral gyrus. These regions are consistent with prior 18F-FDG\u00a0PET findings showing parietal hypometabolism as a hallmark of early AD50,51. These results demonstrate the potential of our model to uncover clinically relevant, disease-associated imaging biomarkers.\n\nWe also analyzed metabolic associations between brain regions and body across 9960 pairs in the epilepsy cohort. Interestingly, we identified significant changes in metabolic associations in 14 pairs (p\u2009<\u20090.001). As illustrated in Fig.\u00a04b, a notable metabolic association change was observed between the \u2018pallidum\u2019 and \u2018vertebrae T1 to T12\u2019. This finding suggests a significant disruption in metabolic connectivity, potentially due to neuronal dysfunctions in epilepsy. These changes in metabolic connectivity, along with their correlation coefficients, are detailed in Supplementary Fig.\u00a02c.\n\nFurthermore, we conducted a significant analysis of metabolic changes within individual organs between the control and patient groups, as detailed in Supplementary Fig.\u00a03. We discovered that epilepsy not only causes metabolic abnormalities in certain brain regions but also leads to significant metabolic changes in other parts of the body, such as in some bones and muscles. Epilepsy increases the cerebral metabolic rate of oxygen and ATP demand, leading to mitochondrial exhaustion, which might help substantiate changes in some brain regions, such as temporal lobe and thalamus52. Additionally, metabolic dysfunctions also affect various bodily functions, including muscle activity53.\n\nAs shown in Fig.\u00a02b, we conducted a multi-modality ablation study by training identical segmentation models under four input settings (CT only, CT+PET, CT+MR, and CT+PET+MR) and evaluated both body and brain tasks. Compared to the CT-only baseline, adding PET or MR each yielded noticeable improvements in Dice and surface\u00a0Dice, reflecting complementary anatomical and functional cues. Finally, the tri-modality model achieved the best performance overall, confirming that integrating multiple modalities provides synergistic improvements in segmentation accuracy.\n\nWe also conducted controlled experiments to assess the impact of training volume on model performance. As shown in Supplementary Fig.\u00a07, the results indicate that segmentation accuracy improves steadily when increasing the dataset size from 30% to 70%, with gains beginning to plateau between 70% and 85%, and nearing saturation at 100%. This trend suggests diminishing returns beyond a certain data threshold. We attribute this plateau to the rich inter-organ supervision in our fully annotated, multi-modality dataset.\n\nThe modality-projection model incorporates a controller module that transforms features from the modality space into feature extraction operators. The distribution of these feature operators across different modalities, as visualized in Fig.\u00a05, enhances the interpretability of the model.\n\nThe black region displays a progression of saliency maps from shallow to deep layers, encompassing nine cases: a, a body CT scan; b-e, brain CT scans; f & g, PET scans; h& i, MR scans. The bottom right subfigure shows the t-SNE visualization of convolutional kernel operators, capturing the feature extraction from shallow to deep layers.\n\nStage 1 (shallowest layer) extracts low-level semantic features (e.g., edges, textures, simple shapes), which are largely modality-independent. In the t-SNE plots, MR and PET are overlapped due to their similar soft-tissue contrast. Since CT is well-suited to capture bone edges and basic texture features, it still shows partial overlap with MR and PET modalities in shallow layers, reflecting shared low-level feature patterns across imaging techniques. In deep convolutional layers (stage 3 and stage 4), high-level semantic features become modality-specific. The t-SNE plots display distinct clustering of feature operators by modality, reflecting each modality\u2019s unique advanced feature extraction. This analysis highlights the contrast between shared low-level universality and modality-specific high-level abstraction, reconciled by the modality-projection controller.\n\nTo illustrate the interpretability of our universal model, we\u00a0visualized saliency maps across network layers. In contrast to traditional gradient-based CAM methods54 that highlight the saliency of the final decision layer, our approach provides layer-wise saliency throughout inference. As shown in Fig.\u00a05, we plot the saliency maps for different regions under various modalities, showing systematic patterns tied to layer depth, modality, and organ category.\n\nIn the shallow layers of the network, saliency is primarily focused on the image\u2019s contours and textures, while in the deeper layers, saliency increasingly concentrates on the ROI regions. As shown in Fig.\u00a05, the saliency map of the shallowest layer (the leftmost column) highlights the edge information of the image. The subsequent layer focuses on the texture information over large areas of the image (second column from the left). In contrast, the deeper network layers progressively refine the ROI regions with increasing precision. This progression indicates that the shallow layers capture global edge and texture information, whereas the deeper layers leverage localized information to improve the delineation of target regions.\n\nAs shown in Fig.\u00a05a, the task of identifying the right femur from a CT image is relatively straightforward due to the high density of bone structures. In contrast, Fig.\u00a05b\u2013e depict the identification of various brain regions, challenging tasks given the difficulty of distinguishing soft tissue areas from CT images. In Fig.\u00a05a, the shallow layers recognize a small amount of contour information, which is sufficient to clearly identify the location of the femur. Conversely, Fig.\u00a05b\u2013e need recognize the overall contour of the skull for preliminary localization, followed by a focus on the subtle variations in the soft tissue across the entire brain region.\n\nFigure\u00a05 a\u2013e represent cases from the CT modality, while Fig.\u00a05f\u2013g are from the PET modality and Fig.\u00a05h\u2013i are from the MR modality. CT saliency maps show continuous contouring, MR maps show soft-tissue emphasis, and PET maps focus on metabolic activity at shallow layers with deeper refinement around the ROI.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64469-w/MediaObjects/41467_2025_64469_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64469-w/MediaObjects/41467_2025_64469_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64469-w/MediaObjects/41467_2025_64469_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64469-w/MediaObjects/41467_2025_64469_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our study demonstrates that the proposed MPUM, employing a modality-projection training strategy, achieves robust segmentation performance. The experimental results highlight significant advantages of this strategy in multi-modality training. This model shows potential in medical imaging tasks3,8,9, extending the applications of traditional universal segmentation models. The experimental results highlight two key advantages of this approach: precise brain segmentation and comprehensive whole-body segmentation. Specifically, the model enhances diagnostic accuracy for ICH, supporting physicians in timely and accurate diagnosis. Additionally, it enables systematic metabolic analysis across both brain-brain and brain-body associations in epilepsy and AD.\n\nPrevious models have struggled with CT-based brain segmentation due to the low soft tissues contrast. Existing methods often rely on knowledge transfer, such as BraSEDA55, which uses GANs to transfer MR brain region knowledge to CT, and UNSB56, which transfers ventricle region knowledge via a diffusion Schr\u00f6dinger bridge. However, these methods are limited by domain shifts and focus on only a few regions. MPUM addresses these issues by delivering high accuracy across 83 distinct brain regions from CT scans.\n\nAdditionally, accurate segmentation of brain ventricles in CT scans is critical for emergency procedures like ventriculostomy, used to treat conditions such as hydrocephalus, brain injury, and tumors56. Although MRI remains the gold standard for ICH detection, CT\u2019s speed and availability make it vital in acute ICH care. MPUM enables precise ventricle segmentation from CT scans, including the third ventricle, lateral ventricles, and temporal horn. This capability offers a significant clinical advantage32,33,34.\n\nFurthermore, the key factors in predicting outcomes and treatment strategies for ICH are midline shift and hemorrhage volume57. Specifically, hemorrhage volume in the brainstem is crucial in determining whether conservative medical treatment or surgical intervention is required57. As shown in Case 2, MPUM is capable of assessing hemorrhage volume in brainstem, providing valuable information for clinical decision-making. While MPUM does not directly detect the midline, it infers midline location by mapping left and right cerebellum regions.\n\nMPUM excels in whole-body segmentation, which supports our research on the brain-body axis\u2014the bidirectional communication between the brain and body that underpins many physiological and psychological processes58,59. While epilepsy has traditionally been viewed as a brain-centric disorder, recent studies suggest it may also involve systemic metabolic effects beyond the brain53,60,61. This insight prompted us to explore whole-body metabolic changes in epilepsy.\n\nLeveraging MPUM\u2019s high-throughput capability, we systematically identified altered metabolic associations across both brain-brain and brain-body ROIs. Notably, significant metabolic changes were observed in brain regions such as the right anterior temporal lobe and right middle/inferior temporal gyrus, which are consistent with existing research on epileptic foci47,48,49. Our whole-body analysis also revealed a statistically significant metabolic association in the epilepsy cohort between pallidum and vertebrae T1-T12. This result highlights MPUM\u2019s utility in uncovering data driven biomarkers for neurological disorders through comprehensive, system level metabolic profiling.\n\nThe capacity and diversity of training data are vital for developing universal segmentation models. Large-scale works like MedSAM2 represent a significant milestone in this direction. However, increasing the number of training subjects must be carefully balanced with considerations of annotation density and label consistency.\n\nWhile our dataset includes 861 subjects (533 CT, 533 PET, 328 MR), each CT scan is annotated with 214 structures, resulting in over 200,000 image-mask pairs across modalities. In contrast, many large-scale public datasets rely on partial, coarse, or prompt-derived annotations (e.g., MedSAM\u2019s reliance on semi-automatic label generation). Supplementary Fig.\u00a09 illustrates that even with 30% of the dataset, our model achieves strong segmentation performance, which improves until performance saturates around 85% of the training data. It demonstrates that our dataset contains rich and dense supervision, enabling effective training even with fewer subjects.\n\nMost public datasets (e.g., BTCV62) annotate only a limited number of structures, leading to label granularity mismatch when combined with our 214-class target. Additionally, the partial annotation problem introduces conflicting supervision. For example, structures labeled as \u201cbackground\u201d in one dataset may be labeled as \u201cforeground\u201d in another, introducing conflicting supervisory signals63,64. Furthermore, sparse annotations lack the anatomical context necessary to model spatial dependencies between neighboring structures, which is a key feature captured by our densely labeled dataset.\n\nDespite the progress our model has made in clinical applications and medical analysis, several challenges remain. Integration into real-world workflows requires not only high accuracy but also seamless user experience, secure deployment, and compatibility with clinical platforms. Current inference runs fully automatically, but basic Python and Linux skills are required. Future versions will integrate with platforms such as 3D Slicer to provide plug-and-play functionality, removing the need for scripting and facilitating deployment in routine radiology workflows.\n\nHere we envision MPUM as a reusable imaging module within broader clinical AI pipelines. Its structured outputs, including whole-body and brain ROI labels, volumetric measurements, and metabolic matrices, serve as standardized inputs for a wide spectrum of downstream tasks. These span rapid localization to support acute clinical workflows; standardized feature extraction for population-scale imaging studies, enabling cross-organ and cross-modality analysis; and automated reporting or decision-support systems, where MPUM outputs feed directly into multi-modal analytics and domain-specific large language models (LLMs). To translate this potential from prototype to bedside, future work will focus on addressing key technical challenges such as domain shift, continual learning, and secure deployment. We also aim to expand multi-task learning and integrate MPUM seamlessly into hospital infrastructure, ultimately enabling a stepwise transition toward AI-assisted, imaging-driven clinical care.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "In Case 1, we employed the 18F-FDG PET/CT dataset20 alongside two MR datasets4,65. Specifically, we utilized 533 negative control subjects from the 18F-FDG PET/CT dataset20. The body region labels were sourced from DAP Atlas Dataset5, covering 142 distinct anatomical structures. DAP label dataset contains the annotations of 533 subjects\u2019 scans. After excluding irrelevant labels such as \u201cbackground\u201d and \u201cleft to annotations\u201d, we utilized 133 effective labels from the DAP dataset. As the DAP dataset focuses primarily on body regions, it lacked detailed brain annotations. To address this gap, we pre-segmented the brain regions in the PET scans using the MOOSE tool66,67, followed by refinement and correction by two experienced radiologists, resulting in 83 annotated brain regions. Consequently, the \u201cbrain\u201d label from the DAP dataset was removed, finalizing 132 non-brain categories and 83 brain region categories. The MR dataset includes 298 MR body scans4 annotated with 43 body regions, and 30 MR brain scans, annotated with the same 83 brain regions65.\n\nFor Case 2, we used the Instance2022 dataset40,41, which includes 100 non-contrast head CT volumes from clinically diagnosed patients with various types of intracranial hemorrhage (ICH), such as subdural, epidural, intraventricular, intraparenchymal, and subarachnoid hemorrhage. These CT volumes were sourced from Peking University Shougang Hospital, China, and meticulously labeled by 10 radiologists with over five years of clinical experience. We fine-tuned our MPUM using the Instance2022 data, enabling it to accurately identify ICH. In addition, we obtained 28 ICH cases along with diagnostic reports from the emergency department of Peking University Third Hospital for further validation. The diagnostic reports include findings confirmed by MRI imaging.\n\nCase 3 involved 50 pediatric epilepsy patients who were enrolled at Qianfoshan Hospital, Shandong, China, between August 3, 2021, and June 4, 2024. The cohort consisted of 27 females and 23 males, with ages ranging from 2 to 18 years (11.78\u2009\u00b1\u20095.94 years). The height of the participants ranged from 0.93 to 1.85\u2009m (1.49\u2009\u00b1\u20090.236\u2009m), and their weight ranged from 14.5 to 90\u2009kg (46.22\u2009\u00b1\u200919.92\u2009kg). The control group comprised 22 participants, including 17 males and 5 females, with data collected from August 18, 2020, to May 9, 2024.\n\nDetailed demographic statistics for both cohorts are provided in Supplementary Table\u00a01. Median ages do not differ significantly between groups (Epilepsy: 12 years [IQR 7\u201315] vs. Control: 13 years [IQR 7\u201314]; Mann\u2013Whitney U, p\u2009=\u20090.782). Height (p\u2009=\u20090.586) and weight (p\u2009=\u20090.959) were likewise comparable, indicating an age- and size-matched control cohort. PET/CT scanning in very young patients was clinically justified. For example, a 2-year-old with drug-resistant epilepsy required whole-body PET to locate an epileptogenic zone ahead of surgery. Supplementary Table\u00a01 summarizes seizure duration, frequency, and semiology (78% focal, 22% generalized). Screening and cohort inclusion details are illustrated in Supplementary Fig.\u00a09.\n\nThe multiple datasets used in this study, along with their descriptions, the division of training and testing data, and download links, are detailed in the Supplementary Table\u00a04.\n\nAll experiments were conducted using Python (v3.10.14). The following packages were used for data preprocessing, analysis, and visualization: PyTorch (v2.6.0), NumPy (v1.26.4), pandas (v1.3.5), SciPy (v1.10.1), scikit-learn (v1.5.2), matplotlib, seaborn (v0.13.2), tqdm (v4.66.5), einops (v0.7.0), SimpleITK (v2.2.1), pydicom (v2.2.2), dicom2nifti (v2.4.8), MONAI (v1.2.0), and nnUNet (v1.7.1).\n\nFor this study, all imaging modalities, including CT, PET, and MR scans, underwent linear interpolation to achieve isotropic voxel sizes with a 2\u2009mm resolution. This standardization was essential to address variations in slice thickness and in-plane resolutions across different datasets12.\n\nTo ensure consistent and stable model training, we normalized the voxel values within these patches. For the CT patches, the voxel values were normalized between 0 and 1. MR images are first intensity-truncated at the 0.5th and 99.5th percentiles to suppress outliers, then linearly scaled to the [0, 1] range. PET data were first converted to Standardized Uptake Value (SUV) and then divided by 20 for normalization. These normalization steps were crucial for maintaining data consistency across the different imaging modalities.\n\nAdditionally, we employed data augmentation techniques, specifically RandGaussianSmooth and RandAdjustContrast, to enhance the diversity and robustness of our training dataset. The RandGaussianSmooth method involves applying Gaussian smoothing to the images with a standard deviation randomly chosen between 0.5 and 1.5. This technique helps in reducing noise and simulating various levels of blurriness. RandAdjustContrast randomly scaled intensity values between 0.5 and 1.5, simulating different contrast conditions across scanners and reconstruction settings.\n\nTo optimize training efficiency, we pre-cropped all CT, PET, and MR scans into fixed-size patches. Loading a 128\u2009\u00d7\u2009128\u2009\u00d7\u2009128 patch is significantly faster than reading the full image and cropping on the fly. This pre-processing step dramatically reduces the I/O time during training, allowing for more efficient use of computational resources. We have made the patch-wise multi-modality training dataset publicly available to facilitate further research and development.\n\nWe employed two evaluation metrics: Dice and surface Dice. Dice measures the overlap between predicted and true segmentations. It is calculated as twice the area of overlap between the two segmentations divided by the total number of pixels in both segmentations, providing an overall accuracy of how well the two align. Surface Dice, on the other hand, is a more specific measure that focuses on the boundary accuracy of the segmentation. It assesses how closely the boundaries of the predicted segmentation conform to the true surface contours of the object being analyzed.\n\nIn recent studies of universal models, such as SAT19 and CDUM8, the modality-mixed strategy is commonly employed during the training phase. This strategy mixes data from various modalities, enabling a single model to adapt to data from multiple modalities. As shown in Fig.\u00a01b, the modality-mixed strategy results in a multi-modality universal model. Although this approach benefits from a larger training data set, it also encounters challenges related to feature interference among modalities. The feature extraction operators (weights and biases) must deal with imaging data from all three modalities. This may lead the model to prioritize modality-independent features, potentially sacrificing modality-specific details. In contrast, the modality-specific strategy involves training separate models for each modality. The performance of these models serves as the baseline for this study. As illustrated in Fig.\u00a01b, there are three types of modality data\u2014MR, PET, and CT\u2014the modality-specific strategy could produce three modality-specific models: a PET model, a CT model, and an MR model. The drawbacks of training individual models for each modality are the limited amount of training data and the lack of feature collaboration between the multi-modality data. In the realm of multi-modality medical imaging, different imaging modalities provide complementary views of the same underlying biological tissues. Each modality captures specific aspects of tissue characteristics due to differences in imaging principles and physical interactions. To effectively integrate information across modalities, we propose a modality projection theory centered on the concept of high-dimensional latent features that represent the comprehensive properties of each tissue type.\n\nThe core of our theory is the assumption that for each tissue, there exists a high-dimensional latent feature vector \\({\\bf{T}}\\in {{\\mathbb{R}}}^{{d}_{T}}\\), where dT denotes the dimensionality of the latent feature space. This latent feature encapsulates all intrinsic properties of the tissue that could be captured across various imaging modalities.\n\nEach imaging modality provides a modality-specific projection of this latent feature into its own feature space. For a given modality m \u2208 CT, MR, PET, we define a projection matrix \\({{\\bf{P}}}_{m}\\in {{\\mathbb{R}}}^{{d}_{T}\\times {d}_{m}}\\), where dm is the dimensionality of the modality feature space. The modality-specific feature vector \\({{\\bf{M}}}_{m}\\in {{\\mathbb{R}}}^{{d}_{m}}\\) is obtained by projecting the latent feature:\n\nThis projection models how each modality interprets the latent tissue representation, capturing modality-specific characteristics.\n\nThe relationship between latent features and modality-specific features suggests that, under certain conditions, it is possible to reconstruct the latent features from the modality-specific representation using the inverse of the projection matrices:\n\nHowever, each modality offers only a partial perspective of the latent tissue state. Reconstructing T from a single modality may miss critical information. Combining multiple modalities yields a more complete and accurate representation.\n\nOptimizing T and Pm simultaneously can lead to training instability, as the condition number of the system matrix may increase due to parameter interdependence. Changes in T propagate to Mm, impacting the eigenvalues of Pm and disrupting gradient flow.\n\nTo address this challenge, we propose incorporating external pre-trained models (e.g., CLIP68) to anchor projections using robust, stable feature embeddings. Let \\({{\\bf{M}}}_{i}\\in {{\\mathbb{R}}}^{{d}_{i}}\\) represent the sub-space features from an external model i, with di being its feature dimensionality. Each external model has its projection matrix \\({{\\bf{P}}}_{i}\\in {{\\mathbb{R}}}^{{d}_{T}\\times {d}_{i}}\\):\n\nBy leveraging these external features, we estimate the latent features by aggregating inversely projected sub-space features:\n\nwhere N is the number of external models used. This approach anchors the latent features to stable, pre-trained representations, mitigating optimization instability. Additionally, the training datasets used by external pre-training models encompass a wide range of textual data, which facilitates the reconstruction of latent features.\n\nIn this generalized framework, the latent features T serves as a central hub connected to various modality features Mm and external model features Mi through their respective projection matrices. This enables effective cross-modal and cross-domain knowledge integration.\n\nBuilding upon the modality projection principle, we introduce the MPUM, a comprehensive framework designed to effectively process and integrate multi-modality medical imaging data. The MPUM leverages the Modality Projection Controller (MPC) within a deep learning architecture to handle diverse imaging modalities\u2014such as CT, MR, and PET\u2014facilitating accurate and efficient image segmentation.\n\nAs illustrated in Supplementary Fig.\u00a04a, the MPUM architecture begins with multi-modality patch inputs (e.g., CT, MR, PET), processed initially through a Head Layer, followed by a series of Dual-Branch Blocks with skip connections, and concluding with a Tail Layer to produce the segmentation output. The skip connections help preserve spatial information and integrate features from different layers, improving segmentation performance.\n\nAt the core of the MPUM is the Modality Projection Controller, illustrated in Supplementary Fig.\u00a04b. The MPC is responsible for dynamically adjusting the feature extraction process based on the specific characteristics of the input modality.\n\nThe MPC process involves the following two steps. Firstly, the latent feature vector T is projected into the modality category space using the modality-specific projection matrix Pm, as shown in Eq. (1). Secondly, the modality features Mm are processed through a Multi-Layer Perceptron (MLP), serving as the Feature Operator Generator (FOG):\n\nresulting in convolutional kernels Km tailored for each modality. This dynamic adjustment allows the MPUM to adapt its convolutional operations to the unique properties of each modality, thus improving feature extraction and segmentation accuracy.\n\nAs illustrated in Supplementary Fig.\u00a04, we detail the operation of the controller-based convolution layer and clarify how the MLP output is shaped into a 3D kernel. Firstly, the input feature map is projected into a latent space via a 1\u2009\u00d7\u20091\u2009\u00d7\u20091 convolution, reducing its channel dimension to a predefined latent size L (e.g., L\u2009=\u200964). This step compresses the feature map into a latent representation while preserving spatial dimensions. The multi-layer perceptron (MLP) in the controller then outputs a flattened kernel of length L\u2009\u00d7\u200933, where 33 corresponds to the spatial dimensions of the kernel. Finally, the filtered feature map is projected back to the target output channel dimension using another 1\u2009\u00d7\u20091\u2009\u00d7\u20091 convolution, ensuring compatibility with downstream operations.\n\nThe MPC uses an MLP to generate convolution kernel parameters, a methodology widely validated in prior work. For instance, HyperNetworks69,70 pioneered the use of auxiliary networks to dynamically generate weights for primary network, demonstrating the feasibility of MLP-based parameter synthesis. Similarly, HyperConvolution71 extended this concept by leveraging MLPs to produce continuous convolution kernels. While our approach adopts this mechanism, our innovation lies in the use of modality-specific embeddings to guide kernel generation, encoding anatomical priors specific to multi-modal medical data.\n\nThe MPUM also incorporates a specialized Dual-Branch Block Structure, illustrated in Supplementary Fig.\u00a04c, which consists of two parallel branches: traditional convolutional branch and controller-based convolutional branch. Traditional convolutional branch employs standard convolutional layers with fixed kernels to extract general features from the input data. It captures modality-invariant features. In contrast, the controller-based branch leverages convolutional kernels dynamically generated by MPC, enabling the network to adapt to specific imaging characteristics of each modality. These controller-driven convolutional layers are non-parametric and adjust their operations according to the modality type, allowing the model to extract highly specific anatomical and functional cues. Together, this dual-branch structure enhances both generalization and specialization.\n\nSaliency maps are vital tools for visualizing the decision-making process of deep learning models. Traditional methods for generating saliency maps primarily rely on gradient-based techniques. Methods such as Vanilla Gradients72 and Grad-CAM54 generate saliency maps by calculating the gradients of the input image with respect to the model\u2019s output, highlighting regions that significantly influence the model\u2019s decisions.\n\nAs shown in Supplementary Fig.\u00a04c, our modality projection block structure enables the generation of saliency maps across all layers of the network, not only the final decision layer. In our framework, the feature operator K from Eq. (5) is a tensor with dimensions \\({{\\mathbb{R}}}^{C\\times H\\times 3\\times 3\\times 3}\\), where C represents categories, H denotes the number of channels, and 3\u00a0\u00d7\u00a03\u00a0\u00d7\u00a03 specifies the kernel size. This structure enables interpretation of the model\u2019s behavior across the entire network hierarchy.\n\nWe applied the Fisher Z-transformation to compare correlation coefficients between two independent groups. This transformation standardizes raw correlation coefficients into values approximating a normal distribution, enabling hypothesis testing between control and patient groups.\n\nIn our analysis, we analyzed metabolic data from organs a and b in a control group (22 individuals), calculating the mean standardized uptake value (SUV) for the regions of interest (ROI) within these organs. The metabolic data for organ a in the control group is denoted as Acontrol\u00a0=\u00a0{an\u2223n\u00a0=\u00a01,\u00a02,\u00a0\u2026,\u00a022}, representing the set of mean SUVs for each individual. Similarly, the metabolic data for organ b in the control group is represented as Bcontrol\u00a0=\u00a0{bn\u2223n\u00a0=\u00a01,\u00a02,\u00a0\u2026,\u00a022}. We calculated the Pearson correlation coefficient, which measures the strength and direction of a linear relationship between two variables:\n\nwhere ai and bi are the individual SUV for organs a and b in the control group, and \\(\\overline{a}\\) and \\(\\overline{b}\\) are the mean SUVs for organ a and b, respectively. We can simplify the Eq. (6) to \\({r}_{a,b}^{group}=pearson({A}^{group},{B}^{group})\\). Similarly, replacing an organ variable with Age yields the Pearson correlations \\({r}_{a,Age}^{group}\\) and \\({r}_{b,Age}^{group}\\).\n\nTo remove the linear effect of age, we calculated the partial correlation between organs a and b, conditional on age, using the closed-form solution:\n\nEq. (7) is equivalent to regressing a and b on age, extracting their residuals, and calculating the ordinary Pearson correlation between the residuals.\n\nWe then apply the Fisher Z-transformation to \\({r}_{a,b}^{control}\\) and \\({r}_{a,b}^{patient}\\):\n\nAnd computed the Z-score:\n\nwhere ncontrol and npatient are the sample sizes of control group and patient group, respectively. Finally, we calculate the p-value assuming a normal distribution:\n\nwhere abs(z) represent the absolute value of the Z-score and cdf stands for cumulative distribution function. The term 1\u00a0\u2212\u00a0cdf(abs(z)) represents the one-tailed p-value, and multiplying by 2 makes it a two-tailed test. Raw p values were adjusted by Benjamini-Hochberg FDR method.\n\nIn large-scale metabolic connectome studies like ours (>20,000 correlations), controlling for false positives is critical46,73,74,75. Although Bonferroni Correction strictly controls the family-wise error rate (FWER), it would require an adjusted threshold of 0.01/20,\u00a0000\u2009=\u20095\u2009\u00d7\u200910\u22127 in our analysis, which is overly conservative. We therefore report both raw and FDR-adjusted p values. All results remain significant at \u03b1\u2009=\u20090.01 post-FDR, reinforcing their reliability (See Supplementary Fig.\u00a02).\n\nThis study was approved by the Ethics Committee of The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital (Approval No. S917). All participants provided informed consent, and the study adhered to all applicable ethical standards and institutional guidelines. Participants received no financial remuneration.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Publicly available datasets used in this study are listed below. Accession links are provided in Supplementary Table\u00a04. 1. AutoPET 18F-FDG PET/CT (533 controls); 2. DAP Atlas body-region labels 3. TotalSegmentator-MRI 4. MRI-Brain 5. Instance2022 ICH head-CT challenge set The pediatric epilepsy PET/CT cohort (50 patients, 22 controls), acquired at Qianfoshan Hospital, Shandong, China, is not publicly available due to patient privacy regulations. Researchers interested in accessing the data should contact the corresponding author (Z. Cheng) with a formal data request and research purpose, which will be reviewed in accordance with institutional policies. Submit a formal request to the Data Access Committee via the corresponding author (Z. Cheng; [czpabc@163.com]), including: (i) institutional affiliation; (ii) a brief research proposal describing the intended analyses; and (iii) a data security plan. Receipt of a complete application will be acknowledged within 10 business days and an access decision is typically issued within 30 calendar days. Approved users must sign a DUA that (a) limits use to the approved research purpose; (b) forbids redistribution to third parties; (c) requires secure storage and access logging; (d) restricts public reporting to aggregate, non-identifying results. A template DUA is provided.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All code used for data processing and performance analysis, including well-trained model weights, is publicly available via GitHub at https://github.com/YixinChen-AI/MPUMunder the MIT licence. The code is archived on Zenodo with the https://doi.org/10.5281/zenodo.16730886. The inference time of MPUM when deployed on different graphics cards as shown in Supplementary Fig.\u00a05.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Kirillov, A. et al. Segment anything. In Proceedings of the IEEE/CVF international conference on computer vision, 4015\u20134026 (2023).\n\nMa, J. & Wang, B. Segment anything in medical images. Nat. Commun. 15, 654 (2024).\n\nWasserthal, J. et al. TotalSegmentator: Robust segmentation of 104 anatomic structures in CT images. Radiology: Artif. 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Mol. imaging 47, 2753\u20132764 (2020).\n\nArticle\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "Zhaoheng Xie discloses support for the research of this work from the Natural Science Foundation of China (62394311 [Z.X.], 62394310 [Z.X.]), Beijing Natural Science Foundation (Z210008 [Z.X.]), National Biomedical Imaging Facility Grant [Z.X.], and from the startup funds of Peking University Health Science Center [Z.X.]. Rui Wang discloses support for the research of this work from the\u00a0Youth Fund of the National Natural Science Foundation of China\u00a0(62301245 [R.W.])\u00a0and from the Guangdong Basic and Applied Basic Research Foundation (2022A1515110674 [R.W.]). Xiangxi Meng discloses support for this work from Peking University Clinical Medicine Plus X - Young Scholars Project.\u00a0The authors gratefully acknowledge Prof. Lin Lu of Peking University Sixth Hospital, Prof. Jiang Yuwu of Peking University First Hospital, and B.E. Ruoyan Xu of Peking University Health Science Center for their valuable discussions and insightful suggestions.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Yixin Chen, Lin Gao.\n\nInstitute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China\n\nYixin Chen,\u00a0Hongbin Han\u00a0&\u00a0Zhaoheng Xie\n\nDepartment of Nuclear Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China\n\nLin Gao,\u00a0Yanhua Duan,\u00a0Leiying Chai\u00a0&\u00a0Zhaoping Cheng\n\nDepartment of Radiology, Peking University Third Hospital, Beijing, China\n\nYajuan Gao,\u00a0Jingge Lian\u00a0&\u00a0Hongbin Han\n\nBeijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Peking University Third Hospital, Beijing, China\n\nYajuan Gao\u00a0&\u00a0Hongbin Han\n\nDepartment of Radiology, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong, China\n\nRui Wang\n\nKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Key Laboratory for Research and Evaluation of Radiopharmaceuticals (National Medical Products Administration), Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, Beijing, China\n\nXiangxi Meng\n\nNational Biomedical Imaging Center, College of Future Technology, Peking University, Beijing, China\n\nZhaoheng Xie\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY. Chen conceived the study, designed the experiments, implemented the code, and drafted the manuscript. L. Gao performed data processing, statistical analysis, and interpretation of the experimental results. Y. Chen and L. Gao contributed equally to this work. Y. Gao collected the ICH dataset and carried out the corresponding data analysis. R. Wang and J. Lian supplied imaging data and contributed to manuscript revisions. X. Meng assisted with methodological development and contributed to manuscript revisions. Y. Duan, H. Han, and L. Chai curated the clinical data and contributed to manuscript revisions. Z. Cheng and Z. Xie jointly supervised the project, secured funding, and approved the final manuscript.\n\nCorrespondence to\n Zhaoping Cheng or Zhaoheng Xie.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Yucheng Tang and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Chen, Y., Gao, L., Gao, Y. et al. Modality-projection universal model for comprehensive full-body medical imaging segmentation.\n Nat Commun 16, 9423 (2025). https://doi.org/10.1038/s41467-025-64469-w\n\nDownload citation\n\nReceived: 10 December 2024\n\nAccepted: 15 September 2025\n\nPublished: 24 October 2025\n\nVersion of record: 24 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-64469-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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light-driven spin injection in MoS2", + "journal": "Nature Communications", + "published": "30 March 2025", + "supplementary_0": [ + { + "label": "Supplemental Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58119-4/MediaObjects/41467_2025_58119_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58119-4/MediaObjects/41467_2025_58119_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58119-4/MediaObjects/41467_2025_58119_MOESM3_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.14976824", + "/articles/s41467-025-58119-4#Sec15" + ], + "code": [ + "https://doi.org/10.5281/zenodo.14976862" + ], + "subject": [ + "Magnetic properties and materials", + "Spintronics", + "Two-dimensional materials" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4397272/v1.pdf?c=1743419155000", + "research_square_link": "https://www.researchsquare.com//article/rs-4397272/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58119-4.pdf", + "preprint_posted": "21 May, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Semiconductor transition metal dichalcogenides are a promising archetype for spintronic devices as they present several spin-to-charge interconversion mechanisms. Nevertheless, the microscopic origin of this interconversion is yet to be\r\nestablished. Here, we report a comprehensive study of light-induced spin pumping in YIG/MoS2 heterostructures. In MoS2 monolayer microsized flakes, two distinct contributions to the spin current injection were identified. One from the metallic edge states and another from the 2D semiconductor states. A competitive interplay between the edge and 2D contributions was observed by changing the average flake size. We demonstrate that a light-driven spin current injection can enhance, attenuate, or even switch on/off the spin pumping depending on the size of the MoS2 flakes. The spin pumping in the MoS2 was independent of the film thickness, consistent with the inverse Rashba-Edelstein effect at the YIG/MoS2 interface. These findings highlight distinct contributions to spin pumping in transition metal dichalcogenides and open avenues for developing opto-spintronic device applications.Physical sciences/Physics/Condensed-matter physics/SpintronicsPhysical sciences/Materials science/Condensed-matter physics/Magnetic properties and materialsSpin pumpingMolybdenum disulfide2D materialsRashba-Edelstein effectYIG/MoS2opto-spintronic", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SIEdgeand2DstatesdisentanglementandlightdrivenspininjectioninMoS2.pdfSI_Edge_and_2D_states_disentanglement_and_light_driven_spin_injection_in_MoS2", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Semiconductor transition metal dichalcogenides are an archetype for spintronic devices due to their spin-to-charge interconversion mechanisms. However, the exact microscopic origin of this interconversion is not yet determined. In our study, we investigated light-induced spin pumping in YIG/MoS2 heterostructures. Our findings revealed that the MoS2 monolayer microsized flakes contribute to spin current injection through two distinct mechanisms: metallic edge states and semiconductor area states. The competition between these mechanisms, influenced by the flake size, leads to different behaviors of spin-pumping. Our calculations of the local density of states, by means of density functional theory, of a flake show that light-driven spin current injection can be controlled based on the intensity of light with a suitable wavelength. We demonstrate that a lightdriven spin current injection can enhance up to very high values, attenuate, or even switch on/off the spin-to-charge interconversion. These results hold promise for developing low energy-consuming opto-spintronic device applications.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Spintronics has emerged as a promising field for developing next-generation devices that intertwine the spin and charge degrees of freedom1,2,3. Research shows that the spin Hall effect (SHE) in metals with high spin-orbit coupling is a key effect on the interconversion of spin and charge currents4,5,6,7. However, other effects might be even greater than the SHE. Systems with orbital degrees of freedom can exhibit a significant orbital Hall effect (OHE)8. Materials with a non-zero Berry curvature are capable of generating spin accumulation, termed as valley Hall effect (VHE)9,10,11. In addition, the Rashba-Edelstein effect (REE), stemming from the breaking spatial inversion symmetry at interfaces, can lead to orbital and spin currents12,13,14,15. Generally, these effects are called Hall effects.\n\nOne of the main challenges in practical applications of spintronics is the energy efficiency control of magnetic and spin states and the interconversion between charge and spin currents, especially at ultra-fast time and small scales. In this scenario, light can play a pivotal role as an energy-efficient way to overcome these issues. However, several challenges still need to be addressed, such as optimizing the efficiency of light-induced magnetic effects, improving the stability of materials, and designing scalable spintronic light drive devices16. A deeper understanding of the fundamental interaction of light with spintronic materials is crucial for actual applications of opto-spintronic devices.\n\nThe 2-dimensional (2D) transition metal dichalcogenides (TMDs), especially semiconductors with hexagonal structures, stand out as an excellent investigation archetype to explore the fundamental origins of spin-to-charge interconversion and the influence of light. Encompassing a distinctive set of features, TMDs can manifest various phenomena, including SHE, OHE, VHE, and REE. Among the TMDs, molybdenum disulfide (MoS2) is particularly interesting. A MoS2 monolayer comprises two S layers sandwiching one Mo layer in a covalently bonded hexagonal structure. This structure leads to a semiconductor state in the bulk and broken inversion symmetry, resulting in a combined spin-momentum locking and high spin-orbit coupling (SOC) required for the Hall effects17,18,19. MoS2 monolayers can exhibit different edge states depending on their termination. When grown in a sulfur-rich environment, the flakes of MoS2 monolayer take on triangular shape and zigzag edge terminations20,21,22,23,24,25. This specific termination results in the emergence of metallic edge states, which have been both experimentally observed and theoretically calculated in a narrow region a few nanometres wide along the edges of triangular flakes26,27,28,29,30. Hence, the semiconductor states in 2D are expected to coexist with the metallic states located at the edges of the flakes. As a result, the edge and the 2D semiconductor area states can contribute differently to the spin-to-charge interconversion through Hall effects and its Kelvin-Onsager thermodynamic reciprocal effects.\n\nSeveral experiments have been recently conducted to measure the spin-to-charge current conversion in MoS2. These experiments were carried out on various MoS2 systems, such as a single flake of monolayer31,32, large areas33, several monolayered flakes34, a few layers thick flakes35,36, and the dependence of its thickness37. These studies provide insights into the fundamental physics of spin transport in MoS2\u00a0based devices. However, since all contributions were observed simultaneously in those works, it was impossible to disentangle the different edge and area contributions.\n\nOne of the most common approaches to analyze the spin-to-charge interconversion is to measure the broadening of the ferromagnetic resonance (FMR) linewidth spectrum of soft ferromagnetic materials in contact with the material under investigation. This process is known as spin pumping, and it occurs due to the injection of angular momentum from the magnetization precession of the ferromagnetic layer to the MoS2 layer, allowing controlled probing of a material\u2019s spin current injection. Since the spin current can only be injected in the TMDs in direct contact with the ferromagnetic layer, this technique can be used to probe small areas, such as tiny flakes, as well as large areas, such as big or abundant flakes38. Spin pumping is a method that has the advantage of averaging the flakes and reducing the impact of local defects in comparison to localized techniques like Kerr microscopy, spin transfer torque, and electrical measurements, which examine only one flake at a time39,40,41,42,43,44,45,46,47. Moreover, spin pumping does not need electrical contacts in the sample, which could suppress some contributions from Hall effects. This is particularly important because of the differences in conductivity between the 2D semiconductor and the metallic edge states present in the MoS2 flakes. In addition, if the material used to inject the angular momentum is a magnetic insulator, such as yttrium iron garnet (YIG), it injects a pure spin current, avoiding unwanted electrical effects48,49.\n\nAlthough significant efforts have been made to develop new tools and theories to better understand TMD spintronics, a study that disentangles the fundamental contributions of the Hall effects and the influence of light has yet to be explored. In this context, we conducted a spin pumping study on the enhancement of YIG\u2019s Gilbert damping, which revealed a competitive interplay between two distinct spin pumping channels: one arising from the metallic edge states and the other originating from the 2D semiconductor area states. This competition was modulated by varying the aspect ratio of MoS2 monolayer flakes. No existing theoretical framework or model, to the best of our knowledge, adequately explains these phenomena. Furthermore, using density functional theory (DFT) to calculate the local density of states (LDOS) in triangular MoS2 flakes, we demonstrated how the balance between these two channels \u2014metallic and semiconductor\u2014 can be finely controlled by adjusting the intensity of illumination with appropriately tuned wavelength. These theoretical insights guided our experiments, which confirmed that it is possible to precisely tune the interplay between the metallic and semiconducting phases.\n\nThese experimental results, in line with the theoretical framework, lead to an unprecedented level of control over the spin-pumping behaviour of the system, allowing us to amplify, diminish, switch on, or completely turn off the spin-pumping effect. We believe that this ability to finely control spintronic effects offers a significant breakthrough in the field of Hall effects and spintronics in general, with potential implications for advanced applications in spin-based devices.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To investigate the different contributions of the Hall effects to the spin pumping in molybdenum disulfide (MoS2), we fabricated several YIG/MoS2 heterostructures, as illustrated in Fig.\u00a01. Monocrystalline yttrium iron garnet (YIG) thin films oriented along the (111) direction were sputtered on the top of a gadolinium gallium garnet (GGG) substrate to act as spin injectors into MoS250,51. The triangular-shaped MoS2 monolayer flakes were synthesized by atmospheric pressure chemical vapor deposition (APCVD) on SiO2/Si substrates20,21. As the growth of MoS2 crystals starts at a nucleation point, the flake size can be controlled, ranging from 1\u2009\u00b5m in lateral size to a continuous monolayer film by increasing the growth time. It is important to note that larger flakes and even films are formed by merging two or more flakes. The previously prepared and magnetically characterized YIG samples were then covered with different amounts and sizes of MoS2 flakes via an etching-free transfer method22.\n\nSpin pumping (\u03b1SP) dependence as a function of the: (a) total edge length (PTotal), (b) total MoS2 area (ATotal), and (e) total MoS2 area divided by the total perimeter (ATotal/PTotal). The normalization on the (e) x-axis is based on the fact that the metallic edge states are proportional to the perimeter multiplied by the width of the metallic edge states, resulting in an adimensional unit. The highlighted samples S1 to S4 were selected for studying the light influence in the SP. Illustrations of the YIG/MoS2 heterostructures with small (red region) and larger flakes (blue region) are shown in (c) and (d), respectively. Representative scanning electron microscopy of MoS2 flakes with small ATotal/PTotal ratio is shown in (f) with a scale bar of 2\u2009\u00b5m and with higher ATotal/PTotal ratio in (g) with a scale bar of 100\u2009\u00b5m.\n\nTo support the flake quality and the transfer method, Section S1 of the Supplementary Information shows and discusses the optical microscopy maps, Raman spectra, Raman map, photoluminescence spectroscopy, atomic force microscopy, and light absorption spectra of samples. The Methods and Experiments section describes sample preparation, Raman and photoluminescence spectroscopy, scanning electron microscopy, ferromagnetic resonance, spin pumping measurements, and density functional theory calculations.\n\nThe spin-to-charge interconversion can be analyzed through the thermodynamic Kelvin-Onsager reciprocal effects when the magnetization precession of the YIG injects a spin current into MoS2. It is important to note that the YIG has very low Gilbert damping and, as a magnetic insulator, the angular momentum injection is expected to occur as a pure spin current, thus avoiding unexpected electrical effects that could lead to artefacts in the measurements48,49. Therefore, as proposed by Tserkovniak et al. 52,53 for SHE (which can be extended to OHE and VHE), the enhancement of the magnetic layer (FM) Gilbert damping when coupled to a metallic layer (M) is proportional to the Hall effects following the equation:\n\nwhere YIG is the FM layer and we are considering the MoS2 (triangular flake or film) in the place of the metallic layer, so \\(\\alpha_{{YIG}/{MoS}_2}\\) and \u03b1YIG are the Gilbert dampings for YIG/MoS2 and YIG, respectively, \u03b5 is the spin-flip probability at each scattering which is proportional to the spin-orbit coupling, S is the interface YIG/MoS2 area, gL is the g factor, \u00b5 is the total film magnetic moment in units of \u00b5B, g\u2191\u2193 is the interfacial mixing conductance, kF is the Fermi wave vector, L is the YIG film thickness, and \u03bbSD is the spin-diffusion length.\n\nIn Eq.\u00a01, it is clear that spin pumping is influenced by the interface area (S) and the thickness (L) of the metallic film. The thickness dependence is related to the Hall effects in MoS2, where spin pumping increases for smaller thicknesses and saturates for thicker films54,55,56,57. There\u2019s an ongoing debate about the mechanism of the spin-to-charge conversion phenomenon in semiconductor MoS2. Experimental studies suggest the Rashba-Edelstein effect as the primary effect31,32,33,34,35, while theoretical works attribute it to Hall effects58,59. Both the REE and its Kelvin-Onsager reciprocal, steamed inverse REE (IREE) results from a broken inversion symmetry, which, for MoS2, is expected only at the YIG/MoS2 interface, making it independent of MoS2 thickness. The Supplementary Information\u00a0Section S2 details the thickness-independent behavior of spin pumping51, leading to the conclusion that the semiconductor state dictates the main spin pumping behavior as expected for IREE.\n\nOn the other hand, the contribution of the MoS2 metallic edge states to the spin pumping remains unexplored. It is worth noting that when MoS2 is grown in a sulfurrich atmosphere, the MoS2 triangular flakes terminate in a zig-zag-type edge23,24,25. Both theoretical and experimental studies state that the MoS2 zig-zag termination produces metallic (conductive) edge-states60,61,62.\n\nTo explore the influence of these edge states in the spin pumping, a set of samples varying the MoS2 triangular size and the number of flakes deposited onto the YIG film were measured by spin pumping. Henceforth, we define two important parameters: the total area of the MoS2 flakes (ATotal) and the total perimeter of the MoS2 flakes (PTotal). These parameters are calculated using the following equations:\n\nThe total area, ATotal, is the sum of the areas of each MoS2 flake, ai, covering the YIG film, where n is the number of flakes. Similarly, PTotal is the sum of the perimeters of the individual MoS2 flakes, pi; w, is a constant width of a few nanometers along the edges of the triangle, to provide a two-dimensional aspect to PTotal. To measure the area ai and perimeter pi of individual flakes, images obtained by optical microscopy were analyzed by the ImageJ software63. The MoS2 merged edges and grain boundaries were not included in the total perimeter estimate. For more information on quantifying the area and perimeter of the samples, as well as their appearance, please refer to Section\u00a0S1.8 of the SI.\n\nThe spin pumping dependence of PTotal and ATotal are shown in Fig.\u00a01a, b, respectively. In previous discussions, it has been established that the semiconductor phase of MoS2 exhibits IREE as the primary mechanism for spin pumping. Since this effect occurs at the interface, the spin pumping in these heterostructures is expected to be directly proportional to the YIG/MoS2 interfacial area (S), as documented in refs. 31,33,34,35,36,37. If the spin pumping in the YIG/MoS2 heterostructures were solely attributable to the semiconductor phase, one would anticipate a linear increase in \u03b1SP with ATotal. However, as illustrated in Fig.\u00a01b, it is evident that \u03b1SP does not exhibit a linear relationship with ATotal, indicating that factors other than interfacial area need to be taken into account. This suggests that the metallic edge states could also play a role in the injection of spin current into the MoS2 flakes.\n\nMoreover, if only the metallic edge states, which are the areas along the sides of the triangles26,27,28,29,30, contribute to spin pumping due to the inverse spin Hall effect (ISHE) as predicted by Eq.\u00a01, for the case where a metallic layer is in direct contact with the FM, there should be a linear relationship between \u03b1SP and the total edge PTotal. Figure\u00a01a contradicts this expectation as there seems to be no correlation between the spin pumping and PTotal. Based on the analyses conducted so far, it appears that \u03b1SP does not depend solely on either ATotal or PTotal. This suggests that a more complex scenario needs to be considered to understand the origin of spin current injection into the MoS2 flakes. To comprehend this point, both semiconductor area and metallic edge states have to be taken into account.\n\nIn order to differentiate between the contributions of the semiconductor area states and the metallic edge states, Fig.\u00a01e illustrates the variation of \u03b1SP as a function of the ratio of the total MoS2 coverage area to the total MoS2 perimeter (ATotal/PTotal). The graph displays a V-shaped curve with two distinct behaviors. The first, highlighted in light red, demonstrates a decrease in spin pumping as the ATotal/PTotal ratio increases. Conversely, after a certain compensation point, where \u03b1SP is extrapolated to zero (highlighted in light blue), the slope becomes positive, and \u03b1SP increases as ATotal/PTotal increases. Notably, the data can be fitted by the absolute value of a single linear function, as shown in Fig.\u00a01e, where the solid line represents the experimental data fit by the equation displayed on the graph. Despite the complexity of the sample preparation, the entire dataset follows a linear behavior with minimal dispersion. These results suggest that both semiconductor area states and metallic edge states contribute to the injection of spin current into the MoS2 triangular flake.\n\nThis V-shaped curve can be explained by a competitive interplay between the two channels contributing to the overall spin current injection. One channel is associated with the semiconductor area states, proportional to ATotal, and the second is associated with the metallic edge states, proportional to PTotal. To better understand those behaviors, it is helpful to visualize the diagram located above the graph in Fig.\u00a01e. This diagram displays the red and blue arrows that represent the metallic edge and the semiconductor area channel contributions to the \u03b1SP, respectively. Meanwhile, the gray arrow indicates the overall measured spin pumping, which corresponds to the difference between these channels.\n\nIt is worth noting that when considering the samples with small ATotal/PTotal ratio (light red region), they have a higher proportion of total edge (PTotal) compared to the area (ATotal) because they have smaller MoS2 flakes. A representative scanning electron microscopy of the flakes of this region (light red) is shown in Fig.\u00a01f with a 2\u2009\u00b5m scale bar. To better visualize the largest metallic edge contribution in relationship to the semiconducting area in this region, a scheme of the spin pumping in these samples is depicted in Fig.\u00a01c. The semiconductor contribution becomes more significant as the ATotal/PTotal ratio increases since the samples in the light blue region have bigger flakes. An illustration showcasing the relationship between these two contributions in samples is depicted in Fig.\u00a01d, and representative scanning electron microscopy of a bigger flake is displayed in Fig.\u00a01g with a 100\u2009\u00b5m scale bar.\n\nTo better understand this picture, first we focus on the light red region, where the ATotal/PTotal ratio is small. Here, the contribution from the metallic edge state channels dominates, as represented by the red arrows in the diagram of Fig.\u00a01e, leaving the semiconductor area state channels a secondary role. This area contribution has an opposite sign and is denoted by the smaller and opposite blue arrows in the diagram. Increasing the ratio, the metallic edge state channel\u2019s dominance remains, but its importance decreases. This means that the spin current injected by the semiconductor area states becomes more noticeable as the ratio increases. Considering the channels as having opposite polarity, a decrease in the overall spin pumping is observed, represented by the gray arrow in the diagram. This trend continues until, by extrapolation, both channels balance each other at the compensation point, where the value of \u03b1SP would be zero, and therefore no spin current is injected into the MoS2. Actually, so far, it is not clear why area and edge states have opposite contributions, but as we will discuss later, ab initio calculations will be useful to clarify this question.\n\nBeyond the compensation point (light blue region), a further increase in the ATotal/PTotal ratio leads to a switch in dominance. Therefore, the semiconductor area states, here represented by the largest blue arrows on the right side of the diagram (see Fig.\u00a01e), dominates the spin current injection, while the metallic edge state, represented by the smaller and opposite red arrows plays a secondary role. A further increase in the ATotal/PTotal ratio implies that the already dominant semiconductor area states become even stronger than the metallic edge states, leading to an overall increase in spin pumping. The V-shaped behavior observed for spin pumping (\u03b1SP) as a function of the ATotal/PTotal ratio appears to be robust. In spin pumping studies, it is also common to consider the mixed spin conductance (g\u2191\u2193) alongside \u03b1SP, as g\u2191\u2193 depends on both \u03b1SP and the magnetic properties of the ferromagnetic material. This makes it easier to compare across different ferromagnetic materials. In Supplementary Information\u00a0Section S3, g\u2191\u2193 is presented in more detail, along with its dependence on the ATotal/PTotal ratio, which also exhibits the same V-shaped behavior.\n\nThe geometric properties of monolayer MoS2 flakes have a significant impact on the competition between semiconductor and metallic edge states in spin injection to MoS2. Each point in the graph in Fig.\u00a01e corresponds to a different sample, so new samples are needed to better track the V-shaped curve in detail and uncover missing information about the origin of the spin injection, especially near the compensation point and in the right region. However, producing new samples with a given ATotal/PTotal in this curve is a challenging task.\n\nTo further investigate the V-shaped curve, we can adjust the balance between the metallic and semiconductor channels by exciting electrons from the valence to the conduction band. This can be done by illuminating the MoS2 flakes with the appropriate light wavelength. By controlling the light intensity, we can regulate the number of electrons promoted to the conduction band, thus controlling the enhancement of the metallic contribution of the spin pumping and precisely tracking the V-shaped curve.\n\nThis approach is supported by density functional theory calculations of the electronic properties of MoS2 flakes64,65 (see Methods). To investigate the metallic behavior of MoS2 triangular flakes, we analyzed atomic projected quantities, such as the LDOS and the real-space representation of the charge density.\n\nThe Fig.\u00a02 panel A shows the LDOS for a MoS2 triangular flake with a lateral distance of 57\u2009nm and zigzag-terminated edges. We calculated the LDOS for two distinct regions: edge and area. The edge region is formed by the two outmost MoS2 triangular flake atomic lines (green curve). Whereas, all other atoms form the area region (red curve). The shaded region corresponds to the MoS2 monolayer bandgap (fully periodic), which corresponds to a value of 1.7\u2009eV. This bandgap value is in good agreement with the literature66 (see Supplementary Information\u00a0Section S4). As shown in panel A, the area LDOS (red curve) is very small within the monolayer bandgap (shaded region). In contrast, the edge LDOS (green curve) exhibits a high density of states within the bandgap, indicating the presence of metallic edge states in the MoS2 triangular flake with zigzag termination, as previously discussed. A deeper understanding of the charge spatial distribution can be obtained by examining the partial charge density (PARCHG) at selected energy levels. The vertical blue lines labeled (i), (ii), (iii), and (iv) in panel A, correspond to energies of 0.00, 0.45, 1.67, and 2.08\u2009eV, respectively.\n\nIn (A), the graph depicts the local density of states (LDOS) as a function of the energy for a MoS2 triangular flake with zig-zag termination and a lateral distance of 57.3\u2009nm. The green curve represents the edge states LDOS, defined as the two outermost atomic lines along the flake\u2019s edge. The red curve is the area states LDOS, which is defined as all other atomic sites on the flake. The grey region highlights the full MoS2 monolayer (fully periodic) bandgap energy region. The vertical blue lines labeled (i), (ii), (iii), and (iv) correspond to selected energies, namely, 0.00, 0.45, 1.67, and 2.08\u2009eV, respectively. B shows the MoS2 triangular flake real space representation of the partial density of states for each energy highlighted in panel (A). The isosurface value is 0.005 electrons/\u00c53. C illustrates the process of electronic excitation in the triangular MoS2 flake when exposed to 2.08\u2009eV light, which promotes electrons from below the Fermi level, as depicted in panel B (i), to the unoccupied states above the bandgap (spatial distribution shown in panel B (iv)). Initially, the system is in its ground state, with electrons occupying states below the Fermi level, as represented by flake (I), leaving the states above the bandgap entirely unoccupied, as shown in flake (II). Upon illumination, electrons are excited from the Fermi level to higher-energy states, filling previously unoccupied states. As the intensity of the light increases, electrons preferentially populate states with the highest LDOS, as illustrated by flake (III). With further increases in light intensity, more electrons are excited from the Fermi level, progressively occupying states that are less probable, as reflected by the increasing red contrast in the flakes (II-V). At maximum light intensity, all available states become occupied, rendering the flake fully metallic, as demonstrated by flake (V). Notably, the spatial distribution of LDOS in the fully illuminated flake (V) mirrors that in panel B (iv), with the key difference being that the states in flake (V) are fully occupied, whereas those in panel B (iv) remain unoccupied. The color scale ranges from white, representing no empty states, to red, representing the occupied states (i.e., the metallic states), as shown on the scale bar.\n\nThe charge distributions at these selected energies are further analyzed in Fig.\u00a02 panel B, from (i) to (iv). These panels depict the real-space representation of the PARCHG for the MoS2 triangular flake at each corresponding energy. Panel B (i), shows the spatial charge density distribution at the Fermi level (E\u2009=\u20090.00\u2009eV), which represents the ground state of the system. This result reveals that the most populated sites are located at the edges of the flake, which confirms its metallic behavior. The flakes in panel B (ii) and (iii) show the spatial distribution of the LDOS at excited energy states within the system\u2019s bandgap. As previously discussed, the LDOS within the bandgap is negligible (as indicated by the red curve in the shaded region of panel A). The edge states dominate, so the spatial distribution of the LDOS at these energies does not change significantly. In contrast, panel B (iv) shows the LDOS at an excited energy state beyond the bandgap. Unlike the previous PARCHG, the LDOS is more evenly distributed across both the surface area and the edges of the flake.\n\nFigure\u00a02 panel C illustrates the process of electronic excitation in the flake when exposed to 2.08\u2009eV light. This light promotes electrons to unoccupied states above the bandgap, as shown in panel C (I) and (II). The red sites represent occupied states, while the white sites represent vacant ones. Initially, the system is in its ground state, with electrons occupying states below the Fermi level, as depicted panel C (I) (or equivalently panel B, (i)), leaving all other states unoccupied. When the flake is illuminated, electrons are excited to higher energy states, filling previously unoccupied states. As the intensity of the 2.08\u2009eV light increases, electrons are first excited to the most probable states\u2014those crystalline sites with the highest LDOS\u2014as shown in panel C (III). With further increases in light intensity, more electrons near the Fermi level are excited to unoccupied states above the bandgap, as depicted in panel C (IV). These electrons gradually occupy less probable sites, i.e., those with lower LDOS. This process is represented by the increasing red contrast in the flakes in panel C (II) to (V), reflecting the growing number of electrons occupying these states. With a further increase in light intensity, the red contrast intensifies (panel C (IV)), indicating a greater number of sites being occupied by electrons, effectively rendering more metallic sites. Eventually, at a given light intensity, all states are occupied, and the flake becomes fully metallic, as represented by the red triangle in panel C (V).\n\nWhen analyzing the effect of the 2.08\u2009eV light illumination (see Fig.\u00a02 panel C (II-V)), it is evident that electronic excitation, leading to the creation of light-induced metallic sites, initiates from the central region of the flakes and spreads towards the edges as the light intensity increases. Notably, the spatial distribution of LDOS in the fully illuminated flake (panel C (V)) resembles that of Panel B (iv), except that initially the states (panel B (v)) are occupied, while those at the second (Panel B, (iv)) remain unoccupied.\n\nOur results indicate that in the absence of any significant doping, meaning the Fermi level is at zero energy, our samples will show a metallic character that is controlled by the edge states.\n\nAccording to this theoretical approach, we can analyze the V-curve by focusing on the compensation point. This involves shining the appropriate light wavelength on the MoS2 flakes, as shown in Fig.\u00a02. As a result, one can expect the semiconductor\u2019s contribution to spin pumping to decrease while the metallic contribution increases. By adjusting the light intensity, it is possible to control the number of electrons moving to the conduction band, which in turn regulates the enhancement of the metallic contribution to spin pumping. Therefore, the V-curve can be accurately tracked by controlling the light intensity and fine-tuning the balance between the semiconducting area and metallic edge states.\n\nTo study the effects of light on the spin current generation, we have performed spin pumping measurements in the presence of light with different wavelengths to populate the conduction band of MoS2, according to the illustration in Fig.\u00a03a. The DFT bandgap of MoS2 was calculated to be 1.7\u2009eV. However, it is well known that DFT tends to underestimate the actual bandgap (see Supplementary Information\u00a0Section S4). Therefore, in the experiments, we chose to use violet light with a wavelength of 405\u2009nm (3.03\u2009eV) which is expected to be above the electronic bandgap. We selected four samples with different ATotal/PTotal ratios to cover the two regimes. These four samples are labeled S1, S2, S3 and S4 in Fig.\u00a01. As can be seen in this figure, through the V-shape, the samples S1 to S4 are in order from left to right, i.e. for smaller to larger flakes. Remembering that this is not the case for either ATotal (Fig.\u00a01b) or PTotal (Fig.\u00a01a). The spin pumping variation of these four samples and a bare YIG sample is shown in Fig.\u00a03(b), where the samples clearly exhibit different behaviors. To provide a more detailed explanation of each sample, the results of S1-S4 are shown in Fig.\u00a03c through f, respectively. The diagram at the top of each figure uses the same notation as before: the red arrows indicate the metallic edge, while the blue arrows represent the 2D semiconductor contributions in the opposite direction. The gray arrows represent the total spin pumping measured, which is the difference between the metallic edges and 2D semiconductor contributions. The purple arrows represent the light-driven contributions.\n\nIn (a) is presented a schematic representation of the light-induced spin pumping. The spin pumping (\u03b1SP) as a function of photon flux (light intensity) is shown in (b) to (f) for violet light (405\u2009nm), (g) for red light (650\u2009nm), and (h) for green light (532\u2009nm). In the diagrams (c) to (f) the overall spin pumping (SP) and the individual contributions from the metallic (M), semiconducting (SC) and metallic excited (ME) states are represented by gray, red, blue and purple arrows, respectively. Samples S1 to S4 are represented by purple squares, yellow pentagons, green circles, and red hexagons, respectively. The solid symbols represent the data recorded on the coplanar waveguide without light incidence, and the open symbols represent the data recorded in the cavity with light incidence. The solid blue circles represent the bare YIG.\n\nThe sample S1 is represented by purple squares in Fig.\u00a03c. It has the smallest ATotal/PTotal ratio, far before the compensation point, lying in the light red region (see Fig.\u00a01e). Here, the contribution of metallic edge state channels to spin pumping is more significant than that of semiconductor area states. On the top of this figure is a diagram that illustrates the contribution to overall spin pumping (the measured \u03b1SP). This sample presents a dominant metallic edge state (red arrwos) while the smaller 2D semiconductor contribution is in the opposite direction. By illuminating the S1 sample with violet light, electrons within the semiconductor regions are excited from the valence to conduction bands, as indicated by the violet arrows. This photoexcitation results in an increase in the density of conductive carriers in the semiconductor areas, thereby enhancing the metallic contribution to the \u03b1SP, which continues to rise with intensified light exposure.\n\nSample S2 is represented by yellow pentagons in Fig.\u00a03d; it has the same behavior as S1. In both samples, the spin pumping is already dominated by the metallic edge state (red arrows in the diagram), which has the same contribution intensified by the light (violet arrows in the diagram). Therefore, the enhancement of spin injection (gray arrow in diagram) is achieved by increasing the photon flux.\n\nIt is worth noting that when MoS2 is illuminated, there is a significant increase in spin injection from YIG to MoS2 in both S1 and S2 samples. For sample S1 the spin injection doubles from \u03b1SP\u2009=\u20093.2\u2009\u00d7\u200910\u22124 to \u03b1SP\u2009=\u20096.5\u2009\u00d7\u200910\u22124, when illuminated. For comparison, this value is in the same order as the YIG/Pt system, among the highest values reported so far50,51. These findings are particularly impressive given the low coverage of TMD (ATotal) in the S1 sample, which only covers about 28% of the YIG\u2019s total area (Fig.\u00a01b). Additionally, one can notice that the light-driven spin pumping enhancement in sample S1 (Fig.\u00a03c) is higher than in sample S2 (Fig.\u00a03d). This is possibly due to the number of available states depending on the average size of flakes. The overall MoS2 covered area of sample S1 is much smaller than that of sample S2, therefore, the light intensity required to achieve the flakes fully metallic (as exemplified in Fig.\u00a02 panel C (V)) could be smaller for smaller flakes.\n\nIn sample S4, represented by red hexagons in Fig.\u00a03e, the ATotal/PTotal ratio is the highest and far beyond the compensation point lying in the light blue region (see Fig.\u00a01e). As illustrated in the diagram the 2D semiconductor state represented by the blue arrows dominates the spin pumping. The metallic edge state contribution to the spin pumping in this region is smaller, with an opposite signal, as illustrated by the smaller switched red arrows. As the light enhances the metallic states, this contribution (represented by the violet arrows) will be additive to the metallic edge contribution. Hence, as the overall spin pumping measured is the difference between metallic and semiconductor channels, the overall spin pumping (gray arrows) will experience a decrease with the increasing photon flux.\n\nThe sample S3 represented by green circles in Fig.\u00a03f is noteworthy. The nonilluminated sample is in the same region as sample S4. However, the ATotal to PTotal ratio is close to the compensation point, where \u03b1SP is zero. As deciped in the diagram, at lower photon flux, the sample S3 behaves similarly to the S4 sample. This means that the light enhances the metallic contribution, which has an opposite signal regarding the semiconductor contribution, reducing the total \u03b1SP, as represented by the leftmost set of arrows on the diagram. This trend continues as the photon flux increases until the contribution of the metallic states reaches the same intensity as the semiconductor. At this point, both contributions cancel each other, and the overall spin pumping is zero. The central set of arrows in the diagram represents this compensation point. As depicted by the rightmost set of arrows, the system switches with a further increase in photon flux. The metallic states become the major contributor to \u03b1SP, in detriment to the 2D semiconductor contribution. From this point, increasing the light intensity will promote the already dominant contribution, the metallic state. At this point, sample S3 will behave like the S1 and S2 samples, meaning that the overall \u03b1SP will increase with the photon flux. In fact, with higher photon flux, the sample S4 is expected to behave similarly to the S3.\n\nFor comparison, Fig.\u00a03b shows the \u03b1SP measure for all samples and the bare YIG (as reference sample) as a function of the violet light (405\u2009nm and 3.03\u2009eV) intensity while keeping the symbols and the color notation of the samples.\n\nA recent theoretical work reported by Habara and Wakabayashi67 has shown that in a monolayer of metallic NbSe2, a TMD with strong spin-orbit coupling field, this field can act as an effective Zeeman field, leading to unconventional topological spin properties. The authors predict that a pure spin Hall current can be generated by light irradiation due to the topological nature of monolayer NbSe2 and its finite spin Berry curvature. As the same ingredients are also present, a similar effect can likely occur in MoS2. Since NbSe2 is metallic, one can conjecture that illumination with a suitable light (violet here) can enforce the already existent metallic edge states spin injection from YIG to MoS2 flakes. This explanation might be the microscopy origin of enhancing the metallic state contribution driven by light. In addition, our results also support the predictions made by the authors, i.e., the light-driven spin-polarized current in TMDs.\n\nFurther tests were performed with the samples under different conditions to ensure that the observed light-induced effects were done by promoting electrons to the conduction band and not due to spurious effects or any measurement artifact. To verify that the promotion of electrons was responsible for the observed effects, \u03b1SP was measured as a function of photon flux for the four samples and a bare YIG sample for distinct wavelengths. Figure\u00a03g, h show the spin pumping with the samples excited by red light (1.91\u2009eV and 650\u2009nm) and green light (2.33\u2009eV and 532\u2009nm), respectively. By using these two wavelengths, no change in \u03b1SP was observed. Therefore, for these energies the electrons cannot be excited into the conduction band, which does not change the measured total spin pumping. It is important to note that although the photon energy of the green light (2.33\u2009eV) is above the underestimated bandgap calculated by DFT, as discussed earlier and in the SI, it is probably still smaller than the actual bandgap. This is in agreement with recent experiments that have measured for the MoS2 monolayer an electronic bandgap as 2.40\u2009eV68. YIG sample represented by the blue circles in Fig.\u00a03 was measured under the same conditions as the previous samples, and no change in the FMR linewidth was found, regardless of whether or not red, green or violet lights were used. This confirms that the observed behavior is due to MoS2.\n\nRecent studies have shown that ultraviolet (UV) light can irreversibly alter the properties of MoS2. For instance, UV exposure can modify the interaction between MoS2 and oxygen, promoting the formation of oxides on the MoS2 surface69,70. Furthermore, UV light can introduce defect states and modify the surface bonding of MoS2, leading to changes in its band structure71. Although these irreversible effects have been observed with UV light, which has a higher photon energy, it is crucial to verify that the violet light used in this study does not induce similar alterations or damage to MoS2. To ensure that violet light did not damage the MoS2 flakes, we performed FMR measurements before and after all experiments with varying incident light power. As shown in Supplementary Information\u00a0Section S5 the FMR spectra and \u03b1SP remained unchanged before and after the experiments with light. This indicates that the light did not damage the MoS2 monolayer and that the experimental procedure is fully reversible.\n\nAll the narratives until now need further explanation about the origin of spin pumping in a semiconductor material. MoS2 has a gap of approximately 2\u2009eV, as discussed in detail throughout the manuscript. Therefore, it is unclear how the excitation employed in the FMR essays, which is in microwave order, can inject the angular momentum from YIG to MoS2.\n\nConsiderable research has been conducted in the field of energy-efficient magnetization manipulation. To minimize energy consumption in magnetization switching, researchers have focused on using light-assisted spin-orbit torque to control magnetization and spin current generation in TMDs72,73,74. Moreover, recent studies have demonstrated the possibility of electrically controlling the modulation of circular polarization and spin injection through magnetization dynamics. This breakthrough is a significant step towards the development of next-generation information and communication technology75,76,77. However, energy-efficient opto-spintronic devices are still far from being achieved.\n\nThis work represents a significant advance in the understanding of the physical nature of spin-charge interconversion in transition metal dichalcogenides. Our results show distinct contributions from 2D semiconductor area and metallic edge states to spin current injection in MoS2 and the role of light-excited states in these contributions. Beyond the fundamental findings, our results allow us unprecedented control over spin current injection. By exploiting the light intensity, the spin pumping can be finely tuned by promoting light-excited states where the spin currents can be amplified, attenuated, or even switched on and off. Therefore, these results mark a significant milestone in opto-spintronics by demonstrating the feasibility of controlling spin currents via light excitation at room temperature.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58119-4/MediaObjects/41467_2025_58119_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58119-4/MediaObjects/41467_2025_58119_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58119-4/MediaObjects/41467_2025_58119_Fig3_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We studied the efficiency of light-driven spin current injection in triangular MoS2 monolayer flakes from YIG thought spin pumping measurements. Alongside the detailed fabrication and comprehensive characterisation, the main experimental results reported are: The identification of two distinct channels contributing to spin pumping, one originating from metallic edge states and the other from semiconductor area states. On this regard, we can conjecture that each may have a different microscopic origin. The semiconductor phase, as demonstrated in this work, is driven by the inverse Rashba Edelstein effect, while the metallic phase likely arises from the spin Hall effect.\n\nThis dual contribution opens up two promising directions for these systems, either in device applications or as fundamental prototypes for deeper studies in spintronics.\n\nOur experimental evidence clearly demonstrates that these two channels operate concurrently. While one channel transfers angular momentum from the ferromagnetic layer (YIG) to the TMD (MoS2, in our particular case), the other channel either returns angular momentum from the TMD to the YIG or inject angular momentum with an opossite sign. This competition gives rise to a compensation point where these spin-pumping contributions cancel each other out. The existence of various channels contributing to spin-to-charge interconversion is well-established in the literature, with these mechanisms typically realized through the use of multiple layers, either within the same material or across different materials. For instance, Pt/FM/W trilayers are often employed to enhance terahertz emission78,79. While Pt and W exhibit opposite spin Hall angles, their placement relative to the FM layer leads to a geometry in which the spin currents from both materials combine to enhance the emission. In contrast, Pt/FM/Pt multilayers have recently been used to investigate the orbital Hall effect in adjacent materials. In this case, the spin current injected by the bottom Pt layer is either partially or fully canceled by the contribution of the top Pt layer. This competition between the two Pt layers facilitates the study of the adjacent material80,81. Despite the distinct mechanisms of spin current injection, which in the case of Pt and W is the spin Hall effect, an analogy can be drawn to better understand the interaction between the two spin current channels in MoS2, the metallic edge and semiconductor area states.\n\nIt is noteworthy that the zigzag metallic edge states studied here may not be the only additional channel capable of spin-to-charge interconversion in TMDs. Recently, the existence of one-dimensional metallic states in 1H-MoS2 grain boundaries has been shown82. However, this particular configuration, called mirror twin boundaries, only occurs when the two adjacent crystals are exactly 60\u00b0 rotated. In our case, the relative crystal orientation of the adjoining flakes was not controlled, so the formation of this specific case is unlikely. In addition, no experimental evidence for boundary influence on spin pumping has been found. Nevertheless, the existence of different one-dimensional metallic channels in TMDs opens the possibility of investigating new channels of spin-to-charge interconversion.\n\nMoreover, we have shown experimentally that by shedding the system with light with suitable energy (wavelength) and adjusting its intensity, we can modulate the balance between metallic and semiconductor channels. This allows the fine control of the spintronic manifestation in this system, either amplifying, diminishing, switching them on, or even completely turning off the spin current injection. Our experimental light-driven disentanglement results between the metallic edge and semiconductor area phases were studied by the local density of state of the MoS2 flake calculations and its real-space representation of the partial charge density (which depicted the spatial distribution of the LDOS), comprehensively conducted by density function theory calculations. These analyses helped to verify the concomitance of the states of the metallic edge and the semiconductor area and how the intensity of the light (with the right energy) can balance each other. In principle, a further spin pumping calculation could be achived by including many body interactions and linear response theory in a combination with DFT and effective Hamiltonians.\n\nIt is crucial to note that although we can offer some plausible insights into the underlying mechanisms, no existing model or theory, to the best of our knowledge, adequately explains this phenomenon. We believe that a study presenting such clear, reproducible, and highly unexpected results\u2014results that challenge conventional thinking and stimulate new questions for the scientific community. Additionally, the ability to control the spintronic properties of this system -amplifying, attenuating, switching on, or completely turning off spin pumping- simply by modulating the intensity of incident light represents a breakthrough innovation. This capability not only introduces a previously unreported mechanism in the field but also opens the door to a wide array of energy-efficient optoelectronic devices.\n\nThe observed light-driven modulation of spintronic effects in MoS2 likely extends across the broader family of transition metal dichalcogenides, including other 1H phase compounds (e.g., MoSe2, MoTe2, WS2) and possibly the T and T\u2019 phases, some of which may exhibit topological insulating states. This generality suggests a significant foundation for further research, potentially expanding control over spintronic properties in two-dimensional materials. As detailed in recent literature83, several TMD compounds may present similar behaviors, although some are still theoretical predictions. By broadening the applicability of our findings, our study paves the way for a new avenue in spintronic research within TMDs.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Thin films of yttrium iron garnet (YIG) were grown by magnetron sputtering on monocrystalline gadolinium gallium garnet (GGG) substrates oriented along the (111) direction. The sputtering chamber pressure was 9.0\u2009\u00d7\u200910\u22128 Torr, and the working argon pressure and flow were 10\u2009mTorr and 15 sccm, respectively. We used an RF power of 75\u2009W. After deposition, an ex situ annealing was performed with oxygen flow. The films had a thickness of 50\u2009nm and a squareshaped pattern of 1\u2009\u00d7\u20091\u2009mm2, which were achieved through photolithography using a laser writer model \u00b5PG101 with 3\u2009\u00b5m resolution and AR-P3510 photoresist50.\n\nTriangular flakes of MoS2 monolayers have been synthesized by atmospheric pressure chemical vapor deposition (APCVD) on SiO2/Si substrates. Flakes with different edge sizes were obtained by varying the growth conditions, ranging from 1\u2009\u00b5m to a continuous film. The growth of the MoS2 crystal starts from a nucleation point and is obtained by sulphurisation of molybdenum oxide. By increasing the growth time, the size of the triangular flakes continues to grow and eventually coalesce to form a uniform continuous MoS2 film. A given number of MoS2 triangles with a chosen edge size were individually transferred from the Si substrate to the YIG using a simple etch-free transfer method. Finally, several samples of YIG with different areas of MoS2 coverage were prepared following the procedure described in refs. 20,21,22.\n\nAll samples were investigated by optical microscopy to evaluate the covered area and the total length of the edges of the MoS2 triangles. Images were analyzed using the ImageJ software63.\n\nA few layers of MoS2 were deposited on YIG films using an automated technique based on mechanical abrasion. We used a soft polymer (polydimethylsiloxane, PDMS) to cover a tip coupled to a computer numerical control (CNC). This tip is then pressed into the MoS2 powder precursor, and the CNC acts as an XY writing pad, performing mechanical abrasion and leaving TMD along the way, providing the deposition. The CNC is coupled to a piezoelectric sensor that controls the z coordinate to achieve excellent uniformity. This technique allows the deposition of TMD thin films on large substrates without damaging the substrate surface. Film properties are controlled through the parameters of the deposition system. The thickness of the film is mainly influenced by the number of exfoliations of the material on the substrate surface. Consequently, this approach made it possible to obtain a certain thickness by adjusting the number of exfoliations51.\n\nRaman and photoluminescence maps and spectra were obtained using a micro-Raman spectrometer (NT-MDT, NTEGRA SPECTRA) in a backscattering configuration equipped with a solid-state laser (473\u2009nm). We performed the experiments using a 100\u00d7 objective and an incident laser power of 0.2\u2009mW. The Raman and PL mapping images were collected using a 10\u2009\u00d7\u200910\u2009\u00b5m2 piezoelectric stage.\n\nThe scanning transmission electron microscopy (STEM) imaging was performed at an acceleration voltage of 3.0\u2009kV on a Jeol 7100FT microscope.\n\nFerromagnetic resonance (FMR) was performed on all samples before and after MoS2 transfer. FMR measurements were performed using a broadband coplanar waveguide from 3 to 14\u2009GHz with AC magnetic field modulation (0.5\u2009Oe and 45\u2009kHz) for lock-in detection. A fixed-frequency cavity configuration (9.8\u2009GHz) was used with the same modulation techniques for light-excited FMR measurements50,51. More details on the FMR and spin pumping analysis can be found in the Supplementary Information\u00a0Section S3.\n\nWe used the plane-wave-based code Vienna ab-initio simulation package (VASP)84,85. With the generalized gradient approximation (GGA)86,87 to treat the exchange and correlation potential. The ionic cores were described using scalar relativistic projected augmented wave (PAW) potentials88. A plane-wave-expansion cutoff of 400\u2009eV was adopted with a 0.01\u2009eV/\u00c5 force criterion for structural optimization. The MoS2 flakes were constructed with a triangular geometry and a zigzag edge termination, to reproduce the experimental setup. Different flake sizes were explored and we are showing results for the largest, with a lateral size of 57.3\u2009\u00c5 and 604 atoms in the supercell. Since VASP uses periodic boundary conditions, we included a vacuum layer in all directions to ensure a minimum of 15\u2009\u00c5 distance between an atom and its image.\n\nOptical images of the Si/SiO2/MoS2 and GGG/YIG/MoS2 samples. Detailed Raman, photoluminescence, and light absorption spectroscopy. Raman frequency maps and atomic force microscopy of single MoS2 flake. Details of the ferromagnetic resonance and spin pumping measurements. A discussion about the bandgap in the MoS2, and the Rashba-Edelstein Effect in the semiconductor states of the MoS2.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The source data is available at https://doi.org/10.5281/zenodo.14976824. 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The authors thank Roberto Bechara, Antonio Costa, Bruno Pimentel, and Igor Evangelista for helpful discussions. R.T.V. is thankful for Grant No. E-26/201.677/2021 from FAPERJ/Brazil. M.C. acknowledges the financial support of CNPq (Grant No. 317320/2021-1) and FAPERJ/Brazil (Grant No. E26/200.240/2023). J.F.F. and J.F.R.M. acknowledge financial support from Fundac\u00b8\u02dcao de Apoio \u2018a Pesquisa do Distrito Federal (FAPDF, Grant No. 193.00001823/2022-10 and 00193-00002418/2023-08). V.C. acknowledges CNPq (Grant No. 311863/2021-3), FAPERJ/Brazil (Grant No. E-26/204.493/2024, E-26/201.394/2021, E-26/210.539/2019), and US Air Force (Grant No. FA9550-23-10329). F.G. acknowledges CNPq (Grant No. 307624/2022-6), FAPERJ/Brazil (Grant No. E26/200.995/2022).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Centro Brasileiro de Pesquisas F\u00edsicas, R. Dr. Xavier Sigaud, 150, Urca, Rio de Janeiro, 22290-180, RJ, Brazil\n\nRodrigo Torr\u00e3o Victor,\u00a0Syed Hamza Safeer,\u00a0Luiz C. Sampaio\u00a0&\u00a0Flavio Garcia\n\nMaterials Science Laboratory, Department of Physics, Quaid-i-Azam University, Islamabad, 45320, Pakistan\n\nSyed Hamza Safeer\n\nInstitute of Physics, LabINS, University of Bras\u00edlia (UnB), Bras\u00edlia, DF, 70910-900, Brazil\n\nJohn F. R. Marroquin\u00a0&\u00a0Jorlandio F. Felix\n\nInstituto de F\u00edsica, Universidade Federal Fluminense, Niter\u00f3i, 24210-346, RJ, Brazil\n\nMarcio Costa\n\nDepartamento de F\u00edsica, Pontif\u00edcia Universidade Cat\u00f3lica do Rio de Janeiro, Rio de Janeiro, 22451900, RJ, Brazil\n\nVictor Carozo\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nR.T.V. contributed to the conceptualization, investigation, project administration, supervision, and writing the original draft. J.F.R.M. and S.H.S. contributed to the investigation. M.C. contributed to the theoretical calculations. J.F.F., V.C., L.C.S., and F.G., contributed to conceptualization, project administration, and supervision. All authors contributed to the formal analysis, review, and editing of the manuscript.\n\nCorrespondence to\n Jorlandio F. Felix or Flavio Garcia.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interest.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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Disentangling edge and bulk spin-to-charge interconversion in MoS2 monolayer flakes.\n Nat Commun 16, 3075 (2025). https://doi.org/10.1038/s41467-025-58119-4\n\nDownload citation\n\nReceived: 09 May 2024\n\nAccepted: 12 March 2025\n\nPublished: 30 March 2025\n\nVersion of record: 30 March 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58119-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Embedding System", + "journal": "Nature Communications", + "published": "19 June 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60802-5/MediaObjects/41467_2025_60802_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60802-5/MediaObjects/41467_2025_60802_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://cocodataset.org/#download", + "https://www.kaggle.com/datasets/adityajn105/flickr8k", + "https://zenodo.org/records/3490684", + "https://archive.ics.uci.edu/ml/machine-learning-databases/00364/dataset_uci.zip", + "https://www.kaggle.com/datasets/penguin0211/twitter-dataset-for-mobile-search", + "https://www.kaggle.com/datasets/dongqicai/mobile-trace-of-viewed-images", + "https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth", + "https://huggingface.co/openai/clip-vit-base-patch16" + ], + "code": [ + "/articles/s41467-025-60802-5#ref-CR51", + "https://github.com/caidongqi/Mobile-Search-Engine/tree/pc", + "/articles/s41467-025-60802-5#MOESM1" + ], + "subject": [ + "Electrical and electronic engineering", + "Energy efficiency" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5686668/v1.pdf?c=1750417532000", + "research_square_link": "https://www.researchsquare.com//article/rs-5686668/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60802-5.pdf", + "preprint_posted": "15 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Forgetting is inevitable in human memory. Recently, multimodal embedding models have been proposed to vectorize multimodal reality into a unified embedding space. The generated embeddings can be easily retrieved to help mobile users remember and recall information when needed. However, as the model\u2019s capacity increases, its resource consumption also rises. The resulting slow throughput and significant computational resource requirements hinder its deployment on mobile devices. In this paper, we present Reminisce, the first efficient on-device multimodal embedding system that enables high-throughput and precise retrieval on resource-constrained mobile devices. The core design draws inspiration from the memory functions of the human brain, utilizing coarse-grained embeddings to identify likely candidates, which are then refined through query-driven fine-grained retrieval. A series of algorithm-hardware orchestrated optimizations automatically navigates this process and strengthens the embedding quality. Experiments show that Reminisce provides high-quality embedding representation with high throughput while operating silently in the background with negligible memory usage and reduced energy consumption.Social science/Language and linguisticsSocial science/History", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Appendix.pdfImplementation details and further discussions", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Forgetting is inevitable in human memory. Recently, multimodal embedding models have been proposed to vectorize multimodal reality into a unified embedding space. Once generated, these embeddings allow mobile users to quickly retrieve relevant information, effectively augmenting their memory. However, as the model\u2019s capacity increases, its resource consumption also rises. The resulting slow throughput and significant computational resource requirements hinder its deployment on mobile devices. In this paper, we present Reminisce, an efficient on-device multimodal embedding system that enables high-throughput embedding and precise retrieval on resource-constrained mobile devices. The core design draws inspiration from the memory functions of the human brain, utilizing coarse-grained embeddings to identify likely candidates, which are then refined through query-driven fine-grained retrieval. A series of algorithm-hardware orchestrated optimizations automatically navigates this process and strengthen the embedding quality. Experiments show that Reminisce provides high-quality embedding representation with high throughput while operating silently in the background with negligible memory usage and reduced energy consumption.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Mobile devices are ubiquitous nowadays. They capture lots of data in users\u2019 daily usage, digitally chronicling every aspect of a person\u2019s life. However, such data has not been fully utilized, attributed not to how to store them, but how to accurately retrieve them1. Specifically, smartphones have abundant storage (up to 1TB for iPhone 15 Pro) to host the information captured at 24\u2009\u00d7\u20097, or local network-attached storage can help accommodate those data as well; yet there has been a lack of method to efficiently locate the data intended at query time2,3. The fundamental challenge is that data generated on devices is multimodal by nature (e.g., text, image, audio, etc.), which are hard to be accurately retrieved in a user-friendly manner, e.g., through natural language4.\n\nFortunately, the recent development of multimodal embedding models (MEM) has shed light on multimodal data retrieval. For example, CLIP unifies text and image modalities into one embedding space5. ImageBind further extends the functionality to 6 modalities through contrastive learning6. At architecture level, those models primarily consist of multi-layer transformer encoders7. In general, MEMs will catelyze two exciting types of mobile applications as shown in Fig.\u00a01: (1) cross-modality searching, which allows users to retrieve data in any modality with user-friendly interface; (2) retrieval-augmented LLM generation, which first identifies the relevant multimodal data (e.g., a picture) in a historical database with user prompt, and uses it to enhance the LLM generation quality, e.g., \u201cin the picture I took for my kid yesterday, is she wearing a blue skirt or yellow?\u201d.\n\nMEM encodes multimodal data streams into a unified embedding space. These embeddings support downstream tasks such as cross-modality search and retrieval-augmented generation. We instantiate MEM-based ubiquitous memory palace on mobile devices with an emphasis on resource-efficient offline embedding to optimize throughput, memory, and energy consumption.\n\nThis work addresses the emerging scenario of on-device multimodal embedding, where MEMs operate as a system service on local devices to embed continuous data streams8,9,10,11, functioning like a memory palace12. The local generation of embeddings is motivated by user privacy concerns, since MEMs can greatly expand the usage of device data, including screen UIs, recorded voices, etc. Offloading such information to the cloud may expose it to unauthorized access. For instance, it was revealed that Apple had been eavesdropping on uploaded user conversations to enhance their Siri model13. With cloud-based MEMs, users risk comprehensive life surveillance, with no way to verify.\n\nDespite on-device MEM is private and generalizable to various downstream tasks6,14,15,16, it comes at a cost of resource intensity. Specifically, our pilot experiments identify two key obstacles towards on-device multimodal embedding: (1) Low embedding throughput. It takes dozens of seconds for billion-sized MEMs to embed a single image, which is significantly slower than the rate at which mobile devices generate data. As a result, even if the device runs continuously throughout the day, only 20% of daily information can be embedded. (2) High energy consumption. The slow inference speed, combined with the immense computing power required, results in high energy consumption. Embedding data from applications consumes even more energy than running the applications themselves. As a result, the battery life of mobile devices is significantly reduced, often to less than 2\u2009h. Even if the embedding process is batched and executed offline (e.g., when the device is idle), its substantial resource demands still hinder practical deployment.\n\nReminisce is an efficient on-device multimodal embedding system. Its key idea is coarse-grained embedding, built upon the early-exiting technique. It draws inspiration from the top-down predictions of cognitive brain17. Embeddings from early-exited MEMs serve as coarse-grained representations to filter likely candidates during retrieval. These candidates are then refined by the remaining layers at query time for final selection. While early exiting avoids full model execution during memorization, three key system challenges remain on mobile devices: low parallelism, limited exiting benefits, and performance degradation. To further promote the practical deployment of Reminisce, we propose three software-hardware co-designs: (1) Data-aware pre-exit predictor is a unified, lightweight early-exit predictor model applicable across all modalities. It facilitates efficient batching and pipeline execution, improving encoding throughput; (2) Progressive LoRA healing retrofits low-rank adaptation (LoRA)18, a popular parameter-efficient fine-tuning method, to ensure high retrieval performance with earlier exits by progressively increasing shared bottom layers. This enables intermediate results to be cached and reused; (3) Speculative fine-grained retrieval. Query embeddings from different exits are used for speculative filtering, with top candidates from each granularity undergoing a second matching round for accurate final retrieval.\n\nOur extensive experiments demonstrate that, with these designs, Reminisce accelerates the multimodal embedding process while ensuring accurate retrieval. We evaluate Reminisceon multiple mobile devices, achieving an average 12.4\u2009\u00d7\u2009improvement in throughput compared to the original MEM. We further conduct a case study using recent Twitter data and a user study based on mobile application traces collected from eight users over one week, demonstrating the practicality of Reminisce in real-world scenarios.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60802-5/MediaObjects/41467_2025_60802_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "As shown in right side of Fig.\u00a01, we prototype an on-device MEM-powered search service to embed multimodal streaming data for future retrieval, functioning like a memory palace12. We specifically target mobile devices, including smartphones and IoT devices with similar computing capabilities. These devices have usable but weaker processing units compared to cloud servers, with limited battery and memory available for long-term background processes19.\n\nFrom the device perspective, the service has two runtimes:\n\nEmbedding runtime (Offline remembering in the background). continuously detects and stores newly generated multimodal content, such as downloaded images, scanned texts, listened-to audio, and logged IMU sensor data. Each item is processed layer by layer through MEMs, as deep learning models are often too large for mobile devices. This can lead the OS to terminate inference processes. Current mobile inference engines support layerwise execution to accommodate large models20,21. A 1024-dimensional embedding is generated for each item in a unified space.\n\nQuery runtime (Online recall in the foreground). is triggered when the user searches for a specific item or performs other tasks based on search results. To retrieve relevant items, the query embedding is compared with stored embeddings to find the most similar matches. If the raw data corresponding to the matched embeddings aligns with the query intent, the query is tagged as successful.\n\nSystem developers prepare the embedding model offline, typically by fine-tuning with powerful cloud GPUs, using widely-used pretrained multimodal embedding models5,6. They define the expected offline costs and online performance for each application by configuring system hyperparameters before deployment.\n\nFirst, we present a preliminary study to demonstrate the utility and efficiency of on-device multimodal embedding in real-world scenarios. We conducted a user study to collect viewed images from daily mobile applications used by 8 volunteers, aged 20 to 52, over the course of a week. To achieve this, we developed an Android application with accessibility services22 to detect and store newly appeared visual content. Images are hashed to include only new content. Images smaller than 100\u2009KB are excluded to avoid capturing icons and minor system elements. One collected trace is illustrated in Fig.\u00a02a.\n\na Viewed-image traces from one mobile user. b MEM inference speeds across different devices, compared to the average image viewing rates of common mobile applications. c MEMs rapidly drain mobile batteries. * indicates testing performed on the GPU of the Jetson ORIN.\n\nMEMs are observed to be contextually expressive. All images and corresponding texts are collected and embedded using ImageBind6. By aligning multimodal embeddings into a unifed space, ImageBind can effectively retrieve semantically relevant content from different modalities using human-friendly inputs (Supplementary Fig.\u00a02).\n\nTo assess the cost of on-device embedding, we ran ImageBind inference on four different mobile devices, ranging from development boards to commodity smartphones.\n\nDespite their contextually expressive capabilities, the embedding speed is too slow to keep pace with the figures generated by applications. As shown in Fig.\u00a02b, on all CPU-based devices, the encoding speed is insufficient for real-time application use. Over a full day of usage, the speed is only sufficient to embed 20% of the figures generated by applications, requiring more than 100\u2009h to process all figures from a single day. Even with a GPU, Jetson NANO23 struggles to handle an entertainment task generating 36.3 images per minute. The only exception is the NVIDIA ORIN24, which performs comparably to a cloud server using an NVIDIA A4025. However, continuously running the CPU or GPU on mobile devices is impractical due to battery depletion.\n\nThe heavy embedding workloads and low throughput strain battery life. Continuous embedding drains the battery even faster than running the app itself. To illustrate, we used ImageBind to continuously embed figures from daily apps. As shown in Fig.\u00a02c, the embedding process consumes more energy than the apps themselves. For example, even when quantized to INT4, MEMs consume 1.8\u2009\u00d7\u2009more energy than gaming. We also measured GPU energy consumption on an NVIDIA ORIN. While GPUs process data faster, they consume more energy than CPUs, making them unsuitable for long-term embedding in the current MEM design.\n\nAs shown in Fig.\u00a03a, the core design of Reminisce is the coarse-grained embedding, built upon the early-exit mechanism. This approach offloads the computation of the full embedding to the less frequent, intent-specific query phase. Specifically, embeddings generated by early-exited MEMs serve as coarse-grained embeddings to filter the most likely candidates during retrieval queries. These candidates are further refined by the remaining layers of the exited MEMs at query time to ensure accurate retrieval. We propose and prototype this mobile-friendly early-exit system for efficient multimodal embedding. Three hardware-software co-design optimizations further enhance the performance of Reminisce, making it practical for mobile devices.\n\na Detailed workflow of Reminisce with system Designs1,2,3. b Illustration of Design 1: Data-aware pre-exit predictor and its advantages over traditional early-exit approaches. c Illustration of Design 2: Comparison of our progressive LoRA approach to previous methods. d Illustration of Design 3: Coarse-grained embeddings are speculatively filtered, and top-ranking candidates are refined into fine-grained embeddings for final retrieval.\n\nThe first optimization is data-aware pre-exit prediction. Traditional early-exit methods determine exits at the end of each branch computation, causing inconsistent workloads and memory fragmentation26, and existing predictive models for CNNs cannot effectively scale to MEM due to their convolution-specific design27,28. Our observation is that different data inherently carry varying amounts of information (Supplementary Fig.\u00a04a), and intermediate multimodal embeddings provide effective cues for determining optimal exit points (Supplementary Fig.\u00a04b). Based on this unique observation, we propose a unified, lightweight early-exit predictor that leverages these intermediate embeddings to preemptively determine the exit layer, enabling batch scheduling for improved parallelism and amortizing loading times (Fig.\u00a03b).\n\nThe second optimization is progressive LoRA healing. Previous early-exit healing approaches29 utilize LoRA18 to fine-tune NLP models for earlier exits. However, these methods fine-tune separate LoRA modules for each exit, preventing the reuse of intermediate results and thereby negating early-exit benefits on mobile devices. As illustrated in Fig.\u00a03c, we propose sharing previously tuned parameters, reducing the number of layers required per token and enabling reuse of intermediate activations. Based on our observation that sharing LoRA weights at top layers is more effective (Supplementary Fig.\u00a05), we propose a progressive LoRA healing method that incrementally increases tuning depth (number of shared layers) at later exits to minimize performance degradation from shared LoRA weights.\n\nThe third optimizations is speculative fine-grained retrieval). Using a full-capacity encoder to generate query embeddings leads to unbalanced retrieval performance when matched with coarse-grained embeddings, resulting in poor top-1 retrieval accuracy (Supplementary Fig.\u00a06). To address this issue, we introduce a speculative fine-grained retrieval mechanism (shown in Fig.\u00a03d) to balance the retrieval process. It first performs speculative filtering using query embeddings at all granularities and then refines the selection through a second, fine-grained matching stage.\n\nThe default MEM model is pretrained ImageBind (huge version)6. ImageBind extends the visual and textual pretrained encoder of CLIP5 with additional capacity that embeds 6 modalities into a shared space. To demonstrate the scalability and versatility of Reminisce, we also evaluate it on CLIP. Over 80% (35 out of 43) of recent multimodal foundation models are based on those two MEM models30.\n\nWe compare Reminisce to the following alternatives: (1) Multimodal Embedding Model (MEM) without any optimization. (2) BranchyNet26, using a traditional early-exit mechanism. (3) Fluid Batching31, an early-exit-aware batching algorithm that allows sample preemption at runtime. For completeness, we also include a naive baseline using monolithic model, i.e., without layer-wise execution, though it incurs nearly unaffordable memory footprint on certain mobile devices. For a fair comparison, all baselines are equipped with ImageBind fine-tuned for the downstream task.\n\nWe evaluate the performance of Reminisce using the following metrics: (1) Accuracy: Retrieval accuracy for each task, with relative accuracy compared to the full-sized MEM model finetuned on the corresponding dataset. (2) Latency: Query latency on mobile devices, defined as the time from query initiation to completion. (3) Throughput: The amount of content processed per second or minute, assuming all samples are buffered in storage. (4) Energy Consumption: Energy consumed during the embedding phase. (5) Memory Usage: Peak memory footprint during the embedding phase.\n\nAs summarized in Table\u00a01, we use four publicly available datasets across four modalities to demonstrate the effectiveness of Reminisce: (1) COCO dataset: Used for text-image retrieval, it contains 123\u2009k images, each paired with five captions. We use the validation subset of COCO to evaluate inference performance, with each caption retrieving its corresponding image. For example, given a caption, 75% of the relevant images are successfully retrieved within the top five results (R@5), based on the full-sized MEM model finetuned on the COCO dataset. (2) FLICKR dataset: Used for image-text retrieval, it consists of images paired with textual descriptions. Absolute retrieval accuracy is 70% for the fine-tuned full-sized MEM model. (3) CLOTHO dataset: Used for text-audio retrieval, it contains audio clips paired with textual descriptions, enabling evaluation across audio and text modalities. Full-sized MEM model achieves 30% retrieval accuracy. (4) HARSMART dataset: Used for IMU retrieval, it employs fine-grained embeddings as queries to assess performance in retrieving IMU data based on embeddings. The MEM model achieves 78% retrieval accuracy.\n\nAdditionally, to demonstrate the effectiveness of Reminisce in real-world scenarios, we conduct a case study using recent internet data that was not seen by the model during pretraining. Following prior empirical literature on Twitter analysis32, we collect a recent publicly available dataset of Twitter memes, referred to as TWITTER. The TWITTER dataset contains 803 images and their corresponding meme descriptions across various up-to-date topics.\n\nWe evaluate Reminisce on the NVIDIA ORIN (ORIN)24, Jetson TX2 (TX2)33, Raspberry Pi 4B (RPI4B)34, and a flagship smartphone with Qualcomm Snapdragon 8Gen3 (8GEN3)35. The default operating mode for ORIN is MAXQ, which is the most cost-effective mode with four large cores disabled. For the Jetson TX2, we select the MAXN mode, the most powerful mode available, to fully utilize GPU computing power. To reduce memory consumption, we quantize the model to INT4 precision for the 8GEN3 smartphone and INT8 precision for ORIN, TX2, and RPI4B. Please refer to Supplementary for more implementation details about hardware specification, executing mode specifications, and quantization. Reminisce runs on the GPU for the ORIN and TX2 boards. For the RPI4B and the 9GEN3 smartphone, Reminisce runs on the CPU due to the lack of CUDA support. Current mobile inference engines cannot effectively utilize GPUs for MEM execution9,20,36.\n\nWe evaluate Reminisce to address the following key questions: (1) How much improvement does Reminisce achieve in terms of embedding throughput and relative retrieval accuracy under different memory budgets across various devices? (2) How much performance improvement does each component contribute? (3) What is Reminisce\u2019s performance under different query latency budgets? (4) What is the system cost of Reminisce? (5) How does Reminisce perform on commodity mobile phones in daily usage scenarios?\n\nFirst, we present the end-to-end embedding throughput performance under the layer-wise inference setting, a more user-friendly approach for always-on daily applications due to its low memory footprint.\n\nReminisce achieves an order of magnitude improvement in throughput. Figure\u00a04 shows that Reminisce can achieve a 12.4\u2009\u00d7\u2009average throughput improvement compared to MEM. This gain is primarily driven by the early-exit mechanism, which allows the model to exit early when the embedding is sufficiently accurate, avoiding unnecessary computations. Additionally, after parameter-efficient healing, the coarse-grained embeddings can convey similar semantics to fine-grained embeddings. For instance, in the text-audio retrieval task CLOTO on Jetson ORIN, Reminisce achieves a 45\u2009\u00d7\u2009throughput improvement with less than 3% relative accuracy loss under the default query latency budget of 1.5\u2009s.\n\na Jetson Orin (INT8). b Jetson TX2 (INT8). c Raspberry Pi 4B (INT8). d 8Gen3 Smartphone (INT4). For fairness, only layerwise baselines are included.\n\nRegarding stronger baselines, Fluid Batching introduces a early-exit-aware batching mechanism, achieving a 3\u2009\u00d7\u2009throughput improvement over the naive early-exiting baseline BranchyNet and 6\u2009\u00d7\u2009over MEM under the layer-wise inference setting. However, Reminisce still outperforms Fluid Batching across all datasets, providing up to 2.4\u2009\u00d7\u2009speedup in throughput. The advantages of Reminisce Arise not only from the early-exit mechanism but also from the pre-exit strategy, which predictively adjusts the embedding granularity based on the sample\u2019s characteristics.\n\nAs illustrated in Fig.\u00a05a, while the zero-shot embedding of ImageBind has the generalization ability across different datasets, the exit healing mechanism is crucial for enhancing Reminisce\u2019s performance. As shown by the green dotted lines, retrieval accuracy improves after healing the exited branches. For instance, compared to zero-shot MEM, exit healing boosts retrieval accuracy by 37.8% and 13.2% on average for the COCO and FLICKR datasets, respectively.\n\na Throughput-to-accuracy trade-off with and without Reminisce\u2019s key designs (1,\u00a02,\u00a03). PE refers to pre-exited coarse-grained embeddings without fine-grained upgrading during the query phase. b Performance under different query latency tolerance.\n\nAfter healing, Reminisce leverages the pre-exit mechanism to dynamically adjust embedding granularity based on each sample\u2019s characteristics. It can predictively exit at the optimal layer to balance the trade-off between accuracy and throughput. As shown in Fig.\u00a05a, compared to exiting all samples at a fixed layer, the data-aware pre-exit mechanism improves retrieval accuracy by up to 19.8%. The higher coarse-grained retrieval performance is crucial for final fine-grained retrieval.\n\nWith a default query candidate pool size of 10, retrieval accuracy using filtered fine-grained embeddings is, on average, 35.5% higher than the previous coarse-grained retrieval accuracy. This improvement is due to the fact that over 95% of the targets retrievable by full-sized MEMs are successfully retrieved from the toplist of coarse-grained embeddings. As a result, the embedding accuracy of Reminisce is comparable to that of the full-sized MEM.\n\nAlthough query costs are negligible compared to embedding costs in the long run\u2014since queries occur less frequently than continuous daily embeddings\u2014they are immediately noticeable to users. Thus, we illustrate Reminisce\u2019s performance under different query latency tolerance in Fig.\u00a05b. During queries, the device holds the entire quantized model in memory without layer-by-layer loading. Given the infrequency of queries, the temporary memory increase is acceptable. Query latency comprises three components: query embedding, matching, and fine-grained embedding. Baseline methods with memory encoders require only the first two steps, typically taking around 1.2\u2009s. Reminisce takes less than 1.5\u2009s (the default latency budget of our evaluation) to achieve acceptable query accuracy. As shown, if the system tolerates higher query delays, performance can be further enhanced. For example, on the FLICKR dataset, the relative retrieval accuracy of Reminisce improves from 92% to 99% after refining an additional 10 candidates (\u2248\u20090.2\u2009s).\n\nAdditionally, similar to web cookies37, the query process can skip the complex fine-grained embedding when repeated, improving efficiency in multi-query scenarios where frequently queried items are retrieved faster. Once a local embedding is queried, its embedding is permanently upgraded. Under these conditions, the system becomes more efficient by skipping the fine-grained embedding process for frequently queried items.\n\nFigure\u00a06 shows the normalized energy consumption of Reminisce and various baselines. Reminisce reduces energy consumption by up to 29\u00d7 and 20\u00d7 on average compared to layerwise-executed baselines. Even compared to naive MEM without layerwise execution, Reminisce still achieves up to 7\u00d7 energy savings on average. This is due to Reminisce\u2019s ability to determine the optimal number of layers for embedding and offload embedding computation to the less frequent querying process.\n\nOur method consistently exhibits the lowest energy usage, highlighting its efficiency and low battery demand. Device: ORIN (INT8).\n\nWe store the embeddings of the items in INT4 precision. Each embedding is 1024-dimensional, resulting in a storage cost of approximately 5\u2009KB per item. Based on the collected mobile application usage statistics, typical users encounter around 6000 images daily. Thus, the storage cost for image embeddings is roughly 29.3\u2009MB per day. Annually, this amounts to about 10.4\u2009GB, which is comparable to the storage required for a high-quality movie. In contrast, the current off-the-shelf solution Rewind38 consumes 14\u2009GB of storage per month on average, as officially reported39.\n\nTo demonstrate the practicality of Reminisce in real-world scenarios, we conducted a case study using daily surfing images and captions collected from Twitter memes. End users filtered the data to ensure privacy, and a total of 805 figures were collected to simulate 30\u2009min of surfing. Our evaluation compares multiple methods\u2014including Naive MEM without layer-wise execution, the MEM baseline, BranchyNet, Fluid Batching, and our Reminisce\u2014in terms of throughput, energy, memory, and retrieval accuracy.\n\nAs shown in Fig.\u00a07, all baseline methods take over 80\u2009min to complete the retrieval task on a fully utilized CPU. Naive MEM incurs a large memory footprint by loading the entire model at once, even with INT4 quantization. Its layer-wise execution counterpart (MEM baseline) reduces memory usage but decreases throughput due to frequent layer-switching overhead. BranchyNet improves throughput by skipping layers but at the expense of lower accuracy. In contrast, Reminisce completes the same task in 28\u2009min\u2014achieving a 3\u00d7 throughput improvement compared to even the strong baseline Fluid Batching, due to our mobile-friendly optimizations.\n\nOur method uses the least CPU time, consumes the least energy, requires under 200MB of memory, and achieves high retrieval accuracy. Device: 8GEN3 (INT4).\n\nOur approach reduces peak memory usage by 7\u00d7 compared to Naive MEM, lowering the footprint below 200\u2009MB. This includes a small buffer (under 50\u2009MB) for pipelined execution and temporary activations\u2014a reasonable tradeoff for performance gains. Energy consumption is reduced by up to 4\u00d7, enabled by fewer layer computations and more efficient batching. The system also achieves higher retrieval accuracy than naive early-exit methods while maintaining an acceptable query latency of just 0.5\u2009s. The additional memory overhead from batching parallelism is justified by the substantial performance improvements.\n\nThese quantitative improvements\u2014from faster processing and lower resource consumption to robust retrieval performance\u2014demonstrate that Reminisce is highly practical for deployment in mobile scenarios, where computational efficiency and low-latency requirements are critical.\n\nTo further validate Reminisce, we conducted a user study by collecting real user data and simulating the system\u2019s performance in embedding images generated during daily mobile app usage. We do not account for charging time or the energy used by the applications themselves to provide a more straightforward comparison between naive MEM and Reminisce. As shown in Fig.\u00a08, without Reminisce, the naive MEM system (in INT4 precision) would require more than 3 battery charges per day, and over 20% of the images would remain unembedded due to time constraints. In contrast, Reminisce reduces the number of required charges by 3\u00d7, allowing all daily generated data to be embedded. This user study highlights Reminisce\u2019s ability to efficiently manage and embed large volumes of data, reducing the burden on battery life and ensuring that the vast majority of daily usage data is preserved and embedded in real-time.\n\na Naive MEM. b Ours. Device: 8GEN3 (INT4).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60802-5/MediaObjects/41467_2025_60802_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60802-5/MediaObjects/41467_2025_60802_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60802-5/MediaObjects/41467_2025_60802_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60802-5/MediaObjects/41467_2025_60802_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60802-5/MediaObjects/41467_2025_60802_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60802-5/MediaObjects/41467_2025_60802_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60802-5/MediaObjects/41467_2025_60802_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this work, we develop Reminisce, an efficient on-device multimodal embedding system to function as a memory augmenting service. Extensive experiments and case studies demonstrate that Reminisce improves embedding throughput and reduces energy consumption while maintaining high retrieval accuracy, making it practical for modern mobile devices.\n\nWe offload the full-sized embedding cost to the query phase, which is infrequent and carries precise retrieval information2. Only coarse-grained key information is preserved using exited embedding models. This mirrors the human brain, which retains key information in long-term memory and recalls details only when necessary40. Different from advanced sparsification or quantization optimizations, which provides little to no benefit during inference due to the limited support of mobile hardware41,42,43,44,45, Reminisce can be seamlessly integrated into off-the-shelf mobile applications to enhance user experience without requiring complex hardware modifications.\n\nThe ability of Reminisce to operate within mobile devices such as smartphones and Raspberry Pi 4B, while maintaining high-quality embeddings, highlights its practicality for real-world applications. For instance, mobile users can now efficiently index and recall multimedia content, fostering new use cases in personal assistants, health tracking, etc.\n\nA pivotal advantage of Reminisce lies in its on-device processing capability, which eliminates the need to offload sensitive data to cloud services. This mitigates risks associated with data breaches and unauthorized access, addressing a critical concern in modern AI systems.\n\nHowever, due to the extra memory overhead of batching parallelism, Reminisce has a slightly higher peak memory footprint compared to the naive layer-wise baseline. Detailed information is provided in the Supplementary Fig.\u00a03. Fortunately, it is still within a practical range, e.g., 82\u2009M for embedding IMU information, which is below the average Android application memory consumption of 100\u2009M as reported in 202019,46. After 5 years, the mobile RAM capacity has increased significantly, with up to 24\u2009GB available on high-end devices47. Less than 200\u2009MB of peaky memory usage is affordable for most modern mobile devices.\n\nThis study provides the following takeaway messages:\n\nWe prototype the first MEM-empowered mobile search service architecture. Through user studies and pilot experiments, we identify the challenges of low embedding throughput and high energy consumption.\n\nWe introduce Reminisce, an efficient on-device multimodal embedding system that addresses these challenges. Reminisce incorporates three techniques: preemptive exit for dynamic execution scheduling, progressive model healing for cache optimization, and speculative retrieval to correct premature exits.\n\nExtensive experiments demonstrate that Reminisce significantly improves throughput and reduces energy consumption while maintaining search performance, making it practical for mobile devices.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "In this work, we develop Reminisce, an efficient on-device multimodal embedding system to address the challenges outlined above. Reminisce is designed to minimize embedding energy costs and query latency while maximizing throughput and achieving near state-of-the-art retrieval accuracy. Additionally, Reminisce shall integrate easily into off-the-shelf mobile applications to enhance user experience without requiring complex hardware modifications. Lastly, Reminisce aims to be both versatile and transferable across a wide range of tasks. To achieve these goals, we leverage early exit, a widely studied optimization technique, as the backbone of our system.\n\nEarly Exiting is the key building block. It terminates the computation of a deep neural network at an intermediate layer based on prediction confidence. Typically, a prediction head is introduced at the end of each layer to serve as a separate exit branch, allowing samples to be correctly classified at the earliest possible layer.\n\nWe choose early exit as the backbone of Reminisce because it aligns with our design principles: (1) Early exit is mobile hardware-friendly: it requires no sparsification kernel compilation and integrates easily into existing multimodal embedding applications. Most mobile devices do not fully support advanced sparsification or quantization optimizations, providing little to no benefit during inference41,42,43,44,45. (2) Early exit preserves the raw structure of MEMs, maintaining their generalization capacity while bypassing only downstream alignment. Additionally, early exit is caching-friendly, as the top layers share the same bottom weights with the exited layers, allowing intermediate activations to be reused and reducing duplicated computations. Other techniques like pruning and quantization cannot fully leverage the intermediate computation of coarse-grained embeddings. This reduction is crucial for Reminisce, as it eliminates redundant forward passes, accelerating both embedding and query phases. (3) Compared to quantization, early exit offers a broader trade-off space. As shown in our experiments (Supplementary Fig.\u00a04a), easy inputs require only one layer (just 3% of total computation) to achieve accurate results. Such a large reduction in cost is not possible with quantization.\n\nAs shown in Fig.\u00a03a, Reminisce provides a memory encoder for clients to build coarse-grained embeddings offline, while the rest of the model functions as a live encoder for precise online retrieval. (1) System developer preparation: Developers first refine widely-used pretrained multimodal models to reduce the number of layers needed for token prediction. The refined model is then deployed to mobile devices for offline embedding. (2) Client offline embedding: Users employ part of the memory encoder to build superficial embeddings for pre-exit prediction. After pre-exit, samples with the same exits are batched and processed layer by layer through pipeline scheduling to generate coarse-grained embeddings. (3) Client online query: During the query phase, the query is embedded for matching. Likely candidates are filtered and refined from the coarse-grained embeddings, which are then matched with the query embedding to finalize retrieval.\n\nIn short, we offload the full-sized embedding cost to the query phase, which is infrequent and carries precise retrieval information2. This mirrors the human brain, which retains key information in long-term memory and recalls details only when necessary40. Retrieval accuracy and latency are sacrificed within acceptable limits to significantly reduce embedding costs, as demonstrated in Fig.\u00a04.\n\nWhile early exit reduces computational load, its application in mobile MEMs introduces several unique challenges: (1) Low parallelism: Early exit is incompatible with batching, as all samples in a batch must exit before processing the next26. This reduces throughput on mobile devices with limited computational resources. Without batching, it is also harder to amortize loading costs, further slowing layer-wise inference. (2) Limited benefits: MEMs are not naturally designed for early prediction and tend to distribute computation across all layers. For instance, ImageBind\u2019s 32-layer vision module requires an average of 21.4 layers to process data, limiting computation savings to 33.1%. MEMs need to reduce the layers required for token prediction and minimize computational resources spent on hesitant or fluctuating predictions. (3) Performance degradation: Despite thorough training of exit branches and predictors, some samples may exit too early, leading to degraded search performance. This is especially problematic in MEMs, where incorrect embeddings can disrupt the unified embedding space, causing unbalanced distributions and inaccurate retrieval.\n\nTraditionally, most early-exit methods decide whether to exit at the end of each branch computation26,48,49. This approach limits hardware acceleration and batching, as exit points vary by data, leading to inconsistent workloads within batches and memory fragmentation26,27,28. Although some predictive models for CNNs27 predict exit values in advance, they cannot scale to MEMs due to their convolution-specific design. In this work, we propose a unified, lightweight early-exit predictor model for all modalities, derived from intermediate data embeddings. The data-aware pre-exit predictor preemptively decides the exit point for MEMs, enabling batch scheduling for better parallelism and helping to amortize and hide loading time.\n\nDifferent data contains varying amounts of information content (Supplementary Fig.\u00a04). Unlike previous work that defines predictive models manually, we propose using intermediate embeddings to predict the exit value without supervision. First, we build the fine-grained embedding Fx for each data point x \u2208 X as a proxy query label. Next, we feed the input into the pre-trained MEM layer by layer, obtaining a set of coarse-grained embeddings \\({{\\mathtt{C}}}_{x}^{i}\\) at different granularities i \u2208 range(layers). We then measure the similarity between the fine-grained and coarse-grained embeddings. When the similarity between Fx and \\({{\\mathtt{C}}}_{x}^{i}\\) becomes the largest among Fx and \\({{\\mathtt{C}}}_{{\\mathtt{X}}}^{i}\\). query retrieves \\({{\\mathtt{C}}}_{x}^{i}\\) from \\({{\\mathtt{C}}}_{X}^{i}\\) successfully. We mark it as a valid embedding exit. The intermediate embeddings are fed into the predictor model, and an MLP model is trained to predict its exit value. This method outperforms fixed early-exit baselines, as shown in Fig.\u00a05a.\n\nAs shown in Fig.\u00a03b, with the data-aware pre-exit predictor, we can predict the exit value before embedding, enabling efficient batching of input data. In addition to early-exit-specific batching, we propose pipelining the layer-by-layer encoding process, where loading and embedding are conducted simultaneously.\n\nOriginal MEMs are not designed for early exit, as they tend to distribute computation across all layers. As a result, most data requires many layers before exiting. We propose a progressive LoRA approach to heal the model, reducing the number of layers needed for each token.\n\nPrevious early-exit healing approaches29 use the parameter-efficient fine-tuning method, LoRA18, to distill knowledge into lower layers, reducing the number of layers required for each token. Naive LoRA tuning fine-tunes a separate LoRA suite for each early-exit layer. For instance, with 32 exits, 32 LoRA suites are required. While this ensures good performance, it has a drawback: the embedding from layer n cannot be reused to compute the embedding for layer n\u2009+\u20091. As illustrated in Fig.\u00a03c, this occurs because LoRA \\({l}_{n}^{1,\\ldots,n}\\) for layer n is not the same as the first n layers of LoRA \\({l}_{n+1}^{1,\\ldots,n+1}\\). Unlike standard embeddings, which complete all layers sequentially, early-exit methods must check whether each layer is the final one. If layer n\u2019s embedding is incompatible with layer n\u2009+\u20091, the early-exit method must recompute the embedding for layer n\u2009+\u20091 from scratch, negating many of the benefits of early exit.\n\nOn cloud servers, computation is not a major issue due to their high processing power, and reducing model weights to alleviate I/O pressure is the primary concern. However, for mobile devices with limited computational power, I/O pressure is less of a concern since they typically serve only one user at a time.\n\nReminisce proposes a progressive LoRA healing method to address this issue, aiming to use a single LoRA suite for all exits. To achieve this, we tune the LoRA layer by layer. For each exit, we tune only the LoRA for the current exit while keeping the previous exits\u2019 LoRA fixed. Since the tunable parameters are fewer than the fixed ones, the healing capacity is weaker compared to using separate LoRA suites, which negatively impacts convergence (i.e., fine-grained embedding) performance (Supplementary Fig.\u00a05b). To mitigate this, instead of tuning one LoRA layer at a time, we progressively tune more LoRA layers at later exits. Similar to the window size in convolutional layers, we define the number of tuned LoRA layers as the LoRA step.\n\nTo determine the optimal step during training, we use information from the predicted exit statistics. We set the training step at the pivot of the predicted exit statistics, ensuring that most exits are healed with an appropriate step size (Supplementary Fig.\u00a05a). This approach prioritizes smaller exits, aligning with the heuristic that most data exits occur at earlier layers, which require more focused healing. At later stages, larger steps enhance fine-grained performance during queries without significantly affecting exit flexibility (Supplementary Fig.\u00a05b).\n\nWith coarse-grained embeddings, we can filter out potential candidates. Further fine-grained embeddings are then processed on these filtered candidates to complete the final retrieval. However, using the default query embedding with a full-capacity encoder does not achieve precise top-1 retrieval (Supplementary Fig.\u00a06a). This poor performance stems from two unique challenges.\n\n# Challenge 1: Reduced embedding capacity. Even if we modify the model to predict early and align it with the full embedding, exiting early during inference inevitably reduces accuracy compared to full-capacity embedding. Fortunately, while coarse-grained embeddings may not achieve precise top-1 retrieval, they can filter out the most likely candidates when expanding the retrieval range to top-10 as shown in Supplementary Fig.\u00a06a. Thus, this challenge can be alleviated by refining the coarse-grained embeddings filtered with query information.\n\n# Challenge 2: Unbalanced embedding distribution. Different data exits at different layers, leading to unbalanced embeddings in storage. Although each embedding is fine-tuned to approximate the full embedding, embeddings from different exit layers retain unique characteristics. Samples from similar exit layers tend to have similar embedding distributions. As a result, a query embedding from a full-capacity encoder cannot retrieve these embeddings precisely (Supplementary Fig.\u00a06).\n\nInspired by speculative decoding50, a popular acceleration technique for language models, we propose feeding the query embedding at different granularities to achieve balanced filtering, as shown in Fig.\u00a03d. (1) Speculative filtering: The top k candidates at each query granularity are preserved for the second round of filtering. (2) Global verifying: The second round selects the final top k candidates from all granularities. If a sample ID is duplicated, the candidate with the next highest score is preserved. (3) Fine-grained correcting: Finally, the coarse-grained embeddings are refined using the rest of the model to generate fine-grained embeddings, which are then matched with the query for more precise retrieval.\n\nAs shown in Fig.\u00a03, coarse-grained embeddings can be reused for fine-grained embeddings. However, due to the down-sampling structure in the output head, it cannot be reused directly. To address this, we store intermediate activations prior to each down-sampling layer. This approach allows coarse-grained embeddings to be reused without recomputation, reducing query latency by up to 70%. We also reuse superficial embeddings to lower the cost of data-aware coarse-grained embedding, improving embedding throughput by up to 30%.\n\nTo efficiently manage intermediate activations and avoid resource waste from stale data, we adopt a cache invalidation strategy as shown in Fig.\u00a09. During offline embedding phase, intermediate activations from superficial embeddings are temporarily stored in RAM to compute coarse-grained embeddings. After each batch, these cached activations are sequentially invalidated from RAM. Coarse-grained intermediate activations are subsequently stored on disk, which has fewer constraints compared to RAM (see\u00a0Supplementary for details). At query phase, cached embeddings matching the incoming query are loaded to compute fine-grained embeddings and are promptly invalidated afterward.\n\nDuring the offline embedding phase, intermediate activations from superficial embeddings are temporarily cached in RAM to compute coarse-grained embeddings. After each batch, these activations are sequentially invalidated from RAM. During the query phase, cached embeddings that match the incoming query are loaded to compute fine-grained embeddings and are immediately invalidated afterward.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60802-5/MediaObjects/41467_2025_60802_Fig9_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "The datasets involved in this study are all publicly available and can be accessed as follows: The COCO dataset used in this study are available in the COCO database under accession code https://cocodataset.org/#download. The FLICKR dataset used in this study are available on Kaggle under accession code https://www.kaggle.com/datasets/adityajn105/flickr8k. The CLOTHO dataset used in this study are available on Zenodo under accession code https://zenodo.org/records/3490684. The HARSMART dataset used in this study are available in the UCI database under accession code https://archive.ics.uci.edu/ml/machine-learning-databases/00364/dataset_uci.zip. The collected Twitter meme dataset have been deposited on Kaggle under accession code https://www.kaggle.com/datasets/penguin0211/twitter-dataset-for-mo bile-search. The collected traces in this study have been deposited on Kaggle under accession code https://www.kaggle. com/data sets/dongqicai/mobile-trace-of-viewed-images. All user data used in this study were anonymized prior to analysis. Personally identifiable information such as names and device identifiers were removed following standard anonymization protocols. The resulting dataset contains only abstracted behavioral features (e.g., app usage timestamps, total ImageView count, and image view throughput per app) that cannot be linked back to individuals. All participants provided informed consent prior to data collection. Each participant was informed about the purpose of the study, the type of data collected, the anonymization procedure, and their rights to withdraw at any time. Furthermore, the open-sourced multimodal embedding models utilized in this paper can be accessed via the following links: ImageBind (https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth) and CLIP-b/16 (https://huggingface.co/openai/clip-vit-base-patch16).", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Codes for this work are available at51: https://github.com/caidongqi/Mobile-Search-Engine/tree/pc. We also provide sufficient details in the methods section and supplementary information for replicating experiments in this work.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Xu, M., Xu, T., Liu, Y. & Lin, F. X. Video analytics with zero-streaming cameras. 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In 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ismsit), (eds \u00d6zseven, T., Ya\u015far, E. & \u00d6nal, S.) 1\u20136 (IEEE, 2018).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by the National Natural Science Foundation of China under grant numbers 62425203 (S.W.) and 62032003 (S.W.); the Royal Academy of Engineering via DANTE (N.D.L.); the European Research Council through the REDIAL project (N.D.L.); SPRIND under the Composite Learning Challenge (N.D.L.); the Google Academic Research Award (N.D.L.); and the CCF-Sangfor \u201cYuanwang\u201d Research Fund (M.X.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China\n\nDongqi Cai,\u00a0Shangguang Wang,\u00a0Chen Peng,\u00a0Zeling Zhang,\u00a0Zhenyan Lu,\u00a0Tao Qi\u00a0&\u00a0Mengwei Xu\n\nDepartment of Computer Science and Technology, University of Cambridge, Cambridge, UK\n\nDongqi Cai\u00a0&\u00a0Nicholas D. Lane\n\nPengcheng Laboratory, Shenzhen, China\n\nZhenyan Lu\n\nFlower Labs, London, UK\n\nNicholas D. Lane\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nD.C. conceived the idea, designed the system, and led the implementation and evaluation. S.W., M.X., and N.D.L. jointly supervised the project and provided high-level guidance. C.P. and Z.Z. contributed to system development and conducted comprehensive experiments. Z.L. contributed to the quantization experiments. T.Q. supported the revision experimental designs. All authors discussed the results and contributed to writing and revising the manuscript.\n\nCorrespondence to\n Shangguang Wang, Nicholas D. Lane or Mengwei Xu.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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Ubiquitous memory augmentation via mobile multimodal embedding system.\n Nat Commun 16, 5339 (2025). https://doi.org/10.1038/s41467-025-60802-5\n\nDownload citation\n\nReceived: 11 January 2025\n\nAccepted: 04 June 2025\n\nPublished: 19 June 2025\n\nVersion of record: 19 June 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60802-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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COVID-19 pandemic in Belgium using perturbation analysis", + "pre_title": "Insights into the role of children in the COVID-19\r\npandemic in Belgium: a longitudinal sensitivity\r\nanalysis", + "journal": "Nature Communications", + "published": "05 March 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57087-z/MediaObjects/41467_2025_57087_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57087-z/MediaObjects/41467_2025_57087_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57087-z/MediaObjects/41467_2025_57087_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-57087-z#ref-CR82", + "https://zenodo.org/records/10549953", + "https://socialcontactdata.org/tools/" + ], + "code": [ + "https://doi.org/10.5281/zenodo.14777392", + "/articles/s41467-025-57087-z#ref-CR83" + ], + "subject": [ + "Computational biology and bioinformatics", + "Epidemiology", + "SARS-CoV-2", + "Viral infection" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4324206/v1.pdf?c=1741266452000", + "research_square_link": "https://www.researchsquare.com//article/rs-4324206/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-57087-z.pdf", + "preprint_posted": "02 May, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Understanding the evolving role of different age groups in virus transmission dynamics is essential for informed pandemic\r\nmanagement. This study delves into the age-related transmission patterns of SARS-CoV-2 in Belgium from November\r\n2020 to February 2022. We employed a next-generation matrix approach, integrating longitudinal social contact data and\r\nnumerical simulations of the evolving population susceptibility. A perturbation analysis of the effective reproduction number\r\n(Rt ) underscored the age-specific transmission patterns. From November to December 2020, adults aged [18,60) were main\r\ncontributors to Rt (\u2248 78%), with children aged [0,12) having a marginal role (\u2248 3.7%). This pattern shifted between January and\r\nMarch 2021, coinciding with in-person education resumption and the Alpha variant emergence; children\u2019s contribution to Rt\r\nincreased to \u2248 38%. Stringent measures in March 2021 significantly reduced transmission levels, substantially downsizing\r\nthe role of the [18,30) age group. Following the summer school break, in September-October 2021, we observed a notable\r\nresurgence in children\u2019s contribution to Rt . Our findings highlight the noteworthy and varying influence of the [0,12) age\r\ngroup on SARS-CoV-2 transmission, offering insights to design nuanced pandemic responses, e.g., that balance public health\r\nneeds with socio-educational implications of interventions like extended school closures. Overall, this study demonstrates the\r\neffectiveness of our methodology in uncovering age-specific transmission patterns in the study of infectious disease spread.Health sciences/Diseases/Infectious diseases/Viral infectionBiological sciences/Computational biology and bioinformatics/Statistical methods", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Understanding the evolving role of different age groups in virus transmission is essential for effective pandemic management. We investigated SARS-CoV-2 transmission in Belgium from November 2020 to February 2022, focusing on age-specific patterns. Using a next generation matrix approach integrating social contact data and simulating population susceptibility evolution, we performed a longitudinal perturbation analysis\u00a0of the effective reproduction number to unravel age-specific transmission dynamics. From November to December 2020, adults in the [18,\u00a060) age group were the main transmission drivers, while children contributed marginally. This pattern shifted between January and March 2021, when in-person education resumed, and the Alpha variant emerged: children aged under 12 years old were crucial in transmission. Stringent social distancing measures in March 2021 helped diminish the noticeable contribution of the [18,\u00a030) age group. By June 2021, as the Delta variant became the predominant strain, adults aged [18,\u00a040) years emerged as main contributors to transmission, with a resurgence in children\u2019s contribution during September-October 2021. This study highlights the effectiveness of our methodology in identifying age-specific transmission patterns.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The COVID-19 pandemic has led to an unprecedented global health crisis, demanding a detailed analysis of the factors driving its transmission dynamics. As such, social contact patterns, immune response, and infectiousness showed strong age-related heterogeneity early on in different contexts1,2,3. These age-related differences have a critical impact on transmission and key epidemiological outcomes such as disease incidence and prevalence, hospitalisation, and mortality4,5,6. Understanding the extent to which these differences influence such vital outcomes is essential for the complex task of developing comprehensive policies that address the evolving challenges posed by a global pandemic like COVID-19. In Belgium, multiple studies7,8,9,10,11,12 have underscored the importance of analysing age-specific transmission patterns to inform effective public health interventions. Our study aligns with this objective by offering analytical tools that aim to clarify the role of age in transmission within the complex landscape of evolving Non-Pharmaceutical Interventions (NPIs), vaccination efforts, and the emergence of virus variants in Belgium. These insights can aid policymakers in crafting balanced strategies that consider not only virus containment but also broader societal implications, such as maintaining economic stability13,14 and safeguarding mental health15,16. In particular, considering younger populations, it is crucial to weigh the trade-offs of stringent measures such as extensive school closures and lockdowns. While these interventions have proven effective in reducing transmission and disease prevalence7,9,11,17, research also highlights their negative impact on children\u2019s mental health and educational outcomes, particularly for those from socio-economically vulnerable backgrounds18,19,20,21. The resulting stress on parents further emphasises the need for robust support systems during these times18,22. We will dwell on the children\u2019s roles in transmission, offering insights to help balance these trade-offs.\n\nWe use data from the CoMix social contact survey7,23,24, a longitudinal, multi-country survey capturing the social interactions of representative samples of individuals including a broad range of demographic variables such as age, gender, region of residence. Specifically, we use Belgian data to inform age-specific social contact matrices over 34 consecutive survey waves from November 2020 to February 2022.\n\nOur approach integrates the evolution of population susceptibility, the emergence of virus variants of concern (VOCs), vaccination rollout, and the impact of NPIs into the analysis. By longitudinally evaluating and interpreting sensitivity indices introduced in a related study25, we aim to clarify the distinct roles of various age groups in virus spread, with particular emphasis on the evolving role of paediatric populations. This study is grounded in an age-structured Susceptible, Exposed, Infectious, and Recovered (SEIR) model8,11, from which we derive our key analytical tool, the Next Generation Matrix (NGM or K)26,27.\n\nThe NGM models how an infected individual from a specific age group contributes to new infections in the same or different age groups over successive generations of disease spread26. Each element kij of the matrix quantifies the expected number of secondary cases in age group j produced by an infectious individual in age group i. The spectral radius of this matrix, \u03c1(K), corresponds to the basic reproduction number, R0, which is crucial for understanding the potential for an outbreak in a fully susceptible population. By updating the NGM to account for changes in susceptibility over time, our study provides insights into the variation of the effective reproduction number (Rt) throughout the study period. The said sensitivity indices are derived from the sensitivity analysis of Rt as a function of the NGM entries. In this context, sensitivity analysis refers to using differential calculus to explore how changes in specific parameters influence a function of those parameters. However, the term is used to describe a variety of exercises, with a common example being the testing of findings robustness against changes in model assumptions. To avoid any confusion, we retain the term sensitivity for the indices but refer to the process as perturbation analysis throughout the study for clarity.\n\nUnravelling age-specific transmission patterns has posed considerable challenges throughout the pandemic, particularly when it comes to understanding the role of children. Early studies suggested that children played a minimal role in transmission compared to adults2,28. However, this may have been underestimated due to the higher incidence of asymptomatic or mild cases in children and their reduced exposure during school closures3,6. Later research highlighted the noteworthy role of children, particularly in school and household settings, in virus transmission11,29,30,31,32. The spread of more transmissible variants, such as Alpha33,34,35,36,37,38, Delta39,40,41,42,43, and Omicron33,44,45 further increased the prevalence and transmission risk within the paediatric population globally32,46. Notably, this became more pronounced during the phased reopening of schools, with studies revealing a strong correlation between school operations and broader transmission dynamics31,47.\n\nOur results build on these findings, highlighting children as key contributors to transmission during school reopenings and the relaxation of NPIs. In addition, young adults under 40 consistently played a substantial role, emerging as the main contributors to transmission when averaged over the entire study period. This study provides a nuanced understanding of how different age groups have contributed to SARS-CoV-2 transmission in Belgium over time.\n\nIn the following Results section, we highlight key shifts in transmission dynamics resulting from the interaction of behavioural factors (evolving NPIs and contact patterns) and epidemiological factors (vaccination and VOCs circulation). We stress that our results are inherently sensitive to the availability and quality of the data and parameter estimates that inform the model. In addition to the uncertainty inherent in the social contact data, a limitation of our methodology is the lack of serial serological survey data, would have enhanced the precision of age-specific susceptibility estimates over the observation period. To address this gap, we employed an extended stochastic compartmental model48, calibrated on early pandemic serological data, COVID-19-related hospitalisations and deaths, to simulate susceptibility across age groups. Further methodological details are available in the Methods Section.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Our analysis explores the role of different age groups in SARS-CoV-2 transmission in Belgium from November 2020 to February 2022. The population is categorised into nine age groups aligned with the Belgian school system. Our observation begins with wave 9 of the CoMix survey, as earlier waves did not include data on contacts of individuals under 18 years of age7, a key focus of our study. Data from survey wave 9, collected from\u00a0November 11\u00a0to\u00a018, 2020, reflect the Belgian population during the second lockdown, which was implemented on November 2, 2020, following a peak in infections, hospitalisations, and deaths around November 449.\n\nWe present the results of our analysis on age-specific transmission dynamics using key indices derived from the NGM, including Cumulative Sensitivity (\\({\\tilde{s}}_{j,t}\\)), Infective Value (vj,t), and Cumulative Elasticity (\\({\\tilde{e}}_{j,t}\\)). These indices, summarised in Table 1, provide insights into how each age group influences Rt and overall transmission. A composite index, Sj,t, integrates these measures to classify age groups according to their contribution to transmission over the study period. The subscript j indicates the age group and the superscript t indicates the specific point in time corresponding to each of the consecutive CoMix survey waves covering the study period (see Table 2).\n\nFor a better comparison, age groups are displayed in four quadrants in Figs.\u00a01, 2, and 3, based on their cumulative elasticity and sensitivity values at specific points in time. Each quadrant corresponds to an epidemiological category that defines the age-specific role in transmission dynamics. The main Contributors are groups with above-average cumulative sensitivity and elasticity, indicating the strongest influence on Rt and key targets for control measures; Effective Spreaders have above-average cumulative sensitivity but below-average cumulative elasticity; Incidental Spreaders have above-average elasticity but below-average cumulative sensitivity; and Marginal Contributors have below-average values for both indices, playing a minimal role in transmission. The division of quadrants is based on average cumulative sensitivity and elasticity values, defined as \\({\\tilde{s}}_{avg}=1\\) and \\({\\tilde{e}}_{avg}=0.11\\). Further explanation on the interpretation and derivation of these indices can be found in the Methods Section and in the\u00a0Supplementary Methods.\n\nAge-specific cumulative sensitivity (\\({\\tilde{s}}_{j,t}\\)) and elasticity (\\({\\tilde{e}}_{j,t}\\)) values are visualised at times t\u2009=\u200913,\u00a014,\u00a017. These correspond to different phases: (a) before-school reopenings, (b) after-school reopenings, and (c) before the Easter break. The quadrants---Main, Incidental, Effective, and Marginal---reflect the various roles in virus transmission based on cumulative sensitivity and elasticity values. The size of the dots represents the infective value vj,t, which averages 0.92 over the observation period.\n\nAge-specific cumulative sensitivity (\\({\\tilde{s}}_{j,t}\\)) and elasticity (\\({\\tilde{e}}_{j,t}\\)) values are visualised at times t\u2009=\u200918,\u00a020,\u00a021. These correspond to different phases: (a) before the Easter Pause, (b) during the Easter Pause, (c) upon control measures relaxation. The quadrants---Main, Incidental, Effective, and Marginal---reflect the various roles in virus transmission based on cumulative sensitivity and elasticity values. The size of the dots represents the infective value vj,t, which averages 0.92 over the observation period.\n\nAge-specific cumulative sensitivity (\\({\\tilde{s}}_{j,t}\\)) and elasticity (\\({\\tilde{e}}_{j,t}\\)) values are visualised at times t\u2009=\u200931,\u00a032,\u00a034. These correspond to different phases: (a) return to school in September 2021, (b) re-opening pubs and dancing clubs,\u00a0(c) new wave of contagions in autumn 2021, with a moderate increase in hospitalisations. The quadrants---Main, Incidental, Effective, and Marginal---reflect the various roles in virus transmission based on cumulative sensitivity and elasticity values. The size of the dots represents the infective value vj,t, which averages 0.92 over the observation period. Panels (d\u2013f) display the social contact matrices for the same survey waves. We underline the marginal role of the [0,\u00a012) group, with their high number of contacts of an extremely assortative nature.\n\nDuring this period, the stringency of the NPIs in place declined, with the stringency index50 dropping from 65.74 in early November 2020 to 60.19 in late December 2020 (Table 2). This variation corresponds to the return to in-person education for primary and secondary schools from November 13, 2020, and the subsequent reopening of non-essential shops from December 1, 202051. This period saw Rt consistently below 1, indicating a controlled virus spread. Despite the easing of restrictions, the population-weighted average of daily reported contacts remained relatively stable, as well as the age-specific proportion of susceptible individuals (see Table 2 and Fig.\u00a04). In this context, adults aged 18\u201360 years emerged as the main contributors to SARS-CoV-2 transmission. The index Sj,t peaked at value 3 for these age groups, due to above-average indices \\({\\tilde{e}}_{j,t}\\), \\({\\tilde{s}}_{j,t}\\), and vj,t (see Fig.\u00a01a).\n\nThe curves depict the estimated daily number of individuals in the susceptible compartment by age group across the study period. This compartment includes individuals naive to both infection and vaccination, as well as those in the four compartments of waning immunity (see Methods Section for details). The points represent the mean estimates of the age-specific number of susceptibles at each point in the study period, with shaded areas showing 95% confidence intervals.\n\nDuring this time interval, the NPIs remained moderately stringent, with an average stringency index of 61.9 (Table 2). This phase was marked by the continuation of in-person education, starting January 6, 2021, in the backdrop of increasing circulation of the Alpha VOC. Notably, the Rt jumped from 0.88 (December 11, 2020) to 1.02 (January 6, 2021). The population-weighted average of daily contacts rose to a local maximum of 6.25 in wave 17 (March 2, 2021), when individuals under 18 years reported an average of\u00a012.75\u00a0contacts per day (see Table 2). In the Supplementary Discussion, we elaborate on how this sharp change in the contact network supports the emergence of children aged 0 to 12 years as incidental virus spreaders. The cumulative elasticity (\\({\\tilde{e}}_{j,t}\\)) and infective value (vj,t) indices remained above average throughout this period, as reflected in an Sj,t consistently equal to 2 or higher. Children\u2019s role in virus transmission was notable in January and March 2021, when Sj,t\u2009=\u20093 for t\u2009=\u200914 (January 20, 2021) and t\u2009=\u200917 (March 2, 2021). Specifically, the cumulative elasticity peaks are observed at \\({\\tilde{e}}_{[0,6),14}=0.32\\) and \\({\\tilde{e}}_{[6,12),17}=0.38\\), as shown in Fig.\u00a01b and c. Children in the age groups [0,\u00a06) and [6,\u00a012) are classified as main contributors, respectively. These findings align with a generalised increase in new cases observed in Belgium from late February onwards52, particularly in the 6\u201311 age band (school grades 1\u20136)46, culminating in a peak at the end of March.\n\nFollowing the surge in cases and hospitalisations, which peaked in late March 2021 and largely originated from school and work settings46,51,52, Belgium implemented a set of stricter NPIs, referred to as the \u201cEaster Pause\u201d51. During this period, from March 25 to April 19, 2021, the stringency index rose sharply to 75.93 on April 16, 2021 (Table 2). The perturbation analysis identifies adults in the age group [18,\u00a030) as main contributors to transmission, as shown in Fig.\u00a02a. On March 18, 2021, the group exhibited an S[18,\u00a030),18\u2009=\u20093, reflecting a high proportional contribution to an Rt of 1.07 (\\({\\tilde{e}}_{[18,30),18}=0.29\\)) and a notably high cumulative sensitivity index (\\({\\tilde{s}}_{[18,30),18}=1.95\\)). Throughout the Easter Pause (waves 19 to 20), we observe an increase in all the indices for adults aged 18 to 60 years, while Rt remains above 1 (see Table 2 and Fig.\u00a02b). The easing of restrictions (April 19, 2021), marked by a drop in the stringency index to 60.19, was driven by a reduction in Rt below 1, a decline in daily new SARS-CoV-2 cases, and increased vaccination uptake49,51,52. Following the relaxation of NPIs, the population-weighted average number of contacts rose from 4.21 (wave 20) to 5.43 (wave 21), with a notable increase in social interactions among individuals under 18 years,\u00a0who reported an average of 11.27 daily contacts (Table\u00a02). On April 28, 2021, the perturbation analysis revealed a marked contrast between young adults ([18,\u00a030) age group) and younger individuals ([0,\u00a012) age group), with a notable decline in elasticity for the former and a steep rise in cumulative sensitivity and elasticity for the latter, as shown in Figs.\u00a02c and 5. This trend is reflected in the values \\({\\tilde{e}}_{[18,30),21}=0.09\\), \\({\\tilde{e}}_{[0,6),21}=0.27\\), and \\({\\tilde{e}}_{[6,12),21}=0.38\\), representing the proportional contributions to an Rt of 0.9. At the same time, above-average cumulative sensitivity indices, allowed us to classify youngsters aged 0 to 12 years as the main contributors to transmission. Concurrently, Fig.\u00a04 illustrates a noticeable drop in the age-specific proportion of susceptible individuals aged 60 and above (wave 21). This trend corresponds to a reduction in the proportional contribution of individuals over 60 to transmission (see Fig.\u00a05). A deeper exploration of the interplay between these factors can be found in the Supplementary Discussion (see Supplementary Fig.\u00a05).\n\nOn the top x-axis, we report the sequence of CoMix waves; on the bottom x-axis, the corresponding calendar date. On the y-axis, we display the corresponding value of the elasticity indices relative to each of the age groups considered, together with the stringency index50 indicating the severity of the social distancing policies in place: here the index is rescaled such that the maximal level of stringency is 1. The dashed horizontal line marks the value 0.11 for the elasticity, i.e., average contribution to Rt. In addition, the effective reproduction number (Rt) is plotted in solid black and on a different scale indicated by the secondary y-axis (right); the solid horizontal black line indicates the critical threshold 1. A bar just above the main x-axis indicates the emerging VOCs of SARS-CoV-2. The length of every coloured bar represents the period of time during which the corresponding VOC was detected in more than 50% of the sequenced SARS-CoV-2 infections.\n\nFrom May to August 2021, the gradual relaxation of NPIs continues in Belgium, as indicated by a further drop in the stringency index (50.9 in May and 47.2 in August 2021) - see Table 2 and Fig.\u00a06. This phase initially corresponded with a reduction in transmission levels marked by an Rt\u2009=\u20090.79 on May 16, 2021(wave 22). However, the Rt increased consistently and remained above 1 starting June 23, 2021 (wave 25). At this time in Belgium, the Delta VOC replaced the Alpha VOC as the predominant circulating SARS-CoV-2 strain. Characterised by increased transmissibility and potential to evade vaccine-induced immunity39,41,42, the Delta VOC represented over 50% of the sequenced new cases at the beginning of July 202153,54. Concurrently, the progress of vaccination across adult age groups, particularly the accelerated rollout for young adults ([18,\u00a030)) from early summer 2021, and its subsequent extension to individuals aged [12,\u00a018) after the EMA approved the Pfizer/BioNTech vaccine for those over 12 years old49,51,52, altered the population\u2019s susceptibility profile. Figure 4 shows a steep decline in susceptibility within the [18,\u00a040) age group during June and July 2021 (waves 25 to 27) and within the [12,\u00a018) age group between July and August (waves 26 to 29). By late August 2021, a gradual rebound in susceptibility is observed across all adult groups, likely due to waning vaccine immunity and increased susceptibility to the Delta VOC39,40,41. During May-June 2021 (waves 22 to 25), individuals aged [12,\u00a018) exhibited high elasticity and an increasing contribution to transmission, in contrast to their marginal role until late April (wave 21), as shown in Fig.\u00a05. While this trend coincides with an increase in observed daily contacts (Supplementary Fig.\u00a013), this alone does not fully explain the shift in age-specific contribution patterns reflected in cumulative elasticity indices. Elasticity patterns at waves 14 (Fig.\u00a01b) and 31 (Fig.\u00a03a) provide a counterexample, demonstrating that the [12,\u00a018) age group can still play a marginal role in transmission despite a high number of contacts. Further analysis of the cumulative elasticity gradient with respect to the NGM\u2019s columns suggests that the shift in social mixing, along with a decreased contribution from the [0,\u00a012) and [18,\u00a030) groups, may have sustained the dominant role of the [12,\u00a018) and [30,\u00a040) groups in transmission in late June 2021. Additional details are provided in the Supplementary Discussion and Supplementary Fig.\u00a07. On June 23, 2021, the [30,\u00a040) age group exhibited a remarkable elasticity of \\({\\tilde{e}}_{[30,40),25}=0.36\\), coinciding with an Rt above 1. In July and August 2021, a consistent Sj,t\u2009=\u20093 across waves 26 to 29 highlights adults in the [18,\u00a040) age group as the main contributors to transmission. Meanwhile, the [12,\u00a018) age group reverted to a marginal role (see Table 2 and Fig.\u00a05) and experienced a noticeable decline in susceptibility starting in late June 2021 (wave 25). However, the shift of age-specific elasticity during this period was primarily driven by changes in contact patterns, as discussed in detail in the Supplementary Discussion (Supplementary Fig.\u00a06).\n\nThe red dashed line corresponds to the average stringency index50.\n\nSeptember and October 2021 marked a phase of further relaxation of NPIs (see Fig.\u00a06), with the stringency index dropping to 32 by October 2021. After school reopened in September, CoMix survey data from waves 30 to 34 revealed notable variations in daily contact numbers, particularly among individuals aged under 18 years. Children under 18 years old reported an average of 8.53\u201312.10 daily contacts, compared to an average of 4.86\u20135.93 for adults (see Table 2 and Supplementary Fig.\u00a013). During this period, adult susceptibility, which had been rising since July, peaked between September and November 2021 (Fig.\u00a04), driven by waning immunity and the increasing circulation of the Delta VOC40. These shifts are reflected in the cumulative sensitivity and elasticity indices, which highlight children under 12 and adults aged [18,\u00a050) as the main contributors to SARS-CoV-2 transmission (Figs.\u00a03 and 5). Interestingly, despite a high overall contact rate within the [12,\u00a018) age group, its contribution to transmission remained marginal. This may be attributed to its highly assortative mixing pattern and lower susceptibility, which together likely limited its role (Fig.\u00a03 and Supplementary Fig.\u00a09b). At the same time, the return to school coincided with an unprecedented elasticity value of \\({\\tilde{e}}_{[0,6),31}=0.43\\), making children in the [0,\u00a012) age group the main contributors to transmission on September 17, 2021 (wave 31), when Sj,\u00a0t peaked at 3 for j\u2009=\u2009[0,\u00a06) and [6,\u00a012). From October 1, 2021 (wave 32), further easing of NPIs, including the reopening of pubs and dance clubs51, corresponded with a shift in transmission dynamics. Adults aged [18,\u00a050), particularly the [30,\u00a040) group, had the highest proportional impact on Rt, joined later by children in the [0,\u00a012) age group on October 29, 2021 (wave 34) (Fig.\u00a03). Defining age-specific contributions to Rt using cumulative elasticity is valuable, especially when Rt exceeds 1, as it signals potential exponential growth. This was the case during this period, with Rt peaking at 1.2 in late October and early November 2021. The analysis indicates that interventions targeting children under 12 years had a high potential to reduce Rt. For example, a hypothetical 14% reduction in the number of susceptibles in the [6,\u00a012) age group at the start of the school year (wave 31) would have lowered Rt just below the critical threshold of 1 (Supplementary Fig.\u00a08).\n\nThe stringency of NPIs slightly increased between mid-November 2021 and early January 2022 due to new restrictions aimed at reducing the winter burden on the healthcare system, triggered by increased hospital admissions51,52. This led to a general reduction in social contacts, although individuals under 18 years of age maintained a relatively high level of contact until November 10, 2021 (wave 37) (Table 2). Concurrently, the Belgian government expedited administering a third vaccine dose, starting in early October 2021, to increase coverage among vulnerable and over-65-year-old individuals before Christmas. This is reflected in a gradual decline in age-specific curves of the susceptible population, counteracting the previously observed rebound (Fig.\u00a04). Our perturbation analysis describes a notable change in SARS-CoV-2 transmission dynamics during this period. Children\u2019s contribution to transmission became marginal, while adults, especially those aged [30,\u00a040), assumed a sustained dominant role. Table 2 shows a consistent S[30, 40),t\u2009=\u20093 during this period, highlighting the above-average cumulative elasticity of this group (Fig.\u00a05) and sensitivity (Supplementary Fig.\u00a014). At wave 40 (January 21, 2022), the Omicron VOC, characterised by increased transmissibility44,45, was the dominant strain, accounting for 99.7% of sequenced cases50,53. At this time, the perturbation analysis identified adults aged [30,\u00a040) as the main contributors to transmission. This is emphasised by a peak elasticity value of \\({\\tilde{e}}_{[30,40),40}=0.36\\). Figure\u00a04 illustrates a marked decrease in the number of susceptibles from January 7, 2022 (wave 39), coinciding with a surge in infections across all age groups49,52. A notable exception is observed in adults over 60 years old, who received booster vaccinations early on. For this group, a linear increase in susceptibility coincides with the emergence of the Omicron VOC, possibly reflecting immunity waning combined with the immune evasion features of the variant45,55.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57087-z/MediaObjects/41467_2025_57087_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57087-z/MediaObjects/41467_2025_57087_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57087-z/MediaObjects/41467_2025_57087_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57087-z/MediaObjects/41467_2025_57087_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57087-z/MediaObjects/41467_2025_57087_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57087-z/MediaObjects/41467_2025_57087_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "This study examines how age-specific susceptibility, vaccination coverage, social behaviour, and NPIs shaped SARS-CoV-2 transmission dynamics in Belgium. By conducting a longitudinal perturbation analysis of the NGM, we provide insights into the role of different age groups over time, offering a valuable tool for targeted pandemic management.\n\nIn our study, children aged [0,\u00a012) emerged among the main contributors to SARS-CoV-2 transmission at specific periods, particularly in early 2021 and September-October 2021, contrasting with their marginal role during the early pandemic stages2,25,28. The said periods coincided with school reopenings, high contact rates, and delayed vaccination efforts for young children, while other age groups benefited from prior immunity through infection or vaccination.\n\nWe emphasise that our analysis offers a relative measure of age-specific contributions to transmission, capturing the dynamic interplay between susceptibility, contact rates, and immunity at each observation point. Vaccination efforts targeting older populations can increase the relative susceptibility of children, amplifying their role in transmission when combined with high contact intensity. Even with stable overall susceptibility of younger groups, local factors such as immunity waning in the population or the emergence of VOCs can significantly enhance children\u2019s influence during specific periods.\n\nMoreover, by the mathematical definition of the indices employed in our analysis, children emerged as potential optimal targets for intervention during specific moments. Their above-average cumulative sensitivity and elasticity indices suggested that changes in their epidemiological parameters were expected to produce larger fluctuations in Rt, particularly when it exceeded the threshold value of 1. This pattern was evident at the beginning of 2021, coinciding with the resumption of face-to-face education in primary and secondary schools. The observed fluctuation in the elasticity of children aged [0,\u00a012) years coincided with shifts in contact patterns (Supplementary Figs.\u00a03 and 13). A similar trend was noted between April 28 and June 12, 2021, following the relaxation of NPIs after Easter. However, during this period, the estimated Rt based on confirmed cases remained safely below the value 1. Children again emerged as main contributors to transmission in September and October 2021, coinciding with another rise in Rt above 1 (Fig.\u00a03). Here, while their high daily contact rates were a factor, the evolving immunity landscape\u2014shaped by the vaccination campaign and the circulation of new viral variants\u2014also played a crucial role in redefining the transmission hierarchy among age groups (see Supplementary Fig.\u00a09). Throughout the study period, high daily contact numbers reported by children aged up to 12 years often aligned with their increased contributions to virus transmission (as indicated by high elasticity values). However, this relationship is not always straightforward. For instance, the [12,\u00a018) group reported consistently high contact rates but contributed marginally to transmission, except during late May-June 2021. During this period, increased disassortative mixing of this group with adults (Supplementary Fig.\u00a07d) corresponded with infective values v[12,\u00a018),t above average (Supplementary Fig.\u00a015). This observation aligns with our mathematical characterisation of the dominant left eigenvector (v) components as the gradient of Rt with respect to the NGM\u2019s rows, which directly involve the group\u2019s contact structure. High infective values indicate age groups with a greater per-susceptible risk of initiating transmission chains, driven by this strong behavioural component. In addition, variations in age-specific susceptibility strongly influence changes in the NGM rows. We consistently observed higher-than-average vj,t values for the [0,\u00a06) and [6,\u00a012) age groups, underscoring the critical need for focused screening and contact tracing in younger age groups to effectively mitigate transmission, as supported by other research11,29,46.\n\nUnderstanding immunity changes within these age groups is crucial for controlling transmission dynamics. Our simulations indicated that before the vaccination campaign in December 2020, children under 18 had approximately half the susceptibility of adults, consistent with studies linking age-related susceptibility increases to stronger initial antibody responses in younger individuals1,5,10,56,57. Our sensitivity indices identified children in the [0,\u00a012) age group among the main contributors to virus transmission between January and March 2021. This period coincided with the Alpha VOC becoming the dominant strain in Belgium, which has been associated with higher transmissibility and increased susceptibility in younger populations36,37,38. Similarly, this group of children exhibited an above-average proportional contribution to transmission between September and October 2021, when the Delta VOC\u2014characterised by higher Rt, increased hospitalisations, and vaccine-evasive properties40,41,58,59\u2014was the predominant circulating variant. By July 2021, vaccination in Belgium had been extended to adolescents aged 12 years and older, but younger children remained unvaccinated until early 2022, leaving them relatively more susceptible and influential in transmission dynamics. In the Supplementary Discussion (Supplementary Figs.\u00a08 and 9), we apply perturbation analysis to demonstrate that earlier vaccination of the [0,\u00a012) age group could have played a key role in reducing Rt, potentially helping to keep it below the value 1. However, during this period, while vaccines were confirmed to be safe for children aged 5 to 11 years60,61, emerging data and public caution\u2014partially fuelled by reports of adverse events62,63\u2014contributed to delays in vaccination rollout for this age group.\n\nAdults aged [18,\u00a060) consistently exhibited high cumulative sensitivity indices, reflecting their larger pool of susceptibles and above-average q-susceptibility and q-infectiousness (Supplementary Table 3 and Supplementary Fig.\u00a014). Within this group, young adults aged [18,\u00a030) were among the main contributors to transmission for most of the observation period, particularly during the rise in hospitalisations observed in March 2021. This dynamic shifted with the introduction of stricter NPIs across Easter (March 31 to April 19), coinciding with a drop in Rt below 1 and a decline in the [18,\u00a030) group\u2019s cumulative elasticity, which fell below average for the first time by the end of April 2021 (Fig.\u00a05).\n\nThe emergence of new VOCs, ongoing vaccination efforts, and the easing of NPIs\u2014along with corresponding shifts in contact patterns\u2014created an intricate landscape of transmission dynamics. Our analysis helped identify that, from late June 2021, as the Alpha strain was replaced by the Delta VOC, the [18,\u00a040) age group played a crucial role in sustaining virus spread. Within this group, the [30,\u00a040) age group emerged as a main contributor, a role that became even more pronounced by the end of December 2021 when the Omicron VOC replaced Delta as the dominant strain. Throughout this period, the [30,\u00a040) group consistently exhibited high elasticity and infective values, surpassing younger age groups in their impact on transmission (see Fig.\u00a05).\n\nOur analytical approach leverages extensive data collected during the COVID-19 pandemic, capturing the evolving transmission dynamics from November 2020 to February 2022. The social contact data from this period provided the interaction network between age groups, while epidemiological parameter estimates determined the likelihood that each interaction resulted in a new infection. This framework shaped the role of each age group in overall transmission. Through our perturbation analysis, we developed indices that capture the hierarchy of these roles, summarising how changes in contact patterns, NPI stringency, age-specific susceptibility, and Rt influenced the contributions of different age groups to virus spread. At the same time, the somehow inverse analysis of pinpointing specific causes behind shifts in these indices is inherently challenging due to the constantly evolving NGM\u2019s structure. In the Supplementary Discussion, we present additional theoretical tools (detailed in the Supplementary Methods) that provide valuable insights into our case study and may serve as a basis for future research into these dynamics. Specifically, applying differential calculus to the sensitivity indices allows for a deeper examination of the factors driving age-related transmission patterns.\n\nWhile our study offers an unexplored perspective on epidemic observation, several other studies have employed social contact data and the next-generation matrix approach to explore the effects of NPIs on age-specific transmission dynamics across various countries. In the UK context, multiple studies64,65,66 have shown that young adults aged 20\u201350 years were the primary contributors to virus transmission overall. However, the role of children, particularly those aged 5\u201317 years, became increasingly important when schools were open. Notably, the reopening of schools after prolonged restrictions was identified as a key driver of transmission increases in these studies. Similarly, studies in the Netherlands67 and Italy68,69 found that reduced school contacts among children aged 0 to 17 years were pivotal in controlling virus spread during closures and driving transmission after reopening. In Canada70 and South Korea71, school closures, alongside social distancing, were highly effective in reducing transmission. In China72,73, prolonged school closures kept the role of the [0,\u00a018) age group minimal, with their relative contribution to virus transmission being much lower than that of the age group of adults [30,\u00a060) during the spread of Omicron variants from March to June 2022. These studies support our findings and emphasise the critical need to accurately assess the impact of different age groups on transmission dynamics when designing control policies.\n\nFinally, it is essential to acknowledge the limitations of our study, which may influence the interpretation and generalisability of our findings. One key limitation is the resolution of available data. Our age structure, aligned with Belgium\u2019s educational settings, relies on multiple data sources to inform age-specific epidemiological parameters1,8,10. However, in early epidemic stages, such detailed data may be unavailable17, limiting the generalisability of our approach to other contexts and the possibility of reconstructing finer age structures from social contact surveys74. On the other hand, efforts to reconstruct social contact data on a finer time scale75 would enhance the potential of our linear analysis25,76. This is particularly crucial when social contact patterns change rapidly, such as with new NPIs or seasonal shifts, an aspect not explicitly addressed in our study. Another limitation concerns data uncertainty and its impact on our results in a heterogeneous framework like ours. Reporting biases, sampling errors, and selection biases can affect the age-specific representativeness of social contact data12. A recent study emphasised the importance of accounting for the frequency and repetitiveness of contacts, as these factors can significantly influence epidemic dynamics75. Uncertainty in contact patterns, susceptibility estimates, and the parameters informing our NGM likely affect the statistical significance of detected age-specific differences. Future work should include comprehensive error propagation analysis while considering contact correlations within and between age groups12,77. Nevertheless, while our approach operates in a deterministic fashion by way of average values, it lays the foundation for future explorations employing perturbation analysis coupled with stochastic models. This creates a pathway for more robust, uncertainty-accounting studies, aligning our approach with evolving needs in pandemic response. The present study focuses on SARS-CoV-2 transmission in Belgium, though the flexibility of our methodology makes it applicable to a wide range of infectious diseases where transmission is tied to contact rates. This adaptable approach offers a valuable tool for analysing disease dynamics and has broad relevance in epidemiology and public health management.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Our research expands upon the concept of NGM perturbation analysis25 by conducting a longitudinal study. We examine dynamic shifts in the epidemiological landscape across distinct time points, from November 2020 to March 2022. The proposed approach allows us to monitor the changes in sensitivity indices over time. The time points in our study align with 34 consecutive waves of social contact data collection24, gathered from a representative sample of the Belgian population. For in-depth information, refer to the \u201cSocial Contacts\u201d section below. The analysis in our study is executed at each time point through a two-step procedure. Initially, we derive the next generation matrix26 from an SEIR compartmental model8,48, which was designed to capture the spread of SARS-CoV-2 in Belgium. Within this framework26,27, the NGM is represented as a square matrix whose dimensions depend on the infectious states considered in the system, in this case, the age groups. Importantly, the spectral radius of the NGM indicates the outbreak potential, equivalent to the average number of secondary infections caused by a typical infected individual while infectious. In a fully susceptible population, this represents the basic reproduction number; otherwise, it reflects the effective reproduction number (Rt). The NGM in our study incorporates the latest data on age-specific social contacts, the count of susceptible individuals per age group, and the Rt derived from available PCR test-based estimates78. Specifically, the effective reproduction number at each time step is the arithmetic mean of the daily Rt estimates over a 7-day period centred on the date of the corresponding social contact survey wave. This ensures that the NGM\u2019s dominant eigenvalue matches the observed Rt values, avoiding reliance on Rt estimates directly derived from the SEIR model simulations. Importantly, this calibration choice does not affect our perturbation analysis, as the sensitivity indices defined in our study are invariant under the multiplication of the NGM (and the Rt) by a nonzero scalar.\n\nThe SEIR model further provides a robust framework for reconstructing the evolving age-specific susceptibility8,48. By accounting for various forms of protection, including natural infection, vaccination, and the impact of viral variants of concern (VOCs), it successfully integrates heterogeneous data sources\u2014such as hospitalisation, seroprevalence, and PCR positivity data\u2014to reliably capture epidemic trends in Belgium. In the second step, the epidemiological characterisation from the first step is employed to conduct a longitudinal perturbation analysis. This process enables us to analytically quantify how local variations in the parameters of the NGM entries influence the Rt. Changes in epidemiological parameters may be due to variations in NPIs, viral mutations, shifts in contact behaviour, depletion of susceptibles from natural infection progression, and the effects of vaccination campaigns.\n\nThe core assumption underpinning the calculation of changes in infected host numbers due to virus transmission relies on the social contact hypothesis79. According to this hypothesis, the number of secondary infections an infected individual generates is proportional to their social contacts. The proportionality constant (indicated as q) and the definition of relevant contact hinge on the specific pathogen under consideration. A contact qualifies as either a face-to-face conversation of at least a few words or skin contact, aligning with the definitions used in the principal studies on social contacts in Belgium7,24. Our study employs social contact data derived from 34 consecutive waves of the CoMix survey conducted in Belgium from November 2020 to March 2022 amid the COVID-19 pandemic. The survey waves were collected every two weeks. Participants logged their daily contacts detailing the type, location, and age of the person contacted. The data were subsequently processed and stratified by age using the open-source tool SOCRATES23,24. Social contacts shape the structure of the next-generation matrix, influencing its overall structure. From the CoMix survey, we obtain the average daily number of contacts that an individual of a particular age (i) makes with individuals of age (j), denoted by mij. This information helps us construct the pivotal social contact matrix. Subsequently, we process these matrices further to:\n\nMeet the reciprocity constraint. Given the nature of the contacts considered, the total number of contacts between two age groups (i and j) should be the same whether derived from mij or mji. In simple terms, Nimij\u2009=\u2009Njmji, where Ni and Nj are the number of individuals in each age group considered.\n\nAccount for the impact of participation fatigue, particularly in longitudinal studies engaging participants over extended periods. A recent study12 conducted on CoMix survey data collected in Belgium revealed that individuals participating in multiple survey waves tended to under-report the number of their daily contacts, indicating a potential impact of participation fatigue on the accuracy of the data collected. In the current research, we incorporate the adjustments suggested by the aforementioned study to correct for this bias. This involves adapting the social contact matrices according to the wave and age of the participants.\n\nAccount for the impact of symptom onset on the infected individuals\u2019 level of social interaction, particularly in non-household environments. This acknowledges that symptoms typically reduce social contacts80, notably influencing the virus transmission dynamics. We refer to the Supplementary Methods for further details.\n\nCrucial to our analysis is the derivation of the NGM from the chosen compartmental model describing disease dynamics. We consider an age-structured Susceptible, Exposed, Infectious, and Recovered (SEIR) model developed by Abrams et al.8, categorising individuals into ten age groups, each spanning ten years. To align with our specific age partitions, \u03a9\u2009=\u2009[0,\u00a06),\u00a0[6,\u00a012),\u00a0[12,\u00a018),\u00a0[18,\u00a030),\u00a0[30,\u00a040),\u00a0[40,\u00a050), [50, 60), [60,\u00a070),\u00a0[70,\u00a0\u221e), we adjust these groups, assuming that the distribution within overlapping intervals reflects the demographic composition. This adjustment is particularly important for those under 18 years, mirroring divisions within the Belgian school system. This age structure is matched by age-specific estimates of key epidemiological parameters, such as q-susceptibility (the probability of a susceptible individual becoming infected after close contact) and q-infectiousness (the probability of an infected individual transmitting the virus during close contact). These quantities, as in Franco et al.10, are defined up to a constant q which is calibrated at each observation point so that the NGM dominant eigenvalue matches the value of Rt estimated from positive PCR tests78. Transition rates through the different infectious states after exposure are also modelled according to this age structure (more details in the Supplementary Methods). The model tracks the progression from a susceptible state (S) to an exposed state (E) upon effective contact with an infectious person. After exposure, individuals enter a pre-symptomatic infectious state, followed by either a symptomatic or asymptomatic state before recovery. Symptomatic cases may progress to severe illness, potentially leading to hospitalisation or ICU admission. Although the model includes disease-related mortality in hospitalised cases, these factors do not affect the NGM structure. It is the choice of infected and infectious compartments and the specific age structure that defines the NGM26. The NGM\u2019s formulation depends on parameters governing transmission (the force of infection) and transitions between various infectious states (occupancy time in each state). Pre-infectious and post-infectious states are irrelevant to the NGM\u2019s formulation; hence, using a different model (e.g., SIR) with the same age structure and infectious state choices would yield an identical NGM. However, it is important to consider the complete model structure when extending our perturbation analysis over time. Since we don\u2019t have comprehensive serological data for Belgium, we use numerical simulations to track the age-specific changes in the susceptible population. This, in turn, affects the structure of the NGM and, consequently, the sensitivity indices. To this end, we use the estimated susceptibility from an SEIRS model developed by Willem et al.48, extending the original SEIR model8 to include VOCs circulation, vaccination uptake, and waning immunity. The model uses a stochastic, binomial chain approach to simulate epidemic progression in discrete generations, with a time resolution of 0.25 days. It specifically accounts for VOC-related changes in transmissibility, hospitalisation risk, and latency period. The model also uses the same social contact data from CoMix surveys to estimate age-specific proportionality factors that translate reported contact rates into transmission rates (under the social contact hypothesis79). Susceptibility in our framework is based on four distinct immune states representing combinations of infection history and vaccination status, as outlined by Willem et al.48 and better detailed in the Supplementary Methods. Individuals are considered susceptible if they have never been infected or vaccinated or have waning immunity. The model transitions individuals from full protection to susceptibility over time, reflecting the gradual decrease in immunity (see Supplementary Table 4). This approach ensures that susceptibility estimates reflect the dynamic nature of population immunity during the pandemic. To effectively integrate various data sources that became available at different stages of the pandemic in Belgium, the model calibration employed a multi-step Bayesian approach with Markov Chain Monte Carlo (MCMC) sampling using 60 chains. Each step focused on different time horizons and specific parameters, which were sequentially updated based on previous calibration results. Transmission-related parameters were initially estimated using data from hospital admissions49, early seroprevalence data (available up to October 17, 2020)81, and genomic surveillance data for Belgium53. Subsequently, parameters affecting hospital and ICU occupancy were estimated by minimising a least squares criterion, ensuring the best fit between observed and simulated hospital loads. Lastly, mortality-related parameters were refined to account for COVID-19-related deaths in hospitalised patients. From 60 MCMC chains, the 40 best-fitting parameter sets were selected based on their agreement with observed data. For each of these sets, 10 stochastic realisations were performed, resulting in 400 estimates of age-specific susceptibility over 730 days (March 1, 2020, to February 28, 2022). The daily age-specific number of susceptible individuals was then calculated as the mean across these estimates. A comprehensive explanation of the model, the calibration process and the result of the calibration are provided in Willem et al.48, especially within the related Supplementary Information.\n\nIn our analysis, we utilise sensitivity indices to elucidate the roles of different age groups in SARS-CoV-2 transmission dynamics. These indices measure the impact of epidemiological changes on the effective reproduction number (Rt), as resulting from the next generation matrix (NGM). They are grounded in the concept of classical sensitivity index (\u2202Rt/\u2202kij\u2009=\u2009sij), which assesses the rate of change in the NGM\u2019s spectral radius due to a variation in a single matrix entry76. The epidemiological implications of these indices are further explained, and their mathematical foundations are detailed in the Supplementary Methods.\n\nKey indices include:\n\nCumulative Sensitivity (\\({\\tilde{s}}_{j,t}\\)): This index measures the impact on Rt resulting from changes in how a single index case in age group j transmits the infection, at time t. Higher \\({\\tilde{s}}_{j,t}\\) values signify a greater sensitivity of Rt to secondary infections from a single infected individual in age group j, thereby identifying effective spreaders in the current infected generation. Each index \\({\\tilde{s}}_{j,t}\\) is proportional to the relative incidence of the infection in age group j, as elaborated in the Supplementary Methods.\n\nCumulative Elasticity (\\({\\tilde{e}}_{j,t}\\)): This index quantifies the proportional contribution to Rt of an age group j in the current generation (assumed to start at time t). High indices pinpoint age groups that substantially contribute to the overall disease propagation and for which proportional variations in the epidemiological parameter set translate into higher proportional shifts of Rt.\n\nInfective Value (vj,t): This index quantifies the impact on the Rt following a perturbation in either the force of infection exerted on individuals in age group j or their susceptibility, at time t. Each vj,t corresponds to the influence a single new case in age group j has on the number of new infections in each future generation. Specifically, one new case in age group j is expected to increase the overall infection count by Rtvj,t in the next generation of infections, and by \\({R}_{t}^{m}{v}_{j,t}\\) after m generations25. Higher values identify age groups with a higher potential for initiating transmission chains.\n\nA composite index, Sj,t, combines the above indices to provide a comprehensive view of each age group's contribution (j) to virus propagation at specific times (t), aligned with CoMix survey waves. This index is derived as the sum of 3 Boolean values, specifically:\n\n\\({\\tilde{e}}_{j,t}\\, > \\,\\frac{1}{n}\\);\n\n\\({\\tilde{s}}_{j,t}\\, > \\,{\\tilde{s}}_{avg}\\) Or vj,t\u2009>\u2009vavg;\n\n\\({\\tilde{s}}_{j,t}\\, > \\,{\\tilde{s}}_{avg}\\) & vj,t\u2009>\u2009vavg.\n\nCondition 1 indicates that the age group j\u2019s contribution to Rt (as represented by \\({\\tilde{e}}_{j,t}\\)) exceeds 11%, corresponding to the average cumulative elasticity value with n\u00a0=\u00a09 age groups (\u2211j\u00a0\\({\\tilde{e}}_{j,t}\\) = 1, see Supplementary Methods). Conditions 2 and 3 assess whether the cumulative sensitivity index \\({\\tilde{s}}_{j,t}\\) and the infective value index vj,t surpass their respective average values, \\({\\tilde{s}}_{avg}\\) and vavg, calculated as the mean of the arithmetic averages of these indices across all survey waves. Throughout the 34 CoMix waves, we note \\({\\tilde{s}}_{avg}=1(0.98,1.03)\\) and vavg\u2009=\u20090.93(0.90,\u00a00.95), indicating the 99% confidence intervals. The resulting Sj,t index, varying from 0 to 3, allows us to deduce the relative transmission roles of different age groups at specific times. A value of Sj,t\u2009=\u20093 indicates a distinctly above-average transmission role, as seen in Table 2. More specifically, individuals in such a high-index age group j are primary contributors to overall virus transmission, likely to drive notable variations in Rt upon experiencing age-specific epidemiological changes (such as through NPIs or vaccination), and possess a higher potential to initiate transmission chains when exposed to infection risks.\n\nFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The datasets analysed in the current study are available in the Zenodo-based repository82, https://zenodo.org/records/10549953, as well as through the CoMix-Socrates App https://socialcontactdata.org/tools/.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The analysis and results presented in this study are fully reproducible using the R code provided in the following GitHub repository: https://doi.org/10.5281/zenodo.1477739283. 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Insights into the role of children in the COVID-19 pandemic in Belgium through perturbation analysis. https://doi.org/10.5281/zenodo.14777392 (2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "L.A., P.B., and N.H. acknowledge funding from the European Union\u2019s Horizon 2020 research and innovation programme \u2013 project EpiPose (Grant agreement number 101003688) and the ESCAPE project (101095619), both funded by the European Union.\u00a0N.H. further acknowledges support from the VERDI project (101045989), also funded by the European Union. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible. L.A. and N.H. acknowledge funding from the Special Research Fund through the Methusalem project BOF08M01 - phase III. C.P.C. acknowledges the funding by Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia (FCT.BD). L.W. and S.A. acknowledge support from the Research Foundation Flanders (FWO) (ACCELERATE project G059423N). CoMix data collection in Belgium was made possible through funding from the European Union\u2019s Horizon 2020 research and innovation programme - project EpiPose (Grant agreement number 101003688), and with financial support from the National Public Health Institute of Belgium, Sciensano and Janssen Pharmaceuticals. This work reflects only the authors\u2019 view. We extend our gratitude to the research teams at the University of Antwerp, Hasselt University, and the London School of Hygiene and Tropical Medicine, involved in the CoMix study within the EpiPose project, for their invaluable contribution in designing the survey and processing, curating, and collecting the social contact data utilised in our study.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium\n\nLeonardo Angeli,\u00a0Nicolas Franco,\u00a0Pietro Coletti,\u00a0Christel Faes,\u00a0Geert Molenberghs,\u00a0Steven Abrams\u00a0&\u00a0Niel Hens\n\nCenter for Computational and Stochastic Mathematics, Instituto Superior T\u00e9cnico, University of Lisbon, Lisbon, Portugal\n\nConstantino Pereira Caetano\n\nNamur Institute for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium\n\nNicolas Franco\n\nInstitute of Health and Society (IRSS), UCLouvain (Universit\u00e9 catholique de Louvain), Brussels, Belgium\n\nPietro Coletti\n\nL-BioStat, KU Leuven, Leuven, Belgium\n\nGeert Molenberghs\n\nCentre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium\n\nPhilippe Beutels,\u00a0Lander Willem\u00a0&\u00a0Niel Hens\n\nDepartment of Family Medicine and Population Health (FAMPOP), University of Antwerp, Antwerp, Belgium\n\nSteven Abrams\u00a0&\u00a0Lander Willem\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nL.A. contributed to conceptualisation, methodology, software development, formal analysis, original draft preparation, visualisation, manuscript review and editing, and project administration. C.P.C. was involved in conceptualisation, methodology, and manuscript review and editing. P.C., G.M., and S.A. provided resources and participated in manuscript review and editing. N.F., C.F., and P.B. reviewed and edited the manuscript. L.W. contributed to software development, conceptualisation, resources, and manuscript review and editing. N.H. contributed to conceptualisation, methodology, resources, original draft preparation, supervision, funding acquisition, project administration, and manuscript review and editing.\n\nCorrespondence to\n Leonardo Angeli.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Yuzhou Chen, C\u00e9cile Tran-Kiem, and the other anonymous reviewer(s) for their contribution to the peer review of this work. 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Assessing the role of children in the COVID-19 pandemic in Belgium using perturbation analysis.\n Nat Commun 16, 2230 (2025). https://doi.org/10.1038/s41467-025-57087-z\n\nDownload citation\n\nReceived: 25 April 2024\n\nAccepted: 10 February 2025\n\nPublished: 05 March 2025\n\nVersion of record: 05 March 2025\n\nDOI: https://doi.org/10.1038/s41467-025-57087-z\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Communications", + "published": "26 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63504-0/MediaObjects/41467_2025_63504_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63504-0/MediaObjects/41467_2025_63504_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63504-0/MediaObjects/41467_2025_63504_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://github.com/dongxu-lei/CLUSTER" + ], + "code": [ + "https://github.com/dongxu-lei/CLUSTER", + "https://doi.org/10.5281/zenodo.16722390", + "/articles/s41467-025-63504-0#ref-CR57" + ], + "subject": [ + "Complex networks", + "Statistical physics" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4963495/v1.pdf?c=1758971251000", + "research_square_link": "https://www.researchsquare.com//article/rs-4963495/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63504-0.pdf", + "preprint_posted": "14 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Modern society takes connectivity for granted. We, therefore, rely quite heavily on\r\nour communication networks, both to sustain interpersonal connections, but also to\r\nsupport our technological infrastructure, e.g., health, power or transportation. This\r\ndependence will further strengthen as 5G technology becomes more pervasive and\r\ncommunication traffic sharply increases. It is, therefore, crucial to develop methods\r\nto optimize the efficiency and reliability of our ever-expanding networks. Such\r\nmethods must account for the interplay between the static network infrastructure\r\nand the dynamic user connection preferences. The problem is that the user preferences\r\narise from each individual\u2019s mobility and communication patterns - data\r\nthat are strictly protected by privacy concerns, and hence cannot be used for the\r\nnetwork optimization. To address this challenge we develop CLUSTER, an interpretable\r\nBayesian non-parametric framework, that uses aggregate, low resolution,\r\nuser data, to detect user groups with predictably correlated connection patterns.\r\nWe show that CLUSTER offers actionable insights on the network management,\r\nsuch as setting each base-station\u2019s activation cycles, detecting critical stations and\r\nguiding the deployment of new stations. All, without violating user privacy. More\r\nbroadly, CLUSTER illustrates a general approach to extract meaningful information\r\nfrom privacy protected data.Physical sciences/Physics/Statistical physics, thermodynamics and nonlinear dynamics/Complex networksPhysical sciences/Physics/Statistical physics, thermodynamics and nonlinear dynamics/Statistical physics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformation.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Modern society takes connectivity for granted, relying heavily on communication networks, both for interpersonal connection and to support critical infrastructure. As Internet- and data-driven technologies become increasingly pervasive, our dependence on fast, reliable communication will only deepen, necessitating advanced tools for optimizing network efficiency and resilience. Such optimization must account for the interplay between the static network infrastructure and the dynamic user preferences. The challenge is that while the infrastructure data is accessible to network operators, the user preferences, tied to personal mobility and communication habits, are protected by privacy laws and are thus heavily restricted. To address this, we introduce CLUSTER: an interpretable Bayesian nonparametric framework that leverages aggregate, low-resolution, unprotected data to identify user groups with correlated connection patterns. By uncovering these patterns, we show, CLUSTER offers actionable insights, from scheduling base-station activation to guiding deployment of new stations - all without compromising user privacy. CLUSTER thus offers a principled approach to extract meaningful insights from restricted data.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Communication is a hallmark of our modern way of life, representing a fundamental driver of socioeconomic activity1, from individual data exchanges2 to autonomous vehicles3,4, sensors5, health-care services6, smart cities7, trade, manufacturing8 and entertainment. In all these applications our ability to provide reliable, continuous connectivity is indispensable. It is, therefore, no surprise that much of our contemporary technological advances focus on enhancing the efficiency of communication systems\u2014a focus that will only accentuate as data-based technology continues to grow. Indeed, future projections anticipate over a tenfold increase in connected devices and a three order of magnitude boost in traffic volume, as we transition from 4G to 5G infrastructure9. This expected expansion further entrenches our communication dependence and its role in our socioeconomic growth.\n\nTo plan ahead, we seek to optimize the network functionality, balancing service quality with operational efficiency. This optimization hinges on two primary strategies. First, deployment, seeking to erect new base-stations (BS) only where needed10,11,12,13,14, thus avoiding unnecessary costs and infrastructure redundancies15,16,17,18,19,20,21. Second, to further enhance efficiency, each BS can be dynamically toggled between sleep and activation \u2014 capturing its duty cycle22,23,24,25. These duty cycles allow the network to conserve energy during periods of low demand26,27,28,29 while still ensuring adequate coverage when regional traffic is expected to increase30,31,32.\n\nThere remains, however, a critical missing link on the path to effective network optimization: the fact that the network\u2019s functionality depends not only on the infrastructure itself, but also on its dynamic interplay with user behavior\u2014in particular, the users\u2019 mobility and their individual connectivity patterns. Without accounting for these factors, it becomes impossible to reliably predict the impact of interventions, from setting the BS duty cycles to planning the deployment of new stations. The challenge is that the necessary data, detailing individual users\u2019 connection patterns, constitutes personally identifiable information (PII). This raises significant legal and ethical barriers that, in practice, preclude us from designing effective network optimization.\n\nOften, these restrictions are bypassed through aggregation and anonymization\u2014for example, by tracking the sheer volume of users at each BS without collecting individual identifiers33,34. While this preserves privacy, it captures only static snapshots, failing to reveal the transfer dynamics between BSs and thus limiting our ability to infer temporal variations in network load. Other strategies balance this dilemma by using distributed learning methods, where no single operator has full access to the entire data35,36. However, these methods still pose privacy risks37, and mitigating these risks often leads to reduced service quality or increased costs36. Finally, statistical models such as deep learning, sidestep the issue by inferring patterns directly from the BS load and activation data, without accessing user-level information38. Yet, these models typically require vast amounts of data, often exceeding what is practically available39. How then can we construct a data-driven approach that balances aggregation, limited data availability, and adheres to the strict privacy constraints?\n\nHere, we develop CLUSTER (Characterizing Latent User Structure Through Evidence Refinement), a hierarchical Bayesian non-parametric model that breaks down the aggregation into clusters of users with correlated communication patterns. Users within the same cluster tend to exhibit similar connection cycles, thus enabling prediction of future BS loads, without the need to track each individual user. CLUSTER is, therefore, sufficiently aggregate to circumvent PII restrictions, but yet detailed enough to allow prediction of the collective load flow between BSs. Using CLUSTER can thus help: (i) Design the BS duty cycles in coordination with the anticipated/desired loads; (ii) provide direct guidelines for deployment of new BSs; (iii) identify crucial BSs, whose failure may significantly impact service40,41. Together, CLUSTER enables network operators to plan ahead their load balancing, accounting for the interplay between the well-mapped infrastructure and the restricted user connection patterns.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Consider a communication network with M users and N spatially located BSs that can alternate between active and sleep modes. As users exchange data they generate demand on each BS, typically driven by geographical proximity. Therefore, the BS load patterns are driven by the interplay between the geographical layout of the BSs and the temporal dynamics of the user connectivity. To capture this we seek to track the connection preferences (CP) of all users. At each point in time, user j\u2019s CP is given by the N-dimensional vector lj(t), whose ith term, \\({l}_{i}^{\\;j}(t)\\), captures the probability of user j to connect to BS i; hence \\({\\sum }_{i=1}^{N}{l}_{i}^{\\;j}(t)=1\\). Collecting all preferences of all users via the matrix L(t)\u00a0=\u00a0[l1(t),\u00a0\u2026,\u00a0lM(t)], we can express the demand at all BSs as\n\nwhere \\({{{\\bf{w}}}}(t)={({w}_{1}(t),\\ldots,{w}_{M}(t))}^{\\top }\\) is the time dependent weight assigned to each user, capturing their varying activity levels.\n\nEquation (1) describes the demand experienced by all BSs as a weighted sum of all CPs over all users. The weights wj(t) \u2208 [0, 1] account for the fact that users generate diverse traffic volumes, and hence the contribution of j\u2019s CP to \\(\\tilde{{{{\\bf{x}}}}}(t)\\) is proportional to the fraction of the total traffic generated by the j-user at time t; we normalize the total traffic volume at all times to follow \\(\\mathop{\\sum }_{j=1}^{M}{w}_{j}(t)=1\\).\n\nEquation (1) represents the incoming demand into all BSs, as determined by the user connectivity patterns. In reality, however, a BS may not always be available to accept this demand. This is because BSs may alternate between active and sleep modes, allowing to save on energy at times of low traffic. If at time t BS i is inactive, its demand will be redirected to one of the other stations, in accordance with all user CPs L(t). Hence, to obtain the actual BS loads we now consider the stations\u2019 duty cycles \\({{{\\bf{u}}}}(t)={({u}_{1}(t),\\ldots,{u}_{N}(t))}^{\\top }\\), in which the ith term describes the fraction of time that BS i is active during the interval (t,\u00a0t\u00a0+\u00a0\u0394t). In simple terms, ui(t)\u00a0=\u00a00 if i is at sleep mode for the entire interval; ui(t)\u00a0=\u00a01 if, during this period, it is fully active; and 0\u2264ui(t)\u22641 if the BS is active for some of the time. In most practical applications u(t) is updated hourly, and hence \u0394t\u00a0=\u00a01 hour, and ui(t) captures the fraction of this one-hour interval in which BS i is active.\n\nThe duty-cycles can be controlled by the network managers, and setting them determines the actual loads on all BSs, which we denote by \\({{{\\bf{x}}}}(t)={({x}_{i}(t),\\ldots,{x}_{N}(t))}^{\\top }\\). These loads, therefore, arise from the interplay between the user CPs, as expressed within (1), and the BS duty cycles, which determine the available BSs at any given time point. For an individual user j, the load they introduce into BS i is proportional to the amount of traffic they generate (wj(t)), their connection preference to the ith BS (\\({l}_{i}^{\\;j}(t)\\)), and the probability that BS i is available to receive their incoming traffic (ui(t)). Summing over all M users collectively, this provides us with \\({x}_{i}(t)\\propto \\mathop{\\sum }_{j=1}^{M}{u}_{i}(t){l}_{i}^{\\;j}(t){w}_{j}(t)\\), capturing the total contribution of all users to BS i\u2019s load at time t. In matrix notation, this product sum can be written as (Supplementary Section\u00a03.4)\n\nwhere D(t) is a normalization factor, following\n\nIn (2) and (3) we use diag(v) to describe a diagonal matrix whose ith term is the element vi, hence, e.g., diag(u(t)) collects all duty cycles into an N\u00a0\u00d7\u00a0N diagonal matrix. The unity vector 1N\u00a0=\u00a0(1,\u00a0\u2026,\u00a01)\u22a4 is an N dimensional vector of ones. Together, the diagonal matrix D(t) in (3) is used to normalize x(t) such that the total load at any time t sums to \\(\\mathop{\\sum }_{i=1}^{N}{x}_{i}(t)=1\\). In case ui(t)\u00a0=\u00a01, i.e. all BSs are constantly active, Eq. (2) recovers the demands of (1), up to the normalization factor D(t).\n\nEquation (2) represents our first key observation. It establishes a direct relationship between the duty cycle settings u(t) and the subsequent load distribution x(t) across all BSs. This mapping provides essential guidance for BS controllers on how to design sleep/activation cycles to achieve a desired load distribution. It also enables to foresee the network\u2019s dynamic response to changes in BS availability, predicting how the loads x(t) will adapt to a station\u2019s shutdowns or sudden failure. Finally, the equation can help forecast the network\u2019s ability to handle growing demand or shifts in user behavior, as, indeed, all these factors are inherently governed by the interplay of u(t),\u00a0L(t) and w(t).\n\nThe challenge is that the path from u(t) to x(t), as expressed in Eq. (2), is incomplete, unless we construct the two missing links: first, L(t), which encapsulates the time dependent CPs of all users, and second, w(t), which describes their usage dynamics. These two components are intrinsically tied to user behavior and thus involve privacy-sensitive information. For instance, user CPs are primarily influenced by geographical proximity to BSs, typically favoring nearby stations. Consequently, access to L(t) would enable network operators to infer user mobility patterns \u2014 a potential breach of individual privacy. Similarly, w(t), reflecting each user\u2019s connectivity over time, reveals usage habits and cellular activity, again touching on protected information, subject to strict regulatory and ethical constraints.\n\nTo bypass these restrictions, we seek to use aggregated data, from which individual user information cannot be identified. Of course, aggregation risks the loss of important details that may impact x(t). Therefore, we wish to implement Eq. (2) based on the appropriate level of aggregation: on the one hand obscuring all PII, while on the other hand maintaining the relevant features by which to predict x(t) in a meaningful fashion.\n\nTo achieve the desired balance between aggregation and individual data, we seek to partition all M users into K \u226a M groups, or clusters, each cluster consisting of an order of \u00a0~\u00a0M/K individuals. Within each of these clusters we collect users that have been found to exhibit correlated CPs. Therefore, individuals within cluster \u03b1 (\u03b1\u00a0=\u00a01,\u00a0\u2026,\u00a0K) have similar lj(t), and can be approximated by the cluster average \\({{{{\\bf{l}}}}}^{\\alpha }(t)={\\langle {{{{\\bf{l}}}}}^{\\;j}(t)\\rangle }_{j\\in \\alpha }\\). Now, instead of tracking individual users, we can model the BS loads by tracking the collective CPs characterizing each cluster.\n\nThis partition helps rewrite Eq. (2) in terms of the K collective CPs and weights. The clustered preference matrix Lc(t) will now have just K columns, i.e. Lc(t)\u00a0=\u00a0[l1(t),\u00a0\u2026,\u00a0lK(t)], each column representing a single group-aggregated CP. Similarly, w(t) will also be clustered into a K-dimensional vector wc(t), capturing the collective traffic generation within each cluster. Using these clustered Lc(t) and wc(t) in (2) we obtain the desired link between u(t) and x(t). Only this time it is predicted by the behavior of the user clusters, rather than that of each individual user. It thus bypasses the PII restrictions.\n\nThe challenge is, therefore, to construct these reduced Lc(t),\u00a0wc(t) in an optimal fashion. The natural approach is to examine all user CPs, and use statistical reduction methods, such as principal component analysis or k-means, to consolidate them into separated clusters. This, however, still requires us to collect data on all individual CPs, leaving us precisely where we started. To overcome this we developed a Bayesian non-parametric (BNP) model \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) that allows us to (approximately) reconstruct Lc(t) and wc(t) directly from the observed BS activation and loads u(t) and x(t), thus circumventing the need to track the individual usage patterns. In explicit terms, we only collect allowable data, which is, indeed, available at each BS, but then we use this data to infer the clustered CPs and weights. This results in Lc(t) and wc(t) - aggregate enough to avoid PII, and yet, as we show below, sufficiently detailed to offer the desired predictive power.\n\nThe challenge of \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) is to translate the observed BS loads x(t) together with the known duty-cycles u(t), into the most likely set of hidden user CPs. In Supplementary Section\u00a04 we show how to do this in an iterative fashion. We begin with an initial rough guess, the ansatz, and then use an iterative algorithm to refine this ansatz, until we reach a CP distribution that is satisfactorily consistent with the observed loads. To reduce the potentially immense search space we induce two prior assumptions. The first, represents the fundamental premise of CLUSTER - stating that the M user CPs can be aggregated into a limited number K of sets that follow statistically similar patterns. The second assumption is that user CPs change gradually over time, and hence exhibit temporal correlations across adjacent time points. After several rounds, \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) retrieves the most likely number of clusters K, their respective weights wc(t) and their aggregated CPs Lc(t).\n\nIn simple terms, \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) takes as input a limited amount of observations of u(t) and x(t), data that contains no PII, and provides as output a well-approximated Lc(t) and wc(t), thus allowing us to construct the N\u00a0\u00d7\u00a0K clustered Eq. (2). All without accessing any user protected data. In Supplementary Section\u00a04.1 we also consider an alternative plain formulation, \\({{{{\\mathcal{M}}}}}_{{{{\\rm{PLN}}}}}\\), in which the number of clusters K is pre-set to some initially assumed value.\n\nWhile the technical details of \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) are explained in Supplementary Section\u00a04.2, here, to gain insight into the conceptual idea of the model, we offer an illustrative description in Fig.\u00a01. We consider, at each specific time point t, the individual users and their CPs as beads in a Galton board. The K groups correspond to K initial release points on the board (funnels), forming clusters of different weights wc(t). These weights are captured here by the number of beads within each funnel. Our observation of x(t) corresponds to the distribution of the beads at the N different bins located at the bottom of the board - each bin representing a specific BS. This final distribution of bin population contains information on the initial release points. However, this information is obscured by the stochastic nature of the bead-paths as they traverse the Galton board (dashed green arrow). Such stochasticity, in our system, originates in the fact that the users within each cluster exhibit some level of variability and randomness, and hence, using our analogy, they do not all begin at the exact same release point (scattered greed/orange beads), and, furthermore, they may follow slightly different paths along their trajectories from these release points to the bins at bottom of the board. Our model \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) uses the observed distribution x(t) across the bins (BSs) to retrieve the most likely number of release points (K), their placement (Lc(t)) and their initial bead population (wc(t)).\n\nIn the CLUSTER framework the individual users are treated as beads in a Galton board. The users j\u00a0=\u00a01,\u00a0\u2026,\u00a0M begin at distinct locations at the top the board, and then follow a stochastic trajectory (green dashed arrow) as they traverse the pins. At the bottom of the board users are distributed across the N base stations (BS1, BS2, \u2026, BSN), resulting in the observable loads xi(t). The BS activation cycles ui(t) determine which BSs are open or closed (blue lids) to receive user loads. The probability of user j to fall within BS i determines that user\u2019s connection preferences (CPs) lj(t). The challenge is that while ui(t) is known and xi(t) are empirically accessible to the network managers (blue background, Observable), the user locations and trajectories across the board, namely their time-dependent CPs, are protected data (red background, Unobservable). We therefore seek to infer this information from the observed u(t) and x(t). CLUSTER takes advantage of the fact that while users behave stochastically, they tend to group into distinct clusters (green vs. yellow beads) that follow statistically similar tracks on the board, and hence exhibit correlated CPs. In this analogy, the clusters behave as funnels c\u00a0=\u00a01,\u00a02,\u00a0\u2026\u2009 located at the top of the board, with the number of beads at each funnel representing the collective cluster weight wc(t). Our \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) and \\({{{{\\mathcal{M}}}}}_{{{{\\rm{PLN}}}}}\\) algorithms retrieve these hidden clusters and allow to construct the average CPs lc(t) associated with each group (green background, Inferred). With these clusters at hand we can use Eq. (2) to link the manager controlled u(t) with their estimated subsequent loads x(t).\n\nTaken together \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) (and \\({{{{\\mathcal{M}}}}}_{{{{\\rm{PLN}}}}}\\)) remove a crucial barrier along the path to effective communication network management. They do this by systematically linking the BS loads x(t) and the operator controlled duty cycles u(t) without the need to track all individual user connection patterns. Network managers can now set the optimal duty-cycle per each BS, attuned to their specifically desired load balance, using the clustered Eq. (2). They can also detect critical BSs, whose removal may significantly affect x(t), or alternatively design strategic BS deployment to reduce loads and address potential coverage gaps. The crucial point is that now, in lieu of the PII-protected M-dimensional L(t) and w(t), they can rely on the reduced K-dimensional Lc(t) and wc(t) to achieve such predictions. However, before we advance to these applications, let us first examine the CLUSTER toolbox to establish its performance against both real and simulated data.\n\n(Supplementary Section\u00a05.1). We first examine \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) under the controlled environment of a numerically simulated population, capturing the dynamics of M\u00a0=\u00a0103 users with distributed weights wj(t), roaming around in a virtual town over the course of 365 days (Fig.\u00a02a,b). Users are partitioned into four different groups, depending on their domestic-occupational routine. For example, group 1 lives in Neighborhood 1 and commutes Downtown daily for work (Fig.\u00a02a,b, yellow); group 2 of the same neighborhood, in contrast, remains homeward bound (blue). These different daily cycles capture realistic routines observed in actual demographic data. We next, spread N\u00a0=\u00a013 BSs at random locations across town, and set, for each BS i, its randomly generated duty-cycle ui(t) (Fig.\u00a02c). As users move across town they connect preferably to the nearest active BS (Fig.\u00a02d). This determines each user\u2019s CP, as illustrated in Fig.\u00a02e.\n\nWe constructed an in silico population of M\u00a0=\u00a0103 users, split into four groups\u2014each with its distinct spatiotemporal behavior patterns. We also deployed N\u00a0=\u00a013 BSs across the town\u2019s three neighborhoods N1,N2,N3 and its downtown area. a At nighttime the majority of users stay within their neighborhoods. b During the day users go about their domestic-occupational routines: Group 1 (blue) and Group 4 (red) remain within their own neighborhoods; Group 2 (yellow) and Group 3 (green) commute daily to work in the downtown area. c Each BS switches from activity to inactivity based on its pre-assigned duty cycle ui(t); here presented for BS blue (circled in a). d The user CPs arise from the interplay between their commuting patterns and the activation cycles of their nearby BSs. Users switch between BSs as they travel from one locale to another (left), or because their nearby BS turned inactive (right). e The resulting CP of user green (circled in a), capturing the probability to connect with each BS as a function of time. The connection probabilities change slightly as the user moves around and enters the coverage area of different BSs. At the 8:\u00a000 to 16:\u00a000 interval, where BS blue is inactive, the CP changes abruptly from BS blue to focus mainly on BS yellow instead. The data in (d, e) are hidden from the network operators, who can only observe u(t) in (c) and the resultant aggregate loads x(t) at all BSs. f Applying \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) to the simulated data, the model detects nine distinct clusters that together account for 99% of user weights. These clusters represent sub-populations with predictably similar CP patterns. Shown are the CLUSTER results at midnight (t\u00a0=\u00a00). g The time-averaged CPs lc extracted for the nine clusters. As predicted, the clusters exhibit visibly different connection patterns (horizontal bar colors match BS colors in (a, b)). h By observing each cluster\u2019s CP we can infer the neighborhood from which the cluster users originate. For example, the CPs of Clusters 4,\u00a05 and 6 condense around BSs orange, blue and green. This suggests that they represent groups of users from N1. Indeed the total weight of these three clusters (solid bars on left) adds up to the weight of N1's inhabitants (orange and blue striped bars). Similar results are also observed for N2 and N3. The cluster numbers (1,\u00a02,\u00a0\u2026,\u00a09) are marked inside their corresponding solid bars; solid bar color matches cluster colors in panels f and g; striped bar color matches user-group colors in (a, b).\n\nNext, we extract the BS loads x(t), as they emerge from the combination of user location and BS availability, and use \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) to uncover the most likely CP-clusters. Note that in our simulation design, just like in the real-world, the clusters are not explicitly introduced into the data. Rather they arise endogenously from the fact that different users have distinct routines, driven by their place of habitat and commuting patterns. Such population dynamics lead to the emergence of natural clusters, that are roughly coordinated with our distinct population groups. We, therefore, test CLUSTER on its ability to retrieve these grouping patterns, without any access to individual user movements - relying on the observed u(t) and x(t) alone. To be clear, while the simulation does model individual usage and movement patterns, our CLUSTER analysis was deliberately rendered blind to that data, hence tracking only the accessible BS parameters u(t) and x(t).\n\nIn Fig.\u00a02f we show the time-averaged clustered weights wc, as predicted by \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\). The algorithm detected K\u00a0=\u00a09 distinct clusters that together account for 99% of the user weights; the remaining small clusters that amount to \u00a0~\u00a01% are ignored. Looking at their collective CPs Lc(t) we find that, indeed, each of these clusters has a unique connection pattern across the 13 BSs (Fig.\u00a02g). This is expressed through the dissimilarity in the BS (color) distribution across the nine inferred CP vectors, indicating that \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) successfully detected statistically distinct population groups.\n\nWe can now examine the extracted cluster CPs and link them to our simulated population. For example, clusters 2,\u00a03 and 9 distribute their CPs across the three BSs located in neighborhood N2. Therefore, they likely represent the inhabitants of this neighborhood, some of whom live closer to BSs purple and light-green (clusters 2,\u00a09), and some in the light and dark-green part of the neighborhood (cluster 3). Indeed, the weights of these three clusters combined sum up to the total weight of neighborhood 2 (Fig.\u00a02h, middle bars). A similar analysis helps retrieve also the remaining population groups in neighborhoods N1 and N3.\n\nTogether, these results demonstrate our algorithm\u2019s ability to successfully recover the hidden patterns in the observed BS load data. The algorithm not only detected the natural partition of the population into neighborhoods, but also exposed the distinct sub-groups within each neighborhood, based on their geographic locales and commuting patterns. Most crucially, it achieved this without relying on any individual user data, but only by tapping into the collective observable load patterns.\n\n(Supplementary Section\u00a05.3). Next, we conduct a similar analysis using empirical data from two real-world networks, a 5G network in Liuzhou and a 4G network in Cixi, both towns located in China (Fig.\u00a03). We begin with the Liuzhou dataset, which captures the behavior of M\u00a0\u2248\u00a07000 users over a span of about 60 days, at \u0394t\u00a0=\u00a03\u2009h resolution (i.e. 8 samples/day). The network comprises N\u00a0=\u00a0302 BSs, divided across five geographic regions (Fig.\u00a03a,b): Countryside (30 BSs, red), Outskirts (46 BSs, green), Downtown (102 BSs, blue), Residential (83 BSs, yellow), and Industrial (41 BSs, violet). For each region, we collected time-series data on the BS duty cycles u(t) and their corresponding loads x(t). For instance, the Countryside region includes 30 BSs monitored over 64 days, thus producing two sets of 30-dimensional time-varying vectors\u2014one for activation (u(t)) and one for load (x(t)). Both vectors span 512 time points (64 days \u00a0\u00d7\u00a08 samples/day). Sample traces of ui(t) and xi(t) for selected BSs\u2014one from each region\u2014are shown in Fig.\u00a03c,d, illustrating typical daily patterns. We also tracked the average population in each region throughout the day (Fig.\u00a03e), offering demographic snapshots that reflect temporal shifts in user distribution.\n\na The map of Liuzhou and its N\u00a0=\u00a0302 BSs (squares) spread across the town\u2019s five urban regions: Countryside (red), Outskirts (green), Downtown (blue), Residential (yellow) and Industrial (violet). Note that some BSs are deployed on the same tower, and hence some of the square symbols may represent more than a single BS. For each BS we collected data on the duty cycle ui(t) and load xi(t) over the course of \u00a0~\u00a02 months. Here we display a snapshot of xi(t) (circle size) and ui(t) (square fill) for five selected BSs on an average day at 9:00 AM. BS purple for example supports a relatively heavy load (large circle) and is active for roughly half of the 9:00 AM time-interval (partly filled square). Data was collected at \u0394t\u00a0=\u00a03\u2009h intervals, and hence ui(t) captures the fraction of these intervals in which BS i was active. b The number of BSs at each region and the time span of the collected data. All regions were tracked for \u00a0~\u00a02 months, except Residential, for which we only had access to 30 days worth of data. c Average daily activation cycle u(t) for the five BSs highlighted in (a). d Average daily load x(t) for these five BSs. In our analysis, we tracked u(t) and x(t) for all 302 BSs in the dataset. e The number of users vs. time as obtained from the five regions (line colors) across an average day. f We used \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) to decompose the town\u2019s M\u00a0\u2248\u00a07000 users into \\({{{\\mathcal{M}}}}\\)-clusters. At 9:00 AM on a typical day we observe a natural partition into 21 separate clusters, across the town\u2019s five regions (colors), ordered here by their collective cluster weights wc. g The predicted \\({{{{\\bf{x}}}}}_{{{{\\mathcal{M}}}}}(t)\\) as obtained from Eq. (4) vs. the experimentally observed xExp(t) collected from the Liuzhou data (average\u2014solid blue line; stochastic\u2014circles, 95% confidence interval\u2014shaded area). The results condense quite accurately around y\u00a0=\u00a0x (solid green line), demonstrating the power of CLUSTER to generate valid BS load forecasts. The mean-absolute error (MAE) is also shown. h The town of Cixi and its (N\u00a0=\u00a08) BS towers. i The predicted \\({{{{\\bf{x}}}}}_{{{{\\mathcal{M}}}}}(t)\\) vs. the experimentally observed xExp(t) as obtained from the Cixi data.\n\nIn principle, had we been able to track all individual user information, we could construct the complete connection preference matrix L(t) and the individual user weights w(t). To illustrate this consider the local population in Countryside at t\u00a0=\u00a09:\u00a000 AM, which, on average, amounts to 630 individuals (Fig.\u00a03e). This sub-population collectively connects to Countryside\u2019s 30 BSs. Therefore, tracking each of these users would provide us with a 30\u00a0\u00d7\u00a0630 matrix L(t) and a 630 dimensional vector w(t). These two observables, had we been able to access them, would together link the duty cycles u(t) with the measured loads x(t) via Eq. (2). Lacking direct access to these data, we instead use \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) to retrieve the clustered Lc(t) directly from the observed u(t),\u00a0x(t). The 630 users in Countryside at 9:\u00a000 AM, we find, break into 3 distinctive clusters, whose collective weights are shown in Fig.\u00a03f (red); the remaining 18 clusters, at that time of day, for the four other regions are also shown. Both the number of clusters K and their specific composition may vary across time, adapting to the evolving dynamics of the user population. Supplementary Table\u00a0S6 details the number of clusters identified in each region throughout the day.\n\nTaking the \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) inferred Lc(t) and wc(t), together with the empirically accessible u(t), we can now use Eq. (2) to predict the loads on all BSs through time. In Fig.\u00a03g do just that. We use \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) to construct \\({p}_{{{{\\mathcal{M}}}}}({{{\\bf{x}}}}(t)| {{{\\bf{u}}}}(t))\\), the probability distribution to observe loads x(t) given the empirically measured u(t). We then extract our predicted loads \\({{{{\\bf{x}}}}}_{{{{\\mathcal{M}}}}}(t)\\) from the inferred distribution as\n\nThe loads in (4) represent the ultimate outcome of our framework: taking only u(t) as input, we predict the anticipated loads \\({{{{\\bf{x}}}}}_{{{{\\mathcal{M}}}}}(t)\\) as output, absent any protected user information. Such prediction is only enabled thanks to our \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) extracted clusters. As Fig.\u00a03g shows, the predicted loads \\({{{{\\bf{x}}}}}_{{{{\\mathcal{M}}}}}(t)\\) (blue solid line) recover, quite accurately, the experimentally observed loads xExp(t) (green solid line). Some variability around the empirical loads is also observed (circles, shaded area), an inevitable outcome of the stochastic nature of our analytical construction. We quantify these discrepancies using the mean absolute error (MAE) of \\({{{{\\bf{x}}}}}_{{{{\\mathcal{M}}}}}(t)\\), which we find, in this dataset, to be MAE\u00a0=\u00a03.599\u00a0\u00d7\u00a010\u22123, capturing a mean discrepancy of 6.69% (see Supplementary Section\u00a06.3 for a rigorous precision analysis). The observed results clearly indicate that CLUSTER can successfully capture empirical user dynamics directly from the collective, low-resolution, non-restricted data accumulated at each BS.\n\nWe now turn to conduct a similar analysis on the 4G Cixi network data. This dataset confronts our algorithm with several challenges that were not present in Liuzhou. First, the database is an order of magnitude smaller, comprising a range of M\u00a0=\u00a0300\u2013800 users, depending on the examined time of day. It therefore exhibits smaller, and hence, noisier, statistics. Second in this dataset we do not have access to the load of each individual antenna. Instead, the data from all antennae on the same tower are aggregated, leaving us with only N\u00a0=\u00a08 BSs, each representing a tower with potentially several antennae. Finally, the modus operandi of the Cixi network control is to leave all BSs almost permanently active, i.e. ui(t)\u00a0=\u00a01 for all t. This excludes all information rooted in the activation variability of the BSs, that \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) can potentially use to enhance its predictive power. Still, despite these limitations, we find in Fig.\u00a03i that our predicted \\({{{{\\bf{x}}}}}_{{{{\\mathcal{M}}}}}(t)\\) (blue solid line) continues to successfully approximate the actual, empirically observed, load patterns (green solid line). This time, quite naturally, with a higher degree of uncertainty (circles, shaded area, MAE\u00a0=\u00a00.014).\n\nTo evaluate CLUSTER\u2019s performance in context, we consider several relevant alternatives, all designed to link x(t) to the operator controlled u(t), while bypassing the hidden individualized user data. This includes traditional linear regression methods,42 alongside more advanced deep learning approaches, based on neural networks and their more sophisticated variants43,44. Using the Liuzhou Countryside data as our benchmark, we find that CLUSTER achieves a smaller MAE as compared to all other examined methods (Fig.\u00a04a, black). This trend is quite consistent throughout all the daily time-intervals (Fig.\u00a04b), barring a few instances in which the Gaussian Process Regression (GPR) method45 (red) features a minor advantage over CLUSTER.\n\nWe consider six relevant methods designed to predict BS loads directly from their given duty cycles. For each method we extract the daily mean absolute error (MAE) between the predicted and the empirically observed loads. a The MAE as obtained from CLUSTER (black), Linear Regression (blue), Random Forest Regression (yellow), Support Vector Regression (green), Gaussian Process Regression (red), Neural Network (pink) and Transformer (violet). CLUSTER achieves the overall optimal score with an MAE of 2.604. b MAE vs. time on an average day. Apart from the two intervals at 3:00 AM and noon, CLUSTER consistently surpasses all other methods. c In the Galton board analogy, the level of clustering C is determined by the spread of the beads into funnels at the top of the board. An even spread represents C\u00a0\u2192\u00a00, i.e. no clusters, whereas a highly condensed bead distribution represents C\u00a0\u2192\u00a01, namely just one or two degenerate clusters. CLUSTER is best suited for the range in between these two extremes. The level of randomness R is captured by the number of pegs. In the limit R\u00a0\u2192\u00a01 we have a large number of pegs and the beads disperse randomly at the bottom of the board. When R\u00a0\u2192\u00a00 there are no pegs, and the beads advance deterministically from their top funnel to the corresponding bottom cell. d The performance gap \u0394MAE between CLUSTER and its leading competitor GPR, in the C,\u00a0R plane, as obtained from 100 simulations. As expected, CLUSTER performs best at intermediate levels of clustering in the range C \u2208 (0.2, 0.8) (dark blue region). We observe two distinct areas where GPR shows a slight advantage (R\u00a0~\u00a00.6, red areas). Note that in this plot, dark blue represents \u0394MAE\u00a0~\u00a010\u22123, while dark red is \u00a0~\u00a010\u22124, hence the two instances were GPR supersedes CLUSTER are minor compared the overall observed CLUSTER advantage. Comparison with the remaining methods of (a) appears in Supplementary Section\u00a05.4.\n\nInterestingly, CLUSTER and its leading contender, the GPR method, address this problem via fundamentally different modeling paradigms. GPR is a general nonparametric method that takes u(t) as input and directly predicts x(t) through a Gaussian process governed by a kernel function. It makes no explicit assumptions about the intermediate variable L(t), and therefore, despite its generality, it offers no interpretable insights into the hidden user CPs. In contrast, CLUSTER imposes a structural assumption on L(t), positing that it can break down into groups of users sharing common behavioral patterns. This design, which is perhaps less general, is specifically tailored to the anticipated characteristics of the user statistics\u2014particularly the tendency of social networks to form homogeneous clusters. Therefore, while GPR benefits from flexible model adaptation, CLUSTER derives its strength from its unique structural alignment with the specific nature of the problem.\n\nTo examine this, we simulated 100 distinct realizations of user-BS interaction with varying levels of randomness R, and clustering C (see Methods Section 5 and Supplementary Section\u00a05.4). To understand these two parameters, we refer, once again, to the Galton board analogy (Fig.\u00a04c). In this illustration the clusters originate in the discrete funnels spread out along the top of the board. Therefore, in a highly clustered system, i.e. where C\u00a0\u2192\u00a01, the users (beads) condense within a small number of funnels, thus following similar trajectories as they advance towards the bottom of the board. In the opposite limit of C\u00a0\u2192\u00a00, they disperse evenly across all funnels. Once the beads are distributed across the funnels, the randomness in their dynamics is controlled by the number of pegs. The system is fully deterministic, i.e. R\u00a0\u2192\u00a00 when there are no pegs, and fully random (R\u00a0\u2192\u00a01) in the limit where the number of pegs approaches infinity.\n\nIn the C,\u00a0R space, CLUSTER is most well-suited for intermediate levels of clustering, i.e. 0 \u2264 C \u2264 1. In this range, since C\u00a0>\u00a00, users can, indeed, be partitioned into meaningful clusters. As C approaches unity, however, we arrive at a system where all users fall within just 1 or 2 clusters, and hence the clustering becomes non-informative. A similar reasoning renders GPR most appropriate for intermediate levels of R. To observe this we measured \u0394MAE\u00a0=\u00a0MAEGPR\u00a0\u2212\u00a0MAECLR, the difference in MAE between the two methodologies. A positive \u0394MAE indicates that CLUSTER outperforms GPR, whereas a negative value implies superior performance by GPR.\n\nIn Fig.\u00a04d we present \u0394MAE for our 100 simulated scenarios, plotted in the C,\u00a0R parameter space. As expected, CLUSTER achieves peak performance at intermediate values of C, specifically within the range C \u2208 (0.2, 0.8), indicated by the dark blue area. Conversely, GPR shows a slight performance advantage around R\u00a0\u2248\u00a00.6 (red regions), once again, in line with our expectations. The crucial point is, that apart from these localized advantages for GPR, CLUSTER consistently outperforms across the entire remainder of the C,\u00a0R plane. Hence, it emerges as the preferred method under a broad range of conditions. A similar performance advantage holds in a systematic comparison of CLUSTER with all other evaluated methods (Supplementary Section\u00a05.4).\n\nTaken together, the results presented in Figs.\u00a02\u20134 clearly demonstrate CLUSTER\u2019s empirical relevance. We can now use its proven predictive power to gain insights towards optimal network planning.\n\nConsider a user j in an area covered by many BSs. This user will likely experience high quality and robust service, benefiting from sufficient connection redundancy. In j\u2019s CP this will be expressed through a non-localized dispersed lj(t), whose entries will be spread across a variety of nearby BSs. In contrast, if j is at an underserved area, with only a small number of nearby BSs, their CP will be localized, with almost all connection probabilities concentrated on j\u2019s few available BSs. The latter points to a service deficiency, and thus suggests that in these areas, where CPs tend to be localized, it is best to increase coverage and add BSs (or increase activation of existing BSs). Hence, once again, access to user CPs proves crucial for network management.\n\nWe, therefore, use CLUSTER to identify such patterns in the collective CP ensemble Lc(t). To achieve this we project all CPs on to a lower dimensional space \\({{{\\mathcal{S}}}}\\), using a combination of two well-established algorithms: the uniform manifold approximation and projection (UMAP) method and the classic k-means clustering algorithm (Supplementary Section\u00a06.2). In the resulting \\({{{\\mathcal{S}}}}\\)-projection, dispersed CPs will likely have much overlap, as they spread their weight over a large number of BSs. Hence, even if users belong to different \\({{{\\mathcal{M}}}}\\)-clusters under \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) (or \\({{{{\\mathcal{M}}}}}_{{{{\\rm{PLN}}}}}\\)) they may still be grouped together within the same \\({{{\\mathcal{S}}}}\\)-cluster. This results in intermixed clusters, i.e. \\({{{\\mathcal{S}}}}\\)-clusters that exhibit a combination of many different CP types, originating in distinct \\({{{\\mathcal{M}}}}\\)-clusters. In contrast, localized CPs will only condense with their own type, those who rely on the same scarce collection of BSs. In \\({{{\\mathcal{S}}}}\\), they will appear as homogeneous and isolated clusters, clearly separated from the primary intermixed cluster.\n\nTo observe this we, once again, simulated the population of a virtual town, featuring domestic and work-related regions (Supplementary Section\u00a05.2). The population included homeward bound individuals, who travel sporadically and irregularly (Fig.\u00a05a, green), alongside regular commuters who move daily from their neighborhood to their workplace (orange). To help examine the \\({{{\\mathcal{S}}}}\\), \\({{{\\mathcal{M}}}}\\) cluster relationship, here we used \\({{{{\\mathcal{M}}}}}_{{{{\\rm{PLN}}}}}\\) to divide the users into a pre-set number of clusters, setting K\u00a0=\u00a0M\u00a0=\u00a0103. Hence, in this simulation each cluster correspond to an individual simulated user.\n\na We constructed a virtual town with domestic and work-related regions, hosting a population of homeward-bound individuals and regular commuters (circles). The population is segregated into distinct \\({{{\\mathcal{M}}}}\\)-clusters (colors), comprising sets of users with correlated CPs. The town\u2019s BSs are also shown (squares). b We used the simulated load patters x(t) to extract a series of \\({{{\\mathcal{S}}}}\\)-projections at different times of the day. We observe a central intermixed \\({{{\\mathcal{S}}}}\\)-cluster (multi-color) coexisting alongside several homogeneous isolated \\({{{\\mathcal{S}}}}\\)-clusters, which correspond to distinct user groups (single color). These isolated \\({{{\\mathcal{S}}}}\\)-clusters, we predict, capture user groups with similar CPs, and hence they represent regional coverage gaps. At t\u00a0=\u00a00 we also observed a partly intermixed \\({{{\\mathcal{S}}}}\\)-cluster (inset), which combines two user groups (blue and green). c We focus on a specific \\({{{\\mathcal{S}}}}\\)-projection capturing the state of the system at t\u00a0=\u00a00, this time clustering the data onto three principal axes U1,\u00a0U2 and U3 (UMAP). We detect 5 isolated \\({{{\\mathcal{S}}}}\\)-clusters (dashed ellipses), 4 that are completely uniform and one that combines two groups (yellow circled). d Mapping these isolated \\({{{\\mathcal{S}}}}\\)-clusters back onto the town map we observe that they indeed correspond to local populations that condense around nearby BSs. Note the dual-colored \\({{{\\mathcal{S}}}}\\)-cluster (blue and green) residing precisely at the geographical overlap of the blue/green BSs. e The coverage gap G(x,\u00a0y) as obtained from the virtual town data. As predicted, we find that the highlighted regions in (d) (dashed ellipses) help detect areas with relatively large gaps (yellow/red patches). Hence, the interplay between the \\({{{\\mathcal{M}}}}\\)-clusters and the \\({{{\\mathcal{S}}}}\\)-clusters can, indeed, offer crucial network planning insights. f, g We ran the same analysis against the five regions of the Liuzhou data at midday (f) and midnight (g). Apart from Countryside at midday (bottom left) all \\({{{\\mathcal{S}}}}\\)-projections include cases of isolated \\({{{\\mathcal{S}}}}\\)-clusters. Mapping these \\({{{\\mathcal{S}}}}\\)-clusters back onto the Liuzhou town map we find that they, indeed, capture locales with suspected coverage gaps (red \u2297s). In some of these cases we can trace the implicated gaps to common human behavioral patterns. For example, in midday we observe a gap on a main avenue, which, quite likely, attracts large volumes of traffic during the day. Similarly, the absence of gaps in Countryside at daytime vs. the observed gap at night, is plausibly a consequence of the domestic nature of this region, whose main network activity occurs in the after-hours.\n\nIn Fig.\u00a05b, we present a series of \\({{{\\mathcal{S}}}}\\) projections captured at different times of the day. In these projections, each point corresponds to a user CP, and the spatial \\({{{\\mathcal{S}}}}\\)-clustering helps group these users based on their correlated spatiotemporal movement patterns. To highlight the CP similarity, we color all points according to each user\u2019s most-preferred BS (see Methods). Thus, a point\u2019s position reflects its \\({{{\\mathcal{S}}}}\\)-cluster membership, while its color indicates its \\({{{\\mathcal{M}}}}\\)-cluster affiliation. As expected, we find a combination of multi-color and mono-chromatic clusters. The former represent intermixed clusters, i.e. users in a confined spatial region that spread their loads across a variety of available BSs. The latter, however, capture isolated clusters, whose users are served by just one or two BSs. These isolated clusters, we argue, indicate potential coverage gaps.\n\nTo gain deeper insight we focus on a specific projection at t\u00a0=\u00a00\u2009h, this time using a 3-dimensional \\({{{\\mathcal{S}}}}\\) clustering (Fig.\u00a05c). The mapping features one central intermixed cluster (colorful), 4 clearly isolated mono-chromatic clusters (green, turquoise, yellow and red), and one dual-colored cluster (green/blue), which, given its low color diversity we also consider to be isolated. Next we project the \\({{{\\mathcal{S}}}}\\)-clusters back onto the two dimensional town map (Fig.\u00a05d). We find that the isolated clusters, indeed, correspond to geographically concentrated populations (dashed ellipses) that are served by a small number of BSs (highlighted). The dual-colored cluster (green-blue) captures a population that is at the boundary between two neighboring BSs, and therefore, quite expectedly, exhibits the observed mixture of two CPs.\n\nThe crucial point is that, according to our prediction, the observed isolated \\({{{\\mathcal{S}}}}\\)-clusters point to areas that experience lacking coverage. To test this we measured the coverage gap G(x,\u00a0y) at each coordinate on the map. This gap quantifies the difference between the user traffic at (x,\u00a0y) and the collective coverage afforded by all BSs at that point (Supplementary Section\u00a06.4). Fig.\u00a05e clearly shows that the detected regions corresponding to the isolated clusters (dashed ellipses) suffer from significant coverage gaps (yellow-red patches), and would, indeed, benefit from additional BSs in their vicinity. Hence, CLUSTER can offer direct insights into future network expansion.\n\nTo examine our analysis in an empirical setting, we once again return to our Liuzhou dataset, and its five regions (Fig.\u00a05f,g): Countryside (red), Outskirts (green), Downtown (blue), Residential (yellow) and Industrial (violet). We constructed five \\({{{\\mathcal{S}}}}\\)-projections for each of these regions, capturing the connectivity patterns at two distinctive times of the daily cycle - noon (Fig.\u00a05f, t\u00a0=\u00a012\u2009h) and midnight (Fig.\u00a05g, t\u00a0=\u00a00\u2009h). As expected, in Countryside at noontime we observe no isolated clusters, and hence no coverage gaps. This is likely due to the fact that there is little demand at these areas in the middle of the workday. In Outskirts we detect two isolated clusters, which indicate a locale with high demand (Fig.\u00a05f, circled). Indeed, we find that these clusters correspond to a main avenue, where there is likely high midday traffic (highlighted on map). In Downtown and Residential we find large intermixed clusters, capturing the concentration of the different demographics at work or at home during the day. We also detect two isolated clusters, one of which is dual-colored, consisting of BSs from both regions (enlarged projection). These smaller clusters capture the constant traffic between residence and downtown throughout the day (e.g., for errands), linking BSs from both regions in the user CPs. Finally, two isolated clusters appear in Industrial, indicating coverage gaps at the interface of that region with Downtown (circled).\n\nThe above CP-patterns are consistent with the population\u2019s peak daytime routine, but then they change dramatically when we shift to midnight (Fig.\u00a05g). Now we observe the emergence of multiple isolated clusters in all areas, including also the peripheral areas Countryside and Residential. This reflects the nocturnal nature of the population, as traffic and mobility cease during the night and all residents remain at their homes. Therefore, with the majority of individuals remaining stationary, their CPs become localized, focusing on just one or two nearby BSs, thus leading to the observed isolated homogeneous \\({{{\\mathcal{S}}}}\\)-clusters.\n\nInterestingly, our methodology detected a significantly higher density of coverage gaps in the simulated network compared to the empirical data. As shown in Fig.\u00a05e, the simulated system with N\u00a0=\u00a015 BSs revealed five underserved regions\u2014a gap-to-BS ratio of 1:3. In contrast, the empirical data featured only 10 gaps across N\u00a0=\u00a0302 BSs, corresponding to a much lower ratio of 1:30\u2014an order of magnitude smaller than in simulation. This outcome is expected: in our simulation, BSs were placed randomly, a deliberate choice aimed at generating a large number of gaps for CLUSTER to detect. As opposed to that, the real-world data from Liuzhou reflects a professionally planned network, where BSs are strategically deployed to minimize such gaps. The fact that this distinction between random and planned deployment is also reflected in our results further underscores CLUSTER\u2019s predictive power.\n\nWhen a BS i malfunctions its user load is redistributed, transferred to nearby BSs. This user transfer can be predicted via \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\), by detecting a user cluster that relied on i and will now transition to its next preferred BS j. This results in a potential uptick in the load observed at j. The intensity of j\u2019s increased load \\({\\tilde{x}}_{j}(t)\\) helps quantify i\u2019s criticality. As long as j\u2019s response is small, service is maintained, and i is said to have little impact on the network functionality. If, however, j\u2019s load spikes significantly following i\u2019s failure, the system may observe service decline, and hence i is deemed critical. We can, therefore quantify a BS\u2019s criticality by tracking its user load transfer to all other BSs46,47,48,49. This leads to\n\nwhere u\u2212i(t) represents the event of i\u2019s failure and \\({f}_{j}(\\tilde{x}| Y)\\) is the probability density that \\({x}_{j}(t)\\in (\\tilde{x},\\tilde{x}+{{{\\rm{d}}}}\\tilde{x})\\), conditional on event Y. The integral on the r.h.s. of (5) quantifies the expected increase in the load of BS j given the current loads x(t), the event of i\u2019s failure u\u2212i(t), and the load transfer patterns from i to j as predicted by \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\). Normalized by the current xj(t), the expression in the summation captures the relative increase in j\u2019s load, which is then aggregated over all j\u00a0\u2260\u00a0i. We, therefore, have Gi\u00a0\u2192\u00a00 if i\u2019s load transfer has no impact on any other BS (\\(\\tilde{x}-{x}_{j}(t)\\to 0\\)), and Gi\u00a0>\u00a00 if i\u2019s failure causes a surge in the load of some BSs.\n\nWe begin by extracting Gi(t) for all BSs in our simulated dataset (Fig.\u00a06a, snapshot at t\u00a0=\u00a00 hours), dividing them into low risk (Gi(t)\u00a0<\u00a01, blue), moderate (1 \u2264 Gi(t) \u2264 2, green) and critical (Gi(t)\u00a0>\u00a02, red). These risk values, however, change throughout the day, as user CPs and weights evolve. To observe this we show in Fig.\u00a06b the daily averaged number of BSs at each category vs. time. We observe an increase in moderate and critical BSs that aligns, quite expectedly, with the peak morning and afternoon hours (red shaded).\n\na Users (circles) and BSs (squares) in our virtual town at t\u00a0=\u00a00. The BS criticality Gi(t) is also shown (inner circles), indicating BSs at low (blue), moderate (green) and critical (red) risk levels. b The daily averaged number of BSs at each risk level: critical (red, top), moderate (middle, green) and low (bottom, blue). As expected, during the peak hours (red shaded), many low risk BSs turn moderate (green maxima, blue minima), as traffic in designated areas increases. At night time, some BSs become critical (top panel, left), likely due to the concentration of users in low coverage residential areas. c The density function Pt(G) vs. G as obtained at four different time points. We observe a majority of low risk BSs (peak around G\u00a0~\u00a01), alongside a small minority of critical BSs (secondary peak to the right). This indicates that the criticality risk is condensed on a small fraction of BSs. d The daily-averaged criticality Gi(t) (solid lines) and load xi(t) (dashed lines) across the town\u2019s N\u00a0=\u00a015 BSs. In most cases, criticality is highly correlated with load (e.g., BS 9), indicating a load-based risk (marked in a). However, in BS 5, for example, the two are uncorrelated. This hints that BS 5's risk is primarily due to its neighbors' capacity, rather than its own load, capturing an environment-driven risk. Most plots exhibit a signature of the population\u2019s diurnal patterns, with some BSs peaking during the working hours, likely around occupational locales, and others peaking mainly at night, driven by domestic intervals. e, f The criticality of all BSs in the five regions of Liuzhou, as obtained from our empirical dataset at both midday (e) and midnight (f). Criticality levels are indicated by color, with a specific color-bar for each region (shown on top/bottom of map). g\u2013k The density Pt(G) at midday (solid lines) and midnight (dashed lines) in each of the five regions. Similarly to our simulated data, also here we observe a majority of low risk BSs, with a tail of risky stations extending to the right.\n\nIn Fig.\u00a06c we show Pt(G), the probability density to observe Gi(t) \u2208 (G,\u00a0G\u00a0+\u00a0dG), as obtained for four different times of the day. The majority of BSs, we find, peak around Gi(t)\u00a0~\u00a01.0, i.e. low to moderate risk. Yet, we also observe a consistent minority of critical BSs, captured by the secondary peak at the r.h.s. of the plot. We observe the highest criticality at midday, e.g., at t\u00a0=\u00a012 (purple), likely due to peaking activity coupled with the fact that users from various locations condense around occupational areas. In all cases, we find that the criticality risks are condensed on a small minority of BSs, indicating that relatively minor interventions can have a major impact on the network resilience. Of course, to take advantage of this fact, we must identify where such interventions are required, namely, pinpoint the critical BSs. This is precisely the power of our CLUSTER analysis, that it can help detect, among the many BSs, which are the network Achilles heels that must be reinforced.\n\nIn Fig.\u00a06d we present the average daily risk cycle of all BSs (solid lines) superimposed with their observed loads x(t) (dashed lines). Interestingly, the critical stations are divided into two types: in BS 9, for example, G9(t) is strongly correlated with the BS load x9(t). Hence, 9\u2019s criticality is rooted in its high traffic volume, which must be redistributed among its neighboring BSs. Such load-based criticality takes a sharp dip at times of the day when 9 experiences lower demand. In contrast, BS 5 exhibits a consistent G5(t), largely detached from its load x5(t). This form of criticality is driven by the 5\u2019s neighborhood, which has typically low demand and hence displays a high sensitivity to 5\u2019s removal. Identifying these two types of criticality\u2014load-based vs. environment-driven, offers direct insight on mitigation. For BS 9 it is best to add an additional BS in the same area, that will help ease its load. In 5\u2019s case, however, it seems the optimal strategy would be to increase the potential load capacity of its neighbors, rather than that of 5 itself, thus helping the neighboring BSs successfully absorb 5\u2019s potentially redistributed load.\n\nNext, we extract Gi(t) from our empirical Liuzhou dataset, examining its five regions at noon and at midnight (Fig.\u00a06e,f). In Countryside, especially in isolated areas, BSs exhibit higher criticality due to sparse BS installation. In Residential, high Gi(t) BSs are often found in more densely populated regions. This observation is consistent with the urban residential patterns in China\u2019s cities, where most residents live in high-density communities within residential buildings, resulting in clusters of high user concentration. Comparing Gi(t) across regions (Fig.\u00a06g-k), we detect more critical BSs in Residential and Downtown at midnight, consistent with simulation results and indicative of the imbalanced geographical distribution of user homes. Interestingly, Pt(G) in Industrial remains nearly constant, possibly due to round-the-clock factory operations.\n\nNetwork planning is a complex, multi-faceted process that must balance service optimization with a range of additional factors, including environmental constraints, economic feasibility, and even geopolitical considerations. Among these diverse concerns, CLUSTER is designed to address the technical dimension \u2014 specifically, predicting BS loads, identifying potential service deficiencies, and assessing the network\u2019s response to failures or interventions. In this capacity, CLUSTER provides professional and technical guidance for network optimization but is not intended to function as a standalone decision-making tool. Rather, it is meant to support and empower planners by enabling more informed decisions that appropriately weigh technical insights alongside economic, social, and geographical priorities\n\nAnother practical consideration is the computational complexity of CLUSTER, particularly how it scales with the number of BSs (N), the covered time period (T\u2009), and the volume of users (M). In Supplementary Section\u00a06.6, we demonstrate that the algorithmic complexity of \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) grows as \\({{{\\mathcal{O}}}}({(NT\\log M)}^{5/4})\\). This reflects a slightly superlinear dependence on N and T, and a slow, logarithmic growth with respect to the user base M.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63504-0/MediaObjects/41467_2025_63504_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63504-0/MediaObjects/41467_2025_63504_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63504-0/MediaObjects/41467_2025_63504_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63504-0/MediaObjects/41467_2025_63504_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63504-0/MediaObjects/41467_2025_63504_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63504-0/MediaObjects/41467_2025_63504_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Communication networks are emerging as one of our most crucial infrastructure systems, having to address the consistently growing traffic and the proliferating diversity of linked devices. These trends are expected to continue in the coming years, requiring us to develop efficient tools to predict, manage and optimize the network efficiency. CLUSTER helps achieve these goals by (i) predicting the expected loads, (ii) offering guidelines for the network expansion, (iii) detecting potentially critical BSs and (iv) extracting the interplay between user demand (x(t) and the BS operational cycles (u(t)). Most crucially, the method fulfills these objectives without compromising user privacy.\n\nOur analysis found that CLUSTER provides exceptionally accurate predictions, both in controlled simulations and against real-world communication data. This success rests on the fact that despite the diversity of users and their potentially idiosyncratic CPs, there are recurring behavioral patterns, rooted in people\u2019s collective life cycles, that our method is able to tap into and exploit. This highlights the potential limitations of CLUSTER, whose effectiveness may decline in the face of unexpected variations in user behavior, or under rapid technological developments that may cause subsequent changes in user CPs. Figure\u00a04, for example, demonstrated that when randomness prevails over clustering, the method\u2019s performance is slightly degraded.\n\nTherefore, we urge future research to focus on enhancing the adaptability and resilience of CLUSTER and other analogous frameworks. For example, integrating real-time data analytics and other machine learning algorithms to continuously update and refine the model. Furthermore, exploring the application of CLUSTER in different geographical and cultural contexts could provide broader insights into its effectiveness and areas for improvement. The integration of emerging technologies, such as transformers, could further augment its predictive capabilities, offering a more dynamic and responsive approach to network optimization.\n\nCLUSTER protects user data through aggregation, eliminating the need to track individual users and thereby limiting access to private information. While effective, this method is not foolproof\u2014some privacy risks may persist even when working with low-resolution aggregate data. We therefore recommend using CLUSTER alongside additional safeguards such as differential privacy or federated learning. Another useful measure is to enforce a minimum cluster size, ensuring that each cluster remains sufficiently aggregated.\n\nIn a broader perspective, from a methodological point of view, CLUSTER\u2019s core innovation lies in its ability to translate aggregate data into actionable predictions. Such an analytical tool is potentially applicable beyond the realms of telecommunication networks. Indeed, the method\u2019s capacity to infer high-dimensional data structures from limited, anonymized datasets positions it as a versatile tool for a variety of fields facing similar challenges. For instance, in healthcare, CLUSTER could help revolutionize patient data analysis, enabling the identification of latent health patterns and treatment responses from fragmented medical records - all without violating patient confidentiality. In urban planning, it could assist in analyzing pedestrian flow and vehicle traffic patterns to inform infrastructure development and public space management. Finally, in environmental science, CLUSTER could detect complex eco-dynamical patterns, using sparse and aggregated observational data50,51,52.\n\nTaken together, CLUSTER\u2019s ability to bridge the gap between high-dimensional latent structures and low-dimensional observations offers crucial opportunities for diverse applications from a range of disciplines. As the world continues to embrace the digital revolution, in health, technology and social sciences, we believe that the need for CLUSTER and CLUSTER-like methodologies will continue to grow, finding applications in an ever-growing variety of fields.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "(Supplementary Sections\u00a03.1 -\u00a03.6). CLUSTER is initiated using a Dirichlet process mixture model (DPMM), which is then iterated until we obtain a satisfactory prediction from u(t) to x(t). The \\({{{{\\mathcal{M}}}}}_{{{{\\rm{BNP}}}}}\\) model structure follows\n\ntaking \\(c \\sim {{\\mathrm{Gamma}}}\\,\\left(1,1\\right)\\). The remaining parameters are \\({{{\\bf{U}}}}\\left(t\\right)={{\\mathrm{diag}}}\\,({{{\\bf{u}}}}\\left(t\\right))\\), \\({{{\\bf{L}}}}\\left(t\\right)=[{{{{\\bf{l}}}}}^{1}\\left(t\\right),{{{{\\bf{l}}}}}^{2}\\left(t\\right),\\ldots ]\\) and \\({{{\\bf{D}}}}\\left(t\\right)={{\\mathrm{diag}}}\\,{({{{{\\bf{1}}}}}_{N}^{\\top }{{{\\bf{U}}}}\\left(t\\right){{{\\bf{L}}}}\\left(t\\right))}^{-1}\\). To initiate the weights we use the stick breaking process53 \\({{{\\bf{w}}}}\\left(t\\right) \\sim {{\\mathrm{StickBreaking}}}\\,\\left(\\alpha \\right)\\) with the parameter \\(\\alpha \\sim {{\\mathrm{Gamma}}}\\,\\left(1,1\\right)\\).\n\nThe CPs \\({{{{\\bf{l}}}}}^{i}\\left(t\\right)\\) follow stochastic temporal dynamics, governed by three constraints: short-term continuity, long-term forgetfulness, and intermediate step-by-step consistency. These conditions help capture the evolving nature of user CPs over time:\n\nShort-term continuity. For incremental time-steps we require that the CPs evolve smoothly over time. This is expressed via the condition\n\nindicating that as \u0394t\u00a0\u2192\u00a00 the two subsequent CPs, l(t) and l(t\u00a0+\u00a0\u0394t), coincide with probability Pr\u00a0=\u00a01. The probabilistic formulation allows for the possibility of occasional abrupt changes due to unpredictable events, as long as these events have measure zero, and hence do not affect Pr.\n\nLong-term forgetfulness. In the opposite limit, as the interval \u0394t\u00a0\u2192\u00a0\u221e, we expect all correlation between l(t) and l(t\u00a0+\u00a0\u0394t) to vanish, and hence\n\nHere q( \u22c5 \u2223 \u22c5 ) is the transition kernel and r( \u22c5 ) describes the stationary distribution, reflecting complete uncertainty about the far-future CPs. Absent any specific information about the long-term behavior of \\({{{\\bf{l}}}}\\left(t+\\Delta t\\right)\\), we set \\(r\\left(\\cdot \\right)={{\\mathrm{Dirichlet}}}\\,\\left({{{\\bf{1}}}}\\right)\\).\n\nIntermediate consistency. For intermediate timescales, we require consistency, often expressed using the Chapman-Kolmogorov equation\n\nThis condition ensures that the transition probabilities over a series of consecutive intervals \u0394ti are consistent with those over the entire period \u2211i \u0394ti.\n\nTo satisfy conditions (i) - (iii), in our implementation, we used the transition kernel\n\nwhere \u03b4( \u22c5 ) represents the Dirac delta function, and \u03bb\u00a0>\u00a00 quantifies the decay rate of temporal correlations. In Supplementary Section\u00a03.6 we show that this kernel satisfies al three conditions above. In the limit where \u0394t \u226b 1/\u03bb the exponential functions in (7) vanish and the r.h.s. approaches the stationary distribution r(l(t\u00a0+\u00a0\u0394t)). This captures the long-term limit where temporal correlations vanish. In our data, the observations occur at regular time intervals, and hence we have \u0394t\u00a0=\u00a0Const. This allows is to simplify (7) into\n\nwhere s\u00a0=\u00a0e\u2212\u03bb\u0394t. The parameter s captures the system\u2019s memory, ranging from a memory-less model where s\u00a0\u2192\u00a00 to one with enduring memory, in which case s\u00a0\u2192\u00a01.\n\n(Fig.\u00a02, Supplementary Section\u00a05.2). In the numerical experiment of Fig.\u00a02, we simulate a town of N\u00a0=\u00a015 BSs, serving M\u00a0=\u00a0103 users over a span of T\u00a0=\u00a0365 days. Using a resolution of \u0394t\u00a0=\u00a00.25 hours, the resulting dataset comprises a total of 35,\u00a0040 entries for the N-dimensional u(t) and x(t). We then partitioned the dataset into two segments: a training set covering the first 300 days, and a test set encompassing the remaining 65 days. Together this captures a 5:1 train to test ratio.\n\n(Fig.\u00a03, Supplementary Section\u00a05.3). We tested CLUSTER using two real-world datasets: one from Liuzhou and the other from Cixi, both located in China. For the Liuzhou dataset, we consider N\u00a0=\u00a0302 BSs serving, on average, M\u00a0\u2248\u00a07,\u00a0000 users per day, over a span of T\u00a0=\u00a060 to 64 days, depending on the region. The only exception is Residential, for which we only had access to 30 days worth of data. Using a resolution of \u0394t\u00a0=\u00a03 hours for the 5 regions, the resulting dataset comprises 6,\u00a0762 entries for the N-dimensional u(t) and x(t). For the Cixi dataset, we have a network of N\u00a0=\u00a08 BSs, supporting M\u00a0=\u00a0300 to 800 users daily across T\u00a0=\u00a030 days. At a resolution of \u0394t\u00a0=\u00a01 hour, this yields 597 entries for the N-dimensional u(t) and x(t). Both datasets were partitioned by a 9:1 train to test ratio.\n\n(Fig.\u00a05, Supplementary Sections\u00a06.1, 6.2). After obtaining the CP vectors we use UMAP to project them onto a low-dimensional space, typically two or three-dimensions. The advantage of UMAP is that it allows us to incorporate a flexible distance metric between the CPs54. This is important since the CP vectors represent probabilities, and therefore, their similarity is more suitably treated by correlation-based metrics rather than the classic Euclidean distance55. To avoid artificial \\({{{\\mathcal{S}}}}\\)-clusters, a risk that is often inherent to UMAP, we cross-validate our UMAP clusters with those obtained from k-means. The latter is highly reliable, whereas the former helps incorporate flexible distance metrics. We, thus, arrive at two-step combined clustering algorithm:\n\nUMAP projection. Employing UMAP to detect the clusters. Each CP is assigned its unique UMAP-cluster affiliation.\n\nk-means validation. We corroborate the UMAP-clusters using k-means. Here, the number of clusters, as well as their specific demarcations, are determined using the elbow method56. A cluster is conclusively identified when it consistently appears in both the UMAP and the k-means analyses.\n\nThis dual step approach ensures that the UMAP detected \\({{{\\mathcal{S}}}}\\)-clusters, indeed, represent a true partitioning within the system\u2019s authentic feature space.\n\nOnce the \\({{{\\mathcal{S}}}}\\)-clusters are mapped, we seek to identify the intermixed vs. isolated \\({{{\\mathcal{S}}}}\\)-clusters. For the ith CP in \\({{{\\mathcal{S}}}}\\)-cluster c, we denote the most preferred BS via mi,c, writing\n\nHere \\({l}_{j}^{i,c}\\) represents the likelihood to connect to BS j, as appears in CP i within cluster c. We visualize these preferred BSs by color coding all points i in Fig.\u00a05 to represent their mi,c. Hence, each point\u2019s position in the two or three dimensional UMAP space is determined by their S-cluster membership, yet its color represents their most preferred BS. This allows us to characterize all \\({{{\\mathcal{S}}}}\\)-clusters based on the set of all their preferred BSs\n\nwhere Mc is the number of users in cluster c. The cardinality of \\({{{{\\mathcal{M}}}}}^{c}\\), visualized through the color-diversity within c, represents the number of unique preferred BSs within that \\({{{\\mathcal{S}}}}\\)-cluster. An intermixed\\({{{\\mathcal{S}}}}\\)-cluster has \\(| {{{{\\mathcal{M}}}}}^{c}| > 1\\); an isolated cluster has \\(| {{{{\\mathcal{M}}}}}^{c}|=1\\). In our analysis we detected one unambiguously intermixed cluster, 4 clearly isolated clusters, and a remaining cluster with \\(| {{{{\\mathcal{M}}}}}^{c}|=2\\) (green and blue). Given its relatively low color-diversity, we classified also this last cluster as isolated.\n\n(Fig.\u00a04c,d, Supplementary Section\u00a05.4). To generate the \u0394MAE plot we simulated 100 distinct systems with different levels of randomness and clustering. To control the level of clustering we consider an infinite array of funnels onto which we distribute beads via a stick-breaking process53 with parameter c. This results in a sequence of funnel weights \\({\\{{w}_{i}\\}}_{i=1}^{\\infty }\\), that can be localized on a small number of funnels (high clustering) or evenly dispersed across all funnels (low clustering). To quantify this we use the funnel entropy\n\nwhich approaches zero in the limit where all the weight is placed on a single funnel, and has HF\u00a0\u2192\u00a0\u221e when they are randomly scattered throughout. To normalize this parameter, we introduce it into a sigmoid function of the form\n\nHence in the limit H\u2009F\u00a0\u2192\u00a00 we have C\u00a0=\u00a0f1(HF)\u00a0=\u00a01, indicating maximal clustering, whereas when H\u2009F\u00a0\u2192\u00a0\u221e we have C\u00a0=\u00a00, i.e. no clustering. In our analysis, for each set value of c, we generate Q realizations and estimate HF via\n\nwhere wi,q denotes the weight of the i-th funnel in the q-th realization, and N is the total number of funnels.\n\nTo control the randomness level we consider beads originating in a single funnel at position \\({{{\\bf{m}}}}=\\left(0,\\ldots,1,\\ldots,0\\right)\\). After passing through the Galton board, we extract their distribution across the bottom cells from a Dirichlet process following\n\nHence, when r\u00a0=\u00a00 the density at the bottom of the board is concentrated fully on m, capturing a state of zero randomness, where the beads fall directly from their funnel to the corresponding cell. In the opposite limit of r\u00a0\u2192\u00a0\u221e we have the Dirichlet process dominated by 1N, a uniform spread over all cells, in which randomness prevails.\n\nOnce again, we characterize the randomness by measuring the entropy at the bottom of the board\n\nIn the limit of no randomness (r\u00a0=\u00a00) we have \\(p({{{\\bf{x}}}})={{\\mathrm{Dirichlet}}}\\,\\left({{{\\bf{x}}}}\\left\\vert \\right.{{{\\bf{m}}}}\\right)\\), yielding a Dirac delta function fully concentrated on the unit vector m. This leads to HB\u00a0\u2192\u00a0\u2212\u00a0\u221e. For the fully random limit (r\u00a0\u2192\u00a0\u221e), the bottom bead distribution follows \\(p({{{\\bf{x}}}})={{\\mathrm{Dirichlet}}}\\,\\left({{{\\bf{x}}}}\\left\\vert \\right.{{{{\\bf{1}}}}}_{N}\\right)\\), for which we write HB\u00a0=\u00a0XN, the entropy of a uniform Dirichlet process of dimension N. Hence the entropy at the bottom of the Galton board, which characterizes the level of randomness in the system, is bounded within HB \u2208 ( \u2212 \u221e,\u00a0XN). This allows us to extract the normalized randomness parameter R\u00a0=\u00a0f2(HB), by setting\n\nwhich ranges from zero, under no randomness, to unity, when randomness is maximal.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data used in this study are available on the GitHub repository at https://github.com/dongxu-lei/CLUSTER.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The codes used in this study are available on the GitHub repository at https://github.com/dongxu-lei/CLUSTER. https://doi.org/10.5281/zenodo.1672239057.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Manyika, J. & Roxburgh, C. The great transformer: The impact of the Internet on economic growth and prosperity. McKinsey Glob. 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B.B. wishes to acknowledge support by the Israel Science Foundation grant no. 499/19, the Israel-China ISF-NSFC joint research program grant no. 3552/21, and by the VATAT grant for data science research.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Dongxu Lei, Xiaotian Lin, Xinghu Yu, Zhihong Zhao.\n\nResearch Inst. of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China\n\nDongxu Lei,\u00a0Fangzhou Liu\u00a0&\u00a0Huijun Gao\n\nYongjiang Laboratory, Ningbo, China\n\nXiaotian Lin\u00a0&\u00a0Songlin Zhuang\n\nNingbo Institute of Intelligent Equipment Technology Company Ltd., Ningbo, China\n\nXinghu Yu\n\nResearch Inst. of Interdisciplinary Intelligent Science, Ningbo University of Technology, Ningbo, China\n\nZhihong Zhao\u00a0&\u00a0Stefano Boccaletti\n\nDepartment of Mechanical Engineering, University of Victoria, Victoria, Canada\n\nYang Shi\n\nDepartment of Mathematics, Bar-Ilan University, Ramat-Gan, Israel\n\nBaruch Barzel\n\nThe Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel\n\nBaruch Barzel\n\nCNR\u2014Institute of Complex Systems, Sesto Fiorentino, Italy\n\nStefano Boccaletti\n\nSino-Europe Complexity Science Center, North University of China, Taiyuan, China\n\nStefano Boccaletti\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.G., B.B. and S.B. conceived the research\u00a0and\u00a0coordinated the study. D.L., X.L. and\u00a0S.Z. developed the methods.\u00a0D.L., X.L., X.Y. and Z.Z. implemented the CLUSTER algorithm. D.L., X.L., F.L. and Y.S. carried out the data analysis and simulations. All authors wrote the manuscript.\n\nCorrespondence to\n Songlin Zhuang or Huijun Gao.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Lei, D., Lin, X., Yu, X. et al. Privacy preserving optimization of communication networks.\n Nat Commun 16, 8501 (2025). https://doi.org/10.1038/s41467-025-63504-0\n\nDownload citation\n\nReceived: 23 August 2024\n\nAccepted: 21 August 2025\n\nPublished: 26 September 2025\n\nVersion of record: 26 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63504-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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molecular and developmental evidence for cell-type diversity in cnidarian mechanosensory neurons", + "journal": "Nature Communications", + "published": "10 February 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM4_ESM.avi" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM5_ESM.mov" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM6_ESM.mov" + }, + { + "label": "Supplementary Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM7_ESM.mov" + }, + { + "label": "Supplementary Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM8_ESM.mov" + }, + { + "label": "Supplementary Movie 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM9_ESM.mov" + }, + { + "label": "Supplementary Movie 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM10_ESM.mov" + }, + { + "label": "Supplementary Movie 8", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM11_ESM.mov" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM12_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_MOESM13_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Evolutionary developmental biology", + "Touch receptors", + "Zoology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4664512/v1.pdf?c=1739279349000", + "research_square_link": "https://www.researchsquare.com//article/rs-4664512/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-56115-2.pdf", + "preprint_posted": "26 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Deploying a conserved mechanosensory neuron known as the concentric hair cell, cnidarians have evolved diverse mechanoreceptors from hydroid filiform tentacles to jellyfish statocysts. However, it is unknown whether cnidarian mechanoreceptor evolution has relied solely on repurposing a single ancestral mechanosensory neuron type. Here we report evidence for cell-type diversity of mechanosensory neurons in sea-anemone cnidarian Nematostella vectensis. Uncovered in the ectoderm of feeding tentacles are conventional type I hair cells and previously unrecognized type II hair cells differing in the structure of apical sensory apparatus and synapses. Moreover, we identify TRP channel-encoding gene polycystin-1 as a type-II-hair-cell-specific essential mediator of gentle touch response. Ontogenically, type I and type II hair cells derive from distinct postmitotic precursors that begin forming at different phases of larval development. Taken together, our findings suggest that anatomically, molecularly, and developmentally distinct mechanosensory neurons diversified within Cnidaria, or prior to the divergence of Cnidaria and Bilateria.Biological sciences/Evolution/Evolutionary developmental biologyBiological sciences/Neuroscience/Somatosensory system/Touch receptorsBiological sciences/Zoology", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SuMovie1Projectionsofkaede.live40xind1250micronscalebarwitharrow.aviSupplementary Movie 1SuMovie2serialzsectionsofbidirectionalsynapsewithscalebar500nm.movSupplementary Movie 2SuMovie3bidirectionalsynapsereconstruction.movSupplementary Movie 3SuMovie4typeIIhaircellwithsynapseslateralviewmovie.movSupplementary Movie 4SuMovie5gentletouchresponseshortwitharrows1.movSupplementary Movie 5SuMovie6gentletouchpkd1f3behaviororaltop.movSupplementary Movie 6SuMovie7gentletouchpkd1f32.movSupplementary Movie 7SuMovie8harshtouchpkd1f33.movSupplementary Movie 8Supplementaryinformation.docxSupplementary informationNCOMMS2441553Ars.pdfReporting SummarySourcedataBaranyketal.xlsxSource data", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Deploying a conserved mechanosensory neuron known as the concentric hair cell, cnidarians have evolved diverse mechanoreceptors from hydroid filiform tentacles to jellyfish statocysts. However, it is unknown whether cnidarian mechanoreceptor evolution has relied solely on repurposing a single ancestral mechanosensory neuron type. Here we report evidence for cell-type diversity of mechanosensory neurons in sea-anemone cnidarian Nematostella vectensis. Uncovered in the ectoderm of feeding tentacles are conventional type I hair cells and previously unrecognized type II hair cells differing in the structure of apical sensory apparatus and synapses. Moreover, we identify TRP channel-encoding gene polycystin-1 as a type-II-hair-cell-specific essential mediator of gentle touch response. Ontogenically, type I and type II hair cells derive from distinct postmitotic precursors that begin forming at different phases of larval development. Taken together, our findings suggest that anatomically, molecularly, and developmentally distinct mechanosensory neurons diversified within Cnidaria, or prior to the divergence of Cnidaria and Bilateria.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "United by the presence of a stinging cell type known as the cnidocyte, Cnidaria is an early-branching animal group represented by sea anemones, corals and jellyfishes. Cnidaria split from its sister group Bilateria (e.g., chordates and arthropods) in the late Precambrian1 and further diverged into two major lineages with distinct life cycles: Anthozoa and Medusozoa. Anthozoa consists of Octocorallia (sea pens and soft corals) and Hexacorallia (sea anemones and scleractinian corals), while Medusozoa comprises Staurozoa (stalked jellyfishes), Hydrozoa (hydromedusae, hydroids and siphonophores), Scyphozoa (true jellyfish), and Cubozoa (box jellyfish)2,3. Cnidarian embryogenesis typically generates a free-swimming planula larva that transforms into a sessile polyp with a single oral opening surrounded by feeding tentacles. Polyps proceed to sexual maturity in Anthozoa, while in Medusozoa they undergo another round of transformation to produce free-swimming medusae that are equipped with feeding tentacles and become sexually mature adults. The cnidocyte-loaded tentacles of polyps and medusae function as sensory structures, detecting water vibration and chemicals emanating from nearby prey to coordinate its capture. In addition, some hydroids develop specialized mechanosensory tentacles known as the filiform tentacles that are thought to mediate a bending reaction of the animal in response to mechanical stimulation of the tentacle4, while a variety of medusae form gravity sensors (e.g., statocysts) to control the balance against gravity5,6,7. Although it is evident that cnidarians evolved diverse mechanosensory structures in parallel with Bilateria, little is known about the cell-type diversity of cnidarian mechanosensory neurons.\n\nIn cnidarian mechanosensory structures such as tentacles,\u00a0the primary sensory neuron thought to transduce mechanical stimuli (e.g., water vibration or touch) is the concentric hair cell that resides in the ectodermal epithelium. The concentric hair cell is characterized by an apical sensory apparatus consisting of a single cilium surrounded by one or multiple collars of stereovilli/microvilli8,9; stereovilli (or stereocilia) are distinguished from microvilli based on the presence of actin rootlets. The concentric hair cell extends basal neuronal processes that likely facilitate the transmission of mechanosensory information to other cells/tissues such as cnidocytes8 and nerve rings of hydrozoan jellyfish (e.g., 5,6,9,10), possibly through unusual synapses with large synaptic vesicles (160\u20131100\u2009nm in diameter)11. Structural evidence for conventional chemical synapses for signal transmission from concentric hair cells to other cells is currently lacking. Nevertheless, consistent with the cells\u2019 purported mechanosensory function, it has been reported that mechanical stimulation of cilia of concentric hair cells, or of the structure that includes the cilia, elicits coordinated movement of hair-cell-bearing tentacles towards the source of the stimulus in hydrozoan and cubozoan polyps as well as in hydrozoan medusae9,12,13, and triggers electrical responses in the nerve ring of hydrozoan medusae9, cnidocytes of hydroid tentacles8, and hair bundle mechanoreceptors\u2014concentric hair cell-support cell complexes\u2014of sea anemone tentacles14,15.\n\nAlthough cell type diversity of non-bilaterians such as cnidarians is generally believed to be limited relative to bilaterians (e.g., 16), morphology of cnidarian hair cells can vary at the subcellular level within a given taxon, raising the possibility that cnidarians have evolved more than one mechanosensory neural cell type. Indeed, the polyp tentacle of the hydrozoan Sarsia tubulosa (formerly Coryne tubulosa) houses two concentric hair cell types that differ in cilia length (15\u2009\u00b5m vs. 1\u20132\u2009\u00b5m) and another concentric hair cell-like cell type with a recessed cilium-microvilli complex17. Likewise, the polyp of box jellyfish Carybdea marsupialis has one concentric hair cell type with a collar of 7\u20139 pronounced stereovilli, and another cell type with a long cilium (40\u2009\u00b5m in length) surrounded by rings of short microvilli12. However, these morphological data are also consistent with alternative interpretations that cells having different morphologies of apical mechanosensor represent morphological variants, or different developmental states, of the same cell type. Thus, it remains unclear whether cnidarians have different mechanosensory neural cell types with distinct mechanotransduction machineries and developmental histories, akin to bilaterian conditions (e.g., gentle touch and harsh touch sensory neurons of C. elegans worms18,19). Consequently, it is unknown whether cnidarian mechanoreceptor evolution has involved diversification of molecularly and developmentally distinct mechanosensory neurons, or been solely dependent on repurposing a single, ancestral mechanosensory neuron type.\n\nWe report here our fortuitous discovery of cell type diversity of mechanosensory neurons in the tentacular ectoderm of the sea anemone Nematostella vectensis, which is a genome-enabled, genetically tractable cnidarian20,21,22. N. vectensis gastrulates by invagination to form an embryo with ectoderm and endoderm separated by an extracellular matrix, the mesoglea23,24. The embryo develops into a ciliated planula larva that swims with the aboral end oriented forward. The planula then undergoes life cycle transition, transforming into a polyp with oral tentacles whose ectoderm houses concentric hair cells with an apical cilium surrounded by a circle of 6-9 stereovilli14,25,26. The apical sensory apparatus of the concentric hair cell is encircled by microvilli of adjacent support cells, forming a structure known as the hair bundle. As noted above, the multicellular structure consisting of a concentric hair cell surrounded by support cells is referred to as the hair bundle mechanoreceptor, and its mechanosensitivity has been confirmed by electrophysiology in N. vectensis14. We have recently shown that the class IV POU\u00a0homeodomain transcription factor (POU-IV)\u2014which is deeply shared across animals excepting Ctenophora27\u2014regulates postmitotic differentiation and maturation of concentric hair cells by activating a specific set of effector genes (e.g., polycystin-1 encoding a conserved transmembrane receptor of the TRP ion channel superfamily), and is necessary for touch-sensitivity of polyp tentacles26. Upon further investigation into the anatomy and development of the tentacular nervous system in N. vectensis, we discovered a previously unrecognized population of concentric hair cells that are anatomically, molecularly, and developmentally distinct from the conventional concentric hair cells of sea anemones, the evidence of which is detailed below.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We first examined the morphological diversity of concentric hair cells in the ectodermal epithelium of oral tentacles in N. vectensis polyps. We used meganuclease-mediated transgenesis28 to develop a pou-iv transgenic reporter line in which a pou-iv promoter (3.2\u2009kb genomic sequence immediately upstream of the start codon of the pou-iv gene) drives the expression of photoconvertible fluorescent protein Kaede (Supplementary Fig.\u00a01). Using this pou-iv::kaede transgenic line, we found that the pou-iv promoter drives Kaede expression in conventional concentric hair cells with a cilium (<15\u2009\u03bcm in length; n\u2009=\u200910) surrounded by a circle of 8-9 pronounced stereovilli (Fig.\u00a01A\u2013E)\u2014as previously reported using F0 pou-iv::kaede mosaic transgenic animals (Ozment et al. 26). Unexpectedly, we also found Kaede expression in concentric hair cell-like cells with a longer erect cilium (<45\u2009\u03bcm in length; n\u2009=\u200913) and less pronounced stereovilli/microvilli (Fig.\u00a01F\u2013J; Supplementary Movie\u00a01). We will herein refer to the conventional concentric hair cell as the type I hair cell, and the morphologically distinct hair cell-like cell characterized by the long cilium as the type II hair cell.\n\nA\u2013J: Confocal sections of the tentacular ectoderm (ec) of pou-iv::kaede transgenic Nematostella vectensis polyp, labeled with antibodies against Kaede (\u201cpou-iv::kaede\u201d) and tyrosinated \u2202-tubulin (\u201ctyrTub\u201d). Filamentous actin is labeled with phalloidin (\u201cF-actin\u201d), and nuclei are labeled with DAPI (\u201cdapi\u201d). The apical epithelial surface faces up. A\u2013E show a type I hair cell (arrowhead), characterized by an apical cilium (ci) surrounded by stereovilli (st) with pronounced rootlets (rl). F\u2013J show a type II hair cell (arrowhead), characterized by a long cilium (ci) surrounded by short stereovilli (st) with rootlets (rl). The presence of type I and type II hair cells have been confirmed in every animal that has been examined (n\u2009>\u200920). K\u2013N: serial block-face scanning electron microscopy (SBF-SEM) images and 3-D reconstruction of type II hair cells (hcII). K shows the apical sensory apparatus consisting of a cilium (ci) surrounded by rings of microvilli (st/mv), a subset of which have actin rootlets that extend about 1\u2009\u03bcm into the cytoplasm and therefore are stereovilli (n\u2009=\u20095 type II hair cells). Similar to type I hair cells (see refs. 14,26), apical microvilli of adjacent support cells (mvs) encircle the apical mechanosensor of the type II hair cell. L is an apical view of a 3-D reconstruction of a type II hair cell. Note that the cilium is surrounded by rings of about 30 stereovilli and microvilli (st/mv). M, N show a two-way (bidirectional) chemical synapse between type II hair cells (arrowhead in M; n\u2009=\u20097 synapses), and a one-way chemical synapse in which a type II hair cell is presynaptic to a type I hair cell (hcI) (arrowhead in N; n\u2009=\u20093 synapses), respectively. These features are unique to type II hair cells; type I hair cells are not presynaptic to type I or type II hair cells. Note the clustering of dense-cored or opaque vesicles adjacent to parallel, relatively electron-dense plasma membranes. Abbreviations: nu nucleus, mi mitochondria Scale bar: 10\u2009\u00b5m (A\u2013J); 500\u2009nm (K, M, N).\n\nTo better resolve the structure of the apical mechanosensory apparatus of the type II hair cell, we carried out serial block-face scanning electron microscopy (SBF-SEM). We discovered that the apical structure consisted of a single cilium surrounded by multiple rings of about 30 stereovilli and microvilli that were approximately 1\u2009\u03bcm long (Fig.\u00a01K, L), contrasting stereovilli of the type I hair cell, which have been reported to range from 6\u20139 in number and 3\u20135\u2009\u03bcm in length in N. vectensis14,26.\n\nTaking advantage of the SBF-SEM volume data, we next searched for chemical synapses of concentric hair cells in order to assess whether the pattern of synaptic connectivity would differ between type I and type II hair cells. Structurally, cnidarian chemical synapses consist of a pair of parallel plasma membranes with clear, dense-cored, or opaque vesicles\u201470\u2013150\u2009nm in diameter\u2014apposing one or both sides of the membranes, with cross filaments sometimes occurring in the intercellular space\u2014 the synaptic cleft\u2014between the parallel plasma membranes (reviewed in ref. 29). The SBF-SEM volume data enabled us to trace thin basal processes to the cell body and unambiguously determine the identity of the cell type to which each chemical synapse belonged. Indeed, we discovered conventional chemical synapses of both type I and type II hair cells in N. vectensis. Synaptic vesicles of type I and type II hair cells were observed to form distinct, dense clusters adjacent to rigid, parallel membranes sandwiching a synaptic cleft that is more electron-dense than the surrounding intercellular space (Fig.\u00a01M, N; Supplementary Figs.\u00a02, 3; Supplementary Movie\u00a02, 3). Synaptic vesicles of type I hair cells were dense-cored (Supplementary Fig.\u00a03A\u2013D), while those of type II hair cells were mostly opaque and sometimes dense-cored (Fig.\u00a01M, N; Supplementary Figs.\u00a02, 3E\u2013I; Supplementary Movie\u00a02). Notably, synaptic vesicles of type I hair cells were significantly larger than those of type II hair cells (mean diameter: type I, 95.04\u2009nm, n\u2009=\u200917; type II, 70.38\u2009nm, n\u2009=\u200962; two-tailed t-test, p\u2009<\u20090.001). In addition, the number of vesicles per synapse was found to be significantly lower in type I hair cells (mean: 13.5 per synapse; n\u2009=\u20094 synapses) than in type II hair cells (mean: 88.3 per synapse, n\u2009=\u20099 synapses) (two-tailed t-test, p\u2009<\u20090.01). Chemical synapses of type I and type II hair cells were found primarily in neurites but sometimes also occur in the basal part of the cell body (i.e., basal to nucleus; see Supplementary Movie\u00a04).\n\nWe then compared the pattern of synaptic connectivity in type I and type II hair cells. Both type I and type II hair cells were observed to form afferent/input synapses with epitheliomuscular cells (n\u2009=\u200910 synapses found in type I hair cells; n\u2009=\u20095 in type II), cnidocytes (nematocytes (n\u2009=\u20092 in type I; n\u2009=\u20091 in type II) and spirocytes (n\u2009=\u20094 in type I; n\u2009=\u20091 in type II)), and ganglion cells (n\u2009=\u20093 in type I; n\u2009=\u20098 in type II) (Supplementary Fig.\u00a03 A\u2013H). In addition, we found that type II hair cells, but not type I hair cells, formed two-way (bidirectional/reciprocal) synapses with each other (n\u2009=\u20097; Fig.\u00a01M) and with ganglion cells (n\u2009=\u20093; Supplementary Fig.\u00a03I). Moreover, type II hair cells formed afferent synapses with type I hair cells (n\u2009=\u20093; Fig.\u00a01N), but not vice versa in any of the type I hair cells that were examined (n\u2009=\u200910 cells). These results indicate that type I and type II hair cells are distinct not only in the structure of apical mechanosensory apparatus, but also in the structure of synapses and the pattern of synaptic connectivity.\n\nNext we asked whether the mechanotransduction mechanisms would differ between the two hair-cell types. As the first step towards tackling this question, we focused our attention on polycystin-1 (pkd1) that encodes a transmembrane receptor of the Transient Receptor Potential (TRP) calcium channel superfamily. This gene was previously identified as a candidate mechanotransduction gene of the concentric hair cells, because it was found to be directly activated by POU-IV transcription factor specifically in hair cells26. However, whether polycystin-1 is expressed in type I hair cells, type II hair cells, or both, was unknown. We aimed to determine 1) whether polycystin-1 was expressed in both types of hair cells, and 2) whether polycystin-1 was essential for normal touch response behavior. To address whether polycystin-1 is expressed in both types of hair cells, we have generated a stable transgenic reporter line for polycystin-1 in which a polycystin-1 promoter (2.1\u2009kb genomic sequence immediately upstream of the start codon of the polycystin-1 gene) drives the expression of Kaede photoconvertible fluorescent protein (Supplementary Fig.\u00a04). We have confirmed by in situ hybridization that the expression pattern of polycystin-1::kaede (pkd1::kaede) indeed recapitulates that of endogenous polycystin-1 at the primary polyp stage (Supplementary Fig.\u00a05). Using this transgenic line, we found that polycystin-1::kaede reporter gene expression specifically occurred in type II hair cells, but not in type I hair cells, of tentacular ectoderm at the polyp stage (Fig.\u00a02A\u2013D), evidencing that type I and type II hair cells are molecularly distinct.\n\nA\u2013D: Confocal sections of the tentacles of pkd1::kaede transgenic Nematostella vectensis polyp. Filamentous actin is labeled with SiR-Actin (A) or phalloidin (B, D) (\u201cF-actin\u201d), and Kaede is labeled with an anti-Kaede antibody in (B, C). The distal end of the tentacle faces up in (A). B\u2013D are sections at the level of the apical ectodermal epithelium. Arrowheads in A show long, often erect, cilia characteristic of type II hair cells. B\u2013D show pkd1::kaede expression in type II hair cells (hcII) with numerous thin stereovilli/microvilli (st/mv), but not in type I hair cells with discrete, large-diameter stereovilli (st) (n\u2009=\u20099 animals). Note also the presence of a prominent contiguous F-actin ring (ar) lining the apicolateral membrane of pkd1::kaede-positive type II hair cells. Consistent with SBF-SEM data, adjacent support cells contribute microvilli (mv) to the apical mechanosensory apparatus of both types of hair cells. Behavior of wildtype (F3 pkd1 +/+, E, F) and mutant (F3 pkd1 \u2212/\u2212, G, H) N. vectensis polyps in response to gentle touch to their oral tentacles. Either a microinjection needle (shown) or a tungsten needle was brought close to the tentacle to elicit gentle touch response\u2014a bending motion of the stimulated tentacle towards the source of the mechanical stimuli. Animals before (E, G) and after (F, H) gentle touch are shown. Most of wildtype animals displayed gentle touch response (83.3%, n\u2009=\u200930; E, F), while the majority of pkd1 homozygous mutants were touch-insensitive (88.3%, n\u2009=\u200960; G, H). The arrowhead in (F) points to a stimulated tentacle exhibiting normal gentle touch response. Scale bar: 50\u2009\u00b5m (A); 5\u2009\u00b5m (B\u2013D); 100\u2009\u00b5m (E\u2013H).\n\nTo assess whether type II-hair-cell-specific expression of polycystin-1 is necessary for normal mechanosensory behavior in N. vectensis, we used CRISPR-Cas9-mediated mutagenesis to generate a polycystin-1 mutant line in which the structure of TRP cation channel and the C-terminal cytoplasmic tail is disrupted (Supplementary Fig.\u00a06). We predicted that if polycystin-1 indeed encoded type II-hair-cell-specific mechanotransduction channels, perturbation of Polycystin-1 channel function should cause defects in type II hair cell function. Concentric hair cells with long, erect cilia\u2014resembling type II hair cells of N. vectensis\u2014occur in the ectoderm of tentacles in the hydroid Coryne pintneri4, in the medusa of the hydrozoan Aglantha digitale9, and in the polyp of the cubozoan Carybdea marsupialis12. They are highly sensitive to gentle mechanical stimulation and water vibration; mechanical stimuli to the hair cell-bearing tentacle induce movement towards the source of the stimuli, involving bending of the stimulated tentacle in Aglantha and Eutonina hydrozoan medusae9,13 and Carybdea polyp12 and of the upper body of the polyp in Coryne4. This behavior presumably facilitates the capture of nearby prey. In Aglantha medusae, this \u2018pointing response\u2019 is distinct from responses to stronger mechanical stimuli; direct contact of the tentacle triggers tentacular contraction and escape swimming behavior9. In N. vectensis polyps, whole-tentacle contraction occurs in response to direct contact of a probe with the tentacular epithelium26. We will here refer to this behavior as a harsh touch response. In addition, we have found that N. vectensis polyps display the pointing response; tentacles respond to an approaching probe, before the probe makes physical contact with the tentacular surface epithelium, by a quick bending motion that brings the tentacle closer to the object (see below). We will refer to this behavior as a gentle touch response. Given that type II hair cell-like cells with long, erect cilia in other cnidarians appear to be highly sensitive to gentle mechanical stimuli, we hypothesized that type II-hair-cell-specific expression of polycystin-1 might be necessary for mediating the gentle touch response behavior in N. vectensis.\n\nTo test whether polycystin-1 is essential for gentle touch response, we crossed polycystin-1\u2009+/-\u00a0heterozygous mutants (F1 or F2) with each other to generate a progeny with expected genotype frequencies of 25% \u2212/\u2212 homozygotes, 50%\u2009+/-\u00a0heterozygotes, and 25% +/+ wildtype siblings. We carried out gentle touch assay on the progeny, whereby a probe (a tungsten needle or a glass needle) is brought close (<1\u2009mm) to a tentacle, without directly contacting the epithelial surface, to elicit bending of the tentacle towards the probe. We then separated the animals based on the presence/absence of the pointing response (Supplementary Movie\u00a05), and genotyped them individually. We found significant enrichment of polycystin-1 homozygous mutants in the gentle-touch-insensitive group (experiment 1, 55.3% (n\u2009=\u200947), hypergeometric test p\u2009<\u20090.001; experiment 2, 69.2% (n\u2009=\u200939), p\u2009<\u20090.001), and significant depletion of polycystin-1 homozygous mutants in the gentle-touch-sensitive group (experiment 1, 10.6% (n\u2009=\u200947), hypergeometric test p\u2009=\u20090.012; experiment 2, 6.25% (n\u2009=\u200932), p\u2009<\u20090.01). Overall, only 11.7% (n\u2009=\u200960) of polycystin-1 homozygous mutants were deemed to exhibit gentle-touch response, while 83.3% (n\u2009=\u200930) of their wildtype siblings showed normal gentle-touch response behavior (Fig.\u00a02E\u2013H; Supplementary Movie\u00a06, 7). In contrast, harsh-touch response occurred in polycystin-1 homozygous mutants (100%, n\u2009=\u200934; Supplementary Fig.\u00a07, Supplementary Movie\u00a08). We note that the gentle-touch-insensitive phenotype of polycystin-1 homozygous mutants is not due to the failure to develop the apical sensory apparatus of type II hair cells (Supplementary Fig.\u00a08). Neither did we find evidence that the phenotype was due to a change in the density of type II hair cells in polycystin-1 homozygous mutants relative to wildtype animals; the estimated linear density of type II hair cells in polycystin-1 homozygous mutants (mean: 14.2 per 100\u2009\u00b5m unit length, n\u2009=\u20095) did not significantly differ from that in their wildtype siblings (mean: 11.6 per 100\u2009\u00b5m unit length, n\u2009=\u20095) (two-tailed t-test, p\u2009=\u20090.5797). Taken together, these results strongly suggest that type II-hair cell-specific expression of polycystin-1 is essential for gentle-touch behavior, but not for harsh-touch behavior, and that molecular mechanisms of mechanotransduction in type I and type II hair cells are not redundant.\n\nWe next considered whether type I and type II hair cells might represent different maturation phases of the same postmitotic cell type. For this possibility, two scenarios were conceivable; type I hair cells give rise to type II hair cells, or vice versa. The scenario whereby type II hair cells transform into type I hair cells seemed highly unlikely because pkd1::kaede expression specifically occurred in type II hair cells and not in type I hair cells (Fig.\u00a02A\u2013D). If type II hair cells gave rise to type I hair cells, Kaede expression\u2014stable enough to persist for months30\u2014would be retained by type I hair cells, which was not observed in any F1 or F2 pkd1::kaede transgenic animals examined (n\u2009=\u200915 animals). However, the possibility that type I hair cells transform into type II hair cells could not be ruled out.\n\nTo further explore the developmental relationship between type I and type II hair cells during maturation, we turned to the pou-iv::kaede transgenic line in which both type I and type II hair cells could be photoconverted and tracked. We photoconverted pou-iv::kaede primary polyps and examined whether the number of photoconverted type I hair cells would change relative to the number of photoconverted type II hair cells after 5 days of tentacular growth (Supplementary Fig.\u00a09A, B). We predicted that if type I hair cells were the source of type II hair cells, the proportion of photoconverted type I hair cells relative to photoconverted type II hair cells should decrease as new type II hair cells formed. Conversely, if type II hair cells were the source of type I hair cells, the proportion of photoconverted type I hair cells relative to photoconverted type II hair cells should increase as new type I hair cells formed. The average proportion of pou-iv::kaede-positive type I relative to type II hair cells in tentacular ectoderm prior to photoconversion\u2014assumed to correspond directly to the proportion immediately after photoconversion\u2014was 1.04 (n\u2009=\u20097 animals), and that of photoconverted type I hair cells relative to photoconverted type II hair cells after 5 days post-photoconversion was 1.00 (n\u2009=\u20096 animals), failing to show any statistically significant change in either direction (two tailed t-test, p\u2009=\u20090.876; Supplementary Fig.\u00a09C). The number of pou-iv::kaede-positive type I or type II hair cells per unit volume did not significantly change either; the mean density of pou-iv::kaede-positive type I hair cells before photoconversion (101.26 per 106\u2009 \u00b5m3; n\u2009=\u20097 animals) did not significantly differ from that of pou-iv::kaede-positive photoconverted type I hair cells at 5 days after photoconversion (117.25 per 106\u2009\u00b5m3; n\u2009=\u20096 animals) (two tailed t-test, p\u2009=\u20090.501), and neither did the mean density of pou-iv::kaede-positive type II hair cells before photoconversion (98.24 per 106\u2009\u00b5m3; n\u2009=\u20097 animals) significantly differ from that of pou-iv::kaede-positive photoconverted type II hair cells at 5 days after photoconversion (118.89 per 106\u2009\u00b5m3; n\u2009=\u20096 animals) (two tailed t-test, p\u2009=\u20090.069). The lack of evidence for transformation of type I hair cells into type II hair cells, or vice versa, is not due to insufficient time for completing the developmental process; we found type I and type II hair cells positive for non-photoconverted Kaede (Supplementary Fig.\u00a09D\u2013I), which indicates that the incubation period was sufficient for generating both types of hair cells anew. We therefore do not find robust support for the hypothesis that type I and type II hair cells represent different maturation states of the same postmitotic cell type.\n\nIf type I and type II hair cells indeed develop from distinct postmitotic precursors, the pattern of development may also differ between the two cell types. To investigate this possibility, we compared the timing of when type I and type II hair cells would begin postmitotic differentiation, taking advantage of the pou-iv::kaede transgenic reporter line in which postmitotic precursors to hair cells, as well as other cell types (e.g., cnidocytes), could be labeled by photoconversion and tracked (Supplementary Fig.\u00a010, 11). Morphologically and/or molecularly differentiated forms of type I and type II hair cells become evident at the tentacle-bud stage (type I26; type II, Supplementary Fig.\u00a04), and therefore, early postmitotic precursors to type I and type II hair cells are expected to form at or prior to the tentacle-bud stage. To test whether postmitotic precursors to type I and/or type II hair cells emerge during planula development, we photoconverted pou-iv::kaede-positive cells at different stages of planula development (2 dpf early planula, 3 dpf mid-planula I, 4 dpf mid-planula II, and 5 dpf late planula; staging based on25), and followed their fate at the tentacle-bud and primary polyp stages (9\u201312 dpf) (Fig.\u00a03). When photoconversion was carried out at the early planula stage, we did not find photoconverted type I or type II hair cells in the tentacular ectoderm of primary polyps, but observed photoconverted cnidocytes (Fig.\u00a03F\u2013H; early planula, n\u2009=\u20095 animals). This confirms that postmitotic precursors to hair cells develop post-embryonically, and suggests that at least a subset of embryonically generated cnidocytes become incorporated into tentacles of primary polyps. When photoconversion was performed at the mid-planula I stage, a subset of morphologically unambiguous type I hair cells, but not type II hair cells, were occasionally found to be photoconverted (Fig.\u00a03I\u2013K; n\u2009=\u20095 animals). Photoconverted type I hair cells were consistently found in animals in which photoconversion occurred at the mid-planula II stage (n\u2009=\u20097 animals). This suggests that postmitotic precursors to type I hair cells emerge between early planula and mid-planula II stages (i.e., between 48-96 hpf at room temperature). Photoconverted type II hair cells with mature morphology, on the other hand, were consistently observed at the tentacle-bud/primary polyp stage in animals where photoconversion was performed at the late planula stage (Fig.\u00a03L\u2013N; n\u2009=\u20097 animals), indicating that postmitotic precursors to type II hair cells begin to develop between mid-planula II and late planula stages (i.e., between 96\u2013120 hpf at room temperature). Taken together, these results suggest that early postmitotic precursors to type I hair cells form before those to type II hair cells during planula development, indicative of temporally distinct generative mechanisms for type I and type II hair cells.\n\nConfocal sections of pou-iv::kaede transgenic N. vectensis at the planula (A\u2013D) and tentacle-bud/primary polyp (E\u2013N) stages. Kaede fluorescent protein (\u201cpou-iv::kaede\u201d) was photoconverted (\u201cpc-pou-iv::kaede\u201d) by ultraviolet illumination during planula larval development (e.g., A\u2013D), and photoconverted Kaede was used as a cell marker to trace the fate of pou-iv::kaede-positive larval cells at life cycle transition (E\u2013N). Filamentous actin (\u201cF-actin\u201d) is labeled with a fluorescent dye SiR-Actin. A\u2013E show sections through the entire animal with the blastopore/mouth facing up. F\u2013H are sections through the center of a tentacle with photoconverted cnidocytes (cn); the distal end of the tentacle faces up. Photoconversion was performed at the early planula stage (2 dpf). Photoconverted type I and type II hair cells were absent (n\u2009=\u20095 animals), confirming their post-embryonic origin. I\u2013K show sections through a photoconverted type I hair cell having characteristically pronounced stereovilli (st) with actin rootlets (rl) in an animal where photoconversion was performed at the mid-planula I stage (3 dpf) (n\u2009=\u20095 animals). The apical epithelial surface faces up. Photoconverted type II hair cells were absent, implying that early-born type I and type II hair cells have temporally distinct developmental origins. L\u2013N show sections at the level of the surface epithelium of the tentacle primordium of an animal in which photoconversion was performed at the 5 dpf late planula stage. Note the presence of photoconverted type II hair cells with characteristically long cilia (>20\u2009\u00b5m; ci) (n\u2009=\u20097 animals), indicating that type II hair cell precursors begin to develop after type I hair cell precursors. Abbreviations: st/mv rings of stereovilli/microvilli. Scale bar: 50\u2009\u00b5m (A\u2013F, J, N); 10\u2009\u00b5m (G\u2013I, K\u2013M).\n\nPostmitotic differentiation and maturation of both type I and type II hair cells are dependent on POU-IV, evidenced by the loss of long stereovillar rootlets and polycystin-1 expression \u2013 defining characteristics of type I and type II hair cells, respectively \u2013 in pou-iv null mutant polyps26. However, the role of POU-IV in specifying and/or maintaining the identity of these cell types may differ. To explore the possibility that pou-iv expression is differentially regulated in the two cell types, we used the pou-iv::kaede transgenic reporter line to compare the degree of persistence of pou-iv promoter activity during the maturation of type I and type II hair cells. We photoconverted pou-iv::kaede-positive, immature type I and type II hair cells at the late planula stage, and allowed the photoconverted animals to develop into primary polyps. We then examined whether photoconverted type I and type II hair cells expressed non-photoconverted Kaede, in order to determine whether the pou-iv promoter activity would persist during maturation. We found that the majority of the photoconverted type I hair cells did not express non-photoconverted Kaede at detectable levels (66.7%, n\u2009=\u200927 cells across 6 animals; Supplementary Fig.\u00a012A\u2013C), suggesting that pou-iv promoter activity in type I hair cells is transient and does not persist through the maturation process. In contrast, photoconverted type II hair cells always expressed non-photoconverted Kaede at levels comparable to those of newly-born type II hair cells (n\u2009=\u200914 cells across 6 animals; Supplementary Fig.\u00a012D\u2013F), implying that pou-iv promoter activity persisted in type II hair cells throughout the maturation process.\n\nNext, we asked whether pou-iv promoter activity persisted in mature type II hair cells. To address this question, we photoconverted pou-iv::kaede-positive cells at the primary polyp stage, to label morphologically mature type II hair cells along with other pou-iv::kaede-positive cells, and allowed the photoconverted animals to continue growth for 5 days. We found that all of the photoconverted type II hair cells that were examined expressed non-photoconverted Kaede (n\u2009=\u200951 cells across 7 animals; Supplementary Fig.\u00a012G\u2013I). Assuming that at least a subset of photoconverted type II hair cells were at a mature state at the time of photoconversion, the results suggest that pou-iv promoter activity continues in mature type II hair cells. Taken together, these findings indicate that pou-iv promoter activity is differentially regulated in type I and type II hair cells, and raise the possibility that the transcriptional regulatory mechanism that establishes and maintains mature cell identity differs between type I and type II hair cells.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56115-2/MediaObjects/41467_2025_56115_Fig3_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Here we have presented anatomical, molecular and developmental evidence for cell type diversity in cnidarian mechanosensory neurons. Specifically, we uncovered in the tentacular ectoderm of the sea anemone N. vectensis previously unrecognized mechanosensory neurons\u2014type II hair cells\u2014that differ in morphology, mechanotransduction mechanism, and development from the conventional mechanosensory neurons of the sea anemone\u2014the type I hair cells. Notably, we have not only identified, for the first time, classical chemical synapses in cnidarian hair cells, but also discovered that type I and type II hair cells have different synaptic structures and patterns of synaptic connectivity. In addition, we have reported the first ion channel-encoding gene\u2014 polycystin-1\u2014essential for mechanosensory behavior in Cnidaria, or any non-Bilateria, and have found that this gene mediates gentle touch response specifically in type II hair cells and not in type I hair cells, consistent with distinct mechanotransduction mechanisms being deployed in the two cell types. Also noteworthy is the first experimental determination of temporal developmental origins of postembryonic cell types in Cnidaria, and the discovery that postmitotic precursors to type I and type II hair cells begin to emerge at different phases of larval development, indicative of temporally distinct developmental mechanisms.\n\nThe molecular mechanisms of development and function of the newly discovered type II hair cells in the sea anemone appear similar to those of mechanosensory cells in Bilateria, indicative of deeply shared ancestry. For instance, the class IV POU homeodomain transcription factor regulates differentiation not only of type II hair cells in the sea anemone N. vectensis26, but also of mechanosensory neurons/cells across bilaterians (e.g., worms and vertebrates31,32,33,34,35,36). Moreover, in N. vectensis, effector genes that are directly activated by POU-IV in type II hair cells\u2014corresponding to a transcriptomically defined adult cell type uniquely expressing polycystin-1 (\u2018metacell c79\u201937)\u2014show significant enrichment of the GO term \u2018sensory perception of sound\u201926. Given that evidence for this GO annotation derives from bilaterian data, it suggests that the effector genes that define the mechanoreceptor identity are broadly shared across the cnidarian type II hair cell and bilaterian mechanosensory cells. Of note, in the hydrozoan cnidarian Hydra vulgaris, pou-iv and polycystin-1 are co-expressed in a specific cluster of transcriptomically similar neurons (\u201cec1\u201d38), concordant with the core gene regulatory network for type II hair cell development being conserved across Cnidaria. It will be important to examine whether pou-iv and polycystin-1 are indeed involved in the development of mechanosensory neurons in Hydra; specialized mechanosensory neurons have not been identified in this cnidarian. Intriguingly, the gene regulatory mechanism for mechanoreceptor development involving pou-iv and polycystin-1 might even predate the divergence of the Cnidaria/Bilateria clade and its likely sister group Placozoa (39,40,41,42; but see refs. 43,44 for an alternative view of phylogeny), as unambiguous orthologs of pou-iv and polycystin-1 are not only present in the Placozoa26,27 but are co-expressed in a transcriptomically defined peptidergic cell type (\u201cmetacell 214\u201d in Trichoplax sp. H245). Investigation of whether these polycystin-1-positive peptidergic cells represent specialized mechanosensory cells of Placozoa is therefore warranted. Taken together, current comparative evidence indicates that the molecular mechanisms of development and function of type II hair cells are not sea anemone lineage-specific, but have deeper evolutionary roots.\n\nOur findings suggest that anatomy, development, and mechanotransduction mechanisms of cnidarian mechanosensory neurons are diverse. As described above, within-individual morphological variation in apical mechanosensors of presumptive mechanosensory neurons occurs across Cnidaria (e.g., 6,9,10,12,17,46), and therefore, it is plausible that diverse types of mechanosensory neurons exist beyond sea anemone cnidarians. In addition, the planula larvae\u2013the post-gastrulation dispersal phase conserved across Cnidaria\u2014are thought to be mechanosensitive (e.g., geotactic47,48; phonotactic49,50,51), although the cellular bases of mechanosensation remain unknown. If mechanosensory neurons indeed existed in N. vectensis planulae, they would be distinct from type I and type II hair cells, as mature forms of type I and type II hair cells are absent in planulae. We suggest that cell type diversity of cnidarian mechanosensory neurons across life cycle stages and lineages likely remains underestimated and merits further investigation.\n\nOur discovery of structurally, molecularly, and developmentally distinct mechanosensory neurons from a cnidarian further implies that evolutionary histories of mechanosensory neurons in non-bilaterian animals are not simple. Repurposing of a single type of mechanosensory neurons as the sole mechanism of mechanosensory neural evolution is no longer tenable for Cnidaria. Instead, cell type diversification of mechanosensory neurons must have occurred in Cnidaria\u2014paralleling mechanoreceptor evolution in Bilateria\u2014and/or in early animal ancestors basal to Cnidaria and Bilateria followed by the retention of ancestrally distinct mechanosensory neuron types in the descendant lineages. Resolving the resultant alternative hypotheses\u2014independent cell type diversification in Cnidaria and Bilateria, ancient radiation predating the divergence of Cnidaria and Bilateria, or a combination of both\u2014necessitates a deeper understanding of the mechanisms of development and function of distinct mechanosensory neuron types within Cnidaria and across deeply-branching animal lineages.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Nematostella vectensis deriving from CH2 males and CH4 females20 were cultured as previously described52,53.\n\nThe pou-iv::kaede and pkd1::kaede transgenic animals were produced by I-SceI-mediated transgenesis as described previously26,28 with modifications. To generate pou-iv::kaede plasmid, 3199\u2009bp genomic sequence upstream of the start codon of the Nematostella vectensis pou-iv (Nematostella vectensis v1.0; scaffold 16: 1,065,408\u20131,068,606; https://mycocosm.jgi.doe.gov/Nemve1/Nemve1.home.html) was cloned in front of the open reading frame of the Kaede gene30 by FastCloning54. To generate pkd1::kaede plasmid, 2145\u2009bp genomic sequence upstream of the start codon of the Nematostella vectensis polycystin 1 (scaffold 353:49,524\u201351,668; https://mycocosm.jgi.doe.gov/Nemve1/Nemve1.home.html) was cloned in front of the open reading frame of the Kaede gene. The plasmid was digested with I-SceI for 15\u201330\u2009min at 37\u2009\u00b0C and injected into zygotes at 50\u2009ng/\u03bcl. The injected animals that were identified as Kaede-positive (F0 animals) were raised to sexually mature adult polyps. F0 animals were then crossed with each other to generate F1 progeny, which was screened to identify carriers. These Kaede-positive F1 animals were raised to adult polyps, which were individually crossed with wildtype animals. In all cases examined, approximately half of the F2 progeny showed Kaede fluorescence, consistent with F1 animals being heterozygous. Both F1 and F2 animals were used in this study.\n\n20 nt-long sgRNA target sites were manually identified in the N. vectensis polycystin-1 genomic locus (Nematostella vectensis v1.0; scaffold 353:51565-82045; https://mycocosm.jgi.doe.gov/Nemve1/Nemve1.home.html), specifically in the region that encodes the TRP ion channel (scaffold 353:76,524\u201380,526). To minimize off-target effects, target sites that had 17 bp-or-higher sequence identity elsewhere in the genome were excluded. Selected target sequences were as follows.\n\n5'- CCCAGTCGTAGAAATTCTCG-3' (Cr1)\n\n5'- TTGTCCATAACTGTAAGACT-3' (Cr2)\n\n5'- TTCCTCTGTCTGACCCAGCT-3' (Cr3)\n\n5'- ATGTTGACCAGGACCCTGAA-3' (Cr4)\n\nThe sgRNA species were synthesized in vitro (Synthego) and mixed at equal concentrations. The sgRNA mix and Cas9 endonuclease (PNA Bio, PC15111, Thousand Oaks, CA, and USA) were co-injected into fertilized eggs at concentrations of 500\u2009ng/\u00b5l and 1000\u2009ng/\u00b5l, respectively.\n\nGenomic DNA from single embryos or from tentacles of single polyps was extracted by using a published protocol22,55,56, and the targeted genomic locus was amplified by nested PCR. Primer sequences used for nested genomic PCR are: \u201c1\u201d Forward 5'- GAGTGCGTTCTTTCGATTCGGTGAG-3', \u201c1\u201d Reverse 5'- GGCAAATACGTCCATGATAATCGTC-3', \u201c2\u201d Forward 5\u2019- TGCTATCATTGATGCTGTTCCAGTGC-3', \u201c2\u201d Reverse 5'- AATCGGACCCAAGATCGGCTGCG-3'. To determine the sequence of mutant alleles, PCR products from genomic DNA extracted from F1 mutant polyps were gel-purified, cloned, and sequenced using a standard procedure. Using the sequence information of the polycystin-1- mutant allele, genotyping primers for F2/F3 animals were designed as follows (Supplementary Fig.\u00a06B).\n\nForward 5'- GACAATTTCTGTAATGTGACGTGACC-3'\n\nReverse (1), 5'- CAGTGAAGCCCACGTCGTACG-3' (polycystin-1\u2009+\u2009specific; expected size of PCR product, 249 bp)\n\nReverse (2), 5'- GAAGAAGACAAGGAAGACCACAGAG-3' (expected size of PCR product: polycystin-1\u2009+\u2009, 3554 bp; polycystin-1-, 829 bp)\n\n10dpf or older, unfed primary polyps with extended tentacles were used for behavioral analyses. For gentle touch assay, a tungsten needle attached to a microdissection needle holder (Roboz Surgical Instrument Co., Gaithersburg, MD, USA), or a microinjection glass needle attached to a micromanipulator (MO-202U; Narishige), was brought close (<1\u2009mm) to a tentacle without direct contact with the epithelium. The response of 2\u20134 tentacles per individual polyp was examined. The polyp that bent its tentacle(s) towards the needle was deemed gentle-touch-sensitive. For harsh touch assay, the needle was moved to directly contact the surface epithelium of the tentacle. The polyp that contracted the stimulated tentacle was deemed harsh-touch responsive. Behavioral experiments were performed and recorded under a Zeiss Stemi 508 microscope or a SteREO Discovery V8 microscope equipped with Nikon DSL-4 camera. Recorded movies were viewed and annotated using ImageJ2 (ver 2.14.0/1.54\u2009f).\n\nSpecimens were transferred from 1/3 (i.e. 12-15 ppt)\u00a0seawater solution to a solution of 2.43% MgCl2 in 1/3 seawater to anesthetize for 15\u2009min at room temperature. Specimens and MgCl2 solution were placed in chamber slides (Lab-Tek II Chambered Coverglass W/Cover #1.5 Borosilicate Sterile, Nalge Nunc International #155360), and imaged using a Zeiss LSM 900 or Nikon Ti2 inverted microscope equipped with Nikon DSL-4 camera. Images were viewed using ImageJ2 (ver 2.14.0/1.54f) or NIS Elements Ar (ver 5.11.01).\n\nTo estimate the linear density of type II hair cells, the number of characteristically long erect cilia (> 20\u2009\u00b5m)\u2014assumed to belong to type II hair cells\u2014projecting perpendicular to the surface ectoderm was measured per unit length (100\u2009\u00b5m) of the distal portion of a tentacle (i.e., from the distal epithelial tentacle tip extending 100\u2009\u00b5m proximally toward the body column). Nikon NIS Elements Advanced Research software (ver. 5.11.01) was used to capture and annotate images for cell counts.\n\nKaede fluorescence was converted from green to red by a 385\u2009nm wavelength violet light using a X-Cite XYLIS LED illumination System (XT720L; Excelitas). Prior to photoconversion, pou-iv::kaede transgenic animals were mounted on a microscope slide in order to immobilize them. Transgenic animals were then exposed to a 385\u2009nm wavelength LED light for 1\u2009min at 100% power using a 10x objective on a Zeiss LSM900.\n\nAnimal fixation and immunohistochemistry were performed as previously described25,57. For immunohistochemistry, we used primary antibodies against Kaede (rabbit; 1:500; Medical & Biological Laboratories, PM012M), and tyrosinated \u2202-tubulin (mouse, 1:500, Sigma T9028), and secondary antibodies conjugated to AlexaFluor 568 (1:200, Molecular Probes A-11031 (anti-mouse) or A-11036 (anti-rabbit)) or AlexaFluor 647 (1:200, Molecular Probes A-21236 (anti-mouse) or A-21245 (anti-rabbit)). Nuclei were labeled using the fluorescent dye DAPI (1:1,000, Molecular Probes D1306), and filamentous actin was labeled using AlexaFluor 488-conjugated phalloidin (1:250, Molecular Probes A12379) or SiR-Actin (1:1000, Cytoskeleton, Inc. CY-SC001). We note that anti-Kaede immunoreactivity occurs in a subset of endodermal neurons (Supplementary Fig.\u00a013), indicating that these neurons express Kaede-like proteins. This cross-reactivity makes it difficult to ascertain Kaede expression in endoderm when immunostaining is performed with an anti-Kaede antibody; we therefore avoided the use of the anti-Kaede antibody for analyses of Kaede expression in the endoderm. Specimens were mounted in ProlongGold antifade reagent (Molecular Probes, P36930) or Vectashield antifade reagent (Vector Laboratories H-1000-10). Fluorescent images were recorded using a Zeiss LSM900. Images were viewed using ImageJ2 (ver 2.14.0/1.54f).\n\nAnimal fixation was performed as previously described25,57. The double fluorescent in situ hybridization procedure was modified from a previously described protocol25. Fixed polycystin-1::kaede transgenic animals were washed in PBST solution (0.1% Tween20 in 1X PBS) then underwent digestion with Proteinase K (final concentration\u2009=\u20090.02\u2009mg/mL), followed by glycine (2\u2009mg/mL) washes, triethanolamine (1%, pH 7.8) washes, and addition of acetic anhydride. Next, samples were washed again in PBST and were refixed in a 4% solution of paraformaldehyde, followed by PBST washes. Specimens were incubated with hybridization buffer for the prehybridization step. Probes were added at a final concentration of 1\u2009ng/\u00b5L and left to incubate for 65\u2009h at 60\u2009\u00b0C. An antisense digoxigenin-labeled riboprobe against N. vectensis polycystin 1 was synthesized from 3' RACE products, and antisense fluorescein-labeled probes were generated against kaede (MEGAscript transcription kit; Ambion). After 65\u2009h, samples were incubated in fresh hybridization buffer and then underwent posthybridization washes consisting of increasing concentrations of 2X SSC (pH 7), then 0.05X SSC, and finally PBST again. Specimens were blocked in blocking buffer (0.5% blocking reagent (Perkin Elmer) in PBST) and then underwent overnight incubation with 1:100 Anti-Digoxigenin-POD (Roche, REF 11633716001) and 1:1000 acetylated \u2202-tubulin (mouse, 1:500, Sigma T6793). Samples were washed again in PBST, then incubated with fluorophore tyramide amplification reagent (HCA ImagAmp 546 Kit NEL774001KT, Revvity; or HCA ImagAmp 647 Kit NEL775001KT, Revvity). After incubation, specimens were washed in 3% H2O2 in PBST, followed by a PBST only wash and blocking in blocking buffer. Specimens were incubated overnight in 1:100 Anti-Fluorescein-POD (Roche, REF 11426346910) and 1:250 AlexaFluor 568 anti-mouse (Invitrogen A-11031) or AlexaFluor 647 anti-mouse (Invitrogen A-21236). After incubation samples were washed in PBST and then incubated with fluorophore tyramide amplification reagent (HCA ImagAmp 488 Kit NEL771001KT, Revvity). Samples were washed again in PBST then nuclei were labeled using the fluorescent dye DAPI (1:1,000, Molecular Probes D1306). Specimens were mounted in ProlongGold antifade reagent (Molecular Probes, P36930). Fluorescent images were recorded using a Zeiss LSM900. Images were viewed using ImageJ2 (ver 2.14.0/1.54f).\n\nPou-iv::kaede transgenic animals were incubated in 1/3 seawater containing 10\u2009\u03bcM of the thymidine analog, EdU (Click-iT EdU AlexaFluor 488 cell proliferation kit, C10337, Molecular Probes), for 20\u2009min to label S-phase nuclei. Following washes in fresh 1/3 seawater, the animals were immediately fixed as described previously25,57. Immunohistochemistry was then carried out as described above. Following the immunohistochemistry procedure, fluorescent labeling of incorporated EdU was conducted according to the manufacturer\u2019s recommendations prior to DAPI labeling.\n\nSample preparation for SBF-SEM was carried out based on a previously established protocol58 with modifications. 10 dpf primary polyps were anesthetized in 2.43% MgCl2 for 20\u2009min, and then fixed in 2.5% glutaraldehyde in 0.1\u2009M sodium cacodylate buffer (pH 7.4) containing 2\u2009mM CaCl2 at 4\u2009\u00b0C overnight. Fixed polyps were washed in 0.1\u2009M sodium cacodylate buffer containing 2\u2009mM CaCl2 for 10\u2009min on ice. They were then washed in 0.1\u2009M sodium cacodylate buffer containing 2\u2009mM CaCl2 and 50\u2009mM glycine for 10\u2009min on ice. Samples were subsequently rinsed in 0.1\u2009M sodium cacodylate buffer containing 2\u2009mM CaCl2 for 10\u2009min on ice twice, and were kept overnight at 4\u2009\u00b0C. Samples were incubated in 2% osmium tetroxide buffered in 0.1\u2009M sodium cacodylate for 45\u2009min at room temperature (RT). The osmium solution was replaced with 1.5% potassium ferrocyanide in 0.1\u2009mM cacodylate buffer, in which samples were incubated for 45\u2009min at RT in the dark. Samples were washed with water for 10\u2009min twice. Samples were incubated in 1% thiocarbohydrazide in water for 15\u2009min at 40\u2009\u00b0C, and were rinsed in water for 10\u2009min twice. They were then incubated in 1% osmium tetroxide for 45\u2009min at RT, and washed in water for 10\u2009min twice. Samples were incubated in 1% uranyl acetate in 25% ethanol for 20\u2009min at RT in the dark, and were washed in water for 5\u2009min three times and were left overnight at 4\u2009\u00b0C. Next, samples were stained with Walton\u2019s lead aspartate for 45\u2009min at 60\u2009\u00b0C, and were dehydrated in a graded ethanol series (30%, 50%, 70%, 90%, 100%), 1:1 ethanol to acetone, and then 100% acetone. Samples were then infiltrated with 3:1 acetone to Durcupan resin (Sigma-Aldrich) for 2\u2009h, 1:1 acetone to resin for 2\u2009h, 1:3 acetone to resin overnight, and then flat embedded in 100% resin on a glass slide and covered with a sheet of Aclar at 60\u2009\u00b0C for 48\u2009h. The resin was shaved to expose the polyp tentacle surface using an ultramicrotome (UC7, Leica), and was remounted to a metal pin with conductive silver epoxy (CircuitWorks, Chemtronics). A polyp tentacle was sectioned and imaged at the Electron Microscopy Core Facility at Max Planck Florida Institute for Neuroscience using 3View2XP and Digital Micrograph (ver. 3.30.1909.0, Gatan Microscopy Suite) on a Gemini SEM300 (Carl Zeiss Microscopy LLC.) equipped with an OnPoint BSE detector (Gatan, Inc.) and Focal Charge Compensation module (Carl Zeiss Microscopy LLC.). Imaging was performed at 2\u2009kV accelerating voltage, 20\u2009\u03bcm aperture, 1.2\u2009\u03bcs pixel dwell time, 30% nitrogen gas flow, 5.149\u2009nm per pixel, and 35.173\u2009nm section thickness. The detector magnification was calibrated within SmartSEM imaging software (ver. 6.0, Carl Zeiss Microscopy LLC.) and Digital Micrograph with a 500\u2009nm cross-line grating-standard following sample acquisition using the same imaging parameters. True Z-section thickness was calculated using the cylindrical diameters method59 on longitudinal sections through microvilli. Serial images were exported as TIFFs to TrakEM260,61 in ImageJ (ver. 1.52p) and aligned using the Scale-Invariant Feature Transform algorithm with linear feature correspondences and rigid transformation (Lowe, 2004). A type II hair cell and synapses were reconstructed in 3D by using segmentation tools and the software ImageJ 3D Viewer62 in TrakEM2. Image analysis tools in TrakEM2 were used to measure the diameter of synaptic vesicles and the total number of synaptic vesicles per synapse. To estimate the total number of synaptic vesicles per synapse, vesicle clouds\u2014defined as a cluster of at least 3 vesicles within about 100\u2009nm of each other (cf. 63) \u2014located at each synapse were segmented. The area of a vesicle cloud was summed across a series of sections for each synapse and was multiplied by the section thickness (35.173\u2009nm) to obtain the total volume of the vesicle cloud. In addition, for each synapse, we measured the diameter of individual vesicles (n\u2009=\u20092 or more per synapse) and calculated the mean diameter, which was used to estimate the expected volume of a vesicle for the synapse. The total volume of the vesicle cloud was then divided by the expected volume of a synaptic vesicle to estimate the total number of synaptic vesicles for each synapse.\n\nStatistical methods were not used to determine sample size. The sample sizes were determined based on the degree and consistency of measurable differences between groups. No data were excluded from the analyses. All of the experimental findings reported in this paper have been successfully reproduced; the sample size is 2 or more per finding. 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This work was supported by Arkansas Bioscience Institute, University of Arkansas, and National Science Foundation Grant No.1931154 and 2042529.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Miguel A. P. Silva\n\nPresent address: Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, Lisbon, Portugal\n\nDepartment of Biological Sciences, University of Arkansas, Fayetteville, AR, USA\n\nJulia Baranyk,\u00a0Kristen Malir,\u00a0Miguel A. P. Silva,\u00a0Sakura Rieck\u00a0&\u00a0Nagayasu Nakanishi\n\nBiology Department, Bowdoin College, Brunswick, ME, USA\n\nGracie Scheve\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: N.N. Investigation: J.B., K.M., M.A.P.S., S.R., G.S., and N.N. Data analysis: J.B., N.N. Methodology: J.B., N.N. Funding acquisition: N.N. Supervision: N.N. Visualization: J.B., N.N. Writing: J.B., N.N.\n\nCorrespondence to\n Nagayasu Nakanishi.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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b/9f650e181c7942cffa317b77cac1564b1a04b7b33a749d47a8af32d42831c844/metadata.json @@ -0,0 +1,176 @@ +{ + "title": "PAM-flexible adenine base editing rescues hearing loss in a humanized MPZL2 mouse model harboring an East Asian founder mutation", + "pre_title": "PAM-flexible adenine base editing rescues hearing loss in a humanized MPZL2 mouse model harboring an East Asian founder mutation", + "journal": "Nature Communications", + "published": "05 August 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_MOESM7_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_MOESM8_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_MOESM9_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_MOESM10_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE299064", + "https://github.com/SNUH-hEARgeneLab/MPZL2", + "/articles/s41467-025-62562-8#MOESM1", + "/articles/s41467-025-62562-8#Sec43" + ], + "code": [], + "subject": [ + "Animal disease models", + "CRISPR-Cas9 genome editing", + "Targeted gene repair" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5353095/v1.pdf?c=1754478347000", + "research_square_link": "https://www.researchsquare.com//article/rs-5353095/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-62562-8.pdf", + "preprint_posted": "24 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Hearing loss is one of the most prevalent sensory disorders, but no commercial biological treatments are currently available. Here, we identified an East Asia-specific founder mutation, the homozygous c.220C>T mutation in MPZL2, that contributes to a significant proportion of hereditary deafness cases in our cohort study. We found that the disease-causing mutation could be targetable by adenine base editors (ABEs) that enable A\u00b7T-to-G\u00b7C base corrections without DNA double-strand breaks. To demonstrate this, we developed a humanized mouse model (hMPZL2Q74X/Q74X) that recapitulates human MPZL2 deafness and leads to progressive hearing loss. A PAM-flexible ABE variant with reduced bystander and off-target effects (ABE8eWQ-SpRY:sgRNA3) was packaged in dual adeno-associated viruses (AAVs) and injected into the inner ear of hMPZL2Q74X/Q74X mice and effectively corrected the mutation. This treatment significantly restored hearing function, improved inner ear structural integrity, and reversed altered gene expression. Base editing may hold therapeutic potential for hereditary deafness, including most cases of MPZL2 deafness.Biological sciences/Biotechnology/Gene therapy/Targeted gene repairBiological sciences/Biological techniques/Genomic analysis/Genome-wide association studiesHealth sciences/Medical research/Experimental models of disease", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "ExtendedDataFigs.pdfExtendeddatafigurelegends.pdfSupplementaryTable1.docxSupplementaryTables25.xlsx", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Hearing loss is one of the most prevalent sensory disorders, but no commercial biological treatments are currently available. Here, we identify an East Asia-specific founder mutation, the homozygous c.220\u2009C\u2009>\u2009T mutation in MPZL2, that contributes to a significant proportion of hereditary deafness cases in our cohort study. We find that the disease-causing mutation can be targetable by adenine base editors (ABEs) that enable A\u00b7T-to-G\u00b7C base corrections without DNA double-strand breaks. To demonstrate this, we develop a humanized mouse model (hMPZL2Q74X/Q74X) that recapitulates human MPZL2 deafness and leads to progressive hearing loss. A PAM-flexible ABE variant with reduced bystander and off-target effects (ABE8eWQ-SpRY:sgRNA3) is packaged in dual adeno-associated viruses (AAVs) and injected into the inner ear of hMPZL2Q74X/Q74X mice and effectively corrects the mutation. This treatment significantly restores hearing function, improves inner ear structural integrity, and reverses altered gene expression. Base editing may hold therapeutic potential for hereditary deafness, including most cases of MPZL2 deafness.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Hearing loss is the most common sensory disorder in humans and affects about 466 million people worldwide (World Health Organization, https://who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss). Although no current treatments are capable of fully restoring biological hearing function1,2, advancements in our understanding of the genetic architecture and molecular mechanisms of hearing loss have led to significant progress in inner ear gene therapies3,4. Notably, CRISPR genome editing technologies have been harnessed to directly correct disease-causing mutations for the fundamental treatment of genetic disorders5. In particular, base editors, including cytosine base editors (CBEs) and adenine base editors (ABEs), are engineered genome-editing tools that enable the precise repair of targeted genomic base pairs without double-strand breaks6,7. Presently, ABEs and CBEs have been successfully used in treating hearing loss in hereditary deafness mouse models8,9, suggesting that base editors hold potential as one-time treatments for hereditary deafness caused by pathogenic point mutations.\n\nRecessive loss-of-function mutations represent approximately 80% of hereditary deafness cases10. One of these, non-syndromic autosomal recessive deafness-111 (DFNB111), is a leading cause of mild-to-moderate progressive hearing loss11. Here, we identify an East Asia-specific founder mutation of DFNB111, the homozygous c.220\u2009C\u2009>\u2009T variant in MPZL2, that contributes to a significant proportion of hereditary deafness cases in our cohort study. To model DFNB111, we generate a humanized MPZL2 c.220\u2009C\u2009>\u2009T knock-in mouse model that replicates the human condition. For in vivo editing, we test various combinations of ABE variants, four types of Cas9 variants with different PAMs, and single-guide RNA (sgRNA)s to correct the c.220\u2009C\u2009>\u2009T mutation. Ultimately, we determine the optimal PAM-flexible ABE variant (i.e., ABE8eWQ-SpRY:sgRNA3), which achieves a 60% editing efficiency in vitro with no detectable bystander or off-target effects. The administration into the inner ear of ABE8eWQ-SpRY:sgRNA3 packaged in dual AAV-ie vectors successfully restores both auditory function and the structural integrity of the inner ear. The development of humanized mouse models and the successful correction of the MPZL2 founder mutation using a single PAM-flexible ABE together represent a significant advancement in base editor gene therapy, suggesting great promise for treating hereditary deafness, including most DFNB111 cases.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We reviewed our sensorineural hearing loss (SNHL) cohort, which consisted of 1437 unrelated families attending the hereditary deafness clinic within the Otorhinolaryngology Division at two tertiary centers (Seoul National University Hospital (SNUH) and the Eye & ENT Hospital, Fudan University (EENT)), between March 2021 and February 2024. Within this cohort, a filtering process yielded 234 pediatric probands (\u2009\u2264\u200918 years old) with symmetric, mild-to-moderate (range 21\u201355\u2009dB hearing threshold), non-syndromic SNHL (Fig.\u00a01a). Through comprehensive genetic testing using exome/genome sequencing, we identified disease-causing mutations in 155 probands (65.7%). The detailed genotypes and their pathogenicity are described in Supplementary Data\u00a01. Collectively, 17 deafness genes that were seen in 2 or more probands were identified as disease-causing within these 155 genetically diagnosed families. GJB2 was the most frequently affected gene (36.8%, 57/155), followed by STRC (18.1%, 28/155) and MPZL2 (9.0%, 14/155) (Fig.\u00a01b). In our cohort, we identified a total of 24 MPZL2-associated DFNB111 patients from 20 unrelated families, regardless of age at ascertainment. The pedigrees, genotypes, and hearing loss phenotypes are presented in Fig.\u00a01c, d. The significant contribution of the MPZL2 c.220\u2009C\u2009>\u2009T mutation to DFNB111 was found. This C\u2009\u2192\u2009T mutation at position c.220 (c.220\u2009C \u22c5G to T\u22c5A) creates a stop codon (TAG) that replaces the Gln codon (CAG) at p.74 (Q74X), likely resulting in nonsense-meditated mRNA decay or a truncated protein. In our DFNB111 patients, 23 (95.8%) harbored at least one c.220\u2009C\u2009>\u2009T allele. Notably, 19 of these patients (79.2%) were homozygous for the c.220\u2009C\u2009>\u2009T mutation (Fig. 1e). The mutational landscape of DFNB111, as documented in the literature and including our cohort, is illustrated in Supplementary Fig.\u00a01a, highlighting the high mutational burden of the c.220\u2009C\u2009>\u2009T mutation. The c.220\u2009C\u2009>\u2009T allele recurred frequently, especially in East Asian populations, suggesting a founder mutation in East Asia (Fig.\u00a01f). In contrast, the second most frequently recurrent allele, c.72delC, was found across different genetic ancestries. Auditory function-gene profiles exhibited significant progressive hearing loss over the course of decades (Fig.\u00a01g), specifically an annual progression of hearing loss of 0.61\u2009dB at low frequencies, 0.57\u2009dB at middle frequencies, and 0.61\u2009dB at high frequencies (Supplementary Fig.\u00a01b). Collectively, we hypothesized that a single ABE that corrects the c.220\u2009C\u2009>\u2009T founder mutation could serve as a \u201cone-and-done\u201d therapy, potentially treating or even curing the majority of DFNB111 patients, which constitute a significant proportion of all hereditary deafness cases.\n\na Schematic flow diagram illustrating the genetic diagnosis study of symmetric, mild-to-moderate, non-syndromic sensorineural hearing loss (ns-SNHL) in children from two tertiary centers. b Gene signatures of symmetric, mild-to-moderate, ns-SNHL in children. The bar plot shows the frequencies of 17 deafness genes that were seen in 2 or more probands from 155 genetically diagnosed families. c Pedigrees and genotypes of 24 affected patients from 20 unrelated DFNB111 families. Arrows, probands; Black filled circles or rectangles, affected patients. d Serial audiograms of 23 affected DFNB111 patients. Red, right ear; Blue, left ear. e MPZL2 mutational landscape on a Lollipop plot (upper), and the prevalence of MPZL2 in trans mutation combinations in our cohort (lower). f The mutational burden of different MPZL2 mutations depending on the genetic ancestry groups in the Chord diagram. g The natural course of hearing loss in DFNB111 patients over decades across different hearing frequencies. Source data for all relevant panels are provided within the Source Data file.\n\nBecause the mouse Mpzl2 and human MPZL2 sequences differ near the c.220\u2009C\u2009>\u2009T mutation, we decided to develop a humanized knock-in mouse model of DFNB111 by inserting 648\u2009bp of human MPZL2 complementary DNA (cDNA) containing the pathogenic c.220\u2009C\u2009>\u2009T mutation into the mouse Mpzl2 locus (Fig.\u00a02a). The integration of the human cassette and successful on-targeting was verified (Supplementary Fig.\u00a02a). Additionally, the sequence of the hMPZL2Q74X mice was confirmed using Sanger sequencing (Fig.\u00a02b). In the humanized knock-in mouse model of DFNB111 (hMPZL2Q74X/Q74X), the mRNA transcript levels of human MPZL2 and mouse Mpzl2 were undetectable across tissues at P4 and P28, in contrast to the normal expression observed in Mpzl2 wild type (WT) mice (Fig.\u00a02c and Supplementary Fig.\u00a02b). Consistent with previous studies12,13, we found that MPZL2 protein was expressed in the organ of Corti, primarily in outer hair cells (OHCs), inner hair cells (IHCs), Deiters\u2019 cells (DCs), and at the contact between DCs and the basilar membrane, as well as pillar cells, the lateral wall of spiral limbus, basal cells of stria vascularis, fibrocyte type II of spiral ligament, and root cells. These findings were observed during both the neonatal period (P4) and the adult period (P28) in Mpzl2 WT mice (Supplementary Fig.\u00a02c, d). Conversely, the MPZL2 protein expression was absent in hMPZL2 Q74X/Q74X mice at both P4 and P28. At 4 weeks of age, hMPZL2 Q74X/Q74X mice exhibited an elevation of 14.5\u2009dB at 24\u2009kHz (P\u2009=\u20090.02) and 23.5\u2009dB at 32\u2009kHz (P\u2009<\u20090.001) in auditory brainstem response (ABR) thresholds compared to Mpzl2 WT mice (Fig.\u00a02d), indicating the onset of hearing loss beginning at 4 weeks, specifically at higher frequencies. Similar to an Mpzl2 knock-out mouse model13, hMPZL2 Q74X/Q74X mice initially displayed mild-to-moderate hearing loss, which progressively worsened to severe hearing loss. At 8 weeks of age, hMPZL2 Q74X/Q74X mice showed elevated thresholds of 24.5\u2009dB at click (P\u2009=\u20090.001), 16.0\u2009dB at 4\u2009kHz (P\u2009=\u20090.007), 16.0\u2009dB at 8\u2009kHz (P\u2009=\u20090.019), 41.0\u2009dB at 16 and 24\u2009kHz, and 42.0\u2009dB at 32\u2009kHz (16-32\u2009kHz, all P\u2009<\u20090.0001) compared to Mpzl2 WT. At 12 weeks of age, the ABR thresholds in hMPZL2Q74X/Q74X mice exceeded 70\u2009dB across the click and 4\u201332\u2009kHz frequencies (P\u2009<\u20090.0001), except at 8\u2009kHz. In contrast, hMPZL2Q74X/WT mice retained ABR thresholds comparable to Mpzl2 WT mice (Fig.\u00a02d). We also performed distortion product otoacoustic emission (DPOAE) measurements to assess the function of OHCs. Similarly, DPOAE thresholds in hMPZL2Q74X/Q74X mice were significantly higher than those in hMPZL2Q74X/WT and Mpzl2 WT mice (Fig.\u00a02e), suggesting that the MPZL2 c.220\u2009C\u2009>\u2009T mutation reduces the functionality of OHCs. In hMPZL2Q74X/Q74X mice, 24\u2009kHz thresholds were elevated by 16.0\u2009dB at 4 weeks (P\u2009=\u20090.01) compared to Mpzl2 WT. At 8 weeks, thresholds increased by 28.0\u2009dB at 16\u2009kHz (P\u2009<\u20090.0001) and 26.0\u2009dB at 24\u2009kHz (P\u2009=\u20090.001). At 12 weeks, elevations were 32.5\u2009dB at 16\u2009kHz (P\u2009<\u20090.0001), 26.5\u2009dB at 24\u2009kHz (P\u2009<\u20090.0001), and 12.5\u2009dB at 32\u2009kHz (P\u2009=\u20090.002) compared to Mpzl2 WT. In hMPZL2Q74X/Q74X mice, a significant loss of OHCs and DCs in the organ of Corti was observed, particularly in the mid- and high-frequency regions of the cochlea, along with cellular disarrangement and a collapsed tunnel of Corti (Fig.\u00a02f and Supplementary Fig.\u00a03a). Quantification of OHCs in the middle and basal turns showed significant differences between Mpzl2 WT and hMPZL2Q74X/Q74X, as well as between hMPZL2Q74X/WT and hMPZL2Q74X/Q74X mice (P\u2009<\u20090.001). DC counts in the basal turn also differed significantly in both comparisons (P\u2009<\u20090.0001). In contrast, no distinct structural abnormalities were observed in the stria vascularis or spiral ligament, and the density of spiral ganglion neurons (SGNs) remained unaffected (Supplementary Fig.\u00a03b\u2013d and Supplementary Fig.\u00a04). To evaluate whether the hearing loss in hMPZL2Q74X/Q74X mice is affected by the insertion of human cDNA into the mouse Mpzl2 locus, we also generated another humanized MPZL2 WT mouse model (hMPZL2WT/WT) using a sequential knock-in strategy (Supplementary Fig.\u00a05a), and we confirmed successful on-target integration and sequence integrity of the human MPZL2 wild-type allele (Supplementary Fig.\u00a05b, c), along with detectable mRNA transcript levels of MPZL2 (Supplementary Fig.\u00a05d). The hMPZL2WT/WT mice exhibited normal thresholds across frequencies at 12 weeks of age, comparable to Mpzl2 WT mice (Supplementary Fig.\u00a06a, b), and displayed normal inner ear structures (Supplementary Fig.\u00a06c, d). These observations indicate that the auditory and inner ear phenotypes in hMPZL2Q74X/Q74X mice are specifically caused by the pathogenic c.220\u2009C\u2009>\u2009T mutation.\n\na Targeting strategy for the humanized MPZL2 mouse model (hMPZL2Q74X) using the CRISPR/Cas9 system. b Sequence verification of hMPZL2Q74X mice and Mpzl2 WT mice using Sanger sequencing. c RT-PCR analysis to detect MPZL2 and Mpzl2 expression in P4 hMPZL2Q74X/Q74X and Mpzl2 WT mice. d, e Comparison of ABR and DPOAE thresholds among Mpzl2 WT (red, n\u2009=\u200910), hMPZL2Q74X/WT (green, n\u2009=\u200910), and hMPZL2Q74X/Q74X (blue, n\u2009=\u200910) mice at 4, 8, and 12 weeks of age. Data are presented as the mean\u2009\u00b1\u2009SEM. Statistical significance was determined using one-way ANOVA with Bonferroni\u2019s multiple comparisons test. Significance levels are indicated as *P\u2009<\u20090.05, **P\u2009<\u20090.01, ***P\u2009<\u20090.001, and ****P\u2009<\u20090.0001. f Representative section images (10\u2009\u03bcm) from 12-week-old Mpzl2 WT mice (n\u2009=\u20091), hMPZL2Q74X/WT mice (n\u2009=\u20091), and hMPZL2Q74X/Q74X mice (n\u2009=\u20092), immunolabeled with Myosin VIIa (HCs, red) and anti-Sox2 (SCs, green) of the organ of Corti in 12 weeks of age. Arrowheads indicate IHCs and DCs, white arrows point to OHCs, and the white line marks SCs. Asterisks indicate loss of OHCs and DCs. Scale bar, 25\u2009\u03bcm. Data are shown as the mean\u2009\u00b1\u2009SEM. Statistical significance was determined using one-way ANOVA with Bonferroni\u2019s multiple comparisons tests, and significance levels are indicated as ****P\u2009<\u20090.0001. g Schematic diagram of the RNA-sequencing analysis (n\u2009=\u20093). h Heatmap analysis of DEGs (upregulation, red; downregulation, blue). i Volcano plot of DEGs (upregulation, red; downregulation, blue), (n\u2009=\u20093, statistical significance was evaluated using a two-sided test (P\u2009<\u20090.05)). j Gene ontology (GO) enrichment analysis in biological processes (BP). k Top 20 GO BP terms associated with cell adhesion, basement membrane organization, and ECM organization. l Quantitative RT-PCR assay in triplicate to validate the DEGs of interest (n\u2009=\u20093, biological replicates): COL9A1/2/3, EMILIN1, IBSP, POSTN, TNXB, LAMA1/2. Statistical significance was determined using one way ANOVA with Kruskal\u2013Wallis test. Data are presented as the mean\u2009\u00b1\u2009SEM. Source data for all relevant panels are provided within the Source Data file.\n\nWe subsequently conducted RNA-seq analysis to explore the molecular pathways involved in the cochlear histopathology caused by the c.220\u2009C\u2009>\u2009T mutation. Cochlear membranous labyrinth tissue from hMPZL2Q74X/Q74X and Mpzl2 WT mice were dissected at P28 and used for RNA-seq analysis (Fig.\u00a02g). Principal Component Analysis of the total RNA-seq data revealed a distinct clustered pattern of gene expression, and a heatmap visualized the patterns and clusters of individual variable values between hMPZL2Q74X/Q74X and Mpzl2 WT mice (Fig.\u00a02h). We identified a total of 1254 differentially expressed genes (DEGs) (Fig.\u00a02i), and Gene Ontology (GO) analysis of the DEGs revealed significant enrichment in biological process, cellular component, and molecular function terms (Fig.\u00a02j and Supplementary Fig.\u00a07). REVIGO analysis further highlighted significant enrichment in biological processes related to cell adhesion, extracellular matrix (ECM) organization, and basement membrane integrity (Fig. 2k). We filtered the DEGs using the gEAR (http://umgear.org) and SHIELD databases (http://shield.hms.harvard.edu), and we further refined the list to focus on DEGs associated with cell adhesion, ECM organization, and basement membrane integrity (Supplementary Data\u00a02). The associated transcripts, including COL9A1/2/3, EMILIN1, IBSP, POSTN, TNXB, and LAMA1/2, identified through the RNA-seq analysis were validated by quantitative RT-PCR (Fig.\u00a02l). Collectively, the RNA-seq results suggested that the MPZL2 c.220\u2009C\u2009>\u2009T mutation disrupts cellular pathways involved in cell adhesion, ECM organization, and basement membrane integrity, thus contributing to the histopathological changes observed in hMPZL2Q74X/Q74X mice.\n\nTo select the best base editing system in vitro, we constructed an endogenous HEK293T cell line that mimics the pathology of human DFNB111 patients by introducing a nucleotide change (c.220\u2009C\u2009>\u2009T) at the MPZL2 genome locus, resulting in a nonsense mutation (p.Q74X), and used it as a model for base editor screening (Fig.\u00a03a and Supplementary Fig.\u00a08). For successful ABE-based base editing, an appropriate PAM is required to position the target base within the optimal editing window, approximately 3\u20138 positions from the 5\u2019 end of the protospacer. Thus, we searched for an NGG PAM sequence that the nCas9 of ABE could recognize near the target adenine. However, the target site only had one sgRNA binding site with the only available NGG PAM, which placed the target adenine at spacer position 2 (i.e., A2), which was outside the editing window where ABE works best (Fig.\u00a03b). Given the lack of suitable NGG PAMs, we explored the use of engineered or evolved Cas9 nuclease-based ABE variants targeting non-NGG PAM. Thus, we included four types of Cas9 variants with different PAM requirements: NG-Cas914 (NG), SpRY15 (NNN; NAN, NTN, and NCN), and eNme2-C16(N4NC) in addition to the canonical SpCas917 (NGG). Moreover, we also considered different ABE platforms including ABEmax, ABE8eWQ, and ABE8e18, which exhibited different editing activities and different editing windows. Consequently, we reconstituted a total of 14 ABE:sgRNA combinations using different adenine deaminases and Cas9 variants, and we designed all possible 6 sgRNAs to more optimally target the c.220\u2009C\u2009>\u2009T mutation in the MPZL2 allele. The target adenine was at position 2, 4, 5, 6 and 7 with the corresponding AGG, AG, TAG, CTA, TCT, and AGGACG PAMs, and these were referred to as sgRNA1\u20136. All ABE:sgRNA combinations were co-transfected into the HEK293T-MPZL2 mutant cells, and the on-target editing efficiency was analyzed by targeted deep sequencing. The results showed that SpRY-based ABE with sgRNA3 (NAN; TAG PAM) and sgRNA4 (NTN; CTA PAM) induced significantly higher levels of A-to-G editing at the target adenine compared to the other ABE:sgRNA combinations (Fig.\u00a03c). We thoroughly evaluated unintended bystander edit events (e.g., A-to-G or C-to-other (T/G/A) edits). Notably, the combination of ABE8eWQ-SpRY:sgRNA3 showed high precision by inducing less bystander editing and preserving high targeted editing efficiency (Bystander A1: 0.14%, A2: 0.15% and Target A5: 51.8%, Silent A9: 0.84%) (Fig.\u00a03c). While ABE8e-SpRY induced base editing at both the target adenine and bystander adenines (Bystander A1: 0.33%, A2: 1.77% and Target A5: 42.41%, Silent A9: 37.30%). Also, no cytosine bystander event was observed at both AC*N and TC*N motifs present within the targetable window (Fig.\u00a03c, d). This indicates that ABE8e had a wider editing window (3\u201310\u2009bp) than ABE8eWQ (4\u20138\u2009bp), and reiterate that ABE-induced cytosine deaminase activity was less significant compared to its adenine deamination activity which is consistent with previous reports19,20. Taken together, our data showed that it would be best to use ABE8eWQ-SpRY and ABEmax-SpRY with sgRNA3 and sgRNA4, respectively, to correct the target c.220\u2009C\u2009>\u2009T in exon 2 of MPZL2. Then we finalized the combination of ABE8eWQ-SpRY:sgRNA3 for further in vivo study considering that ABE8eWQ is known to have fewer sgRNA-independent RNA off-target effects compared to ABEmax21,22.\n\na Schematic representation of a correction strategy using PAM-flexible ABEs in HEK293T monoclonal cells harboring the homozygous C\u2009>\u2009T nonsense mutation (c.220\u2009C\u2009>\u2009T: p.Q74X) at the MPZL2 locus. b Design of sgRNAs targeting the MPZL2 c.220\u2009C\u2009>\u2009T mutation. For more effective editing, the target adenine A needs to be within ABE\u2019s preferred targeting window. In the table below, 6 sgRNA candidates featuring different PAMs are listed, each placing the target A (red) at different positions on the protospacer: A2, A4, A5, A6, and A7. c Comparison of the A-to-G editing efficiencies in vitro at the target adenine and bystander adenines for all ABEs used in this study (n\u2009=\u20093 for all conditions, except for ABE8eWQ:sgRNA3, for which n\u2009=\u20094). d C-to-other editing efficiencies at either AC*N and TC*N motifs within the targetable window of the selected SpRY-based ABEs. The data is from c (Mean values, same as in c, n\u2009=\u20093, except for ABE8eWQ:sgRNA3, for which n\u2009=\u20094). e Potential off-target sites identified by Cas-OFFinder and GUIDE-seq. The venn diagram shows the degree of off-target site overlap between the two methods (f) Heatmap showing A-to-G conversion rates at off-target sites predicted using Cas-OFFinder following ABE (sgRNA3\u2009+\u2009) treatment. g Validation of A-to-G conversion rates at GUIDE-seq-detected off-target sites after ABE (sgRNA3\u2009+\u2009) treatment. Sites that showed differences between control (untreated) and ABE (sg3\u2009+\u2009) samples are marked with an asterisk (*) and labeled by genomic locus, and coding region is shown in purple text. A purple asterisk (*) indicates overlapping site detected by both methods. h Targeted RNA off-target effects in \u00a0HEK293T cells mimicking the MPZL2 c.220\u2009C\u2009>\u2009T variant with three SpRY ABEs that were expressed using NAN PAM sgRNA. The average frequency of A-to-I transitions in three mRNA transcripts (CCNB1IP1, AARS1, and TOPORS) with each of the SpRY-mediated ABE variants. Source data for all relevant panels are provided within the Source Data file.\n\nTo evaluate potential sgRNA-dependent off-target reactions in HEK293T-MPZL2 mutant cells treated with ABE8eWQ-SpRY:sgRNA3, we screened for genome-wide off-target sites for SpRY using Cas-OFFinder software23, and we performed a PAMless NNN search that allowed for up to 2 mismatches and/or 1 DNA/RNA bulge outside the seed region at position 10\u201318 from the 5\u2019 end. We identified 18 potential off-target sites (OT1\u2013OT18) in the human genome (Fig.\u00a03e, Supplementary Data\u00a03). In all, base editing rates at the off-target sites were similar to controls, but we only detected a slight increase in the A-to-G conversion rate at position 5 at OT14, which is in the exon regions of the CDC34 gene (Fig.\u00a03f). However, there have been no clinical phenotypes associated with the CDC34 gene (https://www.omim.org/). We were also able to detect new potential off-target sites by GUIDE-seq24, in addition to the previously identified sites. One of these sites overlapped with the off-target sites, OT14; CDC34 gene identified by Cas-OFFinder (Fig.\u00a03e, Supplementary Data\u00a03). To determine whether base-editing events induced by ABE occurred at these sites, we performed targeted deep sequencing on sites that had a GUIDE-seq read count >5. Of these, except for overlapping site, the real off-target activity was observed at 4 sites (OT2, OT3, OT8 and OT11), which are in non-coding regions such as intergenic and intronic regions (Fig.\u00a03g).\n\nWe next evaluated sgRNA-independent RNA off-targets of ABE8eWQ-SpRY compared with two other ABE variants, ABEmax-SpRY and ABE8e-SpRY. We co-transfected each ABE variant along with sgRNA3 into HEK293T-MPZL2 mutant cells and analyzed the frequency of A-to-I conversion in three representative RNA transcripts (CCNB1IP1, AARS1, and TOPORS)22,25. Across all three mRNA transcripts, ABE8eWQ-SpRY induced lower RNA off-target effects compared to ABEmax-SpRY and ABE8e-SpRY, and these were similar to controls (Fig.\u00a03h). These data further support that ABE8eWQ-SpRY was the best option in our case.\n\nTo efficiently deliver ABE8eWQ-SpRY:sgRNA3 to the cochlea, we used AAV-ie, a variant with superior inner ear tropism and minimal ototoxicity that is known to target cochlear hair cell (HCs) and supporting cell (SCs) more effectively than other AAV serotypes12,26. Given the limitation of AAVs\u2019 small packaging capacity (\u2009~\u20094.7\u2009kb), we used a split intein-mediated AAV system to generate the dual vector of ABE8eWQ-SpRY:sgRNA3. The ABE was split into the amino-terminal (N) and carboxy-terminal (C) halves at amino acid residue 574 of SpRY (Fig.\u00a04a). Dual plasmids encoding C terminal and N terminal of ABE8eWQ-SpRY:sgRNA3 were co-transfected into these two cell lines, and on-target editing efficiency and bystander edits were analyzed before administering the dual AAV system in vivo. The editing efficiency of ABE8eWQ-SpRY:sgRNA3 in endogenous HEK293T-MPZL2 c.220\u2009C\u2009>\u2009T cell line was: Bystander A1: 0.12%, A2: 0.60% and Target A5: 50.71%, Silent A9: 0.57%, and no cytosine bystander event was observed (Fig.\u00a04b). We also tested editing efficiency and bystander editing events in an exogenous clonal MPZL2 c.220\u2009C\u2009>\u2009T mutation HEK293T cell line using dual plasmids that encoding the N-terminal and C-terminal of ABE8eWQ-SpRY:sgRNA3, and got the similar results. The editing efficiency of ABE8eWQ-SpRY:sgRNA3 in an exogenous clonal MPZL2 c.220\u2009C\u2009>\u2009T mutation HEK293T cell line was: Bystander A1: 0.10%, A2: 0.10% and Target A5: 56.1%, Silent A9: 0.67%, C1-to-other: 0%, C2-to-other: 0%, C3-to-other: 0%, and no cytosine bystander event was observed too (Supplementary Fig.\u00a09). Our results indicate that the editing efficiency of ABE8eWQ-SpRY:sgRNA3 in these two cell lines are similar, and the ABE8eWQ-SpRY:sgRNA3 shows high precision without detectable bystander edits and with relevant targeted editing efficiency.\n\na Schematic representation of the dual AAV constructs utilizing split-intein for ABE delivery in vivo, resulting in intein-mediated assembly of complete ABE:sgRNA complexes. The dual plasmids were packaged into the AAV serotype AAV-ie. b Assessment of the editing efficiencies of the MPZL2 target adenine and other bystander adenines or cytosines using dual AAV vectors that encoded split-intein ABE8eWQ-SpRY and sgRNA3 (n\u2009=\u20092). c Experimental overview of the in vivo base editing. Dual AAV-ie was used to induce split intein assembly, with the N-terminal and C-terminal components each prepared at 5.0\u2009\u00d7\u20091013\u2009vg/mL. These were mixed at a 1:1 ratio, and 2000 nl was injected into the RWM of P2 hMPZL2Q74X/Q74X mice. BioRender was used to make the Figure. \u201cCreated in BioRender. \ub0a8, \ubc30. (2025) https://BioRender.com/kz4zbsm\u201d. The images of the syringe, cochlea, and postnatal pup used in the figure were created using BioRender. d In vivo efficiency of A\u2009\u2219\u2009T to G\u2009\u2219\u2009C editing at on-target sites (P\u2009=\u20090.001) and bystander effects in DNA extracted from the organ of Corti (n\u2009=\u20098, P\u2009<\u20090.0001). Statistical analyses were performed using a two-tailed unpaired Student\u2019s t-test. In the box-and-whisker plot, the whiskers mark the minimum and maximum, the box includes the 25th to 75th percentiles, and the line within the box indicates the median of the data set. Significance levels are indicated as *P\u2009<\u20090.05, **P\u2009<\u20090.01, ***P\u2009<\u20090.001, and ****P\u2009<\u20090.0001. e In vivo potential off-target sites relative to the target site. The potential off-target sites were identified using Cas\u2013OFFinder, including those with up to two mismatches and/or up to one DNA/RNA bulge. f RNA off-target test in vivo at 8 weeks after dual AAV-ie-ABE8eWQ-SpRY:sgRNA3 injection. RNA off-target test in the injected mice was performed, and no RNA off-target editing was detected in vivo. n\u2009=\u20092 (Treated) and n\u2009=\u20092 (Control) per group. Source data for all relevant panels are provided within the Source Data file.\n\nAfter optimizing the injection volume (Supplementary Fig.\u00a010) and injection time point for dual AAV-ie-ABE8eWQ-SpRY:sgRNA3, a total of 2\u2009\u00b5L dual AAV (1:1 mixed) at a titer of 5.0\u2009\u00d7\u20091013\u2009vg/mL was injected into the inner ear of neonatal hMPZL2Q74X/Q74X mice at P1-2 via the round window membrane (Fig.\u00a04c) as previously described12. Fourteen days after injection, the organ of Corti was dissected from the cochleae of treated hMPZL2Q74X/Q74X mice. We sequenced the DNA from the cochleae of treated hMPZL2Q74X/Q74X mice and tested the base editing efficiency at the MPZL2 c.220\u2009C\u2009>\u2009T locus in the organ of Corti of eight treated mice. Our results showed that the on-target editing efficiency of treated hMPZL2Q74X/Q74X mice was 2.04\u2009\u00b1\u20090.44% (range: 1.28\u20133.66%, P\u2009=\u20090.001) (Fig.\u00a04d), and the bystander editing efficiencies of A0, A1, A2, and A9 were significantly low (Fig.\u00a04d). Additionally, we performed targeted deep sequencing to measure base editing efficiencies at the RNA level. RNA levels (hereafter referred to as cDNA) were assessed by extracting RNA from the organ of Corti of treated mice and untreated mice at P16, followed by reverse transcription into cDNA. The average cDNA editing efficiency in the organ of Corti of hMPZL2Q74X/Q74X mice injected with dual AAV-ie-ABE8eWQ-SpRY:sgRNA3 was 3.75% (range: 1.87\u20135.84%) (Supplementary Fig.\u00a011). We further used the Cas-OFFinder software to search for potential off-target sites of SpRY in mice and implemented high-throughput sequencing to analyze six potential off-target sites. In three treated mice aged 16 days, no obvious off-target editing was detected at the six predicted off-target sites compared with untreated mice (Fig.\u00a04e). We also performed RNA off-target test in the injected mice in vivo, and no RNA off-target editing was detected by ABE8eWQ-SpRY:sgRNA3 (Fig.\u00a04f). These results indicated that dual AAV-ie-ABE8eWQ-SpRY:sgRNA3 performed with relatively high precision without detectable bystander edits and with clinically relevant editing efficiency.\n\nTo investigate the in vivo base editing efficacy on auditory function, ABR and DPOAE thresholds were recorded at 4 weeks, 8 weeks, and 12 weeks post-injection. Representative click ABR waveforms at 12 weeks from Mpzl2 WT, untreated hMPZL2Q74X/Q74X, and treated hMPZL2Q74X/Q74X mice are shown in Fig.\u00a05a. At 4 weeks, treated hMPZL2Q74X/Q74X mice exhibited slightly lower ABR thresholds compared to untreated mice, showing reductions of 4.1\u2009dB, 10.4\u2009dB, and 3.2\u2009dB at 16\u2009kHz, 24\u2009kHz, and 32\u2009kHz, respectively, although these reductions were not statistically significant (Fig.\u00a05b). We continued to assess hearing function over time and observed more pronounced improvements at 8 weeks and 12 weeks post-injection. At 8 weeks, the ABR thresholds in the treated ears improved by 2.5\u2009dB to 23.3\u2009dB across all frequencies (Fig.\u00a05c). Specifically, a significant difference in hearing thresholds at 16\u2009kHz was observed between the treated ears of hMPZL2Q74X/Q74X mice and untreated hMPZL2Q74X/Q74X mice (49.2\u2009\u00b1\u20095.0\u2009dB vs. 72.5\u2009\u00b1\u20096.1\u2009dB, P\u2009=\u20090.007) (Fig.\u00a05c). By 12 weeks, the treated hMPZL2Q74X/Q74X ears showed greater recovery compared to untreated hMPZL2Q74X/Q74X mice, with threshold improvements from 12.2\u2009dB to 28.6\u2009dB across all frequencies, showing significant differences. Treated hMPZL2Q74X/Q74X mice exhibited significantly reduced thresholds by 20.4\u2009dB at click (P\u2009<\u20090.001), 11.2\u2009dB at 4\u2009kHz (P\u2009=\u20090.012), 26.9\u2009dB at 8\u2009kHz (P\u2009<\u20090.0001), 28.6\u2009dB at 16\u2009kHz (P\u2009=\u20090.001), 14.2\u2009dB at 24\u2009kHz (P\u2009=\u20090.028), and 12.5\u2009dB at 32\u2009kHz (P\u2009=\u20090.022) compared to untreated hMPZL2Q74X/Q74X mice (Fig.\u00a05d). The ABR threshold at 8\u2009kHz in treated hMPZL2Q74X/Q74X mice was comparable to that of Mpzl2 WT mice (Fig.\u00a05d). At 16 weeks, ABR measurements showed that the treated ears exhibited sustained recovery across most frequencies, with improvements ranging from 3.3\u2009dB to 46.48\u2009dB, except at 4\u2009kHz. Specifically, treated hMPZL2Q74X/Q74X mice recovered ABR thresholds by 22.5\u2009dB at click (P\u2009<\u20090.001), 23.7\u2009dB at 8\u2009kHz (P\u2009=\u20090.0001), 35.2\u2009dB at 16\u2009kHz (P\u2009<\u20090.0001), 13.6\u2009dB at 24\u2009kHz (P\u2009=\u20090.04) and 9.1\u2009dB at 32\u2009kHz (P\u2009=\u20090.012) compared to untreated hMPZL2Q74X/Q74X mice (Fig.\u00a05e). At 20 weeks, the treated ears maintained long-term recovery compared to the untreated ears, particularly at click, 8\u2009kHz, 16\u2009kHz, and 32\u2009kHz (P\u2009=\u20090.032) (Fig.\u00a05f). Notably, significant differences between treated and untreated ears ranged from 8.4\u2009dB to 36.6\u2009dB at click (P\u2009=\u20090.001), 9.6\u2009dB to 42.2\u2009dB at 8\u2009kHz (P\u2009=\u20090.001), 17.1\u2009dB to 44.0\u2009dB at 16\u2009kHz (P\u2009<\u20090.0001), and 0.5\u2009dB to 12.9\u2009dB at 32\u2009kHz. These findings confirm the long-term effectiveness and stability of AAV-ie-ABE8eWQ in restoring auditory function in hMPZL2Q74X/Q74X mice. The DPOAE thresholds did not show obvious differences between treated and untreated hMPZL2Q74X/Q74X mice at 4 weeks (Fig.\u00a05g). At 8 weeks, the DPOAE thresholds of the treated ears decreased by 3.3\u2009dB and 7.1\u2009dB at 8\u2009kHz and 16\u2009kHz, respectively (Fig.\u00a05h). At 12 weeks, the decreases were 6.1\u2009dB, 11.64\u2009dB, and 5.0\u2009dB at 8\u2009kHz, 16\u2009kHz, and 24\u2009kHz (Fig.\u00a05i). At 16 weeks, the treated ears exhibited a reduction of 8.8\u2009dB at 8\u2009kHz, 16.2\u2009dB at 16\u2009kHz, and 6.6\u2009dB at 24\u2009kHz compared to the untreated mice, with a persistent significant difference at 8\u2009kHz (P\u2009=\u20090.023) and16 kHz (P\u2009=\u20090.003) (Fig.\u00a05j). At 20 weeks, the treated ears showed a significant reduction of 9.1\u2009dB at 8\u2009kHz compared to the untreated ears (P\u2009=\u20090.048) (Fig.\u00a05k).\n\na Representative ABR waveforms from three groups of mice (Mpzl2 WT, hMPZL2Q74X/Q74X, and treated hMPZL2Q74X/Q74X) in response to click stimuli recorded at 12 weeks post-injection. The bold line in each group represents the threshold. b\u2013f Comparison of ABR thresholds among Mpzl2 WT (red line), hMPZL2Q74X/Q74X (blue line), and treated hMPZL2Q74X/Q74X (green line) mice at 4 weeks (b), 8 weeks (c),12 weeks (d), 16 weeks (e), and 20 weeks (f). Comparison of DPOAE thresholds among Mpzl2 WT (red line), hMPZL2Q74X/Q74X (blue line), and treated hMPZL2Q74X/Q74X (green line) mice at 4 weeks (g), 8 weeks (h),12 weeks (i), 16 weeks (j), and 20 weeks (k). At 16 weeks, 8\u2009kHz (P\u2009=\u20090.023) and 16\u2009kHz (P\u2009=\u20090.003); at 20 weeks, 8\u2009kHz (P\u2009=\u20090.048) showed significant differences between hMPZL2Q74X/Q74X and treated hMPZL2Q74X/Q74X mice. Statistical significance was calculated using one-way ANOVA with Bonferroni\u2019s correction for multiple comparisons. Significance levels are indicated as *P\u2009<\u20090.05, **P\u2009<\u20090.01, ***P\u2009<\u20090.001, and ****P\u2009<\u20090.0001. Data are presented as the mean\u2009\u00b1\u2009SEM. Source Data file. Source data for all relevant panels are provided within the Source Data file.\n\nTo confirm the safety of our therapeutic strategy, dual AAV-ie-ABE8eWQ-SpRY:sgRNA3 was injected into the inner ears of Mpzl2 WT mice. The ABR thresholds of injected Mpzl2 WT ears showed no deterioration compared to untreated Mpzl2 WT mice and un-injected (contralateral) Mpzl2 WT ears at 4 weeks, 8 weeks, or 12 weeks (Supplementary Fig.\u00a012a). Additionally, no degeneration of OHCs or DCs was observed in the organ of Corti of treated mice (Supplementary Fig.\u00a012b, c). Thus, in vivo base editing with the dual AAV-ie-ABE8eWQ-SpRY:sgRNA3 system demonstrated a favorable safety profile with no observed ototoxicity. Furthermore, to determine whether editing occurs in non-cochlear tissues, we performed on-target DNA editing analysis in the non-cochlear tissues, including liver, brain, and thigh muscle, collected at P16 following inner ear local injection of the dual AAV-ie-ABE8eWQ-SpRY:sgRNA3 system at P2. The results showed no dissemination in the non-cochlear tissues, with nearly undetectable levels, except for low-level editing (0.4\u20130.5%) observed in the liver of some treated mice (Supplementary Fig.\u00a013a). However, we observed the absence of potential off-target sites of SpRY in the liver tissues of mice with low-level editing (0.4\u20130.5%), as predicted by Cas-OFFinder software, consistent with findings in untreated mice (Supplementary Fig.\u00a013b).\n\nThe hMPZL2Q74X/Q74X mice treated at P1-2 were euthanized at P28, and RT-PCR and immunofluorescence analyses were performed. Human MPZL2 mRNA expression was significantly restored post-injection compared to untreated mice (Fig.\u00a06a). Consequently, MPZL2 expression in the cochlea, particularly in the organ of Corti of the treated mice, was significantly rescued, reaching levels comparable to those observed in Mpzl2 WT mice (Fig.\u00a06b, Supplementary Fig.\u00a014). These data suggest that normal MPZL2 protein was properly constructed and expressed in the target cells post-injection. Furthermore, at 12 weeks the number of OHCs in middle turn (P\u2009=\u20090.043) and the number of DCs in the middle (P\u2009=\u20090.019) and basal turns (P\u2009=\u20090.016) of treated mice were significantly greater than those in untreated mice (Fig.\u00a06c). Similarly, we observed increased survival of OHCs and DCs, as well as preserved organ of Corti, in treated mice compared with untreated mice (Fig.\u00a06d). Additionally, treated mice exhibited visible hair bundles, particularly in the middle and basal turns (Fig.\u00a06e). We subsequently performed RNA-seq to identify alterations in gene expression profiles in hMPZL2Q74X/Q74X mice treated with dual AAV-ie-ABE8eWQ-SpRY:sgRNA3. Principal Component Analysis of the RNA-seq data revealed a distinct clustering pattern of gene expression between treated and untreated mice (Fig.\u00a06f). A total of 5773 DEGs (3693 upregulated and 2080 downregulated) were identified between treated and untreated mice (Supplementary Fig.\u00a015a). GO analysis of the DEGs revealed significant enrichment in biological processes related to cell adhesion, ECM organization, and immune system process (Fig.\u00a06g). Through a filtering process, we pinpointed the upregulated DEGs in treated hMPZL2Q74X/Q74X mice relative to untreated hMPZL2Q74X/Q74X mice, identifying 80 transcripts that were also significantly downregulated in untreated hMPZL2Q74X/Q74X mice compared with the Mpzl2 WT mice (Fig.\u00a06h). These transcripts were notably enriched in biological processes related to cell adhesion and ECM organization (Supplementary Fig.\u00a015b). Specifically, we identified 14 transcripts associated with cell adhesion and ECM organization, and gene expression profiles in treated hMPZL2Q74X/Q74X mice were restored to the levels observed in Mpzl2 WT mice, in contrast to the downregulated expression in untreated hMPZL2Q74X/Q74X mice (Fig.\u00a06i). Collectively, these findings demonstrate that dual AAV-ie-ABE8eWQ-SpRY:sgRNA3 treatment not only restores MPZL2 mRNA and protein expression levels but also rescues the altered cellular pathways underlying the c.220\u2009C\u2009>\u2009T mutation, resulting in significant improvements in both hearing function and inner ear structural integrity.\n\na RT-PCR showed enhanced MPZL2 expression in cochlear membranous tissues at P28 following injection at P2. b Representative section images of MPZL2 expression in organ of Corti at P28 following injection at P2, immunolabeled with an anti-MPZL2 antibody (green). Scale bar: 25\u2009\u03bcm. c Representative whole-mount images of the HC and SC layer from Mpzl2 WT, untreated hMPZL2Q74X/Q74X, and treated hMPZL2Q74X/Q74X mice at 12 weeks of age, along with the quantification of surviving OHCs (n\u2009=\u20094) and DCs (n\u2009=\u20094) immunolabeled with anti-Myosin VIIa (HCs, green) and anti-Sox2 (SCs, red). Scale bar: 20\u2009\u03bcm. Data are presented as the mean\u2009\u00b1\u2009SEM. In the bar graphs, Mpzl2 WT is shown in red, untreated hMPZL2Q74X/Q74X in blue, and treated hMPZL2Q74X/Q74X in green. Statistical significance was determined using one-way ANOVA with Bonferroni\u2019s correction for multiple comparisons. Significance levels are indicated as *P\u2009<\u20090.05, **P\u2009<\u20090.01, and ***P\u2009<\u20090.001. d Representative section images of the organ of Corti at 12 weeks of age in Mpzl2 WT (n\u2009=\u20091), untreated hMPZL2Q74X/Q74X (n\u2009=\u20091), treated hMPZL2Q74X/Q74X mice (n\u2009=\u20092) after injection at P2, immunolabeled with anti-Myosin VIIa (HCs, red) and anti-Sox2 (SCs, green) antibodies. Scale bar: 25\u2009\u03bcm. e Scanning electron microscope images from Mpzl2 WT, hMPZL2Q74X/Q74X, and treated hMPZL2Q74X/Q74X mice in the middle and basal turns of the cochlea. Scale bar: 10\u2009\u03bcm. f Heatmap analysis of DEGs between untreated hMPZL2Q74X/Q74X mice (n\u2009=\u20093) and treated hMPZL2Q74X/Q74X mice (n\u2009=\u20093) (upregulation, red; downregulation, blue). g Biological processes (BP) terms associated with cell adhesion, ECM organization, and immune system processes. h Schematic diagram of RNA-sequencing analysis (n\u2009=\u20093). i Quantitative RT-PCR assay of the 14 genes of interest related to cell adhesion and ECM organization across the three groups (n\u2009=\u20093). Statistical significance was determined using one-way ANOVA with Kruskal\u2013Wallis test for multiple comparisons. Data are presented as the mean\u2009\u00b1\u2009SEM. Source data for all relevant panels are provided within the Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62562-8/MediaObjects/41467_2025_62562_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Recessive mutations are the most frequent cause of hereditary deafness10. Recessive mutations can be treated with AAV-mediated gene replacement, which delivers wild-type cDNA into inner ear cells to correct the underlying genetic defects27. We recently used the AAV-ie capsid to deliver the mouse Mpzl2 cDNA controlled by a ubiquitous promoter into the inner ear of Mpzl2 knock-out mice and rescued their hearing12. However, because the MPZL2 protein is expressed across multiple cell types within the inner ear, and because there is currently a lack of AAV serotypes and specific promoters that can precisely target these cells, gene replacement therapy may result in both overexpression of MPZL2 in targeted cells and ectopic expression in non-targeted cells, potentially leading to cytotoxic effects28,29. In addition, it is known that AAV-mediated gene therapies often fail to sustain their therapeutic effects over extended periods30,31.\n\nAn alternative treatment strategy for hereditary deafness is gene editing, which corrects the mutated DNA bases and addresses the genetic cause of hearing loss at its \u201croot\u201d rather than replacing the mutant gene32. Previously, in vivo CRISPR nuclease-based gene editing was used to disrupt the reading frame of dominant-negative alleles in dominant deafness mouse models33,34,35 and to restore the nonhomologous end joining-mediated frame in the Pcdh15 mouse model of DFNB2336. However, CRISPR-based disruption of the disease locus is ineffective for ameliorating recessive alleles. Moreover, CRISPR-driven double-strand breaks lead to unintended consequences such as large DNA deletions, chromosomal depletion, and p53-driven programmed cell death37,38. To bypass this issue, base editors offer a one-time therapeutic strategy to permanently correct pathogenic mutations without generating double-strand breaks39,40. However, a significant limitation of base editing gene therapy is the need to design distinct sgRNA systems for different mutation sites within the same gene, which demands substantial human and financial resources. In this regard, mutational hotspots or founder mutations present attractive targets for base editors. In DFNB111 patients, the predominant MPZL2 c.220\u2009C\u2009>\u2009T mutation enables universal application of the dual AAV-ie-ABE8eWQ-SpRY:sgRNA3 system without redesign.\n\nBecause inner ear HCs lack regenerative ability in mammals, their degeneration leads to permanent hearing loss. Congenital deafness caused by gene mutations induces disrupted inner ear structural integrity at birth or even during embryonic development, and in such cases gene transfer early in the prenatal period to correct genetic defects and prevent irreversible degeneration is an effective treatment strategy4. However, this strategy is complex and carries significant risks, precluding progress in preclinical research on gene therapies for congenital deafness. Preclinical research and clinical translation have advanced more rapidly for genes like OTOF, where the mutations do not disrupt the structural integrity of the inner ear2,41. Additionally, progressive hereditary deafness is particularly favorable for the development of gene therapy because the target cells in the cochlea remain structurally intact and functionally competent, providing a wider therapeutic window for intervention. Our study, along with previous studies, suggest that Mpzl2 mutants in mice do not interfere with cochlear development or cause the degeneration of normal HCs and SCs at P412,42. In hMPZL2Q74X/Q74X mice, hearing loss and the degeneration of OHCs and DCs both progress from P28 onward, providing a sufficient time window between the onset of hearing loss, the genetic diagnosis, and treatment43. Similarly, children with DFNB111 often experience milder forms of hearing loss during their teenage years, with some even passing newborn hearing screenings13. Notably, the extended therapeutic window for late-onset progressive deafness might reduce the risk of gene editing delivery vectors dispersing into the brain parenchyma, which is mitigated by the progressive occlusion of communication between the cochlear perilymph and cerebrospinal fluid in the human cochlea44.\n\nKey factors\u2014including advanced editing efficacy, safety, and optimized vector tropism\u2014are pivotal in correcting mutant alleles and restoring auditory function. For ABEs, a wild-type tRNA-specific adenosine deaminase (TadA) from Escherichia coli was engineered to operate on DNA instead of RNA, and ABEmax with TadA7.10 and ABE8e with TadA-8e variants were developed to have higher base editing activities. However, both ABEmax and ABE8e also have unwanted C-to-T conversion activities at a preferred motif (TC*N) and have transcriptome-wide sgRNA-independent off-target effects20. To address these issues, we previously developed a novel ABE variant (ABE8eWQ) by introducing two variants (V106W and D108Q) into ABE8e, which resulted in significantly reduced bystander effects and RNA off-target activity20,45. We found that the c.220\u2009C\u2009>\u2009T mutation could be targeted by ABE8eWQ-SpRY, which demonstrated high editing precision and robust editing efficiency in vitro. SpRY has the potential to broadly target the genome, and this is associated with off-target effects at the DNA level15. However, ABE8eWQ-SpRY exhibited no significant DNA off-targets at predicted sgRNA-dependent sites, along with undetectable RNA off-targets in the human cells. Using the dual AAV-ie-ABE8eWQ-SpRY:sgRNA3 system, we herein achieved high precision without detectable bystander effects and with relevant editing efficiency in vivo. This correlated with the restoration of hearing function, inner ear structures, and the expression of MPZL2 and related genes. Nevertheless, the ABE-sgRNA therapeutic system still requires further optimization to enhance its editing efficiency in vivo, with the goal of restoring higher levels of auditory function and with long term effects.\n\nIn summary, we identified an East Asia-specific founder mutation, the homozygous c.220\u2009C\u2009>\u2009T mutation in MPZL2, as a promising target for ABE-based gene therapy. The development of humanized mouse models and the successful correction of this mutation using a single PAM-flexible ABE may be a step toward clinical translation of base editing gene therapy for treating hereditary deafness, including most cases of MPZL2 deafness.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "This research complied with all relevant ethical regulations. Animal experiments in this research were approved by the Ethics Committee at Fudan University, China and the Institutional Animal Care and Use Committees (IACUC; MS-2022-01, MS-2023-01) at the GEM Center of Macrogen (Seoul, Republic of Korea) and at Seoul National University Hospital. The human data collection was approved by the Institutional Review Board of Seoul National University Hospital (no. IRB-H-0905-041-281 and IRB-H-2202-045-1298) and by the Eye, Ear, Nose and Throat Hospital affiliated with Fudan University Review Board of the Office of Research Compliance through protocol 2020122-1 and 2024073. We obtained written informed consent from either the parents of the children or from the participants themselves.\n\nIn this study, we used a retrospective research design and focused on pediatric participants attending the Hereditary Hearing Loss Clinic within the otorhinolaryngology departments of two institutions. 234 pediatric cases (\u2009\u2264\u200918 years of age, regardless of sex) with symmetric, mild-to-moderate (hearing threshold range 21\u201355\u2009dB), non-syndromic SNHL were involved. The demographic data and audiological phenotypes were retrieved from the electronic medical records.\n\nGenomic DNA was extracted from peripheral blood samples using a Chemagic 360 instrument (Qiagen, Venlo, Netherlands) or a DNeasy 96 Blood & Tissue Kit (Qiagen, Germany). We first applied whole-exome sequencing to sequence the exonic regions. The target regions were captured using SureSelectXT Human All Exon V5 (Agilent Technologies, Santa Clara, CA, USA) or CapTruth Human Exome V2.0 (Medical Laboratory of Nantong ZhongKe Co., Ltd., Nantong, China). A library was prepared following the manufacturer\u2019s instructions and was paired-end sequenced using a NovaSeq 6000 sequencing system (Illumina, San Diego, CA, USA) or DNBSEQ-T7 platform (MGI, Shenzhen, China) with an average depth of coverage of 100\u00d7. Sequencing reads were aligned to the human reference genome (GRCh38) and processed according to the Genome Analysis Toolkit best-practice pipeline for calling single nucleotide variants and short insertions/deletions (indels)46. The ANNOVAR program was used for variant annotation using the RefSeq gene set and Genome Aggregation Database (gnomAD)47,48. Rare non-silent variants were selected as candidates, including nonsynonymous single nucleotide variants, coding indels, and splicing variants. We also used the Korean Reference Genome Database (KRGDB) and KOVA (Korean Variant Archive; Korean population database) databases for further filtration of ethnic-specific variants49,50. Additionally, the ClinVar and HGMD databases were screened to check whether candidate variants had been previously identified in other patients51,52. For individuals exhibiting non-syndromic, symmetric, mild-to-moderate SNHL, we evaluated copy number variations using the SALSA Multiplex Ligation-dependent Probe Amplification Probemix P461-B1 STRC-CATSPER2-OTOA (MRC-Holland, Amsterdam, the Netherlands)53.\n\nPatients who remained undiagnosed after whole-exome sequencing were subjected to whole-genome sequencing. DNA libraries were prepared using TruSeq DNA PCR-Free Library Prep Kits (Illumina) or AngTruth-seq EZ DNA Library Preparation Module (for MGI) v2 (Medical Laboratory of Nantong ZhongKe Co. Ltd., Nantong, China) and sequenced on an Illumina NovaSeq6000 or DNBSEQ-T7 platform with an average depth of coverage of 30\u00d7. The obtained genome sequences were aligned to the human reference genome (GRCh38) using the BWA-MEM algorithm. PCR duplicates were removed using SAMBLASTER54. The initial mutation calling for base substitutions and short indels was performed using HaplotypeCaller and Strelka2, respectively55. Structural variations were identified using Delly56. We classified candidate variants according to the American College of Medical Genetics and Genomics and Association for Molecular Pathology guidelines for hearing loss57.\n\nC57BL/6\u2009N and ICR mice were purchased from Orientbio Inc. (Sungnam, Republic of Korea) and were housed under specific pathogen-free conditions in individually ventilated cages. The temperature and humidity of the breeding environment were maintained at 22\u2009\u00b1\u20091\u2009\u00b0C and 50%, respectively, with a 12-h light:dark cycle. All feed and individually ventilated cages were sterilized, and all air conditioners had filters. The protocols for the generation of hMPZL2Q74X and hMPZL2WT mice were performed according to the Korean Food and Drug Administration (KFDA) guidelines and were reviewed and approved by the Institutional Animal Care and Use Committees (IACUC; MS-2022-01, MS-2023-01) at the GEM Center of Macrogen (Seoul, Republic of Korea) and at Seoul National University Hospital. Female and male mice were randomly chosen for all experiments.\n\nsgRNA searches and potential off-target analyses were performed using the CRISPR design tools CHOPCHOP58 and CRISPOR59. To generate the hMPZL2Q74X mouse, the sgRNA was searched based on the NCBI database (NC_000075.7, NM_007962.4) within the partial regions of exon 1 and intron 1, including the 5\u2019 UTR-ATG region. For the hMPZL2WT mouse, the search was based on human MPZL2 (hMPZL2) transcript NM_005797.4, within the regions of Exon 2 and 3. The sequences of the sgRNAs used are listed in Supplementary Data\u00a04.\n\nThe hMPZL2Q74X targeting donor was designed based on the target site (GRCm39; chr9: 44,954,049) and incorporated approximately 600-bp homology arms on both sides of the hMPZL2Q74X expression cassette. The 5\u2019 and 3\u2019 homology arms included sequences from the upstream-5\u2019UTR and from intron 1 of the mouse Mpzl2 gene, respectively. The designed donor sequences were synthesized in vector DNA form (Invitrogen, Waltham, MA, USA). For the hMPZL2WT mouse, single-stranded DNA (ssDNA) was used for the donor. The ssDNA was designed with the hMPZL2 mutation site (c.220\u2009T\u2009>\u2009C:p.X74Q) at the center and 60\u2009bp homology arms on both sides and was synthesized.\n\nTo induce superovulation, 7.5 IU each of pregnant mare serum gonadotropin (PMSG; Dsmbio, Uiwang, Republic of Korea) and human chorionic gonadotropin (hCG; Dsmbio) were intraperitoneally injected for 5\u20138 weeks in female mice at 48-hour intervals. Subsequently, zygotes were obtained from the oviducts of these female mice after mating with males to produce hMPZL2Q74X mice. To generate hMPZL2WT mice, superovulation was induced in the same manner, and then unfertilized embryos were collected from female mice that had not mated with male mice. The collected embryos were in vitro fertilized using sperm from hMPZL2Q74X homozygous mice to ensure the development of zygotes. Previously reported detailed procedures for in vitro fertilization were referenced60. The zygotes were washed in Quinn\u2019s Advantage Medium with HEPES (ART-1024, Cooper Surgical, Trumbull, CT, USA) and then transferred to droplets of KSOM medium (MR-121-D, Sigma-Aldrich, St. Louis and Burlington, MA, USA) in a CO2 incubator at 37\u2009\u00b0C.\n\nsgRNA was synthesized using the GeneArt Precision sgRNA Synthesis Kit (A29377, Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer\u2019s instructions. Microinjection was performed by injecting a mixture composed of Cas9 protein (Macrogen), purified sgRNAs, and linearized hMPZL2Q74X vector or hMPZL2WT ssDNA into the pronucleus of 1-cell zygotes in Quinn\u2019s Advantage Medium with HEPES at concentrations of 20\u2009ng/\u00b5l, 25\u2009ng/\u00b5l, and 20\u2009ng/\u00b5L, respectively. Microinjected zygotes were incubated at 37\u2009\u00b0C for 2\u2009h and were transplanted by surgical methods into the oviducts of pseudopregnant recipient ICR mice at the 1-cell or 2-cell stages.\n\nGenomic DNA for genotyping was extracted from the tails of 2-week-old mice using an Axen Total DNA mini kit (MG-P005-50, Macrogen). The PCR assay was conducted using the EF-Taq DNA Polymerase (SEF16-R250, Solgent, Daejeon, Republic of Korea) and Axen High-Q Taq DNA Polymerase (MG-E-003-250, Macrogen). The PCR was performed in a total volume of 20\u2009\u00b5L consisting of 2\u2009\u00b5L of 10\u2009\u00d7 reaction buffer, 4\u2009\u00b5L of 5\u2009\u00d7 GC enhancer, 0.4\u2009\u00b5L of 10\u2009mM dNTP mixture, 2\u2009\u00b5L each of 10\u2009\u00b5M forward and reverse primers, about 200\u2009ng of extracted genomic DNA, and PCR-grade water to make up the final volume. The PCR cycling conditions were an initial denaturation at 95\u2009\u00b0C for 5\u2009min followed by 35 cycles at 95\u2009\u00b0C for 10\u2009s, 56\u201360\u2009\u00b0C for 20\u2009s, and 72\u2009\u00b0C for 30\u2009s/kb. For genotyping hMPZL2WT mice, PCR products using the hMPZL2Q74X allele as a template were added to create heteroduplexes through re-annealing, followed by treatment with T7 endonuclease 1 (M0302S, NEB, Ipswich, MA, USA). For hMPZL2WT mice, PCR cloning (E1203S, NEB) was additionally performed for allele separation, and Sanger sequencing was performed for sequence verification of the two types of humanized mice. The sequences of the primers used for genotyping are listed in Supplementary Data\u00a05.\n\nTo generate an endogenous cell line carrying a point mutation at a MPZL2 locus, identical to that found in DFNB11 patients, prime editing was employed. HEK293T (ATCC CRL-3216) cells were seed into a 24-well plate and transfected with PE and the corresponding pegRNA-expressing plasmid. After editing, successfully edited cells were isolated and expanded. Genomic DNAs of single-cell clones were individually purified, and the clone containing intended pathogenic single mutation (MPZL2 c.220\u2009C\u2009>\u2009T) was validated by Sanger sequencing. The primer pairs used are as follows: Forward primer- ATGGGGCATCTCAGTTTCACT and Reverse primer- CATGGGTTGGAAGGGATCTA.\n\nTo generate exogenous clonal MPZL2 c.220\u2009C\u2009>\u2009T mutation HEK293T cell line, the HEK293T-MPZL2 c.220\u2009C\u2009>\u2009T stable cell line was established by cloning a 201-bp disease-associated gene fragment containing the C\u00b7G-to-T\u00b7A mutation from the ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar/) by assembling the fragments into a modified lentivector from lentiCRISPR v2 (#52961), thus yielding the lentivector plasmid Lenti MPZL2-P2A-puro. HEK293T (ATCC CRL-3216) cells were seeded into 6-well plates (Corning) at approximately 85% confluency per well and were co-transfected with 2.25\u2009\u03bcg of the lentivector plasmid, 2.25\u2009\u03bcg psPAX2 (#12260, encoding the viral packaging proteins), and 1.5\u2009\u03bcg pMD2.G (#12259, encoding the VSV-G envelope protein) using Lipofectamine 3000 (L3000015, Thermo Fisher Scientific) following the manufacturer\u2019s instructions. Virus-containing supernatant was collected after 48\u2009h of transfection and centrifuged at 2000\u2009\u00d7\u2009g for 10\u2009minutes to remove cell debris and filtered through a 0.45-\u03bcm polyvinylidene difluoride (PVDF) filter (IPVH00010, Millipore). A total of 150\u2009\u03bcL filtered virus-containing supernatant was added to HEK293T cells at approximately 40\u201350% confluency cultured in 6-well plates. After 24\u2009h of transduction with lentivirus, the cells were split into the wells of a new plate supplemented with puromycin (2.5\u2009\u03bcg/ml). After 72\u2009h of puromycin selection, a stable cell line with the hMPZL2 c.220\u2009C\u2009>\u2009T mutation was successfully established. To ensure single-copy integration, cells with the fewest surviving colonies were collected and subsequently expanded for further transfection procedures.\n\nCultured cells used in this study, including HEK293T-MPZL2 c.220\u2009C\u2009>\u2009T and patient-derived cell lines, were maintained in a humidified atmosphere containing 5% CO2 at 37\u2009\u00b0C. All culture media consisted of DMEM supplemented with heat-inactivated FBS (10% for HEK293T cells and 20% for patient-derived fibroblasts), 100 units/mL penicillin/streptomycin, and 2 mM L-glutamine. Cells were routinely tested for mycoplasma contamination.\n\nThe ABE8e (no.138489, Addgene, Watertown, MA) and NG-ABE8e (no.138491, Addgene) used in this study were separately obtained from previously reported plasmids available from Addgene. Other ABE variant expression plasmids were constructed by replacing the deaminase and Cas-nuclease domains using a Gibson assembly of linearized destination vector and PCR amplicons with Gibson-compatible ends for each assembly junction. PCR was performed using KOD-Multi & Epi (KME-101, TOYOBO).\n\nThe sgRNA expression plasmids for the various Cas effectors were generated using custom oligonucleotides and restriction enzyme-based classical cloning methods based on pRG2 (no. 104174, Addgene). Briefly, for each guide RNA sequence, a pair of complementary oligos with 4-bp overhangs were annealed and inserted via a cut-ligation reaction with BsaI and T4 DNA ligase in the pRG2-hU6 plasmid. All sgRNA sequences were designed with a G preceding 19-bp or 23-bp spacer targets for transcription initiation from the Polymerase III promoter. To produce a vector expressing sgRNA for the other SpCas9 ortholog, eNm2-C, an sgRNA scaffold was synthesized from Macrogen and integrated with the pRG2-hU6 plasmid. The gRNA was inserted again into the plasmid, where the original sgRNA scaffold was swapped with the scaffold tailored for eNme2-C. The sgRNAs were inserted into the PSK-mU6-sgRNA plasmid between two Bbs1 restriction sites. All plasmids used in this study were purified using Nucleobond Xtra Midi EF (MN, 740420.5) for Midi-prep or Exfection plasmid LE (111-102, GeneAll) for Mini-prep. The sequences of the sgRNAs are listed in Supplementary Data\u00a04.\n\nTo evaluate the on/off-target editing efficiency, genomic DNA was amplified by a three-step nested PCR using KOD-Multi & Epi (KME-101, TOYOBO). PCR 1\u20132 steps were performed with primers specifically targeting the genomic region of interest. The total PCR cycles were kept to a minimum to avoid PCR bias. The primers used for PCR step 2 included the minimal adapters for adding barcodes in the following PCR step 3. Finally, the barcoded PCR products were pooled, purified using the Expin PCR SV mini kit (103-102, GeneAll), and sequenced using an Illumina Miniseq according to the manufacturer\u2019s instructions. Sequencing results were analyzed using a BE-Analyzer (http://www.rgenome.net/be-analyzer/)61.\n\nHEK293T cells were seeded in a 24-well plate, and the next day they were transfected with 390\u2009ng ABE-encoding plasmid and 125\u2009ng sgRNA using JetOPTIMUS\u00ae (Polyplus) according to the manufacturer\u2019s protocol. Briefly, the plasmids were mixed with JetOptimus reagent in JetOptimus buffer, then the mixture was incubated for 10\u2009min and added into each well. For RNA extraction, cells were harvested by treating with TRIzol reagent 24\u2009h after transfection. To synthesize cDNA, reverse transcription was performed using ReverTra Ace-\u03b1- (FSK-101, TOYOBO) according to the manufacturer\u2019s instructions. The target region was amplified using KOD-Multi & Epi (KME-101, TOYOBO), and the PCR products were analyzed using an Illumina MiniSeq instrument. To obtain the percentage of adenosines edited to inosines, the number of adenosines converted to guanosines was divided by the total number of adenosines in the products. The sequences of the primers are listed in Supplementary Data\u00a05.\n\nFor AAV production, we used the split intein-mediated ABE constructs designed in a previous study62, with some modifications as follows. Dual ABE constructs were amended sequentially in two steps each. The N-terminal vector was first modified by installing an additional A61R mutation (GCG to AGG) in the 5\u2032 half of the Cas9 coding sequence (2\u20131368 aa) for SpRY using the QuickChange mutagenesis method63. The TadA8eWQ domain and linkers were then subcloned into the N-terminal vector between the NotI and BgIII sites. The C-terminal vector was modified to encode the final selected sgRNA3 (NAN PAM) from the in vitro screening and to contain the remainder of the SpRY mutations. A pair of recombinant vectors contained either inverted terminal repeat (ITR)-CMV promoter-nucleic localization signal (NLS)-ecTadA8eWQ-SpRY(N)-Npu(N)-bGHpA-ITR or ITR-CMV promoter-Npu(C)-SpRY(C)-NLS-1xHA-bGHpA;U6-sgRNA-ITR. The dual viral plasmids for AAV packaging were purified using Nucleobond Xtra Midi EF (MN, 740420.5) for Midi-prep, and the dual viral plasmids were separately packaged into two AAV-ie viruses (PackGene Biotech).\n\nNeonatal mice of either sex were used for injections, and the mice were randomly assigned to the different experimental groups. All surgical procedures were performed in a clean, dedicated space, and all instruments were thoroughly cleaned with 70% ethanol and autoclaved prior to surgery. P1-2 hMPZL2Q74X/Q74X mice were used for AAV-ie-ABE8eWQ injection. Mice were anesthetized by hypothermia on crushed ice, and a skin incision was made behind the ear of the mouse to expose the tympanic ring and the stapedial artery under an operating microscope (OPMI pico, ZEISS). Glass micropipettes (WPI) held by a Nanoliter 2020 Microinjection System (WPI) were inserted through the round window membrane, which allows access to inner-ear cells. The total injection volume was 2\u2009\u03bcl per cochlea for round window membrane injection, and the release rate was 7 nL/s under the control of a MICRO2T SMARTouch microinjection controller (WPI). The skin was closed with a 6-0 nylon suture (NB617P, Ailee).\n\nIsolated tissues, including the Corti, liver, brain and thigh-muscle, were transferred into microtubes and finely minced and weighed. The following samples were then homogenized using a mini homogenizer and incubated in lysis buffer containing Proteinase K until complete lysis is obtained. For DNA extraction, silica column-based kit (NucleoSpin\u00ae Tissue, Macherey-Nagel, Germany) was used according to the manufacturer\u2019s protocol.\n\nGenomic DNA was purified from the organ of Corti of injected hMPZL2 c.220\u2009C\u2009>\u2009T mice and untreated hMPZL2 c.220\u2009C\u2009>\u2009T mice (negative control). Purified DNA was amplified by PCR for each condition using primers for the genomic DNA. HTS amplicon libraries containing an adapter sequence (forward, 5\u2032-ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TCT-3\u2032; reverse 5\u2032- ACT GGA GTT CAG ACG TGT GCT CTT CCG ATC T -3\u2032) at the 5\u2032 end was prepared by PCR using KOD-Plus-Neo DNA Polymerase (KOD-201, Toyobo). The aforementioned products were processed through another round of PCR amplification with distinct barcode sequences added to the primers. To test cDNA editing efficiency, RNA was extracted from a same tissue using AllspinTM (GeneAll, Korea) after homogenization of cochlear tissues. Reverse transcription to cDNA was performed by using ReverTra Ace qPCR RT master Mix (TOYOBO, FSK-101). Then, 1\u2009\u03bcL of cDNA was used for RT-PCR. PCR amplification was conducted using the same conditions as for genomic DNA. The sequences of the primers are listed in Supplementary Data\u00a05. The resulting libraries were pooled and subjected to 150\u2009bp paired-end sequencing on an Illumina HiSeq platform. The A-to-G conversions in the HTS data were analyzed with a BE-Analyzer61.\n\nMice were anesthetized via intraperitoneal injection with a mixture of dexmedetomidine hydrochloride (200\u2009mL/kg) and Zoletil (35\u2009mg/kg). ABR and DPOAE thresholds were measured using a BioSigRZ system (Tucker-Davis Technologies, Alachua, FL, USA) in a soundproof chamber. Anesthetized animals were first recorded to collect ABR, and then moved to a second setup to record DPOAE.\n\nABR signals were collected via subdermal needle electrodes placed at the mastoid portion of the skull, the vertex of the skull, and the dorsal rump as the recording electrode, reference electrode, and ground electrode, respectively. The ABR signals were evoked and filtered through a 300\u2009Hz to 3\u2009kHz passband and averaged at 512 repeats of each sound pressure level (SPL). Click stimuli (90 to 10\u2009dB, 2.5\u2009ms, 21 pps rate) and tone-burst stimuli (4, 8, 16, 24, and 32\u2009kHz, 2.5\u2009ms, 21 pps rate) were applied from 90 to 10\u2009dB SPL in 10\u2009dB steps. The ABR threshold was defined as the lowest sound level at which any waveform peak could be observed.\n\nThe f1 and f2 primary tones were generated at a frequency ratio of 1.2 with f2 levels at 4, 8, 16, 24, and 32\u2009kHz and L1 \u2013 L2\u2009=\u200910\u2009dB SPL. The f2 levels were swept in 5\u2009dB steps from 80\u2009dB to 20\u2009dB SPL. The DPOAE threshold was defined from the average spectra at the f2 levels that produced a DPOAE with a magnitude 5\u2009dB SPL above the noise floor.\n\nHistological and immunofluorescence analyses were conducted on cochleae from 12\u201315-week-old mice. After anesthetization via intraperitoneal injection, the mice were perfused with 4% paraformaldehyde (BPP-9004-001L, T&I) and the cochleae were dissected from the temporal bone. For histology, the cochleae were decalcified and embedded in paraffin blocks that were then sectioned at a thickness of 4 \u03bcm using a rotary microtome. The tissue sections were mounted on glass slides, deparaffinized in Histo-clear II (HS-202, National Diagnostics) for 10\u2009min, washed in 100%, 95%, 90%, and 70% ethanol, and finally washed for 10\u2009min in tap water. Hematoxylin and eosin staining was performed, with the slides being incubated in hematoxylin for 2\u2009min and then stained with eosin for 20\u2009s. The slides were subsequently mounted using a mounting medium and analyzed for cytoarchitecture evaluation at 4\u2009\u00d7 20\u2009\u00d7 and 100\u2009\u00d7 magnification using an ECLIPSE Ci microscope (Nikon).\n\nFor immunofluorescence assays of 12\u201315-week-old cochlea, tissue samples were post-fixed with 4% paraformaldehyde overnight at 4\u2009\u00b0C. The cochleae were rinsed with PBS and decalcified using 10% ethylene diamine tetraacetic acid (EDTA) overnight at room temperature (RT). The decalcified cochleae were cryoprotected with a sucrose gradient in PBS (10%, 20%, and 30% sucrose) for 1\u2009h at RT, then soaked in 30% sucrose overnight at 4\u2009\u00b0C. The specimens were embedded in Tissue-Tek OCT, and mid-modiolar cryosections were made at 10\u2009\u00b5m thickness and mounted on glass slides. The tissue sections were permeabilized with 1% Triton X-100 and blocked with 4% BSA in PBS for 20\u2009min at RT and then incubated overnight at 4\u2009\u00b0C with the following primary antibodies: rabbit anti-MYO7A (1:400 dilution, PA1-936, Invitrogen), mouse anti-SOX2 (1:400 dilution, sc-365823, Santa Cruz), rabbit anti-MPZL2 (1:300 dilution, 11787-1-AP, Proteintech), and mouse anti-TUBB3 (1:300 dilution, 801202, Biolegend). After washing with PBS, the sections were incubated with the corresponding Alexa-conjugated secondary antibodies for 1\u2009h at RT. After rinsing with PBS, specimens were mounted on microscope glass slides using mounting medium with DAPI (AB104139, Abcam). Images were captured using a Zeiss Axioskop light microscope and a Leica TCS SP5 confocal microscope (Leica, Wetzlar, Germany). For whole-mount immunofluorescence staining, the decalcified cochleae were dissected into three pieces in ice-cold PBS. Samples were permeabilized, blocked, and incubated overnight with rabbit anti-MYO7A and mouse anti-SOX2 or rabbit anti-MPZL2 primary antibodies at 4\u2009\u00b0C. Appropriate Alexa-conjugated secondary antibodies were applied for 1\u2009h at RT, and mounting medium with DAPI was used to mount the specimens. Images were obtained using a Leica TCS SP8 confocal microscope (Leica, Wetzlar, Germany). We adjusted the confocal z-stack range (1 to maximum 46) to ensure that staining patterns aligns consistently between whole mounts and cryosection samples. The numbers of MYO7A+ inner hair cells and OHCs and SOX2+ DCs were counted in 100\u2009\u03bcm cochlear sections from 10 cryosection images (two mid-modiolus sections per cochlea) obtained from five cochleas across the apical, middle, and basal turns, and the counted cells were analyzed as percentages. The number of SGNs in the apical, middle, and basal turns was counted using ImageJ software. For SGN quantification, TUBB3+ SGNs were counted per 72,900\u2009\u03bcm\u00b2. SGN density in H&E-stained paraffin sections was determined by counting the number of SGNs per 8000\u2009\u03bcm\u00b2.\n\nCochleae were dissected and gently perfused with 2.5% glutaraldehyde (G7651-10ML, Sigma-Aldrich) in PBS through the round and oval windows and into the apex. Samples were fixed in 2.5% glutaraldehyde in PBS overnight at 4\u2009\u00b0C. After washing with PBS, the cochleae were decalcified and dissected into three turns. The specimens were post-fixed in 1% osmium tetroxide (19172, EMS) in PBS for 2\u2009hours and dehydrated in an ethanol gradient (50%, 60%, 70%, 80%, 90%, and 100% ethanol) at 10-minute intervals. The samples were then rinsed with a mixture of 100% ethanol and hexamethyldisilazane (HMDS, Sigma-Aldrich) in a 1:1 ratio for 30\u2009min, followed by drying with HMDS only for 30\u2009min. The dried samples were mounted on metal disks and gold sputter-coated (Cressington sputter coater 208HR), and images were obtained at 5.0\u2009kV magnification using an electron microscope (Hitachi S-4700 FESEM).\n\nCochlea, kidney, and lung tissues were freshly dissected from P4 or P28 wild-type and mutant mice. These tissues were immediately frozen in liquid nitrogen and stored at \u201380\u2009\u00b0C until processing. Total RNA was isolated using TRIzol Reagent (Invitrogen) as per the manufacturer\u2019s instructions. cDNA synthesis was performed from 2\u2009\u03bcg of total RNA using Accupower RT-pre-mix (Bioneer, Daejeon, Republic of Korea). PCR was then performed using diluted cDNA and 10 pmol of specific primers to assess the mRNA levels. The thermal cycling conditions consisted of an initial denaturation at 95\u2009\u00b0C for 5\u2009min followed by 30 cycles of 95\u2009\u00b0C for 30\u2009s, 55\u2009\u00b0C for 30\u2009s, and 72\u2009\u00b0C for 1\u2009min. The amplified DNA was visualized using agarose gel electrophoresis with Loading STAR (DYNE Bio, Seongnam, Republic of Korea). For quantitative RT-PCR reactions, 1/20 diluted cDNA was combined with SYBR qPCR master mix as the reporter dye and 10 pmol of primers to detect mRNA expression of specific genes. The thermal cycling conditions were as follows: 95\u2009\u00b0C for 3\u2009minutes for enzyme activation followed by 40 cycles of 95\u2009\u00b0C for 10\u2009seconds, 53\u2009\u00b0C for 15\u2009seconds, and 72\u2009\u00b0C for 30\u2009s. The primer sequences are listed in Supplementary Data\u00a05.\n\nRNA sequencing analyses were conducted as previously reported64. Briefly, total RNA was extracted from P28 mouse cochleae (three pairs of cochleae from wild-type and mutant mice) utilizing TRIzol (Invitrogen, USA) following the manufacturer\u2019s protocol. RNA sequencing libraries were prepared using a SMARTER stranded total RNA-seq in accordance with the manufacturer\u2019s protocols (Takara, Japan). After confirming the library size (approximately 300\u2013500\u2009bp) and fluorophore-based quantification of each library concentration (Quantus; Promega, USA), the samples were sequenced on a Nextseq 2000 under paired-end conditions (Illumina). After filtering, fastq files were mapped against the reference genome GRCm39 (ensemble) and pseudo-aligned by Kallisto (version 0.51.0)65. Transcript counts were imported at the gene level using txiimport66, and DEGs were identified using DEseq267. Individual samples were further analyzed to list genes with a fold change greater than 1.5 and a p\u2009<\u20090.05. DAVID GO analysis was performed to examine GO terms for biological processes, cellular components, and molecular functions68. Representative GO terms were summarized and visualized using REVIGO69. DEGs were further compared with the mouse cochlear single-cell RNA-seq database (http://umgear.org) and the SHIELD database (http://shield.hms.harvard.edu) in order to refine the list of genes implicated in specific cellular pathways. The RNA-seq findings were validated by quantitative RT-PCR analysis using specific primers for selected DEGs. The sequences of the primers are listed in Supplementary Data\u00a05.\n\nStatistical analyses and visualizations were conducted using R Version 4.2.2. and GraphPad Prism (Version 9.0.2). The code for human genetics plots (Fig.\u00a01 and Supplementary Fig.\u00a01) is available at https://github.com/SNUH-hEARgeneLab/MPZL2. Differences between two groups were analyzed using an unpaired Student\u2019s t-test, while one-way ANOVA with Bonferroni correction and the Kruskal\u2013Wallis test were conducted for comparisons among more than two groups, as appropriate. All data are presented as the mean\u2009\u00b1\u2009SEM. Statistical significance is indicated in the figures as follows: ns, not significant; *P\u2009<\u20090.05; **P\u2009<\u20090.01; ***P\u2009<\u20090.001; ****P\u2009<\u20090.0001.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The DNA sequencing data reported in this paper cannot be deposited in a public repository because of the patient\u2019s genetic information, the possibility of privacy invasion, psychosocial risks (such as social stigma and discrimination), and the IRB\u2019s restrictions on public data release. To access the data, please submit a request to the lead contact (yilai_shu@fudan.edu.cn, maru4843@hanmail.net). The request will be reviewed and, if approved, the lead contact will work with the requestor on sharing the data, and by adhering to the consent agreements established with the study participants. The RNA-seq data generated in this study have been deposited in the GEO database under accession code GSE299064.\n\nStatistical analyses and visualizations were conducted using R Version 4.2.2 and GraphPad Prism Version 9.0.2, and the corresponding code can be accessed at https://github.com/SNUH-hEARgeneLab/MPZL2. Any additional information required to reanalyze the data reported in this paper is available from the lead contact (maru4843@hanmail.net) upon request.\n\nAll other data supporting this work are included in this article, the\u00a0Supplementary Information, or source data files.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Wang, H. et al. 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This research was supported and funded by Phase III (Postdoctoral fellowship) grant of the SPST (SNU-SNUH Physician Scientist Training) Program (S.-Y.L.), SNUH Kun-hee Lee Child Cancer & Rare Disease Project, Republic of Korea (No.25C-059-0100 to S.-Y.L.), National Research Foundation of Korea (NRF) and funded by the Ministry of Education (No.2022R1C1C1003147 to S.-Y.L.), SNUH Research Fund (No.37-2023-0120 & 04-2021-0670 to S.-Y.L.), Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (No.RS-2025-02213987 to S.-Y.L.),\u00a0and Seoul R&BD Program (No.BT240056 to S.-Y.L.). This research was supported by NRF (No.2021M3A9H3015389, SRC-NRF2022R1A5A102641311 to S.B.) and by the Korean Fund for Regenerative Medicine (KFRM) grant (No. RS-2024-00332601 to S.B.). The study was also supported by the National Natural Science Foundation of China (grants 82225014, 82171148 to Y.S., grant 82301318 to B.Z., and grant 82301332 to H.T.), the National Key R&D Program of China (grant 2020YFA0908201 to Y.S., and grant 2023YFA0915004 to H.T. and B.Z.), the Science and Technology Commission of Shanghai Municipality (grants 23J31900100 to Y.S.), the Shanghai Municipal Education Commission (grant 2023ZKZD12 to Y.S.),\u00a0National Key Research and Development Program of China (grant 2020YFA0908201 to Y.S.), Xuhui District Hospital and Site Cooperation Project (Life and Health field, 23XHYD-05, to Y.S.), Shanghai Municipal Health Commission (grant 2024CXJQ02 to Y.S.) and the Chenguang Program of the Shanghai Education Development Foundation and the Shanghai Municipal Education Commission (grant 23CGA08 to B.Z.). We wish to thank Aavatar Therapeutics (Seunghee Cho, Sangho Park, Dongwoo Song, and Beomseok Choi) for their invaluable technical support during this research.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Shao Wei Hu, Sohyang Jeong, Luoying Jiang, Hansol Koo.\n\nENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China\n\nShao Wei Hu,\u00a0Luoying Jiang,\u00a0Zijing Wang,\u00a0Biyun Zhu,\u00a0Dan Mu,\u00a0Huixia Guo,\u00a0Ziyi Zhou,\u00a0Yingting Zhang,\u00a0Liheng Chen,\u00a0Luo Guo,\u00a0Yang Xiao,\u00a0Honghai Tang,\u00a0Xi Chen,\u00a0Ai Chen\u00a0&\u00a0Yilai Shu\n\nDepartment of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea\n\nShao Wei Hu,\u00a0Sohyang Jeong,\u00a0Won Hoon Choi,\u00a0Min Gu Kim,\u00a0Sung Ho Jung,\u00a0Ho Byung Chae,\u00a0Myung-Whan Suh,\u00a0Moo Kyun Park,\u00a0Jae-Jin Song,\u00a0Jun Ho Lee\u00a0&\u00a0Sang-Yeon Lee\n\nNHC Key Laboratory of Hearing Medicine, Shanghai, China\n\nShao Wei Hu,\u00a0Luoying Jiang,\u00a0Zijing Wang,\u00a0Biyun Zhu,\u00a0Dan Mu,\u00a0Huixia Guo,\u00a0Ziyi Zhou,\u00a0Yingting Zhang,\u00a0Liheng Chen,\u00a0Luo Guo,\u00a0Yang Xiao,\u00a0Honghai Tang,\u00a0Xi Chen,\u00a0Ai Chen\u00a0&\u00a0Yilai Shu\n\nState Key Laboratory of Brain Function and Disorders and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China\n\nShao Wei Hu,\u00a0Luoying Jiang,\u00a0Zijing Wang,\u00a0Biyun Zhu,\u00a0Dan Mu,\u00a0Huixia Guo,\u00a0Ziyi Zhou,\u00a0Yingting Zhang,\u00a0Liheng Chen,\u00a0Luo Guo,\u00a0Yang Xiao,\u00a0Honghai Tang,\u00a0Xi Chen,\u00a0Ai Chen\u00a0&\u00a0Yilai Shu\n\nShanghai Key Laboratory of Gene Editing and Cell Therapy for Rare Diseases, Fudan University, Shanghai, China\n\nShao Wei Hu,\u00a0Luoying Jiang,\u00a0Zijing Wang,\u00a0Biyun Zhu,\u00a0Dan Mu,\u00a0Huixia Guo,\u00a0Ziyi Zhou,\u00a0Yingting Zhang,\u00a0Liheng Chen,\u00a0Luo Guo,\u00a0Yang Xiao,\u00a0Honghai Tang,\u00a0Xi Chen,\u00a0Ai Chen\u00a0&\u00a0Yilai Shu\n\nInstitutes of Biomedical Sciences, Fudan University, Shanghai, China\n\nShao Wei Hu,\u00a0Luoying Jiang,\u00a0Zijing Wang,\u00a0Biyun Zhu,\u00a0Dan Mu,\u00a0Huixia Guo,\u00a0Ziyi Zhou,\u00a0Yingting Zhang,\u00a0Liheng Chen,\u00a0Luo Guo,\u00a0Yang Xiao,\u00a0Honghai Tang,\u00a0Xi Chen,\u00a0Ai Chen\u00a0&\u00a0Yilai Shu\n\nCancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea\n\nHansol Koo\u00a0&\u00a0Sangsu Bae\n\nDepartment of Transdisciplinary Research and Collaboration, Genomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea\n\nHeeyoung Seok\n\nDepartment of Otorhinolaryngology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China\n\nYi Zhou\n\nGEM Division, Macrogen Inc., Seoul, South Korea\n\nSung-Yeon Lee\n\nLaboratory of Theriogenology, College of Veterinary Medicine, Seoul National University, Seoul, South Korea\n\nSung-Yeon Lee\n\nDepartment of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea\n\nSangsu Bae\n\nGenomic Medicine Institute, Seoul National University College of Medicine, Seoul, Republic of Korea\n\nSangsu Bae\n\nSensory Organ Research Institute, Seoul National University Medical Research Center, Seoul, Republic of Korea\n\nSang-Yeon Lee\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY.S., S.-Y.L., and S.B. jointly conceived and supervised the project. S.-Y.L., and S.H.J. performed the sequencing and bioinformatics analysis. S.W.H., H.K., W.H.C., H.S., B.Z., D.M., H.G., Y.Z., H.T., L.C., and X.C. performed the in vitro experiments. S.-Y.L., S.J., L.J., M.G.K., H.B.C., S-Y.L., Z.W., Y.Z., Z.Z., Y.X., and L.G. performed the in vivo experiments and analyzed the data. S.-Y.L., Y.S., S.B., S.W.H., S.J., L.J., and H.S. wrote the manuscript. Y.S., S.-Y.L., S.B., S.W.H., L.J., Z.W., A.C., M.-W.S., M.K.P., J.-J.S., J.H.L., and B.Z. reviewed and revised the manuscript. All authors read and approved the final manuscript.\n\nCorrespondence to\n Sangsu Bae, Sang-Yeon Lee or Yilai Shu.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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PAM-flexible adenine base editing rescues hearing loss in a humanized MPZL2 mouse model harboring an East Asian founder mutation.\n Nat Commun 16, 7186 (2025). https://doi.org/10.1038/s41467-025-62562-8\n\nDownload citation\n\nReceived: 15 November 2024\n\nAccepted: 23 July 2025\n\nPublished: 05 August 2025\n\nVersion of record: 05 August 2025\n\nDOI: https://doi.org/10.1038/s41467-025-62562-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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fraction of Antarctic supraglacial melt lakes with physics-based parameterizations", + "pre_title": "Predicting Mean Depth and Area Fraction of Antarctic Supraglacial Melt Lakes with Physics-Based Parameterizations", + "journal": "Nature Communications", + "published": "15 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61798-8/MediaObjects/41467_2025_61798_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61798-8/MediaObjects/41467_2025_61798_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.15467941", + "/articles/s41467-025-61798-8#ref-CR34", + "/articles/s41467-025-61798-8#ref-CR35" + ], + "code": [ + "https://github.com/dgrau13/meltlake-parameterizations" + ], + "subject": [ + "Cryospheric science", + "Statistical physics" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5410978/v1.pdf?c=1752664053000", + "research_square_link": "https://www.researchsquare.com//article/rs-5410978/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61798-8.pdf", + "preprint_posted": "10 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Supraglacial melt lakes have been implicated in decreasing albedo and increasing flow speed of the Greenland Ice Sheet and ice shelf disintegration in Antarctica. However, supraglacial melt lakes are largely not represented in current large-scale climate and ice sheet models due to their small size compared to typical model grid spacing. In this study, we first use extensive surface elevation measurements from the ICESat-2 satellite altimetry mission to show that roughness on the Antarctic Ice Sheet surface is largely self-affine, consistent with prior observations of bed roughness beneath ice sheets and geomorphic surfaces more broadly. This self-similarity of ice sheet surfaces across scales enables us to develop a broadly applicable set of simple mathematical expressions parameterizing the average supraglacial melt lake area fraction and lake depth. These parameterizations depend only on two ice sheet roughness parameters and the depth of water supplied as runoff from the surface melt. We derive these parameterizations from statistical fitting of large Monte Carlo ensembles of numerical simulations of water flow on random, self-affine surfaces and show that they provide predictions that are generally consistent with observations. Finally, we predict that on large portions of Antarctic ice shelves supraglacial lakes are likely to, on average, stay less than one meter deep and occupy less than 40% of the ice area, absent changes in ice shelf surface roughness.Earth and environmental sciences/Climate sciences/Cryospheric sciencePhysical sciences/Physics/Statistical physics, thermodynamics and nonlinear dynamics/Statistical physics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "APhysicsBasedParameterizationSupplementary.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Despite the importance of supraglacial melt lakes to the future evolution of polar ice sheets, they are not represented in current large-scale climate and ice sheet models. In this study, we use ICESat-2 satellite surface elevation measurements to show that roughness on the Antarctic Ice Sheet surface is largely self-affine. Estimation of ice sheet surface roughness statistics then enables the development of a set of simple mathematical expressions parameterizing the average supraglacial melt lake area fraction and lake depth from statistical fitting of large simulation ensembles of water flow on random, self-affine surfaces. These parameterizations provide predictions that are generally consistent with observations, with some exceptions. Finally, we predict that on large portions of Antarctic ice shelves supraglacial lakes are likely to, on average, stay less than one meter deep and occupy less than 40% of the ice area, absent changes in ice shelf surface roughness.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Over the past three decades, observations have shown widespread surface melting and supraglacial lake formation on Antarctic ice shelves1, driven by atmospheric warming2. Atmospheric temperatures over Antarctica are expected to continue increasing in the future, along with an increasing prevalence of melt lakes on the ice sheet surface3,4,5,6. Supraglacial melt lakes have been implicated in the thinning and collapse of floating ice shelves in Antarctica, with the prominent example of the Larsen B ice shelf collapse during the 2001\u20132002 melt season7,8,9. In the days before the collapse, many supraglacial melt lakes formed on the ice shelf surface, which is hypothesized to have caused hydrofracturing across the ice shelf and its eventual disintegration7,9,10.\n\nIce sheet surface melt affects the surface energy balance of glacial surfaces. Darkening of ice by water (i.e., decreasing surface albedo) causes increased absorption of incoming shortwave radiation by the ice and snow. Ice sheet surface melting initiates a positive feedback, whereby a lower surface albedo allows for more shortwave absorption, further enhancing meltwater production and deepening lakes. After refreezing, albedo is permanently altered from its original value due to snowpack and firn density changes from meltwater saturation, which promotes future melt lake formation at these locations11. Additionally, supraglacial melt lakes alter the latent and sensible heat fluxes through air-ice sheet interactions12,13.\n\nMeltwater from supraglacial lakes may also fill surface fractures in the ice sheet, causing them to propagate deeper9,14,15,16. Incorporating supraglacial melt lakes into large-scale models is thus vital in properly simulating the potential interaction between surface melt and ice sheet fracturing and calving. Supraglacial melt lakes are currently omitted from large-scale models because they are typically small (101\u2013103 meters across)17 compared to the grid spacing in climate (104\u2013105 meters) and ice sheet models (103\u2013104 meters). Existing numerical models of supraglacial hydrology are computationally expensive to run over entire ice sheets at decadal or longer time scales11,18,19. Furthermore, no physically-based theories or empirical parameterizations exist that capture the area-averaged effects of supraglacial melt lakes on the surface energy balance and ice sheet fracturing. This study aims to remedy these gaps through the development of supraglacial melt lake parameterizations that can be implemented in large-scale models.\n\nIn this study, we develop simple equations parameterizing the spatially averaged size of supraglacial melt lakes, which can be added to large-scale models at negligible computational expense. The core idea behind these parameterizations derives from prior theoretical advances from percolation physics20,21 and terrestrial hydrology studies which find robust statistical relationships between lake size and parameters describing surface roughness (on land or otherwise)22. In this work, we define this surface roughness through the self-affinity of the glacier surface. Self-affinity is a fractal property exhibited by most of the Earth\u2019s surface22,23. This property, quantified by the Hurst exponent, measures how the vertical scale of topography varies across horizontal scales23.\n\nWe start by using satellite altimetry measurements to show that surface roughness is self-affine over the vast majority of the Antarctic Ice Sheet. We quantify relevant roughness parameters for the grounded and floating portions of the ice sheet. We then develop a set of simple mathematical parameterizations through a large Monte Carlo ensemble of physics-based simulations of supraglacial meltwater flow over self-affine surfaces. We validate these parameterizations by comparing melt lake area fraction and depth predictions to observations from satellite imagery on two Antarctic ice shelves. Finally, we estimate the maximum attainable mean melt lake depth and area fraction for the current roughness characteristics of the Antarctic Ice Sheet. The methods used throughout this study are described in more detail in the \u201cMaterials and Methods\u201d section. In short, this study confirms that ice sheet surfaces, like other geomorphic surfaces, are self-affine and provides a set of supraglacial melt size parameterizations that can be implemented in large-scale models.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Self-affinity describes a repeating pattern within an object, sequence, or surface that occurs over a wide range of scales (also referred to as \u201cfractal\u201d or \u201cself-similar\u201d) (Fig.\u00a01). Analysis of land surface elevation measurements shows Earth\u2019s surface roughness is largely self-affine23,24, extending even to the land surface beneath ice sheets25. The self-affinity of Earth\u2019s land surface has been used to develop elegant predictions for the size distribution of terrestrial lakes filling depressions on this surface22,24,26. In this section, we investigate whether the Antarctic ice sheet surface also exhibits such self-affinity, and use satellite altimetry measurements to quantify two statistical roughness properties of the ice sheet surface: Hurst exponent (H) and standard deviation (\u03c3).\n\nIllustration demonstrating the relationship between the vertical and horizontal scales of a glacial self-affine surface. The horizontal length scale decreases moving down in the Figure. Blue areas are supraglacial melt lakes filling local depressions. This figure is inspired by Figure 1 of\u200622.\n\nWe estimate surface roughness properties across the Antarctic ice sheet, using the entire Antarctic ICESat-2 ATL06 land-ice elevation data catalog from June 2021 to June 2022 (described further in \u201cMaterials and Methods\u201d). This one-year period includes four repeats for each track27, and we found little temporal variation in surface roughness parameters over that period. All sub-tracks with calculated Hurst exponents less than zero or greater than one are discarded, since they cannot be reliably described as self-affine (i.e., outside the acceptable confines of the range for the Hurst exponent). Typically, such cases are related to spurious elevation data causing large deviations in the power spectral density. Additionally, any linear regressions with R2 values less than 0.7 are discarded to ensure that the only sub-tracks considered are those in which the data strongly indicate roughness is self-affine. Of the 1.8 million sub-tracks that were analyzed, 7.3% of the sub-tracks were discarded during the analysis process. 7.1% were discarded due to poor R2 values when fitting, and the remaining 0.2% were discarded due to the calculated Hurst exponents being outside the acceptable range of [0,1]. As a result, over 92% of all the subtracks analyzed exhibit the qualities of a self-affine surface. Consequentially, we conclude that the Antarctic Ice Sheet surface is largely self-affine.\n\nFigure\u00a02 also shows little spatial variation of the Hurst exponent across the ice sheet. The average Hurst exponent across the continent is \u00a0\u22480.41 with 95% of Hurst exponents estimated to be between 0.35 and 0.48. Floating ice has an average Hurst exponent of 0.47, and grounded ice has an average Hurst exponent of 0.41 (Supplementary Fig.\u00a04). This difference between the mean of the two distributions is statistically significant as assessed by Welch\u2019s t-test, with a reported P value of 0. With higher mean Hurst exponent, ice shelf surfaces have larger scale roughness features, since basal melt rates tend to be higher on steep small-scale basal roughness features28,29 and the buoyant flexure of floating ice filters small scale roughness at the base from surface expression. The Hurst exponent is less than previously reported Hurst exponents for subglacial topography in Greenland as observed via ice penetrating radar in25, which has a mean of 0.65. This indicates either a consistent difference between roughness in Greenland and Antarctica, or that processes related to snow redistribution and densification may influence surface topography, which creates smaller-scale topographical variability at the ice sheet surface than at the base30.\n\na The calculated Hurst exponents across land ice in Antarctica within a single deviation of the mean. b Standard deviation of topography across Antarctica. c The probability distribution function of the calculated Hurst exponents over Antarctic land ice. d Cumulative Density Function of the standard deviation of topography.\n\nThe standard deviation, \u03c3, quantifies the average amplitude of roughness along the ice sheet surface within each ICESat-2 sub-track and is plotted in Fig.\u00a02b for the whole of the Antarctic ice sheet surface. \u03c3 mainly varies over grounded ice due to the varying subglacial bed topography, over which ice flows. Greater roughness is notable over subglacial topographic features including the Trans-Antarctic mountains, Palmer Island, and Roosevelt Island. 74% of sub-tracks have relatively smooth ice sheet surfaces with \u03c3\u00a0<\u00a010 m. Grounded ice has an average \u03c3 of 13.0 meters, which is considerably rougher than floating ice with an average \u03c3 of 4.7 meters (Supplementary Fig.\u00a05).\n\nWe also investigated whether the estimated roughness parameters are biased by the track orientation or \u201ccapture\u201d angle of the ICESat-2 satellite. We found that the Hurst values do not vary much based on the orientation angle of the satellite, and most of the measurements center around the value of \u00a0~0.4, which the average difference in Hurst exponent values at intersection points being 0.0218. For \u03c3, the values between the two different sets of capture angles were also found to center around a single value. Details of this crossover analysis can be found in the\u00a0supplementary material (Supplementary Fig.\u00a01).\n\nIn the prior section, we confirmed that roughness on nearly the entirety of the Antarctic ice sheet surface is self-affine. Since water gathers in surface depressions, the statistics of roughness on the surface can be used to determine the statistics of lake size on that surface, assuming that the surface topography does not change temporally22,31. While ice sheet surface melt may deepen depressions in which melt lakes form over many years, at first order, the pre-existing topography plays a strong role in determining melt lake size as validation with observations confirms in the validation section. Prior work in the statistical physics community has tackled the related problem of percolation on uncorrelated potential surfaces20, producing analytical predictions for the size of clusters (melt lakes in our case) using mean-field mathematical approaches. However, such analytical approaches have been shown21 to be intractable for strongly correlated surfaces (H\u00a0>\u20090; i.e., self-affine) and surfaces that are not completely flooded (i.e., at their maximum capacity for water retention), such as those commonly found on Earth. For self-affine and partially flooded surfaces, numerical methods are necessary to calculate the statistics of lake size.\n\nIn this section, we use a cellular automaton (details described in \u201cMaterials and Methods\u201d), a discrete algorithm that divides a larger surface into state-dependent cells, to simulate the progressive development of lakes on randomly generated self-affine surfaces. We run this cellular automaton for thousands of randomly generated self-affine surface, as a function of \u03c3,\u00a0\u2009H, and meltwater available for ponding (referred to as \u201csupply\u201d hereafter). From these numerical results, we calculate two melt lake size metrics, average area fraction (\\(\\bar{F}\\)) and average supraglacial lake depth (\\({\\bar{w}}_{l}\\)). Once we have a set of model results encompassing the full parameter space, we seek to find a simple mathematical relationship between each of the melt lake size metrics, and the three input parameters: surface roughness Hurst exponent (H), and amplitude (\u03c3), and the average depth of meltwater supplied to the surface (ws). However, first, we fit a relationship between the surface roughness parameters and the average depth of water over the entire surface when it is at its retention capacity, which we call \\({\\bar{w}}_{d}^{*}\\). This quantity is frequently referred to as the \u201cmaximum depression storage\u201d in terrestrial hydrology32. We calculate the maximum depression storage in a separate set of simulations using the full range of \u03c3 and H by provided an excess of water supply for the generated surface and extracting the mean depth across the entire surface. We then fit a simple mathematical expression that can determine the maximum average water depth of any self-affine surface at its maximum water retention capacity utilizing only surface roughness parameters:\n\nConsistent with prior studies in hydrology and percolation physics21,33, this equation predicts a linear increase (with a similar coefficient) in retained water depth with roughness amplitude and a sub-linear decrease in retained water depth with Hurst exponent.\n\nWe use this maximum depth to define S, a dimensionless \u201csupply ratio\u201d\n\nDefining this ratio normalizes differences between surfaces with strongly different retention capacities, and thus greatly improves the parameterization fit to numerical simulations over a wide range of roughness statistics.\n\nWe fit spatially averaged supraglacial melt lake size metrics to the topographical Hurst exponent (H), roughness amplitude (\u03c3), and supply ratio (S). We know that average lake depth (\\({\\bar{w}}_{l}\\)) should be linearly dependent on \u03c3 since the depth of lakes is set by the depth of depressions on the surface which are described by the roughness amplitude. The numerical results from the cellular automaton confirmed this linear dependency. By definition, the average horizontal extent of vertical topographic relief on a self-affine surface does not depend on the average roughness height22. Therefore, we expect that the area fraction of supraglacial melt lakes (\\(\\bar{F}\\)) should have no dependence on \u03c3. The numerical results from the cellular automaton also confirmed that \\(\\bar{F}\\) is not dependent on \u03c3 despite the slight variation in Fig.\u00a04(c) across the \u03c3 space, which may be due to the sample size of surfaces leading to spurious residuals from the individual surfaces. With these results in mind, we were able to use standard least squares regression tools to fit a set of two parameterizations that describe a relationship between each melt lake characteristic (\\({\\bar{w}}_{l}\\) and \\(\\bar{F}\\)) and the three parametric inputs (\u03c3,\u00a0\u2009H,\u00a0\u2009S). The form of these equations is chosen to enforce certain basic physical constraints (described in \u201cMaterials and Methods\u201d). We found the following parameterizations for supraglacial melt lake size metrics:\n\nThese parameterizations all have an R2 value greater than 0.82 compared to the generated numerical results from the cellular automaton. Figures\u00a03 and 4 show the numerical predictions for melt lake metrics, the predictions from parameterizations, and the relative difference between the numerical results and parameterizations. The relative difference between the numerical and parameterized predictions is generally less than 25% for the lake depth parameterization and less than 10% for the area fraction parameterization. For both melt lake size metrics, the relative difference throughout the parameter space falls under 10% when the Hurst value is equivalent to 0.4, i.e., the mean Hurst exponent of the Antarctic ice sheet surface.\n\na Parameter space of the mean lake depth-averaged over 500 randomly generated self-affine surfaces. b The parameter space of the mean lake depth of parameterizations fits with the input variables of the numerical simulations. c The relative difference between the numerical and predicted mean lake depth. The Hurst value is held constant at 0.4 for this parameter space.\n\na Parameter space of the mean area fraction-averaged over 500 randomly generated self-affine surfaces. b The parameter space of the mean area fraction of parameterizations fits with the input variables of the numerical simulations. c The relative difference between the numerical and predicted mean area fraction. The Hurst value is held constant at 0.4 for this parameter space.\n\nTwo random self-affine surfaces with the same Hurst exponent and standard deviation can be quite different from each other and produce fairly different supraglacial melt lake distributions. Thus, when applying these parameterizations to a particular self-affine ice sheet surface area, they may depart from the melt lake size statistics due to random surface variation alone. In the supplementary material, we plot the root-mean-square deviation between the parameterizations and each realization from the numerical results, to quantify the expected natural variation among randomly generated self-affine surfaces (Supplementary Fig.\u00a03). Since we have generated enough random self-affine surfaces in our Monte Carlo ensemble to reach statistical convergence in mean melt lake size metrics, the resulting parameterizations represent the typical size statistics expected for a given ice sheet area. Therefore, these parameterizations are useful for efficiently predicting spatially averaged melt lake metrics over large ice sheet areas. To predict the exact location and depth of melt lakes over a surface with known topography, a high-fidelity model of ice sheet hydrology should be used e.g.,19, but at considerably greater computational expense which may be infeasible over larger spatial and temporal scales.\n\nAfter developing a set of physics-based parameterizations, we aim to validate the parameterizations against observations of melt lakes in Antarctica. To do so, we focus on two ice shelves with regular summer surface melt in the past 10 years: the Larsen C ice shelf in the Antarctic Peninsula and the Amery ice shelf in East Antarctica. To provide inputs to the parameterizations, we use measurements of H and \u03c3 for these ice shelves from the\u00a0ICESat-2 analysis in the first Results subsection, and meltwater runoff supply as predicted by the RACMO regional climate model (described in \u201cMaterials and Methods\u201d). We aim to validate our developed parameterizations by computing a range of mean melt lake depth and area fraction predictions for the Amery and Larsen C ice shelves, with inputs taken from the ICESat-2 derived topographical parameters (H and \u03c3) and the simulated meltwater runoff supply from the RACMO v2.3p2 model34. Due to the difference in time steps and spatial grid of the observational products and predictions, we include all ICESat-2 derived topographical parameters and RACMO seasonal accumulated runoff over the regions of each of the two ice shelves where melt lakes were observed in ref. 35. From RACMO meltwater runoff, we calculate the accumulated mean water runoff depth over the region where melt lakes are observed from the beginning of each melt season and then reset the accumulated supply to zero at the end of each melt season under the assumption that refreezing occurs. Additionally, we assume that the surface roughness has no changes and remains static over time. This results in a distribution of monthly predictions calculated from equations (3) and (4), with inputs taken over the area where supraglacial lakes are observed in Landsat, and including: surface roughness parameters estimated in the first Results subsection, and surface melt supply from RACMO. We compute the interquartile, 5th, and 95th percentiles of this distribution in each month over the years 2014\u20132019. This prediction assumes that the surface is impermeable and that no refreezing occurs during the melt season. Additionally, we assume that topographical parameters measured by the ICESat-2 mission in 2021\u20132022 apply to predictions over the 2014\u20132018 period when observations are available due to the relatively small temporal variations in these parameters during the ICESat-2 mission. Future validation exercises could use new melt lake observations derived directly from ICESat-236 to compare against predictions made from ICESat-2 topographical parameters. Here we use the35 dataset to match the period covered by existing RACMO simulations. All further details of these validation exercises are detailed in the \u201cMaterials and Methods\u201d section.\n\nIn Fig.\u00a05, we plot observed (black dots) and predicted (blue shading) melt lake depth and area fraction at Amery and Larsen C ice shelves. Observed mean melt lake depth on both ice shelves (Fig.\u00a05B) generally falls right within the 25%\u201375% range at the center of the distribution of predictions, though the melt season in RACMO seems to occur consistently later than the observed appearance of lakes (by 1\u20132 months). In contrast, observations of mean area fraction for both Larsen C and Amery ice shelves are consistently below the lowest quartile of observations. One possible reason for this over-prediction of the area fraction is that, in reality, meltwater may percolate into the snow and firn packs early in the melt season instead of flowing over the surface into lakes. Another possible explanation is that refreezing may still occur early in the season, thus preventing meltwater from accumulating at the ice shelf surface. As discussed above, any particular ice sheet surface may depart from the parameterization prediction due to random natural variability of self-affine surfaces, however, we consider melt lake metrics over a sufficiently large part of these ice shelves and determine that this is\u00a0an unlikely explanation for these deviations. Additionally, the dependence of the area fraction parameterization on Hurst exponent is weak enough that it is unlikely that relatively small errors in roughness estimation from ICESat-2 can explain the validation mismatch. We further discuss these and other potential explanations for the mismatch between predicted and observed area fraction of supraglacial melt lakes in the Discussion section.\n\nA, B Predicted vs. Observed Mean Lake Depth and Mean Area Fraction for Amery Ice Shelf from 2013-2019. C, D Predicted vs. Observed Mean Lake Depth and Mean Area Fraction for Larsen C Ice Shelf from 2013\u20132019.\n\nWe also derive another set of predictions by inversely solving for the optimal water supply (wS) to fit the observed mean area fraction using the mean Hurst exponent and mean standard deviation of topography for both Amery and Larsen C ice shelves. We scale the original runoff estimates from RACMO with a ratio of the aforementioned optimal water supply and mean runoff for each melt season under the assumption that either: some runoff percolates into the snow, some runoff refreezes, or RACMO overpredicts runoff. With this altered water supply, we perform the same calculations to develop an additional set of predictions (Fig.\u00a06). Notably, the observations of area fraction primarily reside within the lower 25% of predicted area fractions. The opposite is true for the mean lake depth where predictions are near the lower end of observed lake depth at Larsen C, and are generally less than observed lake depths at Amery ice shelf.\n\nSimilar to Fig.\u00a05, (A, B) display the predicted and observational mean melt lake depth and area fraction for Amery Ice shelf, and (C, D) display the mean melt lake depth and area fraction for Larsen C ice shelf. These predictions are made with an altered water supply, derived from an inversion of the mean area fraction parameterization and RACMO runoff, with a bias to better fit the mean area fraction of both ice shelves.\n\nThe maximum capacity of a rough surface to retain water is purely a function of roughness statistics20,21. As the water supply increases (\\({S\\to \\infty}\\)), the size of supraglacial melt lakes, as captured in equations (10) and (11), eventually reaches a maximum:\n\nFrom equations (5) and (6), we see that the standard deviation of roughness (\u03c3) directly influences the depth of water filling surface depressions. As \u03c3 increases, the maximum spatially-averaged lake depth of the surface increases linearly. This is evident in the maximum-potential lake depth estimations mapped in Fig.\u00a07 which largely resemble the map of roughness amplitude (Fig.\u00a02b.), with a spatial average across the continent of 5.67 meters. As expected, ice shelves are predicted to have a lower maximum-potential lake depth, having a spatially averaged depth of 2.6 meters. Grounded ice has a maximum-potential lake depth of 5.9 meters averaged over space.\n\nMap of mean maximum potential lake depth across the Antarctic Ice Sheet and ice shelves. Sub-figures show a closer look at the estimated mean maximum lake depth at Amery, Larsen C, Ronne, and Ross Ice Shelves. Most ice shelves have a significantly lower mean maximum lake depth than the ice sheet\u2019s interior.\n\nLower Hurst exponents produce higher maximum area fraction of supraglacial melt lakes as shown at the Larsen C ice shelf (Fig.\u00a08). This is due to a greater abundance of small-scale depressions in the topography. As the Hurst exponent increases, the maximum-potential area fraction decreases as there are fewer smaller depressions where melt gathers on the surface. As Antarctica has a narrow range of Hurst exponents, there is little variation in the maximum-potential area fraction with a spatial average of 0.38 over the entire ice sheet area, which is the same when averaged over grounded ice or floating ice.\n\nMap of the mean maximum area fraction a surface can have across the Antarctic Ice Sheet and ice shelves. Values for the mean maximum area fraction are uniform across the continent with some local variations observed on the ice shelves, which is largely responsible for its sole dependence on the Hurst exponent.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61798-8/MediaObjects/41467_2025_61798_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61798-8/MediaObjects/41467_2025_61798_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61798-8/MediaObjects/41467_2025_61798_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61798-8/MediaObjects/41467_2025_61798_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61798-8/MediaObjects/41467_2025_61798_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61798-8/MediaObjects/41467_2025_61798_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61798-8/MediaObjects/41467_2025_61798_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61798-8/MediaObjects/41467_2025_61798_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "A more complex model that simulates more surface and hydrological processes may produce different results from the parameterized predictions here. Processes such as the percolation of meltwater through the firn and snowpack and refreezing of meltwater likely play an important role in determining the water available for lake formation at the ice sheet surface. The parameterizations developed in this study are first-order approximations of the relationship between surface roughness statistics and supraglacial melt lake characteristics. While these parameterizations may not have perfect accuracy in predicting what is found in observations, they can be used to improve our understanding and ability to easily include a representation of supraglacial melt lakes in large-scale climate and ice sheet models.\n\nAs shown in the\u00a0validation section, the parameterization of spatially averaged melt lake depth is consistent with observations at Amery and Larsen C ice shelves. However, the correspondence of area fraction to observations would likely be improved if these parameterizations were paired with a simple model of water storage in firn such as 37. Limitations in the observational products that measure melt lake depth and area fraction used in the\u00a0validation section could also potentially explain some of the mismatches. The current method for measuring these features is from satellite imagery and aerial imagery35. This method may not resolve smaller features such as slush and smaller or shallow lakes at the current functioning resolution and sensitivity of the sensors aboard imaging satellites. Visible satellite imagery is also known to underestimate lake depths due to saturation of red bands at low depths38. Another approach to validate these parameterizations could be to utilize a different observational platform, such as ICESat-2, which can measure smaller surface features.\u00a0Reference 39 compares lake depth measurements of the surface between ICESat-2 and satellite imagery (though a comprehensive ice-sheet-wide open-access product has not yet been released using ICESat-2 data) and concludes that using ICESat-2 improves the accuracy of the melt lake depth, with the satellite imagery underestimating the depth of the melt lakes. Future work could also repeat this validation exercise for melt observed on the Greenland ice sheet, which has more extensive, better-observed surface melt.\n\nOur melt lake parameterization suggests that the albedo effect of melt lakes is not any greater on rougher ice surfaces than on smoother ones, since the area fraction of melt lakes does not depend on the surface roughness amplitude. Our parameterizations (and numerical water routing results) predict that once melt begins on a previously dry surface, melt lake depth quickly increases in the first 10\u2019s of cm of water supply, before reaching a maximal value at relatively low water supply levels. This may explain why even at relatively low levels of surface melt currently observed in Antarctica (\u00a0~cm), melt lakes of several meters depth have been observed in specific regions1,35,36. Given the current ice sheet topography, the formation of such deep lakes may expand to new areas as surface melt spreads to these areas under future atmospheric warming, though the onset of melt lake formation may be delayed by percolation into available pore space in firn. However, our results suggest that the depth of existing lakes are unlikely to deepen further without increasing surface roughness. This may explain why, in the Amery and Larsen C ice shelf cases considered in the\u00a0validation section, observed lake depths span a relatively narrow range, even under inter-annual variability in runoff meltwater supply. Still, this does not consider potential melt feedbacks that melt lake bottoms, causing them to further deepen through modification of surface topography.\n\nMelt lake depth can confidently be predicted to be a linear function of surface roughness, indicating that rougher portions of the ice sheet surface are likelier to be locations of deep meltwater lakes, and therefore more susceptible to fracture propagation and glacier damage by hydrofracture. Parts of the ice sheets that are already deeply crevassed, such as the near-front regions of Antarctic ice shelves in Fig.\u00a02a, are indicated by higher roughness in ICESat-2 measurements, and thus are more likely to retain ponded meltwater. However, it is important to also note that in some regions, the prevalence of surface fractures may also cause the surface to depart from self-affinity (i.e., Eastern Ross ice shelf in Fig.\u00a02).\n\nGiven the relatively minor variation in Hurst exponents across the Antarctic ice sheet (Fig.\u00a02), the variation in melt lake characteristics due to this factor is not expected to be significant. To a good approximation, the Hurst exponent in our derived parameterizations can be assumed to be 0.4 for grounded ice and 0.47 for floating ice. Adopting a single value of Hurst exponent will cause at most 5% error in the predicted melt lake size metrics.\n\nIn25, the self-affinity of bed topography in northern Greenland is calculated, finding the average Hurst exponent to be \u00a0~0.65, though with more spread across space than we estimate for the entirety of Antarctica. Besides the Hurst estimates of the subglacial and supraglacial topography, there are many studies estimating the Hurst exponent for terrestrial geomorphic surfaces, such as mountains, lakes, and coastlines, generally finding them to be between 0.4 and 0.522,23,40. Our estimate of the Hurst exponent is thus similar to many other geomorphic surfaces, but perhaps not Greenland subglacial topography. This perhaps indicates that surface processes, such as those related to random deposition and coarsening of snow topography, may set the Hurst exponent of the ice sheet surface, as has been indicated by prior work on snow microtopography30. It is notable that the classic Kadar-Parisi-Zhang (KPZ) model for the growth of rough surfaces41 produces surfaces with a Hurst exponent of 0.4. Thus, future work may explore whether this is a viable model for the production of ice sheet surface topography.\n\nIn this study, we develop simple mathematical expressions that predict spatially averaged supraglacial melt lake area fraction and depth using readily observable or modeled quantities as inputs. Most current large-scale models either drain all surface melt as runoff or retain all of it as a uniform water sheet on the surface (e.g.,3,42). The parameterizations developed in this study provide a simple way to include a first-order quantitative representation of the influence of supraglacial melt lakes on climate and ice sheets. Specifically, the area fraction parameterization could be implemented within albedo and surface energy balance schemes in global or regional climate/SMB models. The lake depth parameterization could be applied to existing crevasse propagation and fracturing schemes in large-scale ice sheet models43.\n\nIn this study, we sought to fit low-order mathematical parameterizations that could readily be incorporated into a wide range of existing numerical models. However, using a similar training dataset, machine learning could also produce accurate parameterizations, using either outputs from a numerical hydrology model as in this study or directly from observed elevation and melt observations. However, simple mathematical parameterizations while potentially less accurate, have the advantage of being easy to understand and implement in large-scale models.\n\nWhilst there has been an increase in observations of surface melting in Antarctica, there are many more studies and observations of supraglacial melting in Greenland. In a future study, the parameterizations should be tested on the Greenland Ice Sheet and could shed light on how the parameterizations perform with a higher melt supply scenario. A similar analysis performed in the first Results subsection would enable the comparison of roughness characteristics across two different ice sheets.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "To quantify the roughness properties of the ice sheet surface, we use measurements from the ICESat-2 satellite altimetry mission. The ATL06 product from ICESat-2 provides surface elevation at high spatial resolution covering nearly the entire ice sheet (less a small hole of missing coverage around the South Pole in the ice sheet interior). ICESat-2 repeats a near-polar orbit, repeating each track over Antarctica every 91 days, with an along-track spatial resolution of 20 meters between elevation measurements. The ATL06 data product measures the land-ice elevation and is a processed version of the raw photon data which measures the travel time between the satellite and the Earth\u2019s surface. Each ATL06 file has elevation estimates for the six beams, three weak and three strong beams. The weak beam estimates are best suited for bright surfaces such as snow and ice and so are used in this study. We select the first available weak beam from each data file in order not to double count data from both tracks. For every ATL06 track analyzed, we filter points in the track that have been registered to have an elevation greater than 1030 meters, which account for the unphysical elevation values.\n\nEach ICESat-2 ATL06 elevation track is divided into sub-tracks (h) 10 km in length to remove the influence of long-range ice sheet shape on the elevation calculation. Since almost all supraglacial melt lakes are less than 10 km in horizontal extent, ice sheet roughness below 10 km is the most relevant for setting the average area and depth of supraglacial melt lakes. Two surface roughness parameters are computed for each sub-track: the standard deviation of elevation (\u03c3), and the Hurst exponent (H). The standard deviation of elevation (\u03c3) measures the square-root of the variance across the elevation of the subtrack.\n\nThe self-affinity of a surface can be quantified through the Hurst exponent, H, which for topographic data measures the relative importance of topographic variations at a range of horizontal length scales\u00a01. Put another way, a surface, z(x,\u00a0y), is self-affine if it can be rescaled by some factor s according to the following equation:\n\nThe Hurst exponent has a value between zero and one. Hurst exponents near zero indicate that topography at short horizontal length scales has similar vertical relief to topography at long horizontal length scales. Hurst exponents near one indicate that topography at short horizontal length scales has much less vertical relief than topography at long horizontal length scales (i.e., the topography is dominated by smooth, large-scale features).\n\nWe calculate the Hurst exponent using the \u201cpower spectral decay\u201d (PSD) method of ref. 24. Other commonly used methods to calculate the Hurst exponent include Detrended Fluctuation Analysis44 and Higuchi\u2019s method45. Here we find the PSD method to be computationally efficient and provides similar results to other methodologies used to compute the Hurst exponent46,47. In our approach, a Fast-Fourier transform (\\({\\hat{h}}(f)\\)) is applied to each sub-track of ICESat-2 ATL06 elevation data (h(x)), where the frequency is the spatial wavenumber along track (f)\n\nThe power spectral density is defined as\n\nWe then fit a power law relationship between the power spectral density (P(f)) and the spatial wavenumber (f) by linear regression of \\(-\\log (P)\\) against \\(\\log (f)\\). Fig.\u00a09 The slope of this linear regression is known as the power spectral decay coefficient,\n\nThis \u03b2 coefficient can be written as a simple algebraic function of the Hurst exponent (H)\n\nThe log-log plot of an ICE-Sat-2 elevation sub-track with an R2 of 0.98. The y axis inverts the logarithm of the power spectral density to make the power spectral coefficient (\u03b2) positive as shown in Equation (10). Blue markers indicated the calculated PSD values. The solid black line is indicative of the estimated linear regression. The slight deviation from the linear regression at higher wave numbers is due to the ATL06 product processing aggregate photon counts in 40 meters intervals50, thus artificially smoothing roughness at the highest resolutions of the product.\n\nOur primary goal in this study is to find simple mathematical expressions relating mean melt lake area fraction and depth to surface roughness characteristics and meltwater supply. Since we have shown in the prior section that the Antarctic ice sheet surface is self-affine, we focus on such surfaces in developing parameterizations. To achieve this goal, we follow a three-step method: (1) synthetically generate many random self-affine surfaces, (2) simulate water flow and ponding on these surfaces, (3) statistically fit simple mathematical relationships between mean melt lake area characteristics and surface roughness parameters and meltwater runoff supply.\n\nWe use a random self-affine surface generator which takes as inputs the Hurst exponent and standard deviation of the height profile. These inputs are used to calculate the power density spectrum and height probability distribution of surface48. As described in ref. 46, the surface is then generated from this height probability distribution through an inverse Fourier Transform method with randomized phases. Each generated surface has boundaries 10 km apart along a flat plain with topographic height with grid spacing of 100 meters.\n\nAfter generating a surface, we simulate the flow of water into depressions on the surface using a \u201cconditioned-walker\" (CW) model developed for this study based on cellular automata49. The CW model takes as inputs: the surface elevation field and the total volume of meltwater supply. In the CW model described originally by ref. 49, a small discrete fraction (a \u201cprecipiton\u201d) of the total melt supply is added randomly at a location on the surface. The model moves the precipiton to one of the eight surrounding grid spaces with the lowest elevation and repeats this process until the precipiton reaches a grid space surrounded by grid spaces of higher elevation (i.e., a local minimum) or runs off the surface. This is under the assumption that the surface is non-porous with none of the precipitons penetrating the subsurface. The precipiton water thickness is now added to the surface elevation at this grid space until it equals the elevation of a surrounding grid space nearest in elevation. If water is still left over in the precipiton, then the movement process continues until no water is left. After a single precipiton has been deposited, a new iteration begins with an additional precipiton of water added to a random location on the surface, over a prescribed number of fill stages. This occurs until the prescribed total volume of meltwater has been distributed across the surface. At prescribed intermediate water supply levels, the CW model outputs a field of water depth.\n\nThe CW model has some features which make it ideal for considering water flow on relatively flat, rough, ice sheets. First, it allows excess water to flow off the edges of the surface, allowing for a surface\u2019s maximum meltwater capacity to be modeled realistically (in contrast to \u201cdepression-filling\u201d water routing algorithms). Additionally, the CW model can simulate internally drained catchments that cause ponding on ice sheet surfaces, unlike many drainage algorithms used in terrestrial hydrology which assume that all water added to the surface is drained off the surface.\n\nUtilizing the self-affine surface generation algorithm and the CW model, we simulate the distribution of water depth on many surfaces with a given Hurst exponent (H) and roughness amplitude (\u03c3) and varied water supply (ws). For each surface, we calculate the average melt lake depth (\\({\\bar{w}}_{l}\\)) over just the water-covered portions of the surface and the average area fraction of melt lakes (\\(\\bar{F}\\)). Since the average melt lake depth (\\({\\bar{w}}_{l}\\)) is just the water depth only over the area fraction of the ice sheet surface covered by water (\\(\\bar{F}\\)), we can, a priori, write a relationship between both characteristics and the mean water depth across the entire surface (\\({\\bar{w}}_{d}\\))\n\nAdditional work is done to calculate the mean water depth across the entire surface in the\u00a0Supplementary Material (Supplementary Fig.\u00a02). Our workflow uses a Monte Carlo approach where all the melt lake metrics described above are averaged over 500 randomly generated surfaces to reduce the influence of single outlier surfaces. The result is a numerically generated dataset of average \\({\\bar{w}}_{l}\\) and \\(\\bar{F}\\) values, each corresponding to inputs of ws,\u00a0\u2009H and \u03c3. This multivariate dataset is fit using standard nonlinear least squares fitting techniques to derive simple mathematical relationships between the melt lake characteristics. We set the functional form of equations for each melt lake characteristic to meet certain physical constraints (defined below), while using the simplest relationship between each independent parameter and the resulting melt lake parameters. In general, we prioritize forms of the parameterizations with relatively few parameters, and where no single term can be removed without substantially reducing the R2 fit to the numerically generated dataset (i.e., simplicity). We also enforce two constraints:\n\nand\n\nThe first constraint ensures that when there is no meltwater supply (ws\u00a0=\u00a00), the melt lake has zero area fraction and depth. The second constraint ensures that all melt lake size statistics asymptotically approach a constant value since finite-scale bumpy surfaces have a finite capacity to store meltwater. To enforce the second constraint while obtaining smooth parameterizations, all terms involving the water supply are parameterized in terms of the error function, \\({{{\\rm{erf}}}}\\,({w}_{s})\\). We emphasize that there are likely other mathematical forms of these functions that would fit the numerical results equally well, but we search for mathematical functions that are relatively simple and easy to implement in large-scale models.\n\nWe generate surfaces with roughness varying over the full range of possible H values ([0,\u00a01] with \u0394H\u00a0=\u00a00.1) and a wide range of \u03c3 values encompassing \u00a0~90% of estimates for the Antarctic Ice Sheet (1\u201320 meters with \u0394\u03c3\u00a0=\u00a01m; see Fig.\u00a02b). For each (H,\u00a0\u03c3) pair, we add meltwater until the surface has reached its maximum water retention capacity (i.e., \\(\\bar{F},\\,{\\bar{w}}_{l}\\) and \\({\\bar{w}}_{d}\\) no longer change). For every possible (H,\u00a0\u03c3,\u00a0ws) value combination, 500 surfaces are generated and processed under the workflow described in the previous section, making a total of 110,000 simulations to make up the entire parameter space. A mean lake depth and area fraction are calculated for each (H,\u00a0\u03c3,\u00a0ws) parameter combination, averaging over each surface domain and all 500 generated surfaces. A schematic of the workflow can be seen in Fig.\u00a010.\n\nSchematic of the Monte-Carlo workflow to produce water depth fields which relate ice sheet roughness parameters to spatially averaged melt lake parameters.\n\nWe validate our parameterizations with observations of melt lake area and depth from the Landsat-derived product of\u200635. This product uses Landsat 8 Level 1 imagery that has been corrected to account for Top of the Atmosphere (TOA) reflectance and Sentinel 2 Level1C products to analyze the surface conditions of ice sheets and shelves35. Utilizes the Normalized Difference Snow Index (NDSI) to differentiate rocks from snow and the Normalized Difference Water Index to determine which pixels contain water. From this classification35, computes the lake depth in each pixel by taking the difference between the reflection of optically deep water and the albedo of the lake bed and the top of atmosphere reflectance of the lake respectively and dividing the difference by the attenuation rate (Equation (15)).\n\nThe area fraction is calculated as the ratio of the number of pixels classified as surface melt to the total number of land pixels, which are pixels classified either as surface melt or ice. This product has been developed for the entirety of Antarctica though here we focus on Larsen C and Amery ice shelves because: (1) this observational dataset indicates recurrent surface melt on these ice shelves in recent melt seasons, and (2) regional climate models also predict some surface melt on these ice shelves. Many regional climate models (e.g., RACMO v2.3p2 which we use here) predict no surface melt runoff on many other Antarctic ice shelves where melt lakes are observed.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61798-8/MediaObjects/41467_2025_61798_Fig9_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61798-8/MediaObjects/41467_2025_61798_Fig10_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "The processed data used in this study are available in the Zenodo repository under https://doi.org/10.5281/zenodo.15467941. NASA Icesat-2 ATL06 data used in this study is publicly available through the NSIDC. The melt runoff data is also publicly available34, and the supraglacial melt lake observations is available within35.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All code used to generate analysis Antarctic surface roughness, simulate melt distribution to develop the parameters, and to develop all the figures in this work can be found as a permanent Zenodo repository, which will be available upon publication. Scripts for numerical simulations are available on the following GitHub repository: https://github.com/dgrau13/meltlake-parameterizations.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Kingslake, J., Ely, J. C., Das, I. & Bell, R. E. Widespread movement of meltwater onto and across Antarctic ice shelves. Nature 544, 349\u2013352 (2017).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nSteig, E. J. et al. 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This work and the lead author were funded by the NASA Modeling, Analysis, and Prediction Program, Award 80NSSC20K1131.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Azeez Hussain, Alexander A. Robel.\n\nSchool of Earth and Atmospheric Sciences, Georgia Institute of Technology, 311 Ferst Dr., Atlanta, GA, USA\n\nDanielle Grau\u00a0&\u00a0Alexander A. Robel\n\nSchool of Physics, Georgia Institute of Technology, 837 State St. NW, Atlanta, GA, USA\n\nAzeez Hussain\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nThis project was conceived by A.A.R. and D.G. D.G. wrote the first draft of the manuscript. D.G., A.A.R., and A.H. commented on and edited the manuscript. D.G. performed surface roughness analysis of the IceSat-2 Data, ran numerical simulations, and generated prediction figures within the validation. A.H. performed validation calculations for observed melt lake characteristics.\n\nCorrespondence to\n Danielle Grau.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Predicting mean depth and area fraction of Antarctic supraglacial melt lakes with physics-based parameterizations.\n Nat Commun 16, 6518 (2025). https://doi.org/10.1038/s41467-025-61798-8\n\nDownload citation\n\nReceived: 07 November 2024\n\nAccepted: 01 July 2025\n\nPublished: 15 July 2025\n\nVersion of record: 15 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61798-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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photonic corner states in synthetic dimensions", + "journal": "Nature Communications", + "published": "30 December 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55236-4/MediaObjects/41467_2024_55236_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55236-4/MediaObjects/41467_2024_55236_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55236-4/MediaObjects/41467_2024_55236_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55236-4/MediaObjects/41467_2024_55236_MOESM4_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55236-4/MediaObjects/41467_2024_55236_MOESM5_ESM.mp4" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55236-4/MediaObjects/41467_2024_55236_MOESM6_ESM.mp4" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.14207240" + ], + "code": [ + "https://doi.org/10.5281/zenodo.14207240" + ], + "subject": [ + "Quantum optics", + "Quantum simulation" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3979948/v1.pdf?c=1735651038000", + "research_square_link": "https://www.researchsquare.com//article/rs-3979948/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-55236-4.pdf", + "preprint_posted": "12 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Non-Hermitian models describe the physics of ubiquitous open systems with gain and loss. One intriguing aspect of non-Hermitian models is their inherent topology that can produce intriguing boundary phenomena like resilient higher-order topological insulators (HOTIs) and non-Hermitian skin effects (NHSE). Recently, time-multiplexed lattices in synthetic dimensions have emerged as a versatile platform for the investigation of these effects free of geometric restrictions. Despite holding broad applications, studies of these effects have been limited to static cases so far, and full dynamical control over the non-Hermitian effects has remained elusive. Here, we demonstrate the emergence of topological non-Hermitian corner states with remarkable temporal controllability and robustness in a two-dimensional photonic synthetic time lattice. Specifically, we showcase various dynamic control mechanisms for light confinement and flow, including spatial mode tapering, sequential non-Hermiticity on-off switching, dynamical corner state relocation, and light steering. Moreover, we establish the corner state's robustness in the presence of intensity modulation randomness and quantitatively determine its breakdown regime. Our findings extend non-Hermitian and topological photonic effects into higher synthetic dimensions, offering remarkable flexibility and real-time control possibilities. This opens avenues for topological classification, quantum walk simulations of many-body dynamics, and robust Floquet engineering, free from the limitations of physical geometries.Physical sciences/Optics and photonics/Optical techniques/Optical manipulation and tweezersPhysical sciences/Physics/Electronics, photonics and device physics/Photonic devices", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Non-Hermitian models describe the physics of ubiquitous open systems with gain and loss. One intriguing aspect of non-Hermitian models is their inherent topology that can produce intriguing boundary phenomena like resilient higher-order topological insulators (HOTIs) and non-Hermitian skin effects (NHSE). Recently, time-multiplexed lattices in synthetic dimensions have emerged as a versatile platform for the investigation of these effects free of geometric restrictions. Despite holding broad applications, studies of these effects have been limited to static cases so far, and full dynamical control over the non-Hermitian effects has remained elusive. Here, we demonstrate the emergence of topological non-Hermitian corner skin modes with remarkable temporal controllability and robustness in a two-dimensional photonic synthetic time lattice. Specifically, we showcase various dynamic control mechanisms for light confinement and flow, including spatial mode tapering, sequential non-Hermiticity on-off switching, dynamical corner skin mode relocation, and light steering. Moreover, we establish the corner skin mode\u2019s robustness in the presence of intensity modulation randomness and quantitatively determine its breakdown regime. Our findings extend non-Hermitian and topological photonic effects into higher synthetic dimensions, offering remarkable flexibility and real-time control possibilities. This opens avenues for topological classification, quantum walk simulations of many-body dynamics, and robust Floquet engineering in synthetic landscapes.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Non-Hermitian systems host a range of intriguing phenomena in physics, such as reconfigurable light routing1, potential for enhanced sensitivity2,3 and unidirectional invisibility4, that are deeply rooted in symmetry and topology. One such phenomenon is the non-Hermitian skin effect (NHSE) where a macroscopic fraction of the eigenmodes of a finite system become exponentially localized at its boundary5,6. This localization is a direct consequence of the nontrivial (topological) winding of the system\u2019s eigenvalues in the complex energy plane7,8,9. Disorder and small variations in the system do not change the winding number which is a topological invariant9.\n\nOver the last few years, the NHSE has been demonstrated on a variety of platforms5,10,11. Exemplary platforms include acoustics and phononics12, topo-electric circuits13, and photonics14. These developments are in part motivated by the profound impact of NHSE on band topology7,15,16, spectral symmetry17, and dynamics18,19. Particularly in photonics, recently the NHSE has enabled intriguing demonstrations of the tuneable directional flow of light20, near-field beam steering21, engineering arbitrary band topology22 and topological funneling of light14. Nevertheless, these demonstrations have been limited to systems that can be effectively described by time-independent Hamiltonians23. The introduction of time-dependent non-Hermitian Hamiltonians can lead to a dynamic control over the skin effect and also lead to fundamental advances in novel non-Hermitian topological phases that are not accessible using time-independent systems. Here we demonstrate dynamical control of the two-dimensional non-Hermitian photonic skin effect, that is, corner skin modes, using purely synthetic temporal dimensions. Specifically, using time-multiplexed light pulses in fiber loops, we show manipulation of the gain/loss in the system at a scale that is faster than the dynamics of light pulses in the lattice. Using this dynamical manipulation, we demonstrate gradual control over the degree of localization of the corner skin modes, gradual tweezing of light where we move the corner skin modes along a predefined trajectory in the lattice, and 2D funneling of light where photons always funnel to the corner skin modes irrespective of their initial position in the 2D lattice. Finally, by introducing controlled disorder in the system in the form of random variations in gain and loss, we quantitatively investigate the robustness of the corner skin modes against such disorders. Our work opens up an avenue to explore the rich physics of time-dependent non-Hermitian models such as non-Hermitian Floquet systems.\n\nOur system simulates a discrete-time quantum walk of photons on a two-dimensional non-Hermitian square lattice, as illustrated schematically in Fig.\u00a01a. Specifically, we implement a split-step walk where the walker first randomly steps to either left or right (corresponding to the X direction) with equal probability, then up or down (corresponding to the Y direction). To introduce non-Hermiticity, we introduce an additional gain e+\u03b4x when the walker steps to the left, and an additional loss e\u2212\u03b4x when the walker steps to the right. Similarly, the walker experiences a gain e+\u03b4y when moving down and a loss of e\u2212\u03b4y when moving up. The parameters \u03b4x and \u03b4y then indicate the degree of non-Hermiticity of the walk.\n\na Example of photonic quantum walk in a 2D synthetic lattice. The blue and red curved arrows show the direction-dependent loss and gain. b Winding of effective eigenenergies \u03f5up/down(kx, ky) in the complex energy plane for a single bulk non-Hermitian lattice with periodic boundary condition, showing line-gapped topology (indicated by the green line). Here we choose five different values ky\u2009\u2261\u20090,\u2009\u00b1\u2009\u03c0/4,\u2009\u00b1\u20093\u03c0/8. c Four bulk lattices with different gain-loss patterns are glued along their edges to form a corner. Note that \u03b4x\u2009>\u20090 implies gain for a step towards \u2212X and loss for a step toward +X. For \u03b4x\u2009<\u20090 the gain-loss is inverted. A similar rule applies for \u03b4y. d Averaged spatial profile of corner skin modes formed in the system shown in c, by taking non-Hermitian parameters \u03b4x\u2009=\u2009\u03b4y\u2009=\u20090.175. The lattice size is 30\u2009\u00d7\u200930. e The time-multiplexed experimental scheme, with which the lattice parameters can be (dynamically) controlled by the intensity modulators. EDFA: Erbium-doped fiber amplifier. PD: Photodiode.\n\nFor this quantum walk, a concept of complex energy can analogously be defined, by solving for the eigenmodes of the non-unitary quantum walk evolution operator \u00db and taking the logarithm of the corresponding eigenvalue uj. Namely, this can be formulated as \u03f5j = ilog(uj), where \u00db |uj\u3009 = uj |uj\u3009. If we further impose periodic boundary conditions (PBCs) in both x and y for the bulk in Fig.\u00a01a, we can apply the Bloch theorem for the walk and obtain the complex energy bands \u03f5up/down(kx, ky). (The two bands seen in Fig.\u00a01b arise due to the up/down channel configuration of our experiment, see Supplementary Information (SI) for derivation details). The non-unitary time evolution of the walk leads to a nontrivial winding of \u03f5(kx, ky) for each band in the complex energy plane as one continuously varies Bloch vector (kx, ky) along a certain curve in the Brillouin zone. To illustrate this, in Fig.\u00a01b, we plot the complex energies \u03f5up/down(kx, ky) of the bulk lattice shown in Fig.\u00a01a as we vary kx from \u2212\u03c0 to \u03c0 while keeping ky fixed to different values 0,\u2009\u00b1\u2009\u03c0/4,\u2009\u00b1\u20093\u03c0/8. As (kx, ky) varies along each of these directed horizontal curves in the Brillouin zone, both \u03f5up(kx, ky) and \u03f5down(kx, ky) winds one loop in the counterclockwise direction, thus exhibiting an integer-valued winding number \u22121. This is a topological invariant for our non-Hermitian quantum walk. Also, the two winding loops contributed from the two bands \u03f5up/down exhibit a line-gapped topology24, such that the two winding loops never cross the line Re(\u03f5) = 0 in the complex plane. Windings of complex energy along other curves in the Brillouin zone are shown in the supplementary information (SI) section\u00a03.\n\nIn a finite system, the nontrivial winding of the complex energies and the associated 2D non-Hermitian skin effect24 is manifested as corner skin modes, that is, localization of the walker can happen at an interface between regions with opposite windings (or bulk band topologies). Figure\u00a01c shows one exemplary case which consists of four distinct regions, represented by the four different color patches. The gray patch is identical to the system described in Fig.\u00a01a. The other three regions exhibit an inverted gain-loss relation (indicated by a change in the sign of the gain parameter) either along the x or y-axis, or both. This inversion of gain-loss leads to different windings for each region. Non-Hermitian skin effect occurs in such a system, and we numerically verify in Fig.\u00a01d that the averaged eigenmodes of the quantum walk exhibit clustering at the junction between the four regions - as indicated by the red dot in Fig.\u00a01c.\n\nTo simulate the quantum walk described above, we use classical light pulses in a time-multiplexed setup shown in Fig.\u00a01e. We note that for this linear system, the evolution of classical light pulses in the lattice exactly follows that of the quantum walk of single photons in the lattice. We map the state space of the 2D square lattice of size 30\u2009\u00d7\u200930 into different time-delays in two fiber feedback loops, as introduced in previous works25,26. To introduce non-Hermiticity, we use four intensity modulators that introduce individually controllable loss when the walker moves along any direction. We also use two erbium-doped fiber amplifiers (EDFAs) that provide gain in the system, and together with the intensity modulators, introduce a gain-loss mechanism that can be controlled at each step of the walker. We specifically choose electro-optic modulators with a high bandwidth to allow reconfigurability of the system\u2019s topology at each step of the quantum walk. A full discussion of the experimental setup is provided in the SI sections 1 and 2.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55236-4/MediaObjects/41467_2024_55236_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "To show the presence of non-Hermitian corner skin modes, we first construct the model as shown in Fig.\u00a01c, with the corner located at the lattice origin (x\u2009=\u20090, y\u2009=\u20090). We inject a single light pulse into the time bin corresponding to the lattice origin and choose non-Hermitian parameters |\u03b4x| and |\u03b4y| to be 0.175 as in Fig.\u00a01c. In Fig.\u00a02a, we plot the snapshots of the light distribution in the lattice for different time steps 1, 9, and 21, which are obtained by measuring the pulse power at each time bin. The evolution of distribution shows that the walker stays localized at the origin, confirming the presence of a corner skin mode. In sharp contrast, when we set \u03b4x and \u03b4y to 0, we observe a significant spreading of the intensity distribution, indicating the absence of any corner skin modes (see Supplementary Sections 4 and 5 in the SI for the experimental data).\n\na Light localization at the corner skin modes located at (x, y) = (0, 0) for non-Hermitian parameter |\u03b4x\u2009|\u2009=\u2009|\u03b4y |\u2009=\u20090.175. Here a single pulse is initialized at (x, y) = (0, 0) in the up channel. b Light funneling for the same lattice parameter and pulse initialization, but the corner skin mode is located at (x, y) = (\u221210, 10). Here the skin effect allows light to flow to the corner skin mode and localize there. In both a and b, from left to right the snapshots are shown for time steps 1, 9, 21, respectively.\n\nHaving shown the localization of light at the corner skin mode, we next demonstrate the skin-effect-induced funneling of light. Namely, the system dynamics bring any initial state towards the corner skin modes. We set the corner skin mode to be at the lattice site (x\u2009=\u2009\u221210, y\u2009=\u200910) while light pulses are still injected at x\u2009=\u20090, y\u2009=\u20090, which is now in the bulk of the lattice (Fig.\u00a02b). As the system evolves, initially light spreads in bulk, but finally converges to the corner site. As shown in the SI for several different lattice configurations, light pulses always converge to the corner regardless of the initialization location. This funneling of light to the corner skin mode is a manifestation of the skin effect where all the eigenmodes of the system are localized at the corner. Schematic illustration of this funneling effect can be seen in Supplementary Movie\u00a01. Our experimental results are in good agreement with our theoretical prediction shown in Fig.\u00a02b.\n\nThe use of time as a synthetic dimension allows us to dynamically reconfigure our non-Hermitian lattice as a function of time. Specifically, by controlling the intensity modulators at each time step of the quantum walk, we achieve temporal modulation of the gain/loss parameters \u03b4x(t) and \u03b4y(t) such that they are time-dependent. Using this time dependence, first, we demonstrate dynamical control over the degree of localization of the corner skin modes. At the start of the evolution, we adopt the configuration as in Fig.\u00a01c and set |\u03b4x(0)| = |\u03b4y(0)| = 0.175, and inject a single light pulse at the corner skin mode situated at the origin. As the system evolves, we reduce both |\u03b4x|,|\u03b4y| by 50% for every four time-steps and continue doing so until step 16 (Fig.\u00a03a). Because of this reduction, we observe that the corner skin modes become less confined to the origin. This is because the smaller non-Hermitian parameter exhibits eigenmodes distributed over a larger area, as predicted theoretically (see Supplementary Fig.\u00a0S4 in the SI). Thereafter, starting from step 17, we reverse the process, that is, we increase the gain /loss parameters |\u03b4x|,|\u03b4y| back to its original value at the same rate. We now observe a relocalization of light at the origin.\n\na Dynamical control of corner skin mode spatial profile. As the non-Hermitian parameter is gradually reduced from |\u03b4x,max\u2009|\u2009=\u2009|\u03b4y,max\u2009|\u2009=\u20090.175 to |\u03b4x,max\u2009|\u2009=\u2009|\u03b4y,max\u2009|\u2009=\u20090.02 and back to |\u03b4x,max\u2009|\u2009= |\u03b4y,max\u2009|\u2009=\u20090.175, the corner skin mode becomes delocalized and then localized. From left to right the snapshots are shown for time steps 1, 9, 17, 25, 37, respectively. b Dynamically tweezing localized light along a designed \u201cL\u201d-shaped trajectory using the skin effect. Localized light is first moved in the +Y direction for 8 steps and then to the \u2212X direction for 10 steps.\n\nNext, we demonstrate gradual repositioning of the corner skin modes in the lattice. We use the same lattice geometry shown in Fig.\u00a01c and fix the non-Hermitian parameter to \u03b4x\u2009=\u2009\u03b4y\u2009=\u20090.175. As the system evolves, we gradually move the interface between the four distinct topological regions, repositioning the corner skin mode as a function of time. We first move the position of the corner skin mode upwards for 8 unit cells, and then leftward for 10 unit cells. As before, we inject light pulses at the corner skin mode. As the system evolves, we observe that the center of the intensity distribution follows the position of the corner skin mode as it gradually moves along the given L-shaped trajectory from its initial location (x\u2009=\u20090, y\u2009=\u20090) to its final location at (x\u2009=\u2009\u221210, y\u2009=\u20098). Furthermore, during this process, the intensity distribution remains tightly localized close to the corner skin mode. Evidently, the corner serves as a non-Hermitian tweezer of light, which allows us to gradually move trapped photons along a given trajectory in the synthetic lattice. Note that non-Hermitian light steering has been demonstrated in real-space lattices1, and our demonstration in synthetic time dimensions portends the potential for such photonic control using the temporal degree of freedom of light.\n\nSchematic illustrations of the tapering and relocation effects can be seen in Supplementary Movies\u00a02 and 3, respectively.\n\nThe topological nature of the non-Hermitian skin effect ensures its robustness against disorder in gain/loss parameters \u03b4x, \u03b4y. To quantitatively investigate this robustness, we introduce a disorder on the gain/loss. At each lattice site, we randomly pick both \u03b4x, \u03b4y from a uniform distribution on the interval [\u03b4max(1 \u2212 \u03b7), \u03b4max], where max is the maximum gain parameter and is the disorder parameter which quantifies the variance of the gain parameter. In our experiment, we vary the disorder parameter between 0 (no disorder) and 2 (max disorder).\n\nWe find that the skin effect is robust when the disorder parameter \u03b7\u2009<\u20091. In Fig.\u00a04a, b, we plot the evolution of light pulses in the lattice for two different values \u03b7\u2009=\u20090.5 and \u03b7\u2009=\u20091. For both cases, we inject light pulses at the corner skin mode located at the origin. We observe that, even though the localization of the intensity distribution reduces as the disorder increases, the distribution still stays confined around the origin, indicating the existence of corner skin modes even in the presence of disorder. Nevertheless, once we increase the disorder parameter to 1.5 and 2 (Fig.\u00a04c, d), the intensity distribution diffusively spreads away from the origin, indicating the breakdown of corner skin mode. Our experimental observation agrees with the intuitively expected behavior that, for \u03b7\u2009<\u20091, even though there is a disorder in the modulation amplitudes, the gain for the step towards the corner is always larger than that of the outwards direction. Thus the four regions maintain their distinct non-Hermiticity and the corner skin mode exists. But, when \u03b7\u2009>\u20091, a direction-dependent gain for the time steps is no longer always valid and therefore the four regions are no longer distinct and the corner skin mode ceases to exist.\n\nThe randomness is introduced to the intensity modulation of the lattice and the pulse is injected at (x, y)\u2009=\u2009(0, 0). a, b Experimental observation of robustness of the corner skin mode and skin effect in a lattice with moderate disorder (\u03b7y\u2009=\u2009\u03b7x\u2009=\u20090.5, 1). Here the disorder leads to a relaxed spatial confinement of light without breaking the localization of light. c, d Breakdown of the localization in the presence of strong disorder (\u03b7y\u2009=\u2009\u03b7x\u2009=\u20091.5, 2). Light can diffuse arbitrarily far away until they are limited by the size of the experiment. In a\u2013d, from left to right the snapshots are shown for time steps 1, 5, 13, respectively.\n\nTo better characterize the robustness and breakdown of the skin effect, we compute the evolution of the mean-square displacement of the intensity distribution in the lattice as a function of time. The mean-square displacement is quantified as \u2009(n)\u2009=\u2009\u2211x,y\u2009Px,y(n)(x2\u2009+\u2009y2), where Px,y(n) is the time-varying intensity distribution of light. Figure\u00a05 shows the calculated \u2009(n) for several values of the disorder parameter for both theoretical calculations and experimentally measured values. Each experimental curve corresponds to an average of eight independent experimental realizations of disorder, while the theory corresponds to eight averages. Due to the limited size of the lattice (30\u2009\u00d7\u200930), we only collect data from step 1 to step 15, and plot \u2009(n) for the odd steps. The violet, red, and green curves correspond to the weak disorder, with disorder parameters being 0, 0.5, and 1, respectively. All three curves saturate to a fixed value which is well below a certain threshold. This behavior thus implies the robustness of the skin effect. However, for larger disorders of 1.5 and 2, corresponding to the yellow and blue curve, the mean squared distance does not converge. Instead, it spreads out until it becomes limited by the finite size of the lattice, indicating a complete breakdown of the skin effect.\n\na Theory and b Experimental evolution of space-averaged displacement \u2009=\u2009 as a function of step number under different lattice disorders. For disorder strength lower than the threshold \u03b7\u2009=\u20091, the disorder increases the effective spatial diameter of the corner skin mode, as shown in the evolution of average displacement with time. For disorders higher than the threshold, light diffusively spreads to large distances on the lattice. Standard deviations are also shown at each step.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55236-4/MediaObjects/41467_2024_55236_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55236-4/MediaObjects/41467_2024_55236_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55236-4/MediaObjects/41467_2024_55236_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55236-4/MediaObjects/41467_2024_55236_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In conclusion, we demonstrated robust dynamical control over the photonic non-Hermitian skin effect in a 2D synthetic lattice. We created a corner skin mode that localizes light and dynamically tuned the degree of light localization. Moreover, we dynamically steered trapped light along any given trajectory in the 2D lattice. We also demonstrate the robustness of the skin effect under lattice disorder below a certain threshold. Our results demonstrate that useful control mechanisms in spatial landscapes such as reconfigurable light steering1 can be extended to synthetic dimensions.\n\nLooking forward, the dynamic techniques developed in this work can be further applied to investigate Floquet non-Hermitian models27,28,29, in particular in synthetic dimensions30. Further, one can create an analogue of on-site interaction by imposing a nonlinear phase shifter after the linear optical transformations, and investigate non-Hermitian models of interacting particles31. Such nonlinearities could also have implications in the recently discovered regime of topological frequency combs32,33,34 as well as temporal mode-locked lasers35 due to the periodic temporal pulses that define our platform. Moreover, the two-fold spin characteristics in our system can potentially be extended to non-Hermitian models for lattice gauge theories with higher spins and non-Abelian statistics, by increasing the number of loops36. Another intriguing direction can be exploring NHSE-enabled morphing of photonic topological modes which was recently demonstrated in mechanical lattices37. Finally, our non-hermitian lattice can be enriched with engineered synthetic gauge fields38 as demonstrated recently for both Hermitian39 and non-Hermitian models20, to explore intriguing proposals such as the quantum Skin Hall effect40.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "To encode the 2D lattice in time we consider two fiber loops shown in Fig.\u00a01, labeled up channel and down channel. The length of each fiber loop is \u223c3 (km), and one circulation of light in the loop is equivalent to one step of the walk. Hence, we can encode the entire 2D lattice within a time-duration (or time-delay) of \u223c15,000 (ns) without mixing time-bins in step n and step (n\u2009+\u20091). We first encode 30 Y-time bins in both the up and down channels, each of time duration 250 (ns) in a total time duration of 7500 (ns). Each Y-time bin is then occupied by 30 X-time bins, each of time duration 7.5 (ns). At any time, the state of the system is thus represented by a complex vector (Ux,y, Dx,y), encoded in the phase and amplitude of the light pulse circulating in the two fiber loops.\n\nTo initialize the system, we inject a single pulse into the down channel of the fiber loop. We use a continuous wave CW laser with 1550 (nm) wavelength (Optilab DFB-1551-SM-10) and by modulation of this laser using a Thorlabs SOA (SOA1013SXS), we have generated pulses of width \u223c6 (ns) at a repetition rate of 1 (pulse/ms). We then control the polarization of the laser with an inline fiber polarization control (PC) before injecting the light into the down channel with a 90/10 beam splitter. Note that we use two identical 90/10 beam splitters, one for each channel. The 90/10 beam splitter in the down channel is used to inject light into the quantum walk, whereas the 90/10 beam splitter in the up channel is used to weakly couple light pulses out of the quantum walk so that we can measure the pulse power after n steps of evolution using the up channel\u2019s PD. Note that the EDFA placed immediately prior to the up channel\u2019s PD is merely used to amplify the light pulses coming out of the quantum walk experiment, making it easier for the PD to detect it.\n\nAs a pulse enters the system, by default we recognize it as entering the (x\u2009=\u20090, y\u2009=\u20090) time bin, and thus the initial state is D0,0\u2009=\u20091. The pulse then sequentially passes through a 50/50 beam splitter denoted as \u00b1X-beam splitter, a pair of time-varying intensity modulators (Optilab IMP-1550\u221220-PM) is used to impose the correct gain/loss as each time bin (x, y) passes through it, controlled by RF signal generated from Teledyne Lecroy arbitrary waveform generator (T3AWG3252). We then impose a delay of 3 (m) in the up channel and no delay in the down channel. The same procedure then repeats for Y.\n\nTo combat photon loss in the walk, we use two Thorlabs erbium-doped fiber amplifiers (EDFA) (EDFA100S), one for each channel. Before amplifying the pulse, we use wavelength division multiplexers (WDM) (DWDM-SM-1-34-L-1\u22122) to couple a 1543 (nm) CW laser (DFB-1543-SM-30) to the pulses so that the spontaneous emission noise during the amplification is reduced. We decouple the 1550 (nm) pulses from the 1543 (nm) CW laser with the same WDM after the amplification is done. Finally, we use PC to ensure the correct linear polarization for the 1550 (nm) signal pulses. After this, a complete quantum walk step is finished.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data used in this study have been deposited in the Zenodo database under https://doi.org/10.5281/zenodo.14207240.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The codes used for the simulations presented in this study have been deposited in the Zenodo database under https://doi.org/10.5281/zenodo.14207240.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Chalabi, H. et al. Synthetic gauge field for two-dimensional time-multiplexed quantum random walks. Phys. Rev. Lett. 123, 150503 (2019).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nChalabi, H. et al. 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This research was supported by The Office of Naval Research ONR-MURI grant N00014\u221220-1\u22122325, AFOSR FA95502010223, NSF OMA1936314, NSF PHY1820938, IMOD NSF DMR\u22122019444 and the Army Research Laboratory grant W911NF1920181.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Xinyuan Zheng, Mahmoud Jalali Mehrabad.\n\nInstitute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD, USA\n\nXinyuan Zheng\u00a0&\u00a0Edo Waks\n\nJoint Quantum Institute, University of Maryland, College Park, MD, USA\n\nMahmoud Jalali Mehrabad,\u00a0Jonathan Vannucci,\u00a0Kevin Li\u00a0&\u00a0Mohammad Hafezi\n\nDepartment of Mechanical Engineering, and Institute for Physical Science and Technology, University of Maryland, College Park, MD, USA\n\nAvik Dutt\n\nDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA\n\nSunil Mittal\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nX.Z. and M.J.M. performed the experiments and theory and analyzed the data. X.Z., M.J.M, and S.M. constructed the experimental setup with help from J.V. and K.L. E.W., S.M., M.H., and A.D. supervised the project and interpretation of the data. E.W., M.J.M., and X.Z. wrote the manuscript with input from all authors.\n\nCorrespondence to\n Mahmoud Jalali Mehrabad, Sunil Mittal or Edo Waks.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Baile Zhang, Alexander Khanikaev and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Zheng, X., Jalali Mehrabad, M., Vannucci, J. et al. Dynamic control of 2D non-Hermitian photonic corner skin modes in synthetic dimensions.\n Nat Commun 15, 10881 (2024). https://doi.org/10.1038/s41467-024-55236-4\n\nDownload citation\n\nReceived: 08 March 2024\n\nAccepted: 04 December 2024\n\nPublished: 30 December 2024\n\nVersion of record: 30 December 2024\n\nDOI: https://doi.org/10.1038/s41467-024-55236-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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probabilistic machine learning using quantum vacuum noise", + "pre_title": "Photonic probabilistic machine learning using quantum vacuum noise", + "journal": "Nature Communications", + "published": "05 September 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51509-0/MediaObjects/41467_2024_51509_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51509-0/MediaObjects/41467_2024_51509_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://codeocean.com/capsule/4025993/tree" + ], + "code": [ + "https://codeocean.com/capsule/4025993/tree" + ], + "subject": [ + "Nonlinear optics", + "Optics and photonics", + "Quantum optics" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4048986/v1.pdf?c=1725620916000", + "research_square_link": "https://www.researchsquare.com//article/rs-4048986/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-51509-0.pdf", + "preprint_posted": "13 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient stochastic photonic elements. Nevertheless, photonic computing hardware which can control these stochastic elements to program probabilistic machine learning algorithms has been limited. Here, we implement a photonic probabilistic computer consisting of a controllable stochastic photonic element \u2013 a photonic probabilistic neuron (PPN). Our PPN is implemented in a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields. We then program a measurement-and-feedback loop for time-multiplexed PPNs with electronic processors (FPGA or GPU) to solve certain probabilistic machine learning tasks. We showcase probabilistic inference and image generation of MNIST-handwritten digits, which are representative examples of discriminative and generative models. In both implementations, quantum vacuum noise is used as a random seed to encode classification uncertainty or probabilistic generation of samples. In addition, we propose a path towards an all-optical probabilistic computing platform, with an estimated sampling rate of ~ 1 Gbps and energy consumption of ~ 5 fJ/MAC. Our work paves the way for scalable, ultrafast, and energy-efficient probabilistic machine learning hardware.Physical sciences/Optics and photonicsPhysical sciences/Physics/Optical physics/Nonlinear opticsPhysical sciences/Physics/Optical physics/Quantum optics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryOPOprobabilisticmachinelearning.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient stochastic photonic elements. Nevertheless, photonic computing hardware which can control these stochastic elements to program probabilistic machine learning algorithms has been limited. Here, we implement a photonic probabilistic computer consisting of a controllable stochastic photonic element \u2013 a photonic probabilistic neuron (PPN). Our PPN is implemented in a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields. We then program a measurement-and-feedback loop for time-multiplexed PPNs with electronic processors (FPGA or GPU) to solve certain probabilistic machine learning tasks. We showcase probabilistic inference and image generation of MNIST-handwritten digits, which are representative examples of discriminative and generative models. In both implementations, quantum vacuum noise is used as a random seed to encode classification uncertainty or probabilistic generation of samples. In addition, we propose a path towards an all-optical probabilistic computing platform, with an estimated sampling rate of \u00a0~1 Gbps and energy consumption of \u00a0~5 fJ/MAC. Our work paves the way for scalable, ultrafast, and energy-efficient probabilistic machine learning hardware.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Probabilistic machine learning can accelerate image generation1,2, heuristic optimization3,4, and probabilistic inference5,6 by leveraging stochasticity to encode uncertainty and enable statistical modeling7,8. These approaches are well suited for real-life applications which must account for uncertainty and variability, including autonomous driving9, medical diagnosis10, and drug discovery11. However, digital complementary metal-oxide-semiconductor (CMOS) technology requires extensive resource overhead to simulate randomness and control probabilities, which leads to significantly increased power consumption and decreased operational speed12. These challenges have sparked recent proposals for beyond-CMOS hardware such as low-barrier magnetic tunnel junctions13 and diffusive memristors14\u2014both of which leverage intrinsic noise as a source of randomness.\n\nConcurrently, optical neural networks (ONNs)15,16 have shown remarkable progress in energy efficiency17,18, speed19, and bandwidth20 for solving deterministic tasks such as image classification21 and speech recognition22. An important feature of ONNs is the inherent presence of noise in their operation. Therefore, photonic computing hardware typically implements computational tasks that are robust to optical noise16. ONNs have also been explored in regimes where deterministic tasks are performed with high accuracy, despite the presence of high levels of inherent noise18. Conversely, ONNs in which optoelectronic noise is intentionally added have also been proposed for optimization23 and generative networks24. Interestingly, quantum optics offers a natural source of randomness in the ground state of electromagnetic field, known as quantum vacuum noise25,26,27. This intrinsic noise source is ubiquitous in optics and has been used to achieve high-data rate random number generation28,29. In addition, optical systems influenced by quantum vacuum noise have shown natural abilities to generate probability distributions30,31,32, which are of strong interest for computing applications13,14. However, the experimental demonstration of a photonic probabilistic machine learning system has remained elusive so far, mostly due to the lack of programmable stochastic photonic elements.\n\nHere, we experimentally demonstrate a probabilistic computing platform utilizing photonic probabilistic neurons (PPNs). Our PPN is implemented as a biased degenerate optical parametric oscillator (OPO), which leverages quantum vacuum noise to generate a probability distribution encoded by a bias field. We realized a hybrid optoelectronic probabilistic machine learning system which combines time-multiplexed PPNs and electronic processors with algorithm-specific measurement-and-feedback strategies. We demonstrate probabilistic inference of MNIST-handwritten digits with a stochastic binary neural network (SBNN), highlighting how quantum vacuum noise can encode classification uncertainty in discriminative models. Additionally, we showcase the generation of MNIST-handwritten digits with a pixel convolutional neural network (pixelCNN), demonstrating how statistical sampling in generative models can be facilitated by quantum vacuum noise. Furthermore, we provide a thorough discussion of the potential of an all-optical probabilistic machine learning system, offering a possible performance enhancement by a factor of 100 in both speed and energy over traditional CMOS implementations, thereby opening new avenues in high-speed, energy-efficient computing applications.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We first provide a brief overview of two probabilistic machine learning models and their optical implementation with PPNs (Fig.\u00a01).\n\nProbabilistic machine learning enabled by physical random sources, solving a inference and b generation tasks. Neural networks learn a decision line for inference tasks and overall distribution for generation tasks. a Random sources encode uncertainty in neural network parameters, allowing statistical interpretation on inference results. b Stochastic image generation seeded by random sources samples new images from certain probability distributions stored in neural networks. Both computational tasks require controllable stochastic photonic elements which can learn probability distribution and perform statistically independent sampling, which we refer to as photonic probabilistic neurons (PPNs). c Schematic of PPNs. One of the output states (\u03b1(0) or \u03b1(1)) of a multistable optical system is randomly selected from a certain state probability distribution p(\u03b1(1)\u2223b) controlled by a bias field level b. Subsequently, a processing unit reads the output state and updates the bias value b for the next sampling. N independent outcomes can be sampled from different probabilities by time-multiplexing the bias signal. Optical elements in (c), originally published by GWoptics; released under a Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0). MNIST images in (a and b), originally published by LeCun, et al.37; released under a Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0).\n\nDiscriminative models learn decision lines that encode classification boundaries between different images (Fig.\u00a01a, left)33. Probabilistic neural networks (Fig.\u00a01a, middle) then impart statistical properties onto network parameters (e.g., weight uncertainty5 or layer nodes34). Therefore, the network can provide a statistical ensemble of classification results, which are shown as different probabilities of the image classified to certain labels (Fig.\u00a01a, right). Probabilistic inference can quantify classification uncertainty, which becomes critical for ambiguous images located near the decision boundary35,36.\n\nOn the other hand, generative models learn the underlying probability distribution of the training dataset (e.g., images) in order to create new ones (Fig.\u00a01b, left)33. When generating new images, generative models use random sources to seed stochastic image sampling based on the probability distribution learned by the network (Fig.\u00a01b, middle). As a result, images with different labels can be generated (Fig.\u00a01b, right).\n\nIn both of these computational tasks, probabilistic machine learning requires stochastic photonic elements whose probability distribution can be tuned, and that can perform statistically independent sampling. We refer to the optical implementation of this capability as PPNs (purple circles in Fig.\u00a01a, b).\n\nThe proposed PPN is depicted in Fig.\u00a01c. The building block consists of a synchronously pumped degenerate OPO30. An OPO consists of a nonlinear medium (e.g., second-order nonlinear crystal, down converting photon frequency) and an optical cavity surrounding it. The phase of the initial optical field is random due to electromagnetic field fluctuations inside the cavity (quantum vacuum noise). When the power of the pump laser exceeds a certain threshold power, phase-sensitive gain of the OPO allows the initial state to fall into one of the bistable output states with either phase (0\u2009rad, or \u03c0\u2009rad)28. In other words, quantum vacuum noise acts as a perfect random source that manifests itself in the output phase. In fact, this random source is an intrinsic noise source ubiquitous in quantum optics25,26,27. When a vacuum-level external bias field b is introduced in the OPO cavity, the probability distribution of the output steady states can be coherently controlled30. Specifically, our OPO-based PPN encodes a Bernoulli trial B(p) with binary outcomes having probability p and 1\u2009\u2212\u2009p. Independent random sampling and processing can be realized by time-multiplexing the bias signal, resulting in N independent outcomes with encoded probabilities as depicted by different heights in Fig.\u00a01c.\n\nThe experimental system realizing the PPN, and its implementation into a probabilistic computing system, is shown in Fig.\u00a02. The system consists of three modules: biased OPO (purple area), detection (green area), and processing unit (blue area). We time-multiplex OPO signals with an amplitude modulator along the pump path to sample multiple binary outputs from a single optical cavity at a rate of 10\u2009kHz. This bit rate is chosen to ensure the statistical independence of each PPN28,30. We use a homodyne detector to measure the optical phase of the steady state and map it to the corresponding bit value (i.e., 0\u2009rad \u2192 0 and \u03c0\u2009rad\u00a0\u2192 1).\n\na Experimental setup, consisting of an ultrafast laser pumping a nonlinear cavity, and homodyne detection to measure the phase of the OPO signal. Electronic processing units (FPGA/GPU) generate electrical signals to tune the probability. AM amplitude modulator, PM phase modulator, FM flat mirror, DM dichroic mirror, ICM in-coupling mirror, (P)BS (polarization) beam-splitter, SM spherical mirror, PPLN periodically poled lithium niobate nonlinear crystal. PZT piezoelectric actuator, \u03bb/2 half waveplate, PD photodiode. b Modulator voltage\u2013probability relationship. Error bar represents the standard deviation. Optical or electronic elements in (a), originally published by GWoptics; released under a Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0).\n\nDuring each cycle, a bit is measured by the homodyne detector (value 0 or 1), conditioned on the bias value b. This bit, or a collection of bit values (\u201cbitstream\u201d), is then fed into an electronic processing unit to update the bias field value and sample the PPN in the next cycle. In our experiment, the processing unit is taken as either a field-programmable gated array (FPGA) or a graphics processing unit (GPU). The FPGA is more adapted for real-time bitstream processing and control of the optical system, while the GPU can accelerate complex machine learning algorithms such as image generation at the cost of a slower system control.\n\nIndividual pi values are encoded in the phase of the bias field bi by applying a calibrated square-wave voltage to a phase modulator in the bias line path. The voltage\u2013probability relation provided by the phase modulator is shown in Fig.\u00a02b. This relation is used in the following computing experiments to control the bias voltage. A detailed description of the experimental setup is discussed in Supplementary Note\u00a01.\n\nWe now perform probabilistic image classification of MNIST-handwritten digits37 using a pre-trained SBNN model on our optical probabilistic computing platform (Fig.\u00a03a). SBNN encodes inference uncertainty by substituting deterministic layer nodes (as found in conventional fully connected neural networks) with stochastic binary nodes38. In a conventional, fully connected neural network, the jth node value in the (n\u2009+\u20091)th layer Xj,n+1 can be calculated in two steps: (1) matrix\u2013vector multiplication (MVM) between weight matrix W and the nth layer Xn (zj,n\u2009\u2261\u2009\u2211iWj,iXi,n); followed by (2) a nonlinear activation function \u03c3(\u22c5) : \\({X}_{j,n+1}=\\sigma ({z}_{j,n})\\).\n\na Hybrid photonic-electronic architecture for stochastic binary neural networks (SBNNs). Original MNIST grayscale handwritten digit is binarized 10 times with PPNs and each binarized image propagates through SBNN (left panel). Binary nodes are sampled by PPNs and their corresponding p values are evaluated by FPGA (middle panel). Because the nodes are stochastic, inference results vary (right panel). b Confusion matrices of image classification results. A total of 1000 binary images (100 grayscale testing images \u2009\u00d7\u200910 times of binarization\u2009=\u20091000 input images) are tested. c Diagnosing inference results with the aid of quantum vacuum noise. Breadth in probability and low classification accuracy reflect the ambiguity of the input image. MNIST images in (a and c), originally published by LeCun, et al.37; released under a Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0).\n\nWithin our SBNN model, each layer node is represented by a PPN, and a single layer (yellow areas in Fig.\u00a03a) is described as a bitstream of time-multiplexed PPNs. Because of the nonlinear nature of the bias-probability relationship (Fig.\u00a02b), sampling a binary output Xj,n with our PPN from the given bias bj,n (or equivalently bias modulator voltage Vj,n in our experiment), naturally corresponds to passing a nonlinear activation function: \\({X}_{j,n}=B({p}_{j,n})=B[\\sigma ({V}_{j,n})]\\). Modulator voltage Vj,n is calculated via MVM between the weight Wn\u22121 and the \\({\\left(n-1\\right)}{{{{{\\rm{th}}}}}}\\) layer Xn\u22121 (gray areas in Fig.\u00a03a, which is performed by the FPGA in our experiment). In other words, each PPN node binarizes the input, which consists of a weighted sum of previous layer nodes, with probability pj,n. Because of the stochastic nature of the nodes, their probabilities change for every inference, leading to a probabilistic interpretation of classification results for an identical input image (Fig.\u00a03a, right).\n\nTo perform image classification of MNIST-handwritten digits with our optical SBNN, we first binarize original MNIST-handwritten digits (Fig.\u00a03a, left). Original MNIST-handwritten digits (grayscale, pixel values ranging from 0 to 255) are normalized between 0 and 1. The resulting pixel values represent the probability value for each PPN. The grayscale images are binarized by sampling the PPNs. The binary images are propagated through the network (784\u00a0\u2192\u00a0128\u00a0\u2192\u00a064\u00a0\u2192\u00a010), with real-time communication between PPNs and the FPGA. The output layer O0,1,...,9 is used to interpret the classification result, higher Oj corresponding to the higher probability of image representing digit \u201cj\u201d. The network is pre-trained in silico and the weights are implemented on the FPGA. A detailed description of the training process and how the FPGA communicates with the optical setup is in Supplementary Note\u00a02.\n\nTo test the performance of our optical SBNN, a batch of grayscale MNIST-handwritten digits (100 images) from the test set is selected. By binarizing each grayscale MNIST-handwritten digit 10 times to encode statistical uncertainty, we prepared 1000 binarized MNIST-handwritten digits in total to be classified by our optical SBNN. While propagating to the output layer, PPNs in the input and hidden layers encode the uncertainty by stochastically sampling the binary values from given probabilities. Once the output layer is reached, we can collect the statistics from 10 different inference results for each input image. Confusion matrices in Fig.\u00a03b show that the overall experimental classification accuracy (96.5%) is in close agreement with the accuracy obtained from the numerical simulations for the single batch (97.0%) and total test images (98.3%) (see Supplementary Note\u00a02). The classification accuracy of our photonic probabilistic computing hardware is also comparable with that of other optical computing platforms reaching more than 95%21,39,40.\n\nFigure\u00a03c shows how our probabilistic neural network can diagnose the reliability of inference results by harnessing quantum vacuum noise. Unlike deterministic neural networks, the variability of layer nodes in SBNNs results in different probability for each inference. One of the factors that can potentially degrade the classification performance is the ambiguity of the image (i.e., how close the image is to the decision boundary, as shown in Fig.\u00a01a). By encoding uncertainty during inference, our photonic probabilistic computing hardware suggests all possible labels that ambiguous images can be classified. We choose two ambiguous images and two unambiguous images from the test dataset and plot the probability of each binarized grayscale MNIST-handwritten digit being classified under a certain label. Because we binarized 10 images each, 10 different probability values are shown for each label.\n\nThree different scenarios are described in Fig.\u00a03c. Unambiguous images such as \u201c0\u201d and \u201c9\u201d (achieving 100% of classification accuracy) show relatively consistent classification results with probabilities of correct classification close to 1. In this scenario, probabilistic neural networks show similar behavior to deterministic neural networks, which always give the same classification result with a fixed probability. When the input image becomes ambiguous (image \u201c5\u201d underlined in red, achieving 50% of classification accuracy), our SBNN model indicates that the image can be either \u201c3\u201d or \u201c5\u201d. Accordingly, the distribution of probabilities on each label broadens with its average value close to 50%. The worst case scenario is depicted by image \u201c2\u201d (underlined in blue), showing low overall accuracy (20%) and strong inconsistency in classification results. Such scenario clearly showcases how probabilistic sampling can provide additional information to the end-user. Classification results for labels that are not included in Fig.\u00a03c can be found in Supplementary Note\u00a02.\n\nOffering both overall accuracy and statistics of classification results, probabilistic neural networks can diagnose inference results by providing a confidence level of the decision. The total classification result for each input image can be found in Supplementary Note\u00a02.\n\nWe now turn to the demonstration of generative models with our photonic probabilistic computing platform (Fig.\u00a04), demonstrating the use of quantum optical randomness as a source for generative machine learning models. We use a type of autoregressive model (pixelCNN), which models a conditional probability of a current pixel value from previous pixels41.\n\na PixelCNN generating binary MNIST-handwritten digits. PPNs sample the pixel value XN from the given pN value and GPU calculates the pN+1 value for the next pixel from the previous pixels Xi\u2264N. b Branching off to different MNIST-handwritten digits, guided by quantum vacuum noise. Stochastic sampling allows generation of images with different digits and features. c Hundred generated images from pixelCNN, starting from a complete empty image. MNIST images in the left side of (b), originally published by LeCun, et al.37; released under a Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0).\n\nOur implementation protocol for pixelCNN with PPNs is described in Fig. 4a. A binary image with the first N\u2009\u2212\u20091 pixels Xi\u2264N\u22121 specified is given as an input to the network. In principle, N can be any natural number, N\u2009=\u20091 corresponding to the case when pixelCNN creates an image only using quantum vacuum noise as a random seed. When the input image is given, a pre-trained pixelCNN model in the GPU evaluates pN to be encoded on the PPN from previous pixels Xi\u2264N\u22121, generating a binary number for the Nth pixel (XN). The probability pN+1 is now computed based on previous pixel values Xi\u2264N. This process is repeated until the full image is generated (28\u2009\u00d7\u200928\u2009=\u2009784 pixels). Our hybrid optoelectronic computing system can generate new images using quantum vacuum noise as a random seed. Details of network structure and training method can be found in Supplementary Note 3.\n\nDifferent MNIST-handwritten digits, all generated from the same incomplete input image, highlight how quantum vacuum noise enables stochastic image sampling (Fig.\u00a04b). Although they all start from the same \u201cancestor\u201d image, the multiple stochastic samples of pixel values from the PPNs branch off into different MNIST-handwritten digits with different labels (\u201cdescendant\u201d images). It is also possible to generate different images with the same label (which is likely to be labeled as \u201c2\u201d).\n\nWe produced 100 examples of handwritten digit images from quantum vacuum noise using our photonic probabilistic computing platform (Fig.\u00a04c). This was done by initiating an empty image as an input to our optical pixelCNN. We also test the negative-log-likelihood (NLL) of the generated images NLL \\(\\equiv -{\\sum }_{i}\\{{X}_{i}\\ln ({p}_{i})+(1-{X}_{i})\\ln (1-{p}_{i})\\}\\), where the sum runs over i\u2009=\u20091,\u00a0\u2026,\u00a0784 pixel indices. A lower value of NLL indicates statistical similarity to the distribution of training images, yielding 71.1\u2009\u00b1\u200918.8 for our experimental results and 64.9\u2009\u00b1\u200915.4 for numerical simulations. This shows that our system has learned an accurate representation of the image distribution. Details of the performance of image generations can be found in Supplementary Note\u00a03.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51509-0/MediaObjects/41467_2024_51509_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51509-0/MediaObjects/41467_2024_51509_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51509-0/MediaObjects/41467_2024_51509_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51509-0/MediaObjects/41467_2024_51509_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In our demonstration of photonic probabilistic machine learning, the speed and energy efficiency were limited by the PPN sampling rate and data transfer bandwidth between electronic processors and PPNs. In the following, we propose an all-optical probabilistic computing platform which can overcome these challenges, and evaluate the potential benefit in terms of speed and energy efficiency compared to the electronic state of the art.\n\nTo increase sampling rate and reduce energy consumption, we propose an all-optical implementation. For instance, PPNs can be implemented with injection-seeded vertical-cavity surface-emitting lasers, reaching \u00a0>1\u2009Gbps42 and providing energy-efficient operation43. Fast control of the probability and state detection can be achieved with high-bandwidth modulators and detectors44,45,46,47,48, suggesting that PPNs achieving 1\u2009Gbps sampling rate are within reach (detailed explanations can be found in Supplementary Note\u00a04).\n\nFurthermore, our programmable stochastic element naturally implements an all-optical nonlinearity through the bias-probability relationship, which has been a historical challenge in the implementation of energy-efficient all-optical ONNs15. Typically, ONNs rely on optoelectronic measurement-feedback schemes to update the network layers39,49. Conversely, in the proposed scheme, an optical signal (vacuum-level bias) controls the nonlinearity of the layer. Because the bias signal can be derived directly from the accumulated PPN outputs, bypassing active components, the scheme can reduce energy consumption per multiply-accumulate (MAC) operation to as low as \u00a0~5\u2009fJ/MAC. State-of-the-art stochastic electronic devices, such as low-barrier magnetic tunnel junctions and diffusive memristors integrated with conventional CMOS technologies are expected to achieve \u00a0~0.1\u2009Gbps50,51 and consume \u00a0~900\u2009fJ/MAC38. Comparatively, our proposed photonic platform can be \u00a0~\u2009\u00d710 faster and \u00a0~\u2009\u00d7100 more energy efficient. A detailed discussion of this all-optical probabilistic computing platform is found in Supplementary Note\u00a04.\n\nWe now compare the speed and energy performance of our photonic platform to a state-of-the-art FPGA52,53, in an image classification task considering a binary neural network. The deterministic FPGA implementation demonstrated a classification of \u00a0~1.6 million images per second with \u00a0~23\u2009W power consumption. Adopting the network structure of our SBNN model in Fig.\u00a03, we can calculate the computation time and the number of MAC operations required for each inference. Our estimation gives \u00a0~4\u2009ns and \u00a0~105 MAC operations per classification, which result in \u00a0~250 million image classifications per second with a power consumption of \u00a0~0.1\u2009W. Therefore, the suggested all-optical probabilistic computing hardware could perform \u00a0\u00d7100 faster while consuming \u00a0\u00d7100 less power. Detailed discussion can be found in Supplementary Note\u00a04.\n\nOne of the possible extensions of our work is to train the network physically54,55. This becomes critical when an accurate digital modeling of the physical system becomes challenging due to its complexity. Without an additional cost of simulating randomness in digital models, several training methods which resort to stochasticity, including stochastic gradient descent56, dropout34, and noise injection57 could potentially be realized with PPNs. Harnessing quantum vacuum noise in optical elements for both training and testing, our PPNs will pave the way of implementing all-optical probabilistic physical neural networks, which can benefit state-of-the-art machine learning applications including large language models58 and diffusion models59.\n\nOur platform could also be used to implement other important computational tasks. The first one is alternative interpretable neural network models with trainable activation functions60, which could be implemented with the PPN by taking advantage of its tunable bias-probability relationship. The second one is Ising model solvers with external magnetic fields, which can be modeled by the injection of a bias field in a network of OPOs61.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data supporting this work are available within the manuscript, the Supplementary Information, the online repository: https://codeocean.com/capsule/4025993/tree. Raw data generated during the study are available once requested to the corresponding authors. Correspondence and requests should be addressed to S.C. (seouc130@mit.edu) and C.R.-C. (chrc@stanford.edu).", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code used in this study is available at https://codeocean.com/capsule/4025993/tree.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Nichol, A. et al. Glide: towards photorealistic image generation and editing with text-guided diffusion models. Preprint at https://doi.org/10.48550/arXiv.2112.10741 (2021).\n\nGoodfellow, I. et al. Generative adversarial nets. In Advances in Neural Information Processing Systems 27 (NIPS, 2014).\n\nRoques-Carmes, C. et al. Heuristic recurrent algorithms for photonic Ising machines. Nat. 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S.C. acknowledges support from the Korea Foundation for Advanced Studies Overseas PhD Scholarship. Y.S. acknowledges support from the Swiss National Science Foundation (SNSF) through the Early Postdoc Mobility Fellowship No. P2EZP2-188091. C.R.-C. is supported by a Stanford Science Fellowship. D.L., Z.C., and M.S. acknowledge support from the National Science Foundation under Cooperative Agreement PHY-2019786 (The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, http://iaifi.org/). M.H. acknowledges funding by the Austrian Science Fund (FWF) through grant J4729. J.S. acknowledges earlier support from a Mathworks Fellowship. This material is based upon work supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Co-design Center for Quantum Advantage (C2QA) under contract number DE-SC0012704. This material is also based upon work sponsored in part by the U.S. Army DEVCOM ARL Army Research Office through the MIT Institute for Soldier Nanotechnologies under Cooperative Agreement number W911NF-23-2-0121.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA\n\nSeou Choi,\u00a0Yannick Salamin,\u00a0Charles Roques-Carmes,\u00a0Rumen Dangovski,\u00a0Jamison Sloan,\u00a0Shiekh Zia Uddin\u00a0&\u00a0Marin Solja\u010di\u0107\n\nDepartment of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA\n\nYannick Salamin,\u00a0Zhuo Chen,\u00a0Michael Horodynski,\u00a0Shiekh Zia Uddin\u00a0&\u00a0Marin Solja\u010di\u0107\n\nE. L. Ginzton Laboratories, Stanford University, Stanford, CA, USA\n\nCharles Roques-Carmes\n\nThe NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, MA, USA\n\nRumen Dangovski,\u00a0Di Luo\u00a0&\u00a0Zhuo Chen\n\nCenter for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA, USA\n\nDi Luo\n\nDepartment of Physics, Harvard University, Cambridge, MA, USA\n\nDi Luo\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.C., Y.S., C.R.-C., J.S., and M.S. conceived the original idea. R.D. and D.L. contributed to the development of machine learning algorithms. S.C. and Y.S. built the experimental setup with contributions from C.R.-C., M.H., and S.Z.U.; S.C. acquired and analyzed the data. S.C. developed the code for the electronic processing unit and trained the neural networks with contributions from R.D., D.L., and Z.C.; M.S. supervised the project. The manuscript was written by S.C., Y.S., and C.R.-C., with inputs from all authors.\n\nCorrespondence to\n Seou Choi or Charles Roques-Carmes.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Leong Chuan Kwek, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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/dev/null +++ b/a429eaadff2c5827b3e3038598b02ea58c59236cae97e1e8231cc6460632f9b2/metadata.json @@ -0,0 +1,125 @@ +{ + "title": "Quantum metric third-order nonlinear Hall effect in a non-centrosymmetric ferromagnet", + "pre_title": "Quantum Metric Third-Order Nonlinear Hall Effect in A Non-Centrosymmetric Ferromagnet", + "journal": "Nature Communications", + "published": "18 August 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63133-7/MediaObjects/41467_2025_63133_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63133-7/MediaObjects/41467_2025_63133_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Quantum Hall", + "Two-dimensional materials" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4988793/v1.pdf?c=1755601753000", + "research_square_link": "https://www.researchsquare.com//article/rs-4988793/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63133-7.pdf", + "preprint_posted": "21 Oct, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Although Berry curvature in the imaginary part of quantum geometry has been confirmed to play a role in the nonlinear Hall effect of Weyl semimetals, exploration of the real component's influence on nonlinear Hall transport has primarily focused on second-order effects at lower temperatures. However, the potential impact of quantum metric on higher-order transport, particularly the room-temperature quantum metric nonlinear Hall effect, remains largely unexplored. In this study, we observed a significant third-order nonlinear Hall effect induced by quantum metric in non-centrosymmetric ferromagnetic Fe5GeTe2 at room temperature. This effect was confirmed through distinct scaling behaviors regardless of scattering time and a third-order signal dependent on the electron spin state. Notably, our Hall device exhibited an ultrahigh third-order conductivity of 72 \u03bcm\u00b7S\u00b7V-2, surpassing previous studies in Berry curvature-induced third-order nonlinear Hall effects by approximately tenfold, thus enhancing the device's third-order current conversion efficiency. Moreover, we extended the second-order quantum metric dipole scaling to derive a novel third-order equation (\u03c7_xxy^(\"3\" \u03c9) = \u03b7_\"2\" \u03c3^\"2\" +\u03b7_\"0\" ), offering a fresh perspective for studying third-order nonlinear Hall effects in emerging material platforms. Our findings lay the groundwork for the development of room-temperature, low-power quantum spintronic devices leveraging the third-order nonlinear Hall effect.Physical sciences/Physics/Condensed-matter physics/Quantum HallPhysical sciences/Physics/Quantum physics/Quantum information", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformation.pdfQuantum Metric Third-Order Nonlinear Hall Effect in A Non-Centrosymmetric Ferromagnet", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Berry curvature in the imaginary part of quantum geometry has been confirmed to play a role in the nonlinear Hall effect of Weyl semimetals. However, exploration of the influence of the real component of the quantum geometry, the quantum metrics, on nonlinear Hall transport has primarily focused on second-order effects at lower temperatures, rather than higher-order transport. In this study, we observed a significant third-order nonlinear Hall effect induced by quantum metric in non-centrosymmetric ferromagnetic Fe5GeTe2 at room temperature. This effect was confirmed through distinct scaling behaviors regardless of scattering time and a third-order signal dependent on the electron spin state. Notably, our Hall device exhibited an ultrahigh third-order conductivity of 72\u2009\u03bcm\u00b7S\u00b7V-2, surpassing previous studies in Berry curvature-induced third-order nonlinear Hall effects by approximately tenfold, thus enhancing the device\u2019s third-order current conversion efficiency. Moreover, we extended the second-order quantum metric dipole scaling to derive a third-order equation. Our findings lay the groundwork for the development of room-temperature, low-power quantum spintronic devices leveraging the third-order nonlinear Hall effect.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The Hall effect is distinguished by the detection of a perpendicular voltage, referred to as the Hall signal, in a specimen subjected to a longitudinal electric current. Research on the Hall effect has been a central focus in the realm of condensed matter physics, resulting in notable breakthroughs including the identification of the quantum Hall effect and the quantum anomalous Hall effect1,2,3,4. In the time-reversal symmetry (T\u2006) systems, the Hall effect is required to be null in the linear response regime in order to adhere to the Onsager reciprocity relation5. Nevertheless, the nonlinear Hall response is not bound by this requirement, suggesting that it can manifest without the presence of a magnetic field or magnetic order6,7,8,9. This unconventional occurrence challenges conventional theoretical frameworks. Quantum geometry, as characterized by the Berry curvature and quantum metric, which intricately captures the fundamental structure of Hilbert space, has emerged as a potent analytical means for investigating quantum transport and electron interactions in condensed matter physics. The Berry curvature arises from the Berry phase, which elucidates the local geometric properties of a quantum state in parameter space. The contribution of Berry curvature and Berry-connection polarization tensor (BPT) to the nonlinear Hall effect has been demonstrated in Weyl Semimetals10,11,12,13,14,15,16. Nevertheless, the quantum metric, which quantifies the distance in the momentum (k) space, has been largely ignored despite its capacity to generate effects beyond those achievable by Berry curvature, such as inherent second-order nonlinear responses17, current noise18, and orbital magnetic susceptibility19.\n\nThe QMD necessitates the absence of both inversion symmetry (P) and time-reversal symmetry (T) within the system, while still upholding P-T symmetry. At the material level, it is imperative for the system to display magnetic or antiferromagnetic properties alongside non-centrosymmetric structural characteristics. In the case of P-T symmetry, it is necessary for the Berry curvature to vanish completely, while the influences of quantum metric can exhibit significant strength. The recent observation of anomalous electronic behavior in antiferromagnetic topological insulator MnBi2Te4, resulting from quantum metric, has been demonstrated through the second-order nonlinear Hall effect17,20,21. The presence of quantum metric causes the k-symmetric component of the perturbation to become finite, resulting in the transverse current being unaffected by electron scattering22, a phenomenon not attributable to Berry curvature13,14,15,16. Consequently, the exploration of second-order effects resulting from quantum metric provides a significant amount of valuable information concerning electron behavior. This has led to an investigation into the potential extension of quantum metric\u2019s influence on nonlinear Hall effects beyond the second-order. What new and unique physical phenomena may arise as a consequence of this higher-order Hall effect? Currently, the Berry connection polarizability tensor (BPT) has been demonstrated to induce the third-order nonlinear Hall effect10,23,24. This effect, however, is notably suppressed in certain two-dimensional point groups, including C3v, C6v, D3, and D6, due to the presence of inherent three-fold rotational symmetry that hinders the contribution of Berry curvature25. This suppression in specific two-dimensional systems impedes the detection of electronic mechanisms in third-order nonlinear transport. However, the suppression of Berry curvature eventually highlights the emergence of the elusive quantum metric. The quantum metric, a fundamental property of eigenstates26 that may be obscured by thermal fluctuations, presents notable difficulties in studying its contribution to the third-order nonlinear Hall effect, especially at room temperature. There is a pressing need for the development of novel materials that enable the exploration of QMD at elevated temperatures. The ferromagnetic van der Waals crystal Fe5GeTe2 has recently attracted considerable interest in the academic community due to its potential for spintronic applications at room temperature27,28. Additionally, studies have indicated that the distribution of Fe vacancies in Fe5GeTe2 may influence the crystal\u2019s symmetry, potentially resulting in the disruption of inversion symmetry29. Therefore, the non-centrosymmetric Fe5GeTe2, with its three-fold rotational symmetry suppressing the Berry curvature dipole (BCD) and exhibits a high magnetic transition temperature (TC\u2009>\u2009290\u2009K), is regarded as a promising room-temperature QMD nonlinear Hall candidate.\n\nIn this work, the third-order nonlinear Hall effect has been observed in 2D Fe5GeTe2, a non-centrosymmetric ferromagnetic material, exhibiting a pronounced signal at room temperature. The successful induction of anomalous electron motion at room temperature was achieved by manipulating the QMD through top-down control of the material\u2019s symmetry, as evidenced by the observed third-order nonlinear hall signal. The third-order nonlinear Hall effect exhibited a significant enhancement below the Curie temperature, contingent upon the material\u2019s ferromagnetic state, with a scaling relationship that remains invariant with respect to scattering time. In Fe5GeTe2, the characteristics of third-order nonlinear transport are governed by the quantum metric, rather than by intrinsic Berry curvature or extrinsic scattering processes. The relative contributions of the QMD and Drude scattering were quantified utilizing the third-order QMD scaling law. Notably, our Fe5GeTe2-based Hall devices exhibited ultrahigh third-order conductivity (\\({\\chi }_{\\perp }^{3\\omega }\\)\u2009=\u200972 \u03bcm\u00b7S\u00b7V\u22122), attributable to their distinctive quantum geometric and topological properties. This study underscores the impact of quantum metric effects on electron dynamics within ferromagnets, suggesting potential advancements in spintronic Hall effects and nonlinear device technologies.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Previous studies have shown that Fe5GeTe2 has two crystal structures27,30, one with higher symmetry in space group R\\(\\bar{3}\\)m (No.166) and the other in space group R3m (No.160). This study focuses on the Fe5GeTe2 structure in the R3m space group to investigate the nonlinear Hall effect. Understanding the crystal structure of Fe5GeTe2 is crucial before studying the nonlinear Hall effect. The crystal structure of Fe5GeTe2 is characterized by its non-centrosymmetric space group R3m, with lattice parameters a\u2009=\u2009b\u2009=\u20094.037\u2009\u00c5 and c\u2009=\u200929.194\u2009\u00c5. The structure exhibits repetitive building blocks arranged along the c-direction and offset in accordance with the R-centered space group. The rhombohedral lattice of the structure contains three Fe5GeTe2 monolayers within each unit cell (Fig.\u00a01a). In terms of monolayers, Fe5GeTe2 monolayers are composed of four distinct atomic layers arranged in a sandwich structure with the sequence A-B-C-B\u2019-D-A\u2019. The atoms within the Fe-Ge layer (C) and Fe layer (D) adopt a positive hexagonal arrangement. The (C) layer is situated between the Fe atomic layer (B-B\u2019), resulting in a van der Waals gap due to the outermost layer consisting of Te atoms. The crystal structure exhibits threefold helical symmetry axes and threefold rotational symmetry axes, effectively suppressing the BCD (Fig.\u00a01b and Figure\u00a0S1)31,32.\n\na Crystal structure of Fe5GeTe2 viewed along the [110]. b The crystal structure of Fe5GeTe2 from the top view. The fragment describes the red-purple shaded area as the threefold helical symmetry axes (31, 32). green sphere: Fe atoms, orange sphere: Ge atoms, blue sphere: Te atoms. c Schematic diagram of the growth of high-quality Fe5GeTe2 single crystals. d STEM image of Fe5GeTe2. e STEM image corresponding fast Fourier translate (FFT) pattern.\n\nCentimeter-sized single crystals of Fe5GeTe2 (Figure\u00a0S2, Fig.\u00a01c) were successfully synthesized via a solid-state reaction with the addition of a mineralizing agent (I2) (see Experimental section for details). The atomic structure of few-layer Fe5GeTe2 nanosheets along the (001) plane (Fig.\u00a01d) was confirmed by atomic-resolution annular dark field scanning transmission electron microscopy (ADF-STEM), in which the corresponding fast Fourier transfer (FFT) pattern, as shown in Fig.\u00a01e, reflecting the high crystal quality of the nanosheets. The XRD pattern of Fe5GeTe2 crystals (Fig.\u00a02a) shows a predominant scattering vector perpendicular to the (110) plane due to its layered structure, consistent with a previous study30. Fe5GeTe2 atomic layers can be obtained through micro-mechanical exfoliation of bulk crystals using adhesive tape, facilitated by the weak van der Waals interlayer coupling. The energy dispersive X-ray spectrum (EDS) results show the elemental atomic ratio of Fe, Ge and Te in as-synthesized crystals are 5.4: 1: 1.9 (Figure\u00a0S3), basically consistent with the stoichiometric ratio of Fe5GeTe2. In addition, the EDS mapping (Figure\u00a0S4) also confirms good distribution uniformity of our synthesized crystals. The X-ray photoelectron spectroscopy (XPS) integrated peak areas, corrected using sensitivity factors (Figure\u00a0S5), yielded a Fe: Ge: Te atomic ratio of approximately 4.6: 1: 1.9. This composition shows negligible deviation from the nominal Fe5GeTe2 stoichiometry determined by EDS. Second harmonic generation (SHG) spectroscopy confirmed the inversion symmetry breaking in few-layer Fe5GeTe2 using an 850\u2009nm laser to generate a stable SHG signal at 425\u2009nm (Fig.\u00a02b). The image of the SHG mapping distinctly delineates the sample region, providing compelling evidence for the inversion symmetry breaking in Fe5GeTe2 (Figure\u00a0S6).\n\na Experimental X-ray powder diffraction (XRD) patterns for Fe5GeTe2 crystals Inset: optical images of Fe5GeTe2 single crystal. b The SHG signal of Fe5GeTe2 under illumination of 850\u2009nm laser. c Colorful contour mapping of the polarized Raman spectra, where the color reflects the intensity of the Raman vibration. d Temperature-dependent Raman spectrum of Fe5GeTe2. e Correlation between temperature and peak height of Raman peaks at different wavenumbers in Fe5GeTe2.\n\nThe vibrational properties of Fe5GeTe2 originate from the interactions between elementary excitations and phonons. However, as a newly discovered van der Waals magnet, the experimental Raman spectrum of Fe5GeTe2 has not yet been reported so far. In this study, we systematically investigated Raman vibrational modes using the density functional theory (DFT) calculations (Figure\u00a0S7) and Raman spectroscopy (Fig.\u00a02c) with a 532\u2009nm laser. Based on Fe5GeTe2 belonging to space group R3m and point group C3v, group theory analysis reveals the presence of three distinct nonzero vibrational modes, specifically the Az, Ex, and Ey modes (Note.\u00a0S6). According to measurements (Fig.\u00a02c), there were only two Raman active peaks, located at 125\u2009cm\u22121 (Az) and 141\u2009cm\u22121 (Ey) for bulk Fe5GeTe2, in agreement with the calculated modes (Figure\u00a0S7). The thickness-dependent Raman spectra exhibit pronounced blueshifts of approximately 3\u20135\u2009cm\u22121 for both AZ and Ey modes as the thickness decreases from 18.8\u2009nm to 9.2\u2009nm, suggesting robust interlayer interactions and hybridization effects within Fe5GeTe2 (Figure\u00a0S7). Given the challenges associated with directly measuring the magnetism of atomically thin magnetic materials, the changes in Raman spectra accompanying magnetic transitions provide a viable alternative method for monitoring the magnetic order in such materials. It is well known that bulk Fe5GeTe2 has a high Curie temperature of 310\u2009K, while atomic layer samples have an intrinsic Curie temperature of 280\u2009K, near room temperature27. To elucidate the correlation between Raman spectra and the ferromagnetic phase transition at the Curie point of Fe5GeTe2, Raman spectra were acquired for film samples in the vicinity of this critical temperature, as illustrated in Fig.\u00a02d. A significant reduction in the Raman peak intensity of Fe5GeTe2 is observed upon cooling to 300\u2009K (Fig.\u00a02e), which corresponds to the Curie point of the sample. Beyond this critical temperature, the peak intensity recovers, progressively returning to its initial value. These variations in peak intensity are indicative of the material\u2019s transition from paramagnetic to ferromagnetic states. To further confirm the magnetic properties of Fe5GeTe2, we combined magneto-optic effect characterization (reflection magnetic circular dichroism, RMCD) with electronic transport measurements probing the anomalous Hall effect (Figure\u00a0S8). Both methods consistently demonstrate ferromagnetic ordering in Fe5GeTe2, exhibiting a Curie temperature of approximately 300\u2009K.\n\nTo evaluate the nonlinear transport properties of Fe5GeTe2, a Hall bar device was fabricated using a few-layer Fe5GeTe2 nanoflake on a SiO2/Si substrate (280\u2009nm oxide layer) (Fig.\u00a03a, b, Note.\u00a0S8), hexagonal boron nitride (h-BN) as the top protective layer, and classical six-symmetric metal contact electrodes consisting of 10\u2009nm Ti and 90\u2009nm Au, through the processes of laser direct writing (LDW) and electron beam evaporation (see Experimental section for details). The utilization of a lock-in amplifier (LIA) alternating current (AC) technique was implemented to measure the nonlinear transport characteristics of the device, with the primary objectives of noise suppression and acquisition of higher order signals. To study the nonlinear Hall effect, a low-frequency (\u03c9\u2009=\u20097.777\u2009Hz) alternating voltages \\({V}_{\\parallel }^{\\omega }\\) was applied to the device along the longitudinal direction under zero magnetic field, the direction transverse voltages \\({V}_{\\perp }^{n\\omega }\\) with the second-harmonic 2\u03c9 and third-harmonic 3\u03c9 frequencies can be measured by a lock-in amplifier in a phase-sensitive way (Fig.\u00a03b and inset of Fig.\u00a03c). Before monitoring nonlinear Hall signals, it is important to eliminate any spurious signals caused by errors. Our devices demonstrate good ohmic contacts between the material and electrodes (Figure\u00a0S11), suggesting that the potential errors, such as the Schottky rectification effect, will not impact any measured nonlinear Hall signals. In addition, nonlinear signals from drive currents at different frequencies are examined to detect and remove artifacts caused by capacitive coupling, as shown in Figure\u00a0S12. During measurements, the first-, second-, and third-order nonlinear signals were acquired by monitoring the lock-in amplifier\u2019s phase near 180\u00b0, 90\u00b0, and 180\u00b0, respectively (Figure\u00a0S13). This study tested numerous Fe5GeTe2 Hall devices to confirm the presence of third-order nonlinear Hall signals (Figure\u00a0S14), providing further evidence that these signals are not random. While earlier research33,34,35 has found that time-reversal symmetry breaking is needed for the first-order response, leading to an anomalous Hall effect without an external magnetic, recent advances10,23,24 reveal that such responses can also arise from intrinsic resistive anisotropy in materials. Fe5GeTe2 was tested for its first-order response (Figure\u00a0S15), but due to its specific symmetry, the cause of this response was not analyzed in detail. The transverse response voltage (\\({V}_{\\perp }^{\\omega }\\)) is linearly related to the longitudinal excitation voltage (\\({V}_{\\parallel }^{\\omega }\\)) under fundamental frequency conditions, indicating good ohmic contact.\n\na The optical image shows a Fe5GeTe2 Hall device with six electrodes, which are protected by h-BN at 100X magnification. b The schematic view of the Fe5GeTe2 Hall device. The nonlinear signals are measured in the direction indicated. c Second and third order signal \\({V}_{\\perp }^{n\\omega }\\) as a function of \\({V}_{\\parallel }^{\\omega }\\) when the driving field is applied along a specific direction at room temperature. Inset: schematic diagram of second and third order signal measurements. d The \\({V}_{\\perp }^{3\\omega }\\) signal as a function of the \\({({V}_{\\parallel }^{\\omega })}^{3}\\) at different temperatures from 150 to 310\u2009K. Inset: Schematic representation of the direction of the applied excitation voltage and the direction of the detected signal. e The \\({V}_{\\parallel }^{3\\omega }\\) signal as a function of the \\({({V}_{\\parallel }^{\\omega })}^{3}\\) at different temperatures from 150 to 310\u2009K. Inset: Schematic representation of the direction of the applied excitation voltage and the direction of the detected signal. f The relationship between \\({V}_{\\perp }^{3\\omega }\\) and \\({V}_{\\parallel }^{3\\omega }\\) with temperature is shown when \\({V}_{\\parallel }^{\\omega }\\)\u2009=\u20090.1\u2009V.\n\nWe detected second- and third-order nonlinear Hall effects in the device, with the third-order signal being ten times stronger than the second-order signal, indicating the dominance of the third-order effect in Fe5GeTe2. This trend was consistent across all devices (Figure\u00a0S16). Theoretical analysis of crystal physics was utilized to derive the second-order and third-order currents in Fe5GeTe2 (see Experimental section for details). It was found that all second and third-order currents in the R3m space group can appear in the a-b plane, but third-order currents are not allowed in the out-of-plane direction. The third-order nonlinear current in the crystal must satisfy symmetry constraints23, with the third-harmonic response exhibiting a twofold angular dependence relative to \u03b8 (Figure\u00a0S17). The crystal axis determined through this fitting procedure shows perfect alignment with the C3v point group of the space group R3m (see Experimental section for details). The excellent agreement between the theoretical calculations and experimental data further confirms that the nonlinear transport behavior in Fe5GeTe2 is intrinsically governed by its C3v symmetry even in the magnetically ordered state, thereby robustly validating the reliability of our experimental results. It is of significance that a strong third-order signal was detected at room temperature (300\u2009K), a rarity within the realm of third-order nonlinear Hall effect research (Fig.\u00a03c).\n\nSubsequently, we investigated the third-order nonlinear Hall response in Fe5GeTe2. In the context of symmetry, the first-order Hall response requires the broken time-reversal symmetry, the second-order Hall response requires the broken inversion symmetry14,15,36, and the third-order Hall response requires the maintenance of time-reversal symmetry while simultaneously breaking the inversion symmetry10,23. Fe5GeTe2 does not follow the same rules as other materials because its time-reversal (T) and inversion (P) symmetries are broken, but the nonlinear Hall response can still occur with breaking time-reversal symmetry. Recent studies17,20,21 have demonstrated that quantum geometry can be segmented into two components: a real part referred to as the quantum metric, and an imaginary part known as the Berry curvature. Both factors play a role in nonlinear transport. The three-fold rotational symmetry intrinsic to Fe5GeTe2 suppresses BCD contributions21, which may allow the QMD to emerge as the dominant intrinsic mechanism for the nonlinear Hall response. Data in Fig.\u00a03 were collected from #Device 4 with samples thicker than 100\u2009nm to increase the ferromagnetic transition temperature above room temperature (Tc\u2009=\u2009310\u2009K)27, making it easier to observe the third-order nonlinear transport of the quantum metric contribution at room temperature. When an excitation voltage is applied to the Hall device, third-order signals are produced and can be measured in both longitudinal and transverse directions (Fig.\u00a03d, e). When the excitation voltage (\\({V}_{\\parallel }^{\\omega }\\)) is constant, \\({V}_{\\parallel }^{3\\omega }\\) and \\({V}_{\\perp }^{3\\omega }\\) have similar intensities. As temperature decreases, their magnitudes gradually increase (Fig.\u00a03f). The nonlinear Hall effect caused by the BCD exclusively generates a transverse nonlinear response (perpendicular to the excitation current)14,15. In contrast, the QMD enables both transverse and longitudinal nonlinear transport20,21. In experiments, we observed a decrease in signal strength at high temperatures above the Curie temperature, but small third-order signals were still detectable (Figure\u00a0S18). The observed phenomena may be attributed to disorder-induced scattering mechanisms. Recent studies37,38,39,40,41 highlight that extrinsic scattering (e.g., skew scattering and side-jump) plays a critical role in nonlinear Hall effects, even dominating their behavior in certain regimes. Consequently, a rigorous analysis of scattering contributions in Fe5GeTe2 is essential.\n\nAs previously discussed, nonlinear Hall effects can be attributed to the BCD, the QMD, or scattering (non-intrinsic origin). To examine the specific microscopic mechanism of the third-order nonlinear response in Fe5GeTe2, we are investigating the correlation between the third-order response and the scattering time (\u03c4). Due to its metallic nature, the longitudinal conductance of Fe5GeTe2 remains unaffected by the application of an external gate voltage. Therefore, the scattering time can be altered by adjusting the sample temperature. Liquid helium (He) was utilized as a cooling agent to examine the relationship between scattering time and third-order signals in challenging environments. Among these, the device in Fig.\u00a04 (labeled #Device3, 50\u2009nm thickness) was a representative example of this analysis. The resistance was quantified utilizing the four-terminal technique, with conductivity calculated using the formula \\(\\sigma \\,=\\,\\frac{{GL}}{S}\\), where L represents the distance between opposing electrodes and S denotes the cross-sectional area of the channel. Fe5GeTe2 demonstrates metallic transport properties, as illustrated in Fig.\u00a04a. The relationship between temperature and conductivity is found to be insignificant above 100\u2009K, with \u03c3 (300\u2009K)\u2009\u2248\u20090.5\u03c3 (30\u2009K), consistent with previous reports27. Consistent with previous findings at temperatures ranging from 150 to 300\u2009K, the variable \\({V}_{\\perp }^{3\\omega }\\) exhibits a linear dependence on \\({({V}_{\\parallel }^{\\omega })}^{3}\\) at all temperatures, as depicted in Fig.\u00a04b. The linear relationship exhibits a marked deviation when the temperature is reduced below 100\u2009K, and this anomalous behavior cannot be attributed to Berry curvature or scattering mechanisms10,23 as the dominant contributions to the third-order signal, as discussed in detail below.\n\na The longitudinal conductivity \u03c3 as a function of temperature. b The dependence of \\({V}_{\\perp }^{3\\omega }\\) on the cubic of \\({V}_{\\parallel }^{\\omega }\\) is linear within the temperature range of 30 to 300\u2009K. c The \\(| {E}_{\\perp }^{3\\omega }| /{({E}_{\\parallel }^{\\omega })}^{3}\\) as a function of temperature. d\\(\\,| {E}_{\\perp }^{3\\omega }| /{({E}_{\\parallel }^{\\omega })}^{3}\\) as a function of \\({\\sigma }^{2}\\) in Fe5GeTe2 but at various temperature intervals, \\(| {E}_{\\perp }^{3\\omega }| /{({E}_{\\parallel }^{\\omega })}^{3}\\) exhibits distinct functional relationships with \\({\\sigma }^{2}\\). e The scaling relationship between the third-order nonlinear conductivity \\({\\chi }_{\\perp }^{3\\omega }\\) and the square of the linear longitudinal conductivity \\({\\sigma }^{2}\\) The scaling is performed within the temperature range of 30\u2013100\u2009K. f Comparison of the third-order conductivity (\\({\\chi }_{\\perp }^{3\\omega }\\)) and third-order current conversion ratio (\\(| {E}_{\\perp }^{3\\omega }| /{({E}_{\\parallel }^{\\omega })}^{3}\\)) of Fe5GeTe2 with other materials.\n\nSince \\({V}_{\\perp }^{3\\omega }\\) cannot be accurately compared under identical conditions, we instead compare the slopes of the linear relationship between \\({V}_{\\perp }^{3\\omega }\\) and \\({({V}_{\\parallel }^{\\omega })}^{3}\\). The relationship between voltage and electric field can be quantified using the equations \\({E}_{\\perp }^{3\\omega }\\,=\\,\\frac{{V}_{\\perp }^{3\\omega }}{{L}_{\\perp }}\\) and \\({E}_{\\parallel }^{\\omega }\\,=\\,\\frac{{V}_{\\parallel }^{\\omega }}{{L}_{\\parallel }}\\), where \\({L}_{\\parallel }\\) and \\({L}_{\\perp }\\) represent the distances between the electrodes in the transverse and longitudinal orientations, respectively. The temperature-dependent scaling relationship between the third-order nonlinear Hall component \\({V}_{\\perp }^{3\\omega }\\) and the cubic power of the linear excitation \\({({V}_{\\parallel }^{\\omega })}^{3}\\), characterized by the dimensionless ratio \\(\\frac{| {V}_{\\perp }^{3\\omega }| }{{({V}_{\\parallel }^{\\omega })}^{3}}\\,=\\,\\frac{{L}_{\\perp }}{{L}_{\\parallel }^{3}}\\frac{| {E}_{\\perp }^{3\\omega }| }{{({E}_{\\parallel }^{\\omega })}^{3}}\\), exhibits distinct thermal evolution across different regimes. Above 100\u2009K, this scaling coefficient demonstrates a progressive temperature-dependent suppression that becomes particularly pronounced near the Curie temperature (TC\u2009\u2248\u2009290\u2009K). However, the correlation between these voltage components shows a notable decoupling in the low-temperature phase below 100\u2009K, suggesting a fundamental change in the underlying dominant contributions (Fig.\u00a04c). The temperature dependence of the scattering time (\u03c4) in Fe5GeTe2 is found to be significant below 100\u2009K, as illustrated in Fig.\u00a04a, due to the linear relationship between the conductivity (\u03c3) and scattering time (\u03c4). The analysis rigorously investigates the relationship between \\(\\frac{| {E}_{\\perp }^{3\\omega }| }{{({E}_{\\parallel }^{\\omega })}^{3}}\\) and \u03c32 across a temperature range of 30\u2009K to 300\u2009K. A linear relationship is seen between \\(\\frac{| {E}_{\\perp }^{3\\omega }| }{{({E}_{\\parallel }^{\\omega })}^{3}}\\) and \u03c32 at temperatures above 100\u2009K, confirming the scaling relationship:\n\nwhere \u03be and \u03b7 are constants. The left-hand expression can be rewritten as the ratio of the third-order nonlinear Hall conductivity to the longitudinal conductivity (\\(\\frac{| {E}_{\\perp }^{3\\omega }| }{{({E}_{\\parallel }^{\\omega })}^{3}}\\,\\approx \\,\\frac{{\\chi }_{\\perp }^{3\\omega }}{\\sigma }\\)), \\({\\chi }_{\\perp }^{3\\omega }\\) is represented as a third-order nonlinear Hall conductivity. The left side can be rewritten as a ratio of two terms, approximately equal to \\({\\chi }_{\\perp }^{3\\omega }\\)/\u03c3. By analyzing the scaling law relationship between nonlinear response current and relaxation time, it is feasible to determine the predominant contribution21,37. Typically, \u03c40 represent QMD contributions, \u03c4 signifies the proportion of BCD and side-jump contributions, whereas \u03c43 denotes the ratio of skew scattering contributions. As shown by the pronounced linear dependence observed in the experimental (blue data points in Fig.\u00a04d) demonstrates that QMD contributions become obscured within this temperature regime (T\u2009>\u2009100\u2009K). This suppression primarily arises from the disruption of magnetic domain ordering combined with the competition contributions. Additionally, the BCD in Fe5GeTe2 is suppressed by its inherent C3v symmetry. As a result, under this symmetry constraints, the dominant contributions to the system are governed by Eqs. (2) and (3).\n\nEquations (5) and (6) show that skew scattering and side-jump contributions are \\(\\xi\\)\u2009=\u20097.0 \u03bcm4\u00b7A\u22122 and \\(\\eta\\)\u2009=\u2009\u22129.3\u2009\u03bcm2\u00b7V\u22122. The ultrahigh conductivity of Fe5GeTe2 (\u03c3\u2009~\u2009104 S\u00b7cm\u22121 at 300\u2009K) establishes skew scattering as the dominant mechanism underlying third-order nonlinear transport above 100\u2009K. Higher temperatures weaken the QMD due to changes in the ordering of magnetic moments in domains. When the temperature rises above the Curie point, the ferromagnetic material loses its magnetism and becomes paramagnetic as the domains disintegrate, and the average magnetic moment becomes zero. The manifestation of QMD contributions becomes increasingly challenging to resolve experimentally as the temperature rises, where thermal fluctuations begin to dominate the nonlinear response characteristics.\n\nNotably, the third-order signal appears to be minimally impacted by the scattering time at temperatures below 100\u2009K, as shown in Fig.\u00a04d (red curve), and exhibits a nonlinear relationship with \u03c32. An abrupt change in the correlation between \\(\\frac{| {E}_{\\perp }^{3\\omega }| }{{({E}_{\\parallel }^{\\omega })}^{3}}\\) and \u03c32 is observed, contrasting significantly with the behavior arising from the Berry-connection polarizability tensor in TaIrTe410. According to the preceding analysis, it seems that QMD may have a substantial impact on the third-order nonlinear transport in Fe5GeTe2. To confirm the influence of the quantum metric, an examination of the \\({\\chi }_{\\perp }^{3\\omega }\\) as a function of \u03c32 (depicted by the blue curve in Fig.\u00a04e) is presented at temperatures ranging from 30\u2009K to 100\u2009K. By expanding the QMD formula21, we can deduce the scaling law:\n\nSimilarly to Eq. (1\u22123), \\({\\eta }_{2}\\) represent non-intrinsic Drude scattering, whereas \\({\\eta }_{0}\\) signifies the normalized QMD in Eq. (4). By fitting the curve, it was determined that \\({\\eta }_{2}\\)\u2009=\u20090.4\u2009\u03bcm3\u00b7S\u22121\u00b7V\u22122 and \\({\\eta }_{0}\\)\u2009=\u20097.1\u2009\u03bcm\u00b7S\u00b7V\u22122, indicating that the contributions of QMD and Drude scattering to third-order nonlinear transport in 50\u2009nm-Fe5GeTe2 sample are comparable in magnitude. Notably, comparable thickness samples (labeled #Device1, 40\u2009nm thickness) exhibit analogous scaling behavior in third-order nonlinear response (Figure\u00a0S19), further confirming the validity of our result. The magnetic state requirement for QMD generation is emphasized, while the intrinsic layer-dependent exchange interactions in Fe5GeTe2 induce thickness-enhanced ferromagnetic ordering, manifested by a rising Curie temperature with increasing sample thickness. To probe the competition between QMD contributions and scattering contributions at elevated temperatures, a sample with a thickness of over 100\u2009nm (labeled #Device2), closer to a Curie temperature of 310\u2009K, was tested (Figure\u00a0S20). Experimental results suggest that the relationship between \\({\\chi }_{\\perp }^{3\\omega }\\) and \u03c32 is expected to be linear within a temperature range of 1.6\u2009K to 300\u2009K, indicating that scattering dominates the nonlinear response. Thicker samples may lead to an increase in lattice disorder and impurities42,43 (Note\u00a0S19), resulting in transverse asymmetric scattering due to effective spin-orbit coupling of electrons or impurities, which can account for this phenomenon. Moreover, non-intrinsic Drude scattering predominantly governs third-order transport, rendering the QMD contributions unobservable.\n\nThe nonlinear response in Fe5GeTe2 reveals a nuanced interplay between scattering dynamics and quantum geometric effects, where cooperative yet competing contributions from non-intrinsic Drude scattering and emergent QMD collectively govern the third-order transport characteristics. To highlight the robustness of the third-order signal in Fe5GeTe2, we compare its third-order conductivity (\\({\\chi }_{\\perp }^{3\\omega }\\)) and third-order current conversion ratio (\\(| {E}_{\\perp }^{3\\omega }| /{({E}_{\\parallel }^{\\omega })}^{3}\\)) with those of previously reported materials10,23,24 and selected Hall devices fabricated in this work (Fig.\u00a04f). Because of its unique electron spin properties, Fe5GeTe2 can function at room temperature. The material has a high third-order conductivity of 72\u2009\u03bcm\u00b7S\u00b7V\u22122, nearly ten times greater than other materials. All device metrics surpass the highest reported values to date suggesting superior third-order current conversion efficiency. This makes Fe5GeTe2 a promising candidate for future room-temperature nonlinear devices in new quantum materials.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63133-7/MediaObjects/41467_2025_63133_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63133-7/MediaObjects/41467_2025_63133_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63133-7/MediaObjects/41467_2025_63133_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63133-7/MediaObjects/41467_2025_63133_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In summary, we have demonstrated the quantum metric-induced third-order nonlinear Hall effect based on non-centrosymmetric ferromagnetic Fe5GeTe2. The intrinsic third-order nonlinear Hall effect serves as a demonstration of the electrical nonlinear phenomenon stemming from the spin of ferromagnetic states, thereby providing a novel perspective for observing the role of quantum geometry in electron motion. The role of quantum metrics in second-order transport has been unveiled in the investigation of nonlinear transport in antiferromagnetic MnBi2Te417,21. These two studies have observed that this effect diminishes at approximately 25\u2009K, and its sensitivity to spin temperature significantly restricts its practical application. This research overcomes this limitation through material design and expands its potential applications to room temperature. This novel physical phenomenon facilitates the discovery of new quantum physical mechanisms. Our work paves the way for future quantum geometry engineering and spintronic device applications.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "High-quality single crystals of Fe5GeTe2 were successfully synthesized via high temperature solid-state reactions using iodine as a mineralizer. Stoichiometric quantities of high-purity Fe, Ge, Te, and I2, each with a purity level of 99.99 %, were enclosed within a 15\u2009cm quartz ampule under vacuum conditions of 10\u22126 Torr and heated it in a muffle furnace. The temperature was elevated from ambient conditions to 750\u2009\u00b0C over a period of 48\u2009h and maintained for a minimum of 14 days, followed by quenching to conclude the reaction. Single crystals were carefully chosen using an optical microscope and any surface iodine residue was eliminated through the use of acetone, resulting in the production of metallic-luster Fe5GeTe2 single crystals (Fig.\u00a01c; Figure\u00a0S2).\n\nThe Fe5GeTe2 thin flakes were obtained using the mechanical exfoliation method in glovebox (The ratio of O2 to H2O is less than 0.01\u2009ppm). After exfoliation, the flakes were transferred onto a silicon substrate featuring a 280\u2009nm-thick SiO2 layer using polydimethylsiloxane (PDMS). Device fabrication was carried out employing laser-direct imaging facilitated by an optical maskless lithography technique (uPG 501, Heidelberg). Ti/Au (10\u2009nm/100\u2009nm) electrodes were subsequently deposited using electron beam evaporation (DE 400, Wavetest). After fabricating the device, h-BN was transferred onto the device surface for encapsulation via optical microscopic dry transfer.\n\nFrom a crystallographic perspective, the relationship between the driving current\u2019s electric field E and the resulting current density J can be expressed as a power series:\n\nwhere a, b, c \u2208 (x, y, z), \\({\\sigma }_{{ab}}\\) (a\u2009\u2260\u2009b) is the linear conductance, \\({\\chi }_{{abc}}\\) is the second-order polarizability tensor, and \\({\\chi }_{{abcd}}\\) is the third-order polarizability tensor. Then, the second-order nonlinear current density can be written as:\n\nThe equation for the third-order nonlinear current density is:\n\nThe Fe5GeTe2 structure is classified under the R3m (No.160) space group. Based on its structural symmetry, it is understood that its second- and third-order polarizability expressions are as follows:\n\nThe voltage applied to the electrode during device preparation is in the in-plane direction of the material, resulting in \\({E}_{z}\\)\u2009=\u20090. We can derive the second- and third-order nonlinear current densities in the x, y, and z directions for Fe5GeTe2 crystals by combining Eqs. (8) and (9) into (6) and (7), respectively. The resulting values are presented in the matrix below:\n\nThe in-plane alignment of the applied electric field induces a current density distribution that follows:\n\nwhere J is the amplitude, \u03b8 denotes the angle with respect to the material\u2019s x-axis, and the resistivity tensor of the material can be expressed as:\n\nThis anisotropic configuration allows the first-order electric field to be formulated as:\n\nThe longitudinal electric field component along the principal current direction is formulated as:\n\nBased on the preceding formalism, the third order induced electric field E(3) can be derived as:\n\nThe in-plane transverse Hall component of the third-order electric field can be expressed as:\n\nBased on the above results, the ratio \\({{E}_{\\perp }}^{(3)}/{{E}_{\\parallel }}^{3}\\) can be derived as follows:\n\nElectrical transport measurements were performed using a physical property measurement system (PPMS DynaCool, Quantum Design) with high magnetic field capability and an automated top-loading cryostat (attoDRY 2100) equipped with a variable-temperature stage and a superconducting magnet, achieving a base temperature of approximately 1.6\u2009K. A lock-in amplifier (Stanford Research Systems Model SR830) was utilized to filter and detect higher-order signals, with the excitation voltage frequency set at 7.777\u2009Hz. The phases of the first, second, and third harmonic signals were measured to be approximately 180\u00b0, 90\u00b0, and 180\u00b0, respectively. The current-voltage (I-V) characterization was conducted using a PDA FS-Pro semiconductor parameter tester.\n\nThe output from a titanium sapphire femtosecond laser (Chameleon Ultra I, Coherent) was routed through reflective optics and tightly focused via a 100\u00d7 microscope objective (spot size ~1.3\u2009\u03bcm at 850\u2009nm). The generated SHG signal was back-collected through the same objective, spectrally isolated using a dichroic mirror, and purified with a short-pass filter before spectral characterization in a high-sensitivity spectrometer (TuoTuo Technology, TTT-03-SHG). Raman spectroscopy and angle-resolved photoemission resonance spectroscopy (ARPRS) measurements were performed using a HORIBA Xplora Plus system equipped with a 532\u2009nm laser excitation source. To ensure sample integrity, the incident laser power was carefully maintained below 100\u2009\u03bcW with a fixed acquisition time of 60\u2009s. Optical configurations included a 100\u00d7 objective lens and a 1200 lines/mm diffraction grating, achieving a spectral resolution better than 1.6\u2009cm\u22121. For the ARPRS measurements, the incident laser was positioned vertically along the beam path, with the scattered light direction aligned parallel to the incident direction. The sample under test was securely mounted on a stationary rotation stage and systematically rotated clockwise from 0\u00b0 to 360\u00b0 in increments of 12\u00b0.\n\nThe first-principle calculations were performed with the Vienna ab initio simulation package (VASP)44, which utilized the generalized gradient approximation (GGA)45,46 in the Perdew-Burke Ernzerhof (PBE) exchange-correlation functionals47. The plane-wave cutoff energy was set to be 400\u2009eV, and a \u0393-central 10\u2009\u00d7\u200910\u2009\u00d7\u200910 k-point was used in structural optimization. In the framework of DFPT, the features of phonon were carried out with PHONOPY code48. The ferromagnetic state was considered in calculations. The convergence criteria for energy and atom forces were chosen as 105\u2009eV and 0.01\u2009eV/\u00c5, respectively.\n\nAll statistical analyses were conducted using OriginPro-2023 (Origin Lab, Northampton, Massachusetts, USA). 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Mater. 108, 1\u20135 (2015).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by the National Key Research and Development Program of China (No. 2021YFA1200903), the National Natural Science Foundation of China (No. 22175203), and Natural Science Foundation of Guangdong Province (Nos. 2022B1515020065, 2023A1515140171).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices, Nanotechnology Research Center, School of Materials Science and Engineering, Sun Yat-sen University, Guangzhou, PR China\n\nHao Yu,\u00a0Peng Yu\u00a0&\u00a0Guowei Yang\n\nState Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, PR China\n\nHao Yu,\u00a0Xinjie Li,\u00a0Ya-Qing Bie,\u00a0Peng Yu\u00a0&\u00a0Guowei Yang\n\nSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, PR China\n\nXinjie Li\u00a0&\u00a0Ya-Qing Bie\n\nSchool of Physics, University of Electronic Science and Technology of China, Chengdu, PR China\n\nLuo Yan\u00a0&\u00a0Liujiang Zhou\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nP.Y. and H.Y. conceived the study and designed the experiments. H.Y. performed synthesis and characterization of bulk and few-layer Fe5GeTe2 crystals, devices fabrication and electrical transport measurements under the supervision of P.Y. and G.W.Y.; L.Y. carried out the first-principle calculations of Raman spectrum under the supervision of L.J.Z. H.Y. and X.J.L performed electrical transport measurements at low temperature under the supervision of P.Y. and Q.Y.B.; H.Y. wrote the manuscript, assisted by P.Y. and G.W.Y. All authors commented on the manuscript.\n\nCorrespondence to\n Peng Yu.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous, reviewers for their contribution to the peer review of this work. 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of Structured Light for High-Capacity Information Transmission", + "journal": "Nature Communications", + "published": "22 August 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63100-2/MediaObjects/41467_2025_63100_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63100-2/MediaObjects/41467_2025_63100_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63100-2/MediaObjects/41467_2025_63100_MOESM3_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-63100-2#Fig2", + "/articles/s41467-025-63100-2#Fig4", + "/articles/s41467-025-63100-2#MOESM1", + "/articles/s41467-025-63100-2#Sec11" + ], + "code": [], + "subject": [ + "Information theory and computation", + "Lasers, LEDs and light sources", + "Optical techniques" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5562627/v1.pdf?c=1755947251000", + "research_square_link": "https://www.researchsquare.com//article/rs-5562627/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63100-2.pdf", + "preprint_posted": "12 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Structured light brings a breakthrough in information capacity carried by the laser field, finding an ideal utility in optical information transmission. Advancements in optical intensity-based imaging have facilitated the use of structured light for simple information decoding. However, the practicality of available structured-light-based encoding methods is limited by the scarcity of easily distinguishable beam structures. What\u2019s more, currently the structured light is confined to digital bits encoding or channel distinguishing that needs the decoding process, due to a single structured pattern still lacking of effective information. Here, in response to these limitations, we propose a method for extremely high-capacity information encoding, as well as image direct transmission, by modulating the structured light to defective states. Hermite-Gaussian (HG) eigenmode in defect states are designed and generated to achieve a large quantity of easily distinguishable patterns. With well-designed two-dimensional binary hologram gratings to generate different defects in a single HG mode, we achieve over 10n (n\u2009>\u200910) of varying laser states for encoding, corresponding to information capacity being tens of bits. These defect states are recognized by image processing method for quick decoding. What\u2019s more, various image patterns can also be generated and are possible to achieve long-distance transmission with high fidelity. It means that the images can be directly transmitted without Fourier lens imaging, which paves a new way for information transmission. Free propagation and atmospheric turbulence performance of the defective mode are investigated to prove the defective mode has a similar performance to the standard eigenmode and is practical for information transmission.Physical sciences/Optics and photonics/Lasers, LEDs and light sourcesPhysical sciences/Optics and photonics/Optical techniques", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementarymaterialNC.docxSupplementary materialChinesecharactersv2.mp4Defective mode for Chinese characters v2DecodingofdefectiveHG77mode.mp4Decoding of defective HG77 modeHGdefectmodegeneration.aviHG defect mode generationChinesecharatersv1.mp4Defective mode for Chinese characters v1digitsletters.mp4Defective mode for digits and letters", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Structured light brings a breakthrough in information capacity carried by the laser field, finding an ideal utility in optical information transmission. Advancements in optical intensity-based imaging have facilitated the use of structured light for simple information enconding and decoding. Here, we propose a method for extremely high-capacity information encoding, as well as image direct transmission, by modulating the structured light to defective states. Using well-designed two-dimensional binary hologram gratings to generate distinct defects within a single Hermite-Gaussian mode, we achieve over 10n (n\u2009>\u200910) of laser states for encoding, corresponding to information capacity being tens of bits. These defective states are recognized by image processing method for quick decoding. In addition, various image patterns can also be generated and are possible to achieve long-distance transmission with high fidelity. It means that images can be directly transmitted for long distance without digital encoding process, which paves a simple way for information transmission.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Structured light, extensively studied for both scientific research1,2,3 and various applications4,5,6,7,8, holds significant promise in addressing the bottleneck problem of the capacity crunch of optical communications9,10,11,12,13,14,15,16,17. One of the most significant applications of structured light in optical communications is to utilize its rich spatial characteristics as recognizable encoding parameters, and so to achieve ultra-high-capacity information transmission by generating a large amount of uniquely structured light modes11,18. Similar to the shift-keying techniques in temporal optical communication, this approach allows the transmission of multibit data within a single time interval. For instance, quadrature amplitude modulation (e.g., 256QAM) has been successfully used to encode 8-bit data, and by further increasing the value of amplitude and phase levels, 12-bit data encoding has been feasible. However, QAM-based techniques face limitations when attempting to scale to higher bit depths. The breakthrough of recent advances in structured light has opened up the possibility of high-capacity shift-keying data transmission in the spatial domain.\n\nMode-encoding using structured light can neglect the orthogonal requirement of the channels and only needs a large number of patterns to be different from each other, with proper decoding methods. To achieve a maximum number of beam patterns within a certain order, leveraging the superpositions of laser eigenmodes both in degeneracy19 or nondegeneracy20 becomes inevitable, albeit resulting in increasingly complex beam structures. Optical methods with gratings21,22,23, hologram gratings24,25 and other optical elements26,27,28 for such complex superposed modes decoding are not suitable, then self-organizing map29,30,31 based shallow neural networks and convolutional neural networks (CNN)20,32,33,34,35,36 based deep learning methods have been widely investigated. However, with the quantity of beam patterns increasing, recognition of complex patterns becomes more challenging, requiring increasingly complex CNN architectures. Despite advancements, the total pattern quantity is still at a relatively low level, with the highest value reported to be 168037, which asks for the highest mode order of 16 and a quite time-consuming network design. Therefore, there is an urgent need for a much more efficient encoding method to provide considerable beam patterns without increasing decoding burdens.\n\nFrom a broader perspective, current optical information transmission works are mainly based on well-developed radio communication schemes, which transmit digital bits in the time domain. However, the most direct way to achieve information transfer using optics is by leveraging the spatial domain. Information of images and texts can be transmitted from A to B by optical waves directly. Imaging technology is the most common method to obtain spatial information on targets, and it belongs to the detection area, but not the communication area. If the image information of both entities and texts can be transferred directly in free space without digital encoding and decoding processes like imaging technology, optical communication may embrace a revolution, while the problem is that the optical waves carrying images are not easy for long-distance non-diffraction propagation to deliver the original images with high fidelity. Thanks to the spatial expansion characteristics of laser-beam-based structured light bringing great advances, one can achieve the generation of both non-diffraction beams38,39,40 and laser patterns carrying complex images41. Nevertheless, the beam lacks image information, or the laser mode is not possible to invariantly propagate too far. Additionally, projectors and holography42,43 may realize the image\u2019s direct transmission, but also with a relatively short distance due to the relatively large divergence angle of the light field. So far, direct image transmission for long distances is still impossible.\n\nIn this work, we define an information encoding method by using a defective state of structured light and reveal that the defective state can propagate through long distances in a uniform medium with high fidelity. By manipulating the structure defects\u2019 distribution, arbitrary image patterns can be generated for spatial information direct transmission and captured by machine vision for fast recognition. Based on this principle, we also propose an effective information transmission link. By manipulating the existence of each bright spot in an Hermite-Gaussian\u00a0(HG) eigenmode, an extremely large number of defective patterns are obtained for high-capacity encoding and fast decoding. It greatly breaks through previous limitations on recognizable structured light, opening useful avenues for the application of structured light in information transmission.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The eigenmodes of lasers are solutions to the Helmholtz equation under the paraxial approximation, which means that their intensity distribution remains invariant during propagation, with only a scaling effect due to beam divergence as the propagation distance changes. This characteristic of the eigenmodes also implies that the spatial intensity information carried by the laser can be transmitted over long distances in a vacuum or a homogeneous medium without significant distortion, providing a solid foundation for direct image transmission. If the two-dimensional intensity information of an image to be transmitted is loaded onto a two-dimensional higher-order eigenmode, the intensity distribution of the eigenmode will be modulated by the corresponding spatial information of the image. Specifically, if one wants to obtain an HGm,n mode with some local defects at a certain distance d, the targeted light field should be given as Etar, and inverse propagation calculation should be made for its near-field Enear, according to the angular spectrum theory. Enear can be calculated as below,\n\nwhere, \\({\\mathbb{F}}\\) and \\({{\\mathbb{F}}}^{-1}\\) are the Fourier and inverse Fourier transformations, T is the transmittance function with specially designed local defects, and \\(H(d)=\\exp (ikd)\\exp [-i\\pi \\lambda d({f}_{x}^{2}+{f}_{y}^{2})]\\) is the transfer function to the distance d. After getting the function of Enear, the binary hologram grating to be loaded on the spatial light modulators (SLM) can be obtained to generate the required light field. Please refer to the \u201cMethods\u201d section for details.\n\nBy further studying the complete propagation features of the generated light field, the one at z\u2009=\u20090 can be propagated forward over an arbitrary distance D, yielding the complex amplitude of the light wave at z\u2009=\u2009D, denoted as Eout,\n\nSubsequently, the complex amplitude of the light wave at z\u2009=\u20090 can be expressed in the convolutional form of Fresnel diffraction, substituted into the wave at z\u2009=\u2009D, i.e. the complex amplitude of the emitting light wave expanded in the same convolutional form,\n\nThe function of the emitting light field can be expressed as the convolution of the complex amplitude of the target field with transformation function A(x,y), followed by multiplication with the phase factor \u03c6,\n\nwhere,\\(A(x,y)=\\frac{1}{{\\lambda }^{2}Dd}{e}^{{j}\\frac{k}{2(D-d)}({x}^{2}+{y}^{2})}\\).\n\nSpecifically, when studying the transmission distance, assuming the target field is placed at a large distance d, the approximation of the Fourier transform of the transformation function A(x, y) to a Dirac\u00a0delta function (when L\u2009=\u2009D\u2013d\u2009\u2248\u20090) ensures that the transmission characteristics of the target field are minimally affected. This allows the designed intensity information to be transmitted over long distances to the target area nearby z\u2009=\u2009d. However, when the Fourier transform of A(x, y) does not approximate to delta function (L\u2009>\u20090), the image encoded in the light field can only be transmitted within a limited range, of which size is related to the value of d, maintaining approximate invariance. However, as HG mode is an eigenmode of laser cavity, its intensity distribution has the property of transmission-invariance (nothing variant but spatial scaling), which significantly increases the similarity-maintaining range L of the target light field (for detailed analysis in Supplementary Information\u00a0S1). And it is important to notice that, the similarity between Eout (x, y) and Etar (x, y) is almost always satisfied when d has a large value (located at the far field, compared with the Rayleigh length), but unrelated to the exact distribution of T(x, y).\n\nThe loaded image function only modulates the intensity of the eigenmode without altering its phase. When the intensity of the eigenmode is modulated by a slowly varying function since the Fourier transform of the modulation function approximates a Dirac delta function, the transmission characteristics of the eigenmode are minimally affected, allowing the intensity information it carries to be transmitted over long distances. However, when the Fourier transform of the modulation function no longer approximates a Dirac delta function, the image loaded onto the optical field will only maintain near-invariant transmission over a limited region. Despite these variations, the common advantage in all these cases is the ability to achieve long-distance image transmission with the laser beam\u2019s small divergence angle. Figure\u00a01 presents a concept diagram of the image transmission method proposed in this paper.\n\nThe position z\u2009=\u2009d corresponds to the location of the target light field, and L represents the transmission range within which the field maintains a high fidelity. I: the design of the target field, with a standard HG eigenmode light field, modulated by a gray-image-based transmittance function; II: the near field of the target field is derived from the inverse propagation (IP) of the target field, and then the two-dimensional grating can be achieved by the computer-generated holography (CGH)\u00a0method.\n\nFollowing the concept above, the HG eigenmode is possible to be spatially modulated by different image patterns. As shown in Fig.\u00a02a, various image patterns are generated successfully, including the digits, letters, Chinese characters and geometries. Here, the HG8,8 eigenmode is used as the carrier beam to be encoded to various relatively simple symbols. Comparing the target characters with the simulated and experimentally measured patterns, a high degree of consistency is observed, demonstrating that this method is effective in obtaining images of simple symbols.\n\na The generation of digits, letters, Chinese characters and cubic geometries. From the first to the fourth row are the target defect mode patterns, the corresponding holograms, the simulated far field of the +1st diffraction order beam and the experimentally measured patterns. b1 Institution emblem images generated using defective states within 10\u2009m inside the laboratory. b2 Character patterns generated within 50\u2009m outside the laboratory. c Variation of the similarities between the random defective target field and the light field with different propagation distances. d Variation of similarity-maintaining range L (SSIM\u2009>\u20090.9) of HG modes (taking HG3,3 as an example) with random defects over long-distance propagation, with \u03c90\u2009=\u20091, 2, and 3\u2009mm, respectively.\n\nBy increasing the indexes m and n of the HG mode to enhance the array resolution, more complex images are possible to be generated. The on-hand digital micromirror device (DMD) (TI, V-7001) system is possible to generate an HG mode with the highest indexes of m\u2009=\u2009n\u2009=\u200924 by the super pixel method with a unit possessing 4\u00d74 pixels (see details in Supplementary Information\u00a0S2). As the intensity modulation function, the institution emblem was used to load onto the eigenmode of HG24,24, and a discrete emblem pattern was obtained, whose transmission properties were subsequently studied. Fig.\u00a02b1 first presents the intensity distributions of discretized institution emblem pattern under different similarities SSIM\u2009=\u20091, 0.95, and\u00a00.9. Under the laboratory condition, the target positions of the emblem pattern were set at d\u2009=\u20092 and 6\u2009m. The figure shows the intensity distributions of the emblem pattern at and around the target positions. It can be seen that the emblem patterns obtained at the target position exhibit a well-preserved structure, with the original sub-spots of the HG mode maintaining their characteristic profile. By comparing the images transmitted before and after the target positions, a variation in similarity (with calculated SSIM to be around 0.8) with respect to the target image can be observed. To further verify the transmission performance over long distances, the generated defective-state light field was propagated outside the laboratory, achieving a 50\u2009m transmission along the building hallway (see details in Supplementary Information\u00a0S3.3). Figure\u00a02b2 presents the character transmission results at various target distances d. It is evident that when d is larger, a broader spatial interval around the target position yields higher similarity, which is consistent with the previous theoretical analyses.\n\nAfter confirming the feasibility and validity of the proposed method, a simulation-based statistical analysis was conducted to investigate the transmission properties of defective-state beams under more general conditions. Figure\u00a02c presents simulations of the transmission behavior of defective HG modes with varying sparsity levels of randomly introduced defects, whose corresponding fundamental Gaussian mode has a beam waist of 3\u2009mm, resulting in a Rayleigh length zR of 53\u2009m. The diagram also shows the evolution of image similarity for HG3,3, HG5,5, HG7,7, and HG9,9 modes under random defect sparsity levels of 1/4, 1/2, and 3/4, with two target positions, d1\u2009=\u2009200\u2009m and d2\u2009=\u2009800\u2009m, selected for evaluation, where SSIM\u2009=\u20091.0. In the SSIM calculation, each curve is obtained by taking the averaged value of 10 different random defective states. The purple arrow in the figure indicates that the curve for d (200 \u20090.9) at d2\u2009=\u2009800\u2009m is significantly larger than that at d1\u2009=\u2009200\u2009m. Second, as the order of the HG mode increases, corresponding to a reduction in the size of individual defect spots, the similarity region tends to decrease accordingly. For instance, at d1\u2009=\u2009200\u2009m, the average similarity region narrows from 130\u2009m for the HG3,3 mode to 47\u2009m for the HG9,9 mode. Additionally, the similarity curves for various defect sparsity levels are found to be closely aligned across all HG modes, especially in the high-fidelity regions, indicating that defect sparsity has a relatively minor impact on the image similarity after transmission. To more intuitively illustrate how the similarity region changes with different target distances d, a numerical statistical analysis is performed, using the HG3,3 mode with random defects at a sparsity of 1/2, as shown in Fig.\u00a02d, where the evolution of the similarity region L with respect to d for Gaussian beam waist radii \u03c90\u2009=\u20091, 2, and\u00a03\u2009mm. It is evident that for all \u03c90 values, the similarity region L increases more rapidly with larger d. A smaller beam waist radius corresponds to a shorter Rayleigh length, allowing the beam to reach the far field earlier. It is worth noting that although the similarity range L of defective modes varies significantly with different selected target positions d, it has little impact on practical information transmission, since the value of d can be accurately controlled according to the known distance between the transmitter and receiver. What\u2019s more, the range L provides a margin of distance tolerance approximately equal to the value of d. Naturally, the larger the value of L, the greater the dynamic range for information transmission, or the more flexibility in supporting multiple receivers located at different distances can be achieved.\n\nSpatial images have been used as identification codes, such as the widely used Quick Respond (QR) code. The investigation in this study makes the image of the QR code possible to transmit directly for identification in the area far away. Considering the large number of QR codes and the high information capacity contained within a single pattern, the HG mode, which has a quite similar structure to the QR code, is quite adapted\u00a0for sub-structure modulations. According to the physical characteristics of HGm,n mode, intensity\u00a0segmented by phase singularity, it has (m\u2009+\u20091)(n\u2009+\u20091) bright spots on its light intensity distribution. As the HGm,n mode is composed of the array of bright spots, and if we specially design the transmission function T (x, y), it\u2019s able to modulate each single spot in the array. Local defects of the spots at particular positions can be generated by eliminating some bright spots in the mode, and then\u00a0a large number of different patterns can be generated for coding.\n\nBy encoding an HG mode with its sub-spots in different defective states, it is theoretically possible to directly generate all the arrangement conditions in the mode pattern. Taking HGm,n as an example, the mode has (m\u2009+\u20091)(n\u2009+\u20091) bright spots, if each bright spot is treated as a binary switch and converted into a digital 0 and 1, each bright spot can be regarded as a carrier of 1-bit of information, and the whole mode can carry data with (m\u2009+\u20091)(n\u2009+\u20091)-bit, corresponding to 2(m+1)(n+1) status in total. It can be derived that as the order of the HG mode increases, the information carried by a mode increases exponentially.\n\nSimilar to the QR code, the distributions of the sub-spots need to be accurately located so that the patterns can be quickly and accurately recognized by the decoding process. For example, when there is only one single sub-spot left in the mode, it is easy to produce large errors when confirming its position. Therefore, to facilitate its positioning, combined with the principle of the coordinate axis, one column and one row of sub-spots need to be all reserved for auxiliary positioning. In the practical strategy, the number of available codes from HGm,m mode is the same as the total number of defective states of HGm-1,m-1 mode. Considering that HG4,4 is upstream based on relatively low order and high information capacity, it is selected to take as an example. When the positioning row and column are anchored, exactly four light spots remain in each row, and two rows of these four sub-spots can form a byte, which is shown in Fig.\u00a03a. Then, each of these defective mode states can carry two bytes of information, shown as in Fig.\u00a03b. By the presented principle, one can easily obtain the capacity for coding with the HGm,m mode, as shown in Fig.\u00a03c. For example, the defective states of HG7,7 mode can obtain the coding capacity of 49-bit, corresponding to a total defect mode number of 0.56\u2009\u00d7\u20091015.\n\na The encoding principle by generating local defects of bright spots in an HG mode. b Examples of encoding with defective states of HG4,4 mode. c Coding capacity with different HGm,m modes. d The calculation process of the diffraction grating on the DMD for the generation of defective states. e Simulated and experimentally generated defective HG4,4 mode.\n\nIn the previous section, a method for loading specific patterns onto structured modes by superposing varying distribution information was discussed. This approach enhances information transmission by embedding pattern data into the beams while preserving higher fidelity during transmission. However, it requires higher-order structured light, which demands higher system design and technical implementation in terms of generation and manipulation. To simplify the system design, the utilization of lower-order light beams with defect structures was investigated. Despite their lower orders, these structured light beams can still carry a large amount of information, thus offering the promise to enhance transmission efficiency through more simplified structures while maintaining information transmission functions. As discussed in the aforementioned QR-code-related section, the exponential increase in information-carrying capacity provides significant potential for using defective HG modes in information transmission.\n\nTo achieve the generation of defective HG beams, the simplified method shown in Fig.\u00a03d was employed. Specifically, the HGm,n mode was first occluded with a mask T to obtain the target light field Etar, then performed an inverse Fourier transform calculation (matching the preset distance) to obtain the corresponding near-field\u00a0of Etar. The calculated holographic pattern by CGH\u00a0was loaded onto a DMD, and the desired target field Etar\u00a0was produced in the +1st-order diffraction of a vertically incident Gaussian beam. Figure\u00a03e shows the simulated and experimental results of defective HG3,3 and HG4,4\u00a0modes, validating that HG modes generated by the method in Fig.\u00a03d can generate defects at any local position.\n\nMore specifically, when using an HG4,4 mode beam with a waist radius of \u03c90\u2009=\u20093\u2009mm for transmission, selecting a location 20 times the Rayleigh range (~1\u2009km) for information loading and occlusion results in a beam size of \u03c9z\u2009=\u200920\u2009mm at that distance. At this range, the transverse distribution of the HG4,4 beam still preserves the target defect mode structure, albeit with an enlarged beam size, which not only improves the recognizability of the pattern, but also enhances the stability of signal propagation. At this distance, occlusion and information loading operations ensure that the HG4,4 beam retains its defect structure during subsequent long-distance transmission. When specific patterns are loaded onto higher-order HG modes, their distinctive defective modes remain stable over considerable distances, despite diffusion in free space. This stability offers significant advantages for transmitting complex information over long distances. Furthermore, utilizing lower-order HG beams reduces the complexity of generating and controlling high-order modes, enhancing the practicality and feasibility of defective structure-based beam transmission in real-world applications.\n\nIn the experiment, the diffraction gratings calculated by CGH and loaded on DMD can generate the desired structured beams. As illustrated in Fig.\u00a04a, the experimental setup enables the direct generation of defective structured beams by modulating the holograms on the DMD. A Gaussian beam produced by a 532\u2009nm solid-state laser, after collimated through a beam expander, is directly incident on the DMD surface and covers the hologram completely. The emerging light, carrying encoded information, is emitted at 24\u00b0 relative to the DMD normal direction and enters the 4f system composed of lens 1, aperture and lens 2 in sequence. Within this system, lens 1 and the aperture filter the beam, selecting the +1st diffraction order light field, and lens 2 collimates the output. After propagating for a certain distance, a focusing lens 3 is used to simulate the far-field propagation effect, and finally, the beam is concentrated onto the charge-coupled device (CCD) detector to capture the beam intensity image.\n\na The experimental setups and the whole procedure for the encoding and decoding strategies. b The image processing procedure of the measured beam pattern. c Real-time pattern recognition accuracy with 50\u2009fps rate. d Measured defective HG4,4 modes. e Measured defective HG7,7 modes.\n\nThe digital image signals acquired by the CCD detector are transmitted to the host computer using the USB 3.0 interface. The real-time digital image signals are acquired by using the OpenCV-Python library in the host computer, and the parameters of the CCD are controlled by using the Software Development Kit of the corresponding CCD with Python. The main parameters of the CCD are the pixel format, acquisition frame rate and exposure degree: the pixel format is Mono8; the acquisition frame rate matches the frame rate of the transmitter; the appropriate exposure degree helps to reduce the effects of the rapid transformation of the light spot residual effects and improve the acquisition clarity. Compared to the machine learning method for complex structured light decoding, the recognition of HG mode with defects requires much simpler algorithms and is much faster. Decoding patterns with local defects can be achieved by an image processing method comprising several key steps, including pre-processing, Suzuki\u2019s algorithm44, circumscribed circles, and matrix scanning, as depicted in Fig.\u00a04b. Before contour detection, images should first undergo some basic pre-processing to increase the accuracy of contour detection. This includes resizing the source images to a uniform scale, converting them to grayscale format, and applying binary thresholding. Morphological transformations, such as closing small holes within foreground objects and noise removal, are used to further refine the images. Suzuki\u2019s algorithm is then employed for contour tracing, resulting in the detection of outer contours, as depicted in the third experimental image of Fig.\u00a04b.\n\nThe real-time information transmission based on defective HG4,4 and HG7,7 modes was accomplished. In the experiment, the entire defective states of HG4,4 mode (with a total number of 216\u2009=\u200965,536) and partial that of HG7,7 mode (the amount is 2962, which were randomly generated) were used to encode the information, and the defective patterns collected by the CCD camera were decoded directly by the algorithm presented above in real-time. For the defective states of HG4,4 mode, all the 65,536 states were divided into 60 groups based on the encoding sequence for decoding experiments, with each of the first 59 groups containing 1100 different states, and the last group containing the remaining 636 states. Compared to the defective states of HG4,4 mode, HG7,7 mode has a finer structure and higher recognition requirements, so it uses a conversion rate of 20\u2009fps, while defective HG4,4 mode has a faster recognition speed with a conversion rate of 50 fps. The recognition speed is now limited by the performance of our personal computer and the ordinary camera device, while it\u2019s possible to improve the data transfer rate by improving the hardware. By recognizing the defective patterns with the method in the \u201cMethods\u201d section, each defective state is identified as a data string with 16-bit to HG4,4 mode and 49-bit to HG7,7 mode. The experimental results for defective states of HG4,4 mode show an average recognition accuracy of 98.04% with a variance of 0.04%, as illustrated in Fig.\u00a04c, indicating no significant difference in recognition accuracy among the defective states with different sparsities. The accuracy of recognition on defective states of HG7,7 mode with the capacity of 2962 is 91.24%. Furthermore, the experimentally obtained defective patterns from HG4,4 and HG7,7 modes are shown in Fig.\u00a04d and e, which correspond very well with the targeted patterns.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63100-2/MediaObjects/41467_2025_63100_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63100-2/MediaObjects/41467_2025_63100_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63100-2/MediaObjects/41467_2025_63100_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63100-2/MediaObjects/41467_2025_63100_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "By utilizing the single HG eigenmode to make spatial modulations, we obtain as many as 1015 different defective patterns for encoding (corresponding to 49 bits), which is tens of orders higher than the current method in structured-light-based shift keying FSO applications. What\u2019s more, this still does not include all regimes in which the defective HG modes could be used. Compared to temporal QAM techniques, this method also exhibits a clear advantage in terms of mode capacity. Admittedly, current SLM and imaging devices still lag behind the temporal devices in modulation and response speed. However, with the continuous development in performance of these spatial optical components, the proposed technique will promisingly become more competitive. Moreover, to further enhance its communication capacity, two additional advanced strategies can be applied to this method. First, temporal modulations can be added to the defective HG modes to achieve the time division multiplexing along with shift keying. So that, the information transmission capacity can be greatly improved again. Second, one can also use the complementarity of the defective distribution of the HG modes to achieve two (or multi) beam space division multiplexing for higher capacity information transmission. To further explore the potential of this strategy, the recombination could be realized at the transmitter end using polarization or spectral combining techniques, while correspondingly, the separation of the two could also be realized in accordance with the chosen combining method. Additionally, it is possible to change the two (or multi) beam multiplexing states freely, which is very difficult to decipher as there are so many complementary combination conditions.\n\nIn this work, the defective state of structured light is proposed for high-capacity information transmission. The generation method and propagation property of high-order defective HG eigenmode are investigated to show its feasibility for spatial information direct transmission. Image information spatially modulated to the HG mode can be well maintained within long-distance propagation. Based on this principle, a method to greatly increase the encoding capacity of structured light for image-based decoding is also presented. Single HG eigenmode in different locally defective states is generated by computer-generated holograms for information encoding. With an HGm,m eigenmode, we obtained defect patterns encoded with an information capacity of m2-bit. A simple image-processing-based decoding method achieved an up to 50\u2009fps image recognition rate, with an accuracy of above 98%. What\u2019s more, the defective HG beams can also have good performance under moderate turbulences (see Supplementary Information\u00a0S4). This work may pave a useful way for structured-light-based optical information transmission, due to its good image transmission ability and extremely high information encoding capacity.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Structured light can be well generated by an SLM. DMD is one of the SLMs, and it can generate the perfect structure of a mode with very fast speed and spectrum robustness. DMD provides a two-dimensional binary grating for the transformations of a Gaussian mode to a target structured mode on the +1st diffraction order. The two-dimensional binary grating is calculated according to the holography principle of the input and output optical fields. For a near field structured mode Eout from a nearly plane wave, the binary grating can be calculated as below45,46,47,48,\n\nHere, it is easy to check that as \\(\\omega (x,y)\\) and \\(p(x,y)\\) are slowly varying, this formula reproduces the pulse train described above. We can find the corresponding \\(\\omega (x,y)\\) and \\(p(x,y)\\) functions for a general complex scalar field \\(A\\,(x,y){e}^{i\\phi (x,y)}\\) according to the relations below,\n\nIn the spot coordinate extraction, we applied two methods. First, the directional projection method can be used to identify the sub-spots in HGm,n mode. The original grayscale image is converted into a binary image, and the pixel points are added and summed in the vertical and horizontal directions on the binary image to obtain two projection histograms. According to the vertical and horizontal projection histogram, the vertical peaks on both sides [Y1, Ym+1] and horizontal peaks on both sides [X1, Xn+1] are obtained. The accuracy of the direction projection method is relatively high. However, due to the randomness in the shape and size of the actual laser spot, the intensity curve\u2019s smoothness is poor, resulting in a large computational load and long computation time when searching for peak points in the intensity curve. After extracting the spot coordinates, the next step is defect spot localization. The simplest location method is the three-point localization. This method uses positioning rows and columns to transform the spots\u2019 coordinates from the digital image coordinate system to the positioning coordinate system, determining whether a laser spot exists at the coordinates (xi, yj). Through spot coordinate extraction, we obtain the coordinates used in three-point localization (X1, Y1), (X1, Ym+1), (Xn+1, Ym+1), the coordinate mark of other possible laser spots in the HGm,n mode as (xi, yj), which is shown in Fig.\u00a05a, and the coordinates are calculated as followed,\n\na Directional projection method to the spot coordinate extraction. The coordinates of blue circles are determined by the intensity peaks, while the coordinates of white circles are calculated from the blue ones. b Coordinate comparative method to the spot coordinate extraction. The coordinates of red circles are given from the standard HG mode\u2019s spots center location (marked with light blue grid), while whether there is a circle is determined by the extraction of circles in (a).\n\nGiven the slow extraction of coordinates by the directional projection method, the spot recognition method is used to replace it, as shown in Fig.\u00a05b. Following pre-processing preparations, Suzuki\u2019s algorithm is employed to trace and detect the outer contours. After identifying the contour of each spot in defective HGm,n mode, the center point of each contour is determined by using the outer circle obtained by contour fitting. With the vertical coordinate Y=[Y1, Y2, Y3,\u2026, Ym+1] with the smallest x value as positioning column, and horizontal coordinate X=[X1, X2, X3,\u2026, Xn+1] with the smallest y value as positioning row, the spots of HGm,n can be marked as K=[(x1, y1), (x2, y2),\u2026, (xk, yk)](1\u2264k\u2264mn).\n\nAlso, in order to speed up the defect spot localization process, a coordinate comparative method is introduced to distinguish different spots. To calibrate the coordinate points in array K. We select the coordinate value in array X that is the closest to coordinate \\({x}_{k}\\) as the value of \\({x}_{i}\\). Similarly, select the coordinate value in array Y that is the closest to coordinate \\({y}_{k}\\) as the value of \\({y}_{j}\\). In the coordinate comparative method, as shown in Fig.\u00a05b, the index mapping formula for coordinates (xi, yj) is as follows,\n\nTherefore, several scattered points in K are all calibrated. The comparison method, relative to the triangulation method, requires less computational effort. It makes more efficient use of the available spot position information, effectively enhancing recognition speed and accuracy. This approach performs well in specific spot recognition tasks. Once the existing spots and the coordinate axes are successfully recognized, the missing spots can be easily localized. By reading the locations of the missing spots, the information on the defective mode can be decoded.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63100-2/MediaObjects/41467_2025_63100_Fig5_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Information. Source data are provided with this paper. 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Eng. 59, 041202 (2020).\n\nADS\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by the National Natural Science Foundation of China (grant No. 62375015).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "School of Optics and Photonics, Beijing Institute of Technology, Beijing, China\n\nZilong Zhang,\u00a0Yuqi Wang,\u00a0Lianghaoyue Zhang,\u00a0Xiangyang Pan,\u00a0Wei He,\u00a0Yunfei Ma,\u00a0Lingyu Kong\u00a0&\u00a0Changming Zhao\n\nKey Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of People\u2019s Republic of China, Beijing, China\n\nZilong Zhang,\u00a0Yuqi Wang,\u00a0Lianghaoyue Zhang,\u00a0Xiangyang Pan,\u00a0Wei He,\u00a0Yunfei Ma,\u00a0Lingyu Kong\u00a0&\u00a0Changming Zhao\n\nKey Laboratory of Photonics Information Technology, Ministry of Industry and Information Technology of People\u2019s Republic of China, Beijing, China\n\nZilong Zhang,\u00a0Yuqi Wang,\u00a0Lianghaoyue Zhang,\u00a0Xiangyang Pan,\u00a0Wei He,\u00a0Yunfei Ma,\u00a0Lingyu Kong\u00a0&\u00a0Changming Zhao\n\nChina Academy of Aerospace System and Innovation, Beijing, China\n\nHongzhi Yang,\u00a0Suyi Zhao\u00a0&\u00a0Lin Xiao\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nZ.Z. conceived the idea and basic theory. Y.W. performed the theoretical calculations and primary simulations. L.Z. performed the decoding process. Z.Z., Y.W., and L.Z. carried out the experiments and analyzed the results. H.Y., S.Z., and W.H. helped with the CGH process. X.P. and Y.M. helped with the equations\u2019 derivation. Z.Z. and Y.W. wrote the primary manuscript, assisted by L.Z., X.P., and L.K., L.X., and C.Z. revised the manuscript. All authors contributed to the final version of the manuscript. Z.Z. supervised this project.\n\nCorrespondence to\n Zilong Zhang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Zhang, Z., Wang, Y., Zhang, L. et al. Defective states of Hermite-Gaussian modes for long-distance image transmission and high-capacity encoding.\n Nat Commun 16, 7857 (2025). https://doi.org/10.1038/s41467-025-63100-2\n\nDownload citation\n\nReceived: 02 December 2024\n\nAccepted: 07 August 2025\n\nPublished: 22 August 2025\n\nVersion of record: 22 August 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63100-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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PMEL Amyloids Reveals Pathogenic Mechanism of Pigment Dispersion Syndrome.", + "journal": "Nature Communications", + "published": "01 July 2025", + "supplementary_0": [ + { + "label": "Supplementary information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_MOESM1_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_MOESM2_ESM.xlsx" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_MOESM3_ESM.avi" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_MOESM4_ESM.avi" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_MOESM5_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_MOESM6_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_MOESM7_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-61782", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-61783", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-61784", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-61785", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-61786", + "https://doi.org/10.2210/pdb9JST/pdb", + "https://doi.org/10.2210/pdb9JSU/pdb", + "https://doi.org/10.2210/pdb9JSV/pdb", + "https://doi.org/10.2210/pdb9JSW/pdb", + "https://doi.org/10.2210/pdb9JSX/pdb", + "/articles/s41467-025-61233-y#Fig6", + "/articles/s41467-025-61233-y#Fig7", + "/articles/s41467-025-61233-y#Fig8", + "/articles/s41467-025-61233-y#MOESM1", + "/articles/s41467-025-61233-y#MOESM1", + "/articles/s41467-025-61233-y#Sec39" + ], + "code": [], + "subject": [ + "Biophysics", + "Cryoelectron microscopy", + "Hereditary eye disease", + "Protein folding" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5540188/v1.pdf?c=1751457264000", + "research_square_link": "https://www.researchsquare.com//article/rs-5540188/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61233-y.pdf", + "preprint_posted": "10 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "PMEL amyloids provide a vital scaffold for melanin deposition in melanosomes, playing a central role in pigmentation. Despite their importance, the high-resolution structure of PMEL amyloids has remained elusive. Here, we determined near-atomic resolution structures of wild-type PMEL amyloids using cryo-electron microscopy, revealing two distinct polymorphic forms with unique structural features. We further examined the pathogenic G175S mutation linked to pigment dispersion syndrome (PDS). Structural analysis showed that the G175S mutation introduces an additional hydrogen bond, stabilizing a novel fibril conformation. In vitro assays demonstrated a fourfold increase in polymerization efficiency for the G175S mutant compared to the wild-type. This enhanced polymerization correlated with a ~70% increase in secreted amyloids in G175S-expressing cells without detectable changes in melanosome morphology or number. These findings suggest that the G175S mutation promotes amyloidogenesis within melanosomes, increasing amyloid load and contributing to PDS pathophysiology. This study provides insights into the molecular basis of PMEL amyloid formation in both physiological and pathological contexts, offering new perspectives on their structural diversity and dysregulation in pigmentation disorders.Biological sciences/Structural biology/Electron microscopy/Cryoelectron microscopyHealth sciences/Diseases/Eye diseases/Hereditary eye disease", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Table 1 is available in the Supplementary Files section.\nThere is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Table1.docxTable 1SupplementalMaterialsNov28.pdfSupplemental Figures", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "PMEL amyloids serve as essential scaffolds for melanin deposition in melanosomes, playing a crucial role in pigmentation. Despite their importance, the high-resolution structure of PMEL amyloids has remained unresolved. Using cryo-electron microscopy, we determine near-atomic resolution structures of wild-type PMEL amyloid core, revealing two distinct polymorphic forms with structural features. We further investigate the pathogenic G175S mutation associated with pigment dispersion syndrome (PDS). Structural analysis reveales that G175S introduces an additional hydrogen bond, stabilizing an alternative fibril conformation. In vitro, the G175S mutant exhibits a fourfold increase in polymerization efficiency compared to the wild type. In cells, G175S expression resultes in a twofold increase in intracellular amyloid content and a\u2009~70% increase in extracellular amyloids, without altering melanosome morphology or number. These results indicate that the G175S mutation enhances amyloidogenesis within melanosomes, elevating amyloid load and potentially contributing to PDS pathophysiology. This study provides molecular insights into PMEL amyloid formation, highlighting its structural diversity and dysregulation in pigmentation disorders.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Amyloids are protein aggregates traditionally associated with neurodegenerative diseases, but they can also play essential roles in normal physiological processes1,2,3,4,5,6. One example is PMEL (Pmel17/gp100), a pigment-cell-specific protein that forms amyloid fibrils in melanosomes to scaffold melanin deposition7,8,9,10,11,12. These fibrils underscore the dual nature of amyloids as both pathological and functional entities.\n\nPMEL amyloidogenesis occurs within specialized organelles called melanosomes, which progress through four distinct stages (I\u2013IV) of maturation (reviewed in ref. 13). During the transition from stage I to stage II, PMEL fibrils form and assemble laterally into sheets, a process essential for the structural organization of melanosomes. While Seiji et al. (1961) first described fibrillar morphology using thin-section electron microscopy14, the three-dimensional sheet-like architecture of PMEL fibrils was revealed by electron tomography in Hurbain et al. (2008)15. These fibrillar sheets are crucial for the transition to stage III, where melanin deposition begins. The structural organization of these fibrils is integral to their function, yet their high-resolution structure has remained unresolved.\n\nPMEL was first demonstrated to be an amyloid-forming protein by Fowler et al. (2006), who established the presence of cross-\u03b2 amyloid fibrils in melanosomes5. However, the precise region of PMEL that forms the core of these amyloids has been the subject of significant debate. Several in vitro studies, primarily involving recombinant, unglycosylated fragments, have proposed that the repeat (RPT) domain of PMEL constitutes the amyloid core, based on its polymerization under artificial conditions16,17,18,19,20,21,22,23,24,25,26,27. Graham et al. (2019) demonstrated that the O-glycosylation of the RPT domain, rather than its peptide backbone, is essential for organizing the lateral sheet architecture of PMEL fibrils by facilitating their proper spacing and alignment within stage II melanosomes28. Leonhardt et al. (2013) further demonstrated that deletion of the RPT domain disrupted sheet organization, supporting its structural role in the supramolecular architecture of fibrils rather than in amyloid core formation29.\n\nIn contrast, multiple lines of biochemical and cellular evidence consistently point to the CAF domain as the physiological amyloid core. Watt et al. (2009) demonstrated that the CAF domain is required for fibrillogenesis in vitro, as its removal abolished amyloid formation30. This was supported in vivo by Hee et al. (2017), who showed that mutations in the CAF domain disrupted PMEL fibril formation in cells31. Leonhardt et al. (2013) also found that deletion of the CAF domain abolished the formation of fibrillar structures in melanocytic cells, though the study did not confirm whether these were amyloid or organized into sheets29. Together, these studies reinforce the view that the CAF domain forms the structural core of PMEL amyloids in vivo, while the RPT domain, via its O-glycosylation, contributes to higher-order sheet organization. This distinction is critical for resolving ongoing controversy over PMEL domain functions in amyloidogenesis and pigmentation.\n\nMutations in the PMEL gene, such as Gly175Ser (G175S), are associated with pigment dispersion syndrome (PDS), a condition in which pigment granules are released into the anterior chamber of the eye. This aberrant pigment release can elevate intraocular pressure, increasing the risk of pigmentary glaucoma (PG), a sight-threatening complication that affects 15\u201320% of PDS patients32,33. Although the G175S mutation has been proposed to alter PMEL amyloid formation, its precise molecular and structural effects remain unclear34.\n\nIn this work, we present near-atomic resolution structures of native PMEL amyloid cores from both wild-type and G175S mutant forms. We reveal fibril polymorphism and show that the G175S mutation introduces an additional hydrogen bond that stabilizes an alternative amyloid conformation.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Native PMEL amyloids were isolated from the human melanoma cell line HMV-II through deglycosylation, protease digestion, and sonication. Cryo-electron microscopy (cryo-EM) and 2D classification revealed two distinct fibrillar forms: thick and thin filaments, corresponding to two-protofilament and single-protofilament structures, respectively (Fig.\u00a01a and Fig.\u00a0S1). High-resolution structures were obtained from two-protofilament fibrils as shown in Fig.\u00a01b. However, our attempts to resolve the single-protofilament structures were unsuccessful, possibly due to their inherent instability or disruption during the extraction and processing stages. This suggests that the single-protofilament forms may represent immature or structurally less stable amyloid species, which are challenging to analyze at high resolution under the conditions applied in this study.\n\na Cryo-EM images of native PMEL amyloids extracted from the human melanoma cell line, showing both thick (two-protofilament) and thin (single-protofilament) fibrils (Fig.\u00a0S1, squares). For each condition, the imaging experiment was independently repeated three times using separately prepared grids, yielding consistent results. b Side views of the reconstructed 3D maps of the two-protofilament fibrils, illustrating the overall helical structure of the amyloids.\n\nTwo polymorphic forms of two-protofilament fibrils, termed Polymorph 1 and Polymorph 2, were identified in the native amyloids (Figs.\u00a01b and 2). Polymorph 1 adopts a two-start helical architecture, characterized by a helical twist of 177.7\u00b0 and a helical rise of 2.34\u2009\u00c5. In contrast, Polymorph 2 forms a one-start helical architecture with C2 symmetry, featuring a helical twist of \u22124.1\u00b0 and a helical rise of 4.67\u2009\u00c5. Despite similar \u03b2-sheet configurations, the main chain morphology between the two polymorphs is markedly different (Figs.\u00a03a\u2013c and 4a). Notably, Polymorph 2 encompasses a central cavity, which is absent in Polymorph 1, thereby rendering Polymorph 1 as a more densely packed structure.\n\nSide and cross-sectional views of cryo-EM maps and atomic models of wild-type PMEL polymorph 1 (top), polymorph 2 (middle), and the G175S mutant (bottom), each composed of two interacting protofilaments. Left panels show filament side views aligned to the fibril axis, with maps in gray and models colored by chain (green and cyan). Right panels show cross-sectional views highlighting inter-protofilament interfaces. Key residues with bulky side chains and/or interfacial contacts are labeled. Red dashed curves indicate the inter-protofilament interface, which is formed predominantly by hydrophobic packing rather than polar contacts. No hydrogen bonds were detected between protofilaments based on ChimeraX hydrogen bond analysis. Prominent nonpolar residues\u2014such as Phe149, Tyr151, Leu163, Pro166, and Gly169\u2014contribute to shape complementarity and tight packing across the interface. Gray: reconstructed maps; carbon: green/cyan; oxygen: red; nitrogen: blue; sulfur: yellow. Scale bars = 1\u2009nm.\n\na Ribbon diagrams of native PMEL amyloid fibrils, illustrating the four-layered structure of \u03b2-strands in the fibril core. b Amino acid sequence of the N-terminal portion of the CAF domain, with the positions of \u03b2-strands (indicated by arrows) predicted using ModelAngelo. c Close-up views of the parallel cross-\u03b2 sheets, highlighting the structural differences between wild-type and G175S mutant fibrils. Insets (left) show cross-sectional views of the fibril core to indicate the positions of the \u03b2-sheets (boxes). In the G175S mutant, Thr155 (box, left) separates \u03b21-1 and \u03b21-2, resulting in the division of the \u03b2-sheet. Note the alignment of aromatic residues along the fibril axis, emphasizing the positioning of these bulky residues in both wild-type and G175S mutant fibrils. Panel (c) highlights intra-protofilament interactions along the fibril axis, which are maintained primarily through backbone hydrogen bonding between stacked \u03b2-strands. Carbon: green; oxygen: red; nitrogen: blue; sulfur: yellow.\n\na Packing schemes of one cross-sectional layer of the fibrils, illustrating the structural arrangement of residues in wild-type and G175S mutant fibrils. Asterisks (*) indicate the inner cavities present in Polymorph 2 and G175S. Blue and red circles and arrowheads highlight the positions of Tyr159 and Gly/Ser175, respectively. Numbers of key residues are labeled. Residues are color-coded by chemical properties: hydrophobic (white), polar (green), basic (blue), glycine (magenta), and proline (purple). b Close-up views of the G175S fibril highlighting the additional hydrogen bond formed between Tyr159 and Ser175. Insets show the cross-sectional orientation of the fibril and the corresponding regions displayed in the top view (top) and side view (bottom).\n\nEach polymorph comprises two protofilaments that pack against each other along a distinct interface. ChimeraX hydrogen bond analysis35 revealed no hydrogen bonds at the protofilament interface in any of the structures. Instead, the protofilaments interact via extensive hydrophobic contacts between bulky nonpolar side chains, such as Phe149, Tyr151, Leu163, Pro166, and Gly169. These residues are laterally positioned at the interface and form a tightly packed hydrophobic surface, which likely stabilizes the fibril architecture through shape complementarity and van der Waals interactions (Fig.\u00a02, red dashed lines). This contrasts with the vertical stacking of \u03b2-strands within each protofilament\u2014illustrated in Fig.\u00a03c\u2014which is stabilized by canonical backbone hydrogen bonds along the fibril axis.\n\nCryo-EM maps revealed that the side chains of aromatic residues, including tyrosine (Tyr), phenylalanine (Phe), and tryptophan (Trp), are aligned along the fibril surface. However, these residues do not form \u03c0-\u03c0 stacking interactions, as their rings are not directly aligned (Fig.\u00a03c).\n\nTo investigate the effects of the G175S mutation on PMEL amyloids, we expressed G175S PMEL in HMV-II melanoma cells in a PMEL-knockout (KO) background and isolated the resulting amyloids (Figs.\u00a01\u20134, G175S). Similar to the wild-type, cryo-EM revealed thick and thin filaments, with high-resolution reconstructions obtained exclusively from the thick filaments (Fig.\u00a0S2, 2-start). The G175S amyloids exhibited a two-start helical architecture with a helical twist of 178.4\u00b0 and a helical rise of 2.35\u2009\u00c5, similar to Polymorph 1 in wild-type amyloids. However, the \u03b2-sheet configuration and packing within the G175S fibrils were markedly altered.\n\nIn G175S amyloids, the first \u03b2-sheet (\u03b21) is divided into three distinct segments (\u03b21-1, \u03b21-2, and \u03b21-3), in contrast to the single continuous \u03b21 observed in wild-type amyloids. \u03b22 and \u03b23 maintain similar configurations to those in the wild-type, but the overall packing density is reduced due to the division of \u03b21 (Fig.\u00a03a\u2013c). A notable structural feature in G175S amyloids is the formation of an additional hydrogen bond between Ser175 and Tyr159, which may contribute to the fragmented \u03b21 configuration and potentially enhance fibril stability (Fig.\u00a04b).\n\nTo further investigate the biochemical properties of PMEL amyloids, we expressed and purified the CAF domain (residues 148\u2013223) of wild-type and G175S PMEL in E. coli. The purified proteins were subjected to in vitro polymerization assays, and the resulting amyloids were analyzed using cryo-EM and thioflavin T (ThT) fluorescence.\n\nCryo-EM analysis revealed that the wild-type CAF domain polymerized into fibrils structurally identical to Polymorph 1 of native PMEL amyloids, confirming that the in vitro polymerization recapitulates the native fibril structure. Similarly, the G175S CAF domain polymerized into fibrils indistinguishable from the G175S native amyloids, further validating that the extraction process from melanoma cells did not disrupt the amyloid structure (Fig.\u00a05a\u2013c, Fig.\u00a0S3, 4). Notably, no one-start helical fibrils were observed in the in vitro polymerized samples, suggesting that thin fibrils represent immature intermediates or are disassembled during the extraction process.\n\na Cryo-EM images of PMEL CAF domain fibrils polymerized in vitro, showing thick fibrils corresponding to two-protofilament structures. Wild-type fibrils are shorter than G175S ones. For each condition, the experiment was independently repeated three times, with fibrils imaged from separately prepared reactions, and yielded similar results. b Side views of the reconstructed 3D maps of the in vitro polymerized fibrils, highlighting the overall helical structure. c Side (top) and cross-sectional (bottom) views of the reconstructed maps (gray), superimposed with models (green and cyan) of the CAF domain. Key residues are labeled as in Fig.\u00a02. Scale bars = 1\u2009nm.\n\nNext, we performed ThT fluorescence assays to measure the amyloid-forming efficiency of wild-type and G175S CAF domains in vitro5 (Fig.\u00a06a, b). The G175S mutant polymerized approximately four times more efficient than the wild-type, as shown by the rapid increase in ThT signal. Additionally, both G175S and wild-type polymerization curves plateaued, demonstrating that the final yield of polymerized amyloids is approximately four times greater for the G175S mutant than for the wild-type. The increased amyloid-forming efficiency of the G175S mutant is consistent with its structural alterations, particularly the possible stabilizing effect of the additional Ser175-Tyr159 hydrogen bond.\n\na Negative-stain EM images of CAF domain fibrils polymerized in vitro. Wild-type fibrils form thick bundles by 24\u201348\u2009h, whereas G175S fibrils show earlier bundling at 14\u2009h and further thickening by 24\u2009h. b Thioflavin T (ThT) fluorescence assay measuring polymerized amyloids over time. Data represent means with individual data points from five independent polymerization reactions (N\u2009=\u20095), each using separately prepared protein. G175S fibrils showed greater fluorescence than wild type. A two-sided Mann-Whitney U test confirmed a significant difference (U\u2009=\u20090, Z = \u20132.6548, p\u2009=\u20090.007937, standardized effect size r\u2009=\u20090.79, 95% CI of acceptance region for U = [3,22]). c ThT fluorescence of intracellular amyloids from detergent-insoluble lysates after 96\u2009h of doxycycline (Dox) induction. Data from six biological replicates (N\u2009=\u20096), each an independently induced and harvested well. G175S-expressing cells showed a twofold increase over wild type. Fluorescence was normalized using propidium iodide signals to account for differences in cell density and lysis efficiency. One-way ANOVA detected a significant effect across groups (F(4,25)\u2009=\u200942.46, p\u2009=\u20098.49 \u00d7 10\u207b\u00b9\u00b9, \u03b7\u00b2 = 0.87, 95% CI for F = [0, 2.76]). Post-hoc Tukey HSD revealed a significant increase in G175S Dox(+) over WT Dox(+) (mean difference = 312.1, p\u2009=\u20092.14 \u00d7 10\u207b\u2075, 95% CI = [161.5, 462.7]) (asterisk). d ThT fluorescence assay of extracellular amyloids in supernatants normalized to 1 \u00d7 10\u2077 cells per replicate. Data from eight biological replicates (N\u2009=\u20098). WT/G175S Dox (\u2013) represents knockout cells; WT/G175S Dox (+) indicates amyloid levels after 0.2\u2009\u00b5g/ml Dox induction for 96\u2009h. G175S Dox (+) samples showed a\u2009~\u200970% increase over WT Dox (+). One-way ANOVA revealed a significant group effect (F(4,35)\u2009=\u200957.22, p\u2009=\u20097.22 \u00d7 10\u221215, \u03b7\u00b2 = 0.87, 95% CI for F = [0, 2.64]). Tukey\u2019s HSD post-hoc test showed that G175S Dox(+) had significantly higher extracellular amyloid than WT Dox(+) (mean difference = 73.50, p\u2009=\u20093.36 \u00d7 10\u207b\u2075, 95% CI = [35.05, 111.95]) (asterisk).\n\nWhile negative stain EM images (Fig.\u00a06a) showed fewer discrete fibrils at 48\u2009h compared to earlier time points, this reflects a natural progression in PMEL fibril assembly rather than a decrease in total amyloid. Over time, fibrils undergo lateral bundling into thicker aggregates or sheets, leading to fewer visible individual filaments despite an overall increase in amyloid mass. This phenomenon is consistent with the quantitative ThT fluorescence measurements (Fig.\u00a06b), which track total amyloid content and show continued accumulation over time. In G175S samples, fibrils exhibit more extensive bundling than in wild type, consistent with enhanced polymerization efficiency. It is also important to note that uneven adhesion of fibrils to carbon film during sample preparation contributes to local variation in fibril density in the EM images and limits direct visual quantification. Accordingly, the EM images are intended to illustrate morphological progression (i.e., bundling and thickness) rather than the absolute number of fibrils.\n\nTo evaluate the impact of the G175S mutation on amyloidogenesis within the cells, we quantified intracellular and extracellular amyloids produced by MNT1 cells expressing wild-type or G175S PMEL using ThT fluorescence assays (Fig.\u00a06c, d). Here, extracellular amyloids refer to PMEL aggregates recovered from the culture medium, reflecting material released into the extracellular space. Cells expressing the G175S mutant PMEL exhibited approximately twofold higher levels of intracellular amyloids compared to wild-type PMEL-expressing cells, consistent with enhanced amyloidogenic efficiency. Similarly, extracellular amyloids were significantly elevated in G175S-expressing cells, showing approximately 70% higher levels than wild-type. Cells lacking PMEL expression showed negligible ThT fluorescence for both intracellular and extracellular fractions, confirming that the observed signals were specifically attributable to PMEL amyloids.\n\nTo rule out the possibility that the elevated extracellular amyloids in G175S-expressing cells were due to increased cell death, we performed a cell viability assay (Fig.\u00a0S5a). No significant differences in viability were observed between wild-type and G175S-expressing cells, indicating that the extracellular PMEL amyloids are not the result of passive leakage from dying cells. Rather, these data suggest that amyloids are actively released into the medium\u2014possibly via secretory pathways such as exosomes or melanosome extrusion10,36,37,38. Importantly, the increase in extracellular ThT signal closely paralleled the increase in intracellular amyloids, suggesting that G175S does not preferentially enhance secretion or release, but instead promotes overall amyloid accumulation both within and outside the cell. These findings are consistent with the enhanced polymerization observed in vitro and further support the conclusion that the G175S mutation enhances amyloid formation and accumulation within melanosomes.\n\nTo investigate whether the G175S mutation affects melanosome architecture, we employed cryo-focused ion beam scanning electron microscopy (cryo-FIB-SEM) and cryo-electron tomography to analyze melanosomes in MNT1 cells expressing wild-type or G175S PMEL (Fig.\u00a07a, Fig.\u00a0S6, Videos\u00a0S1, 2). Stage III melanosomes, characterized by pigmented fibrillar lamellae, were the primary focus of this analysis15,28. Tomographic slices revealed no discernible differences in the overall structural organization of melanosomes between wild-type and G175S mutant cells. Both cell types displayed lamellar structures, which were reconstructed into three-dimensional models to further examine the spatial arrangement of the fibrillar networks.\n\na Tomographic slices (top panels) and 3D models (bottom panels) of stage III melanosomes from MNT1 cells expressing either WT or G175S PMEL, with Dox induction. Green represents the melanosomal membrane, and cyan indicates the fibrillar lamellae within the melanosome. Scale bars, 100\u2009nm. b Quantification of melanosomal structural features from 12 melanosomes per condition (N\u2009=\u200912), derived from three independent biological replicates. Parameters measured include lamella number, thickness, cross-sectional area, length, curvature, and inter-lamellar distance. No significant differences were observed between WT and G175S. Two-sided Mann-Whitney U test results were: \u2013 Lamella number: U\u2009=\u200973.5, Z\u2009=\u20090.0588, p\u2009=\u20090.9531, standardized effect size r\u2009=\u20090.012, 95% CI = [38.65, 105.35]. \u2013 Lamella thickness: U\u2009=\u200963, Z\u2009=\u2009-0.482, p\u2009=\u20090.6297, r\u2009=\u20090.1, 95% CI = [38, 106]. \u2013 Cross-sectional area: U\u2009=\u200952, Z\u2009=\u2009-1.113, p\u2009=\u20090.266, r\u2009=\u20090.23, 95% CI = [38, 106]. \u2013 Lamella length: U\u2009=\u200965, Z\u2009=\u2009-0.369, p\u2009=\u20090.7125, r\u2009=\u20090.077, 95% CI = [38, 106]. \u2013 Lamella curvature: U\u2009=\u200974, Z\u2009=\u20090.0849, p\u2009=\u20090.9323, r\u2009=\u20090.018, 95% CI = [38, 106]. \u2013 Inter-lamellar distance: U\u2009=\u200957, Z\u2009=\u2009-0.825, p\u2009=\u20090.4095, r\u2009=\u20090.17, 95% CI = [38, 106]. Individual data points are shown with horizontal bars indicating mean values. These results indicate that G175S expression does not significantly alter the ultrastructure of stage III melanosomes compared to wild type.\n\nThe ultrastructure visualized by cryo-electron tomography was highly consistent with the lamellar architecture previously described in osmium-stained thin-section electron microscopy and tomographic reconstructions15, confirming that PMEL fibrils are laterally arranged into sheets within stage III melanosomes. The use of cryo-ET allowed visualization of these structures in a near-native, unstained state, providing complementary confirmation of prior findings.\n\nQuantitative analysis demonstrated no significant differences in the number of lamellae per melanosome, the thickness of the lamellae, the length of the lamellae, or their average curvature between wild-type and G175S melanosomes (Fig.\u00a07b). Additionally, the interlamellar distance and the cross-sectional area of melanosomes were consistent across the two groups. These results suggest that the G175S mutation does not significantly alter the gross morphological properties of melanosomes or their lamellar organization.\n\nTo determine whether the G175S mutation affects melanosome maturation, we quantified melanosome density and analyzed the distribution of melanosome stages (II, III, IV) using ultrathin-section electron microscopy of MNT1 cells expressing wild-type or G175S PMEL (Fig.\u00a08a, b, and Fig.\u00a0S7)39,40. Melanosome density, calculated as the number of melanosomes per square micrometer of cytoplasm, showed no significant difference between wild-type and G175S mutant cells, indicating that the mutation does not alter melanosome abundance (Fig.\u00a08c, left panel).\n\na Representative images of melanosomes in stages I\u2013IV. Stage I: multi-vesicular bodies. Stage II: vesicles containing fibrillar lamellae without melanin deposition. Stage III: vesicles containing fibrillar lamellae with melanin deposition. Stage IV: vesicles with fully melanized, electron-dense granules. b Representative ultrathin-section electron microscopy images of MNT1 cells expressing wild-type (WT) or G175S PMEL with Dox induction. Melanosomes throughout the cytoplasm are annotated with red numerals to indicate stages I\u2013IV. c Quantification of melanosomes. Left: Melanosome density (number per square micrometer of cytoplasm). Right: Percentage of melanosomes in stages II, III, and IV. Horizontal bars represent mean values, and dots represent individual data points (N\u2009=\u200939 cells for each condition). No significant difference in melanosome density was observed between groups. A two-sided Mann-Whitney U test using the normal approximation with tie correction yielded U\u2009=\u2009766.5, Z\u2009=\u20090.055, p\u2009=\u20090.9562, standardized effect size r\u2009=\u20090.0062, 95% CI of acceptance region for U = [564.44, 956.56]. A two-sided Wilcoxon signed-rank test revealed a significant increase in stage III melanosomes in G175S cells compared to WT cells (W\u207a = 690, p\u2009=\u20092.575 \u00d7 10\u207b\u2077, r\u2009=\u20090.84, 95% CI for W\u207a = [236, 505], single asterisk). Additionally, the proportion of stage II melanosomes was significantly decreased in G175S cells compared to WT (W\u207a = 35, p\u2009=\u20091.568 \u00d7 10\u22128, r\u2009=\u20090.91, with 95% CI for W\u207a = [250, 530], double asterisk), while no significant differences were observed in the proportion of stage IV melanosomes (W\u207a = 324, Z = \u20130.91, p\u2009=\u20090.3607, r\u2009=\u20090.15, with 95% CI for W\u207a = [0, 780]). Stage I melanosomes were not included in the quantification due to their low abundance and difficulty in identification.\n\nNext, we assessed the proportion of melanosomes in stages II, III, and IV. The G175S mutation significantly increased the proportion of stage III melanosomes compared to wild-type, as determined by a Wilcoxon signed-rank test (Fig.\u00a08c, single asterisk). Additionally, the proportion of stage II melanosomes was significantly decreased in G175S cells compared to WT (Fig.\u00a08c, double asterisk), while no significant differences were observed in the proportion of stage IV melanosomes.\n\nTo assess whether the G175S mutation affects melanin production, we measured intracellular melanin content at 96\u2009h after Dox induction. No significant differences in melanin content were observed between cells expressing wild-type or G175S PMEL (Fig.\u00a0S5b), suggesting that the increase in stage III melanosomes in G175S cells does not result from altered melanin synthesis. These results collectively indicate that the G175S mutation modulates melanosomal stage distribution without affecting intracellular melanin levels.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61233-y/MediaObjects/41467_2025_61233_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we present the near-atomic resolution structures of PMEL amyloids, revealing two distinct polymorphic forms and the structural alterations induced by the G175S mutation. PMEL amyloids play a critical role in melanosomes by providing a scaffold for melanin deposition, which is essential for pigmentation. While PMEL amyloid formation has been studied extensively for its physiological relevance, their high-resolution structure has remained elusive until now9,12,16,31. Our cryo-EM analysis addresses this gap, offering crucial insights into PMEL amyloid architecture and the structural consequences of the G175S mutation associated with PDS.\n\nOur analysis of native PMEL amyloids revealed two polymorphic forms, Polymorph 1 and Polymorph 2, both adopting a two-protofilament helical architecture. Despite their similar \u03b2-sheet configurations, the main chain morphology differs significantly. Polymorph 2 exhibits an inner cavity, absent in Polymorph 1, resulting in a more loosely packed structure. Such polymorphism highlights the structural plasticity of PMEL amyloids, which may facilitate interactions with melanin precursors or melanosomal proteins under varying physiological conditions. This flexibility is a common feature of functional amyloids and may be critical for the regulation of melanosomal architecture and pigmentation41,42,43.\n\nFactors such as post-translational modifications or variations in the cellular environment may influence the formation of these polymorphs. The observation of multiple fibril forms within a single cell type suggests greater structural heterogeneity in PMEL amyloids than previously recognized. Future studies are needed to determine how polymorphism affects interactions with melanin precursors or melanosomal proteins under diverse physiological conditions.\n\nOur cryo-EM structures of wild-type PMEL fibrils (Polymorphs 1 and 2) and the G175S mutant fibril reveal critical roles for specific residues within the 148\u2013183 region in stabilizing the amyloid core. Trp160 consistently forms the inner core of the \u03b2-strand in all three fibril structures, underscoring its essential role in maintaining structural integrity. Tyr151 and Trp153, although not engaged in \u03c0\u2013\u03c0 stacking, exhibit aligned aromatic rings along the fibril length, contributing to \u03b2-sheet stabilization. These findings align with previous studies showing that mutation of these residues disrupts the formation of PMEL fibril-like structures in cells, underscoring their importance in fibril assembly29,31.\n\nOur cryo-EM structures reveal extra densities within the inner cavities of wild-type polymorph 2 and the G175S mutant fibrils (Fig.\u00a0S8). These densities, present in both native and in vitro polymerized structures, are unlikely to originate from melanin pigments, particularly in melanin-free in vitro samples. While these observations suggest potential pigment-binding sites, further studies are required to confirm their functional relevance.\n\nOur cryo-EM structures resolve the N-terminal half of the PMEL CAF domain (residues 148\u2013183) in the fibril core, while additional densities distal to Thr183 were observed but could not be reliably resolved due to noise. This suggests that the C-terminal portion of the CAF domain may be more flexible relative to the structured N-terminal part. The flexibility of this region could contribute to its inability to adopt a consistent conformation suitable for cryo-EM analysis.\n\nThe PMEL amyloids described in this study exhibit an unusually high density of hydrophobic and bulky residues, such as Trp and Tyr, on their exterior surface. This is distinct from the hydrophilic or charged external surfaces often seen in pathological amyloids like A\u03b2 fibrils, AL, ATTR, SOD1 amyloids44,45,46,47,48,49,50. While these pathological amyloids utilize external hydrophilic interactions to promote aggregation and toxicity, PMEL amyloids may rely on the glycosylated RPT domain to shield their hydrophobic surfaces and prevent non-specific aggregation.\n\nThe G175S mutation in PMEL, strongly associated with PDS, introduces an additional hydrogen bond between Ser175 and Tyr159, altering the \u03b2-sheet configuration. The mutation divides the first \u03b2-sheet (\u03b21) into three shorter segments while preserving the configuration of \u03b22 and \u03b23. These changes enhance amyloid polymerization, as demonstrated by the four-fold increase in polymerization efficiency in vitro. Furthermore, cells expressing G175S PMEL exhibited increased levels of both intracellular and extracellular amyloids compared to wild-type, suggesting more efficient amyloidogenesis.\n\nDespite these biochemical changes, cryo-electron tomography revealed no significant differences in melanosomal architecture between wild-type and G175S cells. This suggests that while the G175S mutation enhances amyloidogenesis, the overall melanosomal structure remains intact. The enhanced amyloidogenic capacity likely enables a greater proportion of PMEL proteins to convert into amyloids, contributing to increased amyloid content without disrupting melanosome morphology.\n\nQuantification of melanosomal stages revealed a significant increase in stage III melanosomes and a corresponding decrease in stage II melanosomes in G175S mutant cells, with no significant changes in stage IV or overall melanosome density. These findings suggest that the G175S mutation enhances amyloid formation efficiency, potentially providing a scaffold for melanin deposition earlier than in wild-type cells. The increased amyloid content in G175S melanosomes likely enhances the efficiency of melanin binding, facilitating the observed shift toward stage III melanosomes. However, no differences in intracellular melanin content were detected (Fig.\u00a0S5b), indicating that amyloidogenic efficiency, rather than melanin synthesis, drives these changes.\n\nPDS is characterized by the release of melanin granules into the anterior chamber of the eye, leading to increased intraocular pressure and an elevated risk of PG. Our findings suggest that the G175S mutation enhances the amyloidogenic efficiency of PMEL, resulting in a higher amyloid content within melanosomes. This structural alteration likely contributes to the rigidity and altered biophysical properties of melanosomes, as evidenced by the requirement for 4\u2009M urea treatment to disintegrate G175S melanosomes during amyloid extraction (Fig.\u00a0S9b).\n\nThese changes in melanosome properties may increase their susceptibility to mechanical stress within the eye, facilitating pigment dispersion under intraocular forces. While our study does not directly demonstrate a link between G175S-induced melanosome rigidity and pigment dispersion, the observed structural and biochemical changes provide a plausible explanation for the increased extracellular amyloids and their potential contribution to PDS pathophysiology. Further investigations into the mechanical properties of G175S melanosomes and their behavior under physiological conditions could shed additional light on this mechanism.\n\nOur analysis focused on the CAF domain as the core structural element of PMEL amyloids. Although several in vitro studies using unglycosylated recombinant fragments have proposed that the RPT domain can form amyloid fibrils under artificial conditions16, in vivo evidence suggests a different role. In particular, Graham et al. (2019) demonstrated that it is not the RPT polypeptide itself, but its O-glycosylation, that is essential for the lateral sheet organization of PMEL fibrils within stage II melanosomes. These findings support a model in which the RPT domain, while not forming the amyloid core, contributes to fibril architecture by spacing and aligning fibrils through its glycan moieties.\n\nA recent review by Buchanan et al. (2023) asserted a field-wide consensus that the amyloidogenic region responsible for the characteristic PMEL fibrillar matrix lies within the RPT domain51. While this view reflects findings from in vitro studies using unglycosylated RPT fragments, it does not incorporate substantial in vivo evidence pointing to the CAF domain as the physiological amyloid core. Our structural data, along with prior cellular and biochemical studies, support the CAF domain as the primary component of PMEL fibrils. Moreover, as shown by Graham et al. (2019)28, the RPT domain contributes to fibril architecture not through its peptide sequence but via O-glycosylation, which mediates lateral sheet organization. These observations call for a more nuanced view of PMEL domain function and underscore the importance of integrating structural and cell-based evidence when defining amyloidogenic regions.\n\nWhile our cryo-EM structures did not reveal RPT-derived structures in the fibril core, we cannot exclude the possibility that our extraction methods, which involved deglycosylation and protease digestion, may have selectively degraded RPT-derived structures or preferentially preserved CAF-derived fibrils.\n\nIn addition to the CAF and RPT domains, the PKD (polycystic kidney disease-1 repeat) domain has also been implicated in PMEL amyloid formation. The PKD domain (residues 235\u2013297) was originally defined by Theos et al. (2005) as part of the M\u03b1 fragment52, and was first shown to be present in PMEL fibrils in 2001 using domain-specific antibodies7,8. Subsequent studies demonstrated that both the PKD and CAF domains are required for amyloid formation in vivo53,54. In vitro, the PKD domain has also been shown to be amyloidogenic30. Biochemical evidence suggests that it may be transiently incorporated into native PMEL fibrils during early stages of maturation, particularly as part of the M\u03b1C fragment55, although it is likely removed or remodeled during later processing steps. In our cryo-EM structures, we did not observe density corresponding to the PKD domain, which may reflect its absence in the final stable amyloid core or degradation during sample preparation involving protease digestion and sonication. This highlights the value of complementary in situ approaches to further define the structural contributions of the CAF, RPT, and PKD domains to PMEL amyloid architecture.\n\nOur cryo-EM analysis reveals that the CAF domain alone can form fibrils resembling those observed in vivo. This suggests that the CAF domain is the primary driver of amyloid core formation. However, the roles of other PMEL domains, such as the N-terminal fragment (NTF), PKD, and RPT domains, cannot be disregarded. Previous studies have shown that the NTF domain is essential for CAF aggregation in vivo, while the PKD and RPT domains are implicated in stabilizing and organizing PMEL fibrils30,55. These domains likely contribute to fibril formation and assembly in the cellular context, where additional factors such as post-translational modifications and environmental conditions may modulate fibril morphology and function. Further studies are needed to elucidate how these domains interact with the CAF domain to regulate PMEL amyloidogenesis.\n\nTaken together, our findings support a model in which the CAF domain serves as the core structural scaffold of PMEL amyloids, while the RPT and PKD domains contribute peripheral or regulatory roles modulated by glycosylation, proteolytic processing, and intracellular context.\n\nAlthough cryo-electron tomography (cryo-ET) has been successfully used to study amyloids in their native cellular contexts, PMEL amyloids pose challenges. Their entangled lamellar organization within melanosomes complicates the isolation and visualization of individual fibrils (Fig.\u00a0S9a). Furthermore, our 2D classification revealed higher-order assemblies that lack helical twists, suggesting a more complex packing arrangement. These findings indicate that in situ structural analysis of PMEL amyloids is technically challenging due to their densely packed and entangled nature.\n\nOur study provides a foundation for understanding the molecular mechanisms underlying PMEL amyloid formation and its dysregulation in PDS. Future research could focus on exploring the effects of other PMEL mutations, elucidating the role of RPT domain glycosylation, and identifying potential therapeutic strategies to mitigate the pathological impact of enhanced amyloidogenesis in PDS.\n\nIn conclusion, our cryo-EM analysis reveals the structural basis of PMEL amyloid polymorphism and the significant impact of the G175S mutation on amyloidogenesis and melanosome maturation. These findings provide insights into the functional and pathological roles of PMEL amyloids and their broader implications in pigmentation biology and disease.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Human melanoma cell lines HMV-II (TKG-0318) and MNT-1 (CRL-3450) were obtained from the Cell Resource Center for Biomedical Research at Tohoku University and the American Type Culture Collection, respectively. HMV-11 cells were maintained in F12 medium (Fujifilm, Osaka, Japan) supplemented with 10% fetal bovine serum (FBS). MNT-1 cells were maintained in DMEM medium (Fujifilm) supplemented with 20% FBS, 10% AIM-V (ThermoFisher Scientific, Waltham, MA), 0.1\u2009mM MEM non-essential amino acids (Fujifilm). Cell lines were authenticated by analysis of short tandem repeat profiling (BEX, Tokyo, Japan).\n\nFor isolating native PMEL fibrils, we used the HMV-II human melanoma cell line. MNT1 cells, while also suitable for PMEL expression, were not used for fibril isolation because their melanin granules are too tightly packed to be efficiently disintegrated during extraction. Conversely, MNT1 cells were used for the rest of the experiments. HMV-II cells, although useful for fibril isolation, become unstable after extended culture and multiple passages, leading to a gradual decrease in their capacity to produce melanin granules. Thus, MNT1 cells were more suitable for experiments requiring stable, long-term granule production.\n\nFirst, we constructed two lentiviral transfer plasmids: pCW57.1-X330-gRNA/SpCas9-mCeru-PuroR and pCW57.1-PMEL-DsRed-HygR-GFP-rTetR (Fig.\u00a0S10a).\n\nThe expression cassette backbone for Cas9 and the gRNA scaffold was derived from the pX330_sgRNA/hSpCas9 plasmid (Addgene number 172832)56. The original gRNA scaffold sequences were replaced with those targeting the PMEL gene.\n\nThe sequences of the sgRNA target sites are listed below, with PAM sites underlined:\n\nAAGTGACTGTCTACCATCGC CGG\n\nCGTGTCCCAGTTGCGGGCCT TGG\n\nTCCATCCAAGGCCCGCAACT GGG\n\nCTCCATCCAAGGCCCGCAAC TGG\n\nFollowing the SpCas9 sequence, we inserted mCerulean3 (synthesized by ThermoFisher Scientific), a T2A sequence, and the puromycin-resistance gene (derived from pCW57.1, Addgene number 99283)57. This plasmid enables knockout of the PMEL gene and selection of clones via mCerulean fluorescence and puromycin resistance. The entire cassette, from gRNA to SpCas9 and mCerulean-PuroR, was inserted into pCW57.1 to convert it into a lentiviral transfer plasmid.\n\nConstruction of pCW57.1-PMEL-DsRed-HygR-GFP-rTetR: The backbone for this plasmid is the Tet-on plasmid pCW57.158. We inserted the PMEL gene with or without G175S mutation, along with DsRed (excised from the tdTomato sequence, derived from pCDH-EF1-Luc2-P2A-tdTomato, Addgene number 72486, a gift from Kazuhiro Oka), under the tight TRE promoter. In addition to introducing the G175S mutation, the PAM sites in the PMEL expression plasmid were mutated to prevent cleavage by Cas9. Following the hPGK promoter, we inserted the hygromycin-resistance gene (derived from pCEP4-AD8gp160, Addgene number 123260)59, a T2A sequence, the EGFP, another T2A sequence, and the rTetR gene. This plasmid enables doxycycline (Dox)-dependent expression of PMEL G175S and allows clone selection via hygromycin resistance and EGFP fluorescence.\n\nWe transfected Lenti-X 293\u2009T cells (Takara Bio, Shiga, Japan) with the transfer plasmids, alongside the packaging plasmid pCMV-dR8.2 delta-vpr (Addgene number 8455),60 and the envelope plasmid pCMV-VSV-G (Addgene number 8454), using the Avalanche-Everyday transfection reagent (EZ Biosystems, College Park, MD). Twenty-four hours post-transfection, the cells were washed three times with PBS and incubated in fresh medium. Virus-containing medium was collected at 48-, 72-, and 96-hours post-transfection. Cell debris was removed by centrifugation at 1000 \u00d7 g, followed by filtration through a 0.45\u2009\u00b5m membrane (Millipore). The lentivirus-containing supernatant was concentrated using polyethylene glycol precipitation61.\n\nTo generate PMEL knockout cell lines, HMV-II and MNT1 cells were incubated with the concentrated lentiviral particles carrying pCW57.1-X330-gRNA/SpCas9-mCeru-PuroR for 24\u2009h. Cells were then selected using 2\u2009\u00b5g/ml puromycin. PMEL gene knockout was confirmed via western blotting using a PMEL-specific antibody (1/2,000 dilution, E-7, Santa Cruz Biotechnology, Dallas, TX) (Fig.\u00a0S10b). PMEL-knockout cells were subsequently transduced with the concentrated lentivirus carrying pCW57.1-PMEL-DsRed-HygR-GFP-rTetR and selected using 200\u2009\u00b5g/ml hygromycin. Expression levels of PMEL were adjusted to match wild-type levels by optimizing Dox concentration (Fig.\u00a0S10b). Primer sequences are summarized in Supplementary Data\u00a01.\n\nWe initially attempted to generate PMEL(G175S)-expressing cells by directly modifying the genomic sequence of the native PMEL gene using CRISPR-Cas9. However, no clones successfully proliferated following drug selection. This outcome is likely due to the additional stress imposed on single isolated cells during the proliferation stage, which is inherently a challenging process. While doxycycline-induced expression of G175S PMEL protein did not result in cell death, its expression may impose a level of cellular stress that interferes with the proliferation of isolated single cells under these conditions. Consequently, we employed the Tet-on system to control the expression of PMEL(G175S) in a PMEL-knockout background, allowing for Dox-inducible expression and avoiding the issues associated with constitutive expression of the mutant protein.\n\nPMEL amyloid fibrils were isolated following a modified version of the previously described method31. Melanoma cells were resuspended in PBS supplemented with 2.5\u2009\u00b5g/ml cytochalasin D (Fujifilm) and 10\u2009\u00b5M nocodazole (Fujifilm) and incubated at 37\u2009\u00b0C for 30\u2009min. The cells were then resuspended in 10\u2009mM Tris-HCl (pH 7.4) containing a protease inhibitor cocktail (Nacalai Tesque, Kyoto, Japan) and incubated on ice for 10\u2009min. The cells were disrupted by Dounce homogenization, followed by centrifugation at 800 \u00d7g for 10\u2009min at 4\u2009\u00b0C to remove cell debris. The membrane fraction was collected by ultracentrifugation at 100,000 \u00d7 g for 60\u2009min at 4\u2009\u00b0C, and the resulting pellets were rinsed twice with PBS. Rinsed membranes were lysed in 2% Triton X-100 in PBS for 1\u2009h at 4\u2009\u00b0C and then layered onto a discontinuous sucrose gradient consisting of 30%, 45%, and 55% sucrose in 50\u2009mM Tris-HCl (pH 7.4), 200\u2009mM NaCl, and 1\u2009mM EDTA. The samples were centrifuged for 2\u2009h at 100,000 \u00d7 g at 4\u2009\u00b0C. The 55% sucrose fraction was diluted 10-fold and ultracentrifuged at 100,000 \u00d7g for 2\u2009h at 4\u2009\u00b0C. The resulting pellet was rinsed twice with PBS and resuspended in 50\u2009mM Tris-HCl (pH 7.4) containing 5\u2009mM CaCl\u2082 and disrupted by sonication for 10\u2009s using a Q125 sonicator (Qsonica, Newtown, CT). For the G175S specimen, the pellet was incubated in 4\u2009M Urea in 50\u2009mM Tris-HCl (pH 7.4) for 10\u2009min at room temperature to loosen the structure before sonication (Fig.\u00a0S9b). Deglycosylation was carried out by adding 10\u2009\u00b5l of O-glycosidase (New England Biolabs, Ipswich, MA) and 10\u2009\u00b5l of neuraminidase (New England Biolabs) to the sample, followed by overnight incubation at 37\u2009\u00b0C. The digested product was centrifuged at 20,000 \u00d7 g for 10\u2009min at 4\u2009\u00b0C, and the pellet was resuspended in 50\u2009mM Tris-HCl (pH 8.0) containing 5\u2009mM CaCl\u2082 and 1\u2009mM DTT. The pellet was then digested with 10\u2009\u00b5g of trypsin for 1\u2009h at 37\u2009\u00b0C16,62. After centrifugation at 20,000 \u00d7 g for 10\u2009min at 4\u2009\u00b0C, the pellet was resuspended in 50\u2009mM Tris-HCl (pH 8.0) containing 5\u2009mM CaCl\u2082 and 1\u2009mM DTT, followed by digestion with 20\u2009\u00b5g of Arg-C endopeptidase overnight at 37\u2009\u00b0C. The digested product was centrifuged at 20,000 \u00d7 g for 10\u2009min at 4\u2009\u00b0C, then disrupted by sonication for 20\u2009s. After ultracentrifugation at 100,000 \u00d7 g for 2\u2009h at 4\u2009\u00b0C, the pellet was sonicated for an additional 20\u2009s. The final specimen was centrifuged at 20,000 \u00d7 g for 10\u2009min at 4\u2009\u00b0C and prepared for cryo-EM analysis (Fig.\u00a0S9).\n\nThe CAF domain (residues 148\u2013223) of human PMEL was subcloned into the pET24a plasmid (Merck Millipore, Darmstadt, Germany) and transformed into E. coli BL21 (DE3) (New England Biolabs). Transformed bacteria were cultured in LB medium at 37\u2009\u00b0C until the optical density (OD600) reached 0.8. Expression of the CAF domain was induced by the addition of 0.5\u2009mM isopropyl \u03b2-D-thiogalactopyranoside (IPTG), followed by overnight incubation at 18\u2009\u00b0C. The cells were harvested and disrupted by sonication. Inclusion bodies were collected and sequentially washed with 2\u2009M, 3\u2009M, 4\u2009M, 6\u2009M, and 8\u2009M urea. The resulting pellets were solubilized in 6\u2009M guanidine hydrochloride (GuHCl), and insoluble debris were removed by centrifugation at 20,000 \u00d7 g for 10\u2009min at 30\u2009\u00b0C. For G175S inclusion bodies, extensive sonication without cooling (up to 40\u2009\u00b0C) was required to achieve solubilization. The clarified supernatant was loaded onto Ni-NTA resin (Nacalai Tesque) pre-equilibrated with 50\u2009mM Tris-HCl (pH 8.0), 8\u2009M urea, and 20\u2009mM imidazole. The resin was washed with the equilibration buffer, and bound proteins were eluted with 50\u2009mM Tris-HCl (pH 8.0), 8\u2009M urea, and 300\u2009mM imidazole. The eluted protein was then diluted with 150\u2009mM sodium acetate (pH 4.4) to reach a final concentration of 0.3\u2009mg/ml, and incubated with vigorous shaking (200\u2009rpm) at 37\u2009\u00b0C for 24\u2009~\u200930\u2009h for wild-type protein. Initially, we incubated the G175S mutant protein at 37\u2009\u00b0C; however, the rapid growth of amyloids at this temperature led to the formation of thick, bundled rods that were unsuitable for analysis. To obtain analyzable fibrils, we incubated the G175S protein at 18\u2009\u00b0C for 30\u2009h. For the wild-type protein, we incubated at 18\u2009\u00b0C for 66\u2009h, resulting in fibrils that exhibited the same structure as those incubated at 37\u2009\u00b0C.\n\nSamples were resuspended in 150\u2009mM sodium acetate (pH 4.4) at a final concentration of 0.3\u2009mg/ml. A 2\u2009\u00b5l aliquot of the sample was applied to each side of freshly glow-discharged Ultra Au foil R1.2/1.3 300 mesh grids (Quantifoil Micro Tools GmbH, Gro\u00dfl\u00f6bichau, Germany). The grids were blotted from both sides for 3\u2009s at 12\u2009\u00b0C under 100% humidity and subsequently plunge-frozen in liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific).\n\nImages were recorded on a CRYO ARM 300 II (JEOL, Tokyo, Japan) at the University of Tokyo, operated at 300\u2009keV. An Omega filter with a slit width of 20\u2009eV and a Gatan K3 direct electron detector in correlated-double sampling (CDS) mode were used for imaging. The nominal magnification was set to 60,000\u00d7, yielding a physical pixel size of 0.8784\u2009\u00c5/pixel. Movies were acquired using the SerialEM software, with a target defocus range of 0.8\u20131.5\u2009\u00b5m. Each movie was recorded for 5.52\u2009s with a total electron dose of 50 e\u207b/\u00c5\u00b2, divided into 50 frames.\n\nImage processing was carried out using CryoSPARC v4.5.3 and Relion v3.1 and v563,64. Raw movie frames were motion-corrected, and the contrast transfer function (CTF) was estimated using the patch-CTF estimation method. Fibrils were automatically picked using the filament tracer tool, and segments were extracted with a box size of 300 pixels and an inter-box distance set to 10% of the box size. Several rounds of 2D classification were performed to exclude bad classes and those that lacked discernible helical twists.\n\nFor the native specimen, we identified both 1-start and 2-start helical fibrils (Fig.\u00a0S1, squares), but only the 2-start helical fibrils were processed further. The 1-start helical particles were discarded due to insufficient resolution. In the native wild-type specimen, two distinct polymorphs were identified and processed separately. After selection, the final segments were re-extracted with a box size of 280 (native fibrils) or 300 (in vitro fibrils) pixels and reconstructed using helical refinement.\n\nInitial references were generated from 2D class averages with a box size of 600 pixels, using the relion_helix_inimodel2D subroutine65. Further refinement included local and global CTF refinement, reference-based motion correction, and 3D classification. Final maps were generated from 3D classes with the highest resolution using helical refinement. The handedness of the fibrils was determined by generating mirror structures of the 3D maps. Models were built using ModelAngelo66 for both the original and mirrored structures. Only the left-handed structure produced reasonable and consistent models, confirming the fibril handedness.\n\nFor the in vitro polymerized specimen, additional refinement steps were performed, including Relion CTF refinement and Bayesian polishing after 3D classification. Resolution estimates were determined using the Fourier shell correlation (FSC) at a threshold of 0.143. Image processing workflows were summarized in Figs.\u00a0S1\u20134.\n\nInitial models were generated using Relion v5.0 ModelAngelo and refined using ChimeraX and ISOLDE tool35,67,68. The refined models were validated using Phenix (v.1.19.2-4158)69 (Table\u00a01). Image rendering was conducted using Chimera X and MaskChains tool70. We used pyem tools for parameter conversion from CryoSPARC to Relion71.\n\nA formvar-coated gold grid stabilized with an evaporated carbon film (FCF100-Au-EC, Electron Microscopy Sciences, Hatfield, PA) was prepared by coating with a 0.1% gelatin solution (Nacalai Tesque) for 1\u2009h at 37\u2009\u00b0C. The grid was then washed with culture medium to remove excess gelatin. MNT1 cells were applied to the prepared grid surface and cultured for 96\u2009h in the presence of 0.2\u2009\u00b5g/mL Dox to induce expression of PMEL. Hoechst-33258 was added to the medium at a final concentration of 1\u2009\u00b5g/mL and incubated with the cells for 10\u2009min to stain nuclei.\n\nFollowing staining, grids were transferred into a TBS buffer (10\u2009mM Tris-HCl, pH 7.4, 150\u2009mM NaCl) supplemented with 0.1% BSA, 5\u2009mM CaCl\u2082, 5\u2009mM MgCl\u2082, and 9% propylene glycol72. The grids were incubated in this buffer for 5\u2009min at room temperature to enhance cryoprotection. After incubation, grids were blotted from both sides for 25\u2009s at 25\u2009\u00b0C in a controlled 100% humidity environment using a Vitrobot Mark IV (Thermo Fisher Scientific). Finally, the grids were plunge-frozen in liquid ethane cooled by liquid nitrogen.\n\nWe used an Aquilos cryo-focused ion beam scanning electron microscope (ThermoFisher Scientific) for preparing lamellae73 (Fig.\u00a0S11a). The preparation was performed in a stepwise manner, beginning with a lamella thickness of 3 \u03bcm, followed by successive milling to reduce the thickness to 2 \u03bcm and finally to 0.88 \u03bcm. These steps utilized gallium ion beam milling currents of 1\u2009nA, 0.5\u2009nA, and 0.3\u2009nA, respectively. For the final polishing step, milling currents between 13 and 50 pA were applied to reach a final lamella thickness of less than 300\u2009nm. After achieving the desired thickness, the lamellae were coated with an additional thin layer of inorganic platinum by sputter coating at 30\u2009mA for 3\u2009s to enhance conductivity and protect the sample during subsequent imaging.\n\nTomographic tilt series were recorded on a Talos Arctica transmission electron microscope (Thermo Fisher Scientific) operating at 200\u2009keV at the University of Tokyo (Fig.\u00a0S11b). The microscope was equipped with a Gatan Quantum-LS Energy Filter set to a slit width of 30\u2009eV and a Gatan K2 BioQuantum direct electron detector in electron counting mode. Imaging was performed at a nominal magnification of 49,000\u00d7, resulting in a physical pixel size of 2.2\u2009\u00c5/pixel. Movies were acquired using Thermo Fisher\u2019s Tomography software, with a target defocus set between 4 and 6\u2009\u00b5m. Tilt series were collected over an angular range from -62\u00b0 to +42\u00b0, with a 4.0\u00b0 increment between images and an initial tilt angle of -10\u00b0. Each movie was recorded for 2.0\u2009s, with a per-frame dose of 2.0 electrons/\u00c5\u00b2, subdivided into 15 frames to capture high-resolution details. The total accumulated dose for one tilt series was 54 electrons/\u00c5\u00b2. This dose was carefully optimized to balance the need for sufficient image contrast and resolution while minimizing electron radiation damage to the carbohydrate-rich structures present in the sample74,75. Raw tilt series were subjected to motion correction using the Alignframes program in IMOD76. Reconstruction of the corrected tilt series was performed with AreTomo277 using a simultaneous iterative reconstruction technique (SIRT) method. Tomograms were denoised using Topaz Denoise3D78. 3D modeling of the tomograms was accomplished using the Drawing tools and Interpolator tool in IMOD.\n\nLamella thickness was measured in the XY plane using 3dmod. The lamellar structure was visualized in Zap View, and the boundaries of the lamella were identified in the XY plane of the tomogram. The Distance Tool in 3dmod was used to measure the width (thickness) of the lamellae in nanometers. Calibration was performed using the tomogram\u2019s pixel size.\n\nThe length of lamellae was measured using Fiji. A midline of each lamella was traced in a 2D slice using the Freehand Tool. The length of the traced midline was calculated using the Analyze > Measure function in Fiji and recorded in \u00b5m.\n\nThe curvature of lamellae was measured using the Kappa Curvature plugin in Fiji. A midline of the lamella was traced in a 2D slice and the curvature was calculated at multiple points along the traced midline, and the average curvature across the entire lamella was reported in \u00b5m-1.\n\nThe inter-lamellar distance was measured in 3dmod. The Distance Tool in Zap View was used to determine the distance between the midlines of adjacent lamellae within the same melanosome. Multiple measurements were taken for each melanosome to account for variability in spacing, and the average distance was reported in nanometers.\n\nThe cross-sectional area of melanosomes was measured from 2D slices extracted from tomograms in Fiji. The melanosome boundary was manually traced in a single Z-slice using the Polygon ROI Tool. The area was calculated using the Analyze > Measure function in Fiji and converted into nm2 based on the pixel calibration.\n\nFor in vitro polymerized fibrils, wild-type and G175S mutant CAF domains (30\u2009\u00b5M) were polymerized at 37\u2009\u00b0C with shaking at 200\u2009rpm. At each time point, 5\u2009\u00b5l of 1\u2009mM ThT solution was added to 100\u2009\u00b5l of the reaction mixture. Fluorescence was measured using a SpectraMax GeminiEM plate reader (Molecular Devices, San Jose, CA) with an excitation wavelength of 444\u2009nm and an emission wavelength of 480\u2009nm.\n\nFor extracellular amyloid quantification, PMEL-knockout MNT-1 cells, transformed with Tet-on-inducible wild-type or G175S PMEL expression plasmids, were cultured in the presence or absence of 0.2\u2009\u00b5g/ml Dox for 96\u2009h. The culture supernatant from 10\u2009cm dishes was recovered and centrifuged at 800 \u00d7 g for 5\u2009min to remove cell debris. The clarified supernatant was further centrifuged at 20,000 \u00d7 g for 15\u2009min at 4\u2009\u00b0C, and the resulting pellets containing melanin granules were resuspended in PBS. After two washes with PBS, the pellets were demembranated by incubating in PBS containing 1% Triton X-100 for 1\u2009h at 4\u2009\u00b0C. The demembranated melanin granules were then washed twice with PBS and resuspended in 100\u2009\u00b5l PBS after disintegration. For ThT fluorescence measurement, 10\u2009\u00b5l of the granule suspension was added to 200\u2009\u00b5l of 50\u2009\u00b5M ThT solution in PBS. Fluorescence was measured as described above using the SpectraMax GeminiEM plate reader. To normalize the ThT fluorescence for extracellular amyloid content, the cell number of each culture was determined using a hemocytometer. The fluorescence values were adjusted to reflect extracellular amyloids from 1 \u00d7 107 cells per data point.\n\nTo quantify the intracellular amyloid content, PMEL-knockout MNT-1 cells with Dox-inducible expression of wild-type or G175S mutant PMEL were cultured in 6-well plates. Dox (0.2\u2009\u00b5g/mL) was added to the medium to induce PMEL expression at time 0\u2009h. Cells were collected at 96\u2009h after induction with six replicates (N\u2009=\u20096) for each condition. Cells were harvested by scraping into 1\u2009mL ice-cold lysis buffer per well (TBS plus 1% Triton X-100, and protease inhibitor cocktail) and homogenized using a homogenization pestle. The lysate was centrifuged at 15,000 \u00d7 g for 10\u2009min at 4\u2009\u00b0C to separate detergent-soluble (supernatant) and detergent-insoluble (pellet) fractions. The detergent-insoluble pellet was resuspended in 500\u2009\u00b5L TBS and washed twice by repeating centrifugation and resuspension. After the final wash, the pellet was disintegrated and resuspended in 100\u2009\u00b5L of ThT assay buffer (10\u2009mM Tris-NaOH pH 8.0, 50\u2009\u00b5M ThT, 1\u2009\u00b5g/mL propidium iodide (PI)). For each measurement, 10\u2009\u00b5L of the detergent-insoluble fraction was mixed with 90\u2009\u00b5L ThT solution in a 96-well black plate. ThT fluorescence was measured using a SpectraMax GeminiEM plate reader with excitation at 444\u2009nm and emission at 480\u2009nm. PI fluorescence was measured as an internal control for cell count with excitation at 535\u2009nm and emission at 615\u2009nm. ThT fluorescence values were standardized using PI fluorescence readings to account for differences in cell density and lysis efficiency. PI fluorescence was assumed to reflect genomic DNA content proportional to the number of cells. Uninduced cells (no Dox) served as negative controls to account for background fluorescence. All measurements were performed with six biological replicates per condition. Normalized ThT fluorescence values were compared between wild-type and G175S PMEL-expressing cells over time to determine differences in intracellular amyloid content.\n\nTo assess the effect of wild-type or G175S PMEL expression on cell viability, we utilized PMEL-knockout MNT-1 cells transduced with Dox-inducible constructs for wild-type or G175S PMEL expression. Cells were seeded in 6-well plates and induced by adding 0.2\u2009\u00b5g/mL Dox to the culture medium. Cell viability was determined by sequential staining first with Propidium iodide (PI, 1\u2009\u00b5g/mL; Fujifilm) and followed by Hoechst 33342 (1\u2009\u00b5g/mL). Propidium iodide was used to label dead cells, as its strong nuclear staining distinguishes it from the cytoplasmic localization of the dsRed-PMEL reporter. Hoechst 33342 was used to stain the nuclei of all cells, providing the total cell count. Imaging and quantification were performed using a fluorescence microscope. Comparisons were made between Dox-treated and untreated cells, as well as between wild-type and G175S PMEL-expressing cells at each time point. Data were collected from six times biological replicates to ensure statistical robustness.\n\nTo quantify intracellular melanin content, PMEL-knockout MNT1 cells with Dox-inducible expression of wild-type or G175S mutant PMEL were cultured in 6-well plates. Dox (0.2\u2009\u00b5g/mL) was added to the medium to induce PMEL expression at time 0\u2009h. Cells were collected at 96\u2009h after induction, with six replicates for each condition. Cells were harvested by trypsinization and split into two equal portions for melanin content measurement and DNA quantification. For melanin measurement, one portion was centrifuged at 500 \u00d7 g for 5\u2009min at room temperature to obtain a cell pellet. Melanin Extraction The pellet was resuspended in 1\u2009mL of 1\u2009N NaOH containing 10% DMSO. The mixture was heated at 80\u2009\u00b0C for 1\u2009h to dissolve melanin79. After heating, the optical density (OD) of the solution was measured at 405\u2009nm. The second portion of the cells was lysed as described in the intracellular amyloid content assay, and genomic DNA content was quantified using PI fluorescence (excitation 535\u2009nm, emission 615\u2009nm). The OD405 values were normalized to genomic DNA content to account for variability in cell number. Uninduced cells (no Dox) were used as negative controls to account for background melanin levels. All measurements were performed with six biological replicates per condition and time point. Normalized melanin content was compared between wild-type and G175S PMEL-expressing cells over time to assess differences in intracellular melanin accumulation.\n\nAfter inducing wild-type or G175S mutant PMEL expression for 96\u2009h, MNT1 cells were collected and fixed with 4% paraformaldehyde and 0.1% glutaraldehyde for 1\u2009h at 4\u2009\u00b0C. The samples were post-fixed with 1% osmium tetroxide, followed by staining with 1% uranyl acetate. Dehydration was performed using a graded ethanol series and acetone, and the samples were embedded in Quetol 812 resin (Nissin EM, Tokyo, Japan). Ultrathin sections of 60\u2009nm were prepared using an ULTRACUT microtome (Reichert Leica) and mounted onto Formvar-coated copper grids. Electron microscopy images were acquired using a JEM-2100F microscope (JEOL, Tokyo, Japan) equipped with an F216 CMOS camera (TVIPS GmbH, Gauting, Germany), operated at 200\u2009keV, at the University of Yamanashi. Melanosomes were identified and counted, and their areas were measured using Fiji software80.\n\nFor statistical comparison of amyloid yield between the wild-type and G175S mutant CAF domains, two analyses were conducted. First, we used the Mann-Whitney U test due to the non-normal distribution of the data. The test was performed as a two-tailed analysis with an exact p-value calculation. The results showed that the G175S mutant yielded significantly higher levels of polymerized amyloids compared to the wild-type (p\u2009=\u20090.0079). The test statistic, Z\u2009=\u2009-2.6548, was outside the 95% confidence interval ([-1.96, 1.96]), indicating a statistically significant difference between the two groups. The observed standardized effect size was 0.79, suggesting a large magnitude of difference in amyloid yield between the wild-type and G175S groups.\n\nFor the quantification of intracellular amyloids, statistical analysis was performed using one-way analysis of variance (ANOVA) to test for overall differences between groups (native MNT1, WT Dox (-), WT Dox (+), G175S Dox (-), and G175S Dox (+), N\u2009=\u20096). A significant difference was found between the groups (p\u2009=\u20098.4903e-11). To further analyze pairwise differences, Tukey\u2019s HSD (Honestly Significant Difference) post-hoc test was performed. Tukey\u2019s HSD revealed that the difference between WT Dox (+) and G175S Dox (+) was statistically significant (p\u2009=\u20092.144e-4).\n\nFor the quantification of extracellular amyloids, statistical analysis was performed using one-way analysis of variance (ANOVA) to test for overall differences between groups (native MNT1, WT Dox (-), WT Dox (+), G175S Dox (-), and G175S Dox (+), N\u2009=\u20098). A significant difference was found between the groups (p\u2009=\u20097.216e-15). To further analyze pairwise differences, Tukey\u2019s HSD (Honestly Significant Difference) post-hoc test was performed. Tukey\u2019s HSD revealed that the difference between WT Dox (+) and G175S Dox (+) was statistically significant (p\u2009=\u20093.36e-5), and there was a highly significant difference between G175S Dox (-) and G175S Dox (+) (p\u2009=\u20096.635e-12).\n\nFor the quantification of melanosome stages, the proportions of stage II, III, and IV melanosomes were compared between wild-type PMEL and G175S mutant PMEL expressing cells using the Wilcoxon signed-rank test, as the proportions of the three stages are dependent (their sum equals 100%). Significant differences were observed in the proportion of stage III melanosomes (p\u2009=\u20092.575e-7, effect size r\u2009=\u20090.8358) and stage II melanosomes (p\u2009=\u20091.568e-8, effect size r\u2009=\u2009-0.9054). However, no significant difference was detected for stage IV melanosomes (p\u2009=\u20090.3607, effect size r\u2009=\u2009-0.1464).\n\nAll the statistical analyses were conducted using an online calculator (https://www.statskingdom.com/).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The cryo-EM maps have been deposited in the Electron Microscopy Data Bank (EMDB) under accession codes: EMD-61782 (native wild-type PMEL fibrils, polymorph 1); EMD-61783 (native wild-type PMEL fibrils, polymorph 2); EMD-61784 (native G175S PMEL fibrils); EMD-61785(in vitro polymerized wild-type CAF domain fibrils); and EMD-61786 (in vitro polymerized G175S CAF domain fibrils). The atomic coordinates have been deposited in the Protein Data Bank (PDB) under accession codes: 9JST (native wild-type PMEL fibrils, polymorph 1); 9JSU (native wild-type PMEL fibrils, polymorph 2); 9JSV (native G175S PMEL fibrils); 9JSW (in vitro wild-type CAF domain fibrils); and 9JSX (in vitro G175S CAF domain fibrils). The source data underlying Figs.\u00a06b\u2013d, 7b, 8c, Supplementary Fig.\u00a05a, b, and 10b are provided as a Source Data file.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Maury, C. P. The emerging concept of functional amyloid. J. Intern. Med. 265, 329\u2013334 (2009).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nJackson, M. P. & Hewitt, E. W. Why are Functional Amyloids Non-Toxic in Humans?. Biomolecules 7, 71 (2017).\n\nArticle\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nChuang, E., Hori, A. M., Hesketh, C. D. & Shorter, J. 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This work was supported by the Takeda Science Foundation (to T.O.), the Japan Society for the Promotion of Science (KAKENHI Grant numbers 21H02654, 24H02285 (to T.O.), 21H04762, 21H04762, and 21H05248 (to M.K.)), and a Research Grant from the Human Frontier Science Program (Grant number RGP006/2023 to T.O.) (https://doi.org/10.52044/HFSP.RGP0062023.pc.gr.168592).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Cell Biology and Anatomy, Graduate School of Medicine, the University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan\n\nHaruaki Yanagisawa,\u00a0Tony Wang\u00a0&\u00a0Masahide Kikkawa\n\nDepartment of Anatomy and Structural Biology, Graduate School of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan\n\nHarumi Arai,\u00a0Hideyuki Miyazawa\u00a0&\u00a0Toshiyuki Oda\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nT.O. conceived and designed the experiments. H.Y. conducted cryo-EM data collection and analysis. H.A. and H. M. conducted cryo-FIB-SEM and cryo-electron tomography. H.A. and T. W. established the protocol for cryo-FIB-SEM milling operation. T.O. prepared samples and analyzed the tomography and cell biology/biochemistry data. T.O. and M.K. wrote the manuscript.\n\nCorrespondence to\n Toshiyuki Oda.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Michael Marks, Ralf Leonhardt and Guillaume van Niel for their contribution to the peer review of this work. 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Cryo-EM of wild-type and mutant PMEL amyloid cores reveals structural mechanism of pigment dispersion syndrome.\n Nat Commun 16, 5411 (2025). https://doi.org/10.1038/s41467-025-61233-y\n\nDownload citation\n\nReceived: 28 November 2024\n\nAccepted: 16 June 2025\n\nPublished: 01 July 2025\n\nVersion of record: 01 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61233-y\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Mermin-Wagner Fluctuations in Two-Dimensional Active Crystals and Glasses", + "journal": "Nature Communications", + "published": "01 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61366-0/MediaObjects/41467_2025_61366_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61366-0/MediaObjects/41467_2025_61366_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.6084/m9.figshare.29234687", + "/articles/s41467-025-61366-0#ref-CR69" + ], + "code": [ + "https://doi.org/10.6084/m9.figshare.29234687", + "/articles/s41467-025-61366-0#ref-CR69" + ], + "subject": [ + "Phase transitions and critical phenomena", + "Statistical physics" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4017223/v1.pdf?c=1751457026000", + "research_square_link": "https://www.researchsquare.com//article/rs-4017223/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61366-0.pdf", + "preprint_posted": "11 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "In two-dimensional (2D) systems, the Mermin-Wagner effect plays a significant role, giving rise to striking dimensionality effects marked by long-range density fluctuations and the divergence of various dynamic properties. This effect also unequivocally negates the possibility of stable crystalline phases in 2D particulate systems with continuous degrees of freedom. This effect has been recently discerned in glass-forming liquids, displaying characteristic signatures like the logarithmic divergence of mean squared displacement in the plateau regime. We explored these long-wavelength fluctuations in crystalline solids and glass-forming liquids in the presence of non-equilibrium active forces. Active systems can be thought of as a minimalistic model for understanding various non-equilibrium systems where the constituent particles' dynamics are controlled by both temperature and internal or external active forces. Such models often offer valuable insights into the dynamical behavior of biological systems, such as collections of cells, bacteria, ant colonies, or even synthetic self-propelled Janus colloids. Our study reveals that fluctuations stemming from active forces get strongly coupled with long wavelength fluctuations arising from thermal effects, resulting in dramatic dynamical effects in 2D systems. We also shed light on how these fluctuations impact dynamical heterogeneity, a defining characteristic of glassy dynamics.Physical sciences/Physics/Condensed-matter physics/Phase transitions and critical phenomenaPhysical sciences/Physics/Statistical physics, thermodynamics and nonlinear dynamics/Statistical physicsPhysical sciences/Physics/Statistical physics, thermodynamics and nonlinear dynamics/Phase transitions and critical phenomena", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "MerminWagnerSupplementary.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "In two-dimensions (2D), the Mermin-Wagner-Hohenberg (MWH) fluctuation plays a significant role, giving rise to striking dimensionality effects marked by long-range density fluctuations leading to the singularities of various dynamical properties. According to the MWH theorem, a 2D equilibrium system with continuous degrees of freedom cannot achieve long-range crystalline order at non-zero temperatures. Recently, MWH fluctuations have been observed in glass-forming liquids, evidenced by the logarithmic divergence in the plateau value of mean squared displacement (MSD). Our research investigates long-wavelength fluctuations in crystalline and glassy systems influenced by non-equilibrium active noises. Active systems serve as a minimal model for understanding diverse non-equilibrium dynamics, such as those in biological systems and self-propelled colloids. We demonstrate that fluctuations from active forces can strongly couple with long-wavelength density fluctuations, altering the lower critical dimension (dl) from 2 to 3 and leading to a novel logarithmic divergence of the MSD plateau with system size in 3D.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The Mermin-Wagner-Hohenberg (MWH) theorem1,2,3 asserts that continuous spontaneous symmetry breaking (SSB) cannot occur at any finite temperature (T\u2009>\u20090) in a 2D equilibrium system. Hohenberg first introduced this for superfluid systems, and later, building on his work, Mermin and Wagner demonstrated that phase transitions involving continuous symmetry breaking are likewise impossible in 2D equilibrium systems consist of continuous spin or particles. In particular, there is no long-range order in the system due to the instability caused by the long wavelength fluctuations in the system below the lower critical dimension (dl); these long wavelength fluctuations are often termed as Mermin-Wagner-Hohenberg (MWH) fluctuations in the literature. As MWH theory concerns only the physics of large length scale or small wave vector, it is natural to expect that the theory will be true for both crystalline and disordered solids as long as long-wavelength phononic excitations are present in the system. It is often argued in the theoretical derivation of the Mermin-Wagner-Hohenberg (MWH) theorem1,2,3 that the dispersion behaviour of phonons determines the thermal vibrations of the molecules or particles in their equilibrium crystalline position4,5, and it is quite straightforward to show that the mean squared displacement (MSD) of particles from their equilibrium position, the Debye-Waller (DW) Factor, diverges logarithmically with the linear dimension (L) of the system in 2D. Recently, the MWH fluctuations are found to be dominant in both supercooled liquids and amorphous solids Shiba et al. first showed this diverging fluctuation effect for a 2D disordered system using computer simulation6. For 2D colloidal systems, the presence of such fluctuations has been confirmed by recent works7,8,9. Again, Li et al.10 have shown that long-wavelength density fluctuations also influence the dynamics in the liquid states in these 2D systems.\n\nIn ref. 10 it was shown that MWH fluctuations can have significantly strong effects even at high temperatures, manifested by the breakdown of the Stokes-Einstein (SE) relation in a manner that is different from the breakdown observed in the supercooled temperature regimes. SE relates the diffusivity (D) of the particles in the medium with the characteristic relaxation time (\u03c4\u03b1) or viscosity (\u03b7) as D\u2009=\u2009KBT/C\u03b7, with KB being the Boltzmann constant, and C being a constant that depends on the details of the probe particle. Assuming \u03c4\u03b1 \u2243 \u03b7/KBT11, one gets D\u00a0~\u00a01/\u03c4\u03b1. On the other hand, in the supercooled temperature regime, one finds: \\(D\\propto {\\tau }_{\\alpha }^{-\\kappa }\\), with \u03ba being smaller than 1. This is often referred to as the fractional SE relation. Fractional SE is known from experiments in various molecular glass-forming liquids and model glass-forming systems in simulations. SE breakdown in 3D can be often explained using the concept of growing dynamic heterogeneity (DH) in these systems12. Whereas in 2D, the picture is very different, even at high temperatures with \u03ba\u2009>\u20091 is reported in the high-temperature normal liquid regimes and \u03ba\u2009<\u20091 being reported in the supercooled temperature regime similar to the 3D case. The observation of \u03ba\u2009>\u20091 at high-temperature liquids in 2D has been shown as purely coming from the MWH like long-wavelength density fluctuations in the system, thereby proving that MWH fluctuations are prevalent even at high temperatures as much as in supercooled and low temperature solid regimes10. This suggests that the study of SE breakdown at high temperatures can be a good way to probe the existence of MWH-like fluctuations in experiments.\n\nA hallmark characteristic of glassy dynamics is the enhanced dynamical heterogeneity, which quantifies the marked difference in relaxation patterns in different parts of a glassy system13,14,15. Dynamic heterogeneity is often highlighted as one of the important features of glassy dynamics. Recent research16 has shown that the DH of glassy systems, calculated using dynamic susceptibility (\u03c74(t); see the Methods Section for its definition), exhibits additional short-time peaks in three-dimensional (3D) systems. These peaks are attributed to collective motions facilitated by long-wavelength phonon modes16. The short-time peak of \u03c74(t) disappears if one does a Brownian dynamics simulation of the same systems in the overdamped limit, where the phonons get suppressed considerably. Notably, these short-time peaks are found to be further enhanced in the presence of coloured active noise at 3D, suggesting that active noise plays an important role in amplifying phonon modes even in 3D17. Thus, it is natural to expect that 2D systems will be influenced even more strongly by these long-wavelength phonon modes when active noises are present. Many experiments are conducted in 2D or quasi-2D geometries due to practical experimental considerations. Even biological systems, such as cell monolayers, which show glass-like dynamics, can be considered quasi-2D systems. Therefore, understanding the presence of MWH fluctuations in non-equilibrium systems like active glasses or active crystalline solids in various dynamical conditions (damping), becomes crucial as synthetic active colloidal particles are in a dynamical regime where inertia is important whereas cell monolayers are in the overdamped regime where inertia can be neglected. The significance of the inertial effect in active systems has been shown in different works17,18,19,20. Furthermore, investigating whether the presence of these fluctuations leads to increased or suppressed mean-squared position fluctuations in the system is essential for interpreting the dynamical information obtained from these 2D or quasi-2D systems.\n\nIn recent times, there has been a surge of research activities in the field of active matter, leading to the emergence of a new direction of studies on disordered systems called active glasses12,21. Active matter is often categorised as a system in which the constituents can move internally, driven by their internal energy, in addition to the environmental influence of thermal fluctuations22,23,24,25,26. Such systems exhibit a plethora of interesting dynamical phenomena, including spontaneous symmetry breaking of the rotational order in two dimensions22, leading to the formation of ordered phases of clusters or flocks. These clusters have coherent collective motion at low noise strength and high particle density23. Many biological systems exhibit collective dynamical behaviour, in which forces generated by ATP consumption drive the dynamics instead of thermal fluctuations. A simple model of these systems that can capture some of the salient dynamical behaviours is a collection of self-propelled particles (SPPs)26. Several studies show that collective dynamics of cells and tissues during cell proliferation, cancerous cell progression, and wound healing27,28,29,30,31,32,33,34,35,36 have dynamical features similar to glassy dynamics. Cell cytoplasm and bacterial cytoplasm show glassy dynamics, which can also modulate the depletion of ATP27,29. These intricate dynamical similarities between various biological systems and glassy systems have fuelled a lot of research activities in modelling active glassy systems to develop an understanding of the emergent dynamical behaviours that are not very sensitive to the details of the system. Instead, they are outcomes of intrinsic non-equilibrium driving due to active forces37,38.\n\nActive systems are inherently out of equilibrium in nature as they do not follow detailed balance and are driven either internally or by external forcing. In recent years, there have been attempts to understand their steady-state dynamical behaviour within equilibrium statistical mechanics using an appropriate effective temperature and generalised fluctuation-dissipation theory (FDT). The work37 shows that in the small activity limit, one can define the effective temperature of the out-of-equilibrium system using the effective FDT analysis in which the time-reversal symmetry is still intact38. Analytical results on the dynamics of an active particle in a harmonic potential, the active Ornstein-Ulhenbeck process, suggest that in a short persistent time limit, an effective potential of the active system can be used to define an effective Boltzmann weight. Similar ideas have been extended to active glasses, which suggest that some dynamical aspects can be well understood using an effective temperature description21,39,40,41,42,43,44. However, higher-order dynamical correlation functions like four-point susceptibility (\u03c74(t)) cannot be understood with the same framework, as reported in ref. 12. Thus, a clear understanding of active systems in their dynamical steady states is still lacking, and the intricate effects of active driving on the dynamics continue to puzzle the scientific community.\n\nIn this article, we have done extensive studies to understand the effect of activity on the dynamics of a model polycrystalline solid and two glass-forming model systems in 2D and 3D. We have also done simulations of a model glassy system in four spatial dimensions (4D) to establish the shift of dl to higher dimensions. These glassy models are referred to as the modified Kob-Anderson model in 2D (2dmKA) with a binary composition of 65:\u00a035 and the Kob-Andersen model in 3D (3dKA) with the composition ratio 80:\u00a02045 (see Methods section). For the polycrystalline solid, we have taken a mono-atomic system and generated the solid by cooling the liquid from a high-temperature melt. Our simulations are carried out in the canonical ensemble, with system sizes ranging from N\u2009=\u2009100 to 105 particles. To introduce activity into the system, we use run and tumble particle (RTP) dynamics12,17,21, which can be tuned using three parameters: c, f0, and \u03c4p. The concentration of active particles is c, which is the ratio of the number of active particles (Na) with respect to the total number of particles (N) in the system, i.e., c\u2009=\u2009Na/N. c, is varied in the range c \u2208 [0, 0.5], while the strength of the active force applied to each particle, f0, is varied in the range f0 \u2208 [0, 2.5]. The persistent timescale, \u03c4p, determines the duration for which the active forces act along a fixed but random direction, and is varied in the range \u03c4p \u2208 [0, 2.0] in our simulations. To understand the general applicability of our results across model systems, we have also performed simulations with active Brownian particles (ABPs) in both damped and overdamped dynamical conditions. Note these systems are also controlled by the same three activity parameters, namely, f0, c and \u03c4p, only the details of how active forces modify the equations of motion are different, as discussed later. Further details of the models and simulation protocols are provided in the Method section.\n\nTo highlight our major findings, we showed that the MWH fluctuation is at play even at higher temperatures, consistent with the findings in passive systems. We have also demonstrated the impact of long wavelength excitations in these systems by calculating an effective dynamical matrix and showing that the results in non-equilibrium can be understood using an effective medium theory using the same MWH arguments, but with enhanced effect. We found that the Debye-Waller (DW) factor diverges as a power-law with increasing activity, instead of the usual logarithmic divergence seen in equilibrium 2D systems. Interestingly, in 3D, we discovered novel logarithmic divergence of DW factor for non-equilibrium systems, in contrast to the absence of any divergence in equilibrium systems. We have demonstrated the robustness of our results across various active matter models at different dynamical conditions. We have also found that the effective phonon dispersion relation becomes non-linear with respect to the wave vector as activity increases, in both the dimensions, leading to the shift of the lower critical dimensions from dl\u2009=\u20092 to 3.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To explore the enhanced effect of long-wavelength phonon modes in active crystals and glasses, we first computed the mean squared displacement (MSD), \u3008\u0394r2(t)\u3009 (see Methods for the definition), as a function of time. For the 2dmKA model glass-forming liquids with activity strength f0\u2009=\u20092.0 at a reduced temperature of T\u2009=\u20090.221, we show the results of increasing MSD plateau with system sizes N\u2009=\u2009103,\u00a0104, and 105 in Fig.\u00a01a. The inset of the same panel shows the data for a passive glass-forming liquid at T\u2009=\u20090.450. We observed a significant increase in the plateau value of MSD for active systems compared to the passive system at similar relaxation times, demonstrating the enhanced effect of long-wavelength fluctuations due to active forcing. In Fig.\u00a01b, we explored the decay profile of the two-point density correlation function Q(t) (see Methods for the definition) for the same system sizes as in panel a. This was done to demonstrate how long-wavelength fluctuations lead to faster relaxation in active systems for larger system sizes. The inset shows the relaxation profile for passive systems. Next, we have computed the MSD plateau of the active glass-forming liquids in the supercooled regime with changing system size for different activity f0, while keeping the concentration of active particles (c\u2009=\u20090.1) and persistent time (\u03c4p\u2009=\u20091.0) fixed. The results are presented in Fig.\u00a01c. We defined the plateau value of MSD as MSD(\u03c4) with \u03c4 being the time at which MSD shows a point of inflection in the plateau regime. The Debye-Waller (DW) factor is proportional to the MSD plateau MSD(\u03c4). We observed logarithmic divergence of the MSD(\u03c4) with system size, i.e., \\(MSD(\\tau ) \\sim \\log (L)\\) for a passive system and in the presence of activity, the divergence of MSD(\u03c4) increases faster than logarithmic and follows power law divergence for higher activity, i.e., MSD(\u03c4)\u00a0~\u2009L\u03b6. The exponent \u03b6 seems to be increasing systematically with increasing activity f0 reaching to \u03b6\u00a0~\u20090.9 for f0\u2009=\u20092.0. We obtained similar results by varying c and \u03c4p while keeping f0 constant (see Supplementary Note\u00a0XV for additional data). Thus, the results appear to be independent of the particular choice of the activity parameter.\n\na Mean square displacement (MSD) as a function of time for activity f0\u2009=\u20092.0 at temperature T\u2009=\u20090.221 with system size ranging from 103 to 105, here it shows increase in MSD plateau much faster than the passive system (Inset) at temperature T\u2009=\u20090.430. b Two-point density correlation Q(t) shows faster relaxation for the case of the active system than the passive system (Inset) for large system size. c MSD(\u03c4) plateau diverges faster than log(L), increasing activity. Inset: At larger activity, it starts to\u00a0show power-law behaviour, unlike the log(L) behaviour of its passive counterpart. For f0\u2009=\u20092.0 the power exponent is \u03b6\u2009=\u20090.86. a\u2013c are for the 2dmKA system. d MSD as a function of time for active polycrystalline system for f0\u2009=\u20092.0 at temperature T\u2009=\u20090.01 for system ranging from 102 to 104, it shows faster increase in MSD plateau than passive system (Inset) at the same temperature, similar to (a). e For an active polycrystal, we can get a similar power-law of MSD plateau divergence, here for activity f0\u2009=\u20092.0, the exponent is \u03b6\u2009=\u20091.65. f Diffusivity as a function of relaxation time shows a power-law exponent \u03ba\u2009=\u20091.35. This breakdown of the Stokes-Einstein relation (\u03ba\u2009>\u20091.0) is possible due to the presence of long-wavelength phonon fluctuation in 2D, which is also valid for active systems as well. We again get back the Stokes-Einstein relation with \u03ba\u2009\u2243\u20091.0 when we do cage-relative diffusivity and relaxation time calculations, again showing the presence of phonon like excitations in active liquids as well. Error bars in the figure panels are measured by computing the standard deviation (SD) of fluctuations in various statistically independent simulations.\n\nThese strong effects of long-wavelength fluctuations in disordered systems led us to investigate their effect on active polycrystalline solids. Our findings, presented in Fig.\u00a01d, show that the plateau value of the mean squared displacement (MSD) significantly increases with system size in the presence of activity (f0\u2009=\u20092.0), while for passive systems, the growth of the MSD plateau follows a logarithmic divergence (see inset). For the polycrystalline model, we keep the temperature of the system at T\u2009=\u20090.01 to ensure that the solid remains in the polycrystalline minimum. We also plotted the MSD plateau values as a function of L in a double logarithmic plot, presented in panel e of the same figure, which shows that the divergence of MSD plateau or the Debye-Waller factor in active crystals grows as a power-law in system size, similar to the results found in disordered systems. The exponent \u03b6\u2009~\u20091.65 seems to be stronger than the exponent obtained in disordered solids. These results suggest that even for out-of-equilibrium systems, the activity-induced enhancement of the MWH fluctuations is not sensitive to details of the structural ordering.\n\nWe now briefly study whether the long wavelength fluctuations affect the dynamics of active liquids at high enough temperatures where the effect of active force is much weaker than the thermal fluctuations, we have plotted the diffusivity (D) as a function of characteristic relaxation time, \u03c4\u03b1 (see Methods Section). Indeed, D vs \u03c4\u03b1 plot in Fig.\u00a01(f) shows a power-law relation with exponent (\u03ba) larger than 1 (\u03ba\u2009\u2243\u20091.35) at high temperatures and then at supercooled temperature regime \u03ba\u2009\u2243\u20090.80 showing the well-known fractional Stokes-Einstein relation. The violation of SE relation at high temperature with \u03ba\u00a0>\u00a01.0 again corroborates with the previous observation of the effect of long wavelength phonon fluctuations in 2D passive liquids, including colloidal glasses in experiments10.\n\nThus, the effect of long-wavelength modes persists even for active liquids. In the inset of the same figure panel, we plot D vs \u03c4\u03b1 after removing the long wavelength fluctuations by computing cage-relative methods as discussed in the Methods section. One sees the validity of the SE relation with \u03ba\u2009\u2243\u20091 at high temperature, this reinforces the presence of phonon-like (long wavelength modes) excitations in active liquids, even at high-temperature. Note that in ref. 10, it was claimed that in the overdamped Brownian limit, the effect of MWH fluctuations are completely suppressed in the liquid dynamics and thus one does not see any effect of MWH fluctuations in high temperature liquid in that limit. However, we show later that MWH fluctuations in solids persist even in the overdamped regime, and activity enhances these fluctuations irrespective of the detailed nature of activity.\n\nTo establish that the non-trivial effect of the increase in MSD plateau is due to long-wavelength excitation modes on a firm ground, we have measured cage relative MSD (CR-MSD) to remove the effect of these modes from our measurements. In Fig.\u00a02a, b, we are showing the CR-MSD as a function of time given for passive and active glassy systems, respectively. The CR-MSD plateau, defined as CR-MSD(\u03c4), shows no divergence with system size for both the passive and active systems uniformly, as illustrated in Fig.\u00a02c on a log-log scale and (d) on a linear-log scale. From this, one can be assured that the increase in MSD plateau is solely due to the long-wavelength mode in the system, which is absent in the cage-relative measurements. Thus, increasing MSD(\u03c4) with the system size is a consequence of the MWH fluctuations even in non-equilibrium systems.\n\nCage-relative MSD for (a) passive and (b) active systems. CR-MSD plateau plotted in log-log (c) lin-log (d) clearly shows no changes with increasing system size. This can be considered as proof that the increase in MSD plateau is due to long-wavelength modes in the system, which is absent in the cage-relative measurements. Error bars in the figure panels are measured by computing the standard deviation (SD) of fluctuations in various statistically independent simulations.\n\nGNF is a well-known characteristics of active systems in which particle density show disproportionately large fluctuations, deviating from typical equilibrium Gaussian statistics. In equilibrium, the standard deviation of the particle number (\\(\\Delta N=\\sqrt{\\langle {N}^{2}\\rangle -{\\langle N\\rangle }^{2}}\\)) and its corresponding average particle number (\\(\\left\\langle N\\right\\rangle\\)) in a sub-volume of linear size l \u00a0<\u2009(L) is related by \\(\\Delta N\\propto {\\left\\langle N\\right\\rangle }^{\\delta }\\), where \u03b4\u2009=\u20091/2 according to the central limit theorem. But the active systems are inherently in the out-of-equilibrium state and thus need not follow the same equilibrium relation; for systems with anisotropic particles, the exponent \u03b1 is found to be greater than 0.546 in the polar order phase. Recently in ref. 47, it was shown that GNF can arise in the fluid phase of active Brownian particles (ABP), where the polar order is absent at a low number density of \u03c1\u2009=\u2009N/V\u2009=\u20090.5. In our system, we also observed the presence of GNFs in the low-density limit, but as we increase the density, GNF disappears.\n\nIn Fig.\u00a03a we have plotted \u0394N vs \u3008N\u3009 for the 2dmKA model system with N\u2009=\u200925000 particles. The exponent \u03b1 increases from 0.3 to 0.46 with increasing activity, indicating the presence of hyper-uniform structures in these density regime. Thus, for the 2dmKA model at the studied density, although the MSD plateau diverges with system size, it does not show any GNFs in the same activity limit. The results in 3D are very similar, with the exponent varying from 0.40 to 0.46 with increasing activity in the 3dKA model (see Fig.\u00a03b). These results are in complete agreement with the results reported in ref. 48 for the same model. It was also pointed out that these class of systems show an effective hyper-uniform behaviour over a certain length scale as evidenced from the structure factor (S(q) data), which shows a power-law like behaviour with wave vector (q) as S(q)\u00a0~\u00a0q\u03b2 over a range of small q but eventually saturates to a constant at much smaller q48,49. The exponent \u03b2 is known to be connected to number fluctuations as \\(\\Delta N\\propto {\\left\\langle N\\right\\rangle }^{(d-\\beta )/2d}\\), where d is the spatial dimensions. Thus, \u03b4\u2009=\u2009(d\u00a0\u2212\u00a0\u03b2)/2d. Thus, the hyperuniform behaviour found in our systems in both 2D and 3D are probably of this effective hyperuniform universality class, showing a moderate suppressed density fluctuations.\n\na In equilibrium, the fluctuations of the number of particles, \u0394N and average number of particles, \\(\\left\\langle N\\right\\rangle\\) in a given sub-volume of linear size l \u00a0<\u2009(L) is related to each other as \\(\\Delta N\\propto {\\left\\langle N\\right\\rangle }^{\\delta }\\) with \u03b4\u2009=\u20090.5 in accordance with the central limit theorem. Interestingly, in a disordered system, this exponent \u03b4 tends to vary from 0.3 to 0.46 with increasing activity in the 2dmKA model. b GNF for the 3DKA in the densed limit with changing activity, which shows the exponent is limiting towards the passive normal liquid case of 0.5. c Shows the variation of \u03b4 from 0.65 to 0.55 with increasing activity in the 2dR10 model with only repulsive inter-particle interactions at \u03c1\u2009=\u20090.5. The curves are shifted by a scale factor of (1.5) with respect to each other for better readability. d Shows that GNFs are absent in high density, even with activity (see text for detailed discussion). e Shows for the 2dR10 model, one sees the exponent \u03b4 to increase beyond 0.5 with decreasing density, indicating some presence of GNFs. But at very high density and low temperature, the exponent (\u03b4) reaches 0.3 for a disordered glassy system similar to the 2dmKA model. Error bars in the figure panels are measured by computing the standard deviation (SD) of fluctuations in various statistically independent simulations.\n\nTo understand it further, we have taken another model of glass-forming liquids with purely repulsive interaction between particles (referred here as the 2dR10 model; see Methods section for details) and varied the number density from low to high. The reason for choosing another model is that the 2dmKA model at low density is known to show phase separation dynamics, and we wanted to study a system which is uniform at all studied density. In Fig.\u00a03c, we show results for the 2dR10 model at \u03c1\u2009=\u20090.50 with increasing activity, the exponent \u03b4 is nearly 0.55, which shows mild GNFs. Figure\u00a03d at high density, \u03c1\u2009=\u20090.85 shows the exponent \u03b1 to increase from 0.3 to 0.5 with increasing activity, which again does not show any sign of GNFs. While changing density, we obtained exponent \u03b4\u2009\u2243\u20090.3 and 0.6 in the high and low-density limit, respectively, as shown in Fig. 3e.\n\nBy choosing \u03b4\u2009=\u20090.3 for the passive 2dmKA system as the baseline value, one might argue that an increase in \u03b4 with activity will hint at GNFs, but our interpretation is that activity systematically reduces the degree of hyperuniformity within the studied activity limit, as \u03b4\u2009>\u20090.5 is referred to as GNFs by definition. Also, faster than logarithmic divergence in 2D and logarithmic divergence in 3D of the Debye-Waller factor clearly highlight that the activity of the system enhances the MWH rather than other possible fluctuations. We do not know any theoretical argument that suggests that GNFs can lead to different types of divergence in the Debye-Waller factor in 2D and 3D systems in the studied parameters regime. Whereas our results can be very well rationalised using MWH fluctuations. Thus, we propose that GNFs do not play a major role on the observed increase in MSD plateau with increasing system size, rather it is the MWH fluctuations that are dominant.\n\nTo investigate the long-wavelength (phonon-like) behaviour of the active crystals and glasses, we have simulated both systems at very low temperatures and employed a method to compute the effective dynamical matrix of the systems. This involved calculating the displacement-displacement covariance matrix, as explained in refs. 50,51. The covariance matrix (\\({{\\mathcal{C}}}\\)) is defined as \\({C}_{ij}^{\\mu \\nu }=\\langle {u}_{i}^{\\mu }{u}_{j}^{\\nu }\\rangle\\), where ui represents the displacement of the ith particle from its average position obtained by averaging over configurations from low-temperature MD data starting from a single minimum energy configuration, this configuration is found to remain close to the initial minimum configuration (minimum position). and \u03bc or \u03bd represent the spatial dimensions (such as x or y in 2D). The symbol \u3008 \u22ef \u2009\u3009 denotes both the ensemble and time averaging. While exploring different approaches to computing the dynamical matrix, we found that the force-force correlation matrix method did not work well for active systems, as active force can not be derived from a potential, as an active system lacks a Hamiltonian structure. However, this method proved to be very useful for passive systems (see detailed discussion in the Supplementary Note VIII, IX). Therefore, we focused on the results obtained using the displacement-displacement covariance matrix, which, although computationally expensive, provided a good description of the system at a coarse-grained timescale. To obtain better convergence, we computed the correlation matrix at a timescale as small as a few molecular dynamics (MD) steps up to the largest timescale accessible. This is important to get good convergence at all frequencies. We performed computations on energy-minimised structures for the disordered systems, as well as polycrystalline solids, which we studied. This approach ensured that the matrix\u2019s eigenvalues were all positive, allowing us to validate the method\u2019s accuracy. Further details on this method can be found in the Method section and the Supplementary Note\u00a0VIII, IX. Note that the energy-minimised structures or the Inherent Structures (IS) are found to be good minimum structures even in the presence of active forces. We have discussed these findings in the Supplementary Note\u00a0XIII.\n\nIn Fig.\u00a04a, we show the vibrational Density of State (vDoS) obtained from the dynamical matrix \\({{\\mathcal{C}}}\\) averaged over 64 independent ensembles. The DoS obtained from the Hessian matrix \\({{\\mathcal{H}}}\\) (see \u201cMethods\u201d for the definition) is labelled as \u201cHessian\", and the one obtained using displacement-displacement correlation is labelled as \u201c\u3008uiuj\u3009 matrix\". Within Harmonic approximation, one can show \\({{\\mathcal{C}}}=(1/T){{{\\mathcal{H}}}}^{-1}\\) (details in the Supplementary Note\u00a0IX), where T is the temperature at which the displacement-displacement correlation matrix is obtained. The dynamical matrix does not depend on the particular choice of temperature as long as the temperature is low enough that the Harmonic approximation is a faithful description of the system, and during the simulation timescale, it does not escape out of the minimum.\n\na vDoS computed for passive systems for N\u2009=\u20091000 particles using exact diagonalization of the Hessian matrix (black circle), averaged displacement-displacement correlation matrix (orange square) and corrected via random matrix procedure (red diamond) (see text for details). The close agreement between these measurements suggests that the displacement-displacement correlation matrix method with random matrix correction is a robust method for the computation of vDoS. b, c vDoS is computed using the displacement correlation matrix method for active systems with increasing activity. The clear appearance of small frequency (\u03c9) peaks in the vDoS with increasing activity signals the increasing dominance of phonon-like modes. The increasing weight of vDoS at small \u03c9 also indicates a jamming to unjamming scenario. d, e vDoS computed for passive and active polycrystalline samples, respectively. f The measured MSD was compared with the computed MSD from the correlation matrix. The excellent agreement suggests the validity of the effective dynamical matrix description of these active systems even at a significant degree of activity. g shows the same comparison for amorphous solids. Inset (f) and (g) highlight the importance of small \u03c9 modes in determining the plateau value of the MSD for all activities. h, i Comparison of plateau values vs system size, L, as obtained from MD simulations and from effective dynamical matrix description. This proves that dramatic Mermin-Wagner-Hohenberg (MWH) fluctuations in the active matter are due to the phonons, and thus, the deviation of the MWH theorem has to come from the details of the phonon dispersion relation. (f) (h) is for a polycrystalline system, and (g, i) is for an amorphous solid. Error bars in the figure panels are measured by computing the standard deviation (SD) of fluctuations in various statistically independent simulations.\n\nIn Fig.\u00a04a, we present a corrected vDoS referred to as \u201cextrapolated\". We corrected the vDoS using a random matrix protocol, as described in detail in refs. 50,51 (see Supplementary Note\u00a0VIII). In brief, we obtained the \\({{\\mathcal{C}}}\\) matrix and calculated the eigenvalues (\u03bb\u2009=\u2009\u03c92) of the matrix via a numerical diagonalization procedure at various degrees of increasing ensemble averaging. Then, we re-estimated these eigenvalues via extrapolation to the infinite ensemble averaging limit based on results on random matrix theory, which suggest that the eigenvalues reach their true limiting values linearly with increasing averaging. The corrected vDoS matches well with the vDoS computed from the Hessian matrix, as shown in panel A. We then used this procedure to obtain the dynamical matrix for active systems with increasing activity, as shown in Fig.\u00a04b. We observed that the vDoS develops significant weight at smaller \u03c9, along with sharp peaks resembling the prominence of phonon-like modes at lower frequencies. The peaks become sharper and stronger with increasing activity. We represented the vDoS in a double logarithm plot in Fig.\u00a04c to highlight the similarity with the vDoS for jammed states approaching the unjamming transition. While it is true that with increasing activity, one approaches fluidisation, the unjamming transition does not promote phonon-like excitations in the system. A detailed analysis is required to understand this similarity. In Fig.\u00a04d, we show our results for polycrystalline samples without activity, and panel (e) shows the same with increasing activity, echoing the similar observation of enhanced phonon excitations with increasing activity.\n\nAfter reliable numerical computation of the vDoS, we wanted to verify the validity of this effective dynamical matrix in describing the dynamics by computing the MSD from the obtained eigenvalues and eigenvectors of the dynamical matrix, \\({{\\mathcal{C}}}\\) as (see Supplementary Note\u00a0VII for the derivation)\n\nwhere \\({{{\\bf{P}}}}_{i}^{a}\\) is the ith component of the eigenvector a and \u03c9a is the corresponding eigenvalue. In Fig.\u00a04f, g, we compare the computed MSD from Eqn. (1) with the one obtained from the molecular dynamics (MD) simulation trajectories for polycrystal and amorphous solids, respectively. We observe a perfect match for the passive case, and a very good match for the active system. However, increasing activity strength shows a mismatch at intermediate timescales when the system transitions from the ballistic to the plateau regime. Despite this, both the short-time ballistic regime and the plateau regime are well-captured by the effective dynamical matrix. Although it is not immediately clear why there is a discrepancy at the intermediate timescale when the system transitions from the ballistic to the plateau regime, we believe that with increasing strength of the active forcing, the system takes a longer time to lose its memory of active driving in the particle displacement. Note that in the dense disordered limit, both f0 and \u03c4p play important role in determining the cage size, which is shown to control the dynamical behaviour of the active glasses52. At a longer timescale, the system will start to move towards the diffusive regime, and we believe that an effective dynamical matrix description will be a poor description. Thus, we expect that if we take only a few of the low-frequency modes of \\({{\\mathcal{C}}}\\) and compute the MSD, we can correctly capture the plateau values for all activities. In the inset of Fig.\u00a04f, g, we show that the MSD computed using Eqn. (1) with only the first 10% of the low-frequency modes indeed correctly captures the plateau value for all activities. These results suggest that the long-time behaviour of the active system confined in a potential minimum can be accurately described by an effective dynamical matrix.\n\nAfter establishing the effective phonon-like description of the active systems at low temperatures and thereby providing strong evidence of phonons being the main reason behind the observed enhancement of MWH fluctuations, we want to understand the primary cause for the breakdown of the MWH theorem. Suppose we assume the basic mechanisms of the MWH theorem arguments hold through. In that case, one expects that the phonon dispersion relation, which gives the dependence of \u03c9 on the wave vector q must get modified due to the presence of active forces as follows:\n\nwhere we have assumed \u03c9(q)\u2009~\u2009q\u03b1. In the limit \u03b1\u00a0\u2192\u00a01, we get back the usual logarithmic dependence, but for 2\u03b1\u00a0>\u2009d, we will have a power-law divergence of the MSD with increasing system size. Thus, a non-linear phonon-dispersion relation in active systems can rationalise the observation. Microscopic understanding of why one expects a non-linear phonon dispersion relation is not clear immediately (see discussion on non-linear phonon dispersion section).\n\nIn Fig.\u00a05a, we show the heat map of the \u03c9 vs q for the longitudinal spectrum of the polycrystalline samples with activity f0\u2009=\u20091.0. In panel (b) shows the peak of the heat map, giving us the phonon dispersion relation of \u03c9(q). The linear phonon dispersion relation for passive systems is very clear, and increasing non-linearity in active systems is also very evident, Inset: log-log plot of the same plot shows the power-law exponent of the dispersion relation. For activity f0\u2009=\u20091.0, \u03b1\u2009~\u20091.8. In panel (c), we show the dispersion relation for the transverse spectrum of the polycrystalline samples, the exponent \u03b1\u2009~\u20091.74. Panel (d) shows the heat map of the \u03c9 vs q for the longitudinal spectrum of the amorphous solid f0\u2009=\u20091.0, and panel (e) shows the dispersion with \u03b1\u2009~\u20091.47, with an inset showing the data in double logarithm. Panel (f) shows similar results but for transverse phonons with \u03b1\u2009~\u20092.0. Results obtained for polycrystalline solids are in close agreement with the results obtained for amorphous solids and corroborate the robustness of an effective dynamical matrix description of the observation. Exponent \u03b1\u00a0~\u00a01.8 for polycrystalline samples quantitatively describes the divergence of the MSD plateau with L as shown by the solid curve in Fig.\u00a01e). Similarly, if we choose \u03b1\u00a0~\u00a01.5 for disordered solids, then we get \u03b4\u2009~\u20091.0, which is close to the exponent obtained from MD data. Further details are given in the Supplementary Note\u00a0XI.\n\na Heat map of the \u03c9 vs q for the longitudinal spectrum of the polycrystalline samples with activity f0\u2009=\u20091.0. b shows the peak of the heat map, giving us the phonon dispersion relation of \u03c9(q). The linear phonon dispersion relation for passive systems is very clear, and increasing non-linearity in active systems is also very evident, where for activity f0\u2009=\u20091.0 exponent is \u03b1 \u2243 1.8. Inset: log-log plot of the same. c the transverse spectrum of the polycrystalline samples for activity f0\u2009=\u20091.0 shows the dispersion exponent \u03b1\u2009\u2243\u20091.74. d we show the heat map of the \u03c9 vs q for the longitudinal spectrum of the amorphous solid samples with activity f\u2009=\u20091.0. e for amorphous solids samples with varying degrees of activities. The spectrum is obtained for the longitudinal phonons, and the inset shows the results in a log-log plot. The exponent of the power law relation for activity f0\u2009=\u20091.0, \u03b1 \u2243 1.47. f shows the similar results, but for transverse phonons with activity f0\u2009=\u20091.0, the exponent value is \u03b1\u2009\u2243\u20092.0. Error bars in the figure panels are measured by computing the standard deviation (SD) of fluctuations in various statistically independent simulations.\n\nTo understand the effect of MWH fluctuations on dimension for both disorder glassy systems and for polycrystalline systems, we have done a similar study in 3D. In Fig.\u00a06a, we have shown the vibrational density of states (vDoS) calculated using the exact hessian (black circle) and the effective dynamical matrix (orange square) for a passive glassy system of system size N\u2009=\u20091000 in 3D. Here, with increasing the frame numbers, the vDoS tends to converge to the exact Hessian curve, which has been shown using the extrapolated vDoS (red diamond). Again, in Fig.\u00a06d, we can observe similar characteristics for polycrystalline solids. Now, taking a large enough number of frames, we can get the dynamical matrix for the active system as well. Figure\u00a06b shows the vDoS computed from the effective dynamical matrix for an active glassy system; here, for higher activity, the weight of the low frequency (\u03c9) modes increases significantly with increasing activity as in 2D systems. Similar outcomes are observed in Fig.\u00a06(e) for polycrystalline solids with an increase in the spectral weight of the low-frequency modes. Thus, in 3D also, activity plays a significant role in enhancing the low-frequency modes. Revisiting the Debye-Waller factor, which is proportional to the MSD plateau shown in Fig.\u00a06c, f for glassy and polycrystalline systems, respectively, we observe a diverging behaviour with system size when activity is present and no divergence for the passive case. The divergence of the MSD plateau increases with increasing strength of activity. Note that for all activity, one observes logarithmic divergence with system size (L). This is the first observation in the 3D system showing the logarithmic divergence of the plateau due to MWH fluctuations in active solids, which is induced solely by the presence of activity. This striking result is a novel observation, showing that MWH fluctuations can persist in higher dimensions (d\u2009>\u20092) for active glassy and polycrystalline systems.\n\na vDoS calculated using exact Hessian (black circle) and the effective dynamical matrix (orange square) for a passive glassy system of system size N\u2009=\u20091000 in 3D. Increasing the frame numbers leads to better convergence to the result from the exact Hessian, which has been shown using the extrapolated vDoS (red diamond). Similarly, results are shown in (d) for polycrystalline solid. b Using the effective dynamical matrix analysis for active glassy systems, we observe that for higher activity the low energy frequency (\u03c9) modes show higher spectral weights. This proves that the activity has a strong effect on the low frequency long-wavelength modes. Similar results are shown in (e) for polycrystalline solid. c MSD plateau shows the diverging behaviour with system size from MD simulation (bold), and MSD computed analytically using all modes of effective dynamical matrix also follows the same behaviour (empty). The strength of the logarithmic divergence increases with activity with no divergence in passive system. This is probably the first observation of logarithmic divergence of MSD plateau in a 3D disordered system indicating a possible shift in lower critical dimensions (dl). Similar results are shown in (f) for a polycrystalline system (see text for details). Error bars in the figure panels are measured by computing the standard deviation (SD) of fluctuations in various statistically independent simulations.\n\nObserved logarithmic divergence of MSD plateau with system size in 3D indicates the phonon dispersion relation to be independent of dimensionality. To investigate the effect of the dimension on the effective dispersion, we explicitly computed the effective dispersion in 3D. This can be thought of as another validity test of the correctness of our proposed effective Hessian description. Using the eigenvectors of the dynamical matrix, we reconstructed the effective dispersion relation of the glassy and polycrystalline solid. We get back the linear dispersion relation (\u03c9 \u221d q) for a passive system in the q\u00a0\u2192\u00a00 limit for both the transverse as well as longitudinal spectrum of the system. For active glassy and polycrystalline solids, it follows \u03c9 \u221d q\u03b1 where, \u03b1\u2009>\u20091. The exponent \u03b1 for the active glassy system for transverse and longitudinal modes are 1.75 and 1.7, respectively, shown in Fig.\u00a07a, b). Similarly, for polycrystalline systems also, transverse and longitudinal modes are non-linear with \u03b1 around 1.96 and 1.56, respectively shown in Fig.\u00a07c and d).\n\nFrom the effective dynamical matrix, we constructed the effective dispersion relation of the glassy and polycrystalline solid. For the passive system, the dispersion relation in the q\u00a0\u2192\u00a00 shows the linear (\u03c9 \u221d q) dispersion relation for both the system for transverse as well as longitudinal spectrum. And for active glassy and polycrystalline solid it follows (\u03c9 \u221d q\u03b1) for active glassy and polycrystalline system. The exponent \u03b1 for glassy system (a) transverse spectrum shows 1.75, (b) longitudinal spectrum 1.7. Similarly, for polycrystalline system, (c) transverse spectrum shows 1.96 and (d) longitudinal spectrum 1.56. And all inset-plots is the log-log scale of its corresponding plots to the increasing nature of the exponent with activity.\n\nTo establish the robustness of the observed results across a broad class of active matter systems, both natural and synthetic active particles, we looked at the MWH fluctuations in active solids with active Brownian particles (ABPs). In a recent work in ref. 53, it was shown that passive colloidal particles (colloidal glasses), which perform Brownian motion, are not in the overdamped limit, as one finds Mermin-Wagner-Hohenberg (MWH) fluctuations in two-dimensional (2D) colloidal assembly. Thus, it is clear that active colloidal systems will be in the limit where inertia still plays an important role, and thus it is imperative to study active systems with Brownian particles both in the underdamped and overdamped limit to see whether results obtained for active solids with RTPs are applicable for active solids with ABPs. Also in ref. 54, it has been shown that RTPs embedded in a gel anneal the gel at an unprecedented rate, which are very hard to achieve via usual thermal annealing, in close agreement with the results reported in ref. 55, where it was demonstrated that annealing by run-and-tumble active particles has a strong similarity with annealing via oscillatory shear. These results suggest that active gels will also have enhanced MWH fluctuations in 2D and probably even in 3D systems. On the other hand, the collection of cells in a monolayer will be in the overdamped regime. Thus, MWH fluctuations in active solids with Brownian particles at various damping conditions will be very relevant for understanding experimental results in the field.\n\nWe performed extensive Brownian dynamics simulations and studied active Brownian particles (ABPs) in the dense glassy regime as well as in polycrystalline solid states in the damped and overdamped limit. Following the protocol used in ref. 53. The equation of motion we use for simulating ABPs are\n\nwhere each particle has mass m interacting via the Lennard-Jones potential as in the 2dmKA model. The ith particle is subjected to a friction \u03b3 and a thermal noise \u03b6i with zero mean and variance \\(2{k}_{B}T\\gamma \\delta (t-{t}^{{\\prime} })\\) that obeys fuctuation-dissipation relation. Each particle has a self-propulsion force f\u2009=\u2009f0n whose direction \\({{\\bf{n}}}\\equiv \\left(\\cos \\theta,\\sin \\theta \\right)\\) undergoes rotational diffusion with time evolution of \u03b8i is being described by an athermal noise \u03bei, with zero mean and correlation \\(\\langle {\\xi }_{i}(t){\\xi }_{j}({t}^{{\\prime} })\\rangle=2{\\tau }_{p}^{-1}{\\delta }_{ij}\\delta (t-{t}^{{\\prime} })\\). We fixed \u03b3\u2009=\u20091.0 and \u03c4p\u2009=\u200920.0 for the damped case. In Fig.\u00a08a we show results for the same poly-crystalline model as before, with a number density of \u03c1\u2009=\u20091.2. For passive system (black circle) we see logarithmic divergence. Then we considered ABPs in two scenario: one where all particles were active (blue diamond) and another where only 50% of the particles have active forcing (red square), for both the cases we fixed f0\u2009=\u20092.0. We see faster than logarithmic divergence in both cases and in the case where all the particles are active, the power-law exponent is \u00a0~\u20092.37. In Fig.\u00a08b, we considered active Brownian particles in dense glassy regime. Again, we observed power-law divergence in the active case, where we set f0\u2009=\u20090.5 with all particles active. The exponent obtained is 1.18. We could not set the active force to a larger value in the polycrystalline case as the particles were escaping from the sallow minimum.\n\na Shows mean squared displacement (MSD) plateau for the polycrystaline system with \u03b3\u2009=\u20091.0 and f0\u2009=\u20092.0, exhibits a logarithmic divergence for passive Brownian particles and a faster-than-logarithmic divergence for active Brownian particles (ABP), with an exponent of 2.37. We vary the active particle concentration c from 0.0 to 1.0. b MSD plateau for 2dmKA model glass with \u03b3\u2009=\u20091.0 has an exponent of 1.18 for active systems and a usual logarithmic divergence for passive systems. c In the extreme overdamped limit (\u03b3\u2009=\u2009100.0), the divergence of the MSD for an active polycrystal has an exponent of 1.32. In inset we show MSD plateau for passive and active Brownian particles in 2dmKA system in the extreme overdamped limit.\n\nSince MWH fluctuations are an equilibrium property, they do not depend on the microscopic dynamics of equilibrium solids. However, the manner in which the mean squared displacement approaches its asymptotic value does depend on the dynamics, but not the asymptotic value itself. Thus, it is much easier to observe signatures of these fluctuations in the dynamic properties with underdamped rather than overdamped dynamics. Hence, the question of possibility of observing these signatures with biologically relevant overdamped dynamics is an important question to address. To study the system in the heavily overdamped limit, we fixed \u03b3\u2009=\u2009100.0 and active particle concentration to be c\u2009=\u20091.0 following53 and studied poly-crsytal and glass with and without active forcing. We see power-law divergence for active systems (see Fig.\u00a08c), for poly-crystalline system we get exponent of \u00a0~\u20091.32. For glassy systems we could not again increase the forces to larger values due to sallowness of the minima compared to the polycrystalline minima, but even there MSD plateau grows as L1/2 in the large damping limit. In ref. 47, a fully damped ABP model was considered at zero temperature (T\u2009=\u20090) to show that MWH fluctuations becomes stronger than passive systems in the infinite persistence limit (\u03c4p\u00a0\u2192\u00a0\u221e). These results clearly suggest that the stronger than logarithmic divergence of position fluctuations with systems size in 2D seem to be very generic result applicable to a broad class of active matter systems including many biological systems.\n\nLastly, to understand whether the lower critical dimensions of system dl is shifted from dl\u2009=\u20092 in passive system to dl\u2009=\u20093 in active solids or it is an intrinsic property of active systems at any dimensions to show anomalous dynamical properties, we also simulated the Kob-Andersen model in four spatial dimensions (4D) at low-temperature T\u2009=\u20090.01 at density \u03c1\u2009=\u20091.2. In Fig.\u00a09, one can observe that the MSD(p) for the passive system decreases with increasing system size, reaching to an asymptotic value that is independent of system size. For large activity, the MSD(p) also decreases with system size from its smaller system size value before saturating to a constant in the large system size limit. Thus, in 4D system, the fluctuations seem to be normal, proving beyond doubt that the lower critical dimensions of the system increased from 2 to 3 in active solids with RTP-type active forcing. This is the first time we can show that the activity can shift the lower critical dimension (dl) of the system to 3 in the studied activity limit. To gain deeper insight, we followed the approach by ref. 56 and conducted a similar analytical calculation to compute the mean-squared displacement for an active system within linear response formalism. In their work, Henkes et al.56 used continuum active linear elasticity and a normal modes formalism to understand the results obtained in epithelial cell monolayers experiments. This allowed them to calculate velocity correlations over distance analytically. Building on this approach, we also derived an analytical expression for the mean squared displacement (MSD) in the following section.\n\nAbsence of MWH fluctuations in amorphous solids in four dimensions (4D). One can observe that the MSD plateau for the passive system decreases with increasing system size, reaching a constant independent of system size. For large activity, the MSD plateau also shows behaviour similar to passive systems, albeit at a higher value, but no divergence with system size is observed. This shows that activity does not destabilise an active solid in 4D. Error bars in the figure panels are measured by computing the standard deviation (SD) of fluctuations in various statistically independent simulations.\n\nAlthough we are not able to offer a microscopic theory to explain the non-linear phonon dispersion relation, an interesting connection to vDoS in fractals is worth considering. If we assume that the underlying structures that support the phononic excitations in these non-equilibrium solids are fractals with fractal dimensions less than 2 in 2D, and then it can be argued that vDoS on these fractals \u00a0~\u2009\u03c91/3\u200957 (also see refs. 58,59,60 on percolating network in d\u2009=\u20092). Assuming such a vDoS, phonon dispersion relation for a finite size system with linear size L will read as \u03c9\u00a0~\u2009q3/2 with q being the wave vector. For a general non-linear phonon dispersion of the form \u03c9(q)\u00a0~\u2009q\u03b1, vDoS will have the following form\n\nSee Supplementary Note\u00a0XII for detailed derivation. If we now assume that \u03b1 \u2243 1.5, then we get \\({{\\mathcal{D}}}(\\omega ) \\sim {\\omega }^{1/3}\\) and cumulative vDoS going as \u03c94/3. In Supplementary Note\u00a0XII, we showed that the cumulative vDoS Pc(\u03c9)\u00a0~\u2009\u03c92 for the passive system, whereas the active system shows much weaker dependence on \u03c9. Thus, an enhanced vDoS at a small frequency could be a possible explanation for the non-linear phonon dispersion relation. Our effective Hessian calculation suggests that the phonon modes remain very similar in the presence of active forces but a lot many modes became de-localised as well as their frequency became smaller. Although the question of underlying fractal networks controlling the vibrational properties of these active solids remains to be validated but enhanced force-chain networks in the presence of active forces and their role in determining the mechanical properties of these solids will be interesting to investigate.\n\nIn ref. 56, the long wavelength modes for a monolayer of soft, self-propelled agents packed densely using a dynamical matrix approach were calculated. Using the same framework, we computed the mean-squared displacement (MSD) for the same damped active Brownian system. Below, we briefly discuss the salient parts of the calculation for the completeness of the discussion in the subsequent sections. The equation of motion for particles can be written as\n\nwhere, \u03b6 is the friction coefficient, \\({{{\\bf{F}}}}_{i}^{{{\\rm{act}}}}\\) is the net active force \\({{{\\bf{F}}}}_{i}^{{{\\rm{int}}}}\\) is the force on i-th particle exerted by its neighbours.\n\nNext, Linearising the interaction forces in the vicinity of an energy minimum, \\(\\{{{{\\bf{r}}}}_{i}^{0}\\}\\) by introducing \\(\\delta {{{\\bf{r}}}}_{i}={{{\\bf{r}}}}_{i}-{{{\\bf{r}}}}_{i}^{0}\\). Eq. (6) becomes\n\nwhere, v0 is the active velocity and \\({{{\\bf{K}}}}_{ij}=\\frac{{\\partial }^{2}V(\\{{{{\\bf{r}}}}_{i}\\})}{\\partial {{{\\bf{r}}}}_{i}\\partial {{{\\bf{r}}}}_{j}}\\) is the dynamical matrix. Now Kij has 2N number of normal modes \u03be\u03bd, with positive eigenvalues \u03bb\u03bd and we can project Eq. (7) onto it, and obtain\n\nwhere, \\({a}_{\\nu }={\\sum }_{i}\\delta {{{\\bf{r}}}}_{i}\\cdot {{{\\mathbf{\\xi }}}}_{i}^{\\nu }\\) and active force are projected onto the modes, \\({\\eta }_{\\nu }=\\zeta {v}_{0}{\\sum }_{i}{\\hat{n}}_{i}\\cdot {{{\\mathbf{\\xi }}}}_{i}^{\\nu }\\) is the active persistence noise with \\(\\langle {\\eta }_{\\nu }(t){\\eta }_{\\nu }^{{\\prime} }({t}^{{\\prime} })\\rangle=C(t-{t}^{{\\prime} }){\\delta }_{\\nu {\\nu }^{{\\prime} }}\\). The correlation function \\(C(t)=\\frac{{\\zeta }^{2}{v}_{0}^{2}}{2}{e}^{-t/{\\tau }_{p}}\\) with \u03c4p being the persistence time. Now, we can integrate Eq. (8) and obtain the moments of a\u03bd and from there we can compute the MSD as\n\nwhere, \u03be\u03bd(q) is Fourier transform of eigenvector \\({\\xi }_{i}^{\\nu }\\).\n\nNext we turn to continuum limit to write Eq. (7) as\n\nwhere, u(R) denotes the elastic deformations from the equilibrium position R, and \\({{\\bf{D}}}({{\\bf{R}}}-{{{\\bf{R}}}}^{{\\prime} })\\) is the continuum dynamical matrix. Fourier transforming Eq. (10) and writing \\({\\tilde{{{\\bf{F}}}}}^{act}({{\\bf{q}}},\\omega )=\\zeta {v}_{0}\\int\\,{d}^{2}{{\\bf{r}}}\\int_{-\\infty }^{\\infty }dt\\hat{n}({{\\bf{r}}},t){e}^{i{{\\bf{q}}}\\cdot {{\\bf{r}}}+i\\omega t}\\), one can calculate the correlation of active noise, \\(\\left\\langle {\\tilde{{{\\bf{F}}}}}^{act}({{\\bf{q}}},\\omega )\\cdot {\\tilde{{{\\bf{F}}}}}^{act}({{{\\bf{q}}}}^{{\\prime} },{\\omega }^{{\\prime} })\\right\\rangle\\). If we then write transverse and longitudinal modes of \\(\\left\\langle \\tilde{{{\\bf{u}}}}({{\\bf{q}}},\\omega )\\cdot \\tilde{{{\\bf{u}}}}({{{\\bf{q}}}}^{{\\prime} },{\\omega }^{{\\prime} })\\right\\rangle\\) in terms of active noise, we will be able to obtain the final expression of MSD for large L in real space and time as (see Supplementary Note\u00a0XIV for the detailed derivation) The generalised mean squared displacement in d-dimension is given by,\n\nwhere, longitudinal and transverse length scale is given by, \\({\\xi }_{L}={\\left[(B+\\mu )\\tau_p /\\zeta \\right]}^{1/2}\\) and \\({\\xi }_{T}={\\left[\\mu \\tau_p /\\zeta \\right]}^{1/2}\\). For d\u2009=\u20092, we get the analytical expression for the MSD as\n\nIf we take L\u00a0\u2192\u00a0\u221e of the above equation, then we get\n\nThis result predicts a logarithmic divergence with system size L for active systems as well (see Supplementary Note\u00a0XIV), which are in contrast with our results. However, if one takes extreme activity limit by making persistence time \u03c4p\u00a0\u2192\u00a0\u221e, one finds the MSD plateau to diverge as \\(\\left\\langle | {{\\bf{u}}}({{\\bf{r}}},t){| }^{2}\\right\\rangle \\sim {L}^{4-d}\\), suggesting the lower critical dimensions to shift to 4 as also claimed in47,61. Note that in the limit \u03c4p\u00a0\u2192\u00a0\u221e, both longitudinal and transverse correlation lengths of the active elasticity theory diverge as \\(\\sqrt{{\\tau }_{p}}\\)47. At finite persistence time, these correlation lengths will be finite and it will predict a crossover behaviour beyond these length scales. Notably, it will predict in 3D, \\(\\left\\langle | {{\\bf{u}}}({{\\bf{r}}},t){| }^{2}\\right\\rangle \\sim L\\) if L\u2009<\u2009\u03beL or \u03beT and become constant beyond this crossover length scale similar to passive systems. In 2D, it will predict \\(\\left\\langle | {{\\bf{u}}}({{\\bf{r}}},t){| }^{2}\\right\\rangle \\sim {L}^{2}\\) for L\u2009<\u2009\u03beL or \u03beT and \\(\\log L\\) beyond these correlation lengths. In contrast, our results in 3D at finite persistence time suggest \\(\\left\\langle | {{\\bf{u}}}({{\\bf{r}}},t){| }^{2}\\right\\rangle \\sim \\log L\\) with the lower critical dimensions to be 3 rather than 4. Although, our initial results in 3D, suggests logarithmic divergence of MSD plateau with increasing system size over the studied \u03c4p range (see Supplementary Fig.\u00a020b for details), it will be very interesting to do a systematic study with varying persistence time over a much larger window including \u03c4p\u00a0\u2192\u00a0\u221e to test some of these theoretical arguments. Thus, we believe that the existing theories based on continuum active linear elasticity and the normal modes formalism is not sufficient to explain power-law divergence and logarithmic divergence of MSD plateau with system size in 2D and 3D, respectively, in different model active solids over a wide range of activity parameters. Theoretical formalism that can incorporate a detailed renormalisation of the dynamical matrix along with the phonon dispersion relation is essential to understand the results presented in this manuscript, and we hope that our work will inspire new theoretical approaches to develop a correct theory of active solids.\n\nWe performed extensive computer simulations to study the effect of long-wavelength Mermin-Wagner-Hohenberg like fluctuations in polycrystalline solid and glasses in liquid and solid regimes, and observe the dimensionality effect on the active systems. We found that active forces modelled as run-and-tumble particles (RTP) and active Brownian particles (ABPs) significantly enhance the long-wavelength density fluctuations in these systems. These long-wavelength density fluctuations also affect liquid dynamics at high temperatures, similar to the results obtained in passive systems53. For an active glassy system in 2D, MWH fluctuations cause a power-law divergence of the Debye-Waller (DW) factor with system size, L, whereas in 3D active solids, one observes logarithmic divergence, which is not reported in the literature before. The exponent increases with increasing activity, universally in both crystalline and disordered solids.\n\nOnce long-wavelength fluctuations are removed from the measurement by calculating cage-relative MSD, one observes the system size divergence of the MSD plateau to vanish. This proves that the observed divergence with system size is solely due to the collective motion of the long-wavelength mode, which gets enhanced in the presence of activity. Moreover, by performing finite-size scaling analysis of the glassy system with changing interactions and density in the presence of activity, we are able to show that the non-trivial fluctuations in the 2D and 3D active system do not stem from giant number fluctuations (GNFs) at the studied density range. Following56, we calculated the displacement fluctuation in active systems within linear response and active elasticity theory and found that it predicts the usual logarithmic divergence even for active systems. This result suggests that existing theoretical formalisms of active solids are not adequate to explain our current observations. Our results indicate that the assumption of the dynamical matrix being the same as the passive system in the presence of active colour noise is probably at fault.\n\nWe proposed an effective dynamical matrix description of the observation by computing the effective Hessian matrix from the displacement-displacement covariance matrix and performing exact diagonalisation to compute the eigenvalues and eigenvectors. The obtained vibrational density of states (vDoS) matched very well for the passive systems after systematically correcting for convergence issues. vDoS results obtained following the same protocols led to the observation of enhanced phonon modes at lower frequencies with increasing activity, as evident from the appearance of sharp peaks in the distribution at small frequencies. The validity of this effective description was verified by computing the mean squared displacement (MSD) of particles in both crystals and amorphous solids using the detailed information of eigenvalues and eigenvectors of the effective Hessian matrix, showing close agreement with MD simulation results. Subsequently, to shed more light on the power-law and logarithmic divergence of DW factors 2D and 3D in crystalline and amorphous solids respectively, we have computed the effective phonon dispersion relation and showed that in passive systems, one gets back the usual linear dispersion as \u03c9(q)\u00a0~\u2009q, whereas in active solids, this relation becomes non-linear as \u03c9(q)\u00a0~\u2009q\u03b1, with exponent \u03b1\u00a0~\u20091.8 in polycrystalline solids and around 1.47\u22122.02 in amorphous solids with exponent increasing systematically with increasing activity in both 2D and 3D. These results qualitatively rationalise the observed power-law and logarithmic divergence of DW in crystalline and amorphous solids in 2D and 3D, respectively, as well as no divergence in 4D solids.\n\nFor an out-of-equilibrium system, one does not expect the MWH theorem to hold like the equilibrium system, and there is plenty of work present in the literature, where for an out-of-equilibrium scenario, the MWH theorem breaks down, i.e., the system shows a long-range crystalline ordered state in 2D. In a recent study62, it has been shown that due to active noise, the 2D system attains long-range crystalline order, which denotes that the MWH fluctuation in the system is suppressed. This result is in agreement with the out-of-equilibrium disorder-to-order transition observed in active systems22,23,25. Unlike these systems, our systems show MWH fluctuations to persist with great strength due to the coloured noise statistics of the self-propelled particles (SPPs) present in the system. Another recent studies55,63 suggests that the RTPs get strongly coupled to the global and local shear modes of relaxation, which are collective in nature, leading to faster annealing similar to the oscillatory shear response of these solids. We believe that if active driving efficiently gets coupled to the local shear modes, then it will also lead to enhanced phonon-like fluctuations in the system. It will be interesting to investigate the effective phonon description in systems where activity suppresses the long-wavelength density fluctuations, as in ref. 62. Such studies may shed insight into whether the phonon dispersion changes consistently in those systems.\n\nOur study reveals for the first time that run-and-tumble particles (RTPs) and active Brownian particles (ABPs) can disrupt the stability of solids in a fundamentally different way, causing destabilisation even in three-dimensional solids, leading to an increase in the lower critical dimensions from dl\u2009=\u20092 to dl\u2009=\u20093. This finding echoes the well-known arguments by Imry and Ma64, which discuss how quenched disorder can alter the lower critical dimension in magnetic systems. Establishing a potential connection between magnetic systems with quenched disorder and active solids with RTPs or ABPs is an exciting prospect. In ref. 61, it was predicted that lower critical dimensions can increase when the driving noise is positively correlated in space and time, and it can decrease when it is anticorrelated. Lower and upper critical dimensions were calculated analytically for the spherical model \\({{\\mathcal{O}}}(n)\\) in the limit n\u00a0\u2192\u00a0\u221e, in the presence of correlated noise. It will be indeed very important to find out whether any such mechanism is at play in our system. Naively, both RTPs and ABPs have temporal correlation in the noise statistics but no spatial correlation, although an emergent dynamical correlation can not be ruled out at this point. Detailed investigation along some of these directions will be very helpful to improve our understanding of the microscopic mechanisms behind the amplified fluctuations observed in these systems. Finally, the validity of our results across various active matter model systems and at different damping conditions suggests that these findings could be applicable to a wide range of active matter systems, both synthetic and biological matters.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61366-0/MediaObjects/41467_2025_61366_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61366-0/MediaObjects/41467_2025_61366_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61366-0/MediaObjects/41467_2025_61366_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61366-0/MediaObjects/41467_2025_61366_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61366-0/MediaObjects/41467_2025_61366_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61366-0/MediaObjects/41467_2025_61366_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61366-0/MediaObjects/41467_2025_61366_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61366-0/MediaObjects/41467_2025_61366_Fig8_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61366-0/MediaObjects/41467_2025_61366_Fig9_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "In this work, we have studied the dynamics of crystalline solids and model glass-forming liquids. The first glass-forming liquid model is the modified Kob-Anderson (2dmKA) Binary model in 2D with a composition ratio of 65:\u00a035, with the other details of the potential same as that of the Kob-Anderson model45 (see Supplementary Note\u00a0I for details). This particular composition ratio ensures that there is no tendency for the system to form local crystalline orders. For the polycrystalline system, we have taken a mono-atomic particle system with energy and the diameter of the particles to be 1.0 in LJ unit, and the interaction potential is smoothed LJ potential, same as the other two glass-forming models. We have performed a simulation with a number of particles in the range N \u2208 [400, 105] in this work. We ran 32 statistically independent ensembles for all the systems except the few large ones (25000\u00a0\u2212\u00a0105); we have taken 8 ensembles for these system sizes. We used a three-chain No\u015be-Hoover thermostat to perform NVT simulations65. The polycrystal model in 3D is the same as the 2d polycrystal model, but simulated in three dimensions. 3dKA and 4dKA models for disorder systems have the same interaction parameters as the 2mdKA but simulated in three (3D) and four dimensions (4D) with a binary particle ratio of 80:\u00a020 (large : small), respectively.\u00a0Another glass-forming liquid model is 2dR10 binary model45 with a composition ratio\u00a0of 50:50, given by the\u00a0repulsive\u00a0potential r\u221210\u00a0with\u00a0smoothed\u00a02nd derivative at cutoff distance. This model\u00a0is studied for varied density and activity.\n\nThe activity in the system is introduced in the form of run and tumble particle (RTP) dynamics12,17,21, where the dynamics of the active particles can be tuned using three parameters such as c, f0, \u03c4p. The equation of motion is given by\n\nwith ri and pi being the position and momentum vector of the ith particle, ni is the active-tag which takes values 0 or 1 depending on whether the particle is passive or active, \u03a6 is the inter-particle potential, and \\({{{\\bf{F}}}}_{i}^{A}\\) is the active force. The active force on the ith particle in 2D can be written as,\n\nwhere the active direction of the ith particle is \\({k}_{\\alpha }^{i}\\) for [\u03b1 \u2208 x,\u00a0y] is chosen from \u00a0\u00b1\u20091. The number of total active particles is taken to be an even number to maintain the total active momentum to be zero along all directions independently, i.e., \\({\\sum }_{\\alpha,i}{k}_{\\alpha }^{i}=0\\). The basis set in 3d and 4d would be [\u03b1 \u2208 x,\u00a0y,\u00a0z] and [\u03b1 \u2208 x,\u00a0y,\u00a0z,\u00a0w] respectively. For setting up the reference temperature, we kept \u03c4p\u2009=\u20091, active force magnitude f0 is selected from 0.0\u00a0\u2212\u00a02.0 for 2dmKA by fixing concentration c\u2009=\u20090.1.\n\nIt is often useful to define the degree of activity in a non-dimensional form. For an active system, the P\u00e9clet number (Pe) is such a quantifier to get the amount of activity in the system. Pe is a non-dimensional number which is the ratio of the transport rate due to advection (activity) and diffusivity (D) of the system. Typically, for any system, Pe can be defined as Lv/D. But for an active system, it can be modified such that it can quantify the distance travelled by a free particle due to activity in terms of velocity, this P\u00e9clet number for an active system is defined as,\n\nwhere \\({\\tilde{v}}_{A}\\) is the persistent velocity of the active particles given by (f0 \u22c5 \u03c4p), and \\(\\tilde{v}\\) is the velocity of the particle due to inertial (velocity) relaxation of the system. The inertial relaxation time (\u03c4\u03b3) of the system is \u03b3\u22121 and \u03c3AA is the diameter of the large particle47,66,67.\n\nFor the deterministic molecular dynamics simulation using the (Nose-Hoover) thermostat, the damping term (\u03b3) is not present. If we use the Einstein relation of the fluctuation-dissipation theorem \u03b3\u2009=\u2009KB \u22c5 T/D, then the P\u00e9clet number in our case will read as\n\nwhere, Boltzmann constant KB and \u03c3AA is 1 in reduced LJ units. If we consider that the damping term is \u03b3\u2009\u2243\u20091, then the persistent length (lp) of the active system will be given by, \\({l}_{p}\\propto {f}_{0}\\cdot {\\tau }_{p}^{2}\\) and as we have taken \u03c4p\u2009=\u20091 and varied f0 from 0 to 2.5 in this study, the persistence length will be roughly 1\u00a0\u2212\u00a02 particle diameter for the largest activity at low densities but due to dense crowded environment, the caging length remains \u00a0~\u20090.7\u03c3AA. There are studies which suggests that in dense disordered systems, caging length controls the dynamical behaviour of the active glasses52. Note that the caging length of around 0.7\u03c3AA is not very different from the persistence length of a cell (around one cell diameter) for an assembly of cells. For ABPs, Pe is well defined via Eq. (16).\n\nTo compute the two-point density-density correlation, we have considered a simpler form of the overlap correlation function Q(t), such as\n\nwhere w(x) is a window function, which is 1 for x\u00a0<\u2009a and 0 otherwise. ri(t) position of the ith at time t. Here parameter \u2018a\u2019 is chosen to remove the vibration due to the caging effect. We chose a\u00a0=\u00a00.3. Relaxation time, \u03c4\u03b1, which is defined as \u3008Q(t\u2009=\u2009\u03c4\u03b1)\u3009\u2009=\u20091/e, where \u3008 \u22ef \u2009\u3009 means ensemble average. The system is equilibrated for 150\u03c4\u03b1, and then we run for 150\u03c4\u03b1 in all our measurements.\n\nFour-point correlation function can be measured from the fluctuation of the two-point correlation function Q(t), which is defined as,\n\nThe mean squared displacement (MSD) is defined as\n\nThe diffusion constant D is computed from the slope of MSD vs t at long timescale using the relation \u3008\u0394r2(t)\u3009\u2009=\u20092dDt in d-dimensional system.\n\nThe cage-relative (CR) displacement8,9,68 of the individual particle i, is defined as\n\nwhere ri,nn(t) is the position of the centre of mass of Nnn nearest neighbours of ith particles. Again, it is defined as,\n\nWe used a cut-off value \\({r}_{c}^{nn}=1.3\\) for the first nearest neighbours Nnn. After identifying the Nnn particles at the initial time (time origin) we track these particles dynamics at later time t. Using these displacement we compute QCR(t), \\({\\chi }_{4}^{CR}(t)\\), and MSD respectively.\n\nThe Hessian matrix is defined as the double derivative of the total potential energy, U({ri}), as\n\nwith i,\u00a0j being the particle index and \u03b1,\u00a0\u03b2 being the dimensionality index.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All the data produced in this work are presented in the manuscript and its Supplementary Information file. Source data for the figures are available in a public repository on Figshare at https://doi.org/10.6084/m9.figshare.29234687 (ref. 69). Raw Molecular Dynamics data will be available from the authors upon request.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All the codes used in this work to produce the data are available in a public repository on Figshare at https://doi.org/10.6084/m9.figshare.29234687 (ref. 69).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Hohenberg, P. C. Existence of long-range order in one and two dimensions. Phys. Rev. 158, 383\u2013386 (1967).\n\nArticle\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nMermin, N. D. & Wagner, H. Absence of ferromagnetism or antiferromagnetism in one- or two-dimensional isotropic heisenberg models. Phys. Rev. Lett. 17, 1133\u20131136 (1966).\n\nArticle\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nMermin, N. D. Crystalline order in two dimensions. Phys. Rev. 176, 250\u2013254 (1968).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nJancovici, B. 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S.K. acknowledges the Swarna Jayanti Fellowship grants DST/SJF/PSA01/2018-19 and SB/SFJ/2019-20/05 from the Science and Engineering Research Board (SERB) and Department of Science and Technology (DST). Most of the computations are done using the HPC clusters procured using Swarna Jayanti Fellowship grants DST/SJF/PSA01/2018-19, SB/SFJ/2019-20/05 and Core Research Grant CRG/2019/005373. S.K. also acknowledges research support from the MATRICES Grant MTR/2023/000079 from SERB.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open access funding provided by Department of Atomic Energy.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Subhodeep Dey, Antik Bhattacharya.\n\nTata Institute of Fundamental Research Hyderabad, Hyderabad, India\n\nSubhodeep Dey,\u00a0Antik Bhattacharya\u00a0&\u00a0Smarajit Karmakar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.K. conceived the project. S.K. supervised the project. S.D. and A.B. performed research and simulations. S.D., A.B. and S.K. designed analysis methods. S.D. and A.B. performed all the analyses. S.D., A.B. and S.K. wrote the paper jointly. S.D. and A.B. contributed in this work equally.\n\nCorrespondence to\n Smarajit Karmakar.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "All the authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Communications", + "published": "07 February 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM3_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM4_ESM.mp4" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM5_ESM.mp4" + }, + { + "label": "Supplementary Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM6_ESM.avi" + }, + { + "label": "Supplementary Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM7_ESM.mp4" + }, + { + "label": "Supplementary Movie 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM8_ESM.mp4" + }, + { + "label": "Supplementary Movie 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM9_ESM.mp4" + }, + { + "label": "Supplementary Movie 8", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM10_ESM.mp4" + }, + { + "label": "Supplementary Data File 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM11_ESM.zip" + }, + { + "label": "Supplementary Data File 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM12_ESM.zip" + }, + { + "label": "Supplementary Data File 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM13_ESM.zip" + }, + { + "label": "Supplementary Data File 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM14_ESM.zip" + }, + { + "label": "Supplementary Data File 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_MOESM15_ESM.zip" + }, + { + "label": "Supplementary Data File 6", + "link": 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"https://www.researchsquare.com//article/rs-4109720/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-56025-3.pdf", + "preprint_posted": "27 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Soft robots excel in safety and adaptability, yet their lack of structural integrity and dependency on open-curve movement paths restrict their dexterity. Conventional robots, albeit faster due to sturdy locomotion mechanisms, are typically less robust to physical insults. We introduce a new multi-material design and printing framework that extends classical mechanism design to soft robotics, synergizing the strengths of soft and rigid materials while mitigating their respective limitations. Using a tool-changer equipped with multiple extruders, we blend thermoplastics of varying Shore hardness into monolithic systems. Our strategy emulates joint-like structures through biomimicry to achieve terrestrial trajectory control while inheriting the resilience of soft robots. We demonstrate the framework by 3D printing a legged soft robotic system, comparing different mechanism syntheses and material combinations, along with their resulting movement patterns and speeds. The integration of electronics and encoders provides reliable closed-loop control for the robot, enabling its operation across various terrains including sand, soil, and rock environments. This cost-effective approach paves the way for a new era of 3D printed soft robots employable in real-world environments.Physical sciences/Engineering/Mechanical engineeringPhysical sciences/Engineering/Electrical and electronic engineeringPhysical sciences/Materials science/Soft materials/Polymers", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "AFrameworkforSoftMechanismDrivenRobotsSupplementary.pdfA Framework for Soft Mechanism Driven Robots - Supplementary InformationDatafileS1.zipData file S1DatafileS2.zipData file S2DatafileS3.zipData file S3DatafileS4.zipData file S4DatafileS5.zipData file S5DatafileS6.zipData file S6DatafileS7.zipData file S7DatafileS8.zipData file S8DatafileS9.zipData file S9DatafileS10.zipData file S10DatafileS11.zipData file S11DatafileS12.zipData file S12MovieS1.zipMovie S1MovieS2.zipMovie S2MovieS3.zipMovie S3MovieS4.zipMovie S4MovieS5.zipMovie S5MovieS6.zipMovie S6", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Soft robots excel in safety and adaptability, yet their lack of structural integrity and dependency on open-curve movement paths restrict their dexterity. Conventional robots, albeit faster due to sturdy locomotion mechanisms, are typically less robust to physical impact. We introduce a multi-material design and printing framework that extends classical mechanism design to soft robotics, synergizing the strengths of soft and rigid materials while mitigating their respective limitations. Using a tool-changer equipped with multiple extruders, we blend thermoplastics of varying Shore hardness into monolithic systems. Our strategy emulates joint-like structures through biomimicry to achieve terrestrial trajectory control while inheriting the resilience of soft robots. We demonstrate the framework by 3D printing a legged soft robotic system, comparing different mechanism syntheses and material combinations, along with their resulting movement patterns and speeds. The integration of electronics and encoders provides reliable closed-loop control for the robot, enabling its operation across various terrains including sand, soil, and rock environments. This cost-effective framework offers an approach for creating 3D-printed soft robots employable in real-world environments.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Soft robots, characterized by their inherent elasticity and impact resistance, have emerged as highly adaptive and resilient technologies, particularly well-suited for challenging environments. Their effectiveness is evident in tasks such as navigating through debris fields1 and overcoming steep obstacles2. Fabricated from a variety of materials including silicone elastomers, polyurethanes, and hydrogels, these robots demonstrate significant flexibility and compliance3. This not only facilitates safer interactions with humans but also enhances their capability to adapt to complex environments. Despite these advantages, the very flexibility that defines soft robots can also detract from their performance. Specifically, it can undermine their structural integrity and limit their range of movement, introducing challenges such as heightened friction and diminished speeds in comparison to their rigid counterparts4,5,6. This paradox underscores the need for ongoing research to balance the benefits of soft robotics with the inherent challenges posed by their material and design properties.\n\nPneumatic actuation, a common method for powering soft robots, allows for basic movements and crawling motions but limits speed and precision5,6,7,8,9. The crawling movements observed in pneumatic robots stem from the adoption of open-curve trajectories by their locomotion mechanisms. These trajectories involve movement patterns where the starting and ending points of an actuator are not contiguous, resulting in increased ground contact and reduced efficiency. Alternative actuation mechanisms like combustion and the use of shape memory alloys or dielectric elastomer actuators introduce unique capabilities for rapid movements and shape changes but face issues with controllability, power efficiency, and safety10,11,12,13,14.\n\nThe operational scope of soft robots has historically been constrained by their dependence on tethers for power and control, leading to a growing interest in soft hybrid systems that integrate soft materials with untethered (rigid) electronic controls. Although soft robotic hybrids have shown promise, they have faced issues in power inefficiency and trajectory control2,5,15,16,17,18. Pneumatic soft robots with integrated fluidic circuitry, devoid of electronic components, have presented a promising avenue for enhancing resilience and autonomy in soft robotics, addressing constraints associated with tethered designs. However, limitations in computational abilities, wireless communication, and reduced locomotion speeds continue to constrain their practical use in real-world applications19.\n\nThis discussion introduces a framework for the design and fabrication of soft hybrid robots. By employing multi-material printing and classical linkage design, we propose an easy-to-implement methodology that leverages the unique properties of soft materials while addressing their inherent limitations. Classical linkage design, in particular, facilitates the creation of closed-curve trajectories, enhancing power efficiency and movement precision without requiring actuators at every joint. This approach, inspired by the movement mechanisms found in legged animals, aims to emulate their efficient locomotion observed in nature. Four-bar linkages controlled by rotary actuators are adept at emulating terrestrial locomotion observed in bipeds20, quadrupeds21, and hexapods22. The majority of legged robots are composed of rigid materials23,24 and are manually fabricated25,26,27,28,29; their implementations have lacked analyses of chassis and mechanisms designs for impact resistance, durability, and adaptability. DeMario et al.\u2019s work similarly focused on multi-material printing of soft materials with varying Shore hardness to create locomotion mechanisms; the work, however, exhibits limitations. The study does not address key soft robotic attributes such as locomotion stability, impact resistance, and oscillation damping, which are essential for performance in varied environments. The use of a specific Klann mechanism with a single actuator and a miniaturized gear train significantly constrains the movement versatility of the robot. There is a limited investigation into material interfaces, with observed challenges like delamination during motion trials. The impact of variations in Shore hardness on locomotion is not explored. The use of PolyJet printing poses challenges regarding accessibility and cost, restricting its fabrication to specialized laboratory settings30. To further reflect upon the variations and capabilities of different printing methods for soft robotics, we created a comparison table in\u00a0Supplementary Information - Comparison of Printing Technologies for Soft Robotics. We also compare the approach of using a single motor with a gear train to the approach of assigning individual motors to each leg in\u00a0Supplementary Information - Comparison of Dependent and Independent Gait Control.\n\nTo merge the benefits of soft robotics with classical linkage design, we present a framework using multi-material fused deposition modeling (FDM). This approach involves the combination of soft and hard materials within unified geometries to achieve distinct compliance variations. This approach enables the creation of mechanisms that are both structurally sound and versatile in design, yet retain their inherent softness and impact resilience, addressing challenges noted in prior research. We systematically design, print, and assemble locomotion mechanisms and robot bodies from combinations of thermoplastic polyurethanes (TPU) of varying Shore hardness (75D, 95A, and 85A), with minimal human intervention. Our designed mechanisms feature joints with increased compliance relative to their links, enabling specific sections to bend under applied forces or moments. These flexible linkages are used as locomotion mechanisms in a soft quadruped (Fig.\u00a01).\n\na Design process. Inspired by nature, we observe the walking trajectories of quadrupeds such as horses. We then select a trajectory from a four-bar atlas. We synthesize the corresponding linkage in the ideal design domain. Finally, we convert the linkage into a multi-material design by replacing ideal joints and links with variations of soft and hard materials. b Application. We assemble the printed parts and integrate the electronics into the robot body, creating a `soft mechanism driven robot'.\n\nOur research undertakes fundamental challenges in soft robotics by directly tackling critical issues related to structural integrity and mobility. By integrating soft and rigid components with cost-effective fabrication techniques, we aim to broaden the capabilities and applications of soft robots. This approach addresses limitations in current actuation methods and improves interactions between robots and their environment, supporting the development of more versatile and power-efficient soft robotic systems (Supplementary Movie\u00a01).\n\nThe goal of this framework is to facilitate the design and implementation of mechanism-driven soft robots suitable for real-world environments, using low-cost materials and accessible desktop FDM printers.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "Effective bonding between diverse materials is essential to enable mechanisms to withstand cyclic loading during operational use. Yin et al. investigated the interfacing properties between TPU and acrylonitrile butadiene styrene31; however, their study did not account for bending or elongation. In our initial experiments, we explored interfaces between polylactic acid (PLA) and TPUs, which led to delamination in our basic motion trials. In this study, we exclusively used TPUs; our approach mitigates the challenges associated with interfacing different materials by harmonizing the composition of our thermoplastics. We selected filaments with Shore hardness levels of 75D, 95A, and 85A due to their commercial availability and the balance between softness and structural integrity (Fig.\u00a02). Filaments with lower Shore hardness are more prone to printing challenges and higher failure rates.\n\na The Shore hardness scale compares our filaments to other common materials. b The fabrication setup with three different filaments assigned to three separate extruders, and the resulting print of the leg mechanisms and the robot body on the print bed.\n\nIn single tool-head FDM printing, a material is deposited onto a layer that has recently been extruded and has not fully cooled, creating favorable fusion characteristics at that moment32. However, in a tool-changer setup, where materials are printed sequentially within a single layer, this process differs. Sequential printing of different materials increases the time between extrusions, allowing the previously extruded material to cool down. The fusion characteristics are less effective compared to single-material prints.\n\nTo address these challenges and understand the behavior of multi-material prints, we fabricated tensile testing specimens employing three distinct interfacing methods: straight, dovetail, and finger joints (Fig.\u00a03a). We selected the dovetail and finger joint sizes and counts to maximize the contact area within the constraints of the specimen\u2019s size and the resolution of our printer. We also created specimens from uniform materials to assess strength degradation between single- and multi-material prints. We detail our tensile testing procedure in the Methods section.\n\na The combinations of different specimens. b Test of a NinjaFlex (85A)/Cheetah (95A) specimen with finger interfaces. c A sample stress-strain graph comparing specimens of uniform Armadillo (75D), uniform Cheetah (95A), uniform NinjaFlex (85A), and Cheetah (95A)/NinjaFlex (85A) with finger interfaces. d Young\u2019s moduli of Cheetah (95A)/NinjaFlex (85A) specimens. e Young\u2019s moduli of Armadillo (75D)/NinjaFlex (85A) specimens. f Young\u2019s moduli of Armadillo (75D)/Cheetah (95A) specimens. Combined structures have Young\u2019s moduli ranging between the material variations.\n\nSpecimens with lower Shore hardness exhibited a capacity for higher strains, whereas specimens with higher Shore hardness demonstrated greater stress tolerance. The stress-strain behavior of two-material specimens is predominantly influenced by the properties of the material with a lower Shore hardness. Even in trials involving our firmest TPU (75D), which exhibited plastic behavior in uniform material trials, the overall behavior of the multi-material tensile testing specimens remained elastomeric. This observation is visible in both the stress-strain curves (Fig.\u00a03c) and the Young\u2019s moduli (Fig.\u00a03d\u2013f) of our material combinations. This outcome underscores that combining materials with plastic behavior with those that display elastomeric properties results in a composite material that predominantly exhibits elastomeric behavior (Supplementary Movie\u00a02).\n\nIn our experiments, straight interfaces, possessing the smallest contact surface area compared to finger and dovetail interfaces and lacking mechanical locking features, exhibited separation at relatively low-stress levels. Although a clear preference for dovetail or finger joints cannot be conclusively established due to their similar performance and the uncertainties inherent in our fabrication method, it was evident that their adhesive qualities surpassed those of straight interfaces (Supplementary Data File\u00a01). Our finite element analysis revealed that the maximum stress exerted on the linkage is ~0.9\u2009MPa during cyclic motion. This is notably lower than the tensile strength of the softest material we utilized (85A), which has a tensile strength of 4\u2009MPa. Our model and experimental evaluation established that straight interfaces demonstrate sufficient interfacing efficacy for walking motions (safety factor \u22654). However, we note that dovetail or finger interfaces might be required in other specific robot applications involving greater force exertions. Supplementary Table\u00a05 in the\u00a0SI provides the ultimate tensile forces that each material and specimen combination can withstand before failure. This table is intended to serve as a reference for selecting appropriate materials and interfaces depending on the operation scenarios, creating new design selections for different robot applications. We also performed cyclic fatigue tests using ASTM standards. We show that all material combinations with all interfaces can endure a minimum of 10,000 cycles. The details of our tests are summarized in\u00a0Supplementary Information - Cyclic Fatigue Testing. Our measurements are provided in Supplementary Data File\u00a02. Published literature indicates that soft/hard multi-material specimens created with a PolyJet printer fail after ~1000 cycles33, while our specimens withstand at least 10,000 cycles. We provide video snippets from fatigue tests in Supplementary Movie\u00a03.\n\nQuantitatively illustrating the stress-strain characteristics of multi-material specimens proves to be challenging. Unlike conventional engineering materials, elastomeric materials do not have a distinct yield point, which complicates the identification of the transition between elastic and plastic zones34. However, Young\u2019s modulus remains easily observable for both plastics and elastomers. We calculated and plotted the tensile modulus for each tested specimen, revealing that combined specimens consistently exhibit Young\u2019s moduli between their constituent materials (Fig.\u00a03d\u2013f). When comprised of an elastomeric and a rigid material, the modulus aligns more closely with the elastomeric material (Fig.\u00a03d, e). When two elastomers are combined, the resulting modulus approximates the average of the two composing materials (Fig.\u00a03f).\n\nFor the remainder of our experiments, we used straight interfaces and the material combination with the highest Shore hardness differential (75D/85A), which meets the requirement of \u226b0.9\u2009MPa for cyclic motion. The analysis in this section complements our overall framework by providing design guidelines for the development of other robots. For example, in applications where the robotic system must endure high stresses, finger or dovetail joints may be preferable to straight interfaces. Alternatively, in scenarios where impact resistance is critical, selecting the softest material combination over harder variants might be advantageous.\n\nWe developed a four-bar locomotion mechanism for a terrestrial quadruped robot to showcase our methodology in a practical setting (Fig.\u00a01a - Full assembly). Our linkage consists of a continuously rotating crank, a rocker that moves back and forth, and a third link that connects the two (Fig.\u00a01b - Robot body assembly). In our Grashof crank-rocker mechanism, the crank, capable of continuous rotation and driven by a DC motor, actuates the entire linkage. The coupler can obtain an arbitrary shape if it connects the crank and rocker links. The design of the coupler determines the coupler points, which influence the overall trajectory of the leg. In our design, the tip of the coupler serves as the foot of the robot, creating a trajectory where the foot lifts at the rear, moves forward in the air, makes ground contact at the front, and then drags back, minimizing ground force exertion and hence friction. To understand how trajectory amplitude and range affect motion, we synthesized three linkages with varying trajectories. Please see Supplementary Data File\u00a03 for the MATLAB scripts of the kinematic analyses of these linkages.\n\nAfter the theoretical synthesis of the mechanisms, we translated the ideal joint-rigid pin structure into a multi-material print variation (Fig.\u00a01a - Multi-material soft conversion). This adaptation was achieved by modulating compliance through the geometry of the linkage. In our design, areas designated for function as joints exhibit greater compliance compared to the links. This distinction was achieved by employing softer TPU materials (85A, 95A) for the joints and harder TPU materials (75D, 95A) for the links, strategically varying material hardness to meet the specific functional needs of each component. We performed deflection simulations using\u00a0finite element analysis (FEA) to compare the displacement differences among three material combinations. We provide a detailed analysis in\u00a0Supplementary Information - Deflection Analysis and the corresponding simulation file in Supplementary Data File\u00a04. We provide an analysis of the power consumption and efficiency of each mechanism in\u00a0Supplementary Information - Mechanism Efficiency Testing.\n\nWe fabricated three distinct configurations: links made of 75D TPU with joints of 85A TPU, links made of 75D TPU with joints of 95A TPU, and links made of 95A TPU with joints of 85A TPU (Fig.\u00a02a). The tuned fabrication profile with the entire printer configuration is shared in Supplementary Data File\u00a05. The crank, serving as the connector between the motor and the rest of the linkage and undergoing a full 360\u00b0 rotation, is unsuitable for a compliance-based multi-material design due to its functional demands. Therefore, we 3D printed it separately using stereolithography (Prusa Tough resin) and subsequently attached it to the linkage with a dowel pin.\n\nIn traditional four-bar mechanisms, an offset is necessary at the joint locations to accommodate the pin connecting the links, preventing a completely planar link design. However, our fabrication technique, which facilitates the differentiation of materials within the same print layer, eliminates this requirement. We can 3D print the leg mechanisms in a streamlined, single-click process using our multi-material tool-changer system. The limitation is not completely diminished but reduced to the only exception of the cams that connect the motors to the linkage (Fig.\u00a01). The cams are fabricated separately and still need an offset; this approach not only simplifies the manufacturing process but also enhances the design efficiency of the leg mechanisms.\n\nThe body of the robot is designed as a monolithic system, comprising the main frame made from a softer variant of TPU (85A) to ensure flexibility. The body is complemented by connection beams for the legs, fabricated from a sturdier TPU variant (75D) to improve structural integrity. This combination of materials optimizes the overall performance of the robot by balancing flexibility and strength in its construction. Although changes in body design can influence robot behavior, this work focuses specifically on the leg mechanisms that drive the robot. Given that the robot body offers a vast design space, we kept the robot body mostly constant throughout this work to control the experimentation of the leg mechanisms. The only exceptions are two test series. In\u00a0Supplementary Information - Body Material Testing, we varied the Shore hardness of the robot body while keeping the leg mechanism unchanged. In\u00a0Supplementary Information - Body Geometry Analysis, we varied the geometry of the robot body while keeping the leg mechanism unchanged.\n\nWe attached the legs to the robot body using the connection beams. The computer aided design (CAD) files of the legs and the robot body are provided in Supplementary Data File\u00a06. The soft body of the robot holds four DC motors, four magnetic encoders, and a custom-made Printed Circuit Board (Supplementary Data File\u00a07) that interconnects the electronic components (Fig.\u00a01b - Adding electronics). We implemented closed-loop controllers for the motors to achieve our walking gait (Supplementary Data File\u00a08).\n\nIn designing the locomotion mechanism, our main objective was to achieve a trajectory resembling a reverse D-shape (Fig.\u00a01a - Inspiration). The flat portion of the D represents the supporting phase, where the foot touches and drags along the ground. Maintaining flatness during this phase is crucial as it minimizes the application of force on the ground, reducing normal forces and friction. The remaining arc depicts the lift-off and forward motion of the foot through the air.\n\nWe derived analytical expressions for linkage motion primarily through kinematic analysis, ensuring that the linkages accurately follow the desired trajectories essential for fast and effective movement. By developing kinematic equations for a standard four-bar linkage using trigonometric identities and vector loop equations, detailed in the Supplementary Fig.\u00a01, our approach enables us to determine the position of the linkage\u2019s endpoint (i.e., the foot) based on specific rotary inputs to the crank (Fig.\u00a04a). We included an analytical model for the rigid versions of our four-bar linkages, along with a MATLAB script for easier analysis, in Supplementary Data File\u00a03.\n\na Analytical synthesis of the mechanism generating the desired trajectory. b The leg mechanism being actuated in mid-air to track the foot for trajectory generation. c Monolithic mechanism duplicated in FEA domain numerically calculates the foot trajectory. d Trajectory data acquired with image processing. e Trajectory data generated in finite element simulations.\n\nHowever, this analysis does not fully capture the motion dynamics of the soft mechanism, as the joints in our multi-material configuration exhibit resistance to motion due to their spring constants and damping effects, unlike ideal joints. We conducted two additional analyses: image trajectory tracking (Supplementary Data File\u00a09) and finite element analysis (Supplementary Data File\u00a010), to compare the trajectory of our linkage with the theoretical (rigid) model. We provide the data from all these experiments in Supplementary Data File\u00a011. Supplementary Movie\u00a04 features a video from our COMSOL simulation, and Supplementary Movie\u00a05 shows the image tracking procedure.\n\nOur findings indicate that the trajectories produced by our mechanisms closely align with their theoretical counterparts in terms of shape (Fig.\u00a04d, e). The trajectories derived from video tracking (Fig.\u00a04d) and finite element analysis (Fig.\u00a04e) are consistent in both shape and size, with an exception for models using stiffer (95A) joints. In our video tracking experiments, we observed that despite immobilizing the motors and body, the stiffer joints introduced resistance that altered the position of the motor and angled the body, significantly deviating the trajectory from the expected outcome. This discrepancy was not observed in finite element analysis, where, due to simplifications made to reduce computational complexity, such physical distortions were not accounted for. We provide the corresponding simulation file in Supplementary Data File\u00a010 and the simulation video in Supplementary Movie\u00a04. The layered nature of 3D printed systems, material flow inconsistencies, and external factors such as humidity further contributed to fabrication-related deviations.\n\nOur observations reveal that increasing joint stiffness leads to a reduced horizontal range in trajectories. The softness of the links appears to have minimal impact on the horizontal range. This suggests that while most deformation occurs at the joints, the links also undergo a slight elastic deformation. However, this deformation is relatively minor compared to that of the joints, affecting the horizontal range of a trajectory only by 2%. We also noticed that our motion curves are smooth, a characteristic we attribute to the damping effects inherent in the soft joint structures. We also performed a mean error analysis to numerically compare the trajectories. We provide details of our analysis in\u00a0Supplementary Information - Mean Error Analysis for Air Trajectories and Supplementary Data File\u00a012.\n\nTo examine the impact of varying compliance (i.e., multi-material combinations) in leg mechanisms, we conducted locomotion tests. Our robot, equipped with four independent actuators and encoders, allowed for individual closed-loop control of each leg mechanism, enabling the execution of any quadruped gait. We implemented the trot gait on the quadruped as it offers a trade-off between speed and stability. In the trot gait, two diagonal legs operate in synchrony, while the other diagonal pair is offset by 180\u2218\u200935.\n\nIn the future, sensory feedback from sources such as cameras could enable adaptive gait changes, allowing the robot to change gaits in response to various obstacles. To prioritize ease of fabrication, reduce costs, and maintain focus on developing mechanism-based soft robots, we conducted all of our experiments using the trot gait.\n\nFor a controlled comparison, we maintained a consistent robot body while testing five distinct leg mechanisms. This set comprised three variations of our synthesized main mechanism (Fig.\u00a05d, designs 2i, 2ii, 2iii), and two additional mechanisms designed for wider and taller trajectories, constructed from Armadillo (75D) and NinjaFlex (85A) materials (Fig.\u00a05c, designs 1 and 3). To track the foot and geometric centroid of each robot during locomotion, we employed an image trajectory processing algorithm shared in Supplementary Data File\u00a09.\n\nMaterial combinations exert a discernible impact on motion smoothness, with softer combinations correlating with diminished oscillations and expanded range in locomotion direction. a Image tracking of leg trajectory for varying synthesis combinations. b Image tracking of leg trajectory for varying material combinations. c Different synthesized mechanisms tested. d Different material combinations tested. e Centroid image tracking for varying synthesis combinations. f Centroid image tracking for varying material combinations.\n\nThe results show that the combinations of materials directly affect the smoothness of locomotion (Fig.\u00a05). We provide our data and videos of the experiments in Supplementary Data File\u00a013 and Supplementary Data File\u00a014. Using combinations of softer materials dampens the amplitude of the vertical movement of both the foot and the centroid of the robot (Table\u00a01). Softer materials, compared to rigid materials, undergo greater elastic deformation under the same applied force, effectively acting as dampers to attenuate motion roughness. We also observed that variations in analytical synthesis translate well into the fabrication domain, with wider and taller trajectories apparent with different geometrical leg variations (Table\u00a01, Fig.\u00a05). In summary, increased material softness enhances speed while also reducing the vertical range of robot movement (Table\u00a01).\n\nAlthough the primary focus of the paper is on soft locomotion mechanisms and the corresponding locomotion tests primarily explore their properties, we recognize the significant influence of body stiffness on motion dynamics. To examine this effect, we performed additional locomotion tests, keeping the locomotion mechanism constant while varying the body material (85A, 95A, and 75D). Detailed descriptions of these tests and the associated data are available in\u00a0Supplementary Information - Body Material Testing and Supplementary Data File\u00a015. The results from these tests demonstrate that softer robot bodies exhibit increased undulations, smoother motion, reduced centroid oscillations compared to non-elastomeric bodies, and, consequently, more stable locomotion. Supplementary Movie\u00a06 provides video footage of these experiments. A robot body made from 85A TPU exhibited greater angulations than a robot body made from 95A TPU, resulting in higher amplitudes in vertical movement. However, angulations did not relate to rough motion. Instead, the increased angulations suggest improved energy dissipation through body movement, resulting in smoother locomotion (Supplementary Movie\u00a06).\n\nAs a final analysis, we explored how geometric differentiation, in terms of cut-out and beam usage, affects the deformation and angulation capabilities of the body. We conducted a finite element simulation comparing three different body models. Our findings indicate that bigger cutouts enable bigger angulation capabilities, but the analysis does not capture information regarding the structural integrity and load-carrying capacities of robot bodies. Our interpretation is that a balance between supports and reliefs is required to establish a successful operation, and this balance may shift with the operation and design needs. The details of these tests are shared in\u00a0Supplementary Information - Body Geometry Analysis, and the simulation files are shared in Supplementary Data File\u00a017.\n\nGiven that our robot chassis and locomotion mechanisms were constructed from a combination of hard and soft materials within unified geometries, our aim was to demonstrate that these overall geometries retained soft characteristics regarding impact resistance and elastic deformation. We devised a hard version of our robot comprising rigid links and conventional joints connected via dowel pins, akin to a standard classical mechanism. We subjected our soft robot and its hard counterpart to compression tests for comparative analysis. We share our test setup in the Methods section and the data in Supplementary Data File\u00a016. We emphasize that this rigid robot was used solely for impact resistance tests, as design and component modifications would have been necessary to achieve locomotion with rigid materials.\n\nThe results presented in Fig.\u00a06 demonstrate that the integration of soft and hard materials in our soft hybrid robot exhibits overall soft behavior. We provide videos of our tests in Supplementary Movie\u00a07. Deformations primarily occur within the soft sections of the body and locomotion mechanisms. In contrast, the hard variant experienced irreversible plastic deformation and fracture. These findings align with our tensile testing results, indicating that monolithic geometries composed of soft and hard materials collectively exhibit soft behavior, with the structural elements of our robot working similarly to those of a soft robot.\n\na The force-versus-displacement graph from the impact test illustrates that the flexible chassis endures deformation before forces escalate. Spikes in the rigid chassis plot denote fracture points. b Post-test, the flexible chassis reverts to its original shape, while the rigid chassis remains flattened due to plastic deformation and fracture. c Picture of the test setup.\n\nFollowing experimentation, we tested our quadruped in different real-life scenarios (Fig.\u00a07, Supplementary Movie\u00a01). We operated the robot on different terrains, including soil with gravel (Fig.\u00a07a), rocks (Fig.\u00a07b), dirt (Fig.\u00a07e), sand (Fig.\u00a07f), and carpet (Fig.\u00a07g). Our locomotion mechanisms enable the robot to achieve different trajectories, and the soft angulation capabilities of the entire robot structure show adaptability in navigating diverse terrains. We demonstrate that variations in the synthesis of locomotion mechanisms are key to adapting to different mission scenarios. For instance, our vertical step linkage (Fig.\u00a05c, design 1) successfully climbed a steep slope (Fig.\u00a07d), a task that our standard linkage (Fig.\u00a05c, design 2) was unable to accomplish. Finally, we show the durability of our structural elements, both the robot body and the locomotion mechanisms, by driving over the robot chassis with a car (Fig.\u00a07c and Supplementary Movie\u00a08).\n\na Robot walking on soil. b Robot walking on a rock. c Robot driven over by a car. d Robot climbing steep rock (vertical trajectory - Fig.\u00a05c). e Robot walking in dirt. f Robot walking on sand. g Robot walking on a carpet.\n\nAcross various demonstrations, the angulation of the body becomes notably evident. This observation is crucial as it highlights how the movement of the body mirrors that of biological systems, with oscillations counteracting the forces generated by the robot\u2019s locomotion, thereby significantly improving stability (Fig.\u00a07g). We describe stability by a combination of different properties such as the vertical amplitude of centroid movement, slope of the curve of the movement plot, and the tip of the plot (being sharp or plateu-like). Elastomeric leg mechanisms and robot bodies exhibit smaller amplitudes in centroid movement; however, comparing two different elastomeric bodies reveals that the softer material may display greater movement due to enhanced angulations. These angulations suggest an increase in energy dissipation through body movements, leading to more stable locomotion. This represents a significant advantage of our fabrication framework over rigid quadrupeds, which face higher normal forces and consequently increased oscillations.\n\nTo demonstrate the effectiveness of our framework in real-world scenarios, we tested the maximum distance our robot could travel on a single battery charge. For this purpose, we changed the initial battery (3.7\u2009V, 750\u2009mAh) with a battery that still fit the form factor of the robot but had a higher discharge capacity (2000\u2009mAh). We programmed our fastest locomotion mechanism, which featured a nominal trajectory with 95A links and 85A joints (Table\u00a01). The robot, with a body length of 98.46\u2009mm, covered an untethered distance of ~250\u2009m (or 2500 times its body length), indicating the practical potential of our framework. As our cyclic fatigue tests showed that our interfaces can withstand at least 10,000 cycles; we deduct that the operational range of the robot is constrained by its battery capacity.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56025-3/MediaObjects/41467_2025_56025_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The field of soft robotics has emerged as a transformative avenue in the realm of robotics, offering consequential advantages in adaptability and impact resistance. Through the synthesis of compliant materials and innovative design principles, a soft robot not only mimics the dexterity and flexibility of biological systems but also competes with a conventional rigid robot in its ability to navigate complex and dynamic environments (Fig.\u00a07).\n\nAlthough soft robotics offers a myriad of advantages, a notable struggle within the field lies in the domain of terrestrial locomotion. The very softness of the materials that endow these robots with unique capabilities also presents challenges when it comes to achieving efficient and precise movements on solid ground. The inherent compliance and deformability of soft materials, which contribute to adaptability and impact resistance, can impede traditional methods of generating controlled and stable locomotion. The lack of rigid structural elements, which is prevalent in conventional robotics (and vertebrates), poses difficulties in maintaining the necessary stability and directional control essential for terrestrial mobility.\n\nOur design and fabrication framework confers a distinct advantage over numerous existing soft robots: practical suitability for real-world scenarios and the preservation of inherent softness while maintaining structural integrity. By facilitating the development of robots capable of adapting to various terrains and trajectory specifications, the field is positioned to introduce soft hybrid robots into practical use. These robots seamlessly transition from the typical crawling motion observed in many soft robots to walking via closed-curve trajectories. The potential customization of these robots with a variety of sensors to fulfill specific application requirements opens up diverse avenues for applications and future research.\n\nOur framework brings classical mechanism design principles into the soft robotics domain; enabling flexible, scenario-specific designs. This approach allows for the replication and conversion of any other mechanism, such as the Watt linkage, which converts rotary to linear motion, into the soft robotics domain. Our goal is to provide the robotics community with a practical method to design and build mechanisms that help soft robots meet specific operational needs.\n\nOur research has also identified key limitations, particularly in integrating conventional electronics and circuits with our robot design. Although structural elements are robust, capable of carrying loads and accommodating angular distortions, the inclusion of rigid electronic components restricts these capabilities. Future work will incorporate flexible electronics and encase electronic components entirely within soft-mechanism-driven robots. By using soft materials to absorb the forces that act on the robot body, fragile electronic components will be protected from stresses. To ensure consistency and eliminate potential errors associated with manual operation, we will explore the opportunity of integrating new tool heads, such as pick-and-place and solder dispensing, with a tool changer, and automate the integration of electronics into the robot chassis.\n\nPrintable TPUs and their material properties, such as glass transition and heat deflection temperatures36, impose constraints on the types of robots that can be produced using our framework. For example, robots developed with this approach may quickly become inoperative in extreme temperature environments.\n\nThe locomotion range and speed of the robots could be further enhanced by incorporating high-performance miniature motors. Waterproofing electronics, including motors, would expand their applicability to water surface and subsurface environments.\n\nIt is important to note that transitioning traditional mechanisms to an entirely compliant basis is not feasible for continuous rotation, as our robot requires distinct rigid cam links for the continuous rotation of its legs. Although alternatives, such as linear actuators to avoid complete rotations, are possible, design constraints will still remain within the system.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "We tested the specimens using an Instron 5567A Universal Testing System (Fig.\u00a03). We designed the specimens in accordance with ASTM D638 Standard and extended them with a 500\u2009mm\u2009min\u20131 rate until they failed or the range of motion of the testing system was reached. We tested the three interfacing methods (straight, dovetail, and fingers) with the combinations of three filaments (85A, 95A, and 75D), leading to nine different combinations for multi-material prints with an additional three single-material prints for comparison. We printed and tested each combination three times.\n\nUpon initial examination, we identified outliers within our data, characterized by premature failure points in contrast to other specimens. Despite using identical materials and print parameters for similar combinations, inconsistencies emerged due to low resolution in layer height, fluctuations in idler tensioning, wear and loosening within the system leading to minor alignment offsets, humidity (given the hygroscopic nature of TPU), and filament path obstructions. Following the removal of outlier data, we obtained meaningful information to facilitate a comparison of interface characteristics.\n\nWe recorded videos of the mechanisms during actuation in mid-air. We marked points of interest for tracking with bright blue tape to ensure contrast against the mechanisms and background (Fig.\u00a04b). These videos were imported into MATLAB, where they were segmented into individual frames for analysis. For each frame of the recorded videos, we captured the RGB value of each pixel. A color thresholding mask was applied to isolate the pixels of interest at the foot of the mechanism. To consolidate the points, we computed their centroids at each time step. This process was replicated for all frames in the video, enabling us to plot the trajectories. This procedure was performed for our nominal four-bar linkage (Fig.\u00a05), with joint differentiation. We tested linkages with joints from TPU with 85A and 95A Shore hardness and links from TPU with 75D Shore hardness to understand the effect of hardness variation on motion.\n\nWe created a CAD model assembly of the linkage, the body, the cam (which couples the DC motor to the linkage), and a dowel pin connecting the cam to the linkage. This model was imported into COMSOL Multiphysics in\u00a0standard for the exchange of product data format, in which we created an analysis with the multibody dynamics tool (Fig.\u00a04c). The multibody dynamics tool allows different bodies to be meshed separately, creating an assembly rather than a unified solid body. We created separate entities of the linkage, the cam, and the pin and connected them with joint definitions.\n\nIn our model, we specified the multi-material structure by developing material models for the different TPU filaments used in fabrication and assigning them to the appropriate sections of the linkage. We conducted simulations on three different material combinations: 85A TPU joints with 95A TPU links, 85A TPU joints with 75D TPU links, and 95A TPU joints with 75D TPU links. In each simulation, we applied a constant rotary motion (speed of \u03c0\u2009rad\u2009s\u20131) to the model at the hinge joint connecting the cam to the motor.\n\nTo assess the impact resistance of the robot, we performed compression tests using a universal testing machine (Fig.\u00a06). For comparison, we fabricated an entirely rigid version of the robot from PLA (Shore hardness 98D37). This rigid model featured rigid links and ideal pin joints connected using 3\u2009mm steel dowels. In both models, electronics and motors were excluded during tests. We performed our tests with an Instron 5567A Universal Testing System, equipped with compression plates and a load cell. Each robot chassis, positioned upright, underwent compression at 30\u2009mm\u2009min\u20131 until structural failure occurred or the lower limit of the Instron was reached.\n\nThe soft chassis was able to withstand a compressive force of ~30\u2009kN after being completely flattened, undergoing only elastic deformation. In comparison, the legs of the rigid chassis fractured after enduring ~17\u2009kN. The body began to deform plastically at ~18\u2009kN. Upon release of the compression platens, the rigid chassis remained flattened with non-reversible damage to the body and near-complete fracture in the legs, whereas the soft mechanism returned identically to its initial configuration and sustained no lasting damage.\n\nWe used generative AI (ChatGPT, 4o) to generate the Python code in the \u201cMean Error Analysis for Air Trajectories\u201d section in\u00a0Supplementary Information. We described the structure of the mathematical method ourselves, and AI created the code block accordingly.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data necessary to evaluate the conclusions in the paper are available in the paper or the\u00a0Supplementary Information.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All code necessary to evaluate the conclusions in the paper are available in the paper or the\u00a0Supplementary Information.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Coad, M. M. et al. Vine robots. IEEE Robot. Autom. Mag. 27, 120\u2013132 (2020).\n\nArticle\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nKal\u0131n, M. A. H. et al. Design, fabrication, and locomotion analysis of an untethered miniature soft quadruped, SQuad. IEEE Robot. Autom. 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Kendre for her support in static tensile testing, and Prof. Gary Leisk for his support in dynamic fatigue testing.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "This work was supported by the National Science Foundation under Grant No. 2237506, given to M.P.N.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Sara A. Frunzi, Brian J. Katz.\n\nDepartment of Mechanical Engineering, Tufts University, Medford, MA, USA\n\nCem Ayg\u00fcl\u00a0&\u00a0Markus P. Nemitz\n\nDepartment of Robotics Engineering, Worcester Polytechnic Institute, Worcester, MA, USA\n\nCan G\u00fcven,\u00a0Sara A. Frunzi\u00a0&\u00a0Brian J. Katz\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nC.A. designed the research and wrote the paper. C.A. and B.J.K. developed the fabrication pipeline. C.A., C.G., and S.A.F. performed experiments. C.A. and C.G. developed the control algorithm. M.P.N. supervised the work and edited the paper.\n\nCorrespondence to\n Markus P. Nemitz.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Bobak Mosadegh who co-reviewed with Majid Roshanfar; Maurizio Follador for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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A framework for soft mechanism driven robots.\n Nat Commun 16, 1426 (2025). https://doi.org/10.1038/s41467-025-56025-3\n\nDownload citation\n\nReceived: 15 March 2024\n\nAccepted: 02 January 2025\n\nPublished: 07 February 2025\n\nVersion of record: 07 February 2025\n\nDOI: https://doi.org/10.1038/s41467-025-56025-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 41.25c-9.8 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pneumonia: a randomised trial", + "pre_title": "Multiplex real-time PCR in non-invasive respiratory samples to reduce antibiotic use in community-acquired pneumonia: a randomised trial", + "journal": "Nature Communications", + "published": "17 August 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51547-8/MediaObjects/41467_2024_51547_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51547-8/MediaObjects/41467_2024_51547_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51547-8/MediaObjects/41467_2024_51547_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Antibiotics", + "Bacterial infection", + "Laboratory techniques and procedures", + "Randomized controlled trials" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4186714/v1.pdf?c=1723979171000", + "research_square_link": "https://www.researchsquare.com//article/rs-4186714/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-51547-8.pdf", + "preprint_posted": "09 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The aetiology of community-acquired pneumonia (CAP) is often not identified and patients are overtreated with antibiotics. We assessed whether multiplex real-time PCR plus conventional microbiological testing is safe and more effective than conventional microbiological testing alone for reducing antibiotic use in community-acquired pneumonia (CAP). In this randomised trial, we recruited adult patients hospitalised with CAP at four Spanish hospitals. Patients were randomly assigned (1:1) to undergo either multiplex real-time PCR in non-invasive respiratory samples plus conventional microbiological testing or conventional microbiological testing alone. The primary endpoint was antibiotic use measured by days of antibiotic therapy (DOT). Between February 20, 2020, and April 24, 2023, 341 patients were assessed for inclusion, of whom 242 were finally enrolled. Of these, 119 were randomly assigned to multiplex real-time PCR plus conventional microbiological testing and 123 to conventional microbiological testing alone. The median DOT was 10.04 (IQR 7.98-12.94) in the multiplex PCR plus conventional microbiological testing group and 11.33 (IQR 8.15-16.16) in the conventional microbiological testing alone group (difference -1.04; 95% CI, -2.42-0.17; p=0.093). No differences between groups were observed in relevant secondary outcomes, including adverse events and 30-day all-cause mortality. Our findings do not support the routine implementation of multiplex real-time PCR in the initial microbiological testing in hospitalised patients with CAP. Further randomised controlled trials are needed to assess the safety and effectiveness of multiplex real-time PCR testing in improving antibiotic use and clinical outcomes in CAP.\u00a0\nClinicaltrials.gov registration: NCT04158492.Health sciences/Health care/Diagnosis/Laboratory techniques and proceduresHealth sciences/Medical research/Clinical trial design/Randomized controlled trials", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "We assessed whether multiplex real-time PCR plus conventional microbiological testing is safe and more effective than conventional microbiological testing alone for reducing antibiotic use in community-acquired pneumonia (CAP). In this randomised trial, we recruited adults hospitalised with CAP at four Spanish hospitals. Patients were randomly assigned (1:1) to undergo either multiplex real-time PCR in non-invasive respiratory samples plus conventional microbiological testing or conventional microbiological testing alone. The primary endpoint was antibiotic use measured by days of antibiotic therapy (DOT). Between February 20, 2020, and April 24, 2023, 242 patients were enrolled; 119 were randomly assigned to multiplex real-time PCR plus conventional microbiological testing and 123 to conventional microbiological testing alone. All but one of the patients allocated to multiplex real-time PCR plus conventional microbiological testing underwent PCR, which was performed in sputum samples in 77 patients (65.2%) and in nasopharyngeal swabs in 41 (34.7%). The median DOT was 10.04 (IQR 7.98, 12.94) in the multiplex PCR plus conventional microbiological testing group and 11.33 (IQR 8.15, 16.16) in the conventional microbiological testing alone group (difference \u22121.04; 95% CI, \u22122.42 to 0.17; p\u2009=\u20090.093). No differences were observed in adverse events and 30-day mortality. Our findings do not support the routine implementation of multiplex real-time PCR in the initial microbiological testing in hospitalised patients with CAP. Clinicaltrials.gov registration: NCT04158492.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Community-acquired pneumonia (CAP) is a major cause of morbidity and mortality worldwide and one of the leading drivers of antibiotic use in hospitalised patients1,2. However, in many cases, the causative agent is not identified and patients are overtreated with antibiotics3,4. Overuse of antibiotics is a major cause of antimicrobial resistance and increases the risk of Clostridioides difficile infection and other antibiotic-related adverse events4,5. Antibacterial resistance is accelerating at an alarming pace and is raising morbidity and mortality rates worldwide6. In this scenario, antimicrobial stewardship is recognised as a crucial component in strategies to deal with the threat of antibiotic resistance7,8.\n\nThe development of multiplex real-time polymerase chain reaction (PCR) in automated platforms currently allows rapid screening of non-invasive respiratory specimens, such as sputum samples and nasopharyngeal swabs, for a wide array of respiratory pathogens9. Several observational studies have found that comprehensive molecular testing significantly improved pathogen detection in CAP, particularly in antimicrobial-exposed patients10,11. Two recent studies investigating the efficacy of multiplex real-time PCR in non-invasive respiratory samples for antimicrobial stewardship in CAP have yielded conflicting findings12,13.\n\nCurrent guidelines for CAP do not incorporate multiplex PCR pneumonia panels into their recommendations for initial microbiological diagnostic testing2,14,15. Furthermore, the guidance regarding conventional microbiological testing methods like sputum culture, blood cultures, and urinary antigen tests lacks consistency and is predominantly grounded in low or very low-quality evidence.\n\nWe conducted a randomised controlled trial to test the hypothesis that multiplex real-time PCR in non-invasive respiratory samples plus conventional microbiological testing is safe and more effective than conventional microbiological testing alone for reducing antibiotic use in hospitalised patients with CAP.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Between February 20, 2020, and April 24, 2023, we assessed 315 patients with CAP for eligibility. After excluding 73 patients who were considered ineligible, the remaining 242 were enrolled and then randomly assigned to undergo multiplex real-time PCR plus conventional microbiological testing (n\u2009=\u2009119; 49%) or conventional microbiological testing alone (n\u2009=\u2009123; 51%). The primary endpoint was analysed by intention-to-treat in all 242 patients and per-protocol in 230. The trial profile is shown in Fig.\u00a01. During the study period, 11,938 patients with suspected or confirmed COVID-19 were admitted in specific emergency areas or buildings of the participating hospitals to which the trial investigators did not have access.\n\nCONSORT diagram indicating participant numbers and dispositions troughout the course of the trial.\n\nThe baseline characteristics in the intention-to-treat population were well-balanced between groups (Table\u00a01). Median age, the percentage of patients older than 75 years, and the frequency of chronic pulmonary and heart diseases were slightly higher in the group undergoing multiplex real-time PCR plus conventional microbiological testing. Charlson comorbidity index score, pneumonia severity index score, and CURB-65 were similar in both groups. The baseline characteristics of the per-protocol population were similar and are provided in Supplementary Table\u00a01. The initial antibiotic therapy was also similar in the two study groups (Table\u00a02). Most patients received conventional antibiotics used in CAP. Only 12 patients in the multiplex real-time PCR plus conventional microbiological group and 17 in the conventional microbiological testing alone group received anti-pseudomonal \u03b2-lactams. No patient received vancomycin or linezolid.\n\nTable\u00a03 shows the microbiological studies performed in each study group in the intention-to-treat population. All but one of the patients allocated to multiplex real-time PCR plus conventional microbiological testing underwent PCR, which was performed in sputum samples in 77 patients (65.2%) and in nasopharyngeal swabs in 41 (34.7%). Twenty-four (31.2%) of the 77 sputum samples were obtained from induced sputum. The proportion of patients who underwent the different types of conventional microbiological examinations was similar in the two study groups. The diagnostic yield based on the sample used for PCR testing is detailed in Supplementary Table\u00a02. Sputum samples and induced sputum samples had a higher yield than nasopharyngeal swabs. The time to positivity for each diagnostic test is presented in Supplementary Table\u00a03. Multiplex real-time PCR results were available more quickly than those from non-PCR-based diagnostic tests. An aetiological diagnosis was established in 76 (63.9%) of 119 patients in the multiplex real-time PCR plus conventional microbiological testing group and in 32 (26.02%) of 123 patients in the conventional microbiological testing alone group (difference 37.85; 95% CI, 25.42\u201350.28; p\u2009<\u20090.0001). The CAP causative organisms identified in each group are shown in Table\u00a04; the most frequent in both groups were Streptococcus pneumoniae, Legionella pneumophila, and Haemophilus influenzae. Polymicrobial infections and those caused by respiratory viruses were more frequent in the multiplex real-time PCR plus conventional microbiological testing group. Gram-negative bacilli, including Pseudomonas aeruginosa, were uncommon in both study groups. There were only three cases of CAP due to Staphylococcus aureus, all in the PCR group, and none of the strains were resistant to methicillin (MRSA).\n\nThe results for primary and secondary endpoints in the intention-to-treat population are shown in Table\u00a05. Median DOT was 10.04 (IQR 7.98, 12.94) in the 119 patients in the multiplex real-time PCR plus conventional microbiological testing group and 11.33 (IQR 8.15, 16.16) in the 123 patients in the conventional microbiological testing alone group (difference \u22121.04; 95% CI, \u22122.42 to 0.17; p\u2009=\u20090.093). The results of the primary endpoint are also shown in Fig.\u00a02. Results for the primary endpoint were confirmed by adjusted analysis (Supplementary Table\u00a04). No significant differences in length of antibiotic therapy (LOT) were found between the groups: the median LOT was 9.00 days (7.42, 11.0) in the experimental group and 8.76 days (6.92, 12.73) in the control group (difference 0.12, 95% CI \u22120.79 to 0.96; p\u2009=\u20090.775).\n\nThe figure presents a box-plot analysis of the primary endpoint, days of antibiotic therapy (DOT), in both study groups; multiplex real-time PCR plus conventional microbiological testing (left, red box-plot) and conventional microbiological testing alone (right, teal box-plot). The median DOT in the multiplex real-time PCR plus conventional microbiological testing is 10.04, with an interquartile range spanning from 7.98 to 12.94. The median DOT in the conventional microbiological testing alone is 11.33, with an interquartile range spanning from 8.15 to 16.16. Outliers were distributed similarly between bout groups with extended DOT up to 60 days.\n\nTime to switch from intravenous to oral antibiotic therapy and time to reach an aetiological diagnosis was significantly shorter in patients undergoing multiplex real-time PCR plus conventional microbiological testing. More patients in the conventional microbiological testing alone group were admitted to the ICU. There were no significant differences in other secondary endpoints, including de-escalation to narrowed spectrum antibiotic treatment, time to clinical stability, days of mechanical ventilation, antibiotic-related side effects, length of hospital stay, hospital readmission (\u226430 days), and death from any cause at 48\u2009h and at 30 days after randomisation. Per-protocol analyses of primary and secondary endpoints produced similar results to those of the intention-to-treat population (Supplementary Tables\u00a05 and 6).\n\nAll patients who received at least one dose of antibiotic treatment were included in the safety analysis. The proportion of patients with any adverse events (17.65% [21/119] vs. 21.14% [26/123]; risk difference \u22123.49; 95% CI, \u221214.27 to 7.28; p\u2009=\u20090.600), serious adverse events (14.29% [17/119] vs. 20.33% [25/123]; risk difference \u22126.04; 95% CI, \u221216.36 to 4.28; p\u2009=\u20090.284), and antibiotic-related events (5.04% [6/119] vs. 4.07% [5/123]; risk difference 0.98; 95% CI, \u22125.11 to 7.06; p\u2009=\u20090.955) were similar in the two study groups. A description of all adverse events according to system organ class reported in both study groups is provided in Supplementary Table\u00a07.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51547-8/MediaObjects/41467_2024_51547_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51547-8/MediaObjects/41467_2024_51547_Fig2_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this randomised, controlled, open-label, multicentre trial, we aimed to evaluate whether multiplex real-time PCR in non-invasive respiratory samples plus conventional microbiological testing is safe and more effective than conventional microbiological testing alone for reducing antibiotic use in hospitalised patients with CAP. The primary endpoint was DOT, chosen as a measure of antibiotic consumption on the basis of the guidelines published by the IDSA and the Society for Healthcare Epidemiology of America for the implementation of antibiotic stewardship programmes16. Specifically, this metric takes into account the use of more than one antibiotic per day by summing up the total days on which any antibiotic is administered.\n\nThe main result of our stuy is that we found a modest reduction in the median number of DOT in patients undergoing PCR testing that was not statistically significant. Furthermore, we did not find significant differences in the LOT between the study groups. The results for most secondary endpoints, including adverse events and 30-day all-cause mortality, were similar. As expected, and in line with the findings of several observational studies, the use of multiplex real-time PCR was associated with an increased microbial yield11,17. We also found that the time to reach an aetiological diagnosis and the time to switch from intravenous to oral antibiotics was shorter in patients undergoing multiplex real-time PCR plus conventional microbiological testing. Importantly, timely switching from intravenous to oral antibiotic therapy can enhance patient outcomes by reducing the risk of catheter-related complications and shortening hospital stays, which can result in cost savings for healthcare facilities18.\n\nThe results of our study are in line with the findings of a randomised trial showing that the routine implementation of urine antigen detection tests did not bring any substantial outcome-related benefit to hospitalised patients with CAP in terms of pneumonia-related complications, length of hospitalisation, or mortality19. Of concern, narrowing the antibiotic treatment according to the urine antigen test results was associated with a higher risk of relapse.\n\nOn the other hand, a randomised trial found that, compared to conventional microbiology, a multiplex bacterial PCR examination of bronchoalveolar lavage shortened the duration of inappropriate antibiotic therapy by 38.6\u2009h in patients admitted to hospital with pneumonia and at risk of Gram-negative rod infection20. However, this result did not translate into a significant difference in terms of time to reach clinical stability, antibiotic-adverse events, length of hospital stay, or in-hospital mortality. An important proviso regarding the applicability of the results of that study in clinical practice is the fact that most institutions do not perform invasive techniques such as bronchoscopy and bronchoalveolar lavage to identify the cause of pneumonia in non-intubated, clinically stable patients. In contrast, our trial included the overall population of non-severely immunosuppressed patients hospitalised with CAP (without focusing on any subgroup of patients at risk for specific pathogens) and used non-invasive respiratory samples that were easy to collect, thus avoiding the risk of complications associated with invasive procedures and patient discomfort and increasing the likelihood of widespread implementation.\n\nTwo recent randomised trials have explored the efficacy of multiplex real-time PCR in non-invasive respiratory samples to reduce antibiotic use in CAP, yielding divergent results12,13. Our main finding aligns with a trial conducted across three Danish medical emergency departments, wherein the integration of point-of-care PCR into the diagnostic regimen did not increase the number of CAP patients with a more targeted and appropriate use of antibiotics12. Conversely, a single-centre randomised trial in Norway revealed that implementing a PCR-based panel for rapid testing in the emergency department facilitated swifter and more tailored antibiotic therapy for individuals with suspected CAP13. However, the difference in the time taken to administer pathogen-directed treatment between patients undergoing PCR and those undergoing standard microbiological diagnostic tests alone was modest. Furthermore, this difference was not correlated with significant variations in length of hospital stay, mortality rates, and hospital readmissions13.\n\nIn our trial, although the multiplex real-time PCR plus conventional microbiological testing group achieved an aetiological diagnosis more frequently and more rapidly than the control group, this did not correlate with a significant reduction in antibiotic consumption. Among the factors that may have contributed to this finding is physician behaviour. Physicians may retain reservations regarding the reliability of PCR results, often placing more confidence in their clinical judgement than in microbiological data when determining antibiotic treatment, particularly in cases where patients show signs of improvement even when a virus is detected. Additionally, concerns may arise regarding the true aetiological significance of certain microorganisms identified through molecular techniques21. We should stress that, in our trial, attending physicians received clinical interpretations of the PCR results, but the research team did not provide specific recommendations regarding antibiotic use based on the microbiological findings. Interestingly, a recent cross-sectional, stepped-wedge, cluster-randomised, non-inferiority trial demonstrated that in patients hospitalised with CAP, a multifaceted antibiotic stewardship intervention might reduce broad-spectrum antibiotic use without improving diagnostic yield22.\n\nOur study has several limitations. The first is its open-label design, which may have introduced a bias in the evaluation of the primary endpoint. The lack of blinding may have influenced researcher behaviours, responses, and assessments. However, DOT is an objective metric of antibiotic consumption, which was assessed by a DSMB blinded to microbiological testing allocation. Second, the multiplex real-time PCR was performed in nasopharyngeal swabs in around one-third of cases. Although concerns have been voiced regarding the value of nasopharyngeal swabs for PCR testing, a growing body of evidence shows the reliability and utility of these easy-to-obtain samples, especially in non-immunocompromised patients and in those for whom sputum samples are difficult to collect23,24. Third, in a planned interim analysis when half the sample size required had been achieved, the DSMB committee proposed to stop recruitment owing to a concern with futility. Therefore, and also in view of the slow recruitment rate due to the impact of the COVID-19 pandemic the steering committee decided to discontinue the trial. It should be noted, that the premature discontinuation of the study might have limited the robustness and generalisability of its results. Additionally, mortality was low in both treatment groups, and the trial was not powered to detect survival differences. Notably, the pandemic may have contributed to the low frequency of viral infections and invasive pneumococcal disease observed in our trial. This effect was likely due to the implementation of universal masking and other non-pharmaceutical interventions, such as social distancing, hand hygiene, and lockdowns25,26,27. Finally, since the number of ICU patients was low, and the trial did not include severely immunocompromised patients, our conclusions do not apply to these populations, that may have risk factors for unusual pathogens and might benefit from comprehensive microbiological testing.\n\nIn conclusion, our study does not support the routine implementation of multiplex real-time PCR in non-invasive respiratory samples in the initial microbiological testing in CAP patients not admitted to the ICU and without severe immunocompromise. Our trial not only broadens the understanding of the difficulties of antibiotic optimisation in CAP management but also highlights the practical implications and considerations for implementing advanced microbiological diagnostic approaches in real-world clinical settings. To thoroughly evaluate the safety and effectiveness of multiplex real-time PCR testing in improving antibiotic use and enhancing relevant clinical outcomes, further studies are needed. These studies should ideally be conducted on adaptive platforms with electronic data capture. They should incorporate prescribers\u2019 qualitative behavioural analysis, cluster-randomised interventions using individual patient data, and include education and audit tools. Such studies are necessary before recommending the integration of this testing method into the initial microbiological assessment of all hospitalised patients with CAP.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "We performed a randomised, controlled, open-label trial with two parallel groups of patients hospitalised for CAP at four Spanish teaching hospitals (the RADICAP trial). Participants were recruited from February 20, 2020, to April 24, 2023. The study was authorised by the Spanish Medicines and Healthcare Products Regulatory Agency (AEMPS; 19-0388) and by the Bellvitge University Hospital Ethics Committee (PR214/18). The protocol has been published elsewhere and followed the SPIRIT initiative28. Patients\u2019 personal and clinical information was managed in line with European Regulation (2016/679). The results are presented in accordance with the Consolidated Standards of Reporting Trials (CONSORT) statement. The trial is registered in ClinicalTrials.gov (NCT04158492) and EudraCT (2018-004880-29).\n\nAll patients aged \u226518 years diagnosed with CAP in the emergency department were screened for eligibility within the first 24\u2009h of admission. CAP was defined as the presence of an infiltrate on the chest radiograph plus one or more of the following: fever (temperature, \u226538.0\u2009\u00b0C) or hypothermia (<35.0\u2009\u00b0C), new cough with or without sputum production, pleuritic chest pain, dyspnoea, and altered breath sounds on auscultation. Exclusion criteria were pregnancy or lactation; severe immunocompromise (i.e., patients receiving antineoplastic chemotherapy or radiotherapy in the previous 90 days, use of immunosuppressive drugs, use of corticosteroids at a minimum dose 15\u2009mg/day in the previous 2 weeks, haematopoetic progenitor transplant, solid organ transplant, patients with HIV infection and CD4 count \u2264200 cells/mm3); pleural empyema; imminent death (life expectancy \u2264 24\u2009h); and participation in another clinical trial. Sex was recorded from the official documentation of each participant. Acute SARS-CoV2 infection and COVID-19 in the previous 90 days were added as exclusion criteria by a protocol amendment after the start of the pandemic. The amendment was approved by the Ethics Committee and by AEMPS. Before inclusion in the trial, all participants or legal representatives provided written informed consent.\n\nPatients were randomly assigned (1:1) to multiplex real-time PCR in non-invasive respiratory samples plus conventional microbiological testing or to conventional microbiological testing alone. A centralised electronic computer randomisation schedule was developed by the Biostatistics Unit at the Bellvitge Biomedical Research Institute (IDIBELL). The randomisation was performed in computed-generated blocks of 10 patients stratified by hospital site so as to conceal the sequence until the intervention was assigned. The code numbers for eligible patients were assigned in ascending sequential order. The allocation list was stored at IDIBELL and was not available to any member of the research team. At each participating hospital, patients who provided written informed consent and met the study criteria were randomised by investigators, who obtained the microbiological testing assigned and code number from a computer-assisted website.\n\nWe randomly allocated participants to undergo either multiplex real-time PCR (Biofire\u00ae Filmarray\u00ae Pneumonia Plus panel, Biofire Diagnostics, LLC, Salt Lake City, Utah, US) plus conventional microbiological testing or conventional microbiological testing alone. In participants assigned to undergo multiplex real-time PCR, sputum samples (either spontaneous or induced) were obtained when available. If sputum samples could not be obtained, nasopharyngeal swabs were collected instead. All samples for PCR testing were obtained within 24\u2009h of randomisation. All participants in both study groups underwent conventional microbiological testing at the discretion of the attending physician, which usually included two sets of blood cultures, sputum for Gram stain and culture when available, and urine for detection of antigens of Streptococcus pneumoniae and Legionella pneumophila serogroup 1. Testing for respiratory viruses (e.g., influenza, respiratory syncytial virus, and human metapneumovirus) was indicated at the discretion of the attending physician. All participants underwent SARS-COV-2 PCR testing. The results of the multiplex real-time PCR were communicated to the attending physicians immediately upon availability. This information, along with the clinical interpretation by the investigators, was shared both via telephone and through the electronic medical record system28. Additionally, the results of conventional microbiological tests were provided to the attending physicians through the electronic medical record system.\n\nThe primary endpoint was antibiotic use measured by days of antibiotic therapy (DOT). DOT refers to the number of days that a patient receives an antimicrobial agent, regardless of the dose, route or frequency of administration16. The secondary endpoints were de-escalation to narrower spectrum antibiotic treatment, time to switch from intravenous to oral antibiotics, time to reach an aetiological diagnosis, days to clinical stability after randomisation, need for intensive-care unit admission, days of mechanical ventilation, antibiotic-related side effects, any adverse event, length of hospital stay, need for hospital readmission within 30 days of randomisation, death from any cause within 48\u2009h and within 30 days of randomisation.\n\nInitial empirical antibiotic therapy was administered in the emergency department in accordance with participating hospitals\u2019 guidelines, which recommend the administration of a \u03b2-lactam agent with or without a macrolide or fluoroquinolone. Initial empirical combination antimicrobial therapy was recommended for patients with severe CAP and/or those without any positive microbiological test. Levofloxacin monotherapy was indicated for Legionella pneumonia and for selected patients such as those with \u03b2-lactam allergy. Carbapenems, piperacillin-tazobactam and cefipime were considered broad-spectrum antibiotics. Narrow-spectrum antibiotics were generally considered when penicillin or ceftriaxone was used. All decisions regarding empirical and definitive antibiotic therapy, de-escalation, switch from intravenous to oral antibiotic therapy, and duration of treatment were made by the attending clinicians. The investigators were not involved in any decisions regarding antibiotic treatment.\n\nAll participants were seen daily during their hospital stay by their attending physicians and by at least one of the study investigators. The investigators recorded all outcome measures. DOTs were calculated as the days elapsed from the initial dose of antimicrobial until the last dose of antimicrobial therapy for the CAP episode. The DOT for a given patient on multiple antibiotics was the sum of DOT for each antibiotic that the patient received. All antibiotics administered to patients for an episode of CAP and its related complications were included in the primary endpoint calculation. A new treatment for CAP was considered if there was an interruption in antibiotic therapy lasting more than 48\u2009h. Antimicrobial de-escalation was considered when a broad-spectrum antimicrobial treatment regimen was replaced with narrower-spectrum antimicrobials or when one or more initial combination empiric antimicrobials were discontinued. Participants attended an outpatient visit 30 days after hospital discharge. The investigators recorded readmissions for any reason or death from any cause in the 30 days after randomisation. The information was obtained from specific searches of hospital databases and was checked by asking patients at the outpatient visit 30 days after hospital discharge. For patients who did not attend this outpatient visit, a structured telephone interview was used to assess outcomes. Adverse events were recorded in all patients who received at least one dose of antibiotic therapy. All adverse events were recorded according to the Common Terminology Criteria for Adverse Events. The study was monitored by the IDIBELL Clinical Research and Clinical Trials Unit. All data were recorded on a secure web application for building and managing databases (REDCap). The study endpoints were assessed by a Data and Safety Monitoring Board (DSMB), which was blinded to study group and patient identity.\n\nSputum samples or nasopharyngeal swabs were processed immediately after reception at the Microbiology Laboratory. The multiplex real-time PCR used was the Biofire\u00ae Filmarray\u00ae Pneumonia Plus panel (Biofire Diagnostics, LLC, Salt Lake City, Utah, US). This panel is an automated multiplex PCR test for the rapid detection of 15 typical bacteria (four Gram-positive, 11 Gram-negative) with a semiquantification result, three atypical bacteria, and nine respiratory viruses (https://www.biomerieux-diagnostics.com/biofire-filmarray-pneumonia-panel). Bacterial load detections were categorised as positive when \u2265106 CFU/mL were detected. In cases of Streptococcus pneumoniae detection, the cut-off point for considering the test as positive was \u2265105 CFU/mL. The results for atypical bacteria (Legionella pneumophila, Mycoplasma pneumoniae, Chlamydiophila pneumoniae) and viruses were reported as detected or not detected. The multiplex real-time PCR results were provided to the attending physicians via the electronic medical record. Conventional microbiological studies in both study groups were carried out by standard methods and usually included Gram stain and culture of good quality sputum samples (<10 squamous cells and >25 leucocytes by low-power field [100X] in the Gram stain examination) when available, two sets of blood cultures (BACTEC\u00ae FX, BD, Madrid, Spain), and culture of pleural fluid when present. Furthermore, S. pneumoniae antigen in urine was detected using a rapid immunochromatographic assay (Binax\u00ae, Abbott, Chicago, Illinois, U.S.) and L. pneumophila serogroup 1 antigen was detected using an immunoenzymatic method (Bartels\u00ae, Trinity Biotech Plc., Bray, Ireland).\n\nBased on our experience using conventional microbiological testing, the expected DOT is about 8 days when the aetiology of CAP is known and 11 days when it is not known. The primary endpoint (DOT) was expected to be non-normally distributed. If the true difference between the two microbiological testing study groups is two DOT, we estimated that we needed 220 participants undergoing multiplex real-time PCR plus conventional microbiological testing and 220 participants undergoing conventional microbiological testing alone to be able to reject the null hypothesis with a probability above 0.8. The type I error probability associated with this test of the null hypothesis is 0.05, assuming an expected dropout rate of 10%. The planned interim analysis was performed on March 27, 2023, when half of the sample required had been recruited, in order to evaluate the safety and to ensure sufficient statistical power. The DSMB, which was blinded to the microbiological testing allocation, raised no concerns regarding safety; however, it mentioned that the difference in DOT specified in the sample size calculation was 2 days and that the difference observed in the interim analysis was around one DOT. The estimated conditional power was 40%, and various simulations of the expected difference at the end of the study yielded a difference of around one DOT, an effect far removed from the expected clinical significance; as a result, the DSMB committee proposed to stop recruitment owing to concerns regarding futility. In view of the DSMB\u2019s recommendation and the slow recruitment rate due to the impact of the COVID-19 pandemic, the steering committee decided to halt the trial on April 24, 2023.\n\nData for the primary and secondary outcomes were analysed by intention-to-treat and per-protocol. The intention-to-treat analysis included all randomly assigned patients, while the per-protocol analysis included all enrolled patients who completed the study without any major protocol deviations. All patients who received at least one dose of antibiotic treatment were included in the safety analysis. The primary endpoint was compared in the two study groups using the Wilcoxon rank sum test, while secondary endpoints were analysed using the chi-squared test or the Wilcoxon rank sum test depending on the type of variable. Median or risk differences were calculated and reported to quantify the observed effect with a 95% confidence interval. An adjusted analysis was performed for the primary endpoint using linear, quantile or logistic regression according to the endpoint. The adjustment variables considered were age, sex, Charlson index score, and pneumonia severity index score. All analyses and data management were performed with R software, v.4.0.529. The most relevant R packages used were dplyr, REDCapDM, rpact and compareGroups30,31,32,33.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Individual data cannot be shared because of privacy restrictions. Raw anonymised data relating to primary and secondary outcomes and safety can be shared upon request to researchers who provide a methodologically reasonable proposal. The request for data can be sent to the corresponding author (J.C.). A period of 18 months after publication of the main study results should elapse before requests are made, to allow authors to publish substudies. Interested researchers should obtain the approval of the Bellvitge University Hospital Ethics Committee.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "GBD 2019 Diseases and Injuries Collaborators. 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A.R. is supported by a predoctoral grant (PFIS contract FI18/00183) by the Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation, Madrid, Spain.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Infectious Diseases, Bellvitge University Hospital, L\u2019Hospitalet de LLobregat, Barcelona, Spain\n\nGabriela Abelenda-Alonso,\u00a0Alexander Rombauts,\u00a0Carlota Gudiol\u00a0&\u00a0Jordi Carratal\u00e0\n\nBellvitge Biomedical Research Institute (IDIBELL), L\u2019Hospitalet de Llobregat, Barcelona, Spain\n\nGabriela Abelenda-Alonso,\u00a0Laura Calatayud,\u00a0Alexander Rombauts,\u00a0Ariadna Padull\u00e9s,\u00a0Jordi Niub\u00f3,\u00a0Carlota Gudiol,\u00a0Carmen Ardanuy\u00a0&\u00a0Jordi Carratal\u00e0\n\nCentro de Investigaci\u00f3n Biom\u00e9dica en Red de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain\n\nGabriela Abelenda-Alonso,\u00a0Ariadna Padull\u00e9s,\u00a0Jordi Niub\u00f3,\u00a0Carlota Gudiol\u00a0&\u00a0Jordi Carratal\u00e0\n\nDepartment of Microbiology, Bellvitge University Hospital, L\u2019Hospitalet de Llobregat, Barcelona, Spain\n\nLaura Calatayud,\u00a0Jordi Niub\u00f3\u00a0&\u00a0Carmen Ardanuy\n\nCentro de Investigaci\u00f3n Biom\u00e9dica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain\n\nLaura Calatayud\u00a0&\u00a0Carmen Ardanuy\n\nInfectious Diseases Unit, Department of Internal Medicine, Hospital de Barcelona, Societat Cooperativa d\u2019Instal.lacions Sanit\u00e0ries, Barcelona, Spain\n\nYolanda Meije\u00a0&\u00a0Alejandra Duarte\n\nDepartment of Internal Medicine, Hospital de Sant Joan Despi Moises Broggi, Sant Joan Despi, Spain\n\nIsabel Oriol\u00a0&\u00a0Judit Aranda\n\nInfectious Diseases Unit, Hospital Universitari Germans Trias i Pujol, Badalona, Spain\n\nNieves Sopena\n\nFundaci\u00f3 Institut d\u2019Investigaci\u00f3 en Ci\u00e8ncies de la Salut Germans Trias i Pujol, Badalona, Spain\n\nNieves Sopena\n\nDepartment of Pharmacy, Bellvitge University Hospital, L\u2019Hospitalet de Llobregat, Barcelona, Spain\n\nAriadna Padull\u00e9s\n\nDepartment of Microbiology, Hospital de Barcelona, Societat Cooperativa d\u2019Instal.lacions Sanit\u00e0ries, Barcelona, Spain\n\nJaume Llaberia\n\nFaculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain\n\nCarlota Gudiol,\u00a0Carmen Ardanuy\u00a0&\u00a0Jordi Carratal\u00e0\n\nBiostatistics Unit, Bellvitge Biomedical Research Institute (IDIBELL), L\u2019Hospitalet de Llobregat, Barcelona, Spain\n\nPau Satorra\u00a0&\u00a0Cristian Teb\u00e9\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.C. was the lead investigator. G.A.A., A.P., C.T., C.A., and J.C. contributed to the study design and development concept. G.A.A. and J.C. drafted the manuscript. P.S. and C.T. performed statistical analysis. J.C. obtained funding. G.A.A., A.R., Y.M., I.O., N.S., A.D., J.A., and C.G. recruited patients for the study and participated in coordination. L.C., J.N., J.L., and C.A. performed the microbiological studies. G.A.A., P.S., C.T., C.A., and J.C. had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the acquisition, analysis or interpretation of data, and performed critical revision of the manuscript for intellectual content.\n\nCorrespondence to\n Jordi Carratal\u00e0.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Multiplex real-time PCR in non-invasive respiratory samples to reduce antibiotic use in community-acquired pneumonia: a randomised trial.\n Nat Commun 15, 7098 (2024). https://doi.org/10.1038/s41467-024-51547-8\n\nDownload citation\n\nReceived: 29 March 2024\n\nAccepted: 12 August 2024\n\nPublished: 17 August 2024\n\nVersion of record: 17 August 2024\n\nDOI: https://doi.org/10.1038/s41467-024-51547-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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0000000000000000000000000000000000000000..dae43701393816222e01a398eb8e515f09698c0c --- /dev/null +++ b/acdb5a4b47e45f817b6663e64dbdb2f6657bcd3265c34d862532882ebc161f9e/metadata.json @@ -0,0 +1,177 @@ +{ + "title": "Mechanistic understanding of UvrA damage detection and lesion hand-off to UvrB in Nucleotide Excision Repair", + "pre_title": "Mechanistic understanding of the Nucleotide Excision Repair: structural bases of damage detection process by UvrA and lesion hand-off to UvrB.", + "journal": "Nature Communications", + "published": "10 April 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_MOESM3_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_MOESM4_ESM.mp4" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_MOESM5_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_MOESM6_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_MOESM7_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ebi.ac.uk/emdb/EMD-51169", + "https://www.rcsb.org/structure/unreleased/9GA2", + "https://www.ebi.ac.uk/emdb/EMD-51168", + "https://www.ebi.ac.uk/emdb/EMD-51170", + "https://www.rcsb.org/structure/unreleased/9GA3", + "https://www.ebi.ac.uk/emdb/EMD-51171", + "https://www.ebi.ac.uk/emdb/EMD-51172", + "https://www.ebi.ac.uk/emdb/EMD-51173", + "https://www.rcsb.org/structure/unreleased/9GA4", + "https://www.ebi.ac.uk/emdb/EMD-51174", + "https://www.rcsb.org/structure/unreleased/9GA5", + "/articles/s41467-025-58670-0#ref-CR74", + "/articles/s41467-025-58670-0#ref-CR75", + "https://www.ebi.ac.uk/empiar/EMPIAR-12274/", + "/articles/s41467-025-58670-0#Sec22" + ], + "code": [], + "subject": [ + "Cryoelectron microscopy", + "DNA-binding proteins" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5098223/v1.pdf?c=1744369695000", + "research_square_link": "https://www.researchsquare.com//article/rs-5098223/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58670-0.pdf", + "preprint_posted": "04 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Nucleotide excision repair (NER) represents one of the major molecular machineries that control chromosome stability in all living species. In Eubacteria, the initial stages of the repair process are carried out by the UvrABC excinuclease complex. Despite the wealth of structural data available, some crucial details of the pathway remain elusive. In this study, we present a structural investigation of the Mycobacterium tuberculosis UvrA-UvrB complex and of the UvrA dimer, both in presence of damaged DNA. Our analyses have yielded new insights into the DNA binding mode of UvrA, as well as an unexplored conformation of some important regions involved in DNA coordination. Furthermore, we determined the molecular events related to the sequential binding of UvrB, leading to the formation of the uncharacterized UvrA2UvrB-DNA and the UvrA2UvrB2-DNA complexes which we interpreted as hierarchical steps initiating the DNA repair cascade in NER pathway.Biological sciences/Structural biology/Electron microscopy/Cryoelectron microscopyBiological sciences/Biochemistry/Proteins/DNA-binding proteins", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "GentaetalNSMBSupplementaryInformation.pdfstructuredatalink.docxData depositionSupmovie1v2noneon.mp4Supplementary movie 1Suppmovie2v2noneon.mp4Supplementary movie 2", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Nucleotide excision repair (NER) represents one of the major molecular machineries that control chromosome stability in all living species. In Eubacteria, the initial stages of the repair process are carried out by the UvrABC excinuclease complex. Despite the wealth of structural data available, some crucial details of the pathway remain elusive. In this study, we present a structural investigation of the Mycobacterium tuberculosis UvrAUvrB complex and of the UvrA dimer, both in complex with damaged DNA. Our analyses yield insights into the DNA binding mode of UvrA, showing an unexplored conformation of Insertion Domains (IDs), underlying the essential role of these domains in DNA coordination. Furthermore, we observe an interplay between the ID and the UvrB Binding Domain (UBD): after the recognition of the damage, the IDs repositions with the concomitant reorganization of UBD, allowing the formation of the complex between UvrA and UvrB. These events are detected along the formation of the uncharacterized UvrA2UvrB1-DNA and the UvrA2UvrB2-DNA complexes which we interpret as hierarchical steps initiating the DNA repair cascade in the NER pathway, resulting in the formation of the pre-incision complex.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Nucleotide excision repair (NER) is a versatile and highly conserved DNA repair pathway responsible for removing bulky DNA-distorting adducts1. In bacteria, two proteins, namely UvrA and UvrB, serve as the initial NER damage sensors1,2,3,4,5. UvrA is an ATPase in the ATP-binding cassette (ABC) superfamily that functions as a dimer. The catalytic core of each protomer of the functional UvrA2 homodimer consists of two composite nucleotide-binding domains (NBD-I and NBD-II) and each moiety contain an ATP/ADP binding site (ATP-I and ATP-II, belonging to NBD-I and NBD-II, respectively) and a Signature domain (SIG-I and SIG-II belonging to NBD-I and NBD-II, respectively). Two distinctive extra domains sprout from the NBD-I of UvrA: the UvrB Binding Domain (UBD) and the Insertion Domain (ID)6,7,8,9,10 (Fig.\u00a01a). UvrB is annotated as a member of the SF2 helicase family for the presence of conserved helicase motifs (I-VI); it contains the classical RecA-like domains (domains 1a and 3), in addition to three auxiliary domains (domains 1b, 2, and 4) and the \u03b2-hairpin motif that projects from domain 1a11,12,13,14,15.\n\na Crystal structure of MtUvrA2 (PDB: 3ZQJ) in two orientations. UvrA domains were colored according to the schematic diagram (the terms ATP-I and ATP-II are used to indicate the proximal and distal ATP binding site, respectively. Similarly, SIG-I and SIG-II are indicative of signature domains, while UBD stands for UvrB binding domain and ID for Insertion Domain). b Schematic representation of the different steps of prokaryotic NER: damage search process (lesion is depicted with a red star) performed by either UvrA2 or UvrA-UvrB complex (1); damage identification and consequent recruitment of UvrB molecules (2); pre-incision complex formation and UvrA detachment (3); UvrC recruitment and damaged DNA incision (4); restoration of the native dsDNA, operated by UvrD, DNA Polymerase I and ligase (5 and 6). Created in BioRender. Ferraris, D. (2024) BioRender.com/f96q610.\n\nThere is consensus on the distinct roles of the two proteins in the early damage detection process: UvrA acts as the primary sensor of DNA double-helix deformation13,16, with the capacity to simultaneously bind damaged DNA and UvrB, behaving as a molecular matchmaker that enables the precise delivery of UvrB to the lesioned site13,17. The discrimination of the lesion and subsequent stable binding represents the first step of the UvrA activity; the binding of UvrB is then the functional prerequisite for the complete pathway to proceed to the pre-incision complex formation, with one UvrB molecule bound to the lesion. Subsequently, UvrC nuclease cleaves the DNA on both sides of the damaged site, the UvrD helicase removes the excised oligonucleotide, and DNA polymerase I fills the gap. Finally, DNA ligase I seals the nick (Fig.\u00a01b).\n\nDespite the amount of available structural information5,6,7,8,10,11,18,19, some critical molecular and structural aspects of the initial steps of bacterial NER remain unclear. In particular, although atomic structures of UvrA bound to damaged DNA7 and UvrA bound to UvrB5 have been reported, a description of the configuration of the UvrA-UvrB complex bound to DNA is still lacking.\n\nSpecifically, the conformational changes occurring in UvrA during genome scanning and the overall architecture of the lesion-sensing complex remain elusive9. On the one hand, it has been proposed that UvrA can locate and verify the DNA lesion independently of UvrB16,20,21. On the other hand, forming an UvrA-UvrB complex has been suggested as the bona fide DNA damage-detecting step, initiating the NER pathway5,9,18,22. The precise oligomerization state of the UvrA-UvrB complex remains unclear since the in vitro stoichiometry has been alternatively estimated as UvrA2UvrB2 or UvrA2UvrB15,13,20,21,22. Indeed, it is possible that both forms could populate the NER pathway. UvrA is essential for damage identification, sensing DNA helix distortion and participating in DNA melting and unwinding23,24. However, whether it is UvrA alone or in complex with UvrB the one responsible for the damage recognition mechanism remains to be established. Moreover, two other outstanding questions regarding the transition toward the pre-incision complex remain to be addressed. First, the only available UvrA2UvrB2 structural model5 is symmetric, with the two UvrB molecules capable of binding complementary strands. However, the pre-incision complex is asymmetric and contains a single copy of UvrB25. It is, therefore, unclear how the lesion-containing strand is identified. Second, the UvrA2UvrB2 places UvrB ~80\u2009\u00c5 from the damage site. Two mechanisms have been hypothesized to explain the localization of UvrB to the lesion: the \u201crecruitment model\u201d model, which suggests a direct delivery of UvrB on the lesion through UvrA2UvrB2 complex reconfiguration, and the \u201ctranslocation model\u201d, which implies that UvrA dissociates before the translocation of UvrB to the damage site; UvrB translocates using its intrinsic ATPase activity5,12.\n\nOur study focuses on the NER pathway in Mycobacterium tuberculosis (MTB), which is essential to maintaining infectivity and survival4,26,27. Using cryogenic electron microscopy (cryo-EM), we determined three distinct macromolecular complexes in the MTB NER pathway: UvrA2 bound to DNA (MtUvrA2-DNA), the UvrA2-UvrB2 complex bound to DNA (MtUvrA2UvrB2-DNA), and a previously undescribed UvrA2-UvrB1 complex bound to DNA (MtUvrA2UvrB1-DNA). Our structural, genetic, and biochemical study came to several conclusions. UvrA in the MtUvrA2-DNA complex was found in a distinct configuration compared to a prior structure. Notably, the two IDs are observed clamping on DNA as they mediate the melting and unwinding of the duplex. We suggest a division of labor between the two UvrA protomers. In the MtUvrA2UvrB1-DNA complex, we establish a link between UvrB binding to a particular UvrA monomer and the concomitant release of this monomer\u2019s ID from contact with DNA. The structure of the MtUvrA2UvrB2-DNA complex reveals how the repositioning of UvrA\u2019s IDs may enable the transfer of the lesion to UvrB. We propose that the three structures (MtUvrA2-DNA, MtUvrA2UvrB1-DNA, and MtUvrA2UvrB2-DNA) operate sequentially in the NER pathway.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "The MtUvrA-MtUvrB complex (prepared as previously described6,11) was vitrified and cryo-EM data were collected using a Talos Arctica (Suppplementary Table\u00a01). The 2D average analysis revealed the presence of particles consistent with the UvrA dimer, but not compatible with the complex (Supplementary fig.\u00a01). Surprisingly, during the 3D reconstruction step, two distinct sets of particles were identified, corresponding to the DNA-free MtUvrA2 and to MtUvrA2 in complex with dsDNA; these were subsequently processed to achieve a final resolution of 4.9\u2009\u00c5 and 4.2\u2009\u00c5, respectively (Supplementary fig.\u00a01, Supplementary Table\u00a01 and Supplementary fig.\u00a02a, b). The structure of MtUvrA2-DNA was reconstructed at higher resolution and will be discussed in a later section, while the structural analysis of MtUvrA2 without DNA is part of the supplementary information.\n\nA posteriori analyses revealed contaminant nucleic acids in the MtUvrA sample used in cryo-EM (Supplementary fig.\u00a03a, b). Given the nature of UvrA as DNA damage detector, we sought to identify the type of lesion possibly present in the copurified DNA. To this end, we conducted a dot blot-based analysis using antibodies specific to prevalent types of DNA lesion, that confirmed the presence of oxidized nucleobases (i.e. 8-oxoguanine), while common UV-induced adducts (i.e. CPDs and 6-4PP) were absent (Supplementary fig.\u00a03c). Taking into account that recombinant MtUvrA was purified in the absence of any intentional external source of DNA damage, we speculate that the exposure of the sample to a pro-oxidative environment resulting from cell lysis, and/or the prolonged binding to a nickel-based matrix, could have induced oxidation of E. coli genomic DNA, which then served as the substrate for UvrA28, overall explaining the abundance of the MtUvrA2-DNA complexes in our sample.\n\nThe optimal superposition of MtUvrA2-DNA reconstruction to the BstUvrA2-UvrB2 complex5 (PDB: 3UWX) revealed that both BstUvrB monomers would collide with the dsDNA molecule present in our model (Supplementary fig.\u00a04a), suggesting that critical structural rearrangements should take place to allow the simultaneous binding of UvrB and dsDNA to MtUvrA2. Therefore, we attempted the long-sought structural characterization of the UvrA-UvrB-DNA complex through the use of a synthetic dsDNA molecule (DNA*) with a 10-nucleotide single-stranded 5\u2019 overhang ends with high affinity for UvrB15 and two internal fluorescein-modified nucleobases separated by four base pairs that mimic bulky lesions recognized by UvrA19 (Supplementary Table\u00a03, Supplementary fig.\u00a04b).\n\nWe assembled and purified the MtUvrA-MtUvrB complex and subsequently mixed it with the DNA*. The sample was vitrified, and cryo-EM data were collected (Supplementary figs.\u00a05, 6 and Supplementary Table\u00a01). Following 3D reconstruction, three kinds of oligomeric assemblies (namely MtUvrA2-DNA, MtUvrA2UvrB1-DNA and MtUvrA2UvrB2-DNA) were identified and independently processed to a resolution of 3.2\u2009\u00c5, 4.3\u2009\u00c5 and 3.7\u2009\u00c5, respectively (Fig.\u00a02, Supplementary fig.\u00a05 and Supplementary fig.\u00a06a).\n\na The indicated cryo-EM reconstructions are color coded and labeled according to the models shown in b; in the MtUvrA2-DNA and MtUvrA2UvrB1-DNA* reconstructions, the yellow and the red arrowhead points out the lack of signal corresponding to a 7-nt long segment of one DNA strand, and to one MtUvrA protomer ID, respectively. b Cartoon representation of the MtUvrA2-DNA, MtUvrA2UvrB1-DNA* and MtUvrA2UvrB2-DNA* models in two orientations; in the side views it is possible to appreciate the initial bending of the DNA in MtUvrA2-DNA and MtUvrA2UvrB1-DNA* and the final position in the groove of MtUvrA2UvrB2-DNA* structure. The unmodeled DNA strand segment on MtUvrA2-DNA is represented with a dashed line.\n\nThe MtUvrA2-DNA reconstruction showed a signal for the dsDNA longer than the one expected for DNA* (Supplementary fig.\u00a07); moreover, the map superposed well (CC\u2009=\u20090.92) with the previously obtained MtUvrA2-DNA map, thus we concluded that it represents the structure of MtUvrA2 associated to the co-purified E. coli DNA. Considering the final resolution of the two reconstructions, we decided to focus our analyses on the latter one.\n\nThe MtUvrA2UvrB2-DNA and MtUvrA2UvrB1-DNA reconstructions exhibited anisotropic behavior due to significant preferential orientation, resulting in a lower map quality than expected for their calculated FSC resolution (Supplementary fig.\u00a06b, c). Nevertheless, the quality of both reconstructions enabled the building of a model through rigid-body fitting of the different domains of UvrA and UvrB (Fig.\u00a02b). Both reconstructions did not exhibit signal for dsDNA since the density is compatible with ssDNA overhangs bound to UvrB, exactly as in DNA* (Supplementary fig.\u00a08). Accordingly, the experimental procedure that we designed allowed us to stabilize two complexes between MtUvrA, MtUvrB and DNA* with two distinct stoichiometries, which, in conjunction with the MtUvrA2-DNA, represent three structures of macromolecular assemblies acting in the initial stages of mycobacterial NER.\n\nRemarkably, in the three different protein-DNA complexes it is possible to distinguish two different DNA configurations: indeed, although the central DNA portion in MtUvrA2-DNA is characterized by lower resolution (Supplementary fig.\u00a06b), it is evident that the oligonucleotide is bent (\u2009~\u200928\u00b0) and locally melted, as in MtUvrA2UvrB1-DNA* complex, while the DNA in MtUvrA2UvrB2-DNA* complex has been observed in a linear configuration, lying in the groove of UvrA dimer with the same positioning of TmUvrA2-DNA7 (Fig.\u00a02b). In all the complexes, the protein-DNA interactions result in a local alteration of the base pair parameters, among which rise and twist angle values diverge from the expected patterns for a canonical B-DNA structure29,30,31.\n\nThe structure of MtUvrA2-DNA shows the same organization of the core catalytic domains observed in the crystallographic structure of the TmUvrA2-DNA (PDB: 3PIH)7, with slight changes in the Signature-I and the C-terminal Zn-finger \u03b2-hairpins761-775 (Supplementary fig.\u00a09a, b). Still, the C-terminal Zn-finger \u03b2-hairpins761-775 adopt the same open conformation that was suggested to be correlated to DNA binding. Protein-DNA contacts along the Signature-II domain are mostly conserved, with a minor variation between the loop704-709 and the phosphate backbone due to the deformation of the DNA (Supplementary fig.\u00a09c). A striking difference is observable at the level of the IDs: while in the TmUvrA2-DNA structure they do not contact the DNA molecule, in our MtUvrA2-DNA reconstruction they clamp both sides of the flat segment in the middle of the DNA double helix, which displays a 28\u00b0 bending and different degrees of unwinding: when compared to the canonical B-DNA, the flat central portion of the DNA displays -16\u00b0 of unwinding and the nucleobases flanking this region (A/T16 and A/T27) 9\u00b0 of unwinding (calculated with w3DNA 2.0 software31). As a result, the major groove-corresponding surface of the unwound dsDNA region faces the catalytic core of the MtUvrA dimer, while the widened minor groove faces upwards (Fig.\u00a03a). Each ID holds in place one of the complementary DNA strands, by using the \u03b1-hairpin381-405 and the \u03b1-hairpin313-335, with the \u03b1-helix320-335 deeply inserted into the DNA minor groove (Fig.\u00a03a and Supplementary fig.\u00a010a).\n\na Closeup of the MtUvrA dimer IDs, showing the structural motifs/elements that are engaged in clamping and deforming the DNA molecule (in orange); in both panels, the region of one DNA strand that is not defined in the model is represented by a dashed line and \u03b2-hairpins363-377 were not modeled. b Detailed view of the flipped-out nucleotide; functionally relevant residues of the recognition-ID hydrophobic pocket are displayed as sticks and labeled, as well as Tyr323. c Flipped-out nucleotide in UvrB-DNA complex (PDB: 6O8F)12, accommodating into the hydrophobic pocket between domains 1a and 1b. d SPR sensorgrams of MtUvrA proteins (wild type and mutated variants) binding to the damaged-DNA probe immobilized on a sensor chip. e Calculated Kon, Koff and KD values are shown for each investigated variant. Source data are provided as a Source Data file.\n\nThe path of the DNA sugar-phosphate backbone is a key attribute particularly well-defined in our reconstruction, except for a 7 nucleotide-long region that contacts one of the two IDs (Figs.\u00a02 and \u00a03a). The poor signal may indicate DNA flexibility, as a probable result of the base-pairing loss due to the presence of the damage; this base-pairing loss as well as base-stacking alteration in the vicinity of the lesion could be the hallmark that UvrA senses to recognize the presence of the damage site. A more detailed analysis of the ID-DNA interface, complementary to the disordered strand, revealed a notable signal oriented backward with respect to the position of the nucleobases (Supplementary fig.\u00a010b). The better definition of the DNA reconstruction with respect to the ID part, together with the fact that nucleobases scatter electrons particularly well, lead us to interpret this signal as a flipped-out nucleobase (Fig.\u00a03b). Interestingly, the reoriented nucleobase is hosted in a hydrophobic pocket within the ID, adopting an overall conformation that matches well the one observed in the structure of UvrB-DNA complex, in which an extruded cytosine occupies an equivalent hydrophobic pocket between domains 1a and 1b12 (Fig.\u00a03c). It is noteworthy that the side-chain of the tyrosine residue at position 323 of the \u03b1-helix320-335 (Y323) appears properly positioned to potentially replace the missing flipped-out base within the DNA \u03c0-stacking. As a matter of fact, sequence alignment showed that the Y323 residue is highly conserved among UvrA of different species (Supplementary fig.\u00a011), supporting a key role in the clamping.\n\nA disordered DNA strand and an extruded nucleobase are structural hallmarks found in other helicases32,33,34 and in the lesion recognition process of other DNA repair enzymes35,36,37. Notably, in our structure these two features are exclusively observable at the level of the ID that is engaged in a potential damage recognition conformation, indicating a division of labor between the two IDs. Consequently, we will refer to the ID that hosts the flipped nucleobase as the \u201crecognition-ID\u201d, and we will indicate the other one as the \u201csupport-ID\u201d (Supplementary fig.\u00a010a).\n\nThe inspection of MtUvrA2-DNA reconstruction revealed a continuous extra density above the minor groove corresponding to the flat face of the dsDNA; the signal is large enough to host the two unmodeled ID \u03b2-hairpins363-377 (Supplementary fig.\u00a010c), which, in this position, would directly contact the DNA. In general, \u03b2-hairpins are conserved motifs in helicases involved in DNA melting24. The MtUvrA ID \u03b2-hairpin363-377 is particularly enriched with conserved arginine and tyrosine residues (Supplementary fig.\u00a011), that have the potential to interact with DNA sugar-phosphate backbone and to establish \u03c0-stacking contacts with nucleobases, thereby interfering with base pairing. Considering the conformation and the amino acid composition of the MtUvrA ID \u03b2-hairpin363-377, we propose that it could play a prominent role in DNA melting. A sequence comparison analysis highlighted a high degree of conservation of this element among species, except for the hyperthermophiles Thermotoga maritima and Aquifex aeolicus. In such cases, the high temperatures of the ecological niche of these organisms may facilitate spontaneous dsDNA local melting, even in the absence of a \u03b2-hairpin, equivalent to the MtUvrA ID \u03b2-hairpin363-377 (Supplementary fig.\u00a011).\n\nAlthough previous works highlighted the role of IDs in stabilizing the complex with damaged DNA through DNA locking and melting8, to date there is no structural model describing the molecular details of this phenomena38,39. Although we were unable to unambiguously establish the identity of the dsDNA component fortuitously captured in the MtUvrA2-DNA complex, the overall molecular architecture revealed by our cryo-EM structure is consistent with a genuine lesion recognition state. To verify the hypothesized contribution of the ID \u03b2-hairpin363-377 and Y323 to the MtUvrA lesion-sensing function, we challenged the damaged-DNA binding activity of two protein variants: MtUvrA-\u0394\u03b2-hairpin, lacking the entire ID \u03b2-hairpin363-377, and MtUvrA-Y323A, in which Y323 is substituted by an alanine. We performed two pilot experiments (Electrophoretic Mobility Shift Assay - EMSA - and Microscale Thermophoresis - MST) using a DNA fragment with the same nucleotide sequence as DNA*, containing 2 fluorescein-dT (FLUdT) mimicking a bulky nucleobase adducts (probe) (Supplementary Table\u00a03); these analyses indicated that the damaged-DNA binding activity of the two variants is severely compromised, compared to the one of wild type MtUvrA (Supplementary fig.\u00a012 a,b). To obtain further detailed information, we characterized the binding of MtUvrA and mutated variants with the damaged DNA with surface plasmon resonance (SPR): the use of a sensor chip functionalized with the same probe revealed that not only MtUvrA, but also its two protein variants can bind damaged-DNA, although displaying different kinetics (Fig.\u00a03d). In particular, UvrA-\u2206\u03b2-hairpin and UvrA-Y323A mutants displayed a dissociation phase sensibly faster than those characterizing the wild-type protein (Fig.\u00a03e), which explains why with non-real-time assays (EMSA, MST) we detected no binding: the dissociation of the mutants is too fast, leading to a non-stabel interactions with the damaged DNA. Furthermore, the binding of the MtUvrA variants to the DNA ligand resulted in a lower response, as evidenced by the reduced resonance units count (Fig.\u00a03d). Therefore, the biochemical data support our structural observations, indicating that the \u03b2-hairpin363-377 and Y323 represent crucial elements for binding damaged DNA by increasing the retention time on the lesion.\n\nOverall, the collective evidence lends support to a model in which the MtUvrA2-DNA structure represents a state wherein UvrA clamps, unwinds and melts dsDNA through the IDs. The recognition-ID is responsible for nucleobase flipping, while \u03b2-hairpin363-377 may facilitate melting by interacting with the minor groove face of the DNA. We suggest that this model represents the end point of the UvrA-promoted lesion recognition process.\n\nConcerning the ATP/ADP binding state, we are able to observe the presence of ADP in the distal sites of the dimer (Supplementary Fig.\u00a013), while the proximal sites are empty. This agrees with previous observations that suggest that UvrA dimer is often more stable in the presence of a mixed nucleotide-bound/free species, in which the proximal site is empty and the distal site may be occupied by ATP or ADP19,22,40. In addition, this correlates well with what observed and proposed previously41: the genome scanning activity and the discrimination between native and damaged DNA require ATP hydrolysis at the distal sites, which are demonstrated to consume ATP in a fast way, releasing phosphate; the proximal sites, instead, have been implicated in interactions between UvrA and UvrB, playing an important role in recruiting UvrB to the damaged site40,42. In our model, we are able to see ADP molecules fulfilling the distal sites, supporting the idea that MtUvrA2-DNA structure describes the moment in which UvrA recognizes the lesion, while the proximal sites are empty, coherently with the fact that no UvrB molecule is present.\n\nThe cryo-EM MtUvrA2-DNA structure provides a molecular snapshot of an early stage in the NER pathway, in which MtUvrA stably binds a potentially damaged DNA. To investigate the structural changes required for the pathway to proceed, we focused on the MtUvrA2UvrB1-DNA* cryo-EM structure that gave us the opportunity to evaluate the impact of the binding of one UvrB molecule on the overall architecture of the protein-DNA complex.\n\nThe reconstruction revealed a loss of signal for the ID belonging to the MtUvrB-bound MtUvrA protomer (Fig.\u00a02a), suggesting that MtUvrB destabilized the MtUvrA ID conformation. The Signature-I domain of the catalytic core of the same MtUvrA protomer underwent a significant repositioning with respect to MtUvrA2-DNA structure (Fig.\u00a04) subsequent to the C-terminal Zn-finger \u03b2-hairpins761-775 repositioning, adopting a conformation that closely resemble the one observed in BstUvrA2UvrB2 structure, the only available structure of UvrA in complex with UvrB5. The Signature-I domain is connected to both the ID and UBD and properly positions the UBD for UvrB binding. The structural arrangement of the UBD upon MtUvrB binding and the MtUvrA ID-DNA interaction appear mutually exclusive, due to steric hindrance, and consequently ID undergoes a conformational change releasing the DNA (Fig.\u00a04 and Supplementary Movie\u00a01). Moreover, the binding of MtUvrB affects the nearby C-terminal Zn-finger \u03b2-hairpin761-775 of the adjacent UvrA protomer that switches from the ID in the MtUvrA2-DNA structure to the Signature-I domain in the MtUvrA2UvrB1-DNA* complex (Fig.\u00a04 and Supplementary Movie\u00a01). This observation led us to propose that the C-terminal Zn-finger \u03b2-hairpin761-775 functions as a scaffolding element, alternatively supporting the Signature-I domain or the ID, along the structural rearrangements that are functional to the protein-DNA complexes remodeling.\n\nComposite graphical representation of MtUvrA2-DNA (left) and MtUvrA2UvrB1-DNA* (right) structures. In both panels, the MtUvrA domains that undergo a substantial repositioning upon MtUvrB binding (ID, UBD, Signature-I and the \u03b2-hairpin761-775) and the DNA are represented as cartoon. The missing domains in each structure (UBD in MtUvrA2-DNA and ID in MtUvrA2UvrB1-DNA*) are represented as plane-colored shapes. The curved arrow in the panel on the right indicates the approx. 40 \u00b0 rotation of the Signature-I domain (as evaluated by comparing the position of the \u03b1-helix463-488 in the two models). In each image, the red arrow points to the contacts established between either the Zn-module (left) or the Signature-I domain (right) of one MtUvrA protomer and the C-terminal Zn finger \u03b2-hairpin761-775 of the opposite one in the dimer. For clarity, the UvrB molecule has been omitted.\n\nDespite the lower resolution of the MtUvrA2UvrB1-DNA* cryo-EM map, we could clearly discern the junction between the single-stranded and the double stranded segments (Fig.\u00a05a and Supplementary fig.\u00a08). This permitted the assignment of the nucleotide sequence of our DNA*. One of the lesion-mimicking fluorescein-modified thymine (FLUdT) of DNA* aligned with the recognition-ID, that is still defined in our structure (Fig.\u00a05b). This evidence suggests that the first MtUvrB binding event induces the release of the support-ID only, while the recognition-ID remains stably associated to the lesioned-strand via the clamping \u03b1-hairpins. Moreover, in the MtUvrA2-DNA complex, the flipped-out nucleotide binds at the hydrophobic pocket of the recognition-ID by adopting a pose that perfectly matches the one displayed by the genuine modified-nucleobase (i.e. FLUdT) in the MtUvrA2UvrB1-DNA* complex (Fig.\u00a05b).\n\na Cartoon representation of the MtUvrA2UvrB1-DNA* with the fluorescein dT highlighted as green spheres. The cryo-EM reconstruction is shown as semitransparent surface. The unpaired-to-paired DNA transition was marked with an arrow. b Superposition of MtUvrA2UvrB1-DNA* and MtUvrA2-DNA structures. The fluorescein dT, the DNA and the recognition-ID in MtUvrA2UvrB1-DNA* appear in green, orange and pink, respectively. The flipped-out nucleobase, the DNA and the recognition-ID in MtUvrA2-DNA are colored in cyan, white and red, respectively. The missing segment of one DNA strand in MtUvrA2-DNA structure is represented with a dashed line.\n\nThe capacity of the recombinant MtUvrA to bind and copurify with endogenous E. coli oxidized DNA, the low dissociation rate showed through SPR data and the extensive interactions shown in the MtUvrA2-DNA structure underpin that MtUvrA is able to lock the lesioned-DNA in a highly stable complex as previously described13,16. On the other hand, in MtUvrA2UvrB1-DNA* reconstruction, we observed the release of one ID (support-ID) with the concomitant binding of one UvrB molecule to the same UvrA protomer. This poses the question on how UvrB recognizes the UBD associated to the support-ID; we speculate that the driving force for the first UvrB binding event relies on a thermodynamic principle: it is likely that the binding energy of each ID is different, because of the presence of the lesion on one side of the DNA; the binding affinity of the recognition-ID is expected to be higher than one of the support-ID. This results in a low free-energy gain during the release of the support-ID and therefore the binding, with the associated conformational changes, is favored in the support-ID-containing UvrA protomer. The absence of a signal for MtUvrB or the UBD in the MtUvrA2-DNA reconstruction suggests that MtUvrB is dispensable to reach such a \u201clocked-in-damage\u201d stage. Moreover, the MtUvrA2UvrB1-DNA* points out that MtUvrB triggers a dramatic structural remodeling of the IDs in MtUvrA2-DNA complex. Our findings support previous works that established that UvrA is responsible for the first damage identification event, exhibiting discrimination between damaged and undamaged DNA based on the residence time on the DNA, subsequently addressing UvrB to the lesion16,40.\n\nThe overall \u201cVenetian gondola\u201d assembly of MtUvrA2UvrB2-DNA* resembled the one observed in the BstUvrA2UvrB2 structure (PDB: 3UWX)5. In both models, each UvrB interacts with a UBD and a Signature-II domain of opposite protomers (Fig.\u00a02b). Both structures exhibited the same conformation for the catalytic core, including the interplay between the Signature-I domain and the C-terminal Zn-finger \u03b2-hairpin761-775, as observed in the MtUvrA2UvrB1-DNA* complex. Additionally, the disengagement of both IDs makes the DNA* free to position inside the MtUvrA2 central groove; the resolution in the central region of the DNA is very low, which is probably caused by the DNA being disordered in this part: indeed, we decided not to model it; nevertheless, a B-DNA cannot fit in the density in that region.\n\nThis conformation in which both IDs are open and two UvrB molecules are bound further confirms the association between MtUvrB binding and ID displacement.\n\nA remarkable similarity between MtUvrA2UvrB2-DNA* and BstUvrA2UvrB2 structures is that the IDs share a similar orientation (Fig.\u00a06a). In both cases, each ID opens wide to approach the ATP-binding domain II. Nevertheless, in BstUvrA2UvrB2 structure the ID repositioning is less pronounced, as a possible consequence of the interactions established between the symmetry mates in the crystal lattice. Given that the same structural configuration was observed in the presence as well as in the absence of DNA, we postulate that UvrB binding not only displaces the IDs, but it also drives the proper IDs repositioning towards the ATP-binding domains II.\n\na The image on the left represents the models of both the UvrA-UvrB heterodimers observed in the MtUvrA2UvrB2-DNA* structure; the UBDs were not represented for a better visualization. The image on the right corresponds to an equivalent heterodimeric component within the BstUvrA2UvrB2 structure. The ID, the ATP-binding domain II (ATP-II) and UvrB are differently colored and labeled. The distances between ID and ATP-II, and the tip of \u03b1-hairpin381-405 and Asn117 of UvrB (Ans116 in BstUvrB) are indicated as double arrows. b Structure-based hypothetical model for the lesion hand-off (two 180\u00b0 rotated images are shown). Upon optimal superposition, the ID of MtUvrA2UvrB2-DNA* structure was superposed and replaced with the ID and the DNA of MtUvrA2-DNA structure; the DNA and the UvrB \u03b2-hairpin98-116 were shifted to avoid clashes and marked with black arrows. Important elements in the model are highlighted and labeled. The missing region in one DNA strand is represented with a dashed line. The green arrow indicates the direction of the short-range translocation of UvrB on the DNA.\n\nRemarkably, the structural rearrangement induced by MtUvrB brings each ID at a 26\u2009\u00c5 distance to the MtUvrB molecule bound to the opposite MtUvrA protomer (Fig.\u00a06a). Given that the recognition-ID represents the primary damage recognition element in MtUvrA and that the support-ID disengages DNA upon the first MtUvrB binding event, we are tempted to speculate that the recognition-ID repositioning could move the lesion in close proximity to the already bound MtUvrB (Fig.\u00a06b). Although this hypothesis would require further demonstration, the proposed mechanism satisfies four important requirements for the formation of the pre-incision complex: i) the UvrB \u03b2-hairpin inserts through the DNA major groove43,44, whereas the recognition-ID of UvrA interacts with DNA through the minor groove; therefore, the repositioning of the recognition-ID, together with the lesion held in place, would make the major groove accessible for the insertion of UvrB \u03b2-hairpin. ii) The damaged nucleobase recognized by the recognition-ID locates in the 3\u2019-to-5\u2019 DNA strand; consequently, the recognition-ID holds the lesion and repositions towards a pre-bound UvrB, which is capable of 5\u2019-3\u2019-directed translocation45. iii) The melted DNA region matches the proposed 6nt-long bubble necessary for UvrB interaction23. Finally, iv) the flipped-out lesion in UvrA agrees with the proposed UvrB lesion recognition mechanism through a base extruded from the DNA helix12,14.\n\nIn our cryo-EM single particle analysis of MtUvrA2UvrB2-DNA*, we detect flexibility between two halves. Each half contains one MtUvrB molecule, a DNA fragment and all domains within one MtUvrA protomer except for the UBD that belongs to the second protomer (Supplementary fig.\u00a05). So, the MtUvrA2UvrB2-DNA* complex is flexible and behaves as two rigid body halves that hinge through the MtUvrA dimerization interface. This is an unprecedented characteristic with respect to previous UvrA structures that showed an extensive interaction between UvrA protomers of 2849 \u00c52. In contrast, in MtUvrA2UvrB2-DNA* the UvrA dimer interface is 1189 \u00c52, a decrease of 60% (Supplementary Table\u00a04). Moreover, this observation agrees with the BstUvrA2UvrB2 structure, which shows a comparable decrease. Therefore, both structures align with the idea that the structural rearrangement produced by the second UvrB molecule binding triggers UvrA interprotomer destabilization; analyzing these three structures in succession (MtUvrA2-DNA, MtUvrA2UvrB1-DNA* and MtUvrA2UvrB2-DNA*), it is possible to observe that the binding of the DNA in MtUvrA2-DNA triggers the opening of the C-terminal Zn-finger \u03b2-hairpins761-775 with respect to MtUvrA2, which causes the repositioning of the SIG-1 domain, leading to the opening of the ID and to the binding of UvrB on the same protomer that exposed the ID; the same events happen for the other protomer, leading to the configuration of MtUvrA2UvrB2-DNA*, in which the dimerization interface is destabilized. It is plausible, therefore, that the destabilization event is caused by a series of conformational changes, triggered by the damage recognition. Since UvrA requires dimerization to bind DNA46, the UvrA dimer destabilization and potential disassembling could culminate in DNA release. DNA release from UvrA is strictly required during the lesion hand-off to UvrB. Therefore, these two key features of MtUvrA2UvrB2-DNA* structure (ID repositioning and UvrA dimer disassembly) support a model in which the recognition-ID repositions to transfer the damage to UvrB.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The structures and biochemical analysis presented here elucidate some of the unanswered questions regarding the damage detection mechanism of UvrA and the transfer of the lesion to UvrB. In particular, we observe three conformations that we interpret as representative of sequential events in the initial stages of NER. This observation leads us to propose a model regarding the process that bridges the damage recognition by UvrA and the formation of the pre-incision complex. As showed in Fig.\u00a07, UvrA first dimerizes to properly bind DNA7 (Fig.\u00a07, step 1 and 2); then, unknown conformational changes, probably triggered by ATP/ADP binding and hydrolysis, lead to UvrA in a locked-in-damage state represented by our MtUvrA2-DNA structure (Fig.\u00a07, step 3), in which it clamps, bends and unwinds DNA through the IDs. In this context, several experimental evidences show that DNA bending and melting is functional for the NER pathway for a proper incision operation47,48,49. Moreover, it has been proposed that UvrA, also independently of UvrB, is able to induce a slight bending of DNA that gradually becomes more severe along the pathway to a point of DNA kinking and wrapping in the pre-incision complex49,50,51. Our observations are in good agreement with single-molecule studies; indeed, Stracy et al. recorded two different modes of UvrA\u2019s interactions with DNA during its searching for lesions16: a slowly moving state in which UvrA makes transient interaction with the DNA (\u2009<\u2009<\u200915\u2009ms) and a more stably bound state. The authors concluded that these long DNA-bound state represents the damage verification process in which UvrA takes time to sample DNA conformations to verify the presence of a lesion. In this sense we can suggest that what we observed in our MtUvrA2-DNA model is not related to the transient binding mechanism while it can be associated to the verification step, the long-bound state, in which UvrA tests the presence of the lesions through conformational changes leading to active distortions of the DNA molecule. Subsequently to the damage recognition, the binding of the first UvrB molecule displaces the support-ID releasing one DNA strand (Fig.\u00a07, step 4 and Supplementary Movie\u00a02). However, in the MtUvrA2UvrB1-DNA* reconstruction, UvrB binds a ssDNA, which deviates from the natural situation where the dsDNA is expected to be present. In such a position, UvrB would clash with an unprocessed dsDNA (Supplementary fig.\u00a04), while it does not when we modeled UvrB as observed in the 6O8E pdb, namely in complex with a bent, unwound DNA showing a flipped-out nucleotide (Supplementary fig.\u00a08). The second UvrB binding event seems to lead to UvrA dimer disassembly, while the recognition-ID repositioning could facilitate UvrB binding to the bubble around the lesion (Fig.\u00a07, step 5 and Supplementary Movie\u00a02). The decreased stability of the UvrA dimer and the ID displacement potentially preparing the lesion hand-off are consistent with the previously reported disassembly of UvrA during the lesion transfer to UvrB25. Therefore, it is plausible that, once primed by the second UvrB binding event, the two phenomena simultaneously occur.\n\nUvrA dimerizes (1) to bind DNA (2); the damaged DNA recognition event triggers unknown conformational changes, leading to the bending, unwinding and melting of the DNA (3), allowing it to assess the presence of the lesion. After damage identification, one UvrB molecule binds and displaces the support-ID, releasing one DNA strand (4); a second UvrB molecule binds, causing the repositioning of the recognition-ID, which holds the DNA and transfers it towards UvrB (5), together with the UvrA dimer disassembly. This conformational state represents a transition state, that allows UvrB to access the lesion bubble and to replace UvrA (5). UvrB translocates the DNA through its ATPase activity to the pre-incision complex final conformation (6). The main events leading to the following conformation were indicated in blue.\n\nSurprisingly, instead of lesion transfer to MtUvrB and complete MtUvrA disassembling, in our MtUvrA2UvrB2-DNA* structure we observed the DNA* deeply inserted in the central groove of the complex with poor signal for the lesion site and only MtUvrA dimer interface destabilization. In this context, our MtUvrA2UvrB2-DNA* structure represents a transition state in which UvrA is unable to transfer the lesion due to the unnatural overhang ends of the DNA*. Therefore, we are tempted to suggest that the DNA bubble containing the lesion would move towards UvrB, dragged by the IDs. The process culminates the pre-incision complex formation through the short-range UvrB translocation to properly position the damage and ready to recruit UvrC (Fig.\u00a07, step 6). It has to be said that this model is speculative: an alternative model may involve the release of DNA by the IDs of UvrA, after the binding of UvrB; this would allow the DNA to slide towards one of the two UvrB molecules positioned on both ends of the complex.\n\nInterestingly, the damage sensing mechanism described here for UvrA is perfectly in line with the available structures of DNA repair proteins that includes modified bases flipping out, DNA bending and, in some cases, unwinding. Particularly remarkable is the agreement with UV-DDB that participates in pyrimidine dimers (CPD and 6-4PP) recognition in eukaryotic NER. UV-DDB bends DNA by 40\u00b0 allowing a \u03b2-hairpin insertion into the double helix and flipping out the modified bases in a lesion-binding pocket52. A second meaningful example is the functional equivalent, but structurally unrelated, yeast protein Rad4 and its human ortholog XPC. As UV-DDB, Rad4 bends DNA by ~40\u00b0 and inserts a \u03b2-hairpin, but in this case, Rad4 flips out the two bases opposite to the lesion53,54.\n\nThe present results offer a structural model of the first steps in NER pathway from UvrA in a locked-in-damage recognition state to the lesion hand-off to UvrB. However, the mechanistic events leading to such a UvrA conformation and the role of ATP hydrolysis remained unanswered. Another important missing piece of information is represented by the description of the molecular bases of the capability of UvrA to discriminate between damaged and undamaged DNA, since an experimental model of UvrA bound to unmodified dsDNA is still lacking.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58670-0/MediaObjects/41467_2025_58670_Fig7_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "M. tuberculosis UvrA was over-expressed in E. coli BL21 (DE3) strain harboring the plasmid pMtHisUvrA6. M. tuberculosis UvrB gene was provided by gene synthesis (GenScript) and was over-expressed in E. coli BL21 (DE3) strain harboring the plasmid pMtHisUvrB. The two proteins were purified as described in Supplementary information. To obtain the samples used in the cryo-EM experiments, pure MtUvrA- and MtUvrB-containing protein solutions were pooled together in a 1.2 molar excess of MtUvrB and 1\u2009mM ATP and 10\u2009mM MgCl2 were added as stabilizing elements of the complex formation18,32,33. The sample mixture was concentrated to 4.3\u2009mg/mL and loaded onto a Superdex 200 Increase 10/300 GL size exclusion chromatography column with a buffer containing 20\u2009mM Tris HCl pH 8.0, 300\u2009mM NaCl. The chromatographic fractions that, on the basis of chromatogram analysis and subsequent SDS-PAGE, contained hetero-tetrameric complexes, were then pooled and concentrated up to 0.5\u2009mg/mL, obtaining the MtUvrA-MtUvrB sample. The MtUvrA-MtUvrB-DNA* was prepared by incubating the MtUvrA-MtUvrB sample with the FLUdT-containing dsDNA (DNA*, Supplementary Table\u00a03), in a 1.2 molar excess of DNA*. The sample was flash-frozen in liquid nitrogen and stored at -80\u2009\u00b0C.\n\nThe MtUvrA-\u2206\u03b2-hairpin and MtUvrA-Y323A variants used in the EMSA-, MST- and SPR-based analyses were produced adopting the same procedure used to express and purify the wild type MtUvrA protein, followed by an additional Size Exclusion Chromatographic step using a Superdex 200 Increase 10/300 GL column with a buffer containing 20\u2009mM Tris HCl pH 8.0, 500\u2009mM NaCl, 5% glycerol as the mobile phase.\n\nVitrification of MtUvrA-MtUvrB and MtUvrA-MtUvrB-DNA* samples at 1.75\u2009\u00b5M was carried out with a Mark IV Vitrobot (Thermo Fisher Scientific). 3\u2009\u03bcl of each sample were applied to a Quantifoil R 1.2/1.3 Cu 300-mesh grid previously glow-discharged at 30\u2009mA for 30\u201d in a GloQube (Quorum Technologies). Immediately after sample application, the grids were blotted in a chamber at 4\u2009\u00b0C and 100% humidity, and then plunge-frozen into liquid ethane.\n\nMtUvrA-MtUvrB sample was transferred to a Talos Arctica (Thermo Fisher Scientific) operated at 200\u2009kV equipped with a Falcon III operating in electron counting mode. 3\u2019642 movies were acquired at a nominal magnification of 120\u2019000x corresponding to a pixel size of 0.889\u2009\u00c5/pixel. The total dose was 40 e-/\u00c52 equally distributed on 40 frames. Data collection statistics are summarized in Supplementary Table\u00a01.\n\nMovies were preprocessed with RELION 3.155 (Supplementary fig.\u00a01). All movies were subjected to motion-correction and dose-weighting using MOTIONCOR256. Estimation of the contrast transfer function (CTF) was performed on aligned dose-weighted sum of power spectra every 4 e-/A2 using CTFFIND 4.1.1057. Motion-corrected micrographs were imported to WARP and particle picking was preformed using the deep convolutional neural network BoxNet2_20180602 using a particle diameter of 60\u2009\u00c5 resulting in a total of 240\u2019304 picked particles. Particles were imported in RELION 3.1 and extracted with a box size of 400\u00d7400 pixels and a pixel size of 0.889\u2009\u00c5/pixel, inverted, normalized and imported into CRYOSPARC 4.2.158 for further processing. After a first round of 2D classification, particles averages above 60\u2009\u00c5 were selected yielding a 148\u2019931 set of particles. The set of particles was further split in two groups depending on their composition through an ab initio job using two models. The two ab initio reconstructed volumes corresponded to MtUvrA2 (68\u2019915 particles) and MtUvrA2-DNA (80\u2019016 particles) and were further cleaned through 3D classification yielding a final number of 22\u2019766 particles and 27\u2019801 particles, respectively. The final resolution of the two structures was 4.9\u2009\u00c5 for MtUvrA2 and 4.2\u2009\u00c5 for MtUvrA2-DNA, according to an FSC of 0.143 (Supplementary fig.\u00a01). Data processing statistics are summarized in Supplementary Table\u00a01 and a schematic representation of the workflow used is in Supplementary fig.\u00a01.\n\nVitrified grids of MtUvrA-UvrB-DNA* sample were sent to the CM01 beamline at the ESRF59,60) for a data collection. Movies were recorded using a Titan Krios (Thermo Fisher Scientific) operated at 300\u2009kV equipped with a K3 detector operating in electron counting mode. 21\u2019232 movies were acquired from two twin grids at a nominal magnification of 105\u2019000x in superresolution mode bin2 corresponding to a pixel size of 0.84\u2009\u00c5/pixel. The dose rate during movie acquisition was 18.8 e-/pix/s and the total dose was 40 e-/\u00c52 equally distributed on 40 frames. Data collection statistics are summarized in Supplementary Table\u00a01.\n\nMovies were preprocessed with RELION 3.155 (Supplementary fig.\u00a05). All movies were subjected to motion-correction and dose-weighting using MOTIONCOR256. Estimation of the CTF was performed on aligned dose-weighted sum of power spectra every 4 e-/A2 using CTFFIND 4.1.1057. Motion corrected micrographs were imported to WARP and particle picking was preformed using the deep convolutional neural network BoxNet2_20180602 using a particle diameter of 60\u2009\u00c5 resulting in a total of 2\u2019158\u2019399 picked particles. Particles were imported in RELION 3.1 and extracted with a box size of 400\u00d7400 pixels and a pixel size of 0.84\u2009\u00c5/pixel, inverted, normalized and imported into CRYOSPARC 4.2.158 for further processing. After a first round of 2D classification, particles averages above 60\u2009\u00c5 were selected yielding 666\u2019671 particles. The set of particles was further split in three groups depending on their composition and an ab initio model was generated with each set. The initial set of 666\u2019671 selected particles was heterogeneous refined using three ab initio models as references. Three classes corresponding to MtUvrA2UvrB2-DNA* (143\u2019862 particles), MtUvrA2UvrB1-DNA* (138\u2019164 particles) and MtUvrA2-DNA (242\u2019593 particles) were further cleaned through 2D classification yielding a final number of 110\u2019916, 99\u2019428 and 229\u2019676 particles, respectively. The final resolution for the three structures was 4.0\u2009\u00c5 for MtUvrA2UvrB2-DNA*, 4.3\u2009\u00c5 for MtUvrA2UvrB1-DNA* and 3.2\u2009\u00c5 for MtUvrA2-DNA, according to an FSC of 0.143 (Supplementary fig.\u00a06). MtUvrA2UvrB2-DNA* reconstruction was split in two halves and each applied on the full reconstruction for particle subtraction first, and then the complementary half on the subtracted particles for a local refinement. The half reconstruction of the MtUvrA2UvrB2-DNA* produced a map at 3.6\u2009\u00c5 and 3.7\u2009\u00c5 of resolution. The two halves were merged in a composite map using chimera61. Data processing statistics are summarized in Supplementary Table\u00a01 and a schematic representation of the workflow used is in Supplementary fig.\u00a05.\n\nFor MtUvrA2-DNA, a previously published structure of TmUvrA (PDB: 3PIH) was used as initial model. UCSF Chimera61 was used to rigidly fit the model in the density map. The changes in sequence and conformations were made using Coot62. The Insertion Domains of MtUvrA crystal structure (PDB: 3ZQJ) were rigid-body fit into the map, with the exception of the non-modeled \u03b2 hairpin domains, and the model was then refined against the map using Phenix Real Space Refine63. A random nucleotide sequence of DNA was modeled and a density fit restraining the Watson-Crick base pairs ad the Pi-stacking between nucleobases was performed. Nevertheless, the electron density map signal of one DNA strand fades away in the central portion and for this reason only a single strand was modeled there. Starting from this configuration, we parameterized MtUvrA2-DNA in the AMBER14SB64 force field with OL15 torsional modifications65 for DNA using the tleap program. The zinc ions coordination has been modeled via the ZAFF force field66. We parameterized ADP via the GAFF2 force field and computed its point charges using the AM1-BCC semiempirical approach. Subsequently, we solvated it with the OPC water model67 and neutralized with sodium ions. This system underwent a two-phases energy minimization (steepest descent with a 1000\u2009kJ/mol energy tolerance and conjugate gradient minimization with a 100\u2009kJ/mol energy tolerance) using GROMACS 2022.368, obtaining the final model. Figures were prepared with CHIMERAX69, PYMOL70, INKSCAPE71 and BIORENDER72.\n\nFor MtUvrA2UvrB1-DNA*, the MtUvrA2-DNA structure and the chain corresponding to UvrB from a previously published model (PDB: 3UWX) was docked into the map, manually adjusted in Coot62 and refined using Phenix Real Space Refine63.\n\nFor the MtUvrA2UvrB2-DNA* structure, the previously published model (PDB: 3UWX) was used and a rigid-body fit was applied in order to get the final model, finally refined using Phenix Real Space Refine63. In analogy with the modeling performed for MtUvrA2-DNA, for MtUvrA2UvrB2-DNA* we parameterized the system with the AMBER14SB-OL15-ZAFF force field and solvated it with OPC water and neutralized with sodium ions. We obtained the final model with the same two-phases minimization algorithm used in MtUvrA2-DNA refinement.\n\nWe generated the plasmid vector that encodes the MtUvrA-Y323A variant by using the pMtHisUvrA6 construct as the DNA template, the primers pair Y323Afor/Y323Arev (see Supplementary Table\u00a03), and the QuikChange II site-directed mutagenesis kit (Stratagene). The protein-encoding open reading frame in the resulting construct was verified by sequencing (Eurofins MWG Operon).\n\nThe expression construct for MtHisUvrA-\u2206\u03b2-hairpin was provided by a custom gene synthesis service (GenScript), encoding the protein variant that lacks the Q361-E381 spanning region, corresponding to the entire ID \u03b2-hairpin.\n\nMutagenic primers and DNA oligonucleotides used in biochemical assays and structural investigation, synthesized by Eurofins Genomics, are reported in Supplementary Table\u00a03.\n\nAll samples were treated with 0.4\u2009M NaOH, 10\u2009mM EDTA pH8 to denature DNA, boiled for 10\u2009min at 94\u2009\u00b0C and finally neutralized with 2\u2009M NH4CH3COO- 1:1\u2009v/v. DNA was then loaded on a nitrocellulose membrane activated with 6X SSC buffer through a Dot Blot vacuum manifold (SRC 96 Schleicher and Schuell), followed by two washes with 2X SSC. The membrane was dried, and DNA/membrane were crosslinked for 2\u2009h at 80\u2009\u00b0C.\n\nThe membrane was then blocked and hybridized with primary antibodies (anti-6-4PPs clone 64M-2; anti-CPDs clone TDM-2; anti-8-oxo-dG (15A3) sc-66036; anti-ssDNA MAB3034) and secondary antibodies HRP-conjugated. Chemiluminescence reaction was induced using Bio-Rad Clarity ECL and developed with Chemidoc Touch (Bio-Rad). SH-SY5Y (ATCC-CRL-2266) cells were untreated or treated with UVC lamp, and then harvested. Genomic DNA was purified using Macherey-Nagel\u2122 NucleoSpin\u2122 Tissue kit, processed as the UvrA sample and loaded on nitrocellulose with serial dilution (125\u2009ng, 62\u2009ng, 31\u2009ng).\n\nAntibody sources and working concentrations are reported: Anti-CPDs TDM-2 (D194-1): CosmoBio, monoclonal, Mouse, 1:1000. Anti-6-4PP (64M2): CosmoBio, monoclonal, Mouse, 1:500. Anti- ssDNA (MAB3034): Merck-Millipore, monoclonal, Mouse, 1:1000. Anti-8OHdG (15A3), Santa Cruz Biotechnology sc-66036, monoclonal, Mouse 1:3000 in 3% BSA. Goat anti-Mouse IgG (H\u2009+\u2009L): ThermoFisher Scientific, Secondary Antibody HRP (Cat # 31430), 1:10000. All of them were used diluted in TBS 0.1% Tween-20.\n\nA biotinylated dsDNA fragment, containing 2 fluorescein-modified thymine nucleobases (FLUdT) and 5\u2019-protruding ends, was mixed, at a fixed 0.15\u2009\u00b5M concentration, with increasing concentrations (0.0-1.5\u2009\u03bcM) of wild type MtUvrA, or MtUvrA-Y323, or MtUvrA-\u2206\u03b2-hairpin, in binding buffer (50\u2009mM Tris HCl pH 8.0, 500\u2009mM NaCl, 5% glycerol; final volume=20\u2009\u00b5L). Upon a 5\u2009min incubation at 25\u2009\u00b0C, samples were electrophoresed on 8% polyacrylamide gel (in 44.5\u2009mM Tris, 44.5\u2009mM boric acid, 1\u2009mM EDTA, pH 8.3) at 100\u2009V for 30\u2009min. The bands were visualized by direct gel imaging using a green light-emitting diode (LED)/605-nm-band-pass filter excitation/emission parameters, respectively.\n\nA Biacore X100 (GE Healthcare) instrument was used for DNA binding experiments. The biotinylated dsDNA fragment containing 2 fluorescein molecules was immobilized onto the surface of a SA sensor chip (cat # BR100012, GE Healthcare), functionalized with streptavidin; the DNA was immobilized onto the active flow cell (#2), while the other flow cell was used as a reference. Recombinant UvrA proteins (wild type and mutant versions) were diluted to a concentration of 50\u2009nM in HBS-EP+ buffer (GE Healthcare). Considering molecular weights (MW) of ligand and analytes of about 25\u2009kDa and 109\u2009kDa respectively, the appropriate ligand density (RL) on the chip was calculated according to the following equation: RL = (ligand MW/analyte MW) \u00d7 Rmax \u00d7 (1/Sm), where Rmax is the maximum binding signal (200 RUs) and Sm corresponds to the binding stoichiometry DNA:protein that is 1:2. Accordingly, the target capture level of the oligonucleotides was of 20 response units (RUs). Increasing concentrations of UvrA proteins were flowed over the SA sensor chip coated with damages dsDNA at a flow rate of 30\u2009\u03bcl/min at 25\u2009\u00b0C with an association time of 120\u2009s and a dissociation phase of 600\u2009s. A double regeneration step with 50\u2009mM Glycine pH 1.5 and 0.4% SDS was performed following each analytic cycle.\n\nThe affinity constant (KD) and the kinetic parameters were evaluated using the BIAcore evaluation software (GE Healthcare) and the reliability of the kinetic constants calculated by assuming a heterogeneous ligand model supported by the quality assessment indicators values.\n\nDNA-binding studies were also performed using MicroScale Thermophoresis (MST) according to previously described methods72,73. Damaged dsDNA fragment was used as the probe and intrinsic fluorescence of the two fluorescein molecules was used to avoid the labeling. Damaged dsDNA was used at a concentration of 10\u2009nM and the ligands (UvrA wild type and mutated proteins) were diluted, starting from the highest concentration of 1\u2009\u00b5M, in 50\u2009mM Tris HCl pH 8, 300\u2009mM NaCl, 5% glycerol; measurements were performed using Premium standard-treated glass capillaries and the instrument Monolith NT.115 (Nano-Temper Technologies). The IR laser-power was set to 40/80% the laser on-and-off times were set at 20 and 5\u2009s, respectively. The amplitude normalized data \u0394Fnorm of each dataset were averaged and plotted against the concentration of the unlabeled ligand on a logarithmic scale. The binding data were analyzed using MO affinity analysis software, as provided by the manufacturer.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The density maps and atomic coordinates reported in this paper have been deposited in the Electron Microscopy Data Bank (EMDB) and RCSB Protein Data Bank (RCSB PDB), respectively, with accession codes as follows: DNA-free MtUvrA2 dimer structure EMD-51169 and PDB ID 9GA2; MtUvrA2 bound to endogenous E. coli DNA at low resolution EMD-51168; MtUvrA2UvrB1 bound to damaged oligonucleotide EMD-51170 and PDB ID 9GA3; MtUvrA2UvrB2 bound to damaged oligonucleotide (half 1) EMD-51171; MtUvrA2UvrB2 bound to damaged oligonucleotide (half 2) EMD-51172; MtUvrA2UvrB2 bound to damaged oligonucleotide EMD-51173 and PDB ID 9GA4; MtUvrA2 bound to endogenous E. coli DNA EMD-51174 and PDB ID 9GA5. The movies were deposited in EMPIAR74,75 under the ascension code: EMPIAR-12274.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Change history", + "section_text": "In the version of this article initially published, due to a processing error, an artifact appeared next to the peak of the green trace in Fig. 3d, which is now amended in the HTML and PDF versions of the article.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Kisker, C., Kuper, J. & Van Houten, B. Prokaryotic nucleotide excision repair. Cold Spring Harb. Perspect. Biol. 5, a012591 (2013).\n\nTruglio, J. J., Croteau, D. L., Van Houten, B. & Kisker, C. Prokaryotic Nucleotide Excision Repair: The UvrABC System. Chem. Rev. 106, 233\u2013252 (2006).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nWozniak, K. J., & Simmons, L. A. Bacterial DNA excision repair pathways. Nat. rev. 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P2022P8KMF received by R.M. and A.C.S.).\u00a0The authors\u00a0would like to thank all the students of the FREE-MOVER project of the University of Piemonte Orientale for the fruitful discussion of\u00a0the scientific data included in the manuscript.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Pharmaceutical Sciences, University of Piemonte Orientale, Via Bovio 6, 28100, Novara, Italy\n\nMarianna Genta,\u00a0Giulia Ferrara,\u00a0Menico Rizzi,\u00a0Franca Rossi\u00a0&\u00a0Riccardo Miggiano\n\nDepartment of Biosciences, University of Milan, Milan, 20133, Italy\n\nRiccardo Capelli,\u00a0Diego Rondelli,\u00a0Sarah Sertic,\u00a0Martino Bolognesi\u00a0&\u00a0Antonio Chaves-Sanjuan\n\nPediatric Clinical Research Center Romeo ed Enrica Invernizzi and NOLIMITS, University of Milan, Milan, 20133, Italy\n\nMartino Bolognesi\u00a0&\u00a0Antonio Chaves-Sanjuan\n\nDepartment of Chemistry and Biochemistry, The City College of New York, New York, NY, 10031, USA\n\nDavid Jeruzalmi\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nR.M. and A.C.S. and D.J. conceptualized the experiments. M.G. and G.F. purified the recombinants proteins for biochemical assays and cryo-EM experiments. M.G., R.M., A.C.S., F.R. and M.R. performed and analyzed the biochemical experiments including surface plasmon resonance, EMSA and microscale thermophoresis. D.R. and S.S. performed dot blot assay and DNA damage quantification. M.G. and A.C.S. performed cryo-EM sample preparation and validation. M.G. and A.C.S. processed and analyzed the cryo-EM datasets. M.G., A.C.S., M.B. and R.C. performed model building and refinement. M.G., R.M. and A.C.S. wrote the manuscript with input from D.J., F.R. and M.B.\n\nCorrespondence to\n Antonio Chaves-Sanjuan or Riccardo Miggiano.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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Mechanistic understanding of UvrA damage detection and lesion hand-off to UvrB in Nucleotide Excision Repair.\n Nat Commun 16, 3416 (2025). https://doi.org/10.1038/s41467-025-58670-0\n\nDownload citation\n\nReceived: 30 September 2024\n\nAccepted: 28 March 2025\n\nPublished: 10 April 2025\n\nVersion of record: 10 April 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58670-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"Visible Light-Mediated Dearomative Spirocyclization/Imination of Nonactivated Arenes through Energy Transfer Catalysis", + "journal": "Nature Communications", + "published": "16 April 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58808-0/MediaObjects/41467_2025_58808_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58808-0/MediaObjects/41467_2025_58808_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58808-0/MediaObjects/41467_2025_58808_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-58808-0#Sec12" + ], + "code": [], + "subject": [ + "Photocatalysis" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4205289/v1.pdf?c=1744888079000", + "research_square_link": "https://www.researchsquare.com//article/rs-4205289/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58808-0.pdf", + "preprint_posted": "11 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Aromatic compounds are one of the most abundant feedstocks in chemical industry, which typically focuses on their functionalization or full reduction. Partial reduction through dearomative sequences has been considerably less explored but would uncover the true potential of aromatic compounds in providing rapid access to complex and higher-value three-dimensional scaffolds. Several dearomative strategies have been established; however, most of these methods either necessitate metal-mediated multistep manipulations or are highly limited in their scope. Herein, an alternative photocatalytic radical cascade approach is disclosed, which exploits dearomative difunctionalization through selective dearomative spirocyclization/imination of nonactivated arenes. Various bifunctional oxime esters and carbonates are utilized in single-step installation of a range of functional groups concomitant with formation of a spiro-center and introduction of an iminyl functionality to furnish dearomatized spiro-imine scaffolds through tandem N\u2013O bond cleavage, hydrogen-atom transfer, radical addition, 5-exo-trig cyclization, and radical cross-coupling sequences. The disclosed methodology allows formation of three or four new chemical bonds, including C\u2212O, C\u2212C, C\u2212N bonds, in a single synthetic step, affording highly decorated three-dimensional structures from readily available starting materials.Physical sciences/Chemistry/Catalysis/PhotocatalysisPhysical sciences/Chemistry/Chemical synthesis/Synthetic chemistry methodology", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupportingInformation20240402final.pdfVisible Light Mediated Dearomative Spirocyclization/Imination of Nonactivated Arenes through Energy Transfer Catalysis", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Aromatic compounds serve as key feedstocks in the chemical industry, typically undergoing functionalization or full reduction. However, partial reduction via dearomative sequences remains underexplored despite its potential to rapidly generate complex three-dimensional scaffolds and the existing dearomative strategies often require metal-mediated multistep processes or suffer from limited applicability. Herein, a photocatalytic radical cascade approach enabling dearomative difunctionalization through selective spirocyclization/imination of nonactivated arenes\u00a0is reported. The method employs bifunctional oxime esters and carbonates to introduce multiple functional groups in a single step, forming spirocyclic motifs and iminyl functionalities via N\u2013O bond cleavage, hydrogen-atom transfer, radical addition, spirocyclization, and radical-radical cross-coupling. The reaction constructs up to four bonds (C\u2212O, C\u2212C, C\u2212N) from simple starting materials. Its broad applicability is demonstrated on various substrates, including pharmaceuticals, and it is compatible with scale-up under flow conditions, offering a streamlined approach to synthesizing highly decorated three-dimensional frameworks.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Aromatic compounds represent one of the most abundant chemical feedstocks and can be easily transformed into a wide range of functionalized two-dimensional scaffolds1,2. Conversely, their conversion into architecturally complex three-dimensional structures through dearomative processes3,4,5,6 is intrinsically challenging due to the resonance stabilization of the aromatic systems7. The classical reduction reactions, such as catalytic hydrogenation8 and Birch reduction9, provide various dearomatized products, but without introducing additional functionalities into the ring system. Reactions that can simultaneously disrupt the aromatic system and introduce additional functionalities \u2014\u00a0dearomative difunctionalizations \u2014 has proved to be challenging. Complex difunctionalized products can be obtained with dearomative strategies via transition-metal\u2013mediated activation, dearomative oxidation, and photocycloaddition manifolds10,11,12,13. Transition-metal\u2013mediated dearomatizations have been widely used and enable elaborate functionalization schemes (Fig.\u00a01, top left) through coordination, electrophilic attack, nucleophilic attack, and decomplexation mechanism14. Dearomative oxidation manifolds include phenol oxidation15 and radical-mediated oxidation16,17,18,19,20 (Fig.\u00a01, top left) that provide cyclohexadienone derivatives from the corresponding phenols or aryl ethers or microbial oxidations of monosubstituted arenes21; however, both of these reactions are hampered by limitations in the substrate scope. Dearomative photocycloaddition reactions have the ability to increase the structural complexity through the formation of bridged or fused bicyclic dearomatized products via [2\u2009+\u20092], [3\u2009+\u20092], and [4\u2009+\u20092] cycloadditions6,11,22,23,24,25,26,27,28,29,30,31, (Fig.\u00a01, top right) and in some cases, the fused bicycles can undergo cycloreversion/fragmentation to yield dearomatized difunctionalized cyclic products (Fig.\u00a01, top right). Although more sophisticated methods have increased the complexity of building blocks accessible from simple arenes32, it is still pivotal to develop mild, selective, and versatile approaches for achieving dearomative difunctionalization of nonactivated arenes into architecturally more complex three-dimensional structures via multicomponent reactions.\n\nTop: Established strategies for dearomative difunctionalization. Middle: Envisioned reaction design for the photocatalytic dearomative difunctionalizations via spirocyclic iminations. Bottom: Mechanistic pathways for the proposed dearomative difunctionalizations.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58808-0/MediaObjects/41467_2025_58808_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "Recently, we disclosed an effective photoredox-mediated dearomative annulation approach to spirocyclic compounds through homolytic C\u2013O bond activation of aromatic carboxylic acids33. The key spirocyclization step was realized through 6-exo-trig or 5-exo-trig intramolecular C-radical addition to a benzene ring, producing a cyclohexadienyl C-radical species, which is converted to the final 1,4-hexadienyl product upon one-electron/one-proton reduction. Despite numerous attempts to form difunctionalized dearomatization products, we were unsuccessful in trapping the cyclohexadienyl C-radical intermediate with different somophiles to form C\u2013C or C\u2013heteroatom bonds. This is in line with the known propensity of this C-radical to undergo either one-electron reduction34,35 or oxidation16,20 followed by a bond-forming reaction with nucleophilic or electrophilic reagents.\n\nTherefore, we sought to circumvent this limitation using the persistent radical effect36,37, which involves trapping of the transient cyclohexadienyl C-radical with a persistent radical generated in situ (Fig.\u00a01, middle). The envisioned strategy is initiated by homolytic bond cleavage in a radical precursor to produce both reactive and persistent radicals, the first of which onsets spirocyclization of the radical acceptor, while the second is utilized for functionalization of the spirocyclic cyclohexadienyl C-radical through radical-radical coupling. Recently, O-functionalized oximes has emerged as a versatile class of bifunctional radical precursors, which can be harnessed to produce a transient O-radical and a persistent iminyl N-radical species upon photocatalytic energy transfer38,39. Typically, these systems employ ubiquitous iridium-based photocatalysts, such as [Ir(dF(CF3)ppy)2(dtbbpy)](PF6), which is excited under visible light irradiation and mediates the intermolecular energy transfer to the radical precursor, onsetting the homolytic N\u2013O bond cleavage process40. Inspired by the pioneering works by Glorius41,42,43, Molander44, and others45,46,47,48,49, we envisioned three different approaches to 1,4-difunctionalization of nonactivated arenes concomitant with dearomative spirocyclization using various oxime radical precursors in conjunction with secondary coupling partners (Fig.\u00a01, bottom).\n\nIn the first of the envisioned systems, oxime esters of carboxylic acids are employed as progenitors to a carboxylate O-radical species, which undergoes facile decarboxylation to afford a nucleophilic C-radical (Fig.\u00a01, bottom, decarboxylation pathway). The latter readily adds to the electron-deficient benzyl acrylamide acceptor to promote the onset of the spirocyclization reaction, followed by coupling of the produced cyclohexadienyl C-radical with the persistent iminyl N-radical to furnish the desired difunctionalized product. Alternatively, the employment of oxime carbonates as the bifunctional radical precursors provides a more kinetically stable carbonate O-radical species, which is less prone to decarboxylation50 and does not undergo direct addition to the electron-deficient benzyl acrylamide acceptor due to the polarity mismatch. In this second envisioned system, the reactivity of such carboxylate O-radical is redirected by the addition to an electron-rich alkene (Fig.\u00a01, bottom, direct radical addition pathway), and the produced C-radical species then onsets the radical addition/spirocyclization/imination sequence leading to the desired product. In the third system, the carbonate O-radical51 is instead utilized as a hydrogen-atom transfer (HAT) agent, allowing generation of the key nucleophilic C-radical species from C\u2013H bonds in both activated and aliphatic substrates (Fig.\u00a01, bottom, HAT pathway). Thereby, our outlined photocatalytic systems provide three alternative routes to the synthesis of highly functionalized spirocyclic lactams decorated with an iminyl group through dearomatization/radical coupling cascade reactions.\n\nTo assess the feasibility of the envisioned photocatalytic systems, we investigated the reactivity of the proposed key radical intermediates using density functional theory (DFT) calculations (for details, see Supplementary Information). The envisioned reaction sequence is initiated by excitation of the model photocatalyst [Ir(dF(CF3)ppy)2(dtbbpy)]+ to the triplet excited state (57.8\u2009kcal\u2009mol\u22121). The latter is quenched by the diphenyl oxime ester 1a through energy transfer to generate the triplet excited state oxime ester 1a* (44.3\u2009kcal\u2009mol-1)38, which undergoes low barrier fragmentation (2.3\u2009kcal\u2009mol\u22121) to carbon dioxide, an N-centered diphenyl iminyl radical and a primary alkyl C-radical (Supplementary Fig.\u00a0S9). The feasibility of the designed mechanistic pathway relies on the difference in reactivities of the formed N- and C-centered radicals, manifested in the difference in Gibbs free energy for their homo- and cross-coupling reactions (N\u2013N: \u221234.3\u2009kcal\u2009mol\u22121, C\u2013N: \u221255\u2009kcal\u2009mol\u22121, C\u2013C: \u221269.9\u2009kcal\u2009mol\u22121). Here, despite the strong driving force for the radical\u2013radical coupling reactions, their rate is greatly limited due to the low steady-state concentration of the coupling components, enabling the trapping of the free radical species with a suitable somophile and onsetting a dearomative radical cascade pathway. According to the DFT calculations, C-centered radicals (aliphatic, acyl, and oxyacyl) display higher reactivity (\u2206G\u2021\u2009=\u200911.6, 10.4, and 11.8 kcal mol\u22121, for the aliphatic, acyl, and oxyacyl radicals, respectively, Supplementary Fig.\u00a0S10) towards the electron-deficient somophile 2a compared to the N-centered iminyl radical (\u2206G\u2021\u2009=\u200918.0\u2009kcal\u2009mol\u22121), corroborating the feasibility of the proposed transformation.\n\nThe reaction conditions for the first of the envisioned systems were optimized with oxime 1a and acrylamide 2a as the model substrates, leading to the difunctionalized spirocyclic product 3a. As the result of the optimization study (for additional details, see the Supplementary Information and Supplementary Table\u00a0S1\u2013S5), imination product 3a was obtained in 56% yield and 1.4:1 dr in ethyl acetate and with [Ir(dF(CF3)ppy)2(dtbbpy)](PF6) as the photocatalyst under visible light irradiation (440\u2009nm LED) (Supplementary Table\u00a0S1). Control experiments confirmed the necessity of both the light and the iridium photocatalyst for the reaction under 440\u2009nm irradiation, and direct excitation at shorter wavelengths (390\u2009nm) gave significantly lower yields. The proposed mechanistic sequence was further supported by fluorescence quenching studies, where only the oxime radical precursor 1a was found to effectively quench the excited iridium photocatalyst (Fig.\u00a02, top), while no appreciable quenching was observed for alkene 2a (see Fig.\u00a02 and Supplementary Fig.\u00a0S1\u2013S2).\n\nTop left: Stern-Volmer fluorescent quenching study with [Ir(dF(CF3)ppy)2(dtbbpy)](PF6) photocatalyst (15\u2009\u03bcM) and compounds 1a and 2a as quenchers. Top right: The main product and side products identified in the reaction mixture by LC-HRMS analysis. Bottom: Gibbs free energy diagram for the proposed transformation optimized at the B3LYP/6-311\u2009+\u2009G(d,p) level using the Grimme correction for dispersion (D3) and the Conductor-like Polarizable Continuum Model (CPCM, UFF, ethyl acetate). Gibbs free energies are given in kcal mol\u20131.\n\nAnalysis of the crude reaction mixture under standard conditions between 1a and 2a by LC-HRMS demonstrated not only the formation of the desired product 3a, but also several side-products consistent with the proposed mechanistic sequence. These side-products include the radical-radical coupling adducts between the proposed intermediates, including one C\u2013N cross-coupling adduct between the iminyl radical and the aliphatic radical, as well as two homodimerization adducts (Fig.\u00a02, top right; Supplementary Fig.\u00a0S6). Additional trapping experiments in the presence of the radical scavenger TEMPO (2 equiv.) under otherwise standard conditions completely inhibited the formation of product 3a and led to the formation of the coupling product between TEMPO and the C-centered aliphatic radical as detected by HRMS (Supplementary Fig.\u00a0S6).\n\nThe DFT calculations suggest that upon homolytic N\u2013O-bond cleavage, the C-centered radical initiates the radical-cascade process through radical addition to somophile 2a via TS2 (\u2206G\u2021\u2009=\u200911.5\u2009kcal\u2009mol-1), forming an \u03b1-carbonyl radical in an exothermic process (IM3, \u2206G\u2009=\u2009\u221215.1\u2009kcal\u2009mol\u22121) (Fig.\u00a02, bottom). The \u03b1-carbonyl radical engages in a rate-limiting 5-exo-trig spirocyclization via TS3 (\u2206G\u2021\u2009=\u200912.7\u2009kcal\u2009mol\u22121) to yield intermediate IM5, which subsequently engages in heterocoupling with the iminyl radical to provide the final imination product as a mixture of diastereomers (trans-3a and cis-3a) with the trans-diastereomer being slightly more thermodynamically favored. The DFT calculations suggest a considerably high reaction barrier for the potential propagation step (TS4, \u2206G\u2021\u2009=\u200926.5\u2009kcal\u2009mol-1), suggesting that the envisioned reaction does not proceed through the radical chain mechanism, which is further supported by light on-off experiments (Supplementary Fig.\u00a0S7).\n\nWith the optimized reaction conditions in hand, the generality of the photocatalytic decarboxylative dearomative difunctionalization was evaluated with a variety of oxime esters (Fig.\u00a03). Primary alkyl C-radicals were smoothly incorporated into the dearomatized products with moderate yields (3a\u20133b, 54\u201360%). Remarkably, the methyl radical produced the desired product in good yield (3c, 46%), which is generally challenging due to its high reactivity.\n\nReaction conditions: oxime 1 (0.3\u2009mmol, 1.5 equiv.), acrylamide 2 (0.2\u2009mmol, 1.0 equiv.), [Ir(dF(CF3)ppy)2(dtbbpy)](PF6) (0.5\u2009mol%), EtOAc (3\u2009mL), N2, blue LEDs (440\u2009nm, 10\u2009W), 2\u2009h, fan cooling (35\u201340\u2009\u00b0C). Diastereomeric ratios (dr) were determined by 1H NMR of the crude reaction mixtures. a4\u2009h reaction time.\n\nSimilarly, secondary C-radicals, including cyclobutyl, cyclohexyl, and gem-difluorinated cyclohexyl radicals, were well-tolerated, affording the expected imination products in moderate yields (3d\u20133f, 56\u201361%). Furthermore, substrates equipped with bulkier tertiary butyl and adamantyl groups provided the expected products in slightly lower yields (3g\u20133h, 44\u201350%). Next, we wanted to investigate the spiroiminative reactivity of the aromatic moiety. It should be noted that the obtained compounds from the unsymmetrically substituted aromatic systems have 4 possible diastereomeric pairs arising from the three stereogenic centers of the product, and thus, the target compounds were either isolated as individual compounds or as a mixture of stereoisomers (see Fig.\u00a03). Gratifyingly, replacing the phenyl ring with aromatic heteroatom containing heterocycles, such as thiophene and dibenzofuran, both gave the expected products in satisfying yield (3i: 52% and 3j:\u00a071%, respectively). The substrate with a fluoro-substituent in the 2-position (3k) of the aromatic ring increased the yield to 73%. A phenyl substituent was tolerated in the para-position of the spirocyclic motif, which gave rise to highly substituted compounds (3l) in good yield (33%), considering the complexity of the synthesized products.\n\nApart from alkyl radicals, acyl and oxyacyl radicals were also suitable coupling partners to yield the corresponding spirocyclic derivatives (3m\u20133p, 57\u201363%). Next, we turned our attention to the exploration of functionalized acrylamide acceptors. Several symmetrical disubstituted substrates bearing methoxy, methyl, and chloro groups at the aromatic functionality furnished the desired products in reasonable yields (3q\u20133t, 39\u201358%). Other modifications of the amide functionality in the somophile, such as the substitution of the methylene group with a carbonyl functionality, provided the corresponding spiro-succinimide product in similar yields (3u, 43%, 3:1 dr). Substitution of the N-tBu group with N-iPr led to a decrease in yield (3v, 29%), while having secondary amide gave no product formation. Previously, the importance of the t-butyl substituent on the amide functionality was attributed to the decreased spatial distance between the two carbons that undergo the spirocyclization35, but it has been shown that the tert-butyl group can be easily removed under mild conditions using copper(II) triflate52.\n\nTo investigate the compatibility of the developed protocol for late-stage functionalization of bioactive molecules, several pharmaceutical drugs, amino acid derivatives, and peptides were converted to oxime esters and subjected to our decarboxylative protocol. Gemfibrozil, Fenofibric acid, and Ciprofibic acid, that are used in the treatment of abnormal blood lipid levels, delivered the expected spirocyclic dearomatized products in reasonable yields (50%, 40%, and 38%, for 3w,\u00a03x, and 3\u2009y, respectively). Other drugs, such as the nonsteroidal anti-inflammatory drug Oxaprozin and the strained \u03b2-lactamase inhibitor Sulfbactam, were compatible with the developed protocol and gave the corresponding spirocyclic compounds in moderate yields (3z: 40% and 3aa: 19%, respectively). Further, the peptide-based Nateglinide (used in diabetes treatment), tosyl-protected glycine, and a sugar-based diprogulic acid were also compatible under these conditions (3ab: 33%, 3ac: 49%, and 3ad: 38%).\n\nThe transformation was also applied to the malonate oxime ester\u00a01ae that directly generates the \u03b1-carbonyl ester without a Giese type coupling reaction which gave the unsubstituted dearomatized spirocyclic/imination product 3ae in 41% yield.\n\nTo demonstrate the versatility of the developed protocol, we wanted to expand the dearomative difunctionalization via a three-component system that utilizes our dearomative spirocyclization/imination of nonactivated arenes (Fig.\u00a01, bottom) in combination with electron-rich and electron-poor alkenes. In this photocatalytic system, the radicals are generated from oxime carbonates via energy transfer from the photocatalyst and reacts through a radical cascade with both a more electron-rich and an electron-poor alkene. This dearomative spirocyclization proceed through five different types of radical intermediates and gives rise to the formation of four chemical bonds (C\u2212O, C\u2212C, C\u2212C, and C\u2212N). The fluorescence quenching studies for this catalytic system demonstrated that only the oxime carbonate 4a effectively quenches the excited iridium photocatalyst (Fig.\u00a02, top), while no appreciable quenching was observed for the electron-rich alkene 5a (see Supplementary Figs.\u00a0S3, S4, and S5). In addition, the DFT calculations revealed that the electron-deficient O-centered carbonate radical preferentially adds to the electron-rich alkene 5a (\u2206G\u2021\u2009=\u20097.8\u2009kcal\u2009mol-1) rather than to the more electron-poor benzyl acrylate-based alkene 2a (\u2206G\u2021\u2009=\u20099.6\u2009kcal\u2009mol-1) (Supplementary Fig.\u00a0S11), which is in line with the control experiment where 4a rather reacted with 5a than with 2a to provide the 1,2-oxyimination product (Supplementary Fig.\u00a0S8a and S8b).\n\nThe formed C-centered radical displayed opposite selectivity, preferentially adding to the electron-deficient benzyl acrylate alkene 2a (\u2206G\u2021\u2009=\u200912.1\u2009kcal\u2009mol\u22121) rather than to the more electron-rich alkene 5a (\u2206G\u2021\u2009=\u200916.1\u2009kcal\u2009mol\u22121) (Supplementary Fig.\u00a0S11). Thus, the DFT calculations indicate that the envisioned three-component reaction cascade is indeed feasible.\n\nThe scope of the direct radical addition\u2013dearomative difunctionalization manifold was initially evaluated with various oxime carbonates using the same standard conditions as in the previous photocatalytic system but with 2 equiv. of the electron-rich alkene somophile (Fig.\u00a04, top). Oxime carbonates derived from methyl, ethyl, and n-butyl alcohols all provided the corresponding products in almost the same yields (6a\u20136c, 50\u201354%) as the two-component system. A range of symmetrical 1,1-disubstituted nonactivated olefins smoothly reacted with 5a and 2a to deliver products 6d\u20136g in similar yields (38\u201342%). Finally, acrylamide acceptors bearing methyl and chloro substituents in the aromatic ring were also well-tolerated (6h\u20136j, 31\u201345%).\n\nReaction conditions: oxime carbonate 4 (0.3\u2009mmol, 1.5 equiv.), acrylamide 2 (0.2\u2009mmol, 1.0 equiv.), non-activated alkene 5 (0.4\u2009mmol, 2.0 equiv.), [Ir(dF(CF3)ppy)2(dtbbpy)](PF6) (0.5\u2009mol%), EtOAc (3\u2009mL), N2, blue LEDs (440\u2009nm, 10\u2009W), 4\u2009h, fan cooling (35\u201340\u2009\u00b0C). Diastereomeric ratios were determined by 1H NMR spectroscopy. Bottom: Scope of the hydrogen atom transfer\u2014dearomative difunctionalization through spirocyclization. Reaction conditions: oxime carbonate 4 (0.4\u2009mmol, 1.5 equiv.), acrylamide 2 (0.2\u2009mmol, 1.0 equiv.), aliphatic substrate 7 (2\u2009mL), [Ir(dF(CF3)ppy)2(dtbbpy)](PF6) (0.5\u2009mol%), EtOAc (1\u2009mL), N2, blue LEDs (440\u2009nm, 10\u2009W), 4\u2009h, fan cooling (35\u201340\u2009\u00b0C). Diastereomeric ratios were determined by 1H NMR spectroscopy. a benzaldehyde (1\u2009mL), EtOAc (2\u2009mL).\n\nThe O-centered carbonate radicals exhibit a versatile reactivity profile that includes decarboxylation and addition to alkenes or aromatic systems51,53,54. Similar to other high-energy O-centered radicals, such as oxyl55,56, oxyacyl57,58, and phosphate59,60 radicals, the O-centered carbonate radicals are potent HAT agents. Hence, unactivated alkanes can be exploited as the source of C-centered radicals that can engage in numerous reactions with somophiles. The reactivity of the carbonate radical was tested in several control experiments. In the absence of an electron-rich alkene, no spirocyclic-imination product was formed suggesting a competing reactivity for the carbonate radical (Supplementary Fig.\u00a0S8a). On the other hand, performing a similar reaction in the absence of an electron-poor alkene and in the presence of an electron-rich alkene yields the difunctionalized product (Supplementary Fig.\u00a0S8b) as previous reported by Molander and Glorius41,42,43,44. Upon irradiation of 4a, the imination product of cyclohexane was detected by HRMS, which shows the potential HAT reactivity of the carbonate radical. This was further supported by DFT calculations that suggest that HAT from cyclohexane (\u2206G\u2021\u2009=\u20097.8\u2009kcal\u2009mol-1) is several orders of magnitude faster than HAT from ethyl acetate (\u2206G\u2021\u2009=\u200910.0\u2009kcal\u2009mol-1) that is used as the solvent under the developed conditions (Supplementary Fig.\u00a0S12). The carbonate radical has only a slightly higher barrier for the competing decarboxylation reaction (9.3\u2009kcal\u2009mol\u22121) compared to HAT from cyclohexane; however, the use of a large excess of cyclohexane is expected to favor the desired HAT reaction (Supplementary Fig.\u00a0S12). Thus, the dearomative difunctionalization manifold discussed above was further extended to employ transient carbonate radical species as potent HAT agents (Fig.\u00a04, bottom). In this photocatalytic system, non-activated alkanes engaged in a competitive HAT reaction, onsetting the formation of the key C-radical species. Several alkane solvents, such as cyclohexane, cyclopentane, and cycloheptane, were easily incorporated into the spirocyclic frameworks without pre-functionalization (8a\u20138c, 30\u201332%). In addition, the \u03b1-C\u2013H bond of ether and even the C(O)\u2013H bond of an aldehyde was selectively activated to furnish the dearomative difunctionalization products, albeit in lower yields (8d\u20138e, 15\u201333%).\n\nTo further demonstrate the synthetic versatility of the developed methodology, we sought to utilize the N-tert-butyl-N-benzyl motif to access bis-spirocyclic products via a radical cascade process involving energy transfer, strain-release activation, spirocyclization and C\u2013N-bond forming imination. Recently, strain-release transformations of bicyclo[1.1.0]butanes (BCB) have facilitated the formation of reactive radical intermediates via photocatalysis to yield molecular frameworks in organic synthesis through the release of substantial ring strain energy31,61,62,63,64,65,66,67,68.\n\nTo our delight, the utilization of the BCB-benzylamide (2m) successfully yielded a tricyclic product with two spirocyclic motif in a single transformation (3af, 36%).\n\nThe synthetic utility was further demonstrated through scale-up reactions using flow chemistry with an in-house designed flow reactor (Fig.\u00a05). The reaction could be easily scaled up from 0.2\u2009mmol to 4\u2009mmol scale under flow conditions with nearly identical yields compared to the small-scale batch reactions, providing the expected products 3a,\u00a03p, and 6b in 53%, 56%, and 49% yields, respectively. Further synthetic manipulations of the dearomative difunctionalization products included the efficient conversion of the iminyl groups to the corresponding cyclohexadienyl amines in the presence of hydroxylamine (9\u201310, 73\u201375%)69 and the reduction to the corresponding amine (11, 81%) using NaBH4, which highlights their expedient access to both primary and secondary amines from the spirocyclic compounds.\n\nTop: BCB-enabled formation of bis-spirocycles. Middle: Large-scale decarboxylative dearomative difunctionalization under continuous flow conditions. Bottom left: Deprotection of the imine functionality for selected products. Bottom right: Reduction of the imine functionality.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58808-0/MediaObjects/41467_2025_58808_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58808-0/MediaObjects/41467_2025_58808_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58808-0/MediaObjects/41467_2025_58808_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58808-0/MediaObjects/41467_2025_58808_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In conclusion, the present work describes a groundbreaking photocatalytic methodology enabling dearomative difunctionalization of nonactivated arenes. The utilization of bifunctional oxime esters and carbonates enabled single-step installation of both spiro- and iminyl-functionalities with large substrate scopes and functional group compatibility. The radical cascade involves N\u2013O bond cleavage, CO2 release, radical addition, 5-exo-trig cyclization, and radical cross-coupling, to form complex spiro-iminyl architectures. This method stands out for its versatility, accommodating a wide substrate scope and functional group compatibility, and its ability to establish up to four chemical bonds (C\u2212O, 2 x C\u2212C, and C\u2212N) in a single synthetic step. Oxime carbonates were also utilized as hydrogen-atom transfer agents to activate C-H bonds from unactivated hydrocarbon solvents in the dearomative spirocyclic/imination and thereby enhancing the protocol\u2019s utility. Several late-stage functionalizations of bioactive molecules and the use of strain-release activation further exemplifies the procedure\u2019s practical relevance and adaptability. This work represents a significant advancement in photocatalytic chemistry, providing a versatile and selective tool for accessing high-value molecular architectures.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "A 10\u2009mL vial equipped with a magnetic stir bar was charged with oxime 1a (0.3\u2009mmol), N-benzyl-N-(tert-butyl)acrylamide 2a (0.2\u2009mmol), [Ir(dF(CF3)ppy)2(dtbbpy)](PF6)\u00a0(0.001\u2009mmol) in EtOAc (3\u2009mL). After the degassing with N2 for 10\u2009min, the mixture was irradiated by blue LEDs (\u03bb\u2009=\u2009440\u2009nm, 10\u2009W) for 2\u2009h at room temperature. After irradiation, the resulting homogenous solution was transferred to a 25\u2009mL round bottom flask with the aid of CH2Cl2 (2 \u00d7\u20093\u2009mL). NEt3 (approx. 0.5\u2009mL) and SiO2 were added to this solution, and the volatiles were removed under reduced pressure, affording a powder which was loaded on the column. Purification by column chromatography using pre-basified silica (NEt3) with PE/EtOAc as eluent afforded the target product 3a.\n\nA 10\u2009mL vial equipped with a magnetic stir bar was charged with oxime 4a (0.3\u2009mmol), N-benzyl-N-(tert-butyl)acrylamide 2a (0.2\u2009mmol), [Ir(dF(CF3)ppy)2(dtbbpy)](PF6)\u00a0(0.001\u2009mmol) in EtOAc (3\u2009mL). The non-activated alkene 5 (0.4\u2009mmol) was added into the mixture after degassing with N2 for 10\u2009min. Then the mixture was irradiated by blue LEDs (\u03bb\u2009=\u2009440\u2009nm, 10\u2009W) for 4\u2009h at room temperature. After irradiation, the resulting homogenous solution was transferred to a 25\u2009mL round bottom flask with the aid of CH2Cl2 (2 \u00d7\u20093\u2009mL). NEt3 (approx. 0.5\u2009mL) and SiO2 were added to this solution, and the volatiles were removed under reduced pressure, affording a powder which was loaded on the column. Purification by column chromatography using pre-basified silica (NEt3) with PE/EtOAc as eluent afforded the target product 6a.\n\nA 10\u2009mL vial equipped with a magnetic stir bar was charged with oxime 4a (0.4\u2009mmol), N-benzyl-N-(tert-butyl)acrylamide 2a (0.2\u2009mmol), cyclohexane 7a (2\u2009mL), [Ir(dF(CF3)ppy)2(dtbbpy)](PF6)\u00a0(0.001\u2009mmol) in EtOAc (1\u2009mL). After the degassing with N2 for 10\u2009min, the mixture was irradiated by blue LEDs (\u03bb\u2009=\u2009440\u2009nm, 10\u2009W) for 4\u2009h at room temperature. After irradiation, the resulting homogenous solution was transferred to a 25\u2009mL round bottom flask with the aid of CH2Cl2 (2 \u00d7\u20093\u2009mL). NEt3 (approx. 0.5\u2009mL) and SiO2 were added to this solution, and the volatiles were removed under reduced pressure, affording a powder which was loaded on the column. Purification by column chromatography using pre-basified silica (NEt3) with PE/EtOAc as eluent afforded the target product 8a.\n\nA 100\u2009mL round bottom flask was loaded with the corresponding oxime (6\u2009mmol), N-benzyl-N-(tert-butyl)acrylamide (4\u2009mmol), [Ir(dF(CF3)ppy)2(dtbbpy)](PF6)\u00a0(0.02\u2009mmol) in EtOAc (60\u2009mL). The mixture was bubbled with a stream of argon for 20\u2009min (for a three-component reaction), methylenecyclohexane\u00a0(8.0\u2009mmol) was added after the degassing), then pumped to a homemade flow reactor at a rate of 0.05\u2009mL/min upon the irradiation of 10\u2009W LEDs (\u03bb\u2009=\u2009440\u2009nm). After irradiation, the resulting homogenous solution was transferred to a 250\u2009mL round bottom flask with the aid of CH2Cl2 (2\u2009\u00d7\u200910\u2009mL). NEt3 (approx. 5\u2009mL) and SiO2 were added to this solution, and the volatiles were removed under reduced pressure, affording a powder which was loaded on the column. Purification by column chromatography using pre-basified silica (NEt3) with PE/EtOAc as eluent afforded the target product.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Materials and methods, detailed optimization studies, computational details, experimental procedures, and mechanistic studies are available in the Supplementary Information. Characterization data and copies of processed NMR spectra for all obtained products and further information (FID) is available from the corresponding author upon request. Source data (xyz coordinates, energies and enthalpies)\u00a0are provided in this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Magano, J. & Dunetz, J. R. 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The National Supercomputer Center (NSC) in Link\u00f6ping is acknowledged for providing computational resources.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open access funding provided by Royal Institute of Technology.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Chemistry, KTH Royal Institute of Technology, Stockholm, Sweden\n\nChao Zhou,\u00a0Elena V. Stepanova,\u00a0Andrey Shatskiy,\u00a0Markus D. K\u00e4rk\u00e4s\u00a0&\u00a0Peter Din\u00e9r\n\nResearch School of Chemistry & Applied Biomedical Sciences, Tomsk Polytechnic University, Tomsk, Russia\n\nElena V. Stepanova\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nC.Z., M.D.K., and P.D. conceptualized and directed the project. C.Z., E.V.S., and A.S. designed, conducted and analyzed the experiments described in this article. All authors contributed to discussing the results and drafting the manuscript.\n\nCorrespondence to\n Peter Din\u00e9r.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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Visible light-mediated dearomative spirocyclization/imination of nonactivated arenes through energy transfer catalysis.\n Nat Commun 16, 3610 (2025). https://doi.org/10.1038/s41467-025-58808-0\n\nDownload citation\n\nReceived: 02 April 2024\n\nAccepted: 03 April 2025\n\nPublished: 16 April 2025\n\nVersion of record: 16 April 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58808-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction", + "journal": "Nature Communications", + "published": "25 November 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54440-6/MediaObjects/41467_2024_54440_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54440-6/MediaObjects/41467_2024_54440_MOESM2_ESM.docx" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54440-6/MediaObjects/41467_2024_54440_MOESM3_ESM.zip" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54440-6/MediaObjects/41467_2024_54440_MOESM4_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54440-6/MediaObjects/41467_2024_54440_MOESM5_ESM.pdf" + }, + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54440-6/MediaObjects/41467_2024_54440_MOESM6_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.10828798", + "https://www.pdbbind-plus.org.cn/download", + "https://www.rcsb.org/structure/6QMT", + "https://www.rcsb.org/structure/6W4K", + "https://www.rcsb.org/structure/7RFS", + "/articles/s41467-024-54440-6#MOESM3", + "/articles/s41467-024-54440-6#Sec27" + ], + "code": [ + "https://github.com/tencent-ailab/Interformer", + "https://doi.org/10.5281/zenodo.10828798" + ], + "subject": [ + "Computational models", + "Molecular modelling", + "Virtual screening" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3995849/v1.pdf?c=1732626439000", + "research_square_link": "https://www.researchsquare.com//article/rs-3995849/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54440-6.pdf", + "preprint_posted": "25 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "In recent years, there has been a growing interest in using deep learning models for protein-ligand docking and affinity prediction, both vital for structure based drug design. However, many of these models overlook the intricate modeling of interactions between ligand and protein atoms, thereby constraining their capabilities in generalization and interpretability. In this paper, we introduce \\textsc{Interformer}, a unified model built upon the Graph-Transformer architecture, which specially crafted to capture non-covalent interactions through the interaction-aware mixture density network. Besides, we incorporated a new strategy that utilizes negative samples, effective interaction distribution correction for affinity prediction. Experimental results on widely-used and our in-house datasets demonstrate the effectiveness and universality of the proposed approach. Extensive analyses confirm our claim that our approach improves performance by modelling protein-ligand specific interactions. Encouragingly, our approach propels the SOTA performance docking tasks forward. We intend to make our code publicly available, hoping to facilitate future research in this field.Biological sciences/Computational biology and bioinformatics/Computational modelsBiological sciences/Computational biology and bioinformatics/Virtual drug screeningBiological sciences/Drug discovery/Drug screening/Virtual screeningProtein-ligand dockingProtein-ligand affinity predictionAI based energy modelStructure based drug design", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "supplmentary.zipaffinity and docking results", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "In recent years, the application of deep learning models to protein-ligand docking and affinity prediction, both vital for structure-based drug design, has garnered increasing interest. However, many of these models overlook the intricate modeling of interactions between ligand and protein atoms in the complex, consequently limiting their capacity for generalization and interpretability. In this work, we propose Interformer, a unified model built upon the Graph-Transformer architecture. The proposed model is designed to capture non-covalent interactions utilizing an interaction-aware mixture density network. Additionally, we introduce a negative sampling strategy, facilitating an effective correction of interaction distribution for affinity prediction. Experimental results on widely used and our in-house datasets demonstrate the effectiveness and universality of the proposed approach. Extensive analyses confirm our claim that our approach improves performance by accurately modeling specific protein-ligand interactions. Encouragingly, our approach advances docking tasks state-of-the-art (SOTA) performance.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "In the intricate journey of drug development, protein-ligand docking and affinity prediction tasks have been important components of the drug discovery process for years1,2. As a crucial task in optimization of drug molecular structures, protein-ligand docking is to predict the position and orientation of a ligand (a small molecule) when it binds to a protein receptor or enzyme. Leveraging accurate binding poses (protein-ligand binding complex conformations), the affinity prediction task offers a computational estimation of the binding strength between a ligand and its target protein, facilitating the screening of ligand with potential affinity.\n\nRecent years have witnessed a surge of interest in using deep learning (DL) approaches for molecular modeling3,4. Treating docking as a generative modeling problem, ref. 5 introduced DiffDock, a graph neural network (GNN) based model that has established a benchmark in binding pose generation. However, the existing DL models often overlook the modeling of non-covalent interactions between protein and ligand atoms, which is essential for interpretability and generalization. As illustrated in Fig.\u00a01 Left, the docking conformation produced by DiffDock remains closely resembles crystal structures but fails to capture the non-covalent interactions. Moreover, although traditional affinity prediction methods excel with crystal structures, their performance dramatically drops when dealing with the less precise binding pose, posing challenges for real-world applications6,7,8.\n\nLeft: The incorrect case generated by DiffDock. Pink and orange arrows indicate improperly formed hydrogen bond and hydrophobic interactions (RMSD: 1.13, PDB ID: 6QMT). Right: The correct binding pose generated by Interformer, where the predicted interaction energy function recovers almost all hydrogen bonds and hydrophobic interactions (RMSD: 0.67).\n\nIn this work, we present Interformer, a computational AI model designed to alleviate the interaction-aware problems on the protein-ligand docking and employs constructive learning for affinity prediction in real-world applications. Firstly, we propose an interaction-aware mixture density network (MDN) to model non-covalent interactions, explicitly focusing on the hydrogen bonds and hydrophobic interactions present in the protein-ligand crystal structure. As illustrated in Fig.\u00a01 Right, Interformer can accurately produce specific interactions in the binding pose. Secondly, we introduce a pseudo-Huber loss function, leveraging the capabilities of contrastive learning to instruct the model in discriminating between favorable and unfavorable binding poses. Thirdly, the proposed model is based on the Graph-Transformer framework9,10, which has demonstrated its superior performance compared to GNN-based models in various graph representation learning tasks11. An additional advantage of Interformer is to interpret the internal mechanisms of protein-ligand interactions by examining the fusion coefficients of the MDN12. When evaluated on protein-ligand docking using two widely-used benchmarks, Interformer achieves a top-1 prediction performance with 84.09% accuracy on the Posebusters benchmark and 63.9% on the PDBbind time-split benchmark with the Root Mean Square Deviation (RMSD) less than 2\u2009\u00c5. The improvement is attributed to the model\u2019s enhanced ability to capture non-covalent interactions between ligands and proteins, which is crucial for generating less ambiguous conformations and essential for successful performance in downstream tasks. Furthermore, the interformer can predict plausible affinity values even when the binding poses are less accurate. Evaluation of our in-house real-world benchmark demonstrates comparable performance to the other models, confirming its pose-sensitive and robust generalization capabilities. While applying to a real-world internal pharmaceutical pipeline, we successfully identify two small molecules, each with affinity IC50 values of 0.7\u2009nM and 16\u2009nM in their respective projects, thus demonstrating its practical value in advancing therapeutic development.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54440-6/MediaObjects/41467_2024_54440_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "Interformer is a deep learning model trained on crystal structure data for protein-ligand docking task, and it redocks docking poses along with corresponding affinity values for the affinity prediction task. Its architecture is inspired by the Graph-Transformer, initially proposed for graph representation learning tasks.\n\nFirstly, the model takes a single initial ligand 3D conformation and protein binding site from the crystal structure as input. Graphs are widely used in various methods to illustrate ligands and proteins, as shown in Fig.\u00a02a, in which nodes represent atoms and edges indicate the proximity between two atoms. We employ pharmacophore atom types13 as node features and use the Euclidean distance between two atoms as edge features. These pharmacophore atom types provide essential chemical information, thus enabling the model to better comprehend specific interactions such as hydrogen bonding or hydrophobic interactions. A more detailed description of these features is provided in Supplementary Table\u00a0S3.\n\na Graph representation, atoms are represented as nodes, and the proximity between two atoms is represented as edges. b Docking pipeline, utilizes node and edge features as inputs, which are processed through Intra and Inter Blocks to update features. These features are then fed into an edge output layer to produce an inter representation, for predicting an interaction-aware MDN. Subsequently, a Monte Carlo sampling procedure utilizes the energy score function to sample multiple docking poses. c Pose Score and Affinity prediction pipeline, leverages the docking pose generated in (b) to update new edge features, which are then fed into Intra and Inter Blocks to update node features. Finally, a virtual node aggregates all node features to predict a pose score and affinity value for the corresponding docking pose.\n\nIn the second stage, the docking pipeline, as illustrated in Fig.\u00a02b, processes the node features and edge features from the protein and ligand through Intra-Blocks. Intra-Blocks are designed to update node features for each atom by capturing the intra-interactions within the same molecule. These updated node features are then fed into Inter-Blocks, which are designed to capture inter-interactions between protein and ligand atom pairs, leading to further updated node and edge features. The edge output layer subsequently combines these two sets of features to generate an Inter-representation for each protein-ligand atom pair. Subsequently, the Inter-representation is processed by an Interaction-aware MDN. This network predicts the parameters of four Gaussian functions for each protein-ligand atom pair, which are constrained separately by different possible specific interactions. The first two Gaussian functions encapsulate all types of pair interactions, while the third one signifies the hydrophobic interactions and the fourth one denotes the hydrogen bond interactions. By integrating these four Gaussian functions, we derive a mixture density function (MDF), representing the conditional probability density function of distance for any given protein-ligand atom pair. This MDF can serve as an energy function to estimate the most probable distance between the protein atom and its corresponding ligand atom. Hydrogen bonds and hydrophobic interactions play a significant role in the binding free energy14. The distribution of these specific interactions differs markedly from that of other typical interactions. We designed individual modeling of each specific interaction distribution, such as the third term for hydrophobic and the fourth term for hydrogen bonding. Consequently, the docking poses generated by our MDF inherently display these specific interactions, much like most natural crystal structures. In contrast, other methods like refs. 15, 16 utilize ten MDFs to model all types of protein-ligand pairs, thereby mixing all types of pairs and overlooking the importance of specific interactions. Alternatively, the methods like DiffDock solely minimize the RMSD as the loss function, resulting in the docking process merely approximating the crystal structure rather than capturing critical specific interactions. Finally, the MDF of all protein-ligand pairs is aggregated into a sum of energy functions, which is then introduced into a Monte Carlo (MC) sampling method17 for generating top-k candidate ligand conformations relative to its protein target. The MC sampling initially positions the ligand in various locations within the protein binding sites and assigns random torsion angles, then seeks to minimize the given energy function with respect to the ligand conformation. By aggregating all candidates sorted by energy values, we can obtain top-k candidate docking poses. A more detailed description of the sampling method is provided in\u00a0Supplementary Information Section\u00a02.\n\nIn the third stage, pose score and affinity prediction pipeline is illustrated in Fig.\u00a02c. The distances and specific interactions between protein and ligand atoms from the generated docking pose update new edge features. The node and edge features are then processed through Intra and Inter-Blocks to create implicit interactions. A virtual node collects all the information about the binding pose through the self-attention mechanism. Finally, the binding embedding of a virtual node is fed into the affinity and pose layer to predict the binding affinity value and the confidence pose score for the corresponding docking pose. By incorporating poor poses, a contrastive pseudo-Huber loss function is utilized to guide the model in discerning whether a pose is good or poor. The training objective ensures that the model predicts a lower value for the poor pose and a higher value for the good pose. The primary distinction between the good and poor pose lies in their interactions. This strategy assists the model in learning crucial interactions rather than artificial features. This characteristic, we refer to as pose-sensitive, has demonstrated superior performance in real-world drug development projects.\n\nWe use the PDBBind time-split test set to determine the success rate of docking poses with an RMSD of less than 2\u2009\u00c5, initially introduced in the DiffDock paper5. We examine two primary scenarios for docking: the first involves providing the entire protein structure as input, termed \u201cblind docking\u201d, and the second is based on a known reference ligand, from which we extract nearby residues using a distance cut-off, termed \u201cpocket residues specified\u201d. Table\u00a01 illustrates the results that our proposed method significantly surpass all previous methods, achieving a top-1 success rate of 63.9%, significantly higher than the SOTA methods, DiffDock and GNINA18 on both scenarios. Including a pose score model, the top-1 success rate decreases to 62.1%. Despite the seemingly decreased RMSD, the selected poses tend to exhibit more accurate specific interactions. A more detailed discussion of this observation is provided in Section \u201cResults\u201d.\n\nMoreover, we consider a recently published PoseBusters benchmark19, which emphasizes the importance of physical plausibility in docking simulations. We evaluate our proposed method against this benchmark, and the results are shown in Fig.\u00a03a. In the conventional docking sampling procedure adopted from the preceding studies, such as those by Vina and other methods5,16,17,20, the initial ligand conformation is based on the reference structure (the ligand structure from crystal structure), but with random position and torsion angles for benchmark evaluations. To ensure fairness, we report two sets of results, each using either the reference or starting conformation (provided by PoseBusters benchmark) as the initial ligand conformation during docking pose sampling. Noteworthy, we apply the starting conformation as the input for energy function prediction in both sets of results to make sure no data leakage. Our method significantly outperforms various SOTA AI and traditional models, achieving a success rate of 84.09%. Nonetheless, 7.8% of the generated poses do not pass the posebusters-validity check, primarily due to steric clashes between the protein and ligand atoms. Despite this, our method notably mitigates this issue of physical plausibility compared to all other AI models. A significant performance decrease is observed when using the starting conformation. We attribute this primarily to incorrect stereochemistry, specifically wrong chiral centers and inaccuracies in the force field. Once these issues are rectified, the performance should align with the result of using reference ligand.\n\na The bar plot of the successful docking rate on PoseBusters version 2 benchmark, * denotes using the provided start conformation as initial ligand sampling conformation. b The docking performance bar plot on three levels of homology sequence similarity subsets. c The histogram depicts the number of specific interactions recovered by various methods within the five recovery rate ranges. d The predicted five MDN distributions, the x-axis is the VdW radius distance of the atoms pair d, and the y-axis is the probability of the MDN. Yellow is a hydrogen bond pair, orange is hydrophobic, and brown are other non-interaction pairs. e The upper diagram illustrates the predicted fusion coefficients \u03b1 for the hydrogen bond interaction between the 20th ligand atom (a hydrogen bond acceptor) and all other protein atoms that are hydrogen bond donors. The model predicts the top-2 highest \u03b1 value for the true hydrogen bonds on 20-143 and 20-280. A similar analysis applies to the lower diagram but for another hydrophobic pair set. f Two docking poses were generated by Interformer. The white represents the crystal structures, the green indicates the docking poses, and the yellow dashed lines signify the hydrogen bond interactions. As can be observed, the poses can directly form hydrogen bond interactions.\n\nFor a more comprehensive understanding of the generalizability of our method, we assess the maximum protein sequence similarity to the training set for each protein within the PDBBind time-split test set. Subsequently, we divide the test set into three subsets, each indicative of low, medium, and high levels of homology. We evaluate the docking accuracy within these subsets, and the results are shown in Fig.\u00a03b. Interformer with pose score achieved an accuracy rate of 63.4% on the low homology subset. This result demonstrates that the model does not merely recall the location of other homologous proteins in the training set but also has the capability of identifying the actual binding position. For the medium and high homology subsets, Interformer with pose score achieves accuracy rates of 56.7% and 63.5%, respectively.\n\nThe primary objective of most deep learning methods is to minimize the RMSD between the docked ligand and the crystal ligand. However, the generated docking poses tend to closely resemble the crystal ligand rather than adhering to physical principles such as non-covalent interaction. The sample we have previously provided demonstrates the ability of our proposed method to predict a reasonable energy function. For a quantitative analysis, we identify the number of the same hydrogen and hydrophobic pairs presented both in the crystal structure and the docking poses. We use the Protein-Ligand Interaction Profiler (PLIP)21 for evaluation, an open-source tool that determines the formation of specific interactions based on physical rules. Upon the evaluation of the PDBBind time-split test set, DiffDock and DeepDock were only capable of recovering an average of 29.42%, 23.55% of hydrogen bonds and 19.36%, 16.26% of hydrophobic interactions. In contrast, Interformer with pose score could recover an average of 57.25% of hydrogen bonds and 43.7% of hydrophobic interactions. However, the average recovery rate slightly decreases to 52.7% and 41.6% for hydrogen bonds and hydrophobic interactions, respectively, without the pose score. This finding suggests that the pose score model can further enhance the accuracy of specific interactions. Therefore, we propose using the docking pose selected by the pose score model instead of the energy model. For a more comprehensive view of this statistic, Fig.\u00a03c displays a histogram that outlines the count of different hit rate ranges. The results further confirm that Interformer relies on non-covalent interactions rather than a simplistic data-driven strategy.\n\nIn drug development processes such as small molecule optimization or virtual screening, it is essential to consider specific protein-ligand interactions as shown in Fig.\u00a03e, the predicted fusion coefficients, denoted as \u03b1, are shown for all potential hydrogen bond and hydrophobic pairs. The \u03b1 values control the weight of the hydrogen bond and hydrophobic terms. The Interformer accurately predicts various interactions between the ligand and protein atoms. Specifically, it identifies the existence of two hydrogen bonds that occur between ligand atom 20 and protein atoms 143 and 280. In addition, it also predicts the presence of hydrophobic interactions between ligand atom 25 and protein atoms 66, 186, and 232. Figure\u00a03d presents the five predicted MDNs. The x-axis represents the probability in this figure, while the y-axis corresponds to the Van der Waals radius distance between two atoms, denoted as d. The MDNs associated with the actual hydrogen bond and hydrophobic pairs are noticeably positioned on the left side of the figure, suggesting the specific interactions and close distance between the two atoms. On the contrary, the remaining three MDNs, which represent potential hydrophobic pairs, hydrogen bonds, and other pairs, are positioned on the right side of the figure. These three MDNs imply that these atom pairs are likely to be relatively distant from each other and are not expected to exhibit any specific interactions. The result indicates that the Interformer can effectively predict reasonable MDNs. Figure\u00a03f showcases two examples of binding poses generated by the Interformer, which exhibit direct hydrogen bonds and hydrophobic interactions. These binding poses can be forwarded seamlessly to Computer-Aided Drug Design software, such as MMGBSA and FEP22,23, without requiring any post-optimization procedures like force-field minimization with OPLS4 or AMBER24,25.\n\nMost methods perform well in predicting affinity based on crystal structure. However, it is hard to obtain such realistic conformation in practical drug development scenarios. The predictions based on poor binding poses often lead to substantial overfitting. To address this issue, we incorporate bad binding poses (negative samples) into the training set and employ a contrastive loss mechanism, as detailed in Method Affinity Module. This strategy brings an ability termed as \u201cpose-sensitivity\u201d that can help to distinguish the different interactions between good and bad poses rather than memorizing the shape of the ligand or binding sites. We evaluate two strategies for our model: one that includes negative samples and another that solely utilizes the crystal structures without incorporating them. On the PDBBind time-split test set, the affinity model using only crystal structures in the training presents a Pearson correlation coefficient R\u2009=\u2009\u20130.174 between the predicted affinity value and the RMSD. However, when negative samples are used, the affinity model achieves a correlation of R\u2009=\u2009\u20130.562, and the pose score model achieves a higher correlation of R\u2009=\u2009\u20130.659. Additionally, Fig.\u00a04a demonstrates that the affinity model without negative samples maintains consistent predictions for both good and poor binding poses because the model does not leverage any non-covalent interaction features. In contrast, when negative samples are incorporated, both affinity and pose score models predict lower values for binding poses with larger RMSD values. The result showcases the capacity of the Interformer to distinguish between good and poor poses.\n\na We utilize RMSD intervals of 1\u2009\u00c5 to calculate the average prediction values of all conformations for various methods. PoseRank is determined by sorting the energy from 1 to 10. To facilitate display, we multiplied the predicted values of PoseScore by 10. b The box plot presents the affinity prediction performance across four real-world test sets, categorized by the maximum similarity of ligands found in the training set. The ChEMBL-Kinase dataset (n\u2009=\u20092539), and the Mpro covalent test (n\u2009=\u2009142), are derived from ChEMBL and patent databases. The LSD1 project (n\u2009=\u200955) and the Mpro project (n\u2009=\u200922) originate from our internal real-world drug design projects. c (left) The most potent compound, Cpd27 in the LSD1 project achieves an affinity of 0.7\u2009nM. c (middle, right) The correlation plot between the Interformer predicted affinity value and experimental pIC50 in LSD1 project and prediction of Interformer without negative samples for training. d (left) The most potent compound, TAD 6-ref, in the Mpro project achieves an affinity of 16\u2009nM. d (middle, right) The correlation plot in the Mpro project and prediction of Interformer without negative samples for training. e (left) The docking pose of Cpd27 aligns with the PDB ID: 6W4K, it holds crucial hydrogen bond interaction with LYS-661 and creating two additional hydrogen bond interactions with GLN-358 and ALA-539. e (right) The docking pose of TAD 6-ref aligns with the PDB ID: 7RFS and is designated to form a macrocyclic to stabilize the ligand. For the box plots in (b), the lower limit represents lower quartile, the center line represents the median and the upper limit represents the upper quartile. The whiskers do not include outliers. For the regression plot in 4c and 4d, the blue line indicates linear regression fit. The light-blue region indicates the corresponding 95% confidence interval computed via bootstrapping mean.\n\nIn drug development, the accurate prediction of binding poses holds significant importance. However, it is equally crucial to predict the affinity based on these poses, as it directly influences the performance of virtual screening and small molecule optimization processes. As the CASF2016 benchmark is often used for comparison, there is a question of the persuasiveness of the benchmark because many models tend to overfit this test set. Therefore, we consider using our internal test set for affinity performance comparison as follows:\n\nChEMBL-Kinase test: We curated data solely from the kinase family available on ChEMBL26. The data points for each target should be no less than 30, resulting in 27 distinct protein targets and 2539 data points. The final assessment was based on the average affinity correlation across each target.\n\nLSD1 project: Our in-house pharmaceutical pipeline is guided by the Interformer model. It focuses on optimizing small molecules for the LSD1 target27. The project produced 54 small molecules, with the most potent one achieving an affinity level of 0.7\u2009nM.\n\nMpro covalent test: We sourced data on small molecules involving four types of covalent bonds from patents for the SARS-CoV-2 main protease (Mpro) target28, yielding 142 data points. The final assessment was based on the average affinity correlation for each covalent bond type.\n\nMpro project: Our in-house pharmaceutical pipeline, guided by the Interformer model, concentrates on optimizing covalent-type small molecules for the SARS-CoV-2 Mpro target29,30. This project has developed nine small molecules, with the most potent one achieving an affinity level of 16\u2009nM. We have also incorporated 12 small molecule data points from the Shionogi31 publication for a comprehensive evaluation.\n\nTable\u00a02 presents the performance of various models on these four real-world internal test sets. All conformations in these test sets, except for the large kinase test set, were generated by various docking programs and verified by humans. On average, of all test sets, the affinity module that does not utilize negative samples training achieves a correlation of 0.124, while the affinity module incorporating negative samples reaches a correlation of 0.454. Within the ChEMBL-Kinase dataset, the Interformer model achieves a correlation coefficient of 0.229, presenting a comparative performance with the SOTA GNINA model. In the LSD1 internal test set, our method achieves a correlation of 0.523, outperforming GNINA. In the Mpro covalent test, we did not test GNINA as it can not handle docking with covalent bonds, and our method achieves a correlation of 0.460, outperforms CovDock32,33. In the Mpro project test set, our method significantly outperforms CovDock by a correlation of 0.604. Due to pose sensitivity, the model substantially outperforms models trained only on crystal structures in terms of generalization.\n\nIn order to demonstrate effectively the practical applicability of our test set, we utilize MMSeq2 to assess the maximum protein sequence similarity within our internal test sets. The average protein sequence similarity are found to be 82.2% for Kinase, 100% for LSD1, and 96% for Mpro. While Mpro shares homology with the SARS-CoV 3C-like protease, it presents notable differences within the binding pocket site. Despite similar homologous proteins in the PDBBind training set, we further examine the ligand similarity using Tanimoto Similarity of Morgan Fingerprint. As depicted in Fig.\u00a04b, the Kinase and Mpro covalent test sets, which are sourced online, exhibit a similarity median of 38% and 50%, respectively. In contrast, the molecules derived from our internal drug development demonstrate a lower homology to the training set, with a similarity median of 33% and 28%.\n\nFor reference, on the Public Benchmark CASF2016, as shown in\u00a0Supplementary Information Table\u00a0S5, the affinity model also demonstrates strong predictive capabilities, achieving a performance of Pearson correlation coefficient R=0.809 and R=0.810 when predicting crystal structures and docking poses.\n\nIn order to validate the effectiveness of Interformer in real-world scenarios and to demonstrate our team\u2019s drug development capabilities, we have independently developed two drug development pipelines. Both projects involve small molecule optimization, where a list of candidate small molecules is designed by medicinal chemistry experts based on the crystal structures and binding modes of reference small molecules. These candidates are then ranked by Interformer for affinity scoring and subjected to various ADMET molecule property prediction models to ensure that the final designed small molecules perform well in terms of properties and affinity.\n\nIn early 2022, we initiated our first project targeting LSD127,34, a potential therapeutic target for cancer. We developed two series of small molecules, resulting in 54 compounds. The most potent demonstrates an affinity of 0.7\u2009nM, as shown in Fig.\u00a04c (left). This compound (Cpd 27 pg 64) was subsequently evaluated for metabolic stability in oral and intravenous mouse trials, exhibiting half-lives (t1/2) of 5.86 and 8.33\u2009h and AUC(0-t) of 6,528 and 11,502\u2009h.ng/mL, respectively. Amid the COVID-19 pandemic that swept across China in late 2022, our focus shifted towards the widely recognized SARS-CoV-2 viral, main protease (Mpro)29,30. We adopted a strategy of macrocyclic modifications35 to small molecules and designed nine small molecules. The most potent among these exhibit an affinity of 16 nM, as shown in Fig.\u00a04d (left). The correlation between actual pIC50 and predicted pIC50 value of Interformer in the LSD1 and Mpro projects was 0.523 and 0.604, respectively, as shown in Fig.\u00a04c (middle) and 4d (middle). Figure\u00a04c (right) and 4d (right) display the correlation of affinity prediction models trained exclusively on crystal structures, which are a mere 0.330 and 0.097. These real-world pharmaceutical scenarios underscore that models trained solely on crystal structures deliver subpar performance when the actual docking pose may not be precise.\n\nIn our LSD1 project, we undertake a structure-activity relationship (SAR) of the docking pose, and analyze the crystal structure of a reference small molecule PDB ID: 6W4K36. It is crucial to consider the presence of another small molecule, the co-factor FAD, within the pocket, as it could interact with the inhibitor. As shown in Fig.\u00a04e (left), the Cpd27 maintains the critical hydrogen bond interaction with LYS-661 of the reference molecule while modifying other functional groups to form two additional hydrogen bond interactions with GLN-358 and ALA-539, both metabolic stability property and affinity are better than the reference molecule. Within our Mpro project, we analyze the crystal structure of a reference small molecule PDB ID: 7RFS37. This inhibitor is a covalent inhibitor. Hence, our series of small molecules also have to consider the positioning of the warhead nitrile group, which must form a covalent bond with CYS-145. Our strategy involve opening the five-membered ring of the reference small molecule to form a macrocyclic modification, thereby circumventing chemical patent protection. As shown in Fig.\u00a04e (right), the TAD 6-ref, a macrocyclic molecule with a six-carbon length, is spatially reasonable through SAR analysis. Nearly all other interactions remain consistent with the reference molecule. Finally, this approach enable us to break through patent protection and find a small molecule that achieves a comparative affinity result with 16\u2009nM.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54440-6/MediaObjects/41467_2024_54440_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54440-6/MediaObjects/41467_2024_54440_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54440-6/MediaObjects/41467_2024_54440_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Determining the structure of protein-ligand complexes represents a significant challenge in the field of drug development.\n\nIn response, we have demonstrated Interformer, a deep-learning generative model specifically designed for protein-ligand docking and affinity prediction. This model integrates a powerful interaction-aware MDF that successfully recovers specific interactions at a high rate. Moreover, the mechanism underlying Interformer can be easily interpreted by users, effectively addressing the common shortfall in state-of-the-art deep learning models, which tend to overlook the critical non-covalent interactions.\n\nBeyond the importance of elucidating binding modes in drug design, the capacity to rank or screen ligands based on their affinity for a particular target is equally critical. Recognizing that many state-of-the-art deep learning models are prone to over-fitting on crystal structures, Interformer employs a training strategy that leverages contrastive learning with negative sampling to enhance pose sensitivity. This approach enables Interformer to distinguish between less accurate and more favorable docking poses by focusing on the specific interactions between protein and ligand atom pairs. Such robust capability allows the model to enhance generalizability for predictions in real-world scenarios.\n\nInterformer has demonstrated consistent improvements in protein-ligand docking across two widely recognized benchmarks, generating physically plausible and reasonable docking poses that enhance the potential for downstream applications. In the realm of affinity prediction, Interformer has shown consistent advancements on four in-house, real-world affinity benchmarks. Further application of Interformer within two internal drug development pipelines has led to the successful identification of two high-potency molecules at the nanomolar level.\n\nOur study underscores the considerable potential of Interformer to impact computational biology and accelerate the drug design process. In the future, we aim to expand the application of Interformer to a broader spectrum of real-world biological challenges and to enhance its performance for diverse molecular interaction types, including protein-protein and protein-nucleic acid interactions. For additional considerations regarding future directions, please refer to Supplementary Information Section\u00a03.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Graph representation is well-suited for depicting the structure of a protein-ligand complex, as shown in Fig.\u00a02a. A complex can be represented as a graph \\({{{\\mathcal{G}}}}=({{{\\mathcal{V}}}},{{{\\mathcal{E}}}})\\), where \\({{{\\mathcal{V}}}}={{{{\\mathcal{V}}}}}_{{{{\\mathcal{L}}}}}+{{{{\\mathcal{V}}}}}_{{{{\\mathcal{P}}}}}\\). Here \\({{{{\\mathcal{V}}}}}_{{{{\\mathcal{L}}}}}\\) is all atoms of the ligand, and \\({{{{\\mathcal{V}}}}}_{{{{\\mathcal{P}}}}}\\) = {\\({v}_{j}| {v}_{i}\\in {{{{\\mathcal{V}}}}}_{{{{\\mathcal{L}}}}},{v}_{j}\\in {{{{\\mathcal{V}}}}}_{{{{\\mathcal{W}}}}}\\,;{{{\\mathcal{D}}}}({v}_{i},{v}_{j})\\, < \\, 7\\) \u00c5} denotes protein pocket atoms. \\({{{{\\mathcal{V}}}}}_{{{{\\mathcal{P}}}}}\\) is determined by the Euclidean distance \\({{{\\mathcal{D}}}}(\\cdot )\\) between reference ligand atoms and \\({{{{\\mathcal{V}}}}}_{{{{\\mathcal{W}}}}}\\) protein atoms. Furthermore, \\({{{\\mathcal{E}}}}=\\{{e}_{ij}| {v}_{i},{v}_{j}\\}\\) denote the set of all edges between each pair of nodes. \\({{{\\bf{X}}}}\\in {{\\mathbb{R}}}^{n\\times {d}_{x}}\\) represent the node features, where Xi corresponding to the node features of the ith atom in the complex. The edge features are denoted as \\({{{\\bf{E}}}}\\in {{\\mathbb{R}}}^{n\\times n\\times {d}_{e}}\\), where n denotes the number of nodes in \\({{{\\mathcal{G}}}}\\). The edge feature \\({e}_{LP}=\\{{e}_{ij}| {v}_{i}\\in {{{{\\mathcal{V}}}}}_{{{{\\mathcal{L}}}}},{v}_{j}\\in {{{{\\mathcal{V}}}}}_{{{{\\mathcal{P}}}}}\\}\\) between ligand and protein pocket atoms are set to zero in the docking pipeline and to the Euclidean distance when a docking pose is available in the pose score and affinity prediction pipeline.\n\nLeveraging a powerful self-attention mechanism9, Graph-Transformer10,11 excels at learning node relationships and has achieved superior performance in various graph tasks. Graph-Transformer adds a bias term to the Multi-Head Self-Attention of Transformer9 to incorporate graph structure information. It takes the node features X and the edge features E as inputs, and the modified self-attention mechanism can be described as follows:\n\nwhere \\({{{\\bf{Q}}}}\\in {{\\mathbb{R}}}^{n\\times d}\\), \\({{{\\bf{K}}}}\\in {{\\mathbb{R}}}^{n\\times d}\\), and \\({{{\\bf{V}}}}\\in {{\\mathbb{R}}}^{n\\times d}\\) are linear transformations of X, referred as queries, keys, and values. \\({{{\\bf{Z}}}}({{{\\bf{E}}}})\\in {{\\mathbb{R}}}^{n\\times n}\\) is the bias term where Z is a linear layer that transforms the edge features E from the dimension de into dimension 1, when two nodes connects; otherwise we set it to \u00a0\u2212\u00a0inf. \\({{{\\bf{A}}}}\\in {{\\mathbb{R}}}^{n\\times n}\\) is a matrix that captures the similarity between the queries Q and the keys K, A updates the query representation through a weighted sum of the values V. For simplicity of illustration, we consider the single-head self-attention mechanism in this literature.\n\nThe classical self-attention mechanism9 permits each node to attend to all other nodes through attention weights, which cannot distinguish between the internal information of ligands and proteins and the information between them. Thus, we use Masked self-attention that restricts certain nodes to attend only to specific designated nodes by a mask M, which is shown in Fig.\u00a05a and can be described as follows:\n\nwhere M is applied to A through element-wise multiplication denotes by \u2299.\n\na Masked self-attention: is a modified self-attention mechanism that uses an attention mask M to restrict nodes from attending to only certain other nodes. Besides, it can incorporate edge features through element-wise addition. \u2a00 denotes as Hadamard product; \u2a01 denotes element-wise summation; \u22a0 denotes dot product. b Edge Output Layer, a module integrates node and edge features into a comprehensive protein-ligand inter-representation. This representation is achieved by summing the node features from protein and ligand with the mean of a learned edge feature, thus forming a two-dimensional feature. Subsequently, the two-dimensional feature is input into a FFN to learn the inter-presentation, which is essential for predicting the MDN.\n\nWe propose Intra-Blocks to capture the intra-interactions of ligands and proteins better. The Intra-Blocks outputs updated node features \\({{{\\bf{H}}}}\\in {{\\mathbb{R}}}^{n\\times d}\\) through applying MSA to the node features X and the edge features E by an Intra-mask \\({{{{\\bf{M}}}}}^{{\\prime} }\\):\n\nTo capture the inter-interactions and predict relationships between the atoms of ligand and protein, we employ an Inter-mask M\u2033 to promote the information exchange between the atoms of ligand and protein:\n\nThe Inter-Blocks outputs updated edge features \\({{{{\\bf{E}}}}}^{{\\prime} }\\) by a residual of A from the previous layer, and both layer normalization (LN) and feed-forward network (FFN) are applied to A in each block. Finally, we obtain the updated node feature \\({{{{\\bf{H}}}}}^{{\\prime} }\\) by applying \\({{{{\\bf{E}}}}}^{{\\prime} }\\) with MSA.\n\nTo predict the energy between protein-ligand atom pairs, the fusion of node and edge features can provide a more comprehensive inter-feature. As shown in Fig.\u00a05b, this can be described as follows:\n\nwhere \\({{{{\\bf{E}}}}}^{o}={{{\\rm{LN}}}}({{{{\\bf{E}}}}}^{{\\prime} }{{{\\bf{W}}}})\\), W is a learnable weight matrix. Initially, we compute the mean of protein-ligand pairs edge feature \\({{{{\\bf{e}}}}}_{ij}^{o}\\) and \\({{{{\\bf{e}}}}}_{ji}^{o}\\) from the semi-positive matrix Eo. Subsequently, this is enhanced through the addition of pair-wise node features, which are obtained by multiplying each ligand node feature \\({{{{\\bf{h}}}}}_{i}^{{\\prime} }\\) with every other protein node feature \\({{{{\\bf{h}}}}}_{j}^{{\\prime} }\\) from \\({{{{\\bf{H}}}}}^{{\\prime} }\\). At the end, we obtain the protein-ligand inter-features \\({{{{\\bf{H}}}}}^{a}\\in {{\\mathbb{R}}}^{| {{{{\\mathcal{V}}}}}_{{{{\\mathcal{L}}}}}| \\times | {{{{\\mathcal{V}}}}}_{{{{\\mathcal{P}}}}}| }\\) by applying a FFN.\n\nThe training procedure consists of two stages. Initially, we train the energy model using the crystal structure, which is subsequently employed to generate negative sample poses. Following this, both positive and negative sample poses are used to train the pose score and affinity prediction model. The training objectives of the three modules will be elaborated in greater detail below (additional details related to training data, training protocol, ablation study and hyper-parameters can be found in\u00a0Supplementary Information Section\u00a01).\n\nTo model specific interactions and output an energy function for the sampling algorithm to generate binding poses, we predict the interaction-aware MDN of each pair of ligand \\(i\\in {{{{\\mathcal{V}}}}}_{{{{\\mathcal{L}}}}}\\) and protein \\(j\\in {{{{\\mathcal{V}}}}}_{{{{\\mathcal{P}}}}}\\) atoms using the inter-feature Ha. This MDN is formed by the weighted sum of four mixed Gaussian functions. The first two can be considered regular interaction forces, whereas the third represents hydrophobic interactions modeled exclusively between hydrophobic atom pairs. The fourth represents hydrogen bonding, which is modeled specifically between hydrogen bond donors and acceptors. Further details are provided as follows:\n\nwhere the fusion coefficient \u03b1, mean \u03bc and variance \u03c3 are learnable parameters of each Gaussian distribution \\({{{\\mathcal{N}}}}\\) in the MDN for each pair of atoms, which obtained by applying a linear weight W to the inter-feature Ha. Given the VdW radius distance d, we can compute the probability p of each term in MDN. In the third term p3, \u03b1 is non-zero only on hydrophobic \\({{{\\mathcal{H}}}}\\) pair, and in the fourth term p4, \u03b1 is non-zero only on hydrogen bond donor \\({{{\\mathcal{D}}}}\\) and acceptor \\({{{\\mathcal{A}}}}\\) pair. The final probability of each atom pair P is derived by summing four \\({{{\\mathcal{N}}}}\\) multiplied by their respective \u03b1. Lastly, the model can be optimized through the negative log-likelihood loss function \\({{{{\\mathcal{L}}}}}_{MDN}\\).\n\nMC sampling generates multiple candidate docking poses based on energy function E (see\u00a0Supplementary Information Section\u00a02). However, the best pose may not necessarily be at the top of the list, necessitating an additional model to further re-rank the docking poses to enhance docking success rate. In this work, we employ a FFN on the virtual node feature v to predict p, which indicates whether the input binding pose is correct. This is determined by calculating if RMSD is less than 2 \u00c5 between the docking pose and the crystal pose. The training objective is a binary loss function.\n\nDocking algorithms typically employ energy functions to determine the affinity value of a ligand13,17,38. These energy functions are generally composed of linear combinations, with the weights of these combinations optimized to minimize the energy of crystal structures rather than directly modeling the experimental affinity values. This practice could result in generating accurate docking poses, but it often falls short in effectively ranking the affinity of the ligands. To address this challenge, we employ a FFN on the virtual node feature v to predict the experimental affinity value y. Affinity units used are IC50, Kd, and Ki, which are normalized by taking the negative logarithm, with higher values indicating stronger affinities. The training objective is a contrastive pseudo-Huber loss function, which ensures the model predicts a lower value for a poor pose and a higher value for a good pose. The primary difference between these two poses lies in their interactions. This strategy aids the model in learning essential interactions rather than artificial features, which we refer to as pose-sensitivity as follows:\n\nwhere \\(\\widehat{y}\\) is the predicted affinity value, we select Huber loss hyperparameter \u03c3 as 4.\n\nWe introduce an extra virtual node v to represent the entire information of the binding pose. v is learnable and is connected to all other nodes within the graph \\({{{\\mathcal{G}}}}\\) via a mask Mv in Inter-Blocks as follows:\n\nPose Score and our contrastive learning affinity module share the same purpose of determining the correctness of a binding pose. Given the strong complementarity between these two tasks, we choose a single model, based on the same virtual node v, to train and predict both the pose score and affinity tasks simultaneously as follows:\n\nSince the affinity value is approximately ten times greater than the binary value, the loss for training the affinity is reduced by a factor of ten.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54440-6/MediaObjects/41467_2024_54440_Fig5_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "Datasets used for all analysis were deposited via Zenodo at https://doi.org/10.5281/zenodo.10828798. Structures data for training are available from the PDBBind website at https://www.pdbbind-plus.org.cn/download. Accession codes for discussed structures from the PDB: 6QMT, 6W4K and 7RFS. The Interformer predicted data generated in this work are provided in Supplementary Data\u00a01. Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Installable source code, associated guidelines, various custom scripts, and interactive data analysis notebooks are available at GitHub https://github.com/tencent-ailab/Interformerand Zenodo https://doi.org/10.5281/zenodo.10828798.", + "section_image": [] + }, + { + "section_name": "Change history", + "section_text": "A Correction to this paper has been published: https://doi.org/10.1038/s41467-025-56973-w", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "\u00d6zt\u00fcrk, H., \u00d6zg\u00fcr, A. & Ozkirimli, E. Deepdta: deep drug\u2013target binding affinity prediction. 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We acknowledge each colleague who helped in this work.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "AI Lab, Tencent, Shenzhen, China\n\nHoutim Lai,\u00a0Longyue Wang,\u00a0Ruiyuan Qian,\u00a0Junhong Huang,\u00a0Peng Zhou,\u00a0Geyan Ye,\u00a0Fandi Wu\u00a0&\u00a0Wei Liu\n\nDepartment of Computer Science, Hunan University, Hunan, China\n\nPeng Zhou\u00a0&\u00a0Xiangxiang Zeng\n\nComputer Science Department, Stanford University, California, USA\n\nFang Wu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.L. conceived the project idea from scratch. H.L. designed and training the model. H.L. performed data analysis. H.L. and W.L. carried out all real world drug design applications. R.Q. and H.L. performed the design of Monte Carlo sampling algorithm. H.L. and L.W. wrote the paper. J.H. and P.Z. enhanced the accessibility of the paper. All authors, including X.Z., G.Y., Fandi W., and Fang W. read and commented on the paper.\n\nCorrespondence to\n Houtim Lai or Longyue Wang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Neeraj Kumar, and the other anonymous reviewer(s) for their contribution to the peer review of this work. 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Interformer: an interaction-aware model for protein-ligand docking and affinity prediction.\n Nat Commun 15, 10223 (2024). https://doi.org/10.1038/s41467-024-54440-6\n\nDownload citation\n\nReceived: 13 March 2024\n\nAccepted: 11 November 2024\n\nPublished: 25 November 2024\n\nVersion of record: 25 November 2024\n\nDOI: https://doi.org/10.1038/s41467-024-54440-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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+{ + "title": "Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids", + "pre_title": "Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids", + "journal": "Nature Communications", + "published": "19 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61952-2/MediaObjects/41467_2025_61952_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61952-2/MediaObjects/41467_2025_61952_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61952-2/MediaObjects/41467_2025_61952_MOESM3_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61952-2/MediaObjects/41467_2025_61952_MOESM4_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61952-2/MediaObjects/41467_2025_61952_MOESM5_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61952-2/MediaObjects/41467_2025_61952_MOESM6_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.2210/pdb5UD5/pdb", + "https://doi.org/10.2210/pdb4TQD/pdb", + "https://doi.org/10.2210/pdb2ZIM/pdb", + "/articles/s41467-025-61952-2#MOESM1", + "/articles/s41467-025-61952-2#ref-CR55", + "https://www.ebi.ac.uk/pride/archive/projects/PXD058768", + "/articles/s41467-025-61952-2#ref-CR56", + "https://github.com/zjuhaoran/FPFORCOM", + "/articles/s41467-025-61952-2#ref-CR57", + "/articles/s41467-025-61952-2#Sec32" + ], + "code": [ + "https://github.com/zjuhaoran/FPFORCOM", + "/articles/s41467-025-61952-2#ref-CR57" + ], + "subject": [ + "Biocatalysis", + "Machine learning", + "Protein design", + "Synthetic biology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5258661/v1.pdf?c=1753359041000", + "research_square_link": "https://www.researchsquare.com//article/rs-5258661/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61952-2.pdf", + "preprint_posted": "20 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The pyrrolysyl-tRNA synthetase (PylRS)/tRNACUA pair is one of the most widely used systems for the incorporation of noncanonical amino acids (ncAAs) into proteins at specific positions. Although directed evolution of PylRS have enabled over 300 ncAAs to be incorporated into proteins, most of the ncAA-containing proteins are expressed in a limited yield due to low activities of PylRS variants. Here, we applied machine learning (ML) to engineer the tRNA-binding domain of PylRS with a fast Fourier transform-partial least square regression (FFT-PLSR) model and three zero-shot prediction ML models. FFT-PLSR was first applied to explore a sequence space composed of pairwise combinations of 12 single mutations, and the best variant, Com1-IFRS, showed an 11-fold increase in activity compared to IFRS, a PylRS variant. The deep learning models ESM-1v, Mutcompute, and ProRefiner were then used to identify new mutation sites impacting the activity of Com1-IFRS. FFT-PLSR was used again to identify a variant, Com2-IFRS, from a sequence space containing 11520 mutations, which showed a 30-fold increase in activity. Com2-IFRS also enhanced enzyme activity against 12 other ncAAs by up to 3944.8-fold. Transplantation of the evolved mutations into 7 other PylRS-derived synthetases improved yields of proteins containing six types of ncAAs, including derivatives of Phe, Tyr, Trp, Cys, His and Lys, by up to 1149.7-fold. Molecular dynamics simulations revealed that mutations reshaped the hydrogen bond network between tRNA and protein, which increased tRNA binding affinity, shortened the reaction distance between tRNA and ncAA, and even enhanced the dynamics correlation network. This paper offers new PylRS variants that increase the utility of the orthogonal translation system and provide a machine learning framework for identifying optimized multiple-point combinatorial mutations in a vast sequence space.Biological sciences/Biochemistry/BiocatalysisBiological sciences/Chemical biology/Synthetic biologyBiological sciences/Chemical biology/Protein designBiological sciences/Computational biology and bioinformatics/Machine learning", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-5258661/v1/e9ad5adf756e92b37261ef7f.png", + "https://assets-eu.researchsquare.com/files/rs-5258661/v1/8f59a7f11ebd872856e8bdd8.png", + "https://assets-eu.researchsquare.com/files/rs-5258661/v1/cf2d03f576eb795fc1c32838.png", + "https://assets-eu.researchsquare.com/files/rs-5258661/v1/7aa771a7f3136dea7e6d79f5.png", + "https://assets-eu.researchsquare.com/files/rs-5258661/v1/a26f02ad0fe622beb37c8425.png" + ] + }, + { + "section_name": "Introduction", + "section_text": "Genetic code expansion is a powerful technique that enables the incorporation of noncanonical amino acids (ncAAs) into proteins at specific positions1. This process typically involves the suppression of an amber stop codon (UAG) by a suppressor tRNA in conjunction with an evolved aminoacyl-tRNA synthetase (aaRS). The pyrrolysyl-tRNA synthetase (PylRS)/tRNAPylCUA pair from Methanosarcina barkeri and Methanosarcina mazei is one of the most widely used systems for genetic code expansion in both bacteria and various eukaryotes, including mammalian cells and multicellular organisms2. Directed evolution of MmPylRS and MbPylRS has generated numerous variants capable of incorporating over 300 ncAAs with diverse chemical and physical properties into proteins3. However, the incorporation efficiency for most ncAAs is low, resulting in significantly lower expression of proteins containing ncAAs compared to wild-type proteins. A powerful directed evolution method has been developed for engineering PylRS to accept a new ncAA or to enhance the incorporation efficiency of a ncAA, which involves both positive and negative selections based on the ability to suppress a nonsense mutation in the presence of the desired ncAA4. However, this method requires multiple rounds of selection for each substrate, making it both time- and labor-intensive. PylRS catalyzes a two-step reaction process5. In the first step, pyrrolysine is activated through adenylation with ATP. In the second step, the resulting aminoacyl-adenylate acts as a substrate for the formation of aminoacyl-tRNAPyl. The enzyme is organized into two conserved domains connected by a variable linker, including an N-terminal domain (NTD) of around 90 residues and a C-terminal domain (CTD) of around 270 residues which harbors the catalytic sites. The tRNA-binding domain (TBD) includes NTD, the linker, and part of CTD, around 240 amino acids in total. Both TBD and the catalytic sites in CTD are required to create a functional PylRS/tRNAPyl pair6. When carrying out the directed evolution of PylRS for the incorporation of structurally diverse ncAAs, mutations were commonly constructed in the CTD to expand the substrate scope. Although mutations in the NTD are not directly involved in catalysis, they have been shown to affect the efficiency of ncAA incorporation in variants of MmPylRS, MbPylRS and their chimeras. For example, a chimeric PylRS variant, IPYE (V31I/T56P/H62Y/A100E), was obtained through a phage-assisted continuous evolution (PACE) selection, which showed improved activity and amino acid specificity7. Transplanting the evolved mutations into MbPylRS and MmPylRS variants greatly increased enzyme activity as well. In another study, a N-terminal mutation of MmPylRS R19H/H29R/T122S was found to enhance the incorporation of ncAA and the yield of ncAA-containing proteins8. Interestingly, since the mutations located in the NTD of PylRS were separated from its catalytic domain, they could be introduced to other PylRS mutants to improve the incorporation efficiency of their corresponding ncAAs. This makes the N-terminal engineering of PylRS a general strategy to enhance the incorporation efficiency of various ncAAs9. However, available N-terminal mutations of PylRS are still relatively rare and their impact on enhancing the efficiency of ncAA incorporation remains limited. Various strategies have been developed to engineer enzymes for improved activity, including directed evolution, rational design, computational design using Rosetta, and machine learning. In the enzyme engineering process, some single mutants are combined with the goal of creating variants with enhanced performance. However, the contributions of these single mutants are affected by mutations made at other sites in the protein, which is known as epistasis10. When mutations that contribute positively on their own are combined into a single protein, two or more mutations often interact in a non-additive manner. Epistatic effects can decrease the efficiency of enzyme engineering by altering the shape of the protein fitness landscape, and it remains challenging to predict this kind of epistatic behavior11. Recently, machine learning has been demonstrated to be useful for navigating epistatic fitness landscape that covers a small sequence space12. A machine learning-assisted directed evolution (MLDE) strategy has been developed to evolve an enzyme to enhance and invert the stereoselectivity of a putative nitric oxide dioxygenase, in which the machine learning method avoids some local fitness traps or long paths to the global optimum for the combinatorial library13. Additionally, an innov\u2019SAR model was developed to predict the enantioselectivity of 512 combinatorial variants based on combinations of 9 single-point mutations for an epoxide hydrolase14. Innov\u2019SAR uses the indexes of the AAindex database to encode the primary protein sequence into a numerical chain which was then converted to protein spectrum using fast Fourier transform (FFT). With the protein spectrum as the encodings of the protein sequence, along with the experimental values, regression models were then trained. Innov\u2019SAR has also been used to predict the thermostability of combinatorial variants15, including a limonene epoxide hydrolase and a transaminase. Despite these advancements, it remains unclear whether these machine learning methods are broadly effective for engineering different enzymes. This uncertainty arises because the protein fitness landscape can vary significantly across different proteins and even among different mutation sites within a single protein. In this study, we applied machine learning to engineer the tRNA-binding domain (TBD) of PylRS, aiming to develop highly improved variants. We anticipated that these variants could be broadly applicable to other C-terminal variants, thereby enhancing the incorporation efficiency of corresponding ncAAs. A MmPylRS variant named IFRS (N346I/C348S) was selected as the model for protein engineering, as it was obtained by screening libraries against 3-iodo-Phe (3IF) and also showed a broad substrate range. Supervised machine learning was first applied to predict the highly active combinatorial variants of 12 single-point mutations. The improved variant was then used as the input for three deep learning models to predict new single-point variants, which were then combined again to obtain the combinatorial variant with the highest activity. The best mutations were then combined with various C-terminal variants to test the generality of these mutations in improving the incorporation efficiency of various ncAAs. We also carried out molecular dynamics (MD) simulations to explore the mechanism behind the enhanced enzyme activity of these variants.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": " Design of combinatorial variants of PylRS using FFT-PLSR model Mutations in the tRNA-binding domain (TBD) of MmPylRS have been demonstrated to improve the efficiency of ncAA incorporation. It was also shown that N-terminal mutations did not significantly influence the substrate specificity of the PylRS and could be transferred to different variants16. We here tested four sets of mutations obtained previously in the TBD of MmPylRS to improve catalytic efficiency of IFRS, which include R61K/H63Y/S193R, R19H/H29R/T122S, D2N/K3N/T56P/H62Y and V31I/T56P/H62Y/A100E6\u20138,17 (Table S1). Based on sequence alignment, we introduced these N-terminal mutations into IFRS and tested their activity for incorporating 3-bromo-Phe (3BrF), a cheaper substrate than 3IF (Figs.\u00a01a&S1). Expression of IFRS was driven by the constitutive, mild-strength E. coli glutaminyl-tRNA synthetase (glnS) promoter, and expression of PylT was controlled by the E. coli lpp gene promoter. Amber suppression of the sfGFPS2TAG gene by 3BrF was investigated by measuring the fluorescence intensity, and the enzyme activity data was presented by the ratio of fluorescence intensity to optical density OD600 (Flu/OD) of cells expressing sfGFP and PylRS. The normailized enzyme activity was calculated by subtracting Flu/OD ratio of cells cultured in the presence of ncAA with that of in the absence of ncAA (Figure S2). We found that R19H/H29R/T122S did not achieve the expected increase in activity, and the IPYE (V31I/T56P/H62Y/A100E) from chPylRS did not increase the activity of IFRS either. Although R61K/H63Y/S193R did achieve a weak increase in activity, the effect was still lower than in wild type MmPylRS. Interestingly, the D2N/K3N/T56P/H62Y from chPylRS increased the activity of IFRS by around 7-fold, confirming that N-terminal mutations can indeed enhance the activity of C-terminal mutants (Figs.\u00a01b). We also tested the activity of 12 single-point mutations and found that only D2N, R61K, and H62Y enhanced the activity of IFRS, with D2N being the most active, exhibiting 3-fold improved activity compared to the wild type (Figs.\u00a01c&S2b). In the combinatorial mutant D2N/K3N/T56P/H62Y, D2N and H62Y enhanced the activity of IFRS, while K3N and T56P reduced the activity, indicating a strong epistasis among the mutations. We then attempted to use machine learning to explore the combinatorial space of these 12 single-point mutations. Fast Fourier transform (FFT)-partial least square regression (PLSR) approach was applied for predicting the fitness of combinatorial variants, which uses FFT for the protein sequence encoding and PLSR as the algorithm of ML model. The FFT-PLSR model has demonstrated the ability to be trained on a small dataset of enzyme mutant activity data to accurately predict the activity across the entire combinatorial space. The variant sequences were first transferred into numerical features and then transformed into two-dimensional energy versus frequency representation using FFT (Figure S3). Based on the transformed data, a PLSR model was trained to predict the activities of other mutants. We first trained the FFT-PLSR model using dataset 1 composed of 12 single-point mutant data. By scoring with leave-one-out cross-validation (LOOCV), we screened 566 amino acid encodings from the AAindex database and selected the index with best scoring to encode the amino acid sequences and build the PLS regression model (Figures S4&5). We constructed 6 double and 19 triple mutants, and measured their activities to form dataset 2 as a test set (N\u2009=\u200925) (Fig.\u00a01d). The trained model achieved an R\u00b2 of 0.843 and an MSE of 2.887 on the test set, indicating that the model showed good prediction ability for high-activity combinatorial mutants (Fig.\u00a01e). Interestingly, the best combinatorial mutant predicted by the model was D2N/R61K/H62Y, which was also the variant with highest activity in the test set (Table S2). To help the model gain a more thorough understanding of epistasis between mutants, we added dataset 2 into the training set, which raised the total number of mutants in the training set to 38. Additionally, we rationally designed and constructed a new test dataset 3 (N\u2009=\u200956) including 56 combinatorial mutants, mainly based on combination of improved variants (Fig.\u00a01f). The retrained model achieved an R\u00b2 of 0.961 on the training set and an R\u00b2 of 0.835 on the test set, still showing high accuracy for predicting the activities of combinatorial mutants (Fig.\u00a01g). We then constructed the top 8 mutants predicted by the model and tested their activities (Fig.\u00a01h & Table S3). All eight variants showed a significant increase in the activity compared to the IFRS, with the mutant Z7 (D2N/V31I/T56P/R61K/H62Y/T122S/S193R, Com1-IFRS) exhibiting the highest activity, which was 11-fold higher than the IFRS (Fig.\u00a01h). We utilized the Uniform Manifold Approximation and Projection (UMAP) algorithm to reduce the dimensionality of the mutation space and visualized it as a two-dimensional scatter plot. Our analysis revealed that mutations with similar activities clustered closely together (Fig.\u00a01i). The mutants from the training set were distributed across the entire sequence space, which was crucial for developing an accurate machine learning model (Fig.\u00a01i). Furthermore, our model's predictions aligned well with the experimental data with high-activity variants in the outer clusters and low-activity variants in the inner clusters (Fig.\u00a01i). This indicates that the model accurately mapped the fitness landscape, allowing us to identify mutants with significantly increased activity in the high-activity clusters. \nFurther improvement of IFRS activity by deep learning models\nTo further improve IFRS activity, we explored new mutation sites in tRNA-binding domain with Com1-IFRS as the template, by employing three deep learning models that enable zero-shot prediction of high-fitness variants including ESM-1v, MutCompute, ProRefiner (Figs.\u00a02a&S6&Table S4). EMS-1v is a protein language model that was trained on extensive datasets of protein sequences spanning the evolutionary tree of life, and hence learned the fundamental principles of protein structure and function18. Given the sequence of Com1-IFRS, each amino acid in TBD region was individually masked and analyzed by the ESM-1v model to predict the impact of potential mutations at that site. We constructed and characterized 16 single-point mutants that were predicted to have a higher likelihood than wild type (Figure S7). MutCompute is a structure-based three-dimensional self-supervised convolutional neural network model that was trained to associate local protein micro-environments with their central amino acid19. Given the N-terminal and C-terminal structures of MmPylRS (5UD5.pdb & 4TQD.pdb), MutCompute predicted single-point mutations optimizing the protein structure, and we characterized the top 44 mutants based on the probability predicted (Figure S7). ProRefiner is a global graph attention model for inverse protein folding that design sequences compatible with a given backbone structure20. We restrict the candidate mutation sites to the TBD of PylRS and leverage sequence design models to compute a quality score for every candidate site. For each site to be examined, we masked this site in the sequence to get the input partial sequence, and the input backbone structure is from Com1-IFRS structure predicted by Alphafold 321. The model then predicted the identity of the masked site in the form of a probability distribution over all amino acid types, and top 42 single-point mutants were selected for characterization (Figure S7). Interestingly, 7 mutants were predicted to have improved activities by both Mutcompute and ProreRiner (Figure S6). Based on three methods, a total of 95 single-point mutants were constructed on 85 amino acids of COM1-IFRS and assayed for enzyme activity (Figs.\u00a02b&S6). Since the enzyme activity is represented by the fluorescence intensity of sfGFP incorporated with 3BrF, the maximum activity that could be detected is fluorescence intensity of wild-type sfGFP. The fluorescence intensity of sfGFPS2TAG in presence of Com-1-IFRS reached 45% of wild-type sfGFP. Among all the single-point variants constructed, we did obtain several variants with higher activities than the Com1-IFRS (Fig.\u00a02b), which were I176S predicted by ESM-1v, D76A, T68V, H28K, T20S predicted by MutCompute, K67S, T68V, V74I predicted by ProRefiner, and H63N predicted by both MutCompute and ProRefiner. The best variant D76A designed by MutCompute, showed a 31% improvement in activity compared to Com-1-IFRS. However, most of the variants designed by the three approaches exhibited reduced or even lost activity, such as W16E showing a 99.7% decrease in activity compared to IFRS. We then wondered if these data are useful for training a supervised ML model to predict high-fitness single-point variant across the protein. With the above single-point mutants activity data as the training set (N\u2009=\u200996 including Com1-IFRS), the FFT-PLSR model was built. There are 566 amino acid encodings in the AAindex database and we tested the performance of the models trained with different number of amino acid encodings. We screened the best amino acids encodings and then used them to train the model. When one index was used, the R2 of the model was only 0.452, while when three amino acids encodings were used, the R2 of the model increased to 0.926 (Figure S8). We then used the three-index model to predict fitness of all single-point mutations in the PylRS TBD region, and the top 20 variants were constructed and characterized for enzyme activity (Figs.\u00a02b&S9). The best variant K67G showed a 31.9% improvement in activity, which was even higher than the D76A, indicating the great effect of the FFT-PLSR model in exploring new mutation sites (Fig.\u00a02c). To further explore the sequence space, we constructed the saturation mutagenesis on the 9 sites where the improved variants were obtained to build a mutability landscape (Fig.\u00a02d). The mutability landscape showed that most of improved variants were found at positions D76, H63 and K67, and the mutations at sites T20, H28 and I176 were mostly detrimental. We screened the variants that showed over a 10% improvement in activity compared to Com1-IFRS (Fig.\u00a02e). There were 3, 7, 5, 1, 2, 9 mutations at positions N7, H63, K67, T68, V73, D75, respectively, meeting this criterion. We hence attempted to use FFT-PLSR model to explore this sequence space containing 11520 (4\u00d78\u00d76\u00d72\u00d73\u00d710) mutations. To achieve epistasis information, the top 3 variants at each mutation site were combined to construct double variants, resulting in a total of 92 combined variants to be used for model training (Figs.\u00a02f). Combination of improved single variants did generate further improved variants, but also the variants with decreased activities, indicating the strong epistasis among the mutations. The best double mutant was N7E/T68F, with activity being 70% higher than the Com1-IFRS (Fig.\u00a02f). We then conducted another round of FFT-PLSR building for predicting high-activity combinatory mutations. There were 27 single-point mutations and 92 combinatorial mutations, plus Com1-IFRS, 120 data in total in the training dataset. These were used to train the FFT-PLSR model. We first used 10-fold cross-validation to evaluate and identify the optimal single-index model. However, this model only achieved an R\u00b2 score of 0.558 on the training set, indicating a poor performance. To improve the model performance, multiple indexes were used for encoding amino acids, which improved the R\u00b2 score to 0.668 for two-index model and 0.677 for three-index model, respectively (Figs.\u00a02g&S10&Table S5). Based on the three-index model, we predicted the activity data of 11520 mutations, and top 20 combinatorial variants were selected for experimental verification (Table S6). All the variants showed comparable or higher activities than the Com1-IFRS, with 15 of them possessing activities improved by more than 2-fold, suggesting the great effect of the ML model. The best Z7-3 variant (N7Y/H63L/K67N/V74W, Com2-IFRS) showed a 2.8-fold increase in activity compared to Com1-IFRS, more than 30-fold higher than IFRS, reaching fluorescence intensity comparable to wild-type sfGFP. Additionally, we tested if the mutations H20S, H28K, I176N and I176S which were not selected for making combinatory mutations could further improve the activity of Com2-IFRS. It was found that none of these single-point mutations improved the activity of Com2-IFRS (Figure S11). Combination of N-terminal mutations with C-terminal mutations to generally enhance the incorporation efficiency of diverse ncAAs As the IFRS is polyspecific that could accept various phenylalanine derivatives22, We hence tested if Com1-IFRS and Com2-IFRS improved incorporation efficiency of other ncAA substrates. It was found that normalized fluorescence of sfGFP-S2TAG incorporated with 12 diverse ncAAs was significantly increased by the two variants compared to IFRS, with the largest 3944.8-fold improvement for 3FF enabled by Com2-IFRS (Fig.\u00a03a). However, since IFRS is a promiscuous enzyme with a certain degree of misincorporation of canonical amino acids (cAAs), Com1-IFRS and Com2-IFRS also increased the incorporation efficiency of cAAs, and different extent of misincoporation was observed in presence of different ncAAs (Figure S12a). C-terminal domain of PylRS containing the catalytic sites have been evolved to accept various ncAAs. The mutations Com1 and Com2 were then combined with different C-terminal mutations to enhance the incorporation efficiency of corresponding ncAAs. To test the universality of Com1 and Com2 in enhancing incorporation efficiency of various ncAAs, we selected C-terminal mutations with large sequence diversity that could accept ncAAs with significantly different side chains, including Phe derivatives, Tyr derivatives, Trp derivatives, Cys derivatives, His derivatives and Lys derivatives (Table S7). C-terminal mutant NACA was combined with Com1 and Com2 to test the incorporation of three Phe derivatives, including 3-bromo-L-phenylalanine (3BrF),2-chloro-L-phenylalanine (2ClF) and 3-L-phenyllactic acid (PLA). The two variants significantly improved efficiency of NACA to incorporate these three ncAAs, and the Com2 led to improvement of 61.1-fold, 68.1-fold and 9.9-fold, for 3BrF, 2ClF, and PLA, respectively (Fig.\u00a03b). IFRS could also accept 3BrF and 2ClF, and Com1, Com2 mutations significantly improved the misincorporation of cAAs in presence of 3BrF and 2ClF (Figure S12a). Interestingly, when combined with NACA, the two variants did not improve the incorporation efficiency of cAAs in presence of 3BrF and 2ClF, compared to IFRS (Figure S12b). This could be due to that NACA exhibited lower activity against cAAs than IFRS, and Com1, Com2 did not influence substrate specificity and generally enhanced the activity of C-terminal mutations against all substrates. This was further confirmed by the performance of Com1 and Com2 introduced in wild-type MmPylRS. MmPylRS showed limited misincorporation of cAAs and the two variants did not increase the misincorporation of cAAs either, compared to the wild type. On the other hand, the two variants significantly increased the incorporation efficiency of N6-(tert-butoxycarbonyl)-L-lysine (BocK) and N6-((allyloxy)carbonyl)-L-lysine (AlocK) and the best Com2 resulted in 47.1-fold and 39.4-fold improvement for BocK and AlocK, respectively (Fig.\u00a03b&S12b). This was further confirmed by the huge increase in expression level of sfGFP incorporated with BocK in presence of Com2-IFRS compared to IFRS (Figure S13). Com1 and Com2 also enhanced activities of other C-terminal mutations toward their corresponding ncAAs. The addition of Com2 increased the incorporation efficiency of N-epsilon-Acetyl-L-lysine (AcK) by 32.2-fold compared to original C-terminal mutations MLAF, and the extent of improvement was significantly higher than the IPVE variant tested previously7. SDS-PAGE also revealed that the amount of sfGFP expressed in presence of Com2-MLAF was significantly higher than that in presence of MLAF (Figure S13). Mass-spectra analysis confirmed the correct incorporation of AcK in sfGFP, but a misincorporation of lysine was also observed (Figure S14). Additionally, addition of Com2 improved activity of GML mutant by 117.3-fold, 587.6-fold and 6.5-fold against 3BrY, 3ClY and 3IY, respectively (Fig.\u00a03b). We checked expression of sfGFP with 3IY incorporated, and found that Com2-GML indeed significantly increased amount of expressed protein compared to GML. Mass-spectra analysis also confirmed the correct incorporation of 3IY (Figure S13&14). Com1 and Com2 mutations were also constructed in BtaRS to explore their effect on incorporation of the Trp derivatives23. The addition of Com2 increased the incorporation efficiency of 3-benzothienyl-L-alanine (Bta) and 3-(1-naphthyl)-L-alanine (1NaA) by 84.4-fold and 1149.7-fold, respectively (Fig.\u00a03b). SDS-PAGE revealed the significantly improved amount of sfGFP with Bta incorporated enabled by Com2-BtaRS compared to BtaRS. Mass-spectra analysis confirmed the correct incorporation of Bta and no misincorporation of cAAs was observed (Table S8). However, misincorporation of cAAs were indeed observed when 1Na was incorporated by Com2-BtaRS (Figure S12b). To explore the effect of Com1 and Com2 on incorporation of Cys derivatives, Com1 and Com2 were combined with CTD mutation WS. Com1 did not exhibit improved effect on fluorescence of sfGFP, while addition of Com2 improved fluorescence intensity of sfGFP with Sac incorporated by 6.1-fold compared to WS (Fig.\u00a03b), and the enhanced amount of sfGFP expressed was confirmed on SDS-PAGE. WS also showed a certain degree of misincorporation of cAAs, which was also observed for Com2-WS (Figure S12b). Com1 and Com2 were combined with two C-terminal mutations including QF and IFGFF to explore their effect on incorporating His derivatives including 3-(2-thienyl)-L-alanine (2ThiA), 2-(5-bromothienyl)-L-alanine (BrThiA) and 3-methyl-L-histidine (3MeH). In the results, the addition of Com2 increased the incorporation efficiency of 2ThiA, BrThiA and 3MetH by 223.1-fold, 61.4-fold and 201.5-fold, respectively, compared to their C-terminal variants (Fig.\u00a03b). SDS-PAGE revealed that Com2-QF and Com2-IFGFF indeed dramatically enhanced the amount of purified sfGFP incorporated with 2ThiA and 2MetH, respectively, compared to C-terminal mutations alone. Moreover, according to mass-spectra analysis, no misincorporation of cAAs were observed for GFP-2MetH and GFP-2ThiA (Figure S13&S14). To explore if the variants obtained were useful in improving expression of other proteins with ncAAs incorporated, we tested expression of myoglobin containing 3MetH (Fig.\u00a04a). 3MetH has been used as a heme ligand to enhance activity of myoglobin or as a catalytic residue of an artificial esterase possessing non-canonical organocatalytic mechanism24. Here, we used MmPylRS variant Com2-IFGFF to incorporate 3MetH into the position His93 of myoglobin as a ligand of heme. The protein expression was explored by carrying out protein purification from same amount of cells in presence of Com2-IFGFF and IFGFF. It was found that concentration of 3MetH-containing myoglobin was 28.3 mg/L for Com2-IFGFF, 6.3-fold higher than 4.5 mg/L of IFGFF (Fig.\u00a04b). We then measured the activities of Mb-3MetH against guaiacol using the purified protein without dilution. The myoglobin catalyzes the oxidation of guaiacol by hydrogen peroxide to generate a stable tetrameric product whose formation can be readily monitored by absorbance at 470 nm. The yield of product was significantly higher for Com2-IFGFF compared to IFGFF (Figure S15). The \u0394OD470 reached 0.063 in the reaction system containing Com2-IFGFF after 40-min reaction, 7.9-fold higher than that using IFGFF (Fig.\u00a04b&S15), confirming the higher expression of target protein aided by the Com2-IFGFF variant. \nSuppression of multiple amber codons of PylRS variants\nWe then characterized the ability of Com1-IFRS and Com2-IFRS to suppress multiple amber codons in sfGFP, which is important for incorporating multiple unnatural amino acids into proteins. One to five consecutive amber codons were inserted second position of sfGFP (Fig.\u00a04c). Both Com1-IFRS and Com2-IFRS exhibited higher fluorescence intensity than IFRS in all situations, while Com2-IFRS showed higher suppression ability than the Com1-IFRS, with 122.4-fold, 99.4-fold, 91.2-fold and 53.3-fold improvement compared to IFRS, for S2TAG\u00d72, S2TAG\u00d73, S2TAG\u00d74 and S2TAG\u00d75, respectively (Figs.\u00a04d&S16a). It was also found that Com1-IFRS and Com2-IFRS improved the incorporation efficiency against native amino acids compared to IFRS. We also tested the incorporation efficiency of multiple unnatural amino acids at different positions of sfGFP (Fig.\u00a04e). When 3BrF was incorporated at the position of D36 of sfGFP, the fluorescence intensity was different from the sfGFP with 3BrF at second position, for both the wild type IFRS and mutant Com1-IFRS and Com2-IFRS (Figs.\u00a04f&S16b). Similar site-dependent incorporation efficiency has previously been observed for other ncAAs7. Despite this, Com2-IFRS still showed higher amber codon suppression efficiency than Com1-IFRS and IFRS, with 3.8-fold, 7.9-fold, 27.3-fold, 4.7-fold and 5.2-fold improvement compared to IFRS, for 1TAG, 2TAG, 3TAG, 4TAG and 5TAG, respectively.\nMD simulations to reveal the mechanisms mediating improved activity of PylRS variants\nThe whole 3D structure of MmPylRS was not available yet since the full-length protein is insoluble. Hence, AlphaFold3 was used to predict the structures of MmPylRS (WT), Com1-WT and Com2-WT in complex with tRNAPyl (Figure S17), and found that the predicted MmPylRS structure aligned well with the separate NTD structure and CTD structure determined previously (Fig.\u00a05a). 50-ns molecular dynamics simulations were then conducted for these structures in complex with Pyl-AMP to understand how the mutations influenced the binding of tRNA and the enzyme activity (Figure S18). The reaction distance between 3'-OH of tRNA A76 and the carboxyl carbon atom of amino acid Pyl was analyzed. It was generally shorter for Com2-WT than wild type and Com1-WT (Fig.\u00a05b&S19). A total of 1000 snapshots were analyzed, and the number of snapshots with a distance shorter than 4 \u00c5 was 462, which is 21-fold and 10-fold higher than that of wild type and Com1-WT, respectively (Fig.\u00a05c). These indicated that Com2 mutations mediated the binding of tRNAPyl to make the aminoacylation reaction more easily happen. Interestingly, in the reaction conformations, we observed new hydrogen bonds formed between Pyl-AMP and tRNA in the two variants compared to WT. Com1-WT showed a new hydrogen bond formed between main chain -NH2 of Pyl and 2'-OH of tRNA A76, while Com2-WT exhibited a hydrogen bond formed between the main chain -NH2 of Pyl and the 3'-OH of tRNA A76. No such hydrogen bonds were found in wild type (Figure S20). These new hydrogen bonds will contribute to the interaction between Pyl-AMP and tRNA, and hence accelerate the reaction. The hydrogen bonds formed between tRNA and the protein were also analyzed. The number of hydrogen bonds in PylRS TBD with occupancy higher than 60% in last 10-ns MD simulations was 22, 19, and 22 for WT, Com1-WT, and Com2-WT, respectively (Figure S21). Specifically, both Com1-WT and Com2-WT formed new hydrogen bonds including LYS3-A58, ARG19-A46, ARG52-G52, ARG193-A5, ARG193-C13 and ARG193-U12, while Com2 formed several extra hydrogen bonds such as ARG55-C45, ARG55-A46, Arg58-A20 (Fig.\u00a05d). Additionally, several hydrogen bonds were disrupted in the variants, such as ASN49-G47, ARG55-A46, ARG55-G21, ARG58-A58, R66-G21 and so forth. Specifically, in the conserved Motif 2 loop that is responsible for tRNA recognition, two hydrogen bonds Lys336-C71 and Lys336-C72 were disrupted, and a new hydrogen bond Asp334-C71 was formed in both the Com1 and Com2 variants (Figure S22). Additionally, an extra H-bond GLU332-C74 was formed in Com1 variant but not in WT and Com2. This indicated that mutations reshaped the interactions between tRNA and protein, and the Com1 and Com2 improved the tRNA binding in a different way. As a result, the interaction energy between tRNA and protein was different for the wild type and variants. The binding free energy between tRNA and different domain of the protein was analyzed. Com1-WT and Com2-WT exhibited lower binding free energy compared to the wild type, which was mainly attributed to the decreased binding free energy of the tRNA binding domain (Figure S23). Interestingly, the binding free energy of full-length Com1-WT was lower than Com2-WT, while that of the tRNA binding domain of Com1-WT was higher than Com2-WT. Residue-level binding energy contribution analysis for both protein and tRNA was carried out. Several amino acids and tRNA bases were indeed found to impact the binding free energy. For example, GLU332 in Com1-WT exhibited reduced binding energy, while no significant changes were observed in WT or Com2-WT, which might be attributed to the newly formed H-bond Glu332-C74 in Com1-WT. S193R mutation formed new salt bridges with tRNA including Arg193-A5, Arg193-C13, Arg193-U12, which led to a significant decrease in the binding free energy (Fig.\u00a05e). Also, Arg55 of Com2-WT showed a significantly low binding energy due to T56P mutation, although it is not the case for Com1-WT. As for the analysis of tRNA binding energy, it was found that the mutations significantly increased the binding free energy for the last three bases C74, C75 and A76, which might facilitate the aminoacylation reaction (Fig.\u00a05e). Dynamics cross-correlation matrices (DCCMs) for the WT and two variants to understand how the mutations impact protein dynamics were computed. Generally, the variants showed more dynamics cross-correlations between residue pairs compared to the wild type, with the Com2-WT exhibiting higher Cij values in most of the regions than Com1-WT and WT (Fig.\u00a05f). Specifically, it was found that the dynamics correlation between NTD and CTD was more significant in the Com2-WT, compared to WT and Com1-WT. NTD and CTD are distant from each other, but are bound together with the tRNA. The mutations on NTD hence impact the dynamics of CTD through modification of interactions with tRNA. The increased correlated dynamical network would help to maintain more reaction conformations and hence enhanced the enzyme activity. ", + "section_image": [] + }, + { + "section_name": "Discussion", + "section_text": "This study utilized machine learning to explore the combinatorial mutation space of MmPylRS tRNA-binding domain and identified great variants, which modified the tRNA binding, enhanced the aminoacylation rate, and subsequently significantly improved stop codon suppression efficiency. PylRS has been engineered for improved activity through N-terminal mutations. However, all previous studies applied directed evolution strategies for the PylRS N-terminal engineering and the methods used included error-prone PCR to construct library coupled with screening based on GFP fluorescence or white/blue colony25, and a phage-assisted continuous evolution7. Although directed evolution is a powerful strategy for enzyme engineering, its success relies on iterative cycles of library construction and screening, and it can be trapped in local fitness optima due to taking one mutation step at a time. In our study, we applied deep learning models capable of zero-shot prediction of high-fitness variants to explore mutation target sites in the whole tRNA-binding domain, and a supervised model FFT_PLSR to explore the sequence space once the mutation target sites were identified. Thanks to the ML models we used, the variants obtained were significantly more active than the variants obtained previously. Due to epistatic interactions between mutations, combining multiple mutations does not always result in positive effects. Investigating the combinatorial effects of mutants involves two main tasks: (1) pairwise combinations of specified mutations, and (2) exhaustive combinations of 20 amino acids at specified mutation sites. Using the FFT-PLSR model, we demonstrated that by using a training data set composed of 38 single, double and triple mutants, it was possible to identify the optimum multiple-point combinatorial mutants in the sequence space of 12 singe-point mutations pairwise combinations, 4096 mutants in total, providing a solution to task 1. We also attempted to apply the ML model to tackle task 2 by restricting the mutant combinatorial space and focusing solely on combinations of single-point improved mutations. We found that using multiple AA indexes to encode protein sequences yielded better results than a single-index model, with the model achieving a fit of 0.677 for the training data. The advantage of the FFT-PLSR model lies in its effective utilization of experimental mutant activity data, guiding the construction of the next set of combinatorial mutants. Zero-shot ML models learn general patterns in proteins, and can predict high-fitness protein variants without requiring any prior knowledge other than protein sequence or structure. These models can help to design an initial variant library or explore new potential mutations, when directed evolution reaches a local minimal, opening new evolutionary pathways. In our study, sequence-based zero-shot model ESM-1V did not perform well, with mutations predicted primarily located in the linker region 90\u2013185 aa, and only a positive mutant I176S obtained showing a minor improvement in activity. The ESM model also suggested the low-activity C42N mutation, while C42, C69, C72, and H24 are critical Zn-binding residues, highlighting the model's limitation in considering structural constraints (Figure S25). Structure-based model MutCompute predicted mutations to optimize the protein structure and 6 improved variants were obtained using this method. Interestingly, MutCompute made predictions based on the crystal structure of wild-type PylRS, and mutations beneficial to the wild-type PylRS do not necessarily work when transferred to PylRS mutants. However, experimental results showed that six mutations predicted by MutCompute still enhanced activity of the Com1-IFRS. Actually, MutCompute has been used to improve activity and stability of several enzymes including PET-degrading enzymes26, DNA polymerase27, and haloalkane dehalogenase28. ProRefiner, developed for inverse protein folding, also predicted two improved Com1-IFRS variants. A similar model, ProteinMPNN has recently been proven to design more soluble, stable and active protein variants than wild type29. The evolved PylRS TBD mutants have demonstrated exceptional effectiveness in increasing the yield of ncAA-containing proteins. The resulting TBD combinatorial mutants improved IFRS activity across its substrate spectrum, and when combined with diverse CTD mutants, they enhanced the incorporation efficiency of six types of ncAAs. Previously, Lin et al. achieved efficient stop codon suppression by fusing the MmPylRS TBD region with the catalytic domain of various cAA RS including histidine, phenylalanine, and alanine30. The TBD mutations obtained in this study are hence promising to increase the activities of diverse chimeric aaRS to enhance the incorporation efficiency of more types of ncAAs into proteins. Moreover, these aaRS obtained are expected to have a marked impact on suppression of multiple TAG stop codons, and hence make the genetic code expansion technology more useful. The wild-type PylRS exhibits poor solubility due to its hydrophobic N-terminal domain, which hindered crystallization of the full-length enzyme. The latest protein-tRNA structure prediction tool, AlphaFold3 was used to model the structure, allowing us to illustrate the mechanism of improved mutations. To better represent the binding conformation, Zn\u00b2\u207a and ATP were included in the system. Analysis of the MD simulations results suggested that the enhanced activity of Com2-IFRS likely stems from the mutations reshaping the binding interactions between protein and tRNA, which reduced the reaction distance between tRNA and Pyl-AMP, thereby enhancing aminoacylation efficiency. Additionally, through DCCM analysis, we found that Com2-WT exhibited a significantly enhanced overall dynamics network, implying that tRNA binding strengthens both intra- and inter-domain couplings. To our knowledge, this is the first study working on MD simulations of the whole structure of PylRS, which paves the way for computational design of more effective PylRS variants. ", + "section_image": [] + }, + { + "section_name": "Declarations", + "section_text": "Code availability\u00a0\nThe source code employed for generating descriptors and training ML models in this research are available at https://github.com/zjuhaoran/FPFORCOM.\nAcknowledgement\u00a0\nThis work was supported by National Natural Science Foundation of China (Grant No. 22108245 & 22378351), the Key Research and Development Program of China (Grant No. 2022YFA0913000) and the Fundamental Research Funds for the Central Universities (226-2022-00214). We thank AI + High Performance Computing Center of ZJU-ICI.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Shandell MA, Tan Z, Cornish VW (2021) Genetic code expansion: A brief history and perspective. Biochemistry 60:3455\u20133469 Wan W, Tharp JM, Liu WR (2014) Pyrrolysyl-tRNA synthetase: An ordinary enzyme but an outstanding genetic code expansion tool. 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BMC Res Notes 5", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SI.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The pyrrolysyl-tRNA synthetase (PylRS) is widely used to incorporate noncanonical amino acids (ncAAs) into proteins. However, the\u00a0yields of\u00a0most ncAA-containing protein \u00a0remain low due to the limited activity of PylRS variants. Here, we apply machine learning to engineer the tRNA-binding domain of PylRS. The FFT-PLSR model is first applied to explore pairwise combinations of 12 single mutations, generating a variant Com1-IFRS with an 11-fold increase in stop codon suppression (SCS) efficiency. Deep learning models ESM-1v, Mutcompute, and ProRefiner are then used to identify additional mutation sites. Applying FFT-PLSR on these sites yields a variant Com2-IFRS showing a 30.8-fold increase in SCS efficiency, and up to 7.8-fold improvement in the catalytic efficiency (kcat/KmtRNA). Transplanting these mutations into 7 PylRS-derived synthetases significantly improves the\u00a0yields of proteins containing 6 types of ncAAs. This paper presents improved PylRS variants and a machine learning framework for optimizing the enzyme activity.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Genetic code expansion enables site-specific incorporation of noncanonical amino acids (ncAAs) into proteins by suppressing an amber stop codon (UAG) using a suppressor tRNA paired with an engineered aminoacyl-tRNA synthetase (aaRS)1. The pyrrolysyl-tRNA synthetase (PylRS)/tRNAPylCUA pair from Methanosarcina barkeri and Methanosarcina mazei is one of the most widely used systems for incorporating ncAAs in bacteria and eukaryotes2. Over 300 ncAAs have been incorporated into proteins by utility of MmPylRS and MbPylRS variants3. However, the incorporation efficiency for most ncAAs remains low, leading to reduced expression of ncAA-containing proteins compared to the wild-type proteins. A powerful directed evolution strategy involving positive and negative selection has been developed to evolve PylRS for new or more efficient ncAA incorporation4. Despite its effectiveness, the method is time- and labor-intensive, requiring multiple rounds of selection for each target ncAA.\n\nPylRS catalyzes a two-step reaction process5. In the first step, pyrrolysine is activated through adenylation with ATP. In the second step, the resulting aminoacyl-adenylate acts as a substrate for the formation of aminoacyl-tRNAPyl. MmPylRS or MbPylRS is organized into two conserved domains connected by a variable linker, including an N-terminal domain (NTD) of around 90 residues and a C-terminal domain (CTD) of around 270 residues, which harbors the catalytic sites. The tRNA-binding domain (TBD) includes NTD, the linker, and part of CTD, around 240 amino acids in total. Both TBD and the catalytic domain (CD), the remaining part in CTD, are required to create a functional PylRS/tRNAPyl pair6. When carrying out the directed evolution of PylRS for the incorporation of structurally diverse ncAAs, mutations were commonly constructed in the CD to expand the substrate scope. Although mutations in the TBD are not directly involved in catalysis, they have been shown to affect the efficiency of ncAA incorporation in variants of MmPylRS, MbPylRS, and their chimeras. For example, a chimeric PylRS variant, IPYE (V31I/T56P/H62Y/A100E), was obtained through a phage-assisted continuous evolution (PACE) selection, and exhibited a 45.2-fold improvement in catalytic efficiency (kcat/KmtRNA) compared to the parent enzyme7. Transplanting the evolved mutations into MbPylRS AcK3RS and MmPylRS AcK3RS variants increased expression of reporter proteins by 9.7-fold and 2.2-fold, respectively, compared to unmodified PylRS counterparts. In another study, a N-terminal mutation of MmPylRS R19H/H29R/T122S was found to enhance the incorporation of ncAA and the yield of ncAA-containing proteins by almost 4-fold compared to wild-type PylRS8. Interestingly, since the mutations located in the TBD of PylRS were separated from its CD, they could be introduced to other PylRS mutants to improve the incorporation efficiency of their corresponding ncAAs. This makes the N-terminal engineering of PylRS a general strategy to enhance the incorporation efficiency of various ncAAs9. However, available N-terminal mutations of PylRS are still relatively rare, and their impact on enhancing the efficiency of ncAA incorporation remains limited.\n\nRecently, a class of highly active PylRS enzymes lacking an NTD has been identified and characterized10,11. These PylRS enzymes function with their cognate PyltRNA and could also be used to direct the incorporation of ncAAs. One of such enzymes is the PylRS from Methanomethylophilus alvus (Ma), which showed a similar amino acid-binding pocket with MmPylRS, and transplanting mutations from MmPylRS to MaPylRS expanded the substrate specificity7,12. MaPylRS/MaPyltRNA was also engineered to be mutually orthogonal to the Mb/MmPylRS system11. However, since these PylRS enzymes were only recently identified, the number of ncAAs incorporated by them was significantly lower than the number directed by Mb/MmPylRS.\n\nVarious strategies have been developed to engineer enzymes for improved activity, including directed evolution, rational design, computational design using Rosetta, and machine learning. In the enzyme engineering process, some single mutants are combined with the goal of creating variants with enhanced performance. However, the contributions of these single mutants are affected by mutations made at other sites in the protein, which is known as epistasis13. When mutations that contribute positively on their own are combined into a single protein, two or more mutations often interact in a non-additive manner. Epistatic effects can decrease the efficiency of enzyme engineering by altering the shape of the protein fitness landscape, and it remains challenging to predict this kind of epistatic behavior14. Recently, machine learning has been demonstrated to be useful for navigating an epistatic fitness landscape that covers a small sequence space15. A machine learning-assisted directed evolution strategy has been developed to evolve an enzyme to enhance and invert the stereoselectivity of a putative nitric oxide dioxygenase, in which the machine learning method avoids some local fitness traps or long paths to the global optimum for the combinatorial library16. Additionally, an innov\u2019SAR model was developed to predict the enantioselectivity of 512 combinatorial variants based on combinations of 9 single-point mutations for an epoxide hydrolase17. Innov\u2019SAR uses the indexes of the AAindex database to encode the primary protein sequence into a numerical chain, which is then converted to a protein spectrum using Fast Fourier Transform (FFT). With the protein spectrum as the encodings of the protein sequence, along with the experimental values, regression models were then trained. Innov\u2019SAR has also been used to predict the thermostability of combinatorial variants18, including a limonene epoxide hydrolase and a transaminase. Despite these advancements, it remains unclear whether these machine learning methods are broadly effective for engineering different enzymes. This uncertainty arises because the protein fitness landscape can vary significantly across different proteins and even among different mutation sites within a single protein.\n\nIn this study, we apply machine learning to engineer the TBD of PylRS, aiming to develop highly improved variants. We anticipate that these variants could be broadly applicable to other catalytic domain variants, thereby enhancing the incorporation efficiency of corresponding ncAAs. A MmPylRS variant named IFRS (N346I/C348S) is selected as the model for protein engineering, as it was obtained by screening libraries against 3-iodo-Phe (3IF) and also shows a broad substrate range. Supervised machine learning is first applied to predict the highly active combinatorial variants of 12 single-point mutations. The improved variant is then used as the input for three deep learning models to predict additional single variants, which are then combined again to obtain the combinatorial variant with the highest activity. The best mutations are then combined with various C-terminal variants to test the generality of these mutations in improving the incorporation efficiency of various ncAAs. We also carry out molecular dynamics (MD) simulations to explore the mechanism behind the enhanced performance of these variants.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Mutations in the TBD of MmPylRS have been demonstrated to improve the efficiency of ncAA incorporation. It was also shown that N-terminal mutations did not significantly influence the substrate specificity of the PylRS and could be transferred to different variants19. We here tested four sets of mutations obtained previously in the TBD of MmPylRS to improve catalytic efficiency of IFRS, which include R61K/H63Y/S193R, R19H/H29R/T122S, D2N/K3N/T56P/H62Y, and V31I/T56P/H62Y/A100E6,7,8,20 (Supplementary Table\u00a01). Based on sequence alignment, we introduced these N-terminal mutations into IFRS and tested their activity for incorporating 3-bromo-Phe (3BrF), a cheaper substrate than 3IF (Fig.\u00a01a, Supplementary Fig.\u00a01). Expression of IFRS was driven by the constitutive, mid-strength E. coli glutaminyl-tRNA synthetase (glnS) promoter, and expression of PylT was controlled by the E. coli lpp gene promoter. Amber suppression of the sfGFPS2TAG gene by 3BrF was investigated by measuring the fluorescence intensity, and the ncAA-containing protein yield was presented by the ratio of fluorescence intensity to optical density OD600 (Flu/OD) of cells expressing sfGFP and PylRS. The normalized protein yield was calculated by subtracting the Flu/OD ratio of cells cultured in the presence of ncAA with that of in the absence of ncAA (Supplementary Fig.\u00a02). We found that R19H/H29R/T122S did not achieve the expected increase in stop codon suppression (SCS) efficiency, and the IPYE (V31I/T56P/H62Y/A100E) from chPylRS did not increase the SCS efficiency of IFRS either. Although R61K/H63Y/S193R in IFRS\u00a0did achieve a modest increase in sfGFPS2TAG expression yield\u00a0compared to the IFRS alone, the effect was still lower than that observed for the mutations\u00a0in wild-type MmPylRS. Interestingly, the D2N/K3N/T56P/H62Y from chPylRS increased the SCS efficiency of IFRS by around 7-fold, confirming that tRNA binding domain mutations can indeed enhance the activity of catalytic domain mutants (Supplementary Fig.\u00a02b). We also tested the activity of 12 single-point mutations and found that only D2N, R61K, and H62Y enhanced the activity of IFRS, with D2N being the most active, exhibiting 3-fold improved SCS efficiency compared to the wild type (Fig.\u00a01b, Supplementary Fig.\u00a02a). In the combinatorial mutant D2N/K3N/T56P/H62Y, D2N and H62Y enhanced the SCS efficiency of IFRS, while K3N and T56P reduced the SCS efficiency, indicating a positive sign epistasis among the mutations (Supplementary Table\u00a02).\n\na Structure of IFRS used for engineering TBD of PylRS, and 3BrF was selected as the substrate of IFRS. b Dataset 1 is composed of activities of 12 single-point mutants. c Dataset 2 is composed of activities of double and triple mutants used as a test set. d Accuracy of ML model in Dataset 2. e Dataset 3 is composed of activities of quadruple and multiple-point mutants used as a test set. f Accuracy of ML model in Dataset 3. g The SCS efficiency of the top 8 variants predicted by the ML model. h Experimental data and predicted data presented in a 2-dimensional sequence space. Error bars represent \u00b1standard deviation of the mean over three independent replicates. Source data are provided as a Source Data file.\n\nWe then attempted to use machine learning to explore the combinatorial space of these 12 single-point mutations, a total of 4096 (212) variants. The FFT-partial least squares regression (PLSR) approach was applied for predicting the fitness of combinatorial variants, which uses FFT for the protein sequence encoding and PLSR as the algorithm of the ML model. The FFT-PLSR model has demonstrated the ability to be trained on a small dataset of enzyme mutant activity data to accurately predict the activity across the entire combinatorial space. The variant sequences were first transferred into numerical features and then transformed into a two-dimensional energy versus frequency representation using FFT (Supplementary Fig.\u00a03). Based on the transformed data, a PLSR model was trained to predict the activities of other mutants. We first trained the FFT-PLSR model using dataset 1, composed of 12 single variants datasets. By scoring with leave-one-out cross-validation (LOOCV), we screened 566 amino acid encodings from the AAindex database and selected the index with the best score to encode the amino acid sequences and build the PLS regression model (Supplementary Figs.\u00a04 and 5). We constructed 6 double and 19 triple mutants, and measured their SCS efficiency to form dataset 2 as a test set (N\u2009=\u200925) (Fig.\u00a01c). The trained model achieved an R2 of 0.843 and an MSE of 2.887 on the test set, indicating that the model showed good prediction ability for high-activity combinatorial mutants (Fig.\u00a01d). Interestingly, the best combinatorial mutant predicted by the model was D2N/R61K/H62Y, which was also the variant with highest activity in the test set (Supplementary Table\u00a03).\n\nIn dataset 2, we observed epistasis between mutations. For example, T56P showed a decreased amber codon suppression efficiency, while H62Y improved the amber codon suppression efficiency compared to the IFRS. However, the T56P/H62Y showed a normalized fluorescence intensity significantly higher than that of H62Y, which led to a positive sign epistasis between T56P and H62Y. The positive sign epistasis effect was also found for R61K and H63Y, and D2N and K3N (Supplementary Table\u00a02). By contrast, R19H and H29R showed a positive reciprocal sign epistasis (Supplementary Table\u00a02). To help the model gain a more thorough understanding of epistasis between mutants, we added dataset 2 to the training set, which raised the total number of mutants in the training set to 38. Additionally, we rationally designed and constructed an additional test dataset 3 (N\u2009=\u200956) including 56 combinatorial mutants, mainly based on combination of improved variants (Fig.\u00a01e). The retrained model achieved an R2 of 0.835 on the test set, still showing high accuracy for predicting the activities of combinatorial mutants (Fig.\u00a01f). We then constructed the top 8 mutants predicted by the model and tested their SCS efficiency (Fig.\u00a01g and Supplementary Table\u00a04). All eight variants showed a significant increase in the activity compared to the IFRS, with the mutant Z7 (D2N/V31I/T56P/R61K/H62Y/T122S/S193R, Com1-IFRS) exhibiting the highest SCS efficiency, which was 11-fold higher than the IFRS (Fig.\u00a01g). We then attempted to apply the ML model to predict single-point variants in the TBD to improve the activity of Com1-IFRS. Fifteen single-point variants were predicted by the model to be more active than Com1-IFRS, with 12 of them located at previously unseen positions. However, all 15 variants failed to further improve the activity of Com1-IFRS, indicating the model\u2019s limited ability to predict effects of mutations at unseen positions (Supplementary Fig.\u00a06). The outcome is reasonable, given that the training set was small, only containing 38 variants across the 12 positions.\n\nWe utilized the Uniform Manifold Approximation and Projection algorithm, along with one-hot encoding for sequence representation, to reduce the dimensionality of the 12 single-point mutations combinational space and visualized it as a two-dimensional scatter plot. Our analysis revealed that mutations with similar activities clustered closely together (Fig.\u00a01h). The mutants from the training set were distributed across the entire sequence space, which was crucial for developing an accurate machine learning model (Fig.\u00a01h). Furthermore, our model\u2019s predictions aligned well with the experimental data with high-activity variants in the outer clusters and low-activity variants in the inner clusters (Fig.\u00a01h). This indicates that the model accurately mapped the fitness landscape, allowing us to identify mutants with significantly increased activity in the high-activity clusters.\n\nTo further improve IFRS activity, we explored additional mutation sites in TBD with Com1-IFRS as the template, by employing three deep learning models that enable zero-shot prediction of high-fitness variants, including ESM-1v, MutCompute, and ProRefiner (Fig.\u00a02a, Supplementary Fig.\u00a07 and Table\u00a05). EMS-1v is a protein language model that was trained on extensive datasets of protein sequences spanning the evolutionary tree of life, and hence learned the fundamental principles of protein structure and function21. Given the sequence of Com1-IFRS, each amino acid in the TBD region was individually masked and analyzed by the ESM-1v model to predict the impact of potential mutations at that site. We constructed and characterized 16 single-point mutants that were predicted to have a higher likelihood than wild type (Supplementary Figs.\u00a07 and 8). MutCompute is a structure-based three-dimensional self-supervised convolutional neural network model that was trained to associate local protein micro-environments with their central amino acid22. Given the N-terminal and C-terminal structures of MmPylRS (5UD5.pdb & 4TQD.pdb), MutCompute predicted single-point mutations optimizing the protein structure, and we characterized the top 44 mutants based on the probability predicted (Supplementary Figs.\u00a07 and 8). ProRefiner is a global graph attention model for inverse protein folding that designs sequences compatible with a given backbone structure23. We restrict the candidate mutation sites to the TBD of PylRS and leverage sequence design models to compute a quality score for every candidate site. For each site to be examined, we masked this site in the sequence to get the input partial sequence, and the input backbone structure is from the Com1-IFRS structure predicted by Alphafold 324. The model then predicted the identity of the masked site in the form of a probability distribution over all amino acid types, and the top 42 single-point mutants were selected for characterization (Supplementary Figs.\u00a07 and 8). Interestingly, 7 mutants were predicted to have improved activities by both Mutcompute and ProreRiner (Supplementary Fig.\u00a07). Based on three methods, a total of 95 single-point mutants were constructed on 85 amino acids of COM1-IFRS and assayed for enzyme activity (Fig.\u00a02b and Supplementary Fig.\u00a07).\n\na Design of single variants using models including ESM-1v, MutCompute, and ProRefiner. b Fitness of single variants is designed. The size of the circle indicates the activity ratio of mutants to Com1-IFRS. The mutants designed by different methods are colored differently. c Activities of nine improved single variants. d The mutability landscape constructed by saturation mutagenesis at nine amino acid sites. The color bar indicates the activity of mutants relative to Com1-IFRS. e The sequences of mutants with at least 10% improved SCS efficiency are higher than Com1-IFRS. The height of each character indicates the relative fitness of the mutant. f The relative activities of double variants constructed by combining the top 3 single variants at each mutation site. The mutants include N7H, N7E, N7Y, T68F, K67G, K67S, K67L, H63L, H63M, H63C, V74F, V74W, D76L, D76F, D76Y. g Accuracy of the FFT-PLSR model built. The R2 value was calculated on the test set, which consisted of 15 double-point mutants. h The SCS efficiency of the top 20 combinatorial variants predicted by the FFT-PLSR model. Error bars represent \u00b1standard deviation of the mean over three independent replicates. Source data are provided as a Source Data file.\n\nSince the enzyme activity is represented by the fluorescence intensity of sfGFP incorporated with 3BrF, the maximum activity that could be detected is the fluorescence intensity of wild-type sfGFP. The fluorescence intensity of sfGFPS2TAG in the presence of Com-1-IFRS reached 45% of wild-type sfGFP. Among all the single-point variants constructed, we did obtain several variants with higher activities than the Com1-IFRS (Fig.\u00a02b), which were I176S predicted by ESM-1v, D76A, T68V, H28K, T20S predicted by MutCompute, K67S, N7S, V74I predicted by ProRefiner, and H63N predicted by both MutCompute and ProRefiner. The best variant D76A, designed by MutCompute, showed a 31% improvement in activity compared to Com-1-IFRS. However, most of the variants designed by the three approaches exhibited reduced or even lost activity, such as W16E, showing a 99.7% decrease in activity compared to IFRS.\n\nWe then wondered if these data are useful for training a supervised ML model to predict high-fitness single-point variants across the protein. With the above single-point mutants\u2019 activity data as the training set (N\u2009=\u200996, including Com1-IFRS), the FFT-PLSR model was built. There are 566 amino acid encodings in the AAindex database, and we tested the performance of the models trained with different numbers of amino acid encodings. To optimize the amino acid encoding, we first screened the single AAindex encoding, which was then fixed for optimizing the second encoding. The third encoding was optimized with the first two encodings fixed. When screening two or three indices, the protein sequence was first subjected to FFT separately, and then the results were combined to train the model. When one index was used, the R2 of the model was only 0.452, while when three amino acids encodings were used, the R2 of the model increased to 0.926 (Supplementary Fig.\u00a09). We then used the three-index model to predict fitness of all single-point mutations in the PylRS TBD region, and the top 20 variants were constructed and characterized for enzyme activity (Fig.\u00a02b, Supplementary Fig.\u00a010). The best variant, K67G, showed a 31.9% improvement in activity, which was even higher than D76A predicted by MutCompute and K67S predicted by ProRefiner, indicating that the FFT-PLSR model was effective in exploring sequence space (Fig.\u00a02c).\n\nTo further explore the sequence space, we constructed the saturation mutagenesis on the nine sites where the improved variants were obtained, to build a mutability landscape (Fig.\u00a02d). The mutability landscape is defined by the impact of all possible point mutations on protein function by substituting the native amino acid at each residue position with each of the 19 non-native amino acids, one at a time25,26. The mutability landscape showed that most of the improved variants were found at positions D76, H63, and K67, and the mutations at sites T20, H28, and I176 were mostly detrimental. We screened the variants that showed over a 10% improvement in SCS efficiency compared to Com1-IFRS (Fig.\u00a02e). There were 3, 7, 5, 1, 2, 9 mutations at positions N7, H63, K67, T68, V73, D75, respectively, meeting this criterion. We hence attempted to use the FFT-PLSR model to explore this sequence space containing 11,520 (4\u2009\u00d7\u20098\u2009\u00d7\u20096\u2009\u00d7\u20092\u2009\u00d7\u20093\u2009\u00d7\u200910) mutations. To achieve epistasis information, the top 3 variants at each mutation site were combined to construct double variants, resulting in a total of 92 combined variants to be used for model training (Fig.\u00a02f). Combination of improved single variants did generate further improved variants, with the best double mutant of N7E/T68F showing a SCS 70% higher than the Com1-IFRS (Fig.\u00a02f). However, the variants with decreased activities were also observed, indicating a strong epistasis among several mutations. For example, both V74W and K67G improved the SCS efficiency, while V74W/K67G showed a significant decrease compared to the Com1-IFRS, which resulted in a strong negative reciprocal sign epistasis effect between V74W and K67G. The antagonistic effect was also observed for V74W and single variants including K67L, D76F, D76L, and D76Y (Supplementary Table\u00a06).\n\nWe then conducted another round of FFT-PLSR building for predicting high-activity combinatory mutations. There were 27 single-point mutations and 92 combinatorial mutations, plus Com1-IFRS, 120 data points in total in the training dataset. These were used to train the FFT-PLSR model. We first used 10-fold cross-validation to evaluate and identify the optimal single-index model. However, this model only achieved an R2 score of 0.558 on the training set, indicating a poor performance. To improve the model performance, multiple indices were used for encoding amino acids, which improved the R2 score to 0.668 for the two-index model and 0.677 for the three-index model, respectively (Supplementary Fig.\u00a011 and Table\u00a07). The retrained FFT-PLSR model achieved an R2 of 0.729 on the test set, which consisted of 15 double-point combinatory mutants not included in the training set (Fig.\u00a02g, Supplementary Fig.\u00a012). Based on the three-index model, we predicted the activity data of 11520 mutations, and the top 20 combinatorial variants were selected for experimental verification (Supplementary Table\u00a08). All the variants showed comparable or higher activities than the Com1-IFRS, with 15 of them possessing activities improved by more than 2-fold, suggesting the great effect of the ML model. The best Z7-3 variant (N7Y/H63L/K67N/V74W, Com2-IFRS) showed a 2.8-fold increase in SCS efficiency compared to Com1-IFRS, more than 30-fold higher than IFRS, reaching fluorescence intensity comparable to wild-type sfGFP (Fig.\u00a02h). Biochemical characterization of IFRS, Com1-IFRS and Com2-IFRS using 3BrF confirmed that the catalytic efficiency (kcat/Km) of Com1-IFRS and Com2-IFRS for tRNA was improved by 1.4-fold and 5.6-fold, respectively, compared to IFRS (Supplementary Table\u00a09). Additionally, we tested if the mutations H20S, H28K, I176N, and I176S, which were not selected for making combinatory mutations, could further improve the activity of Com2-IFRS. It was found that none of these single-point mutations improved the activity of Com2-IFRS (Supplementary Fig.\u00a013).\n\nAs the IFRS is polyspecific and could accept various phenylalanine derivatives27, we hence tested if Com1-IFRS and Com2-IFRS improved the incorporation efficiency of other ncAA substrates. It was found that normalized fluorescence of sfGFPS2TAG incorporated with 12 diverse ncAAs was significantly increased by the two variants compared to IFRS, with the largest 101.9-fold improvement for 3FF enabled by Com2-IFRS (Fig.\u00a03a). However, since IFRS is a promiscuous enzyme with a certain degree of misincorporation of canonical amino acids (cAAs), Com1-IFRS and Com2-IFRS also increased the incorporation efficiency of cAAs, and different extent of misincorporation was observed in presence of different ncAAs (Fig.\u00a03a). Subtracting fluorescence intensity of sfGFP2TAG in presence of 3FF with that in absence of 3FF revealed a 3944.8-fold improvement in SCS efficiency for Com2-IFRS compared to IFRS (Supplementary Fig.\u00a014a). Biochemical characterization of IFRS, Com1-IFRS and Com2-IFRS using 3FF confirmed that the catalytic efficiency (kcat/Km) of Com1-IFRS and Com2-IFRS for tRNA was improved by 1.8-fold and 8.8-fold, respectively, compared to IFRS (Supplementary Table\u00a09).\n\na The SCS activity of IFRS, Com1-IFRS, and Com2-IFRS toward various substrates. b The SCS activity of combinatorial variants against various ncAAs. Light purple, absence of ncAAs in growth medium; Dark purple, presence of ncAAs in growth medium. Error bars represent \u00b1standard deviation of the mean over 4 independent replicates. NcAAs include 3-fluoro-L-phenylalanine (3FF), 2,3-difluoro-L-phenylalanine (23FF), 2,4-difluoro-L-phenylalanine (24FF), 2,5-difluoro-L-phenylalanine (25FF), 3,4,5-trifluoro-L-phenylalanine (345FF), 2,3,6-trifluoro-L-phenylalanine (236FF), 2,3,4,5,6-pentafluoro-L-phenylalanine (PFF), 5-bromo-2-chloro-L-phenylalanine (5Br2ClF), 2-chloro-L-phenylalanine (2ClF), 3,4-dichloro-L-phenylalanine (34ClF), 3-(2-thienyl)-L-alanine (2ThiA), 2-(5-bromothienyl)-L-alanine (BrThiA), N6-(tert-butoxycarbonyl)-L-lysine (BocK), N6-((allyloxy)carbonyl)-L-lysine (AlocK), N-epsilon-Acetyl-L-lysine (AcK), 3-L-phenyllactic acid (PLA), 3-bromo-L-tyrosine (3BrY), 3-chloro-L-tyrosine (3ClY), 3-iodo-L-tyrosine (3IY), 3-benzothienyl-L-alanine (Bta), 3-(1-naphthyl)-L-alanine (1NaA), S-allyl-L-cysteine (Sac), 3-methyl-L-histidine (3MeH). Source data are provided as a Source Data file.\n\nCTD of PylRS containing the catalytic sites has evolved to accept various ncAAs. The mutations Com1 and Com2 were then combined with different catalytic domain (CD) mutations to enhance the incorporation efficiency of the corresponding ncAAs. To test the universality of Com1 and Com2 in enhancing the incorporation efficiency of various ncAAs, we selected CD mutations with large sequence diversity that could accept ncAAs with significantly different side chains, including Phe derivatives, Tyr derivatives, Trp derivatives, Cys derivatives, His derivatives, and Lys derivatives (Supplementary Table\u00a010). CD mutant NACA was combined with Com1 and Com2 to test the incorporation of three Phe derivatives, including 3-bromo-L-phenylalanine (3BrF),2-chloro-L-phenylalanine (2ClF), and 3-L-phenyllactic acid (PLA). The two variants significantly improved efficiency of NACA to incorporate these three ncAAs, and the Com2 led to improvement of 38.9-fold, 29.2-fold and 7.7-fold, for 3BrF, 2ClF, and PLA respectively (Fig.\u00a03b). When the fluorescence intensity was normalized to cAA incorporation, the fold change was 61.1-fold, 68.1-fold and 9.9-fold, for 3BrF, 2ClF, and PLA, respectively (Supplementary Fig.\u00a014b). IFRS could also accept 3BrF and 2ClF, and Com1, Com2 mutations significantly improved the misincorporation of cAAs in presence of 3BrF and 2ClF (Fig.\u00a03a). Interestingly, when combined with NACA, the two variants did not improve the incorporation efficiency of cAAs in presence of 3BrF and 2ClF, compared to IFRS (Fig.\u00a03b). This could be due to that NACA exhibited lower activity against cAAs than IFRS, and Com1, Com2 did not influence substrate specificity and generally enhanced the activity of CD mutations against all substrates.\n\nThis was further confirmed by the performance of Com1 and Com2 introduced in wild-type MmPylRS. MmPylRS showed limited misincorporation of cAAs, and the two variants did not increase the misincorporation of cAAs either, compared to the wild type. On the other hand, the two variants significantly increased the incorporation efficiency of N6-(tert-butoxycarbonyl)-L-lysine (BocK) and N6-((allyloxy)carbonyl)-L-lysine (AlocK) and the best Com2 resulted in 40.2-fold and 32.7-fold improvement for BocK and AlocK, respectively (Fig.\u00a03b, Supplementary Fig.\u00a014b). This was further confirmed by the huge increase in expression level of sfGFP incorporated with BocK in presence of Com2-WT compared to WT (Supplementary Fig.\u00a015). Mass-spectra analysis also confirmed the correct incorporation of BocK and no misincorporation of cAAs was observed (Supplementary Fig.\u00a016 and Table\u00a011). Interestingly, the incorporation efficiency of BocK directed by Com2-WT was 2.7-fold higher than chPylRS-IPYE obtained previously7. Additionally, the SCS efficiency of Com2-WT against BocK was nearly 17.9-fold higher than MaPylRS, the PylRS enzyme lacking an NTD, although a previous study showed that MaPylRS exhibited higher activity than wild-type MmPylRS10 (Supplementary Fig.\u00a017a). Biochemical characterization of WT and Com2-WT using BocK confirmed that the kcat of Com2-WT improved 2.6-fold, the Km for BocK weakly increased, such that the catalytic efficiency (kcat/KmBocK) of the evolved variant was enhanced by 2.2-fold compared to wild-type MmPylRS (Supplementary Fig.\u00a018). The kinetic parameters were also measured for tRNA, and the catalytic efficiency (kcat/KmtRNA) of Com2-WT was improved by 1.8-fold compared to wild type (Supplementary Table\u00a09). Interestingly, Km for tRNA was increased by 1.4-fold for Com2-WT than wild type. Similarly, the binding affinity of Com2-WT with tRNA was around 2.1-fold lower than that of WT, suggesting that the mutations on the TBD improved the enzyme activity by modifying the tRNA binding conformation instead of enhancing the binding affinity (Supplementary Fig.\u00a019).\n\nCom1 and Com2 also enhanced the activities of other CD mutations toward their corresponding ncAAs. The addition of Com2 increased the incorporation efficiency of N-epsilon-Acetyl-L-lysine (AcK) by 13.3-fold compared to the original CD mutations MLAF, and the fold change was 32.2-fold when the incorporation efficiency was normalized to cAA incorporation (Fig.\u00a03b, Supplementary Fig.\u00a014b). The extent of improvement was significantly higher than the IPYE variant tested previously7. The SDS-PAGE also revealed that the amount of sfGFP expressed in presence of Com2-MLAF was significantly higher than that in presence of MLAF (Supplementary Fig.\u00a015). Mass-spectra analysis confirmed the correct incorporation of AcK in sfGFP, but a misincorporation of lysine was also observed (Supplementary Fig.\u00a016). Additionally, addition of Com2 improved incorporation efficiency of GML mutant by 5.4-fold, 5.8-fold and 3.9-fold against 3BrY, 3ClY and 3IY, respectively, while the fold change reached 117.3-fold, 587.6-fold and 6.5-fold when the SCS efficiency was normalized to cAA incorporation (Fig.\u00a03b, Supplementary Fig.\u00a014b). We checked expression of sfGFP with 3IY incorporated, and found that Com2-GML indeed significantly increased amount of expressed protein compared to GML. Mass-spectra analysis also confirmed the correct incorporation of 3IY (Supplementary Fig.\u00a016).\n\nCom1 and Com2 mutations were also constructed in BtaRS to explore their effect on the incorporation of the Trp derivatives28. The addition of Com2 increased the incorporation efficiency of 3-benzothienyl-L-alanine (Bta) and 3-(1-naphthyl)-L-alanine (1NaA) by 56.8-fold and 63.3-fold, respectively (Fig.\u00a03b). SDS-PAGE revealed the significantly improved amount of sfGFP with Bta incorporated enabled by Com2-BtaRS compared to BtaRS. Kinetic parameters measurement using Bta revealed that the catalytic efficiency (kcat/Km) of Com2-BtaRS for tRNA was 4-fold higher than that of BtaRS (Supplementary Table\u00a09). Mass-spectra analysis confirmed the correct incorporation of Bta and no misincorporation of cAAs was observed (Supplementary Table\u00a011). However, misincorporation of cAAs was indeed observed when 1Na was incorporated by Com2-BtaRS (Fig.\u00a03b). To explore the effect of Com1 and Com2 on the incorporation of Cys derivatives, Com1 and Com2 were combined with CD mutation WS. Com1 did not exhibit improved effect on fluorescence of sfGFP, while the addition of Com2 improved fluorescence intensity of sfGFP with Sac incorporated by 2.8-fold compared to WS (Fig.\u00a03b), and the enhanced amount of sfGFP expressed was confirmed on SDS-PAGE (Supplementary Fig.\u00a015). WS also showed a certain degree of misincorporation of cAAs, which was also observed for Com2-WS (Fig.\u00a03b).\n\nCom1 and Com2 were combined with two CD mutations, including QF and IFGFF, to explore their effect on incorporating His derivatives, including 3-(2-thienyl)-L-alanine (2ThiA), 2-(5-bromothienyl)-L-alanine (BrThiA), and 3-methyl-L-histidine (3MeH). In the results, the addition of Com2 increased the incorporation efficiency of 2ThiA, BrThiA, and 3MetH by 93.7-fold, 41.5-fold, and 40.9-fold, respectively, compared to their CD variants. The fold change reached to 223.1-fold, 61.4-fold and 201.5-fold, respectively, when the amber codon suppression activity was normalized to cAA incorporation (Fig.\u00a03b, Supplementary Fig.\u00a014b). SDS-PAGE revealed that Com2-QF and Com2-IFGFF indeed dramatically enhanced the amount of purified sfGFP incorporated with 2ThiA and 3MetH, respectively, compared to CD mutations alone (Supplementary Fig.\u00a015). Kinetic parameters measurement using 3MetH confirmed that the catalytic efficiency of Com2-IFGFF for tRNA was improved by 4.3-fold compared to IFGFF (Supplementary Table\u00a09). Moreover, according to mass-spectra analysis, no misincorporation of cAAs was observed for GFP-2MetH and GFP-2ThiA (Supplementary Fig.\u00a016).\n\nTo explore if the variants obtained were useful in improving the expression of other proteins with ncAAs incorporated, we tested the expression of myoglobin containing 3MetH (Fig.\u00a04a). 3MetH has been used as a heme ligand to enhance the activity of myoglobin or as a catalytic residue of an artificial esterase possessing a non-canonical organocatalytic mechanism29. Here, we used MmPylRS variant Com2-IFGFF to incorporate 3MetH into the position His93 of myoglobin as a ligand of heme. The protein expression was explored by carrying out protein purification from the same amount of cells in the presence of Com2-IFGFF and IFGFF. It was found that the concentration of 3MetH-containing myoglobin was 28.3\u2009mg/L for Com2-IFGFF, 6.3-fold higher than 4.5\u2009mg/L of IFGFF (Fig.\u00a04b). We then measured the activities of Mb-3MetH against guaiacol using the purified protein without dilution. The myoglobin catalyzes the oxidation of guaiacol by hydrogen peroxide to generate a stable tetrameric product whose formation can be readily monitored by absorbance at 470\u2009nm. The yield of product was significantly higher for Com2-IFGFF compared to IFGFF (Supplementary Fig.\u00a020). The \u0394OD470 reached 0.063 in the reaction system containing Com2-IFGFF after a 40-min reaction, 7.9-fold higher than that using IFGFF (Fig.\u00a04b, Supplementary Fig.\u00a020), confirming the higher expression of target protein aided by the Com2-IFGFF variant.\n\na Introduction of 3MetH at His93 position of myoglobin as a ligand of heme. b Product yield of reaction catalyzed by myoglobin-3MetH after 40-min reaction, and expression of myoglobin-3MetH detected on SDS-PAGE. The incorporation of 3MetH was enabled by IFGFF and Com2-IFGFF. +, with 3MetH added; \u2212, no 3MetH added. Myoglobin-WT indicates no ncAA incorporation in myoglobin. c Suppression of multiple amber codons by PylRS variants, with multiple consecutive amber codons inserted at the second position of sfGFP. d Fluorescence intensity of sfGFP with multiple 3BrF inserted at the second position, enabled by IFRS, Com1-IFRS, and Com2-IFRS. e The positions where multiple 3BrF are inserted in sfGFP. f Fluorescence intensity of sfGFP with multiple 3BrF inserted at different positions, enabled by IFRS, Com1-IFRS, and Com2-IFRS. Error bars represent \u00b1standard deviation of the mean over four independent replicates. Source data are provided as a Source Data file.\n\nWe then characterized the ability of Com1-IFRS and Com2-IFRS to suppress multiple amber codons in sfGFP, which is important for incorporating multiple unnatural amino acids into proteins. One to five consecutive amber codons were inserted second position of sfGFP (Fig.\u00a04c). Both Com1-IFRS and Com2-IFRS exhibited higher fluorescence intensity than IFRS in all situations, while Com2-IFRS showed higher suppression ability than the Com1-IFRS, with 122.4-fold, 99.4-fold, 91.2-fold and 53.3-fold improvement compared to IFRS, for S2TAG\u2009\u00d7\u20092, S2TAG\u2009\u00d7\u20093, S2TAG\u2009\u00d7\u20094 and S2TAG\u2009\u00d7\u20095, respectively (Fig.\u00a04d, Supplementary Fig.\u00a021a). It was also found that Com1-IFRS and Com2-IFRS improved the incorporation efficiency against native amino acids compared to IFRS. We also tested the incorporation efficiency of multiple unnatural amino acids at different positions of sfGFP (Fig.\u00a04e). When 3BrF was incorporated at the position of D36 of sfGFP, the fluorescence intensity was different from that of sfGFP with 3BrF at the second position, for both the wild-type IFRS and mutant Com1-IFRS and Com2-IFRS (Fig.\u00a04f, Supplementary Fig.\u00a021b). Similar site-dependent incorporation efficiency has previously been observed for other ncAAs7. Despite this, Com2-IFRS still showed higher amber codon suppression efficiency than Com1-IFRS and IFRS, with 3.8-fold, 7.9-fold, 27.3-fold, 4.7-fold and 5.2-fold improvement compared to IFRS, for 1TAG, 2TAG, 3TAG, 4TAG and 5TAG, respectively.\n\nThe whole 3D structure of MmPylRS was not yet available as the full-length protein is insoluble. Hence, AlphaFold3 was used to predict the structures of MmPylRS (WT), Com1-WT, and Com2-WT in complex with tRNAPyl (Supplementary Fig.\u00a022). The predicted MmPylRS structure aligned well with the separate NTD structure and CTD structure determined previously (Fig.\u00a05a). 50-ns MD simulations were then conducted for these structures in complex with Pyl-AMP to understand how the mutations influenced the binding of tRNA and the enzyme activity (Supplementary Figs.\u00a023\u201325). The root-mean square deviation (RMSD) values revealed that the trajectories were well equilibrated at the last 10\u2009ns, which were used for further analysis (Supplementary Fig.\u00a025a). The reaction distance between the 3\u2032-OH of tRNA A76 and the carboxyl carbon atom of amino acid Pyl was first analyzed. It was generally shorter for Com2-WT than wild type and Com1-WT (Fig.\u00a05b, Supplementary Fig.\u00a026). A total of 1000 snapshots were analyzed, and the number of snapshots with a distance shorter than 4\u2009\u00c5 was 462, which is 21-fold and 10-fold higher than that of wild type and Com1-WT, respectively (Fig.\u00a05c). These indicated that Com2 mutations mediated the binding of tRNAPyl to make the aminoacylation reaction happen\u00a0more easily. Interestingly, in the reaction conformations, we observed new hydrogen bonds formed between Pyl-AMP and tRNA in the two variants compared to WT. Com1-WT showed a new hydrogen bond formed between the main chain -NH2 of Pyl and 2\u2032-OH of tRNA A76, while Com2-WT exhibited a hydrogen bond formed between the main chain -NH2 of Pyl and the 3\u2032-OH of tRNA A76. No such hydrogen bonds were found in wild type (Supplementary Fig.\u00a027). These new hydrogen bonds will contribute to the interaction between Pyl-AMP and tRNA, and hence accelerate the reaction.\n\na The MmPylRS structure predicted by Alphafold 3. b The reaction distance between the 3\u2032-OH of tRNA A76 and the Ca atom of amino acid Pyl, calculated through analysis of 10\u2009ns equilibrated trajectories. c The number of snapshots with a distance shorter than 4\u2009\u00c5. d Hydrogen bonds occupancy in WT, Com1-WT, Com2-WT, calculated through analysis of 10\u2009ns equilibrated trajectories. e The binding free energy of amino acids within 4\u2009\u00c5 of the tRNA as ligand and the tRNA bases. f Dynamics cross-correlation map for the C\u03b1 atom and tRNA P atom pairs within MmPylRS and variants calculated with the last 150\u2009ns MD trajectory. Protein contains 454 amino acids, and the tRNA contains 72 bases (Supplementary Fig.\u00a034). The correlation coefficient (Cij) was shown in different colors. Cij with values from 0 to 1 represents positive correlations, whereas Cij with values from \u22121 to 0 represents negative correlations.\n\nThe hydrogen bonds formed between tRNA and the protein were also analyzed. In last 10-ns MD simulations, the number of hydrogen bonds in PylRS TBD with occupancy over 60% was 22, 19, and 22 for WT, Com1-WT, and Com2-WT, respectively (Supplementary Fig.\u00a028). Specifically, both Com1-WT and Com2-WT formed new hydrogen bonds including LYS3-A58, ARG19-A46, ARG52-G52, ARG193-A5, ARG193-C13 and ARG193-U12, while Com2 formed several extra hydrogen bonds such as ARG55-C45, ARG55-A46, Arg58-A20 (Fig.\u00a05d). Additionally, several hydrogen bonds were disrupted in the variants, such as ASN49-G47, ARG55-A46, ARG55-G21, ARG58-A58, R66-G21 and so forth. Specifically, in the conserved Motif 2 loop that is responsible for tRNA recognition, two hydrogen bonds Lys336-C71 and Lys336-C72 were disrupted, and a new hydrogen bond Asp334-C71 was formed in both the Com1 and Com2 variants (Supplementary Fig.\u00a029). Additionally, an extra H-bond GLU332-C74 was formed in Com1 variant but not in WT and Com2. This indicated that mutations reshaped the interactions between tRNA and protein, and the Com1 and Com2 improved the tRNA binding in a different way. As a result, the interaction energy between tRNA and protein was different for the wild type and variants.\n\nThe binding free energy between tRNA and different domains of the protein was analyzed. Com1-WT and Com2-WT exhibited lower binding free energy compared to the wild type, which was mainly attributed to the decreased binding free energy of the tRNA binding domain (Supplementary Fig.\u00a030). Interestingly, the binding free energy of full-length Com1-WT was lower than Com2-WT, while that of the tRNA binding domain of Com1-WT was higher than Com2-WT. The binding free energy determined in MD simulations seemed to contradict with binding affinity and Km values measured for WT and Com2-WT. This could be attributed to the different ncAA substrate used. In the MD simulations, the substrate was native substrate pyrrolysine, while the substrate used in biochemical characterization was BocK. Kinetic parameters measurement using different ncAA did reveal that Com2 influenced the Km for tRNA in a different way in the presence of different ncAA (Supplementary Table\u00a09).\n\nResidue-level binding energy contribution analysis for both protein and tRNA was carried out. Several amino acids and tRNA bases were indeed found to impact the binding free energy. For example, GLU332 in Com1-WT exhibited reduced binding energy, while no significant changes were observed in WT or Com2-WT, which might be attributed to the newly formed H-bond Glu332-C74 in Com1-WT. S193R mutation formed new salt bridges with tRNA, including Arg193-A5, Arg193-C13, Arg193-U12, which led to a significant decrease in the binding free energy (Fig.\u00a05e). Also, Arg55 of Com2-WT showed a significantly low binding energy due to the T56P mutation, although it is not the case for Com1-WT. As for the analysis of tRNA binding energy, it was found that the mutations significantly increased the binding free energy for the last three bases C74, C75, and A76, which might facilitate the aminoacylation reaction (Fig.\u00a05e).\n\nDynamics cross-correlation matrices (DCCMs) were also computed for the WT and two variants to understand how the mutations impact protein dynamics. Since the MD simulation systems are large and complex, a robust coupling analysis of dynamic cross-coupling correlation might need simulations of a longer timescale than 50\u2009ns. We hence carried out 200-ns simulations for WT, Com1-WT, and Com2-WT (Supplementary Figs.\u00a031\u201333, Supplementary Note\u00a01), and the last 150\u2009ns trajectories were used to compute DCCM of the protein C\u03b1 atom pairs and tRNA P atom pairs. Generally, the variants showed more dynamics cross-correlations between residue pairs compared to the wild type, while Com1-WT and Com2-WT exhibited similar Cij values in most of the regions (Fig.\u00a05f). Specifically, it was found that the dynamics correlation between NTD and CTD was more significant in the Com1-WT and Com2-WT, compared to WT. Although NTD and CTD are distant from each other, they are connected together with the tRNA. The mutations on tRNA binding domain hence impact the dynamics of CTD through modification of interactions with tRNA. The increased correlated dynamical network would help to maintain more reaction conformations, thereby enhancing the enzyme activity of Com1-WT and Com2-WT.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61952-2/MediaObjects/41467_2025_61952_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61952-2/MediaObjects/41467_2025_61952_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61952-2/MediaObjects/41467_2025_61952_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61952-2/MediaObjects/41467_2025_61952_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61952-2/MediaObjects/41467_2025_61952_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "This study utilized machine learning to explore the combinatorial mutation space of MmPylRS TBD and identified great variants including Com1-IFRS and Com2-IFRS (Supplementary Table\u00a012), which modified the tRNA binding, enhanced the aminoacylation rate by up to 5.6-fold against 3BrF, and subsequently significantly improved SCS efficiency by up to 101.9-fold improvement for 3FF. PylRS has been engineered for improved activity through N-terminal mutations. However, all previous studies applied directed evolution strategies for the PylRS N-terminal engineering, and the methods used included error-prone PCR to construct a library coupled with screening based on GFP fluorescence or white/blue colony11, and a PACE7. Although directed evolution is a powerful strategy for enzyme engineering, its success relies on iterative cycles of library construction and screening, and it can be trapped in local fitness optima due to taking one mutation step at a time. In our study, we applied deep learning models capable of zero-shot prediction of high-fitness variants to explore mutation target sites in the whole TBD, and a supervised model, FFT-PLSR, to explore the sequence space once the mutation target sites were identified. Thanks to the ML models we used, the variants obtained were significantly more active than the variants obtained previously.\n\nDue to epistatic interactions between mutations, combining multiple mutations does not always result in positive effects. Investigating the combinatorial effects of mutants involves two main tasks: (1) pairwise combinations of specified mutations, and (2) exhaustive combinations of 20 amino acids at specified mutation sites. Using the FFT-PLSR model, we demonstrated that, by employing a training dataset composed of 38 single, double, and triple mutants, it was possible to identify multi-point combinatorial mutants with significantly improved activity within the sequence space of 12 single-point mutations and their pairwise combinations, totaling 4,096 mutants, thereby providing a solution to Task 1. We also attempted to apply the ML model to tackle task 2 by restricting the mutant combinatorial space and focusing solely on combinations of single-point improved mutations. We found that using multiple AA indexes to encode protein sequences yielded better results than a single-index model, with the model achieving a fit of 0.729 for the test data set. The advantage of the FFT-PLSR model lies in its effective utilization of experimental mutant activity data, guiding the construction of the next set of combinatorial mutants. With the Com1-IFRS as the parent sequence, only 20 variants predicted by the ML model were tested. Among them, the variant Com2-IFRS showed a 2.8-fold increase in activity. In the future, the activity data of these variants could be fed back and used to update the ML model, which would then be employed to predict the next round of variants. Additionally, the construction and characterization of PylRS variants could be carried out by an automatic biofoundry, ensuring high reproducibility and efficient data collection. This would accelerate the protein engineering efficiency of PylRS, as successfully demonstrated with the Methanocaldococcus jannaschii tyrosyl-tRNA synthetase30.\n\nZero-shot ML models learn general patterns in proteins and can predict high-fitness protein variants without requiring any prior knowledge other than protein sequence or structure. These models can help to design an initial variant library or explore additional potential mutations when directed evolution reaches a local minimum, opening additional evolutionary pathways. In our study, sequence-based zero-shot model ESM-1V did not perform well, with mutations predicted primarily located in the linker region 90\u2013185\u2009aa, and only a positive mutant I176S was obtained, showing a minor improvement in activity. The ESM model also suggested the low-activity C42N mutation, while C42, C69, C72, and H24 are critical Zn-binding residues, highlighting the model\u2019s limitation in considering structural constraints (Supplementary Fig.\u00a035). Previously, ESM models were successfully applied to guide the affinity maturation of antibodies, and optimization of an uracil-N-glycosylase variant activity that enables programmable T-to-G and T-to-C base editing31. Several ESM models are currently available, with each trained on different protein sequence datasets with varying numbers of parameters21,32,33. We also tested the effect of ESM2_t33_650M_UR50D and ESM2_t36_3B_UR50D on the prediction of high-fitness variants of IFRS, which predicted seven single variants, and six of them have been predicted by ESM-1v, and none of the six variants tested showed improved activity (Supplementary Table\u00a013). The poor performance of ESM models on engineering IFRS might indicate that although the protein language models, such as ESM trained on vast datasets of natural proteins, allowed zero-shot optimization of specific proteins, it remains a challenge to generally select promising variants for various enzymes possessing remarkable diversity in terms of their classes and catalytic mechanisms. Instead of being used for zero-shot prediction of high-fitness variants, the protein large language models (PLMs) such as ESM could be used to encode protein sequences for building ML models. EVOLVEpro, a few-shot active learning framework that combines PLMs and regression models, has been developed to rapidly improve protein activity34. The single-point variant data of IFRS predicted by the zero-shot ML models could serve as the input of EVOLVEpro, enabling further prediction of high-fitness single-point variants. Through multiple rounds of this iterative process, the model can generate variants with significantly improved activity.\n\nStructure-based model MutCompute predicted mutations to optimize the protein structure, and six improved variants were obtained using this method. Interestingly, MutCompute made predictions based on the crystal structure of wild-type PylRS, and mutations beneficial to the wild-type PylRS do not necessarily work when transferred to PylRS mutants. However, experimental results showed that six mutations predicted by MutCompute still enhanced the activity of the Com1-IFRS. Actually, MutCompute has been used to improve activity and stability of several enzymes, including PET-degrading enzymes35, DNA polymerase36, and haloalkane dehalogenase37. ProRefiner, developed for inverse protein folding, also predicted two improved Com1-IFRS variants. A similar model, ProteinMPNN, has recently been used to generate myoglobin and tobacco etch virus protease designs with improved expression, elevated melting temperatures, and enhanced function38. However, these designs contain multiple mutations and only have 41% to 85% sequence identity to the parent sequences. In contrast, the ProRefiner has been validated for designing single-point mutants with improved activity for transposon-associated transposase, which aligns closely with our research task23.\n\nThe evolved PylRS TBD mutants have demonstrated exceptional effectiveness in increasing the yield of ncAA-containing proteins. The resulting TBD combinatorial mutants improved IFRS activity across its substrate spectrum, and when combined with diverse CD mutants, they enhanced the incorporation efficiency of six types of ncAAs.\n\nMaPylRS, the enzyme lacking an NTD, has been shown to be slightly more active than the wild-type MmPylRS10. However, the incorporation efficiency of BocK directed by the Com2 variant of MmPylRS was 17.9-fold higher than that of MaPylRS, indicating the important role of the NTD of MmPylRS in catalysis. Since MaPylRS lacks the NTD, the strategy of engineering the NTD to enhance enzyme activity might not be applicable to MaPylRS. Previously, Lin et al. achieved efficient SCS by fusing the MmPylRS TBD region with the CD of various cAA RS, including histidine, phenylalanine, and alanine39. We transplanted Com2 mutations to chHisRS, which contained an NTD from MbPylRS-IPYE, MmPylRS, and a CTD from HisRS. The newly formed variant Com2-HisRS exhibited a 6.8-fold improvement in SCS efficiency characterized by fluorescence intensity of sfGFP2TAG, compared to the chHisRS (Supplementary Fig.\u00a017b). The TBD mutations obtained in this study are hence promising to increase the activities of diverse chimeric aaRS to enhance the incorporation efficiency of more types of ncAAs into proteins. Moreover, these aaRS obtained are expected to have a marked impact on the suppression of multiple TAG stop codons, and hence make the genetic code expansion technology more useful.\n\nWe carried out the aminoacylation reaction in vitro to characterize the activity and catalytic efficiency of PylRS variants. However, the extent of improvement in catalytic efficiency (kcat/Km) of the Com2 variant relative to the wild type was not as high as the ncAA incorporation efficiency determined by sfGFP2TAG fluorescence intensity. This might be because the mutations improved the PylRS expression, which hence enhanced the suppression of the amber codon. There is a mutation D2N in Com1-IFRS and Com2-IFRS, which showed a 3.6-fold improvement in sfGFP2TAG expression yield compared to IFRS. Based on the N-terminal rule that governs the rate of protein degradation, the N-terminal amino acid of a protein determines its half-life in vivo40. The first methionine of MmPylRS variant could be removed after translation, and the Asn2 became the N-terminal residue, which might enhance the half-life of PylRS, and hence affect the protein expression of sfGFP.\n\nAdditionally, the activity of PylRS was quantified based on the production of AMP. However, we observed a strong AMP production background during the measurement of kinetic parameters for tRNA (Supplementary Fig.\u00a036a). We tested the stability of ATP in the reaction buffer and found that ATP was stable in the reaction buffer before adding the enzyme, and hydrolysis only happen in presence of PylRS (Supplementary Fig.\u00a036b). Further characterization of WT and Com2-WT revealed that the ATP hydrolysis led to the production of AMP, and this could occur even in absence of tRNA and ncAA (Supplementary Fig.\u00a036c). And, interestingly, Com2-WT exhibited improved ATP hydrolysis capability compared to WT. This might cause some error when using the production AMP to quantify the enzyme activity, as part of the AMP determined might be from ATP hydrolysis instead of the aminoacylation reaction.\n\nThe wild-type PylRS exhibits poor solubility due to its hydrophobic NTD, which hinders crystallization of the full-length enzyme. The latest protein-tRNA structure prediction tool, AlphaFold3, was used to model the structure, allowing us to illustrate the mechanism of improved mutations. To better represent the binding conformation, Zn\u00b2\u207a and ATP were included in the system. Analysis of the MD simulation results suggested that the enhanced activity of Com2-IFRS likely stems from the mutations reshaping the binding interactions between protein and tRNA, which reduced the reaction distance between tRNA and Pyl-AMP, thereby enhancing aminoacylation efficiency. Additionally, through DCCM analysis, we found that Com2-WT exhibited a significantly enhanced overall dynamics network, implying that tRNA binding strengthens both intra- and inter-domain couplings. To our knowledge, this is the first study working on MD simulations of the predicted MmPylRS structure containing both N-terminal and C-terminal domains, paving the way for computational design of more effective PylRS variants.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Primers and genes were synthesized by Tsingke Biotechnology Co., Ltd. (Beijing, China). The high-fidelity DNA polymerase was from Vazyme Biotech Co., Ltd (Nanjing, China), while the Dpn I enzyme was obtained from Takara (Kusatsu, Japan). The T7 RNA polymerase was from ABclonal Technology (Wuhan, China). The Easy RNA Cleanup Kit was from Zhejiang Easy-Do Biotechnology Co., Ltd (Hangzhou, China). The AMP-GloTM assay was from Promega (Beijing, China). The Ni NTA Beads 6FF were from LABLEAD (Beijing, China). The pBK-PylRS plasmid and the pMyo4TAG-PylT plasmid are a kind gift from Dr. Jason Chin, Medical Research Council Laboratory of Molecular Biology. All ncAAs used in these studies were purchased from Aladdin (Shanghai, China), Bidepharm (Shanghai, China), and Macklin (Shanghai, China). The concentrations of tRNA, DNA, and protein were measured using NanoDrop One (Thermo Fisher Scientific Inc., USA). Disposable fiber-optic streptavidin-coated tips were purchased from ForteBio Inc. (USA).\n\nThe gene sequences encoding each of the MmPylRS mutants were codon-optimized for expression in E. coli and were cloned between the NdeI site and PstI sites of the pBK-PylS plasmid41. The pBK-MmPylRS plasmid was chosen as a template for mutant construction with the plasmid PCR approach. The primer sequences used for PylRS variant construction are supplied in the Supplementary Data. The polymerase chain reaction (PCR) mix used was a high-fidelity DNA polymerase Mix (P525, Vazyme). The PCR conditions are 95\u2009\u00b0C 3\u2009min, (95\u2009\u00b0C 30\u2009s, 60\u2009\u00b0C 30\u2009s, 72\u2009\u00b0C 90\u2009s)\u2009\u00d7\u200930 cycles, 72\u2009\u00b0C 10\u2009min. After PCR, 2\u2009\u03bcL Dpn I was added to a 50\u2009\u03bcL PCR reaction mixture, and the digestion was carried out at 37\u2009\u00b0C for 2\u2009h and 75\u2009\u00b0C for 15\u2009min. After Dpn I digestion, 10\u2009\u03bcL of PCR products were directly transformed into chemically competent E. coli BL21(DE3) for the following experiments.\n\nThe sfGFP expression plasmid, named pGFP-PylT, was constructed by replacing the myoglobin gene in pMyo4TAG-PylT (a kind gift from Dr. Jason Chin, Medical Research Council Laboratory of Molecular Biology) with the sfGFP gene. The plasmids pGFP2TAG-PylT, pGFPS2XTAG-PylT, and pGFPNTAG-PylT, containing multiple amber codons, were constructed by multiple rounds of PCR using the pGFP-PylT plasmid as a template. The plasmids encoding MmPylRS mutants were co-transformed with pGFP2TAG-PylT, pGFPS2XTAG-PylT, or pGFPNTAG-PylT into E. coli DH10B cells for assessment of PylRS activity by the expression of sfGFP. After incubation in lysogeny broth (LB) medium at 37\u2009\u00b0C for 60\u2009min, the co-transformed cells were spread onto LB agar containing 50\u2009\u03bcg/mL kanamycin and 12.5\u2009\u03bcg/mL tetracycline. After cultivation for 12\u2009h at 37\u2009\u00b0C, individual colonies were inoculated into 5\u2009mL LB with required antibiotics at 37\u2009\u00b0C and grown to an OD600 of 0.4\u20130.6. The cells were harvested by centrifugation (7104\u2009\u00d7\u2009g, 10\u2009min), washed, and resuspended in antibiotics-supplemented minimal medium. The sfGFP expression was induced by arabinose with a final concentration of 0.2%. 200\u2009\u03bcL aliquots of induced cells were transferred into the 96-well plates in the presence or absence of the corresponding ncAAs in each well. After 12\u2009h induction at 37\u2009\u00b0C, the fluorescence was quantified using a BioTek Synergy H1 microplate reader (excitation/emission: 485/515\u2009nm). Signals were background-corrected and normalized to cell density (OD\u2086\u2080\u2080) measured on the same instrument.\n\nFor sfGFP expression and purification, the DH10B cells containing pBK-PylRS and pGFP2TAG-PylT were grown overnight in 5\u2009mL LB with required antibiotics at 37\u2009\u00b0C. 2\u2009mL of cells were then inoculated into 200\u2009mL of fresh LB medium supplemented with the same antibiotics, and grown to an OD600 of 0.3. sfGFP expression was induced by adding L-arabinose to a final concentration of 0.2%, followed by incubation at 30\u2009\u00b0C and 220\u2009rpm for 20\u2009h, either with or without the corresponding ncAAs. Cells were harvested by centrifugation at 4000\u2009\u00d7\u2009g, 10\u2009min, 10\u2009\u00b0C. The pellets were resuspended in ice-cold phosphate-buffered saline (PBS) buffer containing 20\u2009mM imidazole (pH 8.0) and then sonicated. After centrifugation at 12,000\u2009\u00d7\u2009g, 4\u2009\u00b0C for 20\u2009min, the supernatant was loaded through Ni-NTA beads equilibrated with PBS buffer containing 20\u2009mM imidazole, pH 8.0, and then washed with PBS buffer containing 50\u2009mM imidazole, pH 8.0. The proteins were eluted with PBS buffer containing 500\u2009mM imidazole, pH 8.0. The purified proteins were concentrated using an Amicon Ultra 10,000 MWCO (Millipore) with PBS buffer pH 8.0, and stored at \u221280\u2009\u00b0C.\n\nFor myoglobin expression and purification, the co-transformed DH10B cells containing pBK-PylRS and pMyoglobin-93TAG were cultured overnight and diluted at a ratio of 1:100 into 200\u2009mL of fresh LB medium supplemented with the required antibiotics and 1\u2009mM 3-methyl-L-histidine. Cells were grown until OD600 reached 0.3. L-arabinose was added with a final concentration of 0.2% to induce myoglobin expression (37\u2009\u00b0C, 220\u2009rpm, 12\u2009h) with or without the addition of 3MeH. Cells were harvested by centrifugation at 4000\u2009\u00d7\u2009g for 10\u2009min at 4\u2009\u00b0C. The resulting cell pellets were suspended in ice-cold Tris-HCl buffer (50\u2009mM Tris-HCl, 300\u2009mM NaCl, 20\u2009mM imidazole, pH 7.6) and then sonicated. The suspension was centrifuged at 12,000\u2009\u00d7\u2009g for 20\u2009min at 4\u2009\u00b0C. For 6xHis tag fusion proteins, the resulting supernatant was purified via Ni2+-affinity chromatography on chelating Sepharose equilibrated with Tris-HCl buffer (50\u2009mM Tris-HCl, 300\u2009mM NaCl, 20\u2009mM imidazole, pH 7.6), and washed with 10 volumes of Tris-HCl buffer (50\u2009mM Tris-HCl, 300\u2009mM NaCl, 50\u2009mM imidazole, pH 7.6). The proteins were eluted with Tris-HCl buffer (50\u2009mM Tris-HCl, 300\u2009mM NaCl, 250\u2009mM imidazole, pH 8.0). The protein was concentrated using an Amicon Ultra 10,000 MWCO (Millipore) with Tris-HCl buffer (50\u2009mM Tris-HCl, 300\u2009mM NaCl, pH 7.6), and stored at \u221280\u2009\u00b0C.\n\nThe purified myoglobin was standardized to the same volume to ensure that the activity differences were due to protein concentration. Guaiacol was used as the reaction substrate at a final concentration of 2.5\u2009mM42. The reaction mixture had a total volume of 200\u2009\u00b5L, consisting of 10\u2009\u00b5L of purified myoglobin, 5\u2009\u00b5L of 0.1\u2009M guaiacol, and 185\u2009\u00b5L of 10\u2009mM H2O2-PBS solution. Product formation was monitored spectrophotometrically at 470\u2009nm. The OD changes of the reaction mixture per minute were recorded using kinetic scanning by a BioTek Synergy H1.\n\nThe genes of PylRS and its variants were cloned into pET28a and transformed into BL21(DE3) cells for expression. Cells were grown in LB medium containing 50\u2009\u03bcg/mL kanamycin and 50\u2009\u03bcM ZnCl2 at 37\u2009\u00b0C until the absorbance at 600\u2009nm (OD600) reached 0.6. The protein was then induced by the addition of isopropyl-\u03b2-D-l-thiogalactopyranoside (IPTG) to a final concentration of 100\u2009\u03bcM and shifted to 18\u2009\u00b0C for ~18\u2009h before harvesting. The cells were harvested and resuspended in Tris-HCl buffer (50\u2009mM Tris-HCl, 300\u2009mM NaCl, 20\u2009mM imidazole, pH 7.6). After sonication, the suspension was centrifuged at 12,000\u2009\u00d7\u2009g for 20\u2009min at 4\u2009\u00b0C. The resulting supernatant was purified via Ni2+-affinity chromatography on chelating Sepharose equilibrated with Tris-HCl buffer (50\u2009mM Tris-HCl, 300\u2009mM NaCl, 20\u2009mM imidazole, pH 7.6) and washed with 10 volumes of Tris-HCl buffer (50\u2009mM Tris-HCl, 300\u2009mM NaCl, 50\u2009mM imidazole, pH 7.6). The proteins were eluted with Tris-HCl buffer (50\u2009mM Tris-HCl, 300\u2009mM NaCl, 250\u2009mM imidazole, pH 7.6). The protein was concentrated using an Amicon Ultra 10,000 MWCO (Millipore), stored at \u221280\u2009\u00b0C.\n\nThe M.mazei tRNAPyl was transcribed using T7 RNA polymerase. The DNA oligonucleotides used to construct double-stranded DNA templates\u00a0for\u00a0tRNAPyl include 5\u2032-primer TAATACGACTCACTATAGGAAACCTGATCATGTAGATCGAATG, middle template CACTATAGGAAACCTGATCATGTAGATCGAATGGACTCTAAATCCGTTCAGCCGGGTTA and 3\u2032-primer TGGCGGAAACCCCGGGAATCTAACCCGGCTGAACGGATTTAGAG\u00a0(reverse), in which the T7 promoter sequence is shown in bold. In vitro transcription was performed for 8\u2009h at 37\u2009\u00b0C in a solution containing T7 RNA polymerase, NTPs (ATP, UTP, CTP, GTP), and double-stranded DNA transcription templates obtained by PCR. Transcribed tRNAs were purified with the Easy RNA Cleanup Kit according to the manufacturer\u2019s instructions. Purified tRNA was dissolved in 10\u2009mM Tris-HCl, 10\u2009mM MgCl2 (pH 7.6) and stored at \u221280\u2009\u00b0C.\n\nThe transcribed tRNAPyl was first refolded by heating at 85\u2009\u00b0C for 2\u2009min, then at 37\u2009\u00b0C for 15\u2009min. A 10\u2009\u03bcL reaction mixture was prepared by mixing 5\u2009\u03bcL 2\u00d7 assay buffer (50\u2009mM Tris pH 7.6, 40\u2009mM KCl, 4\u2009mM DL-dithiothreitol, 20\u2009mM MgCl2, 0.2\u2009mg/ml bovine serum albumin in RNAse-free water), 1\u2009\u03bcL RNAse-free water, 1\u2009\u03bcL 20\u2009mM ncAA, 1\u2009\u03bcL tRNA, 1\u2009\u03bcL synthetases, and 1\u2009\u03bcL 1\u2009mM ATP. For WT and Com2-WT, synthetases were diluted to 100\u2009nM in 1\u00d7 assay buffer. For IFRS, Com1-IFRS, Com2-IFRS, BtaRS, Com2-BtaRS, IFGFF, and Com2-IFGFF systems, synthetases were diluted to 200\u2009nM in 1\u00d7 assay buffer. The reaction was carried out at 37\u2009\u00b0C for 30\u2009min. Various concentrations of tRNA (0.5-32\u2009\u03bcM) were used to determine kinetic parameters for the corresponding synthetases. Various concentrations of BocK (0\u201310\u2009mM) were also used to determine kinetic parameters for WT and Com2-WT with 5\u2009\u03bcM tRNA in the reaction system. The AMP generated by the reaction was then measured with the AMP-GloTM assay43 by following the instructions from the manufacturer.\n\nThe tRNAPyl with 5\u2032 end RNA biotinylation was synthesized by Tsingke Biotech. Wild-type PylRS (WT) and its Com2-WT variant were expressed and purified as above. Before measurement, the tRNAPyl was refolded by heating at 85\u2009\u00b0C for 2\u2009min, followed by incubation at 37\u2009\u00b0C for 15\u2009min. Octet platform (ForteBio Inc. USA) was used to monitor and quantify the binding affinity between tRNAPyl and either WT or Com2-WT. The procedure consisted of five sequential steps: (1) baseline; (2) loading; (3) washing; (4) association; (5) dissociation. In a 96-well microtiter plate, the following solutions were prepared (a total of 200\u2009\u03bcL per well) respectively: baseline solution (PBST buffer), loading solution (PBST buffer with 200\u2009nM tRNA-Probe), washing solution (i.e., baseline solution), association solution (PBST buffer with various synthetase concentrations), and dissociation solution (i.e., baseline solution). A disposable fiber-optic streptavidin-coated sensor tip was first dipped into the baseline solution for 60\u2009s with gentle automated shaking, then loaded in the tRNA-Probe solution for 300\u2009s to allow coupling of the biotinylated tRNAPyl. The probe-saturated tip was then washed for 120\u2009s to remove any nonspecifically adsorbed tRNA-Probe. During the association phase, the sensor tip was immersed in the solution containing target synthetase for 300\u2009s, allowing it to bind with the immobilized tRNAPyl probe in a sandwich-like format involving the tethered probe, pendant tRNA structure, and target protein. Finally, the binding tip was in the dissociation solution for 300\u2009s. The resulting binding curves were analyzed using Octet Analysis Studio. Data from control wells (without synthetase in the association step) were subtracted as background, and KD values were determined based on the known concentrations of the synthetases.\n\nFor each model, the process from training to prediction is divided into three stages: data processing, encoding index searching, and comprehensive model training.\n\nThe amino acid sequences of mutants are transformed into 566 numerical sequences using 566 different AAindex representations44,45. Prior to applying FFT, mean standardization is performed, where each element in the numerical sequence is adjusted by subtracting the sequence\u2019s mean value. Subsequently, FFT is applied to each numerical sequence, as represented by Eq.\u00a0146:\n\nwhere fj is the protein output spectrum of complex numbers, j is an index of the Fourier Transform. The numerical sequence includes N value(s) denoted as xk, with 0 \u2264 k\u2009\u2264\u2009N\u22121 and N\u2009\u2265\u20091; k is the frequency in the spectrum; i defines the imaginary number such that i2\u2009=\u2009\u22121.\n\nThe output of the FFT is a complex sequence, with each element represented as a\u2009+\u2009bi, where a is the real component, b is the imaginary part, and i is the imaginary unit. From these two components, both a real and imaginary protein spectrum can be derived, along with an absolute spectrum (or power) spectrum46. The absolute spectrum was the spectrum of choice for the encoding strategy studied. Due to the symmetry of the transformed sequence, the absolute spectrum of the first half is applied. The absolute spectrum is represented as:\n\nWhere fj is the output of the FFT, M is the number of protein sequences.\n\nAfter FFT processing, a spectral form of the protein called the protein spectrum is generated. The amplitudes of the spectrum were then normalized to be between 0 and 1 by Min-max scaling. These normalized protein spectra, along with protein activity data, formed the ML model training set.\n\nThis stage aims to score the encoding method for each AAindex. To enhance the model\u2019s fit, a GridSearchCV approach is employed to optimize the hyperparameter n_components of PLSR, with a search range of 2 to 10. Depending on the dataset size, either LOOCV or k-fold Cross-Validation (k-fold CV) is chosen. The evaluation metrics include the cross-validated Mean Squared Error (cvMSE) and the coefficient of determination (R2). Here, cvMSE is derived from the average of mean squared errors during cross-validation, which facilitates the construction and selection of the most robust models, while R2 quantifies the correlation between the predicted and observed values based on the optimal hyperparameter model.\n\nWhere \\({y}_{i}\\) is the measured activity of the ith sequence, \\({\\hat{y}}_{i}\\) is the predicted activity of the ith sequence, \\(\\bar{y}\\) is the average of measured activities, and n is the number of sequences.\n\nIncorporating the aforementioned process with different AAindex encoding methods allows us to retrain the model and score each of the 566 indices. We then select the highest-scoring index to be used as the final index for model construction.\n\nUtilizing the optimal index selected in the previous step, we encode the data and train the model using the entire dataset. This trained model is subsequently employed for further exploration of the prediction space.\n\nAn Extended Sequence (Ext_SEQ) method has been proposed to enhance the FFT-PLSR model, demonstrating improved model fitting capabilities. In this study, we applied this method to models constructed using single and double mutants data from Com1. The core of this approach lies in the combination of sequences encoded by multiple indices. The best index, index1, is identified as the one that yields the lowest cross-validated Root Mean Square Error (cvMSE) and is selected as the first index for constructing the Ext_SEQ. The protein sequence is encoded using this index, generating a corresponding protein spectrum. In the second iteration, another index, index2, is chosen to construct an Ext_SEQ composed of two elemental sequences. The index2 is selected based on a second ranking that excludes the previously used index, index1. This process is repeated iteratively in each subsequent iteration to identify the optimal index for modeling and to expand the Ext_SEQ to include three or more indices. Ultimately, this concatenation process is performed based on purely statistical criteria.\n\nThe full-length structure of the PylRS dimer was predicted by AlphaFold3, including PylRSs, tRNAs, zinc ions, and ATPs. The structure of Pyl-AMP was obtained from the crystal structure of the CTD of PylRS (PDB: 2ZIM). Upon alignment, it was found that the position of AMP in Pyl-AMP closely overlaps with the position of ATP in the predicted structure. Therefore, Pyl-AMP was directly substituted for ATP. All simulations were performed using GROMACS 2022.0247 using an Amber14sb/parmbsc1 force field48. The force field parameters of the substrate (Pyl-AMP) were constructed using ACPYPE49. Periodic boundary conditions were applied in all simulations. The initial structure was solvated in a cubic simulation box with a layer of water at least 10.0\u2009\u00c5 from the protein surface. Sufficient counter ions (Na+) were added to neutralize the system. The protonation states of titratable residues (histidine, glutamic acid, and aspartic acid) were assigned based on the default setting of the program pdb2gmx in GROMACS in combination with careful visual inspection of local hydrogen-bonded networks. Pdb2gmx took the protonation states of amino acids free in solvent at pH 7 as the default. The Lys and Arg were protonated, while the Asp and Glu were unprotonated. For His, the proton could be either on \u03b4 position, on \u03b5 position, or on both, and the selections were done automatically based on optimal hydrogen bonding conformations. Histidines 63 and 392 in both chain A and chain B were protonated at the \u03b4 position. Histidines 28, 29, 45, 62, 338, 369, and 432 at both chain A and chain B were protonated at the \u03b5 position. Cysteines 42, 69, and 72 in both chain A and chain B were deprotonated due to their binding to Zn2+.\n\nThe simulations were carried out for three systems at 300\u2009K, including WT, Com1-WT, and Com2-WT. At the beginning, the entire system was minimized using the steepest descent method with no positional restraints. The minimization process was configured to stop when the maximum force acting on any atom in the system fell below a threshold of 1000\u2009kJ\u2009mol\u22121\u2009nm\u22121. An initial step size of 0.01\u2009nm was set for the minimization process. The maximum number of minimization steps allowed was set to 5000. No other constraints were applied during the minimization. In the pre-equilibrium stage, the system was gradually heated to 300\u2009K over 1\u2009ns in the NVT ensemble, followed by 2\u2009ns in the NPT ensemble at 1\u2009atm. The potential energy curve and RMSD of the protein were analyzed to confirm the proper equilibration and stabilization of the system. During the production run, the NPT ensemble was employed for 50\u2009ns, at 300\u2009K and 1\u2009atm. The RMSD of protein backbone atoms was calculated using the initial structure of the equilibrium run as the reference. Due to the irregular motion of the linker region affecting the RMSD calculation, as well as the fact that the linker area does not influence catalysis, the linker region was excluded from the RMSD calculation. The reaction distance, hydrogen bonds occupancy, and binding free energy were analyzed for the single chain, maintaining correct conformations of tRNA and substrate, using the last 10\u2009ns equilibrated trajectories. The NPT ensemble was further employed for another 200\u2009ns production simulations, and the dynamics cross-correlation map (DCCM) was calculated from the trajectory spanning from 50 to 200\u2009ns, with data collected at 150\u2009ps intervals.\n\nThe velocity-rescaling thermostat50 with a time constant equal to 0.1\u2009ps was employed throughout the simulations to keep the temperature constant. To maintain the pressure, the Parrinello-Rahman pressure coupling51,52 was utilized in the equilibrium and production run, with the pressure time constant and isothermal compressibility set to 2\u2009ps and 4.5\u2009\u00d7\u200910\u22125\u2009bar\u22121, respectively. A time step of 2\u2009fs for integration of the equations of motion was used throughout the simulation. A cutoff of 10\u2009\u00c5 was used for nonbonded interactions. The particle mesh Ewald algorithm53 was used to calculate long-range electrostatic interactions.\n\nLiquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS) was applied to detect the incorporation of ncAAs into sfGFP as described previously30. LC-ESI-MS analysis was performed on an Agilent 1290 Infinity II LC system coupled with a 6545 Q-TOF mass spectrometer (Agilent, UK). Protein samples (5\u2009\u00b5L, 0.4\u2009\u00b5g/\u00b5L) were injected onto a PLRP-S column (50\u2009mm\u2009\u00d7\u20092.1\u2009mm, 1000\u2009\u00c5, 5\u2009\u00b5m) at 30\u2009\u00b0C. A binary gradient using mobile phase A (5% MeCN, 0.1% formic acid) and B (95% MeCN, 0.1% formic acid) was applied at 0.3\u2009mL/min. The column was equilibrated at 15% B for 1.9\u2009min, held for 1\u2009min, then ramped to 90% B over 16\u2009min, followed by a rapid return to 15% B in 0.1\u2009min. The Q-TOF scanned m/z 100\u20133100 using positive ESI with the following settings: capillary voltage 4000\u2009V, nozzle 500\u2009V, fragmentor 175\u2009V, skimmer 65\u2009V, and octopole RF peak 750\u2009V. Nitrogen was used as nebulizer gas (45\u2009psi, 5\u2009L/min). Spectra were processed using MassHunter Bioconfirm (vB.10.00) and deconvolved via maximum entropy.\n\nFor ESM prediction, the publicly available ESM-1v scripts were used to retrieve \u201cwt-marginals\u201d for each of the five ESM-1v and ESM2 models. Mutations exhibiting enhanced probability scores relative to wild-type residues were systematically screened across all models. For Mutcompute prediction, the target protein\u2019s PDB ID was submitted to the web server (https://mutcompute.com). Results were retrieved via automated email delivery. Notably, the tool exclusively supports pre-existing crystallographic structures from the PDB database and cannot process custom-predicted structural models. For ProRefiner prediction, the package was deployed locally via its GitHub repository (https://github.com/veghen/ProRefiner). AlphaFold2-predicted variants\u2019 structures served as input to calculate mutant amino acid probabilities. Mutants with scores exceeding wild-type thresholds were prioritized for further analysis.\n\nEquation (5) was used to quantify the epistasis between mutations.\n\nWhere NF is Normalized Fluorescence intensity of the sfGFP with ncAA incorporated at position 2 per OD600, resulting from aminoacylation by PylRS and its mutants, \u0394NFexp is the difference in NF between the double variant and the wild type experimentally obtained, and \u0394NFi and \u0394NFj are the differences in the NF between single variants and the wild type. In any case, additivity occurs when \u0394NFij\u2009=\u20090. Positive magnitude epistasis (+ME) or negative magnitude epistasis (\u2212ME) occurs when \u0394NFij\u2009>\u20090 or \u0394NFij\u2009<\u20090, respectively. Positive sign epistasis (+SE) or negative sign epistasis (\u2212SE) occurs when \u0394NFij\u2009>\u20090 if \u0394NFi\u2009<\u20090 or \u0394NFj\u2009<\u20090, or \u0394NFij\u2009<\u20090 if \u0394NFi\u2009>\u20090 or \u0394NFj\u2009>\u20090, respectively. Positive reciprocal sign epistasis (\u2009+\u2009RSE) or negative reciprocal sign epistasis (-RSE) occurs when \u0394NFij\u2009>\u20090 if \u0394NFi\u2009<\u20090 and \u0394NFj\u2009<\u20090, or \u0394NFij\u2009<\u20090 if \u0394NFi\u2009>\u20090 and \u0394NFj\u2009>\u20090, respectively54.\n\nData were analyzed using Prism (GraphPad) and Origin 2025. Data are presented as mean\u2009\u00b1\u2009standard deviation with error bars. All experiments were repeated independently with similar results at least three times. No data were excluded from the analyses. The investigators were not blinded to allocation during experiments and outcome assessment.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Authors declare that all data supporting the findings of this study are available within the paper and its supplementary information files. The PDB structures used in this work include 5UD5, 4TQD, and 2ZIM. The LC-MS data underlying Supplementary Fig.\u00a016 have been deposited to the ProteomeXchange Consortium via the PRIDE55 partner repository with the dataset identifier PXD065336 [https://www.ebi.ac.uk/pride/archive/projects/PXD058768]. The enzyme data has been provided in two JSON format files prepared with the EnzymeML tool56, which are available at Github [https://github.com/zjuhaoran/FPFORCOM] and at Zenodo57.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The source code employed for generating descriptors and training ML models in this research is available at https://github.com/zjuhaoran/FPFORCOM. The source code has also been deposited to Zenodo57.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Shandell, M. A., Tan, Z. & Cornish, V. W. Genetic code expansion: a brief history and perspective. Biochemistry 60, 3455\u20133469 (2021).\n\nArticle\u00a0\n PubMed\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nWan, W., Tharp, J. M. & Liu, W. R. Pyrrolysyl-tRNA synthetase: an ordinary enzyme but an outstanding genetic code expansion tool. Biochim. Biophys. 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We would like to thank iBioFoundry and the Core Facility at the Institute for Intelligent Bio/Chem Manufacturing, ZJU-Hangzhou Global Scientific and Technological Innovation Center.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Qunfeng Zhang, Ling Jiang.\n\nInstitute of Bioengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China\n\nQunfeng Zhang,\u00a0Ling Jiang,\u00a0Yadan Niu,\u00a0Yujie Li,\u00a0Wanyi Chen,\u00a0Jingxi Cheng,\u00a0Haote Ding,\u00a0Binbin Chen,\u00a0Ke Liu,\u00a0Jiawen Cao,\u00a0Junli Wang,\u00a0Shilin Ye,\u00a0Lirong Yang,\u00a0Jianping Wu,\u00a0Gang Xu,\u00a0Jianping Lin\u00a0&\u00a0Haoran Yu\n\nZJU-Hangzhou Global Scientific and Technological Innovation Centre, Hangzhou, Zhejiang, 311200, China\n\nLing Jiang,\u00a0Binbin Chen,\u00a0Lirong Yang,\u00a0Jianping Wu\u00a0&\u00a0Haoran Yu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.Y. and Q.Z. designed research; Q.Z., Y.N., Y.L., W.C., J.C., H.D., K.L., S.Y., J.C., and J.W. performed studies; B.C. and L.J. provided guidance on molecular dynamics simulation; L.Y., J.W., G.X., J.L., and H.Y. supervised the studies; H.Y. and Q.Z. analyzed data; H.Y., L.J., and Q.Z. wrote the paper.\n\nCorrespondence to\n Haoran Yu.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Carlos Acevedo-Rocha and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids.\n Nat Commun 16, 6648 (2025). https://doi.org/10.1038/s41467-025-61952-2\n\nDownload citation\n\nReceived: 12 November 2024\n\nAccepted: 07 July 2025\n\nPublished: 19 July 2025\n\nVersion of record: 19 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61952-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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transformation product of a widely used aminopolyphosphonate complexing agent", + "journal": "Nature Communications", + "published": "11 March 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57473-7/MediaObjects/41467_2025_57473_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57473-7/MediaObjects/41467_2025_57473_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Environmental chemistry" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4692988/v1.pdf", + "research_square_link": "https://www.researchsquare.com//article/rs-4692988/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-57473-7.pdf", + "preprint_posted": "15 Aug, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "We demonstrate for the first time that the broad-spectrum herbicide glyphosate is a stable transformation product during manganese-driven oxidation of diethylenetriamine penta(methylenephosphonate) (DTPMP), a complexing agent widely used in household and industry applications. Glyphosate formation was observed at circumneutral pH (i) in the presence of MnO2 (with and without dissolved O2) as well as (ii) in the presence of Mn2+ and O2. Maximum glyphosate yields varied with reaction conditions and ranged from 0.06 to 0.16 mol-%. Given the ubiquitous presence of manganese in the environment and wastewater treatment systems, Mn-driven transformation of DTPMP likely contributes to glyphosate formation under environmentally relevant conditions. Our results support recent reports of municipal wastewater as a previously neglected source of glyphosate in European surface waters with aminopolyphosphonates as suspected precursors. Therefore, the current approach to protecting water resources from glyphosate contamination needs to be revised, which has significant environmental, legal and economic implications.Earth and environmental sciences/Environmental sciences/Environmental chemistryPhysical sciences/Chemistry/Environmental chemistry", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SIRoehneltetalGlyphosate.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Diethylenetriamine penta(methylenephosphonate) (DTPMP) and related aminopolyphosphonates (APPs) are widely used as chelating agents in household and industrial applications. Recent studies have linked APP emissions to elevated levels of the herbicide glyphosate in European surface waters. However, the transformation processes and products of APPs in the environment are largely unknown. We show that glyphosate is formed from DTPMP by reaction with manganese at near neutral pH in pure water and in wastewater. Dissolved Mn2+ and O2 or suspended MnO2 lead to the formation of glyphosate, which remains stable after complete DTPMP conversion. Glyphosate yields vary with the reaction conditions and reach up to 0.42\u2009mol%. The ubiquitous presence of manganese in natural waters and wastewater systems underscores the potential importance of Mn-driven DTPMP transformation as a previously overlooked source of glyphosate in aquatic systems. These findings challenge the current paradigm of herbicide application as the sole source of glyphosate contamination and necessitate a reevaluation of water resource protection strategies.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The aminomonophosphonate glyphosate is the most widely used herbicide worldwide1. It is a non-selective herbicide, mostly used to kill weeds that compete with crops. Its utilization has significantly increased following the introduction of genetically modified herbicide-tolerant crops, which allowed for extended glyphosate application periods1,2. According to the US National Pesticide Information Center (NPIC) the typical half-life of glyphosate is relatively short (1.5 months)3 but highly variable in soils ranging from few days to several years4,5,6,7. Environmental persistence of glyphosate is reflected by its frequent detection in ground and surface waters8,9,10,11,12,13. The major transformation product (TP) of glyphosate in the environment is aminomethylphosphonate (AMPA), which exhibits a longer half-life than glyphosate7,11.\n\nGlyphosate pollution of water bodies so far has exclusively been attributed to herbicide applications14. Yet, a recent study revealed negative removal rates for glyphosate and AMPA in municipal wastewater treatment plants15, while another study highlighted municipal wastewaters as a significant source for glyphosate and AMPA in surface waters in Europe12. As the high and rather constant loads of glyphosate and AMPA in wastewater effluents over the year are not compatible with herbicide applications, a different source was suspected12. Aminopolyphosphonates (APPs), which are widely used in laundry detergents in the EU16, are known precursors of AMPA17,18,19,20,21. Since the basic structure of glyphosate is already present in certain APPs (see Fig.\u00a01), they are suspected precursors for glyphosate, too12,22.\n\nSchematic representation of the formation of phosphate, AMPA, IDMP and glyphosate (proposed) from DTPMP. Phosphate can form via one C\u2013P bond cleavage (iv), IDMP is formed via one C\u2013N bond cleavage (ii), while AMPA is formed via two C\u2013N bond cleavages (i, ii). We propose one pathway for the formation of glyphosate from DTPMP via two C\u2013N bond cleavages (i, iii) and oxidation of the terminal C first to the aldehyde (v) and then to the carboxylic acid (vi). The symmetry of the DTPMP molecule, which contains five phosphonate groups, allows multiple equivalent bond cleavages to lead to the same resultant product. For clarity, only one representative option for each potential cleavage is illustrated. All compounds are depicted in their fully deprotonated forms.\n\nPhosphonate consumption (APPs as well as N-free analogues) in Europe was 49,000 metric tons per year in 201216. Detergents and bleaches are major applications for phosphonates. 7613 metric tons were used in household detergent and cleaning applications in Germany in 201923. The three commercially most relevant APPs are aminotris(methylene phosphonate) (ATMP), ethylenediaminetetra(methylene phosphonate) (EDTMP) and diethylenetriamine penta(methylene phosphonate) (DTPMP)16.\n\nWhile the predominant removal process for DTPMP and other polyphosphonates in wastewater treatment plants (WWTPs) is commonly attributed to sorption onto sewage sludge16,24,25, recent studies underscore the need to critically examine also the transformation pathways of these compounds12,15.\n\nTransformation of APPs (photolysis, Mn2+/O2, MnOOH) with AMPA, iminodi(methylene phosphonate) (IDMP) and phosphate as major TPs (see Fig.\u00a01) is well documented in the literature17,18,19,26,27,28. However, evidence for glyphosate formation is limited to ozonation of EDTMP in drinking water22 and is lacking under environmentally relevant conditions. The formation of glyphosate from APPs requires the presence of an ethylene moiety on the nitrogen atom of the APP molecule, whose terminal carbon can ultimately be oxidized to a carboxylic acid. Among the high-volume APPs, only EDTMP and DTPMP exhibit this structural feature, the latter one being quantitatively the most significant29.\n\nKlinger et al. 22 proposed a reaction scheme for the formation of glyphosate from EDTMP by ozone, a strong oxidant widely used in water treatment. The authors proposed an aldehyde as an intermediate after a C\u2013N bond cleavage within the ethylene moiety and further oxidation of this aldehyde to the carboxylic acid required for the formation of glyphosate20.\n\nBesides ozone, manganese oxide minerals (MnOx) are strong oxidizing agents that are important not only in technical systems but also in the environment30. They are often formed by microbial oxidation of dissolved MnII, leading to amorphous structures with high surface areas30,31. Manganese minerals are ubiquitous, not only present in soils and sediments but also in substantial concentrations in sewage sludge32. The significance of manganese for the oxidation of ATMP and other APPs including DTPMP has early been recognised by Nowack & Stone27,28,33. Recently, the transformation of IDMP by manganese dioxide was investigated in detail revealing AMPA and PO43- as main TPs26. The transient formation of N-formyl-AMPA (F-AMPA) suggests oxidation of the terminal carbon of an N-methyl-AMPA intermediate to an aldehyde after C\u2013P bond cleavage of IDMP. In analogy to the oxidation of EDTMP by ozone, further oxidation of this terminal carbon might lead to the carboxylic acid \u2013 in this case with a methylene moiety as IDMP is the precursor.\n\nTherefore, it is conceivable that manganese oxide minerals or dissolved manganese may lead to glyphosate formation from APPs bearing an ethylene moiety (see Fig.\u00a01) in technical and environmental systems.\n\nHere, we present the results of systematic laboratory experiments on the formation of glyphosate and AMPA as TPs of DTPMP oxidation by manganese dioxide and dissolved manganese and oxygen. The study was designed to capture relevant environmental conditions regarding pH and the presence or absence of dissolved oxygen.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57473-7/MediaObjects/41467_2025_57473_Fig1_HTML.png" + ] + }, + { + "section_name": "Results and Discussion", + "section_text": "We report the results of laboratory batch experiments designed to elucidate the effects of MnO2 mineral concentrations (0.1\u2009g/L vs. 1\u2009g/L), dissolved Mn2+ (1\u2009mM) and the presence or absence of O2 (atmosphere/ 21 vol.-% O2 vs N2 atmosphere) on the transformation of 1\u2009mM DTPMP. The experiments were carried out in purified water (buffered at pH 6) as well as in sterile-filtered wastewater (pH 8) as matrix. The pH values were monitored and are depicted in Fig.\u00a0S1. Control experiments without manganese oxides or dissolved Mn2+ generally showed no reactivity towards DTPMP (see Supplementary Fig.\u00a0S2).\n\nIn aqueous solution buffered at pH 6, DTPMP was completely transformed by MnO2 within \u2264\u200924\u2009h under all conditions studied (see Fig.\u00a02). With 1.0\u2009g/L MnO2 under oxic conditions (fastest reaction kinetics), complete DTPMP transformation was observed after 20\u2009min, while for 0.1\u2009g/L MnO2 under anoxic conditions (slowest reaction kinetics) complete DTPMP transformation was observed after 24\u2009h. Under oxic conditions with 1\u2009mM Mn2+ the slowest transformation kinetics of DTPMP were observed and complete DTPMP transformation was reached only after >130\u2009h (see Fig.\u00a0S3).\n\nTotal (aqueous and sorbed) DTPMP concentrations quantified using IC-IPAD as a function of time and pseudo-0th order fits for all four experiments with MnO2 in aqueous MES buffer (pH 6). a 1.0\u2009g/L MnO2 oxic conditions, (b) 0.1\u2009g/L MnO2 oxic conditions, (c) 1.0\u2009g/L MnO2 anoxic conditions, (d) 0.1\u2009g/L MnO2 anoxic conditions. Error bars represent absolute errors between experimental duplicates.\n\nTo evaluate the DTPMP transformation kinetics, pseudo 0th-order rate constants were determined as no higher reaction order adequately described the kinetics across all four MnO2 experiments. These constants were derived by linear regression considering the time intervals described in Table\u00a01. This approach allowed for a comparative kinetic analysis of the four MnO2 experiments.\n\nIn the absence of dissolved oxygen, the transformation of DTPMP followed a pseudo-0th order rate law with R2 values of >\u20090.98. The presence of dissolved oxygen accelerated the reaction and changed the rate law as indicated by the deviation from pseudo-0th order kinetics (R2 \u2245 0.85). Both MnO2 concentration and the presence of O2 concentrations enhanced DTPMP transformation kinetics, with the fastest rates for 1.0\u2009g/L MnO2 under oxic conditions, followed by 1.0\u2009g/L MnO2 under anoxic conditions. Both experiments (oxic and anoxic) containing 0.1\u2009g/L MnO2 exhibited slower reaction kinetics than 1.0\u2009g/L, while the reaction containing 0.1\u2009g/L under anoxic conditions showed the slowest reaction. Normalization of the reaction rate constants to the specific surface area (knorm) provided further insights into the role of oxygen and available surface area. knorm values showed that the oxic experiments were faster compared to their anoxic counterparts.\n\nThe rate enhancing role of oxygen may be related to multiple processes involving different redox states of manganese as reported in literature. Under oxic conditions, Mn2+ (formed by the reduction of MnIV26,30) is known to catalyze DTPMP (and ATMP, EDTMP) oxidation by O2 (Mn2+/O2) in homogeneous solution28,34. However, the much slower reaction kinetics of DTPMP in homogeneous solution (1\u2009mM Mn2+ and O2) compared to the heterogeneous systems found in this study clearly show that the strongly enhancing role of O2 in MnO2 experiments must be due to processes other than mere Mn2+/O2 interaction, probably involving the formation of MnIII on the mineral surface.\n\nManganese minerals with elevated MnIII content appear to be more reactive oxidants30,35,36. In heterogeneous systems containing Mn2+ and MnO2, MnIII can be formed by comproportionation and be associated with the mineral surface or reside in solution35. Furthermore, MnO2 can catalyze the oxidation of MnII by O2 to MnIII37. Finally, MnO2 itself may act as a direct oxidant but also as a catalyst in connection with O238.\n\nPrevious research on IDMP transformation using the same sample of MnO2 demonstrated that roughly two electrons are accepted by MnO2 per transformation of one IDMP molecule26. Thus, the electron-accepting capacity of 0.1\u2009g/L MnO2, which corresponds to 1.1\u2009mM MnO2, cannot explain the complete transformation of 1\u2009mM DTPMP to smaller transformation products, which are partially further oxidized. Moreover, as normalized reaction rate constants both under oxic and anoxic conditions were higher for low mineral concentrations (0.1\u2009g/L vs. 1.0\u2009g/L) a catalytic role of MnO2 next to its direct oxidation activity is evident.\n\nDuring DTPMP transformation, the formation of various phosphorus-containing TPs was monitored in the aqueous phase using ion chromatography (IC) coupled to inductive-coupled plasma mass spectrometry (ICP-MS). Figure\u00a03 shows an exemplary chromatogram (1\u2009g/L MnO2, anoxic conditions, reaction time of 2\u2009h, aqueous phase) next to a mix-standard of 30 ppb P (0.97\u2009\u00b5M) per compound. The main TPs identified based on retention times and reference compounds in all experiments were IDMP and phosphate, consistent with previous studies16,17,18,28. Based on the initial DTPMP concentration, IDMP formation reached up to 97 mol-% (0.1\u2009g/L MnO2, anoxic conditions), while PO43- formation peaked at 153 mol-% (1.0\u2009g/L MnO2, oxic conditions). Regarding phosphate, the maximum molar yield amounts to 500 mol-%, due to DTPMP\u2019s five phosphonate moieties. DTPMP, IDMP, and PO43- concentrations over time in the aqueous phase are depicted in Supplementary Fig.\u00a0S4.\n\nPhosphorus-selective IC-ICP-MS chromatogram of an exemplary sample overlayed by the chromatogram of a standard mix including the denoted compounds. The sample (aqueous fraction of duplicate A of the experiment containing 1.0\u2009g/L MnO2 under anoxic conditions, reaction time 2\u2009h) is presented in red color, while the standard mix (30 ppb P per compound) is presented in black. The sample was diluted 1:1000 to match the calibration range. Abbreviations for standard compounds not described in the text: 2-AEP\u2009=\u20092-aminoethylphosphonate, MPA methylphosphonate, PAA phosphonoacetate.\n\nIn the exemplary chromatogram shown in Fig.\u00a03, a double peak is visible at the retention time of AMPA (83.0\u2009s), while a triple peak is observed around the retention time of glyphosate (167.5\u2009s). Both peaks, even if considered to be the respective compounds, were below the instrumental LODs (0.9 ppb P for AMPA and 1.7 ppb P for glyphosate) and therefore represent an almost negligible fraction within the TP spectrum. Thus, it is not surprising that the formation of glyphosate has so far been mostly overlooked.\n\nTo verify glyphosate and AMPA formation during DTPMP transformation, we used FMOC derivatization and subsequent quantification by means of reversed-phase high-performance liquid chromatography (RP-HPLC) coupled to a triple-quadrupole (QQQ) mass spectrometer, an established trace analysis method for glyphosate and AMPA39,40,41.\n\nThe formation of glyphosate as well as AMPA was observed in all experiments with MnO2 (see Fig.\u00a04). While AMPA is the main TP of glyphosate in the environment7,11,42, the main path for AMPA formation from DTPMP is via two C\u2013N bond cleavages (see Fig.\u00a01).\n\nTotal DTPMP, glyphosate and AMPA concentrations during DTPMP oxidation by MnO2 in four different experiments in MES buffer (pH 6). DTPMP (black) was quantified using IC-IPAD (see Fig.\u00a02), AMPA (blue) and glyphosate (red) using LC-QQQ. a 1.0\u2009g/L MnO2 oxic, (b) 0.1\u2009g/L MnO2 oxic, (c) 1.0\u2009g/L MnO2 anoxic, (d) 0.1\u2009g/L MnO2 anoxic. Error bars represent absolute errors between experimental duplicates.\n\nGlyphosate and AMPA yields were calculated in mol-% based on the analyzed initial DTPMP concentration. The maximum molar yields for the experiments conducted at pH 6 within the timespans presented in Fig.\u00a04 were 0.16 mol-% (1.6\u2009\u00b5M) for glyphosate and 10.13 mol-% (95\u2009\u00b5M) for AMPA. Clearly, the concentration of oxygen and MnO2 affected the glyphosate and AMPA formation kinetics and maximum observable yields.\n\nBoth compounds formed fastest in the presence of 1.0\u2009g/L MnO2 under oxic conditions (Fig.\u00a04a), reaching their maximum concentration after 1\u2009h. A similar maximum concentration of glyphosate was reached with 0.1\u2009g/L MnO2 under oxic conditions but only after approximately 28\u2009h compared to 1\u2009h (Fig.\u00a04b).\n\nWith 0.1\u2009g/L MnO2 under anoxic conditions (Fig.\u00a04d) no AMPA was observed within the first four hours. After 6\u2009h, AMPA was detected in comparably low concentrations around 12\u2009\u03bcM increasing up to 98\u2009\u03bcM after 24\u2009h, while a maximum glyphosate yield of 0.3\u2009\u03bcM was detected after 24\u2009h.\n\nContinued glyphosate and AMPA formation after complete DTPMP consumption (see Fig.\u00a04a\u2013c) suggests the presence of intermediates that are further transformed to glyphosate and/or AMPA. The presence of such intermediates is further supported by a lag phase before glyphosate and AMPA formation, as observed for 0.1\u2009g/L MnO2 under oxic conditions (Fig.\u00a04b). Glyphosate and AMPA concentrations only rose after 1.5 and 1\u2009h, respectively, even though DTPMP was almost completely transformed at that time.\n\nThe maximum concentrations of AMPA and glyphosate formed differed between the four experiments (see Table\u00a02). Under anoxic conditions, significantly lower glyphosate yields (0.03 and 0.06 mol-%) compared to oxic conditions (both 0.16 mol-%) were observed, either due to lower glyphosate formation or rapid subsequent transformation. However, for AMPA this trend is not discernible (see Table\u00a02), potentially due to the numerous reaction pathways leading to AMPA.\n\nTo elucidate the significance of heterogeneous (MnO2) and homogeneous (Mn2+/O2) oxidation reactions on product formation, an experiment with 1\u2009mM DTPMP and 1\u2009mM dissolved Mn2+ (MnCl2) was conducted under oxic conditions (buffered at pH 6). Neither glyphosate nor AMPA formation was observed within the first 24\u2009h (see Fig.\u00a05a). After 137\u2009h (approximately 5.5 days), however, 6.3\u2009\u00b1\u20090.2 mol-% AMPA and 0.06\u2009\u00b1\u20090.01 mol-% glyphosate were quantified. AMPA and glyphosate concentrations stayed almost constant until 185\u2009h yielding 6.8\u2009\u00b1\u20090.7 mol-% (AMPA) and 0.07\u2009\u00b1\u20090.00 mol-% (glyphosate).\n\nDTPMP, glyphosate and AMPA concentrations during oxidation of 1\u2009mM DTPMP by 1\u2009mM Mn2+ and oxygen in two different matrices. The matrices consisted of (a) ultrapure water with 20\u2009mM MES buffer (pH 6) and (b) sterile-filtered wastewater (pH 8). DTPMP (black) was quantified using IC-IPAD, glyphosate (red) and AMPA (blue) were quantified using LC-QQQ. Error bars represent absolute errors between experimental duplicates.\n\nTo account for longer reaction times, the experiment for the most reactive system (1\u2009g/L MnO2, oxic) was repeated but now sampled over 96\u2009h (see Supplementary Fig.\u00a0S5). AMPA concentrations increased until the end of the experiment and reached a maximum of 206\u2009\u00b5M (24 mol-%) after 96\u2009h (4 days). Glyphosate was detected up to a maximum of 1.1\u2009\u00b5M (0.1 mol-%), but no clear trend in concentrations could be deduced.\n\nThis experiment demonstrates that AMPA and glyphosate \u2013 even in the most reactive suspension after 4 days \u2013 are not completely transformed. This is remarkable, as the oxidation of glyphosate and AMPA on manganese oxides has been extensively studied and both compounds can be oxidized by MnOx43,44,45,46. Thus, further investigations are required to better understand the stability and further transformation of glyphosate and AMPA in consecutive reactions. The accumulation of TPs during the experiment creates a complex matrix that may impede the reaction between AMPA or glyphosate and MnO2. These TPs potentially occupy the mineral surface, reducing the active sites available for further reactions. Two scenarios are consistent with these findings: (i) the built-up of a TP matrix hinders glyphosate and AMPA transformation by diminishing the reactivity of MnO2 through sorption, or (ii) the rate of glyphosate and AMPA formation exceeds their transformation rate in parallel reactions.\n\nTo address the environmental relevance of the observations in pure water, experiments containing 1\u2009g/L MnO2 and 1\u2009mM Mn2+ were conducted in wastewater (pH 8, sterile filtrated; see the Methods section for details on the wastewater sample). In control experiments with unspiked wastewater with and without MnO2 or Mn2+, negligible glyphosate and AMPA concentrations were occasionally detected (see Fig.\u00a0S6). Dissolved manganese in the wastewater sample was below the detection limit (\u2009<\u20090.04\u2009mg/L).\n\nDTPMP transformation kinetics with 1.0\u2009g/L MnO2 in the wastewater matrix were slower than in MES buffer at pH 6 (see Fig.\u00a0S7 a versus b) in line with a lower oxidation potential of manganese oxides at higher pH47,48, as well as increased electrostatic repulsion between DTPMP and the mineral surface (point of zero charge of 5.6)30,49. Furthermore, differences between the experiments with MES buffer and wastewater are likely due to the complex wastewater-matrix containing organics (such as other complexing agents) and cations (e.g., calcium), which were reported to influence APP transformation and sorption18,50. Glyphosate and AMPA yields with 1\u2009g/L MnO2 in wastewater were up to 0.06 mol-% (glyphosate) and 10.3 mol-% (AMPA) after 168\u2009h. However, in wastewater spiked with 1\u2009mM Mn2+, DTPMP transformation kinetics were faster and had higher glyphosate and AMPA yields compared to MES-buffered pure water at pH 6 (see Fig.\u00a05.) In wastewater spiked with 1\u2009mM Mn2+, the highest glyphosate yield (0.42 mol-%) of all experiments conducted in this study was observed (see Table\u00a02), albeit only after 240\u2009h. Possibly, the reaction kinetics are faster at pH 8 due to stronger complex formation of MnII and DTPMP, such as shown for ATMP28. At higher pH, less protons compete with metal ions present in solution and DTPMP is more negatively charged51. Furthermore, MnIII complexes might play a role at higher pH. While the stability of MnIII complexes varies depending on the ligand, certain ligands including desferrioxamine B show higher stability of MnIII complexes at pH values between 7 and 1152.\n\nPolyphosphonates as replacements for polyphosphates and polycarboxylates in detergents, laundry products and other applications have been considered critical due to their persistence. However, they have been accepted in applications because they were not considered toxicologically relevant. Despite their reportedly high recalcitrance attributed to the high stability of the C\u2012P bond29,53,54,55, APPs can be transformed in the presence of manganese, which occurs ubiquitously in WWTPs, soils & sediments, yielding phosphate, IDMP and AMPA as major products.\n\nOur study demonstrates that manganese potentially plays a key role in converting the widely used complexing agent DTPMP in a multi-step reaction to the herbicide glyphosate. The reaction proceeds at circumneutral pH at MnO2 minerals both in the absence and presence of dissolved oxygen but also in homogeneous solution in the presence of Mn2+/O2, even in wastewater. Under all conditions studied, AMPA and glyphosate were transformation products, AMPA up to 27.1 mol-% and glyphosate up to 0.42 mol-%. Both the kinetics and yield of DTPMP transformation products were heavily influenced by the reaction conditions (MnO2 concentration,\u00a0presence of O2 and pH/matrix). Once DTPMP was completely transformed, the concentrations of glyphosate and AMPA remained constant or even increased within 1 to 10 days of kinetic experiments. The persistence of glyphosate and AMPA towards the end of the experiment in the presence of excess MnO2 is remarkable as both compounds can be oxidized by unreacted MnO2 minerals42,43,44,45,46.\n\nThe comparatively high glyphosate yield of the experiment containing 1\u2009mM Mn2+ in wastewater under oxic conditions, relative to all other experiments, underlines the relevance of both MnO2 and aqueous Mn2+ in environmental and technical systems. This finding underlines the relevance of manganese-driven oxidation reactions in glyphosate and AMPA formation from DTPMP. The distribution between MnII and MnIII/MnIV species in the environment is known to depend on various conditions such as pH, microbial activity, and O2 levels56,57. Our results demonstrate that both MnII/O2 and MnIV can lead to glyphosate and AMPA formation, suggesting that these processes may occur under a wide range of environmental conditions.\n\nOverall, this study provides experimental evidence for conversion of a widely used non-toxic commodity compound into a highly debated pesticide1,2,5,58 under environmentally relevant reaction conditions. While we could demonstrate that the reaction is chemically feasible in the laboratory, future research should elucidate in detail how environmental conditions affect glyphosate formation from DTPMP and related APPs, the formation and identification of key intermediates and field studies including yields in wastewater treatment plants.\n\nIn addition, our findings may also provide clues for revisiting APP biotransformation studies. All bacterial growth media used in published APP biotransformation studies contain dissolved manganese55,59,60,61,62,63. Thus, manganese-driven oxidation may occur in parallel with or instead of biotransformation of DTPMP and other APPs. Martin et al. (2022)34 showed that even at a molar ratio of 1:100 (Mn:ATMP), ATMP was completely degraded within 30\u2009h. Thus, it is conceivable that in biotransformation studies under oxic conditions in the presence of dissolved Mn2+, microorganisms may utilize chemical transformation products of APPs, such as phosphate, IDMP, or AMPA, as phosphorus source rather than or in addition to directly metabolizing the target APPs.\n\nOverall, our work offers a scientific basis to rationalize recent and unexpected findings of elevated glyphosate concentrations in European WWTP effluents12,15 and suggests that manganese may play a crucial role in this phenomenon, potentially serving as a key factor in understanding the underlying processes.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57473-7/MediaObjects/41467_2025_57473_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57473-7/MediaObjects/41467_2025_57473_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57473-7/MediaObjects/41467_2025_57473_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57473-7/MediaObjects/41467_2025_57473_Fig5_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "All chemicals were purchased from Merck (Darmstadt, Germany) in the highest available purity, if not described differently. The manganese dioxide (ManganeseIVoxide, \u226598%; MnO2, Batch No. 168267405) was purchased from Carl Roth (Karlsruhe, Germany). MnCl2 tetrahydrate (p.a., \u2265\u200999%) was purchased from Merck (Darmstadt, Germany) The MnO2 specifications can be found in \u201cMethods \u2013 Mineral Characterization\u201c and the Supplementary Information. DTPMP was purchased as solid acid from Zschimmer and Schwarz (Lahnstein, Germany) under the name \u201cCublen D 900 GR\u201d (CAS: 15827-60-8). In order to ascertain the purity of the DTPMP, a 31P-{1H}-NMR measurement was conducted, showing a purity of >98.6% regarding P (nuclear magnetic resonance spectroscopy (NMR) measurements, for details, see below). Glyphosate PESTANAL (\u2009\u2265\u200998.0%), AMPA (99%), IDMP (\u2009\u2265\u200997%), ATMP (\u2009\u2265\u200997.0%), 2-aminoethylphosphonic acid (99 %), methylphosphonic acid (99 %) and phosphonoacetic acid (98 %) solids were purchased from Sigma Aldrich (St Louis, MO, USA). EDTMP was purchased as solid acid from Zschimmer and Schwarz (Lahnstein, Germany) under the name \u201cCublen ELC 950\u201d. The purity of EDTMP was determined to be >98.6 % regarding P using 31P-{1H}-NMR (see below). Isotope-labelled glyphosate (i) and AMPA (ii) were purchased from LGC Standards Ltd. (Teddington, England) (i) and HPC Standards GmbH (Cunnersdorf, Germany) (ii). Phosphate standard solution (1000\u2009mg/L PO43\u2212 in H2O) for preparation of IC-ICP-MS phosphonate standards was purchased from Merck (Darmstadt, Germany).\n\nSodium hydroxide for IC-IPAD eluent preparation and analyte desorption from the manganese dioxide was purchased as a 49\u201351% aqueous solution from Supelco (Merck, Darmstadt, Germany), NaH2PO4 (p.a., \u226599%) for analyte desorption from Roth (Karlsruhe, Germany) and sodium acetate trihydrate for IC-IPAD eluent preparation from Chemsolute (Renningen, Germany). 2-(N-morpholino)ethanesulfonic acid (MES) buffer (\u2009\u2265\u200999%) for the DTPMP transformation experiments was purchased from Carl Roth. Sodium acetate for IC-IPAD eluent preparation was delivered by Chemsolute (Renningen, Germany).\n\nThe cation exchange resin in proton form (Dowex 50\u2009W X 8, 100\u2013200 mesh, \u2265\u20091.7 eq/L) used to remove dissolved manganese was purchased from Carl Roth.\n\nThe water used for the experiments and IC-IPAD measurements was purified by an ultrapure water purification system (Barnstead, GenPure Pro, Thermo Scientific, Waltham, MA, USA) down to a conductivity below 0.06\u2009\u03bcS/cm. For IC-ICP-MS analysis (dilutions and eluent preparation), doubly distilled water from an Aquatron A4000D system (Barloworld Scientific, Nemours, France) was used.\n\nFluorenylmethoxycarbonyl chloride (FMOC Cl, 98 %) and sodium tetraborate decahydrate (borate, p.a.) used for the derivatization of glyphosate and AMPA were purchased from Carl Roth resp. Honeywell/Fluka (Charlotte, NC, USA). Dichloromethane (DCM, HPLC grade) for washing the derivatized samples was bought from Fisher Scientific (Waltham, MA, USA). For LC analysis with a triple quadrupole mass spectrometer (LC-QQQ) the eluent was prepared using LC/MS-grade acetonitrile (ACN, \u226599.9%) from Honeywell/Riedel-de Ha\u00ebn (Seelze, Germany), LC/MS grade water (Fisher Scientific, Loughborough, UK) and NH4Ac (p.a., \u226598%) from Sigma Aldrich (St Louis, MO, USA).\n\nNitric acid (HNO3, 65%, for analysis) for IC-ICP-MS measurements was purchased from Thermo Fisher Scientific (Bremen, Germany) and purified with a DST-1000 acid purification system from Savillex (Eden Prairie, MN, USA). For IC-ICP-MS eluent preparation, aqueous ammonia solution (25\u201327 %, for trace analysis) was purchased from VWR International LLC (Radnor, PA, USA) and diethylenetriaminepentaacetic acid (DTPA) from Honeywell/Fluka (Charlotte, NC, USA). The IC-ICP-MS post-column internal standard (1000\u2009\u00b5g/L indium in 2% aqueous HNO3) was purchased from Sigma-Aldrich (St. Louis, MO, USA).\n\nDeuterium oxide (D2O, 99.9 atom% D) for NMR measurements was obtained from Sigma- Aldrich (Steinheim, Germany).\n\nThe experiments (duplicates with one control) were conducted in 50\u2009mL centrifugation tubes (polypropylene, Fisher Scientific, Waltham, MA, USA) in the presence of ambient air or in the glovebox (N2 atmosphere, c(O2)\u2009<\u200910 ppm) (Unilab from MBRAUN, Garching, Germany) at room temperature (21\u2009\u00b1\u20091\u2009\u00b0C). For the glovebox experiments, DTPMP stock solution, MES buffer solution and deionized water were purged with N2 for one hour under rapid stirring before transfer into the glovebox. Then, DTPMP, MES buffer and water were mixed to yield concentrations of 1\u2009mM DTPMP and 20\u2009mM MES. After mixing, the first aliquot of 5\u2009mL was taken as t0 sample. Then, solid MnO2 was added, to reach a concentration of 0.1 or 1.0\u2009g/L. The reaction suspensions were shaken in an overhead-shaker at a speed of 25\u2009rpm. At defined time points derived from pilot tests, a well suspended 4\u2009mL aliquot of the suspension was taken, centrifuged (15\u2009min, 20,000 rcf), and filtered (0.2\u2009\u03bcM PES syringe filter, BGB Analytik, L\u00f6rrach, Germany)26. To desorb residual analytes from the mineral pellet after centrifugation and sampling the supernatant (aqueous phase), the mineral pellet was treated with 0.1\u2009M NaOH and 0.1\u2009M NaH2PO4 in the ultrasonic bath for 30 minutes64. To account for possible parallel homogenous MnII-catalyzed oxidation by O2 initiated by formed Mn2+, an additional experiment with 1\u2009mM MnCl2 (no MnO2) under oxic conditions was conducted. The experiments using 1.0\u2009g/L MnO2 and 1\u2009mM Mn2+ /O2 as oxidant/catalyst were repeated in wastewater (pH 8).\n\nThe homogeneous reactions were quenched by the addition of cation exchange resin to the aliquot taken from the reaction solution to bind Mn2+.\n\nSamples were stored in the dark at \u221220\u2009\u00b0C until analysis. Prior to analysis, samples were thawed, treated with cation exchange resin (if not done before freezing) and diluted for the respective measurement purpose.\n\nThe wastewater was sampled at 10 am on September 9, 2024, from the municipal wastewater treatment plant in Lustnau (T\u00fcbingen, SW Germany), after the screen and grit chamber, but before the primary settling tank. The wastewater was then filtered with different filter systems: I) coffee filter (Melitta, Minden, Germany), II) folded filters 595 \u00bd (Whatman Int. Ltd, Buckinghamshire, UK), III) glass fibre round filters GF 55 (Schleicher & Schuell, Dassel, Germany) and finally IV) sterile S-PAK 0.22 \u03bcm filters (Merck, Darmstadt, Germany). This filtered wastewater was used undiluted as matrix for the experiments. The initial pH of the wastewater was 8. The changes in pH development over time are shown in Fig.\u00a0S1. Detailed information regarding the composition of the wastewater is provided below.\n\nQuantification of DTPMP was performed according to the IC-IPAD (ion chromatography coupled to integrated amperometric detection) method published by R\u00f6hnelt et al. (2025)65. A 930 Compact IC Flex ion chromatograph (Metrohm, Herisau, Switzerland) was used, equipped with an anion exchange column (Dionex IonPac AS16, 2\u2009\u00d7\u2009250\u2009mm, Thermo Fisher Scientific, Waltham, United States), a suitable guard column (Dionex IonPac AG16, 2\u2009\u00d7\u200950\u2009mm) and an amperometric detector (Wall-Jet Cell, Metrohm). The Wall-Jet cell was equipped with a gold working electrode, a Pt auxiliary electrode and an Ag/AgCl reference electrode (all Metrohm). The detector method potential vs. time profile is depicted in Supplementary Fig.\u00a0S8. The dosing units for i) sample uptake and ii) concentration gradient were both of the type \u201c800 Dosino\u201d (Metrohm), with i) 2\u2009mL and ii) 5\u2009mL cylinder volume.\n\nTo prevent CO2 dissolution into the eluents, an overpressure of 0.5\u2009bar N2 was applied to both eluent bottles (gas-tight plastic bottles, Metrohm). 15\u2009mM NaOH served as eluent A, while 50\u2009mM NaOH with 400\u2009mM sodium acetate served as eluent B. The gradient profile is depicted in Supplementary Table\u00a0S1.\n\nQuantification was performed by external calibration (0.01\u201320\u2009\u03bcM) and normalization to repeatedly injected control standards.\n\nTotal DTPMP concentrations were analyzed by combining sorbed and aqueous fractions prior to measuring.\n\nAMPA and glyphosate were quantified using reversed phase (RP) liquid chromatography (LC) coupled to triple quadrupole mass spectrometry (QQQ-MS) after derivatization using FMOC chloride39,40,41. The sample was diluted 1:2 (glyphosate) respectively 1:100 (AMPA) with ultrapure water and the respective isotope-labelled standard was added to reach a final concentration of 50\u2009\u03bcg/L. Then, 10\u201315\u2009mg of the cation exchange resin were added to 1\u2009mL of the diluted sample and shaken for 30\u2009min. After sedimentation of the resin, 900\u2009\u03bcL of the supernatant were sampled and mixed with 200\u2009\u03bcL 80\u2009mM borate buffer and 200\u2009\u03bcL 40\u2009mM FMOC chloride in acetonitrile. The sample, which turned milky immediately, was let to rest for 30\u2009min. Afterwards, the clear aqueous phase was washed twice using 2\u2009mL DCM, each. The now derivatized samples were stored in the dark at 4\u2009\u00b0C until measurement. Repeated measurements showed the stability of the derivatized samples over several months.\n\nThe liquid chromatography (1290 Infinity II, Agilent, Santa Clara, CA, USA) was coupled to a triple quadrupole (QQQ) mass spectrometer (6470 LC/TQ, Agilent, Stadt/LAND), equipped with an AJS source. A reversed phase column (Agilent Poroshell 120 EC-C18 2.7\u2009\u00b5m, 2.1\u2009x\u2009100\u2009mm + 2.1\u2009x\u20095\u2009mm pre-column) was used to separate the derivatized compounds. The derivatized analytes were measured in positive ionization mode with a cell accelerator voltage of 5\u2009V.\n\n2.5\u2009mM aqueous ammonium acetate (A) and acetonitrile (B) served as mobile phases. The concentration gradient profile is provided in Supplementary Table\u00a0S2. A sample of 1\u2009\u03bcL was injected together with 0.2\u2009\u03bcL internal standard (200\u2009\u03bcg/L glufosinate-FMOC). The column was heated to 40\u2009\u00b0C and the flow rate was set to 0.3\u2009mL/min. The MS parameters and fragment ions used for quantification can be found in Supplementary Table\u00a0S3.\n\nLimits of detection (LOD) and quantification (LOQ) were calculated for each measurement sequence based on the standard deviation of the linear response and the slope of the calibration curve (5\u2013200\u2009\u03bcg/L (glyphosate), 5\u2013500\u2009\u03bcg/L (AMPA), ISTD 50\u2009\u03bcg/L each) following the International Council for Harmonisation (ICH) Q2(R1) guideline66. Table\u00a0S4 provides the LOD/LOQ values derived for every measurement sequence.\n\nThe aqueous and sorbed fractions were analyzed separately, with the sorbed fractions representing a minor part of the investigated compounds (see Supplementary Fig.\u00a0S9).\n\nFor the detection of all phosphorus-containing compounds, a prepFAST IC system (Elemental Scientific, Omaha, NE, USA) was connected to an iCAP TQ inductively coupled plasma mass spectrometer (ICP-MS) (Thermo Fisher Scientific, Bremen, Germany).\n\nThe prepFAST IC system was equipped with an anion exchange column CF-Cr-01 (50\u2009\u00d7\u20094\u2009mm) (Elemental Scientific, Omaha, NE, USA) and the injection volume was set to 50\u2009\u00b5L. The chromatographic separation was performed with a flow rate of 1000\u2009\u00b5L/min and a post-column internal standard flow rate of 100\u2009\u00b5L/min indium in 2% nitric acid (1\u2009\u00b5g/L). 300\u2009\u00b5g/L DTPA in water (pH 9.2) served as eluent A, while eluent B consisted of 150\u2009mM ammonium nitrate with 300\u2009\u00b5g/L DTPA in water (pH 9.2). For the chromatographic separation, a five-step gradient was employed, which is described in Table\u00a0S5.\n\nAnalysis was conducted in QQQ mode with oxygen as a reaction gas. Phosphorus was detected as 31P16O+ and indium as 115In+ with individual dwell times of 100\u2009ms. Quantification was performed by external calibration. Unknown TPs were quantified using the calibration function of the nearest polyphosphonate standard. The LOD was determined by the 3\u03c3 criteria, and the LOQ was determined by the 10\u03c3 criteria respectively.\n\nDue to the desorption protocol using 0.1\u2009M NaH2PO4 as competitive sorbent, with this method, just the aqueous phase of the heterogenous experiments could be analyzed.\n\nTo assure the purity of the DTPMP (EDTMP) purchased commercially, a 31P-{1H}-NMR measurement was conducted (NMR department, Chemistry Department, University of T\u00fcbingen). 31P-NMR-spectroscopy is a suitable analytical tool to characterize the purity of phosphonates, due to the 100% natural abundance of 31P and the wide range of chemical shifts and high sensitivity of the method67.\n\n10\u2009mg DTPMP (EDTMP) and 600\u2009\u00b5L of deuterated water (D2O) were mixed and vortexed for 5\u2009s. Afterwards, 600\u2009\u00b5L were transferred to an NMR glass tube. The measurement was performed on a Bruker AMX 600\u2009MHz NMR spectrometer (Bruker, Billerica, MA, USA), operating at 242.94\u2009MHz for phosphorous observation with a zgpg30 pulse program. The acquisition parameters used for this experiment with 1D sequence with power-gated decoupling and a 30 \u00b0 flip angle were as follows: number of scans: 64, spectral width: 96153.84\u2009Hz, offset (O1): \u221212146.85\u2009Hz, acquisition time: 0.34\u2009seconds, relaxation delay (d1): 2.00\u2009s. The spectrum was quantitatively evaluated using the Bruker Top Spin 4.1.4 software.\n\nIn the 31P-{1H}-NMR-spectrum (Fig.\u00a0S10), two main signals with an intensity ratio of 1:4 can be seen, which represent the phosphonate-group in the middle of DTPMP (\u03b4 (ppm): 12.94) and the four phosphonate groups of DTPMP attached to the outer amine-moieties (\u03b4 (ppm): 9.23). Impurities are marked with an asterisk. The sum of all signal-integrals is normalized to 100. Impurities of DTPMP contributed with 1.37%. Thus, the purity of the DTPMP regarding the P content was >\u200998.6%.\n\nIn the 31P-{1H}-NMR-spectrum of EDTMP, shown in Fig.\u00a0S11, we can see one signal attributed to EDTMP representing all chemically equivalent phosphonate-groups (\u03b4 (ppm): 8.76). Impurities are marked with an asterisk. The sum of all signal-integrals is normalized to 100. Impurities containing phosphorous of the analyzed EDTMP amount to only 3.40%. The used EDTMP in the experiments is therefore of high purity with 96.6%.\n\nThe point of zero charge (pHPZC) of the manganese dioxide was determined to be pH 5.6\u2009\u00b1\u20090.1 by zeta potential measurements. The X-ray diffractogram (see Fig.\u00a0S12) showed a mostly amorphous structure, interspersed with some crystalline domains (pyrolusite, akhtensite). The specific surface area (SSA) was determined to be 64.5\u2009\u00b1\u20090.2\u2009m2/g using the Brunauer-Emmett-Teller (BET) method. Measurement details as well as further specifications can be found in the Supplementary Information.\n\nThe wastewater sample taken from the WWTP in Lustnau (T\u00fcbingen, Germany) on September 9, 2024, had a pH value of 7.94. The dissolved organic carbon (DOC) measured as non-purgeable organic carbon (NPOC), was determined to be 11.6\u2009mg/L. Dissolved iron and manganese concentrations were both below the detection limit of 0.04\u2009mg/L (Mn) and 0.02\u2009mg/L (Fe). Supplementary Table\u00a0S6 summarizes the results of the wastewater sample characterization using IC with conductivity detection and MP-AES (for analytical methods see the Supplementary Information). Supplementary Table\u00a0S7 contains additional information on a 24\u2009h-mixed wastewater sample monitored by the WWTP Lustnau. The sampling point for the latter lies after the screen but before the grit chamber. 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Lett. 46, 1910\u20131921 (2013).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We thank Nora Simon, Hannah Arnold and Sara Wild (all University of T\u00fcbingen) for assistance in the laboratory, especially for sample derivatization. Furthermore, we thank Volker Karius from the University of G\u00f6ttingen for conducting the X-ray diffractograms of the used manganese dioxide and Markus Kramer from the University of T\u00fcbingen for conducting the NMR measurements. Mathis Athmer thanks The Chemical Industry Fund (FCI, Fonds der Chemischen Industrie, Germany) for his Kekul\u00e9 fellowship. This research was financially supported by the Germany Research Foundation (BU 782/2-1, D.B.; HA 3453/17-1, S.B.H.) and the University of T\u00fcbingen (PRO-MARTIN-2023-11; P.R.M).", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Philipp R. Martin\n\nPresent address: Division of Environmental Geosciences, Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria\n\nGeo- and Environmental Research Center, Department of Geosciences, University of T\u00fcbingen, T\u00fcbingen, Germany\n\nAnna M. R\u00f6hnelt,\u00a0Philipp R. Martin,\u00a0Daniel Buchner\u00a0&\u00a0Stefan B. Haderlein\n\nInstitute of Inorganic and Analytical Chemistry, University of M\u00fcnster, M\u00fcnster, Germany\n\nMathis Athmer\u00a0&\u00a0Uwe Karst\n\nInstitute of Physical and Theoretical Chemistry, Department of Chemistry, University of T\u00fcbingen, T\u00fcbingen, Germany\n\nSarah Bieger\u00a0&\u00a0Carolin Huhn\n\nInstrumental Analytical Chemistry and Center for Water and Environmental Research (ZWU), University of Duisburg-Essen, Essen, Germany\n\nTorsten C. Schmidt\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nA.M.R.: Conceptualization, Methodology, Validation, Formal Analysis, Investigation, Writing \u2013 Original Draft, Visualization, Project administration. P.R.M.: Conceptualization, Methodology, Validation, Funding acquisition, Writing \u2013 Review & Editing. M.A.: Methodology, Investigation, Formal Analysis, Writing \u2013 Review & Editing. S.B.: Investigation, Formal Analysis, Writing \u2013 Review & Editing. D.B.: Conceptualization, Funding Acquisition, Writing \u2013 Review & Editing. U.K.: Supervision, Writing \u2013 Review & Editing. C.H.: Conceptualization, Writing \u2013 Review & Editing. T.C.S.: Supervision, Writing \u2013 Review & Editing. S.B.H.: Conceptualization, Supervision, Resources, Writing \u2013 Review & Editing\n\nCorrespondence to\n Philipp R. Martin or Stefan B. Haderlein.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "R\u00f6hnelt, A.M., Martin, P.R., Athmer, M. et al. Glyphosate is a transformation product of a widely used aminopolyphosphonate complexing agent.\n Nat Commun 16, 2438 (2025). https://doi.org/10.1038/s41467-025-57473-7\n\nDownload citation\n\nReceived: 05 July 2024\n\nAccepted: 20 February 2025\n\nPublished: 11 March 2025\n\nVersion of record: 11 March 2025\n\nDOI: https://doi.org/10.1038/s41467-025-57473-7\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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b/b46b8d2eb3030c20fb151d48a71b46752fa49c1c102bc842028043ad26768782/metadata.json @@ -0,0 +1,141 @@ +{ + "title": "In-situ probing of the Fischer-Tropsch reaction on Co single crystal surfaces up to 1\u2009bar", + "pre_title": "Operando Probing of the Fischer-Tropsch Reaction on Co Single Crystal Surfaces up to 1 bar", + "journal": "Nature Communications", + "published": "24 January 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56082-8/MediaObjects/41467_2025_56082_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56082-8/MediaObjects/41467_2025_56082_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56082-8/MediaObjects/41467_2025_56082_MOESM3_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-56082-8#Sec15" + ], + "code": [], + "subject": [ + "Catalytic mechanisms", + "Heterogeneous catalysis" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3970719/v1.pdf?c=1737810324000", + "research_square_link": "https://www.researchsquare.com//article/rs-3970719/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-56082-8.pdf", + "preprint_posted": "02 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The surface chemistry of the Fischer-Tropsch catalytic reaction over Co has still several unknows. Here, we report an operando X-ray photoelectron spectroscopy study of Co(0001) and Co(1014), and operando high energy surface X-ray diffraction of Co(0001), during the Fischer-Tropsch reaction at 0.15 bar \u2212\u20091 bar and 406 K \u2212\u2009548 K in a H2/CO gas mixture. We find that the Co surfaces remain metallic under all conditions and that the coverage of chemisorbed species ranges from 0.4\u20131.7 monolayers depending on pressure and temperature. The adsorbates include CO on-top, C/-CxHy and various longer hydrocarbon molecules, indicating a rate-limiting direct CO dissociation pathway and that only hydrocarbon species participate in the chain growth. The accumulation of hydrocarbon species points to the termination step being rate-limiting as well. Furthermore, we demonstrate that the intermediate surface species are highly dynamic, appearing and disappearing with time delays after rapid changes in the reactants\u2019 composition.Physical sciences/Chemistry/Catalysis/Heterogeneous catalysisPhysical sciences/Chemistry/Green chemistry/SustainabilityFischer-TropschCobaltHydrogenationHeterogeneous Catalysis", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "CoFTsynthesisSupplementaryMaterials.docx", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The surface chemistry of the Fischer-Tropsch catalytic reaction over Co has still several unknows. Here, we report an in-situ X-ray photoelectron spectroscopy study of Co(0001) and Co(101\u00af4), and in-situ high energy surface X-ray diffraction of Co(0001), during the Fischer-Tropsch reaction at 0.15\u2009bar - 1\u2009bar and 406\u2009K - 548\u2009K in a H2/CO gas mixture. We find that these Co surfaces remain metallic under all conditions and that the coverage of chemisorbed species ranges from 0.4\u20131.7 monolayers depending on pressure and temperature. The adsorbates include CO on-top, C/-CxHy and various longer hydrocarbon molecules, indicating a rate-limiting direct CO dissociation pathway and that only hydrocarbon species participate in the chain growth. The accumulation of hydrocarbon species points to the termination step being rate-limiting also. Furthermore, we demonstrate that the intermediate surface species are highly dynamic, appearing and disappearing with time delays after rapid changes in the reactants\u2019 composition.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The Fischer-Tropsch (FT) reaction is an important industrial process, as it produces higher hydrocarbons from synthesis gas (syngas, \u22481:2 CO:H2 gas mixture) over Co, is an important industrial process1. The FT reaction was used during earlier time as a way to avoid oil embargos for some countries during World War II and the Apartheid regime in South Africa. In the current era it can become an important avenue for a sustainable chemical industry if CO is generated from CO2 via the reverse water gas shift reaction where the CO2 has been captured either directly from the atmosphere or at an intense carbon source. The hydrogen can be produced, not from the current steam reforming process of methane, but instead through electrolysis of water where the electricity is coming from a renewable source such as wind and solar. Presently, the Fischer-Tropsch reaction utilizes Fe, Ru or Co-based catalysts that yield different hydrocarbon distributions (i.e., with regard to the abundance of shorter or longer C-chains in the product stream). Depending on the material the reactions follow these main pathways:\n\nFurther, the created water can react with the CO by the water gas shift reaction:\n\nThe latter reaction creates more H2 at the expense of CO, but on Co catalysts it is not of significant importance thus the gas mixture of 1:2 CO:H2 is commonly employed1. The Co-based FT reaction typically generates long-chain hydrocarbons and waxes and operates at a temperature of 470-510\u2009K and pressures of a few tens of bars1.\n\nThe chemical state of the Co catalyst has previously been investigated through post-reaction analysis of single crystal surfaces to be in a metallic state2,3,4. However, bulk-sensitive measurements under high temperatures and pressures during operando of the FT reaction of Co nanoparticles have shown the existence of small amounts of oxides5,6,7,8,9,10. Furthermore, it has also been proposed that a partially oxidized Co catalyst can be responsible for a high activity11. Although no operando measurements during the FT reaction has detected any major presence of a Co carbide bulk phase it has been demonstrated that CoC2 nano prisms shows a high selectivity to olefin formation12. Recent in-situ surface sensitive measurements of the FT reaction on Fe show a growing carbide phase starting immediately after the reaction is initiated13 and on Ni at low temperatures dissolution of carbon into the bulk as a dilute carbide phase has been observed14. An open key question is if the state of the Co catalyst in the surface region remains fully in a metallic state or if surface oxide and near surface carbide can be present during the reaction conditions. Addressing this question necessitates detection using surface sensitive techniques performed while the reaction is turning over.\n\nThe reaction mechanism of the FT reaction consists of a sequence of elementary reaction steps15. The first step after CO adsorption is the dissociation of CO generating carbon monomeric species. Afterwards such C can both attach to other carbon atoms as well as adsorbed hydrogen and thus grow the hydrocarbon chain. The final step is the termination of the growth through the attachment to hydrogen atoms that results in enough weakening of the bond between the carbons and the surface, ultimately leading to desorption. The CO activation has resulted in two major hypotheses based on theoretical calculations: there is either a direct dissociation, often denoted carbide mechanism16,17, or hydrogen-assisted dissociation via the generation of a CHxO species18,19. It has been proposed that the hydrogenation of adsorbed C20 and the termination step are partly rate limiting21 as well as hydrogenation of atomic O and OH22,23. Here it would be essential to probe the adsorbates on the surface, to determine intermediates that accumulate as the reaction proceeds, as a pointer towards specific rate-limiting steps.\n\nAll chemical sensitive studies over the FT reaction of Co under operando conditions have been conducted with methods mostly probing the bulk, such as X-ray absorption spectroscopy (XAS) and X-ray powder diffraction (XRD)5,6,7,8,9,10. There have been efforts to detect adsorbed species with Infrared Spectroscopy but their observation exclusively showed adsorbed CO24 or hydrocarbons that were likely not on the Co surface25. Scanning tunneling microscopy (STM) have probed Co single crystal surfaces under FT at atmospheric conditions where the morphology of steps and terraces could be followed but without directsensitivity towards the reaction intermediates and adsorbates3,4. However, the observed smoothness of the surface in the STM studies indirectly infers that no large rearrangement of substrate atoms has occurred related to oxide or carbide formation. In one STM study conducted at 4\u2009bar and 492\u2009K on the Co(0001) surface stripes were observed during the FT reaction interpreted as the appearance of long chain hydrocarbon molecules26. A number of surface science studies of model molecules under vacuum have been conducted on Co single crystal surfaces22,27,28,29 but it is unclear if the model molecules are relevant for reactions occurring at many orders of magnitude higher pressures and temperatures.\n\nX-ray photoelectron spectroscopy (XPS) is a unique surface sensitive method to investigate the chemical state of catalytic surfaces and adsorbed intermediates through core-level shifts. The high inelastic scattering cross-section of photoelectrons in the gas phase makes vacuum conditions necessary. Post analysis with XPS has been conducted of Co single crystal surfaces that have been in a reactor with atmospheric pressure3,4 or 4 bar2, at temperatures where the reaction is turning-over, followed by evacuating the reactor to vacuum and then transferring the sample to the spectrometer chamber, where the measurement was conducted. Although adsorbed CO, adsorbed carbidic carbon and hydrocarbon species were observed it is unclear if species may decompose or desorb when the system is evacuated and the temperature reduced. Near-ambient XPS (NAPXPS) studies of Co foil have been restricted to 0.1 mbar23 \u2014 far from the conditions of atmospheric pressure where the FT reaction occurs. These studies have detected significant oxidation of the Co foil at low temperatures, while atmospheric pressure single crystal studies showed the production of methane and other short-chain alkanes and alkenes3,4,30.\n\nHere, we used an ambient-pressure XPS (APXPS) instrument called POLARIS operating at pressures up to 1\u2009bar for CO/H2 mixtures and as high temperatures as 506\u2009K. The POLARIS instrument is based on the virtual pressure cell, where we create a\u2009~\u200930 micron thick local high-pressure cushion and utilize grazing incidence of the incoming hard X-rays to provide surface sensitivity, despite high kinetic energy of the photoelectrons31. The combined effect of X-ray penetration depth and electron inelastic mean free path yields an effective inelastic mean free path comparable with laboratory XPS systems of about 1.4\u2009nm at the C 1\u2009s core-level and 1.3\u2009nm at the Co 2p core-level13. The virtual pressure cell is established by introducing a high-velocity gas jet onto the catalyst and building up a dynamic pressure, such that the gas in contact with the catalyst typically interacts roughly for times on the millisecond scale. This in turn brings the FT reaction over Co into a early steady state, far away from the chemical equilibrium, with low concentrations of products in the effluent gas stream. We have used flat Co(0001) and stepped Co(101\u00af4) single crystal substrates that have been shown previously to turn-over the FT reaction towards mainly methane but also minor fractions of C2+ hydrocarbon species at close to 1\u2009bar and 500 K4. Since the FT reaction is known to be structure sensitive32,33,34,35 (i.e. a Co stepped crystal, Co(101\u00af15) gives much higher turn-over than the terrace surface3) we have thus directly compared the Co(0001) with the Co(101\u00af4) surface to elucidate the influence of steps on the reaction. In particular, the size-dependent effects that show high activity for certain size nanoparticles have in the 2010s been shown to be linked to the relative abundance of B5 sites that appear at the intersection of threefold and fourfold coordinated sites34,36,37,38,39. These sites can be found on the Co(101\u00af4) stepped surface and thus further the explanation why stepped surfaces are observed with higher activities (See Supplementary Information\u00a0S6 and Supplementary fig.\u00a09). As FT reactions have been demonstrated at the same conditions as in the current study, we will denote the experiments as in-situ. Furthermore, we show the facile appearance and disappearance of CxHy adsorbates as seen in the last subheading in Results and Discussion as indicators of a state where the reaction should occur. Our complementary in-situ surface X-ray diffraction experiments yield atomic surface structure information under reaction conditions.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "First, we address the chemical state of Co: whether it is metallic, oxidic or carbidic in the near-surface region. This information would not necessary be observable in bulk-sensitive measurements, as this active phase exists only close to the surface (i.e. the first few monolayers). Figure\u00a01a-c shows the in-situ Co 2p3/2, C 1\u2009s, and O 1\u2009s signal of the Co(0001) catalyst at a pressure of 1\u2009bar with a reaction mixture of 1:2 CO:H2 and a temperature of 406\u2009K and 506\u2009K (the higher corresponding to the typical FT high yield conditions) measured in the POLARIS instrument at a photon energy of 4600\u2009eV (for samples, gases and experimental setup turn to Methods). In this work we apply the following procedure to justify the application of a peak in the spectra: The peak needs to be clearly visible in at least one instance of our measurements to be appended to the peak model. These individual observations we compile into a global fit model which we use for all spectra. We apply this global fit model to each series of spectra such that the defining peak shapes are common to all spectra as described below.\n\nSurface state of Co(0001) sample at indicated temperatures in an atmosphere of CO:H2 1:2 at 1\u2009bar studied by hard X-ray photoelectron spectroscopy (HAXPES) utilizing 4600\u2009eV photons at 0.3\u00b0 incidence. a shows Co 2p3/2 core-level spectra b displays the C 1\u2009s region and c depicts the O 1\u2009s region. Subplots d, e, f show the same conditions as a, b, c but for a Co(101\u00af4) surface. The columns of XPS data have constant scaling of the vertical axis. Subplot g shows a representative figure of the high energy surface X-ray diffraction HESXRD data at 67.4\u2009keV of the Co(0001) crystal (full set is shown in SI). The detector is protected by beam stops at the bulk Bragg peak positions. h X-ray structure factor extracted from the 2D diffraction data shown in g at reaction conditions at 496\u2009K and 1\u2009bar reaction mixture (1:2 CO:H2), data from the hcp part of the surface used for the fit (orange circles with vertical lines indicating an estimation 10% relative error), data from the fcc part (grey circles), fit result (solid line). Source data are provided as a Source Data file.\n\nThe Co 2p3/2 spectrum is composed of a single peak at 778.0\u2009eV that shows a completely metallic state with no shoulder at 780.0 eV40 that would indicate an oxide. A carbide modification of Co would be expected at somewhat higher binding energy compared to the Co0 metal peak if the shift follows as seen in Ni 2p3/2 upon carbon dissolution into bulk Ni14 and the formation of Fe carbides13. The peaks in the C 1\u2009s region at 285.7\u2009eV and in the O 1\u2009s region at 531.7\u2009eV (blue) correspond to adsorbed CO in top position29. The C 1\u2009s feature at 284.1\u2009eV at 506\u2009K is related to hydrocarbon species and exemplifies the reaction intermediates. Other states are only observable at 406\u2009K which will be further discussed below. A carbide species would be seen at around 283.0\u2009eV and oxides at around 529.3 eV41 and none are detected in the spectra.\n\nThe stepped Co(101\u00af4) surface is probed at the same conditions as the Co(0001) and the results there of are presented in Fig.\u00a01d-f for the Co 2p3/2, C 1\u2009s, and O 1\u2009s core-levels. Co is metallic during the reaction also on this facet. Adsorbates in the C 1\u2009s region, however, show a striking difference. Peaks at 284.1\u2009eV and 284.7\u2009eV are now observed at 406\u2009K and as well at 506\u2009K. Also, there is intensity in the region of 283.5\u2009eV for both temperatures. Overall, the total C coverage is higher than for the flat surface. The O 1\u2009s intensity again shows only significant contributions of COtop adsorbates at 506\u2009K and additionally intensity in the 533.0\u2009eV region at 406\u2009K. The origin of the 533.0\u2009eV peak is still an open question. Since we observe no changes in the C 1\u2009s spectrum, which follow the variation of the 533.0\u2009eV peak we can exclude an origin in oxygenated carbon-containing species. Chemisorbed water, either from contaminations in the purified gas or as a product of the FTS reaction, are other hypotheses which agree with the binding energy value of the peak. The known rapid desorption kinetics of water from Co surfaces\u2014which has been observed even at temperatures as low as 170K42\u2014decreases the likelihood of these hypotheses.\n\nAs XPS measurements are sensitive to the chemical state we have completed this data set with structure-sensitive high energy surface X-ray diffractometry (HESXRD) on a Co(0001) single crystal at 200\u2009mbar and 456\u2009K with 1:2 CO:H2 under flow conditions (further conditions are shown in Supplementary Information\u00a0S1). In Fig.\u00a01g we display a maximum intensity per pixel from an angular scan rotating the sample around the surface normal in the range of the Co(0001) (1,0) crystal truncation rod (full experimental details are given in Methods). Our key observation is the appearance of a single surface rod at (1,0) reciprocal lattice units that indicates an unreconstructed hcp surface. A more detailed analysis shows that the behavior at partial pressures of 200\u2009mbar and 1\u2009bar of CO:H2 1:2 mixtures the surfaces do not reconstruct (see Supplementary Information\u00a0S1), which is in line with previous findings of operando STM observations4. No indication for the formation of ordered carbide formation is found. In Fig.\u00a01h we present the X-ray structure factor extracted from the data in g. From the fit we can deduce, that the surface is atomically smooth under all gas mixtures studied with a slight inward relaxation of the topmost layer of ~0.04\u2009\u00c5. The fit improves by including CO molecules on the surface, but due to the small data set available, the occupancies and position could not be further refined. The full data set gives also evidence, that a few percent of the surface is fcc (111) terminated. Due to the low number of fcc-terminated sites the contribution from fcc can therefore be neglected for the XPS data analysis.\n\nWe can thereby conclude that on a Co(0001) single crystal at 1\u2009bar and around 500\u2009K during the operation of the FT reaction the Co surface remains fully metallic and retains an ordered, flat surface which exhibits considerable crystal truncation rod signal. Furthermore, there are no signs of a surface carbide or surface oxide indicating that the C 1\u2009s and O 1\u2009s spectral intensities are related to chemisorbed species.\n\nWhen inspecting the XPS spectra from the adsorbate at different conditions we have curve-fitted the data into specific components. Since many different conditions in terms of pressure and temperature are measured an assigned spectroscopic component should at least be clearly visible as a peak or strong shoulder in one spectrum. The chemical assignment is based either on experimental spectra obtained from model compounds in ultrahigh vacuum (UHV) on Co(0001)27,29,43,44 or on density functional theory (DFT) binding energy calculations (Supplementary Information\u00a0S4). The binding energy scale potentially could differ by twotenths of an eV due to recoil effects at high kinetic energies that depends on the bonding strength (for discussion the reader is referred to Supplementary Information\u00a0S2.d), however, we estimate these effects to be negligible.\n\nFigures\u00a02a and 2b shows the C 1\u2009s spectra from the reaction of CO and H2 with a mix ratio of 1:2 at a pressure of 500\u2009mbar and 200\u2009mbar, respectively, at temperatures in the range of 406\u2009K to 523\u2009K over Co(0001). The corresponding O 1\u2009s spectra are shown in the Supplementary Information\u00a0S2.a. We assign the 283.2\u2009eV (green) feature to chemisorbed C or CH on the surface based on XPS spectra obtained from either decomposition of ethylene on Co(0001) as observed at 283.2 eV43,44 or at 282.8 eV29 and seen in exposure of CO and H2 at 4\u2009bar followed by evacuation at 283.3 eV2. The DFT calculation (Supplementary Information\u00a0S4) gives a binding energy of 283.1\u2009eV for adsorbed C, (energy scale corrected against experimental value of adsorbed CO in on-top position), whereas adsorbed CH has a somewhat lower value of 283.0\u2009eV. With only such a small difference in C 1\u2009s binding energy between adsorbed C and CH and since there is a variation of the experimental value between 282.8 \u2013 283.3\u2009eV it is not possible to distinguish the two adsorbates we thus denote this peak C/CH at 283.2\u2009eV (green).\n\nWe show Co(0001) at a 500\u2009mbar and b 200\u2009mbar. In c we show 150\u2009mbar on Co(101\u00af4). All mixtures are 1:2 CO:H2. Subplot d shows a comparison of 406\u2009K data as function of pressure on the Co(0001) crystal, and subplot e displays a direct comparison of stepped Co(101\u00af4) and flat Co(0001) crystals at 406\u2009K. All y-axes have the same scaling. Data has been normalized according to SI Section S6c. The color code is the same as in Fig.\u00a01b/e. Source data are provided as a Source Data file.\n\nThe component observed at 283.5\u2009eV (yellow) we assign to chemisorbed CH2 species based on DFT calculations (see Supplementary Information\u00a0S4). This binding energy has previously been reported as related to the CH3 group in ethylidyne29 but spectra of the chemisorbed ethylidyne molecule also include a peak corresponding C to the group bonded to the surface at 282.9\u2009eV. Since we observe the 283.5\u2009eV feature at several conditions without any low binding energy component the 283.5\u2009eV peak cannot be associated with ethylidyne. With a similar argument, the 283.5\u2009eV feature cannot be one of the carbons in adsorbed ethylene, where the adsorption site generates two inequivalent C atoms, since then it should be accompanied by a second carbon peak at 283.9 eV29. Next component, located at 284.1\u2009eV (light red) we assign to hydrocarbon fragments, such as -CH2- and -CH3 groups on the surface based on DFT calculations and previous post-analysis experiments2. While some part of these hydrocarbon chains are in contact or in the proximity of the surface through undersaturated monomers, fully saturated parts are most likely pointing away from the surface and would correspond to the 284.7\u2009eV (light blue) feature45. The energy difference between the initial and final states in a photoionization event is much smaller when the hydrocarbon group is directly bonded to the surface allowing for metallic screening of the core-hole state resulting in a lower binding energy for the parts of the hydrocarbon in direct contact with the surface (light red peak) than for the parts pointing away from the surface (light blue)45. Lastly, a clear peak originating from CO adsorbed in on-top configuration, denoted COtop, is observed at 285.7\u2009eV (dark blue). All these species are exemplarily depicted in the Supplementary Information in section S7. We find no indication of CO at other adsorption sites, such as the bridge or hollow sites, as commonly reported in UHV studies at liquid nitrogen temperatures22 or in AP-XPS studies at ~1000x lower pressures46, yet we observe only COtop in our experiments. The O 1\u2009s spectra contains mostly a feature associated with adsorbed CO in on-top position (see also Supplementary Information\u00a0S2.a). No clear indication of any significant amount adsorbed O, OH, CHO, COH or CH3O species are detected on these single-crystal surfaces.\n\nFigure\u00a02a shows the C 1\u2009s spectra at a pressure of 500\u2009mbar from 406\u2009K to 523\u2009K on Co(0001). The coverage of the adsorbates has been determined through a specific normalization procedure (See Supplementary Information\u00a0S2.c). We observe the largest total coverage of carbon containing species at the lowest temperature of 406\u2009K corresponding to 1.5\u2009ML with the -CH2- peak (yellow) clearly dominating the spectrum, but also intensity is observed in the region of non-screened hydrocarbon chains (light blue). A \u201cmonolayer\u201d is here defined relative to the surface atoms of the Co substrate. What is clearly noted is that the total coverage decreases with increasing temperature to below monolayer coverage for T\u2009>\u2009480\u2009K. We observe at 406\u2009K a large amount of the hydrocarbon species with C atoms both bonded to the surface and with CH2 and CH3 groups away from the surface as well as surface bound CH2 groups. As we reach the highest temperature of 523\u2009K there is almost only CO on the surface and some small amount of adsorbed C/CH. Chain growth requires a considerable coverage of carbon species which are not fully saturated by hydrogen, which on the (0001) surface occurs below 485\u2009K. This process can consequently occur at the lower temperature where there is a higher coverage of CH2 groups and various adsorbed hydrocarbon species.\n\nFigure\u00a02b shows the same trend of temperatures but with a total pressure of 200\u2009mbar on Co(0001). At the lowest temperature, we have an almost similar total coverage of 1.3\u2009ML. We notice the same trend where the amount of species decreased with increasing temperature. What is mainly different is that the amount of CH2-adsorbed species is now much higher in comparison to hydrocarbon molecules. The chain growth becomes less efficient with lower coverage. Again, the CO coverage (\u2009~\u20091/3\u2009ML) is almost independent of temperature.\n\nFigure\u00a02c shows the trend with the stepped Co(101\u00af4) surface at total pressure of 150\u2009mbar. In general, we again observe an almost constant CO coverage but an increase in the hydrocarbon content and decrease of CH2 adsorbed species indicating more efficient chain growth at steps compared to terraces. The total coverage at the lowest temperature of 425\u2009K is 1.7\u2009ML and compares well with our previous observations on the Co(0001) surface. The observation of coverages above 1\u2009ML signifies that at these conditions we expect an amount of C on the surface able to cover more than all Co surface sites. The increase of atoms on the surface is due to the appearance of hydrocarbon chains linked to the surface but sticking out into the gas phase.\n\nFigure\u00a02d shows a comparison of spectra at different pressure on the Co(0001) surface at the FT temperature of 406\u2009K. We observe an increase in the total coverage of adsorbed hydrocarbon species on the surface going to 1\u2009bar, however, the relative distribution of different molecular fragments is somewhat similar at this low temperature. We can relate that the production of hydrocarbon at this temperature is limited by the desorption of products and only becomes more efficient to a smaller degree with the increase in pressure since the surface is blocking its active sites due to kinetic hindrance.\n\nFigure\u00a02e shows a comparison of the C 1\u2009s spectra at the temperature of 506\u2009K between the flat Co(0001) and the stepped Co(101\u00af4) surfaces. At this temperature, the catalyst is expected to be active for the FT reaction. There is a striking increase in the hydrocarbon content with the presence of steps. It is interesting to note that also the production of methane and minor hydrocarbon species increased by almost an order of magnitude between flat and stepped Co surfaces in a recent STM reactor study3. This increased reactivity was attributed to the lowering of the energy barriers for a rate limiting CO dissociation, and the increased hydrocarbon presence at all examined temperatures on the stepped crystal is fully consistent with this hypothesis3.\n\nOur data supports the view that CO dissociates most efficiently on the steps through a direct dissociation route and not the hydrogen-assisted mechanism. The direct route hypothesis is strengthened by not observing any CHO species on the surface that would be visible at 285.0\u2009eV and 530.1\u2009eV (O 1\u2009s spectra see: Supplementary Information\u00a0S2.a). Although a weak component at the C 1\u2009s position could possibly be overlapping with adsorbed CO and hydrocarbon species, but there is no appreciable intensity at the low binding energy in the O 1\u2009s spectra47. Furthermore, ultrafast measurements using X-ray lasers have demonstrated that the CHO species could only exist in an extremely short lived transient regime with a life time of only a few picoseconds and could never build up any appreciable coverage during steady-state reaction conditions47. We do not observe any significant CH2O species with a calculated binding energy of 286.5\u2009eV and 530.2\u2009eV or CH3O species at 286.5\u2009eV and 531.2\u2009eV (O 1\u2009s spectra see: S2.a) pointing to that non-dissociated CO does not significantly contribute to the chain growth. Finally, as the coverage of adsorbed hydrocarbon species with more than two attached hydrogens per carbon is quite high the hydrogenation termination step leading to desorption would also be rate limiting. We therefore predict that both CO dissociation and the final hydrogenation leading to desorption is rate limiting under the current conditions on the stepped surface. On the flat surface the different hydrocarbon hydrogenation steps seem to be limiting as well, indicating an overall less active surface.\n\nFigure\u00a03 depicts the time dependence of the C 1\u2009s spectra related to the FT reaction of the Co(0001) surface by applying and removing CO while keeping a constant H2 flow on the sample at 200\u2009mbar total pressure. We performed experiments at 406\u2009K (panel a with line extracts shown in b) and at 506\u2009K (panel c with line extracts shown in d). At 406\u2009K the sample surface is covered with a tiny amount of species at 284.5\u2009eV initially. Upon exposure with CO there immediately appears intensity in the CH2 and COtop regions (283.5 and 285.7\u2009eV, respectively), indicating that some CO is dissociated and hydrogenated. Over an interval of approximately 30\u2009min there is a continuous growth of the 284.5\u2009eV state, indicating the appearance of longer chain hydrocarbons due to chain growth. Upon removal of the CO in the reaction mixture this component remains for a certain time while COtop and CH2 are reacted away within the time resolution of this experiment. Continuing in this configuration, the 284.5\u2009eV hydrocarbon peak intensity reduces, indicating facile reaction and departure from the surface aided by the presence of H2, which supports our claim that a reaction is ongoing. We are here observing the rate limitation of the final hydrogenation step that removes the hydrocarbon species on the surface. A competing reaction to the hydrocarbon chain growth is the fast CH2 hydrogenation into methane, which also explains the swift vanishing of the CH2 contribution upon CO gas removal from the reaction mixture27.\n\na 2D time-resolved spectrum of the C 1\u2009s region at 406\u2009K (c for 506\u2009K) total pressure under CO flow is 200\u2009mbar with a 1:2 CO:H2 mixture. In the beginning and end the sample is subjected to a H2 flow alone. The lines on the right are extracted from a and b at indicated times and resemble these reaction conditions from bottom to top: pure H2, first 6\u2009minutes under CO:H2 mixture, last 6\u2009min under CO:H2 mixture, only H2 after removal of CO from the mixture (red) and at the end of the experiment after at least 15\u2009min in H2(light blue). b & d: Spectra recorded at indicated times. Decomposition follows the Colo scheme of Fig.\u00a01&2. Background: in hydrogen (cream), in 1:2 CO:H2 (light blue). Source data are provided as a Source Data file.\n\nAt 506\u2009K, we observe mainly low COtop surface coverage and only to a negligible degree CxHy species as compared to the 406\u2009K experiment during reaction conditions, which is in line with the trends in the static measurements shown in Fig.\u00a02. After CO is removed from the reaction mixture the intermediate hydrocarbons desorb or react away and the corresponding peak diminishes to baseline intensity. The dynamic response is much faster at the higher temperature. From these temperatures we derive that the surface is highly dynamic (i.e. turning over and in in-situ) and that changes in the conditions needs time to establish a steady state. Moreover, we observe that the rate limiting step changes from the formation of carbon chains at lower temperatures to the dissociation of CO at higher temperatures.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56082-8/MediaObjects/41467_2025_56082_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56082-8/MediaObjects/41467_2025_56082_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56082-8/MediaObjects/41467_2025_56082_Fig3_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We have studied the two Co single crystal surfaces of (0001) and (101\u00af4) using in-situ XPS at almost 1000 times higher pressures than traditional NAPXPS and can directly probe adsorbates on the surface during the reaction. The C 1\u2009s and O 1\u2009s spectra shows only adsorbed species even at pressures close to 1\u2009bar and the Co 2p3/2 spectra have no sign of an oxide or a carbide component. The surface X-ray diffraction results on Co(0001) demonstrate that the surface stays atomically smooth under reaction conditions. Thereby, there is no indication of any chemical or structural changes of the Co substrate surface region as the reaction proceeds. Our observations tip the scales in the discussion regarding the nature of the CO dissociation towards the direct or often denoted carbide mechanism since no sign of hydrogen-assisted dissociation in terms of CHO-detected species in the C or O 1\u2009s spectra. Furthermore, the chain growth involves only hydrocarbon species, since undissociated CO participation should show up as detected CH2O spectral components, which we did not observe. There are also no ethylidene or adsorbed ethylene intermediates detected pointing to simple chain growth of -CHx- species resulting in an increasing amount of hydrocarbon species with groups both bonded directly to the surface but also pointing away towards the gas phase. Several of the current observations in terms of adsorbed CO and presence of hydrocarbon species on the surface have also been seen in the previous NAP study conducted at 100-1000 times lower pressures46. However, the higher pressure condition here also resulted in major differences as evidenced by the fact that CO adsorbs in the top site only, significantly smaller C/CH coverage, CxHy species observed already at 406\u2009K, no oxidation of the surface and no buildup of C at higher temperatures. We notice that the CO dissociation is more facile on the stepped Co single crystal surface. Due to the fast gas exchange rate in the virtual cell (See Supplementary Material section S2.f) accumulation of products in the gas phase and consequentially their reabsorption to the surface are of little significance. On the whole, the increased abundance of hydrocarbon species at 406\u2009K on both surfaces, shows that a reaction is ongoing, yet the partly hydrogenated intermediates are not leaving the surface as rapidly as they are being produced. We thus argue that the final termination step in terms of hydrogenation (that is exchanging the carbon bond with a local C-H bond) is rate-limiting in this regime. At a temperature of 506\u2009K the coverage of hydrocarbons on the surface is lower which indicates that the desorption of products is not limiting the reaction anymore. Since we observe an almost constant CO coverage in all our temperatures the availability of CO is also not a limit in this reaction. Thus, we infer that at this elevated temperature, the CO dissociation limits the rate of the reaction to a larger degree. It has been proposed that also the removal of oxygen atoms is a rate-limiting step2,46 but in the current study no adsorbed oxygen was detected. We associate this lack of oxygen compared to the previous studies as most likely due to higher hydrogenation activity in in-situ studies at high pressures. Finally, our observation of the Co-based Fischer-Tropsch reaction is highly dynamic meaning that the involved species (despite a potentially long residence time) show changing adsorbate compositions as a direct consequence of changes in the reactant mixtures. The time for the delay is strongly temperature dependent and can be on the tens of minutes scale.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The experiments were performed on two hat-shaped cobalt crystals (N4.7) with the flat (0001) and the stepped (101\u00af4) direction exposed on the surface (Ra<30\u2009nm, miscut <0.1\u00b0) with a top surface of 7\u2009mm diameter (Surface Preparation Laboratory, SPL). It is worth noting that the stepped crystal exposes a high density of B536 sites that are regarded as an active site for the Co-based Fischer-Tropsch reaction20. The gases were obtained with a purity of 5\u2009N for all gases except CO where the purity available could not exceed 4.7\u2009N. For the X-ray photoelectron spectroscopy (XPS) experiments the gases were purified in the appropriate apparatus (SAES Getters/Entegris). Under the measurements we recorded the temperature at the back of the sample with a type N thermocouple. Sample front temperature has been calibrated under various gas loads for a comparable specimen with a thermocouple spot-welded to the front. We expect that our temperature measurements agree to be better than \u0394T<15K.\n\nThe POLARIS setup is placed at beamline P22, DESY. The spectrometer utilizes a virtual cell approach, where a gas stream is directed onto a flat specimen and X-rays reach the interaction volume under grazing incidence conditions of 0.3\u00b031. In this way a radially symmetric local high-pressure cushion is formed (d\u2009~\u20092\u2009mm), while the rest of the chamber experiences much lower pressures (i.e. when the pressure in the probed volume is ~1000\u2009mbar the chamber pressure is 10\u2009mbar). The excited electrons are collected at distances of approximately 30\u2009\u00b5m by a line array of circular apertures, well matching the stretched-out X-ray footprint due to the grazing incidence geometry.\n\nFor the XPS experiment we used a double bounce monochromator with Si(311) crystals tuning the photon energy to 4600\u2009eV. The electron analyzer was used with an 800\u2009\u00b5m curved slit and a pass energy of 100\u2009eV. The total energy resolution where the expected photon energy bandwidth and the electron analyser resolution is folded together account for \u0394EFWMH\u2264300meV. The beamline optics use a horizontally bent elliptical mirror and a vertical cylindrical focusing mirror to achieve a beam footprint on the order of 15\u00d715\u03bcm2, which was measured with a polished YAG crystal at regular intervals during the experiment.\n\nDue to the specific design of this experimental setup (i.e. an outward-flowing gas jet), no contamination from the heater or chamber can reach the sample surface when gas flow is applied. The pressure in the virtual pressure cell was estimated from a calibration done by a previously used method31 (See Supplementary Information\u00a0S5).\n\nAll XPS spectra were acquired in an add-dimension mode indicating that a list of short C 1\u2009s, O 1\u2009s, Co 2p3/2 spectra are recorded repeatedly and summed together for statistics. This method allows to distribute slow surface changes under the reactions into all three spectra in similar weights such that distortions between spectra are expected to be negligible and developments can be traced quasi simultaneously in several core-levels.\n\nAll spectra are recorded with a binding energy calibration to the Fermi edge of a clean metal. A correction for the adsorbate binding energies due to the recoil effect has been neglected (see Supplementary Information\u00a0S2d). The spectra were first converted to counts per second and were plotted in each panel with offsets for visibility. For comparison, we normalized the spectra as described in Supplementary Information\u00a0S2c. We observe 4 distinct peaks in O 1\u2009s spectra. These are observed at 532.5\u2009eV, 531.7\u2009eV, 530.2\u2009eV, and 529.1\u2009eV. The first peak is unassigned and the second peak assigned to COtop, whereas the last two are tentatively assigned to chemisorbed O and OH states, which, however, are not observed in an appreciable quantity. In the C 1\u2009s spectra we notice 5 distinct features observed at 285.7\u2009eV, 284.7\u2009eV, 284.1\u2009eV, 283.5\u2009eV and 283.2\u2009eV.\n\nThe quantification of the peaks is performed by applying a global fit model that finds a maximum likelihood optimization using the Levenberg-Marquardt algorithm of the peak positions and shapes for all spectra of a set. The constraints of the fitting parameters are given in Table\u00a01 (for the C 1\u2009s region), Table\u00a02 (for the O 1\u2009s region) and Table\u00a03 (for the Co 2p region). The background estimation has been performed by subtracting a Shirley function where the Shirley parameter is a free variable in every spectrum48.\n\nThe surface X-ray diffraction (SXRD) experiment49 was performed at beamline P21.2 at DESY using a beam with ~3\u00d710\u2009\u00b5m (VxH) FWHM, an energy of 67.4\u2009keV and a glancing angle of 0.05\u00b050,51. The detector (VAREX XRD 4343CT, 150\u2009\u00b5m pixel size) was placed 1.4\u2009m away from the interaction zone. The sample temperature was controlled using a BN-encapsulated graphite heater. The gases of 5\u2009N purity for Ar, and H2 and 4.7\u2009N purity CO (same purities as in the XPS experiment) were delivered to an X-ray transparent Be dome and therein directed onto the same Co(0001) single crystal as for the XPS experiment. The CO gas was additionally purified from Ni(CO)4 using a copper carbonyl trap. The experimental setup is in details described elsewhere49. Scans were taken over a range of 105\u00b0.\n\nPrior to the reaction studies the Co(0001) and Co(101\u00af4) samples were cleaned by established procedures of repeated sputtering (1\u2009keV, ~1\u2009\u00b5A, 30\u2009min) and annealing (523\u2009K, 30\u2009min) processes in ultra-high vacuum. In the XPS experiments the single crystals were also chemically cleaned by dosing of 0.16 liter per minute O2 for 5\u2009s followed by annealing in 1 liter per minute H2 at a sample distance of 30\u2009\u00b5m and 473\u2009K. In between each measurement the samples were kept in H2 flows greater or equal 0.16lpm (p\u2009<\u200950\u2009mbar, negligible scattering in gas) to limit contamination from the vacuum environment, while we evaluated the surface cleanliness of C 1\u2009s, O 1\u2009s, S 1\u2009s, Si 1\u2009s containing species. The intensity sum of these was kept below 5at% of the Co 2p3/2 signal. 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The DFT calculations were performed using resources provided by the Swedish National Infrastructure for Computing (SNIC) at the HPC2N center. The authors would like to acknowledge the help of the P22 beamline engineer Katrin Ederer, and the Technical Division at Stockholm University.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open access funding provided by Stockholm University.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Peter Amann\n\nPresent address: Eduard-Zintl-Institute of Inorganic and Physical Chemistry, Technical University of Darmstadt, Peter-Gr\u00fcnberg-Str. 8, 64287, Darmstadt, Germany\n\nLeon Jacobse\n\nPresent address: Department of Interface Science, Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, 141 95, Berlin, Germany\n\nDepartment of Physics, Stockholm University, 10691, Stockholm, Sweden\n\nPatrick L\u00f6mker,\u00a0David Degerman,\u00a0Christopher M. Goodwin,\u00a0Mikhail Shipilin,\u00a0Peter Amann,\u00a0Gabriel L. S. Rodrigues,\u00a0J\u00f6rgen Gladh,\u00a0Hsin-Yi Wang,\u00a0Markus Soldemo,\u00a0Alexander Holm\u00a0&\u00a0Anders Nilsson\n\nWallenberg Initiative Materials Science for Sustainability, Department of Physics, Stockholm University, 114 28, Stockholm, Sweden\n\nPatrick L\u00f6mker\u00a0&\u00a0Anders Nilsson\n\nPhoton Science, Deutsches Elektronen-Synchrotron DESY, 22607, Hamburg, Germany\n\nPatrick L\u00f6mker,\u00a0Fernando Garcia-Martinez,\u00a0Zoltan Hegedues\u00a0&\u00a0Christoph Schlueter\n\nALBA Synchrotron Light Facility, Carrer de la Llum 2-26, 08290, Cerdanyola del Vall\u00e9s, Barcelona, Spain\n\nChristopher M. Goodwin\n\nLehrstuhl f\u00fcr Physikalische Chemie, Montanuniversit\u00e4t Leoben, 8700, Leoben, Austria\n\nRaffael Rameshan\n\nPULSE Institute, SLAC National Accelerator Laboratory, Menlo Park, 94305, California, CA, USA\n\nJ\u00f6rgen Gladh\u00a0&\u00a0Alexander Holm\n\nLaboratory of Organic Electronics, Department of Science and Technology (ITN), Link\u00f6ping University, SE-60174, Norrk\u00f6ping, Sweden\n\nAlexander Holm\n\nCentre for X-Ray and Nanoscience CXNS, Deutsches Elektronen-Synchrotron DESY, 22607, Hamburg, Germany\n\nSteffen Tober,\u00a0Jan-Christian Schober,\u00a0Leon Jacobse,\u00a0Vedran Vonk,\u00a0Robert Glei\u00dfner,\u00a0Heshmat Noei\u00a0&\u00a0Andreas Stierle\n\nPhysics Department, University of Hamburg, 20148, Hamburg, Germany\n\nSteffen Tober\u00a0&\u00a0Andreas Stierle\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nP.L. with input from A.N. planned the experiments at Petra III. P.L., D.D., Mi.Sh., Ma.So, F.G.M., C.M.G., J.G., H.-Y.W., A.H., R.R., A.S., C.S., A.N., P.A. participated in the XPS experimental work, while Z.H., A.S., H.N., V.V., J.-C.S., R.G., S.T. and L.J. participated in the SXRD measurements. P.L. extracted and plotted the SXRD data and A.S. fitted it. G.L.S.R. performed theoretical calculations. P.L. and Mi.Sh developed the data analysis software and P.L. did the data analysis. P.L. and AN wrote the manuscript. All authors contributed to the literature research, result discussion and manuscript improvement.\n\nCorrespondence to\n Patrick L\u00f6mker or Anders Nilsson.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Bert Weckhuysen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "L\u00f6mker, P., Degerman, D., Goodwin, C.M. et al. In-situ probing of the Fischer-Tropsch reaction on Co single crystal surfaces up to 1\u2009bar.\n Nat Commun 16, 1005 (2025). https://doi.org/10.1038/s41467-025-56082-8\n\nDownload citation\n\nReceived: 25 March 2024\n\nAccepted: 06 January 2025\n\nPublished: 24 January 2025\n\nVersion of record: 24 January 2025\n\nDOI: https://doi.org/10.1038/s41467-025-56082-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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in Ultra Low-Data Regimes", + "journal": "Nature Communications", + "published": "14 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://challenge.isic-archive.com/data/", + "https://www.fc.up.pt/addi/ph2", + "http://db.jsrt.or.jp/eng.php", + "https://www.kaggle.com/datasets/anasmohammedtahir/covidqu", + "http://archive.nlm.nih.gov/repos/chestImages.php", + "https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset?select=Dataset_BUSI_with_GT", + "https://www.ucl.ac.uk/interventional-surgical-sciences/fetoscopy-placenta-data", + "https://www.ucl.ac.uk/interventional-surgical-sciences/weiss-open-research/weiss-open-data-server", + "https://datasets.simula.no/kvasir/", + "https://www.kaggle.com/datasets/balraj98/cvcclinicdb", + "https://github.com/uwm-bigdata/wound-segmentation/tree/master", + "https://www.kaggle.com/datasets/zeeshanahmed13/intraretinal-cystoid-fluid", + "https://github.com/AlaaLab/ETAB/tree/main", + "https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2", + "/articles/s41467-025-61754-6#Sec34" + ], + "code": [ + "https://github.com/importZL/GenSeg", + "https://zenodo.org/records/15427671", + "/articles/s41467-025-61754-6#ref-CR71", + "/articles/s41467-025-61754-6#ref-CR72" + ], + "subject": [ + "Cancer imaging", + "Machine learning", + "Medical imaging", + "Software" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4982456/v1.pdf?c=1752577562000", + "research_square_link": "https://www.researchsquare.com//article/rs-4982456/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61754-6.pdf", + "preprint_posted": "05 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Semantic segmentation of medical images is pivotal in applications like disease diagnosis and treatment planning. While deep learning has excelled in automating this task, a major hurdle is the need for numerous annotated segmentation masks, which are resource-intensive to produce due to the required expertise and time. This scenario often leads to ultra low-data regimes, where annotated images are extremely limited, posing significant challenges for the generalization of conventional deep learning methods on test images. To address this, we introduce a generative deep learning framework, which uniquely generates high-quality paired segmentation masks and medical images, serving as auxiliary data for training robust models in data-scarce environments. Unlike traditional generative models that treat data generation and segmentation model training as separate processes, our method employs multi-level optimization for end-to-end data generation. This approach allows segmentation performance to directly influence the data generation process, ensuring that the generated data is specifically tailored to enhance the performance of the segmentation model. Our method demonstrated strong generalization performance across 9 diverse medical image segmentation tasks and on 16 datasets, in ultra-low data regimes, spanning various diseases, organs, and imaging modalities. When applied to various segmentation models, it achieved performance improvements of 10-20% (absolute), in both same-domain and out-of-domain scenarios. Notably, it requires 8 to 20 times less training data than existing methods to achieve comparable results. This advancement significantly improves the feasibility and cost-effectiveness of applying deep learning in medical imaging, particularly in scenarios with limited data availability.Health sciences/Medical researchBiological sciences/Computational biology and bioinformatics/Machine learningMedical image segmentationGenerative AIUltra low-data regimesEnd-to-end data generation", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Semantic segmentation of medical images is pivotal in applications like disease diagnosis and treatment planning. While deep learning automates this task effectively, it struggles in ultra low-data regimes for the scarcity of annotated segmentation masks. To address this, we propose a generative deep learning framework that produces high-quality image-mask pairs as auxiliary training data. Unlike traditional generative models that separate data generation from model training, ours uses multi-level optimization for end-to-end data generation. This allows segmentation performance to guide the generation process, producing data tailored to improve segmentation outcomes. Our method demonstrates strong generalization across 11 medical image segmentation tasks and 19 datasets, covering various diseases, organs, and modalities. It improves performance by 10\u201320% (absolute) in both same- and out-of-domain settings and requires 8\u201320 times less training data than existing approaches. This greatly enhances the feasibility and cost-effectiveness of deep learning in data-limited medical imaging scenarios.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Medical image semantic segmentation1,2,3 is a pivotal process in the modern healthcare landscape, playing an indispensable role in diagnosing diseases4, tracking disease progression5, planning treatments6, assisting surgeries7, and supporting numerous other clinical activities8,9. This process involves classifying each pixel within a specific image, such as a skin dermoscopy image, with a corresponding semantic label, such as skin cancer or normal skin.\n\nThe advent of deep learning has revolutionized this domain, offering unparalleled precision and automation in the segmentation of medical images1,2,10,11. Despite these advancements, training accurate and robust deep learning models requires extensive, annotated medical imaging datasets, which are notoriously difficult to obtain9,12. Labeling semantic segmentation masks for medical images is both time-intensive and costly, as it necessitates annotating each pixel. It requires not only substantial human resources but also specialized domain expertise. This leads to what is termed as ultra low-data regimes\u2014scenarios where the availability of annotated training images is remarkably scarce. This scarcity poses a substantial challenge to the existing deep learning methodologies, causing them to overfit to training data and exhibit poor generalization performance on test images.\n\nTo address the scarcity of labeled image-mask pairs in semantic segmentation, several strategies have been devised, including data augmentation and semi-supervised learning approaches. Data augmentation techniques13,14,15,16 create synthetic pairs of images and masks, which are then utilized as\u00a0supplementary training data. A significant limitation of these methods is that they treat data augmentation and segmentation model training as separate activities. Consequently, the process of data augmentation is not influenced by segmentation performance, leading to a situation where the augmented data might not contribute effectively to enhancing the model\u2019s segmentation capabilities. Semi-supervised learning techniques8,17,18,19,20 exploit additional, unlabeled images to bolster segmentation accuracy. Despite their potential, these methods face limitations due to the necessity for extensive volumes of unlabeled images, a requirement often difficult to fulfill in medical settings where even unlabeled data can be challenging to obtain due to privacy issues, regulatory hurdles (e.g., IRB approvals), among others. Recent advancements in generative deep learning21,22,23 have opened new possibilities for overcoming such challenges by generating synthetic data. Compared to traditional augmentation methods, generative models have the potential to produce more realistic and diverse samples. However, most existing data generation or augmentation approaches13,14,15,16 do not incorporate feedback from the segmentation performance itself. Some recent studies24 have proposed multi-level optimization (MLO) frameworks in which the data generation process is guided by downstream tasks, such as classification. Yet, applying such optimization effectively to segmentation tasks remains underexplored. Moreover, unlike semi-supervised segmentation methods8,17,18,19,20, generative approaches have the advantage of not requiring additional unlabeled data\u2014an important benefit in sensitive medical domains.\n\nIn this work, we introduce GenSeg, a generative deep learning framework designed to address the challenges of ultra low-data regimes in medical image segmentation. GenSeg generates high-fidelity paired segmentation masks and medical images through a MLO process directly guided by segmentation performance. This ensures that the generated data not only meets high-quality standards but is also optimized to improve downstream model training. Unlike existing augmentation methods, GenSeg performs end-to-end data generation tightly coupled with segmentation objectives; unlike semi-supervised approaches, it requires no additional unlabeled images. GenSeg is a versatile, model-agnostic framework that can be seamlessly integrated into existing segmentation pipelines. We validated GenSeg across 11 segmentation tasks and 19 datasets spanning diverse imaging modalities, diseases, and organs. When integrated with UNet1 and DeepLab10, GenSeg significantly boosts performance in ultra low-data settings (e.g., using only 50 training examples), achieving absolute gains of 10\u201320% in both same-domain and out-of-domain (OOD) generalization. Additionally, GenSeg demonstrates strong data efficiency, matching or exceeding baseline performance while requiring 8\u201320\u2009\u00d7\u2009fewer labeled samples.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "GenSeg is an end-to-end data generation framework designed to generate high-quality, labeled data, to enable the training of accurate medical image segmentation models in ultra low-data regimes (Fig.\u00a01a). Our framework integrates two components: a data generation model and a semantic segmentation model. The data generation model is responsible for generating synthetic pairs of medical images and their corresponding segmentation masks. This generated data serves as the training material for the segmentation model. In our data generation process, we introduce a reverse generation mechanism. This mechanism initially generates segmentation masks, and subsequently, medical images, adhering to a progression from simpler to more complex tasks. Specifically, given an expert-annotated real segmentation mask, we apply basic image augmentation operations to produce an augmented mask, which is then inputted into a deep generative model to generate the corresponding medical image. A key distinction of our method lies in the architecture of this generative model. Unlike traditional models22,23,25,26 that rely on manually designed architecture, our model automatically learns this architecture from data (Fig.\u00a01b, c). This adaptive architecture enables more nuanced and effective generation of medical images, tailored to the specific characteristics of the augmented segmentation masks.\n\na Overview of the GenSeg framework. GenSeg consists of (1) a semantic segmentation model that predicts a segmentation mask from an input image, and (2) a mask-to-image generation model that synthesizes an image from a segmentation mask. The latter features a neural architecture that is both learnable in structure and parameterized by trainable weights. GenSeg operates through three end-to-end learning stages. In stage I, the network weights of the mask-to-image model are trained with real mask-image pairs, with its architecture tentatively fixed. Stage II involves using the trained mask-to-image model to synthesize training data. Real segmentation masks are augmented to create new masks, from which synthetic images are generated. These synthetic image-mask pairs are used alongside real data to train the segmentation model. In stage III, the trained segmentation model is evaluated on a real validation dataset, and the resulting validation loss\u2014which reflects the performance of the mask-to-image model\u2014is used to update this architecture. Following this update, the model re-enters Stage I for further training, and this cycle continues until convergence. b Searchable architecture of the mask-to-image generation model. It comprises an encoder and a decoder. The encoder processes an input mask into a latent representation using a series of searchable convolution (Conv.) cells. The decoder employs a stack of searchable up-convolution (UpConv.) cells to transform the latent representation into an output medical image. Each cell, as shown in (c) contains multiple candidate operations characterized by varying kernel sizes, strides, and padding options. Each operation is associated with a weight \u03b1 denoting its importance. The architecture search process optimizes these weights, and only the most influential operations are retained in the final model. d The weight parameters of the mask-to-image generator are trained within a generative adversarial network (GAN) framework, in which a discriminator learns to distinguish real images from generated ones, while the generator is optimized to produce images that are indistinguishable from real images. All qualitative examples are sourced from publicly available datasets.\n\nGenSeg features an end-to-end data generation strategy, which ensures a synergistic relationship between the generation of data and the performance of the segmentation model. By closely aligning the data generation process with the needs and feedback of the segmentation model, GenSeg ensures the relevance and utility of the generated data for effective training of the segmentation model. To evaluate the effectiveness of the generated data, we first train a semantic segmentation model using this data. We then assess the model\u2019s performance on a validation set consisting of real medical images, each accompanied by an expert-annotated segmentation mask. The model\u2019s validation performance serves as a reflection of the quality of the generated data: if the data is of low quality, the segmentation model trained on it will show poor performance during validation. By concentrating on improving the model\u2019s validation performance, we can, in turn, enhance the quality of the generated data.\n\nOur approach utilizes a MLO24 strategy to achieve end-to-end data generation. MLO involves a series of nested optimization problems, where the optimal parameters from one level serve as inputs for the objective function at the next level. Conversely, parameters that are not yet optimized at a higher level are fed back as inputs to lower levels. This yields a dynamic, iterative process that solves optimization problems in different levels jointly. Our method employs a three-tiered MLO process, executed end-to-end. The first level focuses on training the weight parameters of our data generation model, while keeping its learnable architecture constant. This training is performed within a generative adversarial network (GAN) framework22 (Fig.\u00a01d), where a discriminator network learns to distinguish between real and generated images, and the data generation model is optimized to fool the discriminator by producing images that closely resemble real ones. At the second level, this trained model is used to produce synthetic image-mask pairs, which are then employed to train a semantic segmentation model. The final level involves validating the segmentation model using real medical images with expert-annotated masks. The performance of the segmentation model in this validation phase is a function of the architecture of the generation model. We optimize this architecture by minimizing the validation loss. By jointly solving the three levels of nested optimization problems, we can concurrently train data generation and semantic segmentation models in an end-to-end manner.\n\nOur framework was validated for a variety of medical imaging segmentation tasks across 19 datasets, spanning a diverse spectrum of imaging techniques, diseases, lesions, and organs. These tasks comprise segmentation of skin lesions from dermoscopy images, breast cancer from ultrasound images, placental vessels from fetoscopic images, polyps from colonoscopy images, foot ulcers from standard camera images, intraretinal cystoid fluid from optical coherence tomography (OCT) images, lungs from chest X-ray images, and left ventricles and myocardial wall from echocardiography images.\n\nWe evaluated GenSeg\u2019s performance in ultra-low data regimes. We conducted three independent runs for each dataset using different random seeds. The reported results represent the mean and standard deviation computed across these runs. GenSeg, being a versatile framework, facilitates training various backbone segmentation models with its generated data. To demonstrate this versatility, we applied GenSeg to two popular models: UNet1 and DeepLab10, resulting in GenSeg-UNet and GenSeg-DeepLab, respectively. GenSeg-DeepLab and GenSeg-UNet demonstrated significant performance improvements over DeepLab and UNet in scenarios with limited data (Fig.\u00a02a and Supplementary Fig.\u00a01). Specifically, in the tasks of segmenting placental vessels, skin lesions, polyps, intraretinal cystoid fluids, foot ulcers, and breast cancer, with training sets as small as 50, 40, 40, 50, 50, and 100 samples respectively, GenSeg-DeepLab outperformed DeepLab substantially, with absolute percentage gains of 20.6%, 14.5%, 11.3%, 11.3%, 10.9%, and 10.4%. Similarly, GenSeg-UNet surpassed UNet by significant margins, recording absolute percentage improvements of 15%, 9.6%, 11%, 6.9%, 19%, and 12.6% across these tasks. The limited size of these training datasets presents significant challenges for accurately training DeepLab and UNet models. For example, DeepLab\u2019s effectiveness in these tasks is limited, with performance varying from 0.31 to 0.62, averaging 0.51. In contrast, using our method, the performance significantly improves, ranging from 0.51 to 0.73 and averaging 0.64. This highlights the strong capability of our approach to achieve precise segmentation in ultra low-data regimes. Moreover, these segmentation tasks are highly diverse. For example, placental vessels involve complex branching structures, skin lesions vary in shape and size, and polyps require differentiation from surrounding mucosal tissue. GenSeg demonstrated robust performance enhancements across these diverse tasks, underscoring its strong capability in achieving accurate segmentation across different diseases, organs, and imaging modalities.\n\na The performance of GenSeg applied to UNet (GenSeg-UNet) and DeepLab (GenSeg-DeepLab) under in-domain settings (test and training data are from the same domain) in the tasks of segmenting placental vessels, skin lesions, polyps, intraretinal cystoid fluids, foot ulcers, and breast cancer using limited training data (50, 40, 40, 50, 50, and 100 examples from the FetReg, ISIC, CVC-Clinic, ICFluid, FUSeg, and BUID datasets, respectively for each task), compared to vanilla UNet and DeepLab. b The performance of GenSeg-UNet and GenSeg-DeepLab under out-of-domain settings (test and training data are from different domains) in segmenting skin lesions (using only 40 examples from the ISIC dataset for training, and the DermIS and PH2 datasets for testing) and lungs (using only 9 examples from the JSRT dataset for training, and the NLM-MC and NLM-SZ datasets for testing), compared to vanilla UNet and DeepLab. In all panels, bar heights represent the mean, and error bars indicate the standard deviation across three independent runs with different random seeds. Results from individual runs are shown as dot points. Source data are provided as a Source Data file.\n\nBesides in-domain evaluation, where the test and training images were from disjoint subsets of the same dataset, we also evaluated GenSeg\u2019s effectiveness in OOD scenarios, wherein the training and test images originate from distinct datasets. The OOD evaluations were also conducted in ultra low-data regimes, where the number of training examples was restricted to only 9 or 40. Our evaluations focused on two segmentation tasks: the segmentation of skin lesions from dermoscopy images and the segmentation of lungs from chest X-rays. For the task of skin lesion segmentation, we trained our models using 40 examples from the ISIC dataset. These models were then tested on two external datasets, DermIS and PH2, to evaluate their performance outside the ISIC domain. In the lung segmentation task, we utilized 9 training examples from the JSRT dataset and conducted evaluations on two additional datasets, NLM-SZ and NLM-MC, to test the models\u2019 adaptability beyond the JSRT domain. GenSeg showed superior OOD generalization capabilities (Fig.\u00a02b). In skin lesion segmentation, GenSeg-UNet substantially outperformed UNet, achieving a Jaccard index of 0.65 compared to UNet\u2019s 0.41 on the DermIS dataset, and 0.77 versus 0.56 on PH2. Similarly, in lung segmentation, GenSeg-UNet demonstrated superior performance with a Dice score of 0.86 compared to UNet\u2019s 0.77 on NLM-MC, and 0.93 against 0.82 on NLM-SZ. Similarly, GenSeg-DeepLab significantly outperformed DeepLab: it achieved 0.67 compared to 0.47 on DermIS, 0.74 vs. 0.63 on PH2, 0.87 vs. 0.80 on NLM-MC, and 0.91 vs. 0.86 on NLM-SZ. Figure\u00a03 and Supplementary Fig.\u00a07 visualize some randomly selected segmentation examples. Both GenSeg-UNet and GenSeg-DeepLab accurately segmented a wide range of disease targets and organs across various imaging modalities with their predicted masks closely resembling the ground truth, under both in-domain (Fig.\u00a03a and Supplementary Fig.\u00a07) and OOD (Fig.\u00a03b) settings. In contrast, UNet and DeepLab struggled to achieve similar levels of accuracy, often producing masks that were less precise and exhibited inconsistencies in complex anatomical regions. This disparity underscores the advanced capabilities of GenSeg in handling varied and challenging segmentation tasks. Supplementary Fig.\u00a08 presents several mask-image pairs generated by GenSeg. The generated images not only exhibit a high degree of realism but also demonstrate excellent semantic alignment with their corresponding masks. GenSeg\u2019s superior OOD generalization capability stems from its ability to generate diverse medical images accompanied by precise segmentation masks. When trained on this diverse augmented dataset, segmentation models can learn more robust and OOD generalizable feature representations.\n\na Visualizations of segmentation masks predicted by GenSeg-DeepLab and GenSeg-UNet under in-domain settings in the tasks of segmenting placental vessels, skin lesions, polyps, intraretinal cystoid fluids, foot ulcers, and breast cancer using limited training data (50, 40, 40, 50, 50, and 100 examples from the FetReg, ISIC, CVC-Clinic, ICFluid, FUSeg, and BUID datasets), compared to vanilla UNet and DeepLab. b Visualizations of segmentation masks predicted by GenSeg-DeepLab and GenSeg-UNet under out-of-domain settings in segmenting skin lesions (using only 40 examples from the ISIC dataset for training, and the DermIS and PH2 datasets for testing) and lungs (using only 9 examples from the JSRT dataset for training, and the NLM-MC and NLM-SZ datasets for testing), compared to vanilla UNet and DeepLab. All qualitative examples are sourced from publicly available datasets.\n\nIn comparing the number of training examples required for GenSeg and baseline models to achieve similar performance, GenSeg consistently required fewer examples. Figure\u00a04 illustrates this point by plotting segmentation performance (y-axis) against the number of training examples (x-axis) for various methods. Methods that are closer to the upper left corner of the subfigure are considered more sample-efficient, as they achieve superior segmentation performance with fewer training examples. Across all subfigures, our methods consistently position nearer to these optimal upper left corners compared to the baseline methods. First, GenSeg demonstrates superior sample efficiency under in-domain settings (Fig.\u00a04a). For example, in the placental vessel segmentation task, GenSeg-DeepLab achieved a Dice score of 0.51 with only 50 training examples, a tenfold reduction compared to DeepLab\u2019s 500 examples needed to reach the same score. In foot ulcer segmentation, to reach a Dice score around 0.6, UNet needed 600 examples, in contrast to GenSeg-UNet, which required only 50 examples, a twelve-fold reduction. DeepLab required 800 training examples for a Dice score of 0.73, whereas GenSeg-DeepLab achieved the same score with only 100 examples, an eightfold reduction. In lung segmentation, achieving a Dice score of 0.97 required 175 examples for UNet, whereas GenSeg-UNet needed just 9 examples, representing a 19-fold reduction. Second, the sample efficiency of GenSeg is also evident in OOD settings (Fig.\u00a04b). For example, in lung segmentation, achieving an OOD generalization performance of 0.93 on the NLM-SZ dataset required 175 training examples from the JSRT dataset for UNet, while GenSeg-UNet needed only 9 examples, representing a 19-fold reduction. In skin lesion segmentation, GenSeg-DeepLab, trained with only 40 ISIC examples, reached a Jaccard index of 0.67 on DermIS, a performance that DeepLab could not match even with 200 examples.\n\na The in-domain generalization performance of GenSeg-UNet and GenSeg-DeepLab with different numbers of training examples from the FetReg, FUSeg, JSRT, and ISIC datasets in segmenting placental vessels, foot ulcers, lungs, and skin lesions, compared to UNet and DeepLab. b The out-of-domain generalization performance of GenSeg-UNet and GenSeg-DeepLab with different numbers of training examples in segmenting lungs (using examples from JSRT for training, and NLM-SZ and NLM-MC for testing) and skin lesions (using examples from ISIC for training, and DermIS and PH2 for testing), compared to UNet and DeepLab. In all panels, bar heights represent the mean, and error bars indicate the standard deviation across three independent runs with different random seeds. Results from individual runs are shown as black triangles. Source data are provided as a Source Data file.\n\nWe compared GenSeg against prevalent data augmentation methods, including rotation, flipping, and translation, as well as their combinations. Furthermore, GenSeg was benchmarked against a data generation approach27, which is based on the Wasserstein Generative Adversarial Network (WGAN)28. For each baseline augmentation method, the same hyperparameters (e.g., rotation angle) were consistently applied to both the input image and the corresponding output mask within each training example, resulting in augmented image-mask pairs. GenSeg significantly surpassed these methods under in-domain settings (Fig.\u00a05a and Supplementary Fig.\u00a02). For instance, in foot ulcer segmentation using UNet as the backbone segmentation model, GenSeg attained a Dice score of 0.74, significantly surpassing the top baseline method, WGAN, which achieved 0.66. Similarly, in polyp segmentation with DeepLab, GenSeg scored 0.76, significantly outperforming the best baselines\u2014Flip, Combine, and WGAN\u2014which scored 0.69. GenSeg also demonstrated superior OOD generalization performance compared to the baselines (Fig.\u00a05c and Supplementary Fig.\u00a03b). For instance, in UNet-based skin lesion segmentation, with 40 training examples from the ISIC dataset, GenSeg achieved a Dice score of 0.77 on the PH2 dataset, substantially surpassing the best-performing baseline, Flip, which scored 0.68. Moreover, GenSeg demonstrated comparable performance to baseline methods with fewer training examples (Fig.\u00a05b and Supplementary Fig.\u00a03a) under in-domain settings. For instance, using only 40 training examples for skin lesion segmentation with UNet, GenSeg achieved a Dice score of 0.67. In contrast, the best performing baseline, Combine, required 200 examples to reach the same score. Similarly, with fewer training examples, GenSeg achieved comparable performance to baseline methods under OOD settings (Fig.\u00a05c and Supplementary Fig.\u00a03b). For example, in lung segmentation with UNet, GenSeg reached a Dice score of 0.93 using just 9 training examples, whereas the best performing baseline required 175 examples to achieve a similar score.\n\na GenSeg\u2019s in-domain generalization performance compared to baseline methods, including Vanilla (without any data augmentations), Rotate, Flip, Translate, Combine, and WGAN, when used with UNet or DeepLab in segmenting placental vessels, skin lesions, polyps, intraretinal cystoid fluids, foot ulcers, and breast cancer using the FetReg, ISIC, CVC-Clinic, ICFluid, FUSeg, and BUID datasets. b GenSeg\u2019s in-domain generalization performance compared to baseline methods using a varying number of training examples from the ISIC dataset for segmenting skin lesions, with UNet and DeepLab as the backbone segmentation models. c GenSeg\u2019s out-of-domain generalization performance compared to baseline methods across varying numbers of training examples in segmenting lungs (using examples from JSRT for training, and NLM-SZ and NLM-MC for testing) and skin lesions (using examples from ISIC for training, and DermIS and PH2 for testing), with UNet and DeepLab as the backbone segmentation models. In all panels, bar heights represent the mean, and error bars indicate the standard deviation across three independent runs with different random seeds. Results from individual runs are shown as dot points. Source data are provided as a Source Data file.\n\nGenSeg outperforms existing data augmentation and generation techniques primarily due to its end-to-end data generation mechanism. Unlike previous methods that separate data augmentation/generation from segmentation model training, our approach integrates them end-to-end within a unified, MLO framework. Within this framework, the validation performance of the segmentation model acts as a direct indicator of the generated data\u2019s usefulness. By leveraging this performance to inform the training process of the generation model, we ensure that the data produced is specifically optimized to improve the segmentation model. In previous methods, segmentation performance does not impact the process of data augmentation and generation. As a result, the augmented/generated data might not be effectively tailored for training the segmentation model. Furthermore, our framework learns a generative model that excels in generating data with greater diversity compared to existing augmentation methods.\n\nWe conducted a comparative analysis of GenSeg against leading semi-supervised segmentation methods18,19,20,29, including cross-teaching between convolutional neural networks and Transformer (CTBCT)30, deep co-training (DCT)29, and a mutual correction framework (MCF)31, which employ external unlabeled images (1000 in each experiment) to enhance model training and thereby improve segmentation performance. GenSeg, which does not require any additional unlabeled images, significantly outperformed baseline methods under in-domain settings (Fig.\u00a06a and Supplementary Fig.\u00a04). For example, when using DeepLab as the backbone segmentation model for polyp segmentation, GenSeg achieved a Dice score of 0.76, markedly outperforming the top baseline method, MCF, which reached only 0.69. GenSeg also exhibited superior OOD generalization capabilities compared to baseline methods (Fig.\u00a06c and Supplementary Fig.\u00a05b). For instance, in skin lesion segmentation based on DeepLab with 40 training examples from the ISIC dataset, GenSeg achieved a Dice score of 0.67 on the DermIS dataset, significantly higher than the best-performing baseline, MCF, which scored 0.58. Additionally, GenSeg showed performance on par with baseline methods using fewer training examples in both in-domain (Fig.\u00a06b and Supplementary Fig.\u00a05a) and OOD settings (Fig.\u00a06c and Supplementary Fig.\u00a05b).\n\na GenSeg\u2019s in-domain generalization performance compared to baseline methods, including Vanilla (UNet/DeepLab), CTBCT, DCT, and MCF, when used with UNet or DeepLab in segmenting placental vessels, skin lesions, polyps, intraretinal cystoid fluids, foot ulcers, and breast cancer utilizing the FetReg, DermQuest, CVC-Clinic, ICFluid, FUSeg, and BUID datasets. b GenSeg\u2019s in-domain generalization performance compared to baseline methods using a varying number of training examples from the ISIC and JSRT datasets for segmenting skin lesions and lungs, with UNet and DeepLab as the backbone segmentation models. c GenSeg\u2019s out-of-domain generalization performance compared to baseline methods across varying numbers of training examples in segmenting lungs (using examples from JSRT for training, and NLM-SZ and NLM-MC for testing) and skin lesions (using examples from ISIC for training, and DermIS and PH2 for testing), with UNet and DeepLab as the backbone segmentation models. In all panels, bar heights represent the mean, and error bars indicate the standard deviation across three independent runs with different random seeds. Results from individual runs are shown as dot points. Source data are provided as a Source Data file.\n\nIn the context of medical imaging, collecting even unlabeled images presents a considerable challenge due to stringent privacy concerns and regulatory constraints (e.g., IRB approval), thereby reducing the feasibility of semi-supervised methods. Despite the use of unlabeled real images, semi-supervised approaches underperform compared to GenSeg. This is primarily because these methods struggle to generate accurate masks for unlabeled images, meaning that they are less effective at creating labeled training data. In contrast, GenSeg is capable of producing high-quality images from masks, ensuring a close correspondence between the images\u2019 contents and the masks, thereby efficiently generating labeled training examples.\n\nWe compared the effectiveness of GenSeg\u2019s end-to-end data generation mechanism against a baseline approach, Separate, which separates data generation from segmentation model training. In Separate, the mask-to-image generation model is initially trained and then fixed. Subsequently, it generates data, which is then utilized to train the segmentation model. The end-to-end GenSeg framework consistently outperformed the Separate approach under both in-domain (Fig.\u00a07a and Supplementary Fig.\u00a06a) and OOD settings (Fig.\u00a07b and Supplementary Fig.\u00a06b). For instance, in the segmentation of placental vessels, GenSeg-DeepLab attained an in-domain Dice score of 0.52, significantly surpassing Separate-DeepLab, which scored 0.42. In lung segmentation using JSRT as the training dataset, GenSeg-UNet achieved an OOD Dice score of 0.93 on the NLM-SZ dataset, considerably better than the 0.84 scored by Separate-UNet.\n\na The in-domain generalization performance of GenSeg, which performs data generation and segmentation model training end-to-end, compared to the Separate baseline, which performs the two processes separately, when used with UNet or DeepLab in segmenting placental vessels, skin lesions, polyps, intraretinal cystoid fluids, foot ulcers, and breast cancer utilizing the FetReg, ISIC, DermQuest, CVC-Clinic, KVASIR, ICFluid, FUSeg, and BUID datasets. b GenSeg\u2019s out-of-domain generalization performance compared to the Separate baseline in segmenting skin lesions (using examples from ISIC for training, and DermIS and PH2 for testing) and lungs (using examples from JSRT for training, and NLM-SZ and NLM-MC for testing), with UNet and DeepLab as the backbone segmentation models. In all panels, bar heights represent the mean, and error bars indicate the standard deviation across three independent runs with different random seeds. Results from individual runs are shown as dot points. Source data are provided as a Source Data file.\n\nWe compared GenSeg-UNet with nnUNet2 - a state-of-the-art method for medical image segmentation - under both in-domain and OOD settings across multiple segmentation tasks. GenSeg-UNet consistently outperformed nnUNet in these data-scarce scenarios (Fig.\u00a08a, b). In in-domain scenarios (Fig.\u00a08a), GenSeg-UNet achieves 1\u20137% (absolute percentages) higher performance scores across all tasks. In OOD evaluations (Fig.\u00a08b), which involve more substantial distributional shifts, GenSeg-UNet demonstrates even greater improvements across all tasks, outperforming nnUNet by 5\u201316% (absolute percentages). For instance, in the lung segmentation task, when trained on only 175 examples from the JSRT dataset and evaluated on the SZ dataset, GenSeg-UNet achieves a Dice score of 94.5%, compared to 78.4% with nnUNet\u2014a substantial gain of 16.1%.\n\na GenSeg-UNet consistently outperforms nnUNet across a range of segmentation tasks under in-domain scenarios. b GenSeg-UNet consistently demonstrates superior performance to nnUNet across diverse segmentation tasks in out-of-domain settings. In the X-Y notation, X refers to the training dataset and Y to the test dataset, where X and Y are from distinct distributions. c GenSeg-SwinUnet outperforms SwinUnet, both trained on 40 examples from the ISIC dataset and evaluated on the test sets of ISIC, PH2, and DermIS. d Extension of the GenSeg framework to 3D medical image segmentation tasks under different training data regimes. \u201cHippo.-low\u201d refers to training with an ultra-low data setting for hippocampus segmentation, while \u201cHippo.-full\u201d refers to training with the full available dataset. The same settings are applied to the liver segmentation task. e Comparison of model performance under ultra-low and high data regimes. \u201cUNet-low\u201d denotes the UNet model trained with an ultra-low amount of data, while \u201cUNet-high\u201d refers to the model trained with the full available dataset. The same training settings are applied to GenSeg-UNet. f GenSeg\u2019s performance on the ISIC and FetReg datasets can be further improved by employing several strategies, including increasing the number of training examples, using task-appropriate segmentation models, and refining augmentation techniques. g The runtime (in hours on an A100 GPU) of GenSeg-UNet was measured for lung segmentation using JSRT as the training data and for skin lesion segmentation using ISIC as the training data. In all panels (except g), bar heights represent the mean, and error bars indicate the standard deviation across three independent runs with different random seeds. Results from individual runs are shown as dot points. Source data are provided as a Source Data file.\n\nThe superior performance of GenSeg over nnUNet in ultra-low data regimes can be attributed to fundamental differences in their augmentation strategies. nnUNet employs standard augmentation techniques such as rotation, scaling, Gaussian blur, and intensity adjustments, which, while effective in moderate- to large-scale data settings, offer limited diversity and adaptability in severely data-constrained scenarios. In contrast, GenSeg trains a deep generative model that synthesizes diverse and semantically consistent image-mask pairs tailored to the specific task and dataset. This generative augmentation approach introduces significantly greater variability into the training data, enabling the segmentation model to learn more robust and generalizable representations. By aligning the data generation process with segmentation performance through end-to-end MLO, GenSeg ensures that the synthesized data is not only realistic but also highly informative for improving downstream segmentation accuracy.\n\nGenSeg is a versatile, model-agnostic framework that can seamlessly integrate with segmentation models with diverse architectures to improve their performance. For example, after applying our framework on UNet and DeepLab, we observed significant enhancements in their performance (Figs.\u00a02\u20137), both for in-domain and OOD settings. Furthermore, we also integrated this framework with a Transformer-based segmentation model, SwinUnet32. Using just 40 training examples from the ISIC dataset, GenSeg-SwinUnet achieved a Jaccard index of 0.62 on the ISIC test set. Furthermore, it demonstrated strong generalization with OOD Jaccard index scores of 0.65 on the PH2 dataset and 0.62 on the DermIS dataset. These results represent a substantial improvement over the baseline SwinUnet model, which achieved Jaccard indices of 0.55 on ISIC, 0.56 on PH2, and 0.38 on DermIS (Fig.\u00a08c).\n\nIn addition to 2D medical image segmentation, GenSeg can be extended to support 3D segmentation tasks. To enable this, we adapted our framework by incorporating 3D UNet33 as the segmentation model and Pix2PixNIfTI34 as the generative model, facilitating joint generation and segmentation in a 3D volumetric setting. We make the architecture of the Pix2PixNIfTI model searchable by replacing the convolution and transposed convolution layers in the original generator with our differentiable convolutional and transposed convolutional cells. The architecture parameters of the modified Pix2PixNIfTI model are optimized by minimizing the segmentation loss on the validation set within our MLO-based framework. During training, the input 3D masks are first augmented using rotation and flipping transformations, and the generator then synthesizes 3D volumes from these augmented masks. We evaluated this 3D extension on two datasets from the Medical Segmentation Decathlon (MSD) challenge4, focusing on hippocampus and liver segmentation tasks. Experiments were conducted under both ultra-low data settings (40 training volumes) and higher data settings using the full available training sets (208 volumes for hippocampus and 98 for liver).\n\nGenSeg consistently improved segmentation performance over the baseline 3D UNet in both regimes (Fig.\u00a08d). Notably, in the ultra-low data setting, GenSeg yielded substantial gains, demonstrating its robustness and effectiveness in data-constrained 3D segmentation tasks. These results confirm that GenSeg generalizes beyond 2D segmentation and remains effective when applied to more complex 3D volumetric data.\n\nWhile GenSeg is designed to enable medical image segmentation in ultra-low data regimes, we further investigated its effectiveness in higher data settings. We conducted experiments on the ISIC, FetReg, BUID, and CVC-Clinic datasets using UNet as the segmentation model. Two training regimes were evaluated: (1) UNet-low and GenSeg-UNet-low, trained under ultra-low data conditions with 40, 50, 100, and 40 training examples from the respective datasets; and (2) UNet-high and GenSeg-UNet-high, trained using the full available training sets, consisting of 1000, 2000, 400, and 400 examples, respectively.\n\nAs shown in Fig.\u00a08e, several key observations emerge. First, GenSeg-UNet-high outperforms UNet-high across all datasets, demonstrating that GenSeg\u2019s generative augmentation strategy continues to provide benefits even in high-data regimes. Second, as expected, segmentation performance improves for all models as the training set size increases. Third, despite being trained on significantly fewer examples, GenSeg-UNet-low achieves performance that is often close to that of UNet-high, highlighting GenSeg\u2019s strength in data-scarce scenarios. These findings underscore the versatility and effectiveness of the GenSeg framework across varying data availability conditions. GenSeg consistently enhances segmentation performance regardless of dataset size by integrating generative augmentation into an end-to-end, task-driven learning paradigm. While particularly valuable in low-data regimes, GenSeg also improves generalization in more data-rich settings by enriching the training signal.\n\nTo further enhance GenSeg\u2019s segmentation performance on challenging datasets such as ISIC and FetReg, we conducted additional experiments by incorporating several targeted strategies. These included increasing the amount of training data, refining augmentation techniques, and employing a more proper segmentation backbone. For the ISIC dataset (UNet was used as the segmentation model), we increased the number of training examples from 40 to 1000, which led to an improvement in Jaccard score from 67.3% to 73.9% (Fig.\u00a08f), reaching a level considered satisfactory for binary segmentation tasks. For the FetReg dataset, which presents unique challenges due to high anatomical variability, low image contrast, and the complexity of placental vessel structures, we implemented three modifications: narrowing the rotation augmentation range to (\u22125\u00b0 to 5\u00b0), replacing UNet with DeepLab as the segmentation model, and expanding the training set size from 50 to 2000 examples. These adjustments resulted in a significant performance gain, improving the Dice score to 71.7% (Fig.\u00a08f). These findings indicate that with sufficient data and appropriate architectural and augmentation refinements, GenSeg can achieve high segmentation accuracy even in complex tasks.\n\nWe conducted ablation studies to investigate how different choices of mask-to-image generative models affect the final segmentation performance. In addition to the GAN-based Pix2Pix model used in our current framework, we evaluated two state-of-the-art alternatives: Soft-Intro VAE35, a variational autoencoder (VAE)36,37,38,39 based model, and BBDM40, a diffusion-based generative model41. We integrated each model into our GenSeg framework by using them to replace the original Pix2Pix mask-to-image generator. We modified both BBDM and Soft-Intro VAE by incorporating our multi-branch convolutional cells into their generator networks, to allow their architectures to be optimized based on segmentation performance. We trained each model using two strategies: (1) Separate, where the generative model is trained independently and then fixed before segmentation model training, and (2) End2End, our proposed MLO framework. Evaluation was performed under both in-domain and OOD scenarios.\n\nBBDM (End2End) achieved the highest performance across all datasets, under both in-domain settings (Fig.\u00a09a) and OOD settings (Fig.\u00a09b). The performance of Pix2Pix (End2End) and Soft-Intro VAE (End2End) was comparable, with both trailing slightly behind BBDM. However, BBDM incurs significantly higher computational cost and model size compared to both Pix2Pix and Soft-Intro VAE under the End2End strategy (Fig.\u00a09c). Considering the trade-off between segmentation performance and computational efficiency, Pix2Pix remains a practical and effective choice for our setting, particularly when computational resources are limited. Furthermore, all three End2End approaches consistently outperformed their respective Separate counterparts, highlighting the advantage of jointly optimizing the generative and segmentation models within an end-to-end training framework. This result reinforces the central premise of GenSeg: that aligning the data generation process with downstream segmentation performance leads to more effective learning.\n\na, b Ablation study evaluating the effectiveness of different generative models - including Pix2Pix (GAN-based), BBDM (diffusion-based), and Soft-Intro VAE (VAE-based) - under separate and end-to-end training strategies. Evaluations were conducted under both in-domain (a) and out-of-domain (b) scenarios, using UNet as the segmentation model. For out-of-domain scenarios, datasets are labeled in the format X-Y, where X denotes the training dataset and Y denotes the test dataset. c Comparison of training time (left) measured on an A100 GPU and model size (right) for Pix2Pix, BBDM, and Soft-Intro VAE within our end-to-end training framework, in skin lesion segmentation with 40 training examples from the ISIC dataset when using UNet as the segmentation model. d Impact of mask-to-image GAN models on the performance of GenSeg-UNet was evaluated on the test datasets of ISIC, PH2, and DermIS, in skin lesion segmentation. GenSeg-UNet was trained using 40 examples from the ISIC training dataset. e, f Ablation study comparing simultaneous image-mask generation with the two-step approach, where masks are first augmented and then used to generate images. The two-step strategy outperforms simultaneous generation. Experiments were conducted under both in-domain (e) and out-of-domain (f) settings. In all panels (except c), bar heights represent the mean, and error bars indicate the standard deviation across three independent runs with different random seeds. Results from individual runs are shown as dot points. Source data are provided as a Source Data file.\n\nIn addition, within the GAN family, we compared the Pix2Pix model with two other GAN-based models: SPADE42 and ASAPNet43. For a fair comparison, we also made the generator architectures of these models searchable by applying the multi-branch convolutional modification (Fig.\u00a01c) to their generators. Pix2Pix and SPADE demonstrated comparable performance, both significantly outperforming ASAPNet (Fig.\u00a09d). This performance gap can be attributed to the superior image generation capabilities of Pix2Pix and SPADE.\n\nIn our current framework, image and mask generation is performed using a two-step approach: we first generate augmented masks from real masks using standard augmentation techniques, and then synthesize images from the augmented masks using a mask-to-image generative model. As an alternative, one can generate both the image and the corresponding mask simultaneously44. To investigate which strategy is more effective, we compared our two-step approach with an ablation setting referred to as Simultaneous, in which images and masks are generated jointly using the WGAN-GP model28, integrated within our framework when using UNet as the segmentation model. In this setting, WGAN-GP takes a random noise vector sampled from a Gaussian distribution as input and simultaneously produces a medical image and its corresponding mask. To maintain architectural consistency with our framework, we modified the original WGAN-GP by replacing its convolutional layers with our multi-branch convolutional cells. We then trained the model using our end-to-end optimization strategy to ensure a fair comparison.\n\nThe two-step approach consistently outperforms the WGAN-GP-based simultaneous generation method in both in-domain (Fig.\u00a09e) and OOD (Fig.\u00a09f) settings. Notably, in the OOD evaluations\u2014where 40 examples from the ISIC dataset were used for training and PH2 and DermIS served as test sets\u2014the two-step method achieved 12.1% and 8.9% higher performance, respectively.\n\nThe superior performance of the two-step approach over the simultaneous generation method can be attributed to the explicit conditioning and structural alignment enforced during the data generation process. In the two-step pipeline, segmentation masks are first augmented and then used as conditioning inputs to guide the image generation process. This explicit conditioning enables the mask-to-image generation model to synthesize images that are tightly aligned with the structural boundaries and semantics defined by the input mask. As a result, the generated image-mask pairs exhibit high spatial coherence and fidelity, which is crucial for effective segmentation model training. In contrast, the simultaneous generation approach, as implemented with WGAN-GP, synthesizes both the image and the mask jointly without enforcing a strong pixel-wise correspondence between the two outputs. This lack of explicit conditioning can lead to weaker structural alignment, especially in low-data regimes where the model may struggle to learn accurate joint representations. Specifically, it does not impose semantic constraints that guarantee the generated masks accurately delineate regions of interest within the corresponding images. This misalignment can reduce the effectiveness of the generated data in training downstream segmentation models.\n\nIn GenSeg, the initial step involves applying augmentation operations to generate synthetic segmentation masks from real masks. We explored the impact of augmentation operations on segmentation performance. GenSeg, which utilizes all three operations\u2014rotation, translation, and flipping\u2014is compared against three specific ablation settings where only one operation (Rotate, Translate, or Flip) is used to augment the masks. GenSeg demonstrated significantly superior performance compared to any of the individual ablation settings (Fig.\u00a010a). Notably, GenSeg exhibited superior generalization on OOD data, highlighting the advantages of integrating multiple augmentation operations compared to using a single operation. By combining various augmentation operations, GenSeg can generate a broader diversity of augmented masks, which in turn produces a more diverse set of augmented images. Training segmentation models on this diverse dataset allows for learning more robust representations, thereby significantly enhancing generalization capabilities on OOD test data.\n\na (Left) Impact of augmentation operations on the performance of GenSeg-UNet was evaluated on the test datasets of JSRT, NLM-MC, and NLM-SZ, in lung segmentation. GenSeg-UNet was trained using 9 examples from the JSRT training dataset. ALL refers to the full GenSeg method that incorporates all three operations. (Right) Impact of augmentation operations on the performance of GenSeg-UNet was evaluated on the test datasets of ISIC, PH2, and DermIS, in skin lesion segmentation. GenSeg-UNet was trained using 40 examples from the ISIC training dataset. b, c Ablation study evaluating the impact of elastic augmentation under in-domain (b) and out-of-domain settings (c). In out-of-domain scenarios, datasets are denoted in the format X-Y, where X represents the training dataset and Y the test dataset. UNet was used as the segmentation model. d Ablation study evaluating the impact of rotation augmentation on placental vessel segmentation using the FetReg and FPD datasets with UNet as the segmentation model. e Ablation study on learnable multi-branch convolutions, with UNet as the segmentation model. f (Left) Impact of the tradeoff parameter \u03b3 on the performance of GenSeg-UNet on the test datasets of JSRT, NLM-MC, and NLM-SZ, in lung segmentation with 9 examples from the JSRT training dataset. (Right) Impact of the tradeoff parameter \u03b3 on the performance of GenSeg-UNet on the test datasets of ISIC, PH2, and DermIS, in skin lesion segmentation with 40 examples from the ISIC training dataset. In all panels (except f), bar heights represent the mean, and error bars indicate the standard deviation across three independent runs with different random seeds. Results from individual runs are shown as dot points. Source data are provided as a Source Data file.\n\nElastic and deformable augmentations have recently shown promise in enhancing medical image segmentation performance45. To evaluate their effectiveness within our framework, we conducted an ablation study assessing the impact of incorporating elastic augmentation into the training pipeline when using UNet as the segmentation model. Specifically, we compared the following three ablation settings: 91) Without Elastic, using only our original set of augmentations (e.g., flipping, rotation, translation), (2) With Elastic, combining our original augmentations with elastic augmentation, and 93) Only Elastic, using elastic augmentation alone, without any other augmentations.\n\nThe combination of elastic and traditional augmentations (With Elastic) resulted in modest performance improvements across both in-domain (Fig.\u00a010b) and OOD (Fig.\u00a010c) settings. However, the Without Elastic setting\u2014using only our original traditional augmentations\u2014consistently outperformed the Only Elastic setting (Fig.\u00a010b, c), which applies elastic deformation alone, across all tasks. One possible explanation is that elastic augmentation, when used in isolation, may result in a narrower range of transformations, focusing primarily on localized shape distortions. While such deformations can be beneficial in mimicking anatomical variability, they may not capture broader appearance and geometric changes\u2014such as orientation, scale, or intensity shifts\u2014that traditional augmentations introduce. As a result, relying solely on elastic transformations might limit the diversity of the training data and reduce generalization. These results suggest that traditional augmentations provide a strong and versatile baseline, and that combining them with elastic augmentations may offer additional benefits depending on the dataset characteristics and task requirements.\n\nIn placental vessel segmentation, the orientation of vessels is highly sensitive, raising concerns that rotation-based augmentations may be unsuitable for such images. To investigate this, we conducted an ablation study on two vessel segmentation datasets: FetReg and FPD, each using 100 training examples. We tested the impact of different degrees of rotation augmentation by comparing five settings: no rotation, small-angle rotation (\u22125\u00b0 to 5\u00b0), moderate rotation (\u221215\u00b0 to 15\u00b0), large rotation (\u221230\u00b0 to 30\u00b0), and very large rotation (\u221245\u00b0 to 45\u00b0).\n\nAs shown in Fig.\u00a010d, on the FPD dataset, all degrees of rotation yielded better performance than the no-rotation baseline. On the FetReg dataset, small-angle rotation (\u22125\u00b0 to 5\u00b0) provided the best performance, while increasing the rotation range gradually led to performance degradation. These observations indicate that large-angle rotations can distort vessel morphology and interfere with fine-grained structural cues essential for accurate segmentation, particularly in tasks requiring high spatial precision. On the other hand, small-angle rotations appear beneficial. They introduce controlled variability that helps improve model generalization without compromising anatomical integrity. We hypothesize that such mild transformations encourage robustness to minor viewpoint changes while still preserving the spatial structure of vessels\u2014an important consideration in vascular imaging. In summary, our results confirm that vessel segmentation tasks are sensitive to large rotational transformations, which can negatively impact performance. However, mild rotations in the range of \u22125\u00b0 to 5\u00b0 strike a balance between augmentation diversity and structural preservation, leading to improved outcomes.\n\nTo quantify the impact of the multi-branch design in Fig.\u00a01c, we conducted an ablation study involving three settings. In the first setting (Single-branch), we trained a standard single-branch Pix2Pix generator to synthesize images, which were then used to train the segmentation model in a separate stage. In the second setting (Fixed Multi-branch), we used a multi-branch Pix2Pix generator with branch weights (i.e., all weights \u03b1 in Fig.\u00a01c) fixed to 1, also trained independently from the segmentation model. In the third setting (Learnable Multi-branch), which corresponds to our full GenSeg framework, the generator was integrated into an end-to-end pipeline, where the branch weights \u03b1 were learned by minimizing segmentation loss on the validation set. We evaluated all three configurations on three representative tasks: skin lesion segmentation (ISIC dataset, 200 training examples), intraretinal cystoid segmentation (ICFluid dataset, 50 training examples), and breast cancer segmentation (BUID dataset, 100 training examples). As shown in Fig.\u00a010e, the Fixed Multi-branch model consistently outperformed the Single-branch model, demonstrating the advantage of using multi-branch convolutions. Moreover, the Learnable Multi-branch model further improved performance, highlighting the benefit of learning the branch weights in a task-adaptive manner. To assess the statistical significance of these improvements, we conducted two-sided paired t-tests on performance scores across three tasks. As shown in Supplementary Table\u00a02, each method was evaluated over three independent training runs with different random seeds, and pairwise comparisons were performed. Most p-values are below 0.05, indicating that the performance gains from the multi-branch architecture\u2014particularly the learnable variant\u2014are statistically significant.\n\nWe attribute these improvements to the increased representational capacity of the multi-branch architecture, which enables the generator to learn a more diverse set of features tailored to varying spatial and structural characteristics across datasets. While the fixed multi-branch design provides architectural flexibility, the learnable version further strengthens performance by enabling end-to-end optimization that aligns synthetic data generation with the segmentation objective. In summary, this ablation study demonstrates that learnable multi-branch convolutions significantly improve segmentation accuracy, demonstrating their role as an important micro-architectural component of the GenSeg framework.\n\nWe investigated the effect of the hyperparameter \u03b3 in Eq. (2) on the performance of our method. This parameter controls the balance between the contributions of real and generated data during the training of the segmentation model. Optimal performance was observed with a moderate \u03b3 value (e.g., 1), which effectively balanced the use of real and generated data (Fig.\u00a010f).\n\nGiven that GenSeg is designed for scenarios with limited training data, the overall training time is minimal, often requiring less than 2 GPU hours (Fig.\u00a08g). To enhance the efficiency of GenSeg\u2019s training, we plan to incorporate strategies from refs. 46,47 for accelerated GAN training and implement the algorithm proposed in ref. 48 to expedite the convergence of MLO. Importantly, our method does not increase the inference cost of the segmentation model. This is because our approach maintains the original architecture of the segmentation model, ensuring that the Multiply-Accumulate (MAC) operations remain unchanged.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_Fig8_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_Fig9_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61754-6/MediaObjects/41467_2025_61754_Fig10_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We present GenSeg, a robust data generation tool designed for generating high-quality data to enhance the training of medical image segmentation models. Demonstrating superior in-domain and OOD generalization performance across nine diverse segmentation tasks and 19 datasets, GenSeg excels particularly in scenarios with a limited number of real, expert-annotated training examples (as few as 50). GenSeg substantially enhances sample efficiency, requiring far fewer expert-annotated training examples than baseline methods to achieve similar performance. This greatly reduces both the burden and costs associated with medical image annotation.\n\nGenSeg stands out by requiring fewer expert-annotated real training examples compared to baseline methods, yet it achieves comparable performance. This substantial reduction in the need for manually labeled segmentation masks significantly cuts down both the burden and costs associated with medical image annotation. With just a small set of real examples, GenSeg effectively trains a data generation model which then produces additional synthetic data, effectively mimicking the benefits of using a large dataset of real examples.\n\nGenSeg significantly improves segmentation models\u2019 OOD generalization capability. GenSeg is capable of generating diverse medical images accompanied by precise segmentation masks. When trained on this diverse augmented dataset, segmentation models can learn more robust and OOD generalizable feature representations.\n\nGenSeg stands out from current data augmentation and generation techniques by offering superior segmentation performance, primarily due to its end-to-end data generation mechanism. Unlike previous methods that separate data augmentation/generation and segmentation model training, our approach integrates them end-to-end within a unified, MLO framework. Within this framework, the validation performance of the segmentation model acts as a direct indicator of the generated data\u2019s usefulness. By leveraging this performance to inform the training process of the generation model, we ensure that the data produced is specifically optimized to improve the segmentation model. In previous methods, segmentation performance does not impact the process of data augmentation and generation. As a result, the augmented/generated data might not be effectively tailored for training the segmentation model. Furthermore, our framework learns a generative model that excels in generating data with greater diversity compared to existing augmentation methods.\n\nGenSeg excels in surpassing semi-supervised segmentation methods without the need for external unlabeled images. In the context of medical imaging, collecting even unlabeled images presents a significant challenge due to stringent privacy concerns and regulatory constraints (e.g., IRB approval), thereby reducing the feasibility of semi-supervised methods. Despite the use of unlabeled real images, semi-supervised approaches underperform compared to GenSeg. This is primarily because these methods struggle to generate accurate masks for unlabeled images, meaning they are less effective at creating labeled training data. On the other hand, GenSeg is capable of producing high-quality images from masks, ensuring a close correspondence between the images\u2019 content and the masks, thereby efficiently generating labeled training examples.\n\nOur framework is designed to be universally applicable and independent of specific models. This design choice enables it to augment the capabilities of a broad spectrum of semantic segmentation models. To apply our framework to a specific segmentation model, the only requirement is to integrate the segmentation model into the second and third stages of our framework. This straightforward process enables researchers and practitioners to easily utilize our approach to improve the performance of diverse semantic segmentation models.\n\nGenSeg presents several limitations that warrant attention. First, although GenSeg generates high-quality synthetic image-mask pairs, its performance may still be dependent on the quality and diversity of the limited real-world training data available. If the small dataset used to guide the generation process is highly biased or unrepresentative, the synthetic data produced may inherit these biases, potentially leading to suboptimal generalization on unseen cases. Additionally, while GenSeg demonstrates strong OOD performance, its generalization capabilities may diminish when faced with divergent datasets or imaging modalities that differ significantly from the training set. Furthermore, although GenSeg does not require extensive unlabeled data like semi-supervised methods, it still relies on a small set of expert-annotated data to initiate the synthetic data generation process, meaning that its utility may be limited in cases where even a small annotated dataset is difficult to obtain. Finally, the integration of GenSeg into clinical workflows would require validation in real-world settings to ensure that the synthetic data does not introduce artifacts or inconsistencies that could affect diagnostic decisions. Addressing these limitations in future iterations of GenSeg would be crucial for broadening its applicability and improving its robustness in diverse clinical environments.\n\nFuture research on GenSeg can progress in multiple directions. A key area is improving synthetic data generation to better represent complex anatomical structures and the variability inherent in diverse imaging modalities. This could involve refining the MLO process to capture finer details or incorporating advanced neural architectures to enhance the quality of synthetic images. Additionally, using generative models that can learn from limited examples may help GenSeg generalize more effectively across a broader range of medical scenarios. Another important direction is applying domain adaptation techniques to improve GenSeg\u2019s robustness when encountering datasets that diverge significantly from the training data, such as novel imaging technologies or underrepresented patient populations. This would ensure more reliable performance in real-world clinical settings. Extending GenSeg\u2019s capabilities beyond segmentation to tackle other medical imaging challenges, like anomaly detection, image registration, or multimodal image fusion, could further expand its utility. Such developments would position GenSeg as a more versatile tool for medical image analysis, addressing a wider array of diagnostic and treatment planning needs. Furthermore, integrating feedback from clinical experts into the synthetic data generation process could increase its clinical relevance, aligning outputs more closely with diagnostic practices. These research directions could enhance GenSeg\u2019s adaptability and effectiveness across diverse medical imaging task.\n\nAn important consideration in evaluating the realism and utility of generated masks is how their variability compares to inter-reader variability observed in expert annotations. While our current study does not include a direct comparison\u2014due to the use of datasets with only a single reference annotation per image\u2014this is a valuable direction for future work. Qualitatively, we find that the augmented masks produced by our generative model exhibit anatomically plausible and semantically consistent variations, often resembling the natural diversity seen across patients and imaging conditions. Quantitatively, the consistent improvements in segmentation accuracy suggest that these synthetic masks enrich the training set with meaningful variability. Nevertheless, a systematic comparison with inter-reader variability would provide deeper insights into the clinical realism of the generated data. Incorporating multi-reader datasets in future evaluations could help assess whether the diversity introduced by generative augmentation aligns with the range of acceptable expert interpretations.\n\nIn summary, GenSeg is a robust data generation tool that seamlessly integrates with current semantic segmentation models. It significantly enhances both in-domain and OOD generalization performance in ultra low-data regimes, markedly boosting sample efficiency. Furthermore, it surpasses state-of-the-art methods in data augmentation and semi-supervised learning.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "GenSeg consists of a data generation model and a medical image segmentation model. The data generation model is based on conditional generative adversarial networks (GANs)49,50. It comprises two main components: a mask-to-image generator and a discriminator. Uniquely, our generator has a learnable neural architecture51, as opposed to the fixed architecture commonly seen in previous GAN models. This generator, with weight parameters G and a learnable architecture A, takes a segmentation mask as input and generates a corresponding medical image. The discriminator, with learnable weight parameters H and a fixed architecture, differentiates between synthetic and real medical images. The segmentation model has learnable weight parameters S and a fixed architecture.\n\nData generation is executed in a reverse manner. Starting with an expert-annotated segmentation mask M, we first apply basic image augmentations, such as rotation, flipping, etc., to produce an augmented mask \\(\\widehat{{{{\\bf{M}}}}}\\). This mask is then fed into the mask-to-image generator, resulting in a medical image \\(\\hat{{{{\\bf{I}}}}}(\\widehat{{{{\\bf{M}}}}},{{{\\bf{G}}}},{{{\\bf{A}}}})\\), which corresponds to \\(\\widehat{{{{\\bf{M}}}}}\\), i.e., pixels in \\(\\hat{{{{\\bf{I}}}}}(\\widehat{{{{\\bf{M}}}}},{{{\\bf{G}}}},{{{\\bf{A}}}})\\) can be semantically labeled using \\(\\widehat{{{{\\bf{M}}}}}\\). Each image-mask pair \\((\\hat{{{{\\bf{I}}}}}(\\widehat{{{{\\bf{M}}}}},{{{\\bf{G}}}},{{{\\bf{A}}}}),\\widehat{{{{\\bf{M}}}}})\\) forms an augmented example for training the segmentation model. Like other deep learning-based segmentation methods, GenSeg has access to a training set comprised of real image-mask pairs \\({{{{\\mathcal{D}}}}}_{{{{\\rm{seg}}}}}^{{{{\\rm{tr}}}}}={\\{{{{{\\bf{I}}}}}_{n}^{({{{\\rm{tr}}}})},{{{{\\bf{M}}}}}_{n}^{({{{\\rm{tr}}}})}\\}}_{n=1}^{{N}_{{{{\\rm{tr}}}}}}\\) and a validation set \\({{{{\\mathcal{D}}}}}_{{{{\\rm{seg}}}}}^{{{{\\rm{val}}}}}={\\{{{{{\\bf{I}}}}}_{n}^{({{{\\rm{val}}}})},{{{{\\bf{M}}}}}_{n}^{({{{\\rm{val}}}})}\\}}_{n=1}^{{N}_{{{{\\rm{val}}}}}}\\).\n\nGenSeg employs a MLO strategy across three distinct stages. The initial stage focuses on training the data generation model, where we fix the generator\u2019s architecture A and train the weight parameters of both the generator (G) and the discriminator (H). To facilitate this training, we modify the segmentation training dataset \\({{{{\\mathcal{D}}}}}_{{{{\\rm{seg}}}}}^{{{{\\rm{tr}}}}}\\) by swapping the roles of inputs and outputs, resulting in a new dataset \\({{{{\\mathcal{D}}}}}_{{{{\\rm{gan}}}}}={\\{{{{{\\bf{M}}}}}_{n}^{({{{\\rm{tr}}}})},{{{{\\bf{I}}}}}_{n}^{({{{\\rm{tr}}}})}\\}}_{n=1}^{{N}_{{{{\\rm{tr}}}}}}\\). In this setup, \\({{{{\\bf{M}}}}}_{n}^{({{{\\rm{tr}}}})}\\) serves as the input, while \\({{{{\\bf{I}}}}}_{n}^{({{{\\rm{tr}}}})}\\) acts as the output for our mask-to-image GAN model.\n\nLet Lgan represent the GAN training objective, a cross-entropy function that evaluates the discriminator\u2019s ability to distinguish between real and generated images. The discriminator\u2019s goal is to maximize Lgan, effectively separating real images from generated ones. Conversely, the generator strives to minimize Lgan, generating images that are so realistic they become indistinguishable from real ones. This process is encapsulated in the following minimax optimization problem:\n\nwhere G*(A) indicates that the optimally trained generator G* is dependent on the architecture A. This dependency arises because G* is the outcome of optimizing the training objective function, which in turn is influenced by A. A is tentatively fixed at this stage and will be updated later. Otherwise, if we learn A by minimizing the training loss Lgan, it may lead to a trivial solution characterized by an overly large and complex architecture. Such a solution would likely overfit the training data perfectly but perform poorly on unseen test data.\n\nIn the second stage, we leverage the trained generator to generate synthetic training examples using the aforementioned process, where expert-annotated masks are from \\({{{{\\mathcal{D}}}}}_{{{{\\rm{seg}}}}}^{{{{\\rm{tr}}}}}\\). Let \\(\\widehat{{{{\\mathcal{D}}}}}({{{{\\bf{G}}}}}{*}({{{\\bf{A}}}}),{{{{\\mathcal{D}}}}}_{{{{\\rm{seg}}}}}^{{{{\\rm{tr}}}}})\\) represent the generated data. We then use \\(\\widehat{{{{\\mathcal{D}}}}}({{{{\\bf{G}}}}}{*}({{{\\bf{A}}}}),{{{{\\mathcal{D}}}}}_{{{{\\rm{seg}}}}}^{{{{\\rm{tr}}}}})\\) and real training data \\({{{{\\mathcal{D}}}}}_{{{{\\rm{seg}}}}}^{{{{\\rm{tr}}}}}\\) to train the segmentation model S by minimizing a segmentation loss Lseg (pixel-wise cross-entropy loss). This training is formulated as the following optimization problem:\n\nwhere \u03b3 is a trade-off parameter.\n\nIn the third stage, we assess the performance of the trained segmentation model on the validation dataset \\({{{{\\mathcal{D}}}}}_{{{{\\rm{seg}}}}}^{{{{\\rm{val}}}}}\\). The validation loss, \\({L}_{{{{\\rm{seg}}}}}({{{{\\bf{S}}}}}{*}({{{\\bf{A}}}}),{{{{\\mathcal{D}}}}}_{{{{\\rm{seg}}}}}^{{{{\\rm{val}}}}})\\), serves as an indicator of the quality of the generated data. If the generated data is of inferior quality, it will likely result in S*(A)\u2014trained on this data - performing poorly on the validation set, reflected in a high validation loss. Thus, enhancing the quality of generated data can be achieved by minimizing \\({L}_{{{{\\rm{seg}}}}}({{{{\\bf{S}}}}}{*}({{{\\bf{A}}}}),{{{{\\mathcal{D}}}}}_{{{{\\rm{seg}}}}}^{{{{\\rm{val}}}}})\\) w.r.t the generator\u2019s architecture A. This objective is encapsulated in the following optimization problem:\n\nWe can integrate these stages into a MLO problem as follows:\n\nIn this formulation, the levels are interdependent. The output G*(A) from the first level defines the objective for the second level, the output S*(A) from the second level defines the objective for the third level, and the optimization variable A in the third level defines the objective function in the first level.\n\nTo enhance the generation of medical images by accurately capturing their distinctive characteristics, we make the generator\u2019s architecture searchable. Inspired by DARTS51, we employ a differentiable search method that is not only computationally efficient but also allows for a flexible exploration of architectural designs. Our search space is structured as a series of computational cells, each forming a directed acyclic graph that includes an input node, an output node, and intermediate nodes comprising K different operators, such as convolution and transposed convolution. These operators are each tied to a learnable selection weight, \u03b1, ranging from 0 to 1, where a higher \u03b1 value indicates a stronger preference for incorporating that operator into the final architecture. The process of architecture search is essentially the optimization of these selection weights. Let Conv-xyz and UpConv-xyz denote a convolution operator and a transposed convolution operator respectively, where x represents the kernel size, y the stride, and z the padding. The pool of candidate operators includes Conv/UpConv-421, Conv/UpConv-622, and Conv/UpConv-823, i.e., the number of operators K is 3. For any given cell i with input xi, the output yi is determined by the formula \\({{{{\\bf{y}}}}}_{i}=\\mathop{\\sum }_{k=1}^{K}{\\alpha }_{i,k}{o}_{i,k}({{{{\\bf{x}}}}}_{i})\\), where oi,k represents the k-th operator in the cell, and \u03b1i,k is its corresponding selection weight. Consequently, the architecture of the generator can be succinctly described by the set of all selection weights, denoted as A\u2009=\u2009{\u03b1i,k}. Architecture search amounts to learning A.\n\nWe develop a gradient-based method to solve the MLO problem in Eq. (4). First, we approximate G*(A) using one-step gradient descent update of G w.r.t \\({L}_{{{{\\rm{gan}}}}}({{{\\bf{G}}}},{{{\\bf{A}}}},{{{\\bf{H}}}},{{{{\\mathcal{D}}}}}_{{{{\\rm{gan}}}}})\\):\n\nwhere \u03b7g is a learning rate. Similarly, we approximate H* using one-step gradient ascent update of H w.r.t \\({L}_{{{{\\rm{gan}}}}}({{{\\bf{G}}}},{{{\\bf{A}}}},{{{\\bf{H}}}},{{{{\\mathcal{D}}}}}_{{{{\\rm{gan}}}}})\\):\n\nThen we plug \\({{{{\\bf{G}}}}}{*}({{{\\bf{A}}}})\\approx {{{{\\bf{G}}}}}^{{\\prime} }\\) into the objective function in the second level, yielding an approximated objective. We approximate S*(A) using one-step gradient ascent update of S w.r.t the approximated objective:\n\nFinally, we plug \\({{{{\\bf{S}}}}}{*}({{{\\bf{A}}}})\\approx {{{{\\bf{S}}}}}^{{\\prime} }\\) into the validation loss in the third level, yielding an approximated validation loss. We update A using gradient descent w.r.t the approximated loss:\n\nAfter A is updated, we plug it into Eq. (5) to update G again. The update steps in Eq. (5)\u2013(8) iterate until convergence.\n\nThe gradient \\({\\nabla }_{{{{\\bf{A}}}}}{L}_{{{{\\rm{seg}}}}}({{{{\\bf{S}}}}}^{{\\prime} },{{{{\\mathcal{D}}}}}_{{{{\\rm{seg}}}}}^{{{{\\rm{val}}}}})\\) can be calculated as follows:\n\nwhere\n\nIn this study, we focused on the segmentation of skin lesions from dermoscopy images, lungs from chest X-ray images, breast cancer from ultrasound images, placental vessels from fetoscopic images, polyps from colonoscopy images, foot ulcers from standard camera images, intraretinal cystoid fluid from OCT images, and left ventricle and myocardial wall from echocardiography images, utilizing 19 datasets. Additionally, we extended GenSeg to 3D image segmentation and evaluated its effectiveness on two 3D medical imaging datasets for hippocampus and liver segmentation. Each dataset was randomly partitioned into training, validation, and test sets, with the corresponding statistics presented in Supplementary Table\u00a01. The number of training examples was determined based on two considerations. The first consideration is consistency with prior work. For well-established benchmarks such as ISIC, we adopted low-data configurations used in previous studies to enable fair comparisons. For example, in the skin lesion segmentation task, we followed the setup used in SemanticGAN20. The second consideration is dataset-specific complexity. For datasets without standardized low-sample training protocols, we selected training set sizes based on task difficulty. Specifically, datasets involving more complex anatomical structures, high intra-class variability, or low contrast typically required more training samples to obtain stable performance. In contrast, datasets with simpler and well-defined structures could be effectively learned using fewer samples.\n\nFor skin lesion segmentation from dermoscopy images, we utilized the ISIC201852, PH253, DermIS54, and DermQuest55 datasets. The ISIC2018 dataset, provided by the International Skin Imaging Collaboration (ISIC) 2018 Challenge, comprises 2,594 dermoscopy images, each meticulously annotated with pixel-level skin lesion labels. The PH2 dataset, acquired at the Dermatology Service of Hospital Pedro Hispano in Matosinhos, Portugal, contains 200 dermoscopic images of melanocytic lesions. These images are in 8-bit RGB color format with a resolution of 768\u2009\u00d7\u2009560 pixels. DermIS offers a comprehensive collection of dermatological images covering a range of skin conditions, including dermatitis, psoriasis, eczema, and skin cancer. DermQuest includes 137 images representing two types of skin lesions: melanoma and nevus.\n\nFor lung segmentation from chest X-rays, we utilized the JSRT56, NLM-MC57, NLM-SZ57, and COVID-QU-Ex58 datasets. The JSRT dataset consists of 247 chest X-ray images from Japanese patients, each accompanied by manually annotated ground truth masks that delineate the lung regions. The NLM-MC dataset was collected from the Department of Health and Human Services in Montgomery County, Maryland, USA. It includes 138 frontal chest X-rays, with manual lung segmentations provided. Of these, 80 images represent normal cases, while 58 exhibit manifestations of tuberculosis (TB). The images are available in two resolutions: 4020\u2009\u00d7\u20094892 pixels and 4892\u2009\u00d7\u20094020 pixels. The NLM-SZ dataset, sourced from Shenzhen No.3 People\u2019s Hospital, Guangdong, China, contains 566 frontal chest X-rays in PNG format. Image sizes vary but are approximately 3000\u2009\u00d7\u20093000 pixels. The COVID-QU-Ex dataset, compiled by researchers at Qatar University, comprises a large collection of chest X-ray images, including 11,956 COVID-19 cases, 11,263 non-COVID infections, and 10,701 normal instances. Ground-truth lung segmentation masks are provided for all images in this dataset.\n\nFor placental vessel segmentation from fetoscopic images, we utilized the FPD59 and FetReg60 datasets. The FPD dataset comprises 482 frames extracted from six distinct in vivo fetoscopic procedure videos. To reduce redundancy and ensure a diverse set of annotated samples, the videos were down-sampled from 25 to 1 fps, and each frame was resized to a resolution of 448\u2009\u00d7\u2009448 pixels. Each frame is provided with a corresponding segmentation mask that precisely outlines the blood vessels. The FetReg dataset, developed for the FetReg2021 challenge, is the first large-scale, multi-center dataset focused on fetoscopy laser photocoagulation procedures. It contains 2718 pixel-wise annotated images, categorizing background, vessel, fetus, and tool classes, sourced from 24 different in vivo TTTS fetoscopic surgeries.\n\nFor polyp segmentation from colonoscopic images, we utilized the KVASIR61 and CVC-ClinicDB62 datasets. Polyps are recognized as precursors to colorectal cancer and are detected in nearly half of individuals aged 50 and older who undergo screening colonoscopy, with their prevalence increasing with age. Early detection of polyps significantly improves survival rates from colorectal cancer. The KVASIR dataset was collected using endoscopic equipment at Vestre Viken Health Trust (VV) in Norway, which consists of four hospitals and provides healthcare services to a population of 470,000. The dataset includes images with varying resolutions, ranging from 720\u2009\u00d7\u2009576 to 1920\u2009\u00d7\u20091072 pixels. It contains 1000 polyp images, each accompanied by a corresponding segmentation mask, with annotations verified by experienced endoscopists. CVC-ClinicDB comprises frames extracted from colonoscopy videos and consists of 612 images with a resolution of 384\u2009\u00d7\u2009288 pixels, derived from 31 colonoscopy sequences. videos.\n\nFor breast cancer segmentation, we utilized the BUID dataset63, which consists of 630 breast ultrasound images collected from 600 female patients aged between 25 and 75 years. The images have an average resolution of 500\u2009\u00d7\u2009500 pixels. For foot ulcer segmentation, we utilized data from the FUSeg challenge64, which includes over 1000 images collected over a span of two years from hundreds of patients. The raw images were captured using Canon SX 620 HS digital cameras and iPad Pro under uncontrolled lighting conditions, with diverse backgrounds. For the segmentation of intraretinal cystoids from Optical Coherence Tomography (OCT) images, we utilized the Intraretinal Cystoid Fluid (ICFluid) dataset65. This dataset comprises 1460 OCT images along with their corresponding masks for the Cystoid Macular Edema ocular condition. For the segmentation of left ventricles and myocardial wall, we employed data examples from the ETAB benchmark66. It is constructed from five publicly available echocardiogram datasets, encompassing diverse cohorts and providing echocardiographies with a variety of views and annotations.\n\nFor 3D medical image segmentation tasks, we utilized two datasets from the MSD challenge4: Task04 (hippocampus segmentation) and Task03 (liver segmentation). The hippocampus segmentation task focuses on segmenting the hippocampal region from single-modality MR images. The hippocampus is a key brain structure involved in memory formation, spatial navigation, and emotion processing. Anatomically, it is often divided into anterior and posterior regions, each associated with distinct cognitive and emotional functions. In our experiments, we merged the anterior and posterior regions into a single segmentation category. The dataset includes MR scans from 394 patients, officially split into 260 training and 131 test cases. Since test annotations are not publicly available, we split the original training set into training and test subsets using an 80:20 ratio. During training, the training set was further split into training and validation sets, also with an 80:20 ratio. The Task03 dataset for liver segmentation contains 201 contrast-enhanced CT scans from patients with primary liver cancers and metastatic disease originating from colorectal, breast, and lung cancers. Among these, 123 cases are officially designated for training. We applied the same data-splitting strategy as used in the hippocampus dataset, resulting in 98 training cases and 25 test cases.\n\nFor all segmentation tasks except skin lesion segmentation, we used the Dice score as the evaluation metric, adhering to established conventions in the field67. The Dice score is calculated as \\(\\frac{2| {{{\\bf{A}}}}\\cap {{{\\bf{B}}}}| }{| {{{\\bf{A}}}}|+| {{{\\bf{B}}}}| }\\), where A represents the algorithm\u2019s prediction and B denotes the ground truth. For skin lesion segmentation, we followed the guidelines of the ISIC challenge68 and employed the Jaccard index, also known as intersection-over-union (IoU), as the performance metric. The Jaccard index is computed as \\(\\frac{| {{{\\bf{A}}}}\\cap {{{\\bf{B}}}}| }{| {{{\\bf{A}}}}\\cup {{{\\bf{B}}}}| }\\) for each patient case. These metrics provide a robust assessment of the overlap between the predicted segmentation mask and the ground truth.\n\nIn our method, mask augmentation was performed using a series of operations, including rotation, flipping, and translation, applied in a random sequence. The mask-to-image generation model was based on the Pix2Pix framework50, with an architecture that was made searchable, as depicted in Fig.\u00a01b. The tradeoff parameter \u03b3 was set to 1. We configured the training process to perform 5000 iterations. The RMSprop optimizer69 was utilized for training the segmentation model. It was set with an initial learning rate of 1e\u20135, a momentum of 0.9, and a weight decay of 1e\u20133. Additionally, the ReduceLROnPlateau scheduler was employed to dynamically adjust the learning rate according to the model\u2019s performance throughout the training period. Specifically, the scheduler was configured with a patience of 2 and set to max mode, meaning it monitored the model\u2019s validation performance and adjusted the learning rate to maximize validation accuracy. For training the mask-to-image generation model, the Adam optimizer70 was chosen, configured with an initial learning rate of 1e\u20135, beta values of (0.5, 0.999), and a weight decay of 1e\u20133. Adam was also applied for optimizing the architecture variables, with a learning rate of 1e\u20134, beta values of (0.5, 0.999), and weight decay of 1e\u20135. At the end of each epoch, we assessed the performance of the trained segmentation model on a validation set. The model checkpoint with the best validation performance was selected as the final model. The experiments were conducted on A100 GPUs, with each method being run three times using randomly initialized model weights. We report the average results along with the standard deviation across these three runs.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The skin lesion segmentation data used in this study are available in the ISIC, PH2 [https://www.fc.up.pt/addi/ph2The lung segmentation data used in this study are available in the JSRT, COVID-QU-Ex [https://www.kaggle.com/datasets/anasmohammedtahir/covidqu], NLM-MC, and NLM-SZ [http://archive.nlm.nih.gov/repos/chestImages.php] databases. The breast cancer segmentation data used in this study are available in the BUID [https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset?select=Dataset_BUSI_with_GT] database. The placental vessel segmentation data used in this study are available in the FPD [https://www.ucl.ac.uk/interventional-surgical-sciences/fetoscopy-placenta-data] and FetReg [https://www.ucl.ac.uk/interventional-surgical-sciences/weiss-open-research/weiss-open-data-server] databases. The polyp segmentation data used in this study are available in the KVASIR and CVC-Clinic [https://www.kaggle.com/datasets/balraj98/cvcclinicdb] databases. The foot ulcer segmentation data used in this study are available in the FUSeg [https://github.com/uwm-bigdata/wound-segmentation/tree/master] database. The intraretinal cystoid segmentation data used in this study are available in the ICFluid [https://www.kaggle.com/datasets/zeeshanahmed13/intraretinal-cystoid-fluid] database. The left ventricle and myocardial wall segmentation data used in this study are available in the ETAB database. The hippocampus and liver segmentation data used in this study are available in the MSD [https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2] database.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The source code used in this study is available at https://github.com/importZL/GenSeg and is archived at https://zenodo.org/records/1542767171. GenSeg is licensed under the Apache 2.0 License72.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Ronneberger, O., Fischer, P. & Brox, T. U-net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI: 18th International Conference (eds Navab, N., Hornegger, J., Wells, W. & Frangi, A.) Vol. 9351, 234\u2013241 (Springer, 2015).\n\nIsensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J. & Maier-Hein, K. 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Apache License 2.0. https://www.apache.org/licenses/LICENSE-2.0 (2004).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "P.X. acknowledges funding support from NSF IIS2405974, NSF IIS2339216, NIH R35GM157217, and NIH R21GM154171.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA\n\nLi Zhang,\u00a0Basu Jindal\u00a0&\u00a0Pengtao Xie\n\nBakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA\n\nAhmed Alaa\n\nDepartment of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA, USA\n\nAhmed Alaa\n\nHamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA\n\nRobert Weinreb\n\nDivision of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA\n\nDavid Wilson\n\nDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel\n\nEran Segal\n\nDepartment of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel\n\nEran Segal\n\nDepartment of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA\n\nJames Zou\n\nDepartment of Computer Science, Stanford University, Stanford, CA, USA\n\nJames Zou\n\nDepartment of Medicine, University of California San Diego, La Jolla, CA, USA\n\nPengtao Xie\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nL.Z. contributed to conceptualization, methodology, software, investigation, analysis, writing-original draft, and writing-editing. B.J. contributed to conceptualization, methodology, and writing-original draft. A.A., R.W., D.W., E.S., and J.Z. contributed to investigation, analysis, and writing-editing. P.X. contributed to conceptualization, methodology, investigation, analysis, writing-original draft, and writing-editing.\n\nCorrespondence to\n Pengtao Xie.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "E.S. is a paid consultant to Pheno.AI, Ltd. The other authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Rami Vanguri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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anticancer mechanisms in long-lived bats", + "pre_title": "Limited Cell-Autonomous Anticancer Mechanisms in Long-Lived Bats", + "journal": "Nature Communications", + "published": "03 May 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59403-z/MediaObjects/41467_2025_59403_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59403-z/MediaObjects/41467_2025_59403_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59403-z/MediaObjects/41467_2025_59403_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59403-z/MediaObjects/41467_2025_59403_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi", + "https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1252734", + "/articles/s41467-025-59403-z#Sec29" + ], + "code": [], + "subject": [ + "Cancer models", + "Senescence" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4001566/v1.pdf?c=1746356852000", + "research_square_link": "https://www.researchsquare.com//article/rs-4001566/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-59403-z.pdf", + "preprint_posted": "07 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Bats are remarkably long-lived for their size with many species living more than 20-40 years, suggesting that they possess efficient anti-aging and anti-cancer defenses. Here we investigated requirements for malignant transformation in primary bat fibroblasts in four bat species - little brown bat (Myotis lucifugus), big brown bat (Eptesicus fuscus), cave nectar bat (Eonycteris spelaea) and Jamaican fruit bat (Artibeus jamaicensis) \u2013 spanning the bat evolutionary tree and including the longest-lived genera. We show that bat fibroblasts do not undergo replicative senescence and express active telomerase. Bat cells displayed attenuated stress induced premature senescence with a dampened secretory phenotype. Unexpectedly, we discovered that bat cells could be readily transformed by only two oncogenic perturbations or \u201chits\u201d: inactivation of either p53 or pRb and activation of oncogenic RASV12. This was surprising because other long-lived mammalian species require up to five hits for malignant transformation. Additionally, bat fibroblasts exhibited increased p53 and MDM2 transcript levels, and elevated p53-dependent apoptosis. The little brown bat showed a genomic duplication of the p53 gene. We hypothesize that bats evolved enhanced p53 activity through gene duplications and transcriptional upregulation as an additional anti-cancer strategy, similar to elephants. In summary, active telomerase and the small number of oncogenic hits sufficient to malignantly transform bat cells suggest that in vivo bats rely heavily on non-cell autonomous mechanisms of tumor suppression.Biological sciences/Cell biology/SenescenceBiological sciences/Cancer/Cancer models", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Several bat species live >20\u201340 years, suggesting that they possess efficient anti-aging and anti-cancer defenses. Here we investigate the requirements for malignant transformation in primary fibroblasts from four bat species Myotis lucifugus, Eptesicus fuscus, Eonycteris spelaea, and Artibeus jamaicensis \u2013 spanning the bat evolutionary tree and including the longest-lived genera. We show that bat fibroblasts do not undergo replicative senescence, express active telomerase, and show attenuated SIPs with dampened secretory phenotype. Unexpectedly, unlike other long-lived mammals, bat fibroblasts are readily transformed by two oncogenic \u201chits\u201d: inactivation of p53 or pRb and activation of HRASG12V. Bat fibroblasts exhibit increased TP53 and MDM2 transcripts and elevated p53-dependent apoptosis. M. lucifugus shows a genomic duplication of TP53. We hypothesize that some bat species have evolved enhanced p53 activity as an additional anti-cancer strategy, similar to elephants. Further, the absence of unique cell-autonomous tumor suppressive mechanisms may suggest that in vivo bats may rely on enhanced immunosurveillance.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Bats exhibit diverse lifespans1,2 with many species showing exceptional longevity. Longevity quotient (LQ) is the ratio of observed maximum longevity to the predicted lifespan normalized to the body size. Across Chiroptera, 65 species have a mean LQ of 3.52. It means that these bats live 3.5 times longer than what is predicted by their body size3 making them excellent models to investigate adaptations for longevity.\n\nSeveral studies have analyzed the association of cancer prevalence with life history traits of organisms across phyla4,5,6,7. Cancer is a multistage process involving the accumulation of genetic and epigenetic mutations in mitotic cells, and the frequency of tumor formation depends on the number of cell divisions over time. Therefore, longer lifespans with more cell divisions, and longer exposure to exo- and endogenous stressors increase cancer incidence8,9. However, despite their exceptional lifespans, few to no tumors have been reported in long-lived wild and captive populations of bats6,10,11,12,13. Several comparative studies have uncovered cell autonomous (determined by intrinsic properties of the cells) and non-cell autonomous (determined by cell microenvironment or cell-cell interactions) adaptations in long-lived species that have anti-cancer functions. These include regulating telomerase activity in large species14,15,16, increase in tumor suppressor gene copies in elephants17,18,19, decreased non-LTR retrotransposition20, transposon-triggered innate immune responses for cell clearance21, enhanced DNA repair in long-lived rodents22, and regulation of uncontrolled proliferation by unique mechanisms such as early contact inhibition in the naked mole rat and massive necrotic cell death in the blind mole rat23,24.\n\nBat genomic studies revealed multiple adaptive changes in their immune systems25,26,27,28,29,30,31,32,33. Many of these changes temper inflammation (reviewed in ref. 34), which may have an anticancer effect. In addition, signatures of positive selection were detected in tumor suppressors35, DNA damage checkpoint and DNA repair pathway genes25,35, and growth hormones36. However, with the exception of a recently published study37, malignant transformation has not been experimentally investigated in bat primary cells.\n\nThe number of oncogenic hits required for malignant transformation in vitro varies by species. Human fibroblasts require 5 mutational hits for malignant transformation, while mouse cells require only 2: inactivation of either pRb or p53 tumor suppressors and activation of the Ras signaling pathway38. We previously investigated the numbers of oncogenic hits required for the transformation of multiple rodent species. The number of hits was shown to increase with animal size and lifespan (ranging between 2 in the mouse and 5 in the beaver), reflecting stricter control over cell proliferation in longer-lived and larger-sized species15.\n\nIn the present study, we investigate anti-cancer mechanisms in bats. We use primary wing fibroblasts from four species of bats; the little brown bat (Myotis lucifugus) with a maximum lifespan (MLS) of 34 years, the big brown bat (Eptesicus fuscus; MLS of 19 years), the cave nectar bat (Eonycteris spelaea; MLS of over 8 years; related species Eidolon helvum have MLS of 22 years), and the Jamaican fruit bat (Artibeus jamaicensis; MLS of 19 years). These species are from four bat families, span both bat suborders, and include the longest-lived genera Myotis39. M. lucifugus and E. fuscus are from the family Vespertilionidae, and A. jamaicensis is from Phyllostomidae, both lineages are within the suborder Yangochiroptera. E. spelaea, from the family Pteropodidae, is placed within the other suborder Yinpterochiroptera. We investigate telomerase activity, number of \u2018hits\u2019 required for malignant transformation, and response to stress-induced premature senescence (SIPS) in bat wing fibroblasts, alongside skin fibroblasts from mice and humans for comparison. Hereafter, we use the word \u2018bats\u2019 to refer to the four species examined in this study. We show that, surprisingly, bat fibroblasts require only 2 oncogenic hits for malignant transformation, which include inactivation of either p53 or pRb pathways and activation of RAS. Furthermore, bat cells exhibit elevated p53 activity and higher p53 transcript levels, which resemble anti-tumor adaptations observed in elephants17,18.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Telomeres in mammals are long tracts of tandem hexameric TTAGGG nucleotide repeats that protect the ends of the chromosomes and usually require a specialized RNA-dependent DNA polymerase, telomerase, to be replicated. Telomerase is repressed in somatic cells of large mammals14 but is reactivated during tumorigenesis. Hence, suppression of somatic telomerase activity represents a tumor suppressor mechanism that evolves with large body size40,41. We used Telomere Repeat Amplification Protocol (TRAP), a PCR-based assay, to test for telomerase activity.\n\nCell extracts tested by the TRAP assay showed that all four bat species possess telomerase activity in their wing fibroblasts. M. lucifugus and E. fuscus fibroblasts showed robust telomerase activity when compared to E. spelaea and A. jamaicensis (Fig.\u00a01a). We also confirmed this by testing several tissues we obtained from two of the bat species. Extracts from lung, spleen, wing, liver, heart, kidney, and brain tissues from E. fuscus and E. spelaea showed telomerase activity. Within each bat, post-mitotic tissues like the heart and kidney showed lower activity than the lung, liver, spleen, and wing tissues, with the spleen showing the highest activity (Fig.\u00a01b). Telomerase activity in the wing tissue extract of E. fuscus was higher than in E. spelaea, similar to the observations with their wing fibroblast cultures. Bat wing fibroblasts proliferated continuously in culture and did not show replicative senescence, which is consistent with the presence of active telomerase (Fig.\u00a01c). We then tested telomerase activity in wing fibroblasts from M. lucifugus and E. spelaea differing by at least 20 population doublings (PDs) (Fig.\u00a01d). We found small to negligible reduction in telomerase activity in fibroblasts differing by 20 PDs from both M. lucifugus and E. spelaea, suggesting that the telomerase activity is sustained over several cell divisions.\n\nTelomerase Repeated Amplification Protocol (TRAP) assay is shown using wing fibroblasts or tissue extracts from bat species listed- M. lucifugus (M.luc), E. fuscus (E.fus), E. spelaea (E.spe) and A. jamaicensis (A.jam). HeLa extract was used as the positive control, and the heat-inactivated extract of each sample was used as a negative control. Internal PCR control is used to show the absence of PCR inhibitors in samples. a Telomerase activity in bat wing fibroblasts derived from four different species. Experiments were repeated with n\u2009=\u20093 individual bats/species with similar results. b Representative TRAP assay showing telomerase activity in several tissues of bats E. fuscus and E. spelaea. Experiments were repeated with n\u2009=\u20092 individual bats/species with similar results. c Population doubling curves for four species of bats showing fibroblast growth rates in culture. Data are presented as mean\u2009\u00b1\u2009SD. M. luc, n\u2009=\u20092; E. fus, n\u2009=\u20093; E. spe, n\u2009=\u20092; A.jam, n\u2009=\u20093 individual bats. d Representative TRAP assay showing telomerase activity in bat wing fibroblasts of M. lucifugus and E. spelaea differing by at least 20 population doublings. Experiments were repeated with n\u2009=\u20092 individual bats/species with similar results. Source data are provided as a Source Data file.\n\nA previous study assessed telomerase expression in blood and wing-punch-derived fibroblast transcriptomes. It hypothesized absence of TERT-mediated telomere maintenance in the long-lived bat Myotis myotis (MLS 37 years) given that relative telomere length did not change with age yet there was no evidence of significant TERT expression in the transcriptome data sets examined1. We tested wing fibroblast cultures from this population of Myotis myotis bats using the TRAP assay and found that fibroblast extracts were positive for telomerase activity (Supplementary Fig.\u00a01e). This suggests that telomere maintenance in Myotis myotis also follows canonical telomerase-mediated mechanisms with small-bodied species expressing active telomerase, but without the expected increase in cancer incidence typical of other small-bodied species.\n\nCell-type and species-specific differences have been demonstrated in the number of pathways to be perturbed (\u201chits\u201d) for oncogenic transformation. The number and type of hits required for transformation may provide insights into species-specific inherent barriers to cancer formation in vivo. Human fibroblasts have been shown to require five to six pathways altered, while mice require two oncogenic hits for malignant transformation38.\n\nWe sought to investigate if bat cells possess intrinsic mechanisms that confer resistance to malignant transformation. Wing fibroblasts from three bat species were used: M. lucifugus, E. fuscus, and E. spelaea. Stably transformed fibroblasts were generated by drug selection, expressing SV40 LT (binds and inactivates both p53 and pRb family of proteins) and its mutants - SV40-LT-K1 (inactivates p53 only) and SV40 LT-\u0394 434-444 (inactivates pRb family only), each in combination with HRasG12V. The combination of HRasG12V with SV40 LT constitutes three hits, while HRasG12V with either of the SV40 LT mutants constitutes two oncogenic hits. Fibroblasts expressing GFP plasmid, SV40 LT-only, or HRasG12V-only were used as controls. Exogenous telomerase was not used because all these bat species showed intrinsic telomerase activity in our TRAP assays.\n\nFirst, we analyzed anchorage-independent growth in soft agar assay. Surprisingly, overexpression of HRasG12V along with either of the mutants of SV40 LT was sufficient for colony formation in all three bat species (Fig.\u00a02a). This suggests that two oncogenic hits -overexpression of HRas and inhibition of either p53 or pRb family of proteins - are sufficient for oncogenic transformation of bat fibroblasts. No colonies formed in controls.\n\na Representative images of colonies formed in soft agar assay by transformed bat wing fibroblasts from 3 bat species, M. lucifugus (n\u2009=\u20093), E. fuscus (n\u2009=\u20092), and E. spelaea (n\u2009=\u20093), at the end of three weeks. n= individual bats. Scale bar, 400 \u03bcm. Overexpression of HRasG12V along with SV40 LT constitutes 3 oncogenic hits, while HRasG12V along with either mutant of SV40 LT constitutes 2 oncogenic hits. b Mouse xenograft assay using bat wing fibroblasts overexpressing oncogenes form tumors in nude mice. Transformed cell lines from one individual bat per species was tested in the xenograft assay. Each cell line was tested with 8 injections in nude mice. Images shown were taken at the experiment endpoint. c Tumor growth curves of transformed bat wing fibroblasts show increase in tumor volumes with time in mouse xenograft assay. Tumor volumes are shown as mean\u2009\u00b1\u2009SD, n\u2009=\u20098 injections/cell line. Error bars show standard deviation (SD). Source data are provided as a Source Data file.\n\nWe further tested if transformed fibroblasts can form tumors in the mouse xenograft assay. No tumors formed in mice injected with control cells expressing SV40 LT or GFP only. Fibroblasts transformed with three forms of SV40 LT antigen along with HRasG12V formed tumors in all three bat species, albeit with different tumor growth kinetics (Fig.\u00a02b, c). HRasG12V with SV40 LT (three hits) grew the fastest (Fig.\u00a02c). Overexpression of HRasG12V with either mutant of SV40 LT was sufficient for tumor formation. Thus, just as with laboratory mouse fibroblasts, a minimum of two hits were sufficient to transform bat fibroblasts.\n\nStress-induced premature senescence\u00a0(SIPS) is the premature induction of senescence in cells using exogenous stressors. Two doses of \u03b3-radiation (10 and 20\u2009Gy) were used to induce SIPs in wing fibroblasts from four species of bats, dermal fibroblasts from humans, and laboratory and wild-caught mice, and the outcomes were compared. Cell proliferation was measured by the BrdU incorporation assay three days post-radiation. Cells from all species underwent cell cycle arrest, with more than 50% reduction in BrdU positive cells at 10 and 20\u2009Gy (Fig.\u00a03a). Day 12 post-radiation, the cells were analyzed for induction of senescence by staining for Senescence-associated \u03b2-galactosidase (SA- \u03b2-gal). At 10\u2009Gy, about 40\u201360% senescent cells were observed in human and mice fibroblast cultures. However, the number of senescent cells in M. lucifugus and A. jamaicensis were four-fold lower (Fig.\u00a03b, c) compared to human cells. We found that the number of senescent cells in E. fuscus was similar to mouse at 10\u2009Gy. No interspecies differences in SA-\u03b2-gal were observed at 20\u2009Gy, where most cells of all species showed positive staining. E. spelaea was an exception, as it did not show positive SA-\u03b2-gal staining at pH-6.0 and therefore could not be quantified (Fig.\u00a03b). This could be because of species-specific differences in SA-\u03b2-gal. However, these fibroblasts showed a flat and enlarged appearance typical of senescent cells. In summary, some bat fibroblasts displayed attenuated levels of SIPS compared to mouse and human cells.\n\na BrdU incorporation assay, day 3 post-radiation in bat wing fibroblasts. n\u2009=\u20093 biological replicates, data are presented as mean\u2009\u00b1\u2009SD. Comparison to intra-species controls was done using a paired two-tailed t test. Interspecies comparisons, between bats and wild-caught mice, for similar conditions were made using unpaired two-tailed t tests and are indicated with symbol \u2018#\u2019. b Representative SA-\u03b2 gal staining images of fibroblasts from all species 12 days post-radiation treatment. n\u2009=\u20093 biological replicates. Scale, 200\u2009\u03bcm. c Quantification of SA-\u03b2 gal staining showing percentage of positively stained cells. Data are presented as mean\u2009\u00b1\u2009SD, n\u2009=\u20093 biological replicates. Comparison to intra-species controls was done using a paired two-tailed t test. Interspecies comparisons, between bats and laboratory mice, for similar conditions was done using unpaired two-tailed t tests and is indicated with the symbol \u2018#\u2019. P-values are indicated. Source data are provided as a Source Data file.\n\nSenescent cells are characterized by the release of autocrine and paracrine secretory factors known as Senescence Associated Secretory Proteins (SASP)42,43. Transcriptomes from fibroblasts treated with \u03b3-radiation and allowed to senesce for 12\u2009d were analyzed for SASP factors and other signatures of senescence. Gene Set Enrichment Analysis (GSEA) was performed using SASP gene sets from published literature44,45,46. At 10\u2009Gy, M. lucifugus showed downregulation of several SASP factors that are generally upregulated during senescence (Fig.\u00a04a). This was also reflected in SA-\u03b2 galactosidase staining, where M. lucifugus showed a lower percentage of senescent cells at 10\u2009Gy. Detailed analysis of SASP gene expression showed that senescent M. lucifugus cells at lower doses of radiation altered expression of only about 30% of SASP genes compared to about 70% altered in mouse (Fig.\u00a04b). E. fuscus, E. spelaea, and A. jamaicensis also showed a lower fraction of genes upregulated in some SASP categories when compared to mice in a similar analysis (Supplementary Fig.\u00a02). Because interferons are one of the components of SASP, we directly compared interferon expression in radiation-treated cells from two different time points, 24\u2009h and 12 days post-radiation. Analysis of interferon expression revealed that senescent bat fibroblasts showed remarkably low interferon responses compared to human and mouse fibroblasts (Fig.\u00a04c).\n\na Gene Set Enrichment Analysis (GSEA) of transcriptome using senescence-related gene sets, day 12 post-radiation. Gene sets were collected from the MsigDB database and published literature (See \u201cMethods\u201d). b Scatter plots showing the expression differences between M. lucifugus and mouse senescent fibroblasts day 12 post-radiation in 3 SASP-related gene sets. Percentages in the left-upper and lower-right in the plot indicate the fraction of genes with higher expression in mouse and M. lucifugus, respectively. c GSEA analysis of inflammation-related gene sets in senescent fibroblasts, day 12 post-radiation. For this analysis, radiation-treated cells from 12 days were compared to those from 24\u2009h post-radiation. Source data are provided as a Source Data file.\n\nGenomic instability caused by \u03b3-radiation treatment may lead to apoptosis. Apoptosis assays on day 3 post-radiation showed that most species of bats displayed significant increase in apoptosis upon irradiation with 10 and 20\u2009Gy doses, in contrast to mice and human cells (Fig.\u00a05a). Elevated apoptosis in bats was also observed with lower doses of \u03b3-radiation (2 and 6\u2009Gy) (Supplementary Fig.\u00a01f). Since we observed elevated apoptosis in bat cells following \u03b3-radiation we hypothesized that bats may display elevated p53 activity. We tested p53 reporter activity in untreated fibroblasts from all species. We found that basal p53 transcriptional activity was higher in bat fibroblasts when compared to humans and mice. The vespertilionid bats, M. lucifugus and E. fuscus, showed at least four-fold higher p53-reporter activity, while E. spelaea and A. jamaicensis showed about 1.5-2-fold higher activity compared to humans and mice (Fig.\u00a05b). Cells transfected with SV40 LT-K1 mutant, which specifically inhibits p53 function, demonstrated the specificity of the reporter activity. We then treated cells with Nutlin-3, an Mdm2 agonist, which stabilizes p53 by inhibiting Mdm2-mediated p53 degradation. Bat fibroblasts showed higher apoptosis than mouse and human fibroblasts following treatment with Nutlin-3 (Fig.\u00a05c).\n\na Annexin V apoptosis assay in bat fibroblasts treated with 10 and 20\u2009Gy \u03b3-radiation, day 3 post-radiation treatment. Interspecies comparisons, between bats and wild-caught mouse, for similar conditions were made using unpaired two-tailed t-tests and are indicated with the symbol \u2018#\u2019. b p53-TA-luciferase reporter activity in normal proliferating bat wing fibroblasts and skin fibroblasts from mice and humans. Interspecies comparisons, between bats and wild-caught mouse, for similar conditions were made using unpaired two-tailed t tests and are indicated with the symbol \u2018#\u2019. c Annexin V apoptosis assay in proliferating cells treated with Nutlin-3 for 6\u2009h. Interspecies comparisons, between bats and laboratory mouse, for similar conditions were made using unpaired two-tailed t tests and are indicated with the symbol \u2018#\u2019. Data are presented as mean\u2009\u00b1\u2009SD. n\u2009=\u20093, biological replicates. Comparison to intra-species controls was done using a paired two-tailed t-test. All p-values are indicated. Source data are provided as a Source Data file.\n\nWe performed RNA sequencing of bat wing fibroblasts from M. lucifugus, E. fuscus, E. spelaea, and A. jamaicensis, along with skin fibroblasts from laboratory mice, wild-caught Mus musculus mice, and humans treated with 10 and 20\u2009Gy doses of \u03b3-radiation, at two time points, 24\u2009h and 12 days post-radiation treatment. Both principal component analysis (PCA) and hierarchical clustering showed species-wise clustering of samples (Supplementary Figs.\u00a01a, b). Transcript levels of p53 were elevated in untreated bat fibroblasts compared to human skin fibroblasts (Fig.\u00a06a,\u00a0b). The transcript profile of TP53 in skin fibroblasts from mice was comparable to that of bats, although they did not show higher transcriptional activity in the functional reporter assay in cells.\n\na, b Boxplots showing TP53 transcript levels in untreated control and radiation-treated cells at (a) 24\u2009h and (b) 12 days post-radiation. The box plots display the median, the 1st, and 3rd quartiles; the whiskers show a 1.5\u2009\u00d7\u2009interquartile range. Data points outside the whiskers are outliers. c, d Boxplots showing the expression changes of (c) MDM2 and (d) WRAP53 in each species at 24\u2009h post-radiation. The box plots display the median, 1st, and 3rd quartiles; the whiskers show a 1.5\u2009\u00d7\u2009interquartile range. Data points outside the whiskers are outliers. M. lucifugus n\u2009=\u20094 (n\u2009=\u20093 in 12\u2009d control); E. fuscus n\u2009=\u20094 (n\u2009=\u20093 in all 12\u2009d samples); A. jamaicensis n\u2009=\u20094 (n\u2009=\u20093 in all 12\u2009d samples); E. spelaea n\u2009=\u20094; H. sapiens n\u2009=\u20097 (n\u2009=\u20093 in all 12\u2009d samples); M. musculus n\u2009=\u20094; M. musculus (wild) n\u2009=\u20094 (n\u2009=\u20093 in 24\u2009h:10\u2009Gy). e Heatmap showing the expression changes of TP53 related genes 24\u2009h after irradiation. f GSEA analysis for cell cycle, DNA repair, and TP53 related gene sets in fibroblasts 24\u2009h post-radiation. Source data are provided as a Source Data file.\n\nWe then analyzed TP53 transcriptional targets and regulators (Fig.\u00a06e). M. lucifugus, E. fuscus, and A. jamaicensis showed upregulation of BAX, a direct transcriptional target involved in p53-mediated apoptosis, along with BCL247. MDM2 (mouse double min 2) is a key p53 antagonist that regulates p53 in a ubiquitination-dependent and independent manner48,49. MDM2 transcripts were upregulated in M. lucifugus and E. fuscus (Fig.\u00a06c). WRAP53 (WD40 encoding RNA anti-sense to TP53) is a natural anti-sense transcript nested in the TP53 gene50,51. All bats showed higher levels of WRAP53 transcripts when compared to humans and mice, in both treated and untreated cells. A. jamaicensis showed higher levels of WRAP53 transcripts compared to other bat species (Fig.\u00a06d).\n\nDNA repair genes have been shown to be positively selected in bats25,52. Genes involved in DNA damage (ATM, ATR, ChEK1, GADD45A, PCNA) and cell cycle arrest genes (CDKN1A, CCNG1) were upregulated in most bat species (Fig.\u00a06e). PCNA showed higher upregulation in M. lucifugus in radiation-treated cells. Basal levels of Cold-induced RNA binding protein (CIRBP) with roles in DNA double-strand repair and stabilization of transcripts involved in cellular stress levels were higher in E. fuscus, E. spelaea, and A. jamaicensis compared to humans and mice53 (Fig.\u00a06e).\n\nWe further performed GSEA using different p53 pathway gene sets. Since p53 is the universal pathway activated on radiation treatment, all species showed enrichment of p53 pathway terms (Fig.\u00a06f). However, M. lucifugus showed higher enrichment for some of the TP53-related pathway terms compared to other species. For example, genes of Reactome terms like \u201cTranscription of cell cycle genes, G2, G1- cell cycle arrest\u201d, \u201cRegulation of TP53 activity by phosphorylation\u201d were upregulated in M. lucifugus. The proapoptotic term \u201cReactome TP53 regulates transcription of genes involved in cytochrome C release\u201d was also highly enriched in M. lucifugus at 10\u2009Gy (Fig.\u00a06f).\n\nGSEA for different pathways involved in DNA repair showed that M. lucifugus cells treated with 20\u2009Gy showed slightly higher enrichment for DNA repair pathway terms at 24\u2009h post-radiation treatment (Fig.\u00a06f). However, overall, the DNA repair pathway terms were not specifically enriched in all bat species in our data sets.\n\nA p53-mediated apoptotic response to SIPS was also observed in a previous study with elephant fibroblasts. It was shown that the expansion of TP53 copy number in elephants (20 copies) with several TP53 retrogenes showing expression could be responsible for this phenotype17,18,54. We analyzed genomes of several bat species to see if TP53 copy number expansion has occurred in Chiroptera (Fig.\u00a07a). We found that several bat species have 2\u20134 copies of TP53. Most intriguingly, the vespertilionid bats in this study, M. lucifugus and E. fuscus, seem to possess 7 and 3 copies of TP53, respectively. Of the seven copies of TP53 in M. lucifugus, one full copy duplication and five short retrocopies seem to exist in the available genome assembly, Myoluc2.0 (GCA_000147115.1). Potentially erroneous copy number estimation can result from a fragmented genome assembly. Therefore, we further analyzed and found the two copies in the HiC-guided assembly of the M. lucifugus genome (Myoluc2.0_HiC)55,56. We additionally performed Cas9-targeted sequencing using guides directed to TP53 copies in the M. lucifugus genome and obtained one long read (118\u2009K) covering the TP53 region of M. lucifugus in our preliminary analysis. This long read sequence suggests the presence of two full-length copies of TP53 (Fig.\u00a07b).\n\na TP53 copy numbers are shown as the bar at the right side. The phylogenetic relationship between species were collected from https://vertlife.org/. b TP53 full copy duplications from genome, HiC-guided assembly, and long-read sequencing in M. lucifugus.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59403-z/MediaObjects/41467_2025_59403_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59403-z/MediaObjects/41467_2025_59403_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59403-z/MediaObjects/41467_2025_59403_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59403-z/MediaObjects/41467_2025_59403_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59403-z/MediaObjects/41467_2025_59403_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59403-z/MediaObjects/41467_2025_59403_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59403-z/MediaObjects/41467_2025_59403_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Bats are very long-lived for their size, and tumors are rarely found in bats6,10,11,12,13. The number of oncogenic hits required for malignant transformation highlights species-specific inherent barriers to cancer development. For example, human fibroblasts have been shown to require five oncogenic hits for transformation (activation of telomerase, oncogenic Ras signaling, inactivation of p53, pRb, and PP2A), whereas mice require only two (inactivation of either pRb or p53 and activation of Ras signaling)38. The number of oncogenic hits generally increases with body size and lifespan. For example, chinchilla and porcupine fibroblasts require 3 hits, naked mole rat fibroblasts require 4 hits, and beaver fibroblasts require 5 hits15. Here we discovered, unexpectedly, that all three species of bats tested, M. lucifugus, E. fuscus, and E. spelaea, representing the two bat suborders, underwent malignant transformation with two hits - the expression of HRasG12V and inactivation of either pRb or p53. This suggested that bat fibroblasts are easily transformed and do not possess any peculiar inherent barriers to cell transformation. We also tested if our observation holds true in bat epithelial cells and if they can be easily transformed. We were able to derive kidney epithelial cells from E. fuscus and tested their ability to undergo oncogenic transformation using HRasG12V and SV40 LT. Like their corresponding fibroblasts, kidney epithelial cells from E. fuscus were easily transformed and were capable of adherence-independent growth in soft agar assay (Supplementary Fig.\u00a01h). While we were drafting this manuscript, a similar study was published reporting that fibroblasts from seven different bat species, native to the Asian continent, undergo malignant transformation with overexpression of HRasG12V and SV40\u00a0LT antigen37, indicating that this ease of malignant transformation is a wide spread phenomenon in bats. Transformed colonies of one of the seven bat species, Myotis pilosus, a member of the longest-lived genera of bats, proliferated more slowly, which led the authors to identify a decrease in expression of HIF5, COPS5, and RPS3 as potential mechanisms of cancer resistance in this particular bat37. However, this does not explain the paradox why species whose cells can be transformed with only two oncogenic hits show cancer resistance in vivo.\n\nDownregulation of somatic telomerase activity is a tumor suppressor mechanism that evolved in mammals with a body mass larger than 5\u201310\u2009kg40. Cancerous cells in these species must re-activate telomerase or use alternate telomerase pathways for survival15,57. The presence or absence of telomerase activity in somatic cells and tissues has been tested only for a few bat species16. It has been observed that transcript levels of TERT are generally low in somatic cells, and PCR-based enzymatic assays are a better readout for telomerase activity. Having tested five bat species representing different clades, using the TRAP assay, we confirm that bats have telomerase activity in their somatic cells and tissues, which is consistent with their body mass being below 5\u2009kg. Within the five bat species that we tested, smaller bats M. lucifugus, Myotis myotis, and E. fuscus (body weight 5\u201340\u2009g) showed higher telomerase activity than larger bats, E. spelaea, and A. jamaicensis (40\u201380\u2009g). This fit the general negative correlation between somatic telomerase activity and body mass, independent of lifespan40.\n\nAlthough bat cells expressed telomerase and did not experience replicative senescence, they could be induced to undergo SIPS using \u03b3-radiation. Senescence is characterized by irreversible cell cycle arrest and release of pro-inflammatory factors called senescence-associated secretory phenotype (SASP) by the senescent cells42,43,58. The senescence program is a double-edged sword, though it prevents the inheritance of mutations precluding potential malignant transformation, it also promotes age-related inflammation contributing to the aging process59. M. lucifugus showed distinct features in our analysis of SASP in senescent bat fibroblasts. Senescent M. lucifugus cells showed a markedly low SASP factor expression, suggesting that this bat may avoid the negative aspects of senescence. All bat species exhibited a blunted inflammatory response, with significantly lower interferon expression than non-bat species (Fig.\u00a04c). Bats show reduced pro-inflammatory responses, contraction of the type I interferon family and constitutive expression of interferon-\u03b1 genes in cells and tissues, loss of PYHIN locus, and dampened activation of NLRP3 inflammasome25,34,60,61. These unusual immune adaptations in bats, which may have evolved to counteract damage due to increased metabolism and higher body temperatures during flight, or for the co-existence with viruses, may have been co-opted against the inflammation induced by senescent cells.\n\nWe observed that bat fibroblasts, when exposed to genotoxic stress, displayed enhanced apoptosis when compared to mice and humans. p53 is the primary responder to genotoxic stress and can induce apoptosis on extensive DNA damage62. We found that bat fibroblasts have enhanced basal TP53 transcript levels. All four species had higher levels of TP53 transcripts and showed two to four times higher basal transcriptional activity than in human or mouse cells. Higher transcript levels were sustained in cells upon radiation treatment and induction of senescence. Direct transcriptional targets of TP53, like CDKN1A, CCNG1 (cell cycle arrest), MDM2 (p53 regulator), and BAX (proapoptotic factor)63, were upregulated in most species, albeit with enhanced levels in some bats. Since skin tissue predominantly consists of epithelial cells, we analyzed TP53 transcript expression in the skin tissue transcriptome of three bats species. When compared to mouse skin, we found that TP53 transcript levels were higher in bats (Supplementary Fig.\u00a02c). We speculated if higher TP53 transcription levels could be caused by transposons inserting nearby and providing promoter on enhancer sequences. Indeed, we found that in both bats and non-bat species, the 20 Kb upstream sequences of the 1st coding exon of TP53 are enriched in TEs, especially in the 3rd intron of WRAP53, which partially overlaps with the TP53. These 20 Kb upstream sequences are predicted to contain many high-confidence promoters in all species examined (Supplementary Fig.\u00a02d), which may include original and TE-derived promoters. Interestingly, we found that bats without TP53 duplication have higher number of predicted promoters (6\u20137 predicted promoters) compared to mice, human, and other mammals (5 predicted promoters) (Supplementary Fig.\u00a02d). Therefore, in these bat species, these extra promoters can potentially enhance TP53 transcript levels even without gene duplications.\n\nHow do bat cells tolerate higher p53 protein levels in their cells? Our transcriptome analysis provides few clues, although no universal pattern applicable for all four bat species emerges. Bat fibroblasts treated with radiation upregulated MDM2, with most upregulation seen in the vespertilionid bats M. lucifugus and E. fuscus (Fig.\u00a06c). MDM2, a transcriptional target of TP53, is an E3 ubiquitin ligase that negatively regulates TP53 activity by ubiquitin-dependent and independent mechanisms48,49. Interestingly, bat-specific nucleotide changes in the nuclear localization signal of TP53 and nuclear export signal of MDM2 are reported, which may alter the pattern of transactivation by changing some of the numerous sites on p53 that undergo posttranslational modifications, providing a fine tuning of p53 activity in response to various stimuli25. WRAP53 is a natural antisense transcript of TP53 and, in a non-reciprocal manner, positively regulates p53 activity by preventing degradation of TP53 mRNA50,51. All four bat species show enhanced basal levels of WRAP53, sustained following radiation treatment, and not observed in humans or mice. A. jamaicensis shows the highest expression of WRAP53 expression\u00a0(Fig. 6d). Additionally, p53 was shown to undergo positive selection in M. davidi, one of the longest-lived bats25. Interestingly, FBX031, which promotes degradation of Mdm2 to increase p53 levels64, underwent massive expansion in microbats with Myotis lucifugus and Myotis brandtii genomes containing over 50 copies33,36,65. This expansion may also increase p53 levels and activity.\n\nEnhanced reliance on apoptosis has been observed in the other long-lived and cancer-resistant species, the elephants. It was shown that the expansion of TP53 copy number in elephants (20 copies) with several TP53 retrogenes showing expression could be responsible for this phenotype17,18,54. We analyzed genomes of several bat species to see if TP53 copy number expansion has occurred in Chiroptera. We found that several microbats have 2\u20134 copies of TP53. Most intriguingly, the vespertilionids in this study, M. lucifugus and E. fuscus, seem to possess 7 and 3 copies of TP53, respectively. Of the seven copies of TP53 in M. lucifugus, one is a full copy duplication and five are short retrocopies. Whether the p53 duplication plays a role in enhanced basal p53 levels remains to be determined. Elevated p53 activity is likely to contribute to cancer resistance in bats, as increasing p53 dosage in mice significantly increased their resistance to cancer66,67.\n\nOverall, bats have been shown to have several pro-tumorigenic characteristics such as flight-related high metabolism and oxidative stress, DNA damage, viruses34,68,69,70, and from this study, lack of replicative senescence, easy oncogenic transformation of cells, and sustained telomerase activity. However, these may be counterbalanced by adaptations that lower oxidative stress, torpor-related lower metabolism, adaptations in GH/IGF1 axis, enhanced DNA repair25,34,36,71,72,73, and from our study, anti-tumorigenic properties like apoptotic response to genotoxic stress, lower SASP and inflammatory response to radiation stress, and enhanced expression of tumor suppressor protein, p53. Furthermore, bats have unique immune systems which allows them to survive a wide range of deadly viruses, and many unique immune adaptations have been described in bats (reviewed in ref. 34). Most knowledge of the bat immune systems comes from studies of bat tolerance to viral infections deadly to humans. However, these or similar immune adaptations may also recognize and eliminate tumors. No direct experimental evidence of immune surveillance or cancer immunoediting is available in bats. Chronic inflammation is known to facilitate tumor formation74, and the common immune adaptation found in bats is reduced inflammation when exposed to pathogens. CD8\u2009+\u2009T cells, Natural Killer (NK) cells, and macrophages have roles in the immune surveillance of tissues. Single-cell and genomics analysis have shown an expanded and diverse KLRC/KLRD family of natural killer cell receptors, MHC class I genes, and type I interferons in Rousettus aegyptiacus27. R. aegyptiacus also showed novel subsets of monocytes in response to pathogenic stimuli and unique leukocyte transcriptional subsets in circulation75,76. A high proportion of CD8\u2009+\u2009T cells, tissue-resident memory T cells, and long-lived effector memory natural killer T-like cells were shown to occur across 19 organs in Rhinolophus sinicus77. More interestingly, an analysis of juvenile and adult Rousettus aegyptiacus revealed an unchanged lymphocyte proliferative capacity with age76.\n\nThe NK cells have been shown to be a critical cell type mediating immune surveillance of tumor cells. We analyzed bulk RNA-seq data from tissues of big brown bat (E. fuscus), cave nectar bat (E. spelaea), Jamaican fruit bat (A. jamaicensis) and rat, mouse, and naked mole rats78 and compared them to the NK cell-related transcriptome signatures from published studies79,80,81 and MsigDB (Supplementary Fig.\u00a01g). Remarkably, all the comparisons show higher expression of the 3 NK signatures in bats, indicating that the NK cell composition in bat skin is higher compared to non-bat species. Although awaiting direct experimental evidence, these studies suggest that bats may rely on enhanced immune surveillance to eliminate pre-malignant cells from their tissues.\n\nIn summary, our study demonstrates that bat fibroblasts undergo malignant transformation with just two oncogenic hits. Bat cells constitutively express telomerase and do not need to bypass the replicative senescence barrier for malignant transformation. We also show that bats have elevated transcriptional levels and signaling through p53 pathways with some species showing genomic duplications (Fig.\u00a08). Given the current knowledge of the unique immune adaptations in bats, we hypothesize, that bats may rely more on non-cell autonomous tumor suppressor mechanisms for elimination and detection of cancerous cells in vivo.\n\nOur findings suggest that bat fibroblasts possess pro-tumorigenic characteristics like a lack of replicative senescence, easy oncogenic transformation, and sustained telomerase activity, and are easily transformed with two oncogenic hits. However, they also possess anti-tumorigenic properties like apoptotic response to genotoxic stress, lower SASP, and enhanced expression of tumor suppressor protein, p53. We hypothesize that a balance of these properties and non-cell autonomous factors, like their unique immune system adaptations, may contribute to their resistance to cancer. Created in BioRender. Athar, F. (2025) https://BioRender.com/b33q059.\n\nLimitations of the study: Fibroblasts have been traditionally used for cross-species studies of malignant transformation, as other cell types require unique culture conditions that, in many cases, are species-specific and have not been established for the exotic species. However, requirements for malignant transformation may differ across cell types. Therefore, the conclusions of this study are limited to fibroblasts.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59403-z/MediaObjects/41467_2025_59403_Fig8_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "All animal experiments were approved by the University of Rochester Committee for Animal Research (UCAR), Protocol number 2017-033, and complied with relevant ethical regulations. Myotis lucifugus wing fibroblasts from three individuals were from Richard Miller collection at the University of Michigan. Eptesicus fuscus fibroblasts were isolated from wing tissue of four individual bats from the Northeast Ohio Medical University colony. Eonycteris spelaea fibroblasts were isolated from wing tissue of four individual bats from a breeding colony housed at Duke NUS in Singapore. Artibeus jamaicensis fibroblasts were isolated from wing tissue of four individual bats from the Colorado State University colony. Maximum lifespan (MLS) information for M. lucifugus, E. fuscus, and A. jamaicensis were obtained from AnAge82. MLS for E. spelaea is from personal communication from bat researchers. Myotis myotis were sampled in Morbihan, Brittany in North-West France, July 2021 in accordance with the permits and ethical guidelines issued by \u2018Arr\u00eate \u0301\u2019 by the Pr\u00e9fet du Morbihan (Brittany) and the University College Dublin ethics committee. This population has been transponded and followed since 2010 as part of on-going mark-recapture studies by Bretagne Vivante and the Teeling laboratory.\n\nPrimary fibroblasts were isolated from wing tissue of big brown bats, cave nectar bats, and Jamaican fruit bats, and little brown bats using a published protocol83. Briefly, about 1\u2009cm2 wing tissue was minced ~1\u2009mm pieces using a scalpel. Minced tissue was digested for 30\u201345\u2009min in 10\u2009ml of DMEM/F12 (Fisher Scientific, 11-320-082) media with LiberaseTM (Millipore Sigma, 5401127001) and 1X antibiotic/antimycotic (Sigma-Aldrich, 15240062) at 37\u2009\u00b0C. The digested tissue was washed 3X with DMEM/F12 containing 15% FBS, 1X antibiotic/antimycotic. The digested tissue pellet was resuspended and plated in the same media and allowed to grow for 3\u20137 days with media replenishment as required83. Isolated wing fibroblasts were then grown in EMEM (ATCC) containing 15% fetal bovine serum (GIBCO), 100\u2009U/mL penicillin, and 100\u2009mg/mL streptomycin antibiotics (GIBCO). Culture conditions were 37\u2009\u00b0C with 5% CO2 and 5% O2. Cells were passaged when 80\u201390% confluent. Myotis myotis primary fibroblasts were obtained from collaborators who generated and expanded from non-lethally sampled 3\u2009mm wing biopsies using methods detailed in ref. 1. At least three individuals of each species were sampled, generating independent cell lines. Cells with PD lower than 30 were used for all experiments. Population doubling was calculated using the formula: [3.32*log (cell number harvested - cell number seeded)]\u2009+\u2009PD of cells seeded.\n\nTo isolate kidney epithelial cells, kidneys were minced and digested with LiberaseTM at 37\u2009\u00b0C for 30\u2009min. Digested tissue was strained using a 100 \u03bcm cell strainer, and clumps were pressed using a syringe plunger. Cells were pelleted and incubated in 1\u2009\u00d7 RBC lysis buffer for 10\u2009min in the dark with occasional shaking. Following a wash with PBS, cells were plated and grown in DMEM with 10 % FBS, 1X Antibiotic-Antimycotic, and 1\u2009\u00d7 Primocin (InvivoGen). Generally, fibroblasts are easily detached during trypsinization when compared to cuboidal epithelial cells. Fibroblast contamination was minimized by a short trypsinization and discard of detached cells during regular passaging.\n\nTRAP assay was performed using the TRAPeze kit (Cat. #S7700, Millipore Sigma). Briefly, 0.5 million cells were resuspended in CHAPs lysis buffer, and the amount of protein was estimated using Pierce BCA Protein Assay Kits (Cat. # 23225, Thermo Fisher Scientific). TRAP assay was performed using 250\u2009ng of cell extracts, and the manufacturer\u2019s instructions were followed. HeLa cell lysates were used as positive controls. Uncropped gel images are provided in the Source Data file.\n\nFollowing plasmids were used in this assay to generate stable cell lines: pCMV-HRasG12V (Clontech), Piggyback vectors, pPB-SV40 LT, pPB-SV40 LT-K1, pPB-SV40 LT- \u0394434-444, pBase, GFP control (Launchpad AVA2590, plasmid #85442). Cells were transfected using Amaxa nucleofector (Lonza) using the manufacturer\u2019s protocol T-20. Stable cell lines were generated using puromycin (0.25-0.5\u2009\u03bcg/mL) or hygromycin (25\u201350\u2009\u03bcg/mL), depending on the plasmids and their combinations. For soft agar assay, 2X complete medium was prepared using 2X EMEM, 30% FBS, and 2X antibiotics as required. A base layer was poured into plates after mixing with 1% agarose (Difco Agar Noble). The top layer was made similarly, where 2X complete medium was mixed with 0.8% agarose and 10,000 cells/35\u2009mm well and layered on top of the base layer. Following the solidification of agarose, 1\u2009mL 1X complete media with antibiotics was added on top and refurbished twice a week. Plates were incubated at 37\u2009\u00b0C in a humidified incubator for three weeks. Plates were then imaged for colonies in soft agar.\n\nAnimal experiments were performed under pre-approved protocols and in accordance with guidelines set by the University of Rochester Committee on Animal Resources (UCAR). Fibroblasts stably expressing oncogenes or a combination of oncogenes were tested for tumor formation in female NIH III nude mice (Charles River, Crl:NIH-Lystbg-JFoxn1nuBtkxid). Mice were 3\u20138 weeks of age and were kept under specific pathogen-free (SPF) conditions at the vivarium of the University of Rochester. Mice were housed in 12\u2009h light/ 12\u2009h dark cycle, at temperatures 18\u201323\u2009C, with 40\u201360% humidity. Each flank of the nude mice was injected with two million cells in 100\u2009\u03bcL PBS mixed with an equal volume of Matrigel using a 22-gauge needle. A total of 8 injections per cell line were tested. Tumor formation was monitored twice a week, and dimensions were measured using Vernier calipers. As per UCAR guidelines, a tumor long diameter <5\u2009mm was considered negative, and 20\u2009mm was considered the tumor burden endpoint. Maximal tumor burden was not exceeded. Mice that did not reach tumor burden endpoints were terminated after a maximum of 60 days. Euthanized mice were photographed, and tumors were excised, photographed, weighed, frozen at \u221280\u2009\u00b0C, and preserved in formalin.\n\nRadiation treatment was performed using a \u03b3-radiator (Model 8114 Shepherd Cs137). Cells were seeded 24\u201348\u2009h before treatments. Following treatment with radiation doses 10 and 20\u2009Gy, the cell culture medium was immediately changed, and cells were returned to the incubator.\n\nFor induction of senescence, radiated cells were incubated for 12 days, and the culture medium was replaced twice a week. For Nutlin-3 (CAS 548472-68-0, Santa Cruz) treatment, different concentrations of Nutlin-3 (10 and 50\u2009\u03bcM) in DMSO were added to cells and incubated for 24\u2009h for apoptosis assay or 6\u2009h for western blotting.\n\nCells were seeded 24\u2009h before treatment and were allowed to grow in the presence of 3\u2009\u03bcg/mL BrdU (BD Pharmingen) for 48\u2009h. Cells were trypsinized and fixed with 70% cold ethanol for 30\u2009min. For staining, fixed cells were washed twice with PBS and treated with 2\u2009N HCl for 30\u2009min for DNA denaturation. Following two more PBS washes, cells were incubated with 5% BSA for 1\u2009h at room temperature (RT). Cells were then incubated with anti-BrdU (Alexa Fluor\u00ae 647 Mouse anti-BrdU, Clone 3D4 (RUO)) antibody overnight at 4\u2009\u00b0C. Following 2\u2009\u00d7 PBS washes, stained cells were analyzed by flow cytometry with appropriate positive and negative controls. Gating for flow cytometry was set using negative control cells (Supplementary Fig.\u00a03b).\n\nApoptosis assay was performed using Annexin V FLUOS staining kit (Roche), and the manufacturer\u2019s instructions were followed with slight modifications. Briefly, culture supernatant and PBS washes were collected. Cells were then trypsinized briefly, and the cell pellet was mixed with the floating cells collected from the supernatant and washes. Following two more washes with PBS, the cell pellet was resuspended in Annexin-V-FLUOS labeling reagent containing Annexin V and propidium iodide. Following a 10\u2009min incubation on ice, cells were immediately analyzed by flow cytometry with appropriate positive and negative controls. Samples from each replicate were analyzed using gates set with untreated controls of that replicate (Supplementary Fig.\u00a03a).\n\nIrradiated cells were incubated for 12 days, and the media was replaced twice a week. After 12 days, approximately 24\u201348\u2009h before staining, cells were seeded to be approximately 40% confluent. Cells were washed 2X with PBS and fixed for 5\u2009min with 2% formaldehyde. Cells were washed gently to remove formaldehyde and staining solution (1\u2009mg/ml X-gal in DMSO, 40\u2009mM Citric acid/Sodium phosphate buffer pH 6, 5\u2009mM potassium ferricyanide, 5\u2009mM potassium ferricyanide, 150\u2009mM NaCl, 2\u2009mM MgCl2) was added to the cells. Cells were incubated for 12\u201324\u2009h at\u00a037\u2009\u00b0C. Following the development of blue color, cells were washed 2X with PBS, overlayed with 70% glycerol, and imaged. Cells were counted under the light microscope, and the percentage of SA-\u03b2-gal positive cells was determined.\n\nCells were seeded in 24-well plates 24\u2009h before transfection. Following manufacturers\u2019 instructions, about 250\u2013300\u2009ng of plasmids were transfected using Fugene (Invitrogen). p53-TA-Luc (Clontech/Takara) was used to assay p53 transcriptional activity, and pRL-CMV (Promega) was used as the transfection control. Reporter activity was analyzed 48\u2009h post-transfection using the Dual luciferase reporter assay system and the GloMax 20/20 Luminometer (Promega). Manufacturers\u2019 instructions were followed, and activity ratios are reported as relative luciferase units (RLU).\n\nTotal RNA was extracted from irradiated or non-irradiated fibroblasts from four bat species, laboratory mice, wild-caught mice, and humans using PureLinkTM RNA Mini Kit (Thermo Fisher Scientific) following the manufacturer\u2019s instructions. NGS-TruSeq Stranded mRNA libraries were generated and sequenced with Illumina NovaSeq 6000 single-end 75\u2009bp sequencing at the University of Rochester Genomics Research Center. Whole skin tissue transcriptome was generated from three species of bats \u2013 E. fuscus (4 individuals), and A. jamaicensis (3 individuals), and E. spelaea (5 individuals).\n\nRaw reads were demultiplexed using conFig.bcl2fastq.pl (v1.8.4). Adapter sequences and low-quality base calls (threshold: Phred quality score <\u200920) in the RNA-seq reads were first trimmed using Fastp (0.23.4)84. For all species, the clean reads were aligned using Salmon (v1.5.1)85 to the longest coding sequence (CDS) of each gene extracted from the corresponding genome assembly based on genome annotations using GffRead (v0.12.7)86. The genome assemblies used in this study include GCA_003508835.1 (Eonycteris spelaea), EptFus1.0_HiC (DNA Zoo Consortium, Eptesicus fuscus), GCA_004027435.1 (Artibeus jamaicensis), Myoluc2.0_HiC (DNA Zoo Consortium, Myotis lucifugus), GCF_000001635.27 (Mus musculus), and GCA_000001405.29 (Homo sapiens). Human-referenced TOGA annotations were used when the original genomic annotations are not available87. The orthologous genes of each species to human reference were identified by performing a reciprocal blast search (BLAST\u2009+ v2.10.1)88 against human longest protein (GRCh38.p13; Ensembl database, release109) with parameters of \u201c-evalue 1e-05; -max_target_seqs 1\u201d, and hits with query coverage\u2009>\u200930% were retained. The values of read count and effective gene lengths for each gene were collected and integrated into gene-sample table according to their orthologous relationship. Salmon transcript counts were used to perform differential expression analysis. Only human genes with orthologs in all species were kept for the downstream analysis. To filter out low-expressed genes, only genes with all sample read counts sum\u2009>\u200910 was retained. The filtered count matrix was normalized using the median of ratios method89 implemented in DESeq2 package (v1.40.2)90. Since orthologous gene lengths could vary among different species, we implemented an additional length normalization step in the DESeq2 pipeline to avoid biased comparative quantifications resulting from species-specific transcript length variations. To do this, the matrix of effective lengths for each gene in each sample was delivered to the DESeq2 \u2018DESeqDataSet\u2019 object so that they are included in the normalization for downstream analysis. Differential expression analysis, using either irradiated cells (24\u2009h or 12 days) versus control (24\u2009h or 12 days) or only irradiated cells from 24\u2009h versus 12 days in each species, was performed using DESeq2. We considered all differentially expressed genes (DEGs) with an adjusted p\u2009<\u20090.05, and 1.5-fold expression change to be statistically significant in our analyses.\n\nHallmark gene sets and C2 curated gene sets from MsigDB were used for GSEA analysis using the ranked fold changes from the DEG analysis. The p-values were calculated using a permutation-based approach. For cellular senescence analysis, in addition to MsigDB gene sets, we also collected gene sets identified as the senescence signature using transcriptomic and proteomic approaches from published literature44,45,46. For NK cell transcriptional signature GSEA analysis in skin tissue transcriptome, bulk RNA-seq data from tissues of bats and rat, mice and naked mole rats78 and NK cell-related transcriptomes reported in published studies and MsigDB79,80,81 were used.\n\nBLAST was used to search for TP53 genes in bats genomes using the human TP53 protein\n\nsequence as a query. The best-matched genomic regions plus 5\u2009kb up- and downstream flanking\n\nsequences were extracted from the genome for GeneWise91 gene structure prediction. Predicted genes with more than 80% coverage of the human TP53 protein were treated as TP53 duplicates. To further confirm that the identified TP53 copies in M. lucifugus are not misidentified homologs from the TP53 gene family, additional blast analysis with human TP63 and TP73 was performed. Promoter prediction in \u2212\u200920\u2009kb upstream sequence of the TP53 gene was performed using Promoter 2.0 (https://services.healthtech.dtu.dk/services/Promoter-2.0/)92. The predicted promoters with score\u2009>\u20091 were considered as high-confidence promoters.\n\nBLAST was used to search for TP53 genes in bats genomes using the human TP53 protein sequences as a query. The best matched genomic regions plus 5\u2009kb up- and downstream flanking sequence were extracted from the genome for GeneWise gene structure prediction. Predicted genes with more than 80% converge of human TP53 protein were treated as the TP53 duplicates. To confirm that the identified TP53 copies in M. lucifugus are not misidentified homologs from the p53 gene family, additional blast analysis with human TP63 and TP73 was performed.\n\nHigh molecular weight (HMW) genomic DNA from a single M. lucifugus fibroblast line was isolated using the Nanobind \u00aeHMW DNA extraction kit for cultured cells (PacBio). M. lucifugus TP53 copy sequences found in the Myoluc2.0 genome assembly were used to design guide RNAs (listed in Supplementary Table\u00a02) using Benchling. Guides were designed to cut within sequences of TP53 gene copies and sequence across part of the gene into flanking regions. The Cas9 Sequencing Kit SQK-CS9109 (Oxford Nanopore Technologies) was used for targeted sequencing according to the manufacturer protocol. Briefly, extracted HMW gDNA was phosphatase-treated to remove pre-existing phosphorylated ends, followed by heat inactivation. Next, target regions were cleaved in vitro using Cas9 ribonucleoprotein complexes consisting of Alt-R\u00ae S.p. HiFi Cas9 Nuclease V3 (Integrated DNA Technologies), Alt-R\u00ae CRISPR-Cas9 tracRNA (Integrated DNA Technologies), and target-specific Alt-R\u00ae CRISPR-Cas9 crRNAs (Integrated DNA Technologies). Following cleavage, Cas9 was heat-inactivated, and adapters were ligated to the cleaved phosphorylated ends. Adapters from Ligation Sequencing Kit V14 SQK-LSK114 (Oxford Nanopore Technologies) were used in place of the adapter included in the Cas9 kit. Following adapter ligation, samples were cleaned up using AMPure XP Beads and loaded onto a R10.4.1 flow cell on a MinION Mk1C (Oxford Nanopore Technologies) for sequencing. Raw data was base-called in Super-High accuracy mode and aligned to M. lucifugus TP53 reference sequences in both Guppy and Dorado with read splitting enabled. The list of guides used is shown in Supplementary Table\u00a02.\n\nGraphPad Prism 10 was used to generate graphs and for statistical analysis for all data, excluding RNA-seq analysis. Error bars show standard deviation (SD). Two-tailed Student\u2019s t test was used to test the statistical significance of the differences between groups unless otherwise indicated. P\u2009<\u20090.05 was set as a threshold for statistical significance. Experiments were reproducible with at least 3 independent biological replicates. No statistical method was used to predetermine sample size, no blinding or randomization were used.\n\nFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The raw RNA sequencing data of all bat fibroblasts and tissues are available in the Gene Expression Omnibus (GEO) under accession: GSE262772. 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The work at Duke-NUS is funded by grants from the Singapore National Research Foundation (NRF-CRP10-2012-05), National Medical Research Council (OFIRG19nov-0050), and Ministry of Education (MOE2019-T2-2-130). This study was supported by grants from the National Institute on Aging to VG and AS, and grants from the Michael Antonov Foundation and Milky Way Research Foundation to VG and from a Science Foundation Future Frontiers (grant no. 19/FFP/6790) awarded to ECT.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Fathima Athar, Zhizhong Zheng.\n\nDepartment of Biology, University of Rochester, Rochester, NY, USA\n\nFathima Athar,\u00a0Zhizhong Zheng,\u00a0Max Zacher,\u00a0J. Yuyang Lu,\u00a0Yang Zhao,\u00a0Valentin Volobaev,\u00a0Andrei Seluanov\u00a0&\u00a0Vera Gorbunova\n\nSchool of Biology and Environmental Science, Belfield, University College Dublin, Dublin, Ireland\n\nSebastien Riquier,\u00a0Dominic Alcock\u00a0&\u00a0Emma C. Teeling\n\nDepartment of Anatomy and Neurobiology, Northeast Ohio Medical University, Rootstown, Ohio, USA\n\nAlex Galazyuk\u00a0&\u00a0Lisa Noelle Cooper\n\nDepartment of Microbiology, Immunology, and Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA\n\nTony Schountz\n\nProgramme in Emerging Infectious Diseases, Duke-NUS Medical School, Singapore 169857, Singapore; SingHealth Duke-NUS Global Health Institute, Singapore, Singapore\n\nLin-Fa Wang\n\nDepartment of Medicine, University of Rochester Medical Center, Rochester, NY, USA\n\nAndrei Seluanov\u00a0&\u00a0Vera Gorbunova\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nDesigned experiments: F.A., A.S., and V.G.; Performed experiments: F.A.; Bioinformatic analysis: Z.Z., S.R., and J.Y.L.; Assisted with experiments: M.Z., Y.Z., and V.V.; Contributed research materials: D.A., A.G., L.N.C., T.S., L.W., and E.C.T.; Data interpretation: F.A., Z.Z., S.R., E.C.T, A.S., and V.G.; Wrote manuscript: F.A., Z.Z., E.C.T., A.S., and V.G. All authors read and approved the manuscript.\n\nCorrespondence to\n Andrei Seluanov or Vera Gorbunova.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "L.W. and E.C.T. serve on the scientific advisory board of Paratus Sciences, a company developing the tools and methods necessary to understand bat biology and apply these insights to develop new therapies. Other authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Zachary Compton, Vadim Fraifeld who co-reviewed with Ekaterina Rudnitsky; Huabin Zhao and Shaying Zhao for their contribution to the peer review of this work. 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The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Athar, F., Zheng, Z., Riquier, S. et al. Limited cell-autonomous anticancer mechanisms in long-lived bats.\n Nat Commun 16, 4125 (2025). https://doi.org/10.1038/s41467-025-59403-z\n\nDownload citation\n\nReceived: 29 February 2024\n\nAccepted: 22 April 2025\n\nPublished: 03 May 2025\n\nVersion of record: 03 May 2025\n\nDOI: https://doi.org/10.1038/s41467-025-59403-z\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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b/b521cb75a3ec904a850ceea2a1378c2b7d71ca0198a1fe7b26f016051a573b40/metadata.json @@ -0,0 +1,124 @@ +{ + "title": "Quantized current steps due to the synchronization of microwaves with Bloch oscillations in small Josephson junctions", + "pre_title": "The quantised current steps due to the synchronisation of microwaves with the Bloch oscillations in small Josephson junctions", + "journal": "Nature Communications", + "published": "29 October 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53600-y/MediaObjects/41467_2024_53600_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53600-y/MediaObjects/41467_2024_53600_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://osf.io/g4x9p/" + ], + "code": [], + "subject": [ + "Nanoscale devices", + "Quantum metrology" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3848621/v1.pdf?c=1730286407000", + "research_square_link": "https://www.researchsquare.com//article/rs-3848621/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53600-y.pdf", + "preprint_posted": "04 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Synchronisation of the Bloch oscillations in a small Josephson junction (JJ) under the microwave radiation leading to the current quantisation at a current equal to Cooper-pair charge \u00d7 frequency \u00d7 integer has been proposed as the effect dual to the Shapiro steps. Experimental confirmation of this phenomenon was delayed for a long time until last year's breakthrough when the current quantisation was demonstrated in the superconducting nanowires. The compact high impedance environment of the nanowires played a key role in the experiment. Direct observation of the current quantization in the JJs would answer the fundamental question of the Bloch oscillations and open a more feasible road for the metrological application. Here we place JJs in the high impedance environment and demonstrate dual Shapiro steps for frequencies up to 24 GHz (I=7.7 nA). The current quantisation exists, however, only in a narrow range of JJ parameters, the critical current and capacitance. We carry out a systematic study to explain this by invoking the model of the JJ in presence of the thermal noise. The findings are important for the fundamental physics and application to quantum metrology.Physical sciences/Nanoscience and technology/Nanoscale devices/Superconducting devicesPhysical sciences/Physics/Quantum physics/Quantum metrologyJosephson Junctioncurrent standardShapiro stepsquantum phase slip", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "RShaikhaidarovSupplement.pdfSupplement: Calculation of the critical voltage", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Synchronization of Bloch oscillations in small Josephson junctions (JJs) under microwave radiation, which leads to current quantization, has been proposed as an effect that is dual to the appearance of Shapiro steps. This current quantization was recently demonstrated in superconducting nanowires in a compact high-impedance environment. Direct observation of current quantization in JJs would confirm the synchronization of Bloch oscillations with microwaves and help with the realisation of the metrological current standard. Here, we place JJs in a high-impedance environment and demonstrate dual Shapiro steps for frequencies up to 24\u2009GHz (I\u2009=\u20097.7\u2009nA). Current quantization exists, however, only in a narrow range of JJ parameters. We carry out a systematic study to explain this by invoking the model of a JJ in the presence of thermal noise. The findings are important for fundamental physics and application in quantum metrology.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "A seminal paper by Likharev and Averin published in 1985 predicted that synchronization of Bloch oscillations in a Josephson junction (JJ) with microwaves (MWs) should result in quantized current steps, Im = 2efm, where e is the electron charge, f is the microwave frequency, m is an integer1. The effect is dual to the well-known quantization of voltage observed in the JJ, so-called the\u00a0Shapiro steps. Significant efforts were made to observe this synchronization, with the most successful work performed by Kuzmin and Haviland who observed peaks in dI/dV curves at I\u2009=\u2009\u00b12ef2. However, the demonstration of true current quantization was not achieved until decades later, when dual Shapiro steps were seen in superconducting nanowires3. This breakthrough experiment was inspired by the theoretical work of Nazarov and Mooij4 and the observation of the coherent quantum phase slip (CQPS) through qubit spectroscopy5.\n\nYet, the accuracy of the quantized steps in nanowires is inferior to that of alternative systems that facilitate a controllable classical charge transfer through a quantum dot6 or turnstile pumps7. A significant drawback of nanowire devices is also their low fabrication yield, which is less than 30%. This comes as a result of their tiny, \u223c 10\u2009nm, dimensions being at the limit of modern nanotechnology capabilities. Even nanowires of identical geometry have wide variations of the superconducting parameters, like critical current, and consequently, the CQPS energy8. Exploring current quantization in more reliable JJs through the synchronization of external MWs with the Bloch oscillations could provide a solution.\n\nWe note that steps were recently reported in ultra-small JJs in a high impedance environment composed of large JJs9. However, the synchronization of the Bloch oscillations (the modulation of the current steps with the MW amplitude), must be proven. We also note that the steps in nanowires discussed in ref. 10 are unlikely to be attributable to superconducting behaviour as it is pointed out in Ref. 11.\n\nIn this work, we focus on the experimental study of dual Shapiro steps in JJs. Different aspects of such a system have been previously studied theoretically12,13,14. It is important to find parameters of the JJs and the surrounding circuit that will protect the Bloch bands from external current noise while simultaneously allowing the coupling of MWs to the JJ. The Bloch bands are formed by the periodic modulation of the system\u2019s energy with the induced charge q/2e, see the central panel of Fig.\u00a01a. The amplitude of this modulation, shown on the right panel of the figure, is the CQPS energy, ES. The system\u2019s energy also oscillates with the superconducting flux, \u03a6/\u03a60. The energies in the graphs are normalised by the JJ energy, EJ\u2009=\u2009\u2206RQ/2RN, where RQ\u2009=\u2009h/4e2\u2009\u2248\u20096.5\u2009k\u03a9 and RN are the quantum resistance and normal resistance of the JJ, \u2206 is the superconducting gap. The quantization of current occurs when the coherent tunnelling of the system with E(q/2e) is synchronized with the external MWs.\n\na The Bloch bands of the JJ in units of the Josephson energy EJ versus normalized flux \u03a6/\u03a60 and charge q/2e (EJ/EC\u2009=\u20094.4). The lowest band shows 2e periodic oscillations in the charge space, with the amplitude corresponding to the phase-slip energy ES. The three lowest Bloch bands are marked by different colours. For current quantization the Bloch oscillations in the lowest band are synchronized with the external MWs; (b) He FIB image of the device and the equivalent electric circuit. A small JJ is embedded into a high impedance environment formed by TiN inductances L1\u2009=\u20091.15\u2009\u00b5H and L2\u2009=\u20090.34\u2009\u00b5H and normal Pd resistors R\u2009=\u20096.3 k\u03a9.\n\nThe greatest challenge is to tackle Landau-Zener excitation between the lowest and the higher Bloch bands, which facilitates frequent switching between the branches of a hysteretic I\u2009\u2212\u2009V curve15,16. In response to this challenge, we explore parameters of the JJ and environmental circuit to find a balance between maximising ES and achieving adequate separation of the Bloch bands. At the same time, the JJ must have a differential quasiparticle resistance at low bias (below the superconducting gap 2\u2206/e) comparable to the RQ, and its Josephson energy close to the charging energy EJ \u223c EC1,17. Additionally, we protect the JJ from the EM noise of the environment by embedding it in an electric circuit with inductances, normal resistors, and quasiparticle traps (discussed in\u00a0Supplementary Notes\u00a03).\n\nWe experimentally demonstrate dual Shapiro steps, i.e., current quantization, in small JJs. The current plateaus are modulated with the MW amplitude, thus confirming the quantum coherent nature of the effect. We carefully explore the range of JJ parameters to maximize the quantized current and improve the accuracy of the quantization. Over 20 devices with different parameters of the JJ and sensitivity to the external MWs are explored. The largest observed quantized current and resistance at the current plateau are I1 \u223c 7.7\u2009nA (\u223c 2e \u00d7 23.895\u2009GHz) and 3.4\u2009k\u03a9 respectively. The quantization is seen only in devices with a JJ area smaller than 100\u2009\u00d7\u2009100\u2009nm2 and having an apparent critical voltage VC\u2217\u2009<\u20097\u2009\u00b5V. The performance of the different devices has been analysed to determine the optimum JJ parameters.\n\nA similar demonstration of the synchronization of Bloch oscillations with the MW and the current quantization in small Josephson Junctions was reported recently by the PTB group18,19.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53600-y/MediaObjects/41467_2024_53600_Fig1_HTML.png" + ] + }, + { + "section_name": "Results & discussion", + "section_text": "A helium FIB image of the JJ with the protective environment is shown in Fig.\u00a01b. The JJ has lateral sizes 40\u2009nm\u2009\u00d7\u200980\u2009nm, which, together with Al pads of size 1\u2009\u00d7\u20091\u2009\u00b5m2 (light areas on the right panel before the dark TiN meander in Fig.\u00a01b), give a capacitance CJ\u2009\u2248\u20090.3 fF, corresponding to a charging energy ECJ\u2009=\u2009e2/2CJ\u2009\u2248\u2009h\u2009\u00d7\u200965\u2009GHz. We found from the simulation of the experimental data that there is an additional stray capacitance CS\u2009\u2248\u20091.2fF between the junction and metallic parts of the circuit. This reduces the charging energy to EC\u2009=\u2009e2/2(CJ\u2009+\u2009CS)\u2009\u2248\u2009h\u2009\u00d7\u200911\u2009GHz. In what follows, we distinguish ECJ and EC. The Josephson energy of the junction, calculated from its normal resistance (RN\u2009\u2248\u20091.9\u2009k\u03a9), is EJ\u2009=\u2009h\u2009\u00d7\u200984\u2009GHz, with \u2206 = h\u2009\u00d7\u200949\u2009GHz being the superconducting gap of aluminium. Importantly, the corresponding plasma frequency is Ep\u2009=\u2009\\(\\sqrt{8{E}_{J}{E}_{C}}\\)\u2009\u2248\u2009h\u2009\u00d7\u200986\u2009GHz. Thus, the upper energy band is separated from the ground band by a large energy gap, close to 2\u2206, which reduces the Landau-Zener tunnelling. The ratio of the Josephson energy to the charging energy in the sample discussed below is EJ/EC\u2009=\u20097.6.\n\nThe measurement circuit is shown on the right side of Fig.\u00a01b. The JJ is embedded in a high impedance electromagnetic environment: there are four inductances arranged in sequence: two L1\u2009=\u20091.15\u2009\u00b5H and two L2\u2009=\u20090.34\u2009\u00b5H, and four Pd resistors of R\u2009=\u20096.3\u2009k\u03a9 screening the JJ from the external circuitry. The Pd/Al pads on both sides of L1 serve as the quasiparticle traps. A dc four-probe measurement scheme is connected with current, I+ and I\u2212, and voltage, V+ and V\u2212, leads passed through a box with a cascade of LTCC low-pass filters with a stop band from 80\u2009MHz to 20\u2009GHz. The filter box is positioned at the 15\u2009mK stage of the refrigerator. The screening circuit frequency band is \u2206fc\u2009=\u2009R/(2\u03c0L)\u2009=\u20090.6\u2009GHz. The MWs are coupled to the JJ by a capacitor with C\u03ba\u2009=\u20090.1 fF. The capacitance makes the highest impedance for the MW at the operation frequency, \u223c30\u2009k\u03a9 at 5\u2009GHz, while the inductance L2 between the C\u03ba and the JJ has only \u223c100\u2009\u03a9 at this frequency. The role of L2 is to screen the JJ from the noise of higher frequencies.\n\nA stationary I\u2009\u2212\u2009V curve of the sample is shown in Fig.\u00a02. In a wide voltage range, the curve shows supercurrent-like behaviour with the apparent critical current IC\u2217\u2009\u2248\u200924\u2009nA (Fig.\u00a02a). However, in a narrow voltage range (Fig.\u00a02b) a current blockade appears. The current blockade up to the apparent critical voltage VC\u2217\u2009=\u20092.33\u2009\u00b5V is followed by a rise in current and voltage re-trapping1,20. The voltage does not go to zero but varies around 0.4\u2009\u00b5V before returning to the usual branch of normal current above IC\u2217. The curve is of great interest in itself due to its unusual properties: coexistence of the apparent supercurrent together with the current blockade at low voltage bias. The excess current of the I\u2009\u2212\u2009V curve is relatively small compared to that in CQPS devices with nanowires3. The bottom inset shows the differential resistance dV/dI, with the arrows indicating IC*.\n\na I\u2009\u2212\u2009V curve in wide voltage range\u00a0 has\u00a0supercurrent-like shape\u00a0with an apparent supercurrent IC*\u2009=\u200924\u2009nA. b I\u2009\u2212\u2009V curve in a narrower voltage range reveals a current blockade region with the apparent critical voltage VC*\u2009=\u20092.33\u2009\u00b5V. Inset: a differential resistance dV/dI. The center peak is due to the current blockade. The two side maxima indicated by red arrows define the apparent current IC*.\n\nThe apparent critical current IC* is likely determined by the Landau-Zener tunnelling current\n\nwhen the transitions from the lower to higher Bloch energy bands have a high probability (IC*\u2009\u2264\u2009IZ). Here, we take ECJ because for the high frequency, f \u223c IC/2e, the inductance provides a high impedance and effectively isolates the junction from the rest of the circuit. From the experiment, we have a ratio IC\u2217/IC \u223c0.25, which is consistent with Eq. 1 when taking IC\u2009=\u2009\u03c0\u2206/2eRJ\u2009=\u2009170\u2009nA. The analysis is valid as long as EJ\u2009<\u20092\u2206.\n\nThe I\u2009\u2212\u2009V curve drastically changes when microwave radiation is applied to the JJ: current steps appear at Im = 2efm. Examples of experimental curves taken at two frequencies, 6.495\u2009GHz and 10.215\u2009GHz, are shown in Fig.\u00a03. Under the influence of 6.495\u2009GHz MWs, current plateaus at m\u2009=\u20091 and m\u2009=\u20092 are developed with a low MW amplitude of Iac\u2009=\u20094.2\u2009nA, while the plateau at m\u2009=\u20093 is seen when Iac\u2009=\u20095.8\u2009nA. There is only one clear quantized plateau under 10.215\u2009GHz MWs. At both frequencies, the quantization is limited by the amplitude of the IC*. We explore JJs with different EJ and EC, and find that the necessary condition for quantization is EJ/EC\u2009>\u20092. Additionally, the JJ should have a reasonably high apparent critical current to accommodate the quantized steps in the I\u2009\u2212\u2009V curve. The quantization at other frequencies is shown in Supplementary Figs.\u00a01 and 2.\n\n(a, b) f = 6.495\u2009GHz, and (c, d) f = 10.215\u2009GHz. The dashed lines correspond to the currents Idc = 2\u2009efm, m\u2009=\u20091,2,3,... The amplitude of MW is given as Iac. The red dashed curve in (d) is the theoretical fit made with Eq. (4) (the thermal noise is taken as \u03b4IT\u2009=\u20091\u2009nA). The resistance at m\u2009=\u20091 plateau of this curve is 2.2\u2009k\u03a9.\n\nThe quantized current plateaus are sensitive to the amplitude of the applied MW, Iac. The intensity plot in Fig.\u00a04 shows modulation of the dV/dI at the position of the quantized current Idc=2efm with Iac. This modulation is dual to the modulation of direct Shapiro steps with Vac. Such a behaviour is typical for the synchronization of the external radiation with the coherent tunnelling effects14,21. Modelling of dV/dI for plateaus with different m is shown in Supplementary Fig.\u00a03.\n\nThe extremes of the dV /dI correspond to the Idc = 2\u2009efm plateaus. They oscillate with MW amplitude\u00a0Iac.\n\nTo understand the theory of the coherent phase slips in a small JJ, we consider a relatively simple limit of the junction with a large capacitance, including the stray capacitance CS, and assume that EJ \\(\\ge\\)EC. It is well known that in this case, the phase slips lead to the energy band formation in the form E(q) = \u2212ES cos(\u03c0q/e), where q is the electric charge flowing through the junction and the tunnelling energy, or the width of the bands, can be written as\n\nwhere Ep\u2009=\u2009\\(\\sqrt{8{E}_{J}{E}_{C}}\\) is the plasma energy of the JJ and \u03b7\u2009=\u2009Ep/EC\u2009=\u2009\\(\\sqrt{8{E}_{J}{/E}_{C}}\\) (in our experiments 0.7\u2009<\u2009EJ/EC\u2009<\u20098.3 and 2.4 <\u03b7\u2009<\u20098.2)1. For the analysis, it is important to know that Eq. (2) works reasonably well even when EJ\u2009>\u2009EC.\n\nAt non-zero bias current the dynamics of charge is affected by the impedance attached to the JJ, refer to Fig.\u00a01b. In our circuit, the JJ is connected to an inductance L and resistor R so that q(t) can be obtained from the Eq. (1):\n\nHere Vdc is the dc component of the applied voltage, Vac is the microwave signal, VC\u2009=\u2009\u03c0ES/e is the critical voltage, and \u03be(t) is the noise. The normalised charge 2\u03c0q/2e is the dual analogue of the superconducting phase \u03d5 in the classical Josephson effect. At sufficiently strong noise \u03be(t), which translates to ES\u2009<\u2009kBT, the I\u2009\u2212\u2009V curve takes the form3:\n\nIn the equation Jm(x) are Bessel functions and Idc, Iac are the dc and ac components of the current through the JJ, I(t) = Idc + Iac cos(2\u03c0ft). The equation describes the quantization of current with plateaus at Idc = 2efm. The plateaus follow the square rather than the first order of the Bessel functions. It is a result of the operation in a regime of strong noise. One would have the first order of the Bessel function in the opposite case when ES\u2009>\u2009kBT. The V0(Idc) in the Eq.(4) is the I\u2009\u2212\u2009V curve measured without the microwave signal. It can be approximated with\n\nfor a thermal current noise \u03b4IT\u2009=\u2009\\(\\sqrt{{k}_{B}T/L}\\)3. We fit the quantized current steps in Fig.\u00a03 using this equation with the thermal noise current \u03b4IT\u2009\u2248\u20091\u2009nA. It should be noted that the noise is relatively large, \u03b4IT is comparable with Iac.\n\nTo start the analysis we make a few comments. It is customary to characterize JJs by their critical current, IC\u2009=\u20092\u03c0EJ/\u03a60. However, in experiments, the apparent (measured) critical current, IC\u2217, is usually smaller due to noise or other effects. We believe that in our sample IC* is determined by the Landau\u2013Zener tunnelling effect (Eq. (1)). For the critical voltage, VC\u2009=\u20092\u03c0ES/2e, the CQPS energy ES depends on both EJ and EC. The latter is calculated by taking the total capacitance, which includes CJ together with the parasitic stray capacitance CS. The apparent critical voltage VC*, observed in our experiments is, however, smaller than the calculated VC. We attribute this to the effect of thermal noise.\n\nTo realise dual Shapiro steps in JJs one has to carefully tune the parameters of the JJs. We study several devices and find the parameter range, where the MW response and the current steps are present, see Fig.\u00a05. The apparent critical voltage, VC*, is shown as a function of JJ area for different sets of EJ and EC. In all these measurements, the rest of the circuit is kept unchanged. We experimentally find that current quantization is seen in JJs with EJ/EC \\(\\ge\\) 2, VC* \u2208 [0.5\u2009\u00b5V, 5\u2009\u00b5V], and IC*above 10\u2009nA. By translating EC, EJ, VC*, and IC* to the parameters of the JJs one can find that the junction size should be of the order of 104\u2009nm2 (e.g. 100\u2009\u00d7\u2009100\u2009nm2). We measure three sets of JJs with different pressures of oxygen during the growth of the insulating layer on Al (oxidation time is fixed to 10\u2009min) - magenta, green, and brown data correspond to 1\u2009\u00b5bar, 40\u2009\u00b5bar, and 1 mbar oxidation pressures. They have low, medium and high r, correspondingly. Each set has its specific normal resistance r\u2009=\u2009RJAJ, where AJ is the junction area in units of nm2.\n\nExperimental VC* versus the JJ area. Three groups of JJs (magenta, green, and brown circles) have different insulating layers with different specific resistances. The solid lines are fitted with Eq. (6). The dashed red line separates devices with and without explicit MW response. The devices demonstrating current quantization are inside the red dashed oval indicated as \u201cSteps\u201d. The characteristic resistance r expressed in units 107\u2009\u03a9\u2009\u00d7\u2009nm2. The resistances for the reference area of 100\u2009\u00d7\u2009100\u2009nm2 are R0\u2009=\u20090.9\u2009k\u03a9 (magenta), 1.8\u2009k\u03a9 (green) and 14\u2009k\u03a9 (brown).\n\nThe data are plotted with solid circles to indicate the measurements, in which the MW response is observed (either through direct step observation or in differential resistance). Crosses show samples where the MW response was not observed. The light red and blue areas separate the data with visible responses to the MW radiation from those without responses. The separation is shown schematically by a red dashed line with VC*\u2009~\u20095\u2009\u00b5V. We also show a narrow area by a dashed oval where the steps are observed directly (specified by \u201cSteps\u201d in the figure). All the steps are observed on the set of samples with the lowest specific resistance r, oxidized at 1 \u00b5Bar. Also, note that the response (with direct steps or through dV/dI measurements) is observed only for devices with apparent critical current IC* above 10\u2009nA. Examples of I\u2009\u2212\u2009V curves of samples with different oxidation parameters are shown in Supplementary Fig.\u00a04.\n\nTo explain the data we derive VC* in the limit of strong noise, kBT \\(\\ge\\) eVC, where T is the effective temperature of the system (e.g. resistors or equivalent noise coming from the outside circuit). If we assume that the I\u2009\u2212\u2009V curve is described by Eq. (5), then\n\nWe fit each set of our data in log-scale by Eq. (6), using the specific resistance r as the fitting parameter. The results of fitting are shown as lines. The specific resistances are 0.9, 1.8, and 14\u2009\u00d7\u2009107\u2009\u03a9nm2, which correspond to 0.9, 1.8, 14\u2009k\u03a9 for the reference junctions of 100\u2009\u00d7\u2009100\u2009nm2 area. These values are close to the experimental values of 0.6, 2, and 15\u2009k\u03a9 taken with the witness JJs.\n\nThe current quantization plateaus have slopes of dV/dI below \u223c3\u2009k\u03a9. The JohnsonNyquist noise of \u20096.3 k\u03a9 Pd resistors can be one reason for this. Another possible reason is the heating of the chip with MWs. In the current design, the MW signal effectively resonates and heats the whole area around the JJ, with only a small portion of the MWs being coupled to the JJ. Erdmanis and Nazarov suggest bringing the MW through the gate electrode to a small island of the split JJ17. Such coupling can be highly efficient, allowing the use of a much weaker MW drive. In such a design, one can also modulate ES with the gate voltage \\({E}_{S}={E}_{S0}\\left|\\cos \\left(\\pi {C}_{G}{V}_{G}/e\\right)\\right|\\). However, the effect of fluctuating charge around the split JJ may pose an issue.\n\nHeating also unavoidably occurs in Pd resistors due to the dc and ac currents. Heating from ac current can be minimized, but dc current cannot be avoided. For example, a dc current of 10\u2009nA produces a load of about 10\u2009pW. This results in the resistor heating to T \u223c 0.2\u2009K, or kBT/e \u223c 10\u2009\u00b5V22. Simple solutions such as moving resistors to a separate chip with better heat dissipation9 may not help, because, this increases the stray capacitance. Immersion cooling in the liquid He3 is another approach to reducing noise23, but the effect may be limited because the temperature in a system with phonon cooling weakly depends on the dissipated power \n\nAnother way to improve quantization lies in increasing the apparent critical voltage. Partially this can be done by reducing the stray capacitance, and, simultaneously increasing the Josephson energy by reducing the RN. In the regime of a larger blockade (kBT \u226a eVC*), the noise contribution will be exponentially smaller. With this approach, one would also have a larger IC* needed for plateau observation at a higher current.\n\nIn summary we demonstrate current quantization (dual Shapiro steps) in small JJs and find the conditions for which the steps are observed. The maximum quantized current observed in our experiment reaches 7.7\u2009nA, when MWs of 23.895\u2009GHz are applied. The quantization is a result of the synchronization of the Bloch oscillations with the MW radiation. The coherent nature of the effect is confirmed by the observation of the modulation of the current plateau width with the MW amplitude. We show that the steps are observed in a limited range of the JJ parameters: junction area of about 0.3\u2009\u00d7\u2009104\u2009nm2, R0 \u223c 0.6\u2009k\u03a9 per 100\u2009\u00d7\u2009100\u2009nm2, eVC*\u2009=\u20090.5\u20135\u2009\u03bcV. The current plateaus, however, are not flat, having slopes of \u223c 3.4\u2009k\u03a9 at best. An advantage of the JJ devices, compared to those based on the nanowires, is their high yield. We suggest a few ways to optimize the devices to improve the accuracy and the amplitude of quantized currents. One approach is to reduce the stray capacitance in the circuit.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53600-y/MediaObjects/41467_2024_53600_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53600-y/MediaObjects/41467_2024_53600_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53600-y/MediaObjects/41467_2024_53600_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53600-y/MediaObjects/41467_2024_53600_Fig5_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "The experimental samples are fabricated in four steps. We start with 5\u2009nm thick TiN film on a Si wafer. The Ti/Au (10\u2009nm/80\u2009nm) macroscopic contact pads are fabricated with standard UV lithography. Electron Beam Lithography (EBL) is used during the next three steps: fabrication of compact TiN inductors (100\u2009nm wide wire meanders) with negative resist and reactive ion etching in CF4 plasma; deposition of compact resistors (15\u2009nm thick and 150\u2009nm wide Pd wire meanders) with the lift-off resist mask; fabrication of Al JJs using the shadow evaporation technique. In-situ ion milling is used to ensure good galvanic contact between the layers of the circuit.\n\nLow-temperature experiments are conducted in a dry dilution refrigerator with a base temperature of 15\u2009mK. Samples were mounted on a PCB with the DC and coplanar waveguide lines. The PCB with a sample is enclosed in a copper box. To suppress high-frequency noise dc lines pass through copper powder filters at room temperature, the thermo-coaxial cables, and low pass filters at low temperatures. The microwave signal is attenuated at various temperature stages of the refrigerator. The I\u2009\u2212\u2009V and dV /dI curves are taken using the differential amplifier kept at room temperature24. An electric circuit for the amplifier is shown in Supplementary Fig.\u00a05. dV/dI curves are measured with a standard lock-in technique.\n\nThe height of zero bias peaks of dV/dI (blockade of the JJ current) depends on the MW current amplitude as a square of the Bessel function of zero order. A significant peak suppression under particular frequencies indicates good coupling of the MW to JJ, refer to Supplementary Fig.\u00a06. We use frequencies with good coupling to demonstrate the current quantization, see examples in Fig.\u00a03. Coupling to MW drastically reduces above 30\u2009GHz due to the high-frequency cut-off of our transmission lines. We explore the modulation of the current quantization by the microwave amplitudes to prove the coherent nature of the effect, see Fig.\u00a04.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data generated in this study have been deposited to the Open Science Framework repository. They can be obtained without any restriction at https://osf.io/g4x9p/. Additional information, experimental curves and schemes are also provided in the Supplementary Information.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Averin, D. V., Zorin, A. B. & Likharev, K. K. Bloch oscillations in small Josephson junctions. Sov. Phys. JETP 61, 407 (1985).\n\nADS\u00a0\n \n Google Scholar\u00a0\n \n\nKuzmin, L. S. & Haviland, D. B. Observation of the bloch oscillations in an ultrasmall Josephson junction. Phys. Rev. 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PhD Dissertation, Chalmers University of Technology, 52 (1990).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by Engineering and Physical Sciences Research Council (EPSRC) Grant No. EP/T004088/1, European Union\u2019s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 862660/Quantum E-Leaps and 20FUN07 SuperQuant.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Royal Holloway University of London, Egham, Surrey, TW20 0EX, UK\n\nRais S. Shaikhaidarov,\u00a0Kyung Ho Kim,\u00a0Jacob Dunstan,\u00a0Ilya Antonov,\u00a0Vladimir N. Antonov\u00a0&\u00a0Oleg V. Astafiev\n\nNational Physical Laboratory, Hampton Road, Teddington, TW11 0LW, UK\n\nRais S. Shaikhaidarov\u00a0&\u00a0Ilya Antonov\n\nHQS Quantum Simulations GmbH, Rintheimer Str. 23, Karlsruhe, 76131, Germany\n\nDmitry Golubev\n\nDepartment of Applied Physics, QTF Centre of Excellence, Aalto, 610101, Finland\n\nDmitry Golubev\n\nSkolkovo Institute of Science and Technology, Bolshoy Boulevard 30, Moscow, 121205, Russia\n\nOleg V. Astafiev\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nO.V.A., R.S.S., and V.N.A. conceived and supervised the experiments. R.S.S., K.H.K., J.D., I.A. contributed to device fabrication and characterization at low temperatures and microwave ranges. All authors contributed to the simulation and analysis of the data. D.G. and V.N.A. wrote the manuscript and all authors contributed to editing the manuscript.\n\nCorrespondence to\n Vladimir N. Antonov.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous, reviewer(s) for their contribution to the peer review of this work. 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Quantized current steps due to the synchronization of microwaves with Bloch oscillations in small Josephson junctions.\n Nat Commun 15, 9326 (2024). https://doi.org/10.1038/s41467-024-53600-y\n\nDownload citation\n\nReceived: 30 January 2024\n\nAccepted: 07 October 2024\n\nPublished: 29 October 2024\n\nVersion of record: 29 October 2024\n\nDOI: https://doi.org/10.1038/s41467-024-53600-y\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"pre_title": "Twin structures and substitutional dopants in ZnSe0.7Te0.3: the effect and transition during sodium ion electrochemistry", + "journal": "Nature Communications", + "published": "12 May 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59707-0/MediaObjects/41467_2025_59707_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59707-0/MediaObjects/41467_2025_59707_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59707-0/MediaObjects/41467_2025_59707_MOESM3_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-59707-0#Sec15" + ], + "code": [], + "subject": [ + "Batteries", + "Materials chemistry" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4270462/v1.pdf?c=1747134408000", + "research_square_link": "https://www.researchsquare.com//article/rs-4270462/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-59707-0.pdf", + "preprint_posted": "23 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Constructing various defects is considered to be a common viable means of improving electrochemical performance. However, it is of significance to thoroughly scrutinize the formation mechanism of defects and their effects and transition during the charge\u2013discharge process. Here, twin structures are introduced into ZnSe0.7Te0.3 nanocrystals by doping of Te heteroatoms. The Te dopants are visualized to locate in the lattices of ZnSe by spherical aberration electron microscopy. The formation of twin structures is thermodynamically promoted by Te heteroatoms partially replacing Se based on the theoretical calculation results. Our findings show ZnSe0.7Te0.3 transforms into ZnSe and Te via the first cycling, which differ from the counterparts prepared by traditional methods and can effectively activate the interfacial and electronic effects due to the incomparable distribution and unmatched compatibility. It can be concluded that even though the initially constructed defects in materials of redox chemistry mechanism can\u2019t resurrect, they play a critical role in tailoring the electrochemistry. We\u2019re firmly convinced to pursue the exploitation of advanced and delicate electrode materials for batteries.Physical sciences/Chemistry/Materials chemistry/Electronic materialsPhysical sciences/Materials science/Materials for energy and catalysis/Batteries", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SI.pdfTwin structures and substitutional dopants in ZnSe0.7Te0.3: the effect and transition during sodium ion electrochemistry", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Compared with lithium-ion batteries (LIBs), sodium-ion batteries (SIBs) are an alternative technology for future energy storage due to their abundant resources and economic benefits. Constructing various defects is considered to be a common viable means of improving the performance of sodium storage. However, it is of significance to thoroughly scrutinize the formation mechanism of defects and their effects and transition during the charge\u2013discharge process. Here, twin structures are introduced into ZnSe0.7Te0.3 nanocrystals by doping of Te heteroatoms. The Te dopants are visualized to locate in the lattices of ZnSe by spherical aberration electron microscopy. The formation of twin structures is thermodynamically promoted by Te heteroatoms partially replacing Se based on the theoretical calculation results. Moreover, calculation results show that with the increase of twin boundaries (TBs), the sodium diffusion energy barrier is greatly reduced, which helps the kinetics of sodium ion diffusion. In the connection, the composition and amount of TBs are optimized via tuning the doping level. The combined effect of point defects and twin structures greatly improves the sodium storage performance of ZnSe0.7Te0.3@C. Our work reveals the mechanism of the point defect on the twin plane defect and systematically investigates their effect on the electrochemical performance.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "In recent years, sodium-ion batteries (SIBs) have made great progress, and are expected to be one of the substitutes for widely commercialized lithium-ion batteries (LIBs), due to the abundant reserves of sodium resources and similar electrochemistry and operation mechanisms1,2. As a typical anode material for LIBs, graphite has been widely used. In view of the limited battery capacity and structural instability of sodium-graphite intercalation compounds, it\u2019s urgent to search for high-capacity and long-life SIB anode materials to meet the needs of large-scale energy storage applications3,4. Thanks to their structural diversity and high theoretical capacity (about 400\u2013600\u2009mAh\u2009g\u22121), transition metal chalcogenides (TMC) have great application potential in the anode of SIBs5. ZnSe, an important member of TMC, is an alloy\u2013conversion combined anode material prospective for SIBs. It has decent electrical conductivity, better than its oxide counterpart, and a weak metal-selenium (Se) bond that facilitates the electrochemical conversion reactions6. But, it has some problems such as poorer electronic conductivity than carbon, sluggish kinetics, and large volume change in the long\u2013term charge\u2013discharge process, which limits its rapid charge\u2013discharge ability and structural stability. To solve these problems, various strategies have been developed, such as combining ZnSe with carbon\u2013based materials and constructing robust three\u2013dimensional nanostructures. Moreover, defect engineering is demonstrated to be an effective method to reasonably design electrode materials for rechargeable batteries to achieve the improved electrochemical performance7.\n\nDefects in crystals can be classified into point defects, line defects, and planar defects according to the dimension of the defects. They have significant effects on the chemical properties, thermal stability, and mechanical properties of materials8,9. Point defects such as vacancies, substitute and interstitial atoms can increase adsorption sites, accelerate the ion diffusion, and improve the electronic conductivity of the LIB and SIB electrode materials10, such as Co and F codoped SnO211, MoS2/C with S vacancies12, Cu-doping cobalt embedded nitrogen-doped porous carbon (CoCu@NC)13. Dislocations, one kind of line defects, can prevent cracking, loss of active materials, and adverse interface reactions with electrolytes by reducing strain during the phase transition of spinel material LiNi0.5Mn1.5O414. In addition, the diffusion rate of Na+ at grain boundaries (planar defects) is much faster than in the bulk phase of Ta5+-substituted Na3V2(PO4)315. As a member of special planar defects, twin boundaries (TBs) also often appear in crystal materials with a twin structure. The existence of twin structure is conducive to the diffusion of lithium ions in electrode materials, which helps to improve the electrochemical dynamics of batteries8,16. For instance, Nie et al. demonstrated that TBs promote the diffusion of lithium ions in single\u2013crystal SnO2 nanowires17. Wang et al. studied the formation of TBs in lithium manganate oxide, and also demonstrated TBs can enable fast lithium-ion diffusion and charging performance18. It follows that the defect investigations have been done thoroughly, especially in LIBs; however, more efforts should be made on the study of anode materials for SIBs. The electrical conductivity of Te is about 2\u2009\u00d7\u2009102\u2009S\u2009cm\u20131, which is much higher than that of Se (1\u2009\u00d7\u200910\u20134\u2009S\u2009cm\u20131)19,20. In addition, Se and Te atoms are in the same main group, and the latter has a slightly larger radius than the former. This indicates that Te heteroatom doping of ZnSe may improve its electrical conductivity and potentially introduce some additional defects. The introduction of two kinds of defects into ZnSe can collectively increase adsorption sites and promote reaction kinetics, contributing to better electrochemical sodium storage. At the same time, the systematic investigations of the formation, effects, and transition of defects during electrochemistry also require further efforts.\n\nHerein, we prepared ZnSe0.7Te0.3 nanocrystals with twin structure as anode material for SIBs using zeolitic imidazolate frameworks (ZIF\u20138) as template and Te heteroatom doping hybridized with thin hollow carbon structure (ZnSe0.7Te0.3@C). The doping of heterogeneous Te increased the energy of the system and lattice distortion. Alternatively, the crystal matrix introduced TB defects to alleviate this tendency and maintain the system stable. Moreover, via the composition adjustment during the synthesis, ZnSe0.7Te0.3 was determined as the optimized Te doping level with optimal TB amount. By a combination of a series of structural characterizations, the electrochemical reactions of ZnSe0.7Te0.3 with sodium ions were confirmed, which also demonstrated the transition of defects in ZnSe0.7Te0.3 during the charging/discharging. ZnSe0.7Te0.3@C electrode shows significantly superior sodium storage properties to the pristine ZnSe@C electrode, including a higher capacity (5\u2009A\u2009g\u22121; 307\u2009mAh\u2009g\u22121 vs 118.8\u2009mAh\u2009g\u22121 after 1000 cycles), better rate performance (20\u2009A\u2009g\u22121; 256.2 vs 121.5\u2009mAh\u2009g\u22121). The good storage performance results from the promotive effect of two defect dimensions, TB (planar defect) and substitution dopant (point defect), in ZnSe0.7Te0.3. When designing the anode materials of SIBs, the defects of two dimensions are introduced at the same time, which will overcome their shortcomings from different aspects, and finally realize the comprehensive and significant improvement of sodium storage performance.", + "section_image": [] + }, + { + "section_name": "Results and discussion", + "section_text": "The preparation flow chart of ZnSe0.7Te0.3@C nanocomposite with twin structure is shown in Fig.\u00a01a. ZIF\u20138 is composed of zinc ions and dimethylimidazole molecules and serves as the precursor. From scanning electron microscope (SEM) and transmission electron microscopy (TEM) images in Supplementary Fig.\u00a01, the as-prepared ZIF\u20138 exhibits solid dodecahedra with a side length of about 1 \u03bcm. Due to the presence of the terminal N\u2013H functional groups, ZIF\u20138 is always sensitive to the mixed solution of ethanol and water21. In our synthesis, ZIF\u20138 dodecahedra grew into smaller solid nanospheres under the action of hydroxyl groups in M-aminophenol and ammonium hydroxide. At the same time, M-aminophenol and formaldehyde polymerized to form MF resin encapsulating ZIF\u20138 nanospheres (ZIF\u20138@MF), as shown in SEM and TEM images in Supplementary Fig.\u00a02. Then, ZIF\u20138@MF was fully ground and mixed with Se and Te powder, followed by annealing at high temperature under the protection of Ar/H2 atmosphere and ZnSe0.7Te0.3@C nanocomposite was harvested. In order to detect the role of defects, the nanocomposite of ZnSe@C was also fabricated for comparison. The morphology and structure of ZnSe@C and ZnSe0.7Te0.3@C were characterized by SEM and TEM. From images of Fig.\u00a01b, c, ZnSe0.7Te0.3@C nanocomposite consists of typical nanobowl-like carbon with a diameter of about 60\u2009nm and ZnSe0.7Te0.3 nanocrystals. Then, aberration-corrected high-angle annular dark-field-scanning TEM (HAADF\u2013STEM) image (Fig.\u00a01d) shows the low contrast of the carbon structure, demonstrating the thin shell of a few nanometers. The energy-dispersive X-ray spectrum (EDS) (Fig.\u00a01e) mapping shows that ZnSe0.7Te0.3@C includes C, N, Zn, Se, and Te elements, and the nanoparticles are composed of Se, Te, and Zn elements, indicating the formation of ZnSe0.7Te0.3. ZnSe@C exhibits similar morphological features, composed of nanobowl-like carbon and ZnSe nanocrystals (Supplementary Fig.\u00a03).\n\na Schematic illustration of the preparation process of ZnSe0.7Te0.3@C. b FESEM and c TEM images of ZnSe0.7Te0.3@C. d and e STEM\u2013EDX elemental mapping of ZnSe0.7Te0.3@C: C (yellow), N (azure), Te (blue), Se (red), and Zn (green).\n\nIn order to further detect the crystallographic structure of ZnSe0.7Te0.3, high\u2013resolution imaging was done by the HAADF\u2013STEM technique. From the Supplementary Fig.\u00a04, a remarkable twin structure and multiple TBs in ZnSe0.7Te0.3 nanoparticle are clearly observed. Figure\u00a02a shows the partial HAADF\u2013STEM image of Supplementary Fig.\u00a04. The twin plane is analyzed to be (111) plane with a spacing of 0.34\u2009nm (Fig.\u00a02a), and the twinning direction can be determined to be [11\u20132]. In contrast, no twin structure is present in ZnSe@C, as shown in Supplementary Fig.\u00a05. Figure\u00a02b\u2013d shows the corresponding atom mapping of Zn, Se, and Te. The lattices of Zn and Se are relatively complete, while Te atoms are randomly dispersed and the distribution doesn\u2019t accord with the periodic lattice structure, indicating the doped Te atoms. The structure of mixed ZnSe atom mapping is shown in Fig.\u00a02e. Figure\u00a02f\u2013h shows that Te heteroatoms are successfully doped into the ZnSe lattices. Furthermore, it can be seen that partial Se atoms are missing and replaced by Te atoms marked by the white circle. The absence of Se atoms is demonstrated by the white circle marked in the combined atom mapping of Se and Zn in Fig.\u00a02e. The white circle marked in the complex atom mapping of Zn and Te in Fig.\u00a02f shows the existence of Zn and Te atom, indicating that Te atom does not replace the Zn atom. In summary, it can be concluded that doped Te atoms partially replace Se atoms. According to the above analysis, the atom model schematic illustration of ZnSe0.7Te0.3 twin structure was described in Fig.\u00a02i.\n\na\u2013h Atomically resolved HAADF\u2013STEM image (a) and the corresponding EDX maps of b Zn, c Se, d Te, e Zn and Se merging, f Zn and Te merging, g Se and Te merging, and h Zn, Se, and Te merging. i Atomic model schematic illustration of the twin ZnSe0.7Te0.3 (Zn: green, Se: red, and Te: blue).\n\nIn order to explain the formation mechanism of the twin structure in ZnSe0.7Te0.3@C, we carried out the density functional theory (DFT)22,23 calculations to detect the underlying impetus. According to the above spherical aberration-corrected electron microscopy results, no obvious vacancy defect was found in ZnSe0.7Te0.3@C, and the arrangement of atoms was highly ordered and complete. Therefore, from the perspective of theory, Te can either replace Se atoms or occupy different interstitial sites in ZnSe, considering Te and Se are in the same group24. In order to further verify theoretically whether Te replaces Se atoms or occupies interstitial sites in ZnSe, we constructed the structural models of different configurations in Fig.\u00a03a\u2013c and then calculated the corresponding phonon spectra, average energy per atom in the final state, and defect formation energy. From Supplementary Fig.\u00a06a, b, it can be seen that the phonon spectra of the sub 1 model with one Te atom replacing one Se atom and the sub 3 model with three Te atoms replacing three Se atoms have no virtual phonon mode, indicating that the structures are dynamically stable. However, the phonon spectrum for the int model in which Te occupies an interstitial position has a virtual phonon mode, proving that its structure is unstable, as shown in Supplementary Fig.\u00a06c\u2013e. The virtual frequency may be caused by the large Te atom occupying the interstitial position, rendering a huge structural distortion and profoundly affecting the normal arrangement of the surrounding atoms. The average energy of each atom in the final state of these different configurations is negative, which indicates that they are thermodynamically stable (Supplementary Fig.\u00a06f). Furthermore, the elastic constants of sub 1, sub 3 and int models have been calculated, and the results indicate that these structural models are mechanically stable (The calculations and result details are in Note\u00a01 in Supplementary information). The defect formation energy of sub 1 model is 0.76\u2009eV, which is much lower than that of sub 3 and int models (2.23 and 3.31\u2009eV), as shown in Fig.\u00a03f. This indicates that Te tends to replace Se atoms rather than occupy interstitial positions, which is consistent with the above atom mapping results. In addition, considering the application of the system for SIB, the ab initio molecular dynamics simulations were used to detect the behaviors of sub 1 and sub 3 at different temperatures. As shown in Supplementary Fig.\u00a07, the results show that, at 298 and 313\u2009K, the sub 1 and sub 3 structures do not change significantly with the increase of temperature. Figure\u00a03d, e shows the atomic interface structure models of ZnSe0.7Te0.3 with TBs and ZnSe0.7Te0.3 without TBs based on the HAADF\u2013STEM imaging results. The total number of atoms of ZnSe0.7Te0.3 models with TBs and without TBs is the same, and the ratio of Se to Te atoms is about 7:3. The average energy per atom in the final state of the ZnSe0.7Te0.3 with TBs is ~\u20132.99\u2009eV, which is lower than that of ZnSe0.7Te0.3 without TBs (\u20132.8\u2009eV), indicating that ZnSe0.7Te0.3 with twin structure is more stable thermodynamically, as shown in Fig.\u00a03g. In addition, the defect formation energy of ZnSe0.7Te0.3 with TBs is relatively small (0.86\u2009eV), indicating that the twin structure is easy to form. Based on the above analyses, it\u2019s known that the introduction of Te substitute atoms will increase the energy of the ZnSe system. But, the formation of twin structure can, to some extent, stabilize the Te-doped ZnSe0.7Te0.3 system. So, the Te substitutional atoms thermodynamically promote the formation of twin structures in ZnSe.\n\na A phase model in which one Te atom replaces one Se atom (sub 1). b A phase model in which three Te atoms replace three Se atoms (sub 3). c A phase model with one Te atom occupying an interstitial position (int). Interface models of ZnSe0.7Te0.3 d with TBs and e without TBs. f Comparison diagram of defect formation energy of three models of sub 1, sub 3, and int. g Comparison diagram of average energy per atom in the final state of interface models. Source data are provided as a Source Data file.\n\nThe crystal phase of the samples was characterized by X-ray diffraction (XRD) measurements, as shown in Supplementary Fig.\u00a08. Both ZnSe@C and ZnSe0.7Te0.3@C show a set of diffraction peaks notably consistent with the face-centered cubic ZnSe with space group F-43m. No impurity peaks were detected in either sample, and sharp Bragg peaks indicated good crystallinity in both ZnSe@C and ZnSe0.7Te0.3@C. Accurate structural information of ZnSe@C and ZnSe0.7Te0.3@C is obtained through Rietveld refinement, and the results are listed in Supplementary Tables\u00a01\u20133. It can be seen from the refinement results that the cell parameters and cell volume of ZnSe0.7Te0.3@C are larger than those of ZnSe@C, indicating that Te atoms are successfully doped into ZnSe (Supplementary Table\u00a03). The occupancy of Se and Te atoms in ZnSe0.7Te0.3@C was 0.029 and 0.013, respectively, which was consistent with the atomic percentage of Se and Te in XPS results (Supplementary Table\u00a02). In addition, compared with ZnSe@C, the diffraction peaks of ZnSe0.7Te0.3@C have different degrees of deviation toward small angles, which was caused by Te heteroatoms successfully doped into ZnSe crystal lattices. The average crystallite size of ZnSe@C and ZnSe0.7Te0.3@C is calculated using Scherrer\u2019s method to be 43.4\u2009nm and 17.8\u2009nm, respectively, which proves that the nanoparticle size of ZnSe0.7Te0.3@C is smaller (Supplementary Table\u00a04). The valence state and chemical composition of ZnSe0.7Te0.3@C nanocomposites were further studied by X-ray photoelectron spectroscopy (XPS). C, N, Se, Te and Zn elements coexist in ZnSe0.7Te0.3@C nanocomposites, and the atomic ratio of Se and Te is about 0.7: 0.3 (Supplementary Fig.\u00a09a). The high\u2013resolution C 1s spectrum in Supplementary Fig.\u00a09b involves three main peaks. One at 284.8\u2009eV corresponds to C\u2013C bonds, while the other two peaks at 286.4 and 288.1\u2009eV represent C\u2013N and C=O bonds, respectively25. In addition, the N 1s XPS spectrum (Supplementary Fig.\u00a09c) is deconvolved into three remarkable peaks at 398.7, 401.0, and 402.7\u2009eV, respectively, pertaining to pyridinic N, pyrrolic N, and graphite N, indicating that the carbon is doped with N atoms26. The binding energy of Se 3d5/2 (53.9\u2009eV) and Se 3d3/2 (54.9\u2009eV) in the spectrum shows the presence of Se2\u2013 in ZnSe0.7Te0.3@C nanocomposites in Supplementary Fig.\u00a09d. The high\u2013resolution Te 3d spectrum is exhibited in Supplementary Fig.\u00a09e, where four main peaks can be observed. The peaks at 572.7 and 583.0\u2009eV result from Te 3d5/2 and Te 3d3/2 orbitals, respectively, indicating the presence of Te2\u2013. Peaks at 576.2 and 586.7\u2009eV are attributable to its oxide, which is caused by the oxidation of the sample surface27. The Zn 2p XPS spectrum (Supplementary Fig.\u00a09f) contains two main peaks at 1044.9 and 1021.9\u2009eV, respectively, coming from Zn 2p1/2 and Zn 2p3/2 orbitals, a token of bivalent Zn28.\n\nThe thermogravimetric analysis (TGA) curve of ZnSe0.7Te0.3@C nanocomposites in the air atmosphere is shown in Supplementary Fig.\u00a010. It can be calculated that the content of carbon in the sample is about 48.6%. Nitrogen adsorption\u2013desorption measurements were carried out to study their porous profiles and specific surface area of ZnSe@C and ZnSe0.7Te0.3@C nanocomposites, as shown in Supplementary Fig.\u00a011. The adsorption isotherms of them are typical type III isotherms, and the H3 hysteresis appears when the relative pressure of P/P0 is greater than 4.5, indicating the presence of mesoporous structures29. According to nitrogen adsorption\u2013desorption measurements, the Brunauer\u2013Emmett\u2013Teller (BET) specific surface area of ZnSe0.7Te0.3@C nanocomposites is 270.6\u2009m2\u2009g\u20131, which is much greater than that of ZnSe@C (172.7\u2009m2\u2009g\u20131), pertaining to the reduced size of nanoparticles after introduction of Te atoms. Supplementary Fig.\u00a011b shows the pore size distribution, and the pore size of both is mainly distributed in the range of 2\u201330\u2009nm. The results demonstrate that ZnSe0.7Te0.3@C has more mesoporous pores than ZnSe@C, which is conducive to promoting the full contact between electrolyte and electrode materials and increasing the transfer rate of sodium ions.\n\nNaPF6, as a conventional sodium electrolyte salt, has good ionic conductivity30. Compared with carbonate solvent, dimethoxyethane (DME) can effectively change the interface and reduce the charge transfer resistance31. A thin but stable sodium ion permeable solid electrolyte interface (SEI) layer is easily formed in the electrolyte NaPF6-DME, facilitating its cycling and rate performance32. So, 1\u2009M NaPF6 in DME was used as the electrolyte for cell testing. To evaluate the electrochemical performance of the samples, the synthesized electrodes were assembled into coin-type cells and tested in a 0.01\u20133\u2009V potential window at 25\u2009\u00b0C. To evaluate the effect of twin structures and substitute Te atoms on the sodium ion storage electrochemistry of ZnSe0.7Te0.3@C nanocomposites, the kinetics analyses were deployed. The diffusion process of sodium ions along and across the TBs of ZnSe0.7Te0.3 was studied by theoretical calculations. As shown in Fig.\u00a04a\u2013c, obviously, the Na+ diffusion energy barrier along the TBs is 0.45\u2009eV for ZnSe0.7Te0.3 with twin structures, much lower than that of the defect\u2013free counterpart (0.66\u2009eV). Not only that, as for the Na+ diffusion across the TBs in Fig.\u00a04d\u2013f, ZnSe0.7Te0.3 with twin structures also shows a lower energy barrier of 0.70\u2009eV than without defects (0.90\u2009eV). Hence, the existence of twin structures is demonstrated to be favorable for reducing the diffusion energy barrier of sodium ions. The kinetics of sodium ion diffusion is improved both along and across the TBs. The existence of a twin structure accelerates the diffusion of sodium ions along different paths. To further verify the simulation conclusions, galvanostatic intermittent titration technique (GITT) curves of the charge/discharge process of ZnSe@C and ZnSe0.7Te0.3@C were measured to dig out their sodium ion diffusion rates (Supplementary Fig.\u00a012). On this basis, the diffusion coefficients of sodium ions (DNa+) were calculated in Fig.\u00a04g, h. It is obvious that the DNa+ of ZnSe0.7Te0.3@C is higher than that of ZnSe@C during the charging and discharging process, in accord with the above theoretical calculation results.\n\nSodium ion diffusion models for ZnSe0.7Te0.3 (a) with and (b) without TBs along the TB (Se: green, Zn: gray, Te: dark yellow, and Na: yellow). The energy barrier of sodium ion diffusing (c) along and (f) across the TB for ZnSe0.7Te0.3 with TBs and without TBs. Sodium ion diffusion models for ZnSe0.7Te0.3 (d) with TBs and (e) without TBs across the TB. The diffusion rate of sodium ions during (g) charging and (h) discharging is calculated by GITT. i The energy barrier of sodium ion for ZnSe0.7Te0.3 with two TBs across the TB. Test temperature: 25(\u00b10.5)\u2009\u00b0C with air convection. Type of electrolyte: 1 M NaPF6 in dimethoxyethane. Source data are provided as a Source Data file.\n\nIn order to theoretically investigate the effect of the number of TBs on the performance, the model of ZnSe0.7Te0.3 with two TBs is constructed (Supplementary Fig.\u00a013). And the sodium ion diffusion energy barrier is calculated, as shown in Fig.\u00a04i. Obviously, the Na+ diffusion energy barrier across the TBs is 0.39\u2009eV for ZnSe0.7Te0.3 with two TBs, much lower than that of the ZnSe0.7Te0.3 with one TB (0.70\u2009eV). The calculation results show that with the increase of the number of TBs, the sodium diffusion energy barrier is greatly reduced, which helps the kinetics of sodium ion reactions. To further investigate the kinetics of ZnSe@C and ZnSe0.7Te0.3@C, electrochemical impedance spectroscopy (EIS) tests were performed. The Nyquist plots are fitted by an equivalent circuit model shown in Supplementary Fig.\u00a014a, and the obtained values of resistance are listed in Supplementary Table\u00a05. As shown in Supplementary Fig.\u00a014a, the Rct of ZnSe0.7Te0.3@C is about only one-third of that of ZnSe@C, implying ZnSe0.7Te0.3@C has a higher charge transfer rate. The Z\u2019\u2013\u03c9\u20131/2 plot derives from the EIS spectra in Supplementary Fig.\u00a014b, and the slope called Warburg coefficient \u03c3 is related to the diffusion of sodium ions in the electrode materials. Obviously, ZnSe0.7Te0.3@C has a slope of 26.3, much lower than that of ZnSe@C (80.4), further demonstrating the faster sodium ion diffusion of ZnSe0.7Te0.3@C.\n\nTo consolidate the above theoretical analyses, different samples were controlled in terms of the number of twin boundaries by tuning the doping level of tellurium. By changing the usage amount of Se and Te during the synthesis, ZnSe0.8Te0.2@C, ZnSe0.7Te0.3@C, and ZnSe0.5Te0.5@C were prepared. Atomic percentages of C, N, O, Zn, Se, and Te in samples of different compositions were shown by the energy spectrometer attached to the SEM, as shown in Supplementary Table\u00a06. According to the component content in the energy spectrum, the atomic percentage of Se and Te in the sample can be determined, so as to obtain ZnSe0.8Te0.2@C, ZnSe0.7Te0.3@C, and ZnSe0.5Te0.5@C. In order to determine the number of twin boundaries in samples of different components, the samples were characterized by the technique of TEM. Supplementary Fig.\u00a015a\u2013c shows the low magnification TEM images of ZnSe0.8Te0.2@C, ZnSe0.7Te0.3@C, and ZnSe0.5Te0.5@C, in which some of the grains have some fine stripes alternating between light and dark. When these streaks are enlarged, they have a typical twin structure, as shown in Supplementary Fig.\u00a015d\u2013f. Therefore, the grains with twin boundary fringes of different compositions are analyzed statistically and quantitatively. The percentage of grains with twin boundary fringes in ZnSe0.8Te0.2@C, ZnSe0.7Te0.3@C, and ZnSe0.5Te0.5@C is 4.5%, 11.4%, and 6.2%, respectively (Supplementary Fig.\u00a015g). Compared with ZnSe0.8Te0.2@C and ZnSe0.5Te0.5@C, the grain twin boundary ratio of ZnSe0.7Te0.3@C is the largest, which may improve the performance the most.\n\nThen, the rate performance of ZnSe@C, ZnSe0.8Te0.2@C, ZnSe0.7Te0.3@C and ZnSe0.5Te0.5@C is tested and shown in Fig.\u00a05c. ZnSe0.7Te0.3@C is capable of releasing reversible specific capacities of 351.1, 333.3, 321.7, 310.2, 294.2, 277.2, 256.2\u2009mAh\u2009g\u20131 at current densities of 0.2, 0.5, 1, 2, 5, 10 and 20\u2009A\u2009g\u20131, respectively. For comparison, ZnSe@C is able to release reversible specific capacities of 245.0, 204.9, 184.5, 169.3, 149.4, 133.9, 121.5\u2009mAh\u2009g\u20131 at the same current density. ZnSe0.8Te0.2@C releases specific capacities of 293, 277.9, 264.7, 246.2, 224.8, 206.7, 185.3\u2009mAh\u2009g\u20131, respectively, and ZnSe0.5Te0.5@C releases specific capacities of 298.2, 270.4, 246.9, 219.9, 191.7, 170, 152.1\u2009mAh\u2009g\u20131, respectively, at the same current density. The rate performance of ZnSe0.7Te0.3@C, ZnSe0.8Te0.2@C, and ZnSe0.5Te0.5@C is better than that of ZnSe@C, which is due to the collective effect of Te atom doping improving conductivity and the twin structure improving sodium ion diffusion dynamics. The rate performance of ZnSe0.7Te0.3@C is better than that of ZnSe0.8Te0.2@C and ZnSe0.5Te0.5@C, indicating that ZnSe0.7Te0.3@C has the most appropriate number of TBs and Te atom doping. The rate performance of ZnSe0.5Te0.5@C is slightly poorer than that of ZnSe0.8Te0.2@C, which is due to the excessive amount of Te atom doping. The rate performance measurements of other batteries for the ZnSe0.7Te0.3@C, ZnSe@C, ZnSe0.5Te0.5@C and ZnSe0.8Te0.2@C are shown in Supplementary Fig.\u00a016. The charge and discharge curves of ZnSe0.7Te0.3@C nanocomposites at different current densities are shown in Fig.\u00a05d. When the current density increases from 0.2 to 20\u2009A\u2009g\u20131, the voltage gap changes slightly between the charge and discharge voltage platforms, indicating the smaller polarization of reactions. Supplementary Fig.\u00a017 compares the rate properties of zinc\u2013based selenides and tellurides with those previously reported, exhibiting better rate capability, especially at higher rates. In order to study the contribution of amorphous carbon to capacity in the sample, the cycle and rate performance were tested, as shown in Supplementary Fig.\u00a018. At a current density of 1\u2009A\u2009g\u22121, amorphous carbon has only a specific capacity of 32.3\u2009mAh\u2009g\u22121 after 1000 cycles (Supplementary Fig.\u00a018a). Moreover, amorphous carbon releases reversible capacities of 58.2, 35.3, 24.8, 17.7, 12.2, 8.9, 11.5\u2009mAh\u2009g\u20131 at current densities of 0.2, 0.5, 1, 2, 5, 10, and 20\u2009A\u2009g\u20131 (Supplementary Fig.\u00a018b). Obviously, it contributes little capacity in these hybrid nanocomposites.\n\na CV curves and b galvanostatic charge and discharge curves at a current density of 0.2\u2009A\u2009g\u20131 of ZnSe0.7Te0.3@C. c Rate performance and e long\u2013term cycling at the current density of 1\u2009A\u2009g\u20131 of ZnSe@C, ZnSe0.5Te0.5@C, ZnSe0.8Te0.2@C and ZnSe0.7Te0.3@C (1\u2009A\u2009g\u20131\u2009=\u20091.6\u2009C). d Charge and discharge curves of ZnSe0.7Te0.3@C at different current densities. f Long cycle performance at the current density of 5\u2009A\u2009g\u20131 of ZnSe@C and ZnSe0.7Te0.3@C. Test temperature: 25(\u2009\u00b1\u20090.5)\u00b0C with air convection. Type of electrolyte: 1\u2009M NaPF6 in dimethoxyethane. Source data are provided as a Source Data file.\n\nFurthermore, Fig.\u00a05e shows the corresponding cycle performance at the current density of 1\u2009A\u2009g\u20131. The first discharge capacities of ZnSe0.7Te0.3@C, ZnSe0.8Te0.2@C, ZnSe0.5Te0.5@C, and ZnSe@C are 388.4, 382.0, 445.3, and 303.4\u2009mAh\u2009g\u22121, respectively. After 800 cycles, the discharge capacity of ZnSe0.7Te0.3@C, ZnSe0.8Te0.2@C, and ZnSe0.5Te0.5@C can retain 317.4, 272.5, and 240.3\u2009mAh\u2009g\u22121, respectively, significantly higher than that of ZnSe@C (191.0\u2009mAh\u2009g\u20131). This is because the Te atom doping introduces twin plane defects, thereby increasing the active sites and improving the kinetics. Besides, the Te atom doping also reduces the size of the nanoparticles, increases the specific surface area of the active material in contact with the electrolyte, and makes the nanoparticles fully react with sodium ions. Compared with ZnSe0.8Te0.2@C and ZnSe0.5Te0.5@C, ZnSe0.7Te0.3@C has the highest specific capacity, indicating that ZnSe0.7Te0.3@C has the optimal Te doping level and number of TBs. The cycle performance of other batteries at the current density of 1\u2009A\u2009g\u20131 for smaples is shown in Supplementary Fig.\u00a019. The stability and capacity of batteries at high currents are still significant, so a high\u2013current cycle performance test of ZnSe0.7Te0.3@C and ZnSe@C was carried out, as shown in Fig.\u00a05f. It can be seen that both ZnSe0.7Te0.3@C and ZnSe@C have good cyclic stability. After 1000 cycles, the discharge capacity of ZnSe0.7Te0.3@C remains 307\u2009mAh\u2009g\u22121, while that of ZnSe@C is only 118.8\u2009mAh\u2009g\u22121. The fine electrochemical performance of ZnSe0.7Te0.3@C is attributed to the twin structures by Te atom doping, the optimal number of TBs, and the improved conductivity. Hence, ZnSe0.7Te0.3@C was determined to continue other electrochemistry testing. The cycle performance of other batteries at the current density of 5\u2009A\u2009g\u20131 for the ZnSe0.7Te0.3@C and ZnSe@C is shown in Supplementary Fig.\u00a020.\n\nConsidering the promotive action of twin structures and substitional Te doping, ZnSe0.7Te0.3@C has a great prospect as the anode material for SIBs. Cyclic voltammetry (CV) tests were conducted to study the electrochemical reactions in a potential window of 0\u20133\u2009V vs. Na+/Na, with a scanning speed of 0.2\u2009mV\u2009s\u20131, as shown in Fig.\u00a05a. During the first cathodic scan, an unobtrusively wide peak appears at 0.80\u2009V, which results from the formation of the SEI film and the insertion of Na ions into the ZnSe0.7Te0.333,34. Two additional strong cathodic peaks at about 0.35 and 0.19\u2009V may be due to the conversion of ZnSe0.7Te0.3 to the metal Zn, Na2Se, and Na2Te, and further alloying of Zn35,36,37. During the first anodic scan, oxidation peaks at about 0.97 and 1.16\u2009V are associated with the dealloying reaction of NaZn13 and the oxidation of metal Zn to ZnSe, respectively37. The small anodic peak at about 2.18\u2009V is related to the oxidation of Na2Te to Te25,38. These analyses will be further demonstrated by the XRD and HRTEM measurements below. A pair of weak redox peaks near 0\u2009V can be attributed to the insertion/extraction of Na+ for the hollow bowl\u2013like carbon39. In subsequent scans, the CV curves almost overlapped, indicating good electrochemical reversibility. ZnSe@C has similar CV profiles to ZnSe0.7Te0.3@C in Supplementary Fig.\u00a021a. For comparison, the presence of two cathode peaks at 0.34\u2009V and 0.10\u2009V in ZnSe@C corresponds to the conversion of ZnSe to Zn and Na2Se, and further alloying of Zn. The galvanostatic charge and discharge curves of ZnSe0.7Te0.3@C and ZnSe@C at the current density of 0.2\u2009A\u2009g\u20131 are shown in Fig.\u00a05b and Supplementary Fig.\u00a021b, whose charge and discharge platforms are consistent with the redox peaks in CV curves. In addition, the initial coulomb efficiency of ZnSe0.7Te0.3@C is 83.6%, while that of ZnSe@C is 65.9%, which pertains to the fast kinetics contributed by the twin structures and substitute Te atoms. After the first cycle, the following charge and discharge curves of ZnSe0.7Te0.3@C basically coincide, while the curves of ZnSe@C separate seriously, indicating that ZnSe0.7Te0.3@C has better cyclic stability.\n\nAs for the electrochemistry of sodium ion reactions at ZnSe0.7Te0.3@C, it was further revealed by ex situ XRD and ex situ TEM characterizations. Figure\u00a06a shows the ex situ XRD pattern of ZnSe0.7Te0.3@C during the first discharge and charge process. Copper foil acting as a current collector shows strong XRD peaks at 43.3\u00b0, 50.3\u00b0, and 74.0\u00b0. The other diffraction peaks are located at 27.1\u00b0, 45.0\u00b0, 53.4\u00b0, 65.7\u00b0, and 72.3\u00b0, demonstrating the pure phase of ZnSe0.7Te0.3@C present in the original electrode. As the discharge continues, these peak intensities of ZnSe0.7Te0.3@C gradually decrease and then disappear completely. When discharged to 0.4\u2009V, new weak peaks appear at 23.8\u00b0 & 34.4\u00b0, assignable to the production of Na2Te, and peaks at 31.8\u00b0 & 36.2\u00b0 are attributable to the production of NaZn13 and Zn. When discharged to 0.01\u2009V, a small peak appears at 37.4\u00b0 due to the formation of Na2Se. This demonstrates that the conversion reaction of ZnSe0.7Te0.3 to Na2Se, Na2Te, and NaZn13 occurs during the discharge process. During the continuous charging process, these characteristic peaks of Na2Se, Na2Te, and NaZn13 gradually weaken and disappear, indicating that Na2Se, Na2Te, and NaZn13 gradually transform into ZnSe and Te. To prove it, the electrode material after fully charging was studied using the TEM technique. In Fig.\u00a06b, we can see a large number of hollow carbon shells and several nanoparticles. Further high\u2013resolution observation in Fig.\u00a06c shows clear lattice fringes with 0.33\u2009nm corresponding to the (111) crystal plane of ZnSe, and 0.32\u2009nm from the (101) crystal plane of the Te phase, demonstrating that the final product is ZnSe and Te. The HAADF\u2013STEM image and elemental mapping images shown in Fig.\u00a06d and e can also verify the product. It can be seen that Zn and Se element mapping overlap very well, denoting ZnSe nanoparticles. However, Te element tends to aggregate away from ZnSe nanoparticles, further indicating the formation of Te phase. Figure\u00a06f shows the schematic diagram of the electrochemistry process of ZnSe0.7Te0.3 during the first cycle. The electrochemical reactions for the first cycle can be summarized as follows:\n\na Ex situ XRD pattern of the ZnSe0.7Te0.3@C during the first charge and discharge process. b TEM image, c HRTEM image, d HAADF\u2013STEM image, and e elemental mapping images of the ZnSe0.7Te0.3@C after two full discharge and charge cycles. f Schematic diagram of sodium ion reaction mechanism of ZnSe0.7Te0.3 during the first charge and discharge process. Test temperature: 25(\u00b10.5)\u2009\u00b0C with air convection. Type of electrolyte: 1\u2009M NaPF6 in dimethoxyethane. Source data are provided as a Source Data file.\n\nDischarging:\n\nCharging:\n\nThe results show that the initial electrochemical reactions destroyed the ZnSe0.7Te0.3 structure, and it cannot retain the original phase. Since the active materials for the following electrochemistry are ZnSe and Te, not ZnSe0.7Te0.3 phase, what\u2019s the use of constructing ZnSe0.7Te0.3 with special defects as the electrode material? Here, we designed a comparative experiment to verify the effect on performance. In view of the low melting point of elemental Te, via the melting\u2013diffusion method, Te nanoparticles of small size were composited with ZnSe@C in the molar ratio of Se: Te\u2009=\u20097: 3 (ZnSe@C/Te). It has the same constituents as the active materials after cycling. The ZnSe@C/Te material in the comparison experiment was characterized by TEM, as shown in Supplementary Fig.\u00a022. It can be seen from the figure that Te and ZnSe nanoparticles are also coated with carbon. Supplementary Fig.\u00a023 shows the cycling performance of ZnSe@C/Te and the parallel battery at the current density of 1\u2009A\u2009g\u20131. It can just maintain the lifetime of 339 cycles and then break. A low reversible capacity of 223\u2009mAh\u2009g\u20131 was offered, much worse than ZnSe0.7Te0.3@C (310.9\u2009mAh\u2009g\u20131). Moreover, ZnSe@C/Te releases reversible capacities of 292.4, 241.9, 191.4, 157.1, 129.9, 109.1, and 96.4\u2009mAh\u2009g\u20131 at current densities of 0.2, 0.5, 1, 2, 5, 10, and 20\u2009A\u2009g\u20131 (Supplementary Fig.\u00a024), exhibiting a poor rate capability. Parallel data for ZnSe@C/Te electrode is also provided in Supplementary Fig.\u00a024. Obviously, the initial ZnSe0.7Te0.3 plays a vital role in sustaining the fine electrochemical performance even though it thoroughly transformed to other materials.\n\nIn conclusion, substitutional defects and twin structures were introduced into ZnSe nanocrystals through Te heteroatomic doping. Based on HAADF\u2013STEM, atomic mapping, and theoretical calculation, it is revealed that tellurium partially replaces selenium in ZnSe, which promotes the formation of twin structures verified by calculations. By tuning the composition of Te-doped ZnSe, the optimal composition and number of TBs are obtained for ZnSe0.7Te0.3. The combined effect of point defects, twin structures, and the optimal number of TBs greatly improves the sodium storage performance of ZnSe0.7Te0.3@C with a capacity of 307\u2009mAh\u2009g\u20131 after 1000 cycles at the current density of 5\u2009A\u2009g\u20131. Our work reveals the mechanism of action of Te substitute atoms on twin plane defects, the effect of Te dopant content on the number of twin boundaries, and its effect on electrochemical performance. This provides the theoretical basis of defect engineering for designing the anode materials of sodium-ion batteries with good performance.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59707-0/MediaObjects/41467_2025_59707_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59707-0/MediaObjects/41467_2025_59707_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59707-0/MediaObjects/41467_2025_59707_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59707-0/MediaObjects/41467_2025_59707_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59707-0/MediaObjects/41467_2025_59707_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59707-0/MediaObjects/41467_2025_59707_Fig6_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Methanol (\u226599.5%), hexadecyltrimethylammonium bromide (C19H42BrN, \u226599.0%), zinc nitrate hexahydrate (Zn(NO3)2\u00b76H2O, \u226599.0%), formaldehyde aqueous solution (CH2O, 37.0\u201340.0%) and ammonia solution (NH4OH, 25.0\u201328.0%) were bought from Sinopharm Chemical Reagent Co., Ltd. 2-methylimidazole (C4H6N2, 98%), selenium (Se, \u226599.99%), Sodium metal (Na, 99.7%), and tellurium (Te, \u226599.9%) were bought from the Aladdin. M-aminophenol (C6H7NO, 98%) was bought from 9ding chemical (Shanghai) limited. Sodium carboxymethyl cellulose and electrolyte (1\u2009M NaPF6 in dimethoxyethane) were bought from Duoduo Chemical Technology Co., Ltd. None of the reagents were further purified.\n\n1.31\u2009g of 2-methylimidazole and 200\u2009mL of methanol were mixed in a 250\u2009mL beaker and magnetically stirred until a clear solution was reached. Then, 2.38\u2009g of zinc nitrate hexahydrate was added to the above solution until it turned milky white under magnetic agitation. The obtained mixture was left for 12\u2009h, then centrifuged, washed with methanol, and dried at 60\u2009\u00b0C to collect the white powder of ZIF\u20138.\n\nThe above-prepared ZIF\u20138 powder, 0.46\u2009g of hexadecyltrimethylammonium bromide, 0.2\u2009mL of ammonia solution, 28\u2009mL of ultra-pure water, and 12\u2009mL of ethanol were magnetically stirred in a 50\u2009mL beaker for 6\u2009h. Then 0.07\u2009g of m-aminophenol and 0.12\u2009mL of formaldehyde aqueous solution were added and stirred magnetically for 12\u2009h. Afterward, the solution was extracted and filtered, washed with ultra-pure water, and dried at 60\u2009\u00b0C to prepare ZIF\u20138@MF.\n\n0.2\u2009g of ZIF\u20138@MF, 0.2\u2009g of Te powder, and 0.12\u2009g of Se powder were ground evenly and then heated in an Ar/H2 mixed atmosphere at 800\u2009\u00b0C for 2\u2009h at a heating rate of 4\u2009\u00b0C/min to obtain ZnSe0.8Te0.2@C. 0.2\u2009g of ZIF\u20138@MF, 0.2\u2009g of Te powder, and 0.1\u2009g of Se powder were ground evenly and then heated in Ar/H2 mixed atmosphere at 800\u2009\u00b0C for 2\u2009h at a heating rate of 4\u2009\u00b0C/min to obtain ZnSe0.7Te0.3@C. 0.2\u2009g of ZIF\u20138@MF, 0.2\u2009g of Te powder, and 0.07\u2009g of Se powder were ground evenly and then heated in an Ar/H2 mixed atmosphere at 800\u2009\u00b0C for 2\u2009h at a heating rate of 4\u2009\u00b0C/min to obtain ZnSe0.5Te0.5@C. 0.2\u2009g of ZIF\u20138@MF and 0.15\u2009g of Se powder were ground evenly and then heat\u2013treated under the same conditions to obtain ZnSe@C. 0.32\u2009g of ZnSe@C and 0.14\u2009g of Te powder were ground evenly and then heated in an Ar atmosphere at 480\u2009\u00b0C for 1\u2009h at a heating rate of 4\u2009\u00b0C/min to obtain ZnSe@C/Te. The black suspension was obtained by fully soaking ZnSe0.7Te0.3@C in aqua regia and ultrasonic treatment. Afterward, the solution was extracted and filtered, washed with ultra-pure water, and dried at 60\u2009\u00b0C to prepare amorphous carbon.\n\nThe crystal structures of samples such as ZnSe@C and ZnSe0.7Te0.3@C were characterized by X-Ray Diffractometer (XRD, Bruker D8 Advance, Germany, Cu K\u03b1 radiation, \u03bb\u2009=\u20091.5418\u2009\u00c5) in the 2\u03b8 range from 10\u00b0 to 80\u00b0. The morphology, composition, and microstructure of the samples were characterized by a field emission scanning electron microscope (FESEM, GeminiSEM 300, Carl Zeiss Microscopy Ltd.) coupled with energy-dispersive X-ray spectroscopy (EDS) and a transmission electron microscope (TEM, Talos F200X, accelerating voltage of 200\u2009kV). High-angle annular dark-field-scanning TEM (HAADF\u2013STEM) imaging was done on a Thermo Scientific Spectra 300 equipped with a spherical aberration correction system, and the microscope was operated at 300\u2009kV. The components and valence states of the ZnSe0.7Te0.3@C were measured by X-ray photoelectron spectroscopy (XPS, Thermo ESCALAB 250 Xi) with a monochromatic Al K\u03b1 X-ray source (h\u03bd\u2009=\u20091486.6\u2009eV). And all binding energies were calibrated using C 1\u2009s signals at 284.8\u2009eV. Thermogravimetric analysis (TGA) of the sample was carried out on a Simultaneous Thermal Analyzer (TGA/DSC3\u2009+\u2009) in an air atmosphere at a heating rate of 20\u2009\u00b0C/min. Nitrogen adsorption\u2013desorption measurements were carried out on the Autosorb 6B instrument at 70\u2009K.\n\nThe coin cells (CR2032) were assembled in a glove box filled with argon gas (with H2O and O2\u2009<\u20090.1 ppm) to evaluate the electrochemical performance of the samples. A two-electrode system was used in the battery test, in which the prepared composite electrode was used as the working electrode, and sodium foil was used as the counter electrode. The assembled 2032 coin battery consists of a positive and negative stainless steel housing, working electrode, separator, electrolyte, two round stainless steel (16\u2009mm\u2009\u00d7\u20090.05\u2009mm), and a stainless steel spring. The active material, acetylene black, and adhesive agent (sodium carboxymethyl cellulose) were dispersed in ultra-pure water at a mass ratio of 7:1.5:1.5 and fully ground in agate mortar to prepare the slurry. The slurry was then cast on a copper foil with a four-sided preparation device (SZQ, Guangzhou Demanyi Instruments Co., Ltd.) and dried in a vacuum oven at 60\u2009\u00b0C for 12\u2009h. The dried electrode sheet was cut into a circular working electrode with a diameter of 10\u2009mm by a manual slicing machine (MSK-T10, Shenzhen Kejing Technology Co., Ltd.) without further calendering. The active material loading on the electrode was approximately 0.68\u2009mg\u2009cm\u22122. The mass basis of the battery refers to the mass of the active material on the electrode. A single clean copper foil with a diameter of 10\u2009mm has a mass of about 6.8\u2009mg. The sodium foil was drilled into a circular sheet with a diameter of 14\u2009mm and a thickness of approximately 0.6\u2009mm. 1\u2009M NaPF6 in dimethoxyethane was used as the electrolyte. The amount of electrolyte added per button cell is approximately 180\u2009\u03bcL. A circular glass fiber (GF/F, Whatman) with a diameter of 19\u2009mm acts as the separator for the battery and is about 0.7\u2009\u03bcm thick. The Land battery test system (LAND\u2013CT2001A, Wuhan, China) was used to conduct galvanostatic charge/discharge tests at various current densities within the voltage range of 0.01\u20133\u2009V vs Na+/Na. All battery tests were carried out in a constant temperature chamber of 25(\u00b10.5)\u2009\u00b0C with air convection. The galvanostatic intermittent titration technique (GITT) was tested on a Land battery testing system (LAND\u2013CT2001A, Wuhan, China) with a potential window of 0.01\u20133\u2009V vs Na+/Na. The cyclic voltammetry (CV) curves of the samples were measured on an electrochemical workstation (Shanghai Chenhua electrochemistry workstation, CHI760D) with a sweep speed of 0.2\u2009mV\u2009s\u22121 and a voltage range of 0\u20133\u2009V. Electrochemical impedance spectroscopy (EIS) measurements were done on an electrochemical workstation (Shanghai Chenhua electrochemistry workstation, CHI760D), and the amplitude of the AC voltage was set at 5\u2009mV at 100\u2009kHz to 0.01\u2009Hz. The nature of the added signal is potentiostatic, and the number of data points per decade of frequency is 12. The applied quasi-stationary potential is the open circuit potential, and the open circuit voltage application time is 2\u2009s. The electrochemical data provided in the manuscript belong only to a specific battery. The electrodes used for ex situ XRD measurements are obtained by disassembling a charge\u2013discharged battery in a glove box filled with argon gas. Then, the electrodes were placed in a sealed bag in the glove box, and the exposure time of the electrodes in the air was about 5\u2009s before the XRD test. Temperature environment: 25(\u00b12)\u2009\u00b0C.\n\nAll calculations were performed on the Vienna ab initio simulation package (VASP)40 within the frame of density functional theory (DFT)22,23 with a cutoff energy of 450\u2009eV. The exchange correlation interaction of electrons was described by the generalized gradient approximation (GGA) of Perdew\u2013Burke\u2013Ernzerhof (PBE) functional41, and the interaction between electrons and ions was described by the projected augmented wave (PAW) method42. The Brillouin zone was sampled by a Monkhorst\u2013Pack k-point mesh43 of 2\u2009\u00d7\u20092\u2009\u00d7\u20092 grid for bulk phase models and 2\u2009\u00d7\u20093\u2009\u00d7\u20091 grid for interface models. In addition, DFT\u2013D3 method44,45 was used to explain the presence of van der Waals forces within the system. The structural optimizations were done, and the total energy converged within 10\u20135\u2009eV. The final force of each ion was below 0.02\u2009eV/\u00c5.\n\nThe defect formation energy of structural models was also calculated with the chemical potential of the components, and the calculation formula was as follows:\n\nwhere \u25b3HD is the defect formation energy, ED is the total energy of the supercell with the defect (D), Eh is the total energy of the ZnSe supercell, \u03bci is the chemical potential of element Te and Se. ni is the number of Te and Se atoms that were removed or added to form the system. Ei is the total energy of the solid structure of Se and Te. Ni is the total number of atoms in a solid structure of Se and Te. The chemical potentials of Te and Se were obtained by the solid structure models of Se and Te after structure optimization, as shown in Supplementary Fig.\u00a025.\n\nEper atom is the average energy per atom in the final state, Etotal is the total energy of the structural model, and Natom is the total number of atoms in the structural model.\n\nThe ab initio molecular dynamics (MD) simulations were carried out via VASP, with a 300\u2009eV cutoff energy and 10\u22124\u2009eV energy convergence. Nose-Hoover thermostat46,47,48 was employed in order to control the system at finite temperatures 298\u2009K and 313\u2009K. The time step was 2\u2009fs, and each simulation lasted for 20\u2009ps.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data supporting the findings of this study are available within this article and its Supplementary Information file, or from the corresponding author upon request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Usiskin, R. et al. Fundamentals, status and promise of sodium-based batteries. Nat. Rev. Mater. 6, 1020\u20131035 (2021).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nChu, Y. et al. Reconfiguring hard carbons with emerging sodium-ion batteries: a perspective. Adv. Mater. 35, 2212186 (2023).\n\nArticle\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nTang, Z. et al. Revealing the closed pore formation of waste wood-derived hard carbon for advanced sodium-ion battery. Nat. 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A 31, 1695 (1985).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by the National Natural Science Foundation of China (U21A2077), the Natural Science Foundation of Shandong Province (ZR2022JQ08, ZR2024MB003), Postdoctoral Innovation Project of Shandong Province (SDCX-ZG-202303012), the Taishan Scholars Program of Shandong Province (tsqn202211028), and the Instrument Improvement Founds of Shandong University Public Technology Platform (ts20230209).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "School of Chemistry and Chemical Engineering, Shandong University, Jinan, China\n\nJingui Zong,\u00a0Yazhan Liang,\u00a0Fan Liu,\u00a0Mingzhe Zhang,\u00a0Kepeng Song,\u00a0Baojuan Xi\u00a0&\u00a0Shenglin Xiong\n\nSchool of Materials Science and Engineering, Shandong University, Jinan, China\n\nMingzhe Zhang\u00a0&\u00a0Jinkui Feng\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.L.X. and B.J.X. conceived and designed the research. J.G.Z., S.L.X. and B.J.X. performed the experiments and the characterization of the materials. J.G.Z., S.L.X. and B.J.X. analyzed the data and wrote the manuscript. K.P.S. conducted the electron microscopy experiments and analyzed the data. J.G.Z., Y.Z.L., F.L., M.Z.Z., K.P.S., J.K.F., B.J.X. and S.L.X. have discussed the results and commented on the manuscript. The authors also want to thank Dr. D. Qi from the Electron Microscopy Centre of Shandong University for the help with TEM experiments.\n\nCorrespondence to\n Kepeng Song, Baojuan Xi or Shenglin Xiong.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Engineering twin structures and substitutional dopants in ZnSe0.7Te0.3 anode material for enhanced sodium storage performance.\n Nat Commun 16, 4406 (2025). https://doi.org/10.1038/s41467-025-59707-0\n\nDownload citation\n\nReceived: 15 April 2024\n\nAccepted: 30 April 2025\n\nPublished: 12 May 2025\n\nVersion of record: 12 May 2025\n\nDOI: https://doi.org/10.1038/s41467-025-59707-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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multichannel meta-infrared imaging", + "journal": "Nature Communications", + "published": "18 June 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49592-4/MediaObjects/41467_2024_49592_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49592-4/MediaObjects/41467_2024_49592_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [ + "https://github.com/We1wu/Multichannel-meta-infrared-imaging", + "https://doi.org/10.5281/zenodo.11544077" + ], + "subject": [ + "Electrical and electronic engineering", + "Nanophotonics and plasmonics" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3929576/v1.pdf?c=1718795368000", + "research_square_link": "https://www.researchsquare.com//article/rs-3929576/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-49592-4.pdf", + "preprint_posted": "17 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Multichannel meta-imaging, inspired by the parallel-processing capability of neuromorphic computing, offers significant advancements in resolution enhancement and edge discrimination in imaging systems, extending even into the mid- to far-infrared spectrum. Currently, high-performance multichannel infrared imaging systems typically consist of separate detector arrays and gratings or multi-camera integration, which require complex circuit design and heavy power consumption, hindering the implementation of advanced human-eye-like imagers. Here, we present a novel approach for printable graphene plasmonic photodetector arrays with zero-bias operation driven by a ferroelectric superdomain for multichannel meta-infrared imaging with enhanced edge discrimination. The fabricated photodetectors exhibited multiple spectral responses by directly rescaling the ferroelectric superdomain instead of reconstructing the gratings. We also demonstrated enhanced and faster shape classification (98.1%) and edge detection (98.2%) using our multichannel infrared images compared with single-channel detectors. Our proof-of-concept photodetector arrays simplify multichannel infrared imaging systems and hold great potential for applications in efficient edge detection in human-brain-type machine vision.Physical sciences/Nanoscience and technology/Nanoscale devices/Nanophotonics and plasmonicsPhysical sciences/Optics and photonics/Applied optics/Optoelectronic devices and componentsmultichannel infrared imagingmeta-imagingferroelectric superdomaindeep learningedge detection", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "supplementaryinformationv2.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Multichannel meta-imaging, inspired by the parallel-processing capability of neuromorphic computing, offers considerable advancements in resolution enhancement and edge discrimination in imaging systems, extending even into the mid- to far-infrared spectrum. Currently typical multichannel infrared imaging systems consist of separating optical gratings or merging multi-cameras, which require complex circuit design and heavy power consumption, hindering the implementation of advanced human-eye-like imagers. Here, we present printable graphene plasmonic photodetector arrays driven by a ferroelectric superdomain for multichannel meta-infrared imaging with enhanced edge discrimination. The fabricated photodetectors exhibited multiple spectral responses with zero-bias operation by directly rescaling the ferroelectric superdomain instead of reconstructing the separated gratings. We also demonstrated enhanced and faster shape classification (98.1%) and edge detection (98.2%) using our multichannel infrared images compared with single-channel detectors. Our proof-of-concept photodetector arrays simplify multichannel infrared imaging systems and offer potential solutions in efficient edge detection in human-brain-type machine vision.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Infrared imaging systems can convert infrared radiation into human-eye-recognizable pseudoviews and are used in diverse applications, including security, surveillance, environmental monitoring, industrial inspections, and medical and health diagnoses1,2,3,4. Current advanced infrared imaging systems are typically based on focal plane array infrared sensors and have shown great potential for target perception5. However, fundamental challenges remain in achieving ultra-high resolution beyond the optical diffraction limit and in bias operation with low power consumption for hardware implementation. The human visual system can rapidly and accurately identify objective features, such as color, depth, and edges, in complex environments through the parallel processing of input light signals (Fig.\u00a01a), and allow the future manufacturing of artificial vision6,7,8,9. Inspired by this, recent efforts have been devoted the collection of multichannel information in terms of hardware, such as heat-assisted detection and ranging (HADAR) and meta-imagers, to enhance infrared image resolution and recognition efficiency10,11. This inevitably faces common limitations in addition to separating grating or sensor components, which necessitates a complex circuit design.\n\na Illustration of the human visual system. The optical pathways are depicted in the top and lateral views in the left and middle panels, demonstrating the components of the human visual system, including the eyes, connecting pathways to the visual cortex, and other regions of the brain. The schematic diagram of the retina in the right panel highlights its remarkable ability to process various types of input light information in parallel. LGN represents the lateral geniculate body. b Schematic of the meta-infrared using type-printing photodetectors. The integrated sensing layer enables selective light detection with a multi-spectral response, which avoids externally separated grating filters. MCA represents the multichannel array detectors.\n\nRegarding the smart perception of future infrared imaging development, graphene plasmonic photodetectors have become a promising candidate for large-scale integration into optoelectronic networks, as they directly detect infrared light with a tunable spectral response and make possible multipixel read-out circuits. Previous experiments demonstrated the excitation and tuning of plasmons in patterned graphene using traditional electrostatic gating techniques, resulting in infrared light detection with a tunable and selective response12,13,14,15. However, achieving high-quality patterned graphene remains a challenge because of the inevitable edge disorder and amorphization that occur during the patterning process using standard techniques such as chemical vapor deposition (CVD) growth, lithography, and ion/chemical etching16,17,18. Although alternative approaches have shown promise for modulating surface plasmons in continuous graphene through substrate patterning or metal gratings19,20,21, precise spatial modulation of the charge carrier density of graphene for tunable infrared light detection has remained challenging. Furthermore, current patterned graphene plasmonic photodetectors employing top- or back-gate layers face limitations in terms of complex fabrication of micro-/nano- structured graphene patterns to contact with gate electrodes and high input consumption16,22,23.\n\nFerroelectric superdomains, characterized by nanoscale domains with alternating up/downward polarization arrays, have the advantage of spatially manipulating the graphene carrier density at nanoscale resolution to construct nonuniform conductivity patterns, thereby confining graphene plasmons for enhanced infrared detection with a tunable spectral response24,25,26, suggesting its unique potential for nanophotonic applications. In this study, we designed a simple two-terminal zero-bias multichannel array (MCA) detector by artificially type-printing graphene carrier density using BiFeO3 (BFO) superdomains with hundreds-nanoscale-wide stripes for a meta-infrared imaging application. Raman signal-based spatial monitoring of the carrier density indicates that non-uniform patterning of graphene conductivity can be achieved by reconfiguring the ferroelectric superdomain. When operating at zero-bias voltage and room temperature, our device array exhibited tunable transmission spectra and selective responsivity in the mid-infrared region. Importantly, we demonstrate the integration of MCA photodetectors for infrared imaging applications, showing enhanced recognition accuracy for both overall target shapes and edge detection, along with faster training and recognition speeds compared with single-channel array (SCA) detectors.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49592-4/MediaObjects/41467_2024_49592_Fig1_HTML.png" + ] + }, + { + "section_name": "Results and discussion", + "section_text": "Conventional infrared imaging using SCA sensors (top panel of Supplementary Fig.\u00a01), which uses a linear response to map the incident intensity, faces the fundamental challenge of achieving ultrahigh spatial resolution beyond the optical diffraction limit. Consequently, textureless images, commonly known as the \u201cghosting effect\u201d, are produced. Recent developments in multichannel imagers have demonstrated their ability to enhance imaging resolution. For example, the integration of different types of sensors (e.g., HADAR) allows an imaging system to emulate the parallel processing functions of the human eye10. Recently, an alternative approach using multichannel meta-imagers with external angular gratings (bottom panel of Supplementary Fig.\u00a01) was developed to accelerate machine vision11. However, both have complex spatial layouts; in particular, different devices require different bias voltages to drive, which hinders the goal of achieving high energy efficiency.\n\nThe scheme of the multichannel meta-infrared imaging technique using ferroelectric superdomain-printed photodetectors is illustrated in Fig.\u00a01b. Unlike the merging of diverse cameras, the pixel points of our meta-imaging were engineered to achieve a parallel multichannel using a single aperture implemented with type-printing detectors. To ensure that the photodetector can provide selective photocurrents along with a multispectral response, we optimized the plasmonic sensing layer by rescaling the geometric shape of the ferroelectric superdomain into a 6-channel-pixel at an imaging point to avoid externally separated grating layouts. This approach can recognize a curled thumb, whereas the conventional approach (using SCA detectors) cannot.\n\nMovable-type printing is divided into the engraving, typesetting, and printing processes27,28,29. Figure\u00a02a illustrates the fabrication process and structure of the photodetector designed using the type-printing technique employed in this study (see Methods). Here, we used a conductive atomic force microscopy (AFM) probe (Bruker) with a platinum coating to create a ferroelectric superdomain, where the epitaxial ferroelectric BFO thin film was periodically switched to an adjacent upward and downward-striped domain array (step 1 corresponds to the engraving process of the type-printing technique). Additionally, the large-area ferroelectric superdomain arrays were fabricated using a water-printing technique in our previous experiments27,28. The crystal structure of the BFO thin film was obtained using annular bright-field high-resolution scanning transmission electron microscopy (STEM) and is depicted in Supplementary Fig.\u00a02a, b. Under the microscope, the iron atoms (depicted as yellow spheres) shifted dramatically in the upward or downward domains, indicating a reversal in the direction of ferroelectric polarization. The phase difference between the adjacent domains was 180\u00b0, as characterized by piezoelectric force microscopy (PFM) (Fig.\u00a02b). The period of polarized strips (a, length of adjacent upward and downward domains) was controlled from 200\u2009nm to 1\u2009\u03bcm within the same BFO film. The widths of the upward and downward domains were equal within each period, that is, the width of the ferroelectric domain stripes (d) ranged from 100 to 500\u2009nm.\n\na Schematic of the experimental setup for printing graphene/BiFeO3 (BFO) superdomain-based plasmonic photodetector. The BFO film epitaxial growth on an SrTiO3 (STO) substrate with a covering LSMO (LaxSr1\u2212xMnO3) electrode layer. b Piezoelectric force microscopy (PFM) phase image of BFO superdomain, showing the periodical ribbons patterned adjacent to upward and downward domains. c G-band frequency mapping for graphene on BFO superdomain. The scale bars in (b) and (c) are 1\u2009\u03bcm, that is, the domain width is 500\u2009nm. d Scanning electron microscopy image of the active area of the fabricated graphene/BFO device. The fake color denotes on the graphene on the BFO film. e Schematic of the graphene plasmon resonator architecture for a plane wave normally infrared incidence. GSPP represents graphene surface plasmon polaritons. a and d represent the period of polarized strips (length of adjacent upward and downward domains) and the width of the ferroelectric domain stripes, respectively. f Simulated electric field intensity for graphene plasmon resonator by a three-dimensional model. The excited GSPP is highly confining in the borders of upward/downward domains at the graphene/BFO interface. SLG denotes single-layer graphene.\n\nSubsequently, we transferred the CVD-grown single-layer graphene onto the BFO film (Step 2, corresponding to the printing process of the type-printing technique) using the wet transfer method30. Subsequently, we employed a noncontact Raman spectroscopy monitoring technique to evaluate the doping level of graphene induced by the electrostatic effect of remnant polarization in the ferroelectric domains. The spatially resolved Raman G-band frequency shifts were used as probes for detecting the graphene carrier density31. In Fig.\u00a02c, Raman G-band frequency mapping revealed periodic stripes with lower and higher frequencies, corresponding to the upward and downward domains, respectively. Additionally, Raman shift analysis (Supplementary Fig.\u00a02b) showed a high 2D-to-G peak intensity ratio (I2D/IG\u2009=\u20092.15) and a low D-band intensity, confirming the high-quality and single-layer characteristics of the transferred graphene. Furthermore, comparative analysis of the Raman G-band for graphene on various BFO substrates (Supplementary Fig.\u00a02c\u2013e) demonstrated that the behavior of graphene on BFO with an upward domain is near-intrinsic graphene, whereas the behavior differs in the downward domain that follows a typical p-doped graphene (Supplementary Fig.\u00a02g). This difference is mainly derived from that the p-type behavior of graphene on the upward BFO domain is suppressed while that of the defect-free graphene surface on the downward BFO domain is enhanced32. The feature of the parallel p\u2013i junctions that are strongly connected to the inherent polarity of the ferroelectric domain32, combined with the reversible advances in nanoscale ferroelectric superdomain arrays (corresponding to the typesetting process of type-printing technique), offer the potential for graphene-based nanophotonic applications.\n\nTo facilitate photocurrent collection and reduce contact resistance, source/drain electrodes were deposited on the graphene sheet (step 3). Typically, if the source/drain electrodes are made of the same metal, the device exhibits an overall zero photocurrent. In this study, we employed an asymmetric metal-doping scheme to disrupt the symmetric built-in electric field profile in the graphene channel, following a previous study on metal-graphene-metal operation at zero bias between the source and drain33. Specifically, we deposited 20-nm-thick layers of palladium (Pd) and titanium (Ti) onto a graphene sheet and covered it with an 80-nm-thick layer of gold (Au) on the contact electrodes. A magnified scanning electron microscopy (SEM) image of the active region of the as-fabricated device is presented in Fig.\u00a02d, and the raw SEM image is shown in Supplementary Fig.\u00a02f.\n\nThe average Raman G-band frequency of graphene on BFO with a striped domain width of 500\u2009nm (Supplementary Fig.\u00a02c) shows that the peak position of the G-band (POG) in Raman shift for graphene on upward and downward domains were 1586 and 1597\u2009cm\u22121, respectively. The periodic Raman frequency shifted from lower to higher frequencies, suggesting that graphene experienced an abrupt transition from lower to higher carrier concentrations at the edges of the upward/downward domains19,20, confirming the feasibility of spatial printing of graphene carrier density without patterning of graphene sheets. Periodic patterns of graphene carrier-density modulation offer potential solutions for type-printing multiple parallel p\u2013i junctions in a continuous graphene sheet without destroying the graphene sheet or applying complex gating electrodes (Supplementary Fig.\u00a02g). Owing to the reversible and nonvolatile characteristics and nanoscale size of the ferroelectric domain, this strategy of integrating continuous graphene with a ferroelectric superdomain makes possibility for printing graphene carrier density into desired patterns, enabling graphene plasmonic resonance and enhancing the photocurrent in the mid- to far-infrared ranges.\n\nTo better understand the achieved selective light detection beyond broadband wavelength response in the designed device, we introduced a plasmonic resonance model (Fig.\u00a02e) in a graphene/BFO superdomain hybrid structure with electromagnetic field simulation using finite element analysis. As shown in Fig.\u00a02f and Supplementary Fig.\u00a02h, the simulated cross-sections of the electric field intensity in our device show that the excited graphene plasmons are highly confined in the BFO superdoamin array, and detailed numerical simulation procedures are presented in\u00a0Supplementary Notes\u00a01\u20133. In this scenario, we set the chemical potential (\u03bcc\u2009=\u2009EF) of graphene doped by the upward and downward domains with a width of 500\u2009nm to +121\u2009meV and \u2212448\u2009meV, respectively. Herein, we used the formula of \\(\\hslash \\Delta \\omega=\\alpha {{\\hbox{'}}}|{E}_{{{{{{\\rm{F}}}}}}}|\\) (ref. 31) as shown in Supplementary Note\u00a01, where \\(\\hslash \\Delta \\omega\\) and \\(\\alpha {{\\hbox{'}}}\\) denote the POG shift of graphene and the integral constant, respectively. The energy of the transverse-magnetic mode is strongly confined at the graphene/ferroelectric interface with ultra-high enhanced electric field intensity. Although the energy flow enters the graphene structure on the upward ferroelectric domain, it is almost perfectly absorbed. This phenomenon may contribute to the electronic behavior near the upward and downward domains of doping graphene junctions. Importantly, for our designed plasmonic device, the resonance frequency (\u03c9spr) could be easily regulated by rescaling the ferroelectric superdomain width (d) according to Eq. (1),\n\nwhere e, \u0127, \u03b50, \u03b5r, and q0 represent the element charge, reduced Planck constant, vacuum dielectric constant, the relative dielectric constant of BFO, free space wave vector of incident light, and N\u2009=\u20091, 2, 3, \u2026, respectively. A detailed derivation of the Eq. (1) is provided in Supplementary Note\u00a04.\n\nTo validate the performance of this printable architecture, we fabricated a device array on the same BFO thin film by rescaling the ferroelectric domain width (corresponding to the typesetting process of the type-printing technique). Two types of device arrays were fabricated (Supplementary Fig.\u00a03a\u2013c) to facilitate the optical (sample size of 5\u2009mm\u2009\u00d7\u20095\u2009mm) and photoelectric (sample size of 10\u2009mm\u2009\u00d7\u200910\u2009mm) measurements. Each unit contained six BFO stripes with domain widths ranging from 100 to 500\u2009nm. The optical and corresponding PFM phase images of the fabricated device array are shown in Fig.\u00a03a. The AFM Raman images (Fig.\u00a03b) and corresponding POG peaks (Supplementary Fig.\u00a02c, e) strongly support our idea of type-printing desired graphene carrier patterns by resetting the ferroelectric domain width.\n\na Integration of graphene photodetector array onto a BFO film. The middle column is the optical image of the fabricated device array with a ferroelectric domain width ranging from 100 to 500\u2009nm. The top and bottom columns are the PFM phase images of the corresponding BFO superdomains. The scale bars for PFM and optical image are 1\u2009\u03bcm and 10\u2009\u03bcm, respectively. b Atomic force microscopy-Raman mapping images for graphene carrier density printed by rescaling the BFO superdomain. c Tunable transmission extinction spectra (1\u2212T/T0) for graphene by rescaling the BFO superdomain, where T and T0 are the measured transmission intensity with and without graphene on BFO film. The hollow squares and the solid lines represent the experimental and corresponding Lorentz fitting data, respectively. The inset is the schematic of transmission measurement. d Comparison of responsivities with typical types of infrared photodetectors using metal-semiconductor-metal structure working at room temperature. The data were collected from refs. 15,35,36,37,38,39,40,41. The hollow and solid scatters represent the photocurrent recorded with and without bias voltages, respectively. The light blue, yellow, and red areas represent three infrared regions defined by the CIE 17-21-004 standard, and the dark red area is the main thermal radiation range of human body.\n\nWe then focused on the spectral response of graphene to the BFO superdomains of different widths. To achieve this, we used an AFM probe to direct incident light precisely to specific microregions. The transmission measurement scheme is illustrated in the inset of Fig.\u00a03c and the experimental setup is schematically shown in Supplementary Fig.\u00a04a, where T and T0 correspond to the transmission values of the BFO film epitaxially grown on the SrTiO3 (STO) substrate with and without graphene, respectively. The measured extinction spectra of the graphene/BFO hybrid structures are shown in Fig.\u00a03c. More quantitative information about the extinction spectra in our fabricated device arrays is shown in Supplementary Fig.\u00a05. Within the periodically printed graphene stripes on the BFO superdomains, we observed two notable features. First, a remarkably selective resonant peak appeared with domain widths ranging from 100 to 500\u2009nm. Second, the resonant peaks shifted from low frequency (1084\u2009cm\u22121) to high frequency (1292\u2009cm\u22121) with a decreasing domain period. The coincidence between the blue-shift phenomenon and Eq. (1) can be attributed to the matching of the wave vector of the incident wave in free space and the wave vector of the excited surface plasmon polaritons in graphene, thereby forming a resonant coupling effect.\n\nThe photocurrent of the device array was measured using an automated wavelength-tuning laser to generate incident signals that were added to the graphene sheet at a 90\u00b0 angle (in the vertical direction) with a zero-bias voltage, and the experimental setup and workflow are schematically shown in Supplementary Fig.\u00a04b. All the photocurrents were obtained by averaging the peak values under illumination at different wavelengths. The photoelectric characteristics demonstrated here are representative of the selective response observed for nine cells (9\u2009\u00d7\u20096 devices, Supplementary Fig.\u00a03d) fabricated for each ferroelectric domain width. All the photocurrent records were extracted from the current dependence of time (I-t) curves and the typical I-t plots characterized from the same device are shown in Supplementary Fig.\u00a06a. The highest photocurrent in each cell was achieved near the resonant wavelength (Supplementary Fig.\u00a06b, c). The fabricated photodetectors also show a good linear relation between photocurrent and incident power (Supplementary Fig.\u00a06d). Additionally, we investigated the responsivity of the photodetectors using R\u2009=\u2009Iph/Pin, where Iph and Pin represent the photocurrent and incident laser power, respectively. The obtained results (Supplementary Fig.\u00a07a, b) demonstrate that our device array exhibits a selective detection factor \u03b2, which is the ratio of the highest and lowest responsivity values in each cell.\n\nWe discuss herein the performances of typical infrared photodetectors based on emerging materials operating at room temperature, as shown in Fig.\u00a03d. Compared with previously reported metal plasmon-enhanced graphene photodetectors, such as metal-graphene-metal structure33, the intrinsic plasmons in patterned graphene offer an incomparable advantage in the mid-infrared range. Its surface plasmons can be used to enhance the absorption for tunable photodetection controlled by the grating effect, providing appealing spectral selectivity with ultra-high tunability15. The photodetector demonstrated herein also achieved an enhanced responsivity of ~30\u2009mA\u2009W\u22121 and a specific detectivity (D*) of the order of 109 Jones (Supplementary Fig.\u00a07c). The specific detectivity was obtained using D* = RA1/2/(2eId)1/2 (in cm\u2009Hz1/2\u2009W\u22121 (Jones))34, where A is the active area of the device, Id is the dark current, and R and e are the responsivity and unit charge defined above. The observations of enhanced light detection can be attributed to the effective plasmon excitations and perfect crystallinity of the graphene sheet. While the obtained responsivity and specific detectivity were not the highest recorded among current infrared photodetectors using graphene and other emerging two-dimensional materials15,35,36,37,38,39,40,41, it demonstrates that two key features in our device array include selective responsivity and zero-bias operating voltage. Another interesting phenomenon is that the device we designed can operate with an extended detection range in the IR\u2013C band (3\u2009\u03bcm to 1\u2009mm, CIE 17-21-004, https://cie.co.at/eilvterm/17-21-004). This coincided with the biological thermal radiation, particularly the fact that over 50% of the energy emitted by the human body was concentrated in the range of 8\u201315\u2009\u03bcm, which our device could handle. Furthermore, the convenience of printing graphene carrier-density patterns by switching ferroelectric domains at the nanoscale without the need for complex nanofabrication, as seen in conventional graphene plasmonic devices, allows for a remarkably selective response over broadband wavelengths and features a simple MCA integration for light detection in the infrared region. The characteristics exhibited by our device provide a promising route for advanced multi-channel infrared imaging that combines low power consumption with high recognition capability, as mentioned earlier.\n\nTo evaluate the imaging capability of the fabricated array of infrared photodetectors, we used the OPENZYNQ open-source project (https://github.com/openzynqhardware/openzynq.git) to simulate the process. A schematic of the simulation procedure is shown in Supplementary Fig.\u00a08. For data collection, we employed a commercial Melexis infrared camera with linear interpolation of 192\u2009\u00d7\u2009144\u2009pixels to capture the desired temperature. The workflow of the proposed multichannel meta-infrared imaging is schematically illustrated in Supplementary Fig.\u00a09 and the objective temperature mapping method is shown in Supplementary Fig.\u00a010. To ensure the image recognition accuracy, we intentionally reduced each channel pixel of the 6-channel imaging outcome to 1/6 of a single-channel pixel layer, while maintaining the total number of pixels unchanged. The simulations have already been demonstrated for both the SCA with a ferroelectric domain width of 500\u2009nm and MCA with ferroelectric domain widths ranging from 100 to 500\u2009nm (Fig.\u00a04a and Supplementary Fig.\u00a011). We observed that MCA detectors excel at recognizing inner edge profiles such as curled fingers and overlapped leaves, whereas SCA detectors fall short in this regard. The images generated by the MCA showed a more pronounced temperature difference between the regions of the fingers and palms, indicating that the MCA can identify more complex edge features of the targets when processing images.\n\na Infrared imaging for different gestures with single-channel array (SCA) and multichannel array (MCA) of type-printing photodetectors. b, c Evolution of classification accuracy for gesture recognition. The inset in (b) shows the enlarged view of the data where the gesture training accuracy exceeded 98%. d, e Changes in the classification accuracy for curled finger recognition. The training process includes 3600 iterations and the recognition process involves 100 epochs. The light red and blue colors in (b\u2013e) represent the error bars of infrared image recognition results using MCA and SCA detectors, respectively, and the solid lines indicate the corresponding mean results.\n\nAn off-chip learning task involving the classification of gesture images (0\u20135, as shown in Fig.\u00a04a) was conducted to evaluate the learning capability of the MCA detectors in infrared imaging. The training accuracies for gesture recognition were 99.2% and ~100% after 3600 iterations based on the SCA and MCA detectors, respectively (Fig.\u00a04b). The MCA detectors exhibited a higher training speed than the SCA detectors because of the intrinsic differences between the edge profiles. However, we analyzed the changes in classification accuracy over 100\u2009epochs for gesture recognition. Figure\u00a04c reveals two key findings. First, when dealing with a small sample size, the MCA performs better than the SCA in gesture recognition accuracy. For example, at 10\u2009epochs, the recognition accuracies of the MCA-based- and SCA-based detectors were 96.7% and 63.7%, respectively. At 40\u2009epochs, they reach 97.8% and 88.9%, respectively. Second, when faced with a large sample size, the difference in gesture recognition accuracies between the two methods became small. For example, at 100\u2009epochs, the recognition accuracies of the MCA and SCA were 98.1% and 93.2%, respectively.\n\nThe second important application of MCA detectors is enhanced edge detection, which was demonstrated using an off-chip learning task involving the recognition of curled fingers. As shown in Fig.\u00a04a, the curled finger edges extracted from the infrared images using the MCA method are much more distinct than the SCA, which failed to capture them. The same trend was observed in the images of the overlapped leaves (Supplementary Fig.\u00a011). The results of training in the presence and absence of curled fingers showed that the MCA and SCA methods had training accuracies equal to 76.3% and 99.8%, respectively (Fig.\u00a04d). This difference was much larger than that of the gesture training, with the discrepancies decreasing from 0.8% to 23.5%. Similar to gesture training, the MCA detectors exhibited higher training speeds than those of the SCA detectors for curled fingers. More importantly, during curled finger recognition, after 100\u2009epochs, the recognition accuracy of the MCA approach reached a high level of 98.2%, whereas that of the SCA remained at ~69.7% (Fig.\u00a04e). These results further demonstrate that the MCA method can improve the accuracy of target recognition in infrared imaging at the hardware level, particularly by enhancing the edge detection accuracy in complex environments.\n\nIn conclusion, we developed a printable photodetector array by integrating monolayer graphene with a BFO thin film that features a nanoscale-wide stripe superdomain and demonstrated that this type of device array was designed for multichannel meta-infrared imaging applications and yielded edge detection enhancements. Graphene was monitored in a non-contact manner for dopants using Raman shifts, and doping patterns on ferroelectric superdomains were observed at the nanoscale. The printable photodetectors operated at a zero-bias voltage and exhibited a high responsivity of ~30\u2009mA\u2009W\u22121 at room temperature. This can be attributed to the resonant coupling of graphene plasmons with incident light. Moreover, the device arrays exhibited a selective response in the mid-infrared region achieved through direct rescaling of the BFO superdomain width under ambient conditions. This study demonstrated the precise spatial control of graphene carrier density by reversing the ferroelectric domains at the nanoscale. The compatibility of graphene sheets with different substrates offers several advantages compared with conventional devices that rely on complex nanofabrication techniques. Additionally, we proved that MCA detectors can enhance shape and edge detection in infrared imaging. These features allow for future integrated optoelectronic platforms with simple circuit designs and low power consumption.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49592-4/MediaObjects/41467_2024_49592_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49592-4/MediaObjects/41467_2024_49592_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49592-4/MediaObjects/41467_2024_49592_Fig4_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "BFO thin films were epitaxially grown on (001)-oriented STO substrates with LSMO as the bottom electrode using pulsed laser deposition. For the deposition of both LSMO and BFO films, a KrF excimer laser, with 248\u2009nm wavelength, 5\u2009Hz repetition rate, and ~1.5\u2009J\u2009cm\u22122 energy density, was employed. The films were grown in an atmosphere of 0.2\u2009mbar oxygen pressure at 700\u2009\u00b0C. The thicknesses of the films were maintained at ~25\u2009nm.\n\nFor ferroelectric polarization switching, the upward and downward domains were switched by scanning the surface with a nanotip subject to a +12\u2009V (\u221212\u2009V) bias exceeding the coercive voltage (Fig.\u00a02a, step 1). The switching of ferroelectric domains in BFO with large area was obtained by water-printing technique27,28, the downward and upward domains of BFO were cyclically switched by exposing to Milli-Q water (pH\u2009=\u20097.0) and acidic solution (pH\u2009=\u20093.0). The process begins by converting the initial upward polarization to downward using an acidic solution. Next, a stripe-patterned coating is created on the BFO surface by photolithography. The exposed downward polarization is then reversed to upward by Milli-Q water treatment, followed by the removal of the photoresist. For the convenience of guiding the incident light to specific areas and fixing the active illuminating regions, the BFO film was patterned into an array with a fixed area, and the etched depth was controlled at ~10\u2009nm in each unit using reactive ion etching before polarization.\n\nSingle-layer graphene was transferred onto a BFO film using an improved wet method30 (Fig.\u00a02a, step 2). Briefly, the polymethyl methacrylate (PMMA, Aladdin) solution (20\u2009mg\u2009mL\u22121) was spin-coated on CVD-grown monolayer graphene/copper foils (SixCarbon Technology Shenzhen) at 3000 revolutions per minute for 30\u2009s and dried at 120\u2009\u00b0C for 90\u2009s in air. Ammonium persulfate (0.1\u2009M) was used to etch the copper substrate, and the PMMA/graphene film was washed several times with deionized water to remove the etchant residue. The prepolarized BFO film was then placed in water at a tilting angle underneath the PMMA/graphene film to support it. After drying in air, the PMMA was removed with an acetone bath at 50\u2009\u00b0C and washed with ethanol. The monolayer graphene was etched to the same area as the marked BFO using oxygen plasmons. Subsequently, the Au/Ti and Au/Ni source and drain electrodes were deposited on the border of the graphene to fabricate the device (Fig.\u00a02a, step 3). The thicknesses of the Au layer and Ti and Pd layers were 80 and 20\u2009nm, respectively.\n\nPFM experiments were performed under ambient conditions at room temperature using an Infinity Asylum Research AFM instrument. The crystal structures of BFO were determined using a transmission electron microscope (JEOL 2100F) operated at 200\u2009keV and equipped with a probe aberration corrector (corrected electron optical system,\u00a0Heidelberg, Germany) and double spherical aberration (Cs) correctors. The spatial resolution of the microscope reached 90\u2009pm at an incident semiangle of 20\u2009mrad. Subsequently, a fast Fourier transform multislice approach was used for the STEM configuration. Raman-AFM mapping images and the corresponding spectral data in the same region were acquired using a fully integrated system based on the Smart SPM state-of-the-art scanning probe microscope and XploRA Raman micro-spectrometer (HORIBA). In all cases, 532\u2009nm laser excitation and tip-enhanced Raman spectroscopic resolution were used. The transmission spectra were collected using a Spotlight 200i FT-IR Microscopy System (PerkinElmer Inc.) with a spot resolution better than 10\u2009\u03bcm. The photocurrents were performed using a Keithley 4200A-SCS Parameter Analyzer (Tektronix), and the incident source was produced by a tunable laser (EKSPLA, 2.3\u221210\u2009\u03bcm), at zero bias voltage between the contact electrodes. The schematics of the experimental setups for infrared transmission microscopy and photocurrent characterization are shown in Supplementary Fig.\u00a04. The laser intensities, including the Raman and electrical measurements, were set to values below 1\u2009mW to avoid artifacts caused by laser-induced heating. All measurements were performed in ambient air at room temperature.\n\nThe electrical properties of graphene were calculated using random phase approximation, and the dynamic optical response of graphene was derived from the Kubo formula (Supplementary Note\u00a02) (refs. 42,43). Electromagnetic field simulation was performed using the finite element method (Supplementary Note\u00a03). Infrared imaging simulations were performed using an open-resource project, and the details are shown in\u00a0Supplementary Notes\u00a05 and 6.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Relevant data supporting the key findings of this study are available within the article and the Supplementary Information file. All raw data generated during the current study are available from the corresponding authors upon request.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The codes used in the current study are available on an open-source project (https://github.com/We1wu/Multichannel-meta-infrared-imaging, https://doi.org/10.5281/zenodo.11544077).", + "section_image": [] + }, + { + "section_name": "Change history", + "section_text": "A Correction to this paper has been published: https://doi.org/10.1038/s41467-024-49948-w", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Ding, Y. C. et al. Uncooled self-powered hemispherical biomimetic pit organ for mid- to long-infrared imaging. Sci. Adv. 8, eaba8432 (2022).\n\nArticle\u00a0\n ADS\u00a0\n \n Google Scholar\u00a0\n \n\nZhao, Y. et al. High-speed scanless entire bandwidth mid-infrared chemical imaging. Nat. 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Mater. 26, 123201 (2014).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "The authors thank Ms. Xiaoxu Lai and Prof. Ronghui Guo from Sichuan University for their support with the Raman mapping and photocurrent measurements. Dr. J.X. Guo thank Dr. Prof. Bin Yu from Zhejiang University for his discussions in the early stage of this work. We also thank Dr. Prof. Lei Bi from the University of Electronic Science and Technology of China for discussions on spectral response. This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 52225205, 62371095, 62201096, and 62074029), the National Key Research and Development Program of China (Grant Nos. 2022YFB3206100, 2021YFA0718700), the Key R&D Program of Sichuan Province (Grant Nos. 2022ZHCG0041, 2022JDTD0020, and 2022YFG0163), the Natural Science Foundation of Sichuan Province (Grant Nos. 2024NSFSC0509 and 2022NSFSC0652) and the Fundamental Research Funds for the Central Universities.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Junxiong Guo, Shuyi Gu, Lin Lin.\n\nSchool of Electronic Information and Electrical Engineering, Institute of Advanced Study, Chengdu University, Chengdu, 610106, China\n\nJunxiong Guo,\u00a0Shuyi Gu,\u00a0Ji Cai,\u00a0Hongyi Cai,\u00a0Ze Liu\u00a0&\u00a0Yafei Zhang\n\nSchool of Integrated Circuit Science and Engineering, National Exemplary School of Microelectronics, University of Electronic Science and Technology of China, Chengdu, 610054, China\n\nJunxiong Guo,\u00a0Lin Lin,\u00a0Xiaosheng Zhang\u00a0&\u00a0Wen Huang\n\nSchool of Integrated Circuits, Tsinghua University, Beijing, 100084, China\n\nYu Liu\n\nCollege of Integrated Circuit Science and Engineering, National and Local Joint Engineering Laboratory for RF Integration and Micro-Packing Technologies, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China\n\nYu Liu\n\nSchool of Physics and Astronomy, Beijing Normal University, Beijing, 100875, China\n\nYu Tian,\u00a0Yuelin Zhang\u00a0&\u00a0Jinxing Zhang\n\nKey Laboratory of Multiscale Spin Physics, Ministry of Education, Beijing, 100875, China\n\nYu Tian,\u00a0Yuelin Zhang\u00a0&\u00a0Jinxing Zhang\n\nInstitute of Physics, Chinese Academy of Science, Beijing National Laboratory of Condensed Matter Physics, Beijing, 100190, China\n\nQinghua Zhang\n\nSchool of Materials and Energy, University of Electronic Science and Technology of China, Chengdu, 610054, China\n\nYuan Lin\n\nSchool of Materials Science and Engineering, Tsinghua University, Beijing, 100084, China\n\nLin Gu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.G., Yu L., W.H., and J.Z. conceived and designed the experiments. Y.T., Yuelin Z., and J.Z. provided the epitaxially grown BFO films. Y.T. and Yuelin Z. wrote the ferroelectric superdomain using a type-printing technique and characterized the PFM images. J.G., Yu L., and L.L. fabricated the devices. Q.Z. and L.G. conducted high-resolution TEM characterization. L.L. conducted SEM characterization. J.G. and L.L. conducted the Raman characterization and optical transmission measurements. J.G. and L.L. conducted the photodetection experiments. J.G., Yu L., L.L., J.C., Z.L., and Yafei Z. performed the theoretical models and calculations for the electromagnetic field simulations. J.G., S.G., Yu L., and H.C. designed and performed the infrared imaging and deep learning. J.G., Yu L., X.Z., Yuan. L., W.H., and J.Z. analyzed the data. J.G., Yu L., W.H., and J.Z. supervised the experiments. J.G. and Yu L. wrote the paper and Supplementary Information. All authors participated in the data analysis and discussion of this work.\n\nCorrespondence to\n Junxiong Guo, Yu Liu, Wen Huang or Jinxing Zhang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Type-printable photodetector arrays for multichannel meta-infrared imaging.\n Nat Commun 15, 5193 (2024). https://doi.org/10.1038/s41467-024-49592-4\n\nDownload citation\n\nReceived: 11 March 2024\n\nAccepted: 12 June 2024\n\nPublished: 18 June 2024\n\nVersion of record: 18 June 2024\n\nDOI: https://doi.org/10.1038/s41467-024-49592-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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0000000000000000000000000000000000000000..90f2ae6affaf7602608ad1cf6451564dd97a561d --- /dev/null +++ b/b6ff781ff26d3bb27437499bd27accc488780f180ff64d9cc3f70071cd5e5930/metadata.json @@ -0,0 +1,147 @@ +{ + "title": "Observation of switchable polar skyrmion bubbles down to the atomic layers in van der Waals ferroelectric CuInP2S6", + "pre_title": "Observation of switchable polar skyrmion bubbles down to the atomic layers in van der Waals ferroelectric CuInP2S6", + "journal": "Nature Communications", + "published": "08 March 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57714-9/MediaObjects/41467_2025_57714_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57714-9/MediaObjects/41467_2025_57714_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57714-9/MediaObjects/41467_2025_57714_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-57714-9#Sec12" + ], + "code": [], + "subject": [ + "Ferroelectrics and multiferroics", + "Two-dimensional materials" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3856814/v1.pdf", + "research_square_link": "https://www.researchsquare.com//article/rs-3856814/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-57714-9.pdf", + "preprint_posted": "27 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Polar skyrmions are topologically nontrivial polarization textures that demonstrate exotic physical phenomena and novel memory applications1-9. Thus far, these textures have primarily been reported in oxide-ferroelectric-based epitaxial heterostructures because their stabilization requires an elastic energy penalty from the epitaxial strains3-8. Here, without the epitaxial-strain engineering, we discover polar skyrmion bubbles in stand-alone van der Waals ferroelectric CuInP2S6 crystal through the combination of piezoelectric force microscopy, high-resolution transmission electron microscopy, and phase-field simulations. In a thick CuInP2S6 flake of over ~100 nm, skyrmion bubbles feature an elliptical hedgehog-like state with center-divergent or center-convergent configurations. Progressively thinning the flake thickness to ~8 nm allows a topological transition from elliptical to circular skyrmionic patterns. Interestingly, the skyrmions can be switched with the change in helicity by probe-applied electrical and mechanical stimuli, which is distinct from the creation and annihilation of other reported skyrmions. Both theoretical and experimental data proves that the formation and thickness-dependence of skyrmion textures primarily stem from charge-related energy penalty. This work opens up a new material system (i.e., two-dimensional layered ferroionic materials) for exploring uncharted polar-topology physics and prospective neuromorphic devices.Physical sciences/Materials science/Condensed-matter physics/Ferroelectrics and multiferroicsPhysical sciences/Materials science/Nanoscale materials/Two-dimensional materials", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nF. X., and B. W. are inventors listed on the Chinese patent ZL202410763157.3 (granted date: 3 September 2024) held by ZJU-Hangzhou Global Scientific and Technological Innovation Center that covers the preparation method of polar skyrmions in ferroelectric materials", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "nreditorialpolicychecklist.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Polar skyrmions are topologically nontrivial polarization textures that demonstrate exotic physical phenomena and novel memory applications. Thus far, these textures have primarily been reported in oxide-ferroelectric-based epitaxial heterostructures because their stabilization requires an elastic energy penalty from the epitaxial strains. Here, without the epitaxial-strain engineering, we discover polar skyrmion bubbles in stand-alone van der Waals ferroelectric CuInP2S6 crystal through the combination of piezoelectric force microscopy, high-resolution transmission electron microscopy, and phase-field simulations. In a thick CuInP2S6 flake of over \u2212100\u2009nm, skyrmion bubbles feature an elliptical hedgehog-like state with center-divergent or center-convergent configurations. Progressively thinning the flake thickness to \u22128\u2009nm allows a topological transition from elliptical to circular skyrmionic patterns. Interestingly, the skyrmions can be switched with the change in helicity by probe-applied electrical and mechanical stimuli, which is distinct from the creation and annihilation of other reported skyrmions. Both theoretical and experimental data proves that the formation and thickness-dependence of skyrmion textures primarily stem from charge-related energy penalty. This work opens up a new material system (i.e., two-dimensional layered ferroionic materials) for exploring uncharted polar-topology physics and prospective neuromorphic devices.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Topological polar textures are topological defects and domain patterns intrinsically embedded in ferroelectric materials. Their recent advances have demonstrated flux-closure domains1,2,3,4, vortices/antivortices5,6,7,8,9,10,11,12,13,14, electrical bubbles15,16,17,18,19, merons20,21, polar skyrmions22,23,24,25,26,27,28,29,30, hopfions31, and others32,33,34,35,36, which provide an excellent opportunity for studying nanoscale topology and emergent polar properties. These complex polar textures are condensed by the delicate balances between elastic, electrostatic, and gradient energies37,38. Notably, via the elastic energy penalty of substrate epitaxial strains, particle-like polar skyrmions with continuous polarization rotation across the cores and peripheries can be stabilized in oxide ferroelectric heterostructures23,24,25,26,27,28,29. Such skyrmionic textures present rich physics (e.g., negative permittivity24 and chirality29), and prospective device applications, including ultrahigh-density memristors above a gigabit per square inch27, both of which so far have garnered considerable interest.\n\nAlthough van der Waals ferroelectric crystals provide a new ingredient beyond oxide ferroelectric heterostructures for fundamental and technological studies, their progress in polar skyrmions remains elusive due to the challenges of manipulating intricate boundary conditions. Here, we observe switchable skyrmions in layered ferroelectric CuInP2S6 crystal with thicknesses ranging from the bulk (i.e., 180\u2009nm) to the atomic layers of about 8\u2009nm. By reducing the crystal thickness, a topological transition occurs and the skyrmions incrementally evolve from elliptical to circular textures. Unlike previously reported polar skyrmions24,25,26,27,28,29, we do not intentionally introduce additional elastic energy from the substrates to support the presence of CuInP2S6 skyrmions. Instead, material intrinsic properties from CuInP2S6 can be leveraged to modulate the boundary conditions and stabilize polar textures. As a typical ferroionic material, CuInP2S6 is usually coupled with the migration of copper ions39,40, leading to the charge accumulation. Phase-field simulations and experimental evidence show that the charge-related energy penalty (e.g., copper ion-induced electrostatic energy) contributes to the stabilization of polar skyrmions in CuInP2S6 crystals.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "In this work, we engineered the composition of van der Waals CuInP2S6 material to increase the amount of copper ions, and attempted to approach the stoichiometric composition. This design can suppress phase separation from CuInP2S6 and In4/3P2S641,42 and thus induce the evolution of ferroelectric domain configurations (Supplementary Fig.\u00a01 and Note\u00a01). Bulk CuInP2S6 crystals were synthesized using the chemical vapor transport (CVT) method, and thin flakes were exfoliated onto gold-layer-covered silicon substrates (see Methods). Basic characterization by second harmonic generation (SHG), Raman spectra, single-crystal X-ray diffraction, and piezoelectric force microscopy (PFM) switching loops were systematically presented in Supplementary Fig.\u00a01, 2 and Tables\u00a01, 2, confirming the stoichiometry, structural asymmetry, and ferroelectricity. Remarkably, the SHG results for the CuInP2S6 crystal used in this work (i.e., S3: Cu0.90In0.99P2S5.90) show highly polar anisotropy, reflecting the distinct noncentrosymmetry that supports our following observations43. Figure\u00a01a shows typically monoclinic CuInP2S6 crystal structures with highlighting two polarization orientations (POOP and PIP). Unlike common ferroelectrics44,45, CuInP2S6 crystals combine both properties of ferroelectric polarization and ionic conductivity (i.e., ferroionic), in which ions serve as a new knob to interact with polarization switching. Under external stimuli, Cu ions can accumulate over the crystal surface and increase the interfacial charge density39,40. Despite many reports on the out-of-plane polarization of CuInP2S6 crystal42,43,46, small in-plane polarization does exist, which has been revealed by PFM measurement and theoretical calculations47. Following previous works47,48,49, in-plane and out-of-plane polarization arises from the opposite motions between the In and Cu atoms. We conjecture that the orthogonal polarization directions (i.e., in-plane and out-of-plane dipoles) and the energy from abundant charges, including copper ions, likely benefit the polarization curling of polar textures.\n\na Schematics of the CuInP2S6 crystal structure. The antiparallel motions of Cu and In atoms result in out-of-plane and in-plane dipoles. b Large-area lateral piezoelectric force microscopy (L-PFM) amplitude and topographic mapping of coffee-bean-like polar textures on the surface of a 160-nm-thick CuInP2S6 crystal. The sample was scanned along the horizontal direction, and the detected polarization by PFM probe is along the arrow (bottom-left). c L-PFM and vertical piezoelectric force microscopy (V-PFM) mapping for the top-left region in b. d Enlarged dipolar configurations marked in red in (c). Schematic of the dipolar configurations is depicted at the bottom of (d). e Annular dark-field STEM image for identifying skyrmionic features. f Phase-field simulation of in-plane and out-of-plane polarization configurations for elongated polar skyrmions. The results resemble L-PFM and V-PFM responses. P represents normalized polarization.\n\nFor exploring topological polar textures, we used the high-resolution vector PFM technique, simultaneously detecting vertical and lateral electromechanical responses, to scan CuInP2S6 flakes. As shown in Fig.\u00a01b and Supplementary Fig.\u00a03, we observe spontaneous particle-like nanodomains which are orderly and self-assembled over a large area. Such an ordered arrangement may stem from material anisotropy, as revealed by the phase-field simulations (Supplementary Note\u00a02). To capture the detailed domain texture, we selected a small region and the acquired vector PFM responses are presented in Fig.\u00a01c. From the lateral PFM (L-PFM) amplitude images, it is seen that the nanodomain structures present a regular coffee-bean-like shape, whose sizes range from ~250\u2009nm in length and ~30\u2009nm in width to a smaller length of less than 50\u2009nm. These different sizes are similar to those of magnetic skyrmions50,51,52, and suggest the possibility of polar texture propagation. Remarkably, L-PFM phase mapping shows a strong contrast of nanodomain responses against the background, in which all nanodomains exhibit distinguishable upper and lower portions with antiparallel in-plane polarizations. However, the nanodomain structures in vertical PFM (V-PFM) phase mapping demonstrate a slightly smaller size than that of L-PFM responses, manifesting polarization curling at the core area of the particle domains. The L-PFM and V-PFM observations are crucial to envisioning and constructing the rotated polarization vector of these nanodomains. We note that these PFM results are not entirely associated with the topographic variation, ruling out the influence of the sample thickness in PFM phase mapping. In addition, we estimated the effective piezocoefficients from PFM amplitude mapping as shown in Supplementary Fig.\u00a03b, in which in-plane and d33 piezocoefficients are around 1\u20133 and 4\u20136\u2009pm/V, respectively. This d33 magnitude roughly coincides with previous works, e.g., 5\u201312\u2009pm/V53, and 2.5\u2009pm/V49; the commensurate in-plane piezocoefficient is likely caused by our composition engineering of CuInP2S6 crystal.\n\nWhen looking into a single nanodomain (Fig.\u00a01d), the in-plane polarization can be considered a head-to-head type (i.e., an upward-to-downward configuration). Their contour size in the L-PFM phase closely resembles the coffee-bean-like amplitude. From V-PFM phase and amplitude, we deduce that the detected polarization should orient inward due to the specific piezoelectric effect in CuInP2S652,54. These observations indicate that the ordered nanodomains are probably in a hedgehog-like state with N\u00e9el-type polarization rotating gradually from the core to the periphery (see the following images for more evidence). To intuitively visualize the texture, we show the schematic of an elliptical N\u00e9el-type skyrmion in the bottom panel of Fig.\u00a01d, which is analogous to those observed in ferromagnetic systems52,55. This emergent topological domain can match with the PFM results well, and has never been observed in conventional ferroelectric thin films. The elliptical shape arises from copper-ion induced anisotropy noncentrosymmetry (See Supplementary Fig.\u00a01 and the following theoretical explanation). Strikingly, over a large area of 35 \u00d7 40 \u03bcm2, such polar skyrmionic solitons can uniformly exist (Supplementary Fig.\u00a03c), which is promising for creating ultrahigh-density memory devices for neuromorphic devices with thousands of resistance states. Moreover, we probed the presence of topological textures by scanning transmission electron microscopy (STEM). From the dark-field STEM image in Fig.\u00a01e, orderly elongated shapes are observed with a length of up to three hundred nanometers, which are analogous to our PFM finding. Selected area electron diffraction (Supplementary Fig.\u00a03d) shows a single-crystal pattern and completely rules out the generation of a new phase at the elliptical area.\n\nAfter experimentally observing CuInP2S6 polar skyrmions, we next rationalize their existence using a phase-field model based on the time-dependent Ginzburg-Landau equation. In order to model surface charges of CuInP2S6 flakes induced by the migration of Cu ions39,40, we employed a charge density of \u22120.0255\u2009C/m2 (Supplementary Note\u00a03), below which the skyrmionic patterns cannot be intrinsically condensed but above which topological patterns undergo a transition (see theoretical simulations in Supplementary Fig.\u00a07). Figure\u00a01f and Supplementary Fig.\u00a03e show the successful acquisition of simulated elliptical polar skyrmions that are orderly aligned in one direction. In skyrmion area, the elongated configurations of in-plane and out-of-plane polarization highly coincide with the experimental L-PFM and V-PFM findings in Fig.\u00a01c. However, away from skyrmionic area, zero in-plane polarization was setted for the simplicity of the simulation. As expected, these results reveal that surface charges likely induced by Cu ions play a crucial role in the formation of polar skrymions, which is experimentally substantiated by Raman spectra (Supplementary Fig.\u00a01). Thus, the canted polarization over the skyrmionic area can be mainly ascribed to the electrostatic energy penalty. In addition to the influence of surface charges, the strong anisotropy in CuInP2S6 flakes is responsible for the ordered arrangement of elliptical polar skyrmions, which is induced due to the anisotropic material parameters in phase-field simulations (see Supplementary Note\u00a02 and Supplementary Table\u00a03). Moreover, the topological nature of skyrmionic polar textures is commonly described by an invariant called the skyrmion number (N)37:\n\nwhere n denotes the unit polarization vector, and the integrand is the Pontryagin charge density in the xy plane. The Pontryagin charge density mapping of the elliptical polar texture is displayed in Supplementary Fig.\u00a03e, which results in an integer skyrmion number of \u22121. From this perspective, the topological classification of the observed elliptical polar skyrmions is equivalent to that of magnetic56 and other polar skyrmions22,23,24,25,26,27,28,29 in previous studies.\n\nTo further confirm the elliptical texture of polar solitons, we performed angle-dependent PFM mappings by rotating the CuInP2S6 crystal with a clockwise direction. Figure\u00a02 exhibits the V-PFM response at 0\u00b0 and the L-PFM responses at 0\u00b0, 45\u00b0, and 90\u00b0, respectively, in which the images were adjusted to accommodate the 0\u00b0-scanned results. Related raw PFM images under different rotation angles are shown in Supplementary Fig.\u00a04, and the rotation can be clearly identified by the two ends of coffee-bean-like shape. High-frequency torsion of the PFM probes allows the precise detection of in-plane electromechanical responses, indicative of in-plane polarization. In particular, for 45\u00b0 and 90\u00b0 scanning, the detected in-plane amplitude responses deviate slightly from the 0\u00b0 coffee-bean-like pattern. For example, the 45\u00b0 L-PFM mapping shows rectangle-like contours in which the bright pattern is divided by a dark diagonal. The 90\u00b0 L-PFM mapping displays two contrasted ends of a rectangle-like shape while most areas exhibit dark responses. As illustrated in Fig.\u00a01d, such divergences in shapes can be well interpreted by the polarization curling for elongated polar skyrmions. Following previous studies27,57,58, we plotted the polarization vector based on PFM mapping with respect to the scanning angles (Fig.\u00a02b and Supplementary Note\u00a04). Five center-convergent configurations can be clearly identified, demonstrating the polarization rotation and vertices over the surfaces. This also signifies the formation of polar skyrmions in ferroelectric CuInP2S6 crystals. Apart from angle-dependent measurement, we also scanned topological solitons with different spring-constant probes and different laser spots to exclude the impact of scanning artifacts. As shown in Supplementary Fig.\u00a05, all scenarios can reproduce the elliptical skyrmions.\n\na. Cantilever-angle dependent PFM mappings with five center-convergent polar skyrmions. The V-PFM responses at 0\u00b0 and L-PFM responses at 0\u00b0, 45\u00b0, and 90\u00b0 are depicted, and \u03b1 indicates the azimuth angle between cantilever and sample orientations. b Polarization vector mapping constructed by the L-PFM responses in (a). See Supplementary Information for construction details. c. MAADF-STEM image showing similar skyrmion shapes with PFM results. d. Atomic resolved HAADF-STEM image showing the boundary between skyrmion and non-skyrmion area. The dashed line indicates the transitional area.\n\nHigh resolution STEM was also employed to verify the topological solitons in CuInP2S6 crystals. Medium-angle annular dark-field (MAADF) STEM image (Fig.\u00a02c) shows elliptical skyrmion shapes with a brighter periphery and darker core, similar to those observed by PFM measurement. For these topological structures, dark regions possessing out-of-plane polarization are surrounded by bright walls with in-plane polarization25,26. By magnifying the skyrmions, high-angle annular dark-field (HAADF) STEM successfully captured atomic-resolved structures (Fig.\u00a02d), in which a white dashed line denotes the boundary between skyrmion and non-skyrmion area. Vector mapping of ion displacement, extracted from atomic-scale STEM images, is typically used to demonstrate polarization rotation, serving as strong evidence for studying skyrmions in oxide ferroelectrics24,25,26,27,28. However, for CuInP2S6 crystal, we argue that collecting ion displacement remains challenging due to the relatively small off-center distance. This can be revealed by the small polarization value of 5~11 \u03bcC/cm2,43,59 one-order-magnitude smaller than those in oxide ferroelectrics24,25,26,27,28.\n\nHaving demonstrated the presence of polar skyrmions, we now explore thickness-dependent topological textures (Fig.\u00a03 and Supplementary Fig.\u00a06). Thickness has been demonstrated as an effective approach to control polar textures in oxide ferroelectric heterostructures due to the strain modulation3,4,5,16,26,27,28. In line with this strategy, we find that, when reducing the CuInP2S6 thicknesses from ~160\u2009nm to ~40\u2009nm and then to \u22128\u2009nm, polar skyrmions can gradually evolve from elongated shapes to a mixture of elongated and circular shapes (Fig.\u00a03a and b) and then to circular patterns (Fig.\u00a03c and d). For the circular skyrmions in Fig.\u00a03d, the diameter is about 50\u2009nm; center-divergent and center-convergent configurations as illustrated in Fig.\u00a03e can be identified by blue and red dashed lines, respectively. These circular skyrmions can be condensed over a large area (Supplementary Fig.\u00a07) and exhibit a higher density compared to elliptical polar skyrmions. Such a transition of skyrmionic shapes with respect to thicknesses is also substantiated by using STEM imaging. Besides the elliptical configuration (Fig.\u00a01e), we find ultra-small circular skyrmions with a diameter of around ~5\u2009nm in thin CuInP2S6 flakes (Fig.\u00a03g), which are highlighted by white arrows. The atomic-resolved MAADF-STEM image (Fig.\u00a03h) recorded the circle contrast on single crystal background, implying a non-trivial polarization structure as revealed in PFM measurement. A discrepancy between the sizes observed by STEM and PFM characterizations is mainly because the latter has a measuring resolution (\u221220\u2009nm) restricted by probe radius27.\n\nTopographic, V-PFM, and L-PFM mappings for a 40-nm (a and b) and 8-nm (c and d) thick CuInP2S6. In (b), both elongated and round polar skyrmions have a center-convergent helicity. In (d), most circular polar skyrmions exhibit center-divergent properties, while only one, as marked in red, is identified as the center-convergent type. e, Schematics of center-convergent and center divergent circular skyrmions. f. Polarization configurations for simulated circular polar skyrmions. g Low-magnification STEM images displaying many circular features, which are marked by white arrows. h Atomic-resolved STEM mapping with skyrmionic lattice. White dashed lines represent typical skyrmions. i Statistic skyrmion sizes as a function of sample thicknesses. The data was extracted from PFM amplitude mappings. j Thickness-dependent Raman spectra. Copper vibrational peaks are labeled by blue dashed lines. k Statistic Raman shifts related to the thickness reduction for copper vibration peaks. The error bars in i and k indicate standard deviation.\n\nTo confirm the thickness-determined topology, we conducted a statistical analysis on the skyrmion sizes taken from PFM mappings. The data includes but is not limited to these from Fig.\u00a01\u20133 (see Supplementary Fig.\u00a06 for additional PFM images). As expected, Fig.\u00a03i shows a remarkable transition of topological textures, marked by blue dashed line. The underlying physics is systematically investigated by Raman spectra (Fig.\u00a03j and k). With the thicknesses decreasing from 230\u2009nm to 8\u2009nm, Fig.\u00a03j exhibits a pronounced red shift for copper vibrational peaks, particularly at around 316 /cm. In order to unambiguously identify the change, we plotted typical Raman shifts of copper ion-related peaks60,61 (i.e., 316 and 103 /cm) as a function of thicknesses. The Raman peaks become softened, displaying a dramatic shift by over 12 wavenumbers to 302.5 /cm and by over 1 wavenumber to 102 /cm. These are predominantly caused by the migration behaviors of copper ions, constituting possible evidence for charge induced topology change.\n\nPhase-field simulations in Supplementary Fig.\u00a07 also support our experimental observations on the thickness-dependent topology of polar textures. By increasing the surface charge density in the simulation, elongated skyrmions can be transformed from the labyrinth domains. When the charge density further increases with reduced thicknesses, the shape of the skyrmion changes from ellipse to circle. We note that the thinner flakes should have a higher local strain gradient during the mechanical exfoliation process, resulting in a more remarkable Cu migration because of the stronger flexoelectric field. Thus, the thinner flakes possess larger surface charge densities induced by Cu migration. As a result, tunable polar topologies with respect to the thicknesses can occur. For both elliptical and circular textures (Supplementary Fig.\u00a07), they have a skyrmion number of unity, confirming their nontrivial topology.\n\nThe switching ability of polar skyrmions is essential for applications in high-density memory devices. We examined the dynamics of switching properties when applying electrical and mechanical stimuli. Figure\u00a04a and b present the evolved dynamics of polar skyrmions with respect to different DC voltages applied onto the PFM probes. When subjected to negative DC voltages, we simultaneously used a small AC driving voltage to capture skyrmion patterns. V-PFM and L-PFM mappings manifest the mixture of elliptical and circular patterns (dashed red lines). We scrutinize the L-PFM phase under a\u2009\u2212\u20092.5-V DC voltage, finding that the typical colors for upper and lower portions of skyrmions are deterministically switched. This signifies that the skyrmion has been reversed to a new state, specifically transitioning center-convergent skyrmion 1 to center-divergent patterns 1\u00b4 (Fig.\u00a04d). When removing DC voltages (i.e., DC\u2009=\u20090\u2009V), skyrmions can reversibly switch back to the initial state, indicating the intermediate, metastable properties of pattern 1\u00b4. With the electrical stimulus, the almost unchanged V-PFM responses in Fig.\u00a04b suggest that skyrmion state 1 and 1\u00b4 only have the switching of polarization-rotation direction over the surface. Referring to magnetic skyrmions62, we term this variation the switching of helicity number from \u03c0 to 0, as illustrated in Fig.\u00a03d. However, with the application of positive DC voltages, we do not observe the similar switching behaviors (Supplementary Fig.\u00a08a). To understand these differences, schematics of electric field-driven ion migration are depicted in Supplementary Fig.\u00a08b. Negative DC voltages can attract copper ions to migrate towards the surface while positive DC voltages compel copper ions to move away from the surface63. Therefore, considering the migration nature of copper ions, the reversible switching of polar skyrmions shall arise from charge-related energy penalty. During electrical modulation, under low DC voltages topological protection prevents polar skyrmions from collapsing, while under high DC voltages polar skyrmions can be completely erased or destroyed. We note that this switching behavior holds immense potential for crafting high-density ferroelectric memory devices.\n\na, b, Topographic and PFM mappings demonstrating electrically switchable elongated and circular polar skyrmions under different DC biases. c Evolution of the dipolar configuration realized by continuous PFM scanning with an estimated force of \u221230 nN over 6 times. d Schematics of the transition between different skyrmionic states. The helicity number is switched between 0 and \u03c0.\n\nInterestingly, as the polar skyrmions were continuously scanned by PFM probes with an estimated force of ~30 nN for over 6 times (see Methods), the phase patterns dramatically change (Fig.\u00a04c). This finding firmly demonstrates that mechanical stimuli can also be used to manipulate the helicity number of polar skyrmions. The underlying mechanism of the mechanical manipulation of polar skyrmions can be explained by the flexoelectric effect64. Due to the large strain gradient under the PFM tip, a localized flexoelectric field and relevant Cu ion migration exists, leading to the polarization switching over the skyrmion area.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57714-9/MediaObjects/41467_2025_57714_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57714-9/MediaObjects/41467_2025_57714_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57714-9/MediaObjects/41467_2025_57714_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57714-9/MediaObjects/41467_2025_57714_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In conclusion, by increasing the density of copper ions in van der Waals ferroionic CuInP2S6 crystals, we have shown evidence for large-area polar skyrmions with charge-related energy penalty, and demonstrated their switching properties and topological transitions with respect to thickness variations. Our work highlights that 2D van der Waals ferroelectrics, without epitaxial-strain constraints, could be a new platform for exploring unexplored polar topology and neuromorphic devices with multilevel states and linear synaptic update.\n\nOur approach of charge-related energy penalty can be generalized to a broad range of ferroionic materials, such as metal seleno- and thio-phosphate materials (i.e., ABP2X6, A = Cu and Ag, B = In, Cr, Bi, and Mn, X\u2009=\u2009S and Se)65 and traditional oxide ferroelectrics with abundant ion vacancies (i.e., BaTiO3)66. By delicately modulating the ion concentrations for achieving a favorable boundary condition, we anticipate that topological defects including polar vortices, polar merons, polar skyrmions, and so on, can also be stabilized in those materials (i.e., ABP2X6 and BaTiO3 with a deficiency of ion vacancies).", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "High-quality CuInP2S6 single crystals were synthesized using the CVT method. Cu powders (99.5%, Sigma-Aldrich), In pieces (99.99%, Alfa Aesar), P chunks (99.99%, Sigma-Aldrich), and S powders (99.5%, Sigma-Aldrich), with different molar ratios, were loaded into a quartz ampoule. The materials were further mixed with a small amount of iodine granules (99.8%, Sigma-Aldrich), which functioned as the transport agent in the CVT process. Then, the ampoule was vacuumized to below 20 mTorr and flame-sealed. During the growth process in a tube furnace, the source zone and growth zone were kept at 750\u2009\u00b0C and 700\u2009\u00b0C for 7 days, respectively. The CuInP2S6 thin flakes were mechanically exfoliated onto conductive substrates coated with Au layer (30\u2009nm) using Scotch tape.\n\nThe PFM measurements were performed using the Asylum Research MFP-3D and Cypher. Both the dual AC resonance tracking PFM mode and vector PFM mode were used to acquire useful electromechanical responses. Commercial soft conductive probes (e.g., AC 240 TS with a spring constant of 2\u2009N/m and ASYELEC-01-R2 with a spring constant of 2.8\u2009N/m) were adopted to avoid damaging sample surfaces. AC biases of 0.2 to 0.5\u2009V were applied onto the PFM probes to image polar textures with scanning points of 320 to 512 and a scanning rate of 1.5\u2009Hz. Vector PFM mode can simultaneously record out-of-plane and in-plane electromechanical responses at a free-air resonance and contact resonance frequency, respectively. In particular, for in-plane responses, the dipole perpendicular to the PFM cantilever can be precisely detected by the torsional vibration of PFM probes. When conducting the angle-dependent PFM measurement, the CuInP2S6 sample was manually rotated, and the exact angles were calibrated by comparing the initial and rotated PFM mapping. When carrying out the mechanical switching measurement (Fig.\u00a03a), the AC probe biases were turned off except for 1st and 6th scanning.\n\nFor the TEM analysis, the exfoliated thin CuInP2S6 flakes were directly transferred onto a silicon nitride membrane grid (CleanSiN) on an accurate transfer platform (Metatest, E1-G) using polydimethylsiloxane. The observations were performed by spherical aberration-corrected electron microscopy on a FEI Titan G2 80-200 ChemiSTEM (30 mrad convergence angle, 0.8\u2009\u00c5 spatial resolution). Annular dark-field scanning transmission electron microscopy was carried out. The collection semi-angles were 37\u2013200 mrad (for MAADF) and 56-200 mrad (for HAADF).\n\nThe SHG measurements were conducted using a 1030\u2009nm femtosecond-pulsed laser (pulse width of \u2212200\u2009fs and pulse frequency of 1\u2009MHz, YactoFiber-FL-20) by a homemade optical microscope with a 50X near-infrared transmission lens with a spot size of less than 1 \u03bcm. The polarization of generated second harmonic 515\u2009nm light was selected in parallel conditions relative to the fundamental beams using individual linear polarizers and analyzed using a compact integrated and aberration-free spectrograph (Princeton Instruments, FERGIE). The polarization direction of the incident light field was adjusted by the rotation of the \u03bb/2 waveplate driven by a rotating motor.\n\nThe Raman spectra were carried out using LabRAM HR Evol (Horiba). The used laser and grating are 532\u2009nm and 1800, respectively.\n\nThe XRD \u03b8-2\u03b8 scan was performed at room temperature using a Bruker D2 PHASER diffractometer with Cu K\u03b1 radiation (\u03bb\u2009=\u20091.54184\u2009\u00c5). The single-crystal XRD experiment was carried out at 120\u2009K on a Bruker D8 Venture diffractometer with Mo K\u03b1 radiation (\u03bb\u2009=\u20090.71073\u2009\u00c5).", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data supporting the findings of this study are available within the manuscript and supplementary information. Any other relevant data are also available from the corresponding author upon reasonable request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All the code or mathematical algorithm files used in this study are available from the corresponding author upon reasonable request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Jia, C.-L., Urban, K. W., Alexe, M., Hesse, D. & Vrejoiu, I. Direct Observation of Continuous Electric Dipole Rotation in Flux-Closure Domains in Ferroelectric Pb(Zr,Ti)O3. Science 331, 1420\u20131423 (2011).\n\nArticle\u00a0\n ADS\u00a0\n PubMed\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nMcQuaid, R. G., McGilly, L. J., Sharma, P., Gruverman, A. & Gregg, J. M. Mesoscale flux-closure domain formation in single-crystal BaTiO3. Nat. 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The authors thank Dr. Lingyuan Gao for the helpful discussion.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Fei Xue, Chenhui Zhang, Sizheng Zheng, Peiran Tong, Baoyu Wang.\n\nCenter for Quantum Matter, School of Physics, Zhejiang University, Hangzhou, China\n\nFei Xue,\u00a0Haoran Xu,\u00a0Hua Wang\u00a0&\u00a0Kai Chang\n\nZJU-Hangzhou Global Scientific and Technological Innovation Center, College of Integrated Circuits, Zhejiang University, Hangzhou, China\n\nFei Xue,\u00a0Baoyu Wang,\u00a0Zhongyi Wang,\u00a0Haoran Xu,\u00a0Youshui He,\u00a0Chu Huan,\u00a0Yang Xu,\u00a0Bin Yu\u00a0&\u00a0Hua Wang\n\nPhysical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia\n\nChenhui Zhang,\u00a0Yinchang Ma\u00a0&\u00a0Xixiang Zhang\n\nDepartment of Engineering Mechanics, Zhejiang University, Hangzhou, Zhejiang, China\n\nSizheng Zheng,\u00a0Peng Han\u00a0&\u00a0Jie Wang\n\nResearch Center for Intelligent Computing Platforms, Zhejiang Lab, Hangzhou, China\n\nSizheng Zheng\u00a0&\u00a0Jie Wang\n\nCenter of Electron Microscopy, School of Materials Science and Engineering, State Key Laboratory of Silicon Materials, Zhejiang University, Hangzhou, China\n\nPeiran Tong\u00a0&\u00a0He Tian\n\nSchool of Materials and Energy, Electron Microscopy Centre of Lanzhou University, Lanzhou University, Lanzhou, China\n\nYong Peng\u00a0&\u00a0Nan Wang\n\nZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, Zhejiang, China\n\nHongzhi Zhou,\u00a0Hongliang Chen\u00a0&\u00a0Haiming Zhu\n\nCore Labs, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia\n\nYouyou Yuan\n\nKey Laboratory for Magnetism and Magnetic Materials of the Ministry of Education, Lanzhou University, Lanzhou, China\n\nSenfu Zhang\n\nStoddart Institute of Molecular Science, Department of Chemistry, Zhejiang University, Hangzhou, China\n\nHongliang Chen\n\nZhejiang-Israel Joint Laboratory of Self-Assembling Functional Materials, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, China\n\nHongliang Chen\n\nSchool of Physics, Central South University, Changsha, China\n\nJian Sun\n\nPhysics Department and Institute for Nanoscience and Engineering, University of Arkansas, Fayetteville, AR, USA\n\nPeng Chen\n\nGuangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China\n\nXingsen Gao\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nF. X. and C.Z. conceived the project. C. Z. synthesized the crystal under the guidance of X. Zhang. F. X., C. Z., B. W., Z. W., H. X., Y. M., Y. Y., and C. H. performed material characterizations. S. Zheng and P.H. carried out the simulation under the guidance of J. Wang. H. T., P. T., Y. P., and N. W. performed TEM measurements. Y. H. constructed the vector mapping. H. Zhou and H. Zhu conducted SHG measurements. F. X., S. Zheng, and C. Z. wrote the paper with the review from all authors. S. Z., H. C., Y. X., B. Y., J. S., H. W., P. C., and X. G. provided resources or comments. F. X.,X. Z., and K. Chang supervised this project.\n\nCorrespondence to\n Fei Xue, Chenhui Zhang, He Tian, Jie Wang or Xixiang Zhang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "F. X. and B. W. are inventors listed on the Chinese patent ZL202410763157.3 (granted date: 3 September 2024) held by ZJU-Hangzhou Global Scientific and Technological Innovation Center that covers the preparation method of polar skyrmions in ferroelectric materials. Other authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Nagarajan Valanoor and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Observation of switchable polar skyrmion bubbles down to the atomic layers in van der Waals ferroelectric CuInP2S6.\n Nat Commun 16, 2349 (2025). https://doi.org/10.1038/s41467-025-57714-9\n\nDownload citation\n\nReceived: 17 September 2024\n\nAccepted: 03 March 2025\n\nPublished: 08 March 2025\n\nVersion of record: 08 March 2025\n\nDOI: https://doi.org/10.1038/s41467-025-57714-9\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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evolution of transmembrane helices reveals mechanisms of cholesterol attraction", + "pre_title": "Physics-Based Evolution of Cholesterol-Attracting Transmembrane Helices: Deciphering Cholesterol Attraction in Native Membrane Proteins", + "journal": "Nature Communications", + "published": "20 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63769-5/MediaObjects/41467_2025_63769_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63769-5/MediaObjects/41467_2025_63769_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63769-5/MediaObjects/41467_2025_63769_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63769-5/MediaObjects/41467_2025_63769_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.15925656", + "https://doi.org/10.5281/zenodo.15925656", + "/articles/s41467-025-63769-5#Sec39" + ], + "code": [ + "https://doi.org/10.5281/zenodo.15925656" + ], + "subject": [ + "Biochemistry", + "Biological physics", + "Computational biology and bioinformatics", + "Membrane biophysics" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4130793/v1.pdf?c=1761044847000", + "research_square_link": "https://www.researchsquare.com//article/rs-4130793/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63769-5.pdf", + "preprint_posted": "26 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The existence of linear cholesterol-recognition motifs in alpha-helical transmembrane domains has long been debated. Our study introduces an innovative approach, evolutionary molecular dynamics (Evo-MD), which utilizes a genetic algorithm guided by coarse-grained molecular dynamics simulations. Through Evo-MD, we successfully determine the thermodynamic optimum for cholesterol attraction within isolated alpha-helical transmembrane domains (TMDs). Our investigation uncovers that cholesterol attraction in membrane proteins features a distinct and well-defined global thermodynamic optimum. This optimum arises from two key structural features: hydrophobic slenderness and hydrophobic mismatch. Additional support for these findings is provided by atomistic simulations and solid-state NMR experiments.Furthermore, we thoroughly analyze membrane protein databases and conduct live cell assays using analogous short hydrophobic sequences to explore the occurrence and feasibility of these features. Our results reveal surprising deviations from thermodynamic optimality in cholesterol attraction within native proteins. In particular, our analysis challenges the conventional belief that linear motifs such as the CRAC/CARC motif enhance cholesterol binding. Instead, we propose a rationalization that conserved aromatic residues, crucial components of the CRAC/CARC motif, likely promote dimerization by modulating membrane solubility in a cholesterol-dependent manner, rather than enhancing the binding of cholesterol.In summary, our study resolves the long-standing debate regarding linear cholesterol-recognition motifs and reveals the presence of sub-optimal cholesterol attraction in native alpha-helical transmembrane domains. These findings significantly contribute to our understanding of cholesterol-protein interactions and offer valuable insights into an alternative functional role played by conserved aromatic residues in membrane protein biologyBiological sciences/Biophysics/Membrane biophysicsPhysical sciences/Physics/Biological physics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "RisseladanatcommunSI.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The existence of linear cholesterol-recognition motifs in transmembrane domains has long been debated. Evolutionary molecular dynamics (Evo-MD) simulations\u2014genetic algorithms guided by (coarse-grained) molecular force-fields\u2013reveal that thermodynamic optimal cholesterol attraction in isolated alpha-helical transmembrane domains occurs when multiple consecutive lysine/arginine residues flank a short hydrophobic segment. These findings are supported by atomistic simulations and solid-state NMR experiments. Our analyses illustrate that linear motifs in transmembrane domains exhibit weak binding affinity for cholesterol, characterized by sub-microsecond residence times, challenging the predictive value of linear CRAC/CARC motifs for cholesterol binding. Membrane protein database analyses suggest even weaker affinity for native linear motifs, whereas live cell assays demonstrate that optimizing cholesterol binding restricts transmembrane domains to the endoplasmic reticulum post-translationally. In summary, these findings contribute to our understanding of cholesterol-protein interactions and offer insight into the mechanisms of protein-mediated cholesterol regulation within membranes.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Cholesterol serves as a major constituent of the mammalian plasma membrane. The overall fraction of cholesterol in the plasma membrane relative to total plasma membrane lipids is about 30% to 40% in leukocytes, epithelial cells, neurons, and mesenchymal cells1. The localization, trafficking, and functionality of membrane proteins involved in cholesterol-dependent pathways and cholesterol homeostasis may critically rely on their ability to attract and bind cholesterol molecules2,3,4,5,6,7,8,9,10,11,12,13,14. Prediction of protein-cholesterol affinity could therefore illuminate their role in diseases that are characterized by loss of cholesterol homeostasis (e.g., neurological diseases and cancer15), and pave the road for novel drug targets and strategies6,11,12,16,17,18,19,20. A compelling amount of data obtained by bioinformatic approaches, molecular modeling and simulations, and experiments have suggested the existence of cholesterol recognition amino acid consensus motifs (CRAC motifs)3,4,14,21,22, as well as its inverse (CARC motif)23, in various membrane protein families, including, for example: viral membrane proteins (e.g., refs. 16,19), ion channels (e.g., refs. 24,25), and G protein-coupled receptors (GPCRs)\u2014the most intensively studied drug target family (e.g., refs. 6,11,26,27,28,29,30).\n\nHowever, the looseness of the CRAC and CARC definitions, represented via the flexible algorithmic rules: (L/V)-X1\u22125-(Y)-X1\u22125-(K/R) and (K/R)-X1\u22125-(Y/F)-X1\u22125-(L/V) respectively, is rather unexpected for a motif that mediates binding to a unique molecule, raising skepticism about its predictive value3,10,23,31. This flexible definition based solely on residue patterning within a single transmembrane motif neglects the overall 3-dimensional protein structure of multipass membrane proteins, including the presence of hydrophobic grooves and cavities formed between helical hairpins and additional adjacent transmembrane helices, which have been shown to actively mediate cholesterol binding7,8,10,31,32. In addition, the large flexibility of these motifs implies that cholesterol recognition does not depend solely on exact molecular shape compatibility, as in protein-ligand docking, but is influenced by other thermodynamic forces primarily dictated by the overall amino acid composition and structural features of transmembrane helices such as hydrophobic length and accessible surface area, similar to the structural determinants that dictate their relative preference for cholesterol-enriched membrane phases5. Hence, such an alternative perspective would account for the variability in the positions of these amino acids within various proposed linear motifs associated with cholesterol binding3,14,23,33.\n\nHigh-throughput screening of transmembrane sequences offers a powerful approach for investigating the existence of linear motifs while simultaneously characterizing their underlying thermodynamic driving forces. However, the accessible chemical space of transmembrane domains is astronomical (about 2020 possibilities), which warrants the use of smart search strategies.\n\nDirected evolution is a method used in protein engineering that mimics the process of natural selection to steer proteins or nucleic acids toward a pre-specified goal34. Evolutionary inverse design strategies see applications in a variety of fields due to their efficient exploration of search-space35. These methods fall within the scope of reinforcement learning, adapting processes for optimal performance by reinforcing desired behavior36. Of special interest are the genetic algorithms (GA), which model the mechanisms of Darwinistic evolution in a computational algorithm, utilizing genetic elements such as recombination, cross-over, mutation, selection, and fitness37. Since directed evolution is both time and labor intensive, it can quickly become intractable in a laboratory setting thereby limiting its value. In such scenarios, molecular dynamics (MD) simulations may provide an alternative in silico route for the high-throughput virtual screening of chemical space.\n\nHere, we demonstrate the ability of GAs guided by coarse-grained MD simulations\u2014a method which we coin evolutionary molecular dynamics (Evo-MD)\u2014to yield unique insights into the driving forces that underpin cholesterol recognition (Fig.\u00a01). Evo-MD effectively reduces the search for optimal ligand consensus motifs to solving a variational problem in high-dimensional chemical space using stochastic operators such as genetic cross-overs and mutations. To this end, we introduce EVO-MD, a highly parallel software package for evolutionary molecular dynamics simulations that incorporates the GROMACS molecular dynamics engine into a custom, Python-based GA wrapper. EVO-MD can adapt any element of MD simulations, be it structural (e.g., atoms, molecules), topological, or simulation parameters (e.g., force field parameters), based on a reinforcement value measured during the simulation (see ref. 38 for a recent perspective on physics-based optimization).\n\nRandom peptide sequences self-evolve into optimal cholesterol attracting transmembrane domains in the course of evolution. Generated peptides are iteratively ranked upon increasing fitness, as determined via ensemble averaging within molecular dynamics simulations.\n\nIn this work, we employ the computational method Evo-MD to explore the thermodynamic driving forces of cholesterol attraction for a fixed-length sequence of 20 amino acids within a transmembrane domain. Our primary objective is to investigate the factors that influence cholesterol binding affinity in transmembrane helices, guided by the hypothesis that the presence of a cholesterol-binding linear motif correlates with optimal cholesterol binding in isolated transmembrane domains. In accordance with the original linear motif concept, we exclude contributions from neighboring helices to cholesterol attraction/binding that could generate correlations extending beyond a single transmembrane domain. Our Evo-MD simulations reveal an intriguing phenomenon in this context: a strong negative hydrophobic mismatch emerges as a predominant factor in cholesterol attraction within isolated membrane helices. The resolved patterning is characterized by a short hydrophobic segment flanked by stacked charged lysine and arginine residues. This finding is further substantiated by atomistic free energy calculations, which underscore the high affinity of cholesterol for this specific hydrophobic configuration. Moreover, solid-state NMR experiments validate the interaction of cholesterol with lysine residues embedded within the hydrophobic interior of the membrane, as evidenced in synthesized transmembrane peptides. Cellular assays reveal that proteins incorporating these optimal motifs localize to the endoplasmic reticulum (ER) membrane post-translationally due to their hydrophobic mismatch.\n\nThe estimated residence time for optimal cholesterol binding is approximately hundreds of nanoseconds, which is remarkably short compared to the timescales of many biological processes. Our findings also underscore that some of the proposed essential hydrophobic aromatic residues within CARC motifs, such as phenylalanine, in fact actively and inherently repel cholesterol, refuting the prevailing assumption of their cholesterol-attracting nature. As a result, our analysis proposes that the responsiveness of specific motifs to increased cholesterol levels might be due to their use of the dual function of cholesterol as both a ligand and a solvent for membrane proteins. This responsiveness appears to rely on a fine balance between amino acids that either attract or repel cholesterol, rather than solely focusing on ligand binding.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63769-5/MediaObjects/41467_2025_63769_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "Artificial evolution is simulated in a system consisting of a 30% cholesterol and 70% 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC) membrane containing a single, 20 amino acid long peptide sequence positioned transversely through the membrane (Fig.\u00a02A, B). The use of a model membrane composed of POPC and 30% cholesterol, though simple, effectively mimics the lipid carbon tail saturation and cholesterol concentration found in many cellular membranes39. We conducted simulations using both the Martini 240,41,42 and the newer Martini 343,44,45 coarse-grained force fields to validate for potential inconsistencies between the force fields. Owing to the symmetry of the here studied bilayer, generated sequences are mirror symmetric, i.e., only the first ten amino acids are independently chosen. Evolution is directed towards peptide sequences that increase the local density of cholesterol, visualized by the percentage cholesterol content of the membrane within a certain range from the peptide (Fig.\u00a02C). In practice, this is obtained by maximizing the ensemble-averaged non-bonded interaction energy between the peptide and cholesterol, i.e., this defines the fitness, in the course of sequence evolution.\n\nA, B Snapshots of a transmembrane protein (yellow) embedded within a POPC (white/brown) membrane containing 30% cholesterol (red). C Ratio of the cholesterol content in a local radius around the protein (see methods). An increase in fitness correlates to an increase in local cholesterol. The baseline cholesterol concentration (30%) is indicated by the dashed line. D Fitness development during protein evolution, shown for various population sizes. The fitness is expressed in terms of the total peptide-cholesterol non-bonded interaction energy. Fitness increases with GA iterations. Size of the population affects the height of the fitness plateau. E The GA converges to different fitness values, depending on the size of the populations. Eventually, evolution converges to an optimal solution for population sizes greater than 128 individuals.\n\nStarting from random peptide sequences, the observed evolution eventually converges to an optimum, as is evident by a plateau in the fitness values (Fig.\u00a02D). Convergence of genetic algorithms depends on a variety of factors, most notably the size of the population\u2014which directly relates to the area of the search space that is sampled each iteration\u2014and the number of iterations that are performed. Either parameter requires some minimum value for convergence to occur. The population size should be large enough (in combination with mutation rate and other diversifying factors) to prevent premature convergence to suboptimal solutions, and, with evolution proceeding between iterations, a certain minimum number of iterations is necessary. Ideally, both parameters are chosen as large as possible.\n\nTo assess whether the convergence of evolution is either suboptimal (i.e., a local solution) or optimal (i.e., a global solution), we conducted a set of evolutionary runs with population sizes ranging from 4 to 320 individuals until no further convergence of fitness was observed. Figure\u00a02D shows how the fitness of the best-performing sequences changes with each generation. As expected, increasing population size increases the optimum fitness, as is evident from a higher plateau value reached after convergence of fitness (Fig.\u00a02E). This increase in optimal fitness leveled off once the population size began exceeding 128 individuals, which we took as the baseline population size for GA convergence. Data from GA runs containing 128+ individuals and at least 40 generations was used for sequence analysis.\n\nAssociated with the convergence in fitness with respect to population size, we observed a similarity in the sequences produced by distinct GA runs. Although GA runs with smaller population sizes (<64) eventually converged to some fitness value, a comparison between these distinct GA runs revealed a large diversity in the respective sequences, indicating that the algorithms converged to local optima in the solution space. This diversity in sequence decreases as population size increases, with very similar sequences being obtained as population sizes increase to 128 individuals and above. Furthermore, at such population sizes, starting the evolution from different initial populations consisting of randomly generated sequences yields a consistent result. On these grounds, we can conclude that the GA successfully converges to a global optimum.\n\nTo gain detailed insights into the resolved evolutionary landscape, high-fitness sequences from all GA runs with populations of 128+ individuals were combined to generate a sequence logo of the sampled sequence space (Fig.\u00a03A). Sequence logos express the degree of amino acid conservation at each position within the sequence in terms of the concomitant Shannon entropy (bits) by scaling the character height of the corresponding amino acid. Randomly occurring amino acids at a certain position contain no information, corresponding to a small letter, whereas a more frequently occurring amino acid encodes information, corresponding to a larger letter.\n\nA Sequence logos computed from high-fitness peptide sequences reveal a highly conserved hydrophobic mismatch pattern (red = hydrophobic; blue = hydrophilic). Owing to the symmetry of the here-used bilayer, sequences are mirrored around the center as indicated by the dashed line. B Peptide-induced hydrophobic mismatch leads to a high local (1.0\u2009nm radius) cholesterol composition of the membrane. This mismatch mechanism is present in both Martini 2 and Martini 3. Sequences adhere to the following motif: D2K(12\u2212x/2)--Lx--K(12\u2212x/2)D2 (x\u2009=\u20090, 2, 4 etc.). C Analysis of side-chain cholesterol affinity across force fields. Martini 2 shows a preference for small hydrophobic side-chains (P, V, L, A). This mechanism is absent in Martini 3 and all-atom, where the emphasis seems to lie on the size of the sidechain. Error bars represent the standard error of the mean. Statistics were obtained from 3 independent replicates. D A rationally designed motif (L11) based on the CG optimal cholesterol attractor. The sequence retains both the conserved poly-lysine patches, and the short hydrophobic section. The corresponding atomistic structure is shown below. E Free energy profiles over the peptide-cholesterol distance are computed in all-atom simulations for the rationally designed motif D3K3L11K3D3 (L11), and the stereotypical transmembrane peptide GK2[LA]7LK2A (KALP21). KALP21 is characterized by a slender hydrophobic motif rich in leucines [LA]7L. Nevertheless, a pronounced cholesterol affinity is only observed for the designed motif L11. Shaded areas represent the standard error of the mean. 3 independent replicates were simulated for each peptide. F Peptide covering the known cholesterol binding \u03b3 M4 transmembrane region4,56. The CARC motif present in this sequence is indicated by the colors4,56. Mutation of the aromatic residue phenylalanine into an alanine is known to impair its cholesterol-dependence4. G Free energy profiles over the peptide-cholesterol distance are computed in all-atom simulations for the \u03b3 M4 peptide, and the non-CARC (F\u00a0\u2192\u00a0A) mutant of the \u03b3 M4 peptide. Mutation of phenylalanine in fact produces a strong increase in cholesterol affinity. Shaded areas represent the standard error of the mean. 3 independent replicates were simulated for each peptide.\n\nIn both the Martini 2 and Martini 3 coarse-grained force fields, the global solution converges to a distinctive pattern featuring a short conserved hydrophobic core centered within the peptide. This core is flanked by two hydrophilic blocks composed of conserved positively charged lysines (K) and arginines (R). Notable differences exist between force fields. Martini 2 exhibits a strong preference for three consecutive lysines, which are the most evolutionarily conserved residues. In contrast, Martini 3 features equal competition between lysines and arginines. Both versions primarily feature negatively charged aspartic acids (D) at terminal positions, which are more highly conserved in the Martini 3 force field. High-fitness sequences resulting from directed evolution in both Martini 2 and Martini 3 force field versions exhibit a consistent hydrophobic pattern. This pattern features positively charged lysines and arginines at positions directly facing a central short hydrophobic block.\n\nIt is important to emphasize that the solution space resolved here is subject to a constraint in secondary structure, i.e., all sequences are assumed to be alpha-helical5. We will address the transferability of solution space in more detail in a later section of this work. Furthermore, while our study primarily investigates cholesterol attraction in simplified POPC model membranes, it is important to note that verification using a coarse-grained model of native epithelial membrane46 demonstrates the universality and persistence of the resolved attraction features in more realistic membrane environments (Supplementary Fig.\u00a02).\n\nThe sharp positional convergence of hydrophilic charged residues deeply located in the hydrophobic core of the membrane prompted us to investigate what role the length of the hydrophobic block plays in the cholesterol-sensing ability of the sequence. To this end, we created dummy peptides according to the D2K(12\u2212x/2)\u2013Lx\u2013K(12\u2212x/2)D2 motif with each peptide consisting of 20 amino acids in total. Here, leucines form the hydrophobic block of the peptides, with lysines functioning as the hydrophilic edges. By varying the number of leucines and lysines, we effectively vary the length of the hydrophobic block. Interestingly, cholesterol affinity increases with decreasing hydrophobic block length, with an optimal effect at 2\u20134 leucines (Fig.\u00a03B). This pattern seems to arise from a trade-off between short block length and transmembrane (meta)stability, with a further decrease in block length resulting in a decline in functionality. Artificially restraining a transmembrane orientation/topology for such motifs (e.g., K9V2K9, and even K20) eliminates the stability factor, thereby restoring the functionality (Supplementary Fig.\u00a03). The cholesterol attraction thus appears to be mediated by positively charged lysine residues deeply embedded in the membrane, as is consistent with their evolutionary conservation. The positioning of these residues, specifically the length of the conserved hydrophobic block, must ensure a transmembrane topology during evolutionary development. Interestingly, despite the Martini 2 force field showing a stronger net attraction than the Martini 3 force field, both exhibit a similar overall gradual decline in relative cholesterol affinity as block size increases toward a hydrophobic length of 20 amino acids.\n\nFinally, we emphasize that our study specifically focuses on maximizing the attraction of free membrane cholesterol. Owing to the membrane thickening effect of cholesterol42, cholesterol-enriched phases such as the liquid ordered (Lo) phase generally favor TMDs characterized by a long rather than short hydrophobic length5,47,48,49,50. The here-resolved motif is therefore not expected to optimally bind toward the interface of cholesterol-enriched liquid ordered domains5,42 (Supplementary Fig.\u00a09). Nevertheless, the clustering of cholesterol is itself membrane phase independent and equally occurs when the resolved TMD is embedded within a liquid-ordered DPPC:cholesterol mixture (Supplementary Fig.\u00a03).\n\nNext, we examined whether the composition of the hydrophobic fraction influences cholesterol attraction. To investigate this, we constructed D2K2X20K2D2 sequences to systematically analyze the native cholesterol affinity of hydrophobic residues in the absence of hydrophobic mismatch for the Martini 2, Martini 3, and the atomistic (AMBER99SB-ILDN with Slipids) force field (Fig.\u00a03C). We measured the local cholesterol composition within a 1.0\u2009nm radius of the transmembrane domain to assess cholesterol attraction.\n\nIn the Martini 2 force field, we observed an unexpectedly strong attraction between cholesterol and certain amino acid residues, particularly proline, valine, and leucine. These residues are modeled using a simplified representation consisting of a small single-bead side chain with variable bond lengths. Our investigation revealed that artificially altering the side chain bond distances significantly impacted cholesterol attraction. Specifically, decreasing the bond length enhanced cholesterol attraction, while increasing it diminished attraction (Supplementary Fig.\u00a020). We attribute this pronounced cholesterol attraction primarily to artifacts arising from the exaggerated interactions between small bead types used to represent both cholesterol and amino acids within the Martini 2 force field43,44.\n\nIn contrast, the Martini 3 force field showed a different pattern. Only alanine and glycine displayed significant net attraction toward cholesterol. However, the atomistic simulations revealed that only glycine may exhibit a weak but significant cholesterol attraction. This finding aligns with the cholesterol binding to glycine zipper motifs observed in atomistic simulations17,51.\n\nSurprisingly, larger hydrophobic aromatic residues such as tyrosine (Y) and phenylalanine (F)\u2014key components of CRAC/CARC motifs\u2014were found to be weakly or strongly cholesterol repulsive across all simulation models, including atomistic simulations. Furthermore, other hydrophobic CRAC/CARC residues like leucine and valine showed either inert or repulsive behavior toward cholesterol, with particularly strong repulsion observed in the atomistic simulations.\n\nOur research across three distinct force fields reveals that the composition of hydrophobic residues may prioritize minimizing cholesterol repulsion over maximizing attraction. Notably, the atomistic and Martini 3 force fields demonstrated greater behavioral similarity compared to the Martini 2 force field. To minimize repulsion, simulations consistently favored small hydrophobic amino acids, such as alanine, and residues with weaker helical propensity, including valine, proline, and glycine. Conversely, larger hydrophobic amino acids like leucine and aromatic amino acids (phenylalanine and tyrosine) enhance repulsion. This pattern suggests that cholesterol affinity appears more dependent on the size rather than the hydrophobicity of the hydrophobic amino acids constituting transmembrane helices. We propose that bulky, highly corrugated proteins disrupt the order within the surrounding cholesterol matrix5,52, resulting in a local depletion of cholesterol. The surprising absence of correlation between amino acid hydrophobicity and (relative) cholesterol affinity suggests that depletion is likely driven by optimizing cholesterol-cholesterol interactions rather than protein-cholesterol interactions. Hydrophobic transmembrane domains therefore tend to show a net repulsion rather than a net attraction toward cholesterol. This repulsion appears to be compensated by negative hydrophobic mismatch via lysines and arginines exposed to the hydrophobic membrane core.\n\nIn this work, we resolved the essential physicochemical driving forces that underpin cholesterol attraction in transmembrane domains within homogeneous model membranes. The here-resolved motif features of the optimal cholesterol attractor are subsequently translated into more realistic peptide sequences by accounting for the following three model approximations:\n\n(I) Given that transmembrane domains are primarily composed of alpha-helices, we imposed an alpha-helical secondary structure constraint on the generated sequences. Although this assumption simplifies the search space by bypassing the challenge of secondary structure prediction, it introduces the potential for amino acids with low alpha-helix propensities (such as proline and valine)53 to appear in the generated sequences, potentially leading to non-helical peptides in unconstrained simulations. Maintaining stable helicity is crucial for preserving membrane stability. Short hydrophobic helical segments flanked by deeply embedded charged amino acids create negative hydrophobic mismatch, which maximizes cholesterol attraction. However, strong membrane elastic forces constantly counteract this stability. To address this, we designed a polyleucine sequence due to its high helical propensity. Note that leucine residues exhibit inherent cholesterol repulsion in our atomic-scale simulations (Fig.\u00a03C).\n\n(II) Electrostatic interactions are underestimated in the coarse-grained simulations, enabling the formation of sequences with a high net charge. To obtain a sequence with net zero charge, we balance the conserved lysines patches by adding three aspartic acids (D) to both terminal ends. This essentially entails a superposition of the conserved features observed in the Martini 2 and 3 force fields.\n\n(III) We anticipate on the notion that the coarse-grained model\u2014and MD simulations in general\u2014underestimate the hydrophobic length where transmembrane domains become thermodynamically stable with respect to experimental conditions. Transmembrane partitioning of polyleucine helices in experiments only becomes favorable over surface partitioning at a length of 10 leucines, in contrast to their atomistic estimation of 7\u20138 leucines54 and our course-grained estimation of 6 leucines (Supplementary Fig.\u00a04).\n\nAltogether, this leads to the more realistic sequences D3K3L11K3D3 (L11) and potentially D3K3L10K3D3 (L10), both of which retain all the design features proposed by the GA. Biophysical characterization in model membranes (POPC and DLPC with 30% cholesterol) using Circular Dichroism (CD) spectra confirms that even the shorter of these two sequences (L10) adopts a helical structure in lipid membranes (Supplementary Fig.\u00a015).\n\nThe L10 peptide was confirmed to associate with cholesterol through its highly conserved lysine patch in NMR experiments that correlate peptide and cholesterol signals when the two are in close contact. The peptide was labeled with 13C at the carbonyl group of the two lysine residues that are directly adjacent to the Leucine motif (position 6 and 17 in the sequence) and 13C4 labeled cholesterol. The use of ether-linked lipids avoids any signal in the carbonyl region coming from the lipids. A cross peak in the PDSD spectrum (Fig.\u00a04A, Supplementary Fig.\u00a05) between the carbonyl group and cholesterol C4 confirms the interaction. A comparable interaction is seen for KALP-21 (Supplementary Fig.\u00a06), which was labeled at the analogous lysine residues. For these measurements, the sample temperature was 100\u2009K to prevent diffusion, allowing a long mixing time of 30\u2009s, which is needed to efficiently observe transfer over the expected distance range of about 6 to 9\u2009\u00c555. Note that the transfer rate in PDSD is expected to scale down with the sixth power of distance, such that the measurement is strongly influenced by any small changes in the pose of the cholesterol molecule relative to the peptide (See Fig.\u00a04 for a depiction of two such poses of close contact between peptide and cholesterol, in which the distance changes substantially).\n\nA All-atom MD snapshots display peptide-cholesterol interaction deep within the membrane. Interaction between cholesterol (C-4) and the deeply located lysine residues (carbonyl of position 6 and its mirror in the sequence logo) is also observed in DNP-enhanced ssNMR (inset). Labeled lysines and the cholesterol C-4's are indicated with black arrows. B (Control) Cholesterol (red) exhibits a lower energy penalty for movement along the membrane normal compared to POPC (beige), allowing for more favorable shielding of deeply located lysine residues. High-fitness cholesterol attractors utilize this effect by increasing deep lysine interactions, leading to local accumulation of cholesterol molecules. B (Restrained) Application of a force to lipid headgroups within a specific distance from the membrane cter prevents cholesterol flip-flopping and movement toward the bilayer center. C Removal of cholesterol vertical mobility (Restrained) leads to a large drop in functionality for cholesterol attractors with short hydrophobic blocks (K8V4K8), while attractors with longer hydrophobic blocks (K1V18K1) are less affected. The dashed line indicates the average cholesterol content of the system (30%). Bars represent the standard error of the mean. Statistics were obtained from 5 independent replicates.\n\nFurthermore, free energy calculations in atomistic MD simulations (see Methods) confirm that this design pattern exhibits a pronounced functionality in cholesterol affinity, as shown in Fig.\u00a03E for the sequence L11. This functionality is particularly evident when compared to (i) the prototypical and somewhat similar model peptide KALP21 (sequence: GKK(LA)7LKKA), (ii) the \u03b3M4 transmembrane domain of the muscle nicotinic acetylcholine receptor\u2014a known strong cholesterol binding sequence with a CARC motif4,56, and (iii) its F-452/A mutant4 (see Fig.\u00a03G and Supplementary Fig.\u00a021). We thus observe that the encoded functionality persists between the different model resolutions. Moreover, the obtained free energy profile illustrates that cholesterol attraction occurs over rather large distances\u2014up to 1.8\u2009nm\u2014suggesting that the attraction is membrane mediated, and thus resulting from an interplay between peptide and membrane.\n\nOptimization of cholesterol binding resulted in a thermodynamic optimum characterized by a small free energy minimum of up to 5\u2009kJ/mol or 2\u2009kBT. Notably, this optimum represents the upper limit of achievable residence time for optimal cholesterol binding. To put such a value into perspective: The binding free energy of typical ligands modifying GPCR function exceeds values of 40\u2009kJ/mol or 16\u2009kBT57 and is thus substantially larger than that of cholesterol acting as a ligand via binding of linear motifs.\n\nNotably, our fitness function effectively maximizes the integral of the free energy profiles shown in Fig.\u00a03 within the cutoff radius of the simulation (1.2\u2009nm). To elucidate its association with the maximum binding affinity of a single cholesterol molecule, we analyzed multiple sequences, including the well-established CRAC and CARC motifs. An overview of the measured fitness and the associated (maximum) binding affinity of a single cholesterol molecule is listed in Supplementary Fig.\u00a017. The linear correlation we observed provides evidence for the correlation between the maximum binding affinity of a single cholesterol molecule and the overall enthalpic interaction. Therefore, optimizing the attraction between cholesterol and the membrane environment simultaneously optimizes the binding affinity for individual cholesterol molecules, and thus we observed the upper thermodynamic limit of cholesterol binding to linear motifs.\n\nOur analysis of the concomitant average first passage times (see Methods), derived from atomistic simulations, reveals that the upper bound for cholesterol-binding residence time falls below 400\u2009ns for the L11 sequence. Although linear motifs within transmembrane domains can facilitate cholesterol binding, the low binding affinity and concomitant short residence time\u2014even when close to the thermodynamic optimum\u2014may significantly limit the ability of such a ligand binding based mechanism to alter protein functionality within GPCRs, given that concomitant changes within the conformational ensemble due to ligand binding occur on microseconds to milliseconds time scales58,59.\n\nThe main question to address remains why the thermodynamically optimal mechanism of cholesterol attraction favors hydrophobic mismatch. Notably, the observed effect is consistent across different force fields, demonstrating robustness and reliability. Specifically, the phenomenon occurs in all three force fields tested, suggesting that the underlying physical principles driving this behavior are not dependent on the particular set of parameters used in molecular simulations. In contrast to POPC lipids, cholesterol exhibits a low free energy barrier when undergoing flip-flopping between the two leaflets of the membrane. As a result, the head group of cholesterol is particularly adept at interacting with the lysines deeply located within the hydrophobic region of the membrane. Such binding mode is confirmed both by our molecular dynamics simulation as well as solid state NMR experiments (Fig.\u00a04A). We hypothesize that by moving toward this hydrophobic region, cholesterol molecules effectively shield the lysine patch from unfavorable interactions with the hydrophobic lipid tails (Fig.\u00a04B). To this end, we conducted simulations within the Martini 2 force field that artificially restricted bilayer flip-flopping of cholesterol in the simulations via the application of an external field (flat-bottom potentials). High-fitness sequences containing a short hydrophobic block, which would rely on the vertical mobility of cholesterol for their functionality, experienced a significant decrease in cholesterol attraction. However, longer attractors with less optimal characteristics, where the attraction of cholesterol primarily depends on the nature of the hydrophobic section, remained relatively unaffected (Fig.\u00a04C). Therefore, we attribute the enhanced attraction of cholesterol to the difference in vertical mobility of lipid head groups in the immediate vicinity of the transmembrane domain (TMD). It is worth noting that the thermodynamically optimal POPE attractor (Martini 2 force field) can also be attributed to a differential vertical mobility effect between POPE and POPC lipids due to the effectively smaller phosphatidylethanolamine (PE) head group. However, in this case the attractors exploit a favorable enthalpic interaction between POPE head groups and the centrally located tryptophan region (Supplementary Fig.\u00a014).\n\nAn interesting question is to what extent a hydrophobic mismatch mediated attraction of cholesterol can be expressed within isolated transmembrane domains in nature. Noting that hydrophobic mismatch is also a known determinant in protein trafficking and sorting49,60, one would therefore intuitively expect a stronger limitation on the evolutionary expression of such a mechanism. To investigate the possible nature of these evolutionary constraints, we performed experiments in live cells (HEK cells) expressing the short hydrophobic sequences D3K3L10K3D3 (L10) and D3K3L11K3D3 (L11), each with a fluorescent tag, as well as KALP21 (GK2[LA]7LK2A). KALP21 is a typical model peptide in membrane biophysical studies and has a (relatively short) hydrophobic length of 15 amino acids. Our experiments revealed that L10 (Fig.\u00a05E), L11 (Fig.\u00a05F), and KALP21 (Fig.\u00a05G) can be effectively expressed in live cells. These transmembrane proteins were found to localize exclusively to the endoplasmic reticulum (ER) and not to other intracellular organelles such as lysosomes or mitochondria (Supplementary Fig.\u00a010). In addition, they did not localize to the plasma membrane, but notably decreased the trafficking of fat transporter and scavenger receptor CD36 to the plasma membrane (Supplementary Figs.\u00a011 and 13). The unique characteristics of the ER membrane make it particularly favorable for the insertion of transmembrane domains (TMDs) with negative hydrophobic mismatch, as it is the thinnest membrane in live cells and incurs the lowest energetic penalty for such insertions60,61. In contrast, the TMDs of SNARE proteins (such as Syntaxin-1), which have longer hydrophobic lengths ranging from 23 to 25 amino acids, can still be successfully expressed throughout the cell using the assay employed in this study49. However, the fact that a prototypical model peptide like KALP21 (with a hydrophobic length of 15 amino acids), which differs by only one amino acid from the shortest native TMD within the TmAlphaFold database (with a hydrophobic length of 16 amino acids), is confined to the ER membrane highlights the existence of an evolutionary barrier related to protein trafficking. This barrier prevents optimal exploitation of the hydrophobic mismatch mechanism, which favors a hydrophobic length toward the limit of transmembrane topology stability (10 to 11 amino acids).\n\nA Comparison of CNN-predicted fitness distributions between single-pass and multi-pass database TMDs. Markers indicate the interquartile range and the median of the data. B Relative presence of CRAC motifs ((L/V)-X1-5-(Y)-X1-5-(K/R), and its inverse) with respect to the CNN-predicted fitness. C Single-pass CNN-predicted fitness distributions with respect to TMD length. Markers indicate the interquartile range and the median of the data. D Multi-pass CNN-predicted fitness distributions with respect to TMD length. Markers indicate the interquartile range and the median of the data. Fluorescence microscopy of transfected HEK cells, expressing L10 (E), L11 (F), and KALP21 (G); as well as fluorophore-tagged Sec61 to mark the ER. For each panel, a line profile was drawn (red), and the normalized fluorescence intensity profiles of peptide and ER are compared in the respective graphs. Scale bars and line profiles in all panels correspond to 10\u2009\u03bcm. All microscopy experiments were performed in three independent replicates.\n\nFinally, to explore the potential exploitation of hydrophobic mismatch-mediated attraction in nature, we systematically analyzed isolated transmembrane domains extracted from 8370 native membrane proteins in the TmAlphaFold database using a CNN trained on EVO-MD fitness-labeled data within the Martini 2 model (see Methods and Supplementary Information). We discovered a weak but significant correlation between predicted fitness and TMD length in single-pass proteins, specifically at the shortest hydrophobic length of 16 amino acids (Fig.\u00a05C). This correlation was absent in multi-pass proteins, likely due to differential TMD lengths diminishing weak evolutionary pressures for the expression of negative hydrophobic mismatch. As a result, cholesterol attraction via linear motifs in nature will be limited toward less efficient mechanisms yielding residence times that likely fall below the timescale of several 100\u2009ns estimated for optimal cholesterol attraction/binding. This raises the question whether these thermodynamically suboptimal mechanisms could remain effective in achieving their biological purpose, specifically the regulation of GPCRs, given that the timescales of conformational responses (relaxation times) within GPCRs upon binding of ligands, being high microsecond to milliseconds58,59, lie far beyond the here estimated range of maximal attainable residence times for cholesterol binding to linear motifs.\n\nThe CRAC/CARC motif has traditionally served as the primary criterion for predicting cholesterol attraction/binding within transmembrane domains (TMDs). However, our study aimed to reassess this motif\u2019s predictive capacity for accurately determining cholesterol attraction, its proposed functional role. Interestingly, our Evo-MD simulations revealed that aromatic residues crucial for the CRAC/CARC motif were not conserved during the evolutionary process aimed at optimizing cholesterol attraction. In addition, systematic atomistic simulations demonstrated that hydrophobic motifs consisting of the aromatic CRAC/CARC residues F and Y strongly repel cholesterol. The most potent cholesterol binding motif described in the scientific literature, as revealed through in silico molecular docking, is a CARC motif found within the \u03b3 M4 transmembrane domain of the muscle nicotinic acetylcholine receptor4,56. Although our atomistic simulations confirmed a modest initial affinity for cholesterol, as indicated by a shallow free energy minimum of approximately 2.3\u2009kJ/mol, the introduction of a putative mutation (F-452/A) in the crucial aromatic residue within the CARC motif, replacing phenylalanine with alanine4, actually enhanced the motif\u2019s ability to attract cholesterol rather than impairing it (Fig.\u00a03G). This finding is consistent with the detrimental effect of phenylalanine on cholesterol attraction when it forms the hydrophobic motif, as observed in our coarse-grained and atomistic simulations. Notably, the characteristic free energy well depth for cholesterol attraction in linear motifs is small (on the order of kBT), leading to considerable variations between replicas in individual umbrella sampling attempts (Supplementary Fig.\u00a021). However, the free energy differences between the different peptide sequences, particularly between L11, KALP21, and \u03b3 M4, are pronounced and substantially larger than the sampling noise.\n\nOur results challenge the current assumption of CRAC/CARC motif functionality in transmembrane domains (TMDs), as the presence of hydrophobic CRAC/CARC residues V, L, F, and Y within hydrophobic motifs\u2014being larger amino acids\u2014intrinsically decreases rather than increases cholesterol attraction (also see Supplementary Fig.\u00a012). The discrepancy between observed behavior and proposed roles in optimizing cholesterol binding affinity raises questions about the true biological functions of these motifs. Key observations include:\n\nShort residence times: The cholesterol binding free energy to CRAC/CARC motifs is 2\u2009kBT or less (<5\u2009kJ/mol), indicating low affinity when compared to the energy of thermal fluctuations. Consequently, the residence time is only several hundred nanoseconds, suggesting rapid dissociation compared to other ligands known to regulate GPCRs57.\n\nCholesterol repulsion: Key residues in CRAC/CARC motifs repel cholesterol, contradicting expectations based on their proposed function.\n\nAbsence of co-crystal structures: No crystal or Cryo-Electron Microscopy (Cryo-EM) structures feature cholesterol bound to CRAC/CARC motifs, suggesting generally weak binding affinities10,31. Despite molecular docking studies in vacuum showing good fit for cholesterol binding to CRAC/CARC motifs3,23, actual experimental support for this interaction remains scarce.\n\nFinally, we conducted a comprehensive analysis using the TmAlphaFold database for membrane proteins, employing a convolutional neural network (CNN) trained on fitness-labeled data generated by EVO-MD using the Martini 2 model (see Methods and Supplementary Information). The Martini 2 coarse-grained force field has been successfully applied in modeling cholesterol binding to CRAC motifs present in serotonin1A receptors and ErbB2 growth factor receptors 227,62. Although this coarse-grained model systematically overestimates cholesterol attraction in transmembrane proteins45, its behavior aligns with the atomistic simulations regarding cholesterol repulsion by aromatic residues. The performed analysis examined the frequency of CRAC/CARC motifs and their correlation with cholesterol attraction (Fig.\u00a05B). Our findings reveal a negative correlation between cholesterol attraction and the occurrence of CRAC motifs in both single-pass and multi-pass transmembrane domains (TMDs). Notably, systematic mutation of these residues to alanine significantly increases cholesterol attraction (Supplementary Fig.\u00a012). This phenomenon can be attributed to the cholesterol-repulsive nature of hydrophobic aromatic residues required to classify a motif as CRAC/CARC.\n\nThese observations collectively indicate that mechanisms governing CRAC/CARC motif function in TMDs may differ significantly from their proposed role in optimizing cholesterol binding affinity. This conclusion highlights the need for further research to elucidate the potential recognition mechanisms of linear motifs. Specifically, it emphasizes the need for further investigation of examples where point mutations in identified CRAC/CARC motifs have impaired cholesterol responsiveness, such as the motifs present in the Programmed death-ligand 1 (PD-L1) and serotonin 1A receptor, or the mitochondrial translocator protein TSPO11,12,13.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63769-5/MediaObjects/41467_2025_63769_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63769-5/MediaObjects/41467_2025_63769_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63769-5/MediaObjects/41467_2025_63769_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63769-5/MediaObjects/41467_2025_63769_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our study applied Evo-MD simulations to investigate the mechanisms and design features responsible for driving optimal cholesterol attraction within transmembrane domains (TMDs). We found that hydrophobic mismatch and the presence of small hydrophobic amino acids play significant roles in facilitating the ideal interaction between cholesterol and TMDs. These mechanisms demonstrated robustness across multiple simulation models, diverse simulated membrane compositions (including a coarse-grained model of the native epithelial membrane46), as depicted in Supplementary Fig.\u00a02, and various membrane environments such as the liquid-disordered and liquid-ordered phases, as shown in Supplementary Fig.\u00a09. These findings emphasize the fundamental importance of these mechanisms in governing cholesterol-membrane interactions within native membrane proteins.\n\nIn the field of cholesterol-binding domains, the CRAC (cholesterol recognition/interaction amino acid consensus) and its inverse motif CARC have gained significant attention and are widely studied in scientific literature. These motifs have been identified in various proteins known to interact with cholesterol, particularly GPCRs (G-protein coupled receptors)3. However, there is an ongoing debate regarding the applicability of CRAC/CARC motifs in GPCRs. It has been observed that cholesterol can crystallize bound to GPCRs that lack a CRAC, CARC, or the equivalent cholesterol consensus motif (CCM) that switches the position of Y/F residue and L/V within the CRAC algorithm30,33,63, and even when these motifs are present, cholesterol often does not occupy them10,14,31,64. This highlights the complexity of cholesterol-protein binding and suggests that additional mechanisms beyond CRAC/CARC motifs may contribute to cholesterol binding in GPCRs. Our study adds to this understanding by exploring the broader mechanisms and design features that govern cholesterol attraction in linear motifs. We demonstrated that isolated transmembrane domains can facilitate cholesterol binding akin to the concept of linear motifs, albeit with very low affinity (up to 2\u2009kBT) and short residence time (up to 400\u2009ns) even in the thermodynamic optimum.\n\nPrevious atomistic simulations have explored how cholesterol modulates the human \u03b22-adrenergic receptor (\u03b22AR), a prototype G protein-coupled receptor, in an allosteric manner63. The proposed mechanism involves cholesterol binding to specific high-affinity sites near transmembrane helices 5-7 of the receptor. Notably, the lifetime of cholesterol in these high-affinity sites was found to be (at least) microsecond-scale, thus significantly longer than the nanosecond lifetimes observed for linear motifs.\n\nThe binding of typical regulatory ligands targeting GPCRs is 42\u2009kJ/mol (10 kcal/mol) or about 16\u2009kBT57 and exceeds the here measured binding free energy of cholesterol to optimal linear motifs (about 2\u2009kBT) by about 14\u2009kBT57. This would therefore result in a concomitant residence time that is, assuming a similar kinetic prefactor, 1.2\u2009\u00d7\u2009106 times longer\u2014thus approaching second time scales. It can be argued that, due to the high abundance of cholesterol within the plasma membrane, the binding occupancy will be high despite weak binding interactions. Nevertheless, it remains questionable whether the rapid ligand binding and unbinding kinetics associated with linear motifs can sufficiently influence the slower relaxation modes within membrane proteins, which are relevant for functionality and occur on and above microsecond timescales65.\n\nAromatic residues are considered the key components in the CRAC, CARC, and CCM motifs3,23,33. Notably, the contribution of aromatic residues to the binding affinity within CARC/CARC motifs has been primarily inferred from the enthalpic interactions observed in docking experiments with a single cholesterol molecule in a vacuum3. Our simulations sought to replicate and extend these findings by maximizing enthalpic interactions within a more realistic membrane environment. In such an environment, the interactions with phospholipids become competitive since the attraction of cholesterol is mediated by relative differences in binding affinity with other lipids, rather than relying solely on absolute cholesterol binding affinity as measured within in vacuo docking experiments.\n\nHaving shown that hydrophobic aromatic residues tend to be detrimental to cholesterol attraction in isolated linear motifs within a lipid environment, the following question emerges: is their presence coincidental, arising from other evolutionary pressures unrelated to cholesterol-mediated regulation of transmembrane proteins (such as structural stability or the decreased packing of lipids in membrane leaflets66,67), or do they actively participate in cholesterol responsivity?\n\nAlthough the co-evolution with cholesterol-repelling aromatic residues could be coincidental, mutating these residues in presumed functional CRAC motifs, like PD-L1 and TSPO, impairs their cholesterol responsiveness12,13. Aromatic residues within these motifs may alternatively facilitate responsivity through the repulsion of cholesterol\u2014with cholesterol acting as a cosolvent for membrane proteins rather than a ligand51\u2014to alter the behavior and functionality of membrane proteins.\n\nSuch a sensing mechanism relying on repulsion rather than attraction of the surrounding lipid environment may reflect the membrane saturation sensing mechanism in the transcriptional regulator Mga2, which relies on the relative rotation of two transmembrane domains (TMDs) to sense lipid packing density68. Tighter lipid packing favors a rotational orientation, with the bulky tryptophan sensing residue \u2019hiding\u2019 in the dimer interface. Less dense lipid packing in membranes with a high proportion of unsaturated lipid acyl chains favors a different relative orientation of the TMDs, with the sensing residue facing hydrophobic lipid acyl chains, thereby weakening dimer formation.\n\nAnalogously, elevated cholesterol levels might induce membrane-exposed aromatic residues and leucines to facilitate dimerization by prioritizing protein-protein interactions over protein-lipid interactions. Dimerization is known to control the functionality of a wide class of both single-pass62 and multi-pass membrane proteins69. Our coarse-grained simulations exploring dimers of the \u03b3 M4 TMD23 highlight the role of phenylalanine within the CARC motif in enhancing protein-protein interactions within cholesterol-enriched lipid membranes (Supplementary Fig.\u00a016).\n\nLikewise, elevated cholesterol levels in multi-pass membrane proteins may alternatively force aromatic residues to rotate inward, enhancing interactions with residues in neighboring helices, thereby shielding them from the unfavorable membrane environment. Such an induced structural change could alter protein (channel) configuration and functionality potentially even via long range allosteric coupling13.\n\nAkin to cholesterol-protein docking studies in a vacuum3, aromatic residues may however favor cholesterol binding under specific conditions where competition from other lipids is absent. For instance, when these residues are situated within a groove between several transmembrane domains10,14,32, deeply embedded within the membrane and inaccessible to other lipids except cholesterol, they can effectively promote cholesterol binding. However, it is important to note that this scenario requires knowledge of the protein\u2019s full three-dimensional structure, especially for multi-pass membrane proteins. Such comprehensive understanding exceeds the predictive capabilities of models solely based on linear motifs.\n\nThe observation of direct binding interactions between aromatic residues within identified CRAC motifs and cholesterol27 in coarse-grained molecular simulations using the Martini 2 force field appears counterintuitive, given the strong cholesterol repulsion of aromatic residues within this force field. In fact, systematic mutation of aromatic residues within identified CRAC/CARC motifs in native proteins actually increases cholesterol attraction, as described by the same Martini 2 force field (Supplementary Fig.\u00a012). This suggests that secondary interactions, including those with other residues and residues in neighboring helices, as well as the overall three-dimensional protein structure (hydrophobic groves), are likely to play a role in facilitating the observed cholesterol binding.\n\nDespite significant advancements, the mechanisms governing cholesterol-dependent protein regulation in GPCRs remain poorly elucidated. Atomistic simulations revealed that cholesterol binding to specific high-affinity sites reduced \u03b22AR conformational variability in a high (40%) cholesterol environment compared to a low (10%) cholesterol environment63. A primary challenge at elevated cholesterol concentrations lies in distinguishing the effects resulting from cholesterol binding as a weak ligand versus its role as a cosolvent of membrane proteins. Additional control simulations in which cholesterol binding is artificially conserved under low cholesterol conditions, as well as point mutations within the specific binding sites, could further clarify the different roles of cholesterol binding versus its effects on lipid membranes such as stiffening and reduced dynamics.\n\nIn summary, our study has demonstrated the ability of Evo-MD to identify evolutionary fingerprints of protein-lipid interactions in membrane proteins. Our methodology relies on the physics-based inverse design of molecules, leveraging the fact that the physical driving forces governing functionality are inherently embedded within the complexity of independently parameterized classical molecular force fields. This approach diverges significantly from prevalent data-driven quantitative structure-activity relationship (QSAR) based inverse design approaches, which employ machine learning based variational encoders to translate optima in an abstract high-dimensional latent space into corresponding chemical structures35.\n\nBy determining the true thermodynamic optimum for cholesterol attraction, Evo-MD has provided insights into the fundamental forces that drive lipid recognition and binding in membrane proteins. This unique ability of Evo-MD enables us to gain a deeper understanding of how proteins recognize and bind specific membrane lipids or lipid-soluble ligands, including hormones and vitamins, within the complex and crowded environment of lipid membranes. We anticipate that physics-based evolution approaches like Evo-MD will unveil insights into the molecular organization of biological membranes and protein trafficking mechanisms38. The synergy with other groundbreaking protein structure prediction methodologies, such as the Alphafold 2 project70, could further facilitate these applications.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Coarse-Grained simulations were performed with the Martini 2.2 and Martini 3 CG force field using the GROMACS 2019.1 molecular dynamics package. EVO-MD is written in Python 3.6.8 and depends on the NumPy and MPI for Python packages for functionality. Peptide topologies are generated using seq2itp71. Input parameters for the coarse-grained simulations are based on the Martini 2 \u2019New-RF\u2019 parameters72 and the Martini 3 recommended parameters43. with exceptions detailed in the sections below.\n\nEVO-MD was developed as a framework for the simulated evolution of MD simulation systems. Simulated evolution is a type of optimization problem involving the optimization of some property of the simulated system, by means of iteratively tuning a set of parameters. The performance (i.e., fitness) of such a parameter set is then measured by means of a fitness function, which generally consists of one or more MD simulations followed by an analysis step.\n\nUsing GAs, we can manage large, hyper-dimensional optimization problems through efficient exploration of the search space. Analogous to the method\u2019s origin in genetics, we envision each possible solution as a chromosome, which consists of a unique set of parameters encoded into a (bit)string sequence. The algorithm iteratively samples parts of the search space by forming a population of chromosomes and measuring their fitnesses. In line with evolution, individuals with high fitnesses are selected to recombine and form a new population. Since the new population is based on a highest fitness subset of the previous population, it is assumed that the average fitness of the population increases each iteration. This process is visualized in Fig.\u00a01.\n\nImplementation of the cholesterol sensing project is illustrated in Fig.\u00a06. Each candidate peptide is encoded as a sequence of one-letter amino acid codes. For faster convergence, the sequence is mirrored to produce a palindromic sequence, effectively reducing the search space for a peptide 20 amino acids in length from 2020 to 2010 (assuming 20 amino acid types). The GA is initialized by generating a random population of Npop sequences, after which each sequence is evaluated in parallel according to the fitness function.\n\nPeptide sequences are evaluated by means of MD simulation. A peptide structure (yellow) is generated from sequence and inserted into a POPC (beige) and cholesterol (red) bilayer membrane. The fitness is then computed from the resulting trajectory. Highest fitness sequences are selected from the evaluated population. Through recombination (involving crossover and mutation operations) of the selected sequences, a new population is generated.\n\nThe fitness function takes a sequence as argument and returns a single float value representing the sequence\u2019s fitness. This function involves several simulation steps: generate_peptide, insert_peptide, production, and compute_fitness. Generate_peptide generates a peptide structure and topology using the seq2itp tool71, followed by energy minimization and peptide-membrane alignment. Insert_peptide combines the peptide structure with an existing equilibrated membrane structure containing 128 lipid molecules (90 POPC, 38 cholesterol) and 1598 Martini water beads, and places the peptide transversely through the membrane. Collisions between peptide and membrane structures are resolved by partially decoupling the non-bonded interactions\u2014combined with soft-core potentials\u2014and running a steepest descent algorithm. The production module adds ions to neutralize any net charge on the system, after which equilibration and production simulations are performed. The compute_fitness module then measures the ensemble-averaged short-ranged Lennard-Jones interactions between peptide and cholesterol molecules from the simulation trajectory, which is returned as the fitness (Coulomb interactions involving cholesterol are absent within the CG model). Notably, such a fitness is the direct outcome of the competition between cholesterol and POPC lipids to interact with the peptide. Therefore, its value is directly proportional to the adopted cholesterol concentration and thus the relative binding free energy.\n\nOnce all sequences in the population have been evaluated, the algorithm proceeds by selecting the best N performers to serve as parents for the next population. A new sequence is generated by recombining two randomly selected sequences from the parent pool, which involves a cross-over operation and a mutation operation. During the cross-over operation, a random position is selected in the new sequence. The part to the left of that position is inherited from the first parent, while the rest of the sequence is inherited from the second parent. Afterwards, the mutation operation ensures that each position in the sequence has a 1/len(sequence) chance of being replaced with a random amino acid. New sequences are created in this manner until a new population of size Npop is produced. This process of population fitness evaluation and recombination of the highest fitness candidates into a new population is then repeated until a desired number of iterations is achieved.\n\nA rerun mechanism was implemented to account for possible undersampling during fitness evaluation. If a sequence reoccurs in a future generation, its fitness value will be computed from the weighted average of the current and all prior fitness evaluations. With the chance of sequence reoccurrence increasing as the algorithm converges, this mechanism serves to increase confidence in the final fitness value.\n\nThe membrane template structure consists of a 5.6\u2009\u00d7\u20095.6\u2009\u00d7\u200910\u2009nm simulation box, containing a bilayer membrane in water solvent. The membrane consists of 90 POPC molecules and 38 cholesterol molecules. The solvent consists of 1598 Martini water beads.\n\nAs the seq2itp tool only produces topology files, a structure file for the peptide is generated by stacking hardcoded amino acid structures along the Z-axis and performing a 1.5\u2009ps simulation at low time step (0.05\u2009fs) using the GROMACS 2019.1 \u2019sd\u2019 stochastic dynamics integrator. This allows the hardcoded structure to slowly relax to a more reasonable conformation according to the generated topology.\n\nInsert_peptide centers the peptide in the membrane box and merges the two structures together. A steepest descent, combined with a partial decoupling of the non-bonded interactions (\u03bb\u2009=\u20090.75) and soft-core potentials, is then performed on the merged structure to remove collisions between the peptide and the membrane structures.\n\nA final steepest descent is performed without soft-core potentials. A short, 1.5\u2009ps simulation is performed at low time step (0.05\u2009fs) using the stochastic dynamics integrator to prevent blowing up of the system before the actual simulation is performed. The production simulation consists of a 500\u2009ns NPT MD simulation with 30\u2009fs time step, of which the first 50\u2009ns are used for equilibration. Temperature is coupled to 300\u2009K using velocity rescaling (\u03c4\u2009=\u20091\u2009ps with separate coupling groups for the membrane, peptide, and solvent), Pressure is coupled semi-isotropically to 1 bar using the Berendsen algorithm (\u03c4\u2009=\u20098\u2009ps), with compressibility set to 4.5\u2009\u00d7\u200910\u22125\u2009bar\u22121.\n\nEvaluation of the sequence\u2019s fitness is finalized by computation of a fitness value from the produced simulation trajectory. GROMACS\u2019 gmx energy tool is used to extract the ensemble average of the non-bonded interaction energies from the production trajectory. The absolute value is then returned to the GA.\n\nQuantification of sequence cholesterol clustering capability was performed by measuring the ratio of cholesterol molecules to membrane molecules within a cylinder of radius r centered on the peptide center-of-mass (COM). GROMACS\u2019 gmx rdf tool was used to compute a cumulative number radial distribution function (gCN(r)) for cholesterol COMs and POPC COMs, both with respect to the peptide COM. The final ratio figures are created by computing:\n\nComparisons between multiple ratio figures (local cholesterol content) were taken at a cylinder radius of 1.0\u2009nm, chosen as a middle-ground between local-sampling (low r) and sufficient sampling (high r).\n\nProduction runs of the GA were performed according to the parameters as described in Table\u00a01. Parents indicates the size of the selection pool, from which parents were selected at random for the recombination step. Iteration elites describe the number of highest fitness sequences which pass unaltered into the next generation. Rerun elites keeps track of a list of sequences which have been evaluated more than once, and allows several highest fitness sequences to proceed to the next generation unaltered. The total number of elites is equal to the sum of iteration and rerun elites.\n\nSimulations for the analysis of side-chain cholesterol affinity were performed using the GROMACS 2019.1 molecular dynamics package. Simulations for the computation of the free energy profiles and the cholesterol binding residence time were performed using the GROMACS 2021.3 molecular dynamics package, with the Plumed 2.7.2 plugin. Peptides were represented using AMBER99SB-ILDN73, while POPC and cholesterol were represented with the Slipids forcefield74,75. For water molecules we used the TIP3P model. Simulations were performed in the NPT ensemble at 303.15\u2009K, maintained with a Nose-Hoover thermostat. Pressure was kept at 1\u2009bar using a semi-isotropic coupling scheme and a Parrinello-Rahman barostat. Long-range electrostatic interactions were calculated using the PME algorithm with a real-space cutoff of 1.4\u2009nm. Van der Waals interactions were calculated with a 1.4\u2009nm cutoff, and dispersion corrections for energy and pressure were applied. The leap-frog algorithm with a time step of 2\u2009fs was used to integrate the equations of motion. The LINCS algorithm was used to constrain hydrogen atom-containing bonds.\n\nLipid bilayer simulation systems were set up consisting of 83 POPC lipids, 35 cholesterol molecules, and 6359 water molecules. Peptides were generated in an initial helical conformation and placed transversely through the membrane, no bias was enforced during the simulations. The systems were equilibrated for 50\u2009ns with the lipids and peptide coupled to a 600\u2009K temperature bath, while water remained at 303.15\u2009K. After initial equilibration, a simulated annealing procedure linearly decreased the temperature of the lipids and peptide from 600\u2009K to 303.15\u2009K over 10\u2009ns, after which the simulations continued for another 50\u2009ns at 303.15\u2009K. 2\u2009\u03bcs measurement simulations were performed for each sequence, of which the first 250\u2009ns were discarded for equilibration purposes. 5 replicates were performed for each sequence according to this procedure. The error bars represent the maximum difference among the five ensemble averages from these five simulations.\n\nUmbrella sampling (US) was used to determine the free energy profile of cholesterol binding to KALP21, L11, \u03b3 M4, and the mutant of \u03b3 M4. As the reaction coordinate, we used the in-plane center-of-mass distance (xy-distance) between the cholesterol ring system and all the peptide C\u03b1 atoms located in the same membrane leaflet as the cholesterol molecule (residues 1\u201311 and 1\u201312 for KALP21 and L11, respectively; residues 1\u201314 were selected for the \u03b3 M4 transmembrane peptide and its mutant). To describe the binding process, we sampled the 0.7\u20132.3\u2009nm range of xy-distance using 9 evenly spaced US windows separated by 0.2\u2009nm. In each window the reaction coordinate was subject to a harmonic bias potential with a spring constant of 250\u2009kJ\u2009mol\u22121\u2009nm\u22122. For each window, 1.5\u2009\u03bcs simulations were performed, and the free energy profiles were calculated using the WHAM method. For each window, the first 400\u2009ns of the trajectory were discarded for equilibration purposes. For each peptide we simulated three replicas, each starting from an independent set of configurations, to produce the final PMFs. The statistical uncertainties of the free energy were estimated using the Monte Carlo bootstrap method, taking into account autocorrelation times.\n\nThe residence time of cholesterol binding to the L11 peptide was calculated according to the following formula (see Zwanzig76):\n\nwhere x is the reaction coordinate (i.e., L11\u2013cholesterol xy-distance), while the integration limits a and b correspond to the bound and dissociated states, respectively (i.e., 0.70 and 1.80\u2009nm). G(x) and D(x) represent the free energy and diffusion coefficient as a function of the reaction coordinate x. x0 represents the position of a reflecting barrier at 0.62\u2009nm.\n\nTo obtain D(x), the diffusion coefficient was computed for each US window separately according to D\u2009=\u2009Var(x)/\u03b877 and interpolated. Here, Var(x) and \u03b8 represent the variance and autocorrelation of the reaction coordinate in a given US window.\n\nTo investigate the hydrophobic mismatch mechanism, the removal of vertical mobility of lipids and lipid flip-flopping was facilitated by applying an inverse flat bottom position restraint to the first beads of POPC (NC3 bead) and cholesterol (ROH bead). The position restraint consists of a layer, parallel to the membrane and centered on the bilayer center. A harmonic force with force constant 1000\u2009kJ\u2009.mol\u22121\u2009.nm\u22122, directed away from the bilayer center, is applied to affected beads that come within 2.0\u2009nm (NC3) or 1.5\u2009nm (ROH) of the center of the bilayer.\n\nThe CNN architecture consisted of a one-hot encoding step, which is fed into 2 convolutional layers (128 nodes each) with max pooling, followed by 2 fully-connected dense layers (36 nodes each) and a single output neuron. The random dropout, which is applied before the output of the convolutional layers enters the dense layers, was set to 0.5%. A dataset of 26769 sequences generated using Evo-MD was used for the development of the CNN model, of which 20% was used as an independent validation set for the final model. The remaining 80% of the dataset was used in a 4-fold cross-validation (each fold using 5353 sequences as a test set, and 16061 sequences for training). The model was trained in 16 epochs, with a batch size of 64 and a learning rate of 0.001. An independent benchmarking of the model\u2019s performance against molecular dynamics simulations over the whole applicability domain (Coefficient of determination: R2\u2009=\u20090.859) is given in Supplementary Fig.\u00a018.\n\nProtein sequences and corresponding transmembrane predictions were downloaded from the TmAlphaFold Transmembrane Protein Structure Database (https://tmalphafold.ttk.hu/downloads). From this database, Homo sapiens (UP000005640), Mus musculus (UP000000589), and Rattus norvegicus (UP000002494) were considered for analysis. We only included proteins that passed all 10 TM prediction quality flags (i.e., categorized as \u2019excellent\u2019), as described in ref. 78. The resulting dataset contained 8370 protein entries in total, which was subsequently split in a single-pass dataset (2084 entries) and a multi-pass dataset (6286 entries, 42436 passes).\n\nWe post-processed these datasets to produce sequences of 20 amino acids, as the CNN was trained on this type of data. TM sequences that exceeded 20 amino acids in length were removed, and TM sequences shorter than 20 amino acids were extended evenly along the edges using the corresponding non-TM amino acids from the protein sequence. We ended up with 902 single-pass sequences and 11,954 multi-pass sequences, which we used for fitness prediction using the CNN, and subsequent analysis.\n\nMembranes were prepared using standard protocols for the hydration of lipid films79. Briefly, 5\u2009mg of 1,2-di-O-dodecyl-sn-glyercero-3-phosphocholine (12:0 ether-linked DLPC lipid), 0.2\u2009mg of labeled peptide (labeled at the carbonyl of the two leucine-proximal lysine residues) and 1.093\u2009mg of cholesterol (13C labeled at C4) were dissolved in chloroform. The chloroform was then dried with gentle N2 flow and the film was stored under vacuum overnight for complete evaporation of chloroform. The film was then hydrated with 250\u2009\u03bcL of buffer (mixture of 5\u2009mM HEPES buffer, pH 7.4 and 100\u2009mM NaCl). The hydrated film was then sonicated (5\u2009min on, 10\u2009min off, 4 cycles in a 25\u2009\u2218C water bath) to prepare the final membranes. The sample was lyophilized and thoroughly mixed with a solution of 13C-depleted d8-Glycerol (60 percent by volume), and 0.13\u2009mg of AmuPoL. A sample without 13C labeling of the peptide provided a control.\n\nCD spectra were recorded in a Jasco J815 spectrometer with a scan rate of 20\u2009nm/min. For the CD measurement, liposomes were prepared in the same way as for the NMR sample, but with 2\u2009mM phospholipids and 0.5 mM cholesterol. The phospholipid composition was an equimolar mixture of 1,2-ditetradecanoyl-sn-glycero-3-phosphocholine (DMPG) and 1 mM 1,2-Dimyristoyl-sn-glycero-3-phospho-rac-(1-glycerol) (DMPG). The peptide concentration was 100\u2009\u03bcM.\n\nAll DNP-enhanced NMR spectra were recorded with a 600\u2009MHz Bruker Avance III HD spectrometer (magnetic field of 14.1 T) equipped with 3.2\u2009mm low temperature (LT) HCN magic angle spinning (MAS) DNP probe. A 395\u2009GHz gyrotron oscillator was deployed to deliver the desired microwave irradiation to the sample through a corrugated waveguide. For the LT MAS probe, variable temperature, bearing and drive gasses were cooled with a second-generation Bruker liquid nitrogen cold cabinet, operating at 100\u2009K. Samples were packed into 3.2\u2009mm zirconia MAS NMR rotors via a custom-made filling device made from a truncated pipette tip. Finally, the rotor was centrifuged to ensure proper packing. 13C Proton-driven spin diffusion (PDSD) spectra80,81 were carried out at 8\u2009KHz MAS. Cross polarization from proton to carbon was implemented with a 1.5\u2009ms Hartmann-Hahn transfer using 66\u201374\u2009kHz (10% linear ramp) on the proton channel, and 71\u2009kHz on the 13C channel. Decoupling, 83\u2009kHz SPINAL-6482, was applied on the proton channel during acquisition. A PDSD mixing time of 30\u2009s was chosen to effect transfer over the expected distance range of about 6\u20139\u2009\u00c5. Spectra were referenced by setting the 13C signal from silicone to 4.3\u2009ppm on the DSS scale83. All spectra were acquired and analyzed in Topspin 3.5 patch level 6.\n\nAmino acid sequences for D3K3L10K3D3 (L10), D3K3L11K3D3 (L11), and GK2[LA]7LK2A (KALP21) were introduced into mScarlet-N1 and mEmerald-N1 by Gibson assembly. The final constructs were all confirmed by sequencing (Supplementary Table\u00a01).\n\nHEK cells were transfected by Lipofectamine 2000 (Thermo Fisher Scientific) following manufacturer\u2019s instructions. Briefly, 3\u2009ml of Lipofectamine 2000 was mixed with max 2\u2009mg of total DNA (in equimolar ratio) in 200\u2009ml OptiMEM (Gibco). Transfection mix was incubated for 30\u2009min at room temperature, then was added to the cells. Cells were transfected and incubated overnight (37\u2009\u2218C and 5% CO2). The day after medium was fully replaced with fresh supplemented DMEM.\n\nPrior to imaging the transfected cells were incubated with Wheat Germ Agglutinin, CF\u00ae405S Conjugate (WGA405, 1:20, stock 2\u2009mg/ml) for 10\u2009min. During acquisition the laser power was kept constant. Exposure time 200\u2009ms and Piezo stage z-motor was used to collect z-stacks.\n\nFor visualizing the intracellular organelles, the transfected cells were incubated with LysoTracker\u2122 Deep Red, (Thermo Fisher Scientific) and MitoTracker\u2122 Deep Red FM, (Thermo Fisher Scientific) for visualization of lysosomes and mitochondria, respectively. Fluorescent dyes were diluted 1:1000 in pre-warmed imaging solution and added to HEK cells 10 min before imaging.\n\nImages were acquired using Acquisition software NIS Elements 5.21.02 and analyzed with ImageJ (NIH). Freehand selection tool in Fiji was used to select a region of interest (ROI) of 3 pixel width following the fluorescence signal of WGA405 (i.e., 405-channel) as reference for plasma membrane from the medial cell plane. Each ROI was assessed for the signal intensity in the 561-channel (for mCherry-CD36) and the mean fluorescent intensity was measured from the ROI for calculating Intensity/length (mm). GraphPad Prism 9 was used to plot the graphs (each value is shown as the average\u2009\u00b1\u2009standard error of the mean). Statistical test performed was unpaired t-Test (p\u2009<\u20090.0001, P value summary ****).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63769-5/MediaObjects/41467_2025_63769_Fig6_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "The datasets generated by Evo-MD used in this work are available at [https://doi.org/10.5281/zenodo.15925656]. The trained CNN model used in this work is available at [https://doi.org/10.5281/zenodo.15925656]. All relevant data supporting the findings of this study are available with the paper and its supplementary information files. Source data is provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The version of Evo-MD used in this work is available at [https://doi.org/10.5281/zenodo.15925656].", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Levental, I. & Veatch, S. L. The continuing mystery of lipid rafts. J. Mol. Biol. 428, 4749\u20134764 (2016).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nMidzak, A. & Papadopoulos, V. Binding domain-driven intracellular trafficking of sterols for synthesis of steroid hormones, bile acids and oxysterols. Traffic 15, 895\u2013914 (2014).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nFantini, J. & Barrantes, F. J. How cholesterol interacts with membrane proteins: an exploration of cholesterol-binding sites including CRAC, CARC, and tilted domains. Front. 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(www.gauss-centre.eu) for funding this project by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS at J\u00fclich Supercomputing Centre (JSC), and\u00a0on the HAWK supercomputer at the High-Performance Computing Center Stuttgart (HLRS). We thank Advanced Medical Bioimaging Core Facility at Charit\u00e9, Berlin, for the support. D.M. is supported by the start-up funds from DZNE, the grants from the German Research Foundation (MI 2104 and SFB1286/B10) and the ERC Grant MemLessInterface (101078172). P.C. and J.C. gratefully acknowledge financial support from the National Science Centre, Poland (grant no. UMO-2021/41/N/ST4/03571). P.C. and J.C. also gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2023/016277. J.M. and H.J.R. thank the NWO Vidi scheme (project number 723.016.005) for funding. J.M. was additionally funded by\u00a0the\u00a0Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant number\u00a0RI 2791/7-1.\u00a0H.J.R. was additionally funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany\u2019s Excellence Strategy-EXC 2033-390677874-RESOLV.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Leiden Institute of Chemistry, Leiden University, Leiden, The Netherlands\n\nJeroen Methorst,\u00a0Nino Verwei,\u00a0Niek van Hilten,\u00a0Dennis Aschmann,\u00a0Alexander Kros\u00a0&\u00a0Herre Jelger Risselada\n\nTechnical University of Dortmund, Department of Physics, Dortmund, Germany\n\nJeroen Methorst\u00a0&\u00a0Herre Jelger Risselada\n\nLaboratory of Molecular Neuroscience, German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany\n\nChristian Hoffmann,\u00a0Roberto Sansevrino,\u00a0Han Wang\u00a0&\u00a0Dragomir Milovanovic\n\nDepartment of Physical Chemistry, Gda\u0144sk University of Technology, Gda\u0144sk, Poland\n\nPawe\u0142 Chodnicki\u00a0&\u00a0Jacek Czub\n\nDepartment of Applied Computer Science, Gda\u0144sk University of Technology, Gda\u0144sk, Poland\n\nPawe\u0142 Chodnicki\n\nDepartment of NMR-based Structural Biology, Max Planck Institute for Multidisciplinary Sciences, G\u00f6ttingen, Germany\n\nPartha Pyne\u00a0&\u00a0Loren Andreas\n\nCardiovascular Research Institute, University of California, San Francisco, USA\n\nNiek van Hilten\n\nDepartment of Pharmaceutical Chemistry, University of California, San Francisco, USA\n\nNiek van Hilten\n\nInstitute of Biochemistry, Charit\u00e9-Universit\u00e4tsmedizin Berlin, Corporate Member of Freie Universit\u00e4t Berlin, Humboldt-Universit\u00e4t Berlin, and Berlin Institute of Health, Berlin, Germany\n\nDragomir Milovanovic\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.M. and H.J.R. designed the research. J.M. developed the Evo-MD code and performed CG MD simulations. N.V., N.v.H\u201e and J.M. developed the neural network code and performed the database analysis. C.H., R.S., H.W., and D.M. performed and analyzed the in-vitro cell experiments. P.C. and J.C. performed and analyzed all-atom MD validation simulations. P.P. and L.A. performed and analyzed the NMR and CD experiments. D.A. and A.K. synthesized peptides for the cell, NMR, and CD experiments. J.M. and H.J.R. wrote the manuscript.\n\nCorrespondence to\n Herre Jelger Risselada.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Adam Lange and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Methorst, J., Verwei, N., Hoffmann, C. et al. Physics-based evolution of transmembrane helices reveals mechanisms of cholesterol attraction.\n Nat Commun 16, 9275 (2025). https://doi.org/10.1038/s41467-025-63769-5\n\nDownload citation\n\nReceived: 19 March 2024\n\nAccepted: 28 August 2025\n\nPublished: 20 October 2025\n\nVersion of record: 20 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63769-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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SARS-CoV-2 BA.2.86 spike upon ACE2 binding for receptor-binding domain up", + "journal": "Nature Communications", + "published": "07 October 2024", + "supplementary_0": [ + { + "label": "Supplementary information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52808-2/MediaObjects/41467_2024_52808_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52808-2/MediaObjects/41467_2024_52808_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52808-2/MediaObjects/41467_2024_52808_MOESM3_ESM.docx" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52808-2/MediaObjects/41467_2024_52808_MOESM4_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52808-2/MediaObjects/41467_2024_52808_MOESM5_ESM.mp4" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52808-2/MediaObjects/41467_2024_52808_MOESM6_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52808-2/MediaObjects/41467_2024_52808_MOESM7_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.gisaid.org", + "https://github.com/TheSatoLab/BA.2.86_RBD", + "https://www.rcsb.org/structure/8WXL", + "https://www.ebi.ac.uk/emdb/EMD-37910", + "https://www.rcsb.org/structure/8XUX", + "https://www.ebi.ac.uk/emdb/EMD-38459", + "https://www.rcsb.org/structure/8XUY", + "https://www.ebi.ac.uk/emdb/EMD-38686", + "https://www.rcsb.org/structure/8XVM", + "https://www.ebi.ac.uk/emdb/EMD-38690", + "https://www.rcsb.org/structure/8XUZ", + "https://www.ebi.ac.uk/emdb/EMD-38687", + "https://www.rcsb.org/structure/8XV0", + "https://www.ebi.ac.uk/emdb/EMD-38688", + "https://www.rcsb.org/structure/8XV1", + "https://www.ebi.ac.uk/emdb/EMD-38689", + "https://www.ebi.ac.uk/emdb/EMD-60905", + "https://www.ebi.ac.uk/emdb/EMD-60904", + "https://www.ebi.ac.uk/emdb/EMD-60906", + "https://www.rcsb.org/structure/9IU1", + "https://www.ebi.ac.uk/emdb/EMD-60886", + "http://www.rcsb.org", + "http://www.ebi.ac.uk/emdb/", + "/articles/s41467-024-52808-2#Sec27" + ], + "code": [], + "subject": [ + "Cryoelectron microscopy", + "SARS-CoV-2", + "Virus structures" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4106877/v1.pdf?c=1728385565000", + "research_square_link": "https://www.researchsquare.com//article/rs-4106877/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-52808-2.pdf", + "preprint_posted": "21 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Since 2019, SARS-CoV-2 has undergone mutations, resulting in pandemic and epidemic waves. The SARS-CoV-2 spike protein, crucial for cellular entry, is believed to bind to the ACE2 receptor exclusively when its receptor-binding domain (RBD) adopts the \u201cup\u201d conformation. However, whether ACE2 exclusively binds to the \u201cup\u201d RBD or also interacts with the \u201cdown\u201d RBD to facilitate the conformational shift to RBD-up remains unclear. Here, we present the structures of the BA.2.86 spike alone and bound to ACE2. The N354-linked glycan contributes to the neutralizing antibody evasion in BA.2.86. Notably, we successfully observed the ACE2-bound \u201cdown\u201d RBD, indicating a trigger structure before the RBD-up conformation. The wider and mobile angle of RBDs in the \u201cup\u201d state provides space for ACE2 to interact with the \u201cdown\u201d RBD, facilitating the transition to the RBD-up state. These structural insights into the spike-protein dynamics would help understand the mechanisms underlying SARS-CoV-2 infection and its neutralization.Biological sciences/Microbiology/Virology/Virus structuresBiological sciences/Structural biology/Electron microscopy/Cryoelectron microscopyBiological sciences/Microbiology/Virology/SARS-CoV-2SARS-CoV-2spikeBA.2.86angiotensin-converting enzyme 2receptor-binding domaincryo-electron microscopy", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryTable1.docCryo-EM data collection, refinement and validation statisticsSupplementaryTable2.xlsxThe huma sera list in this studySupplementaryMovie1.mp4The results of 3D Flexible Refinement for SARS-CoV-2 BA.2.86 S RBD-down bound to ACE2 stateSupplementaryMovie2.mp4A schematic of the transition from two RBD-up to three RBD-up for BA.2.86 S upon ACE2 binding", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Since 2019, SARS-CoV-2 has undergone mutations, resulting in pandemic and epidemic waves. The SARS-CoV-2 spike protein, crucial for cellular entry, binds to the ACE2 receptor exclusively when its receptor-binding domain (RBD) adopts the up-conformation. However, whether ACE2 also interacts with the RBD in the down-conformation to facilitate the conformational shift to RBD-up remains unclear. Herein, we present the structures of the BA.2.86 and the JN.1 spike proteins bound to ACE2. Notably, we successfully observed the ACE2-bound down-RBD, indicating an intermediate structure before the RBD-up conformation. The wider and mobile angle of RBDs in the up-state provides space for ACE2 to interact with the down-RBD, facilitating the transition to the RBD-up state. The K356T, but not N354-linked glycan, contributes to both of infectivity and neutralizing-antibody evasion in BA.2.86. These structural insights the spike-protein dynamics would help understand the mechanisms underlying SARS-CoV-2 infection and its neutralization.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19), a respiratory infectious disease that led to a global pandemic in 2020. According to data retrieved from Nextstrain (https://nextstrain.org/ncov/gisaid/global/6m) regarding the global status of SARS-CoV-2 evolution, the BA.2.86 variant of SARS-CoV-2 was first identified in July 2023. Notably, BA.2.86 acquired over 30 amino acid substitutions in the spike (S) protein compared to those present in the previously predominant lineage XBB and its parent BA.2, raising concerns about potential immune evasion. Acknowledging its heightened mutational load, the World Health Organization promptly designated BA.2.86 as a variant under monitoring on August 17, 20231. By December 2023, BA.2.86 had become globally prevalent, with over 7500 reported sequences on GISAID. Furthermore, JN.1, a descendant of BA.2.86, with an amino acid substitution in the S-protein, was also spreading rapidly, and, a month later was designated as a variant of interest with BA.2.86 by the WHO2,3. To date, the BA.2.86 lineage, including JN.1, has been detected in over 30 countries. The emergence of BA.2.86 coincides with a surge in cases across several countries. Therefore, studying the structure of the BA.2.86-S-protein, along with the reported virological characteristics of BA.2.864,5,6,7,8, may contribute to a better understanding of this virus.\n\nThe S-protein of SARS-CoV-2 exists as a trimer with a pivotal role in viral entry into target cells. During this cellular entry process, the S-protein\u2014comprising the S1 and S2 subunits\u2014initially binds to receptors such as angiotensin-converting enzyme 2 (ACE2), neuropilin-1, and TMEM106B9,10,11,12. Among these receptors, ACE2 is the most established receptor associated with pathophysiology13,14. The ACE2 receptor is also utilized by SARS-CoV, the etiologic pathogen of an epidemic that primarily impacted Asia in 2003, and coronavirus NL63, which causes common cold symptoms15. The S1 subunit of the S-protein contains a receptor-binding domain (RBD), which is the primary target of neutralizing antibodies. Meanwhile, the S2 subunit mediates membrane fusion between the viral envelope and the target cell membrane, facilitating the delivery of the viral genome into the host cell cytoplasm. To bind to the ACE2 receptor, the RBD of the S-protein must be in the up conformation16. However, the full-length structure of the S-protein, including the transmembrane domain, reportedly adopts an all-down conformation of the RBD17. Structures of the S-protein of the recent Omicron lineages, predominantly the ectodomain, also reportedly exhibit the all-down conformational state of RBDs18,19,20, suggesting the requirement for a passive trigger to transition the RBD to the up-conformation. Therefore, the structure of the SARS-CoV-2-S undergoes sequential conformational transitions, progressing from an all-RBD down state to one RBD-up, followed by two RBD-up, and ultimately reaching the three RBD-up states upon ACE2 binding. To activate its fusion ability, the S-protein is cleaved into S1 and S2 subunits by the host protease furin during viral budding21, and the S2 subunit is further cleaved by host proteases, such as transmembrane protease, serine 2 (TMPRSS2), or cathepsin L, during viral entry22. However, an in-depth exploration of ACE2 binding to both up- and down-RBD conformations in the SARS-CoV-2 spike protein is lacking. Consequently, the understanding of critical conformational shifts essential for cellular entry and neutralizing-antibody evasion, particularly in the context of the BA.2.86 variant, remains limited.\n\nIn this work, we perform the single-particle analyses of the BA.2.86 S-protein alone and the BA.2.86 and JN.1 S-proteins in complex with the ACE2 receptor using cryo-electron microscopy (cryo-EM). The primary objective is to gain insights into the structural dynamics of ACE2-receptor recognition and neutralizing-antibody evasion in SARS-CoV-2 variants. Additionally, we monitor interactions between ACE2 and RBDs to identify any potential intermediate structural conformations. We believe that our findings can contribute to an enhanced understanding of the mechanism underlying SARS-CoV-2 infection and aid in the development of effective neutralization strategies.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The BA.2.86 lineage, including JN.1, significantly diverged from other SARS-CoV-2 variants, including XBB (Fig.\u00a01A). The BA.2.86 lineage, particularly JN.1, demonstrated rapid global spread, effectively out-competing XBB and establishing itself as the dominant lineage as of February 2024 (Fig.\u00a01B). These evolutionary and epidemic characteristics of BA.2.86 highlight the critical need for its detailed characterization, including the determination of its S-protein structure.\n\nA Maximum likelihood tree depicting SARS-CoV-2 evolution. B Detection frequency plot of XB.1.5, XBB.1.6, EG.5.1, BA.2.86, JN.1, and other variants. C Cryo-EM maps of the BA.2.86-S-protein trimer, closed-2 state (left) and one-RBD-up state (right); protomers are sky blue, yellow, and dark olive green. D Cryo-EM maps obtained for each Omicron subvariants. Protomers for BA.2, XBB.1/XBB.1.5, BA.2.75/EG.5.1, and BA.2.86 are shown in dark green, brown, red, and sky blue, respectively. Other protomers are shown in dark gray and light gray. E Superposition of the main-chain structure as protomers of the BA.2.86-S closed-2 state (sky blue), BA.2-S closed-1 state (deep pink), and XBB.1.5-S closed-2 state (raspberry), respectively. F The position of amino-acid substitutions in the BA.2.86-S-protein compared to the XBB.1.5-S-protein. The BA.2.86-S protomer structure (left) and its sequence schematic (right) are shown as the S1 subunit (sky blue), the S2 subunit (pink), and the RBD (bright yellow). G Structure of BA.2.86-S-protein trimer (same colors as in C); close-up view presents the N354-linked glycan (left) and residues 621\u2013640 (right), showing interactions with surrounding residues and glycosylation sites as sticks (leaf green). Dashed lines represent hydrogen bonds.\n\nTo gain structural insights into the evolution of the BA.2.86 S-protein, we performed cryo-EM analysis of the BA.2.86 S ectodomain (Fig.\u00a01C, Supplementary Fig.\u00a01A,\u00a0B, and Supplementary Table\u00a01). We previously reported closed-1 and closed-2 conformational states in the S-protein structures of SARS-CoV-2 variants that emerged after BA.2.75 (BA.2.75, XBB.1, XBB.1.5, and EG.5.1)18,19,20,23 (Fig.\u00a01D). Notably, closed-2 typically has a more open RBD structure than that of closed-1; however, their S2 subunit is relatively identical. However, structural analysis in this study revealed only the closed-2 state and not the closed-1 state in the BA.2.86 S-protein. Meanwhile, the S-protein structure of BA.2 has only been resolved in the closed-1 state24,25, whereas that of BA.2.86 has only been observed in the closed-2 state, resulting in poor overlap of the main chains of BA.2 and BA.2.86 S-proteins (Fig.\u00a01E). In contrast, the main chains of the closed-2 states of the S-proteins in the BA.2.75, XBB.1, XBB.1.5, and EG.5.1 variants overlapped well with that of the BA.2.86 S-protein (Fig.\u00a01E). One RBD-up state, frequently observed in the S-protein structures of other SARS-CoV-2 variants, was rarely observed in the XBB.1 and XBB.1.5 S-proteins. However, the one RBD-up state was observed again in EG.5.1 and in BA.2.86 (Fig.\u00a01D). The spike protein of BA.2.86 acquired 39 amino acid substitutions (37 in the S1 subunit, 12 in the RBD, and 2 in the S2 subunit) compared with that in the amino acid sequence of the XBB.1.5 S-protein (Fig.\u00a01F); however, the overall structure did not significantly differ from that of the closed-2 state of the previous variants (Fig.\u00a01E). Hence, the survival strategy of SARS-CoV-2 involves the gradual modification of its S-protein structure or conformation, facilitating evolutionary adaptation without undue stress.\n\nSubsequently, we focused on three specific areas where significant structural alterations were observed in BA.2.86 S compared with those in XBB.1.5, given that both S-proteins can adopt a closed-2 state. First, a consensus sequence of N-linked glycosylation was obtained by K356T substitution in the RBD, and the presence of an N-linked glycan structure at N354 was confirmed in this study (Fig.\u00a01G and Supplementary Fig.\u00a01C). Second, the structure corresponding to amino acid residues 621\u2013640 surrounding the furin-cleavage site was observed in the BA.2.86 S-protein (Fig.\u00a01G and Supplementary Fig.\u00a01C); this region was not visible in S-proteins of other variants, except for the S-protein of alpha26. This observation is likely attributed to the influence of P621S, which marks the starting point for structural visibility. Third, a large EM density was observed above V445H at the apex of the closed-2 state of the BA.2.86 S-protein, suggesting an interaction between H445 and this unidentified EM density (Supplementary Fig.\u00a02). Analysis of raw EM images indicated that the formation of a dimer-of-trimer with head-to-head orientation, as reported for the Kappa S-protein27, is unlikely. The disappearance of the unidentified EM density during cryo-EM analysis in EDTA (1\u2009mM)-containing phosphate-buffered saline (PBS) suggests that His accumulation at the S-protein trimer apex may cause metal ion coordination, leading to nonspecific interactions with substances in the solution.\n\nTo elucidate the molecular mechanism of ACE2 receptor recognition by BA.2.86 S-protein, we performed cryo-EM analysis of the BA.2.86 S\u2013ACE2 complex (Fig.\u00a02A,\u00a0B, Supplementary Fig.\u00a03, and Supplementary Table\u00a01). In SARS-CoV-2 variants that emerged after Omicron, most ACE2-bound S-protein structures exhibited one-RBD-up and/or two-RBD-ups. However, in the BA.2.86 S\u2212ACE2 complex, the presence of one-RBD-up was not observed, whereas conformations with two- and three-RBD-ups were evident (Fig.\u00a02A). Notably, the ACE2 receptor was bound to all three RBDs, including two-RBD-ups and one-RBD-down, which is an unprecedented observation. This structure may represent an intermediate state wherein the two-RBD-up transitions to three-RBD-up. The two-RBD-up and one-RBD-down conformation (two-RBD-up\u2013one-RBD-downthree-ACE2) were not modeled owing to unclear EM density during conventional refinement (Fig.\u00a02B and Supplementary Fig.\u00a03, and Supplementary Table\u00a01).\n\nA\u2013C Cryo-EM maps of BA.2.86-S-protein bound to human ACE2 (S; same colors as Fig.\u00a01C, ACE2; dark gray). A Two-RBD-up state (top), and three-RBD-up state (bottom). B Two-RBD-up\u2212one-RBD-downthree-ACE2 state. C Comparison of the angles formed by the three residues: N164 of the NTD and two G476 of the up-RBD for two-RBD-uptwo-ACE2 (34.2\u00b0, left) and two-RBD-up\u2212one-RBD-downthree-ACE2 (110.9\u00b0, right), respectively. D Close-up view of the two-RBD-up\u2212one-RBD-downthree-ACE2 state. The N165-linked glycan in the NTD and the N322-linked glycan in ACE2 are situated close to each other.\n\nConsequently, we employed 3D Flexible Refinement (3DFlex)28 to assess the flexibility of the two up-RBDs, revealing significant swaying motion (Fig.\u00a02C and Supplementary Fig.\u00a04A\u2013C, and Supplementary Movie\u00a01). Compared with the two RBD-up bound to ACE2 (two-RBD-uptwo-ACE2; one RBD is down state and not bound to ACE2) (Fig.\u00a02C left top), the two up-RBDs in the two-RBD-up\u2013one-RBD-downthree-ACE2 exhibited increased mobility and wider angles (Fig.\u00a02C right). The angles formed by the three points, N164 of the N-terminal domain (NTD) and G476 of two up-RBDs, were different at 34.2\u00b0 and 110.9\u00b0 in the two-RBD-uptwo-ACE2 and two-RBD-up\u2013one-RBD-downthree-ACE2, respectively (Fig.\u00a02C). This heightened mobility of the two up-RBDs in the two-RBD-up\u2013one-RBD-downthree-ACE2 enables ACE2 access. Moreover, binding to a down-RBD may contribute to the down-to-up transition of this domain. In contrast, the RBD-down\u2212ACE2 complex remained stable, allowing for model building. Apart from the RBD\u2013ACE2 interface in the RBD-down\u2212ACE2 complex, we observed a weak stacking interaction between the NTD N165-linked glycan and ACE2 N322-linked glycan, alternated to avoid steric hindrance, which may contribute to the RBD-down\u2212ACE2 stabilization (Fig.\u00a02D).\n\nFor the two-RBD-up\u2013one-RBD-downthree-ACE2 conformation, we assume that the up-RBDs become very flexible/mobile in exchange for the stabilization of the down-RBD, and that the balance between the up and down conformations has led to this non-canonical conformation. To test this possibility, the BA.2.86 S-ACE2 complex was treated at a higher temperature of 42\u2009\u00b0C and subjected to cryo-EM analysis (Supplementary Fig.\u00a05). The two-RBD-up\u2013one-RBD-downthree-ACE2 conformation was observed in BA.2.86 S\u2212ACE2 treated at 37\u2009\u00b0C for 1\u2009hour, but not in that treated at 42\u2009\u00b0C for 1\u2009hour. These results may suggest that an intermediate structure, two-RBD-up\u2013one-RBD-downthree-ACE2, has been thermally eliminated, which indicates instability as an S-trimer bound to ACE2. However, the canonical two-RBD-uptwo-ACE2 structure was retained under this condition. Unexpectedly, another non-canonical conformation of one-highly-open RBD and one-partially-open RBD was observed in the BA.2.86 S\u2212ACE2 complex treated at 42\u2009\u00b0C, in addition to the canonical two-RBD-uptwo-ACE2 (Supplementary Fig.\u00a05).\n\nNotably, we observed a complex with ACE2 bound to the BA.2.86 S RBD-down conformation. Hence, we compared two structures in which ACE2 was bound to BA.2.86 S RBD-up or RBD-down. Local refinement was performed on both the RBD-up\u2013ACE2 and RBD-down\u2013ACE2 complexes to comprehensively examine the interactions between ACE2 and RBD-up or RBD-down, and the structures were reconstructed at resolutions of 3.00\u2009\u00c5 and 3.05\u2009\u00c5, respectively (Fig.\u00a03A, B, Supplementary Fig.\u00a03, and Supplementary Table\u00a01). No significant differences were detected in the overall structures of either ACE2\u2013RBD; however, slight variations were observed at the interface between ACE2 and the up- or down-RBD (Fig.\u00a03B and Supplementary Fig.\u00a04D, E). First, the main chain from F514 to T522 was shifted outwardly in the up-RBD compared with that in the down-RBD (Fig.\u00a03B). The F514 to T522 loop in the down-RBD was located close to the NTD of the adjacent protomer. In contrast, the loop in the up-RBD was exposed toward the solvent (Fig.\u00a03B and Supplementary Fig.\u00a04D, E). Second, ACE2 H34 interacted bidirectionally with RBD Y453 and Q493 in the RBD-up\u2013ACE2, whereas ACE2 H34 exhibited a unidirectional interaction with RBD Y453 in the\u00a0RBD-down\u2212ACE2.\n\nA Cryo-EM maps of the RBD\u2013ACE2 interface in the RBD-up (sky blue)\u2212ACE2 (dark gray), and RBD-down (orange)\u2212ACE2. B Structure of BA.2.86 S RBD-ACE2 complex (same colors as in A). Close-up views represent residues involved in the corresponding interaction of the BA.2.86 RBD-up\u2013ACE2 complex structure, which differs from the BA.2 or BA.2.86 RBD-down\u2013ACE2 complex structure (BA.2; PDB, 8DM6; BA.2 RBD, deep pink; ACE2, dark gray, BA.2.86 RBD-down; same color as A), are shown. Dashed lines represent hydrogen bonds. C Sensorgrams of SPR analysis evaluating the binding affinities of ACE2 for BA.2.86 S-RBD (sky blue), JN.1 S-RBD (blue-green), and XBB.1.5 S- RBD (raspberry). D Cryo-EM maps of JN.1-S-protein bound to human ACE2 (S; blue-green, raspberry and khaki, ACE2; dark gray). The two-RBD-uptwo-ACE2 state (left), the RBD-ACE2 interface in the RBD-up\u2212ACE2 (middle), and the two-RBD-up\u2212one-RBD-downthree-ACE2 state (right). E Close-up view of the JN.1 S\u2013ACE2 interface (JN.1 S; blue-green, ACE2; dark gray). Dashed lines represent hydrogen bonds.\n\nSubsequently, to comprehend the altered interactions of SARS-CoV-2 with ACE2 caused by SARS-CoV-2 evolution, we compared the RBD\u2013ACE2 interface interactions between BA.2 and BA.2.86. A notable disparity among the variants was primarily observed in the interaction between RBD R/Q493 and ACE2 H34 (Fig.\u00a03B and Supplementary Fig.\u00a04D). In variants predating Omicron, represented by the ancestral strain, the amino acid residue 493 of the S-proteins was Gln. However, the Q493R substitution occurred in the S-proteins of BA.1 and BA.2, leading to the loss of interaction with ACE2 H34 and an alternative interaction with ACE2 E3529,30 (Fig.\u00a03B). Subsequently, in BA.4/5 and subsequent variants, it reverted to Q493; although variations exist in the interactions of each variant with ACE2, the substitution did not occur until BA.2.86 (Fig.\u00a03B). We observed that V445H, a unique characteristic of the BA.2.86 S structure, formed a novel stacking intramolecular interaction with P499 in the RBD (Fig.\u00a03B and Supplementary Fig.\u00a04D). Furthermore, BA.2.86 RBD R403K formed a novel intramolecular interaction with RBD N405 (Fig.\u00a03B and Supplementary Fig.\u00a04D). R403K reportedly enhances the membrane fusion ability4, likely owing to the influence of the altered interaction.\n\nWe also compared the binding affinity of ACE2 to the S-RBD of JN.1, which has recently been observed to be prevalent along with BA.2.86, with that of BA.2,86. However, similar to a previous report3, the ACE2-binding affinity of JN. 1 was lower than that of BA.2.86 and higher than that of XBB.1.5 (Fig.\u00a03C). This suggests that the ACE2-binding ability of JN.1 did not influence its prevalence.\n\nTo determine the structural basis for the differences in ACE2 recognition between the S-proteins of BA.2.86 and JN.1, we performed cryo-EM analysis of JN.1-S\u2013ACE2 complex (Fig.\u00a03D, Supplementary Fig.\u00a06, and Supplementary Table\u00a01). In the BA.2.86 S\u2013ACE2 complex, two- and three-RBD-ups were observed; however, only the two-RBD-uptwo-ACE2 structure was found in the JN.1-S\u2013ACE2 complex. Notably, the structure of ACE2 binding to all RBDs in the two-RBD-up\u2013one-RBD-down, which is a characteristic in the BA.2.86 S\u2013ACE2 complex, was observed in JN.1 as well. Similar to the two up-RBDs in the two-RBD-up\u2013one-RBD-downthree-ACE2 conformation of the BA.2.86 S, those of JN.1 also exhibit increased mobility and wider angles (termed highly-open RBDs). ACE2 bound to the highly-open RBDs in the JN.1 S was partially observed, like BA.2.86, owing to the unclear EM density. Accordingly, the highly-open RBDs in the S-proteins may be a shared property in the BA.2.86 lineage, which includes the JN.1 subvariant.\n\nTo gain structural insights into the reduced ACE2-binding affinity in the JN.1 S, we performed local refinement on the RBD-up\u2013ACE2 complex, and the structure was reconstructed at resolutions of 4.30\u2009\u00c5 (Fig.\u00a03D, Supplementary Fig.\u00a06, and Supplementary Table\u00a01). In the JN.1 S, the L455S substitution occurred, which did not happen in BA.2.86. Neither L455 in the BA.2.86 S nor S455 in the JN.1 S interacts with ACE2; however, the adjacent RBD Y453 forms a hydrogen bond with ACE2 H34 in both S-proteins of BA.2.86 and JN.1 (Fig.\u00a03B, E). S455 in the JN.1 S also forms a hydrogen bond with the adjacent RBD N417. However, the interaction around L455S could not explain the reduced ACE2 affinity in the JN.1 S. Therefore, based on the determined structures, we calculated the shape complementarity (Sc) between RBD and ACE2 in the BA.2.86 and the JN.1. The Sc (0.42) of JN.1 S-RBD\u2013ACE2 was lower than that (0.52) of BA.2.86. The binding free energy (dG= \u201317.88) of JN.1 S-RBD-ACE2 was increased compared to that (dG= \u201324.30) of BA.2.86. These values indicate that JN.1 S is less favorable for binding to ACE2 compared to BA.2.86.\n\nStructural analysis revealed that the three amino acid substitutions, K356T, V445H, and P621S, characteristic of the BA.2.86-S structure, were especially responsible for the structural alterations compared to that in XBB.1.5. Therefore, the impact of these amino acid substitutions on infectivity and neutralization was evaluated in vitro (Fig.\u00a04A\u2013D). First, we introduced single amino acid substitutions reverting to the BA.2 type into the pseudotyped virus bearing the BA.2.86-S- protein and compared its entry efficiency into HOS-ACE2/TMPRSS2 cells. Viruses bearing BA.2.86-S T356K or S621P exhibited decreased infectivity compared with that bearing BA.2.86-S WT (Fig.\u00a04A). Conversely, the virus bearing BA.2.86-S H445V showed increased infectivity compared with that bearing BA.2.86-S WT (Fig.\u00a04A). These findings suggest that K356T and P621S contribute to the increased infectivity of BA.2.86; however, V445H has a negative impact.\n\nA, B Lentivirus-based pseudovirus assay. HOS-ACE2/TMPRSS2 cells were infected with pseudoviruses bearing each S-protein of BA.2.86 and its derivatives. The amount of input virus was normalized to that of HIV-1 p24 capsid protein. The percent infectivity of BA.2.86 derivatives compared to that of BA.2.86 is shown. The horizontal dashed line indicates the mean value of the percentage infectivity of BA.2.86. Assays were performed in quadruplicate, and a representative result of four independent assays is shown. Data are presented as the mean\u2009\u00b1\u2009SD. Each dot indicates the result of an individual replicate. Statistically significant differences versus BA.2.86 are determined by two-sided Student\u2019s t tests. The p values for the difference of each infectivity are indicated in A (vs BA.2.86\u2009+\u2009T356K, p\u2009=\u20090.0095; vs BA.2.86\u2009+\u2009H445V, p\u2009=\u20090.0005; vs BA.2.86\u2009+\u2009S621P, p\u2009<\u20090.0001; and vs BA.2.86\u2009+\u2009T356K\u2009+\u2009H445V\u2009+\u2009S621P, p\u2009=\u20090.0003) and in (B) (vs BA.2.86\u2009+\u2009T356K, p\u2009=\u20090.0047 and vs BA.2.86\u2009+\u2009N354Q, p\u2009=\u20090.59). Increased and decreased infectivity are shown in red and blue, respectively. C, D Neutralization assay. Assays were performed with pseudoviruses harboring the S-proteins of BA.2.86 and its derivatives. Convalescent sera were used, which were from fully vaccinated individuals who had been infected with XBB.1.5 (four 3-dose vaccinated, three 4-dose vaccinated, two 5-dose vaccinated, and one 6-dose vaccinated; total =10 donors). Assays for each serum sample were performed in quadruplicate to determine the 50% neutralization titer (NT50). Each dot represents one NT50 value, and the geometric mean and 95% confidence interval are shown. Numbers in parentheses indicates the geometric mean of NT50 values. The horizontal dashed line indicates the detection limit (40-fold). Statistically significant differences vs. BA.2.86 were determined by two-sided Wilcoxon signed-rank tests. The p-values less than 0.05 for the difference of each NT50 are indicated in C (vs BA.2.86\u2009+\u2009T356K, p\u2009=\u20090.0020; vs BA.2.86\u2009+\u2009H445V, p\u2009=\u20090.22; vs BA.2.86\u2009+\u2009S621P, p\u2009=\u20090.32; and vs BA.2.86\u2009+\u2009T356K\u2009+\u2009H445V\u2009+\u2009S621P, p\u2009=\u20090.10) and in D (vs BA.2.86\u2009+\u2009T356K, p\u2009=\u20090.027 and vs BA.2.86\u2009+\u2009N354Q, p\u2009=\u20090.77). NT50 fold changes compared with BA.2.86 are indicated by X.\n\nSubsequently, we compared neutralizing-antibody evasion using the same mutant pseudotyped viruses with XBB.1.5 breakthrough-infection human sera (Fig.\u00a04C). Only the virus bearing BA.2.86-S T356K exhibited decreased neutralizing-antibody evasion (Fig.\u00a04C). This indicates that the K356T substitution acquired in the BA.2.86-S-protein contributes to enhanced neutralizing-antibody evasion. K356T represents a novel site for acquired N354-linked glycosylation of the S-protein of BA.2.86 (Fig.\u00a01G); therefore this glycosylation might contribute to the heightened neutralizing-antibody evasion ability of BA.2.86. To evaluate the possibility that the novel N354-linked glycosylation might play an important role in neutralizing-antibody evasion, we further compared infectivity and neutralizing-antibody evasion in a similar manner using the pseudotyped virus bearing BA.2.86-S N354Q (Fig.\u00a04B,\u00a0D). A substitution to Gln, which exhibits the most similar side chain structure to Asn, was employed to evaluate only the effect of the glycosylation defect. Both of infectivity and neutralizing-antibody evasion by pseudotyped virus bearing BA.2.86-S N354Q were comparable to those of BA.2.86-S WT (Fig.\u00a04B,\u00a0D). Taken together, the K356T acquired in the BA.2.86 S would contribute to the enhancement of infectivity and neutralizing-antibody evasion by itself, rather than through the effect of glycosylation.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52808-2/MediaObjects/41467_2024_52808_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52808-2/MediaObjects/41467_2024_52808_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52808-2/MediaObjects/41467_2024_52808_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52808-2/MediaObjects/41467_2024_52808_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "During the evolution of SARS-CoV-2, the S-protein has undergone recurrent amino acid substitutions. Correspondingly, various S-protein structures have been determined, demonstrating both closed-1 and closed-2 states, as observed in BA.2.75, XBB.1, XBB.1.5, and EG.5.118,19,20,23; S-proteins adopting the closed-1 state, exemplified by BA.224; and those displaying the closed-2 state, as demonstrated by BA.2.86 in this study, all within the context of RBD all-down states. Note that because the inability to identify a specific state within a cryo-EM dataset can typically be a function of the data processing workflow, these structures are not the only available structure in each variant. Nonetheless, these conformational states of S-proteins appear to be gradually altering with SARS-CoV-2 evolution (Fig.\u00a01D). Although these conformational state variations may influence cell entry and neutralizing-antibody evasion, the impact of specific conformational states remained unclear owing to technical challenges in fixing these states in vitro and in vivo. Despite the BA.2.86-S-protein acquiring 39 amino acid substitutions compared with that in the XBB.1.5-S-protein, its overall structure did not significantly differ from that of the closed-2 states of other variants (Fig.\u00a01D, E). This suggests that the survival strategy of SARS-CoV-2 may not allow amino acid substitutions that would substantially alter the overall structure.\n\nStructural insights and neutralization assays using XBB.1.5 breakthrough-infection human sera revealed that the newly acquired K356T in the BA.2.86 S contributes to its enhanced neutralizing-antibody evasion (Fig.\u00a04C). However, modification of the coupled N354-linked glycan did not contribute to it (Fig.\u00a04D). K356T also contributes to increased infectivity in BA.2.86 (Fig.\u00a04A), rendering it a crucial mutation responsible for its prevalence. The S50L substitution, alongside K356T, facilitates efficient entry into lung cells31 without inducing structural changes. Additionally, P621S allows the amino acid residues 621\u2013640, adjacent to the furin-cleavage site, to be a visible structural region in BA.2.86 (Fig.\u00a01G). The cleavage efficiency of the furin-cleavage site in this region is reportedly higher in BA.2.86 than that in BA.24, suggesting that structural stabilization in this region of the BA.2.86 S-protein may influence furin-cleavage efficiency.\n\nOn neutralizing-antibody evasion in BA.2.86, we only examined the amino acid substitutions, K356T, V445H, and P621S, in the BA.2.86 S, where the structural alterations were obvious compared to that in XBB.1.5. Hence, other substitutions acquired by BA.2.86 that are reportedly to be important for changes in neutralizing potential were mapped onto the determined BA.2.86-S structure32,33,34 (Supplementary Fig.\u00a07A). Notably, that the used sera and the panel of monoclonal antibodies differ from that in our experiments. Based on the mapping, nine substitutions, H245N, K356T, R403K, V445H, L452W, N481K, A484K, E554K, and P621S enhanced antibody evasion potential, and one, I332V, reduced it. To assess the neutralizing-antibody evasion potential in BA.2.86 with a different approach, we employed a computational evaluation of the antibody binding potential to the S-proteins of ancestral strain, XBB.1.5, and BA.2.86. A total of 34 antibodies whose RBD-bound structures are registered in the Protein Data Bank (PDB), which are either therapeutic antibodies (in clinical trials or on the market) or neutralizing antibodies, were utilized (Supplementary Fig.\u00a07B and Supplementary Table\u00a02). The interaction energies against these anti-S antibodies were significantly reduced in the BA.2.86-S-protein compared to those of ancestral strain, but not significantly different from those of XBB.1.5 (Supplementary Fig.\u00a07C). In fact, most of the 39 substitutions occurred in the S1 subunit. However, the substitutions were dispersed throughout the structure, not concentrated in the receptor-binding motif and specific RBD epitopes classified as class 1 to 4 epitopes35 (Fig.\u00a01F and Supplementary Fig.\u00a07D). It implies that BA.2.86 may not escape greatly from neutralizing antibodies at serum levels in vaccinated and XBB lineage-infected individuals.\n\nUpon examining the SARS-CoV-2 S-RBD\u2013ACE2 interface, the most notable difference among variants lies in the interaction centered on RBD R/Q493 and ACE2 H34 or E35 (Fig.\u00a03B). The Q493R substitution, occurring only in BA.1 and BA.2, forms an interaction with ACE2 E3536,37,38. In other variants, a unique hydrogen-bonding network is formed or lost, including surrounding residues centered on RBD Q493 and ACE2 H34, likely having a significant role in ACE2 binding. In fact, the interaction of ACE2 with the up- or down-RBD in BA.2.86 S exhibited a slight difference in the orientation of H34 in ACE2 (Fig.\u00a03B). Hence, SARS-CoV-2 S-RBD R/Q493 and ACE2 H34 may act as modulators of amino acid substitutions within the RBD when the S-protein of each variant binds to ACE2.\n\nTo transition from an all-RBD-down state to one RBD-up, the down-RBD must first adopt the up-conformation and bind to ACE2. Our findings suggest that a similar principle applies to the transition from two-RBD-up to three-RBD-up. However, in the transition from two-RBD-up to three-RBD-up, we found that the two up-RBDs bound to ACE2 exhibit considerable flexibility, resulting in a swaying motion, creating space for an approaching ACE2 receptor. ACE2 binding to the down-RBD then may assist the transition to three-RBD-up (Fig.\u00a02B,\u00a0C and Supplementary Movie\u00a01 and 2). Indeed, some antibodies reportedly bind to down-RBDs39; our results support the significance of neutralizing antibodies binding to down-RBDs for viral neutralization. Distinct RBD opening angles have also been reported in the cryo-EM structures of SARS-CoV bound to ACE2 under conditions that mimic its entry process40. Of the angles formed between long axes of the SARS-CoV RBD and horizontal plane, the smallest angle is 51.2\u00b0. Compared to an angle of 22.7\u00b0 formed by the SARS-CoV RBD in the closed state, an angle of 51.2\u00b0 would render a partially-open RBD state (Fig.\u00a05A). In contrast, the angles formed by the down-RBD of the ACE2-bound and apo form in BA.2.86-S-proteins are nearly identical, with values of 32.4\u00b0 and 31.7\u00b0, respectively (Fig.\u00a05B). Accordingly, the structure of BA.2.86-S-RBD-down bound to ACE2 suggests a unique ACE2-S structure. The angle of the highly-open RBD observed when the BA.2.86-ACE2 complex was treated at 42\u2009\u00b0C was 83.6\u00b0, and the angle of the partially-open RBD was 56.3\u00b0 (Fig.\u00a05C). The variation in the open angles of RBDs may reflect the nature of variants and the entry process. Furthermore, a unique ACE2-binding mode of the BA.2.86 and JN.1 S-proteins may contribute to neutralizing-antibody evasion because they can bind to ACE2 even though the RBD is down-state and does not require exposure of its RBM that a major target of neutralizing antibodies.\n\nA\u2013C Protomer of S-proteins in SARS-CoV and SARS-CoV-2 BA.2.86 S\u2500ACE2 complex. Angles between the axis of RBDs in the SARS-CoV-2 BA.2.86 or SARS-CoV and the horizontal plane are shown to the right of each conformation. A Surfaces of SARS-CoV S\u2500ACE2 complex. Left: ACE2-bound conformation 1 (S; light pink). Middle: Unbound-down conformation (S; blue gray). Right: ACE2-bound conformation 3 (S; blue). B Surfaces of BA.2.86 S\u2013ACE2 complex. Left: RBD-down\u2013ACE2 conformation (S; orange). Middle: RBD-down conformation in the apo form (S; sky blue). Right: RBD-up\u2013ACE2 (S; dark olive green). C Surfaces of BA.2.86 S\u2500ACE2 complex treated at 42\u2009\u00b0C for 1\u2009h. Left: highly-open RBD conformation (S; mint green). Right: partially-open RBD conformation (S; dark yellow).\n\nCollectively, our findings suggest that comprehensively investigating and elucidating the structural details of the SARS-CoV-2 S-protein, including those from different variants, is crucial to understanding the complex mechanisms underlying SARS-CoV-2 cellular entry. Additionally, they may provide valuable insights for the designing of therapeutics, developing vaccines, and informing public health strategies.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52808-2/MediaObjects/41467_2024_52808_Fig5_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "All protocols involving specimens from human subjects recruited at Interpark Kuramochi Clinic were reviewed and approved by the Institutional Review Board of Interpark Kuramochi Clinic (approval ID: G2021-004). All human subjects provided written informed consent. All protocols for the use of human specimens were reviewed and approved by the Institutional Review Boards of The Institute of Medical Science, The University of Tokyo (approval IDs: 2021-1-0416 and 2021-18-0617). We have obtained consent to publish information that identifies individuals (including three or more indirect identifiers such as exact age, sex, medical history, vaccination history or medical center of the study participants).\n\nConvalescent sera were collected from fully vaccinated individuals who had been infected with XBB.1.5 (four 3-dose vaccinated, three 4-dose vaccinated, two 5-dose vaccinated, and one 6-dose vaccinated; the time interval between the last vaccination and infection, 44\u2013435 days; 15\u201346 days after testing. n\u2009=\u200910 in total; average age: 50.4 years, range: 18\u201374 years, 30% male). The SARS-CoV-2 variants were identified as previously described2,18,41,42. Sera were inactivated at 56\u2009\u00b0C for 30\u2009minutes and stored at \u201380\u2009\u00b0C until use. Details of the convalescent sera are summarized in Supplementary Table\u00a03.\n\nWe calculated the detection frequency of SARS-CoV-2 isolates that circulated from November 1, 2022 to February 19, 2024 with the GISAID viral genomic surveillance data (https://www.gisaid.org; EPI SET ID: EPI_SET_240301rn)43. We excluded the data of the SARS-CoV-2 isolate that (i) lacks collection date and PANGO lineage information; (ii) was retrieved from non-human animals; and (iii) was sampled by quarantine. The lineage of each isolate was reassigned using Nextclade v2.14.044. The Nextclade classification system was used to classify the variants, with 23\u2009A as XBB.1.5; 23B and 23\u2009G as XBB.1.16; 23\u2009F and 23H as EG.5.1; 23I without S: L455S mutation as BA.2.86; 23I with S:L455S mutation as JN.1; and other clades as other. The detection frequency plot was created using ggplot2 v3.4.4.\n\nWe obtained genomic sequences and surveillance data of 729 SARS-CoV-2 isolates used to reconstruct the representative SARS-CoV-2 phylogenetic tree in our previous paper4 from the GISAID database. We excluded the data of SARS-CoV-2 isolates that meet the filtering criteria mentioned earlier (see Detection frequency) and whose genomic sequences are no longer than 28,000 base pairs and contain \u22652% of unknown (N) nucleotides. We randomly selected 20 genomic sequences of the SARS-CoV-2 JN.1 variant that were classified based on the Nextclade lineage classification system and satisfy the same filtering criteria. We then pooled the data of the selected JN.1 genomic sequences with those in our previous paper\u2019s dataset. Next, the genomic sequences were aligned to the genomic sequence of the Wuhan-Hu-1 SARS-CoV-2 isolate (NC_045512.2) using the reference-guide multiple pairwise alignment strategy implemented in ViralMSA v1.1.2445. Gaps in the alignment were removed automatically using TrimAl v1.4.rev22 with -gappyout mode46, and the flanking edges at positions 1\u2013292 and 29,588\u201329,771 were removed manually. The genomic sequence of Wuhan-Hu-1 was subsequently omitted from the alignment. Then, a preliminary phylogenetic tree of SARS-CoV-2 sublineages was reconstructed from the alignment using maximum likelihood-based IQ-TREE v2.2.047. The best-fit nucleotide substitution model was selected automatically using ModelFinder48. Branch support was assessed using ultrafast bootstrap approximation49 with 1000 bootstrap replicates. We identified genomic sequences causing branch length outliers in the preliminary tree using the Rosner test implemented in the EnvStats R package v2.7.050 using R v4.2.2 (https://www.r-project.org/). These genetic sequences were discarded from the final tree reconstruction. The final tree, consisting of 720 SARS-CoV-2 genomic sequences (EPI SET ID: EPI_SET_240301bk), was then reconstructed using the methods described earlier. The tree was visualized using ggtree R package v3.6.251.\n\nS-protein ectodomain (BA.2.86, JN.1), S-protein RBD (BA.2.86, JN.1, XBB.1.5) and human ACE2 were expressed and purified as previously described52. Briefly, pHLsec expression plasmids, encoding the BA.2.86 S-protein ectodomain with six proline substitutions (F817P, A892P, A899P, A942P, K986P, and V987P)53 and deleting the furin-cleavage site (RRAR to GSAG substitution) with a T4-foldon domain, the S-protein RBD (residues 322\u2013536), or soluble human ACE2 (residues 19\u2013617), were constructed. Plasmids expressing the BA.2.86-S ectodomain were generated by DNA synthesis (Eurofins). Plasmids expressing the JN.1-S ectodomain were generated by site-directed overlap extension PCR using pHLsec BA.2.86-S as the template. The resulting PCR fragments, S-protein RBDs and soluble human ACE2, were subcloned into the AgeI-KpnI site of the pHLsec vector using the In-Fusion HD Cloning Kit (Takara, Cat# Z9650N). Nucleotide sequences were determined by DNA sequencing services (Eurofins), and the sequence data were analyzed by SnapGene software v6.1.1 (www.snapgene.com). Details of the primers are summarized in Supplementary Table\u00a04. Each plasmid was transfected into HEK293S GnTI(\u2212) cells, and the expressed proteins in the cell culture supernatant were purified using a cOmplete His-Tag Purification Resin (Roche, Cat# 5893682001) affinity column and either Superose 6 Increase 10/300 GL size-exclusion chromatography (Cytiva, Cat# 29091596) for the BA.2.86-S and JN.1-S ectodomains or Superdex 75 Increase 10/300 GL size-exclusion chromatography (Cytiva, Cat# 29148721) for the S-protein RBD or soluble human ACE2.\n\nSoluble human ACE2 was covalently immobilized onto a CM5 chip (Cytiva, Cat# 29104988) covalently. A serial dilution of S-protein RBD (BA.2.86, JN.1,and XBB.1.5) ranging in concentrations from 100 to 6.25\u2009nM was prepared in the HBS-EP buffer (Cytiva, Cat# BR100188) and injected on the chip. The response units were recorded in a single cycle mode at 25\u2009\u00b0C using the Biacore T200 system. The resulting data were fitted to a 1:1 binding model using Biacore T200 Evaluation software.\n\nThe BA.2.86 and JN.1 S-protein solutions were incubated at 37\u2009\u00b0C for 1\u2009h before cryo-EM grid preparation. An equal volume of PBS with 2\u2009mM EDTA was added to the BA.2.86 S-protein solution (final 1\u2009mM EDTA) to clarify the unknown cryo-EM density. To prepare the BA.2.86 and JN.1 S\u2013ACE2 complex solutions, the purified ACE2 was incubated with BA.2.86- and JN.1-S-proteins at a molar ratio of 1: 3.2 molar ratio (spike: ACE2) at 18\u2009\u00b0C for 15\u2009min. Another BA.2.86 S\u2013ACE2 complex solution was incubated at 42 \u00b0C for 1\u2009hour to increase the mobility of RBD-ACE2. The samples were then applied to a Quantifoil R2.0/2.0 Cu 300 mesh grid (Quantifoil Micro Tools GmbH), which was freshly glow-discharged for 60\u2009s at 10\u2009mA using PIB-10 (Vacuum Device). The grids were plunged into liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific) with the following settings: temperature 18\u2009\u00b0C, humidity 100%, blotting time 5\u2009s, and blotting force 5.\n\nMovies were collected on a Krios G4 (Thermo Fisher Scientific) operated at 300\u2009kV with a K3 direct electron detector (Gatan) at a nominal magnification of 130,000 (0.67 per physical pixel) using a GIF-Biocontinuum energy filter (Gatan) with a 20\u2009eV slit width. The movies were collected with a total exposure of 1.5\u2009s and a total dose of 52.45 (BA.2.86 S), 51.16 (BA.2.86 S with 1\u2009mM EDTA), 51.39 (BA.2.86 S\u2013ACE2; dataset 1), 51.1 (BA.2.86 S\u2013ACE2; dataset 2), 51.0 (BA.2.86 S\u2013ACE2 at 42 \u00b0C for 1\u2009hour), and 51.2 (JN.1 S\u2013ACE2) e/\u00c52 over 50 frames. A total of 3,500 (BA.2.86 S), 801 (BA.2.86 S with 1\u2009mM EDTA), 6,000 (BA.2.86 S\u2013ACE2; dataset 1), 6,719 (BA.2.86 S\u2013ACE2; dataset 2), 4,813 (BA.2.86 S\u2013ACE2 at 42 \u00b0C for 1\u2009hour), and 9,018 (JN.1 S\u2013ACE2) movies for BA.2.86 S\u2013ACE2 complexes were collected at a nominal defocus range of 0.8 \u2013 1.8\u2009\u00b5m using EPU software (Thermo Fisher Scientific).\n\nAll datasets were processed using cryoSPARC v4.3.154. For BA.2.86 S, movie frames were aligned, dose-weighted, and CTF-estimated using Patch Motion correction and Patch CTF estimation. A total of 1,156,425 particles were blob-picked, and reference-free 2D classification (K\u2009=\u2009150, batch = 200, iteration = 30) was performed to remove the junk particles. A total of 465,894 particles were used for ab-initio reconstruction to obtain the initial models. Two rounds of heterogeneous refinement were performed to classify the closed and one-up conformations of the RBD. For the one-up conformation, 3D classification without alignment focused on the up RBD (K\u2009=\u20094, force hard classification, input mode = simple) was performed, and a class that clearly showed up RBD conformation. A final map was reconstructed by non-uniform refinement, and a local refinement focusing on the RBD was carried out to support model building. For the closed conformation, once the particles were aligned and expanded by non-uniform refinement with C3 symmetry, they were further expanded with C3 symmetry. A 3D classification without alignment focused on the down RBD (K\u2009=\u20096, force hard classification, input mode = simple) was performed, which clearly showed down RBD conformation. A final map was reconstructed by non-uniform refinement with C3 symmetry, and a local refinement focusing on the down RBD was carried out to support model building.\n\nFor BA.2.86 S under PBS condition with 1\u2009mM EDTA, movie frames were aligned, dose-weighted, and CTF-estimated using Patch Motion correction and Patch CTF estimation. A total of 280,627 particles were blob-picked, and reference-free 2D classification (K\u2009=\u2009150, batch = 200, iteration = 30) was performed to remove junk particles. Heterogeneous refinement was performed using EMD-3562320 (SARS-CoV-2 XBB.1 spike closed state) as a reference map, followed by 3D classification without alignment focused on the down RBD (K\u2009=\u20094, force hard classification, input mode = simple). The final map was reconstructed by non-uniform refinement with C3 symmetry.\n\nFor the BA.2.86 S\u2013ACE2 complex, movie frames were aligned, dose-weighted, and CTF-estimated using Patch Motion correction and Patch CTF estimation. A total of 1,537,510 (dataset 1) and 1,909,117 (dataset 2) particles were blob-picked, and reference-free 2D classifications (K\u2009=\u2009150, batch = 200, iteration = 30) were performed to remove junk particles on each dataset separately. The initial models were reconstructed using 403,943 particles belonging to dataset 1; two rounds of heterogeneous refinement were performed on each dataset to reconstruct the BA.2.86 S\u2013ACE2 complex map using the initial models as references.\n\nFor the RBD-up state bound to ACE2, to address the flexibility of the RBD-up and ACE2 interface, once the particles were aligned by non-uniform refinement with C3 symmetry, the particles were expanded with C3 symmetry. 3D classification (K\u2009=\u20096, force hard classification, input mode = simple) focused on the RBD-up; ACE2 interface without alignment was performed to remove those appearing unclear on the RBD-ACE2 map. The duplicated particles were removed, followed by heterogeneous refinement, and non-uniform refinements were performed to obtain maps of the two-up or three-up RBD states bound to ACE2. A local map of the RBD-up and ACE2 interface was obtained by the iterative runs of local refinement and 3D classification without alignment.\n\nFor the RBD-down state bound to ACE2, once the particles were aligned by non-uniform refinement followed by 3D classification (K\u2009=\u20094, force hard classification, input mode = simple) focused on RBD-down and ACE2 interface without alignment was performed to obtain the particles that clearly showed the RBD-down and ACE2. An additional 3D classification (K\u2009=\u20094, force hard classification, input mode = simple) was performed, and global and local maps were obtained by non-uniform refinement or local refinement.\n\nTo address the flexibility of the ACE2-bound RBD-up state, 3D Variability Analysis, and subsequent 3D Flexible Refinement28 were performed. 3D Variability Analysis (Number of modes: 4, Filter resolution: 10\u2009\u00c5) was performed using the downsampled particles (96 pixels, 4.02\u2009\u00c5/pix). For the 3D Flex training, the four components solved by 3D Variability analysis were used to initialize the latent coordinates, and the particles were downsampled to 128 pixels (3.01\u2009\u00c5/pix). The mesh was segmented into seven subregions corresponding to three RBDs, three NTDs, and an S2 subunit. All RBD and NTD segments were connected to the S2 subunit. After the training was completed, the volume series was generated by 20 frames on each latent coordinate.\n\nFor the BA.2.86 S\u2013ACE2 complex incubated at 42 \u00b0C for 1\u2009hour, movie frames were aligned, dose-weighted, and CTF-estimated using Patch Motion correction and Patch CTF estimation. A total of 1,541,394 particles were blob-picked, and reference-free 2D classification (K\u2009=\u2009150, batch = 200, iteration = 30) was performed to remove junk particles. Heterogeneous refinement was performed using the initial model of BA.2.86 S\u2013ACE2 complex as a reference map. For unclearly RBD-ACE2 state, 3D classification without alignment focused on the down RBD (K\u2009=\u20094, force hard classification, input mode = simple). For the two-RBD-uptwo ACE2, 3D classification without alignment focused on the RBD-up (K\u2009=\u20094, force hard classification, input mode = simple). The final map was reconstructed by non-uniform refinement. For a non-canonical two-up state (one-highly-open RBD and one-partially-open RBD) bound to ACE2, 3D classification without alignment focused on the two-up RBDs (K\u2009=\u20094, force hard classification, input mode = simple). The global map was obtained by non-uniform refinement.\n\nFor the JN.1 S\u2013ACE2 complex, movie frames were aligned, dose-weighted, and CTF-estimated using Patch Motion correction and Patch CTF estimation. A total of 1,099,575 particles were blob-picked, and reference-free 2D classifications (K\u2009=\u2009150, batch = 200, iteration = 30) were performed to remove junk particles on each dataset separately. Two rounds of heterogeneous refinement were performed using the initial model of BA.2.86 S\u2013ACE2 complex as a reference map. For the two-RBD-uptwo ACE2, 3D classification (K\u2009=\u20094, force hard classification, input mode = simple) focused on the RBD-up and ACE2 interface without alignment was performed to remove the classes showing the unclear RBD-ACE2 map. A local map of the RBD-up and ACE2 interface was obtained by the iterative runs of local refinement and 3D classification without alignment. For the two-RBD-up\u2212one-RBD-downthree ACE2, once the particles were aligned by non-uniform refinement followed by 3D classification (K\u2009=\u20094, force hard classification, input mode = simple) focused on RBD-down and ACE2 interface without alignment was performed to select the particles that clearly showed the RBD-down and ACE2. The global map was obtained by non-uniform refinement.\n\nThe reported global resolutions are based on the gold-standard Fourier shell correlation curve (FSC\u2009=\u20090.143) criteria. Local resolutions were calculated using cryoSPARC55. Figures related to data processing and reconstructed maps were prepared using UCSF Chimera (version 1.16)56 and UCSF Chimera X (version 1.4)57.\n\nThe structures of the SARS-CoV-2 XBB.1 S-protein closed-2 state (PDB: 8IOT19) and/or human ACE2 protein (PDB:7XB038) were fitted to the corresponding maps using UCSF Chimera. Iterative rounds of manual fitting in Coot (version 0.9.8.7)58 and real-space refinement in Phenix (version 1.20.1)59 were performed to improve the non-ideal rotamers, bond angles, and Ramachandran outliers. The final model was validated using MolProbity software60. The structural models shown in the surface, cartoon, and stick presentations in the figures were prepared using the PyMOL Molecular Graphics System, Version 2.5.0 (http://pymol.sourceforge.net).\n\nTo calculate the angles of RBD-apo or RBD-ACE2 complexes, the axis of RBDs was generated. Then the angles between horizontal plane and the axis were calculated with UCSF Chimera X angle command.\n\nDocking simulations between 34 therapeutically relevant antibodies61,62 and RBD variants were performed using Rosetta release 3.1363. Only high-resolution refinement was employed. The amino acid sequences of each variant were obtained from the GISAID database (https://gisaid.org/lineage-comparison/). Initial backbone coordinates were obtained from the structures of each antibody/RBD complex in the PDB. The side chains of variants were modeled using PyMOL 2.5.0 (http://pymol.sourceforge.net), and deletions in the RBDs were modeled using Modeller 10.564 when necessary. In each docking run, we generated 20 docking poses and averaged the binding energies (I_sc) of the top ten poses to evaluate the physicochemical compatibility of each antibody/RBD complex. For group comparisons, statistical tests were performed using the Friedman test followed by a post-hoc Nemenyi test, utilizing Python libraries such as scipy and scikit_posthocs. The level of significance was set at 5%.\n\nDocking simulations between RBDs and human ACE2 were also performed, as described above. In addition to binding energies, shape complementarity (Sc)65 was assessed using InterfaceAnalyzer in Rosetta, based on the top ten docking poses.\n\nPlasmids expressing the SARS-CoV-2 spike proteins of BA.2.86, and its derivatives were prepared in our previous studies42,66,67. Plasmids expressing the spike proteins of BA.2.86 derivatives were generated by site-directed overlap extension PCR using pC-SARS2-S BA.2.86 as the template. The resulting PCR fragments were subcloned into the KpnI-NotI site of the pCAGGS vector68 using the In-Fusion HD Cloning Kit (Takara, Cat# Z9650N). Details of the primers are summarized in Supplementary Table\u00a04. Nucleotide sequences were determined by DNA sequencing services (Eurofins), and the sequence data were analyzed by SnapGene software v6.1.1 (www.snapgene.com).\n\nThe Lenti-X 293\u2009T cell line (Takara, Cat# 632180) and HOS-ACE2/TMPRSS2 cells (gifted by Dr. Kenzo Tokunaga), a derivative of HOS cells (a human osteosarcoma cell line; ATCC CRL-1543) stably expressing human ACE2 and TMPRSS269,70, were maintained in high-glucose Dulbecco\u2019s modified Eagle\u2019s medium (DMEM; Wako, Cat# 044- 29765) containing 10% fetal bovine serum (Sigma-Aldrich Cat# 172012-500\u2009ML), 100 units of penicillin and 100\u2009\u00b5g/mL streptomycin (Sigma-Aldrich, Cat# P4333-100ML).\n\nPseudoviruses were prepared as previously described42,66,71. Briefly, lentivirus (HIV-1)-based, luciferase-expressing reporter viruses were pseudotyped with the SARS-CoV-2 S. One day prior to transfection, the LentiX-293T cells were seeded at a density of 2 \u00d7 106 cells. Subsequently, they were cotransfected with 1\u2009\u03bcg of psPAX2-IN/HiBiT (a packaging plasmid encoding the HiBiT-tag-fused integrase70), 1\u2009\u03bcg pWPI-Luc2 (a reporter plasmid encoding a firefly luciferase gene70) and 500\u2009ng of plasmids expressing parental S or its derivatives using TransIT-293 transfection reagent (Mirus, Cat# MIR2704) according to the manufacturer\u2019s protocol. Two days post-transfection, the culture supernatants were harvested and filtered. The amount of produced pseudovirus particles was quantified using the HiBiT assay and the Nano Glo HiBiT lytic detection system (Promega, Cat# N3040) as previously described70. In this system, HiBiT peptide is produced with HIV-1 integrase and forms NanoLuc luciferase with LgBiT, which is supplemented with substrates. In each pseudovirus particle, the detected HiBiT value is correlated with the amount of the pseudovirus capsid protein, HIV-1 p24 protein70. Therefore, we calculated the amount of HIV-1 p24 capsid protein based on the HiBiT value measured, according to a previously described method70. To measure viral infectivity, the same amount of pseudovirus normalized to the HIV-1 p24 capsid protein was inoculated into HOS-ACE2/TMPRSS2 cells. At two days postinfection, the infected cells were lysed with a Bright-Glo luciferase assay system (Promega, Cat# E2620), and the luminescent signal produced by the firefly luciferase reaction was measured using a GloMax explorer multimode microplate reader 3500 (Promega). The pseudoviruses were stored at \u201380\u2009\u00b0C until use.\n\nNeutralization assays were performed as previously described2,42,66,67\u00a0with some modifications. The assays were mainly conducted by a semi-automated high-throughput method using Fluent780 (Tecan)72. The SARS-CoV-2 spike pseudoviruses (counting ~100,000 relative light units) and serially diluted (40-fold to 29,160-fold dilution at the final concentration) heat-inactivated sera were manually prepared in a 2-ml 96-well plate (Greiner, Cat# 780271) and in 96-well microplates (ThermoFisher Scientific, Cat# 168136), respectively. The pseudoviruses were dispensed and mixed with the sera in 384-well plates (ThermoFisher Scientific, Cat# 164610) on Fluent780 (Tecan). Pseudoviruses without sera were included as controls. After incubation at 37\u2009\u00b0C for 1\u2009hour, HOS-ACE2/TMPRSS2 cells (3000 cells/30\u2009\u03bcL) were added to the 20\u2009\u03bcL mixture of pseudovirus and serum in a 384-well white plate on the device. Two days post- infection, the infected cells were lysed with a Bright-Glo luciferase assay system (Promega, Cat# E2620) on Fluent780 (Tecan), and the luminescent signal was measured and processed using an Infinite200 and a Magellan (TECAN). The assay of each serum sample was performed in quadruplicate, and the 50% neutralization titer (NT50) was calculated using Prism 9 (GraphPad Software).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The GISAID datasets used in this study are available from the GISAID database (https://www.gisaid.org; EPI_SET_240301rn and EPI_SET_240301bk). The supplemental tables for the GISAID datasets are available in the GitHub repository (https://github.com/TheSatoLab/BA.2.86_RBD). The atomic coordinates and cryo-EM maps of the structures of BA.2.86\u2009S-protein closed state (PDB: 8WXL, EMD-37910), RBD 1-up state (PDB: 8XUX, EMD-38459), RBD 2-up state in complex with ACE2 (PDB: 8XUY, EMD-38686), RBD 3-up state in complex with ACE2 (PDB: 8XVM, EMD-38690), RBD 2-up and 1-down state in complex with ACE2 (PDB: 8XUZ, EMD-38687), up-RBD and ACE2 interface (PDB: 8XV0, EMD-38688), and down-RBD and ACE2 interface (PDB: 8XV1, EMD-38689), treated at 42\u2009\u00b0C (EMD-60905[https://www.ebi.ac.uk/emdb/EMD-60905]) and JN.1-S-protein RBD 2-up state in complex with ACE2 (EMD-60904[https://www.ebi.ac.uk/emdb/EMD-60904]), RBD 2-up and 1-down state in complex with ACE2 (EMD-60906 [https://www.ebi.ac.uk/emdb/EMD-60906]) and up-RBD and ACE2 interface (PDB: 9IU1, EMD-60886) generated in this study have been deposited in the Protein Data Bank (www.rcsb.org), and Electron Microscopy Data Bank (www.ebi.ac.uk/emdb/).\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "WHO. 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Hashiguchi); AMED SCARDA Japan Initiative for World-leading Vaccine Research and Development Centers UTOPIA (JP223fa627001, to K. S.), AMED SCARDA Program on R&D of new generation vaccine including new modality application (JP223fa727002, to K. S., and JP243fa727002, to Y. T.); AMED SCARDA Hokkaido University Institute for Vaccine Research and Development (HU-IVReD) (JP223fa627005, to K. M. and JP243fa627005, to Y. T.); AMED BINDS (JP17am0101093 and JP22ama121037, to K. M.); AMED CREST (JP21fk0108463 and JP22gm1810004, to K. M. and JP23gm1810004 to D. K. and Y. T.); AMED (JP20ae0101047 to K. M. and JP23wm0325047 to D. K., T. Hashiguhi, and Y. T.); JSPS KAKENHI Fund for the Promotion of Joint International Research (International Leading Research) (JP23K20041 to K. T. K., and K. S.); The Scientific Research on Innovative Areas and International Group from the MEXT/JSPS KAKENHI (JP20H05873 to K. M.); JSPS Core-to-Core Program (A. Advanced Research Networks) (JPJSCCA20240006, to T. Hashiguchi); JST CREST (JPMJCR20H4, to K. S. and JPMJCR20H8, to T. Hashiguchi); The Cooperative Research Program (Joint Usage/Research Center program) of Institute for Life and Medical Sciences, Kyoto University (to K. S. and K. M.); Hokkaido University Biosurface project (to K. M.); The Naito Foundation (to T. Hashiguchi); Takeda Science Foundation (to K. M.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Laboratory of Medical Virology, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan\n\nHisano Yajima,\u00a0Kanako Terakado Kimura,\u00a0Yoshiko Nakada-Nakura,\u00a0Yusuke Atarashi,\u00a0Takuya Hemmi,\u00a0Jiei Sasaki\u00a0&\u00a0Takao Hashiguchi\n\nLaboratory of Biomolecular Science and Center for Research and Education on Drug Discovery, Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan\n\nYuki Anraku,\u00a0Shunsuke Kita\u00a0&\u00a0Katsumi Maenaka\n\nDivision of Systems Virology, Department of Microbiology and Immunology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan\n\nYu Kaku,\u00a0Arnon Plianchaisuk,\u00a0Kaho Okumura,\u00a0Naoko Misawa,\u00a0Ziyi Guo,\u00a0Alfredo A. Hinay Jr.,\u00a0Kaoru Usui,\u00a0Wilaiporn Saikruang,\u00a0Spyridon Lytras,\u00a0Keiya Uriu,\u00a0Ryo Yoshimura,\u00a0Shusuke Kawakubo,\u00a0Luca Nishumura,\u00a0Yusuke Kosugi,\u00a0Shigeru Fujita,\u00a0Jarel Elgin M.Tolentino,\u00a0Luo Chen,\u00a0Lin Pan,\u00a0Wenye Li,\u00a0Maximilian Stanley Yo,\u00a0Kio Horinaka,\u00a0Mai Suganami,\u00a0Mika Chiba,\u00a0Kyoko Yasuda,\u00a0Keiko Iida,\u00a0Adam Patrick Strange,\u00a0Naomi Ohsumi,\u00a0Shiho Tanaka,\u00a0Eiko Ogawa,\u00a0Tsuki Fukuda,\u00a0Rina Osujo,\u00a0Jumpei Ito\u00a0&\u00a0Kei Sato\n\nFaculty of Liberal Arts, Sophia University, Tokyo, Japan\n\nKaho Okumura\n\nResearch Center for Drug and Vaccine Development, National Institute of Infectious Diseases; Shinjuku-ku, Tokyo, 162-8640, Japan\n\nYusuke Atarashi,\u00a0Daisuke Kuroda\u00a0&\u00a0Yoshimasa Takahashi\n\nInstitute for Vaccine Research and Development (IVReD), Hokkaido University, Sapporo, Japan\n\nYoshimasa Takahashi\u00a0&\u00a0Katsumi Maenaka\n\nResearch Administration Office, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan\n\nHiromi Sumita\n\nInternational Research Center for Infectious Diseases, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan\n\nJumpei Ito\u00a0&\u00a0Kei Sato\n\nDivision of Pathogen Structure, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan\n\nKatsumi Maenaka\n\nGlobal Station for Biosurfaces and Drug Discovery, Hokkaido University, Sapporo, Japan\n\nKatsumi Maenaka\n\nKyushu University, Fukuoka, Japan\n\nKaori Sasaki-Tabata\u00a0&\u00a0Katsumi Maenaka\n\nGraduate School of Medicine, The University of Tokyo, Tokyo, Japan\n\nKei Sato\n\nGraduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan\n\nKei Sato\n\nCREST, Japan Science and Technology Agency, Kawaguchi, Japan\n\nKei Sato\u00a0&\u00a0Takao Hashiguchi\n\nInternational Vaccine Design Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan\n\nKei Sato\n\nCollaboration Unit for Infection, Joint Research Center for Human Retrovirus Infection, Kumamoto University, Kumamoto, Japan\n\nKei Sato\n\nMRC-University of Glasgow Centre for Virus Research, Glasgow, UK\n\nKei Sato\n\nKyoto University Immunomonitoring Center, Kyoto University, Kyoto, Japan\n\nTakao Hashiguchi\n\nHokkaido University, Sapporo, Japan\n\nKeita Matsuno,\u00a0Naganori Nao,\u00a0Hirofumi Sawa,\u00a0Keita Mizuma,\u00a0Jingshu Li,\u00a0Izumi Kida,\u00a0Yume Mimura,\u00a0Yuma Ohari,\u00a0Shinya Tanaka,\u00a0Masumi Tsuda,\u00a0Lei Wang,\u00a0Yoshikata Oda,\u00a0Zannatul Ferdous,\u00a0Kenji Shishido,\u00a0Hiromi Mohri,\u00a0Miki Iida,\u00a0Takasuke Fukuhara,\u00a0Tomokazu Tamura,\u00a0Rigel Suzuki,\u00a0Saori Suzuki,\u00a0Shuhei Tsujino\u00a0&\u00a0Hayato Ito\n\nTokyo Metropolitan Institute of Public Health, Tokyo, Japan\n\nKazuhisa Yoshimura,\u00a0Kenji Sadamas,\u00a0Mami Nagashima,\u00a0Hiroyuki Asakura\u00a0&\u00a0Isao Yoshida\n\nTokai University, Kanagawa, Japan\n\nSo Nakagawa\n\nKyoto University, Kyoto, Japan\n\nKazuo Takayama,\u00a0Rina Hashimoto,\u00a0Sayaka Deguchi,\u00a0Yukio Watanabe,\u00a0Yoshitaka Nakata,\u00a0Hiroki Futatsusako,\u00a0Ayaka Sakamoto,\u00a0Naoko Yasuhara,\u00a0Tateki Suzuki\u00a0&\u00a0Yukari Nakajima\n\nHiroshima University, Hiroshima, Japan\n\nTakashi Irie\u00a0&\u00a0Ryoko Kawabata\n\nKumamoto University, Kumamoto, Japan\n\nTerumasa Ikeda,\u00a0Hesham Nasser,\u00a0Ryo Shimizu,\u00a0M. S. T. Monira Begum,\u00a0Michael Jonathan,\u00a0Yuka Mugita,\u00a0Sharee Leong,\u00a0Otowa Takahashi,\u00a0Takamasa Ueno,\u00a0Chihiro Motozono\u00a0&\u00a0Mako Toyoda\n\nUniversity of Miyazaki, Miyazaki, Japan\n\nAkatsuki Saito,\u00a0Anon Kosaka,\u00a0Miki Kawano,\u00a0Natsumi Matsubara\u00a0&\u00a0Tomoko Nishiuchi\n\nCharles University, Vestec-, Prague, Czechia\n\nJiri Zahradnik,\u00a0Prokopios Andrikopoulos,\u00a0Miguel Padilla-Blanco\u00a0&\u00a0Aditi Konar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nA. P., K. O., and J. I. performed phylogenetic and bioinformatics analyses. H. Y., Y. Anraku, K. T. K., Y. N. N., Y. Atarashi, T. Hemmi, D. K., Y. T., S. K., J. S., H. S., K. M., and T. Hashiguchi performed structural and protein-science analyses. Y. K. and K. S. performed cell culture experiments. J. I. performed statistical analyses. H. Y., Y. Anraku, J. I., K. S., and T. Hashiguchi designed the experiments and interpreted the results. H. Y., K. T. K., J. I. and T. Hashiguchi wrote the original manuscript. All authors reviewed and approved the final manuscript for publication.\n\nCorrespondence to\n Kei Sato or Takao Hashiguchi.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks George Gao and the other anonymous reviewer(s) for their contribution to the peer review of this work. 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The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Yajima, H., Anraku, Y., Kaku, Y. et al. Structural basis for receptor-binding domain mobility of the spike in SARS-CoV-2 BA.2.86 and JN.1.\n Nat Commun 15, 8574 (2024). https://doi.org/10.1038/s41467-024-52808-2\n\nDownload citation\n\nReceived: 15 March 2024\n\nAccepted: 18 September 2024\n\nPublished: 07 October 2024\n\nVersion of record: 07 October 2024\n\nDOI: https://doi.org/10.1038/s41467-024-52808-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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b/bed9abfca8d6ad9c1cee2a7c50e5b8b7e262390a93611e6b220c68fb369d5b0d/metadata.json @@ -0,0 +1,168 @@ +{ + "title": "Sarbecovirus RBD indels and specific residues dictating multi-species ACE2 adaptiveness", + "pre_title": "Sarbecovirus RBD indels and specific residues dictating multi-species ACE2 adaptiveness", + "journal": "Nature Communications", + "published": "14 October 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53029-3/MediaObjects/41467_2024_53029_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53029-3/MediaObjects/41467_2024_53029_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53029-3/MediaObjects/41467_2024_53029_MOESM3_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53029-3/MediaObjects/41467_2024_53029_MOESM4_ESM.docx" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53029-3/MediaObjects/41467_2024_53029_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53029-3/MediaObjects/41467_2024_53029_MOESM6_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53029-3/MediaObjects/41467_2024_53029_MOESM7_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.iucnredlist.org/search?query=bat&searchType=species", + "/articles/s41467-024-53029-3#MOESM5", + "/articles/s41467-024-53029-3#MOESM1", + "/articles/s41467-024-53029-3#Sec28" + ], + "code": [], + "subject": [ + "SARS virus", + "Viral evolution", + "Virus\u2013host interactions" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3948650/v1.pdf?c=1728990354000", + "research_square_link": "https://www.researchsquare.com//article/rs-3948650/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53029-3.pdf", + "preprint_posted": "13 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Sarbecoviruses exhibit varying abilities in using angiotensin-converting enzyme 2 (ACE2) receptor1-3. However, a comprehensive understanding of their multi-species ACE2 adaptiveness and the underlying mechanism remains elusive, particularly for many sarbecoviruses with various receptor binding motif (RBM) insertions/deletions (indels)4-11. Here, we analyzed RBM sequences from 268 sarbecoviruses categorized into four RBM indel types. We extensively examined the capability of 14 representative sarbecoviruses and their derivatives in using ACE2 orthologues from 51 bats and five non-bat mammals. We revealed that most sarbecoviruses with longer RBMs (type-I), present broad ACE2 tropism, whereas viruses with single deletions in Region 1 (type-II) or Region 2 (type-III) generally exhibit narrow ACE2 tropism, typically favoring their hosts\u2019 ACE2. Sarbecoviruses with double region deletions (type-IV) exhibit a complete loss of ACE2 usage. Subsequent investigations unveiled that both loop deletions and critical RBM residues significantly impact multi-species ACE2 tropism in different ways. Additionally, fine mapping based on type-IV sarbecoviruses elucidated the role of several clade-specific residues, both within and outside the RBM, in restricting ACE2 usage. Lastly, we hypothesized the evolution of sarbecovirus RBM indels and illustrated how loop length, disulfide, and adaptive mutations shape their multi-species ACE2 adaptiveness. This study provides profound insights into the mechanisms governing ACE2 usage and spillover risks of sarbecoviruses.Biological sciences/Microbiology/Virology/Virus–host interactionsBiological sciences/Microbiology/Virology/Viral evolutionACE2sarbecovirusesindelACE2 adaptivenessreceptor binding motif", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementarydataS1table1SarbecovirusandACE2.xlsxSupplementary data S1SupplementarydataS2.pdfSupplementary data S2", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Our comprehensive understanding of the multi-species ACE2 adaptiveness of sarbecoviruses remains elusive, particularly for those with various receptor binding motif (RBM) insertions/deletions (indels). Here, we analyzed RBM sequences from 268 sarbecoviruses categorized into four RBM indel types. We examined the ability of 20 representative sarbecovirus Spike glycoproteins (S) and derivatives in utilizing ACE2 from various bats and several other mammalian species. We reveal that sarbecoviruses with long RBMs (type-I) can achieve broad ACE2 tropism, whereas viruses with single deletions in Region 1 (type-II) or Region 2 (type-III) exhibit narrower ACE2 tropism. Sarbecoviruses with double region deletions (type-IV) completely lost ACE2 usage, which is restricted by clade-specific residues within and outside RBM. Lastly, we propose the evolution of sarbecovirus RBM indels and illustrate how loop lengths, disulfide, and residue determinants shape multi-species ACE2 adaptiveness. This study provides profound insights into the mechanisms governing ACE2 usage and spillover risks of sarbecoviruses.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The Severe Acute Respiratory Syndrome (SARS) outbreak and COVID-19 pandemic significantly raised global awareness of the zoonotic risks posed by sarbecoviruses1,2. The Sarbecovirus subgenus, also known as lineage B \u03b2-coronaviruses, encompasses hundreds of SARS-related coronaviruses exhibiting varying RBM sequences3,4,5,6,7,8,9,10,11,12,13,14. Most sarbecoviruses naturally infect Rhinolophus (horseshoe) bats, the primary natural reservoir15,16,17. Additionally, sarbecoviruses sharing high receptor binding domain (RBD) similarity to SARS-CoV-2 have been identified in pangolins, such as GX/P2V/2017 (GX_P2V), GD/1/2019, and GX_P1E10,18. Sarbecoviruses exhibit extensive genetic diversity in RBM, likely arising from frequent recombination and the high selective pressure associated with inter-species host jumping in bats and pangolins, underscoring the risks of the emergence and outbreak of new human sarbecovirus19,20,21,22,23. However, many sarbecoviruses are known only as viral sequences and their ability to jump species and spillover to humans remains unclear.\n\nAlthough ACE2 has been documented as a receptor for selected groups of setracovirus (e.g., NL63) and merbecoviruses (e.g., NeoCoV)24,25, it has been primarily studied as the receptor for sarbecoviruses1,9,24. Notably, not all sarbecoviruses have been confirmed to use ACE2 as their receptor, especially RBD clade 2 sarbecoviruses, which are proposed to utilize a distinct (yet unknown) receptor3,4,15. Nevertheless, ACE2 usage has been demonstrated in most representative sarbecoviruses other than clade 2 sarbecoviruses3,6,7,16,22,26. Structural analysis of ACE2 in complex with RBDs from various sarbecoviruses reveals a similar interaction mode, albeit with variations in specific residues involved in recognition20,22,27,28. Specifically, the bridge-shaped RBM spanning amino acids (aa) 439-508 of SARS-CoV-2, formed by an extended loop connecting two \u03b2 strands of the RBD core subdomain and stabilized with disulfide-bridging, interacts with ACE2 at two distinct patches29,30. The interface on ACE2 mainly comprises the amino-terminal (N-terminal) \u03b11\u00a0helix, along with limited interactions with the \u03b12 helix and a loop connecting the \u03b23 and \u03b24 strands29.\n\nGiven the importance of receptor recognition in determining host barriers, assessing multi-species ACE2 tropism for sarbecoviruses with distinct RBM features is crucial for understanding their zoonotic potential31,32. Previous studies have provided substantial insight into distinct receptor preferences among bats and other mammalian species for SARS-CoV-1, SARS-CoV-2, GX_P2V, RaTG13, NeoCoV, and others20,22,25,33,34,35,36,37,38,39,40,41,42. Varying entry-supporting abilities have also been observed in ACE2 orthologues from the same bat species but with different polymorphisms, particularly in residues involved in sarbecovirus binding21,43,44,45,46,47,48.\n\nSarbecoviruses are commonly classified into several clades based on the RBD phylogeny and ACE2 usage3,4,23. Despite sharing a similar RBD core subdomain, sarbecoviruses exhibit diversity in RBM sequences, particularly the presence of various indels in Region 1 (residues 443-450SARS-CoV-2) or Region 2 (residues 470-491SARS-CoV-2)4,23. Clade 1 sarbecoviruses are all ACE2-using sarbecoviruses which can be further divided into subclades 1a, 1b, and 1c based on RBD phylogeny3. Most clade 1a (SARS-CoV-1 lineage) and 1b (SARS-CoV-2 lineage) sarbecoviruses have the longest RBM and lack RBM deletions of more than one amino acid3. Several sarbecoviruses with Region 1 or Region 2 single RBM deletions that are identified as clade 1b viruses, such as RshSTT182, RshSTT20049, Rc-o319, and Rc-kw85, which were recently found in Cambodia and Japan, respectively. Clade 1c sarbecoviruses include a subgroup of recently reported Asian sarbecoviruses carrying single Region 1 deletion, such as RmYN05, RaTG15, and RsYN04, which are also known as clade 4 sarbecoviruses in some studies6,7,50. Here, we\u00a0described these sarbecoviruses as 1c subclade considering their RBD phylogeny, geographical distribution, and ACE2 usage compared with 1a and 1b. Clade 2 sarbecoviruses are phylogenetically close to clade 1 and characterized by the two deletions (or indels) within RBM3,4,15,16,32,51,52,53. Clade 3 sarbecoviruses, such as BM48-31, Khosta-1/2, BtKY72, PDF-2386, and PRD-0038, discovered in Africa and Europe are considered closer to the sarbecovirus ancestors and all carry single deletions (indels) in RBM Region 13,9,14,54,55,56,57. Several clade 3 sarbecoviruses have been demonstrated as ACE2-using viruses, supporting this receptor usage as an ancestral trait of sarbecoviruses3,28,51,58. Although proposed to have evolved from ACE2-using ancestors through the subsequent receptor switch3,54, whether all clade 2 sarbecoviruses have lost ACE2 usage across all ACE2 orthologues remains an open question.\n\nOur understanding of the key determinants affecting sarbecoviruses multi-species ACE2 adaptiveness and the factors restricting ACE2 usage remains incomplete. With an increasing number of sarbecoviruses identified with various single RBM indels, addressing the impact of these indels on multi-species ACE2 tropism becomes crucial. Moreover, sarbecoviruses with similar RBM deletion patterns exhibit marked differences in ACE2 tropism profile, emphasizing the role of critical RBD residues impacting multi-species ACE2 recognition beyond loop deletions20,21,45,51.\n\nIn this study, we analyze the 268 sarbecovirus S sequences to delineate the overall RBM indel features and categorize them into four RBM indel types. Employing an ACE2 library consisting of 56 orthologues, we extensively evaluate cellular RBD binding and pseudovirus entry of 20 representative sarbecoviruses and various derivatives, encompassing RBM loop chimera and mutations. We elucidate multiple key determinants for ACE2 recognition and show how loop lengths, disulfide, and restrictive residues dictate ACE2 tropism and adaptiveness to ACE2 orthologues from various species. Our data lead to a more comprehensive understanding of the multi-species ACE2 adaptiveness across sarbecoviruses, as well as the coevolution of RBM indels and ACE2 adaptiveness.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We retrieved 2318 Non-human \u03b2-coronavirus S glycoprotein sequences from the NCBI and GISAID databases, with 876 distinguished as sarbecovirus based on phylogenetic analysis. After reducing redundancy by excluding identical sequences and those highly similar to SARS-CoV-1 and SARS-CoV-2 (>99% identity), we obtained 265 sarbecovirus S sequences for further investigation, with 17 from pangolins, 1 Hipposideros\u00a0bat and 247 Rhinolophus bats. We also included 3 representative human sarbecovirus sequences (SARS-CoV-1, SARS-CoV-2-WT, and SARS-CoV-2-Omicron BA.1) for analysis (Fig.\u00a01a and Supplementary Data\u00a01). Phylogenetic analysis based on RBD protein sequences revealed five sub-clades (RBD clades 1a, 1b, 1c, 2, and 3), with clade 2 accounting for the largest (Fig.\u00a01b, Supplementary Fig.\u00a01a). Multi-sequence alignment and Sequence Logo analysis highlighted three highly variable regions in RBMs, with Regions 1 and 2, but not Region 3, being the hot spots of loop indels (Fig.\u00a01c, and Supplementary Data\u00a02).\n\na Flow diagram illustrating the retrieval of 265 non-redundant S glycoprotein sequences from non-human sarbecoviruses and three additional human sarbecoviruses. b The RBD clade information of the 268 sarbecoviruses. c RBM sequence logo plot illustrating the three high variable regions. The SARS-CoV-2 residue numbering is shown. d Phylogenetic tree based on RBD amino acids sequence\u00a0for the 268 sarbecoviruses (See also Supplementary Fig.\u00a01a, b for the complete tree and selection strategy) and multi-sequence alignment of 35 selected sarbecoviruses displayed with four RBM indel types with relevant sequences were boxed with corresponding colors. The 20 sarbecoviruses for subsequent functional analysis are indicated in red. The two cysteines for disulfide bridging are highlighted in orange. e Summary of the deleted residue numbers in Region 1 and Region 2 compared to the SARS-CoV-2 RBM sequence. The counts of each deletion length among the 268 sarbecoviruses are indicated in parentheses. f The counts of S glycoprotein sequences of the four RBM indel types. g Distribution plot of RBD clades grouped by RBM\u00a0indel types. h Analysis of the deleted residue numbers of Region 1 and Region 2 indels grouped by different RBM types. i Structural display of the two interaction patches in the SARS-CoV-2 RBD/hACE2 complex (6M0J). Selected residues involved in receptor recognition and the C480-C488 disulfide are indicated in the close-up views of the two interaction patches. j Superimposing of SARS-CoV-2 RBD (6M0J) with RBDs from the indicated sarbecoviruses belonging to each RBM\u00a0indel type. The discrepancies in loop lengths are highlighted with red dashed ellipses. Disulfide bridges are indicated in orange.\n\nFor the five subclades, RBM sequences of 35 representative sarbecoviruses from each main phylogenetic branch were selected to demonstrate the diversity of RBM sequences. The selection criteria include RBD phylogeny, RBM sequence similarity, indel types, indel lengths, and preference for sequences with cryo-EM structure availability (Fig.\u00a01d, and Supplementary Fig.\u00a01a\u2013d). To better investigate the impact of RBM indels on multi-species ACE2 adaptiveness, we categorized sarbecoviruses into four RBM indel types in addition to the clade-based classification. Specifically, RBM type-I describes most clade 1a and 1b sarbecoviruses without RBM deletions relative to SARS-CoV-1 or SARS-CoV-2 and are considered RBM prototypes, RBM type-II and type-III are viruses with single RBM deletions in Region 1 or Region 2, respectively, RBM type-IV viruses correspond to clade 2 sarbecoviruses with dual RBM deletions (Fig.\u00a01d).\n\nIn analyses of RBM deletions of the 268 sequences, it was found that deletions of 1, 2, 3, 4, or 5 amino acids (aa) can occur in Region 1, and deletions of 1, 9, 13, or 14 aa can occur in Region 2 (Fig.\u00a01e). Notably, the 5aa deletion in Region 1 is always accompanied by a 13 or 14 aa deletion in Region 2. Accordingly, four RBM types were defined based on the presence of deletions (>1aa) in Region 1 (type-II), Region 2 (type-III), or both (type-IV), while sequences with no deletion or only 1aa deletion were classified as type-I. This classification led to different subgroups of sarbecovirus (RBM types) compared to the clades based on RBD phylogeny (Fig.\u00a01f\u2013g). For example, all clade 1a sarbecoviruses (SARS-CoV-1 lineage) are RBM type-I, all clade 3 and clade 1c sarbecoviruses are RBM type-II, while clade 1b (SARS-CoV-2 lineage) sarbecoviruses can be divided into RBM type-I, II, and III (Fig.\u00a01g). The deleted amino acid numbers in Region 1 and 2 of different sarbecoviruses are summarized based on different RBM types (Fig.\u00a01h). After reducing redundancy by removing sarbecoviruses with high RBM sequence similarity, 20 representative sarbecoviruses were selected for subsequent functional profiling of multi-species ACE2 tropism, encompassing sarbecoviruses from all five subclades and covering all kinds of deleted numbers in either Region 1 or Region 2 (Supplementary Fig.\u00a01b, c). Amino acid sequence identity analyses revealed that these sarbecoviruses share at least 57.07% RBD identity and 21.54% RBM identity in amino acid sequences, suggesting greater genetic variation in RBM than in RBD (Fig.\u00a01d and Supplementary Fig.\u00a01d).\n\nFrom a structural perspective, the spatially proximate Region 1 and Region 3 loops form interaction patch 2, while the majority of residues in Region 2 loop contribute to interaction patch 1 (Fig.\u00a01i). Interestingly, the conserved disulfide bridge for stabilizing loop in Region 2 is absent in RBM type-III and IV sarbecoviruses (Fig.\u00a01d, i)59. Superimposition of the solved or AlphaFold2-predicted RBDs with that of SARS-CoV-2 highlighted shortened extended loops due to specific deletions (Fig.\u00a01j). Given that the two deletions are situated in critical RBM extensions for ACE2 interaction, their presence might impact multi-species ACE2 tropism.\n\nWe utilized a well-established RBD-hFc-based assay to assess the live cell receptor binding (Supplementary Fig.\u00a02a\u2013c). Due to the unavailability of the authentic sarbecovirus strains, we employed a dual reporter-based vesicular stomatitis virus (VSV) pseudotyping system assembled with sarbecovirus S glycoproteins to assess receptor functionality of various ACE2 orthologues (Supplementary Fig.\u00a02d\u2013f)4. The S glycoproteins from these sarbecoviruses incorporated into the pseudovirus particles were adjusted at comparable levels for entry assays (Supplementary Fig.\u00a02g). The two different functional assays provide cross-validation and migrate the potential impact of other S components on viral entry efficiencies, such as NTD and S2.\n\nTo illustrate a comprehensive ACE2 usage spectrum of each sarbecovirus, we examined 56 ACE2 orthologues from 51 bat and 5 representative non-bat mammalian species (Supplementary Fig.\u00a03a, b). The bat species represent a broad genetic diversity spanning 11 bat families with global distribution and genetic diversity, including eight Rhinolophus bats geographically across Europe, Africa, and Asia (Fig.\u00a02a, and Supplementary Fig.\u00a03c)33. Sequence analysis of these ACE2 orthologues exhibited significant variations in residues potentially involved in sarbecovirus interactions (Supplementary Fig.\u00a03a, b). HEK293T cells stably expressing ACE2 orthologues were established and maintained with verified expression33 (Supplementary Fig.\u00a04). RBD binding assays based on the 20 selected sarbecoviruses with distinct RBM features were conducted to investigate their multi-species ACE2 binding spectra (Fig.\u00a02a). According to the binding data, a subset of 17 sarbecoviruses were tested with the pseudovirus entry assays to evaluate their ability to utilize different ACE2 orthologs for entry. (Fig.\u00a02b).\n\na, b Multi-species ACE2 usage spectra of sarbecoviruses of different indel types. RBD binding of 20 sarbecoviruses (a) and PSV entry of 17 sarbecoviruses (b) were assessed in HEK293T cells stably expressing the 56 ACE2 orthologues from bats and selected mammals. Rhinolophidae as the natural sarbecovirus reservoir is indicated in red. Asterisk: sarbecoviruses not tested in PSV entry experiments. c Efficiencies of selected sarbecovirus RBD-hFc recombinant proteins binding to HEK293T cells stably\u00a0expressing the indicated ACE2 orthologs from their hosts or preferred species. d BLI analyses of binding kinetics of the soluble dimeric ACE2 ectodomains to immobilized viral RBD-hFc. e, f Structural demonstration (7DRV for RaTG13 and 7DDP for GX_P2V) (e) and S glycoprotein pseudovirus packaging efficiencies (f) of RaTG13 and GX_P2V swap mutants. For f, data are representative blots from two independent experiments. g, h Heat map displaying PSV entry efficiencies of RaTG13\u00a0(g) and GX_P2V\u00a0(h) swap mutants (SARS-CoV-2 residue numbering) in HEK293T cells expressing the indicated ACE2 orthologues. PSV entry > 5% control ACE2 is considered an effective entry and the numbers of supportive orthologues are shown in parentheses. i Negative charged surface of the consensus ACE2 (based on 56 ACE2 orthologues) spatially proximate to residue 501 of RaTG13 and GX_P2V. The AlphaFold2 predicted structure based on consensus ACE2 sequence, with interaction predicted by HDOCK. RLU: Relative Luminescence Units; RFU: Relative Fluorescence Units. The amino acid usages of the selected surface-exposed residues are indicated. Heatmap plotted by the mean values (n\u2009=\u20092 independently infected cells) in a, b, g, and h, which are representative results out of two independent experiments. Source data are provided as a Source Data file.\n\nThese two assays displayed generally consistent ACE2 usage patterns, with a few exceptions. These apparent discrepancies in two different assays are commonly observed when testing other coronaviruses. Except for the RBD concentrations or viral titer/infectivities that may result in differences in assay sensitivity, other factors also contribute to the discrepancies. For example, the multi-step entry process is not always guaranteed by RBD binding alone, weak RBD binding can sometimes be sufficient for entry as the binding is a dynamic process and multivalent binding through spike trimmers in pseudoviral particles can increase binding avidity. Except for the type-IV RBM sarbecoviruses, the other sarbecoviruses displayed confirmed ACE2 usage, albeit with different ACE2 usage spectra. Several type-I RBM viruses, like SARS-CoV-1, SARS-CoV-2, and GX_P2V, efficiently use most orthologues, including human ACE2 (hACE2) (Fig.\u00a02a). In contrast, RBM type-II or type-III sarbecoviruses generally showed narrower ACE2 tropism, and most are unable to use hACE2, representing a relatively low ACE2 adaptiveness to achieve broad ACE2 tropism compared to RBM type-I sarbecoviruses. Notably, GX_P1E, an RBM type-II pangolin sarbecovirus closely related to GX_P2V, with only 2aa (NY) deletion in RBM Region 1 displayed narrower ACE2 tropism, suggesting that 2aa reduction in Region 1 length is sufficient to affect the breadth of multi-species ACE2 tropism. Although PRD-0038 has been proposed as a sarbecovirus with broad ACE2 tropism among Rhinolophus bats28, this virus, along with four other clade 3 sarbecoviruses (Khosta-2, BtKY72, PDF-2386, and BM48-31) with 2 to 4aa deletions in Region 1, display a narrow or moderate breadth of ACE2 tropism in our study (Fig.\u00a02a). The RBD binding of the five sarbecoviruses to their optimal ACE2 orthologues, mostly from their hosts, was further analyzed through flow cytometry and Bio-layer interferometry (BLI) (Fig.\u00a02c, d).\n\nNotably, two close-related RBM type-I sarbecoviruses with identical RBM lengths, GX_P2V and RaTG13, displayed contrasting breadth of ACE2 tropism (Fig.\u00a02a, b). We hypothesize that the narrow tropism of RaTG13 could be attributed to suboptimal residues restricting its binding with ACE2 orthologues. Supporting this hypothesis, the previously reported RaTG13 mutations, T372A43,47,60 (removing a glycan at N370) and T403R/K45,46 (enabling additional ACE2 interaction), significantly expanded the spectrum of ACE2 usage in our study (Supplementary Fig.\u00a05a, b). Furthermore, analysis based on eight swap mutants exchanging RBM residues on 493, 498, 501, and 505 (SARS-CoV-2 numbering) reveals point mutation at position 501, in addition to position 372 and 403, can also markedly expand the breadth of ACE2 tropism of RaTG13, suggesting a relatively high muli-species ACE2 adaptiveness of this virus (Fig.\u00a02e\u2013h, Supplementary Fig.\u00a05c)3,43,61,62.\n\nTo investigate the mechanism of restrictive effect of D501, residue usage analysis was conducted on six ACE2 positions spatially close to position 501, which revealed an overall negatively charged surface among the 56 orthologues, thereby disfavoring D501 due to electrostatic repulsion (Fig.\u00a02i). This hypothesis is further supported by similar phenotypes of RshSTT200, SARS-CoV-1, and SARS-CoV-2 carrying D/T mutations at this position (Supplementary Fig.\u00a05d,e and Supplementary Fig.\u00a06a\u2013d). Since the N501Y mutation became dominant during the spread of SARS-CoV-2 in humans, we also compared the multi-species ACE2 usage spectra of SARS-CoV-1 and SARS-CoV-2 carrying N or Y at position 501SARS-CoV-263. The Y mutation at this position slightly increased the number of acceptable ACE2 orthologues for SARS-CoV-2 while dramatically reducing acceptable ACE2 to only four orthologues for SARS-CoV-1 (Supplementary Fig.\u00a06a\u2013d). Structural analysis suggests Y487SARS-CoV-1 may cause steric hindrance with local Y41hACE2 and K353hACE2, whereas the Y501SARS-CoV-2 instead forms a \u03c0-\u03c0 stacking interaction with Y41hACE2 and cation-\u03c0 interaction with K353. This indicates the virus-specific phenotypes can be attributed to the structural discrepancies of residues at equivalent positions (Supplementary Fig.\u00a06e)64. Similarly, the 501\u2009T mutations in RmYN05 or Rc-o319 have no significant impact on their breadth of ACE2 tropism (Supplementary Fig.\u00a05d, e). Moreover, the different efficiencies of SARS-CoV-2, SARS-CoV-2-N501Y, and SARS-CoV-2-Omicron BA.1 in using various ACE2 orthologues indicated the presence of residues, other than the 501 residues, affecting the ACE2 tropism of Omicron BA.1, a phenotype that also observed in authentic SARS-CoV-2 infection assays (Supplementary Fig.\u00a07a, b). Fine mapping of the mutations in BA.1 underscores the critical contribution of residues at position 493 in affecting multi-species ACE2 tropism (Supplementary Fig.\u00a07c\u2013g).\n\nCollectively, these data indicated that the RBM type-I sarbecoviruses exhibited superior multi-species ACE2 adaptiveness among the tested 20 sarbecoviruses, although the tropism breadth can be modulated by critical residues within or near the interaction surface, such as residues at positions 403, 501, and 493 (SARS-CoV-2 numbering).\n\nTo investigate the impact of loop lengths on multi-species ACE2 tropism, we generated chimeras with specific loop substitutions in Region 1 or Region 2. These include SARS-CoV-2 with single deletions in each region and other sarbecoviruses carrying partial or entire loop substitutions with SARS-CoV-2 equivalent sequences (Fig.\u00a03a). The VSV packaging efficiency of all S chimeras was validated by Western blot (Fig.\u00a03b).\n\na Schematic illustration of the Region 1 and Region 2 substitutions in sarbecoviruses of different indel types. Light pink background: insertions corresponding to SARS-CoV-2 counterparts; gray background: deletions corresponding to sequences from type-II or type-III sarbecoviruses. (*) RBM swaps based on the indel boundaries. b Western blot detecting the S glycoprotein packaging efficiency in PSV particles. c, d\u00a0RBD binding (c) and PSV entry (d) efficiencies of indicated\u00a0sarbecoviruses and their swap mutants in HEK293T\u00a0cells stably expressing the 56 ACE2 orthologues. e\u2013h Disulfide bond in Region 2 is critical for multi-species ACE2 usage of sarbecoviruses. Schematic illustration of the Region 2 disulfide-related mutants based on SARS-CoV-2, Rc-o319, and their Region 2 substitution mutants (e), S glycoprotein packaging efficiency (f), RBD binding efficiency (g), and PSV entry efficiency (h) of SARS-CoV-2 and Rc-o319 mutants in HEK293T cells\u00a0stably expressing the 56 ACE2 orthologues (dots in g and h). Dashed lines: background cut-off of RBD binding and PSV entry assays. For b and f, data are representative of two independent experiments with similar results. Heatmap plotted by the mean values (duplicates of independently bound or infected cells) in c and d, which are representative results out of two independent experiments. Two-sided Chi-squared test was used for statistical analysis of significance for g and h (n\u2009=\u200956, each point represents a value based on an ACE2 orthologue from a specific species). *P\u2009<\u20090.05, ****P\u2009<\u20090.0001. RLU Relative Luminescence Units, RFU Relative Fluorescence Units. Source data are provided as a Source Data file.\n\nSARS-CoV-2 with a KVNY deletion in Region 1 (\u0394Region1*, relative to RshSTT200) displayed reduced multi-species ACE2 tropism but still can use ACE2 from a subset of species, including humans (Fig.\u00a03c, d). However, the 9aa deletion in Region 2 (\u0394Region2*, relative to Rc-o319) abolished its ability to use any tested ACE2 orthologues. BM48-31 and Rc-o319 with regions substituted by SARS-CoV-2 equivalents can use more ACE2 orthologues, yet remain unable to achieve a broad tropism as RBM type-I sarbecoviruses. For RshSTT200 and Rc-o319, the increase of multi-species ACE2 tropism was not achieved by filling the deletions unless the entire RBM region was substituted with SARS-CoV-2 counterparts. This indicates the importance of side chains or local conformations apart from loop lengths (Fig.\u00a03c, d). Notably, the highly conserved RBM disulfide bridge is present in Rc-o319-R2 but absent in Rc-o319-R2*, the importance of which was confirmed by the loss of infectivity of SARS-CoV-2 C480S and Rc-o319-R2 C462S mutants in using all tested ACE2 orthologues59,65. However, introducing a disulfide to Rc-o319-R2* via K454C mutation remains insufficient to restore its ability to use ACE2, suggesting the presence of incompatible residues for Region 2-ACE2 interaction (Fig. 3e\u2013h).\n\nSubstituting both R1 and R2 regions, which also introduced the featured disulfide, in the three RBM type-IV (clade 2) sarbecoviruses (ZC45, RmYN02, HKU3) failed to achieve any detectable ACE2 usage signal in both binding and pseudovirus entry assays (Fig. 3c, d). This indicates the presence of determinants other than loop deletions that restrict ACE2 usage in RBM type-IV sarbecoviruses4.\n\nIn earlier attempts to identify determinants restrict ACE2 usage for clade 2 sarbecoviruses, we unexpectedly found that HKU3 and ZC45 remained unable to bind any ACE2 even with the entire RBM (aa439-508) replaced with SARS-CoV-2 RBM, indicating the presence of determinants restricting ACE2 recognition outside the RBM (Supplementary Fig.\u00a08a). When comparing RBD sequences from 172 RBM type-IV (clade 2) sarbecoviruses with the other 96 ACE2-using sarbecoviruses, 22 clade 2-specific residues situated within or outside the RBMs were identified (Fig.\u00a04a). It has been proposed that two residues (D496 and P502) within the Region 3 of RBM type-IV sarbecoviruses may restrict potential ACE2 interaction based on structural modeling, while the impact of these two residues and other clade 2-specific residues outside RBM on ACE2 recognition remains to be investigated using cell-based functional assays66.\n\na RBD residue usage (SARS-CoV-2 residue numbering) of 268 sarbecoviruses grouped by ACE2 dependence. Red fonts and arrows: strict clade 2-specific residues. Orange fonts and arrows: limited clade 2 specificity. b RBD amino acid sequences alignment of SARS-CoV-2, SARS-CoV-1, and HKU3. Red: HKU3-specific; Orange: shared by HKU3 and SARS-CoV-1 only. The boundaries of three fragments (Frag.) for subsequent mapping are indicated. c\u2013g Fine mapping of residues restricting ACE2 usage outside the RBM. Mapping strategy for narrowing down the range of determinants restricting ACE2 recognition (c). Orange and green circles: capability of using hACE2 for entry (>1% of SARS-CoV-2 entry) and binding (background subtracted RFU\u2009>\u20090.2), respectively. Gray: unable to use hACE2. Underlines: two critical residues limiting ACE2 binding. PSV entry (d) and RBD binding (e) of the HKU3 mutants carrying SARS-CoV-2 corresponding sequences in HEK293T-hACE2. Yellow highlighted the mutants critical for analyzing ACE2-restricting determinants. Data representative of three independent experiments for d and e. RBD binding (f) and PSV entry (g) of HKU3 mutants with restored ACE2 binding affinity. h Dose-dependent inhibition of amplification of recombinant pcVSV-CoV carrying HKU3 S\u2009+\u2009NNSVGD\u2009+\u2009RBMSARS-CoV-2 spike with h11B11 in Caco2-hACE2 cells. i, j\u00a0pcVSV-HKU3 serves as a negative control. PSV entry (i) and RBD binding (j) of SARS-CoV-2 mutants carrying clade 2-specific restricting residues in HEK293T-hACE2. k BLI analyses of binding kinetics of the soluble dimeric ACE2 ectodomains to immobilized viral RBD-hFc. Scale bars, 200 \u03bcm. Heatmap plotted by the mean values (n\u2009=\u20093 independently infected cells) in f and g. Data are mean \u00b1 s.d. for d and i (n\u2009=\u20093 biologically independent cells), one-way ANOVA analysis, followed by Dunnett\u2019s test for statistical analysis. *P\u2009<\u20090.05, ****P\u2009<\u20090.0001; NS, not significant (P\u2009>\u20090.05). Data representative of at least two independent experiments for h, i, and j with similar results. Source data are provided as a Source Data file.\n\nTo identify the determinants restricting ACE2 recognition, we conducted chimeric HKU3 S mutants with corresponding RBD sequences substituted with SARS-CoV-2 equivalents. HKU3 was selected as it represents the clade 2 sarbecovirus showing the highest SARS-CoV-2 RBD amino acid sequence identity (63.92%) in our study (Supplementary Fig.\u00a01d). Sequence alignment of SARS-CoV-1, SARS-CoV-2 and HKU3 RBD displayed 16 HKU3-specific residues upstream of the RBM region (Fig.\u00a04b). The subsequent functional dissection initiated from large fragment swaps and then proceeded to fine mapping of single residue combinations (Fig.\u00a04b, c). In addition to SARS-CoV-2 RBM replacement (HKU3-RBMSARS2), further substituting fragment A (aa 385-417) enabled HKU3 to use hACE2 for entry but remained deficient in binding hACE2 efficiently. Extending by fragment B (aa354-417) and fragment C (aa349-417) underscore the critical contribution of S349SARS-CoV-2 for efficient ACE2 binding (Fig.\u00a04d, e). Fine mapping of fragment A highlighted the crucial role of six clade 2-specific residues at positions 388, 394, 399, 401, 404, 405 (SARS-CoV-2 numbering) that restricting hACE2 usage, with S349\u2009+\u2009V401G404D405 (S\u2009+\u2009VGD) being the minimal combination (Fig.\u00a04c\u2013e and Supplementary Fig.\u00a08b). Similar results were obtained when testing two other clade 2 (RBM type-IV) sarbecoviruses, ZC45 and RmYN02 (Supplementary Fig.\u00a08b, d\u2013h). The expanded multi-species ACE2 usage spectra of HKU3-RBMSARS2 carrying S\u2009+\u2009NNSVGD or S\u2009+\u2009VGD mutations were demonstrated by RBD binding and pseudovirus entry assays (Fig. 4f, g). Furthermore, the ACE2 dependence of HKU3 S mutant (HKU3-RBM S\u2009+\u2009NNSVGD) was verified by the efficient amplification of a propagation-competent recombinant VSV genetically encoding the S mutant in Caco2-hACE2 cells, which could be dose-dependently neutralized by the hACE2-specific antibody h11B11 (Fig.\u00a04h and Supplementary Fig.\u00a08c)67.\n\nThe restrictive effect of these clade 2-specific residues was further demonstrated by the loss of ACE2 usage of SARS-CoV-2 carrying mutations at equivalent positions. SARS-CoV-2 carrying the corresponding mutants within or outside the RBM region (S349N, V401L, V401L\u2009+\u2009G404S\u2009+\u2009D405S, G496D\u2009+\u2009G502P, and P507A\u2009+\u2009Y508T) all underwent a significantly reduced efficiency in using hACE2 (Fig.\u00a04i,\u00a0j). The loss of hACE2 binding affinity of SARS-CoV-2 RBD harboring S349N or V401L point mutations was further verified by BLI assays (Fig.\u00a04k). The restrictive effect of residues at these two positions can also observed in RmYN02 (clades 2) and SARS-CoV-1 (clade 1a), but is less pronounced in BM48-31 (clade 3) with higher RBD sequence divergence (Supplementary Fig.\u00a08d\u2013j).\n\nInterestingly, unlike R403SARS-CoV-2 which directly interacts with ACE2, the two clade 2-specific residues crucial for ACE2 binding, S349SARS-CoV-2 and V401SARS-CoV-2, are situated underneath the canonical RBM. Thus, S349N and V401L mutations (SARS-CoV-2 numbering) in HKU3 may slightly alter the RBM conformation due to their relatively larger side chains (Supplementary Fig.\u00a08i). The resulting conformational shift may lead to the mismatch of critical residues for ACE2 interaction, thereby restricting ACE2 usage of clade 2 sarbecoviruses even when the entire RBM region is replaced by SARS-CoV-2 counterparts.\n\nTo trace the coevolution of sarbecoviruses RBM indels and ACE2 adaptation, we integrated biological functional data on multi-species ACE2 tropism with in silico analyses based on RBD clades, RBM types, and residue usages in the two indel-containing regions (Fig.\u00a05a\u2013c and Supplementary Fig.\u00a09a, b). The breadth of multi-species ACE2 tropism and hACE2 fitness jointly affect the overall spillover risks of sarbecoviruses of different clades and RBM indel types (Fig.\u00a05a, b). The scrutiny of the sequence features reveals an intriguing indel pattern in Region 1, characterized by one or two conserved and centrally located glycine (G), while Region 2 displays a complex indel rather than straightforward 9aa or 13/14aa deletions in RBM type-III and IV sarbecoviruses, respectively (Fig.\u00a05c and Supplementary Fig.\u00a09b). Notably, a potential evolutionary trace of Region 1 insertion was identified by the likely duplication of NY/NF sequences on the right side of the G (Fig.\u00a05c).\n\na The phylogenetic tree based on RBD amino acid sequences using maximum likelihood analysis (See also Supplementary Fig.\u00a01a). The red lines mark the sarbecoviruses tested in this study. b The numbers of supportive ACE2 orthologues (data based on Fig.\u00a02a with RLU\u2009>\u20092\u2009\u00d7\u2009105) and hACE2 compatibility of the indicated sarbecoviruses. Coloring is based on RBD clades. c Region 1 sequence logoplot (SARS-CoV-2 numbering) of sarbecoviruses grouped by different indel types in each clade. The conserved D442, F/Y451 for defining the boundary of Region 1 are highlighted in black. The featured glycine (G) and H-bond-associated tyrosine (Y) are highlighted in red and orange, respectively. d The proposed evolutionary pathway of sarbecoviruses RBD clades and RBM indel types. e Details of Region 1 sequence changes along with the emergence of different clades during the evolution of sarbecoviruses in bats, pangolins, and humans. The Region 1 lengths in each group are indicated in blue. The conserved glycine (1\u2009G) and the double G (2\u2009G) in most clade1b sarbecoviruses are highlighted in red. f The RBD-ACE2 complex structures or models of sarbecoviruses with distinct Region 1 sequences. Region 1 loops of various lengths are highlighted in green without transparency, the featured G is marked in red. Close-up views of the interface between Region 1 loops and the indicated ACE2 orthologues are shown. ACE2 orthologs are displayed in light gray. Orange dashed lines: H-bonds. g PSV entry of SARS-CoV-2 R1 mutants in HEK293T expressing the indicated ACE2 orthologues. Data are presented by mean values (n\u2009=\u20093 independently infected cells). Dashed lines indicate the events with low confidence for d and e. Source data are provided as a Source Data file.\n\nCombining these data, we proposed an evolutionary pathway delineating the emergence of various RBM types, highlighted by critical events driving the evolution (Fig.\u00a05d). While the origin of the common ancestor of sarbecoviruses remains elusive, Africa/Europe sarbecoviruses (clade 3) maintained a relatively ancient state of RBM indel type-II3. The Asia sarbecoviruses underwent extensive evolution and developed into clade 1 and 2 sarbecoviruses with significant genetic diversity. These viruses evolved in three different directions with distinct ACE2 adaptiveness. Clade 1c sarbecoviruses maintained RBM type-II with limited genetic diversities based on currently known sequences. Clade 2 sarbecoviruses underwent R1 (-5aa) and R2 (-13/14aa) deletions (or indels) and switched to a non-ACE2 receptor, coupled with the emergence of clade 2-specific residues that further restricted ACE2 usage. In contrast, clade 1a and 1b viruses underwent Region 1 insertion (or indels with increased residue numbers), generating the longest (8aa) Region 1 for underpinning the interactions, thereby achieving the highest multi-species ACE2 adaptiveness. Some clade 1b viruses may have undergone further or different indels events in Region 1 (-2 to -4aa) and Region 2 (-9aa), resulting in RBM type-II (e.g. RshSTT200 and GX_P1E) and type-III (e.g. Rc-o319 and Rc-kw8), respectively, with moderate multi-species ACE2 adaptiveness. (Fig.\u00a05d, e).\n\nWhile Region 1 is shorter than Region 2, it exhibits more diverse sequence changes in ACE2-dependent sarbecoviruses, fine-tuning the species-specific ACE2 adaptiveness. By contrast, no further RBM indel change was observed among all 172 clade 2 sarbecoviruses. Intriguingly, despite high sequence variability in Region 1, only two out of the 268 sequences, BM48-31 and BB9904, lack a G in this region. In RBM type-I sarbecoviruses with 8aa length, most clade 1b viruses have two conserved G (2\u2009G), whereas most 1a viruses have an SG or TG (Fig.\u00a05c, e).\n\nFrom a structural aspect, indels in Region 1 resulted in different loop lengths, with a G close to the turn of the loop conferring flexibility. The elongated Region 1 loop allows closer distance and potential H-bond formation with ACE2, reinforcing the patch 2 ACE2 interaction network along with the Region 3 loop, which explains the superior multi-species ACE2 adaptiveness of RBM type-I sarbecoviruses (Fig.\u00a05f).\n\nThe importance of residue usage in Region 1 is supported by the data showing a dramatic decrease in multi-species ACE2 usage in SARS-CoV-2 carrying a G447Y mutation (Fig.\u00a05g and Supplementary Fig.\u00a010a\u2013c). Additionally, we observed a reduced multi-species ACE2 adaptiveness of the \u201cG-free\u201d 4aa deletion mutant, SARS-CoV-2-\u0394GGNY (Region 1: DSKVNY), compared to SARS-CoV-2-\u0394KVNY (Region 1: DSGGNY) (as shown in Fig.\u00a03), the former only recognize R.alc ACE2, similar to the phenotype of BM48-31 which also lacks G in region 1 (Fig.\u00a05g and Fig.\u00a02a, b). SARS-CoV-2-\u0394GGNY may employ a similar ACE2 recognition mode as BM48-31, considering the importance of position 31\u2009R.alcACE2 for both viruses in a swap mutagenesis experiment based on R.alc ACE2 and its closely related orthologue, R.fer ACE2 (Supplementary Fig.\u00a011a\u2013d). This suggests that Region 1 deletion might be more tolerable for binding with multiple ACE2 orthologues if smaller side chains, like G, are maintained in this region.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53029-3/MediaObjects/41467_2024_53029_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53029-3/MediaObjects/41467_2024_53029_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53029-3/MediaObjects/41467_2024_53029_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53029-3/MediaObjects/41467_2024_53029_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53029-3/MediaObjects/41467_2024_53029_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The persistent evolution of sarbecoviruses in Rhinolophus bats drives the emergence of sarbecovirus clades with varying RBM sequences. Frequent sequence changes, particularly indels within the RBM, pose challenges in predicting the potential of sarbecoviruses to cross species barriers and spillover to humans. To better investigate the influence of indels and other determinants on multi-species ACE2 usage, we propose a new RBM indel-based classification, categorizing all currently identified sarbecoviruses into four distinct RBM indel types.\n\nOur functional data, combined with extensive sequence analyses, provide a comprehensive profile of the multi-species ACE2 tropism of sarbecoviruses belonging to specific clades and RBM indel types (Supplementary Fig.\u00a012). Despite narrower ACE2 tropism, all tested sarbecoviruses carrying single RBM deletion in either Region 1 or Region 2 exhibited confirmed ACE2 usage, typically adapted to ACE2 from their hosts. Furthermore, we hypothesize an evolutionary history regarding the emergence of sarbecoviruses with distinct RBM indel types. As the number of sarbecovirus sequences from different clades increases, the intricate evolutionary history of sarbecoviruses remains to be updated or amended.\n\nThe driving force behind the emergence of different sarbecovirus Region 1 and Region 2 indels remains elusive. Virus recombination may play a crucial role, as the RBM or even Region 1 has been predicted as a breaking point for combinations between sarbecoviruses54. Although various NTD-indels emerged in SARS-CoV-2 during the pandemic, no indels have been detected in RBM Region 1 or Region 2 in prevalent variants, suggesting a different evolution mechanism of RBM indels formation of various sarbecoviruses in bats compared to SARS-CoV-2 in humans68.\n\nOur results reveal a coevolution between sarbecovirus indels and multi-species ACE2 adaptiveness. Remarkably, the fine-tuning of RBM Region 1 through various indels and specific side chains promotes the emergence of sarbecoviruses with distinct multi-species ACE2 usage spectra. This could be attributed to the dispensable trait of Region 1 for interaction with a specific ACE2 orthologue (including hACE2), compensated by the Region 3 loop without indels. Additional interactions mediated by the extended Region 1 loop in RBM type-I sarbecoviruses might be crucial for achieving broader host tropism and facilitating host jumping. Interestingly, a conserved G within Region 1 suggests that greater flexibility or the absence of a large side chain in this site may confer certain evolutionary advantages. Comparatively, indels in Region 2 are less diverse than in Region 1 and generally have a more dramatic impact on ACE2 recognition, or even result in a receptor switch to a yet-unknown receptor. It is worth noting that limited multi-species ACE2 adaptiveness may restrict viral host jumping ability. However, this does not preclude the ability to use or adapt to hACE2, as is exemplified in Khosta-2 or other clade 1b or 3 sarbecovirus mutants described in this and prior studies3,28,45,57,58.\n\nFilling RBM deletions with SARS-CoV-2 counterparts does not guarantee a broader ACE2 usage spectrum and may sometimes result in reduced or lost ACE2 usage. This underscores the enhanced ACE2 adaptiveness achieved during adaptive evolution, with both length and residues being optimized in specific indels. Consequently, substituting the entire loop sometimes is necessary for achieving higher ACE2 compatibility. However, RBM type-IV sarbecoviruses, even after gap-filling or entire RBM substitution, remained unable to use any ACE2 orthologues, which led to the identification of clade-specific determinants outside the RBM that restrict ACE2 usage, likely due to adaptation to another receptor usage. Some critical RBD core residues are underneath the RBM, indirectly restricting ACE2 binding by affecting the RBM conformation. Future structural analysis could elucidate how these determinants affect receptor recognition.\n\nA main limitation of this study is our analyses are based on the publicly available sarbecovirus sequences which may not recapitulate all authentic sarbecoviruses in nature. There could be additional RBM indel types yet to be discovered and evaluated. Although we tried to include most S glycoprotein sequences with distinct features, there might still be exceptions. For example, certain RBM type-II sarbecoviruses not tested might achieve a broad ACE2 tropism through compensatory interactions not contributed by RBM region 1. Moreover, we acknowledge that it is insufficient to predict tropism patterns solely based on RBD clades and RBM indel types, as the pattern can be affected by single restriction residues. Considering the current data of restrictive clade 2-specific RBD core residues are based on RBM type IV chimeras with SARS-CoV-2 RBM, other ways of acquiring ACE2 binding may exist for these viruses harboring different RBM sequences. It should also be noted that although ACE2 compatibility is a primary barrier for sarbecoviruses to cross species, efficient ACE2 recognition alone does not guarantee susceptibility at the animal level. Other factors, such as host protease, immune response, and viral replication efficiency, also affect host tropism, which can be verified by authentic viruses and in vivo studies in the future50,69,70.\n\nIn conclusion, our RBM indel-type classification offers a more precise way to describe sarbecoviruses when combined with RBD phylogenetic information. Our functional ACE2 usage data elucidate the mechanism governing multi-species ACE2 usage and adaptiveness, shaped by multiple factors such as the presence and features of RBM loop deletions, RBM disulfide bridges, critical RBM residues for direct interaction, and restrictive residues within and outside the RBM. These findings establish a solid scientific foundation for risk assessment and viral surveillance to mitigate potential future zoonoses caused by these viruses.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "BHK-21 (ATCC, CCL-10), Caco2 (ATCC, HTB-37), HEK293T (ATCC, CRL-1586) cells\u00a0and their derivatives were maintained in Dulbecco\u2019s modified eagle medium (DMEM; Gibco) supplemented with 10% fetal bovine serum (FBS), 2.0\u00a0mM L-Glutamine, 110\u2009mg/L sodium pyruvate, and 4.5\u2009g/L D-glucose. I1-Hybridoma (CRL-2700), secreting a monoclonal antibody targeting VSV glycoprotein (VSV-G), was maintained in Minimum Essential Medium with Earle\u2019s salts and 2.0 mM L-Glutamine (MEM; Gibco). All cells were cultured at 37\u00b0C in 5% CO2 with regular passage every 2-3 days.\n\nSarbecovirus S glycoprotein sequences are retrieved from NCBI Virus and GISAID databases. The keywords used for sequence search include \u201cBetacoronavirus\u201d, \u201cSequence length between 1000-1400\u201d \u201cprotein\u201d and \u201cNOT Homo sapiens\u201d for NCBI and \u201cbat\u201d, \u201cpangolin\u201d, \u201ccivet\u201d, coronaviruses for GISAID. A comprehensive collection of 2,318 Betacoronavirus S glycoprotein sequences was obtained. After extracting 876 sarbecovirus sequences through phylogenetic analysis using Geneious, the dataset was refined to 265 unique sequences for further analysis by excluding redundant entries. The ACE2 orthologues sequences were summarized by previous reports33. Several additional ACE2 orthologues tested in this study include Rhinolophine Malayanus (Provided by Professor Huanbin Zhao, Wuhan University, China), Rhinolophus shameli (GenBank: MZ851782), Rhinolophus cornutus (GenBank: BCG67443.1), Rhinolophus sinicus isolate Rs-3357(GenBank: KC881004.1), Rhinolophus affinis (GenBank: QMQ39227.1), Manis javanica (Pangolin)(GenBank: XP_017505752.1), Mouse (GenBank: NP_001123985), Camelus (GenBank: XP_006194263), Civet (GenBank: Q56NL1), and\u00a0Rhinolophus alcyone (GenBank: ALJ94035.1). Human Aminopeptidase N precursor (APN) (GenBank: NP_001141.2) was included as a negative control. The sources and accession numbers of the receptors and the 268 sarbecoviruses were summarized in Supplementary Data\u00a01.\n\nAmino acid or nucleotide sequences from viruses or ACE2 orthologues were aligned using Mafft v7.45071. Phylogenetic trees were generated with IQ-Tree (version 2.0.6)72 using a Maximum Likelihood model with 1000 bootstrap replicates. Tree annotations were performed using iTOL (https://itol.embl.de/). Sequence identities were analyzed by Geneious Prime (https://www.geneious.com/) after being aligned by Mafft. The residue usage frequency (Sequence Logo analysis) was generated by the Geneious Prime.\n\nThe coding sequences of various coronavirus S glycoproteins and their derivatives were human codon optimized and cloned into the pCAGGS vector with C-terminal 18-amino acids replaced with an HA tag (YPYDVPDYA) for improving VSV pseudotyping efficiency and enabling detection73,74. The plasmids for expressing ACE2 orthologues are constructed by inserting human codon-optimized ACE2 sequences into a lentiviral transfer vector (pLVX-IRES-puro) with a C-terminal 3\u00d7FLAG-tag (DYKDHD-G-DYKDHD-I-DYKDDDDK) for detection. The plasmids expressing the recombinant coronaviruses RBD human IgG Fc (RBD-hFc) fusion proteins were constructed by inserting RBD sequences corresponding to SARS-CoV-2 (aa331\u2212524) containing an N-terminal CD5 secretion signal peptide (MPMGSLQPLATLYLLGMLVASVL) and C-terminal hFc-twin-strep tandem tags for purification and detection.\n\nACE2 expressing stable cell lines were established by lentiviral transduction33,75. Briefly, lentivirus carrying the ACE2 genes was generated by co-transfecting pLVX-IRES-puro-ACE2 orthologues, pMD2G (plasmid no. 12259; Addgene), and psPAX2 (plasmid no. 12260; Addgene) into HEK293T cells. HEK293T or Caco2 cells were subsequently transduced with the lentiviruses, and the stable cells expressing ACE2 orthologues were selected in the presence of puromycin (1\u2009\u03bcg/mL). The expression levels of ACE2 orthologues were assessed using an immunofluorescence assay33. Briefly, HEK293T cells were fixed with 4% paraformaldehyde for 10\u2009min at room temperature, permeabilized with 0.2% Triton X-100/PBS for 10\u2009min, and blocked with 1% BSA for 30\u2009min at 37\u2009\u00b0C. Subsequently, the cells were incubated with M2 antibody (anti-FLAG-tag, catalogue no. F1804A-5MG; Sigma) at 4\u2009\u00b0C for 1\u2009hour. After three washes with PBS, the cells were treated with 2\u2009\u03bcg/mL Alexa Fluor 594-conjugated goat anti-mouse IgG (catalogue no. A11032; Thermo Fisher Scientific). Nucleus was stained blue with Hoechst 33342 (1:5,000 dilution in PBS). Images were captured with a fluorescence microscope (MI52-N; Mshot). Relative fluorescence unit of Alexa Fluor 596 and Hoechst 33342 was quantified by Thermo Varioskan LUX. The expression of most ACE2 orthologues was also verified by Western Blot analysis in our previous reports33.\n\nRecombinant RBD-hFc fusion proteins or ACE2 ectodomains (amino acid sequences 18-740 correspond to Human ACE2) fused with FLAG-strep-tag proteins were generated through transient transfection of HEK293T cells using Lipofectamine 2000. The transfected cells were cultured in SMM 293-TIS Expression Medium (Serum-free, without L-Glutamine) (Sino Biological). The supernatant, containing the recombinant proteins, was collected at 2, 4, and 6 days post-transfection, and the expression was confirmed through Western Blot analysis using the Goat Anti-Human IgG-Fc secondary Antibody (HRP) (SinoBiological Inc, SSA002) for RBD or the M2 antibody for ACE2. Protein purification was performed using Protein A/G Plus Agarose (Thermo Fisher Scientific) for RBD and Strep-Tactin\u00aeXT 4Flow\u00ae high capacity resin (IBA) for ACE2 ectodomains. The protein concentration was quantified using the BCA protein determination kit (EpiZyme) and SDS-PAGE with Coomassie blue staining was employed for analysis.\n\nHEK293T cells stably expressing ACE2 were seeded in poly-D-lysine-treated 96-well plates. After 12\u2009hours, with cells were incubated with RBD-hFc protein (2\u2009\u03bcg/mL) in growth medium for 0.5\u2009hours at 4\u2009\u00b0C. Subsequently cells were washed with Hanks\u2019 Balanced Salt Solution (with Ca2+ & Mg2\u2009+\u2009)(HBSS)\u00a0twice, and then treated with Alexa Fluor 488-conjugated goat anti-human IgG (catalogue no. A11013; Thermo Fisher Scientific) at a concentration of 2\u2009\u03bcg/mL in DMEM with 2% FBS for 30\u2009minutes (min) at 4\u2009\u00b0C. Hoechst 33342 (1:5,000 dilution in PBS) was utilized for nuclear staining. Following fixation with methanol at 25\u2009\u00b0C for 5\u2009minutes and washed once by HBSS, images were captured by fluorescence microscopy (MI52-N; Mshot), and the fluorescence intensity was analyzed using Thermo Varioskan LUX Alexa. RFUs of each sample were normalized by subjecting background signals in control cells (expressing hAPN) before analysis. The RFUs showing negative values after subtraction were presented as zero.\n\nHEK293T cells stably expressing ACE2 orthologues (R.aff, R.sha, R.alc, R.mal, and R.cor) were cultured in 6-well plates for 12\u2009hours. Cells were detached by 5\u2009mM EDTA and washed twice by PBS, and then incubated with indicated proteins (RaTG13 RBD, RshSTT200 RBD, BM48-31 WT RBD, BM48-31 A480Y RBD, RmYN05 RBD, Rc-o319 RBD with hFc tags) at a concentration of 20\u2009\u03bcg/mL for 30\u2009min at 4\u2009\u00b0C. Following three PBS washes, cells were stained with 488-conjugated goat anti-human IgG (1:1000, Alexa Fluor) for 30\u2009min. Subsequently, flow cytometry analysis was performed using a CytoFLEX analyzer, collecting 10,000 events per sample. In a separate assay demonstrating the sensitivity of live cell binding assay, HEK293T cells expressing hACE2 were plated 12\u2009hours before incubation with two-fold serial diluted SARS-CoV\u22122 RBD-hFc (from 20\u2009\u03bcg/mL) for 30\u2009min. After three PBS washes, cells were stained with 488-conjugated goat anti-human IgG (1:1000, Alexa Fluor) and subjected to Flow cytometry analysis. For the pseudoviruses entry assays, GFP expressing VSV pseudotypes was 10-fold serial diluted from 1\u2009\u00d7\u2009106 TCID50/mL. After 12\u2009hours post-infection incubation, cells were washed with PBS and trypsinized for analysis. FlowJo V10 software was employed for data analysis. Gating strategy is illustrated in Supplementary Fig.\u00a013.\n\nThe Octet RED96 system (ForteBio, Menlo Park, CA) was employed to determine the apparent affinity (KD, app, due to the potential dimerization of\u00a0ACE2) between the RBD and ACE2. The buffer for analysis was phosphate buffer saline with 0.05% Tween20 (PBST). The RBD (10\u2009\u03bcg/mL) was captured on ProA biosensors, followed by binding of ACE2 ectodomains at 2-fold serial dilutions ranging from 500\u2009nM for 120\u2009s, followed by dissociated in the PBST for additional 300\u2009s. Analysis was conducted with curve-fitting kinetic with global fitting with a 1:1 binding mode using ForteBio Octet analysis software v12.2.0.20 (ForteBio, Menlo Park, CA). Mean KD, app values were derived by averaging all binding curves that conformed to the theoretical fit with an R2 value\u2009\u2265\u20090.95.\n\nPseudovirus incorporating coronaviruses S glycoproteins were produced using a vesicular stomatitis virus (VSV)-based system with slight modifications to a well-established protocol73,76,77. In general, HEK293T cells were transfected with plasmids expressing S proteins through Lipofectamine 2000 (Biosharp, China). After 24\u2009hours, the transfected cells were infected with VSV-\u0394G-fLuc-GFP (1\u00d7106 TCID50/mL) diluted in DMEM followed by a two-hour incubation on a shaker at 37\u2009\u00b0C, the cells were replenished with DMEM containing anti-VSV-G monoclonal antibody (I1, 1\u2009\u03bcg/mL). After 24\u2009hours, the pseudovirus-containing supernatant was harvested, centrifuged at 13,523 \u00d7 g for 5\u2009min at 4 \u00b0C, and stored at \u221280 \u00b0C. For the viral entry assay, the HEK293T cell lines expressing various ACE2 orthologues were inoculated with pseudotyped viruses in DMEM with 10% FBS. In general, 3\u2009\u00d7\u2009104 trypsinized cells were incubated with pseudovirus (1.5\u2009\u00d7\u2009105 TCID50/100\u2009\u03bcL) in a 96-well plate to allow cell attachment and pseudovirus entry. At 16-20 hpi\u00a0(hours post infection), images of the infected cells were captured by a fluorescence microscope (MI52-N; Mshot). Intracellular luciferase activity was determined using a Bright-Glo Luciferase Assay Kit (Promega Corporation, E2620) and measured with a Thermo Varioskan LUX, SpectraMax iD3 Multi-well Luminometer (Molecular Devices) or a GloMax 20/20 Luminometer (Promega Corporation).\n\nPlasmids for rescuing propagation-competent (pc) VSV-CoV genetically encoding HKU3 and HKU3 S\u2009+\u2009NNSVGD\u2009+\u2009RBM spike glycoproteins were generated by replacing the fLuc coding sequences in the vector pVSV-\u0394G-fLuc-GFP\u00a0with coronavirus spike coding sequences75. Reverse genetics experiments were conducted to rescue recombinant pcVSV-CoV according to a previously described protocol77. BHK-21 cells of 80% confluence in a 6-well plate were infected with recombinant vaccinia virus expressing T7 RNA polymerase (VVT7, a gift from Mingzhou Chen\u2019s lab, Wuhan University) at a multiplicity of infection (MOI) of 5 for 45\u2009minutes at 37\u2009\u00b0C. Subsequently, the vvT7 was removed by PBS wash, and the cells were transfected with the pVSV-dG-GFP-S plasmids and helper plasmids in a ratio of 5:3:5:8:1 (pVSV-dG-GFP-S: pBS-N: pBS-P: pBS-G: pBS-L). Supernatant containing pcVSV-CoV (P0) was collected at 48\u2009hours post-transfection and filtered through a 0.22-\u03bcm filter to remove vvT7. Subsequently, Caco2 cells transfected with plasmids expressing VSV-G for 24\u2009hours were further infected with P0 supernatant for VSV-G efficient virus amplification assisted by VSV-G, which generated the passage 1 (P1) supernatant. P1 viruses were further amplified in Caco2 cells stably expressing either hACE2 or an HKU3-customized viral receptor, in the presence of anti-VSV-G antibody (I1-Hybridoma supernatant), resulting in passage 2 (P2) viruses carrying HKU3 S\u2009+\u2009NNSVGD\u2009+\u2009RBMSARS-CoV-2 or HKU3 S glycoproteins, respectively78. For a typical virus amplification assay, 3\u2009\u00d7\u2009104 trypsinized cells were incubated with pcVSV-CoV (1\u2009\u00d7\u2009104 TCID50/100\u2009\u03bcL) in a 96-well plate. For the hACE2 antibody h11B11 neutralization assay, cells were incubated with indicated concentrations of antibody for 1\u2009hour at 4\u2009\u00b0C, washed by PBS, and then incubated with viruses. GFP images were captured after 24\u2009hours post-infection.\n\nThe SARS-CoV-2 WT strain (IVCAS 6.7512) was provided by the National Virus Resource, Wuhan Institute of Virology, Chinese Academy of Sciences. The BA.1 strain (YJ20220223) was provided by Hubei Provincial Center for Disease Control and Prevention. SARS-CoV-2 authentic viruses-related experiments were conducted in ABSL-3 facility at Wuhan University with the approval from the Biosafety Committee of ABSL-3 lab. HEK293T cells expressing ACE2 orthologues were seeded in poly-lysine-treated 96-well plates (1.25\u2009\u00d7\u2009105 cells/well). At 12\u2009hours post seeding, SARS-CoV-2 strains (WT and Omicron BA.1) were introduced to different stable cells and incubated for 1-2\u2009hours. Following a medium change to DMEM with 2% FBS, cells were cultured for 24\u2009hours, fixed with methanol, and treated with anti-SARS-CoV-2 Nucleocapsid (N) antibody (catalogue no. 40143-MM05; Sino Biological) at 1:1000 for one hour at 37\u2009\u00b0C. After PBS wash, cells were treated with a secondary antibody (Alexa Fluor 594) and Hoechst 33342 (1:10,000 dilution in PBS) for nuclei staining. Images were captured using a fluorescence microscope (MI52-N, Mshot, China).\n\nProtein structures were predicted by AlphaFold2 and HDOCK79,80,81. Briefly, AlphaFold2, implemented in ColabFold, was utilized with default settings for predicting the protein structures of various sarbecovirus RBDs and ACE2 orthologues. The top-ranked model was used for all subsequent analyses. The docking of the ACE2 ectodomain in complex with RBD was accomplished using HDOCK (v.1.1). Structural representations and analyses were carried out within ChimeraX\u00a0(v.1.8). The hydrogen bonds and clashes between the displayed amino acids were analyzed using the H-bonds and clashes command. The following cryo-EM complex structures in the PDB database were also used for structural analysis in this study: human ACE2/SARS-CoV-2-RBD (Protein Data Bank 6M0J), human ACE2/SARS-CoV-1-RBD (3SCI), human ACE2/RaTG13-RBD (7DRV), human ACE2/GX_P2V-RBD (7DDP), human ACE2/SARS-CoV-2 alpha variant-RBD (7EKF), human ACE2/RshSTT200-RBD (7XBH), Rhinolophus alcyone ACE2/PRD-0038-RBD (8U0T), and RsYN04 RBD/antibody S43 (8J5J).\n\nTo examine the intracellular sarbecoviruses S glycoprotein expression levels, HEK293T cells were transfected with plasmids encoding the viral S glycoproteins fused with a C-terminal HA-tag. After 24\u2009hours, cells were washed with PBS, lysed on ice for 10\u2009min in 2% TritonX-100/PBS containing 1\u2009mM PMSF (Beyotime, ST506). The cell lysates were clarified by centrifugation at 13,523 \u00d7 g for 5\u2009mins at 4\u2009\u00b0C. The supernatants were mixed with 1:5 (v/v) 5\u00d7 SDS-loading buffer and incubated at 95\u2009\u00b0C for 5\u2009min. For evaluating the S glycoprotein levels in pseudovirus (PSV) particles in the cultured medium, PSV was concentrated with a 30% sucrose cushion (30% sucrose, 15\u2009mM Tris\u2013HCl, 100\u2009mM NaCl, 0.5\u2009mM EDTA) at 20,000 x g for 1.5\u2009hours at 4\u2009\u00b0C. The concentrated PSV was then resuspended in 1\u00d7SDS loading buffer and incubated at 95 \u00b0C for 30\u2009min. Following SDS-PAGE and PVDF membrane transfer, the blots were blocked with 10% milk in TBST containing 0.1% TBS (20\u2009mM Tris-HCl pH 8.0, 150\u2009mM NaCl) supplemented with 0.05% Tween-20 at room temperature for 1\u2009hour. Primary antibodies targeting HA (MBL, MBL-M180-3), \u03b2-tubulin (Immunoway, YM3030), or VSV-M (Kerafast, EB0011) were applied at a 1:10,000 dilution in TBST with 1% milk. After three washes with TBST, blots were incubated with the secondary antibody Peroxidase AffiniPure Goat Anti-Mouse IgG (H\u2009+\u2009L) (Jackson Immuno Research, 115-035-003). Blots were further washed three times before chemiluminescence detection (SQ201, Yamei Biotech) using the ChemiDoc MP Imaging System (Bio-Rad). Uncropped scans of all blots in the Figures are supplied in the Source Data file, and the uncropped scans of those presented in the Supplementary Figs. were included at the end of the Supplementary Information file (Supplementary Fig.\u00a014).\n\nThe global distribution data of bat species were obtained from the IUCN Red List of Threatened Species 2020, the base layer of the map (version 5.1.1) was sourced from Natural Earth, available at (https://www.naturalearthdata.com/downloads/110mcultural-vectors/). GeoScene Pro 21 was utilized to visualize and analyze the bat distribution data.\n\nMost experiments were independently conducted 2\u22124 times with similar results, each with 2-3 biologically independent replicates. Representative results were shown. Heat maps were plotted based on combined data of mean values (at least n\u2009=\u20092 biologically independent wells of cells) of RLU or RFU that were obtained in one or several experiments, with background (control cells expressing APN) signals subtracted. Most data are presented as means \u00b1 standard deviation (s.d.) as indicated in the figure legends. All statistical analyses were conducted using Prism 9 software (GraphPad). Two-tailed unpaired (Student\u2019s) t-test was performed if only two conditions were compared. One-way ANOVA analysis, followed by Dunnett\u2019s test, was employed for multiple comparisons. The association between the entry/binding efficiency and the presence of RBM disulfide was assessed using the two-sided chi-squared test. P\u2009<\u20090.05 was considered significant. *P\u2009<\u20090.05, **P\u2009<\u20090.01, ***P\u2009<\u20090.005, and ****P\u2009<\u20090.001; NS, not significant (P\u2009>\u20090.05).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. Bat distribution shapefiles are available at https://www.iucnredlist.org/search?query=bat&searchType=species. The structural models of the ACE2 protein and RBD protein were downloaded from Protein Data Bank with PDB ID: human ACE2/SARS-CoV-2-RBD (6M0J), human ACE2/SARS-CoV-1-RBD (3SCI), human ACE2/RaTG13-RBD (7DRV), human ACE2/GX_P2V-RBD (7DDP), human ACE2/SARS-CoV-2 alpha variant-RBD (7EKF), human ACE2/RshSTT200-RBD (7XBH), Rhinolophus alcyone ACE2/PRD-0038-RBD (8U0T), and RsYN04 RBD/antibody S43 (8J5J). The accession numbers of 268 sarbecovirus spike proteins and 56 ACE2 orthologues used in this study are available in the Supplementary Data\u00a01. The data generated in this study are provided in the Supplementary Information. Uncropped scans of all blots in the Supplementary Figs. were included at the end of the Supplementary Information file (Supplementary Fig.\u00a014).\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Change history", + "section_text": "A Correction to this paper has been published: https://doi.org/10.1038/s41467-025-61790-2", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Ksiazek, T. G. et al. A novel coronavirus associated with severe acute respiratory syndrome. N. Engl. J. Med. 348, 1953\u20131966 (2003).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nZhou, P. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270\u2013273 (2020).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nStarr, T. N. et al. ACE2 binding is an ancestral and evolvable trait of sarbecoviruses. 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We thank Ming Guo (Wuhan University) for his help in conducting SARS-CoV-2 authentic viruses-related experiments in ABSL-3. We thank Qiang Ding (Tsinghua University) for his kind offer of several plasmids expressing mammalian ACE2 orthologues. We also want to express our gratitude to the core facilities and ABSL-3 laboratory of the Key Laboratory of Virology, Wuhan University.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Jun-Yu Si, Yuan-Mei Chen, Ye-Hui Sun.\n\nState Key Laboratory of Virology, College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei, China\n\nJun-Yu Si,\u00a0Yuan-Mei Chen,\u00a0Ye-Hui Sun,\u00a0Meng-Xue Gu,\u00a0Mei-Ling Huang,\u00a0Lu-Lu Shi,\u00a0Xiao Yu,\u00a0Xiao Yang,\u00a0Qing Xiong,\u00a0Cheng-Bao Ma,\u00a0Peng Liu\u00a0&\u00a0Huan Yan\n\nWuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, China\n\nZheng-Li Shi\n\nGuangzhou Laboratory, Guangzhou International Bio Island, Guangzhou, China\n\nZheng-Li Shi\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization, H.Y., and J.Y.S.; methodology, J.Y.S., Y.M.C, Y.H.S, M.X.G, C.L.W., C.B.M, P. L., Q.X., L.L.S., M.L.H., X.Yu., X.Yang., Z.X.M., Y.C.S.; data analysis, J.Y.S., Y.M.C., Y.H.S., M.X.G.; writing\u2014original draft, H.Y., J.Y.S., Y.M.C.; writing\u2014review & editing, H.Y., J.Y.S., Z.L.S.; supervision and funding acquisition, H.Y.\n\nCorrespondence to\n Huan Yan.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Frank Kirchhoff and the other anonymous reviewer(s) for their contribution to the peer review of this work. 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Sarbecovirus RBD indels and specific residues dictating multi-species ACE2 adaptiveness.\n Nat Commun 15, 8869 (2024). https://doi.org/10.1038/s41467-024-53029-3\n\nDownload citation\n\nReceived: 06 March 2024\n\nAccepted: 24 September 2024\n\nPublished: 14 October 2024\n\nVersion of record: 14 October 2024\n\nDOI: https://doi.org/10.1038/s41467-024-53029-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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b/bf2909ca9a3a873bfac9782db9aa0cc1aa2e0b4db29e341a0f54f631a83dc88c/metadata.json @@ -0,0 +1,158 @@ +{ + "title": "Structural basis for synthase activation and cellulose modification in the E. coli Type II Bcs secretion system", + "pre_title": "Structural basis for synthase activation and cellulose modification in the\r\nE. coli Type II Bcs secretion system", + "journal": "Nature Communications", + "published": "11 October 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53113-8/MediaObjects/41467_2024_53113_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53113-8/MediaObjects/41467_2024_53113_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53113-8/MediaObjects/41467_2024_53113_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-50584", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-50595", + "http://doi.org/10.2210/pdb9FMT/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-50567", + "http://doi.org/10.2210/pdb9FMZ/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-50581", + "http://doi.org/10.2210/pdb9FMV/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-50571", + "http://doi.org/10.2210/pdb9FNN/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-50599", + "http://doi.org/10.2210/pdb9FP0/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-50563", + "http://doi.org/10.2210/pdb9FO7/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-50619", + "http://doi.org/10.2210/pdb9FP2/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-50633", + "http://doi.org/10.2210/pdb6YB3/pdb", + "http://doi.org/10.2210/pdb6TJ0/pdb", + "http://doi.org/10.2210/pdb6YBB/pdb", + "http://doi.org/10.2210/pdb6PCZ/pdb", + "http://doi.org/10.2210/pdb5FGN/pdb", + "http://doi.org/10.2210/pdb6WLB/pdb", + "http://doi.org/10.2210/pdb4P00/pdb" + ], + "code": [], + "subject": [ + "Bacterial structural biology", + "Cryoelectron microscopy", + "Multienzyme complexes", + "Polysaccharides" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4560365/v1.pdf?c=1728731148000", + "research_square_link": "https://www.researchsquare.com//article/rs-4560365/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53113-8.pdf", + "preprint_posted": "18 Jun, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Bacterial cellulosic polymers constitute a prevalent class of biofilm matrix exopolysaccharides that rely on conserved cyclic diguanylate (c-di-GMP)-dependent cellulose synthases. Polymer structure and modifications, however, depend on the ensemble of synthase modules and accessory subunits, thus defining several types of bacterial cellulose secretion (Bcs) systems. In E. coli, a BcsRQABEFG macrocomplex, encompassing the inner membrane and cytosolic subunits, and an outer membrane porin, BcsC, secure the biogenesis of phosphoethanolamine (pEtN)-modified cellulose. Resolution-limited studies have proposed different macrocomplex stoichiometries and its assembly and regulation have remained elusive. Using cryo-EM, we visualize the molecular mechanisms of BcsA-dependent recruitment and stabilization of a trimeric BcsG pEtN-transferase for polymer modification and a dimeric BcsF-dependent recruitment of an otherwise cytosolic BcsE2R2Q2 regulatory complex. We further demonstrate that BcsE, a secondary c-di-GMP sensor, remains dinucleotide-bound and retains the essential-for-secretion BcsRQ partners onto the synthase even in the absence of direct c-di-GMP-synthase complexation, likely lowering the threshold for c-di-GMP-dependent synthase activation. Such \u2018activation-by-proxy\u2019 mechanism could allow Bcs secretion system activation even in the absence of dramatic intracellular c-di-GMP increase and is reminiscent of other widespread synthase-dependent polysaccharide secretion systems where c-di-GMP sensing and/or synthase stabilization are carried out by key co-polymerase subunits.Biological sciences/Microbiology/BiofilmsBiological sciences/Structural biology/Electron microscopy/Cryoelectron microscopy", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "AnsoBcsMacroSuppInfoCombined540.pdfPDBfiles.docxAccess to PDB files, cryo-EM maps and validation reports", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Bacterial cellulosic polymers constitute a prevalent class of biofilm matrix exopolysaccharides that are synthesized by several types of bacterial cellulose secretion (Bcs) systems, which include conserved cyclic diguanylate (c-di-GMP)-dependent cellulose synthase modules together with diverse accessory subunits. In E. coli, the biogenesis of phosphoethanolamine (pEtN)-modified cellulose relies on the BcsRQABEFG macrocomplex, encompassing inner-membrane and cytosolic subunits, and an outer membrane porin, BcsC. Here, we use cryogenic electron microscopy to shed light on the molecular mechanisms of BcsA-dependent recruitment and stabilization of a trimeric BcsG pEtN-transferase for polymer modification, and a dimeric BcsF-dependent recruitment of an otherwise cytosolic BcsE2R2Q2 regulatory complex. We further demonstrate that BcsE, a secondary c-di-GMP sensor, can remain dinucleotide-bound and retain the essential-for-secretion BcsRQ partners onto the synthase even in the absence of direct c-di-GMP-synthase complexation, likely lowering the threshold for c-di-GMP-dependent synthase activation. Such activation-by-proxy mechanism could allow Bcs secretion system activity even in the absence of substantial intracellular c-di-GMP increase, and is reminiscent of other widespread synthase-dependent polysaccharide secretion systems where dinucleotide sensing and/or synthase stabilization are carried out by key co-polymerase subunits.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Bacteria have evolved sophisticated nanomachines for the biogenesis of extracellular biofilm matrix components, which allow them to achieve cooperative multicellularity, increased fitness, and homeostasis1,2,3,4. Across the bacterial domain of life, and especially in Gram-negative pathogens and eukaryotic host-associated microbes, such as E. coli or S. enterica serovar Typhimurium, biofilm formation is typically controlled by the RNA-based second messenger c-di-GMP, which is able to elicit multiple pathway-specific physiological responses via spatially restrained intracellular signaling mechanisms5. Bacterial synthase-dependent exopolysaccharide secretion systems are prevalent c-di-GMP sensor-effectors, which incorporate modules for dinucleotide sensing, glycan polymerization, transmembrane export, synthase regulation, and polymer modification, and thus determine the physicochemical properties of the mature biofilms and their interactions with environment and/or eukaryotic hosts4,6.\n\nBacterial cellulosic polymers are a widespread class of biofilm matrix exopolysaccharides that facilitate colonization of both biotic and abiotic environments and can have either beneficial or harmful effects on human health and economy6. Examples of beneficial cellulose production include the secretion of crystalline cellulose by acetic acid bacteria, which finds an increasing number of biotechnological applications6,7, or the secretion of acetylated cellulose by plant-colonizing biocontrol microorganisms8,9. Interestingly, while secretion of pEtN-cellulose by probiotic bacteria such as the E. coli Nissle 1917 strain can positively affect the intestinal epithelial barrier10,11, the lack of cellulose secretion by the deadly enteroaggregative E. coli O104:H4 strain in vivo has been associated with increased virulence3, indicating overall beneficial, antivirulence and/or anti-inflammatory properties for the polymer in the gut. In contrast, a combination of pEtN-cellulose and curli secretion by uropathogenic E.coli has been shown to increase adhesion to host bladder cells12 and thus likely contributes to chronic urinary tract infections. Therefore, the mechanistic understanding of bacterial cellulose synthesis and modifications across species and across cellulose secretion systems can find a number of applications, from materials science through the selection or engineering of crop-protective biocontrol symbionts to the development of infection-specific antimicrobial compounds.\n\nBacterial cellulose is synthesized by dedicated cellulose synthase enzymes1,6. The latter\u2019s core fold of a glycosyl transferase and a transmembrane export domains (GT and TMD, respectively) is structurally conserved across kingdoms6,13, however, bacterial BcsA orthologs typically incorporate an additional PilZ domain for c-di-GMP-dependent synthase regulation14,15 (Supplementary Fig.\u00a01a). Nevertheless, cellulose secretion and the actual polymer structure and modifications are determined not only by the ensemble of synthase modules, but also by a multitude of accessory subunits, which can assemble in several distinct types of Bcs secretion systems4,6,16 (Supplementary Fig.\u00a01b). In particular, type I Bcs systems are characterized by the expression of BcsD proposed to engage in a variety of intracellular scaffolds for both crystalline and modified cellulose secretion, type II systems feature the secondary c-di-GMP sensor BcsE and the pEtN-transferase BcsG components discussed below, and type III systems lack all BcsD, BcsE and BcsG subunits and often feature BcsK (periplasmic scaffolding only) rather than BcsC (scaffolding and outer membrane export) homologs in the periplasm4,16. In addition to the pEtN-modification conferred by BcsG in some Bcs secretion systems, others have been proposed to secrete acetylated cellulose thanks to a co-expressed alginate acetylation-like Wss complex4,16 (Supplementary Fig.\u00a01b).\n\nIn E. coli and other enterobacteria, the biofilm matrix is composed primarily of proteinaceous fimbriae, such as non-motile flagella and amyloid curli, and of cellulosic polymers produced by an E. coli-like or type II Bcs secretion system, which incorporates additional c-di-GMP-sensing and polymer modification modules3,6 (Fig.\u00a01a). In particular, E. coli cellulose biogenesis requires the concerted expression of two adjacent bcs operons (bcsRQABZC and bcsEFG), whose protein products assemble into a multicomponent BcsRQABEFG synthase macrocomplex embedded in the inner membrane, a periplasmic cellulase (BcsZ) and an outer membrane porin with periplasmic scaffolding repeats (BcsC)17 (Fig.\u00a01a). Whereas in vitro cellulose synthesis can be carried out with only the BcsA synthase and a C-terminal tail-anchor (TA) from the co-polymerase BcsB, micromolar concentrations of activating c-di-GMP, bivalent ions (e.g., Mg++) and uridine diphosphate glucose (UDP-glucose) as energetically preloaded substrate18, the rest of the Bcs macrocomplex components are either essential or act as enhancers for cellulose biosynthesis in vivo17. Of these, the BcsG subunit has been shown to interact with a E. coli type-specific N-terminal domain of the BcsA synthase17 and to introduce phosphoethanolamine (pEtN) moieties onto the nascent polymer via a pEtN-transferase domain in the periplasm19; the transmembrane peptide BcsF has been shown to recruit the secondary c-di-GMP-sensing protein BcsE20; and the latter\u2014together with an essential-for-secretion BcsRQ ATPase complex\u2014has been shown to form a cytosolic vestibule around the synthase\u2019s intracellular modules17,21 (Fig.\u00a01a). Whereas fragmentary insights into Bcs macrocomplex formation and components\u2019 structures have been obtained from several crystallographic and electron microscopy studies17,20,21,22, the overall stoichiometry, assembly and regulatory mechanisms have remained enigmatic.\n\na Left, E. coli bcs operon organization, BcsA domain architecture and thumbnail representation of the secretion system topology in the E. coli envelope. Middle and right, current structural insights into complex assembly from X-ray crystallographic and electron microscopy structures17,20,21,22. Adapted with modifications from Krasteva 20244 under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode). NTD N-terminal domain (green), TMD transmembrane domain (wheat), GT glycosyl transferase domain (light green), PilZ c-di-GMP-sensing PilZ domain (dark red), CT C-terminal tail with amphipathic helices (orange), OM outer membrane, PG peptidoglycan, IM inner membrane, ATP adenosine triphosphate, c-di-GMP cyclic diguanylate, pEtN phosphoethanolamine, NTPase* (light gray) catalytically incompetent nucleoside triphosphatase domain, REC* (orange) phosphorylation-incompetent receiver domain, GGDEF* (dark red) degenerate diguanylate cyclase domain. Multidomain BcsB hexamerizes to form a periplasmic crown shown in two different views. The carbohydrate-binding domains are shown in shades of light purple, the flavodoxin-like domains in blue and pink, and the C-terminal tail-anchor (TA) in dark purple. Densities for BcsANTD (green), BcsG (light blue), BcsE (tricolor) and BcsF (dark blue) have remained practically unresolved in the macrocomplex and are represented as thumbnails, whereas crystallographic snapshots have captured two different conformations of BcsE, shown on the right20,21.\u00a0The\u00a0relative REC* domain displacement and rotation are indicated\u00a0(45\u00a0\u00c5 and 144\u00a0degrees, respectively). The formation of a composite c-di-GMP binding site by RxxD (arginine-two residues-aspartate) motifs from both the degenerate REC* and GGDEF* domains increases the affinity for dimeric c-di-GMP from the low micromolar to nanomolar range21 (bottom right). b Cartoon representations of the here-in-resolved cryo-EM structure of the assembled, c-di-GMP-saturated Bcs macrocomplex in five different views. c Cartoon representations of the here-in-resolved cryo-EM structure of the assembled Bcs macrocomplex featuring a c-di-GMP-free BcsA.\n\nHere we use cryogenic electron microscopy (cryo-EM) to settle conflicting reports on the macrocomplex stoichiometry21,22 and reveal the molecular mechanisms of regulatory subunit recruitment and function. We demonstrate that BcsA\u2019s N-terminal domain adopts an amphipathic fold to recruit three copies of the pEtN-transferase BcsG, stabilized opposite of the previously reported hexameric BcsB crown. We further demonstrate that the single-pass inner membrane polypeptide BcsF folds into an X-shaped dimer to recruit and retain an asymmetric BcsE2R2Q2 complex around the synthase\u2019s cytosolic modules. In this so-formed vestibule, the N-terminal domain of a BcsR protomer plugs into a hydrophobic pocket at the BcsAGT-PilZ domain interface, and BcsQ buttresses the PilZ domain, likely stabilizing the catalytically competent state. Finally, we demonstrate that through a composite, interdomain c-di-GMP binding site BcsE acts as a higher-affinity dinucleotide sensor that can adopt discrete dimerization interfaces to maintain the activating vestibule components even in the absence of direct c-di-GMP-BcsA interactions. Together, our structural data suggest that the E. coli-like Type II Bcs secretion systems have evolved a cooperative activation-by-proxy mechanism to lower the threshold for c-di-GMP-dependent activation, as well as an additional synthase module for pEtN-transferase recruitment and efficient co-synthetic polymer modification.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53113-8/MediaObjects/41467_2024_53113_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "Bacterial BcsA orthologs are processive GT2 family synthases with a single cytosolic GT domain that uses UDP-glucose as substrate in an inverting, divalent metal ion-dependent mechanism of glycan polymerization, best studied in vitro in the Rhodobacter sphaeroides BcsAB heterodimeric complex15,23,24. Polymerization is coupled with inner membrane polysaccharide extrusion through a narrow pore in the BcsATMD-BcsBTA inner membrane complex translocating a non-modified, non-hydrated homopolymer. In the resting state, the BcsAGT active site is capped by a so-called gating loop, whose conformation is stabilized by interactions with the N-proximal BcsAPilZ domain linker that senses c-di-GMP23. In the presence of micromolar concentrations of dinucleotide the PilZ domain undergoes an ~18\u00b0 rotation and 4.4\u2009\u00c5 displacement around a C-proximal \u03b1-helical hinge, and the linker-gating loop-stabilizing interactions are released to yield a catalytically competent state15. Processive cycles of active site opening, substrate entry, gating loop closure, polymerization, and translocation are then determined by the presence of product vs. substrate in the active site and minute movements of a so-called finger helix in the bottom of the enzyme\u2019s active site6,24.\n\nWhereas BcsA itself is highly conserved, the secretion-competent synthase macrocomplexes are strikingly diverse across the bacterial clade4,6. In E. coli, in particular, the catalytic BcsAB tandem has been shown to associate with the ensemble of the inner membrane and cytosolic subunits in an approximately megadalton-sized secretory assembly. In it, the synthase associates in a non-canonical BcsA:BcsB stoichiometry with up to six BcsB copies whose donut-shaped periplasmic modules assemble into a superhelical crown with stacking carbohydrate-binding modules likely guiding the extruded polysaccharide into the periplasm and towards the BcsC periplasmic scaffold17,21. Additionally, the synthase has been found to associate stably with an essential-for-secretion BcsRQ tandem, with the secondary c-di-GMP-sensing protein BcsE, the inner membrane polypeptide BcsF and the pEtN-transferase BcsG17,21,22 (Fig. 1a). The stoichiometry and recruitment mechanisms of all of these latter components have remained under debate, mostly due to the limited resolutions of previously reported structural models in the literature17,21,22.\n\nHere we present cryo-EM structures of the E. coli Bcs macrocomplex positioning all seven BcsRQABEFG partners and multiple c-di-GMP-binding sites (Fig.\u00a01b, c and Supplementary Fig.\u00a02). We show that the Bcs macrocomplex contains a single BcsA synthase, which associates in the membrane with hexameric BcsB on one side and a trimeric BcsG pEtN-transferase complex on the other. We further show that BcsA\u2019s catalytic and c-di-GMP-sensing domains engage in extensive cytosolic interactions with the essential-for-secretion BcsRQ complex, present as a heterotetrameric BcsR2Q2 assembly. The latter is further retained by direct interactions between BcsQ and the C-terminal modules of dimeric BcsE, whose degenerate receiver (REC*) and diguanylate cyclase (GGDEF*) domains bind an intercalated dimeric c-di-GMP per BcsE protomer. The previously uncharacterized N-terminal domains (NTD) of BcsE, on the other hand, form a membrane-proximal head-to-head dimer of P-loop nucleotide triphosphatase (NTPase)-like modules. Remarkably, the latter\u2019s central \u03b2-sheets are complemented at each distal side by an additional \u03b2-strand from the extended cytosolic tails of an inner membrane-embedded BcsF dimer, whose X-shaped transmembrane helices positioned near the tail-anchors of synthase-distal BcsB copies from the crown. Together, these results demonstrate a definitive BcsR2Q2AB6E2F2G3 stoichiometry for the assembled E. coli Bcs macrocomplex, which binds up to six c-di-GMP molecules for maximal synthase activation and nascent polysaccharide modification (Fig.\u00a01b, c). Finally, we visualize the complex\u2019s intrinsic conformational plasticity, in which the regulatory BcsRQEF vestibule, and BcsE in particular, can resort to alternative protein interaction interfaces to maintain the activating BcsRQ partners onto the synthase\u2019s cytosolic modules even in non-saturating dinucleotide concentrations (Fig.\u00a01b, c).\n\nWe demonstrated previously that contrary to the canonical 1:1 BcsAB assemblies observed in purified samples from R. sphaeroides (Type III Bcs secretion system)23 and G. hansenii (Type I Bcs secretion system)25, in the assembled E. coli Bcs macrocomplex BcsA associates with a BcsB hexamer whose periplasmic modules of alternating carbohydrate-binding and flavodoxin-like domains (CBD1-FD1-CBD2-FD2) polymerize via a \u03b2-sheet complementation mechanism between the FD1n:FD2n+1 domains of adjacent BcsB protomers21. The structure of the hexameric periplasmic crown is refined here to 2.35\u2009\u00c5 resolution and reveals to near-atomic detail the molecular mechanism of BcsB polymerization, with more than 3300 \u00c52 interface surface and a free energy gain of \u221222.5\u2009kcal/mol between each pair of adjacent protomers (Supplementary Fig.\u00a03).\n\nWhereas earlier studies have visualized the overall fold of the transmembrane regions of the BcsA synthase and the C-terminal tail-anchor of the co-polymerase BcsB1 protomer21,22, here we position the transmembrane anchors for most of the remaining BcsB subunits and present the first structures of the regulatory BcsG and BcsF partners at side-chain resolution. The first and second BcsB copies engage in contacts with the BcsA synthase, where the C-terminal TA of BcsB1 fits in a groove formed by BcsA TM\u03b11\u20133 and is further encased by BcsANTD to complete the membrane export module, while BcsB2-TA engages in limited hydrophobic contacts with the loop connecting BcsA TM\u03b13 and TM\u03b14 at the periplasmic side of the inner membrane. Near the synthase-distal BcsB copies, on the other hand, positions an X-shaped transmembrane BcsF dimer. In particular, the BcsF tandem positions near the fourth and third BcsB protomers in the c-di-GMP-saturated macrocomplex (Fig.\u00a02b), and near the fifth and the fourth BcsB copies in the context of a dinucleotide-free synthase (Fig.\u00a02c). Each BcsF subunit features a single transmembrane helix, which upon exit from the inner membrane kinks into an amphipathic helical extension and a cytosolic C-terminal tail engaged in interactions with a BcsE N-terminal domain as further described below. Interestingly, neither BcsE, nor BcsF engage in direct protein-protein contacts with their intraoperon partner BcsG.\n\na Different views of a locally refined cryo-EM structure of the c-di-GMP-free BcsA-BcsBTA-BcsG3 assembly (BcsAG3 for simplicity) with corresponding electron densities (left) and cartoon representation (right). b, c Zoom-ins on the specific protein-protein interfaces with key residues shown as sticks and the electron density as a mesh. d Composite predicted structure of full-length BcsG (catalytic domain: X-ray structure of the E. coli BcsGCTD; NTD and linker, AlphaFold (AF)) and crystal structure of the lipid A pEtN-transferase from Neisseria meningitidis EptA. The flexible interdomain linkers are colored in purple. e Model for independent function of the three BcsG copies for substrate-extraction and cellulose modification. IM inner membrane, PE phosphatidyl-ethanolamine.\n\nUsing bacterial two-hybrid functional complementation (BACTH) assays, we demonstrated previously that the E. coli type-specific N-terminal domain of the synthase interacts specifically with the BcsG enzyme in cellulo17. In addition, BcsG has been shown to directly affect BcsA integrity in the membrane22,26 and in some strains to be essential for cellulose secretion17,26. Indeed, using local refinement to resolve the more dynamically associated pEtN-transferase (Fig.\u00a02 and Supplementary Fig.\u00a04), we show here that BcsANTD adopts an amphipathic fold and recruits three copies of the BcsG pEtN-transferase whose transmembrane N-terminal domains are tightly packed between BcsATMD and the sixth BcsB protomer of the crown, whereas the C-terminal catalytic BcsG modules remain unresolved in the structures. The BcsA N-terminus folds into a W-shaped series of amphipathic \u03b1\u2013helices whose connecting loops coordinate the BcsG protomers via conserved amino\u00a0acid motifs in an otherwise weakly conserved primary structure (Fig.\u00a02a\u2013c). Each of the BcsGNTD folds into 5 transmembrane helices (TM\u03b11\u20135, which anchor the protein in the inner membrane and, via the TM\u03b14\u2013TM\u03b15 connecting loop interact with BcsANTD, which in turn packs against an \u03b1-helical amphipathic hairpin formed by the BcsA C-terminus (Fig.\u00a02a\u2013c). The BcsG TM\u03b13\u2013TM\u03b14 linker region, on the other hand, folds into a short amphipathic \u03b1-helical loop at the periplasmic membrane interface, whereas TM\u03b15 is predicted to extend into a 48 residue-long flexible linker27, followed by a crystallographically characterized26,28 but here unresolved C-terminal pEtN-transferase domain (Fig.\u00a02a, d). Based on homology with other alkaline phosphatases, such as the Neisseria meningitidis lipid A pEtN-transferase EptA (NmEptA)29, the amphipathic helical loop and the extended interdomain linker could potentially assist the C-terminal catalytic domain in substrate-extraction by interactions with the polar headgroups of periplasm-facing phospholipids (phosphatidyl-ethanolamine (PE) in the case of BcsG) and/or could allow for significant conformational flexibility in substrate delivery to the target acceptor. Interestingly, in BcsG the amphipathic helical loops point outwards relative to the crown\u2019s lumen, where BcsB\u2019s stacked carbohydrate-binding domains are proposed to form the polysaccharide extrusion path (Fig.\u00a01). This suggests major conformational gymnastics of the catalytic C-terminal domains for pEtN extraction and transfer onto the nascent cellulosic polymer (Fig.\u00a02e), and could potentially explain the lack of resolved BcsGCTD-corresponding regions in the averaged electron density maps.\n\nRemarkably, the presence of three BcsG copies is in contrast with a previous assignment of densities from a low-resolution cryo-EM map of the macrocomplex to a dimeric BcsG enzyme22 and most of the reported mechanistic studies on active pEtN-transferases (including on the C-terminal periplasmic module of BcsG) present no substrate- or product-determined prerequisite for catalytic domain oligomerization26,28,29,30,31,32,33,34. This suggests that the three BcsG copies visualized here likely act independently from each other to dynamically sample the membrane for, extract, and transfer pEtN moieties from inner membrane PE onto the nascent polysaccharide (Fig.\u00a02e). Importantly, while this\u00a0work was under review a separate study reported independently the recruitment of trimeric BcsG via BcsANTD, based on lower-resolution cryo-EM data, subcomplex purification and AlphaFold modeling35. Together, these results further validate the experimental structural data presented here, and the two studies integrate and redress the structure-function model of pEtN-transferase association and function.\n\nWe previously demonstrated that the cellulose secretion enhancer BcsE can form equimolar BcsE2R2Q2 complexes with the essential-for-secretion BcsRQ tandem in solution, that BcsE is sequestered by BcsF to the membrane and that BcsE\u2019s N-terminal domain is necessary for stable cytosolic complex association with the synthase macrocomplex20. Nevertheless, how BcsE and BcsF interact, what structures they adopt in the secretory assembly, and even their actual membrane-bound stoichiometries have remained unresolved21,22.\n\nHere we show BcsE and BcsF interact in an asymmetric and heterotetrameric BcsE2F2 complex (Fig.\u00a01b, c and Fig.\u00a03a). In particular, BcsF adopts an X-shaped dimeric conformation within the inner membrane, stabilized by a hydrophobic N-proximal transmembrane interface burying 626 \u00c52 of surface area with free energy gain of \u221215.5\u2009kcal/mol (Fig.\u00a03b). At the C-termini, each BcsF protomer recruits a BcsE partner copy via cytosolic \u03b2-sheet complementation interactions with the central 9-stranded \u03b2-sheet of the interacting BcsENTD (Fig.\u00a03b). The BcsF C-terminal tail threads along a shallow hydrophobic patch onto BcsE\u2019s degenerate NTPase* domain and provides an overall charged solvent-exposed surface for the assembly (\u223c837\u2009\u00c5 buried interface with free energy gain of \u221212.9\u2009kcal/mol) (Fig. 3c). Consistent with the observed complex, BcsF truncations before or after P43 preceding the C-terminal cytosolic tail lead to incomplete Bcs macrocomplex assembly and corroborate the requirement for stable BcsF-BcsENTD interaction for vestibule complex recruitment (Fig.\u00a03c and Supplementary Fig.\u00a05). Importantly, the observed \u03b2-sheet complementation mechanism for BcsF-driven BcsE recruitment and BcsENTD dimerization (see below) is likely conserved across enterobacteria as shown in ColabFold and AlphaFold3-predicted models of a consensus BcsE2F2 complex derived from representative homologs across the enterobacterial clade (Supplementary Fig.\u00a06).\n\na Locally refined cryo-EM structure of the BcsE2F2 assembly from the c-di-GMP-saturated synthase macrocomplex shown as electron density and in cartoon. IM, inner membrane. b BcsF dimerization shown as Coulombic electrostatic potential-colored surface (left, default \u221210 to 10 range) and in cartoon and sticks (right). c BcsE-BcsF interactions. Left, BcsENTD is shown as a lipophilicity-colored surface (default \u221220 to 20 range), BcsF residues\u2014including the hydrophobic plug residues V46 and L52\u2014are shown as sticks. Right, recombinant expression and purification of the Bcs macrocomplex with various BcsF variants (BcsHisRQAHA-FLAGB + BcsstrepEF*G). Protein-specific bands are identified as previously17,21. BcsE and BcsA-specific signals are further detected by western blotting with epitope tag-specific antibodies in the bottom (representative data from three independent experiments). d The BcsENTD dimerization interface. e The BcsEREC* dimerization interface. f The c-di-GMP-binding dual I-site pocket in closed BcsE. All interface parameters were calculated with PISA57.\n\nThe cryo-EM structures presented here are consistent with the previously characterized tripartite architecture of BcsE, comprising a degenerate trio of an NTPase*, REC*, and GGDEF* domains (Fig.\u00a01a). Nevertheless, rather than engaging in head-to-tail interactions as proposed previously based on indirect BACTH interaction assays20,21, the two BcsENTD modules pack against each other in a head-to-head dimer, stabilized primarily by hydrophobic and \u03c0\u2013stacking interactions in the center and by the peripheral BcsF C-terminal tails at the periphery (747\u2009\u00c5 buried with free energy gain of \u22122.7\u2009kcal/mol at the BcsENTD dimer interface) (Fig.\u00a03d).\n\nThe REC*-GGDEF* domain tandem interacts with BcsQ via an extended C-terminal tail trailing along the BcsQ surface, as observed in crystallographic snapshots previously21. However the REC* domains, which are not in contact in the crystallized states, engage in head-to-head dimerization interactions mediated by a \u03b14\u2013\u03b25\u2013\u03b15 interface (Fig.\u00a03e), observed as a canonical REC domain dimerization interface in many phosphorylation competent response regulators36,37. An intercalated c-di-GMP dimer is found at each cis-interdomain interface of a closed BcsE21, stabilized by a composite R306ATD-R415TGD I-site tandem contributed by the corresponding REC* and GGDEF* domains, respectively (see below) (Fig.\u00a01a and Fig.\u00a03f). Finally, the two GGDEF* domains adopt different orientations relative to the apical BcsR2Q2 tandem consistent with the overall macrocomplex asymmetry. In the c-di-GMP-saturated macrocomplex, one BcsEGGDEF* copy adopts an overall interaction interface consistent with the previously reported crystallized states and contacts BcsAGT via its REC* module. The second BcsEGGDEF*, on the other hand, positions above the BcsQ dimer interface and is further stabilized by the \u03b2-strand connecting loops at the bottom of the BcsAPilZ domain barrel (Fig.\u00a03a). In the macrocomplex featuring a c-di-GMP-free synthase, the relative orientation of the REC* and GGDEF* BcsE modules are yet different and discussed in detail below.\n\nWe previously showed that, upon co-expression, BcsR and BcsQ stabilize and act as chaperones to each other via the formation of a heterotetrameric BcsR2Q2 complex20 with essential roles in Bcs system positioning, assembly, stability, and function17,21,38. Using X-ray crystallography and cryo-EM, we positioned the latter at the apical densities of the cytosolic vestibule formed around the synthase\u2019s PilZ domain, however, the limited electron density map resolution prevented us from deciphering the specific protein-protein interactions and their roles in cellulose secretion21. Here we locally refined the structure of the crownless Bcs macrocomplex complex to an average resolution of 2.85\u2009\u00c5, visualizing all interaction interfaces and coordinated nucleotide co-factors. An assymetric BcsR2Q2 complex is recruited to the membrane complex via BcsE\u2019s C-terminal elongated tails, where both BcsQ copies interact with the synthase\u2019s PilZ module (Fig.\u00a04a, b) and adopt the nucleotide-driven sandwich dimer conformation characteristic for the SIMIBI (SIgnal recognition particle, MinD, and BioD) family of protein-sorting NTPases to which BcsQ belongs21. Consistent with the previously reported crystal structures of the BcsR2Q2 complex21, the BcsR copies stabilize the ATP-bound BcsQ apical dimer via their V-shaped C-terminal tandem of \u03b1-helices (\u03b1C1 and \u03b1C2). Importantly, whereas one of the BcsR protomers is solvent-exposed and features an unresolved N-terminal domain, the other BcsR copy also interacts with the back of the BcsA catalytic module (Fig.\u00a04a\u2013c). Contrary to the crystallized states where BcsRNTD can adopt a \u03b2-hairpin conformation that threads onto the surface of dimeric BcsQ (Supplementary Fig.\u00a07), here residues D21-S30 fold into an N-proximal \u03b1-helix (\u03b1N) that U-turns into an extended linker before adopting the V-shaped C-terminal domain onto the BcsQ dimer interface (Fig.\u00a04c and Supplementary Fig.\u00a07). The resulting N-terminal hairpin nestles into a hydrophobic BcsAGT pocket via a L25-F29-L31-I34 plug at the tip and a I22:Y36 stabilizing interaction at the base. The latter isoleucine:tyrosine pair are thus brought to a distance of less than 4\u2009\u00c5, as compared to the \u223c40\u2009\u00c5 that separates them in the BcsQ-interacting crystallized state (Supplementary Fig. 7). The strictly conserved D21 positions between R367 from the BcsAGT domain and R792 in the middle of the C-proximal hinge that enables PilZ rotation upon BcsA:c-di-GMP complexation (Fig.\u00a04c). Overall, the BcsR:BcsA interaction interface buries 1059\u2009\u00c5 and contributes a free energy gain of \u22125.8\u2009kcal/mol (Fig.\u00a04b, c). The BcsAPilZ domain orientation is further stabilized by interactions between the \u03b24\u2013\u03b25 connecting loop of the PilZ barrel and N-proximal residues from BcsR-\u03b1C1, as well as by an extensive interface with the underlying BcsQ protomer (682\u2009\u00c5 buried with a free energy gain of \u22124.1\u2009kcal/mol) (Fig.\u00a04b, c). On the other side of the \u03b2-barrel, an intercalated c-di-GMP dimer is found coordinated between the arginines from the canonical R696RxxR motif in the N-proximal PilZ domain linker, the active site gating loop is unstructured, and the active site is substrate-accessible (Fig.\u00a04d). Together, the BcsEF-stabilized BcsRQA complex appears to induce or stabilize the synthase into a catalytically competent state, which is consistent with previous in vitro activity data demonstrating dramatic synthase activation in the presence of excess cytosolic vestibule components, with stimulatory effects observed even in the absence of c-di-GMP22. Consistent with the observed BcsR:BcsA interactions, plasmid-based overexpression of BcsR leads to overproduction of matrix pEtN-cellulose (Fig.\u00a04b), whereas mutations in the N-terminal domain, which do not affect BcsRQ complex formation per se21, led to severe or complete loss of pEtN-cellulose secretion (Fig.\u00a04e).\n\na Locally refined cryo-EM map and fitted structure of the crownless c-di-GMP-saturated synthase macrocomplex in two different views. IM inner membrane. b Cartoon representation of the same assembly, summary of the BcsA interactions with the cytosolic vestibule partners and stimulatory effects of BcsR overexpression as detected by binding and UV-fluorescence of E. coli macrocolonies grown on Congo Red-supplemented plates. c A zoom-in on the BcsA-BcsR interface with key residues shown as sticks. The R-D-R triad is indicated with a yellow arrowhead. d c-di-GMP coordination, together with its corresponding electron density, and overall core synthase fold showing unstructured gating loop and an accessible active site. e Effects on cellulose secretion upon BcsRNTD mutagenesis using plasmid-based complementation with various BcsR mutants. KDDA D21K-L25D-F29D-L31D, ADDDA D21A-L25D-F29D-L31D-Y36A. CR Congo Red, CF calcofluor. Data representative of three independent experiments with two biological replicates each. f Consensus ColabFold structural models of Type II BcsA-BcsR (based on multiple BcsA homologs encoded by bcsR- and bcsEF-positive enterobacterial bcs clusters), Type III BcsA (derived from bcsK-positive bcs clusters) and Type I and hybrid BcsA-BcsPNTD (derived from bcsPDQ-positive bcs clusters). BcsA is shown as Coulombic electrostatic potential-colored surface, and BcsR (magenta) and BcsPNTD (cyan) are shown in cartoon. The stabilizing pairs of hydrophobic residues in BcsR and BcsPNTD are shown as sticks.\n\nThe experimentally determined BcsR:BcsA interaction interface via a surface-exposed hydrophobic pocket at the back of the synthase\u2019s GT and PilZ modules is likely conserved across the enterobacterial clade. Indeed, a ColabFold model of a consensus BcsRA complex based on protein sequences from representative cellulose-secreting enterobacteria demonstrates an overall conserved BcsR fold and interface residues, including a hydrophobic plug at the tip, stabilizing F:Y \u03c0\u2013stacking interactions at the base of the BcsRNTD hairpin (corresponding to the isoleucine:tyrosine pair discussed above), and a conserved R:D:R triad at the GT:BcsR:hinge interface (Fig.\u00a04f). These are accompanied by a hydrophobic BcsR-binding surface pocket on BcsA, supporting synthase-partner activator coevolution. Interestingly, similar fold prediction based on a consensus BcsA sequence derived from homologs encoded by bcsK-containing Type III bcs clusters which typically lack cytosolic Bcs regulators4,16 lacks a corresponding hydrophobic pocket despite overall high conservation of the BcsA sequence and fold (Fig.\u00a04f).\n\nWe recently showed that most \u03b2-Proteobacteria featuring bcsD in a Type I or hybrid bcs operon architecture, also encode proline-rich BcsP homologs39 (Supplementary Fig.\u00a01b). Similarly to the proline-rich cellulose crystallinity factor BcsH/CcpAx from Gluconacetobacter hansenii, which determines the formation of a longitudinal BcsHD cytoskeletal scaffold (a.k.a. cortical belt) and the respective linear alignment of the synthase terminal complexes for cellulose secretion and crystalline ribbon formation40,41, \u03b2-proteobacterial proline-rich BcsP recruits BcsD into distinct cytoskeletal assemblies that are key to cellulose biogenesis and the mature biofilm architecture39. Interestingly, the N-terminal regions of BcsP homologs show homology to enterobacterial BcsR16 and, similarly to the latter, BcsP expression and/or stability appeared enhanced in the presence of co-expressed and interacting BcsQ39. We, therefore, retrieved multiple sequences of representative and co-occurring \u03b2-proteobacterial BcsA and BcsP homologs and modeled the consensus complex between the synthase and BcsPNTD. Indeed, the predicted structure confirms both the presence of a conserved hydrophobic pocket on the synthase and a BcsR-like hairpin-shaped plug for BcsPNTD, suggesting a common mechanism for synthase regulation among widespread Type I and Type II Bcs secretion systems (Fig.\u00a04f).\n\nBcsE was originally defined as a GIL-, or GGDEF I-site like-, domain protein due to a conserved C-terminal region sensing c-di-GMP via an RxxD (R415TGD in E. coli BcsE) motif similar to the product-sensing I-sites, which are found on many catalytically active diguanylate cyclases and are involved in feedback inhibition or dinucleotide signal relay37,42. We demonstrated previously that the so-called\u00a0GIL domain is, in fact, a degenerate and conformationally dynamic REC*-GGDEF* domain tandem, where the R415TGD sequence corresponds to the canonical I-site in an otherwise catalytically incompetent diguanylate cyclase module20. Whereas this motif is absolutely necessary for dinucleotide complexation, the phosphorylation-incompetent REC* domain can undergo significant conformational rearrangements to contribute a second I-site motif (R306ATD) for an intercalated c-di-GMP dimer complexation21 (Fig.\u00a01a). This corresponds to a relatively compact or closed BcsE conformation observed in a BcsRQEREC*-GGDEF* crystal structure reported previously21 and is also consistent with the cryo-EM structure presented above. The dissociation constants for dimeric c-di-GMP complexation thus change from the low micromolar (\u223c2.5\u2009\u03bcM, for the contribution of the GGDEF* I-site alone) to the nanomolar range (\u223c140\u2009nM, for dual I-site coordination)21 (Fig.\u00a01a). The latter c-di-GMP-binding affinity is significantly higher than the affinity for activating c-di-GMP complexation by the BcsA synthase itself, previously reported in the low micromolar range and orders of magnitude higher than the global cytosolic c-di-GMP concentrations in the early stages of biofilm formation3,18,43.\n\nThis raises the question of whether and how c-di-GMP binding to the higher-affinity sensor BcsE could have stimulatory effects on synthase activity and cellulose biogenesis in non-saturating dinucleotide concentrations. One possible mechanism is that the molecular breathing of the Bcs macrocomplex during the processive cycles of glucose polymerization could cause reiterative conformational changes in BcsE and the synthase, thus leading to diametric changes in their respective dinucleotide binding affinities and c-di-GMP recycling for reiterative synthase activation. Alternatively, the higher-affinity c-di-GMP binding to BcsE, associated with the latter\u2019s compact conformation within the multicomponent cytosolic vestibule could stabilize the synthase in a catalytically competent conformation regardless of its direct dinucleotide complexation.\n\nTo gain mechanistic insights into the c-di-GMP-dependent regulation, we kept low micromolar concentrations of the dinucleotide (2\u20134\u2009\u00b5M) throughout the purification procedure and prepared the cryogrids after a final fast concentration step to \u223c2:1 c-di-GMP:Bcs macrocomplex ratio. As these conditions are close to the predetermined dissociation constants for dimeric c-di-GMP complexation to both the BcsA and the BcsE GGDEF* domain alone (i.e., consistent with splayed BcsE without contributions of the secondary REC* domain I-site to dinucleotide binding)18,21 but more than an order of magnitude higher than that for compact, tandem I-site-contributing BcsE21, we hypothesized that they could allow us to capture either a BcsE-saturated/BcsA non-saturated state or, inversely, a splayed, non-saturated BcsE accompanying a c-di-GMP-bound synthase.\n\nAbout half of the structurally resolved particles featured the fully c-di-GMP-saturated state shown above, where all three c-di-GMP-sensing subunits (BcsA and BcsE2) are bound to an intercalated dinucleotide dimer in a preserved 2:1 dinucleotide-to-protein binding site ratio. Interestingly, the remaining particles showed a c-di-GMP-free BcsA synthase and a more extended vestibule conformation (Figs.\u00a01c and\u00a05a\u2013c and Supplementary Fig.\u00a02), where the central BcsEREC* domains engage in a different dimerization interface mediated by the pairs of \u03b21\u2013\u03b22 connecting loops (melted \u03b11 relative to canonical response regulators) and the C-proximal \u03b1-helices (canonical \u03b15) (Fig.\u00a05d). Densities for the PilZ-proximal GGDEF* domain feature markedly lower-resolution (Supplementary Fig.\u00a08), however, the conformation for both BcsEREC*-GGDEF* tandems is still consistent with the closed BcsE state and intercalated c-di-GMP complexation (Fig.\u00a05c, e and Supplementary Fig.\u00a02d). The overall BcsE fold features a more extended conformation along the NTPase*-REC* domain linkers, neither BcsE protomer contacts the cytosolic synthase modules and the X-shaped BcsF dimer is found shifted near the fifth and fourth BcsB protomer as opposed to the c-di-GMP-saturated complex shown above (Fig.\u00a01c). Nevertheless, the BcsRQ tandem is retained as an apical complex and the synthase-proximal BcsQ and BcsR protomers engage in similarly extensive contacts with the catalytic and PilZ modules (Fig.\u00a05f). The latter is only partially rotated around the hinge helix relative to the c-di-GMP-bound state (12.8\u00b0 rotation and 1.7\u2009\u00c5 displacement) and the N-proximal PilZ domain linker is partially unstructured but remains far from gating loop-stabilizing interactions with the BcsAGT core. Conversely, the gating loop remains unresolved, and the active site appears substrate-accessible, suggesting an overall preserved catalytically competent state in the assembled macrocomplex (Fig.\u00a05g).\n\na Locally refined cryo-EM map and fitted structure of the crownless synthase macrocomplex featuring a c-di-GMP-free synthase in two different views. IM inner membrane. b Cartoon representation of the same assembly. c The cryo-EM map and model of a locally refined BcsRQEF assembly. d A zoom-in on c-di-GMP binding by a composite, dual I-site pocket in closed BcsE. e REC* domain dimerization interface in the non-saturated macrocomplex. f A zoom-in on the BcsA:BcsR interface and summary of the synthase\u2019s interactions with the cytosolic vestibule partners57. g Overall core synthase fold showing unstructured gating loop and an accessible active site.\n\nTogether, these data suggest that even lower, non-saturating c-di-GMP concentrations would allow dinucleotide binding to the nanomolar-affinity sensor BcsE via contributions of both its REC* and GGDEF* domain I-sites and would lead to sufficient BcsE compaction, assembly of the cytosolic vestibule and stabilization of the synthase modules in a BcsRQ-preactivated state. The specific BcsE REC* domain dimerization interface and overall vestibule conformation would also be likely influenced by the lateral diffusion and BcsF partner stabilization among the synthase-distal BcsB copies of the crown. Processive substrate addition and product release by BcsA would thus depend primarily on minute movements of its gating loop and finger helix\u2014as observed in crystallo in saturating dinucleotide concentrations for the catalytic cycle of the R. sphaeroides BcsAB tandem\u2014rather than be absolutely dependent on direct synthase-c-di-GMP complex formation. Overall, this is consistent with a model where the secondary c-di-GMP sensor BcsE serves as a proxy for dinucleotide-dependent regulation by effectively lowering the threshold for activating c-di-GMP concentrations and stabilizing the catalytically competent synthase state, rather than by circulating dinucleotide in and out of its PilZ-linker pocket (Fig.\u00a06).\n\nIn addition to direct c-di-GMP complexation at micromolar dinucleotide concentrations, BcsA can be activated or stabilized in a catalytically competent conformation by a high-affinity c-di-GMP-sensing BcsRQEF cytosolic vestibule complex or by macromolecular intracellular scaffolds. In the periplasm, the polymer can undergo chemical modifications by the pEtN-transferase BcsG or by a multicomponent Wss cellulose acetylation complex. Finally, the polymer can undergo limited hydrolysis by the periplasmic endoglucanase BcsZ. OM outer membrane, PG peptidoglycan, IM inner membrane.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53113-8/MediaObjects/41467_2024_53113_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53113-8/MediaObjects/41467_2024_53113_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53113-8/MediaObjects/41467_2024_53113_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53113-8/MediaObjects/41467_2024_53113_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53113-8/MediaObjects/41467_2024_53113_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In many free-living and eukaryotic host-associated bacterial species, secreted cellulosic polymers represent key building components in the three-dimensional architecture of collaborative multicellular biofilms3,6. In E. coli, the pEtN decoration of secreted cellulose influences not only the physicochemical properties of the polymer itself, but also favors higher-order, long-range fibrillation of the other major biofilm matrix component\u2014amyloid curli\u2014and thus provides for markedly increased biofilm cohesion and elasticity12,44. Importantly, the mature biofilm is a highly heterogeneous environment with stark gradients of oxygen, nutrients, moisture, and/or shear stress. This leads to local stratification and/or compartmentalization of the quantity and type of secreted adherence factors and yields a self-organized division of labor where subsets of cells engage in extracellular matrix production, while others provide for cell proliferation and/or biofilm dispersal3,45.\n\nIn general, younger, nutrient-exposed biofilm layers are characterized by post-exponential growth metabolism, rod shape, enmeshed or no flagella, preserved proliferation, and very low c-di-GMP levels (\u223c40\u201380\u2009nM)3. Conversely, older, stationary-phase biofilm strata activate a cascade of c-di-GMP-metabolizing enzymes for gradual c-di-GMP increase, thus leading to non-dividing rounded cells embedded in a dense extracellular mesh of pEtN-cellulose and amyloid curli3. In intermediate layers, separate pockets or pillars of cells can activate specifically curli or pEtN-cellulose secretion, suggesting localized regulatory events that can selectively override the global c-di-GMP deficit3. Indeed, at least in some E. coli strains, the Bcs macrocomplex has been shown to directly interact with a cellulose-specific diguanylate cyclase (DgcC/AdrA), which would dramatically increase the probability of c-di-GMP-BcsA encounters in comparison to dinucleotide diffusion from an overall depleted cytosolic pool43. In addition to the spatial sequestration of a pathway-specific diguanylate cyclase and in light of the structural data presented here, we propose that E. coli and related enterobacteria have evolved a highly cooperative nanomachine for efficient c-di-GMP-sensing, cellulose synthase activation, and polymer modifications.\n\nConsistent with our earlier but indirect BACTH results17, we reveal here that the E.\u00a0coli-like BcsA synthases have evolved a specific N-terminal amphipathic domain, whose W-shaped fold recruits three copies of the BcsG pEtN-transferase. The latter is an enzyme that is proposed to use inner membrane PE as a pEtN donor and to transfer the moiety via a S278-linked covalent intermediate; its catalytic domain and enzymatic mechanism have been extensively characterized structurally, in vitro, and in vivo19,26,28,46. It is important to note that whereas up to half of the glucose residues can be pEtN-modified in the processively secreted cellulose19, PE is a small-headgroup zwitterionic phospholipid that is generally enriched in the inner, rather than the outer, leaflet of the inner membrane47. The evolution of enterobacterial BcsANTD for the recruitment of multiple BcsG copies per synthase could thus provide efficient substrate mining for extensive polymer modification during processive synthase activity, where individual BcsG protomers are likely to act independently of each other. The membrane sampling and significant conformational changes, which would be required for pEtN-transfer onto a nascent polymer processively extruded through the periplasmic BcsB crown, are possibly enabled by the 48 amino-acid long interdomain linker that could at least theoretically extend more than 10\u201315\u2009nm in the periplasmic space. Highly dynamic, large-scale structural transitions have been proposed based on molecular dynamics simulations for other pEtN-transferases, such as the lipid A pEtN-transferase NmEptA29, however, in the latter both the pEtN donor and acceptor (lipid A) are expected to be still embedded in the inner membrane. Further mechanistic work is thus necessary to capture substrate-, intermediate- and product-bound states across the catalytic cycle of full-length BcsG in the context of the multicomponent secretory assembly and translocating cellulosic polymer.\n\nIn addition to recruitment of the BcsG complex, we further reveal the recruitment and interactions of the rest of the E. coli-characteristic Bcs subunits, which are either essential for (e.g., BcsRQ) or greatly affect cellulose biogenesis (BcsEF) in vivo17. We previously demonstrated that, in the absence of BcsEFG or BcsENTD, BcsRQ are not stably retained in the macrocomplex, and the periplasmic crown features a pentameric, rather than hexameric BcsB17,20. In contrast, we reveal here that the assembly of a wild-type macrocomplex and a hexameric BcsB crown likely contributes not only to the stabilization of the trimeric pEtN-transferase complex between BcsATMD on one side, the synthase-distal BcsB copy on the other and BcsANTD at the inner membrane-cytosol interface; but also to the recruitment of dimeric BcsF via discreet interactions with the C-terminal tail-anchors of synthase-distal BcsB copies from the crown. We further demonstrate a cytosolic, BcsF-dependent \u03b2-sheet complementation mechanism for recruitment of the catalytically incompetent NTPase-like domain of BcsE, which itself leads to the stabilization of the entire BcsE2R2Q2 cytosolic vestibule around the catalytic and c-di-GMP-sensing modules of the synthase. Although this vestibule is observed in two discreet c-di-GMP-bound conformations dependent on dinucleotide abundance and stabilizing BcsB-BcsF interactions, BcsA remains BcsRQ-bound and, as a result, presents a catalytically competent conformation even in the absence of direct dinucleotide complexation.\n\nTogether, these data highlight the possibility of two additional regulatory inputs for efficient synthase activation, which could have widespread implications across enterobacteria and beyond. On the one hand, the nanomolar-affinity, tandem I-site-presenting c-di-GMP sensor BcsE could effectively lower the threshold for activating c-di-GMP concentrations in the assembled Bcs macrocomplex (Fig.\u00a06). Such activation by a separate c-di-GMP sensor is reminiscent of other widespread EPS secretion systems where activating c-di-GMP-sensing is carried out either with the contributions of (e.g., in the poly-N-acetylglucosamine secretion system of E. coli) or fully by separate co-polymerase subunits (e.g., in the Pel or alginate secretion systems of P. aeruginosa)4. In E. coli and other related bacteria, such activation-by-proxy could provide an important boost to cellulose secretion in the early stages and/or intermediate layers of biofilm development where cytosolic c-di-GMP is particularly low3,5 and where functional differentiation between cell proliferation vs. biofilm matrix secretion provides the foundations of the three-dimensional matrix architecture without inhibiting overall macrocolony growth. On the other, the observed PilZ domain-stabilizing BcsA-BcsR interactions are likely preserved in a wide range of BcsP-encoding Bcs secretion systems that do not necessarily feature a bcsEFG cluster but could\u00a0rather rely on BcsA-interacting BcsPDQ scaffolds for stabilization of the catalytically competent synthase state and enhanced polymer secretion39 (Fig.\u00a06 and Supplementary Fig.\u00a01b). Both the more widespread and the idiosyncratic BcsA-regulatory mechanisms presented here can thus be harnessed for the selective targeting of a variety of cellulose secretion systems across free-living, pathogenic and symbiotic bacteria, as well as for the bioengineering of hybrid systems for the enhanced production of biotechnologically relevant polymers.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "No statistical methods were used to predetermine the sample size. The experiments were not randomized, and the investigators were not blinded during experimental design, execution, or outcome assessment.\n\nOligonucleotides, construct design, and bacterial strains are listed in Tables\u00a0S1 and S2. All plasmids for recombinant protein expression (see below) were propagated in and isolated from E. coli DH5\u03b1 cells. Recombinant Bcs macrocomplex expression for structural studies was carried out in NiCo21(DE3) competent E. coli cells (New England Biolabs). Recombinant expression for assessment of BcsF roles in macrocomplex assembly was carried out in a T1 phage-resistant \u0394bcs BL21*(DE3) strain, featuring a deletion of both bcs operons (bcsEFG and bcsRQAB), as well as the corresponding interoperon region (see below). Phenotypic assays of colony morphology and calcofluor binding were carried out in the wild-type E. coli 1094 strain, and the E. coli 1094 \u0394bcsR strains were transformed with variants of the low-copy pAM-238 plasmid. All bacterial strains and plasmids used in this study are available upon request.\n\nDNA manipulations were carried out using standard protocols for polymerase chain reaction (PCR), molecular cloning, transformation, and DNA analysis. Procedures for cloning of bcsHISRQAHA-FLAGB and bcsStrepEFG for co-expression from pACYCDuet1 and pRSFDuet1* are similar to those previously described. Briefly, the genomic region corresponding to bcsRQAHA-FLAGB was amplified using genomic DNA from the E. coli 1094 bcsAHA-FLAG strain as a template and a high-fidelity DNA polymerase (Phusion, New England Biolabs) with appropriate restriction sites introduced in the 5\u2032 primer overhangs (sense/antisense PstI/NotI). In parallel, the pACYCDuet1 vector was also PCR-amplified to include the respective restriction sites for in-frame ligation under the pACYCDuet1 Promoter 1, including the incorporation of an N-terminal polyhistidine tag-coding sequence on bcsR. The genomic region corresponding to bcsEFG was PCR-amplified with appropriate restriction sites introduced in the 5\u2032 primer overhangs (sense/antisense BamHI/NotI), and the pRFSDuet1 vector was amplified to introduce the respective restriction sites for in-frame ligation under the pRSFDuet1 Promoter 1 and to remove the polyhistidine tag-coding sequence (*). All PCR products were subsequently digested with the respective restriction enzyme pair (New England Biolabs), gel-purified, ligated using T4 DNA ligase (New England Biolabs), transformed into chemically competent DH5\u03b1 cells, and plated on LB agar plates containing an appropriate antibiotic (34\u2009\u03bcg\u2009ml\u22121 chloramphenicol and 40\u2009\u03bcg\u2009ml\u22121 kanamycin for the pACYCDuet1 and the pRSFDuet1 constructs, respectively). Single colonies were grown in 5\u2009ml liquid LB medium at 37\u2009\u00b0C overnight, and the plasmid DNA was extracted using NucleoSpin\u00ae Plasmid preparation kit according to the manufacturer\u2019s instructions (Macherey-Nagel). Positive clones were identified by restriction digestion and DNA sequencing. For introduction of a N-terminal STREP II tag-coding sequence in bcsE, the purified bcsEFG-pRSFDuet1* was inverse PCR-amplified with oligonucleotides including the epitope tag-coding sequence, the PCR product was gel-purified, 5\u2019 phosphorylated using T4 polynucleotide kinase (New England Biolabs), ligated by the addition of T4 DNA ligase and transformed in E. coli DH5\u03b1 cells for plasmid selection and amplification as above21.\n\nThe BL21*(DE3) \u0394bcs mutant was generated using a modified protocol of a one-step inactivation procedure48. First, an FLP recognition target sites (FRT)-flanked kanamycin resistance (KmR) cassette was generated by PCR using the pKD4 plasmid as a template and a pair of oligonucleotides carrying 50-nucleotide extensions homologous to regions adjacent to the target bcs gene cluster. In parallel, BL21*(DE3) was transformed with the pKD46 plasmid, and transformants were selected on LB agar plates supplemented with 100\u2009\u00b5g/ml ampicillin and grown at 30\u2009\u00b0C. Of these, a single colony was grown in liquid LB at 30\u2009\u00b0C, in the presence of ampicillin and 0.05% arabinose for induction of phage \u03bb Red recombinase prior to chemically competent cell preparation. The PCR product was then transformed into the resulting BL21*(DE3) cells, and transformants were selected on LB agar plates supplemented with 40\u2009\u00b5g\u00a0ml\u20131 kanamycin and grown at 37\u2009\u00b0C, allowing for the loss of the pKD46 helper plasmid. Replacement of the bcs gene cluster by the kanamycin-resistance cassette was confirmed by colony PCR. The resulting \u0394bcs::KmR strain was then transformed with the pCP20 helper plasmid, encoding Flp recombinase, and transformants were selected on ampicillin (100\u2009\u00b5g\u00a0ml\u20131), then incubated for 24\u2009h at 30\u2009\u00b0C to allow excision of the cassette by the expressed Flp recombinase. Plasmid pCP20 was then eliminated by growth at 37\u2009\u00b0C in the absence of antibiotics, and the cells were verified for kanamycin and ampicillin sensitivity.\n\nOverexpression of the Bcs macrocomplex was performed by co-expression of the pACYCDuet1-bcsHisRQAHA-FLAGB and pRSFDuet1*-bcsStrepEFG constructs in chemically competent NiCo21(DE3) cells and plated on LB agar plates with antibiotic concentrations reduced to two-thirds of the ones stated above. After overnight incubation of the plates at 37\u2009\u00b0C, multiple colonies of the transformed NiCo21(DE3) cells were picked and grown together at 37\u2009\u00b0C in antibiotics-supplemented terrific broth (TB) medium to optical density at 600 nanometers (OD600) of 0.8\u20131.2, upon which the cultures were transferred to 17\u2009\u00b0C and induced with 0.7\u2009mM isopropyl-\u03b2-D-thiogalactopyranoside (IPTG, Neo Biotech) for 16\u2009h or overnight. Cells were pelleted by centrifugation (5000\u2009\u00d7\u2009g, 20\u2009min, 4\u2009\u00b0C) and the pellets were resuspended in ice-cold buffer A containing 20\u2009mM HEPES pH 8.0, 120\u2009mM NaCl, 10% glycerol, 5\u2009mM MgCl2, 10\u2009\u03bcM adenosine-5\u2032-[(\u03b2,\u03b3)-methyleno]triphosphate (AppCp, Jena Bioscience), 2\u2009\u03bcM cyclic diguanylate (c-di-GMP, Sigma-Aldrich), 250\u2009\u03bcM cellobiose, 0.5\u2009mg\u2009ml\u22121 Aspergillus niger cellulase (Sigma-Aldrich), 100\u2009\u03bcg\u2009ml\u22121 lysozyme, and 1 tablet per 50\u2009ml complete EDTA-free protease inhibitors (Roche). The cells were subsequently disrupted using an Emulsiflex-C5 high-pressure homogenizer (Avestin) and the lysates were pre-cleared by a low-speed centrifugation step (10,000\u2009\u00d7\u2009g, 15\u2009min, 4\u2009\u00b0C). Membranes were pelleted by high-speed centrifugation using an SW 28 Ti or an SW 41 Ti Beckman rotor (26,500\u2009rpm/126,000\u2009\u00d7\u2009g or 38,000\u2009rpm/247,000\u2009\u00d7\u2009g, respectively, for 1\u2009h at 4\u2009\u00b0C) and resuspended in solubilization buffer containing all buffer A components except for lysozyme and cellulase, as well as a mix of detergents at the following final concentrations: 0.6% w/v digitonin (Sigma-Aldrich), 0.35% w/v n-dodecyl-\u03b2-D-maltopyranoside (anagrade \u03b2-DDM, Anatrace), and 0.45% w/v lauryl maltose neopentyl glycol (LM-NPG, Anatrace). After incubation for 90\u2009min at 22\u2009\u00b0C and under mild agitation, the solubilized membrane fraction was cleared by a second high-speed centrifugation (50,000\u2009\u00d7\u2009g, 40\u2009min, 4\u2009\u00b0C). The supernatant was incubated with ANTI-FLAG\u00ae M2 affinity gel (100\u2009\u03bcl resin per litre of induced culture, Sigma-Aldrich), under mild agitation at 4\u2009\u00b0C for 1\u2009h. After gravity elution of the non-bound fraction, the resin was washed extensively (>30 column bed volumes) with affinity buffer containing 20\u2009mM HEPES pH 8.0, 120\u2009mM NaCl, 5\u2009mM MgCl2, 10\u2009\u03bcM AppCp, 4\u2009\u03bcM c-di-GMP, 250\u2009\u03bcM cellobiose and 0.01% w/v LM-NPG. The bound complexes were eluted using four-column bed volumes of elution buffer (affinity buffer supplemented with 3\u00d7 FLAG\u00ae peptide at 100\u2009\u03bcg\u2009ml\u22121), concentrated on a 100\u2009kDa cutoff Amicon\u00ae Ultra (MerckMillipore) centrifugal filter. Samples were analyzed by SDS-PAGE and western blots. For cryo-EM grid preparation, the Bcs macrocomplex was concentrated to \u223c2\u20134\u2009mg ml\u20131, spotted on glow-discharged (ELMO, Cordouan Technologies) gold UltrAuFoil R 1.2/1.3 cryogrids, blotted, and plunge-frozen in liquid ethane using a Vitrobot Mark IV device (Thermo Fisher Scientific) at 4\u2009\u00b0C and 100% humidity.\n\nCryogrids were prescreened and optimized on the Elsa Talos Arctica transmission electron microscope (Thermo Fisher Scientific) at the European Institute of Chemistry and Biology (IECB, Bordeaux) operated at 200\u2009kV and equipped with a Gatan K2 Summit direct electron detector. For structure resolution, cryo-EM data was collected at the CM01 beamline at the European Synchrotron Radiation Facility (ESRF, Grenoble) on a Titan Krios transmission cryo-electron microscope, operated at 300\u2009kV and equipped with a GATAN K3 direct electron detector and a Quantum LS imaging filter. 20,022 movies (two movies per grid hole, 50 frames per movie) were recorded in electron counting mode with a total electron dose per movie of 49.35 electrons/\u00c52, corrected pixel size of 0.839\u2009\u00c5/pixel, and defocus spread from \u22122.1 to \u22120.3\u2009\u03bcm. The movies were motion-corrected using MotionCor249 within the ESRF autoprocessing pipeline and the resulting micrographs were imported in CryoSPARC50 v4.4.1 for Patch-CTF correction and downstream processing. Particles were autopicked using the software\u2019s Template Picker function and 2D templates as previously reported21 and, after extraction (box size 500 pixels, Fourier crop 200) and a round of 2D classification, a total of 1,359,795 particles with resolved structural features were selected for further processing. Ab-Initio Reconstruction and Heterogeneous Refinement among three classes yielded a model consistent with the previously reported Bcs macrocomplex structure integrating 834,077 or 61% of the preselected particles. The corresponding particles were re-extracted without downsampling, and non-uniform refinement led to a 3D reconstruction featuring well-resolved crown densities and less-resolved inner membrane and cytosolic regions. The hexameric BcsB periplasmic crown was locally refined after subtracting the inner membrane and cytosolic densities from the particles dataset using the Particle Subtraction function. An inverse Particle Subtraction was also used to subtract the periplasmic densities from the initial particles dataset in order to retain only the inner membrane and cytosolic regions. The latter subtracted particles were then subject to another round of Ab-Initio Reconstruction with three classes yielding two well-resolved classes corresponding to the c-di-GMP-bound and the c-di-GMP-free synthase, whereas a third class featured poorly resolved structural features. Each of the resulting classes was input as a search model for heterogeneous refinement (3D classification) of the full macrocomplex, yielding the two states\u2014c-di-GMP-saturated or not\u2014for the global assembly. Corresponding particles were subject to another round of Ab-Initio modeling or each 3D class, followed by resolution-limited non-uniform refinement to avoid oversharpening and loss of the more dynamic/less\u00a0resolved features. The respective crown regions were subtracted again, and separate regions of interest were further refined via Local Refinement jobs after map segmentation and mask generation within Chimera51. Additional map sharpening for density interpretation was performed using Deep EMhancer52 via the CryoSPARC interface. Atomic model building and refinements were performed iteratively using previously reported BcsB, BcsRQ, and BcsEREC*-GGDEF* structures20,21 and AlphaFold353 or ColabFold54-generated models as inputs for manual building in Coot55 and automated real-space refinement in Phenix56. Interface analyses were carried out with the PISA server57. Details of the data collection and refinement statistics are listed in Tables\u00a0S3 and S4, and Supplementary Figs.\u00a03, 4, 8, and 9. Structure visualization was performed in ChimeraX58.\n\nProtein fractions were analyzed by standard denaturing SDS-PAGE electrophoresis using 4\u201320% gradient mini-gels (Bio-Rad), InstantBlue Coomassie protein stain (Abcam), and a Bio-Rad GelDoc Go Infinity imager. For Western blot analyses, SDS-PAGE\u2013migrated proteins were directly transferred using a standard mini-gel transfer protocol, polyvinylidene difluoride membranes, and a Trans-blot Turbo transfer system (Bio-Rad). Blocking and antibody incubations were performed in the presence of 5% skim milk or bovine serum albumin (the latter for STREP II tag detection) in TPBS (1\u00d7 phosphate-buffered saline supplemented with 0.1% Tween-20 detergent); all washes between and after antibody incubations were performed with 1\u00d7 TPBS buffer. Mouse anti-HA (hemagglutinin) (Thermo Fisher Scientific, #26183; dilution 1:1000) and mouse anti-STREP II (QIAGEN, #34850; dilution 1:1000) antibodies were used as primary antibodies; horseradish peroxidase-conjugated rabbit anti-mouse antibody (Abcam, ab6728; dilution 1:10,000) was used as secondary antibody. Signals were visualized using the Clarity Western ECL substrate and a ChemiDoc imaging system (Bio-Rad).\n\nBcsA, BcsP, BcsE, and BcsF protein sequences encoded by operons coding for BcsR-BcsE-BcsF (Type II bcs clusters, 20 representative sequences for each protein), BcsP-BcsD-BcsQ (Type I and hybrid bcs clusters, 30 representative sequences for each protein) or BcsK (Type III bcs clusters, 121 representative sequences for BcsA) were identified with the help of webFlaGs59 and the STRING60 and NCBI Nucleotide databases and aligned separately using Clustal Omega61. The alignments were visualized in JalView62 and trimmed for non-conserved N- or C-terminal extensions and internal sequence gaps. The corresponding consensus sequences were then retrieved, and the proteins or protein complexes were modeled using the AlphaFold53 or ColabFold54 web server and visualized in ChimeraX58.\n\nTo test for the functional effects of the BcsA-interacting BcsR region, chemically competent cells were prepared from E. coli 1094 wild-type and \u2206bcsR deletion strains. The latter was transformed with a low-copy-number plasmid (pAM-238) carrying none, wild-type or mutant bcsR genes and plated on LB agar plates (Miller) supplemented with 60\u2009\u03bcg\u00a0ml\u20131 streptomycin. Single colonies were inoculated in 3\u2009ml LB-streptomycin medium and left to grow overnight at 37\u2009\u00b0C with agitation. On the following morning, 4\u2009\u03bcl of each culture was spotted onto low-salt LB agar plates (1.5\u2009g\u00a0L\u20131 NaCl) supplemented with streptomycin, 0.1\u2009mM IPTG, and 0.02% calcofluor (fluorescent brightener 28; Sigma-Aldrich) or 25\u2009\u03bcg\u00a0ml\u20131 Congo Red (Sigma-Aldrich). The spots were allowed to air dry, and the plates were incubated at 30\u2009\u00b0C. After 24\u2009h, the plates were photographed under brief illumination with long-wave UV light (365\u2009nm) for calcofluor fluorescence and with a GelDoc Go imaging system (Bio-Rad) under trans-UVB illumination (UV tray and ethidium bromide mode) for pEtN-cellulose-specific Congo Red fluorescence.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Information. Refined structural models and electron density maps are deposited in the electron microscopy and protein databanks with accession codes as follows: EMD-50584 and EMD-50595 for the low-pass filtered global assemblies of the c-di-GMP-saturated and non-saturated Bcs macrocomplex, respectively; 9FMT/EMD-50567 for the locally refined BcsB periplasmic crown; 9FMZ/EMD-50581 and 9FMV/EMD-50571 for the locally refined c-di-GMP-bound and c-di-GMP-free BcsAG3 complex, respectively; 9FNN/EMD-50599 and 9FP0/EMD-50632 for the locally refined crownless the c-di-GMP-saturated and non-saturated Bcs macrocomplex, respectively; 9FO7/EMD-50619 for the locally refined BcsE2F2 regulatory subcomplex from the c-di-GMP-saturated state; and 9FP2/EMD-50633 for the locally refined BcsRQEF vestibule complex from the non-saturated Bcs macro complex. Previously published structural models discussed in this work refer to entries 6YB3 (crystal structure of E. coli BcsRQ), 6TJ0 (crystal structure of splayed BcsE), 6YBB (crystal structure in closed BcsE, in a BcsRQ-bound complex), 6PCZ (a BcsGCTD crystal structure), 5FGN (crystal structure of N. meningitidis EptA), 6WLB (cryo-EM structure of poplar CesA8), 4P00 (a crystal structure of R. sphaeroides BcsAB). AlphaFold and ColabFold-generated models used in initial model building or structure analyses are deposited as an open-access dataset in Zenodo (DOI:10.5281/zenodo.13732043).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Moradali, M. F. & Rehm, B. H. A. Bacterial biopolymers: from pathogenesis to advanced materials. Nat. Rev. Microbiol. 18, 195\u2013210 (2020).\n\nArticle\u00a0\n PubMed Central\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nHobley, L., Harkins, C., MacPhee, C. E. & Stanley-Wall, N. R. 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This research has received funding from the ERC Executive Agency under grant agreement 757507 BioMatrix-ERC-2017-StG (to P.V.K.) and the Agence Nationale de Recherche (ANR, France) under grant agreements CelluSec (to P.V.K.) and T-ERC CoG 2024\u00a0BacFilm (to P.V.K.). Finally, Itxaso Anso is further supported by the Postdoctoral Program under the Order of 20 June 2023 of the Ministry of Education (Basque Country, Spain), which regulates and coordinates new grants and grant renewals for the Advancement of Doctorate-level Investigators (Programa Posdoctoral de Perfeccionamiento de Personal Investigador Doctor; to I.A.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Samira Zouhir\n\nPresent address: Laboratoire de Biologie et Pharmacologie Appliqu\u00e9e (LBPA), CNRS UMR8113, ENS Paris-Saclay, Universit\u00e9 Paris-Saclay, Gif-sur-Yvette, F-91190, France\n\nUniv. Bordeaux, CNRS, Bordeaux INP, CBMN, UMR 5248, F-33600, Pessac, France\n\nItxaso Anso,\u00a0Samira Zouhir,\u00a0Thibault G\u00e9ry Sana\u00a0&\u00a0Petya Violinova Krasteva\n\nStructural Biology of Biofilms Group, European Institute of Chemistry and Biology (IECB), 2 Rue Robert Escarpit, Pessac, F-33600, France\n\nItxaso Anso,\u00a0Samira Zouhir,\u00a0Thibault G\u00e9ry Sana\u00a0&\u00a0Petya Violinova Krasteva\n\nDepartment of Biochemistry and Molecular Biology, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940, Leioa, Spain\n\nItxaso Anso\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nP.V.K. conceived the project. I.A., S.Z., T.S., and P.V.K. designed, performed, and optimized the experimental procedures. I.A., S.Z., T.S., and P.V.K. analyzed the data. I.A. and P.V.K. secured funding and wrote the paper.\n\nCorrespondence to\n Petya Violinova Krasteva.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Joel Weadge and the other, anonymous, reviewer for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Anso, I., Zouhir, S., Sana, T.G. et al. Structural basis for synthase activation and cellulose modification in the E. coli Type II Bcs secretion system.\n Nat Commun 15, 8799 (2024). https://doi.org/10.1038/s41467-024-53113-8\n\nDownload citation\n\nReceived: 10 June 2024\n\nAccepted: 24 September 2024\n\nPublished: 11 October 2024\n\nVersion of record: 11 October 2024\n\nDOI: https://doi.org/10.1038/s41467-024-53113-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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field-free unconventional spin-orbit torque magnetization switching dynamics in van der Waals heterostructures", + "journal": "Nature Communications", + "published": "30 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64109-3/MediaObjects/41467_2025_64109_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64109-3/MediaObjects/41467_2025_64109_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Electronic and spintronic devices", + "Magnetic devices", + "Two-dimensional materials" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4964769/v1.pdf?c=1759316735000", + "research_square_link": "https://www.researchsquare.com//article/rs-4964769/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-64109-3.pdf", + "preprint_posted": "25 Sep, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The van der Waals (vdW) heterostructure of emerging two-dimensional (2D) quantum materials, with control over their quantum geometries, crystal symmetries, spin-orbit coupling, and magnetic anisotropies, provides a new platform for generating unconventional nonlinear Hall effects, spin polarization and efficiently controlling the magnetization dynamics for non-volatile spin-based computing. However, so far, the generation of a large out-of-plane spin polarization is limited to achieve energy-efficient field-free magnetization switching and spin dynamics measurements in all-2D vdW heterostructure are so far missing, where the interplay between spins and magnetization dynamics should enable the design of ultrafast spintronic devices. Here, we demonstrate magnetization dynamics and energy-efficient field-free spin-orbit torque (SOT) switching of out-of-plane magnet Fe3GaTe2 due to unconventional Berry curvature-induced out-of-plane spin polarization from a topological Weyl semimetal TaIrTe4 in a vdW heterostructure at room temperature. We observed a large non-linear 2nd harmonic Hall signal at room temperature and evaluated the SOT-induced magnetization dynamics with a large damping-like torque of 4.83\u00b10.59 mT per MAcm(-2). Deterministic field-free SOT magnetization switching in vdW heterostructure of TaIrTe4/Fe3GaTe2 is observed at room temperature with a low current and power density of 1.81 \u00d71010 A/m2 and 0.175\u00d71015 W/m3 , respectively, which is an order of magnitude better than that of conventional systems. From the magnetization switching experiments, the SOT efficiency is found to be 3.95 with a very large spin Hall conductivity of 7.39\u00d7106 \u0127/2e (\u03a9\u2009m)(\u20131) . These findings on all-vdW heterostructures offer a promising route to energy-efficient and external field-free ultrafast spintronic technologies.Physical sciences/Physics/Electronics, photonics and device physics/Electronic and spintronic devicesPhysical sciences/Nanoscience and technology/Nanoscale devices/Magnetic devicesPhysical sciences/Materials science/Nanoscale materials/Two-dimensional materialsQuantum materialsBerry curvatureBroken symmetriesSpin-orbit torque (SOT)magnetization dynamicsvan der Waals materialsWeyl semimetals2D ferromagnetsUnconventional SOT2D materialsRoom temperature2 nd HarmonicsTaIrTe4Fe3GaTe2", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryTaIrTFGTSOT.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Two-dimensional quantum material heterostructures can offer a promising platform for energy-efficient non-volatile spin-based technologies. However, spin dynamics experiments to understand the basic spin-orbit torque phenomena are so far lacking. Here, we demonstrate unconventional out-of-plane magnetization dynamics, and energy-efficient and field-free spin-orbit torque switching in a van der Waals heterostructure comprising out-of-plane magnet Fe3GaTe2 and topological Weyl semimetal TaIrTe4. We measured non-linear second harmonic Hall signal in TaIrTe4/Fe3GaTe2 devices to evaluate the magnetization dynamics, which is characterized by large and tunable out-of-plane damping-like torque. Energy-efficient and deterministic field-free SOT magnetization switching is achieved at room temperature with a very low current density. First-principles calculations unveil the origin of the unconventional charge-spin conversion phenomena, considering the crystal symmetry and electronic structure of TaIrTe4. These results establish that van der Waals heterostructures provide a promising route to energy-efficient, field-free, and tunable spintronic devices.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "In quantum materials, the interplay between spin-orbit coupling and magnetism, with additional control over the band topology, quantum geometries, and crystal symmetries can offer the potential for next-generation universal memory and computing technologies1,2. Specifically, enhanced functionalities can be achieved using efficient charge-spin conversion (CSC) phenomena in such quantum materials to enable spin-orbit torque (SOT) induced magnetization switching of a ferromagnet (FM)3. In conventional SOT memory devices, commonly used spin-orbit materials (SOM) exhibit moderate CSC efficiency and primarily generate in-plane SOT torque components, limiting their application in switching a magnet with perpendicular magnetic anisotropy (PMA)4.\n\nRecently developed van der Waals (vdW) heterostructures of two-dimensional (2D) SOMs and FMs can offer an alternative framework to address the challenges in SOT technologies5. Interestingly, low crystal symmetries of vdW SOMs can generate out-of-plane SOT components, making them suitable for field-free switching of ferromagnets with PMA6,7,8,9. Meanwhile, vdW magnets such as Fe3GeTe2 and Fe3GaTe2 with strong PMA are also developed, showing promise for reliable SOT device operations10,11,12,13. Taking advantage of such quantum materials, all-2D vdW heterostructures have been explored for field-free SOT magnetization switching14,15,16,17. However, the SOT switching parameters are two to three orders of magnitude lower than required for energy-efficient switching and most of the experiments were limited to cryogenic temperatures.\n\nTo circumvent this issue, Weyl semimetal TaIrTe4 with low crystal symmetry, large spin-orbit coupling (SOC), and large Berry curvature dipole was explored to generate a larger out-of-plane SOT component for energy-efficient and field-free SOT switching of conventional magnets8,9,18. Therefore, all-2D vdW heterostructure combining the best vdW quantum materials with a large current-induced out-of-plane spin polarization and above room temperature vdW ferromagnet with an out-of-plane magnetization is encouraging for energy-efficient non-volatile spintronic technologies. Furthermore, the investigation of magnetization dynamics in all-2D vdW heterostructures is critical for understanding the interplay between broken crystal symmetries, unconventional CSC, and SOT-induced magnetization dynamics, ultimately enabling the design of efficient and ultrafast spintronic devices.\n\nHere, we show strong unconventional out-of-plane SOT magnetization dynamics using harmonic measurements and demonstrate energy-efficient field-free SOT magnetization switching using the all-vdW heterostructures of TaIrTe4/Fe3GaTe2 at room temperature. Weyl semimetal TaIrTe4 with a tunable canted spin polarization combined with a vdW ferromagnet Fe3GaTe2 with strong PMA enables the exploration of magnetization dynamics and their tunable SOT efficiency. The 2nd harmonic measurements with detailed magnetic field and angle-dependent measurements at various temperatures reveal a large and tunable unconventional out-of-plane SOT torque in the TaIrTe4/Fe3GaTe2 all-vdW heterostructure. The SOT components are observed to vary with temperature and correlate with the measured spin canting angle. Moreover, we observed a field-free deterministic SOT magnetization switching with a very low critical switching current density of \\(1.81\\times {10}^{10}{{{\\rm{A}}}}/{{{{\\rm{m}}}}}^{2}\\), demonstrating energy-efficient non-volatile spintronic memory device. To unveil the origin of the unconventional CSC phenomena in TaIrTe4, detailed first-principles calculations were performed considering crystal symmetry and electronic structures.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We investigated TaIrTe4/Fe3GaTe2 vdW heterostructures (Fig.\u00a01a)19 due to their promising properties, anticipating that their combination could yield contemporary phenomena such as large non-linear Hall effects and unconventional spin-orbit torque (SOT) magnetization dynamics. TaIrTe4 is a vdW topological Weyl semimetal (WSM) candidate, with a significant Berry curvature dipole and large spin splitting of the electronic bands20. In addition, it provides unconventional charge-spin conversion with an out-of-plane spin polarization component that can induce an out-of-plane SOT18 on the adjacent PMA ferromagnet to induce a magnetic field-free switching. On the other hand, Fe3GaTe2 is a unique vdW topological nodal line metallic ferromagnet with strong PMA above room temperature with Tc around 370 K10. We fabricated Hall-bar devices based on TaIrTe4/Fe3GaTe2 vdW heterostructures, along with individual Hall bars on TaIrTe4 and Fe3GaTe2 crystals, to characterize properties, such as the anomalous Hall effect (AHE), 2nd harmonics measurements and SOT-driven switching experiments (details in Methods section and Supplementary Fig.\u00a0S1). Figure\u00a01b presents a typical optical microscope image of a representative TaIrTe4/Fe3GaTe2 Hall-bar device.\n\na Schematic diagram of a van der Waals (vdW) heterostructure of Weyl semimetal TaIrTe4 and out-of-plane ferromagnet Fe3GaTe2. Band structure of typical type-II Weyl semimetal with two Weyl nodes. b Optical image of representative TaIrTe4/Fe3GaTe2 vdW heterostructure Hall bar device with a scale bar of 5\u2009\u00b5m. c 2nd harmonic transverse Hall voltage \\({V}_{{xy}}^{2\\omega }\\) in response to an applied alternating current \\({I}^{\\omega }\\) along a-axis at different temperatures for a device with 20\u2009nm\u00a0thin TaIrTe4. The inset illustrates the crystal structure of Td-TaIrTe4, characterized by low crystal symmetry and a mirror plane along the crystallographic b-axis. d 2nd harmonic transverse Hall voltage \\({V}_{{xy}}^{2\\omega }\\) with temperature at an \\({I}^{\\omega }\\) of 0.1\u2009mA of TaIrTe4. Insets show the energy dispersion curve of type-II Weyl semimetal and tuning of Fermi level energy (EF) with temperature. e 2nd harmonic voltage \\({V}^{2\\omega }\\) response measured in TaIrTe4 device as a function of angle between current applied along a-axis of TaIrTe4 (|\\({I}^{\\omega }\\)\u2009|\u2009\u2009\u2009=\u2009\u20090.1\u2009mA) and external magnetic field (13\u2009T). The device is rotated in XY and ZY planes, as depicted in the schematics. In the XY rotation, the device rotates such that the magnetic field aligns parallel to the sample surface and making \\({\\varPhi }_{B}\\) angle with a-axis of TaIrTe4, whereas in ZY rotation, the device rotation changes magnetic field direction from a-axis of TaIrTe4 to c axis and making \\({\\theta }_{B}\\) angle with c-axis with TaIrTe4. The solid lines are the fits. f Temperature dependence of shift (\u0394) in the maxima or minima of \\({V}^{2\\omega }\\) vs \u03a6B and \u03b8B curves. This shift is denoted as out-of-plane canting angles, as illustrated in schematics. Such shift is directly correlated to the out-of-plane spin canting angle, which is estimated to be \u2212 (27\\(\\pm\\)0.76)\u00b0 at room temperature. Error bars in f are obtained by fitting experimental data in e using sin\u03a6B and sin(\u03b8B\u2009+\u2009\u0394) functions.\n\nTaIrTe4 exhibited a strong nonlinear Hall effect, characterized by a 2nd harmonic Hall voltage that nonlinearly depends on driving currents sourced along the a-axis of the crystal, perpendicular to its mirror plane at room temperature (Fig.\u00a01c). Unlike linear Hall effects observed in systems with broken time-reversal symmetry, the nonlinear Hall effect in TaIrTe4 arises from the large Berry curvature dipole in the absence of inversion-symmetry (also see Supplementary Note\u00a02). Notably, the nonlinear Hall voltage changed sign near ~200\u2009K (Fig.\u00a01c, d), indicating temperature-induced shift in the chemical potential, consistent with the Weyl semi-metallic properties of TaIrTe421. The current induced spin polarization in TaIrTe4 are probed using bilinear magnetoelectric resistance (BMER) technique9,22, measuring 2nd harmonic voltage while rotating the samples in XY and ZY planes (Fig.\u00a01e). In XY rotation, the magnetic field vector remains in the ab crystallographic plane sweeping azimuthal angle (\u03a6B) with respect to the a-axis of TaIrTe4, whereas in ZY rotation, the field vector sweeps polar angle (\u03b8B) with respect to the c-axis of TaIrTe4 in the ac plane. Figure\u00a01e depicts the temperature dependence of 2nd harmonic voltage with \u03a6B and \u03b8B. The direction of resultant spin angular momentum arises due to CSC effects in TaIrTe4 being equivalent to angular shift (\u0394) of BMER curves measured along XY and ZY geometries. The \u0394 is found to be \\(-\\)(\\(27\\pm 0.76\\))\u00b0 at room temperature, indicating the presence of an out-of-plane spin density induced in TaIrTe4. Such spin-polarization can help in generating unconventional out-of-plane SOT in adjacent ferromagnetic layer Fe3GaTe2 with PMA resulting in field-free deterministic switching. The temperature dependence of \u0394 (Fig.\u00a01f) suggests that the polarity and magnitude of the spin canting angle in TaIrTe4 are highly tunable by the position of chemical potential/Fermi level9.\n\nTo verify the magnetic property and anisotropy of Fe3GaTe2, the anomalous Hall resistance Rxy is measured at different temperatures ranging from 2 to 300\u2009K (Fig.\u00a02a, b). A square-shaped magnetic hysteresis loop is observed with coercivity around 100 mT and anomalous Hall resistance (RAHE) of around 1.5 \u03a9 at room temperature, where the latter is directly proportional to saturation magnetization (Ms) of Fe3GaTe2. The RAHE vs T curve, shown in Fig.\u00a02c, is fitted with \\({R}_{{xy}}\\left(T\\right)={R}_{{xy}}\\left(0\\right){\\left(1-{\\left(\\frac{T}{{T}_{c}}\\right)}^{2}\\right)}^{\\beta }\\) analogues to Bloch equation for magnetization vs temperature curve to estimate Curie temperature (\\({T}_{c}\\)\u2009=\u2009\\(369.14\\pm 7.73{{{\\rm{K}}}}\\))10 and critical magnetization exponent \\(\\beta=0.35\\,\\)22,23,24,25,26. Figure\u00a02d shows the anomalous Hall resistance of Fe3GaTe2 as a function of in-plane magnetic fields at different temperatures from 2 to 300\u2009K. A magnetic hysteresis loop is observed at all temperatures, with finite remanence and coercivity, consistent with the typical behavior of PMA magnets along their hard axis (Fig.\u00a02e).\n\na, b Anomalous Hall resistance of Fe3GaTe2 as a function of out-of-plane magnetic fields at 300\u2009K and temperature dependence ranging from 2 to 300\u2009K. c Anomalous Hall amplitude at the saturated field as a function of temperature. Solid line is fit to extract the Curie temperature (\\({T}_{c}=369.14\\pm 7.73{{{\\rm{K}}}}\\)) d Anomalous Hall resistance of Fe3GaTe2 as a function of in-plane magnetic fields at different temperatures ranging from 2 to 300\u2009K. e Comparison of anomalous Hall effect measurement for field swept parallel to sample plane (i.e., \\(H{{{\\perp }}}c\\)) vs perpendicular (i.e., \\({H||c}\\)) to sample plane at 2\u2009K temperature. The anisotropic field (HK) is ~7.9\u2009T, indicating strong perpendicular magnetic anisotropy present in Fe3GaTe2. f Variation of coercive fields and anisotropic fields with temperature extracted from (\\({R}_{{xy}}\\) vs \\({\\mu }_{0}{H}_{{{{\\perp }}}}\\)) and (\\({R}_{{xy}}\\) vs \\({\\mu }_{0}{H}_{{||}}\\)) measurements. g AHE signals \\({R}_{{xy}}\\) with different out-of-plane angles (\u03b8) between the magnetic field and the c-axis of the sample plane at 300\u2009K. h Variation of AHE signals \\({R}_{{xy}}\\) with positive and negative DC bias currents.\n\nFigure\u00a02f shows the variation of magnetic coercivity (Hc) in both field directions (i.e., \\(H\\, {{\\perp }}\\, c-axis\\) and \\({H||}c-axis\\)) and anisotropic field with temperature. The anisotropic field (HK), defined as the difference in saturation between in-plane and out-of-plane magnetic fields, reaches ~7.9\u2009T at 2\u2009K and ~3.8\u2009T at 300\u2009K. Such a high value of HK suggests that Fe3GaTe2 has a very high magnetic anisotropy energy density with a very strong PMA. The coercive field (Hc) is also quite high along in-plane direction as compared to out-of-plane direction. Both the Hc and HK decrease with an increase in temperature approaching the Curie temperature of Fe3GaTe2. Figure\u00a02g illustrates AHE signals Rxy measured at varying out-of-plane angles (\u03b8) between c-axis of sample and magnetic field. It can be noted here that the magnitude of AHE signal (\\({R}_{{xy}}^{{AHE}}=\\frac{{R}_{{xy}}\\left(+{H}_{S}\\right)-{R}_{{xy}}\\left(-{H}_{S}\\right)}{2}\\)) remains almost constant till \\(\\pm {80}\\)\u00b0; beyond that AHE loop disappears between \\(\\pm 600\\,{{{\\rm{mT}}}}\\) field range. Again, this indicates a strong out-of-plane magnetic anisotropy present in Fe3GaTe2. Figure\u00a02h shows the variation of AHE signals Rxy with positive and negative DC bias currents. We observed that the magnitude of anomalous Hall signal, the coercivity and saturation fields remain unchanged with positive or negative current bias varied from\u00a0\\(\\pm 0.1 \\;{{\\rm{mA}}}\\; {{\\rm{to}}} \\pm 1 \\; {{\\rm{mA}}}\\), indicating the robustness of perpendicular anisotropic magnetic moment against dc current within these bias ranges.\n\nThe harmonic Hall measurements are performed on TaIrTe4/Fe3GaTe2 heterostructures to quantitatively evaluate the non-linear effects and magnetization dynamics driven by SOT. When a sinusoidal current (\\({I}^{\\omega }\\)) is applied to the vdW heterostructure, composed of the spin-orbit material TaIrTe4 and a ferromagnet Fe3GaTe2, spin-orbit torques \\(({{{\\boldsymbol{\\tau}} }}_{{{\\bf{SOT}}}})\\) are exerted on the magnetization (m) of the Fe3GaTe2. This effect originates from the spin accumulation at the vdW interface due to efficient CSC in TaIrTe4. Typically, two mutually orthogonal torques are generated: the damping-like torque (\\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{DL}}}}} \\sim {{{\\bf{m}}}}{{\\times }}({{{\\boldsymbol{\\sigma }}}}{{\\times }}{{{\\bf{m}}}})\\)) and the field-like torque (\\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{FL}}}}}\\,{{{\\boldsymbol{ \\sim }}}}\\,{{{\\boldsymbol{\\sigma }}}}\\times {{{\\bf{m}}}}\\))13,27.\n\nIn these measurements, applying a sinusoidal current (\\({I}^{\\omega }\\)) with a fixed frequency of 213.3\u2009Hz induces SOT-driven magnetization oscillation, generating harmonics in both the longitudinal and transverse resistance signals. The 1st and 2nd harmonic signals are measured and analyzed across various angles (\\({\\varPhi }_{B}\\)) between the in-plane magnetic field (\\(H{{{\\perp }}}c\\)) and the applied sinusoidal current (\\({I}^{\\omega }\\)), as well as under varying external magnetic fields (\\({H}_{{ext}}\\)). This analysis provides information about the current-induced effective SOT fields and torques.\n\nSince the spin Hall effect (SHE) in TaIrTe4 induces both in-plane and out-of-plane spin polarizations (\\({\\sigma }^{X,Y,Z}\\)), the applied \\({I}^{\\omega }\\) along the a-axis of TaIrTe4 generates corresponding components of the damping-like (\\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{DL}}}}}^{{{{\\bf{X}}}},{{{\\bf{Y}}}},{{{\\bf{Z}}}}}\\)) and field-like (\\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{FL}}}}}^{{{{\\bf{X}}}},{{{\\bf{Y}}}},{{{\\bf{Z}}}}}\\)) torques. The 2nd harmonic transverse voltage generated from these current-induced effective SOT fields componenets (\\({H}_{{DL}}^{X,Y,Z},{H}_{{FL}}^{X,Y,Z}\\)) and torques (\\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{DL}}}}}^{{{{\\bf{X}}}},{{{\\bf{Y}}}},{{{\\bf{Z}}}}},{{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{FL}}}}}^{{{{\\bf{X}}}},{{{\\bf{Y}}}},{{{\\bf{Z}}}}}\\)) in PMA ferromagnets is expressed as28,29,\n\nHere, damping-like torque components generated by X, Y and Z spin polarization contribute to coefficients \\({V}_{{DL}}^{X,Y,Z}\\), and the field-like torque counterparts give rise to coefficients \\({V}_{{FL}}^{X,Y,Z}\\). The estimation of SOT fields (\\({H}_{{DL}}^{X,Y,Z},{H}_{{FL}}^{X,Y,Z}\\)) from coefficients (\\({V}_{{DL}}^{X,Y,Z},{V}_{{FL}}^{X,Y,Z}\\)) is detailed in Supplementary Note\u00a07 (Supplementary Eqs.\u00a0S1\u2013S7).\n\nThe 2nd harmonic Hall signal as a function of \\({\\varPhi }_{B}\\) at constant fields (H\u2009>\u2009HK) is plotted in Fig.\u00a03b. The \\({V}_{{xy}}^{2\\omega }\\) vs \\({\\varPhi }_{B}\\) curve is fitted with Eq.\u00a02 to estimate SOT in materials containing only conventional in-plane spins30,31. However, \\({V}_{{xy}}^{2\\omega }\\) vs \\({\\varPhi }_{B}\\) curve of TaIrTe4/Fe3GaTe2 could not be well fitted with Eq.\u00a02 (see Fig.\u00a03b). For proper fitting, we need to include \\({V}_{{DL}}\\cos {2\\varPhi }_{B}\\) and \\({V}_{{DL}}\\sin {\\varPhi }_{B}\\) terms as in Eq.\u00a01, which consider additional torque components due to current-induced out-of-plane spin canting in TaIrTe4. The coefficients \\({{V}_{{xy}}^{2\\omega }}_{\\cos {\\Phi }_{B}}\\), \\({{V}_{{xy}}^{2\\omega }}_{\\sin {\\Phi }_{B}}\\) and \\({{V}_{{xy}}^{2\\omega }}_{\\cos {2\\Phi }_{B}}\\) are hyperbolic functions of the magnetic field (see Supplementary Note\u00a07, Eqs S2\u2013S4), implying linear function on 1/(H-HK) or 1/H. The values of these coefficients were extracted by fitting the experimental 2nd harmonic transverse voltage and plotted in Fig.\u00a03c-e. From these slopes, \\({H}_{{DL}}^{X,Y,Z}\\) are estimated (using RAHE\u2009=\u20091 \u03a9 and RPHE\u2009=\u20090.0113 \u03a9; see Supplementary Note\u00a06) and plotted as a function of current densities Ja.c. in Fig.\u00a03g. The slope of \\({H}_{{DL}}^{X,Y,Z}\\) vs \\({J}_{a.c.}\\) are found out to be \\({H}_{{DL}}^{X}/{J}_{a.c.} \\sim \\left(3.09 \\pm 0.37\\right)\\times {10}^{-12}\\;{{{\\rm{T}}}}{{{{\\rm{A}}}}}^{-1}{{{{\\rm{m}}}}}^{2}\\), \\({H}_{{DL}}^{Y}/{J}_{a.c.} \\sim \\left(2.43\\pm 0.15\\right)\\times {10}^{-12}\\;{{{\\rm{T}}}}{{{{\\rm{A}}}}}^{-1}{{{{\\rm{m}}}}}^{2}\\) and \\({H}_{{DL}}^{Z}/{J}_{a.c.} \\sim \\left(6.78\\pm 0.44\\right)\\times {10}^{-12}\\;{{{\\rm{T}}}}{{{{\\rm{A}}}}}^{-1}{{{{\\rm{m}}}}}^{2}\\). This analysis indicates that current-induced effective SOT fields or torques in TaIrTe4/Fe3GaTe2 heterostructure originated from the out-of-plane spin polarization \\(({\\sigma }^{Z})\\), and it is larger than its in-plane counterparts \\(({\\sigma }^{{XY}})\\).\n\na Schematic of the TaIrTe4/Fe3GaTe2 heterostructure, illustrating the effects of damping-like torques (\\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{DL}}}}}^{{{{\\bf{XY}}}}}\\) and \\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{DL}}}}}^{{{{\\bf{Z}}}}}\\)) and field-like torques (\\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{FL}}}}}\\)) on Fe\u2083GaTe\u2082 magnetization when the current is applied along the a-axis of TaIrTe\u2084 layer19. The 2nd harmonics Hall voltage (\\({V}_{{xy}}^{2\\omega }\\)) measurement scheme is shown with an external in-plane magnetic field at angle \u03a6B relative to the a.c. current direction Iac. b \\({V}_{{xy}}^{2\\omega }\\) vs \u03a6B of Dev1 at magnetic field 7\u2009T and temperature 300\u2009K. The solid lines are fitted with Eqs.\u00a01 and 2. The second panel shows \\({V}_{{xy}}^{2\\omega }\\) vs \u03a6B for varied magnetic fields (7-12\u2009T). c\u2013e Coefficient \\({{V}_{{xy}}^{2\\omega }}_{\\cos {\\varPhi }_{B}}\\)(\\(\\cos {\\varPhi }_{B}\\) dependent in \\({V}_{{xy}}^{2\\omega }\\)), \\({{V}_{{xy}}^{2\\omega }}_{\\sin {\\varPhi }_{B}}\\)(\\(\\sin {\\varPhi }_{B}\\) dependent in \\({V}_{{xy}}^{2\\omega }\\)) and \\({{V}_{{xy}}^{2\\omega }}_{\\cos 2{\\varPhi }_{B}}\\)(\\(\\cos {2{\\phi }}_{B}\\) dependent in Vxy2\u03c9) as a function of 1/(H-Hk) and 1/H under different current densities Ja.c.. The error bar in c, d and e are obtained from fitting of experimental data in (b) using Eq.\u00a01. f Angle sweep of \\({V}_{{xy}}^{2\\omega }\\) at different temperatures (2\u2013325\u2009K) at a constant magnetic field of 10\u2009T. Solid lines are fit to experimental data using Eq.\u00a01. g Damping-like field components (\\({H}_{{DL}}^{X},{{H}_{{DL}}^{Y},H}_{{DL}}^{Z}\\)) as a function of current density, with linear fits estimating HDL/ Ja.c., whereas error are obtained from the linear fit of c, d and e data. h Temperature dependence of HDL/ Ja.c. for TaIrTe4/Fe3GaTe2 device. Insets show the energy dispersion curve of type-II Weyl semimetal and tuning of Fermi level energy (EF) with temperature. The error bars in (h) are obtained by fitting experimental data in (f) using Eq.\u00a01.\n\nThe polarity and magnitude of spin accumulation generated by TaIrTe4 are influenced by the chemical potential9, resulting in temperature dependence changes in the tilt angle (as shown in Fig.\u00a01f). A similar trend is expected in the current-induced effective SOT fields or torques. Hence, to observe the temperature dependence of SOT efficiency from the 2nd harmonic Hall signal of TaIrTe4/Fe3GaTe2, angle sweep second harmonic Hall (SHH) measurements are conducted at different temperatures. Figure\u00a03f illustrates the \\({V}_{{xy}}^{2\\omega }\\) vs \\({\\varPhi }_{B}\\) curve at 10\u2009T across different temperatures. Damping-like SOT effective fields (\\({H}_{{DL}}^{X,Y,Z}\\)) are estimated using Eq.\u00a01. \\({H}_{{DL}}^{Z}/{J}_{a.c.}\\) is highly tunable with temperature (Fig.\u00a03h), it decreased from 2\u2009K to 100\u2009K, reached a minimum between 100\u2013200\u2009K, and increased from 200\u2009K to 325\u2009K. This behavior aligns with the temperature dependence of current-induced spin accumulation (Fig.\u00a01f), showing large out-of-plane spin polarization at 2\u2009K and room temperature with a minimum near 100\u2009K. Hence, the out-of-plane damping-like torque is observed to be tunable by the chemical potential of TaIrTe4.\n\nTo further validate and estimate SOT components, we measured the 1st and 2nd harmonic transverse Hall resistance Rxy signal as a function of magnetic field applied parallel to the sample surface (\\(H{{{\\perp }}}c\\)) and perpendicular to the applied current direction. In the 1st harmonic \\({R}_{{xy}}^{{{{\\rm{\\omega }}}}}\\) vs H, a hysteresis loop with a magnetic anisotropic field \\({H}_{K}\\) of ~ 1.5\u2009T is observed (Fig.\u00a04a). The 2nd harmonics transverse Hall resistance signal \\({R}_{{xy}}^{2\\omega }\\) varied with the external magnetic field applied parallel to the sample surface. The measurements are conducted with the field oriented either perpendicular (\\({H}_{y},{\\varPhi }_{B}=90^\\circ \\; {or}\\;270^\\circ\\)) or parallel (\\({H}_{x},{\\varPhi }_{B}=0^\\circ\\) or 180\u00b0) to the direction of current (or a-axis of TaIrTe4). These results are displayed in Fig.\u00a04b, c. The resistance exhibited a hyperbolic dependence on the field for |H\u2009|\u2009>Hk, however became discontinuous for \\({|H|} < {H}_{K}\\).\n\na 1st harmonic transverse resistance (\\({R}_{{xy}}^{1\\omega }\\)) as a function of magnetic field swept parallel to the sample surface (\\(H{{{\\perp }}}c\\)) and perpendicular to current direction, measured at 300\u2009K on Dev 2. b, c 2nd harmonic transverse resistance \\({R}_{{xy}}^{2\\omega }\\) varied as a function of the external magnetic field applied along parallel to the sample surface, with \\({H}_{y}\\) representing \\(H{{{\\perp }}}c\\) and perpendicular to the current (\\(H{{{\\perp }}}{J}_{a.c.}\\)), and \\({H}_{x}\\) representing \\(H{{{\\perp }}}c\\) and parallel to the current (\\({H||{J}_{a.c.}}\\)). d, e Dependence of the 2nd harmonic transverse resistance \\(({R}_{{xy}}^{2\\omega })\\) on the in-plane magnetic field (\\({H}_{y}\\) and \\({H}_{x}\\)) for different magnitudes of constant write current density (\u2009Ja.c.). The data is fitted using equations simplified from Supplementary Eq\u00a0S1\u2013S7 (also see Supplementary Eq.\u00a0S8\u2013S9 and Supplementary Note\u00a08). f Extracted effective damping-like field components (\\({H}_{{DL}}^{X,Y,Z}\\)) corresponding to spin polarization (\\({{{{\\boldsymbol{\\sigma }}}}}^{{{{\\bf{X}}}}},{{{{\\boldsymbol{\\sigma }}}}}^{{{{\\bf{Y}}}}},{{{{\\boldsymbol{\\sigma }}}}}^{{{{\\bf{Z}}}}}\\)) as a function of Ja.c. along with corresponding error bars are obtained from fits to the 2nd harmonic signal.\n\nFigure\u00a04d, e shows 2nd harmonics transverse resistance \\(({R}_{{xy}}^{2\\omega })\\) versus \\({H}_{y}\\) and \\({H}_{x}\\) for different applied sinusoidal current densities (\\({J}_{a.c.}\\)). The hyperbolic curvature of these plots sharpens with increasing current density. For \\({R}_{{xy}}^{2\\omega }\\) vs \\({H}_{y}\\) data at \\({\\varPhi }_{B}=90^\\circ\\) and \\(270^\\circ\\), Eq. (1) reveals that only x and z components of the SOT fields contribute to the 2nd harmonics signal (see Supplementary Eq.\u00a0S8). Therefore, from the analysis of \\({R}_{{xy}}^{2\\omega }\\) vs \\({H}_{y}\\) data at \\({\\varPhi }_{B}=90^\\circ\\), we have calculated \\({H}_{{DL}}^{X},\\) \\({H}_{{DL}}^{Z}\\), \\({H}_{{FL}}^{X}\\) and \\({H}_{{FL}}^{Z}\\). Similarly, for \\({R}_{{xy}}^{2\\omega }\\) vs \\({H}_{x}\\) data (\\({\\varPhi }_{B}=0^\\circ\\)) and using the extracted \\({H}_{{DL}}^{Z}\\) and \\({H}_{{FL}}^{Z}\\) values, we have estimated \\({H}_{{DL}}^{Y}\\) and \\({H}_{{FL}}^{Y}\\) (see Supplementary Eq.\u00a0S9 and also see Supplementary Note\u00a06 and 8). The extracted values of \\({H}_{{DL}}^{X,Y,Z}\\) with different current densities \\({J}_{a.c.}\\) are plotted in Fig.\u00a04f. The slopes of HDL vs \\({J}_{a.c.}\\) are found to be: \\({H}_{{DL}}^{X}/{J}_{a.c.} \\sim (0.348\\pm 0.081)\\times {10}^{-12}{{{\\rm{T}}}} {{{{\\rm{A}}}}}^{-1}{{{{\\rm{m}}}}}^{2}\\),\u00a0\\({H}_{{DL}}^{Y}/{J}_{a.c.} \\sim (0.061\\pm 0.002)\\times {10}^{-12}{{{\\rm{T}}}}{{{{\\rm{A}}}}}^{-1}{{{{\\rm{m}}}}}^{2}\\) and \\({H}_{{DL}}^{Z}/{J}_{a.c.} \\sim (3.50\\pm 0.27)\\times {10}^{-12}{{{\\rm{T}}}}{{{{\\rm{A}}}}}^{-1}{{{{\\rm{m}}}}}^{2}\\). These findings also confirm that effective damping like the field corresponding to Z spin polarization is significantly larger than that from XY polarized spins.\n\nIt should be noted that TaIrTe4 alone also exhibits a 2nd harmonic voltage signal as function of \\({\\varPhi }_{B}\\), arising from broken mirror symmetry and finite Berry curvature dipole. This signal follows a \\(\\cos {\\varPhi }_{B}\\) or \\(\\sin {\\varPhi }_{B}\\) dependence (Fig.\u00a01e). So, the \\({H}_{{DL}}^{X}\\) and \\({H}_{{DL}}^{Y}\\) values from the fitting of \\({V}_{{xy}}^{2\\omega }\\) vs \\({\\varPhi }_{B}\\) data can be overestimated (Eq.\u00a01). However, the estimation of Z-component damping-like field \\({H}_{{FL}}^{Z}\\) using coefficient \\({{V}_{{xy}}^{2\\omega }}_{\\cos 2{\\varPhi }_{B}}\\) (Eq.\u00a01), central to the conclusion of second harmonic measurements, remains consistent. Furthermore, only TaIrTe4 also has a unique field dependence of 2nd harmonics signal as shown in Supplementary Fig.\u00a0S6d, which is quite different from SOT-induced \\({R}_{{xy}}^{2\\omega }\\) vs H curve (see Fig.\u00a04b\u2013e). At large magnetic field, the \\({R}_{{xy}}^{2\\omega }\\) vs H curve from TaIrTe4 appears to be linear function of magnetic field; hence, to account for the 2nd harmonics field contribution of TaIrTe4 and thermal effects31,32, a linear polynomial term is included while fitting the field-dependent curves (also see Supplementary Note\u00a08). Also, the field-like torque for Dev 1 and 2 and Nerst effect voltages for Dev1 are shown in Supplementary Fig.\u00a0S7 and Supplementary Note\u00a07.\n\nSOT magnetization switching experiments are crucial for investigating magnetization switching characteristics, such as determining the critical switching current density, assessing the need for an external field to aid in switching, and identifying whether the process is deterministic or non-deterministic. A series of pulse currents (Ipulse) applied along the a-axis in the TaIrTe4/Fe3GaTe2 heterostructure can induce an unconventional spin current along the z-axis, with spin polarization \\({\\sigma }^{Z}\\) oriented along the z-axis in TaIrTe49. This spin current generates an unconventional SOT on Fe3GaTe2, consisting of both field-like (\u03c4FL) and damping-like (\u03c4DL) torques, facilitating the switching of the magnetization direction M. The field-like torque \u03c4FL\u2009~\u2009\\({{{\\boldsymbol{\\sigma }}}}\\times {{{\\bf{m}}}}\\) induces the precession of M around the exchange field generated by spin polarization, while the damping-like torque \u03c4DL\u2009~\u2009\\({{{\\bf{m}}}}\\times ({{{\\boldsymbol{\\sigma }}}}\\times {{{\\bf{m}}}})\\) aligns M with the spin polarization \\({{{\\boldsymbol{\\sigma }}}}\\), predominantly driving the magnetization switching (Fig.\u00a05a)33. Figure\u00a05b shows the AHE at 300\u2009K of Dev3 used for switching experiments. Figure\u00a05c presents SOT-induced magnetization switching, measured by applying a pulsed write current (Ip) along the a-axis with a pulse duration of 50 ms. This is followed by a small D.C. read current (Ir~500 \u03bcA) to determine the magnetization state via the Hall resistance Rxy=Vxy/Ir. Due to a large unconventional SOT, fully deterministic field-free magnetization switching could be observed at room temperature with Ip\u2009=\u2009\u00b13.5\u2009mA. Since the signal Rxy is proportional to the out-of-plane magnetization Mz, the SOT Rxy signal indicates a current-induced magnetization change between +Mz and -Mz. Notably, deterministic SOT switching of TaIrTe4/Fe3GaTe2 heterostructure is observed at Hx\u2009=\u20090\u2009T, which indicates the creation of \\({\\sigma }^{Z}\\) spin polarization in TaIrTe4 leading to an out-of-plane SOT component. The magnitude of the switching signal is comparable to the AHE signal magnitude with field sweep, showing a full magnetization switching34,35,36,37.\n\na Diagrammatic representation of TaIrTe4/Fe3GaTe2 heterostructure. This configuration leads to a significant out-of-plane antidamping torque (\\({\\tau }_{{AD}}^{{OOP}}\\)), which is symmetric with respect to the current direction, facilitating field-free deterministic switching of the Fe3GaTe2 magnetization19. b AHE of the TaIrTe4/Fe3GaTe2 heterostructure device 3 with magnetic field sweep at 300\u2009K. c Field-free full deterministic switching achieved at 3.5\u2009mA pulse current and 500\u2009\u00b5A current is used as reading current to measure magnetization states keeping external field zero at 300\u2009K temperature. The current is applied along the symmetry axis (a-axis) of TaIrTe4. d Current-driven magnetization switching of TaIrTe4/Fe3GaTe2 under different bias fields parallel to the sample surface and current (Hx). The forward and backward current sweeps are distinguished by arrows. The data is\u00a0vertically shifted\u00a0to avoid overlap. e The benchmark of SOT spin Hall conductivity vs. power consumption with state-of-the-art results8,9,16,28,31,34,35,36,37. Ellipse represents error and device to device variation in the calculated parameters.\n\nWe further investigated the impact of deterministic SOT switching on the external in-plane magnetic field parallel to the current direction (Fig.\u00a05d). The external in-plane magnetic field (Hx) can break the symmetry of deterministic SOT switching. As the strength of Hx increases, the switching mechanism transitions from being predominantly driven by the out-of-plane spin torque component (\\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{DL}}}}}^{{{{\\bf{z}}}}}\\)) to being influenced by the in-plane components \\(({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{DL}}}}}^{{{{\\bf{x}}}},{{{\\bf{y}}}}})\\). We observed that a small positive Hx has minimal effect on the SOT switching signal, however, increasing Hx beyond 100 mT results in a noticeable reduction of the signal magnitude. Despite this reduction, the switching efficiency was maintained at 50%, demonstrating some robustness against the external magnetic field. In contrast, when Hx is applied in the negative direction, the switching efficiency drops significantly to about 50% even at \u221210 mT, and it nearly diminishes to ~10% at -300 mT. Interestingly, the switching polarity remains unchanged up to 100 mT, indicating the effectiveness of the out-of-plane spin polarization of TaIrTe4 in counteracting the external magnetic field9,14. In conventional SOT, where magnetization switching is driven purely by in-plane spin current, the switching polarity typically reverses abruptly with Hx23,32. However, this was not observed in our experiments, highlighting the larger contribution of \\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{DL}}}}}^{{{{\\bf{z}}}}}\\) from TaIrTe4 in the magnetization dynamics of Fe3GaTe2. In device 4 (data provided in Supplementary Fig.\u00a0S3), we observed that the switching polarity remained unchanged even up to 200 mT when pulse current of \u00b14\u2009mA was applied along the a-axis of TaIrTe4. However, it abruptly reversed when both the current and magnetic field of similar magnitude were applied along the b-axis of TaIrTe4.\n\nFurthermore, to examine the presence of \\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{DL}}}}}^{{{{\\bf{z}}}}}\\) and calculate unconventional SOT driven switching efficiency, we have performed AHE loop shift measurement with bias current (see Supplementary Fig.\u00a0S4)38,39. The out-of-plane antidamping torque can shift the AHE hysteresis loop when a positive and negative dc bias current beyond a threshold value equivalent to switching current density is applied along the a-axis of TaIrTe4. Such AHE loop shift (Hshift) is observed for compensating \\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{DL}}}}}^{{{{\\bf{z}}}}}\\) driven intrinsic damping in Fe3GaTe29,14,38. The SOT efficiency (\u03b5SOT) due to unconventional \\({{{{\\boldsymbol{\\tau }}}}}_{{{{\\bf{DL}}}}}^{{{{\\bf{z}}}}}\\) torque is defined by equation39,40,41\n\nIn our device, the \u03b5SOT is 1.76, with the Hshift and \\({J}_{{switch}}\\) calculated to be 2 mT and \\(1.81\\times {10}^{10}{{{\\rm{A}}}}{{{{\\rm{m}}}}}^{-2}\\), respectively and Ms taken as 0.97\u00d7105 Am\u22121 (see Supplementary Note\u00a05). The switching efficiency parameter (\\(\\eta\\)), defined as the ratio of switching current-driven and magnetic field-driven AHE, is observed to be 1 (Fig.\u00a05c). Using the device parameters (\\({\\varepsilon }_{{SOT}}=1.76\\) and charge conductivity of TaIrTe4 \u03c3c\u2009=\u20099.4\u2009\u00d7\u2009105 S/m), we estimate the spin Hall conductivity in TaIrTe4/Fe3GaTe2 heterostructure to be \u03c3SH\u2009\u2009=\u2009\u0127/2e\\(({\\varepsilon }_{{SOT}}.{\\sigma }_{c})=\\)1.65\u00d7106\u2009\u0127/2e\u2009(\u03a9m)\u20131. By employing both SOT-induced magnetic switching and 2nd harmonic Hall measurements, we have established that the magnetization of Fe3GaTe2 in heterostructure with TaIrTe4 can be effectively manipulated with a switching current density of Jswitch\u2009~\u2009\\(1.81\\times {10}^{10}{{{\\rm{A}}}}/{{{{\\rm{m}}}}}^{2}\\) and power density P (\\(={J}_{{switch}}^{2}/{\\sigma }_{c}\\)) of \\(0.348\\times {10}^{15}\\frac{{{{\\rm{W}}}}}{{{{{\\rm{m}}}}}^{3}}\\) at room temperature. The benchmarked of the spin Hall conductivity \u03c3SH and power density P of TaIrTe4/Fe3GaTe2 devices along with literature available on state-of-the-art SOT devices8,9,14,15,16,31,32,37,42 are shown in Fig.\u00a05e and Supplementary Table\u00a01.\n\nOur experimental observations strongly suggest the presence of unconventional spin Hall effect in TaIrTe4, which originates from the in-plane charge current and results in an out-of-plane spin-polarized spin current across the interface, corresponding the \\({\\sigma }_{{ZX}}^{Z}\\) component of the spin Hall conductivity (SHC) tensor (\\({I}_{Z}^{{S}_{Z}}={\\sigma }_{{ZX}}^{Z}{I}_{X}^{C}\\) where \\({I}^{S}\\) and \\({I}^{C}\\) are spin and charge currents, respectively). While similar effects were also found in another low-symmetry Weyl semimetal (TD-WTe2)6,7,15,16,18, the symmetry constraints theoretically prohibit this configuration, and the experimental results have not been explained. Like WTe2, TaIrTe4 has low crystal symmetry described by space group (SG) 31 (Pmn21), consisting of a mirror plane perpendicular to the a axis (See Fig.\u00a06a), as well as glide reflection and two-fold screw rotation, which prevents an unconventional SHC component43.\n\na Orthorhombic crystal unit cell of TaIrTe4. b Electronic structure of TaIrTe4 calculated via density functional theory along the high-symmetry lines in the Brillouin zone shown in c. d\u2013f Calculated intrinsic spin Hall conductivity, representing configurations of spin current with spin polarization along a-, b-, and c-axis, respectively. The magnitude of the unconventional spin Hall conductivity \u03c3zzx at the Fermi level is determined mostly by the band marked as red in b.\n\nTo unveil the origin of the unconventional SHE, we have performed first-principles calculations of TaIrTe4 (see Methods for computational details). As shown in Fig.\u00a06, our results reveal a large spin splitting of bands near the Fermi level due to SOC, and the presence of seven spin Hall conductivity components: six conventional (\\({\\sigma }_{{jk}}^{i}\\) with i\u2009\u2260\u2009j\u2009\u2260\u2009k) and one unconventional component \\({\\sigma }_{{ZX}}^{Z}\\), with the latter showing a magnitude comparable to the conventional components. The unconventional SHE at the Fermi level reaches \u03c3SH\u2009\u2009=\u20091.56\u2009\u00d7\u2009104\u2009\u0127/2e\u2009(\u03a9m)\u20131, in agreement with experimental values reported for TaIrTe4 (1.47\u20135.44)x104\u2009\u0127/2e\u2009(\u03a9m)\u20131\u20098,9,18, which vary depending on experimental conditions and sample characteristics. Although previous studies attributed its presence to the topological properties of the surface8, our calculations show that a large unconventional SHE occurs even in the bulk.\n\nWe analyzed the crystal symmetry in more detail by directly applying symmetry operations, revealing that the relaxed structures exhibit a slight deviation from SG 31. This small structural distortion reduces the symmetry to either SG 6 (Pm) or SG 1 (P1), depending on the specified numerical precision (see Supplementary Note\u00a09 for details). In both cases, the two-fold screw symmetry which normally prohibits unconventional SHE is absent, thus allowing for out-of-plane spin polarization of spin current. Structural distortion could further increase near the surface, potentially enhancing the generated spin accumulation. Therefore, from a symmetry perspective, the unconventional SHE component is justified, while its magnitude arises from the electronic properties, as discussed in Supplementary Materials.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64109-3/MediaObjects/41467_2025_64109_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64109-3/MediaObjects/41467_2025_64109_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64109-3/MediaObjects/41467_2025_64109_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64109-3/MediaObjects/41467_2025_64109_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64109-3/MediaObjects/41467_2025_64109_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64109-3/MediaObjects/41467_2025_64109_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our experiments indicate even larger spin Hall conductivity (SHC) values than previously reported and reveal an additional component, \\({\\sigma }_{{ZY}}^{Z}\\), induced by charge current along the mirror plane. This component was absent in the previous studies and does not emerge in bulk calculation, suggesting a possible role of interfacial effects. The overall enhancement of SHC could arise from the spin and orbital effects at the TaIrTe4/Fe3GaTe2 interfaces, and also from the individual constituents12,42,44,45,46,47,48,49,50,51,52. This highlights the unique behaviors of the vdW heterostructure and suggests unfamiliar avenues for exploring SOT in low-symmetry materials.\n\nIn summary, we demonstrated the potential of TaIrTe4/Fe3GaTe2 vdW heterostructures for generating a large and tunable nonlinear 2nd harmonic Hall effect, and energy-efficient deterministic field-free magnetization switching at room temperature. By leveraging the unique properties of the topological Weyl semimetal TaIrTe4 and the magnetic Fe3GaTe2 with strong PMA, our findings reveal a large non-linear Hall effect, substantial unconventional out-of-plane damping-like torque and a remarkably low switching current density, outperforming conventional systems. To unveil the origin of unconventional charge-spin conversion phenomena in TaIrTe4, detailed first-principles calculations were performed considering crystal symmetry and its impact on the energy-dependent electronic structure and spin Hall conductivity. Finally, we measured a substantial and tunable damping-like torque and observed deterministic field-free magnetization switching at a very low current density offering a promising route to energy-efficient and external field-free spintronic technologies.\n\nNote: After preparation of this manuscript, we came across reports on magnetization switching in TaIrTe4/Fe3GaTe2 system34,53. However, spin dynamics experiments to understand the spin-orbit torque phenomena in vdW heterostructures are so far lacking. In our manuscript, in addition to energy-efficient magnetization switching, we report a detailed understanding of unconventional and tunable SOT magnetization dynamics using 2nd harmonic measurements in all-vdW heterostructures.\n\nWe have observed a larger out-of-plane damping-like torque compared to the in-plane components in heterostructures of TaIrTe4/Fe3GaTe2. This conclusion is drawn from measurements on various devices (Dev1-Dev5) across different experimental setups, thereby reinforcing the reproducibility and robustness of this finding. However, the ratio of magnitude of \\({{H}_{{DL}}^{Z}/H}_{{DL}}^{{XY}}\\) varies among devices, indicating a more profound role of spin Hall conductivity that can arise from the spin and orbital effects at the TaIrTe4/Fe3GaTe2 interfaces. This variation may be influenced by the relative twist angle between the incommensurate heterostructures of TaIrTe\u2084 and Fe\u2083GaTe\u2082, which requires further investigation54.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "TaIrTe4 single crystals were synthesized by evaporating tellurium from a Ta-Ir-Te melt, with the crystal growth conducted at 850\u2009\u00b0C and Te condensation at 720\u2009\u00b0C55. Fe3GaTe2 single crystals were grown via a self-flux method using Fe, Ga, and Te with 99.99% purity in the molar ratio of 1:1:2 in an evacuated and sealed quartz tube. The solid reactions took place for 24\u2009h at 1273\u2009K, followed by cooling to 1153\u2009K within 1\u2009h and slowly cooling down to 1053\u2009K within 100\u2009h10.\n\nThe van der Waals heterostructure samples were prepared by mechanically exfoliating nanolayers of TaIrTe4 and Fe3GaTe2 crystals on top of each other on a SiO2/Si wafer using the Scotch tape method inside a glove box. The top sample surface was immediately capped with a 2\u2009nm Al2O3 layer to protect from degradation with time. For the Devs1-3 nearly rectangular-shaped flakes were selected and the TaIrTe4/Fe3GaTe2 heterostructures were patterned to Hall-bar geometry using electron-beam lithography (EBL) and Ti (15\u2009nm)/Au (250\u2009nm) contacts were prepared by EBL and electron beam evaporation. For Dev4 and Dev5, flakes are in arbitrary shapes, therefore, dry physical etching by Ar ion milling was used to fabricate well-defined Hall-bar devices.\n\nSpin-orbit torque was characterized using an in-plane 2nd harmonic Hall lock-in measurement technique. The \\({R}_{{xy}}^{1\\omega }\\) and \\({R}_{{xy}}^{2\\omega }\\) for an a.c. current \\({I}^{\\omega }\\) of 213.3\u2009Hz were simultaneously measured while rotating the sample in the plane (azimuthal angle \u03c6B) under an external field \u03bc0Hext. The harmonic measurements were conducted using a Lock-in SR830 to detect the in-phase 1st and out-of-phase 2nd harmonic voltages. The 2nd harmonic measurements in the high magnetic field range were performed with a Quantum Design cryogen-free PPMS DynaCool system, interfaced with the SR830 to record the 1st and 2nd harmonic voltages. The 1st harmonic signal is detected by putting the voltmeter in phase with the oscillator, whereas the 2nd harmonic signal is out of phase with the source signal.\n\nSpin-orbit torque switching measurements were conducted in a vacuum cryostat with a magnetic field strength of up to 0.7\u2009T. Electronic measurements were carried out using a Keithley 6221 current source and a Keithley 2182\u2009A nanovoltmeter. To monitor the longitudinal and transverse Hall resistances, Keithley 2182\u2009A nanovoltmeters were employed. For SOT-induced magnetization switching, the Keithley 2182\u2009A nanovoltmeters were used to observe the Hall resistances responses, while a Keithley 6221 A.C. source applied a pulse current of 50 millisecond (ms) through the device, followed by a D.C. read current of magnitude 500\u2009\u00b5A.\n\nDensity functional theory calculations for bulk TaIrTe4 were performed using the Quantum Expresso package56,57 by employing the Perdew, Burke, and Ernzerhof (PBE) generalized gradient approximation (GGA) for exchange-correlation functional58. We used fully relativistic pseudopotentials and expanded the electron wave functions in a plane-wave basis with the energy cutoff of 80\u2009Ry. We adopted an orthorhombic unit cell with the experimental lattice constants a\u2009=\u20093.77\u2009\u00c5, b\u2009=\u200912.42\u2009\u00c5, and c\u2009=\u200913.18 \u00c559. The atomic positions were relaxed with the force and energy convergence thresholds set to 10\u22123\u2009Ry/Bohr and 10\u22124\u2009Ry, respectively. The Brillouin Zone (BZ) was sampled following the Monkhorst-Pack scheme with the k-grids of 20\u2009\u00d7\u20098\u2009\u00d7\u20098 and adopting a Gaussian smearing of 10\u22123\u2009Ry. For the post-processing analysis, we used the python package PAOFLOW, which projects the ab initio wavefunctions onto pseudo-atomic orbital (PAO) basis to construct tight-binding Hamiltonians60,61, further interpolated to a denser grid of 80\u00d740\u00d740. The charge-to-spin conversion response tensors were calculated using the approaches implemented in PAOFLOW and described in the previous works62,63,64.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data that support the findings of this study are available from the corresponding authors on a reasonable request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Manchon, A. et al. Current-induced spin-orbit torques in ferromagnetic and antiferromagnetic systems. Rev. Mod. Phys. 91, 035004 (2019).\n\nArticle\u00a0\n MathSciNet\u00a0\n ADS\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nShao, Q. et al. Roadmap of spin\u2013orbit torques. IEEE Trans. Magn. 57, 1\u201339 (2021).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nHan, W., Otani, Y. & Maekawa, S. Quantum materials for spin and charge conversion. npj Quant. 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B 107, 165140 (2023).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "Authors acknowledge funding from European Comission (EU) Graphene Flagship, European Innovation Council (EIC) project 2DSPIN-TECH (No. 101135853), 2D TECH VINNOVA competence center (No. 2019-00068), Wallenberg Initiative Materials Science for Sustainability (WISE) funded by the Knut and Alice Wallenberg Foundation, EU Graphene Flagship (Core 3, No. 881603), Swedish Research Council (VR) grant (No. 2021\u201304821, No. 2018-07046), FLAG-ERA project 2DSOTECH (VR No. 2021-05925) and MagicTune, Carl Tryggers foundation, Graphene Center, Chalmers-Max IV collaboration grant, VR Sweden-India collaboration grant, Areas of Advance (AoA) Nano, AoA Materials Science and AoA Energy programs at Chalmers University of Technology, Dutch Research Council (NWO grant OCENW.M.22.063),\u00a0QRDI Project 676\u00a0No. ARG01-0516-230179 and\u00a0National Key Research and Development Program of China (No. 2022YFE0134600). The fabrication of devices was performed at Nanofabrication laboratory MyFab at Chalmers University of Technology. The calculations were carried out on the Dutch national e-infrastructure with the support of SURF Cooperative (EINF\u221210786) and on the H\u00e1br\u00f3k high-performance computing cluster of the University of Groningen.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open access funding provided by Chalmers University of Technology.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Microtechnology and Nanoscience, Chalmers University of Technology, G\u00f6teborg, Sweden\n\nLalit Pandey,\u00a0Bing Zhao,\u00a0Roselle Ngaloy,\u00a0Himanshu Bangar,\u00a0Lars Sj\u00f6str\u00f6m,\u00a0Prasanna Rout\u00a0&\u00a0Saroj P. Dash\n\nWallenberg Initiative Materials Science for Sustainability, Department of Microtechnology and Nanoscience, Chalmers University of Technology, G\u00f6teborg, Sweden\n\nLalit Pandey\u00a0&\u00a0Saroj P. Dash\n\nZernike Institute for Advanced Materials, University of Groningen, Groningen, The Netherlands\n\nKarma Tenzin,\u00a0Veronika Lamparsk\u00e1\u00a0&\u00a0Jagoda S\u0142awi\u0144ska\n\nDepartment of Physical Science, Sherubtse College, Royal University of Bhutan, Kanglung, Trashigang, Bhutan\n\nKarma Tenzin\n\nCenter for Advanced Materials Research, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, United Arab Emirates\n\nAya Ali\n\nDepartment of Applied Physics and Astronomy, University of Sharjah, Sharjah, United Arab Emirates\n\nMahmoud Abdel-Hafiez\n\nDepartment of Physics and Astronomy, Uppsala University, Uppsala, Sweden\n\nMahmoud Abdel-Hafiez\n\nDepartment of Physics, Faculty of Science, Fayoum University, Fayoum, 63514, Egypt\n\nMahmoud Abdel-Hafiez\n\nSchool of Materials Science and Engineering, Huazhong University of Science and Technology, Hubei, China\n\nGaojie Zhang,\u00a0Hao Wu\u00a0&\u00a0Haixin Chang\n\nGraphene Center, Chalmers University of Technology, G\u00f6teborg, Sweden\n\nSaroj P. Dash\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nL.P. and S.P.D. conceived the idea and designed the experiments. L.P. fabricated and characterized the devices with support from B.Z., R.N., L.S., H.B., and P.R. The TaIrTe4 single crystals were grown by A.A. and M.A.H., while G.Z., H.W., and H.C. grew the Fe3GaTe2 single crystals. K.T., V.L., and J.S. performed, analyzed and described the density functional theory calculations. J.S. supervised theoretical calculations. L.P. and S.P.D. analyzed and interpreted the experimental data and wrote the manuscript, with comments from all the authors. S.P.D. coordinated and supervised the project.\n\nCorrespondence to\n Lalit Pandey or Saroj P. Dash.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Deblina Sarkar who co-reviewed with Shivam Kajale and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Pandey, L., Zhao, B., Tenzin, K. et al. Tunable unconventional spin orbit torque magnetization dynamics in van der Waals heterostructures.\n Nat Commun 16, 8722 (2025). https://doi.org/10.1038/s41467-025-64109-3\n\nDownload citation\n\nReceived: 11 September 2024\n\nAccepted: 09 September 2025\n\nPublished: 30 September 2025\n\nVersion of record: 30 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-64109-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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fiber with aligned mesogens-induced thermopower for four-dimensional dynamically adaptive heat harvesting", + "pre_title": "An actuatable ionogel thermoelectric fiber with aligned mesogens-induced unprecedented thermopower for four-dimensional dynamically adaptive heat harvesting", + "journal": "Nature Communications", + "published": "01 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_MOESM1_ESM.pdf" + }, + { + "label": "Description of Addtional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_MOESM2_ESM.pdf" + }, + { + "label": "Movie S1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_MOESM3_ESM.mp4" + }, + { + "label": "Movie S2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_MOESM4_ESM.mp4" + }, + { + "label": "Movie S3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_MOESM5_ESM.mp4" + }, + { + "label": "Movie S4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_MOESM6_ESM.mp4" + }, + { + "label": "Movie S5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_MOESM7_ESM.mp4" + }, + { + "label": "Movie S6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_MOESM8_ESM.mp4" + }, + { + "label": "Movie S7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_MOESM9_ESM.mp4" + }, + { + "label": "Movie S8", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_MOESM10_ESM.mp4" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_MOESM11_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Organic molecules in materials science", + "Thermoelectric devices and materials", + "Thermoelectrics" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5606102/v1.pdf?c=1751454584000", + "research_square_link": "https://www.researchsquare.com//article/rs-5606102/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60103-x.pdf", + "preprint_posted": "16 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Thermoelectric (TE) ionogel is promising material for harvesting low-grade heat owing to their nature of quasi-solid state and giant thermopower. However, current high-performance ionogel at low humidity present multicomponent systems, resulting in a trade-off between TE property, mechanics, and cost, and their device are integrated on planar substrates, erasing their advantage of adapting to complex-shaped geometry. A TE conversion system dynamically adaptive to any curved heat surface while achieving high intrinsic TE performance remains a formidable challenge. Here, an actuated ionogel TE fiber is designed, where the fine-tuning mesogen orientations can generate unprecedented~3-fold thermopower boost (25.8 mV K-1) at low humidity via enlarging the thermal mobility difference of ion inside the liquid crystal elastomer network, accompanied by a~30-fold electrical conductivity boom. Moreover, benefiting from their actuatable and excellent mechanical properties, and internal torque inside weaving structure, a woven gripper-structured TE device achieves a four-dimensional dynamically adaptive ability to complex geometrical heat source, and thus a stable output over time regardless of changing size or temperature of the heat source. Decoupled identification of size/shape and temperature of the heat source can also be enabled. The design concepts of the actuatable TE ionogel and device pave new ways for commercial ionic thermoelectrics.Physical sciences/Materials science/Materials for energy and catalysis/ThermoelectricsPhysical sciences/Energy science and technology/Thermoelectric devices and materialsThermoelectric ionogelLiquid crystal elastomerActuated thermoelectric fiberFour-dimensional device", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Supportingvideo.1.mp4Supporting video.1Supportingvideo.2.mp4Supporting video.2Supportingvideo.3.mp4Supporting video.3Supportingvideo.4.mp4Supporting video.4Supportingvideo.5.mp4Supporting video.5Supportingvideo.6.mp4Supporting video.6SupportingInformation.docxSupporting Information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Thermoelectric (TE) ionogel have emerged as promising materials for harvesting low-grade heat owing to their flexibility and giant thermopower. However, current high-performance TE ionogel requires multi-component systems, resulting in trade-offs between TE performance, mechanics, and ion leakage risk. Moreover, the humidity-dependent thermopower and two-dimensional device architectures restrict their practical applications. Here, a thermally actuated TE ionogel fiber is designed by tailoring the interactions between liquid crystal elastomer (LCE) network and ionic liquid. Fine tuning the mesogen orientation of LCE network ensures ~3-fold thermopower boost (25.8\u2009mV\u2009K\u22121) and ~30-fold electrical conductivity boom (21.5\u2009mS\u2009m\u22121) at low humidity (<30% RH). Furthermore, an actuatable gripper-structured TE device can be successfully integrated, which could four-dimensional dynamically adapt to complex-geometry heat source and enable decoupled recognition of size/shapes and temperatures of the heat source. The design concepts of actuatable thermoelectrics pave ways for their commercial successes in smart wearables and soft robots.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Low-grade heat below 373\u2009K is about 2/3 of the total waste heat in daily life, such as industrial waste heat, body-released sensible heat, and electronics-dissipated heat, etc., the effective utilization of which is expected to relieve energy crisis1. Thermoelectric (TE) conversion system can directly convert heat into electricity, showing considerable promise for harvesting low-grade heat2. In comparison to traditional TE materials based on inorganic semiconductors and semi-metals, which suffer from low thermopower, rigid, and high cost, the emerging ionic thermoelectric (i-TE) materials in recent years, including TE ionogels, polyelectrolytes, and hydrogels, etc., have attracted much attention owing to their nature of eco-friendly property, flexibility, and high thermopower (>10\u2009mV\u2009K\u22121)3,4.\n\nAmong the i-TE materials, TE ionogels - that is, polymeric networks swollen with salts, especially have emerged as a potential material for harvesting heat from complex geometrical heat source, owing to their quasi-solid state, excellent thermal stability, and good mechanical properties. Over the past few years, great effort has been made to extend the scope of TE ionogels material and optimize the TE performance. Polymeric systems including polyethylene oxide (PEO), polyvinyl alcohol (PVA), poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP), and bacterial cellulose (BC) swollen with salts have been proven effective for constructing TE ionogels5,6,7,8. And thermal mobility difference between anions and cations inside these polymeric systems accounts for the generation of thermopower. To further enhance the thermopower, enlarging the thermal mobility difference between the anions and cations is required. The strategies including selective combination of polymeric networks and multiple ionic species, incorporation of inorganic additives into polymeric networks, and modulation of humidity conditions, have been confirmed effective to enlarge this thermal mobility difference. For example, Li et al. prepared a hybrid i-TE gel containing 1-ethyl-3-methylimidazolium tetrafluoroborate (EMIM BF4) ionic liquid (IL), polyethylene glycol (PEG), and poly(acrylamide)/ alginate (Pam-alginate) double-network. By introducing PEG9, the electrostatic attraction interaction between PEG and EMIM+, as well as the electrostatic repulsion interaction between PEG and BF4-, enabled a larger ionic mobility differential, thereby ensuring an enhanced thermopower of 19.3\u2009mV\u2009K\u22121. Besides, as reported by Jang et al. the incorporation of SiO2 additives into polyaniline: poly(2-acrylamido-2-methyl-1-propanesulfonic acid): phytic acid (PANI:PAAMPSA:PA) ternary polymer10 not only promoted ionic dissociation within the system, but also weakened the binding between cations and polymers, enhancing the p-type thermopower from 14.9 to 17.9\u2009mV\u2009K\u22121. In addition, Liu et al. incorporated EMIM Cl into the EMIM TFSI/PVDF-HFP ionogel to enable a thermopower of 9.5\u2009mV\u2009K\u22121 at humidity of 40% RH, and the thermopower could be further improved to 19\u2009mV\u2009K\u22121 under high humidity (70% RH)6. Despite the effectiveness of the forementioned strategies in enlarging thermopower, the complexity of multicomponent systems and high humidity conditions inevitably lead to trade-offs between thermopower, mechanical properties, homogeneity, and liquid leakage, limiting the practical application11,12. Thus, the exploration of advanced polymeric network, that can intrinsically enable high ionic thermal mobility difference to contribute a high thermopower, would be highly desirable.\n\nTo promote the TE ionogels toward practical application, beyond materials performance, rational-design device that can adapt to heat source with complex-shaped geometry, at the same time without compromising the TE performance, are equally important. However, the development of i-TE devices is still in infancy, whose advances fall far below that of conventional electronic TE device. The current i-TE devices are integrated by connecting i-TE films in series on a planar substrate13. However, the substrates unavoidably limit the practical application owing to four aspects: (1) The softness of the substrate is far inferior to quasi-solid TE ionogels itself, weakening the intrinsic advantages of ionogels in contacting complex-shaped thermal sources; (2) The substrate will inevitably cause thermal shunting, which will damage the final output performance of the device; (3) During long-term heat harvesting, the different thermal expansion of ionogle and the substrate will result in unreliably contacting low-grade (<373\u2009K) heat source, causing device failure; (4) Planar two-dimensional devices cannot capture vertical heat flow in space. Therefore, how to establish an effective i-TE device, that can self-adapt to complex-geometric heat sources and capture vertical heat flow, remains particularly important yet challenging.\n\nTaking the above challenges of TE materials and devices in mind, the integration of thermally actuated function and high thermoelectric function in TE ionogels come into being, which is expected to enable dynamically adaptive heat harvesting from low-grade heat source. A recently reported work developed an ionic thermoelectric actuator14, but thermopower is only 0.6\u2009mV\u2009K-1, and the actuating feature originates from the physically integrated multilayers, which limited the application in efficient heat harvesting. How to achieve both high TE performance and actuated function in monolithic materials, at the same time ensure effectiveness at device level is challenging. A thermally actuating material - liquid crystal elastomer (LCE) may provide the entrance15. LCE possesses a tunable network composed of mesogenic units chemically incorporated into soft polymer chains, in which the soft chain with functional groups (including ether bonds C-O-C) could provide platform tailoring the ion-polymer interactions, at the same time the mesogen alignment could provide platform regulating ion transport. Thus, LCEs may not only serve as an effective polymer matrix for high thermopower via tuning ionic thermal mobility, but also construct a dynamically adaptive TE device attributing to the thermally responsive properties of LC, providing an innovative idea for future designing thermoelectric ionogels16,17.\n\nHere, we report a LCE-based p-type TE ionogel fiber, composed of LCE network and 1-Ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMIM TFSI), to address the above challenges in both i-TE materials and devices. The fiber molding contributes to aligned mesogens, which in turn induce a remarkable ~3-fold boom in thermopower (25.8\u2009mV\u2009K\u22121) and a dramatic ~30-fold electrical conductivity boom (21.5\u2009mS\u2009m\u22121). Meanwhile, combining the LC phase-induced axial actuation inside the i-TE fibers and the specific weaving patterns, a gripping-like and textiles-based actuatable i-TE device was successfully fabricated, which could achieve dynamically adaptive heat harvesting from complex-shaped heat source, and exhibit decoupled identification of size/shape and temperature of heat source. This design of LCE i-TE fibers not only provides an innovative approach for optimizing i-TE performance, but also unlocks the integration of actuator science with TE ionogels, offering a unique entry point for the application of TE ionogels.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The thermal voltage of TE ionogels is produced by the difference in migration rate between cations and anions in a polymeric network under temperature difference, thus how to enlarge the advantageous migration of anion or cation as possible in polymer network is significant for achieving high thermopower. In order to achieve the above vision, a LCE network is synthetized and designed (Supplementary Fig.\u00a01 and Fig.\u00a01a), in which 2,2\u2019-(ethylenedioxy) diethanethiol (EDDET) with ether bond C-O-C is served as the soft chain, aiming to provide the possibility of selective/preferential reactions with cations to produce ion migration difference, at the same time the orientation of the mesogen (1,4-Bis-[4-(3-acryloyloxypropyloxy) benzoyloxy]-2-methylbenzene-RM257) in LC phase is fine-tuned and aligned, aiming to further expand this migration difference. As a result, a strategy of synergies of oriented channel and intermolecular interaction is processed to enhance the thermopower via a \u201chighway\u201d for the rapid passage of ions. This synergistic effect is expected to promote a high intrinsic thermopower of the LCE ionogels.\n\na Design principle. b 1D intensity profiles of LCE fibers and films. Inset picture are 2D WAXD pattern of LCE films (down) and fibers (top). c Schematic of molecular chain inside LCE fibers and films. d\u2013e Local graph of FTIR spectra for LCE fibers showing the stretching vibration band of S=O and bending vibration band of C-O-C. (f) Raman spectra for TFSI- in LCE fibers at wavenumber range from 800\u2013600\u2009cm\u22121. g\u2013h MD snapshots of LCE i-TE fibers and films. i The binding energy between EMIM+/TFSI- and molecular chains in LCE i-TE fibers and films. j Diffusion coefficients (MSD) of EMIM+ and TFSI- in LCE i-TE fibers and films. Error bars denote mean\u2009\u00b1\u2009standard deviation.\n\nAccording to the design principle, to optimize mesogen alignment for ion motion inside LCE materials, fiber molding process were carried out as schematically exhibited in Supplementary Fig.\u00a01. During injection, the shear force generated at the nozzle aligns the mesogen, ultimately forming ordered channels with oriented molecular chains18. In order to confirm the effect of fiber molding on the molecular chain structure, the two-dimensional wide-angle x-ray scattering (2D WAXD) analysis is performed on both fiber and film samples. It can be observed from the 2D WAXD color map (Fig.\u00a01b) that both fiber and film state exhibit isotropic diffraction loops. In addition, the scattering intensity (I) versus scattering vector (q) profiles (q\u2013I curves) in Fig.1b reveals distinct differences in the main peak positions between the fiber and film forms. Then, the molecular chain spacing (d) is concluded via the equation [1] \\(d=2\\pi /q\\), and the concluded result shows that d of LCE fibers (4.58\u2009\u00c5) is slightly larger than that of the films (4.33\u2009\u00c5). Meanwhile, the order parameter (S) is further calculated, whose results indicate a larger S of LCE fibers (S\u2009=\u20090.34) than that of LCE films (S\u2009=\u20090.28)19,20. These larger d and S indicate a wider molecular chain spacing and smoother diffusion channels of fiber compared with film due to the higher liquid crystal orientation, which is expected to accelerate the rapid passage of ions (Fig.\u00a01c).\n\nThen, the selective/preferential reactions of cations from EDDET soft chain segment inside LCE materials are tailored by immersing LCE fibers in EMIM TFSI ionic liquid. And the Fourier transform infrared spectroscopy (FTIR) and Raman spectra are further carried out to provide physicochemical insights into molecular interaction mechanisms between the soft chain and ions. By comparing the FTIR spectra of EMIM TFSI ionic liquid, pure LCE fiber and EMIM TFSI treated LCE fibers for 1, 3 and 6\u2009hours (Supplementary Fig.\u00a02 and Fig.\u00a01d, e), it is discovered that the S=O stretching band of TFSI- shifts from 1350\u2009cm\u22121 to 1355\u2009cm\u22121 after being introduced into LCE fibers21. And the characteristic absorption peaks of C-O-C of soft chain in 1247\u2009cm-1 shifts to the higher wavenumbers after immersing in ionic liquid. These results indicate a hydrogen-like non-covalent bonding interaction between the TFSI- and soft chain18,22. In addition, a tiny characteristic peaks at 1188\u2009cm\u22121 from C-N-C of EMIM+ is observed in EMIM TFSI treated LCE fibers, but the peak position remains unchanged (Supplementary Fig.\u00a03)23. Meanwhile, the C-O-C bending vibrational peak of LCE at 1071\u2009cm\u22121 shows no peak shifts and no disordered phenomenon in EMIM TFSI treated LCE fibers, which means no obvious hydrogen bond between EMIM+ and LCE molecular chain (Supplementary Fig.\u00a04)19. Furthermore, as shown in the Raman spectra (Fig.\u00a01f), the characteristic peaks of TFSI- of EMIM TFSI appears in 747\u2009cm\u22121, while this peek shifts to 733\u2009cm\u22121 when introducing EMIM TFSI into LCE fibers, at the same time, the half-peak width of TFSI- peaks gradually increase with the extension of immersion time, suggesting a strong interaction between TFSI- and soft chain inside LCE i-TE fibers24,25.\n\nFinaly, molecular dynamics simulations (MD) are conducted to further validate the molecular chain ordering and ion/molecule interactions in both LCE i-TE films and i-TE fibers. From the MD snapshots (Fig.\u00a01g, h), it can be clearly observed that LCE i-TE fibers exhibit more ordered molecular chain alignment compared to the disordered arrangement in LCE i-TE films, potentially providing effective channels for ionic diffusion. Then the binding energy between cations/anions (EMIM+/TFSI-) and molecular chains, as well as ion diffusion coefficients in LCE i-TE fibers/films are calculated. As displayed in Fig.\u00a01i, molecular chains in LCE i-TE films, owing to their complex disordered structure, show higher binding energy with ions compared to those in LCE i-TE fibers, and TFSI- exhibits stronger binding energy with molecular chains than EMIM+ in both systems. This result suggests that ionic migration in LCE i-TE films may encounter greater hindrance than fibers. Meanwhile, ionic diffusion coefficients in LCE i-TE fibers are significantly higher than those in LCE i-TE films, with cation migration being dominant in both cases (Fig.\u00a01j). Thereby, as can be concluded from the above experimental and computational results, owing to the synergistic effect of the intermolecular interaction and the mesogen orientation, ion migrates faster inside LCE i-TE fibers than LCE i-TE films, and EMIM+ migrates faster than TFSI- in LCE network, which is more obvious inside LCE i-TE fibers than LCE i-TE films. These effects are predicted to contribute to a high intrinsic thermopower in LCE i-TE fibers.\n\nTo verify the synergistic effect of the intermolecular interaction and the orientation pathway on the intrinsic thermopower, the thermoelectric properties are further investigated in detail. Since the thermopower of i-TE materials are still measured by homemade experimental setup in the field of i-TE, we conducted quantitative benchmarking correction with previously reported i-TE materials, confirming the testing apparatus accuracy, as shown in Supplementary Fig.\u00a053,8. The LCE i-TE fibers prepared by impregnation method are firstly compared with that prepared by a one-step synthesis method, where ionic liquid was directly added during the LCE synthesis. It can be discovered that the LCE i-TE fibers synthesized by one-step method appear as white gelatinous, and show low thermopower below 5\u2009mV\u2009K\u22121 ((Supplementary Figs.\u00a06 and 7)). Meanwhile, neither the modulation of synthesis parameters nor ion species can significantly increase the thermopower, which may be attributed to the selective reactions of Michael addition26. Unexpectedly, the simple impregnation method in this paper addresses the above questions. As shown in Fig.\u00a02a, the thermopower of LCE i-TE fibers fabricated by impregnation method with the identical ion species are superior to that by one-step synthesis, confirming the effectiveness and applicability of impregnation method in improving the thermopower of LCE i-TE materials. In addition, the ion species can alter the polarity of the thermopower, where impregnation of BMIM PF6 or LiBF4 endows n-type characteristics, while impregnation of BMIM OAC, EMIM TFSI,\u00a0AMIM TFSI\u00a0or EMIM Cl presents the opposite case. And the value of thermopower reaches its maximum (9.6\u2009mV\u2009K-1) in the case of EMIM TFSI (Fig.\u00a02a).\n\na Thermopower of LCE fibers immersed in different types of ionic liquids. b Thermopower of LCE i-TE films and fibers immersed in different concentrations of EMIM TFSI. c Thermopower of LCE i-TE fibers synthesized with various crosslinker contents at different immersion time. d Exploded schematic of the layer at different depths in LCE i-TE fibers. e\u2013f Two-dimensional statistical kernel density estimates of the N and F elements contents along the radial direction of the LCE i-TE fibers in the case of different soaking time, respectively. g Comparison in thermopower of ionic thermoelectric materials measured at humidity less than 30% RH between the reported literatures and this work, inner graph shows a comparison in the thermopower of TE fibers between reported literatures and this work6,8,9,12,13,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58. h Ionic electrical conductivity of LCE i-TE fibers and films synthesized with different concentrations of EDDET and PETMP. i Voltage response curves of LCE i-TE fibers impregnated with different concentrations of ionic liquids over 10,000\u2009s. j Cyclic voltage-temperature curves of LCE i-TE fibers. k Capacitor mode working mechanism diagram. All error bars denote mean\u2009\u00b1\u2009standard deviation.\n\nThen, the thermoelectric performance of LCE i-TE fibers and films treated with EMIM TFSI is investigated and compared (Note that LCE i-TE materials immersing in different EMIM TFSI concentration are named as LCE-x EMIM TFSI with x\u2009=\u20090.5, 1.0, 1.5, 2.0, 2.5 and 3.0\u2009M, respectively). As shown in Fig.\u00a02b, the thermopower of both the LCE i-TE films and i-TE fibers increases at low EMIM TFSI concentrations and then decreases at high EMIM TFSI concentrations, saturating at 1\u2009M. And the thermopower of LCE i-TE films can reach 10.4\u2009mV\u2009K\u22121 at 1\u2009M EMIM TFSI concentrations, while that of LCE i-TE fibers reach 17.4\u2009mV\u2009K\u22121. This pronounced increase in the thermopower of LCE i-TE fibers in contrast to films confirms the important effect of mesogen orientation on the intrinsic thermopower, as revealed in Fig.\u00a01b\u2013h, which is attributed to the more favorable thermal diffusion of free cation and thus the larger difference of thermal mobility between anions and cations. To further prove that the above higher thermopower of LCE i-TE fibers compared with films indeed stems from different mesogen orientation, other than different shape between fibers and films, we fabricated LCE i-TE fibers with different diameters (1\u2009mm, 2\u2009mm, 3\u2009mm) that suffered from different shear force, and studied the effect of mesogen orientation on the thermopower of these fibers. From the WAXD results of these LCE i-TE fibers (Supplementary Fig.\u00a08), it can be seen that diameters alter the mesogen orientation, and the LCE i-TE fiber with diameter of 2\u2009mm exhibits superior molecular chain spacing (d\u2009=\u20094.58\u2009\u00c5) and order parameter (S\u2009=\u20090.34) compared to its 1\u2009mm (d\u2009=\u20094.47\u2009\u00c5, S\u2009=\u20090.30) and 3\u2009mm (d\u2009=\u20094.39\u2009\u00c5, S\u2009=\u20090.29) counterparts. At the same time, the thermopower exhibits closely positive correlation with the order parameter, where the highest order parameter in the case of 2\u2009mm diameter enable the highest thermoelectric performance, and the smallest order parameter (3\u2009mm diameter) lead to the smallest thermopower, confirming the crucial role of molecular orientation in improving thermoelectric properties. More importantly, it is found that LCE i-TE fibers with diameter of 3\u2009mm (S\u2009=\u20090.29) and LCE i-TE films (S\u2009=\u20090.28, as shown in Fig.\u00a01b) with the similar order parameter contributes a similar thermopower (10\u2009mV\u2009K\u22121, the thermopower of LCE i-TE films is shown in Supplementary Fig.\u00a09). It means that the same molecular orientation whatever appearing in fiber-format or film-format will cause the same thermopower, further confirming the impact of molecular orientation on TE performance.\n\nIn addition, the thermopower of LCE i-TE fibers is further optimized via regulating synthesis parameter and post-immersion time. From Fig.\u00a02c and Supplementary Fig.\u00a010, it can be found that LCE i-TE fibers show best thermopower when the crosslinker Pentaerythritol Tetra (3-mercaptopropionate) (PETMP) is 0.11\u2009M and soft chain EDDET is 1.7\u2009M. The thermopower versus crosslinker and soft chain can be attributed to the change in the viscosity and crosslinking degree of LCE materials, where excessively low viscosity and crosslinking at low crosslinker content decrease the thermopower by preventing the binding between ions and soft chain, unduly high viscosity and crosslinking degree also deteriorate the thermopower via restricting the thermal diffusion behavior of cations and anions. Besides, the thermopower of LCE i-TE fibers obviously increase and then decreases with the elongation of immersion time, saturating at 6\u2009h in the case of 0.11\u2009M crosslinker. This change trend in thermopower has been also reported in the profile of thermopower versus ion concentration6,12. To figure out the reason why the highest thermopower appears at a moderate immersion time, we study the ionic liquid distribution using energy dispersive x-ray spectroscopy (EDS) and layer-by-layer x-ray photoelectron spectroscopy (XPS). From the result of EDS, four elements of IL (C, N, O, and S) are uniformly distributed on both the fiber surface and cross-section after 6\u2009h immersion, directly demonstrating that the IL can effectively and uniformly penetrate into the fiber interior (Supplementary Fig.\u00a011). However, it remains unclear whether the concentration of cations and anions at different depths affects the performance. Therefore, as shown in Fig.\u00a02d\u2013f, the two-dimensional statistical kernel density estimation, which is plotted using the R language, ggplot2 based on the data of layer-by-layer XPS, is further carried out, demonstrating a non-positive correlation between immersion time and the content of ionic species (N elements for cations and F elements for anions). During the initial immersion, ions diffuse from the liquid into both the surface and interior of the fibers, leading to increased ion content. As immersion time extends, fibers swelling reaches an optimal state. However, excessive immersion causes over-swelling, leading to relaxation and deterioration of the internal network structure. Ultimately, the highest ion content at each layer (excluding the surface layer) and the most uniform ion distribution are observed at immersion time of 6\u2009h. This phenomenon reveals that the ionic content and distribution accounts for the immersing time-controlled thermopower.\n\nIn brief, the thermopower of LCE i-TE fibers consistently exceed that of LCE i-TE films, and this superior performance can be attributed to the crucial role of mesogen orientation induced ion migration difference27,28. Ultimately, the optimized thermopower of the LCE i-TE fiber reach 25.8\u2009mV\u2009K\u22121 (Fig.\u00a02c and Supplementary Fig.\u00a012), while that of the i-TE film is just 10\u2009mV\u2009K\u22121. Particularly, the thermopower of this LCE i-TE fiber is higher than that of the reported ionic thermoelectric materials measured at less than 30% RH, and is the largest one among all reported thermoelectric fibers (Fig.\u00a02(g))6,8,9,12,13,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58.\n\nThen, the ionic electrical conductivity (\u03c3i) of LCE i-TE films and fibers are studied. As shown in Fig.\u00a02(h), the \u03c3i of the LCE i-TE films remain relatively stable regardless of synthesis parameter variations, with a maximum value of only 0.7\u2009mS\u2009m\u22121. However, a breakthrough increase of the \u03c3i can be found in the LCE i-TE fibers, whose highest value reaches 21.5\u2009mS\u2009m\u22121 at the same condition (1.7\u2009M EDDET and 0.11\u2009M PETMP). The enormous differences in \u03c3i between LCE i-TE films and fibers is mainly related to differences in ionic diffusion channels arising from mesogen orientation. Furthermore, the conductivity of the LCE i-TE fibers gradually increases with both ionic liquid concentration and immersion time, reaching a maximum value of 32.7\u2009mS\u2009m\u22121 (Supplementary Fig.\u00a013). Meanwhile, thermal conductivity tests reveal that the LCE i-TE fibers exhibit notably low thermal conductivity of 0.11\u2009~\u20090.12\u2009W\u2009m\u22121\u2009K\u22121 (Supplementary Fig.\u00a014). And this low thermal conductivity can contribute an effective utilization of the environmental temperature gradient. To prove this high utilization, an external temperature difference of 6\u2009K is imposed on the fiber, as the three-dimensional waterfall plot of the temperature shown (Supplementary Fig.\u00a015), the temperature difference along the fiber maintained 5.6\u2009K, achieving a utilization of 93%. Finally, the good thermopower, electrical conductivity, and low thermal conductivity result in a maximum ZTi value of 0.035 (Supplementary Fig.\u00a016).\n\nApart from the high TE performance, reliability and stability of this high TE property is also essential to practical application. Therefore, long-term voltage stability and cycling performance tests are further conducted to evaluate the practicality of LCE i-TE fibers. As observed in Fig.\u00a02i, the voltage profile of LCE i-TE fibers immersed in different IL concentration exhibits remarkable stability under a long measuring time up to 10,000\u2009s, without fluctuations. Meanwhile, Fig.\u00a02j shows that LCE i-TE fibers display stable voltage response to cyclic stimuli of temperature difference. And the air stability of LCE i-TE fibers is further studied. As shown in Supplementary Fig.\u00a017, the thermopower of LCE i-TE fibers remains above 80% after a week-long performance monitoring, indicating excellent air stability. Moreover, thanks to the advantages of the post-immersion method, the LCE i-TE fibers do not suffer from ionic liquid leakage issues. When wiping the LCE i-TE fibers with lint-free wipes, no overflow or leakage of ionic liquid is observed (Supplementary Movie\u00a0S1). Therefore, the above results of long-term voltage stability, cycling performance tests, air stability, and leakage measurement all demonstrated the excellent reliability of LCE i-TE fibers.\n\nAccordingly, compared to currently reported ionic thermoelectric materials, the LCE i-TE fibers possess inherently ordered network structures for high TE performance, operability under low humidity conditions, simple components, no leakage risk, long-term stability, cyclability, and air stability, all of which demonstrates exceptional practicality and application potential in low-grade heat harvesting.\n\nLastly, we investigate the capacitive characteristics of LCE i-TE fibers, as shown in Fig.\u00a02k. A complete working model is divided into four stages, an open-circuit voltage of ~41\u2009mV is generated by LCE i-TE fibers, due to the diffusion and accumulation of ions on the hot/cold side when a temperature gradient of 1.8\u2009K is applied at stage I. At stage II, electrons flow through the external circuit and neutralize the unbalanced charge on the electrodes, causing a thermal voltage decay to zero. Nevertheless, as the temperature gradient disappears and the external load is removed (stage III), the ions accumulate on the electrodes gradually return to their original random state, while the electrons remaining on the cold side generate a reverse voltage. Eventually, the external load is reconnected, dropping the reverse voltage of the stage III to zero (stage IV)39. Meanwhile, by calculating the energy obtained in the second and fourth stages using the equation [2] \\({Energy}=\\int {U}^{2}/{Rdt}\\), the LCE i-TE fibers exhibit promising output energy of 69.3\u2009nJ when being connected to an external load of 20\u2009M\u03a9 (Supplementary Fig.\u00a018)5.\n\nBeyond superior TE performance, the excellent mechanical property and scalable fabrication ability are crucial for i-TE materials and devices. As shown in Fig.\u00a03a, b, large-scale fabrication of continuous LCE i-TE fibers longer than two meters, with inherently deformable, stretchable, homogeneous, and unfolding properties, can be easily achieved. This demonstrates a promising commercial production.\n\na\u2013b Physical drawings of large-scale produced LCE i-TE fibers. SEM and POM images of LCE i-TE fibers before (c\u2013f) and after (g\u2013j) impregnation with ionic liquid solution. k Tensile stress-strain curves of LCE i-TE fibers with different impregnation time at stretching rate of 50\u2009mm\u2009min\u22121. l Storage modulus (G\u2032) and loss modulus (G\u2033) of LCE i-TE fibers with different impregnation time. m Physical images of LCE i-TE fibers puncture test. n Displacement-stress curve corresponding to the LCE i-TE fibers puncture test in (m). o, p Physical diagram of Chinese knots suffering from horizontal and vertical tensile. q LCE i-TE fibers-based bracelet suffering from tensile and twist testing.\n\nThe polarized optical micrographs (POM) and scanning electron microscopy (SEM) are employed to examine the polarization and surface morphology of the fabricated LCE i-TE fibers. The LCE i-TE fibers exhibit smooth surfaces without obvious impurities and defects before (Fig.\u00a03c, d) and after immersion process (Fig.\u00a03g, h), the diameter of fibers remains 2\u2009mm regardless of impregnation times (Supplementary Fig.\u00a019). Meanwhile, before immersion (Fig.\u00a03e, f), POM observations reveal colorful birefringence when the LCE fiber is oriented at a 45\u00b0 angle to the polarizing filter, while turning to completely dark at 0\u00b0. This phenomenon remains unchanged after immersion (Fig.\u00a03i, j), indicating a characteristic of monodomain aligned and anisotropic of both the LCE fibers before and after immersion59,60. Furthermore, cross-sectional POM images of the LCE fibers before and after immersion demonstrate fully dark fields during heating and cooling cycles, attributed to the fact that molecular chains aligned parallel to the fiber axis resulting in disappearance of birefringence, which directly prove the highly ordered arrangement inside the LCE i-TE fibers (Supplementary Fig.\u00a020).\n\nIn addition, the mechanical properties of LCE i-TE fibers do not show significant changes with varying IL concentrations (Supplementary Fig.\u00a021), but displays obvious changes with IL immersion time. As the immersion time increased, the strain of LCE i-TE fibers gradually decreases from 255.2% to 137%, combined with tensile stress reducing from the 524.5\u2009KPa to 308.9\u2009KPa (Fig.\u00a03k). The same tendency of changes in the strain and stress can be seen for LCE i-TE films (Supplementary Fig.\u00a022). The main reason for these decays in mechanical properties may be attributed to the decrease of interchain force and mechanical properties induced by the LCE swelling61. Nonetheless, the LCE i-TE fibers exhibit superior mechanical properties compared to LCE i-TE films after the immersion process. Specifically, the LCE i-TE fibers still possess stress and strain values of 444.5\u2009KPa and 202.6% following 6\u2009h immersion, surpassing those of LCE i-TE films by 54.4% and 50.9%, respectively. And the excellent mechanical properties of the LCE i-TE fibers enable readily lifting 500\u2009g weights without breaking. The LCE i-TE fibers also have good stretchability, which can return to their original state after being stretched to a strain of 100%, as shown in Supplementary Fig.\u00a023. Dynamic mechanical measurements in Fig.\u00a03l display higher storage modulus G\u2032 than the loss modulus G\u2033 of the LCE i-TE fibers impregnated for 1, 3, 6\u2009h, indicating the quasi-solid gel behavior of LCE i-TE fibers46. Additionally, LCE i-TE fibers and films demonstrate excellent puncture resistance, as shown in Fig.\u00a03m and Supplementary Fig.\u00a024. When applying stress with a 1\u2009mm sharp needle, the fiber exhibits a maximum puncture displacement of 11.4\u2009mm and a stress of 3.1\u2009N (Fig.\u00a03n).\n\nUltimately, the dyed LCE i-TE\u00a0fibers are woven into Chinese knots, which show good endurance to a certain amount of tensile force applied in the transverse and longitudinal directions, with no inter-fiber fracture phenomenon (Fig.\u00a03o, p). Similarly, no fracture or damage occurs when applying tensile and torsional stresses to the bracelet woven by LCE i-TE fibers, as shown in Fig.\u00a03q. The above results confirm the excellent mechanical properties and good weavability of the LCE i-TE fibers, highlighting their significant potential for applications in wearable electronics and smart textile technologies.\n\nBesides the above TE function, the liquid crystal primitive inside the LCE i-TE fibers can contribute the thermal-induced reversible phase transition, which will drive reversible shape change at the macro level62. Here differential scanning calorimetry (DSC) is employed to characterize the phase transition behavior of the LCE i-TE fibers. As shown in Supplementary Fig.\u00a025, heat capacity increases rapidly at 38.9\u2009\u00b0C, corresponding to the glass transition. Meanwhile, an additional slight change of the heat capacity at the 58.7\u2009\u00b0C can be observed, which accounts for the nematic\u2014isotropic transition point in monodomain LCE (clearing point)63. Accordingly, the liquid crystalline range of the fabricated LCE i-TE fiber is from 38.9\u2009\u00b0C to 58.7\u2009\u00b0C. Thus, temperature higher than 38.9\u2009\u00b0C will cause entangle and curl behavior of LCE molecular chain, while gradually lowing temperature will lead to stretching and flattening behavior, achieving a reversible deformation, as the mechanism diagram shown in Fig.\u00a04a. Based on this mechanism, the shrinkage in the LCE i-TE fibers driven by 60\u2009\u00b0C heating temperature can reach 14.5%, and gradually return to original state as the temperature decreases (Fig.\u00a04b). Meanwhile, as the physical exhibit in Fig.\u00a04c, LCE i-TE fibers attach with a red sphere undergo obvious stretching (from 40\u2009\u00b0C to 60\u2009\u00b0C) and recovery (from 60\u2009\u00b0C to 30\u2009\u00b0C) phenomena. These results indicate the favorable thermally actuating responsiveness of the prepared LCE i-TE fibers, and this controlled deformation is especially important for the application of i-TE materials in the field of wearable and smart textiles.\n\na Mechanism diagram of reversible thermal braking of LCE molecular chains. b The shrinkage variation ratio of LCE i-TE fibers at different temperatures. c Thermal actuation diagram of LCE i-TE fibers attached with a red sphere at different temperatures. d Diagram of plain stitch structure and deformation image. e Diagram of rib knit pattern structure and deformation image. f The deformation of plain stitch- and rib knit pattern-structured fabrics at different temperatures. g Radargram of LCE i-TE fibers in contrast with the areas of thermopower, tensile stress, fiber format i-TE materials, and actuation8,9,29,43,46,48,64.\n\nAfterwards, two special weaving structures, plain stitch and rib knit pattern, are utilized to fabricate textiles, aiming to convert the one-dimensional thermally actuating deformation along the fibers into three-dimensional deformation inside the textiles. As shown in Fig.\u00a04d, it can be found that the plain stitch-structured LCE textile is composed of a single knitting ring arrangement, and the internal torque is accumulated in each knitting coil. When heating the sample, the LCE i-TE fibers shrink, so that the braided ring shrinks and thus the fabric is bent. In contrast, as shown in Fig.\u00a04e, the LCE textile woven with rib knit pattern shows non-deformation, due to the mutual cancellation of the torque between the adjacent knitting rings. Then, both of the LCE fabrics woven by plain stitch and rib knit are put into the oven, and the deformability of these fabric are examined at different temperatures (Fig.\u00a04f). The LCE fabric of these two structures has no obvious change when the oven temperature is 30\u2009\u00b0C, but with the temperature over 40\u2009\u00b0C, fabric woven by plain stitch shows gradually bending deformation, and when the temperature reaches 70\u2009\u00b0C, the fabric realizes complete curling deformation. In contrast, the fabric woven by rib knit pattern exhibits no obvious deformation over temperature ranging from 40\u2009\u00b0C to 70\u2009\u00b0C. The braided structure-induced thermal actuating properties provides the basis for the subsequent construction of four-dimensional dynamically adaptive devices.\n\nBased on the above results, as shown in Fig.\u00a04g, the proposed LCE i-TE fibers not only exhibit the highest value of thermopower at low humidity, but also demonstrate excellent mechanical and actuation properties, providing a unique actuating TE material for smart wearables, soft robots, and flexible electronics8,9,29,43,46,48,64.\n\nBased on the thermal response characteristics of fabrics, we developed self-adaptive thermoelectric devices with gripper-like configurations using plain stitch and rib knit patterns. These all-fiber devices integrate thermoelectric, actuating, and sensing capabilities. As illustrated in Fig.\u00a05a, b and Supplementary Fig.\u00a026, the top part of the device is made of rib knit pattern, whose deformation is not affected by temperature, while the four gripping arms were made of plain stitch-structured fabrics with excellent thermally actuating ability. The thermoelectric device demonstrates remarkable self-deformation ability. As evidenced in Supplementary Movie\u00a0S2 and Supplementary Movie\u00a0S3, it exhibits autonomous deformation at elevated temperatures. When being placed near a heat source, the device shows excellent grasping capabilities (Supplementary Movie\u00a0S4). Importantly, the device returns to its original state as temperatures decrease (Fig.\u00a05c and Supplementary Movie\u00a0S5), enabling dynamic and adaptive heat harvesting capabilities. And when being exposed to an environmental temperature of 60\u2009\u00b0C, the prepared TE device can easily grasp a red plastic ball within 90\u2009s, as shown in Fig.\u00a05d. Notably, this thermoelectric device demonstrates remarkable grasping capability by lifting a steel ball, whose weight is 17 times higher than the device\u2019s (as shown in Supplementary Fig.\u00a027). Additionally, the device exhibits reliable cyclic performance in its grasping functionality, as demonstrated in Supplementary Movie\u00a0S6 and Fig.\u00a05f, it can perform repeated cycles of gripping and releasing through temperature elevation and reduction processes.\n\na, b Physical drawing for all fiber-based self-adaptive thermoelectric devices of top view and front view. c Recovery process of bending angle of devices after temperature reduction. d Flowchart of the system\u2019s capture of a plastic red ball. e The deformation of plain stitch-structured TE device at different temperatures. f Flowchart for cyclic gripping properties of TE devices. g, h Gripping cylinders heat sources with different pipe diameters (\u03a6thick: 16\u2009mm, \u03a6thin: 10\u2009mm). i\u2013j \u0394R/R0 curves for cyclic bending (top) and stretching (down) of TE device/LCE i-TE fibers at different degrees of deformation. k Output voltage and \u0394R/R0 of the device at different temperatures under a bending angle of 45\u00b0. l Output voltage and \u0394R/R0 of the device at different bending angles under a heat source of 150\u2009\u00b0C. m, n The current-voltage curves and localized plots of the TE device simultaneously enduring bending angles and temperature. o, p The \u0394R/R0 of system captures the different-sized spheres and various shapes. All error bars denote mean\u2009\u00b1\u2009standard deviation.\n\nTo enable precise heat harvesting and deformation sensing, we embedded an LCE i-TE fiber into the grasping arm as its active part. As demonstrated in Fig.\u00a05e, integrating fibers will not compromise the overall device\u2019s thermally deformation properties. Then, the ability to dynamically adapt to low-grade heat source with complex-shaped geometry is studied, by employing a double-armed gripper-like TE device. It can be seen from Fig.\u00a05g, h that the TE device can tightly self-wrap cylinder heat sources with different tube diameters and generate a voltage of 1.01\u2009V at 160\u2009\u00b0C, regardless of 10\u2009mm or 16\u2009mm (Supplementary Movie\u00a0S7 and Supplementary Movie\u00a0S8). Accordingly, the thermoelectric device can keep closely attaching to the heat source and output a stable voltage over time regardless of dynamically changing size or increasing temperature of the heat source, without extra treatment between the device and the heat source including sticking, pressing, etc. The dynamically adaptive TE device in four-dimensional space promotes their practical applications in harvesting low-grade heat.\n\nBesides, the resistance changing rate (\u0394R/R0) of LCE i-TE fibers embedded in gripping arms and the \u0394R/R0 of single i-TE fibers show stable and reversible cyclically response to different bending angles and strain, respectively (Fig.\u00a05i, j). Thus, the TE device can sense temperature and deformation via voltage signal and resistance signal, respectively. Then, the feasibility of decoupled sensing of temperature and deformation is verified. From the Fig.\u00a05k, the corresponding output voltage of TE devices shows proportional relationship to the temperature difference when the heat sources are heated from 120\u2009\u00b0C to 160\u2009\u00b0C under a bending angle of 45\u00b0, while the \u0394R/R0 remains constant as the TE devices tightly grabbed the heat source all the time. Simultaneously, we also measure the changes in output voltage and \u0394R/R0 at different bending angles. As shown in Fig.\u00a05l and Supplementary Fig.\u00a028, the variation of output voltage and thermopower at different bending angles keep stable, which are only 3.5% and 5.2%, respectively, while the \u0394R/R0 continuously increase with increasing angle. Therefore, the TE devices can directly and accurately reflect the bending angle through \u0394R/R0, while the temperature is reflected by the output voltage, achieving decoupled recognition. To further display the decoupled sensing capability, current-voltage (I\u2013V) curves are tested under simultaneous thermal and bending stimuli (Fig.\u00a05m), it can be revealed that the I\u2013V curves maintain parallel relationships with consistent slopes (resistance values) under fixed bending angles, while their Y-intercepts increase proportionally with temperature (Fig.\u00a05n). With constant temperature differentials, the I\u2013V curves demonstrate increasing slopes corresponding to larger bending angles. This behavior confirms the decoupled sensing of temperature and deformation, where temperature is detected through voltage response (Y-intercepts) and deformation is detected through resistance changes (slopes).\n\nFinally, a TE device with four griping arms is integrated, whose two adjacent arms (named x arm and y arm, respectively) are the active sensing parts. Inside this device, the \u0394R/R0 of the inlaying LCE i-TE fibers on the two adjacent arms (x arm and y arm) of the devices is recorded to detect the sizes and shapes of the captured objects. In the case of capturing a sphere with different size, it can be found in Fig.\u00a05o, the bending angle of the grasping arm gradually decreases with the gradual increase of the sphere size, and the corresponding \u0394R/R0 show a certain linear relationship with the size of the grasping object. Meanwhile, in the case of capturing a sphere with different shape (Fig.\u00a05p), the \u0394R/R0 shows significant variations capturing five objects (sphere (small), sphere (big), triangular, cuboid, cube), due to different bending angle when grasping arm touches objects of different shapes. Thus, the combined data of the \u0394R/R0 from the two arms allows for accurate identification of object size and shape.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60103-x/MediaObjects/41467_2025_60103_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In conclusion, we have investigated a p-type LCE ionic thermoelectric fiber with a high thermopower (25.8\u2009mV\u2009K\u22121) via the synergistic effect of the intermolecular interaction originating from the soft chain and the orientation pathway originating from the LC phase, achieving the highest reported thermopower at low humidity in the field of ionic thermoelectrics and thermoelectric fibers. Based on the specific thermal actuated property, high TE performance, and excellent mechanical properties, an all fiber-based, self-adaptive, and griper-liked thermoelectric devices was successfully fabricated. And this griper-liked device can accurately recognize the shape/size and temperature of the capturing objects in a decoupled mode. The proposal of this high-performance ionic thermoelectric fiber and the dynamically self-adaptive thermoelectric devices provide an innovative idea for the future research of ionic thermoelectrics and have a highly promising application in the fields of smart wearables and soft robots.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "RM257 (98%) was purchased from Shanghai Bide Pharmaceutical Technology Co., Ltd., EDDET (95%), PETMP (95%) and ethyl acetate were supplied by Shanghai Titan Scientific Co., Ltd. DPA (99%) was bought from Meryer (Shanghai) Chemical Technology Co., Ltd. The EMIM Cl/TFSI, BMIM OAC/PF6, AMIM TFSI and LiBF4 were purchased from Lanzhou Greenchem ILS, LIPC, ACS (Lanzhou China). All reagents were used as received without any further purification.\n\nThe preparation of LCE i-TE could be described in the following two step: the first step was the mold forming of LCE fibers, where the 2.4\u2009g RM257, 0.7\u2009g EDDET and 0.1\u2009g PETMP were dissolved in 2.4\u2009ml of ethyl acetate solution and stirred at 70\u2009\u00b0C for 1\u2009h until complete dissolution, meanwhile, the LCE solution was obtained after the 9\u2009\u03bcl of DPA was added and stirred for 1\u2009min. LCE films were obtained by pouring the LCE solution into culture dishes and letting it stand at room temperature for 12\u2009h. For LCE fibers, the LCE solution was transferred to a syringe and injected into a polytetrafluoroethylene tube by the mold method, and horizontally placed at room temperature for 12\u2009h. Finally, the LCE fibers were obtained via stripping from the tube. The final step in the preparation of LCE i-TE fibers/films was the impregnation of LCE fibers/films in ionic liquid solution. Firstly, the ionic liquid solutions with a concentration of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0\u2009M were prepared, and then, the prepared LCE fibers/films were immersed in this solution for 1, 3, 6, 9,\u00a012, 24\u2009h to get the LCE i-TE fibers/films impregnation with different time.\n\nThe all fiber-based self-adaptive thermoelectric devices consisted of four fabric arms (double-armed gripping system was two fabric arms) and a top fabric, where the fabric arms as bending actuator modules were prepared from LCE i-TE fibers by plain stitch weave, while the top fabric was fabricated via rib knit pattern. Then the LCE i-TE fibers were used to sew the top fabric and the curved arms together, where the curved sides of the fabric arms faced each other and sewn to the four edges of the top fabric to create the gripping deformation. And the LCE i-TE fibers, which serves as a temperature recognition and sensing module, was directly embedded in one of the four curved arms to enable all fiber-based self-adaptive thermoelectric devices preparation.\n\nThe micro morphology and optical properties of LCE i-TE fibers were conducted using FE-SEM (SU8010, Hitachi) and POM (DM750P, Leica). The FTIR spectroscopy (Nicolet6700, Thermo Fisher) with the attenuated total reflection accessory and Raman spectra (inVia-Reflex, Renishaw) with a 633\u2009nm laser were recorded to analyze the ion and molecular interchain interaction. Thermopower was measured by a homemade equipment based on the equation [3] \\({Thermopower}=-\\varDelta V/\\varDelta T\\), here potential differences \u0394V arising from eight temperature differences \u0394T was recorded by Keithley 2182\u2009A. The linear correlation (R2) between \u0394V and \u0394T should be > 0.999. Test items were tested at 3 times of each sample for an average value. The ionic conductivity \u03c3i was calculated as follows: \\({\\sigma }_{i}=d/(A * R)\\), where the d, A, and R in the formula represent the thickness, area, and ionic resistance, respectively. The ionic resistance was tested by electrochemical impedance spectroscopy on an electrochemical workstation (DH7006,) with the frequency ranging from 0.1 to100000 Hz. Stress-strain curves were performed on an Instron (5969) testing instrument with a speed of 50\u2009mm\u2009min\u22121 at room temperature. And the gelatin properties of LCE i-TE fibers, which the angular frequency dependencies of the storage modulus (G\u2032) and loss modulus (G\u2033) were performed by DMA (Q800, TA).\n\nModeling and Simulation Methods: All ionic species and small molecules were parameterized using the next-generation general AMBER force field (GAFF2), with specific force field parameters generated using the sobtop software. The initial configurations were constructed using the packing optimization for molecular dynamics simulations (Packmol) program with a periodic simulation box of 40\u2009\u00d7\u200940\u2009\u00d7\u200940\u2009nm. All molecular dynamics (MD) simulations were performed using the groningen machine for chemical simulations (GROMACS) 2022.5 package. The simulation protocol consisted of three main stages:\n\nEnergy Minimization\n\nThe system was initially minimized using a combination of 5000 steps of steepest descent followed by 5000 steps of conjugate gradient algorithms to eliminate unfavorable contacts.\n\nConstant number of particles, pressure, and temperature ensemble (NPT) Pre-equilibration\n\nThe system was pre-equilibrated in the NPT ensemble using the V-rescale thermostat at 298\u2009K and the Parrinello-Rahman barostat at 1\u2009atm. Non-bonded interactions were treated with a cutoff radius of 1.2\u2009nm, and the integration time step was set to 1\u2009fs.\n\nMD Simulation\n\nFollowing equilibration, the temperature coupling was switched to the Berendsen thermostat. Bond lengths and angles were constrained using the linear constraint solver (LINCS) algorithm. 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We acknowledge the technicians at Shenzhen HUASUAN Technology Co.,Ltd. for assistance with theoretical calculations.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Liuqi Cao, Tingting Sun.\n\nState Key Laboratory of Advanced Fiber Materials, College of Materials Science and Engineering, Donghua University, Shanghai, China\n\nLiuqi Cao,\u00a0Huiru Zhao,\u00a0MengHan Shang,\u00a0Lianjun Wang\u00a0&\u00a0Wan Jiang\n\nCollege of Biological Science and Medical Engineering, Donghua University, Shanghai, China\n\nTingting Sun\n\nEngineering Research Center of Advanced Glass Manufacturing Technology, Ministry of Education, Donghua University, Shanghai, China\n\nLianjun Wang\n\nInstitute of Functional Materials, Donghua University, Shanghai, China\n\nWan Jiang\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nL.W., W.J. and T.S. conceived the ideas and designed the work. L.C. carried out the experiments including material preparation and characterization, device fabrication, and measurements. L.C. and T.S. contributed to microstructural characterization. T.S. carried out the density functional theory and molecular dynamics calculations. L.C. assisted with the power-generation measurements. T.S. contributed to the drawings. L.C. and T.S. wrote the draft. H.Z. and M.H. contributed to the discussion and editing. All authors approve the final version of the manuscript.\n\nCorrespondence to\n Tingting Sun, Lianjun Wang or Wan Jiang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Hesheng Xia, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Cao, L., Sun, T., Zhao, H. et al. An actuatable ionogel thermoelectric fiber with aligned mesogens-induced thermopower for four-dimensional dynamically adaptive heat harvesting.\n Nat Commun 16, 5445 (2025). https://doi.org/10.1038/s41467-025-60103-x\n\nDownload citation\n\nReceived: 09 December 2024\n\nAccepted: 15 May 2025\n\nPublished: 01 July 2025\n\nVersion of record: 01 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60103-x\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Carbon-Neutral European Energy System", + "journal": "Nature Communications", + "published": "12 June 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60652-1/MediaObjects/41467_2025_60652_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60652-1/MediaObjects/41467_2025_60652_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60652-1/MediaObjects/41467_2025_60652_MOESM3_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.14872184", + "/articles/s41467-025-60652-1#ref-CR131", + "https://github.com/PyPSA/technology-data/releases/tag/import-benefits-v2", + "https://doi.org/10.5281/zenodo.14872325", + "/articles/s41467-025-60652-1#ref-CR132", + "/articles/s41467-025-60652-1#Sec25" + ], + "code": [ + "https://github.com/fneum/import-benefits/tree/v3.0.1", + "https://doi.org/10.5281/zenodo.14872325", + "/articles/s41467-025-60652-1#ref-CR132", + "https://pypsa-eur.readthedocs.io/en/v0.13.0-docs-fix" + ], + "subject": [ + "Energy economics", + "Energy grids and networks", + "Energy modelling", + "Energy policy", + "Renewable energy" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4218656/v1.pdf?c=1749812768000", + "research_square_link": "https://www.researchsquare.com//article/rs-4218656/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60652-1.pdf", + "preprint_posted": "07 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Importing renewable energy to Europe offers many potential benefits, including reduced energy costs, lower pressure on infrastructure development, and less land-use within Europe. However, there remain many open questions: on the achievable cost reductions, how much should be imported, whether the energy vector should be electricity, hydrogen or hydrogen derivatives like ammonia or steel, and their impact on Europe's domestic energy infrastructure needs. This study integrates the TRACE global energy supply chain model with the sector-coupled energy system model for Europe PyPSA-Eur to explore scenarios with varying import volumes, costs, and vectors. We find system cost reductions of 1-14%, depending on assumed import costs, with diminishing returns for larger import volumes and a preference for methanol, steel and hydrogen imports. Keeping some domestic power-to-X production is beneficial for integrating variable renewables, utilising waste heat from fuel synthesis and leveraging local sustainable carbon sources. Our findings highlight the need for coordinating import strategies with infrastructure policy and reveal maneuvering space for incorporating non-cost decision factors.Business and commerce/EconomicsPhysical sciences/Energy science and technology/Energy modellingPhysical sciences/Energy science and technology/Energy infrastructure/Energy grids and networksPhysical sciences/Energy science and technology/Renewable energyPhysical sciences/Engineering/Energy infrastructure", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "importbenefitssi.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Importing renewable energy to Europe may offer many potential benefits, including reduced energy costs, lower pressure on infrastructure development, and less land use within Europe. However, open questions remain: on the achievable cost reductions, how much should be imported, whether the energy vector should be electricity, hydrogen, or derivatives like ammonia or steel, and their impact on Europe\u2019s infrastructure needs. This study integrates a global energy supply chain model with a European energy system model to explore net-zero emission scenarios with varying import volumes, costs, and vectors. We find system cost reductions of 1-10%, within import cost variations of \u00a0\u00b1\u00a020%, with diminishing returns for larger import volumes and a preference for methanol, steel and hydrogen imports. Keeping some domestic power-to-X production is beneficial for integrating variable renewables, leveraging local carbon sources and power-to-X waste heat. Our findings highlight the need for coordinating import strategies with infrastructure policy and reveal maneuvering space for incorporating non-cost decision factors.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Importing renewable energy to Europe may offer several advantages for achieving a swift energy transition. It might lower costs, help circumvent the slow domestic deployment of renewable energy infrastructure and reduce pressure on land usage in Europe. Many parts of the world have cheap and abundant renewable energy supply potentials that they could offer to existing or emerging global energy markets1,2,3,4,5,6,7,8. Partnering with these regions could help Europe reach its carbon neutrality goals while stimulating economic development in exporting regions.\n\nHowever, even if energy imports are economically attractive for Europe, a strong reliance may not be desirable because of energy security concerns. Awareness of energy security has risen since Russia constrained fossil gas supplies to Europe in 20229, at a time when the 27 member states of the European Union (EU27) imported around two-thirds of their fossil energy needs10. Europe must take care to avoid repeating the mistakes of previous decades when it became dependent on a small number of exporters with market power and reliant on rigid pipeline infrastructure.\n\nEurope\u2019s strategy for clean energy imports will also strongly affect the requirements for domestic energy infrastructure. Previous research found many ways to develop a self-sufficient energy system11,12,13. To support such scenarios without energy imports into Europe, reinforcing the European power grid or building a hydrogen network was often identified as beneficial14,15. However, depending on the volumes and vectors of imports (electricity, hydrogen, or hydrogen derivatives) and levels of industry migration, Europe might not need to expand its hydrogen pipeline infrastructure. Most hydrogen is used to make derivative products (e.g., ammonia for fertilisers, sponge iron for steel, or Fischer-Tropsch fuels for aviation and shipping)14. If Europe imported these products at scale, much of the hydrogen demand would fall away. In consequence, this would reduce the need for hydrogen transport. However, if hydrogen itself were imported and to be transported to today\u2019s industry clusters, this would require a pipeline topology tailored to connecting these to the hydrogen arriving from North Africa or maritime entry points across Europe.\n\nPolicy has reflected these different visions for imports in various ways. In particular, hydrogen imports have recently attracted considerable interest, with plans of the European Commission16 to import 10 Mt (333 TWh, lower heating value) hydrogen and derivatives by 2030. New financing instruments, like the European Hydrogen Bank17 or H2Global18 are set up to support the scale-up of green hydrogen imports. The desire to import hydrogen and derivative products is also present in various national strategies19. In particular, Germany\u2019s new import strategy plans to cover up to 70% of its demand for hydrogen and its derivatives through imports by 2030 and highlights bilateral partnerships as well as the expansion of import infrastructure as a means to accomplish this20,21. Conversely, hydrogen roadmaps of Denmark22, Ireland23, Spain24, and the United Kingdom (UK)25, recognise these countries\u2019 potential to become major exporters of renewable energy, whereas France\u2019s strategy focuses on local hydrogen production to meet domestic needs26. Beyond direct energy imports, the Draghi report27 also raises broader concerns about European industrial competitiveness and discusses the benefits of relocating energy-intensive industries to renewable-rich regions inside Europe. In addition, European grid development plans28 reveal renewed enthusiasm for electricity imports via ultra-long high-voltage direct current (HVDC) cables, evolving from early DESERTEC29 ideas to contemporary proposals like the Morocco-UK Xlinks project30.\n\nWhile many previous academic studies have evaluated the cost of \u2018green\u2019 renewable energy and energy-intensive material imports in the form of electricity5,31,32,33,34,35 hydrogen2,6,36,37,38,39,40,41,42, ammonia7,43,44,45, methane2,46,47, steel48,49,50, carbon-based fuels4,51,52, or a broader variety of power-to-X fuels1,3,8,53,54,55,56, these do not address the interactions of imports with European energy infrastructure requirements. On the other hand, among studies dealing with the detailed planning of net-zero energy systems in Europe, some do not consider energy imports11,13,15, while others only consider hydrogen imports or a limited set of alternative endogenously optimised import vectors14,57,58,59,60,61. Only a few consider at least elementary cost uncertainties42,62, and none investigate a larger range of potential import volumes across subsets of available import vectors.\n\nIn this study, we explore the full range between the two poles of complete self-sufficiency and wide-ranging renewable energy imports into Europe in scenarios with high shares of wind and solar electricity and net-zero carbon emissions. We investigate how the infrastructure requirements of a self-sufficient European energy system that exclusively leverages domestic resources from the continent may differ from a system that relies on energy imports from outside of Europe. For our analysis, we integrate an open optimisation model of global energy supply chains, TRACE54, with a spatially and temporally resolved sector-coupled open-source energy system optimisation model for Europe, PyPSA-Eur63, to investigate the impact of imports on European energy infrastructure needs. We evaluate potential import locations and costs for different supply vectors, by how much system costs can be reduced through imports, and how their inclusion affects deployed transport networks, storage and backup capacities. For this purpose, we perform sensitivity analyses interpolating between very high levels of imports and no imports at all, exploring low and high costs for imports to account for associated uncertainties, and system responses to the exclusion of subsets of import vectors, in order to identify the cost-effective manoeuvreing space.\n\nAs possible import options, we consider electricity by transmission line, hydrogen as gas by pipeline and liquid by ship, methane as liquid by ship, liquid ammonia, crude steel and its precursor, hot briquetted iron (HBI), methanol and Fischer-Tropsch fuels by ship. Each energy vector has unique characteristics with regard to its production, storage, transport and consumption. Electricity offers the most flexible usage but is challenging to store and requires variability management if sourced from wind or solar energy. Hydrogen is easier to store and transport in large quantities, but at the expense of conversion losses and less versatile applications. Large quantities could be used for backup power and heat, steel production, industry feedstocks and the domestic synthesis of shipping and aviation fuels. On the other hand, imported synthetic carbonaceous fuels like methane, methanol and Fischer-Tropsch fuels could largely substitute the need for domestic synthesis. There is more experience with storing and transporting these fuels, and part of the existing infrastructure could potentially be reused or repurposed. However, they require a sustainable carbon source and, particularly for methane, effective carbon management and leakage prevention64. Ammonia is similarly easier to handle than hydrogen, but does not require a carbon source. However, it faces safety and acceptance concerns due to its toxicity and potentially adverse effects on the global nitrogen cycle65,66. Its demand in Europe is mostly driven by fertiliser usage. Crude steel and HBI represent the import of energy-intensive materials and offer low long-distance transport costs.\n\nThe PyPSA-Eur63 model co-optimises the investment and operation of generation, storage, conversion and transmission infrastructures in a single linear optimisation problem. The model is further given the opportunity to relocate ammonia and primary crude steel production within Europe, capturing potential renewables pull effects within Europe and abroad67,68,69. We resolve 115 regions comprising the European Union without Cyprus and Malta, as well as the United Kingdom, Norway, Switzerland, Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, Serbia, and Kosovo. In combination with a 4-hourly-equivalent time resolution for the weather year of 2013, grid bottlenecks, renewable variability, and seasonal storage requirements are sufficiently captured. The model includes regional demands from the electricity, industry, buildings, agriculture and transport sectors, international shipping and aviation, and non-energy feedstock demands in the chemicals industry. Transmission infrastructure for electricity, gas and hydrogen, and candidate entry points like existing and prospective liquefied natural gas (LNG) terminals, as well as cross-continental pipelines, are also represented. However, no pathways are modelled in this overnight scenario, and the model has perfect operational foresight. We utilise techno-economic assumptions for 2040 and enforce net-zero CO2 emissions and limit the annual carbon sequestration to 200 \\({{{{\\rm{Mt}}}}}_{{{\\mbox{CO}}}_{2}}\\) a\u22121, similar to the 250 \\({{{{\\rm{Mt}}}}}_{{{\\mbox{CO}}}_{2}}\\) a\u22121 highlighted in the European Union\u2019s carbon management strategy70. This suffices to offset unabated industrial process emissions and limits the use of fossil fuels beyond that, whose emissions are compensated either through capturing emissions at source or by carbon dioxide removal. More details are included in the Methods section.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Green fuel and steel import costs seen by the model are based on an extension of recent research by Hampp et al.54, who assessed the levelised cost of energy exports for different green energy and material supply chains from various world regions to Europe. Our selection of exporting regions comprises all 53 coloured or dotted regions in Fig.\u00a01a. In the TRACE optimisation model54, regional wind and solar potentials are assessed based on prevailing weather conditions and land availability while prioritising projected domestic demand. In combination with the techno-economic modelling of the various fuel-specific supply chains stages (Supplementary Fig.\u00a01), the lowest levelised supply cost for each carrier, exporter and importer combination are determined for a reference volume of 500 TWh a\u22121 (or 100 Mt a\u22121 of steel/HBI), thus incorporating the trade-off between import cost and import location (Fig.\u00a01b). Unlike domestic electrofuel synthesis in Europe, which could use captured CO2 from point sources, direct air capture is assumed to be the only carbon source of imported fuels. Concepts involving the shipment of captured CO2 from Europe to exporting regions for carbonaceous fuel synthesis or permanent sequestration of CO2 captured by direct air capture abroad are not considered71,72.\n\na shows the regional differences in the cost to deliver green methanol to Europe (choropleth layer), the cost composition of different import vectors (bar charts), an illustration of the wind and solar availability in Morocco, and an illustration of the land eligibility analysis for wind turbine placement in the region of Buenos Aires in Argentina. b depicts considered potential entry points for energy imports into Europe like the location of existing and planned liquefied natural gas (LNG) terminals and gas pipeline entry points, the lowest costs of hydrogen imports in different European regions (choropleth layer), and the considered connections for long-distance high-voltage direct current (HVDC) import links and hydrogen pipelines from the Middle East and North Africa (MENA) region, Turkey, Ukraine and Central Asia. c displays the distribution and range of import costs for different energy carriers and entry points with indications for selected origins from the TRACE model (violin charts), i.e., differences in identically coloured markers are due to regional differences in the transport costs to alternative entrypoints. These are more variable for liquid hydrogen as transport distance is a more substantial cost factor for this import vector. Costs are given for techno-economic assumptions for 2040. Supplementary Fig.\u00a03 shows the world map for the lowest hydrogen import costs by pipeline or ship into Europe. Source data are provided as a Source Data file. Maps made with Natural Earth. Subnational regions created from geoBoundaries133. LH2\u2009=\u2009liquefied hydrogen; PV\u2009=\u2009photovoltaics.\n\nThe import costs for each combination of carrier, exporter and importer are then included as supply options in the PyPSA-Eur model. Hydrogen and methane can be imported where there are LNG terminals in operation or under construction, or where pipeline entry points exist (except for entry points from Russia). Due to higher volatility, electricity imports are endogenously optimised, meaning that the capacities and operation of wind and solar generation, as well as storage in the respective exporting regions and the HVDC transmission lines, are co-planned with the rest of the European system. Ammonia, carbonaceous fuels, and ferrous materials are not spatially resolved in the model, assuming they can be transported within Europe at negligible cost. Thus, their specific import location is not determined. An import limit of 500 TWh per region for the sum of all exports is imposed to prevent over-reliance on single exporters.\n\nFor imports of hydrogen by pipeline, North African regions offer the lowest cost (ca. 74-88 \u20ac MWh\u22121, Supplementary Fig.\u00a03). Importing hydrogen by ship is substantially more expensive due to liquefaction and evaporation losses, with a cost difference of 18% between each vector\u2019s lowest cost supplier (Supplementary Fig.\u00a07). For hydrogen derivatives, Argentina and Chile offer additional potential for low-cost imports, for instance, 125\u2013132 \u20ac MWh\u22121 for Fischer-Tropsch fuels or 548\u2013566 \u20ac t\u22121 for steel. These values are similar to those achieved in the Maghreb region. Further notable regions include Australia and Canada. Methanol is slightly cheaper than the Fischer-Tropsch route because it is assumed to be more flexible with a 20% minimum part load compared to 50% for Fischer-Tropsch synthesis73. The lower process flexibility shifts the energy mix towards solar electricity and causes higher levels of curtailment and battery storage, increasing costs (Supplenmentary Fig.\u00a09). The transport costs of CH4(l) are lower than for H2(l) since the liquefaction consumes less energy and individual ships can carry more energy with CH4(l). Pipeline imports of CH4(g) were also considered, but costs were higher than for CH4(l) shipping under the assumption that new pipelines would have to be built or renewed.\n\nIn Fig.\u00a02, we first explore the cost reduction potential of various energy and material import options. Without energy imports, total energy system costs add up to 836 bn\u20ac2020 a\u22121. By enabling imports from outside Europe and considering all import vectors, we find a potential reduction of total energy system costs by up to 37 bn\u20ac2020 a\u22121, using technology assumptions for 2040. This corresponds to a relative reduction of 4.4%, which remains nearly unchanged regardless of the domestic crude steel and ammonia production relocation potential. With more long-term (2050) or near-term (2030) technology assumptions, the cost savings range from 3.6% to 5.4%, with higher cost savings achieved with near-term technology assumptions (Supplementary Fig.\u00a012).\n\nSubsets of available import options are sorted by ascending cost reduction potential. a shows the profile of total system cost savings. Shaded ranges show cost savings (%) for technology assumptions for 2030 and 2050, in addition to the default assumptions for 2040. Cost savings (%) are also shown for scenarios without crude steel and ammonia relocation, in addition to the default case where relocation is allowed. b shows the composition and extent of imports in relation to total energy system costs. Percentage numbers in the bar plot indicate the share of total system costs spent on domestic energy infrastructure. Alternative scenarios of this figure with higher and lower import cost assumptions are shown in Supplementary Figs.\u00a013 and 14. Source data are provided as a Source Data file. HBI\u2009=\u2009hot briquetted iron; HVDC\u2009=\u2009high-voltage direct current; bn\u2009=\u2009billion.\n\nFor cost-optimal imports, around 77% of these costs are used to develop domestic energy infrastructure. The remaining 23% are spent on importing a volume of 50 Mt of green steel and around 1498 TWh of green energy, which is around 13% of the system\u2019s total energy supply (Fig.\u00a03). Our results show a cost-effective import mix consisting primarily of liquid carbon-based fuels, hydrogen, and steel imports with small volumes of ammonia and electricity imports.\n\na shows the optimised import shares by carrier for the import scenario with flexible carrier choice and volume, technology assumptions for 2040, and allowed relocation of crude steel and ammonia production. b shows the total supply for each carrier for the same scenario. c shows trade flows for the same scenario as a Sankey diagram. The flows are aggregated to broader regions to emphasise that alternative origin-destination pairs could often yield similar results. Import shares for further import scenarios are included in Supplementary Figs.\u00a020 and 21. Steel is included in energy terms, applying a factor of 2.1\u2009kWh kg\u22121 as released by the oxidation of iron. Source data are provided as a Source Data file. HBI = hot briquetted iron; HVDC\u2009=\u2009high-voltage direct current.\n\nNext, we investigate the impact of restricting the available import options to subsets of import vectors. We find that if only hydrogen can be imported, cost savings are reduced to 20 bn\u20ac2020 a\u22121 (2.4%), with pipeline-based hydrogen imports being preferred to imports as liquid by ship. By importing a larger volume of hydrogen as an intermediary carrier (1338 TWh instead of 576 TWh, Supplementary Fig.\u00a020), low-cost renewable energy from abroad can still be leveraged for the synthesis of derivative products in Europe. However, the benefit is reduced as domestic CO2 feedstocks from industrial sources are depleted.\n\nConversely, when direct hydrogen imports are excluded from the available import options, cost savings are close to the maximum with 34 bn\u20ac2020 a\u22121 (4.1%). This indicates that the benefit of using domestically captured biogenic or fossil CO2 is similar to tapping into low-cost renewable resources abroad. Focusing imports exclusively on liquid carbonaceous fuels derived from hydrogen, i.e., methanol or Fischer-Tropsch fuels, still achieves high cost savings of 31 bn\u20ac2020 a\u22121 (3.7%), which is due to the smaller demand or variety of applications for ammonia, methane, and steel compared to liquid carbonaceous fuels. Thus, excluding them has a small effect on cost savings. This aligns with the finding that restricting options to only methane, ammonia, or ferrous material imports yields negligible to small cost savings below 5 bn\u20ac2020 a\u22121 (0.6%). Negligible cost savings were also found for the direct import of electricity, as it poses more challenges for integration into the European system.\n\nOverall, while varying import costs within \u2009\u00b1\u200920% affects the magnitude of attainable cost savings, the relative impact of restricting specific import options remains broadly consistent (Supplementary Figs.\u00a013 and 14). Likewise, using more long-term (2050) or near-term (2030) technology assumptions do not affect the dynamics substantially (Fig.\u00a02).\n\nFigure\u00a03a, b outline which carriers are imported in which quantities in relation to their total supply under default assumptions when the vector and volume can be flexibly chosen (\u2018all imports allowed\u2019 in Fig.\u00a02). In energy terms, cost-optimal imports comprise around 45% carbonaceous fuels, 35% hydrogen, and less than 10% electricity. Noticeably, all primary crude steel and ammonia for fertilisers is imported, whereby steel imports are preferred over HBI imports. Around half of the total hydrogen supply is imported, matching the ratio of the 2030 REPowerEU targets16. Hydrogen is imported so that it can be processed into derivative products domestically rather than used for direct applications for hydrogen. Smaller import shares are observed for electricity, which is largely supplied from domestic resources, because of higher costs and losses in electricity transmission than other import vectors, and for methane, which is supplied from domestic fossil and biogenic sources (Supplementary Fig.\u00a024).\n\nIn terms of trade flows (Fig.\u00a03c), we observe carbonaceous fuel imports by ship from South America \u2013 leveraging low transport costs of dense liquid fuels \u2013 as well as ammonia, steel, and hydrogen imports from the Maghreb region. Hydrogen is mainly received by pipeline in Spain. Moreover, due to its proximity to Italy, some electricity imports are received by HVDC connections from Tunisia. While the model suggests trade routes from particular regions, we aggregate these to broader regions to emphasise that alternative origin-destination pairs within the regions could often yield similar results (Supplementary Figs.\u00a07\u201310).\n\nTo explain the import shares in Fig.\u00a03a in more detail, we compare import costs with average domestic production cost split by cost and revenue components in Fig.\u00a04. First, for the scenario without imports, imported fuels appear substantially cheaper than domestic production, which is mostly driven by levelised cost differences of wind and solar electricity supply. The high demand for hydrogen derivatives (Supplementary Fig.\u00a04) means that the most attractive domestic potentials for renewable electricity and captured carbon dioxide have been exhausted. Consequently, power from wind and solar needs to be produced in regions with worse capacity factors and higher levelised costs.\n\nThe three panels (a\u2013c) refer to different import scenarios. In each panel, the bar charts show the production-weighted average costs of domestic production of steel, hydrogen and its derivatives, split into its cost and revenue components. These have been computed using the marginal prices of the respective inputs and outputs for the production volume of each region and snapshot. Capital expenditures are distributed to hours in proportion to the production volume. Missing bars indicate that no domestic production occurred in the scenario, e.g., for the case of methane, where all demand is met by biogenic and fossil methane and no synthetic production occurred (cf. energy balances in Supplementary Figs. 24\u201325). All hydrogen is produced from electrolysis; i.e., the model did not choose to produce hydrogen via steam methane reforming with or without carbon capture. For each bar, the yellow error bars show the range of time-averaged domestic production costs across all regions. The black error bars show the range of import costs across all regions. The maps on the right of each panel relate the hydrogen production volume to the weighted cost of domestic hydrogen production (colorbar). The shown scenarios use technology assumptions for 2040, allow crude steel and ammonia relocation, and do not constraint import volume in (b, c). Confer Supplementary Fig.\u00a022 for information on the domestic cost supply curves. Source data are provided as a Source Data file. Maps made with Natural Earth. HBI\u2009=\u2009hot briquetted iron.\n\nPart of this gap is closed when hydrogen imports are allowed. By sourcing cheaper hydrogen from outside Europe, the domestic costs of derivative fuel synthesis are reduced. However, the large remaining volume of CO2 handled in the European system for use and sequestration (Supplementary Fig.\u00a025) means that direct air capture is still the price-setting technology for CO2, as economic applications for biogenic and industrial carbon capture (i.e., those with high full load hours) are depleted.\n\nWith all import vectors allowed, we see minimal cost differences between domestic production and imports as the supply curves reach equilibrium (Supplementary Fig.\u00a022). This is because imports of hydrogen and derivative products lower the strain on the domestic supply curves for hydrogen and carbon dioxide. Thereby, domestic production would only ramp up where it competes with imports and associated infrastructure costs. This was the case for hydrogen, methanol, and Fischer-Tropsch fuels in the British Isles and parts of Southern Europe and Nordic countries (Fig.\u00a04). Consequently, not all hydrogen is imported, but some domestic production is retained.\n\nThus far, the presented findings originate from a central estimate for the import cost. However, the cost-optimal import mix strongly depends on the assumed import costs. This uncertainty is addressed in Fig.\u00a05. Figure\u00a05a highlights the extensive range in potential cost reductions if higher or lower import costs could be attained and underlines the resulting variance in cost-effective import mixes. Within\u2009\u00b1\u200930% of the default import costs applied to all carriers, total cost savings vary between 2 bn\u20ac2020 a\u22121 (0.3%) and 112 bn\u20ac2020 a\u22121 (13.5%). Within this range, import volumes vary between 500 and 2646 TWh. Across most scenarios, there is a stable role for ammonia and liquid carbonaceous fuel imports. Within a narrower\u2009\u00b1\u200920% range, hydrogen imports also appear in larger quantities, while steel imports become less attractive with cost increases of 10% or more. Electricity imports grow with declining costs.\n\nIn (a, b), indicated relative import cost changes are applied uniformly to all vectors. In (c, d), cost changes are applied uniformly to all vectors but electricity imports. In (e, f), cost changes are only applied to carbonaceous fuels (methane, methanol and Fischer-Tropsch). a, c, e show potential system cost savings compared to the scenario without imports. b, d, f show the share and composition of different import vectors in relation to total energy system costs. The information is shown both in absolute terms and relative terms compared to the scenario without imports. Ranges in (a, c, e) show cost savings (%) for technology assumption years 2030 and 2050, in addition to 2040. All shown scenarios allow relocation of crude steel and ammonia production within Europe. HBI\u2009=\u2009hot briquetted iron; HVDC\u2009=\u2009high-voltage direct current.\n\nHowever, not all carriers are equally affected by technology cost variations. Fuel synthesis technologies do not influence electricity imports, and only carbon-based fuels are subject to the cost of CO2 supply. We find that when the relative cost variation is not applied to electricity imports (Fig.\u00a05b), they remain less attractive than other vectors, even when those alternative vectors face a 20% cost rise.\n\nOne central assumption regarding costs for carbon-based fuels is that imported fuels rely on direct air capture (DAC) as a carbon source. Arguments for this assumption relate to the potential remoteness of the ideal locations for renewable fuel production or the absence of industrial point sources in the exporting region. In contrast, domestic electrofuels can mostly use less expensive captured biogenic or fossil carbon dioxide from industrial processes. Therefore, the higher cost for DAC partially cancels out the savings from utilising better renewable resources abroad. This is one of the reasons why there is substantial power-to-X production in Europe, even with corresponding import options. However, the availability of cheaper (biogenic) CO2 in exporting regions would lower the costs of carbonaceous fuel imports (Table\u00a01).\n\nWhen the relative cost variation is only applied to carbon-based fuels (Fig.\u00a05c), reflecting cost uncertainty in carbon provision, hydrogen imports are quickly displaced by Fischer-Tropsch and methanol imports with falling costs. Only when import costs rise by 20% do domestically produced liquid hydrocarbons \u2013 derived mainly from imported hydrogen \u2013 become more cost-effective than direct imports. In all three cases of import cost variations, methane imports become relevant only with substantial cost reductions of 40%, replacing biogas and residual fossil gas consumption.\n\nOverall, Fig.\u00a05 also demonstrates that the impact of import cost variations on savings remains stable with more near-term (2030) and long-term (2050) technology assumptions.\n\nWhat is consistent for many scenarios with higher or lower import costs is the flat solution space around the respective cost-optimal import volumes. Increasing or decreasing the total amount of imports from the optimum barely affects system costs within \u00a0\u00b1\u20091000 TWh. This is illustrated in Fig.\u00a06 and extended Supplementary Figs.\u00a015\u201318, which show the system cost as a function of enforced import volumes and different import costs for hydrogen and its derivatives. A wide range of scenarios with import volumes below 4100 TWh (2300 TWh for 20% higher import costs, 5800 TWh for -20% lower import costs) have lower total energy system costs than the no-imports scenario. These ranges of import values are two to three times as large as the corresponding cost-optimal import volumes, which are indicated by the red markers in Fig.\u00a06 and correspond to the bars previously shown in Fig.\u00a05b. Naturally, the cost-optimal volume of imports increases as their costs decrease, but with noticeably varying slopes for system cost savings per unit of additional imported energy.\n\na Solid lines show the total system cost as a function of enforced import volumes for higher (brown scale) or lower (blue scale) import costs. The dashed lines indicate the corresponding shares of the domestic system cost. The red markers denote the maximum cost reductions and cost-optimal import volume for given import cost levels (extreme points of the curves). The cost alterations are uniformly applied to all import options but direct electricity imports. Steel is included in energy terms, applying 2.1\u2009kWh\u2009kg\u22121 as released by the oxidation of iron. b shows the composition of the total system cost as a function of enforced import volumes for the central import cost estimate. The dashed line splits the system costs into costs for imports and the domestic system. All shown scenarios use technology assumptions for 2040 and allow relocation of crude steel and ammonia production within Europe. Cost compositions for the alternative import cost scenarios are presented in Supplementary Figs.\u00a015\u201318. Source data are provided as a Source Data file. HBI\u2009=\u2009hot briquetted iron; HVDC\u2009=\u2009high-voltage direct current.\n\nAs we explore the effect of increasing import volumes on system costs, we find that already 56% (48\u201380% within \u00a0\u00b1\u200920% import costs) of the 4.4% (1.3\u20139.0%) total cost benefit can be achieved with the first 500 TWh of imports. This corresponds to 31% (25\u201349%) of the cost-optimal import volumes, highlighting the diminishing returns of large amounts of energy imports in Europe. The initial 1000 TWh realise 90% (80\u2013100%) of the highest cost savings, for which primary crude steel and liquid carbonaceous fuel imports are prioritised, followed by ammonia and hydrogen and, subsequently, larger volumes of electricity beyond cost-optimal import levels. Once more than 5500 TWh (5000\u20138200 TWh) are imported, less than half the total system cost would be spent on domestic energy infrastructure.\n\nAs imports increase, there is a corresponding decrease in the need for domestic power-to-X (PtX) production and renewable capacities. A large share of the hydrogen, methanol, and primary steel production is outsourced from Europe, reducing the need for domestic wind and solar capacities. This trend is further characterised by the displacement of biogas usage in favour of hydrogen imports around the 4000 TWh mark (3000\u20135000 TWh within \u00a0\u00b1\u200920% import costs) as demand for domestic CO2 utilisation drops and methane use for power and heat provision is displaced by hydrogen. The increase in hydrogen imports results in the build-out of more hydrogen fuel cell combined heat and power (CHP) units for power and heat supply in district heating networks. Regarding electricity imports from the Middle East and North Africa (MENA) region, Fig.\u00a06 reveals a mix of wind and solar power with some batteries to establish favourable feed-in profiles for the European system integration and higher utilisation rates for the long-distance HVDC links. For instance, for imports of 4000 TWh in Fig.\u00a06, the capacity-weighted average utilisation rate was 85%. This is because a considerable share of the electricity import costs can be attributed to power transmission.\n\nAs import costs are varied, the composition of the domestic system and import mix for different import volumes is primarily similar (Supplementary Figs.\u00a015\u201318). The main difference is a less prominent role for steel imports with higher import costs. What is furthermore noteworthy is that reducing import costs from \u2212\u200930% to \u2212\u200950% only marginally reduces domestic infrastructure costs, indicating largely saturated import potentials. Regarding available import options, the windows for cost savings are more limited if only subsets are available (Supplementary Fig.\u00a019). However, up to an import volume of 1500 TWh for the central cost estimate, excluding electricity imports or constraining imports to methanol and Fischer-Tropsch fuels only, would not substantially diminish the cost-saving potential.\n\nAcross the range of import scenarios analysed, we find that the decision which import vectors are used strongly affects domestic energy infrastructure needs (Fig.\u00a07).\n\nPanels (a, c, e) show the regional electricity supply mix (pies), added HVDC and HVAC transmission capacity (lines), and the siting of battery storage (choropleth). Panels (b, d, f) show the hydrogen supply (top half of pies) and consumption (bottom half of pies), net flow and direction of hydrogen in newly built pipelines (lines), and the siting of hydrogen storage subject to geological potentials (choropleth). Total volumes of transmission expansion are given in TWkm, which is the sum product of the capacity and length of individual connections. The half circle in the Bay of Biscay indicates the imports of hydrogen derivatives that are not spatially resolved: ammonia, steel, HBI, methanol, Fischer-Tropsch fuels. Hydrogen imports are shown at the entry points. All shown scenarios use technology assumptions for 2040, allow steel and ammonia relocation and have no import volume constraint (when available). Maps for more scenarios are included in Supplementary Figs.\u00a026\u201329. Source data are provided as a Source Data file. Maps made with Natural Earth. HBI\u2009=\u2009hot briquetted iron; HVDC\u2009=\u2009high-voltage direct current; HVAC\u2009=\u2009high-voltage alternating current; AC\u2009=\u2009alternating current; DC\u2009=\u2009direct current; CHP\u2009=\u2009combined heat and power.\n\nIn the fully self-sufficient European energy supply scenario, we see large PtX production within Europe to cover the demand for hydrogen and hydrogen derivatives in steelmaking, fertilisers, high-value chemicals, green shipping, and aviation fuels. Production sites are concentrated mainly in and around the North and Baltic Seas, using wind-based electrolysis, and some additional hubs in Southern Europe using solar-based electrolysis. Electricity grid reinforcements, representing around 50% of the current transmission capacity, are focused in Northwestern Europe, with numerous long-distance HVDC connections, but are broadly distributed overall.\n\nWith a total of 57 TWkm, the hydrogen pipeline build-out is smaller, mostly serving regional connections. For several reasons, it is also considerably smaller than the 204\u2013306 TWkm observed previously in Neumann et al.14 or the European Hydrogen Backbone reports74 which envisioned a similar order of magnitude. Besides assumed full electrification of heavy-duty road transport and assuming low CO2 transport costs from point sources to low-cost hydrogen sites75, one reason is the considered relocation of crude steel and ammonia production to where hydrogen is cheap and abundant, reducing the need to transport hydrogen (Supplementary Fig.\u00a06). Not considering relocation of primary crude steel and ammonia production would result in a slightly larger hydrogen network of 71 TWkm (Supplementary Fig.\u00a026), while increasing system costs by 2.5 bn\u20ac2020 a\u22121 (0.3%) in the no-imports scenario. With permitted relocation of ammonia and crude steel production, primary steel production shifts to the British Isles and Spain, while ammonia production moves to the Nordic-Baltic region. Both sectors become more strongly localised, with individual regions capturing a market share surpassing 30%.\n\nHowever, the main reason why hydrogen consumption is mainly concentrated in regions with low-cost production is that over 80% of the hydrogen is used to produce electrofuels for aviation, shipping, and chemical feedstocks, compared to about 10% for crude steel and ammonia production. These liquid fuels can be transported at a lower cost to airports, ports, and industrial sites across Europe than hydrogen. Consequently, there is low impetus for transporting hydrogen directly, resulting in a hydrogen network that is much smaller than envisioned in the European Hydrogen Backbone74.\n\nConsidering imports of renewable electricity, green hydrogen, and electrofuels substantially alters the magnitude of energy infrastructure in Europe. Imports displace much of the European power-to-X production capacities and, particularly, domestic solar energy generation in Southern Europe. Much of the remaining derivative fuel synthesis in Southern Spain uses imported hydrogen, assuming the delivery of captured CO2 from other parts of Europe at low cost75. In contrast, the British Isles retain some domestic electrolyser capacities to produce synthetic fuels locally. Electricity imports of 131 TWh, compared to total imports of 1609 TWh, mainly enter from Tunisia at multiple nodes in Mallorca, Corsica, Sardinia, Sicily, and mainland Italy. This distribution facilitates grid integration without strong reinforcement needs in the Italian peninsula.\n\nWhile the broad regions of domestic power grid reinforcements are not significantly affected by the import of electricity and other fuels, the volume of power grid expansion is reduced by 20%. The reduction in network infrastructure is even more pronounced with the hydrogen network; the hydrogen network size is reduced by 70% with many of the North and East European connections omitted. Compared to the self-sufficiency scenario, the cost-benefit of the hydrogen network shrinks from 3 bn\u20ac2020 a\u22121 (0.4%) to less than 1 bn\u20ac2020 a\u22121 (0.1%). This is caused by substantial amounts of hydrogen derivative imports or direct processing of imported hydrogen at the entry points, which diminishes the demand for hydrogen in Europe and, hence, the need to transport it. With further 10% cheaper carbonaceous fuel imports, the hydrogen network would then shrink to 9 TWkm (Supplementary Fig.\u00a027).\n\nChanges in the magnitude of domestic PtX production also affect Europe\u2019s backup capacity needs. Less PtX production means lower wind and solar capacities, which reduces the amount of available generation in dark wind lulls. Next to energy storage and demand-side management of electric vehicles and heat pumps, the operational flexibility of electrolysers and derivative fuel production yields significant benefits for integrating variable wind and solar feed-in and reduces reserve capacity requirements. In a theoretical scenario without imports, where all PtX processes must run inflexibly at full capacity, system costs rise by 8.8%. Between the main scenarios with and without imports, we observe that as imported fuels displace some flexible domestic power-to-X, domestic thermal backup capacities increase from 129 GWel. (no imports allowed) to 276 GWel. (all imports allowed). Instead of curtailing the domestic production of electrofuels, backup power plants need to be dispatched. Most of these power plants are CHPs fuelled by fossil gas, providing backup heat alongside backup power when electricity prices are high during winter (Supplementary Fig.\u00a032). The resulting emissions are then compensated elsewhere in the system through biogenic carbon dioxide removal (Supplementary Fig.\u00a025). Spatially, these are distributed across Central Europe, while batteries provide backup power in Southern Europe (Supplementary Fig.\u00a033). The model leverages Europe\u2019s extensive power grid to widely distribute centralised backup power, even though, in reality, individual nations may prefer maintaining domestic reserve capacities.\n\nA further observation is the high potential value of PtX waste heat and its role in siting fuel synthesis plants (Supplementary Figs.\u00a026 and 28). Alongside the flexible operation of electrolysis to integrate variable wind and solar feed-in and the broad availability of industrial and biogenic carbon sources in Europe, waste heat usage in district heating networks is a potential revenue stream that could make electricity and hydrogen imports with subsequent domestic conversion more attractive relative to the direct import of derivative products. Our default assumption that only 25% of the waste heat can be utilised stems from potential challenges in co-locating PtX plants with district heating networks within the 115 model regions. If all waste heat could be leveraged, notable system cost savings of 20 bn\u20ac2020 a\u22121 (2.4%) could be achieved in the no-imports scenario compared to a scenario where waste heat is fully vented. To realise these benefits, Fischer-Tropsch and Haber-Bosch plants tend to be geographically distributed where space heating demand is high (e.g., Paris or Hamburg) (Supplementary Fig.\u00a026), which increases hydrogen network build-out to 98 TWkm (+\u200972%) compared to the reference scenario with 25% waste heat utilisation. This is not the case for methanolisation plants, which have lower waste heat potential.\n\nIn Table\u00a01, we present a breakdown of some potential causes for import cost variations compared to domestic supply chains relating to technology costs, financing costs, excess power and heat revenues, fuel synthesis flexibility, and the availability of geological hydrogen storage and alternative sources of CO2.\n\nFor example, we show that a higher weighted average cost of capital (WACC) than the uniformly applied 7%, e.g., due to higher project financing risks, and lower WACC, e.g., due to the government-backing of projects, strongly affect import costs76. An increase or decrease by just one percentage point already alters the unit costs by around \u00a0\u00b1\u00a07%. Likewise, technology cost variations abroad for electrolysers and DAC units have a strong influence. Biogenic CO2 \u2013 or fossil CO2 from industrial processes that is largely cycled between use and synthesis and, hence, not emitted to the atmosphere \u2013 can reduce the levelised fuel cost by 16% if it can be provided for 50 \u20ac t\u22121.\n\nBy default, we assume islanded fuel synthesis sites, which causes curtailment rates of 8%. If surplus electricity production could be sold and absorbed by the local power grid in exporting regions, additional cost reductions could be achieved. Furthermore, process integration with waste heat usage and flexible operation can also reduce fuel cost by 3\u22126%. Import costs are also reduced by 4% where geological hydrogen storage is available by reducing the need for flexible power-to-X operation.\n\nIn contrast, the cost impact is low if the cheapest exporting region withdraws from the market. Within a cost premium of 10% in relation to the lowest cost exporting region, Chile, ten other regions could step in if these regions were unavailable for exports (Supplementary Fig.\u00a09).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60652-1/MediaObjects/41467_2025_60652_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60652-1/MediaObjects/41467_2025_60652_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60652-1/MediaObjects/41467_2025_60652_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60652-1/MediaObjects/41467_2025_60652_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60652-1/MediaObjects/41467_2025_60652_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60652-1/MediaObjects/41467_2025_60652_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60652-1/MediaObjects/41467_2025_60652_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our analysis offers insights into how renewable energy imports might reduce overall systems costs and interact with European energy infrastructure. Our results show that imports of green energy reduce the costs of a carbon-neutral European energy system by 37 bn\u20ac2020 a\u22121 (4.4%), noting, however, that the uncertainty range is considerable. While we find that some imports are beneficial within a \u00a0\u00b1\u200920% variation of import costs, system cost savings range between 1% and 10%. However, what is consistent within this range are the diminishing returns of energy imports for larger quantities, with peak cost savings below imports of 3000 TWh/a (equivalent to 90 Mt of hydrogen). We also find that there is value in pursuing some power-to-X production in Europe as a source of flexibility for wind and solar integration and as a potential source of waste heat for district heating networks. Another siting factor favouring European power-to-X is the wide availability of sustainable biogenic and industrial carbon sources, which helps reduce reliance on more costly direct air capture.\n\nWith reference to the meta-study by Genge et al.55, our import costs to Europe for lowest-cost to median-level-cost exporters mostly conform to the review\u2019s interquartile ranges for different carriers and technology assumptions for 2030 and 2050. Some higher costs, e.g., for ammonia, can be attributed to our updated electrolyser cost assumptions (1100/950/800\u20ac \\({{{{\\rm{kW}}}}}_{\\,{\\mbox{el.}}\\,}^{-1}\\) in 2030/2040/2050), which reflect recent market developments77. Also for crude steel imports, our central cost estimate of 531 \u20ac t\u22121 for the lowest-cost exporter is positioned between studies with lower50 and higher67 cost estimates. Among other studies investigating the relationship between energy imports and the European energy system, several analyses report lower import shares in the range of 10\u201320% of total hydrogen supply58,61,62. For instance, Kountouris et al.61 see limited hydrogen imports of 182 TWh a\u22121 from the Maghreb region and Ukraine despite favourable import costs of 33 \u20ac \\({{{{\\rm{MWh}}}}}_{{H}_{2}}^{-1}\\). Conversely, Wetzel et al.59 find higher import shares of 53% for methane and 43% for hydrogen. The latter closely aligns with our 49% import share for hydrogen. Wetzel et al.59 find that imports reduce system costs by 2.8%, which is also comparable to our 2.4% system cost reduction when only direct hydrogen imports are considered. The most pronounced import dependency we found in Schmitz et al.42, with import shares beyond 90% for Germany. Results in Kountouris et al.61 further substantiate our finding that derivative imports and demand relocation could diminish hydrogen network benefits.\n\nSeveral limitations of our study should be noted. First, the optimisation results represent a long-term equilibrium that disregards potential transition-related infrastructure lock-ins or mid-term ramp-up constraints of export capacities or domestic infrastructure development. The development speed of key technologies is also uncertain and could affect cost-optimal infrastructure and import strategies. A further limitation is that our cost-based analysis of imports, which best reflects long-term bilateral purchase agreements, neglects price impacts of intensifying global competition for green fuel imports and exports56. Besides unclear market developments, local challenges in exporting regions, such as public acceptance for export-oriented energy projects78 and potential water scarcity41,79 to produce large amounts of hydrogen in renewable-rich but arid regions are not addressed. We also do not assess potential impacts on the regional economy and local employment effects within Europe, as some ammonia production and steel manufacturing relocates in the model. Furthermore, the model\u2019s lack of spatial resolution for CO2 means that carbonaceous hydrogen derivatives are sited where H2 is cheapest, implicitly assuming that the CO2 from biogenic or industrial sources can be transported there. However, such required CO2 pipelines could be built at relatively low additional system cost75. In the context of carbon management, more lenient assumptions on sustainable biofuel potentials, allowed levels of geological carbon sequestration, or plastic landfill could alter the results, shifting the system away from synthetic electrofuels towards more fossil fuel use with carbon capture or carbon dioxide removal75,80.\n\nOverall, we find that the import vectors used strongly affect domestic infrastructure needs. For example, only a smaller hydrogen network would be required if hydrogen derivatives were largely imported and the domestic ammonia and crude steel industry is allowed to relocate. We also identify higher electricity backup requirements in the absence of large power-to-X flexibilities. These findings underscore the importance of coordination between energy import strategies and infrastructure policy decisions. Our results present a quantitative basis for further discussions about the trade-offs between system cost, carbon neutrality, public acceptance, energy security, infrastructure buildout, and imports.\n\nThe small differences in cost observed between some scenarios are particularly relevant because factors other than pure costs, which are not reflected in our infrastructure optimisation model, might then drive import strategies. To some, the relatively limited cost benefit of imports and offshoring of industrial production may speak against imports. Concerns about energy security could motivate more domestic supply and diversified imports. For instance, shipborne imports of hydrogen derivatives could be preferred to reduce pipeline lock-in and to mitigate the risks of sudden supply disruptions and the exercise of market power. From a practical perspective, it may also be more appealing to focus on carriers that are already globally traded commodities and to prefer infrastructure offering quick deployment.\n\nPolicymakers in Europe might prefer alternative systems featuring, for instance, lower domestic infrastructure requirements, reuse of existing infrastructure, lower technology risk, and reduced land usage for broader public support than the most cost-effective solution. Moreover, policies favoring local energy supply chains and importing intermediary products like sponge iron could be favoured to preserve European jobs while outsourcing only the most energy-intensive processes. However, in shifting potential land use and infrastructure conflicts to abroad, where population densities are often lower, potential exporting countries would need to weigh the prospect of economic development against internal social and environmental concerns, particularly in countries with a history of colonial exploitation81. Ultimately, Europe\u2019s energy strategy would likely seek to balance cost savings from green energy and material imports with broader concerns like geopolitics, economic development, public opinion, and the willingness of potential exporting countries in order to ensure a swift, secure, and sustainable energy future. Our research shows that there is manoeuvreing space around Europe\u2019s energy import strategies to accommodate such non-cost concerns.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "For our analysis, we use the European sector-coupled high-resolution energy system model PyPSA-Eur82 (derivative of v0.13.0) based on the open-source modelling framework PyPSA83 (Python for Power System Analysis), covering the energy demands of all sectors including electricity, heat, transport, industry, agriculture, as well as non-energy feedstock demands, international shipping, and aviation. An overview of considered supply, consumption, and balancing technologies per carrier is shown in Supplementary Fig.\u00a02.\n\nThe model simultaneously optimises spatially explicit investments and the operation of generation, storage, conversion, and transmission assets to minimise total system costs in a single linear optimisation problem, which assumes perfect operational foresight and is solved with Gurobi (v11.0.1)84. To manage computational complexity, no pathways with multiple investment periods are calculated, but overnight scenarios targeting net-zero CO2 emissions. The capacity expansion is based on technology cost and efficiency assumptions for 2040 (see \u2018Data availability\u2019), acknowledging that much of the required infrastructure must be constructed well before reaching net-zero emissions. Figures\u00a02 and 5 and Supplementary Fig.\u00a029 feature additional scenarios using technology assumptions for 2030 and 2050.\n\nExisting hydro-electric power plants85 are included, as well as nuclear power plants built after 1990 or currently under construction according to Global Energy Monitor\u2019s Global Nuclear Plant Tracker (52 GW total of 106 GW in current operation)86. While hydroelectricity is assumed to be non-extendable due to geographic constraints, additional nuclear capacities can be expanded where cost-effective. We assume the existing nuclear fleet is operated inflexibly and apply country-specific historical availability factors from 2021 to 202387.\n\nTemporally, the model is solved with an uninterrupted 4\u2009h equivalent resolution for a single year (2190 time steps), using a segmentation clustering approach implemented in the tsam toolbox on all time-varying data88. While weather variations between years are not considered for computational reasons, the chosen weather year 2013 is representative in terms of wind and solar availability and heat demand89. Some demands are associated with a time-varying profiles (e.g., residential/services electricity, electric vehicles, and heating demand) based on travel patterns or ambient weather conditions, while the other exogenous demands are assumed to be time-constant (e.g., kerosene, naphtha, methanol, ammonia, and industry electricity).\n\nSpatially, the model resolves 115 European regions90, covering the European Union, the United Kingdom, Norway, Switzerland, and the Balkan countries without Malta and Cyprus. For computational reasons, only electricity, heat, and hydrogen are modelled at high spatial resolution, while oil, methanol, methane, ammonia, and carbon dioxide are treated as easily transportable without spatial constraints. Of the total final energy and non-energy demand (Supplementary Fig.\u00a05), only some demands are spatially fixed (Supplementary Fig.\u00a04). These include electricity for residential, industry, services, and agriculture; heat; electric vehicles; solid biomass for industry; naphtha/methanol feedstocks; and hydrogen for crude steel and ammonia production unless these industries can relocate.\n\nMost other hydrogen demands are spatially variable. Only a small demand of 5 TWh a\u22121 in the chemicals industry (excluding liquid feedstocks) remains, which is offset by spatially fixed hydrogen production of around 10 TWh a\u22121 from chlor-alkali electrolysis for chlorine production. High-temperature industrial heat is supplied by methane, shipping and aviation use carbonaceous fuels, and land transport is fully electrified. In district heating and the power sector, backup hydrogen capacities are endogenously sized and sited just as the production capacities of hydrogen derivatives (Fischer-Tropsch, methane, methanol), which account for more than 80% of the hydrogen consumption. Since the model optimises the siting and operation of these fuel synthesis plants and electrolysers, many demands are spatially variable (e.g., electricity demand for electrolysers or hydrogen demand for methanolisation). Existing hydrogen production capacities from fossil gas reforming are not considered, as they are expected to reach the end of life over the model horizon.\n\nA mathematical description of PyPSA-Eur can be found in Supplementary Note\u00a01, adapted from Neumann et al.14\n\nNetworks are considered for electricity, methane, and hydrogen transport. Existing gas pipelines taken from SciGRID_gas91, can be repurposed to hydrogen in addition to new hydrogen pipelines14. Data on the gas transmission network is further supplemented by the locations of fossil gas extraction sites and gas storage facilities based on SciGRID_gas91, as well as investment costs and capacities of LNG terminals in operation or under construction from Global Energy Monitor\u2019s Europe Gas Tracker92. Geological potentials for hydrogen storage are taken from Caglayan et al.93, restricting where this low-cost storage option is available. In modelling gas and hydrogen flows, we incorporate electricity demands for compression of 1% and 2% per 1000km of the transported energy, respectively94. Existing high-voltage grid data is taken from OpenStreetMap95. For HVDC transmission lines, we assume 2% static losses at the substations and additional losses of 3% per 1000\u2009km. The losses of high-voltage AC transmission lines are estimated using the piecewise linear approximation from Neumann et al.96, in addition to applying linearised power flow equations97. Up to a maximum capacity increase of 30%, we consider dynamic line rating (DLR), leveraging the cooling effect of wind and low ambient temperatures to exploit existing transmission assets fully98. To approximate N\u00a0\u2212\u00a01 resilience, transmission lines may only be used up to 70% of their rated dynamic capacity99. To prevent excessive expansion of single connections, power transmission reinforcements between two regions are limited to 15 GW, while an upper limit of 50.7 GW is placed on hydrogen pipelines, which corresponds to three 48-inch pipelines94.\n\nRenewable potentials and time series for wind and solar electricity generation are calculated with atlite100, considering land eligibility constraints like nature reserves, excluded land use types, topography, bathymetry, and distance criteria to settlements. Given low onshore wind expansion in many European countries in recent years101, a deployment density of 1.5\u2009MW\u2009km\u22122 is assumed for eligible land for onshore wind expansion102. For reference, this assumption leads to an onshore wind potential for Germany of 244 GW. The temporal renewable generation potential for the available area is then assessed based on reanalysis weather data, ERA5103, and satellite observations for solar irradiation, SARAH-3104, in combination with standard solar panel and wind turbine models provided by atlite.\n\nBiomass potentials are restricted to residues from agriculture and forestry, as well as waste and manure, based on the regional medium potentials specified for 2050 in the JRC-ENSPRESO database105. Continued use of energy crops or biomass imports are not considered. The finite sustainable biomass resource can be employed for low-temperature heat provision in industrial applications, biomass boilers, and CHPs, and (electro-)biofuel production for use in aviation, shipping, and the chemicals industry. In addition, we allow biogas upgrading, including capturing the CO2 contained in biogas, which unlocks all considered uses of regular methane (Supplementary Fig.\u00a02). The total assumed bioenergy potentials are 1372 TWh, which splits into 358 TWh/a for biogas and 1014 TWh/a for solid biomass. The total carbon content corresponds to 605 \\({{{{\\rm{Mt}}}}}_{{{\\mbox{CO}}}_{2}}\\) a\u22121, which is not fully available as a feedstock for fuel synthesis or sequestration for negative emissions due to imperfect capture rates of up to 90%. Biogenic CO2 can be captured from biogas upgrading, biomass CHPs and biomass-based low-temperature heat provision in industrial use, if the added cost of carbon capture is economically viable.\n\nThe carbon management features of the model trace the carbon cycles through various conversion stages: industrial emissions, biomass and gas combustion, carbon capture in numerous applications, direct air capture, intermediate storage, electrofuels, recycling, landfill or long-term sequestration. The overall annual sequestration of CO2 is limited to 200 \\({{{{\\rm{Mt}}}}}_{{{\\mbox{CO}}}_{2}}\\) a\u22121, similar to the 250 \\({{{{\\rm{Mt}}}}}_{{{\\mbox{CO}}}_{2}}\\) a\u22121 highlighted in the European Commission\u2019s carbon management strategy70. This number allows for sequestering the industry\u2019s unabated fossil emissions (e.g., in the cement industry) while minimising reliance on carbon removal technologies. A carbon dioxide network topology is not co-optimised since CO2 is not spatially resolved. This means that the location of biogenic or industrial point sources of CO2 is not a siting factor that this model version considers for PtX processes, implicitly assuming that the CO2 would be transported there at low cost75,106.\n\nWhile the shipping sector is assumed to use methanol as fuel, given its high technology-readiness level compared to hydrogen or ammonia107, land-based transport, including heavy-duty vehicles, is fully electrified in the presented scenarios108. Aviation can use green kerosene derived from Fischer-Tropsch fuels or methanol, owing to the lower technology readiness levels of fuel cell or battery-electric aircraft107. Alternative uses for methanol and Fischer-Tropsch fuels extend beyond transport, including power-to-methanol73, diesel for agriculture machinery and as feedstock for high-value chemicals.\n\nWe consider potential flexibility restrictions in the synthesis processes to obtain more realistic operational patterns of green electrofuel synthesis plants. We apply a minimum part load of 20% for methanolisation and 50% for methanation and Fischer-Tropsch synthesis109,110,111,112. The assumed lower operational flexibility is a potential disadvantage of Fischer-Tropsch over methanol synthesis, where theses fuels compete. These \u2018green\u2019 options then compete with \u2018blue\u2019 and \u2018grey\u2019 options, such as steam methane reforming of fossil gas with or without carbon capture for hydrogen (Supplementary Fig.\u00a02). Some carriers also feature a biogenic production route (e.g., methane and oil).\n\nHeating supply technologies like heat pumps, electric boilers, gas boilers, and combined heat and power (CHP) plants are endogenously optimised separately for decentral use and central district heating. District heating shares of demand are exogenously set to a maximum of 60% of the total urban heat demand with sufficiently high population density. Besides the options for long-duration thermal energy storage, district heating networks can further be supplemented with waste heat from various power-to-X processes: electrolysis, methanation, ammonia synthesis, and Fischer-Tropsch fuel synthesis. Because the thermal discharge from the methanol synthesis is primarily used to distillate the methanol-water output mix73, its waste heat potential is not considered for district heat. Here, we assume a utilisable share of waste heat of 25%, considering that within the 115 regions, only a fraction of fuel synthesis plants might be connected to district heating systems. In further sensitivity analyses, we explore the effect of no or full waste heat utilisation.\n\nThe model includes a variety of options for providing backup power and heating in periods of low renewable generation and high demand (Supplementary Fig.\u00a02). Backup power options include hydrogen, gas and methanol turbines. Backup heat options include gas boilers and resistive heaters. For combined backup heat and power, we consider biomass, hydrogen, and gas CHPs. Furthermore, flexible demands like electric vehicles, heat pumps and fuel synthesis units, as well as batteries and thermal storage in district heating, can be utilised to reduce the need for backup capacities.\n\nUnless indicated otherwise, all scenarios also allow the model to relocate the crude steel and ammonia industry within Europe endogenously. This allows the best sites within Europe to compete with outsourced production abroad. While this captures some of the most energy-intensive industry sectors, other sectors, like concrete and alumina production, are not considered for relocation.\n\nWithout relocation of crude steel and ammonia production allowed, the production volumes of primary crude steel, by direct iron reduction (DRI) and electric arc furnace (EAF), and ammonia for fertilisers, by Haber-Bosch synthesis, are spatially fixed. This results in exogenous hydrogen demand per region. Total production volumes are based on current levels113,114. For the spatial distribution, we use data on the existing integrated steelworks listed in Global Energy Monitor\u2019s Global Steel Plant Tracker115 and manually collected data on the location and size of ammonia plants in Europe.\n\nWith the relocation of crude steel and ammonia production allowed, the model endogenously chooses the regional production volumes of primary crude steel, HBI, and ammonia, subject to the availability of cheap hydrogen. Thereby, the regional capacities and operation of Haber-Bosch, DRI, and EAF plants are co-optimised with the rest of the system, similar to the siting of Fischer-Tropsch or methanolisation plants. For DRI and EAF, investment costs and specific requirements for fuels and iron ore are taken from the Steel Sector Transition Strategy Model (ST-STSM) of the Mission Possible Partnership116,117. and assume steel can be stored and transported without constraints within Europe.\n\nFor both cases, we assume a rise in the steel recycling rate from 40% today to 70% in our carbon-neutral scenarios118. We assume that the electric arc furnaces for secondary steel remain, in proportion, at current locations and do not relocate.\n\nA limitation of the relocation modelling of crude steel and ammonia production is that it only considers the cost of energy in the siting of these industries. Other factors, such as impacts on regional economies and local jobs, integration with other production processes, or availability of other existing infrastructure, are not considered, largely due to a lack of data. The resulting relocation patterns should therefore be interpreted with caution, as they might underestimate total relocation costs and frictions. We allow domestic relocation, nevertheless, in most scenarios, as it would be inconsistent to allow crude steel and ammonia imports from abroad while preventing relocation within Europe.\n\nThe European energy system model is extended with data from the TRACE model (derivative of v1.1) used in Hampp et al.54 to assess the unit costs of different vectors for importing green energy and material to entry points in Europe from various world regions. For consistency with the European model, the techno-economic assumptions were aligned, using the same values for 2040 (plus 2030 / 2050 in Fig.\u00a02 and 5 and Supplementary Fig.\u00a029 and a uniform weighted average cost of capital (WACC) of 7%119. As possible import vectors, we consider electricity by transmission lines, hydrogen as a gas by pipelines and as a liquid by ship, methane as a liquid by ship, liquid ammonia, crude steel and HBI, methanol and Fischer-Tropsch fuels by ship. Liquid organic hydrogen carriers (LOHC) are not considered as export vectors due to their lower technology readiness level (TRL) compared to other vectors1.\n\nOur selection of 53 potential exporting regions broadly comprises countries with favourable wind and solar resources and large enough potential for substantial exports above 500 TWh a\u22121 in addition to domestic consumption. We exclude some countries due to political instability (e.g., Sudan, Somalia, Yemen), using a Fragile States Index120 value of 100 as a threshold, or due to severe imposed sanctions (e.g., Russia, Iran, Iraq), following the EU Sanctions Map121. Landlocked countries without access to seaports or realistic pipeline connections are excluded. For landlocked regions within pipeline reach, we only exclude shipborne vectors. Some large countries are split into multiple subregions for a more differentiated view (e.g., USA, Argentina, Brazil, and China). The resulting regions are marked in Fig.\u00a01A.\n\nTo determine the levelised cost of energy for exports, the methodology first assesses the regional potentials for solar, onshore, and offshore wind energy. These potentials and time series are calculated using atlite100, applying similar land eligibility constraints as in PyPSA-Eur (but using other datasets with global coverage) and applying the same wind turbine and solar panel models to ERA5103 weather data for 2013 in eligible regions. Since TRACE evaluates whole regions without further transmission network resolution, the renewable potentials and profiles within a region are split into different resource classes to reduce smoothing effects. We consider 30 classes each for onshore wind and solar, and 10 for offshore wind, where applicable. Based on these calculations, levelised cost of electricity (LCOE) curves can be determined for each region. A selection of LCOE curves is shown in Supplementary Fig.\u00a022.\n\nIn the next step, potentials are reduced by the projected future local energy demand, starting with the lowest LCOE resource classes. With this approach, domestic consumption is prioritised and supplied by the regions\u2019 best renewable resources, even though we do not model the energy transition in exporting regions in detail. To create the demand projections, we use the GEGIS122 tool, which utilises machine learning on historical time series, weather data, and macro-economic factors to create artificial electricity demand time series based on population and gross domestic product (GDP) growth scenarios following the SSP2 scenario of the Shared Socioeconomic Pathways123. From these time series, we take the annual total and increase it by a factor of two to account for further electrification of other sectors, which the GEGIS tool does not consider.\n\nThe remaining wind and solar electricity supply can then be used to produce the specific energy or material vector according to the flow chart of conversion pathways shown in Supplementary Fig.\u00a01. Considered technologies include water electrolysis for H2, direct air capture (DAC) for CO2, synthesis of methane, methanol, ammonia or Fischer-Tropsch fuels from H2 with CO2 or N2, and H2 direct iron reduction (DRI) for sponge iron with subsequent processing to green steel in electric arc furnaces (EAF) from iron ore priced at 97.7 \u20ac t\u22121116. Other CO2 sources than DAC are not considered in the exporting regions. Furthermore, while batteries and hydrogen storage in steel tanks are considered, underground hydrogen storage is excluded due to the uncertain availability of salt caverns in many potential exporting regions124,125. We also assume that the energy supply chains dedicated to exports will be islanded from the rest of the local energy system, i.e., that curtailed electricity or waste heat could not be used locally.\n\nFor each vector, an annual reference export demand of 500 TWhLHV or 100 Mt of crude steel and HBI is assumed, mirroring large-scale energy and material infrastructures and export volumes, corresponding to approximately 40% of current European LNG imports126 and 66% of European steel production127. Transport distances are calculated between the exporting regions and the twelve representative European import locations using the searoute Python tool128 for shipborne vectors or crow-fly distances for pipeline or HVDC connections, and modified by a mode-specific detour factor. The chosen representative import locations are based on large ports and LNG terminals in the United Kingdom, the Netherlands, Poland, Greece, Italy, Spain, and Portugal, as well as pipeline entry points in Slovakia, Greece, Italy, and Spain. All energy supply chains are assumed to consume their energy vector as fuel for transport to Europe, except for HBI and crude steel, which use externally bought green methanol as shipping fuel. The capital costs of the ships and pipelines are also included, following the metholodogy of Hampp et al.54.\n\nFor each combination of carrier, exporter, and importer, a linear capacity expansion optimisation is performed to determine cost-optimal investments and the operation of generation, conversion, storage, and transport capacities for all intermediary products to deliver 500 TWh a\u22121 (or 100 Mt a\u22121 for materials) of the final carrier to Europe. Dividing the total annual system costs by the targeted annual export volume yields the levelised cost of energy or material as seen by the European entry point. To match the multi-hourly resolution used for the European model, the TRACE model was configured to use a 3-hourly resolution for 2013, resulting in similar balancing requirements. Considering the reference export volume of 500 TWh a\u22121 (or 100 Mt a\u22121 for materials), the resulting levelised cost curves of imports for different import vectors and exporting regions are presented for the respective lowest-cost entry point to Europe in Supplementary Figs.\u00a07\u201310. The curves show the varying cost composition of the country-carrier pairs. In this step, each import vector combination of carrier, exporter, and importer is optimised separately. Further constraints, like constraints on total export volumes per country, are imposed in the coupling to the European model.\n\nA mathematical description of TRACE can be found in Section S3 in Hampp et al.54\n\nThe resulting levelised unit cost for each combination of carrier, exporter, and reference importer is then used as an exogenous input to the European model. For each candidate entry point in the 115 European model regions, we match the closest reference import location from TRACE and add the corresponding import cost curve as a supply option (Supplementary Figs.\u00a07\u201310). Moreover, we limit energy exports from any one exporting region to Europe for the sum of all carriers to 500 TWh a\u22121. This is to both prevent a single country from dominating the import mix and be consistent with the target export volume assumed in TRACE. Beyond that, the decision about the origin, destination, vector, volume, and timing of imports is largely endogenous to PyPSA-Eur.\n\nHowever, imports may be further restricted by the expansion of domestic import infrastructure. For each vector, we identify locations where the respective carrier may enter the European energy system by considering where LNG terminals and cross-continental pipelines are located (Fig.\u00a01b). For hydrogen imports by pipeline, imports must be near-constant, varying between 90\u2013100% of peak imports, aligning with the high pipeline utilisation rates observed in the TRACE model. For methane imports by ship, existing LNG terminals reported in Global Energy Monitor\u2019s Europe Gas Tracker92 can be used. For hydrogen by ship, new terminals can be built in regions where LNG terminals exist. To ensure regional diversity in potential gas and hydrogen imports and avoid vulnerable singular import locations, we allow the expansion beyond the reported capacities only up to a factor of 2.5, taking the median value of reported investment costs for LNG terminals129. A premium of 20% is added for hydrogen import terminals due to the lack of practical experience with them. For electricity, the capacity and operational patterns of the HVDC links can be endogenously optimised. Imports for carbonaceous fuels, ammonia, HBI, and steel are not spatially allocated to specific ports, given their low transport costs relative to value. Port capacities are assumed unconstrained since these commodities, particularly carbonaceous fuels, are comparable to the large fossil oil volumes currently handled at European ports.\n\nFurther conversion of imported fuels is also possible once they have arrived in Europe, e.g., hydrogen could be used to synthesise carbon-based fuels, ammonia could be cracked to hydrogen, methane could be reformed to hydrogen, and methane or methanol could be combusted for power generation. However, conversion losses can make it less attractive economically to use a high-value hydrogen derivative merely as a transport and storage vessel, only to reconvert it back to hydrogen or electricity.\n\nThe supply chain of electricity imports is endogenously optimised with the rest of the European system rather than using a constant levelised cost of electricity for each export region. This is because, owing to the greater challenge of storing electricity, the hourly variability of wind and solar electricity leads to higher price variability than hydrogen and its derivatives, and the intake needs to be more closely coordinated with the European power grid. The endogenous optimisation comprises wind and solar capacities, batteries and hydrogen storage in steel tanks, and the size and operation of HVDC link connections to Europe based on the renewable capacity factor time series as illustrated in Fig.\u00a01b. Europe\u2019s connection options with exporting regions are confined to the 4% nearest regions, with additional ultra-long distance connection options to Ireland, Cornwall, and Brittany following the vision of the Xlinks project between Morocco and the United Kingdom30. Connections through Russia or Belarus are excluded. In addition to excluded entry points, some connections from Central Asia are affected by additional detours beyond the regularly applied detour factor of 125% of the as-the-crow-flies distance. Similar to intra-European HVDC transmission, a 3% loss per 1000km and a 2% converter station loss are assumed.\n\nFinally, we note that all mass-energy conversion is based on the lower heating value (LHV). To present energy and material imports in a common unit, the embodied energy in steel is approximated with the 2.1\u2009kWh kg\u22121 released in iron oxide reduction, i.e., energy released by combustion130. All currency values are given in \u20ac2020.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "A dataset of the model results is available on Zenodo under https://doi.org/10.5281/zenodo.14872184131. Data on techno-economic assumptions for years can be found at https://github.com/PyPSA/technology-data/releases/tag/import-benefits-v2\u00a0and has been archived on Zenodo under https://doi.org/10.5281/zenodo.14872325132.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code to reproduce the experiments is available at https://github.com/fneum/import-benefits/tree/v3.0.1\u00a0(v3.0.1), which uses a derivative of TRACE v1.1 and PyPSA-Eur v0.13.0. The code has been archived on Zenodo under https://doi.org/10.5281/zenodo.14872325132. We also refer to the documentation of PyPSA-Eur at https://pypsa-eur.readthedocs.io/en/v0.13.0-docs-fix\u00a0for more details.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "IRENA. 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F.N. and J.H. jointly developed the code. F.N. curated data, created visualisations, and drafted the manuscript. J.H. and T.B. reviewed and edited the manuscript.\n\nCorrespondence to\n Fabian Neumann.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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@@ +{ + "title": "Inequitable distribution of risks associated with occupational heat exposure driven by trade", + "pre_title": "Inequitable Distribution of Heat Exposure Risks Driven by Trade", + "journal": "Nature Communications", + "published": "09 January 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55483-5/MediaObjects/41467_2024_55483_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55483-5/MediaObjects/41467_2024_55483_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55483-5/MediaObjects/41467_2024_55483_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55483-5/MediaObjects/41467_2024_55483_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://worldmrio.com", + "https://webapps.ilo.org/ilostat-files/WEB_bulk_download/html/bulk_indicator.html", + "https://dataverse.nl/dataset.xhtml?persistentId=doi:10.34894/QT5BCC", + "https://climate-adapt.eea.europa.eu/en/metadata/indicators/thermal-comfort-indices-universal-thermal-climate-index-1979-2019", + "https://sedac.ciesin.columbia.edu/data/collection/gpw-v4", + "https://hub.arcgis.com/datasets/esri::world-countries-generalized", + "/articles/s41467-024-55483-5#Sec18" + ], + "code": [ + "https://doi.org/10.6084/m9.figshare.25624338.v1" + ], + "subject": [ + "Climate-change adaptation", + "Developing world", + "Environmental health" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4463391/v1.pdf?c=1736514405000", + "research_square_link": "https://www.researchsquare.com//article/rs-4463391/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-55483-5.pdf", + "preprint_posted": "30 May, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The exposure to extreme heat at workplaces may result in great risks to the involved labour. This issue becomes more prominent due to the global dispersion of labour-intensive work via trade. Here we combine a high-resolution climate model with an input\u2013output model to investigate the exposure to extreme heat at work due to global trade. We find an 89% surge in trade-related labour exposure to extreme heat, escalating from 221.5 to 419.0\u00a0billion person-hours between 1995 and 2020. The lower-middle-income and low-income economies constituted 53.7% and 18.3% of global exposure, while only 5.7% and 1.0% in global labour compensation. In countries highly susceptible to extreme heat conditions, workers could spend up to about 50% of their working hours in heated conditions. Our findings uncover the disproportionate trade effects in redistributing global benefits and costs, which leads to the inequality in heat exposure between rich and poor economies. In striving for equitable and safe work conditions and social justice, workers vulnerable to heat extremes should be protected through the development of climate adaptation infrastructure in developing economies, especially those engaged in international trade.Scientific community and society/Developing worldEarth and environmental sciences/Environmental social sciences/Climate-change impacts/Environmental healthEarth and environmental sciences/Environmental social sciences/Climate-change adaptationScientific community and society/Social sciences/Climate change/Climate-change impacts/Environmental health", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Supplementary.docxInequitable Distribution of Heat Exposure Risks Driven by Trade", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The exposure to extreme heat at workplaces poses substantial threat to human effort and manual labour. This becomes more prominent due to the global dispersion of labour-intensive production activities via trade. We combine a climate model with an input\u2013output model to quantify the risks associated with trade-related occupational extreme heat exposure. Here we show an 89% surge in trade-related labour exposure to extreme heat, escalating from 221.5 to 419.0 billion person-hours between 1995 and 2020. Lower-middle-income and low-income economies constituted 53.7% and 18.3% of global exposure but only 5.7% and 1.0% of global labour compensation. In countries highly susceptible to extreme heat conditions, workers perform tasks in heated conditions for up to about 50% of their working hours. The disproportionate trade effects in redistributing global benefits and costs leads to the inequality in heat exposure between developed and developing economies. In striving for equitable and safe work conditions, workers vulnerable to heat extremes in developing economies should be protected by climate adaptation infrastructure, given their critical roles in the global production system.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Climate change has resulted in extreme heatwaves in more regions, where more populations are exposed to such adverse conditions1,2,3,4. Extreme temperature events have increased by 232% and caused 13% of all disaster deaths worldwide during the past two decades5, which is a major risk to global sustainable development6. In particular, heatwaves caused over 70000 excess deaths in Europe during the summer of 2003 and over 55000 excess deaths in Russia in 20107,8. To date, extreme heat waves have become a huge threat to the global labour force9,10. Billions of workers are exposed to unsafe heat, many of whom are working in the poorest and warmest regions11. Meanwhile, the rapid development of global trade has led to the redistribution of both production and job opportunities12,13. Due to lower labour costs, labour in developing economies is encountering new job opportunities; however, they are also facing unprecedented risks associated with occupation heat exposure, especially those living in the most vulnerable regions14. Such exposure leads to significant adverse impacts, such as heightened health risks15,16, increased mortality rates17,18, and reduced labour productivity19,20. The exposure to extreme heat at workplaces poses a substantial threat to the achievement of Sustainable Development Goals (SDG), especially SDG8 (decent work and economic growth), SDG10 (reduced inequalities), and SDG13 (climate action).\n\nUnderstanding the vulnerability of the global labour force to extreme heat is crucial for devising effective adaptation strategies. Existing studies have made significant progress in quantifying workplace heat exposure9,21,22, estimating labour productivity and economic loss23,24,25,26, measuring heat-related morbidity and mortality27,28,29,30, and projecting future costs and losses based on various climate scenarios31,32. However, it is still unclear how international trade contributes to the distribution and extent of extreme heat exposure among the global labour force. From the production side, a country\u2019s vulnerability to extreme heat is shaped by its domestic production activities and its role in the international trade system33, especially the climate risks faced by its labour force in producing traded goods34. From the consumption side, consumers should recognise the externalities of their demand, including the impact beyond local labour to foreign workers exposed to extreme heat challenges through international trade35, to foster a more equitable understanding of the true costs of their consumption. This study aims to enrich the existing literature by uncovering the nexus between international trade and exposure to extreme heat among the global labour force.\n\nWe propose a comprehensive analytical framework that integrates a high-resolution global climate model36, socio-economic and demographic information, and a global multi-regional input-output model to quantify the occupational extreme heat exposure among the labour force from 1995 to 2020 (Supplementary Figs.\u00a01, 2). Using an extended multi-regional input-output model with a labour satellite account, we estimate labour employment embodied within the global production networks, which was downscaled to the grid level using gridded population datasets. High-resolution climate data enabled the identification of annual extreme heat hours for each grid cell. Incorporating country-specific working hours from the Penn World Table, we obtain the number of hours an average labourer was exposed to extreme heat at each grid cell per year. Linking labour embodied in production networks with extreme heat exposure, we quantify occupational heat exposure within the global production system with both spatial and temporal dimensions. We find that trade accounts for nearly one-quarter of the global labour force\u2019s total exposure to extreme heat, with a large flux of heat exposure transferred from developed economies to developing economies (Supplementary Table 1)37. These results indicate a high level of inequality between who causes climate change through historical emissions and who bears its negative consequences38,39.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Over the past three decades, heat exposure of the global labour force has increased substantially due to the changing climates11,40, sustained long working hours41, and increased international trade. The outsourced production via trade in labour-intensive sectors usually happens in countries with abundant labour force already subject to high ambient temperatures. Figure 1 illustrates the evolution of global heat exposure, categorised by production types\u2014purely domestic production, where goods are produced and consumed within the same country, and via international trade, where goods are produced in one country and consumed in another42. The total heat exposure has surged by 60%, growing from 1142.3 billion person-hours in 1995 to 1826.2 billion person-hours in 2020. This equates to an annual rise of 27.3 billion person-hours, which would place such an increase among the top fifteen countries globally in terms of heat exposure if it were a country.\n\na World\u2019s total exposure to heat stress classified by domestic production and international trade. The connected lines represent percentage change compared to the base year 1995. b World\u2019s total labour employment is classified by domestic production and international trade. The connected lines represent the percentage change compared to 1995. c The average annual exposure per capita for domestic production-related and trade-related workers. The connected lines represent the percentage change compared to 1995. Source data are provided as a Source Data file.\n\nTrade-related exposure played a significant role, which increased from 221.5 billion person-hours to 419.0 billion person-hours during this study period (increased by 89%). Such rapid trade-related exposure growth has even outpaced the expansion in trade-related labour employment, with a 52% increase over this study period. The global average per capita working hours in heat-exposed conditions expanded from 485.6\u2009h in 1995 to 578.1\u2009h in 2020, meaning a substantial increase of 19.1%. To be more specific, the per capita exposed working hours for pure domestic production increased from 489.0\u2009hours in 1995 to 575.4\u2009hours in 2020, while such a figure for international trade increased from 471.7\u2009hours in 1995 to 587.3\u2009hours in 2020. The rapid increase in exposure per capita underscores the significant impact of climate change during the past three decades, potentially leading to even more severe risks associated with occupational heat exposure over this century under various Shared Socioeconomic Pathways (SSP) scenarios (Supplementary Figs.\u00a03\u20136). Using the structural decomposition analysis (SDA)43, we found that climate change, trade-related production structure, and trade-related final demand structure effect account for 102, 347, and 134 billion person-hours of the total increase (Supplementary Fig.\u00a07).\n\nExposure to extreme heat is highly unequal, with production-side exposure more concentrated in developing economies. Trade plays disproportionate roles in redistributing global benefits and costs by generating more exposure than value-added or labour compensation (Fig.\u00a02). In 1995, trade accounted for 18.9% of the global exposure, 19.9% of global labour employment, 13.8% of the global labour compensation, and 14.8% of the global value-added. However, the share of trade in the global labour compensation and value-added was still less than that in the global exposure by the end of 2020. Following the World Bank\u2019s method for classifying economies based on Gross National Income (GNI) per capita37, this study classifies all economies into four income groups: high-income, upper-middle-income, lower-middle-income, and low-income groups (Supplementary Table 1), with the latter three categories encompassing developing economies. Despite the changes in economic classification for some countries over time, this study abstracts from such shifts and uses a consistent categorisation to minimise bias associated with changes in group membership (Supplementary Table 1). This approach ensures that the analysis remains robust and comparable across different time periods. Figure 2e\u2013l shows the inequitable exchange of heat exposure and socio-economic benefits between different income groups. The left side represents the production-based amount\u2014the heat exposure borne by the labour within the territories and the right side represents the consumption-based amount\u2014the relative footprint generated by their consumption of goods and services. From the production side, the high-income group accounted for an extremely small portion of global heat exposure (5.9%) and labour supply (17.8%) but a significantly larger share in global labour compensation (68.5%) and value-added (61.0%) in 2020. On the contrary, the lower-middle-income and low-income groups constituted the majority of global exposure (53.7% and 18.3%), and labour supply (32.6% and 13.0%), but their shares in global labour compensation (5.7% and 1.0%) and value-added (8.7% and 1.3%) were markedly low. Specifically, within the 5.9% of global exposure attributed to the production of high-income groups, 5.4% was for their own consumption and 0.5% was for exports. However, the goods and services they consumed resulted in 19.4% of the global total exposure. Of this, only 5.4% occurred within their own territories, with 2.8%, 7.0%, and 4.2% in upper-middle-income, lower-middle-income, and low-income groups, respectively. International trade has greatly improved global economic growth, but it is coupled with unequal resource, environmental and labour exchanges.\n\na\u2013d The share of (a) exposure, (b) labour employment, (c) labour compensation, and (d) value-added that is transferred embodied in trade compared to the global total value. e\u2013h Global flows between high-income, upper-middle-income, lower-middle-income, and low-income economy groups in 1995 for (e) exposure, (f) labour employment, (g) labour compensation, and (h) value-added. i\u2013l Global flows between high-income, upper-middle-income, lower-middle-income, and low-income economy groups in 2020 for (i) exposure, (j) labour employment, (k) labour compensation, and (l) value-added. Source data are provided as a Source Data file.\n\nUnequal exposure to extreme heat is stark across countries (Fig.\u00a03 and Supplementary Figs.\u00a08\u201315), with developing economies facing significantly higher exposure for their production activities (Supplementary Figs.\u00a016, 17, 20). India, China, Indonesia, Nigeria, and Bangladesh were the top five countries in terms of total exposed hours in 2020, accounting for 24.7, 13.4, 7.3, 4.3, and 4.2% of the global total, respectively. These countries are labour-intensive, contributing to the global labour force with respective shares of 14.1, 22.8, 4.1, 2.0, and 2.1%, totalling 45.2%. In addition, they are highly susceptible to heat-exposed conditions. For instance, in Nigeria, an average worker might perform their tasks under heat stress for up to 1186.8\u2009hours, or 59.4% of their working hours, during the year of 2020. Comparable figures for India, China, Indonesia, and Bangladesh are 47.6, 15.6, 50.8, and 48.4%, respectively. Wealthier economies (dark blue distributed on the left side) had fewer per capita working hours exposed to extreme heat compared to developing economies (light blue on the right side). High, upper-middle, lower-middle, and low-income economy groups were subject to 192.5, 348.5, 952.1, and 814.2\u2009hours of heat exposure per capita. In particular, south-east Asia and Africa had the highest exposure rates. For instance, the average per capita exposed hours were 1319.5 in Thailand and 1186.8 in Nigeria in 2020, while the average per capita exposed hours were only 28.1 in Germany and 260.9 in the United States in 2020. More exposure hours in developing economies mean that those workers have to work longer under adverse climate conditions than their counterparts in developed economies.\n\na, c Exposure rate per capita and their share in the global labour employment on (a) production side and (c) consumption side. b, d Negative relationship between exposure rate per capita and GDP per capita on (b) production side and (d) consumption side. All the per capita GDP values are purchasing-power-parity-based (constant 2017 international $). The size of the bubbles indicates the total heat exposure of the country\u2019s labour. The colour intensity of the bubbles represents the exposure per capita. The blue line represents the mean regression prediction, which estimates the expected exposure level for each value of GDP per capita. The shaded area indicates the 95% confidence interval around these mean predictions. Source data are provided as a Source Data file.\n\nCompared with production side exposure, the distribution of consumption side exposure is different as exposure is shifted from developing to developed economies (Supplementary Figs.\u00a018, 19, 21). The consumption side exposure in the United States accounted for 6.2% of the global total exposure in 2020, which is nearly three times their production side exposure (only 2.2%). The per capita exposure hours in high, upper-middle, lower-middle, and low-income economy groups were 389.0, 384.6, 931.3, and 847.0\u2009h in 2020. In general, the consumption side exposure rate in the high-income group is twice as much as their production side exposure rate, indicating that they should take higher associated environmental costs.\n\nThe socio-economic characteristics play an essential role in such disparities, such as economic development level, industrial structure, average labour working hours, and trade patterns (Supplementary Fig.\u00a022). Normally, a lower per capita Gross Domestic Product (GDP) is often related to a higher share of heavy work and longer annual per capita working hours, which all contribute to regional disparities. The per capita net exposure rate\u2013measured as the difference between consumption-based exposure footprint and production-based exposure\u2013is positive in wealthier economies, but negative in developing economies, indicating that developed economies have outsourced their environmental burdens (in this case, heat exposure) to developing economies via international trade. Such disparity is underscored by the significant differences in heat exposure per unit of value-added embodied in exports and imports across countries (Supplementary Figs.\u00a023\u201328), highlighting the uneven distribution of costs borne by various labour forces engaging in global trade.\n\nFigure\u00a04 shows top exposure flows embodied in trade (Supplementary Fig.\u00a029). Economies shaded in red have net exposure transferred to other places, such as those in North America and Europe; while economies shaded in blue have net exposure transferred from other places, such as those in South America, Africa, and Asia. The United States had the largest net exposure import embodied in trade from 2000 to 2020, from 29.2 billion person-hours in 1995 to 73.2 billion person-hours in 2020. China experienced a decrease in its net exposure export, from being the largest exporter (30.7 billion person-hours) in 1995 to only 0.3 billion person-hours in 2020. India was the largest net exposure exporter in 2020 (46.6 billion person-hours), followed by Vietnam, Indonesia, Bangladesh, and Ethiopia. During the past three decades, more developing economies have experienced an increase in their heat exposure, meaning that their workers have to take more public health consequences induced by climate change.\n\nIn (a) 1995 and (b) 2020, the filled colour represents the exposure balance measured by the difference between their consumption side exposure and production side exposure. Countries/regions filled in red have net exposure import (outsourcing to other places), while those filled in blue have net exposure export (original production place). These arrows represent the top ten exposure flows between different countries/regions, in which the colour and arrow width correspond to the size of each flow.\n\nExposure embodied in trade is closely related to the industrial structure in each economy. Figure 5a, b presents industry-specific exposure to extreme heat embodied in both exports and imports for the top twenty economies from 1995 to 2020 (Supplementary Fig.\u00a030). The agriculture sector, characterised by labour-intensive activities conducted predominantly in non-sheltered settings, has a pivotal role in the global dispersal of vulnerability to extreme heat conditions. For instance, India recorded an alarming 59.8 billion person-hours of export-embodied exposure in 2020, of which 19.0 billion person-hours originated from its agricultural sector, accounting for 31.9% of its total exported exposure. The petroleum, chemical, and non-metallic mineral products and the retail sector followed this agriculture sector, but with figures of only 6.2 and 4.6 billion person-hours, respectively, which accounted for 10.4% and 7.7% of India\u2019s total export-embodied exposure.\n\na, b Exposure embodied in exports and imports for the top twenty economies with the largest exposure trading volume in 1995 and 2020. The connected red dots show the net exposure balance. c, d Exposure embodied in global exports by sector and economy in 1995 and 2020 on the production side. Source data are provided as a Source Data file.\n\nFigure 5c, d illustrate the structural compositions of trade-embodied exposure, categorised by both sector and exporting/importing economy for 1995 and 2020, respectively. The agriculture sector was the dominated sector for trade-related exposure in 1995, with a figure of 121.9 billion person-hours and accounting for 55.0% of the global total, followed by both retail and wholesale sectors with figures of 9.7 and 9.2 billion person-hours and accounting for 4.4% and 4.2% of the global total. After three decades of globalisation, this agriculture sector remained its dominance, with a figure of 165.1 billion person-hours in 2020, but only accounting for 39.4% of the global total in 2020. Conversely, both retail and wholesale sectors increased their exposure hours, with figures of 28.92 and 28.21 billion person-hours, collectively contributing to 13.6% of the global total. China\u2019s agricultural sector, which was responsible for 6.4% of the global exposure trade in 1995\u2014second only to India at 7.0%\u2014experienced a significant decline to 1.7% in 2020 due to its gradual industrial transition toward manufacturing and services.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55483-5/MediaObjects/41467_2024_55483_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55483-5/MediaObjects/41467_2024_55483_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55483-5/MediaObjects/41467_2024_55483_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55483-5/MediaObjects/41467_2024_55483_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55483-5/MediaObjects/41467_2024_55483_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we investigate the global exposure to extreme heat at work using an integrated framework combined with world climate derived from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis for the global climate and weather (ERA5), a multi-regional input-output model to link international production and consumption, as well as socioeconomic and demographic information (Supplementary Figs.\u00a01, 2). Our study builds on previous studies on population-weighted heat exposure44,45, to advance our understanding of the nexus between labour, trade, and climate change. The result that global trade has led to unequal labour heat exposure is consistent with the asymmetric effects of heat stress, where developing countries disproportionately suffer from health losses and labour productivity losses33. The results reveal a substantial human cost: in some countries, labour may work nearly half of their time under heat stress and such burden has increased by 89% globally. The rapidly increasing risks associated with occupational heat exposure align with the significant human cost of global warming\u2014especially in countries like India, Indonesia, and Nigeria46\u2014such as the nearly 200% stark increase in global urban exposure from 1983 to 201647 and the multifold population heat exposure in the rest of this century40,48.\n\nThe results indicate an unequal burden of climate change, particularly across different countries, genders, and vulnerable groups49. Labour force in tropical and impoverished regions have to work under extreme heat conditions to struggle for a living, but neither their input nor their losses are adequately compensated50,51,52. The insufficient adaptation infrastructure further increased their risks and losses, especially for those most at-risk regions53,54,55,56. Without a concerted global effort, labour working in worse conditions may suffer from associated health risks, which may lead to reduced productivity of the global labour force. Gender also plays a crucial role in the unequal distribution of climate change effects27. In 2020, women\u2019s per capita working hours in heat-exposed conditions exceeded those of men (Supplementary Fig.\u00a031). Women are particularly vulnerable due to their involvement in labour-intensive sectors, such as agriculture, Southeast Asian textile industries, and African artisanal mining. Social norms and gender wage gaps may limit women\u2019s access to adaptation resources, worsening their vulnerability to climate change. Also, despite SDG8 targets to end child labour in all its forms by 2025, there were 160 million children in child labour in 202057, constituting a substantial share of the global workforce and heat-exposed workers. The child labour\u2014often in impoverished tropical regions\u2014are especially vulnerable to heat exposure, bearing significant challenges to their health status and educational attainment. Moreover, the lack of governance in vulnerable countries may aggravate such risks58,59. Other factors, such as the lack of public awareness or backward medical conditions, further contribute to reduced labour productivity and increased economic losses60,61, especially those engaging in labour-intensive tasks (Supplementary Figs.\u00a031\u201334).\n\nEconomic instruments are essential for mitigating the risks faced by the labour force due to extreme heat. A primary policy should be the collection and expansion of climate change funds, ensuring sufficient resources to assist developing economies and enhancing their climate adaptation infrastructure62. The disproportionate impact of climate change on developing economies necessitates a dedicated fund to compensate for climate-related losses and damages. To alleviate the financial burden, revenues from carbon pricing or tariffs could be allocated to reimburse the Loss and Damage funds to offset the costs of climate adaptation, mitigate the climate change losses, and internalise the environmental and health externalities. Dialogues between developed and developing economies should be initiated so that all the stakeholders can seek potential solutions through integrated efforts.\n\nFostering sustainable supply chains is another key measure to ensure the co-achievement of SDG8, SDG10, and SDG1363. In many developing economies, weak workplace safety regulations leave workers exposed to climate-related hazards, especially in sectors like agriculture and manufacturing that are critical for trade. Profit-driven supply chains may \u201crace to the bottom\u201d by outsourcing production to regions with the least regulations and lowest labour costs, thereby exacerbating the risks for workers. Therefore, it is crucial to establish global standards that protect workers\u2019 rights and safety. Such measures not only safeguard the labour force but also contribute to the broader goals of decent work, reducing inequalities, and combating climate change.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "This study uses a multi-regional input-output model with a labour satellite account to estimate labour hours embodied in the global production networks, which is further combined with the gridded population to obtain grid-level labour in production networks. By combining this with a climate model that provides grid-level heat exposure, as well as with other data such as average working hours, labour\u2019s exposure to heat stress can be accounted for. How these modules are integrated is illustrated in Supplementary Information (Supplementary Figs.\u00a01, 2).\n\nThe input-output (IO) model has been widely adopted to investigate the interconnections between different sectors. By applying multi-regional input-output (MRIO) models, researchers can uncover how countries/regions are connected bilaterally through trade. These MRIO models can be extended with satellite accounts to trace emissions, resources, pollution, water, land, as well as many other aspects transferred through the international production system42,64,65. Here, we use a global multi-regional input-output model to trace labour hours embodied in trade. Assuming that there are \\(m\\) regions and \\(n\\) sectors involved in international trade, the equilibrium between product supply and demand can be expressed in Eq. (1).\n\nwhere, \\({{\\bf{A}}}\\) is a direct input coefficient matrix, \\({{\\bf{X}}}\\) is the total output matrix, and \\({{\\bf{Y}}}\\) is the final demand matrix. The Leontief model links final demand with gross outputs, which is shown in Eq. (2).\n\nwhere \\({{\\bf{B}}}\\) is the Leontief inverse matrix. Denote the labour intensity matrix by \\({{\\bf{E}}}\\), which is a diagonalized matrix representing labour employment per unit of gross output for each region-sector, we can get Eq. (3).\n\n\\({{{\\bf{L}}}}_{{mn}\\times {mn}}\\,\\) is the global labour employed, in which its element \\({l}_{{isjr}}\\) is the labour employment in region \\(i\\) sector \\(s\\) created by region \\(j\\) sector \\(r\\). Thus, by summing the matrix \\({{\\bf{L}}}\\) by rows, we obtain production side labour employment of each region-sector; summing the matrix \\({{\\bf{L}}}\\) by columns, we obtain consumption side labour induced by each region-sector.\n\nThere are 26 sectors in the Eora multiregional input-output tables, and 14 economic activities in the International Labour Organisation (ILO) labour employment database. To match the labour employment categories with Eora economic sectors we follow a widely adopted approach, namely matching these two data sources by using a concordance table (Supplementary Table\u00a02).\n\nTo measure labour force exposure hours to extreme heat, we combine climate models with socio-economic information. The climate model is based on the ERA566, from which researchers have developed the universal thermal climate index (UTCI)36. UTCI is a human biometeorology parameter\u2014an equivalent temperature (\u00b0C)\u2014that measures human physiological response to the thermal environment. By considering how the human body experiences atmospheric conditions (air temperature, humidity, wind, and radiation), the UTCI describes synergistic heat exchanges between the thermal environment and the human body. Four variables from the ERA5 are necessary to build the UTCI index, including 2\u2009m air temperature, 2\u2009m dew point temperature (or relative humidity), wind speed at 10\u2009m above the ground level and mean radiant temperature (MRT). There are 10 UTCI thermal stress categories that correspond to specific human physiological responses to the thermal environment. The categories related to UTCI degree Celsius (\u00b0C) are as follows: above +46: extreme heat stress; +\u200938 to +\u200946: very strong heat stress; +\u200932 to +\u200938: strong heat stress; +\u200926 to +\u200932: moderate heat stress; +\u20099 to +\u200926: no thermal stress; +\u20099 to 0: slight cold stress; 0 to \u2212\u200913: moderate cold stress; \u2212\u200913 to \u2212\u200927: strong cold stress; \u2212\u200927 to \u2212\u200940: very strong cold stress; below \u2212\u200940: extreme cold stress (Supplementary Table\u00a03). Thus, working hours each day can be classified into different heat stress levels (Supplementary Figs.\u00a035\u2013222). Defining the exposure to heat exposure as the number of hours exposed to extreme/very strong/strong heat stress, the UTCI index can be converted to a binary heat exposure indicator, which is shown in Eq. (4).\n\nwhere \\({{{\\rm{UTCI}}}}_{{gydh}}^{c}\\) is the converted UTCI index for the gth grid on hour \\(h\\) in day \\(d\\) of year \\(y\\), by subtracting 273.15 from the original UTCI index stored in Kelvin (K). Then we make two assumptions. First, following the idea of 12\u2009h exposure time in previous studies, we set the maximum daily working hour to be 12\u2009h, which is equivalent to 2400 annual working hours for 200 work days/year and 3000 annual working hours for 250 work days/year. Second, as different grids may differ in annual work hours and average daily work hours, we assume that the annual work hours are evenly distributed throughout the 12\u2009h available work time per day. Then, the daily and annual heat exposure cap can be summed by using Eq. (5).\n\nThus, the average exposed work hours to heat can be obtained by using Eq. (6).\n\nwhere \\({\\omega }_{{gy}}\\) is the average work hours of the labour force in gth grid for year \\(y\\), which is obtained from region-specific database and estimation.\n\nWe use a population density module to link the interregional embodied labour flow with grid cell annual average per capita work hours exposed to extreme heat. Thus, the labour force within each grid cell can be linked from a production network perspective, which is shown in Eq. (7).\n\nwhere \\({p}_{{gy}}\\) is the population density of the gth grid in year \\(y\\), \\({a}_{g}\\) is the area of the gth grid, and \\({G}_{i}\\) is all grid cells in country/region \\(i\\). Then the labour force exposure to extreme temperatures (LET) linked with the production network on the grid cell level and country/region level can be expressed in Eqs. (8, 9).\n\nHere, \\({{{\\rm{LET}}}}_{{isjr}}\\) represents the embodied heat exposure of the labour force, \\({l}_{{isjr}}\\) represents the embodied labour employment, and \\(\\frac{{\\sum }_{g\\in {G}_{i}}{w}_{{gy}}\\times {p}_{{gy}}{\\times a}_{g}}{{\\sum }_{g\\in {G}_{i}}{p}_{{gy}}\\times {a}_{g}}\\) reflects the average per capita exposure. Summing \\({{\\rm{LET}}}\\) by \\(i\\) and \\(s\\), we obtain the total heat exposure induced by sector \\(r\\) in region \\(j\\), which is a consumption side exposure; while summing \\({{\\rm{LET}}}\\) by \\(j\\) and \\(r\\), we obtain the total heat exposure induced by sector \\(s\\) in region \\(i\\), which is a production side exposure.\n\nWe use an SDA to attribute the role of each driving factor in the total changes in labour force heat exposure (Supplementary Method 1). Based on Eqs. (3) and (9), we quantify \\({{\\bf{LET}}}\\) based on the input-output framework in a matrix form,\n\nwhere \\({{\\bf{H}}}\\) is the diagonal matrix for average heat exposure index per labour employed in each country, \\({{\\bf{E}}}\\) is a diagonal matrix for labour intensity per gross output for each country-sector, \\({{\\bf{B}}}\\) is the Leontief inverse matrix representing the production structure, \\({{{\\bf{Y}}}}_{s}\\) is a \\({mn}\\times m\\) matrix with the sum of all elements equal to 1, representing the final demand structure, and \\({{{\\bf{Y}}}}_{t}\\) is the diagonal matrix with all diagonal elements as the same value\u2014the deflated global total demand. Thus, the changes \\({{\\bf{LET}}}\\) can be attributable to its driving factors, including the climate change effect \\(\\Delta {{\\bf{H}}}\\), the labour intensity effect \\(\\Delta {{\\bf{E}}}\\), the production structure effect \\(\\Delta {{\\bf{B}}}={\\Delta {{\\bf{B}}}}^{d}+{\\Delta {{\\bf{B}}}}^{e}\\), the final demand structure effect \\(\\Delta {{{\\bf{Y}}}}_{s}=\\Delta {{{{\\bf{Y}}}}_{s}}^{d}+{{{{\\bf{Y}}}}_{s}}^{e}\\), and the total final demand growth effect \\(\\Delta {Y}_{t}\\),\n\nThen, we apply the SDA method on constant price input-output tables obtained by taking the Producer Price Indices (PPIs) as deflators and the Logarithmic Mean Divisia Index (LMDI) approach to estimate the role of each driving factor between time periods (Supplementary Fig.\u00a07).\n\nTo test the robustness of the results, this study further conducts several sensitivity analyses. First, a comparison between this study and previous studies was conducted (Supplementary Table\u00a04). Second, we use alternative data sources by replacing the input-output table from Eora with Inter-Country Input-Output tables compiled by the Organisation for Economic Co-operation and Development (OECD-ICIO), which consists of 67 economies and 45 sectors from 1995 to 2018. The results drawn from these two data sources are compared on the global and national levels both on the production and consumption sides from 1995 to 2015 (Supplementary Figs.\u00a0223\u2013225). Third, we also use Monte Carlo simulation to test the robustness of the original heat exposure estimation, based on whether it falls within the expected range of values derived from the simulations. We conduct 1000 Monte Carlo simulations, where the heat stress drawn from the ERA5 is set to a normal distribution with a mean equal to the baseline values and a standard deviation set to 10% of each respective baseline value. The results indicate that the original estimation falls well within the range of expected values generated by these simulations, and the global labour force\u2019s total heat exposure remain robust from 1995 to 2020 when heat stress varies within an acceptable level (Supplementary Figs.\u00a0226\u2013228).\n\nMultiregional Input-output tables. To measure trade flows between countries/regions, we need to use information from MRIO tables. There are several available MRIO databases, including Eora67, World Input-Output Database (WIOD)68, OECD-ICIO69, and Exiobase70, as well as other newly emerging databases. In this study, the global MRIO table is collected from the Eora global supply chain database. Eora provides high-resolution multiregional IO tables with matching environmental and social satellite accounts for 190 economies and 26 sectors. Its full geographical coverage, especially with detailed information on tropical and low-income economies, allows all the global labour employment to be analysed. In addition, it has a long temporal coverage, which helps uncover the trends and compare them across different time periods.\n\nLabour employment. Labour employment data were drawn from the International Labour Organisation (ILO) via ILOSTAT explorer. The ILO-modelled estimates provide a complete set of internationally comparable labour statistics, which is a balanced panel data set with consistent country/region coverage. The data is mainly based on nationally reported observations, and the missing data is imputed using the ILO model. ILO evaluates existing self-reported data, selects only those observations deemed sufficiently comparable across countries/regions and runs the model to obtain the ILO-modelled estimates. For the working-age population who is at least 15 years old, the labour employment data from ILO provides a breakdown of total employment by economic sector and gender. The economic activity classification is based on the International Standard Industrial Classification (ISIC) Rev.4, which includes 14 sectors.\n\nWorking Hours. The average number of annual working hours for the labour force in each country/region is originally obtained from Penn World Table version 10.01. Penn World Table is a database with information on relative levels of income, output, input and productivity, covering 183 countries/regions between 1950 and 201941. We use variable avh in the database\u2014average annual work hours by labour engaged\u2014as the work hours for each country/region. For those countries that have missing data for a given year, we use the random forest to fill in the missing values (Supplementary Method 2 and Supplementary Fig.\u00a0229). For the year 2020, the relevant data from 2019 was applied in the analysis.\n\nGridded population density data. To capture heterogeneous levels of exposure to extreme heat geographically, we need to split those country-level employment data to the grid level. To do this, we use the population density in each grid from the fourth version of the Gridded Population of the World data (GPWv4). In GPWv4, population input data were collected at the detailed spatial resolution available from the results of the 2010 round of Population and Housing Censuses and then extrapolated to produce population estimates for years 2000, 2005, 2010, 2015, and 2020. For the year 1995, which is not covered by GPWv4, the population density of the year 2000 is used instead. The population density raster data sets are gridded with an output resolution of 30 arc-seconds (~\u20091\u2009km at the equator).\n\nClimate data. To obtain gridded extreme heat exposure, we draw climate data from the ECMWF ERA5 reanalysis. The ERA5 provides UTCI\u2014an equivalent temperature that measures human physiological response to the thermal environment. To obtain the daily extreme heat hours in each grid, we separate the UTCI index by categories: (1) above +\u200946: extreme heat stress; (2)\u2009+\u200938 to +\u200946: very strong heat stress; (3)\u2009+\u200932 to +\u200938: strong heat stress; (4)\u2009+\u200926 to +\u200932: moderate heat stress; (5)\u2009+\u20099 to +\u200926: no thermal stress; (6)\u2009+\u20099 to 0: slight cold stress; (7) 0 to \u2212\u200913: moderate cold stress; (8) \u2212\u200913 to \u2212\u200927: (9) strong cold stress; (10) \u2212\u200927 to \u2212\u200940: very strong cold stress; below \u2212\u200940: extreme cold stress.\n\nFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Multiregional input-output tables were collected from the Eora global supply chain database, which is accessible on https://worldmrio.com with a valid license. Labour employment data were drawn from ILO. The data for labour employment can be accessed at https://webapps.ilo.org/ilostat-files/WEB_bulk_download/html/bulk_indicator.html. The average annual work hours in different countries were obtained from the Penn World Table (version 10.01). The Penn World Table can be accessed at https://dataverse.nl/dataset.xhtml?persistentId=doi:10.34894/QT5BCC. Climate data and gridded extreme heat exposure data were drawn from the ECMWF ERA5 reanalysis. The ERA5 data can be accessed at https://climate-adapt.eea.europa.eu/en/metadata/indicators/thermal-comfort-indices-universal-thermal-climate-index-1979-2019). Gridded population density data were obtained from GPWv4. The GPWv4 data can be accessed at https://sedac.ciesin.columbia.edu/data/collection/gpw-v4. The country boundaries data is from Esri, Garmin International, the US Central Intelligence Agency, and the National Geographic Society. World Countries (Generalised). 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Ecol. 23, 946\u2013958 (2019).\n\nArticle\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China for grants 72204159 (M.L.), 72088101 (Y.G., X.C., and M.L.), and 42341205(B.M.), the National Key R&D Programme of China for grant 2019YFC1908500 (Y.G. and M.L.), the Japanese Grants-in-Aid for Scientific Research for grants 20K01674 (B.M.) and 20KK0033 (B.M.), and the IDE-JETRO GVC for project V 2024\u22122025 (B.M.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China\n\nMeng Li\u00a0&\u00a0Yong Geng\n\nInstitute of Developing Economies, Japan External Trade Organization, Chiba, Japan\n\nBo Meng\u00a0&\u00a0Kimiko Uno\n\nCollaborative Innovation Center for Emissions Trading System Co-constructed by the Province and Ministry, Wuhan, China\n\nBo Meng\n\nSchool of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, P.R. China\n\nYong Geng\n\nSchool of Economics and Management, Beihang University, Beijing, China\n\nFan Tong\n\nLab for Low-carbon Intelligent Governance, Beihang University, Beijing, China\n\nFan Tong\n\nPeking University Ordos Research Institute of Energy, Ordos, China\n\nFan Tong\n\nSchool of Public Policy and Management, Tsinghua University, Beijing, China\n\nYuning Gao\n\nThe Organization for Economic Co-operation and Development, Paris, France\n\nNorihiko Yamano\n\nDepartment of Agricultural Economics & Agribusiness, Baton Rouge, US\n\nSunghun Lim\n\nInternational Monetary Fund, Washington D.C., US\n\nJoaquim Guilhoto\n\nGakushuin Women\u2019s College, Tokyo, Japan\n\nKimiko Uno\n\nHunan University of Technology and Business, Changsha, China\n\nXiaohong Chen\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nM.L., B.M., and Y.G. designed the research. M.L. determined the methods and carried out the calculation and analysis. N.Y. contributed to the data. B.M., F.T., YN.G., N.Y., S.L., J.G., and K.U. improved the analysis and the figures. M.L., B.M., and Y.G. wrote the manuscript. M.L., Y.G., F.T., S.L., and X.C. revised the manuscript. Y.G. refers to Yong Geng. YN.G. refers to Yuning Gao.\n\nCorrespondence to\n Bo Meng or Yong Geng.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Nina Knittel, Sangwon Suh, and XIANCHUN TAN for their contribution to the peer review of this work. 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Supercapacitors Surpassing Dynamic Limit of Electrical Double Layer Effects", + "journal": "Nature Communications", + "published": "18 April 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59015-7/MediaObjects/41467_2025_59015_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59015-7/MediaObjects/41467_2025_59015_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59015-7/MediaObjects/41467_2025_59015_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-59015-7#Sec14" + ], + "code": [], + "subject": [ + "Electrical and electronic engineering", + "Electronic devices", + "Supercapacitors" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5032527/v1.pdf?c=1745060723000", + "research_square_link": "https://www.researchsquare.com//article/rs-5032527/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-59015-7.pdf", + "preprint_posted": "15 Oct, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The prosperity of microelectronics has intensified the requirement for miniaturized power systems using capacitors with high capacity and broad frequency ranges. Electrochemical supercapacitors (SCs) stand out with their superior capacitance density, surpassing traditional electrolytic capacitors (ECs) by at least two orders of magnitude. However, the intrinsic slow ion dynamics of electrical double layer effects greatly limit SCs\u2019 characteristic frequency, constraining their applicability in microsystems. This work initially constructs a near-ideal micro electrochemical SC, featuring the monolayer graphene as a working electrode, to reveal the ceiling of electrochemical capacitance characteristic frequency. To address this limitation, we introduce a novel Hybrid Electrochemical Electrolytic Capacitor (HEEC) design, which asymmetrically coupling the electrochemical and dielectric effects. At low frequencies, the HEEC's electrochemical segment provides sufficient capacity, while its electrolytic segment takes over at high frequencies, broadening the frequency range. Consequently, the HEEC boasts considerable capacitance density across a broad frequency range. Employing our developed wafer-scale microfabrication techniques, we showcase a micro HEEC device, achieving a groundbreaking characteristic frequency of 44 kHz and a volume capacitance density of 800 \u03bcF/cm\u00b3. To demonstrate its practicality in microsystems, the HEEC device is integrated with a power management chip and buck circuit module, respectively, with only 2 % space usage compared to commercial AECs, achieving the same performance.Physical sciences/Energy science and technology/Energy storage/SupercapacitorsPhysical sciences/Materials science/Materials for devices/Electronic devicesPhysical sciences/Engineering/Electrical and electronic engineering", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformationFinal.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The prosperity of microelectronics has intensified the requirement for miniaturized power systems using capacitors with high capacity and broad frequency ranges. Electrochemical supercapacitors stand out with their superior capacitance density, surpassing traditional electrolytic capacitors by at least two orders of magnitude. However, the intrinsic slow ion dynamics of electrical double layer effects greatly limit supercapacitors characteristic frequency, constraining their applicability in microsystems. This work constructs a near-ideal micro electrochemical supercapacitor, featuring the monolayer graphene as a working electrode, to reveal the ceiling of electrochemical capacitance characteristic frequency. To address this limitation, we introduce a Hybrid Electrochemical Electrolytic Capacitor design, which asymmetrically coupling the electrochemical and dielectric effects. At low frequencies, the electrochemical segment provides sufficient capacity, while its electrolytic segment takes over at high frequencies, broadening the frequency range. Consequently, the hybrid design boasts considerable capacitance density across a broad frequency range. Employing our wafer-scale microfabrication techniques, we showcase a device, achieving a characteristic frequency of 44\u2009kHz and a volume capacitance density of 800 \u03bcF/cm3. To demonstrate its practicality in microsystems, the device is integrated with a power management chip and buck circuit module, respectively, with only 2 % space usage compared to commercial electrolytic capacitor, achieving the same performance.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The proliferation of portable and wearable electronics has created a considerable demand for miniaturized electronic systems. Among these microsystems, traditional electrolytic capacitors stand out as the bulkiest components due to the limited capacitance density brought by dielectric effect1,2,3,4,5,6. Electrochemical supercapacitors (SCs), which are based on the electric double layer (EDL) effect, have emerged as a promising alternative, offering significantly higher capacitance densities and the capability for on-chip fabrication7,8,9. However, SC\u2019s significant capacitance attenuation with increasing frequency brings a poor frequency response: Generally, the characteristic frequency is below 1\u2009Hz, far below 10\u2009kHz required for most modern circuits10,11,12,13.\n\nExtensive research has focused on improving the frequency response of SCs, with a primary guiding theory to reduce the device impedance14,15,16,17,18,19. Over the past decade, the application of high-conductivity electrode material, such as graphene20,21, carbon nanotubes22,23, conductive polymers24, and transition metal carbides (nitrides)19,25,26, has been proven effective in decreasing device electrical resistance. Meanwhile, it has been observed that electrodes with low porosity and open structures significantly diminish ion diffusion resistance. Notably, Han et al. developed a 3D interconnected open-structured SC electrode using carbon tubes with high conductivity, achieving a characteristic frequency of 1.3\u2009kHz, which is among the highest reported for SCs22. Nevertheless, further improving characteristic frequency to meet the demand of modern circuits remains a formidable challenge. Research on the EDL dynamic theory suggests that the diffuse layer impedance and an inherent relaxation time are inevitable during EDL formation, establishing a fundamental dynamic limitation27,28. This implies that simply optimizing electrode materials and structures may not be sufficient to surmount the current frequency barrier.\n\nHybrid electrode designs have demonstrated their efficacy in addressing the performance limitations of SCs29,30. This approach involves the combination of electrodes with distinct charge storage mechanisms to harness the benefits of both. For instance, merging electrodes with varying voltage windows can extend the device\u2019s voltage endurance to the sum of the two individual windows31,32. Another example is the fusion of an EDL-type electrode with a Li-ion-battery-type electrode to create a Li-ion capacitor, which harnesses the intercalation effect of Li ions to substantially enhance charge storage efficiency and energy density33,34,35. These successful examples inspire us to explore the possibility of combining the dielectric effect possessing fast-response property with the EDL effect, potentially overcoming the frequency bottleneck of SCs.\n\nIn this paper, we experimentally reveal the upper bound of EDL-based SC\u2019s characteristic frequency, and propose the Hybrid Electrochemical Electrolytic Capacitor (HEEC) design, offering dual breakthrough in capacity and frequency. A near-ideal micro electrochemical cell is constructed using single-layer graphene as the active electrode, which is highly conductive and of no porous structure, thereby demonstrating that surpassing 10\u2009kHz for the SC\u2019s characteristic frequency is challenging. In view of this challenge, the HEEC design is introduced, integrating the advantages of both EDL and dielectric effects. The former provides excellent capacitance density at lower frequencies (below 1\u2009kHz), whereas the latter extends the frequency range, elevating the characteristic frequency to surpass 10\u2009kHz. By meticulously developing a selective atomic layer deposition technique, we achieved a planar micro HEEC with facile fabrication processes, showing a high characteristic frequency of 44\u2009kHz and a stable volume capacitance density of 800 \u03bcF/cm3. Furthermore, the micro HEEC was encapsulated and integrated with a power management integrated chip to create a compact power module with a size of only 6\u2009\u00d7\u20094\u2009\u00d7\u20092 mm3, which is over an order of magnitude smaller than traditional equivalents. The compact power module has been successfully utilized in the power management of a triboelectric nanogenerator, demonstrating the HEEC\u2019s broad application potential.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "As illustrated in Fig.\u00a01a, devices possessing both high characteristic frequency and capacitance density have the potential to bridge the existing gap and greatly broaden the applicability. Commercial electrolytic capacitors operate at higher frequencies but limited by low capacitance density because of small surface area and the thick dielectric layer (C\u2193=\u03b5A\u2193d\u2191). Though \u201cvalley-like\u201d anodes and cathodes increase the specific surface area, the capacitance density is still deficient, not to mention the thick dielectric layer formed by anodization (Fig.\u00a01b). However, electrochemical supercapacitors have large capacitance density due to mesoporous electrodes with high specific surface area but only work at quasi-static low frequencies due to intrinsic long relaxation time and large diffusion layer impedance (long distance, \u03d5\u2193=arctan\u2061|ZimZre\u2191|)28. The fundamental theory of HEECs relies on reasonably combining dielectric behavior (high frequency) of electrolytic capacitors with electrochemical processes (high capacitance) of supercapacitors. On one side, the thin dielectric layer by atomic layer deposition (ALD) stimulates electrolyte ions to accumulate and dissipate swiftly leading to low equivalent series resistance (short distance) as electrolytic ones, which secures higher characteristic frequency. On the other side, perfect conducting electrodes with uniformly distributed mesopores (porous Au) ensure large specific surface area and fast electron channels as electrochemical ones, which are prerequisites for high capacity in electrochemical devices. From a perspective of theory (Fig.\u00a01c), the Inner Helmholtz Plane (IHP) capacitance, Outer Helmholtz Plane (OHP) capacitance, diffusion layer capacitance, and charge transfer resistance consist of the EDL impedance with a large real part (resistive component), resulting in a smaller phase angle. On the contrary, the impedance of anodes is only composed of the dielectric capacitance including cation, electric dipole, and positive charge, which leads to a negligible resistance and better frequency performance. The potential distribution (light red line) of the HEEC also demonstrates similar correspondence and consequence: the potential drops much slower with a smaller gradient from the cathode to electrolyte compared to that from the anode to electrolyte, mainly due to the influence of diffusion layer, which means a higher localized and mean electrical field (the derivative of potential to distance) for the anode region. A higher electrical field also means a larger filed stress (electromagnetic force) and higher velocities for the anions and cations moving through the electrolyte. Consequently, the rapid movement of electrolyte ions in the anode localized region results in a better frequency response of the electrolytic electrode in comparison to the cathode. In conclusion, our design of the HEEC differs from electrolytic and electrochemical capacitors in terms of the working principle, electrode microstructure, fabrication processes, and device layout (Fig.\u00a01).\n\na HEECs fill gaps of operating frequency and capacitance density between supercapacitors and electrolytic capacitors by both electrochemical and electrolytic processes. b Different microstructures and equivalent circuit models of the electrolytic capacitor (CEC electrolytic ideal capacitor), electrochemical supercapacitor (CPESC constant phase element for supercapacitor), and HEEC (combination of electrolytic and supercapacitor, ALD atomic layer deposition). c Theory of electrochemical and electrolytic processes including the potential profile (red line: electric potential, purple dash line: x-distance) along the cathode to anode cross section path and the equivalent circuit (IHP Inner Helmholtz Plane, OHP Outer Helmholtz Plane) of the HEEC.\n\nMeanwhile, to further clarify the necessity of such a hybrid design, measurement of the Electrochemical Impedance Spectroscopy of the monolayer graphene, an EDL material, is adopted to uncover the intrinsic frequency limitation of EDL capacitors. Limited by the ion diffusion in the porous structure and the extra binder or/and conductive material, traditional three electrode systems fail to measure the intrinsic frequency limitation of common carbon materials. Hence, a monolayer graphene with an open microcell device is an ideal platform to study the frequency limit of an EDL capacitor benefited by no ion intercalation in multilayer material36. The monolayer graphene is defined as a 200\u2009\u00d7\u2009200 \u03bcm2 square window (electrolyte added) by photoresist (PR) connected with a pair of Ti/Pt electrodes as working electrodes (Fig.\u00a02a, details in Method). As shown in the top view (Fig.\u00a02b), except the test window region, monolayer graphene (inside the red dash square) is isolated from the electrolyte and connects to Ti/Pt electrodes. Results of the Bode plot suggest that the intrinsic characteristic frequency of monolayer graphene is 6.5\u2009kHz and the dissipation factor reaches 1.0, where the quantity of real capacitance (Zre) equals dissipated capacitance (Zim), at 6.8\u2009kHz (Fig.\u00a02c). Even though these values are considerable for electrochemical capacitors, they are theoretical maxima already, which is hard to continue to improve. Hence, electrolytic capacitors and dielectric processes are introduced and combined with EDL capacitors to achieve a breakthrough.\n\na Intrinsic limitation test of the electrochemical process (EDL) with fixed-size (200\u2009\u00d7\u2009200 \u03bcm2) monolayer graphene on Si/SiO2 substrate. b Optical microscopic top view of the test set-up (Red dash square: Graphene). c Intrinsic limitations of characteristic frequency (6.5\u2009kHz) and dissipation factor (6.8\u2009kHz) of electrochemical processes (EDL).\n\nInterdigital electrodes are allocated to represent two different types of physico-chemical behavior, electrochemical EDL and electrolytic, with constant gaps (30 \u03bcm) between these electrodes. Electrodes representing electrolytic behavior (low capacitance density) are designed to be several times wider than electrochemical electrodes (high capacitance density) to balance the capacity. Otherwise, the performance will be compromised because of the series characteristics of capacitors in circuits (i.e., capacitance depends on the smallest one). Pre-experiments using sandwiched devices fabricated by the same process as HEECs determine the specific dimensions of each electrode. The capacitance densities at 1\u2009kHz are used as references for both types since it is the frequency where the capacitance of supercapacitor-type electrodes dramatically decreases. Matching capacitance density at higher frequencies is meaningless because the electrolytic-type capacitance will be orders of magnitude larger in this region. Afterward, only the width of electrolytic-type electrodes is manipulated to meet the matching demand as mentioned previously. The capacitance density of supercapacitor electrodes is two and a half times as large as that of electrolytic ones. To further optimize the device performance, different ratios of electrodes, from 0.5: 1 to 1: 4 (electrochemical cathodes: electrolytic anodes), are also proposed.\n\nMeanwhile, circuit design at the device level is also critical. We change the traditional layout of hybrid devices, one type on each side, to a parallel design, which means that electrolytic and electrochemical capacitors are connected in parallel leading to an add-up instead of a cask effect. The characteristic frequency of such devices is also higher than the typical hybrid ones. Typical hybrid capacitors are subjected to the inevitable equivalent series resistance of electrochemical electrodes. However, in the design, the equivalent series resistance of each supercapacitor-type electrode is shorted at high-frequency regions due to the pure dielectric process of electrolytic capacitor electrodes. In summary, all designs above widen the operating frequency and enlarge the capacitance density simultaneously, which further fill the gap of capacitors feasible region (Fig.\u00a01a).\n\nThe asymmetrically designed HEECs were fabricated using 8 steps, including standard optical lithography, physical vapor deposition (PVD), atom layer deposition (ALD), and metal lift-off processes. The entire fabrication process is fully compatible with standard semiconductor processes and does not require any special treatment of the electrodes\u2019 active material. Specifically, the fabrication processes are in Fig.\u00a03a (details in Method and Supplementary Fig.\u00a01):\n\na Step-by-step illustration of Microfabrication processes of HEECs (blue: Si, purple: SiO2, gold: Cr/Au, light cyan: photoresist, yellowish white: Au-Ag, gradient yellow: porous Au, gradient blue: Al2O3 @ porous Au, light gray: Encapsulation, PVD physical vapor deposition, ALD atomic layer deposition). b Encapsulated HEECs on Si/SiO2 wafer substrate (Top: s-HEEC, Bottom: p-HEEC). c Cross-sectional and top view SEM image of anodes of HEECs (thin Al2O3 @ porous Au surface, thickness: 2.5 \u03bcm). d High-resolution TEM image of conformally-covered Al2O3 (thickness: 9.3\u2009nm)-Au (111) interface (EC electrolytic capacitor, SC supercapacitor) e Grazing Incidence XRD results of cathodes (EC, Au) and anodes (SC, Au-Al2O3). f XPS chemical shifts of Au4f7 (cathodes: 84.34\u2009eV, anodes: 85.49\u2009eV) and Al2p (cathodes: N/A, anodes: 75.39\u2009eV).\n\nFirst, interdigital electrodes are patterned with specific widths and gaps to optimize capacitance densities and ion transportation using photolithography. Next, a current collector is deposited on a substrate, followed by co-sputtering of Au and Ag with appropriate power. The wafer is then immersed in acetone to complete the lift-off process, forming the interdigital structure. Subsequently, metal Ag is selectively etched away to create a porous Au cathode by nitric acid (HNO3), while a thin dielectric Al2O3 layer is grown on the porous Au anode using atomic layer deposition (ALD). An asymmetric structure is achieved by selectively removing Al2O3 on the cathode, while the anode is protected by a thick photoresist during the wet etching procedure. Finally, the thick photoresist that protects the anode is removed by acetone and the HEEC is encapsulated in electrolyte by the SU-8 photoresist, finishing the fabrication process. This fabrication enables the wafer-level production and circuit integration of HEECs through fully CMOS-compatible processes.\n\nThe series-connected and parallel-connected HEECs (denoted as s-HEEC and p-HEEC) mainly consist of Si/SiO2 wafer substrate, electrochemical cathodes, electrolytic anodes, electrolyte, and photoresist encapsulation (Fig.\u00a03b). Illustrations in the Fig.\u00a03 have revealed the benign fabrication of both devices. All HEECs are designed to have different cathode-to-anode ratios as described above. Cross-sectional scanning electron microscopy (SEM) images demonstrate the ideal porous structure for our devices, whose mesoscopic pores range from 40\u2009nm to 200\u2009nm forming excellent ion transportation paths, which is also verified in the top view (Fig.\u00a03c). Au, Al, and O are evenly distributed in electrodes after ALD deposition with the ratio of 5:2:3 (Supplementary Figs.\u00a02 & Supplementary Table\u00a01), corresponding to the stoichiometric ratio of the Al2O3 dielectric layer. To protect the dielectric layer on the anode during the wet etching, a thick photoresist is patterned as Supplementary Fig.\u00a03 suggests. Meanwhile, the successful removal of the dielectric layer on cathodes can also be verified in the EDS mapping of the anode, electrode gap, and cathode (Fig.\u00a03d). It shows that the anode is composed of Au, Al, and O, while the cathode only consists of Au (98.5\u2009wt. %) without Al and O element (Supplementary Table\u00a0S1). The High-resolution Transmission Electron Microscopy (HRTEM) image of the anode also shows the conformal coverage of porous Au surfaces by the 9.3-nm-thick Al2O3 layer (several dots on the surface are Pt particles, details in the Method), creating a benign Au-O-Al interface (Fig.\u00a03e). The anode and cathode are further characterized by GIXRD using small incident angles because of the thin dielectric layer (Fig.\u00a03f). Both results clearly show features of (111) Au, (200) Au, (220) Au, and (311) Au, while difficult to observe features representing the existence of Al2O3 due to its rather thin properties. Therefore, we confirm the thin Al2O3 layer and the interface property by a typical film detection method \u2013 X-ray Photoelectron Spectroscopy (XPS). As shown in Figs.\u00a03g and Supplementary Fig.\u00a04, peaks of 2p and 1\u2009s electrons of Al and O at 75.39\u2009eV and 532.3\u2009eV determine the successful deposition and elimination of the Al2O3 dielectric layer, respectively37. In contrast, the XPS spectrum of the cathode has no sign of Al and O peaks for Al-O chemical bonds at the same binding energy. Besides, the chemical shift of Au4f electrons in anodes is 1.15\u2009eV larger than that in cathodes (Fig.\u00a03g), corresponding to the strong binding energy of Au-O-Al chemical bond at the interface as mentioned previously38.\n\nElectrochemical Impedance Spectroscopy (EIS) is primarily used to evaluate the frequency performance of HEECs. A small sinusoidal signal input (normally less than 5\u2009mV) is added to the device to output the current and voltage, calculating the impedance of the tested sample. First, we want to use the EIS numerical simulation to verify the different interface properties of the anode and cathode, echoing the theoretical analysis above. The simulation of s-HEEC1/3 demonstrates a good matching with tested results at all frequency regions as shown in Fig.\u00a04a. Here, we use a simple equivalent circuit for the simulation of s-HEECs: a resistor (R1) and a constant phase element (CPE1) parallel-connected to a charge transfer resistance (R3) representing the EDL capacitor, a resistor (R2) representing the electrolyte resistance, but only an ideal capacitor (C1) without any other resistive components representing the dielectric capacitor, distinguishing EDL and dielectric processes, respectively. Such equivalent circuit model reduces the simulation difficulty and further guides the circuit layout design. The simulation result of characteristic frequency also fits well with experimental results, showing an excellent value of around 120,000\u2009Hz (Figs.\u00a04b & Supplementary Table\u00a02).\n\na Electrochemical Impedance Spectroscopy (EIS) simulation result of s-HEEC1/3 with small areal resistance. b Bode plots simulation of s-HEEC1/3 and equivalent circuits used for the simulation. c EIS of s-HEECs with different electrode ratios (cathode to anode). d Bode plots of different s-HEECs with highest characteristic frequency over 1\u2009MHz. e Capacitance density versus operating frequency of s-HEEC1/3. f s-HEECs dissipation factor at different frequencies.\n\nFor all s-HEECs, near-vertical spectral lines indicate the capacitive behavior in this frequency region (Figs.\u00a04c & Supplementary Fig.\u00a05). Specifically, in the high-frequency region, the intersection of these lines and real part impedance (x-axis) identifies the equivalent series resistance (ESR) since capacitor values can be ignored under such high frequency. As shown in the Fig.\u00a04c inset, the ESRs of different devices with various electrode ratios are ranging from 36.1 to 40.5 \u03a9. Since HEECs are designed to be compact enough (from 0.0032 to 0.0076\u2009cm2), areal resistances are still acceptable for these devices among which the smallest one is 0.11 \u03a9\u22c5cm2. Such ESRs are essential to excellent capacitive performance in high-frequency regions because imaginary impedance provided by capacitors is negligible compared to constant real impedance by ESRs, especially over kilohertz. As a result, characteristic frequencies of HEECs with different ratios are all impressive for high-frequency applications. The highest characteristic frequency among devices is far beyond 1\u2009MHz with the 1:1.5 electrode ratio corresponding to its smallest ESR (Fig.\u00a04d). The lowest characteristic frequency in this series-configuration batch is still over 105\u2009Hz (Supplementary Fig.\u00a05), which is about 3 times larger than the ceramic and film capacitor, 9 times larger than the tantalum capacitor (Supplementary Fig.\u00a06c), and even about 26 times larger than the commercial electrolytic capacitors (AEC47\u03bcF). Though the HEEC shows phase loss at the low-frequency region because of larger absolute ESR and the insulating substrate (Si/SiO2), it can still perform well in practical high-frequency applications due to its higher characteristic frequency and capacitance density. The characteristic frequency represents the watershed of resistive and capacitive behavior. Thus, a higher characteristic frequency of capacitors indicates a potential to work in power electronic bulk-boost circuits and other highly responsive applications.\n\nThe capacitance density is the most important technical index of the entire design. Normally, tested imaginary impedance is not on behalf of the capacitance value in circuits. Calculations shown in the method are applied to tested results, leading to the relationship between real capacitance density and frequency (Fig.\u00a04e). Rather larger capacitance density at low frequencies and slower loss rate can be seen for s-HEECs with electrode ratios of 1:2.5 and 1:3. This is predictable since it matches the preliminary experimental results that the capacitance density of SCs and ECs are equal at 1\u2009kHz when the electrode ratio is selected as 1:2.4. Consequently, the capacitance density of the hybrid capacitor is optimized when the values of both SC and EC are equivalent because the total capacitance depends on the smallest one in a series configuration. In the quasi-static frequency region (\u2009<1\u2009Hz), the capacitance density of the s-HEEC reaches 600 \u03bcF/cm3, which is 3 times larger than commercial electrolytic capacitors (AEC47\u03bcF), 10 times larger than the ceramic capacitor, 14 times larger than the tantalum capacitor, and two order of magnitude larger than the film capacitor (Supplementary Fig.\u00a06d). Besides, the loss rates of these two s-HEECs are also slower than the commercial one, whose capacitance remains relatively stable over one kilohertz, suggesting a better frequency performance.\n\nThe dissipation factor (DF) of capacitance density is also a significant index to evaluate the performance of HEECs along with operating frequency. It is calculated as the absolute value of imaginary capacitance (Cim) over real capacitance (Cre). As can be seen in Fig.\u00a04f, DFs for s-HEECs are small when the frequency is lower than 104\u2009Hz, which are less than 0.5. When the operating frequency is over 30,000\u2009Hz, DFs gradually start to increase and finally reach 1.0 at the lowest 121\u2009kHz. At this frequency, the contributing capacitance (Cre) equals the dissipation capacitance (Cim), marking a critical turning point. The smallest of such turning points of s-HEECs is still higher than the aluminum capacitor (AEC47\u03bcF, 4.6\u2009kHz), ceramic capacitor (32.4\u2009kHz), film capacitor (35.8\u2009kHz), tantalum capacitor (13.3\u2009kHz, Supplementary Fig.\u00a06e), and the previous work based on 3D interconnected carbon electrodes (around 1,000\u2009Hz), revealing a good frequency performance and a small areal internal resistance22.\n\nLong-term stability and multi-environmental performance are also critical to feasibility of the s-HEEC in integrated circuits. To further validate the long-term stability of single devices, electrochemical tests are applied to the s-HEEC that has been exposed to normal environment for five months. Electrochemical properties of the s-HEEC remain almost unchanged after such a long time (Supplementary Fig.\u00a07), showing its perfect long-term stability. Moreover, we also subjected the s-HEEC to a variety of different temperatures to test its thermal stability. However, the s-HEEC shows a susceptible property under different temperatures in terms of electrochemical performance (Supplementary Fig.\u00a08). Such phenomenon is inseparable from the layout-design of HEECs. The s-HEEC forces SCs and ECs to stay only at the cathode and anode, respectively, which contributes to an unbalanced network and instability, especially when it confronts dramatic changes.\n\nTo further improve the high-frequency performance, including characteristic frequency and capacitance density, p-HEECs are designed using a parallel-connected layout, which means that SCs (cathode) and ECs (anode) are in parallel. In such a design, the capacitance density is doubled, but the ESR is demultiplicated, improving the frequency performance in two ways. We still conduct the numerical simulation for p-HEECs as shown in Fig.\u00a05a, b. The equivalent circuit of p-HEECs uses seven different components to simulate such behavior, including R1, R2, R3, R4, R5, C1, C2, CPE1, and CPE2, where R1, R4, and CPE1 represent one SC in the cathode; R2, R5, and CPE2 represent another SC in the anode; C1 and C2 represent ideal ECs in both electrodes; R3 represents the electrolyte resistance. Using the simple second-order model, EIS and bode plots simulation of p-HEEC1/2 perfectly match experimental results (Fig.\u00a05c), showing a small areal resistance (0.23 \u03a9\u22c5cm2) and great characteristic frequency (44\u2009kHz). Normalized values for these components in simulation have been listed in Supplementary Table\u00a0S2, which are equivalent to tested results at low and high frequencies suggesting a good match with HEECs. Most importantly, coincident simulation results of s-HEEC1/3 and p-HEEC1/2 prove that EC electrodes can be well represented by the simple ideal capacitors with little or even without any other parasitic resistance, confirming the feasibility of our design theory to improve frequency performance.\n\na Electrochemical Impedance Spectroscopy (EIS) simulation result of p-HEEC1/2 and equivalent circuits used for the simulation. b EIS simulation result in the high frequency region with areal equivalent series resistance (ESR) of 0.23 \u03a9\u22c5cm2. c, Bode plots simulation result of p-HEEC1/2 achieving near-ideal fit. d EIS results of p-HEECs with different electrode ratios (cathode to anode). e EIS results of p-HEECs and CSCs in the high frequency region. f Bode plots of different p-HEECs with highest characteristic frequency of 344\u2009kHz. g Capacitance density versus operating frequency of p-HEEC1/2. h p-HEECs and commercial AECs dissipation factor at different frequencies. i Dissipation factors of commercial supercapacitors at different frequencies.\n\nExcept for commercial aluminum, ceramic, film and tantalum capacitors, we also investigate the performance of commercial supercapacitors (denoted as CSC1 and CSC2) as contrasts with p-HEECs. In Fig.\u00a05d, e, it is obvious that EIS spectral lines are more vertical compared with s-HEECs and CSCs, showing a better high-frequency capacitive behavior. Areal ESRs of p-HEECs are smaller than s-HEECs (Fig.\u00a05e), ranging from 0.11 to 0.3 \u03a9\u22c5cm2, which are perfect values in terms of on-chip capacitors8,11,17. Besides, such a small ESR is also predominant when it is compared with the areal ESR of the film capacitor (2.66 \u03a9\u22c5cm2, Supplementary Fig.\u00a010b) and CSCs (CSC1: 11.4 \u03a9\u22c5cm2, CSC2: 9.79 \u03a9\u22c5cm2), which are about 10 times and 100 times larger than p-HEECs, respectively. The highest characteristic frequency among p-HEECs is 348\u2009kHz, observed in the p-HEEC0.5/1 (Fig.\u00a05f). The lowest value of p-HEECs (35\u2009kHz) is comparative to that of the ceramic and film capacitor (Supplementary Fig.\u00a010c), but smaller than that of s-HEECs, possibly due to capacitance mismatch between the electrodes, leading to a reduction in characteristic frequency. However, the lowest characteristic frequency of p-HEECs remains higher than that of the tantalum capacitor (13.3\u2009kHz) and AECs (4.6\u2009kHz & 17.2\u2009kHz) and significantly better than that of CSCs, which even exhibit phase angles of less than 20\u00b0. As expected by our design theory, the highest capacitance density of p-HEECs is over 34% higher than that of s-HEECs, achieving the volumetric capacitance density of 800 \u03bcF/cm3 and the areal capacitance density of 40 \u03bcF/cm2, all of which include the thickness of substrate (electrochemical inert) during calculation (Fig.\u00a05g). When the operating frequency is below 20,000\u2009Hz, the capacitance of p-HEECs also remains stable and declines slowly as the frequency increases, surpassing the continuously decreasing capacitance density of s-HEECs (Supplementary Fig.\u00a013). The p-HEEC1/2 also outperforms the ceramic, film, and tantalum capacitor (Supplementary Fig.\u00a010d), commercial AECs with higher capacitance density and slower dissipation rate as well. Though the capacitance density of CSCs (1140 \u03bcF/cm3) is initially larger than that of p-HEECs, it experiences a drastic 90% reduction within a narrow frequency range of 15\u2009Hz (from 2.2\u2009Hz to 14.7\u2009Hz) and even drops to the same capacity of HEECs (725 \u03bcF/cm3) only at 3.16\u2009Hz, beyond which the capacitance of CSCs will be significantly lower than that of p-HEECs. As shown by the dotted arrows in Fig.\u00a05g, p-HEECs successfully implemented the concept of broadening the frequency response range of CSCs while increasing the AECs capacitance density. Such performance of p-HEECs is exactly what we expected in the concept map (Fig.\u00a01a) and even better, plugging the gap in capacitors feasible region. DFs are also important to p-HEECs, identifying the watershed of capacitive storage and dissipation. As shown in Fig.\u00a05h, DFs of p-HEECs remain below 0.25 up to 5000\u2009Hz, which is superior to those of CSCs, whose DFs largely exceed 1.0 across all tested operating frequency ranges (Fig.\u00a05i). The capacitance storage and dissipation are equal at 35\u2009kHz for the worst-performing device, which are similar to the ceramic and film capacitor (32.4 and 35.8\u2009kHz), however, still surpasses the tantalum capacitor (13.3\u2009kHz, Supplementary Fig.\u00a010e), AECs (17.2\u2009kHz) and significantly outperforms previous studies22. Notably, the frequency threshold (at DF\u2009=\u20091.0) exceeds 45\u2009kHz for the best-performing p-HEEC1/2. Although the frequency threshold of p-HEEC1/2 is lower than that of s-HEEC1/3, p-HEEC1/2 still demonstrates significantly higher capacitance density compared to both s-HEECs and AECs.\n\nSimilar as the s-HEEC above, long-term stability and multi-environmental tests are also applied to the p-HEEC after an exposure to air for five months (Supplementary Fig.\u00a011). As is the case with s-HEECs, the p-HEEC keeps the near-vertical EIS curve as before and remains a good consistency in terms of the areal ESR. The bode plot maintains the same trend before and after five months. Though the characteristic frequency drops a bit, the device still normally operates in the circuit. The capacitance density of the p-HEEC slightly differs about 7.4% which is acceptable. After 10,000 cyclic voltammetry (CV) cycles at a scan rate of 20\u2009V/s, the p-HEEC still remains 98.3% capacity, exhibiting the electrochemical stability. In addition, electrochemical properties of the p-HEEC also vary along with temperatures, but it is more resistant in comparison to the s-HEEC (Supplementary Fig.\u00a012). The relatively stable performance at different temperatures should attribute to the uniform distribution of SCs and ECs on the cathode and anode, resulting in a much more balanced capacitor-resistor network. Therefore, the p-HEEC can be a better and more practical design in comparison to the s-HEEC. From all experimental and simulation results above, the performance of HEECs has been displayed in extenso, which is superior to other normal or hybrid devices, ceramic, film, tantalum, and electrolytic capacitors.\n\nThe p-HEEC1/2 and AEC are first connected to a rectifier-filter circuit with an output load of 1 k\u03a9. To verify the superiority of the HEEC operating in high frequency region in comparison to AECs, the frequencies of input voltage signal (light blue line) are selected as 10\u2009kHz, 25\u2009kHz, 50\u2009kHz, and 100\u2009kHz, respectively and the amplitude of input is selected as 2\u2009V for all input signals (Fig.\u00a06a and Supplementary Fig.\u00a014). The output voltage of p-HEEC1/2 (light red line) is comparable to that of the AEC (light yellow line) in all circumstances. Square and ramp input signals are also applied to both the AEC and HEEC. Such comparison can be quantified using the ripple factor (RF), the DC voltage over the AC voltage. Reasonably, RFs of output voltage of the HEEC are comparable or superior to that of the AEC, which are 4.20 %, 1.33 %, and 1.88 % operating under sinusoidal, square, and ramp input signals of 100\u2009kHz, respectively (Fig.\u00a06f). For square and ramp input signals, all RFs of p-HEEC1/2 are smaller than that of the AEC at all frequencies. Outcomes strongly demonstrate the high-frequency advantage of HEECs, surpassing that of commercial AECs. The same filtering demonstration is also conducted on other conventional capacitors (e.g., ceramic capacitor, film capacitor, and tantalum capacitor) to compare practical performance between these capacitors and the p-HEEC1/2. Basically consistent with the above results, the performance of p-HEEC1/2, in terms of the output stability and ripple factor, is comparable to the ceramic capacitor and film capacitor and much better than that of the tantalum capacitor, indicating the feasibility of HEECs in practical circuits. (Supplementary Fig.\u00a015). The p-HEEC1/2 is then integrated with a power management chip to create a compact power module. In this demonstration, the triboelectric nanogenerator (TENG) is applied to charge the HEEC and further actuate the PMIC as shown in Fig.\u00a06b and Supplementary Fig.\u00a016. The voltage of HEEC is charged to 1.2\u2009V with the input voltage signal of 2\u2009Hz and 50 Vpeak generated from the TENG (Fig.\u00a06c). After the fast-charging period, the voltage of the HEEC is stabilized around 1.2\u2009V with a small leakage current. The dimension of such compact power module is only 6\u2009\u00d7\u20094\u2009\u00d7\u20092 mm3 due to the utilization of standard microfabrication processes and compact mini package, demonstrating the integratability of HEECs with other circuits and power management ICs (Figs.\u00a06b & Supplementary Fig.\u00a017). Finally, we replace a commercial bulky AEC (9\u2009\u00d7\u200910\u2009\u00d7\u200915\u2009mm) with the selected micro HEEC (8\u2009\u00d7\u20098\u2009\u00d7\u20090.45\u2009mm) in a PCB-board level buck circuit to verify the feasibility of HEECs in a real commercial module (Fig.\u00a06d). The circuit is shown in the inset in which we substitute the AEC at output end for demonstration because of limitation of HEEC voltage window. The output signal of substituted p-HEEC is the same as the original commercial AEC. The output voltage RFs of two modules are 1.11 % and 0.87 % (Fig.\u00a06f), which means that the HEEC qualified for the same job with only 2 % space usage of the AEC volume, showing microelectronic integratability. Furthermore, we also apply different input signals with multiple frequencies for the buck circuit demonstration whose results are consistent with the outcomes above, showing the same efficacy (Supplementary Fig.\u00a018). We summarize the characteristic frequency and volumetric capacitance (calculating without substrate) of different devices in Fig.\u00a06g20,23,24,39,40,41,42,43,44,45,46,47,48,49,50. The volumetric capacitance density at 120\u2009Hz is the most representative index for devices aiming to high-frequency operation. Our works have comparable capacitance density (\u2009>\u200910 mF/cm3 without substrate) in the same order of magnitude with most micro supercapacitors and significantly higher capacitance density in comparison to other conventional capacitors. Furthermore, we achieve the record-high characteristic frequency over 20\u2009kHz, which verifies the previous assumption that EDL capacitors cannot surpass 10\u2009kHz derived from the monolayer graphene (Fig.\u00a02c).\n\na Utilization of AEC and HEEC for high frequency filtering of sinusoidal (left), square (center), and ramp (right) signals with inputs of 50\u2009kHz (top) and 100\u2009kHz (bottom), respectively. b Power management IC integrated with the triboelectric nanogenerator (TENG) and HEEC. c Voltage of the HEEC along with charging time (about 1\u2009s to be fully charged) and output voltage of TENG (50\u2009V, 2\u2009Hz, inset: power management module circuit). d Power management buck circuit substituting the bulky output AEC by the micro HEEC. e DC output voltage of buck circuit using the AEC and HEEC, respectively (input voltage: 5 VDC, output voltage: 1 VDC, inset: buck circuit diagram). f Ripple factors versus operating frequency of the AEC and HEEC in rectifier-filter and buck circuits with different input signals. g Comparison of capacitance density and characteristic frequency with other research works and conventional capacitors.\n\nTo clarify the long-term stability and performance under various load and thermal conditions of power management circuits using HEECs, the rectifier-filter circuit and buck circuit are retested after five months with the same configuration. Output signals of the rectifier-filter and buck circuit are still stable DC signal as before (Supplementary Fig.\u00a019). Ripple factors are all below 4.5% (Supplementary Fig.\u00a020) under any different load conditions (from 1 k\u03a9 to 1 M\u03a9). Additionally, different operating temperatures, from 0 \u00b0C to 85 \u00b0C, are applied to the circuits to verify the performance. These power management circuits still work well in all thermal conditions with small ripple factors of output signals. (Supplementary Fig.\u00a021).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59015-7/MediaObjects/41467_2025_59015_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59015-7/MediaObjects/41467_2025_59015_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59015-7/MediaObjects/41467_2025_59015_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59015-7/MediaObjects/41467_2025_59015_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59015-7/MediaObjects/41467_2025_59015_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59015-7/MediaObjects/41467_2025_59015_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "This research initiates a great span bridge between commercial electrolytic and electrochemical capacitors, which meets requirements for micro capacitors operating at over kilohertz frequencies, filling the blank of this frequency region in the Ragone Plot. The Hybrid Electrochemical Electrolytic Capacitor (HEEC) design successfully overcomes the frequency limitations of micro capacitors, achieving an impressive 44\u2009kHz characteristic frequency with 800 \u03bcF/cm3 volume capacitance density by hybrid incorporation of electrolytic and electrochemical capacitors at the circuit level. By integrating the HEEC into a compact power module and replacing the commercial AEC in the buck circuit, we demonstrate its practical utility in power management applications. The HEEC\u2019s potential to revolutionize high-frequency power management for portable and integratable electronics is evident, offering a promising pathway for more efficient and compact microelectronic devices.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Step 1: The interdigital electrodes are patterned by optical lithography using a bi-layer photoresist. The widths of the cathode and anode are designed to be 50 \u03bcm and 120 \u03bcm, respectively, ensuring the capacitance densities of the cathode and anode are approximately equal at 1\u2009kHz. The gap between these electrodes is 30 \u03bcm ensuring fast ion transportation. Step 2: The Cr (20\u2009nm)/Au (50\u2009nm) current collector is deposited onto the Si/SiO2 (300\u2009nm) substrate. Then, Au and Ag are co-sputtered on the SiO2/Cr/Au substrate for 40\u2009minutes with different sputtering power, which are radio frequency and direct current power, respectively. Step 3: The fabricated wafer is immersed in acetone for half an hour to finish the lift-off process, which accomplishes the interdigital structure of HEECs. Step 4: Selective etching away metal Ag from the Au/Ag compound by 70\u2009wt.% nitric acid (HNO3) forms the porous Au cathode (15\u2009minutes). The porous Au electrode is one part of the HEEC that represents the supercapacitor type with an electrochemical EDL effect. Step 5: The atomic layer deposition (ALD) conformally generates the thin dielectric Al2O3 layer (10\u2009nm) on the surface of the porous Au anode, forming the electrolytic capacitor type that utilizes the dielectric behavior. Step 6: The thicker photoresist (AZ-12XT) is used to protect the anode of HEECs, leaving only the cathode exposed to the later wet etchant. After photolithography, the thick photoresist is post-baked under high temperature (120 \u00b0C) for 45\u2009minutes to consolidate. Step 7: The asymmetric structure is achieved by selectively removing Al2O3 on the cathode with hydrochloric acid (HCl), while the anode is well protected by the thick photoresist. Step 8: The wafer is immersed in acetone solution to remove the thick photoresist covered on the anode. To remove the photoresist completely, low-power ultrasonic (30\u2009W) is applied during the whole procedure. Step 9: The encapsulation region is defined by SU8-2025 negative photoresist using ultraviolet photolithography, creating square barriers surrounding HEECs. Step 10: The gel electrolyte is prepared by adding 2\u2009wt.% PVA (Mw: 50,000; 87\u201389% hydrolyzed) to 1.5\u2009mol/L Na2SO4 solution (98\u2009wt.%) and magnetic stirring (800\u2009rpm) at 120 \u00b0C for 2\u2009h. Then, the gel electrolyte is spin coated on the wafer, which fills the encompassed area. Step 11: Cap the defined encapsulation region with the same negative photoresist (SU8-2025) and clean the residual gel electrolyte in other area using warm water. As illustrated above, HEECs are fabricated by wafer-level fully CMOS-compatible processes, ensuring mass production and chip integration capabilities. The thickness of the metal electrodes is 3 \u03bcm, while the electrode region (including the interdigitated electrodes and the gaps) ranges from 0.32 mm2 to 0.76 mm2. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) of HEECs were conducted under ambient atmospheric conditions at room temperature. Two-electrode test system was used for HEECs tests with the working electrode connected to anodes while the counter electrode and the reference electrode connected to cathodes (DC bias: 0\u2009V, amplitude: 5\u2009mV, frequency range: 1\u2009Hz\u2013106\u2009Hz).\n\nThe monolayer graphene utilized in this study, measuring 1\u2009\u00d7\u20091\u2009cm\u00b2 and obtained from Shenzhen 6Carbon Technology Co., Ltd., exhibited a coverage exceeding 99%. To facilitate graphene wet transfer, the graphene layer on the reverse side of the copper (Cu) foil underwent a 4-minute O2 plasma treatment, employing O2 at a flow rate of 300 sccm and a power of 300\u2009W. Subsequently, the Cu foil, along with the graphene and poly(methyl methacrylate) (PMMA) on the front side, was etched using a 3\u2009wt.% ammonium persulphate solution for ~4\u2009h until complete removal of the Cu foil. The graphene with PMMA was rinsed in deionized water six times to eliminate any residual ammonium persulphate before being transferred onto the target substrate. Following a 12-h air-drying period in a clean room, the wafer containing the graphene/PMMA composite was subjected to a 30-minute heat treatment at 110 \u00b0C to enhance the graphene-substrate adhesion. Subsequently, the PMMA layer was removed by immersing the substrate in acetone for 30\u2009minutes.\n\nThe fabrication of the monolayer graphene module proceeded as follows: Ti (5\u2009nm)/Pt (35\u2009nm) layers were deposited onto a highly phosphorus-doped silicon wafer with a 300\u2009nm thermally grown oxide layer using E-beam evaporation, followed by a lift-off process involving acetone. Subsequently, the substrate underwent a 1-minute O2 plasma treatment, employing O2 at a flow rate of 150 sccm and a power of 150\u2009W to render it hydrophilic prior to graphene transfer. The graphene active area was defined using AZ601 photoresist as a mask, after which the excess graphene was etched using O2 plasma for 4\u2009minutes with O2 at 300 sccm and power at 300\u2009W. Finally, AZ601 photoresist served as a passivation layer to insulate the metal electrode from the electrolyte, exposing only the graphene testing window and electrode pad area.\n\nElectrochemical impedance spectroscopy (EIS) of the graphene was conducted under ambient atmospheric conditions at room temperature using a CHI 760E three-electrode system, with the metal electrode as the working electrode, a homemade Ag/AgCl (1\u2009M KCl) electrode as the reference electrode, and Pt wire as the counter electrode. A 1\u2009M KCl electrolyte was employed, with testing frequencies ranging from 1\u2009Hz to 20000\u2009Hz and direct voltages varying from -0.4\u2009V to 0.4\u2009V, incremented by 0.1\u2009V (vs. Ag/AgCl).\n\nThe relationship between capacitance density of HEECs and operating frequencies is calculated using the following equation,\n\nwhere Cre represents the real part of capacitance density, Cim represents the imaginary part of capacitance density, Zre is the real part of the impedance, Zim is the imaginary part of the impedance, Z represents the total impedance, A represents the area of the device, and f represents the operating frequency.\n\nThe dissipation factor (DF) is calculated by the ratio of the imaginary part and the real part of the total impedance with the following equation,\n\nThe ripple factor (RF) is calculated by the ratio of the AC component and the DC component of the output voltage with the following equation,\n\nwhere VAC is the AC component, VDC is the DC component, VRMS is the root-mean-square of the output voltage, and VMEAN is the mean of the output voltage.\n\nZView software is employed for the equivalent circuit fitting of the electrochemical impedance spectroscopy (EIS) data, and the classic Randles circuit is utilized in this process. The Constant Phase Element (CPE) used in the simulation is a passive component that maintains a constant phase difference between voltage and current in alternating current (AC) circuits. In comparison to ideal components, the CPE provides a more accurate representation of actual circuit behavior of EDL capacitors. The impedance of the CPE is given by the equation,\n\nwhere Q represents the CPE parameter with the numerical value of the admittance, \u03c9 is the angular frequency, and \u03b1 is the phase angle parameter. Specifically, \u03b1=\u22121 corresponds to an ideal inductor, \u03b1=0 to an ideal resistor, \u03b1=1 to an ideal capacitor where Q=C, \u03b1=0.5 to Warburg impedance, and \u22121<\u03b1<1 to non-ideal dielectric behavior. The irregular structure of porous electrodes leads to non-ideal capacitive behavior, which is more accurately described using a CPE. To model an EDL supercapacitor, a CPE and an ideal resistor (equivalent series resistor, ESR) are connected in series. Electrolytic capacitors, on the other hand, can be represented by an ideal capacitor C due to their negligible equivalent series resistance and superior frequency response. The electrolyte resistance is modeled with an ideal resistor. Thus, the equivalent circuit of the s-HEEC consists of a supercapacitor CPE1, an electrolyte resistance R1 in the porous electrode, a charge transfer resistance R3 is in parallel with CPE1, an electrolyte resistance R2 in the gap between the cathode and anode, and an electrolytic capacitor C1 connected in series. For the p-HEEC, one electrode is represented by an EDL supercapacitor in parallel with an electrolytic capacitor. The two electrodes are then connected in series with the electrolyte resistance to form a parallel structure. Specifically, for the cathode, R1 is electrolyte resistance in the porous electrodes for EDL capacitor, CPE1 is impedance of the EDL capacitor, R4 is the charge transfer resistance of the EDL capacitor, and C1 is the capacitance of the electrolytic capacitor. For the anode, R2, R5, CPE2, and C2 have the same meaning of R1, R4, CPE1, and C1, respectively. R3 is the electrolyte resistance of the gap between two electrodes.\n\nAECs and CSCs are directly purchased from different manufacturers. The model of AEC4.7\u03bcF is DR003541 and the model of AEC47\u03bcF is DR000586, both produced by Hunan Aihua Group Co., Ltd. The model of CSC1 is SE-5R5-D224VYH and the model of CSC2 is SE-5R5-D224VY, both produced by Jinzhou Kaimei Power Co., Ltd. AECs and CSCs are tested with the same equipment (CHI-760E) as HEECs under the same circumstance.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Source data in this study are provided in the Source Data file with this paper.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "El-Kady, M. F. & Kaner, R. B. Scalable fabrication of high-power graphene micro-supercapacitors for flexible and on-chip energy storage. Nat. Commun. 4, 1475 (2013).\n\nArticle\u00a0\n ADS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nZhang, J., Gao, F. & Hu, P. A vertical transistor with a sub-1-nm channel. Nat. Electron. 4, 325\u2013325 (2021).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nZhao, Y. et al. A wearable freestanding electrochemical sensing system. Sci. 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Power Sources 614, 234951 (2024).\n\nArticle\u00a0\n CAS\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This research was supported by the National Natural Science Foundation of China under Grant No. 62174097 (X.W.) and Grant No. 62204082 (X.W.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Zhangshanhao Li, Minghao Xu.\n\nSchool of Integrated Circuits, Tsinghua University, Beijing, 100084, China\n\nZhangshanhao Li,\u00a0Minghao Xu,\u00a0Yier Xia,\u00a0Ziyun Yan,\u00a0Bingmeng Hu,\u00a0Haizhao Feng\u00a0&\u00a0Xiaohong Wang\n\nCollege of Semiconductors (College of Integrated Circuits), Hunan University, Changsha, 430001, China\n\nJianyou Dai\u00a0&\u00a0Sixing Xu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nZ.L., S.X., and X.W. conceived the project. Z.L., M.X., and Z.Y. designed the monolayer graphene verification module, HEECs structures and layout, and microfabrication processes. M. X. fabricated the monolayer graphene module. Z.L., Y.X., Z.Y., and H.F. fabricated HEECs. Z.L., Y.X., Z.Y., and B.H. conducted the characterization and electrochemical performance tests. J. D., X. Y., and Z. L. carried out the demonstration of HEECs. S. X., Z.L., and X.W. co-wrote the paper and all authors discussed the result and commented on the manuscript.\n\nCorrespondence to\n Sixing Xu or Xiaohong Wang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "Authors declare no competing interests", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Pietro Zaccagnini and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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High-frequency supercapacitors surpassing dynamic limit of electrical double layer effects.\n Nat Commun 16, 3704 (2025). https://doi.org/10.1038/s41467-025-59015-7\n\nDownload citation\n\nReceived: 04 September 2024\n\nAccepted: 08 April 2025\n\nPublished: 18 April 2025\n\nVersion of record: 18 April 2025\n\nDOI: https://doi.org/10.1038/s41467-025-59015-7\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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"journal": "Nature Communications", + "published": "02 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_MOESM1_ESM.pdf" + }, + { + "label": "Description Of Additional Supplementary File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_MOESM3_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_MOESM4_ESM.mp4" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_MOESM5_ESM.mp4" + }, + { + "label": "Supplementary Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_MOESM6_ESM.mp4" + }, + { + "label": "Supplementary Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_MOESM7_ESM.mp4" + }, + { + "label": "Supplementary Movie 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_MOESM8_ESM.mp4" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_MOESM9_ESM.pdf" + }, + { + "label": "Supplementary Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_MOESM10_ESM.zip" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-61896-7#ref-CR63", + "https://figshare.com/s/04953ebc5c87d0a2b4a7" + ], + "code": [ + "/articles/s41467-025-61896-7#ref-CR64", + "https://github.com/Mybzlab/faabp-cooperative-transport" + ], + "subject": [ + "Applied physics", + "Entomology", + "Mechanical engineering", + "Nonlinear phenomena" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3831488/v1.pdf?c=1756897771000", + "research_square_link": "https://www.researchsquare.com//article/rs-3831488/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61896-7.pdf", + "preprint_posted": "26 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Cooperative transport is a striking phenomenon where multiple agents join forces to transit a\r\npayload too heavy for the individual. While social animals such as ants are routinely observed to\r\ncoordinate transport at scale [1\u20135], reproducing the effect in artificial swarms remains challenging, as it\r\nrequires synchronization in a noisy many-body system [6\u201327]. Here we show that cooperative transport\r\nspontaneously emerges in swarms of stochastic self-propelled agents, without requiring any form of\r\nsensing, feedback, or control. We show that a minute modification to the mechanical design of the\r\nindividual agent dramatically changes its alignment response to an external force. We experimentally\r\ndemonstrate that with the proper design, a swarm of active particles spontaneously cooperates in the\r\ndirectional transport of larger objects. Surprisingly, transport increases with increasing payload size.\r\nA mechanical, coarse-grained description reveals that force-alignment is intrinsic and captured by\r\na signed, charge-like parameter with units of curvature. Numerical simulations of swarms of active\r\nparticles with a negative active charge corroborate the experimental findings. We analytically derive\r\na geometrical criterion for cooperative transport which results from a bifurcation in a non-linear\r\ndynamical system. Our findings generalize existing models of active particles [28\u201337], offer new design\r\nrules for distributed robotic systems, and shed light on cooperative transport in natural swarms.Physical sciences/Physics/Applied physicsPhysical sciences/Engineering/Mechanical engineeringBiological sciences/Zoology/EntomologyBiological sciences/Zoology/BiomechanicsPhysical sciences/Physics/Statistical physics, thermodynamics and nonlinear dynamics/Nonlinear phenomena", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Video1CooperativeTransportExperiment.mp4Video 1 - Cooperative Transport - ExperimentVideo2CooperativeTransportSimulation.mp4Video 2 - Cooperative Transport - SimulationVideo3HighSpeedImagingNegativeForceAlignmentAscending.mp4Video 3 - High Speed Imaging Negative Force-Alignment - AscendingVideo4HighSpeedImagingPositiveForceAlignmentDescending.mp4Video 4 - High Speed Imaging Positive Force-Alignment - DescendingVideo5DiffusiveMotionofaPassivePayloadExperiment.mp4Video 5 - Diffusive Motion of a Passive Payload - ExperimentVideo6DiffusiveMotionofaPassivePayloadSimulation.mp4Video 6 - Diffusive Motion of a Passive Payload - SimulationAMechanicalOriginofCooperativeTransportSI.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Cooperative transport is a striking phenomenon where multiple agents join forces to transit a payload too heavy for the individual. While social animals such as ants are routinely observed to coordinate transport at scale, reproducing the effect in artificial swarms remains challenging, as it requires synchronization in a noisy many-body system. Here we show that cooperative transport spontaneously emerges in swarms of stochastic self-propelled robots. Robots deprived of sensing and communication, are isotropically initialized around a passive circular payload, where directional motion is not expected without an external cue. And yet it moves. We find that a minute modification to the mechanical design of the individual agent dramatically changes its alignment response to an external force. We then show experimentally that by controlling the individual\u2019s friction and mass distribution, a swarm of active particles autonomously cooperates in the directional transport of larger objects. Surprisingly, transport increases with increasing payload size, and its persistence surpasses the persistence of the active particles by over an order of magnitude. A mechanical, coarse-grained description reveals that force-alignment is intrinsic and captured by a signed, charge-like parameter with units of curvature. Numerical simulations of swarms of active particles with a negative active charge corroborate the experimental findings. We analytically derive a geometrical criterion for cooperative transport which results from a bifurcation in a non-linear dynamical system. Our findings generalize existing models of active particles, provide design rules for distributed robotic systems, and shed light on cooperation in natural swarms.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Foraging ants teaming up to transport a large payload is a hallmark of agile cooperation in nature1,2,3,4,5. Groups of ants can forage synergetically, transporting items heavier than the summed capacity of the individuals2. The significance of cooperative transport in living systems, and the potential industrial applications of coordinating transport using simple agents with only local sensing attracted interest beyond entomology, inspiring researchers across disciplines including swarm robotics6,7,8,9,10,11,12,13,14,15, non-equilibrium physics16,17,18,19,20,21, and biology of social animals22,23,24,25,26,27.\n\nRecent research in collective behavior successfully captured emergent effects such as flocking or aggregation by treating individual agents as stochastic self-propelled particles with simple interaction rules28,29,30,31,32. This approach, however, proved limited in describing cooperative transport: a passive payload introduced to a swarm of active particles shows moderate, diffusive dynamics. Unless the payload has an explicit shape asymmetry, it only exhibits Brownian motion16,17,18,20,21.\n\nReplacing the primitive active agents with robotic swarms augmented with sophisticated circuitry and advanced artificial intelligence also had limited outcomes without the aid of an external cue or manual positioning10: robots with electronic feedback, proximity sensors, and communications, struggle to respond to the rapidly changing environment owing to frequent mechanical collisions of the robots with the payload and with one another9,11,12,13,14,15,19,22. At the absence of an external cue, cooperative transport was restricted to small swarms, and required a manual pre-arrangement of the swarm relative to the payload11. Robots programmed to avoid collisions displayed suppressed collective dynamics, leading many times to swarm-scale deadlocks33,34.\n\nIn this work, we show that collective transport can spontaneously emerge in a swarm of rudimentary self-propelled particles, without any form of sensing, feedback, or control. This is achieved via a minor adjustment to the mechanical design of the self-propelled particle, which dramatically alters its orientation response to an external force. We show experimentally that it is possible to achieve negative force-alignment in which particles orient themselves opposite to the external force, and that a swarm of such robots cooperate in the directional transport of a larger, passive object (see Fig.\u00a01 and Supplementary Movies\u00a01 and 2). An inspection of the contact dynamics of an active particle with the ground (Supplementary Movies\u00a03 and 4) allows us to derive from first principles the pivotal contribution of mechanics to force-alignment (Fig.\u00a02), thereby extending, and generalizing previous phenomenological descriptions for the equations of motion of self-propelled particles13,35,36,37,38,39,40,41,42,43,44. We find that the force-alignment is intrinsic to active particles, and can be described by a signed, charge-like parameter with units of curvature, which we term \u201ccurvity\u201d.\n\nA A robot with a stiff rear leg and two soft front legs driven using vibration motors (center of mass,\u00a0red dot, is behind the soft legs: \u03b4\u2009<\u20090). B Time laps of a swarm of robots that spontaneously push a larger payload (diameter 2a\u2009=\u200928\u2009cm) moving it all the way to the arena\u2019s boundary. C A robot design with opposite leg polarity, where the stiff leg is in the front, and the soft legs are in the back (center of mass is forward of the soft legs \u03b4\u2009>\u20090). D Robots with this design are deflected by the payload which shows only moderate, rather diffusive displacement. E Mean square displacement of the payload shows near ballistic motion ( \u221d t2) for \u03b4\u2009<\u20090 design (green), while with robots with \u03b4\u2009>\u20090 (blue), the payload show orders of magnitude slower, near diffusive, motion ( \u221d t1). Scale bar is 20\u2009cm.\n\nA A top view of an active particle self-propelled along its heading (\\(\\hat{e}\\)) subjected to an in-plane external body force (\\(\\vec{f}\\)) acting on the center of mass (CoM, red dot). B The quasi-two-dimensional motion of a bristle bot has three characteristic phases: (I) At Rest all legs are on the ground and the external force is balanced by static friction, the robot does not move. (II) In the Aerial phase, the robot is completely aloft with constant linear acceleration along the external force. (III) Having softer legs creates a Pivot phase, where the robot is partially touching the ground, and the external force creates a torque around the pivot axis. The softer legs are in front of the CoM, and the robot turns against an external force (see also Supplementary Movie\u00a03). C A robot with a stiffer front leg (\u03b4\u00a0>\u00a00, soft legs at the back) goes through a similar sequence but rotates in the opposite direction, i.e., along the external force (see also Supplementary Movie\u00a04). D A robot with soft front legs goes against an external force and climbs up an inclined plane. (E) A robot with soft rear legs goes down an inclined plane (see also Supplementary Movie\u00a04). F Trajectories of numerical simulation of Eqs. (1), (2) show that particles with negative curvity (\u03ba\u2009<\u20090) turn and move against an external force, like robots with soft front legs (green), and particles with positive curvity (\u03ba\u2009>\u20090), turn along the external force, like robots with soft rear legs (blue). A particle with a theoretical zero curvity (like ABP), drifts along the external force, but does not reorient its heading. Scale bars are 10\u2009cm.\n\nWe use this model in numerical simulations of stochastic active particles, where we observe cooperative transport when particles have a negative curvity, corroborating the experimental observations (see Fig.\u00a03 and Supplementary Movies\u00a01,2,5, and 6). Surprisingly, in both experiments and simulations the transport propensity increases with increasing payload size (see Fig.\u00a04). We analytically derive a condition for transport, which depends on the geometrical curvature of the payload as well as the intrinsic curvity of self-propelled particles. The condition is consistent in both simulations and experiments, offering a geometrical criterion for cooperative transport.\n\nA Schematic of the motion of a self-propelled particle with negative curvity (\u03ba\u2009<\u20090) that turns its heading (\\(\\hat{e}\\)) against an external force. B Time sequence from a simulation of 1000 particles shows a progressive accumulation of the particles on one side of the payload followed by its transport. An active wake is formed at the rear of the payload and continually exchanges particles with the surroundings in a dynamic steady-state (see also Supplementary Movie\u00a02). C Schematic of the motion of a self-propelled particle with positive curvity, that turns its heading along an external force. D Time sequence of a payload with one thousand active particles with positive curvity shows a diffusive trajectory (see Supplementary Movie\u00a06). E Mean square displacement of the payload\u2019s trajectory shows near ballistic motion ( \u221d t2) when \u03ba\u2009<\u20090 (green) but near diffusive motion ( \u221d t1) when \u03ba\u2009>\u20090 (blue).\n\nA Individual trajectories become increasingly persistent for larger payloads and negative curvity in both experiments and simulations. B The power law (n) of the mean square displacement is closer to ballistic motion (n\u2009>\u20091.5), when \u03baa\u2009<\u2009\u22121, and closer to diffusive (n\u2009<\u20091.4), when \u03baa\u2009>\u2009\u22121. Each point is the average n of four experiments with error bars showing standard error (see SI Section\u00a0I for details). C Simulations of a payload of radius a, in a swarm of 200 FAABPs of curvity \u03ba, moves an order of magnitude faster when \u03baa\u2009<\u2009\u22121 (simulation results measure mean speed of payload \u3008vp\u3009 relative to nominal speed of FAABPs, v0, see SI Section\u00a0II for details). Circles show experimental results with near ballistic MSD (n\u2009>\u20091.5), and \u00a0\u00d7 denotes experiments where power law is closer to diffusive (n\u2009<\u20091.4). Dashed line follows the analytical prediction for cooperative transport (\u03baa\u2009=\u2009\u22121, see Eq. (6)), and found at the boundary between the two dynamical regimes.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_Fig4_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "Stochastic self-propelled robots were built following a modified bristle bot design36,38,40,44. A robot (sizing 5\u20136\u2009cm in diameter) is driven by two vibration motors mounted on a tripod with one stiff leg and a pair of asymmetric soft legs (see Fig.\u00a01 and SI Section\u00a0I). Vibrations induce noisy forward motion which defines the robot\u2019s heading, \\(\\hat{e}\\) (see Fig.\u00a01A, C). In the experiments, a large circular passive payload was placed in the middle of a symmetrical arrangement of robots, which were then turned on, setting the swarm into motion. With the traditional design (soft legs placed at the back), robots sporadically push the passive payload which exhibits Brownian-like motion\u2014with each collision, robots turn away from the payload (Fig.\u00a01C\u2013E, and Supplementary Movie\u00a05). In contrast, when the soft legs are placed at the front, the swarm spontaneously breaks symmetry and propels the payload in a near ballistic trajectory (see Fig.\u00a01A, B, E and Supplementary Movie\u00a01). Here, with each collision, robots tend to turn into the payload and progressively push it until reaching the perimeter of the arena (150\u2009cm diameter). The cooperative transport emerges autonomously, and does not require an external, directional cue, nor manual pre-arrangement. We further find the effect to increase with payload diameter (2a\u2009=\u20097\u201332\u2009cm), swarm size (N\u2009=\u20091\u2009\u2212\u200953 robots) in both a custom-made and a modified commercial multi-robot platform33,44 (see Fig.\u00a04 and SI Section\u00a0IB 4).\n\nHigh-speed video imaging offered insight into the origin of force-alignment (see Fig.\u00a02, Supplementary Movies\u00a03, 4, and SI Section\u00a0I A 2). While moving, the robot\u2019s stiff and soft legs interact differently with the ground\u2014the stiff leg has higher restitution, and spends a longer duration in the air, whereas the softer legs show only moderate hopping. This difference leads to a differential fore-aft friction which lies at the heart of the mechanical origin of the force-alignment\u2014robots with soft legs at the back align with an external force (descend downhill), whereas robots with soft legs at the front align against the force (ascend uphill). The difference between the two designs is revealed in the presence of an external force or boundaries. In the absence of such, their dynamics are qualitatively indistinguishable. Force-alignment is generic to self-propelled particles regardless of the locomotion mechanism and should be expected in general both on the macroscopic and the microscopic scale. In the next section, we derive the mechanical origin of force-alignment in granular hoppers from first principles.\n\nThe microscopic origin of force-alignment is revealed by considering the instantaneous acceleration, \\(\\vec{a}\\), of a vibrationally propelled robot under an external body force, \\(\\vec{f}\\), acting in the plane of motion. Below we outline the coarse-graining of the rapid hopping dynamics, and derive effective equations of motion of a granular active particle dominated by inertia and dry friction. The equations only require the mean value of the different parameters, with no particular significance to the order of the three phases. We will reproduce previous work that assumed overdamped dynamics, and force alignment based on symmetry37, however we will show that their effective parameters (mobility and force-alignment) are controlled by inertial quantities (mass and moment of inertia).\n\nWe consider the motion to have three characteristic phases: I \u2014 rest, II \u2014 aerial, and III \u2014 pivot, with a mean overall duration T (see Fig.\u00a02). A robot starts at rest (I) with all contact points on the ground, thereby the external force is perfectly balanced by static friction and there is no motion (\\(m\\vec{a}=0\\)). The robot then jumps forward (along \\(\\hat{e}\\)) with an instantaneous horizontal speed of vh, having a typical time aloft of \u03c4A. While in the aerial phase (II) the robot accelerates by the external force \\(m\\vec{a}=\\vec{f}\\). For simplicity, we treat contact with the substrate as having perfect static friction, and when the robot lands it loses all its momentum. Combining phases I and II results in a coarse-grained velocity proportional to the sum of active velocity and the external force (Eq. (1)), where the nominal speed is \\({v}_{0}\\equiv \\frac{{\\tau }_{A}}{T}{v}_{h}\\) and the mobility is \\(\\mu \\equiv \\frac{{\\tau }_{A}^{2}}{2mT}\\). This formalism is similar to Drude\u2019s model that leads to linear Ohm\u2019s conductivity where charge carriers in a conductor lose their momentum during collisions. Inevitably, contact friction is not equal on all legs, and empirically we find that the robot spends a longer duration, \u03c4P, on the softer legs acting as a pivot. During the pivot phase (III), static friction with the contacting feet restrict linear motion (\\(m\\vec{a}\\approx 0\\)), however, the robot can rotate, as it experiences a torque \\(\\vec{\\tau }\\). The torque is the result of the external force acting on the center of mass which in general is displaced from the axis of rotation, \\(I\\vec{\\alpha }=\\vec{\\tau }=\\delta \\hat{e}\\times \\vec{f}\\), where I is the moment of inertia around the rotation axis, and the lever arm, \u03b4, is the offset of the center of mass from the axis along the orientation vector \\(\\hat{e}\\) (see Fig.\u00a02B, C). The offset, \u03b4, can be positive or negative, respectively resulting in positive or negative force-alignment.\n\nPhase III gives the microscopic basis for force-alignment on which our model rests. Combining the instantaneous dynamics of phases I-III results in coarse-grained equations of motion\n\nwhere \u03ba acts as an effective charge-like parameter of an active particle, and defined from the microsopic properties of the hopper\n\nBeing a key result of our model we name \u03ba curvity, as it has units of curvature, and stems from the particle\u2019s activity (for details see SI Section\u00a0III). Similarly to an electric charge, \u03ba is signed, and its sign is controlled by an internal symmetry. The sign and magnitude of the curvity follow \u03b4, the signed offset of the center of mass.\n\nDespite not having any formal viscus drag, Eq. (1) has the same structure as the equations used to describe drag-dominated micro-swimmers acting in the low Reynolds number regime20, where inertial quantities are justifiably neglected, and velocity is proportional to the external force through a mobility constant (\u03bc). Previous work on granular active matter already assumed that dry macroscopic objects can be described using overdamped dynamics37,40. The derivation above shows that while the equations of motion take the same structure, they are controlled by effective parameters that directly depend on inertial quantities, like mass (m) and moment of inertia (I). Equations (1) is also found in the extensively used model of Active Brownian Particles (ABP)29,35,45,46,47,48,49. Our derivation shows that the rotation of the active particle results from an external force, and does not require self-propulsion. Previous work used similar equations37,40,42,44,50 to describe particles that undergo velocity alignment as self-aligning active particles (SAAP). Originally introduced to offer a mechanism for flocking whereby a bird\u2019s heading tends to align on its velocity50. There, and in subsequent work the alignment strength described using positive quantities such as \u2018relaxation-time\u2019 or \u2018alignment rate\u201937,40,50, and more recently, \u2018alignment-length\u201942,44. That description successfully captured important collective behavior such as flocking (positive curvity). Since the alignment parameter can be negative, it is more naturally treated as a curvature (signed inverse length). For negative curvity (\u03ba\u2009<\u20090), there is no-self alignment: when subjected to a strong force (\u03bcf\u2009>\u2009v0), a particle\u2019s heading will settle against its velocity (Eqs. (1), (2)). Moreover, the microscopic derivation shows that the curvity does not depend on self-propulsion (Eq. (3)): an external force can rotate an active particle even at zero nominal speed (v0\u2009=\u2009vh\u2009=\u20090, see\u00a0SI). Therefore we call particles which follow Eqs. (1),(2) Force-Aligning Active Brownian Particles (FAABP), as their alignment stems from the external force (rather than self-propulsion).\n\nAn important consequence of the microscopic model presented here is to identify that \u03ba is signed and to offer a powerful design rule. For example, in the point mass limit (I\u2009=\u2009m\u03b42), the curvity is inversely proportional to the offset \u03ba\u2009\u221d\u20091/\u03b4, and when the offset is negative (center of mass is behind the soft legs, \u03b4\u2009<\u20090), robots turn against an external force. Describing the robots\u2019 mass distribution as a disc-shaped core (battery and electronics) embedded in a ring shaped frame (3D printed chassis), shows that the mechanical model predicts the measured curvity to within a factor of 1.8 (see Movies\u00a03\u20134, and Supplementary Figs.\u00a01 and 2, and Section\u00a0I A 4 in the SI). The curvity can be also computed for an arbitrary shape (e.g., rod-like) and mass distribution, by evaluating the robot\u2019s moment of inertia relative to the pivot axis, as the balance of the increased lever arm (\u03ba \u221d \u03b4), with the increased moment of inertia (\u03ba\u2009\u221d\u20091/I). The derivation above also shows that the effective parameters (v0, \u03bc, and \u03ba) are not independent of one another, and how they are linked by the robot\u2019s inertial properties. It is interesting to note that previous work showed that some ciliated and flagellated micro-swimmers tend to swim up51,52. Specifically E.coli53 and Paramecium54 showed an increasing radius of curvature of their trajectories with their own size, even on the micro-scale. Super diffusion of a passive particle was even observed in bath of E.Coli55. When combined, these suggest a potential extension of the collective dynamics described here to the domain of micro-swimmers20.\n\nWe tested numerically swarms of FAABPs by adding orientational noise to Eq. (2) and short-range repulsion, in a simulation engine using 5th-order Runge-Kutta integration. The orientational noise has zero mean, \\(\\langle \\vec{\\xi }\\rangle=0\\), with a Gaussian distribution of width \u3008\u03be\u20092\u3009\u2009=\u20092\u0394tkBT (\u0394t is the simulation time step, and kBT sets the magnitude of the noise, thereby tuning their persistence length, l0), with particles modeled as soft discs of radius b (see SI Section\u00a0II for details).\n\nSimulated dynamics of individual FAABPs with a constant force reproduce experimental trajectories of robots moving on an inclined plane (see Fig.\u00a02D\u2013F). FAABPs with a positive curvity (\u03ba\u2009>\u20090) turn in the direction of the force similarly to robots having their center of mass in front of their soft legs (\u03b4\u00a0>\u00a00), whereas FAABPs with a negative curvity (\u03ba\u2009<\u20090) turn anti-parallel and move against the external force, like the robots with their center of mass behind the soft legs (\u03b4\u2009<\u20090). The singular, zero curvity FAABP (\u03ba\u2009=\u20090), is simply an ABP \u2014 its heading is unaffected as it drifts in the direction of the external force (Fig.\u00a02F).\n\nWe tested the effect of a passive particle of radius a on a randomly distributed swarm of FAABPs ranging in sizes between N \u2208 [1, 1000], with negative curvities, \u03ba\u2009<\u20090. We observe that after a short transient where the swarm homogeneously accumulates around the passive particle, symmetry is spontaneously broken and particles form an active wake on one side that propels the passive payload (see Fig.\u00a03 and Supplementary Movie\u00a02). In line with the experimental findings, the transport emerges autonomously, despite the initial isotropic random arrangement of the active particles. The passive particle shows elongated trajectories, larger than its size, and larger than the simulation box (for periodic boundary conditions). The direction of transport is different from one run to the other, and the active wake is in a dynamic steady state, constantly exchanging the participating FAABPs. A similar effect is also observed in the non-periodic simulation, excluding the effect of the boundary. Transport is also observed when FAABPs are non-interacting (can pass through one another but not through the passive particle) excluding the role of Motility Induced Phase Transition29,56. This means that cooperation emerges not by direct robot-to-robot interaction, but instead by a proxy\u2014the passive payload. Transport is completely absent for FAABPs with positive curvity (\u03ba\u2009>\u20090) or for smaller payloads, where the passive particle only shows a diffusive trajectory (see Figs.\u00a03C, D, 4, and Supplementary Movie\u00a06).\n\nCounter-intuitively, cooperative transport is enhanced with increasing payload radius, a. By contrast to thermal fluctuations, where a particle\u2019s diffusion decreases with its size57, here we find that both the payload\u2019s speed (vp) and persistence length (lp) increase with its size, with an overall increased long term effective diffusion ( \u221d vplp)35. Performing 116 experiments and over 1000 simulations (varying payload size, curvity, robot count, and orientational noise, see\u00a0SI), we found that in both experiments and simulations, larger payloads are better transported, provided that the curvity of the active particles is sufficiently negative (see Fig.\u00a04). In the experiments, the payload\u2019s weight is proportional to the payload\u2019s radius (m \u221d a, see SI Section\u00a0I B 2), for a combined super-linear increase in mass transport. Trajectories of a larger payload in a swarm of robots with negative curvity show more persistent motion compared to that when the curvity is positive or the payload is smaller (Fig.\u00a04A). Experimentally, there is a considerable increase in the average power-law of the mean square displacement of the payload when \u03baa\u2009<\u2009\u22121 (see Fig.\u00a04B). Exploring the \u03ba-a phase space in simulation reveals two phases, with an order of magnitude increase in the mean speed of the payload (Fig.\u00a04C). The phase boundary for both experiments and simulations lies at \u03baa\u2009=\u2009\u2212\u00a01.\n\nWe next show that the condition for cooperative transport is geometrical and stems from the interplay of the active particles\u2019 curvity, \u03ba, and the curvature of the circular passive particle, 1/a. Despite the deprecate dynamics underlying the numerical and experimental systems the condition for cooperative transport is identical: simulated particles are in the over-damped limit, with drag proportional to the particle\u2019s diameter, smooth self-propulsion, Gaussian orientational noise, and strictly two-dimensional motion, whereas in experiments objects are inertial, making an effective active granular gas, with solid friction with the ground and with one another, an intermittent vibrational self-propulsion, a non-Gaussian orientational noise, and quasi-two-dimensional hopping.\n\nWe start by modeling a circular payload as a repulsive, two-dimensional, radially symmetric potential fixed at the origin, \\(U=U\\left(r\\right)\\), exerting a repulsive force on the active particles: \\(\\vec{f}=-\\vec{\\nabla }U(r)=\\Gamma \\left(r\\right){v}_{0}/\\mu \\hat{r}\\) (see Fig.\u00a05A). Active particles interact with this circular obstacle following Eqs. (1) and (2). \u0393(r) sets the radial profile of the force and is chosen such that the magnitude of the repulsive force at the payload\u2019s perimeter exactly balances the nominal speed of the active particle, \u0393(r\u00a0=\u00a0a)\u2009=\u20091, effectively setting the payload\u2019s size. Inspired by previous work on repulsive particles58, keeping the potential profile implicit (exponential decay, soft-core, Yukawa, etc.), makes the result below more general. Circular self-propelled particles in 2D have three degrees of freedom (see Fig.\u00a05B): a radial and azimuthal position \\(\\left(r,\\varphi \\right)\\), and a heading relative to the x-axis, \\(\\hat{e}\\equiv \\left(\\cos \\theta,\\sin \\theta \\right)\\). The system has rotational symmetry and the dynamics depend only on the orientation of the heading relative to the center of the potential \u03c8\u2009\u2261\u2009\u03b8\u00a0\u2212\u00a0\u03c6. Plugging the radial force term into the FAABPs\u2019 equations of motion (Eqs. (1), (2)) gives a dynamical system described by two non-linear coupled first-order differential equations:\n\n(see SI Section\u00a0IV A for a detailed derivation). At \u03c8\u2009=\u20090 the active particle points away from the payload, and at \u03c8\u2009=\u2009\u03c0, it fronts the payload. When the prefactor in Eq. (5) switches sign (\\(\\tilde{u}(r)\\equiv \\kappa \\Gamma (r)+1/r=0\\)), an active particle is effectively attracted to the repulsive potential (see Fig.\u00a05C). Given the definition of \\(\\Gamma \\left(r\\right)\\), this can be satisfied when\n\nThe condition in Eq. (6) presents a geometrical criterion for cooperative transport: once the curvity is sufficiently negative, instead of being scattered away (\u03c8\u00a0\u2192\u00a00), an active particle colliding with the obstacle re-orients sufficiently fast into the receding repulsive hill to continually push against the obstacle (\u03c8\u00a0\u2192\u00a0\u03c0). The inequality in Eq. (6) is agnostic to whether the curvity or the curvature is negative, and could be applied more generally. Even if the force alignment is non-negative (positive or zero), a self-propelled particle could display effective attraction to a concave boundary, provided that its curvature is sufficiently negative (1/a\u2009<\u20090). This has been previously observed in self-propelled particles interacting with a concave obstacle16, in a single confined active particle interacting with the inner concave walls of a harmonic trap40, and more recently, in active particles interacting with one another59.\n\nA Illustration of an active particle near a repulsive potential. B Configurational coordinates [position \\(\\vec{r}=\\left(r,\\varphi \\right)\\) and heading \u03b8] of an active particle (green) near a circular repulsive potential. \u03c8 is the angle of the heading relative to the potential center. C An effective attraction well is formed when \u03baa\u2009<\u2009\u22121. \\(\\tilde{u}\\left(r\\right)\\) is the prefactor in Eq. (5), and in regions where \\(\\tilde{u}\\left(r\\right) < 0\\), self-propelled particles are effectively attracted to an otherwise repulsive potential. D Experiments where \u03baa\u2009<\u2009\u22121 show an effective attraction as an increase in the mean linear density, \u03bb, of robots at the perimeter of the payload. Each point is the average \u03bb of 4 runs with standard error. E Phase portraits of Eqs. (4) and (5) in the distance and relative orientation plane (r-\u03c8) display a basin of attraction (gray region) when \u03baa\u2009=\u2009\u22122: an active particle is effectively attracted to a repulsive potential. At the linearly stable fixed point (r\u2009=\u2009a, \u03c8\u2009=\u2009\u03c0, filled circle) self-propulsion is balanced by the repulsive force. F When \u03baa\u2009=\u20091, the fixed point is a saddle (empty circle), and there is no activity-induced attraction.\n\nPhase portraits of the dynamical systems above (\u03baa\u2009=\u20091) and below (\u03baa\u2009=\u2009\u22122) the transition, show a local topological change at the fixed point where the active particle is pushing against the payload, r/a\u2009=\u20091, \u03c8\u2009=\u2009\u03c0 (see Fig.\u00a05E, F). When Eq. (6) is satisfied, the dynamical system undergoes a bifurcation, and the saddle point turns into a linearly stable sink that attracts active particles (see SI Section\u00a0IV A 2). In both experiments and simulations, this effective attraction is manifested in an enhanced kissing number Nkiss of robots touching the payload (see Figs.\u00a01, 3, 4 and Supplementary Movies\u00a01, 2 and 5, 6), with an increased linear filling fraction, \u03bb\u2009\u2261\u2009Nkissb/a (see Fig.\u00a05D). In a system that meets the condition in Eq. (6), the effective attraction and the resulting cooperative transport are robust over a range of orientational noises (see Fig.\u00a06).\n\nA Phase diagram of the mean speed of the payload shows an order of magnitude increase when the condition in Eq. (6) is met, over a range of persistence lengths of the active particles (l0) (see SI Section\u00a0II). B Individual trajectories of the payload and the ensemble averages of the MSD (inset) shows that at low orientational noise of the active particles (l0\u2265\u20091) the passive payload moves in extended trajectories (blue, green) with near ballistic MSD ( \u221d t2, inset). Cooperative transport is suppressed (red blob) with a diffusive MSD of the payload ( \u221d t1, inset), when the persistence length of the active particles approaches their own size (l0\u2009=\u20090.05\u00a0\u2248\u00a0b).\n\nWe find that the persistence of the payload (lp) increases with the number of active particles, and can even surpass the persistence of the active particles themselves (l0, Fig.\u00a07A, B). This effect becomes clear when measuring the amplification of the persistence length (lp/l0): with increasing interaction (\u03baa more negative), the amplification increases faster with increasing swarm size (Fig.\u00a07D). Moreover, at a given interaction strength, the amplified persistence increases super-linearly with the number of robots (N), a hallmark of a cooperative swarm60. Overall, both the speed (Figs.\u00a04, 6), and the persistence length (Fig.\u00a07) of the payload increase with its size (a). The effective equations of motions derived above can explain this pronounced effect.\n\nA The trajectories and (B) MSDs of a simulation of N\u2009=\u20091000 active particles (gray) and one passive payload (green, \u03baa\u2009=\u2009\u22126) show that the payload moves more slowly (shorter trajectory, lower MSD) but is more persistent (MSD \u221d t2) than the active particles. C The persistence of a passive payload is amplified by over an order of magnitude relative to the active particles (lp/l0\u2009>\u200910) with increasing interaction strength (\u03baa) and swarm size (N). Sections of the \u03baa\u00a0\u2212\u00a0N phase space show a sharper increase in the amplification with increasing swarm size (D), and a super linear marginal contribution of swarm size to the persistence length of the passive particle (E), expected from a cooperative system.\n\nOnce the payload starts moving, the dynamics are no longer isotropic. A velocity\u00a0fluctuation driving the passive particle to the right, \\(u\\hat{x}\\) (w.l.o.g.), spontaneously breaks symmetry and introduces an explicit dependence on the azimuthal coordinate in Eqs. (4) and (5), as well as an additional dynamical equation for the azimuth itself, leading to a dynamical system with three variables:\n\n(see SI Section\u00a0IV B 1 for derivation). In the isotropic case (Eqs. (4) and (5)), there was a fixed point for any combination of the heading (\u03b8) and azimuth (\u03c6) that satisfy: \u03c8\u2009\u2261\u2009\u03b8\u00a0\u2212\u00a0\u03c6\u00a0=\u00a0\u03c0. When the payload is already moving, this is no longer the case. There are only two fixed points for the azimuthal degree of freedom, either pushing against the payload\u2019s motion (\u03c6\u2009=\u20090, unstable) or along its motion (\u03c6\u2009=\u2009\u03c0, stable). This means that when a group is transiting a payload, it preferentially recruits further individuals to push in the same direction, facilitating cooperative transport. Since the direction of the payload guides recruitment, a single \u2018leader\u2019 could, in theory, synchronize the group\u2019s behavior, by adjusting the payload\u2019s movement. Whereas in previous work, the manual pre-arrangement of a small number of robots dictated the direction of transport11,44. Here, the recruitment effect emerges directly from symmetry-breaking, supporting cooperation at scale.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61896-7/MediaObjects/41467_2025_61896_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this work, we found that a mechanical response to external forces alone can directly lead to cooperative transport. We showed that transport emerges spontaneously and autonomously in a swarm of stochastic, self-propelled particles with no explicit sensing or decision-making, nor external cue whatsoever, important in the growing field of multi-robot systems. We identified the key role of force-alignment response and traced its mechanical origin by coarse-graining the equations of motion from first principles. We found that an intrinsic parameter, which we term curvity, controls the sign and magnitude at which the orientation of an active particle responds to an external force, thereby setting the characteristic curvature of its trajectory. We discovered that particles with negative curvity tend to turn against an external force and push against obstacles. We experimentally fabricated such particles and presented a mechanical design rule for their construction, offering a route for engineering cooperative transport.\n\nWe then compared experiments and simulations over a range of parameters (including payload size, swarm size, curvity, and system noise) finding a consistent criterion for the emergence of cooperative transport as spontaneous symmetry breaking. The criterion is given by geometrical quantities, where self-propelled particles can become attracted to an otherwise repulsive potential. The criterion shares a mathematical structure with the Young-Laplace equation61, where the stability of a three-dimensional fluid interface is conditioned by two local curvatures, suggesting a link between interfacial phenomena, boundaries, and active matter.\n\nBeing of a geometrical origin, force-alignment can be tuned on the micron-scale: analogous dynamics were also observed in bacteria53. A marriage of force-alignment with emerging colloidal technologies of artificial microswimmers harbors a potential for designing cooperative transport at the cellular level20,31,62. Moreover, effective attraction and repulsion can be further tuned by designing non-circular payloads with variable curvature (positive and negative) in tandem with the curvity of the self-propelled particles. Tunable attraction and repulsion, combined with cooperative transport, offer an activity-based architecture for unlocking new paradigms in modeling, as well as programming, far-from-equilibrium self-assembly.\n\nFinally, although foraging ants are not simple stochastic particles, and make complex decisions based on sensory information, our findings suggest an underlying mechanism for scalable cooperation in nature.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Experimental data required to reproduce the results found in the manuscript is found on an online repository63. https://figshare.com/s/04953ebc5c87d0a2b4a7.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Code required to reproduce the simulations in the manuscript is found on an online repository64. https://github.com/Mybzlab/faabp-cooperative-transport.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Traniello, J. 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Mybzlab/faabp-cooperative-transport: Cooperative transport in force aligning active brownian particles. github https://doi.org/10.5281/zenodo.15338098 (2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We acknowledge I. Kolvin, C. Kelleher, and O. Dauchot for critical reading of the manuscript, and Y. Roichman for supplying Kilobots. This work was supported in part by the Israel Science Foundation grants 2096/18 and 2117/22 and the Israeli Ministry of Aliya, and by the project Dutch Brain Interface Initiative (DBI2), project number 024.005.022 of the research programme Gravitation financed by the Dutch Research Council (NWO).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "School of Physics and Astronomy, and the Center for Physics and Chemistry of Living Systems, Tel Aviv University, Tel Aviv, Israel\n\nEden Arbel,\u00a0Naomi Oppenheimer\u00a0&\u00a0Yoav Lahini\n\nDepartment of Artificial Intelligence, Donders Center for Cognition, Radboud University, Nijmegen, The Netherlands\n\nLuco Buise,\u00a0Charlotte van Waes\u00a0&\u00a0Matan Yah Ben Zion\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nE.A. performed and analyzed the robotic swarm experiments, C.v.W. contributed to the development of the analytical model, L.B. contributed to the development and analysis of the numerical simulations, Y.L. contributed to the experimental design, analysis, and funding, N.O. developed the numerical and analytical models, M.Y.B.Z. conceived the project, contributed to the development of the experimental, analytical, and numerical models, to the project\u2019s funding, and oversaw the project\u2019s execution.\n\nCorrespondence to\n Matan Yah Ben Zion.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous, reviewer(s) for their contribution to the peer review of this work. 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A mechanical route for cooperative transport in autonomous robotic swarms.\n Nat Commun 16, 7519 (2025). https://doi.org/10.1038/s41467-025-61896-7\n\nDownload citation\n\nReceived: 11 March 2024\n\nAccepted: 03 July 2025\n\nPublished: 02 September 2025\n\nVersion of record: 02 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61896-7\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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b/c9737008b18c23ba767d22e1ac78d292fdd130b60598596274e817b2ab868dca/metadata.json @@ -0,0 +1,139 @@ +{ + "title": "Role of the Labrador Current in the Atlantic Meridional Overturning Circulation response to greenhouse warming", + "pre_title": "Roles of the Labrador Current in the Atlantic Meridional Overturning Circulation responses to greenhouse warming", + "journal": "Nature Communications", + "published": "27 August 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51449-9/MediaObjects/41467_2024_51449_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51449-9/MediaObjects/41467_2024_51449_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://ihesp.github.io/archive/products/ihesp-products/data-release/DataRelease_Phase2.html", + "http://ihesp.qnlm.ac", + "https://www.o-snap.org/", + "https://doi.org/10.5281/zenodo.12249262" + ], + "code": [ + "https://doi.org/10.5281/zenodo.3637771", + "https://doi.org/10.5281/zenodo.12249262" + ], + "subject": [ + "Climate and Earth system modelling", + "Physical oceanography", + "Projection and prediction" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3950226/v1.pdf?c=1724843323000", + "research_square_link": "https://www.researchsquare.com//article/rs-3950226/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-51449-9.pdf", + "preprint_posted": "18 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Anthropogenic warming is projected to enhance Arctic freshwater exportation into the Labrador Sea. This extra freshwater may weaken deep convections and contribute to the Atlantic Meridional Overturning Circulation (AMOC) decline. Here, by analyzing an unprecedented high-resolution climate model simulation for the 21st century, we show that the Labrador Current strongly restricts the lateral spread of freshwater from the Artic Ocean into the open ocean such that the freshwater input has a limited role in weakening the overturning circulation. In contrast, in the absence of a strong Labrador Current, the extra freshwater is allowed to spread into the interior region and eventually shut down deep convections in the Labrador Sea. Given that the Labrador Sea overturning makes a significant contribution to the AMOC in many climate models, our results suggest that the AMOC decline during the 21st century could be overestimated in these models due to the unresolved Labrador Current.Earth and environmental sciences/Ocean sciences/Physical oceanographyEarth and environmental sciences/Climate sciences/Climate change/Climate and Earth system modellingEarth and environmental sciences/Climate sciences/Climate change/Projection and prediction", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformation.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Anthropogenic warming is projected to enhance Arctic freshwater exportation into the Labrador Sea. This extra freshwater may weaken deep convection and contribute to the Atlantic Meridional Overturning Circulation (AMOC) decline. Here, by analyzing an unprecedented high-resolution climate model simulation for the 21st century, we show that the Labrador Current strongly restricts the lateral spread of freshwater from the Arctic Ocean into the open ocean such that the freshwater input has a limited role in weakening the overturning circulation. In contrast, in the absence of a strong Labrador Current in a climate model with lower resolution, the extra freshwater is allowed to spread into the interior region and eventually shut down deep convection in the Labrador Sea. Given that the Labrador Sea overturning makes a significant contribution to the AMOC in many climate models, our results suggest that the AMOC decline during the 21st century could be overestimated in these models due to the poorly resolved Labrador Current.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Increased freshwater supply to the Arctic Ocean in a warming climate makes high-latitude regions susceptible to dramatic changes in ocean circulation. As anthropogenic warming continues in the 21st century, Arctic summer sea ice is likely to disappear in a few decades1,2, increasing the Arctic liquid freshwater storage. A stronger hydrological cycle in the atmosphere will also put more freshwater into the Arctic through an increase in net precipitation and river runoff3,4. Coupled climate models suggest that these extra freshwater sources to the Arctic will lead to a larger freshwater exportation to the subpolar North Atlantic in the 21st century5,6. The additional freshwater will increase ocean stratification and potentially slow down the Atlantic Meridional Overturning Circulation (AMOC), with serious consequences for regional and global climates7,8,9,10.\n\nIn this study, we focus on the influence of the extra freshwater from the Arctic on the ocean circulation in the Labrador Sea, a critical region for AMOC changes11,12,13,14. Climate models consistently project a slowdown of the AMOC during the 21st century due to warming and freshening in the high-latitude North Atlantic15,16,17. However, the overturning and deep convection responses to freshwater input are crucially impacted by the boundary current that typically circulates around open-ocean convection regions, where deep water forms. For example, there is significant freshwater input into the Weddell Sea, a key region for bottom\u00a0water formation in the Southern Ocean, due to the melting of Antarctic ice sheet and sea ice. In the presence of a well-resolved Antarctic Slope Current, the extra freshwater largely stays on the shelf region as the slope current restricts the lateral spread of freshwater, with limited influence on open-ocean convection18. The Labrador Current can also restrict the lateral exchange of freshwater between the shelf and open ocean19,20. Thus, we hypothesize that when the extra freshwater enters the Labrador Sea from the Arctic through the Canadian Arctic Archipelago21, the Labrador Current restricts the freshwater from spreading into the open ocean and weakening the overturning circulation. Typical climate models for the Coupled Model Intercomparison Project (CMIP) of Intergoverment Panel for Climate Change (IPCC) assessment report, mostly at 1\u00b0 resolution, are unable to resolve the Labrador Current22 (supplementary Fig.\u00a01), and thus could overestimate overturning responses to the freshwater forcing due to anthropogenic warming23.\n\nHere, we study the role of the Labrador Current in regulating the overturning responses to an increased Arctic freshwater export in the Labrador Sea and its influence on AMOC changes in an unprecedented high-resolution coupled simulation over the 21st century (2006\u20132100) under the high-emission scenario (RCP8.5) from the CMIP5 protocol. The simulation (HighRes)24 is conducted using the Community Earth System Model version 1 (CESM1), with a nominal horizontal resolution of 0.1\u00b0 for the ocean, ~6.5\u2009km in the Labrador Sea, and 0.25o for the atmosphere (see \u201cCESM simulations\u201d in \u201cMethod\u201d). HighRes performs well in reproducing the narrow Labrador Current25 (supplementary Fig.\u00a01) and the Labrador Sea overturning circulation26 (supplementary Fig.\u00a02) when compared to observations. These processes are often biased in coarse-resolution climate models (supplementary Figs.\u00a01 and 2). To quantify the role of the Labrador Current in regulating the overturning circulation changes, we also look at the overturning responses in a coarse-resolution counterpart of HighRes, LowRes, with a nominal 1\u00b0 resolution as in most climate models in CMIP5 and CMIP6. HighRes and LowRes differ from each other primarily in their horizontal resolutions. We show that the well-resolved Labrador Current in HighRes strongly restricts freshwater to the shelf and leads to a much weaker response in the Labrador Sea overturning circulation than LowRes, in which the Labrador Current is poorly resolved. Thus, we conclude that, without resolving the Labrador Current, coarse-resolution climate models may overestimate the AMOC decline during the 21st century.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Consistent with climate models from CMIP66 and previous generations5, HighRes projects a significant increase in liquid freshwater input to the Arctic Ocean due to anthropogenic warming (Fig.\u00a01a\u2013c). Under the RCP8.5 scenario in HighRes, the annual mean sea ice volume decreases by 98.5% from 16,298\u2009km3 in 2006\u20132015 to 243\u2009km3 in 2091\u20132100; summer sea ice is completely lost in the 2060s. Net precipitation and river runoff also increase significantly due to a stronger atmospheric hydrological cycle by ~161% and 39%, respectively. The larger Arctic freshwater input will necessarily lead to an increased liquid freshwater exportation into the subpolar North Atlantic, including the Labrador Sea.\n\nChanges (2091\u20132100 minus 2006\u20132015) in a equivalent sea-ice thickness, b precipitation minus evaporation (P-E), and c river runoff in the Arctic in HighRes. d Surface ocean circulation in the Labrador Sea in HighRes over 2006\u20132015. The shading shows mixed layer depth in March during the same period. The 1000\u2009m, 2000\u2009m, and 3000\u2009m isobaths are indicated by blue contours. The black box encloses the Labrador Sea region. LC is the Labrador Current. WGC is the West Greenland Current. Time series of anomalous freshwater flux (FWF) into the Labrador Sea (defined as positive) from e the west of Greenland across Davis Strait (red line) and Hudson Strait (yellow line) and f from the east of Greenland. The freshwater flux anomaly is relative to the year 2006. The thin lines in e and f show the annual mean anomaly. The thick lines represent 20-year running mean.\n\nWe estimate the freshwater transport into the Labrador Sea (Fig.\u00a01d) from the north across Davis Strait, from the west across Hudson Strait, and from the east by the West Greenland Current in HighRes (see \u201cFreshwater flux\u201d in \u201cMethod\u201d). Salinity averaged in the Labrador Sea (denoted by the box in Fig.\u00a01d) in the year 2006 is used as the reference salinity. The increase in the freshwater flux from Davis Strait is most significant, at a rate of about 1.4\u2009mSv\u2009yr\u22121 over the 21st century (Fig.\u00a01e). The freshwater flux across Hudson Strait also enhances with a smaller rate of 0.35\u2009mSv\u2009yr\u22121 (Fig.\u00a01e). Both the trends are significant at the 95% confidence level according to a two-tailed Student\u2019 t test. The freshwater flux coming from the east weakly increases in 2020\u20132040 and decreases after the year 2040 (Fig.\u00a01f). The freshwater pathway into the Labrador Sea is determined by several processes, including the Arctic circulation and the location of freshwater sources. The differing freshwater flux trends between the western and the eastern routes are likely related to the structure of liquid freshwater increases in the Arctic Ocean in HighRes: liquid freshwater content increases in the Canadian basin but decreases in the Eurasian basin (supplementary Fig.\u00a03).\n\nThe extra freshwater in the Labrador Sea reduces sea surface salinity, but the freshening is mostly confined to the shelf close to Newfoundland and Labrador of Canada (Fig.\u00a02a). The freshening on the shelf is ~0.67\u2009psu in 2091\u20132100 relative to 2006\u20132015. In contrast, in the interior Labrador Sea with depth deeper than 2000\u2009m, where deep convection occurs, the surface freshening is only ~0.27\u2009psu. We quantify the salinity changes along the Atlantic Repeat Hydrography Line 7 West line\u00a0(AR7W\u00a0line)25 (black dashed line in Fig.\u00a02a). The freshening is most obvious in the upper 150\u2009m on the shelf (Fig.\u00a02b). The exchange of freshwater between the shelf and the open ocean is strongly restricted by the narrow Labrador Current (Fig.\u00a02b, supplementary Fig.\u00a01), consistent with previous studies19,20. The Labrador Current may also help flush the freshwater out into the subpolar North Atlantic, contributing to reducing the freshening effects. Coarse-resolution models (e.g., LowRes), on the other hand, cannot fully resolve the Labrador Current, and thus may misrepresent the freshening of the interior Labrador Sea with excessive freshwater input from the shelf. Indeed, with similar increase in total freshwater flux that is mostly due to enhanced freshwater flux from the western route via Davis Strait and Hudson Strait (supplementary Fig.\u00a04), the surface freshening is almost uniform in the Labrador Sea in LowRes, with a 1.52\u2009psu decrease in the interior (Fig.\u00a02c). The broader surface salinity decrease is related to the too weak and wide Labrador Current (Fig.\u00a02d, supplementary Fig.\u00a01), which allows freshwater to enter the interior Labrador Sea. Thus, we suggest that the coarse-resolution CESM model overestimates the freshwater influence on surface salinity changes in the Labrador Sea.\n\na, c Changes (2091\u20132100 minus 2006\u20132015) in sea surface salinity (SSS) in a HighRes and c LowRes. The black dashed line indicates the Atlantic Repeat Hydrography Line 7\u00a0West line (AR7W line). b, d Salinity changes (2091\u20132100 minus 2006\u20132015) along the AR7W line in b HighRes and d LowRes. The contour lines show velocity of currents across the\u00a0AR7W line\u00a0with interval of 12\u2009cm\u2009s\u22121. The dashed (solid) lines represent currents out of (into) the Labrador Sea.\n\nThe Labrador Current also regulates ocean stratification and mixed layer depth (MLD) changes in the interior Labrador Sea due to surface freshening. Ocean stratification, quantified as the density difference between the sea surface and 1\u2009km depth, increases by 91% from 2006\u20132015 to 2091\u20132100 in HighRes (supplementary Fig.\u00a05). The strengthening is surface intensified and almost equally attributed to surface warming and freshening (Fig.\u00a03a, b, supplementary Fig.\u00a06). In contrast with HighRes, the upper-ocean stratification increases more dramatically in the 21st century by 158% in LowRes (supplementary Fig.\u00a05). The larger stratification increase in LowRes can be mostly (~80%) attributed to the widespread surface freshening in the Labrador Sea that leads to a dramatic decrease in surface density (Fig.\u00a03c, d, supplementary Fig.\u00a06). Similar conclusions can be drawn for MLD changes (supplementary Fig.\u00a07). In HighRes, the March MLD in the interior Labrador Sea decreases by 56% from 430\u2009m in 2006\u20132015 to 190\u2009m in 2091\u20132100. While in LowRes, the March MLD shoals by 91% from 928\u2009m to 83\u2009m during the same period. We note that the present-day MLD in LowRes is overestimated as in many coarse-resolution models27,28,29. The results highlight the role of the Labrador Current in future ocean stratification changes in the Labrador Sea and suggest that coarse-resolution models may overestimate the stratification increase due to freshwater forcing.\n\na, c Changes of potential density (\u03c3, reference pressure at sea surface) profiles in the Labrador Sea in a HighRes and c LowRes. Solid (dashed) lines indicate the mean in 2006\u20132015 (2091\u20132100). b, d Strengthening of the upper-1000\u2009m ocean stratification, N2, calculated as g/\u03c31000\u2009m\u00b7(\u03c31000\u2009m\u2013\u03c30m)/1000 and its contributions due to temperature changes, \u0394NT2, and salinity changes, \u0394NS2, in b HighRes and d LowRes. The temperature stratification, NT2, is calculated as -g\u03b1500m(T1000\u2009m\u2013T0m)/1000, where T is the potential temperature and \u03b1 is the thermal expansion coefficient. The haline stratification, NS2, is calculated as g\u03b2500m(S1000\u2009m\u2013S0m)/1000, where S is salinity and \u03b2 is the haline contraction coefficient. Only regions in the Labrador Sea (denoted by the box in Fig.\u00a01d) deeper than 2000 m are considered here.\n\nThrough its impacts on surface freshening and stratification changes, the Labrador Current regulates overturning circulation changes in a warming climate. The stratification increase will decrease deep water formation in the Labrador Sea and potentially contribute to the AMOC weakening during the 21st century. We quantify the Labrador Sea overturning at the west leg of Overturning in the Subpolar North Atlantic Program (OSNAP) observing system (Fig.\u00a04a, c, see \u201cOSNAP overturning streamfunction\u201d in \u201cMethod\u201d), at the exit of the Labrador Sea. The Labrador Sea overturning is weakened by ~55% in HighRes from 2006\u20132015 to 2091\u20132100. In comparison, the weakening of the Labrador Sea overturning in LowRes is more substantial by ~ 90% during the same period. The deep convection in LowRes almost completely shuts down at the end of the 21st century. The much stronger weakening of the Labrador Sea overturning in LowRes can be attributed to the overly stratified ocean in the Labrador Sea. We calculate the surface water mass transformation (SWMT), which is dynamically connected to the overturning13,14,30, using surface buoyancy flux and surface density (see \u201cSurface water mass transformation\u201d in \u201cMethods\u201d section). The SWMT largely reproduces the Labrador Sea overturning circulation as well as its changes during the 21st century (supplementary Fig.\u00a08). Decomposing the SWMT changes into contributions due to changes in the surface buoyancy flux and surface density (see \u201cSurface water mass transformation\u201d in \u201cMethod\u201d), we show that the differing Labrador Sea overturning responses between HighRes and LowRes are mainly owing to changes in surface density structure (supplementary Fig.\u00a08). The surface buoyancy flux is not significantly different between HighRes and LowRes at 98% confidence level (supplementary Fig.\u00a09) and cannot explain their difference in the overturning changes.\n\na, c Changes of overturning across the west leg of Overturning in the Subpolar North Atlantic Program observing system (OSNAP West) in a HighRes and c LowRes. Solid (dashed) lines indicate the mean in 2006\u20132015 (2091\u20132100). The percentage changes in the maximum overturning streamfunction between 2006\u20132015 and 2091\u20132100 is denoted on the upper-right corner in a and c. b, d Percentage changes in the AMOC from 2006\u20132015 to 2091\u20132100 in b HighRes and d LowRes. The percentage changes are calculated as the AMOC streamfunction differences between 2091\u20132100 and 2006\u20132015, divided by the maximum AMOC at 40oN in 2006\u20132015 in each simulation. The AMOC is calculated in density space and then remapped into depth space using the time and zonal mean depth of each density layer.\n\nThe weakening of the Labrador Sea overturning circulation contributes to the AMOC decline during the 21st century. We calculate the North Atlantic overturning in density space and then remap it to depth space following previous studies31,32 (see \u201cDensity-space AMOC\u201d in \u201cMethod\u201d, supplementary Fig.\u00a010). Consistent with the Labrador Sea overturning circulation changes, the AMOC decline appears to be faster in LowRes (Fig.\u00a04d) than HighRes (Fig.\u00a04b). Overturning changes across OSNAP East, which dominates the North Atlantic overturning in observations, may also contribute to the faster AMOC decline in LowRes (supplementary Figs.\u00a011 and 12). However, the difference in OSNAP East overturning changes between LowRes (53%) and HighRes (39%) is less dramatic as compared to OSNAP West (Fig.\u00a04a, c).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51449-9/MediaObjects/41467_2024_51449_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51449-9/MediaObjects/41467_2024_51449_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51449-9/MediaObjects/41467_2024_51449_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51449-9/MediaObjects/41467_2024_51449_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we highlight the role of the Labrador Current in regulating the Labrador Sea responses to increased liquid freshwater input due to anthropogenic warming. The narrow Labrador Current strongly restricts the lateral exchange of freshwater between the continental shelf and open ocean. In the absence of this narrow boundary current, the extra freshwater input from the Arctic spreads into the open ocean and causes a much stronger increase in ocean stratification, leading to an overestimated weakening of the Labrador Sea overturning circulation. The impact of the Labrador Current in restricting lateral freshwater exchange might evolve as the climate continues to warm. HighRes predicts a weakening of the Labrador Current due to surface wind changes (supplementary Fig.\u00a013), suggesting a slightly diminishing role of the Labrador Current in the future climate. Nevertheless, given that the Labrador Sea overturning circulation makes a significant contribution to the AMOC in many climate models of coarse resolution27,28,29, our results suggest that the AMOC weakening may be overestimated in these climate models.\n\nA similar dependence of the AMOC decline rate on model resolutions has been found in GFDL models33. However, the overturning responses to anthropogenic forcing are complex and involve many processes (e.g., ice sheet melting) that are not resolved even in the high-resolution CESM. Our discussions are mostly based on one single climate model under the previous CMIP5 protocol. It is possible that the dependence of the AMOC decline rate on model resolution is model-dependent. In this regard, HighResMIP34 may be useful for a more comprehensive comparison of AMOC decline among various climate models. However, the high-resolution models in HighResMIP, mostly based on NEMO ocean model35,36, only partially resolve the boundary current at 0.25o resolution and simulate the climate until year 2050, beyond which the AMOC decline is more significant15,16. High-resolution projections with various model configurations until 2100 are highly desired to validate our results and enable a more comprehensive understanding of the AMOC changes during the 21st century.\n\nAs anthropogenic warming continues, more freshwater is expected to enter the subpolar North Atlantic from the Greenland ice sheet melting and Arctic freshwater release. The extra freshwater input will necessarily interact with deep convection and cause a slowdown of the AMOC37,38,39. However, our results suggest that future AMOC responses will be sensitive to how the extra freshwater input is distributed in the high-latitude regions. To address this question, we need to monitor the freshwater sources and their exportation pathways through Arctic-subpolar North Atlantic gateways towards regions that could impact the AMOC. High-resolution models are also desired for a more accurate representation of freshwater transports associated with narrow boundary currents40,41,42,43 and oceanic eddies44,45,46,47 that are not resolved in coarse-resolution models.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The simulations used in this paper were carried out by CESM1.3 at the International Laboratory for High-Resolution Earth System Prediction (iHESP)24. CESM comprises the Community Atmosphere Model version 5 (CAM5), the Parallel Ocean Program version 2 (POP2), the Community Ice Code version 4 (CICE4), and the Community Land Model version 4 (CLM4). We make use of the configuration with high and low model horizontal resolutions. For HighRes, the resolutions of atmosphere, ocean, and sea-ice components are 0.25\u00b0, 0.1\u00b0, and 0.1\u00b0, respectively. For LowRes, the nominal horizontal resolutions are 1\u00b0. Both CESM simulations were run under the representative concentration pathway 8.5 (RCP8.5) forcing from 2006 to 2100 in accordance with the CMIP5 experimental protocol.\n\nThe freshwater flux (FWF) is defined as follows\n\nwhere v is the cross-section velocity (in units of m\u2009s\u22121), S is the salinity (in units of psu), dA is the cross-section area at each grid point (in units of m2), Sref is the reference salinity which is set as the salinity averaged in the Labrador Sea (denoted by the box in Fig.\u00a01d) in the year 2006. The Sref is similar in the two simulations with 34.7\u2009psu (34.8\u2009psu) for HighRes (LowRes). The time series of freshwater flux into the Labrador Sea shown in Fig.\u00a01 and supplementary Fig.\u00a04 is computed by summing up annual FWF at grid points with S\u2009<\u2009Sref and currents into the Labrador Sea along each section denoted in Fig.\u00a01d. The unit of FWF is converted from m3\u2009s\u22121 to mSv (1\u2009mSv\u2009=\u2009103\u2009m3\u2009s\u22121) in this paper.\n\nDense waters that reside in the lower limb of the AMOC are produced mainly in the eastern subpolar North Atlantic (i.e., the Irminger and Iceland basins) in observations, and to a lesser extent, in the western subpolar North Atlantic (i.e., the Labrador Sea)48. The strength of dense water formation can be measured by overturning across OSNAP West and OSNAP East in density space. In this paper, OSNAP overturning is calculated in density space with the density referenced to 2000-m depth (\u03c32) as the vertical coordinate (in units of kg\u2009m\u22123, after subtracting 1000\u2009kg\u2009m\u22123). Volume fluxes are integrated from west to east and from higher to lower density.\n\nThe overturning streamfunction in the subpolar North Atlantic and its low-frequency variability are largely determined by the SWMT13,14,30. The SWMT is the transformation of water from one density class to another due to buoyancy losses and gains at the sea surface. The SWMT is calculated by integrating the surface density flux over outcrop regions in each density bin as follows\n\nwhere B is the surface density flux (in units of kg seawater m\u22122\u2009s\u22121, defined as positive for ocean density increase), \u03c32 is the density referenced to 2000-m depth (in units of kg\u2009m\u22123, after subtracting 1000\u2009kg\u2009m\u22123), dA is the surface outcrop area (in units of m2, corresponding to densities in the range from \u03c32-\u0394\u03c32/2 to \u03c32\u2009+\u2009\u0394\u03c32/2). The unit of SWMT is converted from m3\u2009s\u22121 to Sv (1\u2009Sv\u2009=\u2009106 m3\u2009s\u22121) in the paper. The density flux comprises heat and salt fluxes that referred to as Bheat and Bsalt, respectively. The heat flux is\n\nwhere \u03b1 is the thermal expansion coefficient (in units of K\u22121), Cp is the specific heat capacity of seawater (in units of J\u2009kg\u22121\u2009K\u22121), Q is the surface net heat flux (in units of W\u2009m\u22122, defined as positive for ocean heat gain), which is the sum of surface radiation and turbulent heat fluxes. The salt flux is\n\nwhere S is the sea surface salinity (in units of msu, 1\u2009msu\u2009=\u200910\u22123\u2009psu), \u03b2 is the haline contraction coefficient (in units of msu\u22121), and F is the surface freshwater flux (in units of kg freshwater m\u22122\u2009s\u22121, defined as negative for ocean salinity increase).\n\nVariations in SWMT can be decomposed into contributions due to changes in surface density flux and surface density. We calculate the latter (referred to as SWMTOCN) in this paper by replacing the time-dependent surface density flux with the monthly-mean surface density flux in 2006 in Eq. (2).\n\nWe calculate the AMOC in density space that better represents the overturning circulation at high latitudes. The AMOC streamfunction is defined as follows\n\nwhere H is the Heaviside function, \u03c3\u2019 represents the density field, x is longitude, y is latitude, z is depth, t is time, \u03c3 is the density at which the streamfunction is calculated, and v is the Meridional velocity. The zonal integral is performed across the Atlantic Ocean from the western boundary (xw) to the eastern boundary (xe), and the vertical integral is performed from the ocean bottom (zbot) to the sea surface. Density referenced to 2000-m depth (\u03c32) is used as the vertical coordinate (in units of kg\u2009m\u22123, after subtracting 1000\u2009kg\u2009m\u22123). We also remap the AMOC streamfunction in density space into depth space using the time and zonally averaged depth of each density layer following previous studies31,32.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The climate model simulations and observation data used in this study are publicly available and can be downloaded from the following websites: CESM model outputs (https://ihesp.github.io/archive/products/ihesp-products/data-release/DataRelease_Phase2.html or http://ihesp.qnlm.ac), OSNAP overturning (https://www.o-snap.org/). The data generated in this study for plotting the figures in the paper are available from https://doi.org/10.5281/zenodo.12249262.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The iHESP version of CESM HighRes code is available at ZENODO via https://doi.org/10.5281/zenodo.3637771. 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Science 363, 516\u2013521 (2019).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "Funding: National Science Foundation Grant OPP-2211691 (to M.S.), Ocean University of China Fellowship for International Postdoctoral Research (to X.S.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Frontiers Science Center for Deep Ocean Multispheres and Earth System and Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, China\n\nXuan Shan\u00a0&\u00a0Lixin Wu\n\nWoods Hole Oceanographic Institution, Woods Hole, MA, USA\n\nXuan Shan\u00a0&\u00a0Michael Spall\n\nLaoshan Laboratory, Qingdao, China\n\nShantong Sun\u00a0&\u00a0Lixin Wu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: S.S., L.W. Investigation: X.S. Visualization: X.S. Supervision: S.S., L.W. Writing\u2014original draft: X.S. Writing\u2014review & editing: S.S., L.W., M.S.\n\nCorrespondence to\n Xuan Shan or Shantong Sun.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Virna Meccia and the other, anonymous, reviewer for their contribution to the peer review of this work. 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Role of the Labrador Current in the Atlantic Meridional Overturning Circulation response to greenhouse warming.\n Nat Commun 15, 7361 (2024). https://doi.org/10.1038/s41467-024-51449-9\n\nDownload citation\n\nReceived: 12 February 2024\n\nAccepted: 06 August 2024\n\nPublished: 27 August 2024\n\nVersion of record: 27 August 2024\n\nDOI: https://doi.org/10.1038/s41467-024-51449-9\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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lymph node", + "pre_title": "Radiation and anti-PD-L1 synergize by stimulating a stem-like T cell population in the tumor-draining lymph node", + "journal": "Nature Communications", + "published": "14 April 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_MOESM3_ESM.docx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_MOESM4_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_MOESM5_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_MOESM6_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-58510-1#Sec23" + ], + "code": [], + "subject": [ + "Cancer therapy", + "Tumour immunology" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3921977/v1.pdf?c=1744715131000", + "research_square_link": "https://www.researchsquare.com//article/rs-3921977/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58510-1.pdf", + "preprint_posted": "05 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Radiotherapy (RT) and anti-PD-L1 synergize to enhance local and distant (abscopal) tumor control. However, clinical results in humans have been variable. With the goal of improving clinical outcomes, we investigated the underlying synergistic mechanism focusing on a CD8+ PD-1+ Tcf-1+ stem-like T cell subset in the tumor-draining lymph node (TdLN). Using murine melanoma models, we found that RT + anti-PD-L1 induces a novel differentiation program in the TdLN stem-like population which leads to their expansion and differentiation into effector cells within the tumor. Our data indicate that optimal synergy between RT + anti-PD-L1 is dependent on the TdLN stem-like T cell population as either blockade of TdLN egress or specific stem-like T cell depletion reduced tumor control. Together, these data demonstrate a multistep stimulation of stem-like T cells following combination therapy which is initiated in the TdLN and completed in the tumor.Biological sciences/Cancer/Tumour immunologyBiological sciences/Cancer/Cancer therapyBiological sciences/Immunology/Tumour immunologyRadiationimmunotherapyPD-1PD-L1checkpoint blockadestem-like T cellsTcf-1tumor- draining lymph nodeabscopal effect", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-921977/v1/093c296f3dfa2ef94533bb59.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-921977/v1/7076cb97ab2271b66d2e7ecb.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-921977/v1/ae7a12ee5abaa1e34d4d70c1.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-921977/v1/7c1c5082839c828c2d1e1a43.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-921977/v1/de532bfce2a61082ca692cf2.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-921977/v1/ad68b9fa386cb91de0ec23e5.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-921977/v1/861b62a696a99f2ff16defbb.jpg%3FmaxDims%3D150x150&w=256&q=75.png" + ] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest. N.C.S. has a consulting role at Checkpoint Surgical, Sensorion, and Synergy Research, Inc, is a member of the advisory board of Regeneron, receives book royalties from Plural Publishing, and has received funding from Astex Pharmaceuticals. GBL has received research funding through a sponsored research agreement between Emory University and Merck and Co., Bristol-Myers Squibb, Boerhinger-Ingelheim, and Vaccinex.\nAll studies were reviewed and approved by the institutional IACUC committee. The IACUC institution was Emory University School of Medicine.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "KeyResourcesTable.docxKey Resources TableSupplementalfigures.pdfSupplemental figuresSupplementaryTable1.csvSupplementaryTable2.xls", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Combination radiotherapy (RT) and \u03b1PD-L1 therapy has potential to enhance local and distant (abscopal) tumor control, however, clinical results in humans have been variable. Using murine melanoma models, we found RT\u2009+\u2009\u03b1PD-L1 increases intra-tumor progenitor CD8+\u2009PD-1+\u2009TCF-1+\u2009T cells. This increase depends on trafficking of the PD-1+\u2009TCF-1+ cells from the tumor-draining lymph node (TdLN) to the tumor. RT alone promotes the expansion and differentiation of the TdLN derived PD-1+\u2009TCF-1+ cells into TIM-3+\u2009GZMB+\u2009TCF-1- effector-like cells in the tumor with further enhancement after the addition of \u03b1PD-L1. In the TdLN, combination therapy enriches for a novel PD-1+\u2009TCF-1+\u2009TOX- LY6A+ subset with expression of a type I interferon and migratory signature. This subset is able to traffic to the tumor and differentiate into TIM-3+\u2009TCF-1- cells. Finally, we found that ablation of the PD-1+\u2009TCF-1+\u2009T cell population attenuates the enhanced tumor control observed with combination RT\u2009+\u2009\u03b1PD-L1. These results suggest that abscopal response failures may be secondary to impaired stimulation of TdLN CD8+\u2009PD-1\u2009+\u2009TCF-1+\u2009T cells or an inability of PD-1+\u2009TCF-1+ cells in the TdLN to traffic to the tumor.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "CD8+\u2009T cells play a critical role in the anti-tumor immune response. However, chronic antigen exposure in cancer leads to CD8+\u2009T cell exhaustion with upregulation of markers including PD-1, TIM-3 as well as epigenetic changes1,2,3. Blockade of PD-1 promotes CD8+\u2009T cell expansion and reinvigoration leading to robust clinical responses in many different types of cancer4,5,6,7. Interestingly, CD8+\u2009PD-1+\u2009T cells within the tumor microenvironment are heterogenous with subsets including progenitor PD-1+\u2009TCF-1+\u2009T cells and PD-1+\u2009TIM-3+\u2009GZMB+ effector-like cells8,9. Following PD-1/L1 blockade, the CD8+\u2009PD-1+\u2009TCF-1+\u2009T cell subset expands and differentiates into\u00a0a TIM-3+\u2009GZMB+ subset which has the capacity for tumor killing10,11. Approaches which enhance this expansion and differentiation process in combination with \u03b1PD-1/L1 have the potential to further improve clinical outcomes.\n\nRadiotherapy (RT) is effective as a local treatment and is known to also have immunomodulatory effects. On occasion, tumor regression outside the radiation field occurs via immune-stimulation, a process known as the abscopal effect12,13,14. RT mediates this effect, in part, by acting as an in-situ vaccine while broadening the T cell receptor repertoire and recruiting na\u00efve/antigen experienced T cells to the anti-tumor immune response14,15,16. Importantly, RT can improve local and distant disease control when combined with immune checkpoint blockade including \u03b1PD-1/L1 in pre-clinical studies, however, clinical trial results have been mixed15,17,18,19,20,21,22,23. Understanding the impact of RT and combination therapy on specific T cell subsets may lead to more sophisticated integration approaches for these two treatment modalities and improved clinical outcomes.\n\nThe tumor-draining lymph node (TdLN) is important for a robust RT or \u03b1PD-1/L1 stimulated immune response24,25,26,27,28. More recent studies have shown that the TdLN acts as a reservoir for PD-1+\u2009TCF-1+\u2009T cells26,29,30. This population of PD-1+\u2009TCF-1+\u2009T cells in the TdLN serve as developmental precursors for the intra-tumoral population, and they continuously migrate from the TdLN to the tumor under basal conditions29. Once in the tumor TCF-1+ cells undergo further differentiation into TIM-3+\u2009TCF-1- subsets. This process is promoted by \u03b1PD-1/L126. Finally, our earlier work suggested this TdLN reservoir of TCF-1+ cells may also be important for the RT alone stimulated immune response25. Together, these findings suggest that the TdLN PD-1+\u2009TCF-1+\u2009T cell population is important for enhanced tumor control with combination RT\u2009+\u2009\u03b1PD-L1.\n\nHere, using murine models of melanoma, we found that RT alone and in combination with \u03b1PD-L1 promoted significant tumor infiltration and differentiation of TdLN derived CD8+\u2009PD-1+\u2009TCF-1+\u2009T cells. In the TdLN, combination therapy enriched for a novel PD-1+\u2009TCF-1+\u2009TOX- LY6A+ subset with expression of a type I interferon and migratory signature. This subset had the capacity to migrate to the tumor and differentiate into a TCF-1- TIM-3+\u2009GZMB+ effector-like population. Finally, ablation of the PD-1+\u2009TCF-1+\u2009T cells worsened tumor control following combination therapy confirming the importance of this population to the anti-tumor activity of combination RT\u2009+\u2009\u03b1PD-L1.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We previously showed that RT alone can enhance the anti-tumor immune response leading to improved tumor control in a CD8+\u2009T cell dependent manner25. Here, to interrogate the impact of combination RT\u2009+\u2009\u03b1PD-L1 on local and abscopal tumor control as well as CD8+\u2009T cell subsets, B16F10 cells expressing the lymphocytic choriomeningitis (LCMV) glycoprotein (B16F10GP), which allow for the identification of tumor-specific T cells25, were sequentially implanted on the bilateral flanks of wt C57BL/6 mice (Fig.\u00a01a). Sequential implantation of the flank tumors was done to model metachronous metastatic disease. Tumor 1, the initially injected tumor, was treated with 10\u2009Gy x 1 fraction of RT (Supplementary Fig.\u00a01a) with or without \u03b1PD-L1 starting day 10 post-implantation25. Mice were sacrificed 9 days after treatment initiation (day 19) for tissue analysis (Fig.\u00a01a). Tumor 1 growth was significantly reduced with RT alone, and tumor 2 growth also exhibited a strong trend towards slowed growth (Fig.\u00a01b, Supplementary Fig.\u00a01b, c). In contrast, \u03b1PD-L1 alone had minimal effect on the growth of tumor 1 or tumor 2 consistent with the known resistance of this cell line to PD-1 based therapy31,32. Combination RT\u2009+\u2009\u03b1PD-L1, however, slowed the growth of both the irradiated tumor 1 and the unirradiated tumor 2 to a greater extent than either monotherapy (Fig.\u00a01b, Supplementary Fig. 1b, c). We performed the same kinetic analysis in the parental B16F10 cell line and found similar robust enhancement with RT\u2009+\u2009\u03b1PD-L1 compared to monotherapy at both the primary and abscopal site (Supplementary Fig.\u00a01d).\n\na Experimental schema. b Tumor growth kinetics for the RT targeted tumor 1 and the distant (abscopal tumor) tumor 2 demonstrating enhanced control with combination therapy. Con, control; RT, radiation therapy. Data reflect 2 separate experiments combined (Con n\u2009=\u20098, RT n\u2009=\u20099, \u03b1PD-L1 n\u2009=\u20098, RT/\u03b1PD-L1 n\u2009=\u200910 total). Statistical significance calculated by two-tailed unpaired t tests. c Representative plots of GP33+\u2009PD-1+\u2009T cells gated on CD8 in tumor 1 and tumor 2 under different treatment conditions. d Quantitation plots for number of GP33+\u2009T cells per gram tumor. e Representative plots of PD-1+\u2009TCF-1+ and PD-1+\u2009TIM-3+ gated on CD8+\u2009PD-1+\u2009GP33+\u2009T cells. f Quantitation plots for number of PD-1+\u2009TCF-1+\u2009T cells per gram tumor. g Quantitation plots for number of PD-1+\u2009TIM-3+\u2009T cells per gram tumor. The vast majority of GP33+\u2009T cells in the tumor are TIM-3+\u2009, therefore, the plots showing total GP33+\u2009T cells (d) and the TIM-3+ subset are very similar. h Representative histogram flow plots. Data reflect 3 separate experiments combined (n\u2009=\u200913 total). GZMB, granzyme B. All data are presented as mean values\u2009\u00b1\u2009SEM. Statistical significance calculated by Kruskal-Wallis test, unless otherwise noted. Source data are provided as a Source Data file.\n\nNext, we investigated the anti-tumor immune response and found the number of bulk CD8+\u2009T cells in tumor 1 and tumor 2 were not significantly increased with RT or \u03b1PD-L1 alone while combination therapy demonstrated significant increases in both tumors (Supplementary Fig.\u00a01e). We then evaluated tumor specific CD8+\u2009PD-1+\u2009GP33+\u2009T cells and again found a significant increase in tumor 1 and tumor 2 following combination treatment compared to untreated or monotherapy (Fig.\u00a01c, d). Given the importance of the PD-1+\u2009TCF-1+ subset for the \u03b1PD-L1 response10,11, we investigated whether this population changed in the tumor following RT\u2009+\u2009\u03b1PD-L1. We found that both the tumor specific TCF-1+ and TCF-1- TIM-3+ populations substantially increased in tumor 1 and tumor 2 after combination therapy compared to either monotherapy alone with no significant changes in their relative frequencies (Fig.\u00a01e\u2013g, Supplementary Fig.\u00a01f, g). PD-1+\u2009TIM-3+\u2009TCF-1- cells were predominantly GZMB+ and TOX+ consistent with the previously described effector-like subset in cancer and chronic LCMV infection (Fig.\u00a01h)8,33. Combination therapy also led to significant increases in both the tumor CD8+\u2009IFN-\u03b3+ and IFN-\u03b3+\u2009TNF-\u03b1+\u2009T cells (Supplementary Fig.\u00a01h\u2013k). We repeated the experiment using 8\u2009Gy \u00d7 3 fractions and again observed slowed tumor growth with combination RT\u2009+\u2009\u03b1PD-L1 at both the primary and distant (abscopal) site (Supplementary Fig.\u00a02a\u2013e). We confirmed these findings with another melanoma cell line, YUMM1.7 (Supplementary Fig.\u00a02f\u2013j).\n\nOur group and others have shown that the tumor-draining lymph node (TdLN) is an important reservoir for PD-1+\u2009TCF-1+\u2009T cells supplying the tumor25,26,29,30. Tumor antigen specific cells are found in the TdLN but not the non-TdLN or other secondary lymphoid organs like the spleen (Supplementary Fig.\u00a03a). We have previously shown that disrupting this reservoir of lymphocytes in the TdLN using fractionated radiation impaired RT alone mediated immunostimulation25. These data suggest that the tumor-specific PD-1+\u2009TCF-1+\u2009T cell reservoir in the TdLN are the source of the increase in tumor PD-1+\u2009TCF-1+ and PD-1+\u2009TCF-1- T cells following combination therapy as well as the enhanced tumor control. To evaluate this hypothesis, we again confirmed that the majority of the GP33+\u2009T cells in the TdLN were TIM-3- TCF-1+ while most of the GP33+\u2009T cells in the tumor were TIM-3+ (Fig.\u00a02a, b). Mice were then treated with FTY720 prior to RT or \u03b1PD-L1 to prevent lymphocyte egress from the TdLN and other secondary lymphoid organs (Fig.\u00a02c). The TdLN analyzed throughout was taken from the RT targeted side (tumor 1 side). The percentage of circulating total lymphocytes, CD4+\u2009, and CD8+\u2009T cells in the blood decreased significantly upon FTY720 administration (Supplementary Fig.\u00a03b\u2013d). In both tumors, administration of FTY720 blocked the increase of total CD8+ and PD-1+\u2009GP33+\u2009T cells observed following combination therapy (Fig.\u00a02d, e, Supplementary Fig.\u00a03e\u2013g). Examination of the subsets found the increased numbers of tumor-antigen specific PD-1+\u2009TCF-1+ and TIM-3+ in both tumors induced by combination therapy was also abolished by FTY720 treatment (Fig.\u00a02f\u2013h, Supplementary Fig.\u00a03h\u2013j). Notably, FTY720 also attenuated the slowing of tumor 1 and tumor 2 growth by combination RT\u2009+\u2009\u03b1PD-L1 (Fig.\u00a02i, Supplementary Fig.\u00a03k).\n\na Representative plots of PD-1+\u2009GP33+\u2009T cells and PD-1+\u2009TCF-1+ cells vs. TIM-3+ in the tumor and its TdLN. b Quantitation of the PD-1+\u2009TCF-1+ cells frequency in the tumor and its TdLN. Combined data from 2 experiments (n\u2009=\u200910 total). c Experimental schema with yellow bar representing FTY720 administration in drinking water. d Representative plots gated on CD8 showing PD-1+\u2009GP33+\u2009T cells in the tumor under different treatment conditions with or without FTY720. e Quantitation of the number of GP33+\u2009T cells per gram tumor. Combined data from 2 experiments (n\u2009=\u200910 total). f Representative plots gated on antigen specific subsets under different treatment conditions with or without FTY720. g Quantitation of the number of antigen specific PD-1+\u2009TCF-1+\u2009T cells per gram tumor. Combined data from 2 separate experiments (n\u2009=\u200910 total per group). h Quantitation of the number of antigen specific PD-1+\u2009TIM-3+ cells per gram tumor. Combined data from 2 separate experiments (n\u2009=\u200910 total per group). i Tumor kinetics under different treatment conditions with and without FTY720. Statistical significance calculated by two-tailed unpaired t tests. Combined data from 2 experiments (n\u2009=\u200915 total). All data are presented as mean values\u2009\u00b1\u2009SEM. Statistical significance calculated by Kruskal-Wallis test, unless otherwise noted. Source data are provided as a Source Data file.\n\nIn the TdLN, the frequency and number of total CD8+\u2009T cells following RT\u2009+\u2009\u03b1PD-L1 remained unchanged with or without FTY720 (Supplementary Fig.\u00a04a, b). In contrast, the frequency and number of CD8+\u2009PD-1+\u2009GP33+\u2009T cells significantly increased with FTY720 treatment. (Supplementary Fig.\u00a04c\u2013e). Importantly, the number of GP33+\u2009PD-1+\u2009TCF-1+\u2009T cells was significantly increased in the TdLN with FTY720 and combination therapy (Supplementary Fig.\u00a04f\u2013h); the TIM-3+ did not reach significance (Supplementary Fig.\u00a04i, j). Together, these results support the hypothesis that the increase in tumor PD-1+\u2009TCF-1+\u2009T cells following RT\u2009+\u2009\u03b1PD-L1 depends on their egress from the TdLN.\n\nPrior data has shown that \u03b1PD-L1 monotherapy promotes the expansion and differentiation of CD8+\u2009PD-1+\u2009TCF-1+\u2009T cells10,11,26. To determine the impact of RT alone and combination therapy on this population\u2019s differentiation, we performed a serial adoptive transfer experiment using P14 T cells. First, we sacrificed mice 14 days after a single tumor injection to confirm that adoptively transferred na\u00efve P14 T cells would activate in the TdLN and differentiate within the TdLN and tumor into TCF-1+ and TCF-1- subsets respectively (Fig.\u00a03a). P14s were recovered in both the TdLN and tumor, and the vast majority of the cells (99%) were PD-1+\u2009TCF-1+\u2009TIM-3- in the TdLN and TIM-3+ in the tumor like endogenous cells (Fig.\u00a03b\u2013d). We then sorted CD44+\u2009PD-1+\u2009TIM-3- P14s from the TdLNs of tumor bearing mice and transferred them into separate B16F10GP tumor-bearing mice (Fig.\u00a03e, Supplementary Fig.\u00a04k). These recipients received RT with or without \u03b1PD-L1 3 days later (Fig.\u00a03e). We did not find any significant difference in the number of total or TCF-1+\u2009P14s in the\u00a0recipient TdLN with either monotherapy or combination (Fig.\u00a03f, g, Supplementary Fig.\u00a04l).\n\na Experimental schema. b Representative flow plots showing the P14 T cells differentiating into PD-1+\u2009TCF-1+ cells in the TdLN and (c) PD-1+\u2009TIM-3+ in the tumor. d Frequency of PD-1+\u2009TCF-1+ cells and PD-1+\u2009TIM-3+ in the tumor versus its TdLN. e Experimental schema with serial adoptive transfer. f Representative flow plot of gating on transferred P14s in the TdLN of tumor 1 under different treatment conditions. g Quantitation of the number of P14s in the TdLN of tumor 1 by treatment condition. Data reflect combined data from two separate experiments (n\u2009=\u20096 total). h Representative flow plot of gating on transferred P14s in the tumor under different treatment conditions. i Quantitation of P14s per gram tumor. Data reflect combined data from two separate experiments (n\u2009=\u20096 total). Statistical significance calculated by Kruskal-Wallis test. j Representative flow plots of P14 T cell subsets in the tumors. k Frequency of PD-1+\u2009TCF-1+ and l PD-1+\u2009TIM-3+ in the tumors. Data reflect combined data from two separate experiments (n\u2009=\u20096 total). All data are presented as mean values\u2009\u00b1\u2009SEM. Statistical significance calculated by one-way ANOVA, unless otherwise noted. Source data are provided as a Source Data file.\n\nIn contrast, we found a significant increase in the number of P14s in tumor 1 for RT alone (Fig.\u00a03h, i). The frequency of PD-1+\u2009TCF-1+ cells significantly decreased in tumor 1 with a concomitant increase in the frequency of TIM-3+ cells demonstrating that RT alone can promote both expansion and differentiation of TdLN PD-1+\u2009TCF-1+\u2009T cells in the RT targeted tumor (Fig.\u00a03j\u2013l). Importantly, combination therapy led to greater expansion of P14s in both tumor 1 and tumor 2 and enhanced differentiation of PD-1+\u2009TCF-1+\u2009T cells into TIM-3+ cells compared to either monotherapy alone (Fig.\u00a03h\u2013l).\n\nTo further interrogate the TdLN, we performed single cell RNA-seq (scRNA-seq) on sorted TdLN na\u00efve and CD8+\u2009PD-1+\u2009T cells under untreated conditions. Published tumor infiltrating CD8+\u2009T cell data from similar tumors models were also introduced into our analysis34. Unsupervised clustering using uniform manifold approximation and projection (UMAP) revealed substantial heterogeneity within CD8+\u2009T cells, identifying at least six distinct subtypes across the TdLN and tumor (Fig.\u00a04a). These clusters included: Cluster 1, na\u00efve T cells, defined by high Tcf7 expression and negative for activation markers including Fos and Jun; Cluster 2, stem-like-1 (TSTEM-1), defined by Tcf7 and Fos expression and the absence of Tox expression; Cluster 3, stem-like-2 (TSTEM-2) co-expressing Tcf7 and Ly6a with low Tox expression; Cluster 4, progenitor exhausted (TPEX), characterized by co-expression of Tcf7 and Tox; Cluster 5, effector-like and terminally differentiated (TD), defined by Tcf7 negativity and positive Tox expression; and Cluster 6, cycling T cells, identified by Mki67 expression (Fig.\u00a04a). In the tumor, the predominant CD8+\u2009T cell subset was Cluster 5, comprising about 50% of the CD8+\u2009PD-1+\u2009T cell population (Fig.\u00a04b). In contrast, Tcf7-positive subsets (Clusters 2, 3, and 4) were primarily found in the TdLN, with the Tcf7+ Tox+\u2009TPEX population (Cluster 4) being the most abundant. The resulting clusters were validated by comparing their transcriptional signatures to known marker genes and previously published datasets, ensuring the identified subsets were biologically meaningful and consistent with established T cell populations (Supplementary Fig.\u00a05a)26.\n\na UMAP (Uniform Manifold Approximation and Projection) identified six major cell populations in the TdLN and tumor. b Quantitation of different subset frequencies in the TdLN and the tumor; with subset identities as indicated in (a). c TCR (T-cell receptor) sequencing demonstrates percentage overlap between antigen experienced polyclonal CD8+\u2009T cell populations in the tumor and TdLN. Each row (M) reflects a different mouse. d Feature plots showing expression levels of relevant markers. e Average expression and percent expressing various markers in the different T cell subsets. f Density plot of stemness module score (Tcf7, Il7r, Sell, Fos, Jun, Cd69). g Density plot of exhaustion module score (Lag3, Ctla4, Tigit, Entpd1, Pdcd1). Source data are provided as a Source Data file and are available on the NCBI Gene Expression Omnibus (GEO) database.\n\nTo evaluate for a clonal relationship between TdLN-derived CD8+\u2009PD-1+\u2009T cells and CD8+\u2009T cells within the tumor, we performed bulk-TCR sequencing on sorted polyclonal CD8+\u2009PD-1+\u2009T cells from paired TdLN and tumor samples from untreated mice. TCR sequencing demonstrated that up to 50% of the tumor TCR repertoire overlaps with the TdLN, suggesting that a significant fraction of antigen-experienced TdLN T cells are tumor-specific, even under untreated conditions (Fig.\u00a04c).\n\nA hallmark of T cell exhaustion is the sustained expression of markers such as Tox, Ctla4, Entpd1, Pdcd1, and Havcr2, many of which were enriched in the tumor-infiltrating subset (Cluster 5) (Fig.\u00a04d, e). Notably, exhaustion markers such as Lag3, Ctla4, and Tox were also expressed by the TPEX subset, distinguishing them from other TCF-1+ populations (Fig.\u00a04e). Additionally, Cluster 5 in the tumor exhibited elevated expression of effector genes, such as Gzmb, Ifng, and Klrk1, linked to cytotoxic T cell functions (Fig.\u00a04d, e). In contrast, TdLN PD-1+\u2009CD8+\u2009T cells (Clusters 2 and 3) were enriched for stemness markers (e.g., Tcf7, Il7r, Sell, Ccr7) and activation markers (e.g., Jun, Fos, Cd69, Junb) (Fig.\u00a04d). We identified a distinct stem-like subset (Cluster 3, TSTEM-2), marked by the co-expression of an interferon-stimulated gene signature including type I interferon response genes (Isg15, Irf7, and Ifitm3), chemokine markers (Ccr5 and Ccrl2), and the murine Ly6 gene complex, including Ly6a and the memory marker Ly6c (Fig.\u00a04e)35. This subset exhibited a unique profile, characterized by the presence of both stemness-associated and effector genes. Notably, the expression of effector genes such as Klrk1 and Gzmb distinguished TSTEM-2 from the canonical TSTEM-1 subset (Cluster 2), which lacked these markers. Given TSTEM-2 exhibits characteristics of both stemness and differentiation, this subset may play a transitional role within the CD8+\u2009T cell response in the TdLN despite the lack of Tox expression like the canonical TPEX (Fig.\u00a04e).\n\nNext, to characterize the broader pattern of T cell phenotypes across the TdLN and tumor, we identified two highly correlated gene modules: a stemness module in the TdLN and an exhaustion module in the tumor, consistent with prior reports (Fig.\u00a04f, g)26,29. While these modules do not directly correspond to cluster-specific marker genes, they provide complementary insights, capturing dynamic transcriptional programs associated with differentiation states. These data support the idea that TSTEM-1 and Cluster 5 occupy opposite ends of the T cell differentiation spectrum.\n\nTo assess the impact of combination therapy on these different antigen experienced TCF-1+ subsets within the TdLN, we performed scRNA-seq on sorted CD8+\u2009PD-1+\u2009T cells from the TdLN seven days post-treatment with \u03b1PD-L1, RT alone, or combination RT\u2009+\u2009\u03b1PD-L1 therapy. A total of 38,578 cells were analyzed, with an average of 1928 cells per sample across five mice per treatment group. Unsupervised clustering of the TCF-1+ cells identified 3 major clusters (TSTEM-1, TSTEM-2, TPEX) in the TdLN under different treatment conditions (Fig.\u00a05a). CD8+\u2009PD-1+\u2009T cell subset frequencies from untreated mice were largely similar to those treated with \u03b1PD-L1 or RT monotherapy. However, combination therapy led to a notable phenotypic shift, marked by an over 10-fold increase in the frequency of the TSTEM-2 subset (Fig.\u00a05a\u2013c, Supplementary Fig.\u00a05b), which was accompanied by a reduction in the frequency of both the TSTEM-1 and TPEX population (Fig.\u00a05a\u2013c, Supplementary Fig.\u00a05b). Differential abundance analysis revealed that the expansion of the TSTEM-2 subset was statistically significant (FDR\u2009<\u20090.05) when comparing RT\u2009+\u2009\u03b1PD-L1 to monotherapies or control (Fig.\u00a05c).\n\na UMAP and quantitation demonstrating the major cell PD-1+ Tcf7-expressing T cell populations in the TdLN under different treatment conditions. b UMAP by treatment condition with the novel population in RT\u2009+\u2009\u03b1PD-L1 group circled. c Proportions of Tcf7-expressing CD8+\u2009PD-1+\u2009T cell subtypes were compared across treatment conditions using a two-sided permutation test (1000 iterations), with empirical P values adjusted by the Benjamini\u2013Hochberg method. Red dots indicate significant differences (FDR\u2009<\u20090.05, |log2FC\u2009|\u2009>\u20090.58); gray dots are non-significant. Exact p values and 95% confidence intervals (bootstrapped, 1000 iterations) are reported. d Density plots showing expression levels of Tcf7, Ly6a, Tox, Klrk1, Ccrl2, and Gzmb in CD8+ Tcf7-expressing PD-1+\u2009T cells from the TdLN, with color intensity representing scaled expression levels (purple = minimum; yellow\u2009=\u2009maximum). e Gene expression patterns across CD8+\u2009PD-1+ Tcf7-expressing T cell subsets under different treatments (RT, \u03b1PD-L1, RT\u2009+\u2009\u03b1PD-L1) compared to controls. Dot size represents percent expression, and color indicates average expression levels for exhaustion, effector, Ly6a, migration, cytokine receptor, stem, and Type I interferon (IFN) genes. f RNA velocity analysis of CD8+\u2009PD-1+\u2009T cell subsets with arrows indicating inferred directional transitions between TSTEM-1, TSTEM-2, TPEX, and Cluster 5 (Effector-like/TD) states. Source data are provided as a Source Data file and are available on the NCBI Gene Expression Omnibus (GEO) database.\n\nGiven these phenotypic changes, we next quantified differentially expressed genes (DEGs) in each treatment group relative to untreated controls to explore therapy-induced changes in gene expression. Combination therapy with RT and \u03b1PD-L1 resulted in a markedly higher number of upregulated DEGs in CD8+\u2009PD-1+\u2009T cells compared to either monotherapy alone (Combo\u2009=\u2009178 genes; RT\u2009=\u20095 genes; \u03b1PD-L1\u2009=\u20096 genes), with minimal overlap in upregulated genes across the three treatment groups (Supplementary Fig.\u00a05c, d). In contrast, relatively few genes were significantly downregulated across all three treatment cohorts (Combo = 13 genes; \u03b1PD-L1\u2009=\u200914 genes; RT\u2009=\u200913 genes) (Supplementary Fig.\u00a05c), emphasizing the unique effect of combination therapy in driving gene upregulation.\n\nTo better understand the functional and transcriptional relevance of the expanded TSTEM-2 subset, we examined a curated panel of key differentially expressed genes between TSTEM-2 and the other Tcf7-expressing subsets (Fig.\u00a05d). Stem-like and memory-associated marker Ly6a was enriched in TSTEM-2 cells, consistent with their progenitor-like identity. Additionally, the migration-associated marker Ccrl2 and the activating receptor Klrk1, commonly associated with NK cells, were upregulated alongside the effector molecule Gzmb. In contrast, exhaustion markers such as Tox were predominantly expressed in the TPEX subset, highlighting distinct transcriptional states.\n\nBuilding on these findings, we explored TSTEM-2-specific transcriptional responses across different treatment conditions. TSTEM-2 cells displayed distinct transcriptional changes with effector genes such as Gzmb and Klrk1 upregulated in all treatment groups (RT\u2009+\u2009\u03b1PD-L1, RT, and \u03b1PD-L1) compared to the control, suggesting enhanced cytotoxic potential across therapeutic contexts. Exhaustion markers (Dapl1, Ctla4, Dusp1, and Btla) were markedly reduced, particularly in the combination therapy group. Stem-related genes (Tcf7, Il7r, and Sell) remained consistently expressed. Of note, Ly6a genes (Ly6c2 and Ly6a), migration-related genes (Cxcr3, Ly6c2, Cxcl10, Ccrl2, and Icam1), cytokine receptors (Il18rap, Ifngr1, and Il18r1), and type I interferon response genes ((Irf7, Isg15, Ifitm3 and Stat3) were elevated in TSTEM-2 cells compared to other subsets under all treatment conditions; however, these pathways were further upregulated in the TSTEM-2 cells of the combination therapy group. Other subsets, including TSTEM-1 and TPEX, exhibited less pronounced transcriptional changes in response to treatment, emphasizing the unique impact of combination therapy on TSTEM-2 cells (Fig.\u00a05e, Supplementary Fig.\u00a05e, f).\n\nGiven the TSTEM-2 cell subset expression of both stemness and effector-like markers, we evaluated the differentiation trajectories of the TdLN TCF-1+ populations. RNA velocity was performed on cells under combination RT\u2009+\u2009\u03b1PD-L1. The velocity vector field, visualized on the UMAP embedding (Fig.\u00a05f), points to distinct pathways originating from TSTEM-1 and progressing towards either TSTEM-2 or TPEX populations. These findings suggest the TSTEM-2 are an intermediate in a non-canonical differentiation program driven by combination RT\u2009+\u2009\u03b1PD-L1 therapy.\n\nNext, to investigate these scRNA-seq findings further and determine if they correlated with protein expression, we adoptively transferred tumor specific CD45.2+ P14s into CD45.1 mice, implanted a single tumor, treated with RT + \u03b1PD-L1, and then, on day 17, we co-stained the transferred cells in the TdLN and tumor. In the TdLN, again gating on PD-1+\u2009CD44\u2009+\u2009P14s, we observed both TCF-1+\u2009LY6A+ and TCF-1+\u2009LY6A- subsets (Fig.\u00a06a). Within the TCF-1\u2009+\u2009LY6A- population, we found a TOX+ and TOX- population which were defined as TSTEM-1 and TPEX respectively22. Within the LY6A+ population, we further identified a CD314+ subset, and consistent our scRNA-seq data, this was termed the TSTEM-2 population. We found a\u2009>\u200910 fold increase in the TSTEM-2 population between control and combination therapy as in the scRNA-seq data. There were also notable, but lower magnitude increases in TPEX and TSTEM-1 (Fig.\u00a06b). In the tumor, a TOX+\u2009TCF-1- subset was observed (Fig.\u00a06c). The MFI for a selection of markers varied somewhat across these different subsets and was consistent with the scRNA-seq data (Fig.\u00a06d). Tumor-specific P14 TSTEM-2 cells were also identified in the blood following RT + \u03b1PD-L1 (Fig.\u00a06e), suggesting they can traffic from the TdLN to other tissues including the tumor. A similar increase in the TdLN TSTEM-2 population was observed with another tumor, YUMMER1.7, treated with combination RT\u2009+\u2009\u03b1PD-L1 (Supplementary Fig.\u00a06a\u2013c), demonstrating the findings are not limited to a single model.\n\na Representative flow plots showing the gating strategy for identification of TCF-1+ subsets among transferred P14 cells in the TdLN following RT + \u03b1PD-L1. b Quantification of P14 subsets in the TdLN. Data are combined from two separate experiments (n\u2009=\u20098 total). c Representative flow plot gating on transferred P14s in the tumor. d Representative histogram plots for the different subsets. e Representative flow plots of P14 TCF\u22121+\u2009LY6A+\u2009CD314+ cells in the blood. f Experimental schema for serial adoptive transfer. g Representative flow plots depicting expression of various markers on TSTEM-2 cells pre- and post-transfer. h Quantification of MFIs (mean fluorescence intensity) of different markers; data reflect combined data from two separate experiments (n\u2009=\u20096 total). All data are presented as mean values\u2009\u00b1\u2009SEM. Statistical significance calculated by two-tailed unpaired t test. Source data are provided as a Source Data file.\n\nSince the tumor-specific TSTEM-2 are identifiable in the blood and express a migratory transcriptional signature, we performed an adoptive transfer experiment to determine whether they can infiltrate the tumor and can further differentiate into effector-like cells (Fig.\u00a06f). P14 TSTEM-2 from the TdLN were sorted and transferred into B16F10GP single tumor-bearing mice (Fig.\u00a06f, Supplementary Fig.\u00a06d). 7 days later, the transferred cells were in the tumor and had downregulated TCF-1, CD62L, increased expression of PD-1 and upregulated GZMB and TIM-3 demonstrating an ability to differentiate into an effector-like subset (Fig.\u00a06g, h).\n\nFinally, given the broad impact of RT/\u03b1PD-L1 on the PD-1+\u2009TCF-1+ subset in the TdLN, we evaluated whether this subset was required for the enhanced tumor control observed with combination therapy. We generated a knock-in mouse allowing for specific depletion of TCF-1+\u2009T cells. A diphtheria toxin receptor (DTR) P2A eGFP gene was inserted into the 3\u2019 untranslated region of the Tcf7 locus using CRISPR technology (Supplementary Fig.\u00a07a). CD45.2 Tcf7DTR-eGFP were then bred with P14 mice to generate CD45.2 P14 Tcf7DTR-eGFP (Supplementary Fig.\u00a07a). To verify that P14 Tcf7DTR-eGFP (P14 DTR+\u2009) activate and differentiate into T cells expressing both PD-1 and TCF-1 as well as eGFP, we adoptively transferred CD45.2 P14 DTR+ cells into CD45.1 mice on day -1. B16F10GP cells were then injected on bilateral flanks on day 0. We found P14 DTR+\u2009T cells in both tumors, TdLN and expressing PD-1 at all sites (Supplementary Fig.\u00a07b\u2013d). eGFP was highly expressed in the TCF-1+ subset in both tumors and TdLN, but not in the TCF-1- TIM-3+\u2009T cells from the tumors on day 19 (Supplementary Fig.\u00a07e, f). Of note, PD-1+\u2009TCF-1+\u2009P14 DTR+ numbers in the TdLN and tumors were similar to P14 DTR- suggesting no differences in response to chronic antigenic stimulation (Supplementary Fig.\u00a07g, h). Tumor growth kinetics for both tumor 1 and tumor 2 in P14 DTR+ and DTR- recipients were indistinguishable (Supplementary Fig.\u00a07i).\n\nNext, we tested whether diphtheria toxin (DT) specifically depleted the TCF-1+\u2009T cell population. DTR- or DTR+\u2009P14 were adoptively transferred followed by bilateral tumor inoculations and DT administration (Supplementary Fig.\u00a08a). In both the TdLN and the bilateral tumors, DT ablated TCF-1+ cells from adoptively transferred P14 DTR+ but not from P14 DTR- littermate controls (Supplementary Fig.\u00a08b\u2013e). There was also a reduction in TIM-3+\u2009TCF-1- population (Supplementary Fig.\u00a08f) attributable to the elimination of the precursor TCF-1+\u2009T cells. Importantly, endogenous PD-1+\u2009TCF-1+\u2009T cells and PD-1+\u2009TIM-3+ were unchanged in either DTR- or DTR+ recipients in the TdLN and both flank tumors (Supplementary Fig.\u00a08g\u2013i).\n\nHaving validated specific depletion of TCF-1+\u2009T cells in CD45.1 recipient mice, we explored the impact of PD-1+\u2009TCF-1+\u2009T cell depletion on RT\u2009+\u2009\u03b1PD-L1. To do this, we again adoptively transferred P14 DTR+ or P14 DTR- from littermate controls into CD45.1 and implanted tumors on bilateral flanks (Fig.\u00a07a) followed by combination therapy starting on Day 12. In the TdLN, the adoptively transferred P14+\u2009DTR- T cells were detectable, had robustly upregulated PD-1 and were nearly all TCF-1+\u2009, while the TCF-1+ cells in P14 DTR+ recipients were all depleted (Fig.\u00a07b\u2013e, Supplementary Fig.\u00a09a). Additionally, in the P14 DTR- recipient mice treated with RT\u2009+\u2009\u03b1PD-L1, we could detect the LY6A+ cells in the TdLN draining the irradiated tumor (tumor 1), and this TCF-1 expressing subset was also ablated in the P14 DTR+ recipients (Fig.\u00a07f, g). Next, we evaluated tumor 1 and tumor 2 and found transferred P14s in both the P14 DTR+ and P14 DTR- recipients with all expressing high levels of PD-1 (Fig.\u00a07h, i). The TCF-1+\u2009T cell subset was again specifically depleted in only P14 DTR+ recipients (Fig.\u00a07j, k, Supplementary Fig.\u00a09b\u2013d). A reduction was also observed in both tumors of the TIM-3+ subset again confirming that the PD-1+\u2009TCF-1+\u2009T cells are necessary for TIM-3+\u2009TCF-1- production (Fig.\u00a07l, Supplementary Fig.\u00a09e). Finally, we evaluated the growth of tumors 1 and 2 and found that with specific PD-1+\u2009TCF-1+ depletion, both local tumor control and the abscopal effect induced by combination RT\u2009+\u2009\u03b1PD-L1 were significantly reduced (Fig.\u00a07m, Supplementary Fig.\u00a09f). It is likely that the endogenous PD-1+\u2009TCF-1+ are responsible for the DTR+ group still demonstrating slowed tumor kinetics compared to controls. The results demonstrate that PD-1+\u2009TCF-1+\u2009T cells are important for optimally\u00a0 enhanced tumor control observed with combination RT\u2009+\u2009\u03b1PD-L1.\n\na Experimental schema with diphtheria toxin (DT) reflecting time points of DT administration. b Representative flow plots of DTR- or DTR+\u2009P14 cells in the TdLN of tumor 1. DTR, diphtheria toxin receptor. c Representative histogram plot of PD-1 expression in the DTR- P14 T cells in the TdLN of tumor 1. d Representative flow plots of P14 subsets in the TdLN of tumor 1 for DTR- and DTR+\u2009. e Quantitation of P14 PD-1+\u2009TCF-1+\u2009T cells in the TdLN. f Representative flow plots of LY6A+\u2009TCF-1+ cells in the TdLN. g Quantitation of LY6A+\u2009TCF-1+ cells in the TdLN. h Representative flow plots of DTR- or DTR+\u2009P14 cells in the tumor. i Representative histogram plots for PD-1 expression in DTR+ and DTR- P14 cells. j Representative flow plots of P14 subsets in the tumors. k Quantitation of the number of P14 PD-1+\u2009TCF-1+\u2009T cells and (l) TIM-3+ per gram tumor. m Tumor growth kinetics under different treatment conditions with DTR+ or DTR- P14 cell transfer. Data shown from a representative experiment n\u2009=\u20095 per group, repeated 3 times. All data are presented as mean values\u2009\u00b1\u2009SEM. Statistical significance calculated by two-tailed unpaired t test. Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58510-1/MediaObjects/41467_2025_58510_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The aim of our study was to mechanistically dissect the abscopal effect mediated by combination RT\u2009+\u2009\u03b1PD-L1 to enhance the translational impact and guide approaches to overcome treatment failure in humans. Here, using murine melanoma models, we found that combination therapy robustly stimulates PD-1+\u2009TCF-1+\u2009T cell migration from the TdLN and expansion/differentiation in the tumor. Within the TdLN, RT\u2009+\u2009\u03b1PD-L1 expanded a novel subset that expresses Tcf7, Klrk1 and Ly6a and has both a migratory and type I interferon signature. This LY6A+\u2009CD314+ subset can migrate to the tumor and differentiate into TIM-3+\u2009GZMB+\u2009TCF-1- effector-like cells. Finally, we showed that PD-1+\u2009TCF-1+\u2009T cells are important for the enhanced tumor control observed with combination RT\u2009+\u2009\u03b1PD-L1. These data have several biological and clinical implications.\n\nBiologically, RT has been previously shown to promote the release of both cryptic/sequestered tumor antigen, type I interferon signaling, and damage associated molecular patterns (DAMPs) leading to enhanced APC maturation and T cell activation12,36. Importantly, prior studies primarily focused on the intra-tumoral T cells, largely neglecting the T cell subsets present in secondary lymphoid organs14. Our findings suggest that the RT induced antigen bolus and/or cytokine production (including type I interferons) is enough, by itself, to promote PD-1+\u2009TCF-1+\u2009T cell expansion and differentiation initiated in the TdLN (Fig.\u00a03i\u2013l)36,37. This is further enhanced and modified by the presence of \u03b1PD-L1. This observation is novel as, to this point, robust PD-1+\u2009TCF-1+\u2009T cell differentiation was thought to be almost exclusively dependent on PD-1/L1 blockade. Whether APC migration from the tumor to the TdLN or whether antigen passively drains to the node following RT is an area of active investigation and the focus of future studies.\n\nAnother intriguing finding from our study is the identification and significant increase of the TSTEM-2 phenotype in the TdLN following combination therapy. This subset appears to differentiate from the TSTEM-1 population and given the type I interferon signature in the TSTEM-2, we speculate interferon-\u03b1/\u03b2 may play a role36,37,38. Although this subset was present at very small numbers at baseline, it also exhibited a distinct transcriptional profile post-treatment. ScRNA-seq analysis revealed elevated expression of Cxcr3, consistent with enhanced tumor-homing capacity, alongside production of Cxcl10, suggesting a chemoattractant role. These attributes suggest that TSTEM-2 cells serve as central coordinators of immune recruitment, particularly for Cxcr3-expressing populations such as CD8+\u2009T cells and NK cells, while simultaneously modulating the tumor microenvironment to optimize effector cell retention and sustain anti-tumor responses. This dual functionality of migration and modulation provides a mechanistic basis for the enhanced effects of combination therapy. Validation of the chemoattractant role of TSTEM-2 cells could clarify their contribution to the immune coordination underlying combination therapy.\n\nOur findings should also be evaluated in the context of an elegant study from Hashimoto et al., which demonstrated that in chronic LCMV, combined IL-2\u2009+\u2009\u03b1PD-L1 treatment induced a unique T cell phenotype co-expressing Tcf7 and effector molecules39. In our study, following combination therapy, the TSTEM-2 subset in the TdLN demonstrated stem and modest levels of effector gene expression suggesting that there may be shared induction mechanisms between those two subsets in different treatment and model systems. Of note, in our study TSTEM-2 cells can migrate to the tumor and continue their differentiation into TIM-3+\u2009GZMB+ cells potentially bypassing the TPEX intermediate state in the TdLN. Although these results are compelling, we wish to avoid overstating these observations, and it is still possible that TSTEM-2 undergo transient TPEX differentiation in the tumor prior to TCF-1 downregulation22. Future studies will evaluate this in more detail, as well as determining whether the TSTEM-2 subset may serve as superior precursor for adoptive cell therapy and whether they can generate TIM-3+\u2009GZMB+ with more potent effector potential.\n\nClinically, a number of trials evaluating combination RT + checkpoint blockade have had mixed to underwhelming results19,21,40. Many of the clinical trials have focused on treating larger volumes with elective nodal irradiation, in particular head and neck cancer40. More recent data has confirmed that elective nodal irradiation or surgical nodal disruption can blunt both the local and distant radio-immunotherapy stimulated anti-tumor response24,25,41,42. Our findings offer a potential explanation for the observed clinical data, providing insight into the underlying mechanisms which may also impact RT with other checkpoint inhibitors including \u03b1CTLA-443. These data also suggest that a neoadjuvant approach to combination therapy, especially for melanoma, where the draining nodes are not disturbed by either surgery or radiation will have the potential for greater anti-tumor immune responses. Similarly, metastatic sites of disease targeted for induction of an abscopal response must have robust nodal drainage to effectively stimulate an immune response.\n\nFinally, future studies will investigate methods to overcome the dependency on the TdLN. As noted, many clinical scenarios have tumors which either lack robust nodal drainage or it is difficult to assess. Therefore, if a TdLN-like microenvironment can be replicated within the tumor or other secondary lymphoid organs, then this anatomical and immunologic limitation may be overcome.\n\nIn this study, we evaluated the importance of the TdLN and PD-1+\u2009TCF-1+\u2009T cells for the enhanced tumor control of RT\u2009+\u2009\u03b1PD-L1 in murine melanoma tumor models. However, human data will ultimately be needed to determine the applicability of these findings to human disease. Clinical trials to evaluate the immunologic impact of neoadjuvant RT\u2009+\u2009\u03b1PD-1/L1 in melanoma are planned. Additionally, given our studies were restricted to melanoma, other cancer types need to be investigated in the future to establish the generalizability of our findings.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Six-week-old female C57BL/6 mice were purchased from the Jackson Laboratory. All mice were used in accordance with the Emory University Institutional Animal Care and Use Committee guidelines (protocol #: PROTO202000109). Mice were housed under the following conditions: a light cycle from 7:00 AM to 7:00 PM, a temperature of between 68 and 72\u2009\u00b0F, and humidity ranging from 30 to 70\u2009g/m3. Mice were sacrificed if they become sick, lethargic or had >10% weight loss prior to tumor volume defined endpoint. The B16F10 cell line was obtained from American Type Culture Collection (ATCC #CRL-6475). A B16F10 cell line expressing the glycoprotein (GP) of the LCMV Armstrong strain was generated by lentiviral transduction. Briefly, the codon- optimized GP was cloned into the bicistronic replication deficient lentiviral vector pLVX- IRES- ZsGreen1 (Takara) followed by production of lentiviral particles in 293\u2009T cells (ATCC) and lentiviral transduction of B16F10 cells. A stable B16F10GP cell line was established by sorting B16F10 cells expressing high levels of the green fluorescent protein ZsGreen1 using a FACS AriaII (BD Biosciences) 2 weeks after transduction. The cell line was grown in Dulbecco\u2019s modified Eagle\u2019s medium (DMEM) supplemented with 10% FBS, 100\u2009U/mL penicillin, 100\u2009\u00b5g/mL streptomycin, and 2\u2009mM glutamine. The cells were cultured at 37\u2009\u00b0C with 5% CO2. YUMM1.7/YUMMER1.7 were a kind gift of the Paulos laboratory. Detailed information on the medium and chemicals used is listed in the key resources table.\u00a0Tcf7DTR-eGFP mice was created by CRISPR/Cas-mediated genome engineering (Taconic Biosciences). The gRNA to mouse Tcf7 gene (target sequence: ATGTTGGTGCTGGCTCCACTGGG), the donor vector containing \u201cIRES-DTR-P2A-EGFP\u201d cassette, and Cas9 mRNA were co-injected into fertilized mouse eggs to generate targeted knock-in offspring. F0 founder animals were identified by PCR followed by sequence analysis, which were bred to wildtype mice to test germline transmission and F1 animal generation. These mice were then bred to P14 mice to generate P14 Tcf7DTR-eGFP. PCR Primers 1: F: 5\u2019-ACTGTGGATTCACCCTCTGTTTAC-3\u2019, R: 5\u2019-ATCTTCATCACCTTAAGAGGACCC-3\u2019. Product size: 2467\u2009bp Wildtype allele: 469\u2009bp. PCR Primers 2: F: 5\u2019-CGAAGAGAAAGTGAAGTTGGGCA-3\u2019, R: 5\u2019-AGCTTGCCGTAGGTGGCATC-3\u2019. Product size: 231\u2009bp. Homozygous: two bands with 231 and 2467\u2009bp. Heterozygous: three bands with 231\u2009bp, 469\u2009bp and 2467\u2009bp. WT: one band with 469\u2009bp.\n\n5\u2009\u00d7\u2009105 B16F10GP cells were injected into the right flanks on day 0 and left flanks on day 3. After the tumor was palpable (10\u201312 days), the right tumors were irradiated using Small Animal Radiation Therapy (SmART\u2009+) system by Precision. During radiation, mice were anesthetized with an isoflurane-based anesthesia system. The radiation dose was 10\u2009Gy x 1 fraction or 8\u2009Gy x 3 fractions. The treatment protocol was planned by SmART ATP \u2013 Advanced Treatment Planning. Tumor sizes were assessed using calipers. Tumor volume was calculated according to the formula length\u2009\u00d7\u2009width\u2009\u00d7\u2009depth\u2009\u00d7 0.52. For FTY720 experiment, FTY720 was provided in the drinking water (2\u2009\u00b5g/mL) 2 days prior to RT. FTY720 treatment was continued throughout the entire experimental course. For \u03b1PD-L1 treatment, it was administered i.p. at a dose of 200\u2009\u00b5g per mouse. For T cell analysis, mice were sacrificed on day 19 when tumor, spleen, blood, and TdLN were harvested. Mice were monitored and euthanized in accordance with the Emory University Institutional Animal Care and Use Committee tumor burden scoring guidelines. Any tumor exceeding 20\u2009mm in length or 2000 mm3\u00a0in volume will result in euthanasia.\n\nP14 cells were obtained from the spleen of P14 mice. C57BL/6 mice (CD45.1) underwent retro-orbital injection with 2.5\u2009\u00d7\u2009105 P14 cells one day prior to B16F10GP tumor cell implantation. P14 DTR\u00b1 were used for stem-like T cell depletion experiments. For the depletion of DTR expressing cells, Diphtheria Toxin (DT) was injected i.p. 3 times at a dose of 50\u2009mg/kg of body weight.\n\nFlow cytometric analysis was performed on a BD FACSymphony A3 or Cytek Aurora. Direct ex vivo staining and intracellular cytokine staining were performed with fluorochrome- conjugated antibodies. Tumor, TdLNs, blood, and spleen were harvested. Tumors were digested in Collagenase IV (300\u2009units/mL) for 60\u2009min in a shaker at 37\u2009\u00b0C. TdLNs, spleen and digested tumor tissue were washed through a 70\u2009\u00b5m filter using wash buffer (RPMI\u2009+\u20092% FBS) to produce a single-cell suspension. Spleen samples were ACK lysed and resuspended in FACS buffer (PBS\u2009+\u20092% FBS\u2009+\u2009EDTA). Tumor and blood samples underwent an additional step using lymphocyte separation medium before staining. Tissues were stained with antibodies. The list of antibodies and assays used is provided in the key resources\u00a0 table. To detect tumor- specific CD8+\u2009T cells, MHC-I tetramers were prepared (The NIH Tetramer Facility). For intracellular detection of transcription factors such as T-cell factor-1 (TCF-1), cells were surface stained for 30\u2009min, fixed and permeabilized using the Foxp3 Fixation/Permeabilization Kit according to manufacturer\u2019s instructions (eBioscience), followed by intracellular staining for 30\u2009min. All staining was performed in a 96 well plate. Splenocytes were used for single color controls. FACS data were analyzed with FlowJo (V10.8) software.\n\nFor single-cell RNA sequencing, CD8+\u2009PD-1+\u2009CD44+ cells from the TdLNs were flow sorted on a FACSAria (BD) flow cytometer. Individual mice samples were hashed (BioLegend) and pooled for sequencing. CD8+\u2009CD44-\u00a0CD62L+ na\u00efve cells from the TdLN of untreated control mice were also sorted and pooled to provide controls for the analysis. For TCR sequencing, CD8+\u2009PD-1+\u2009CD44+ cells from tumors were flow sorted on a FACSAria (BD) flow cytometer. For TCF-1+\u2009P14s transfer, CD8+\u2009PD-1+\u2009CD44+\u2009TIM-3- cells from TdLNs were flow sorted on a FACSAria (BD) flow cytometer. For TSTEM-2 cell transfer, CD8+\u2009PD-1+\u2009CD44+\u2009LY6A+\u2009CD314+ cells from TdLNs were flow sorted on a FACSAria (BD) flow cytometer.\n\nSingle-cell RNA sequencing was performed by 10x Genomics Chromium Controller.\n\nPre-processing of single cell RNA-seq data: The Cell Ranger Single Cell Software Suite (version 5.0.1) by 10x Genomics was used to perform de-multiplexing, barcode processing, and single-cell 3\u2019 gene counting. Reads from each pool were then aligned to the mm10-2020 mouse genome (2020 release). The count data was processed and analyzed in R (version 4.2.1) as described below. To deconvolute the cells belonging to each sample we used the Seurat package (version 4.1.1) in R. The outputs derived from CellRanger were used to create two separate objects (one with the transcriptome alignment and one with the antibody plus hashtags (HTO) alignment). Initial objects were created using the function \u201cRead10X\u201d. We filtered both objects based on the cell barcode to keep only cells which were identified in both the transcriptome and in the antibody alignments. After this cell filtering, we used the function \u201cCreateSeuratObject\u201d to create a transcriptome-based Seurat object. The antibody derived data was filtered to maintain only the hashtag counts; later it was appended as a specific assay using the \u201cCreateAssayObject\u201d function. For cell demultiplexing we used the function \u201cHTODemux\u201d with default parameters in order to maximize the number of singlets detected. Individual single cells were finally filtered based on their assigned \u201cHTO_classification.global\u201d= \u201cSinglet\u201d. Antibody reads were then normalized using the Seurat function \u201cNormalizeData\u201d with the parameters \u201cnormalization.method\u201d = \u201cCLR\u201d and \u201cmargin\u201d=\u201d2\u201d, to indicate a normalization across cells.\n\nQuality control of the scRNA-seq: Low quality cells with a high percentage of mitochondrial gene counts (>\u2009~10%) and with <500 measured genes were excluded. To mitigate potential doublet inclusion, cells with UMI count above 40,000 and detected genes above 5000 were removed. A total of 20 samples were sequenced and 38,578 single cells (Untreated, 9239 cells; RT, 8932 cells; \u03b1PD-L1, 10,502 cells; RT\u2009+\u2009\u03b1PD-L1, 9905 cells) were kept for subsequent analyses. In addition, the Miller et al. and Huang et al. single-cell datasets were imported without modification as validation sets26,34. After filtering, data in each cell was log normalized using Seurat\u2019s \u2018NormalizeData\u2019 function (method = \u2018LogNormalize\u2019, scale.factor = 10,000), the 2000 most variable genes were identified, and the \u2018ScaleData\u2019 function was used to scale and center the gene expression matrix after regressing out the heterogeneity associated with cell cycle and mitochondrial contamination. For each dataset, the number of principal components used for neighborhood graph construction and dimensional reduction was set at 20. Uniform Manifold Approximation and Projection (UMAP) visualization indicated cells from different samples were well mixed into the shared space44.\n\nAnnotation of cell clusters: To identify cell subsets, we utilized publicly available single-cell RNA sequencing datasets with comprehensive cell type annotations, specifically those from Huang et al. and Miller et al.26,34. Unbiased cluster identification was conducted using the Leiden algorithm, a graph-based method for community detection that optimizes modularity. This method was selected over gene enrichment score cutoffs to ensure objective cluster identification. Identified clusters were validated against known cell type markers, and differential expression analysis using the \u2018FindAllMarkers\u2019 function in Seurat (test.use\u2009=\u2009\u2018wilcox\u2019, min.pct\u2009=\u20090.1, logfc.threshold\u2009=\u20090.5) was performed to confirm the accuracy and consistency of the clusters with established cellular profiles. This combination of approaches reinforced the reliability of cell subset identification and analysis.\n\nscProportion Analysis: To quantify the proportions of CD8+\u2009PD1+\u2009T cell subsets from TdLN across treatment conditions, we employed the scProportionTest package45 which utilizes permutation-based statistical tests to detect significant differences in cell abundance. Proportion data were calculated based on the relative frequency of each subset. Results were visualized using ggplot2, illustrating the relative changes in their proportions across treatment conditions\n\nCalculation of Gene Signature Density Plots: Gene signature density plots were generated to visualize the distribution of specific cell state signatures across cell populations. Gene signatures were curated from established literature and enrichment scores were calculated for each cell using the R package UCell46 which applies a rank-based method to estimate the expression of gene sets within single cells. To create smoothed density plots, the Nebulosa package47 was used, employing kernel density estimation to provide clear visualization of the distribution of signature scores across the cell populations. This approach allowed for the comprehensive assessment of various gene signature distributions in the analyzed dataset.\n\nRNA velocity analysis: We performed RNA-velocity analysis on the TdLN dataset using velocyto (v0.17) and scvelo (v0.2.3)40. BAM files as generated by the BD Rhapsody WTA analysis pipeline were preprocessed with samtools to make them compatible with velocyto. Loom files generated by velocyto were loaded into scvelo to estimate and visualize RNA velocities according to the scvelo tutorial. Partition-based graph abstraction (PAGA)41 was computed based on the RNA velocity graph, using CD8+\u2009PD-1+\u2009T cell subclusters as grouping variable and the option minium_spanning_tree=False. The result was visualized as a graph showing the transition confidences as directed edges. The tumor cells were derived from the Miller et al. dataset33.\n\nGenomic DNA (gDNA) was extracted from sorted CD8+\u2009PD-1+\u2009T cells using AllPrep DNA/RNA Micro Kit (QIAGEN) according to the manufacturer\u2019s instructions. The isolated gDNA was sent to Adaptive Biotechnologies (Seattle, WA, USA) for TCR sequencing by immunoSEQ assays. In the analysis, the percentage of unique TCRs in the tumor which were overlapping and non-overlapping with the TdLN TCRs were calculated.\n\nAll experiments were analyzed using Prism 9 (GraphPad Software). Summary graphs show means\u2009\u00b1\u2009SEM. Statistical significance was determined as described in the figure legend.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Source data are provided as a Source Data file. scRNA-seq data are available in the NCBI Gene Expression Omnibus (GEO) database under the accession number GSE256178. TCR-seq data are available in the NCBI Gene Expression Omnibus (GEO) database under the accession number GSE291836.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Custom code for scRNA-seq data analysis is available from the corresponding author on reasonable request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Hashimoto, M. et al. 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Kesarwala,\u00a0Alexandre Orthwein,\u00a0Mohammad K. Khan,\u00a0David S. Yu\u00a0&\u00a0Zachary S. Buchwald\n\nBioinformatics Graduate Program, Georgia Institute of Technology, Atlanta, GA, USA\n\nErin Connolly\n\nDepartment of Urology and Winship Cancer Institute, Emory University, Atlanta, GA, USA\n\nMaria Cardenas\u00a0&\u00a0Haydn Kissick\n\nMarc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai (ICMMS), New York City, NY, USA\n\nNataliya Prokhnevska\n\nDepartment of Pathology and Immunology, University of Geneva, Geneva, Switzerland\n\nAnnapaola Mariniello\n\nDepartment of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA\n\nAnnapaola Mariniello\n\nWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA\n\nIsabelle De Bruyker,\u00a0Susan N. Thomas\u00a0&\u00a0J. Brandon Dixon\n\nMedical Scientist Training Program, University of California San Diego, La Jolla, CA, USA\n\nMeghana S. Pagadala\n\nDepartment of Hematology and Medical Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA\n\nSarwish Rafiq\u00a0&\u00a0Gregory B. Lesinski\n\nGeorge W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA\n\nSusan N. Thomas\u00a0&\u00a0J. Brandon Dixon\n\nParker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA\n\nSusan N. Thomas\u00a0&\u00a0J. Brandon Dixon\n\nDepartment of Cell Biology, Emory University School of Medicine, Atlanta, GA, USA\n\nShirley L. Zhang\n\nDepartment of Surgery and Winship Cancer Institute of Emory University, Atlanta, GA, USA\n\nMichael C. Lowe\u00a0&\u00a0Chrystal M. Paulos\n\nDepartment of Otolaryngology - Head and Neck Surgery and Winship Cancer Institute, Emory University, Atlanta, GA, USA\n\nNicole C. Schmitt\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY.S., E.C. and Z.S.B. devised the concept. Y.S., E.C. and Z.S.B. designed experiments. Y.S., E.C., M.A., C.Z., P.C., H.S., P.T., T.C. and I.D.B. performed experiments. Y.S., E.C. and Z.S.B. performed data analysis. D.S.Y., M.C., N.P., A.M., M.S.P., V.R.D., S.R., A.H.K., A.O., S.N.T., S.L.Z., M.K.K., J.B.D., G.B.L., M.C.L., H.K., C.M.P. and N.C.S. contributed by providing feedback and their expertise. Y.S., E.C., and Z.S.B. wrote the manuscript with input from other authors. All authors reviewed the manuscript.\n\nCorrespondence to\n Zachary S. Buchwald.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "N.C.S. has a consulting role at Checkpoint Surgical, Sensorion, and Synergy Research, Inc, is a member of the advisory board of Regeneron, receives book royalties from Plural Publishing, and has received funding from Astex Pharmaceuticals. G.B.L. has received research funding through a sponsored research agreement between Emory University and Merck and Co., Bristol-Myers Squibb, Boerhinger-Ingelheim, and Vaccinex. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Yina Huang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Combination radiation and \u03b1PD-L1 enhance tumor control by stimulating CD8+\u2009PD-1+\u2009TCF-1+\u2009T cells in the tumor-draining lymph node.\n Nat Commun 16, 3522 (2025). https://doi.org/10.1038/s41467-025-58510-1\n\nDownload citation\n\nReceived: 12 February 2024\n\nAccepted: 19 March 2025\n\nPublished: 14 April 2025\n\nVersion of record: 14 April 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58510-1\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 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point-by-point arbitrary waveform synthesis beyond tera sample per second", + "pre_title": "Temporal point-by-point arbitrary waveform synthesis beyond tera sample per second", + "journal": "Nature Communications", + "published": "21 March 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58052-6/MediaObjects/41467_2025_58052_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58052-6/MediaObjects/41467_2025_58052_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Fibre optics and optical communications", + "Microwave photonics" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4373572/v1.pdf?c=1742641569000", + "research_square_link": "https://www.researchsquare.com//article/rs-4373572/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58052-6.pdf", + "preprint_posted": "05 Sep, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Arbitrary waveform synthesizers play an essential role in modern information technology, yet electronic synthesizers face limitations due to the speed of analog-to-digital converters, typically in the range of tens to hundreds of giga samples per second (GSa/s). While photonic-assisted waveform synthesizers show promise in surpassing this ceiling, but the system reconfigurability remains a challenge. Here, we propose and demonstrate a temporal point-by-point arbitrary waveform synthesizer with a sampling rate beyond tera sample per second (TSa/s), leveraging an optical temporal Vernier caliper in the photonic synthetic dimension. The temporal Vernier caliper, consisting of a mode-locked laser (MLL) and a fiber loop, utilizes a slight detuning between the optical pulse period of the MLL and the round-trip time delay of the loop to control the sampling rate of a synthesized waveform with high flexibility. The amplitude of each sampling point is manipulated at a low speed corresponding to the reciprocal of the pulse period, thus massive point-by-point waveform synthesis can be achieved with full reconfigurability. In the experiment, arbitrary waveforms with widely tunable sampling rates from 5 GSa/s to 1 TSa/s are synthesized, which is an order of magnitude higher than state-of-the-art electronic counterparts. To show the capability of massive point-by-point synthesis, communication signals for high-speed wireless communications and linearly chirped microwave waveforms for high-resolution multi-target detection are synthesized. The capabilities of tunable sampling rate and massive point-by-point synthesis make the temporal Vernier caliper a universal solution for high-speed microwave sources, offering significant promise for broad applications requiring high-speed signal sources.Physical sciences/Optics and photonics/Applied optics/Microwave photonicsPhysical sciences/Optics and photonics/Applied optics/Fibre optics and optical communications", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Arbitrary waveform synthesizers are indispensable in modern information technology, yet electronic counterparts are limited by the speed of analog-to-digital converters to hundreds of GSa/s. While photonic-assisted synthesizers offer potential to surpass this ceiling, scalability and reconfigurability remain challenges. Here, we propose a temporal point-by-point arbitrary waveform synthesizer beyond TSa/s, leveraging an optical temporal Vernier caliper in the photonic synthetic dimension. The system, combining a mode-locked laser and a fiber loop, controls the sampling rate of synthesized waveforms by exploiting a slight detuning between the pulse period and the round-trip delay of the fiber loop. The experiment demonstrates generated waveforms with ultra-high, tunable sampling rate up to 1 TSa/s, an order of magnitude higher than state-of-the-art electronic counterparts. Additionally, the system supports up to 10.4 kilo-points in memory depth. As application examples, the generation of communication waveforms for high-speed wireless communications and linearly chirped microwave waveforms for high-resolution multi-target detection is demonstrated.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The synthesis of high-speed arbitrary waveform plays a vital role for applications such as spectroscopy1, radar and lidar2,3, optical communications4,5,6, and biological imaging7,8. The sampling rate and memory depth are two of the most critical parameters of an arbitrary waveform generator (AWG), indicating the maximum operation speed and number of points one can define9,10. Using optoelectrical modulation for waveform generation usually presents good phase noise performance11,12, but the speed is limited by the bandwidth of an electro-optical modulator13,14, usually tens of gigahertz. Line-by-line spectral shaping utilizes a spectral shaper to control the spectrum of a target temporal waveform15,16,17,18, thus avoiding the high-speed modulation of the temporal waveform. However, the spectral shapers have limited spectrum resolution, such as spatial light modulators15,19,20, and lack reconfigurability, such as non-programmable and predesigned metasurfaces16,17. Furthermore, since a point change in the spectral domain represents the change to all points in the temporal domain, point-by-point temporal control is not possible16,17,19. The above limitations result in the synthesized waveform with a limited speed, a small number of points, and a limited type of shapes12,16,17,21,22,23.\n\nRecently, the concept of synthetic dimension has been introduced to the photonics field, by which high-speed manipulation of optical signals was realized24,25,26. In ref. 26, a photonic Galton board (PGB) in the synthetic dimension was used to control the temporal distribution of photons in a seed pulse for the generation of an arbitrary waveform at a high speed. Since a seed pulse with a finite number of photons undergoes cascaded splitting and recombination in the PGB, a generated waveform has a limited number of sampling points, a low power and a poor signal-to-noise ratio (SNR) after the pulse passes multiple layers of the PGB. This limitation restricts the number of layers that can be implemented in a PGB and hinders the capability of massive point-by-point synthesis of high-speed arbitrary waveforms.\n\nVernier caliper, invented by Pierre Vernier in 1631, is a common tool that performs fine length measurement by mechanical interpolation of two slightly different linear graduation divisions: the main scale division (MSD) and the Vernier scale division (VSD)27. The interpolation of the MSD and VSD results in the least count (LC, LC\u2009=\u2009MSD-VSD), which is small and thus can be used to improve the measurement accuracy. In the optics community, the divisions are usually implemented by two resonators of slightly detuned free spectral ranges (FSRs)27,28, such as two interferometers29,30, two Sagnac loops31, or two ring resonators32,33, where the large and small FSRs function as the MSD and the VSD, respectively. The overlap of the signals from two resonators will bring out a new larger FSR, which enhances the sensitivity for sensing (such as LIGO for gravity wave detection30), facilitates mode selection of lasers32,34 and oscillators33,35, and promotes channelization accuracy of broadband optical receivers36.\n\nIn this paper, we propose and demonstrate a massive temporal point-by-point arbitrary waveform synthesizer with a sampling rate beyond TSa/s, leveraging an optical temporal Vernier caliper in the photonic synthetic dimension. The temporal Vernier caliper is implemented by a mode-locked laser (MLL) and a fiber loop. A pulse train with a period of Tp (VSD) generated by the MLL is injected into the fiber loop, which has a tunable round-trip time Tr (MSD). A slight detuning \u0394 of the time difference between Tp and Tr forms a temporal LC in the synthetic dimension, which enables the synthesis of arbitrary waveforms with a tunable and ultra-high sampling rate. Meanwhile, the amplitude of each pulse is manipulated at a low speed, which is\u00a0reciprocal to the pulse period Tp, and\u00a0thus massive point-by-point waveform synthesis can be achieved. The operation of the proposed temporal Vernier caliper is evaluated experimentally. Arbitrary waveforms at a sampling rate of 1 TSa/s are synthesized, which is an order of magnitude higher than the state-of-the-art electronic counterparts. Point-by-point synthesis capability also facilitates the synthesis of sophisticated arbitrary waveforms. In the experiment, two types of waveforms, communication signals with different modulation formats for high-speed wireless communications and linearly chirped microwave waveforms for high-resolution multi-target detection, are synthesized. The proposed waveform synthesizer, with its combined capabilities of ultra-high speed and tunable sampling rate and massive point-by-point control, stands as a new solution for high-speed arbitrary waveform generation.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Figure\u00a01 shows the concept of spectral and temporal Vernier calipers. In a spectral Vernier caliper, as shown in the left part of Fig.\u00a01, two optical resonators with slightly detuned FSRs (FSR1 and FSR2) are employed. The reciprocals of FSR1 and FSR2 correspond to the VSD (VSD\u2009=\u20091/FSR1) and MSD (MSD\u2009=\u20091/FSR2), respectively. A new FSR (FSR3) will be brought out after the overlap of the VSD and MSD, which is the reciprocal of the LC (FSR3\u2009=\u20091/LC = (FSR1 \u00d7 FSR2) / (FSR1 \u2013 FSR2)). FSR3 can be very large for a slight detuning between FSR1 and FSR237. In a temporal Vernier caliper, as shown in the right part of Fig.\u00a01, the MSD and VSD are represented by two pulse trains with slightly different temporal intervals, INT1 and INT2. For example, INT1 and INT2 can be implemented by an optical pulse train in a fiber loop and another optical pulse train generated by a laser source, respectively. When the two optical pulse trains interpolate with each other, a new pulse train will be produced. The temporal interval, INT3, between two adjacent pulses (a red and a green pulse) in the new pulse train is linearly increasing, which is the LC (INT3\u2009=\u2009LC\u2009=\u2009INT1 \u2013 INT2) in the temporal Vernier caliper. LC can be very small when INT1 and INT2 are slightly detuned. Thus, the temporal Vernier effect can be used to realize fully reconfigurable temporal point-by-point synthesis of high-speed arbitrary waveforms by producing well-controlled and small time delays between temporal pulses that are otherwise difficult to achieve.\n\nA Vernier caliper has two slightly different linear graduation divisions (MSD and VSD). The interpolation of the MSD and VSD results in a very small LC (LC\u2009=\u2009MSD-VSD). For a spectral Vernier caliper, as shown in the left part, the joint operation of two optical resonators with slightly detuned FSRs (FSR1 and FSR2) results in a new FSR (FSR3), which can be very large. For a temporal Vernier caliper, the interpolation of two pulse trains with slightly detuned temporal intervals (INT1 and INT2) produces a new pulse train, which has a linearly increased temporal interval (INT3). INT3 can be very small when INT1 and INT2 are detuned with a small temporal difference.\n\nFigure\u00a02a shows the experimental implementation of an optical temporal Vernier caliper (also See Methods). An optical pulse train with a repetition rate of 20\u2009MHz or a pulse period of 50\u2009ns is generated by a mode-locked laser (MLL) source (See Supplementary Material\u00a01), as shown at point (A) in Fig.\u00a02b. The repetition rate is reduced to 5\u2009MHz (pulse period Tp\u2009=\u2009200\u2009ns, corresponding to the VSD in the caliper) by using an acousto-optic modulator (AOM1) to allow a pulse to pass after blocking three pulses. AOM1 is also used to control the pulse amplitude profile according to the target waveform to be synthesized, as shown at point (B) in Fig.\u00a02b. Then, the pulse train with the designated amplitude profile is injected into a fiber loop with a length of 40\u2009m through a 2 \u00d7 2 optical coupler (OC). In the loop, a tunable delay line (TDL) is incorporated to fine tune the loop length L between 40\u2009m and 40.112\u2009m, or a round-trip time Tr between 200\u2009ns and 200.56\u2009ns (corresponding to the MSD). An erbium-doped fiber amplifier (EDFA), a tunable optical filter (TOF), and a dispersion compensating fiber (DCF) are also incorporated into the loop to enable the fiber loop to have a high Q-factor (See Methods and Supplementary Material\u00a02). The temporal distribution of the pulses between the OC during the first three round trips is illustrated at point (C) in Fig.\u00a02c. As the first pulse (red) recirculates in the fiber loop for one round trip, a second pulse (orange) will be injected into the loop, leading to two recirculating pulses in the fiber loop with a small temporal interval, which is the detuning \u0394 between the pulse period Tp and the round-trip time Tr, i.e., the LC in the temporal Vernier caliper. The detuning \u0394 is given by\n\nwhere n is the refractive index of the optical fiber core, L is the physical length of the fiber loop, and c is the velocity of light in vacuum. As the first (red) and the second pulse (orange) recirculate in the loop for the second round trip, a third pulse (blue) will be injected into the loop, which will also have a temporal interval of \u0394 with the second pulse. After multiple round trips and injecting multiple pulses with a designated amplitude profile into the fiber loop, a pulse sequence with a designated amplitude profile, i.e., a sampled optical arbitrary waveform s(t), is synthesized in the synthesized dimension, as shown in Fig.\u00a02c. Mathematically, the synthesized sampled waveform is given by\n\nwhere M is the total number of optical pulses injected into the loop, Am is the amplitude of the m-th injected optical pulse, which is controlled by a voltage signal applied to AOM1 through a low-speed AWG. The amplitudes of the pulses, Am, determine the amplitude profile of the synthesized arbitrary waveform, and p(t) denotes an optical pulse in the pulse train at the output of AOM1. The sampling rate fs of the arbitrary waveform s(t) is given by\n\na The temporal Vernier caliper is implemented by a mode-locked laser (MLL) source and a fiber loop, in which a tunable delay line (TDL), an acousto-optic modulator (AOM2), an erbium-doped fiber amplifier (EDFA), a tunable optical filter (TOF), and a dispersion compensating fiber (DCF) are incorporated. b The optical pulse train generated by the MLL has a repetition rate of 20\u2009MHz, as shown at point (A). A new pulse train with a designated amplitude profile and a pulse period of 200\u2009ns (VSD) is achieved after AOM1, as shown at point (B). As the first pulse (red) recirculates in the fiber loop for one round trip, a second pulse (orange) will be injected into the loop, resulting in two recirculating pulses in the fiber loop with a small temporal interval, which is the detuning \u0394 between the pulse period Tp and the round-trip time Tr, i.e., the LC in the caliper. After injecting multiple pulses, a pulse sequence with a designated shape, i.e., a sampled arbitrary waveform, is synthesized, as shown at point (C). c As multiple pulses are injected into the loop, an additional dimension, the pulse location, is synthesized, which has a scale of LC.\n\nIt can be seen from Eq.\u00a03 that the sampling rate of the synthesized waveform fs is determined by the detuning \u0394 between the pulse period Tp and the round-trip time Tr, which can be ultra-small due to the temporal Vernier effect. Meanwhile, the sampling rate can be continuously tuned by controlling the loop length L, which can be tuned by the TDL in the loop. Thus, by introducing the temporal Vernier effect, the challenge in high-speed synthesis of a temporal waveform is subtly addressed. The amplitude of each sampling pulse in the synthesized waveform is manipulated by AOM1 at a low symbol rate, which is\u00a0the reciprocal of the pulse period Tp, thus enabling massive point-by-point shaping in the synthesized waveform. Finally, the synthesized optical arbitrary waveform is directed out of the loop through the OC and sent to a photodetector (PD) where the optical arbitrary waveform is converted to an electrical arbitrary waveform, which is displayed by a high-speed oscilloscope (OSC).\n\nBased on the setup shown in Fig.\u00a02, an experiment is performed to show the synthesis of arbitrary waveforms with tunable and high sampling rates. By controlling the amplitude of each injected pulse through AOM1, different waveforms with 30 sampling points, including a Gaussian, a triangular, a rectangular, and a sawtooth waveform, are synthesized, as shown in Fig.\u00a03. By tuning the TDL in the loop, the loop length is tuned from 40.04 to 40.0008\u2009m, corresponding to the MSD moving from 200.2 to 200.004\u2009ns. Thus, an LC ranging from 0.2 to 0.004\u2009ns is resulted as the VSD is fixed at 200\u2009ns. When the LC is 0.2, 0.1, 0.033, and 0.004\u2009ns, the sampling rates of the synthesized waveforms are 5, 10, 30, and 250 GSa/s, respectively. Each pulse in the synthesized waveform is a sampling point, as shown in Fig.\u00a03a1\u2013d1 and Fig.\u00a03a2\u2013d2. The sampling points in the waveforms at 30 and 250 GSa/s are not visible due to the limited bandwidth of the PD, which functions as a lowpass filter to select the spectra of the waveforms at lower frequency ranges. The fidelity of the synthesized waveforms is evaluated by calculating the average root mean square error (RMSE) excluding the impact of the limited bandwidth of the PD, which is as small as 0.0481, confirming the high vertical resolution of the synthesized waveform (See Supplementary Material\u00a03).\n\nThe synthesized waveforms (solid-blue lines) with 30 sampling points at four different sampling rates: Gaussian at (a1) 5, (a2) 10, (a3) 30, and (a4) 250 GSa/s. Triangular at (b1) 5, (b2) 10, (b3) 30, and (b4) 250 GSa/s. Rectangular at (c1) 5, (c2) 10, (c3) 30, and (c4) 250 GSa/s. Sawtooth at (d1) 5, (d2) 10, (d3) 30, and (d4) 250 GSa/s. The target waveforms are shown as dashed-red lines for comparison. The average RMSE is calculated to be 0.0481.\n\nAs the length of the fiber loop is further reduced, the MSD can be tuned much closer to the VSD, i.e., the LC in the synthetic dimension becomes much smaller, and\u00a0then arbitrary waveforms with much higher sampling rates can be synthesized. To show that, we tune the length of the loop to 40.0002\u2009m (MSD\u2009=\u2009200.001\u2009ns) and fix the VSD to 200\u2009ns, which results in an LC as small as 1\u2009ps. Then, 150 pulses are injected into the loop. By controlling the amplitude of each injected pulse, a Gaussian, a triangular, a rectangular, and a sawtooth waveform with 150 sampling points and a sampling rate of 1 TSa/s are synthesized, as shown in Fig.\u00a04. The average RMSE is calculated to be 0.0337, again confirming the high fidelity (See Supplementary Material\u00a03). The results show that waveforms with up to 150 levels with high distinguishability can be achieved by the caliper-based synthesizer, which shows more than 7 bits of vertical\u00a0resolution.\u00a0\n\nThe synthesized a Gaussian, b triangular, c rectangular, and d sawtooth waveforms (solid-blue lines) with 150 sampling points at sampling rates of 1TSa/s. The target waveforms are shown as dashed-red lines for comparison. The average RMSE is calculated to be 0.0337.\n\nIn communication systems, arbitrary waveforms with thousands of data points are usually required. To show the capability of massive point-by-point waveform synthesis of sophisticated arbitrary waveforms, optical signals with sampling points of 10.4 kpts having different modulation formats for wireless communications are demonstrated, as shown in Fig.\u00a05. Figure\u00a05a shows a proposed wireless communication system using the temporal Vernier caliper as a signal synthesizer. Optical signals synthesized by the temporal Vernier caliper are sent to a PD for optoelectronic conversion. Then, the signals are amplified by an electrical amplifier (EA) and mixed with a local oscillation (LO) signal to up-convert the frequency of the signal to the designated communication frequency band. The mixed signals are sent to the free space by a transmitting antenna (TA). Different devices will receive their interest signals with their receiving antennas (RAs). Finally, the received signals are amplified by another EA and undergo digital signal processing (DSP) for demodulation. By duplicating the temporal Vernier caliper in multiple temporal parameter subspaces (See Supplementary Material\u00a04), an on-off keying (OOK) and a four-level pulse amplitude modulation (PAM-4) signal with sampling points of 10.4 kpts and sampling rates of 260 MSa/s are synthesized, as shown in Fig.\u00a05b1 and b2, respectively. The zoom-in view of the signals is shown in Fig.\u00a05b2 and c2, where the two-level (on & off) and four-level (0-1-2-3) voltages can be seen. The cross markers indicate the sampling points corresponding to the target waveform in the experimental results. Figure\u00a05b3 and c3 shows the eye diagram of the synthesized OOK and PAM4 signals, respectively. By successively synthesizing the in-phase and the quadrature components and combining them with the in-phase component being delayed synchronizing with the quadrature component, a quadrature phase-shift keying (QPSK) signal is synthesized (See Supplementary Material\u00a04). Figure\u00a05d1 and e1 shows the in-phase and the quadrature components of a QPSK signal, respectively. The zoom-in views are shown in Fig.\u00a05d2 and e2, where the two-level voltages can be seen. The in-plane diagram is shown in Fig.\u00a05f, which shows an error vector magnitude (EVM) of 12.96%. By using a MLL laser source with more stable temperature control and more precise cavity length control, a mode-locked pulse train with more stable amplitude can be produced, thus further reducing the EVM of the synthesized signal. Note that, in the experiment, to ensure each sampling pulse can be seen on the OSC, especially the level \u201c0\u201d pulse which may become invisible due to the overlap of two adjacent \u201c1\u201d pulses, the LC is increased to be 3.84\u2009ns, corresponding to a sampling rate of 260 MSa/s. This does not hinder the fact that the sampling rate can be further increased to TSa/s when the detune between the pulse period and the round-trip time of the loop is controlled to be 1\u2009ps, as shown in Figs.\u00a03 and 4.\n\na A wireless communication system. The optical signals with different modulation formats are first sent to a PD for optoelectronic conversion. Then the signals are amplified by an EA and mixed with a local oscillation (LO) signal. The mixed signals are then sent to the free space by a transmitting antenna (TA). Different devices will receive their interest signals with their receiving antennas (RAs). Finally, the received signals are amplified by another EA and undergo digital signal processing (DSP) for demodulation. b1,\u00a0b2 The OOK signal with 10.4 kpts at 260 MSa/s and its zoom-in view. The cross markers indicate the sampling points corresponding to the target waveform in the experimental results. The eye diagram is shown in (b3). c1, c2 The PAM-4 signal with 10.4 kpts at 260 MSa/s and its zoom-in view. The eye diagram is shown in (c3). d1,\u00a0e1 The in-phase and the quadrature components of a QPSK signal with 10.4 kpts at 260 MSa/s. d2, e2 The zoom-in views. f The in-plane diagram of the QPSK signal, which shows an EVM of 12.96%.\n\nLinearly chirped microwave waveforms (LCMWs) are widely used in radar systems to improve the detection range while maintaining a high range resolution through pulse compression2,12,38,39. An LCMW can be generated based on photonics to create a pulse burst having linearly increasing or decreasing pulse interval with an ultrawide bandwidth38,40. By filtering the pulse burst using a bandpass filter (BPF) to select the 1st-order spectral channel, an LCMW is generated38,40. Here, an LCMW is generated and employed in a radar system for target detection for both static and moving targets. Figure\u00a06a shows the schematic of the radar system. The temporal Vernier caliper is configured to generate a pulse burst with 150 sampling points at a sampling rate of 60 GSa/s, as shown in Fig.\u00a06b1, which is sent to a PD for optoelectronic conversion. Figure\u00a06b2 shows the spectrum of the pulse burst. As can be seen, the 1st-order spectral channel has a bandwidth of 4.7\u2009GHz between 2.5 and 7.2\u2009GHz, which is selected by a BPF to generate an LCMW, as shown in Fig.\u00a06c1. Figure\u00a06c2 shows the time-frequency spectrogram. Then, the LCMW is amplified by an electrical amplifier (EA) and radiated to free space via a TA. When reflected by a target, an echo signal with a time delay determined by the distance of the target is received by an RA. Finally, by performing pulse compression, a compressed pulse is obtained, as shown in Fig.\u00a06d1, and the distance of the target is estimated by measuring the time delay between the transmitted pulse and the compressed pulse. Figure\u00a06d2 gives a zoom-in view of the compressed pulse. The use of the radar system to detect multiple targets is also studied. Figure\u00a06e shows the detection of a target moving within 0.2\u2009m at a speed of 8\u2009mm/s and 16\u2009mm/s. The compressed pulses for the moving target are shown in Fig.\u00a06f. Figure\u00a06g and h shows the simultaneous detection of a moving target and a static target. As can be seen, target #1 is moving and target #2 is kept static. Simultaneous detection of three static targets at five different locations is also performed, as shown in Fig.\u00a06i\u00a0and j. Determined by the bandwidth of the BPF, the generated LCMW has a bandwidth of 4.7\u2009GHz, resulting in a range resolution of 3.2\u2009cm (See Supplementary Material\u00a05). For a synthesized pulse burst at 60 GSa/s, the theoretical maximum bandwidth of the 1st-order spectral channel is 20\u2009GHz, yielding a maximum range resolution as small as 0.75\u2009cm, which surpasses that of both state-of-the-art electrical2 and photonic41,42,43 radars. Beyond its proof-of-principle demonstration in radar systems, the temporal Vernier caliper, as a versatile solution for high-speed waveform generation, holds significant potential across a range of applications, such as LiDAR3,44, medical imaging45, and optical computing46,47.\n\na The schematic of the radar system. An optical pulse burst is generated by the temporal Vernier caliper, shown in (b1), which is sent to a PD for optoelectronic conversion. b2 The 1st-order spectral channel has a width of 4.7\u2009GHz between 2.5 and 7.2\u2009GHz, which is selected by a BPF to generate an LCMW, as shown in (c1) and (c2). The LCMW is amplified by an electrical amplifier (EA) and radiated to free space via a TA. When reflected by a target, an echo signal with a time delay is received by an RA. By performing pulse compression, a compressed pulse as shown in (d1) and (d2) is obtained, and the distance of the target is estimated. e,\u00a0f The detection of a target moving within 0.2\u2009m at 8\u2009mm/s and 16\u2009mm/s. g,\u00a0h Simultaneous detection of a moving target (#1) and a static target (#2). i, j Simultaneous detection of three static targets at five different locations. Xcorr: cross-correlation.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58052-6/MediaObjects/41467_2025_58052_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58052-6/MediaObjects/41467_2025_58052_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58052-6/MediaObjects/41467_2025_58052_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58052-6/MediaObjects/41467_2025_58052_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58052-6/MediaObjects/41467_2025_58052_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58052-6/MediaObjects/41467_2025_58052_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "By utilizing a temporal Vernier caliper in the photonic synthetic dimension, temporal point-by-point shaping enables the massive and complicated manipulation of the sampling points in a synthesized waveform at a speed of TSa/s. The key to achieving a high sampling rate without using high-speed modulation is the introduction of the VSD and MSD in the temporal Vernier caliper, which correspond to the period Tp of an optical pulse train and the round-trip time Tr of a fiber loop, respectively. A slight detuning \u0394 between Tp and Tr forms a small and tunable LC in the temporal synthetic dimension, which is inversely proportional to the sampling rate of the synthesized waveform. In the experiments, arbitrary waveforms with widely tunable sampling rates from 5 GSa/s to 1 TSa/s were synthesized. An average RMSE of 0.0409 and a high vertical resolution of 7 bits were calculated to show the high fidelity of the synthesized waveforms. To show the capability of massive point-by-point temporal synthesis of arbitrary waveforms, two application examples are provided. The first one is to generate OOK, PAM-4, and QPSK signals with sampling points of up to 10.4 kpts for a high-speed wireless communications. The second one is the synthesis of LCMWs for multi-target detection in a radar system.\n\nTable\u00a01 presents a comparison between our approach and those reported previously in literature for waveform synthesis. As shown, earlier approaches primarily synthesized waveforms based on pulse shaping, including the use of Talbot effect48,49,50 and time lens51,52. Since a temporal18,48,49,50,51,53,54 or spectral shaper16,17,20,52 has a limited bandwidth or resolution, the generated waveforms have low speeds, small number of shaping points, low vertical resolution, limited reconfigurability, and poor waveform fidelity. While our proposed approach utilizing a temporal Vernier caliper provides better performance in terms of sampling rate, tunability, memory depth, vertical resolution, reconfigurability and waveform fidelity. The proposed caliper-based waveform synthesizer is, to the best of our knowledge, the first TSa/s waveform synthesizer with high commercial readiness.\n\nNote that the sampling rate at 1 TSa/s in our demonstrations was achieved in the optical domain. For microwave applications, the generated waveforms should be in the electrical domain, which can be accomplished by using high-speed PDs supporting THz operation for optical to electrical conversion55,56. In addition, for a synthesized waveform with a temporal duration of Tw and a sampling rate of SR, the synthesis time is (Tw\u00d7SR) \u00d7Tr. A solution to reduce the synthesis time is the use of a multi-channel relaying. By sequencing the outputs from M different modules, the synthesis time of a waveform can be shortened by M times (See Supplementary Material\u00a06). In addition, implementing the system on photonic integrated circuits can significantly reduce the length of the loop, and\u00a0thus the synthesis time can be significantly reduced47,57,58,59,60,61.\n\nThe proposed temporal point-by-point waveform synthesis offers a new solution for high-speed waveform generation, providing significant promise for the next-generation high-speed AWG. The massive point-by-point synthesis offers high-speed all-optical data encoding in optical computing and optical neural networks avoiding limited-speed electro-optical modulation, and\u00a0thus promoting the efficiency in data processing to beyond peta operation per second (POPS)46,47,62. Through photonic integration, the temporal Vernier caliper can achieve a much smaller footprint and enhanced stability47,57,60,61, enabling various high-speed or high-precision manipulation of time-domain signals, such as fine phase matching in nonlinear optical systems63,64, super-resolution time measurements in LiDAR systems3,44, and ultra-precise clock synchronization in modern communication systems65,66.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The temporal Vernier caliper is implemented using commercial off-the-shelf optical and optoelectronic components. The optical pulses generated by the MLL (Calmar Optcom FPL-03CFFJNU) have a 3-dB spectral width of 10.72\u2009nm at 1550.92\u2009nm (See Supplementary Material\u00a01). The Sycn port of the MLL is connected to the Trigger port of a two-channel low-speed AWG (Rigol DG822) with a sampling rate of 125 MSa/s. Before being injected into the fiber loop, a pulse is allowed to pass through AOM1 (CETC SGTF200-1550-1P) with a designated amplitude after blocking three pulses. This is done by applying a voltage waveform generated by the AWG from the first channel to AOM1. The duration of each voltage waveform is 5\u2009MHz (1/Tp\u2009=\u20091/200\u2009ns), which is much larger than the full width at half maximum (FWHM\u2009<\u2009500\u2009fs) and the time jitter (<50\u2009fs) of an optical pulse, ensuring that\u00a0the optical pulse will always be located within the modulation window. Then, the pulses are injected into the fiber loop through the OC. The AOM is used since the AOM has a lower insertion loss of less than 3\u2009dB and can have an ultra-high extinction rate of over 70\u2009dB, which makes it possible to fully block the unused pulses and avoid the residual pulses being injected into the loop, which may affect the SNR of a synthesized waveform.\n\nIn the fiber loop, the TDL (General Photonics MDL-002) with a maximum time delay of 560\u2009ps and a time resolution of 1\u2009fs is employed for fine tuning the loop length, which allows continuous tuning of the sampling rate of a synthesized arbitrary waveform with precision from 12.8 kSa/s @ 3.57 GSa/s to 1 GSa/s @ 1 TSa/s. To avoid lasing within the fiber loop, AOM2 (Brimrose TEM-210-50-10-1550-2FP) with a shifting frequency of 200\u2009MHz is employed in the fiber loop to shift the frequency of a recirculating pulse by 200\u2009MHz for each round trip. Note that AOM2 also works as an optical switch by applying a gate signal to terminate the pulse recirculation in the loop after a target waveform is synthesized. To ensure that\u00a0the pulses can recirculate in the loop for a sufficiently large number of round trips, an EDFA (PYOE-EDFA-C) pumped at 68\u2009mA is incorporated to compensate for the round-trip loss. A TOF (Santec OTF-350) with a passband of 7.26\u2009nm at around 1545.22\u2009nm was also employed to remove the amplified spontaneous emission noise generated by the EDFA to minimize the reduction in SNR. In the experiments, the noise figure is calculated to be 3.61\u2009dB after 150 round trips (See Supplementary Material\u00a02). The SNR of the pulse is still large enough after 150 round trips, which ensures that\u00a0the synthesized waveforms have high quality and fidelity. A DCF of about 7\u2009m is also employed to compensate for the residual chromatic dispersion in the loop.\n\nA synthesized arbitrary waveform is directed out of the loop through the OC and sent to a PD (PIN/TIA20T) where the optical waveform is converted to an electrical waveform. The waveform is then displayed on the OSC (Teledyne Labmaster 10-36Zi-A). The PD has a bandwidth of 23\u2009GHz. The OSC has a bandwidth of 36\u2009GHz and a sampling rate of 80 GSa/s.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data necessary to evaluate the conclusions of this study are included in the paper and/or the Supplementary Materials. Any further details related to this study can be requested from the corresponding authors.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Yan, M. et al. Mid-infrared dual-comb spectroscopy with electro-optic modulators. Light Sci. 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hippocampus-dependent memory", + "pre_title": "GolpHCat (TMEM87A), a unique voltage-dependent cation channel in Golgi apparatus, contributes to Golgi-pH maintenance and hippocampus-dependent memory", + "journal": "Nature Communications", + "published": "11 July 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_MOESM4_ESM.txt" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_MOESM5_ESM.txt" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_MOESM6_ESM.txt" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_MOESM7_ESM.txt" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_MOESM8_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_MOESM9_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.2210/pdb8HSI/pdb", + "https://doi.org/10.2210/pdb8HTT/pdb", + "https://doi.org/10.2210/pdb8KB4/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-34998", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-350178", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-37069", + "http://doi.org/10.2210/pdb8CTJ/pdb", + "http://doi.org/10.2210/pdb7W9W/pdb", + "http://doi.org/10.2210/pdb7DRT/pdb", + "http://doi.org/10.2210/pdb5YQZ/pdb", + "http://doi.org/10.2210/pdb5EGI/pdb", + "/articles/s41467-024-49297-8#Sec52" + ], + "code": [], + "subject": [ + "Biophysics", + "Electron microscopy", + "Ion channels", + "Learning and memory" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4015466/v1.pdf?c=1720782460000", + "research_square_link": "https://www.researchsquare.com//article/rs-4015466/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-49297-8.pdf", + "preprint_posted": "19 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Impaired ion channels regulating Golgi pH lead to structural alterations in the Golgi apparatus, such as fragmentation, which is found, along with cognitive impairment, in Alzheimer\u2019s disease. However, the causal relationship between altered Golgi structure and cognitive impairment remains elusive due to the lack of understanding of ion channels in the Golgi apparatus of brain cells. Here, we identify that a transmembrane protein TMEM87A, renamed Golgi-pH-regulating cation channel (GolpHCat), expressed in astrocytes and neurons that contributes to hippocampus-dependent memory. We found that GolpHCat displays unique voltage-dependent currents, which is potently inhibited by gluconate. Additionally, we gained structural insights into the ion conduction through GolpHCat at the molecular level by determining three high-resolution cryogenic-electron microscopy structures of human GolpHCat. GolpHCat-knockout mice show fragmented Golgi morphology and altered protein glycosylation and functions in the hippocampus, leading to impaired spatial memory. These findings suggest a novel molecular target for Golgi-related diseases and cognitive impairment.Biological sciences/Structural biology/Electron microscopyBiological sciences/Biochemistry/Ion channelsBiological sciences/Neuroscience/Learning and memory", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformationNCOMMS2361122A.docxSupplementary figures and tablesSourceDataNCOMMS2361122A.xlsxSource DataNCOMMS2361122Ars.pdfReporting Summary", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Impaired ion channels regulating Golgi pH lead to structural alterations in the Golgi apparatus, such as fragmentation, which is found, along with cognitive impairment, in Alzheimer\u2019s disease. However, the causal relationship between altered Golgi structure and cognitive impairment remains elusive due to the lack of understanding of ion channels in the Golgi apparatus of brain cells. Here, we identify that a transmembrane protein TMEM87A, renamed Golgi-pH-regulating cation channel (GolpHCat), expressed in astrocytes and neurons that contributes to hippocampus-dependent memory. We find that GolpHCat displays unique voltage-dependent currents, which is potently inhibited by gluconate. Additionally, we gain structural insights into the ion conduction through GolpHCat at the molecular level by determining three high-resolution cryogenic-electron microscopy structures of human GolpHCat. GolpHCat-knockout mice show fragmented Golgi morphology and altered protein glycosylation and functions in the hippocampus, leading to impaired spatial memory. These findings suggest a molecular target for Golgi-related diseases and cognitive impairment.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Golgi pH is critical for its proper morphology and function, such as the modification, packaging, and transport of proteins. Impaired Golgi pH alters protein trafficking and glycosylation1,2 and induces morphological changes in the Golgi apparatus, such as fragmentation3. This morphological alteration is frequently found in neurodegenerative diseases, such as Alzheimer\u2019s and Parkinson\u2019s diseases4,5,6. Cognitive impairment is also a common symptom of these diseases7,8. However, the relationship between altered Golgi structures and cognitive impairment is poorly understood.\n\nGolgi pH has been proposed to be regulated by an adenosine triphosphate (ATP)-mediated proton pump, proton leak exchanger, proton leak channel, anion channel, and cation channel9. ATP-mediated proton pump and protein leak exchanger have been identified as vacuolar-type ATP hydrolase10 and Na+/H+ exchanger (NHE)7/811,12, respectively. The anion channel that regulates Golgi pH and morphology has been identified as Golgi pH regulator (GPHR)3, which maintains normal neuronal morphology and circuitry13. However, the molecular identity and function of Golgi-resident cation channels in the brain remain elusive.\n\nA potential candidate for the Golgi-resident cation channel is a transmembrane (TMEM) protein with unknown functions expressed in the plasma membrane or intracellular organelle membranes. Over the past decade, several TMEMs and their cryogenic-electron microscopy (cryo-EM) structures have been extensively investigated and found to be functional ion channels14,15,16,17,18,19,20. A TMEM with the generic name TMEM87A, identified as a predominantly Golgi-localized protein, has been proposed to play a potential role as a mechanically activated (MA) channel or an accessory subunit that modulates MA channels21. In a subsequent study, the cryo-EM structure of its lipid-bound form was elucidated at a resolution of ~4.7\u2009\u00c522. However, despite these structural insights, the functionality of TMEM87A as an ion channel remains unclear due to the absence of single-channel currents in TMEM87A-reconstituted liposome patch recordings22. Thus, the role of TMEM87A as an ion channel remains controversial.\n\nIn this study, we identify and characterize the unique cation channel TMEM87A as a Golgi pH-regulating cation channel (GolpHCat), determine its atomic-level structure, delineate its ion conduction pathway, and ascertain its functions in Golgi homeostasis, protein glycosylation, and biological processes, and investigate its contribution to hippocampal spatial memory by employing multidisciplinary cutting-edge technologies, including Golgi-specific pH imaging, proteoliposome-single-channel recordings, cryo-EM structural analysis, molecular dynamic modeling, proteomics/glycomics approaches, and animal behavior tests.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We first analyzed the protein sequence of full-length TMEM87A and found that TMEM87A contains a GYG sequence, which is a signature selectivity filter of classical K+ channels23 (Supplementary Fig.\u00a01a), raising the possibility that full-length TMEM87A may be a cation channel. Full-length TMEM87A encodes a 63\u2009kDa protein with a predicted N-terminal Golgi-targeting motif and seven transmembrane (TM) domains (Supplementary Fig.\u00a01b, c). In humans, TMEM87A encodes three isoforms: isoform 1 is full-length with a predicted Golgi-targeting motif and TMs, isoform 2 has no TM, and isoform 3 has no predicted Golgi-targeting motif (Supplementary Fig.\u00a01d). According to the brain RNA-seq database, full-length TMEM87A (isoform 1) is highly expressed in both, neurons and astrocytes24,25. Thus, based on bioinformatics analysis, TMEM87A is a potential candidate for the Golgi-resident cation channel in the brain.\n\nTo examine the protein expression of TMEM87A in the Golgi apparatus, we performed immunocytochemistry (ICC) against TMEM87A, using golgin-97 as a Golgi marker in cultured human astrocytes (Fig.\u00a01a). We found that TMEM87A strongly colocalized with Golgin-97 (Pearson\u2019s correlation coefficient R: 0.75) in cultured human astrocytes (Fig.\u00a01a), indicating that TMEM87A is mainly localized in the Golgi. To investigate whether the predicted signal sequence is indeed responsible for Golgi localization, we overexpressed individual constructs carrying the full DNA sequence each of TMEM87A isoform 1, predicted Golgi-targeting motif deleted isoform 1 (isoform 1\u2206), and TMEM87A isoform 3, which we predict lacks the Golgi-targeting motif (Supplementary Fig.\u00a01d) in cultured human astrocytes. We observed distinct and strong fluorescence signals indicating Golgi localization for isoform 1. In contrast, isoform 1\u2206 and isoform 3 exhibited weak fluorescence signals with different localization, even when the fluorescence intensity was saturated (Supplementary Fig.\u00a01e). In the presence of 5\u2009\u00b5M MG132, the expression levels of TMEM87A-iso1\u2206/iso3-EGFP were increased compared to the absence of MG132 (Supplementary Fig.\u00a01f), indicating that the Golgi-targeting motif contributes to not only Golgi localization but also protein stability by potentially increasing degradation. Further, Next-generation sequencing (RNA-seq) was used to examine the expression levels of each isoform in cultured human astrocytes (Supplementary Fig.\u00a01g). We found that TMEM87A isoform 1 had the highest proportion of total TMEM87A expression (Supplementary Fig.\u00a01g). Taken together, these results indicate that the major form of TMEM87A, isoform 1, is localized to the Golgi apparatus in cultured human astrocytes owing to its N-terminal signal sequence.\n\na Colocalization of TMEM87A with the Golgin-97, Golgi marker, in cultured human astrocytes. b Comparison of resting Golgi luminal pH and buffer capacities under the absence and presence of 50\u2009mM NH4Cl in Scrambled (gray, n\u2009=\u200914 cells) or TMEM87A shRNA (blue, n\u2009=\u200918 cells) transfected cultured human astrocytes expressing B4GALT1-RpHluorin. Arrows indicate time points in (c, d). c Golgi resting pH values. d Golgi pH values after treating the 50\u2009mM NH4Cl at 30\u2009s. c, d n\u2009=\u200921 cells for Scrambled and n\u2009=\u200918 cells for TMEM87A shRNA. e Schematic diagram of whole-cell patch-clamp recording from TMEM87A WT or TMEM87A-AAA (a.a. 318\u2013320) transfected CHO-K1 cell. Inset: fluorescence image of EGFP-tagged TMEM87A. f Averaged I-V relationship from Control (gray), TMEM87A WT (pink), or TMEM87A-AAA (blue) transfected cells under voltage-ramp protocol (from +100 to \u2212150\u2009mV). g, h Current densities measured at \u2212150\u2009mV in (g) and +100\u2009mV in (h). i The rectification index is calculated as the absolute ratio of amplitude at \u2212150\u2009mV over at +100\u2009mV. g\u2013i n\u2009=\u200927 cells for Control, n\u2009=\u200916 cells for TMEM87A WT, or n\u2009=\u200912 cell for TMEM87A-AAA. j Representative currents from Control and TMEM87A WT transfected cells under voltage-step protocol (from +100\u2009mV to \u2212150\u2009mV, 25\u2009mV step). k Averaged I-V relationship from TMEM87A WT transfected cells with bath solutions containing Na+, NMDG+, K+, or Cs+. l, m Current densities measured at \u2212150\u2009mV in (l) and +100\u2009mV in (m). k\u2013m n\u2009=\u200921 cells for Na+ n\u2009=\u20098 cells for NMDG+, n\u2009=\u200910 cells for K+, n\u2009=\u20098 for Cs+. n Reversal potentials measured from TMEM87A WT transfected cells with bath solutions containing Na+ (n\u2009=\u200913 cells), K+ (n\u2009=\u200910 cells), or Cs+ (n\u2009=\u20098 cells). o Relative permeability ratio of Na+ to K+ (PK+/PNa+, n\u2009=\u200910 cells) or Cs+ (PCs+/PNa+,n\u2009=\u20098 cells). p Representative I-V relationship from TMEM87A WT transfected cell with or without gluconate in the bath solution. q Dose-response curve for percentage currents at \u2212150\u2009mV for gluconate concentrations (n\u2009=\u20095 cells). r Representative I-V relationship from TMEM87A WT transfected cell under various pH. s Normalized currents at \u2212150\u2009mV under various pH, normalized to current at pH 7.3 (n\u2009=\u20097 cells). Data were presented as the mean\u2009\u00b1\u2009SEM. Statistical analyses were performed using two-tailed unpaired t-test in (c) (t\u2009=\u20098.453, df\u2009=\u200937); two-tailed unpaired t-test with Welch\u2019s test (d) (t\u2009=\u20092.189, df\u2009=\u200937); Kruskal\u2013Wallis test followed by Dunn\u2019s multiple comparisons test in g (H\u2009=\u200925.34), h (H\u2009=\u200921.98), i (H\u2009=\u200915.61); one-way ANOVA followed by Dunnett\u2019s multiple comparisons test in l (F(3,43)\u2009=\u200944.10), m (F(3,43)\u2009=\u20092.164), and n (F(2,28)\u2009=\u20097.552). Source data and exact p values are provided as a Source Data file.\n\nTo examine whether TMEM87A contributes to Golgi pH, we expressed a Golgi luminal-targeting pH sensor construct, B4GALT1-RpHluorin226, for real-time imaging of pH in cultured human astrocytes (Fig.\u00a01b and Supplementary Fig.\u00a01h). We found that gene silencing of TMEM87A by shRNA led to a more basic resting Golgi pH than non-silenced (scrambled) conditions (Fig.\u00a01c and Supplementary Fig.\u00a01i (top)). Furthermore, Golgi pH buffering capacity, as measured by the change in pH upon 50\u2009mM NH4Cl application, was little but statistically significantly lower in TMEM87A shRNA-transfected cells (Fig.\u00a01d and Supplementary Fig.\u00a01i (bottom)), indicating that TMEM87A contributes to Golgi pH buffering capacity. Taken together, these results indicate that TMEM87A, a candidate cation channel, localizes to the Golgi and contributes to Golgi pH homeostasis.\n\nNext, to investigate whether TMEM87A mediates current in the heterologous expression system, we transfected human TMEM87A (hTMEM87A) into CHO-K1 cells, that have minimal endogenous ion channel expression, and recorded whole-cell currents under voltage-clamp conditions (Fig.\u00a01e). Although native TMEM87A is mainly localized in the Golgi of human astrocytes, we observed that EGFP-tagged TMEM87A under heterologous overexpression was found not only in the Golgi but also in the plasma membrane (Fig.\u00a01e), as previously reported21. First, we measured the voltage-dependent membrane current under a voltage-ramp protocol ranging from \u2212150 to +100\u2009mV with a 140\u2009mM NaCl-containing external solution and 130\u2009mM K-gluconate-containing internal solution (Fig.\u00a01f). TMEM87A wild-type (WT)-mediated current displayed a non-linear current-voltage (I-V) relationship with a reversal potential near \u22127.7\u2009mV and pronounced inward-rectification near \u2212150\u2009mV (Fig.\u00a01f\u2013h). The average rectification index value of TMEM87A WT from +100 to \u2212150\u2009mV was 2.7\u2009\u00b1\u20090.3 (Fig.\u00a01i). In contrast to the WT TMEM87A-carrying cells, both outward and inward currents were completely abolished in cells carrying a mutant form of TMEM87A with the pore GYG sequence mutated to Ala-Ala-Ala (TMEME87A-AAA) (Fig.\u00a01f\u2013i). We performed a surface biotinylation assay and confirmed the surface expression of both TMEM87A WT and AAA mutant forms (Supplementary Fig.\u00a02a). These results indicate that the GYG sequence in TMEM87A may play a critical role in mediating the current. Furthermore, we recorded currents with voltage-step pulses from +100 to \u2212150\u2009mV and found that TMEM87A-mediated currents displayed voltage-dependent inward rectification with no time- or voltage-dependent inactivation (Fig.\u00a01j). Collectively, TMEM87A mediates voltage-dependent inwardly rectifying membrane currents in a heterologous expression system.\n\nTo investigate whether TMEM87A-mediated inward currents were carried by Na+ ions, we replaced Na+ with N-methyl-D-glucamine (NMDG) (Fig.\u00a01k). The inward current was mostly abolished, suggesting that TMEM87A may be a Na+-permeable cation channel (Fig.\u00a01k\u2013m). To determine the relative permeability ratio of TMEM87A-mediated currents to different cations such as K+ and Cs+, we replaced Na+ with K+ or Cs+ (Fig.\u00a01k\u2013m) and found that reversal potentials were slightly shifted to more positive potentials: from Na+ (\u22127.7\u2009\u00b1\u20091.5\u2009mV) to K+ (0.5\u2009\u00b1\u20091.6\u2009mV) and Cs+ (\u22123.5\u2009\u00b1\u20091.1\u2009mV) (Fig.\u00a01n). Using a modified Goldman\u2013Hodgkin\u2013Katz equation, we calculated the permeability ratios to be PK+/PNa+ = 1.4\u2009\u00b1\u20090.1 and PCs+/PNa+ = 1.6\u2009\u00b1\u20090.1 (Fig.\u00a01o), suggesting that TMEM87A might be a nonselective cation channel with a slightly higher permeability to K+ and Cs+ compared to Na+. We further confirmed that TMEM87A-mediated current was not carried by Cl\u2212 (Supplementary Fig.\u00a02b, c). Taken together, these results provide a series of evidence that TMEM87A might be a nonselective cation channel.\n\nTo investigate the pharmacological properties of TMEM87A-mediated currents, we tested the inhibitory effects of gadolinium (Gd3+), a well-known nonselective cation channel blocker. We found that Gd3+ effectively blocked TMEM87A-mediated currents in a dose-dependent manner with a half-maximal inhibition (IC50) of 2.98\u2009\u00b5M (Supplementary Fig.\u00a02d, e). Similar to hTMEM87A, mouse TMEM87A mediated similar magnitudes of membrane currents with similar gadolinium sensitivity (Supplementary Fig.\u00a02f, g). While we were substituting Cl\u2212 with various large anions, we serendipitously discovered that gluconate potently blocked TMEM87A-mediated currents with IC50 of 0.10\u2009\u00b5M (Fig.\u00a01p, q).\n\nTo investigate if the TMEM87A-mediated current was sensitive to pH, we recorded currents in bath solutions having different pH values (Fig.\u00a01r). We found that the TMEM87A-mediated inward currents were significantly reduced at acidic pH, whereas they were significantly enhanced at basic pH (Fig.\u00a01s), indicating that TMEM87A-mediated current was pH-sensitive. Despite its pH sensitivity, TMEM87A exhibited negligible permeability to H+ (Supplementary Fig.\u00a02h, i). Taken together, these results suggest that TMEM87A might be a voltage- and pH-dependent, nonselective, and inwardly rectifying cation channel.\n\nTo investigate whether TMEM87A is a bona fide functional ion channel, we performed blister-attached patch recordings to study single-channel activity with 200\u2009mM KCl symmetric solutions using reconstituted TMEM87A proteins in liposomes (proteoliposomes) (Fig.\u00a02a). For liposome preparation, we employed a combination of both neutral and negatively charged lipids, such as 1-palmitoyl-2-oleoyl-sn-glycero\u22123-phosphocholine (POPC) and palmitoyloleoylphosphatidylglycerol (POPG), which distinguishes our approach from a previous study22 that reported a lack of single-channel activity using only soy phosphatidylcholine (PC) (Fig.\u00a02a). Full-length hTMEM87A (M1\u2009~\u2009E555) with a cleavable C-terminus EGFP-tag and a Twin-strep tag was expressed in Expi293F cells, solubilized, purified with n-dodecyl \u03b2-d-maltoside (DDM) and cholesteryl hemisuccinate (CHS), and reconstituted into proteoliposome for the analysis of in vitro channel activity (Supplementary Fig.\u00a02j and Methods).\n\na Schematic diagram showing the procedure for reconstitution of TMEM87A into liposome (8:2, POPC:POPG) with dehydration/rehydration method for single-channel recording. b Representative spontaneous single-channel currents from the reconstituted proteoliposome of TMEM87A under voltage steps (from +90 to \u2212150\u2009mV, 30\u2009mV step) from the same patch condition. c Voltage-dependent channel-opening probability (Po) of TMEM87A at each holding potential (n\u2009=\u20093). d I-V relationship of TMEM87A single-channel unitary current activities (n\u2009=\u20093). Data were fitted with a polynomial. e The unitary current\u2009\u00d7\u2009open probability-voltage relationship of TMEM87A (n\u2009=\u20093). f Amplitude histogram of TMEM87A single-channel unitary current activities with open (O) and closed (C) states at +90\u2009mV (orange) and \u2212150\u2009mV (purple) from (b). Distribution data are fitted with a sum of two Gaussians at each holding potential. Data were presented as the mean\u2009\u00b1\u2009SEM. The n numbers are from three independent proteoliposome experiments. Source data are provided as a Source Data file.\n\nTMEM87A in proteoliposome displayed conspicuous stochastic single-channel openings at positive or negative holding potentials from +90 to \u2212150\u2009mV, but not at 0\u2009mV (Fig.\u00a02b). A detailed analysis revealed that the channel\u2019s open probability (Po) starts from Po\u2009=\u20090 at 0\u2009mV and increases non-linearly at both negative and positive potentials, with a maximum Po \u2248 0.6 at +90\u2009mV and Po \u22480.3 at \u2212150\u2009mV (Fig.\u00a02c). These results indicate that purified TMEM87A is a voltage-dependent channel with activation voltages at both negative and positive potentials and a much higher Po at positive potentials (sixfold higher Po at +90\u2009mV compared to Po at \u221290\u2009mV; Fig.\u00a02c). The analysis of amplitude showed weak inward rectification (Fig.\u00a02d), which was in marked contrast to the strong inward rectification observed in the whole-cell patch results (Fig.\u00a01f). However, when we multiplied the unitary current and open probability at each holding potential, we obtained a strongly rectifying I-V relationship (Fig.\u00a02e), implying that the whole-cell patch results originated from the ensemble average of the single-channel activities of TMEM87A. Interestingly, TMEM87A showed no subconductance opening at negative potentials, including \u2212150\u2009mV, whereas there were numerous subconductance-level openings (Osub) at +90\u2009mV (Fig.\u00a02b, f). In addition, we found that the single-channel conductance gradually increased at negative potentials and gradually decreased at positive potentials (Fig.\u00a02b). These results indicate that the voltage-dependent gating and ion permeation of TMEM87A are profoundly different at positive and negative potentials. The open- and closed-time distribution plots showed that TMEM87A frequently opened and closed for brief periods at +90\u2009mV with time constants \u03c4open\u2009=\u200926\u2009ms and \u03c4close\u2009=\u200941\u2009ms (Supplementary Fig.\u00a02l, m), whereas it less frequently opened and closed for longer periods at \u2212150\u2009mV with time constants \u03c4open\u2009=\u2009421\u2009ms and \u03c4close\u2009=\u2009505\u2009ms (Supplementary Fig.\u00a02n, o), again indicating a profoundly different channel gating at positive and negative potentials. Using liquid chromatography-mass spectrometry (LC-MS), in the purified TMEM87A solutions, we found negligible amounts of other pore-forming ion channel proteins (Supplementary Fig.\u00a02k and Supplementary Table\u00a01), which could have confounded our conclusion that TMEM87A is a bona fide ion channel. Taken together, these results provide direct evidence that TMEM87A is a voltage-dependent cation channel.\n\nTo address the molecular mechanism of hTMEM87A as a voltage-dependent cation channel, we determined the structure of hTMEM87A at an overall resolution of 3.1\u2009\u00c5 (Fig.\u00a03a, Supplementary Fig.\u00a03, and Supplementary Table\u00a02) using single-particle cryo-EM in the detergent condition. In the final density maps, we reliably assigned most of the side chains (D38-P473) as well as three N-linked oligosaccharides (N62, N79, and N127), except for two loops (L148\u2013K167 and S193\u2013L202) and the C-terminal tail (L474-E555), probably due to their structural flexibility (Fig.\u00a03b, c and Supplementary Fig.\u00a03j\u2013l).\n\na Cryo-EM density map of hTMEM87A, colored in slate gray. The density of the micelle (contoured at 0.161\u03c3) is presented as light gray. b Overall structure of hTMEM87A, with ELD (light gray) and TMD [rainbow color from TM1 (red) to TM7 (purple)]. Disulfide bridges (orange, C74\u2013C128, and C89\u2013C431) and N-linked glycans (green, N62, N79, and N127) are shown as sticks. PE and cholesterol are indicated as pink and cyan sticks, respectively. c Topology of hTMEM87A. ELD consists of two \u03b1-helices and seven \u03b2-strands arranged in an anti-parallel \u03b2-sandwich. The secondary structure elements (cylinder for helix and arrow for strand) are colored as in (b). Disulfide bonds are shown as an orange line. Dashed lines denote regions where density was insufficient for model building. d Two different views of the vertical cross-section of the PE-binding pocket in TMD. The electrostatic surface potential of the central cavity is shown. The upper hydrophilic and lower hydrophobic cavities are indicated as cyan and yellow dashed circles, respectively. e Open-book views of the PE-binding pocket and the interaction details. Interaction residues with PE are shown as sticks. The hydrogen and ionic bonds are depicted as a dashed line. Cyan and yellow colored circles represent hydrophilic and hydrophobic cavities in TMD, respectively. f Structural comparison of hTMEM87A TMD with other seven transmembranes (7TM) proteins ChRmine (PDB:7W9W, pale green), Wntless (PDB:7DRT, cyan), and Glucagon receptor (PDB:5YQZ, light purple), shown as a side view (top) and top view (bottom). In the top view, ELD (hTMEM87A), luminal domain (LD, Wntless), and extracellular domain (ECD, glucagon receptor) are omitted for clarity. PE in hTMEM87A, ATR in ChRmine, and Phosphatidylcholine (PC) in Wntless are displayed as pink, cyan, and lime sticks, respectively. g Superimposition of hTMEM87A TMD and TM region of ChRmine (pale green), Wntless (cyan), or glucagon receptor (light purple). The view is a 90\u00b0 rotation view of (b). along the y-axis to show the lateral opening between TM5 and TM6 of hTMEM87A TMD.\n\nContrary to our expectation that TMEM87A would form a tetramer similar to other K+ channels, such as KcsA and HCN channels containing a GYG motif23,27, our data revealed that hTMEM87A is a monomer, not a tetramer (Fig.\u00a03a). The structure of hTMEM87A contains two distinct domains: the globular domain [termed the extracellular/luminal domain (ELD), D38-K213] containing three glycans, N62, N79, and N127, and the transmembrane domain (TMD, Y224-P473) having seven transmembrane helices (7TM) (Fig.\u00a03b, c and Supplementary Fig.\u00a04a, b). The 7TM of the TMD are arranged counterclockwise and connected by three intracellular loops (ICL1-ICL3) and three extracellular/luminal loops (ELL1-ELL3) (Fig.\u00a03b, c). In the TMD, TM1, TM6, and TM7 form a flat plane perpendicular to the lipid membrane (hereafter referred to as the TM plane). ELD has broad interactions with the top edges of the flat TM plane and the extracellular loop connecting TM2 and TM3 (ELL1) (Supplementary Fig.\u00a04d, e; interaction patch 1\u20134). Combined with the inter-domain disulfide bond (C89\u2013C431), these broad interactions are likely to restrain ELD movement, thus maintaining a fixed orientation of the ELD relative to the TM plane.\n\nSince voltage-dependent inwardly rectifying currents were detected in the proteoliposomes reconstituted with hTMEM87A (Fig.\u00a02), we were intrigued to find a central cavity buried deep in the TMD for ion conduction. Two cavities are located between the TM plane and the tilted/twisted TM2-TM5 helices; an upper hydrophilic cavity (cyan dashed circle) and a lower hydrophobic cavity (yellow dashed circle) (Fig.\u00a03d). The upper hydrophilic cavity opens to the luminal side near the ELD and is exposed to the upper leaflet of the lipid bilayer through a gap between TM5 and TM6. Compared to the previously reported hTMEM87A structure (PDB ID: 8CTJ)22, we observed a well-resolved electron density in the TMD pocket, while overall architecture is similar (overall root mean square deviation (RMSD)\u2009=\u20091.03\u2009\u00c5, Supplementary Fig.\u00a03i\u2013k). Based on the shape of the electron density and the lipid composition of the Golgi apparatus membrane28, we modeled phosphatidylethanolamine (PE, 1-palmitoyl-2-oleoyl-sn-glycerol-3-phosphoethanolamine, or PE-16:0-18:1) to this density (Fig.\u00a03b and Supplementary Fig.\u00a03k). Although no phospholipids had been added during protein purification, and the protein was dissolved in LMNG/CHS detergent, PE was co-purified with hTMEM87A, suggesting that PE tightly and stably binds to TMEM87A. Indeed, the lateral side of the central cavity of the TMD was partially sealed by PE, of which the R2-fatty acid chain fully occupied the lower hydrophobic cavity (Fig.\u00a03d). In particular, the terminal amine of PE formed a charge-interaction with TM4 E347 and TM7 W445, and its phosphate group was coordinated by R305 and R309 of TM3, Y340 of TM4, and D371 of TM5 (Fig.\u00a03e). In addition, the oxygen atoms in the ester group were stabilized by TM5 D371 and TM6 S415. Side chains of TM helices [\u03b12 (I258, I262, and V265), \u03b13 (A308, L311, V312, and V315), \u03b15 (I378), \u03b16(F404, L408, A411, and V412) and \u03b17 (F449, I452, L453, and I456)] make hydrophobic interactions with the R2-fatty acid chain. The remaining R1-fatty acid chain of PE protrudes through the gap between TM5 and TM6, where it interacts with C375, W376, and F379 of TM5 and is likely to interact with other lipid molecules in the membrane bilayer. The residues participating in these extensive interactions with PE are highly conserved (Fig.\u00a03e and Supplementary Fig.\u00a05a), indicating a physiological role for PE in maintaining the structural and functional integrity of hTMEM87A. Taken together, these results indicate that hTMEM87A has a monomer architecture composed of an ELD and seven TMD, with a well-resolved PE in the TMD pocket.\n\nTo investigate the structural basis of the TMEM87A ELD and TMD for their physiological roles, we initially searched for structural homology using the Dali server29. As discussed in the previous study22, we found that ELD resembles a Golgi Dynamics (GOLD) domain present in p24 family proteins and SEC14-like protein 3, which are implicated in the secretory pathway such as cargo sorting and membrane trafficking (Supplementary Fig.\u00a04f)22,30,31. Interestingly, the sequence identity of ELD across hTMEM87 family members is relatively low compared to that of the TMD (Supplementary Fig.\u00a05b). However, the results of structure prediction with AlphaFold232 indicated that the ELD structure of hTMEM87A and hTMEM87B were highly similar (Supplementary Fig.\u00a04c), suggesting functional redundancy between TMEM87 family members. Given the roles of GOLD domain-containing proteins in the secretory pathway, the ELD of the hTMEM87 family may play a role in protein trafficking by interacting with unidentified partners. Sequence comparison of TMEM87A with eukaryote orthologs33 suggests that the conserved evolutionary TMD is more relevant to the physiological function of TMEM87A (Supplementary Fig.\u00a05a). From a Dali server search with hTMEM87A TMD, we found that structural homologs of hTMEM87A TMD included microbial channelrhodopsin (ChRmine34, PDB: 7W9W, TM1-TM7 among 7TM, Z-score 13.7, RMSD 3.3 over 214 residues), Wnt transport protein (Wntless35, PDB: 7DRT, TM2-TM8 among eight TM helices, Z-score 14.2, RMSD 4.1 over 233 residues), and glucagon G-protein coupled receptor (Glucagon receptor36, PDB: 5YQZ, TM1-TM7 among eight TM helices, Z-score 11.7, RMSD 4.0 over 223 residues) (Fig.\u00a03f), although their sequence identity is relatively low (8\u201313%). hTMEM87A is similar to Wntless and glucagon receptors in having a central cavity buried in the TMD located below the globular extracellular domain (ECD) and opening to the luminal side. However, the position of the ECDs differs substantially from that of the hTMEM87A ELD. Moreover, TM4 and TM5 of hTMEM87A are tilted toward the cavity core, resulting in a smaller central cavity compared to the cavities of Wntless and glucagon receptors, which can accommodate a lipidated Wnt3a/8a hairpin and a peptide ligand, respectively (Fig.\u00a03f, g). The overall arrangement of hTMEM87A 7TM was much closer to that of ChRmine, although it does not have an ECD. ChRmine and hTMEM87A are superimposed with an overall RMSD of 3.3\u2009\u00c5 over 7TM (214 residues) and two of their corresponding TMD layers (TM1/6/7 and TM2/3/4/5) are more tightly packed with each other than those of Wntless and glucagon receptor (Fig.\u00a03g). Similar to all-trans-retinal (ATR) in ChRmine, PE is surrounded by TM3-TM7 in hTMEM87A, although their binding orientations are quite different (Fig.\u00a03f, g). Taken together, the 7TM of hTMEM87A is structurally similar to ChRmine, even though its extracellular globular domain resembles the GOLD domain.\n\nNext, we examined the configuration of the hTMEM87A cavity to delineate the location and shape of the ion conduction pathway. No continuous channel pores were observed in the structure (Fig.\u00a04a). The water-accessible cavity was physically blocked by the R2-fatty acid chain of PE, which extends from the lateral opening between TM5 and TM6 to the lower hydrophobic cavity. These data suggested that the observed conformation was presumably in a closed state. However, analysis of the electrostatic surface potentials of hTMEM87A revealed that negatively charged residues (D38 in ELD and E222, D223, E279, E298, D441, and D442 in TMD), main-chain carbonyl groups of L438 and W439, and the hydroxyl group of ELD Y90 are distributed on the funnel-shaped luminal vestibule (Fig.\u00a04a\u2013c; hereafter negatively charged luminal vestibule (NLV)). Similar to the extracellular vestibule of ChRmine34, the electronegative surface potential of hTMEM87A NLV may attract and stabilize positively charged ions, thereby effectively increasing the local cation concentration. To examine the potential role of these negatively charged NLV residues in ion conduction, we performed mutagenesis and measured hTMEM87A channel activity using whole-cell patch clamping. Among the three negatively charged residues, the E279A mutation resulted in almost complete elimination of channel activity, whereas the E298A mutation showed partially decreased channel activity, and the D442A mutation showed no change compared to WT (Fig.\u00a04d and Supplementary Fig.\u00a06a, b). Further investigation of the role of the NLV in cation attraction with accelerated Gaussian molecular dynamics (GaMD) simulations37,38 revealed that potassium cations (K+) are recognized by the superficial region of the NLV (D38, Y90, E222, and D223), stabilized in deeper NLV regions upon interaction with additional residues (D441 and D442), and are able to travel a short distance through the channel (Fig.\u00a04e and Supplementary Fig.\u00a06g). In fact, K+ binding events occur frequently, as we explored up to 12 bindings within the 1.5\u2009\u00b5s GaMD simulations. Collectively, the attraction of the cation for ion conduction was orchestrated by the negatively charged residues on the funnel-shaped luminal vestibule, whereas complete cation permeabilization was not observed, possibly because we started with a closed structure, and the expected timescale of the channel pore opening is long (~10\u2009ms).\n\na Organization of ion-pathway in hTMEM87A. Water-accessible cavities are shown as a cyan surface, with the putative ion conduction pathway indicated by a red arrow. Negative-charged luminal vestibule (NLV) and constriction site (CS, black-lined box) are labeled. b The surface electrostatic potential of the NLV (yellow dotted circle). ELD and ten residues (R351-S361) are omitted for clarity. c Close-up views of NLV and key negative-charged residues are shown as sticks. d Current density measured at \u2212150\u2009mV for hTMEM87A WT (n\u2009=\u20095) and NLV mutants (n\u2009=\u200915 for E279A, n\u2009=\u200910 for E298A, and n\u2009=\u200910 for D442A). e Representative structures of the K+ conformational dynamics in the NLV of hTMEM87A obtained from GaMD simulations 1. K+ atoms (purple sphere) and their interacting residues (light gray stick) are displayed. TM4 and TM5 are omitted for clarity. f Close-up view of gluconate binding site in hTMEM87A. Gluconate (yellow stick) with cryo-EM density map (contour level\u2009=\u20090.118) and interaction residues (gray sticks) are shown. The hydrogen and ionic bonds are depicted as a dashed line. Helices of hTMEM87A TMD are displayed as transparent cartoons. g Close-up views of the constriction site and the interaction details. Key interaction residues (gray) and PE (pink) are shown as sticks. Cavities are shown as cyan surfaces. Helices of hTMEM87A TMD are displayed as transparent cartoons. TM4 is omitted for clarity. h Current density measured at \u2212150\u2009mV for hTMEM87A WT (n\u2009=\u20095) and CS mutants (n\u2009=\u20097 for Y237A, n\u2009=\u200911 for E272A, n\u2009=\u20099 for K273A, n\u2009=\u20099 for S301A, n\u2009=\u20097 for K304A, n\u2009=\u20098 for R305A, and n\u2009=\u200910 for R309A). Data were presented as the mean\u2009\u00b1\u2009SEM. Statistical analyses were performed using student one-way ANOVA followed by Dunnett\u2019s multiple comparisons test in d (F(3,36)\u2009=\u200916.64) and h (F(7,58)\u2009=\u200910.84). Source data and exact p values are provided as a Source Data file.\n\nIt has been shown that gluconate can effectively block both outward and inward currents of hTMEM87A with 0.10\u2009\u00b5M IC50 (Fig.\u00a01p, q). Thus, to decipher the binding pocket for gluconate and the putative ion conduction pathway of hTMEM87A, we incubated purified hTMEM87A with 10\u2009mM sodium gluconate and determined the cryo-EM structure of hTMEM87A-Gluconate (hTMEM87A-Gluc) at ~3.6\u2009\u00c5 resolution (Supplementary Fig.\u00a03m\u2013t). While the overall structure of hTMEM87A-Gluc was essentially identical to hTMEM87A structure, of potential significance, the observed density indicated that a gluconate ion occupied the hydrophilic cavity of hTMEM87A via electrostatic interactions with R305, R309, D371, and W445, in a manner similar to the head group of PE (Fig.\u00a04f, g and Supplementary Fig.\u00a03t). Based on these observations, we hypothesized that the extended electrostatic-interaction networks (Y237, E272, K273, S301, K304, R305, R309, S344, D371, Y340, and S415) underneath the NLV, while also mediating the binding of the PE head group, can form a constriction and hence, be implicated in hTMEM87A-mediated ion conduction upon the channel-opening stimulus. To test this hypothesis, we expressed hTMEM87A mutants (Y237A, E272A, K273A, S301A, K304A, R305A, and R309A) and recorded their channel activity. Despite their robust cell membrane expression, current amplitudes at \u2212150\u2009mV were significantly decreased for all mutants compared to WT (Fig.\u00a04h and Supplementary Fig.\u00a06a, d, f). Moreover, these residues in the constriction sites are highly conserved (Supplementary Fig.\u00a05a). These data suggest that a channel-opening stimulus (high voltage in our experiments) can trigger rearrangements in the electrostatic-interaction networks underneath the NLV, ultimately leading to ion conduction. Conceivably, such a mechanism resembles that of channelrhodopsin photocurrents, which are initiated by retinal isomerization and drive conformational changes in transmembrane helices39,40,41.\n\nNext, we asked why endogenous PEs bind to TMEM87A, despite phosphatidylcholine (PC) being the most abundant lipid in the Golgi membrane (PC, ~50%; PE, ~20%)28. To this end, we compared the binding free energies of PC and PE to hTMEM87A using MD simulations and assessed them using a linear interaction energy (LIE) model42 (Supplementary Fig.\u00a06h\u2013l). The results showed that the additional methyl groups of PC, while providing greater van der Waals interaction energy, also reduce the coulombic interaction energy due to their screening effect. However, the calculated binding free energy of PC to hTMEM87A is higher than that of PE by 19.2\u2009kJ/mol (\u0394Fm->p (PC)\u2009=\u2009\u221213.2\u2009kJ/mol and \u0394Fm->p (PE)\u2009=\u2009\u221232.4\u2009kJ/mol), suggesting that under physiological condition, PE is more likely to bind to hTMEM87A. Importantly, these data support that the PE observed in the cryo-EM structure was co-purified with hTMEM87A from the Golgi membrane. However, future studies employing techniques like mass spectrometry could further strengthen our conclusions and provide complementary evidence for PE in TMD. To characterize the entry of PE and binding to hTMEM87A, we simulated five trajectories up to 1 \u03bcs starting from a system (named Sp*/L) constructed by removing the bound PE lipid (L) from the hTMEM87A structure and then embedding the bare hTMEM87A (p*) in a lipid bilayer composed of only PE (Fig.\u00a05). To represent the binding process, we defined two distances, dP and dR2-Cent, as those from the phosphorus atom and the center of mass of the R2-fatty acid chain of PE to the smallest-moment principal axis (Pz) of TMD, respectively (Fig.\u00a05a). In the two-dimensional histograms of dP and dR2-Cent for all five trajectories, we identified seven highly populated states of PE (from S1 to S7), among which the fully bound state (S1) was the most probable. The conformational snapshots corresponding to these seven states are shown in Fig.\u00a05c. In one example trajectory, for which variations in dP and dR2-Cent as a function of time are presented, the PE lipid starts entering the TMD cavity quickly (~25\u2009ns), passes through two intermediate partially bound states (S3 and S4), and arrives at the fully bound state (S1) after ~500\u2009ns (Fig.\u00a05b). These data demonstrate that lipid entry occurs in a stepwise manner. Moreover, the proximity between the state corresponding to the cryo-EM structure (S*) and S1 indicates a clear tendency for PE to be inserted at this unique position of hTMEM87A.\n\na 2D histogram of distances (dP and dR2-Cent) for five MD trajectories (5\u2009\u00d7\u20091\u2009\u03bcs from system Sp*/L). dP and dR2-Cent are the distances from the phosphorus atom and the center of mass of the R2-fatty acid chain to the smallest-moment principal axis (Pz) of TMD, respectively. PE in cryo-EM structure (red) and seven highly populated states of PE are labeled (from S1 to S7). b Variations of distances of dP and dR2-Cent as a function of time along one trajectory. States of S4, S3, and S1 are indicated by horizontal lines. c The conformational snapshots of PE in seven different states (S1\u2013S7). PE from cryo-EM structure (pink) and MD simulations are displayed as sticks [the phosphorus atom (orange), R1-fatty acid chain (light blue), and R2-fatty acid chain (teal)]. Dashed lines indicate Pz of TMD. Calculated distances of P and R2-Cent are indicated. Cyan and yellow colored circles represent hydrophilic and hydrophobic cavities in TMD, respectively. d, e Cross-sectional view of hTMEM87 WT and A308M. The A308M, which blocks the PE chain from entering the inner cavity, is highlighted in red. A modeled lipid in A308M is shown as a pink stick. f Current density measured at \u2212150\u2009mV for hTMEM87A WT and A308M (bottom, n\u2009=\u20095 WT and n\u2009=\u200910 for A308M). g Close-up view of lipid binding site in hTMEM87A A308M. h Voltage-sensing TM4 of TRIC-B1 (left, PDB: 5EGI) and potential voltage sensor in TM3 of TMEM87A (right). The conserved basic residues (cyan), nearby acidic residues (yellow), and phospholipids (pink) are shown as sticks. i Current density measured at \u2212150\u2009mV for hTMEM87A WT (n\u2009=\u20095) and mutants on either ends of TM3 (n\u2009=\u20097 for E288R, and n\u2009=\u20096 for AAA). j Water-accessible cavities (yellow surfaces) with putative ion-pathway (red arrow). PE is omitted to show the unblocked lower hydrophobic cavity. Potential voltage-sensing helix TM3 and conserved basic residues are shown as cyan. Data were presented as the mean\u2009\u00b1\u2009SEM. Statistical analyses were performed using a two-tailed unpaired t-test in (f) (t\u2009=\u20094.605, df\u2009=\u200913); one-way ANOVA followed by Dunnett\u2019s multiple comparisons test (i) (F(2,15)\u2009=\u200924.74). Source data and exact p values are provided as a Source Data file.\n\nAlthough we investigated the ion conduction of TMEM87A, we could not identify an ion conduction pathway beyond the constriction site due to the presence of the R2-fatty acid chain of PE occupying the hydrophobic TMD cavity (Fig.\u00a03d, e). Previous studies have suggested that hydrophobic regions in ion conduction pathways, such as those found in KcsA43 and TWIK-144, contribute to ion permeation and gating processes45,46. Based on this, we hypothesized that the lower hydrophobic cavity of TMEM87A might be a potential pathway for ion conduction and that our structure represents a non-conducting state in which the lipid chain obstructs ion conduction. To verify this, we determined the cryo-EM structure of a hTMEM87A mutant A308M, which we predict would block the ion conduction pathway at the entrance of the lower hydrophobic cavity (Fig.\u00a05d and Supplementary Fig.\u00a07). Indeed, A308M effectively sealed the lower hydrophobic cavity, resulting in reduced channel activity compared to that of the WT (Fig.\u00a05e, f and Supplementary Fig.\u00a06c). Interestingly, the PE density in hTMEM87A A308M was displaced to a position similar to that of the S6 state in MD simulation (Fig.\u00a05g and Supplementary Fig.\u00a07i). This suggests that the lower hydrophobic cavity of TMEM87A is a potential ion conduction pathway, and that PE displacement in hTMEM87A by the external energy, such as high voltage and mechanical pressure steps, could affect channel opening.\n\nWe demonstrated that the channel activity of hTMEM87A was regulated in a voltage-dependent manner (Figs.\u00a01, 2). Voltage-regulated ion channels, such as Kv channels, Nav channels, and trimeric intracellular cation (TRIC) channels, usually have a voltage-sensing domain (helix) wherein positively charged lysine or arginine residues are enriched47,48,49,50. The TRIC-B1 channel contains three conserved basic residues in its TM4 helix. These interact with the phosphate group of phosphatidylinositol-4,5-biphosphate (PIP2) and nearby negatively charged residues, which occlude its ion permeation pathway (Fig.\u00a05h). Moreover, the artificial disulfide bonds that lock the TM4 helix of TRIC-B1 in a restrained conformation cause it to remain closed upon depolarization, demonstrating an essential mechanism by which the voltage-sensing TRIC TM4 helix is coupled to channel activation48. Although the structure of hTMEM87A is different from that of TRIC channels, three conserved basic residues (K304, R305, and R309) lining the hTMEM87A TM3 interact with an electronegative PE head group and the neighboring E272 residue, which closely resembles the voltage-sensing TM4 helix of TRIC-B147 (PDB: 5EGI) (Fig.\u00a05h). Indeed, electrophysiological analysis with mutations of these residues (K304A, R305A, and R309A) showed reduced channel activity (Fig.\u00a04h). In addition, the AAA mutation of the GYG sequence of TMEM87A at the cytoplasmic end of TM3 resulted in reduced channel activity (Figs.\u00a01e, f, \u00a05h, i and Supplementary Fig.\u00a06a, e). Moreover, we found that mutations in the loop residues flanking the TM3 helix (E288R on ELL1) resulted in increased channel activity than WT (Fig.\u00a05i and Supplementary Fig.\u00a06e). As noted, E288 interacts with nearby basic residues, maintaining the structural integrity of hTMEM87A (Supplementary Fig.\u00a04d, e, Patch4). Replacing the negatively charged glutamate with positively charged arginine disrupts interactions with the basic residues, potentially introducing the conformational changes to TM3 and influencing channel activity. Alternatively, the mutation might alter electrostatic interactions within the TMD, leading to an environment more conductive to ion flow. To unravel the precise mechanism, further investigations are needed. Collectively, these results suggest a critical role for the hTMEM87A TM3 in influencing channel activity, probably by participating in voltage sensing and gating (Fig.\u00a05j). However, we did not observe any conventional channel structure from the geometric pore analysis and cation permeation pathway among all simulated trajectories (~1\u2009\u03bcs). Possible explanations for the lack of cation permeation pathway may include (1) longer simulation times (>1\u2009\u03bcs) needed and (2) the lack of structures for open state upon the voltage stimulation. Taken together, these structural analyses suggest that TMEM87A is a monomeric ion channel with a putative ion conduction pathway that can be opened by voltage stimulation and closed by PE.\n\nBased on the electrophysiological and structural results, we renamed TMEM87A as GolpHCat to represent a Golgi-resident, pH-regulating cation channel. To characterize the in vivo functions of GolpHCat in mice, we generated a GolpHCat knockout (KO) mouse line using CRISPR/Cas9 gene editing (Supplementary Fig.\u00a08a). Tmem87A of Mus musculus is located on the reverse strand of mouse chromosome 2 (Chr2, 120,185,793\u2013120,234,594) and consists of 20 exons (Supplementary Fig.\u00a08a). Two guide RNAs (gRNAs) targeting the introns flanking exon 10 were designed to delete exon 10 containing the GYG motif sequence, which made a 255\u2009bp deletion in the gene (Supplementary Fig.\u00a08a). To confirm the generated mouse genotypes, we performed PCR genotyping around the deletion and observed three types of genotypes: WT with one band at 484\u2009bp, heterozygote (HT) with two bands at 484\u2009bp and 229\u2009bp, and homozygote (KO) with one band at 229\u2009bp (Supplementary Fig.\u00a08b). GolpHCat HT and KO mice were viable with no gross abnormalities, possibly due to compensation by TMEM87B.\n\nTo examine the expression pattern of GolpHCat, we performed immunohistochemistry (IHC) using antibodies against GolpHCat and Glial Fibrillary acidic protein (GFAP) or \u201cNeuronal Nuclei\u201d (NeuN), which are astrocytic and neuronal markers, respectively. Strong fluorescence intensity was observed in the hippocampus, but not in other brain regions (Fig.\u00a06a and Supplementary Fig.\u00a08c). Furthermore, we observed the absence of GolpHCat expression in GolpHCat KO mice using IHC and western blotting (Fig.\u00a06a and Supplementary Fig.\u00a08h). The observed GolpHCat immunoreactivity in both GFAP- and NeuN-positive cells in WT mice was virtually absent in GolpHCat KO mice (Fig.\u00a06b, c). These results indicate that the antibody against GolpHCat is highly specific and that GolpHCat is majorly expressed in both astrocytes and neurons of the hippocampus. To investigate whether GolpHCat is localized in the Golgi of astrocytes and neurons in the mouse brain, we performed IHC with antibodies against GolpHCat and different Golgi markers (Fig.\u00a06d), such as Golgin-97 and giantin, since both markers show different immunoreactivities in astrocytes51, neuronal dedrites52 and soma53. We found that GolpHCat was colocalized with Golgin-97 and giantin in astrocytes and soma of neurons, respectively (Fig.\u00a06d). Pearson\u2019s correlation coefficient R of 0.5 for GFAP positive astrocytes and 0.6 for NeuN-positive neurons (Fig.\u00a06e) indicated that GolpHCat was moderately localized in the Golgi of astrocytes and soma of neurons.\n\na Immunostaining for GolpHCat, GFAP, and NeuN in the hippocampus of WT and GolpHCat KO mice (left). High-magnified images showing colocalization of GolpHCat with GFAP or NeuN (right). b Fluorescence intensity of GolpHCat immunoreactivities in GFAP+ cells of WT (n\u2009=\u2009206 cells from four mice) and GolpHCat KO (n\u2009=\u2009102 cells from three mice) mice. c Fluorescence intensity of GolpHCat immunoreactivities in NeuN+ cells of WT (n\u2009=\u2009241 cells from four mice) and GolpHCat KO (n\u2009=\u2009259 cells from three mice) mice. d Colocalization of GolpHCat with Golgin-97 or Giantin in hippocampal astrocyte (GFAP) and neuron (NeuN) of WT mice, respectively. e Pearson\u2019s correlation coefficient for colocalization of GolpHCat and Golgi markers in hippocampal astrocytes (n\u2009=\u200914 cells) and neurons (n\u2009=\u200915 cells) of WT mice. f TEM images of the Golgi apparatus in hippocampal astrocytes and neurons of WT and GolpHCat KO mice. Yellow arrows indicate Golgi. g Diagram of Golgi structure for analysis used in (h, i). h Maximum length of Golgi cisternae (left) and width of Golgi (right) in hippocampal astrocytes of WT (n\u2009=\u200919 cells from three mice) and GolpHCat KO (n\u2009=\u200915 cells from three mice) mice. i Maximum length of Golgi cisternae (left) and width of Golgi (right) in hippocampal neurons of WT (n\u2009=\u200914 cells from three mice) and GolpHCat KO (n\u2009=\u200917 cells from three mice) mice. Data were presented as the mean\u2009\u00b1\u2009SEM. Statistical analyses were performed using two-tailed Mann\u2013Whitney test in b (U\u2009=\u2009365.5), c (U\u2009=\u20092), i width (U\u2009=\u200913); two-tailed unpaired t-test in (h)-length (t\u2009=\u20093.778, df\u2009=\u200932), (h)-width (t\u2009=\u20096.739, df\u2009=\u200932), (i)-length (t\u2009=\u20094.741, df\u2009=\u200929). Source data and exact p values are provided as a Source Data file.\n\nIt has been previously reported that genetic ablation of the Golgi pH regulating anion channel, GPHR, disorganizes the Golgi structure with fragmentation and swelling of the cisternae3. To examine Golgi morphology in the hippocampal astrocytes and neurons of WT and GolpHCat KO mice, we performed transmission electron microscopy (TEM) (Fig.\u00a06f). We observed that most of the Golgi exhibited disrupted stacks and dilated cisternae in both cell types of GolpHCat KO mice compared to WT mice (Fig.\u00a06f). In addition, we analyzed the length of Golgi cisternae and width of Golgi apparatus (Fig.\u00a06g) in each cell type and found that the maximum length of cisternae was significantly decreased, whereas the width of Golgi was significantly increased in both hippocampal astrocytes and neurons of KO mice (Fig.\u00a06h, i). This is consistent with the morphological alterations observed in GPHR KO cells3. In contrast, the mitochondria showed normal morphology in the cells of GolpHCat KO mice (Supplementary Fig.\u00a08d), indicating that GolpHCat is critical for maintaining normal Golgi morphology in cells. Taken together, our results suggest that the lack of GolpHCat results in impaired Golgi homeostasis, leading to impaired Golgi functions, such as protein glycosylation.\n\nTo investigate the glycosylation patterns, we analyzed glycans by LC-MS using hippocampal brain samples from WT and GolpHCat KO mice (Fig.\u00a07a), as previously described54,55. We found that among the detected 99 N-glycans some were downregulated, while others were upregulated in the KO mice (Fig.\u00a07a). We performed principal component analysis (PCA) and observed a complete separation of WT and KO mice by principal component 1 (Fig.\u00a07b). Subsequently, we determined the glycan types that most strongly influenced principal component 1, highlighting the significance of C/H-F and C/H-FS glycans in our findings (Fig.\u00a07c). Furthermore, to investigate the specific altered glycan patterns that significantly distinguished the WT and GolpHCat KO mice, we featured 14 specific glycans that displayed significant differences, as depicted in the volcano plot (Fig.\u00a07d). Consistent with Fig.\u00a07c, C/H-F and C/H-FS glycans exhibited significant alterations in KO samples compared to WT samples. To gain further insight into the changes in fucosylation, we examined the number of fucose residues in C/H-F and C/H-FS glycans. This analysis revealed that the that the normalized absolute peak intensity (NAPI) of mono-fucosylated glycans was little but statistically significantly higher in KO samples, whereas the NAPI of multi-fucosylated glycans (>2 fucose residues) was notably lower in KO than in WT samples (Fig.\u00a07e). Taken together, these results indicate that the lack of GolpHCat alters protein glycosylation, specifically fucosylation, in the hippocampus.\n\na\u2013e Comparison of 99 N-glycans found in the hippocampus of WT (n\u2009=\u20093 mice) and GolpHCat KO (n\u2009=\u20093 mice) mice. a Heat map for the hierarchical clustering of N-glycans. The scale bar indicates z-scores of standardized glycan value with higher or lower expressed glycans depicted in red or blue, respectively. (Sugar code: Hex_HexNAc_Fuc_NeuAc_(HexA)_sulfation(S)). b PCA analysis of hippocampus samples between WT and GolpHCat KO. c Feature influence strength for principal component 1 in (b). d Volcano plot. The red dots represent significant expression C/H-F glycans, the orange dots represent significantly expression C/H-FS glycans, and the gray dots represent insignificant expressed glycans. e Comparative distribution of all fucosylated glycans NAPI by the number of fucose residues (n\u2009=\u20093 mice per group). Data were presented as the mean\u2009\u00b1\u2009SEM. Statistical analyses were performed using two-tailed t-test in (d); two-tailed unpaired t-test in (e) (#0, t\u2009=\u200910.18, df\u2009=\u20094; #1, t\u2009=\u20090.6246, df\u2009=\u20094; #>2, t\u2009=\u20093.827, df\u2009=\u20094). Source data and exact p values are provided as a Source Data file.\n\nTo explore the changes in biological functions arising from the lack of GolpHCat, we conducted a proteomic analysis on membrane proteins from hippocampal brain samples of both WT and GolpHCat KO mice using LC-MS (Supplementary Fig.\u00a08e). We found 29 differentially expressed proteins present only in WT and 53 differentially expressed proteins present only in GolpHCat KO mice (Supplementary Fig.\u00a08f and Supplementary Table\u00a03). Among these, the expression of certain ion channels, including inward rectifier potassium channel 4 (KCNJ4) and potassium voltage-gated channel subfamily C member 1 (KCNC1) increased, whereas the expression of sodium channel subunit beta-3 (SCN3B) decreased (Supplementary Fig.\u00a08g, h and Supplementary Table\u00a03). To examine the roles of these differentially expressed proteins, we analyzed their molecular functions using Gene Ontology (GO) analysis (Supplementary Fig.\u00a08i). We found that \u201cTransporter activity\u201d was decreased in KO mice (Supplementary Fig.\u00a08i). To predict the effect of these differentially expressed proteins on biological functions, we performed a protein network cluster analysis based on the results of the GO analysis (Supplementary Fig.\u00a08j). The results predicted that these differentially expressed proteins play vital roles in learning and memory (Supplementary Fig.\u00a08j). Taken together, these results indicate that the lack of GolpHCat alters glycosylation in the hippocampus, which is expected to alter biological functions, especially hippocampus-related learning and memory.\n\nTo test if the lack of GolpHCat causes altered hippocampus-related learning and memory, we performed detailed electrophysiological and behavioral analyses of the well-established hippocampal spatial memory circuit of CA3\u2009\u2192\u2009CA1 synapses. Unexpectedly, the intrinsic neuronal excitability of CA1 pyramidal neurons was not altered in GolpHCat KO mice compared to WT mice (Supplementary Fig.\u00a09a\u2013c), although the shape of the action potential was slightly altered in KO mice (Supplementary Fig.\u00a09d\u2013i). To examine synaptic transmission and plasticity, we measured extracellular field excitatory postsynaptic potentials (fEPSP) at the CA3-CA1 synapses of WT and GolpHCat KO mice (Fig.\u00a08a). We then examined the basal synaptic transmission with increasing stimulus intensities at the Schaffer collaterals and found no difference between WT and GolpHCat KO mice (Fig.\u00a08b). To examine the presynaptic release probability, we measured paired-pulse ratios (PPRs) with increasing interpulse intervals and consistently found no significant differences between WT and KO mice at any interpulse interval (Fig.\u00a08c), indicating that the basal synaptic connectivity and transmission were unaltered in KO mice. Finally, to examine the potential role of GolpHCat in synaptic plasticity, we performed high-frequency stimulation (HFS)-induced long-term potentiation (LTP) and found that it was significantly impaired in GolpHCat KO mice (Fig.\u00a08d, e). Taken together, these results indicate that the lack of GolpHCat alters hippocampal LTP while leaving basal synaptic transmission unchanged. Finally, to investigate whether the lack of GolpHCat affects hippocampus-dependent memory, we subjected WT and GolpHCat KO mice to hippocampus-dependent spatial memory-related behavioral tasks, such as novel place recognition (NPR) and contextual fear tests (Fig.\u00a08f). GolpHCat KO mice showed a significant impairment of both contextual spatial memory in NPR (Fig.\u00a08g, h) and contextual fear memory in the fear test (Fig.\u00a08i), with no change in anxiety levels in the elevated plus maze test (Supplementary Fig.\u00a09j\u2013o). Taken together, these results indicate that GolpHCat is required for hippocampal spatial and contextual memory, which is consistent with predictions based on the glycomics and proteomics analyses (Fig.\u00a07).\n\na Schematic diagram of fEPSP recording in the Schaffer-collaterals pathway. b Input-output curve for fEPSP slope obtained with increasing stimulus intensities in WT and GolpHCat KO mice. c Paired-pulse ratios (PPR) obtained with increasing interpulse intervals. Inset: representative fEPSP traces. Scale bar: 1\u2009mV and 500\u2009ms. d HFS (1\u2009s at 100\u2009Hz)-induced LTP. Inset: representative fEPSP traces from before and after HFS-induced LTP. Scale bar: 0.5\u2009mV and 10\u2009ms. e Slope of fEPSP over the last 5\u2009min. n\u2009=\u200910 cells from three mice for WT and n\u2009=\u200914 cells from three mice for GolpHCat KO in (b\u2013e). f Schematic diagram of the NPR and contextual fear task. g Total object exploration time during the test phase of the NPR task. h Discrimination index during the test phase of the NPR task. i Percentage of freezing during the acquisition phase of contextual fear task (left) and retrieval phase during the 5\u2009min (right). The orange rectangular indicates the time point of shock. n\u2009=\u20099 mice for WT and n\u2009=\u20099 mice for GolpHCat KO in (g\u2013i). j Illustration of virus injection for Scrambled (n\u2009=\u20099) and astrocyte-specific GolpHCat KD (Astrocytic KD; n\u2009=\u20099) in the stratum radiatum of the hippocampal CA1 region. k Total object exploration time during the test phase of the NPR task. l Discrimination index during the test phase of the NPR task. m Percentage of freezing during the acquisition phase of contextual fear task (left). Percentage of freezing during the retrieval phase during the 5\u2009min (right). The pink rectangular indicates the time point of shock. n\u2009=\u20099 mice for Scrambled and n\u2009=\u20099 mice for Astrocytic KD in (k\u2013m). n Illustration of virus injection for Scrambled and neuron-specific GolpHCat KD (Neuronal KD) in the pyramidal layer of the hippocampal CA1 region. o Total object exploration time during the test phase of the NPR task. p Discrimination index during the test phase of the NPR task. q Percentage of freezing during the acquisition phase of contextual fear task (left). Percentage of freezing during the retrieval phase during the 5\u2009min (right). The purple rectangular indicates the time point of shock. n\u2009=\u20098 mice for Scrambled and n\u2009=\u200910 mice for Neuronal KD in (o\u2013q). Data were presented as the mean\u2009\u00b1\u2009SEM. Statistical analyses were performed using two-way ANOVA followed by \u0160\u00edd\u00e1k\u2019s multiple comparisons test in b (F(6,32)\u2009=\u20090.8259), c (F(6,132)\u2009=\u20091.021), i acquisition, (F(10,160)\u2009=\u20090.7738), m acquisition (F(10,160)\u2009=\u20090.2676), q acquisition (F(10,160)\u2009=\u20091.002); two-tailed unpaired t-test in e (t\u2009=\u20092.426, df\u2009=\u200922), i retrieval (t\u2009=\u20092.367, df\u2009=\u200916), l (t\u2009=\u20092.786, df\u2009=\u200916), m retrieval (t\u2009=\u20092.502, df\u2009=\u200916), and p (t\u2009=\u20092.523, df\u2009=\u200916); two-tailed paired t-test in g (WT (t\u2009=\u20094.361, df\u2009=\u20098), KO (t\u2009=\u20092.275, df\u2009=\u20098), k (Scrambled (t\u2009=\u20092.969, df\u2009=\u20098), Astrocytic KD (t\u2009=\u20090.3143, df\u2009=\u20098), o (Scrambled (t\u2009=\u20092.600, df\u2009=\u20097), Neuronal KD (t\u2009=\u20090.1794, df\u2009=\u20099); two-tailed Mann\u2013Whitney test in (h) (U\u2009=\u20090), and q retrieval (U\u2009=\u200916). Source data and exact p values are provided as a Source Data file.\n\nBecause GolpHCat is expressed in both hippocampal astrocytes and neurons (Fig.\u00a06a, d), we investigated the cell type that majorly contributes to memory impairment in GolpHCat KO mice. We performed cell-type specific gene silencing (KD) using GolpHCat-shRNA-carrying viruses, followed by behavioral tests (Fig.\u00a08j\u2013q and Supplementary Fig.\u00a09p, q). A Cre-dependent shRNA expressing virus (Lenti-pSico-scrambled/GolpHCat shRNA-EGFP) and a cell type-specific Cre-expressing virus (AAV-GFAP-Cre-mCh for astrocyte; Fig.\u00a08j, and Supplementary Fig.\u00a09r or AAV-CaMKII\u03b1-Cre-mCh for neuron; Fig.\u00a08n, and Supplementary Fig.\u00a09s) were co-injected in the hippocampal CA1 region. Both astrocytic and neuronal GolpHCat gene-silenced mice showed impaired spatial and contextual memory in the NPR test recall (Fig.\u00a08k, l, o, p) and contextual fear test (Fig.\u00a08m, q). Taken together, these results indicate that GolpHCat in both astrocytes and neurons is critical for spatial and contextual memory in the hippocampus.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49297-8/MediaObjects/41467_2024_49297_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In the present study, we provide unprecedented insights into the identification and function of TMEM87A as a bona fide cation channel within the Golgi apparatus. We also provide a comprehensive understanding of TMEM87A\u2019s structure-activity relationship by elucidating the ion conduction pathway and gating mechanism through the determination of two distinct cryo-EM structures bound to PE and gluconate. Contrary to a previous study that failed to observe single-channel currents in TMEM87A-reconstituted liposome patch recordings22, our investigation employing different lipid compositions successfully demonstrates genuine voltage-dependent single-channel currents, providing unequivocal evidence that TMEM87A is a bona fide ion channel. Our research surpasses previous structural studies by achieving higher resolution cryo-EM structures at 3.1 and 3.6\u2009\u00c5, in comparison to the previously published structure at 4.7\u2009\u00c522. Based on this enhanced resolution, we are the first to identify the mysterious phospholipid as PE, while Hoel et al. only suggested a phospholipid-bound structure without specifying the type of phospholipid22. We also propose the PE-mediated voltage-dependent gating mechanism of TMEM87A, thereby shedding light on its intricate functionality. Moreover, we introduce gluconate as a potent blocker of TMEM87A and demonstrate its inhibitory effect on TMEM87A-mediated currents. Finally, we propose a critical function for TMEM87A in the brain and cognition, specifically highlighting its involvement in hippocampal spatial memory. In summary, our comprehensive structural and molecular studies significantly contribute to a deeper understanding of TMEM87A\u2019s role as a cation channel in the Golgi apparatus and the brain.\n\nWe have identified a unique voltage-dependent nonselective cation channel, which we named GolpHCat. According to the brain RNA-seq database, it is highly expressed in the brain24,25. Single-channel analysis of GolpHCat demonstrates that, unlike any other known voltage-dependent channels, GolpHCat shows a skewed U-shaped voltage-dependent open probability curve, centered around 0\u2009mV (Fig.\u00a02c). Other voltage-gated channels typically exhibit a unidirectional sigmoidal activation curve56, whereas GolpHCat displays a unique bidirectional activation curve at both negative and positive potentials. The one-and-only resembling channel is perhaps the voltage-dependent anion channel VDAC, which displays an upside-down U-shaped voltage-dependent open probability curve, centered around 0\u2009mV57,58. Based on this unique voltage-activation property, one can make several interesting conjectures about how the Golgi membrane potential is maintained by GolpHCat; (1) GolpHCat\u2019s primary function might be to clamp the resting Golgi membrane potential at 0\u2009mV by opening at both negative and positive offset voltages and resetting the voltage to near 0\u2009mV (by depolarization and hyperpolarization upon channel opening, respectively), (2) the unique U-shaped voltage-dependent open probability curve should render the resting Golgi membrane potential set to 0\u2009mV, independent of the concentration changes in luminal Na+ and K+ ions, and (3) GolpHCat might be able to achieve this voltage-clamping effect, regardless of the presence of any leak channels.\n\nIn this study, we determined the high-resolution cryo-EM structures of hGolpHCat complexed with the Golgi membrane phospholipid PE and the pharmacological inhibitor gluconate. While future studies are required to examine its function, our structural analysis of hGolpHCat, complemented by MD simulations and electrophysiological analyses, provides crucial insights into the ion conduction pathway of hGolpHCat as a nonselective voltage-dependent cation channel. Although residues on the funnel-shaped luminal vestibule can in principle attract cations, the R2-fatty acid chain of PE and key residues that interact with its head group appear to occlude the ion conduction pathway, indicating that hGolpHCat is impermeable to ions under cryo-EM conditions (resting state). Notably, the putative ion conduction pathway is lined with highly conserved hydrophobic and electropositive residues. Although currently unclear, we speculate that conformational changes in the voltage sensor TM3 in response to Golgi membrane depolarization under physiological conditions may lead to the opening of the ion conduction pathway in hGolpHCat (Fig.\u00a05j). Further structural and functional studies are needed to elucidate the gating dynamics of GolpHCat.\n\nWe observed the GOLD domain in the ELD of TMEM87A, similar to transmembrane Emp24 domain-containing protein (TMED/p24), acyl-coenzyme A binding domain-containing protein 3 (ACBD3), and translocating chain-associating membrane protein (TRAM), in addition to Wntless. Many GOLD-containing protein families are well-known, and their functions have been studied. For example, TMED/p24 has been implicated in various cellular processes, including the biogenesis of coat protein vesicles59 and has been suggested to act as a cargo receptor that facilitates transport along the secretory pathway60,61. The human Golgi-resident protein, ACBD3 plays a role in preserving the integrity of the Golgi structure through its interaction with giantin, which in turn affects the movement of proteins between the endoplasmic reticulum and Golgi apparatus62. A splice variant of Toll/interleukin-1 receptor/resistance protein domain-containing adapter molecule 2 (TICAM2) called TRAM with a GOLD domain is involved in signal transduction pathways, where it participates in intracellular signaling events related to Golgi functions63,64. Therefore, like other GOLD-containing proteins, GolpHCat may also play a role in several functions, such as trafficking, secretion, and sorting of yet unidentified proteins. Future work is needed to identify proteins interacting with the GOLD domain of TMEM87A using deeper proteomic analysis of mouse brain samples and to determine the structure of interacting protein complexes.\n\nOur study proposes a counter-cation channel, GolpHCat, that facilitates proper luminal acidification of the Golgi apparatus for its normal morphology and functions. We generated GolpHCat KO mice and observed disrupted Golgi morphology, such as fragmentation and swelling, and altered protein glycosylation. It has been reported that a well-organized, ribbon-shaped Golgi structure is necessary for proper glycosylation in each cisterna of the Golgi apparatus1,2,65. When GolpHCat is removed, it is possible that the lack of GolpHCat aberrantly drives excessive Cl- and H+ influx into the lumen through GPHR3 and NHE711,12, respectively, thereby lowering the resting pH to 6.3. Simultaneously, excessive Cl- and H+ influx creates an osmotic pressure, thereby allowing water molecules to flow in through the aquaporins (AQPs) and, owing to an osmotic pressure, causing the Golgi to swell. These effects may impair Golgi homeostasis, leading to altered protein glycosylation. Golgi pH homeostasis is also important for the distribution and activity of glycosylation enzymes. Elevated pH within the Golgi apparatus disrupts N-glycosylation by causing Golgi glycosyltransferases to be mislocalized66,67. Deviation of pH from its normal range can greatly alter the activity of enzymes within the Golgi, depending on their specific pH sensitivity68,69. In our study, it is possible that the distribution and activities of fucosyltransferase enzymes were altered in GolpHCat KO mice, leading to the altered pattern of fucosylated glycans (Fig.\u00a07a\u2013e). We observed altered glycan patterns, with an increase in mono-fucosylated glycans and a decrease in multi-fucosylated glycans in the GolpHCat KO samples (Fig.\u00a07e). This suggests that fucosyltransferase (FUT)8, associated with core fucosylation (1 fucose), may become more active, whereas other FUTs may be less active, due to Golgi pH perturbations. Future studies are needed to investigate whether FUT enzymes are altered in GolpHCat-KO mice. Taken together, our results propose that GolpHCat, as a counter-cation channel, is a key molecule for the maintenance of Golgi homeostasis and structure, allowing for normal Golgi functions, such as protein glycosylation, especially fucosylation, in the brain.\n\nOur study raises an importance of Golgi pathology such as morphological and functional alteration in cognitive impairments. We firstly propose that not only neuronal but also astrocytic Golgi pathology, caused by the impaired Golgi pH, is largely responsible for cognitive impairment. Understanding the molecular mechanism of Golgi pathology is expected to shed light on therapeutic approaches for cognitive impairment found in various neurodegenerative diseases.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "All experimental procedures were approved by the Institutional Animal Care and Use Committees (IACUCs, #2018-IBS-20) of the Institute for Basic Science (IBS, Daejeon, South Korea).\n\nAll mice were maintained under a 12:12-h light-dark cycle (lights on at 8:00 AM) and had ad libitum access to food and water. All mice were housed in groups of 4\u20135 per cage. Adult male C57BL/6\u2009J wild-type mice (6\u20137 weeks old) were used for behavioral tests with or without virus injection. All experiments were done with sex- and age-matched controls.\n\nWe requested the generation of a TMEM87A knockout mice line from GHBio (Daejeon, Korea). TMEM87A KO allele was generated by using the CRISPR/Cas9-mediated homologous recombination. Two guide RNAs (gRNAs), which bound to intron 9 or intron 10 and deleted the whole exon 10, including the GYG sequence, were designed with the following spacer sequences: spacer of gRNA1, 5\u2032-TAAGCCAAGTACTAGCACGT-3\u2032; spacer of gRNA2: 5\u2032-GATGAAAGAAGTAGTGAGCT-3\u2032. Protospacer adjacent motif (PAM), AGG, for the corresponding gRNA was located on the 3\u2009bp downstream of each targeted DNA sequence. The targeting construct was injected into a C57BL/6\u2009J mouse ES cell line with the two gRNAs. PCR and sequencing analysis were used to identify ES cell clones with proper targeting. Obtained female knockout mice were maintained by crossing with male WT mice. Genotypes were determined by PCR using the following primers; Forward: 5\u2032-TGTGCACATAACTGAGGTCAT-3\u2032; Reverse: 5\u2032-GCTTCACTGCAATCTTTCTGC-3\u2032.\n\nGolpHCat KO mice (9\u201315 weeks) and WT littermates were used for IHC, multi-omics, slice patch, and behavior tests.\n\nChinese hamster ovary-K1 (CHO-K1, referred to as CHO) cells and human astrocytes were purchased from the Korean Cell Line Bank and abm (Richmond), respectively. CHO cells and human astrocytes were maintained in F-12 (Gibco), and DMEM (Corning) respectively. Both media were supplemented with 25\u2009g/L glucose, 4\u2009g/L l-glutamine, 1\u2009g/L sodium pyruvate, 10% heat-inactivated fetal bovine serum (FBS, Gibco), and 1% penicillin-streptomycin (Gibco). Cells were incubated in a humidified 5% CO2 incubator at 37\u00b0C.\n\nThe partial sequence alignment of the selectivity filter of the potassium channels with hTMEM87A/B and the multiple sequence alignments of TMEM87A isoforms was performed using Clustal Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/). Accession numbers are as follows: hTMEM87A isoform 1, NP_056312.2; isoform 2, NP_001103973.1; isoform 3, NP_001273416.1; hTMEM87B, NP_116213.1; Kv1.1, NP_000208.2; TWIK-1, NP_002236.1; TASK-1, NP_002237.1; TRAAK, NP_201567.1; THIK-1, NP_071337.2; HCN1, NP_066550.2.\n\nHydrophobicities of human and mouse TMEM87A were calculated in ProtScale (https://web.expasy.org/protscale/) using the Kyte&Doolittle, window size:21, 100%. The signal peptide in the N-terminal of TMEM87A and cleavage site position were predicted in the SignalP-5.0 (https://services.healthtech.dtu.dk/service.php?SignalP-5.0).\n\nOpen reading frame of human TMEM87A-transcript variant 1 (NM_015497; SC108269), 3 (NM_001286487; SC336449), and mouse TMEM87A-transcript variant 1 (NM_173734; MC201598) were purchased from Origene. The coding sequences of these genes were PCR amplified and then subcloned into CMV-pIRES2-DsRed/iRFP vector using the SalI/BamHI restriction enzyme sites or CMV-EGFP-N1 vector using the EcoRI/AgeI restriction enzyme sites using the cloning kit (EZ-Fusion\u2122 HT Cloning core Kit, Enzynomics). Mutant forms, shRNA-insensitive form, and isoform 1 truncation of human TMEM87A were made using the mutagenesis kit (EZchange\u2122 Site-directed Mutagenesis kit, Enzynomics). Information on oligomer sequences for cloning and mutagenesis were listed in Supplementary Table\u00a04.\n\nThe pSicoR and pSico vectors were used for shRNA knockdown in vitro and in vivo, respectively. The targeted sequences for human TMEM87A and mouse TMEM87A were 5\u2032-GGATTGGTGCTGTCATCTTCC-3\u2032 and 5\u2032-GGATTGGTGCTGTCATCTTTC-3, respectively.\n\nFor making the human TMEM87A clone construction that is not sensitive to shRNA (shRNA-insensitive human TMEM87A), six nucleotides that make the same amino acid due to the redundancy of genetic code were changed in the human TMEM87A shRNA target site (shRNA-insensitive human TMEM87A sequence 5\u2032- GGATTGGTGCTGTCATCTTCC-3\u2032 included six mismatches).\n\nHuman astrocytes were cultured on 0.1\u2009mg/ml poly-d-Lysine (PDL; P6407, Sigma-Aldrich)-coated coverslips in 24 wells for 24\u2009h. Cells were fixed with 4% paraformaldehyde (PFA) in PBS for 20\u2009min at room temperature before washing three times with PBS and incubating in blocking solution (2% donkey serum (Genetex, GTX27475), 2% goat serum (Abcam, ab7481) and 0.03% Triton-X100 (Sigma, 93443) for permeabilization in PBS) at room temperature (RT) for 1\u2009h. For immunostaining, the samples were incubated overnight at 4\u2009\u00b0C with primary antibodies: rabbit anti-TMEM87A (1:100, Novus Biologicals, NBP1-90531), mouse anti-Giantin (1:100, Abcam, ab37266), mouse anti-Golgin-97 (1:100, Invitrogen, A-21270) chicken anti-GFAP (1:200, Millipore, AB5541) diluted in blocking solution. After incubation overnight, the samples were washed three times with PBS and then incubated for 1\u2009h at RT with secondary antibodies: anti-rabbit IgG Alexa Fluor 488 (1:500, Jackson lab), anti-mouse IgG Alexa Fluor 594 (1:500, Jackson lab), and anti-chicken IgG Alexa Fluor 647 (1:500, Jackson lab) diluted in the blocking solution. After secondary antibody incubation, samples were washed three times with PBS, and DAPI solution (1:2,000, Pierce, 62248) was added during the second washing step. Finally, samples were mounted with a fluorescent mounting medium (Dako, S3023). Fluorescence images were taken using a Zeiss LSM 900 confocal microscope with a 63X lens and all images are z-maximum projection. Quantification was done by ImageJ software. Representative figures were obtained from three independent experiments.\n\nHuman astrocytes were cultured on PDL-coated coverslips for 24\u2009h before transfection. Cells were transfected with TMEM87A shRNA-mCh with each shRNA-insensitive EGFP-tagged TMEM87A isoform for 48\u2009h. Cells were washed once with PBS, then fixed with 4% paraformaldehyde in PBS for 20\u2009min at RT and washed three times. DAPI solution was added during the second washing step. Finally, samples were mounted with a fluorescent mounting medium. Fluorescence images were taken at 405\u2009nm for DAPI, 488\u2009nm for EGFP, and 594\u2009nm for mCh using a Zeiss LSM 900 confocal microscope with a 63X lens.\n\nRNA was isolated from cultures of human astrocytes using a Qiagen RNEasy Kit (Cat. No #74106). Sample libraries were prepared using the Ultra RNA Library Prep kit (NEBNext, E7530), Multiplex Oligos for Illumina (NEBNext, E7335), and polyA mRNA magnetic isolation module (Invitrogen, Cat. No. #61011). Full details of the library preparation and sequencing protocol are provided on the website and previously described70. The Agilent Bioanalyser and associated High Sensitivity DNA Kit (Agilent Technologies) were used to determine the quality, concentration, and average fragment length of the libraries. The sample libraries were prepared for sequencing according to the HiSeq Reagent Kit Preparation Guide (Illumina, San Diego, CA, USA). Briefly, the libraries were combined and diluted to 2\u2009nM, denatured using 0.1\u2009N NaOH, diluted to 20\u2009pM by addition of Illumina HT1 buffer, and loaded into the machine along with read 1, read 2, and index sequencing primers. After the 2\u2009\u00d7\u2009100\u2009bp (225 cycles) Illumina HiSeq paired-end sequencing run, the data were base called, and reads with the same index barcode were collected and assigned to the corresponding sample on the instrument, which generated FASTQ files for analysis.\n\nBCL files obtained from Illumina HiSeq2500 were converted to FastQ and demultiplexed based on the index primer sequences. The data was imported to Partek Genomics Suite (Flow ver 10.0.21.0328; copyright 2009, Partek, St Louis, MO, USA), where the reads were further processed. Read quality was checked for the samples using FastQC. High-quality reads were aligned to the Homo sapiens (human) genome assembly GRCh37 (hg19, NCBI using STAR (2.7.8a). Aligned reads were quantified to the human genome assembly transcript model (hg19 - RefSeq transcripts 93) and normalized to obtain fragments per kilobase million (or FPKM) values of positively detected and quantified genes. Alternate splice variants for the genes were detected during quantification and also normalized to obtain FPKM values for the alternatively spliced variants.\n\nFor the pH measurement, pSicoR-Scrambled/TMEM87A shRNA-DsRed and B4GALT1-Ratiometric pHluorin2 were transiently co-transfected into human astrocytes one day before imaging. For the measurement of the Golgi pH buffer capacity, we obtained basal pH images and then treated 50\u2009mM NH4Cl. Fluorescence live images were excited at wavelengths of 405 and 475\u2009nm, and the emitted fluorescence was captured through a spectral slit for wavelengths of 508\u2009nm. The data were imported into Microsoft Excel for calculation of the intensity ratios (405\u2009nm:475\u2009nm) and further analyses. Golgi pH values were estimated using a pH calibration curve based on modified methodology from the previous study71. Briefly, calibration was performed with human astrocyte cells expressing GPI(plasma membrane targeting)-Ratiometric pHluorin2. The cells were first washed with PBS, followed by treatment with various extracellular solutions of different pH levels for 10\u2009min, after which the fluorescence intensity was measured. This process is replicated with all calibration buffers to establish a standard curve.\n\nAlthough endogenous human TMEM87A mainly localized to the Golgi membrane, overexpressed hTMEM87A is partially localized to the plasma membrane21 (Supplementary Fig.\u00a06f). Therefore, whole-cell patch clamping was performed to measure the channel activity of hTMEM87A localized at the plasma membrane.\n\nFor whole-cell patch-clamp recording, TMEM87A WT or mutants cloned into pIRES2-DsRed vectors (Addgene) were transiently transfected into CHO-K1 cells one day before patch recording. A transfection reagent (Lipofectamine 3000; Invitrogen, L3000001) was used for transfection in all experiments. After 24\u2009h, cells were seeded onto 0.1\u2009mg/ml PDL-coated coverslips and used for whole-cell patch-clamp recording within 12\u2009h.\n\nUnless otherwise indicated, the bath solution contained (in mM) 150 NaCl, 3 KCl, 10 HEPES, 5.5 glucose, 2 MgCl2, and 2 CaCl2 with pH adjusted to 7.3 by NaOH (320\u2013325 mOsmol/kg). Borosilicate glass pipettes (Warner Instrument Corp., USA, GC150F-10) were pulled and had a resistance of 4\u20136\u2009M\u03a9 in the bath solution when filled with a pipette solution contained (in mM) 130 K-gluconate, 10 KCl, 10 HEPES, 10 1,2-Bis(2-aminophenoxy)ethane-N, N, N\u2019, N\u2019-tetraacetic acid (BAPTA) with pH adjusted to 7.3 by KOH (290\u2009~\u2009310 mOsmol/kg). Cells were held at \u221260\u2009mV. For recording with voltage-clamp ramp protocol, currents were measured under the 1000-ms-duration voltage ramps descending from +100 to \u2212150\u2009mV with 10\u2009s time intervals. For recording with voltage-clamp step protocol, currents were measured under the step pulses from +100 to \u2212150\u2009mV with 100\u2009ms with 1\u2009s time intervals. Electrical signals were amplified using MultiClamp 700B (Molecular Devices, USA). Data were acquired by Digitizer 1550B (Molecular Devices) and pClamp 11 software (Molecular Devices, USA) and filtered at 2\u2009kHz. These machines were used in all electrophysiology experiments in this paper.\n\nTo determine the contribution of Na+ in the bath solution for the inward currents, 150 NaCl-containing bath solution was replaced with 150 NMDG-Cl under the same other condition. For measuring the cation permeability of TMEM87A, 150 Na+ in the bath solution was substituted with 150 X, where X was K+ or Cs+ (pH 7.3 was adjusted with KOH and CsOH, respectively). The pipette solution was prepared as described above. For calculating the relative permeability ratio of TMEM87A, we measured the differences in reversal potential between two cationic I-V curves. The permeability ratio, PX/PNa, was estimated using the modified Goldman\u2013Hodgkin\u2013Katz Eq. (1) as reported previously:\n\nTo determine the impermeability of anion, 150 NaCl containing bath solution was replaced to 150 Na-isethionate under the same other condition. The I-V curves in this experiment were corrected with the LJP (LJP: NaCl, +13\u2009mV; Na-isethionate, \u221211.6\u2009mV).\n\nTo assess the inhibitors as a TMEM87A blocker, they were contained in the bath solution and then recorded using the following concentration: Gadolinium chloride (GdCl3) (0.3, 1, 3, 10, and 30\u2009\u00b5M, Sigma), Na-gluconate (gluconate) (0.01, 0.03, 0.1, 1, and 10\u2009\u00b5M, Sigma). The IC50 values were estimated by non-linear regression analysis, using the following Eq. (2):\n\nwhere \\(X\\) is the inhibitor concentration, \\({{IC}}_{50}\\) is the concentration required for half-maximal inhibition, and \\(h\\) is the Hill slope constant (\\(h\\): gadolinium, \u22121.498; gluconate, \u22121.301).\n\nTo measure the extracellular pH-dependency of TMEM87A currents, different pH solutions were made using the composition previously described, and the pH was manipulated with HCl or NaOH.\n\nWe selected HEK293T cells for our surface biotinylation experiments due to their superior transfection efficiency compared to CHO-K1 cells. The biotinylation assay was performed using the PierceTM Cell Surface Protein Biotinylation and Isolation Kit (Thermo Scientific) as per manufacturer\u2019s instructions. In detail, hTMEM87A WT or its point mutants (E279A, E298A, D442A, Y237A, E272A, K273A, S301A, K304A, R305A, R309A, S415F, E288R, and G318AY319AG320A) cloned into the pIRES2-DsRed vector were transiently transfected into HEK293A cells (5\u2009\u00d7\u2009106 cells in 100\u2009mm dish). After 40\u2009h, cells were rinsed with PBS and then incubated with 0.25\u2009mg/ml Ez-LinkTM-Sulfo-NHS-SS-Biotin, a membrane-impermeable reagent, in PBS for 10\u2009min at room temperature. Biotinylated cells were washed with ice-cold TBS three times, and harvested cell pellets were resuspended in 250\u2009\u03bcl lysis buffer containing a complete protease inhibitor cocktail (Roche). After centrifugation (15,000\u00d7g for 5\u2009min at 4\u2009\u00b0C), the supernatant was collected, and protein concentration was measured using the Bradford protein assay. 800\u2009\u03bcg of protein was incubated with 250\u2009\u03bcl of NeutrAvidin agarose resin (Thermo Scientific) on a shaker for 30\u2009min at room temperature. After three times washing using the washing buffer in the PierceTM Cell Surface Protein Biotinylation and Isolation Kit, biotinylated proteins were eluted using 100\u2009\u03bcl elution buffer containing 10\u2009mM DTT, separated by 10 % SDS\u2013PAGE and transferred to PVDF membranes. The membranes were blocked with 5% skim milk in TBST for 1\u2009h at RT, and washed with TBST three times. Then, the membranes were incubated with primary antibodies: rabbit anti-TMEM87A antibodies (1:1000, Novus Biologicals) and mouse anti-\u03b2-actin antibodies (1:2000, Santa Cruz) overnight at 4\u2009\u00b0C. The membranes were washed with TBST three times and incubated with the corresponding horseradish peroxidase-conjugated secondary antibodies [HRP-linked anti-rabbit IgG (Cell Signalling) and HRP-linked anti-mouse IgG, (Thermo Scientific) for 1\u2009h at RT. After washing three times with TBST, immune-reactive protein bands were detected using EzWestLumiOne (ATTO).\n\nThe cDNA encoding human TMEM87A (hTMEM87A, NP_056312.2, M1-E555) followed by a TEV protease cleavage sequence (ENLYFQG), a PreScission Protease cleavage sequence (LEVLFQGP), EGFP (M1-239K), a thrombin cleavage sequence (LVPRGS) and a Twin-strep-tag were cloned into the BamHI and XhoI sites of a pcDNA3.4 (Invitrogen). All hTMEM87A mutants were created by site-directed mutagenesis using the WT construct as a template. Constructs and primers are listed in Supplementary Table\u00a05.\n\nRecombinant hTMEM87A protein was transiently expressed in Expi293F cells (Thermo Fisher Scientific) according to the manufacturer\u2019s instructions. Briefly, 200\u2009\u03bcg of plasmid DNA was transfected into 200\u2009ml of Expi293F cells (3.0\u2009\u00d7\u2009106 cells/ml) using Expifectamine (Thermo Fisher Scientific). Cells were cultured in Expi293 expression medium (Thermo Fisher Scientific) at 37\u2009\u00b0C and 8% CO2 with shaking (orbital shaker, 120 rpm). After 20\u2009h, the enhancer (Thermo Fisher Scientific) was supplemented to the culture, then further incubated for 30\u201334\u2009h at 30\u2009\u00b0C.\n\nCell pellets were resuspended in 20\u2009ml HN buffer [50\u2009mM HEPES pH 7.5, 250\u2009mM NaCl, and 1x complete protease inhibitor cocktail (Roche)] and lysed by sonication (total 2\u2009min, 1\u2009s with intervals of 5\u2009s, 20% amplitude). After ultracentrifugation (Beckman Ti70 rotor, 150,000\u00d7g for 1\u2009h), the collected membrane fraction was homogenized with a glass Dounce homogenizer in 20\u2009ml buffer [HN buffer\u2009+\u20091% (w/v) n-dodecyl \u03b2-d-maltoside (DDM; Anatrace) and 0.2% (w/v) cholesteryl hemisuccinate (CHS; Anatrace)] and solubilized for 2\u2009h at 4\u2009\u00b0C. The insoluble cell debris was removed by ultracentrifugation (Beckman Ti70 rotor, 150,000\u00d7g for 1\u2009h), and the supernatant was incubated with 2\u2009ml Strep-Tactin resin (IBA Lifesciences) for 30\u2009min at 4\u2009\u00b0C. After washing with 10 column volumes of wash buffer [HN buffer\u2009+\u20090.05% (w/v) DDM and 0.01% (w/v) CHS], hTMEM87A-EGFP-Twin strep tag was eluted with 5\u2009ml elution buffer [HN buffer\u2009+\u20090.05% (w/v) DDM/CHS\u2009+\u200910\u2009mM desthiobiotin]. After concentration using an Amicon Ultra centrifugal filter (100-kDa cut-off; Millipore), hTMEM87A-EGFP-Twin strep was further purified by size exclusion chromatography (SEC) using a Superose 6 Increase 10/300 GL column (Cytiva) equilibrated with a final buffer [HN buffer\u2009+\u20090.01% (w/v) DDM and 0.002% (w/v) CHS]. The peak fractions were immediately used for preparing proteoliposomes.\n\nFor structural studies, the incubated supernatant with 2\u2009ml Strep-Tactin resin was washed with 10 column volumes of wash buffer [TN buffer (50\u2009mM Tris pH 9.0, 250\u2009mM NaCl, and 1x complete protease inhibitor cocktail)\u2009+\u20090.05% (w/v) lauryl maltose neopentyl glycol (LMNG; Anatrace) and 0.01% (w/v) CHS], hTMEM87A-EGFP-Twin strep tag was eluted with 5\u2009ml elution buffer [TN buffer, 0.05% LMNG, 0.01% CHS, and 10\u2009mM desthiobiotin]. After concentration using an Amicon Ultra centrifugal filter, hTMEM87A-EGFP-Twin strep was further purified by size exclusion chromatography (SEC) using a Superose 6 Increase 10/300 GL column equilibrated with a final buffer [TN buffer\u2009+\u20090.01% (w/v) LMNG and 0.002% (w/v) CHS]. The collected peak fractions were concentrated to ~0.8\u2009mg/ml using an Amicon Ultra centrifugal filter and immediately used for the cryo-EM grid preparation for the hTMEM87A structure. For the complex cryo-EM structure of hTMEM87A with gluconate (hTMEM87A-Gluc) and hTMEM87A A308M, HN buffer was used instead of Tris buffer during purification. Other protein solubilization and purification conditions were the same as those for hTMEM87A. Before freezing grids, 10\u2009mM of sodium gluconate (Sigma-Aldrich) was added to purified hTMEM87A (0.7\u2009mg/ml) and incubated for 1\u2009h on ice.\n\nA total of 10\u2009mg of lipids (8:2, POPC:POPG; Avanti Polar Lipids) was dissolved in 1\u2009mL chloroform in the glass tube, dried to a thin film under a nitrogen stream, and further dried overnight under a vacuum. A total of 2\u2009mL dehydration/rehydration (D/R) buffer (5\u2009mM HEPES, 200\u2009mM KCl pH 7.2 adjusted by KOH) was added to the lipids, and the solution was vortexed for 60\u2009s before being bath sonicated until transparent for 20\u2009min. Purified hTMEM87A was added to 2\u2009mg lipids in D/R buffer and reconstituted into liposomes at a 1:100 protein-to-lipid ratio. D/R buffer was used to bring the volume to 1\u2009mL and roller mix at room temperature for 1\u2009h. After the beads settled to the bottom of the tube to eliminate detergents, the supernatant was collected and ultracentrifuged for 45\u2009min at 4\u2009\u00b0C and 250,000\u00d7g. Pelleted proteoliposomes were resuspended in 80\u2009\u03bcL D/R buffer by gently pipetting and used on the day. Three to four spots of 20\u2009\u03bcL of proteoliposomes were placed on a glass coverslip coated with 0.1\u2009mg/ml PDL. Proteoliposomes were vacuum-dried at room temperature for 6\u2009h at 4\u2009\u00b0C and then rehydrated with 20\u2009\u03bcL DR buffer to each spot with wet filter paper overnight at 4\u2009\u00b0C (8\u201324\u2009h) for patch-clamp recording.\n\nA previously reported protocol72 was used for the TMEM87A single-channel recordings in the liposome. All recordings were performed with the attached liposome patch. For the TMEM87A single-channel recording, the symmetric solution was used in bath and pipette solutions followed by: (in mM) 200 KCl, 5 HEPES with pH adjusted to 7.2 by KOH. Notably, for making the blister, 40\u2009mM MgCl2 was added in bath solution 30\u2009min before the patch and was maintained throughout the recording. Borosilicate glass pipettes were pulled and polished to a resistance of 3\u20136\u2009M\u03a9 in the bath solution. The currents were recorded at RT and holding current as indicated in the data. The single-channel analysis was performed in Clampfit 10.7.\n\nQuantifoil R 1.2/1.3 Cu 200-mesh holey carbon grids (SPI SUPPLIES) were glow-discharged for 75\u2009s at 15\u2009mA (PELCO easiGlow Glow Discharge Cleaning system, Ted Pella). Then, 4\u2009\u03bcl of the purified hTMEM87A, hTMEM87A-Gluc, hTMEM87A A308M were applied to the grid at 100% humidity at 4\u2009\u00b0C. After 7\u2009s blotting, grids were plunged into liquid ethane using a FEI Vitrobot Mark IV (Thermo Fisher Scientific). Micrographs were acquired on a Titan Krios G4 TEM operated at 300\u2009keV with a K3 direct electron detector (Gatan) at the Institute for Basic Science (IBS), using a lit width of 20\u2009eV on a GIF-quantum energy filter. EPU software was used for automated data collection at a calibrated magnification of \u00d7105,000 under the single-electron counting mode and correlated-double sampling (CDS) mode73, yielding a pixel size of 0.849\u2009\u00c5/pixel. The micrograph was dose-fractionated to 57 frames under a dose rate of 7.95 e\u2013/pixel/sec with a total exposure time of 6.14\u2009s, resulting in a total dose of about 67.72 e\u2013/ \u00c52. A total of 10,377 movies for hTMEM87A, 13,099 movies for hTMEM87A-Gluc, and 4565 movies for hTMEM87A A308M were collected with a nominal defocus range from \u22120.8 to \u22121.9\u2009\u03bcm. Detailed parameters are summarized in Supplementary Table\u00a02.\n\nThe detailed image processing workflow and statistics are summarized in Supplementary Fig.\u00a03b\u2013h, 3m\u2013s, Supplementary Fig.\u00a07, and Supplementary Table\u00a02. Micrographs were subjected to patch motion correction and patch CTF estimation in cryoSPARC v.3.3.274. For the hTMEM87A data set, 96,330 particles were first picked using a blob picker of cryoSPARC. Then, 2D class average images were generated as templates for subsequent reference-based auto-picking. A total of 8,101,104 particles from the complete datasets were binned four times, and to identify higher quality particles, subsequent 2D classification, Ab initio, and heterogeneous refinement were performed in cryoSPARC. The resulting 445,198 particles from the 3D classes showing good secondary structural features were re-extracted into the original pixel size for further 3D refinements. Non-uniform refinement75 and CTF refinement76 improved the particle alignment and map quality. The final refinement yielded a map at an overall ~3.1\u2009\u00c5 resolution according to the 0.143 cut-off criterion77. For hTMEM87A-Gluc, reference-based picked 6,035,205 particles were processed similarly to hTMEM87A data processing. The final non-uniform refinement from 201,915 particles yielded a map at an overall ~3.6\u2009\u00c5 resolution. For hTMEM87A A308M, reference-based picked 4,758,336 particles were processed similarly to hTMEM87A data processing. The final non-uniform refinement from 360,876 particles yielded a map at an overall ~3.1\u2009\u00c5 resolution. The mask-corrected Fourier shell correlation (FSC) curves were calculated in cryoSPARC, and reported resolutions were based on the gold-standard Fourier shell correlation (FSC)\u2009=\u20090.143 criteria. Local resolutions of density maps were estimated by Blocres78. Model building for hTMEM87A was initiated using the module \u2018Map to model\u2019 in PHENIX package79 and a model generated by AlphaFold32,80. The model was then subjected to iterative manual and automated refinement rounds in PHENIX and Coot81. The final refinement statistics are summarized in Supplementary Table\u00a02.\n\nA cavity search using a Solvent Extractor from the Voss Volume Voxelator server82 was performed using an outer-probe radius of 5\u2009\u00c5 and an inner-probe radius of 1.2\u2009\u00c5. The Dali server29 was used to search protein structures having a similar fold. All molecular graphics figures were prepared with UCSF ChimeraX83 and PyMOL84.\n\nGaussian accelerated molecular dynamics (GaMD) is an unconstrained enhanced sampling method that smooths the potential energy surface and reduces the energy barriers of biomolecular processes by adding a harmonic boost potential37,38.\n\nWhere \\({V}^{*}({r}^{ \\rightharpoonup })\\) is the modified potential, \\(V({r}^{ \\rightharpoonup })\\) is the system potential and \\(\\Delta V({r}^{ \\rightharpoonup })\\) is the harmonic boost potential. Along the simulation time, the harmonic boost potential is only added when system potential drops below reference energy:\n\nWhere \\(E\\) is the reference energy, and \\(k\\) is the harmonic force constant. The two adjustable parameters \\(E\\) and \\(k\\) can be determined by applying the following criteria:\n\nWhere \\({V}_{max }\\) and \\({V}_{min }\\) are the maximum and minimum potential energies, respectively.\n\nThe PE-bound hTMEM87A structure was used as a starting point and minimized in a two-stage geometry optimization approach using Gaussian accelerated molecular dynamics (GaMD) simulation. First, a short minimization of the water molecules positions, with positional restraints on the protein, ligand, and P31 atoms of the membrane, was performed with a force constant of 10\u2009kcal/mol\u2009\u00c5\u22122 at constant volume periodic boundary conditions. Second, an unrestrained minimization including all atoms in the simulation cell was carried out. The minimized system was gently heated in two phases. First, the temperature was increased from 0 to 100\u2009K in a 20\u2009ps step. Harmonic restraints of 10\u2009kcal/mol\u2009\u00c5\u22122 were applied to the protein, ligand, and membrane. Second, the temperature was slowly increased from 100\u2009K to the production temperature (310.15\u2009K) in a 100\u2009ps step. In the second phase, harmonic restraints of 10\u2009kcal/mol\u2009\u00c5\u22122 were applied to the protein, ligand, and P31 atoms of the membrane. The Langevin thermostat was used to control and equalize the temperature. The initial velocities were randomized in the heating step. In the heating and following steps, bonds involving hydrogen were constrained with the SHAKE algorithm, and the time step was set at 2\u2009fs, allowing potential inhomogeneities to self-adjust. The equilibration step was performed in three stages. First, 5\u2009ns of MD simulation under NVT ensemble and periodic boundary conditions were performed to relax the simulation temperature. Second, 5\u2009ns of MD simulation under an NPT ensemble at a simulation pressure of 1.0\u2009bar was performed to relax the density of the system. The semi-isotropic pressure scaling using the Monte Carlo barostat was selected to control the simulation pressure. Third, an additional 5\u2009ns of MD simulation was performed to relax the system further. A cutoff value of 11\u2009\u00c5 was applied to Lennard-Jones and electrostatic interactions.\n\nAfter equilibration, an extra short 5\u2009ns of MD simulation followed by 45\u2009ns GaMD simulation (the boost potential is applied) was carried out in order to collect potential statics for calculating the acceleration parameters. Finally, a 500\u2009ns of GaMD production run was performed. In total, we performed three independent simulations (i.e., 1.5\u2009\u00b5s accumulated time). All GaMD simulations were performed using the AMBER2021 package85 and applying the \u2018dual-boost\u2019 potential, where one boost potential is applied to the dihedral energetic term and the other to the total potential energetic term of the force field. The reference energy was set to the upper bound, which provides a more aggressive boost. The upper limit of the boost potential standard deviation, \u03c30, was set to 6.0\u2009kcal/mol.\n\nAll MD simulation systems were built from the cryo-EM structure of hTMEM87A (PDB ID: 8HSI) using Membrane Builder in CHARMM-GUI86. The missing loops (L148\u2013K167 and S193\u2013L202) were reconstructed using Modeller87. Using the PropKa program, the protonation state for K273 was determined as LYN, H187, and H403 as HID, and other histidine residues as HIE. All systems were solvated in ~150\u2009mM KCl solution, and the periodic simulation boxes were about 10\u2009\u00d7\u200910\u2009\u00d7\u200914\u2009nm3 large. The Amber FF14SB, Lipid17, and TIP3P force fields were used for protein, lipid, and water, respectively88,89. The standard CHARMM-GUI equilibration protocol was followed to equilibrate the systems. The Particle-mesh Ewald method90 was used for the electrostatic interaction, and a cut-off length of 0.9\u2009nm for the van der Waals interaction. The production trajectories were integrated with a time step of 2\u2009fs using OpenMM91. The temperature was kept at 310.15\u2009K via the Langevin dynamics with a friction coefficient of 1 ps-1, and the pressure was retained at 1\u2009bar via a Monte Carlo barostat with a coupling frequency of 5 ps-1. The trajectory analysis was performed with MDAnalysis92, and the first 100\u2009ns was discarded when calculating interaction energies. Supplementary Data\u00a01\u20134 contain the initial and final structures of MD simulations.\n\nFrom simulations of the lipid binding process (m->p), m-L + p* -> m + p-L, where m stands for membrane, we estimate the timescale for the hTMEM87A-PE binding process to be ~100\u2009ns. We then try to estimate the timescale of the unbinding process by calculating the binding free energy \\(\\varDelta {F}_{m\\to P}\\left(L\\right)\\) of hTMEM87A-lipid binding process according to the linear interaction energy (LIE) model42. For that purpose, we simulated five 1 \u03bcs trajectories for the solvated system (Sp-L) of the cryo-EM structure embedded in a simple Golgi model membrane (m), which has a PC-to-PE ratio of 3:1, and one 1 \u03bcs trajectory for the solvated system (Sm-L) that consists of only the membrane. From the p-L trajectories, we computed average coulombic and van der Waals interaction energies corresponding to the L->p process of L(g) + p* -> p-L as\n\nFrom the m-L trajectory, we similarly computed for the L->m process of L(g) + m -> m-L.\n\nWe can then obtain the average interaction energies for the m->p process by\n\nFinally, the binding free energy is estimated by the LIE formula of\n\nAdult mice were deeply anesthetized with isoflurane and transcardially perfused with saline, followed by cold 4% paraformaldehyde in 0.1\u2009M PBS. Brains were post-fixed in 4% paraformaldehyde for 24\u2009h at 4\u2009\u00b0C and 30% sucrose for 48\u2009h at 4\u2009\u00b0C. Frozen brains in OCT embedding compound solution were cut into 30 \u03bcm coronal sections. Sectioned brains were washed three times in PBS and incubated in a blocking solution (2% donkey serum, 2% goat serum, and 0.3% Triton-X100 for permeabilization in PBS) at room temperature (RT) for 1\u2009h. Samples were incubated in the blocking solution (2% donkey serum, 2% goat serum, and 0.3% Triton-X100 for permeabilization in PBS) at RT for 1\u2009h. For immunostaining, the samples were incubated overnight at 4\u2009\u00b0C with primary antibodies: rabbit anti-TMEM87A (1:100, Novus Biologicals, NBP1-90531), mouse anti-Giantin (1:100, Abcam, ab37266), chicken anti-GFAP (1:200, Millipore, AB5541), guineapig anti-NeuN (1:200, Millipore, ABN90) diluted in blocking solution. After incubation overnight, the samples were washed three times with PBS and then incubated for 1\u2009h at RT with secondary antibodies: anti-rabbit IgG Alexa Fluor 488 (1:500, Jackson lab), anti-mouse/chicken IgG Alexa Fluor 594 (1:500, Jackson lab), and anti-chicken/guineapig IgG Alexa Fluor 647 (1:500, Jackson lab) diluted in blocking solution. After secondary antibody incubation, samples were washed three times with PBS, and DAPI solution was added during the second washing step. Finally, samples were mounted with a fluorescent mounting medium. Fluorescence images were taken using a Zeiss LSM900 confocal microscope with a 20X and 63X lens and obtained Z-stack images in 1\u20132\u2009\u00b5m steps and the fluorescent intensity was analyzed using the Image J software. For colocalization analysis, fluorescence images were taken using the Zeiss Elyra 7 Lattice SIM with 63X lens and Pearson\u2019s correlation coefficient R was analyzed using the colocalization tools in ZEN Blue software.\n\nBrains were fixed with 2% glutaraldehyde and 2% PFA in 0.1\u2009M PBS for 12\u2009h at 4\u00b0C and washed in 0.1\u2009M PBS, and then post-fixed with 1% OsO4 in 0.1\u2009M PBS for 1.5\u2009h. The samples were then dehydrated with increasing concentrations of ethanol (50\u2013100%), infiltrated with propylene oxide for 10\u2009min, embedded with a Poly/Bed 812 kit (Polysciences, USA), and polymerized for 18\u2009h at 60\u2009\u00b0C. The samples were sectioned into 200\u2009nm with a diamond knife in the ultramicrotome (EM-UCT, Leica, USA) and stained with toluidine blue for observation with an optical microscope. Thin sections (70\u2009nm) were double-stained with 5% uranyl acetate for 10\u2009min and 1% lead citrate for 5\u2009min. Images were taken using the transmission electron microscope (JEM-1011, JEOL, Japan) at the acceleration voltage of 80\u2009kV and photographed with a digital CCD camera (Megaview III). Golgi morphologies were analyzed using the ZEN Blue software.\n\nTo test the intrinsic properties of the hippocampal neurons and astrocytes, slice recording was performed using a modified protocol from the previous reports93. Briefly, the brain was excised from the skull and sectioned in an ice-cold, oxygenated (95% O2/5% CO2) sucrose-based dissection buffer containing (in mM) 212.5 sucrose, 5 KCl, 1.23 NaH2PO4, 26 NaHCO3, 10 glucose, 0.5 CaCl2, 10 MgSO4, pH 7.4. Brain slices were transversely cut into 300\u2009\u00b5m thick sections containing hippocampus using a vibrating microtome (DSK Linearslicer\u2122 Pro7, DSK, Japan). Prepared brain slices were recovered and recorded in oxygenated (95% O2/5% CO2) artificial cerebrospinal fluid (aCSF) containing (in mM) 124 NaCl, 5 KCl, 1.25 NaH2PO, 26 NaHCO3, 2.5 CaCl2, 1.5 MgCl2, and 10 glucose for at least 1\u2009h at 28\u2009\u00b1\u20091\u00b0 prior to recording. Brain slices for the astrocyte patch were co-loaded with 0.5\u2009\u00b5M SR-101 (Sigma; S7635) dye to identify the location of astrocytes in the CA1 region.\n\nFor rheobase and action potential recording in hippocampal pyramidal neurons, a patch electrode (6\u20138 M\u03a9) was filled with an internal solution (in mM): 145 K-gluconate, 10 HEPES, 5 KCl, 0.2 EGTA, 5 Mg-ATP, and 0.5 Na2-GTP, pH adjusted to 7.3, and osmolarity 295 mOsmol/kg). Measurement was performed in a whole-cell current-clamp configuration, with no membrane potential adjustment. Rheobase and action potential were measured by giving 5 or 20\u2009pA depolarizing steps for 1\u2009s injection with 3\u2009s between steps, respectively. Frequency, spike half-width, spike rise, and spike decay values of Rheobase were analyzed by Mini analysis software (Synaptosoft). For passive conductance recording in hippocampal astrocytes, measurement was performed in a whole-cell voltage-clamp configuration, and we used the holding potential of \u221280\u2009mV. Patch electrode (6\u20138\u2009M\u03a9) was filled with an internal solution (in mM): 140 KCl, 10 HEPES, 5 EGTA, 2 Mg-ATP, 0.2 NaGTP, adjusted to pH 7.4 with KOH). Currents were measured under the 1000-ms-duration voltage ramps descending from +100 to \u2212150\u2009mV with 10\u2009s time intervals. Passive conductance data analysis was performed using Clampfit (Molecular Devices)\n\nTo test basal synaptic transmission, paired-pulse ratio (PPR), and long-term potentiation (LTP), brain slice preparation and fEPSP experiments were performed as described previously93. Briefly, the mouse was anesthetized with isoflurane and decapitated. Isolated brain from decapitation was cut into 400-\u03bcm-thick transverse hippocampal slices using a vibrating microtome (DSK) in ice-cold, oxygenated (95% O2/5% CO2) sucrose-based dissection buffer containing 5 KCl, 1.23 NaH2PO4, 26 NaHCO3, 10 glucose, 0.5 CaCl2, 10 MgSO4, and 212.5 sucrose (in mM).\n\nBrain slices were recovered in oxygenated aCSF containing 124 NaCl, 5 KCl, 1.25 NaH2PO4, 2.5 CaCl2, 1.5 MgCl2, 26 NaHCO3, and 10 glucose (in mM) at 28\u2009\u00b1\u20091\u2009\u00b0C for at least 1\u2009h and subjected to the fEPSP recordings. To evoke fEPSP from the Schaffer collateral pathway, an electrical stimulus was delivered with a concentric bipolar electrode (CBBPE75, FHC, Bowdoin, ME, USA).\n\nTo record fEPSP in the Schaffer collateral pathway, an aCSF-filled recording pipette, fabricated from a borosilicate glass capillary (1\u20133\u2009M\u03a9, Harvard Apparatus, USA), was placed in stratum radiatum of hippocampal CA1. The slope of fEPSP was acquired and analyzed with WinLTP v2.01 software (WinLTP Ltd., The University of Bristol, UK). For the basal synaptic transmission, stimulus intensity was increased by 50\u2009pA from 0 to 300\u2009pA. In subsequent experiments, the stimulus intensity was set to 40\u201345% of the maximum response. For the PPR experiment, two pulses were delivered at intervals of 10, 25, 50, 100, 250, 500, and 1000\u2009ms, and the ratio was calculated by dividing the fEPSP slope from the second response by the one from the first response. For the LTP experiment, the slope of fEPSPs was monitored at 0.067\u2009Hz (one pulse per 15\u2009s) during the experiment. After obtaining a stable fEPSP response for at least 20\u2009min, LTP was induced with a single high-frequency stimulation (HFS) (1\u2009s at 100\u2009Hz). To quantify the degree of potentiation, the fEPSP slopes over the last 5\u2009min from each slice were averaged.\n\nBrain tissue samples (WT, n\u2009=\u20093; GolpHCat KO, n\u2009=\u20093) were homogenized with a buffer consisting of 0.25\u2009M sucrose, 20\u2009mM HEPES-KOH pH 7.4, and a 1:100 protease inhibitor mixture by sonication. The protein concentration of the homogenized samples was determined using a Qubit 2.0 Fluorometer, and 250\u2009\u00b5g of protein was used for membrane extraction. The lysates containing 250\u2009\u00b5g protein were pelleted by ultracentrifugation at 200,000\u00d7g for 45\u2009min in a homogenization buffer, then resuspended in 0.2\u2009M Na2CO3 (pH 11), and centrifugated at 200,000\u00d7g for 45\u2009min. Finally, the membrane fraction was extracted by once more resuspending 0.2\u2009M Na2CO3 and centrifuging at 200,000\u00d7g for 45\u2009min94.\n\nEach membrane fraction resolubilized in 50\u2009\u00b5L deionized (DI) water was mixed with an equal volume of 200\u2009mM NH4HCO3 and 10\u2009mM dithiothreitol (Sigma-Aldrich) and then denatured for the thermal cycle (100\u2009\u00b0C, 2\u2009min). After cooling, 2\u2009\u00b5L of peptide N-glycosidase F (New England Biolabs) was added, and the entire mixture was incubated in a water bath at 37\u2009\u00b0C for 16\u2009h. The mixture was then chilled in 80% (v/v) ethanol (Merck) at \u2013 42\u2009\u00b0C for 1\u2009h, and the glycan-rich supernatant was collected by centrifugating and precipitating out the deglycosylated proteins. The supernatant fraction was vacuum-dried and followed by purifying the released N-glycans using porous graphitized carbon-solid phase extraction (PGC-SPE). Briefly, PGC cartridges (Agilent Technologies, USA) were washed with 6\u2009mL of DI water and 6\u2009mL of 80% acetonitrile (ACN) and 0.1% trifluoroacetic acid (TFA) in DI water (v/v). The cartridge was conditioned with 6\u2009mL of DI water, followed by loading an aqueous N-glycan solution onto the cartridge. Continuously, N-glycans were eluted stepwise with 6\u2009mL of 10% ACN (v/v), 20% ACN (v/v), and 40% ACN and 0.05% TFA (v/v) in DI water after washing with 8\u2009mL of DI water. All fractions were vacuum-dried and resolubilized with 15\u2009\u00b5L of DI water before LC/MS analysis.\n\nPurified glycans were analyzed at nano-LC/Q-TOF MS with a nano-LC chip consisting of a porous graphitized carbon analytical column (5\u2009\u00b5m, 0.075\u2009\u00d7\u200943\u2009mm i.d.). Glycans were separated at 0.3\u2009\u00b5L/min with a 65\u2009min gradient using Buffer A (3.0% ACN with 0.1% formic acid (v/v) in water and Buffer B (90% ACN with 0.1% formic acid (v/v) in water). The LC gradient used was as follows: 2.5\u2009min, 0% B; 20\u2009min, 16% B; 30\u2009min, 44% B; 35\u2009min, 100% B; 45\u2009min, 100% B; 45.01\u2009min, 0% B. MS spectra were acquired in positive ionization mode with a mass range of m/z 500\u20132000 and an acquisition time of 0.63\u2009s per spectrum.\n\nAfter data acquisition, raw LC/MS data were processed by the Molecular Feature Extractor algorithm of MassHunter Qualitative Analysis software B.07.00 (Agilent Technologies). A list of all N-glycans was extracted using the previously optimized application of the spatial mouse brain glycome database55. N-glycan compositions were identified with a mass error tolerance of 10 ppm using computerized algorithms.\n\nThe dried membrane fraction was mixed with 100\u2009\u00b5L of 50\u2009mM ammonium bicarbonate. About 2\u2009\u00b5L of 550\u2009mM dithiothreitol was added, and samples were incubated in a water bath at 60\u2009\u00b0C for 50\u2009min. Chilled samples mixed with 4\u2009\u00b5L of 450\u2009mM indole-3-acetic acid (IAA) were incubated at room temperature for 45\u2009min in the dark. The mixture was then digested with 10\u2009\u00b5L of 0.2\u2009g/L trypsin in a water bath at 37\u2009\u00b0C for 16\u2009h. Digested proteins were purified by C18 SPE. C18 cartridges (Thermo Fisher Scientific) were first washed with 6\u2009mL of 0.1% TFA in DI water (v/v) followed by 6\u2009mL of 80% ACN, and 0.1% TFA in DI water (v/v). The cartridge was conditioned with 6\u2009mL of 0.1% TFA in DI water (v/v), followed by loading an aqueous digested protein solution onto the cartridge. Peptides were continuously eluted with 6\u2009mL of 80% ACN, and 0.1% TFA in DI water (v/v) after washing with 6\u2009mL of 0.1% TFA in DI water (v/v). The fractions were dried under vacuum and resolubilized with 100\u2009\u00b5L of 0.1% TFA in DI water (v/v) before LC-MS/MS analysis.\n\nProteins and glycoproteins were analyzed using a Thermo Scientific Ultimate 3000 RSLCnano system coupled to a Thermo Scientific Q Exactive Plus hybrid quadrupole Orbitrap mass spectrometer with a nano-electrospray ion source. Mobile phases A and B were water with 0.1% formic acid (v/v) and ACN with 0.1% formic acid (v/v), respectively. The samples were separated at a 0.3\u2009\u00b5L /min flow rate for 130\u2009min using PepMap RSLC C18 column (Thermo Scientific, 2.0\u2009\u00b5m, 75\u2009\u00b5m\u2009\u00d7\u200950\u2009cm). The Orbitrap MS parameters were set as follows: survey scan of peptide precursors was performed at 70\u2009K FWHM resolution in the range of m/z 350\u20131900. HCD fragmentation was performed on 27 at a resolving power setting of 17.5\u2009K. Resulting fragments detected in the range of m/z 200\u20132000 were used.\n\nThe raw data of proteins were processed using MaxQuant v2.0.1.0. Data were searched against the UniProt/SwissProt mouse (Mus musculus) protein database with 17,127 total entries and contaminant proteins. Searches were performed with the following parameter: Data were filtered with a peptide-to-spectrum match (PSM) of 0.01 false discovery rates (FDR), 7 in minimum peptide length, 1 in minimum unique peptide, modification including oxidation and acetylation in protein N-terminal, and true of iBAQ and match between runs. LFQ intensity data was statistically calculated concentrations of proteins using Perseus. Gene Ontology (GO) term enrichment was performed using PANTHER, and network clusters were by Cytoscape using ClueGO and CluePedia.\n\nA novel place recognition task was performed as previously described95. Animal moving in real time was recorded with EthoVision XT software (Noldus). For the experiment, mice were placed in an open field with two identical objects and given 10\u2009min to explore these objects and returned to their home cage. After 1\u2009h, mice were placed back into the open field with two identical objects with one object relocated to a novel place in the field (corner directly opposite to the object\u2019s previous location). Mice were recorded while exposed to this condition for 10\u2009min to observe their spatial recognition memory. The Discrimination index was calculated as the percentage of time spent examining the object in the novel place over the total time spent examining both objects.\n\nOn the training day, mice were placed in a standard fear conditioning shock chamber. Mice were allowed to explore the chamber freely for 5\u2009min and then received the first electrical foot shock (0.5\u2009mA, 2\u2009s duration, followed by four more shocks at 1\u2009min and 30\u2009s intervals. After 24\u2009h, on the test day, mice were placed back into the same chamber for 5\u2009min to assess freezing during the retrieval phase. The mice\u2019s movements in the fear conditioning chamber were recorded using a near-infrared camera and analyzed in real-time with EthoVision XT software (Noldus). Freezing behavior was defined as immobility for more than 2\u2009s. The freezing percentage was calculated as the immobile time divided by the total time.\n\nAll viruses used in this study were produced at the Institute for Basic Science virus facility (IBS virus facility). Mice were placed in stereotaxic frames after being anesthetized with vaporized isoflurane (Kopf). The scalp was incised, and a hole was drilled into the skull above the CA1 (anterior/posterior, \u22121.5\u2009mm; medial/ lateral, \u00b11.5\u2009mm from bregma, dorsal/ventral, \u22121.8\u20132.0\u2009mm from the brain surface). The virus was loaded into a glass needle and injected bilaterally into the CA1 at a rate of 0.1\u2009\u00b5l/min for 5\u2009min (total 0.5\u2009\u00b5l) using a syringe pump (KD Scientific). In each experiment, AAV-GFAP-Cre-mCh, AAV-CaMKIIa-Cre-mch, Lenti-psico-Scramble-GFP, and Lenti-pSico-TMEM87A shRNA-GFP viruses were used. Three weeks after the virus injection, mice were used for behavioral experiments and Immunohistochemistry.\n\nAll experiments in this study are performed with at least three biological replicates (mice or cell culture experiments). Representative image data for Figs.\u00a01a, 6a, d, f were also obtained from at least three biological replicates. All data were presented as the mean\u2009\u00b1\u2009SEM and significant symbol and value were represented in each figure, legend of figure, and source data, respectively. GraphPad Prism 9.4.1 software was used for statistical analysis. Normal distribution was first assessed using the D\u2019Agostino-Pearson omnibus normality test for all experiments. Parametric tests (Student\u2019s two-tailed paired or unpaired t-test, one-way ANOVA) were used for data following a normal distribution. Non-parametric tests (Mann\u2013Whitney test, Kruskal\u2013Wallis test) were used for data not following a normal distribution. Samples that passed the normality test, but not equal variance test was assessed with Welch\u2019s correction. Two-way ANOVA followed by \u0160\u00edd\u00e1k\u2019s post hoc test was used. The significance was represented as asterisks (*p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, ****p\u2009<\u20090.0001, and ns non-significant).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data that support this study are available from the corresponding authors upon request. The atomic coordinates have been deposited to the Protein Data Bank (PDB) under the accession numbers 8HSI (hTMEM87A); 8HTT (hTMEM87A with gluconate); and 8KB4 (hTMEM87A A308M). The cryo-EM maps have been deposited in the Electron Microscopy Data Bank under accession codes EMD-34998 (hTMEM87A); EMD-350178 (hTMEM87A with gluconate); and EMD-37069 (hTMEM87A A308M). The accession codes for the PDB structures are human TMEM87a, PDB: 8CTJ, ChRmine, PDB: 7W9W, Wntless, PDB: 7DRT, Glucagon receptor, PDB: 5YQZ, and TRIC-B1, PDB: 5EGI. Raw files obtained in the Next Generation RNA Sequencing experiments are available on NCBI GEO (Accession number GSE228084). The source data underlying all figures are provided as a Source Data file.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Rivinoja, A., Hassinen, A., Kokkonen, N., Kauppila, A. & Kellokumpu, S. J. J. Elevated Golgi pH impairs terminal N\u2010glycosylation by inducing mislocalization of Golgi glycosyltransferases. J. 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We are grateful to the staff of the Research Solution Center at IBS for help with cryo-EM data collection. Computational work for this research was performed on the data analysis hub (Olaf) in the IBS Research Solution Center. This work was supported by grants from the Institute for Basic Science (IBS-R030-C1 to H.M.K. and IBS-R001-D2 to C.J.L.). This work was also supported by Young Scientist Fellowship (IBS-R001-Y1) to W.K. from the Institute for Basic Science, the Bio & Medical Technology Development Program (NRF-2022M3E5F3080873), the Medical Research Center (MRC) grant (NRF-2018R1A5A2025286), and the Brain Pool Program (NRF- 2021H1D3A2A02038434, NRF-2021H1D3A2A02081370) funded by the Ministry of Science and ICT (MSIT) through the National Research Foundation of Korea (NRF) to S.C. We also thank the Korea Institute of Science and Technology Information (KISTI) Supercomputing Center (KSC-2021-CRE-0469).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Hyunji Kang, Ah-reum Han, Aihua Zhang.\n\nCenter for Cognition and Sociality, Life Science Cluster, Institute for Basic Science (IBS), 55 Expo-ro, Yuseong-gu, Daejeon, 34126, Republic of Korea\n\nHyunji Kang,\u00a0Wuhyun Koh,\u00a0Jung Moo Lee,\u00a0Hayeon Lee,\u00a0Mridula Bhalla,\u00a0Jea Kwon,\u00a0Woo Suk Roh\u00a0&\u00a0C. Justin Lee\n\nIBS School, University of Science and Technology (UST), 217 Gajeong-ro, Yuseong-gu, Daejeon, 34113, Republic of Korea\n\nHyunji Kang\u00a0&\u00a0C. Justin Lee\n\nCenter for Biomolecular and Cellular Structure, Life Science Cluster, Institute for Basic Science (IBS), 55 Expo-ro, Yuseong-gu, Daejeon, 34126, Republic of Korea\n\nAh-reum Han,\u00a0Jimin Yang\u00a0&\u00a0Ho Min Kim\n\nGlobal AI Drug Discovery Center, College of Pharmacy and Graduate School of Pharmaceutical Science, Ewha Womans University, Seoul, 03760, Republic of Korea\n\nAihua Zhang,\u00a0Miguel A. Maria-Solano\u00a0&\u00a0Sun Choi\n\nGraduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Korea\n\nHeejin Jeong,\u00a0Hee Young Jo\u00a0&\u00a0Hyun Joo An\n\nDepartment of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea\n\nHo Min Kim\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.K. and A.-r.H. contributed equally and can be listed first in bibliographic documents. H.K., A.-r.H., H.M.K., and C.J.L. designed the experiments and analyzed the data; H.K. performed whole-cell patch recording, ICC, IHC, and pH imaging. H.K. and J.M.L. performed proteoliposome patch recording. A.-r.H. purified proteins and determined the cryo-EM structure; A.Z., M.A.M.-S., and S.C. designed,\u00a0performed MD simulation and analyzed the data; J.Y. and H.K. performed surface biotinylation assay and western blot. H.K., W.K., and W.S.R. performed slice patch recordings. H.K. and M.B. conducted RNA sequencing. H.K. and H.L. conducted behavioral tests. H.J., H.Y.J., and H.J.A. performed proteomics and glycomics. J.K. analyzed glycomics data. H.K., A.-r.H., A.Z., S.C.,\u00a0H.M.K., and C.J.L. wrote the manuscript.\n\nCorrespondence to\n Sun Choi, Ho Min Kim or C. Justin Lee.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Dirk Schneider and the other, anonymous, reviewers for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Kang, H., Han, Ar., Zhang, A. et al. GolpHCat (TMEM87A), a unique voltage-dependent cation channel in Golgi apparatus, contributes to Golgi-pH maintenance and hippocampus-dependent memory.\n Nat Commun 15, 5830 (2024). https://doi.org/10.1038/s41467-024-49297-8\n\nDownload citation\n\nReceived: 14 December 2023\n\nAccepted: 30 May 2024\n\nPublished: 11 July 2024\n\nVersion of record: 11 July 2024\n\nDOI: https://doi.org/10.1038/s41467-024-49297-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 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stable zinc air batteries via Fe and W dual atom sites", + "pre_title": "3d(Fe)-5d(W) diatomic hybrid oxygen electrocatalysts enables ultra-stable operation of rechargeable zinc-air batteries for over 10,000 h", + "journal": "Nature Communications", + "published": "29 August 2025", + "supplementary_0": [ + { + "label": "Supplementary information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63540-w/MediaObjects/41467_2025_63540_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63540-w/MediaObjects/41467_2025_63540_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63540-w/MediaObjects/41467_2025_63540_MOESM3_ESM.zip" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63540-w/MediaObjects/41467_2025_63540_MOESM4_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63540-w/MediaObjects/41467_2025_63540_MOESM5_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-63540-w#Sec22" + ], + "code": [], + "subject": [ + "Batteries", + "Electrocatalysis" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5530445/v1.pdf?c=1756551991000", + "research_square_link": "https://www.researchsquare.com//article/rs-5530445/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63540-w.pdf", + "preprint_posted": "14 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Durable and highly active oxygen electrocatalysts are crucial to the large-scale application of rechargeable zinc-air batteries. In this work, we utilized the N4 unit in Pc molecule to trap the tungsten atoms scratched off from the tungsten carbide milling balls\u00a0and placed the obtained W-N4 unit close adjacent to the Fe-N4 units in FePc, resulting in\u00a0highly active Fe-N4/W-N4 diatomic sites with well-pronounced 3d-5d hybrid for efficient and durable oxygen electrocatalysis. The electron distribution of the Fe-N4 site is optimized by the neighboring W-N4 site, which facilitates the O2 activation and the desorption of *OH and enhances the catalytic activity of the\u00a0Fe-N4 site. Meanwhile, the unsaturated 5d orbitals and tunable valence of the W atoms could modulate the electronic state of the Fe species, prevent leaching, and further enhance the catalytic stability. The resulting zinc-air battery with Fe,W-N-C air cathode exhibited unprecedented cycling stability and repeatability for over 10,000 hours. This remarkable stability improvement not only provides new perspectives for the commercialization of ultra-stable zinc-air batteries\u00a0but also highlights the critical step in developing 5d metal-boosted 3d metal active sites to fabricate efficient oxygen electrocatalysts.Physical sciences/Chemistry/Catalysis/ElectrocatalysisPhysical sciences/Energy science and technology/Energy storage/BatteriesPhysical sciences/Materials science/Materials for energy and catalysis/Batteries", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Supplementaryinformation.docxSupplementary Information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Durable and highly active oxygen electrocatalysts are crucial to the large-scale application of rechargeable zinc-air batteries. Here we utilize the N4 unit in phthalocyanine molecule to trap the tungsten atoms scratched off from the tungsten carbide milling balls and place the obtained W-N4 unit adjacent to the Fe-N4 units from iron (\u2161) phthalocyanine, resulting in highly active Fe-N4/W-N4 diatomic sites with well-pronounced 3d\u22125d hybrid for efficient and durable oxygen electrocatalysis. The electron distribution of the Fe-N4 site is optimized by the neighboring W-N4 site, which facilitates the O2 activation and the desorption of *OH and enhances the catalytic activity of the Fe-N4 site. Meanwhile, the unsaturated 5\u2009d orbitals and tunable valence of the W atoms could modulate the electronic state of the Fe species, prevent leaching, and further enhance the catalytic stability. The resulting zinc-air battery with Fe,W-N-C air cathode exhibits notable cycling stability and repeatability for over 10,000\u2009h. This enhanced stability highlights the possibility of developing 5\u2009d metal-boosted 3\u2009d metal active sites for the fabrication of efficient oxygen electrocatalysts and stable zinc-air batteries.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The oxygen reduction reaction (ORR) is an important cathodic reaction in emerging energy technologies such as metal-air batteries and fuel cells, which involves multi-electron and proton-coupling processes1,2. Its sluggish kinetics and the high cost of commercial Pt-based electrocatalysts severely limit the widespread commercial applications3,4. Among the atomically dispersed metal catalysts, the Fe-N4 sites with a porphyrin-like structure are widely recognized as one of the most promising sites for ORR5,6,7,8. However, the symmetric electron distribution of the Fe-N4 site and the limited orbital overlap between Fe 3\u2009d and O 2p orbitals increase the energy requirements to activate the O2 (*O2\u2009\u2192\u2009*OOH)9,10,11. The strong affinity of OH\u2212 on the Fe-N4 sites hinders the desorption of *OH, resulting in large overpotentials due to the accumulation of OH\u221212,13,14,15,16,17. Moreover, at high potentials, the FeNC/Fe2+ thermodynamic equilibrium will shift toward the formation of Fe2+, and the leaching and dissolution of the Fe sites during the ORR process will cause decrements of the catalytic stability and further limit the commercial applications of the Fe-based catalysts18,19,20.\n\nInspired by the Cu-Fe bimetallic atomic structure of cytochrome c oxidase, a natural oxidoreductase often found in animals and plants, researchers realized that they might need another atom with different radii and electronegativities forming bimetallic configuration with Fe to break the electronic density plane symmetry of the Fe-N4 sites21. The interaction between two different atoms may mitigate the dissolution of the Fe center and enhance the catalytic stability22. Recently, numerous efforts have been devoted to designing dual-atom catalysts with bimetallic active sites, such as Fe-Co, Fe-Cu, and Fe-Ni23,24,25,26. Although these diatomic catalysts exhibit improved ORR catalytic activity relative to single-atom catalysts due to the synergistic effect of the bimetallic sites, the electronic structure of these second metals (3\u2009d) is quite close to Fe, making them less efficient in breaking the symmetrical charge distribution27. Therefore, introducing metal atoms that cause more asymmetric electron distribution seems to be a straightforward solution. Wang et al. found that the Ir possesses increased 5\u2009d electronic wave function spatial extent, and can effectively modulate the electronic structure and local coordination environment of 3\u2009d transition metals Ni-Fe oxyhydroxides28, because of the stronger hybridization with neighboring ligand orbitals29. In addition, 5\u2009d metals have additional orbital degrees of freedom to tailor the electronic band structure and the adsorption and desorption energy of the intermediates30. Xin et al. also utilized the difference in electronegativity between the two metals in a diatomic catalyst to optimize the electron distribution at the active site31. Therefore, it is reasonable to speculate that if a 5\u2009d metal with large radii, multiple valence states, and different electronegativity can be introduced as the second metal near the Fe-N4 site to form a Fe-5d M diatomic site, it will be more conducive to the occurrence of ORR. Among the 5\u2009d transition metals, W is a promising candidate with demonstrated electrocatalytic activity in single-atom form, and the activity is sensitive to coordination number32,33,34. The challenge of using W as the second metal site lies in synthesizing such electrocatalysts with rationally designed 3d\u22125d diatomic sites using minimal resources and facile processes suitable for large-scale industrial applications. In fact, the identification of the hetero-diatomic structure itself is complicated enough. Aberration-corrected high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) analysis often shows many diatomic couples, and EELS analysis is required to confirm the coexistence of two metals in a small zone (e.g., 1\u00d71\u2009nm). Unless a significant portion of diatomic couples is individually analyzed by EELS, which is often impractical, it is risky to conclude that the dominating structure is hetero-diatomic (A-B) or homo-diatomic (A-A or B-B). The 3d\u22125d diatomic sites can be judged more intuitively through the difference in brightness of the HAADF-STEM image, which could provide favorable support for the precise synthesis of diatomic catalysts.\n\nHerein, we utilized the shear and impact forces during the high-energy ball milling to strip tungsten atoms from tungsten carbide milling balls and constructed a 3d\u22125d hybrid Fe,W-N-C catalyst with Fe-N4/W-N4 diatomic sites in carbon black support. The symmetrical charge distribution of the Fe-N4 site is optimized by the neighboring W-N4 site, and the Fe,W-N-C catalyst exhibits an ORR half-wave potential as high as 0.90\u2009V and effectively four-electron ORR activity. Meanwhile, the mechanistic investigations reveal that the *O2 activation energy and *OH desorption energy on Fe-N4 sites could be significantly optimized by introducing the neighboring W-N4 site. Importantly, the introduction of large radius 5d-W atoms adjacent to the Fe-N4 site was found to be crucial to reduce the irreversible leaching of the Fe catalytic center and achieve unexpected oxygen catalytic stability. The zinc-air battery (ZAB) with Fe,W-N-C air cathode demonstrated a repeatable discharge/charge cycling stability for more than 10,000\u2009h, which highlights its practical application potential.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "High-energy ball milling has enough power to break and reform chemical bonds, often used to construct defects on the support, which could anchor isolated metal atoms/clusters, while the surface wearing of the milling balls is often overlooked35. Figure\u00a01a illustrates the tungsten atoms falling off the tungsten carbide balls due to enormous shear and impact forces. When the raw material contains only carbon black, the fresh tungsten atoms with higher surface energy tend to combine with carbon atoms and aggregate into tungsten carbide nanoparticles during ball milling or subsequent pyrolysis steps (WC-C, Supplementary Figs.\u00a01 and 2, Supplementary Table\u00a01). In contrast, when sufficient phthalocyanine (Pc) complex is added, the tungsten atoms will stay isolated on the carbon black support (W-N-C, Supplementary Figs.\u00a03 and 4). This could be attributed to the unique macrocyclic structure of Pc, with a central cavity occupied by two hydrogen atoms and surrounded by four nitrogen atoms in square planar geometry. This cavity is well-known for forming many stable metal complexes by replacing the two hydrogen atoms with metal atoms, such as Cu, Fe, Co, Ni, etc. When the two hydrogen atoms are knocked off during the ball milling process, the vacant cavity becomes an active trap for the fresh tungsten atoms, resulting in atomically dispersed W-N4 sites.\n\na Tungsten carbide nanoparticles-carbon black catalyst (WC-C) and single-atom tungsten catalyst (W-N-C) obtained by stripping W atoms from tungsten carbide milling balls. b 3d-5d hybrid diatomic M,W-N-C catalysts (M = Fe/Co/Ni) obtained by co-introducing exogenous N-containing complex and 3\u2009d metal-containing complex into the raw materials.\n\nInspired by the successful construction of the W-N4 site, we introduced one more component during the ball milling process: FePc, which possesses a similar structure to that of Pc but with two hydrogen atoms replaced by a Fe atom in the cavity, and constructed a 3d-5d Fe-W diatom catalytic site. As illustrated in Fig.\u00a01b, in the highly energetic environment of ball milling (e.g., huge impact faces and high localized temperatures), the adsorption of both Pc and FePc molecules on the carbon black surface tends to reach thermodynamic equilibrium. Driven by the electron donor-acceptor interactions, the Pc and FePc tend to partially overlap and form N4/FeN4 on the carbon surface at equiblium36,37. When the N4 portion of the N4/FeN4 site traps a highly active tungsten atom scratched off from the milling balls, the Fe-N4/W-N4 diatomic site is successfully constructed. After that, a pyrolysis process at 900\u2009\u00b0C for 2\u2009h in Ar atmosphere stabilizes the sites without sacrificing the coordinating N atoms or causing the aggregation of metal atoms38,39,40. It must be emphasized that 100% of the metals were utilized in the synthesis process. In the catalyst design, the Fe content is determined by the maximum amount of FePc allowed by atomic dispersion on carbon black, and the W content depends on the ball milling time to achieve the 1:1 atomic ratio. Extra milling time will introduce more W than that can be trapped and lead to the formation of tungsten carbide nanoparticles. Similarly, if the amount of Pc is insufficient, it will be difficult to introduce enough N4 sites on the carbon black surface to fix the W atoms that fall off the ball mill, resulting in some W atoms forming WC nanoparticles (Supplementary Fig.\u00a05). In the obtained catalysts, named Fe,W-N-C, the contents of Fe and W were optimized at 1.25\u2009wt% and 4.02\u2009wt% (the atom ratio of Fe and W is close to 1:1) as determined by inductively coupled plasma-optical emission spectroscopy (ICP-OES, Supplementary Table\u00a01). To demonstrate the versatility of this catalyst construction strategy, we extended it to other representative 3d-metals (such as Co and Ni) by simply replacing FePc with other metal phthalocyanine complex and successfully synthesized Co,W-N-C and Ni,W-N-C 3d\u22125d hybrid dual-atom catalysts (Supplementary Figs.\u00a06 and 7, Supplementary Table\u00a01).\n\nThe dual-atom configuration of Fe,W-N-C catalyst was investigated using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). As shown in Fig.\u00a02a, b the Fe,W-N-C catalyst is composed of a series of pearl-like spherical carbon particles with diameters of 50-80\u2009nm, and there are no obvious metal/ metal Pc nanoparticles in the carbon matrix. These results are consistent with the X-ray diffraction (XRD) patterns, in which only two broad diffraction peaks belonging to the (002) and (101) crystal planes of carbon were detected (Fig.\u00a02c). The atomically dispersed Fe,W atom pairs were directly imaged by HAADF-STEM at the atomic scale. As shown in Fig.\u00a02d, a large number of isolated bright-faint dot pairs are uniformly dispersed on the carbon black surface, marked by green rectangles. The pronounced brightness difference in each pair of atoms is due to the sensitive Z-contrast of heavy elements. Since only two metal elements are possible in the system, the pair can be safely identified as 3d-Fe (faint) and 5d-W (bright) atomic pair without guesswork. Supplementary Fig.\u00a08 exhibits the corresponding intensity profile for two typical Fe-W bimetallic pairs at site 1 and site 2 in Fig.\u00a02d, and it was found that the distance between the two metal atoms is about 0.55\u2009nm, which is consistent with the distance in the atomic structure model of the 3d-5d hybridized Fe-N4/W-N4 diatomic site of the Fe,W-N-C catalyst pointed out in the density functional theory calculations part (Supplementary Fig.\u00a09). In addition, the characteristic peaks of Fe and W atoms were both found in the electron energy loss spectroscopy (EELS) spectrum corresponding to the 1\u2009nm \u00d7 1\u2009nm small area HAADF-STEM image, providing another direct evidence of the existence of Fe and W diatomic sites (Fig.\u00a02e\u2013g)41,42. Since HAADF-STEM images describe two-dimensional projections, the projected distances between Fe and W atoms could differ (tagged by blue circles in Fig.\u00a02d), depending on the angle between the W-Fe axis and the incident beam43. The lower magnification HAADF-STEM image and corresponding energy-dispersive X-ray spectroscopy (EDX) mapping also revealed the uniform distribution of C, N, W, and Fe elements in the Fe,W-N-C catalyst (Fig.\u00a02h and Supplementary Fig.\u00a010). High-resolution X-ray photoelectron spectroscopy (XPS) results confirmed the existence of sufficient N (from Pc and FePc molecules) in the Fe,W-N-C catalyst, which not only promoted the anchoring of Fe and W atoms on the carbon black surface but also generated graphitic nitrogen to improve electron transfer in the carbon skeleton (Supplementary Fig.\u00a011)44.\n\na SEM image. b TEM image. c XRD patterns of Fe,W-N-C catalyst, FePc complex, and Pc complex. d Aberration-corrected HAADF-STEM image. e HAADF-STEM image and the corresponding STEM-EELS mapping (f) taken from the orange boxed area in (e). g The corresponding EELS spectrum of the 1\u2009nm\u00d71\u2009nm selected small area in (f). h HAADF-STEM image and the corresponding EDX elemental mapping images for C (red), N (blue), W (yellow), and Fe (green).\n\nTo gain more insight into the electronic structure and coordination environment of the 3d-Fe atom and 5d-W atom in Fe,W-N-C catalyst, X-ray absorption spectroscopy (XAS) was collected. As shown in Fig.\u00a03a, a pre-edge peak at around 7114\u2009eV was observed in the Fe K-edge X-ray absorption near edge structure (XANES), which is characteristic of the 1\u2009s to 4pz electric dipole transition, along with the charge transfer from ligand to the metal center, and could be recognized as the fingerprint of the porphyrin-like planar Fe-N445,46,47,48. The absorption edge of the Fe,W-N-C catalyst is situated between FeO and Fe2O3, indicating that the oxidation state of Fe is between +2 and +3. To elucidate the effect of the neighboring 5d-W site on the chemical state of the Fe center, a single atom Fe-N-C catalyst without W was prepared as a reference through a similar procedure (Supplementary Figs.\u00a012 and 13). Notably, the Fe K-edge absorption of the Fe,W-N-C catalyst is significantly lower than that of the Fe-N-C catalyst, implying that the introduction of 5d-W site neighboring to the 3d-Fe site can effectively regulate the oxidation state of the Fe atom, which could prevent the electrochemical dissolution of the Fe center and enhance its electrocatalytic stability (vide infra, Supplementary Fig.\u00a014). The Fourier transformed k3-weighted Fe K-edge extended X-ray absorption fine structure (EXAFS) spectrum of the Fe,W-N-C catalyst exhibited a prominent peak at around 1.5\u2009\u00c5, which could be assigned to the Fe-N contributions in the first shell (Fig.\u00a03b). However, the main peak of the Fe-N-C catalyst is located at 1.41\u2009\u00c5 (similar to the position shown in the FePc complex, Supplementary Fig.\u00a015). The difference in peak position of Fe,W-N-C catalyst and Fe-N-C catalyst is due to the introduction of W-N4 sites close to the Fe-N4 sites in the Fe,W-N-C catalyst, which reduces the electron transfer from the Fe atoms to the surrounding environment, resulting in a decrease in the oxidation state of the Fe atoms and an increase in the Fe-N distance in Fe,W-N-C. Compared with Fe Foil, there is no observable Fe-Fe scattering peak at 2.2\u2009\u00c5 in both of the Fe,W-N-C and Fe-N-C catalysts. This confirms the absence of Fe aggregates and verifies that Fe atoms exist in an atomically dispersed form. Due to the high resolution in R-space and k-space, the wavelet transform (WT)-EXAFS analysis was carried out to further reveal the isolated state of the Fe atoms. The Fe,W-N-C catalyst demonstrated an intensity maximum at k\u2009~\u20094.7\u2009\u00c5 responding to the Fe-N bonds, and no metallic Fe-Fe scattering signal can be detected (Fig.\u00a03c, and Supplementary Fig.\u00a016). As shown in Fig.\u00a03d\u2013f and Supplementary Fig.\u00a017, the W atoms also exhibited an atomically dispersed nature with an oxidation state between 0 and +6.\n\na Normalized Fe K-edge X-ray absorption near-edge structure (XANES) spectra of Fe foil, FeO, Fe2O3, and Fe,W-N-C catalyst. b Fourier transform k3-weighted Fe K-edge extended X-ray absorption fine structure (FT-EXAFS) spectra at R space and FT-EXAFS fitting curves of Fe,W-N-C. c 3D contour wavelet transformed Fe K-edge EXAFS map of the Fe,W-N-C catalyst. d Normalized W L3-edge XANES spectra of W foil, WO3, and Fe,W-N-C. e k2-weighted W L3-edge FT-EXAFS spectra and FT-EXAFS fitting curves of Fe,W-N-C. f 3D contour wavelet transformed W L3-edge EXAFS map of the Fe,W-N-C catalyst. g The atomic structure model of the Fe,W-N-C catalyst. h Comparison between the experimental Fe K-edge XANES spectrum of Fe,W-N-C and the theoretical spectrum calculated for the atomic structure in (g). i Comparison between the experimental W L3-edge XANES spectrum of Fe,W-N-C and the theoretical spectrum calculated for the atomic structure in (g).\n\nTo clearly elucidate the coordination configurations of Fe and W atoms in the Fe,W-N-C catalyst, EXAFS fitting curves were simulated. As displayed in Fig.\u00a03b, e and Supplementary Table\u00a02, the fitting results indicated that both Fe and W atoms are coordinated with four N atoms in the first shell, with bond lengths of 1.90\u2009\u00c5 and 2.09\u2009\u00c5, respectively. Since weak peaks at above 2.0\u2009\u00c5 are observed in both the Fe K-edge EXAFS and W L3-edge EXAFS spectra, combined with the weak WT-EXAFS intensity maximum in the k range of 5\u20137\u2009\u00c5\u22121, which is higher than that of the coordination with C and different from the Fe-Fe or W-W peak, suggesting that a nonnegligible long-range interaction between the Fe and W atoms may occurred (Fig.\u00a03b, c, e, f and Supplementary Fig.\u00a017)23,49. We included the outer shells of Fe and W in EXAFS fitting. Although the distance between Fe and W is too long (about 5.5\u2009\u00c5 revealed by HAADF-STEM) to be accurately fitted in EXAFS, the simulated spectra fitted to the 4th shell of Fe (R\u2009=\u20093.33\u2009\u00c5) and the 5th shell of W (R\u2009=\u20094.29\u2009\u00c5) overlapped well in the experimental spectra, which further proved that the atomic structure model of the 3d\u22125d hybridized Fe-N4/W-N4 diatomic site of the Fe,W-N-C catalyst pointed out in Fig.\u00a03g is valid. Furthermore, in order to further verify the structure features, the XANES simulations for this representative structure (R\u2009=\u20097\u2009\u00c5 cluster) using the FDMNES code were calculated and shown in Fig.\u00a03h, i. It turned out that the theoretically calculated spectra showed similar features to the experimental spectra, particularly for the shape and the position of the peaks, demonstrating the well-defined structure of Fe,W-N-C catalyst.\n\nThe electrochemical ORR performance of the Fe,W-N-C catalyst was assessed using a rotating disk electrode (RDE) in oxygen-saturated 0.1\u2009M KOH electrolytes. To verify the vital role of the Fe-N4/W-N4 diatomic sites in oxygen electrocatalysis, Fe,WC-N-C catalyst (with isolated Fe atom and tungsten carbide nanoparticles coexisting) was synthesized using a similar method (Supplementary Figs.\u00a018 and 19). As shown in Fig.\u00a04a, the diatomic Fe,W-N-C catalyst exhibited a notable ORR catalytic activity with the most positive onset potential (1.03\u2009V) and half-wave potential (E1/2, 0.90\u2009V) among other synthesized catalysts and the commercial Pt/C catalyst. Specifically, the E1/2 of the Fe,W-N-C catalyst is 60\u2009mV and 30\u2009mV higher than that of the single atom Fe-N-C catalyst and the Fe,WC-N-C catalyst, confirming the positive effect of the 5d-W species on the single atom 3d-Fe catalyst which can be maximized when the 5d-W species is also in single atom form. Fe,W-N-C also possessed the highest kinetic current density up to 17.14\u2009mA\u2009cm\u22122 at 0.82\u2009V, nearly two times higher than the commercial Pt/C catalyst (Fig.\u00a04b and Supplementary Table\u00a03). Compared with the single atom Fe-N-C catalyst, the 3d-5d hybrid Fe-N4/W-N4 diatomic site exhibited a lower Tafel slope (94\u2009mV dec\u22121), revealing its lower oxygen binding energy and faster ORR kinetics (Supplementary Fig.\u00a020, and Supplementary Table\u00a04)15,50,51. The electrochemically active surface areas (ECSAs) of Fe,W-N-C and commercial Pt/C catalysts were estimated and compared by calculating the double-layer capacitance values (Cdl) via cyclic voltammetry curves (Supplementary Fig.\u00a021). The higher Cdl value of the Fe,W-N-C catalyst compared to that of the commercial Pt/C catalyst, indicates its larger ECSA and more approachable active sites. Given its limited BET specific surface area of 75.6 m2 g\u22121 (Supplementary Fig.\u00a022), it can be concluded that most of the active sites exist on the carbon black surface.\n\na LSV curves without iR correction for Fe,W-N-C, Fe,WC-N-C, Fe-N-C, W-N-C, WC-N-C, and commercial 20\u2009wt% Pt/C catalysts in oxygen-saturated 0.1\u2009M KOH electrolyte (25\u2009\u00b1\u20091\u2009\u00b0C, pH=13.0\u2009\u00b1\u20090.5, the resistance of the solution was 45\u2009\u2009\u00b1\u2009\u20095 \u03a9) at 1600\u2009rpm with scan rate of 5\u2009mV\u2009s\u22121. b Onset potential (Eonset), half-wave potential (E1/2) and kinetic current density (JK) (0.82\u2009V, V versus RHE) for the different catalysts. c ORR polarization curves of the Fe,W-N-C catalyst at different rotating speeds. d Koutecky-Levich plots and electron transfer numbers at different potentials of the Fe,W-N-C catalyst. e H2O2 yield and electron transfer number of Fe,W-N-C and Pt/C catalysts measured by RRDE. f Chronoamperometric response curves for Fe,W-N-C and Pt/C catalysts.\n\nThe electron transfer numbers at various potentials were calculated using the linear sweep voltammetry curves collected at different RDE rotating speeds. As shown in Fig.\u00a04c, d, the limiting current density increases with the rotation speed, and the electron transfer numbers were calculated to be ~ 4 in the potential range of 0.2-0.8\u2009V. Also, a nearly complete 4-electrons transfer pathway and less than 2.55 % H2O2 yield could be observed in a wide potential range from 0.2 to 1.0\u2009V by rotating ring disk electrode (RRDE) measurements, further certifying the high selective to the 4-electrons transfer pathway (Fig.\u00a04e). This is highly desirable since the competing 2-electrons transfer pathway not only reduces the energy efficiency, but also poisons the Fe active site through the Fenton reaction between the generated H2O2 and the Fe sites52,53,54. To confirm the effect of the W-N4 site in the Fe,W-N-C catalyst after the Fe-N4 site being poisoned, the nitrite stripping tests were conducted55. As shown in Supplementary Fig.\u00a023, the reduced activity after introducing nitrite indicates that the Fe-N4 site in the Fe, W-N-C catalyst is the adsorption site of O2/ ORR intermediates. It is worth noting that the ORR catalytic activity did not decrease completely to the metal-free level, indicating that the W-N4 site can also drive the ORR process at a higher overpotential. After that, the gravimetric site density (MSD) of Fe-N4 site in Fe,W-N-C catalyst was roughly estimated as at least 10.18 \u00b5mol g\u22121 (due to the high hydrogen evolution catalytic activity of the W site at high overpotentials56,57, which will mask the dissolution peak55) by analyzing the current density difference between the unpoisoned and poisoned curves. Since the ORR kinetic current density of the Fe,W-N-C catalyst at 0.95\u2009V (vs RHE) in 0.1\u2009M KOH electrolyte is 0.83\u2009mA\u2009cm\u22122, the turnover frequency (TOF) of the Fe,W-N-C catalyst is estimated to be 4.2\u2009s\u22121.\n\nBesides catalytic activity and selectivity, methanol tolerance and durability are also essential indices for evaluating the ORR catalytic performance. As displayed in Supplementary Fig.\u00a024, the Fe,W-N-C catalyst exhibited high methanol tolerance ability with negligible current density decay. In contrast, the commercial Pt/C catalyst suffered from a sharp drop in the current. Additionally, the Fe,W-N-C catalyst demonstrated a satisfying ORR long-term catalytic stability with a relative current density retention of 97.94% after a 10-h chronoamperometric test (Fig.\u00a04f). Due to the unique 3d-5d hybrid Fe-N4/W-N4 dual-atom site, the ORR catalytic performance of Fe,W-N-C is comparable to other reported high-activity noble/non-noble metal catalysts (Supplementary Table\u00a05). The stability of Fe,W-N-C catalyst has been further tested by potential cycling from 0.2\u2009V to 1.1\u2009V. As shown in Supplementary Fig.\u00a025, the 1500th CV curve is largely consistent with the initial one, and the LSV curves indicate that despite the catalyst undergoing 1500 cycles of CV tests, there is no obvious decay in E1/2 and limited current density, suggesting the outstanding ORR stability of the Fe,W-N-C catalyst. By comparing the CV and ICP results before and after the accelerated durability test (It was found that the Fe content in the catalyst before and after the accelerated durability test were accounted for 1.25\u2009wt% and 1.24\u2009wt%, respectively), negligible metal leaching was observed, which further demonstrated the enhanced catalytic stability of the Fe,W-N-C catalyst.\n\nInspired by the good ORR catalytic performance, we further examined its catalytic performance in oxygen evolution reaction (OER) to evaluate its potential application as a cathode for rechargeable ZABs. As illustrated in Supplementary Fig.\u00a026 and Supplementary Table\u00a06, the Fe,W-N-C catalyst exhibited the lowest potential as 1.56\u2009V to deliver a current density of 10\u2009mA\u2009cm\u22122, among Fe,WC-N-C (1.68\u2009V), Fe-N-C (1.81\u2009V), W-N-C (1.64\u2009V), WC-N-C(1.72\u2009V), and the benchmark IrO2 (1.59\u2009V) catalysts. Additionally, the smallest Tafel slope of Fe,W-N-C catalyst confirmed its more favorable OER kinetics (87\u2009mV dec\u22121, Supplementary Fig.\u00a027). Most importantly, the Fe,W-N-C catalyst also demonstrated high OER catalytic stability for over a 15-h chronopotentiometry test, far exceeding the commercial IrO2 catalyst (Supplementary Fig.\u00a028).\n\nGiven the enhanced ORR and OER catalytic activity and stability, rechargeable ZABs with Fe,W-N-C catalyst loaded on the cathode were assembled to demonstrate their practicability. As shown in Supplementary Figs.\u00a029 and 30, the ZAB with Fe,W-N-C cathode delivered a high specific capacity of 781\u2009mAh\u2009g\u22121 and a corresponding energy density up to 953\u2009Wh\u2009kg\u22121, outperforming the ZAB with commercial Pt/C\u2009+\u2009IrO2 catalyst (specific capacity: 678\u2009mAh\u2009g\u22121; energy density: 780\u2009Wh\u2009kg\u22121). Notably, the cell exhibited a stable and repeatable discharge/charge cycling curve for over 10,000\u2009h (over 30,000 cycles) at the current density of 5\u2009mA\u2009cm\u22122 with negligible decay on the discharge/charge voltage (the voltage gap is nearly unchanged and maintained at around 0.72\u2009V) and areal energy density (based on the area of the air cathode), which is comparable to other reported ZABs (Fig.\u00a05a, Supplementary Figs.\u00a031-33). Even when operated at a high current density of 50\u2009mA\u2009cm\u22122, the Fe,W-N-C based ZAB still can deliver stable performance for over 2000\u2009h (12,000 cycles), far exceeding the ZAB with commercial Pt/C\u2009+\u2009IrO2 as the cathode and other reported ZABs (Supplementary Figs.\u00a034 and 35). Surprisingly, the Fe,W-N-C based ZAB exhibited a relatively stable discharge/charge cycling curve of more than 550\u2009h at the current density of 100\u2009mA\u2009cm\u22122, revealing its possibility of stable operation in high-current power-consuming facilities (Supplementary Figs.\u00a036 and 37). Such high ZAB stability has seldom been achieved to date (Supplementary Table\u00a07). In addition, the Fe,W-N-C cathode ZAB presented a high discharged voltage plateau with a maximum peak power density of 252\u2009mW\u2009cm\u22122, in contrast to only 140\u2009mW\u2009cm\u22122 for the Pt/C\u2009+\u2009IrO2 based ZAB (Fig.\u00a05b), demonstrating its practical application potential as an alternative catalyst to Pt-based catalysts in ZABs. To meet the demand for flexible energy devices, we also assembled a flexible solid-state ZAB with Fe,W-N-C as the cathode. As shown in Fig.\u00a05c and Supplementary Figs.\u00a038 and 39, the solid-state ZAB remains stable even after the iterative bending test. It can also light up a series of LED lights on a luminous wristband, promising its practical application in flexible electronics (Supplementary Fig.\u00a040).\n\na Galvanostatic discharge/charge cycling stability tests for liquid-state ZAB based on Fe,W-N-C air cathode at a current density of 5\u2009mA\u2009cm\u22122. b Discharge polarization and power density plots of Fe,W-N-C and Pt/C-IrO2 based ZABs. c Cycling stability test of the flexible solid-state ZAB with Fe,W-N-C air cathode at a current density of 5\u2009mA\u2009cm\u22122, and the insets are digital photos of the ZAB at flat/bent/revert flat states. d Simplified schematic illustration of the solid-state ZAB during the in situ XAS tests. e Fe K-edge XANES spectra of Fe,W-N-C based ZAB during the discharging and charging processes at different current densities. f Fe K-edge XANES spectra of Fe,W-N-C based ZAB in resting state after several discharge/charge cycles.\n\nTo trace the origin of the notable stability, solid-state ZAB with Fe,W-N-C cathode was assembled and in-situ XAS analyses were carried out as illustrated in Fig.\u00a05d. The ZAB was first discharged and charged at 5\u2009mA\u2009cm\u22122 and then cycled at a higher current density of 10\u2009mA\u2009cm\u22122 after a 2-min rest. As shown in Fig.\u00a05e, the Fe adsorption edge slightly shifts to lower energy compared to the blank state during the first discharge process, and then moves back to higher energy during the charge process. This low-high energy shift repeated in the second discharge/charge cycle, indicating that the discharge/charge processes of ZAB indeed have certain fluctuations in the valence state of the Fe center, which could be attributed to the adsorption of reactants/reaction intermediates on Fe site. The intensity of the pre-edge peak in Fe K-edge XANES is slightly lower than that in ex-situ measurement, suggesting the adsorption of reactants on Fe. Based on the fluctuation of the valance state and the decrease in pre-edge intensity, Fe can be safely identified as the central metal of the active site in both ORR and OER processes. Despite fluctuations in the valence state of Fe during the discharge/charge cycles, the oxidation state remained between +2 and +3 without over-oxidization or over-reduction to cause Fe aggregation or dissolution (Supplementary Fig.\u00a041). Furthermore, nine XANES curves were recorded at the resting state of the ZAB after each discharge/charge cycle, and the oxidation state of the Fe remained stable, which further demonstrates its catalytic stability (Fig.\u00a05f).\n\nThe density functional theory (DFT) calculations were conducted to clarify the regulation of 5d W-N4 sites on neighboring Fe-N4 sites. Based on the HAADF-STEM-EELS, XPS, and XAS analysis, the atomic configurations of the main active sites in Fe-N-C and Fe,W-N-C catalysts are illustrated in Figs.\u00a06a and 6b (Supplementary data\u00a01). By analyzing the charge density differences of the Fe-N4 and 3d-5d hybrid Fe-N4/W-N4 sites, it could be found that after modification with the neighboring 5d W-N4 site, the electron transfer from Fe atoms to the surrounding decreased, indicating the reduced oxidation state of the Fe atom, consistent with the XAS results. Given the oxygen-rich environment in ORR, we analyzed the adsorption and transition pathways of O2 on the W-N4 site. As shown in Supplementary Fig.\u00a042, the adsorption of O2 molecules on W will spontaneously convert from the end-on adsorption to the side-on adsorption, and the O-O bond will break and form a stable W-(O)2 configuration, which remains the same during the catalytic process. Therefore, the actual active site in Fe,W-N-C catalyst should be denoted as Fe-N4/W-N4O2. To further clarify the regulation of 5d W-N4O2 sites on Fe-N4 sites and the effects on ORR catalytic activity, we investigated the ORR catalytic process at pure Fe-N4 sites and 3d-5d hybrid Fe-N4/W-N4O2 sites. As illustrated in Fig.\u00a06c, due to the strong adsorption of *OH on the Fe center of the Fe-N-C catalyst, the potential-determining step is the desorption of *OH. Also, the positive \u2206G for O2 activation (*O2\u2009\u2192\u2009*OOH) step reveals its inertness. In contrast, after introducing the neighboring 5d W-N4O2 site, the intramolecular hydrogen bond forms between the H atom in *OOH and the O atom in the W-N4O2 site, accelerating the activation of O2 (Fig.\u00a06d). Most importantly, the energy required for the desorption of *OH (rate-determining step) was reduced by 0.14\u2009eV, suggesting that the formation of 3d-5d hybrid Fe-N4/W-N4O2 is beneficial for optimizing the adsorption energies of ORR intermediates. This maybe due to the electron-withdrawing effect of the adjacent W-N4O2 site on Fe site, which leads to a decrease in the electron density of Fe (the positive charge on Fe in the Fe-N-C catalyst and Fe,W-N-C catalyst are 1.085 and 1.103, respectively, Supplementary Fig.\u00a043), thereby affecting the amount of charge that can transferred to *OH, which is beneficial to the desorption of *OH. The Bader charge transfer results revealed that the *OH accepted less charge from Fe-N4/W-N4O2 site than pure Fe-N4 site, which also confirmed the weak adsorption and stronger desorption ability of *OH on Fe-N4/W-N4O2 site. (Fig.\u00a06e, f). The Projected density of state (PDOS) analysis also corroborated that the introduction of neighboring 5d W-N4O2 site reduces the overlap between the Fe-3d orbitals and O-2p orbitals (in *OH intermediates), especially the overlap between Fe-3dz2 orbital and O-2pz and 2py orbitals, which leads to the weak adsorption of *OH (Fig.\u00a06g, h, Supplementary Fig.\u00a044 and 45).\n\nAtomic configurations and charge density differences of (a) Fe-N4 and (b) Fe-N4/W-N4 configurations in Fe-N-C and Fe,W-N-C catalysts, where charge depletion and accumulation were depicted by cyan and yellow, respectively. c The free energy diagram of ORR through a 4e\u2212 pathway on the active sites of Fe-N-C and Fe,W-N-C catalysts under the electrode potential of 0\u2009V at pH=13. d The atomic configuration of *OOH intermediate on Fe-N4/W-N4 site, where the dash line represents the generated hydrogen bond between the H atom in *OOH intermediate and the absorbed O atom from the W-N4O2 site. The charge density difference and Bader charge transfer diagrams of *OH on (e) Fe-N4 and (f) Fe-N4/W-N4O2 sites. g Projected density of state (PDOS) analysis of Fe-3d orbital with *OH intermediates on Fe-N4 and Fe-N4/W-N4O2 sites. h PDOS analysis of Fe-3dz2 orbital with O 2px/2py/2pz orbitals in *OH intermediate on Fe-N4 and Fe-N4/W-N4O2 sites. i The valence changes of Fe atoms in Fe,W-N-C catalyst during ORR process and the demetallation energy differences between Fe,W-N-C and Fe-N-C catalysts.\n\nAs for the OER process, since the initial system is not an oxygen-saturated environment, the active site of Fe,W-N-C catalyst is first assumed to be the original Fe-N4/W-N4 site of the catalyst. However, when the W-N4 site serves as the adsorption site of the H2O/OER intermediates, the desorption of *O2 from the W-N4 site requires higher energy, which reveals the inertness of OER on the W-N4 site in the Fe, W-N-C catalyst (Supplementary Fig.\u00a046). This also proves that in the OER reaction, the W-N4 site will eventually form the configuration of W-N4O2. Also, under the modification of the W-N4O2 site, lower energy is required for Fe-N4 site to drive the OER processes. It is further proved that during the OER reaction, the Fe-N4 site is also the real adsorption site of the H2O/OER intermediates, while the W-N4O2 site is used to optimize its electronic structure to promote the occurrence of OER.\n\nTo reveal the catalytic stability of the Fe-N4/W-N4O2 site, the oxidation state transition of Fe atoms and the demetallation energy of the Fe site during the ORR process were calculated. As shown in Fig.\u00a06i, the oxidation state of Fe atoms tends to be stable in each step under the regulation of the neighboring 5d W-N4O2 site, indicating that Fe atoms will not be over-oxidized or over-reduced by the adsorbed oxygen-containing intermediates. Notably, the positive demetallation energy differences between the Fe-N4/W-N4O2 and Fe-N4 sites in all reaction stages demonstrate the strong binding energy of Fe-N bonds in Fe-N4/W-N4O2 site, which well explains the enhanced catalytic stability of the Fe,W-N-C catalyst.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63540-w/MediaObjects/41467_2025_63540_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63540-w/MediaObjects/41467_2025_63540_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63540-w/MediaObjects/41467_2025_63540_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63540-w/MediaObjects/41467_2025_63540_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63540-w/MediaObjects/41467_2025_63540_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63540-w/MediaObjects/41467_2025_63540_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "This work reports a sustainable and scalable approach to construct highly active Fe-N4/W-N4 dual-atomic sites, utilizing 100% of the precursors and generating no byproducts. Under the highly energetic environment of ball milling, we utilized the electron donor-acceptor interactions between Pc and FePc to bring them in close adjacent, and used the N4 site in Pc to trap the fresh W atoms scratched off from milling balls. This strategy has been successfully adapted to construct other M-N4/W-N4 dual-atomic sites by replacing FePc with other metal phthalocyanines. The W atom may even be replaced to fabricate more genetic M1-N4/M2-N4 sites if specific milling balls containing desired metals are manufactured. In the ORR reaction, the neighboring W-N4 site facilitates the desorption of *OH on the Fe center and increases the demetallization energy of FeN4 sites, resulting in a zinc-air battery with a high energy density of 953\u2009Wh\u2009kg-1 and cycling stability for more than 10,000\u2009h. This low-cost catalyst not only paves the road for the large-scale commercial deployment of zinc-air batteries but also provides a feasible approach for the design of advanced 3d-5d metal hybrid electrocatalysts.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Carbon black super P (99%, Thermo Fisher Scientific Chemicals), phthalocyanine crystalline (Pc, 98%, Thermo Fisher Scientific Chemicals), iron (\u2161) phthalocyanine (FePc, 96%, Thermo Fisher Scientific Chemicals), cobalt (\u2161) phthalocyanine (CoPc, 93%, Tokyo Chemical Industry Co. Ltd), nickel phthalocyanine (NiPc, 95%, Thermo Fisher Scientific Chemicals), Nafion dispersion D520 (Fuel Cell Store), 2-propanol (C3H8O, 99.5%, Fisher Chemical), methanol (CH3OH, 99.8%, Fisher Chemical), potassium hydroxide (KOH, 90%, Sigma-Aldrich), zinc acetate dihydrate (98%, Thermo Fisher Scientific Chemicals), iridium (\u2163) oxide (IrO2, Premion 99.99%, Alfa Aesar), 20% platinum on carbon (Fuel Cell Etc.) were used as received. The 18.2 M\u03a9-cm ultrapure water was obtained from the Mili-Q system.\n\nIn a typical synthesis of dual-atom Fe,W catalyst, an appropriate amount of tungsten carbide grinding balls were put into the tungsten chamber of a planetary ball mill equipment. Then, 500\u2009mg carbon black powder, 200\u2009mg phthalocyanine crystalline and 101.5\u2009mg iron (\u2161) phthalocyanine complex were added. The mixture was ground for 15 cycles at room temperature with a rotation speed of 150\u2009rpm, a 5\u2009min stop was set to change the direction of rotation during the 5\u2009min clockwise and 5\u2009min counter-clockwise cycle. The precursor mixture was then annealed at 900\u2009\u00b0C for 2\u2009h under a gas flow of 100 sccm Ar. After cooling down to room temperature, the dual-atom Fe,W catalyst was obtained and named as Fe,W-N-C.\n\nOther dual-atom catalysts, i.e., Co,W-N-C and Ni,W-N-C were prepared by using the same synthesis procedures with different phthalocyanine-metal complexes (cobalt (\u2161) phthalocyanine for Co,W-N-C and nickel phthalocyanine for Ni,W-N-C) instead of iron (\u2161) phthalocyanine complex.\n\nFe single atom-tungsten carbide nanoparticle coexistence catalyst (Fe,WC-N-C) and W single atom catalyst (W-N-C) were synthesized by using a similar method to Fe,W-N-C catalyst, except for without the addition of phthalocyanine crystalline and iron (\u2161) phthalocyanine complex in the ball milling step, respectively. Similarly, tungsten carbide nanoparticle catalyst (WC-C) was prepared by using the same method with Fe,W-N-C catalyst, except for without the addition of both phthalocyanine crystalline and iron (\u2161) phthalocyanine complex. As a comparison, Fe single atom catalyst (Fe-N-C) was also synthesized by using the same method with Fe,W-N-C catalyst, except that agate grinding balls were used instead of tungsten carbide grinding balls to avoid introducing tungsten into the Fe-N-C catalyst.\n\nThe morphology and detailed structure of catalysts were investigated by using field-emission scanning electron microscopy (Hitachi S-4800) and transmission electron microscopy (JEOL JEM-ARM200CF S/TEM) equipped with energy-dispersive X-ray spectroscopy (EDX) and electron energy loss spectroscopy (EELS). The phase composition was revealed by X-ray diffraction pattern with Bruker D8 Discover diffraction system. X-ray photoelectron spectroscopy experiments were performed by using Kratos Axis (Ultra) spectrometer with monochromatized Al K\u03b1 (h\u03c5\u2009=\u20091486.71\u2009eV). The spectrometer was calibrated by the binding energy (84.0\u2009eV) of Au 4f7/2 with reference to the Fermi level. Charge effects were corrected by using C 1\u2009s peak at 284.8\u2009eV. The inductively coupled plasma-optical emission spectroscopy (iCAP 6300) was used to detect the metal content in different samples. The synchrotron X-ray absorption spectra of Fe and W were recorded on the hard X-ray microanalysis beamline (HXMA-061D) of Canadian Light Source. XANES data analysis, EXAFS fitting and wavelet transformation were performed with Athena, Artemis and HAMA Fortran version software packages, respectively58,59,60. All spectra were collected in ambient conditions.\n\nThe catalyst inks were prepared by dispersing 2\u2009mg catalyst in a mixture of 400\u2009\u00b5L isopropanol/ultrapure water/5\u2009wt % Nafion solution (190\u2009\u00b5L/190\u2009\u00b5L/20\u2009\u00b5L). The obtained mixture was ultrasonicated for 120\u2009min before use. Subsequently, 8\u2009\u00b5L of the suspension was dropped by a pipettor onto the surface of a polished glassy carbon rotating disk electrode (RDE, 0.19625\u2009cm\u22122) or rotating ring-disk electrode (RRDE) and naturally dried in air. The working electrode for the oxygen reduction reaction (ORR) electrochemical test was catalyst-loaded RDE/RRDE with a catalyst loading of 0.2\u2009mg\u2009cm\u22122. As for the oxygen evolution reaction (OER), a carbon paper electrode (1\u2009cm\u22122) coated with 0.2\u2009mg\u2009cm\u22122 catalyst was used as the working electrode.\n\nAll of the ORR and OER electrochemical measurements were carried out in a conventional three-electrode system by a Bio-Logic electrochemical workstation equipped with a pine modulated speed rotator. The catalyst modified electrode, a graphite rod and Ag/AgCl electrode (3.5\u2009M KCl) were employed as the working electrode, counter electrode, and reference electrode, respectively. All obtained potentials were converted to the reverse hydrogen electrode (RHE) potentials according to Nernst Eq. (1) and without iR-compensation:\n\nThe ORR linear sweep voltammetry (LSV) polarization curves were conducted in O2-saturated freshly-prepared 0.1\u2009M KOH electrolyte (pH=13.0\u2009\u00b1\u20090.5, the resistance of the solution was 45\u2009\u2009\u00b1\u2009\u20095 \u03a9) and recorded at different rotation speeds from 400 to 2500\u2009rpm with a scan rate of 5\u2009mV\u2009s\u22121. The electron transfer number (n) was calculated based on Koutecky-Levich Eqs. (2)-(4):\n\nwhere J is the measured current density (mA cm\u207b2), JL is the diffusion limiting current density (mA cm\u207b2), JK is the kinetic current density (mA cm\u207b2), \u03c9 is the angular velocity of the disk, n is the electron transfer number, F is the Faraday constant (96485\u2009C\u2009mol\u207b1), CO is the saturated O2 concentration (1.2 \u00d7 10\u207b6 mol cm\u207b3), DO is the diffusion coefficient of O2 in the electrolyte (1.9 \u00d7 10\u207b5 cm2 s\u207b1), and V is the kinematic viscosity (0.01\u2009cm2\u2009s\u207b1).\n\nThe number of electron transfer and the yield of hydrogen peroxide on RRDE were calculated on the basis of the currents of the disk electrode and ring electrode by using the following Eqs. (5)-(6):\n\nwhere Id is the disk current, Ir is the ring current, N is the H2O2 collection efficiency of the ring.\n\nThe double-layer capacitance values (Cdl) was analysis by recording the cyclic voltammetry curves between 1.06 V-1.16\u2009V with different scan rates of 10\u2009mV\u2009s\u22121 to 100\u2009mV\u2009s\u22121. The ORR stability tests were performed by measuring the current change at an operation potential of 0.8\u2009V (vs. RHE) for 10\u2009h.\n\nFor OER electrochemical measurements, freshly-prepared 1.0\u2009M KOH electrolyte was used as the electrolyte (the resistance of the solution was 1.7\u2009\u2009\u00b1\u2009\u20090.2 \u03a9), and the long-term stability test was completed by chronopotentiometry at a constant current density of 10\u2009mA\u2009cm\u207b2.\n\nTo obtain a non-changing oxygen reduction performance and cyclic voltammograms (CV) for the duration of the experiment, extensive cycling (20 cycles in Ar-saturated electrolyte at 100\u2009mV\u2009s-1, 10 cycles in Ar-saturated electrolyte at 10\u2009mV\u2009s-1, and 6 cycles in O2-saturated electrolyte at 5\u2009mV\u2009s-1 with the potential range of 1.05 to -0.4\u2009V) were repeated performed to make the layer hydrophilic and allow a complete wetting. All experiments for active site density determination were performed in a 0.5\u2009M acetate buffer at pH=5.2 with a catalyst loading amount of 0.27\u2009mg\u2009cm\u22122.\n\na. The LSV curve was recorded in O2-saturated electrolyte within the voltage range from 1.0\u2009V to 0.3\u2009V with a scan rate of 5\u2009mV\u2009s\u22121. b. The CV curve was recorded in Ar-saturated electrolyte within the voltage range of 0.45\u2009V to -0.15\u2009V with a scan rate of 10\u2009mV\u2009s\u22121.\n\na. The working electrode was dipped in 0.125\u2009M NaNO2 solution for 300\u2009s at open circuit potential (OCP) with a rotation rate of 300\u2009rpm. b. The working electrode was washed in deionized water for 60\u2009s, electrolyte for 300\u2009s, and deionized water for 60\u2009s with a rotation rate of 300\u2009rpm at OCP.\n\na. The LSV curve in O2-saturated electrolyte within the voltage range from 1.0\u2009V to 0.3\u2009V with a scan rate of 5\u2009mV\u2009s\u22121 was recorded again. b. The CV curve was also recorded in Ar-saturated electrolyte within the voltage range of 0.45\u2009V to -0.15\u2009V with a scan rate of 10\u2009mV\u2009s\u22121.\n\nThe gravimetric site density (MSD) can be calculated by the following Eq. (7):\n\nThe turnover frequency (TOF) at 0.95\u2009V in 0.1\u2009M KOH electrolyte can be calculated via the following Eq. (8):\n\nWhere Qstrip is the excess coulometric charge associated with the stripping peak, nstrip is the number of electrons associated with the reduction of one adsorbed nitrosyl per site and its value is 5, F is the Faraday constant (96500\u2009C\u2009mol\u22121), JK is the kinetic current density of the catalyst at 0.95\u2009V in 0.1\u2009M KOH electrolyte.\n\nThe performance of the rechargeable liquid-state ZABs was evaluated by the Bio-Logic electrochemical workstation and LAND testing system in a homemade air cell at a temperature of 25\u2009\u2009\u00b1\u2009\u20091\u2009\u00b0C (relative humidity 45-65%), which consisted of an air cathode (Fe,W-N-C catalyst coated carbon paper, 0.5\u2009mg\u2009cm\u207b2), a polished zinc plate anode (1\u2009cm \u00d7 3\u2009cm), and electrolyte (6.0\u2009M KOH with 0.2\u2009M zinc acetate). For comparison, the commercial Pt/C and IrO2 catalysts (mass ratio of 1:1) were also loaded on carbon paper with a loading amount of 1.0\u2009mg\u2009cm\u207b2 and used as the air electrode of the ZABs. The specific capacity and energy density were determined by the galvanostatic discharge at a current density of 10\u2009mA\u2009cm\u207b2 and normalized by the mass of consumed Zn foil, as Eq. (9).\n\nwhere, Idischarge is the discharge current density (10\u2009mA\u2009cm\u22122), t is the time when the reaction stops, mZn1 and mZn2 are the weights of the Zn foil before and after the discharge process, respectively.\n\nThe discharge and charge cycling durability of the batteries were evaluated at constant current densities of 5\u2009mA\u2009cm\u207b2 (catalyst loading: 0.5\u2009mg\u2009cm\u207b2), 50\u2009mA\u2009cm\u207b2 and 100\u2009mA\u2009cm\u22122 (catalyst loading: 1.0\u2009mg\u2009cm\u207b2).\n\nThe rechargeable flexible solid-state ZAB was assembled with a catalyst-coated carbon cloth as the cathode, a polished zinc plate as the anode, and a PVA-KOH-Zn(CH3COO)2 hydrogel as the solid-state electrolyte. The discharge and charge cycling durability of the battery was evaluated at a constant current density of 5\u2009mA\u2009cm\u207b2 with LAND battery testing system.\n\nThe operando XAS spectra of flexible solid-state ZABs were recorded in fluorescence mode. In order to ensure that the Fe element in the cathode of flexible solid-state ZABs could be clearly detected, 200\u2009\u00b5L Fe,W-N-C catalyst ink was dropped on the gas diffusion layer and served as the air cathode. The Zn mesh was used as the anode and the glass fiber membrane impregnated with KOH-Zn(CH3COO)2 electrolyte was served as the solid electrolyte. To obtain the evolution information of the active site in the working state of ZAB, discharge and charge cycle tests were conducted at the current densities of 5\u2009mA\u2009cm\u207b2 and 10\u2009mA\u2009cm\u207b2. During the XAS spectrum collection process, the X-ray absorption edge position was calibrated using standard Fe foil.\n\nAll DFT calculations were performed by using the Vienna Ab initio Simulation Package (VASP)61. The projector augmented wave (PAW) method was adopted to describe the electron-ion interaction62. The Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional within a generalized gradient approximation (GGA) was employed to describe the electron-electron interaction, and a cut-off energy for the plane-wave was set as 450 eV63. Moreover, the semiempirical DFT-D3 dispersion correction was applied to describe the weak interactions, including Van der Waals interactions in molecule adsorption and hydrogen bonding64. A \u0393-centered 2\u00d72\u00d71 k-point mesh was selected to sample the Brillouin zone of hexagonal cells. The convergence criteria for the energy and force were set to 10\u22126 eV and 0.01\u2009eV\u2009\u00c5\u22121, respectively. To avoid the interactions between two neighboring images, the vacuum layer thickness was set to be 20\u2009\u00c5. All of the atoms were fully relaxed during the structural optimizations.\n\nThe considered reaction steps for the 4e\u2212 electrochemical ORR processes under base conditions are generally reported as following Eqs. (10)-(14):\n\nwhere * represents the catalytic active sites of vacant surface or the intermediate species adsorbed on the active sites. The Gibbs free energy of ORR can be calculated with the following Eq. (15)13:\n\nwhere \u2206E represents the adsorption energy difference for each species adsorbed on the catalyst calculated by VASP, the \u2206ZPE and \u2206S are the zero-point energy and entropy difference between the adsorbed state and corresponding free-standing state, respectively. neU and \u2206GpH are the free energy contributions related to the electrode potential U and pH value, respectively. pH value was set as 13 to describe an alkaline media, and the effect of pH on the \u0394G was obtained by the following Eq. (16):\n\nThe nudged elastic band (NEB) climbing image NEB (CI-NEB) methods were used to estimate the diffusion barrier65. 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We also greatly appreciate Manabu Fujii for the help.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada\n\nYifan Li,\u00a0Hanlin Wang,\u00a0Xuesong Xie,\u00a0Yang Yang,\u00a0Xuehai Tan,\u00a0Keren Jiang,\u00a0Hao Zhang\u00a0&\u00a0Zhi Li\n\nEngineering Research Center of Advanced Rare Earth Materials, Department of Chemistry, Tsinghua University, Beijing, China\n\nChang Chen\n\nHard X-Ray Micro Analysis BL, Canadian Light Source Saskatoon, Saskatoon, SK, Canada\n\nNing Chen\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY.L., H.Z., and Z.L. conceived and designed the project. Y.L. carried out the sample synthesis, characterizations, electrochemical measurements, battery tests and manuscript writing. H.W. carried out the computational investigation and provided the analyses. C.C. contributed to the ICP, STEM-EELS characterization and battery performance analysis. X.X. and Y.Y. assisted with the XAS tests. X.T. and K.J. helped to discuss the experimental data. N.C. contributed to the XAS tests and XANES analysis. Z.L. and H.Z. are responsible for the overall supervision of the project. All the authors participated in preparing the manuscript and contributed to the discussion.\n\nCorrespondence to\n Hao Zhang or Zhi Li.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Mohsin Muhyuddin, Mai Thanh Nguyen and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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Ten thousand hour stable zinc air batteries via Fe and W dual atom sites.\n Nat Commun 16, 8085 (2025). https://doi.org/10.1038/s41467-025-63540-w\n\nDownload citation\n\nReceived: 09 January 2025\n\nAccepted: 22 August 2025\n\nPublished: 29 August 2025\n\nVersion of record: 29 August 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63540-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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the Precambrian continental crust", + "journal": "Nature Communications", + "published": "23 October 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53438-4/MediaObjects/41467_2024_53438_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53438-4/MediaObjects/41467_2024_53438_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53438-4/MediaObjects/41467_2024_53438_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.13798759", + "/articles/s41467-024-53438-4#ref-CR53" + ], + "code": [], + "subject": [ + "Carbon cycle", + "Stable isotope analysis" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3722826/v1.pdf?c=1729768055000", + "research_square_link": "https://www.researchsquare.com//article/rs-3722826/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53438-4.pdf", + "preprint_posted": "14 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The deep continental crust represents a vast potential habitat for microbial life where its activity remains poorly constrained. A common characteristic of these ecosystems is the presence of organic acids like acetate, but the role of these molecules in the subsurface carbon cycle - including the mechanism and rate of their turnover - is still unclear. Here, we developed an isotope-exchange \u2018clock\u2019 based on the temperature-dependent abiotic equilibration of H-isotopes between acetate\u2019s methyl-group and water, which can be used to define the maximum in situ residence time for acetate. We applied this technique to the fracture fluids in Birchtree and Kidd Creek mines within the Canadian Precambrian crust. At both sites, we found isotopic disequilibrium between acetate and water, indicating acetate residence times <1 million years and a rate of turnover that could theoretically support microbial life. However, radiolytic water-rock reactions could also contribute to acetate production and degradation, a process that would have global relevance for the deep biosphere. More broadly, our study demonstrates that isotope-exchange clocks can constrain in situ residence times of biomolecules with possible applications to other environments.Earth and environmental sciences/Biogeochemistry/Carbon cycleEarth and environmental sciences/Ecology/Stable isotope analysis", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SubsurfaceAcetateSIFINAL.pdfSupplementary Materials", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The deep continental crust represents a vast potential habitat for microbial life where its activity remains poorly constrained. Organic acids like acetate are common in these ecosystems, but their role in the subsurface carbon cycle - including the mechanism and rate of their turnover - is still unclear. Here, we develop an isotope-exchange \u2018clock\u2019 based on the abiotic equilibration of H-isotopes between acetate and water, which can be used to define the maximum in situ acetate residence time. We apply this technique to the fracture fluids in Birchtree and Kidd Creek mines within the Canadian Precambrian crust. At both sites, we find that acetate residence times are <1 million years and calculated a rate of turnover that could theoretically support microbial life. However, radiolytic water-rock reactions could also contribute to acetate production and degradation, a process that would have global relevance for the deep biosphere. More broadly, our study demonstrates the utility of isotope-exchange clocks in determining residence times of biomolecules with possible applications to other environments.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Fluid-bearing fractures within crystalline rocks of the Precambrian continental crust have been identified globally at sites from the Canadian Shield to the South African Craton and may store as much as one-third of the Earth\u2019s groundwater1. Surface meteoric water mixes with fracture fluids in the top 1\u20132 kilometers of the crust sustaining diverse populations of microorganisms. Here, we focus on still deeper fluids that are generally characterized by anoxia, high salinities (up to 325\u2009g/L), low cell densities (<103\u2013105 cells/L) and variable hydrogeologic recharge rates2,3,4. At the Kidd Creek Cu-Zn-Ag Mine (Timmins, Ontario), noble gas-derived mean residence times of fracture fluids can exceed 109 years3. Long fluid residence times allow the products of water-rock reactions to accumulate to a greater extent than elsewhere. Despite the accumulation of these potential substrates, cell densities in the fluids are low, making the Kidd Creek Deep Fluid and Deep Life Observatory a prime window into abiogenic synthesis4. Most notably, radiolysis produces abundant H2 while simultaneously generating oxidants like sulfate5,6,7,8,9. At sufficiently high concentrations, H2 can reduce inorganic carbon to generate methane and higher hydrocarbons through abiotic Sabatier and polymerization reactions10,11,12,13. It was recently suggested, based on laboratory experiments, that radiolysis in Kidd Creek may also generate simple organic acids such as acetate, formate and oxalate from water and dissolved inorganic carbon14,15,16. Indeed, the dissolved organic carbon pool in Kidd Creek\u2019s fracture waters is over 2\u2009mM and up to 68% of this pool is composed solely of acetate and formate16. Through observations of Kidd Creek and other subsurface continental sites, it has become clear that abiotic water-rock reactions including radiolysis can provide a chemical framework \u2013 organic carbon, oxidants and reductants \u2013 that could support microbial communities17.\n\nThe synthesis mechanism of these chemical species has been studied for over thirty years at Kidd Creek, yet estimates of their turnover times are to date limited. Methane and sulfur cycling have been examined through isotopic analyses, but these measurements provide binary statements about production and consumption rather than quantitative rates10,18. Substrate turnover times are instead estimated via bottom-up models of radiolytic yields that come with large uncertainties5,6,7,9. Direct measurements of carbon turnover are needed for accurate evaluation of the net productivity and thus habitability of hydrogeologically isolated systems like Kidd Creek. Moreover, environmental measurements of abiogenesis rates could elucidate the quantitative importance of these reactions in other deep biosphere locations both on Earth and potentially other planets or moons.\n\nHere, we constrain the turnover time of acetate in two deep subsurface fracture fluid systems by developing and applying an isotope-exchange clock for dissolved acetate. First, we experimentally constrained the rate of uncatalyzed (abiotic) H-isotope exchange between water and acetate methyl-H, which is presumed to occur through a tautomerization reaction19,20. We found that the rate of this exchange reaction follows a first-order Arrhenius relationship with temperature (Fig.\u00a01A). Since acetate is synthesized out of H-isotopic equilibrium with surrounding fluids and exchange drives it towards equilibrium at a known rate, the apparent 2H-fractionation between acetate and water can serve as a clock: If acetate turnover is slower than abiotic isotopic exchange, acetate\u2019s methyl-site \u03b42H composition will be defined by the water \u03b42H and the equilibrium isotope effect (EIE) between them. Alternatively, if turnover is comparatively high, it will have a disequilibrated signature from the water. Although we do not (yet) know the magnitude of starting disequilibrium upon acetate synthesis, preventing a fully quantitative estimate of residence time, the mere presence of isotopic disequilibrium between acetate and water must indicate a residence time that is shorter than the equilibration time.\n\nA Arrhenius plot of hydrogen isotope exchange rates with a linear regression through experiments at 60\u2009\u00b0C (n\u2009=\u20093), 100\u2009\u00b0C (n\u2009=\u20091), 150\u2009\u00b0C (n\u2009=\u20092) and 200\u2009\u00b0C (n\u2009=\u20092) (solid circles). Extrapolated reaction rates are projected to 25\u2009\u00b0C (open circle). Shaded region represents 2 RMSD. B Carbon and hydrogen isotope composition of acetate from Kidd Creek and Birchtree mines. Shaded regions represent \u03b413C of total organic carbon from the metasedimentary rocks of the Kidd Creek formation34. Error bars reflect standard deviation on analytical triplicates.\n\nWe applied this approach to fracture fluids at Kidd Creek Mine and \u2013 for comparison \u2013 at Birchtree Mine, a site with lower salinity and higher microbial activity in the Canadian Shield16. A suite of microbial communities with diverse metabolisms have been enriched from fluids from Thompson Mine, adjacent to the Birchtree site, including fermentation and organoclastic sulfate reduction21. Whereas only alkane-oxidizing and hydrogenotrophic sulfate reducers could be enriched from Kidd Creek fluids4. Cell densities are also higher in Thompson fluids (103\u2212107 cells/mL) than in Kidd Creek (<104 cells/mL)4,21. The distinct carbon isotope ratios of acetate in Birchtree (\u221227\u2030) and Kidd Creek (\u22127\u2030) fluids further supported the hypothesis that microbial communities were actively turning over dissolved organic molecules like acetate in Birchtree fluids, while Kidd Creek fluids represented an abiotic endmember with long organic residence times16. We used our isotope exchange clock method to test this hypothesis and found acetate-water 2H disequilibria at Birchtree that confirm acetate turn over, likely by microbial metabolisms. More notably, acetate-water disequilibria was also identified in Kidd Creek fluid, indicating relatively short acetate residence times (<1\u2009Myr) despite fluid residence times that are 1000-times longer. Our results from Kidd Creek provide insights into an active carbon cycle within isolated deep continental fracture fluids and suggest tentative constraints on the importance of radiolytic acetate production as an abiotic reaction in the deep biosphere.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53438-4/MediaObjects/41467_2024_53438_Fig1_HTML.png" + ] + }, + { + "section_name": "Results and discussion", + "section_text": "Acetate was incubated at temperatures between 60\u2009\u00b0C and 200\u2009\u00b0C in the presence of 5% deuterated water in pressurized gold bags (see Methods). To derive the kinetic rate constant for hydrogen exchange between acetate\u2019s methyl group and ambient water, the 2H/1H ratio (\u03b42H value) of acetate\u2019s methyl group was measured periodically throughout the incubations via ESI-Orbitrap mass spectrometry (See Methods)22. Under every condition tested, acetate \u03b42H values increased with time reflecting exchange with the 2H-enriched aqueous medium. At high temperatures (\u2265150\u2009\u00b0C), the rate of acetate 2H enrichment over time was initially linear then gradually flattened as it approached isotopic equilibrium with water (Fig.\u00a0S2). At lower temperatures, the exchange kinetics were too slow to allow full equilibration of acetate and water within the runtime of the experiments. The fitted half-times for exchange increased exponentially with decreasing temperature from 3\u2009hours to 810 years, following an Arrhenius relationship (R2\u2009=\u20090.999, EA\u2009=\u2009138\u2009kJ/mol, Fig.\u00a01). Replicate incubations, which were performed for all conditions except 100\u2009\u00b0C, resulted in similar reaction rates (overlapping data points in Fig.\u00a01, Table\u00a0S3). Exchange between acetate\u2019s methyl-site and water is presumed to occur through a reversible tautomerization between ethanoate and ethenol moieties (Fig.\u00a0S4). Regardless of the exact mechanism, the excellent fit to an Arrhenius relationship between 60\u2009\u00b0C and 200\u2009\u00b0C suggests that the mechanism of exchange does not change within the tested temperature range. Extrapolating to the ambient temperature for samples collected at Kidd Creek and Birchtree (25\u2009\u00b0C), the predicted exchange half-time was 250,000\u2009\u00b1\u200970,000 years (2xRMSD).\n\nEquilibrium 2H-isotope effects (EIEs) for acetate-water were calculated using density functional theory (DFT) across a range of temperatures (see Methods). These indicated a temperature-dependent change in the EIE from \u2212108\u2030 at 250\u2009\u00b0C to \u2212192\u2030 at 25\u2009\u00b0C (Fig.\u00a02B). Four high temperature incubations at 200\u2009\u00b0C were designed to experimentally test these calculations. Incubations were started with varying magnitudes and direction of isotopic disequilibrium, but in each case acetate \u03b42H values changed with time until the experiments converged to similar EIEs. Water was present in excess and so did not change in \u03b42H value. Equilibrium was reached in less than one day at 200\u2009\u00b0C and remained there for two days (Fig.\u00a02A). On average, the measured EIE (0.888 \u00b1 0.012) was within analytical error of the DFT-calculated value (0.882). While the two experimental series did not perfectly converge in \u03b42H values, they came within ~20\u2030 of each other. This offset is potentially due to analytical artifacts associated with measuring the high \u03b42H value of acetate in the 2H2O spiked sample and is small in comparison to the scale of natural hydrogen isotope variations (blue, Fig.\u00a02A). Thus, at 200\u2009\u00b0C, the empirically determined EIE corroborates the DFT calculations.\n\nA Observed isotope effect between acetate and water throughout a three-day 200\u2009\u00b0C exchange experiment with water at either \u221250\u2030 or +110\u2030. Dashed line represents the calculated EIE between acetate and pure water. Error bars represent standard deviation on analytical replicates (n\u2009=\u20093). B Hydrogen isotope fractionation between acetate and water (2\u03b5acetate/water) at both sites. Solid line is the calculated EIE between the Ca-acetate complex and brine water. Dashed line is the EIE between free acetate and brine water as a function of temperature. Error bars are covered by the data points and represent standard deviation on analytical replicates (n\u2009=\u20093).\n\nThe \u03b413C and \u03b42H values of acetate extracted from Kidd Creek and Birchtree fracture fluids were measured via the ESI-Orbitrap method, revealing different isotopic compositions at the two sites22. Samples collected from three separate boreholes in Kidd Creek between 2008 and 2018 yielded \u03b413C values of \u221210.0\u2030 to \u22126.6\u2030 (VPDB) and \u03b42H values of \u2212142\u2030 to \u2212130\u2030 (VSMOW). In contrast, acetate extracted from three fracture fluid samples from Birchtree yielded \u03b413C values of -26.7\u2030 to \u221227.4\u2030 and \u03b42H values of \u2212167\u2030 to \u2212170\u2030 (Fig.\u00a02 and Table\u00a0S2). All \u03b413C values match the range of values previously reported for these two sites16. When compared to the previously-measured \u03b42H values of water from Kidd Creek and Birchtree (\u221236\u2030 and \u221274\u2030, respectively)19, a similar apparent hydrogen isotope fractionation between acetate and water exists at both sites. This fractionation ranges from \u2212115\u2030 to \u221290\u2030 (Fig.\u00a02B) and differs from isotopic equilibrium at 25\u2009\u00b0C by over 50\u2030. These data demonstrate that acetate in Kidd Creek and Birchtree fracture fluids is far from the calculated H-isotopic equilibrium with water and must therefore have rates of production and consumption that are faster than the rate of abiotic exchange.\n\nThe identical apparent acetate-water hydrogen isotope effect (2\u03b5acetate/water) from the two sites is notable (Fig.\u00a02B). One possibility that we considered is whether complexation of acetate by the abundant (>1\u2009M) dissolved cations4 could significantly alter the EIE, i.e. a \u2018matrix effect\u2019. In this case, a shared 2\u03b5acetate/water value between the sites would be possible if acetate at both sites was in equilibrium with water and the 2\u03b5acetate/water value matched the shifted EIE. Calcium is the most abundant cation in Kidd Creek and Birchtree fluids that complexes with free acetate, thus the Ca-acetate complex represents the most likely acetate complexation in these systems. To test whether complexation shifts the calculated EIE, we calculated the partition function ratio of a calcium-acetate bidentate complex and for high ionic strength brines then combined these to define an EIE for the complex-brine equilibrium. Conservatively assuming that all the acetate is ligated to calcium cations and is in equilibrium with a CaCl2 brine, the calculated EIE is \u2212167\u2030 at 25\u2009\u00b0C, which is 60\u2030 offset from the fractionation observed in Kidd Creek and Birchtree (Fig.\u00a02B). Thus, a comparison of DFT calculations and environmental data suggest that acetate and water in Kidd Creek and Birchtree are in substantial isotopic disequilibrium, whether acetate exists as a free anion or is complexed to calcium in solution. The identical value of 2\u03b5acetate/water values observed at both sites (Fig.\u00a02B) may instead reflect kinetic isotope effects that provide insight into acetate turnover mechanisms.\n\nThe turnover times of organic molecules can provide important constraints on the productivity and habitability of isolated systems like the continental deep biosphere, but to date such timescales have been difficult to measure17. Water-rock reactions influencing the geochemistry of Kidd Creek and other sites often operate too slowly to replicate through experimentation. Similarly, microbial growth rates and metabolic fluxes typical of these settings are inaccessibly slow on laboratory timescales2. While these processes can be identified through isotope geochemistry and genomic analyses, rates of abiogenesis and/or microbial metabolism remain elusive23. Our new H-isotope exchange clock helps to fill that gap by setting upper limits on residence times (i.e. lower limits on production and consumption rates) for acetate. Moreover, the general approach should be directly applicable to other organic molecules in the environment\n\nincluding many potentially important organic substrates and biomolecules.\n\nIn fracture fluids from both Kidd Creek and Birchtree, isotopic disequilibrium between acetate and water implies active production and consumption of acetate by physical, chemical, and/or biological processes. These processes must generate and consume acetate faster than the abiotic exchange reaction can establish H-isotope equilibrium with water. Given that equilibration of hydrogen atoms occurs in less than four half-times, acetate residence times must be less than one million years, at least 1000-fold shorter than that of Kidd Creek fracture fluids (>1\u2009Gyr). Normalizing by the concentrations of acetate (Table\u00a0S2) and assuming present-day concentrations are at steady-state, these turnover times require acetate production and consumption rates of >1\u2009nM/year and >0.1\u2009nM/year in Kidd Creek and Birchtree, respectively. Since estimated physical fluid recharge rates are slower than acetate turnover times3, our data suggest active production and consumption of acetate by microbial metabolisms and/or abiotic reactions.\n\nMany anaerobic microorganisms use acetate as a carbon and electron source. The rates of acetate consumption implied by our residence time estimates provide an opportunity to quantify the amount of metabolic power potentially available to microbes consuming this substrate in the continental deep biosphere. Anaerobic respiration \u2013 represented here as sulfate reduction \u2013 and methanogenesis are common acetate consumption pathways in anoxic environments24,25. Considering the lower threshold of 1\u2009nM/year for acetate consumption in Kidd Creek, acetate would supply 10\u221211.5 W/L or 10\u221212\u2009W/L via sulfate reduction or methanogenesis, respectively (Fig.\u00a03). Assuming a range of cell-specific maintenance powers (the flux of energy required to maintain a cell)26,27,28, this rate could support between 102 to 106 cells/mL (Fig.\u00a03). In saline fracture fluids of the continental subsurface, microbial cells must synthesize organic osmolytes to combat high osmotic pressures, increasing their basal power demands29,30. Our results suggest that even with these higher power requirements, at least 103 cells/mL could theoretically survive solely on acetotrophic metabolic pathways in Kidd Creek (Fig.\u00a03). However, such calculations only reveal the viability of these prospective metabolic pathways and cannot be used as sole evidence of microbial acetotrophy. Further evidence is required to determine whether acetate is actively being consumed by biotic processes.\n\nTheoretical cell densities for sulfate reducers (left) and acetoclastic methanogens (right) that could be supported in the fracture fluids\u00a0over a range of acetate production rates.\n\nThe processes producing and degrading acetate can be constrained using its steady-state isotopic composition. In anoxic settings, acetate typically has a \u03b413C value similar to that of the surrounding total organic carbon (TOC). This is commonly attributed to minimal isotope effects associated with the production of acetate by microbial fermentation and consumption by anaerobic respiration31,32,33. Acetate in Birchtree fracture fluids has \u03b413C values that match this expectation, but it does not have the characteristic 13C and 2H depletion associated with chemolithoautotrophic acetogenesis22,33,34,35. This suggests that acetate turnover in Birchtree fluids is driven by heterotrophic microbial metabolisms.\n\nIn contrast, acetate in Kidd Creek is 13C-enriched relative to TOC36. If microbial activity is similarly responsible for acetate turnover in Kidd Creek fracture fluids, the reactions(s) consuming acetate must have larger (normal) carbon isotope effects than those in Birchtree. Acetoclastic methanogenesis exhibits such an isotope effect (25-30\u2030)37. When the fermentation of organic matter to acetate is coupled with methanogenic consumption, acetate can indeed be 13C-enriched relative to TOC; however, this enrichment is not consistent across environments and the mechanisms behind it are still unclear25,32,38,39. Furthermore, the isotopic composition of methane and low ratio of methane-to-higher-alkanes in Kidd Creek fluids are not consistent with the significant rates of acetoclastic methanogenesis required to generate the observed 13C enrichment in acetate11,37. During cultivation studies, autotrophic and alkane-oxidizing sulfate reducers were enriched from Kidd Creek fluids, but fermentative and acetoclastic methanogenic microorganisms were not4. Importantly, the lack of microbial growth does not preclude these metabolic niches from being an important component of the ecosystem. When culture-independent 16S rRNA sequencing was performed on the same borehole fluids, a variety of putatively chemolithoautotrophic and organisms were identified, including Fuchsiella ferrireducens, an iron-reducing bacterium capable of reductive acetogenesis40. While acetogenesis is a possible source of acetate in these systems, cultured acetogens consistently generate 13C and 2H depleted acetate, the opposite signal to what is observed here in Kidd Creek fluids22,31,32,33,34,35. As such, other mechanisms should be considered to explain acetate turnover in this system.\n\nThe identical hydrogen isotope fractionations between acetate and water at Kidd Creek and Birchtree could indicate turnover mechanisms that are shared between the mines, such as radiolytic reactions. Radiolysis is well documented in the deep biosphere and has been shown to both produce and degrade acetate in laboratory experiments14,15. Radiolytic reactions occur when alpha, beta and gamma irradiation from natural decay of U, Th and K in the rock matrix triggers reactions with surrounding water, solutes, and minerals8,35. Since radiolysis drives substantial abiotic chemistry in subsurface fluids (i.e. H2 production5,7), it could produce acetate in situ as well14,15,16.\n\nIf radiolytic synthesis is the source of acetate in these fracture fluids, it operates at a rate that far exceeds those observed in laboratory studies. Maximum net yield during in vitro experiments is 6\u2009nM acetate per joule of alpha radiation, corresponding to 0.007\u2009nM/yr acetate generation rate in Kidd Creek fluids (see Methods), well below the minimum production rate estimated here15. These results should be interpreted with caution though. Radiolytic synthesis of organic acids is not a single production reaction but a network of reactions that both creates and degrades acetate14,15. The net yield measured in vitro represents a balance of production and degradation fluxes, whereas gross yields could be much higher. Thus, if radiolysis is both producing and degrading acetate in situ, it could support fast turnover times without having high net generation rates. Kinetic isotope effects associated with this turnover could then explain the constant hydrogen isotope fractionation from water observed at both sites. However, while radiolysis is likely cycling acetate in the continental subsurface to some extent, we cannot presently determine whether it is solely responsible for acetate turnover based on our current understanding.\n\nFuture work should carefully examine radiolytic reactions under conditions that match the subsurface to assess their rates of acetate turnover and associated isotope effects. Given that the substrates for radiolysis \u2013 water and DIC \u2013 are ubiquitous, this process could provide a means to fuel acetotrophic metabolisms in environments well beyond the Precambrian continental subsurface, including global marine sediments, groundwaters, and the subsurface of other planets or moons.\n\nIsotope-exchange clocks may also be relevant for other molecules and environments. For isolated systems characterized by slow turnover (i.e. subsurface environments of Earth, Mars or Europa), the acetate H-exchange reaction introduced here could be a useful constraint on acetate residence times or could simply confirm the presence of an active carbon cycle. However, for more biologically productive environments with fast turnover of organics (i.e. shallow marine sediments41), this particular clock is insensitive. Isotope exchange in organic molecules that experience more rapid equilibration of C-bound H would provide more useful information about substrate turnover in these systems. Molecules containing acidic alpha-H atoms, which can undergo tautomerization more easily than acetate (i.e. longer chain organic acids and aldehydes), are potential targets42. Conversely, molecules with yet slower exchange (e.g. alkanes) could provide information about turnover in hotter environments43. Our study provides the analytical and experimental basis for developing these techniques and directly constraining the turnover of small biomolecules in situ using their hydrogen isotope composition, one that could be applied to diverse environments.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53438-4/MediaObjects/41467_2024_53438_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53438-4/MediaObjects/41467_2024_53438_Fig3_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Organic acids were extracted following the procedure developed by Mueller et al. 22 with minor changes to account for the high concentrations of chloride in the fracture waters. Briefly, samples of fracture fluid were titrated to pH >6 with NaOH if necessary. Samples were run through a Dionex Ag/H cartridge at 0.5\u2009mL/min to remove chloride after the cartridge had been washed with 300\u2009mL purified water (MilliQ) at 2\u2009mL/min. The first 0.5\u2009mL of eluent from the cartridge was discarded as it represented the dead volume. The remaining sample was collected until almost all the resin was used, carefully avoiding over-filling the cartridge, which would cause chloride to leak through. The cartridge eluent was injected onto a Dionex high performance ion chromatography instrument with an AG-11HC column and a KOH gradient from 1 to 20\u2009mM. The organic acid fraction of the chromatogram was collected into vials using manual fraction collection. This step was repeated for samples with lower acetate concentration and collected into the same vial. The collected acids were titrated to pH >6 with degassed, anoxic NaOH and then dried down under nitrogen. Samples were redissolved in LC-MS grade methanol.\n\nThe majority of samples were analyzed on a heated electrospray ionization (HESI) Orbitrap QExactive HF (Thermo Fisher, Bremen, Germany) following the protocol of Mueller et al. 22. This technique quantifies the molecular-average \u03b413C (VPDB) and methyl-specific \u03b42H of acetate by comparison to an working standard of sodium acetate (\u03b413C\u2009=\u2009\u221219.2\u2030, \u03b42H\u2009=\u2009\u2212127\u2030). Certain samples were measured on an electrospray ionization (ESI) Orbitrap Exploris 240, but the mass spectrometry parameters were identical and the same standard was used for all measurements. Multiple sample introduction methods into the Orbitrap were used throughout the course of this study.\n\nFor direct infusion measurements, 500\u2009\u03bcL syringe (Hamilton) was filled with sample or standard solution (in LC-MS grade methanol) and attached to a syringe pump (Chemyx). Solution was infused into the mass spectrometer at 5\u2009\u03bcL/min. After a 7-minute acquisition, the syringe and its tubing were washed with 2\u2009mL of LC-MS grade methanol and the next sample or standard was loaded into the syringe pump. This was repeated to achieve bracketed, sample-standard comparisons (AAAABBBBAAAA, A = standard replicates, B = sample replicates). This method was used when memory effects between sample and standard due to large differences in \u03b42H or \u03b413C were a concern. This was especially important for 2H-enriched acetate samples from exchange experiments.\n\nFor dual inlet measurements, two 500\u2009\u03bcL syringes (Hamilton) were filled, one with sample and the other with standard solution (in LC-MS grade methanol) and attached to a syringe pump (Chemyx). The solution was infused into the mass spectrometer at 5\u2009\u03bcL/min. Using a Rheodyne 6-port valve, sample and standard were alternated while achieving continuous flow of both (after Hilkert et al.)44. Each acquisition block was 12\u2009minutes with 4-5\u2009minute switch times between blocks cut out of the data acquisition to avoid carryover effects. This was repeated to achieve bracketed, sample-standard comparisons (ABABABA, A = standard replicates, B = sample replicates). This method was used for the majority of Kidd Creek and Birchtree samples. Acetate standard was diluted to match sample ion current.\n\nFor in-flow injection measurements, samples were infused into the mass spectrometer using a Vanquish Horizons HPLC Split Sampler Autosampler and a Vanquish Horizons Pump set to 5\u2009\u03bcL/min with degassed LC-MS grade methanol as an eluent. An injection volume of 50\u2009\u03bcL was used to insert this sample into the flow of methanol which carried it to the Orbitrap for 14\u2009min. At that time the flow rate was increased to 30\u2009\u03bcL/min to clear residual sample from the transfer lines. At 18.5\u2009minutes, the flow rate was dropped again to 5\u2009\u03bcl/min and after 90\u2009s, the next injection began. Data acquisition included all 20\u2009min of the run but only integrated between 2 and 12\u2009min to calculate isotope ratios. This was repeated to achieve bracketed, sample-standard comparisons (ABABABA, A = standard replicates, B = sample replicates). Acetate standard was diluted to match sample ion current.\n\nIn all of the above methods, the following ESI parameters were used as default. Minor adjustments were made daily to tune the instrument for spray stability. Polarity = negative, spray voltage = 3.0\u2009kV, spray current <0.2\u2009\u03bcA, Auxiliary gas = 1 (arbitrary units), sweep gas = 1 (arbitrary units), sheath gas = 10 (arbitrary units), auxiliary gas temperature = 100\u2009\u00b0C, RF lens = 60%, capillary temperature = 320\u2009\u00b0C. The following Orbitrap parameters were used for all analyses. Automated gain control = 1e6, resolution = 60,000, microscans = 1, quadrupole range = 57\u201362\u2009m/z, lock mass = off. Raw data off the Orbitrap was extracted using the software IsoX (Thermo Fisher, Bremen, Germany) and converted to isotope ratios using a Python script. This script uses the Makarov equation outlined in Mueller et al. 22 to convert from ion intensities to ion counts. It then culls scans that are >99th percentile or <1st percentile in total ion current to avoid integrating scans with ion source aberrations.\n\nHigh-temperature acetate-water exchange experiments were conducted using a customized Dickson-type flexible reaction cell setup (Parr Instruments) with no vapor phase present. Each flexible gold bag was filled with 90\u2009mL of 1\u2009mM sodium acetate in MilliQ water (pH 6-7) that was sparged with nitrogen and pressurized to 30\u2009MPa. Two experiments were performed at 150\u2009\u00b0C in 5% 2H2O. One was run for a week, sampling every 24\u2009h, while the other was run for a month, sampling every 3\u20135 days. Another month-long experiment with 5% 2H2O was performed at 100\u2009\u00b0C, sampling every 3\u20135 days. Acetate-water exchange experiments were also performed at 60\u2009\u00b0C in 60\u2009mL serum vials. Each vial was filled with 50\u2009mL of 1\u2009mM sodium acetate in 5% 2H2O (pH 7) that had been sparged with nitrogen and sealed with a butyl rubber stopper and crimped with an aluminum cap. At each timepoint, 1\u2009mL of sample was collected via needle and syringe. The sample was immediately frozen and stored at \u221220\u2009\u00b0C and the solution was sparged with nitrogen again to remove any air introduced during sampling. These experiments were done in triplicate. All exchange experiments were performed at pH 6\u20137 to match environmental conditions.\n\nAdditional high temperature flexible gold bag experiments were performed to determine the equilibrium isotope effect at 200\u2009\u00b0C (30\u2009MPa). Each reaction cell was filled with 90\u2009mL of 1\u2009mM sodium acetate (pH 6\u20137) in either \u221250\u2030 or +110\u2030 \u03b42H water. Each condition was measured in duplicate, resulting in four total experiments. Samples were taken every hour for the first six hours to measure the extent of isotopic exchange with time and then every ~6\u201312\u2009h for the next 66\u2009h. At each time point, 1.5\u2009mL of the sample was collected and discarded to remove the dead volume from the sampling apparatus and then an additional 1.5\u2009mL of sample was taken for acetate \u03b413C and \u03b42H analyses. Collected aliquots were immediately frozen and stored at \u221220\u2009\u00b0C until they were analyzed.\n\nThe kinetic rate constants for H-isotope exchange were calculated using the formulation from Sessions et al. 43:\n\nwhere Ft is the 2H fractional abundance (i.e., mole fraction) at a given timepoint, Fi is the initial fractional abundance and Fe is the fractional abundance at equilibrium. The latter was calculated using the fractional abundance of the water and the equilibrium isotope effect from DFT models at the corresponding temperature. In experiments where the isotope composition approaches or reaches equilibrium, data points close to the equilibrium value were discarded from the calculation of rate constant due to the large propagated errors when the natural logarithm of the value Fe \u2013 Ft was close to zero.\n\nThe apparent hydrogen isotope fractionation between acetate and water (\u03b5acetate/water) was calculated as:\n\nThe free energy (\u0394G) available to microbial metabolisms was calculated by adjusting the standard free energy (\u0394G\u00b0) for the activity of the reactants and products found in Kidd Creek fracture fluids following the equation:\n\nwhere R is the ideal gas constant (kJ/mol/K) and T is temperature (K), set to 298\u2009K at 500\u2009bar pressure. Q is the reaction quotient defined as:\n\nwhere a is the activity of a substrate defined as the product of its concentration (molar) and gamma value and v is the stoichiometric coefficient which is negative for reactants. Gamma values for sulfate, methane and bicarbonate were found on the Geochemists Workbench with the thermo-hmw.dat database, which uses a Pitzer equation based Harvie-M\u00f8ller-Weare activity model owing to the high ionic strength of the fracture fluid (4.9 molal). Acetate is not part of this database, so it was calculated with extended Debye Hueckel equation using the thermo.dat database. The concentrations used in these calculations were taken from data in Lin et al.10. Sulfate, bicarbonate, acetate and methane concentrations were set to 620 \u03bcM, 57 \u03bcM, 1.3\u2009mM and 2.1\u2009mM, respectively. Methane concentration was calculated from fluid flow rate, gas exsolution rate from the fluid, and the concentration of methane in the gas (from Lin et al.10). It was assumed that all methane was dissolved fully in solution due to the high (500\u2009bar) in situ pressure of the fracture fluids (after Sherwood-Lollar et al. 11). Sulfide was below detection limits (<2 \u03bcM). Its concentration was set to 10\u2009nM but increasing its concentration to the detection limit did not change the implications of the cell densities (\u2009>\u200910 cells/mL at all maintenance energies simulated).\n\nCell density (cells/L) is calculated by combining the acetate turnover rate (M/s), the free energy of the reaction (J/mol), and the maintenance energy of a cell (J/s/cell).\n\nwhere \u03c4AC is the turnover time and \u03c1 is the cell density.\n\nTemperature-dependent 2H/1H equilibrium fractionation between acetate and water was estimated using density functional theory. Liquid-phase acetate and water molecular models were optimized in the GAUSSIAN(TM) program, revision D.01 and GAUSSIAN 16, revision B.01 using basis set 6-311\u2009G(d,p)45,46 and functional B3LYP under Tight optimization criteria (maximum/RMS atomic displacement 0.00006/0.00004 Bohr, maximum/RMS force 0.000015/0.00001 Hartrees/Bohr or Hartrees/Radian), with an Ultrafine integration grid mesh. The integral equation-formalism polarizable continuum model was used to represent the solvation environment47,48. Following optimization, frequency calculations were carried out for the monoisotopic isotopologues and with a single 2H/1H substitution to determine the effect of 2H/1H substitution on vibrational frequencies. The Urey-Bigeleisen-Mayer equation was used to calculate the temperature-dependent reduced partition function ratio of each species under 2H/1H substitution49. Corrected ratios were computed using the temperature-dependent regression of Wang et al. 48 to account for the effects of anharmonicity50. The equilibrium fractionation factor was then computed as the ratio of the corrected ratios at the desired temperature.\n\nTemperature-dependent 2H/1H equilibrium fractionation between the Ca-acetate complex and water was estimated using an empirically derived molecular geometry for the complex, which was then optimized using the same level of theory and basis sets as in the DFT calculations above51. More details regarding these calculations can be found in the Supplemental Information (Tables\u00a0S4-S6). The partition function ratio of water was adjusted to account for the \u2018salt effect\u2019 of a 3\u2009M CaCl2 brine, which was empirically determined to be 15\u2030 at 25\u2009C by Horita et al. 52. The beta factor for water (\u03b2water) was multiplied by 1.015 to ascertain the beta factor of the brine (\u03b2brine):\n\nThe beta factors for free acetate (\u03b2acetate) and Ca-acetate complex (\u03b2Ca-acetate), calculated from the DFT simulations, were then used to calculate the EIE between acetate and water (\u03b1acetate/water), between acetate and brine (\u03b1acetate/brine) and between the Ca-acetate complex and brine (\u03b1Ca-acetate/brine). For example, the EIE of Ca-acetate complex and brine is calculated as such:\n\nTo estimate the radiolytic yield (nM/J) of acetate production by alpha, gamma and beta irradiation in Kidd Creek needed to support a given rate of acetate production, modified calculations from Warr et al. 9 were used. The total acetate yield (YAC) in nM/s is defined as:\n\nwhere i represents either alpha, gamma or beta radiation and Enet is the dose rate (Gy/s) and G is the radiolytic yield (G). The bulk rock density (\u03c1bulk) was set to 2.98\u2009kg/dm3. \\({\\phi }\\) is the porosity, typically ~1% at crystalline rocks sites like Kidd Creek9. Here, we assume that beta and gamma radiation does not produce acetate, since it has not been measured, such studies have not yet been done, so only \u03b1 radiation is considered. Consequently, this represents a conservative estimate of radiolytic acetate production. Alpha radiolytic yields were taken from Vandenborre et al. 15. In experiments with 200\u2009\u03bcM dissolved carbonate in pure water, acetate accumulated to 8\u2009\u03bcM within 1400\u2009Gy of absorbed radiation and plateaued at this concentration up to 5600\u2009Gy, due to competing production and consumption reactions reaching a steady state. This results in a range of 1.3 to 6.0\u2009nM/J for alpha radiation yields.\n\nThe dosage rate of alpha radiation is calculated as:\n\nWhere E\u03b1 is the dosage of alpha radiation emitted (Gy/s) and X represents the specific elemental source of that radiation. S\u03b1 is the stopping power of rock to alpha radiation set at 1.5 after Warr et al.9. W is the water-rock ratio set to 0.37% calculated following Warr et al. 9, using water and rock density of 1.11\u2009g/cm3 and 2.98\u2009g/cm3, respectively, and a porosity value of 1%5,9.\n\nAt 1% K, 1\u2009ppm Th and 1\u2009ppm U, these elements emit 0, 1.93\u2009\u00d7\u200910\u221212 and 6.9\u2009\u00d7\u200910\u221212\u2009Gy/s of alpha radiation, respectively9. To estimate E\u03b1 for each of these elements in Kidd Creek, they were linearly increased based on the actual concentration in the deposit, which are 1.5\u2009ppm, 6.7\u2009ppm and 1.7% for U, Th and K, respectively9. 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Zenodo. https://doi.org/10.5281/zenodo.13798759 (2024).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We would like to thank Nathan Dalleska (Caltech) for helpful discussions about sample processing as well as Andreas Hilkert and Dieter Juchelka (Thermo Fisher, Bremen) and the Caltech Proteome Exploration Lab for use of their Orbitrap facilities. Funding for this work came from an NSF Gradaute Research Fellowship DGE-1745301 (to E.P.M.), a European Association of Organic Geochemistry Research Award (to E.P.M.), the NASA Astrobiology Institute grant # 80NSSC18M0094 (to J.M.E. and A.L.S.), a CIFAR Earth 4D grant (to B.S.L. and V.O). This work was also supported by the Deutsche Forschungsgemeinschaft through the Cluster of Excellence \u201cThe Ocean Floor \u2013 Earth\u2019s Uncharted Interface\u201c (project 390741603) to V.H.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA\n\nElliott P. Mueller,\u00a0Juliann Panehal,\u00a0Alexander Meshoulam,\u00a0John M. Eiler,\u00a0Victoria Orphan\u00a0&\u00a0Alex L. Sessions\n\nDepartment of Earth Sciences, University of Toronto, Toronto, ON, Canada\n\nMin Song,\u00a0Oliver Warr\u00a0&\u00a0Barbara Sherwood Lollar\n\nMARUM Centre for Marine Environmental Sciences, University of Bremen, Bremen, Germany\n\nChristian T. Hansen,\u00a0Verena B. Heuer,\u00a0Wolfgang Bach\u00a0&\u00a0Kai-Uwe Hinrichs\n\nDepartment of Earth Sciences, University of Ottawa, Ottawa, ON, Canada\n\nOliver Warr\n\nDepartment of Earth, Environmental, and Resource Sciences, University of Texas at El Paso, El Paso, TX, USA\n\nJason Boettger\n\nInstitut de Physique du Globe de Paris (IPGP), Universit\u00e9 Paris Cit\u00e9, 1 rue Jussieu, Paris, France\n\nBarbara Sherwood Lollar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nE.P.M. conceptualized and designed the study and performed data analysis. E.P.M., J.P. and M.S. performed sample chemical preparation and Orbitrap analysis. E.P.M., C.H. and V.H. performed isotope exchange reactions. J.B. and A.M. performed DFT calculations. J.E., A.L.S, V.O, B.S.L, K.H., O.W. and W.B. provided laboratory analytical facilities and samples as well as important scientific insights. All authors contributed to data interpretation and manuscript writing.\n\nCorrespondence to\n Elliott P. Mueller.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors claim no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Tori Hoehler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Mueller, E.P., Panehal, J., Meshoulam, A. et al. Isotopic evidence of acetate turnover in Precambrian continental fracture fluids.\n Nat Commun 15, 9130 (2024). https://doi.org/10.1038/s41467-024-53438-4\n\nDownload citation\n\nReceived: 30 December 2023\n\nAccepted: 08 October 2024\n\nPublished: 23 October 2024\n\nVersion of record: 23 October 2024\n\nDOI: https://doi.org/10.1038/s41467-024-53438-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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glioblastoma depend on plastic and reprogrammable cell states", + "journal": "Nature Communications", + "published": "19 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Dataset 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM7_ESM.xlsx" + }, + { + "label": "Supplementary Dataset 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM8_ESM.xlsx" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM9_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM10_ESM.mp4" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM11_ESM.avi" + }, + { + "label": "Supplementary Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM12_ESM.mp4" + }, + { + "label": "Supplementary Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM13_ESM.mp4" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM14_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM15_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_MOESM16_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE270083", + "https://zenodo.org/records/15682177", + "/articles/s41467-025-61999-1#Sec35" + ], + "code": [], + "subject": [ + "Cancer microenvironment", + "CNS cancer", + "Reverse engineering" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4466481/v1.pdf?c=1754566128000", + "research_square_link": "https://www.researchsquare.com//article/rs-4466481/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61999-1.pdf", + "preprint_posted": "18 Jun, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Glioblastoma (GBM) is the most common primary brain cancer. It causes death mainly by local invasion via several routes, including infiltration of white matter tracts and penetration of perivascular spaces. However, the pathways that mediate these invasion routes are only partly known. Here, we conduct an integrative study to identify cell states and central drivers of route-specific invasion in GBM. Combining single-cell profiling and spatial protein detection in patient-derived xenograft models and clinical tumor samples, we demonstrate a close association between the differentiation state of GBM cells and their choice of invasion route. Computational modeling identifies ANXA1 as a driver of perivascular invasion in GBM cells with mesenchymal differentiation and the transcription factors RFX4 and HOPX as drivers of diffuse invasion in cells with neural stem cell or astrocyte-like differentiation. Ablation of these new targets in tumor cells alters their invasion route, redistributes the cell states, and extends survival in xenografted mice. Our results define a close association between GBM cell differentiation states and invasion routes, identify new functional biomarkers of route-specific invasion, and point toward targeted modulation of specific invasive cell states as a therapeutic strategy in GBM.Health sciences/Oncology/Cancer/CNS cancerBiological sciences/Systems biology/Reverse engineeringBiological sciences/Cancer/Cancer microenvironment", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "InvasionRoutesSupplement.pdfSUPPLFile1.xlsxSUPPLFile2scregclustresults.xlsxSUPPLFile3.xlsxSUPPLFile4Vectors.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Glioblastoma (GBM) is the most common primary brain cancer. It causes death mainly by local invasion via several routes, including infiltration of white matter tracts and penetration of perivascular spaces. However, the pathways that mediate these invasion routes are only partly known. Here, we conduct an integrative study to identify cell states and central drivers of route-specific invasion in GBM. Combining single-cell profiling and spatial protein detection in patient-derived xenograft models and clinical tumor samples, we demonstrate a close association between the differentiation state of GBM cells and their choice of invasion route. Computational modeling identifies ANXA1 as a driver of perivascular involvement in GBM cells with mesenchymal differentiation and the transcription factors RFX4 and HOPX as orchestrators of growth and differentiation in diffusely invading GBM cells. Ablation of these targets in tumor cells alters their invasion route, redistributes the cell states, and extends survival in xenografted mice. Our results define a close association between GBM cell differentiation states and invasion routes, identify functional biomarkers of route-specific invasion, and point toward targeted modulation of specific invasive cell states as a therapeutic strategy in GBM.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Glioblastoma (GBM), the most common primary brain cancer in adults, is characterized by rapid progression and a lack of effective therapeutic options for patients with recurrent disease. Unlike other difficult forms of cancer, GBM causes death not by distant metastasis but by rapid local invasion. The recurrence of GBM is attributed to infiltrative cells found in perivascular spaces, white matter, or brain parenchyma, also known as Secondary Scherer structures1,2. The amount of infiltration is negatively correlated with overall survival and tumor growth rate, as supported by surgical3, radiological4, mathematical5, and animal model studies. Yet, infiltrating cells are largely out of reach for current therapy. Comparisons between present-day patients and historical cases suggest that while the severe mass effect appears to be less common in GBM patients today, dissemination, including life-threatening brainstem invasion, is now more pronounced6.\n\nThese observations raise several pertinent questions regarding GBM invasion. Specifically, is the observed impact of invasion on survival driven by particular subpopulations of invading cells? What cell-intrinsic and extrinsic factors mediate these invasions, and do they vary among patients? Importantly, can targeting these invading cells or mitigating invasion extend survival in recurrent GBM? Recent molecular studies, including single-cell profiling, have identified transcriptionally distinct GBM subpopulations, influenced by both genetic mutations and the microenvironment7,8,9,10,11,12,13,14,15. Notably, GBM cells exhibit four main states: mesenchymal-like (MES-like), oligodendrocyte precursor cell (OPC)-like, neural progenitor cell (NPC)-like, and astrocyte (AC)-like8. The mesenchymal state, associated with increased invasion, has been found to rise over time in recurrent tumors16. Interestingly, Venkataramani et al.17 reported that OPC/NPC-like states are prominent in invasion in vivo. Various pathways, including Eph- and epidermal growth factor receptor signaling, stemness pathways, and transcription factors like SOX10 and CEBPB, have been linked to GBM invasion14,18,19,20,21,22. However, the genetic regulation and therapeutic targeting of GBM invasion remain largely unresolved.\n\nHere, we investigate the hypothesis that GBM cell invasion routes are closely tied to their transcriptional states. Specifically, we aim to delineate which cell states favor perivascular versus diffuse invasion, identify key functional properties of these states, and pinpoint genes essential for each invasion type. By utilizing patient-derived cell culture xenograft (PDCX) models with diverse invasion patterns, we integrate single-cell transcriptomics and spatial proteomics to uncover distinct migration behaviors of GBM cell subpopulations.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The Human Glioblastoma Cell Culture (HGCC) Resource consists of extensively studied patient-derived cell (PDC) cultures, thoroughly investigated at genomic and pharmacological levels23,24. In our ongoing research, we have been systematically characterizing the invasion phenotypes of 64 GFP/luciferase-tagged HGCC cultures in nude mice, assessing the extent of perivascular and diffuse invasion, along with other morphological characteristics. The two predominant phenotypes identified in these studies (based on Principal Component Analysis) are either a consolidated tumor with perivascular invasion or a diffuse growth pattern, frequently involving the corpus callosum25. In order to investigate different modes of invasion of GBM cells, we picked six representative HGCC cell cultures with either predominant perivascular invasion or diffuse growth pattern, as suggested from their location in the PCA of phenotypic profiles of all cases in HGCC collection. We evaluated the growth structures using multiplexed immunofluorescence staining, employing STEM121 to identify tumor cells, and specific markers such as CD31 for blood vessels, MBP for white matter, AQP4 for astrocytes, and NeuN for neurons. Three of the chosen cultures (U3013MG, U3054MG, and U3220MG) produced bulky tumors with dense perivascular growth (Fig.\u00a01A), whereas the other three (U3031MG, U3179MG, and U3180MG) produced a diffuse infiltration phenotype (Fig.\u00a01B). Several different secondary Scherer structures were evident in our models (Fig.\u00a01C), including leptomeningeal spread (U3220MG) and perineuronal satellitosis (U3031MG and U3179MG). Of note, the phenotypes demonstrated high reproducibility among mouse individuals (Supplementary Figs.\u00a01 and 2), with concordance levels of 96% for diffuse infiltration, 88% for perivascular invasion, and 96% for perineuronal invasion (Supplementary Data\u00a01). Interestingly, mouse survival rates varied between cases, with diffusely invading HGCC cultures showing a tendency toward longer survival times compared to those with bulk and perivascular growth and invasion phenotypes (Fig.\u00a01D, logrank test: \u03c72\u2009=\u20099.08, df\u2009=\u20091, p\u2009=\u20090.0026, n\u2009=\u200945 mice). The selected cultures had a spectrum of characteristic GBM mutations (Fig.\u00a01E). In conclusion, these selected xenografts serve as representative examples of GBM with specific invasion routes.\n\nA Coronal sections of mouse brains xenotransplanted with U3013MG, U3045MG, and U3220MG. B U3031MG, U3179MG, and U3180MG PDCs. Tumor cells are visualized in black (STEM121), blood vessels in red (CD31), white matter in yellow (MPB), astrocytes in cyan (AQP4), and neurons in magenta (NeuN). The scale bar indicates 1000\u2009\u03bcm. Black squares indicate the zoomed-in location in (C). In both (A and B), representative scans were selected from 10 mice injected with each cell line. (I) Bulk formation, (II) perineuronal satellitosis, (III) diffuse infiltration, (IV) vascular proliferation, (V) Perivascular invasion route, (VI) White matter invasion route, (VII) Subpial invasion route, (VIII) Leptomeningeal spread. The scale bar indicates 100\u2009\u03bcm. D Mouse survival in days for each injected patient-derived cell line. (n\u2009=\u200915 mice (U3013MG), n\u2009=\u200910 mice (U3054MG, U3220MG, U3031MG, U3179MG); and, 14 mice (U3180MG), E Table detailing patient data such as sex (F Female, M Male), age, survival (shown in days), subtype (MES Mesenchymal, PN Proneural, CL Classical), cm2 cross-section referring to the tumor volume, tumor location (T Temporal lobe, O Occipital lobe, C Cortex, F Frontal lobe, P Parietal lobe) mutational profiles, CNAs, observed phenotype, and invasion route. Source data are provided as a source data file.\n\nNext, we aimed to elucidate the connection between the cell state distribution and the invasion phenotype in our PDCX models, utilizing single-cell RNA sequencing (scRNA-seq) for each culture. This encompassed samples from adherent cultures before injection and tumor cells isolated from mouse brains at experimental endpoint. The final data contained 119,766 cell transcriptomes, covering the six lines under in vitro and in vivo conditions, i.e., 12 groups (samples specified in \u201cMethods\u201d). The UMAP dimensionality reduction (Fig.\u00a02A, B) and gene set enrichment of markers obtained by graph-based cell clustering (Fig.\u00a02C) revealed distinct regions within the gene expression space for cells derived from the two classes of PDCXs. Specifically, PDCX models with bulk-forming and perivascular invading tumors populated a transcriptional subspace enriched for injury response and macrophage-like expression signatures, while diffusely growing PDCX models occupied a region enriched for neurodevelopmental, neuronal-like signatures. Oligodendrocyte-like signatures were observed for both invasion routes. Notably, the diffusely growing models were also enriched for outer radial glial cell markers and astrocytic markers (Fig.\u00a02C)26. PDCX and PDC cells grouped together with cell cycle-related programs in a UMAP dimensionality reduction, confirming that all PDCX and PDC include cycling and non-cycling cells (Fig.\u00a02A, C). Notably, U3220MG, which displays a high degree of leptomeningeal invasion (Fig.\u00a01A, C), also harbored a distinct transcriptional cluster (Fig.\u00a02A), suggestive of a unique cell state potentially linked to this invasion route.\n\nA UMAP separation of GBM cells by source patient suggests separation by invasion phenotype. (scRNAseq runs of n\u2009=\u20091 sample of in vitro cultured cells, n\u2009=\u20092 independent samples of in vivo PDCX-isolated tumor cells (from different mouse individuals) for each of the 6 GBM lines, except U3031MG, and U3179MG, which were run as n\u2009=\u20091 sample of in vitro cultured cells, n\u2009=\u20091 in vivo sample of PDCX-derived cells. The full data comprises a total of 119,766 single-cell transcriptomes and all cells are shown). B UMAP of the same GBM cells by growth condition, note that PDCX-derived cells occupy a greater set of transcriptional states. Same number of samples (n) and cells plotted as in (A). C UMAP of the same GBM cells as in (A, B), displaying enrichment of different gene signatures, measured by the Normalized Enrichment Score (NES) in each cell cluster. Note the differential distribution of injury response, oligodendrocyte, and macrophage signatures, versus neurodevelopmental signatures. Same number of samples (n) and cells plotted as in (A). D, E 4-state embedding (cf. Neftel et al.) shows MES/OPC enrichment of perivascular invading cells and NPC/AC enrichment of diffusely growing GBM cells. Same number of samples (n) and cells plotted as in (A). F Mosaic plot quantifying the relationship between transcriptional cell state and preferred invasion route, with coloring indicating observed frequencies compared to expected. A Pearson\u2019s chi-squared test of independence was performed (p\u2009<\u20092.22\u2009\u00d7\u200910\u221216, df\u2009=\u20096, two-sided), with standardized residuals used for shading. No adjustments were made for multiple comparisons. Same number of samples (n) and cells plotted as in (A). Darker shades indicate greater deviation from expected frequencies.\n\nInterestingly, the cells transplanted into mice showed a wider variety of cell states compared to those cultured in vitro (Fig.\u00a02B, E and Supplementary Fig.\u00a03). A possible explanation for this is that exposure to the mouse brain environment activates latent differentiation potential of the cells, whereas the cells stay less differentiated in vitro, which is maintained in stem cell conditions. We further computed cell state plots (cf. ref. 8), showing that the perivascular invading cultures showed a strong bias towards OPC-like and MES-like states, whereas the diffusely invading cultures were associated with NPC-like and AC-like states (Pearson\u2019s chi-squared test: p\u2009<\u20092.22\u2009\u00d7\u200910\u221216, df\u2009=\u20096, Fig.\u00a02D, F). This is intriguing since a previous characterization of invasive GBM, which focused on electrophysiological connectivity of the cells, found an important separation between unconnected NPC-like and OPC-like cells on the one hand, and connected AC-like and MES-like cells on the other hand17. This finding suggests that the preference for perivascular vs. diffuse invasion routes is orthogonal to the electrophysiological phenotype concerning cell state.\n\nTaken together, we found a clear correlation between the invasion patterns of PDCX models and the unique cellular states they exhibited. Notably, perivascular invasion was marked by an abundance of OPC-like and MES-like states, while diffuse invasion was characterized by an NPC-like and AC-like state dominance. Of note, while this key difference was more evident in cells sampled from mouse brains, it was also seen before injection, underscoring that the tendency towards a particular invasion phenotype and cell state distribution are intrinsic properties of GBM PDCs.\n\nOur initial scRNA-seq analysis revealed a significant correlation between transcriptional states and in vivo invasion routes (Fig.\u00a02F). Subsequently, we employed a data-driven approach to identify potential regulators of GBM invasion.\n\nWe have previously described a method, termed single-cell regulatory-driven clustering (scregclust), to simultaneously cluster genes into modules and predict regulators (such as transcription factors and kinases) of these gene modules27. Applying scregclust to the scRNA-seq data from our PDCX and PDC models resulted in a regulatory landscape, where the different gene modules cluster based on their association with predicted upstream regulators (Fig.\u00a03A and Supplementary Data\u00a02). We assessed the modules by quantifying their similarity with established gene signatures of transcriptional states from ref. 8, as well as signatures of diffuse, perivascular, and leptomeningeal invasion routes fitted from our data (Fig.\u00a03B). Upon inspection of the landscape, it became evident that groups of modules\u2014referred to as metamodules\u2014emerged across different PDCX models, displaying shared functional profiles and regulation (Supplementary Fig.\u00a04A). By projecting the metamodule gene signatures onto a single cell atlas of human cortical development28, we could also classify them according to their resemblance to normal cell types in the human brain, e.g., oligodendrocytes or astrocytes (Supplementary Fig.\u00a04B). As a positive control of our regulatory predictions, we confirmed modules corresponding to the cell cycle programs G1/S and G2/M, predicted to be regulated by known cell cycle markers such as E2F1 and TK1 (G1/S), and AURKA/B (G2/M).\n\nA Heatmap of the regulatory landscape, with rows representing regulators (transcription factors and kinases) and columns representing gene modules, indicating sample origin. B Overlap between module gene content and cell state or invasion route signatures. C\u2013E Barplots displaying regulators selective for growth condition, patient, and invasion route. One-way ANOVA tests were used to assess the effects of growth condition, patient origin, and invasion route. All ANOVA tests were two-sided with no corrections applied for multiple comparisons. F MA plot of differentially expressed genes, with labeled regulators from (A) with an absolute log2 fold greater than 0.5. Pv stands for perivascular invasion and Diff stands for diffuse invasion. The underlying data in (A\u2013F) comprises scRNAseq runs of n\u2009=\u20091 sample of in vitro cultured cells, n\u2009=\u20092 independent samples of in vivo PDCX-isolated tumor cells (from different mouse individuals) for each of the 6 GBM lines, except U3031MG, and U3179MG, which were run as n\u2009=\u20091 sample of in vitro cultured cells, n\u2009=\u20091 in vivo sample of PDCX-derived cells. The full data comprises a total of 119,766 single-cell transcriptomes) Source data are provided as a source data file.\n\nWe used one-way ANOVA tests to analyze how the regulators of gene modules were influenced by factors such as growth conditions (in vitro vs. in vivo) (Fig.\u00a03C), patient source (Fig.\u00a03D), or invasion routes (Fig.\u00a03E). Subsequently, we conducted a differential gene expression analysis between the perivascular and diffuse invasion routes to identify genes strongly associated with each route (Fig.\u00a03F).\n\nOur analysis identified a total of 53 regulators associated with invasion routes: 36 linked to growth condition or source patient, and an additional 17 with differential expression between perivascular and diffuse invasion (padj\u2009<\u20090.01, and differential expression log2 fold change\u2009>\u20090.5). Key regulators attributed to the perivascular route were ANXA1 and ANXA2, two members of the annexin family, that play roles in inflammation and apoptosis29. Regulators for the diffuse invasion route included HOPX, CKB, RFX4, and OLIG1. HOPX, a homeodomain-containing transcription factor, is involved in stem cell maintenance30. CKB, an enzyme, regulates cellular energy homeostasis and is linked to cancer31. RFX4, a transcription factor recently identified as sufficient for the directed differentiation of CNS cell types from embryonic stem cells32, OLIG1, essential for oligodendrocyte differentiation, contributes to central nervous system myelination33. Finally, for the leptomeningeal invasion route, HMGA1 and PRRX1 appeared as selective regulators. HMGA1, a chromatin-binding protein, is implicated in transcriptional regulation and cancer progression34. PRRX1, a transcription factor, contributes to embryonic development and cancer invasiveness35. IFI16 and HBGEF are growth condition selective regulators, suggesting that these genes might be explored as markers of GBM cells responding to the tumor microenvironment in future independent work. Intriguingly, the transcription factor MITF and some of its known targets (DCT, MLANA, PLT1, and S100A1) - genes implicated in melanogenesis\u2014were detected as a module active in bulk and perivascular invading cells. Moreover, JUND, PDGFRA, and SCX exhibited a high degree of patient selectivity, suggesting that these genes might have applications as robust biomarkers of inter-tumoral variation.\n\nIn summary, Scregclust identified a concise set of genes with a possible upstream role in determining cell states associated with GBM invasion. To substantiate our findings, we compiled a shorter list of promising regulators to move forward with and validate at the protein level, as discussed next.\n\nOur next objective was to validate the candidate regulator genes at the protein level by assessing their expression in different regions of the mouse GBM xenografts. For this, we combined 6-plex multi immunofluorescence staining with computational image segmentation to measure the expression of each protein in different spatial contexts (Fig.\u00a04A, Supplementary Data\u00a03, and Supplementary Fig.\u00a05). To identify such contexts, we co-stained each protein of interest with markers for tumor cells (anti-human STEM121/NCL), blood vessels (CD31), and white matter (MBP). Utilizing morphological criteria and image k-means clustering, we segmented each slide into 9 different spatial compartments: high-density tumor (1), medium-density tumor (2), low-density tumor (3), circle-shaped aggregates (4), tumor cells growing in close proximity to vasculature (5), diffusively growing cells in the corpus callosum (6), diffusely-growing elongated tumor cells (7), mouse endothelial cells (8) and the mouse brain parenchyma (9) (Fig.\u00a04B). As positive controls, we observed that CD31 produced a selective signal in the vascular spatial compartment (number 8), whereas STEM121/NCL was selective for all tumor-containing spatial compartments (Fig.\u00a04C). Furthermore, in support of our computational segmentation, we confirmed that it accurately scored the relative abundance of different spatial compartments (e.g., the amount of dense tumor or perivascular cells), consistent with the manually observed phenotype of each PDCX model, grouping the cultures into dense/perivascular and diffuse clusters, respectively (Fig.\u00a04D and Supplementary Fig.\u00a06).\n\nA Multispectral IHC of U3054MG PDCX, with example staining of STEM121 in black, ANXA1 in brown, CD31 in red, MKI67 in yellow, and MBP in blue. Representative section from a total of n\u2009=\u200910 independent mouse replicates injected with U3054MG. B We segmented scans into 9 compartments (high-density tumor (1), medium-density tumor (2), low-density tumor (3), circle-shaped aggregates (4), tumor cells growing within close proximity to the vasculature (5), diffusely-growing elongated tumor cells in the corpus callosum (6), other diffusely invading cells (7), blood vessels (8), and mouse brain parenchyma (9). Created in BioRender. Nelander, S. (2025) https://BioRender.com/lpyogrt. C Scoring all PDCX models using 35 antibodies; upregulated and downregulated expression of proteins in named compartments for perivascular and diffusively invading cells. (Sections from n\u2009=\u20092 independent biological replicates (individual mice) were stained for each antibody, for each of the 6 cell lines). D Relative area of segmented compartments per PDCX cell line (n\u2009=\u20093 independent biological replicates (individual mice). E Volcano plot indicating key differentially expressed proteins. The log10 p-values are obtained from a two-sided heteroscedastic t-test, not adjusted for multiple comparisons (Sections from n\u2009=\u20092 independent biological replicates (individual mice) were stained for each antibody, for each of the 6 cell lines). Source data are provided as a source data file.\n\nAnalysis of all 6 PDCX models (n\u2009=\u2009240 scans) showed that perivascular invading GBM cells exhibited higher expression of ANXA1 and CAV1 protein in their perivascular compartments (numbers 4 and 5) compared to diffusely invading cells (Fig.\u00a04E). In line with the neurodevelopmental transcriptional phenotype observed for the diffusely invading cell lines, they displayed a relatively higher abundance of RFX4, AQP4, and HOPX (number 7). Notably, OLIG2 protein was enriched in elongated cells in sparse areas of the tumor, identifying it as a marker of cells that individually penetrate the brain parenchyma (Fig.\u00a04C).\n\nThese findings underscore the heterogeneity of protein expression in GBM and further support ANXA1 protein as a marker associated with perivascular localization and dense growth patterns, and HOPX and RFX4 as candidate protein markers for diffuse route-invading GBM.\n\nTo assess the translational value of our laboratory findings, we investigated potential regulator expressions in human tissue microarray (TMA) samples from the HGCC biobank (n\u2009=\u2009148) (Supplementary Fig.\u00a07 and Supplementary Data\u00a06). Given the strong correlation of invasion routes with ANXA1, HOPX, and RFX4, these markers were chosen. Samples showed high expression of ANXA1 in cells surrounding blood vessels, whereas cells with HOPX expression were found away from the blood vessels, which is in accordance with our PDCX data (Fig.\u00a05A). RFX4 expression was present in both normal brain tissue and the tumor core area. Next, we asked whether these markers were associated with patient survival. Cox regression analysis (or multivariate survival analysis) with age, sex, and subtype as covariates revealed that ANXA1 protein expression (observed as the fraction of ANXA1-positive cells) had a slight association with worse survival (HR\u2009=\u20091.011, 95% confidence interval\u2009=\u2009[1.003,1.020], p\u2009=\u20090.00802), while a high fraction of RFX4 protein positive cells was associated with worse survival (HR\u2009=\u20091.021, 95% confidence interval\u2009=\u2009[1.005,1.037], p\u2009=\u20090.00856). No association between HOPX protein expression and survival was found (Fig.\u00a05B). Extending the set of covariates further with individual key mutations (c.f. Fig.\u00a01E) did not substantially affect these trends (Supplementary Data\u00a04).\n\nA Human tissue microarray (TMA) staining of the tumor core, including patients U3013MG, U3180MG, and healthy brain tissue from HGCC. Staining includes CD31 in red, ANXA1 in brown, HOPX in blue, and RFX4 in cyan. The upper panel scale bar indicates 100\u2009\u03bcm, while the lower panel scale bar is 20\u2009\u03bcm. The stainings were repeated twice. Representative images chosen from n\u2009=\u20094 TMA cores from each patient. B Multivariate survival analysis using Cox regression on survival data from the HGCC, with age, sex, and transcriptional subtype as covariates, indicate associations between high ANXA1 protein (measured as the fraction of ANXA1-positive cells) and shorter survival, and between high RFX4 protein (measured as the fraction of RFX4-positive cells) and shorter survival. HR Hazard ratios, CI confidence intervals, and p-values are indicated in the figure. Two-sided test; no corrections for multiple comparisons were made. C Staining of the tumor core and D edge from three patients from BrainUK. Staining includes CD31 in red, ANXA1 in brown, HOPX in blue, RFX4 in cyan, and DAPI in black. The scale bar indicates 50\u2009\u03bcm. The stainings were performed once. Representative images from n\u2009=\u20091 tumor sample section from each patient, and 7 neuropathologist-inspected fields per section. E ANXA1 and HOPX proteins are selectively found in perivascular and diffuse regions in BrainUK samples, as determined by a two-sided z-test for proportions, assessing differences in marker expression between perivascular and diffusely invading GBM cells. Asterisks indicate statistical significance: p\u2009=\u20090.0098 (*), p\u2009=\u20090.0012 (**). N\u2009=\u200914 patients, all listed in the table. No corrections were applied for multiple comparisons. (N/A means that this type of invasion was absent in the sample). Source data are provided as a source data file.\n\nAlthough extensive, the HGCC biobank consists of samples of mostly European ancestry patients from a single hospital, and the tumor samples are from an unannotated core region. Therefore, to avoid bias from a single cohort, we also investigated patient tumor samples from an independent cohort, the Queen Square/NHNN repository (ethical approval was obtained via BrainUK, ref:21/014). Also, in this cohort, ANXA1 expression was observed localized to tumor cells near blood vessels, both within the tumor core and outside the tumor bulk. Since HOPX is also expressed in normal brain tissue, the distinction of its expression in the tumor core or border region is more challenging. Nevertheless, HOPX was expressed in neurons and glial cells, reflecting our PDCX findings. RFX4 expression was found in normal healthy brain tissue and scattered in the tumor core in some patient cases (Fig.\u00a05C, D). Importantly, the expressions of ANXA1 and HOPX were found to be invasion route specific and not patient-specific also within this cohort (Fig.\u00a05E).\n\nANXA1 has been investigated before in different cancer types29. In gliomas, ANXA1 has been shown to play a role in glioma progression36, to be present in the immune microenvironment and to be correlated with survival and metastasis potential37. Less, however, is known about this protein\u2019s role in perivascular invasion in GBM. HOPX plays a critical role during normal development and is strongly expressed in radial astrocyte stem cells38 and outer radial glial-like cells26. RFX4 functions as a transcription factor and may serve as a potential marker of GBM stem cells39, with increased expression observed in gliomas40 and implicated in astrocyte differentiation in cell models41,42. Furthermore, it correlated with poor GBM prognosis39. Our confirmation of these markers in two independent patient sample cohorts underscores their value in delineating cell populations potentially driving distinct types of invasion in GBM.\n\nNext, we sought to evaluate the impact of ANXA1, HOPX, and RFX4 on GBM cell invasion and survival. ANXA1, the predicted regulator of perivascular invasion, and HOPX and RFX4, the predicted regulators of diffuse invasion, were knocked out (KO) with CRISPR/Cas9 in U3013MG and U3180MG, respectively. These two cell lines were chosen due to their capacity for lentiviral modification. Cells were transduced with scramble (SCR) guide RNAs as controls. The KO was confirmed by PCR and sequencing of the flanked region, and by loss of protein expression for the markers expressed in vitro. Cell identity was confirmed with STR profiling (Supplementary Figs.\u00a08 and 9). Before injecting the cells into mice, we conducted proliferation and self-renewal assays in vitro to ensure that ANXA1-KO, HOPX-KO, and RFX4-KO cells exhibited no discernible advantages in growth or tumor-forming capabilities (Supplementary Fig.\u00a010).\n\nWe orthotopically injected nude mice with ANXA1-KO U3013MG cells, HOPX-KO U3180MG cells, and RFX4-KO U3180MG cells, along with corresponding SCR control. We then assessed survival, pathology, and gene and protein expression changes.\n\nMice grafted with ANXA1-KO U3013MG cells showed significantly extended median survival time (Fig.\u00a06A, p-value\u2009<\u20090.0001) as compared to SCR control. To further analyze the impact of ANXA1-KO, we evaluated the brain of the xenografted mice histologically. We observed that ANXA1-KO U3013MG tumors did not form a bulk tumor as SCR-U3013MG and U3013MG-WT did (Fig.\u00a06D, E). Specifically, the high-density tumor (1), medium-density tumor (2) areas abundance was decreased in ANXA1-KO, as well as a decrease of tumor cells growing within close proximity of the vasculature (5). ANXA1-KO cells had a higher tendency to grow as a low-density tumor (3), and their morphology shifted from cell aggregates (4) to more diffusely growing elongated tumor cells (7) (Fig.\u00a06D, E, I). In summary, the absence of ANXA1 in tumor cells reduced tumor bulk formation and significantly reduced association with vascular structures, with tumor cells shifting toward a more diffusely infiltrative phenotype. We did not observe significant changes in the number of proliferating cells compared to SCR controls (Fig.\u00a06L).\n\nA, B Mouse survival for ANXA1-KO U3013MG (n\u2009=\u200910 mice), HOPX-KO U3180MG (n\u2009=\u200910), and RFX4-KO U3180MG (n\u2009=\u200910). C Automated segmentation into 8 compartments. Created in BioRender. Nelander, S. (2025) https://BioRender.com/lpyogrt. D\u2013H Whole brain scans and staining for each genotype. n\u2009=\u20093 brains were analysed per group and the stainings were repeated four times. I\u2013K Change in compartment area for each PDCX-KO compared to SCR control. In (I, J), N\u2009=\u20094 independent replicate mice were used, and in (J), N\u2009=\u20093 independent replicate mice were used. Each mouse is shown as a point. Error bars are 90% confidence intervals obtained from a two-sided t-test, based on independent mouse replicates. L Percentage of KI67+ cells in each genotype (n\u2009=\u20094 independent biological replicates (individual mice) in each group were used in the ANXA1 knockout vs control comparison, and n\u2009=\u20093 independent biological replicates (individual mice), were used in the RFX4 and HOPX knockout vs control comparison. Points represent individual mice, the distribution represents all counted fields in all mice. * indicates two-sided t test, p\u2009=\u20090.0375, calculated for the mouse independent replicates). M Percentage of stellate cells in each genotype. (n\u2009=\u20094 independent biological replicates, i.e., individual mice, in each group were used in the ANXA1 knockout vs control comparison, and n\u2009=\u20093 independent biological replicates, i.e., individual mice, were used in the RFX4 and HOPX knockout vs control comparison. Points represent individual mice, the distribution represents all counted fields in all mice; * indicates two-sided t test, p\u2009=\u20090.0139, calculated for the mouse independent replicates). Source data are provided as a source data file.\n\nIn the diffusely growing U3180MG xenografts, targeting of either HOPX or RFX4 prolonged survival, decreased tumor cell density, and (in the case of RFX4) led to altered morphology of the tumor cells. The KO of HOPX in U3180MG increased median survival (Fig.\u00a06B, p-value\u2009=\u20090.0002) and these tumors appeared less aggressive than the control, as judged by reduction of tumor density (Fig.\u00a06J). We saw no obvious phenotypic change of the tumor cells, except a possible increase in individual tumor cells making contact with blood vessels (Fig.\u00a06G, J). The KO of RFX4 also increased median survival time significantly (Fig.\u00a06B, p-value\u2009<\u20090.0001). The RFX4-KO PDCX showed a marked reduction of tumor cells density (Fig.\u00a06H, K). Additionally, a notable number of invading cells, often seen in the striatum, had a stellate phenotype, reminiscent of lower grade glioma (Fig.\u00a06H, M).\n\nTo broaden our understanding of the dynamics underlying the invasion phenotypes, particularly in the case of ANXA1, we performed real-time analyses comparing GFP-tagged U3013MG cells with wild-type ANXA1 versus ANXA1se knockout (KO) cells across three complementary experimental systems (co-culture, zebrafish xenografts, and mouse brain slice grafts). In mouse brain slice assays, time-lapse confocal microscopy revealed significantly reduced migration of ANXA1-KO U3013MG cells along blood vessels compared to wild-type U3013MG cells, as quantified by single-cell tracking analyses (Supplementary Fig.\u00a011 and Supplementary Movie\u00a01). Consistently, in zebrafish xenografts, ANXA1-KO cells exhibited a pronounced tendency toward diffuse dispersion, whereas wild-type cells preferentially co-localized with vessels and demonstrated collective migration along these structures (Supplementary Fig.\u00a012 and Supplementary Movies\u00a02, 3, and 4). Similar observations were made in the co-culture system, where ANXA1-KO cells displayed markedly reduced interaction and adhesion to endothelial vessels (Supplementary Fig.\u00a013). To assess the role of ANXA1 further, we overexpressed ANXA1 in U3013-ANXA1-KO and U3180MG cells. In the co-culture system, we saw that the ANXA1-KO phenotype was recovered and vascular association restored (Supplementary Fig.\u00a013). Furthermore, when we overexpressed ANXA1 in U3180MG cells, we saw that the diffuse growth phenotype was abolished in zebrafish xenografts. Cells instead formed a bulk (Supplementary Fig.\u00a012 and Supplementary Movie\u00a05). Additionally, an in vitro collagen sphere invasion assay comparing ANXA1-wild-type and ANXA1-KO U3013MG lines supported ANXA1\u2019s involvement in regulating invasive behaviors, with knockout cells exhibiting significantly reduced invasive potential (Supplementary Fig.\u00a010). Taken together, these complementary dynamic analyses support a role for ANXA1 in promoting dynamic tumor cell association with blood vessels.\n\nTo understand the mechanisms underlying the altered growth and invasion phenotypes following KO interventions, we performed single-cell profiling of ANXA1-KO, RFX4-KO, and HOPX-KO tumor cells extracted from mouse brains. Cells from the diffusively invading ANXA1-KO tumors exhibited a transition from the MES- and OPC-like states observed in control ANXA1-WT tumor cells, favoring NPC- and AC-like states (Fig.\u00a07A, B). This trend toward astrocytic differentiation was further supported by differential expression analysis and gene set enrichment analysis (Fig.\u00a07C). Additionally, we observed an upregulation of GAP43, a marker of regenerating neurons and reactive glial cells suggested to play a role in GBM invasion43. Anecdotally, the transcription factor MITF and some of its known targets (DCT, MLANA, PLT1, and S100A1)\u2014genes implicated in melanogenesis\u2014were downregulated upon ANXA1 loss (Supplementary Data\u00a05 and Supplementary Fig.\u00a014).\n\nA\u2013C Comparison of ANXA1-KO U3013MG cells with wild-type shows a shift towards NPC-like and AC-like differentiation. (scRNAseq data from n\u2009=\u20092 pools of tumor cells isolated from PDCX brains, total of 12069 cells). D\u2013F Shift of cell state distribution in RFX4-KO U3180MG cells, towards an NPC-like, low-proliferating state. (scRNAseq data from n\u2009=\u20092 pools of cells isolated from PDCX brains, total of 10824 cells). G\u2013I Shift of cell state distribution in HOPX-KO U3180MG cells towards MES-like state. (scRNAseq data from n\u2009=\u20092 pools of cells isolated from PDCX brains, total of 13270 cells).\n\nIn contrast, knockout of RFX4 in U3180MG xenografts significantly shifted cells toward NPC-like and OPC-like states, with gene signatures enriched for neuronal differentiation (Fig.\u00a07D\u2013F). Additionally, RFX4-KO reduced the proportion of AC-like and MES-like states, alongside suppression of proliferation-related pathways. This decrease in proliferative cell populations corresponds with lower tumor density (Fig.\u00a06K) and reduced KI67 positivity (Fig.\u00a06L). Interestingly, RFX4-KO also resulted in decreased HOPX expression, suggesting regulatory interdependence between these transcription factors (Supplementary Fig.\u00a014). The HOPX knockout in U3180MG cells prompted a notable transition toward MES-like states, accompanied by decreased activation of developmental and proneural signatures (Fig.\u00a07G\u2013I). Although fewer cells adopted an AC-like phenotype after HOPX-KO, this shift was insufficient to fully eliminate astrocytic characteristics, potentially explaining the continued diffuse invasion behavior (Fig.\u00a07G). The transition to a MES-like state was supported by increased vascular association observed histologically (Fig.\u00a06G, J) and confirmed in co-culture assays showing enhanced endothelial interactions compared to controls (Supplementary Fig.\u00a013). Together, these findings establish ANXA1 as a crucial factor maintaining MES-like states linked to perivascular invasion and identify RFX4 and HOPX as critical regulators of proliferation and differentiation states in diffusely invading GBM cells. To further explore these regulatory dynamics, we compared knockout-induced transcriptional changes to an atlas of human early brain development by Eze et al.44. Mapping ANXA1-KO cells onto this developmental atlas revealed significant enrichment for radial glial-like phenotypes, marked by genes including SOX9 and PAX6, with a concomitant gain of neuronal-like phenotypes. Cells with RFX4-KO predominantly exhibited upregulation of neuronal signatures, marked by e.g., MAP2 and TUBB3. The results for HOPX-KO were more complex, highlighting enrichment for neuroepithelial clusters characterized by mesenchymal genes such as ANXA2 and ID3, indicating a partial mesenchymal transition (Supplementary Fig.\u00a015). In conclusion, knocking out ANXA1 prompted GBM cells to adopt diffuse invasion accompanied by astrocytic differentiation. In contrast, while RFX4 and HOPX knockouts also retained diffuse invasion, they distinctly altered the transcriptional landscape, affecting proliferation, differentiation, and mesenchymal traits. These findings highlight potential therapeutic implications, emphasizing the plasticity of MES-like states and the robustness of AC-like states, which could inform strategies targeting invasive GBM populations.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61999-1/MediaObjects/41467_2025_61999_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Extensive invasion, a hallmark characteristic of GBM, contributes to poor prognosis, patient mortality, and relapse. While various invasion routes exist, such as perivascular, diffuse infiltration, or perineuronal satellitosis, the underlying mechanisms remain elusive. Our results provide several pieces to the puzzle of brain tumor progression. Upon injecting patient-derived cells into the mouse brain, distinct known invasion patterns emerge that correlate with specific transcriptional states: MES-like cells exhibit perivascular invasion, while AC-like and NPC-like cells display diffuse invasion. Using a data-driven modeling strategy, we predicted possible regulators of these states, which were validated in patient samples, in vivo experiments, and in-depth molecular profiling.\n\nIn this study, ANXA1 emerged as strongly associated with perivascular growth patterns in GBM. Knocking out ANXA1 in perivascular invading cells induced notable phenotypic shifts, including the loss of tumor bulk and perivascular involvement, while acquiring an AC-like cell state and diffuse invasion, ultimately leading to increased median survival in mice. This observation suggests that the ANXA1+ perivascular invading phenotype potentially drives a reactive cell state, possibly linked to genes associated with injury response45. Furthermore, the over-expression of ANXA1 in diffusely invading U3180MG cells caused bulk formation in zebrafish xenografts emphasizing the importance of ANXA1 in forming bulky tumors. Mechanistically, ANXA1 plays roles in inflammation and tumor cell migration46. Cleavage of ANXA1 protein at the cell membrane generates a ligand for formyl peptide receptors, a class of G protein-coupled receptors involved in cell movement. Notably, targeting ANXA1 increases radiosensitivity in GBM cell lines36 and is expressed downstream of the ephrin B2 receptor (EFBN2), which is implicated in mouse (G26) models of perivascular invasion14. In our data, EFBN2 was not selected by scregclust as a regulator because of its low expression in the human-derived PDCX models. The microenvironment, particularly the perivascular niche, significantly influences phenotypic expression, augmenting the perivascular invading phenotype and enriching proteins linked to mesenchymal transformation47,48. Our results appear consistent with the oncostreams phenotype49, described as the collective invasion of COL1A1-positive tumor cells with mesenchymal properties. We propose that targeting ANXA1 may offer a strategy to suppress COL1A1-positive oncostreams in GBM, possibly with enhanced selectivity compared to targeting collagen 1 directly (since ANXA1 is less abundant in the normal brain than COL1A1). In epithelial cancers, epithelial-to-mesenchymal transition (EMT) has been linked to growth along blood vessels50. In accordance, we found intriguing overlaps between previously described regulators of EMT and genes detected in ANXA1-positive cells, including TCF447,48, and S100A1051. Future research endeavors include delineating regulatory dependencies and exploring the efficacy of targeting ANXA1 with small peptides52 or exploiting it as a surface antigen for perivascular invading GBM cells. The association of NPC-like and AC-like cells to diffuse growth found in this work aligns well with the phenotype of non-malignant NPCs and astrocytes. Astrocytic and neural precursor migration is an integral part of brain development and injury response53. In response to injury, astrocytes transition from a quiescent to a migratory state, contributing to tissue repair and neuronal survival45.\n\nIn contrast to the absolute loss of perivascular invasion and bulk formation upon ANXA1 ablation, targeting the predicted diffuse invasion drivers HOPX and RFX4 did not result in a complete loss of the phenotype in question, but rather a more complex shift in cell state linked to reduced proliferation and extended median survival in mice. The difference between the intervention experiments may point to the AC as a more robust cell state, consistent with what Schmitt et al. observed, that MES-like cells are more sensitive to reprogramming cues than other GBM states, which are more \u201chardwired\u201d54. Knockout of RFX4 suppressed AC-like transcriptional signatures and protein expression of GFAP, together with a higher expression of NPC-like signatures employing a more progenitor profile. RFX4 drives the maturation of neural stem cells and neural structures55,56, and our results point to a possible role in promoting AC-like states and growth in GBM. The presence of stellate cells in the RFX4 knockout brains is intriguing and may point to a particular subpopulation that will require further investigation. Our knockout results point to HOPX as being downregulated upon RFX4 targeting, potentially suggesting partially a shared mechanism between these two interventions on GBM invasion and growth. U3180 cells were pushed towards the MES-like state upon HOPX knockout. This was accompanied with an increase of vascular association in both in vitro (Supplementary Fig.\u00a013) and in vivo (Fig.\u00a06). When projected on the atlas of the developing brain, HOPX knockout cells were enriched for a neuroepithelial cluster (Supplementary Fig.\u00a015). HOPX has been implicated in suppressing EMT in another cancer model before ref. 57. We have not investigated the exact mechanism underlying this mesenchymal shift however, all our findings support the connection between mesenchymal phenotypes and vessel association.\n\nEach of the ANXA1, RFX4, and HOPX-KOs extended mouse survival times. We propose that the extended survival observed in mice grafted with ANXA1-KO cells is attributable to the absence of tumor bulk growth and perivascular invasion and the subsequent shift towards diffuse invasion. In these mice, the tumor cells appear more integrated into the brain tissue without forming a bulky mass that exerts pressure. As for the mice lacking HOPX and RFX4, although the exact mechanism is less clear, it\u2019s probable that the reduction in actively cycling cells contributes to their increased survival. While it\u2019s premature to extrapolate these findings directly to human patients, the correlation with improved survival outcomes suggests a potential clinical relevance. In the present cohort, we noted an association between RFX4 protein expression and shorter survival in unselected GBM patients, also after correcting for age, sex, and transcriptional subtype. RFX4 was also associated with survival within the subgroup of patients with a diffuse growth phenotype in mice (Supplementary Data\u00a04). These findings may warrant validation in larger, independent patient cohorts.\n\nOur results extend and complement previous studies aimed at relating cell differentiation to invasive growth in GBM. Firstly, Brooks et al. proposed a model wherein oligodendrocytic differentiation, dependent on SOX10, is observed among cells invading axonally in white matter tracts21. While the white matter invading phenotype was not a main focus of this study, our scregclust analysis detected a cluster expressing oligodendrocytic markers, present in two of the cell cultures that we have characterized as bulky and perivascular. While oligodendrocytic protein markers were expressed in these PDCXs, we did not, however, see a specific expression of these markers in white matter-located cells (Supplementary Fig.\u00a016). Secondly, Venkataramani et al. suggested that diffuse invasion is primarily driven by OPC-like and NPC-like cells17. Further examination revealed that a significant portion of diffusively invading unconnected cells consists of AC-like and NPC-like cells, supporting our observation that these cells utilize a diffuse invasion route. Lastly, Varn et al. identified two distinct GBM recurrence phenotypes: one characterized as neuronal and the other as mesenchymal, both linked with invasiveness16. This further supports both a mesenchymal mode of invasion and a neuronal mode of migration for the invasive GBM cells remaining in the normal brain parenchyma. Further work is needed to refine the nomenclature around cell states and invasion routes in GBM, and the association between AC-like cells and diffuse growth consistently observed across our three diffusely growing PDCX models extends previous observations. Towards this goal, studying GBM invasion across a larger clinical repertoire will be crucial. This would, for instance, open for robust statistical associations between tumor genetic and epigenomic features and their morphological presentation.\n\nPDCX models must be used with an awareness of potential limitations. While the models recapitulate key invasion phenotypes observed in glioblastoma, they do not fully capture the clinical context of human disease. In particular, patients typically undergo surgical resection, radiotherapy, and mount adaptive immune responses against the tumor\u2014factors absent in our immunocompromised mouse models. These differences likely contribute to the observed discrepancies in survival patterns between mice and patients, and we have interpreted our findings with these limitations in mind.\n\nMethodologically, our study introduces a framework for uncovering invasive cell states and their regulators. In this study, we employ scregclust to identify key gene regulators implicated in perivascular or diffuse invasion, leveraging scRNA-seq data. Subsequently, we validate the protein expression of these regulators within the invasion niche of patient samples from two independent cohorts using multiplex immunofluorescence staining. Upon perturbation of a potential regulator in the invasion route of interest, we observe significant alterations in both RNA and protein expression, impacting the invasion route, migratory behavior, and morphology of these cells in vivo. Just like the observed transcriptional states are much more pronounced in the brain environment compared to adherent cultures, the effect of gene targeting is more pronounced in vivo than in vitro. It thus appears crucial to anchor the discovery of regulators of invasion in sufficiently complex models that recapitulate at least crucial parts of the brain environment. We acknowledge that our immunodeficient mouse models lack central aspects, which makes it important to validate the discovered functional biomarkers in independent patient materials, as was done here.\n\nTaken together, this work presents a scalable approach to uncover critical genes that underlie specific cell states linked to brain tumor invasion. Looking ahead, it will be important to extend investigations to larger clinical repertoires, and to leverage our understanding of invasion regulators to interfere with these processes in a tailored manner. We reserve this for future work.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Patient-derived glioblastoma cell lines were established from tumor tissue as previously explained (ref. 23). All samples were collected with the informed consent of the patients, and the collection was approved by the Uppsala Regional Ethical Board, under number 2007/353. The cells were seeded on 1% laminin-coated flasks and maintained in serum-free neural stem cell medium with B-27 and N2 supplements, as well as EGF and FGF growth factors. The experiments adhered to the principles outlined in the WMA Declaration of Helsinki and the Department of Health and Human Services Belmont Report.\n\nAll mouse experiments were conducted in strict accordance with an ethical permit granted by the Uppsala Animal Research Ethical Board, bearing reference numbers C41/14 and 5.8.18-06726/2020. Female NMRI nude (NMRI-Foxn1 nu/nu) mice were procured from Janvier Labs, while Hsd:Athymic nude-Foxn1 mice were procured from Envigo. Mice falling within the age range of 6 to 9 weeks were selected for the experiments. They were housed in individually ventilated cages, with each cage accommodating up to 5 mice. Appropriate housing enrichment, bedding material, food, and drinking water were provided ad libitum, and the mice were maintained on a 12/12-h light cycle. Human glioma cell cultures demonstrating verified tumor growth and the desired phenotype were systematically labeled with a lentivirus expressing GFP-Luciferase to enable subsequent tracking. All cell lines were STR profiled before injections to confirm their genetic identity (Eurofins Genomics). Orthotopic tumor injections were carried out by transplanting 100,000 labeled cells into the striatum of each mouse. The mice were monitored using in vivo bioluminescence imaging (luciferase monitoring) and regular weight measurements for up to 40 weeks post-injection. Humane endpoints were defined in accordance with approved animal ethics protocols and were used to minimize suffering. Mice were euthanized if they exhibited signs of significant weight loss (exceeding 15% of peak body weight maintained for more than one week), hunched posture, reduced activity (e.g., burrowing, social withdrawal), mild piloerection, or the onset of neurological symptoms such as incoordination or crouching. Additionally, body scoring was applied, with a termination threshold set on a pre-defined assessment scale. In cases where mice were monitored by luciferase imaging, elevated luminescent signal indicating progressive tumor growth also served as a humane endpoint, even in the absence of clinical symptoms. All mice were closely observed every 3\u20134 days, with additional veterinary consultation if the animals\u2019 conditions were unclear. Upon reaching the defined scientific or humane endpoints, mice were euthanized, and brains were harvested for histological or cellular analysis.\n\nFor histology, mouse brains were processed in an automated tissue processor under the following conditions: 1\u2009h 70% EtOH, 2 \u00d7 1\u2009h 96% EtOH, 3 \u00d7 1\u2009h 100% EtOH, 2 \u00d7 1\u2009h Xylene, and 3 \u00d7 1\u2009h Paraffin at 60\u2009\u00b0C. The paraffin-embedded brains were then sectioned into 5\u2009\u03bcm slides. Each block was analyzed for protein expression using a standard IHC protocol. In brief, after deparaffinization, antigen retrieval using Antigen Unmasking Solution Citrate-Based pH 6 (Vector Laboratories #H-3300) with 0.05% Tween 20 (Biorad #1610781) was commenced in 2100 Antigen Retriever for 15\u2009min followed by cooling down to room temperature. Then sections were incubated in 3% H2O2 (30% H2O2 (Thermo Scientific #10687022) diluted in TBS) for 10\u2009min, followed by washes with TBS-T (washing buffer). The sections were blocked with Normal Antibody Diluent (ImmunoLogic WellMed #UD09) for 30\u2009min at room temperature, and primary antibodies STEM121 (1:500) (Takara #Y40410), ANXA1 (1:400) (CST, #32934S), RFX4 (1:500) (HPA #050527), and HOPX (1:1000) diluted in Normal Antibody Diluent were applied and incubated for 60\u2009min at RT. We used BrightVision, a 1-step detection system, Goat Anti-Rabbit HRP (WellMed #DPVR110HRP), and anti-Mouse HRP (WellMed #DPVM110HRP) detection systems followed by incubation with Bright-DAB substrate kit (WellMed #KBS04-110). Slides were then counterstained with Myers\u2019 Hematoxylin and permanently mounted with Pertex (HistoLab #00811).\n\nMultiplex staining was performed using the Opal 6-Plex Manual Detection kit (Akoya Biosciences, NEL861001KT). Procedures were conducted according to the protocol with a deviation, where anti-mouse HRP (Immunologic #DPVM110HRP) and anti-rabbit HRP (Immunologic #DPVR110HRP) were used for antibody detection instead. Antibodies were stripped after each Opal incubation using the microwave, and the procedure was repeated for the next primary antibody-Opal pairing. Every antibody-Opal pairing was independently validated as per the manufacturer\u2019s instructions. The validated antibody-Opal pairings are available in Supplementary Data\u00a03. Slides were mounted with ProLong Diamond Antifade Mountant (ThermoFisher #P36970), imaged using the PhenoImager whole slide workflow, and unmixed using InForm 4.8 (Akoya Biosciences) software.\n\nUpon the experimental endpoint, mouse brains were harvested into cold HBSS buffer containing 1% Pen/Strep, 0.6% glucose, and 25\u2009nM HEPES. Then, the brain was sliced using a 1\u2009mm coronal section matrix, cut into about 1\u20132\u2009mm pieces using a surgical blade, and dissociated into single cells using the Tumor Dissociation kit, human (Miltenyi, #130-095-929), used in combination with the Mouse Cell Depletion Kit (Miltenyi, #130-104-694) according to the manufacturer\u2019s protocols. Red blood cells were removed using the Red Blood Cell Lysis Solution (Miltenyi, #130-094-183).\n\nThe generation of single-cell RNA sequencing libraries followed the manufacturer\u2019s guidelines, utilizing the Chromium Single Cell 3\u2032 Library and Gel Bead Kit v2, v3, and v3.1 (analysis of KO cells) (10\u00d7 Genomics, Pleasanton, CA). Cryo-preserved cells underwent washing and re-suspension in 0.1% BSA in PBS just before loading onto a Chromium Single Cell B Chip (10\u00d7 Genomics) with the aim of capturing 10,000 cells. Subsequently, the quality of the libraries was assessed using Agilent High Sensitivity DNA Kit and Agilent Bioanalyzer 2100 DNA Kit (Agilent Technologies). Libraries were sequenced on an Illumina NovaSeq 6000 with the sequencing configurations recommended by 10\u00d7 Genomics. Demultiplexing, counting, and alignment to the human (GRCh38) reference genome were performed using Cell Ranger 3.0.2 (10\u00d7 Genomics).\n\nWe profiled a total of 19 samples. Each of the cell lines U3013MG, U3180MG, and U3220MG were run as one in vitro sample, and two in vivo samples (from different mice). U3031MG, and U3179MG were run as a single in vitro sample. U3054MG was run as two replicate in vitro samples and four replicate in vivo samples (from different mice).\n\nWe performed single-cell analysis using the Seurat package (v. 4) (Butler et al., 2018). We filtered out cells expressing fewer than 500 genes and genes that were expressed by fewer than 10 cells. We filtered out potential doublets by setting nFeature_RNA parameters at greater than 7200 for v3 of the kit and greater than 5100 for the v2 kit. We removed low-quality cells that contained more than 30% mitochondrial genes, resulting in 110,458 cells retrieved (85.6% of the original population). We also removed highly expressed genes that are not related to the study, such as abundant ribosomal, mitochondrial, and hemoglobin genes. Lastly, to mitigate the effect of the cell cycle on cell groupings, we assigned each cell scores based on gene markers for the S- and G2/M-phases, and the difference between these scores was regressed out, as suggested by ref. 58. Then, we used the reciprocal PCA method to integrate the data and clustered cells using the Louvain algorithm with multilevel refinement. We used a range of resolutions from 0.01 to 1 to unravel cell subpopulations, and based on a directed graph calculated using the Clustree (v. 0.5.0) package to assess cluster separation, we continued with resolution 0.3, which grouped the cells into 21 subpopulations. Note that batch integrated data was only used for visualization and clustering, not for downstream analyses described below.\n\nThe scregclust algorithm27 was applied to the scRNA-seq data from each sample individually. For each run, the initial cluster number was set to 20, a minimum number of genes per cluster to 10, and a range of penalization values were tested (0.01, 0.05, and 0.1). The final penalization was chosen to 0.1 based on the metrics \u201cpredictive R2\u201d and \u201cregulator importance,\u201d as described in ref. 27. For each sample, this resulted in a regulatory table with regulators (transcription factors and kinases) as rows and gene modules as columns. The regulatory tables for all samples were merged into a common table for the entire sample set, and the data were z-transformed (Fig.\u00a03A). Modules (columns) were clustered using hierarchical clustering, using the hclust-package in R with default settings (complete linkage) and Euclidean distance. Gene modules were characterized by quantifying their overlap with gene signatures of GBM cell states and cell cycle phases8, as well as signatures representing the invasion routes (diffuse, perivascular, leptomeningeal). The overlap was quantified using the Jaccard index. Analysis of variance (ANOVA) tests were performed to assess the specificity of the predicted regulators in regard to growth condition, patient, and invasion route (Fig.\u00a03B). Modules were given categorical annotations; the first two were derived from their sample origin (growth condition: in vitro/in vivo, and patient: U3013MG, U3031MG, etc.). The third, invasion route, was derived from the above-described scoring.\n\nTo compile the shorter list of regulators for experimental validation, we applied a combination of statistical and practical criteria. First, we prioritized genes that were significantly associated with invasion route (perivascular, diffuse, or leptomeningeal), based on ANOVA and differential expression analyses (adjusted p-value\u2009<\u20090.01, absolute log2 fold change\u2009>\u20090.5). We excluded regulators that were predominantly associated with patient identity or growth condition, as shown in Fig.\u00a03C\u2013E, to avoid confounding effects. From the resulting list, we selected regulators with established or plausible roles in invasion, differentiation, or transcriptional control, and for which high-quality antibodies were available for spatial protein validation. This process led us to focus on ANXA1, RFX4, and HOPX, which emerged as strong and feasible candidates.\n\nMetamodules were defined through hierarchical clustering of the merged regulatory table described above, and cutting the dendrogram at height 36, which resulted in 13 metamodules. Signatures were defined by, for each metamodule, merging the gene content of each individual gene module and keeping genes that were common for four gene modules or more. These metamodule signatures were then used to assign a metamodule score to each cell in the dataset provided by ref. 28, using the AddModuleScore()-function in the Seurat R-package.\n\nTo score the intensity of different protein markers in different anatomical niches, we processed the VP images as follows. We regard each pixel as a point in 8-dimensional space (z1,\u00a0z2,\u00a0.\u00a0.\u00a0.\u00a0,\u00a0z8) where four of the channels were common to all analyzed images: nuclei (DAPI), auto-fluorescence (AF), tumor cells STEM121/NCL, and endothelial cells (CD31). The four remaining channels were used to evaluate proteins of interest. Images were loaded from qptiff format using bioformats toolbox in Matlab. For each channel, we used L2-regularized regression to correct for shared variation with the other channels. The L2 penalty was set to 0 for DAPI and AF channels, and to a tuning constant for the others. After correction, we segmented a four-channel image consisting of the DAPI, AF, STEM121/NCL and CD31 channels using image k means segmentation (Matlab image analysis toolbox), with k set to 5. This consistently resulted in a 5-cluster solution with easily identifiable centroids representing tumor cells (high STEM121/NCL) and endothelial cells (high CD31). Pixels assigned to these centroids were used to obtain binary images T and V representing the tumor (T) and vascular (V) parts of the section. The endothelial niche was defined as the set of positive pixels in the V image. High, medium, and low-density regions of T were found as positive pixels of the T image in regions of different density, as measured by the Matlab imboxfilter method. We subsequently used a set of morphological property filters to detect elongated tumor cells, tumor cells near blood vessels, tumor cells near vasculature, and tumor cells in dense bundles. After these steps, we had obtained a labeling matrix L, that provided the class of each pixel. We subsequently scored each protein i by measuring its average intensity in each class j, correcting for cellularity using the DAPI channel, i.e.,\n\nwhere Sj is the set of pixels (x,y) in class j.\n\nThe stitched pyramidal OME-TIFF files were loaded into QuPath 4.3 software59, and TMA was disarrayed to assign coordinates to the TMA cores. The nuclei were segmented using the StarDist 4.0 extension60. Then, for each protein marker, we evaluated staining specificity and set up manual classification thresholds depending on their localization. These fixed thresholds for each marker protein were then used for the classification of all TMAs within the set. The process was separately iterated for each staining set.\n\nTo assess whether marker expression was associated with patient survival, we conducted multivariate Cox proportional hazards regression using the R survival package (v3.4-0). The model incorporated the fraction of positively stained cells per core (i.e., positive cells/total nuclei) as the predictor, with age, sex, and transcriptional subtype as covariates. Multiple cores per patient were accounted for by clustering on patient ID, using the R syntax: coxph(Surv(time, status) p\u0303rotein\u2009+\u2009age\u2009+\u2009sex\u2009+\u2009subtype, data\u2009=\u2009dataset, cluster\u2009=\u2009patient_ID). Transcriptional subtype12 was included as a categorical variable with three levels (classical, mesenchymal, proneural) with classical used as the baseline subtype by construction. We included subtype as a covariate due to its association with survival in univariate Cox regressions (p\u2009<\u20090.05). We also explored adding common mutations (c.f. Fig.\u00a01E) as covariates, and performing the survival analysis in subsets of patients, defined by their mouse xenograft growth pattern (Supplementary Data\u00a04). Mutation and mouse growth data was obtained from refs. 24,25 and phenotypic subsets found by 2-class k means clustering. All patients in this analysis were deceased at the time of data collection (i.e., no censored observations) and all cases were IDH wild-type.\n\nTo extend our collection and provide material that consisted of invasive regions of glioblastoma, we applied for access to samples from BrainUK (BRAIN UK Ref: 21/014). We then checked the expression of our top candidate proteins ANXA1, RFX4, and HOPX using mIF staining as described above. The material was carefully analyzed and scored by neuropathologists in 7\u201310 fields of view per section, selected in tumor-invaded niches.\n\nFor generating knockout clones of target genes (HOPX, ANXA1, RFX4, and SCR) for U3013MG and U3180MG, cell cultures were transduced using a reverse-transduction method. Briefly, cells were detached using TrypLe, washed in PBS, and counted. Then, 100,000 cells were co-transduced with the Cas9-nickase and gRNA vectors. To minimize off-target effects, cells were transduced with the Cas9-nickase vector at MOI 3 and the gRNA vectors at MOI 5. After vector addition, cells were incubated for 2\u2009h at 37\u00b0, then plated onto laminin-coated 6-well plates. The virus-containing medium was replaced after 24\u2009h, and selection medium was added 3 days post-transduction. Cultures were treated with antibiotic selection medium for 7\u201310 days and then passaged for seeding each of them into 96-well plates as single-cell clones using FACS. See Supplementary Figs.\u00a017 and 18 vectors and guideRNA sequences.\n\nSingle-cell clones constituting colonies were genotyped to identify knockout clones. DNA was isolated using lysis buffer and incubated for 2\u2009h at 60\u00b0. DNA was precipitated using precipitation buffer for 30\u2009min at RT and washed 3 times with 70% EtOH. The pellet was air-dried for 30\u2009min and then resuspended in TE buffer (pH 8). Clones with visible alterations in amplicon size from high-throughput PCR were selected for Sanger sequencing. In the second step, KAPA HiFi HotStart ReadyMix was used to amplify the DNA, and the amplicons were separated on a 2% agarose gel. The purified amplicons were then subjected to Sanger sequencing. Details of primers used are in Supplementary Fig.\u00a019.\n\nSanger sequencing results were qualified and analyzed using SnapGene and the ICE CRISPR analysis tool. Clones with knockout indication were expanded, and about 10 million cells were collected from each clone to create FFPE cell pellets for IHC analysis of protein. A small pellet was also collected for second genotyping PCR and sent for Sanger sequencing. FFPE cell pellets were sectioned and stained with antibodies and protocol indicated in Section \u201cResults\u201d. From clones with confirmed protein loss, a pellet of 100 thousand cells was collected and sent for STR profiling. Three to ten knockout clones per target were mixed in equivalent numbers 6 days before injection in mice.\n\nTo assess the proliferation and self-renewal capacities of knockout cells, we used CyQuant Cell Proliferation Assay and Extreme Limiting Dilution Assay (ELDA). In the proliferation test, cells were seeded in a range of serial dilutions in duplicates and allowed to grow for 72\u2009h. After that, the Cyquant Protocol was performed according to the manufacturer\u2019s instructions. Self-renewal was tested by seeding cells in dilutions ranging from 200 to 1 cell per well in 96-well ultra-low attachment plates over the period of 7 days, two biological replicates were used. ELDA analysis was conducted using software accessible at http://bioinf.wehi.edu.au/software/elda/, following the specified procedure. Invasion was assessed by seeding 3000 cells/well to 96-well S-Bio plates and spheres were allowed to form for three days. After sphere formation, fresh media was added followed by addition of 1:1 Matrigel (Corning, #356234) and media mixture on top on ice. The plate was then transferred to 37\u00b0 and followed up to 10 days. The invasion capacity was assessed using Incucyte software.\n\nLive PDCX slice culture assay was performed as described most recently by us61. Briefly, GFP-tagged xenograft tumors from U3013MG-ANXA1 SCR and knockout lines were grown in nude mice. Brains with optimal luciferase signals were extracted and placed in ice-cold HBSS (Gibco, #24020117), then embedded in low-melting agarose-HBSS in square molds. Using a Leica VT 1200 S vibratome, 300\u2009\u03bcm brain slices were cut at speeds of 20\u2013200 \u03bcm/second and transferred onto transwell membranes in 12-well plates (Corning, #3460) with brain slice culture medium containing 2.5\u2009mM HEPES, 10 mM glucose, and 2\u2009\u03bcg/ml Tomato lectin-DyLight 594 for vasculature visualization. Excess medium around the slices was removed to maintain an air-liquid interface. Slices were incubated at 37\u00b0, 5% CO2. The next day, plates were placed in the Image\u2009\u00d7\u2009press Micro Confocal system (Molecular Devices), capturing time-lapse images every <2\u2009h for up to 5 days. Media was changed every 48\u2009h. Images were processed, stitched, and overlaid in MetaXpress 6.5, and frame-to-frame registration was done using custom MATLAB scripts for analysis. Peritumoral regions of the slice were subjected to cell detection with image analysis operations for blob detection, followed by single-cell tracking with a Kalman-filter based framework written in MATLAB (Image Processing Toolbox, Computer Vision Toolbox, Version 23.2, Release 2023b, The MathWorks, Inc., Natick, Massachusetts, United States). A convolutional neural network was used to classify cells as vessel-associated or other. The proportion of cells in either class was calculated as the average proportions of classes from several peritumoral regions. Cell speed (microns per hour) for each cell was calculated as the average speed for the whole life-time of the cell track.\n\nHuman brain microvascular endothelial cells (HBEC-5i) (ATCC, #CRL-3245\u2122), at a density of 13,055 cells per well, were seeded on top of a Matrigel base in a \u03bc-slide 15 well for 3D culture (Ibidi, #81506). The plate was incubated in a humidity chamber at 37\u00b0 with 5% CO2 for endothelial tube formation. The GBM PDCs were labeled with Qtracker\u2122 525 cell tracking dye (Invitrogen, #Q25041) by incubating 1 million cells in a 0.1\u2009nM labeling solution for 1\u2009h at 37\u00b0. Soon after the formation of vessel-like scaffolds by endothelial cells, GBM PDCs, at a density of 4100 cells per well, were seeded on top of the endothelial network. The plate was transferred to the Image\u2009\u00d7\u2009press Micro Confocal system for live imaging. Time-lapse Z-stack images (step size: 5\u2009\u03bcM) were acquired at 15-min intervals for a maximum of 31\u2009h and saved as 2D maximum projection images for cell migration and statistical analysis. Using a custom written analysis framework written in MATLAB (Image Processing Toolbox, Computer Vision Toolbox, Version 23.2, Release 2023b, The MathWorks, Inc., Natick, Massachusetts, United States), cell centers were identified and tracked throughout the time-lapse using a Kalman-filter based framework as described above. A binary mask based on the endothelial cells and enlarged with morphological dilation was used to categorize cells as being associated or non-associated with the vessel-like network. The proportion of cells associated with the network for each perturbation was estimated by sampling cells with replacement from replicates to obtain a distribution of values for statistical testing.\n\nAn incross of Tg(kdrl:mCherry) labelling vasculature on pigmentless Casper strain (nacre\u2212/, roy orbison\u2212/\u2212) background was generated to obtain embryos for tumor injections. The glioblastoma cells, expressing GFP and luciferase, were resuspended in NSC medium containing 20\u2009mg/ml polyvinylpyrrolidone (PVP; Sigma #PVP360) and injected into zebrafish embryos at 1 day post fertilization (dpf). Stills were obtained by TCS SP8 DLS LightSheet microscope (Leica) and 24-h time-lapse imaging was performed with Confocal SP8 (Leica), both at 24\u2009h post injection (hpi). By using surface rendering, 3D representations of the tumor cells and blood vessels were generated. Colocalization analysis was performed using Imaris (Bitplane v.9.5), by identifying the regions of colocalization between these rendered surfaces and calculating their proximal distance.\n\nCell-state plots. Cell-state plots were generated as described. Barplots in Fig.\u00a07B, D and F were generated by counting the number of cells in each quadrant. Mosaic plot. To statistically assess the relation between cell state and invasion route, a chi-square test was performed and visualized as a mosaic plot. Differential gene expression analysis and MA plots. Differential gene expression (DGE) analysis was performed using the FindAllMarkers-function in the Seurat package. MA plots were created by plotting the log2FC-values from the DGE analysis against the log2 total gene count for each gene across cells.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The single cell RNA sequencing data generated in this study have been deposited in GEO database under accession ID GSE270083. The source data is available in Supplementary figures and the source data file. 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Preprint at https://www.biorxiv.org/content/early/2025/03/22/2025.03.20.644331 (2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We thank the involved patients and their families for the support and donation of materials to the Human Glioblastoma Cell Culture (HGCC) biobank. We also thank the HGCC team for invaluable contributions in collecting and providing the patient-derived cell cultures used in this study. We thank the ongoing HGCC Tissue Microarray (Tobias Bergstr\u00f6m, unpublished) and HGCC Phenobank initiative (Cecilia Krona, unpublished) for sharing TMAs and phenotypic data, respectively. We thank the Brain UK biobank for making patient materials investigated in Fig.\u00a05 available. We thank the National Genomics Infrastructure (NGI) for providing the sequencing service and the BioVis Platform for providing assistance with FACS sorting and microscopy. We thank FoUU for assistance with sectioning and scanning tissue slides and Artur Mezheyeuski for sharing expertise on multiplex staining, multispectral image acquisition, and data analysis. We thank Finn Hallb\u00f6\u00f6k, Karin Forsberg Nilsson, and Veronica Rendo for their valuable comments and feedback during the writing process. This research was supported by the Swedish Cancer Society (20 0839 PjF), the Swedish Research Council (2021-03224), Knut and Alice Wallenberg Foundation (2022-0057),\u00a0and Swedish Foundation for Strategic Research (CCS23-011).", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open access funding provided by Uppsala University.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Milena Doroszko, Rebecka Stockgard.\n\nDepartment of Immunology, Genetics and Pathology, Uppsala University, Program for Neurooncology and Neurodegeneration, Uppsala, Sweden\n\nMilena Doroszko,\u00a0Rebecka Stockgard,\u00a0Irem Uppman,\u00a0Josephine Heinold,\u00a0Faidra Voukelatou,\u00a0Hitesh Bhagavanbhai Mangukiya,\u00a0Madeleine Skepp\u00e5s,\u00a0Mar Ballester Bravo,\u00a0Ramy Elgendy,\u00a0Maria Berglund,\u00a0Ludmila Elfineh,\u00a0Cecilia Krona,\u00a0Soumi Kundu,\u00a0Ida Larsson\u00a0&\u00a0Sven Nelander\n\nDepartment of Immunology, Genetics and Pathology, Uppsala University, Beijer Gene and Neuro Laboratory, Uppsala, Sweden\n\nFaidra Voukelatou\u00a0&\u00a0Katarzyna Koltowska\n\nBrain Tumour Research Centre, Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK\n\nThomas O. Millner\u00a0&\u00a0Silvia Marino\n\nDepartment of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA\n\nIda Larsson\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nExperiments were performed by M.D., R.S., I.U., J.H., F.V., H.B.M., M.B.B., L.E. and R.E. Mouse experiments were performed by S.K., R.S., I.U., M.B.B., M.B., M.D., H.B.M. and C.K. (coordination). Profiling and imaging data were analyzed by M.D., I.L., M.S. and S.N. Human tissue was analyzed by T.M. and S.M. Zebrafish experiments were analyzed by F.V. and K.K. The first manuscript draft was prepared by R.S., I.U., I.L., S.K., M.B.B., M.D. and S.N., with input from the other authors. S.N. guided the study.\n\nCorrespondence to\n Sven Nelander.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Nourhan Abdelfattah, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Doroszko, M., Stockgard, R., Uppman, I. et al. The invasion phenotypes of glioblastoma depend on plastic and reprogrammable cell states.\n Nat Commun 16, 6662 (2025). https://doi.org/10.1038/s41467-025-61999-1\n\nDownload citation\n\nReceived: 07 June 2024\n\nAccepted: 08 July 2025\n\nPublished: 19 July 2025\n\nVersion of record: 19 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61999-1\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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cluster-level trajectory inference", + "journal": "Nature Communications", + "published": "07 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61628-x/MediaObjects/41467_2025_61628_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61628-x/MediaObjects/41467_2025_61628_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61628-x/MediaObjects/41467_2025_61628_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-61628-x#ref-CR38", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132188", + "/articles/s41467-025-61628-x#ref-CR39", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95753", + 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+ "/articles/s41467-025-61628-x#ref-CR47", + "/articles/s41467-025-61628-x#ref-CR48", + "https://www.10xgenomics.com/resources/datasets/fresh-embryonic-e-18-mouse-brain-5-k-1-standard-1-0-0", + "/articles/s41467-025-61628-x#ref-CR49", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE140203", + "/articles/s41467-025-61628-x#ref-CR50", + "/articles/s41467-025-61628-x#ref-CR51", + "/articles/s41467-025-61628-x#ref-CR52", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70677", + "/articles/s41467-025-61628-x#ref-CR53", + "/articles/s41467-025-61628-x#ref-CR54", + "/articles/s41467-025-61628-x#ref-CR55", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE162170", + "/articles/s41467-025-61628-x#ref-CR56", + "/articles/s41467-025-61628-x#ref-CR57", + "https://doi.org/10.6084/m9.figshare.27643494" + ], + "code": [ + "https://github.com/cuhklinlab/TIVelo", + "/articles/s41467-025-61628-x#ref-CR58", + "https://doi.org/10.5281/zenodo.15637938", + "https://tivelo.readthedocs.io/en/latest/" + ], + "subject": [ + "Computational models", + "Machine learning", + "Statistical methods" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5438462/v1.pdf?c=1751972827000", + "research_square_link": "https://www.researchsquare.com//article/rs-5438462/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61628-x.pdf", + "preprint_posted": "25 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "RNA velocity inference is a valuable tool for understanding cell development, differentiation, and disease progression. However, existing RNA velocity inference methods typically rely on explicit assumptions of ordinary differential equations (ODE), which prohibits them to capture complex transcriptomic expression patterns. In this study, we introduce TIVelo, a novel RNA velocity estimation approach that first determines the velocity direction at the cell cluster level based on trajectory inference, before estimating velocity for individual cells. TIVelo calculates an orientation score to infer the direction at the cluster level without an explicit ODE assumption, which effectively captures complex transcriptional patterns, avoiding potential inconsistencies in velocity estimation for genes that do not follow the simple ODE assumption. We validated the effectiveness of TIVelo by its application to 16 real datasets and the comparison with five benchmarking methods.Biological sciences/Computational biology and bioinformatics/Computational modelsBiological sciences/Computational biology and bioinformatics/Machine learningBiological sciences/Computational biology and bioinformatics/Statistical methods", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "TIVelomanuscriptsupp.pdfSupplementary figures of \u201cTIVelo: RNA velocity estimation leveraging cluster-level trajectory inference\u201d", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "RNA velocity inference is a valuable tool for understanding cell development, differentiation, and disease progression. However, existing RNA velocity inference methods typically rely on explicit assumptions of ordinary differential equations (ODE), which prohibits them from capturing complex transcriptomic expression patterns. In this study, we introduce TIVelo, a RNA velocity estimation approach that first determines the velocity direction at the cell cluster level based on trajectory inference, before estimating velocity for individual cells. TIVelo calculates an orientation score to infer the direction at the cluster level without an explicit ODE assumption, which effectively captures complex transcriptional patterns, avoiding potential inconsistencies in velocity estimation for genes that do not follow the simple ODE assumption. We validated the effectiveness of TIVelo by its application to 16 real datasets and the comparison with six benchmarking methods.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular diversity and complexity by enabling transcriptome analysis at the individual cell level1. Trajectory inference (TI) leverages scRNA-seq data to order individual cells/cell clusters along a trajectory based on gene expression, thereby providing insights into cellular differentiation and development2,3. TI methods can be classified into two main categories: The first category, such as Monocle4, Slingshot5, and Palantir6, involves the direct construction of a cell graph, where each node represents a cell and each edge denotes a connection between cells. The second category, such as PAGA7, involves an initial clustering of the dataset, followed by the construction of a cluster graph, where each node represents a cell cluster and each edge denotes a connection between these clusters. One method closely related to TI is pseudotime analysis, which aims to order individual cells along a continuous trajectory representing a biological process. The continuous trajectory is often based on the result of TI analysis. A significant challenge associated with pseudotime analysis is its requirement for prior information, where a starting cell or cluster needs to be chosen with pseudotime set as 0. Acquiring this information can be difficult in practice, presenting a substantial obstacle to the effective application of pseudotime analysis methods.\n\nRNA velocity is a concept that provides a dynamic view of cellular behavior. Compared with pseudotime analysis, RNA velocity estimation typically does not require prior information such as the starting cell or cluster, and can provide developmental velocity direction and intensity for each cell, instead of the single pseudotime value. The basic premise of RNA velocity lies in leveraging the information contained within the two forms of RNA for each gene: unspliced (nascent) RNA and spliced (mature) RNA, denoted by u and s, respectively. Typically, spliced RNA s is produced from unspliced RNA u, and the rate of spliced RNA s production \\(\\frac{ds}{dt}\\) is often referred to as RNA velocity. The sign (positive or negative) of the RNA velocity of a specific gene within a cell can indicate the future regulation (upregulation or downregulation) of that gene.\n\nThe concept of RNA velocity was first proposed by velocyto8, which provides a standard pipeline from extracting unspliced and spliced RNA counts from sequencing data, to estimating RNA velocity by a steady-state model based on the ordinary differential equation (ODE) assumption. scVelo9, a successor to velocyto, still relies on the ODE assumption but leverages the EM algorithm to iteratively update the ODE rate parameters of each gene and the pseudotime of each cell, thereby fitting u-s expression of each gene through an ODE curve. Following this, UniTVelo10 introduces a radial basis function-based curve fitting strategy, and supports a unified pseudotime of each cell across different genes. Contrarily, VeloAE11 does not directly infer velocity in the high-dimensional gene expression space. Instead, it projects the u-s expression into a low-dimensional embedding space through an autoencoder (AE), and further estimates the velocity based on the latent embedding. Analogous methods include VeloVAE12, which leverages a variational autoencoder13 (VAE). It infers latent pseudotime and ODE rate parameters by an encoder using u and s as the input, and then generates u and s expression by a decoder to minimize the reconstruction loss of u and s. VeloVI14 shares a similar approach but employs a Bayesian deep generative model, outputting posterior distributions of ODE rate parameters and thus velocities. Another category of RNA velocity inference methods applies Neural ODE15, such as scTour16 and LatentVelo17. These methods first embed u and s expression into a low-dimensional latent space, then use Neural ODE to fit the developmental process for latent embedding along the cell trajectory. Instead of building an ODE model to fit the u-s expressions of all cells for each gene, and then infer RNA velocity for each cell according to the fitted velocities of all genes, DeepVelo18 and cellDancer19 infer RNA velocity for each cell directly, based on the u-s expressions in that cell\u2019s nearest neighborhood. In addition, RNA velocity estimation methods that combine unspliced and spliced RNA with other biological information have been developed. For instance, MultiVelo20 uses chromatin accessibility, protaccel21 uses protein abundances, Dynamo22 uses new/total labeled RNA-seq, PhyloVelo23 uses phylogenetic trees, and TFvelo24 uses transcription factors (TFs).\n\nCurrent RNA velocity estimation methods come with several limitations. Firstly, most RNA velocity inference methods are based on the ODE assumption, which assumes that the transcription process follows a simple ODE model, with constant rate parameters of each gene. Although it is a rough approximation to the transcription process and can easily achieve analytical solutions in practice, the naive ODE model fails to deal with complicated transcription dynamics25,26. Some variants of the ODE model attempt to address this by introducing variable rate parameters, including DeepVelo and cellDancer; their limitations are discussed in the Supplementary Note\u00a01. Secondly, the ODE model and its variants are usually fitted for the expression of different genes independently, and the inferred velocities for individual genes are aggregated together as the final estimation. In practice, this strategy may lead to inconsistent or even reversed velocity estimation to the expected direction10,25,26,27.\n\nIn this study, we introduce TIVelo for RNA velocity estimation. Rather than fitting an ODE model to individual cells and genes, TIVelo initially infers the velocity direction among different cell clusters. This cluster-level velocity estimation provides insights into the broader velocity trends, thereby preventing the potential misdirection in developmental trajectories that can occur when velocities are aggregated from independently fitted genes. To infer the direction on the cell cluster level, TIVelo leverages a model-free strategy based on an intrinsic property of the unspliced-spliced RNA relationship: the unspliced RNA should always be expressed and repressed earlier than the spliced RNA. This strategy allows for comprehensive mining of signals embedded in the u-s expression profiles, without complicated mathematical modeling for the RNA transcription process. In this way, it mitigates the issue that ODE rate parameters may vary over different cell stages, which is hard to deal with through an ODE model with constant parameters. We validated TIVelo\u2019s capability to accurately infer RNA velocity in 16 real datasets, in comparison with six benchmarking RNA velocity estimation methods.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The basic idea of TIVelo is to first infer the direction on a cluster-level graph and then use this direction to supervise the velocity estimation of each cell. There are three primary steps in TIVelo: main path selection, orientation inference, and RNA velocity estimation.\n\nIn the main path selection step, the cluster graph will first be constructed for the dataset. In this cluster graph, each cell cluster is a node and each edge represents the connectivity between a pair of nodes (Fig.\u00a01a). Subsequently, we identify possible \u201cterminal states\" in this cluster graph, which refers to either root cluster or end cluster in the data. One terminal state most likely to be a root/end cluster will be selected as the \u201corigin node\" (Methods; Fig.\u00a01b). Finally, we select a \u201cmain path,\" which refers to a path in the cluster graph beginning from the origin node, and involving as many cells as possible (Methods; Fig.\u00a01c).\n\na Constructing PAGA cluster graph for the dataset. b Finding possible cluster by root/end score as origin node. c Selecting main path beginning from the origin node. d Assigning diffusion pseudotime for cells along the main path. e Extracting time series \\(\\tilde{u}\\) and \\(\\tilde{s}\\) of each gene according to pseudotime t. Here \\(\\tilde{u}\\) and \\(\\tilde{s}\\) are rescaled to sum to 1 for each gene. f Calculating orientation score for G genes within the dataset. Identifying expected direction of the main path and setting new origin node. g Assigning level and child node(s) for each node in the cluster graph. This facilitates directed trajectory inference (DTI) analysis. h Building directed nearest neighbor (dNN) for each cell. i. Inferring RNA velocity for each cell by dNN.\n\nIn the orientation inference step, each cell along the main path will be assigned a pseudotime (Fig.\u00a01d). Cells along the main path are then ordered by this pseudotime, forming time series of u and s expression of each gene g, which are denoted by \\({\\tilde{{{{\\boldsymbol{u}}}}}}_{g}\\) and \\({\\tilde{{{{\\boldsymbol{s}}}}}}_{g}\\). Then a specially designed orientation score Sg will be calculated for gene g based on time series \\({\\tilde{{{{\\boldsymbol{u}}}}}}_{g}\\) and \\({\\tilde{{{{\\boldsymbol{s}}}}}}_{g}\\), using the intrinsic property of u-s relationship, such that the unspliced RNA u should always be expressed earlier than the spliced RNA s and decrease earlier than s (Methods). The scores from G pre-filtered genes will be aggregated together to evaluate if the current direction on the main path (from origin node to the end of the main path) is correct or not. If \\(\\frac{1}{G}{\\sum }_{g}{S}_{g} < 0\\), the direction is considered incorrect, the origin node will be reset (Fig.\u00a01e, f).\n\nIn RNA velocity estimation step, we begin from the new origin node and assign a level to each node in the cluster graph. A smaller number of level signifies closer proximity to the root cluster, and the level of the origin node is set at 0. Based on this level, we draw a directed edge from each node to its child nodes, providing a directed trajectory inference (DTI) analysis (Fig.\u00a01g). To infer RNA velocity for each cell, we construct directed nearest neighborhood (dNN) for each cell n, referring to the near future state of that cell (Methods; Fig.\u00a01h). The velocity vector of each cell is compelled to point toward the mean expression in the dNN, leading to the RNA velocity estimation results (Fig.\u00a01i).\n\nThe efficacy of TIVelo comes from two aspects. The first aspect is that TIVelo calculates an orientation score for each gene to infer the direction on the main path, which is a simpler task than directly fitting RNA velocity for individual genes. Directly fitting RNA velocity for individual genes is a commonly used strategy by ODE-based methods, but may fail when there exist genes with expression patterns not agreeing with the assumptions in the ODE equation. Moreover, TIVelo divides the entire developmental process into short pseudotime sections and aggregates local transcription patterns in different sections. This strategy can fully exploit transcription features from each gene\u2019s u-s profiles, instead of constructing an ODE model. On the other hand, ODE-based methods like scVelo assume that all cells should follow an ODE process with constant coefficients, ignoring the fluctuation during the transcription.\n\nTo show this point, we compared the performance of scVelo and TIVelo on the mouse gastrulation (erythroid) dataset. In the mouse gastrulation (erythroid) dataset, the expected direction of development is from cell type Blood progenitors 1 to Erythroid 3 (Fig.\u00a02a, b). In addition, there exist multiple rate kinetics genes (MURK genes) in this dataset, which refer to genes whose transcription rate \u03b1 is non-constant and will suddenly increase during cell development28. For convenience, we use the term \u201cnon-MURK genes\" to denote all other genes that do not exhibit these MURK characteristics.\n\na Gene Mllt3 in mouse gastrulation (erythroid) fitted by ODE curves of scVelo. Mllt3 is a gene with top likelihood fitted by scVelo. b Gene Gclm in mouse gastrulation (erythroid) fitted by ODE curves of scVelo. Gclm is a multiple rate kinetics gene (MURK gene), with rate parameter \u03b1 suddenly increasing at some time point25,28 (top left panel). c RNA velocity stream plot inferred from scVelo, based on genes with top 500 likelihood except MURK genes. d RNA velocity stream plot inferred from scVelo, based on MURK genes. e The u-s pattern along the main path for corresponding two genes. In each panel the colorbar below indicates the cell type, which is identical to the annotation in (a). f The velocity graph for selected cells created by scVelo. There is a unit length arrow showing the direction from selected cell n to cell \\(n^{\\prime}\\) on the UMAP embedding plot if \\(n^{\\prime}={{{\\mathrm{argmax}}}\\,}_{n^{\\prime} }{\\pi }_{n,n^{\\prime} }\\). The line width of each arrow is proportional to its corresponding \\({\\pi }_{n,n^{\\prime} }\\). g RNA velocity stream plot produced by scVelo. h TIVelo first infers the direction on the cluster level, according to the orientation scores. i In RNA velocity estimation, for each cell and each gene, the inferred velocity should follow the direction inferred in (h). j The velocity graph for selected cells created by TIVelo. k RNA velocity stream plot produced by TIVelo.\n\nAs shown in Fig.\u00a02a, the ODE curve fitted by scVelo (dynamical mode) for non-MURK genes with large likelihood, such as Mllt3, accurately represents the expected developmental lineage from cell type Blood progenitors 1 to Erythroid 3. However, MURK genes, including Gclm exhibit a non-constant transcription rate (\u03b1) over pseudotime (Fig.\u00a02b, top left), which leads to a fitted ODE curve that contradicts the expected developmental direction (Fig.\u00a02b). This limitation of scVelo can be further demonstrated by the velocity stream plot based on non-MURK genes and MURK genes, respectively. While the velocity stream based on non-MURK genes with top likelihood in scVelo can depict the expected direction (Fig.\u00a02c), the velocity stream based on MURK genes presents a completely reversed and unexpected developmental direction (Fig.\u00a02d).\n\nInstead of fitting velocity for individual genes, TIVelo calculates an orientation score for each gene, which is used to infer the direction along the main path. To calculate the orientation score, TIVelo constructs a linear tree29,30 model for each gene based on its unspliced and spliced RNA counts. Linear tree can help to split the entire cell development process into short pseudotime sections, and within each section TIVelo calculates the corresponding orientation score according to the intrinsic property that unspliced RNA u should always be expressed earlier than spliced RNA s and decrease earlier than s. This orientation score provides guidance for inferring direction at the cluster level, and the sign of the score indicates if the initial direction should be kept or reversed (Methods).\n\nIn the mouse gastrulation (erythroid) dataset, for non-MURK gene Mllt3, TIVelo captures the local transcription pattern from Blood progenitors 2 to Erythroid 1, yielding an orientation score of 18.03 (Fig.\u00a02e, top). Similarly, for MURK gene Gclm, it accurately captures the transcription pattern from Erythroid 2 to Erythroid 3, and the orientation score is 13.93 (Fig.\u00a02e, bottom). Consequently, TIVelo successfully obtains positive orientation scores for both genes, supporting the expected cell development trajectory from cluster Blood progenitors 1 to Erythroid 3.\n\nThe second aspect of TIVelo\u2019s effectiveness lies in its strategy to infer RNA velocity of each cell and each gene following the direction on the cluster graph. Specifically, TIVelo infers the direction on the cluster graph based on the orientation score (Fig.\u00a02h); subsequently, for each cell TIVelo constructs a dNN according to the inferred direction on the cluster graph, which refers to the future state of that cell. For each gene of that cell, the inferred velocity should point toward the mean spliced expression in dNN (Method; Fig.\u00a02i). The superiority of this strategy can be seen from the visualization of the velocity graph9, which is the correlation between velocity and cell-to-cell transition for each pair of cells \\((n,n^{\\prime} )\\). For mouse gastrulation (erythroid) dataset, the inferred velocity vector of cell n can be written as \\({{{{\\boldsymbol{v}}}}}_{n}={({{{{\\boldsymbol{v}}}}}_{n,1}^{\\top },{{{{\\boldsymbol{v}}}}}_{n,2}^{\\top })}^{\\top }\\), where vn,1 and vn,2 are the velocities corresponding to MURK and non-MURK genes, respectively. For scVelo, since the velocities from MURK and non-MURK genes may have completely opposite directions (Fig.\u00a02c, d), vn,1 and vn,2 can indicate totally different descendant cell populations for cell n. This leads to the lack of clear directionality toward expected future state in the velocity graph for the cells (Fig.\u00a02f). In contrast, TIVelo\u2019s strategy will ensure both vn,1 and vn,2 pointing toward cells \\(n^{\\prime}\\) within the dNN of cell n. As a result, only cells \\(n^{\\prime}\\) within the dNN of cell n will become the descendant cell populations of cell n, and these cells will have a large value of velocity graph. This leads to a velocity graph correctly reflecting the cell developmental direction (Fig.\u00a02j).\n\nFinally, by using the embedding position of each cell n, the embedding velocity stream plot can be drawn based on the velocity graph (Methods). The embedding velocity stream plot produced by scVelo presents a pattern contradictory to the expected developmental trajectory (Fig.\u00a02g). TIVelo, in contrast, yields a velocity stream plot that aligns with the expected trajectory, with each cell\u2019s velocity more consistent with the velocity stream in its neighborhood (Fig.\u00a02k).\n\nTo evaluate the effectiveness of TIVelo, we investigated its application to the dentate gyrus dataset. In this dataset, the expected root cell cluster is intermediate progenitor cells for neurons (nIPC). This dentate gyrus dataset contains two primary lineages: one is from nIPC to Granule mature, and the other is from nIPC to Astrocytes (Fig.\u00a03a). There are several island-like cell clusters in the UMAP visualization of this data, such as oligodendrocyte precursor cells (OPC) and oligodendrocytes (OL) (Fig.\u00a03a), which add an extra layer of complexity to RNA velocity estimation.\n\na Comparative RNA velocity stream plots of TIVelo, UniTVelo, and veloVI, highlighting TIVelo\u2019s inference of the velocity stream from immature to mature Granule (indicated by the red box) and from OPC to OL (blue box). b The cluster graph after graph pruning and main path selection. c The \\(\\tilde{u}\\)-\\(\\tilde{s}\\) variation along the main path and the linear tree model constructed for gene Grin2b, with the orientation score of primary rising/falling section annotated below. The colorbar indicates the cell type, which is identical to the annotation in (a). d Velocity fitting for genes Map1b, Cplx2 and Ak5 from TIVelo. Top row: u-s scatter plots colored by cell type, with fitted ODE curve from scVelo (dynamical mode). Bottom row: fitted velocity vectors of sampled cells from TIVelo. The Granule mature cells are colored by red, and other cell types are colored by light blue. e Directed trajectory inference (DTI) analysis by TIVelo.\n\nThe comparison of RNA velocity stream plots of TIVelo, UniTVelo, and veloVI are shown in Fig.\u00a03a. TIVelo, UniTvelo and veloVI provide generally accurate velocity estimation from nIPC to other lineages. However, the result of TIVelo highlights the developmental trajectory from Granule immature to Granule mature (Fig.\u00a03a, red box). Conversely, the result of UniTVelo and\u00a0veloVI displays an apparent reversed velocity flow from Granule mature to Granule immature. Another expected velocity flow from OPC to OL in the oligodendrocyte lineage is observed in the velocity stream plot of TIVelo (Fig.\u00a03a, blue box), which is not depicted in the result of veloVI. The velocity stream plots produced by scVelo (stochastic mode), scVelo (dynamical mode), DeepVelo and cellDancer are shown in Supplementary Fig.\u00a01c. The reversed velocity flow from Granule mature to Granule immature exists in results produced by both modes of scVelo, while the result from cellDancer cannot reflect the expected developmental trajectory.\n\nThe improvement of such details in TIVelo is primarily due to its strategy of first inferring the direction on the cluster graph. Figure\u00a03b shows the cluster graph of the dentate gyrus after graph pruning and main path selection (Methods). The expected root cluster nIPC is selected as the origin node (orange node), from which a main path is selected (red path). For cells along this main path, we computed the orientation score for each gene, yielding an average score of 1.138. This positive average orientation score indicates that the actual developmental direction on the main path should be from nIPC to Granule mature. Consequently, the velocity vector of cells in the Granule immature cluster is compelled to point toward cells in the Granule mature cluster, thereby eliminating the reversed velocity flow mentioned earlier.\n\nTo show the details of orientation score calculation, the unspliced counts \\(\\tilde{{{{\\boldsymbol{u}}}}}\\) and spliced counts \\(\\tilde{{{{\\boldsymbol{s}}}}}\\) along the main path of a typical gene Grin2b, which has been reported to play a significant role in neurogenesis31, together with the fitted linear trees, are shown in Fig.\u00a03c. Grin2b demonstrates a clear induction pattern (\\(\\tilde{{{{\\boldsymbol{u}}}}} > \\tilde{{{{\\boldsymbol{s}}}}}\\)) during development from Neuroblast to Granule immature, and a repression pattern (\\(\\tilde{{{{\\boldsymbol{u}}}}} < \\tilde{{{{\\boldsymbol{s}}}}}\\)) during development from Granule immature to Granule mature (Methods). The orientation score for this gene is 13.20, which provides solid evidence for us to infer the correct main path direction.\n\nTIVelo\u2019s strategy to infer the direction on the cluster graph not only produces velocity stream plots consistent with the expected cell development, but also achieves better RNA velocity estimation for individual genes. For some genes in dentate gyrus, cells in Granule immature tend to have larger u and s expression values than those in Granule mature, such as Map1b, Cplx2, and Ak5 (Fig.\u00a03d, top row). When fitted by ODE-based methods like scVelo, velocity vectors from Granule mature to Granule immature are observed, leading to reversed velocity stream compared to the expected developmental trajectory, which is shown in Fig.\u00a03d (top row) and Supplementary Fig.\u00a01c. Conversely, the velocity vectors in those three genes inferred from TIVelo correctly point to cells in Granule mature (Fig.\u00a03d, bottom row). This further underscores the advantage of RNA velocity estimation of individual cells supervised by cluster-level direction.\n\nFinally, TIVelo can provide DTI analysis at the cluster level. Specifically, a directed edge is drawn from each node in the cluster graph and pointing toward its child nodes (Methods). Figure\u00a03e presents the DTI analysis result of the dentate gyrus, clearly illustrating the developmental relationship of cell clusters, including island-like clusters.\n\nSimilar results from several datasets where the orientation score on the main path is positive, are shown in supplementary information, including pancreatic endocrinogenesis (Supplementary Fig.\u00a02), mouse gastrulation (erythroid) (Supplementary Fig.\u00a03), mouse hindbrain (Oligo) (Supplementary Fig.\u00a04), dentate gyrus development 2 (Supplementary Fig.\u00a05) and mouse embryonic fibroblast reprogramming (Supplementary Fig.\u00a06).\n\nTo further assess the performance of TIVelo, particularly in scenarios where ODE models may fail, we applied it to the intestinal organoid dataset. This dataset has two developmental lineages, namely the secretory lineage (mouse intestinal stem cells (Stem cells) to Paneth cells) and the enterocyte lineage (Stem cells to Enterocytes)32 (Fig.\u00a04a).\n\na Comparative velocity stream plots produced by TIVelo, UniTVelo, and veloVI. b The cluster graph after graph pruning and main path selection. The end cluster Enterocytes is selected as the origin node. c The \\(\\tilde{u}\\)-\\(\\tilde{s}\\) variation along the main path and the linear tree model constructed for gene Muc13, with the orientation score of primary rising/falling section annotated below. The colorbar indicates the cell type, which is identical to the annotation in (a). d Velocity fitting for five different genes from TIVelo. For each gene, the velocity direction of each cell generally points to Enterocytes from Stem cells.\n\nA comparison of the velocity stream plots generated by TIVelo, UniTVelo, and veloVI is shown in Fig.\u00a04a. Only the velocity stream inferred by TIVelo accurately reflects the bifurcation from Stem cells into the secretory lineage and the enterocyte lineage. As a comparison, velocity plots inferred from UniTVelo and veloVI display reversed velocity flow, originating from Paneth cells and Enterocytes and moving toward Stem cells. Of note, the result of UniTVelo is different from its original report, probably due to the use of (default) random initialization here, while the original study used a warm initialization. The velocity stream plots produced by scVelo (stochastic mode), scVelo (dynamical mode), DeepVelo and cellDancer are shown in Supplementary Fig.\u00a01d: while they can generally predict the development in the secretory lineage, the development in the enterocyte lineage is not accurately estimated except for DeepVelo.\n\nThe better performance of TIVelo comes from its inference of direction on the cluster graph of intestinal organoid, which is shown in Fig.\u00a04b. The origin node initially selected by TIVelo is Enterocytes, an end cell cluster in the data. The main path is selected as the path from Enterocytes cells to Stem cells, which has the opposite direction for the enterocyte lineage. The average orientation score computed on the main path is \u22120.650, suggesting that the direction on the main path should be reversed, and cluster Stem cells should be set as the new origin node. This enables TIVelo to correctly infer the direction along the main path. Muc13, which has been reported to be involved in the maintenance of gastrointestinal epithelium33, provides explicit evidence for us to infer the direction on the main path, as is illustrated in Fig.\u00a04c. In the early stage of transition from Enterocytes to TA cells, the expression levels of \\(\\tilde{{{{\\boldsymbol{u}}}}}\\) and \\(\\tilde{{{{\\boldsymbol{s}}}}}\\) are nearly identical as both are increasing. However, in the later stage from TA cells to Stem cells, where both \\(\\tilde{{{{\\boldsymbol{u}}}}}\\) and \\(\\tilde{{{{\\boldsymbol{s}}}}}\\) are decreasing, \\(\\tilde{{{{\\boldsymbol{s}}}}}\\) is overtaken by \\(\\tilde{{{{\\boldsymbol{u}}}}}\\). Consequently, the orientation score of Muc13 is computed as \u221213.74, which strongly supports the reversal of the direction on the main path.\n\nMoving from the velocity stream estimation to the velocity inference for individual genes, TIVelo can accurately infer the velocity direction on the enterocyte lineage. We illustrate this by showing the u-s scatter plot of several genes in this data, along with some sampled velocity vectors inferred from TIVelo (Fig.\u00a04d). These genes, including Ndrg1, Dhrs1, Gramd3, Cdr2, and Slc7a9, exhibit differential u-s expression across cells in the enterocyte lineage. As shown in Fig.\u00a04d, for each gene, the velocity direction of each cell generally points to Enterocytes from Stem cells, thereby accurately indicating the developmental trajectory of the enterocyte lineage.\n\nTo demonstrate how TIVelo can provide insights for biological studies, we carried out several downstream analyses based on TIVelo\u2019s inferred velocities, including fate probability visualization, lineage-specific driver gene identification, identification of macrostates with functional insights, and kinetic rates inference. The details are discussed in Supplementary Note\u00a02.\n\nSimilar results from several datasets where the orientation score on the main path is negative and the origin node is corrected, are shown in supplementary information, including mouse retina development (Supplementary Fig.\u00a07), scNT-seq neuron KCl stimulation (Supplementary Fig.\u00a08) and mouse hindbrain (GABA, Glial) (Supplementary Fig.\u00a09).\n\nWe next investigated the performance of TIVelo on two cell-cycle datasets of fluorescent ubiquitination-based cell-cycle indicator (FUCCI) RPE1 and U2OS cells32,34. These datasets were employed by veloVI14 to evaluate the RNA velocity estimation performance. One advantage of comparing different methods on these two datasets is that they provide additional validation of the inferred velocity/pseudotime through a cell-cycle score: the inferred pseudotime should be positively correlated to the cell-cycle score, and the inferred RNA velocity of cells with a lower cell-cycle score should ideally point toward cells with a higher cell-cycle score (Fig.\u00a05a).\n\na UMAP plots for RPE1-FUCCI and U2OS-FUCCI, colored by cell-cycle score of each cell. b Comparative velocity stream plots produced by TIVelo, veloVI and scVelo (stochastic mode) on RPE1-FUCCI. c Violin plots showing the comparison of sign accuracy across all positions (n\u2009=\u2009290 positions) in RPE1-FUCCI, produced by TIVelo and six other benchmarking methods. The mean sign accuracy of each method is annotated below. In the box plot inside each violin, the lower bound, center and upper bound of the box plot stand for the first quartile (Q1), median and the third quartile (Q3) of the sign accuracy, respectively. The lower whisker and the upper whisker stand for Q1\u2009\u2212\u20091.5\u2009\u00d7\u2009IQR and Q3\u2009+\u20091.5\u2009\u00d7\u2009IQR, where IQR\u2009=\u2009Q3\u2009\u2212\u2009Q1. d Violin plots showing the comparison of sign accuracy across all positions (n\u2009=\u2009996 positions) in U2OS-FUCCI, produced by TIVelo and six other benchmarking methods. The mean sign accuracy of each method is annotated below. In the box plot inside each violin, the lower bound, center and upper bound of the box plot stand for the first quartile (Q1), median and the third quartile (Q3) of the sign accuracy, respectively. The lower whisker and the upper whisker stand for Q1\u2009\u2212\u20091.5\u2009\u00d7\u2009IQR and Q3\u2009+\u20091.5\u2009\u00d7\u2009IQR, where IQR\u2009=\u2009Q3\u2009\u2212\u2009Q1.\n\nSince there is no cell type annotation provided with these two datasets, we first employed the clustering algorithm leiden35 to obtain the cell clusters (Methods). Then, RNA velocity is estimated for the two datasets following the TIVelo pipeline. Figure\u00a05b shows the comparison of RNA velocity stream plots of the RPE1-FUCCI dataset from TIVelo, veloVI and scVelo (stochastic mode). TIVelo and veloVI accurately predict the velocity direction following the increase of cell-cycle score, while scVelo (stochastic mode) incorrectly identifies an intermediate orange cluster as the end cluster, leading to reversed velocity flow from the blue cluster to the orange cluster. The cluster graph after graph pruning and main path selection, and the velocity stream plots from additional methods, are shown in Supplementary Fig.\u00a010.\n\nIt is worth noting that in Fig.\u00a05b, it seems that the inferred velocity streams\u00a0of TIVelo in cluster 1 lack coherence in their orientation. However, this directional pattern is not indicative of an inconsistency in TIVelo\u2019s velocity estimation but rather reflects the underlying biological dynamics. The details are discussed in Supplementary Note\u00a03.\n\nTo quantify the performance of different methods, we calculated the velocity sign accuracy proposed by veloVI for each cell-cycle position in both datasets (Methods). Figure\u00a05c, d illustrate the comparison of sign accuracy calculated for both datasets using TIVelo and six other benchmarking methods. In both datasets, the mean sign accuracy of velocity inferred from TIVelo outperformed all six other methods.\n\nSingle-cell multi-omics datasets with both the modalities of RNA and ATAC are available36. Although TIVelo only uses the modality of RNA, it can achieve comparable or better performance compared to methods that use both modalities. More specifically, we compared the performance of TIVelo and MultiVelo20 on four single-cell multi-omics datasets that included both modalities of RNA and ATAC from four different tissues (embryonic mouse brain, SHARE-seq mouse skin, hematopoietic stem and progenitor cells (HSPCs) and developing human brain). While MultiVelo constructs an ODE of chromatin accessibility level c, unspliced RNA u and spliced RNA s for each gene, TIVelo only utilizes unspliced RNA u and spliced RNA s for RNA velocity estimation.\n\nWe first focused on the RNA velocity comparison on the HSPCs dataset, as shown in Fig.\u00a06a. The RNA velocity stream plot produced by TIVelo accurately depicts the differentiation from hematopoietic stem cells (HSC) into three lineages: myeloid lineage, erythroid lineage and platelet lineage (Fig.\u00a06c, top row). In contrast, the RNA velocity inferred by MultiVelo does not reflect the actual developmental trajectory of the erythroid lineage, and scVelo (dynamical mode) incorrectly identifies progenitors and megakaryocyte\u00a0(Prog MK) as the root cluster.\n\na Comparative velocity stream plots of the HSPCs dataset generated by TIVelo, MultiVelo, and scVelo (dynamical mode). b The cluster graph after graph pruning and main path selection. The end cluster, Platelet, is selected as the origin node. The mean orientation score along the main path is \u22127.773. c Top: The depiction of three distinct lineages in the HSPCs dataset. Bottom: The u-s scatter plots and fitted velocity from TIVelo of three genes AZU1, KEL, and VWF. For each gene, u and s are expressed more differentially across cells in its above lineage than cells in other lineages. For each gene, cells not in its corresponding lineage are shown in the panel at the bottom right. The velocity vectors of three genes from TIVelo accurately reflect the developmental trajectory in their corresponding lineage. d A comparison of the cross-boundary direction correctness (CBDir) (UMAP space) score across four datasets. e A comparison of the cross-boundary direction correctness (CBDir) (Gene space) score across four datasets.\n\nThe enhanced performance of TIVelo can be better understood through the visualization of the HSPCs cluster graph (Fig.\u00a06b). The origin node initially selected by TIVelo is the cell cluster Platelet, which is an end cluster in the HSPCs dataset. The main path selected by TIVelo is a path from Platelet to the expected root cluster HSC. The mean orientation score on the main path is \u22127.773, indicating that the direction should be reversed, and the origin node is reset as HSC. The direction inference at the cluster level facilitates the RNA velocity estimation for individual genes. Figure\u00a06c shows the velocity vectors of three genes inferred from TIVelo in the bottom half, namely AZU1, KEL, and VWF, corresponding to myeloid lineage, erythroid lineage and platelet lineage, respectively, which are displayed in the upper half of the figure. For each gene, u and s are expressed more differentially across cells in its corresponding lineage than cells in other lineages. The velocity vectors of three genes from TIVelo accurately reflect the developmental trajectory in their corresponding lineage.\n\nFinally, we compared TIVelo and MultiVelo across the four datasets employed by MultiVelo. We measure their performance by CBDir (UMAP space) and CBDir (Gene space), which are two metrics testing if the estimated velocity follows the expected developmental trajectory (Methods). TIVelo achieved much higher CBDir (UMAP space) scores for the mouse brain and HSPCs datasets, and comparable CBDir (UMAP space) scores for the mouse skin and human brain datasets (Fig.\u00a06d). Furthermore, TIVelo consistently outperformed MultiVelo on all four datasets in terms of CBDir (Gene space) (Fig.\u00a06e).\n\nThe cluster graphs used in TIVelo and the velocity stream plots comparisons between TIVelo and MultiVelo from another three single-cell multi-omics (RNA\u2009+\u2009ATAC) datasets, are shown in supplementary information, including embryonic mouse brain (Supplementary Fig.\u00a011a), SHARE-seq mouse skin (Supplementary Fig.\u00a011b) and developing human brain (Supplementary Fig.\u00a011c).\n\nTo thoroughly assess TIVelo\u2019s performance, we did a comparison on ten datasets commonly used by existing RNA velocity inference methods, as shown in Fig.\u00a07. The comparison includes TIVelo and six other benchmarking methods, namely scVelo (stochastic mode), scVelo (dynamical mode), UniTVelo, cellDancer, veloVI and DeepVelo. The metrics to measure their performance include cross-boundary direction correctness (CBDir) (Gene space), transition cosine similarities (TransCosine), and velocity coherence (VeloCoh)14. CBDir (Gene space) and TransCosine assess if the estimated velocity vectors follow the expected developmental trajectory, while VeloCoh tests if the inferred velocity vectors are consistent with the differences of spliced counts between the expected future state and current state (Methods). As is illustrated in Fig.\u00a07a, b the CBDir (Gene space) and TransCosine scores of TIVelo demonstrate superior or comparable performance to those of other methods across all ten datasets. The VeloCoh score of TIVelo is the highest in five datasets, while in the pancreas, dentate gyrus, mouse gastrulation (erythroid) and dentate gyrus 2, TIVelo achieves a score comparable to veloVI or DeepVelo (Fig.\u00a07c). In summary, TIVelo outperforms or matches the benchmarking methods, underlining its superior performance in RNA velocity inference.\n\na Comparison of TIVelo and six benchmarking methods by cross-boundary direction correctness (CBDir) (Gene space) score across ten datasets. b Comparison of TIVelo and six benchmarking methods by transition cosine similarities (TransCosine) across ten datasets. c Comparison of TIVelo and six benchmarking methods by velocity coherence (VeloCoh) across ten datasets.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61628-x/MediaObjects/41467_2025_61628_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61628-x/MediaObjects/41467_2025_61628_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61628-x/MediaObjects/41467_2025_61628_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61628-x/MediaObjects/41467_2025_61628_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61628-x/MediaObjects/41467_2025_61628_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61628-x/MediaObjects/41467_2025_61628_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61628-x/MediaObjects/41467_2025_61628_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this paper, we present TIVelo for RNA velocity estimation. TIVelo is based on TI at the cluster level, by inferring velocity direction on the cluster graph and supervising RNA velocity estimation for individual cells. TIVelo stands out from current RNA velocity inference methods due to several innovative features.\n\nFirstly, TIVelo employs a linear tree model to determine the direction of the main path of the cluster graph. This approach allows for the comprehensive mining of signals embedded in gene expression profiles. By leveraging the intrinsic property of the unspliced-spliced relationship, TIVelo effectively mitigates the impact of genes with varying ODE rate parameters during development, enabling more reliable velocity estimation.\n\nSecondly, TIVelo infers the RNA velocity vector of individual cells by referring to the direction on the cluster graph. This ensures consistency and avoids potential contradictions that may arise when aggregating velocity from independently fitted genes. By considering the direction of the cellular trajectory rather than relying solely on individual gene expression patterns, TIVelo enhances the reliability of velocity estimation.\n\nThirdly, TIVelo only requires unspliced and spliced RNA for RNA velocity inference, eliminating the need for additional prior information or other biological modalities. This simplifies the workflow and reduces the burden of data acquisition and preprocessing, making TIVelo more accessible and user-friendly.\n\nDespite its advantages, TIVelo does have some limitations that should be considered and further improved in the future. First, TIVelo may fail to accurately estimate velocity for large datasets with complex cluster graph topologies. As TIVelo\u2019s approach relies on inferring the direction on the cluster level graph, the presence of complicated connections and branching patterns within the graph can pose challenges. In such cases, it may be beneficial to reapply the clustering algorithm using a lower resolution, resulting in larger cell clusters and a simplified structure of the corresponding cluster graph.\n\nSecond, it is worth noting that the velocity vectors produced by TIVelo may not exhibit the same level of smoothness as those generated by ODE-based methods. This can be observed through the velocity inferred from TIVelo for genes in dentate gyrus and organoid datasets (Figs.\u00a03d and 4d). The strategy of constructing dNN for each cell and using the mean expression level in dNN as the future state can introduce noise into the velocity inference (Methods). By using the fit function of the RNA velocity inference step, which introduces a regularization term to enhance the velocity consistency, and increasing the hyperparameter \\(\\lambda ^{\\prime}\\) controlling the weight of the regularization term, this issue can be alleviated (Methods).", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The dataset should undergo preprocessing prior to the utilization of TIVelo. TIVelo encompasses three main steps: main path selection, orientation inference, and RNA velocity estimation. During the main path selection, the cluster graph for the data is constructed, and a long, primary path within the graph is chosen. In the orientation inference step, we utilize the unspliced RNA u and spliced RNA s counts to infer the direction of the main path, based on the intrinsic characteristics of transcription: the unspliced RNA u should always be expressed earlier than the spliced RNA s and decrease earlier than s. This bypasses the need to explicitly specify an ODE model for u and s. The assumptions in the ODE models may not be realistic and can be violated in real datasets. Our model-free approach provides robustness in inferring the direction along the main path. This is the pivotal step of TIVelo. In the step of RNA velocity estimation in TIVelo, the velocity vector of each cell will be inferred, adapting to the direction at the cluster level.\n\nDatasets are preprocessed through scVelo9 standard preprocessing pipeline. This pipeline includes filtering genes with at least 20 cells with non-zero unspliced and spliced counts, selecting the top 2000 highly variable genes, log-transforming, and data smoothing through averaging over the 30 nearest neighbors. The preprocessing can be implemented directly through scvelo.pp.filter_and_normalize and scvelo.pp.moments functions.\n\nIn this step, we first construct a cluster graph for the dataset, followed by graph pruning to simplify the graph topology. Subsequently, we select an origin node in this cluster graph, from which a main path in the graph will be selected.\n\nThe cluster graph, represented by cell clusters as nodes and connecting weights as edges, is initially constructed for the dataset using PAGA7. If cell annotation is included in the dataset, it is utilized as the group key for PAGA. If not, we employ Leiden for clustering with resolution\u2009=\u20090.6, and subsequently use the clustering label as the group key.\n\nAfter the construction of the cluster graph, we perform graph pruning, where the purpose is to filter out small or isolated clusters (nodes) in the cluster graph.\n\nThe first step in graph pruning is to select the major nodes with a large number of cells in the graph and drop the other nodes. Suppose we have a dataset with N cells and C clusters, and cluster i has Ni cells where \u2211iNi\u2009=\u2009N. Then (1) order clusters by cell numbers. Select clusters from large to small until the selected cell number \\({\\sum }_{i\\in {{{\\mathcal{S}}}}}{N}_{i} > 0.5N\\), where \\({{{\\mathcal{S}}}}\\) is the set of selected clusters. \\({{{\\mathcal{S}}}}\\) should include at least five clusters or all clusters if C\u2009<\u20095; (2) if \\(i^{\\prime}\\) is connected to \\(i \\in {{{\\mathcal{S}}}}\\) with weight \\({w}_{i^{\\prime},i} > 0.6{\\max }_{i\\ne i^{\\prime} }{w}_{i^{\\prime},i}\\), then \\(i^{\\prime}\\) should be added to \\({{{\\mathcal{S}}}}\\). The graph is denoted as G after this first step of graph pruning.\n\nThe second step in graph pruning is to reconnect disjoint sub-graphs in graph G. If there is more than one disjoint sub-graph in graph G, then for each pair of sub-graphs (G1,\u00a0G2), G1,\u00a0G2 \u2286 G, we add back one cluster i not included in graph G, which can connect G1 and G2, based on the following rule: let wi1 and wi2 denote the weights between cluster i and its closest clusters in sub-graphs G1 and G2, respectively; we select cluster i with the largest value in wi1wi2. If such a cluster i does not exist, we recommend the user to run the subsequent steps of TIVelo for the subgraphs separately, because this indicates that there may be several independent lineages in the dataset.\n\nOrigin node refers to a node in the cluster graph, beginning from which we select the main path (see \u201cMain path selection\"). We initially select the origin node from terminal states, which refers to either root clusters or end clusters in the cluster graph. It is crucial to avoid selecting an intermediate cluster as an origin node, as this will affect the performance of inferring the direction on the main path (Supplementary Fig.\u00a012). In the orientation inference step, the origin node can be reset based on the orientation score on the main path (see \u201cInfer orientation\").\n\nWe first implement the scvelo.tl.terminal_states function in the package scVelo to obtain the root and end score for each single cell. After obtaining the root and end score for each cell, the mean root and end scores, Ri and Ei, are computed for each cluster i\u2009(1 \u2264 i \u2264 C). Subsequently, we rank the mean root score Ri and end score Ei and get k1, \u22ef \u2009, kC and \\(k^{{\\prime} }_{1},\\cdots \\,,k^{{\\prime} }_{C}\\) such that \\({R}_{{k}_{1}} > \\cdots > {R}_{{k}_{C}}\\) and \\({E}_{k^{{\\prime} }_{1}} > \\cdots > {E}_{k^{{\\prime} }_{C}}\\), where k1, \u22ef \u2009, kC and \\(k^{{\\prime} }_{1},\\cdots \\,,k^{{\\prime} }_{C}\\) are two sets of indices for the clusters. The origin node is then selected based on the following procedure:\n\nFor i\u2009=\u20091:\u00a0C select the first ki with \\({R}_{{k}_{i}} > 0.1\\) and \\({E}_{{k}_{i}} < {R}_{{k}_{i}}\\);\n\nIf no ki is selected then for i\u2009=\u20091:\u00a0C select the first \\(k^{{\\prime} }_{i}\\) with \\({E}_{k^{{\\prime} }_{i}} > 0.1\\) and \\({R}_{k^{{\\prime} }_{i}} < {E}_{k^{{\\prime} }_{i}}\\);\n\nIf no \\(k^{{\\prime} }_{i}\\) is selected then select k1.\n\nThe detailed steps for selecting the origin node are presented in the Supplementary Note\u00a04.\n\nThe main path in a cluster graph refers to a path that begins from the origin node and involves as many cells as possible. Let o denote the origin node. We define down(i) as the downstream nodes for node i, which represent nodes that are directly connected to i, excluding nodes in the path between node o and i; and define successor(i) as all nodes being successors to node i, which are nodes directly or indirectly connected to i, except for all nodes in the path between node o and i (Supplementary Fig.\u00a013). The nodes in the main path are then selected sequentially as follows:\n\nChoose i \u2208 down(o) to let \\({N}_{i}+{\\sum }_{i^{\\prime} \\in {{{\\rm{successor}}}}(i)}{N}_{i^{\\prime} }\\) attain its maximum. Then add i to the main path;\n\nChoose \\(i^{\\prime} \\in {{{\\rm{down}}}}(i)\\) to let \\({N}_{i^{\\prime} }+{\\sum }_{i^{\\prime\\prime} \\in {{{\\rm{successor}}}}(i^{\\prime} )}{N}_{i^{\\prime\\prime} }\\) attain its maximum. Then add \\(i^{\\prime}\\) to the main path;\n\nRepeat this process until no node can be selected.\n\nAfter the main path is selected, we proceed to infer the orientation/direction along the main path. We first assign a pseudotime to cells along the main path, beginning from the origin node. Consequently, for each gene, the corresponding unspliced RNA u and spliced RNA s can be ordered by this pseudotime to form two time series. Then we fit a linear tree model to capture the rising/falling time sections of each time series. Based on the linear tree model, an orientation score can be calculated to facilitate the inference of the direction along the main path. Finally, the orientation score from each gene will be aggregated, according to which we select a new origin node (root node) for the cluster graph.\n\nWe set one root cell in the origin node as time 0, and infer a pseudotime for each cell along the main path, by using diffusion pseudotime37. The root cell with time 0 is chosen by the default way indicated by diffusion pseudotime: adata.uns['iroot'] = np.flatnonzero(adata.obs['cell_types']=='Origin')[0]. Specifically, t1, \u22ef \u2009, tN are corresponding pseudotime for N cells along the main path, with t1 < \u22ef < tN. For each gene g, we reorder the u-s expression vector \\({{{{\\boldsymbol{u}}}}}_{g},{{{{\\boldsymbol{s}}}}}_{g}\\in {{\\mathbb{R}}}^{N}\\) according to the pseudotime of the cells, forming the time series of u and s: \\({\\tilde{{{{\\boldsymbol{u}}}}}}_{g}=({\\tilde{u}}_{1,g},\\cdots \\,,{\\tilde{u}}_{N,g})\\) and \\({\\tilde{{{{\\boldsymbol{s}}}}}}_{g}=({\\tilde{s}}_{1,g},\\cdots \\,,{\\tilde{s}}_{N,g})\\). To alleviate the issue of noise, ug and sg will be smoothed by a 1-d convolution with a kernel of \\((\\frac{1}{K},\\cdots \\,,\\frac{1}{K})\\in {{\\mathbb{R}}}^{K}\\), where K is the kernel size and is set to be 100 by default.\n\nNow we would like to see if the current cell pseudotime t1, \u22ef \u2009, tN is correct or should be reversed. To do this, we utilize an intrinsic property of u and s, that is, u should be expressed earlier than s during transcription induction, and decrease earlier than s during transcription repression, since u is the precursor of s. To use this property, it is crucial to find the time section in which both \\({\\tilde{{{{\\boldsymbol{u}}}}}}_{g}\\) and \\({\\tilde{{{{\\boldsymbol{s}}}}}}_{g}\\) are increasing (induction phase) or decreasing (repression phase), which can be achieved by constructing the linear tree model for each time series. More precisely, for \\({\\tilde{{{{\\boldsymbol{u}}}}}}_{g}\\) and \\({\\tilde{{{{\\boldsymbol{s}}}}}}_{g}\\), the unspliced and spliced RNA for each gene g, we\n\nFit a linear regression model for \\({\\tilde{{{{\\boldsymbol{u}}}}}}_{g}\\) and \\({\\tilde{{{{\\boldsymbol{s}}}}}}_{g}\\) against cell index x \u2208 [1, N] ordered by the pseudotime of the cells;\n\nPerform grid searching for a split point xs \u2208 [1, N], so that when we fit a piece-wise linear regression model on both sides of the split point, i.e., on [1,\u00a0xs] and (xs,\u00a0N], the total mean squared error (MSE) is the lowest;\n\nSplit the data into two parts [1,\u00a0xs] and (xs,\u00a0N], and for each part repeat the above steps until convergence. The convergence is defined by two conditions: either MSE(r)\u00a0\u2212\u00a0MSE(r+1)\u00a0<\u00a0100, where MSE(r) is the total MSE of iteration r, or the interval between any two split points becomes shorter than 10.\n\nBased on the linear tree constructed for each gene, an orientation score can be calculated, indicating if the current direction (determined by cell pseudotime) is correct or not. More specifically, for a gene g the score is calculated by\n\nwhere \\({{{{\\mathcal{T}}}}}_{g}\\) is the collection of rising time sections (in which both \\({\\tilde{{{{\\boldsymbol{u}}}}}}_{g}\\) and \\({\\tilde{{{{\\boldsymbol{s}}}}}}_{g}\\) are increasing) or falling time sections (in which both \\({\\tilde{{{{\\boldsymbol{u}}}}}}_{g}\\) and \\({\\tilde{{{{\\boldsymbol{s}}}}}}_{g}\\) are decreasing), \\({k}_{u,\\tau }^{(g)}\\) and \\({k}_{s,\\tau }^{(g)}\\) are the slopes of the fitted linear tree for \\({\\tilde{{{{\\boldsymbol{u}}}}}}_{g}\\) and \\({\\tilde{{{{\\boldsymbol{s}}}}}}_{g}\\) in section \u03c4, \\({d}_{n,g}={\\tilde{u}}_{n,g}-{\\tilde{s}}_{n,g}\\), and l\u03c4 is the length of the section \u03c4.\n\nThe rationale behind this score is as follows. During transcription induction, both u and s are increasing, and the condition \\({I}_{({k}_{u,\\tau }^{(g)}\\ > \\ 0,{k}_{s,\\tau }^{(g)}\\ > \\ 0)}=1\\) should hold. If dn,g\u2009>\u20090, it indicates that u is expressed earlier than s. Conversely, during transcription repression, both u and s are decreasing, and \\({I}_{({k}_{u,\\tau }^{(g)} < 0,{k}_{s,\\tau }^{(g)} < 0)}=1\\) should hold. If dn,g\u2009<\u20090, it indicates that u decreases earlier than s. In both cases the score will be positive, which supports that the given direction (cell pseudotime) is correct. Otherwise the given direction is wrong and should be reversed. The factor l\u03c4 ensures that longer rising/falling time sections, which possess a stronger signal, carry a larger weight when calculating the score.\n\nTypically, in scRNA-seq datasets the magnitude of s is much larger than that of u9. In this case, when we calculate the orientation score, the difference \\({d}_{n,g}={\\tilde{u}}_{n,g}-{\\tilde{s}}_{n,g}\\) will always be negative. To address this issue, \\({\\tilde{{{{\\boldsymbol{u}}}}}}_{g}\\) and \\({\\tilde{{{{\\boldsymbol{s}}}}}}_{g}\\) should be normalized before constructing the linear tree. In this normalization, \\({\\tilde{{{{\\boldsymbol{u}}}}}}_{g}\\) and \\({\\tilde{{{{\\boldsymbol{s}}}}}}_{g}\\) are rescaled to sum to 1 for each gene g, i.e., \\({\\tilde{u}}_{n,g}\\leftarrow {\\tilde{u}}_{n,g}/{\\sum }_{n}{\\tilde{u}}_{n,g}\\) and \\({\\tilde{s}}_{n,g}\\leftarrow {\\tilde{s}}_{n,g}/{\\sum }_{n}{\\tilde{s}}_{n,g}\\). This is to facilitate the comparison between the relative expression levels of u and s. We further evaluated an alternative normalization approach, in which u and s are rescaled by the maximum values of u and s for each gene. The details are discussed in Supplementary Note\u00a05.\n\nTo validate the robustness of TIVelo in practical applications, we carried out several experiments, including the evaluation of TIVelo\u2019s performance on the perturbed main path, and on the main path with reversed direction. Furthermore, we can enhance TIVelo\u2019s robustness with respect to the origin node through the refinement of the origin node selection strategy. The details are discussed in Supplementary Notes\u00a06 and7.\n\nTo accurately infer the new origin node (root node), we only aggregate orientation scores from velocity genes9,10 with high u-s expression data quality. Briefly speaking, velocity gene refers to one with a positive coefficient (\u03b3\u2009>\u20090.01) between u and s, and a positive coefficient of determination (R2\u2009>\u20090.01). In addition, the ratio of standard deviations of u and s should be moderate (0.03\u00a0<\u00a0\u03c3ratio\u2009<\u20093). For cells along the main path, the orientation score can be calculated for each velocity gene g, i.e., S1, \u22ef \u2009, SG, where G is the number of velocity genes in the data.\n\nUsually there exist some branches in the cluster graph, which refer to all paths except the main path (Fig.\u00a03b, green paths). We infer the new origin node by two cases, determined by the relationship between the main path and the branches.\n\nIn the first case, both endpoints of the main path are not connected to the branches (Supplementary Fig.\u00a014, left). In this case, once \\(\\frac{1}{G}{\\sum }_{g}{S}_{g} < 0\\), we consider the direction on the main path to be incorrect, and a new origin node should be set as the other end of the main path. Otherwise when \\(\\frac{1}{G}{\\sum }_{g}{S}_{g}\\ge 0\\), the origin node remains unchanged.\n\nIn the second case, one endpoint i of one branch b is also an endpoint of the main path (Supplementary Fig.\u00a014, right). If this happens, we check the direction on branch b similar to the way for the main path. Specifically, we assign a pseudotime to cells along branch b by diffusion pseudotime. Time series \\({\\tilde{{{{\\boldsymbol{u}}}}}}_{g}^{b}\\) and \\({\\tilde{{{{\\boldsymbol{s}}}}}}_{g}^{b}\\) of gene g are extracted for calculating orientation score \\({S}_{g}^{b}\\). The average score \\(\\frac{1}{G}{\\sum }_{g}{S}_{g}^{b}\\) is used to see if the direction of b should be corrected. If the corrected direction on the main path is aligned with the corrected direction on branch b, the new origin node is set as the other endpoint j of branch b; otherwise the new origin node is set as node i (Supplementary Fig.\u00a014, right).\n\nAfter the new origin node (root node) of the cluster graph is determined, we infer the RNA velocity for each cell. The velocity vector of each cell should follow the direction of the cluster graph. We assign a level to each node in the cluster graph and identify the child nodes of each node based on their levels. In addition, a new pseudotime \\(t^{\\prime}\\) is assigned to each cell, beginning from the new origin node. RNA velocity of each cell in node i is inferred according to its pseudotime and child nodes.\n\nFirstly, we assign a level for each node in the original cluster graph without graph pruning. We rerun PAGA on the whole dataset and obtain connectivities weight wi,j for each pair of clusters (i,\u00a0j). The levels for the nodes are assigned as follows:\n\nThe level of new origin node o is set to be 0;\n\nAssume that node i has level k. For any node \\(i^{\\prime}\\) if (1) \\({w}_{i,i^{\\prime} } > {z}_{1}\\); (2) there does not exist node j such that \\({w}_{i,i^{\\prime} } < {z}_{2}{w}_{i,j}{w}_{j,i^{\\prime} }\\), where z1 and z2 are prespecified thresholds (the default values for z1 and z2 are 0.1 and 1, respectively), then the level of \\(i^{\\prime}\\) is assigned as k\u2009+\u20091.\n\nContinue the above steps until every nodes have a level.\n\nSecondly, based on the level assigned for each node, we determine the child nodes for each node in the graph as follows:\n\nFor every node i, node \\(i^{\\prime}\\) is the child node of i if (1) \\({w}_{i,i^{\\prime} } > {z}_{1}\\); (2) level(\\(i^{\\prime}\\))-level(i)\u2009=\u20091;\n\nIf node j is not the child node of any node, it will be the child node of \\(j^{\\prime}={{{\\mathrm{argmax}}}\\,}_{j^{\\prime} }{w}_{j,j^{\\prime} }\\), i.e., each node should have at least one parent node, except for the origin node;\n\nIf node j has two or more parent nodes, e.g., i1,\u00a0i2 \u22ef ik then the parent node of j will be \\({i}_{k}={{{\\mathrm{argmax}}}\\,}_{k}{w}_{j,{i}_{k}}\\), i.e., each node is not allowed to have more than one parent node.\n\nAfter the child nodes are determined, we can draw a directed edge from any node i to its child node \\(i^{\\prime}\\). This gives us the result of DTI for the cell clusters in the dataset.\n\nTo infer RNA velocity of each cell, we create a dNN graph for each cell n, which signifies the future state of cell n in its k-nearest neighborhood (k-NN) (as illustrated in Fig.\u00a01h). We construct a k-NN graph for each cell n denoted as \\({{{{\\mathcal{N}}}}}_{n}\\), through scvelo.pp.neighbors from scVelo package, setting k\u2009=\u200930 by default. We also reapply diffusion pseudotime for the entire dataset (setting one root cell in the new origin node as time 0 by the default way as before: adata.uns['iroot'] = np.flatnonzero(adata.obs['cell_types']=='Origin')[0]) to assign a pseudotime \\(t^{{\\prime} }_{n}\\) for each cell. Then cell \\(n^{\\prime}\\) is considered to be in the dNN of cell n if (1) \\(n^{\\prime} \\in {{{{\\mathcal{N}}}}}_{n}\\); (2) \\(t^{{\\prime} }_{n} < t^{{\\prime} }_{n^{\\prime} }\\); (3) \\(i^{\\prime} \\in {{{\\rm{child}}}}(i)\\) or \\(i=i^{\\prime}\\), where clusters i and \\(i^{\\prime}\\) are the clusters that cells n and \\(n^{\\prime}\\) belong to, respectively.\n\nThe velocity vector for cell n should point toward the mean expression level of cells within dNN of cell n. That is equivalent to minimizing the following objective Lg for each gene g:\n\nwhere dnn(n) is the dNN of cell n, Nn is the number of cells in dnn(n), and \u03bb is a hyperparameter controlling the weight of loss from u and s, which is set as 1 by default. \\({\\tilde{v}}_{n,g}^{(u)}\\) and \\({\\tilde{v}}_{n,g}^{(s)}\\) are inferred velocities for u and s of cell n and gene g.\n\nOne straightforward way to infer \\({\\tilde{v}}_{n,g}^{(u)}\\) and \\({\\tilde{v}}_{n,g}^{(s)}\\) is to let Lg in Equation (2) be 0 directly, to obtain:\n\nand\n\nThis is the simple_fit function of TIVelo.\n\nInstead of directly calculating \\({\\tilde{v}}_{n,g}^{(u)}\\) and \\({\\tilde{v}}_{n,g}^{(s)}\\) by Equation (3) and (4), we can add some regularization to the objective in order to enhance the consistency of the velocity vector of similar cells. Specifically, the regularization term is designed as\n\nwhere \\({{\\tilde{{\\boldsymbol{v}}}}}_{n}={(\\tilde{v}_{n,g})}_{g}\\) and \\({{\\tilde{{\\boldsymbol{v}}}}}_{n^{\\prime} }={(\\tilde{v}_{n^{\\prime},g})}_{g}\\) are RNA velocity vectors of cell n and \\(n^{\\prime}\\), and \\({{{\\mathcal{C}}}}(n)\\) is the cluster cell n belongs to. We update matrices \\({(\\tilde{v}_{n,g})}_{n,g}\\) to minimize \\({\\sum }_{g}{L}_{g}+\\lambda ^{\\prime} {L}_{c}\\), where \\(\\lambda ^{\\prime}\\) is another hyperparameter controlling the weight of the regularization term. This is the fit function of TIVelo. This function is used by default with \u03bb\u2009=\u20091 and \\(\\lambda ^{\\prime}=0.1\\).\n\nIn addition to the default strategy for selecting the root cell when applying the diffusion pseudotime, we refined this strategy for two datasets: mouse gastrulation (erythroid) and RPE1-FUCCI, for improved velocity inference in their root cluster. The details are discussed in Supplementary Note\u00a08.\n\nKinetic rate mode of TIVelo enables simultaneous inference of cell-specific kinetic rates (\u03b1, \u03b2 and \u03b3) when inferring RNA velocity for each cell. In this mode, instead of updating \\({\\tilde{v}}_{n,g}^{(u)}\\) and \\({\\tilde{v}}_{n,g}^{(s)}\\) in Equation (2) directly, we calculate the inferred velocity \\({\\tilde{v}}_{n,g}^{(u)}\\) and \\({\\tilde{v}}_{n,g}^{(s)}\\) by cell-specific kinetic rates \u03b1n,g, \u03b2n,g, and \u03b3n,g as follows:\n\nFurthermore, we infer the kinetic rates \u03b1n,g, \u03b2n,g, and \u03b3n,g from unspliced and spliced expressions as follows:\n\nwhere f\u03b8 is fully connected layers with parameters \u03b8, \u03b1n, \u03b2n, and \u03b3n are G-dimensional kinetic rates for cell n across all G genes, and un and sn are G-dimensional unspliced and spliced expressions of cell n. This approach provides natural regularization, ensuring cells with similar expression profiles have comparable kinetic rates.\n\nBy default, we use a f\u03b8 with three layers in Equation (7). The input dimensions, (two) hidden dimensions and output dimensions of f are 2G, (256,\u00a064) and 3G, respectively. We further calculate the inferred velocities \\({\\tilde{v}}_{n,g}^{(u)}\\) and \\({\\tilde{v}}_{n,g}^{(s)}\\) by Equation (6), and minimize the objective Lg in Equation (2) with respect to \u03b8. The model is trained for 300 epochs with a learning rate 0.001.\n\nThe velocity graph \\({{{\\mathbf{\\Pi }}}}=({\\pi }_{n,n^{\\prime} })\\) and transition matrix \\(\\tilde{{{{\\mathbf{\\Pi }}}}}=({\\tilde{\\pi }}_{n,n^{\\prime} })\\) for each pair of cells \\((n,n^{\\prime} )\\) can be constructed as follows9:\n\nand\n\nwhere sn is the spliced RNA expression of cell n, vn is the inferred velocity vector of cell n, \\({Z}_{n}={\\sum }_{n^{\\prime} }\\exp \\left(\\frac{{\\pi }_{n,n^{\\prime} }}{{\\sigma }_{n}^{2}}\\right)\\), and \u03c3n is the kernel width parameter.\n\nBy using the embedding position en of each cell n, the embedding velocity vector of cell n can be derived by\n\nwhere \\({\\tilde{{{{\\boldsymbol{\\delta }}}}}}_{n,{n}^{{\\prime} }}=\\frac{{{{{\\boldsymbol{e}}}}}_{n}^{{\\prime} }-{{{{\\boldsymbol{e}}}}}_{n}}{\\parallel {{{{\\boldsymbol{e}}}}}_{n}^{{\\prime} }-{{{{\\boldsymbol{e}}}}}_{n}\\parallel }\\), and N is the total number of cells.\n\nWe construct the velocity graph and visualize embedding velocity stream plots using scvelo.tl.velocity_graph and scvelo.pl.velocity_embedding_stream from scVelo.\n\nTo compare the performance of different RNA velocity estimation methods, we compute several metrics to assess if the estimated velocity vectors are consistent and have the correct direction following the expected developmental trajectory.\n\nThe first one is CBDir11. This measures if the velocity vector of each cell has a correct direction to the downstream cells along the developmental trajectory. Specifically, assume that the expected developmental trajectory is from cluster A to cluster B, then (A,\u00a0B) is called a pair of cluster edge and\n\nwhere \\({{{{\\mathcal{N}}}}}_{n}(n)\\) is the k-NN for cell n, xe,n and \\({{{{\\boldsymbol{x}}}}}_{e,n^{\\prime} }\\) are the embedding expression vectors of cell n and \\(n^{\\prime}\\) in 2-dimensional space, and ve,n is the embedding velocity vector for cell n using the same dimensional-reduction algorithm as for expression, which is calculated as in Equation (10). To better check the situation in high-dimensional gene space, we also propose a new metric CBDir (Gene space) using the original expression vector xn and velocity vector vn instead of the embedding version:\n\nTo distinguish CBDir (Gene space) from the original metrics, we refer to the initially proposed version as CBDir (UMAP space).\n\nThe second metric is transition cosine similarities (TransCosine), which is calculated by\n\nfor each pair of cluster edge (A,\u00a0B), where \\({\\pi }_{n,n^{\\prime} }\\) is the \\((n,n^{\\prime} )\\) element of velocity graph9. \\({\\pi }_{n,n^{\\prime} }\\) is the cosine similarities between velocities and potential cell state transitions, calculated as in Equation (8). TransCosine measures if the velocity graph constructed is consistent with the cluster edge (A,\u00a0B).\n\nThe third metric is velocity coherence (VeloCoh)14, which is calculated by\n\nwhere \\(\\tilde{{{{\\mathbf{\\Pi }}}}}=({\\tilde{\\pi }}_{n,n^{\\prime} })\\) is the cell-cell transitions probability matrix as in Eq. (9), S is the spliced RNA expression matrix and \\({[\\tilde{{{{\\mathbf{\\Pi }}}}}{{{\\bf{S}}}}]}_{n,:}\\) is the n-th row of \\(\\tilde{{{{\\mathbf{\\Pi }}}}}{{{\\bf{S}}}}\\), which is the predicted future state of cell n. It measures if the predicted displacement of a cell \\({[\\tilde{{{{\\mathbf{\\Pi }}}}}{{{\\bf{S}}}}]}_{n,:}-{{{{\\boldsymbol{s}}}}}_{n}\\) is coherent with the inferred velocity vector. To simplify the calculation, we use a version of \\(\\tilde{{{{\\mathbf{\\Pi }}}}}\\) as \u03a0 divided by its row sums.\n\nFor each cell n (VeloCoh) and each pair of cluster edge (A,\u00a0B) (CBDir (Gene space), CBDir (UMAP space), TransCosine), the aforementioned scores are calculated and the mean values are used as the comparative metrics.\n\nFor cell-cycle datasets of FUCCI, we compare the performance of different methods using velocity sign accuracy proposed by veloVI. Specifically, assume that each cell in cell-cycle datasets of FUCCI has a cell cycle position pi with pi\u00a0<\u00a0pi+1 (each cell cycle position may correspond to several cells). The empirical velocity \\({\\hat{{{{\\boldsymbol{v}}}}}}^{({p}_{i})}\\) at position pi is calculated by \\({\\hat{{{{\\boldsymbol{v}}}}}}^{({p}_{i})}\\propto {\\bar{{{{\\boldsymbol{s}}}}}}^{({p}_{i+1})}-{\\bar{{{{\\boldsymbol{s}}}}}}^{({p}_{i})}\\), where \\({\\bar{{{{\\boldsymbol{s}}}}}}^{({p}_{i})}\\) is the mean spliced expression level of cells at position pi. The estimated velocity at position pi is calculated by \\({\\tilde{{{{\\boldsymbol{v}}}}}}^{({p}_{i})}\\propto {\\bar{{{{\\boldsymbol{v}}}}}}^{({p}_{i+1})}-{\\bar{{{{\\boldsymbol{v}}}}}}^{({p}_{i})}\\), where \\({\\bar{{{{\\boldsymbol{v}}}}}}^{({p}_{i})}\\) is the mean of inferred velocity of cells at position pi. Sign accuracy of position pi is calculated as the fraction of components that the signs of \\({\\hat{{{{\\boldsymbol{v}}}}}}^{({p}_{i})}\\) and \\({\\tilde{{{{\\boldsymbol{v}}}}}}^{({p}_{i})}\\) agree, accounting for positive velocity, negative velocity and zero velocity.\n\nscVelo. scVelo implements preprocessing by the following functions:\n\n\nscvelo.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)\nscvelo.pp.moments(adata, n_neighbors=30, n_pcs=30)\n\n\nFor both stochastic mode and dynamical mode, we use the default setting of scVelo following the tutorial: https://scvelo.readthedocs.io/en/stable/.\n\nNotably, the inherent randomness in scVelo\u2019s dynamical mode and veloVI may lead to differences between the reproduced results and the results shown in the original paper. The details are discussed in Supplementary Note\u00a09.\n\nUniTVelo. UniTVelo follows the preprocessing procedure of scVelo. The default configuration settings of UniTVelo are used as follows (https://unitvelo.readthedocs.io/en/latest/index.html):\n\n\nvelo_config=unitvelo.config.Configuration()\nvelo_config.R2_ADJUST=True\nvelo_config.IROOT=None\nvelo_config.FIT_OPTION='1'\nvelo_config.AGENES_R2=1\n\n\nTo ensure fairness, we set velo_config.IROOT to None for all datasets by default.\n\nIn UniTVelo, the preferred mode for datasets with cell cycle phase included (pancreas, retina, RPE1-FUCCI and U2OS-FUCCI) or with sparse cell types included (dentate gyrus) should be the independent mode (mode 2). For such datasets, we reproduced the results of UniTVelo by mode 2, using velo_config.FIT_OPTION='2'.\n\nThe details of configuration settings in UniTVelo are discussed in Supplementary Notes\u00a010 and11.\n\ncellDancer. We adopted the same preprocessing procedure and hyperparameters in the model for all datasets when we implemented cellDancer:\n\nUsing scvelo.pp.filter_and_normalize and scvelo.pp.moments for preprocessing with default arguments. Using \u201cvelocity genes\" defined in UniTVelo for RNA velocity analysis.\n\nInferring RNA velocity for each cell following the instructions in the tutorial page for mouse gastrulation (erythroid)(https://guangyuwanglab2021.github.io/cellDancer_website/notebooks/case_study_gastrulation.html): \n\nUsing celldancer.velocity with permutation_ratio=0.125.\n\nUsing celldancer.compute_cell_velocity with projection_neighbor_size=10.\n\nFor consistency across methods, we used the scvelo.pl.velocity_embedding_stream function in the scVelo package to visualize the results from cellDancer. To provide additional context, we also include the built-in visualization approaches from cellDancer.\n\nThe implementation details of cellDancer, including the preprocessing procedure, hyperparameter settings and visualization methods, are documented in Supplementary Note\u00a012.\n\nveloVI. veloVI follows the preprocessing procedure of scVelo. The model training and the velocity inference are performed using default model settings according to its tutorial: https://velovi.readthedocs.io/en/latest/index.html.\n\nDeepVelo. DeepVelo follows the preprocessing procedure of scVelo. The model training and the velocity inference are performed using default model settings according to its tutorial: https://github.com/bowang-lab/DeepVelo.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All datasets used in this research are openly accessible to the public.\n\nPancreatic endocrinogenesis: The pancreatic endocrinogenesis38 data used in this study is available in the Gene Expression Omnibus (GEO) under accession code GSE132188. The dataset can be downloaded by running scvelo.datasets.pancreas in Python.\n\nDentate gyrus development: The dentate gyrus development39 data (dentate gyrus 2) used in this study is available in GEO under accession code GSE95753. A sub-dataset (dentate gyrus) comprising two time points (P12 and P35) can be accessed by running scvelo.datasets.dentategyrus in Python.\n\nMouse gastrulation (erythroid): The mouse gastrulation (erythroid)40 data used in this study is available in ArrayExpress under accession code E-MTAB-6967. The dataset can be downloaded by running scvelo.datasets.gastrulation_erythroid in Python.\n\nMouse hindbrain (Oligo): The mouse hindbrain (Oligo)41 data can be downloaded from the website of Kharchenko Lab at https://pklab.med.harvard.edu/ruslan/velocity/oligos/.\n\nMouse hindbrain (GABA, Glial): The mouse hindbrain (GABA, Glial)42 data is available in GEO under accession code GSE118068. The processed loom files are available from Figshare43 of DeepVelo.\n\nIntestinal organoid: The intestinal organoid32 data is available in GEO under accession code GSE128365. The dataset can be downloaded from the Dynamo package by running dynamo.sample_data.scEU_seq_organoid in Python.\n\nMouse retina development: The mouse retina development44 data is available in GEO under accession code GSM3466902. The dataset can be downloaded from the website of Kharchenko Lab at http://pklab.med.harvard.edu/peterk/review2020/examples/retina/.\n\nscNT-seq neuron KCl stimulation: The scNT-seq neuron KCl stimulation45 data is available in GEO under accession code GSE141851. The dataset can be downloaded from GitHub page at https://github.com/wulabupenn/scNT-seq.\n\nMouse embryonic fibroblast reprogramming: The mouse embryonic fibroblast reprogramming46 data is available in GEO under accession code GSE99915. The dataset can be downloaded from the CellRank package by running cellrank.datasets.reprogramming_morris in Python.\n\nFUCCI: The RPE1-FUCCI34 and U2OS-FUCCI32 data can be downloaded from Figshare47,48 of veloVI.\n\nEmbryonic mouse brain: The embryonic mouse brain data from 10x Genomics is available at 10x website.\n\nSHARE-seq mouse skin: The SHARE-seq mouse skin49 data is available in GEO under accession code GSE140203. The RNA and ATAC datasets can be downloaded from Figshare50,51 of MultiVelo.\n\nHuman HSPCs: The human HSPCs52 data is available in GEO under accession code GSE70677. The RNA and ATAC datasets can be downloaded from Figshare53,54 of MultiVelo.\n\nDeveloping human brain: The developing human brain55 data is available in GEO under accession code GSE162170. The RNA and ATAC datasets can be downloaded from Figshare56,57 of MultiVelo.\n\nThe processed datasets used in this study have been deposited to Figshare and can be accessed at: https://doi.org/10.6084/m9.figshare.27643494.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The TIVelo tool is developed as a Python package and is openly available for use at https://github.com/cuhklinlab/TIVelo, as well as deposited in Zenodo58 (https://doi.org/10.5281/zenodo.15637938). Comprehensive documentation, including detailed installation guidelines and steps to reproduce the results presented in this study, is available at https://tivelo.readthedocs.io/en/latest/.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Kharchenko, P. V. The triumphs and limitations of computational methods for scrna-seq. Nat. 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This work has been supported by the Chinese University of Hong Kong startup grant (4930181 to Z.L.), the Chinese University of Hong Kong Science Faculty\u2019s Direct Grant for Research 2023/2024 (to Z.L.), the Chinese University of Hong Kong Science Faculty\u2019s Collaborative Research Impact Matching Scheme (CRIMS 4620033 to Z.L.), the Hong Kong Research Grant Council (GRF 14301120 to Z.L., GRF 14300923 to Z.L.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China\n\nMuyang Ge,\u00a0Jishuai Miao,\u00a0Ji Qi,\u00a0Xiaocheng Zhou\u00a0&\u00a0Zhixiang Lin\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nZ.L. supervised this study. M.G. and Z.L. proposed and developed TIVelo\u2019s computational method. M.G. conducted data analysis. J.Q., J.M., and X.Z. provided advice on data analysis. M.G. and J.M. developed and released TIVelo\u2019s package. M.G. and Z.L. drafted the manuscript. J.Q. and X.Z. revised the manuscript.\n\nCorrespondence to\n Zhixiang Lin.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": ": Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Ge, M., Miao, J., Qi, J. et al. TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference.\n Nat Commun 16, 6258 (2025). https://doi.org/10.1038/s41467-025-61628-x\n\nDownload citation\n\nReceived: 12 November 2024\n\nAccepted: 26 June 2025\n\nPublished: 07 July 2025\n\nVersion of record: 07 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61628-x\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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receptors to enable erythropoietin-free erythropoiesis", + "journal": "Nature Communications", + "published": "29 January 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supporting Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_MOESM2_ESM.docx" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_MOESM4_ESM.xls" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_MOESM5_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_MOESM6_ESM.pdf" + }, + { + "label": "Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_MOESM7_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_MOESM8_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE285656", + "https://www.ncbi.nlm.nih.gov/bioproject/PRJNA31257/", + "/articles/s41467-025-56239-5#Sec33" + ], + "code": [], + "subject": [ + "CRISPR-Cas9 genome editing", + "Erythropoiesis", + "Erythropoietin", + "Molecular medicine", + "Synthetic biology" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4986623/v1.pdf?c=1738242490000", + "research_square_link": "https://www.researchsquare.com//article/rs-4986623/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-56239-5.pdf", + "preprint_posted": "23 Oct, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Blood transfusion plays a vital role in modern medicine. However, availability is contingent on donated blood, and frequent shortages pose a significant healthcare challenge. Ex vivo manufacturing of red blood cells (RBCs) derived from universal donor O-negative pluripotent stem cells emerges as a solution, yet the high cost of recombinant cytokines required for ex vivo erythroid differentiation remains a major barrier. Erythropoietin (EPO) signaling through the EPO receptor is indispensable to RBC development, and EPO is one of the most expensive components in erythroid-promoting media. Here, we used design-build-test cycles to develop highly optimized small molecule-inducible synthetic EPO receptors (synEPORs) which were integrated at a variety of genomic loci using homology-directed repair genome editing. We found that integration of synEPOR at the endogenous EPOR locus in an induced pluripotent stem cell producer line enabled culture with small molecule to yield equivalent erythroid differentiation, transcriptomic changes, and hemoglobin production compared to cells cultured with EPO. Due to the dramatically lower cost of small molecules vs. recombinant cytokines, these efforts eliminate one of the most expensive elements of ex vivo culture media\u2014EPO cytokine. Because dependence on cytokines is a common barrier to ex vivo cell production, these strategies could improve scalable manufacturing of a wide variety of clinically relevant cell types. More broadly, this work showcases how protein engineering and genome engineering may be combined to introduce precisely regulated and tunable behavior into cells, an advancement which will pave the way for increasingly sophisticated synthetic biology applications.Biological sciences/Developmental biology/Haematopoiesis/Erythropoiesis/Haematopoietic stem cellsBiological sciences/Genetics/CRISPR-Cas systems/CRISPR-Cas9 genome editingBiological sciences/Stem cells/Pluripotent stem cells/Induced pluripotent stem cellsBiological sciences/Biotechnology/Molecular engineering/Synthetic biologyBiological sciences/Developmental biology/Differentiation", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-4986623/v1/799c45b9d08a2b84922918f3.png", + "https://assets-eu.researchsquare.com/files/rs-4986623/v1/a1b5cf6b37ce3a25921d6ef7.png", + "https://assets-eu.researchsquare.com/files/rs-4986623/v1/474b2f8acbb40e25da9132f8.png", + "https://assets-eu.researchsquare.com/files/rs-4986623/v1/073e2137783aed8a3479c825.png", + "https://assets-eu.researchsquare.com/files/rs-4986623/v1/97593f3e83f7bb558bb29e67.png" + ] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nM.H.P. is a member of the scientific advisory board of Allogene Therapeutics. M.H.P. has equity in CRISPR Tx and Kamau Tx. C.T.C., M.H.P., and M.K.C. have filed provisional patent no. PCT/US2023/076969.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "ExtendedDatasynEPORDEGs.xlsDataset 1ExtendedDatasynEPORTPMsv3.xlsxDataset 2ExtendedDataiEPORGOAnalysis.xlsxDataset 3ExtendedDatasynEPORSpearmanCorrelation.xlsDataset 4NCBsynEPORSupplementalFiguresv2.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Blood transfusion plays a vital role in modern medicine, but frequent shortages occur. Ex vivo manufacturing of red blood cells (RBCs) from universal donor cells offers a potential solution, yet the high cost of recombinant cytokines remains a barrier. Erythropoietin (EPO) signaling is crucial for RBC development, and EPO is among the most expensive media components. To address this challenge, we develop highly optimized small molecule-inducible synthetic EPO receptors (synEPORs) using design-build-test cycles and genome editing. By integrating synEPOR at the endogenous EPOR locus in O-negative induced pluripotent stem cells, we achieve equivalent erythroid differentiation, transcriptomic changes, and hemoglobin production using small molecules compared to EPO-supplemented cultures. This approach dramatically reduces culture media costs. Our strategy not only addresses RBC production challenges but also demonstrates how protein and genome engineering can introduce precisely regulated cellular behaviors, potentially improving scalable manufacturing of a wide range of clinically relevant cell types.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Blood cell transfusion plays an essential role in modern medicine. In support of surgery, obstetrics, trauma care, and cancer chemotherapy, approximately 35,000 units of blood are drawn daily in the U.S., contributing to an annual provision of 12 million red blood cell (RBC) units1. However, availability is contingent on donated blood, resulting in supply constraints and safety concerns. Blood shortages pose a significant global healthcare challenge, expected to worsen with aging populations and decreasing donor numbers2. Moreover, patient populations with especially rare blood types constitute up to 5% of blood transfusion cases3 and are most vulnerable to these shortages. From a financial perspective, the cost of RBC transfusion has been steadily increasing over the past two decades, accounting for nearly 10% of total inpatient hospital expenditure4. Collectively, these factors are expected to worsen the significant unmet medical need for transfusable blood.\n\nTo address these challenges, ex vivo manufacturing of RBCs in bioreactors from producer cell lines, such as pluripotent stem cells (PSCs), emerges as a potentially renewable and scalable solution5. Early clinical trials have shown that ex vivo-derived RBCs may be delivered to patients with no reported adverse events6. In addition, ex vivo-derived RBCs offer potential benefits compared to donor blood, including a lower risk of infectious disease transmission, streamlined production, product uniformity, and ability to source or genetically engineer antigen-negative cells2. However, ex vivo RBC production is still prohibitively expensive, owing in large part to the high cost of recombinant cytokines required to stimulate producer cells to expand and differentiate into erythroid cells7. Erythropoietin (EPO) signaling through the EPO receptor (EPOR) is indispensable to RBC development8, and of all components in erythroid-promoting media, EPO is one of the most expensive7. Given prior success manipulating the EPOR to increase erythropoietic output9 and the ease with which erythroid development is modeled ex vivo10, in this work, we use synthetic biology tools and genome editing technology to de-couple EPOR signaling from the EPO cytokine.\n\nThe cellular mechanisms that regulate erythroid differentiation from hematopoietic stem and progenitor cells (HSPCs) are well understood, and efficient differentiation requires activation of the EPOR/JAK/STAT signaling cascade by EPO11. In its native form, two EPOR monomers dimerize in the presence of EPO to activate downstream signaling12. Prior work has shown that EPOR dimerization may be initiated by a range of dimer orientations and proximities using agonistic diabodies or in the context of chimeric receptors12,13,14. Because mutant FK506 binding proteins (FKBP)-based dimerization domains have been deployed to create small molecule-inducible safety switches15, we hypothesize that FKBP domains may be repurposed to create synthetic EPOR receptors to place EPO signaling under control of a small molecule.\n\nHere, we demonstrate that EPOR signaling can be induced by small molecule stimulation of highly optimized chimeric receptors\u2014hereafter termed synthetic EPORs (synEPORs). We then use homology-directed repair genome editing to integrate these synEPORs under regulation of various endogenous and exogenous promoters to identify strategies that best recapitulate endogenous EPOR signaling. In this way synthetic biology is enabled by both protein engineering and precision genome engineering through the integration of full gene cassettes in a variety of genomic locations.\n\nThis work establishes synEPORs as tools that enable highly efficient ex vivo production of RBCs using a low-cost small molecule. By removing dependence on one of the most expensive elements of ex vivo erythrocyte production, these efforts address one of the major barriers to meeting the global demand for blood with ex vivo-manufactured RBCs. More broadly, this work demonstrates how synthetic biology and genome editing may be combined to introduce precisely regulated and tunable behavior into cells for a wide variety of therapeutic applications.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Perhaps the simplest genome engineering approach to replace the demand for exogenous EPO from ex vivo erythroid differentiation would be to engineer HSPCs to secrete their own EPO cytokine. To test this, we created a strategy to integrate a cassette at the CCR5 safe harbor locus that expresses EPO cDNA under the strong, constitutive SFFV promoter (Supplementary Fig.\u00a01A). We then edited HSPCs and performed ex vivo erythroid differentiation in the absence of EPO. We found that while edited cells analyzed at d14 effectively acquired erythroid markers, total cell expansion was >50-fold lower than that achieved by the addition of exogenous EPO on unedited cells (Supplementary Fig.\u00a01B-D). In addition, we tested a commercially available EPO mimetic (EMP17) and found that it mediated little cell expansion or acquisition of erythroid markers above unedited cells cultured without EPO. This is in line with the reported low activity and specificity of EPO mimetics in comparison to recombinant EPO cytokine16,17.\n\nTherefore, we posited that engineering of the EPOR itself could better enable EPO-free erythropoiesis. To do so, we explored the possibility of repurposing FKBP domains to dimerize EPOR monomers and initiate downstream EPOR signaling by first designing a set of seven candidate FKBP-EPOR chimeras. We selected an FKBP-based system for dimerization due to their clinical relevance, since FKBP-Caspase9-based safety switches are currently being tested in clinical trials for their ability to eliminate engineered immune cells after transplantation (NCT01494103). Because our specific application was to enable EPO-free production of erythroid cells ex vivo, we avoid the potential for any pleiotropic effects of administering the small molecule in vivo. Therefore, we developed a variety of FKBP-EPOR chimeras that placed the FKBP domain at the N-terminus, C-terminus, at various locations within the native EPOR, and as a full replacement of the EPOR extracellular domain (Fig.\u00a01A). DNA donor templates corresponding to each design were packaged in AAV6 vectors and integrated into the CCR5 safe harbor site in human primary HSPCs using combined CRISPR/AAV6-mediated genome as previously described18,19,20. Expression of each FKBP-EPOR chimera was driven by the strong, constitutive SFFV promoter followed by a 2A-YFP to allow fluorescent readout of edited cells (Fig.\u00a01A). Edited HSPCs were then subjected to an established 14-day ex vivo erythrocyte differentiation protocol21,22 in the absence of EPO and with or without 1\u2009nM of FKBP dimerizer AP20187 small molecule (hereafter referred to as \u201cBB\u201d dimerizer)15. Since EPO is essential for differentiation, we hypothesized that erythroid differentiation would only occur when BB stimulated a functional synEPOR to activate downstream signaling (Fig.\u00a01B).\n\nA Schematic of chimeric FKBP-EPOR transgenes integrated at the CCR5 locus via CRISPR/AAV-mediated editing. Red boxes represent the location of FKBP within the EPOR. Expected functional ligands are displayed above each construct. B Schematic of HSPC editing and subsequent erythroid differentiation in the presence or absence of BB. Created in BioRender. Lesch, B. (2025) https://BioRender.com/k36m210. C Percentage of edited HSPCs that acquired erythroid markers (CD34-APC-/CD45-V450-/CD71-PE-Cy7+/GPA-PE+) +/\u2212BB normalized to unedited cells +EPO at d14 of differentiation. Bars represent median +/\u2212SEM; *p\u2009=\u20090.0196 by unpaired two-tailed t test across distinct samples. N\u2009=\u20095 biological replicates for all 1.5 conditions; N\u2009=\u20093 biological replicates for all 1.1, 1.2, 1.3, 1.4, 1.6, and 1.7 conditions; N\u2009=\u20091 biological replicate for all Mock conditions. D Fold change of edited allele frequencies over the course of differentiation +/\u2212BB and +/\u2212EPO. Bars represent median +/\u2212SEM. N\u2009=\u20095 biological replicates for \u2212BB/\u2212EPO and +BB/\u2212EPO conditions; N\u2009=\u20093 biological replicates for \u2212BB/+EPO and +BB/+EPO conditions. Source data are provided as a Source Data file. E Representative flow cytometry staining and gating scheme for synEPOR 1.5-edited HSPCs at d14 of differentiation \u2212EPO and +/\u2212BB. Arrows indicate that only gated cells are displayed on the subsequent plot.\n\nAt the end of differentiation, we stained cells for established erythroid markers and analyzed by flow cytometry (Supplementary Fig.\u00a02). As expected, we found that unedited \u201cMock\u201d conditions yielded no erythroid cells (CD34-/CD45-/CD71+/GPA+), while HSPCs edited with synEPOR designs 1.4 and 1.5 showed BB-dependent erythroid differentiation (Fig.\u00a01C and Supplementary Fig.\u00a03). Although FKBP-EPOR design 1.4 appeared to be most effective, for downstream optimizations we iterated on design 1.5 due to the smaller cassette size and because removal of the entire EPOR extracellular domain is expected to eliminate potential activation by EPO cytokine. This allowed us to create a receptor that could activate the EPOR pathway only when dimerizer was present but not when endogenous cytokine was present. Further investigation of synEPOR 1.5 found a\u2009>\u20094x selective advantage imparted to edited cells by the end of erythroid differentiation when cells were cultured in the presence of BB without EPO as indicated by increasing edited allele frequency measured by droplet digital PCR (ddPCR) (Fig.\u00a01D). In addition, virtually all cells that acquired erythroid markers in the synEPOR 1.5 condition were YFP+ (Fig.\u00a01E), indicating that only edited cells were capable of differentiation.\n\nTo investigate why certain FKBP-EPOR designs were non-functional, we used AlphaFold223 to generate in silico structure predictions of each candidate synEPOR monomer in comparison to wild-type EPOR monomers. We observed a high-confidence structure generated across wild-type EPOR extracellular and transmembrane domains, with low-confidence scores given to signal peptide and intracellular regions (Supplementary Figs.\u00a04, 5). For candidate synEPORs, we observed a high-confidence structure corresponding to the FKBP domain at the anticipated location among all designs. Although this analysis did not reveal any obvious protein structure disruption caused by addition of FKBP domains to the EPOR protein, our experiments demonstrated that FKBP placement within the EPOR has a great bearing on signaling potential. We found that only those constructs with FKBP placed immediately upstream of the EPOR transmembrane domain could initiate BB-dependent signaling. Therefore, it is possible that designs with FKBP within the intracellular domain may interfere with JAK/STAT signaling, while FKBPs placed further upstream of the transmembrane domain may not mediate sufficient proximity of EPOR intracellular domains to achieve sustained signaling.\n\nInitial synEPOR designs 1.4 and 1.5 both mediated BB-dependent erythroid production, yet they were unable to achieve a level of differentiation equivalent to unedited cells cultured with EPO (mean of 78.9% and 32.2% of the amount of differentiation achieved with unedited cells +EPO for synEPOR 1.4 and 1.5, respectively; Fig.\u00a01C). Therefore, we engineered second-generation synEPORs to determine if addition of a signal peptide (SP) onto synEPOR 1.5 could enhance potency, since elimination of the entire EPOR extracellular domain also removes the native SP at the N-terminus. To test the effect of these modifications, we designed and built constructs that added the native EPOR SP or the IL6 SP24 onto the N-terminus of synEPOR 1.5. This comparison was performed because SPs for cytokines are known to be particularly strong25,26. These DNA donor templates were packaged into AAV6 and integrated into the CCR5 locus as before (Fig.\u00a02A). We then performed ex vivo erythroid differentiation in the presence or absence of EPO and BB. We found that addition of EPOR SP and IL6 SP both improved mean erythroid differentiation in the presence of BB alone (44.5% and 62.6%, respectively) compared to the original synEPOR 1.5 design (32.2%) (Fig.\u00a02B and Supplementary Fig.\u00a06). These vectors also yielded a further selective advantage in the presence of BB (both with and without EPO), achieving a mean 10.0- and 10.6-fold increase in edited allele frequency by the end of erythroid differentiation with addition of EPOR and IL6 SPs, respectively (Fig.\u00a02C and Supplementary Fig.\u00a07A).\n\nA Schematic of second-generation synEPORs integrated at CCR5 locus. Red boxes represent the FKBP domain; yellow and green triangles indicate EPOR and IL6 SPs, respectively; dashed line represents EPOR truncation. Expected functional ligands are displayed above each construct. B Percentage of edited HSPCs that acquired erythroid markers (CD34-APC-/CD45-V450-/CD71-PE-Cy7+/GPA-PE+) +/\u2212BB normalized to unedited cells +EPO at d14 of differentiation. synEPOR 1.5 data from Fig.\u00a01C shown for comparison. Bars represent median +/-SEM; *p\u2009=\u20090.0052 and **p\u2009=\u20090.0006 by unpaired two-tailed t test across distinct samples. C Fold change of edited allele frequencies over the course of differentiation +/\u2212BB and +/\u2212EPO. Bars represent median +/\u2212SEM; *p\u2009=\u20090.0178 and **p\u2009=\u20090.000048 comparing d0 vs. d14 within treatment by unpaired two-tailed t test across distinct samples. N\u2009=\u20094 biological replicates for all 1.5.IL6SP samples as well as \u2212BB/\u2212EPO and +BB/\u2212EPO conditions edited with t1.5.IL6SP; N\u2009=\u20096 for \u2212BB/+EPO and +BB/+EPO conditions edited with t1.5.IL6SP. D Representative flow cytometry staining and gating scheme for synEPOR-edited HSPCs at d14 of differentiation \u2212EPO and +/\u2212BB. Arrows indicate that only gated cells are displayed on the subsequent plot. E Representative hemoglobin tetramer HPLC plots at d14 of erythroid differentiation. +BB and \u2212BB/\u2212EPO conditions were from cells edited with synEPOR; +EPO condition was from unedited cells. All plots normalized to 1e6 cells. Source data are provided as a Source Data file. F AlphaFold2-based structure prediction of truncated EPOR and synEPOR. SP was removed since this sequence will be cleaved following translocation to the membrane. TMD labeled with an arrow as a reference point.\n\nGiven the greater efficacy of synEPOR 1.5 with IL6 SP, we investigated whether incorporation of a naturally occurring nonsense mutation (EPORW439X) that truncates the 70 C-terminal amino acids of EPOR and eliminates a negative inhibitory domain may additionally increase receptor potency9. Therefore, we designed a vector with this truncated EPOR intracellular domain as well as IL6 SP and observed a further enhancement, achieving a mean of 90.9% erythroid differentiation compared to EPO-cultured HSPCs (Fig.\u00a02B). This significantly increased the selective advantage of edited cells cultured in the presence of BB, achieving a mean 11.9-fold increase in edited alleles by the end of erythroid differentiation (Fig.\u00a02C). As before, virtually all cells that acquired erythroid markers were YFP+, indicating that only edited cells stimulated with BB were able to initiate EPOR signaling (Fig.\u00a02D). Notably, a substantial portion of cells also differentiated in the absence of BB, which we addressed in downstream experiments. We will hereafter refer to our optimized FKBP-EPOR design 1.5 with IL6 SP and naturally occurring EPOR truncation as \u201csynEPOR\u201d).\n\nTo ensure that synEPOR-stimulated erythroid cells produce functional hemoglobin, we performed hemoglobin tetramer high-performance liquid chromatography (HPLC) at the end of erythroid differentiation. We found that cells edited with the optimized synEPOR and cultured with BB yielded a hemoglobin production profile consisting primarily of adult and fetal hemoglobin (HbA and HbF, respectively). This hemoglobin production profile was indistinguishable from that produced by unedited cells culture with EPO (Fig.\u00a02E).\n\nFinally, we used AlphaFold2 to predict the structure of the optimized synEPOR and find remarkable similarity to the predicted structure of the naturally occurring truncated EPOR (Fig.\u00a02F). As expected, we observe a shortening of the low-confidence intracellular domain for the truncated EPOR compared to wild-type EPOR (Supplementary Fig.\u00a08) as well as a high-confidence structure corresponding to the FKBP domain in the expected location for the optimized synEPOR. As with native EPOR SP, we also observe a low-confidence region corresponding to the IL6 SP.\n\nWhile our optimized synEPOR was effective at mediating small molecule-dependent erythroid differentiation and hemoglobin production, we observed some erythroid differentiation and hemoglobin production in the absence of BB as well (Fig.\u00a02B, E). This could be due to the strong, constitutive viral SFFV promoter driving supraphysiologic levels of receptor expression that induced ligand-independent dimerization. In contrast to the potent SFFV promoter, prior work has shown that CD34+ HSPCs express low levels of the endogenous EPOR, and expression increases modestly over the course of ex vivo erythroid differentiation27. Therefore, in the next round of optimizations, we explored the impact of various expression profiles on synEPOR activity. To do so, we developed targeted integration strategies that placed an identical optimized synEPOR under expression of: 1) an exogenous yet weaker, constitutive human PGK1 promoter following integration at the CCR5 locus (hereafter referred to as \u201cPGK(synEPOR)\u201d); 2) the strong erythroid-specific HBA1 promoter following integration into the start codon of the HBA1 locus10 (hereafter referred to as \u201cHBA1(synEPOR)\u201d); and 3) the endogenous EPOR locus following integration into the 3\u2032 end of the gene and linked by a 2A cleavage peptide (hereafter referred to as \u201cEPOR(synEPOR)\u201d) (Fig.\u00a03A). We chose these additional integration strategies to investigate whether extremely high synEPOR expression or simply constitutive expression throughout differentiation was most responsible for the dimerizer-independent activity of SFFV(synEPOR). These experiments also investigated whether erythroid-specific expression of synEPOR from the highly expressed HBA1 locus may elicit the most dramatic pro-erythroid effect or if, alternatively, integration of synEPOR at the endogenous EPOR locus may best recapitulate endogenous EPOR signaling\u2014analogous to the effective regulation of synthetic T cell receptors when knocked into the endogenous TRAC locus28.\n\nA Schematic of third-generation synEPORs that drive expression from: (1) PGK promoter from CCR5 safe harbor site; (2) erythroid-specific HBA1 locus; and (3) endogenous EPOR locus. B Percentage of edited HSPCs that acquired erythroid markers (CD34-APC-/CD45-V450-/CD71-PE-Cy7+/GPA-PE+) +/-BB normalized to unedited cells +EPO at d14 of differentiation. SFFV(synEPOR) data from Fig.\u00a02B shown here for comparison. Bars represent median +/-SEM; *p\u2009=\u20090.0434, **p\u2009=\u20090.0354, ***p\u2009=\u20090.0006, ****p\u2009=\u20090.000034, and *****p\u2009<\u20090.00001 by unpaired two-tailed t test across distinct samples. C Representative flow cytometry staining and gating scheme for edited HSPCs at d14 of differentiation \u2212EPO and +/\u2212BB. Arrows indicate that only gated cells are displayed on the subsequent plot. D Representative hemoglobin tetramer HPLC plot of edited HSPCs at d14 of differentiation -EPO and +/\u2212BB. E Cumulative cell count fold change of edited HSPCs over the course of differentiation. Bars represent median +/\u2212SEM. N\u2009=\u20096 biological replicates for all Mock conditions; and N\u2009=\u20093 biological replicates for all synEPOR-edited conditions. F Dose response of edited HSPCs cultured over a range of [BB] at d14 of differentiation normalized to unedited cells +EPO. Bars represent median +/\u2212SEM; *p\u2009=\u20090.000481, **p\u2009=\u20090.000126, and ***p\u2009<\u20090.00001 comparing +BB/+EPO to +BB/-EPO conditions by unpaired two-tailed t test across distinct samples. N\u2009=\u20097 biological replicates for PGK(synEPOR) condition +EPO at 0\u2009M [BB]; N\u2009=\u20096 biological replicates for PGK(synEPOR) conditions +EPO at 10-9M [BB], \u2212EPO at 0\u2009M [BB], and \u2212EPO at 10-9M [BB], HBA1(synEPOR) conditions +EPO at 0\u2009M [BB], +EPO at 10-9M [BB], and -EPO at 10\u22129M [BB]; N\u2009=\u20095 biological replicates for HBA1(synEPOR) condition -EPO at 0\u2009M [BB], EPOR(synEPOR) conditions +EPO at 0\u2009M [BB], +EPO at 10-9M [BB], and \u2212EPO at 10\u22129M [BB]; N\u2009=\u20094 biological replicates for PGK(synEPOR) condition +EPO at 10-10M [BB], HBA1(synEPOR) conditions +EPO at 10\u221210M [BB], -EPO at 10\u221210M [BB], EPOR(synEPOR) condition \u2212EPO at 0\u2009M [BB]; N\u2009=\u20093 biological replicates for PGK(synEPOR) conditions +EPO at 10\u221212M [BB], +EPO at 10\u221211M [BB], +EPO at 30\u221211M [BB], \u2212EPO at 10\u221212M [BB], -EPO at 10\u221211M [BB], and \u2212EPO at 10-10M [BB], HBA1(synEPOR) conditions +EPO at 10\u221212M [BB], +EPO at 10\u221211M [BB], +EPO at 30\u221211M [BB], -EPO at 10\u221212M [BB], \u2212EPO at 10\u221211M [BB], and \u2212EPO at 30\u221211M [BB], EPOR(synEPOR) conditions +EPO at 10\u221210M [BB], and \u2212EPO at 10\u221210M [BB]; N\u2009=\u20092 biological replicates for PGK(synEPOR) condition +EPO at 10\u22128M [BB], \u2212EPO at 30\u221211M [BB], and -EPO at 10\u22128M [BB], HBA1(synEPOR) conditions +EPO at 10\u22128M [BB], and \u2212EPO at 10\u22128M [BB], EPOR(synEPOR) conditions +EPO at 10\u221212M [BB], +EPO at 10\u221211M [BB], +EPO at 30\u221211M [BB], +EPO at 10\u22128M [BB], \u2212EPO at 10\u221212M [BB], -EPO at 10\u221211M [BB], \u2212EPO at 30\u221211M [BB], \u2212EPO at 10\u22128M [BB]; and N\u2009=\u20091 biological replicate for all conditions at 30\u221210M [BB], 30\u22129M [BB], and 30\u221210M [BB]. Source data are provided as a Source Data file.\n\nFollowing the integration of each vector into the intended site in primary HSPCs, we performed ex vivo erythroid differentiation in the presence or absence of EPO and BB. We observed that all three integration strategies yielded effective erythroid differentiation in the presence of BB compared to unedited cells cultured with EPO (Fig.\u00a03B, C). However, the greatest differences were found in edited conditions cultured without BB or EPO. Compared to the mean 26.3% erythroid differentiation we observed previously in the SFFV(synEPOR)-edited condition without EPO or BB, expression of synEPOR from the PGK and EPOR promoters both reduced BB-independent activity (mean of 2.2% and 20.0% in PGK(synEPOR) and EPOR(synEPOR) conditions, respectively) (Fig.\u00a03B, C and Supplementary Fig.\u00a07B). In contrast, we found that expression of synEPOR from the HBA1 promoter drove high frequencies of erythroid differentiation in the presence and absence of BB, indicating constitutive activity (Fig.\u00a03B, C). Because HBA1 is expressed much more highly than EPOR by the end of ex vivo differentiation29, it is possible that this BB-independent activity is a result of supraphysiologic levels of synEPOR expression from the HBA1 promoter that leads to spontaneous signaling even in the absence of dimerizing ligand. However, further experiments would be required to determine whether this is the case. Interestingly, deeper characterization of synEPOR-edited cells by d14 of differentiation revealed that HBA1(synEPOR) conditions yielded a less mature phenotype, with a higher percentage of CD36+ cells (a marker of immature erythroblasts)30 (Supplementary Fig.\u00a09) and significantly fewer enucleated erythrocytes (Supplementary Fig.\u00a010). On the other hand, we found that PGK(synEPOR) and EPOR(synEPOR) conditions yielded the opposite\u2014fewer CD36+ cells and a higher percentage of enucleated erythroblasts by d14 of differentiation. Cresyl blue staining confirmed these findings, while also indicating that the vast majority of cells in all conditions had reached terminal differentiation, becoming either normoblasts, reticulocytes, or enucleated erythrocytes (Supplementary Fig.\u00a011).\n\nIn spite of these subtle differences, we found that each synEPOR integration strategy yielded normal production of adult and fetal hemoglobin when edited cells were cultured in the presence of BB without EPO (Fig.\u00a03D). Due to the high level of erythroid differentiation observed in the HBA1(synEPOR) condition, it was unsurprising that edited cells cultured with neither EPO nor BB also produced a substantial amount of adult and fetal hemoglobin. Finally, we found that total erythroid cell production from all synEPOR-edited HSPCs cultured with BB was comparable to HSPCs cultured with exogenous EPO (Fig.\u00a03E).\n\nNext, we determined whether expression of synEPOR from these different promoters has a bearing on the dose response to BB. While prior work using BB found 1\u2009nM to be most effective at activating small molecule-inducible safety switches15, we observed substantial erythroid differentiation at levels well below 1\u2009nM of BB. In fact, we found that 1pM and 10pM of BB yielded erythroid differentiation that was comparable to EPO in cells edited with EPOR(synEPOR) and PGK(synEPOR) strategies, respectively (Fig.\u00a03F). However, to achieve mean differentiation that was identical to or greater than EPO-stimulated cells required a dose of 0.1\u2009nM for PGK(synEPOR)- and EPOR(synEPOR)-edited populations. In contrast, we found that cells edited with HBA1(synEPOR) yielded efficient erythroid differentiation across the entire dose range, including in the absence of BB (Fig.\u00a03F), consistent with constitutive activity of this integration strategy.\n\nIn its native form, EPO cytokine dimerizes two EPOR monomers, leading to a JAK/STAT signaling cascade culminating in translocation of phosphorylated STAT5 to the nucleus, which initiates a pro-erythroid transcriptional program11. While we have shown that synEPOR-edited cells stimulated with BB acquire classic erythroid markers and yield normal hemoglobin profiles, an open question is whether this synthetic stimulus recapitulates the complex transcriptional response of endogenous EPOR signaling (Fig.\u00a04A). To investigate this, we edited HSPCs with our various synEPOR integration strategies (Fig.\u00a03A) and performed bulk RNA-sequencing (RNA-Seq) at d14 of erythroid differentiation in absence of EPO and presence of BB. For comparison, we also performed RNA-Seq on unedited cells at the beginning (d0) and end (d14) of erythroid differentiation in the presence of EPO. These efforts yielded an average of 55.1\u2009M reads per sample with 98.5% of reads aligned to the genome and 97.2% with Quality Score \u226520 (Supplementary Fig.\u00a012).\n\nA Schematic of well-characterized endogenous EPO\u2009+\u2009EPOR signaling effects vs. undefined BB+synEPOR signaling effects. Created in BioRender. Lesch, B. (2025) https://BioRender.com/z58z349. B Transcripts per million (TPM) from RNA-Seq with annotations for globin, EPOR, and synEPOR genes. C Volcano plot comparing unedited and edited HSPCs at d14 of differentiation v. unedited HSPCs at d0. Dashed lines are drawn at +/\u22121 log2 fold change and adjusted p-value\u2009=\u20090.01 by Wald test. Total number of significantly down- and upregulated genes is shown in top left and top right of each plot, respectively. D Volcano plot comparing edited HSPCs at d14 +BB v. unedited HSPCs at d14 +EPO. Dashed lines are drawn at +/\u22121 log2 fold change and adjusted p-value\u2009=\u20090.01 by Wald test. Total number of significantly down- and upregulated genes is shown in top left and top right of each plot, respectively. E Principal component analysis of all conditions with covariance ellipses. F Summary of gene ontology (GO) enrichment analysis comparing all d14 conditions v. d0 control. Top 50 differentially expressed genes were used as input. Plotted are significantly enriched GO pathways (Benjamini-Hochberg False Discovery Rate adjusted p-value\u2009\u2264\u20090.05) that were binned into broader categories with enrichment score derived by Enrichr software. Count refers to the number of genes within each GO pathway that contributed to enrichment. Source data are provided as a Source Data file.\n\nIn analyzing these data, we found that alpha-, gamma-, and beta-globin are among the most significantly upregulated genes in unedited cells at d14 vs. d0 (Fig.\u00a04B, C). Similarly, for all cells edited with synEPOR, these globins are also among the most significantly upregulated genes (Fig.\u00a04B, C). In fact, by the end of differentiation these globins comprise a mean of 86.5%, 76.9%, 63.3%, and 83.7% of all reads for unedited cells cultured with EPO as well as PGK(synEPOR)-, HBA1(synEPOR), and EPOR(synEPOR)-edited cells cultured with BB, respectively (Supplementary Fig.\u00a013A and Supplementary Data\u00a01). As previously observed, we found that EPOR expression increases over the course of erythroid differentiation27 (38.8-fold from d0 to d14 in unedited cells; Fig.\u00a04B and Supplementary Fig.\u00a013B). In all synEPOR-edited cells we observe similar levels of EPOR compared to unedited cells, which is expected since each integration strategy preserves endogenous EPOR expression. As for synEPOR expression, we find that both PGK and EPOR promoters drive expression comparable to that of endogenous EPOR at d14 in unedited cells (Fig.\u00a04B and Supplementary Fig.\u00a013C). However, the HBA1 promoter drives supraphysiologic levels of synEPOR, with expression approaching that of the globins. Since the HBA1(synEPOR) integration strategy replaces a full copy of the HBA1 gene with synEPOR transgene, it is not surprising to find a significant decrease in HBA1 expression in this condition as well (Fig.\u00a04B, Supplementary Fig.\u00a013A, and S13D). Consistent across donors, genes most highly expressed in HSPCs are uniformly downregulated in all d14 samples while erythroid-specific genes are uniformly upregulated (Fig.\u00a04B, C, Supplementary Fig.\u00a013D\u2013H, and Supplementary Data\u00a02). Because of this, we find that d0 HSPCs and all d14 samples segregate into two distinct hierarchies (Supplementary Fig.\u00a014A), indicating a high degree of similarity across all d14 samples regardless of whether these were unedited cells cultured with EPO or synEPOR-edited cells cultured with BB.\n\nAlthough consistent differences were observed comparing all conditions to d0 HSPCs, we next determined whether significant differences existed at the end of differentiation between unedited cells cultured with EPO and synEPOR-edited conditions cultured with BB. This comparison revealed an extremely high degree of similarity between unedited cells cultured with EPO and PGK(synEPOR)-edited cells cultured with BB; only three genes were differentially expressed, including upregulation of the synEPOR transgene (Fig.\u00a04D). In contrast, the transcriptome of HBA1(synEPOR)-edited cells departed more substantially from unedited cells, with a total of 47 differentially expressed genes. As expected, in this condition we observed significant upregulation of synEPOR as well as downregulation of HBA1. Remarkably, we find that the only differentially expressed gene in EPOR(synEPOR)-edited conditions is the synEPOR transgene, indicating that this condition best recapitulated endogenous EPOR signaling. These conclusions were further supported by principal component analysis, which found that all d14 samples clustered separately from d0 samples and that EPOR(synEPOR)-edited cells stimulated with BB most closely resemble unedited cells cultured with EPO (Fig.\u00a04E). Gene co-expression network analysis additionally revealed a high degree of similarity between synEPOR-edited conditions and unedited cells cultured with EPO (Supplementary Fig.\u00a014B).\n\nTo determine which cellular processes were activated by EPO compared to edited cells cultured with BB, we performed gene ontology enrichment analysis of differentially expressed genes in each condition compared to unedited cells at d0 (Fig.\u00a04F and Supplementary Data\u00a03). At d14, the most highly enriched pathways were hydrogen peroxide (H2O2) catabolism\u2014a critical function of erythrocytes to process the significant amounts of superoxide and H2O2 that occur during oxygen transport31. We also find gas transport and erythroid differentiation processes to be highly enriched across all d14 samples. From this analysis, the HBA1(synEPOR) condition shows the most substantial departure from endogenous EPOR signaling, with a number of significantly enriched pathways unrelated to erythrocyte function. On the other hand, we find that EPOR(synEPOR) most closely resembles endogenous EPOR signaling, leading us to conclude that expression of synthetic receptors from the endogenous promoter is likely to best recapitulate the transcriptomic changes initiated by native cytokine signaling.\n\nAll prior work was done in primary HSPCs to determine whether we could successfully engineer small molecule-inducible EPORs that recapitulate native erythroid development and function. However, while primary hematopoietic HSPCs may be sourced from umbilical cord blood and mobilized peripheral blood to produce RBCs ex vivo, their expansion capacity is limited2. As a solution, induced pluripotent stem cell (iPSC) producer lines provide a potentially unlimited source of patient-derived RBCs6. Therefore, in downstream experiments we used an iPSC line called PB005 derived from a healthy donor with O-negative blood type32 to determine if synEPORs could effectively produce erythroid cells from a universal blood donor.\n\nTo test this, we integrated our most effective synEPOR expression strategies\u2014PGK(synEPOR) and EPOR(synEPOR)\u2014into the PB005 iPSC line and isolated homozygous knock-in clones (Fig.\u00a05A and Supplementary Fig.\u00a015A). These clones were then subjected to an established 12-day differentiation into hematopoietic progenitor cells (HPCs). Surprisingly, we found that EPOR(synEPOR)-edited clones yielded a substantially greater number of CD34+ cells compared to both unedited and PGK(synEPOR)-edited clones (Supplementary Fig.\u00a015B\u2013D), although this condition had a higher proportion of cells staining for erythroid markers (Supplementary Fig.\u00a015E, F). Following iPSC-to-HPC differentiation, we performed a 14-day RBC differentiation without EPO and +/\u2212BB (Fig.\u00a05A). We found that all cells (unedited and edited) effectively differentiated in the presence of EPO, whereas virtually no erythroid differentiation was observed in unedited cells in the absence of EPO (Fig.\u00a05B and Supplementary Fig.\u00a016A). PGK(synEPOR)-edited cells stimulated with BB yielded a high percentage of erythroid cells, but differentiation efficiency was significantly less than that achieved by EPO in these clones at every timepoint (Fig.\u00a05B). In addition, overall cell proliferation was substantially lower than that achieved with EPO (Fig.\u00a05C). In contrast, EPOR(synEPOR) clones achieved a differentiation efficiency that was indistinguishable from clones cultured with EPO; cell proliferation over the course of differentiation was also nearly equivalent to that achieved with EPO (Fig.\u00a05B, C). Given frequent clonal differences observed in proliferation capacity, we also examined cell proliferation from the best PGK(synEPOR)- and EPOR(synEPOR)-edited clones. By day 14, the most highly proliferative PGK(synEPOR)-edited clone only achieved 37.3% of the proliferation of that same clone when cultured with EPO (Supplementary Fig.\u00a016B). However, the most effective EPOR(synEPOR)-edited clone achieved even greater proliferation (107.8%) compared to the same clone cultured with EPO. We note that this proliferation rate was normalized to the number of HPCs at the beginning of erythroid differentiation and therefore does not take into account the increase in CD34+ HPCs achieved within the EPOR(synEPOR)-edited condition over the course of iPSC-to-HPC differentiation (Supplementary Fig.\u00a015B\u2013D).\n\nA Schematic of iPSC-to-erythroid cell differentiation strategy and subsequent analysis. Created in BioRender. Lesch, B. (2025) https://BioRender.com/e82e687. B Percentage of cells that acquired erythroid markers (CD34-APC-/CD45-V450-/CD71-PE-Cy7+/GPA-PE+) over the course of differentiation. Bars represent mean +/\u2212SEM; ns = not statistically significant, *p\u2009=\u20090.0353, **p\u2009=\u20090.00469, ***p\u2009=\u20090.00444, ****p\u2009=\u20090.000236, *****p\u2009=\u20090.00004, ******: p\u2009=\u20090.000034, and *******p\u2009<\u20090.00001 comparing \u2212BB/\u2009+\u2009EPO to +BB/-EPO conditions by unpaired two-tailed t test across distinct samples. N\u2009=\u20096 biological replicates for all PGK(synEPOR) conditions; N\u2009=\u20094 biological replicates for all EPOR(synEPOR) conditions; and N\u2009=\u20093 biological replicates for all Mock conditions. C Percentage of total cell proliferation normalized to clones cultured +EPO over the course of differentiation. Bars represent mean +/SEM; ns = not statistically significant, *p\u2009=\u20090.0229, **p\u2009=\u20090.0168, ***p\u2009=\u20090.00193, ****p\u2009=\u20090.000569, and *****p\u2009<\u20090.00001 comparing +BB to +EPO conditions by unpaired two-tailed t test across distinct samples. N\u2009=\u20096 biological replicates for all PGK(synEPOR) conditions; N\u2009=\u20094 biological replicates for all EPOR(synEPOR) conditions and Mock condition \u2212BB/\u2212EPO; and N\u2009=\u20093 biological replicates for Mock conditions \u2212BB/\u2212EPO and +BB/-EPO. D Representative hemoglobin tetramer HPLC plots of edited and unedited iPSC-derived erythroid cells at end of differentiation. Delta mV was calculated by subtracting background mV value. E Ratio of HbF production in +BB v. +EPO conditions of synEPOR-edited iPSC-derived erythroid cells at end of differentiation. Source data are provided as a Source Data file. F Cost comparison of EPO and BB (lowest price per mg commercially available for purchase as of 2/9/24).\n\nNext, we measured the hemoglobin profiles of these iPSC-derived erythroid cells using HPLC and observed fetal hemoglobin to be the most prevalent tetramer in the presence of EPO, which is consistent with prior studies33. We found this to be the case as well for clones edited with both PGK(synEPOR) and EPOR(synEPOR) conditions cultured with BB (Fig.\u00a05D). While PGK(synEPOR) conditions cultured with BB almost uniformly expressed lower fetal hemoglobin than their EPO-cultured counterparts, we observed the opposite for EPOR(synEPOR)-edited conditions, with clones cultured with BB typically producing elevated levels of fetal hemoglobin relative to those same clones cultured with EPO (Fig.\u00a05D, E). However, there appeared to be some clonal variation since not all clones conformed to these trends (Fig.\u00a05E). These findings were further confirmed by quantifying hemoglobin production per cell, which was done using HPLC to quantify the amount of heme released by hemoglobin based on a standard curve. This analysis revealed generally elevated hemoglobin production across EPOR(synEPOR)-edited clones cultured with BB compared to the same clones cultured with EPO (median of 33.1 vs. 24.4\u2009pg hemoglobin per cell, respectively; Supplementary Fig.\u00a016C). In contrast, clones edited with PGK(synEPOR) showed higher hemoglobin production when cultured with EPO (median of 21.1 vs. 32.1\u2009pg hemoglobin per cell with BB vs. EPO, respectively). Importantly, these levels of hemoglobin production are within the range expected for normal RBCs in the blood stream (25.4\u201334.6\u2009pg/cell)34,35, with fetal hemoglobin being the predominant hemoglobin as reported using current iPSC differentiation protocols33,36. Finally, while transfused RBCs typically produce more adult than fetal hemoglobin, the healthy phenotype of patients with hereditary persistence of fetal hemoglobin (HPFH) and the recent approval of Casgevy to induce high levels of HbF to treat sickle cell disease and \u03b2-thalassemia provide support that a blood product with high HbF should be both safe and effective37,38.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56239-5/MediaObjects/41467_2025_56239_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this work, we combined synthetic protein engineering with the specificity of homology-directed repair genome editing to enable small molecule control of cell differentiation and behavior. By first optimizing highly effective small molecule-responsive receptors and then integrating them into endogenous regulatory machinery, we effectively recapitulated native receptor signaling. These efforts enable cell signaling to be stimulated by low-cost small molecules instead of recombinant cytokines currently required for ex vivo cell manufacturing. In this specific instance, EPO is one of the most expensive components of erythroid-promoting media7. Here, we demonstrate that EPOR(synEPOR)-edited cells cultured with a small molecule are capable of achieving equivalent erythroid differentiation, transcriptomic changes, and hemoglobin production compared to cells cultured with EPO. For comparison, we determined the cost per mg of the largest commercially available units of recombinant human EPO and AP20187 (BB) small molecule. We found that the price per mg of BB was nearly 50-fold less than that of recombinant EPO (Fig.\u00a05F). In addition, 1/10th the amount of BB compared to recombinant EPO was required to yield equivalent erythroid production from EPOR(synEPOR)-edited iPSCs. Taken together, the corresponding estimates for cost of EPO required to produce a single unit of RBCs at a culture density of 5e7/mL is $1,246.50, conversely the cost to produce an equivalent amount of RBCs using BB is $2.25.\n\nWhile the tools and editing strategies defined in the work enable the replacement of recombinant EPO with low-cost small molecules, the cost of EPO-free media still greatly exceeds the cost of donated blood7. However, there are instances where donated blood is not readily available, such as for those with exceptionally rare blood types, or among patients with chronic diseases that require repeated transfusions, such as sickle cell disease, where minor differences in antigen prevalence lead to the development of alloimmunization in up to 30% of patients3. Therefore, our technology could be integrated into patient-derived iPSCs to produce a renewable supply of autologous RBCs at reduced cost for these currently unmet medical needs. Yet, there are major advances still required before this approach will be biologically and economically feasible. These include further reducing the cost of erythroid-promoting media, better replicating high-density RBC production that occurs in vivo33,39, and improving enucleation of adult hemoglobin-producing RBCs40. This is a multi-faceted problem and will require sustained efforts to further reduce production expenses. Nevertheless, by combining protein engineering and genome engineering to eliminate the requirement of EPO cytokine in erythroid-promoting media, this work demonstrates how synthetic biology may be used in the future to eliminate the need for other exogenous cytokines currently required for efficient ex vivo RBC production. For instance, orthogonal dimerizer systems could be leveraged in the future to create small molecule-inducible MPL, KIT, IL3R, and INSR to remove the need for TPO, SCF, IL-3, and insulin, respectively. We therefore believe this work brings us one major step closer to establishing ex vivo RBC production as a scalable and renewable source of blood cells for transfusion medicine.\n\nMore broadly, we envision a future where clinically relevant cell types may be manufactured off-the-shelf and at scale to meet the broad spectrum of patient needs. However, significant advances are needed to improve affordability and accessibility to patients. Given the complexities of large-scale cell manufacturing, many innovations have been accomplished by mechanical engineers who have developed improved bioreactors33,41,42. Our work demonstrates how challenges within this space may also be addressed by genome engineers to create more effective producer cells to seed these advanced bioreactors. Because dependence on expensive cytokines is a common barrier to scalable production of any cells ex vivo, the strategies defined in this work may be readily adapted to enable large-scale production of platelets, neutrophils, T cells, and many other clinically relevant cell types. This will ensure that advancements in cell engineering may be rapidly translated to patients at a cost that is both affordable and accessible.\n\nFinally, this work demonstrates the power of iterative design-build-test cycles to rapidly improve the function of synthetic proteins. In this work, test cycle 1 defined the ideal placement of an FKBP domain within the EPO receptor. Test cycle 2 enhanced the efficacy of synEPOR designs by incorporation of signal peptides and a naturally occurring EPOR mutation. Finally, test cycle 3 defined the ideal expression profile of our optimized synEPOR cassette when placed under a variety of exogenous and endogenous promoters. Perhaps unsurprisingly, we find that integration of the optimized synEPOR at the endogenous EPOR locus best recapitulates endogenous EPOR signaling, an engineering attribute enabled by homology-directed repair genome editing. In addition, given the incredible modularity of membrane-bound receptors43, it is possible that the small molecule-inducible architecture defined in this work may inform the design of other potentially useful small molecule-inducible receptors to modulate a wide variety of cell signals. If so, it is likely that synthetic receptor function may be fine-tuned by design-build-test cycles as well as genome editing to mediate precise integration into the genome. Gaining precisely regulated and tunable control over cells will thus pave the way for increasingly sophisticated cell engineering applications.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Informed patient consent was acquired, and patients were recruited in accordance with protocol number 33813, which was approved by the NHLBI Institutional Review Board. HSPCs derived from both male and female donors were included in this study with no exclusion criteria applied regarding ethnicity. All samples were de-identified immediately following collection.\n\nNo statistical method was used to predetermine sample size. However, we conducted experiments with sample sizes sufficient to determine statistically significant differences across treatments. No data were excluded from the analyses. The experiments were not randomized, and investigators were not blinded to allocation during experiments or outcome assessment. All statistical tests on experimental groups were performed using GraphPad Prism software (v9).\n\nIntegration vectors were designed such that the left and right homology arms (LHA and RHA, respectively) are immediately flanking the cut site in exon 2 of the CCR5 locus or exon 8 of the EPOR locus. For HBA1 integration, full gene replacement was achieved using split homology arms\u2014the LHA corresponding to the region immediately upstream of the start codon and RHA corresponding to the region immediately downstream of the cut site in the 3\u2019 UTR of the HBA1 gene10. Homology arm length ranged from 400\u20131000\u2009bp. For FKBP-EPOR chimeras, flexible GGGGS linkers were added between FKBP domains and SPs and the EPOR gene. When placing the FKBP domain immediately adjacent to the EPOR transmembrane domain, the TM domain was defined as amino acid sequence PLILTLSLILVVILVLLTVLALLSH. EPOR SP was defined as amino acid sequence MDHLGASLWPQVGSLCLLLAGAAW. IL6 SP was defined as amino acid sequence MNSFSTSAFGPVAFSLGLLLVLPAAFPAP. The FKBP corresponded to amino acid sequence MLEGVQVETISPGDGRTFPKRGQTCVVHYTGMLEDGKKVDSSRDRNKPFKFMLGKQEVIRGWEEGVAQMSVGQRAKLTISPDYAYGATGHPGIIPPHATLVFDVELLKLE. Finally, to avoid the possibility of unintended recombination of synEPOR with the endogenous locus for EPOR(synEPOR)-edited conditions, we disguised homology of synEPOR by creating silent mutations within the EPOR domains at every possible codon, with a preference for codons that occurred more frequently throughout the human genome44. All custom sequences for cloning were ordered from Integrated DNA Technologies (IDT; Coralville, Iowa, USA). Gibson Assembly MasterMix (New England Biolabs, Ipswich, MA, USA) was used for the creation of each vector as per the manufacturer\u2019s instructions.\n\nAll AAV6 vectors were cloned into the pAAV-MCS plasmid (Agilent Technologies, Hayward, CA, USA), which contains inverted terminal repeats (ITRs) derived from AAV2. To produce AAV6 vectors, we seeded HEK293T cells (CRL-1573, ATCC, Manassas, VA, USA) in 2-5 15\u2009cm2 dishes at 13-15\u00d7106 cells per plate; 24\u2009h later, each dish was transfected using 112\u2009\u03bcg polyethyleneimine (cat.: 23966; Polysciences, Warrington, PA, USA), 6\u2009\u03bcg of ITR-containing plasmid, and 22\u2009\u03bcg of pDGM6 (gift from D. Russell, University of Washington), which contains the AAV6 cap genes, AAV2 rep genes, and Ad5 helper genes. After 48\u201372\u2009h of incubation, cells were collected and AAV6 capsids were isolated using the AAVPro Purification Kit (All Serotypes, Takara Bio, San Jose, USA), as per the manufacturer\u2019s instructions. AAV6 vectors were titered using a Bio-Rad QX200 ddPCR machine and QuantaSoft software (v.1.7, Bio-Rad, Hercules, CA, USA) to measure the number of vector genomes.\n\nCD34+ HSPCs were sourced from fresh cord blood (generously provided by the Stanford Binns Family Program for Cord Blood Research) and Plerixafor- and/or G-CSF-mobilized peripheral blood (AllCells, Alameda, CA, USA or STEMCELL Technologies, Vancouver, Canada). CD34+ HSPCs were cultured at 1-5\u00d7105 cells/mL in StemSpan SFEMII (STEMCELL Technologies) or Good Manufacturing Practice Stem Cell Growth Medium (SCGM, CellGenix, Freiburg, Germany) base medium supplemented with a human cytokine (PeproTech, Rocky Hill, NJ, USA) cocktail: stem cell factor (100\u2009ng/mL), thrombopoietin (100\u2009ng/mL), Fms-like tyrosine kinase 3 ligand (100\u2009ng/ml), interleukin-6 (100\u2009ng/mL), streptomycin (20\u2009mg/mL) (ThermoFisher Scientific, Waltham, MA, USA), and penicillin (20\u2009U/mL) (ThermoFisher Scientific, Waltham, MA, USA), and 35\u2009nM of UM171 (cat.: A89505; APExBIO, Houston, TX, USA). The cell incubator conditions were 37\u2009\u00b0C, 5% CO2, and 5% O2.\n\nChemically modified CRISPR guide RNAs (gRNAs) used to edit CD34+ HSPCs at CCR5, HBA1, and EPOR were purchased from Synthego (Redwood City, CA, USA). The gRNA modifications added were 2\u2019-O-methyl-3\u2019-phosphorothioate at the three terminal nucleotides of the 5\u2019 and 3\u2019 ends45. The target sequences for gRNAs were as follows: CCR5: 5\u2019-GCAGCATAGTGAGCCCAGAA-3\u2019; HBA1: 5\u2019-GGCAAGAAGCATGGCCACCGAGG-3\u2019; and EPOR: 5\u2019-AGCTCAGGGCACAGTGTCCA-3\u2019. All Cas9 protein was purchased from Aldevron (Alt-R S.p. Cas9 Nuclease V3; Fargo, ND, USA). Cas9 ribonucleoprotein (RNP) complexes were created at a Cas9:gRNA molar ratio of 1:2.5 at 25\u2009\u00b0C for a minimum of 10\u2009min before electroporation. CD34+ cells were resuspended in P3 buffer plus supplement (cat.: V4XP-3032; Lonza Bioscience, Walkersville, MD, USA) with complexed RNPs and electroporated using the Lonza 4D Nucleofector (program DZ-100). Cells were plated at 1-2.5\u00d7105 cells/mL following electroporation in the cytokine-supplemented media described above. Immediately following electroporation, AAV6 was supplied to the cells between 2.5-5e3 vector genomes per cell. The small molecule AZD-7648, a DNA-dependent protein kinase catalytic subunit inhibitor, was also added to cells immediately post-editing for 24\u2009h at 0.5\u2009nM to improve homology-directed repair frequencies46.\n\nFollowing editing, HSPCs derived from healthy patients or iPSC-derived HPCs were cultured for 14\u2009d at 37\u2009\u00b0C and 5% CO2 in SFEMII medium (STEMCELL Technologies, Vancouver, Canada)21,22. SFEMII base medium was supplemented with 100\u2009U/mL penicillin/streptomycin (ThermoFisher Scientific, Waltham, MA, USA), 10\u2009ng/mL stem cell factor (PeproTech, Rocky Hill, NJ, USA), 1\u2009ng/mL interleukin-3 (PeproTech, Rocky Hill, NJ, USA), 3\u2009U/mL EPO (eBiosciences, San Diego, CA, USA), 200\u2009\u03bcg/mL transferrin (Sigma-Aldrich, St. Louis, MO, USA), 3% antibody serum (heat-inactivated; Sigma-Aldrich), 2% human plasma (isolated from umbilical cord blood provided by Stanford Binns Cord Blood Program), 10\u2009\u03bcg/mL insulin (Sigma-Aldrich), and 3\u2009U/mL heparin (Sigma-Aldrich). In the first phase, at days 0\u20137 of differentiation (d0 being 2\u20133\u2009d post editing), cells were cultured at 1\u00d7105 cells/mL. In the second phase (d7\u201310), cells were maintained at 1\u00d7105 cells/mL and IL-3 was removed from the culture. In the third phase (d11\u201314), cells were cultured at 1\u00d7106 cells/mL and transferrin was increased to 1\u2009mg/mL. For -EPO conditions, cells were cultured in the same culture medium listed above except for removal of EPO from the media. For conditions with the addition of BB homodimerizer (AP20187; Takara Bio, San Jose, USA), 1\u2009\u03bcL of 0.5\u2009mM BB was diluted in 999\u2009\u03bcL PBS (HI30; BD Biosciences, San Jose, CA, USA), of which 2\u2009\u03bcL of the dilution was added for every 1\u2009mL of differentiation media to reach a desired concentration of 1\u2009nM. Fresh BB was added at each media change (d0, 4, 7, 11). For experiments requiring additional dilutions, BB was diluted further in PBS to reach the required concentration (as low as 1pM). For conditions with EPO mimetic, EMP17 (Anaspec, Fremont, CA, USA) was added to differentiation media at a molar ratio equivalent to 3\u2009U/mL of EPO (618pM) in place of recombinant EPO.\n\nHSPCs subjected to erythroid differentiation were analyzed at d14 for erythrocyte lineage-specific markers using a FACS Aria II and FACS Diva software (v.8.0.3; BD Biosciences, San Jose, CA, USA). Edited and unedited cells were analyzed by flow cytometry using the following antibodies: CD34-APC (1:50 dilution; 561; BioLegend, San Diego, CA, USA), CD45-V450 (1:50 dilution; 2\u2009\u00b5L in 100\u2009\u00b5l of pelleted RBCs in 1\u00d7PBS buffer; HI30; BD Biosciences), CD36-PE (1:50 dilution; 5\u2013271; BioLegend), CD71-PE-Cy7 (1:500 dilution; OKT9; Affymetrix, Santa Clara, CA, USA), and CD235a (GPA)-PE (1:500 dilution; GA-R2; BD Biosciences) or GPA-PE-Cy5 (1:500 dilution; GA-R2; BD Biosciences). In addition to cell-specific markers, cells were also stained with Ghost Dye Red 780 (Tonbo Biosciences, San Diego, CA, USA) to measure viability and DRAQ5 to quantify enucleation frequencies (BioLegend). All data visualization was performed using the FACS Aria II cytometer and FACS Diva software (v.8.0.3) and subsequent data analysis was performed using FlowJo (v.10.6.1).\n\nBetween 2\u20134\u2009d post editing, HSPCs were harvested and QuickExtract DNA extraction solution (Epicentre, Madison, WI, USA) was used to collect genomic DNA (gDNA). Additional samples were collected at various stages of erythroid differentiation (d4, 7, 11, and 14) and gDNA was digested using BamHI-HF as per the manufacturer\u2019s instructions (New England Biolabs, Ipswich, MA, USA). Percentage of targeted alleles within a cell population was measured with a Bio-Rad QX200 ddPCR machine and QuantaSoft software (v.1.7; Bio-Rad, Hercules, CA, USA) using the following reaction mixture: 1-4\u2009\u03bcL of digested gDNA input, 10\u2009\u03bcL of ddPCR SuperMix for Probes (no dUTP) (Bio-Rad), primer/probes (1:3.6 ratio; Integrated DNA Technologies, Coralville, IA, USA) and volume up to 20\u2009\u03bcL with H2O. ddPCR droplets were then generated following the manufacturer\u2019s instructions (Bio-Rad): 20\u2009\u03bcL of ddPCR reaction, 70\u2009\u03bcL of droplet generation oil, and 40\u2009\u03bcL of droplet sample. Thermocycler (Bio-Rad) settings were as follows: 98\u2009\u00b0C (10\u2009min), 94\u2009\u00b0C (30\u2009s), 57.3\u2009\u00b0C (30\u2009s), 72\u2009\u00b0C (1.75\u2009min), return to step 2 \u00d7 40-50 cycles, and 98\u2009\u00b0C (10\u2009min). Analysis of droplet samples was performed using the QX200 Droplet Digital PCR System (Bio-Rad). To determine percentages of alleles targeted, the numbers of Poisson-corrected integrant copies/mL were divided by the numbers of reference DNA copies/mL. The following primers and 6-FAM/ZEN/IBFQ-labeled hydrolysis probes were purchased as custom-designed PrimeTime quantitative PCR (qPCR) assays from Integrated DNA Technologies: All HBA1 vectors: forward: 5\u2032-AGTCCAAGCTGAGCAAAGA-3\u2032, reverse: 5\u2032-ATCACAAACGCAGGCAGAG-3\u2032, probe: 5\u2032-CGAGAAGCGCGATCACATGGTCCTGC-3\u2032; all CCR5 vectors: forward: 5\u2032-GGGAGGATTGGGAAGACAAT-3\u2032, reverse: 5\u2032-TGTAGGGAGCCCAGAAGAGA-3, probe: 5\u2032-CACAGGGCTGTGAGGCTTAT-3\u2032. The primers and HEX/ZEN/IBFQ-labeled hydrolysis probe, purchased as custom-designed PrimeTime qPCR Assays from Integrated DNA Technologies, were used to amplify the CCRL2 reference gene: forward: 5\u2032-GCTGTATGAATCCAGGTCC-3\u2032, reverse: 5\u2032-CCTCCTGGCTGAGAAAAAG-3\u2032, probe: 5\u2032-TGTTTCCTCCAGGATAAGGCAGCTGT-3\u2032.\n\nOn d14 of RBC differentiation, up to 1e4 cells were loaded on a glass slide in no more than 5\u2009\u03bcL volume of PBS. Then 9\u2009\u03bcL of Brilliant Cresyl blue staining solution (cat.: #16035; Sigma-Aldrich) was added onto a coverslip and allowed to air dry. The coverslip was then placed over the cells on the glass slide to stain for reticulocytes. Brightfield images were taken at \u00d720 magnification.\n\nEnergy-predicted structures were derived by applying AlphaFold2 (v2.3.2)23 on wild-type EPOR, truncated EPOR, and synEPOR sequences. Five differently trained neural networks were applied to produce unrelaxed structure predictions. Energy minimization was applied to the best predicted unrelaxed structure (highest average predicted distance difference test (pLDDT) and lowest predicted aligned error) to produce the optimal relaxed structure.\n\nFrozen pellets of approximately 1e6 cells ex vivo-differentiated erythroid cells were thawed and lysed in 30\u2009\u03bcL of RIPA buffer with 1x Halt Protease Inhibitor Cocktail (ThermoFisher Scientific, Waltham, MA, USA) for 5\u2009min on ice. The mixture was vigorously vortexed and cell debris was removed by centrifugation at 20,700\u2009g for 10\u2009min at 4\u2009\u00b0C. HPLC analysis of hemoglobins in their native form was performed on a cation-exchange PolyCAT A column (35 \u00d7 4.6mm2, 3\u2009\u00b5m, 1500\u2009\u00c5; PolyLC Inc., Columbia, MD, USA) using a Perkin-Elmer Flexar HPLC system (Perkin-Elmer, Waltham, MA, USA) at room temperature and detection at 415\u2009nm. Mobile phase A consisted of 20\u2009mM Bis-tris and 2\u2009mM KCN at pH 6.94, adjusted with HCl. Mobile phase B consisted of 20\u2009mM Bis-tris, 2\u2009mM KCN, and 200\u2009mM NaCl at pH 6.55. Hemolysate was diluted in buffer A prior to injection of 20\u2009\u03bcL onto the column with 8% buffer B and eluted at a flow rate of 2\u2009mL/min with a gradient made to 40% B in 6\u2009min, increased to 100% B in 1.5\u2009min, returned to 8% B in 1\u2009min, and equilibrated for 3.5\u2009min. Quantification of the area under the curve of peaks was performed with TotalChrom software (Perkin-Elmer) and raw values were exported to GraphPad Prism v9 software for plotting and further analysis.\n\nTotal RNA was extracted from frozen pellets of approximately 1e6 cells per condition using RNeasy Plus Micro Kit (Qiagen, Redwood City, CA, USA) according to the manufacturer\u2019s instructions. Sequencing was provided by Novogene (Sacramento, CA, USA) and raw FASTQ files were aligned to the GRCh38 reference genome extended with the synEPOR target sequence and quantified using Salmon (v1.9.0)47 with default parameters. Quality control was performed by Novogene.\n\nThe estimated gene expression counts were used with DESeq248 to conduct differential gene expression analysis between sample groups. Mitochondrial and lowly expressed genes were removed (sum NumReads <1). The top 50 up- and down-regulated genes based on adjusted p-value using Wald test were isolated and analyzed with Enrichr49 to yield functional annotations.\n\nMitochondrial genes were removed from the gene expression matrix (TPM) and the remaining genes were used to conduct principal component analysis with all samples. Gene expression for experimental and control groups were averaged and log-normalized. Average gene expression distributions were plotted using Seaborn (https://github.com/atsumiando/RNAseq_figure_plotter_python).\n\nThe TPM-normalized gene expression matrix of all PGK(synEPOR)-, HBA1(synEPOR)-, and EPOR(synEPOR)-edited conditions (n\u2009=\u200910) was used to construct a pairwise gene similarity matrix where each entry represented the Spearman correlation coefficient between a pair of genes. The correlation between a specified set of EPOR-related genes was compared for both EPOR and synEPOR to determine which genes synEPOR adequately mimics in the immediate gene co-expression network of endogenous EPOR.\n\nA previously published iPSC line, PB005 derived from peripheral blood of a donor with O- blood type was used in this study50. iPSCs were cultured and maintained in mTeSR1 medium (cat.: 85850; STEMCELL Technologies, Vancouver, Canada) on Matrigel (cat.: 354277; Corning, NY, USA)-coated plates. For passaging, cells at a confluency of 80-90% were incubated with Accutase (cat.: AT104; Innovative Cell Technologies, San Diego, USA) for 5\u20137\u2009min to dissociate into single cells and replated in mTeSR1 medium supplemented with 10\u2009mM of ROCKi (Y27632; cat.: 10005583; Cayman Chemical, Ann Arbor, MI, USA). After 24\u2009h, cells were maintained in fresh mTeSR1 medium with daily media changes. For freezing iPSCs, STEM-CELLBANKER freezing medium (cat.: 11924; Amsbio, Cambridge, MA, USA) was used.\n\niPSCs were genome edited using the CRISPR/AAV platform19,46 as follows: Cas9 RNP complex was formed by combining 5\u2009\u03bcg of Cas9 (Alt-R S.p. Cas9 Nuclease V3; Fargo, ND, USA) and 2\u2009\u03bcg of gRNA (Synthego, Redwood City, CA, USA) and incubating at room temperature for 15\u2009min. iPSCs pre-treated with ROCKi (Y27632; cat.: 10005583; Cayman Chemical, Ann Arbor, MI, USA) for 24\u2009h were dissociated with Accutase (cat.: AT104; Innovative Cell Technologies, San Diego, USA) into single cells. 1-5e5 iPSCs were resuspended in 20\u2009\u03bcL of P3 primary cell nucleofector solution plus supplement (cat.: V4XP-3032; Lonza Bioscience, Walkersville, MD, USA) along with the RNP complex and electroporated using Lonza 4D Nucleofector (program CA-137). After electroporation, iPSCs were plated in mTeSR1 medium supplemented with ROCKi, 0.25\u2009\u03bcM AZD7648 (cat.: S8843; Selleck Chemicals, Houston, TX, USA) and AAV6 donor at 2.5e3 vector genomes per cell, based on ddPCR titers as above. After 24\u2009h, cells were switched to medium with mTeSR1 and ROCKi. From the following day, cells were maintained in mTeSR1 medium without ROCKi.\n\nTo isolate single-cell\u2032\u2032 clones, genome-edited iPSCs were plated at a density of 250 cells per well of a 6-well plate in mTeSR1 medium supplemented with 1x CloneR2 reagent (cat.: 100-0691; STEMCELL Technologies, Vancouver, Canada). After 48\u2009h, cells were switched to fresh mTeSR1 medium with 1x CloneR2 and incubated for 2\u2009d. Following this, iPSCs were maintained in mTeSR1 medium without CloneR2 with daily media changes. At d7\u201310, single-cell colonies were picked by scraping and propagated individually. The isolated single cell iPSCs were genotyped using PCR with primers annealing outside the homology arms to identify clones with bi-allelic knock-in. The following primers were used for genotyping: CCR5 integration: forward: 5\u2019-CTCATAGTGCATGTTCTTTGTGGGC-3\u2019, reverse: 5\u2019-CCAGCCCAGGCTGTGTATGAAA-3\u2019; EPOR integration: forward: 5\u2019-GCCACATGGCTAGAGTGGTAT-3\u2019, reverse: 5\u2019-CTTTCTTAGAACATGGCCTGATTCAGA-3\u2019.\n\niPSCs were differentiated into CD34+ HPCs using the STEMdiff Hematopoietic Kit (cat.: 05310; STEMCELL Technologies, Vancouver, Canada) according to the manufacturer\u2019s protocol. Briefly, iPSCs at 70-80% confluency were dissociated into aggregates using ReLeSR (cat.: 100-0484; STEMCELL Technologies). Aggregates were then diluted 10-fold, and 100\u2009\u03bcL of the diluted suspension was aliquoted into a 96-well plate for quantification. Approximately 80 aggregate colonies were subsequently plated per well of a 12-well plate pre-coated with Matrigel and maintained in mTeSR1 medium. 24\u2009h post-plating, the number of colonies per well was manually quantified, and the medium was replaced with differentiation medium A. The medium was then changed according to the kit\u2019s instructions for a total of 12 days. On d12, suspension cells were harvested by pipetting cells up and down to ensure a homogeneous cell suspension. To assess the efficiency of differentiation, as determined by CD34+/CD45+ expression, cells were analyzed using flow cytometry with the erythrocyte flow panel described above for HSPCs. Following this, CD34+ cells were further differentiated into erythroid cells using the three-phase system described above, either in the presence or absence of EPO and BB.\n\nQuantification of the amount of hemoglobin produced in cells was obtained by quantitative detection of the heme peak released from hemoglobin. Lysate were obtained from 1-2e5 cells as frozen pellets, as described for hemoglobin tetramer analysis. The relationship between heme and hemoglobin was established from serially diluted hemolysate made with a blood sample of a known hemoglobin content. Detection of heme was performed by reverse-phase PerkinElmer Flexar HPLC system (PerkinElmer) with a Symmetry C18 column (4.6 \u00d7\u200975\u2009mm, 3.5\u2009\u00b5m; Waters Corporation, Milford, MA, USA) at 415\u2009nm. Mobile phase\u2009 A consisted of 10% methanol made in acetonitrile and mobile B of 0.5% trifluoroacetic acid in water adjusted at pH 2.9 with NaOH. Samples were injected at a flow rate of 2\u2009mL/min in 49% A, followed by a 3\u2009min gradient to 100% A. The column was then equilibrated to 49% A for 3\u2009min.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "RNA-seq data have been deposited in the NCBI Gene Expression Omnibus database (accession no. GSE285656). Sequencing reads were aligned to the GRCh38 reference human genome (NCBI Sequence Read Archive database; accession no. PRJNA31257). The data for all figures in this study are provided in the Source Data file.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Klein, H. G., Hrouda, J. C. & Epstein, J. S. Crisis in the sustainability of the U.S. blood system. N. Engl. J. Med. 377, 1485\u20131488 (2017).\n\nArticle\u00a0\n PubMed\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nTrakarnsanga, K. et al. 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We also would also like to thank the Stanford Binns Program for Cord Blood Research for providing CD34+ HSPCs and the FACS Core Facility at the Stanford Institute of Stem Cell Biology and Regenerative Medicine as well as University of California, San Francisco Flow Core for access to flow cytometry machines. Finally, we would like to thank Caleb Grossman for helpful discussions and feedback as we planned initial experiments.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Aadit P. Shah, Kiran R. Majeti\n\nSchool of Medicine, Stanford University, Stanford, CA, USA\n\nAadit P. Shah,\u00a0Freja K. Ekman\u00a0&\u00a0Sofia E. Luna\n\nDepartment of Pediatrics, Stanford University, Stanford, CA, USA\n\nAadit P. Shah,\u00a0Kiran R. Majeti,\u00a0Freja K. Ekman,\u00a0Sridhar Selvaraj,\u00a0Sofia E. Luna\u00a0&\u00a0Matthew H. Porteus\n\nDepartment of Genetics, Stanford University, Stanford, CA, USA\n\nFreja K. Ekman\u00a0&\u00a0Carsten T. Charlesworth\n\nDepartment of Surgery, University of California, San Francisco, San Francisco, CA, USA\n\nDevesh Sharma,\u00a0Roshani Sinha,\u00a0Travis McCreary,\u00a0Benjamin J. Lesch,\u00a0Tammy Tran,\u00a0Simon N. Chu\u00a0&\u00a0M. Kyle Cromer\n\nEli & Edythe Broad Center for Regeneration Medicine, University of California, San Francisco, San Francisco, CA, USA\n\nDevesh Sharma,\u00a0Roshani Sinha,\u00a0Travis McCreary,\u00a0Benjamin J. Lesch,\u00a0Tammy Tran,\u00a0Simon N. Chu\u00a0&\u00a0M. Kyle Cromer\n\nBenioff Children\u2019s Hospital Oakland, University of California, San Francisco, San Francisco, CA, USA\n\nEric Soupene\n\nDepartment of Biological & Medical Informatics, University of California, San Francisco, San Francisco, CA, USA\n\nPrathamesh Chati\n\nDepartment of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA\n\nM. Kyle Cromer\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nM.H.P. and M.K.C. supervised the project. A.P.S., K.R.M., C.T.C., M.H.P., and M.K.C. designed experiments. A.P.S., K.R.M., F.K.E., S.S., D.S., R.S., E.S., S.E.L., T.M., B.J.L., T.T., S.N.C., and M.K.C. carried out experiments. A.P.S., K.R.M., F.K.E., P.C., M.H.P., and M.K.C. analyzed data. A.P.S., K.R.M., and M.K.C. wrote the manuscript.\n\nCorrespondence to\n Matthew H. Porteus or M. Kyle Cromer.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "M.H.P. is a member of the scientific advisory board of Allogene Therapeutics. M.H.P. has equity in CRISPR Tx and Kamau Tx. C.T.C., M.H.P., and M.K.C. have filed provisional patent no. PCT/US2023/076969, which includes all synEPOR designs, genomic integration strategies, and the application of EPO-independent erythropoiesis. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Shah, A.P., Majeti, K.R., Ekman, F.K. et al. Engineering synthetic signaling receptors to enable erythropoietin-free erythropoiesis.\n Nat Commun 16, 1140 (2025). https://doi.org/10.1038/s41467-025-56239-5\n\nDownload citation\n\nReceived: 18 October 2024\n\nAccepted: 10 January 2025\n\nPublished: 29 January 2025\n\nVersion of record: 29 January 2025\n\nDOI: https://doi.org/10.1038/s41467-025-56239-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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the 2017 Ebola outbreak response in DR Congo", + "pre_title": "Using real-time modelling to optimise an outbreak response: Insights from the 2017 Ebola outbreak in the Democratic Republic of the Congo", + "journal": "Nature Communications", + "published": "06 July 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49888-5/MediaObjects/41467_2024_49888_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49888-5/MediaObjects/41467_2024_49888_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49888-5/MediaObjects/41467_2024_49888_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://github.com/robin-thompson/EbolaReponseTeam", + "/articles/s41467-024-49888-5#ref-CR42" + ], + "code": [ + "https://github.com/robin-thompson/EbolaReponseTeam", + "/articles/s41467-024-49888-5#ref-CR42" + ], + "subject": [ + "Computational models", + "Ebola virus", + "Ecological modelling", + "Epidemiology", + "Viral infection" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3951663/v1.pdf?c=1727734648000", + "research_square_link": "https://www.researchsquare.com//article/rs-3951663/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-49888-5.pdf", + "preprint_posted": "18 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Important questions for policy makers during infections disease outbreaks include: i) How effective are public health measures?; ii) When can resource-intensive and restrictive interventions be removed? We used mathematical modelling to address these questions during the 2017 outbreak of Ebola virus disease in Likati Health Zone, Democratic Republic of the Congo. The index case developed symptoms on 27th March 2017, and eight cases occurred in total prior to the arrival of the Ebola Response Team (ERT) on 15th May 2017. We used a branching process transmission model to estimate that before the arrival of the ERT, the reproduction number was R=1.49 (95% credible interval (0.67,2.81)). Based on the full distributional estimate of R, the risk of further cases occurring if the ERT had not been deployed was estimated to be 0.97 (i.e., there was a 97% chance of additional cases in the absence of the ERT). Following the arrival of the ERT, no further cases arose, suggesting that interventions implemented by the ERT were effective. We then used the same transmission model to estimate in real-time when the ERT could be withdrawn. By the time of the end-of-outbreak declaration and withdrawal of the ERT (2nd July 2017), the risk of future cases in the absence of the ERT was only 0.01, indicating that the decision to withdraw the ERT was safe. We also evaluated the sensitivity of our modelling results to the estimated value of R, and considered different criteria for determining when the ERT could be withdrawn. As well as providing insights into interventions during the 2017 EVD outbreak, this research provides a modelling framework that can be used during future infectious disease outbreaks to determine the effectiveness of control measures and to guide when to relax or remove interventions.Health sciences/Diseases/Infectious diseases/Viral infectionBiological sciences/Ecology/Ecological modellingEbola virus diseaseInfectious disease outbreakPublic health measuresInterventionsEnd-of-outbreak declaration", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformation.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Important policy questions during infections disease outbreaks include: i) How effective are particular interventions?; ii) When can resource-intensive interventions be removed? We used mathematical modelling to address these questions during the 2017 Ebola outbreak in Likati Health Zone, Democratic Republic of the Congo (DRC). Eight cases occurred before 15 May 2017, when the Ebola Response Team (ERT; co-ordinated by the World Health Organisation and DRC Ministry of Health) was deployed to reduce transmission. We used a branching process model to estimate that, pre-ERT arrival, the reproduction number was R=1.49 (95% credible interval (0.67,2.81)). The risk of further cases occurring without the ERT was estimated to be 0.97 (97%). However, no cases materialised, suggesting that the ERT\u2019s measures were effective. We also estimated the risk of withdrawing the ERT in real-time. By the actual ERT withdrawal date (2 July 2017), the risk of future cases without the ERT was only 0.01, indicating that the ERT withdrawal decision was safe. We evaluated the sensitivity of our results to the estimated R value and considered different criteria for determining the ERT withdrawal date. This research provides an extensible modelling framework that can be used to guide decisions about when to relax interventions during future outbreaks.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The 2014\u201316 Ebola virus disease (EVD) epidemic in West Africa was a stark reminder of the need for rapid response to control emerging infectious disease outbreaks1,2,3,4. In recent years, epidemiological modelling has increasingly been used to track pathogen transmission and to guide public health measures against a range of diseases5,6,7,8,9. However, beyond the timely deployment of interventions, identifying the adequate level of outbreak response and understanding how to adjust measures over time are challenging issues10,11,12,13,14.\n\nAn EVD outbreak occurred in 2017 in Likati Health Zone, Democratic Republic of the Congo (DRC)15,16,17. The index case developed symptoms on 27 March 2017. After eight cases had arisen in total, a response team (the Ebola Response Team; ERT) co-ordinated by the DRC Ministry of Health and the World Health Organisation was deployed and implemented a range of measures, including community engagement and risk communication, contact tracing, active case finding, isolation and treatment of suspected patients, and PCR and serology testing. After the deployment of the ERT, the key question was whether these actions were sufficient to prevent further transmission or whether additional measures were required. Around one month later, when no further cases had occurred, attention turned to when public health measures could be relaxed and the ERT could be withdrawn safely with only a small risk of outbreak resurgence. Efficient removal of control resources, when they are no longer necessary, is essential to reduce economic and social costs18.\n\nMathematical models can be used both to assess the effectiveness of public health measures and to determine when interventions can be lifted safely. For example, Funk et al.19 analysed data from the beginning of the 2014\u201316 Ebola epidemic and demonstrated that expansion of the treatment centre and an increase in healthcare-seeking behaviour limited transmission in Lofa County, Liberia. Similarly, a range of studies attempted to unpick the effects of different interventions during the COVID-19 pandemic20,21,22. In terms of deciding when to remove interventions, other modelling analyses have been used to assess the \u201cend-of-outbreak probability\u201d: namely, the probability that an outbreak is over and no further cases will occur in future23,24,25,26,27,28,29,30. Estimation of the end-of-outbreak probability is useful for informing when outbreaks can be declared over and when interventions can be removed, since this decision-making involves balancing the benefits of relaxing stringent and resource-intensive measures against the risk of additional cases.\n\nIn this article, we report mathematical modelling that we undertook during and after the 2017 EVD outbreak in Likati Health Zone, DRC, to address two key questions, specifically: (i) How effective was the ERT at reducing transmission (Fig.\u00a01\u2014Key Question 1)?; (ii) When could the ERT be withdrawn and associated public health measures relaxed without a substantial risk of further cases (Fig.\u00a01\u2014Key Question 2)? We used a branching process outbreak model to infer the level of virus transmission prior to the arrival of the ERT, as quantified by the reproduction number, R. Following the deployment of the ERT, each day, we estimated the risk of additional cases occurring if the ERT was withdrawn under the assumption that R reverts to its original value (i.e., the value estimated using data from before the arrival of the ERT) in the absence of the ERT. As we show, this quantity, which we term the \u201crisk of withdrawing the ERT\u201d, can be used to assess the effectiveness of the ERT and to guide in-real time when the ERT can be withdrawn according to an acceptable level of risk (to be determined in a context-specific fashion). As well as providing analyses of the 2017 EVD outbreak in Likati Health Zone, our research provides a general epidemiological modelling framework that can be used during future outbreaks to assess interventions and to determine when measures can be relaxed or removed safely.\n\nFollowing eight EVD cases occurring between 27 March and 11 May 2017, the ERT was deployed on 15 May 2017. Mathematical modelling was used to assess: (i) The effectiveness of the ERT at reducing transmission (Key Question 1), and; (ii) The risk of withdrawing the ERT, quantified in terms of the probability that further cases would occur if the ERT was withdrawn (Key Question 2; this quantity was evaluated every day until the ERT was withdrawn on 2 July 2017). In this figure, and in all subsequent figures in the main text, dates are expressed in DD/MM format (all in 2017).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49888-5/MediaObjects/41467_2024_49888_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "To analyse transmission in the absence of the ERT, we began by estimating the value of R using: (i) the incidence data from 27 March to 14 May 2017 and (ii) a distributional estimate of the EVD serial interval (see the \u201cMethods\u201d section and Fig. S1). This range of dates represents the time period from the onset of disease in the index case up to the day before the arrival of the ERT (Fig.\u00a01). In the absence of the ERT, the median reproduction number estimate was R=1.49 (95% credible interval 0.67\u20132.81; Fig.\u00a02A).\n\nA The estimated value of R prior to the arrival of the ERT (calculated using Eq. (3)). B The risk of withdrawing the ERT (blue, calculated each day using Eq. (5); i.e., the probability of future cases occurring if the ERT is withdrawn on each date on the x-axis, based on the distributional estimate of R in panel A) and the actual date of withdrawal of the ERT (black dashed). In panel B, the first date shown is the ERT deployment date (15 May 2017).\n\nFollowing the arrival of the ERT, no further cases occurred in the outbreak. On each day, we estimated the probability that future cases would occur if the ERT had been withdrawn (i.e., the risk of withdrawing the ERT), under the assumption that R reverts to its original value (i.e., the value estimated using the disease incidence time series data from before the arrival of the ERT) when the ERT is withdrawn (Fig.\u00a02B). As described in the \u201cMethods\u201d section (Eq. (5)), in this analysis the risk of withdrawing the ERT was calculated using the full distributional estimate of R in the absence of the ERT (Fig.\u00a02A).\n\nTwo things are notable from the estimated risk of withdrawing the ERT shown in Fig.\u00a02B. First, on the day that the ERT arrived, there was a high estimated probability of future cases (0.97) in the absence of the ERT. The fact that no subsequent cases occurred suggests that the ERT was effective at reducing transmission. Second, there was a very small probability of future cases in the absence of the ERT by the actual date on which the ERT was withdrawn (0.01 on 2 July 2017). Policy makers could, therefore, be confident (around\u00a099% sure) that no further cases would occur when the outbreak was declared over and the ERT was withdrawn.\n\nThe analysis in Fig.\u00a02 demonstrates that further cases were unlikely by the time that the ERT was withdrawn from the Likati Health Zone (2 July 2017). Such analyses can be used in real-time to guide when interventions can be relaxed or removed. For example, the ERT could theoretically have been withdrawn as soon as the risk of withdrawing the ERT fell below a pre-specified threshold value. While such a threshold was not specified for this outbreak, smaller threshold values should be set when policy makers have a lower tolerance for future cases occurring. In this outbreak, if a threshold value of 0.05 had been chosen (corresponding to a <5% chance of future cases), then the ERT could have been withdrawn on 21 June 2017, whereas if a more risk-averse threshold value of 0.01 had been chosen (corresponding to a <1% chance of future cases), then the ERT could instead have been withdrawn on 3 July 2017, which corresponds very closely to the actual date of ERT withdrawal (2 July 2017). In practice, the outbreak was declared over and the ERT was withdrawn based on the one-size-fits-all rule of declaring EVD outbreaks over after a period of 42 days without cases (following the recovery or burial of the previous case)31.\n\nWhile the results in Fig.\u00a02B are based on the full distributional estimate of R, other assumptions are possible. For example, if a policy maker wishes to be risk averse, they may choose to base their decision-making on analyses conducted with a high value of R rather than the full distributional estimate of R. For example, if the analysis in Fig.\u00a02B is repeated but with R=2.56 (corresponding to the 95th percentile estimate of R in Fig.\u00a02A), then the result shown in purple in Fig.\u00a03A is obtained. Similar results but for different percentile estimates of R from the distributional estimate in Fig.\u00a02A are also shown in Fig.\u00a03A (these results correspond to applying Eq. (4) with different values of R).\n\nA The risk of withdrawing the ERT each day (Eq. (4)) for different percentile values of the distributional estimate of R (see Fig.\u00a02A) and the actual date of withdrawal of the ERT (black dashed). B The date on which the ERT could theoretically have been withdrawn, if the ERT was withdrawn as soon as the risk of withdrawing the ERT fell below different threshold values (0.1\u2014blue; 0.05\u2014green; 0.01\u2014red). A threshold value of 0.01 corresponds to (at most) a 1% chance of cases arising following ERT withdrawal. Results are shown for different percentile values from the distributional estimate of R (see Fig.\u00a02A). In both panels, the percentile R values shown correspond to: 50% \u2013 R=1.49; 60% \u2013 R=1.63; 70% \u2013 R=1.79; 80% \u2013 R=1.99; 90% \u2013 R=2.29; 95% \u2013 R=2.56; 99% \u2013 R=3.11.\n\nWe find that the higher the assumed value of R, the longer it would have been necessary to wait before the inferred risk of future cases in the absence of the ERT was low enough for the ERT to be withdrawn (Fig.\u00a03B). Similarly, if the ERT can only be withdrawn when the risk of future cases reduces below a pre-specified threshold value, then as expected a lower value of this threshold would require policy makers to wait longer before withdrawing the ERT. For example, if the median estimate of R was used, and interventions were removed as soon as the risk of future cases fell below 0.1 (i.e., 10%), then the ERT could have been withdrawn on 16 June 2017. If, instead, the 95th percentile estimate of R was used, and interventions were removed as soon as the risk of future cases fell below 0.01 (i.e., 1%), then the ERT withdrawal date would have been 6 July 2017.\n\nTo demonstrate how our modelling approach can be extended for use in different settings, we conducted a range of Supplementary Analyses.\n\nIn the branching process model used to generate the results shown in the main text (Figs.\u00a02 and 3), we assumed that the number of cases each day was drawn from a Poisson distribution (see the \u201cMethods\u201d section). While this assumption is made frequently in such models32,33,34, other probability distributions could be used. For example, use of a negative binomial distribution with a low value of the dispersion parameter, k, allows the possibility of super-spreading events to be accounted for35. In Supplementary Analysis\u00a01 and Fig. S2, we reproduced the results shown in Fig.\u00a02 but under this alternative transmission model. We found that our main conclusions were unchanged. First, soon after the ERT was deployed, there was a high risk of future cases in the absence of the ERT (0.97 under both the assumption that k=0.2 and under the alternative assumption that k=10). The fact that no cases went on to occur, despite the high risk of additional cases in the absence of the ERT, suggests that the ERT was effective. Second, by the time that the ERT was withdrawn, the risk of ERT withdrawal was estimated to be low (0.02 when k=0.2 and 0.01 when k=10), indicating that the decision to withdraw the ERT was safe (Fig. S2B).\n\nTo demonstrate the generalisability of our modelling approach, we also applied it to data from a second larger outbreak of EVD in which the ERT was deployed. Specifically, in Supplementary Analysis\u00a02 and Fig. S3, we estimated the risk of ERT withdrawal each day for an EVD outbreak with 54 cases that occurred in \u00c9quateur Province of DRC in 2018. Because this was a larger outbreak than the one analysed in the main text, contact tracing allowed sufficient data to be recorded for the serial interval distribution to be estimated for this specific outbreak (rather than the general EVD serial interval estimate that we used in our main analyses\u2014see the \u201cMethods\u201d section). Again, in the 2018 EVD outbreak, our estimates suggest that the ERT was only withdrawn when it was safe to do so (Fig. S3E).\n\nIn our main analyses, we assumed that all cases were recorded. While this was a plausible assumption for the 2017 EVD outbreak in Likati Health Zone due to its small size and the extensive case finding that was undertaken, for larger EVD outbreaks underreporting of cases is common36. In Supplementary Analysis\u00a03 and Fig. S4, we, therefore, extended our analysis of the larger 2018 EVD outbreak from Supplementary Analysis\u00a02 to consider the effect of underreporting. This enabled us to demonstrate how unreported cases can be accounted for when estimating the risk of withdrawing the ERT (Fig. S4). As expected, under the assumption of a higher number of unreported cases, the estimated risk of withdrawing the ERT increased. By assuming a larger extent of underreporting, more risk-averse decisions about when to withdraw the ERT can be made.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49888-5/MediaObjects/41467_2024_49888_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49888-5/MediaObjects/41467_2024_49888_Fig3_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Assessing the effectiveness of control measures and determining when interventions can be removed safely is essential during infectious disease outbreaks18,37. Relaxing interventions too soon presents a risk of a resurgence in cases, but leaving measures in place for longer than necessary is costly. In addition, interventions are, at best, inconvenient and, at worst, highly damaging, to the local population. In this study, we have presented analyses of the 2017 outbreak of EVD in Likati Health Zone, DRC, in which we used mathematical modelling to: (i) assess the effectiveness of the ERT for reducing transmission and (ii) estimate the risk of withdrawing the ERT each day (i.e., the risk that further cases would occur if the ERT was withdrawn) to guide decision making.\n\nThe available quantitative evidence suggests that the ERT was effective at reducing transmission during this outbreak. Based on transmission prior to the arrival of the ERT, we estimated that, had the ERT not been deployed, the risk of additional cases occurring was 0.97 (Fig.\u00a02B). Since no cases went on to occur, our analysis suggests that the ERT was effective. The ERT was then in place from 15 May to 2 July 2017. By the time the outbreak was declared over and the ERT was withdrawn, the risk of further cases had fallen to 0.01. While it was only possible to infer that the ERT was effective after the fact (given the additional information that no cases occurred following its arrival), we were able to estimate the risk of withdrawing the ERT in real-time by inferring the probability that additional cases would occur if it was removed, based on the level of transmission observed prior to the ERT\u2019s arrival.\n\nPolicy makers could choose to remove control interventions as soon as the risk of future cases falls below a context-dependent threshold value. Lower values of this threshold correspond to a more risk-averse strategy (interventions must be maintained for longer, but with a lower risk of subsequent cases occurring, compared to when a higher threshold value is used). If a threshold value of 0.05 had been used to determine when the ERT could be withdrawn in the 2017 EVD outbreak in Likati Health Zone, then the ERT could have been withdrawn on 21 June 2017 (11 days prior to the actual date of withdrawal of the ERT; Fig.\u00a02B).\n\nOur main results relating to the risk of withdrawing the ERT were based on our full distributional estimate of the reproduction number, R, in the absence of the ERT (Fig.\u00a02). We then explored how our results differed for individual values of R within the distributional estimate (Fig.\u00a03). Basing policy decisions on a high percentile value of R would be a more risk averse choice than using the full distributional estimate of R. We found that, if the ERT is withdrawn when the risk of future cases falls below 0.01, then using the 99th percentile value of R to guide decision making would have required the ERT to be deployed for longer than using the 50th percentile value of R (until 7 July 2017, rather than 2 July 2017; Fig.\u00a03B). When the full distributional R estimate was used, the corresponding withdrawal date was 3 July 2017 (Fig.\u00a02B).\n\nThe fact that higher assumed values of R require policy makers to wait longer before safe withdrawal of the ERT would have been deemed to be possible highlights the fact that considering uncertainty in the value of R in end-of-outbreak analyses is essential. Basing decisions on mean or median estimates of R alone, without considering uncertainty, could lead to interventions being removed on dates that are not supported by all available quantitative evidence. We note that the results shown in Fig.\u00a02B are based on the full distributional estimate of R derived directly from the disease incidence time series data and, therefore, account rigorously for uncertainty in the precise value of R.\n\nIn our analyses of the 2017 Likati Health Zone EVD outbreak, we attributed the fact that no cases occurred after the arrival of the ERT, despite the high estimated probability of future cases in the absence of the ERT, to the ERT\u2019s activities. While we contend that this interpretation is supported by the available quantitative evidence (as noted above, we estimated the risk of additional cases occurring had the ERT not been deployed to be 0.97, yet no further cases arose), other factors may have contributed to preventing transmission. For example, behavioural changes may have occurred in the local population to reduce viral spread even without the ERT being deployed in response to a growing awareness of the outbreak.\n\nAs with any epidemiological modelling study, the results presented here are based on simplifying assumptions. An assumption of the transmission model underlying most of our analyses (Figs.\u00a02, 3, S3 and S4) is that the number of cases occurring each day is drawn from a Poisson distribution. While this assumption is common to many renewal equation models32,33,34, consideration of other distributions is possible, including accounting for the possibility of super-spreading events23,38. For a fixed expected number of events on a particular day, more overdispersed distributions would be more likely to lead to zero cases on that day, but with a higher chance of a large number of cases occurring on that day reflective of a super-spreading event. To relax the assumption of a Poisson-distributed number of cases each day, we reproduced the results shown in Fig.\u00a02 but instead assumed that the daily number of cases is drawn from a negative binomial distribution, allowing for the possibility of super-spreading events (Supplementary Analysis\u00a01 and Fig. S2). In a scenario in which the ERT is withdrawn as soon as the inferred risk of withdrawing the ERT reaches a pre-specified low value, we found that the ERT withdrawal date was not sensitive to our choice of dispersion parameter in the negative binomial distribution.\n\nAnother key assumption underlying our main analyses is that all cases were detected. As noted in the \u201cResults\u201d\u00a0section, this may have been a reasonable assumption for the 2017 EVD outbreak in Likati Health Zone. However, underreporting of cases is common, not only for EVD but also for a range of other pathogens36,39,40. We therefore conducted supplementary analyses of a different (much larger) EVD outbreak (Supplementary Analysis\u00a02; Fig. S3), including considering the effect of unreported cases (Supplementary Analysis\u00a03; Fig. S4), showing that the risk of withdrawing the ERT is higher when underreporting is accounted for (Fig. S4B).\n\nDespite the simplifications in our epidemiological model, our results strongly suggest that the measures implemented by the ERT were effective at reducing transmission during the 2017 EVD outbreak in Likati Health Zone, DRC, and that the ERT was only withdrawn when it was safe to do so. The timely application of effective control measures is extremely important during infectious disease outbreaks. Similarly, the relaxation of interventions as soon as the risk of future cases is sufficiently low allows limited control resources to be conserved. The research conducted here provides a quantitative framework that can be used to guide this decision-making in real-time during future infectious disease outbreaks.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The 2017 EVD outbreak in Likati Health Zone, DRC, comprised eight cases (five confirmed cases and three probable cases15,16) occurring between 27 March and 11 May 2017. The ERT arrived in Likati on 15 May 2017, and no cases arose subsequently. A disease incidence time series was made available in real-time and consisted of symptom onset dates for all reported cases (Fig.\u00a01).\n\nPathogen transmission in the absence of the ERT was modelled using a renewal equation. In the version of the model used in our main analyses, the number of cases, It, occurring on day t is drawn from a Poisson distribution with mean\n\nIn this expression, R is the reproduction number (the expected number of secondary cases generated by each case over the course of their infectious period). The notation ws represents the probability that the (discrete) serial interval takes the value s days (i.e., the probability that the interval between an infector and infectee appearing in the disease incidence time series is s days).\n\nWe assumed that the continuous serial interval distribution for EVD is a gamma distribution with a mean of 15.3 days and a standard deviation of 9.3 days41 (we considered an alternative serial interval distribution in Supplementary Analyses\u00a02 and 3). We then discretised this distribution to obtain ws (for s=1,2,\u2026) using the method described in web appendix 11 of the Supplementary Data of the article by Cori et al.32. Specifically, if the probability density function of the continuous serial interval distribution is denoted by g(.), then\n\nin which N is a normalising constant so that the sequence of values {ws}s=1\u221e represents a valid probability mass function. The resulting discretised serial interval distribution is shown in Fig. S1.\n\nFor the purpose of our analyses of the 2017 EVD outbreak in Likati Health Zone, we labelled the date of the first case (27 March 2017) as t=1. The ERT then arrived on day t=50. We used the incidence data up to the day before the arrival of the ERT to estimate R in the absence of the ERT. Specifically, we assumed that the index case arose as a result of transmission from outside the local population (e.g., infection from an animal reservoir), and then calculated the normalised likelihood of the incidence data observed following the index case as a result of local transmission up to (and including) day t=49,\n\nIn these equations, the variables M1 and M2 are normalising constants so that L(R) represents a valid probability distribution (this is equivalent to the posterior estimate for R assuming a uniform prior). This corresponds to a gamma distribution with shape parameter \u03b1=1+\u2211t=249It and rate parameter \u03b2=\u2211t=249\u2211s=1t\u22121\u2061It\u2212sws=\u2211s=148\u2061I49\u2212sFs, in which Fs represents the cumulative distribution function of the discretised serial interval distribution (i.e., the probability that the serial interval takes a value less than or equal to s days). The resulting distribution is shown in Fig.\u00a02A. Additional description about the approach used to estimate R is provided in the Supplementary Information (Supplementary Text\u00a01).\n\nFollowing the arrival of the ERT, no further cases occurred in the 2017 EVD outbreak (this was not true for the larger outbreak considered in Supplementary Analyses\u00a02 and 3). However, deployment of the ERT is costly and the interventions implemented by the ERT can be restrictive to the local population. There are, therefore, incentives to withdraw the ERT and relax or remove associated interventions as quickly as possible when the risk of a resurgence of cases is sufficiently low. We used the distributional estimate of R in the absence of the ERT (Fig.\u00a02A) to determine the risk of future cases if the ERT is withdrawn on day t.\n\nFor a fixed value of R, the probability of future cases occurring at any time from day t onwards, if the ERT is withdrawn on day t, is given by\n\nHere,we define \u03b3(t)=\u2211j=t\u221e\u2061\u2211s=1j\u22121\u2061Ij\u2212sws=\u2211s=1t\u22121\u2061It\u2212s(1\u2212Fs\u22121). In these expressions, since we are calculating the probability that no cases occur from day t onwards, we set Ij\u2212s=0 whenever j\u2212s\u2265t.\n\nEquation (4) can be used to calculate the risk of withdrawing the ERT when the value of R is known or a single value of R is assumed. To account for uncertainty in the value of R, a distributional estimate of R can be incorporated into calculations of this risk using the expression\n\nHere, \u03b1 and \u03b2 are the shape and rate parameters of the likelihood, L(R), as given above.\n\nAn ethics application was submitted by co-author Dr M Keita to allow continued access to and use of multiple Ebola datasets for research purposes. This application was approved by the ethics committee at the Kinshasa School of Public Health, DRC (approval number ESP/CE/03/2021).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data used in our analyses are available within the relevant code in the following Github repository: https://github.com/robin-thompson/EbolaReponseTeam42. The disease incidence time series from the 2017 EVD outbreak in Likati Health Zone, DRC, has been published previously and there are no restrictions on its use.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The computing code used to perform the analyses in this article is available in the following GitHub repository: https://github.com/robin-thompson/EbolaReponseTeam42. All code was written in Matlab (compatible with version 2022a).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "WHO Ebola Response Team. Ebola virus disease in West Africa\u2014the first 9 months of the epidemic and forward projections. N. Engl. J. Med. 371, 1481\u20131495 (2014).\n\nLindblade, K. A. et al. Decreased Ebola transmission after rapid response to outbreaks in remote areas, Liberia, 2014. Emerg. Infect. 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Using Real-time Modelling to Inform the 2017 Ebola Outbreak Response in DR Congo (Github Repository, 2024).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We acknowledge the support of the JUNIPER partnership, funded by MRC (grant number MR/X018598/1; R.T.). Thanks to members of the Wolfson Centre for Mathematical Biology at the University of Oxford for helpful discussions about this research.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Mathematical Institute, University of Oxford, Oxford, UK\n\nR. Thompson\u00a0&\u00a0W. Hart\n\nWorld Health Organization, Regional Office for Africa, Brazzaville, Democratic Republic of the Congo\n\nM. Keita,\u00a0A. Gueye\u00a0&\u00a0D. Chamla\n\nInstitute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland\n\nM. Keita\n\nGlobal Neglected Tropical Diseases Programme, World Health Organization, Geneva, Switzerland\n\nI. Fall\n\nInstitut National de Sant\u00e9 Publique, Ministry of Public Health, Hygiene and Prevention, Kinshasa, Democratic Republic of the Congo\n\nM. Mossoko\u00a0&\u00a0J. Nsio-Mbeta\n\nInstitut National de Recherche Biom\u00e9dicale, Kinshasa, Democratic Republic of the Congo\n\nS. Ahuka-Mundeke\n\nMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London, UK\n\nT. Jombart\n\nGeneva Centre of Humanitarian Studies, University of Geneva, Geneva, Switzerland\n\nJ. Polonsky\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nR.T.: conceptualisation, methodology, formal analysis, investigation, visualisation, validation, writing\u2014original draft, writing\u2014review and editing. W.H.: methodology, validation, writing\u2014review and editing. M.K.: fieldwork, writing\u2014review and editing. I.F.: fieldwork, writing\u2014review and editing. A.G.: fieldwork, writing\u2014review and editing. D.C.: fieldwork, writing\u2014review and editing. M.M.: fieldwork, writing\u2014review and editing. S.A.-M.: fieldwork, writing\u2014review and editing. J.N.-M.: fieldwork, writing\u2014review and editing. T.J.: conceptualisation, methodology, writing\u2014review and editing. J.P.: conceptualisation, fieldwork, methodology, writing\u2014review and editing.\n\nCorrespondence to\n R. Thompson.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Benjamin Dahl and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Using real-time modelling to inform the 2017 Ebola outbreak response in DR Congo.\n Nat Commun 15, 5667 (2024). https://doi.org/10.1038/s41467-024-49888-5\n\nDownload citation\n\nReceived: 12 February 2024\n\nAccepted: 19 June 2024\n\nPublished: 06 July 2024\n\nVersion of record: 06 July 2024\n\nDOI: https://doi.org/10.1038/s41467-024-49888-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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0000000000000000000000000000000000000000..1faaa4ae1f216fabd5802189930b0508ad39fd2d --- /dev/null +++ b/cf54f2c9c8eccd9ab0a9e6ea0bb095520b22ea05538ba32dd5c8a67189e51c12/metadata.json @@ -0,0 +1,144 @@ +{ + "title": "ITER full model in MCNP for radiation safety demonstration", + "pre_title": "ITER full model in MCNP for radiation safety demonstration", + "journal": "Nature Communications", + "published": "03 October 2024", + "supplementary_0": [ + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52667-x/MediaObjects/41467_2024_52667_MOESM1_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [ + "https://mcnp.lanl.gov/mcnp_how_to_get_to_mcnp.shtml", + "https://zenodo.org/records/8409685" + ], + "subject": [ + "Civil engineering", + "Energy security", + "Nuclear fusion and fission", + "Power stations" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4107910/v1.pdf?c=1728039961000", + "research_square_link": "https://www.researchsquare.com//article/rs-4107910/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-52667-x.pdf", + "preprint_posted": "19 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Development of nuclear fusion as a safe and virtually limitless power source receives growing attention in the context of looming energy crisis and climate change. ITER project stands as the flagship international project and advances steadily. Construction of the Tokamak Complex is nearly finished, and the assembly of core components has started in the site. In parallel, design is getting finalized and the safety case gets more concrete. Current approaches for radiation safety demonstration based in 3D nuclear analysis require sophisticated artifacts due to the consideration of separate MCNP models for the Tokamak and the rest of the facility, resulting in cumbersome studies and thus, debilitated conclusions. To improve this situation, we have built the first integral MCNP model of the ITER facility: the ITER full model. Accompanied by improvements to the D1SUNED code, we illustrate its computational practicality and pertinence in two meaningful simulations for ITER safety case. This work represents the culmination of a two-decades-long effort of ITER modelling seeking to demonstrate the radiation safety. Beyond supporting the remaining design tasks, this model represents a noticeable simplification in the approach to produce the corresponding 3D nuclear analysis. It improves the robustness of the first-of-a-kind ITER safety case.Physical sciences/Energy science and technology/Nuclear energy/Nuclear fusion and fissionPhysical sciences/Mathematics and computing/Scientific data", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The development of nuclear fusion as a safe and virtually limitless power source is receiving growing attention in the context of looming energy crisis and climate change. ITER project stands as the flagship international initiative and is advancing steadily. The construction of the Tokamak Complex is nearly finished, and the assembly of core components has begun on site. Simultaneously, the design is being finalized, and the safety case is becoming more concrete. Current approaches to radiation safety demonstration using 3D nuclear analysis with the Monte Carlo code MCNP require sophisticated artifacts to sew together simulations in separate models for the Tokamak and the rest of the facility. This results in cumbersome studies and, consequently, challengeable conclusions. To address this issue, we have built the an integral MCNP model of the ITER facility: the ITER full model. Along with improvements to the D1SUNED code, we illustrate its computational practicality and pertinence in two meaningful simulations for ITER safety case. This work represents the culmination of a two-decade-long effort of ITER modelling aiming to demonstrate adequate radiation safety. Beyond supporting the remaining design tasks, this model simplifies the corresponding 3D nuclear analysis and improves the robustness of the ITER safety case.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "ITER will be the largest Tokamak ever constructed, and it will produce orders of magnitude more neutrons than its predecessor (up to 107 times more), JET1, and other current private initiatives2. According to French nuclear licensing procedures, ITER is classified as Installation Nuclear de Base INB-174 under French regulation. Ensuring that the expected radiation conditions do not pose challenges and risks to equipment and humans, has made nuclear analysis a relevant computational discipline in support of the ITER design and licensing during the last two decades. The radiation safety case of ITER, structured around the computational codes MCNP3 and D1SUNED4, receives growing attention as the construction progresses. Monte Carlo method accuracy and code robustness accumulated over decades of use are the basis for MCNP selection by ITER Organization, while D1SUNED is an extension to boost MCNP performance needed to deal with ITER nuclear analysis computational particularities. Independent examination has explicitly identified the need to provide additional robustness to the prediction of three-dimensional radiation fields5.\n\nITER presents particularities requiring adaptations and innovations in the discipline. Geometry representation in MCNP stands as one of the most relevant ones. Materials as different as steel, water, beryllium, copper and air are deployed in intricate cm-scale shapes over dozens of meters. Homogenization of materials can result in large distortions of the radiation field prediction, the sense and size of which cannot be anticipated, thus representing an undesired approach6. Thanks to the development of tools such as Spaceclaim (www.spaceclaim.com) for geometry handling and SuperMC7 for computer-aided design (CAD) geometry translation to MCNP format, an ever-growing degree of detail has been captured over the years in successive models.\n\nHowever, this approach implied an increased workload to prepare simulation inputs. To save time and to standardize the modeling of the environment to any simulation, ITER Organization coordinated a strategy of making reference models available to any user. They are arranged in two families: the Tokamak models8,9,10 and the Tokamak Complex models11,12,13 (representing the nuclear buildings). Despite the increased computational demand, most of the analyses conducted for ITER have been successfully carried out using them. Nevertheless, due to the usability of the models and computational limitations, it was necessary to keep both families apart.\n\nConsidering separate reference models for the machine and the building entails raising concerns. Situations in which the source of radiation is represented in a different model than the region of interest cannot be characterized with one single simulation. The same applies to situations in which the sources and/or regions of interest transcend the boundary between reference models of both families. Topics of relevance for the ITER safety case are affected.\n\nTo date, different artifacts have been considered to deal with this limitation on a case-by-case basis. All of them present drawbacks regarding the practicality, accuracy, codes licensing policies, compatibility with the ITER-specific environment of nuclear analysis tools, and/or simplicity of the analysis. They represent obstacles for an agile design process and affect the robustness of the safety case5.\n\nThe development of a heterogeneous model of the complete ITER Tokamak10 advances in the computational performance of D1SUNED, and a set of tools in support of weight windows technique14,15 have brought the possibility of joining the reference model families in a single integral model of the ITER facility, including both the Tokamak and the Tokamak Complex: the ITER full model. We present the model and two illustrative applications of high relevance for the ITER design and the safety case assessment. Model usability and computational viability to provide clean evaluations while avoiding the use of any artifact is thus demonstrated.\n\nThis work represents the culmination of a two-decade-long effort of ITER modeling8,9,10,11,12,13, methods development11,16, and implementation of tools7,17,18 to predict the radiation conditions in the ITER facility. It involves the simplification of the safety-related analysis for the benefit of clarity and standardization of the approach and reduces both human and computational resource demands. In practical terms, it means abandoning artifacts for the benefit of improved robustness of the nuclear analysis.\n\nThe reference models of the ITER Tokamak have been in use and constant improvement for over two decades following or even triggering methodological advances. The number of MCNP surfaces is a good indicator of the complexity of a model. Initial versions consisted of <2000 surfaces to represent a single 20\u00b0 sector of the machine. The adoption of CAD-to-MCNP translation tools, prominently SuperMC7, the inclusion of SpaceClaim (www.spaceclaim.com) in the workflow, and the development of guidelines and standardized approaches represented steep advances and boosted international collaboration. A successful 40\u00b0 model series was developed over 12 years for applications related to regular sectors of the machine: A-lite, B-lite, C-lite, and C-model8. A similar development took place for the 80\u00b0 irregular sector of the machine9 (containing the Neutral Beam Injector). In 2020, the first 360\u00b0 model of the ITER Tokamak was built: E-lite10. In parallel, diverse MCNP models of the ITER Tokamak Complex were created in 201011, 201612, and 202013. Details on the number of surfaces are given in Table\u00a01.\n\nThe complexity captured in the Tokamak models increased by a factor of ~100 in ~12 years. A similar trend is observed for the Tokamak Complex models, involving rising computational loads in either case19. This was the motivation to keep the Tokamak and the Tokamak Complex reference models apart.\n\nNote the mentioned ITER reference models contain, by definition, a non-exhaustive representation of the facility. They include either sectors of the machine inside the bio-shield, or the Tokamak Complex beyond the bio-shield. The same logic applies to radiation source representations considered within these models and to the regions of interest that can be studied: their maximum spatial extent will be delimited by the boundaries of the models. This feature is referred to as a partial representation captured in the models. Working in such conditions is referred to as a local approach, and it becomes more valid as the range of the dominant interactions gets narrower.\n\nA myriad of local studies used the partial reference models to successfully address specific design topics and have constituted the core of the discipline. Numerous aspects of the radiation field of ITER were identified and accurately characterized thanks to the complexity captured in the reference models. For example, the role of the particles streaming through the gaps in the shutdown dose rates20 (SDDR), SDDR cross-talks between adjacent ports21, the nuclear heating peak values in the Vacuum Vessel inner shell22, or integral heating in the Blanket Shield Modules6. Note, however, that all of them are conducted in a local approach since they consider models dismissing regions either from the geometry or the radiation source. This has been widely acceptable since a relative evaluation or a confined phenomenon was addressed, rather than an absolute judgment of long-range aspects of the radiation field. Note this approach is sufficient, and even efficient, under certain circumstances.\n\nLocal approach shows, however, limitations when the geometries, radiation sources and/or regions of relevance become wider and eventually transcend the model's boundaries. This situation has manifested in the last years in a set of works, and diverse attempts to mitigate it have been set in place.\n\nA group of works23,24,25,26,27 took the approach of locally expanding the ITER reference models (A-lite23,24, B-lite25,26 and C-model27) in terms of geometry to address nuclear analysis of the Torus Cryopump Port #12 port cell25,26, the equatorial port bio-shield plug23 or the Neutral Beam Injector Cell24 and a dedicated shielding cabinet for the Radial Neutron Camera27. This permitted to include radiation sources of relevance and tallies over regions of interest. To some extent, this approach can be understood as increasing the study domain by extending the models. This is, building ad hoc dedicated larger-but-still-partial models. While it serves the purpose of facilitating a given study, the lack of generality of this approach is evident. Further, it remains difficult to quantify the impact of this approach.\n\nDifferently, other works explored the possibility of sewing simulations using different models to build a wider domain of study by concatenating studies in subsequent domains. This is made by transferring radiation transport information from one simulation to the next one. The radiation impinging on the boundary of a model needs to be characterized and reconstructed in the next one. The simplest procedure is the setting of a tally, inspecting it, and inferring an SDEF card (the standard approach for sources definition in MCNP) from it, which was made in some cases to study the upper launchers28, the upper port #1829 and a bio-shield plug generic design30. Note, however, that the SDEF cards are limited to one-level variable dependencies, which prevents proper modeling of the dominating effect of radiation streaming (focused, locally intense and highly energetic radiation beams). Furthermore, this approach is accompanied by an avoidable field distortion due to binning unapproachable to the date. Methods for uncertainty propagation associated with this approach are largely missing.\n\nMore generally, the use of WSSA files (i.e. binary surface source writing file), recording weight, coordinates, velocity vectors, and energy of impinging histories, permits to carrying out of such an operation, capturing the full complexity of the radiation field in the model's boundary, as it was considered in to study the equatorial port #1131 and the In-vessel Viewing System32,33. Its main drawback is, however, the cap in the sampling of source particles. The number of particles registered in the initial simulation is the maximum number of particles that can be simulated in the subsequent one. This can entail convergence problems. In addition, it may involve the manipulation of source description files occupying GBs, sometimes impractical.\n\nLastly, the limitations of the use of WSSA files were addressed with a dedicated tool, named SRC-UNED34. It automatically infers probability distribution functions in user-defined bins from WSSA files, which permits unlimited sampling with lighter files (~MB). On the other hand, in comparison with the SDEF card approach, it permits higher-level variable dependencies, while the field distortion due to binning remains. Note that SRC-UNED is an artifact embedded within an already complex workflow, requiring a documented verification and validation (V&V) plus a case-by-case binning validation. SRC-UNED has been used in works such as the shielding design to protect electronics35, the design of lower ports bio-shield plugs36, the assessment of the radiation conditions during in-vessel components extraction13, the bio-shield lid design37 and the production of radiation atlases16.\n\nThus, attempts to overcome the partial nature of the local studies derived from the use of the current ITER reference models exist, reaching what can be considered pseudo-integral representations. With different degrees of success, none of them is fully satisfactory, bringing different problems such as the proliferation of new models, lack of generality, or unassessed distortions in the radiation field. The underlying problem to all of them is the constant avoidance of an obvious situation: the integral representation of the facility requires models including everything.\n\nDifferently, to design tasks, a local approach based on partial representations results unsatisfactory for the safety case assessment. Most of the safety-relevant responses must be determined all through the facility and considering all relevant sources of radiation, with the aim of reaching a global and absolute perspective. It will demonstrate compliance with the limits for radiation exposure to the public, workers and electronics, as well as the minimization of the occupational radiation exposure (ORE) known as the As Low As Reasonably Achievable (ALARA) strategy. This will involve a collection of studies considering a few radiation sources and large regions of the facility.\n\nThe safety case will deal with several neutron and photon sources spread all across the entire facility (shown in Figs.\u00a01 and \u00a02): (1) plasmas of different species, (2) Deuterium\u2014Deuterium (DD) and Deuterium\u2013Tritium (DT) reactions in the Neutral Beam Injector components38, (3) high-energy particles following runaway electrons events, (4) activated structures and components39, (5) activated corrosion products across the Tokamak cooling water system (TCWS)40, (6) activation of water in the TCWS41 (16N and 17N, photon and neutron emitters), and (7) activated Tokamak dust impregnating components42.\n\nThe plasma neutron source and the Tokamak Cooling Water System 16N photon source geometrical arrays are shown both in orange in the context of the ITER facility. The bio-shield and Lid are highlighted in blue. Note that the HV deck stands for a high-voltage deck, and the NB cell stands for a neutral beam cell. Courtesy of ITER Organization.\n\nThe bio-shield is highlighted in a white dashed line. Note that NBI stands for Neutral Beam Injector.\n\nTimewise, instances of these radiation sources may dominate the radiation field during the pre-fusion power operation, during the fusion power operation phase, during machine shutdown and maintenance, as well as during the decommissioning. Space-wise, the full Tokamak pit, over 600 rooms in the Tokamak complex, which includes over 4500 penetrations in the walls, as well as the full extension of the ITER site up to the fence, are subject to study.\n\nFacing the ITER safety case as a collection of local studies is unsatisfactory in terms of robustness, simplicity, and clarity5. Composing a global view as a patching of hundreds of local studies results is impractical. As explained, it requires artifacts introducing unassessed uncertainties and resulting more cumbersome as the study domain becomes wider or global for a simple reason: representing a single facility with two mutually exclusive models to determine the long-range situation is artificial from a methodological perspective. There is a growing perspective that an integral representation in a unified model might offer a more streamlined process for safety demonstration.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52667-x/MediaObjects/41467_2024_52667_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52667-x/MediaObjects/41467_2024_52667_Fig2_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "The natural boundary between the Tokamak models and the Tokamak Complex models has been the bio-shield: a cylindrically shaped reinforced concrete structure with a 2.5\u2009m-thick wall (Fig.\u00a01). The ITER full site MCNP model has been built, inserting an updated version of E-lite10 inside the Tokamak Complex model 202013 bio-shield (copy/pasting MCNP cells and surfaces definitions). Note the last one already contained representations of the neutral beam cell, high-voltage deck (HV deck) and the TCWS. Further updates were later implemented to the resulting model.\n\nUpdates to E-lite43 before inclusion in the ITER full model are depicted in Table\u00a02. With respect to bio-shield, occurring in both models, the bio-shield cells used in the Tokamak Complex model were preserved. Some components transcending the boundary between the two models were present in either model. A cookie-cutter approach was followed to extend them and preserve them in the resulting joined model.\n\nOnce assembled, the resulting model has been amended. All the bio-shield plugs MCNP models have been updated. MCNP models of the Drain Tank Room were added to the model, the contents of all the In-Vessel Viewing System ports, plus LP #2 and #4, EP #8, #10, #15, #16, UP #1 and #16 have been updated from plasma to the gallery and included in the new model (both inside and outside the bio-shield). These models account for the extra number of cells and surfaces observed in Table\u00a03. The ITER full model is shown in Fig.\u00a03.\n\nCross-section view of the ITER full model. The MCNP model in horizontal plane corresponding to Z\u2009=\u200960\u2009cm is shown in the figure.\n\nThus, the construction of the ITER full model in MCNP has been a process of updating E-lite, assembling it with the Tokamak Complex model, and updating the resulting model. Such a relatively standard process resulted in challenges, given the size of the models involved. Identifying cells (required, e.g., to void the in-bio-shield region of the Tokamak Complex model or to set priority in clashes) and debugging lost particles were particularly tedious. Loading time and plotting times in the range of hours were incompatible with the usual approach to visualize the geometry. And the inexistence of a CAD model of E-lite added difficulty to the task. This was resolved by three strategies. First, both E-lite and the Tokamak Complex models are profusely commented on. Studying and using the headings and comments was particularly useful in identifying cells. Secondly, batches of hundreds of MCNP plots of the two separate models and the joint one were produced by sending tasks to a queue manager to produce them in parallel, one picture per processor. Third, the information that MCNP reports in the output following a lost particle in the case of a cell clash was processed with a dedicated script. It resulted, thus, relatively immediate to identify which cells clash; the resolution of the clash was then implemented by negating one cell from another with a \u201c#\u201d operator according to the hierarchy just explained.\n\nThe process to construct the ITER full model was tailored since it was a prototype starting from two already large models. The strategies just mentioned suffice in the unlikely event that another prototype is to be built again. Nevertheless, should the ITER full model in MCNP get consolidated, it should be built from the scratch with a different approach incorporating lessons learnt which are all explained the section named \u201cFuture work\u201d.\n\nThe computational consumption of the ITER full model is shown in Table\u00a03 in comparison with its constituents, considering no tally in any of the cases. The loading and running time referred to herein were obtained using Intel Xeon 8160 processors. To make the model usable, two improvements were developed in D1SUNED v4.1.2. First, OMP-based parallelization was implemented to handle the large RAM memory needs of the model. OMP (Open Multi-Processing) is a library for parallel programming in the symmetric shared-memory processors model, so all threads share memory and data. Second, some parts of the geometry loading and processing have also been parallelized in OMP. The loading time has been reduced by a factor of 4 from pure message passing interface (MPI) to OMP\u2009+\u2009MPI in 48-processor nodes, permitting a more agile debugging process.\n\nThe practicality and robustness of the consideration of the ITER full model are shown in coming examples intended to illustrate its advantages to address long-range simulations without artifacts to couple models. We show it considering the two most pertinent sources of radiation expected in ITER: the DT plasma and the 16N radioisotopes in the TCWS. Note these are representative instances of the studies still needing to be addressed satisfactorily for the safety case assessment. The simulations shown here are only to illustrate the practicality and benefits of the ITER full model. They are not intended to forecast any creditable ITER radiation field. Thus, considerations about results crediting and safety margin are irrelevant and excluded.\n\nThe DT plasma source will be fully contained within the ITER Tokamak vacuum chamber (thus, in E-lite model currently), thus excluded and relatively far from the Tokamak Complex MCNP model.\n\nBefore this work, the most advanced approach to determine its influence across the entire Tokamak Complex involves the use of SRC-UNED34 to reconstruct the respective radiation conditions recorded in the bio-shield, the natural boundary where the Tokamak Complex MCNP model starts10. Despite recent improvements to tool44, SRC-UNED relies on a dedicated V&V, and it introduces case-dependent unapproachable uncertainties due to the space, energy, and angle of user-defined binning, as mentioned. SRC-UNED has been instrumental in the production of the two last editions of the ITER radiation atlases, but it also stands as one of the improvable points of the safety case.\n\nThe consideration of the ITER full model renders the use of SRC-UNED, WSSA files, or any other approach, unnecessary to simulate the influence of the plasma source in the Tokamak Complex. Furthermore, any distortion to the neutron field prediction due to binning is simply avoided. The neutron flux due to the DT plasma source during a 500\u2009MW pulse in the entire facility, simulated in a single direct run, is shown in Fig.\u00a04, avoiding any artifact to sew simulations (simplicity) and the associated distortions (enhanced robustness). This is a quantitative difference with respect to the state-of-the-art approaches with marked benefits for the preparation of the safety case.\n\nIt considers a threshold at 10\u2009n\u2009cm\u22122\u2009s\u22121, and it is computed with the ITER full model. Note B1, L1, and L2 levels are stories of the edifice hosting the lower, equatorial and upper levels of the Tokamak.\n\nElaboration on the computational viability is pertinent. Showing the compatibility of radiation levels with the allocation of safety-related electronics (currently assumed as 10\u2009n\u2009cm\u22122\u2009s\u22121) is one of the most computationally demanding calculations. It represents a reduction of ~13 orders of magnitude in the neutron flux from the dominant radiation source, the plasma. Figure\u00a04, showing acceptable statistical errors (\u03b5) for all the values above the limit, confirms it can be achieved with the ITER full MCNP model. Most of the map shows reliable (\u03b5\u2009<\u20090.1) or questionable (0.1\u2009<\u2009\u03b5\u2009<\u20090.2) results. Minor regions show values creditable within a factor of a few (0.2\u2009<\u2009\u03b5\u2009<\u20090.5) and only, very exceptionally, useless values are observed (\u03b5\u2009>\u20090.5). The calculation required a number of Monte Carlo histories of 1.2\u2009\u00d7\u20091011 considering only the transport of neutrons (mode N) and considering the global variance reduction technique14. The simulation required ~460,000\u2009cpu\u2009h, while no stopper was identified, preventing affordable further running of histories to reach higher convergence if needed. This indicates the practicality of the ITER full model to address similar safety-related studies derived from the influence of the plasma source across the Tokamak Complex.\n\nThe 16N source contained in the TCWS following the irradiation of cooling water with 14.1\u2009MeV DT fusion neutrons presents a span of 10 orders of magnitude in intensity41 in an intricate distribution across the entire facility crossing the bio-shield. The high-energy photons following the decay of 16N are known to compete with, or even outpace the plasma source influence in diverse locations beyond the bio-shield in the Tokamak Complex and outside it. It produces a remarkable leakage upwards37, and the subsequent sky-shine phenomenon. It is relevant since it affects the spatial domain of safety to public, starting in the facility fence. While the highest specific activity is found in the pipes inside the bio-shield, most of the activated water volume of the TCWS lies outside the bio-shield. Simulating the TCWS influence with state-of-the-art approach considering the Tokamak Complex reference model presents a challenge: the dominant region of the source (inside the bio-shield) would need to be included in an unrepresented region of the model: the region inside the bio-shield is empty in the Tokamak Complex reference model. This is using a model beyond its scope, which consequences have been unapproachable so far. WSSA or SRC-UNED could also be considered with the already mentioned limitations.\n\nAlternatively, a tailored extension of the Tokamak Complex MCNP model with the pertinent geometry could be implemented. However, tailoring MCNP models for specific safety demonstrations is subtly challenging. The need to document all the models, to carry out independent verifications of each of them, and the potential design evolution requiring simultaneous updates to all the models, are undesired workload multiplication factors. Resources limitation often stalls this approach; some models are abandoned or more worryingly, unconsciously obsoleted. These reasons are, once more, the manifestation of the unsatisfactory approach of considering separate reference models for the regions inside and outside the bio-shield.\n\nThe use of the ITER full model resolved most of the previous considerations. In Figs.\u00a05 and 6 the biological dose due to the 16N radioisotopes contained in the TCWS during a 500\u2009MW pulse is shown. The pattern shown clearly corresponds to a sky-shine phenomenon that originated in the pipes immediately below the bio-shield lid carrying the highest concentrations of 16N. At ground level, the \u03b5 is lower than 10% near the fence. The calculation required a number of Monte Carlo histories of 5\u2009\u00d7\u20091010 with global variance reduction and a computational load of ~260,000\u2009cpu\u2009h, showing the practicality of the ITER full model once more.\n\nThe gray-out area corresponds to the Hot Cell complex region unrepresented in the ITER full model and excluded to avoid misleading conclusions.\n\nFront view of the biological dose rate during operation in the middle plane due to 16N decay (X\u2009=\u20090).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52667-x/MediaObjects/41467_2024_52667_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52667-x/MediaObjects/41467_2024_52667_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52667-x/MediaObjects/41467_2024_52667_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52667-x/MediaObjects/41467_2024_52667_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "A heterogeneous MCNP model, including the Tokamak and the Tokamak Complex of ITER, is presented in this work: the ITER full model. We have shown how it permits to address integral nuclear analysis across the entire facility. It permits avoiding sophisticated artifacts to sew partial simulations and the associated unassessed uncertainties. This results in reinforced analysis robustness which will facilitate the preparation of a more reliable safety case in the years ahead. Relevant examples of qualitative improvements thanks to this model are given for (i) the determination of the neutron flux across the Tokamak Complex due to plasma, and (ii) the determination of the biological dose rate outside the Tokamak Complex due to the 16N in the TCWS. Both cases, run in single dedicated simulations, were subject to cumbersome patching and noticeable assumptions before the present work. Note that the ITER safety case is important for the rest of the industry in many aspects.\n\nThe consideration of this full model as a basis for future works may save computational resources and facilitate the explanations of radiation conditions predictions. It represents an important support to the ITER safety case assessment.\n\nWe identify the need to produce a consolidated model to promote the adoption of the approach by the community and to smooth its application to the ITER safety case. On the one hand, it should be built based on a new and specifically conceived universe structure, reconcile the numbering of cells, surfaces, materials and universes implemented in E-lite, and it must be implemented with a GIT-like version control system. It must be fully commented as well to facilitate its use. Importantly, it should permit easy traceability of the assumptions, so the model can be switched from a \u201cbest estimate model\u201d to a \u201cconservative model\u201d. On the other hand, the model computational load can be reduced with the implementation of negative universes, the conception of simpler universe containers, the elimination of redundant geometry words, flattened negations, and cell splitting. It would permit addressing relevant scoping studies for uncertainties quantification. Finally, it is also possible to implement modifications that could nearly automate the extraction of partial models for local/specific non-safety studies, thereby facilitating the overall management of ITER nuclear analyses by centralizing studies around a single, traceable, verified and validated nuclear analysis model.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The MCNP models other than E-lite10 and the Tokamak Complex 202013 that have been incorporated into the ITER full model have been produced following the sequence: (i) simplification, healing, and refurbishment of CAD model with Spaceclaim (www.spaceclaim.com), (ii) translation to MCNP with SuperMC7, (iii) lost particles debugging with MCNP3 and D1SUNED4. Automatic void generation was considered. The models were integrated by universe allocation.\n\nAll the calculations conducted for this work have been executed with D1SUNED4 v4.1.2. Global Variance Reduction technique14 was considered. The nuclear data considered for neutron transport corresponds mainly to FENDL3.1 c/d45. The photon transport library considered was MCPLIB8446 in any case. Results in MCNP mesh format were converted to vtk format and plotted with Paraview47. The voxel size considered for the determination of neutron flux inside the Tokamak Complex was 1\u2009\u00d7\u20091\u2009\u00d7\u20091\u2009m3. The voxel size considered for the determination of the biological dose rate outside the Tokamak Complex was 5\u2009\u00d7\u20095\u2009\u00d7\u20095\u2009m3.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Data and the model are the intellectual property of the ITER Organization. Data will be made available upon request after the recipients confirm in writing that the purpose of obtaining the data is only to reproduce the results and after the recipients have signed and returned a non-disclosure agreement confirming that no part of the data will be distributed in any way. Data for the tritium breeding module ports will not be made available, as they are subject to additional intellectual property restrictions.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The codes MCNP6 and D1SUNED v4.1.2 are required to obtain the results presented. The MCNP6 code is distributed by the Radiation Safety Information Computational Center (RSICC, Oak Ridge National Laboratory) under user licenses, following the procedure provided online (https://mcnp.lanl.gov/mcnp_how_to_get_to_mcnp.shtml). The D1SUNED v.4.1.2 code, which is developed by UNED, is a proprietary patch-code to MCNP6. The code will be made available on request after the recipients confirm in writing that the purpose of obtaining the code is only to reproduce the results and after the recipients have signed and returned a non-disclosure agreement confirming that no part of the code will be distributed in any way. With respect to color scales for maps, license terms establish that: Permission is granted, free of charge, to any person obtaining a copy of these color scale files and associated documentation files (https://zenodo.org/records/8409685), to deal in the color scale files without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the color scale files, and to permit persons to whom the color scale files are furnished to do so, subject to the following conditions: The color scale files are provided \u201cas is\u201d, without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and non-infringement. In no event shall the authors or copyright holders of the color scale files be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the color scale files or the use or other dealings in the color scale files.", + "section_image": [] + }, + { + "section_name": "Change history", + "section_text": "A Correction to this paper has been published: https://doi.org/10.1038/s41467-024-54683-3", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Mailloux, J. et al. \u201cOverview of JET results for optimising ITER operation\u201d. Nucl. Fusion 62, 042026 (2022).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nBall, P. The race to fusion energy. Nature 599, 362\u2013366 (2021).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nX-5 Monte Carlo Team. 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ParaView: An End-User Tool for Large Data Visualization, Visualization Handbook (Elsevier, 2005).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was carried out using an adaption of the C-model MCNP model that was developed as a collaborative effort between the AMEC company (international), Culham Centre for Fusion Energy (United Kingdom), Frascati Research Centre of the National Agency for New Technologies, Energy and Sustainable Economic Development (Italy), FDS Team of the Institute of Nuclear Energy Safety Technology (People\u2019s Republic of China), ITER Organization (France), Japan Atomic Energy Agency in Naka (Japan), National Distance Education University (UNED) (Spain), Fusion for Energy (Spain) and University of Wisconsin\u2013Madison (United States). This work was performed under ITER contract IO/19/CT/43-1948 between the ITER Organization and the consortium consisting of UNED and the companies Orano project and Jacobs (R.J., M.B., A.K., V.L., J.A., G.P., A.J.L.-R., P.M., M.D., P.G., J.S.). We appreciate the support given by the Comunidad de Madrid for funding under I+D en Tecnolog\u00edas (TECHNOFUSI\u00d3N (III)-CM, project S2018/EMT-4437) (R.J., M.B., A.K., V.L., J.A., G.P., A.J.L.-R., P.M., M.D., P.G., J.S.), by the Escuela T\u00e9cnica Superior de Ingenieros Industriales-UNED 2022, 2023 and 2024 program (R.J., M.B., A.K., V.L., J.A., G.P., A.J.L.-R., P.M., M.D., P.G., J.S.), and by UNED for funding of the predoctoral contract (Formaci\u00f3n Personal Investigador) (P.M., M.D., P.G.). We also appreciate the support given by the Ministry of Science, Innovation and Universities and the European Union\u2014Next Generation EU for the funding of the \u201cAyudas Margarita Salas\u201d contracts (G.P., J.A.) and \"Juan de la Cierva 2022\" contract (G.P.). The views expressed in this publication are the sole responsibility of the authors and do not necessarily reflect the views of Fusion for Energy or of the ITER Organization. Neither of these institutions nor any person acting on their behalf is responsible for the use that might have been made of the information in this publication. The content of this paper does not commit the ITER Organization to being a nuclear operator. We would like to thank Fabio Crameri for providing the Scientific color maps.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: R. Juarez, M. Belotti.\n\nDepartamento de Ingenier\u00eda Energ\u00e9tica, Universidad Nacional de Educaci\u00f3n a Distancia (UNED), Madrid, Spain\n\nR. Juarez,\u00a0M. Belotti,\u00a0A. Kolsek,\u00a0V. L\u00f3pez,\u00a0J. Alguacil,\u00a0G. Pedroche,\u00a0A. J. L\u00f3pez-Revelles,\u00a0P. Mart\u00ednez-Albertos,\u00a0M. De Pietri,\u00a0P. Guijosa\u00a0&\u00a0J. Sanz\n\nInstituto de Fusi\u00f3n Nuclear \u201cGuillermo Velarde\u201d, Universidad Polit\u00e9cnica de Madrid, Madrid, Spain\n\nJ. Alguacil\u00a0&\u00a0G. Pedroche\n\nITER Organization, St. Paul Lez, Durance, France\n\nY. Le Tonqueze\u00a0&\u00a0E. Polunovskiy\n\nOak Ridge National Laboratory (ORNL), Knoxville, TN, USA\n\nM. J. Loughlin\n\nFusion for Energy (F4E), Barcelona, Spain\n\nR. Pampin\u00a0&\u00a0M. Fabbri\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nR.J., A.J.L.-R., G.P., and J.S. conceived the work and wrote the paper. M.B. assembled E-lite and the Tokamak Complex MCNP models with the support of A.J.L.-R. and P.G. He also implemented the acceleration of the loading time in D1SUNED with support from V.L. and J.A. A.K., M.D., and P.M. implemented new sub-models some of them in coordination with M.F., R.P., M.J.L., E.P., and Y.L. R.J. and A.K. ran the calculations.\n\nCorrespondence to\n R. Juarez.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Lee Packer, Rosaria Villari, who co-reviewed with Simone Noce, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Juarez, R., Belotti, M., Kolsek, A. et al. ITER full model in MCNP for radiation safety demonstration.\n Nat Commun 15, 8563 (2024). https://doi.org/10.1038/s41467-024-52667-x\n\nDownload citation\n\nReceived: 15 March 2024\n\nAccepted: 17 September 2024\n\nPublished: 03 October 2024\n\nVersion of record: 03 October 2024\n\nDOI: https://doi.org/10.1038/s41467-024-52667-x\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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0000000000000000000000000000000000000000..5bd64c384440c618987fcf0eca1e63c6f7b4f7e0 --- /dev/null +++ b/cf82a8b02a7bfcad5ae10a025763e66af8a4b70103ddda7b1e8cc605e55d09ee/metadata.json @@ -0,0 +1,188 @@ +{ + "title": "Active site remodeling in tumor-relevant IDH1 mutants drives distinct kinetic features and potential resistance mechanisms", + "pre_title": "Active site remodeling in tumor-relevant IDH1 mutants drives distinct kinetic features and potential resistance mechanisms", + "journal": "Nature Communications", + "published": "06 May 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_MOESM6_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_MOESM7_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_MOESM8_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.2210/pdb8VHC/pdb", + "https://doi.org/10.2210/pdb8VH9/pdb", + "https://doi.org/10.2210/pdb8VHD/pdb", + "https://doi.org/10.2210/pdb8VHB/pdb", + "https://doi.org/10.2210/pdb8VHA/pdb", + "https://doi.org/10.2210/pdb8VHE/pdb", + "https://doi.org/10.2210/pdb1T0L/pdb", + "/articles/s41467-024-48277-2#ref-CR13", + "https://doi.org/10.2210/pdb4KZO/pdb", + "/articles/s41467-024-48277-2#ref-CR14", + "https://doi.org/10.2210/pdb6PAY/pdb", + "/articles/s41467-024-48277-2#ref-CR26", + "https://doi.org/10.19061/iochem-bd-6-320", + "https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=d24eb2fc5c0a4a2d9437dc1598212530", + "/articles/s41467-024-48277-2#MOESM4", + "/articles/s41467-024-48277-2#MOESM6", + "/articles/s41467-024-48277-2#Sec21" + ], + "code": [ + "/articles/s41467-024-48277-2#ref-CR45" + ], + "subject": [ + "Cancer metabolism", + "Enzyme mechanisms", + "Mass spectrometry", + "X-ray crystallography" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3889456/v1.pdf?c=1715080514000", + "research_square_link": "https://www.researchsquare.com//article/rs-3889456/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-48277-2.pdf", + "preprint_posted": "22 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Mutations in human isocitrate dehydrogenase 1 (IDH1) drive tumor formation in a variety of cancers by replacing its conventional activity with a neomorphic activity that generates an oncometabolite. Little is understood of the mechanistic differences among tumor-driving IDH1 mutants. We previously reported that the R132Q mutant uniquely preserves conventional activity while catalyzing robust oncometabolite production, allowing an opportunity to compare these reaction mechanisms within a single active site. Here, we employed static and dynamic structural methods and found that, compared to R132H, the R132Q active site adopted a conformation primed for catalysis with optimized substrate binding and hydride transfer to drive improved conventional and neomorphic activity over R132H. This active site remodeling revealed a possible mechanism of resistance to selective mutant IDH1 therapeutic inhibitors. This work enhances our understanding of fundamental IDH1 mechanisms while pinpointing regions for improving inhibitor selectivity.Biological sciences/Structural biology/X-ray crystallographyBiological sciences/Biochemistry/Enzymes/OxidoreductasesBiological sciences/Biochemistry/Enzyme mechanismsEnzyme mechanismIDH1structure-function relationshipsHDX-MSoncometabolite", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Mealka2024suppNSMB.pdfSupplementary materialMealka2024ExtendedDataNSMB.pdfExtended Data File", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Mutations in human isocitrate dehydrogenase 1 (IDH1) drive tumor formation in a variety of cancers by replacing its conventional activity with a neomorphic activity that generates an oncometabolite. Little is understood of the mechanistic differences among tumor-driving IDH1 mutants. We previously reported that the R132Q mutant unusually preserves conventional activity while catalyzing robust oncometabolite production, allowing an opportunity to compare these reaction mechanisms within a single active site. Here, we employ static and dynamic structural methods and observe that, compared to R132H, the R132Q active site adopts a conformation primed for catalysis with optimized substrate binding and hydride transfer to drive improved conventional and neomorphic activity over R132H. This active site remodeling reveals a possible mechanism of resistance to selective mutant IDH1 therapeutic inhibitors. This work enhances our understanding of fundamental IDH1 mechanisms while pinpointing regions for improving inhibitor selectivity.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "IDH1 is a highly conserved, homodimeric enzyme that reversibly converts isocitrate (ICT) to \u03b1-ketoglutarate (\u03b1KG) through NADP+-dependent oxidative decarboxylation. Tumor-driving IDH1 mutants catalyze a NADPH-dependent conversion of \u03b1KG to the oncometabolite D-2-hydroxyglutarate (D2HG), while typically ablating the conventional reaction1,2,3. D2HG competitively inhibits \u03b1KG-dependent enzymes like TET2 and JmjC lysine demethylases, causing DNA and histone hypermethylation and cellular de-differentiation4,5. Mutations at R132 drive >85% of lower grade and secondary gliomas6 and ~40% of cartilaginous tumors7, with R132H typically the most common8,9. Mutated IDH1 has been successfully therapeutically targeted, with several FDA-approved selective inhibitors in use and more in clinical trials (reviewed in refs. 10,11,12).\n\nWhile early kinetic characterization of IDH focused on bacterial forms, recent efforts have illuminated details of human IDH1. As wild type (WT) IDH1 binds its substrates, a conformational change occurs where the large domain (residues 1\u2013103, 286\u2013414) and small domain (residues 104\u2013136, 186\u2013285) move towards each other owing to a hinge (residues 134\u2013141) within the clasp domain (residues 137\u2013185)13. This movement closes the active site cleft with the concomitant opening of a back cleft13. In the absence of bound substrates, the \u03b110 helix (residues 271\u2013285) helps stabilize IDH1 in its open, inactive conformation13. This critical regulatory element undergoes a conformational change to help properly orient the active site residues upon substrate binding-driven closure13. These structural features are generally preserved in IDH1 R132H1,3,14, but inherent catalytic deficiencies coupled with improved NADPH binding result in this mutant catalyzing D2HG production, albeit inefficiently though at great benefit to the tumor environment.\n\nTo better understand how D2HG production occurs, there is tremendous value in studying a variant of IDH1 with more robust neomorphic reaction activity. IDH1 R132C/S/L/G/Q mutations have been reported in patients at lower frequencies15,16,17,18,19 and support distinct tumor D2HG levels20. We have demonstrated that these mutants display distinct kinetic profiles for both neomorphic and conventional reactions21,22, suggesting that their kinetic features may drive some of the variability of patients\u2019 D2HG levels22. We identified one mutant, R132Q, that maintained weak conventional catalytic activity, drove robust D2HG production21, and was resistant to mutant IDH1 inhibitors via a mechanism not yet understood22. Additionally, IDH1 R132Q drove enchondroma tumor formation in mouse models23. By identifying distinct features of R132Q and R132H, we can uncover selectivity handles for improved mutant IDH1 inhibitors, as an H-to-Q mutation requires only a single base change. Investigating the atomic-level mechanisms that drive diverse kinetic activity and inhibition among tumor-relevant IDH1 mutants can also inform chemical features that guide the field of enzyme design24.\n\nHere, we report the static and dynamic structural features that drive the distinct kinetic properties among tumor-relevant IDH1 mutants, capitalizing on the unusual active site attributes that allow R132Q to maintain conventional and enhance neomorphic activities. We observe by X-ray crystallography that the neomorphic substrate \u03b1KG, but not the conventional substrate ICT, binds via multiple conformations to R132Q. Solution-based kinetics and structural experiments demonstrate that ability of R132Q to explore multiple conformations and substrate binding modes depends upon a relatively immobile, solvent-inaccessible enzyme that is better optimized for substrate binding, hydride transfer, and mutant IDH1 inhibitor resistance compared to R132H.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We previously demonstrated that IDH1 R132Q maintains weak catalytic efficiency for the conventional reaction (ICT to \u03b1KG), while also displaying higher catalytic efficiency for the neomorphic reaction (\u03b1KG to D2HG) relative to R132H21,22. Steady-state kinetics analysis (Supplementary Fig.\u00a01) revealed a 5.9-fold increase in catalytic efficiency for the conventional reaction in R132Q versus R132H, driven primarily by an increase in kcat. R132Q catalyzed the neomorphic reaction 9-fold more efficiently than R132H via optimization of both kcat and Km. This suggested that R132Q exhibits a more stable transition state and provides more optimized on/off paths of the reactants and products compared to R132H.\n\nPre-steady-state kinetics experiments indicated that hydride transfer, or a step preceding it, was rate-limiting for the conventional reaction catalyzed by WT and R132Q, and for the neomorphic reaction catalyzed by R132Q and R132H (Fig.\u00a01). NADPH consumption by R132H showed an initial lag that was eliminated when using higher concentrations of \u03b1KG (Supplementary Fig.\u00a02). A lag has been reported previously with IDH1 WT, which was eliminated via pre-incubation of both ICT and metal25,26,27,28. Interestingly, we did not observe a lag in the neomorphic reaction catalyzed by R132Q, despite using a concentration of \u03b1KG that was 10-fold lower than the concentration associated with a lag in R132H. This suggested that \u03b1KG is more proficient at driving R132Q from an inactive to an active state compared to R132H, though it was not apparent through these experiments whether this was achieved by a more catalytically primed ground state or a faster conformational change.\n\nNADPH formation in the conventional reaction and consumption in the neomorphic reaction was monitored over the course of a single turnover (top plot) and compared with a control experiment lacking enzyme (bottom plot, in green). Traces represent an average of four technical replicates. Residuals (middle plot) were obtained to assess the goodness of a single exponential equation fit in the top plots. Kinetic parameters were calculated and reported as +/\u2212SEM resulting from deviation of the mathematical fit. A IDH1 WT, conventional reaction. B IDH1 R132H, neomorphic reaction. C IDH1 R132Q, conventional reaction. D IDH1 R132Q, neomorphic reaction.\n\nWe were unable to capture rates of conformational change when monitoring intrinsic protein fluorescence. However, we measured rates of NADPH binding to IDH1 WT, R132H, and R132Q using enzyme that was stripped of cofactor14 (Supplementary Fig.\u00a03). We found that all three IDH1 proteins displayed single-step binding events, with an NADPH binding on rate (kon) that was ~2-fold faster for WT than R132Q, while kon rates for R132H were profoundly slower. We also used isothermal titration calorimetry (ITC) to measure equilibrium binding affinity of NADPH for IDH1 (Supplementary Fig.\u00a04). We found that both mutants exhibited a decrease in Kd compared to WT, suggesting that a slower koff rate drove the improved affinity for NADPH observed for R132H despite the slow kon rate. Taken together, these kinetic data further supported the finding that when compared to R132H, IDH1 R132Q has a lower barrier to adopting the closed, active conformation that is driven by substrate and metal binding.\n\nTo illuminate possible mechanisms behind the time-resolved changes exhibited by IDH1 R132Q versus those in WT and R132H, we first used hydrogen/deuterium exchange-mass spectrometry (HDX-MS) analysis. We probed solvent accessibility as indicated by deuterium uptake in the binary IDH1:NADP(H) form, as WT and mutant IDH1 are known to copurify bound to NADP(H)26,27. We also measured deuterium uptake upon the addition of substrate (ternary complex, IDH1:NADP(H):ICT/\u03b1KG), or upon the addition of substrate and Ca2+ (quaternary complex, IDH1:NADP(H):ICT/\u03b1KG:Ca2+). By far the most substantial change in deuterium uptake for WT, R132H, and R132Q occurred in the quaternary form, indicative of closed, catalytically competent conformations among all enzyme species (Supplementary Fig.\u00a05). This is consistent with previous findings that both substrate (ICT, but also presumably \u03b1KG in the neomorphic reaction) and divalent metal binding are required to drive IDH1 into its fully closed, active conformation25,26,27,28. Deuterium uptake generally showed the following trend: R132H:NADPH:\u03b1KG:Ca2+\u2009\u226b\u2009WT:NADP+:ICT:Ca2+\u2009>\u2009R132Q:NADPH:ICT:Ca2+\u2009>\u2009R132Q:NADPH:\u03b1KG:Ca2+ (Fig.\u00a02, Supplementary Figs.\u00a05 and 6), with R132Q overall appearing to have a less structurally dynamic, more closed conformation compared to R132H.\n\nA Plots of deuterium uptake encompassing residues 86\u2013120, 168\u2013182, and 269\u2013291 (left) are shown with the structural features of these residues shown in cartoon (right) for IDH1 R132Q, WT13, and R132H14. B Plots of deuterium uptake for residues 168\u2013191, 217\u2013227, 257\u2013267, and 305\u2013354 (left) are shown, with the structural features of IDH1 R132Q, WT13, and R132H30 encompassing these regions indicated in cartoon (right). Each point represents the mean of three technical replicates.\n\nSince our kinetic studies suggested IDH1 R132Q had a lower barrier to achieve the closed conformation compared to R132H, we hypothesized that binary R132Q:NADP(H) would be in a more quaternary-like state. To test this, we compared deuterium uptake among the binary states, predicting that the R132Q:NADP(H) complex would experience less deuterium uptake than R132H:NADP(H). Unsurprisingly, in general the IDH1:NADP(H) form of all three proteins had high deuterium uptake, particularly in the substrate binding pocket, clasp, and dimer interface (Fig.\u00a02, Supplementary Figs.\u00a05\u20137). As predicted, R132Q:NADP(H) and WT:NADP(H) had the least deuterium uptake overall, while R132H:NADP(H) exhibited, by far, the most uptake. As this suggested that NADP(H)-bound R132Q had a more closed/less mobile conformation compared to R132H, we wondered if the temporal features of our HDX-MS data suggested a faster closing upon substrate binding for R132Q. This would provide one mechanism of the improved catalytic efficiency shown by IDH1 R132Q relative to R132H in the conventional and neomorphic reactions. To address this, we inspected peptides that included residues within 4\u2009\u00c5 of bound NADP(H) and ICT/\u03b1KG to compare deuterium exchange rates, as uptake plots represent combined exchanged rates for all amides in the peptide, to compare the composition of exchange rates in R132Q versus R132H. We expect that fewer amides would be exchanging at slower exchange rates for R132Q if this enzyme had a primed ground state that reached a closed conformation more easily29. Indeed, many active-site peptides had fewer amides with slower/intermediate exchange rates for IDH1 R132Q and WT compared to R132H (Supplementary Fig.\u00a07). Specifically, peptides 210\u2013216 (including catalytic residue K212), 240\u2013253, and 257\u2013267 all showed contributions of amides exchanging at faster rates for R132Q versus R132H. This favors a model where the ground state of R132Q is a more closed conformation that follows a simpler path to a catalytically competent state compared to R132H.\n\nSeeking to pair the dynamic, intermediate-resolution HDX-MS data with static, high-resolution X-ray crystal structures, we report here six crystallographic models representing the structures of IDH1 R132Q: binary IDH1 R132Q bound to NADP(H) (R132Q:NADP(H), PDB 8VHC, PDB 8VH9; R132Q bound to conventional reaction substrates (R132Q:NADP(H):ICT:Ca2+, PDB 8VHD); R132Q bound to neomorphic reaction substrates (R132Q:NADP(H):\u03b1KG:Ca2+, PDB 8VHB), PDB 8VHA, and R132Q bound to a NADP-TCEP adduct (R132Q:NADP-TCEP:Ca2+, PDB 8VHE). These structures facilitated comparisons with previously solved IDH1 WT13 and R132H structures14,30, including among binary and ICT- and \u03b1KG-bound models.\n\nBinary structures of IDH1 R132Q (Fig.\u00a03A) were valuable to help us understand differences among the mutant active sites. While R132Q:NADP(H) showed no major global structural alterations upon alignment with previously solved structures of WT:NADP(H)13 and R132H:NADP(H)30, local shifts were observed (Fig.\u00a03B\u2013D). Unsurprisingly, NADP(H)-bound R132Q had the typical open, inactive conformation seen in WT and R132H, with a larger active site cleft and smaller back cleft relative to the quaternary complexes (Supplementary Table\u00a01). These distances in the binary R132Q structure more closely resembled binary WT than R132H, supportive of a more closed, catalytically competent ground state for R132Q. However, R132Q exhibited notable differences compared to WT and R132H binary complexes. In particular, the clasp domain and helices proximal to the substrate and cofactor binding site were shifted, with the \u03b11, \u03b12, \u03b14, \u03b15, and \u03b111 helices adjusted upwards and inwards in R132Q versus WT and R132H, resulting in a similar shift of the NADP(H) molecule itself in dimer-based alignments (Fig.\u00a03B). Importantly, this inward shifting of the \u03b11 helix is a feature of closed, catalytically competent IDH1 conformations. R132Q also contained longer, more intact \u03b2 strands in the clasp domain, which plays a major role in maintaining the dimer, compared to WT and R132H (Fig.\u00a03C). The fully intact \u03b27 and \u03b28 strands in R132Q were reminiscent of quaternary, fully substrate-bound forms of IDH1 WT and R132Q (vide infra). Consistent with such stable secondary structure, peptides in the \u03b28 strand of R132Q:NADP(H) had lower deuterium uptake than WT:NADP(H) and R132H:NADP(H) (Fig.\u00a02, Supplementary Fig.\u00a06). IDH1 R132Q also maintained an extensive hydrogen bonding network enveloping the NADP(H) molecule; this network was far less robust in R132H (Supplementary Fig.\u00a08). Together, dynamic and static structural data suggested that the IDH1 R132Q active site pocket and surrounding features have greater rigidity and more defined structural features typical of fully-substrate-bound forms of IDH1, suggesting a more catalytically primed state for R132Q:NADP(H) compared to R132H:NADP(H).\n\nA The binary R132Q:NADP(H) complex is shown with each monomer highlighted using a slight color change. B Dimer-based alignments of R132Q:NADP(H) (red), WT:NADP(H)13 (black), and R132H:NADP(H) (light green)30. C Monomer-based alignments of the structures in (B). D The view show in (C) was simplified to highlight catalytic residues Y139 and K212 (though the latter residue drives catalysis in the monomer not shown as this is a monomer-based alignment), residue R132(H/Q), and the cofactor.\n\nHere, we also report an ICT-bound quaternary structure of IDH1 R132Q (R132Q:NADP(H):ICT:Ca2+, Fig.\u00a04A). Upon alignment with WT:NADP(H):ICT:Ca2+\u200913 (Fig.\u00a04B, C, Supplementary Fig.\u00a09B), there was obvious overlap in both global features and active site details. ICT-bound IDH1 R132Q also aligned well with R132H bound to its preferred substrate, \u03b1KG (R132H:NADP(H):\u03b1KG:Ca2+)14 (Fig.\u00a04B, C, Supplementary Fig.\u00a09D). Like ICT-bound WT and \u03b1KG-bound R132H structures, ICT-bound R132Q adopted a catalytically competent, closed conformation, with ICT maintaining many of the same polar interactions with the protein and divalent ion as observed with WT. This is supportive of our kinetic data showing R132Q\u2019s preservation of the conventional activity.\n\nA The R132Q:NADP(H):ICT:Ca2+ complex is shown with each monomer highlighted using a slight color change. B Monomer-based alignments of R132Q:NADP(H):ICT:Ca2+/R132Q:NADP(H):Ca2+ monomers (dark and light cyan) with WT:NADP(H):ICT:Ca2+13 (dark green); R132H:NADP(H):ICT30 (wheat); and R132H:NADP(H):\u03b1KG:Ca2+14 (dark purple). C For clarity, only the catalytic residues, residue R132X, cofactor, substrates, Ca2+ and hinge are shown in the same orientation for the structures shown in (B).\n\nThough alignment of ICT-bound WT and R132Q was strikingly similar (Supplementary Fig.\u00a09B), the 220-fold decrease in catalytic efficiency suggested that maintaining hydrogen bonding features and active site structuring was not sufficient for robust conventional activity in R132Q. Interestingly, ICT was observed only in one monomer of the R132Q quaternary complex, resulting in a shift of the \u03b111 helix and the NADP(H) molecule upward and outward in the ICT-absent R132Q monomer (Fig.\u00a04C), reminiscent of the WT:NADP(H) binary structure (Fig.\u00a03). This lack of active site saturation suggested a lower affinity toward ICT for R132Q versus WT. Though Km values are not affinity measurements, it is noteworthy that there was a 32-fold increase in Km when comparing R132Q to WT (Supplementary Fig.\u00a01). To address differences in binding affinity, we again turned to ITC experiments. ICT binding affinity for IDH1 R132H was too poor to be detected, while R132Q exhibited ~170-fold worse affinity for ICT compared to WT (Supplementary Fig.\u00a04). Structural studies provided a possible mechanism for ICT\u2019s poor binding to R132H versus R132Q; in contrast to the closed, catalytically competent conformation of ICT-bound R132Q, a previously solved ternary R132H:NADP(H):ICT30 structure revealed quasi-open monomers that had \u03b14 and \u03b111 helices shifted upwards and outwards from the dimer interface and an unraveled \u03b110 helix (Fig.\u00a04B, Supplementary Fig.\u00a09C), regions we and others have shown to be highly flexible13,30,31,32. Notably, ICT was found in a posited pre-binding site that was shifted to the left of its catalytically-competent position (Fig.\u00a04C)30. This resulted in limited polar interactions by ICT to R132H30 in contrast to ICT\u2019s extensive polar contacts to R132Q, including hydrogen bonding to catalytic residue Y139 in R132Q that indicated a catalytically-ready binding conformation (Supplementary Fig.\u00a08). As further evidence that ICT-bound R132H was ill-prepared for catalysis, its catalytic residues were swung away from the active site (Fig.\u00a04C), akin to the positioning found in binary, catalytically incompetent IDH1 structures. Though this R132H structure did not include a divalent metal that may be required for full closure30, it is nonetheless unsurprising that R132H, in contrast to R132Q, is essentially unable to convert ICT to \u03b1KG.\n\nSince IDH1 R132Q distinctly maintains both conventional and neomorphic catalytic abilities, we asked how the binding conformations for ICT, the conventional reaction substrate, and \u03b1KG, the neomorphic reaction substrate, compared. Here, we report two \u03b1KG-containing R132Q quaternary structures (R132Q:NADP(H):\u03b1KG:Ca2+). These co-crystallization experiments led to a variety of complexes, with monomer asymmetry observed (Fig.\u00a05). One structure contained a dimer that had \u03b1KG and a covalent NADP-\u03b1KG adduct bound in its monomers (Fig.\u00a05A). Cleft measurements in both monomers indicated a slightly more open conformation when compared to the closed quaternary R132Q (ICT-bound), WT (ICT-bound) and R132H (\u03b1KG-bound) structures, with the \u03b111 helix shifted out away slightly from the substrate binding pocket (Fig.\u00a05, Supplementary Fig.\u00a010). As a result, the NADP(H) itself shifted outwards compared to the ICT-bound R132Q structure, resulting in a semi-closed conformation (Fig.\u00a05D, Supplementary Table\u00a01).\n\nIn (A\u2013C) and (E), a description of the ligands present is listed below each monomer. A R132Q:NADP(H):\u03b1KG:Ca2+/R132Q:NADP-\u03b1KG:Ca2+ dimer. Each R132Q monomer is highlighted using a slight change in color. B R132Q:NADP-\u03b1KG:Ca2+/ R132Q:NADP(H):\u03b1KG:Ca2+ dimer 1 (yellow) aligned with the dimer shown in (A) (magenta). C R132Q:NADP-\u03b1KG:Ca2+/R132Q:NADP(H):Ca2+ dimer 2 (orange) aligned with the dimer shown in (A) (magenta). D Monomer-based alignment of ICT- and \u03b1KG-containing R132Q monomers. E R132Q:NADP-TCEP:Ca2+/R132Q:NADP-TCEP:Ca2+ dimer. F Monomer-based alignment of adduct-containing R132Q monomers.\n\nA second \u03b1KG-bound structure had distinct features among two dimers in the crystallographic asymmetric unit. One catalytic dimer contained one NADP-\u03b1KG adduct and one \u03b1KG molecule (Fig.\u00a05B), and again appeared as an intermediate between the R132Q:NADP(H) and the R132Q:NADP(H):ICT:Ca2+ structures (Supplementary Table\u00a01, Supplementary Fig.\u00a010). A second dimer contained an NADP-\u03b1KG adduct in one monomer, and no \u03b1KG-containing molecule in the other monomer (Fig.\u00a05C). This dimer was in a more closed, catalytically competent conformation, reminiscent of the fully closed WT quaternary structure (Supplementary Table\u00a01, Supplementary Fig.\u00a010). The Ca2+ ion clearly led to extensive restructuring, as the R132Q:NADP(H):Ca2+ monomer aligned relatively poorly with the R132Q:NADP(H) complex despite the only difference being the metal ion (Supplementary Fig.\u00a010G). Thus, closing of R132Q to the \u03b1KG-bound form may be driven just as much by metal binding as by substrate binding. This finding was recapitulated by the overall decrease seen in deuterium uptake upon treatment of substrate-bound R132Q with Ca2+ (Supplementary Fig.\u00a05). Overall, we were able to capture snapshots of stable conformations of \u03b1KG binding ranging from semi-closed (\u03b1KG-bound) to essentially fully closed (NADP-\u03b1KG adduct-bound).\n\nClosed conformations are seen for WT13 and R132H14 when bound with their preferred substrates (ICT and \u03b1KG, respectively). As \u03b1KG-bound R132Q was often not as fully closed as the ICT-bound form, we wondered how \u03b1KG-bound R132Q compared to these WT and R132H closed conformations. In alignments of R132Q:NADP(H):\u03b1KG:Ca2+ with quaternary WT and R132H structures, the catalytic residue Y139 in R132Q was shifted away from the \u03b1KG molecule (Supplementary Fig.\u00a010), with this molecule making fewer hydrogen bond contacts within the R132Q active site compared to R132H (Supplementary Fig.\u00a08). In R132Q, the \u03b1KG binding site was shifted upwards towards NADP(H) and away from the substrate binding sites seen in the ICT-bound WT and \u03b1KG-bound R132H structures. This shift might be facilitated by one surprising feature of all non-\u03b1KG-containing R132Q monomers -- the nicotinamide ring could not be reliably modeled due to missing electron density. This suggests that when \u03b1KG was absent (such as in the R132Q:NADPH:Ca2+ monomer in Fig.\u00a05C) or, more unexpectedly, even when \u03b1KG was bound (R132Q:NADPH:\u03b1KG:Ca2+ monomers), this portion of NADP(H) was more dynamic. Since the \u03b1KG-containing R132Q structures did not appear in a catalytically-ready form, it is possible that the enzymatic mechanism may rely on different amino acids used in the conventional reaction, or, since \u03b1KG serves as a substrate and product for R132Q, we may have a view into a product-bound conformation.\n\nWe found that the \u03b110 regulatory segment underwent the expected notable restructuring upon substrate binding, with this segment forming a helix in both the ICT- and \u03b1KG-bound quaternary forms of R132Q (Fig.\u00a05D), just like in ICT-bound WT and \u03b1KG-bound R132H. However, our HDX-MS experiments captured more subtle differences in R132Q that depended on which substrate was bound. The \u03b110 regulatory segment and nearby \u03b19 helix were more protected from deuterium exchange in both \u03b1KG and \u03b1KG\u2009+\u2009Ca2+ conditions in R132Q than in the ICT and ICT\u2009+\u2009Ca2+ conditions (Figs.\u00a02 and 6). Beyond its proximity to the regulatory segment, the \u03b19 helix has an additional role in active site remodeling in that it helps form a \u201cseatbelt\u201d enveloping the NADP(H) cofactor (reviewed in ref. 33). This seatbelt was observed in the WT:NADP(H):ICT:Ca2+ quaternary structure13, with residue R314 in the \u03b111 helix shifted inward to form polar contacts with D253\u2019 and Q256\u2019 in \u03b19 of the adjacent monomer and with a water molecule (Fig.\u00a07). The absence of the seatbelt was not limited to binary R132Q, R132H, and WT structures; no seatbelt was observed in the ternary ICT-bound or, more surprisingly, in the closed, quaternary \u03b1KG-bound R132H structures14,30. As no \u03b1KG-bound WT structure is available at this time, we compared a structure of a non-R132 mutant, G97D, which generates D2HG but exhibits a high degree of structural similarities with IDH1 WT14. The \u03b1KG-bound form of G97D also did not show a seatbelt conformation, suggesting this is a distinct feature of ICT-bound, fully closed structures.\n\nDeuterium uptake is shown as a gradient from red (high uptake) to blue (low uptake). A Deuterium uptake by IDH1 WT, R132Q, and R132H upon no ligand treatment. These HDX-MS data were overlaid on NADP(H)-only bound forms of WT13 in all three cases, as the \u03b1KG helix was disordered in the NADP(H)-only bound forms of IDH1 R132Q and R132H30. B Deuterium uptake by WT and R132Q upon treatment with NADP+ and ICT, and by IDH1 R132Q and R132H upon treatment with NADPH and \u03b1KG. These HDX-MS data were overlaid on WT:NADP(H):ICT:Ca2+\u200913, R132Q:NADP(H):ICT:Ca2+ and R132Q:NADP(H):\u03b1KG:Ca2+, or R132H:NADP(H): \u03b1KG:Ca2+\u200914. C Deuterium uptake by IDH1 WT and R132Q upon treatment with NADP+, ICT, and Ca2+, and by IDH1 R132Q and R132H upon treatment with NADPH, \u03b1KG, and Ca2+. These HDX-MS data were overlaid on the structures described in (B).\n\nA Unlike the binary structure of IDH1 WT13 and quaternary structure of G97D:NADP(H):\u03b1KG:Ca2+14, the quaternary IDH1 WT complex13 forms a seatbelt over the NADP(H). B Binary R132Q:NADP(H) and quaternary R132Q:NADP(H):\u03b1KG:Ca2+ structures do not form a seatbelt, while R132Q:NADP(H):ICT:Ca2+ and the most closed conformation of R132Q:NADP-\u03b1KG:Ca2+ form a seatbelt. C No seatbelt is formed in the binary R132H:NADP(H), ternary R132H:NADP(H):ICT, or quaternary R132H:NADP(H):\u03b1KG:Ca2+ structures of IDH1 R132H14,30.\n\nIDH1 R132Q behaved like WT (Fig.\u00a07A) when binding the conventional reaction substrate (ICT), with a seatbelt forming over the cofactor since residue R314 was in position to contact Q256\u2019, D253\u2019, and, distinct in this protein, E247\u2019 in \u03b211 of the adjacent monomer, as well as a water molecule (Fig.\u00a07B). However, R132Q behaved more like R132H (Fig.\u00a07C) when binding the neomorphic substrate, with \u03b1KG-bound monomers showing residue R314 swung away from the \u03b19\u2019 helix, precluding the necessary polar contacts (Fig.\u00a07B). Interestingly, the closed R132Q:NADP-\u03b1KG:Ca2+/R132Q:NADP(H):Ca2+ dimer (Fig.\u00a05C) had an intact seatbelt over the NADP-\u03b1KG adduct (Fig.\u00a07B), suggesting that a fully closed conformation of \u03b1KG-bound R132Q is possible if the nicotinamide ring of NADP(H) is stabilized in some way, such as via adduct formation. Interestingly, HDX-MS dynamics showed seatbelt formation was associated with an increase in deuterium uptake, with the \u03b111 helix, which contains the seatbelt-forming R314 residue, being more protected in the \u03b1KG-bound R132Q and R132H (seatbelt-lacking) complexes relative to the ICT-bound WT and R132Q (seatbelt-forming) complexes (Fig.\u00a06). We note that all of these mutant structures (both R132H and R132Q) describe mutant:mutant homodimers; as these mutations are found heterozygously in patients, a possibility exists for WT:mutant heterodimers, which could result in still different structural features and conformations. Overall, multiple conformations were possible with \u03b1KG-containing R132Q structures, including those associated with fully closed forms.\n\nIn addition to the NADP-\u03b1KG adduct, we encountered an NADP-tris(2-carboxyethyl)phosphine (NADP-TCEP) adduct when attempting to crystallize ICT-bound R132Q (Fig.\u00a05E, Supplementary Fig.\u00a011). There may be catalytic relevance to these adducts since the TCEP and \u03b1KG carboxylates helped coordinate Ca2+ and maintained many hydrogen bonds in their respective active sites, though the metal ion was slightly shifted to accommodate these adducts (Supplementary Figs.\u00a010, 12). All TCEP and \u03b1KG adducts appeared as hybrids between the semi-closed, \u03b1KG-bound and fully closed, ICT-bound R132Q complexes (Supplementary Table\u00a01). In general, one NADP-\u03b1KG adduct-containing monomer (Fig.\u00a05C) aligned well to the fully closed ICT-bound R132Q structure in all regions except the clasp domain, where the adducted monomer was shifted towards the dimer interface and the \u03b29 strand was more intact (Fig.\u00a05, Supplementary Fig.\u00a010). As further evidence of its fully closed conformation, this NADP-\u03b1KG adduct-containing monomer also had an intact seatbelt (Fig.\u00a07B).\n\nTo better understand how these adducts were forming, we performed density functional theory (DFT) calculations for model NADP-TCEP and NADP-\u03b1KG adducts (Supplementary Tables\u00a02 and 3), which suggested that adduct formation would not occur if not for the constraining environment of the crystal structure. We considered an alternative possibility that the R132Q active site favored adduct formation and binding. If the NADP-TCEP adduct could form in the R132Q active site, it would act as a competitive inhibitor. Thus, we treated R132Q with varying concentrations of three reducing agents (TCEP, dithiothreitol (DTT), and \u03b2-mercaptoethanol (BME)) to determine the effects of conventional reaction catalysis (Supplementary Fig.\u00a013, Supplementary Table\u00a04). Dose-dependent inhibition of R132Q catalysis was profound with TCEP, while DTT and BME had minimal effects. More modest, though notable effects on catalysis were also observed when challenging WT with the highest concentration of TCEP tested (10\u2009mM, Supplementary Fig.\u00a014). Together, these results support the hypothesis that adduct formation occurred outside of the non-physiologically-relevant crystal packing environment, with the adducts mimicking \u03b1KG binding, ICT binding, or transition between the two.\n\nAs these adduct-containing structures showed hybrid binding features of \u03b1KG and ICT, we wondered if transition state features could be extrapolated. Here, the nicotinamide ring of the adduct lent an interesting clue. Calculations suggested that the nicotinamide ring is likely planar in the oxidized form34,35. During NADP+ activation for hydride transfer, the enzyme is predicted to distort the nicotinamide ring to form a puckered transition state as a partial positive charge on C4N develops34,35,36 (Supplementary Fig.\u00a015). NAD(P)-adducts with reducing agents have been reported previously, including with TCEP37 and DTT38, and were found to have a more puckered nicotinamide ring, reminiscent of a transition state. Here, unlike the planar ring observed in our non-adducted forms of NADP(H) (R132Q:NADP(H):ICT:Ca2+), both the \u03b1KG- and TCEP-containing NADP-adducts showed a more puckered nicotinamide ring (Supplementary Fig.\u00a012, Supplementary Table\u00a03), suggestive of a transition-state-like conformation.\n\nIn summary, we highlight discrete catalytic and structural features among two tumor-relevant IDH1 mutants, with the R132Q mutant serving as an invaluable tool to probe the journey through substrate turnover of two reactions that typically cannot be performed by the same enzyme. Together, our kinetics experiments and static and dynamic structural data suggested that substrate binding and conformational changes associated with the conventional and the neomorphic reactions have distinct paths through turnover that can be described in terms of differences in substrate affinity, substrate binding site location, solvent accessibility, and propensity for conformational activation and active site remodeling (summarized in Fig.\u00a08). IDH1 R132Q\u2019s accommodation of catalytically-relevant adducts, perhaps due to its active site appearing better optimized for catalysis compared to R132H, illuminate snapshots of substrate and substrate analogs in varying degrees of catalytic readiness.\n\nHelices displaying profound differences in alignment of the three forms of IDH1 are highlighted. The seatbelt feature is indicated on the \u03b111 and \u03b19 helices. A Binary WT:NADP(H)13 collapses to a closed conformation upon ICT binding, though moderate levels of deuterium exchange are still permitted. B Binary R132Q:NADP(H) collapses to a closed conformation upon ICT binding, showing improved catalytic efficiency for the conventional reaction and lower deuterium uptake compared to R132H. However, catalytic activity is much lower compared to WT. C Binary R132H:NADP(H)30 collapses to a fully closed conformation only upon \u03b1KG binding14, but a seatbelt is not formed and deuterium uptake remains high. D Binary R132Q:NADP(H) forms semi-closed and closed conformations upon binding \u03b1KG and NADP-\u03b1KG, respectively, with a seatbelt successfully formed in the closed state in some of our crystallographic snapshots. The \u03b1KG binding site was shifted away from the \u03b19 helix, though catalytic activity was much higher than that seen in R132H.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Kinetic, HDX-MS, and crystallography experiments revealed fundamental differences in the catalytic mechanisms of WT and tumor-relevant IDH1 mutants (Fig.\u00a08). We were surprised to identify adducts binding to and, in the case of the TCEP adduct, inhibiting R132Q. While we do not expect this adduct to form under physiologically conditions, our experiments measuring reducing agent inhibition were supportive of the possibility of these adducts representing catalytically meaningful conformations. Determining that the NADP-TCEP adduct was competitive with ICT was unsurprising as the TCEP portion of the adduct mimicked features of ICT binding to R132Q (Supplementary Fig.\u00a012). While this experiment itself does not differentiate whether the adduct forms outside the protein and then binds, or whether the active site pocket drives adduct formation, our DTT experiments may support a model where the enzyme drives adduct formation. NADP-DTT adducts have been previously reported with yeast xylose reductase38. While DTT has some similar structural features compared to ICT, it does not recapitulate the carboxylates that the TCEP and \u03b1KG adducts contain. These adducts preserved many polar contacts that non-adducted NADP(H) and ICT form with Ca2+ and active site residues. Further supportive of this adduct being distinct to R132Q, and thus perhaps formed with the help of this enzyme, TCEP was much less effective at inhibiting WT (Supplementary Fig.\u00a014). This work also highlights a liability for using TCEP as a reducing agent in kinetic and structural studies on dehydrogenases, as adduct formation with NAD(P)+ may complicate structure/function analysis.\n\nWe reported previously that IDH1 R132Q has distinct catalytic profiles for the conventional and neomorphic reactions compared to more common tumor-driving IDH1 mutants (R132H, R132C)21,22. Though both mutations retain a polar amino acid, a mutation to a glutamine versus a histidine would be expected to be less disruptive due to a more similar size and shape relative to arginine, though it is unsurprising that WT is far more efficient at catalyzing the conventional reaction than the mutants since R132 coordinates the C3 carboxylate of ICT3,13. As neither mutant can directly participate in this coordination with ICT, we asked why the conventional reaction was more efficient in R132Q than R132H. We found that R132Q employed an active site water that mitigated the loss of hydrogen bonding to ICT resulting from the R to Q mutation by imperfectly mimicking the polar interactions with the substrate normally afforded by R132 (Supplementary Fig.\u00a016). Despite the shifting of the \u03b1KG binding site, we noticed a similar compensatory mechanism in our \u03b1KG-bound R132Q structure, with a water molecule again recapitulating these polar interactions. Here, however, the water molecule did not appear to hydrogen bond with the substrate. Instead, a second water molecule was found at the same location as the Ca2+ ion in the quaternary ICT-bound WT IDH1 structure (Supplementary Fig.\u00a016A), which presumably helped stabilize the \u03b1KG substrate in R132Q. We previously reported the importance of water molecules in facilitating mutant IDH1 inhibition31, and this current work highlights the importance of water in substrate binding by providing a possible mechanism by which R132Q is more catalytically efficient compared to R132H.\n\nIn addition to affecting catalysis, the \u03b110 regulatory segment may serve as a selectivity filter for mutant IDH1 inhibitor binding39. We have shown previously that selective mutant IDH1 inhibitors bind poorly to R132Q, with IC50 profiles consistent with WT rather than R132H22. We predicted that a more stable \u03b110 regulatory segment in R132Q:NADP(H) drove this resistance. However, here we found that while this unfolded loop indeed had stronger electron density compared to R132H:NADP(H)30, it still appeared less stable than the partially folded features of WT:NADP(H)13. We now believe that the more activated, quaternary-like state of the binary R132Q:NADP(H) complex helped drive inhibitor resistance. In this complex, regions including the \u03b111 and \u03b14 helices were shifted inwards, with R132Q experiencing less deuterium uptake (Fig.\u00a08). Using compound 24 as a prototypical selective mutant IDH1 inhibitor40, the small increase in the stability of the \u03b110 regulatory segment in R132Q did not appear to have much effect on inhibitor binding (Fig.\u00a09A). Instead, our alignments showed residues 111-121 in the inhibitor binding pocket, which form a loop between the \u03b24 and \u03b25 strands, likely had a larger role in the loss of affinity towards inhibitors for R132Q. While this region accommodated the inhibitor in the R132H:NADP(H) complex (Fig.\u00a09D), these residues interfered with inhibitor binding to R132Q:NADP(H) (Fig.\u00a09C). Interestingly, unlike in R132Q, these residues didn\u2019t appear to preclude inhibitor binding in WT:NADP(H) (Fig.\u00a09E). Thus, while the \u03b110 regulatory segment likely precludes inhibitor binding in WT, residues 111-121 perform this function in R132Q (Fig.\u00a09A). Thus, the essentially kinetically identical inhibitory characteristics of WT and R132Q22 develop through two very different mechanisms. Importantly, as this loop would not have been readily apparent as a selectivity gate when only examining the WT structure, it is only through our R132Q:NADP(H) structure that we were able to identify a possible resistance strategy and selectivity handle.\n\nWe have reported previously that IDH1 R132Q binds selective mutant IDH1 inhibitors poorly. A A previously solved structure of a selective IDH1 R132H inhibitor (6O2Y)40 was aligned to WT13, R132H30, and R132Q binary complexes. In (B\u2013E), residues A111-V121 are shown as a surface. B The structure of the inhibitor bound to a R132H:NADP(H) complex40. Residues A111-V121 in R132Q:NADP(H) (C) and in WT:NADP(H) (D) obstruct the inhibitor binding pocket. E The inhibitor could be accommodated in the structure of R132H:NADP(H)30.\n\nWhile much effort has been devoted to understanding the distinct catalytic and structural features of IDH1 WT versus R132H, our discovery of the unusual kinetic properties of the R132Q mutant allowed a valuable opportunity to establish the static and dynamic structural adjustments required to maintain conventional and neomorphic activities within the same active site. Compared to R132H, our findings show that the R132Q binding pocket and surrounding areas are better primed for substrate binding and hydride transfer steps. Rather than simply acting as a hybrid of WT and R132H, R132Q employed distinct strategies to yield improved catalytic parameters for both ICT and \u03b1KG turnover as compared to R132H. These structural and dynamic discoveries not only highlight mechanistic properties of important tumor drivers, but also identify regions that may serve as selectivity handles when designing mutant IDH1 inhibitors requiring increasing selectivity or optimization against resistance mutants.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48277-2/MediaObjects/41467_2024_48277_Fig9_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Dithiothreitol (DTT), isopropyl 1-thio-\u03b2-D-galactopyranoside (IPTG), Triton X-100, \u03b1-ketoglutaric acid sodium salt (\u03b1KG), DL-isocitric acid trisodium salt hydrate, and magnesium chloride (MgCl2) were obtained from Fisher Scientific (Hampton, NH). BME was obtained from MP Biomedicals (Santa Ana, CA). \u03b2-Nicotinamide adenine dinucleotide phosphate reduced trisodium salt (NADPH), \u03b2-nicotinamide adenine dinucleotide phosphate disodium salt (NADP+) and tris(2-carboxyethyl)phosphine) (TCEP) was purchased from Millipore Sigma (Burlington, MA). Nickel-nitrilotriacetic acid (Ni-NTA) resin was obtained from Qiagen (Valencia, CA). Stain free gels (4\u201312%) were obtained from Bio-Rad Laboratories (Hercules, CA). Protease inhibitor tablets were obtained from Roche Applied Science (Penzberg, Germany). Phenylmethylsulfonyl fluoride (PMSF) salt was purchased from Thermo Scientific (Waltham, MA).\n\nThe E. coli BL21 Gold DE3 strain was used for all protein expression. Human IDH1 WT, R132H, and R132Q homodimers were expressed from a pET-28b(+) plasmid in E. coli BL21 Gold DE3 cells. The R132Q construct was made in the WT IDH1 background using site-directed mutagenesis with the following primers: forward primer, 5-GTTAAACCGATCATTATTGGTCAGCATGCCTATGGTGATCAGTATC; reverse primer, 5-GATACTGATCACCATAGGCATGCTGACCAATAATGATCGGTTTAAC. As described previously21, incubation in 0.5-1\u2009L of terrific broth with 30\u2009\u00b5g/mL of kanamycin (37\u2009\u00b0C, 200\u2009rpm) occurred until reaching an A600 of 0.9\u20131.2. Protein expression was induced with 1\u2009mM IPTG after briefly cooling to 25\u2009\u00b0C. Following 18\u2009h of incubation (19\u2009\u00b0C, 130\u2009rpm), cell pellets were harvested and resuspended in lysis buffer (20\u2009mM Tris pH 7.5 at 4\u2009\u00b0C, 500\u2009mM NaCl, 0.1% NaCl, 0.1% Triton X-100, and a protease inhibitor tablet), and cells were lysed using sonication. Crude lysates were clarified via centrifugation (12,000\u2009rpm, 1\u2009h, 4\u2009\u00b0C). Lysate was loaded on to a pre-equilibrated Ni-NTA column and washed with 150\u2009mL of wash buffer (20\u2009mM Tris pH 7.5 at 4\u2009\u00b0C, 500\u2009mM NaCl, 15\u2009mM imidazole, 5\u2009mM BME), and protein was eluted with elution buffer (50\u2009mM Tris pH 7.5 at 4\u2009\u00b0C, 500\u2009mM NaCl, 500\u2009mM imidazole, 5% glycerol, 10\u2009mM BME). Protein was dialyzed overnight in 50\u2009mM Tris pH 7.5 @ 4\u2009\u00b0C, 100\u2009mM NaCl, 20% glycerol, and 1\u2009mM DTT, and >95% purity was ensured via SDS-PAGE analysis. Finally, IDH1 protein was flash-frozen using liquid nitrogen and stored at \u221280\u2009\u00b0C. All kinetic analysis was performed <1 month from cell pelleting.\n\nFor pre-steady-state kinetics and HDX-MS experiments, protein was loaded onto a pre-equilibrated (50\u2009mM Tris-HCl 7.5 at 4\u2009\u00b0C and 100\u2009mM sodium chloride) Superdex 16/600 size exclusion column (GE Life Sciences, Chicago, IL) following Ni-NTA affinity chromatography to remove any protein aggregates. Protein was eluted with 50\u2009mM Tris-HCl pH 7.5 at 4\u2009\u00b0C, 100\u2009mM NaCl, and 1\u2009mM DTT. The fractions were pooled and concentrated for use in pre-steady-state experiments, or pooled and dialyzed in Tris-HCl pH 7.5 at 4\u2009\u00b0C, 100\u2009mM NaCl, 20% glycerol, and 1\u2009mM DTT and used immediately for HDX-MS analysis32. For R132Q X-ray crystallography experiments, two 1\u2009L cultures of terrific broth supplemented with 50\u2009\u03bcg/ml of kanamycin were incubated at 37\u2009\u00b0C and 180\u2009rpm until an A600 of 0.4 was reached. Cultures were removed and placed onto stir plates and allowed to cool to 25\u2009\u00b0C. Expression was induced when cultures reached an A600 of 0.8\u20131.0 with 1\u2009mM IPTG and incubated for an additional 16\u201318\u2009h. Cell pellets were harvested and resuspended in lysis buffer (20\u2009mM Tris pH 7.5 at 4\u2009\u00b0C, 500\u2009mM NaCl, 0.2% Triton X-100, 5\u2009mM imidazole, 1\u2009mM PMSF, and 5\u2009mM BME). Following cell lysis via sonication, crude lysate was clarified via centrifugation at 14,000\u2009\u00d7\u2009g for one hour. The lysate was loaded on to a pre-equilibrated Ni-NTA column. The column was washed with 100\u2009mL of wash buffer (20\u2009mM Tris pH 7.5 at 4\u2009\u00b0C, 500\u2009mM NaCl, 15\u2009mM imidazole, 5\u2009mM BME). Protein was eluted using elution buffer (50\u2009mM Tris pH 7.5 at 4\u2009\u00b0C, 500\u2009mM NaCl, 500\u2009mM imidazole, 5% glycerol, 10\u2009mM BME). For the NADP(H)-stripped experiments, all required substrates for catalysis for the reverse (WT required MgCl2, \u03b1KG, and bicarbonate) and neomorphic (R132H and R132Q required MgCl2 and \u03b1KG) reactions were added to the Ni-NTA affinity column-bound IDH1 to convert any tightly binding NADPH to NADP+, followed by extensive column washing to remove the more weakly bound NADP+ as described in previous work14. In all cases, eluted protein was loaded onto a HiPrep 26/10 desalting column (GE Healthcare) containing 25\u2009mM Tris pH 7.5 at 20\u2009\u00b0C, 500\u2009mM NaCl, 5\u2009mM EDTA, 2\u2009mM DTT, and placed on ice overnight to remove any remaining metals from purification. Fractions containing IDH1 were concentrated (MilliPore Amicon Ultra 15 30\u2009kDa NMWL concentrator) and loaded onto a Superdex 26/600 (GE Healthcare) pre-equilibrated with 20\u2009mM Tris pH 7.5 at 20\u2009\u00b0C, 200\u2009mM NaCl, and 2\u2009mM DTT. Fractions containing pure IDH1 were pooled and concentrated to a final concentration of 14\u201320\u2009mg/mL, flash frozen using liquid nitrogen, and stored at \u221280\u2009\u00b0C. In all cases, the purity of the protein (>95%) was confirmed using SDS-PAGE analysis.\n\nStructure figures were prepared using PyMOL v. 2.5.541.\n\nTo measure steady-state activity of homodimer WT, R132H, and R132Q, only minor modifications were made from previous studies21,22. For the conventional reaction (ICT to \u03b1KG), IDH1 buffer (50\u2009mM Tris HCl pH 7.5 at 37\u2009\u00b0C, 150\u2009mM NaCl, 10\u2009mM MgCl2, 1\u2009mM DTT) and homodimer IDH1 (100\u2009nM IDH1 WT, or 200\u2009nM IDH1 R132H and R132Q), as well as various concentrations of ICT and 200\u2009\u00b5M NADP+ were preincubated separately for 3\u2009min at 37\u2009\u00b0C. Following addition of substrates at 37\u2009\u00b0C, the increase of absorbance at 340\u2009nm due to production of NADPH was monitored using an Agilent Cary UV/Vis 3500 spectrophotometer (Santa Clara, CA). For the neomorphic reaction (\u03b1KG to D2HG), IDH1 buffer and homodimer mutant IDH1 (200\u2009nM) as well as various concentrations of \u03b1KG at pH 7.5 and 200\u2009\u00b5M NADPH were separately preincubated for 3\u2009min at 37\u2009\u00b0C. Following addition of substrates at 37\u2009\u00b0C, the decrease of absorbance at 340\u2009nm due to consumption of NADPH was monitored. As described previously21,22, the kinetic parameters, which were obtained using two or three individual protein preparations (biological replicates), were determined by plotting the slope of the linear range of the change in absorbance over time. The change in absorbance was converted to nanomolar NADPH using the molar extinction coefficient for NADPH of 6.22\u2009cm\u22121 mM\u22121 to determine kobs (i.e. nM NADPH/nM enzyme s\u22121) at each substrate concentration. Each kobs was fit to the Michaelis-Menten equation using Graphpad Prism v.10 to calculate kcat and Km, and technical replicate points are indicated.\n\nFor the reducing agent inhibition steady-state studies, the conventional reaction conditions described above were repeated except one of three reducing agents (DTT, TCEP, or BME) were added at varying concentrations during the pre-incubation step with the enzyme before substrates were added. Here, three protein preparations were used (biological replicates), with each point representing a single technical replicate. Upon obtaining Michaelis-Menten plots at various reducing agent concentrations, the inverse of both kobs and substrate concentration were plotted in Lineweaver-Burk analysis.\n\nSingle-turnover, pre-steady-state kinetic assays were performed for the neomorphic reaction at 37\u2009\u00b0C using an RSM stopped-flow spectrophotometer (OLIS, Atlanta, GA). For the neomorphic reaction, hydride transfer (NADPH to NADP+ conversion) was monitored as a change in fluorescence as a function of time via measuring the depletion of NADPH signal by exciting the sample at 340\u2009nm and scanning the emission spectrum from 410 to 460\u2009nm. Final concentrations after mixing were as follows: 40\u2009\u00b5M IDH1 R132Q or R132H, 10\u2009\u00b5M NADPH, 10\u2009mM \u03b1KG (IDH1 R132H) or 0.5\u2009mM \u03b1KG (IDH1 R132Q), 50\u2009mM Tris-HCl (pH 7.5 at 37\u2009\u00b0C), 150\u2009mM NaCl, 0.1\u2009mM DTT, and 10\u2009mM MgCl2. The change in fluorescence as a function of time was fit to a single exponential equation (Y\u2009=\u2009A0e-kt) using Graphpad Prism to obtain kobs. For IDH1 R132H, a higher concentration of \u03b1KG (20\u2009mM) was used since 1\u2009mM \u03b1KG showed an initial lag.\n\nSingle turnover pre-steady-state kinetics were also performed for the conventional reaction at 37\u2009\u00b0C to obtain rate constants associated with steps after NADP+ binding through hydride transfer using an RSM stopped-flow spectrophotometer. NADPH formation as a function of time was similarly monitored by exciting at 340\u2009nm and scanning the emission spectrum from 410 to 460\u2009nm. Final concentrations after mixing were as follows: 30\u2009\u00b5M IDH1 WT or R132Q, 10\u2009\u00b5M NADP+, 0.5\u2009mM ICT (IDH1 WT) or 1\u2009mM ICT (IDH1 R132Q), 50\u2009mM Tris-HCl (pH 7.5 at 37\u2009\u00b0C), 150\u2009mM NaCl, 0.1\u2009mM DTT, and 10\u2009mM MgCl2. The change in fluorescence as a function of time was fit to a single exponential equation (Y\u2009=\u2009A0e\u2212kt) using Graphpad Prism and kobs values were obtained.\n\nRates associated with NADPH binding corresponding to the first step of the catalytic cycle for the neomorphic reaction were performed using an RSM-stopped flow spectrophotometer (OLIS, Atlanta, Georgia). However, due to low sensitivity of our stopped-flow spectrophotometer, the concentrations of NADPH and IDH1 were increased, which in the case of IDH1 WT led to rates too fast to be detected by our instrument (\u2264100\u2009s\u22121). Therefore, glycerol (40%) and temperature (10\u2009\u00b0C) were used to slow NADPH binding rates to IDH1. NADPH binding as a function of time was monitored by exciting at 340\u2009nm and scanning the emission spectrum from 410 to 460\u2009nm. Final concentrations after mixing were as follows: 4\u2009\u00b5M IDH1, varying concentration of \u00b5M NADP+, 100\u2009mM Tris-HCl pH 7.5, 150\u2009mM NaCl, 0.1\u2009mM DTT, 10\u2009mM MgCl2, and 40% glycerol. The change in fluorescence as a function of time was fit to a single exponential equation (Y\u2009=\u2009A0e\u2212kt) using Graphpad Prism, and kobs values were obtained and plotted as a function of NADPH concentration using the equation kobs\u2009=\u2009k1[NADPH]\u2009+\u2009k-1. This yielded a linear graph indicating one-step binding, with the slope equal to k1 and the Y-intercept equal to k-1, though the Y-intercept slope was too high to do so reliably. For all pre-steady-state kinetics experiments except NADPH binding measurements, a single protein preparation was used with each trace representing an average of 4 technical replicates. For NADPH binding, 10 technical replicates were averaged. This work described here based on previously described experiments14.\n\nIsothermal titration calorimetry (ITC) experiments were conducted at the Sanford Burnham Prebys Protein Production and Analysis Facility using a Low Volume Affinity ITC calorimeter (TA Instruments). For NADPH titrations, experiments were performed at 25\u2009\u00b0C in 20\u2009mM Tris pH 7.5, 100\u2009mM NaCl, 10\u2009mM MgCl2, and 2\u2009mM BME, injecting 0.25\u2009mM NADPH into the cell containing 0.025\u2009mM for IDH1 WT, 0.025\u2009mM or 0.04\u2009mM IDH1 R132H; and injecting 0.15\u2009mM NADPH into the cell containing 0.034\u2009mM or 0.026\u2009mM IDH1 R132Q. For ICT titrations, experiments were performed at 25\u2009\u00b0C in 20\u2009mM Tris pH 7.5, 100\u2009mM NaCl, 10\u2009mM CaCl2, and 2\u2009mM BME, injecting 0.6\u2009mM ICT into the cell containing 0.12\u2009mM IDH1 R132Q or 0.13\u2009mM IDH1 R132H. Baseline control experiments were performed by injecting the ligand into a cell with buffer only. In all cases, ITC data were analyzed using the Nanoanalyze software package by TA Instruments.\n\nHDX-MS data collection and analysis was performed at the Biomolecular and Proteomics Mass Spectrometry Facility (BPMSF) of the University California San Diego using a Waters HDX-1 system which consists of a Leap PAL HTX-xt dual pipette autosampler controlled by HDxDirector software (v1.0.4.0) (Leap Technologies Inc, Carrboro, NC), which manages sample preparation for injection into a Waters UPLC HDX Manager temperature-controlled (0.1\u2009\u00b0C) LC box (v1.50.1314), with solvent management by the combination of a Waters nanoAcquity UPLC Auxillary Solvent Manager (v1.50.2601) and a Waters nanoAcquity UPLC Binary Solvent Manager (v1.50.1327) delivering the sample into the Lockspray ESI source of a Waters Synapt G2-Si (UEA) quadrupole time-of-flight mass spectrometer, all Waters instruments being controlled by MassLynx 4.1 SCN917 (Waters Corporation, Milford, MA). Experiments were performed as previously described, using a sample of IDH1 WT without substrates to be analyzed alongside every experiment to allow direct experiment to experiment comparisons32,42. Deuterium exchange reactions were conducted using a Leap HDX PAL autosampler (Leap Technologies, Carrboro, NC). The D2O buffer was prepared by lyophilizing sample buffer (50\u2009mM Tris buffer at pH 7.5 at 4\u2009\u00b0C, 100\u2009mM NaCl, and 1\u2009mM DTT) either alone (IDH1:NADP(H) condition) or with the following ligands: for the conventional reaction experiments, IDH1 WT and R132Q were treated with 0.01\u2009mM NADP+ and 10\u2009mM ICT (ternary complexes), or with 0.1\u2009mM NADP+, 10\u2009mM ICT, and 10\u2009mM CaCl2. For the neomorphic reaction experiments, IDH1 R132Q and R132H were treated with 0.1\u2009mM NADPH and 10\u2009mM \u03b1KG (ternary complexes), or with 0.1\u2009mM NADPH, 10\u2009mM \u03b1KG, and 10\u2009mM CaCl2 was also included (quaternary complexes). The buffer was first prepared in ultrapure water, lyophilized, and then redissolved in an equivalent volume of 99.96% D2O (Cambridge Isotope Laboratories, Inc., Andover, MA) just prior to use. Deuterium exchange measurements were performed in triplicate for every time point (in the order of 0\u2009min, 0.5\u2009min, 1\u2009min, 2\u2009min, 5\u2009min); each run took ~30\u2009min to complete, including a blank run to ensure no carryover from run to run. Samples were prepared ~30\u2009min prior to experimental setup and stored at 1\u2009\u00b0C until dispensing into reaction vials at the start of the reaction, resulting in samples that were exposed to their substrates for between 2\u2009h (0.5\u2009min timepoint) and 7.5\u2009h (last replicate of the 5\u2009min timepoint) at 1\u2009\u00b0C. IDH1 proteins were diluted to 5\u2009\u00b5M in MS vials, to which the appropriate concentration of substrate(s) was added as indicated above (final volute of 150\u2009\u00b5M), and this sample was placed in the 0.1\u2009\u00b0C tray of the Leap autosampler. Sample (4\u2009\u00b5L) alone or with substrate(s) were then removed by the autosampler from the original vial and transferred to a 25\u2009\u00b0C tube in the other block of the autosampler, where they were equilibrated for 5\u2009min at the reaction temperature (25\u2009\u00b0C) before mixing with either H2O (control) or D2O buffer (56\u2009\u00b5L) for the indicated times. Fifty \u00b5L of the H2O- or D2O-incubated sample was then transferred to a tube at 1\u2009\u00b0C into which 50\u2009\u00b5L 3\u2009M guanidine hydrochloride had been pre-aliquoted (final pH 2.66). The sample was incubated for 1\u2009min at 1\u2009\u00b0C to quench deuterium exchange and denature the protein prior to injection of 90\u2009\u00b5L of the sample into a 100\u2009\u00b5L sample loop for in-line digestion at 15\u2009\u00b0C using an immobilized pepsin column (Immobilized Pepsin, Pierce). Peptides were then captured on a BEH C18 Vanguard precolumn at 200\u2009\u00b5L/min and then separated by analytical chromatography (Acquity UPLC BEH C18, 1.7\u2009\u00b5m 1.0\u2009\u00d7\u200950\u2009mm, Waters Corporation) at 40\u2009\u00b5L/min over 7.5\u2009min using a 7\u201385% acetonitrile gradient containing 0.1% formic acid. The resultant elution was injected by electrospray into the Waters Synapt G2Si quadrupole time-of-flight mass spectrometer. Data were collected in the Mobility, ESI+ mode using a mass acquisition range of 200\u20132000\u2009m/z, a scan time of 0.4\u2009s, and the following settings: detector 2950\u2009V, source temperature 80\u2009\u00b0C, desolvation temperature 175\u2009\u00b0C, sample cone 30\u2009V, sample cone gas 50\u2009L/h, desolvation gas 600\u2009L/h, nebulizer gas 6.0\u2009L/h. An infusion of leu-enkephalin (m/z\u2009=\u2009556.277) every 30\u2009s was used for continuous lock mass correction (mass accuracy of 1 ppm for calibration standard).\n\nTo identify peptides, data was collected on the mass spectrometer in mobility-enhanced data-independent acquisition (MSE), mobility ESI+ mode. Peptide masses were determined from triplicate analyses, and resulting data were analyzed using the ProteinLynx global server (PLGS) version 3.0 (Waters Corporation). We identified peptide masses using a minimum number of 250 ion counts for low energy peptides and 50 ion counts for their fragment ions, with the requirement that peptides had to be larger than 1500\u2009Da in all cases. Peptide sequence matches were filtered using the following cutoffs: minimum products per amino acid of 0.2, minimum score of 7, maximum MH+ error of 5 ppm, and a retention time RSD of less than 5%. To ensure high quality, we required that all peptides were present in two of the three experiments. After identifying peptides in PLGS, we then used DynamX 3.0.0 data analysis software (Waters Corporation) for peptide analysis. Here, relative deuterium uptake for every peptide was calculated via comparison of the centroids of the mass envelopes of the deuterated samples with non-deuterated controls per previously reported methods43, and used to obtain data for coverage maps. Data are represented as mean values\u2009+/\u2212\u2009SD of the three technical replicates due to processing software limitations, but we note that the LEAP autosampler robot provides highly reproducible data for biological replicates. Back-exchange was corrected for in the deuterium uptake values using a global back exchange correction factor (typically ~25%) determined from the average percent exchange measured in disordered termini of varied proteins44 and validated through examination of highly disordered IDH1 peptides, with adjustment for slight differences in each experiment via comparison of the IDH1 WT without substrate control included in each experimental set. Given the very short run time (7\u2009min total, 4\u2009min window of peptide elutions), we have previously determined that a global correction based on fully exposed peptides suffices for back exchange correction45. Significance among differences in HDX data points was assessed using ANOVA analyses and t tests (p value cutoff of 0.05) within DECA (v 116)45 to determine a minimum significant difference of 0.25\u2009Da for all peptides, as reported in the compliance table (Supplementary Table\u00a05, Supplementary Fig.\u00a019). Individual peptides of interest were compared via t-test of bound versus apo IDH1 using DECA45 to validate significance. We generated deuterium uptake plots in DECA [github.com/komiveslab/DECA]45, with data plotted as deuterium uptake (back exchange-corrected) versus time. Deuterium uptake plots show the maximum possible deuterium uptake on the y axis, which is preferable to plotting the percent uptake or percent difference because it accounts for the size of the peptide. An HDX-MS data summary table is shown in Supplementary Table\u00a05, and additional data are provided in Supplementary Data\u00a01, 2, and 3.\n\nFor the NADP(H)-only bound IDH1 R132Q crystals (PDB 8VHC, PDB 8VH9), enzyme (14\u201320\u2009mg/mL) was incubated on ice with 10\u2009mM NADPH. Crystals of R132Q:NADP(H) were grown via hanging drop vapor diffusion at 4\u2009\u00b0C. 2\u2009\u03bcL of IDH1 were mixed with 2\u2009\u03bcL of well solution containing either 220\u2009mM ammonium sulfate, 100\u2009mM bis-tris pH 6.5, and 20% (w/v) PEG 3350 (PDB 8VHC), or well solution containing 200\u2009mM ammonium citrate tribasic pH 7.0 and 26% (w/v) PEG 3350 (PDB 8VH9). Though both forms aligned very well and appeared otherwise identical, we feared the citrate buffer could nonetheless promote more substrate-bound-like features due to its structural similarity to isocitrate. Thus, the binary structure crystallized in sulfate was used for all further comparisons and alignments.\n\nIDH1 R132Q crystals containing ICT (PDB 8VHD) were grown by first incubating the enzyme at 20\u2009mg/mL with 10\u2009mM NADP+, 10\u2009mM CaCl2, and 200\u2009mM DL-isocitric acid at 20\u2009\u00b0C for 1\u2009h. Then, 2\u2009\u00b5L of IDH1 were mixed with 2\u2009\u03bcL of well solution containing 100\u2009mM bis-tris propane pH 6.5, 200\u2009mM NaI, and 24% (w/v) PEG 3350 and stored at 4\u2009\u00b0C. Crystals were harvested using a nylon-loop and cryo-protected using a solution of 100\u2009mM bis-tris propane pH 6.5, 200\u2009mM NaI, 26%(w/v) PEG 3350, and 20%(v/v) glycerol. Crystals were flash-frozen in liquid nitrogen and stored until data collection.\n\nIDH1 R132Q crystals containing \u03b1KG and/or \u03b1KG-adducts were generated by incubating enzyme (14\u201320\u2009mg/mL) on ice with 10\u2009mM NADPH, 20\u2009mM CaCl2, 75\u2009mM \u03b1KG Fisher Scientific (Hampton, NH) for 1\u2009h. For the PDB 8VHB structure, crystals were grown at 4\u2009\u00b0C via hanging drop vapor diffusion, where 2\u2009\u03bcL of IDH1 were mixed with 2\u2009\u03bcL of the well solution containing 200\u2009mM NaSCN and 21%(w/v) PEG 3350. Crystals were cryo-protected using a solution of 20% (v/v) glycerol, 25% (w/v) PEG 3350 and 200\u2009mM NaSCN, and flash-frozen in liquid nitrogen and stored until data collection. For the PDB 8VHA structure, IDH1 R132Q was incubated at 20\u2009\u00b0C with 10\u2009mM NADPH, 10\u2009mM CaCl2, 10\u2009mM \u03b1KG, and then crystals were grown at 4\u2009\u00b0C by mixing 2\u2009\u03bcL of IDH1 R132Q with 2\u2009\u03bcL of well solution containing 160\u2009mM NaNO3 and 20% (w/v) PEG 3350. Crystals were harvested using a nylon-loop and cryo-protected in a solution containing 22% (v/v) glycerol and 26% (w/v) PEG 3350.\n\nFor IDH1 R132Q crystals containing the NADP-TCEP adduct (PDB 8VHE), enzyme (14\u201320\u2009mg/mL) was incubated on ice with 10\u2009mM NADP+, 20\u2009mM CaCl2, and 75\u2009mM DL-isocitric acid for 1\u2009h. Crystals were grown at 4\u2009\u00b0C via hanging drop vapor diffusion, with 1.5\u2009\u03bcL of IDH1 mixed with 1.5\u2009\u03bcL of well solution containing 200\u2009mM KSCN, 24% (w/v) PEG 6000, and 5\u2009mM TCEP pH 7.4.\n\nData were collected at 100\u2009K using synchrotron radiation at the Advanced Photon Source, beamline 24-ID-E or at the Stanford Synchrotron Radiation Lightsource, beamline BL12-2. All datasets were processed with XDS v6/30/2346. Structure solutions were obtained by molecular replacement using PHASER-MR in Phenix 1.247,48. For \u03b1KG and/or \u03b1KG-adducts (PDB 8VHB, PDB 8VHA), isocitrate (PDB 8VHD), and NADP-TCEP (PDB 8VHE) co-crystals, PDB ensembles of PDB 1T0L13, PDB 4KZO14, and PDB 6PAY26 were used for molecular replacement by generating ensembles using Phenix Ensembler47,48. For IDH1 R132Q apo structures, PDB 1T0913 and PDB 4UMX49 were used as search models. The models were optimized via iterative rounds of refinement in Phenix Refine and manual rebuilding in Coot 1.150,51. Ligand restraints were generated in Phenix eLBOW47,48. Data collection and refinement statistics are summarized in Supplementary Table\u00a06, a stereo-image of the electron density maps for each structure reported here are shown in Supplementary Fig.\u00a017, and mFo-DFc omit maps contoured at 3 sigma for all ligands are shown in Supplementary Fig.\u00a018.\n\nDensity functional theory (DFT) calculations52 were carried out to model the NADP-TCEP binding energetics and geometry using the Gaussian 16 vC.01 suite of programs53. The NADP+ was modeled as the nicotinamide ring plus a pendant dihydroxy furan to represent the sugar. The model NADP+ and NADP-TCEP adduct were each given a +1 charge. To better model the effects of the solvent, three explicit water molecules were included in calculations on the adducts, distributed at the likeliest sites for hydrogen bonding. The B3LYP54, \u03c9B97XD55, and M0656 hybrid functionals were used with the cc-pVDZ57,58 and pc-n59,60 basis sets, with the latter obtained from the online Basis Set Exchange61. In all of these calculations, implicit solvation was applied using the COSMO model with water as the solvent62,63 and empirical dispersion was added using the D3 version of Grimme\u2019s dispersion along with Becke-Johnson damping64,65. This treatment of solvation effectively models the species as though they were in solution rather than crystalline form. Harmonic frequency analysis was carried out to obtain the vibrational corrections needed to calculate the free energies. Finally, because basis set superposition error can be substantial relative to intermolecular bond energies, the counterpoise correction was applied to our final energies of reaction66,67. The transition state (TS) for the TCEP\u2009+\u2009NADP+ binding was identified and confirmed by analysis of the single imaginary vibrational frequency. The DFT calculations for the model NADP-TCEP adduct predicted values of 25\u00b0 for \\(\\Delta {\\theta }_{{{{{{\\rm{C}}}}}}}\\) and \u221211\u00b0 for \\(\\Delta {\\theta }_{{{{{{\\rm{N}}}}}}}\\), where the experimental values in the X-ray structure were \\(\\Delta {\\theta }_{{{{{{\\rm{C}}}}}}}\\)\u2009=\u200929.2\u00b0 and \\(\\Delta {\\theta }_{{{{{{\\rm{N}}}}}}}\\)\u2009=\u2009\u22121.1\u00b0 (Supplementary Table\u00a02). For the NADP-\u03b1KG adduct, agreement was similar, with DFT predicting \\(\\Delta {\\theta }_{{{{{{\\rm{C}}}}}}}\\)\u2009=\u200929\u00b0 and \\(\\Delta {\\theta }_{{{{{{\\rm{N}}}}}}}\\)\u2009=\u2009\u221214\u00b0 as compared to \\(\\Delta {\\theta }_{{{{{{\\rm{C}}}}}}}\\)\u2009=\u200925\u00b0 and \\(\\Delta {\\theta }_{{{{{{\\rm{N}}}}}}}\\)\u2009=\u2009\u221225\u00b0 in the X-ray structure (Supplementary Table\u00a02). The binding was energetically favored, and appeared to occur without barrier when vibrational effects were included, with a calculated binding energy of 9.4\u2009kcal\u2009mol\u22121 at 298\u2009K. However, the calculated free energies indicated that in solution, the entropy decrease would preclude spontaneous binding. Quenching the translational entropy of the species in the crystal may be what allowed the process to occur. We noted that the counterpoise corrections to the transition state and adduct energies were essential, having magnitudes of 7\u20138\u2009kcal\u2009mol\u22121 and comparable to the uncorrected energy differences.\n\nFor the dihedral angles, the deviation from planarity \\(\\Delta \\theta\\) of the NADP pyridine ring in the adduct was reported using the average of two dihedral angles. Numbering the carbon atoms in the ring by convention as shown in Supplementary Fig.\u00a015, the C-P bond in NADP-TCEP formed at atom 4. The positions of the N atom 1 and the opposite C atom 4 are referenced to the plane defined by the roughly coplanar atoms 2, 3, 5, and 6. The average of the dihedral angles 2-3-5-4 and 6-3-5-4 (Supplementary Fig.\u00a015) was subtracted from 180\u00b0 to yield \\(\\Delta {\\theta }_{{{{{{\\rm{C}}}}}}}\\) as a metric for the deviation from planarity of C4, while the average of 3-2-6-1 and 5-2-6-1 subtracted from 180\u00b0 is used to calculate \\(\\Delta {\\theta }_{{{{{{\\rm{N}}}}}}}\\) for N1. A sign convention was applied such that if \\(\\Delta {\\theta }_{{{{{{\\rm{C}}}}}}}\\) and \\(\\Delta {\\theta }_{{{{{{\\rm{N}}}}}}}\\) had the same sign, the two corners of the ring bend away each other in chair fashion, whereas opposite signs indicate a boat-like conformation. Comparison of the results from the different functionals and basis sets showed very little difference in the geometry. Optimized geometries obtained with the pc-2 basis set on a smaller geometry (omitting sugar and explicit waters) were not significantly different from those obtained with pc-1, so we chose to report the B3LYP/pc-1 results here, with the sugar and explicit waters included (Supplementary Table\u00a02). An additional geometry optimization was run on the NADP-\u03b1KG adduct with two explicit waters and a -2 charge, employing the aug-pc-1 basis set59,68 to obtain the diffuse functions necessary to adequately model anions.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Crystallographic data and protein structure coordinates have been deposited with the Protein Data Bank (PDB) public repository: PDB 8VHC; PDB 8VH9; PDB 8VHD; PDB 8VHB; PDB 8VHA; and PDB 8VHE. Previously solved structures are also available: PDB 1T0L13, PDB 4KZO14, and PDB 6PAY26. Output files from the computational work are available at the ioChem-BD database [https://doi.org/10.19061/iochem-bd-6-320]. HDX-MS data can be found at the MassIVE FTP server [https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=d24eb2fc5c0a4a2d9437dc1598212530]. Supplementary Information is included with Supplementary Figs. and Tables. Supplementary Data\u00a01\u20133 with additional HDX-MS data are also included. Source Data is also included. Additional information and requests for resources and reagents should be directed for fulfillment by the corresponding author Christal D. Sohl (csohl@sdsu.edu).\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Deuterium uptake plots were generated using DECA, which can be accessed using the following link: github.com/komiveslab/DECA. 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The Sanford Burnham Prebys Protein Production and Analysis Facility is supported by NCI Cancer Center Support Grant P30 CA030199. The Northeastern Collaborative Access Team beamlines are funded by NIH/NIGMS (P30GM124165) and the Eiger 16\u2009M detector at the 24-ID-E beam line is funded by a NIH-ORIP HEI grant (S10OD021527). The Advanced Photon Source is a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. Use of the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. DOE Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. The SSRL Structural Molecular Biology Program is supported by the DOE Office of Biological and Environmental Research, and by NIH/NIGMS (P30GM133894). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Chemistry & Biochemistry, San Diego State University, San Diego, CA, USA\n\nMatthew Mealka,\u00a0Nicole A. Sierra,\u00a0Diego Avellaneda Matteo,\u00a0Elene Albekioni,\u00a0Rachel Khoury,\u00a0Timothy Mai,\u00a0Brittany M. Conley,\u00a0Nalani J. Coleman,\u00a0Kaitlyn A. Sabo,\u00a0Andrew L. Cooksy,\u00a0Tom Huxford\u00a0&\u00a0Christal D. Sohl\n\nDepartment of Chemistry & Biochemistry, University of California San Diego, La Jolla, CA, USA\n\nElizabeth A. Komives\u00a0&\u00a0Steve Silletti\n\nSanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA\n\nAndrey A. Bobkov\n\nVividion Therapeutics, San Diego, CA, USA\n\nJamie M. Schiffer\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nM.M., N.A.S., D.A.M., E.A., B.M.C., A.A.B., A.L.C, and S.S. contributed to the methodology, data curation, data analysis, visualization, validation, and editing; R.K., T.M., N.J.C., K.A.S. contributed to the methodology, data curation, experimental analysis, and validation; E.A.K. contributed to funding acquisition, data analysis, and editing; J.M.S. contributed to the experimental analysis and editing; T.H. contributed to the conceptualization, data analysis, supervision, visualization, and editing; C.D.S. contributed to the conceptualization, data analysis, data curation, supervision, visualization, funding acquisition, writing, editing, and project administration.\n\nCorrespondence to\n Christal D. Sohl.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "J.M.S. is an employee at Vividion Therapeutics and owns stock in Schr\u00f6dinger. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Jianping Ding and the other, anonymous, reviewers for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Mealka, M., Sierra, N.A., Avellaneda Matteo, D. et al. Active site remodeling in tumor-relevant IDH1 mutants drives distinct kinetic features and potential resistance mechanisms.\n Nat Commun 15, 3785 (2024). https://doi.org/10.1038/s41467-024-48277-2\n\nDownload citation\n\nReceived: 07 February 2024\n\nAccepted: 26 April 2024\n\nPublished: 06 May 2024\n\nVersion of record: 06 May 2024\n\nDOI: https://doi.org/10.1038/s41467-024-48277-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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histomorphological atlas of resected mesothelioma discovered by self-supervised learning from 3446 whole-slide images", + "journal": "Nature Communications", + "published": "07 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63846-9/MediaObjects/41467_2025_63846_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63846-9/MediaObjects/41467_2025_63846_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63846-9/MediaObjects/41467_2025_63846_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63846-9/MediaObjects/41467_2025_63846_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Mesothelioma%20(MESO)", + "https://www.cancer.gov/ccg/research/genome-sequencing/tcga/studied-cancers/mesothelioma-study", + "https://github.com/measty/MesoGraph", + "/articles/s41467-025-63846-9#Sec18" + ], + "code": [ + "https://github.com/FarzanehSeyedshahi/Histomorphological-Phenotype-Learning", + "/articles/s41467-025-63846-9#ref-CR55" + ], + "subject": [ + "Image processing", + "Machine learning", + "Mesothelioma", + "Tumour biomarkers" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5678715/v1.pdf?c=1759921541000", + "research_square_link": "https://www.researchsquare.com//article/rs-5678715/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63846-9.pdf", + "preprint_posted": "16 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Mesothelioma is a highly lethal and poorly biologically understood disease which presents diagnostic challenges due to its morphological complexity. This study uses self-supervised AI (Artificial Intelligence) to map the histomorphological landscape of the disease. The resulting atlas consists of recurrent patterns identified from 3446 Hematoxylin and Eosin (H&E) stained images scanned from resected tumour slides. These patterns generate highly interpretable predictions, achieving state-of-the-art performance with 0.65 concordance index (c-index) for outcomes and 85% AUC in subtyping. Their clinical relevance is endorsed by comprehensive human pathological assessment. Furthermore, we characterise the molecular underpinnings of these diverse, meaningful, predictive patterns. Our approach both improves diagnosis and deepens our understanding of mesothelioma biology, highlighting the power of this self-learning method in clinical applications and scientific discovery.Biological sciences/Cancer/MesotheliomaBiological sciences/Computational biology and bioinformatics/Machine learning", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SUPHPLMeso.pdfSupplementary Info", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Mesothelioma is a highly lethal and poorly biologically understood disease which presents diagnostic challenges due to its morphological complexity. This study uses self-supervised AI (Artificial Intelligence) to map the histomorphological landscape of the disease. The resulting atlas consists of recurrent patterns identified from 3446 Hematoxylin and Eosin (H&E) stained images scanned from resected tumour slides. These patterns generate highly interpretable predictions, achieving state-of-the-art performance with 0.65 concordance index (c-index) for outcomes and 88% AUC in subtyping. Their clinical relevance is endorsed by comprehensive human pathological assessment. Furthermore, we characterise the molecular underpinnings of these diverse, meaningful, predictive patterns. Our approach both improves diagnosis and deepens our understanding of mesothelioma biology, highlighting the power of this self-learning method in clinical applications and scientific discovery.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Mesothelioma is a highly lethal cancer almost always caused by asbestos exposure1,2. Early detection, critical for effective treatment, remains challenging3,4. Mesothelioma\u2019s biological diversity complicates histopathological diagnosis, as early malignancy can be difficult to distinguish from reactive changes. Diagnosing mesothelioma from H&E (Hematoxylin and Eosin) images is a subjective and time-intensive process even for skilled subspecialty histopathologists. Definitive diagnosis often remains elusive5,6, even with immunohistochemistry or FISH (fluorescence in situ hybridisation). These diagnostic challenges are at least in part due to the difficulties in devising robust, manually applicable systems of morphological characterisation, in addition to the well-known issues of inter-pathologist agreement. The emergence of AI methods provides an opportunity to comprehensively describe the morphological complexity of mesothelioma to generate a quantitative visual dictionary of the disease.\n\nRecent AI methods in mesothelioma primarily focus on tile-based or cell-based approaches, using supervised or weakly-supervised learning. MesoNet7 used Whole Slide Images (WSI) tiles to predict patient survival through a risk score based on malignant morphologies but lacked insight into the diversity of the tumour microenvironment. Cell-based methods like MesoGraph8 and SpindleMesoNET9 quantified malignancy through tumour cell shapes, especially spindle cells, but required extensive annotations and were computationally intensive for slide-level applications. While these approaches provide valuable insights, their findings are restricted by the nature and quality of their human annotations. Recent self-supervised models, such as Hierarchical Image Pyramid Transformer (HIPT)10, CTransPath11, HistoSSLscaling12, UNI13, and Histomorphological Phenotype Learning(HPL)14 as well as other self-supervised models such as RNAPath15, which focus on healthy tissue analysis, have been developed for H&E WSI histopathological analysis. Uniquely among these approaches, HPL focuses on identifying recurrent histomorphological patterns through clusters known as histomorphological phenotype clusters (HPCs). HPL leverages the Barlow Twins self-supervised framework16, using ResNet for feature extraction from 224\u2009\u00d7\u2009224 WSI patches, followed by clustering of tile feature vectors via the Leiden algorithm17. Each HPC represents a unique morphological pattern that can be associated with specific molecular landscapes or used to predict patient outcomes and mesothelioma subtypes by quantifying HPC frequencies. (Further details in the 'Online Methods' section.) Previously, HPL has been applied to lung cancer, revealing significant underlying patterns and giving impressive prognostic performance14, but it has not been implemented in mesothelioma. Self-supervised methods such as HPL depend upon accessing large volumes of training data, preferably from resected tumour material which offers large tissue areas with full morphological variance. This is especially challenging in mesothelioma, which is so often diagnosed from tiny biopsies and subsequently treated medically rather than surgically.\n\nIn this work, we curated 3446 whole slide images of 485 resected mesothelioma cases to generate a uniquely powerful training resource called Leicester Archival Thoracic Tumour Investigation Cohort-Mesothelioma (LATTICe-M), as shown with further details in Fig.\u00a01a. We then applied the HPL pipeline to our dataset to build a comprehensive atlas of mesothelioma H&E morphology. (Fig.\u00a01b)\n\na An overview of the LATTICe-M dataset clinical information, highlighting key demographic and pathological information. The forest plot displays log hazard ratios (centre) with 95% confidence intervals (error bars), derived from the Cox proportional hazards model for clinical variables including age, subtype, and TNM stage. Survival probability Kaplan-Meier survival curves are stratified by mesothelioma subtype, with time displayed in months. Shaded areas represent 95% confidence intervals around each survival curve. Risk groups were defined by median predicted hazard from the Cox model. The overview plots are based on n\u2009=\u2009512 patients (biological replicates), each representing an independent clinical record. The unit of study is patient and no technical replicates is used. Other clinical variables are presented with their corresponding frequencies and percentages in two summary tables. b HPL pipeline workflow: Each WSI is divided into 224 by 224 pixel tiles. After applying various data augmentation distortions, these tiles served as input for the Barlow Twins self-supervised learning model. Once the model is trained, the ResNet backbone network generated 128-dimensional feature vectors per tile, representing prominent histopathological features. These vectors were then grouped using the Leiden community clustering algorithm to identify morphologically distinct patterns. At the patient level, the clusters representing different histopathological patterns were analysed to quantify the proportion of each HPC within each WSI. This quantitative information was subsequently used to predict mesothelioma subtypes and patient outcomes. Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63846-9/MediaObjects/41467_2025_63846_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "We have identified 47 recurrent histomorphological phenotype clusters (HPCs) (Fig.\u00a02a) based on morphological features encoded by self-learned neural networks. These HPCs are identified from 3,239,939 tiles extracted from 3446 images at 5x equivalent resolution. 41 of 47 HPCs are shared in more than 20% of cases, and none of them are case-specific. A threshold of >1% abundance was applied to call an HPC \u201cpresent\u201d in a case. HPCs were then binned by patient prevalence groups as well as coloured by rare and frequent (<20% and \u00a0>\u00a080%) in grey, intermediate (20\u221280%) in blue. As a result of this, two complementary bar charts summarise these distributions: one showing the percentage of cases per HPC and another counting HPCs within 10%-wide patient-prevalence bins. Rare HPCs (<20% prevalence) represent either normal tissues (open lung/muscle, which are minor tissue components in the tumour-rich blocks selected for scanning), reactive changes which are either unusual or not targeted for scanning (dense lymphocytes from tertiary lymphoid structures, pleural plaque), and a couple of the less common tumour phenotypes (cold, solid pattern epithelioid disease and plump disorganised spindle cells). The near-universal HPCs (>80%) represent features which are either very widespread in a surgical resection (e.g. talc pleurodesis, vessels, collagen) or quite broad ubiquitous malignant morphologies (e.g. infiltrated fat, sparse epithelioid disease). Interestingly, these more common HPCs often display lower \u2018purity\u2019, reflecting a broader morphological composition.\n\nA team of subspecialty expert pathologists from 3 centres, who had no access to the WSI images or labels (blinded assessment), examined every HPC to achieve consensus morphological annotations for each one, derived from their defining features: epithelioid vs spindled morphology, inflammation, necrosis, cellularity, desmoplasia, atypia, and cluster purity and each HPC was given a summary title. They evaluated inflammation levels in each HPC, categorising them as None-Sparse, Mild-Moderate, or Marked. Most HPCs were None-Sparse, but some displayed notable patterns. Assessments of inflammation and necrosis exhibited the highest levels of consensus, with at least 50% of the HPCs receiving unanimous agreement in these categories. For epithelioid growth patterns, we also observed a relatively high level of full agreement. However, for spindle architecture in our non-epithelioid clusters (orderly/less orderly/disorderly), agreement among the pathologists was lower, perhaps reflecting the subjectivity of this measure. Across 47 HPCs with 3 raters, Fleiss\u2019 Kappa scores (reported in the last row of Fig.\u00a02c\u2014with variable category definitions per component) for individual histopathological components ranged from 0.2 to 0.6, indicating fair to moderate agreement based on the interpretation scale proposed by ref. 18. This degree of agreement is in line with kappa scores for several diagnostic tasks in mesothelioma19. These annotations reveal areas of the UMAP containing multiple HPCs with broad similarities, such as spindled/collagenous HPCs, epitheloid tumour growth patterns, and lymphocytic infiltration, as well as peripheral and projecting clouds of morphologically highly distinct lung tissue and chest wall muscle tiles (Fig.\u00a02d). This grouping is supported by the general co-occurrence of HPCs within each slide in the\u00a0Supplementary Figs.. These HPCs enable the detailed automated spatial annotation of any mesothelioma whole slide image, as illustrated in Fig.\u00a02e, highlighting two cases with highly divergent outcomes and morphologies. The first case, a sarcomatoid malignancy which resulted in death at 66 days, is highly morphologically diverse and contains abundant tiles in spindle cell-associated clusters, while the second is predominantly made up of a single epithelioid cluster.\n\na A HPC color-coded UMAP (Uniform Manifold Approximation and Projection) plot displaying the spatial distribution of 47 distinct HPCs. b Distribution and patient prevalence of HPCs: Percentage of LATTICe-M cases exhibiting each HPC, with colouring indicating rare and ubiquitous (<20% or \u00a0>80%, grey) and intermediate (20\u221280%, blue) patterns. Also on the right, distribution of HPCs categorised by patient prevalence decile. c Majority consensus for pathologists' panel\u2019s annotations for HPCs. Asterisks (*) indicate complete agreement among all pathologists. d Main pattern color-coded UMAP, where annotations were provided by a pathologist following the clustering step to group HPCs with similar morphologies. e WSI from a poor outcome and good outcome case overlaid and quantified by HPCs, demonstrating the differences in HPC composition and showing the percentage of the top 10 HPCs for each sample, alongside their histomorphology annotations (scale bar, 21mm). Source data are provided as a Source Data file.\n\nThe crucial histopathological distinction in mesothelioma subtyping lies between epithelioid and non-epithelioid (i.e. sarcomatoid/biphasic) variants. To classify this, we generated a numerical vector representing the percentage or frequency of each HPC for every WSI. This vector was transformed using the centred log-ratio (clr) transformation to enhance stability and interpretability, then feed into a logistic regression model for classification as either non-epithelioid or epithelioid. Tumours labelled as biphasic and sarcomatoid were combined into a single group, creating a binary classification task. 8 HPCs are significantly associated with the epithelioid subtype (HPCs 14, 39, 24, 25, 27, 40, 8, and 18), containing epithelioid malignancy, predominantly characterised by tubular patterns and solid sheets of epithelioid tumour cell growth. Of the 9 HPCs linked to the non-epithelioid subtype, 3 HPCs (15, 16, and 22, mostly containing disorderly spindle cells) are unanimously classified as non-epithelioid malignancy by our pathologists as well. The other 6 HPCs (6, 7, 35, 37, 45, and 28, mostly solid epithelioid growth pattern) contain more diverse appearances, including epithelioid HPCs, pleural plaque and muscle. This might be due to the inclusion of biphasic cases and lethal epithelioid patterns in this group and also suggests a possible link between sarcomatoid growth and invasion into the chest wall. (Fig.\u00a03a) Our logistic regression classifier (Likelihood Ratio test statistic(40) = 1219.3, p = 1.195e-229) achieved 88% 5-fold cross-validated AUC (Area Under the Curve) Score on the LATTICe-M dataset and 80% on The Cancer Genome Atlas (TCGA) mesothelioma dataset and robust across varied clustering configurations (Fig.\u00a03b). We visualised HPC compositions at the case level using a PCA plot, colour-coding cases by subtype at diagnosis. The transition from epithelioid cases to sarcomatoid cases through biphasic cases is clearly visible. (Fig.\u00a03c)\n\na Forest plot for the logistic regression model used in subtype classification, showing log odds ratios (centre) with 95% confidence intervals (error bars), derived from logistic regression coefficients as each HPC's contribution towards subtype prediction, along with significant HPCs for each class (epithelioid versus non-epithelioid), including their p-values, confidence intervals, log odds ratios, and pathologist annotations on HPC histomorphology. This analysis includes n = 3446 WSIs (biological replicates) with subtype labels derived from clinical data. WSIs from epithelioid and non-epithelioid cases (n = 2565 and n = 881, respectively) were treated as independent samples. No technical replicates were included (scale bar, 400\u2009\u03bcm). b The ROC (Receiver Operating Characteristic) curve for LATTICe-M test and TCGA-MESO datasets showing their subtype classification performance, including AUC-ROC (Area Under the Curve for ROC), Precision, Sensitivity and Specificity scores. c The PCA (Principal Component Analysis) plot shows patient-level vector representations, color-coded by mesothelioma subtype labels. Source data are provided as a Source Data file.\n\nWe aggregated HPC frequencies across all samples per patient, summarising each case into a readily interpretable composition of morphologies. The 5-fold cross-validated c-index values for patient prognosis outcomes were 0.67 and 0.65 for the training and test LATTICe-M primary datasets, respectively, and 0.65 for the fully unseen TCGA cohort as an external dataset. The addition of clinical information, including mesothelioma subtype, TNM stage, and age, only modestly improved the ability of the algorithm to predict outcomes, yielding an increment in C-index of 0.01. We further verified that our approach is robust across other clustering configurations. Compared to similar research on mesothelioma outcome prediction using WSIs, such as MesoNet7, our model achieves at least a comparable c-index score for the same additional dataset (TCGA). While MesoNet reported a score of 0.656 for TCGA, we matched this performance, with c-index scores ranging from 0.64 to 0.7 across different folds, however, prioritising model interpretability through our morphology-based HPCs, also using a fully self-supervised pipeline.\n\nWe identified HPCs 10 (Log Hazard Ratio = \u22120.089, p = 0.001, Confidence Interval = [\u22120.145, \u22120.034]) and 27 (Log Hazard Ratio = \u22120.062, p = 0.008, Confidence Interval = [\u22120.109, \u22120.016]) (\"epithelioid nests in bland stroma\" and \u201cdense lymphocytes\") as positive survival factors, while HPCs 15 (Log Hazard Ratio = 0.052, p = 0.026, Confidence Interval = [0.006,0.098]) and 22 (Log Hazard Ratio = 0.042, p = 0.016, Confidence Interval = [0.008, 0.077]) (\"disorderly spindle cells\" and \u201ctransitional mesothelioma\") emerge as strong predictors of poor outcome. (Fig.\u00a04a) A comparison of tile-level UMAP plots colour-coded by hazard ratio reveals a high degree of similarity, further underscoring the strong links between sarcomatoid transformation and poor patient outcomes. The map highlights red areas (higher hazard ratio) like HPC 15 and 16 (Sarcomatoid HPCs) and blue areas (lower hazard ratios) like HPC 45, 27 and 5 (either non-tumourous tissue or lymphocyte HPCs) (Fig.\u00a04b).\n\na Forest plot for the Cox proportional hazards model used in survival prediction and values represent log hazard ratios (centre) with 95% confidence intervals (error bars), obtained from the Cox model, including Significant HPC's p-values, confidence intervals, log hazard ratios, and pathologist annotations on HPC morphology. The Cox model was trained on n\u2009=\u2009512 independent patients, each linked to a survival outcome. All samples are biological replicates, and the unit of study is the patient. No technical replicates were used in the analysis (scale bar, 400\u2009\u03bcm). b UMAP plot of tile vector representations, color-coded by Cox model predicted hazard ratio for each HPC. c Kaplan-Meier plots showing low-risk and high-risk patient groups for LATTICe-M and TCGA-MESO datasets, with their reported p-value performance scores. Shaded bands represent 95% confidence intervals. d Kaplan-Meier plots comparing the performance of our survival predictor, pre-trained on the LATTICe-M dataset, tested on TCGA-MESO epithelioid cases, versus predictions based on the traditional systemic overall grade of the TCGA-MESO epithelioid cases. Shaded bands represent 95% confidence intervals. e An example of a SHAP decision plot for a high-risk (red line) and low-risk (blue line) patient, displaying the percentage of HPCs within their samples that contribute to their outcomes. Source data are provided as a Source Data file.\n\nNext, we categorised patients into high- and low-risk groups based on their calculated hazard ratios for 60 months. Kaplan-Meier plots were generated for LATTICe-M train (Log-rank test statistic(1) = 62.41, p = 2.79e-15) and test (Log-rank test statistic(1) = 15.14, p = 9.96e-05) datasets, as well as the TCGA-Meso additional cohort (Log-rank test statistic(1) = 10.24, p = 0.00138), as shown in Fig.\u00a04c. The model achieves impressive separation, predicting outcomes with surprising power in this very poor-prognosis population who face all the complex hazards of radical surgery and impaired respiratory physiology alongside the biology of their tumour burden. Figure\u00a04d shows a comparison between classical histological grading of epithelioid pleural mesothelioma (as suggested in ref. 20) and our model, both applied to the TCGA mesothelioma just epithelioid sample cases. Our pipeline demonstrates superiority (Log-rank test statistic(1) = 5.02, p = 0.025) against human grading (Log-rank test statistic(1) = 1.16, p = 0.282) for patient outcomes in this dataset. Figure\u00a04e shows the SHAP (SHapley Additive exPlanations)21 decision plots for our Cox model, comparing a high-risk sarcomatoid case (red) and a relatively low-risk epithelioid case (blue). The plot shows how the model assigns high or low-risk labels for these patients based on the abundance/scarcity of influential HPCs, such as the highly lethal HPC 15 (\"disorderly spindle cells\"), or the protective HPC 27 (\"dense lymphocytes\"), which both contribute to the calculated risk in these two cases.\n\nWe further demonstrate the ability of our model to predict patient outcomes within disease subtypes (epithelioid vs non-epithelioid) groups. HPC frequencies were calculated, and survival was predicted separately for each group, identifying HPCs which underscored subtype-specific traits (Fig.\u00a05a). For epithelioid cases, HPC 10 (\"epithelioid nests in bland stroma\") and HPC 22 (\"transitional mesothelioma\") emerged as significant predictors of good and bad outcomes, respectively. HPC 10 is likely to represent relatively indolent well-differentiated classically epithelioid disease. Interestingly, HPC 22, which is very enriched for the appearances of transitional mesothelioma, is not uncommon in cases diagnostically subtyped as epithelioid and is strongly predictive of poor outcomes in this group. This supports the view that transitional appearances signal early stages of transition to sarcomatoid growth22, and its presence in this group highlights the difficulty in human identification of this pattern23. For biphasic/sarcomatoid cases, HPC 23 (\"bland spindle cells and collagen\") predicts poor outcome, perhaps identifying areas of cytologically bland desmoplastic differentiation, while the good prognostic association of HPC 27 (\"dense lymphocytes\") suggests towards a particular role for the immune system in sarcomatoid disease. We also show example tiles for specific HPC groups of interest based on pathologist annotations, including inflamed clusters, classical desmoplastic appearances, and necrosis (Fig.\u00a05b).\n\na HPC frequency plot for epithelioid (n = 372) and biphasic/sarcomatoid (n = 140) cases, showing the co-occurrence of HPCs over cases within each group. Log hazard ratios are drawn based on a Cox model trained on subtype-filtered cases, 5-fold cross validated, and significant HPCs (p\u00a0<\u00a00.05) are highlighted in bold colors. b Grouped HPCs based on pathologist annotations, highlighting key histopathological features such as necrosis, desmoplastic components, and inflammation in the tumour microenvironment. 6 random tiles are displayed for each HPC. c Correlations between HPC proportions and positivity for IHC markers per TMA cores (n\u2009=\u2009711) were assessed using two-sided Spearman rank correlation tests. Positive correlations (red colors) indicate that the marker is enriched in that HPC; negative correlations (blue colors) indicate depletion. Only HPCs with significant adjusted p-values, alongside associated HPCs, are shown. d Representative IHC-stained cores showing high expression (top row) and low expression (bottom row) for associated IHC markers (scale bars, 400\u2009\u03bcm (a\u2013c), 180\u2009\u03bcm (d)). Source data are provided as a Source Data file.\n\nTo further assess the biological significance of the identified HPCs, we investigated their associations with quantitative Immunohistochemistry (IHC) markers reflecting tumour cell proliferation and aberrant mRNA translation activity. HPCs with significant associations to previously obtained quantitative IHC markers24 are shown in Fig.\u00a05c. Notably, the HPCs with upregulation of mRNA translation, proliferation, and oxidative phosphorylation are nearly all associated with poor patient outcome and are all either sarcomatoid or poorly differentiated epithelioid in morphology, further underlining the linkage of these processes to tumour virulence. eIF4A1, the ubiquitous pro-proliferation translation initiation factor, is particularly closely related to poor outcome HPCs, supporting possible therapeutic targeting of this molecule. Negative associations with markers of oxidative phosphorylation and pro-translation mTOR signalling are only seen in areas of low-grade disease, or crush/diathermy artefact likely to degrade IHC signal. Figure\u00a05d represents chromogenically IHC-stained tissue cores for each marker. The top row shows examples with high expression of the corresponding marker, while the bottom row shows cores with low expression. For each case, both the IHC-stained image and the corresponding H&E scan are displayed side by side. Additionally, a representative tile from each core is shown to highlight the cellular-level resolution of the tissue.\n\nWe next investigated the biological underpinnings of HPC morphology to further explain our model\u2019s predictive capabilities in mesothelioma prognosis and subtyping. This was achieved by quantifying associations between gene expression signatures and HPC composition in the TCGA Mesothelioma RNASeq dataset25.\n\nWe used the MCPcounter algorithm to estimate cell types, including fibroblasts, endothelial cells, T cells, B lineage cells, myeloid dendritic cells, NK cells, and CD8 T cells from RNASeq data. (Fig.\u00a06a) Expression of the proliferation marker Ki67 is also mapped, revealing especially high proliferation in spindle cell-enriched HPCs (HPCs 22, 16, 15, and 6). Critically, these HPCs are the most predictive of non-epithelioid subtype (3a). In contrast, HPCs defined by well-differentiated epithelioid disease and normal tissue (e.g., HPCs 17, 3, 10, and 19) show low proliferation.\n\nTwo-sided Pearson correlation between WSI level HPC compositions and transcriptomic signatures in bulk RNA-seq data from the TCGA-MESO cohort. Only correlation coefficients with associated p-value lower than 0.05 are represented; supercluster annotations were provided by an expert pathologist. The subtype row shows the log odds ratio from the logistic regression model, while survival row is based on the partial hazards ratio predicted by the Cox model. The inflammation row was generated using inference from the HoVer-Net model. a Ki67 (Proliferation Marker) expression and MCPcounter (Microenvironment Cell Populations-counter) tumour microenvironment signatures. b KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway signatures. c MSigDB (Molecular Signatures Database) hallmark gene sets. Source data are provided as a Source Data file.\n\nFibroblast signatures are strongly pronounced in multiple clusters which either significantly determine sarcomatoid disease or contain fibroblastic/collagenous/stroma-rich morphology indicative of fibroblast-like mesenchymal dedifferentiation of mesothelioma cells. Fibroblast signatures are minimal in HPCs representing papillary or micropapillary epithelioid mesothelioma, solid pattern disease, lung tissue, and large-vessel-rich HPCs consistent with more specialised epithelioid/tissue-specific phenotypes.\n\nLymphocyte-rich HPC 27 shows strong correlations with T cells, B lineage cells, and myeloid dendritic cells. Similarly, inflamed HPCs (HPCs 29, 1, 24, 27), identified by Hover-Net, indicate active immune environments linked to better prognosis and likely improved immunotherapy response. HPCs 1 (inflamed fat) and 27 (dense lymphocytes) are high in both B- and T-cell signatures and show strong inter-correlation, suggesting dense inflammation and tertiary lymphoid structure formation in chest wall fatty tissues, supporting previous observations that tertiary lymphoid structures are related to good outcome.26\n\nKEGG pathway correlations across HPCs again show clear separation between non-epithelioid and epithelioid subtypes (Fig.\u00a06b), with generally heightened mitogenic signalling pathway activity in a group of sarcomatoid and fibroblastic clusters associated with aggressive biology. In contrast, HPCs linked to epithelioid growth and normal tissues exhibit relative down-regulation.\n\nAn analysis of cancer hallmark pathways further identifies the most sarcomatoid HPCs as a group with strong positive links to multiple proliferation-associated pathways (Fig.\u00a06c), in addition to mitogenic signalling and multiple EMT-related pathways. Notably, the same group exhibits downregulation of oxidative phosphorylation components, indicating a metabolic shift towards hypoxia.\n\nTo assess the generalisability of our self-supervised model trained on the LATTICe-M dataset, we benchmarked its performance on the St. George\u2019s Hospital TMA dataset from the MesoGraph study8. This external evaluation is significant for two reasons. First, no additional training was applied to the new dataset, so the results represent the pre-trained model\u2019s performance on a fully unseen wholly exterior cohort. Second, although the model was trained on WSIs, it maintained strong performance on tissue microarray cores. These fragments are not only tiny (\u00a0\u2248\u00a01 millimetre) but are selected to represent pure tumour tissue. In contrast, our model was trained on large diagnostic WSIs including background tissues, and used unsupervised clustering to filter artefacts.\n\nAs the image size in TMA cores is insufficient to support the previous frequency-based method, we employed multiple instance learning (MIL) to predict mesothelioma subtypes by summarising information across tiles from a core or a biopsy. We called this method HPL-MIL and benchmarked HPL-MIL against state-of-the-art methods, max-MIL and naive-MIL (patch-based MIL methods), PINS27, CLAM28, MesoGraph, on the 235 cores from St. George\u2019s Hospital TMA cohort8 (Table\u00a01). Each TMA core was treated as a bag of instances, where the instances are individual tile embeddings extracted from the core. Using an attention-based multiple instance learning approach, we obtained a core-level representation by computing a weighted average of tile embeddings. We then performed subtype classification of each core using the core-level labels available for the TMA dataset. HPL-MIL achieved higher AUC, Average Precision, Sensitivity and specificity scores across all the methods without pre-training on the cohort tissues.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63846-9/MediaObjects/41467_2025_63846_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63846-9/MediaObjects/41467_2025_63846_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63846-9/MediaObjects/41467_2025_63846_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63846-9/MediaObjects/41467_2025_63846_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63846-9/MediaObjects/41467_2025_63846_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we applied our self-supervised HPL pipeline to the LATTICe-M cohort, which we believe to be the largest image collection in terms of area of mesothelioma tissue yet employed for AI training. We achieved state-of-the-art accuracy in two key clinically important tasks: a C-index of 0.65 in survival prediction across a 5-fold cross-validation and 88% AUC in subtype classification (epithelioid vs sarcomatoid/biphasic). Furthermore, our method outperformed human grading in prognostication for epithelioid cases, and we identified survival-linked histomorphological patterns within each subtype, emphasising the interpretability of self-supervised methods and identifying recurrent morphologies worthy of future study. Quantitative visual maps of HPCs (Fig.\u00a02e) and SHAP decision plots (Fig.\u00a04e) offer clinical utility for understanding AI diagnoses and future selection of therapies.\n\nOur approach eliminates the need for retraining to retain performance on external mesothelioma datasets, thus addressing a key computational challenge in self-supervised models, as proven by its efficacy across three independent cohorts. It effectively extracts relevant morphological patterns from small TMA cores (e.g. the St. George\u2019s dataset) and WSIs of varied origins and quality (e.g. TCGA and LATTICe-M), enabling real-time clinical decision-making without extensive preprocessing. CLAM28 was benchmarked against HPL on both the TCGA and LATTICe-M datasets (full results in\u00a0Supplementary Data). HPL consistently outperformed CLAM in both subtype classification and survival prediction, while maintaining high interpretability and biological relevance. This suggests the possibility of a robust diagnostic tool for both resection material and mesothelioma biopsies, which remain a major diagnostic challenge.\n\nOur model has essentially created a morphological atlas of mesothelioma, discovering ab initio the characteristic recurrent H&E morphologies which comprise the disease. The fact that these morphologies have clear biological and clinicopathological significance proves their meaning and value. For example, the discovered linkage of the tumour microenvironment to patient survival shows how crucial the morphology of immune system engagement is to tumour virulence and biology, and suggests biomarker potential in predicting responses to immunotherapy. Furthermore, RNASeq data annotation of histomorphological clusters further illustrates connections between tumour microenvironment signatures, molecular pathways, and survival, offering valuable molecular insights into the biology of the disease.\n\nThe molecular associations of HPCs help us to understand tumour virulence and suggest numerous hypotheses for mechanistic testing. For example, we see numerous mRNA cancer hallmark pathways linked to high-risk sarcomatoid HPCs, helping to explain links between morphology and outcome in molecular terms and highlighting possible areas of target discovery. Sarcomatoid clusters are directly linked to signatures of proliferation, hypoxia, and EMT in bulk sequence data, without any requirement for spatial methods or microdissection. This is in keeping with biological knowledge that sarcomatoid mesothelioma cells can proliferate rapidly under hypoxic conditions29 and supportive of the idea that sarcomatoid dedifferentiation represents co-option of a physiological EMT pathway.\n\nAdditionally, subtle transitional morphologies in cases classified as being epithelioid overall appear to have significant prognostic value in our survival analysis. This highlights the continuous nature of epithelioid to sarcomatoid transition, and suggests the importance of accurate identification of transitional states, which is a challenging task by eye, and which is likely to benefit from our approach. Furthermore, HPL could also be used to target therapy by identifying such cases with subtle sarcomatoid changes, which are likely to be more responsive to immunotherapies30).\n\nThis study also has several limitations that warrant acknowledgment. First, staging data were missing in 32.23% of Leicester cases (165 patients), probably reducing the power of T/N/M-related analysis (Fig.\u00a01a). Second, smoking history was incomplete in 43.55% of cases (223 patients), limiting cohort-wide assessment of its impact. These data gaps highlight the need for consistent clinical documentation in retrospective studies and constrain the use of these variables in survival and subtype prediction models alongside AI-derived features (HPC frequencies).", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The primary dataset used in this study is the Leicester Archival Thoracic Tumour Investigation Cohort-Mesothelioma (LATTICe-M)31, comprising 512 patients diagnosed with pleural mesothelioma who underwent surgical resection. Study clinical data were collected and managed using REDCap electronic data capture tools32,33 hosted on secure research servers at University Hospitals of Leicester NHS Trust. Cases are histologically subtyped into epithelioid (n = 372), sarcomatoid (n = 107), and biphasic (n = 33). The cohort includes 436 male and 76 female patients (85.2% and 14.8%, respectively), consistent with mesothelioma incidence at the collection site. Sex was self-reported at the time of intake. Patient age ranged from 36 to 85 years (64.3\u00a0\u00b1\u00a08.6). No information on race, ethnicity, or other socially relevant variables was collected. Participants were not financially compensated. Sex and gender were reported in the study; however, no sex-based analysis was performed with the aim of training a self-supervised model. Disaggregated sex counts are available in the source data files.\n\nEthical approval was obtained from the UK National Health Service Research Ethics Committee (ref. no. 14/EM/1159). No prospective recruitment, interventions, or international data transfers were involved. There were no risks to participants or researchers, as only archived histopathology material was used under standard governance. Pathology annotation support was provided by S.K. (Adelaide, Australia), whose contributions were formally recognised through authorship. Data ownership is held by the Greater Glasgow and Clyde Biorepository, under governance via an amendment granted by the Leicester South REC. All research procedures were conducted in compliance with relevant ethical regulations, and written informed consent was obtained from all participants.\n\nFigure\u00a01 a presents additional clinical details. The WSIs were sectioned and stained with Hematoxylin and Eosin at Leicester University Hospital, scanned at 10X, 20X, or 40X magnifications. After tiling and background removal, WSIs with fewer than 100 tiles were excluded, leaving 485 patients and 3446 WSIs for the pipeline and downstream analysis. To identify significant clinical factors in this cohort, we employed a Cox proportional hazards model and found that age, mesothelioma subtype, and TNM stage significantly contributed to survival prediction. (Fig.\u00a01a)\n\nTo validate our results, we used the publicly available Cancer Genome Atlas (TCGA)-mesothelioma cohort25, an entirely differently-scanned dataset, still comprising WSIs but obtained from multiple centres. It includes 86 samples from 74 patients with both WSIs and RNAseq data available. This cohort was primarily used to discover links between HPCs and tumour microenvironment features, pathways, and hallmarks. All HPL pipeline steps were performed on the primary dataset (LATTICe-M), and evaluation scores were reported on the fully unseen additional TCGA dataset, without any further training.\n\nFinally, we utilised the St. George\u2019s Hospital dataset, consisting of H&E-stained TMAs from tumour biopsies collected at St. George\u2019s Hospital, London. This dataset includes four TMA slides scanned at 20x magnification using a Hamamatsu Nanozoomer S360 scanner, comprising 235 cores labelled as epithelioid, biphasic, or sarcomatoid, as the only available clinical information. The dataset, introduced in the Mesograph study8, was used for training and testing. We employed it to demonstrate the robustness and generalisability of our trained WSI model by benchmarking and comparing its performance on TMA cores against different methods reported in the study. (Section 3)\n\nHPL is a tool developed to detect and categorise histomorphological patterns within large collections of whole-slide images. HPL employs an automated, self-supervised deep learning approach, eliminating the need for expert pathologists to prelabel or manually define histomorphological patterns. Once these patterns are identified, new whole-slide images can be introduced to the trained model and classified according to the pre-established patterns. This feature allows pathologists to quantify specific patterns in new patient samples precisely. The clustering of each whole-slide image into meaningful histomorphological patterns follows several sequential steps, which are described below (Fig.\u00a01b)\n\nWhole-slide images pre-processing: In this first step, whole-slide images are segmented into non-overlapping 224 \u00a0\u00d7 224-pixel tiles at 5X magnification, which corresponds to a pixel size of approximately 1.8 micrometres. Tiles that do not contain at least 60% tissue coverage are filtered out to maintain relevance. Consistent pixel size and magnification are ensured during the tile processing phase to guarantee uniformity in the resulting tiles. The tiling code used for this process is accessible on DeepPATH GitHub34 for further details.\n\nFeature extraction: HPL employs a self-supervised learning technique known as Barlow Twins16, which matches or even exceeds the performance of other self-supervised methods. Barlow Twins delivers state-of-the-art results in standard pathology tasks compared to DINO35, MoCo36, and SwAV37 mothods38. We previously compared Barlow Twins with DINO within HPL framework, and it showed improved performance with the cohort size similar to this study14. One key feature of HPL is its ability to maintain consistent image representations, even with slight colour or zoom level variations. This capability ensures that differences in image scanning or processing across datasets do not affect the results. The aim is to capture diverse visual patterns in tissue samples and represent them as feature vectors, capturing distinct characteristics like texture. Each 224\u2009\u00d7\u2009224-pixel tile is converted into a vector representation, denoted as {z \u2208 RD;\u00a0D\u00a0=\u00a0128}. During training, the model is optimised to produce consistent outputs for twin inputs, ensuring robustness in vector representation.\n\nClustering: After generating the vector representations, we employed the Leiden community detection algorithm17 (from the Python ScanPy library39) to cluster tiles or vector representations with similar histomorphological features. Since neighbouring vector representations in high-dimensional space exhibit similarity, this method effectively groups the tiles based on shared morphological patterns captured by their feature vector representations.\n\nWe began with a subsample of 750,000 tiles and constructed a nearest-neighbour graph between the tiles. From this initial set of detected clusters, we assigned the remaining vector representations to these clusters (or graph nodes) based on their distance. The number of clusters identified depends on the chosen Leiden algorithm resolution. For our analysis, multiple resolutions were applied to capture varying levels of granularity. The resulting histomorphological phenotype clusters (HPCs) enabled the quantification of patients or whole-slide images (WSIs) based on these clusters, streamlining further analysis and simplifying the understanding of complex tissue patterns.\n\nPreparing compositional vectors: At this stage, using the identified HPCs, we can characterise the entire tissue or patient by quantifying the frequency of each HPC (1). To achieve this, each whole-slide image (WSI) is transformed into a compositional vector A, where the dimensionality is equal to the total number of HPCs (c). Each element within the vector represents the percentage of the tissue area attributed to a specific HPC. This approach quantifies the contribution of each HPC to the overall tissue composition, allowing for a detailed analysis of the histomorphological landscape within a patient or a sample.\n\nFor statistical compositional analysis and to prepare for the use of linear models, we apply the Centred Log-Ratio (clr) transformation40 to our compositional vector A to minimise correlation between HPC frequencies. This transformation maps the vector composition from the c-part simplex into a c-dimensional Euclidean vector space. Additionally, to address zero elements in the dataset, we use multiplicative replacement41.\n\nSubtype classification: For the diagnostic task, we employed the clr transformed compositional vectors derived from whole-slide images (WSI) and fed them into a logistic regression model (Scikit-learn42 and Statsmodels Python library43). This approach is weakly-supervised, utilising patient-level labels assigned by pathologists, where each patient label is applied to both the patient and their corresponding slides. We combined sarcomatoid and biphasic mesothelioma into a single class (non-epithelioid class) and compared it against the majority class, primarily consisting of epithelioid samples. Also, to address the class imbalance in the primary dataset (1:3 ratio for non-epithelioid to Epithelioid subtypes), we applied an undersampling strategy using the Edited Nearest Neighbour (ENN) technique44 (Imbalanced-learn Python library45). This method reduced the majority class by removing redundant and noisy samples.\n\nUltimately, the logistic regression model used the compositional vectors of WSIs to classify mesothelioma subtypes based on the contributions of HPCs. In this approach, individual HPCs serve as distinct features for our logistic regression classifier, enabling us to rank the importance of each HPC and its role in predicting specific tumour subtypes within each sample. The predicted probability of being two classes is given by:\n\nWhere b0 is the bias or intercept term and b1 is the coefficient for compositional vector (A).\n\nSurvival analysis: In the clinical outcome aspect of our study, we created a clr-transformed compositional vector for each patient, reflecting the overall HPCs composition. We then used the Cox proportional hazards regression model46 to analyse patient survival in relation to the HPC composition vector. Finally, Kaplan-Meier plots47 were employed to visually distinguish between high-risk and low-risk patient groups within each dataset. For this step, we used Lifelines48 and SciPy Python libraries49. For both subtype classification and survival prediction tasks, we employed five-fold cross-validation to ensure robust evaluation. The reported scores represent the average performance across all folds. Furthermore, we ensured no overlap of patients between the training and test sets, maintaining strict separation to prevent data leakage and guarantee unbiased assessments. However, for providing annotations and associations with the tumour microenvironment, we focused on a single fold for consistency and detailed analysis.\n\nAdditionally, we engaged three expert pathologists to independently and blindly annotate HPCs without access to patient clinical data or additional HPC details. Each HPC was classified into one of three categories: epithelioid tumour, spindle cells/extracellular matrix, or non-tumour. The pathologists assessed each HPC\u2019s primary and secondary architectural features, HPC purity, inflammation, necrosis, nuclear atypia and biphasic components (in malignant groups). Also, they evaluated patterns such as desmoplastia and cellularity in spindle cell HPCs, as well as the tumour-stroma ratio and stromal cellularity in epithelioid HPCs.\n\nTo assess agreement in our multi-centre annotation process, we used majority voting among the three expert pathologists who annotated the HPCs. Instances of unanimous agreement, where all three pathologists selected the same category and are marked with an asterisk (*). In contrast, cases of complete disagreement, where each pathologist chose a different category, are highlighted in grey in Fig.\u00a02c. Also, inter-rater reliability was assessed using Fleiss\u2019 Kappa. For each HPC i (N\u00a0=\u00a047), we counted the number of raters (n\u00a0=\u00a03) assigning it to each category j, and computed the marginal probability of category j as:\n\nWe then calculated the average proportion of agreeing rater-pairs across clusters (observed agreement \\(\\bar{P}\\)), estimated the agreement expected by chance and scaled the excess agreement relative to the maximum possible beyond chance:\n\nA Kappa of 1 indicates perfect concordance, 0 reflects agreement no better than random, and values \u22640 denote worse-than-chance agreement. It is important to note that the number of categories available for annotation (denoted as j) varied across the different histomorphological components we selected, such as inflammation, necrosis, etc. As a result, the Fleiss\u2019 Kappa values are inherently influenced by this variability and are not directly comparable across components. Specifically, components with more annotation categories introduce greater choice complexity, which tends to lower agreement scores. To prevent misinterpretation, we recommend referring to the majority voting results and the asterisk indicators of full agreement as complementary measures of reliability.\n\nWe used the deep learning model HoVer-Net50 to segment cells in each tile within the HPCs and calculate the abundance of only inflammatory cells in every HPC. While the tiles used in the HPL framework were at 5x magnification, the HoVer-Net model was trained on 20x tiles. To bridge this difference, we first applied HoVer-Net to 20x tiles, then combined 16 tiles (arranged in a 4\u2009\u00d7\u20094 grid) to create 5x equivalents. This allowed us to map HoVer-Net\u2019s segmentation results, particularly the inflammation annotations, to specific tiles. Each tile was subsequently assigned to a corresponding HPC by calculating the average number of inflamed cells detected across the related tiles. Approximately 900 whole-slide images (WSIs) at 20x magnification were annotated using the HoVer-Net model for this analysis.\n\nWe correlated HPCs with tumour microenvironment features, hallmark pathways, and relevant biomarkers (Section 3). All WSIs from each patient were used to calculate the clr-transformed HPC compositional vectors. We then applied the single-sample Gene Set Enrichment Analysis (ssGSEA) to quantify pathway expression in both the Kyoto Encyclopedia of Genes and Genomes (KEGG)51 and Molecular Signatures Database (MSigDB)52 hallmark datasets. We also estimated immune cell subpopulation abundance using MCPcounter53. Ki67 RNA expression levels were also utilised across the entire sample set to assess cellular proliferation in each case, providing further insight into tumour growth activity within different morphological HPCs. Correlations between the clr-transformed HPC compositions and pathway expression levels were calculated, with only correlations having a p-value below 0.01 retained, ensuring statistical significance.\n\nTo further evaluate the biological relevance of the identified HPCs, we sought associations between HPCs and quantitative IHC measures of tumour cell proliferation and dysregulation of mRNA translation. We used data previously generated from a study of the LATTICe-M TMA cohort which revealed the importance of translational dysregulation to mesothelioma development24. Data were available from 8 TMAs, comprising 711 cores after quality control. To link molecular phenotype with spatial composition, we calculated the proportional representation of each HPC within each TMA core and then assessed the association between HPC proportions and marker expression. Marker positivity scores were derived from automated quantification pipelines applied to scanned IHC images. The correlation was calculated using the two-sided Pearson test and multiple comparison correction was applied using the Benjamini-Hochberg false discovery rate (FDR) method with \u03b1 = 0.05.\n\nWe also benchmarked and compared the HPL model (trained on WSIs) against other state-of-the-art AI methods using an independent small dataset of tissue microarray (TMA) cores to demonstrate its robustness. We used the St. George\u2019s Hospital dataset, publicly released with the MesoGraph study8, comprising 235 cores with associated mesothelioma subtype labels. The study benchmarked methods such as max-MIL, naive-MIL (patch-based MIL approaches), PINS27, CLAM28, and MesoGraph on this dataset. Employing gated attention in MIL54, we predicted the probability of each core belonging to a specific mesothelioma subtype, naming this approach HPL-MIL. In our weakly-supervised MIL setting, each TMA core is treated as a bag B\u00a0=\u00a0{h1,\u00a0h2,\u00a0.\u00a0.\u00a0.\u00a0,\u00a0hk} of k tile embeddings (Instances). Each tile hk is obtained from our HPL ResNet-128 encoder trained using the Barlow Twins framework on the LATTICe-M dataset. To derive a representation for the entire core, we use attention-based pooling:\n\nwhere the attention weight ak is computed as:\n\nThis allows the model to learn which tiles are more informative for core-level prediction. The resulting representation z is passed to a linear classifier for subtype classification. While MIL lacks full interpretability, it allowed us to benchmark mesothelioma subtype classification against existing MIL-based methods, representing the generalisability and scalability of the HPL pipeline. Despite this success, the remainder of the study prioritises the more interpretable histomorphological clusters and their morphological insights to address the complexity of mesothelioma.\n\nWe additionally benchmarked the CLAM (Clustering-constrained Attention Multiple Instance Learning)28 framework using 128-dimensional tile embeddings extracted from our Barlow Twins-trained ResNet. Subtype classification was performed using a linear layer on top of CLAM outputs, while survival prediction was based on risk scores generated by the network and evaluated via a Cox proportional hazards model, enabling a fair comparison with HPL-based survival predictions. CLAM was trained for 50 epochs with early stopping, using the Adam optimiser with a binary loss and a learning rate of 10\u22124. The total loss combined slide and instance-level objectives with coefficients c1\u00a0=\u00a00.9 and c2\u00a0=\u00a00.3, as follows:\n\nThe number of clusters was fixed at 8, consistent with the original CLAM configuration. WSIs were treated as bags, with subtype labels assigned at the bag level, and a gated attention, was used to compute instance-level attention. Five-fold cross-validation, aligned with the HPL evaluation, was applied throughout.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The LATTICe cohort (histology whole slide images and clinical data) used in this study is not publicly available due to their extremely large size and ethical limitations according to the LATTICe agreement, which makes public hosting technically impractical. However, we are delighted to make the data available for academic research purposes upon request. Interested researchers may contact the corresponding author via the email provided. Access will be granted for a limited period based on a clear research purpose and mutual agreement, with data use restricted to non-commercial research. We aim to respond to access requests as soon as possible. TCGA mesothelioma RNAseq data have been retrieved from UCSC Xena [https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Mesothelioma%20(MESO)] and images from Genomic Data Commons (GDC) portal [https://www.cancer.gov/ccg/research/genome-sequencing/tcga/studied-cancers/mesothelioma-study]. St. George Hospital TMA Dataset is available on MesoGraph GitHub [https://github.com/measty/MesoGraph].\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The source code for this study is openly available under the MIT License on GitHub [https://github.com/FarzanehSeyedshahi/Histomorphological-Phenotype-Learning] and archived on Zenodo55. Reproducible figures can be generated using the provided Jupyter notebooks. A step-by-step README on GitHub details installation and execution.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Wagner, J. C., Sleggs, C. A. & Marchand, P. Diffuse pleural mesothelioma and asbestos exposure in the north western cape province. Br. J. Ind. Med. 17, 260 (1960).\n\nCAS\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nMolinari, L. 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J.L.Q. is supported by the Mazumdar-Shaw Molecular Pathology Chair endowment at the University of Glasgow. KY acknowledges support from Cancer Research UK (EDDPGM-Nov21\\100001 and DRCMDP-Nov23\\100010), BBSRC BB\\V016067\\1, Prostate Cancer UK MA-TIA22-001 and EU Horizon 2020 grant ID: 101016851.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Kai Rakovic, Nicolas Poulain.\n\nSchool of Cancer Sciences, University of Glasgow, Glasgow, Scotland, UK\n\nFarzaneh Seyedshahi,\u00a0Kai Rakovic,\u00a0Adalberto Claudio Quiros,\u00a0Daniel Murphy,\u00a0Ke Yuan\u00a0&\u00a0John Le Quesne\n\nCancer Research UK Scotland Institute, Glasgow, Scotland, UK\n\nFarzaneh Seyedshahi,\u00a0Kai Rakovic,\u00a0Nicolas Poulain,\u00a0Ian R. Powley,\u00a0Leah Officer-Jones,\u00a0Catherine Ficken,\u00a0Fiona Ballantyne,\u00a0Daniel Murphy,\u00a0Ke Yuan\u00a0&\u00a0John Le Quesne\n\nPathology Department, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, Scotland, UK\n\nKai Rakovic\u00a0&\u00a0John Le Quesne\n\nSchool of Computing Science, University of Glasgow, Glasgow, Scotland, UK\n\nAdalberto Claudio Quiros\u00a0&\u00a0Ke Yuan\n\nUniversity Hospitals of Leicester, Leicester, UK\n\nCathy Richards,\u00a0Hussein Uraiby\u00a0&\u00a0Apostolos Nakas\n\nFlinders Health and Medical Research Institute, Adelaide, Australia\n\nSonja Klebe\n\nCRUK Lung Cancer Centre of Excellence, UCL Cancer Institute, London, UK\n\nDavid A. Moore\n\nDepartment of Cellular Pathology, University College Hoapital, London, UK\n\nDavid A. Moore\n\nLeicester Medical School, University of Leicester, Leicester, UK\n\nClaire R. Wilson\n\nUniversity of Leicester, Leicester, UK\n\nMarco Sereno\n\nBirmingham Tissue Analytics, University of Birmingham, Birmingham, UK\n\nAna Teodosio\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nF.S. conceived the study, conducted the research, performed data analysis, and wrote the manuscript. K.R. provided pathology expertise, insights, and contributed to TCGA data annotations. N.P. performed R codings and TCGA sequencing data associations. A.C.Q. provided computational guidance insights for HPL analysis. C.R., S.K., and J.L.Q. performed histopathological annotations and provided mesothelioma subspecialty biological expertise. K.Y., D.M., and J.L.Q. supervised the project, provided guidance throughout, and contributed insights for running the project. I.R.P., H.U., D.A.M., A.N., C.W., M.S., L.O., C.F., A.T., and F.B. contributed to LATTICe-M dataset curation. All authors reviewed and approved the final manuscript.\n\nCorrespondence to\n Ke Yuan or John Le Quesne.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "D.A.M. has received speaker fees from AstraZeneca, Eli Lilly, BMS, Takeda and Boehringer Ingelheim; consultancy fees from AstraZeneca, ThermoFisher, Takeda, Amgen, Janssen, MIM software, Bristol-Myers Squibb and Eli Lilly; and educational support from Takeda and Amgen. All other authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Haining Yang and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Seyedshahi, F., Rakovic, K., Poulain, N. et al. A histomorphological atlas of resected mesothelioma discovered by self-supervised learning from 3446 whole-slide images.\n Nat Commun 16, 8891 (2025). https://doi.org/10.1038/s41467-025-63846-9\n\nDownload citation\n\nReceived: 05 January 2025\n\nAccepted: 29 August 2025\n\nPublished: 07 October 2025\n\nVersion of record: 07 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63846-9\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning", + "pre_title": "Autonomous Design of Ordered Gas Diffusion Layers for High-performing Fuel Cells via Bayesian Machine Learning", + "journal": "Nature Communications", + "published": "15 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61794-y/MediaObjects/41467_2025_61794_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61794-y/MediaObjects/41467_2025_61794_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61794-y/MediaObjects/41467_2025_61794_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61794-y/MediaObjects/41467_2025_61794_MOESM4_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.15622653", + "/articles/s41467-025-61794-y#ref-CR63", + "/articles/s41467-025-61794-y#Sec22" + ], + "code": [ + "https://doi.org/10.5281/zenodo.15622653", + "/articles/s41467-025-61794-y#ref-CR63" + ], + "subject": [ + "Fuel cells", + "Mechanical engineering" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5235975/v1.pdf?c=1752663963000", + "research_square_link": "https://www.researchsquare.com//article/rs-5235975/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61794-y.pdf", + "preprint_posted": "05 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Rational design of gas diffusion layers (GDL) is an example of a long-standing pursuit to increase the power density and reduce the cost of proton exchange membrane fuel cells (PEMFC). However, current state-of-the-art GDLs are designed by trial and error, which is a time-consuming endeavor. Here, we propose an autonomous Bayesian machine learning approach to optimize the design of GDL structures. With the artificial neural network accelerating the calculation of anisotropic transport properties of reconstructed 7621 fibrous GDLs, Bayesian optimization algorithm identifies optimal structures in only 40 steps, maximizing the PEMFC\u2019s limiting current density. Results suggest that the optimal GDL structure consists of highly orientated fibers with moderate diameters (~10 \u00b5m), which is successfully fabricated with a controlled electrospinning technique. Impressively, the PEMFC demonstrates a record high power density of 2.17 W cm-2 and a limiting current density of ~7200 mA cm-2, far exceeding that with commercial GDL which only achieves 1.33 W cm-2 and ~2700 mA cm-2. The approach reported here represents how the advanced algorithms can aid the innovative material design to address the critical water flooding issues in PEMFCs, leading to significant performance improvements, which paves the way for further application across various scientific fields.Physical sciences/Energy science and technology/Fuel cellsPhysical sciences/Engineering/Mechanical engineeringCellsGas Diffusion LayerMachine LearningBayesian OptimizationWater flooding.", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "supportinginformation.docxSupporting information", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Rational design of gas diffusion layers (GDL) is an example of a long-standing pursuit to increase the power density and reduce the cost of proton exchange membrane fuel cells (PEMFC). However, current state-of-the-art GDLs are designed by trial-and-error, which is a time-consuming endeavor. Here, we propose a closed-loop workflow of Bayesian machine learning approach to guide the design of GDL structures. With artificial neural network accelerating the calculation of anisotropic transport properties of reconstructed GDLs, Bayesian optimization algorithm identifies optimal structures in only 40 steps, maximizing the PEMFC\u2019s limiting current density. Results suggest that the optimal porous\u00a0GDL structure consists of highly orientated fibers with moderate diameters, which is successfully fabricated with a controlled electrospinning technique. The PEMFC demonstrates a high power density of 2.17\u2009W\u2009cm-2 and a limiting current density of ~7200\u2009mA\u2009cm-2, far exceeding that with commercial GDL (1.33\u2009W\u2009cm-2 and ~2700\u2009mA\u2009cm-2).", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "To achieve net-zero emission (NZE) targets, producing green hydrogen through water electrolysis powered by renewable energy is seen as a promising solution to address carbon emissions1. The NZE Scenario by 2030 forecasts that nearly 8\u2009Mt of hydrogen will be utilized in transportation. Proton exchange membrane fuel cells (PEMFCs) represent the primary devices employed to convert hydrogen into electricity, primarily in heavy-duty vehicle applications due to their higher energy density and refueling advantages over battery electric vehicles2,3. Beyond mobile applications, the roadmap from renewable electricity to hydrogen via electrolyzers and back to electricity through fuel cells underscores the crucial role of PEMFCs in stationary energy storage applications or for distributed/back-up power4. However, the industrial implementation is heavily dependent on the reduction of the cost and its operation stability5. To meet the cost and durability targets set by the United States Department of Energy for light-duty vehicles ($30 per kW and 8000\u2009h) and heavy-duty vehicles ($60 per kW and 30,000\u2009h), respectively6,7, it is imperative that the power density of PEMFCs be significantly advanced. This necessitates meeting ambitious targets, such as the 2.5\u2009W\u2009cm\u22122 goal set by the New Energy and Industrial Development Organization of Japan8, by the year 2030.\n\nEnhancing the power density of PEMFC demands reducing three types of losses: activation, ohmic, and mass transport losses. Over recent decades, advances in catalyst and membrane electrode assembly (MEA) design have significantly reduced activation loss9,10, but research on tackling mass transport issues, specifically \u201cwater flooding\u201d at high current densities, is not keeping pace. When liquid water accumulates in the pores of catalyst layer (CL), it\u00a0blocks the transport pathways for oxygen to reach the reactive sites11, deteriorating the fuel cell performance with reduced power density and limiting current density (LCD)12 (Supplementary Fig.\u00a01). Excess water content in the cell also negatively affects the durability of PEMFCs13. Nevertheless, the issue of water flooding remains an intricate and poorly understood multi-phase transport phenomenon.\n\nThe transport of liquid water is driven by capillary force in porous media, and the two-phase transport phenomenon is significantly influenced by the geometric structure and wettability of the gas diffusion layer (GDL) and microporous layer (MPL)14,15. However, the randomly connected fibers of the GDL lead to nondirectional and tortuous pathways for water transport, resulting in the accumulation of water in the pores, which blocks the gas transport. Modifying the structures (e.g., constructing perforations16,17) and tuning wettability are typical strategies adopted to boost water drainage, despite the recent advance in proposing a GDL-less configuration with electrode-flow field integration18. For example, the geometric parameters of perforations on carbon paper are systematically optimized, such as density, length, and width19. In addition, constructing patterned wettability20,21, water highways22 by introducing both hydrophilic and hydrophobic regions in GDL regulated the pathways for gas/water transport. However, the erratic paths still exist in these modified GDLs, and their water removal capability under high current density (e.g., >5\u2009A\u2009cm\u22122) is yet to be validated23.\n\nMore significantly, it is reported that water tends to aggregate in the under-rib region in the GDL to flow channel configuration, as evidenced by X-ray imaging and modeling24,25, which limits the efficacy of modifications on existing carbon paper GDLs. Efforts have been made to tackle the under-rib water accumulation. For example, deterministic perforations that connect channels and ribs are proven to facilitate water removal8 and the adoption of patterned GDL, such as carbon cloth, can better alleviate in-plane water accumulation over carbon paper-based GDLs. In addition, Csoklich et al. adopted woven fabrics (gold-sputtered polyethylene) with a deterministic structure with improved transport properties for gas/water transport26. However, the trial-and-error optimization of GDLs based on existing structures and materials is ineffective and laborious in searching for the best structures from numerous structures with varied geometric properties.\n\nArtificial intelligence (AI) and machine learning (ML) are powerful tools for solving complex problems. ML has shown advantageous performance over trial-and-error experiments in tasks such as predicting material properties27, optimizing electrode manufacturing28, and analyzing three-dimensional pore structures29. ML has been applied across various PEMFC components, including electrocatalysts30, membrane31, channels32, and GDLs33. For example, Li and Liu et al. applied AI-aided models to design nonprecious metal electrocatalysts and achieved good accuracy for improving the efficiency of MEA design. ML has been applied to optimize the properties of proton exchange membranes, emphasizing the role of ML models in predicting membrane performance and durability. In addition, there are several trials to adopt ML to optimize the GDL microstructures. A multi-objective optimization method was developed for the design of GDL microstructure for improving several key properties of GDL microstructure by searching manufacturing parameters33. Besides, a Bayesian optimizer was developed for optimization of the microstructure of carbon felt electrodes combined with the lattice Boltzmann method34. In addition to pore-scale optimization, there are explorations to optimize the GDL parameters, such as porosity and thickness, in the cell-scale models35,36. However, the task of searching for the best GDL structures involves complex multi-scale and multi-physics modeling. This includes intricate pore-scale modeling linking geometric structure to transport properties, and performance assessment in cell-scale multi-physics modeling, making the optimization over a library of structures a significant challenge. Therefore, it is necessary to develop an efficient optimization strategy that integrates multi-scale and multi-physics modeling.\n\nIn this study, we propose a closed-loop design of GDL via multi-scale modeling coupling Bayesian ML in a three-step workflow. As shown in Fig. 1, ML is employed to expedite the calculation of anisotropic transport properties of GDLs. An Artificial Neural Network (ANN) with independent input parameters (porosity, fiber orientation, and fiber radius) is trained on a comprehensive dataset constructed by pore network modeling, which significantly reduces the computational cost of the time-consuming pore-scale modeling. The anisotropic transport properties are incorporated into the three-dimensional two-phase multi-physics cell-scale model to evaluate cell performance, with the LCD as the key descriptor for optimization. Bayesian optimization (BO), a powerful tool for multidimensional optimization of expensive, difficult-to-evaluate, or noisy functions/tasks, serves as an effective approach for optimizing the key geometric parameters of GDLs by maximizing the LCD of PEMFC. The link between microstructure and cell performance is effectively established by integrating pore-scale with cell-scale modeling, which is combined with ANN and BO in a closed-loop workflow for rapid, low-cost optimization (Supplementary Fig.\u00a02). The optimization results suggest that optimal\u00a0porous GDLs consist of highly orientated fibers of moderate diameters. The aligned GDL is fabricated using an electrospinning method with designed precursor solutions and properly adjusted conditions, which are assessed in PEMFC tests with a comprehensive study on the influence of geometric parameters and the cell operating conditions. When the aligned fibers are placed connecting the channels and ribs, water can continuously\u00a0be transferred from CL to the flow field along the through-plane direction and be fast removed along the in-plane direction, resulting in enhanced PEMFC performances over existing carbon paper-based GDLs.\n\na Workflow of closed-loop Bayesian machine learning which integrates calculation of anisotropic transport properties of GDLs using machine learning, numerical simulation incorporating pore-scale modeling with cell-scale simulation, and Bayesian optimization. Optimized GDL is synthesized with a controlled electrospinning method and evaluated through fuel cell tests under different testing conditions. b Pore network modeling to calculate anisotropic transport properties. Bayesian optimization\u00a0(BO) process including c, d optimum fund and BO sampling with limiting current density (LCD) as an indicator. Corresponding geometric properties including fiber porosity, diameter, and orientation are mapped in e. Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61794-y/MediaObjects/41467_2025_61794_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "The first step in the workflow is the property calculation of fiber-based GDLs, which are reconstructed from key features (porosity, fiber diameter and orientation) (Supplementary Fig.\u00a03). Anisotropic transport properties, including permeability, tortuosity, and diffusivity are determined using pore network modeling, where the equivalent pores and throats are extracted based on complex porous structure and followed by Stokes flow simulation in x, y, z directions, as shown in Fig.\u00a01b. However, case-by-case determination of the transport properties is time-consuming and laborious. To accelerate the property calculation process, an ANN is developed as shown in Fig.\u00a01a, with the input of three key features and five output transport properties. The ANN is carefully trained to avoid overfitting and accurately capture the anisotropic transport properties of complex porous structures, with an R-squared of 0.98 in prediction (Supplementary Fig.\u00a04, 5). The unique GDL structure represented by mass transport properties is evaluated in a 3D PEMFC multi-physics model (Supplementary Fig.\u00a06), with LCD used as the descriptor for representing the overall cell-scale performance, which is given by:\n\nwhere ilim represents the current density of fuel cells approaching zero operating voltage, caused by polarization by activation, ohmic, and concentration loss.\n\nBO algorithm integrates pore-scale property calculation of GDLs and cell-scale numerical simulation of fuel cells to speed up the search for optimal GDL structures. The algorithm is given by:\n\nBayesian Optimization\n\n1\u2003Input: Objective function f, domain \u03c7, initial observation D0\n\n2\u2003Output: The best input x* \u2208 \u03c7 and its function values f(x*)\n\n3\u2003Initialize Gaussian Process (GP) with prior p(f)\n\n4\u2003Define acquisition function \u03b1(x; D)\n\n5\u2003D \u2190 D0\n\n6\u2003for t\u2009=\u20091,2, \u2026, T do\n\n7\u2003\u2003Select xt \u2208 \u03c7 by optimizing the acquisition function:\n\n8\u2003\u2003\u2003\u2003\u2003\u2003\u2003\u2003\u2003\u2003\u2003\u2003\u2003\u2003\u2003\u2003\u2003\\({x}_{t}={{\\mbox{arg}}\\,\\max }_{x\\in \\chi }\\alpha (x;D)\\)\n\n9\u2003\u2003Evaluate the objective function yt = f(xt) by multi-scale modeling:\n\n10\u2003\u2003\u20031. Pore-scale calculation of GDLs\n\n11\u2003\u2003\u2003\u20092. Cell-scale simulation of fuel cells\n\n12\u2003\u2009\u2009\u2009\u2006Augment the data: D \u2190 D \u222a {(xt, yt)}\n\n13\u2003Update the GP model with the new data D\n\n14\u2003end for\n\n15\u2003\\({x}^{\\,* }\\leftarrow {{\\mbox{arg}}\\,\\max }_{x\\in \\chi }f(x)\\) based on the GP posterior\n\n16\u2003return x*, f(x)\n\nBO is used to maximize the LCD with the objective function by multi-scale modeling, within the domain (\u03c7) of porosity, pore radius, and fiber orientation. During each iteration in the BO algorithm, we employed the acquisition function for selecting the next xt, evaluated the objective function and updated the Gaussian process model, to progressively approach the global optimum for complex fuel cell simulations. Figure\u00a01c, d shows the convergence of optimum LCD within 40 iterations, where the optimal structures mainly lie in the regions with porosity in the range of 0.7\u20130.9, fiber radius of 3\u20136\u2009\u00b5m, and a fiber orientation near 0\u00b0, representing a highly aligned fiber structure (Fig.\u00a01e) with the reconstructed structure as a representative. However, fabricating aligned carbon fiber electrodes remains challenging, as fiber-fabricating techniques like wet-spinning and melt-spinning typically result in fibers with diameters much larger than 10\u2009\u03bcm and poorly adjusted fiber orientation, and traditional electrospinning technique is commonly used for fabricating nanoscale fibers that are randomly dispersed.\n\nTo obtain the optimal GDL structure, we developed a controlled electrospinning method to fabricate aligned GDLs with different fiber diameters. Polyacrylonitrile (PAN) and cobalt salt are dissolved in DMF solutions to form the precursor solution for electrospun MPL and GDL. The addition of cobalt salt can facilitate the formation of aligned fibers during the electrospinning process37,38,39, and later can be reduced to cobalt particles and catalyze the graphitization of carbon fibers40,41. The small amount of Co particles in GDLs is well wrapped inside the fiber or a layer of graphitized carbon (Supplementary Figs.\u00a07, 8), which will not influence PEMFC performance. The fabrication of the dense polymer mat for MPL and aligned polymer fiber mat with a relatively large fiber diameter for GDLs is achieved by adjusting the concentration of precursor solution and electrospinning conditions. As shown in Fig.\u00a02a, a low PAN concentration (e.g., 8\u2009wt%) is used to derive MPL, which exhibits relatively low viscosity. Under the high voltage, the ejected jet becomes highly dispersed, rendering the formation of thin fibers that randomly land on the collecting drum. After stabilization and carbonization, the as-synthesized MPL shows a dense morphology (Fig.\u00a02b), with a fiber diameter in the range of 400\u20131000\u2009nm (Supplementary Fig.\u00a09) that is randomly displaced. When increasing PAN concentration (e.g., 11, 13, and 15\u2009wt%), the viscosity of the precursor solution increases, and the jet tends to stick together and form standing fiber bundles, as shown in Fig.\u00a02a. Under this condition, the concentrated fiber jets land on the collector in a fixed position and interweave into the aligned fibrous morphology, as shown in Fig.\u00a02c-e. It is also worth pointing out that the fiber diameter increases with increased PAN concentration. As measured from the magnified figures (Supplementary Fig.\u00a010), the diameter of aligned carbon fiber derived from the precursor solution with 11, 13, and 15\u2009wt% PAN is ~3, ~6, and ~10\u2009\u03bcm, respectively. Carbon fibers which are heat treated at 1100\u2009\u00b0C typically shows a relatively poor hydrophobicity, with the contact angle measured to be below 120\u00b0 (Supplementary Fig.\u00a011). The subsequent hydrophobic treatment using 1H,1H,2H,2H-Perfluorooctyltriethoxysilane (PFTS)/ethanal solution is critical in increasing the hydrophobicity which significantly increases the contact angle to around 144\u00b0, which is comparable to that of the commercial GDLs (~147\u00b0), as shown in Fig.\u00a02f. Compared with the fibers without hydrophobicity treatment, the morphologies of the fibers after treatment remain almost the same (Supplementary Fig.\u00a012). The energy-dispersive X-ray spectroscopy (EDS) mapping shows that the hydrophobic product is uniformly distributed on the fiber surface, as evidenced by the appearance of uniformly distributed F and Si element (Fig.\u00a02g and Supplementary Fig.\u00a013)42. The carbonization and graphitization degree of the as-synthesized GDL is compared with the commercial ones using Raman spectroscopy43. As shown in Fig.\u00a02h, the commercial GDL (cGDL) which is typically graphitized at temperature over 2000\u2009\u00b0C44,45 shows a relatively high graphitization degree, with sharp peaks corresponding to G band and G\u2019 (or 2D) band visible at the Raman shift of around ~1580 and ~2700\u2009cm\u22121, respectively. In comparison, the graphitization degree of home-made electrode is slightly inferior to the commercial ones with a relatively high peak intensity of disordered carbon (D band) at ~1350\u2009cm\u22121, since only a carbonization at 1100\u2009\u00b0C is adopted during the heat treatment process. Supplementary Fig.\u00a014 illustrates three-dimensional morphology of GDLs from computed tomography (CT) results. The rendering results reveal that aligned GDL (aGDL) exhibits well-aligned fibers and interconnected pores, whereas cGDL displays random pores within the stochastic fibers and uneven pore distribution. The pore size distributions of GDLs are further analyzed based on CT results using pore network algorithms, showing that the equivalent pore diameter of aGDL is larger than that of cGDL and has distributions over a wider range.\n\na Schematic illustration of electrospinning jets for the formation of dense and random fibers and aligned fibers. E, applied voltage. Scanning electron microscopy (SEM) images of the carbon fiber mat derived from b 8\u2009wt%, c 11\u2009wt%, d 13\u2009wt% and e 15\u2009wt%, which are respectively repeated with the same structures over three times. f Contact angle of commercial GDL (cGDL) and aligned GDL (aGDL) after hydrophobicity treatment. g Energy-dispersive X-ray spectroscopy (EDS) mapping of the treated aGDL. h Raman spectra of aGDL and cGDL. Source data are provided as a Source Data file.\n\nWith the proposed strategy to synthesize MPL and different GDLs, we systematically investigated the influence of the GDL fiber diameter, with/without MPL, the aligned fiber orientation with respect to the flow field, and fiber randomness, on PEMFC performance. As illustrated in Fig.\u00a03a, three GDLs named as aGDL_d1, aGDL_d2, and aGDL_d3 are respectively derived from PAN precursors of concentration of 11, 13, and 15\u2009wt%, and stack with MPL. The aGDL_wo is free of MPL and has the same aligned GDL (aGDL_d3). With the aGDL_d3, we further compared two arrangements relative to the flow channels, one is placing the aGDL vertical to the flow channel and the other is placing the aligned GDL in parallel to the flow channels, which we name the latter as aGDL_parallel. To compare the aligned GDL with its randomly arranged counterpart without involving the influence of other factors, such as varied fiber diameter, we stack several thin layers of aligned GDLs in different orientations, as shown in Supplementary Figs.\u00a015 and 16, and get rGDL_d3. Lastly, the aGDLs are compared with commercial PTFE-treated carbon paper-based GDLs that are coated with particle-based MPL. With all these GDLs, the pore structures are characterized by mercury intrusion porosimetry (MIP), as shown in Fig.\u00a03b. It is found that the as-prepared aGDL_d3 exhibits large pores in the same range as commercial GDL, which are around 30\u2009\u03bcm. When varying the fiber diameter of the GDL, the pore size decreases in the sequence that aGDL_d1 0.05\\)). On the first day of tracking, individuals were, on average, less active compared to their long-term mean (\u22127.8\u2009\u00b1\u200919.2%; mean\u2009\u00b1\u2009SD), whereas daily displacements were higher (6.9\u2009\u00b1\u200923.8%; mean\u2009\u00b1\u2009SD), with large SD attributed to strong intra- and interspecific variability. Net deviations, i.e., absolute deviations on the first day, were 14.2 \u00b1\u200915% for activity and 18.7\u2009\u00b1\u200916.3% for displacements. The activity level of 25 species differed substantially immediately after release compared to subsequent days, with a gradual stabilization during the initial days (Table\u00a02). This trend was particularly evident in omnivores (R2\u2009=\u20090.374, Dev. explained\u2009=\u200946.4%). While omnivores and carnivores were less active during the initial days, pooled herbivore data revealed both increased and decreased activity rates. A similar pattern was found for displacements, as most species traveled longer distances after collaring events compared to the long-term mean (days 11\u201320; R2\u2009=\u20090.25, Dev. explained\u2009=\u200937.7).\n\nDaily differences to the long-term mean of activity (upper) and displacements (lower) split by diet: herbivores (left), omnivores (middle), and carnivores (right) for 42 mammal species, n\u2009=\u20091585. All species with p\u2009\u2264\u20090.05 are shown as solid lines and species with p\u2009>\u20090.05 or n\u2009<\u20095 as dotted lines. Activity: R2\u2009=\u20090.374, Dev. explained\u2009=\u200946.4%, displacements: R2\u2009=\u20090.25, Dev. explained\u2009=\u200937.6%. Predictions are derived from two Generalized Additive Mixed Models with Gamma error distributions to assess the effect of disturbance intensity on activity and displacements of the focal species over time. The dotted blue line represents the long-term mean (average for days 11\u221220). In the legend following each species name, the first number refers to the number of individuals for activity and the second for displacements.\n\nOn the first day post-release, moose (Alces alces) exhibited the largest increases in displacement distance, moving 63% further compared to the long-term mean, followed by common eland Tragelaphus oryx (52%), and spotted hyena Crocuta crocuta (44%). In contrast, leopards Panthera pardus were found to have the largest reductions in displacement distances, reducing their movement distances by-65%, followed by wolves Canis lupus (\u221244%), and Eurasian lynx Lynx lynx (\u221243%). Moose also had the largest increases in activity on day one (44%), followed by red deer Cervus elaphus (26%), and Mongolian khulan Equus hemionus hemionus (9%). Wolves had the largest decreases in activity on day one (\u221248%), followed by the white-tailed mongoose Ichneumia albicauda (\u221241%), and leopard Panthera pardus and golden jackal Canis aureus (\u221241%). In general, carnivores traveled shorter distances post-release, aside from the spotted hyena (Deviance day1GPS\u2009=\u200944%) and fossa Cryptoprocta ferox (Deviance day1GPS\u2009=\u200912%). In this study, we did not investigate mortality rates as only individuals that survived for at least 20\u2009days post-tagging were included in our analysis.\n\nRecovery speed in activity is best explained by a high human footprint index of the respective study site, the individuals\u2019 sex [+male] (Fig.\u00a03AB, Table\u00a03) and a larger body mass (competing model, \u0394AIC\u2009<\u20092, Tab.\u00a0S2). A fast recovery in displacements was best explained by the species-specific diet [+carnivore] and its body mass, with large species recovering considerably faster (Fig.\u00a03CD, Table\u00a04). During the first 10\u2009days of tracking, the difference from the long-term mean of displacements decreased from 33\u2009\u00b1\u200917% on day 1 to 21\u2009\u00b1\u200920% on day 10, while activity decreased from 24\u2009\u00b1\u200914%\u201312\u2009\u00b1\u20096%; devianceday1 vs.\u00a0devianceday10 for all species with p\u2009\u2264\u20090.05, Fig.\u00a03, Table\u00a01. Comparing individual days in days 11\u201320 to the mean of this period indicated mean routine variations of 14% for activity and 35% for displacements.\n\nA, B Recovery speed (of activity) described in relation to sex and the Human Footprint index (HFi), n\u2009=\u20091241. High recovery speed values indicate a fast recovery. High HFi values indicate a strong anthropogenic influence, and low values indicate a high degree of remoteness. The inset (A) shows the density plots of the sample size distribution for each dietary guild in regard to HFi. B Predictions are presented for values of the lower (12.37), median (18.68), and upper (25) quartiles of HFi. Insets here (B) present exemplary satellite imagery of sites with differing HFi; left to right: an area with little infrastructure and some habitat fragmentation [HFi: 10]; agricultural fields with small forest patches, road infrastructure, and some settlements [HFi: 17]; a more degraded landscape with a quarry and an adjacent solar park [HFi: 25] (\u24d2Landsat / Copernicus, GoogleEarth 2020-202388). Landscapes with extreme HFi values (close to zero: representing pristine, undisturbed areas; close to 50: representing dense populated urban areas) were less present in the dataset and, as such, examples are not shown. C, D Recovery speed (of displacements) described in relation to body mass (C) and dietary type (D), n\u2009=\u20091014. Recovery speed describes the speed of change in activity or displacements as a percentage of the respective long-term mean on day one. Dots (A, C) represent calculated values. Dots (B, D) and the solid lines (A, C) represent mean modeled values, and bars (B, D) as well as the gray shaded area (A, C) are 95% confidence intervals. Note that the y-axis is sqrt-transformed.\n\nRecovery duration also differed between dietary types. Omnivores and carnivores returned to their mean long-term behavior in both disturbance intensity measures after 5\u20136\u2009days (Table\u00a02), with data beyond this period being less influenced by collaring events. In contrast, herbivores were the quickest to return to their mean long-term displacement behavior but were slowest to return to their long-term activity levels: 3.6\u2009\u00b1\u20091.0\u2009days (displacements), and 6.6 \u2009\u00b1\u20090.9\u2009days (activity), mean\u2009\u00b1\u2009SD).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52381-8/MediaObjects/41467_2024_52381_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52381-8/MediaObjects/41467_2024_52381_Fig3_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our findings revealed widespread evidence of post-collaring behavioral changes in animal activity and displacements. Animals displayed a general trend in their responses, marked by the most pronounced deviations in behavior immediately following successful collar deployment. Subsequently, their behavior stabilized, converging on their long-term mean within four to 7\u2009days (Tab.\u00a02). This recovery duration represents the initial period of more pronounced data bias. Responses found in our dataset are consistent with the findings of case studies from the respective species: In moose\u00a0A. alces, the observed reaction is in accordance with Neumann et al.57, who identified larger spatial displacements for up to 4.5\u2009days after capture. In wild boars Sus scrofa, similar to our findings, the first post-capture days were characterized by low activity and low mobility levels, which then gradually restored to stable levels at approximately 10\u2009days28. Additionally, we observed increased movement rates for red deer immediately after release, as found by Becciolini et al.46. We can not make any conclusions about the effect of tagging on survival rates, as only data from individuals that survived the study period were considered.\n\nMales recovered on average 1.3\u2009days faster than females from collaring-induced changes in activity, aligning with findings in roe deer26, yet this effect was not detected in displacements. Females may require a longer recovery time due to gestation, birth, and rearing of offspring (as only 5\u201310% of mammalian species engage in paternal care58). These factors may aggravate negative impacts associated with the attachment of tracking devices, potentially leading to increased stress levels, reduced foraging efficiency, or, as a consequence, compromised reproductive success26,27,28. We expect this effect to be even more pronounced in pregnant or lactating females. However, due to the heterogeneous nature of the dataset, with various species captured over different times across continents, we did not account for an individual\u2019s physiological or behavioral season.\n\nOmnivores and carnivores were generally less active than herbivores after release. In cases where animals are caught with bait, as is sometimes done for carnivores and omnivores, individuals may not need to carry out foraging movements in the following days as they would under normal circumstances. A more proximate explanation for the reduced movement and activity could also be a reaction to chemical immobilization. In contrast, 65% of the herbivores increased their activity on the first day post-release. Resting to conserve energy does not seem like a legitimate reaction to being chased and immobilized because their natural response to being chased by predators is escaping by moving. The recovery speed of activity and displacements after collaring events was slower in herbivores than omnivores and carnivores. From an evolutionary perspective, this is surprising since predators frequently chase many wild herbivores, and therefore, herbivores may be expected to be better adapted to and recover faster from disturbances. Yet, these responses may be offset by the potent anesthesia used, particularly for large herbivores (e.g., Bison sp., A. alces, Tragelaphus strepsiceros, C. elaphus). For all species, we found strong intraspecific variation in the response behavior, which may be context-specific or linked to animal personalities59, traditionally assessed along a bold-shy continuum60,61.\n\nStress-related activity of wildlife is often categorized as either fight or flight62. This can also hold true for the post-capture response of wildlife to either the capture event or the collar. Characterization of fight-flight was first identified in human psychology63, but as Bracha et al.64 noted, the addition of \u201cfreeze\u201d to the term is needed. In wildlife, this can be extended to include hiding in response to disturbance65. Post-release behavior likely includes a complex blend of all these responses, as well as additional stressors they encounter during that timeframe. To add to this complexity, in places with significant anthropogenic influence, animals frequently display enhanced tolerance and adaptation to human presence50,51. Animals that adapt to human presence may experience reduced competition for resources compared to natural habitats66.\n\nManagement practices, such as supplementary feeding, which can cause habituation and changes in space use, mobility, or activity (e.g., ref. 67), may also influence behavior after collaring. Furthermore, some species demonstrate behavioral flexibility and can adjust their activity patterns or habitat preferences68 and their movement behavior69 to avoid direct conflicts with humans. For example, some mammals, such as raccoons Procyon lotor and coyotes Canis latrans, thrive in urban areas by utilizing human-associated food resources and adapting their behavior to coexist with humans50,51, yet, the impact of anthropogenic influence is species-specific70. Previous studies have shown that human interactions can strongly influence animal behavior. For example, the coexistence of humans and wildlife in urban areas often selects individuals with bold personalities71,72,73. On the other hand, animals inhabiting remote areas have less exposure to human presence and, consequently, encounters. Hence, when such animals encounter humans, they might show an exacerbated response toward the disturbance and remain alert for a prolonged time. While this assertion is speculative, it is supported by our finding here, where individuals in remote areas recovered slower from collaring than those in highly anthropogenically influenced areas. With numerous deployment methods like helicopter darting, chasing, or trapping being applied in the field, analyzing their effect was not feasible within the scope of this study. The effect of the deployment method remains unclear, and the selected method may even change along an HFI gradient. For example, helicopter darting may be the only option in areas with little infrastructure, whereas in more urban areas, alternative options are preferred. Interpreting the effect of the human footprint should take into account that deployment type could be influenced by the respective study area. Therefore, we strongly recommend documenting these methodological decisions for future research.\n\nThere exists a fine balance between obtaining valuable data and ensuring the well-being of tracked animals. Researchers must consider these ethical dilemmas carefully and implement tracking methods that minimize harm and maximize animal welfare. Omitting initial data can contribute to reducing biased results, thereby generating more accurate outcomes that could better inform conservation efforts. Yet, it may be difficult to detect the effects of collars during short-term deployments, as the data obtained is highly time-constrained. While our study was confined to assessing behavioral alterations associated with collaring events, it is important to note that even short-term modifications in behavior can incur energetic costs, reduce energy intake, or influence predation risk and, as such, potentially impact animal survival and fitness74,75,76. As we only considered data from individuals that survived for at least 20\u2009days post-tagging, we could not account for possible mortality rates. The inclusion of such data in future studies could contribute to an even more holistic understanding of the consequences of tagging.\n\nWhile established animal welfare guidelines and regulatory requirements that allow for such invasive studies exist, many of these rely on findings from isolated case studies. Our study of post-release telemetry data of 42 terrestrial mammalian species reveals potential biases in wildlife GPS and ACC data during the initial days of animal tracking, likely due to invasive immobilization and tagging procedures, which may influence movement ecology findings. These impacts, however, fade within a relatively short time frame of four to 7\u2009days, suggesting that the overall impact of collaring is minimal and short-lived, which is good news for animal tracking science. In studies where longer tracking is not feasible, researchers should be aware of these disturbance biases. Particularly, short-term studies, lasting <7\u2009days, may be significantly compromised. These studies are prevalent in certain research areas, for example, where battery weight strongly limits tracking duration. Based on our findings, we strongly advocate extending animal tracking periods well beyond 7\u2009days whenever possible. Further efforts relating the findings of this study to other important variables such as method of capture, type of tag, drug combinations, and post-release behavior could provide valuable insights into best practices in reducing capture myopathy, stress, and data bias. By understanding and addressing these limitations, researchers can maximize GPS-collaring advantages while limiting adverse effects on study animals. Undoubtedly, animal tracking will continue to contribute to our understanding of the environment, with progress in this field being propelled by ongoing technological developments, improved techniques, and heightened ethical considerations.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Animal tracking data (GPS and ACC, see Supplementary Note\u00a01 for permits) from multiple data providers were either directly sourced from tables or downloaded from the Movebank data repository77 with the help of the R package Move78. In the first step, we omitted individuals with missing data during the initial 20\u2009days, resulting in 1585 unique individuals. We defined data as missing if any discontinuation resulted in <1 GPS fix per hour and less than one activity measurement per 30\u2009min. The resulting number of individuals per terrestrial mammal species ranged from 4 to 672 (mean nacc\u2009=\u200936.4, mean ngps\u2009=\u200932.6) out of total individuals nacc\u2009=\u20091452 of 41 species across 57 study sites, and total individuals ngps\u2009=\u20091262 of 40 species across 55 study sites.\n\nWe classified the data into two periods: the initial 10\u2009days following the individual\u2019s release and days 11\u201320. We considered the latter timeframe representative of \u2019long-term\u2019 behavior, expecting that the response to the collaring/handling process had subsided within the initial 10\u2009days, as shown in previous studies (e.g., A. alces\u2009\u2264\u20094.5\u2009days57, C. capreolus\u2009\u2264\u200910\u2009days26, C. elaphus\u2009\u2264\u200910\u2009days46).\n\nWe calculated mean daily ODBA values for each individual with the R package moveACC79 as ODBA\u2009=\u2009\u2223Ax\u2223\u2009+\u2009\u2223Ay\u2223\u2009+\u2009\u2223Az\u2223 for tri-axial measurements; and as ODBA\u2009=\u2009\u2223Ax\u2223\u2009+\u2009\u2223Ay\u2223 for bi-axial measurements, where Ax, Ay, and Az are the derived dynamic accelerations corresponding to the three perpendicular axes of the sensor13. Downsampling from three to two axes to compare ACC measurements was not necessary, as the raw data were used per individual to calculate the disturbance intensity, which is then expressed in percent. Acceleration records obtained from individuals with only one axis (Acinonyx jubatus) were not considered. The temporal resolution of both GPS and ACC data was adjusted by rounding timestamps to the nearest 5\u2009min interval. Then, displacements were calculated using the R package adehabitatLT80 as each individual\u2019s mean displacement (m) from one GPS fix to the next within each 24\u2009h interval. For each study site, we extracted the Human Footprint index (HFi:81,82) and calculated the mean HFi for a 5\u2009km radius around the center of the study site (mean longitude, mean latitude).\n\nSubsequently, we related daily averaged values (displacement, activity) to the respective mean during days 11\u201320 to calculate the disturbance intensity (Fig.\u00a01). We applied two Generalized Additive Mixed Models with Gamma error distributions for the disturbance intensity in activity and displacements to estimate the effect on the focal species in combination with time (i.e., days 1\u201310) on daily differences to the long-term mean using the R package mgcv83. Since we did not expect a linear relationship, we specified the predictor variable time as a smooth term for each species and a first-order auto-regressive correlation structure corAR1 among the residuals of the model associated with each individual. Sex was included as a random smoothing effect, allowing for a smooth relationship between sex and the dependent variable. This allows for individual-specific effects of sex on the response, which can be useful when assuming that the relationship between sex and the response is not strictly linear but varies smoothly across individuals or species. The disturbance intensity model was specified as follows:\n\nThus, the linear predictor \u03b7id,t includes an autoregressive process of order one (AR[1]). Here, the parameter \u03c1 accounts for the temporal autocorrelation, id represents the animal identifier, and t is the corresponding time point. In addition, u indicates the use of random intercepts. Deviance was calculated and modeled separately for both activity and displacement.\n\nFor all individuals of species with significant disturbance effects (Fig.\u00a02, Table\u00a01), we calculated the \u2223slope\u2223 on day one after the release as a measure of recovery speed, i.e., how fast individuals adapt throughout the first days. The slope was calculated for each individual as the first derivative for x\u2009=\u20091 from the ID-specific fitted curve with y\u2009~\u2009log(x). Recovery speed, expressed in units of percentage per day, quantifies the rate of adaptation of individuals. The steeper the slope (i.e., the higher the values), the faster individuals were at adapting or acclimating. We applied separate linear mixed effect models for activity and displacement to estimate the recovery speed in both activity and displacements, using the R package lme484 using the respective measurements, \u2223slope day 1\u2223 as the dependent variable. We included sex, dietary type (herbivore, omnivore, carnivore), body mass derived from literature values85 (Table\u00a0S1), and the Human Footprint index of the study area as independent variables and study species as a random effect. Due to incomplete data and many different levels, we did not consider the deployment procedure as an independent variable. The dependent variable, as well as the independent variables, body mass and HFi, were log-transformed. The model was calculated using Gaussian error distribution and a natural logarithm link function. Subsequently, we selected models using the R package MuMIn86. By ranking model combinations via the Akaike Information Criterion (AIC), we considered all independent variables in the best-fit models within 2 AIC units in the final model and report the respective summary. Models were calculated using all gap-less data available for the independent and dependent variables, resulting in minor variations in sample size and species analyzed for activity and displacements.\n\nTo assess the stabilization period of collaring effects on activity and displacement, we used the fitted disturbance intensity model (Eq. 1) to calculate the period until individuals reverted to their average long-term behavior for both disturbance intensity measures (activity and displacements) for the first time post-release. For this, we included all individuals of species in which significant patterns were identified with the disturbance intensity model above.\n\nThe recovery speed model was specified as a linear mixed effect model:\n\nwhere sex and diet were specified as categorical variables; slopeday1 was calculated and modeled separately for activity and displacement.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The datasets generated in this study to create the respective figures have been provided in the Source Data file. The GPS and acceleration datasets used and analyzed in this study are available in the Movebank Data Repository77 at www.movebank.org (Antidorcas marsupialis, ID: 904829042; Chlorocebus pygerythrus, ID: 17629305; Erinaceus europaeus, ID: 354843286; ID: 348067475; ID: 490547558; ID: 1371906275; Felis silvestris, ID: 40386102; Genetta genetta, ID: 19814565; Ichneumia albicauda, ID: 158898881; Lepus europaeus, ID: 918554628; ID: 1138520346; ID: 4048590; ID: 25727477; ID: 43360515; ID: 71038468; ID: 73514179; Lynx rufus, ID: 501787846; ID: 475878514; Panthera pardus, ID: 17629305; Papio anubis, ID: 17629305; Procyon lotor, ID: 4048590; Taurotragus oryx, ID: 904829042; Tragelaphus strepsiceros, ID: 904829042; Viverra tangalunga, ID: 57540673; Vulpes vulpes, ID: 4048590; ID: 326682415; ID: 173932849); data from Euromammals87 can be accessed by logging into their website or via a contact form at https://euromammals.org/ (Capra ibex; Capreolus capreolus; Cervus elaphus; Lynx lynx; Sus scrofa); or can be obtained from data providers upon request through the corresponding author.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Hebblewhite, M. & Haydon, D. 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Environ. 202, 18\u201327 (2017).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We are very thankful for the support of Karatina University, Smithsonian Institution\u2019s National Museum of Natural History, and Mpala Research Centre; the staff of Polish and Slovak Tatra National Parks for their help in bear trapping; all Carpathian Brown Bear Project members who assisted in the field during captures, handling and data collection; Zbigniew Krasinski from the Bia\u0142owieza National Park and Tomasz Kaminski from the Mammal Research Institute PAS for help in European bison collaring; the BioMove RTG including associated helpers in the field, the workers at the ZALF research station; the Forest and Wildlife Research Center at Mississippi State University; the members of Euromammals including Eurodeer, Euroboar, and Euroreddeer; Junta de Castilla y Leon, Gobierno Principado Asturias, Ministerio Transicion Ecologica, Tragsatec; the Oyu Tolgoi\u2019s Core Biodiversity Monitoring Program, implemented by the WCS through a cooperative agreement with Sustainability East Asia LLC for their help in Khulan capture, marking and radiotracking; N. Sharma, G. Basson, D. Medeiros, J. McGraw, R. Reed, A. Johnston, H. Maschmeyer, R. King, B. Nichols, J. Suraci, for essential support in animal tracking; D. Simpson, S. Ekwanga, M. Mutinda, G. Omondi, W. Longor, M. Iwata, A. Surmat, M. Snider, W. Fox, and K. VanderWaal for field assistance, M. Crofoot, D. Rubenstein, and L. Frank for sharing their field equipment, and M. Kinnaird and T. Young for logistical support; L. Purchart, M. Kutal, J. Krojerov\u00e1, and K. Purchartov\u00e1 for scientific background and project coordination and P. Forejtek for veterinarian support; the Danau Girang Field Centre research group in collaboration with the Sabah Wildlife Department, Veterinarian support provided by Drs. M. Gonzalez, S. Guerrero-Sanchez, D. Ramirez, L. Benedict, and P. Nagalingam; field assistants and personnel at Zackenberg Research Station; the Office Fran\u00e7ais de la Biodiversit\u00e9, especially Jean-Luc Hamann and Vivien Siat and the Office National des Forets, including the wildlife technicians, the foresters, and the many volunteers for their help in the capture of red and roe deer; field collaborators and veterinarians of the Leibniz-IZW, Berlin, especially Janina Radwainski; the Namibian Ministry of Environment, Forestry and Tourism, the Namibian farmers, and the entire team of the Cheetah Research Project of the Leibniz-IZW, Berlin; K. Boyer, S. Peper, C. Wilson, Z. Johnson, H. Greenburg, K. Haydett, D. Warren, D. Payne, J. Hoffman, M. Proctor, J. Gaskamp for assistance with trapping wild pigs and white-tailed deer in Oklahoma; the University of California, Santa Cruz and the California Department of Fish and Wildlife for their partnership in the Santa Cruz Puma project; and all non-mentioned technicians, and workers in the field. This work was supported by the DFG-funded research training group \u201cBioMove\" (DFG-GRK 2118/1); by a National Science Foundation Postdoctoral Research Fellowship in Biology (DBI-1402456) awarded to Adam W. Ferguson and Paul W. Webala; the Polish-Norwegian Research program administered by the National Research Centre for Research and Development in Poland (POL-NOR/198352/85/2013), Tatra National Park own funding; by the German Federal Ministry of Education and Research BMBF within the Collaborative Project \u201cBridging in Biodiversity Science-BIBS\" (grant number: 01LC1501); by the Polish Ministry of Sciences and Information Technology (grant no 2P04F 011 26); Frankfurt Zoological Society \u00a0\u2212 Help for Threatened Wildlife and the EU LIFE program (project no LIFE06 NAT/PL/000105); by the DFG: KA 1082/17-1; by the DFG: KA 1082/16-1; by Safari Club International Foundation, Michigan Department of Natural Resources, and the Federal Aid in Wildlife Restoration Act under Pittman-Robertson project W-147-R; by grant QK1910462 and CZ021010.00.0160190000803; by Ministerio de la Transicion Ecologica; by the Peninsula Open Space Trust, Land Trust of Santa Cruz County, California Department of Fish and Wildlife, Santa Clara Open Space Authority; by the National Geographic Society Committee for Research and Exploration #9385-13; by the Washington University in Saint Louis ICARES grant 2015; by the Dean\u2019s office of the Faculty of Forestry and Wood Technology, Mendel University in Brno and Training Forest Enterprise Masaryk Forest Kr^tiny; by Houston Zoo; the Sime Darby Foundation; Ocean Park Conservation Foundation Hong Kong (TM01.1718); and Phoenix Zoo; by the \u2018Mov-It\u2019 Agence Nationale de la Recherche grant ANR-16-CE02-0010-02 to NM; by the Federal Ministry of Education and Research, Germany, FKZ: 01LL1804A; by the Office Fran\u00e7ais de la Biodiversit\u00e9 (OFB); by the \u201cStiftung Naturschutz Berlin\"; by the Noble Research Institute, LLC; by 15. Juni Fonden and Copenhagen Zoo; by the Italian Ministry of Education, University and Research (PRIN 2010-2011, 20108 TZKHC, 418 J81J12000790001); by the Foreste Casentinesi National Park; by the Regione Autonoma della Sardegna, Provincia di Sassari, and Fondazione Banco di Sardegna; by the National Science Foundation; by the Messerli Foundation Switzerland; CS was supported by the Elsa-Neumann foundation; by the US National Science Foundation (grant nos. BCS 99-03949, BCS 1266389), the Leakey Foundation, and the Committee on Research, University of California, Davis to Lynne A. Isbell, and the Wenner-Gren Foundation (grant no. 8386) to Laura R. Bidner; by the Natural Sciences and Engineering Research Council of Canada (NSERC), NAB. PGSD3-404001-2011; by the National Institutes of Health (NIH), WMG. GM83863, and the University of KwaZulu-Natal.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, 14469, Potsdam, Germany\n\nJonas Stiegler,\u00a0Cara A. Gallagher,\u00a0Robert Hering,\u00a0Florian Jeltsch,\u00a0Christoph Lobas,\u00a0Jan Pufelski,\u00a0Manuel Roeleke,\u00a0Carolin Scholz,\u00a0Maxi Tomowski,\u00a0Wiebke Ullmann\u00a0&\u00a0Niels Blaum\n\nAnimal Ecology, Institute of Biochemistry and Biology, University of Potsdam, 14469, Potsdam, Germany\n\nJonas Stiegler\u00a0&\u00a0Jana A. Eccard\n\nEcology and Macroecology Laboratory, Institute for Biochemistry and Biology, University of Potsdam, 14469, Potsdam, Germany\n\nRobert Hering\n\nSenckenberg Biodiversity and Climate Research Centre, Senckenberg Gesellschaft f\u00fcr Naturforschung, 60325, Frankfurt (Main), Germany\n\nThomas M\u00fcller\n\nDepartment of Biological Sciences, Goethe University, 60438, Frankfurt (Main), Germany\n\nThomas M\u00fcller\n\nSmithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA, USA\n\nThomas M\u00fcller\n\nDepartment of Environmental Science, Radboud Institute for Biological and Environmental Sciences, Radboud University, P.O. Box 9010, 6500, GL Nijmegen, Netherlands\n\nMarlee Tucker\n\nDepartment of Veterinary Medicine, University of Sassari, Via Vienna 2, 07100, Sassari, Italy\n\nMarco Apollonio,\u00a0Francesca Brivio\u00a0&\u00a0Rudy Brogi\n\nWildlife Research Unit, Agricultural Centre Baden-Wuerttemberg (LAZBW), 88326, Aulendorf, Germany\n\nJanosch Arnold\u00a0&\u00a0Peter Linderoth\n\nSchool of Life Sciences, University of KwaZulu-Natal, Durban, South Africa\n\nNancy A. Barker\u00a0&\u00a0Abi T. Vanak\n\nLeibniz Institute for Zoo and Wildlife Research (IZW), Berlin, Germany\n\nLeon Barthel,\u00a0Anne Berger,\u00a0Konstantin B\u00f6rner,\u00a0Sophia Kimmig,\u00a0Stephanie Kramer-Schadt,\u00a0Anette Krop-Benesch,\u00a0J\u00f6rg Melzheimer,\u00a0Carolin Scholz,\u00a0Leif S\u00f6nnichsen\u00a0&\u00a0Bettina Wachter\n\nGran Paradiso National Park, Turin, Italy\n\nBruno Bassano\n\nDepartment of Ecoscience, Aarhus University, Roskilde, Denmark\n\nFloris M. van Beest,\u00a0Lars Haugaard,\u00a0Niels M. Schmidt\u00a0&\u00a0Peter Sunde\n\nDepartment of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA\n\nJerrold L. Belant,\u00a0Dean E. Beyer Jr,\u00a0Jarred F. Duquette,\u00a0Nicholas L. Fowler,\u00a0Todd M. Kautz\u00a0&\u00a0Tyler R. Petroelje\n\nDepartment of Anthropology, University of California, Davis, CA, 95616, USA\n\nLaura R. Bidner\u00a0&\u00a0Lynne A. Isbell\n\nMpala Research Centre, 555-10400, Nanyuki, Kenya\n\nLaura R. Bidner,\u00a0Adam W. Ferguson\u00a0&\u00a0Dedan K. Ngatia\n\nDepartment of Biology, St. Louis University, St. Louis, MO, USA\n\nStephen Blake\n\nWildCare Institute, Saint Louis Zoo, 1 Government Drive, Saint Louis, MO, 63110, USA\n\nStephen Blake\n\nWildlife Conservation Society, Mongolia Program, Ulaanbaatar, Mongolia\n\nBayarbaatar Buuveibaatar\u00a0&\u00a0John C. Payne\n\nResearch and Innovation Centre, Animal Ecology Unit, Fondazione Edmund Mach, San Michele all\u2019Adige, Trento, Italy\n\nFrancesca Cagnacci\n\nNBFC, National Biodiversity Future Centre, Palermo, 90133, Italy\n\nFrancesca Cagnacci\n\nBionet Natuuronderzoek, Stein, Netherlands\n\nJasja Dekker\u00a0&\u00a0Ren\u00e9 Janssen\n\nTexas A&M Natural Resources Institute, and Department of Rangeland, Wildlife and Fisheries Management, Texas A&M University, College Station, TX, 77843-2138, USA\n\nJane Dentinger\u00a0&\u00a0Stephen L. Webb\n\nDepartment of Forest Ecology, Faculty of Forestry and Wood Technology, Mendel University, 613 00, Brno, Czech Republic\n\nMartin Du\u013ea\n\nDanau Girang Field Centre, Sabah Wildlife Department, 88100, Kota Kinabalu, Sabah, Malaysia\n\nMeaghan N. Evans\u00a0&\u00a0Benoit Goossens\n\nOrganisms and Environment Division, School of Biosciences, Cardiff University, Cardiff, CF10 3AX, UK\n\nMeaghan N. Evans\u00a0&\u00a0Benoit Goossens\n\nDepartment of Biological Sciences, Chicago State University, 9501 S. King Drive, Chicago, IL, 60628, USA\n\nAdam W. Ferguson,\u00a0Peter Lokeny\u00a0&\u00a0Molly M. McDonough\n\nGerman Primate Center, Behavioral Ecology and Sociobiology Unit, 37077, G\u00f6ttingen, Germany\n\nClaudia Fichtel,\u00a0Peter Kappeler,\u00a0Mia-Lana L\u00fchrs\u00a0&\u00a0Lennart Pyritz\n\nDepartment of Biology, University of British Columbia, 1177 Research Road, Kelowna, British Columbia, Canada\n\nAdam T. Ford\n\nDepartment of Evolutionary Biology and Environmental Studies, University of Zurich, 8057, Zurich, Switzerland\n\nBenedikt Gehr\n\nDepartment of Environmental Science Policy & Management, 130 Mulford Hall, University of California at Berkeley, Berkeley, CA, 94720-3112, USA\n\nWayne M. Getz\n\nSchool of Mathematical Sciences, University of KwaZulu-Natal, Private Bag X54001, Durban, 4000, South Africa\n\nWayne M. Getz\n\nDepartment of Zoology and Physiology, University of Wyoming, Laramie, WY, 82071, USA\n\nJacob R. Goheen\n\nDepartment of Life Science and Biotechnology, University of Ferrara, Via Borsari 46, I-44121, Ferrara, Italy\n\nStefano Grignolio\n\nBiodiversity Research Centre, Agriculture and Natural Resources Sciences, Namibia University of Science and Technology, Windhoek, Namibia\n\nMorgan Hauptfleisch\n\nNorwegian Institute for Nature Research, P.O. Box 5685 Torgarden, NO-7485, Trondheim, Norway\n\nMorten Heim,\u00a0Petra Kaczensky,\u00a0Kirk A. Olson,\u00a0Christer M. Rolandsen\u00a0&\u00a0Erling J. Solberg\n\nDepartment of National Park Monitoring and Animal Management, Bavarian Forest National Park, Freyunger Str. 2, 94481, Grafenau, Germany\n\nMarco Heurich\u00a0&\u00a0Joseph Premier\n\nChair of Wildlife Ecology and Management, Faculty of Environment and Natural Resources, University of Freiburg, Tennenbacher Stra\u00dfe 4, 79106, Freiburg, Germany\n\nMarco Heurich,\u00a0Max Kr\u00f6schel\u00a0&\u00a0Joseph Premier\n\nInstitute of Forestry and Wildlife Management, Inland Norway University of Applied Science, NO-2480, Koppang, Norway\n\nMarco Heurich\n\nUniversit\u00e9 de Toulouse, INRAE, CEFS Castanet-Tolosan, France\n\nMark A. J. Hewison\u00a0&\u00a0Nicolas Morellet\n\nAnimal Behavior Graduate Group, University of California, Davis, CA, 95616, USA\n\nLynne A. Isbell\n\nSchool of Business, Innovation and Sustainability, Halmstad University, Halmstad, Sweden\n\nAnders Jarnemo\n\nDepartment of Game Management and Wildlife Biology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Kam\u00fdck\u00e1 129, Prague 6-Suchdol, 165 00, Czech Republic\n\nJezek Milo\u0161\u00a0&\u00a0V\u00e1clav Silovsk\u00fd\n\nResearch Institute of Wildlife Ecology, University of Veterinary Medicine Vienna, A-1160, Vienna, Austria\n\nPetra Kaczensky\n\nMammal Research Institute, Polish Academy of Sciences, Stoczek 1, 17-230, Bia\u0142owie\u017ca, Poland\n\nTomasz Kami\u0144ski,\u00a0Katharina Kasper,\u00a0Rafa\u0142 Kowalczyk,\u00a0Krzysztof Schmidt\u00a0&\u00a0Leif S\u00f6nnichsen\n\nDepartment of Sociobiology/Anthropology, University of G\u00f6ttingen, 37077, G\u00f6ttingen, Germany\n\nPeter Kappeler\n\nGrims\u00f6 Wildlife Research Station, Department of Ecology, Swedish University of Agricultural Sciences, 730 91, Riddarhyttan, Sweden\n\nPetter Kjellander\n\nInstitute of Ecology, Chair of Planning-Related Animal Ecology, Technische Universit\u00e4t Berlin, Potsdam, Germany\n\nStephanie Kramer-Schadt\n\nB\u00fcro Renala, G\u00fclper Hauptstr. 4, 14715, Havelaue, Germany\n\nMia-Lana L\u00fchrs\n\nCenter for Integrated Spatial Research, Environmental Studies Department, University of California, Santa Cruz, CA, 95060, USA\n\nStephanie S. Matsushima\u00a0&\u00a0Christopher C. Wilmers\n\nDepartment of Integrative Biology and Biodiversity Research, University of Natural Resources and Life Sciences, Vienna, Gregor-Mendel-Stra\u00dfe 33, 1180, Vienna, Austria\n\nLeopold Obermair\u00a0&\u00a0Robin Sandfort\n\nDepartment of Integrative Biology and Evolution, Research Institute of Wildlife Ecology, University of Veterinary Medicine, Savoyenstra\u00dfe 1, 1160, Vienna, Austria\n\nLeopold Obermair\n\nHunting Association of Lower Austria, Wickenburggasse 3, 1080, Vienna, Austria\n\nLeopold Obermair\n\nTechnische Universit\u00e4t M\u00fcnchen, Arcisstra\u00dfe 21, 80333, M\u00fcnchen, Germany\n\nKidan C. Patanant\n\nTragsatec, C. de Juli\u00e1n Camarillo, 6B, San Blas-Canillejas, 28037, Madrid, Spain\n\nManuel Pina\u00a0&\u00a0Josep Piqu\u00e9\n\nDipertimento di agronomia, animali, alimenti, risorse naturali e ambiente, Universit\u00e0 degli Studi di Padova, 35020, Legnaro PD, Italy\n\nMaurizio Ramanzin\n\nOffice Fran\u00e7ais de la Biodiversit\u00e9, Montfort, 01330, Birieux, France\n\nSonia Sa\u00efd\n\nArctic Research Centre, Aarhus University, Aarhus, Denmark\n\nNiels M. Schmidt\n\nDepartment of Behavioural Ecology, Bielefeld University, Bielefeld, Germany\n\nNadine Schubert\n\nInstitute of Nature Conservation, Polish Academy of Sciences, 31-120, Krak\u00f3w, Poland\n\nNuria Selva\u00a0&\u00a0Agnieszka Sergiel\n\nDepartamento de Ciencias Integradas, Facultad de Ciencias Experimentales, Centro de Estudios Avanzados en F\u00edsica, Matem\u00e1ticas y Computaci\u00f3n, Universidad de Huelva, Huelva, Spain\n\nNuria Selva\n\nEstaci\u00f3n Biol\u00f3gica de Do\u00f1ana, Consejo Superior de Investigaciones Cient\u00edficas, Sevilla, Spain\n\nNuria Selva\n\nPanthera, 8 W 40th St, 18th Floor, New York, NY, 10018, USA\n\nLaurel E. K. Serieys\n\nAmarula Elephant Research Programme, School of Life Sciences, University of KwaZulu-Natal, Durban, 4041, South Africa\n\nRob Slotow\n\nDepartment of Genetics, Evolution and Environment, University College, London, WC1E 6BT, UK\n\nRob Slotow\n\nCopenhagen Zoo, Frederiksberg, Denmark\n\nMikkel Stelvig\n\nDepartment of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Mississippi State, MS, USA\n\nGarrett M. Street\n\nAlaska Department of Fish and Game, Wildlife Division, 11255 W. 8th Street, AK, USA\n\nNathan J. Svoboda\n\nCenter for Ecological Sciences, Indian Institute of Science, Bengaluru, 560012, India\n\nMaria Thaker\n\nEvolutionary Biology / Systematic Zoology, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany\n\nMaxi Tomowski\n\nCentre for Biodiversity and Conservation, Ashoka Trust for Research in Ecology and the Environment, Bangalore, India\n\nAbi T. Vanak\n\nWellcome Trust/DBT India Alliance, Clinical and Public Health Program, Bengaluru, India\n\nAbi T. Vanak\n\nTatra National Park, Zakopane, Poland\n\nFilip Zieba\u00a0&\u00a0Tomasz Zwijacz-Kozica\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch 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on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nNiels Blaum & Jonas Stiegler developed the idea; Marlee Tucker & Francesca Cagnacci facilitated data collection, Robert Hering helped analyze the data; Thomas M\u00fcller, Marlee Tucker, Niels Blaum, and Jonas Stiegler conducted initial manuscript structuring, internal revision, and outline; Cara A. Gallagher designed the conceptual figure and strongly contributed to revising the manuscript; Nancy Barker, Anne Berger, Niels Blaum, Francesca Cagnacci, Meaghan N. Evans, Cara A. Gallagher, Morgan Hauptfleisch, Robert Hering, Robert Hering, Marco Heurich, Lynne A. Isbell, Stephanie Kramer-Schadt, Thomas M\u00fcller, Krzysztof Schmidt, Nuria Selva, Laurel E.K. Serieys, Agnieszka Sergiel, Marlee Tucker, Bettina Wachter, Stephen Webb, Christopher C. Wilmers, Tomasz Zwijacz-Kozica commented on the manuscript. The main persons responsible for providing the data are Nancy A. Barker (C. crocuta, P. leo), Floris M. van Beest (O. moschatus), Jerrold L. Belant (C. latrans, C. lupus, U. americanus), Anne Berger (C. capreolus, E. europaeus), Stephen Blake (B. bison), Niels Blaum (A. marsupialis, L. europaeus, T. oryx, T. strepsiceros), Francesca Brivio (S. scrofa), Bayarbaatar Buuveibaatar (G. subgutturosa), Francesca Cagnacci (C. capreolus, C. elaphus, C. ibex, L. lynx, S. scrofa), Meaghan N. Evans (V. tangalunga), Adam W. Ferguson (G. genetta, I. albicauda), Claudia Fichtel (C. ferox, E. rufifrons, P. verreauxi), Adam T. Ford (M. guentheri), Wayne M. Getz (C. crocuta, P. leo), Stefano Grignolio (C. ibex), Morgan Hauptfleisch (A. marsupialis, T. oryx, T. strepsiceros), Robert Hering (A. marsupialis, T. oryx, T. strepsiceros), Marco Heurich (C. elaphus), Lynne A. Isbell (C. pygerythrus, P. pardus, P.anubis), Ren\u00e9 Janssen (F. silvestris), Petra Kaczensky (E. hemionus), Sophia Kimmig (V. vulpes), Rafa\u0142 Kowalczyk (B. bonasus), Stephanie Kramer-Schadt (P. lotor, V. vulpes), Mia-Lana L\u00fchrs (C. ferox), J\u00f6rg Melzheimer (A. jubatus), Nicolas Morellet (C. capreolus), Manuel Roeleke (P. lotor), Christer M. Rolandsen (A. alces), Sonia Sa\u00efd (C. capreolus), Niels M. Schmidt (O. moschatus), Nuria Selva (U. arctos), Laurel E. K. Serieys (L. rufus), Rob Slotow (P. leo), Jonas Stiegler (L. europaeus), Garrett M. Street (O. virginianus, S. scrofa), Wiebke Ullmann (L. europaeus), Abi T. Vanak (C. aureus, F. chaus, V. bengalensis), Bettina Wachter (A. jubatus), Stephen L. Webb (O. virginianus, S. scrofa), Christopher C. Wilmers (P. concolor). All other co-authors contributed to data collection and approved the final manuscript.\n\nCorrespondence to\n Jonas Stiegler.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Mark Boyce and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Mammals show faster recovery from capture and tagging in human-disturbed landscapes.\n Nat Commun 15, 8079 (2024). https://doi.org/10.1038/s41467-024-52381-8\n\nDownload citation\n\nReceived: 23 January 2024\n\nAccepted: 29 August 2024\n\nPublished: 15 September 2024\n\nVersion of record: 15 September 2024\n\nDOI: https://doi.org/10.1038/s41467-024-52381-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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b/d6279081b51dd7e3c752648f72b286e9a4d83df160cbe2513b95804ff2fe7f90/metadata.json @@ -0,0 +1,156 @@ +{ + "title": "The identification of XPR1 as a voltage- and phosphate-activated phosphate-permeable ion channel", + "pre_title": "The identification of XPR1 as a voltage- and phosphate-activated phosphate-permeable ion channel", + "journal": "Nature Communications", + "published": "15 May 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59678-2/MediaObjects/41467_2025_59678_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59678-2/MediaObjects/41467_2025_59678_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59678-2/MediaObjects/41467_2025_59678_MOESM3_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59678-2/MediaObjects/41467_2025_59678_MOESM4_ESM.mp4" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59678-2/MediaObjects/41467_2025_59678_MOESM5_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59678-2/MediaObjects/41467_2025_59678_MOESM6_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59678-2/MediaObjects/41467_2025_59678_MOESM7_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.2210/pdb9CKZ/pdb", + "https://doi.org/10.2210/pdb9CL0/pdb", + "https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-45656", + "/articles/s41467-025-59678-2#Sec20" + ], + "code": [], + "subject": [ + "Cryoelectron microscopy", + "Ligand-gated ion channels" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4457423/v1.pdf?c=1747393822000", + "research_square_link": "https://www.researchsquare.com//article/rs-4457423/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-59678-2.pdf", + "preprint_posted": "10 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Maintaining a balance of inorganic phosphate (Pi) is vital for cellular functionality due to Pi's essential role in numerous biological processes. Proper phosphate levels are managed through Pi import and export, facilitated by specific Pi transport proteins. Although the mechanisms of Pi import have been extensively studied, the processes governing Pi export remain less understood. Xenotropic and Polytropic retrovirus Receptor 1 (XPR1) has been identified as the only known Pi export protein in mammals, playing a key role in facilitating Pi efflux from cells. Malfunctions in XPR1 are associated with human diseases, such as primary familial brain calcification and certain cancers, highlighting its critical role in maintaining Pi homeostasis. In this study, we introduce the cryogenic electron microscopy structure of human XPR1 (hXPR1), unveiling a structural arrangement distinct from that of any known ion transporter, with a topology not identified in previous computational predictions. Our structural results suggest that hXPR1 may operate as an ion channel, a hypothesis supported by patch clamp recordings revealing hXPR1's voltage- and Pi-dependent activity and large unitary conductance. Using proteoliposomal uptake assays, we demonstrate that purified and reconstituted hXPR1 catalyzes transport of Pi. Further analysis, including the structure of hXPR1 in presence of Pi, and functional effects of mutating a putative Pi binding site, leads us to propose a plausible ion permeation pathway. Together, our results provide novel perspectives on the Pi transport mechanism of XPR1 and its homologues.Biological sciences/Structural biology/Electron microscopy/Cryoelectron microscopyBiological sciences/Biochemistry/Ion channels/Ligand-gated ion channels", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Maintaining a balance of inorganic phosphate (Pi) is vital for cellular functionality. Proper phosphate levels are managed through Pi import and export; and the processes governing Pi export remain the least understood. Xenotropic and Polytropic retrovirus Receptor 1 (XPR1) has been identified as the only known Pi export protein in mammals. In this study, we introduce the cryogenic electron microscopy structure of human XPR1 (hXPR1), unveiling a structural arrangement distinct from that of any known ion transporter. Our structural results suggest that hXPR1 may operate as an ion channel, a hypothesis supported by patch clamp recordings revealing hXPR1\u2019s voltage- and Pi-dependent activity and large unitary conductance. Further analyses, including the structure of hXPR1 in presence of Pi, and mutagenesis studies at one of\u00a0the putative Pi binding sites, lead us to propose a plausible ion permeation pathway. Together, our results provide novel perspectives on the Pi transport mechanism of XPR1.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Xenotropic and polytropic retrovirus receptor 1 (XPR1), also known as SLC53a1 of the solute carrier (SLC) superfamily, is a multi-pass membrane protein initially identified in mice as the cell surface entrance receptor for murine xenotropic and polytropic retroviruses1,2. The function of XPR1 was later found to mediate inorganic phosphate (Pi) export from the cytosol to the extracellular space3,4,5. The protein is well conserved phylogenetically across all eukaryotes, and the Pi-exporting activity has been demonstrated in various orthologues6,7,8,9,10.\n\nGiven the role of Pi in many key cellular processes, including energy production, biosynthesis, and cell signaling, its intracellular concentration is tightly regulated, in part through controlling Pi import and export11,12. XPR1 is the only known inorganic phosphate exporter in mammals, is present in most cell types12, and thus plays a central role in maintaining cellular Pi homeostasis. XPR1 mutations have been associated in patients with primary familial brain calcification (PFBC)4,5,13,14,15,16,17, a genetic neurodegenerative disorder marked by progressive bilateral calcification distributed primarily in the basal ganglia region18. In addition, the upregulation of XPR1 has been implicated in several cancers, facilitating cancer proliferation, migration, and invasion19,20,21,22,23,24,25. Considering the critical role of XPR1 in regulating Pi homeostasis and the current knowledge gaps in our understanding of its connection to XPR1-related diseases, a systematic study to explore its structural-functional mechanisms is of great importance.\n\nAll XPR1 homologs are composed of two major functional domains: the N-terminal cytosolic SPX (SYG1/PHO81/XPR1) domain and the transmembrane domain (TMD). The SPX domain was discovered as an intracellular phosphate sensor26, and the XPR1-mediated Pi export activity is regulated by SPX binding to inositol polyphosphates27,28,29. Secondary structure predictions proposed that the XPR1 TMD is composed of 8 transmembrane \u03b1-helices4. Part of the TM region belongs to the EXS (ERD1/XPR1/SYG1) domain family, which was demonstrated to be essential for proper localization to the plasma membrane and Pi export activity for the plant orthologue PHO130. Whereas crystal structures have been reported for the SPX domain26, a detailed structure-function relationship study of the full-length XPR1 protein could enhance the understanding of the transport mechanism of XPR1 and its critical role in regulating phosphate homeostasis. In this study, we present the cryo-EM structures of full-length human XPR1 and demonstrate that it functions as a voltage- and Pi-dependent ion channel permeable to Pi.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We expressed full-length human XPR1 (hXPR1) in HEK293S GnTI- cells, purified the protein in a detergent mixture (Supplementary Fig.\u00a01), and determined the structure by cryogenic electron microscopy single particle analysis (cryo-EM SPA) in the absence of any Pi or known ligands. The overall resolution of the apo-hXPR1 (ligand-free) map reached 3.4\u2009\u00c5 with the transmembrane region extended to 2.7\u2009\u00c5. The quality of the map was sufficient to allow accurate assignments of backbones and side chains within the TMD (Supplementary Figs.\u00a02 and 4a). The cytosolic domain of apo-hXPR1 was relatively poorly resolved compared to the TMD, possibly due to flexibility, but we were able to perform rigid body docking and flexible fitting to accommodate the previously determined SPX crystal structure26 (PDB:5IJH) into the density map. The identification of the position of the SPX domain enables the unambiguous assignment of TMD topology.\n\nhXPR1 in a detergent mixture forms a homodimer. The overall TM domain has a trapezoidal shape with dimensions of 110\u2009\u00d7\u200940\u2009\u00d7\u200950\u2009\u00c5 (Fig.\u00a01a and Supplementary Movie\u00a01). For each protomer, the cytosolic SPX domain connects to the TMD via an unresolved flexible linker, and the C-terminus is situated in the cytosol as well (Fig.\u00a01b). The TM domain of each protomer consists of 10 transmembrane \u03b1-helices as opposed to 8 helices hypothesized previously (Fig.\u00a01b, c). The dimeric interaction is mediated predominantly within the TM region by TM1, and the dimer interface has a buried surface area of 449\u2009\u00c52. It is interesting to note that the previously predicted EXS domain, which was hypothesized to contain a long cytoplasmic loop and three TM helices, spans the TM5 to TM10 segments with the predicted loop amounting to TM6 and TM7 (Fig.\u00a01b). There are 4 long intracellular loops connecting TM2 to TM3, TM4 to TM5, and TM8 to TM9, and the C-terminal region is also intracellular. The only substantial extracellular loop connects TM5 to TM6 (Fig.\u00a01c).\n\na\u00a0The composite Cryo-EM density map (left) and cartoon representations of the atomic model (middle and right) of apo-hXPR1 dimer viewed in the membrane plane from two orthogonal directions. Two protomers are colored magenta and lavender. The densities of the cytosolic domain and TMD are displayed at a contour level of 8.17\u03c3 and 5.04\u03c3, respectively. The gray box in the background indicates the membrane bilayer. b Cartoon representations of an hXPR1 monomer viewed from the side and from top-down. The SPX domain is colored in yellow, the EXS domain in light blue, and the rest of the protein in gray. c Topology of a monomeric hXPR1.\n\nIt was previously reported that XPR1 adopts a unique fold compared to other members of the SLC family31. To determine if the helix arrangement of the TM domain belongs to any other known structural fold, which might potentially provide insight into the Pi transport mechanism, we used the structure similarity search engine DALI32 to compare the TM domain structure of hXPR1 to known proteins in the Protein Data Bank. Strikingly, the result indicated that the hXPR1 is not similar to any known Pi transporters or any other secondary transporters with \u201calternating-access\u201d mechanisms in general33. This structural distinction from ion transporters suggests hXPR1 could potentially mediate Pi permeation via an uncommon mechanism. The closest resemblance of hXPR1 is to the archaeal ion-translocating rhodopsin family, where the topological arrangement of TM5-10 from hXPR1 loosely matches to TM2-7 from a light-driven chloride ion-pumping rhodopsin34 (Supplementary Fig.\u00a05). Such structural similarity suggests that TM5-10 might carve out an isolated space from the membrane lipid environment that creates a pathway for ion permeation, as seen in ion-translocating rhodopsin with its TM2-7. In addition, the absence of any blockade (e.g., retinal) within this isolated space may allow a continuous path, which could potentially facilitate passive diffusion as seen with ion channels.\n\nTo further investigate the ion channel hypothesis, patch clamp electrophysiology experiments were conducted using giant unilamellar vesicles (GUVs) reconstituted with purified hXPR1. Macroscopic currents recorded from excised inside-out patches in response to voltage ramps exhibited a strongly rectifying behavior with large 0.5\u2009nA inward currents at voltages near \u2212100\u2009mV but little or no current at positive voltages (Fig.\u00a02a). Inward currents evoked by 1\u2009s pulses to different voltages following a prepulse to +95\u2009mV activate to a steady state, with faster activation at more negative voltages (Fig.\u00a02b, lower panel). Small steps and stochastic fluctuations in current are evident, suggestive of ion channel activity. Importantly, control experiments lacking hXPR1, but with the same concentration of detergent, exhibited no appreciable current over the same voltage range and ionic condition (e.g., Fig.\u00a02b, upper panel). Transient outward (tail) hXPR1 currents could be evoked by stepping to +15\u2009mV following activation of inward current at negative voltages (Fig.\u00a02c). The voltage-dependence of steady-state activation (Fig.\u00a02d) was determined by plotting the mean normalized conductance-voltage relation (G-V), measured from the tail current amplitude following 1\u2009s pulses to different voltages, and is fit by a Boltzmann function with apparent charge of \u22121.8 e and half-activation voltage of \u221234\u2009mV. A similar voltage-dependence of macroscopic current was observed in whole cell recording of HEK293S cells transfected with hXPR1 (Supplementary Fig.6a, b). This voltage-dependence implies that the tail current decay at +15\u2009mV reflects deactivation (i.e., channel closure) and that the rectifying behavior observed with voltage ramps is due to voltage-dependent channel gating as opposed to a strong dependence of open channel conductance on voltage. The latter is evident from hXPR1 currents recorded from excised patches from XPR1-expressing HEK293S cells during voltage ramps (Fig.\u00a02e, upper panel), which reproduce the rectifying behavior in GUVs (Fig.\u00a02a) but with an order of magnitude smaller current and stochastic activity consistent with a small number of channels. Dashed lines indicate three open levels of equal conductance, whereas the red trace with no current fluctuations represents a sweep where channels remained closed. Stochastic closing events can also be observed following steps to +40\u2009mV (Fig.\u00a02e, lower panel), and a different patch shows steady-state activity at \u201350\u2009mV with the corresponding all-points histogram (Fig.\u00a02f). The linear relation between current and voltage indicates that the conductance of the open channel is voltage-independent with a unitary conductance of 134 pS, based on the difference in slopes of the dashed lines in Fig.\u00a02e.\n\na Inwardly-rectifying currents evoked from an excised GUV patch with hXPR1 by voltage ramps (\u2212105\u2009mV to +75\u2009mV) from +15\u2009mV. b Inward currents are evoked by hyperpolarizing voltage-pulses (\u221215 to \u2212105\u2009mV, in 10\u2009mV intervals) from +95\u2009mV in a GUV patch with hXPR1, but not in detergent control. c Outward tail current at +15\u2009mV following 1\u2009s hyperpolarizing pulses. d Normalized hXPR1 G-V relation from tail currents (mean\u2009\u00b1\u2009SEM, n\u2009=\u20093 replicates from one GUV) fit by a Boltzmann function (z\u2009=\u2009\u22121.8\u2009\u00b1\u20090.2 e, V1/2\u2009=\u2009\u221234\u2009\u00b1\u20092\u2009mV, mean\u2009\u00b1\u2009SD). e Unitary current activity during voltage ramps or +40\u2009mV pulses from a HEK293S GnTI- cell expressing hXPR1. a\u2013e were from inside-out patches with external 0 Pi NMDG-MSA and internal 20 Pi, 0.07 Ca2+, K-MSA solutions. f Unitary XPR1 current recorded from HEK293 cell with single open level and associated all-points histogram at \u221250\u2009mV with external 1 Pi NMDG-citrate and internal 20 Pi, 0.07 Ca2+, K-MSA solutions. g XPR1 currents evoked from a GUV patch by voltage ramps with 75\u2009mM Pi as the sole internal anion (black) or with 75\u2009mM Pi + 10 Cl\u2212 (red) are superimposable. XPR1 currents evoked from a GUV patch by voltage ramps (h) or \u201350\u2009mV pulses (i) are enhanced as internal Pi is increased from 10\u2009mM (blue) to 75\u2009mM (red), and almost undetectable in 0 Pi (black) using internal 10 Cl K-MSA with external 0 Pi NMDG-MSA solutions. Thick curves represent an average of 5 (g) or 10 (h, i) traces (thin curves). j G-V relations in 10 and 75\u2009mM Pi (mean\u00b1SEM, n\u2009=\u20093 patches). G-Vs at both [Pi] were normalized to the maximal conductance in 75\u2009mM Pi. k Schematics of the [32P] Pi transport assay with proteoliposomes. A membrane voltage potential difference was generated using a potassium gradient and valinomycin. l Time-dependent accumulation (mean\u00b1SEM, n\u2009=\u20096 independent assays) of [32P] Pi in hXPR1-containing proteoliposomes without valinomycin (blue), with valinomycin (red), and in empty liposomes with valinomycin (black) as control.\n\nThe negative reversal potential of XPR1 current (Fig.\u00a02a\u2013e; arrows) indicates that the channel is not selective for Pi, as the intracellular and extracellular solutions contained 20\u2009mM and 0 Pi, respectively. However, this observation does not rule out the possibility that the channel conducts Pi together with other ions. Indeed, large inward currents were recorded with 75\u2009mM Pi as the sole internal anion (Fig.\u00a02g), supporting that XPR1 is permeable to Pi. In addition, increasing internal Pi from 10 to 75\u2009mM greatly increased peak current during voltage ramps or \u221250\u2009mV pulses (Fig.\u00a02h, i) without altering unitary current amplitude at \u221250\u2009mV (Supplementary Fig. 6c\u2013e). This indicates that XPR1 channel activity is Pi-dependent. The enhanced activity of XPR1 in 75\u2009mM Pi is due to an 18\u2009mV shift in the steady-state G-V relation to more positive voltages relative to 10\u2009mM Pi without change in maximal conductance (Fig.\u00a02j), as well as a speeding of activation kinetics (Supplementary Fig.\u00a06c). Small macroscopic XPR1 currents could also be recorded in 0 Pi (Supplementary Fig.\u00a06c) but only at voltages more negative than \u221270\u2009mV suggesting a further difference in the voltage-dependence of activation between 0 and 10\u2009mM Pi. Unitary current fluctuations at \u221275\u2009mV in 0 Pi (Supplementary Fig.\u00a06f) were comparable in magnitude to those observed in 10 or 75\u2009mM Pi at \u221250\u2009mV, implying that the small macroscopic XPR1 current in 0 Pi (Supplementary Fig.\u00a06c) reflects a low open probability and failure to maximally activate the channel at the most negative voltages tested.\n\nXPR1 does not appear to be selective for Pi versus methanesulfonate. The currents recorded in 0 Pi, with methanesulfonate as the primary internal anion, indicate that the channel is permeable to this ion. Furthermore, the change from 75\u2009mM to 10\u2009mM Pi, which involved substitution of 100\u2009mM methanesulfonate for Pi, had no appreciable effect on the unitary current amplitude (Supplementary Fig.\u00a06d, e). The internal solution for this experiment also included 10\u2009mM Cl\u2212. However, switching from 0 to 10\u2009mM Cl\u2212 in the presence of 75\u2009mM Pi had no effect on mean current amplitude following XPR1 activation (arrow Fig.\u00a02g), implying that Cl\u2212 at this low concentration makes little or no contribution to XPR1 conductance. The selectivity of the channel was not investigated in detail owing in part to the strong dependence of channel activity on internal [Pi]. Currents in Fig.\u00a02 were recorded with extracellular solutions containing NMDG as the main cation and methanesulfonate (Fig.\u00a02a\u2013e) or citrate (Fig.\u00a02f) as the main anion and low Cl\u2212, to reduce the number of potential permeant ions and to minimize conductance through native channels in HEK293 cells.\n\nOne advantage of the strong dependence of XPR1 activity on internal [Pi] is that in GUV recordings, only channels oriented with their cytoplasmic side facing the vesicle lumen should be activated under typical inside-out recording conditions where only the luminal side is exposed to high Pi. To test this hypothesis and confirm that XPR1 in GUVs is reconstituted in both orientations, we recorded from inside-out patches with intracellular (20\u2009mM Pi) solution in the pipette and external (NMDG-methanesulfonate) solution in the bath. Under these conditions, outward XPR1 currents were recorded at positive voltages, exhibiting rectification consistent with channels oriented with the cytoplasmic side out. (Supplementary Fig.\u00a06g). That our GUV data in Fig. 2 reproduces results from HEK cells therefore can be accounted for by the fact that we only applied high Pi on the luminal side.\n\nTo test whether the isolated protein is functional for Pi transport, we conducted proteoliposome flux assays and found that under the same buffer condition with citrate in which currents were observed by patch clamp recordings in Fig.\u00a02f, liposomes reconstituted with hXPR1 protein, but not empty liposomes, showed time-dependent accumulation of [32P] Pi. This transport was enhanced when the membrane potential was perturbed using a potassium gradient and the potassium ionophore valinomycin (Fig.\u00a02k, l). These results could be accounted for by the voltage increasing the Pi driving force or hXPR1 open probability, consistent with the electrophysiological experiments. That is, since the external side of the vesicles was exposed to high (25\u2009mM) Pi, only XPR1 with the cytoplasmic side facing out should have been activated, and the imposed voltage (negative on the external side relative to the lumen) should favor increased channel activity as well as increased driving force for Pi entry.\n\nThe ion channel-like conductance of hXPR1 observed by patch clamp recording elicited a closer examination of the TM domain of hXPR1 to identify potential ion permeation pathways. We found that each of the transmembrane segments TM1-4 is surrounded by the detergent environment individually and thus relatively isolated, suggesting that these four helices might not participate in ion translocation across the membrane. On the other hand, the TM5-10 are organized sequentially into a 6-helix bundle in a clockwise arrangement (viewed from the cytoplasmic side), forming a barrel-shaped structure. Aside from TM9, all helices within the barrel are oriented roughly perpendicular to the membrane surface. TM9, on the other hand, is tilted to ~45\u00b0 with respect to the membrane. The protein\u2019s electrostatic surface reveals a highly positively charged vestibule at the center of the 6-helix bundle (Fig.\u00a03a). This tunnel-like pathway is open to the cytoplasmic side and extends to the center of the protein. The positive surface of this vestibule arises from a series of positively charged residues, including Arg459, Arg466, Lys482, Arg570, Arg603, Arg604, and Arg611, and this overall positivity of the cavity is consistent with a pore that can conduct anions. To visualize the putative ion permeation pathway, we used the CAVER program35. The identified pore generally overlaps with the positive vestibule. The pore is accessible to solvent on the cytosolic side but is closed to the extracellular side in the apo-hXPR1 structure (Fig.\u00a03b). The first ~one-third of the pore leading from the cytosolic entrance is formed by TM5a, 6, 7, 8, and 10. The tilted helix, TM9, meets the others in the middle, and all TM5-9 contribute to the central portion of the pore. The portion leading to the extracellular exit is closed by insertion of TM9 into the 6-helix barrel (Fig.\u00a03b). The overall diameter of the pore is around 4\u2009\u00c5, with a narrowest restriction of 3\u2009\u00c5. (Fig.\u00a03c). Many of the surface-lining residues within this putative pore are conserved across different species among hXPR1, plant PHO1, and yeast SYG1 (Fig.\u00a03d, e, Supplementary Fig.\u00a07), suggesting this passage may be conserved among XPR1 homologs.\n\na (left) The solvent-accessible surface of the hXPR1 TMD colored by \u00b15\u2009kT/e electrostatic potential calculated using APBS62. The secondary structures of TM1-4 are shown in gray and TM5-10 in light cyan. The positively charged vestibule formed at the center of the barrel-shaped helical bundle of TM5-10 is boxed in red. Two solid black lines indicate the membrane boundary. (right) The electrostatic surface\u2013potential map depicts the same vestibule alone, viewed orthogonally from the extracellular space. b The putative pore location in hXPR1 inside the 6-helix barrel colored light cyan, and the pore pathway is depicted as a purple mesh. The residues on the TM5-10 that are conserved among hXPR1, atPHO1, and scSYG1 are colored in dark green. c Pore radius along the z coordinate. d, e Detailed view of the green-colored conserved pore-lining residues shown in stick model on TM5, 7, and 9 (d), and TM6, 8, and 10 (e).\n\nTo identify potential phosphate binding sites, we solved the structure of hXPR1 in buffer containing 25\u2009mM sodium phosphate and 1\u2009mM phytic acid (InsP6), as inositol polyphosphates are known to facilitate Pi export upon binding to the SPX domain28. The soluble SPX domains of Pi/InsP6-hXPR1 map were poorly resolved compared to the TMD, as evident from the 2D classification analysis (Supplementary Fig.\u00a03b), 3D reconstructions with either C1 or C2 symmetry imposed did not yield a structured and resolvable soluble domain. Thus, C2 symmetry was imposed for the final reconstruction of the Pi/InsP6-hXPR1 map. The resolution of the resulting density map reached 2.3\u2009\u00c5, which is sufficient to recognize ions in the density (Supplementary Fig.\u00a03, 4b, and Supplementary Movie\u00a01). The overall TMD structure of Pi/InsP6-hXPR1 does not differ significantly from that of apo-hXPR1, with an RMSD of only 0.271\u2009\u00c5. However, in the Pi/InsP6-hXPR1 map, we identified a string of isolated, non-protein densities within the putative pore surrounded by TM5-10, that were not observed in the apo-hXPR1 map (Fig.\u00a04a). It is highly likely that these densities represent locations for Pi ions as they travel through the pore. Based on these densities, we identified two locations along the putative pore which could serve as Pi coordination sites (Fig.\u00a04b). The first site is situated near the narrowest restriction of the pore, where two positive charged residues Lys482 and Arg604 sandwich the putative Pi density, with side chains of other surrounding conserved residues Asp398, Tyr483, and Asp533 located more distally (Fig.\u00a04c). The second site is near the extracellular end of the putative pore, in which the putative ion density is surrounded by three positive residues Arg604, which also participates in the first putative coordination site, in addition to Arg603 and Arg570 (Fig.\u00a04d). These three positively charged core residues form a sequential arrangement with two consecutively on one helix and the other on an adjacent helix. Interestingly, this type of core interaction pattern is similar to the phosphate recognition region in triose-phosphate/phosphate translocator of plants, which also has three positively charged residues Lys204, Lys362, and Arg363 organized into a similar pattern (Supplementary Fig.\u00a08)36. As such, these core residues may form a key Pi coordination site in the XPR1 putative pore. Located above the three arginine residues is Trp573, the aromatic residue whose side chain is positioned perpendicular to the pore. Although the string of densities extends beyond Trp573 (Fig.\u00a04b), these extended densities are surrounded by non-conserved neutral residues.\n\na The density map of TM5-10 of Pi/InsP6-hXPR1, with each TM helix colored individually at a contour level of 10.96\u03c3. The non-protein isolated densities within the pore are colored in pink red. Densities from TM5 and TM10 (right), or TM7 and TM8 (left), are removed to expose the pore. b The string of putative ion densities in gray depicted at a 10.96\u03c3 contour level with the cartoon representation of Pi/InsP6-hXPR1 TM5-10 structure. Densities corresponding to the two putative ion coordination sites are boxed in red and blue. c, d Close-up views of the two putative ion coordination sites indicated in the colored boxes in (b), with the ion density shown at a 5.35\u03c3 contour level. e Relative Pi transport of the alanine mutations of three arginine residues within the red-colored putative Pi binding in (c). The relative transport was measured at the 20-min time point with the addition of valinomycin. Means\u2009\u00b1\u2009SEM plotted (n\u2009=\u20094 independent assays). f XPR1 R570A currents evoked from a GUV patch by voltage ramps are decreased as internal Pi is increased from 10\u2009mM (black) to 75\u2009mM (red), using 10 Cl K-MSA internal solutions with external 0 Pi, 10 NMDG-Cl. Each curve represents the average of 5 traces (as in Fig.\u00a02g).\n\nMutations of each of the three arginine residues (Arg570, Arg603, and Arg604) in the second putative Pi coordination site to alanine significantly impaired the Pi uptake in the flux assay (Fig.\u00a04e). In addition, in patch clamp assays, while large currents could be recorded from R570A in 10\u2009mM Pi with methanesulfonate as the main anion, currents were greatly reduced in 75\u2009mM Pi (Fig.\u00a04f), an effect opposite to that observed with WT XPR1 (Fig.\u00a02g). This suggests the mutation selectively reduces Pi permeability, consistent with a role of R570 in Pi coordination.\n\nThe positions of residues lining the surface of the putative pore have little difference between the apo-hXPR1 and Pi/InsP6-hXPR1 maps, as evident from a TM5-10 RMSD of 0.267\u2009\u00c5 between the two structures. Thus, the dimension of this pore in the Pi/InsP6-hXPR1 structure is very similar to that of apo-hXPR1, with the narrowest diameter of 3\u2009\u00c5. In addition, in both structures, the TM9 forms a single continuous transmembrane helix, with the top segment inserted directly into the pore, effectively blocking the exit towards the extracellular space. Thus, we propose that both structures represent the closed state of hXPR1. Consistent with a failure of InsP6 to open the channel, we observed no appreciable effect of 1\u2009mM InsP6 on XPR1 current or Pi flux (Supplementary Figs.\u00a012b, c).\n\nIn the cytosolic helical bundle of one of the protomers of the apo-hXPR1, we identified a short \u03b1-helix that does not map to the SPX domain. The density map of this protomer displays a well-resolved connection between this short cytoplasmic helix and the end of TM10, the last TM helix of TMD (Supplementary Fig.\u00a09a). This connection allows us to build a portion of this protomer\u2019s C-terminal cytoplasmic tail. This short helix, which we denote as intracellular loop 4 (IL4), was assigned to residues 636\u2013646 (Supplementary Fig.\u00a09b) linked directly to TM10 via a loop. Given the different orientations of SPX domains with respect to the TMD between two protomers in the apo structure (Supplementary Fig.\u00a09c) and the unresolvability of SPX in the Pi/InsP6 structure, we hypothesize that the SPX domain is flexible and might undergo conformational changes in response to different conditions. The C-terminal cytoplasmic tail, with one end connecting directly to TMD and the other forming a short helix that bundles with the SPX domain, potentially serves to bridge between the SPX domain and TMD and provides the architectural basis for the allosteric regulation of the SPX domain on TMD.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59678-2/MediaObjects/41467_2025_59678_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59678-2/MediaObjects/41467_2025_59678_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59678-2/MediaObjects/41467_2025_59678_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59678-2/MediaObjects/41467_2025_59678_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we investigated the structure-function relationship of human XPR1. Our structures revealed that hXPR1 is dissimilar to known transporters but has features consistent with ion channel function: TM5-10 form a helical barrel, and within this barrel a central cavity is identified which reveals a partial pathway with appropriate diameter and charge to conduct anions; the additional densities seen coordinated to positively charged side chains within that pathway in the presence of Pi likely represent Pi coordination sites.\n\nElectrophysiological recordings from hXPR1 in excised patches revealed large unitary currents with a linear open channel I-V relation in HEK293 cells, and large macroscopic inward currents in GUVs, including in the absence of Pi or with Pi as the sole internal anion, all supporting the conclusion that XPR1 can function as an ion channel that is permeable to Pi and relatively non-selective for anions. The Pi transport activity was further confirmed using proteoliposomal flux assays. The lack of structural similarity between XPR1 and known transporters, together with the identification of channel-like structural topology including a pore architecture with putative Pi binding sites, supports that XPR1 transports Pi as a channel rather than as a Pi transporter with uncoupled ion channel activity, a hypothesis further supported by observations that mutations of the key arginine residues within one of the putative Pi coordination sites impaired Pi uptake in the flux assay, and one of them showed reduced Pi permeability in patch clamp recordings.\n\nThe rate of Pi transport (~10 Pi per XPR1 s\u22121, from the 1\u2009min time point) is orders of magnitude less than the charge movement through the open channel measured with patch clamp at \u201350\u2009mV (Fig.\u00a02f), owing to several factors that cannot all be quantified. First, in the flux assay, the initial rate is likely to be underestimated owing to the time resolution of the measurement. Second, the membrane voltage is not controlled and is likely to favor a low Po (<0.1) if V is near 0 based on the V-dependence of activation (Fig.\u00a02d). Third, the fraction of XPR1 protein molecules that are functional in the flux assay and have correct membrane orientation to be activated by high external Pi is unknown. Finally, in the patch clamp assay, Pi flux represents only a fraction of the total charge movement as the channel is not selective for Pi over the predominant anion, methanesulfonate.\n\nStrong inward rectification and large unitary conductance clearly distinguished XPR1 activity from native channels occasionally observed in HEK293 cells. The inward rectification arises from voltage-dependent activation of the channel at negative voltages. Activation is also Pi-dependent, with little activity in the absence of Pi and shifts in the G-V relation to more positive voltages as [Pi]i is increased. The channel appears to attain a high open probability, near unity, at maximally effective voltages in high [Pi]i, as unitary currents activated at \u2212100\u2009mV in 20\u2009mM Pi during voltage ramps exhibit no sign of transient closure (Fig.\u00a02e), and the maximal macroscopic conductance at 10 or 75\u2009mM Pi is constant (Fig.\u00a02j).\n\nThe mechanistic basis of voltage-dependent activity is unknown but is unlikely to simply reflect voltage-dependent block of the pore by impermeant ions, given the slow activation kinetics that required up to 0.5\u2009s to reach equilibrium (Fig.\u00a02b). Alternative possibilities include voltage-dependent conformational changes in the protein (i.e., a voltage-sensor domain), or a dependence of channel opening or closing on voltage-dependent binding of a permeant ion to a site or sites within the pore, a mechanism which has been proposed by various groups to contribute to the voltage-dependent activation of CLC0 chloride channels37. That the apparent gating charge of XPR1 activation (z\u2009=\u2009\u22121.8 e, Fig.\u00a02d) is identical to the average charge of Pi at pH 7.4 and activation is shifted to more positive voltages as internal [Pi] is increased is consistent with the notion that Pi binding in the pore may contribute to the voltage-dependence of activation. This hypothesis may also explain why a recent study38 reported that XPR1 conductance is voltage-independent (i.e., has a linear macroscopic I-V relation), as they used internal and external solutions that both contain 100\u2009mM Pi. Likewise, the use of high Pi might explain why this group reported an increase in XPR1 conductance by 1\u2009mM InsP6, although we saw no appreciable effect of InsP6 at this concentration on current or Pi flux under our assay conditions.\n\nWith our structures, we could map the locations of PFBC mutations (Supplementary Fig.\u00a011a). Many of the mutations were known to be located on the SPX domain, which could potentially disrupt the SPX regulation of the Pi export activity. On the other hand, our structures provide novel perspectives on how mutations in other parts of hXPR1 could lead to diseases. Three mutations are located within the TM5-10 helical barrel forming the putative ion permeation pore: Arg459, Arg570, and Ile575 (Supplementary Fig.\u00a011b). Specifically, mutations of Arg459 and Arg570 have been shown to lead to reduced Pi export without affecting the protein expression levels5,15. These two arginine residues are conserved (Supplementary Fig.\u00a07), with Arg459 located near the narrowest constraint and Arg570 within the putative Pi coordination site (Figs.\u00a03e and\u00a04c), in support of the hypothesis that Pi permeates through the putative pore. Moreover, both our flux assay and electrophysiological recordings showed that the mutation of R570A impaired Pi transport, which supports its role in the putative Pi coordination site and may help explain the pathological mechanism of the PFBC-causing variant R570L.\n\nIn addition, three disease-associated mutation sites, Asn619, Arg624, and Ile629, are located within the C-terminal cytoplasmic tail on the loop connecting TM10 to IL4 (Supplementary Figs.\u00a010, 11b). Asn619 and Arg624 are also conserved across XPR1 homologs (Supplementary Fig.\u00a07), and these mutations were documented to reduce XPR1-mediated Pi efflux as well5. Combined with the potential flexibility of the SPX domain, these results suggest a novel role for the C-terminal cytoplasmic tail in bridging the SPX domain with the TMD to achieve allosteric regulation. Indeed, it has been hypothesized that part of the C-terminal tail could serve as the plug that restricts the entrance into the pore39,40,41, and the binding of InsP8 could alter the SPX conformation to rearrange the structure of the C-terminal tail and lessen its restriction of the pore40,41.\n\nBoth our structures likely represent a closed state based on the pore size and the TM9 blockade towards the extracellular side. It is possible that an alternative, perhaps transient, state not observed in our data, allows Pi exit to the extracellular side. It is still unclear how the pore would open. To explore reasonable alternative structures, we used Alphafold2 to predict structures for the transmembrane domain42. When compared, the helix arrangements in our experimental structures and the most probable prediction are quite similar, with one major difference focusing on TM9. AlphaFold2 predicts that TM9 is broken into two segments with a kink in the middle, and the segment closer to the extracellular space is rotated away from the 6-helix bundle (Supplementary Fig.\u00a010a). In this conformation, TM9 no longer blocks the ion permeation pathway, and the pore is open to both sides of the membrane (Supplementary Fig.\u00a010b). Trp573 resides next to the kink, and in our closed structure the side chain of Trp573 is situated directly above the putative Pi binding site, with Arg570 being one helical turn away (Supplementary Fig.\u00a010a). Thus, we propose the hypothesis that a bent conformation of TM9 at Trp573 may open hXPR1 to allow Pi efflux. In Pi transporter SLC20, a kink is observed in a helix lining the Pi binding pocket at a conserved tryptophan residue, and the helix-bending mechanism was proposed to control the opening and closing of the gate that allows the Pi release43. Thus, we propose the hypothesis that a bent conformation of TM9 at Trp573 may open hXPR1 to allow Pi efflux. After the submission of this manuscript, two groups reported cryo-EM studies of XPR1, with both describing lower-resolution structures of XPR1 in which the TM9 is kinked similarly to the aforementioned AlphaFold prediction38,39. These results support our hypothesis of an open XPR1 conformation that entails a continuous tunnel allowing the observed ion channel activity. However, the exact mechanism by which the bent conformation of TM9 is triggered and whether it is coupled to the binding of inositol polyphosphate species or to additional undiscovered factors requires further studies.\n\nIn summary, our structural and functional data established that hXPR1 transports Pi as an ion channel whose activity is regulated by intracellular Pi concentration and membrane voltage. It is likely that if XPR1 functions as a non-selective anion channel in cells, its activity must be tightly regulated. The requirement that XPR1 activates only at negative voltages with high intracellular Pi assures that the channel will only be open under conditions where the electrochemical gradient favors Pi efflux. In addition, the Pi export activity of XPR1 in cells is thought to be critically dependent upon the presence of higher-order intracellular inositol pyrophosphates such as InsP7 and InsP827,28,29, which are only transiently generated as a result of excess Pi conditions44,45,46. Additional means of regulating XPR1 activity have also been reported10,19,47. These regulatory mechanisms may allow the channel to act as an \u201cescape valve\u201d for Pi that is only transiently activated, and this pattern of Pi efflux could potentially be linked to the phenomena of rapid Pi release documented in various cell types48,49, specifically in pancreatic \u03b2-cells in which XPR1 was established to mediate the \u201cphosphate flush\u201d50. Our results provide insights into XPR1\u2019s role in maintaining intracellular Pi homeostasis and reveal the structural and functional impacts of mutations causing PFBC, enabling further investigations into their mechanisms and approaches to therapeutics.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The cDNA of human XPR1 (Uniprot: Q9UBH6) was synthesized with a Strep-tag II peptide fused at the C-terminus, and cloned into the pBacMam vector for expression in HEK293S GnTI- cells51.\n\nThe purification was carried out at 4\u2009\u00b0C. - The cell pellet from 2\u2009L of HEK293S GnTI culture was resuspended in 100\u2009mL lysis buffer containing 20\u2009mM Tris, 150\u2009mM NaCl, and 2\u2009mM MgCl2 buffered at pH 7.4, supplemented with 1 protease inhibitor cocktail tablet (Roche) and 5\u2009\u03bcL of nuclease (Thermo Fisher) per 50\u2009mL buffer. The cells were directly solubilized by adding 1.5% (w/v) n-dodecyl-\u03b2-d-maltoside (DDM, Anatrace) and 0.15% (w/v) cholesteryl hemisuccinate (CHS, Anatrace) for two hours and were centrifuged at 180,000\u2009\u00d7\u2009g for 1\u2009h. The supernatant containing detergent-solubilized hXPR1 protein was loaded onto StrepTactin HP affinity purification column (Cytiva) and washed with wash buffer containing 20\u2009mM Tris pH 7.4, 150\u2009mM NaCl, 0.005% (w/v) glyco-diosgenin (GDN, Anatrace), 0.005% (w/v) lauryl maltose neopentyl glycol (LMNG, Anatrace), and 0.0001% CHS. hXPR1 protein was then eluted with wash buffer supplemented with 5\u2009mM desthiobiotin (Sigma Aldrich). The eluted protein was concentrated using a centrifugal filter unit with a 50\u2009kDa cut-off down (Milipore) to 500\u2009\u03bcL volume.\n\nFor apo-hXPR1 structural studies, the concentrated protein was loaded onto Superose 6 10/300 GL size-exclusion column (Cytiva) pre-equilibrated with wash buffer. For hXPR1 in the presence of Pi/InsP6, the size-exclusion column was pre-equilibrated using 25\u2009mM sodium phosphate, 150\u2009mM NaCl, and 1\u2009mM phytic acid (InsP6) at pH 7.4 with the same GDN/LMNG/CHS detergent mixture concentration.\n\nData were collected using a Dawn Ambient light scattering instrument equipped with a 661\u2009nm laser (Wyatt). The whole system is linked to an HPLC system with UV absorbance detection at 280\u2009nm (Agilent) and an Optilab (Wyatt) for differential refractive index (dRI) measurements. Approximately 100\u2009\u03bcg of purified hXPR1 protein was injected and flowed through a Superose 6 10/300 GL column (Cytiva) equilibrated with 20\u2009mM Tris pH 7.4, 150\u2009mM NaCl, 0.005% (w/v) GDN, 0.005% (w/v) LMNG, and 0.0001% CHS. Data was analyzed using the Astra software (Wyatt). A dn/dc of 0.185 is used for the detergent mixture, and \u03b5 is set to 1.64\u2009ml/mg.cm.\n\nhXPR1 samples in different conditions were concentrated to 10\u201320\u2009mg/mL for cryo-EM grid preparation. Cryo grids were prepared using the Thermo Fisher Vitrobot Mark IV, maintained at 8\u2009\u00b0C and 100% relative humidity. Quantifoil R1.2/1.3 Cu 300 mesh grids were glow-discharged in air for 15\u2009s using Pelco Easyglow. 3.5\u2009\u03bcL hXPR1 sample was applied to each glow-discharged grid. After blotting with filter paper (Ted Pella, Prod. 47000-100) for 3.5\u20134.5\u2009s, the grids were plunged into liquid ethane cooled with liquid nitrogen.\n\nCryo-EM data were collected using Thermo Fisher Titan Krios microscope at 300\u2009kV with a Quantum energy filter (Gatan) with 15\u2009eV slit width, and a K3 Summit direct electron detector (Gatan). Movie stacks were collected in super-resolution mode with defocus values ranging between \u22122.2\u2009\u03bcm and \u22120.8\u2009\u03bcm at 105,000\u00d7 nominal magnification (calibrated per pixel size of 0.416\u2009\u00c5 in super-resolution). The exposure time for each stack was 2.6\u2009s, fractionated into 40 frames, with a total accumulated dose of 50e\u2212/\u00c52. A total of 16,297 movies were collected for the apo-hXPR1 dataset, and 15,802 movies for the Pi/InsP6-hXPR1 dataset.\n\nFor apo-hXPR1, the movie stacks were motion-corrected with MotionCor252 and the aligned final images were binned (2\u2009\u00d7\u20092) to 0.832\u2009\u00c5 per pixel size. Dose weighting was performed during motion correction, and the defocus values were estimated with CTFFIND453. After manual curation, a total of 14,168 micrographs were selected, which had a CTF-fitted resolution value below 4\u2009\u00c5. A total of 8,468,502 particles were automatically picked using templates from preliminary analysis and extracted for 2D classifications in cryoSPARC54. 813,777 particles were selected from the good 2D classes for ab initio 3D reconstruction and imported into Relion4.0 for 3D classification55. Two good classes with recognizable structural features containing 230,861 particles were selected and imported back to cryoSPARC for non-uniform refinement using C1 symmetry with CTF refinement56, which yielded a map with an overall resolution of 3.4\u2009\u00c5. Resolutions were estimated using the gold-standard Fourier shell correlation with a 0.143 cut-off. Local resolution was estimated using ResMap57.\n\nThe data processing for Pi/InsP6-hXPR1 followed a similar workflow. A total of 14,603 micrographs were selected, which had a CTF-fitted resolution value below 4\u2009\u00c5 after motion correction and CTF estimation. 11,247,130 particles were automatically picked, with 2,428,881 particles selected from the good 2D classes. A final set containing 536,955 particles was selected after 3D classifications and used for non-uniform refinement using C2 symmetry with CTF refinement, which yielded a map with an overall resolution of 2.3\u2009\u00c5.\n\nThe transmembrane domain of apo-hXPR1 was built using the AlphaFold prediction42 as the initial model. Carbon backbones and the side chains were adjusted based on the density map. The SPX domain of apo-hXPR1 was built using the solved crystal structure26 (PDB: 5IJH) as a template to perform rigid body docking into the density maps and modified with flexible fitting. The model of Pi/InsP6-hXPR1 was built using the apo-hXRP1 as the initial reference and adjusted based on the density map. Model building was conducted in Coot58. Structural refinements were carried out in PHENIX59 in real space with secondary structure and geometry restraints. The channel was calculated using CAVER 3.0.335 with a minimum probe radius of 1.2, shell depth of 3, shell radius of 2, and clustering threshold of 3.5.\n\nFor proteoliposomes used in Pi transport assay, brain polar lipid extract (Avanti) was mixed with 3% (w/w) cholesterol (Avanti) in chloroform, dried under argon gas stream, and further dried overnight in vacuum. Lipids were then hydrated at 10\u2009mg/mL with assay buffer containing 140\u2009mM N-Methyl-D-glucamine (NMDG, Sigma Aldrich), 20\u2009mM HEPES, 1\u2009mM phosphoric acid, 10\u2009mM hydrochloric acid, adjusted to pH 7.4 with citric acid. The lipids were flash frozen in liquid nitrogen, thawed for a total of five freeze-thaw cycles, and then extruded 21 times using polycarbonate filters with a pore size of 50\u2009nm (Whatman) to obtain unilamellar vesicles. 0.01% of DDM was added to destabilize the lipid, and then purified wild-type or mutant hXPR1 proteins in 0.03% DDM were added with a 1:500 (w/w) protein-to-lipid ratio. The mixture was incubated for 1\u2009h, and detergent was removed by the addition of Bio-Beads SM-2 (Bio-Rad). Collected liposomes were flash frozen and stored at \u221280\u2009\u00b0C until further use.\n\nGiant unilamellar vesicles (GUVs) used in patch clamp were made from 20\u2009\u03bcL brain polar lipid extract with 10% (w/w) cholesterol in chloroform at 5\u2009mg/mL by electroformation using the Vesicle Prep Pro (Nanion Technologies) in 250\u2009\u03bcL buffer containing 2\u2009mM HEPES at pH 7.4, 1\u2009mM EGTA, 400\u2009mM sorbitol. Purified hXPR1 protein in 0.03% DDM at 0.1\u2009mg/mL was mixed with GUV solution and diluted to a final concentration of approximately 50\u2013500\u2009ng/mL (~1:90,000 to ~1:900,000 protein-to-lipid molar ratio) and incubated overnight at 4\u2009\u00b0C with SM-2 Bio-Beads (Bio-Rad).\n\nIonic currents were recorded using the patch clamp technique in the inside-out or whole cell configuration. Data were acquired and analyzed as previously described60. Traces shown in the figures are digitally filtered at 5\u2009kHz. Voltages have been corrected for liquid junction potentials, calculated according to the stationary Nernst\u2013Planck equation using LJPcalc61. The bath was grounded through an agar bridge. All experiments were performed at room temperature (22\u201324\u2009\u00b0C). External solutions contained in mM: NMDG-MSA \u2212140 N-methyl-D-glucamine (NMDG), 20 HEPES, 5 EGTA, 10 HCl, with or without 1 Pi added as phosphoric acid and adjusted to pH 7.2 with 112 methanesulfonic acid (MSA). NMDG-citrate \u2212140 NMDG, 20 HEPES, 1 phosphoric acid, 10 HCl, adjusted to pH 7.2 with 17.4 citric acid. NMDG-Cl \u2212 10 NMDG, 20 HEPES, 260 Sucrose, pH 7.2 with 8.72 HCl. Internal solutions contained in mM: 20 Pi, 0.07 Ca, K-MSA \u2212 110 KOH, 10 K2HPO4, 10 KH2PO4, 0.42 HCl, 4.79 CaCl2,5 HEDTA, pH to 7.2 with 92.5 MSA, and free Ca2+\u2009=\u200966.0\u2009\u00b1\u20095.9\u2009\u00b5M measured by Ca2+ electrode (Orion Research). 0 Pi, 10 Cl, K-MSA \u2212 140 KOH, 20 HEPES, 5 EGTA, 10 HCl, pH 7.2 with 115.1 MSA. 75 Pi, 10 Cl, K-MSA \u2212 54 K2HPO4, 21 KH2PO4, 20 HEPES, 5 EGTA, 10 KCl, pH 7.2 with 16 KOH. 10 Pi, 0 Ca, K-MSA - as a 13:2 mixture of 0 and 75 Pi, 10 Cl K-MSA. 75 Pi, 0 Cl, K-MSA \u2212 54 K2HPO4, 21 KH2PO4, 20 HEPES, 5 EGTA, pH 7.2 with 16 KOH.\n\nPi uptake activity was measured with reconstituted proteoliposomes containing either wild-type or mutant hXPR1. The control was empty liposomes. To generate a potassium gradient used to perturb the membrane potential, thawed liposomes in assay buffer containing 140\u2009mM NMDG, 20\u2009mM HEPES, 1\u2009mM phosphoric acid, 10\u2009mM hydrochloric acid adjusted to pH 7.2 with citric acid, were added with 60\u2009mM NaCl and 5\u2009mM KCl. The mixture underwent an additional five freeze-thaw cycles using liquid nitrogen. The liposomes were extruded again using a 200\u2009nm filter membrane for homogeneity, yielding sealed XPR1-containing liposomes with 5\u2009mM internal KCl. The extruded liposomes were exchanged into assay buffer containing 60\u2009mM KCl and 5\u2009mM NaCl using a PD-10 desalting column (Cytiva). A total volume of 5\u2009\u03bcl of liposomes was added to a 50-\u03bcl reaction solution. Carrier non-radioactive sodium phosphate (1\u2009M stock, pH 7.4) was added at a final concentration of 25\u2009mM, along with 0.1\u2009mCi/mL [32P] orthophosphate (5\u2009\u00b5Ci total, diluted from stock of 8500\u20139120\u2009Ci/mmole; carrier-free, PerkinElmer) to initiate the reaction. For experiments in which the membrane potential was perturbed, 200\u2009nM valinomycin was added to the reaction mixture. The mixture was incubated for various time points at 37\u2009\u00b0C. The reaction was rapidly filtered with a G-25 spin column (Cytiva) to remove unincorporated Pi. Radioactivity was determined by liquid scintillation counting.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The atomic coordinates have been deposited in the Protein Data Bank with the accession codes 9CKZ (hXPR1 in apo state (ligand-free)) and 9CL0 (hXPR1 in the presence of inorganic phosphate and phytic acid). The cryo-EM density maps have been deposited in the Electron Microscopy Data Bank (EMDB) with the accession codes EMD-45656 (hXPR1 in apo state (ligand-free)) and EMD-45657 (hXPR1 in the presence of inorganic phosphate and phytic acid). All electrophysiological data needed to evaluate the conclusions in the paper are present in the paper, and the raw data are available upon request.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Change history", + "section_text": "The following sentence was omitted from the acknowledgments section of this paper, \u2018We thank Dr. Gaya P. Yadav for cryoEM data collection at the Laboratory for Biomolecular Structure and Dynamics (LBSD) of Texas A&M University. The LBSD is supported, in part, by the Department of Biochemistry & Biophysics, AgriLife, and Texas A&M University\u2019. The original article has been corrected.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Tailor, C. S., Nouri, A., Lee, C. G., Kozak, C. & Kabat, D. Cloning and characterization of a cell surface receptor for xenotropic and polytropic murine leukemia viruses. Proc. Natl Acad. Sci. 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Ludtke and Yongcheng Song for their valuable insights and thoughtful suggestions throughout the course of this work. We thank Dr. Gaya P. Yadav for cryoEM data collection at the Laboratory for Biomolecular Structure and Dynamics (LBSD) of Texas A&M University. The LBSD is supported, in part, by the Department of Biochemistry & Biophysics, AgriLife, and Texas A&M University.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Hongjiang Wu, Liang Sun.\n\nVerna and Marrs McLean Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX, USA\n\nHongjiang Wu,\u00a0Tong Huo,\u00a0Theodore G. Wensel\u00a0&\u00a0Zhao Wang\n\nDepartment of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA\n\nLiang Sun\u00a0&\u00a0Frank T. Horrigan\n\nDepartment of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA\n\nZhao Wang\n\nCryoEM Core (Advanced Technology Core), Baylor College of Medicine, Houston, TX, USA\n\nZhao Wang\n\nDepartment of Materials Science and NanoEngineering, Rice University, Houston, TX, USA\n\nZhao Wang\n\nDepartment of Molecular and Cellular Oncology, Division of Basic Science, The University of Texas MD Anderson Cancer Center, Houston, TX, USA\n\nZhao Wang\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nZ.W. and F.T.H. conceived the study. Z.W., H.W., F.T.H., L.S., and T.G.W. designed the experiments. T.H. performed preliminary protein\u00a0construct screenings. H.W. performed biochemistry and structural biology experiments. L.S. and H.W. performed electrophysiology experiments. All authors analyzed the results and wrote the manuscript.\n\nCorrespondence to\n Frank T. Horrigan or Zhao Wang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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The identification of XPR1 as a voltage- and phosphate-activated phosphate-permeable ion channel.\n Nat Commun 16, 4519 (2025). https://doi.org/10.1038/s41467-025-59678-2\n\nDownload citation\n\nReceived: 10 December 2024\n\nAccepted: 29 April 2025\n\nPublished: 15 May 2025\n\nVersion of record: 15 May 2025\n\nDOI: https://doi.org/10.1038/s41467-025-59678-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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+Wuhan University https://orcid.org/0000-0001-8744-8665 +Chen Liu +Wuhan University +Young-Jun Park +University of Washington https://orcid.org/0000-0003-2901-6949 +Chengbao Ma +Wuhan University +Cameron Stuart +University of Washington +Risako Gen +University of Washington +Yu-Cheng Sun +Wuhan University +Xiao Yang +Wuhan University +Mei-Yi Lin +Wuhan University +Qing Xiong +Wuhan University +Junyu Si +Wuhan University +Peng Liu +Wuhan University +David Veesler +University of Washington https://orcid.org/0000-0002-6019-8675 + +Article + +Keywords: HKU25, MERSr-CoV, EjCoV-3, DPP4, ACE2, Receptor, Cryo-EM +Posted Date: March 13th, 2025 + +DOI: https://doi.org/10.21203/rs.3.rs-6097445/v1 + +License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Microbiology on October 30th, 2025. See the published version at https://doi.org/10.1038/s41564-025-02152-y. +ACE2 utilization of HKU25 clade MERS-related coronaviruses with broad geographic distribution + +Chen Liu1,#, Young-Jun Park2,3,#, Cheng-Bao Ma1, Cameron Stuart2, Risako Gen2, Yu-Cheng Sun1, Xiao Yang1, Mei-Yi Lin1, Qing Xiong1, Jun-Yu Si1, Peng Liu1, David Veesler2,3,*; Huan Yan1.* + +1State Key Laboratory of Virology and Biosafety, College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University; Wuhan, Hubei, 430072, China. +2Department of Biochemistry, University of Washington; Seattle, WA 98195, USA. +3Howard Hughes Medical Institute, University of Washington; Seattle, WA 98195, USA. +#C.L. and Y.-J.P. contributed equally to this work. +*To whom correspondence may be addressed. Email: huanyan@whu.edu.cn (H.Y.) or dveesler@uw.edu (D.V.) + +Summary +Dipeptidyl peptidase-4 (DPP4) is a well-established receptor for several MERS-related coronaviruses (MERSr-CoVs) isolated from humans, camels, pangolins, and bats 1–6. However, the receptor usage of many genetically diverse bat MERSr-CoVs with broad geographical distributions remains poorly understood. Recent studies have identified angiotensin-converting enzyme 2 (ACE2) as an entry receptor for multiple merbecovirus clades. Here, using viral antigen and pseudovirus-based functional assays, we demonstrate that several bat merbecoviruses from the HKU25 clade previously thought to utilize DPP4 7, employ ACE2 as their functional receptor. Cryo-electron microscopy analysis revealed that HsItaly2011 and VsCoV-a7 recognize ACE2 with a binding mode sharing similarity with that of HKU5 but involving remodeled interfaces and distinct ortholog selectivity, suggesting a common evolutionary origin of ACE2 utilization for these two clades of viruses. EjCoV-3, a strain closely related to the DPP4-using MERSr-CoV BtCoV-422, exhibited relatively broad ACE2 ortholog tropism and could utilize human ACE2 albeit suboptimally. Despite differences in entry mechanisms and spike proteolytic activation compared to MERS-CoV, these viruses remain sensitive to several broadly neutralizing antibodies and entry inhibitors. These findings redefine our understanding of the evolution of receptor usage among MERSr-CoVs and highlight the versatility of ACE2 as a functional receptor for diverse coronaviruses. + +Keywords: HKU25 | MERSr-CoV | EjCoV-3 | DPP4 | ACE2 | Receptor | Cryo-EM +Introduction +Middle East respiratory syndrome coronavirus (MERS-CoV) is a highly pathogenic virus with a case fatality rate of 36% \( ^8 \). Since its emergence in 2012, sporadic MERS-CoV infections have been reported annually in the Middle East \( ^9 \). Recently, the World Health Organization (WHO) expanded its list of prioritized coronaviruses to include the entire Merbecovirus subgenus, due to their epidemic and pandemic potential \( ^{10,11} \). According to the International Committee on Taxonomy of Viruses (ICTV) taxonomy (August 2023)\(^{12}\), the merbecovirus subgenus includes four species: Betacoronavirus cameli (MERSr-CoVs), Betacoronavirus erinacei (EriCoV), Betacoronavirus pipistrelli (HKU5), and Betacoronavirus tylonycteridis (HKU4). Although MERS-CoV is part of Betacoronavirus cameli, along with diverse viruses circulating in vespertilionid bats (Vespertilionidae), there is a phylogenetic gap connecting these merbecoviruses \( ^{13,14} \). The closest known relative of human and camel MERS-CoV is NeoCoV, which was discovered in Neoromicia capensis (Cape serotine bat) in Africa and only shares 85.5% whole genome nucleotide sequence identity with MERS-CoV and exhibits significant divergence in the Spike (S) glycoprotein S\(_1\) subunit \( ^{15-17} \). + +DPP4 was first identified as the entry receptor for MERS-CoV in 2013 and was later shown to also mediate entry of HKU4-related viruses (\( Betacoronavirus tylonycteridis \)), which includes strains from Tylonycteris bats and pangolins \( ^{1-5} \). While HKU4 and a few bat MERSr-CoVs, such as BtCoV-422 \( ^6 \), share similar RBD features with human/camel MERS-CoV, many other MERSr-CoVs exhibit highly divergent receptor-binding domain (RBD) sequences, suggesting the use of alternative receptors \( ^{18} \). Indeed, the extraordinary genetic diversity observed in merbecovirus RBDs emphasizes the challenges associated with predicting zoonotic risks of these viruses \( ^{14,18,19} \). As a result, we classified the phylogenetic diversity of merbecovirus RBDs into six distinct clades to provide a framework to understand receptor usage and support vaccine design and pandemic preparedness efforts \( ^{20} \). + +We and others recently revealed that merbecoviruses from the NeoCoV, MOW15-22, and HKU5 clades, comprising viruses found on three continents, have independently evolved the ability to utilize ACE2 as a receptor using entirely distinct binding modes \( ^{17,19-23} \). The receptor switch history of merbecovirus remains unclear but recombination appears to play a crucial role in these events \( ^{14,17,24,25} \). Therefore, merbecovirus receptor usage can markedly deviate from the taxonomy of viral species based on the conservation of five concatenated replicase domains in ORF1ab \( ^{26,27} \). + +Although bat MERSr-CoV HKU25 has been proposed to use DPP4 for entry, the supporting data is not strong, and structural evidence supporting this claim is lacking\( ^7 \). Consequently, there is uncertainty as to the nature of the receptor used for cell entry by merbecoviruses from the HKU25 clade, including viruses discovered in Italy \( ^{28} \), Switzerland \( ^{29} \), China \( ^{6,7,30,31} \), and Japan\( ^{32} \), limiting our ability to predict the spillover potential of these important pathogens. Here, we hypothesized, that members of the HKU25 clade of coronaviruses utilize ACE2 rather than DPP4 as their receptor based on phylogenetic relatedness to HKU5. Screening an ACE2 ortholog library revealed that most, but probably not all, HKU25 clade coronaviruses can engage ACE2 from several bat species and a subset of non-bat mammals, particularly those +from the Artiodactyl and Rodent orders. EjCoV-3, a strain closely related to the DPP4-using MERSr-CoV BtCoV-422 at the whole genome level, demonstrated broad ACE2 ortholog tropism and a weak ability to utilize human ACE2 (hACE2). Cryo-electron microscopy analysis of the ACE2-bound HsItaly2011 and VsCoV-a7 RBDs showed that these viruses engage ACE2 with a binding pose reminiscent of that observed for HKU5, but involving remodeled interfaces and distinct ortholog selectivity, suggesting a common evolutionary origin of ACE2 utilization for these two clades of viruses \(^{20}\). +Results +Prediction of ACE2 utilization by HKU25 clade coronaviruses +To investigate receptor usage among diverse merbecoviruses, we retrieved publicly available β-coronavirus S sequences from the National Center for Biotechnology Information (NCBI) database. Phylogenetic analysis based on amino acid sequences identified 1,117 S sequences classified as merbecoviruses. After removing redundant sequences with identical amino acid compositions and over-sampled human MERS-CoV strains, we selected 152 S sequences (Dataset S1) for multiple sequence alignment and phylogenetic tree construction to identify representative strains (Extended Data Fig.1a). Further phylogenetic analyses of S (Extended Data Fig. 1a) and receptor-binding domain (RBD) sequences (Fig. 1a) were conducted on representative strains spanning four species, with a focus on viruses without confirmed receptors. These included the bat coronavirus NsGHA2010 33, hedgehog coronaviruses (EriCoVs) 34–36, and 15 non-redundant bat coronaviruses classified as members of the HKU25 clade 6,7,31. Comparative analysis of trees based on whole-genome nucleotide sequences and S/RBD amino acid sequences revealed phylogenetic incongruencies (Fig. 1b). For example, the three geographically separated MERSr-CoV strains EjCoV-3 32, BtCoV-422 6,7, and VmSL2020 29, which exhibit significant divergence in their S/RBD region, clustered together and share 84.5~89.4% genome-wide nucleotide sequence identity. Analysis of amino acid sequences from five concatenated domains in the replicase region (3CLpro, NIRAN, RdRp, ZBD, and HEL1) within ORF1ab confirmed that all HKU25 clade coronaviruses are classified as MERSr-CoVs (>92.4% identity compared to MERS-CoV) 26,27 (Fig. 1b). Phylogenetic analysis of RBD sequences revealed a close relationship between HKU5- and HKU25 clade coronaviruses, suggesting that members of the HKU25 clade of coronaviruses may also utilize angiotensin-converting enzyme 2 (ACE2) as receptor, similar to HKU5 20,22,23. Whereas HKU5 was predominantly sampled in Pipistrellus abramus (P.abr) bats in Southeast China, HKU25 clade coronaviruses have been identified in a wide range of vespertilionid bat species across Eurasia. These include: VmSL2020 and VmSL2021 from Vespertilio murinus (V.mur) in Switzerland 29; PaGB01 from Plecotus auritus (P.aur) in the United Kingdom 37, HsItaly2011 from Hypsugo savii (H.sav), and PkItaly2011 from Pipistrellus kuhlii (P.kuh) in Italy 28, SC013 from Vespertilio superans (V.sup) 30, GD2016-Q249 from Pipistrellus abramus (P.abr) 31; HKU25 strains from Hypsugo pulveratus (H.pul) 6 in China; VsCoV-1, VsCoV-kj15, VsCoV-a7 from Vespertilio sinensis (V.sin, same species as Vespertilio superans) and EjCoV-3 from Eptesicus japonensis (E.jap) in Japan 32 (Fig. 1c). + +Pairwise amino acid sequence analysis showed that S glycoproteins from HKU25 clade coronaviruses share 65-68% identities with MERS-CoV S, 67-70% identities with HKU4-1 S, and 69-73% identities with HKU5-1 S, respectively. Furthermore, HKU25 clade RBDs share 48-54% identity with HKU4-1 and 62-73% with HKU5-1, but markedly lower homology (32-36%) with NeoCoV and MOW15-22, suggesting distinct receptor recognition modes17,19 (Extended Data Fig. 1c). HKU25 clade RBDs harbor insertions-deletions (indels) similar to that found in HKU5 (e.g. two indels at HKU5 S residues 513-522 and 543-552, respectively), but distinct from that of MERS-CoV, HKU4-1, BtCoV-422, NeoCoV, MOW15-22, and HKU31 (Extended Data Fig. 1d). Additional insertions-deletions (indels) can be found in PaGB01, SC2013, VsCoV-kj15, and other viruses. Up to 15 out of 24 ACE2-interacting HKU5-19s residues are conserved in +HKU25 clade RBDs, suggesting a potentially shared receptor usage (Fig. 1d). Simplot analysis comparing several viral genome sequences with EjCoV-3 reveals high similarity to BtCoV-422 and VmSL2020 with marked divergence in the S_1 region. Accordingly, the EjCoV-3 RBD is more closely related to the ACE2-using HKU5-1 RBD than to the DPP4-utilizing BtCoV-422 RBD \( ^1 \), suggesting possible recombination events among ancestral strains (Extended Data Fig. 1e). Furthermore, AlphaFold3-predicted structures of HKU25-related RBDs highlight their similarity to HKU5-1 in terms of overall RBM architecture, especially the RBM indel 2 and 3 located at the tip \( ^{20} \), except for PaGB01 which harbors a short indel 3 (Fig. 1e). + +Overall, these findings suggest that members of the HKU25 clade of MERSr-CoVs may use ACE2 as host receptor through a binding mode similar to that of HKU5 \( ^{20} \), setting them apart from DPP4-using MERS-CoV and HKU4 clade coronaviruses or other ACE2-using MERSr-CoVs. +Fig 1 + +a +Tree scale: 1 +Merbecovirus +RBD +Sarbecovirus +Receptor usage +ACE2 (group 2) +ACE2 (group 1) +ACE2 confirmed (group 3) this study +DP4 +Genome +Virus Species +EnCoV +HKU4-CoV +HKU5-CoV +MERSr-CoV (>92.4%) +NeoCoV +PDF-2180 +HKU31 +SC2013 +HKU25 +HKU4-1 +HKU5 +VsCoV-1 +VsCoV-kj15 +VsCoV-a7 +EGCoV-3 +EnCoV174 +PaGB01 +MOW15-22 +HKU4-1B +VmsL2020 +HsItaly2011 +Pkitay2011 + +c +▲ Unidentified +■ DPP4 +■ ACE2 + +d +HKU5-19s numbering +Indel 1 +Indel 2 +Indel 3 +(AlphaFold3) + +e +Indel 1 +Indel 2 +Indel 3 +MERS-CoV (4KQZ) +HKU4 (9D32) +HKU5 (9D32) +HKU4 (4QZV) +HKU25 +SC2013 +HsItaly2011 +PaGB01 +NeoCoV (7WPO) +MOW15-22 (9C6O) +Fig. 1 | Genetic features and geographic distribution of HKU25 clade MERSr-CoVs. a,b, Maximum-likelihood phylogenetic trees of representative merbecoviruses, generated using IQ-tree2. Trees are based on amino acid sequences of the RBD (a) or genomic nucleotide sequences (b), with SARS-CoV-2 as the outgroup. Information on receptor usage, binding mode, host, and viral species based on amino acid sequence identities of five replicase domains (3CLpro, NiRAN, RdRp, ZBD, and HEL1) for coronavirus taxonomy (MERSr-CoV defined as diverged by less than 7.6% identity to MERS-CoV, NC_019843.3) are annotated. The known ACE2-using viruses were classified according to the three distinct binding modes identified in the NeoCoV- (group 1), MOW15-22- (group 2), and HKU5-related (group 3) clades 27. The scale bars denote genetic distance (1 substitution per nucleotide/amino acid position). c, Geographic distributions of bat hosts (left) and sampling locations of merbecoviruses with annotated receptor usage (right). Data from the IUCN (International Union for Conservation of Nature) Red List were visualized using GeoScene Pro. Squares: ACE2-using; Circles: DPP4-using; Triangles: receptor-unidentified. Color coding is the same as panel 1B. HKU25 clade strains were outlined in magenta. Host abbreviations: V.mur/V.sup (Vespertilio murinus/V.superans), H.pul (Hypsugo pulveratus), E.jap (Eptesicus japonensis), H.sai (Hypsugo savii), V.sin (Vespertilio sinensis), P.kuh (Pipistrellus kuhlii), P.aur (Plecotus auritus), P.abr (Pipistrellus abramus). d, RBM sequence alignment of the indicated merbecoviruses with manual adjustment to optimize indel positioning. Fully and partially conserved residues were indicated as red and green backgrounds, respectively. Dashed boxes highlight indels. Residues involved in HKU5-ACE2 interactions are marked with stars; positions that are conserved or non-conserved compared to HKU25 clade viruses are colored in red and blue, respectively. HKU5-19s residue numbering is shown. e, Experimentally determined structures or AlphaFold3-predicted RBD structures of representative merbecoviruses. The putative RBMs are indicated in magenta and three featured indels described in panel D are labeled in orange (indel 1), light blue (indel 2), and dark blue (indel 3), respectively. Sequences between indel 2 and indel 3 are labeled in purple to facilitate observation. +Multi-species ACE2 tropism of HKU25 clade coronaviruses +To investigate the receptor usage of HKU25 clade coronaviruses, we first tested binding of RBD-human IgG Fc (RBD-hFc) fusion constructs from eleven HKU25 clade strains to ACE2 and DPP4 orthologs from P.aur, P.kuh, V.mur, and P.abr, which are reported host species of HKU25 clade coronaviruses. None of these ACE2 or DPP4 orthologs supported RBD-hFc binding of virus strains (PaJX2020-Q274, PkItaly2011, VmSL2020, VmSL2021, and PaGB01) identified in the corresponding host species. However, P.aur ACE2, but not DPP4, unexpectedly bound RBD-hFc constructs from six HKU25 clade strains efficiently (Extended Data Fig. 2). + +To further explore the multi-species ACE2 tropism of HKU25 clade coronaviruses, we subsequently assessed RBD-hFc binding and VSV pseudovirus entry of eight representative strains using a well-established ACE2 library comprising 113 ACE2 orthologs from 59 bats and 54 non-bat mammalian species with validated expression21 (Fig. 2a,b and Extended Data Fig. 3). We found that HKU25, EjCoV-3, SC2013, HsItaly2011, and VsCoV-a7 can efficiently bind to and utilize multiple ACE2 orthologs from bats and non-bat mammalian species with an overall preference for Murina feae (M.fea), Eptesicus fuscus (E.fus), and P.aur bat ACE2 orthologs along with several Artiodactyl and Rodent ACE2s. Although the EjCoV-3 RBD-hFc only weakly bound to select ACE2 orthologs, EjCoV-3 S VSV pseudovirus exhibited a broad ACE2 tropism across diverse mammalian orders and was the only HKU25 clade coronavirus tested capable of utilizing hACE2 for cell entry. In contrast, we did not detect meaningful binding or pseudovirus entry for any ACE2 ortholog tested with VmSL2020, VsCoV-1, or PaGB01 (Fig. 2a,b). + +Concurring with the above data, flow cytometry analysis further showed that the RBD-hFc of HKU25 clade coronaviruses bound to ACE2 orthologs from E.fus and M.fea, but not to human or E.fus DPP4, contradicting a previous report which proposed that HKU25 can bind and utilize hDPP4 7,21 (Fig. 2c). Using biolayer interferometry (BLI), we found that the soluble dimeric R.nor ACE2 bound to the immobilized HsItaly2011 RBD with apparent affinity (K_0,app) of 121 nM and that the dimeric E.fus ACE2 ectodomain bound to the immobilized VsCoV-a7 RBD with a K_0,app of 816 nM. We could not detect binding of dimeric hACE2 ectodomain to either RBDs (Fig. 2d). Together, these results demonstrate that a subgroup of HKU25 clade coronaviruses utilize ACE2 as entry receptor while largely excluding the role of DPP4 in cell entry. +Fig 2 + +a +b +Species +Rhinolophus malayanus +Rhinolophus affinis +Rhinolophus shanellii +Rhinolophus pearsoni +Rhinolophus comatus +Rhinolophus megaphyllus-3357 +Rhinolophus singapurensis +Rhinolophus thomasi +Rhinolophus ferrumequinum +Rhinolophus alcyone +Asellia tridens +Hippopotamus amphibius +Hippopotamus gauselitus +Hippopotamus pratti +Hippopotamus armiger +Mystacinus armatus +Rousettus aegyptiacus +Eonycteris spelaea +Cynopterus sphinx +Cynopterus brachyotis +Macroglossus minimus +Echolus helveticus +Pteropus giganteus +Pteropus alecto +Taphozous melanopogon +Nycticeius leisleri +Nyctophilus gouldi +Platyrrhinus parnellii +Platyrrhinus davisi +Mormopterus blanfordii +Mormopterus hirsuta +Tadarida brasiliensis +Desmodus rotundus +Phyllostomus discolor +Trachops cirrhosus +Vampyriscus spectrum +Artibeus carolinensis +Carollia perspicillata +Sturirna hondurensis +Artibeus jamaicensis +Tadarida brasiliensis +Mops condylurus +Miniopterus schreibersii +Miniopterus natalensis +Nycticeius humeralis +Myotis daubentonii +Myotis myotis +Myotis davidii +Myotis lucifugus +Myotis brandtii +Eptesicus fuscus +Plecotus auritus +Vespertilio murinus +Pipistrellus abramus +Pipistrellus nathusii +Pipistrellus pipistrellus +Pipistrellus kuhlii +Lasiurus borealis +Aerostes cinereus +Antrozous pallidus +Homo sapiens (Human) +Vector +Fig. 2 | ACE2 ortholog utilization of HKU25 clade MERSr-CoVs. a,b, Heat map representing the magnitude of HKU25 clade RBD-hFc binding to (green) and VSV pseudovirus (PSV) entry into (red) HEK293T cells transiently expressing bat (A) or non-bat b, mammalian ACE2 orthologs. Mammalian orders are color-coded: Carnivora, Primates, Artiodactyla, Rodentia, Cetacea, Perissodactyla, Diprotodontia, Pholidota, Erinaceomorpha, Lagomorpha, Chiroptera. Data represent mean values (\( n = 3 \) biological replicates). PSVs were pretreatment with 100 \( \mu \)g ml\(^{-1}\) TPCK-treated trypsin (Try). c, Flow cytometry analysis of binding of HKU25 clade RBDs to HEK293T transiently expressing the indicated ACE2 or DPP4 orthologs. Grey: vector control. Dashed lines: background threshold. Data are means of three technical repeats from three tubes of cells. d,g, BLI analysis of binding kinetics of dimeric ACE2 ectodomains (*R.nor* ACE2 in panel d, *E.fus* ACE2 in panel f, and hACE2 in panel e/g) to immobilized RBD-hFc of indicated strains. Analysis was conducted using curve-fitting kinetic with global fitting (1:1 binding model). +Molecular basis of HsItaly2011 and VsCoV-a7 utilization of ACE2 +To understand the molecular basis of HKU25 clade coronavirus engagement of ACE2 host receptors, we characterized the E.fus (Bat) ACE2-bound VsCoV-a7 and R.nor (Rodent) ACE2-bound HsItaly2011 RBDs complexes using single particle cryoEM (Fig. 3a, Extended Data Fig.4-5 and Extended Data table 1). The use of natively dimeric ACE2 ectodomain constructs enabled leveraging the applied C2 symmetry to perform symmetry expansion yielding reconstructions at 2.5 Å resolution. + +Strikingly, the VsCoV-a7 and HsItaly2011 RBDs engage the ACE2 peptidase domain with comparable binding poses to that recently described for the ACE2-bound HKU5 RBD complex with which they can be superimposed with r.m.s.d. values of 0.9 and 1.0 Å over 776 and 774 aligned Ca positions. The interfaces of VsCoV-a7 RBD - E.fus ACE2 complex and HsItaly2011 RBD - R.nor ACE2 complexes bury an average surface of 875 Å^2 and 1018 Å^2, respectively, as compared to 950 Å^2 for the HKU5 RBD - P.abr ACE2 complex 20. + +E.fus ACE2 and R.nor ACE2 respectively interact with the VsCoV-a7 RBD and the HsItaly2011 RBD through both shared interactions and contacts specific to each complex involving the molecular determinants of receptor species tropism previously identified for HKU5. For instance, P324_{E.fusACE2}, P325_{R.norACE2}, and P324_{P.abrACE2} insert in a comparable crevice at the surface of each RBM through Y429_{VsCoV-a7}, Y461_{HsItaly2011}, and Y464_{HKU5-19s}. Conversely, neighboring interactions are profoundly remodeled relative to HKU5, as exemplified by N328_{E.fusACE2} harboring an N-linked oligosaccharide, which is directly interacting with Y425_{VsCoV-a7}, or T329_{R.norACE2} which is hydrogen-bonded to the equivalent Y457_{HsItaly2011} residue through a water molecule (Fig. 3b). The N353_{E.fusACE2} side chain is hydrogen-bonded to the T474_{VsCoV-a7} backbone carbonyl (similar to N353_{P.abrACE2} and T510_{HKU5}) whereas G354_{R.norACE2} cannot form similar interactions with the HsItaly2011 RBD. The VsCoV-a7 and HsItaly2011 RBDs respectively accommodate an ACE2 glycan at position N90_{E.fusACE2} and N90_{R.norACE2} through F482_{VsCoV-a7} and Y514_{HsItaly2011}, setting them apart from the P.abr ACE2 - HKU5 interface (Fig. 3b). + +To functionally validate the contribution of the identified interacting residues in receptor recognition, we examined the influence on HsItaly2011 RBD-hFc binding of R.nor ACE2 substitutions at key positions (Fig. 3c). Most point mutations evaluated reduced HsItaly2011 RBD-hFc binding with the exception of the N90A_{R.norACE2} glycan knock-out and Q322N_{R.norACE2} glycan knock-in mutations, suggesting a minor role of these glycans in modulating HsItaly2011 receptor engagement (Fig. 3c). Consistent with the structural data, several alanine/glycine substitutions or charge reversals in the HsItaly2011 RBM dampened RBD-hFc binding and pseudovirus entry efficiency except for the Y514A, D517K, and T544A mutations, probably due to the remodeling of the contacts (Fig. 3d-f). +Fig 3 + +a + +![Comparison of RBD structures from HsItaly2011, VsCoV-a7, and R.nor ACE2/E.fus ACE2](page_186_120_1077_340.png) + +b + +![Close-up views of RBD-ACE2 complexes for HsItaly2011, VsCoV-a7, and HKU5](page_186_495_1077_340.png) + +c + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
WTE22KE26KN30AD38KY41AN90AQ96AQ322NP325GT329AN330AH353DK387DVector
Anti-hFc———————————————
HsItaly2011———————————————
+ +d + +![Immunofluorescence images of HsItaly2011 RBD-hFc mutants binding](page_186_1040_1077_120.png) + +e + +![Bar graph showing HsItaly2011 mutants PSV entry](page_186_1160_1077_120.png) + +f + +![Western blot showing HsItaly2011 S mutants](page_186_1280_1077_120.png) +Fig. 3 | Structural basis for HsItaly2011 and VsCoV-a7 RBD interaction with bat or rat (R.nor) ACE2 orthologs. a, Ribbon diagrams in two orthogonal orientations of the cryo-EM structures of the R.nor ACE2 peptidase domain (green) bound to HsItaly2011 RBD (gold) and E.fus ACE2 peptidase domain (green) bound to VsCoV-a7 RBD (plum). b, Zoomed-in views and comparisons of the interface key interactions of the HsItaly2011 RBD/R.nor ACE2, VsCoV-a7 RBD/E.fus ACE2 and HKU5 RBD/P.abr ACE2 (PDB ID: 9D32). HKU5 RBD and P.abr ACE2 peptidase domain are colored in light blue and green, respectively. Selected interface interactions are shown as black dotted lines. c, Analysis of HsItaly2011 RBD-hFc binding to membrane-anchored wildtype and mutants R.nor ACE2 transiently transfected in HEK293T cells analyzed by immunofluorescence. d, e RBD-hFc binding (d) and pseudovirus (pretreatment with 100 μg ml⁻¹ TPCK-treated trypsin) entry (e) efficiencies of HsItaly2011 S mutants in HEK293T cells transiently expressing R.nor ACE2. f, VSV packaging efficiencies of HsItaly2011 S mutants. VSV-M was used as a loading control. Unpaired two-tailed t-tests for E, data are presented as means ± s.d. for for n = 3 biological replicates. *P < 0.05,**P < 0.01, ***P < 0.005. Scale bars in c and d: 100 μm. RLU: relative light unit. +Critical residues and glycans for ACE2 tropism determination +To investigate the host species tropism determinants of HKU25 clade coronaviruses, we engineered chimeric ACE2 constructs by swapping sequence regions between functional orthologs from P.aur, M.erm, and the non-functional hACE2. Our structural analysis revealed that direct virus-interacting amino acids are located between residues 1-100 and 301-400. We therefore generated chimeras by swapping three consecutive regions: residues 1-100, 101-300, and 301-400. Swaps in residues 101-300 had minimal impact on receptor functionality compared to wild-type (WT) controls whereas substitutions in residues 1-100 or 301-400 altered receptor functionality (Fig. 4a). For example, chimeras containing hACE2 residues 1-100 lost their RBD-binding ability, indicating the crucial role of these residues in receptor recognition. We further set out to determine the molecular determinants restricting the functionality of hACE2. Replacing residues 1-100 in hACE2 with sequences from P.aur or M.erm ACE2 restored binding to several HKU25 clade RBDs. Swaps in residues 301-400 caused milder phenotypic changes (Fig. 4a). However, swaps of residues 1-50 (relative to P.aur ACE2) or substituting five key residues (relative to M.fexas ACE2) failed to rescue hACE2 functionality (Figs. 4b and Extended Data Fig. 6a,b). Through testing several hACE2 mutations within residues 301-400, we identified that the Q325P mutation promoted detectable HKU25-NL140462 RBD binding which was further enhanced by the additional E329R/N330D substitutions, more closely matching the P.abr ACE2 residues (HKU5 receptor, Fig. 4b). Moreover, M.erm ACE2 residue 354, a critical determinant of HKU5 receptor species tropism 20, also influenced EjCoV-3 and VsCoV-a7 RBD binding efficiency, underscoring shared molecular determinants between HKU25 and HKU5 clades (Extended Data Fig. 6c). + +Prior studies have highlighted the key role that some ACE2 N-linked glycans located near RBD-interacting interfaces can play in modulating receptor recognition through binding enhancement or steric restriction 17,19,20. To assess the functional impact of ACE2 glycans on HKU25 clade receptor utilization, we mutated each of the four glycosylation sites (hACE2 numbering positions 90, 322, 329, and 387) near or within the interaction interface (Fig. 4c,d). HKU25 clade RBD binding assays showed that removing glycans at these sites can enhance binding to varying degrees in several mutants, while glycan knock-in abolished binding in several mutants (Fig. 4e). Furthermore, the glycan knockout P.abr ACE2 S388P and the hACE2 N322S/Q325A/E329N mutations promoted detectable HKU25-NL140462/EjCoV-3 and SC2013 RBD binding, respectively (Fig. 4f,g). These results suggest that glycans primarily act as host-tropism barriers for HKU25 clade coronaviruses, as opposed to promoting binding as is the case for the NeoCoV-P.pip ACE2 interaction 17. + +In summary, ACE2 ortholog specificity and host range of HKU25 clade coronaviruses are governed by key critical interface residues and modulated by glycan shields. +Fig 4 + +a + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Chimera ACE2 of P.aurChimera ACE2 of M.ermChimera ACE2 of hACE2Chimera ACE2 of hACE2
VectorP.aurM.ermhACE2hACE2hACE2P.aurP.aurM.ermM.ermM.ermM.erm
AntiFlag–––––––––––
HKU2S462–––––––––––
SC2013––––––––––––
VsvCoV E[CoV3]a7–––––––––––
+ +b + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
WTQ325PE329RG354N387NQSE329R/N330D+N322Y+Q325P+G354R
AntiFlag–––––––––
HKU2S462–––––––––
+ +c +Footprint of Hsitaly2011 +K387 (P.abr N386 glycan) +Q322 (hACE2 N322 glycan) +T329 (E.fus N328 glycan) +N90 +N53 +R.nor +Aligned Footprint of Hsitaly2011 +N386 (E.fus N386 glycan) +M321 (P.abr N322 glycan) +A387 (hACE2 N322 glycan) +R328 (E.fus N328 glycan) +N90 +N53 +D90 (E.fus N90 glycan) +Footprint of VeCoV-a7 +T386 (P.abr N386 glycan) +S321 (E.fus N328 glycan) +N90 +N53 +N24 +E.fus +Aligned Footprint of Hsitaly2011 +N322 (E.fus N328 glycan) +N90 +N53 +EACE2 + +d + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
WTQ325PE329RG354N387NQSE329R/N330D+N322Y+Q325P+G354R
hACE21-1001-1001-1001-1001-1001-1001-1001-1001-100
P.aur101-300101-300101-300101-300101-300101-300101-300101-300101-300
M.erm301-400301-400301-400301-400301-400301-400301-400301-400301-400
+ +e + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
WTT92IS321NN329R/N329DWTP91L/T92IS321NN329R/N329DWTT92IS321NN329R/N329DVector
AntiFlagGlycan(-)Glycan(+)Glycan(-)FlagGlycan(+)Glycan(+)Glycan(-)FlagGlycan(-)Glycan(+)Glycan(-)–
HKU2S882––––––––––––
SC2013–––––––––––––
VsvCoV E[CoV3]a7––––––––––––
+ +f + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
WTD90NLR328NS388P
AntiFlag–––
HKU2S462–––
E[CoV3]a7–––
+ +g + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
M.feaWTK26A/H34SF40S/Y41H/T92IQ325PQ325P/E329N Q325P/G354NQ325A/E329NN322SN322S/Q325PE329N/G354N329Glycan(+)329Glycan(+)
AntiFlag–––––––––––
SC2013––––––––––––
+Fig. 4 | ACE2 tropism determinants for HKU25 clade coronavirus. a, Immunofluorescence analysis of RBD-hFc binding to HEK293T cells transiently expressing ACE2 chimeras (swaps between hACE2/P.aur ACE2 or hACE2/M.erm ACE2. ACE2 expression were validated by detecting the C-terminal fused FLAG tags. b, HKU25-NL140462 RBD-hFc binding to hACE2 mutants with equivalent residues in P.abr ACE2. c, N-glycans proximal to or within the HKU25 clade RBD-ACE2 interface (residues 1–100, 301–400). HsItaly2011 (yellow) and VsCoV-a7 (pink) RBD footprints are mapped onto ACE2 orthologs (gray surface). Glycans actually present on the surface of indicated WT ACE2 orthologs or predicted glycans through glycan-knock in mutations (based on hACE2, PDB 6M0J) are rendered in blue and green, respectively. d, Glycosylation sequons (green) at positions 53, 90, 322, 329, and 387 (hACE2 numbering). Cryo-EM confirmed glycans are marked with ¥. Please note although several glycosylation sequons are present, no glycan is present in these sites according to the cryo-EM map. e,g, RBD-hFc binding assay evaluating the impact of N-Glycan mutations on E.fus/M.fea/P.aur ACE2 (e), P.abr ACE2 (f) or hACE2 (g) orthologs. Red/blue dashed outlines: enhanced/reduced binding. Scale bars: 100 μm. +Characterization of S-mediated entry of HKU25 clade coronaviruses +A notable difference between HKU25-related coronaviruses and HKU5 or MERS-CoV is the absence of a polybasic (furin) cleavage site at the S1/S2 junction, a feature critical for proteolytic processing during viral biogenesis \(^{38,39}\) (Fig. 5a). Accordingly, pseudotyped particles carrying HKU25 clade S glycoproteins were uncleaved when produced in HEK293T cells, with the exception of EjCoV-3 S exhibiting minimal cleavage (Fig. 5b). We found that HKU25 clade S glycoproteins promoted cell-cell fusion in Caco-2 cells expressing functional ACE2 orthologs from several mammalian species, but not with hACE2, in a trypsin-dependent manner, highlighting a requirement for exogenous protease priming under the tested conditions (Fig. 5c). + +To evaluate S-mediated viral propagation in human cells, we used a replication-competent VSV-CoV-S (rcVSV-S) pseudotyping system in Caco-2 cells expressing human or bat ACE2 orthologs. Seven rcVSV-HKU25r-S viruses were rescued and amplified efficiently in Caco-2 cells stably expressing ACE2 orthologs from E.fus, M.fea, or R.nor (Fig. 5d). Moreover, only EjCoV-3 exhibited detectable (weak) propagation in Caco-2 cells endogenously expressing hACE2 or overexpressing hACE2 at 72 hours post-infection (hpi), consistent with the results from single-round VSV-S pseudovirus entry assays (Fig. 2a,b). + +To delineate entry pathways and therapeutic targets, we tested rcVSV-HKU25r-S amplification in the presence of inhibitors or neutralizing antibodies targeting distinct steps of the coronavirus entry pathway. Broadly neutralizing antibodies S2P6 \(^{40}\) and 76E1 \(^{41}\) against the S2 subunit, and the OC43 HR2-derived EK1C4 lipopeptide \(^{42,43}\) effectively suppressed propagation of rcVSV-HsItaly2011-S, rcVSV-SC2013-S, and rcVSV-HKU25 NL140462-S. However, sensitivity to the cathepsin B/L inhibitor E64d and the TMPRSS2 inhibitor Camostat \(^{44,45}\), varied across rcVSV-S pseudoviruses, suggesting distinct host protease preference and entry pathways among HKU25 clade coronaviruses (Fig. 5e). Additionally, the hACE2-targeting monoclonal antibody h11B11 \(^{46,47}\) neutralized viruses bearing S glycoproteins from HKU25-NL13892, EjCoV-3, and SC2013, but not MERS-CoV, in Caco-2 cells or Caco-2 cells expressing the indicated hACE2 mutants (Fig. 5f). These findings underscore the potential of broad-spectrum entry inhibitors and antibodies as countermeasures against zoonotic spillovers of ACE2-using HKU25 clade MERSr-CoVs. +Fig 5 + +a + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
735745748751760
MERS-CoVTPSILITRVKRASGVPGENR
HKU5-19sTPSILITRVKRASGASDVQIR
HKU25-892TPSILITRVKRASGASDVQIR
EJCoV-3TPSILITRVKRASGASDVQIR
SC2013TPSILITRVKRASGASDVQIR
VsCoV-kj15TPSILITRVKRASGASDVQIR
HsItaly2011TPSILITRVKRASGASDVQIR
VsCoV-a7TPSILITRVKRASGASDVQIR
VmSL2020TPSILITRVKRASGASDVQIR
VsCoV-arTPSILITRVKRASGASDVQIR
PgB01TPSILITRVKRASGASDVQIR
+ +Furin site + +b +![Western blot showing MW(kDa) and anti-HA bands for MERS-CoV, HKU5-19s, HKU25-892, EJCoV-3, SC2013, VsCoV-kj15, HsItaly2011, VsCoV-a7, VmSL2020, PgB01, and Vector](page_1012_120_377_246.png) + +c + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
HKU5-19sHKU25-892EJCoV-3SC2013VsCoV-kj15HsItaly2011VsCoV-a7
0P.abrE.fusO.ariM.feaM.feaR.norE.fus
20P.abrE.fusO.ariM.feaM.feaR.norE.fus
20hACE2hACE2hACE2hACE2hACE2hACE2hACE2
+ +d + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
HKU25-462HKU25-892SC2013VsCoV-kj15HsItaly2011VsCoV-a7
TPCK-Trypsin (μg/ml)1030103010301030
E.fus (24 hpi)hACE2 (24 hpi)hACE2 (24 hpi)hACE2 (24 hpi)hACE2 (24 hpi)hACE2 (24 hpi)hACE2 (24 hpi)hACE2 (24 hpi)hACE2 (24 hpi)
TPCK-Trypsin (μg/ml)1001020300102030
Caco-2 BatACE2 orthologsE.fus (24 hpi)Caco-2 (72 hpi)Caco-2 (72 hpi)Caco-2 (72 hpi)Caco-2 (72 hpi)Caco-2 (72 hpi)Caco-2 (72 hpi)Caco-2 (72 hpi)Caco-2 (72 hpi)
+ +e + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
100 nM100 μM1%50 μg/ml
HKU5-1CamostatE64dDMSOBSAS2P676E1
HKU25-462CamostatE64dDMSOBSAS2P676E1
HKU25-892CamostatE64dDMSOBSAS2P676E1
SC2013CamostatE64dDMSOBSAS2P676E1
HsItaly2011CamostatE64dDMSOBSAS2P676E1
+ +f + + + + + + + + + + + + + + + + + +
50 μg/ml
SC2013EJCoV-3 HKU25-892h11B11 BSAh11B11 BSAh11B11 BSAh11B11 BSAh11B11 BSAh11B11 BSAh11B11 BSAh11B11 BSA
+Fig. 5 | The Characterization and inhibition of the ACE2-mediated entry of rcVSV pseudotypes with HKU25 clade S glycoproteins. a, S1/S2 junction sequence alignment of HKU25 clade S glycoproteins with MERS-CoV residue numbering. The arginines (R) were highlighted in bold fronts. Furin cleavage sites are highlighted in red dashed boxes. b, S glycoprotein incorporation into VSV pseudoviral particles by detecting the C-terminal fused HA tags. VSV-M was used as a loading control. c, Cell-cell fusion mediated by HKU25 clade coronaviruses S in Caco-2 cells stably expressing indicated ACE2 orthologs with the treatment of TPCK-treated trypsin. d, Propagation of rcVSV-HKU25r-S in Caco-2 cells or Caco-2 hACE2 cells in the presence of the indicated concentrations of TPCK-treated trypsin. e,f, Inhibition of rcVSV-HKU25r-S propagation by small molecular inhibitors, S2 antibodies (e) or hACE2-targeting antibodies h11B11 (f) in Caco-2 cells stably expressing indicated hACE2 mutants. BSA: Bovine serum albumin, 50 μg ml⁻¹. hpi: hours post-infection. Scale bars: 200 μm. + +![Diagram showing evolutionary relationships and receptor usage acquisition and switching among merbecovirus lineages](page_374_682_1092_388.png) + +Fig. 6 | Proposed evolutionary model of merbecovirus receptor usage acquisition and switching. Geographical regions (orange) and receptor binding modes of specific merbecovirus RBD clades (gray: unidentified; red: ACE2; blue: DPP4) are indicated. Genome lineage evolution and RBD clade diversification involve RBM indels and S1 recombination (green) are annotated. Light and blue dashed lines propose the origins of DPP4-using MERS-CoV and BtCoV-422. Strains of the same species are grouped within black boxes. Spillovers to non-bat mammalian species are indicated. Indel: sequence insertions and deletions. +Discussion +DPP4 had been established as a canonical receptor for Merbecovirus subgenus members since its identification as entry receptor for MERS-CoV. While this likely applies to the entire HKU4 clade, only a limited number of MERS-related coronaviruses have been experimentally confirmed to use DPP4 \(^{1,3,4,7,48}\). The inability of most merbecoviruses to engage DPP4 has hindered the development of robust infection models, limiting our understanding of their entry mechanisms and our ability to develop and evaluate the efficacy of antiviral therapeutics and vaccines. + +Recent studies demonstrated that ACE2 recognition has evolved independently in multiple merbecovirus clades with distinct geographic distributions \(^{17,19,20}\). Our findings extend this paradigm by identifying a subset of HKU25 clade coronaviruses as group 3 ACE2-using merbecoviruses, challenging the previously proposed use of DPP4 by HKU25 clade viruses \(^7\). These results unveil the prevalence of ACE2 usage among merbecoviruses and the overall similar but divergent ACE2 engagement mode utilized by HKU5 and HKU25 clades concurs with their close RBD phylogenetic relationships \(^{18,20}\). The recognition of partially overlapping surfaces for multiple merbecoviruses, sarbecoviruses, and setracoviruses using entirely distinct RBM architectures suggest fulfillment of specific geometric constraints leading to viral entry \(^{49,50}\). + +Notably, some merbecoviruses may employ receptors other than ACE2 or DPP4. For instance, EriCoVs, NsGHA2010, and several merbecoviruses from the HKU25 clade exhibit distinct RBM indels and are not found to use any tested ACE2 or DPP4 orthologs (Extended Data Fig. 1d). However, while three HKU25 clade coronaviruses (VsCoV-1, VmSL2020, and VmSL2021) were not confirmed as ACE2 dependent in this study, their reliance on ACE2 cannot be excluded due to untested ACE2 orthologs from *V. mur* and *V. sin*. Similarly, polymorphisms in the *P. aur* ACE2 allele may have influenced receptor functionality for PaGB01 \(^{51}\). Confirming the host ACE2/DPP4 receptor orthologs from the sampling host or identifying new receptors for these viruses will be essential to obtain a comprehensive understanding of receptor utilization and species tropism for merbecoviruses. + +The evolutionary history of receptor usage among merbecoviruses remains unclear. Accumulating evidence suggests that S recombination with breaking points between NTD and S$_2$ subunit plays a key role in receptor switching \(^{14,52}\). For instance, DPP4-using MERS-CoV and BtCoV-422 are proposed to have acquired their RBDs through recombination with HKU4 clade viruses from NeoCoV-like and EjCoV-3-like ACE2-using ancestors, respectively \(^{14}\). + +Whereas RBD recombination facilitates receptor switching in coronaviruses, this mechanism does not establish novel receptor recognition modalities. Instead, remodeling of interactions via critical adaptive sequence changes, such as RBM indels and antigenic drift, appear to drive the acquisition of new receptor binding modes \(^{53}\). Although ACE2 utilization has been proposed to have emerged independently across diverse bat species, the evolutionary origins of the conserved DPP4-binding mode in merbecoviruses remain enigmatic. The limited genetic diversity and restricted geographic distribution of HKU4 clade viruses support an evolutionary trajectory involving RBM indel-driven divergence from ancestral HKU25- or HKU5-like lineages. +In agreement with a prior hypothesis 14, HKU4 clade recombination with ACE2-using lineages ultimately generated phylogenetically discrete DPP4-using MERSr-CoVs including MERS-CoV and BtCoV-422 (Fig. 6). This evolutionary model is supported by shared distinct sequence signatures of indels 2 and 3 among DPP4-using viruses (e.g. MERS-CoV, HKU4, BtCoV-422) in Fig. 1 d,e. The RBD phylogenetic gap between MERS-CoV and currently known HKU4 strains, alongside the absence of viruses highly similar to MERS-CoV in extensive bat virome surveys54–60, implies the existence of unsampled reservoirs of HKU4-like viruses in uncharacterized ecological niches, which may have donated the DPP4-binding RBD sequences to MERS-CoV. + +The HKU25 clade encompasses genetically diverse MERSr-CoVs circulating across vespertilionid bats in Eurasia. Host migration and cross-species transmission appear to drive viral diversification and recurrent recombination events. Despite being phylogenetically classified within MERSr-CoVs based on ORF1ab sequences, HKU25 clade coronaviruses resemble HKU5 clade viruses in terms of ACE2 binding mode and ortholog tropism. As observed for other merbecovirus clades, critical interface residues and glycans govern their ACE2 specificity and zoonotic potential 17,19,21. For example, the P.abr ACE2-specific N386-glycan, which is accommodated upon HKU5 RBD binding, appears to sterically prevent the recognition of HKU25 clade coronaviruses (Fig. 4c,f). Unlike HKU5, which has spilled over into minks and evolved hACE2-compatible lineages23,61, currently sampled HKU25 clade coronaviruses remain bat-restricted and poorly adapted to human ACE2 (hACE2). The absence of furin cleavage sites in HKU25 clade S proteins may constitute additional constraints on human adaptation. Nevertheless, the broad ACE2 ortholog tropism and hACE2 utilization of EjCoV-3 (albeit weak) reveals pre-adaptive potential for host switching of this clade and thus warrants close monitoring. Lessons learned from the COVID-19 pandemic, proactive investigations of transmissibility, pathogenicity, and therapeutic vulnerabilities of these ACE2-using merbecoviruses should be prioritized for our preparedness for potential future outbreaks. +Methods + +Cell lines +HEK293T (CRL-3216), HEK293T (ATCC, CRL-11268), Caco2 (HTB-37), BHK-21 (CCL-10), and I1-Hybridoma (CRL-2700) cells were obtained from the American Type Culture Collection (ATCC). Expi293F (Thermo Fisher Scientific, A14527) was used for protein production. All the above cells were cultured in Dulbecco's Modified Eagle Medium (DMEM, Monad, China) supplemented with 1% PS (Penicillin/Streptomycin) and 10% Fetal Bovine Serum (FBS). The I1-Hybridoma cell line, which produces a neutralizing antibody targeting the VSV glycoprotein (VSV-G), was maintained in Minimum Essential Medium (MEM) with Earles's balances salts, 2.0 mM of L-glutamine (Gibico), and 10% FBS. All cell lines were incubated at 37°C with 5% CO2 and routinely passaged every 2-3 days. HEK293T or Caco-2 cell lines overexpressing various receptors were generated using lentivirus transduction followed by the puromycin (1 μg ml⁻¹) selection. + +Construct design +Plasmids of WT or mutated mammalian ACE2 orthologs or ACE2 chimera were constructed by inserting human codon-optimized coding sequences into the lentiviral transfer vector (pLVX-EF1a-Puro, Genewiz) with C-terminus 3 × FLAG tags (DYKDHD-G-DYKDHD-I-DYKDDDDK) for bat ACE2 and single FLAG tags (DYKDDDDK) for non-bat mammalian ACE2 orthologs 21,65,66. The constructions expressing human or bat DPP4 orthologs encoding residues 1 to 766 corresponding to hDPP4 were generated similarly to ACE2 orthologs with 3 × FLAG tags. For S-incorporated VSV pseudovirus production, human codon-optimized S sequences from MERS-CoV (YP_009047204.1), HKU5-1 (YP_001039962), HKU5-19S (AGP04932.1), HKU25-NL140462 (ASL68953.1), HKU25-NL13892 (AWH65943.1), EjCoV-3 (BDD37132.1), SC2013 (AHY61337.1), VsCoV-kj15 (BDI08820.1), VsCoV-a7 (BDI08829.1), HsItaly2011 (AUM60014.1), VmSL2020 (USF97409.1), VsCoV-1 (BBJ36008.1), PaGB01 (WDQ26963.1) were cloned into the pCAGGS vector with C-terminal residues 13–15 replaced by a HA tag (YPYDVPDYA) to facilitate S incorporation 67. For the expression of recombinant CoVs RBD-hFc fusion proteins, plasmids were constructed by inserting RBD coding sequences from HKU5-19s (residues 385-586), HKU25-462 (residues 385-587), HKU25-892 (residues 384-586), HKU25-305 (residues 385-587), PaGD2016-Q249 (residues 383-584), EjCoV-3 (residues 383-585), SC2013 (residues 382-588), VsCoV-kj15 (residues 388-594), HsItaly2011 (residues 383-586), Pkltaly2011 (residues 383-586), VsCoV-a7 (residues 385-587), VmSL2020 (residues 389-590), VmSL2021 (residues 387-588), VsCoV-1 (residues 382-583), and PaGB01 (residues 377-572) into the pCAGGS vector containing an N-terminal CD5 secretion signal peptide (MPMGLSQPLATLYLLGMLVASVL) and C-terminal hFc-twin-strep-3 × FLAG tags (WSHPQFEKGGGGSGGGSGGSAWSHPQFEK-GGGRSDYKDHDGYKDHDIDYKDDDDK) for purification and detection. Plasmids expressing soluble ACE2 ectodomain proteins were generated by inserting sequences from hACE2 (residues 18-740), E.fus ACE2 (residues 18- +746), and R.nor ACE2 (residues 18-740) into the pCAGGS vector, with an N-terminal CD5 secretion signal peptide and a C-terminal twin-strep-3 × FLAG tag. DNA fragments for cloning chimera or mutants were generated by overlap extension PCR or gene synthesis and verified by commercial DNA sequencing. + +For cryo-EM analysis, the ACE2 ectodomain encoding residues of R.nor (1-741) and E.fus (1-747) were subcloned into the pcDNA3.1(+) plasmids with C-terminal Avi and octa-histidine tag. The RBD encoding residues of HsItaly2011 (388-589) and VsCoV-a7 (356-556) were subcloned into the pcDNA3.1(+) with N-terminal signal peptide (MGILPSPGMPALLSLVLLSVLLMGCVAAETGT) and C-terminal thrombin cleavage sequence, 8 flexible GS linker sequence and an Avi tag followed by octa-histidine tag. + +Recombinant protein production +For producing proteins or antibodies for biochemical or neutralization assays, corresponding plasmids expressing proteins were transfected using GeneTwin reagent (Biomed, TG101-01) in HEK293T cells or Expi293F cells. After 4-6 hours post-transfection, culture medium was replenished with the SMM 293-TII Expression Medium (Sino Biological, M293TII). Protein-containing supernatant was collected every three days for 2-3 batches. Fc-tagged proteins (Antibodies and recombinant RBD-hFc) were purified using Pierce Protein A/G Plus Agarose (Thermo Scientific, 20424). In general, proteins were enriched by the agarose, washed with wash buffer (100 mM Tris/HCl, pH 8.0, 150 mM NaCl, 1 mM EDTA), eluted using the Glycine buffer (100 mM in H2O, pH 3.0), and immediately neutralized with 1/10 volume of 1M Tris-HCl, pH 8.0 (15568025, Thermo Scientific). Proteins with twin-strep tag were purified using Strep-Tactin XT 4Flow high-capacity resin (IBA, 2-5030-002), washed with wash buffer (100 mM Tris/HCl, pH 8.0, 150 mM NaCl, 1 mM EDTA), and then eluted with buffer BXT (100 mM Tris/HCl, pH 8.0, 150 mM NaCl, 1 mM EDTA, 50 mM biotin). Purified proteins were concentrated using ultrafiltration tubes, buffer-changed to PBS, and stored at -80°C. Concentrations were determined by the Omni-Easy Instant BCA Protein Assay Kit (Epizyme, ZJ102). + +For recombinant glycoprotein production for cryo-EM analysis, each construct was expressed in Expi293F cells (Thermo Fischer Scientific), cultured at 37°C with constant rotation at 130 RPM in a humidified incubator with 80% relative humidity and 8% CO2. DNA was transfected following the protocol outlined by the manufacturer (Thermo Fischer Scientific) and grown for four days prior to harvest. Cell culture supernatants were clarified by centrifugation and harvested using either HisTrap HP Ni Sepharose Columns (Cytiva) or Ni Sepharose excel resin (Cytiva). The resin was washed with 10-50 CVs of 25 mM Tris 150 mM NaCl 10 mM Imidazole pH 8.0, followed by a 15 CVs wash using 25 mM Tris 150 mM NaCl 400 mM Imidazole pH 8.0 to elute the protein. Afterwards, the proteins were buffer exchanged into 25 mM Tris 150 mM NaCl pH 8.0 using 10KDa or 100KDa Amicon Ultra-15 Centrifugal Filter Units (Millipore) for RBDs or ACE2s respectively. A portion of proteins were also set aside and biotinylated using a biotin ligase (BirA) reaction kit (Avidity). These biotinylated RBDs were adjusted to a final concentration of 40 μM with all the provided reagents and the reaction was carried out at room temperature for 30 minutes followed by 10 hours at 4°C. Subsequently, proteins were each purified by gel filtration using a Superose-6 Increase 10/300 column (ACE2s) or Superdex-200 10/300 column (RBDs) (Cytiva) equilibrated in a buffer containing 25 mM Tris, 150 mM NaCl pH +8.0. The main peak was collected, flash-frozen using liquid nitrogen, and stored at -80°C until use. + +RBD-hFc live-cell binding assay +HEK293T cells transiently expressing receptors were incubated with RBD-hFc proteins (diluted in DMEM) for 30 minutes at 37°C (36 hours post-transfection). Subsequently, cells were washed once with HBSS and incubated with 1 µg mL^{-1} of Alexa Fluor 488-conjugated goat anti-human IgG (Thermo Fisher Scientific; A11013) diluted in HBSS/1% BSA for 1 hour at 37°C. After additional washing with HBSS, nuclei were stained with Hoechst 33342 (1:10,000 dilution in HBSS) for 30 minutes at 37°C. The images were captured using a fluorescence microscope (Mi52-N). Relative fluorescence units (RFUs) were quantified using a Varioskan LUX Multi-well Luminometer (Thermo Scientific). Heatmap presentations in Figures 3A and 3B were plotted by subtracting background RLUs from control cells without ACE2 expression (Vector). + +Pseudovirus production and entry assays +VSV-dG pseudovirus (PSV) carrying trans-complemented S glycoproteins from various coronaviruses were produced following a modified protocol as previously described^{62}. Briefly, HEK293T cells were transfected with plasmids encoding S glycoproteins. At 24 hours post-transfection, cells were transduced with VSV-G trans-complemented VSV-dG encoding GFP and firefly luciferase (VSV-dG-lLuc-GFP, constructed and produced in-house) at 1.5×10^6 TCID_{50}, diluted in DMEM with 8 µg mL^{-1} polybrene, and incubated at 37 °C for 4–6 hours. After three washes with PBS, the culture medium was replaced with SMM 293-TII Expression Medium (Sino Biological, M293TII), along with the presence of the I1 neutralizing antibody targeting the VSV-G to eliminate background entry signal from the residual VSV-G-harboring pseudovirus. Supernatants containing S-incorporated VSV pseudovirus were harvested 24 hours later, centrifuged at 12,000 × g for 5 minutes (4°C), aliquoted, and stored at −80°C. The TCID_{50} of the pseudovirus was calculated using the Reed-Muench method^{63,64}. +For single-round VSV pseudovirus entry assays, HEK293T or Caco2 cells transiently/stably expressing different receptors (3×10^4 cells/well in 96-well plates) were incubated with pseudovirus (2×10^5 TCID_{50}/100 µL). Pseudoviruses produced in serum-free SMM 293-TII Expression Medium were typically pretreated with TPCK-trypsin (Sigma-Aldrich, T8802) for 10 minutes at room temperature, followed by 10% FBS in the culture medium to inactive the protease activity. The I1-neutralizing antibody was added to the trypsin-treated pseudoviruses again to reduce the background before use. Luciferase activity (Relative light units, RLU) was measured at 18 hpi using the Bright-Glo Luciferase Assay Kit (Promega, E2620) and detected with a GloMax 20/20 Luminometer (Promega) or Varioskan LUX Multi-well Luminometer (Thermo Fisher Scientific). +To examine the S glycoprotein packaging and cleavage efficiency, the S-incorporated VSV pseudoviruses were concentrated using a 30% sucrose cushion (30% sucrose, 15 mM Tris-HCl, 100 mM NaCl, 0.5 mM EDTA) at 20,000 × g for 1 hour at 4°C. Pellets were resuspended in 1×SDS loading buffer, vortexed, boiled (95°C, 10 minutes), and followed by western blot detecting the S glycoproteins by C-terminal HA tags and with the VSV-M serving as a loading control. +Cell-cell fusion assays +Dual-split proteins (DSPs) based fusion assays were performed in Caco-2 cells stably expressing ACE2 receptors. Two group cells stably expressing the indicated receptors were transiently transfected with different plasmids for assessing the HKU25-related coronaviruses S and receptor interaction-mediated membrane fusion. Group A cells were transfected with plasmids encoding S and rLucN(1-155)-sfGFP1-7(1-157), while group B cells were transfected with plasmids encoding S and sfGFP8-11 (158-231)-rLuc (156-311) expressing plasmids. At 12 hours post-transfection, two groups of cells were trypsinized, mixed, and seeded into a 96-well plate at \( 8 \times 10^4 \) cells per well. At 24 hours post-transfection, the cells were washed once with DMEM and then incubated with DMEM with or without indicated concentrations of TPCK-treated trypsin (Sigma-Aldrich, T8802) for 10 minutes at room temperature. After washing with DMEM, the cells were replenished with DMEM/10% FBS to neutralize trypsin activity. Syncytia formation with green fluorescence was assessed 6 hours later using Hoechst 33342 nuclear staining (1:5,000 dilution in Hanks' Balanced Salt Solution (HBSS) for 30 minutes at 37 °C) and fluorescence microscopy (MI52-N; Mshot). + +Immunofluorescence assay +For assessing the expression levels of ACE2 or DPP4 orthologs tags, cells transiently or stably expressing the indicated receptors with C-terminal fused FLAG tags were fixed and permeabilized by incubation with 100% methanol (10 minutes at room temperature), washed by HBSS, and incubated with a mouse antibody M2 (Sigma-Aldrich, F1804) diluted in PBS/1% BSA for one hour at 37°C. After one HBSS wash, the cells were incubated with Alexa Fluor 594-conjugated goat anti-mouse IgG (Thermo Fisher Scientific, A32742) secondary antibody diluted in 1% BSA/PBS for one hour at 37°C. The images were captured with a fluorescence microscope (Mshot, MI52-N) after the nuclei were stained with Hoechst 33342 reagent (1:1,000 dilution in HBSS). + +Biolayer interferometry (BLI) +For dimeric hACE2 ectodomain proteins binding to immobilized HKU25-NL13892 RBD-hFc or HsItaly2011 RBD-hFc, recombinant RBD-hFc proteins were immobilized on Protein A (ProA) biosensors (ForteBio, 18-5010), which were then incubated with the indicated soluble Dimeric hACE2-ectodomain proteins (two-fold serial-diluted in PBST starting from 2,000 nM or 1000 nM) with wells incubated with kinetic buffer (PBST) only as a background control. Protein binding kinetics was assessed using an Octet RED96 instrument (Molecular Devices) at 25°C and shaking at 1,000 rpm. The kinetic parameters and the apparent binding affinities (due to ACE2 dimerization) were analyzed using Octet Data Analysis software 12.2.0.20 with global curve fitting using a 1:1 binding model. +Biotinylated HsItaly2011 and VsCoV-a7 RBD were diluted into 10x Octet Kinetics Buffer (Sartorius) and loaded onto pre-hydrated streptavidin biosensors to a 1 nm shift. The tips were then re-equilibrated in the kinetics buffer before being dipped into a serial dilution of R.nor or E.Fus ACE2 dimers for 300 to 500 seconds followed by another incubation in kinetics buffer to assess the dissociation. The ACE2 starting concentrations were as high as 3,000 nM to as low as 900 nM, and diluted either two or three-fold in the kinetics buffer leaving one well without any +dilution as a background control. Kinetics were assessed at 30\(^\circ\)C and 1,000 rpm using an Octet Red96. The binding kinetics were baseline subtracted and assessed using Octet Data Analysis 11.1 software with a global curve fitting in a 1:1 binding model and plotted in GraphPad 10.4. + +Flow cytometry +Cells transiently expressing ACE2 or DPP4 orthologs were washed twice with cold PBS and incubated with 10 \( \mu \)g mL\(^{-1}\) indicated RBD hFc proteins at 4\(^\circ\)C for 30 minutes at 36 hours post-transfection. Subsequently, cells were incubated with Alexa Fluor 488-conjugated goat anti-human IgG to stain the bound RBD-hFc (Thermo Fisher Scientific; A11013) at 4\(^\circ\)C for 1 hour. Subsequently, cells were detached with 5 mM EDTA/PBS, fixed with 4% PFA, permeabilized with 0.25% Triton X-100, blocked with 1% BSA/PBS at 4\(^\circ\)C, and then incubated with mouse anti-FLAG tag antibody M2 (Sigma-Aldrich, F1804) diluted in PBS/1% BSA for 1 hour at 4\(^\circ\)C, followed by incubation with Alexa Fluor 647-conjugated goat anti-mouse IgG (Thermo Fisher Scientific; A32728) diluted in 1% BSA/PBS for 1 hour at 4\(^\circ\)C. For all samples, 10,000 receptor-expressing live cells (gated based on SSC/FSC and FLAG-fluorescence intensity and SSC/FSC) were analyzed using a CytoFLEX Flow Cytometer (Beckman). + +rcVSV-CoV amplification and inhibition assays +The experiments of replication-competent VSV-S (rcVSV-S) were authorized by the Biosafety Committee of the State Key Laboratory of Virology and Biosafety, Wuhan University, and conducted under BSL2 conditions. To construct plasmids for rescuing replication-competent (rc) VSV-CoV expressing HKU25-clade S glycoproteins, the firefly luciferase (fLuc) encoding sequences of pVSV-dG-fLuc-GFP \(^{50}\) were replaced with the indicated coronavirus spike sequences. Reverse genetics was applied to rescue rcVSV-CoV-S pseudotypes expressing HKU25-clade S glycoproteins along with a GFP reporter, following a modified protocol from previous descriptions \(^{62}\). Briefly, BHK-21 cells were seeded in a 6-well plate at 80% confluence and inoculated with 5 MOI of recombinant vaccinia virus expressing T7 RNA polymerase (vvT7, a kind gift from Mingzhou Chen's lab, Hubei University) for 45 minutes at 37\(^\circ\)C. Subsequently, cells were transfected with pVSV-dG-GFP-S vector plasmids and helper plasmids (pVSV-dG-GFP-S: pBS-N; pBS-P: pBS-G: pBS-L=5:3:5:8:1) after washing by DMEM. The rcVSV-CoV containing supernatant (P0) was filtered (0.22 \( \mu \)m) and amplified in Caco-2 cells transiently expressing VSV-G (P1). Subsequently, P2 viruses were generated in Caco-2 cells stably expressing indicated ACE2, without the ectopic expression of VSV-G and in the presence of anti-VSVG antibody (I1-Hybridoma supernatant) to produce viruses without VSV-G contamination. For amplification assay, 3×10^4 trypsinized Caco-2 cells stably expressing the indicated ACE2 were incubated with rcVSV-CoV (1×10^4 TCID_{50}/100 \( \mu \)L) in a 96-well plate in DMEM supplemented with 2% FBS with or without the treatment of indicated concentrations of TPCK-treated trypsin. At the indicated time post-infection, the cell nuclei were stained with Hoechst 33342 (1:10,000 dilution in HBSS) for 30 minutes at 37\(^\circ\)C, and the fluorescence images were taken by a fluorescence microscope (MI52-N). + +Cryo-electron microscopy data collection, processing, and model building +The E.fus ACE2 ectodomain-bound VsCoV-a7 RBD complex was prepared by mixing at 1:1.2 molar ratio followed by a 1 hour incubation at room temperature. 3 μL of 5 mg ml⁻¹ complex with 6 mM 3-[(3-Cholamidopropyl)dimethylammonio]-2-hydroxy-1-propanesulfonate (CHAPSO) were applied onto freshly glow discharged R 2/2 UltrAuFoil grids⁶⁸ prior to plunge freezing using a vitrobot MarkIV (ThermoFisher Scientific) with a blot force of 0 and 5.5 sec blot time at 100% humidity and 22°C. The data was acquired using an FEI Titan Krios transmission electron microscope operated at 300 kV and equipped with a Gatan K3 direct detector and Gatan Quantum GIF energy filter, operated in zero-loss mode with a slit width of 20 eV. Automated data collection was carried out using Leginon⁶⁹ at a nominal magnification of 105,000× with a pixel size of 0.843 Å. The dose rate was adjusted to 9 counts/pixel/s, and each movie was acquired in counting mode fractionated in 100 frames of 40 ms. A total 14,595 micrographs were collected with a defocus range between -0.2 and -3 μm. Movie frame alignment, estimation of the microscope contrast-transfer function parameters, particle picking, and extraction were carried out using Warp⁷⁰. Particles were extracted with a box size of 192 pixels with a pixel size of 1.686Å. Two rounds of reference-free 2D classification were performed using cryoSPARC⁷¹ to select well-defined particle images. Initial model generation was carried out using ab-initio reconstruction in cryoSPARC and the resulting maps were used as references for heterogeneous 3D refinement. Particles belonging to classes with the best resolved RBD and ACE2 density were selected. To further improve the data, the Topaz model⁷² was trained on Warp-picked particle sets belonging to the best classes after 2D classification and particles picked using Topaz were extracted and subjected to 2D-classification and heterogenous 3D refinements. The two different particle sets from the Warp and Topaz picking strategies were merged and duplicates were removed using a minimum distance cutoff of 90Å. After two rounds of ab-initio reconstruction-heterogeneous refinements, 3D refinement was carried out using non-uniform refinement in cryoSPARC⁷³. The dataset was transferred from cryoSPARC to Relion using the pyem program package and particle images were subjected to the Bayesian polishing procedure implemented in Relion⁷⁴ during which particles were re-extracted with a box size of 320 pixels and a pixel size of 1.0 Å. To further improve the map quality, ab-initio reconstruction in cryoSPARC was used to classify the data in three bins and the generated models were used as references for heterogeneous 3D refinement. The final 3D refinements of the RBD bound ACE2 peptidase dimer structure were carried out using non-uniform refinement along with per-particle defocus refinement in cryoSPARC to yield the reconstruction at 2.7 Å resolution comprising 941,663 particles. To further improve the density of the RBD and ACE2 domain interface, the particles were symmetry expanded and subjected to focus 3D classification without refining angles and shifts using a soft mask encompassing the RBD and ACE2 domain interface using a tau value of 40 in Relion. Particles belonging to classes with the best resolved RBD-ACE2 domain interface density were selected and then subjected to local refinement using CryoSPARC. The final dataset contained 578,871 asymmetric units used for the final local refinement with a soft mask comprising one ACE2 peptidase domain and the bound RBD resulting in a 2.5 Å resolution reconstruction. Reported resolutions are based on the gold-standard Fourier shell correlation (FSC) of 0.143 criterion and Fourier shell correlation curves were corrected for the effects of soft masking by high-resolution noise substitution⁷⁵,⁷⁶. Local resolution estimation, filtering, and sharpening were carried out using cryoSPARC. +For the R.nor ACE2 ectodomain bound HsItaly2011 RBD structure, The complex was prepared by mixing at 1:1.2 molar ratio followed by a 1 hour incubation at room temperature. 3 µL of 5.3 mg ml⁻¹ complex with 6 mM CHAPSO were applied onto freshly glow discharged R 2/2 UltrAuFoil grids prior to plunge freezing using a vitrobot MarkIV (ThermoFisher Scientific) with a blot force of 0 and 5.5 sec blot time at 100% humidity and 22°C. 8,298 movies were collected with a defocus range comprised between -0.2 and -3.0 µm. The overall data processing methods were the same as that for the E.fus ACE2 ectodomain-bound VsCoV-a7 RBD complex. The final dataset contained 831,432 asymmetric units used for the final local refinement with a soft mask comprising one R.nor ACE2 peptidase domain and the bound HsItaly2011 RBD resulting in a 2.5 Å resolution reconstruction. More details are shown in Figures S5 and S6. UCSF Chimera77, Coot78, AlphaFold379, and Phenix80 were used to fit, build, and refine the model using the sharpened and unsharpened cryo-EM maps. Validation used Phenix80, Molprobity81, EMRinger82 and Privateer83. + +Bioinformatic and structural analysis +Merbecovirus S glycoprotein sequences were retrieved from the NCBI Virus database (https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/) on 9th August 2024. A total of 3,308 unique Betacoronavirus S glycoprotein entries was obtained through search terms “Betacoronavirus” or “BetaCoV” with advanced filters “sequence length between 1,200-1,400” and “exclude SARS-CoV-2”. Phylogenetic analysis in Geneious software identified merbecovirus sequences, which were refined to 150 non-redundant entries after excluding over-sampled MERS-CoV strains (retaining two representatives: one human-derived, one camel-derived). Subsequent NCBI BLAST searches using MERSr-CoV S protein sequences identified two additional MERS-related coronaviruses (PDF-2180 and VsCoV-1), yielding a final dataset of 152 merbecoviruses (sources and accession numbers in Supplementary Data 1). The RBM sequences in Fig. 1d and Extended Data Fig. 1d were aligned via MAFFT with manual adjustments to optimize indel positioning. Fully and partially conserved residues were highlighted with red and green backgrounds, respectively. The RBD sequences used for evolutionary analysis in Fig. 1A were aligned via MAFFT-DASH. Phylogenetic trees were generated with IQ-Tree (version 2.0.6) using a Maximum Likelihood model with 1000 bootstrap replicates. Pairwise sequence identities were calculated in Geneious Prime (https://www.geneious.com/) following MAFFT alignment. The structures of HKU25-NL13892, SC2013, HsItaly2011, PaGB01 and HKU31 RBDs were predicted using AlphaFold379. Experimentally resolved structures included the NeoCoV RBD–Pipistrellus pipistrellus ACE2 complex (PDB: 7WPO), MERS-CoV RBD (4KQZ), HKU4 RBD (4QZV), HKU5 RBD (9D32), and MOW15-22 RBD (9C6O). All these RBDs were visualized and analyzed in ChimeraX (v1.7.1). + +Statistical analysis +Most experiments were performed 2–3 times with 3 biological repeats unless otherwise specified. Representative results were shown as means ± SD as indicated in the figure legends. Unpaired two-tailed t-tests were conducted for statistical analyses using GraphPad Prism 10. P < 0.05 was considered significant. *P < 0.05,**P < 0.01, ***P < 0.005, and ****P < 0.001. + +Data and materials availability +The cryo-EM maps and model have been deposited to the electron microscopy data bank and protein data bank with accession numbers EMD-49092, PDB-9N7D (R.nor ACE2-bound HsItaly2011 RBD), and EMD-49093, PDB-9N7E (E.fus ACE2-bound VsCoV-a7 RBD). Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. + +All reagents generated in this study are available from the lead contact with a completed Materials Transfer Agreement. Correspondence and requests for resources and reagents should be directed to the lead contact, Huan Yan (huanyan@whu.edu.cn) + +Code availability +This study did not generate custom computer code. + +Declaration of interests +The authors declare no competing interests. + +Supplementary Information is available for this paper. +Fig S1 + +a + +b + +Tree scale: 1 + +Spike + +c + +MERSr-CoV + +MERS-CoV + +HKU5r-CoV + +HKU4r-CoV + +HKU5-CoV + +HKU5-FG + +Amino acid identity + +Spike + +d + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
RBD cladeReceptor usageStrainsIndel 1Indel 2Indel 3S50
MOW ACE2 Group2MOW15-2ZP-LGGNVNSYSGANKVGDFAFWGDDQIPFEFVEVESKPSRVGLELNDNTYALVUGD1WREVALTKQPIDSR
Neo ACE2 Group1NeoCoVSSSISYSGANKVGDFAFWGDDQIPFEFVEVESKPSRVGLELNDNTYALVUGD1WREVALTKQPIDSR
EriGHA2010TVNDAQCGYINGDEQIISIPFCSWKGNLKSVQI
?HKU31-F5VSSLNFTANNQGFDGKDILLQSLPNIASGVLGLSNDYDFGIFN
DPP4 Group1BTCv-4Z2??????????
HKU4HKU4-1??????????
MERS DPP4 Group1MERS-CoV??????????
HKU5PaGB01??????????
?VeCoV-1??????????
?VmSL2020??????????
ACE2 Group3HKU5-1??????????
ACE2 Group3EJCoV-3??????????
ACE2 Group3HKU5-Y9??????????
+ +E + +SimPlot-Query: EJCoV-3 whole-genome + +RBD (aa353–555) + +Window: 200bp, Step: 20bp, GapStrip: On, Kimura(2-parameter), Tt: 2.0 +Extended Data Fig.1 Amino acid sequence analysis of merbecovirus S glycoproteins. a,b, phylogenetic trees based on amino acid sequences of S glycoproteins from all retrieved non-redundant merbecoviruses (a) or selected representative merbecoviruses S (b) were generated by IQ-tree2. SARS-CoV-2 was set as an outgroup. c, Heat plot of pairwise RBD and S amino acid sequence identities of indicated merbecoviruses. d, Manually adjusted multiple sequence alignment of RBM residues 496-565 (HKU5-19s residue numbering) from the indicated merbecoviruses with the three indels marked by dashed boxes. Fully and partially conserved residues were highlighted with red and green background, respectively. e, SimPlot analysis of whole-genome nucleotide similarity of indicated merbecoviruses relative to EjCoV-3. The right panel magnifies the RBD region (EjCoV-3 positions 22608–23216 nt). Dashed lines indicate RBD boundaries; nucleotide identities to EjCoV-3 are labeled. + +![Heat plot and alignment of merbecovirus S glycoproteins](page_370_624_1002_624.png) + +Extended Data Fig.2 Binding of HKU25 clade RBD-hFc constructs to ACE2 or DPP4 orthologs from selected bat host species. HKU25-clade coronaviruses RBD-hFc binding to +HEK293T cells transiently expressing the indicated receptors assessed by immunofluorescence. The Red dashed boxes highlight data that shows the RBD binding of indicated viruses with receptors from their reported host species (marked in red). Scale bars: 100 μm. + +A +Expression of Bat ACE2 orthologs (Flag) + + + + + + + + + + + + + + + + + + + + + + + +
R.maiR.affR.shaR.peaR.corR.sin3357R.sinR.thoR.ferR.aic
A.stoH.pomH.galH.praH.armM.lyrR.eagE.speC.sphC.bra
M.sobE.helP.gigP.aleT.melN.tepP.parP.davM.blaM.hir
T.sauD.rotP.disT.cirV.speA.cauC.perS.honA.jamT.bra
M.molM.schM.natN.humM.feaM.myoM.davM.lucM.braE.fus
P.aurV.murP.abrP.natP.pipP.kuhL.borA.cinA.palhACE2
Vector
+ +B +Expression of non-bat mammalian ACE2 orthologs (Flag) + + + + + + + + + + + + + + + + + + + + +
P.larL.canF.catP.conP.pardV.vulC.famU.arcA.melM.erm
M.putN.schE.jubZ.calP.anuT.gelM.fasR.roxP.tepP.tro
G.gorP.abeN.leuC.jacS.apaS.bolC.ferS.scrB.tauB.mut
B.bubC.hirO.ariO.virP.leuC.griM.musR.norJ.jacI.tri
O.orcT.truG.melL.vexN.aslP.catC.simE.cabP.cinM.jav
E.eurO.cunE.telE.fusM.feaP.abrP.aurhACE2Vector
+ +Extended Data Fig.3 The expression level of ACE2 orthologs from various mammalian species. a,b, Immunofluorescence analysis of the expression of bat (a) or non-bat mammalian (b) ACE2 orthologs in HEK293T cells by detecting the C-terminal fused FLAG tags. Scale bars: 100 μm. +Extended Data Fig.4 Cryo-EM data processing of the E.fus ACE2 bound VsCoV-a7 RBD data set. a,b, Representative electron micrographs (a) and 2D class averages (b) of the E.fus ACE2-bound VsCoV-a7 RBD complex embedded in vitreous ice. Scale bars: 100 nm (a) and 130 Å (b). c, Gold-standard Fourier shell correlation curve of the E.fus ACE2-bound VsCoV-a7 RBD reconstruction. The 0.143 cutoff is indicated by a horizontal dashed line. d, Local resolution estimation calculated using cryoSPARC and plotted on the sharpened map. e, Data +processing flowchart. CTF: contrast transfer function; NUR: non-uniform refinement. The angular distribution of particle images calculated using cryoSPARC is shown as a heat map. + +Extended Data Fig.5 Cryo-EM data processing of the R.nor ACE2 bound HsItaly2011 RBD data set. a,b, Representative electron micrographs (a) and 2D class averages (b) of the R.nor ACE2-bound HsItaly2011 RBD complex embedded in vitreous ice. Scale bars: 100 nm (a) and 130 Å (b). c, Gold-standard Fourier shell correlation curve of the R.nor ACE2-bound +HsItaly2011 RBD reconstruction. The 0.143 cutoff is indicated by a horizontal dashed line. d, Local resolution estimation of the R.nor ACE2-bound HsItaly2011 RBD reconstruction calculated using cryoSPARC and plotted on the sharpened map. e, Data processing flowchart. CTF: contrast transfer function; NUR: non-uniform refinement. The angular distribution of particle images calculated using cryoSPARC is shown as a heat map. + + + + + + + + + + + + + + + + + + + + + + + +
aChimera ACE2 of M.feaChimera ACE2 of P.aurChimera ACE2 of hACE2
WThACE2 1–100WThACE2 1–100WThACE2 1–90
Anti-Flag------
HKU25 462------
EjCoV-3------
SC2013------
VsCoV-kj15------
HsItaly2011------
VsCoV-a7------
+ + + + + + + + + + + + + + + + + + + +
bhACE2 mutants
E.fusM.feaP.aurWTK26A/H34S/F40S/Y141H/T92IVector
Anti-Flag------
HKU25 892------
EjCoV-3------
SC2013------
VsCoV-a7------
+ + + + + + + + + + + + + + + + + + +
cM.erm ACE2 mutants
WTR364NR364GVector
Anti-Flag----
HKU5-19s----
HKU25 462----
EjCoV-3----
SC2013----
VsCoV-a7----
+ +Extended Data Fig.6 Molecular determinants of ACE2 host species tropism overlapping with HKU5. a, Immunofluorescence assay analyzing HKU25r-CoV RBD binding to HEK293T cells transiently expressing ACE2 chimeras with indicated sequence swaps between hACE2 and P.aur ACE2 or M.fea ACE2. The expression levels were validated by detecting the C-terminal fused FLAG tags. b, HKU25 clade viruses RBD-hFc binding to HEK293T cells transiently expressing hACE2 mutants. c, HKU25 clade viruses RBD-hFc binding to HEK293T cells transiently expressing M.erm ACE2 mutants. Scale bars: 100 μm. +Extended data table 1, Cryo-EM data collection and refinement statistics. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
VsCoV-a7 RBD – E.fus ACE2
PDB 9N7E
EMD-49093		HsItaly2011 RBD – R.nor ACE2
PDB 9N7D
EMD-49092
Data collection and processing
Magnification105,000105,000
Voltage (kV)300300
Electron exposure (e-/Å2)6060
Defocus range (μm)-0.2 - -3.0-0.2 - -3.0
Pixel size (Å)0.8430.843
Symmetry imposedC1C1
Initial particle images (no.)5,485,4552,124,204
Final particle images (no.)578,871831,432
Map resolution (Å)
FSC threshold		2.5
0.143		2.5
0.143
Refinement
Model resolution (Å)
FSC threshold		2.6
0.5		2.6
0.5
Map sharpening \( B \) factor (Å2)-86-77
Model composition
Non-hydrogen atoms70687245
Protein residues883897
Ligands1615
Water163109
\( B \) factors (Å2)
Protein36.730.2
Ligand59.048.8
Water23.820
R.m.s. deviations
Bond lengths (Å)0.0090.003
Bond angles (°)0.9420.593
Validation
MolProbity score1.461.33
Clashscore5.622.33
Poor rotamers (%)0.972.04
Ramachandran plot
Favored (%)97.1497.64
Allowed (%)2.632.25
Disallowed (%)0.230.11
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Privateer: software for the conformational validation of carbohydrate +structures. Nat. Struct. Mol. Biol. 22, 833–834 (2015). + +Author information +State Key Laboratory of Virology and Biosafety, College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University; Wuhan, Hubei, 430072, China. +Chen Liu, Cheng-Bao Ma, Yu-Cheng Sun, Xiao Yang, Mei-Yi Lin, Qing Xiong, Jun-Yu Si, Peng Liu, Huan Yan. + +Department of Biochemistry, University of Washington; Seattle, WA 98195, USA. +Howard Hughes Medical Institute, University of Washington; Seattle, WA 98195, USA. +Young-Jun Park, Cameron Stuart, Risako Gen, David Veesler. + +Contributions +C.L., Y.-J.P., D.V., and H.Y. conceived the project. Y.-J.P. and C.S. designed glycoprotein constructs and recombinantly expressed glycoproteins. C.L., C.-B.M., Y.-C.S., and M.Y.L. cloned S, RBD-hFc, and ACE2 mutants and conducted RBD-hFc binding assays. C.L. and C.-B.M. conducted S cleavage and cell-cell fusion assays. C.L. and X.Y. rescued the rcVSV-HKU25r-S pseudotypes and C.L. performed rcVSV propagation and inhibition assays. C.-B.M., C.S., and R.G. conducted biolayer interferometry binding experiments. C.L., C.-B.M., and Y.-C.S. carried out VSV pseudovirus entry and neutralization assays. Y.-J.P. carried out cryo-EM sample preparation, data collection, and processing. Y.-J.P. and D.V. built and refined the structures. DV and HY wrote the manuscript with input from all authors. H.Y., D.V., C.L., Y.-J.P., C.S., R.G., and C.-B.M. analyzed the data. C.L. conducted phylogenetic and conservation analysis. + +Corresponding author +Correspondence to Huan Yan + +Acknowledgments +This study was supported by National Natural Science Foundation of China (NSFC) projects (82322041, 32270164 to H.Y., 323B2006 to C.-B.M.), the National Key R&D Program of China (2023YFC2605500 and 2023YFC2607300 to H.Y.), Natural Science Foundation of Hubei Province (2023AFA015 to H.Y.), the Fundamental Research Funds for the Central Universities (to H.Y.) and TaiKang Center for Life and Medical Sciences (to H.Y.). Yan Lab thanks Lu Lu (Fudan University) for providing EK1C4 peptides; Qiang Ding (Tsinghua University), Qihui Wang (CAS Key Laboratory of Pathogenic Microbiology & Immunology, China), and Zheng-Li Shi (Guangzhou National Laboratory) for sharing some ACE2 plasmids with H.Y. used in this study. +This study was also supported by the National Institute of Allergy and Infectious Diseases (P01AI167966, DP1AI158186 and 75N93022C00036 to D.V.), a Shurl and Kay Curci Foundation Graduate Scholarship Award (to R.G.), the National Institute of General Medical Sciences, an Investigators in the Pathogenesis of Infectious Disease Awards from the Burroughs Wellcome Fund (D.V.), the University of Washington Arnold and Mabel Beckman cryo-EM center and the National Institute of Health grant S10OD032290 (to D.V.). D.V. is an +Investigator of the Howard Hughes Medical Institute and the Hans Neurath Endowed Chair in Biochemistry at the University of Washington. +Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +• DataS1MerbecovirusSAlignment.txt \ No newline at end of file diff --git a/d6b3eb9c7edeb508b52afd144afe10ea531cc2b56b328cd2ec9ecc014f7e8a01/metadata.json b/d6b3eb9c7edeb508b52afd144afe10ea531cc2b56b328cd2ec9ecc014f7e8a01/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..318d586610eab39454429775e89316d982858040 --- /dev/null +++ b/d6b3eb9c7edeb508b52afd144afe10ea531cc2b56b328cd2ec9ecc014f7e8a01/metadata.json @@ -0,0 +1,122 @@ +{ + "title": "Edge polarization topology integrated with sliding ferroelectricity in Moir\u00e9 system", + "pre_title": "Edge Polarization Topology Integrated with Sliding Ferroelectricity in Moir\u00e9 System", + "journal": "Nature Communications", + "published": "15 April 2025", + "supplementary_0": [ + { + "label": "supplementary_information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58877-1/MediaObjects/41467_2025_58877_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58877-1/MediaObjects/41467_2025_58877_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Ferroelectrics and multiferroics", + "Two-dimensional materials" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5377031/v1.pdf?c=1744715239000", + "research_square_link": "https://www.researchsquare.com//article/rs-5377031/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58877-1.pdf", + "preprint_posted": "25 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Van der Waals moir\u00e9 heterostructure have been found to exhibits a robust interfacial ferroelectricity down to atomic thickness, and discovering and understanding the complex polarization state in moir\u00e9 systems is of fundamental interest to condensed-matter research. In this study, we examined the moir\u00e9 ferroelectricity in twisted h-BN heterostructure by piezoresponse force microscopy. Due to atomic reconstruction, triangular moir\u00e9 patterns are detected, and we have directly observed sliding ferroelectricity in the center of triangular moir\u00e9 patterns as well as robust in-plane polarization topology emerging at the boundary of adjacent triangles, which we call edge ferroelectricity. The edge ferroelectricity possesses non-trivial vortex polarization topology. Our calculations trace the origin of this phenomenon to joined piezoelectric effects with sliding ferroelectricity. The present work provides intuitive insights to explore the unique moir\u00e9 ferroelectricity in a non-polar background matrix, paving the way for potential ultrathin nonvolatile memory applications.Physical sciences/Physics/Condensed-matter physics/Ferroelectrics and multiferroicsPhysical sciences/Materials science/Nanoscale materials/Two-dimensional materialsPhysical sciences/Physics/Condensed-matter physics/Topological matter/Topological defects", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "NNSI.pdfSUPPLEMENTARY MATERIAL", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Van der Waals moir\u00e9 heterostructure have been found to exhibits a robust interfacial ferroelectricity down to atomic thickness, and discovering and understanding the complex polarization state in moir\u00e9 systems is of fundamental interest to condensed-matter research. In this study, we examine the moir\u00e9 ferroelectricity in twisted h-BN heterostructure by piezoresponse force microscopy. Due to atomic reconstruction, triangular moir\u00e9 patterns are detected, and we directly observe sliding ferroelectricity in the center of triangular moir\u00e9 patterns as well as robust in-plane polarization topology emerging at the boundary of adjacent triangles, which we call edge polarization. The edge polarization possesses non-trivial and robust vortex polarization topology. Our calculations trace the origin of this phenomenon to joined piezoelectric effects with sliding ferroelectricity. This work provides intuitive insights to explore the unique moir\u00e9 ferroelectricity in non-polar background matrix, and the inherent stability of the topological structures ensures reliable and durable performance of electronic devices.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Superlattices formed by stacking two-dimensional van der Waals (vdW) materials have garnered significant interest due to their intriguing physics that significantly differs from conventional physics, which possesses potential applications in nanotechnology1. By controlling the twist angle, various optical and electronic characteristics, such as superconductivity2,3, quantum anomalous Hall effect4, negative capacitance5, and so on6,7,8 can be obtained. Interestingly, if layering sheets slide or twist minor, sliding or moir\u00e9 ferroelectricity with atomic reconstruction can be obtained9. The induced long-range polar structure is generally considered to originate from charge transfer or electron orbital distortion among layers, thereby inducing vertical polar vectors10. The nanoscale characterization based on scanning probe microscopy (SPM) is capable of detecting the localized moir\u00e9 domains, polarization, and charges.\n\nThe strong moir\u00e9 ferroelectricity can emerge in different non-polar systems11, such as boron nitride (BN)12,13,14,15,16,17, WSe216,18,19,20, and graphene9,21,22, etc. These ultrathin two-dimensional layered materials lack dangling bonds and can be easily exfoliated to a monolayer. The layered structure ensures unique electronic properties, and their ferroelectricity can persist at atomic-level thickness, thereby overcoming traditional size effects in ferroelectric materials and providing pathways for next-generation storage device applications. However, the physical origins of the moir\u00e9 systems are much more complicated. More than sliding ferroelectricity, other mechanisms including flexoelectricity23,24, piezoelectricity25, are also put forward to explain the unique stacking-induced ferroic properties. Recently, several nontrivial ferroelectric topological configurations such as skyrmions, merons, and vortices26 have been theoretically demonstrated in twisted low-dimensional vdW heterostructure27,28. The long-period moir\u00e9 superlattice generates rich moir\u00e9 band structures, which possess great potential for applications in actuators, mechanical sensors, transducers, and random-access memories29,30,31. Although the topological structure is depicted in twisted MoS2 with large twist angles (>1.05\u00b0)32, when the twist angles are decreased below the magic angle (<1.05\u00b0), more complex and diverse physical behaviors typically occur. However, identifying the continuous polar structures in the moir\u00e9 system at microscopic twist angles remains a significant challenge.\n\nIn this study, we demonstrated a moir\u00e9 ferroelectricity in twisted h-BN (t-BN) system. The relaxed atomic structure of t-BN is given in Fig.\u00a01A, which are superimposed by BN layers with a small twist angle, \u03b8. They interfere with each other, and create triangular moir\u00e9 domains, due to atomic reconstruction. Based on systematically investigation, we identify that the moir\u00e9 superlattice displays a complex polarization characteristic, and the polarization in configuration space is presented in Fig.\u00a01B. By employing piezoelectric force microscopy (PFM), it is experimentally demonstrated that the sliding ferroelectric polarization in the centers of adjacent triangles is opposing due to a notable 180\u00b0 phase difference. More interestingly, the in-plane (IP) polarization rotates clockwise or counterclockwise at the edges of adjacent triangles, and the edges are divided into two parts with opposite out-of-plane polarization, forming a more complex topological polarization network in the moir\u00e9 system that is different from the traditional topologically non-trivial meron-antimeron structure (Fig. S1). In this case, this creates a toroidal or donut shaped configuration when viewed along the axis of rotation (Fig.\u00a01C). The transition regions labeled by the black dash in Fig.\u00a01B, like a Bloch-type domain walls in a moir\u00e9 superlattice, is reported in the current work. The orientation of the polarization gradually rotates from down to up, then rotate back to down, and continuously from down to up (Fig.\u00a01D). It should be noted that the electromechanically response at edge of triangle moir\u00e9 patterns are much stronger than center regions, which is significantly different from conventional ferroelectrics, indicating a competition of underlying origins such as atomic reconstruction and charge redistributing etc. at the observed edges of moir\u00e9 patterns.\n\nA Top view of the relaxed atomic structure of t-BN. B Perspective view of moir\u00e9 structure. C Top view of IP polarization with clockwise and counterclockwise like merons and anti-merons. OOP polarization at the edge is displayed in the form of blue and red colors. The color bar represents the direction of OOP polarization. D Unusual Bloch-type domain walls with down-up-down-up polarization transitions.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58877-1/MediaObjects/41467_2025_58877_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "The t-BN samples are fabricated using the tear-and-stack method with both top and bottom layers of ~3\u2009nm BN (Supplementary Fig. S2). The optical image is shown in Supplementary Fig. S3. The domain structures of the t-BN were characterized by PFM (Fig.\u00a02A), in which the principle of lateral PFM is illustrated in Fig.\u00a02B (principle of vertical PFM is given in Supplementary Fig. S4). When the tip encounters a region with different polarization vectors, it experiences a change in amplitude and phase of its vibration and torsion, which can be decomposed into two directions vertical (\\(d{{{\\rm{P}}}}_{\\perp }\\)) and parallel (\\(d{{{\\rm{P}}}}_{\\parallel }\\)) to the tip. The moir\u00e9s in the region squared by the white dash in Fig.\u00a02C is examined. It exhibits triangle moir\u00e9 patterns with a length of 550\u2013820\u2009nm, and an edge width of ~100\u2009nm (Fig.\u00a02D, E). The lateral amplitude (L-Amp.) and phase contrast (L-Pha.) of each edge exhibit distinct behaviors when the scan angle between the tip and triangle\u2019s sides changes. The L-amp. response is the weakest when the cantilever (gray polygon) is parallel to the edge, and the strongest when the components are perpendicular to the cantilever (red and blue dash arrows). Since the polarization can point in any direction in three-dimensional space, information about triangle lengths and PFM response directions must be consulted in order to fully determine the polarization distribution in t-BN.\n\nA Schematic of PFM measurements. B Principle of lateral PFM. C Topography of t-BN. D L-Amp. and E L-Pha. of moir\u00e9 domains at a tip-sample angle of 0\u00b0. F L-Amp. and G L-pha. signals measured at the same position with a tip-sample angle of 60\u00b0. H L-Amp. and I L-pha. responses obtained at a tip-sample angle of \u221260\u00b0.The gray tip represents the direction of the cantilever beam, and the red and blue dashed arrows in the phase image indicate the direction of IP polarization. J Polar plot of L-Amp. magnitude extracted from L-amp. images obtained at different tip-sample angles (from 0\u00b0 to 360\u00b0 with a step of 30\u00b0). K The profiles of L-Amp. and L-Pha. along the red dashed line in (D) and the same position in (E).\n\nTaking the data in Fig.\u00a02D, E as reference where the sample alignment angle is set as 0\u00b0, we conducted PFM imaging by rotating the sample counterclockwise in the increments of 30\u00b0 for a set of given angles (Entire process is given in Supplementary Fig. S5). Figure\u00a02D\u2013I exhibit the typical L-Amp. and L-Pha. results with tip scanning along the three triangle\u2019s sides. The polar plot of edge L-amp. values can be found in Fig.\u00a02J, and the profile values of L-amp. and L-pha. are given in Fig.\u00a02K by extracting data from the red dash in Fig.\u00a02D and the same position in Fig.\u00a02E (Larger range is given in Supplementary Figs. S7-8). Figure\u00a02K shows strong L-amp. responses at edge (blue curve) and ~180\u00b0 phase difference between adjacent edges (red curve), indicating significant edge effects. The results that IP polarization is detectable close to the edge of triangular moir\u00e9 patterns, demonstrating IP polarization is aligned to the sides of triangular moir\u00e9 patterns. Moreover, IP polarizations appear to be connected head-to-tail along the edges and rotate along the center axis of each triangle, suggesting a clockwise and counterclockwise IP polarizations. In a word, it exhibits a topological polarization network like merons and antimerons (Supplementary Fig. S6) in t-BN superlattice. The schematic of domain and domain wall in x-y plane is given in Fig.\u00a01C, D, which is highly identical to the L-PFM results shown in Fig.\u00a02.\n\nDifferent from IP topological domains in t-BN, out-of-plane (OOP) polarizations for the same edges are surprisingly split into two parts, as shown in Fig. 3C\u2013F. The domains are also checked by rotating the sample 90\u00b0 to exclude the IP vectors and topography crosstalk (Supplementary Fig. S9). However, the OOP phase difference between adjacent patterns with AB and BA stacking is much smaller than 180o, consisting with previous works23, which is contrast with the proposed opposite polarization in AB and BA stacking. To suppress the electrostatic influence, we employed PFM technique (Quadrature Phase Differential Interferometry, QPDI) measurement, and Fig.\u00a03A, B show the OOP PFM results. The region near the edge within the black and white labeled in Fig.\u00a03A, B is zoomed in Fig.\u00a03C\u2013F, and values along the red dashes labeled in Fig.\u00a03E, F are extracted in Fig.\u00a03J, H. The distinct amplitude and phase contrast in the adjacent centers of triangular moir\u00e9 pattern is, namely, regions I and IV. The phase contrast between them is ~180\u00b0, and the difference of amplitude response (Fig.\u00a03A) is obvious, suggesting opposite OOP polarization direction. Meanwhile, OOP PFM responses at edge, regions II and III, are also distinguished, which is very consistent with IP signals. The amplitude intensity of edge is higher than the centers, while the phase contrast between II and III is ~180\u00b0, indicating another opposite OOP polarization direction.\n\nA V-Amp. and B V-Pha. images at the same region in Fig.\u00a02. C The enlarged V-Amp. and D V-Pha. at the regions within the black dashed boxes in (A) and (B). E Zoomed V-Amp. and F V-Pha. at the edge labeled by black dash squares in (C) and (D). G The profiles of V-Amp. and H V-Pha. extracted along the red dashes highlighted in (E) and (F).\n\nNotably, the adjacent center and edge also present opposite polarization direction (region I and II). The polarization vectors near the edge experience OOP down-up-down-up switching and keep the IP component direction the same (unusual Bloch-type). The schematic of OOP edge is given in Fig.\u00a01D (z-x plane and perspective view), which is in accordance with both V- and L-PFM. By evaluating the L- and V-PFM results (Figs.\u00a02 and 3), diagram can be built in 3D space, as shown in Fig.\u00a01B. The polarization topology in t-BN possess a complex network like merons and anti-merons assembled in the edge of triangle moir\u00e9s, and unusual Bloch-type domain walls at the edges (Fig.\u00a01D). Distinguish from strong center polarization in conventional ferroelectric materials, it should be noted that polarization in t-BN system is more prominent in edge of moir\u00e9 patterns. In addition to t-BN, similar phenomena including IP and OOP were also observed in twisted WSe2 (Supplementary Figs. S10, 11). Moreover, the complex polar topological structure is consistent in moir\u00e9 patterns with different twist angles (twist angle\u226a1\u00b0) (Supplementary Figs. S12\u201315), indicating that such configuration is suitable for different moir\u00e9 systems.\n\nTo understand the moir\u00e9 ferroelectricity in t-BN, a deep potential model using machine learning methods combining with first-principle calculations24 is adopted to handle the twisted systems which generally contain tens of thousands of atoms (details are given in Methods and Supplementary Fig. S16, 17). We get the relaxed atomic distribution for h-BN moir\u00e9 structure at a twist angle of 0.99\u00b0 (Fig.\u00a01A), consisting of triangular domains of dominant AB or BA stackings, and saddle point (SP) at the boundary of adjacent triangles, which is corresponding to middle of moir\u00e9 pattern edge33. In contrast with AB/BA stackings, SP has the highest IP polarization and zero OOP polarization, which is identical with our experimental results as shown in Figs. 2 and 3. Then, a DP-JAX model, mapping the dipole moments of bilayer h-BN with the local atomic structure, is trained to predict the local polarization distribution in the relaxed h-BN moir\u00e9 structure. Local polarization in adjacent triangles before relaxation is given in Supplementary Fig. S18, exhibiting vortex distributions in reverse directions27. IP polarization under relaxation effects, as presented in Fig.\u00a04A, is mainly concentrated along the edges of the triangles, which is consistent to our L-PFM results (Fig.\u00a02). However, OOP polarization predicted by the model is merely exist in triangle domains, no edge polarization around SP regions is observed (Fig.\u00a04B), contradictory to V-PFM measurements (Fig.\u00a03).\n\nA IP polarization distribution in relaxed h-BN with twist angle of 0.99\u00b0. The length and color intensity of the arrows represent the magnitude of polarization. B OOP ferroelectricity distribution, as predicted by the DP-JAX model, in relaxed h-BN with a twist angle of 0.99\u00b0. C OOP polarization induced by piezoelectric effects.\n\nNote that although DP-JAX model is effective to capture the contribution from short-range sliding ferroelectricity10, it ignores long-range flexoelectric and piezoelectric effects in the SP regions of moir\u00e9 systems34. These might play an important role in global polarization distribution, and thus explaining the unique edge polarization. According to the OOP displacement field, flexoelectric effects is ruled out as a key factor in this case since it would induce polarization in the same direction between adjacent triangle domains, and is anticipated to be countered by the opposing reconstruction between top and bottom layers. This is contradicting to our experimental findings that edge possesses two opposite OOP polarizations (Fig.\u00a03). Therefore, model analysis of piezoelectric effect in the dipole distribution within this system is carried out. For each layer, atomic reconstruction causes an IP strain field (Supplementary Fig. S19), resulting in the distribution of IP polarization due to piezoelectric effect (Supplementary Fig. S20). However, two adjacent layers have opposite atomic displacement patterns (Supplementary Fig. S21), leading to compensated IP polarization, and thus leaving no effect on the global IP polarization.\n\nNevertheless, IP polarization induced by piezoelectric effect can induce IP charge distribution (Supplementary Fig. S22). The opposite IP polarization in each layer can lead to reversed piezoelectric charge distribution between them35. Meanwhile, the charge pattern induced by piezoelectric effects is found to be angle-dependent. The induced charges accumulate in the center (triangular domains) when the twist angle is large (Supplementary Fig. S22E, F), while distribute along the edge if the twist angle is small (Supplementary Fig. S22A\u2013D)28. Interestingly, the piezoelectric charges with opposite signs and uniform IP projection in adjacent layers can create a vertical potential drop, and thus bringing out OOP polarization36. Moreover, the introduced polarization direction at the two-lobe edge is opposite (one lobe is polarized upward while the other one with downward polarization), and the polarization intensity decrease from the edges to the centers (Fig.\u00a04C), which aligns well with our experimental observations (Fig.\u00a03). In the experiment, the tested moir\u00e9 pattern twist angles are all less than 1\u00b0.\n\nThe moir\u00e9 structure constituted by the unique edges induced by piezoelectric and sliding effects (Figs.\u00a02, 3) is inherently stable due to the nontrivial topology. Although the fact that the generation and manipulation of vortex (AB/BA)-antivortex (AA) pairs can be facilitated through localized electric fields, the robustness of the vortex and antivortex cores usually exhibit resistance to movement, providing a robust framework capable of withstanding perturbations. As depicted in Fig.\u00a05A, the moir\u00e9 topological pattern persists under a minor OOP bias, preserving the triangular architecture unchanged. When a more substantial bias of \\(\\pm\\)1.5\u2009V is applied, edge dynamics are initiated, causing AB, BA, and edge regions to adjust and reach energy equilibrium, with energy-unfavorable triangles contracting while the adjacent ones expand. Notably, despite the deformation of edges induced by an external electric field, the cores of antivortices and vortices remain stable, and the AA regions remain almost unchanged. These findings are in accordance with our theoretical model, where the core of the vortex/antivortex polarization pattern remains in the same stacking modes under the effect of electric field (Fig.\u00a05B). Additionally, the IP atomic displacement after lattice reconstruction also shows minimal variation upon the application of electric field (Fig.\u00a05C), indicating the piezoelectricity-induced edge polarization is robust to such external influences.\n\nA V-Amp. images under different biases. B Expansion (composed of BA stacking) and contraction (composed of AB stacking) of ferroelectric domains under an electric field of 0.5\u2009V/\u00c5, while the area of AA stacking does not show significant change. C In-plane atomic displacement of upper layer after structural reconstruction under the effect of electric field.\n\nIn conclusion, we propose a robust edge topology that features IP like meron-antimeron polarization. The edge consists of two splitting regions with opposite OOP polarizations and a complete IP topological structure. The adjacent triangular moir\u00e9 patterns in the center only possess opposite OOP polarizations. Through our theoretical investigation, we identify that the combined sliding ferroelectricity and piezoelectric effects are the physical origin of the complex polarization characteristics in twisted BN. The stability of vortex-antivortex structures with unique edges is significantly influenced by their topological characteristics, which is essential for the development of devices that rely on these topological features, ensuring remarkable reliability and durability even under varying external perturbations. Our discovery provides new insights into unconventional interfacial ferroelectrics in moir\u00e9 systems, opening up new opportunities for fundamental research and applications in electronics and data storage.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58877-1/MediaObjects/41467_2025_58877_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58877-1/MediaObjects/41467_2025_58877_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58877-1/MediaObjects/41467_2025_58877_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58877-1/MediaObjects/41467_2025_58877_Fig5_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Sample fabrication and hexagonal boron nitride (h-BN) are exfoliated and identified through optical microscopy. The thickness of these thin layers is then determined using an atomic force microscope (AFM). A polycarbonate (PC) film on PDMS is used to extract graphene from the SiO2 substrate to serve as the bottom gro cation. The twisted boron nitride (t-BN) is prepared using the tear-and-rotate technique (Supplementary Fig. S1). On a SiO2 (300\u2009nm)/Si substrate, grapheneund electrode. Meanwhile, the AFM probe is utilized to cut the BN layer into two pieces, BN1 and BN2. The sample stage is heated to 90\u2009\u00b0C, and with careful manipulation using the pre-constructed PDMS/PC/graphene setup, BN1 is extracted and moved parallel to the sample stage to cover BN2. The PC film is peeled off from PDMS and transferred onto the SiO2/Si substrate, then heated to 100\u2009\u00b0C to enhance adhesion between the substrate and the film. Thick graphite extracted using PDMS is placed as an electrode on top of the underlying graphene. The optical image of the obtained t-BN is shown in Supplementary Fig. S2.\n\nA commercial SPM (Cypher S, Asylum Research) including Piezoelectric force microscopy (PFM) is employed in this investigation. All experiments were conducted under ambient atmospheric conditions at room temperature. Conductive probes (NSC18, MicroMasch) with platinum (Pt) coating were used. The vertical PFM (V-PFM) contact resonance frequency was set to approximately 340\u2009kHz, while the lateral PFM (L-PFM) contact resonance frequency was about 620\u2009kHz. An alternating current (AC) voltage of 1.5\u2009V was applied throughout the PFM measurements. The PFM phase originates from polarization reversal, which can be verified by QPDI (Quadrature Phase Differential Interference) -PFM measurements (a 180\u00b0 PFM phase difference exists in oppositely polarized regions). QPDI allows for the measurement of pure vertical probe displacement, unaffected by cantilever bending and avoiding electrostatic artifacts. The QPDI-PFM measurements were conducted using a commercial Vero AFM (Asylum Research, Oxford Instruments), as shown in Fig.\u00a03, during the testing process, an alternating current of 1\u2009V was applied.\n\nEnergy model. Deep neural network-based potential model is used to describe atomic interactions in moir\u00e9 systems, effectively resolving the challenge of balancing accuracy and efficiency in large-scale atomistic relaxations. Using the training datasets from first-principle calculations, we employ the deep potential (DP) method37,38 to train a deep neural network potential for h-BN bilayer. In other words, the DP model is to learn the connection between atomic distribution and the interatomic potential energy and force field, thus selecting appropriate configurations for the training dataset is vital for an accurate model39,40. Moir\u00e9 bilayer system contains all characteristic structures of various stacking modes, including the intermediate states of interlayer sliding, IP distortion as well as interlayer warping. Manual construction of all these configurations is very laborious. Therefore, we explored configurations by running AIMD simulations on VASP. The initial dataset contains AB, AA, AA\u2019, SP along with seven other bilayer stacking modes which have all been optimized by DFT calculations and expand to 4*4 supercells with 64 atoms. 2000-step AIMD simulations using the canonical ensemble were performed at 300\u2009K for each initial stacking configurations (such as AB). It is worth mentioning that the MD trajectories have covered all stacking configurations, which is strong evidence of the comprehensive coverage of our datasets. After AIMD simulations, we got 22000(11*2000) configurations for the datasets. Next, we trained the DP model by the DeePMD-kit code37 in which the total potential energy of a configuration is assumed to be a sum of atomic energies mapped from a descriptor through an embedding network. 4400 configurations of the training data are used for validations. The sizes of the embedding and fitting networks are (25, 50, 100) and (240, 240, 240), respectively, and the cutoff radius for each atom is 6.0\u2009\u00c5. The DP model was trained with 1,760,000 steps with a batch size of 1, thereby minimizing the loss function that including energy and atomic force contributions. To evaluate the performance of the DP model, we compared the energies and atomic forces for all configurations in the validation datasets using both DFT and the DP model (Supplementary Fig. S11A\u2013C). The DP model is used to relax the superlattice within the LAMMPS package41.\n\nBased on the method training the intrinsic BN energy model, we trained an energy model under an applied electric field of 0.5\u2009V/\u00c5. First-principles datasets of the structure and energy were obtained by running AIMD simulations in VASP, with an 0.5\u2009V/\u00c5 electric field applied along the out-of-plane axis. The DP model was trained over 1,200,000 steps. Same relaxation method was then used to get the moire structure under the influence of electric field.\n\nWe use the Vienna ab initio simulation package (VASP)42 with projector augmented wave method43,44 and the generalized gradient approximation (GGA) with Perdew-Burke-Ernzerh (PBE) of exchange-correlation functional45. A plane-wave cutoff energy of 500\u2009eV in the structural relaxation calculations was adopted. Van der Waals corrections are employed using DFT-D2 method of Grimme46. A large distance of \\({{\\rm{c}}} > 15{{\\text{\\AA }}}\\) along the out-of-plane direction is applied to eliminate interlayer interactions. A \\(2\\times 2\\times 1\\) k-point mesh is sufficient to converge the energy and atomic force for the \\(4\\times 4\\times 1\\) supercell with 64 atoms for AIMD calculation, preparing datasets for model training. The optimized lattice constant of AB stacked bilayer h-BN is \\({{\\rm{a}}}={{\\rm{b}}}=2.509{{\\text{\\AA }}}\\) with interlayer distance \\(3.08{{\\text{\\AA }}}\\). The Berry phase method results in a calculated perpendicular polarization of the h-BN\u2019s AB domain of \\({{{\\rm{P}}}}_{{{\\rm{z}}}}=2.06\\times {10}^{-12}{{\\rm{C}}}/{{\\rm{m}}}\\), consistent with previous theoretical work10. To validate the accuracy of our energy model, we also calculate phonon properties of a relaxed h-BN moir\u00e9 structure with a large twist angle using density-functional perturbation theory47 (Supplementary Fig. S11E, F)).\n\nApart from scalar properties like force and energy, deep potential can also be used to fit high-dimensional physical quantities like local dipole moments38. For a better understanding of the topological polarization in a real place of a relaxed moir\u00e9 system, we use DeepMD-JAX package to train a model fitting the local dipole distributions of the h-BN bilayer moir\u00e9 structure. Compared with the DP model with tensor flow backbend, DP-JAX model, implementing the new scheme with JAX, performs better on the training accuracy as well as training speed34. The total polarization of a crystal \\({{\\rm{\\mu }}}\\) can be conveniently expressed as the sum of the dipole moments of ions and of the wannier centres(\\({{{\\rm{w}}}}_{{{\\rm{k}}}}\\)), the latter can be calculated by the maximally localized wannier functions(MLWFs)48,49. In a spin-saturated system, the MLWFs describe electron pairs, thereby assigning charges of \\(2{{{\\rm{e}}}}^{-}\\) to wannier centres, accordingly, the microscopic polarization is:\n\nwhere \\({{\\rm{e}}}\\) is the unit electronic charge, \\({{{\\rm{Z}}}}_{{{\\rm{i}}}}\\) are atomic numbers, \\({{{\\bf{r}}}}_{{{\\bf{i}}}}\\) are the position vectors of the nuclei. The Wannier centers are obtained from a unitary transformation that minimizes the spatial spread in the occupied orbital subspace. We use a valence-only pseudopotential approach, where the nuclear charges \\({{{\\rm{e}}}Z}_{i}\\) represent the ions consisting of the nuclei and the frozen core electrons, while the Wannier centers correspond to the valence electrons. Specializing to h-BN, which contains boron (\\({{{\\bf{r}}}}_{{{{\\bf{B}}}}_{{{\\bf{i}}}}}\\)) and nitride (\\({{{\\bf{r}}}}_{{{{\\bf{N}}}}_{{{\\bf{i}}}}}\\)) ions, wannier centers are only associated with N, the most electronegative atoms during molecular evolution, the polarization vector is:\n\nBased on this principle, we calculated the polarization of the relaxed bilayer h-BN unit cell with different stackings. The results closely matched the polarization calculated using the Berry phase method, as shown in Supplementary Fig.\u00a0S6a, demonstrating the feasibility of using Wannier centers to compute local polarization. Using the polarization dataset obtained through the Wannier center approach, we trained the DP-JAX model capable of predicting dipole distribution in bilayer h-BN superlattices. The dipole distribution for unrelaxed twisted bilayer h-BN is consistent with previous work. We have benchmarked our model by comparing the polarization results of the DP model with DFT calculations for all configurations in the validation dataset (Supplementary Fig. S12A\u2013C). Besides, the polarization predicted by our model for the relaxed bilayer h-BN unit cell with different stackings, as shown in Supplementary Fig. S12D, also aligns well with the results obtained using the Berry phase method. Consequently, it is reasonable to expect that our model will perform well on relaxed moir\u00e9 systems as well.\n\nSince monolayer h-BN has the symmetry of C3, the only independent non-zero piezoelectric coefficient is \\({\\widetilde{e}}_{11}\\equiv {\\widetilde{e}}_{111}\\). Other non-zero coefficients are related to \\({\\widetilde{e}}_{11}\\) by50\n\nUsing the berry phase method in VASP51, \\({\\widetilde{e}}_{11}\\) can be calculated from the change of IP polarization with respect to the strain. Density functional perturbation theory can also be used to obtain \\({\\widetilde{e}}_{11}\\)52,53, the result is consistent with the berry phase method as well as previous calculations50. The piezoelectric polarizations in the moir\u00e9 superlattice can be expressed as the function of strain tensor \\({{\\rm{u}}}\\),\n\nwhere \\({u}_{{xx}},{u}_{{yy}},{u}_{{xy}}\\) arise from IP atomic displacement under the relaxation effects. Accordingly, the piezoelectric charge density is the divergence of the piezoelectric polarization,\n\nDue to the equal magnitude and opposite sign of the charge densities at the same IP position for the two layers, vertical polarization is created by the interlayer potential drop,\n\nwhere \\(d\\) is the interlayer distance at the local unit cell in the moir\u00e9 structure. This is the polarization induced by piezoelectric effects.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. Additional data are available from the corresponding author upon reasonable request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Han, L. et al. High-density switchable skyrmion-like polar nanodomains integrated on silicon. 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sustainable chemiosmotic strategy for driving solute transport in synthetic cells", + "journal": "Nature Communications", + "published": "12 September 2024", + "supplementary_0": [ + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52085-z/MediaObjects/41467_2024_52085_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52085-z/MediaObjects/41467_2024_52085_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52085-z/MediaObjects/41467_2024_52085_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52085-z/MediaObjects/41467_2024_52085_MOESM4_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-52085-z#Fig1", + "/articles/s41467-024-52085-z#Fig6", + "/articles/s41467-024-52085-z#MOESM3", + "/articles/s41467-024-52085-z#MOESM3", + "/articles/s41467-024-52085-z#MOESM3", + "/articles/s41467-024-52085-z#MOESM3", + "/articles/s41467-024-52085-z#Tab1", + "/articles/s41467-024-52085-z#MOESM3", + "/articles/s41467-024-52085-z#Sec40" + ], + "code": [], + "subject": [ + "Biochemistry", + "Biotechnology", + "Membrane proteins", + "Nanobiotechnology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4283667/v1.pdf?c=1727452612000", + "research_square_link": "https://www.researchsquare.com//article/rs-4283667/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-52085-z.pdf", + "preprint_posted": "23 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Cellular homeostasis requires sustained provision of metabolic energy in the form of ATP and electrochemical ion gradients. Primary and secondary active transporters are prominent consumers of cellular energy, and couple ATP hydrolysis and ion gradient dissipation, respectively, to translocation of molecules across biological membranes. Active transport is essential for the translocation of most charged and/or large hydrophilic molecules, both for nutrient uptake into and waste export from living cells. Endeavours to build synthetic cells crucially depend on simulating real cell behaviour by supplying stable and sustained energy sources and deploying them for membrane transport. Here, we provide synthetic cells with long-lasting metabolic energy supply in the form of an electrochemical proton gradient. Leveraging the L-malate decarboxylation pathway from Lactococcus lactis we generate a stable proton gradient and electrical potential in lipid vesicles by electrogenic L-malate/L-lactate exchange coupled to L-malate decarboxylation. By co-reconstitution of the pathway with the Escherichia coli transporters GltP and LacY, the synthetic cells maintain accumulation of L-glutamate and lactose over periods of hours, mimicking nutrient feeding in living cells. This study underscores the potential of harnessing a proton motive force via a simple metabolic network, involving electrogenic substrate/product exchange and substrate decarboxylation, paving the way for the development of more complex synthetic systems.Biological sciences/BiochemistryBiological sciences/Biochemistry/Proteins/Membrane proteinsSynthetic cellssynthetic vesiclesL-malate decarboxylationproton motive force generationglutamate and lactose transportmetabolic reaction network", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupportingInformationAsustainablechemiosmoticstrategyfordrivingsolutetransportinSCs.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Cellular homeostasis depends on the supply of metabolic energy in the form of ATP and electrochemical ion gradients. The construction of synthetic cells requires a constant supply of energy to drive membrane transport and metabolism. Here, we provide synthetic cells with long-lasting metabolic energy in the form of an electrochemical proton gradient. Leveraging the L-malate decarboxylation pathway we generate a stable proton gradient and electrical potential in lipid vesicles by electrogenic L-malate/L-lactate exchange coupled to L-malate decarboxylation. By co-reconstitution with the transporters GltP and LacY, the synthetic cells maintain accumulation of L-glutamate and lactose over periods of hours, mimicking nutrient feeding in living cells. We couple the accumulation of lactose to a metabolic network for the generation of intermediates of the glycolytic and pentose phosphate pathways. This study underscores the potential of harnessing a proton motive force via a simple metabolic network, paving the way for the development of more complex synthetic systems.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Living cells require energy to fuel essential biosynthetic processes, to grow and divide, and to maintain homeostasis and an out-of-equilibrium metabolic state. The two main metabolic energy currencies of a cell are ATP and H+ (or Na+) electrochemical gradients; the latter are referred to as proton and sodium motive force (SMF), respectively. A proton motive force (PMF) can be generated by respiration, light-driven electron transfer reactions, or ATP hydrolysis1,2. The PMF is composed of a H+ chemical gradient, \u0394pH (typically alkaline inside), and an electrical potential, \u0394\u03a8 (typically negative inside):\n\nwhere R, T, and F correspond to the gas constant, temperature, and Faraday constant, respectively, and \u0394pH\u2009=\u2009pHi\u2013pHo.\n\nFermentative bacteria are unable to form a PMF by respiration or photosynthetic reactions, and the PMF can be formed via ATP hydrolysis by F1F0-ATPase3,4. However, it is also possible to generate a PMF without involvement of high-energy intermediates like ATP, using electrogenic uniport or electrogenic precursor-product exchange in combination with metabolic breakdown of the substrate inside the cell3,5. An example is the internal decarboxylation of substrate (precursor), catalyzed by a soluble decarboxylase, coupled to the uptake of precursor and extrusion of product, mediated by a specific transport protein6.\n\nBacteria of the genera Lactobacillus, Lactococcus, Leuconostoc, and Pedicoccus possess an L-malate decarboxylation pathway, also known as malolactic fermentation, which generates a PMF and counterbalances intracellular acidification7,8. In Lactococcus lactis, the cytosolic L-malate decarboxylase (malolactic enzyme, MleS) catalyzes the decarboxylation of L-malate to L-lactate plus CO2, while a membrane-embedded secondary transporter, MleP, exchanges di-anionic L-malate for L-lactate or mono-anionic L-malate for L-lactic acid. The decarboxylation reaction results in an inward gradient for L-malate and an outward gradient for L-lactate, establishing the driving forces for the L-malate/L-lactate exchange5. The CO2 may leave the cell by passive diffusion without affecting pH7. Consumption of scalar protons during the decarboxylation reaction leads to an intracellular alkalinization and, therefore, generates a \u0394pH across the plasma membrane. The gradual decrease in external L-malate and increase in L-lactate can rise the external pH, because the molecules have a different acidity (L-malate: pKa1\u2009=\u20093.4, pKa2\u2009=\u20095.1; and L-lactate: pKa\u2009=\u20093.8)7,9, but generally the impact of L-malate decarboxylation will be highest for the internal pH.\n\nThe exchange of di-anionic L-malate for L-lactate or mono-anionic L-malate for L-lactic acid is electrogenic and thus generates a membrane potential (\u0394\u03a8, inside negative). Both components of the PMF are generated in different but coupled steps, which is mechanistically very different from how the PMF is generated in respiration or photosynthesis or upon ATP hydrolysis by F1F0-ATPase. We refer to the combined action of MleP and MleS as the L-malate decarboxylation pathway. The compartmentalization of the L-malate decarboxylation pathway makes it possible to conserve the low amount of free energy from the decarboxylation reaction (\u221217 to \u221225 kJ mol\u22121)6, chemiosmotically into a PMF10. The free energy change of a carboxylation reaction is too small for the synthesis of ATP from ADP plus Pi, but the formed PMF can be used to supply the cell with ATP and fuel other essential functions like the transport of nutrients. The PMF can also facilitate processes like cell division11, (membrane) protein insertion/secretion12 and intercellular communication13,14. Various other PMF-generating precursor-product exchange\u2013decarboxylation pathways have been described (oxalate2\u2212/formate\u2212, citrate2\u2212/L-lactate\u2212, arginine+/agmatine2+, ornithine+/putrescine2+, glutamate\u2212/\u03b3-aminobutyrate, histidine/histamine+, tyrosine/tyramine+, aspartate\u2212/alanine)15,16,17,18,19,20,21,22.\n\nIn this work, we explore the potential of the L-malate decarboxylation pathway for the generation of a PMF in submicrometer-size lipid vesicles. We co-reconstituted the pathway with Escherichia coli glutamate transporter GltP23,24 and lactose transporter LacY25, and we show long-lasting transport and high steady-state levels of these solutes. We also demonstrate the utilization of L-malate-dependent lactose accumulation in downstream metabolic reactions. The sustainable energy conversion by the L-malate decarboxylation pathway enables more complex cell-like metabolic functions and sets the foundations for further out-of-equilibrium networks in synthetic cells.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The L-malate decarboxylation pathway generates a proton motive force by the action of two proteins: the integral membrane L-malate/L-lactate exchanger (MleP) and the soluble, luminal, L-malate decarboxylase (MleS). To guide the reconstitution of this system in lipid vesicles we characterized both proteins. A summary of the data obtained and in literature is presented in Table\u00a01.\n\nMleP belongs to the 2-hydroxycarboxylate transporter family (2HCT), which function as symporters or exchangers26. MleP has been described as a L-malate/L-lactate exchanger7 with a molecular weight of 47.9\u2009kDa and 9\u201314 predicted TMS26,27. We overexpressed the 10\u00d7 His-tagged MleP in L. lactis, purified the protein via immobilized metal-affinity chromatography (IMAC) and incorporated the protein in lipid vesicles composed of dioleoyl-phospholipids DOPE:DOPG:DOPC 1:1:2 (mol ratio) or E. coli polar lipids: egg PC 3:1 (mol ratio). Figure\u00a01a shows that MleP is reconstituted with an efficiency of 51\u2009\u00b1\u20099% (Supplementary Fig.\u00a01); the double band is assigned to different structural conformations and incomplete denaturation by SDS. We also observe some dimeric MleP, similar to what has been reported for other members of the 2HCT family26.\n\na SDS-polyacrylamide gel of MleP in E. coli polar lipids:egg PC 3:1 (mol ratio). (Uncropped gel in Supplementary Fig.\u00a01a). b Cartoon of MleP-liposomes loaded onto SSM, indicating the direction of charge transfer during the L-malateinflux/L-lactateefflux (violet box) and L-lactateinflux/L-malateefflux exchange (orange box). c Current traces recorded by SSM-based electrophysiology of MleP LPR 100 proteoliposomes (n\u2009=\u20093) or empty liposomes (n\u2009=\u20092) for L-malateinflux/L-lactateefflux (violet, L-malate jump) and exchange in the opposite direction (orange, L-lactate jump). d Normalized peak currents obtained from ON signals for different concentrations of L-malate (black, n\u2009=\u20093) and L-lactate (blue, n\u2009=\u20092) jumps on L-lactate- and L-malate-loaded MleP liposomes, respectively. Data of peak currents represent the mean from independent experiments with n different preparations of proteoliposomes. Error bars represent \u00b1 SD. Solid lines correspond to a Michaelis\u2013Menten fit (black R2\u2009=\u20090.971, blue R2\u2009=\u20090.985). e pH dependence of peak currents obtained from L-malate jumps at the indicated external pHs on MleP LPR 250 liposomes loaded with L-lactate at pH 7. Data are normalized to the value at pH 6 and correspond to the average from n independent experiments with different preparations of proteoliposomes (n\u2009=\u20092). Solid line represents a sigmoidal function fit of the data (R2\u2009=\u20090.999). f 14C-L-malate efflux measurements performed on MleP-liposomes (LPR 200 in DOPE:DOPG:DOPC 1:1:2 (mol ratio)) diluted in buffer containing L-lactate (violet, exchange Malout/Lacin), L-malate (red, homologous exchange Malout/Malin) or without counter substrate (black, Mal efflux). Val indicates valinomycin addition and the generation of a \u2212100\u2009mV K+ diffusion potential. g Cartoon of half turnover transport in MleP-liposomes. h Current traces recorded upon a L-malate (blue, n\u2009=\u20093) or L-lactate (red, n\u2009=\u20093) jump on MleP LPR 100 or empty liposomes loaded with sulfate or acetate, respectively. Current traces in c, h. are presented as the average from independent experiments with different preteoliposome preparations and different SSM sensor chips. Shaded areas\u2009=\u2009\u00b1 SD. Mal\u2009=\u2009L-malate, Lac\u2009=\u2009L-lactate. b, g were created with Biorender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.\n\nWe determined the electrogenic nature of L-malate/L-lactate exchange by solid-supported membrane (SSM)-based electrophysiological measurements (Fig.\u00a01). The net transfer of charge by the exchange of divalent L-malate for monovalent L-lactate is detected as a transient current via the capacitive coupling between the supported membrane and the vesicles28. A negative current is observed when external L-lactate (non-activating solution) is replaced with L-malate (activating solution) with L-lactate-loaded MleP-vesicles adsorbed to the supporting membrane (Fig.\u00a01b, c). A positive peak current is obtained when a L-lactate jump is triggered on L-malate-loaded MleP vesicles, because the charge transport is now in the opposite direction (Fig.\u00a01b, c). There is no substantial current when the same solution exchange is performed in liposomes without MleP (empty liposomes) (Fig.\u00a01c). We reduced the possibility of obtaining electrical artifacts from differences in ionic strength between the activating and non-activating solutions by replacing lactate and malate with acetate and sulfate, respectively, which carry the same charge but are not recognized as substrates by MleP. These results confirm the electrogenic character of the L-malate/L-lactate exchange.\n\nWe found that MleP reconstituted at a lipid-to-protein (LPR) ratio of 100 was able to exchange L-malate for L-lactate in the chemically defined synthetic lipid mixture DOPE:DOPG:DOPC 1:1:2 (mol ratio), but the activity was 7 times higher in liposomes composed of E. coli polar lipids: egg PC 3:1 (Supplementary Table\u00a01, Supplementary Fig.\u00a02). Therefore, we used the E. coli polar lipid/egg PC mixture for the majority of the reconstitutions at LPR 100 and further SSM measurements. The amplitude of the peak current (Ip) is proportional to the steady state L-malate/L-lactate exchange activity29. The peak current amplitude increases in a hyperbolic manner with the increment in the outside concentration of L-malate or L-lactate, while keeping the internal concentration of L-lactate or L-malate at 30\u2009mM (Fig.\u00a01d and Supplementary Fig.\u00a03a\u2013c). We find that the Kmapp for L-lactate is 7-fold higher than for L-malate (Table\u00a01 and Fig.\u00a01d).\n\nSince the L-malate decarboxylation leads to an internal as well as external pH change, (see Introduction and7), we performed L-malate jumps on L-lactate-loaded vesicles at pH values between 6 and 8.5 (Supplementary Fig.\u00a03d). Here, we used MleP LPR 250 vesicles to have a similar number of transporters per vesicle as in the experiments with the L-malate decarboxylation pathway. The L-malate/L-lactate activity is highest at pH 6 and decreased at more alkaline pHs (Fig.\u00a01e). The pH dependence most likely reflects the activity of MleP and not the availability of substrate, because the change in concentration of di-anionic L-malate is only 4% between pH 6 and pH 8.5. Besides L-malate/L-lactate exchange, MleP facilitates uniport of L-malate27, which would also be electrogenic and enable L-malate decarboxylation because L-lactate can leave the vesicles in the protonated form (L-lactic acid) by passive diffusion. Indeed, transport assays with vesicles loaded with radiolabelled L-malate (Fig.\u00a01f) show efflux of L-malate but with a rate at least one-order of magnitude slower than L-malate/L-lactate exchange. The electrogenic nature of both the uniport and exchange is shown by the increase of activity in the presence of the K+ ionophore valinomycin, which dissipates the membrane potential. Thus, two different methodologies (SSM-based electrophysiology and efflux of radiolabelled substrate) confirm that MleP is an electrogenic secondary antiporter/uniporter.\n\nThe slow kinetics of the uniport reaction complicates the SSM measurements, but we recorded small peak currents when L-malate or L-lactate jumps were applied on MleP vesicles without counter-substrate (Fig.\u00a01g, h). These peak currents can be interpreted as pre-steady state currents that originate from a half turnover, i.e. L-malate or L-lactate influx, which is followed by a slow return of the empty carrier. Interestingly, the peak current from the L-lactate jump is not only 5-fold larger in magnitude but also has a positive direction, indicative of movement of positive charge in or negative charge out of the vesicles.\n\nL-malate decarboxylase MleS catalyzes the decarboxylation of L-malate to L-lactate, releasing carbon dioxide and consuming a proton (Fig.\u00a02a). Protons are used to compensate for the free electron pair remaining in the organic intermediate after the heterolytic cleavage that releases CO2 (Supplementary Fig.\u00a04)6. MleS is a homodimeric protein with a molecular weight of 60\u201365\u2009kDa per subunit and has NAD+ and Mn2+ as bound cofactors30,31,32. The decarboxylation reaction proceeds in three consecutive steps without detectable accumulation of intermediates: (i) L-malate oxidation to oxaloacetate; (ii) decarboxylation of oxaloacetate to pyruvate; and (iii) pyruvate reduction to L-lactate (Supplementary Fig.\u00a04)26. The NAD+ consumed in the first step is recycled in the third step. The proton consumption leads to alkalinization of the cytoplasm.\n\na L-malate decarboxylation reaction catalyzed by MleS. b Representative size-exclusion chromatogram of MleS and SDS-polyacylamide gel showing purified MleS. Similar results were obtained from three independent purification trials. (Uncropped gel in Supplementary Fig.\u00a027). c pH traces were recorded with a pH microelectrode for the L-malate decarboxylation reaction at 30\u2009\u00b0C and pH 7. The reaction started with the addition of 5\u2009mM Na-L-malate at t\u2009=\u20090. pH traces in the absence of Mn2+ and NAD+ and in the presence of 5\u2009mM and 25\u2009mM of Na-L-lactate are indicated. pH was recorded at intervals of 1\u2009s. d pH curves obtained for the decarboxylation reaction at different concentrations of Na-L-malate using 150\u2009nM MleS and pH 7. pH curves correspond to the mean from independent experiments with different enzyme preparations (n\u2009=\u20093). Shaded regions correspond to \u00b1 SD. e L-malate dependence of MleS calculated from the initial rates of alkalinization (first 10\u2009seconds) obtained from pH curves in d, and using a titration curve (Supplementary Fig.\u00a05) to convert pH changes into \u00b5mol of H+. Solid line corresponds to a Michaelis-Menten fit of the experimental data (R2\u2009=\u20090.987). f pH dependence of the initial rates of H+ consumption obtained for the decarboxylation of 5\u2009mM Na-L-malate in low buffered solution at 30\u2009\u00b0C and using 150\u2009nM of enzyme. For pHs 4\u20135 the buffer solution was 2\u2009mM of K-acetate, while for pHs 6\u20138 the buffer consisted of 2\u2009mM K-phosphate. Initial rates of H+ consumption were calculated as indicated in e, using distinct titration curves for every pH. The solid line corresponds to the fitting of experimental data to a logistic peak function (R2\u2009=\u20090.992). Data points in e represent the mean of the H+ consumption rate \u00b1 SD (n\u2009=\u20093) from independent experiments with different enzyme preparations. Data points in f represent the mean of the H+ consumption rate \u00b1 SD (n\u2009=\u20093) from independent experiments with the same enzyme preparation. a was created with Biorender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.\n\nWe overexpressed L. lactis MleS and purified the protein by IMAC and size-exclusion chromatography (SEC). A single and symmetrical peak in the SEC and SDS-polyacrylamide gel confirms the production of a monodisperse protein (Fig.\u00a02b). The L-malate decarboxylation activity of MleS was determined by pH measurements in 2\u2009mM of potassium phosphate and is presented as H+ consumption rate (\u00b5mol H+ consumed\u2009min\u22121\u2009mg\u2009MleS\u22121) (Fig.\u00a02c\u2013f). The H+ consumption was calibrated by titration of the reaction buffer with NaOH (Supplementary Fig.\u00a05). We noticed that on longer timescales the amount of consumed H+ was lower than expected for a reaction with a Keq of 5.5\u2009\u00d7\u2009102, calculated by eQuilibrator33 (Supplementary Fig.\u00a06d). To verify if the enzymatic reaction was not running to completeness, we followed the production of L-lactate by HPLC after derivatization with 9-chloromethyl anthracene (Supplementary Fig.\u00a06). Virtually complete decarboxylation of 5\u2009mM L-malate was confirmed by the production of approximately 5\u2009mM of L-lactate (Supplementary Fig.\u00a06 d). We explain the leveling off of the pH by the dissolution of CO2 and the formation of bicarbonate plus a proton, which opposes the alkalinization of the decarboxylation reaction. The pH recordings and L-lactate measurements show excellent correspondence for the initial 30\u2009s of the reaction (Supplementary Fig.\u00a06d, inset), and therefore, the initial rates of H+ consumption were used to determine the kinetic parameters of MleS (Table\u00a01 and Fig.\u00a02c\u2013f). There is no deleterious effect of L-lactate up to 5\u2009mM (Fig.\u00a02c and Supplementary Fig.\u00a07), but the initial rate of H+ consumption was reduced by 50% in 25\u2009mM L-lactate (Fig.\u00a02c). The Kmapp for L-malate is 1.4\u2009\u00b1\u20090.4\u2009mM and Kmapp for NAD+ is 42\u2009\u00b5M (Supplementary Fig.\u00a08) at pH 7. In summary, we show that MleS is a relatively fast enzyme with a kcat of 266\u2009\u00b1\u200914\u2009s\u22121 around pH 7.\n\nWe determined the coupled activities of MleP and MleS in vesicles in which we first reconstituted MleP and then encapsulated MleS, along with NAD+ plus MnCl2 (Fig.\u00a03a). We used MleS concentrations and MleP LPRs that would yield at least one dimer, even in the smallest vesicles (~100\u2009nm); the other components were encapsulated in a large excess (Table\u00a02). We encapsulated the hydrophilic fluorescent probe pyranine (8-hydroxypyrene-1,3,6-trisulfonic acid or HPTS) for ratiometric quantification of the intravesicular pH (Supplementary Fig.\u00a010). Preliminary experiments with liposomes showed that a small fraction of pyranine is retained at the outer surface of the vesicles, even after extensive washing (two cycles of ultracentrifugation and resuspension, and a final gel filtration step) (Supplementary Fig.\u00a011). Therefore, we included the collisional quencher DPX (p-xylene-bis-pyridinium bromide) in the external medium for every measurement34. Additionally, we inhibited any MleS, possibly adsorbed to the outer surface of the vesicles, by using EDTA to chelate Mn2+ ions that are required for activity (Supplementary Fig.\u00a012a). Due to the low rate of L-malate uniport by MleP (Fig.\u00a01f), we included 2\u2009mM of L-lactate inside the vesicles to enable rapid L-malate/L-lactate exchange. We kept the same concentration in the external medium, because L-lactic acid (in fast equilibrium with L-lactate) rapidly permeates the membrane (Supplementary Fig.\u00a018a). Indeed, when L-lactate is not initially present in the external medium, the alkalinization is slower because initially, only L-malate uniport is possible, but the internal pH reaches a higher point than in the presence of external L-lactate (Supplementary Fig.\u00a013).\n\na Cartoon of the L-malate decarboxylation pathway in liposomes. The consumption of H+ leads to an internal alkalinization and thus a \u0394pH (alkaline inside) across the membrane (violet). The electrogenic exchange of internal L-lactate by external L-malate mediated by MleP generates a \u0394\u03a8 (negative inside) (green). b SDS-polyacrylamide gel of MleP LPR 250 (w/w) proteoliposomes in E. coli polar lipids:egg PC 3:1 (mol ratio) with 2.5\u2009\u00b5M MleS encapsulated. (Uncropped gel in Supplementary Fig.\u00a09). c Internal pH of full system (MleP+MleS) reported by pyranine (n\u2009=\u20095) or only MleS (No MleP, n\u2009=\u20092) or only MleP (No MleS, n\u2009=\u20092). Na-L-malate was added at t\u2009=\u20090 to start the decarboxylation pathway (downward arrow). d Effect of pH on the \u0394pH formed by the L-malate decarboxylation pathway reconstituted in liposomes. e Effect of dissipation of \u0394\u03a8 (red, n\u2009=\u20093) and \u0394pH (dark yellow) on the internal pH (as indicated in c) with valinomycin or nigericin, respectively. Valinomycin was present before addition of L-malate and nigericin addition is indicated by an upward arrow. f Total L-lactate produced from the L-malate decarboxylation pathway (as in c), quantified by RP-HPLC after 9-CMA derivatization. Data points correspond to the mean of L-lactate concentration from independent replicates with different sample preparations (n\u2009=\u20092). Internal pH curves in c\u2013e correspond to the mean of pH from n independent experiments with different preparations of proteoliposomes. pH curves were calculated from the ratio of the pyranine fluorescence intensities at the excitation wavelengths 450\u2009nm and 405\u2009nm, using the calibration curve in Supplementary Fig.\u00a010. Shaded areas represent\u2009\u00b1\u2009SD. a was created with Biorender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.\n\nUpon addition of 10\u2009mM L-malate to the MleP-MleS containing vesicles, the internal pH increased from 7.0 to 7.50\u2009\u00b1\u20090.03 (Fig.\u00a03c) and then over a period of 10\u2009h gradually decreased to 7.34\u2009\u00b1\u20090.07 (Supplementary Fig.\u00a014). The drop in internal pH was not observed at pH 6.0 (Fig.\u00a03d). No alkalinization was observed when either MleP or MleS were absent (Fig.\u00a03c), indicating that the formation of a pH gradient (\u0394pH) requires the coupled activities of MleP and MleS. The rate of alkalinization increases with lower LPR (more MleP per vesicle) and higher amounts of encapsulated MleS (Supplementary Fig.\u00a015). Although the kcat of the enzymatic reaction is \u224810\u00d7 higher than the estimated turnover number of MleP, the MleP/MleS ratio (in molecules per vesicle) was always higher than 1 (range 2\u201313), explaining the increase in activity with MleS concentration (Supplementary Fig.\u00a015). However, the rate of acidification shows a stronger dependence on the MleP than MleS concentration. A slight decrease in internal pH was observed upon addition of L-malate to vesicles lacking MleS (Fig.\u00a03c), which may reflect uniport of L-malateH\u2212 and dissociation of the proton in the vesicle lumen.\n\nTo demonstrate that the internal alkalinization results in a H+ gradient across the membrane we used the ionophore nigericin, which exchanges K+ for H+. Indeed, nigericin collapses the H+ gradient (Fig.\u00a03e). The formation of a membrane potential by L-malate decarboxylation is evident from the accelerated alkalinization in the presence of the K+-selective ionophore valinomycin, which dissipates the membrane potential \u0394\u03a8 (Fig.\u00a03e). The \u0394\u03a8 (inside negative) slows down the L-malate/L-lactate exchange decreasing thereby the activity of the L-malate decarboxylation pathway.\n\nInterestingly, when the L-malate decarboxylation pathway runs at pH 6, which is the optimal pH for MleS and MleP (Figs.\u00a01e and 2f), the H+ gradient is maintained constant for longer periods of time (Fig.\u00a03d). Finally, CO2 can leave the vesicles by passive diffusion but it can also be converted into bicarbonate plus a proton and thus contribute to acidification of the vesicle lumen.\n\nNext, we monitored the formation of the \u0394\u03a8, using the fluorescent probe DiSC3(5) (3,3\u2019-dipropylthiadicarbocyanine iodide)35,36. This carbocyanine distributes uniformly over the inner and outer leaflet when \u0394\u03a8\u2009=\u20090 (Fig.\u00a04a). \u0394\u03a8 <0 leads to accumulation of the probe in the inner leaflet and quenching of fluorescence (Fig.\u00a04a, b). After equilibration of DiSC3(5) in the membrane of L-lactate-loaded MleP vesicles, the addition of 10\u2009mM L-malate results in a fast quenching of fluorescence (Fig.\u00a04b). This indicates the generation of \u0394\u03a8 <0 as a consequence of the L-malate/L-lactate exchange. The exchange rapidly reaches electrochemical equilibrium, in which the L-malate gradient is opposed by the \u0394\u03a8, after which the membrane potential slowly decreasing (Fig.\u00a04b). When the same experiment is performed with MleP vesicles containing MleS, a larger quenching is observed (Fig.\u00a04b). The internal conversion of L-malate into L-lactate, catalyzed by MleS, maintains the inward gradient of L-malate and hence, the membrane potential is larger and sustained for a longer period of time (Fig.\u00a04b). Competition between L-malate and L-lactate leads to a gradual decrease in \u0394\u03a8, because fewer molecules of L-malate are transported per unit of time when the L-lactate concentration in the external medium increases. The dissipation of \u0394\u03a8 starts earlier than the dissipation of \u0394pH, which reflects the low capacitance of the lipid bilayer as the translocation of a few charges is sufficient to reduce \u0394\u03a8 substantially.\n\na The fluorescent probe 3,3\u2019-dipropylthiadicarbocyanine iodide (DiSC3(5)) distributes in response to a membrane potential (\u2206\u03a8). b DiSC3(5) fluorescence curves generated for MleP LPR 250 proteoliposomes containing MleS (n\u2009=\u20093) or no MleS (n\u2009=\u20092). After equilibration of DiSC3(5), L-malate was added at t\u2009=\u20090 and the fluorescence quenching effect was recorded. At the end valinomycin was added to dissipate the \u2206\u03a8. c Effect of pH on the DiSC3(5) fluorescence curve for the L-malate decarboxylation pathway (n\u2009=\u20091). Conditions of the measurements at pH 6 are the same as those at pH 7, but the internal and external buffer was K phosphate pH 6. Measurements were performed at 30\u2009\u00b0C. Solid lines correspond to the fluorescence data normalized to the point immediately before L-malate addition and are presented in arbitrary units (arb. u.). Data in green curves represent the mean of fluorescence from independent experiments with n different preparations of proteoliposomes. Shaded areas in (b, c). indicate \u00b1 SD. Membrane potential was calculated from calibration data (Supplementary Fig.\u00a016) and is presented in the right axis. d Gradient forces calculated from the pH gradient and membrane potential data at pH 7. e Gradient forces calculated from the \u0394pH and membrane potential data at pH 6. pH curves after addition of 10\u2009mM L-malate (violet) were taken from Fig.\u00a03d and the driving forces were calculated from Z\u0394pH=2.303(RT/F)\u0394pH, where R is the gas constant (8.31\u2009J\u2009mol\u22121\u2009K\u22121), T is temperature in Kelvin (303\u2009K) and F is the Faraday constant (96485\u2009C\u2009mol\u22121). \u0394pH was determined assuming that at t\u2009=\u20090 pHi\u2009=\u2009pHo, and that the external pH does not change substantially. Since \u0394pH is a positive value (pHi \u2013 pHo), the plotted curve corresponds to \u2212Z\u0394pH. Membrane potential (\u0394\u03a8) data (green) were calculated by interpolation of the fluorescence quenching from panel c and Supplementary Fig.\u00a018b, using the calibration curve from Supplementary Fig.\u00a016b. Proton motive force (PMF) curves were calculated from \u2212Z\u0394pH plus \u0394\u03a8. a was created with Biorender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.\n\nThe dequenching of DiSC3(5) fluorescence upon addition of valinomycin confirms that the L-malate-induced quenching corresponds to the formation of a \u0394\u03a8 across the membrane. We calibrated the fluorescence quenching by comparison of the signal generated with K+ diffusion potentials of varying magnitudes (Supplementary Fig.\u00a016). We find \u0394\u03a8\u2009=\u2009\u221279\u2009\u00b1\u20099\u2009mV three min after addition of L-malate when the decarboxylation reaction is done at pH 7 (Fig.\u00a04b, d). Similar as seen with the \u0394pH, the build-up of \u0394\u03a8 is faster and subsequent dissipation occurs at a lower rate when the pathway is operated at pH 6 (Fig.\u00a04c, e), which is in line with the activity of MleP and MleS.\n\nThe dynamics of the PMF generated by the L-malate decarboxylation at pH 7 and 6 follows from the corresponding \u0394pH and \u0394\u03a8 curves, using Eq.\u00a01 (Fig.\u00a04d, e, orange curves). The PMF shows similar dynamics as the membrane potential and is maintained at a higher level at pH 6 than pH 7. In line with the low electrical capacitance of lipid bilayers and the relatively high buffer capacity of the internal medium the \u0394\u03a8 is formed faster than the \u0394pH and is initially the main component of the PMF at pH 7.\n\nNext, we used the \u0394\u03a8 and \u2206pH formed by L-malate decarboxylation to drive the accumulation of L-glutamate and D-lactose. We overexpressed and purified GltP of E. coli and co-reconstituted the protein with MleP in vesicles composed of E. coli polar lipids:egg PC 3:1 (Fig.\u00a05a). Both proteins were co-reconstituted at relatively high LPR (250 each) to assure a high reconstitution efficiency37,38. Cryo-TEM shows the size-distribution and the predominantly unilamellar nature of the vesicles (Supplementary Fig.\u00a017a). Estimation of the incorporation efficiency of GltP and MleP in the same vesicles was not possible by SDS-PAGE, because both proteins migrate similarly. Individually, they were reconstituted with an efficiency of 60 and 50%, respectively (Fig.\u00a05b & Supplementary Fig.\u00a01a, b). MleS, NAD+, Mn2+, sodium-L-lactate plus pyranine were encapsulated in the MleP-GltP vesicles. Co-incorporation of GltP did not significantly affect the performance of the L-malate decarboxylation pathway (Supplementary Fig.\u00a019a). Addition of L-glutamate leads to a small drop in the pH gradient, which is in agreement with 3H+ symported with L-glutamate by GltP (Supplementary Fig.\u00a019b).\n\na Cartoon of the co-reconstituted L-malate decarboxylation pathway and GltP in liposomes. The coupled transport of 3H+ and 1 L-glutamate by GltP is driven by the PMF from the L-malate decarboxylation pathway. b SDS-polyacrylamide gel of purified GltP in DDM (Lane 1), reconstituted at LPR 100 (w/w) (Lane 2), and co-reconstituted with MleP at LPR 100 (w/w) (Lane 3) in E. coli polar lipids:egg PC 3:1 (mol ratio) liposomes. MleS was encapsulated in the vesicles of Lane 2 and 3. (Uncropped gel in Supplementary Fig.\u00a01b). c Glutamate transport upon addition of 10\u2009mM L-malate in MleP LPR 250 - GltP LPR 250 proteoliposomes containing L-malate decarboxylation components. In curves blue and black, proteoliposomes were pre-incubated for 5\u2009min with external 20\u2009\u00b5M Na-L-14C-glutamate before addition of 10\u2009mM of L-malate (blue) or succinate (black) (Glu\u2192Mal and Glu\u2192Succ). In the orange curve, 10\u2009mM L-malate was added at t\u2009=\u20090, and, after 30\u2009min of incubation, the uptake was started by addition of 20\u2009\u00b5M Na-L-14C-glutamate (Mal\u2192Glu). d Dissipation of \u0394\u03a8 and \u0394pH by 1\u2009\u00b5M valinomycin and 1\u2009\u00b5M nigericin, respectively, at t\u2009=\u20093\u2009h. Data correspond to an individual experiment with a single preparation of proteoliposomes. e Cartoon of the L-glutamate transport (in symport with H+) driven by \u0394\u03a8 and \u0394pH, which are formed by valinomycin-mediated K+ diffusion (\u0394\u03a8) and acetate (AcO\u2212)/acetic acid (AcOH) diffusion (\u0394pH) potentials in GltP liposomes. f Comparison of the L-glutamate transport driven by the PMF from the L-malate decarboxylation pathway (blue) with that from K+ and acetate diffusion potentials in MleP-GltP proteoliposomes (n\u2009=\u20091). All the experiments were performed at 30\u2009\u00b0C and pH 7. Data for the blue curves in (c, d, and f). are presented as the mean of L-glutamate uptake (nmol of internalized L-glutamate per mg of GltP)\u2009\u00b1\u2009SD from independent replicates (n\u2009=\u20095) with different preparations of proteoliposomes. a, e were created with Biorender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.\n\nFigure\u00a05c shows the uptake of L-glutamate, driven by the PMF that is generated by L-malate decarboxylation. After 40\u201350\u2009min a steady state is reached, which lasts at least 4\u2009hours; the accumulation level ([Glu]IN/[Glu]OUT) is ~140 (based on a specific internal volume of 3\u2009\u00b5L/mg of lipid39) (Fig.\u00a05c & Supplementary Fig.\u00a020). The uptake of L-glutamate is sigmoidal, because it takes some time to generate the PMF. When the transport reaction is initiated 30\u2009min after the start of the L-malate decarboxylation (i.e., pre-formed PMF), there is no delay and the initial glutamate uptake increases linearly with time (compare blue and orange lines, Fig.\u00a05c). There is no transport of L-glutamate when succinate instead of L-malate is used (Fig.\u00a05c, inset). The rate of L-glutamate uptake depends on the L-glutamate concentration with a Kmapp\u2009=\u200933\u2009\u03bcM (Supplementary Fig.\u00a020). In line with the effect on the PMF (Figs.\u00a03d and\u00a04c), the rate of glutamate transport driven by the L-malate decarboxylation at pH 6 is not substantially different from that at pH 7 but the accumulation level is higher at pH 6 (Supplementary Fig.\u00a021), which is in agreement with the higher driving force. The accumulation level of ~140 matches the \u0394pH (0.5\u2009pH units) and \u0394\u03a8 (\u221220\u2009mV) at pH 7 obtained 50\u2009min after L-malate addition (Fig.\u00a04d) and the H+/glutamate\u00af stoichiometry of 3. Equation\u00a02 yields a [Glu]IN/[Glu]OUT of ~150-fold for the PMF generated by L-malate decarboxylation, indicating a good correspondence between the generated driving force and the formed glutamate gradient via GltP.\n\nAccumulated L-glutamate leaves the vesicles when \u0394\u03a8 and \u0394pH are dissipated by the action of the ionophores valinomycin and nigericin (Fig.\u00a05d). Addition of valinomycin (a highly selective K+ ionophore) leads to a transient pH increment and dissipation of the membrane potential, which results in a small efflux of glutamate. The total PMF is dissipated upon subsequent addition of nigericin (an ionophore that exchanges K+ for H+), and efflux of glutamate to equilibration levels is observed. Since the \u0394pH acts three times as driving force, whereas the \u2206\u03a8 acts twice (See Eq.\u00a02), the dissipation of \u2206\u03a8 has less effect on the steady state levels of glutamate than \u0394pH dissipation. In line with this, lower but sustained levels of glutamate are observed when valinomycin was present from the beginning of the experiment (Supplementary Fig.\u00a022b).\n\nThe power of the L-malate decarboxylation pathway is not only demonstrated by the high levels of L-glutamate uptake, but also by the maintenance of large solute gradients for hours. This is especially clear when L-glutamate accumulation driven by the L-malate decarboxylation is compared with the transport driven by a K+ diffusion potential together with an acetic acid diffusion potential, which is the generic approach to study PMF-dependent transport processes40,41 (Fig.\u00a05e). Dilution of MleP-GltP vesicles, containing Na-acetate, into a solution with a lower concentration of Na-acetate establishes an inward H+ gradient as a consequence of the outward passive diffusion of acetic acid (Fig.\u00a05e). The \u0394pH is proportional to the in/out ratio of the acetate concentration (See Methods). Along with a negative-inside \u0394\u03a8, generated by valinomycin-mediated outward K+ diffusion, the two gradients yield a transient PMF (Fig.\u00a05e) that we used as benchmark for the PMF from the L-malate decarboxylation pathway. With an artificially-imposed pH gradient of 0.5 (alkaline inside) and \u0394\u03a8 varying from 0 to \u2212100 mV, we determined the dependence of L-glutamate transport on the driving force (Supplementary Fig.\u00a023b, c). The maximal rate is higher than with L-malate decarboxylation but L-glutamate leaks out after 10\u2009min, because the \u0394\u03a8 and \u0394pH are transient (Fig.\u00a05f and Supplementary Fig.\u00a023b). Moreover, the acetate gradient yields a \u0394pH \u2248 0.4 that decreases slowly in the absence and rapidly in the presence of \u0394\u03a8 (inside negative) (Supplementary Fig.\u00a023d). Thus, the transient nature of diffusion potentials and the interdependence of \u0394\u03a8 on \u0394pH and vice versa prohibit thermodynamic analyzes of transport reactions as exemplified here by Eq.\u00a02. By contrast, the L-malate decarboxylation pathway yields smaller gradients but they can be kept constant for hours (Figs.\u00a03 and\u00a04).\n\nBy comparing the initial rate of L-glutamate uptake driven by the L-malate decarboxylation with the initial rates of glutamate uptake driven by diffusion potentials (Supplementary Fig.\u00a023b\u2013c), we estimate that the driving force from the L-malate decarboxylation is comparable to a \u0394pH of 0.5 (by acetate diffusion) and a membrane potential of ~\u221240\u2009mV (by valinomycin-mediated potassium diffusion) and thus a PMF of \u221270\u2009mV, which is in line with direct measurements of \u0394pH and \u0394\u03a8 by the fluorometric probes pyranine and DiSC3(5) (Fig.\u00a04d).\n\nWe also co-reconstituted the L-malate decarboxylation pathway with E. coli lactose permease, LacY25, which functions as a H+/galactoside symporter (Fig.\u00a06a). The (co-)reconstitution protocol for LacY-MleP42,43,44 differs from the one we used for GltP-MleP, but we obtained mostly unilamellar vesicles as shown by cryo-TEM (Supplementary Fig.\u00a017b). We find that co-reconstitution mediated by octyl-\u03b2-D-galactopyranoside (OG), and detergent removal via rapid dilution, generated the largest L-malate dependent D-lactose uptake (Supplementary Fig.\u00a024). The \u0394pH formed by L-malate decarboxylation was comparable with and without LacY in the vesicles (see Supplementary Fig.\u00a025 and Fig.\u00a03d). D-lactose uptake reached its maximal level after 2\u2009hours (Fig.\u00a06c); [lactose]IN/[lactose]OUT\u2009~\u200920 (or 75\u2009mV). A slight reduction in accumulation level was observed at later times, presumably due to a decrease in PMF or as a result of an uncoupled lactose efflux. Since lactose is taken up with 1 proton the accumulation is much lower than for glutamate, which is symported with 3 protons; the [lactose]IN/[lactose]OUT gradient of 75\u2009mV is in line with a PMF of \u221270\u2009mV (see Eq.\u00a03).\n\na Cartoon of the co-reconstituted L-malate decarboxylation pathway and LacY in liposomes. b SDS-polyacrylamide gel of purified LacY (Lane 1), reconstituted at LPR 100 (w/w) (Lanes 2 and 3). Lane 4, co-reconstituted MleP LPR 100 (w/w) and LacY LPR 100 (w/w). Samples of lanes 3 and 4 contained MleS. Lipid system: E. coli polar lipids: egg PC 3:1\u00a0(mol ratio). Uncropped gel in Supplementary Fig.\u00a01c. c Comparison of the L-malate-induced 14C-D-lactose transport driven by the L-malate decarboxylation (red) with transport driven by a \u0394pH generated from acetate diffusion with (blue) and without (yellow) valinomycin-mediated \u0394\u03a8 in MleP LPR 250\u2013 LacY LPR 200 (w/w) proteoliposomes. Inset: zoom in on the initial D-lactose uptake curve. Data in the red curve represent the mean of D-lactose uptake (nmol of D-lactose\u2009mg\u22121 LacY) from n\u2009=\u20092 independent replicates with different preparations of proteoliposomes. d Cartoon of vesicles with L-malate decarboxylation pathway plus reaction for hydrolysis of lactose (LacZ), phosphorylation of glucose by hexokinase (HK), using Mg-ATP, and oxidation of glucose-6-phosphate (G6P-DH), using NADP+. e NADPH production in vesicles with MleP-LacY (black and blue) or only MleP (green) plus MleS, LacZ, HK, and G6P-DH. L-malate decarboxylation was started by addition of 10\u2009mM Na-L-malate 30\u2009min before D-lactose (100\u2009\u00b5M) addition at t\u2009=\u20090 in absence (black and green) or presence (blue) of 20\u2009mM of non-hydrolysable thiodigalactoside (TDG). Data correspond to the mean of fluorescence from 5 (black), 3 (blue) and 2 (green) technical replicates. f Effect of 20\u2009mM methyl-\u03b2-D-thiogalactoside (TMG) or TDG on the lactose metabolism. Error bars indicate \u00b1 SD from 5 (gray) and 3 (blue) technical replicates. g D-lactose dependence of NADPH production in MleP-LacY vesicles. h D-lactose dependence of NADPH production presented as the initial rate of NADPH fluorescence change. Solid line represents a Michaelis-Menten fit to experimental data (R2\u2009=\u20090.991). The half saturation constant\u2009=\u2009~0.2\u2009mM. NADPH fluorescence was followed at excitation of 350\u2009nm and emission of 460\u2009nm (slit width of 5\u2009nm). a, d were created with Biorender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.\n\nThus, the L-malate decarboxylation pathway forms a PMF that is stable on the timescale of hours and allows the accumulation of lactose and L-glutamate to a point of good correspondence between the generated PMF and the established solute gradient from the active transport.\n\nNext, we encapsulated \u03b2-galactosidase (LacZ), hexokinase (HK) plus glucose-6-phosphate dehydrogenase (G6P-DH) in MleP-LacY vesicles with L-malate decarboxylation pathway. These enzymes catalyze the hydrolysis of D-lactose into galactose and glucose, phosphorylation of glucose to glucose-6-phosphate (G6P, entry point for glycolytic pathway) and formation of 6-phosphoglucono-\u03b4-lactone (pentose phosphate pathway) plus NADPH (Fig.\u00a06d). The reduction of NADP+ to NADPH was used to monitor the activity of the overall pathway (Fig.\u00a06e). We ran the L-malate decarboxylation for 30\u2009min to pre-form a PMF and then added D-lactose, which elicits an increase in NADPH fluorescence that is not observed in vesicles without LacY (Fig.\u00a06e). The NADPH fluorescence is reduced in the presence non-hydrolysable substrates methyl-\u03b2-D-thiogalactoside (TMG) and thiodigalactoside (TDG) (Fig.\u00a06e, f), which act as low (KD\u2009\u2248\u20091\u2009mM) and high (KD\u2009=\u200930\u201350\u2009\u00b5M) affinity competitive inhibitors of lactose transport, respectively45,46,47. NADPH production is dependent on the D-lactose concentration with a half saturation constant (Kmapp) of ~0.2\u2009mM), which is close to the apparent Km for LacY (Fig.\u00a06g, h and Table\u00a01). In our design of the reaction network, the maximal levels of NADPH formed are determined by the amount NADP+ encapsulated in the vesicles; additional control experiments are shown in Supplementary Fig.\u00a026a. Finally, NADPH was also formed in the absence of L-malate decarboxylation albeit at a lower rate. Since lactose is internally hydrolyzed, an out-to-in lactose gradient is maintained to facilitate the uptake of lactose. Furthermore, the galactose formed upon hydrolysis of lactose by LacZ is a substrate of LacY and enables lactose/galactose exchange, which is even faster than lactose-H+ symport driven by the PMF (Supplementary Fig.\u00a026b)45,48. Yet, the fastest metabolism of lactose is observed when a PMF is formed by the L-malate decarboxylation pathway and used to drive the uptake of lactose. We thus show full functionality of a reaction network involving (i) PMF generation by electrogenic transport and decarboxylation of L-malate; (ii) PMF consumption by lactose-H+ symport; (iii) metabolism of lactose to galactose plus 6-phosphoglucono-\u03b4-lactone; and (iv) reduction of NADP+ to NADPH.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52085-z/MediaObjects/41467_2024_52085_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52085-z/MediaObjects/41467_2024_52085_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52085-z/MediaObjects/41467_2024_52085_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52085-z/MediaObjects/41467_2024_52085_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52085-z/MediaObjects/41467_2024_52085_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-52085-z/MediaObjects/41467_2024_52085_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We have developed a minimal PMF-generating system in lipid vesicles that enables the efficient uptake of nutrients via secondary active transporters. We show that transport of the amino acid L-glutamate and the sugar lactose, mediated by GltP and LacY, respectively, reach maximal accumulation levels in good agreement with the generated driving force. Furthermore, we have shown coupling of this chemiosmotic system to the initial steps of lactose metabolism and the formation of NADPH. This work is part of our efforts to construct synthetic cells from molecular components and the development of pathways for long-term fueling of energy requiring processes. In comparison to strategies to feed synthetic cells with building blocks by diffusion via pore-forming toxins49, nanopores50,51 or low-selectivity channels52, the L-malate decarboxylation pathway and ion-linked transporters allow the accumulation of nutrients against their concentration gradient. This is an essential feature of living cells, not only to transport molecules in but also to pump metabolic end products out. Additionally, the components of the system presented here are proteins that can be expressed and regenerated from their genetic units by an encapsulated transcription-translation machinery, contributing to the construction of an autonomous synthetic cell53,54.\n\nBesides the sustained chemiosmotic transport of nutrients, our study offers kinetic and mechanistic insights into the two protein components of the L-malate decarboxylation pathway (Figs.\u00a01 and 2). Particularly, the association of the half turnover electrical signals (Fig.\u00a01g, h) with the presence of a highly conserved positively charged residue in the binding pocket of 2HCT family members55,56 suggests that the preferred species transported by MleP are the monoprotonated forms of L-malate (HMal\u2212) and L-lactate (L-lactic acid). Thus, we conclude that MleP generates a membrane potential by mono-anionic L-malate/L-lactic acid antiport or at a much lower rate by mono-anionic L-malate uniport.\n\nThe L-malate decarboxylation pathway is a particularly useful and robust system for the provision of metabolic energy in the form of a PMF, because: (i) The L-malate decarboxylation pathway is constituted of only two proteins: an integral membrane protein that works as an electrogenic MleP and a soluble enzyme that catalyzes the decarboxylation of L-malate to L-lactate (MleS). Decarboxylation pathways involving an electrogenic exchange or antiport reaction are arguably the simplest mechanisms to generate an electrochemical proton gradient across the membrane. (ii) MleP exchanges structurally related substrates, L-malate and L-lactic acid, which have in common the 2-hydoxycarboxylate unit. The direction of transport is determined by the substrate concentration gradients and not by the orientation of MleP in the membrane57,58. Indeed, we show that an inside negative potential is formed when L-malate is taken up in exchange for internal L-lactate, whereas a positive potential is formed for L-malate exit in exchange for external L-lactate (Fig.\u00a01). This is a technical advantage over primary transporters that facilitate H+ translocation in a light-dependent process59,60, a redox reaction61 or ATP hydrolysis62. (iii) MleP and MleS are more active at pH 6 (Figs.\u00a01 and 2) than at pH 7 or higher, implying a built-in mechanism for pH homeostasis. Thus, more protons are taken up by the decarboxylation pathway when the internal pH decreases, e.g. as a result of a lower external pH or import of protons via secondary active transporters such as GltP and LacY. The relative simplicity and versatility of the pathway to generate a PMF and to maintain the internal pH relatively constant is an advantage for the further integration of metabolic modules in synthetic cells. Indeed, we show successful integration of the L-malate decarboxylation pathway with a metabolic network for the formation of precursors of the glycolytic (glucose-6P) and pentose phosphate pathway (6-phosphoglucono-\u03b4-lactone) from the internalized lactose. The main function of the pentose phosphate pathway is to provide the cell with precursors for nucleotide synthesis and reducing power (NADPH)63. Hence, the here presented system constitutes a platform for the integration of other essential functions like redox or ATP/ADP homeostasis64,65.\n\nWhat are the limitations of the L-malate decarboxylation pathway? First, L-lactic acid (present in low amounts at pH 7 but in rapid equilibrium with L-lactate) is highly membrane permeable66,67,68, which in our system results in competition between external L-lactic acid with L-malate. This reduces the exchange rate and, hence, limits the generation of PMF. Second, the decrease in activity in MleP and MleS at pH\u2009>\u20096 may impose a limit on the capacity of the L-malate decarboxylation pathway to generate a PMF at alkaline pH values.\n\nThe PMF in living cells fluctuates and responds to changes in the external medium69,70. PMF values in cells have been reported to fall within the range \u2212100 to \u2212270\u2009mV, depending on the specific organism and conditions71. The PMF from the L-malate decarboxylation pathway in liposomes at pH 7 was maximally\u2009\u2248\u2009\u2212100\u2009mV, i.e., 8\u2009min after L-malate addition and reached\u2009\u2248\u2009\u2212120\u2009mV after 12\u2009min at pH 6 (Fig.\u00a04d, e). Subsequently, the PMF decreased to ~\u221270\u2009mV at pH 7, mostly as a consequence of the partial dissipation of the membrane potential (Fig.\u00a04d). Hence, at longer times, the contribution of \u0394pH to the PMF increased relative to that of the \u0394\u03a8. We used in most of our studies a pH of 7; the PMF is higher at lower pH values reflecting the pH regulation of MleP and MleS.\n\nThe accumulation of L-glutamate and D-lactose nicely follows the transients in the PMF, suggesting the energy from the PMF is actively transformed into a substrate gradient by the symporters and, remarkably, we obtain H+/solute stoichiometries of ~3 for GltP and ~1 for LacY. These values nicely match the mechanistic stoichiometries of these proteins. In older literature, the relationship between the magnitude of the PMF and lactose accumulation has been found condition-dependent, which has led to the suggestion that the mechanistic stoichiometry of LacY (and other transporters) may vary. Our estimates match the mechanistic stoichiometry, which may be due to the fact that we work at relatively low PMF and close to pH 7, where leak pathways may be less prominent72,73.\n\nThe internal L-glutamate concentration reaches 1\u2009mM, when the external concentration is only 20\u2009\u00b5M. For biosynthesis, the cellular amino acid concentrations are typically in the low millimolar range; the glutamate concentrations are typically higher because this amino acid also serves a role as compatible solute, and its oxidation can feed oxidative phosphorylation with reducing equivalents for in vitro protein synthesis74,75,76,77,78. Reaching intravesicular concentrations in the GltP vesicles similar to those in E. coli would require an external concentration of L-glutamate of 0.6\u20130.8\u2009mM. We also note that, at 20\u2009\u00b5M of L-glutamate, GltP operates at <40% of its maximal rate.\n\nA reference value for the intracellular concentration of lactose is not available, because in cells the disaccharide is quickly broken down to galactose plus glucose, which are further metabolized via the Leloir and glycolytic pathway79, respectively. We have determined the uptake of lactose at an external concentration of 50\u2009\u03bcM, which is well below the Km of LacY (0.5\u2009mM, Table\u00a01). Hence, a much faster uptake is feasible at higher substrate concentration. We also notice slow lactose efflux at higher internal lactose concentrations (at t\u2009>\u20092\u2009h, Fig.\u00a06c), which is possibly linked with H+:lactose uncoupling events that are more prominent at alkaline pH values73,80. Indeed, uncoupling of solute-H+ symport has been reported as strategy to prevent unconstrained accumulation of solutes81,82.\n\nIn summary: we show sustainable and long-term accumulation of an amino acid and metabolism of a sugar, driven by a PMF-generating pathway that could be implemented in various types of synthetic cells and used to sustain far-from-equilibrium metabolism. A synthetic cell requires 20 amino acids (but also other nutrients), which could be taken up by separate amino acid transporters. To limit the number of transporters to be reconstituted and later on to be produced by in-vesicle synthesis and membrane insertion, we envisage the use of a broad specificity di-/tripeptide transporter such as DtpT together with luminal peptidases to supply the cell with all amino acids41,83,84. Similarly, the integration of H+:ribonucleoside symporters along with ribonucleoside kinases or H+:nucleotide symporters can provide building blocks for the synthesis of DNA and RNA85,86. Co-reconstitution of the L-malate decarboxylation pathway along with Na+/H+ antiporters is a strategy to fine-tune the pH homeostasis or convert the PMF into a SMF. This would extend the possibilities of building block carriers to symporters driven by a Na+ electrochemical gradient, e.g., as present in mammalian cells. Given the importance of the PMF (and SMF) as energy carrier in all organisms from all kingdoms of life, the here-developed chemiosmotic network may inspire other studies of molecular and cellular processes that require electrochemical ion gradients.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The plasmids bearing the mleP and mleS genes were constructed following the method from87 as follows: The mleP and mleS genes were amplified from the genome of L. lactis IL1403 by PCR with primers mleP_clic_fw and mleP_clic_rev for MleP, and mleS_clic_fw and mleS_clic_rev for MleS (Supplementary Table\u00a02), using Phusion HF DNA polymerase (Thermo Fisher Scientific, Inc.). This yielded plasmids pNZ_clic_MleP and pNZ_clic_MleS. Both genes are under the nisin A-inducible Pnis promoter, and the proteins have a TEV cleavable 10 His-tag at the C-terminus. Both pNZ_clic_MleP and pNZ_clic_MleS were transformed into L. lactis NZ9000.\n\nExpression of L. lactis MleP and MleS was performed in a batch culture as follows: L. lactis NZ9000 cells transformed with pNZ_clic_MleP or pNZ_clic_MleS were grown in 3\u2009L of rich media (2% (w/v) Gistex, 65\u2009mM sodium phosphate pH 7, 1% (w/v) glucose) supplemented with 5\u2009\u00b5g\u2009mL\u22121 chloramphenicol at 30\u2009\u00b0C (without stirring) after inoculation with 50\u2009mL of an overnight pre-culture. At an optical density (OD600) of 0.5, the expression was induced with 0.05% (v/v) of culture supernatant from a nisin A-producing strain. The strains were grown for an additional 2\u2009hours and then harvested by centrifugation (6000\u2009\u00d7\u2009g, 4\u2009\u00b0C, 15\u2009minutes), washed once, resuspended in ice-cold 100\u2009mM potassium phosphate pH 7 to an OD600\u2009\u2248\u2009100, frozen in liquid nitrogen, and stored at \u221280\u2009\u00b0C.\n\nGltP was produced from plasmid pBad24\u2010GltP. Expression was performed in Escherichia coli MC1061 cells, grown in LB broth at 37\u2009\u00b0C and shaken at 200\u2009rpm. Ampicillin was added to the cultures to a final concentration of 100\u2009\u00b5g\u2009mL\u22121. At an optical density at 600\u2009nm of 0.8\u20131.0, L\u2010arabinose was added to a final concentration of 0.01% (w/v) and the temperature was switched to 25\u2009\u00b0C. Five hours after induction, the cells were harvested (6268\u2009\u00d7\u2009g, 10\u2009min, 4\u2009\u00b0C, Beckman JLA 9.1000 rotor), washed once, and resuspended in ice-cold 20\u2009mM Tris\u2010HCl pH 8 to an OD600\u2009\u2248\u2009150, frozen in liquid nitrogen, and stored at \u221280\u2009\u00b0C.\n\nE. coli BL21 cells transformed with the plasmid pT7C3H-lacY (containing the coding region of LacY and the protein tagged with 10\u00d7 His at the C-terminus) were grown in LB medium supplemented with 100\u2009\u00b5g\u2009mL\u22121 of ampicillin at 37\u2009\u00b0C and stirring at 150\u2009rpm. Induction of protein expression was performed at 30\u2009\u00b0C at an OD600 of 0.5 with 0.4\u2009mM of isopropyl \u03b2-D-1-thiogalactopyranoside for a period of 3\u2009h. Cells were harvested by centrifugation (6000\u2009\u00d7\u2009g, 4\u2009\u00b0C, 15\u2009minutes), washed once, resuspended in ice-cold 100\u2009mM potassium phosphate pH 7 to an OD600\u2009\u2248\u2009100, frozen in liquid nitrogen, and stored at \u221280\u2009\u00b0C.\n\nL. lactis cells overexpressing MleS were thawed and lysed at 30 kPsi in a high-pressure homogenizer (HPL6, Maximator) in the presence of 100\u2009\u00b5g\u2009mL\u22121 DNAse, 2\u2009mM MgSO4 and 1\u2009mM PMSF. After lysis, 5\u2009mM of sodium-EDTA was added. Cell debris was removed by centrifugation (15\u2009min, 22,000\u2009\u00d7\u2009g, 4\u2009\u00b0C) and the supernatant was centrifuged for 90\u2009min at 125,000\u2009\u00d7\u2009g and 4\u2009\u00b0C. Protein concentration in the cell lysate was determined by the bicinchoninic acid assay (BCA) (ThermoFisher Scientific Protein Assay kit) using BSA as standard, the cell lysate was frozen in liquid nitrogen and stored at \u221280\u2009\u00b0C. Ni2+-Sepharose resin (Cytiva) was washed with milliQ water and equilibrated with buffer A (200\u2009mM NaCl, 50\u2009mM potassium phosphate pH 7.5) plus 10\u2009mM imidazole. Lysate was thawed on ice and incubated with Ni2+-Sepharose resin (0.5\u2009ml column volume per 50\u2009mg of total protein content) for 1\u2009h with gentle mixing at 4\u2009\u00b0C. The suspension was transferred to a glass chromatography column (Bio-Rad). The resin was washed with 20 column volumes of buffer A plus 50\u2009mM imidazole. Protein was eluted with buffer A containing 500\u2009mM of Imidazole, and the protein concentration was determined from absorbance measurements at 280\u2009nm using a NanodropTM (ThermoFisher Scientific). Fractions with the highest protein concentration were used for size-exclusion chromatography on a Superdex 200 Increase 10/300 GL column (GE Healthcare) in 100\u2009mM NaCl, 50\u2009mM potassium phosphate pH 7.0. Protein was supplemented with 10% glycerol, aliquoted, flash-frozen in liquid nitrogen, and stored at \u221280\u2009\u00b0C.\n\nL. lactis cells overexpressing MleP or E. coli cells overexpressing LacY were thawed and lysed at 30 kpsi (L. lactis) or 20 kPsi (E. coli) in a high-pressure homogenizer (HPL6, Maximator) in the presence of 100\u2009\u00b5g\u2009mL\u22121 DNAse, 2\u2009mM MgSO4 plus 1\u2009mM PMSF. After lysis, 5\u2009mM of sodium-EDTA was added. Cell debris was removed by centrifugation (15\u2009min, 22,000\u2009\u00d7\u2009g, 4\u2009\u00b0C) and the supernatant was centrifuged for 90\u2009min at 205,000\u2009\u00d7\u2009g, and 4\u2009\u00b0C. Supernatant was discarded, and the pellet of cell membranes was resuspended in ice-cold potassium phosphate pH 7 to a total protein concentration of 10\u2009mg\u2009mL\u22121 (Determined by the BCA assay). Resuspended membranes were flash-frozen in liquid nitrogen and stored at \u221280\u2009\u00b0C. Membrane vesicles containing 20\u2009mg of total protein were thawed on ice, and solubilized for one hour with n-dodecyl-\u03b2-D-maltoside (DDM) [0.5% (w/v) for MleP, 1.0% for LacY] in 200\u2009mM NaCl, 50\u2009mM potassium phosphate pH 7.5. Non-solubilized membranes were removed by centrifugation (25\u2009min, 270,000\u2009\u00d7\u2009g, 4\u2009\u00b0C). Ni2+-Sepharose resin (Cytiva) was washed with milliQ water, equilibrated with buffer A (200\u2009mM NaCl, 50\u2009mM potassium phosphate pH 7.5) supplemented with 10\u2009mM imidazole plus 0.03% (for MleP) or 0.05% (for LacY) (w/v) of DDM and added to the solubilized membrane vesicles. The suspension was nutated for 1\u2009h and subsequently transferred to a Poly-Prep chromatography column (Bio-Rad). The resin was washed with 20 column volumes of buffer A plus 50\u2009mM imidazole and 0.03% (for MleP) or 0.05% (for LacY) (w/v) of DDM. Proteins were eluted with buffer A plus 350\u2009mM imidazole and 0.03% (for MleP) or 0.05% (for LacY) (w/v) of DDM. Protein concentration was determined from absorbance measurements at 280\u2009nm using a NanodropTM (ThermoFisher Scientific). 2\u2009mM of \u03b2-mercaptoethanol was added to all the buffers for the purification of LacY.\n\nE. coli cells overexpressing GltP were thawed and lysed at 25\u2009kPsi (E. coli) in a high-pressure homogenizer (HPL6, Maximator) in the presence of 100\u2009\u00b5g\u2009mL\u22121 DNAse, 2\u2009mM MgSO4 plus 1\u2009mM PMSF. Unbroken cells and cell debris were pelleted (30\u2009min, 12,074\u2009\u00d7\u2009g, 4\u2009\u00b0C), and the supernatant was subjected to ultracentrifugation (150\u2009min, 193,727\u2009\u00d7\u2009g, 4\u2009\u00b0C). Membrane pellets were resuspended in 20\u2009mM Tris\u2010HCl pH 8, and stored at \u201080\u00b0C. The protein concentration in the membranes was determined using BCA method, with BSA as a standard. Proteins were solubilized from membrane vesicles in buffer B (300\u2009mM NaCl, 50\u2009mM HEPES pH 8.0), containing 15\u2009mM imidazole pH 8.0 plus 1% n\u2010decyl\u2010\u03b2\u2010maltoside (w/v) (DM), at a final protein concentration of 3\u2009mg\u2009mL\u22121. After incubation on a rocking platform for 60\u2009min, the solution was centrifuged (30\u2009min, 286,286\u2009\u00d7\u2009g, 4\u2009\u00b0C). Supernatants were incubated on a rotating platform for 60\u2009min at 4\u2009\u00b0C with Ni2+\u2010Sepharose slurry (Fast\u2010flow, GE Healthcare, bed volume of 0.5\u2009ml), pre\u2010equilibrated with buffer A. The mixture was loaded on a BioRad Poly\u2010Prep column, and unbound protein was allowed to flow through. Columns were washed with 20 column volumes of buffer C (500\u2009mM KCl, 0.15% DM (w/v), 50\u2009mM MES pH 6.0), supplemented with 150\u2009mM imidazole pH 6.0 and continued washed with 5 column volumes of buffer C, supplemented with 200\u2009mM imidazole pH 6.0. Protein was eluted from the column in three fractions of 350, 800, and 400\u2009\u03bcL using buffer C, supplemented with 500\u2009mM imidazole pH 6.0. The second elution fraction from the affinity chromatography contained most of the purified protein. Protein concentration was determined from absorbance measurements at 280\u2009nm using a NanodropTM (ThermoFisher Scientific).\n\nE. coli polar lipids: egg PC (3:1). E. coli polar lipids were prepared by precipitation with acetone and then extraction with diethyl ether from a commercial extract of E. coli total lipids (Avanti Polar Lipids), according to ref. 88. E. coli polar lipids and egg PC (Avanti Polar Lipids) dissolved in chloroform were mixed to a molar ratio of 3:1.\n\nDOPE:DOPG:DOPC (1:1:2). Synthetic lipids 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE), 1,2-dioleoyl-sn-glycero-3-phospho-(1\u2019-rac-glycerol) (DOPG) and 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) (Avanti Polar Lipids) were dissolved in chloroform to a concentration of 25\u2009mg\u2009mL\u22121 and mixed in a molar ratio of 1:1:2.\n\nFor the formation of vesicles (liposomes), the chloroform in the lipid mixtures was evaporated in a rotary evaporator (B\u00fcchi Labortechnik AG), the lipids were washed with diethyl ether, and the lipid film was hydrated and resuspended in 50\u2009mM K-phosphate at pH 7 to a concentration of 20\u2009mg\u2009mL\u22121. Lipid resuspension was facilitated by sonication with a tip sonicator at 70% amplitude, 15\u2009s ON, 45\u2009s OFF for 16 cycles, while the suspension was kept cold in ice-water and subjected to three cycles of freezing (in liquid nitrogen)\u2014thawing (in a water bath at room temperature). Large-unilamellar vesicles (LUVs) were formed by extrusion of the lipid suspension through a 400\u2009nm pore-size polycarbonate filter (Whatman, GE Healthcare).\n\nThe protocol was adapted from ref. 89. Briefly, an aliquot (1\u2009mL) of 20\u2009mg\u2009mL\u22121 of a mixture E. coli polar lipids:egg phosphatidylcholine (PC) 3:1 (mol ratio) or DOPE:DOPG:DOPC 1:1:2 (mol ratio) lipids were resuspended in 50\u2009mM potassium phosphate pH 7.0 and 13\u00d7 extruded through a 400\u2009nm polycarbonate filter (Whatman, GE Healthcare) to form LUVs, diluted to a lipid concentration 4\u2009mg\u2009mL\u22121 in 50\u2009mM potassium phosphate pH 7.0, and destabilized with 10% Triton X\u2212100 by titration to OD540\u2009=\u20090.6 \u00d7 Rsat (Rsat is point of maximal OD540). Detergent-purified membrane protein (\u22481\u2009mg\u2009mL\u22121) was added to the lipid-Triton X\u2212100 mixture at the desired LPR ratio (w/w), and the protein-detergent-lipid mixture was incubated at room temperature for 30\u2009min with gentle agitation. Detergent was removed by consecutive additions of polystyrene beads (BioBeads SM-2, BioRad) with continuous and gentle agitation as follows: 25\u2009mg\u2009mL\u22121 of polystyrene beads were added and incubated for 30\u2009min at room temperature. A second portion of 15\u2009mg\u2009mL\u22121 was added and incubated for 60\u2009min at 4\u2009\u00b0C. A third portion of 20\u2009mg\u2009mL\u22121 was added and incubated overnight at 4\u2009\u00b0C. After the last addition of 40\u2009mg\u2009mL\u22121 the mixture was incubated for 2\u2009h at 4\u2009\u00b0C. Polystyrene beads were discarded, proteoliposomes were harvested by centrifugation (25\u2009min, 270,000\u2009\u00d7\u2009g, 4\u2009\u00b0C) and resuspended to a lipid concentration of 50\u2009mg\u2009mL\u22121 in 50\u2009mM potassium phosphate pH 7.0.\n\nThe protocol was adapted from refs. 42,43,44. Briefly, 1\u2009mL of 20\u2009mg\u2009mL\u22121 of E. coli polar: egg PC 3:1 (mol ratio) lipids were resuspended in 50\u2009mM potassium phosphate pH 7.0 plus 1\u2009mM dithiothreitol (DTT) and dissolved by adding OG to a final concentration of 1.8% (w/v) and incubation at room temperature for 2\u2009h with agitation. DDM-purified membrane protein (\u22481\u2009mg\u2009mL\u22121) was added, and the mixture was incubated on ice for 10\u2009min. The protein-detergent-lipid mixture was diluted at least 100\u00d7 in ice-cold 50\u2009mM potassium phosphate pH 7.0 plus 1\u2009mM DTT, and the proteoliposomes were harvested by centrifugation (120\u2009min, 205,000\u2009\u00d7\u2009g, 4\u2009\u00b0C). The pellet was resuspended in 50\u2009mM potassium phosphate pH 7.0 plus 1\u2009mM DTT to a lipid concentration of 10\u2009mg\u2009mL\u22121, polystyrene beads (BioBeads SM-2, BioRad) were added (50\u2009mg\u2009mL\u22121), and the mixture was incubated overnight at 4\u2009\u00b0C with gentle agitation. Polystyrene beads were discarded, proteoliposomes were harvested by centrifugation (25\u2009min, 270,000\u2009\u00d7\u2009g, 4\u2009\u00b0C) and resuspended to a lipid concentration of 50\u2009mg\u2009mL\u22121 in 50\u2009mM potassium phosphate pH 7.0 plus 1\u2009mM DTT.\n\nMleP LPR 100 vesicles composed of E. coli polar lipids: egg PC 3:1 (mol ratio) was the standard for the characterization of MleP by SSM measurements. This LPR yields about 60 MleP molecules per vesicle of diameter 200\u2009nm, which is sufficient for large and robust electrical signals even at very low concentrations of substrate. Previous studies have shown that the reconstitution efficiency decreases with decreasing LPR37,38. Hence, we increased the LPR to 250 for experiments when multiple membrane proteins had to be reconstituted (MleP with GltP or LacY) and MleS was encapsulated. We tested two different lipid compositions to demonstrate that, besides E. coli polar lipids+egg PC (3:1), MleP also functions in synthetic lipid mixtures like DOPE:DOPG:DOPC (1:1:2) (Fig.\u00a01f and Supplementary Fig.\u00a02), albeit less efficiently (Supplementary Fig.\u00a02).\n\nLiposomes or proteoliposomes were adsorbed onto an SSM pre-formed on a gold sensor chip and transport currents were detected via capacitive coupling, as explained elsewhere28,29. Proteoliposomes with MleP in E. coli polar: egg PC lipids 3:1 (mol ratio) at the indicated LPR (w/w) were used for the encapsulation of sodium-L-lactate or sodium-L-malate in 100\u2009mM potassium phosphate pH 7.0 by 5\u00d7 freeze-thaw cycles (flash-freezing in liquid nitrogen, thawing in an ice-water bath at \u224810\u2009\u00b0C), followed by gentle mixing and 13\u00d7 extrusion through a 200\u2009nm polycarbonate filter (Whatman, GE Healthcare). Extruded proteoliposomes were collected by centrifugation (25\u2009min, 270,000\u2009\u00d7\u2009g, 4\u2009\u00b0C) and resuspended to a final lipid concentration of 5\u2009mg\u2009mL\u22121 in non-activating (NA) solution (see below) containing either L-malate or L-lactate.\n\n10\u2009\u00b5L of MleP proteoliposomes were applied onto an SSM, pre-formed on an octadecanethiol-functionalized gold sensor chip (3\u2009mm diameter, Nanion) by the addition of 1,2-diphytanoyl PC (15\u2009mg\u2009mL\u22121 in n-decane) (Avanti Polar Lipids) and NA solution (see below). The adsorption of proteoliposomes to the SSM was accelerated by centrifugation of the sensor chip at 2500\u2009\u00d7\u2009g, 30\u2009min at room temperature. The sensor chip with the proteoliposomes adsorbed on the SSM\u00a0were loaded into the chamber of a SURFE2R N1 device (Nanion) and a jump in the concentration of substrate was triggered via a software-controlled fast solution exchange protocol29 at room temperature.\n\nThe solution exchange protocol consisted of an initial perfusion of NA solution for 1\u2009sec followed by the perfusion of activating solution (A) for 1\u2009sec and a final perfusion of NA solution for 1\u2009sec. The A solution contained the substrate that initiates the transport. The composition of the NA solution was the same as that in the lumen of the proteoliposomes. Current signals obtained during the perfusion of A solution were taken for quantification purposes (ON signals). For all the measurements, we used 100\u2009mM potassium phosphate at the indicated pH. Ionic strength and osmolarity were made similar for the A and NA solutions; acetate\u2212 and sulfate2\u2212 were used as non-transported anions to replace L-lactate\u2212 and L-malate2\u2212, respectively. For pH dependence, proteoliposomes adsorbed onto the SSM were incubated for 20\u2009min in NA solution at the corresponding pH before the solution exchange for pH equilibration. Supplementary Table\u00a03 summarizes the transport modes and conditions of the solution exchange.\n\nFor the L-malate dependence, the A solution was x mM Na-L-malate, (30\u2212x)\u2009mM Na-sulfate plus 30\u2009mM Na-acetate, while the NA solution was 30\u2009mM Na-L-lactate plus 30\u2009mM of Na-sulfate. For the L-lactate dependence, the A solution was x mM Na-L-lactate, (30\u2212x)\u2009mM Na-acetate plus 30\u2009mM Na-sulfate, while the NA solution was 30\u2009mM Na-L-malate plus 30\u2009mM of Na-acetate.\n\nFor the efflux assays 20\u2009mg of MleP proteoliposomes (lipid-to-protein ratio 400:1) were used. The vesicles were extruded 13 times through a 200\u2009nm pore-size polycarbonate filter, diluted to 6\u2009mL in 100\u2009mM potassium phosphate pH 7.0, and collected by centrifugation (25\u2009min, 270,000\u2009\u00d7\u2009g, 4\u2009\u00b0C). The vesicles were resuspended in as little solution as possible (<100\u2009\u00b5L). To load the vesicles with substrate, 1\u2009mM of 14C-L-malate was added (final specific activity: 600\u2009MBq\u2009mmol\u22121). This suspension was incubated at room temperature for 1\u2009hour, followed by overnight incubation at 4\u2009\u00b0C. The vesicles were diluted to a final concentration of 3.34\u2009mg of lipid mL\u22121 in 100\u2009mM potassium phosphate pH 7.0 or sodium phosphate pH 7.0. Next, 0.2\u2009\u00b5M valinomycin (stock solution of 100\u2009\u00b5M in ethanol) was added to either dissipate any formed membrane potential, or to create a membrane potential (inside negative) in vesicles resuspended in sodium phosphate buffer. 100\u2009\u00b5L samples were taken at given time intervals, diluted in 2\u2009ml of ice-cold quenching buffer (100\u2009mM potassium phosphate pH 7.0), and filtered over 0.45\u2009\u00b5m pore size cellulose nitrate filters. The filters were washed with 2\u2009mL quenching buffer. Radioactivity was quantified by liquid scintillation counting using Ultima Gold MV scintillation fluid (PerkinElmer) and a Tri-Carb 2800TR scintillation counter (PerkinElmer).\n\nL-malate decarboxylation activity catalyzed by MleS was estimated from the H+ consumption with a pH combination microelectrode (BlueLine 16, SI Analytics) in a solution with low buffer capacity. 700\u2009\u00b5L of reaction solution containing 0.5\u2009mM NAD+, 0.1\u2009mM MnCl2, 100\u2009mM KCl, and 2\u2009mM potassium phosphate at pH 7.0 were incubated for 3\u2009min at 30\u2009\u00b0C. The enzyme was added at the concentration indicated in Fig.\u00a02 (50\u2013150\u2009nM), and a temperature equilibration step of 3\u2009min was performed. The decarboxylation reaction was initiated (t\u2009=\u20090) by the addition of sodium-L-malate to a final concentration of 5\u2009mM (or otherwise indicated) from a stock solution whose pH was adjusted to pH 7.0 with NaOH. For the pH dependence, 2\u2009mM potassium phosphate was used at pH 6\u20138, while sodium-acetate was used for experiments at pH 4 and 5, keeping the L-malate concentration at 5\u2009mM and MleS concentration at 150\u2009nM. For the L-malate dependence, the enzyme concentration was 150\u2009nM. The L-malate decarboxylation activity was calculated as the H+ consumption expressed in \u00b5mol H+\u2009min\u22121\u2009mg\u22121, using a titration curve with NaOH to calibrate the buffer capacity. The pH was recorded during the progression of the reaction with the software MultiLab Pilot V5.21 (Xylem Inc.).\n\nThe protocol was adapted from65. 2\u2009mM sodium-L-lactate, 1.0\u2009mM sodium-\u03b2-nicotinamide adenine dinucleotide (NAD+), 0.5\u2009mM MnCl2, 0.1\u2009mM HPTS, pyranine, (ThermoScientific), and 2.5\u2009\u00b5M MleS was mixed with 10\u2009mg of proteoliposomes resuspended to a lipid concentration of 25\u2009mg\u2009mL\u22121 in 50\u2009mM potassium phosphate pH 7 to a final volume of 400\u2009\u00b5L. Encapsulation of soluble components was performed by a 5\u00d7 freeze-thaw cycle (flash-freezing in liquid nitrogen, and thawing in an ice-water bath at \u224810\u2009\u00b0C) followed by gentle mixing and 13\u00d7 extrusion through a 400\u2009nm polycarbonate filter (Whatman, GE Healthcare). External components were removed by gel filtration on a 22\u2009cm long column with Sephadex G-75 (Sigma) pre-equilibrated with 2\u2009mM sodium-L-lactate plus 50\u2009mM potassium phosphate pH 7.0. Proteoliposomes were washed twice with 60\u00d7 volume dilution, followed by centrifugation (25\u2009min, 270,000\u2009\u00d7\u2009g, 4\u2009\u00b0C) and resuspension to a final lipid concentration of 125\u2009mg\u2009mL\u22121. 2\u2009mM of DTT was added to all buffers when vesicles with LacY were used.\n\nThe internal pH of the vesicles was determined from fluorescence of encapsulated pyranine (trisodium-8-hydroxypyrene-1,3,6-trisulfonate, HPTS (ThermoFisher Scientific))90. Pyranine was encapsulated in proteoliposomes (along with the soluble components of the L-malate decarboxylation pathway) at a concentration of 0.1\u2009mM, as described in the previous section. Liposomes or proteoliposomes containing internal pyranine were diluted 50 times in an external solution (2\u2009mM sodium-L-lactate plus 50\u2009mM potassium phosphate pH 7, unless otherwise indicated) to a lipid concentration of 2.5\u2009mg\u2009mL\u22121 in a quartz fluorescence cuvette (105.250 QS, Hellma Analytics). To eliminate the fluorescence from traces of pyranine on the outside, 5\u2009mM of the collisional quencher p-xylene-bis-pyridinium bromide (DPX) was included in the external solution. The mixture was incubated for 10\u2009min at 30\u2009\u00b0C, and the L-malate decarboxylation reaction started by the addition of 10\u2009mM (unless otherwise indicated) of sodium-L-malate (from a 2\u2009M stock pre-adjusted to pH 7 with NaOH). Where the effect of ionophores was to be determined, valinomycin and nigericin dissolved in dimethyl sulfoxide (DMSO) were added to a concentration of 1\u2009\u00b5M (lipid:ionophore \u22483000:1 (mol ratio)). Fluorescence measurements were performed in an FP-8300 spectrofluorometer (Jasco, Inc). Excitation spectra of pyranine between 380 and 480\u2009nm (\u03bbexc) at an emission wavelength (\u03bbem) of 512\u2009nm were taken at intervals of 1\u2009min. The ratio between the fluorescence intensities at \u03bbexc\u2009=\u2009450\u2009nm and 405\u2009nm (F450nm/F405nm) was calculated and interpolated in a calibration curve to obtain the pH values. For the calibration curve: Pyranine was diluted 1000 times in the solution of 50\u2009mM potassium phosphate at the indicated pH to a final concentration of 0.1\u2009\u00b5M, and the excitation spectra were measured as described above. The pH of the buffer was measured right before starting the fluorescence measurements with a pH microelectrode. F450nm/F405nm was plotted against the measured pH (Supplementary Fig.\u00a010c), and the data were fitted to a logistic equation of the form:\n\nWhere y\u2009=\u2009F450/F405, x\u2009=\u2009pH, a\u2009=\u20093.741, k\u2009=\u20092.223 and xc\u2009=\u20097.883. To calculate the pH from the fluorescence excitation spectra of pyranine we used the following equation:\n\nTo measure L-lactate production by MleS in solution, the enzyme was diluted to a concentration of 50\u2009nM in 0.5\u2009mM NAD+, 0.1\u2009mM MnCl2, 100\u2009mM KCl plus 2\u2009mM potassium phosphate pH 7.0 and incubated at 30\u2009\u00b0C for 3\u2009min. The reaction was initiated by the addition of 5\u2009mM sodium-L-malate (pre-adjusted to pH 7 with NaOH). 50\u2009\u00b5L aliquots were taken at 0\u2009s, 10\u2009s, 30\u2009s, 60\u2009s, 2\u2009min, 3\u2009min, 5\u2009min, 10\u2009min, 20\u2009min, and 30\u2009min, and processed as described below.\n\nMleP-proteoliposomes containing 2\u2009mM sodium-L-lactate, 1.0\u2009mM NAD+, 0.5\u2009mM MnCl2 plus 50\u2009mM potassium phosphate pH 7.0 were diluted 50 times in external solution containing 2\u2009mM sodium-L-lactate and 50\u2009mM potassium phosphate pH 7.0 and incubated at 30\u2009\u00b0C for 3\u2009min. The L-malate decarboxylation pathway was initiated by the addition of 10\u2009mM sodium-L-malate (from a 2\u2009M stock pre-adjusted to pH 7 with NaOH), and 50\u2009\u00b5L aliquots were taken at the indicated times over a period of 4 hours and processed as described below. A sample before the addition of L-malate was also taken.\n\nThe protocol for the analysis of L-lactate was adapted from ref. 91. L-malate decarboxylation in solution or in MleP-proteoliposomes was stopped by transferring 50\u2009\u00b5L of samples into 20\u2009\u00b5L of quenching solution (7% perchloric acid plus 4.5\u2009mM EDTA). The excess acid was neutralized by the addition of 15 \u00b5L of 1\u2009M KOH plus 1\u2009M KHCO3, and samples were incubated overnight at \u221220\u2009\u00b0C. Samples were centrifuged at 16,000\u2009\u00d7\u2009g for 5\u2009min at room temperature in a table top centrifuge and 10\u2009\u00b5L of the supernatant was transferred to 20\u2009\u00b5L of 5% (w/v) triethanolamine (TEA) in acetonitrile (ACN) plus 90\u2009\u00b5L of 90\u2009mM tetra-n-butylammonium bromide in acetonitrile. 380\u2009\u00b5L of derivatization reagent (10\u2009mM 9-chloromethyl anthracene (9-CMA, ThermoFisher Scientific) in acetonitrile) was added, and the reaction was run at 70\u2009\u00b0C for 30\u2009min. The solution 9-CMA was previously sonicated in a water bath for 15\u2009min and filtrated through a PTFE filter. Derivatized samples at room temperature were centrifuged for 5\u2009min at 16,000\u2009\u00d7\u2009g and 10\u2009\u00b5L of a 2\u00d7 diluted supernatant was analyzed by RP-HPLC on a 1260 LC HPLC system (Agilent) composed of a G1311B binary pump, G1329B autosampler, G1316A thermostated column compartment and a G1315C diode array detector, using a Shimadzu XR-ODS 3\u2009\u00d7\u200975\u2009mm C18 column. Samples were run with a binary gradient between ACN and water as follows: start was at 30% ACN, 80% ACN at 10\u2009min, 95% ACN from 11 to 15\u2009min, 30% ACN from 16\u2009min to the end (20\u2009min), flow rate 0.9\u2009\u00b5L\u2009min\u22121. The column was kept at 40\u2009\u00b0C, while samples in the autosampler were kept at 10\u2009\u00b0C. Samples were analyzed by detection of absorbance at 365\u2009nm. L-lactate derivative was detected based on the retention time and quantified by interpolation of the peak area from a calibration curve (Supplementary Fig.\u00a06).\n\nThe membrane potential in liposomes and proteoliposomes was estimated using 3,3\u2019-dipropylthiadicarbocyanine iodide (DiSC3(5), Invitrogen) as a fluorescent probe.\n\nMleP at LPR 250 (w/w) in E. coli polar lipids:egg PC 3:1 (mol ratio) liposomes containing 2.5\u2009\u00b5M MleS, 1\u2009mM NAD+, 0.5\u2009mM MnCl2, 2\u2009mM sodium-L-lactate, (\u00b10.1\u2009mM pyranine) plus 50\u2009mM potassium phosphate pH 7.0 were diluted 650 times in 0.98\u2009mL of 2\u2009mM sodium-L-lactate (unless otherwise indicated) plus 50\u2009mM potassium phosphate pH 7.0 to a final concentration of lipids of 0.2\u2009mg\u2009mL\u22121 in a cuvette for fluorescence measurements. The suspension was incubated at 30\u2009\u00b0C for 3\u2009min with constant stirring (700\u2009rpm) using a glass-covered magnetic bar. DiSC3(5) was added to a final concentration of 1\u2009\u00b5M from a 1\u2009mM stock in DMSO. After an equilibration time of 5\u2009min, L-malate decarboxylation was initiated by addition of 10\u2009mM sodium-L-malate (from a 2\u2009M stock pre-adjusted to pH 7.0 with NaOH) and the fluorescence quenching (Fq) was followed for 30\u201360\u2009min. Valinomycin was added to a final concentration of 50\u2009nM from a 50\u2009\u00b5M stock in DMSO to dissipate the membrane potential, which is observed as an increment in fluorescence (Fval). The percentage of fluorescence quenching (\u0394F %) was calculated according to Eq. (6) and interpolated in a calibration curve (obtained for the same liposome samples) to estimate the magnitude of the L-malate-induced membrane potential.\n\nMleP at LPR 250 (w/w) in E. coli polar lipids:egg PC liposomes containing 50\u2009mM of potassium phosphate pH 7.0 were 650 times diluted in 0.98\u2009mL of 50\u2009mM potassium phosphate, 50\u2009mM sodium phosphate or mixtures of these buffers to obtain the desired K+ concentration on outside of the vesicles (Supplementary Table\u00a04). DiSC3(5) was added to a final concentration of 1\u2009\u00b5M from a 1\u2009mM stock in DMSO and, after equilibration, valinomycin was added to a final concentration of 50\u2009nM from a 50\u2009\u00b5M stock in DMSO. As a response to the negative inside membrane potential established from the diffusion of K+ along its concentration gradient, the fluorescence was quenched to a final level (Fq) depending on the magnitude of the K+ gradient. Nigericin was added in order to dissipate the membrane potential causing a fluorescence dequenching (FNig). The calibration curve was constructed by plotting the percentage of quenching (Fluorescence quenching %), calculated according to Eq. (7) and using the imposed potassium diffusion potential.\n\nThe K+ diffusion potential was calculated according to Eq. (8),\n\nwhere R is the gas constant, T is the temperature in Kelvin, and F is the Faraday constant. Data were fitted to an exponential equation (Supplementary Fig.\u00a016b). The membrane potential was calculated from fluorescence quenching curves using Eq. (9),\n\nwhere A\u2009=\u200930.7, k\u2009=\u2009108.4, y0\u2009=\u2009\u221230.9 are fitting parameters of the data in Supplementary Fig.\u00a016b, and \u0394F(%) corresponds to the fluorescence quenching.\n\nFluorescence measurements were performed in an FP8300 spectrofluorometer (JASCO) using a 10\u2009\u00d7\u20094\u2009mm QS cuvette (Hellma Analytics, 109.004\u2009F) at a temperature of 30\u2009\u00b0C under constant stirring (700\u2009rpm) with a glass-covered magnetic bar. Excitation and emission wavelengths were 662 and 680\u2009nm, respectively.\n\nProteoliposomes with MleP, GltP, or LacY containing the soluble components of the L-malate decarboxylation pathway (2\u2009mM sodium-L-lactate, 1.0\u2009mM NAD+, 0.5\u2009mM MnCl2 plus 50\u2009mM potassium phosphate pH 7.0) were 50 times diluted in external solution containing 2\u2009mM sodium-L-lactate, 50\u2009mM potassium phosphate pH 7.0 and 20\u2009\u00b5M of 14C-radiolabelled sodium-L-glutamate (Perkin Elmer) (specific activity 15\u2009mCi/mmol, 555 MBq/mmol) for GltP or 50\u2009\u00b5M of 14C-radiolabelled D-lactose (Amersham) (specific activity 15.4\u2009mCi/mmol, 570.4 MBq/mmol) for LacY and incubated for 5\u2009min at 30\u2009\u00b0C with continuous stirring. 10\u2009mM of sodium-L-malate was added (from a 1\u2009M stock adjusted to pH 7 with NaOH) to start the L-malate decarboxylation. 100\u2009\u00b5L samples were taken at indicated times before and after the addition of L-malate for up to 4\u2009hours, diluted into 2\u2009mL of ice-cold quenching solution (0.1\u2009M LiCl), and filtered over 0.45\u2009\u00b5m pore size nitrocellulose filters to stop the transport. The filter was washed, and radioactivity was quantified by liquid scintillation counting using Ultima Gold MV scintillation fluid (PerkinElmer) and a Tri-Carb 2800TR scintillation counter (PerkinElmer). As an alternative protocol, proteoliposomes were diluted in an external solution without substrate, and the radiolabelled substrate was added 30 min after the addition of L-malate; here, the L-glutamate or D-lactose transport is initiated after the generation of the PMF. When the effect of membrane potential or pH gradient dissipation was to be evaluated, valinomycin or nigericin was added to a final concentration of 1\u2009\u00b5M from a 1\u2009mM stock in DMSO.\n\nTo determine the L-glutamate and D-lactose transport driven by the \u0394\u03a8 and \u0394pH from valinomycin-facilitated K+ diffusion and acetic acid diffusion potentials, respectively, the corresponding proteoliposomes (MleP plus GltP or MleP plus LacY) were loaded with 2\u2009mM sodium-L-lactate, 70\u2009mM potassium acetate and 25\u2009mM of potassium phosphate at pH 7.0. The external composition was 2\u2009mM sodium-L-lactate, 22\u2009mM sodium acetate, 25\u2009mM sodium phosphate pH 7.0, 20\u2009\u00b5M of radiolabelled L-glutamate for GltP (or 50\u2009\u00b5M of radiolabelled D-lactose for LacY), 1\u2009\u00b5M of valinomycin and different ratios of potassium-D-gluconate and sodium-D-gluconate to establish an outward K+ concentration gradient, according to Table\u00a03. In this manner, the internal concentrations of K+ ([K+]IN) and acetate\u2212 ([AcO\u2212]IN) are 109\u2009mM and 70\u2009mM, respectively. The transport was initiated by a 50-fold dilution of proteoliposomes into the external solution (pre-incubated for 3\u2009min at 30\u2009\u00b0C). 100\u2009\u00b5L samples were taken at the indicated time intervals and processed as described above for quantification of radioactivity.\n\nThe driving forces from the pH gradient and membrane potential were calculated, using Eqs. (10) and (8), respectively.\n\n\u03b2-galactosidase (LacZ) from E. coli (Sigma. G5635, lyophilized) was hydrated in 50\u2009mM K-phosphate pH 7, 5\u2009mM \u03b2-mercaptoethanol, 10\u2009mM MgCl2 plus 10% glycerol to a final concentration of 10\u2009mg\u2009mL\u22121 on ice until a translucid solution was obtained. Hexokinase (HK) from yeast (Roche, 11426365001) and glucose-6-phosphate dehydrogenase (G6P-DH) from yeast (Sigma, G7877) were acquired as suspensions in 3.2\u2009M of ammonium sulfate. An aliquot of every resuspension was centrifuged at 15,000\u2009\u00d7\u2009g for 10\u2009min at 4\u2009\u00b0C, and the supernatant was discarded. The pellet was dissolved in 50\u2009mM\u2009K-phosphate pH 7, 5\u2009mM \u03b2-mercaptoethanol, 10\u2009mM MgCl2 plus 10% glycerol to a final concentration of 8.5\u2009mg\u2009mL\u22121 (HK) and 15\u2009mg\u2009mL\u22121 (G6P-DH). Protein concentration was determined from absorbance measurements at 280\u2009nm using a NanodropTM (ThermoFisher Scientific).\n\nSoluble components of the L-malate decarboxylation pathway (2.5\u2009\u00b5M MleS, 1\u2009mM NAD+, 0.5\u2009mM MnCl2 plus 2\u2009mM Na-lactate) along with 7\u2009\u00b5M \u03b2-galactosidase, 7\u2009\u00b5M hexokinase, 4\u2009\u00b5M G6P-DH, 10\u2009mM MgATP, 2\u2009mM NADP+ were encapsulated in MleP LPR 250 (w/w)-LacY LPR 200 (w/w) or MleP LPR 250 vesicles with E. coli polar lipids: egg PC 3:1 (mol ratio) via OG-mediated reconstitution and detergent removal by rapid dilution42,43. Encapsulation, washing and resuspension was performed according to the protocol described in \u201cEncapsulation of soluble components of the L-malate decarboxylation\u201d.\n\nProteoliposomes with encapsulated components were diluted 40\u00d7 in 50\u2009mM K-phosphate pH 7, Na-lactate 2\u2009mM, 1\u2009mM DTT plus 2.5\u2009mM EDTA, unless otherwise indicated, and the suspension was incubated for 5\u2009min at 30\u2009\u00b0C. 10\u2009mM of Na-malate was added from a 1\u2009M stock and the mixture was incubated for 30\u2009min to pre-form a PMF from the L-malate decarboxylation pathway. Then, D-lactose was added to a final concentration of 100\u2009\u00b5M from a 4\u2009mM stock (40\u00d7 dilution). For inhibition of lactose transport, TMG or TDG were added to a final concentration of 20\u2009mM before the addition of D-lactose. Fluorescence measurements were performed in a FP-8300 spectrofluorometer (Jasco, Inc). Emission spectra of NADPH between 380 and 500\u2009nm (\u03bbem) at an excitation wavelength (\u03bbexc) of 350\u2009nm were taken at intervals of 1\u2009min during the whole experiment.\n\nUncropped gels for the images presented in Figs.\u00a01a, 2b, 3b, 5b, 6b can be found in Supplementary Figs.\u00a01 and 9 and as Source Data. The band patterns observed in the SDS-polyacrylamide gels are representative from at least two independent experiments (different sample preparations). In Supplementary Fig.\u00a01a, c, three and two independent uncropped gels are presented for the analysis of MleP and LacY in liposomes, respectively, showing that images in Figs.\u00a01a and 6b are representative from independent sample preparations. Lanes 7 and 8 in Supplementary Fig.\u00a01b and Lanes 8 and 9 in Supplementary Fig.\u00a09 correspond to independent reconstitution samples and show that images in Figs.\u00a05b and 3b are representative of independent preparations.\n\nIndependent replicates with different sample preparations are indicated by n number in the Figure legends, and the standard deviation (SD) is indicated when n\u2009>\u20092.\n\nData and statistical analysis were performed using OriginPro Lab v8.5. Cartoons in Fig.\u00a01b, g, 2a, 3a, 4a, 5a, e, 6a, d. were created with Biorender.com released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license. Figures were designed with Adobe Illustrator.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data in this study are available within the main text, Supplementary Information and Source Sata files. Source data is available for Figs.\u00a01\u20136, Supplementary Figs.\u00a01\u20133 and 5\u201327, Table\u00a01 and Supplementary Table\u00a01 in the associated source data file.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Konings, W. N., Poolman, B. & van Veen, H. W. Solute transport and energy transduction in bacteria. Antonie Van Leeuwenhoek 65, 369\u2013380 (1994).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nMitchell, P. Chemiosmotic coupling in oxidative and photosynthetic phosphorylation. Biol. 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Pati\u00f1o-Ruiz, Zaid Ramdhan Anshari.\n\nDepartment of Biochemistry, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG, Groningen, The Netherlands\n\nMiyer F. Pati\u00f1o-Ruiz,\u00a0Zaid Ramdhan Anshari,\u00a0Bauke Gaastra,\u00a0Dirk J. Slotboom\u00a0&\u00a0Bert Poolman\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nM.P.R. performed the SSM measurements, analysis of enzymatic decarboxylation reaction in solution, pH, and membrane potential determination in the vesicles. M.P.R. and Z.R.A. performed the uptake of radiolabeled substrates and fluorescence measurements. B.G. cloned and expressed the mleS and mleP genes and performed the radiolabel transport experiments with MleP. M.P.R., Z.R.A., B.G., D.J.S., and B.P. designed the research and analyzed the data. M.P.R., Z.R.A., D.J.S., and B.P. wrote the paper. D.J.S. and B.P. supervised the research.\n\nCorrespondence to\n Bert Poolman.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Kwanwoo Shin and the other, anonymous, reviewers for their contribution to the peer review of this work. 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skeletal muscle", + "journal": "Nature Communications", + "published": "02 January 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55565-4/MediaObjects/41467_2024_55565_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55565-4/MediaObjects/41467_2024_55565_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55565-4/MediaObjects/41467_2024_55565_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55565-4/MediaObjects/41467_2024_55565_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-55565-4#ref-CR57", + "/articles/s41467-024-55565-4#Sec23" + ], + "code": [], + "subject": [ + "Animal physiology", + "Metabolism", + "Translational research" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4271626/v1.pdf?c=1735996004000", + "research_square_link": "https://www.researchsquare.com//article/rs-4271626/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-55565-4.pdf", + "preprint_posted": "25 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "In mammals, loss of food intake and reduced mechanical loading/activity of skeletal muscles leads to a very rapid loss in mass and function. However, during hibernation in bears, despite spending months without feeding and with very modest muscle activity, only moderate muscle wasting is observed. Part of this tissue sparing is due to a highly reduced metabolic activity in almost all tissues, including skeletal muscle. Myosin, one of the most abundant proteins in skeletal muscle, has different metabolic activities in resting muscle. To evaluate the ATPase activity of myosin in hibernating bears, we performed an analysis on a single muscle fiber level. Individual fibers were taken from biopsies of the same bears either during hibernation or during the active phase in the summer. We confirm that muscle fibers from hibernating bears show no loss of fiber size and a mild reduction in force generating capacity. Interestingly, we find a significant reduction in ATPase activity of single muscle fibers taken from hibernating bears, which is caused by a reduced myosin ATP turnover. Single fiber proteomics analysis shows a major remodeling of their proteome, which is similar between different fiber types. Both type 2A and type 1/2A mixed fibers show a marked reduction in mitochondrial proteins during hibernation, with a decrease in proteins linked to the TCA cycle and mitochondrial translation. Western blotting, electron microscope and immunohistochemical analyses confirm mitochondrial alterations in winter muscles.\r\nUsing bioinformatical approaches based on the significant proteome changes, we found a decrease in Myosin Light Chain Kinase (MYLK2) targets in winter muscles compared to summer samples. This outcome was confirmed by western blotting analyses of both phosphorylated myosin light chain and MYLK2, which is a known stimulator of basal myosin ATPase activity. These results suggest that reduced myosin ATPase activity is one of the evolutionary adaptations adopted by resting skeletal muscle during hibernation to minimize energy expenditure. Interestingly, this suggests modulation of myosin ATPase activity as a new possible target to combat muscle wasting diseases, particularly those linked to altered metabolism.Health sciences/Medical research/Translational researchBiological sciences/Zoology/Animal physiologyBiological sciences/Physiology/Metabolism", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Hibernating brown bears, due to a drastic reduction in metabolic rate, show only moderate muscle wasting. Here, we evaluate if ATPase activity of resting skeletal muscle myosin can contribute to this energy sparing. By analyzing single muscle fibers taken from the same bears, either during hibernation or in summer, we find that fibers from hibernating bears have a mild decline in force production and a significant reduction in ATPase activity. Single fiber proteomics, western blotting, and immunohistochemical analyses reveal major remodeling of the mitochondrial proteome during hibernation. Furthermore, using bioinformatical approaches and western blotting we find that phosphorylated myosin light chain, a known stimulator of basal myosin ATPase activity, is decreased in hibernating and disused muscles. These results suggest that skeletal muscle limits energy loss by reducing myosin ATPase activity, indicating a possible role for myosin ATPase activity modulation in multiple muscle wasting conditions.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The regulation of muscle mass and function is affected by changes in activity levels, hormonal stimulation, and mechanical stress1. For example, muscle disuse, as occurs when people are bedridden for prolonged periods, or after bone fractures, are accompanied by a significant reduction in muscle mass and function within days/weeks2. Surprisingly, despite not eating or moving for months, hibernating bears only lose a moderate amount of muscle mass3,4, allowing them to look for food after arousal or to get out of the den in an emergency situation5. This sparing of body mass is not due to a drastic reduction in body temperature (only a few degrees), as they are able to reduce their basal metabolism independently from body temperature, in part through a very significant reduction in heart and respiratory rate6,7. With regard to skeletal muscle, the mechanisms underlying this reduction in basal metabolism and the preservation of mass and function are not completely understood. Some mechanisms have been proposed, like reduced oxidative stress8, miRNA-dependent regulation of protein synthesis9, or circulating factors modulating protein turnover10, however, no clear mechanism is currently known. As a significant portion of body mass is accounted for by skeletal muscle, reductions in energy consumption by the muscle contractile apparatus can induce major alterations in whole body energy consumption. We have shown that the molecular motor of skeletal muscle, i.e. the myosin protein, can have different ATPase activity based on its conformation in relaxed muscle11. It can be found in a biochemical state characterized by very low ATPase activity which is known as the super relaxed state (SRX). On the other hand, if a myosin head is out of the SRX, but still in a relaxed muscle, this has been described as the Disordered Relaxed State (DRX)12. These two states have about a tenfold of magnitude difference in energy consumption, as can be measured by ATP hydrolysis rate, with the SRX having a time constant of approximately 200\u2009s-1 and the DRX of 20\u2009s-1. Many different factors, ranging from changes in pH, temperature, or phosphorylation of myosin regulatory light chains, can modulate the stability of the SRX and alter basal ATPase activity of resting muscle. Interestingly, it was shown to be also modulated in small hibernators13. To understand if modulation of myosin ATPase activity in resting muscle contributes to the drastic reduction in whole body metabolic rate in hibernating bears, we analyzed summer and winter freshly skinned biopsies taken from the same bears. Using two different approaches we observed indeed a significant reduction in ATPase activity in muscle fibers taken from winter biopsies. To gain mechanistic insight into the underlying processes regulating this, we performed a single fiber proteomics analysis. This revealed a significant reduction in mitochondrial proteins in winter muscle, confirming results obtained previously on snap frozen muscle14. We also observed a decreased myosin light chain kinase activity, possibly contributing to an increased SRX stability, as reported in other experimental systems15. Altogether, our results show that part of the energy saving mechanism in hibernating bears is through the reduction of ATP consumption by myosin, likely contributing to whole body energy expenditure.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Loss of muscle mass and function in hibernating bears is known to be less severe than that observed in humans or mice during muscle disuse. To determine how muscle size and function is affected at the single fiber level, we analyzed muscle biopsies taken from the same bears captured in summer or in winter, around the middle of the denning period. We collected these samples for two years in a row allowing us to analyze 11 winter and 10 summer muscle biopsies. As can be seen in Table\u00a01, bear 4 and 5 were captured for two years in a row, while almost all other bears were captured in one winter and summer. Only bear 6 was not localized again in summer, as young males can migrate to find their own territory when they become young adults. The bears evaluated in this study are not yet sexually mature and have been followed from birth.\n\nTo analyze functional properties of muscle fibers, we permeabilized muscle biopsies by placing them in a skinning solution. For this we isolated mechanically from each muscle 8\u201310 single fibers and determined fiber cross-sectional area. As can be seen in Fig.\u00a01a, there are significant differences in absolute fiber size comparing different bears, however, these can be at least in part explained by the variability in body weight of the different animals. Indeed, bear 2 and 3 (61 and 67\u2009kg) are substantially bigger than number 4 and 6 (31 and 40\u2009kg). Despite these differences in starting weight, average overall fiber size is not changed when comparing summer and winter biopsies. This is in line with the relatively stable body mass between summer and winter for the same bear as shown in Table\u00a01. These cross-sectional areas are obtained on permeabilized fibers which are known to exhibit a 20\u201330% swelling caused by the chemical permeabilization process. Our results suggest that fiber swelling is not different between summer and winter bears as shown on the right in Fig.\u00a01A. Indeed, the cross-sectional areas of chemically skinned (and swollen) fibers and those measured on snap-frozen muscle sections of the corresponding bears, did not show a significant difference between summer and winter samples (Supplementary Fig.\u00a01b).\n\na Average cross-sectional area of skinned fibers taken from individual bears in winter and summer (n\u2009=\u20097\u221217, Source data are provided as a Source Data file). Each dot corresponds to the CSA of a single fiber taken from that specific bear. On the right the average CSA of all bears examined is divided by season (summer n\u2009=\u20099, winter n\u2009=\u200911,mean\u2009\u00b1\u2009SEM, two-sided Unpaired t-test; P value 0.3225). b maximal isometric tension produced from skinned fibers taken from individual bears (n\u2009=\u20097-15, Source data are provided as a Source Data file) and divided by season summer and winter bears show a small decrease in normalized tension in hibernating bears (summer n\u2009=\u200914, winter n\u2009=\u200911, mean\u2009\u00b1\u2009SEM, two-sided Unpaired t-test, P value 0.0115). c Representative traces of force redevelopment (kTR) after a 10% length shortening in a fiber from a winter biopsy compared to summer biopsy, on the right the average kTR for individual animals (summer n\u2009=\u20098, winter n\u2009=\u200910. mean\u2009\u00b1\u2009SEM, two-sided unpaired t-test P value 0.0454). All data related to summer samples are reported in orange/red color, while those related to winter are in light blue/blue.\n\nNext, we analyzed force production from isolated skinned fibers taken from each biopsy. As shown in Fig.\u00a01b, there was variability in the force production from fibers taken from each individual biopsy. Despite this, we were able to uncover a slight, yet significant 12\u00b12% decrease between summer and winter biopsies, suggesting some alterations in sarcomere contractility during the hibernation period. These observations are in line with those that were reported when measuring twitch force in vivo, showing a 29% reduction after 110 days of denning16. It was also reported that twitch kinetics in vivo were reduced during the denning period. To address if part of these alterations in contractile kinetics in vivo can be due to changes in the core contractile apparatus, and not just a consequence of altered calcium handling, we performed a slack test on skinned fibers. This allows us to determine force re-development after a rapid shortening of 10% in an activated fiber, which is sufficient to reduce tension close to zero. In the left side of Fig.\u00a01c two representative force redevelopment traces are reported, showing the slower kinetics of the winter fiber compared to the summer. Force re-development kinetics, expressed as Ktr (Fig.\u00a01c right), are significantly slower in hibernating muscle as compared to control tissue, in line with the observations performed previously in vivo. While it is not trivial to determine which alterations at the sarcomeric level are responsible for these changes in kinetics, it suggests that part of the alterations observed in vivo can be due to changes in the contractile apparatus.\n\nThe lack of food intake and movement, suggests that muscles reduce energy consumption to a minimum. As was postulated more than a decade ago, muscle myosin can be found in two different resting states, the super relaxed state (SRX) and the disordered relaxed state (DRX). Estimates suggest that the SRX, has an ATPase activity which is approximately 5-10-fold lower than that observed when myosin heads are less stable and more disordered (DRX), in both cases without leading to force generation11. To determine if relaxed muscle fibers have a lower basal ATPase activity during the winter, we isolated single fibers and performed an ATPase activity assay. This assay is based on two enzymatic reactions that couple ADP produced by myosin-dependent spontaneous nucleotide turnover to NADH oxidation17. This assay reflects the activity of myosin, as the myosin inhibitor blebbistatin eliminates most ATPase activity. It is important to point out that these skinned fiber preparations have a highly permeabilized and altered plasma membrane, therefore a lot of ion pumps are no longer functioning. In Fig.\u00a02a on the left, each dot corresponds to the ATPase activity of one single fiber, while the histograms show the average for each bear. On the right, each dot corresponds to the average of each individual bear. As shown in b.\u00a02a, basal ATPase rate of bear muscle fibers in summer is 0.046\u00b10,02\u2009s-1, a range also observed in most other species, like mouse, rabbit and human18. Interestingly, fibers taken from the same animals during hibernation show a significant reduction in ATPase rate of 0.033\u00b10,02\u2009s-1 (P\u2009<\u20090.0001). Treatment of fibers with the myosin inhibitor blebbistatin (blebb) reduces ATPase activity to 0.006\u2009s-1 in both summer and winter muscles.\n\na Average ATPase activity of skinned fibers taken from individual bears in winter and summer. Each point represents a single fiber taken from a specific biopsy (n\u2009=\u20098-27, Source data are provided as a Source Data file). On the right the average ATPase activity divided by season, in which a decrease in ATPase activity is measured in winter compared to summer (summer n\u2009=\u20099, winter n\u2009=\u200911. mean\u2009\u00b1\u2009SEM, two-sided Mann-Whitney test P value\u2009<\u20090.0001). b Decaying fluorescence traces of mantATP chasing experiments in skinned fibers from summer and winter bear muscle biopsies (n\u2009=\u200919 single fibers for summer samples and n\u2009=\u200919 single fibers for winter samples, each fluorescence decay are fitted with the triple exponential decay shown in the material and methods). Fitted parameters are reported on the right, as winter biopsies showed a significant reduction in both DRX (T1. mean\u2009\u00b1\u2009SEM. Two-sided Unpaired t-test, P value 0.0058) and SRX (T2. mean\u2009\u00b1\u2009SEM. Two-sided Unpaired t-test, P value 0.0023) time constants, indicating a slower overall nucleotide release. All data related to summer samples are reported in orange/red color, while those related to winter are in light blue/blue.\n\nWhile these results clearly show that energy consumption by resting myosin is reduced in hibernating bear muscles, it does not allow us to determine the relative distributions of SRX/DRX in these fibers. To address this issue, we performed a mantATP chasing experiment, whereby we incubate fibers with a fluorescently labeled form of ATP (mantATP) and determine the decay in fluorescence after changing of the medium. The subsequent observed reduction in fluorescence can be fitted with a triple exponential decay function. The first exponential is very short, since it represents the nonspecific binding of the nucleotide present in solution, the second exponential is associated to myosin in DRX, while the last one corresponds to myosin in SRX. Each exponential is characterized by two parameters: the population (P) and the time constant (T). Populations are the fraction of myosin heads associated with each state, namely P1 for DRX and P2 for SRX, while the time constant represents the stability of myosin in terms of nucleotide turnover rate. As can be seen in the representative traces shown on the left in Fig.\u00a02b, nucleotide release is slower in hibernating muscles then in summer biopsies, supporting the data presented in Fig.\u00a02a. The altered decay kinetics are caused by significant differences in the time constants T1 and T2, respectively that of myosin DRX and SRX (Fig.\u00a02b, right), being larger in winter and indicating an increased myosin stability. Relative populations of DRX and SRX are not changed in hibernating muscle, as can be observed in Supplementary Fig.\u00a02a.\n\nHow is the proteome affected in hibernating fibers, and how do these changes affect their functional properties? To address these questions, we performed a single fiber proteomics analysis on 8\u201310 single fibers taken from each biopsy. One of the major strengths of this single fiber approach is that one can compare the changes of the proteome within the same fiber type. We conducted our analysis on skinned fibers, which lack many soluble proteins, as our focus was on structural proteins influencing the functional shifts observed. Through this approach, we identified between 700\u20131200 proteins per fiber (Fig.\u00a03a). HEK cell lysates were analyzed as LC-MS performance control (CV of <5%) and blank controls were injected every 15 samples, identifying minimal protein carry-over per sample (<50) (Supplementary Fig.\u00a03a\u2013c). Using unique peptides, we determined the relative proportion of MYH isoforms in each fiber. Fibers were classified based on whether they contained more than 70% of a specific MYH isoform. Fiber type distribution remained stable, with Type 2\u2009A fibers being the most dominant in both summer and winter single muscle fibers (Fig.\u00a03b). We confirmed this finding also by an electrophoresis analyses showing how bear MYH isoforms run very similar to those taken from mouse muscles (Supplementary Fig.\u00a02b).\n\na Number of identified proteins per fiber (mean\u2009\u00b1\u2009SEM; n\u2009=\u20094 bears for summer 1, winter 2; n\u2009=\u20095 for winter 1, n\u2009=\u20096 bears for summer 2) (b) MYH isoform distribution analysis of isolated single fibers from different bears over four different seasons (orange summer, blue winter). Each bar represents the relative abundance of MYH isoforms in one fiber in relation to the total sum of intensities for MYH1 (blue), MYH2 (red), MYH4 (purple), and MYH7 (green). Fibers from different bears are sorted into the four seasons and ranked from left to right of highest MYH2 abundance. c volcano plot for Type 2\u2009A Fibers summer vs winter (n\u2009=\u200941\u201338). Differentially abundant proteins are highlighted in light blue (Welch\u2019s t-test: FDR\u2009<\u20090.05, s0\u2009>\u20090.1) d examples of proteins going up in summer (left) or winter (right). e changes in GO terms comparing summer and winter type 2\u2009A fibers. The box plot represents the upper (75%) and lower (25%) whiskers scores outside the middle 50% with outliers from 0, 100% f Volcano plot for mixed (type1/2\u2009A) Fibers summer vs winter (n\u2009=\u200920-30). Differentially abundant proteins are highlighted in light blue (Welch\u2019s t-test: FDR\u2009<\u20090.05, s0\u2009>\u20090.1). g Principal component analysis of protein expression patterns from isolated skinned single fibers of Type 2A (triangle) and mixed fibers (circle) collected in winter (blue) and summer (orange).\n\nWhile MYH isoforms showed no major alterations (Fig.\u00a03b), significant changes were observed in the proteome of type 2\u2009A fibers between hibernating and active bear muscle fibers (Fig.\u00a03c, Supplementary Fig.\u00a03d). We identified 402 significantly differentially regulated proteins, with 244 upregulated proteins in summer and 158 in winter. Notable examples like the mitochondrial anion channel (VDAC1/2) are in both summer seasons more abundant while certain myofibrillar components like Laminin-A and Desmin are more abundant in the winter months (Fig.\u00a03d). Fisher exact test performed on the significantly changed proteins showed an enrichment of the gene ontology (GO) terms linked to mitochondrial content and function in summer muscle fibers (Fig.\u00a03e). These comparisons, limited to type 2A fibers, do not reflect shifts in fiber type and concomitant changes in mitochondrial content.\n\nTo determine if these changes were specific to type 2A fibers, we also performed a single fiber proteomics analysis on mixed fibers (Fig.\u00a03f, Supplementary Fig.\u00a04). Mixed fibers were defined as those expressing less than 70 % of any MYH isoform. However, they could be categorized as Type 1/2a mixed fibers due to their predominant isoforms being MYH7 and MYH2. Interestingly, many proteins regulated in type 2A fibers showed similar patterns in mixed fibers, indicating a consistent muscle remodeling during hibernation regardless of fiber type (Fig.\u00a03f, Supplementary Fig.\u00a03d). Indeed, principal component analysis demonstrated clear separation between winter and summer single muscle fibers regardless of MYH content (Fig.\u00a03g). ANOVA analysis confirmed clustering of the fibers based on the season rather than fiber types (Supplementary Fig.\u00a04a). Overlapping significantly changed proteins revealed 95 upregulated and 129 down regulated in both fiber type 2\u2009A and mixed fibers (Supplementary Fig.\u00a04b, c).\n\nTo better understand how these changes in mitochondrial GO terms reflect their organization and content within the fibers, we first performed a western blotting analysis for different proteins of the five respiratory complexes. In line with the observations in the single fiber proteomics, we observed a small, yet significant reduction in specific proteins from each complex (Fig.\u00a04a). Next, we performed an immunohistochemistry analysis for Tom20, a mitochondrial import receptor subunit, to determine mitochondrial distribution within the fiber. As can be seen in Fig.\u00a04c, summer fibers showed a more intense staining with a relatively normal distribution pattern. Electron microscope analyses of summer and winter skinned fibers showed, as expected, increased spaces between sarcomeres and swollen mitochondria in both conditions, as compared to freshly fixed tissue (Supplementary Fig.\u00a04d). More in general, a schematic representation of differentially regulated mitochondrial proteins shows how proteins linked to lipid oxidation are reduced, while glycolytic proteins are maintained, similar to previous observations14 (Fig.\u00a04d).\n\na, b Western blotting for proteins of the different respiratory complexes shows a significant reduction in all complexes in winter (W, blue dots) compared to summer (S, orange dots) skinned fibers. Data are presented as individuals\u2019 values with mean bars \u00b1 SEM (n\u2009=\u20099 bears/season, the same individuals were sampled and analyzed in summer and winter) and normalized to the total protein content (Ratio paired t test two-sided, CV P value\u2009=\u20090.0145; CIII P value\u2009=\u20090.0202; CIV P value\u2009=\u20090.0095; CII P value\u2009=\u20090.0018; CI P value\u2009=\u20090.0338; a.u=arbitrary units). Representative Western blots are shown for four couples of bears. Samples are derived from the same experiment and blots were processed in parallel. Source data are provided in the Source Data file c. Immunohistochemistry for TOM20 shows mitochondrial distribution, while Phalloidin shows actin localization in summer and winter fibers (n\u2009=\u20093 fibers for animal, n\u2009=\u20093 bears for season. The same individuals were used for summer and winter fibers). d Regulation of metabolism-related factors in skinned fibers of hibernating bear muscles. The relative abundance of proteins in winter (hibernating) versus summer (active) brown bears (n\u2009=\u20096 per season) is shown using the following color code: significantly (Welch T-test analysis; P\u2009<\u20090.01) up- and down-regulated proteins are shown in red and green boxes, respectively; white boxes show proteins that were unchanged between winter and summer and black boxes show proteins that could not be detected. Created in BioRender. Cussonneau, L. (2024) https://BioRender.com/t32y880.\n\nAs mentioned, there are 244 proteins with a significantly higher expression in summer muscles. To better understand what these proteins have in common, we performed an analysis using the ENRICHR software. One of the most suggestive enrichments we observed in this list is related to the so-called kinases co-expression analysis. In this analysis it is possible to determine the kinases which are co-expressed with regards to the list of proteins/genes examined. Interestingly, we observed that MYLK2 is the kinase with the strongest correlation to the enriched proteins in the summer muscles (Fig.\u00a05a). This captured our interest, as a reduced MYLK2 expression or activity during the winter induces a decrease in myosin regulatory light chain (RLC) phosphorylation, with subsequent stability of the SRX and a reduced ATPase activity. As skinned fibers are permeabilized, they do not allow for the determination of changes in phosphorylation levels. Therefore, we used snap frozen muscle tissue from the same bears and performed a western blotting analysis for both the total kinase levels and the phosphorylation of its main target, the myosin regulatory light chain (MLC). As shown in Fig.\u00a05b and 5c, we find a very consistent decrease in both MKL2 content and the phosphorylation of RLC on serine 19, the site known to be involved in the regulation of SRX stability. As it is not straightforward to find a specific protein which is unaltered during hibernation, blots were normalized for total protein content as identified by ponceau staining. Interestingly, this reduction in MYLK2 is also in line with a transcriptional reduction observed in a previous study from hibernating bears19, or during torpor in zebrafish20. To understand if a similar reduction in MYLK2 levels also occurs in other models of muscle disuse, we analyzed its expression levels in a recently developed murine model of unilateral hindlimb casting21. As can be seen in Fig.\u00a05d and e, there is a significant reduction in MYLK2 after 7 and 14 days, suggesting a preserved mechanism between mice and bears during muscle unloading.\n\na ENRICHR kinases co-expression analysis shows an increase in MYLK2 in summer bears. b, c Western blotting analysis on frozen muscle tissue taken from the same bears as the skinned fibers shows decrease in MYLK2 and the phosphorylation of its target, P-RLC. Data are presented as individuals\u2019 values with mean bars \u00b1 SEM (n\u2009=\u200910 bears/season, the same individuals were sampled and analyzed in summer and winter) and normalized to the total protein content. Orange and blue dots are for bear muscles, respectively in summer and winter (Ratio paired t-test two-sided, pRLC P value\u2009=\u20090.0147, and MYLK2 P value\u2009=\u20090.0001; a.u.=arbitrary units). Representative Western blots are shown for five couples of bears and a control mouse muscle (M). d, e Western blotting analysis on gastrocnemius muscle tissue from immobilized (Boot D7 or D14, black dots) or the controlateral muscle (Ctrl, grey dots) from the same mouse show a significant decrease in MYLK2 and a tendency for the phosphorylation of its target P-MLC in immobilized leg compared to the contralateral one. Data are presented as individuals\u2019 values with mean bars \u00b1 SEM (n\u2009=\u20096 mice for one week, D7 and n\u2009=\u20094 mice for 2 weeks, D14 immobilization) and normalized to the total protein content. Ratio paired t test two-sided (pRLC D7, P value\u2009=\u20090.1822; MYLK2 D7, P value\u2009=\u20090.02; pRLC D14, P value\u2009=\u20090.2511; MYLK2 D14, P value\u2009=\u20090.0120; a.u = arbitrary units). Representative Western blots are shown for 3 couples of mice. Samples are derived from the same experiment and blots were processed in parallel. Source data are provided as a Source Data file.\n\nTaken together, our results suggest that part of the reduced ATPase activity in winter muscle is a consequence of an increased SRX stability due to reduced RLC phosphorylation.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55565-4/MediaObjects/41467_2024_55565_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55565-4/MediaObjects/41467_2024_55565_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55565-4/MediaObjects/41467_2024_55565_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55565-4/MediaObjects/41467_2024_55565_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55565-4/MediaObjects/41467_2024_55565_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "It is well known that muscle disuse leads to a very rapid loss in skeletal muscle mass and function in a matter of days. In this sense, hibernation represents a unique form of disuse, during which animals abstain from eating or engaging in physical activity for months, yet remarkably, experience only minimal muscle loss. For comparison, a 90-days immobilization in humans leads to a decrease in force of 54%22. On average, the effect of bed-rest and immobilization periods of 4\u201328 days showed a muscle loss in the range of 0.2\u20132.3 %/day, which is always exceeded by a force loss, ranging between 1.1\u20133.5 %/day2,23. In a disuse model in rodents caused by 3\u2009d printed cast, the gastrocnemius muscle exhibits a 25% in weight loss and a 40% drop in muscle force over the course of 2 weeks21. Also in this study, despite not measuring muscle mass or function in vivo, we find no atrophy in single fibers and only a 12% reduction in force-generating capacity, suggesting significant protection against disuse muscle wasting.\n\nIt has been reported that this remarkable tissue sparing during hibernation is achieved, among other mechanisms, by drastically reducing metabolic rate3,6. Indeed heart rate and respiratory rate are reduced 3\u20135 fold, despite maintaining body temperatures relatively high6. Here, we determined if skeletal muscle myosin, one of the most abundant proteins in the body, has a reduced ATPase activity in hibernating bears. Using a single fiber ATPase assay, we find that ATPase activity in relaxed skinned fibers of hibernating muscle is significantly lower (-28%) than what is observed in muscle biopsies taken from the same bears in summer. Using a mantATP chasing approach we find that time constant for nucleotide exchange is prolonged for all myosin heads in winter samples. Performing a single fiber proteomics analysis, we find, despite no major loss in fiber size and function during hibernation, that there is a very significant remodeling of the proteome. Mitochondrial proteins are altered and show a reduction in winter muscle, independently from fiber type. Interestingly, winter fibers show a lower content and activity of MYLK2, a kinase responsible for myosin RLC phosphorylation which induces SRX destabilization, possibly explaining the different resting ATPase activity in the two seasons.\n\nIn a landmark paper published over 20 years ago, it was shown that hibernating bears only lose around 20% of muscle strength in vivo during hibernation24. Later, the same authors confirmed these observations and also showed that contractile kinetics of twitch tension was reduced16. As these measurements were performed in vivo, it is difficult to determine where muscle dysfunction or changes in kinetics occurred. Muscle disuse is known to lead to alterations in muscle innervation, calcium dynamics and dysfunction of the contractile apparatus2,25,26. In our study, we evaluated contractile function and kinetics of single fibers, without the requirement of muscle innervation or calcium release through normal excitation-contraction coupling cycles. Interestingly, we find that most of the muscle dysfunction which occurs in hibernating bears is due to a reduced force production from the contractile apparatus. A possible explanation for a reduced normalized force in single fibers could potentially be through a different fiber swelling27, as reduced swelling in winter fibers would lead to an underestimation of skinned fiber cross sectional area. However, as winter fibers show an increase in structural proteins like desmin and lamin A, likely reinforcing the cytoskeleton, it is unlikely winter fibers swell more. Indeed, comparisons of snap frozen and skinned CSA do not show differences between summer and winter fibers. Also, electron microscopy analysis does not show increased space between sarcomere in parallel or the presence of damaged sarcomeres. This suggests that reduced force is most likely due to alterations in the force generating capacity of the contractile proteins in the fiber. One of the few modifications known to affect functional properties of skinned fibers is oxidation, leading to reduced contractile force and kinetics28. Indeed, incubation of permeabilized fibers with a nitric oxide donor, is sufficient to reduce contractile force and kinetics, but even resting ATPase activity, suggesting this oxidative stress acts on the myosin head and not on the actin-myosin interaction. It has been reported that antioxidant defense and general oxidative stress in hibernating muscles is reduced, even though overall nitrosylation is increased8. However, it is possible that proteins with a very high half-life, like structural muscle proteins26, can accumulate these relatively stable oxidative modifications over time, leading to the observed functional decrease. This issue is likely to be particularly pronounced during hibernation, as it has been reported that general protein turnover is strongly reduced4.\n\nWhile we were not able to link the functional deficit to changes in a specific protein, the proteomics analyses performed on single fibers show a major remodeling of the proteome in both fast and slow fibers. The most obvious alteration is observed in the mitochondrial proteome, with many proteins showing a significant reduction. Similar results have been reported in many hibernating species29, and could contribute to some redox-dependent modifications of contractile proteins. While many proteins show a decrease in winter muscle, there are also interesting proteins which strongly increase during hibernation that can potentially explain some of the protective processes that occur under these extreme conditions. One interesting example is Cold-inducible RNA binding protein (CIRBP), an mRNA binding protein, which can stabilize specific transcripts to improve survival under prolonged cold conditions. Indeed, loss of CIRBP reduces hypothermic cardio protection30, a critical issue for heart transplant. Interestingly, this protective effect of CIRBP appears to be mediated by its inhibitory effect on ferroptosis31, a specific type of cell death accompanied by iron accumulation and lipid peroxidation32. While the regulation of ferroptosis as a protective mechanism during hibernation has been suggested33, it is interesting to note that similar changes might also occur in hibernators which do not lower their body temperature that much, like the bears.\n\nThe major finding in this study is that relaxed skeletal muscle myosin consumes less ATP during hibernation. Both an ATPase activity measurement (NADH-oxidation) and a MantATP chasing approach clearly showed that myosin in single muscle fibers taken from hibernating bears consumes less ATP. While summer fibers showed some variability, winter fibers had a consistently reduced ATPase activity in all biopsies examined. Some of this variability observed in the ATPase activity in the summer group can be due to the high variability in activity patterns and daily energy expenditure reported in bears34. In non-denning adult polar bears daily energy expenditure can vary 5-10-fold, while activity patterns can show an even wider range. The more homogeneous values obtained in winter muscles are most likely due to the more uniform conditions at the moment all bears are captured. Changes in ATPase activity and ATP/ADP levels are also known to affect contractile kinetics, linking metabolic changes directly to contractile characteristics. Indeed, using an in vitro motility assay it was shown that sliding velocity of myosin is strongly affected by ADP dissociation from actomyosin and decreased in dystrophic muscle, a condition known to have an increased oxidative stress35,36. A reduction in ATPase activity of myosin is also supported by findings from snap frozen biopsies that show a reduction in both the production and use of ATP in the muscles of hibernating bears14. Proteomics studies suggest that this is accompanied by a preferred use of lipids, albeit with a lower rate of oxidation, while sparing glycogen stores14. Furthermore, in a recent study it was found that the ATPase activity in small hibernators was altered, however, no evidence for changes in bear muscle was observed13. While this might seem in contrast to our results, we believe that an important reason for the discrepancy is that our samples were skinned starting with fresh material, without experiencing snap freezing. It has been shown that the freezing/thawing process reduces force production by roughly 50%37,38,39, likely altering the ordered pattern of thick filaments, which has an important role in the SRX cooperative nature.\n\nMYLK2 activity through RLC phosphorylation is a well-known mechanism that causes an increased calcium sensitivity and an altered thick filament conformation, with more disordered myosin heads arrays40. In response to this phosphorylation, an increase in resting myosin ATP consumption has been reported for cardiac myofibrils41, while its energetic contribution in skeletal muscle is controversial, with some literature stating little-to-no effect42 and others suggesting an increase only at low calcium levels43. It must be stated that divergent outcomes may be due to an undetermined initial sample phosphorylation level, as well as the marginal modulation of nucleotide turnover in a resting muscle compared to active contraction. In fact, myosin light chain phosphorylation does not alter the stability of the SRX in a linear fashion and does not abolish it completely11. MYLK2 is rapidly activated by the calmodulin-calcium complex in a contracting muscle, while it is slowly deactivated by its autoinhibitory alpha helix in absence of free cytosolic calcium. In skeletal muscle, the phosphatase kinetics are quite slow44, leaving myosin RLC phosphorylation as a molecular memory of muscle activation, and inducing post tetanic potentiation45. As muscles are highly inactive during hibernation, it is reasonable to assume that the lack of calcium release from the SR is one of the major reasons for the reduced MYLK2 activity, even though changes in phosphatase activity cannot be excluded either for the reduced RLC phosphorylation.\n\nAccording to our data, the decrease in ATP consumption from myosin during hibernation is caused by a prolongation of myosin DRX and SRX time constants, as shown by the mantATP chasing experiments. Even in the absence of a change in DRX and SRX populations, an increased time constant reflects a different myosin heavy chain heads stability. In fact, in skinned fibers from winter biopsies the DRX constant (T1) is increased by about 25% while the SRX constant (T2) is almost double. The corresponding difference in nucleotide exchange rate causes the increased stability of myosin in the DRX population to contribute in a stronger manner to the decrease in ATP hydrolysis, followed by the increased stability of myosin in the SRX population. These data support the idea that an energy preservation mechanism is acting on the biochemical equilibrium of the thick filament. It has been reported that age could affect myosin SRX in the same fashion we showed here46, by altering time constant rather than myosin populations. It is known that, upon oxidation, myosin produces less force, but its effect on nucleotide exchange and possibly on myosin heads dragging on the thin filament in a lightly attached configuration is not well established. Temperature is unlikely to have a major impact on the seasonal differences reported in this work. Indeed, bears during hibernation do not experience a massive drop in body temperature, not enough to induce a structural destabilization and the appearance of myosin heads in the refractory state47. Also, it is known that with the lowering of the temperature myosin heads undergo an order-to-disorder transition48, increasing DRX and consequentially futile ATP hydrolysis. To give an estimation of the contribution of the SRX/DRX balance to whole body energy consumption, we performed a rough calculation based the energy consumption of resting muscle myosin (Supplementary Fig.\u00a05). These suggest that energy sparing in muscle tissue is around 30% in hibernating muscle compared to summer muscle.\n\nTaken together, we find that in large, hibernating mammals, like the brown bear, skeletal muscle myosin reduces ATPase activity, highlighting a new energy saving mechanism during extreme situations. Interestingly, also a murine model of muscle disuse showed a similar reduction in MYLK2 levels. While additional mechanistic exploration is required to link MYLK2 levels to resting energy consumption in muscle fibers, it does suggest that myosin ATPase activity might also play a role in other conditions of muscle wasting, particularly those affected by major modulations of muscle metabolism.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Biopsies from the vastus lateralis muscle were collected from 9 free-ranging brown bears, 2\u20133 years old (Ursus arctos), from Dalarna and G\u00e4vleborg counties, Sweden, from 2022 to 2023 (Table\u00a01). The samples were immediately frozen on dry ice until storage at \u221280\u2009\u00b0C or processed to obtain a chemically skinned sample. Each year, the same bears were captured during winter hibernation (February) and recaptured during their active period by helicopter darting49 (June). Two bears were captured in two consecutive years. Bear immobilization was performed as previously described50. The study was conducted according to the guidelines of the Declaration of Helsinki and of the European Directive 2010/63/EU and approved by the Institutional Review Board (or Ethics Committee) of (1) the Swedish Ethical Committee on Animal Experiment (Dnr 5.8.18-03376/2020), the Swedish Environmental Protection Agency (NV-01278-22), the ARRIVE guidelines, and the Swedish Board of Agriculture (Dnr 5.2.18\u20133060/17). All procedures complied with Swedish laws and regulations. Capture, anesthesia, and sampling were carried out according to an established biomedical protocol51. For skinned fibers, bundles of bear muscle were harvested and stored at 4\u2009\u00b0C in a skinning solution (150\u2009mM potassium propionate, 5\u2009mM potassium dihydrogen phosphate, 5\u2009mM magnesium acetate, 5\u2009mM EGTA, 2\u2009mM DTT, 2.9\u2009mM ATP, 0.5\u2009mM sodium azide, proteases and phosphatases inhibitors, pH 7.0) for 24\u2009h and then transferred to a storage solution (same as skinning but 50% glycerol) at -20\u2009\u00b0C17,52.\n\nMice were housed in independent cages in an environmentally controlled room (23\u2009\u00b0C, 12-h-light-dark cycle) with ad libitum access to food and water. Gastrocnemius muscles of 3-month-old C57BL/6 mice were collected at different time points: after 1 week (D7, n\u2009=\u20096 mice) and 2 weeks (D14, n\u2009=\u20094 mice) of unilateral immobilization using custom-made 3D-printed boot as previously described21. Sex has not been considered in this study, both male and female mice have been included. Gastrocnemius muscles of the controlateral leg were collected at the same time and all the muscles were snap-frozen in liquid nitrogen for subsequent analyses. The study was conducted according to the Guide for the Care and Use of Laboratory Animals (NIH; National Academies Press, 2011), to the ARRIVE guidelines (https://arriveguidelines.org/) as well as the Italian law for the welfare of animals. The Italian Ministero della Salute approved all animal experiments, Allegato VI (Rome, Italy; authorization number 448/2021 PR).\n\nVastus lateralis muscles or skinned fibers from 9 bears (paired samples collected in summer and winter each year for the same individual; Table\u00a01), and gastrocnemius muscles from mice after 1 week (D7, n\u2009=\u20096) or 2 weeks (D14, n\u2009=\u20094) of unilateral immobilization or the controlateral leg (Ctrl), were used. Samples were homogenized using metallic beads in 100\u2009\u00b5L of an ice-cold buffer (50\u2009mM Tris pH 7.5, 150\u2009mM NaCl, 1\u2009mM EDTA, 10\u2009mM MgCl2, 0.5\u2009mM DTT, 10% Glycerol, 2% SDS) containing inhibitors of proteases (complete Tablets EDTA-free, Roche) and phosphatases (phosphoSTOP, Roche, Monza, Italy). The homogenates were lysed for 3\u2009min at 30\u2009Hz in TissueLyser II (Qiagen, Milan, Italy) and then centrifuged at 10,000\u2009g for 15\u2009min at 4 \u25e6C. The resulting supernatants were then stored at \u221280\u2009\u00b0C until further use. The concentration of proteins was determined using the PierceTM BCA Protein Assay (Thermo Fisher Scientific, Parma, Italy). Proteins were then diluted in Laemmli buffer and stored at \u221280\u2009\u00b0C until further use. Protein extracts were subjected to SDS-PAGE (sodium dodecyl sulfate-polyacrylamide gel electrophoresis) using BoltTM 4 to 12%, Bis-Tris Plus WedgeWellTM gels (Thermo Fisher Scientific, Parme, Italy) and transferred onto a nitrocellulose membrane. Blots were blocked for 1\u2009h at room temperature with 5% bovine serum albumin in TBS buffer with 0.1% Tween-20 (TBS-T, pH 7.8), then washed thrice in TBS-T and incubated (stirring overnight at 4\u2009\u00b0C) with appropriate primary antibodies against Phospho-Myosin Light Chain 2 (Ser19)(Rabbit, 1:1000 5%BSA, CST#3671, Lot: 7, Cell Signaling Technology, Milan, Italy), MYLK2 (Rabbit, 1:1000 5%BSA, HPA059704, Lot: R84232, Atlas antibodies, Rome, Italy) and OXPHOS (Mouse, 1:2000 5%BSA, #ab110413, Lot: 2101006616, Abcam, Milan, Italy). Blots were washed and incubated for 1\u2009h with an appropriate secondary horseradish peroxidase-conjugated antibody at room temperature, goat Anti-Mouse IgG-HRP Conjugate (1:5000) and Goat Anti-Rabbit IgG-HRP Conjugate (1:4000), respectively #1706516 (Lot: L005680) and #1706515 (Lot: L005679), Biorad, Segrate MI, Italy. Signals were detected after incubation with Luminata Classico Western HRP substrate (Millipore, Burlington, MA, USA) and visualized using iBright750 imaging system (Thermo Fisher Scientific, Parme, Italy). Signals were quantified using the ImageJ software 1.53f5151 and normalized against the total amount of proteins determined by Ponceau signals to correct for uneven loading. Protein data were presented as individual values. The bilateral ratio paired Student\u2019s t-test was used to compare the muscles of bears during summer and winter (S and W, respectively). Statistical analysis was performed using Prism 8 (GraphPad Prism 9, San Diego, CA, USA).\n\nMuscle samples for protein electrophoresis were solubilized in the SDS-PAGE sample buffer (62.5\u2009mM Tris pH 6.8, 2.3% SDS, 5% Beta-mercaptoethanol,10% glycerol) containing the Complete Protease Inhibitor Cocktail (Roche, Basel, Swiss) and analyzed by SDS-PAGE on 8% polyacrylamide gels according to the method described by Talmadge and Roy52. MyHC protein bands were revealed by Coomassie Blue (EZBlue Gel Staining Reagent, Sigma Aldrich) and isoform percentage composition was evaluated by densitometry using ImageJ.\n\nSingle fibers were dissected from biopsies and then mounted on aluminum T-clips on a 3D printed setup (Supplementary Fig.\u00a01A). The setup was then placed on the stage of a Nikon Eclipse inverted fluorescence microscope equipped with a Hamamatsu ORCA Flash-4.0 camera. The sarcomere length was adjusted between 2.4\u20132.5\u03bcm using a micromanipulator. The experiment was performed at room temperature (26\u2009\u00b0C). The fiber was incubated in a rigor buffer solution (potassium acetate 120\u2009mM, MOPS 50\u2009mM, EGTA 4\u2009mM, potassium dihydrogen phosphate 5\u2009mM, magnesium acetate 5\u2009mM, DTT 1\u2009mM, pH 6.8) for 5\u2009min. Then, the fiber was moved to another chamber containing rigor buffer plus 250\u2009\u03bcM mantATP (NU-202L, Jena Bioscience, Germany) and incubated for 10\u2009min. At the beginning of the recording, the fiber was moved to a new chamber containing fresh relaxing buffer (rigor buffer with the addition of 4\u2009mM ATP). Pictures were taken using the DAPI filters set through a 10X magnification objective (PLAN FLUOR 10X/0.30 WD 16.0), plus additional 1.5X magnification of the Nikon Eclipse stage. The recording protocol was the following: 10 frames were taken every 2\u2009s, then 20 frames every 10\u2009s, and 20 additional frames every 20\u2009s, for a total measurement time of 620\u2009s. Every frame exposure was 400\u2009ms with the camera set to binning 4\u00d74. The recording protocol and camera setting has been optimized to reduce photobleaching during the total illumination time of about 50\u2009s. The decrease in fluorescence intensity was fitted with the following three exponential decay function:\n\nwhere P0, P1 and P2 are population intensities, while T0, T1 and T2 are population time constants. Initial fitting values are set to P0\u2009=\u20090.25, T0\u2009=\u20092, P1\u2009=\u20090.375, T1\u2009=\u200920, P2\u2009=\u20090.375, T2\u2009=\u2009200 and fitting is run for 1000 iterations using GraphPad Prism (version 10.0.2 for Mac, GraphPad Software, Boston, Massachusetts USA). P1 and P2 are expressed as a percentage of P1\u2009+\u2009P2, thus the total fitted populations, excluding the non-specific P0.\n\nSingle fibers were dissected and pipetted into a 384well plate in relaxing buffer (Potassium propionate 100\u2009mM, MOPS 50\u2009mM, EGTA 12\u2009mM, magnesium acetate 6\u2009mM, potassium dihydrogen phosphate 6\u2009mM, ATP 4\u2009mM, DTT 2\u2009mM, Triton X-100 0.025%, protease inhibitor, pH 7.4) plus the coupled reaction buffer (NADH 1.6\u2009mM, PEP 5\u2009mM, pyruvate kinase 40U/ml and lactate dehydrogenase 40\u2009U/mL) to a final volume of 30\u2009\u03bcL. The plate was covered with an optically clear seal and placed in a temperature-controlled multiplate reader set to 27\u2009\u00b0C (Multiskan SkyHigh, ThermoFisher Scientific). The coupled reaction act as the following: myosin hydrolyses ATP to ADP and inorganic phosphate, ADP and phosphoenolpyruvate are converted by pyruvate kinase to ATP and pyruvate, pyruvate and NADH are converted to lactate and NAD+ by Lactate dehydrogenase17,53. The oxidation rate of NADH was measured every 2\u2009min as the decreasing absorbance at 340\u2009nm, total run time 25\u2009min. The linear decrease was estimated using NADH absorbance epsilon of 6300\u2009mol-1 cm-1 and using a calibration curve obtained by the addition of a growing concentration of ADP. A concentrated KCl solution was added to each well at the end of the assay to reach the final concentration of 0.4\u2009M and extract myosin from the bear fiber, the protein amount was measured using PierceTM 660\u2009nm Protein Assay Reagent (Thermofisher nr. 22660) for normalization purposes.\n\nSingle skeletal muscle fibers were dissected in cold storage solution, clipped with aluminum T-clips at each end and mounted on an Aurora Scientific Permeabilized Fibers 802D setup (Aurora Scientific, Ontario, Canada). A thin flow of 5% toluidine blue and 8% glutaraldehyde54 was used to crosslink each end of the fiber. After an extensive wash with relaxing solution, the diameter was measured, and sarcomere length was adjusted to 2.5 \u03bcm using a camera mounted on an inverted microscope. The maximal tension of the fibers was measured at 21\u00b0C. For single fiber shortening the sarcomere length was adjusted to 2.9 \u03bcm, so during the activation of the fiber a steady shortening of 10% L0 was imposed at the plateau. Relaxing, preactivating and activating buffers, as well as calcium sensitivity buffers are derived from48. Force is normalized to cross sectional area using the measured diameter and assuming circular geometry. The time constant of the force redevelopment after a slack test has been obtained through a single exponential fitting. The tension-time trace has been analyzed between the time of the initial rise in tension just above the zero level, reached after a shortening of 10% of the SL, to the time when the plateau was fully reached. The build-in MATLAB\u00ae function fit has been used with the custom function \\({{{\\rm{T}}}}={{{\\rm{a}}}}(1-{{{{\\rm{e}}}}}^{-\\frac{{{{\\rm{t}}}}}{{{{{\\rm{t}}}}}_{{{{\\rm{c}}}}}}})\\), being \\({{{\\rm{T}}}}\\) the tension, \\({{{\\rm{t}}}}\\) the time, \\({{{\\rm{a}}}}\\) the tension at the plateau and \\({{{{\\rm{t}}}}}_{{{{\\rm{c}}}}}\\) the time constant. R2 values were higher than 0.9 for all the traces.\n\nKtr is described as the rate of force redevelopment following a rapid shortening of the fiber after reaching maximal isometric tension. The rapid shortening allows the tension to be redeveloped from zero in a saturating calcium condition so that the kinetic is not affected by Ca2+ diffusion, the thin filament is activated, and the force increases proportionally to the thick filament activation and myosin recruitment. The experimental traces are fitted using a single exponential function as reported extensively in the literature55.\n\nIsolated single fibers (n\u2009=\u20098) of n\u2009=\u20094 (summer 1, winter 2), n\u2009=\u20095 (winter 1), n\u2009=\u20096 (summer 2) biological replicates per season were placed in a 96-well plate. On each plate, one column was used for the quality standard based on 20k HEK cells to process sample preparation and the performance of the LC-MS instrumentation. 40\u2009\u00b5l of 4% SDS in PBS containing 5\u2009mM TCEP and 10\u2009mM CAA were added to each well. Next, 96 well plates were placed on 95\u2009\u00b0C for 10\u2009min followed by sonication in a Bioraptor sonicator set to 20\u2009\u00b0C water temperature with 10 cycles of 30 on/30 off. Samples were digested following the standard SP3 protocol56. Briefly, 20\u2009\u00b5g of each washed SP3 beads were added to each well followed by immediately adding one sample volume of acetonitrile (ACN). After the incubation period of 8\u2009min and 2\u2009min on a magnet, supernatant was discarded and magnetic beads were washed 2x with 70% EtOH, 1x with 100% ACN. Samples were digested with 10\u2009\u00b5l of 20\u2009ng LysC and 40\u2009ng trypsin dissolved in 50\u2009mM ammonium bicarbonate at 37\u2009\u00b0C, 750\u2009rpm overnight. Samples were acidified by using 100\u2009\u00b5l 0.1% FA followed by a clean-up with house-made SDB-RPS tips. Purified peptides were loaded with indexed retention time peptides (iRT) on EvoTips Pure and applied a 60 SPD method on an EvoSep One system (both EvoSep, Denmark) with an 8\u2009cm PepSep Column. Mobile phases were compromised of 0.1% FA as solvent A and 0.1% FA in ACN as solvent B. The HPLC system was coupled to a timsTOF pro 2 using a CaptiveSpray source (both Bruker). Samples were measured randomly in dia-PASEF mode with daily ion mobility calibration using three ions of Agilent ESI-Low Tuning Mix following vendor specifications. The DIA-PASEF window was ranging in dimension 1/k0 0.7\u20131.35, with 24 \u00d7 25 Th windows and in dimension m/z from 350 to 1250. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD05098057. Files were processed with DIA-NN 1.8.1 using library free search against UniProt Ursus Arctos database (2019) complemented with protein sequences from myosin heavy chain variants. Database was blasted against existing GOterms with GOblast. Mass ranges were set according to the settings of the mass spectrometer, mass deviation was automatically determined from the first data file for thermos files and set to a mass deviation of 15 ppm for files acquired by bruker instruments. Further calculations were performed within R (version 4.2.2) using the following libraries: diann, tidyverse, data.table, samr, vsn, and ggplot2, gprofiler, missForest. Data was further processed using an in house modified R-script based on the version by V. Demichev (Github page, cit MaxLFQ). Data input was filtered for unique peptides, q-Value\u2009<\u20090.01, Lib.Q.Value\u2009<\u20090.01, PG.Q.Value\u2009<\u20090.01, Global.Q.Value\u2009<\u20090.01, Quantity.Quality > 0.7, Fragment.count >= 4. Fiber with less than 500 identified proteins were excluded. Protein intensities of each fiber was normalized to the total fiber intensity. Fibers were characterized into Fiber type I (>70% MYH7), Fiber type IIa (>70% MYH2), Fiber type IIx (>70% MYH1), and mixed Fiber (<70% MYH1, <70% MYH2, <70% MYH4, <70% MYH7). Data completeness of 70% was calculated on each group. One group is specified by season and fiber type. Missing values were imputed by random forest algorithm for groups with 70% data completeness, with more than 30% missing values random forest algorithm was downshifted of 0.3, width 1.5. Further analysis was performed in perseus (V 1.6.5.0) and InstantClue (V 0.10.10.20211105)58. ANOVA analysis and Welch\u2019s T-test analysis was performed with an FDR with less than 0.05 and 500 randomizations, Quality control of iRT peptides were performed in Skyline-Daily (V 22.21.391).\n\nSmall bundles (about 15\u201320 myofibers) were dissected from each skinned bear biopsy and pinned, slightly stretched, over a silicone support (SYLGARD 184 Silicone; GMID 01673921). The pinned bundles were then washed with relaxing buffer (potassium propionate 100\u2009mM, MOPS 50\u2009mM, EGTA 12\u2009mM, magnesium acetate 6\u2009mM, potassium dihydrogen phosphate 6\u2009mM, ATP 4\u2009mM, DTT 2\u2009mM, protease inhibitor, pH 7.4) to remove the storage solution while avoiding spontaneous fiber contraction. Still stretched, the samples were fixed overnight at 4\u2009\u00b0C with 2.5% glutaraldehyde (EMS; Cat. N\u00b016220) in 0.1\u2009M sodium cacodylate buffer (pH 7.4). Subsequently the samples ware postfixed with 1% in 0.1\u2009M sodium cacodylate buffer for 1\u2009h at 4\u2009\u00b0C. After three water washes, samples were dehydrated in a graded ethanol series and embedded in an epoxy resin (Sigma-Aldrich 46345). Ultrathin sections (60\u201370\u2009nm) were obtained with Leica Ultracut EM UC7 ultramicrotome, counterstained with uranyl acetate and lead citrate and viewed with a Tecnai G2 (FEI) transmission electron microscope operating at 100\u2009kV. Images were captured with a Veleta (Olympus Soft Imaging System) digital camera.\n\nEach fiber was dissected from the bear biopsies and pinned over a silicone support (SYLGARD 184 Silicone; GMID 01673921) and washed with relaxing buffer (Potassium propionate 100\u2009mM, MOPS 50\u2009mM, EGTA 12\u2009mM, magnesium acetate 6\u2009mM, potassium dihydrogen phosphate 6\u2009mM, ATP 4\u2009mM, DTT 2\u2009mM, protease inhibitor, pH 7.4) and slightly stretched. The fibers were then fixed in paraformaldehyde 2% for 5\u2009min and permeabilized with PBS Triton X-100 1% for 3\u2009min. The samples were blocked with mouse-on-mouse blocking reagent (Vector laboratory MKB-2213) for 1\u2009h at room temperature. TOM20 primary antibody (Proteintech, Nr 11802-1-AP, lot 00128108) (dilution rate 1:50) was incubated as a cocktail solution of PBS (Sigma-Aldrich, Life science, P4417) 0.5% BSA (Sigma-Aldrich, Life science, A3912) and 2% goat serum (Sigma-Aldrich, Life science, G9023) overnight at 4\u2009\u00b0C. Alexa Fluor\u00ae 647 conjugated Anti-Rabbit IgG (Alexa Fluor\u00ae 647 AffiniPure\u2122 Goat Anti-Rabbit IgG H\u2009+\u2009L, Jackson ImmunoResearch, 111-605-144, lot 000000142480; 1:100 dilution) secondary antibody was subsequentially incubated together with a cocktail solution of Phalloidin (Alexa Fluor 568 phalloidin, Invitrogen, Life Technologies corporation, A1238; lot 2077757, 1:1000 dilution), BSA 0.5% and 4% goat serum for 1\u2009h at 37\u2009\u00b0C. After each step the fibers were washed three times in PBS. Lastly, each fiber was carefully placed on microscope slides (series 3 adhesive, Trajan, T7611) and mounted with Elvanol (0.01\u2009g/vol polyvinyl alcohol, 30% glycerol, PBS). Images were acquired with a confocal scanning laser microscope (Zeiss LSM900 upright confocal).\n\nStatistical analysis has been carried out using GraphPad Prism software (version 10.0.2 for Mac, GraphPad Software, Boston, Massachusetts USA). Individual datasets were tested for outliers using the ROUT method Q\u2009=\u20091%, then tested for normal distribution using D\u2019Agostino & Pearson test before proceeding to parametric or nonparametric unpaired Student t-test. Statistical significance is reported for p-value\u2009<\u20090.05. Data are presented as means \u00b1 standard error of the mean (SEM), and individual data points are shown to visualize distribution. In Figs.\u00a01a, 1b and 2a the histograms on the right report a single value for each bear which was obtained as the mean of the multiple single fiber analysis represented in the histograms on the left. In Figs.\u00a01c and 2b each dot represents a single fiber belonging to all the subjects analyzed within the corresponding season (n\u2009=\u20092\u00b15 fibers per subject per season). 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French Space Agency (CNES, #7906 and #8072) to F.B., and Agence Nationale de la Recherche (ANR-22-CE14-0018) to F.B. and E.L. This work was funded by the European Union via the Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie grant agreement no. 886232 to L. M. The authors thank the field capture team of the Scandinavian Brown Bear Research Project (SBBRP). The long-term funding of SBBRP has come primarily from the Swedish Environmental Protection Agency, the Norwegian Environment Agency, the Austrian Science Fund, and the Swedish Association for Hunting and Wildlife Management.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Cosimo De Napoli, Luisa Schmidt.\n\nVenetian Institute of Molecular Medicine (VIMM), Padova, Italy\n\nCosimo De Napoli,\u00a0Mauro Montesel,\u00a0Laura Cussonneau,\u00a0Samuele Sanniti,\u00a0Leonardo Nogara\u00a0&\u00a0Bert Blaauw\n\nDepartment of Biomedical Sciences, 35131, University of Padova, Padova, Italy\n\nCosimo De Napoli,\u00a0Mauro Montesel,\u00a0Laura Cussonneau,\u00a0Lorenzo Marcucci,\u00a0Elena Germinario,\u00a0Marco Narici,\u00a0Leonardo Nogara\u00a0&\u00a0Bert Blaauw\n\nInstitute for Genetics, Cologne Excellence Cluster on Cellular Stress Responses in Aging\u2010Associated Diseases (CECAD), University of Cologne, Cologne, Germany\n\nLuisa Schmidt\u00a0&\u00a0Marcus Kr\u00fcger\n\nNorwegian Institute for Nature Research, Trondheim, Norway\n\nJonas Kindberg\n\nDepartment of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, Ume\u00e5, Sweden\n\nJonas Kindberg\n\nDepartment of Forestry and Wildlife Management, Faculty of Applied Ecology and Biotechnology, Inland Norway University of Applied Sciences, Koppang, Norway\n\nAlina Lynn Evans\n\nFrench Space Agency, Centre National d\u2019Etudes Spatiales (CNES), Paris, France\n\nGuillemette Gauquelin-Koch\n\nUniversit\u00e9 de Strasbourg, CNRS, IPHC UMR 7178, 7, Strasbourg, Cedex 2, France\n\nFabrice Bertile\n\nNational Proteomics Infrastructure, ProFi, Strasbourg, France\n\nFabrice Bertile\n\nUniversit\u00e9 Clermont Auvergne, INRAE, UNH UMR 1019, CRNH Auvergne, Clermont-Ferrand, France\n\nEtienne Lefai\n\nDepartment of Pharmaceutical Sciences, 35131, University of Padova, Padova, Italy\n\nLeonardo Nogara\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nL.N., B.B., M.K. conceived the project and wrote the manuscript. C.DN., L.S., M.M., L.C., S.S., L.M., E.G., J.K., AL.E., G. GK., M.N. F.B., and E.L. performed experiments and analyzed data. All authors discussed the results and commented on the manuscript.\n\nCorrespondence to\n Marcus Kr\u00fcger, Leonardo Nogara or Bert Blaauw.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "De Napoli, C., Schmidt, L., Montesel, M. et al. Reduced ATP turnover during hibernation in relaxed skeletal muscle.\n Nat Commun 16, 80 (2025). https://doi.org/10.1038/s41467-024-55565-4\n\nDownload citation\n\nReceived: 15 April 2024\n\nAccepted: 13 December 2024\n\nPublished: 02 January 2025\n\nVersion of record: 02 January 2025\n\nDOI: https://doi.org/10.1038/s41467-024-55565-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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virus induces B cell migration and diapedesis", + "journal": "Nature Communications", + "published": "19 May 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_MOESM3_ESM.avi" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_MOESM4_ESM.avi" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_MOESM5_ESM.avi" + }, + { + "label": "Supplementary Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_MOESM6_ESM.avi" + }, + { + "label": "Supplementary Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_MOESM7_ESM.avi" + }, + { + "label": "Supplementary Movie 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_MOESM8_ESM.avi" + }, + { + "label": "Supplementary Movie 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_MOESM9_ESM.avi" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_MOESM10_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_MOESM11_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_MOESM12_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1255185", + "/articles/s41467-025-59813-z#Sec46" + ], + "code": [], + "subject": [ + "B cells", + "Chemotaxis", + "Herpes virus", + "Neuroimmunology" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5267381/v1.pdf?c=1747739347000", + "research_square_link": "https://www.researchsquare.com//article/rs-5267381/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-59813-z.pdf", + "preprint_posted": "24 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Infection with the Epstein-Barr virus (EBV) is a major risk factor for the development of cancer and autoimmune disorders. The virus enters the body in the pharynx, but EBV causes disease in distant organs, including the gut and the brain. Here we show that infected B cells display features of homing cells. First, they undergo migration through chemokinesis induced by paracrine CCL4 release and CCR1 activation, two molecules induced by the virus. CCR1 knockout inhibited migration and, unexpectedly, proliferation of infected B cells. Second, migrating EBV-infected cells disrupted the integrity of endothelial barriers and underwent diapedesis very efficiently. This process was again dependent on CCL4 and its ability to induce ICAM-1 on endothelial cells. Migration and diapedesis converged on the FAK kinase whose inhibition blocked cell growth and survival of EBV-transformed B cells, but also their spreading to spleen and brain in an animal model in vivo. Moreover, IL-10 secreted by EBV-infected B cells attracted and facilitated diapedesis of EBV-negative CD52high primary B cells. Among these, CD11c+ cells that have been implicated in the pathogenesis of autoimmune diseases were preferentially represented. Curbing migration offers an opportunity to reduce the pathogenicity of EBV-infected B cells in diseased individuals.Biological sciences/Microbiology/Virology/Herpes virusHealth sciences/Pathogenesis/Infection", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Infection with the Epstein-Barr virus (EBV) is a major risk factor for the development of cancer and autoimmune disorders. The virus enters the body in the pharynx, but EBV causes disease in distant organs, including the gut and the brain. Here we show, using in vitro culture and mouse infection models, that EBV-infected B cells display features of homing cells. Infected B cells undergo migration following paracrine CCL4 release and CCR1 induction, while CCR1 deficiency inhibits migration and, unexpectedly, proliferation of infected B cells. Furthermore, migrating EBV-infected B cells undergo CCL4-dependent diapedesis, induce ICAM-1 on endothelial cells, and disrupt the integrity of endothelial barriers. Both migration and diapedesis are regulated by FAK, with FAK inhibition blocking growth and survival of EBV-transformed B cells, as well as their spreading to spleen and brain in an animal model in vivo. Moreover, IL-10 secreted by EBV-infected B cells attracts and facilitates diapedesis of EBV-negative CD52highCD11c+ B cells, which have reported autoimmune properties. Our results thus provide mechanistic insight on EBV-induced B cell dysregulation, and also hint curbing migration as a potential target for reducing the pathogenicity of EBV-infected B cells.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The Epstein\u2013Barr virus was the first discovered human tumor virus1. It infects the large majority of the population and persists for life in infected hosts1. We currently lack preventative vaccines against this virus and infected cells cannot be eliminated. EBV mainly infects B cells and causes multiple types of lymphoproliferations, e.g posttransplant lymphoproliferative disorders (PTLD), Burkitt\u2019s lymphoma or Hodgkin\u2019s lymphoma1.\n\nPrimary infection with the Epstein\u2013Barr virus (EBV) can also cause an infectious mononucleosis (IM) syndrome, a self-limiting lymphoproliferation1. An EBV infection, particularly if complicated by an infectious mononucleosis, increases the risk of developing multiple sclerosis (MS), the most frequent demyelinating disease, over 30-fold2. Moreover, this infection typically precedes by several years the development of neurological lesions in patients with MS. Conversely, individuals who do not carry EBV have a negligible risk of developing MS. Altogether, this points towards EBV infection as a major event in MS pathogenesis. EBV is also suspected to be implicated in the pathogenesis of systemic lupus erythematosus and of other autoimmune diseases3.\n\nEBV is thought to enter the body in the oropharynx1. Accordingly, tonsils are frequently infiltrated by infected B blasts during infectious mononucleosis syndrome or in PTLD that arise in transplanted children and low numbers of infected resting B cells are commonly found in tonsils from healthy sero-positive individuals4,5,6. However, EBV-infected B cells can also be detected at distant anatomical sites, for example in lymph nodes, or in the gut-associated lymphoid tissue7,8. Furthermore, PTLDs develop frequently in the liver and GI tract of adult transplant recipients and in some transplanted children5,9. EBV-infected B cells are found in the brain of healthy individuals, but are more abundant in the brain of individuals with MS10,11. These observations imply that infected B cells must be able to migrate outside the lymphoid tissues they initially colonized.\n\nNormal B cells migrate extensively, in particular during development, germinal center maturation and homing to sites of inflammation12,13. However, this migration is controlled by multiple chemokines typically secreted by stromal cells or inflammatory cells that interact with cognate receptors at the surface of B cells.\n\nEBV-infected B cells secrete multiple chemokines including CCL3, CLL4, CCL5, CCL22, or CXCL8, but also chemokine receptors such as CCR1 or CCR714,15,16. Interestingly, CCR1 is a receptor for CCL3 and for CCL4 secreted by lymphocytes17. Thus, EBV-infected cells could in principal initiate an autocrine chemotactic or chemokinetic loop. Chemokine receptors are G-protein-coupled proteins that upon ligand binding activate the SRC and FAK kinases to engage the MAP kinase signaling pathway18.\n\nHere we report that cytokines secreted by B cells upon EBV infection induce migration and diapedesis of both infected and resting B cells. In infected B cells this process is driven by CCL4 that activates CCR1 and FAK in an autocrine manner. Inhibition of this pathway also blocks proliferation of infected B cells, establishing a link between B cell migration and proliferation. Moreover, Il-10 produced by infected B cells recruits resting B cells and allows their diapedesis, in the absence of specific chemotactic signals. Given the importance of endothelial cell barriers to maintain the integrity of most organs, we anticipate that these properties of infected B cells substantially contribute to the pathogenesis of EBV-associated diseases whilst offering new therapeutic opportunities against them.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We determined the migrating abilities of various types of B cells embedded in a 3D collagen mesh using time lapse microscopy. We first generated an in vitro model of chemokine-induced migration by exposing primary B cells to CD40L, IL-4 and CXCL12 (referred hereafter to as B blasts) to serve as a positive control. Both these blasts and EBV-infected B cells moved with a high average velocity (8\u2009\u00b5m/min) (Supplementary Movie\u00a01 and 2, Fig.\u00a01a\u2013c). In contrast, resting B cells and B cells stimulated with CD40L and IL-4 alone were nearly immobile (Supplementary Movie\u00a03 and 4, Fig.\u00a01a\u2013c). The tracks formed by the migrating infected B cells and migrating B blasts consisted of long linear segments separated by a few angles, suggesting that they were predominantly directed, i.e. ballistic in nature, and not purely random Brownian motions19,20. The recorded trajectories of these single cells displayed an intermediate degree of directionality for both B cell subtypes (60%), supporting this hypothesis (Fig.\u00a01d, see Supplementary Note\u00a01 for a detailed explanation). We then generated mean displacement plots from for the B cell subtypes (Fig.\u00a01e, see Supplementary Note\u00a01 for a detailed explanation)20. While the movement of control B cells generated a linear graph that is characteristic of a random Brownian migration, EBV-infected cells and blasts covered larger distances per time unit and generated a \u2018sublinear\u2019 graph that is again indicative of a sustained directed migration (Fig.\u00a01e). We next developed a correlated random walk simulation program that generates walks with various degrees of directionality (from purely random to purely directed). This model confirmed that EBV-infected cells and B blasts have predominantly directed paths (60 and 70%, respectively) that significantly deviate from purely random walks (p\u2009<\u20090.001) (Fig.\u00a01f)20.\n\na\u2013e B cells stimulated with CD40L, IL-4 and CXCL12, EBV-infected B cells, primary resting B cells or B cells stimulated with CD40L and IL-4 were seeded in a collagen matrix at a concentration of 3 \u00d7 10E5 cells/ml and subjected to time lapse microscopy for 15\u2009min (n\u2009=\u200950, one representative example from 5 experiments with independent B cell samples). a Generated paths were projected onto a 2D surface to generate tracks with or without alignment (n\u2009=\u200950, one representative example from 5 experiments with independent B cell samples). Created in BioRender. Poirey, R. (2025) https://BioRender.com/hgal8k4. b Analysis of the generated tracks revealed the percentage of motile cells. Bar graphs give the mean and standard deviation (n\u2009=\u200950, one representative example from 5 experiments with independent B cell samples). c The velocity of migrating cells is given in the bar graph as mean with standard deviation (n\u2009=\u200950, one representative example from 5 experiments with independent B cell samples). d The directionality of migrating cells is given in the bar graph indicating the mean and standard deviation (n\u2009=\u200920, one representative example from 5 experiments with independent B cell samples). e Square root time profile of migrating B cells. The graph shows the mean displacement of the cells as a function of the square root of time with mean and standard deviation (n\u2009=\u200950, one representative example from 5 experiments with independent B cell samples). f The original tracks were compared to a modelized random walk to determine their percentage of directionality. We show 2D tracks generated by EBV-infected B cells, together with modelized paths harboring 70% directionality. Statistical significance was determined using one-way analysis of variance in (b) and (c) and unpaired two-sided t tests in (d). **p\u2009<\u20090.01, ****p\u2009<\u20090.0001. Source data are provided as a Source Data file.\n\nDirected paths are generated by lymphoid cells under chemotactic or chemokinetic stimulation19. However, because the paths generated by EBV-infected B cells do not converge, these cells are subjected to chemokinesis rather than chemotaxis. This suggests that the concentration of chemokines in the extracellular milieu is homogeneous, rather than concentrated in a discrete region of the culture. Thus, the chemotactic signals in a culture of EBV-infected B cells probably originate from many members of the cell culture, if not all of them. These observations prompted us to canvass the literature for cytokines or chemokines that are expressed together with their receptor by EBV-infected cells. We identified human IL-10 (hIL-10), EBV-encoded IL-10 homolog (also referred to as viral IL-10 or vIL-10), CCL3, CCL4 and CCL5 as potential candidates21. ELISA-based assays confirmed that all these cytokines are secreted in the supernatant of EBV-infected B cells, with CCL3 and CCL4 being secreted at concentrations more than hundred times higher than in supernatants from primary B cell controls (Supplementary Fig.\u00a01a). Chemotaxis chamber assays with these cytokines showed that they all attract infected B cells, with CCL4 showing the strongest effects (Fig.\u00a02a, Supplementary Fig.\u00a01b). Moreover, when EBV-infected B cells embedded in a collagen mesh were subjected to a CCL4 gradient, they clearly moved towards increasing concentrations of the chemokine (Supplementary Fig.\u00a01c). Reciprocally, incubation of infected B cells with neutralizing antibodies specific to CCL4 approximately halved the proportion of infected B cells undergoing migration (Supplementary Fig.\u00a01d, e). Similarly, CXCL12\u2019s effect on B blasts\u2019 migration largely disappeared after exposure to a CXCR4 inhibitor (AMD3100), confirming that their directional movement resulted from a chemokinetic stimulus (Supplementary Fig.\u00a01f). To clarify CCL4\u2019s role in EBV-infected B cells, we knocked out its gene by CRISPR/Cas9. The CCL4null cells lost 90% of their ability to migrate and did not grow, except if they were supplemented with exogenous CCL4 (Fig.\u00a02b, Supplementary Fig.\u00a02a). If cell movement is dependent on chemokine secretion and the infected cells themselves produce the chemokines, infected cells cultured at low concentration should move less efficiently. Therefore, we repeated the above-described experiments with EBV-infected B cells seeded at low cell density (3 \u00d7 10E4/ml). Under these conditions, the majority of infected B cells were immobile and the few cells in motion showed short and slow trajectories (Fig.\u00a02c, d and Supplementary Movie\u00a05). Thus, infected cells could move only if they were close enough to other infected B cells. However, if exogenous CCL4 was added to the culture medium, cells resumed migration and became indistinguishable from cells seeded at higher concentration (Fig.\u00a02c, d). Altogether, CCL4 appears to play a central role in migration of EBV-infected cells. We then attempted to inhibit CCR1, the only CCL4 receptor expressed by infected cells (Supplementary Fig.\u00a03a, b)16. Treatment of infected cells with the CCR1 antagonist BX471 efficiently reduced the percentage of migrating cells and their speed, but only at concentrations above 10\u2009\u00b5M (Supplementary Fig.\u00a02b and Supplementary Fig.\u00a03c). This treatment did not increase apoptosis after 24\u2009h, but blocked cell growth and eventually induced cell death (Supplementary Fig.\u00a02b, c). In the same vein, CCR1null cells were unable to migrate and grow and progressively died after three weeks in culture (Fig.\u00a02b, Supplementary Fig.\u00a02a). In summary, interaction between CCL4 and CCR1 orchestrates EBV-induced cell migration, growth and survival.\n\na Transwell assay with CCL4 as an attractor. 5 \u00d7 10E4 infected B cells were seeded in the top chamber. Medium containing CCL4 (10\u2009ng/ml) was placed in the bottom chamber. The number of infected cells that have reached the bottom chamber after 1\u2009h of stimulation is given, relative to the number of spontaneously migrating cells (2.5 \u00d7 10E3). Medium devoid of chemokines served as a negative control. The bar graph gives the mean with standard deviation (n\u2009=\u20096 independent B cell samples). b The bar graph shows the mean percentage and standard deviation of motile CCL4null or CCR1null EBV-infected cells as observed by time-lapse microscopy (n\u2009=\u20095 independent B cell samples). The impact of exogeneous CCL4 on the mobility of CCL4null cells is also shown. c EBV-infected B cells were seeded in a collagen matrix at low concentration (3 \u00d7 10E4 cells/ml) in the presence or absence of CCL4. Cells were subjected to live cell imaging for 15\u2009min to generate 2D tracks with or without alignment (n\u2009=\u200950 one out of 5 individual experiments). The same cells seeded at high concentration (3 \u00d7 10E5 cells/ml) served as a positive control. d We determined the percentage and the velocity of mobile cells analyzed in (c). Results are given as bar graphs with mean and standard deviation (n\u2009=\u200950 one out of 5 individual experiments). e\u2013g EBV latent genes and CCL4-CCR1 expression. e CCR1 surface expression on Burkitt cell line MUTU I and III clones was determined by FACS (left panel) and CCL4 concentration in supernatants from these cells was determined by ELISA (right panel) (n\u2009=\u20093 independent transfections). The results are summarized in bar graphs (mean with standard deviation). f B cells stimulated with CD40L\u2009+\u2009IL-4 were transfected to express LMP1 or EBNA2. The bar graphs show CCR1 surface expression and CCL4 release in transfected cells and in empty vector controls. Results were normalized for the percentage of transfected cells. Bar graphs show mean and standard error of the mean (n\u2009=\u20093 transfected independent B cell samples). g Differential CCR1 expression at the surface of B cells infected with a LMP1null virus or wild type controls (left panel) and CCL4 release in the supernatants of these cells (right panel) (n\u2009=\u20093 independent B cell samples). The results are summarized in bar graphs (mean with standard deviation). h EBV-infected B cells were subjected to scanning electron microscopy. i EBV-infected B cells were prefixed in PFA and stained with antibodies specific to CDC42 and phosphoPKC Zeta (green), together with a membrane dye (red) and nuclear dye DAPI (blue). Representative pictures are shown (n\u2009=\u200930, one representative example from 3 experiments with independent B cell samples). Statistical significance was determined using two-sided paired t tests in (a), (e), and (g) and one-way analysis of variance in (b), (d), and (f). *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001, and ****p\u2009<\u20090.0001. Source data are provided as a Source Data file.\n\nLatently infected B cells proliferate under expression of the latent genes, among which the Epstein\u2013Barr nuclear antigen 2, a transactivator that activates the Notch pathway and the latent membrane protein 1, a permanently active viral homolog of CD40, play a crucial role1. To establish whether these viral genes control CCL4 and CCR1 expression in infected B cells, we first monitored their expression in an early and a late passage of the MUTU Burkitt\u2019s lymphoma cell line. While an early passage of this line (MUTU I) has a restricted latent protein expression pattern largely limited to EBNA1 (latency I), late passage cells (MUTU III) express all latent genes, including EBNA2 and LMP1 (latency III)22. CCR1 surface expression and CCL4 release in MUTU III was respectively 8 (CCR1) and 600 (CCL4) times higher than in MUTU I, suggesting that expression of these proteins is associated with latency III (Fig.\u00a02e). To determine which latent gene is responsible for these effects, we transfected EBNA2 or LMP1 in CD40L\u2009+\u2009IL4 activated B blasts. These assays revealed that LMP1 is a potent inducer of CCL4 and CCR1 expression (Fig.\u00a02f). Interestingly, EBNA2 also activated CCL4 release, but was unable to induce CCR1 expression (Fig.\u00a02f). Similar results were obtained after transfection of the cell line BL41 with LMP1 or EBNA2 (Supplementary Fig.\u00a03d). To confirm LMP1\u2019s role in this process, we infected primary B cells with a LMP1null mutant (M81/\u2206LMP1). Relative to B cells infected with wild-type viruses, B cells infected with M81/\u2206LMP1 released CCL4 and expressed CCR1 at 10- and 5-time lower levels, respectively (Fig.\u00a02g). This approach was unfortunately not possible for EBNA2 as the M81/\u2206EBNA2 mutant fails to initiate transformation. Altogether, we conclude that LMP1 and EBNA2 collaborate to initiate the chemokine loop that leads to migration.\n\nExamination of our B cell panel by scanning electron microscopy (SEM) or confocal light microscopy combined to a membrane dye revealed that both EBV-infected B cells and our control B blasts displayed typical features of migrating B cells (Fig.\u00a02h, Supplementary Fig.\u00a04a). These cells displayed a well-developed uropode at one pole, and a large lammelipodia intertwined with filopodiae at the opposite pole. These features were visible in the control blasts, but not in the negative controls (Supplementary Fig.\u00a04a). Treatment of EBV-infected B cells with specific inhibitors showed that polarization results from F-actin polymerization and requires myosin II (Supplementary Fig.\u00a05a, b). Lamellipodiae drove migration of both infected B cells and control B blasts (Supplementary Movie\u00a01 and 2). We found that CCR1 is preferentially expressed at the surface of the lamellipodiae (Supplementary Fig.\u00a03b). Morphological features of polarization were confirmed by the study of specific molecular markers. Immunofluorescence staining showed that the small G protein CDC42 or phosphoPKC\u00a0Zeta were concentrated between the uropode and the nucleus of infected B cells, but were randomly distributed in B cell controls (Fig.\u00a02i and Supplementary Fig.\u00a04b). This is relevant as these molecules induce polarization and are involved in cell migration23,24. Furthermore, centrin and live tubulin stains showed that the microtubule organizing center (MTOC) was also located in this area, as were cellular organelles such as mitochondria or Golgi apparatus (Supplementary Fig.\u00a04c\u2013e)23. Similar features were observed in our control B blasts, but not in non-moving controls (Supplementary Fig.\u00a04b\u2013e). Interestingly, polarization and lamellipodia developed slowly post-infection and reached their full development only 2 weeks after infection, a time at which cells start to migrate and proliferate efficiently (Supplementary Fig.\u00a04f). After that time point, these morphological features and the ability to migrate persisted unchanged, even after several months in culture.\n\nG protein-coupled receptors such as CCR1 have been reported to signal through the FAK pathway with which CDC42 and the Rho-associated kinase (ROCK) interact18,25,26. EBV-infected cells treated with FAK2, CDC42 or ROCK inhibitors lost polarization and evinced markedly reduced mobility (Supplementary Movie\u00a06, Fig.\u00a03a, Supplementary Fig.\u00a05a, b). The FAK2 inhibitor defactinib was particularly interesting as it immediately and strongly reduced cell mobility and cell growth, but also cell survival after 2 weeks at moderate concentrations (3.5\u2009\u00b5M) (Fig.\u00a03a). Defactinib has previously been tested in clinical trials at doses delivering similar seric concentrations27,28. Defactinib induced low levels of apoptosis after one day at this concentration (Supplementary Fig.\u00a02c). Defactinib did not affect growth of activated non-infected B cells (Supplementary Fig.\u00a02d). Interestingly, EBV-infected B cells expressed phosphoFAK2 at levels three times higher than those of the stimulated B blasts (Fig.\u00a03b). This suggests that growth of EBV-infected cells is dependent on higher phosphoFAK2 expression levels and thus is more sensitive to defactinib (Fig.\u00a03b). Because defactinib inhibits both FAK1 and FAK2, we assessed expression of these proteins in EBV-infected B cells. Immunostains revealed that these cells nearly exclusively express the FAK2 protein, although some residual expression of the non-lymphoid kinase FAK1 was visible (Supplementary Fig.\u00a06). Moreover, while FAK2 was localized at the uropode, FAK1 was mainly perinuclear in location. We then assessed the effect of CCR1 inhibition on phosphoFAK2 expression levels in infected B cells. Exposure of infected B cells to CCR1 inhibitors at 10\u2009\u00b5M more than halved phosphoFAK2 expression levels (Fig.\u00a03c). Although low doses of the CCR1 inhibitor BX471 (5\u2009\u00b5M) and of defactinib (0.5\u2009\u00b5M) were unable to influence growth of infected B cells, their combination showed clear synergistic effects (Fig.\u00a03d, Supplementary Fig.\u00a02e). We confirmed synergy between BX471 and defactinib first by drawing a dose-response curve for these drugs (Fig.\u00a03e). This analysis showed a very steep dose-response slope for the latter drug that limited the range of concentrations that could be used to draw isobolograms. Nevertherless, isobolograms for E10 (10% of complete growth reduction) and E30 (30% of complete growth reduction) showed clear synergistic effects (Fig.\u00a03e). At higher doses, both drugs were individually able to reduce cell growth. Under these conditions, as expected, synergistic effects could not be observed. Altogether, these data confirm that CCR1 and FAK2 are located in the same pathway and are essential for EBV-infected B cell polarization and cell migration.\n\na EBV-infected B cells were exposed to the FAK2 inhibitor defactinib (Def.) for 30\u2009min (3.5\u2009\u00b5M) and seeded in collagen at a concentration of 3 \u00d7 10E5 cells/ml. 2D cell tracks with or without alignment were generated from time lapse microscopy recordings. The bar graphs show the mean percentage of motile cells with standard deviation in treated and untreated cells (n\u2009=\u20093 independent B cell samples). Treated EBV-infected B cells were also observed by scanning electron microscopy. Curves showing EBV-infected cell growth in the presence of defactinib (Def.) at a 3.5\u2009\u00b5M concentration over two weeks. Mean cell count and standard deviation are indicated (n\u2009=\u20095 independent B cell samples). b Total and phosphoFAK2 expression levels in B cells stimulated with CD40L, IL-4 and CXCL12 or after EBV infection were determined by western blot with specific antibodies. The bar graph summarizes the mean phosphoFAK2 expression levels and standard deviation, relative to the loading control tubulin (n\u2009=\u20094 independent B cell samples). c Western blot showing phosphoFAK2 expression levels in EBV-infected B cells exposed to the CCR1 inhibitor BX471\u00a0at 10 \u03bcM for 30\u2009min. The arrow refers to phosphoFAK2, the asterisk refers to a non-specific signal. The bar graph summarizes the mean phosphoFAK2 expression levels and standard deviation. Values are given relative to the values observed in the untreated population. An immunoblot against tubulin was used as loading control (n\u2009=\u20094 independent B cell samples). d Curves showing EBV-infected cell growth over time in the presence of defactinib (Def.) at a 0.5\u2009\u00b5M concentration. Infected cells were alternatively treated with BX471 (5\u2009\u00b5M) alone or with a combination of low doses defactinib (0.5\u2009\u00b5M) and BX471 (5\u2009\u00b5M). Mean cell count and standard deviation are indicated (n\u2009=\u20095 independent B cell samples). e dose-response curve for defactinib and BX471 (n\u2009=\u20093 independent B cell samples, each point showing mean and standard deviation), together with isobolograms (ED10\u2009=\u200910% of maximal effect and ED30\u2009=\u200930% of maximal effect). The linear curve shows additivity of the drug effects, the curve below it is indicative of synergistic effects. Statistical significance was determined using two-sided paired t tests in (a) (growth curve at day 14), b and (c) and one-way analysis of variance at day 7 in (d). *p\u2009<\u20090.05, **p\u2009<\u20090.01. Source data are provided as a Source Data file.\n\nAutoimmune diseases associated with EBV, and in particular MS, frequently show tissular infiltration with immune cells, but the cause of their recruitment is not clear29. Thus, we investigated whether the large amounts of chemokines secreted by EBV-infected B cells could influence movement of non-infected lymphoid cells. Migration assays indeed showed that EBV-infected B cells attract primary B cells, but not activated B cells (Fig.\u00a04a, Supplementary Fig. 7a). Next, we attempted to identify the cytokines responsible for this effect. Because primary B cells express the IL-10 receptor but not the receptors for CCL3, CCL4 and CCL5, we performed chemotaxis assays with human IL-10 and EBV-encoded IL-10. These cytokines increased migration of resting, but not activated control B cells (Fig.\u00a04b, Supplementary Fig.\u00a07a). Reciprocally, an EBV-infected B cell line that lacks both EBV-encoded and human IL-10 (Fig.\u00a04b) lost its ability to attract resting B cells. Although IL-10 does not act as a chemotactic factor, it has been reported to modulate the action of chemokines and to increase chemokinesis in B cells30. Indeed, incubation of primary B cells with a mix of EBV-encoded and human IL-10 or coculture with EBV-transformed cells led to an increased proportion of migrating cells (Supplementary Fig.\u00a07b). In an attempt to characterize the mobile B cell population attracted by EBV-infected B cells, we performed single-cell RNAseq (scRNAseq) and FACS stains on the mobile (move) and immobile (stay) subpopulations. UMAP visualization based on clustering analysis identified 3 clusters within the primary B cell population. These were defined on the basis of a NF-kB activation signature, of a BCR response with SYK expression or of a combined IL-4R and IL-7R expression, but we did not find any evidence that this clustering differed between the move and stay population (Fig.\u00a04c, d). However, gene set enrichment analysis (GSEA) revealed that moving primary B cells strongly express interferon alpha and gamma response genes, which probably results from the contact with infected B cells that produce interferons (Fig.\u00a04e)31,32, as well as an increased oxidative phosphorylation and a decreased entry into mitosis. ScRNA analysis also identified CD52 and CD53 expression as enhanced in the majority of migrating B cells, relative to their immobile counterparts (Fig.\u00a04f, Supplementary Table\u00a02). We could confirm that CD52, a protein involved in endothelial diapedesis, is more strongly expressed in the migrating cells, although the difference in expression between the migrating and non-migrating populations was limited in intensity (Fig.\u00a04f)33. Altogether, this suggests that the motile B cell subset is metabolically active and primed for migration and diapedesis. Furthermore, we found that migrating cells were enriched in CD11c-positive cells, relative to the initial B cell population (Fig.\u00a04g). However, coculture of primary B cells with infected B cells could not reproduce this effect, excluding that contact with EBV-infected B cells upregulated CD11c (Supplementary Fig.\u00a07c). CD11c is a marker of atypical B cells that play an important role in autoimmune diseases and are enriched in the brain of patients with MS34,35.\n\na Transwell migration assay with EBV-infected B cells in the bottom well and labeled primary resting B cells in the top well. The graph shows the mean number and the standard deviation of labeled primary B cells that reached the bottom chamber after 24\u2009h, relative to spontaneous B cell migration in the absence of infected B cells that served as a negative control (n\u2009=\u20095 independent B cell samples, each experiment in duplicate). On average approximately 2.5 \u00d7 10 E3 primary B cells spontaneously migrated to the bottom chamber. Created in BioRender. Poirey, R. (2025) https://BioRender.com/qnmhdf1. b Primary B cells were placed in the top chamber of a transwell device and human IL-10 (hIL-10, 100\u2009ng/ml) or EBV-encoded IL-10 (vIL-10, 100\u2009ng/ml) or both cytokines combined (10\u2009ng/ml each) were added to the bottom chamber. EBV-infected B cells placed in the bottom chamber served as a positive control. The number of primary B cells that reached the bottom chamber after 24\u2009h is indicated relative to spontaneous B cell migration (left panel, n\u2009=\u20097). Same experiment as in (a) was repeated with B cells transformed with a virus that lacks CCL4 (CCL4null), EBV-encoded IL-10 (vIL-10null) or viral and human IL-10 (h/vIL-10null) placed in the bottom chamber. B cells infected with wild-type EBV served as a positive control (right panel, n\u2009=\u20095 independent B cell samples). Bar graphs give the mean and standard deviation. c Uniform Manifold and Projection (UMAP) plot of scRNA-seq analysis performed on all primary B cells and colored by annotation. d UMAP visualization of scRNA-seq data from mobile (move) and immobile (stay) B cell populations. e Top five enriched (orange) and depleted (blue) Gene Ontology terms in the mobile B cell population, relative to the immobile B cells. f The graph shows CD52 differential expression level in the move and stay B cell populations after scRNA analysis. Differential CD52 expression in both cell populations was confirmed by flow cytometry (n\u2009=\u20097 independent B cell samples, 2000 cells recorded). The bar graph gives the mean fluorescence intensity (MFI) and standard deviation of CD11c expression in these subpopulations. g Same experiment as in (a), primary B cells that remained in the top chamber and those that migrated to the bottom chamber were stained for CD11c. The bar graph gives the mean and standard deviation of CD11c expression in these subpopulations, relative to the proportion of CD11c in the initial primary B cell population (n\u2009=\u20095 independent B cell samples, 2000 cells recorded). Statistical significance was determined using two-sided paired t tests in (a), (f) and (g), one-way analysis of variance in (b), non-parametric two-sided Wilcoxon Rank Sum test in (e). P-values were adjusted using the Bonferroni correction. *p\u2009<\u20090.05, **p\u2009<\u20090.01, ****p\u2009<\u20090.0001. Source data are provided as a Source Data file.\n\nLymphoid cells homing to tissues under chemotactic stimulus perform diapedesis to cross endothelial barriers and reach, for example, inflamed tissues36. We first used a transwell assay in which the upper and lower chambers are separated by a layer of activated human endothelial cells derived from brain, dermis, or umbilical vein (HBMEC, HDMEC or HUVEC) cells. HBMEC cells are typically used to model the blood-brain barrier (BBB)37. Addition of infected B cells to the top chamber led to an efficient crossing of the endothelial barrier within 24\u2009h with all three types of endothelia (Fig.\u00a05a, Supplementary Fig.\u00a07d). Diapedesis of primary B cells, CD40L\u2009+\u2009IL-4-stimulated B cells and CXCL12-activated B blasts through this HUVEC or HBMEC barrier was ten-fold less efficient (Fig.\u00a05a, Supplementary Fig.\u00a07d). Diapedesis of EBV-infected B cells through HUVEC and HBMEC cells increased endothelial permeability, as assessed by a decreased trans-endothelial electrical resistance (TEER) (Supplementary Fig.\u00a08a, b). We then tested the ability of EBV-infected B cells to adhere to endothelial layers stimulated with TNF-\u03b1 under constant laminar shear stress. EBV-infected B cells, and to a lesser extent CXCL12-stimulated B blasts, adhered strongly to both types of endothelia and showed evidence of rolling in these flow experiments (Fig.\u00a05b, Supplementary Movie\u00a07, Supplementary Fig.\u00a08c, d). Moreover, only EBV-infected B cells bound to unstimulated HBMEC, albeit at very reduced levels (Fig.\u00a05b). However, EBV-infected B cells treated with defactinib lost their ability to adhere to the endothelium (Fig.\u00a05b, Supplementary Fig.\u00a08c). Coculture of confluent endothelial cells with infected B cells, but not with CXCL12-activated B blasts, activated calcium signaling within 10\u2009min, as expected from endothelia experiencing diapedesis (Supplementary Fig.\u00a09a)38. Coculture of EBV-transformed B cells with the three types of endothelial barriers led to a relocation of the zona occludens protein 1 (ZO-1), a marker of endothelial barrier integrity, from the intercellular areas to the cytoplasm, and to a disruption of the cytoskeletal structure around cell-to-cell contact areas (Fig.\u00a05c, Supplementary Fig.\u00a09b)39. Rolling of EBV-infected B cells on endothelial cells was followed by a loosening of endothelial junctions within 10\u2009min, with the development of large clefts, as shown by live labeling of the F-actin network (Supplementary Fig.\u00a09c, d). This suggested that EBV-infected B cells perform transcytosis.\n\na Spontaneous diapedesis of primary resting B cells, B cells stimulated with CD40L and IL-4 or CD40L, IL-4 and CXCL12, and of EBV-infected B cells through a layer of human brain microvascular endothelial cells (HBMEC) cells. Bar graphs give the mean and standard deviation of cells that had migrated after 24\u2009h of culture (n\u2009=\u20096 independent B cell samples). Created in BioRender. Poirey, R. (2025) https://BioRender.com/qnmhdf1. b We determined the ability of various B cell populations (see a)) to adhere to HBMEC cells under constant flow conditions, with or without prior stimulation with TNF-\u03b1. The graph shows the concentration of cells bound to the endothelial layer achieved by the different B cell populations. The effect of defactinib (Def.) (3.5 \u03bcM) or of an LFA-1 inhibitor (BIRT377 50 \u03bcM) on EBV-infected B cell adhesion is also described. Bar graphs give the mean and standard deviation (n\u2009=\u20095 independent B cell samples). c HBMEC and HUVEC endothelial cells were cocultured with EBV-infected B cells for 30\u2009min. The effect of coculture on ZO-1 expression was evaluated 24\u2009h later by immunofluorescence. Its localization was determined by intensity profiling of gray values, membrane peaks being marked with arrows (middle panel). Bar graphs give the percentage and the standard deviation of cells that have a membrane peak (n\u2009=\u20095 independent B cell samples). d Same as in (c), but cells were analyzed for ICAM-1 expression. The bar graphs give the raw integrated density of fluorescence (Raw/Int/Den) in the different samples (n\u2009=\u20095 independent B cell samples, graphs showing mean and standard deviation). e Primary B cells labeled with a green fluorescent dye were mixed or not with EBV-infected B cells and placed above an endothelial cell layer. The relative number of primary B cells that crossed the endothelial barrier under both conditions after 24\u2009h is given in the bar graph as mean with standard deviation (n\u2009=\u20096 independent B cell samples). f Same experiment as described in (e), but the stay and move populations were stained for CD11c. The relative proportion of CD11c+ cells in these two subsets was determined (n\u2009=\u20095 independent B cell samples, 1000 cells recorded). The graph shows the mean of these proportions with their standard deviation in cell populations that underwent coculture with EBV-infected B cells. The results are given relative to the CD11c+ proportions in the absence of coculture. Statistical significance was determined using one-way analysis of variance in (a) and (b) and two-sided paired t tests in (c), (e), and (f) and unpaired t test in (d). *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001 and ****p\u2009<\u20090.0001. Source data are provided as a Source Data file.\n\nDiapedesis requires interactions between surface molecules, e.g. the integrin LPAM-1 expressed by lymphoid cells that home to the GALT and MAdCAM-1, an addressin expressed by high endothelial cells within this lymphoid tissue40,41. In tissues infected by pathogens, chemokines and inflammatory mediators such as TNF-\u03b1 can also upregulate ICAM-1 and VCAM-1 expression at the surface of endothelial cells42. This facilitates binding and diapedesis of activated homing lymphocytes43. Exposure of primary endothelial layers to EBV-infected B cells led to a clear increase in ICAM-1, but not VCAM-1 or MAdCAM-1 expression (Fig.\u00a05d, Supplementary Fig.\u00a010a, b). EBV-infected B cells express LFA-1, a protein that efficiently interacts with ICAM-1, suggesting that this interaction might contribute to diapedesis (Supplementary Fig.\u00a010c). We tested this hypothesis by blocking ICAM-1-LFA-1 interactions using the BIRT377 LFA-1 inhibitor (Fig.\u00a05b). Addition of this inhibitor to endothelial cells co-cultured with EBV-infected B cells indeed reduced adherence. We then treated HUVEC with some chemokines produced by EBV-infected B cells. CCL4, human IL-10 and EBV-encoded IL-10 all upregulated ICAM-1 expression, with the latter two inducing ICAM-1 nearly as efficiently as TNF-\u03b1, the key cytokine in this process (Supplementary Fig.\u00a010d, e, g). HBMEC spontaneously express low levels of\u00a0ICAM-1 that increased after CCL4 exposure, but were not sensitive to stimulation with IL-10 (Supplementary Fig.\u00a010d, f, g). Coculture of HUVEC cells with CCL4null or IL-10null infected B cells approximately halved ICAM-1 expression, relative to levels obtained with wild-type cells (Supplementary Fig.\u00a010e, g). Similar results were obtained with HBMEC cells cocultured with CCL4null infected B cells (Supplementary Fig.\u00a010f, g). ICAM\u2019s induction by CCL4 is congruent with the ability of this chemokine to disrupt the neurovascular endothelium44. CCL4null and double human and viral knockout h/vIL-10null EBV-infected B cells had a reduced propensity to cross a continuous HUVEC layer, confirming their role in diapedesis (Supplementary Fig.\u00a010h). However, this observation could not be reproduced with HBMEC, probably because they spontaneously express low ICAM-1 levels. The observation that EBV-infected B cells attract primary B cells suggests that they might also be able to facilitate their diapedesis. Therefore, we exposed an HBMEC endothelial barrier to EBV-infected B cells admixed with primary B cells. The presence of EBV-infected B cells doubled diapedesis of primary B cells (Fig.\u00a05e). Among the primary B cell population that crossed the endothelial barrier together with infected B cells, CD11c-positive B cells were enriched, relative to the total primary B cells (Fig.\u00a05f).\n\nBecause defactinib blocks EBV-infected B cells migration and cell viability in vitro, we tested whether this molecule can prevent EBV-induced B cell migration and growth in immunosuppressed NSG mice. Half of the mice received defactinib two weeks after injection of primary B cells exposed to a low virus dose (4 \u00d7 104 infected cells per mouse) for a duration of four weeks (Fig.\u00a06a). This prevents the development of large EBV-induced tumors within the observation period. Six weeks after injection of the infected B cells, all mice were alive, but splenic infiltration by EBV-infected lymphoid cells was obvious only in the untreated population. Mice treated with defactinib did not show evidence of splenic invasion, neither after in situ hybridization on splenic tissue using the EBV-specific non-coding RNA probe EBER, nor after qPCR with EBV-specific probes (Fig.\u00a06b). In the same vein, blood samples drawn from defactinib-treated mice at termination of the experiment were devoid of detectable viral sequences. However, the blood of three out of 5 non-treated infected mice tested positive for the EBV-specific qPCR (Fig.\u00a06b). We also subjected brain and meningeal samples from the two mice groups to the same assay. We found that only mice treated with defactinib were free of detectable virus infection (Fig.\u00a06b). This suggests that EBV-infected B cells can independently reach the central nervous system, though at very low levels.\n\na NSG mice were injected with primary B cells coated with low doses EBV. Two weeks later they were treated for 4 weeks with defactinib intra-peritoneally twice daily at a dose of 15\u2009mg/kg. Untreated mice served as a negative control. Created in BioRender. Poirey, R. (2025) https://BioRender.com/me9d7ci. b Spleen tissue sections showed nodular infiltration by EBER-positive cells (red, marked with white arrows) in the control mice (upper panel, n\u2009=\u20095). In contrast, spleens from treated mice were completely devoid of EBV-infected B cells (lower panel, n\u2009=\u20095). Spleen tissue or peripheral blood after completion of the experiment were subjected to an EBV-specific PCR. Tissue sections from the brain and meninges of investigated mice were similarly analyzed for the presence of EBV sequences. The bar graphs give the mean EBV copies per 100\u2009ng of DNA extracted from tissues after logarithmic transformation and their standard deviation. Statistical significance was determined using two-sided unpaired t tests. ***p\u2009<\u20090.001; *p\u2009<\u20090.05; ****p\u2009<\u20090.0001. Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59813-z/MediaObjects/41467_2025_59813_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Understanding the contribution of EBV to the development of autoimmune diseases, and in particular of multiple sclerosis, is seen as key for the understanding of their pathogenesis and the development of targeted therapies45. In particular, whether and how EBV accesses the brain and other organs is a central question. In the present paper, we found that EBV infection induces polarization, and confers to these cells the ability to migrate and undergo diapedesis with high efficiency, two cardinal features of homing cells. All these features can be largely explained by expression of the well-characterized CCR1 and CCL4 molecules that induced an unexpected paracrine chemokinesis that allowed cells to move independently from the external stimuli that are required for cell homing in general. This is possible because infected B cells simultaneously induced the expression of a chemokine (CCL4) and of its receptor (CCR1). It is also remarkable that the viral infection activated expression of two molecules that are typically expressed by T cells, although CCR1 has been detected in a subpopulation of non-germinal center B cells and CCL4 is released after B cell receptor stimulation46,47. CCR1 binding led to an engagement of the FAK2 pathway, but it remains possible that other stimuli also play a role in its activation in EBV-infected B cells. Importantly, cell migration in the context of EBV infection influenced cell growth and ultimately cell survival. Migration blockers eventually eliminated infected B cells, including those undergoing spontaneous lytic replication, a virus infection mode previously associated with multiple sclerosis48. This reflects the effects of G protein-coupled receptors such as CCR1 and its downstream effector FAK2 on cell growth and division and offers new therapeutic perspectives49,50,51. Our data expand the\u00a0current view on EBV-mediated B cell transformation, in which EBV latent proteins directly interact with signaling pathways to initiate cell growth1. Previous work showed that LMP1 can induce CCL4 release and that CCR1 surface expression in EBV-positive Burkitt\u2019s lymphomas requires full latent gene expression21,52. We confirm that CCR1 and CCL4 expression in Burkitt\u2019s lymphoma cells requires full latent gene expression. Furthermore, we now show that both EBNA2 and LMP1 induce CCL4 release and that LMP1, but not EBNA2, induces CCR1 surface expression in primary B cells. These findings highlight how EBV latent genes influence the biology of their target. In particular, it shows that LMP1\u2019s functions extend beyond a mere induction of proliferation, and includes an activation of cell migration and diapedesis.\n\nOur data also revealed new potential therapeutic targets as the CCL4-CCR1-FAK2 pathway\u00a0and its downstream effectors can be relatively easily targeted as shown with the\u00a0already available inhibitors. We found here in particular that defactinib, a molecule that has been extensively tested in clinical trials for multiple malignancies with variable efficacy, but with a good safety profile is able to eliminate infected B cells and prevent migration in the spleen in an animal model27,28,53. In order to parallel the clinical conditions as closely as possible, we used these inhibitors in vitro and in vivo at concentrations measured in the blood of treated patients. Notably, the combination of one CCR1 inhibitor previously used in clinical trials with very low doses of defactinib was able to block cell growth in vitro very efficiently. These two concentrations can probably be reached safely in patients with EBV-associated diseases. Previous clinical testing of the CCR1 inhibitors has shown their excellent safety profiles54.\n\nEBV-infected B cells\u2019 ability to induce ICAM-1 expression at the surface of endothelial cells, independently of an external chemotactic stimulus, and to disrupt confluent endothelial tissues is likely to have pathogenic consequences. PTLD are frequently extranodal in location and involve organs such as the liver, the gastro-intestinal tract or the central nervous system7,8,10. The ability of EBV-infected B cells to disorganize and cross endothelial barriers could explain their propensity to enter non-lymphoid tissues. ICAM-1 expression at the surface of endothelial cells is a crucial step in diapedesis and is likely to facilitate specific or non-specific transcytosis of inflammatory cells as we showed for resting primary B cells, in particular if the endothelium structure has been simultaneously damaged by EBV-infected cells. In particular, we found that EBV-infected B cells recruit and facilitate diapedesis of CD52highCD11c+ B cells, a process that is EBV-encoded IL-10-dependent. This could potentially explain why EBV-infected B cells and primary B cells, in particular T-bet+CD11c+ B cells infiltrate the central nervous system of patients with multiple sclerosis34.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "This research complies with all relevant ethical regulations. The Ethics Committee of the University of Heidelberg approved all uses of human material (approval S/752/2022). All recruited volunteers provided written informed consent.\n\nPeripheral blood CD19+ B cells were isolated from fresh buffy coats by Ficoll density gradient followed by selection with anti-CD19 PanB Dynabeads and beads detachment, as recommended by the manufacturer (#11143D, Invitrogen, Germany).\n\nPeripheral blood CD19+ B cells were cultured in RPMI-1640 medium (#11875093, ThermoFisherScientific, Germany) supplemented with 10% fetal calf serum (FCS) and stimulated with 20\u2009ng/ml human recombinant IL-4 (#A42602, Life Technologies, Germany) and 50\u2009ng/ml soluble CD40L (#PPT-310-02-100, Peptrotech, Germany) (designated as CD40L/IL4) over 5 days and subjected to subsequent assays. In other experiments we used cells that were additionally stimulated with CXCL12 (CD40L/IL4/CXCL12). For stimulation with CXCL12 (#300-28\u2009A, Peptrotech, Germany) 100\u2009ng/ml of the chemokine were added to 4 days old CD40L/IL4 stimulated B cells and analyzed 24\u2009h later.\n\nInfections were performed with wild-type M81 or a mutant with a deletion in the BCRF1 gene, the EBV-encoded Il-10 (B828)55. We constructed the BCRF1 knockout mutant by deleting the BCRF1 gene locus from the TATA box to the end of the BCRF1 ORF (strain M81, KF373730.1 coordinates 9594 to 10183), using homologous recombination with the M81 bacmid. The deletion was obtained by exchanging this segment of the EBV genome with a kanamycin cassette cloned in the pCP15 plasmid. This was achieved by PCR amplification of the kanamycin cassette (primers see Supplementary Table\u00a01).\n\nWe also constructed an EBV LMP1null virus (B738) by exchanging the LMP1 sequence (EBV coordinates 167717-168892) with a kanamycin resistance cassette by homologous recombination in the M81 wildtype BAC (KF373730.1). This cassette was amplified from the pCP15 plasmid by PCR (primers see Supplementary Table\u00a01). After successful recombination, the kanamycin resistance gene was excised from the LMP1null virus using the FLP recombinase (pCP20) as described before56\n\nFor recombinant virus production, lytic replication of 293 cells containing the EBV recombinant virus (M81, EBV LMP1null, EBV-encoded Il-10null) were induced by transfection of a BZLF1 expression plasmid with co-transfection of a BALF4 expression plasmid57. The supernatants were collected 4 days post-transfection and filtered through a 0.45-\u03bcm filter to remove cell debris.\n\nPeripheral blood CD19+ B were exposed to various viruses to generate new virus-transformed cell lines (designated as EBV-infected cells) and were routinely cultured in RPMI-1640 medium supplemented with 10% fetal calf serum.\n\nPrimary Human Brain Microvascular Endothelial Cells (HBMEC) were purchased from Innoprot (P10361, Innoprot, Spain) and cultured on fibronectin (2 \u03bcg/cm2, #354008, Corning, USA) coated 75\u2009cm2 flasks in Endothelial basal medium supplemented with FCS, endothelial cell growth supplement and penicillin/streptomycin solution (#P60104, Innoprot, Spain). Human Umbilical Vein Endothelial Cells (HUVEC) and Human Dermal Microvascular Endothelial Cells (HDMEC, isolated from the dermis of juvenile foreskin and adult skin) were purchased from Promocell (C-12200 and C-12212, Promocell, Germany). HUVEC cells were grown in Endothelial Cell Growth Medium, HDMEC cells in Endothelial Cell Growth Medium MV (C-22110, Promocell, Germany). EBV-infected cells were cultured in RPMI-1640 (ThermoFisherScientific, Germany) and 10% FCS. For coculture experiments EBV-infected cells were kept in media required by endothelial cells.\n\nThe EBV-negative Burkitt lymphoma cell line BL41 (kind gift from Gilbert Lenoir, IARC, France) and the EBV-positive MUTU cell lines (clones I and III) (kind gift from A. Rickinson, CRUK, Birmingham, UK) were cultured in RPMI-1640 and 10% FCS. BL41 was electroporated using 10\u2009\u00b5L Neon tips with the following settings: 1350\u2009V, 20\u2009ms, 1 pulse. CD40L\u2009+\u2009IL-4 stimulated B cells were electroporated using 10\u2009\u00b5L Neon tips with the following settings: 1900 V, 20\u2009ms, 1 pulse. For transfection the Neon MPK5000 transfection system was used (#10431915, ThermoFisherScientific, Germany)\n\nChemokines CCL3 (#300-08), CCL4 (#300-09), CCL5 (#300-06), hIL-10 (#AF-200-10) were purchased from Peprotech (Peptrotech, Germany), vIL-10 was purchased from Biotechne (#915-VL-010, Biotechne, Germany) and used at concentrations of 10\u2013100\u2009ng/ml. Neutralizing antibody against CCL4 was purchased from R&D (#MAB271, clone 24006, R&D, USA) and added at concentrations of 0.5\u20131.0 \u03bcg/ml 30\u2009min prior to time lapse experiments.\n\nIn some experiments we added inhibitors 30\u2009min prior to our analysis. These inhibitors were BX471 (S7604, Selleckchem, USA) at 1-10\u2009\u03bcM, defactinib (S7654, VS-6063, Selleckchem, USA) at 0.5-10\u2009\u03bcM, Latrunculin A at 5\u2009\u03bcM (10010630, Cayman Chemical, USA), BDM (2,3-Butanedione-2-monoxime, 20828, Cayman Chemical, USA) at 50\u2009mM, Blebbistatin (24169, Cayman Chemical, USA) at 50\u2009\u03bcM, Y27632 (ROCK Inhibitor, 10005583, Cayman Chemical, USA) at 5\u2009\u03bcM, ZCL278 (CDC42 Inhibitor, 14849, Cayman Chemical, USA) at 50\u2009\u03bcM, LFA-1 Inhibitor BIRT377 (30112547, ThermoFisherScientific, Germany) at 50\u2009\u03bcM. We also used AMD3100, a CXCR4 inhibitor at a 1\u2009\u00b5M concentration (HY-10046, Medchem, Germany).\n\nCCL3 (DY270), CCL4 (DY271), CCL5 (DY278) and human IL-10 (DY217B) were quantified by ELISA (DuoSet ELISA, R&D) in the supernatants of 1 \u00d7 10E6 EBV infected B-cells, of B cells stimulated with CD40L/IL4 or of unstimulated B cells that were cultured in 1\u2009ml of RPMI/10%FCS for 24\u2009h.\n\nWestern blotting was performed as described before8. In brief, protein extracts were generated by resuspending cells in extraction buffer (50\u2009mM Tris at pH 7.5/150\u2009mM NaCl/0.1% SDS/1% sodium deoxycholate/1% Triton X-100) and sonification. After heat denaturation, extracts were loaded onto 10% or 12% SDS acrylamide gels. After electrophoresis, proteins were electroblotted onto an ECL-Membrane (GE10600001, Amersham Pharmacia) for 90\u2009min at 25\u2009V. After preincubation of the blot in PBS/5% dry milk powder containing 0.1% Tween 20, antibodies were added overnight at 4\u2009\u00b0C. After washings in PBS, the blots were incubated for 1\u2009h with horseradish peroxidase-coupled secondary antibodies (goat anti-mouse, w402b, Promega, final dilution 1:20000). Bound Abs were revealed by using the ECL detection reagent (#1705061, BioRad, Germany). We used antibodies against FAK2 (ab32571, clone YE353, 1:1000, Abcam), pFAK2 (Y402) (#592918, clone MAB6210, 1:1000, Cell Signaling), tubulin (T6557, clone GTU-88, 1:5000, SIGMA) and CCR1 (#53504, clone MAB145, 1:1000\u2009R&D).\n\nFor studies of suspension cells (EBV-infected and controls), cells were pre-fixed in 1% paraformaldehyde (PFA) for 5\u2009min. PFA-pre-fixed cells were dropped and dried on glass slides, fixed with 4% paraformaldehyde for 5\u2009min at room temperature and permeabilized in phosphate-buffered saline (PBS) or tris buffered saline (TBS) 0.5% Triton X-100 for 2\u2009min. Adherent cells were fixed in 4% PFA for 10\u2009min and permeabilized as described for suspension cells. Fixed cells were incubated with the first antibody at 4\u2009\u00b0C overnight, washed in PBS thrice, and incubated at 37\u2009\u00b0C for 30\u2009min with the secondary antibody and counterstained with DAPI and a blue or red membrane dye (BOT-30023, CellBrite Cytoplasmic Membrane Dye, Biomol, Germany) to visualize cell membranes and lamellipodia. Slides were embedded in 90% glycerol and visualized with a confocal Olympus Fluoview 1000 microscope. We used antibodies against CDC42 (ab187643, clone EPR15620, 1:100 Abcam, UK), pPKCzeta (Thr410) (PA5-104967, polyclonal, 1:100, Invitrogen, Germany), ZO-1 (#33-9100, clone 1A12, 1:1000, Invitrogen, Germany), ICAM-1(#BMS1011, clone R6.5., 1:1000, ThermoFisherScientific, Germany), gm130 (#12480, clone D6B1, 1:100, Cell Signalling, USA), FAK1 (#3285, polyclonal, 1:1000, Cell Signalling, USA) and FAK2 (ab32571, clone YE353, 1:250, Abcam, UK). For stainings of ZO-1 and ICAM-1 after coculture, endothelial cells were seeded into a 18 Well \u00b5-Slide at a concentration of 3 \u00d7 10E5 cells/ml and grown until confluency. 2 \u00d7 10E4 EBV-infected cells or control cells were added and incubated as cocultures for 30\u2009min at 37\u2009\u00b0C 5%C02 and washed off thereafter. 24\u2009h after coculture cells were stained for ZO-1 or ICAM-1, respectively. We used the same algorithm to analyze intercellular spaces that appear after coculture and stained endothelial cells with Rhodamin-Phalloidin (A22287, Invitrogen, Germany).\n\n1 \u00d7 10E5 EBV-infected cells or controls were stained with MitoTracker green or LysoTracker green (#M7514 and #L7526, ThermoFisherScientific, Germany) according to the manufacturer\u2019s protocol. We used a concentration of 200\u2009nM for MitoTracker labeling. Cells were seeded into ibidi \u00b5-Slide 18 Well Glass Bottom (#81816, Ibidi, Germany) in RPMI-1640 and 10% FCS and visualized using a confocal microscope (Olympus FluoView FV1000). We used live cell probes (SIR-actin and SIR-tubulin) for actin and tubulin staining and imaging in living cells. Both probes were purchased from Spirochrome (#CY-SC001 and #CY-SC002, Spirochrome, Switzerland) and used as to the manufacturer\u2019s instructions with an incubation time of 60\u2009min at a 1\u2009\u03bcM final concentration. For centrin, 7 \u00d7 10E5 EBV-infected cells were transfected with the pEGFP-centrin-1 plasmid (#72641, Addgene, USA) using Neon Transfection system (#10431915, ThermoFisherScientific, Germany). For each sample, we electroporated one time using 10\u2009\u00b5L Neon tips with the following settings: 1350\u2009V, 30\u2009ms. One day after transfected cells were analyzed with a confocal microscope (Olympus FluoView FV1000).\n\nCells grown in suspension were fixed with cacodylate-buffered aldehyde (2% freshly prepared Formaldehyde plus 2% glutaraldehyde), post-fixed with 1% buffered OsO4, dehydrated in graded steps of ethanol following critical point drying (Balzers 030) using porous pods (Baltic preparation, Wetter, Germany) as containers. The dried cells were shed onto gluey carbon tabs (science services, Munich, Germany) for mounting on standard Al-subs and sputter coated with Au/Pd 80:20 (Batic preparation, Wetter, Germany). Micrographs were taken with a Zeiss Auriga SEM (Carl Zeiss Oberkochen, Germany) at 2\u2009kV acceleration Voltage and about 2\u2009mm work distance using an inlens detector for secondary electrons.\n\nFor time lapse experiments, 3 \u00d7 10E5 cells/ml (EBV-infected high) or 3 \u00d7 10E4 cells/ml (EBV-infected low) were seeded on Ibidi chemotaxis slides (#13478749, Ibidi GmbH, Germany) into a bovine collagen matrix with a concentration of 1.5\u2009mg/ml bovine collagen I (#A106444-01, ThermoFisherScientific, Germany). The slide was inserted into a 37\u2009\u00b0C heating and incubation system for the whole duration time lapse analysis. Images were acquired every minute over a period of 15\u2009min using a confocal Olympus Fluoview 1000 microscope. Manual tracking was performed using the Fiji Tracking tool and presented as an overlay of dots and lines for 2D tracks. Directionalities and velocities from manual trackings were calculated using Ibidi chemotaxis and migration tool. We generated aligned 2D trajectory plots (\u201cspider plots\u201d) by setting all (x,y) coordinates of the cells\u2019 starting points to (0,0). The data were statistically analyzed by Rayleigh test using the Ibidi chemotaxis and migration tool. We plotted the mean cell displacement by the square root of time to generate square root time profiles.\n\nBMEC or HUVEC cells were seeded at a concentration of (3\u20135) \u00d7 10E5 cells/ml onto ibidi \u00b5-Slides VI 0.4 (#80606, ibidi, Germany) and cultured until confluency. Slides were precoated with 100\u03bcg/ml with fibronectin (2 \u03bcg/cm2, #354008, Corning, USA) upon seeding. Slides were placed in a thermostated hood (37\u2009\u00b0C) and time lapse microscopy was performed using a confocal microscope (Olympus FluoView FV1000). Pictures were taken every 10\u2009seconds over a period of 15\u2009min. \u03bc-slides were connected to the ibidi Pump system (ibidi, Gr\u00e4felfing, Germany) and exposed to various cell suspensions (EBV-infected B cells, stimulated and resting B cells) at a concentration of 1 \u00d7 10E6/ml. After initial perfusion of the flow chamber at 0.6 dynes/cm2 for 2\u2009min for equilibration, the total cell suspension was perfused through the chamber at a constant flow rate (1.5 dynes/cm2) and images recorded using a time lapse recording system connected to the microscope (Olympus FluoView FV1000). After 10\u2009min of perfusion, the flow rate of the cell suspension was raised so that wall shear stress increased from 1.5 to 3.0 dynes/cm2. Adherent leukocytes were identified and counted at 2-min intervals during the 10-min perfusion at 1.5 dynes/cm2 as previously described58. In some experiments the cells were cultured in LFA-1 Inhibitor BIRT377 50 \u03bcM or defactinib 3.5 \u03bcM 30\u2009min prior to the experiment. For some experiments BMEC and HUVEC cells were stimulated with TNF-\u03b1 at a concentration of 100\u2009ng/ml 24\u2009h (#300-01\u2009A, Peptrotech, Germany) prior to the experiment.\n\n2 \u00d7 10E4 cells of EBV-infected and/or uninfected CD19+ B cells were seeded into the upper compartment of transwell insets with an area of 0,32\u2009cm2 and 3\u2009\u00b5m pore size. Chemokines CCL3, CCL4, CCL5, hIL-10 and vIL-10 were added at different concentrations (10-100\u2009ng/ml) into the lower compartment and the number of migrating cells determined after 1 to 24\u2009h. In experiment where EBV-infected cells served as attractant for B cells, B cells were labeled with CytoTrace Green CMFDA (#22017, AAT Bioquest Inc., USA) and seeded into the upper compartment of the transwell inset. EBV-infected cells were seeded at a concentration of 2 \u00d7 10E4 per ml into the lower compartment and numbers of migrating cells determined after 24\u2009h. In other experiments endothelial cells HUVEC, BMEC or HDMEC were seeded at a concentration of 3 \u00d7 10E5/ml into the upper compartment of the inset and cultured until confluency (2\u20133 days). 2\u20133 days after seeding EBV-infected cells were seeded into the upper compartment and migration determined after 24\u2009h. In some experiments B cells and EBV-infected cells were seeded simultaneously into the upper compartment of the inset. In this case B cells were labeled with CytoTrace Green CMFDA (#22017, AAT Bioquest Inc., USA) and the number of migrating cells determined after 24\u2009h.\n\nHBMEC or HUVEC cells were seeded as described above for transwell migration assays with endothelial barrier. Trans-endothelial electrical resistance (TEER) was measured until stable values were reached59. For coculturing, insets were placed upside down and 5 \u00d7 10E5 EBV-infected cells applied onto the basolateral side of endothelial cells in a volume of 50 \u03bcl of endothelial media. Cells were left for 30\u2009min and gently washed off. TEER was measured the subsequent day and given as TEERREPORTED relative to the day before coculture was performed. For HUVEC cells barriers were induced by adding DBcAMP 250\u03bcM (Dibutryl cAMP, #HY-B0764G, Medchem, Germany) one day before contact with EBV-infected cells.\n\nThe chemotaxis assays were performed using blind well chemotaxis chambers (Neuro Probe, #BW100), with compartments separated by a 5 \u03bcm polycarbonate filter (Neuro Probe, #PFA8). The lower compartments were filled with either 10%FCS/RPMI or chemokines (CCL3, CCL4, CCL5, human and/or EBV-encoded IL-10) in 10%FCS/RPMI. The upper compartment was filled with 100\u2009\u03bcl 10%FCS/RPMI containing 5 \u00d7 104 cells. The chambers were incubated for 1\u2009h at 37\u2009\u00b0C/5% CO2 and afterwards the cells in the lower compartment were counted.\n\nCells were washed and resuspended in 100\u2009\u00b5L of PBS and 0.1% BSA incubated with primary antibodies against CD11c conjugated to APC (# 337208, clone Bu15, 1:20, Biolegend, USA), CD52 conjugated to APC (#318904, clone QA19A22, 1:20, Biolegend, USA) or ICAM-1 conjugated to PE (#MCA1615PE, clone 15.2, 1:20, Biorad, Germany) for 30\u2009min on ice. After washing, cells were resuspended in 200\u2009\u00b5L of PBS and 0.1% BSA and analyzed using a BD FACS calibur (Becton, Dickinson and Company). In coculture experiments or experiments were EBV-infected B cells were used to attract B cells, these cells were labeled with CytoTrace Green CMFDA (#22017, AAT Bioquest Inc., USA), 1000 (CD11c), 2000 (CD52) or 10000 (ICAM-1) cells were recorded. Using FSC and SSC gating dead cells were excluded and in case of coculture experiments a second gating was used for green cells and the percentage/MFI of these cells were reported (CD11c and CD52) Post-acquisition analysis was performed using the FlowJo Software (Becton, Dickinson and Company). Gating strategies are shown in Supplementary Fig.\u00a011.\n\nHUVEC, BMEC or HDMEC were seeded at a concentration of 3 \u00d7 10E5/ml into ibidi \u00b5-Slide 18 Well Glass Bottom (Ibidi, Germany) and cultured until confluency. Cells were labeled with FLUO4-AM (#F14201, ThermoFisherScientific, Germany) at a final concentration of 2.5 \u03bcM. Before dilution into the loading medium equal volume of 20% (w/v) Pluronic in DMSO (P3000MP, ThermoFisherScientific, Germany) was added. Cells were incubated with the AM ester for 60\u2009min at 37\u2009\u00b0C. Before fluorescence measurements, cells were washed in indicator-free medium and then incubated for a further 15\u2009min to allow complete de-esterification of intracellular AM esters. Baseline pictures were acquired and 2 \u00d7 104 EBV-infected or control cells were added and changes in signals detected using a confocal microscope (Olympus FluoView FV1000). Mean fluorescent intensities were determined using Fiji image software60.\n\n2 \u00d7 10E4 uninfected CD19+ B cells were labeled with CytoTrace Green CMFDA (#22017, AAT Bioquest Inc., USA) and seeded into the upper compartment of a transwell insets with an area of 0.32\u2009cm2 and 3 \u03bcM pore size in 200 \u03bcl of RPMI-1640 and 10% FCS. 2 \u00d7 10E4 unlabeled EBV-infected cells were added to the lower compartment in 500 \u03bcl of RPMI-1640 and 10% FCS. Cells were incubated for 24\u2009h and green labeled B cells of the upper (designated as \u201cstay\u201d) and lower (designated as \u201cmove\u201d) compartment each sorted into two 384 well plates by Fluorescence Activated Cell Sorting using a BD FACSAria (Becton, Dickinson and Company). Cells were subjected to SMART-seq 2 scRNA-seq platform. Transcript reverse transcription and amplification were performed following the protocol of Smart-seq2. Libraries were constructed with the Nextera XT DNA Library Prep kit (Illumina, San Diego, CA) and sequenced on Novaseq paired end 100pbSP (Illumina, USA).\n\nFor Creation of the Seurat object and normalization raw FASTQ files (R1: 101 base pairs, R2: 101 base pairs) were aligned using STAR on the GRCh38 human reference genome with the soloType function \u201cSmartSeq\u201d (v2.7). Quality controls were used to exclude cells with a number of detected genes below 3500 and above 5500 and 6300 (move and stay conditions, respectively) and cells with more than 15% of transcripts encoded by the mitochondrial genome. The resulting count matrices were log-normalized using Seurat NormalizeData with a scale factor of 10.000 and a scaling step was performed using Seurat ScaleData with all genes from the matrices as features to cell cycle calculation. Each filtered count matrix was normalized a second time with Seurat SCTransform (vst.flavor = \u201cv2\u201d) and dimension reduction was performed with RunPCA (npcs = 5).\n\nFor Metadata creation cell phenotype annotations were identified using singleR and Celldex R packages against cell markers from the MonacoImmuneData database. All calculations were made from the \u201cSCT\u201d assay of the Seurat merged object.\n\nFor Data merging, we merged the condition-specific objects (move and stay) using Seurat v4.3. Variable features from scale.data slot were used to center and reduce the merged object with RunPCA and embedded in two dimensions with RunUMAP, excluding BCR- and TCR-encoding genes [Supp. Ref\u00a02,3] from the lists of variable genes (regex: IG[HKL][VDJ] |IGHG[1-4]|IGH[MDE] |IGKC|IGLL|IGLC[1-7] |IGHA[1-2] |TR[ABGD][CV]) determined by the Seurat function VariableFeatures.\n\nLouvain clustering was performed with the FindClusters function, with a resolution of 0.6. To annotate each cluster, we ran a \u2018one-versus-all\u2019 Differential Expression Analysis (DEA) for each cluster (Seurat, FindAllMarkers, Wilcoxon rank-sum test), keeping only upregulated genes with a avg_log2FC\u2009>\u20090.8, pct.1\u2009>\u20090.6 and a Bonferroni-adjusted P value\u2009<\u20090.001. The resulting list of genes was used as input to the \u2018enrichGO\u2019 function of clusterProfiler package (v4.12.0, parameters: ont = \u201cBP\u201d, OrgDb = org.Hs.eg.db, keyType = \u201cSYMBOL\u201d and pvalueCutoff\u2009=\u20090.05). We then removed redundancy in the output list of GO terms with the \u2018simplify\u2019 function with a cutoff of 0.70. For the annotation of cluster 2, all the upregulated genes from the differential expression analysis were considered (logfc.threshold = 0.25).\n\nAt the single-cell level, enriched pathway visualization was performed using the DEenrichRplot function of the Seurat R package. The maximum number of genes to perform the enrichment calculation was set to 500 and only pathways with FDR\u2009<\u20090.05 and logfc.threshold = 0.6 were kept, applying GO_Biological_Process_2023 database.\n\nSingle-cell differential gene expression list between move and stay conditions was calculated using Seurat FindMarkers (assay = \u201cSCT\u201d) from merged matrix. The LogFC threshold was set to 0.6 with p_val_adj below 0.05. All B cells were retained for testing.\n\nThe pU6-(BbsI)-CBh-Cas9-T2A-mcherry-P2A-Ad4E4orf6 plasmid (Addgene #64222) was digested with XhoI and EcoRI to remove the Ad4E4orf6 cassette and a stop codon was inserted through primer annealing (Supplementary Table\u00a01) and ligation. Subsequentially, the modified plasmid was digested with BpiI and the guide DNA sequences were introduced using primer annealing (Supplementary Table\u00a01) and ligation. The correctness of the cloning was verified via sequencing using the primer listed in Supplementary Table\u00a01. The design of the guide DNA was performed using publicly available tools: Chopchop (https://chopchop.cbu.uib.no/) and the IDT design tool (https://eu.idtdna.com/site/order/designtool/index/CRISPR_SEQUENCE). The gDNA sequences were chosen based on the on- and off-target scores predicted by the different tools.\n\nCCL4 and CCR1 plasmids were transfected into cells infected with EBV WT (M81), hIL-10 plasmids were transfected into an EBV-encoded IL-10 deficient mutant of the M81 virus (B828). Transfection was performed using Neon Transfection system (ThermoFisherScientific, Germany). For each sample, we electroporated one time using 10\u2009\u00b5L Neon tips with the following settings: 1350\u2009V, 20\u2009ms, 2 pulses. One day after transfected cells were sorted for mCherry by Fluorescence Activated Cell Sorting. Cells were cultured in RPMI-1640 and 10%FCS (CCR1null) and supplemented with 100\u2009ng/ml CCL4 (CCL4null) or 10\u2009ng/ml Il-10 (human IL-10null) and used for transwell or time lapse microscopy assays.\n\n1 \u00d7 10E6 B cells were infected with the M81 Virus at an MOI of 3 per cell and injected into 5-week-old male NSG mice (NOD.Cg-Prkdcscid Il2rgtm1Wjl, DKFZ in house breeding). Defactinib or carrier solution (50% PG300, 5% Tween20, 40% H20, 5% DMSO) was given i.p. at a dosage of 15\u2009mg/kg twice daily starting 14 days after infected cells were given. The infected mice (n\u2009=\u20095) were monitored for 6 weeks post-infection and then euthanized by CO2 inhalation. Animals were housed in an BSL2 SPF facility. Control mice (n\u2009=\u20095) were hosted separately from treated animals. Animal experiments were approved by the Regierungspr\u00e4sidium Karlsruhe (G-160/22) and are compliant with the institutional laboratory animal research guidelines. All efforts were made to minimize animal suffering and to reduce the number of animals used. Mice were maintained in a specific-pathogen-free, standardized environment with 22\u2009\u2009\u00b1\u2009\u2009\u20092\u2009\u00b0C temperature, 55\u2009\u2009\u2009\u00b1\u2009\u2009\u200910% humidity, 12\u2009h light/dark cycles and fed with a standard diet according to the German Cancer Research Center guidelines.\n\nPurification of total DNA from fixed, paraffin-embedded spleen tissue was carried out with DNeasy Blood & Tissue kits (#69504, QIAGEN, Germany). Briefly, paraffin was removed by extraction with xylene. Spin-Columns were used to isolate total DNA. EBV BALF5 gene locus was amplified from total DNA by qPCR of 100\u2009ng tissue DNA. EBV copy number was calculated using a standard curve.\n\nMice organs were fixed in 10% formalin overnight and embedded in paraffin blocks. Three-micrometer-thick continuous sections were prepared. The presence of EBV was detected by in situ hybridization with an EBER-specific peptide nucleic acid probe, in conjunction with a PNA detection kit (K5201, Dako, USA) following the manufacturer\u2019s protocol. In parallel, adjacent sections were stained with H&E. Images were taken with an Aperio Digital Pathology Slide Scanner (Leica, Germany) and analyzed using QuPath Software65.\n\nGraphPad Prism 9 was used to conduct all statistical analysis. The error bars represent the standard deviation of the data sets. Statistical significance was determined using Student\u2019s t test or ANOVA analyses combined with Dunnett\u2019s multiple comparisons test. Bar graphs include means and their standard deviations. P values are displayed by asterisks with *p\u2009<\u20090.05, **p\u2009<\u20090.01, ***p\u2009<\u20090.001 and ****p\u2009<\u20090.0001.\n\nDose response analysis was performed by first fitting a four-parameter log-logistic function to both defactinib and BX471. Isobolograms were computed by first determining the average observed result at several substance mixtures, and then using the available dose\u2013response fits to identify the corresponding doses required by the component doses alone. All analyses were performed using R Version 4.3.0 and the DRC package66 as well as the Graphpad Prism software.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The scRNAseq data generated in this study have been deposited in the ncbi database under accession code Bioproject\u201d PRJNA1255185. All other data are included in the Supplementary Information. The raw numbers for charts and graphs are available in the Source Data file whenever possible\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Munz, C. 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PLoS ONE 10, e0146021 (2015).\n\nArticle\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "The authors are very grateful to the members of the core facility imaging and microscopy at DKFZ. We are particularly indebted to Dr. Karsten Richter for his tremendous help and expertise with electron microscopy. We also thank the genomics core facility of the DKFZ and Dr. Ivo Buchhalter for their help with scRNAseq. The study was funded by DKFZ, DZIF, and Inserm. This project has partially received funding from the European Union\u2019s Horizon Europe Research and Innovation Actions under grant no. 101137235 (BEHIND-MS). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union nor the granting authority. Neither the European Union nor the granting authority can be held responsible for them.", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Unit D400, DKFZ, Heidelberg, Germany\n\nSusanne Delecluse,\u00a0Francesco Baccianti,\u00a0Alina Steffens,\u00a0Daniel Judt,\u00a0Remy Poirey\u00a0&\u00a0Henri-Jacques Delecluse\n\nInserm joint unit, Heidelberg, Germany\n\nSusanne Delecluse,\u00a0Francesco Baccianti,\u00a0Alina Steffens,\u00a0Daniel Judt,\u00a0Remy Poirey\u00a0&\u00a0Henri-Jacques Delecluse\n\nDepartment Nephrology, University of Heidelberg, Heidelberg, Germany\n\nSusanne Delecluse\n\nGerman Center for Infection Research (DZIF), Braunschweig, Germany\n\nSusanne Delecluse\n\nCentre International de Recherche en Infectiologie (Team LIB), Universit\u00e9 Lyon, INSERM, U1111, Universit\u00e9 Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, UMR5308, ENS de Lyon, Lyon, France\n\nManon Zala\u00a0&\u00a0Pierre Sujobert\n\nFacult\u00e9 de M\u00e9decine Lyon-Sud, Universit\u00e9 de Lyon, Oullins, France\n\nManon Zala\u00a0&\u00a0Pierre Sujobert\n\nUniversity of Heidelberg, Heidelberg, Germany\n\nAlina Steffens\n\nBiostatistics Unit C60, DKFZ, Heidelberg, Germany\n\nCarolin Drenda\u00a0&\u00a0Tim Holland-Letz\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.D. and H.J.D. planned experiments, analyzed data and wrote the paper. S.D., F.B., A.S., D.J., and R.P. conducted experiments. C.D. and T.H.L. performed statistical analyses and migration modeling. M.Z. and P.S. conducted bioinformatic analyses.\n\nCorrespondence to\n Henri-Jacques Delecluse.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Haruko Hayasaka, Micah Luftig and the other anonymous reviewer(s) for their contribution to the peer review of this work. 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Entails a U-Shaped Trajectory in Human Brain Structure Linked to Hormones and Maternal Attachment", + "journal": "Nature Communications", + "published": "16 January 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-55830-0/MediaObjects/41467_2025_55830_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-55830-0/MediaObjects/41467_2025_55830_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-55830-0/MediaObjects/41467_2025_55830_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://github.com/URNC-Lab/UShape-Pregnancy" + ], + "code": [ + "https://github.com/URNC-Lab/UShape-Pregnancy", + "https://doi.org/10.5281/zenodo.14361671", + "/articles/s41467-025-55830-0#ref-CR59" + ], + "subject": [ + "Neuroscience", + "Psychology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4124712/v1.pdf?c=1737119228000", + "research_square_link": "https://www.researchsquare.com//article/rs-4124712/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-55830-0.pdf", + "preprint_posted": "29 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Growing evidence places the gestational period as a unique moment of heightened neuroplasticity in adult life. In this longitudinal study, we unveiled a U-shaped trajectory in gray matter (GM) volume, which dips in late pregnancy and partially recovers during postpartum. These changes were most prominent in brain regions associated with the Default Mode and Frontoparietal Network, which also showed an increased global efficiency and density connectivity. The U-shaped trajectory was predominantly linked to gestational factors, as it was only present in gestational mothers and correlated with fluctuations in estrogens over time. Finally, the mother\u2019s mental health status mediated the relationship between postpartum GM volume recovery and maternal attachment at six months postpartum. This research sheds light on the complex interplay between hormones, brain development, and behavior during the transition to motherhood. It addresses a significant knowledge gap in the neuroscience of human pregnancy and opens new possibilities for interventions aimed at enhancing maternal health and well-being.Biological sciences/NeuroscienceBiological sciences/Psychology", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformationNN.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Growing evidence places the gestational period as a unique moment of heightened neuroplasticity in adult life. In this longitudinal study spanning pre, during, and post pregnancy, we unveil a U-shaped trajectory in gray matter (GM) volume, which dips in late pregnancy and partially recovers during postpartum. These changes are most prominent in brain regions associated with the Default Mode and Frontoparietal Network. The U-shaped trajectory is predominantly linked to gestational factors, as it only presents in gestational mothers and correlates with fluctuations in estrogens over time. Finally, the mother\u2019s mental health status mediates the relationship between postpartum GM volume recovery and maternal attachment at 6 months postpartum. This research sheds light on the complex interplay between hormones, brain development, and behavior during the transition to motherhood. It addresses a significant knowledge gap in the neuroscience of human pregnancy and opens new possibilities for interventions aimed at enhancing maternal health and well-being.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Each year, nearly 140 million women give birth worldwide1. Pregnancy represents a transformative journey marked by critical psychological adaptations to motherhood2. In humans, neuroimaging studies scanning women before and after pregnancy and around the peripartum suggest that first-time mothers experience a remodeling of brain architecture3,4,5 that predicts postpartum maternal attachment towards the newborn4. Concurrently, murine research suggests that maternal brain changes are driven by gestational hormones, including steroid hormones, and facilitate maternal behavior6,7,8. A promising hypothesis posits that human brain changes during the maternal transition follow a U-shaped trajectory, with an initial decrease in cortical gray matter (GM) volume during pregnancy, followed by a partial recovery in the postpartum period9. Such neural trajectory could be driven by mirroring fluctuations in steroid hormones before and after childbirth10 and could be further influenced by parenting experience11,12. Despite these observations, no previous study has charted the complete trajectories of human brain change from pre-conception throughout pregnancy and postpartum, integrating multimodal neuroimaging data, endocrine assessments, and neuropsychological information (see Pritschet et al., for a precision imaging study of a single-subject13).\n\nIn this prospective study, women completed a Magnetic Resonance Imaging (MRI) scanning protocol, hormonal analyses, and neuropsychological evaluations before, during, and after pregnancy, along with a group of nulliparous women with no plans to become pregnant. The study also included a group of non-gestational mothers, who were partners of the gestational mothers, to discern the effects of pregnancy from those of the parenting experience. This landmark design allowed to uncover the brain trajectory that unfolds during the transition to motherhood, as well as its connection with steroid hormones and maternal attachment, filling a critical void in the human maternal brain literature.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "This longitudinal study included 127 women undergoing their first pregnancy, henceforth referred to as gestational mothers. We acquired structural MRI scans at five sessions: before conception, at the second and third trimesters of pregnancy, and during the postpartum, at one and 6 months after birth, as well as resting state MRI scans before conception and at one and 6 months after birth. To distinguish the impact of gestational factors from parenting-related factors, we also collected longitudinal data at similar time intervals from 20 female partners of the women in the gestational mothers\u2019 group, henceforth, non-gestational mothers. Finally, to account for brain changes unrelated to motherhood, we scanned 32 women without children nor plans of going through pregnancy or maternity; henceforth, nulliparous women. For every group and session, structural MRI data was paired with urine samples for endocrine determinations and questionnaires to assess mental health, and maternal attachment toward the infant (see the demographic information in table\u00a0S1).\n\nTo unravel the complete trajectory of structural brain changes during pregnancy and postpartum, we analyzed the participants\u2019 global cortical GM volume, thickness, and surface area over the five sessions. There were no pre-existing differences in global cortical GM volume, thickness, and surface area among the three groups of gestational mothers, non-gestational mothers, and nulliparous controls (cortical GM volume: F(2,176)\u2009=\u20090.461, \u03b72\u2009=\u20090.005, p\u2009=\u20090.631; cortical thickness: F(2,176)\u2009=\u20090.258, \u03b72\u2009=\u20090.003, p\u2009=\u20090.773; surface area: F(2,176)\u2009=\u20090.583, \u03b72\u2009=\u20090.007, p\u2009=\u20090.559).\n\nCompared to nulliparous women, gestational mothers displayed a U-shaped quadratic trajectory in global cortical GM volume from before pregnancy to 6 months postpartum, with the inflection point at late pregnancy (Group x Session [Quadratic Term] interaction: B\u2009=\u2009207,552.67, 95% CI = [177,360.29, 237,723.63], SE\u2009=\u200915,457.46, t\u2009=\u2009666.43, p\u2009<\u20090.001, q\u2009<\u20090.001) (Fig.\u00a01A and table\u00a0S2). The observed trajectory was statistically significant even after controlling for the effects of the participants\u2019 age, total intracranial volume (eTIV), image quality, and time between sessions (Table\u00a0S2). Changes in Body Mass Index (BMI) during pregnancy and the postpartum did not yield a statistically significant effect in the results (Table\u00a0S3). This quadratic model had a significantly better fit than a linear model of the cortical GM volume trajectory over time (Table\u00a0S4), and adding the eTIV into the interaction term did not improve the fit of the quadratic model either (Table\u00a0S5). Moreover, a generalized additive model further confirmed the quadratic nature and symmetry of the pattern of GM volume changes (fig.\u00a0S1).\n\nLongitudinal changes were derived from the group x session2 fixed effect term of the adjusted linear mixed effect model: Cortical GM Volume \u223c Group + Session + Session2 + Group*Session + Group*Session2\u2009+\u2009eTIV + Age + Euler + Inter-session Interval + (1 \u2223 participant ID). A Mean percentage of cortical GM volume change per group and session in relation to the baseline (i.e., Session 1 - Pre-pregnancy Session). Error bars correspond to 95% confidence intervals around the sample\u2019s mean at each experimental group and session, respectively. The gray shading corresponds to the pregnancy period. B Vertex-wise signed effect size maps (\u03b7p2) of the group x session2 interaction (q value\u2009<\u20090.05). Indicating larger quadratic effects (red) and smaller quadratic effects (blue) in gestational mothers than in nulliparous or non-gestational mothers. Signed effect size maps were projected to the inflated fsaverage template provided by the FreeSurfer software. Colors were collapsed to \u00b10.14, which indicated a large effect size. Gest, gestational mothers; nGest, non-gestational mothers; Null, nulliparous women.\n\nThe U-shaped trajectory comprised a global 4.9 % GM volume decrease during pregnancy (95% CI = [\u22125.2, \u22124.6]) (table\u00a0S6), followed by a 3.4 % GM volume increase from late pregnancy to 6 months postpartum (95% CI = [3.1, 3.6]). In the second trimester of pregnancy cortical GM volume had already decreased by 2.7% (95% CI = [\u22123.0, \u22122.5]) (Table\u00a0S6). GM volume did not fully return to pre-pregnancy levels at 6 months postpartum (B\u2009=\u2009\u22127693.05, 95% CI = [\u22129371.04, \u22126010.90], SE\u2009=\u2009857.18, t\u2009=\u2009\u22128.97, p\u2009<\u20090.001) (Table\u00a0S7), although the GM volume change between these sessions did not significantly differ compared to nulliparous women (B\u2009=\u200972.22, CI = [\u22123362.64, 3,520.97], SE\u2009=\u20091768.46, t\u2009=\u20090.04, p\u2009=\u20090.967 (Table\u00a0S8). Hence, we found support for a U-shape trajectory of cortical GM volume reductions during pregnancy, with a partial recovery at 6 months into postpartum.\n\nA vertex-wise analysis indicated that, compared to nulliparous women, the cortical GM volume quadratic trajectory affected widespread bilateral regions (94% of brain vertices surviving a q\u2009<\u20090.05 False Discovery Rate (FDR) correction) (Fig.\u00a01B). The most prominent GM volume changes, measured as partial eta squared greater than 0.06, were observed in the inferior parietal, superior frontal, supramarginal, precuneus, and superior temporal (Table\u00a0S9). Similar findings were observed when comparing the quadratic GM volume trajectory in gestational versus non-gestational mothers (Fig.\u00a01B). Moreover when decomposing GM volume into area and thickness, both global and vertex-wise analyses revealed a comparable U-shaped pattern in gestational mothers (Figs.\u00a0S2 and S3 and Tables\u00a0S10\u2013S11). The quadratic trajectories observed in GM volume, area, and thickness followed a normal distribution, with no discernible subgroups among gestational mothers showing distinct brain trajectories (Fig.\u00a0S4). Thus, the observed brain changes were highly consistent, affected both hemispheres and were observed in both cortical surface and thickness measures.\n\nNo significant linear or quadratic changes in GM volume, area, or thickness were found in non-gestational mothers (versus nulliparous women) during their transition to motherhood (Tables\u00a0S2, S10, and S11), suggesting that the observed cortical trajectory was primarily influenced by gestational factors (Fig.\u00a01). Additional supplementary analyses on gestational mothers indicated that type of conception, nor the biological sex of the baby, affected the quadratic trajectory for cortical GM volume (Tables\u00a0S12\u2013S13). Moreover, the type of parturition and type of breastfeeding did not play a significant role in the cortical GM volume increases occurring during postpartum (Tables\u00a0S14\u2013S15). Therefore, the observed changes were unique to gestational mothers and were independent of situational factors.\n\nFor completeness, changes in cerebral white matter (WM) volume and global cerebrospinal fluid (CSF) were also examined throughout the transition to motherhood, although it should be noted that T1 MRI images are not optimal for analyzing these metrics. Compared to nulliparous women, gestational mothers displayed a U-shaped quadratic trajectory in global WM volume from before pregnancy to 6 months postpartum (Fig.\u00a0S5A), and an inverse U-shaped trajectory in CSF (Fig\u00a0S5B), both reaching an inflection point at late pregnancy (WM volume: Group x Session [Quadratic Term] interaction: B\u2009=\u200948,457.14, 95% CI = [37,294.88, 59,623.65], SE\u2009=\u20095,717.89, t\u2009=\u20098.47, p\u2009<\u20090.001, q\u2009<\u20090.001; CSF: Group x Session [Quadratic Term] interaction: B\u2009=\u2009\u2212477.48, 95% CI = [\u2212699.73, \u2212255.27], SE\u2009=\u2009113.82, t =\u2009\u22124.20, p\u2009<\u20090.001, q\u2009<\u20090.001) (Tables\u00a0S16\u2013S17). These trajectories were statistically significant after controlling for the effects of the participants\u2019 age, eTIV, image quality, and time between sessions (Tables\u00a0S16\u2013S17). An analysis of variance confirmed that both WM volume and CSF changes were better explained by the quadratic model than by a linear model (Tables\u00a0S18\u2013S19).\n\nWe then examined whether there could be differences in the cortical GM volume trajectory based on the functional location of the changes by parcellating the brain in Yeo\u2019s seven large-scale functional brain networks14 (Fig.\u00a02A). Pre-post pregnancy changes have been mainly reported in Default Mode and Frontoparietal regions4,5, which also seem to undergo smaller GM volume increases during early postpartum3. Hence, we hypothesized that there might be two different neuroanatomical trajectories: one for regions belonging to higher-order cognition networks (Default Mode and Frontoparietal) and another including the rest of the networks (Visual, Somatomotor, Dorsal Attention, Ventral Attention, and Limbic).\n\nThe different trajectories were assessed by parcellating the brain in Yeo\u2019s seven large-scale functional brain networks14. A Cortical parcellation of Yeo\u2019s seven large-scale functional brain networks. B Mean percentage of cortical GM volume change per group and session and functional location in relation to the baseline (i.e., Session 1 - Pre-pregnancy Session). The gray shading corresponds to the pregnancy period. C Vertex-wise signed effect size maps (\u03b7p2) of the group x session2 interaction (q value\u2009<\u20090.05 and \u03b7p2\u2009>\u20090.06 to capture medium to big effect sizes). Indicating larger quadratic effects (red) and smaller quadratic effects (blue) in gestational mothers than in nulliparous. Signed effect size maps were projected to the inflated fsaverage template provided by the FreeSurfer software. Colors were collapsed to \u00b10.14, which indicated a large effect size. Gest, gestational mothers; Null, nulliparous women. D Spin test for the signed effect sizes of the vertex-wise Group [gestational mothers vs nulliparous women]*Session2 interaction in cortical volume within the seven large-scale functional brain networks. Black horizontal bars represent the observed values and the violin plots reflect the null distributions obtained using 1000 spin-permutations of the maps. The exact one-tailed p values are reported when p\u2009<\u20090.05. No multiple comparisons corrections were applied. The black dot on the center of the boxplot represents the median, the box encloses the lower and upper quartiles, and the whiskers extend to the minimum and maximum values within a range of 1.5 times the interquartile range.\n\nWe observed the U-shaped pattern in all networks (Fig.\u00a02B). However, using a model that differentiated the cortical GM volume trajectory occurring in higher-order networks (Default Mode and Frontoparietal) from the remaining networks (Model A\u2014see \u201cFormula Nine\u201d from \u201cMethods\u201d), we observed a steeper curve for the trajectory in higher-order regions (Nested Networks [Default Mode + Frontoparietal] x Session [Quadratic Term] interaction: B\u2009=\u200922893.02, 95% CI = [20,574.91, 25,211.13], SE\u2009=\u20091180.91, t\u2009=\u200919.36, p\u2009<\u20090.001) (Fig.\u00a02B and Table\u00a0S20). Indeed, this model had a better fit than the one assessing a single trajectory for all networks (Model B\u2014see \u201cFormula Ten\u201d from \u201cMethods\u201d and table\u00a0S21), therefore outperforming the one-size-fits-all trajectory approach (BIC Model A\u2009<\u2009BIC Model B: 71395.88\u2009<\u200972353.42). When evaluating the spatial correspondence between the signed effect size maps of the vertex-wise analysis and Yeo\u2019s networks, we observed a significant above-chance quadratic effect in the Default Mode network (p\u2009=\u20090.006) (Fig.\u00a02C, D). There was also a significant below-chance cortical quadratic effect in the Limbic network (p\u2009=\u20090.012). Hence, we observed U-shaped trajectories with varying magnitudes based on the functional location of the GM volume changes.\n\nWe next explored whether pregnancy also entails changes in women\u2019s functional connectome. We used Schaefer\u2019s cortical parcellation (400 parcels) mapped to Yeo\u2019s 7 large-scale functional networks14,15 to evaluate changes in functional network connectivity before and after pregnancy. Specifically, we measured changes in the functional organization and network segregation using whole-brain and within-network modularity, mean participation coefficient, and system segregation graph theory metrics16,17. Modularity allowed us to capture network compartmentalization. That is the extent to which a specific network is subdivided into communities of strong within-module connectivity and weak between-module connectivity. Using system segregation, we assessed the balance between connections within each Yeo\u2019s network and those spanning different networks. Finally, through the mean participation coefficient we quantified the extent of intermodular connectivity across Yeo\u2019s 7 large-scale functional networks.\n\nNo significant shifts in the whole-brain modularity were observed when considering Yeo\u2019s 7-large-scale functional networks as the initial communities (Table\u00a0S22). Similarly, there were no functional changes in the system segregation, modularity organization or mean participation coefficient of the network\u2019s nodes within each network (Table\u00a0S23). Overall, no significant alterations in the segregation/integration properties of functional networks were observed during the transition to motherhood.\n\nSince murine models suggest that gestational steroid hormones are key drivers of neuroplasticity18,19, we assessed their contribution to the observed cortical trajectory in human pregnancy. Specifically, we tested the association between the longitudinal trajectories of percentage of cortical GM volume change and the levels of a wide array of steroid metabolites, considering data from before pregnancy to the first month postpartum (i.e., sessions 1, 2, 3, and 4). Among the 49 analyzed hormones, 39 of them, including six estrogens, twelve progestogens, fourteen glucocorticoids, and seven androgens, followed a quadratic trajectory during pregnancy and early postpartum (Table\u00a0S24). We observed two types of trajectories: an inverse U-Shape with either a turning point at early or late pregnancy and a U-Shape with a turning point at early or late pregnancy (Figs.\u00a0S6\u2013S9).\n\nWhen evaluating the linked evolution between neuroanatomical and hormonal trajectories, only the trajectory of two sulfated estrogens (estriol sulfate and estrone sulfate) showed significant negative correlations with the GM volume trajectory found in gestational mothers, surviving a hierarchical multiple testing correction within each steroid family (estriol sulfate: Spearman\u2019s R\u2009=\u2009\u22120.32, p\u2009=\u20090.001, q\u2009=\u20090.006; estrone sulfate: Spearman\u2019s R\u2009=\u2009\u22120.24, p\u2009=\u20090.016, q\u2009=\u20090.049; Fig.\u00a03) (see Table\u00a0S25 for all 49 correlations). Thus, the observed U-shape in cortical GM volume changes in gestational mothers was associated with the mirroring trajectory of sulfated estrogens.\n\nFrom our sample of 127 gestational mothers, 100 provided urine samples at sessions 1, 2, 3, and 4 and were therefore included in these analyses. A On the left Y-axis, there is the global percentage of cortical GM volume change in each session in relation to the baseline (i.e., Session 1 - Pre-pregnancy Session). On the right Y-axis, the relative levels in estriol 3-sulfate and estrone sulfate per session relative to the maximum change (% of max) are represented. Mean values are represented as circles and their 95%-confidence intervals as error bars. B Two-sided correlations between the quadratic parameter coefficients of GM volume change (Y-axis, extracted from the adjusted model: cortical GM volume change \u223c Session + Session2\u2009+\u2009(Session2\u2223 participant ID)) and the two steroid concentrations (X-axes, extracted from the adjusted model: Steroid concentration \u223c Session + Session2\u2009+\u2009(Session2\u2223 participant ID)). P-values were corrected within each metabolite family using FDR. The green lines and the gray shaded area represent the least squares regression lines and the 95% confidence intervals around the smooth line, respectively. Spearman\u2019s R, Spearman\u2019s correlation coefficient; p, uncorrected p-value; and q, hierarchical FDR corrected p-value.\n\nIn rodents, the brain remodeling during pregnancy orchestrates the onset of maternal behavior at birth6. Hence, we next assessed the link between the observed global neuroanatomical changes in gestational mothers and their maternal attachment toward the newborn. With this aim, we divided the cortical GM volume trajectory into two components: the percentage of GM volume decreases from pre-pregnancy to late pregnancy (sessions 1 and 3), and the percentage of GM volume increases from late pregnancy to 6 months postpartum (sessions 3 and 5). Both components of the trajectory - the GM decrease from pre-pregnancy to late pregnancy, and the GM increase from late pregnancy to 6 months postpartum- were positively associated with the mother-to-infant attachment scale at 6 months postpartum. Specifically, smaller GM volume decreases during pregnancy and higher GM volume recovery during postpartum predicted lower levels of hostility towards their baby (Fig.\u00a04A). However, only the latter association remained significant after adjusting for multiple comparisons (Pearson\u2019s R (96)\u2009=\u20090.29, CI = [0.10, 0.46], t\u2009=\u20093.00, p\u2009=\u20090.003, q\u2009=\u20090.028). No other subscales of antenatal or postnatal maternal attachment were correlated with the GM volume changes (Table\u00a0S26). Thus, a greater recovery of maternal brain changes from late pregnancy to 6 months postpartum was associated with higher levels of attachment with the infant at 6 months postpartum, particularly in terms of reduced hostility.\n\nFrom our sample of 127 gestational mothers, 98 completed the MRI session at 6 months postpartum, and were therefore included in these analyses (see Fig.\u00a0S7 for the dropout scheme). A Two-sided Pearson\u2019s correlation between the percentage of cortical GM volume recovery during postpartum\u2014from the 34th week of pregnancy to 6 months postpartum- (X-axis) and absence of hostility at 6 months postpartum (Y-axis). P-values are corrected for multiple testing, using FDR. The orange line and the gray shaded area represent the least squares regression line and the 95% confidence intervals around the sample\u2019s mean at each experimental session, respectively. Pearson\u2019s R, Pearson\u2019s correlation coefficient; p, uncorrected p-value; and q, False-Discovery-Rate corrected p-value. B Path diagram of the mediation model between the percentage of cortical gray matter volume recovery (% GMV) during postpartum\u2014from the 34th week of pregnancy to 6 months postpartum -, maternal well-being, and absence of hostility at 6 months postpartum. P-values used for the path diagram are uncorrected p-values. Numbers represent the coefficient estimates, asterisks indicate the significance of each pair of associations (*, p\u2009<\u20090.05; **, p\u2009<\u20090.01; ***, p\u2009<\u20090.001), and the positive symbol (+) indicates the positive association between each pair of variables.\n\nFinally, given the impact of mental health on maternal behavior, we sought to investigate whether the relationship between global cortical GM volume recovery from late pregnancy to 6 months postpartum and maternal attachment was mediated by maternal mental health outcomes at 6 months postpartum, including well-being, perceived stress, and postnatal depression. The three measures significantly correlated with absence of hostility at 6 months postpartum in gestational mothers (Well-being: Pearson\u2019s R (96)\u2009=\u20090.55, CI = [0.40, 0.68], t\u2009=\u20096.48, p\u2009<\u20090.001, q\u2009<\u20090.001; Depression: R (95)\u2009=\u2009\u22120.31, CI = [\u22120.48, \u22120.12], t\u2009=\u2009\u22123.22, p\u2009=\u20090.002, q\u2009=\u20090.002; Stress: R\u2009=\u2009\u22120.44, CI = [\u22120.58, \u22120.26], t\u2009=\u2009\u22124.75, p\u2009<\u20090.001, q\u2009<\u20090.001). Moreover, the mother\u2019s well-being mediated 51% of the effect of GM volume recovery on absence of hostility (B\u2009=\u20090.51, 95% CI = [0.22, 1.13], p\u2009=\u20090.003, q\u2009=\u20090.008; Table\u00a0S27; Fig.\u00a04B). The mother\u2019s lower perceived stress mediated 29% of the effects of GM volume recovery and absence of hostility, however, this result did not survive multiple testing correction (B\u2009=\u20090.29, 95% CI = [0.01 0.71], p\u2009=\u20090.045, q\u2009=\u20090.069, Table\u00a0S27). Depression scores did not have a significant mediating effect (B\u2009=\u20090.13, 95% CI = [\u22120.08, 0.39], p\u2009=\u20090.167, q\u2009=\u2009167, Table\u00a0S27). In sum, the positive association between maternal attachment in gestational mothers and cortical GM volume recovery was partly explained by higher levels of maternal well-being.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-55830-0/MediaObjects/41467_2025_55830_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-55830-0/MediaObjects/41467_2025_55830_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-55830-0/MediaObjects/41467_2025_55830_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-55830-0/MediaObjects/41467_2025_55830_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "This prospective study uncovered a U-shaped trajectory in cortical GM volume, area, and thickness in first-time gestational mothers during pregnancy and postpartum, peaking in the peripartum period. GM reductions were evident as early as the second trimester, suggesting an early onset of brain remodeling during pregnancy. At 6 months postpartum, GM volume had not returned to pre-pregnancy levels, supporting the notion that maternal brain changes may be a long-lasting phenomenon that persists beyond the postpartum period4,5,20,21. These findings indicate that gestation and postpartum induce opposite effects on the cortical mantle, resolving a long-debated puzzle for scholars in the field. In particular, our results suggest that previous studies reporting volume increases in cortical GM during postpartum21,22,23,24,25 and less pronounced cortical GM decreases before and after pregnancy (\u22122.7% at 3 months postpartum vs \u22124.9% at late pregnancy)4,26, were indeed capturing part of the postpartum recovery process. We also discovered a similar U-shaped pattern in WM volume, alongside an inverse U-shaped pattern in CSF volume, suggesting that at least part of the observed changes could result from a compensatory effect, where increased brain fluid compresses cortical tissue. These patterns, along with specific subcortical changes, warrant further validation via more appropriate MRI sequences, such as diffusion imaging for WM microstructure, arterial spin labeling for cerebral blood flow changes, and T2 high-resolution images for subcortical structures.\n\nExternal factors related to the parental experience minimally influenced this trajectory, as it solely manifested in women undergoing pregnancy but was absent in non-gestational mothers. This suggests that the U-shaped trajectory observed from before pregnancy to 6 months postpartum is mainly due to gestational factors. Importantly, these findings do not preclude parenting-related brain adaptations in non-gestational mothers nor do they exclude such adaptations as contributing factors of the neuroanatomical changes observed in gestational mothers. The impact of childrearing and environmental factors on the human maternal brain may be less pronounced and more localized than the effects of pregnancy, in line with previous research on the paternal brain27,28. Given that motherhood is a life-long journey, parenting-related factors may also contribute to brain changes observed beyond the immediate postpartum4,20,25, and in middle-aged and older mothers29. Lastly, our study did not measure parenting investment or behavior; thus, we cannot determine whether gestational mothers were more engaged in parenting behaviors than non-gestational mothers.\n\nThe dynamic neuroanatomic changes observed in gestational mothers were associated with fluctuations in two types of estrogens: estriol and estrone sulfate. Similarly, a previous study reported estradiol levels in the third trimester to be associated with GM volume changes before and after pregnancy in humans5. Our study offers a more complete picture, showing that both trajectories evolve together yet in opposite directions. The larger the increase and posterior decrease in estriol and estrone, the larger the decrease and posterior recovery in GM volume change. Estrogen surge during pregnancy is mainly due to placental production30 and consequently plummets after placental expulsion at childbirth. In line with this, we observed a turning point in estrogenic and cortical trajectories around childbirth. These observations suggest that parturition is a critical phase in maternal brain remodeling that deserves more research attention31. The brain-hormone associations revealed in our study bridge findings in humans with the mechanistic insights gained from animal models6,8, reinforcing the idea that estrogens critically influence neuroplasticity processes during human pregnancy. Research should confirm these results using blood samples, which will more closely reflect the circulating free-hormonal levels and their conjugates.\n\nThe U-shaped trajectory of GM volume affected numerous regions across the brain\u2019s cortex, encompassing 94% of its surface. Particularly striking changes were observed in higher-order cognitive networks such as the Default Mode and Frontoparietal networks, which exhibited a steeper decrease during gestation, reaching the lowest point at late pregnancy. After childbirth, these networks recovered at a similar rate as the rest of the networks and thus remained at a lower level at 1 and 6 months postpartum. These findings align with previous studies showing that late pregnancy GM decreases affect all networks3 and that reductions in Default Mode and Frontoparietal networks persist longer compared to other networks3,4,5. However, none of the networks exhibited changes in their functional segregation properties, suggesting that the distinct GM volume trajectories might not be reflected in a functional network reorganization. Of note, the lack of changes observed in these metrics do not preclude the occurrence of numerous other functional changes during this life transition or at later stages of the postpartum period. Using network coherence as their measure of interest, Hoekzema and colleagues reported pre-post-pregnancy increases in network connectivity within a cluster located in the Default Mode Network. Moreover, cross-sectional studies have reported differences in the effective connectivity between key nodes of the parental caregiving network in first-time mothers across the early and late postpartum periods32,33. Together, a more in-depth analysis of functional changes, including the examination of other network functional properties and even alternative network constructions, is essential to characterize connectomic changes in mothers. Furthermore, future investigations should explore the structural-functional coupling across the perinatal period.\n\nBehaviorally, the percentage of GM volume recovery during the postpartum was associated with a higher absence of hostility towards the infant at 6 months postpartum. This positive association suggests that the brain remodeling experienced by gestational mothers might be adaptive, facilitating facets of maternal behavior. Our results agree with a prior study reporting an association between pre-to-post-pregnancy GM volume changes and higher scores on attachment quality and absence of hostility4. Here, we reveal that maternal attachment at 6 months postpartum depends more on the recovery of GM volume during the postpartum period than on the decrease in GM volume during pregnancy. Forthcoming work should incorporate precise assessments of childrearing involvement and parent-infant interactions to further understand the functional meaning of these brain changes. Additionally, studies integrating cognitive and neuroimaging assessments should also explore if this pronounced neural remodeling is linked to the increased cognitive load and subsequent cognitive reserve of new mothers34.\n\nPregnancy and postpartum are defined as stages with high risk for mental health disorders35,36. Mental health can, in turn, impact mother-to-infant attachment and the infant\u2019s cognitive development37. We found that even in a sample of healthy mothers, higher well-being, lower perceived stress, and lower depression scores correlate with a higher absence of hostility towards the newborn. Our results further reveal that the mother\u2019s general well-being mediates more than 50% of the relationship between GM volume recovery and attachment at 6 months postpartum. This suggests that the neuroanatomical changes occurring after pregnancy affect the mental well-being in mothers, which in turn facilitates adaptive maternal attachment. Maternal well-being has also been shown to mediate the relationship between improved cognitive performance and reduced top-down corticolimbic inhibition in first-time mothers at one year postpartum32. Together, these findings open the door to identifying specific periods during pregnancy and postpartum when experiences and interventions may have the greatest impact on maternal brain health and psychological well-being. Research should also explore if these neuroanatomical trajectories are disrupted in psychiatric disorders such as perinatal depression.\n\nIn sum, our study offers a comprehensive view of the brain\u2019s adaptations to motherhood by linking brain structure, hormonal dynamics, and maternal attachment. Leveraging the largest longitudinal neuroimaging dataset of mothers to date, we unveil a distinctive U-shaped trajectory of cortical changes during pregnancy and the postpartum. Notably, this U-shaped trajectory was associated with dynamic fluctuations in gestational estrogen levels, while the postpartum recovery of GM volume was associated with increased maternal attachment. Along with these widespread neuroanatomical changes, we discerned a more pronounced trajectory within the Default Mode and the Frontoparietal networks. By revealing the dynamic brain changes during pregnancy, the possible hormonal drivers behind these changes, and how their interplay impacts the mother\u2019s psychological well-being, this study marks a crucial advance in maternal brain research.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "This research was approved by the Ethics Committee at the Hospital del Mar Research Institute (Ref: 2017/7450/I), the Hospital Cl\u00ednic de Barcelona (Ref: HCB/2018/0357), and the Hospital Universitari Quir\u00f3n Dexeus (Ref: 7/2/2017), in accordance with the Declaration of Helsinki guidelines. All participants signed a consent form before participating in the study and received monetary compensation for their participation.\n\nFor this prospective cohort study, first-time mothers participated in an MRI acquisition protocol before, during, and after their first pregnancy. This longitudinal design allowed us to use each woman\u2019s baseline state as her control. First-time gestational mothers were also compared to a group of non-gestational mothers and nulliparous women. Participants completed a total of five sessions: (1) pre-conception, (2) at 18 weeks of pregnancy (mean\u2009\u00b1\u2009sd = 18.25\u2009\u00b1\u20090.94 weeks), (3) at 34 weeks of pregnancy (mean\u2009\u00b1\u2009sd = 34.20\u2009\u00b1\u20090.90 weeks), (4) at one month postpartum (mean\u2009\u00b1\u2009sd = 1.09\u2009\u00b1\u20090.27 months), and (5) at 6 months postpartum (mean\u2009\u00b1\u2009sd = 6.05\u2009\u00b1\u20090.32 months).\n\nParticipants were recruited in the Barcelona area through word-of-mouth, local hospitals, and social media ads. We recruited nulliparous women who were planning on becoming pregnant in the near future (i.e., gestational mothers), nulliparous women whose female partners were planning to become pregnant in the near future (i.e., non-gestational mothers), and nulliparous women without any plans of having children soon (i.e., nulliparous women). Exclusion criteria for all participants included being over 45 years old, being pregnant at the pre-conception session, previous pregnancies, current major neurological or psychiatric disorders assessed by the MINI International Neuropsychiatric Interview38, intake of psychiatric medication, and MRI incompatibilities. Gestational mothers and their non-gestational partners who did not achieve pregnancy after the first MRI session were discontinued from the study.\n\nThe initial sample at the pre-conception session comprised 317 gestational mothers, 56 non-gestational mothers, and 60 nulliparous women. However, we only included participants who completed at least sessions 1, 3, and 4 (pre-conception, 34 pregnancy weeks, and one month postpartum), met the above-mentioned inclusion criteria, and whose MR images were not affected by artifacts in the structural MRI data analyses. Participants with an artifact or no MRI acquisition at sessions 2 and 5 (18 pregnancy weeks and 6 months postpartum) were included in the analyses, excluding only the session with the affected MRI acquisitions. Our final experimental sample consisted of 127 first-time gestational mothers (mean age \u00b1 sd = 33.67\u2009\u00b1\u20094.04 years), 20 non-gestational mothers (mean age \u00b1 sd = 32.15\u2009\u00b1\u20093.53 years), and 32 nulliparous women (mean age \u00b1 sd = 30.38\u2009\u00b1\u20093.23 years). Of note, except for one, all non-gestational mothers of the final sample were partners of women from the gestational mothers group. For an overview of the group allocation and dropout per group and session, please see Fig.\u00a0S10.\n\nIn the gestational mothers\u2019 group, 45.67% of participants became pregnant via natural conception (58 women), and 54.33% conceived via assisted reproduction methods (69 women). Of those who used assisted reproduction, 23.19% used artificial insemination (16 women), 40.58% used in-vitro fertilization (28 women), and 36.23% used intracytoplasmic sperm injection (25 women). At childbirth, 61.90% of women delivered their babies via vaginal birth (78 women), 11.11% had a scheduled C-Section (14 women), and 26.98% had an unplanned C-Section (34 women). One woman gave birth to monozygotic twins, and one delivered dizygotic twins. Of the total 129 babies delivered, 56.80 % were males and 43.20% were females. Lastly, 84.68% of gestational mothers breastfed exclusively in the first month postpartum, 7.26% mixed breastfeeding and baby formula, and 8.06% did not breastfeed (Table\u00a0S1).\n\nGestational mothers and non-gestational mothers did not differ in age (B\u2009=\u2009\u22121.52, p\u2009=\u20090.232) nor did non-gestational mothers and nulliparous women (B\u2009=\u20091.77, p\u2009=\u20090.242). However, we did observe significant age differences between gestational mothers and nulliparous women (B\u2009=\u20093.29, p\u2009<\u20090.001). Groups were homogenous in education level (\u03c72(4, N\u2009=\u2009179)\u2009=\u20092.7, p\u2009=\u20090.608). Finally, there were no significant group differences in the time intervals between sessions, except for a difference in the interval between the first and second sessions in gestational and non-gestational mothers when compared to nulliparous women (B\u2009=\u200911.32, p\u2009<\u20090.001 & B\u2009=\u200910.67, p\u2009<\u20090.001). To account for the potential confounding effects of age and time interval between Session 1 and 2, both variables were included as covariates in all analyses involving group comparisons.\n\nAll participants in this study self-reported as females and identified as women. As such, sex- and gender-based analyses were not applicable. In the manuscript, we use the term \u201cwomen\u201d to refer to females whose sex aligns with their gender identity. We acknowledge that this terminology reflects the characteristics of our specific sample and recognize the importance of evolving language to be more inclusive as research in this field expands to encompass gestational individuals of diverse sex characteristics and gender identities. The study sample is broadly representative of the Spanish population of mothers in terms of education level, conception and parturition methods, and the baby\u2019s biological sex. Additionally, the sample is inclusive regarding participants\u2019 sexual orientation, encompassing lesbian mothers\u2014both gestational and non-gestational\u2014as well as heterosexual partners. However, we acknowledge that greater representativity could be achieved, particularly in terms of racial and ethnic diversity, which remains an important goal for future research.\n\nEach session consisted of an MRI acquisition, a collection of urine samples for hormonal determinations, and the completion of self-report neuropsychological questionnaires. The neuroimaging sessions before and after pregnancy (sessions 1, 4, and 5) comprised both structural and resting-state functional MRI acquisitions (long protocol). The neuroimaging sessions that took place during pregnancy (i.e., sessions 2 and 3) included only a structural acquisition (short protocol) to ensure the comfort of our participants. We decided to exclude hormonal measurements at 6 months postpartum based on previous literature indicating a plummet in sex steroids after childbirth10.\n\nData acquisition\n\nWe acquired three-dimensional T1-weighted images on a 3 Tesla Philips Ingenia CX with a Head-and-Neck 32-channel coil. We used a Turbo Field Echo (TFE) sequence in sagittal orientation and the following parameters: Voxel size\u2009=\u20090.75\u2009\u00d7\u20090.75\u2009\u00d7\u20091\u2009mm3; field of view (FOV)\u2009=\u2009240\u2009\u00d7\u2009240\u2009\u00d7\u2009180\u2009mm3; echo time (TE)\u2009=\u20094.6\u2009ms; repetition time (TR)\u2009=\u20099.9/2300\u2009ms; prepulse delay\u2009=\u2009900\u2009ms; flip angle (FA)\u2009=\u20098\u00b0; acceleration factor = 1.9; percent sampling\u2009=\u200978%; acquisition time\u2009=\u2009259\u2009s. The MR technician performed an on-site visual inspection of the images, and the acquisition was repeated in case of significant head movement. A technical error in the FOV affected two acquisitions: one during a participant\u2019s second session and one during a participant\u2019s fourth session. We excluded the affected session of the first participant and completely excluded the second participant (Fig.\u00a0S10).\n\nImage processing\n\nTo analyze the structural images, we employed the recon-all longitudinal stream within FreeSurfer, version 7.2.039. Initially, the individual brain images from each session were processed cross-sectionally. This pipeline extracted outer (pial) and inner (white matter) cortical boundaries to construct the cortical surfaces and the corresponding vertex-wise maps, including volume, cortical thickness, and white matter surface area. The pipeline also computed the Euler number, whose average across hemispheres is an excellent proxy for image quality40 (Table\u00a0S1). Then, we used a longitudinal workflow to process each participant\u2019s brain image at each subsequent session to create a participant-specific unbiased template based on individual native images. This longitudinal workflow ensured uniformity in the number of vertices and faces of cortical surfaces for every participant across sessions, improving intra-participant precision of metrics. The participant-specific templates were used to initialize the reconstruction of the surfaces at each session. Additionally, at the final stage, this workflow allowed us to compute the participants\u2019 estimated intracranial volume (eTIV), which did not differ among groups (F(2,176)\u2009=\u20091.44, p\u2009=\u20090.240).\n\nCortical metrics were studied at both global and vertex-wise levels. To assess global metrics, cortical maps in the subjects\u2019 anatomical space were employed to compute total cortical gray matter (GM) volume, mean cortical thickness, and total surface area. Additionally, Yeo\u2019s parcellation was projected onto the cortical surfaces obtained during the longitudinal processing to compute the aforementioned cortical metrics within the seven functional networks described by Yeo14. For the vertex-wise analysis, the subjects\u2019 cortical maps were projected onto the common fsaverage space and then smoothed with a 10\u2009mm full-width-at-half-maximum (FWHM) Gaussian kernel. As a quality control, we identified outliers using non-parametric methods (within each group and session) to detect failures in the hemispheric parcellation process. The parcellation of such outliers was visually inspected, and those participants in which the process failed were excluded (fig.\u00a0S10).\n\nStatistical Analyses\n\nQuadratic trajectory of the neuroanatomical changes. Data was analyzed using linear mixed effects (LME) models. In both global and vertex-wise analyses, we fitted separate LME models using total cortical volume, mean cortical thickness, and total surface area as dependent variables. Across models, we used group (gestational mother, non-gestational mother, and nulliparous women), a linear and quadratic term for session, and two interactions (i.e., group*linear term for session, group*quadratic term for session) as fixed effects. To account for confounding factors, we also included z-standardized covariates of age at session 1, eTIV, mean Euler number of each session, and time interval between sessions 1 and 2. Moreover, we incorporated a random intercept to control for subject-specific differences (see Formula 1, where cortical metric corresponds to cortical GM volume, thickness, or surface area). In the vertex-wise analysis, we additionally orthogonalized the interaction terms to avoid collinearity with the simple terms of the model. Our contrasts of interest were the following: (1) linear effect of session on gestational mothers (compared to nulliparous women) (2) linear effect of session on non-gestational mothers (compared to nulliparous women) (3) quadratic effect of session on gestational mothers (compared to nulliparous women) (4) quadratic effect of session on non-gestational mothers (compared to nulliparous women) (5) differences between gestational and non-gestational mothers in the linear effect of session, and (6) differences between gestational and non-gestational mothers in the quadratic effect of session. \n\nWe explored global differences using the lmer function (lmer library), within the Rstudio software under R version 4.2.1. The quadratic term for session was assessed using the poly function (stats library version 4.3.1). The covariables were standardized to z-scores using the scale function (base library version 4.3.1). We corrected p-values for the three different metrics (volume, thickness, and surface area) using the Benjamini & Hochberg False Discovery Rate (FDR)41 p.adjust function built on the stats library (version 4.3.1). In this article, corrected p-values using FDR correction are referred to as q-values. Moreover, we confirmed that the above-mentioned quadratic model (Formula 1) provided a better fit than the one only including linear changes over sessions by performing an analysis of variance using the anova function of the R stats library.\n\nTo better describe the symmetry of gray matter volume trajectory over pregnancy and postpartum we performed a generalized additive model using the gam function built on the mcgv library (version 1.8.42) (see supplementary text I for methodological details). To visually inspect the intervariability of the U-shaped brain changes within the gestational mothers group, we modeled the quadratic term for Session as a random effect in each cortical metric (gray matter volume, cortical thickness, and surface area, see \u201cFormula 2\u201d). Then, we extracted the conditional means of the random effects in these models using the ranef function and plotted them using an histogram.\n\nFor completeness, we also analyzed the cerebral white matter volume and cerebrospinal fluid changes using the same model as in Formula 1. We corrected p-values for these two supplementary metrics using the Benjamini & Hochberg False Discovery Rate (FDR)41 p.adjust function built on the stats library (version 4.3.1). We also confirmed that the quadratic model provided a better fit than the one only including linear changes over sessions by performing an analysis of variance using the anova function of the R stats library.\n\nFor the vertex-wise analysis, we used MATLAB\u2019s LME vertex-wise tool distributed within FreeSurfer42. For each contrast of interest, we corrected vertex-wise p-value maps using an FDR correction across hemispheres and cortical metrics. For all analyses, we considered q-values below a threshold of 0.05 significant. Lastly, for the FDR-corrected vertex-wise maps, we calculated effect sizes as partial eta squared (\u03b7p2), considering the sign of the parameter associated with each contrast. We used the Desikan-Killiany atlas43 to obtain a listing of the anatomical spatial distribution of those GM volume quadratic changes modeled by formula 1 with an effect greater than 0.06 (i.e., moderate effect size). Similarly, we used the Yeo atlas to obtain a listing of the functional spatial distribution14.\n\nFor the sake of simplicity, in the following analyses we focused only on cortical volume.\n\nGM volume recovery at 6 months postpartum. To confirm whether GM volume at 6 months postpartum had returned to pre-pregnancy levels in gestational mothers, we fitted an LME model using cortical volume as the dependent variable, and session as a two-level factor (Session 1 and Session 5) fixed effect. To account for confounding factors, we also included z-standardized covariates of age at session 1, eTIV, mean Euler number of each session, and the time interval between sessions 1 and 2. Moreover, we incorporated a random intercept to control for subject-specific differences (see Formula 3).\n\nAdditionally, to assess group differences in GM volume at 6 months postpartum, we built a similar LME but with group (gestational mothers, non-gestational mothers, and nulliparous women) as another fixed factor (see Formula 4).\n\nEffect of situational and gestational factors of the GM volume changes. To take into account relevant situational or gestational factors that could be affecting the cortical GM volume trajectory observed in gestational mothers, we built four separate models assessing: the type of conception (natural or assisted), the baby\u2019s biological sex (male or female), the parturition type (vaginal, scheduled c-section, or unplanned c-section) and the type of breastfeeding at one month postpartum (no breastfeeding, exclusive breastfeeding, or mixed breastfeeding). For each variable, we fitted an LME model using total cortical GM volume as the dependent variable. Then, for the models assessing the type of conception and the baby\u2019s biological sex, we used said variables, a linear and quadratic term for session, and two interactions (i.e., conception/baby\u2019s biological sex* linear term for session, conception/baby\u2019s biological sex* linear term for session) as fixed effects. Due to the temporality of childbirth and breastfeeding regarding the quadratic GM volume trajectory, we decided to model the impact of these variables on GM recovery from late pregnancy to 6 months postpartum rather than on the quadratic trajectory. Hence, we used the type of childbirth/breastfeeding type, session (3 to 5), and an interaction between both (i.e., type of childbirth/breastfeeding type * Session (3 to 5)) as fixed effects. Moreover, we incorporated a random intercept to control for subject-specific differences\u00a0(see formulas 5 to 8).\n\nSpatial correspondence with large-scale functional networks. To assess the spatial distribution of the GM volume trajectory in gestational mothers\u2019 brains, we fitted an LME model using cortical volume as the dependent variable and linear and quadratic term for session as fixed effects. Then, we added Yeo\u2019s 7 large-scale functional networks as a random factor nested within the participant\u2019s random intercept. Additionally, we included standardized covariates for age at the pre-conception session, total intracranial volume, mean Euler number of each session, and the time interval between sessions 1 and 2 as covariables. This model allowed us to fit a unique curve for all networks (see Formula 9). Based on prior literature3,4,5, we hypothesized that there might be two different trajectories in the brain: one including higher-order cognition networks (Default Mode and Frontoparietal) and another one including the rest of the networks. Hence, we fitted a second LME model with nested networks (Default Mode and Frontoparietal versus others) as an additional fixed factor. That is, we used network group, a linear and quadratic term for session, and the interactions between network group and the linear and quadratic terms for session as fixed effects, and a random factor with the 7 Yeo Networks nested within the participant\u2019s random intercept (see Formula 10). This model allowed us to fit a different curve for each group of networks. Once the models were fitted, we used the Bayesian Information Criteria (BIC) to compare both models and select which model better explained the brain\u2019s GM volume trajectories. The model with a lower BIC was selected as the better-fitted model.\n\nWe also calculated the mean signed effect size within each of Yeo\u2019s seven large-scale functional networks and compared them to suitable null distributions to determine which networks exhibited significantly higher or lower spatial correspondence with the observed quadratic GM volume trajectory in gestational mothers compared to nulliparous women (See Group*Session2 interaction from Formula 1). To generate null distributions, we used spin-permutations (rotations) of the maps and then recomputed the mean values in each network (which remained unrotated). We performed 1,000 uniformly distributed random rotations of the fsaverage vertex indices using the spin-test toolbox (https://github.com/spin-test/spin-test). Finally, we calculated p-values for each map and network as the proportion of rotations that produced higher or lower values than our original maps. We considered p-values below a threshold of 0.05 significant.\n\nResting-state functional MRI (rs-fMRI) data was collected before and after pregnancy (sessions 1, 4, and 5). The rs-fMRI analyses only included participants who had completed the first (pre-pregnancy) and fourth (1-month postpartum) sessions, and whose MR images were not affected by artifacts. Participants with an artifact or no MRI acquisition at session 5 (6 months postpartum) were included in the analysis, and only the affected session was excluded. The final experimental sample for the functional analyses consisted of 123 gestational mothers, 20 non-gestational mothers, and 40 nulliparous women. For an overview of the group allocation and dropout per session, please see Fig.\u00a0S11.\n\nData acquisition\n\nWe obtained rs-fMRI images on the same 3 Tesla Philips Ingenia CX with a Head-and-Neck 32-channel coil. We acquired T2*-weighted whole-brain single shot echo-planar images (EPIs, with 225 images, the first three serving as dummy scans to account for T1 saturation effects equilibration) with the following parameters: TR\u2009=\u20091.6\u2009s; TE\u2009=\u200935\u2009ms; Flip Angle\u2009=\u200975\u00b0; Field of View = 240\u2009\u00d7\u2009240\u2009\u00d7\u2009138\u2009mm; voxel size\u2009=\u20093\u2009\u00d7\u20093\u2009mm; 46 slices; and slice thickness\u2009=\u20093\u2009mm. Subsequently, two extra images were acquired to address image distortions induced by the magnetic field. These images were acquired using single-shot spin-echo EPI sequences in opposing phase encoding directions\u2014one with phase encoding in the anterior-posterior direction and the other with phase encoding in the posterior-anterior direction. Each image comprised two volumes with the same spatial resolution and matrix dimensions as the resting-state images and the acquisition parameters were TR\u2009=\u20091600\u2009ms; TE\u2009=\u200935\u2009ms; and a flip angle\u2009=\u200990\u00b0.\n\nImage processing\n\nTo process the rs-fMRI images, we applied the standard preprocessing procedures for rs-fMRI signals using a Nipype pipeline implemented in Python (version 3.9.7). This procedure involved a fieldmap intensity correction, head motion realignment, spatial co-registration and normalization, 8\u2009mm-FWHM spatial smoothing, and temporal filtering (0.009\u20130.08\u2009Hz). Then, six head motion parameters, signals from white matter, and cerebrospinal fluid were regressed from the images to control for head motion and physiological noises. All steps were performed using FS44,45, except the temporal filtering, which was performed using AFNI46. Subjects with evident poor image quality (measured as large signal loss or extended hyperintensities) or excessive head motion (mean framewise displacement (FWD)\u2009>\u20090.25\u2009mm) in any of the evaluated sessions were excluded from the analysis (Fig.\u00a0S11).\n\nGraph construction\n\nThe organization of functional brain networks was examined using graph theory, which characterizes the topological properties of large-scale brain networks47. Nodes were defined based on Schaefer\u2019s 400-region cortical parcellation15, which are assigned to one of the networks of Yeo\u2019s original parcellation14. We calculated a 400\u2009\u00d7\u2009400 functional connectivity matrix for each participant and session, which indicated the Pearson correlation coefficients between each pair of nodes. Then, we applied a Fisher z transformation to each correlation matrix and set the diagonal elements and negative connections to 0. We chose Pearson r values to represent functional connectivity between nodes because of their simplicity in interpretation and extended usage in human network neuroscience48.\n\nGraph theoretical metrics\n\nGraph theoretical metrics were computed for each participant and session using in-house scripts based on the Brain Connectivity Toolbox17. Moreover, we calculated each metric over a range of costs (0.05\u20130.15 at 0.01 intervals, a range and interval widely used in graph theory analyses48). All graph metrics reported here are the average values across all costs.\n\nModularity. We calculated the whole-brain modularity under the Louvain community detection algorithm. As inputs, we used the previously calculated undirected, weighted connectivity matrices and a 1\u2009\u00d7\u2009400 vector indicating each column\u2019s correspondence to Yeo\u2019s 7-large-scale functional networks as the initial community affiliation vector. Whole-brain modularity quantified the strength of segregation into Yeo\u2019s 7-large-scale functional networks. In addition, we calculated a within-network modularity index for each of Yeo\u2019s 7-large-scale functional networks, quantifying the modular structure of each network.\n\nStrength of system segregation. System segregation was used to describe the relative strength of within-network connections compared to between-network connections. We calculated the strength of system segregation for each of Yeo\u2019s 7-large-scale functional networks. Following the methodology described by Cohen and D\u2019esposito, 201649, within-network connectivity strength was calculated as the mean connectivity strength between all pairs of nodes within the same network. Between-network connectivity strength was calculated as the mean connectivity strength between all pairs of nodes that connected two networks.\n\nParticipation coefficient. Lastly, we used the mean participation coefficient to measure intermodular connections within Yeo\u2019s 7-large-scale functional networks. The participation coefficient was first calculated for each node on the 400\u00d7400 weighted connectivity matrices. Then, each node was assigned to one of Yeo\u2019s functional networks, and the participation coefficient values were averaged for each network.\n\nNull model correction\n\nAll the graph theory metrics were compared with the same measures computed on a reference network. We randomized the undirected weighted matrices for each subject, session, and cost, preserving the degree distribution. Each edge was rewired approximately 100 times. Then, we calculated the same graph theory metrics on the randomized matrices (i.e., null graph theory metrics). For each subject and session, we used the averaged values across all costs. Finally, we corrected our metrics by subtracting the values obtained from the null graph theory metrics subject-wise. These were the values used in our analyses.\n\nStatistical analyses\n\nTo evaluate between-group differences in functional connectivity changes at a whole-brain level, we fitted an LME model using modularity as a dependent variable. We used group (nulliparous women, gestational mother, and non-gestational mother), session (Pre-pregnancy, 1-month postpartum, and 6-month postpartum), and group*session interaction as fixed effects. To account for possible confounding factors, we also included z-standardized covariates of age at session 1, the FWD of each session, and the time interval between sessions 1 and 4. Lastly, to account for subject-specific differences, we incorporated random intercepts into the models (Formula 11).\n\nTo determine changes in the within-network functional connectivity, we built a separate LME model per network using modularity, system segregation, and mean participation coefficient as predictors. In all models, we used session (pre-pregnancy, 1-month pp, and 6-month pp), group (nulliparous women, gestational mother, and non-gestational mother), and a session*group interaction as fixed effects. We also included z-standardized covariates of age at session 1, the FWD of each session, and the time interval between sessions 1 and 4 to account for possible confounding factors. Lastly, to account for subject-specific differences, random intercepts were incorporated into the models (Formula 12). P-values were corrected for all built models using FDR correction (21 models: 3 graph theory measures*7 networks). We considered significant corrected q-values below a threshold of 0.05.\n\nFrom the 127 gestational mothers, 20 non-gestational mothers, and 32 nulliparous women who underwent neuroanatomic scans in sessions 1, 2, 3, and 4, 100 gestational mothers, 15 non-gestational mothers, and 29 nulliparous women provided urine samples during these same sessions. One sample was missing from one nulliparous woman in session 2.\n\nSample preparation and determination\n\nUrinary steroid metabolite levels were measured at sessions from pre-conception to one month postpartum. Each sample of spot urine was collected by the participant the day before the MRI session after 3\u2009pm to avoid the morning cortisol peak concentration50. In case the participant forgot to bring the urine sample to the visit, the sample was either taken during the visit if it was after 3\u2009pm or it was collected that same afternoon at home and sent to the laboratory via private courier service. After the visit, the urine samples were aliquoted and stored at \u221280\u2009\u00b0C. On the analysis day, an aliquot of urine samples was thawed at room temperature and proceeded for steroid extraction and concentration using the solid-phase extraction (SPE) method. We used liquid chromatography-tandem mass spectrometry as a targeted approach to analyze urine steroids. To detect and quantify unconjugated steroids we used a previously published methodology by our team51. Additionally, to detect and quantify conjugated steroids\u2014including monosulfated, monoglucuronidated, bisulfated, and sulfo-glucuronidated steroids - we used a recently developed methodology (see supplementary text IV for methodological details).\n\nDue to sample collection and processing constraints, we processed urine samples in eight batches. Batches one to three included urine samples from the first session, and batches four to eight included urine samples from the first to the fourth session. For each batch, we calculated the corresponding calibration curves using solid phase extraction-stripped urine and a set of quality control samples. Quality control samples were injected at least twice per each batch-analysis.\n\nData processing\n\nUsing targeted metabolomics, we obtained urinary hormonal concentrations from 50 steroid metabolites. Seven steroids belonged to the estrogens family, twelve to the progestogens family, another twelve to the androgens family, and nineteen to the corticoids family (see Table\u00a0S24). To correct hormonal values below the detection limit, we performed a half-minimum (HM) imputation for each metabolite. Specifically, we replaced undetected levels (noted as zeros) with half of the minimum value detected among the non-zero hormonal concentrations.\n\nAs concentration levels for most of these metabolites increase during pregnancy (i.e., at 18 and 34 pregnancy weeks), we excluded one metabolite that was detected in less than 80% of pregnancy samples. After the imputation, we adjusted the steroid metabolites\u2019 concentration in each sample for creatinine levels. Lastly, we applied a logarithmic transformation to our data to account for the typically skewed distributions of hormones. All in all, we used creatinine normalized and logarithmically corrected steroid metabolites\u2019 concentrations in our analyses.\n\nStatistical analysis\n\nSteroidal data was analyzed using LME models. We fitted separate LME models using each steroid metabolite concentration as the dependent variable. For each model, we used group (gestational mother, non-gestational mother, and nulliparous women), a linear and quadratic term for session, and two interactions (i.e., group*linear term for session, group*quadratic term for session) as fixed effects. To account for confounding factors, we also included z-standardized covariates of age at session 1, body mass index (BMI), and the time interval between sessions 1 and 2. Moreover, we incorporated a random intercept to control for subject-specific differences (see Formula 13). We corrected p-values within each steroid family (i.e., estrogens, progestogens, corticoids, and androgens) using FDR.\n\nThen, to assess the possible joint evolution of GM volume in gestational mothers and the different steroid changes along the transition to motherhood, we computed a Spearman correlation between the quadratic parameter coefficients of GM volume change and steroid concentrations. To conduct this correlation, we first calculated the percentage of GM volume change for each participant session compared to pre-conception. Then, we fitted an LME model using the percentage of GM volume change at each session (i.e., 1,2,3,4) as the dependent variable. This model contained a linear and quadratic term for sessions as fixed effects and a random intercept to account for subject-specific differences. Lastly, we added a random slope for the quadratic term component, which allowed us to extract a coefficient for the quadratic GM volume trajectory of each participant (see Formula 14). Similarly, we fitted an LME model for each metabolite which significantly followed a quadratic trajectory over sessions (Table\u00a0S24). These models contained a linear and quadratic term for session as fixed effects, a random intercept for each participant, and a random slope for the quadratic time component (See Formula 15). We then computed a two-sided Spearman correlation between the quadratic parameter coefficients of both models. Lastly, within each metabolite family, we corrected p-values using FDR (i.e., hierarchical FDR). We applied a hierarchical FDR correction because, in these analyses, each metabolite is not an isolated feature but a part of a subfamily of the data set52.\n\nHormonal dense sampling\n\nTo confirm the steroidal trajectories correlating with the observed neuroanatomical trajectory, we collected urine samples from five extra gestational mothers at a higher sampling frequency. The hormonal sampling started at the pre-pregnancy session and continued every two weeks during pregnancy up to the first two postpartum weeks (pregnancy weeks: 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40). All women, except one, gave birth before the 40th week of pregnancy. The sample preparation, steroidal determination, and processing were identical to the above-explained methodology (see sample preparation and determination and data processing sections). The steroidal trajectories of both the extra sample and the main sample are depicted in Fig.\u00a0S12.\n\nBefore each MRI session (mean\u2009\u00b1\u2009sd\u2009=\u2009\u22121\u2009\u00b1\u20095 days), participants completed a series of self-reported questionnaires administered online. Questionnaires administered during pregnancy measured antenatal mother-to-infant attachment (Maternal Antenatal Attachment Scale, MAAS53), while postpartum questionnaires measured postnatal mother-to-infant attachment (Maternal Postnatal Attachment Scale, MPAS54) among gestational mothers. Moreover, we obtained sociodemographic, lifestyle, and health information, as well as perceived stress (Perceived Stress Scale, PSS55), depressive symptoms (Edinburgh Depression Antenatal Scale, EDAS and Edinburgh Depression Postnatal Scale, EDPS56,57), and general well-being (Well-being Index, WHO-558) measures across all time points and experimental groups. Two participants (one gestational mother and one non-gestational mother) did not complete these questionnaires at session 4.\n\nStatistical analysis\n\nTo assess the potential link between GM volume changes during pregnancy and postpartum and antenatal and postnatal attachment, we used the subscales of MAAS (i.e., intensity of preoccupation, and attachment quality) assessed at 34 pregnancy weeks and MPAS (i.e., absence of hostility, attachment quality, and pleasure in interaction) at 6 months postpartum. Moreover, we divided the GM volume trajectory into two components: the percentage of GM volume change from before pregnancy to late pregnancy (sessions 1 to 3), and the percentage of GM volume change from late pregnancy to 6 months postpartum (sessions 3 to 5). The percentage of GM volume change was calculated as: ((GM volume at session 3\u2014GM volume at session 1)/GM volume at session 1)*100 and ((GM volume at session 5 - GM volume session 3)/GM volume at session 3)*100. Then, we assessed separate two-tailed Pearson\u2019s correlations between each component using the attachment measures and the percentage of GM volume change during pregnancy or postpartum. In total, we had 8 correlation tests (two assessing the effect of GM volume change during pregnancy on antenatal attachment, three assessing the effect of the GM volume change during pregnancy on postnatal attachment, and three assessing the effect of the GM volume change during postpartum on postnatal attachment). For all tests, p-values were corrected by FDR59.\n\nMoreover, we analyzed if the relationship between the absence of hostility at 6 months postpartum and the GM volume recovery at postpartum was causally mediated by mental health outcomes. Thus, we built three separate mediation models with mediate R function (mediation library version 4.5.0) using well-being, perceived stress, and depressive symptoms as possible mental health mediators. For each mediation, we modeled the effect of the independent variable and the mediator on the dependent variable (Absence of hostility ~ % GM volume recovery + Mental health mediator) and the effect of the independent variable on the mediator (Mental health mediator\u00a0~\u00a0% GM volume recovery). We then estimated the average causal mediation effect, the direct effect, the proportion mediated, and the total effect. For each mediation model, we applied nonparametric bootstrapping with 10,000 permutations to estimate the 95% confidence intervals. All p-values were corrected by FDR across the three mediation models.\n\nStatistical analyses were performed in Rstudio (version 2023.06.0\u2009+\u2009421), under R version 4.3.1, with the following libraries: lmer (version 1.1.33) for LME models, including global neuroanatomical, functional connectomics, neuropsychological and hormonal data; emmeans (version 1.8.7) for computing estimated marginal means for assessing contrasts among modeled factors; stats (version 4.3.1) for assessing quadratic effects in neuroanatomical and hormonal data, to apply FDR corrections, and to perform pearson and Spearman correlations between neuroanatomical and neuropsychological or hormonal data; and mediation (version 4.5.0) for the mediation analysis. Vertex-wise FDR-corrected maps were computed using the lme_mass_FDR function included in the MATLAB\u2019s LME vertex-wise tool distributed within FreeSurfer. Figures were plotted using ggplot2 R library. The in-house Rscript also used the following libraries: htmltools (version 0.5.5), tydiverse (version 2.0.0), readxl (version 1.4.3), kableExtra (version 1.3.4.9000), doBy (version 4.6.17), ggpurb (version 0.6.0), corrplot (version 0.92), dyplr (version 1.1.2), MASS (version 7.3.60), flextable (version 0.9.4), officer (version 0.6.3), sjPlot (version 2.8.14). Vertex-wise analyses were plotted using the R library fsbrain (version 0.5.4).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The datasets including the global cortical neuroanatomical and resting-state metrics, hormonal variables, demographic information, obstetric data, and neuropsychological information generated and analyzed in the current study are available in the GitHub repository (https://github.com/URNC-Lab/UShape-Pregnancy). Effect sizes and significance vertex-wise maps reported in the manuscript are also available there. All the data and code necessary to replicate and extend our findings are available in the repository. Source data for this article\u2019s tables and figures are provided within the databases and scripts published in the repository. The transfer of the raw and processed MRI images of the study participants requires additional data treatment agreement including the purpose of the use, and thus, are only available upon reasonable request to the corresponding author. The timeframe for response to access requests is one month. Once granted, data will remain available indefinitely, provided that the terms of the data use agreement are adhered to.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Image processing and vertex-wise statistical analyses of the neuroimaging data are based on pipelines integrated within the softwares referenced in the \u201cMethods\u201d section. Custom code generated for additional statistical analyses and figure representations is available in the GitHub repository (https://github.com/URNC-Lab/UShape-Pregnancy), along with the necessary datasets to replicate them. The public repository must be cited using the following https://doi.org/10.5281/zenodo.1436167159.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "World Health Organization. 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Benet for helping with the data collection, project management, and dissemination of our work. This work has received funding from the European Research Council under the European Union\u2019s Horizon 2020 research and innovation program (Grant Agreement No. 883069), \u2018la Caixa\u2019 Foundation under the project code LCF/PR/HR19/52160001, FEDER/Ministerio de Ciencia e Innovaci\u00f3n \u2013 Agencia Estatal de Investigaci\u00f3n (RTI2018-093952-B-I00), Instituto de Salud Carlos III project PI22/01365 and co-funded by European Regional Development Fund. M.M.-G. was funded by Ministerio de Ciencia, Innovaci\u00f3n y Universidades, Instituto de Salud Carlos III, Predoctorales de Formaci\u00f3n en Investigaci\u00f3n en Salud (PFIS) (contract FI18/00255) and S.C. was funded by a Miguel Servet Type II research contract (CPII21/00016). M.M.-G. and S.C. were co-funded by European Social Fund \u2018Investing in your future\u2019. A.S. was supported by the Ministerio de Ciencia e Innovaci\u00f3n (PRE2019\u2013091422).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Camila Servin-Barthet, Magdalena Mart\u00ednez-Garc\u00eda.\n\nThese authors jointly supervised this work: Susana Carmona, Oscar Vilarroya.\n\nUnitat de Recerca en Neuroci\u00e8ncia Cognitiva, Departament de Psiquiatria i Medicina Legal, Universitat Aut\u00f2noma de Barcelona, Barcelona, Spain\n\nCamila Servin-Barthet,\u00a0Anna Soler\u00a0&\u00a0Oscar Vilarroya\n\nHospital del Mar Research Institute, Barcelona, Spain\n\nCamila Servin-Barthet,\u00a0Luis Marcos-Vidal,\u00a0Anna Soler,\u00a0Olha Khymenets,\u00a0Daniel Berg\u00e9,\u00a0Oscar J. Pozo,\u00a0Clara Pretus\u00a0&\u00a0Oscar Vilarroya\n\nInstituto de Investigaci\u00f3n Sanitaria Gregorio Mara\u00f1on, Madrid, Spain\n\nMagdalena Mart\u00ednez-Garc\u00eda,\u00a0Mar\u00eda Paternina-Die,\u00a0Daniel Mart\u00edn de Blas\u00a0&\u00a0Susana Carmona\n\nDepartment of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA\n\nMagdalena Mart\u00ednez-Garc\u00eda\n\nDepartamento de Bioingenier\u00eda, Universidad Carlos III de Madrid, Madrid, Spain\n\nMar\u00eda Paternina-Die\u00a0&\u00a0Daniel Mart\u00edn de Blas\n\nCIBER de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain\n\nMar\u00eda Paternina-Die,\u00a0Daniel Mart\u00edn de Blas,\u00a0Daniel Berg\u00e9\u00a0&\u00a0Susana Carmona\n\nMedicine and Life Sciences Department, Universitat Pompeu Fabra, Barcelona, Spain\n\nDaniel Berg\u00e9\n\nAssisted Reproduction Unit, Clinical Institute of Gynecology, Obstetrics and Neonatology, Hospital Cl\u00ednic de Barcelona, Barcelona, Spain\n\nGemma Casals\n\nAugust Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain\n\nGemma Casals\n\nFaculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain\n\nGemma Casals\n\nServei de Obstetricia, Departament de Obstetricia, Ginecologia i Medicina de la Reproducci\u00f3, Hospital Universtiari Dexeus, Barcelona, Spain\n\nPilar Prats\n\nDepartament de Psicobiologia i de Metodologia de les Ci\u00e8ncies de la Salut, Universitat Aut\u00f2noma de Barcelona, Barcelona, Spain\n\nClara Pretus\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: C.S-B, M.G-M., O.P., S.C., O.V. Data curation: C.S-B, M.G-M, M.P-D, D.M-B, A.S., O.K., D.B. Formal analysis: C.S-B, D.M-B. Funding acquisition: M.G-M, A.S, O.P, S.C, O.V. Investigation: C.S-B, M.G-M, M.P-D, A.S. Methodology: C.S-B, M.G-M, M.P-D, L.M-V, D.M-B, O.K, O.P, C.P. Project administration: S.C, O.V. Resources: G.C, P.P, A.S, O.K, O.P, S.C, O.V. Software: C.S-B., L.M-V, D.M-B. Supervision: S.C., O.V. Validation: M.P-D, D.M-B, O.K. Visualization: C.S-B, M.G-M. Writing - original draft: C.S-B, M.G-M. Writing - review and editing: All authors.\n\nCorrespondence to\n Susana Carmona or Oscar Vilarroya.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Jakob Seidlitz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Servin-Barthet, C., Mart\u00ednez-Garc\u00eda, M., Paternina-Die, M. et al. Pregnancy entails a U-shaped trajectory in human brain structure linked to hormones and maternal attachment.\n Nat Commun 16, 730 (2025). https://doi.org/10.1038/s41467-025-55830-0\n\nDownload citation\n\nReceived: 16 May 2024\n\nAccepted: 30 December 2024\n\nPublished: 16 January 2025\n\nVersion of record: 16 January 2025\n\nDOI: https://doi.org/10.1038/s41467-025-55830-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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+1,150 @@ +{ + "title": "Minimal presynaptic protein machinery governing diverse kinetics of calcium-evoked neurotransmitter release", + "pre_title": "A minimal presynaptic protein machinery mediating synchronous and asynchronous exocytosis and short-term plasticity", + "journal": "Nature Communications", + "published": "30 December 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54960-1/MediaObjects/41467_2024_54960_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54960-1/MediaObjects/41467_2024_54960_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54960-1/MediaObjects/41467_2024_54960_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54960-1/MediaObjects/41467_2024_54960_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-54960-1#Sec19" + ], + "code": [ + "https://github.com/ChrisAlexNorman/SytSim_Matlab", + "https://github.com/ChrisAlexNorman/SytSim" + ], + "subject": [ + "Short-term potentiation", + "Synaptic vesicle exocytosis" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3991952/v1.pdf?c=1735651033000", + "research_square_link": "https://www.researchsquare.com//article/rs-3991952/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54960-1.pdf", + "preprint_posted": "22 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Neurotransmitters are released from synaptic vesicles with remarkable precision in response to presynaptic Ca2+ influx but exhibit significant heterogeneity in exocytosis timing and efficacy based on the recent history of activity. This heterogeneity is critical for information transfer in the brain, yet its molecular basis remains poorly understood. Here, we employ a biochemically-defined fusion assay under physiologically-relevant conditions to delineate the minimal protein machinery sufficient to account for different modes of Ca2+-triggered vesicle fusion and short-term facilitation. We find that Synaptotagmin-1, Synaptotagmin-7, and Complexin, synergistically restrain SNARE complex assembly, thus preserving vesicles in a stably docked state at rest. Upon Ca2+ activation, Synaptotagmin-1 induces rapid vesicle fusion, while Synaptotagmin-7 mediates delayed fusion. Competitive binding of Synaptotagmin-1 and Synaptotagmin-7 to the same SNAREs, coupled with differential rates of Ca2+-triggered fusion clamp reversal, govern the kinetics of vesicular fusion. Under conditions mimicking sustained neuronal activity, the Synaptotagmin-7 fusion clamp is destabilized by the elevated basal Ca2+ concentration, thereby enhancing the synchronous component of fusion. These findings provide a direct demonstration that a small set of proteins is sufficient to account for how nerve terminals adapt and regulate the Ca2+-evoked neurotransmitter exocytosis process to support their specialized functions in the nervous system.Biological sciences/Neuroscience/Synaptic transmission/Synaptic vesicle exocytosisBiological sciences/Neuroscience/Synaptic plasticity/Short-term potentiation", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "BoseetalSupplements.pdfSupplementary Figures", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Neurotransmitters are released from synaptic vesicles with remarkable precision in response to presynaptic calcium influx but exhibit significant heterogeneity in exocytosis timing and efficacy based on the recent history of activity. This heterogeneity is critical for information transfer in the brain, yet its molecular basis remains poorly understood. Here, we employ a biochemically-defined fusion assay under physiologically relevant conditions to delineate the minimal protein machinery sufficient to account for various modes of calcium-triggered vesicle fusion dynamics. We find that Synaptotagmin-1, Synaptotagmin-7, and Complexin synergistically restrain SNARE complex assembly, thus preserving vesicles in a stably docked state at rest. Upon calcium activation, Synaptotagmin-1 induces rapid vesicle fusion, while Synaptotagmin-7 mediates delayed fusion. Competitive binding of Synaptotagmin-1 and Synaptotagmin-7 to the same SNAREs, coupled with differential rates of calcium-triggered fusion clamp reversal, govern the overall kinetics of vesicular fusion. Under conditions mimicking sustained neuronal activity, the Synaptotagmin-7 fusion clamp is destabilized by the elevated basal calcium concentration, thereby enhancing the synchronous component of fusion. These findings provide a direct demonstration that a small set of proteins is sufficient to account for how nerve terminals adapt and regulate the calcium-evoked neurotransmitter exocytosis process to support their specialized functions in the nervous system.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Information transfer in the brain depends on the release of neurotransmitters stored in synaptic vesicles (SVs) within the presynaptic terminals. SV fusion with the presynaptic membrane and neurotransmitter release are tightly regulated by changes in the presynaptic Ca2+ concentration ([Ca2+]) and can occur in less than a millisecond after the action potential (AP) invades a presynaptic terminal1,2. In addition to fast, synchronous release that keeps pace with APs, many synapses also exhibit delayed asynchronous release that persists for tens to hundreds of milliseconds1,2. Synapses also vary in terms of how the probability of neurotransmitter release is altered by the recent history of AP firing3,4. The balance between synchronous and asynchronous release, and the degree of synaptic facilitation or depression of release, differs not only among neurons but also among synapses supplied by a single axon according to their postsynaptic targets. This heterogeneity is important in coordinating activity within neuronal networks5,6,7.\n\nThe key components of the synaptic vesicular exocytosis machinery have been identified1,2,8. These include the SNARE proteins that catalyze SV fusion (VAMP2 on the SV, and Syntaxin/SNAP25 on the presynaptic membrane); Ca2+ release sensors that couple SV fusion to Ca2+ signal (Synaptotagmins); and proteins that regulate SV docking and the organization of vesicular release sites (e.g., Complexin (CPX), Munc13, Munc18). Ca2+-evoked neurotransmitter release occurs from a readily releasable pool (RRP) of vesicles docked at the presynaptic active zone1,2,9. A consensus has emerged that, at an individual RRP vesicle, multiple SNARE complexes are arrested (\u2018clamped\u2019) in a partially-assembled state (SNAREpins) by Synaptotagmins and CPX. Ca2+ activation of Synaptotagmins releases the fusion clamp allowing SNAREpins to fully assemble and drive SV fusion10,11,12. Despite this general scheme, the reasons for variations in the synchrony of exocytosis or the occurrence of short-term facilitation or depression among synapses, remain poorly understood.\n\nSynchronous neurotransmitter release, occurring within a few milliseconds of the arrival of an AP, is triggered by a transient high local [Ca2+] ([Ca2+]peak\u2009~\u200910-100\u2009\u00b5M) at vesicular release sites, and genetic deletion and substitution experiments have shown that it requires a fast, low-affinity Ca2+ sensor such as Synaptotagmin 1, 2 or 9 (Syt1, Syt2, Syt9)13,14. Asynchronous neurotransmitter release can occur in response to a single AP but is particularly prominent during and following high-frequency bursts of APs. This delayed release requires a persistent elevation of presynaptic [Ca2+] and this [Ca2+]residual is thought to reach a low micromolar concentration1,15,16. At many synapses, accumulation of [Ca2+]residual also leads to transient facilitation of the fast synchronous release component1,4. The slow, high-affinity Ca2+ sensor Synaptotagmin 7 (Syt7), which is activated by both [Ca2+]peak and [Ca2+]residual, has been implicated in regulating both asynchronous release and short-term facilitation17,18,19,20. Indeed, previous studies have shown that genetic removal of Syt7 reduces short-term synaptic facilitation and asynchronous release13,18,19. However, the Syt7\u2019s role in asynchronous release remains a topic of debate as other Ca2+-release sensors (e.g. Syt1, Syt3 and Doc2A) have also been implicated in mediating asynchronous release component17,21,22,23.\n\nAn inherent limitation of genetic studies is their inability to demonstrate whether Syt1 and Syt7 alone are sufficient to regulate the timing and plasticity of neurotransmitter release, as the contribution of other presynaptic proteins cannot be ruled out. Furthermore, because vesicular exocytosis involves an interplay of presynaptic Ca2+ dynamics and Ca2+ sensors, a quantitative account of synchronous/asynchronous release kinetics and short-term plasticity requires precise control and measurement of [Ca2+]. This is difficult to achieve in intact synapses owing to the small size of the active zone and the high speed of Ca2+ diffusion and buffering24,25.\n\nHence, we sought to determine whether Syt1 and Syt7 (along with SNAREs and CPX) are sufficient to determine the kinetics and activity-dependent changes in Ca2+-evoked SV release. Additionally, we aimed to uncover the underlying molecular mechanisms governing the cooperative action of Syt1 and Syt7. To achieve this, we took a reductionistic approach of combining an in vitro reconstituted fusion assay26,27,28,29 with quantitative computational modeling12. Specifically, we utilized a biochemically-defined high-throughput assay based on a suspended lipid membrane platform that uses fluorescence microscopy to track the docking, clamping (equivalent to the delay from docking to spontaneous fusion), and Ca2+-triggered fusion of individual vesicles at tens of milliseconds precision. Critically, this setup allowed precise control over the identity and density of the included proteins, as well as the [Ca2+] signal26,28,29,30.\n\nWe report that under resting conditions, Syt1 and Syt7 along with CPX clamp vesicle fusion to produce docked vesicles. Upon Ca2+-influx, Syt1 and Syt7 act as \u2018fast\u2019 and \u2018slow\u2019 release sensors respectively and govern the overall fusion kinetics by competitively binding to the same SNARE complex. Computational modeling suggests that the slower Ca2+-triggered reversal of the Syt7 fusion clamp, in contrast to Syt1, accounts for the delayed fusion kinetics. When [Ca2+]basal is elevated to mimic neuronal activity, Syt7 enhances Ca2+-synchronized vesicle fusion independent of Syt1, due to selective destabilization of the Syt7 clamp by elevated [Ca2+]basal. In summary, our data suggest that Syt1, Syt7, SNAREs, and CPX constitute the minimal protein machinery necessary to support the diverse kinetics and activity-dependent dynamics of Ca2+-evoked neurotransmitter release.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Recently, using the in vitro experimental setup, we demonstrated that under physiologically relevant conditions, Syt1 and CPX are sufficient to produce clamped (RRP-like) vesicles, and these stably docked vesicles can be triggered to fuse rapidly by Ca2+ addition30. Building on this advance, we designed the reconstitution conditions to investigate the cooperative action of Syt1 and Syt7 as follows: in all experiments, we used small unilamellar vesicles containing VAMP2 and Syt1 and included CPX in the solution (Fig.\u00a01a, Supplementary Fig.\u00a01). We reconstituted pre-formed t-SNAREs (a 1:1 complex of Syntaxin1 and SNAP-25) and Syt7 (when warranted) in the suspended lipid membrane (Fig.\u00a01a, Supplementary Fig.\u00a01). We incorporated Syt7 in the suspended lipid membrane reflecting its predominant localization in the presynaptic membrane in central synapses31,32. Since the concentration of Syt7 within the active zone is unknown and likely varies among different types of synapses, we tested the effect of varying Syt7 concentration. In all cases, we monitored large ensembles of vesicles (\u2009~\u2009150 \u2212 200 vesicles) and used fluorescently labeled lipid (2% ATTO647N-PE), introduced in the vesicles to track the docking and fate of individual vesicles (Fig.\u00a01a and Methods). To trigger the fusion of docked vesicles, we chose a [Ca2+] of 100\u2009\u00b5M. This concentration aligns with the [Ca2+]peak observed at presynaptic vesicular release sites24 and is sufficient to saturate both Syt1 and Syt710, therefore mitigating possible variability stemming from differential activation of Syt1 and Syt7.\n\na In a typical in vitro fusion experiment, vesicles containing VAMP2 (~70 copies) and Syt1 (~20 copies) were added to a suspended bilayer membrane (formed on a silicon substrate with 5\u2009\u03bcm holes) reconstituted with Syntaxin/SNAP25 (1:400 protein-to-lipid ratio) \u00b1Syt7 in the presence of Complexin (2\u2009\u00b5M) in solution. The fate of each vesicle before and after the addition of 100\u2009\u00b5M Ca2+ was monitored by a confocal microscope using a fluorescent (ATTO647N) marker included in the vesicle. b Syt7 (included in the t-SNARE containing bilayer) had no impact on the fusion competence of docked Syt1/VAMP2 vesicles, with ~85% fusing within 5\u2009s after the arrival of 100\u2009\u03bcM Ca2+ signal at or near the docked vesicles. The hatched bar represents the percent fusion occurring with 2 frames (~300\u2009ms) following Ca2+ arrival. c Syt7 altered the Ca2+-triggered fusion kinetics of docked Syt1/VAMP2 vesicles. Top, Representative time-lapse image of Ca2+-evoked fusion of docked vesicles shows that without Syt7 the vesicles fuse rapidly and synchronously following Ca2+ addition. The inclusion of Syt7 (1:200 protein-to-lipid ratio) introduces variable delays in Ca2+-evoked fusion kinetics. Individual vesicles (white circles) docked within 5\u2009\u00b5m suspended bilayer are shown. Bottom, quantitative analysis of Ca2+-evoked fusion of Syt1/VAMP2 vesicles shows that Syt7 introduces a concentration-dependent delay in the Ca2+-evoked fusion kinetics, resulting in a significant reduction in the proportion of vesicles fusing within the first 2 frames (~300\u2009ms) following Ca2+ arrival at time t\u2009=\u20090\u2009s. Data (mean\u2009\u00b1\u2009standard deviation) are from 5 independent experiments (N\u2009=\u20095) for each condition (~40\u201350 vesicles per experiment). One-way ANOVA revealed statistically significant difference (***p\u2009<\u20090.001) in Ca2+-coupled fusion occurring within ~300\u2009ms (hatched bar) between groups. The data from ANOVA and Tukey\u2019s HSD post-hoc comparing specific groups is shown in Supplementary Table\u00a01. The source data is provided as a \u2018Source Data\u2019 file. Figure\u00a01a created in BioRender. Krishnakumar, S. (2023) BioRender.com/f92r389.\n\nIn the absence of Syt7, the majority (~95%) of the Syt1/VAMP2 containing vesicles that docked to the t-SNARE bilayers were \u2018immobile\u2019 and remained unfused during an initial 10\u2009min observation window (Supplementary Fig.\u00a02). Addition of Ca2+ triggered the fusion of ~90% of the stably clamped vesicles within 5\u2009s as measured by lipid mixing (Fig.\u00a01b). Notably, a significant portion of fusion events (~70%) occurred within 2 frames (~300\u2009ms) following the initial arrival of Ca2+ signal (Fig.\u00a01b, c), even though the [Ca2+] reached 100\u2009\u00b5M over time scale of ~750 milliseconds (Supplementary Fig.\u00a03).\n\nInclusion of Syt7 in the t-SNARE-containing bilayer (at concentrations ranging from 1:2000 to 1:200 protein-to-lipid ratio) had no discernable effect on the number or the fate of the docked Syt1/VAMP2 vesicles (Supplementary Fig.\u00a02). Hence, the vast majority (~90%) of the vesicles remained stably docked in an immobile clamped state (Supplementary Fig.\u00a02). Likewise, Syt7 did not impact the Ca2+-induced fusion competence as ~90% of the docked vesicles fused within 5\u2009s following the addition of Ca2+ (100\u2009\u00b5M) (Fig.\u00a01b). However, we observed significant delays in the kinetics of Ca2+-triggered fusion, and these delays correlated with the amount of Syt7 included in the bilayer (Fig.\u00a01c). The proportion of \u2018coupled release\u2019 i.e. vesicles undergoing fusion within 2 frames (~300\u2009ms) following the initial arrival of Ca2+ signal progressively declined from approximately 70% to 10%, as the concentration of Syt7 in the bilayer was increased from 1:2000 to 1:200 (Fig.\u00a01b, c). Noteworthy, this impact was specific to Syt7, as inclusion of Syt1 (instead of Syt7) in the suspended bilayer, even at a protein-to-lipid ratio of 1:200, did not alter the likelihood or kinetics of Ca2+-triggered fusion of Syt1/VAMP2 vesicles (Fig.\u00a01b, c). Taken together, these data indicate that Syt7 influences the kinetics of Ca2+-triggered fusion for Syt1/VAMP2 vesicles in a concentration-dependent manner, without altering the fusion competence of docked vesicles.\n\nThe clamping efficiency of docked vesicles was similar in the absence or presence of Syt7, with approximately 90% of docked vesicles stably clamped under both conditions (Supplementary Fig.\u00a02). Therefore, it remained unclear whether Syt7 also contributes to the fusion clamp (in addition to Syt1 and CPX) under these conditions. Simple removal of Syt1 and/or CPX from the reaction mixture was not feasible, as omitting CPX potentiated spontaneous fusion, while leaving out Syt1 significantly reduced the number of docked vesicles, precluding any meaningful analysis26,30. Hence, we developed reconstitution conditions specifically tailored to investigate the role of Syt7 as a fusion clamp.\n\nIn previous work, we demonstrated that CPX could be omitted under low VAMP2 copy number conditions (i.e. vesicles containing ~13 copies of VAMP2 and ~22 copies of Syt1), as Syt1 alone could produce stably clamped Ca2+-sensitive vesicles27,30. We further showed that disrupting the Syt1-SNARE interaction at the \u2018primary\u2019 interface using the well-established mutations in the Syt1 C2B domain (R281A, E295A, Y338W, R398A, R399A; referred to as Syt1Q)33,34 specifically abrogates the Syt1 fusion clamp without affecting vesicle docking30. Given this background, we investigated Syt7\u2019s impact on the fusion clamp by utilizing the Syt1Q mutant in the CPX-free, low VAMP2 condition (i.e. Syt1Q/VAMP2low vesicles).\n\nAs anticipated, in the absence of Syt7, the majority (>90%) of docked Syt1Q/VAMP2low vesicles fused spontaneously. Inclusion of Syt7 in the bilayer restored the clamp on Syt1Q/VAMP2low vesicles in a dose-dependent manner, with approximately 40% and 90% of vesicles remaining stably docked in an immobile state with Syt7 included at 1:800 and 1:200 (protein-to-lipid ratio) respectively (Fig.\u00a02a). Furthermore, these stably docked vesicles could be triggered to fuse by the addition of 100\u2009\u00b5M Ca2+ (Fig.\u00a02b), but the fusion kinetics were desynchronized from the Ca2+ signal, with a temporally distributed vesicle fusion pattern (Fig.\u00a02c). This suggests that Syt7 can independently establish a calcium-sensitive fusion clamp and may act in conjunction with Syt1 and CPX under physiologically relevant conditions.\n\nThe involvement of Syt7 in the fusion clamp was evaluated using vesicles containing low-copy VAMP2 (~15 copies) and a non-clamping Syt1 mutant, Syt1Q (carrying R281A,E295A,Y338W,R398A,R399A mutations that disrupt the Syt1-SNARE primary interface) in the absence of CPX. a The time between docking and spontaneous fusion was measured for each docked vesicle and the \u2018docking-to-fusion\u2019 latency time was cumulatively expressed as the survival percentage. This \u2018survival analysis\u2019 provided the measure of the strength of the fusion clamp. In the absence of Syt7 (gray), the majority of the docked VAMP2low/Syt1Q vesicles proceed to fuse spontaneously with a half-time of ~1\u2009s. The inclusion of Syt7 in the bilayer resulted in stably docked vesicles in an immobile state, with clamping efficiency correlating with the amount of Syt7 included. Approximately 40% of vesicles were clamped under low Syt7 concentration (1:800, light blue) and this increased to ~90% under high Syt7 concentration (1:200, dark blue). b, c Syt7 clamped VAMP2low/Syt1Q vesicles remained fusion competent and could be triggered to fuse by the addition of Ca2+ (100\u2009\u00b5M) and the observed fusion was desynchronized to the Ca2+ signal. In the absence of Syt7, a very small percent of the docked VAMP2low/Syt1Q vesicles underwent fusion which precluded meaningful kinetic analysis. Data (mean\u2009\u00b1\u2009standard deviation) are from 3 independent experiments (N\u2009=\u20093) for each condition (~40\u201350 vesicles per experiment). One-way ANOVA revealed statistically significant difference (***p\u2009<\u20090.001) in % Ca2+-evoked fusion of docked vesicles in the presence of Syt7 as compared to the condition without Syt7 in the bilayer. The data from ANOVA and Tukey\u2019s HSD post-hoc comparing specific groups are shown in Supplementary Table\u00a02. The source data is provided as a \u2018Source Data\u2019 file.\n\nNext, we investigated the impact of Ca2+-binding-deficient Syt1 and Syt7 mutants to understand the mechanisms behind their synergistic action (Fig.\u00a03). Specifically, we employed Syt1 with D309A, D363A, D365A mutations in the C2B domain (Syt1DA), and Syt7 with D225A, D227A, D233A, D357A, D359A mutations in the C2AB domains (Syt7DA). The introduction of Ca2+-insensitive Syt7DA in the bilayer resulted in a concentration-dependent reduction in Ca2+-evoked fusion of docked vesicles containing Syt1WT/VAMP2, with ~30% decrease at a low (1:800 protein-to-lipid) and ~70% reduction at high (1:200) Syt7DA concentrations (Fig.\u00a03a). However, Syt7DA had no discernable effect on the fusion kinetics, with the majority of vesicles fusing within the first 2 frames following Ca2+ arrival (Fig.\u00a03a).\n\na The inclusion of the Ca2+ binding deficient Syt7 mutant (Syt7DA) in the bilayer inhibited Ca2+ (100\u2009\u00b5M) evoked fusion of Syt1WT/VAMP2 vesicles in a dose-dependent manner, without altering the overall fusion kinetics. b Syt7WT from the bilayer rescued the Ca2+-evoked fusion of Syt1DA /VAMP2 vesicles but the fusion events were desynchronized to the Ca2+ signal. Complexin (2\u2009\u00b5M) in solution was included in all experiments. c Quantitative pull-down and Western-blot analysis with Syt7 as \u2018bait\u2019 and CPX-SNARE complex as \u2018prey\u2019 demonstrate that Syt1 disrupts Syt7-SNARE interaction in a concentration-dependent manner. Data (mean\u2009\u00b1\u2009standard deviation) are from 5 independent experiments (N\u2009=\u20095) for each condition (~40\u201350 vesicles per experiment) in (a) and (b) and from 4 independent experiments (N\u2009=\u20094) in (c). One-way ANOVA revealed statistically significant difference in % Ca2+-evoked fusion of docked vesicles in the presence of Syt7DA (***p\u2009<\u20090.001) or Syt7WT (***p\u2009<\u20090.001) as compared to condition without Syt7 in the bilayer. The data from ANOVA and Tukey\u2019s HSD post-hoc comparing specific groups are shown in Supplementary Tables\u00a03 and 4 respectively. The source data is provided as a \u2018Source Data\u2019 file. Figure\u00a03c (top) created in BioRender. Krishnakumar, S. (2023) BioRender.com/p26b995.\n\nAs expected, the disruption of Ca2+ binding to Syt1 (Syt1DA) eliminated Ca2+-triggered vesicular fusion (~7%) without altering the docking or clamping of the vesicles. However, the inclusion of Syt7WT into the bilayer restored Ca2+-evoked fusion to levels corresponding with the concentration of Syt7WT in the bilayer (~40% and ~75% with 1:800 and 1:200 Syt7WT respectively) (Fig.\u00a03b). Notably, the observed fusion was desynchronized from the Ca2+ signal (Fig.\u00a03b). Taken together, these data suggest that Syt1 and Syt7 act on the same vesicles, likely targeting the same SNARE complexes, and their cooperative action in regulating Ca2+-evoked fusion stems from a competitive binding of Syt1 and Syt7 to the same SNARE complex. Furthermore, these results unequivocally demonstrate that Syt1 acts as a \u2018fast\u2019 Ca2+-sensor to trigger rapid Ca2+-evoked vesicle fusion, whereas Syt7 functions as a \u2018slow\u2019 Ca2+-sensor that mediates release over longer time intervals.\n\nSubsequently, we employed a quantitative pull-down assay to directly test the competitive interaction between Syt1 and Syt7 with the same SNARE complex. While the binding of Syt1 to Syntaxin/SNAP25 is well-documented34,35,36, the Syt7-SNARE interaction remains poorly understood. Hence, we initially conducted a pull-down experiment using Syt7 immobilized on agarose beads as \u2018bait\u2019 and pre-formed CPX-SNARE complex at varying concentrations as the \u2018prey\u2019. Western-blot analysis confirmed direct molecular interaction between the CPX-SNARE complex and Syt7, revealing a saturable dose-response curve with an estimated apparent affinity (Kd)\u2009~\u200920\u2009\u00b5M (Supplementary Fig.\u00a04). We then examined the binding of 30\u2009\u00b5M CPX-SNARE complex to Syt7-coated beads in the presence of varying concentrations (ranging from 1\u2009\u00b5M to 50\u2009\u00b5M) of Syt1. The inclusion of Syt1 disrupted the Syt7-SNARE interaction, resulting in near complete abrogation of binding at Syt1 concentrations \u2265 30\u2009\u00b5M (Fig.\u00a03c). This analysis directly demonstrates the competitive nature of the binding between Syt1 and Syt7 to the SNARE complex. In summary, our data argue that the kinetics of Ca2+-triggered fusion are governed by the number of Syt1 or Syt7 associated SNAREpins, which is in turn determined by the relative abundance of these two proteins.\n\nHow do Syt1 and Syt7 shape the kinetics of vesicular fusion? It has been proposed that the cooperative action of Syt1 and Syt7 in regulating vesicular release can be explained by a \u2018release of inhibition\u2019 model10,11,12. According to this model, Syt1 and Syt7 along with CPX bind to SNAREpins at docked SVs and clamp vesicular fusion at rest. Ca2+ activation of Syt1 and Syt7 leads to the release of the fusion clamp. Thereby, the rate of the Ca2+-triggered removal of the fusion clamp determines the overall efficacy and kinetics of SV fusion (Fig.\u00a04a). The model further posits that the differential Ca2+/membrane binding properties of Syt1 and Syt7, along with the relative numbers of Syt1 or Syt7 bound SNAREs on a given vesicle, fine-tune the release properties in response to Ca2+ signals. Indeed, our experimental data with Ca2+-insensitive Syt1DA and Syt7DA mutants support the \u2018release of inhibition\u2019 model, as both mutants blocked the Ca2+-triggered fusion of docked vesicles, consistent with the model predictions (Fig.\u00a03).\n\na Schematic illustration of the release of inhibition model. At rest, fusion of vesicles is inhibited (\u2018clamped\u2019) by binding of Syt1 and Syt7 along with CPX to partially assembled SNAREpins. Upon Ca2+ binding, the C2 domains of Syt1 and Syt7 insert into the membrane, leading to the removal of the fusion clamp. This allows the complete zippering of the SNARE complexes, resulting in vesicular fusion. Inset shows that two clamp architectures are considered in the default model: dual Syt1/Syt1 or dual Syt1/Syt7 clamp (see Supplementary Fig.\u00a06 for additional clamp architectures tested). b Kinetic reaction schemes describing Ca2+-triggered release of the fusion clamp. Each modeled C2 domain sequentially binds two Ca2+ ions which triggers the insertion of its aliphatic loop into the membrane. Scheme 1 assumes that membrane insertion results in the instantaneous removal of the Synaptotagmin fusion clamp, while Scheme 2 assumes a delay between membrane insertion and the removal of the clamp. S0, S1, S2 refer to 0, 1 or 2 Ca2+ bound state of the C2 domains, while I2 refers to membrane inserted state of the Ca2+-bound C2 domain. The prefixes c and u refer to the \u2018clamped\u2019 or \u2018unclamped\u2019 state respectively. c The time course of vesicular fusion (Model Output) simulated in response to the experimentally constrained Ca2+ signal (Supplementary Fig.\u00a03) for models with different clamp architecture and kinetics of clamp reversal. Experimental data (mean\u2009\u00b1\u2009standard deviation from Fig.\u00a01c) for the Ca2+-triggered fusion of Syt1 containing vesicles in the absence (Syt1EXP) or the presence of saturating levels of Syt7 (Syt7EXP) are plotted for comparison. The model suggests that experimentally observed fusion kinetics can be explained by the mechanism with differential rates of fusion clamp removal for Syt1 (instantaneous) and Syt7 (delayed). For each modeled condition a minimum of 1000 stochastic simulations were performed to calculate the average response. The source data is provided as a \u2018Source Data\u2019 file. Figure\u00a04a created in BioRender. Krishnakumar, S. (2023) BioRender.com/x49d271.\n\nTo investigate whether the differences in Ca2+/membrane binding properties of Syt1 and Syt7 could explain our current results, we adapted the previously developed computational framework12. This modeling framework enables us to simulate SV fusion in response to specific Ca2+ signals for different Synaptotagmin fusion clamp architectures. Drawing on structural studies, within the default model, we assumed that each vesicle contains six SNARE complexes37 and each SNARE complex can bind two Syt1 molecules34. Additionally, we postulated that Syt7 might compete with Syt1 for one of these binding sites. Consequently, we considered two limiting cases for the fusion clamp\u2019s architecture: either Syt1/Syt1 or Syt1/Syt7 (Fig.\u00a04a). These scenarios correspond to experimental conditions without Syt7 or with a saturating level of Syt7 in the lipid bilayer respectively. As in our previous work12, we assumed that Ca2+ binding and membrane loop insertion of the C2B domain of Syt1 or C2A domain of Syt7 leads to the instantaneous removal of the fusion clamp (Fig.\u00a04b, Scheme 1). The release of the clamp enables the full zippering of freed SNAREs, and each SNARE complex independently contributes towards lowering the fusion barrier, thereby catalyzing SV fusion.\n\nAs a model input, we incorporated experimentally estimated changes in [Ca2+] at the lipid bilayer, corresponding to a ramped increase of [Ca2+] from 0 to 100\u2009\u00b5M (Supplementary Fig.\u00a03). In the absence of Syt7 (Syt1/Syt1 clamp), the standard model closely reproduced the kinetics of vesicular fusion in response to the addition of Ca2+ (Fig.\u00a04c). Indeed, the time course of Syt1-mediated vesicular fusion closely follows the kinetics of the [Ca2+] signal (Fig.\u00a04c). This suggests that Ca2+ diffusion is likely the rate-limiting step governing vesicular fusion kinetics under our experimental conditions. However, this model failed to replicate the slower vesicular fusion kinetics observed when Syt7 was included (Syt1/Syt7 clamp).\n\nUnder our experimental conditions, Syt1 and Syt7 are predicted to exhibit comparable Ca2+-activation patterns, with Syt7 being slightly more sensitive than Syt1 (Supplementary Fig.\u00a05). This suggests that the Ca2+-triggered membrane insertion of Syt7 is not the rate-limiting step in the removal of the Syt7 fusion clamp. Consequently, we adapted the model to include a delay between the Ca2+-triggered membrane insertion of the Syt7 C2A domain and the removal of the fusion clamp (Fig.\u00a04b, Scheme 2). This modification allowed us to reconcile the model with the experimental data under Syt1/Syt7 clamp conditions (Fig.\u00a04c).\n\nGiven the ongoing debate surrounding the exact number of SNARE complexes on an RRP vesicle37,38, as well as the architecture of the Synaptotagmin fusion clamp30,34,35, we explored alternative fusion clamp configurations. Specifically, we varied the number of SNAREpins in the vesicles from six to twelve and examined scenarios where a single Synaptotagmin molecule - either Syt1 or Syt7 - could bind to and clamp an individual SNAREpin (Supplementary Fig.\u00a06). As expected, increasing the number of SNAREpins, or using a single clamp instead of a dual clamp accelerated fusion rates at low [Ca2+]. However, at saturating [Ca2+], the models\u2019 outputs closely aligned with those of the default dual-clamp model with six SNAREpins (Supplementary Fig.\u00a06). Taken together, our data argue that the mechanisms of clamp removal are distinct for Syt1 and Syt7 and that the differing rates of clamp removal \u2013 rapid for Syt1 and slower for Syt7 \u2013 are key factors determining the Ca2+ triggered vesicular fusion kinetics.\n\nIn addition to modulating fusion kinetics, Syt7 has also been implicated in the facilitation of synchronous neurotransmitter release during neuronal activity17,18. This short-term plasticity of vesicular release has been linked to the accumulation of [Ca2+]residual in low micromolar range within the presynaptic terminal due to sustained neuronal activity1,15,16. Hence, we investigated the effect of elevated basal [Ca2+]basal on Ca2+-triggered release properties of Syt1/VAMP2 vesicles in the absence and presence of Syt7.\n\nThe inclusion of 0.5\u2009\u03bcM [Ca2+]basal during the vesicle docking phase had little to no effect on vesicle docking or clamping (i.e., spontaneous fusion) of vesicles across all conditions tested (Supplementary Fig.\u00a07). It also did not affect vesicle fusion triggered by 100\u2009\u03bcM [Ca2+] under control (no Syt7 in the bilayer) conditions (Fig.\u00a05a). However, when Syt7 was included in the bilayer (at 1:200 protein-to-lipid ratio), it enhanced the fast component of Ca2+-evoked release (within the first 300 milliseconds after the initial arrival of Ca2+ signal), increasing it from ~8% to ~35%, without changing the overall level of fusion over the 5-second interval (Fig.\u00a05a). Likewise, the computational model incorporating a delay in the removal of the Syt7 fusion clamp (Scheme 2) also replicated the synchronization of vesicular fusion when Syt7 was pre-activated with low micromolar [Ca2+] (Fig.\u00a05b). We only tested 0.5\u2009\u00b5M [Ca2+]basal in our in vitro assay as higher [Ca2+]basal significantly increased spontaneous fusion of docked vesicles, preventing meaningful analysis. However, the degree of synchronization predicted by the computational model correlated with the concentration of Ca2+ utilized for pre-activation (Fig.\u00a05b). Together, these data show that when pre-activated by low micromolar [Ca2+]basal, Syt7 enhances Ca2+-synchronized vesicle fusion.\n\na Comparison of the Ca2+ (100\u2009\u00b5M) evoked fusion characteristics without (green) or with (pink) 0.5 \u03bcM [Ca2+]basal included during vesicle docking reveals that when pre-activated, Syt7 (included in the bilayer at 1:200 protein-to-lipid ratio) increases the proportion of Ca2+-coupled release of Syt1-containing vesicles (Syt1WT/Syt7WT) without changing the overall fusion levels. This enhancement was not observed with Syt1WT alone. Notably, a similar degree of enhancement of Ca2+-synchronized release was observed with vesicles containing Ca2+-binding deficient Syt1DA (Syt1DA/Syt7WT). Data (mean\u2009\u00b1\u2009standard deviation) are from 5 independent experiments (N\u2009=\u20095) for each condition (~40\u201350 vesicles per experiment). Complexin (2\u2009\u00b5M) in solution was included in all experiments. b The dual Syt1/Syt7 clamp model, incorporating the delayed release of the clamp for Syt7, reproduces the experimentally observed enhancement of synchronous release following pre-activation with low micromolar [Ca2+]. The extent of facilitation correlated with the level of pre-activating [Ca2+] used. For each modeled condition a minimum of 1000 stochastic simulations were performed to compute the average response. ***p\u2009<\u20090.001 using the one-sided students\u2019 t-test comparison to the control condition with no [Ca2+]basal. The source data is provided as a \u2018Source Data\u2019 file.\n\nInterestingly, disrupting Ca2+-binding to Syt1 (Syt1DA) did not abolish the Syt7-dependent synchronization of vesicular release with elevated [Ca2+]basal (Fig.\u00a05a). Indeed, we observed a similar proportion of Ca2+-synchronized release between Syt1WT/Syt7 and Syt1DA/Syt7 conditions (Fig.\u00a05a). This indicates that when primed by elevated [Ca2+]basal, Syt7 is capable of independently mediating fast Ca2+-synchronized release.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54960-1/MediaObjects/41467_2024_54960_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54960-1/MediaObjects/41467_2024_54960_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54960-1/MediaObjects/41467_2024_54960_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54960-1/MediaObjects/41467_2024_54960_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54960-1/MediaObjects/41467_2024_54960_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our study presents one of the first in vitro reconstitution of different modes of Ca2+-triggered SV fusion with minimal protein components. We demonstrate that Syt1 and Syt7, along with SNAREs and CPX, are sufficient to recapitulate fast and delayed Ca2+-evoked vesicular fusion as well as Ca2+-dependent facilitation of vesicular release. Our data show that under resting conditions, Syt1, Syt7, CPX work together to arrest SNARE assembly and produce stably, docked RRP-like vesicles. When activated by the Ca2+ signal, the Syt1 triggers the fast component, whilst Syt7 drives the slow component of the resultant vesicular fusion. Mutational analysis with Ca2+-insensitive Syt1 and Syt7 mutants indicates that the synergistic action of Syt1 and Syt7 in the regulation of Ca2+-evoked vesicular fusion can be described by the \u2018release of inhibition\u2019 model, which\u00a0posits that the rate of vesicular fusion is governed by the release of the Syt1 and/or Syt7 fusion clamp upon their Ca2+ activated membrane insertion10,11,12. Furthermore, we observe that the kinetics of Syt7-mediated vesicular fusion are significantly influenced by [Ca2+]basal. We demonstrate that Syt7 is capable of mediating fast, Ca2+-synchronized release, independent of Syt1, when [Ca2+]basal is elevated into low micromolar range. We posit that this phenomenon underlies the critical role of Syt7 in short-term facilitation of fast synchronous release component during sustained neuronal activity1,4.\n\nThe temporal resolution of our fusion assay is limited by the shape of the Ca2+ signal, which ramps up to 100\u2009\u00b5M within approximately 750 milliseconds. Indeed, we find that the rate of fast Syt1-mediated fusion closely mirrors the rate of [Ca2+] increase at the lipid bilayer. Due to technical limitations, our in vitro assay cannot replicate the rapid, millisecond-scale Ca2+ transients evoked by action potentials at the presynaptic active zone. Hence, we utilized a computational modeling framework, capable of simulating vesicular fusion kinetics in response to specific [Ca2+] transients, to relate our findings to neurotransmitter release kinetics in neuronal synapses. The computational implementation of the \u2018release of inhibition\u2019 model with the experimental ramped [Ca2+] signal as an input, closely reproduced the kinetics of Syt1-mediated vesicular fusion observed in our reconstituted fusion assay. Notably, the same model implementation reproduced the millisecond kinetics of vesicular fusion in response to fast [Ca2+] transients observed in live synapses12. This indicates that the reconstituted fusion assays replicate the functionality of the component proteins in living synapses, with comparable operational efficacy. Thus, our data strongly suggest that Syt1 and Syt7 are likely sufficient to describe the synchronous and asynchronous components of AP-evoked neurotransmitter release in neuronal synapses.\n\nThe computational modeling further suggests that the differential effects of Syt1 and Syt7 on vesicular release kinetics can be attributed to the differential strength and kinetics of Ca2+-triggered reversal of their respective fusion clamps. Specifically, our data indicate that activation leads to almost instantaneous removal of the Syt1 fusion clamp but delayed release of the Syt7 clamp. Both Syt1 and Syt7 are expected to bind and clamp the SNARE complexes via their C2B domains34,36. However, the critical distinction in their roles in the regulation of SV fusion arises from the Ca2+ activation of the Syt1 C2B domain compared to the Syt7 C2A domain19. The fast removal of the Syt1 clamp may be attributed to the rapid dissociation of Syt1 from the SNARE complex at the primary interface upon Ca2+-triggered membrane insertion of its C2B domain, as demonstrated in biochemical and structural studies35,36. In contrast, for Syt7 the decoupling of SNARE binding (C2B domain) and Ca2+ activation (C2A domain) may contribute to the slower disassembly of the Syt7 fusion clamp.\n\nWhile our data indicates that Syt1, Syt7, and CPX all are involved in establishing the fusion clamp, the precise molecular composition of the fusion clamp on a RRP vesicle remains unknown. In particular, the clamping function of CPX and Syt7 remains a topic of debate. For example, genetic removal of CPX potentiates spontaneous events in invertebrate model systems39,40, but acute removal of CPX in cultured mouse neurons abates both spontaneous and evoked neurotransmitter release41 suggesting that CPX is principally a positive regulator of fusion in mammalian synapses. However, a recent study showed that CPX mutants that disrupt the clamping function under in vitro conditions26,42 selectively potentiate spontaneous neurotransmitter release, while leaving the evoked release largely untouched42. This suggests that the inhibitory function of CPX might be normally masked by a more pronounced positive function in mammalian synapses. Similarly, in central synapses the genetic deletion of Syt7 does not alter frequency of spontaneous release19. However, the overexpression of Syt7 reverses the increased mini frequency observed in Syt1 knockout neurons19. This suggests that Syt7 can substitute for Syt1 in clamping mini release, but its clamping role may not be apparent under normal physiological conditions due to low expression levels of Syt7 in presynaptic terminals. Additional research is needed to delineate the precise molecular organization of the pre-fusion \u2018clamped\u2019 state and the mechanisms of Ca2+-triggered reversal of the fusion clamp.\n\nAdditionally, we find\u00a0that Syt1 and Syt7 compete to bind the same SNARE complexes. Consequently, the relative abundance of these Ca2+ sensors shape the kinetics and plasticity of Ca2+-evoked vesicular fusion in our assay. This observation offers a mechanistic explanation for physiological data demonstrating that the relative expression levels of Syt1 and Syt7 regulate both the kinetics and plasticity of neurotransmitter release in neuronal synapses43,44. The number of Syt1 per vesicle is tightly controlled (~15\u201320 copies per SV) and local Syt1 concentration under a docked vesicle is estimated to be in the range of 5\u201310\u2009mM45,46. Syt1 interacts with SNARE complex at the primary interface with an affinity of ~10\u2009\u00b5M, suggesting a near-complete saturation of Syt1-SNARE binding under physiological conditions. However, the precise concentration of Syt7 is unknown and varies across synapses. Consequently, the number of Syt1- or Syt7-bound SNAREpins under a given RRP vesicle is likely determined by local abundance of Syt7. This implies that controlling the local abundance of Syt7 molecules could be a straightforward mechanism by which synapses can dynamically adjust the strength and efficacy of synaptic transmission during sustained activity.\n\nWhile the precise mechanism of Syt7-mediated facilitation is not fully understood, it is hypothesized that activation of Syt7 could enhance the release by two different mechanisms: (i) by increasing the probability of RRP vesicles and/or (ii) by enhancing the activity-dependent docking of SVs4,47. Our in vitro reconstitution experiments and modeling demonstrate that, when pre-activated, Syt7 enhances the synchronous release of pre-docked vesicles. Our previous computational modeling study provided insights into the possible molecular mechanisms underlying Syt7-mediated facilitation12. It revealed that the rate of vesicle fusion is dictated by the time taken for three SNAREpins to be released from the fusion clamp on a specific vesicle. Due to its high Ca2+/membrane affinity, Syt7 is partially activated at low micromolar [Ca2+], weakening the fusion clamp. This in turn, accelerates the liberation of three SNAREpins upon the arrival of [Ca2+] signal, thereby enhancing the fast release component. Consistent with this hypothesis, we observed a comparable level of facilitation when [Ca2+]basal levels were elevated either during or after the vesicles reached the immobile clamped state (Supplementary Fig.\u00a07).\n\nWe note that, apart from the distinct Ca2+-release sensors, other proteins and mechanisms also play a role in regulating the timing and plasticity of neurotransmitter release. For example, the genetic deletion of Syt7 does not fully eliminate asynchronous release or short-term facilitation in some synapses43,48. Consequently, it is suggested that other Ca2+-release sensors (e.g., Syt1, Syt3 and Doc2A) may also contribute to the asynchronous release component21,22,23,49. Additionally, short-term facilitation of synchronous neurotransmitter release may result from enhanced Ca2+ transients at release sites, driven by the saturation of Ca2+ buffers during repetitive activity25,50. Furthermore, the strength and efficacy of neurotransmitter release is also regulated by Ca2+-dependent SV docking, priming, and recycling. As such, SV priming factors (e.g. RIM, Munc13) could also influence neurotransmitter release dynamics9,51. Nonetheless, our results highlight the central role of Syt1 and Syt7 in decoding presynaptic Ca2+ dynamics and translating this into complex patterns of vesicular release.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "In this study, we used the following clones that have been described previously26,30 including full-length VAMP2 (human VAMP2-His6, residues 1\u2013116); full-length t-SNARE complex (mouse His6-SNAP25B, residues 1\u2013206 and rat Syntaxin1A, residues 1\u2013288); CPX (human His6-Complexin 1, residues 1\u2013134); Syt1 wild-type (rat Synaptotagmin1-His6, residues 57-421) and mutants (D309A, D 363A, D365A; Syt1DA) and (R281A, E295A,Y338W,R398A,R399A, Syt1Q) in the same background.\n\nOur initial experiments were conducted with the full-length Syt7 wild-type protein (His6-SUMO-rat Synaptotagmin-7, residues 17\u2013403). However, this construct posed technical challenges due to its low and highly variable membrane reconstitution efficiency. Hence, we modified the construct by adding a second transmembrane domain (TMD) from Syt1 with a flexible 16 residue GSGS linker, resulting in His6-SUMO-Syt1TMD-Syt7 construct (referred to as Syt7WT in this manuscript). The inclusion of Syt1TMD (in addition to the Syt7TMD) improved the reconstitution efficiency of the Syt7WT protein into the membrane, while the flexible GSGS linker ensured the proper orientation of the two TMDs and Syt7 C2AB domains (Supplementary Fig.\u00a01). Control experiments showed that effect of Syt7, whether containing one or two TMDs, on Ca2+-evoked fusion of Syt1/VAMP2 vesicles were indistinguishable (Supplementary Fig.\u00a01).\n\nWe also generated Syt7 mutant (D225A, D227A, D233A, D357A, D359A; Syt7DA) in the same background. We purchased the cDNA to produce the SUMO nanobody (nanoCLAMP SMT3-A1) from Nectagen (Lawrence, KS). The lipids used in the study, including 1,2-dioleoyl -snglycero-3-phosphocholine (DOPC), 1,2-dioleoyl-sn-glycero-3- (phospho-L-serine) (DOPS), and phosphatidylinositol 4, 5-bisphosphate (PIP2) were purchased from Avanti Polar Lipids (Alabaster, AL). ATTO647N-DOPE and ATTO465-DOPE were purchased from ATTO-TEC, GmbH (Siegen, Germany) and Calcium Green conjugated to a lipophilic 24-carbon alkyl chain (Calcium Green C24) was purchased from Abcam (Cambridge, UK). All other research materials and consumables, unless specified, were purchased from Sigma-Aldrich (St Louis, MO) and Thermo Fisher Scientific (Waltham, MA)\n\nAll proteins were expressed and purified in a bacterial expression system as described previously26,30 (Supplementary Fig.\u00a01). In summary, proteins were expressed in E. coli BL21(DE3) cells (Novagen, Madison, WI) under 0.5\u2009mM IPTG induction for 4\u2009h. Bacterial cells were pelleted and then lysed using a cell disruptor (Avestin, Ottawa, Canada) in lysis buffer containing 25\u2009mM HEPES, 400\u2009mM KCl, 4% Triton X-100, 10% glycerol, pH 7.4 with 0.2\u2009mM Tris[2-carboxyethyl] phosphinehydrochloride (TCEP), and EDTA-free Complete protease inhibitor cocktail (Merck, Rahway, NJ). The resulting lysate was clarified using a 45Ti rotor (Beckman Coulter, Atlanta, GA) at 40,000 RPM for 30\u2009min and subsequently incubated with pre-equilibrated Ni-NTA resin overnight at 4\u2009\u00b0C. The resin was washed with wash buffer containing 25\u2009mM HEPES pH 7.4, 400\u2009mM KCl, 0.2\u2009mM TCEP. The wash buffer was supplemented with 1% octylglucoside (OG) for Syt1 and SNARE, and with 0.2% Triton-X-100 for Syt7. Proteins were eluted from beads using 400\u2009mM Imidazole and their concentrations were determined using a Bradford Assay (BioRad, Hercules, CA) with BSA standard. The Syt1 and Syt7 proteins were further treated with Benzonase (Millipore Sigma, Burlington, MA) at room temperature for 1\u2009h with Syt1 additionally being run through ion exchange (Mono S) to remove DNA/RNA contamination. SDS-PAGE analysis was done to check the purity of the proteins, and all proteins were flash-frozen in small aliquots and stored at \u221280\u2009\u00b0C with 10% glycerol.\n\nSmall unilamellar vesicles containing VAMP2 and Syt1 were prepared using rapid detergent dilution and dialysis method, followed by additional purification on discontinuous Optiprep gradient by ultracentrifugation26,30. To mimic synaptic vesicle lipid composition, we used 88% DOPC, 10% DOPS, and 2% ATTO647N-PE, with the protein-to-lipid input ratio of 1:100 for VAMP2 for physiological density, 1:500 for VAMP2 at low copy number, and 1:250 for Syt1. Informed by previous work26,30 that characterized the reconstitution efficiency and inside/outside ratio of these proteins, we estimate the vesicle contains ~70 copies of outside facing VAMP2 and ~20 copies of outside facing Syt1 (at physiological conditions) and ~15 copies of VAMP2 and ~20 copies of Syt1 (under low VAMP2 conditions).\n\nTo form the suspended lipid bilayer, we first prepared giant unilamellar vesicles (GUVs) containing t-SNARE \u00b1 Syt7 were prepared using the osmotic shock protocol as described previously52. To mimic the presynaptic plasma membrane, the lipid composition of the GUVs was 80% DOPC, 15% DOPS, 3% PIP2%, and 2% ATTO465-PE. The t-SNARE complex (1:1 Syntaxin/SNAP25) was included at the protein-to-lipid input ratio of 1:200 to yield a final concentration of 1:400. Incorporating the t-SNARE complex enabled us to circumvent the necessity for the SNARE-assembling chaperones Munc18 and Munc1353. When warranted, Syt7 was added at a protein-to-lipid input ratio of 1:50, 1:100, 1:200, and 1:1000 to yield the defined concentrations of Syt7 tested, based on the reconstitution efficiency (~50%) and 50-50 inside/outside ratio determined by protease (Chymotrypsin) accessibility assay (Supplementary Fig.\u00a01).\n\nSubsequently, t-SNARE (\u00b1Syt7) containing GUVs were burst on freshly plasma-cleaned Si/SiO2 chips decorated with a regular array of 5\u2009\u00b5m diameter holes in HEPES buffer (25\u2009mM HEPES, 125\u2009mM KCl, 0.2\u2009mM TCEP, 5\u2009mM MgCl2 pH 7.4). The bilayers were then extensively washed with the same HEPES buffer containing 1\u2009mM MgCl2. For each experiment, the fluidity of the bilayers was verified using FRAP of the Atto-465 fluorescence (Supplementary Fig.\u00a08). As a control, we tested and confirmed that the mobility of Alexa488 labeled t-SNAREs is not affected by the inclusion of Syt7 (Supplementary Fig.\u00a08).\n\nThe vesicle docking and fusion experiments were carried out as described previously26,28,30. Typically, in each experiment, approximately 100\u2009nM lipids worth of vesicles, along with CPX (2\u2009\u00b5M final concentration) were added using a pipette and then allowed to interact with the suspended bilayer for 5\u2009mins. ATTO647N-PE fluorescence was used to track the fate of individual vesicles, i.e. vesicle docking, post-docking diffusion, docking-to-fusion delays, and spontaneous fusion events. Docked immobile vesicles that remained un-fused during the initial 10\u2009min observation period were defined as \u2018clamped\u2019. Fusion was identified as a sharp, rapid decrease in fluorescence intensity, as the lipids from the vesicles diffused into the bilayer. After the initial 5-minute observation period, the excess vesicles in the chamber were removed by buffer exchange, and 100\u2009\u00b5M CaCl2 was added to quantify the Ca2+-triggered fusion of the pre-docked vesicles. To cover large areas of the planar bilayer and simultaneously record lipid mixing in large ensembles of vesicles (~40\u201350 per experiment), the movies were acquired at a speed of 147\u2009ms per frame.\n\nCa2+ typically reached the vicinity of vesicles docked on the bilayer approximately 1-2 frames post-addition26,30 and this correlated with the minima of the transmittance signal (Supplementary Fig.\u00a03). For select experiments, we also included Calcium Green C24 in the bilayer to directly quantify the arrival of Ca2+ at the bilayer and confirmed that it matched with the transmittance signal change (Supplementary Fig.\u00a03). As Calcium-green is a high-affinity Ca2+ sensor (Kd of ~100\u2009nM), its fluorescence signal is typically saturated within a single frame following the arrival of Ca2+ at the bilayer (Supplementary Fig.\u00a03). Hence, we utilized a soluble Alexa647 dye (~25\u2009nM) mixed with 100\u2009\u03bcM CaCl2 to track the diffusion of Ca2+ into the chamber. Assuming similar diffusion of Ca2+ and the Alexa647 dye, the changes in Alexa647 fluorescence provided a reliable indicator for estimating alterations in the [Ca2+] signal at or near the vesicles docked on the bilayer (Supplementary Fig.\u00a03).\n\nAll experiments were carried out at 37\u2009\u00b0C using an inverted laser scanning confocal microscope (Leica-SP5) equipped with a multi-wavelength argon laser including 488\u2009nm, diode lasers (532\u2009nm and 641\u2009nm), and a long-working distance 40X water immersion objective (NA 1.1). The emission light was spectrally separated and collected by photomultiplier tubes.\n\nTo investigate the binding of Syt7 to SNAREs and assess the competitive binding of Syt1/Syt7 to the same SNARE complex, we used a pull-down analysis coupled with western-blot analysis. Briefly, we purified a SUMO-nanobody and covalently attached it to a CNBR-activated Sepharose resin. The nanobody-Sepharose resin was incubated (4\u2009hr at 4\u2009\u00b0C) with SUMO-Syt7 protein and subjected to extensive wash with HEPES buffer (50\u2009mM HEPES, 400\u2009mM KCl, 0.2\u2009mM TCEP, 0.2% Triton-X-100, pH 7.4) to form the \u2018bait\u2019. The CPX-SNARE complex was assembled and purified on the Superdex-200 column as described previously54,55 in the HEPES buffer and used as the \u2018prey\u2019. For the binding experiment, ~15\u2009\u00b5M of Syt7-resin was incubated with pre-formed CPX-SNARE complex at varying (1\u201350\u2009\u00b5M) concentrations overnight at 4\u2009\u00b0C with minimum agitation. The resin was washed extensively (5X) with HEPES buffer, followed by a stringent wash with HEPES buffer containing 1\u2009M KCl to eliminate unbound proteins. The resin samples were subjected to SDS-PAGE gel electrophoresis, followed by western blotting using a Syntaxin monoclonal antibody (Abcam, Cambridge, UK) to quantify the amount of SNARE bound to the Syt7-resin. We used the same protocol for the competition assay, with the following modification: 15\u2009\u00b5M Syt7-resin was incubated with 30\u2009\u00b5M of CPX-SNARE complex, along with 1\u201360\u2009\u00b5M Syt1 included in the solution overnight at 4\u2009\u00b0C with minimum agitation.\n\nVesicular fusion in response to experimentally estimated changes in [Ca2+] was simulated using the computational modeling framework established in our previous work12. [Ca2+] stimulation profile was approximated based on the diffusion kinetics of Alexa 647 (Supplementary Fig.\u00a03). Each RRP vesicle was associated with either six or twelve partially assembled SNAREpins which were clamped in this state by either one (Syt1 or Syt7) or two Synaptotagmins (Syt1/Syt1 or Syt1/Syt7). The dynamics of each Synaptotagmin C2 domain were described by the Markov kinetic schemes shown in Fig.\u00a04b using the parameters we previously constrained. We assumed that kon was limited by diffusion to 1\u2009\u00b5M\u22121 ms\u22121 and koff\u2009=\u2009150\u2009ms\u22121 based on the intrinsic Ca2+ affinity Kd\u2009=\u2009150\u2009\u00b5M, which is similar for both Syt1 and Syt7 C2 domains20,56,57. kin\u2009=\u2009100\u2009ms\u22121 based on the characteristic time for Synaptotagmin C2 domain rotation and membrane insertion45. kout\u2009=\u20090.67\u2009ms\u22121 for Syt1 and kout\u2009=\u20090.02\u2009ms\u22121 for Syt7 were determined from the apparent rates of C2 domain dissociation from lipid membranes (kdiss) measured in the presence of EGTA using stopped-flow experiments57,58,59 as described in our previous work12. In reaction Scheme 1 (Fig.\u00a04b), we considered that C2 domain membrane insertion leads to the instantaneous release of the fusion clamp. In reaction Scheme 2 (Fig.\u00a04b), we assumed that a delay between the Ca2+-triggered membrane insertion of the Syt7 C2A domain and the removal of the fusion clamp is described by a first-order reaction with the rate ku\u2009=\u20090.0002\u2009ms\u22121 (this value was chosen as it best fits the experimental data within the tested ku range of 0.0001\u22120.01\u2009ms\u22121). We further assumed that the clamp could be restored following membrane dissociation at an identical rate kc\u2009=\u20090.0002\u2009ms\u22121. The rate of vesicular fusion was determined by assuming that the repulsive forces between a docked vesicle and the plasma membrane amount to an energy barrier E0\u2009=\u200926 kBT60. Once both of its Synaptotagmins are in an unclamped state a SNAREpin contributes \u0394E\u2009=\u20094.5 kBT of work towards overcoming this energy barrier61. With n uninhibited SNAREpins the fusion barrier is spontaneously overcome through thermal fluctuations at a rate given by the Arrhenius equation\u00a0\\({R}_{rate}(n)=A\\cdot \\exp (-\\frac{{E}_{0}-n\\varDelta E}{{k}_{B}T})\\), where the pre-factor A\u2009=\u20092.17\u2009\u00d7\u2009109\u2009s\u22121 considering that a single SNARE complex can mediate fusion in vitro on a timescale of 1 sec45,61. Monte Carlo estimates of the cumulative probability of vesicle fusion in response to a Ca2+ activation signal were derived from at least 1000 stochastic simulations of individual vesicles in all scenarios. We estimate that this restricts prediction error due to stochastic variation to less than 1%. All simulations were carried out in MATLAB 2020b (The MathWorks Inc.) and Python 3.10. To accommodate the observed variability in the timing of Ca2+ signal arrival, which can span up to three frames under our experimental conditions (see Supplementary Fig.\u00a03), we applied a temporal blurring of the model output, by smoothing the data across a time window of 0.45\u2009s, equivalent to the duration of three imaging frames.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All relevant data that support the findings of this study has been included in the \u2018Source Data\u2019 file. Additional supporting information is available from the corresponding author upon request. Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The MATLAB and Python codes used in this study has been deposited in the GitHub repository: (https://github.com/ChrisAlexNorman/SytSim_Matlab); (https://github.com/ChrisAlexNorman/SytSim).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Kaeser, P. S. & Regehr, W. G. Molecular mechanisms for synchronous, asynchronous, and spontaneous neurotransmitter release. Annu Rev. Physiol. 76, 333\u2013363 (2014).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nSudhof, T. C. Neurotransmitter release: the last millisecond in the life of a synaptic vesicle. Neuron 80, 675\u2013690 (2013).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nRegehr, W. G. Short-term presynaptic plasticity. Cold Spring Harb. Perspect. 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USA 116, 2435\u20132442 (2019).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "We are grateful to Drs Dimitri Kullmann, James Rothman, and Dmitri Rusakov for reading the manuscript and providing critical feedback. This work was supported by National Institute of Health (NIH) grant NS133091 (S.S.K. and K.E.V.); UKRI MRC Project Grant MR/T002786/1 (Y.T. and K.E.V.); UKRI BBSRC/NC3R Project Grant NC/X002233/1 (K.E.V.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Dipayan Bose, Manindra Bera.\n\nNanobiology Institute, Yale University, West Haven, CT, USA\n\nDipayan Bose,\u00a0Manindra Bera\u00a0&\u00a0Shyam S. Krishnakumar\n\nDepartment of Neurology, School of Medicine, Yale University, New Haven, CT, USA\n\nDipayan Bose\u00a0&\u00a0Shyam S. Krishnakumar\n\nDepartment of Cell Biology, School of Medicine, Yale University, New Haven, CT, USA\n\nManindra Bera\u00a0&\u00a0Kirill E. Volynski\n\nDepartment of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK\n\nChristopher A. Norman,\u00a0Kirill E. Volynski\u00a0&\u00a0Shyam S. Krishnakumar\n\nDepartment of Computer Science, University of Warwick, Coventry, UK\n\nChristopher A. Norman\u00a0&\u00a0Yulia Timofeeva\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.S.K. and K.E.V. conceived the project; D.B. and M.B. carried out the in vitro functional analysis; C.A.N. and Y.T. contributed to the implementation of the computational model; C.A.N. performed all model simulations. S.S.K. and K.E.V. wrote the manuscript. All authors discussed the results and commented on the manuscript.\n\nCorrespondence to\n Kirill E. Volynski or Shyam S. Krishnakumar.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Minimal presynaptic protein machinery governing diverse kinetics of calcium-evoked neurotransmitter release.\n Nat Commun 15, 10741 (2024). https://doi.org/10.1038/s41467-024-54960-1\n\nDownload citation\n\nReceived: 23 March 2024\n\nAccepted: 25 November 2024\n\nPublished: 30 December 2024\n\nVersion of record: 30 December 2024\n\nDOI: https://doi.org/10.1038/s41467-024-54960-1\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Mucociliary Clearance In Human Airways", + "journal": "Nature Communications", + "published": "12 March 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_MOESM4_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_MOESM5_ESM.mp4" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_MOESM6_ESM.mp4" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_MOESM7_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_MOESM8_ESM.zip" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.6084/m9.figshare.24989700", + "/articles/s41467-025-57667-z#Sec26" + ], + "code": [ + "/articles/s41467-025-57667-z#Fig3", + "/articles/s41467-025-57667-z#Fig5", + "/articles/s41467-025-57667-z#MOESM1", + "/articles/s41467-025-57667-z#MOESM1", + "/articles/s41467-025-57667-z#MOESM1", + "/articles/s41467-025-57667-z#MOESM1", + "/articles/s41467-025-57667-z#MOESM1", + "https://doi.org/10.5281/zenodo.14684411" + ], + "subject": [ + "Biophysics", + "Data integration", + "Mechanisms of disease" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4164522/v1.pdf?c=1741777749000", + "research_square_link": "https://www.researchsquare.com//article/rs-4164522/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-57667-z.pdf", + "preprint_posted": "24 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Our study focuses on the intricate connection between tissue-level organization and ciliated organ function in humans, particularly in understanding the morphological organization of airways and their role in mucociliary clearance. Mucociliary clearance is a key mechanical defense mechanism of human airways, and clearance failure is associated with many respiratory diseases, including chronic obstructive pulmonary disease (COPD) and asthma. While single-cell transcriptomics have unveiled the cellular complexity of the human airway epithelium, our understanding of the mechanics that link epithelial structure to clearance function mainly stem from animal models. This reliance on animal data limits crucial insights into human airway barrier function and hampers the human-relevant in vitro modeling of airway diseases. This study, for the first time, maps the distribution of ciliated and secretory cell types along the airway tree in both rats and humans, noting species-specific differences in ciliary function and elucidates structural parameters of airway epithelia that predict clearance function in both native and in vitro tissues alike. By uncovering how tissue organization influences ciliary function, we can better understand disruptions in mucociliary clearance, which could have implications for various ciliated organs beyond the airways.Biological sciences/BiophysicsBiological sciences/Cell biology/Mechanisms of diseaseBiological sciences/Computational biology and bioinformatics/Data integration", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "RothSahin2024SIMethods.pdfRothSahin2024SITablesFigures.pdfSIVideoS1HumanAirway.mp4Video S1SIVideoS2RatAirway.mp4Video S2SIVideoS3InVitroCiliaXClearance.mp4Video S3", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Mucociliary clearance is a vital defense mechanism of the human airways, protecting against harmful particles and infections. When this process fails, it contributes to respiratory diseases like chronic obstructive pulmonary disease (COPD) and asthma. While advances in single-cell transcriptomics have revealed the complexity of airway composition, much of what we know about how airway structure impacts clearance relies on animal studies. This limits our ability to create accurate human-based models of airway diseases. Here we show that the airways in female rats and in humans exhibit species-specific differences in the distribution of ciliated and secretory cells as well as in ciliary beat, resulting in significantly higher clearance effectiveness in humans. We further reveal that standard lab-grown cultures exhibit lower clearance effectiveness compared to human airways, and we identify the underlying structural differences. By combining diverse experiments and physics-based modeling, we establish universal benchmarks to assess human airway function, interpret preclinical models, and better understand disease-specific impairments in mucociliary clearance.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Mucociliary clearance (MCC) is a critical mechanical barrier mechanism of the human airways1,2,3. In MCC, the beating of specialized multiciliated cells propel a layer of mucus across the epithelial surface, effectively trapping and removing inhaled particles and pathogens4,5. The mucus is produced by the submucosal glands and a variety of secretory cells interspersed among ciliated cells. These secretory cells include mucin-secreting goblet cells, club cells, and their intermediate stages6. The composition of secretory cell types has a profound impact on the rheology and flowability of mucus7,8,9. Failure of MCC contributes to the debilitating pathophysiology of many respiratory diseases, including chronic obstructive pulmonary disease (COPD), primary ciliary dyskinesia, asthma, and cystic fibrosis10,11. However, our understanding of how diseases impair MCC remains limited by an incomplete knowledge of the mechanics that link secretory and ciliated cell organization to MCC in native human airway tissues3 since most in vivo and ex vivo data has come from animal models12,13,14. Obtaining data on live ciliary beat and clearance function in human tissue poses challenges and remains exceedingly rare in the literature15,16. Furthermore, the organization of secretory and ciliated cells at the epithelial surface is underexplored, as conventional histology typically reveals cross-sectional rather than surface-lining tissue architecture. While providing important insights about cellular heterogeneity, transcriptional profiling commonly does not resolve spatial organization17,18 and may not accurately reflect protein expression19.\n\nThe lack of such crucial data limits not only our understanding and capability to model human MCC but may also undermine correct diagnosis and early intervention in disease. For instance, a recent study revealed that while luminal surface ciliation in the mouse trachea is relatively low (~40%), the specific distribution and orientation of ciliary beat ensure robust and efficient particle clearance14. However, the authors could only speculate whether these design principles directly translate to humans, where such quantitative insights could help refine treatments for conditions impairing ciliary beat, such as PCD20, or causing loss of ciliated cells, such as asthma21. The limited human-relevancy of animal studies has spurred the development of innovative in vitro human airway epithelial models22,23,24. Nevertheless, without quantitative benchmarks of MCC in humans, the ability of these models to accurately represent human physiology remains unproven. Furthermore, the proportions of ciliated and various secretory cell types are believed to vary along the airway tree within and between humans and rats25. It remains unclear whether this heterogeneity results in species-specific regional differences in MCC, which is pertinent for studying diseases that alter the regional proportions of airway epithelial cell types26.\n\nTo address these gaps, we have established a quantitative map of the distribution of ciliated, club, and goblet cells along the surface lining of the human and rat airway tree. We have evaluated species-specific differences in the associated particle clearance and utilized these data to develop quantitative metrics and physics-based computational models capturing key MCC characteristics in lower airway epithelia. Finally, we deploy our comprehensive quantitative framework to benchmark the structural and functional properties of a variety of in vitro human airway epithelial culture conditions against native human airway epithelia. The data presented significantly enhances our understanding of MCC mechanics and, by providing means of MCC quantification and quality control, will improve the translational potential of in vitro models for studying diseases and therapeutic interventions affecting MCC.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Live ciliary beat and particle clearance15,27 was measured in freshly isolated airway epithelial tissue originating from the ventral wall of the respiratory tree branching generation (BG)0 through BG6 derived from 4 human whole lungs with no prior history of chronic lung disease, and BG0 through BG5 from healthy rat whole lungs (Fig.\u00a01a; for detailed donor information and numbers see Supplementary Tables\u00a01 and 2). The human BG6 samples were at, or below, 2\u2009mm in diameter (Supplementary Fig.\u00a01) and therefore constituted the transition to small airway morphology28. While such a definition of small airways does not exist for rodents, our analysis covered a comparable fraction of the conducting airways along the proximal-distal axis in both systems (7 BGs of 16 BGs in humans29, and 6 BGs of 14 BGs in rats30). Subsequently, we quantified the luminal (i.e., surface-lining) cell type composition in these samples and, to increase donor numbers, in additional fixed human bronchial rings. Where unknown, their BG was estimated based on their diameters (Supplementary Table\u00a01) using a calibration curve (Supplementary Fig.\u00a01). The percentage of cells at the mucosal surface expressing cell-specific markers was quantified using mucin 5AC (MUC5AC) to mark goblet cells, secretoglobin family 1\u2009A member (SCGB1A1) to label club cells, and acetylated alpha-tubulin (ATUB) to stain ciliated cells4 (Fig.\u00a01b, c). Luminal cell boundaries were revealed by F-actin staining of the cytoskeleton, allowing us to segment the cell outlines using image processing and thereby determine total cell numbers in each field of view. By overlaying the signal of each marker onto the cell outlines, we computed the percentages of different cell types and their overlaps (See Methods and Supplementary Figs.\u00a02 and 3). The results are summarized in Fig.\u00a01d, for details see Supplementary Tables\u00a03 and 5. Intriguingly, human airways exhibited a robustly high ciliated cell proportion, or cilia coverage, of 86\u2009\u00b1\u20099% (mean\u2009\u00b1\u2009STD) across all analyzed BGs, whereas cilia coverage in rat airways gradually increased along the airway tree, ranging from 49\u2009\u00b1\u200912% in trachea to 92\u2009\u00b1\u20092.3% in BG5. Of note, the trachea in rat exhibited a pattern of alternating lower (ca. 30\u201340%) and higher (ca. 45\u201360%) ciliation levels, corresponding to cartilage rings and the interspersed tracheal ligaments, respectively31. We did not observe such regular ciliation patterns in the human airways (Supplementary Fig.\u00a04). The dense luminal ciliation of the human airways contrasted with results obtained from classic histology or gene expression data, which, likely by including both luminal and subluminal cell populations in the analysis, have suggested a lower ciliation level of 30\u201350% in human trachea and increasing ciliation in higher BGs25,32,33,34,35 (Supplementary Fig.\u00a04). Our data hence suggest that surface ciliation cannot be easily predicted from bulk tissue percentages. On the other hand, our analysis of secretory cell levels matched trends reported in these studies. In all human BGs examined, 10\u201320% of luminal cells were positive for either MUC5AC, SCGB1A1, or both. Within this secretory cell population, the proportion of MUC5AC\u2009+\u2009SCGB1A1- (goblet) and MUC5AC- SCGB1A1+ (club) cells varied as a function of BG (Fig.\u00a01d, inset). Goblet cells dominated the trachea-bronchial region whereas club cells became increasingly frequent in BG3 and higher. On average, 15 and 30% of the labeled secretory population were MUC5AC\u2009+\u2009SCGB1A1+ (double-positive hybrid/transitionary) cells, consistent with transcriptional and histological studies32,36,37 (for additional staining see Supplementary Fig.\u00a06A, B). A comparable analysis was inconclusive for the rat secretory cells due to the spatially variable, but overall low (<10%), abundance of cells positive for MUC5AC and/or SCGB1A1, which matches literature25. As noted by others31, there was spatial variability of secretory cell density (Supplementary Fig.\u00a06C). Since rat airways are thought to contain a high abundance of so-called \u201cserous\u201d secretory cells25 for which no protein markers have been reported, additional secretory cells could be present.\n\na Airway branching generations in human and rat investigated in this study included BG0-6 in humans and BG0-5 in rats. b Workflow for imaging luminal epithelial cell type composition and ciliary beat and clearance function in airway samples. Schematic: Created in BioRender. Nawroth, J. (2025) https://BioRender.com/a01i578). c Example IF staining of cilia (ATUB, magenta) and secretory cells (SCGB1A1, green; MUC5AC, gray) in human and rat airway epithelium in BG0 (trachea) and BG5/6. Scale bar: 20\u2009\u00b5m. d Quantification of luminal cell proportions labeled with ATUB (ciliated cells) or with MUC5AC and/or SCGB1A1 (secretory cells) as a function of airway branching generation in human and rat airway epithelium. Inset: Percentage of human secretory cell population positive for only MUC5AC (gray), only for SCGB1A1 (green), or for both (white) as a function of branching generation. Solid line: mean, shaded region: SEM. Numbers of human donors (2\u20133 FOVs each): BG0, n\u2009=\u20093; BG1, n\u2009=\u20093; BG2, n\u2009=\u20095; BG3, n\u2009=\u20093; BG4, n\u2009=\u20097; BG5, n\u2009=\u20092; BG6, n\u2009=\u20092; Numbers of rat donors (2\u20133 FOVs each): BG0, n\u2009=\u20095; BG1, n\u2009=\u20093; BG2, n\u2009=\u20092; BG4, n\u2009=\u20091; BG5, n\u2009=\u20091. For full donor information see Supplementary Tables\u00a01 and 2. Source data for (d) are provided as a Source Data file.\n\nThe differences in cilia coverage between species suggested differences in particle clearance, which we proceeded to explore by measuring live ciliary beat and fluorescent bead transport in the densely ciliated human airways (BG0 to BG6) compared to the more sparsely ciliated rat airways (BG0 and BG1). (Fig.\u00a02a, b, Supplementary Movie S1 and S2). These regions correspond to the trachea, main stem bronchi, and bronchial regions in humans and rats. Matching cell type composition analysis, we sampled multiple fields of views along the ventral airway tube. As the human explant tissues had been submerged in buffer for many hours prior to live recordings, the naturally occurring periciliary liquid and mucus layers were likely diluted or removed. Hence, in order to enable a direct comparison of ciliary beat and clearance between human explants and other samples, we chose to gently wash all apical surfaces to remove any build-up of mucus, and to then image the samples submerged in aqueous buffer. Ciliary beat frequency (CBF) recorded at room temperature was 2.6\u2009\u00b1\u20090.5\u2009Hz in human tissue and 4.5\u2009\u00b1\u20091.3\u2009Hz in rat tissue. Associated particle clearance speed reached 16.5\u2009\u00b1\u20098.2\u2009\u00b5m\u2009s\u22121 in humans and 4.8\u2009\u00b1\u20092.0\u2009\u00b5m\u2009s\u22121 in rats. Therefore, clearance speed was significantly higher in humans despite significantly lower CBF than in rat tissues. To assess this imbalance further, we derived the normalized \u201cclearance speed per beat\u201d (CPB) by dividing the average clearance speed by the average CBF for each field of view. CPB measures how far a particle is transported per ciliary beat cycle and has the units \u00b5m per beat. CPB can be considered a measure of stroke effectiveness and is independent of CBF. Our analysis revealed a significantly higher CPB in human tissue (6.2\u2009\u00b1\u20092.5\u2009\u00b5m per beat) compared to rat tissue (1.1\u2009\u00b1\u20090.3\u2009\u00b5m per beat) (Fig.\u00a02c). We also investigated particle clearance directionality D as a function of distance R, defined as \\(D\\left(R\\right)=\\frac{|\\left\\langle {{{\\bf{v}}}}\\right\\rangle |}{\\left\\langle |{{{\\bf{v}}}}|\\right\\rangle },\\) where \u3008\u3009 indicates the mean, || indicates the magnitude, and \\({{{\\bf{v}}}}\\) indicates flow velocity. D(R) ranges from 0 to 1, where a number near zero indicates highly convoluted flow and a number near 1 indicates straight and unidirectional flow, and it decays with increasing distance. Each trace was fitted with a decaying exponential, \\(D\\left(R\\right)\\,{e}^{\\frac{-R}{{R}_{0}}}\\), to estimate the correlation length R0. This analysis revealed a similar characteristic length scale of the correlated flow in human (R0\u2009=\u200923\u2009\u00b1\u20098\u2009\u03bcm) and rat airways (R0\u2009=\u200929\u2009\u00b1\u20097\u2009\u03bcm) (Fig.\u00a02d, left). We also compared mean particle clearance directionality over multiple cell lengths at R\u2009=\u200980\u2009\u03bcm, which was significantly higher in human compared to rat tissues, indicating straighter transport (Fig.\u00a02d, right). All measurements were completed in tissues from healthy, adult lungs that were washed, mucus-free and submerged in aqueous saline buffer, suggesting that the differences in CPB and clearance directionality were due to species-specific properties of ciliary beat and organization14 rather than defective38 or immature ciliary beat16, or altered mucus properties39,40.\n\na Representative measurement of ciliary beat frequency (CBF) and associated particle clearance trajectories and speed in a human airway epithelial sample (BG2). b Same measurements in rat airway epithelial sample (BG1). Scalebar in (a, b): 100\u2009\u00b5m. c Quantification of average CBF, particle clearance speed, and clearance per beat (CPB) in human airways BG0-6 and rat airways BG0-1. Number of human donors: n\u2009=\u20094. Number of rat donors: n\u2009=\u20096. d left: Clearance directionality as a function of distance in human (red) and rat (blue) airways. Thick lines are average curves. Right: Mean directionality over a flow distance of 80\u2009\u00b5m. Number of human donors n\u2009=\u20094; number of rat donors: n\u2009=\u20094. Boxplots: Each solid dot is the mean value of one donor (1\u20133 BGs, 2\u20134 FOVs each); red line indicates median, bottom and top edges of the box indicate 25th and 75th percentiles, whiskers indicate minimum and maximum; significance was assessed with two-sided unpaired t-test. Source data for (c, d) are provided as a Source Data file.\n\nTo capture these species-to-species differences in ciliary activity, we measured multiple ciliary properties at the single cell and tissue level. On the cellular level, we assessed average ciliary beat orientation, i.e., the angle of the ciliary beat axis, as well as ciliary beat amplitude and cilia length (Fig.\u00a03a). At the tissue-level, we analyzed the spatial distribution of ciliated cells using the spatial correlation function C(R) (see Methods) to find \u03bb, the average gap distance between ciliated areas14 (Fig.\u00a03b). We computed the relative variability of \u03bb using the crystalline order parameter (COP), defined as crystalline OP\u2009=\u2009\\(1-\\frac{\\sigma \\sqrt{2}}{\\left\\langle \\lambda \\right\\rangle }\\) where \u03c3 is the standard deviation of \u03bb between field of views14. We also determined the degree of alignment of ciliary beat in each field of view using the director-free orientational order parameter defined as ciliary beat OP = \\(\\sqrt{{{\\left\\langle \\sin 2\\theta \\right\\rangle }^{2}+\\left\\langle \\cos 2\\theta \\right\\rangle }^{2}}\\), where \u03b8 are the measured ciliary beat angles across the field of view. The ciliary beat OP ranges from 0 to 1, where 0 indicates randomly distributed ciliary beat orientations, and 1 indicates spatial alignment of beat.\n\na Cell-level analysis of ciliary beat orientation based on beat trajectories (scalebar: 20\u2009\u00b5m), ciliary beat amplitude based on kymograph span (scalebar: 10\u2009\u00b5m), and cilia length based on histology sections (scalebar: 10\u2009\u00b5m). b Tissue-level analysis of ciliation gap size \u03bb using spatial correlation function C(R) of ciliated regions in multiple fields of view (left plot) where the first local maximum of the mean correlation curve reveals mean \u03bb (right plot). Scalebar: 20\u2009\u00b5m. c Average cilia coverage, ciliary beat order, ciliary beat amplitude, cilia length, ciliation gap size and crystalline order parameter measured in human BG0-6 and rat BG0-1. Numbers of human donors from left to right: n\u2009=\u200911, 4, 3, 9, 4, 4. Numbers of rat donors from left to right: n\u2009=\u20099, 4, 4, 5, 7, 6. Boxplots: Each solid dot is the mean value of one donor (1\u20133 BGs, 2\u20134 FOVs each); red line indicates median, bottom and top edges of the box indicate 25th and 75th percentiles, whiskers indicate minimum and maximum except outliers, red crosses denote outliers (defined as exceeding \u00b12.7 times standard deviation). Significance was assessed with two-sided unpaired t-test. Schematic of ciliary input metrics on cell- and tissue-level used to predict output metrics of tissue-level clearance using physics-based computational model. e Predicted and measured clearance per beat and clearance directionality in human (red, BG0-6) and rat (blue, BG0-1). Solid line represents mean prediction, shaded area shows uncertainty based on spread of input metrics, and individual data points indicate measurements from human and rat airways. Different marker shapes indicate different donors (n\u2009=\u20093 human donors; n\u2009=\u20094 rat donors), illustrating presence of two different ciliation levels in trachea of same rat donor (e.g., blue circles). Black data points and error bars represent experimental human and rat benchmark (mean\u2009\u00b1\u2009SEM of individual data points), indicating a reasonable match of the model predictions. Source data for (b\u2013d) are provided as a Source Data file.\n\nUsing these metrics, we compared the human airways at BG0-6 with the rat airways at BG0-1. Shown by the proximal-distal cell type analysis (Fig.\u00a01d), coverage with ciliated cells in these regions was significantly higher in the human airways (86.1\u2009\u00b1\u20099.2%) compared to rat airways (53.1\u2009\u00b1\u200914.4%) (Fig.\u00a03c). We found that cilia length was significantly higher in human compared to rat tissue (human: 7.1\u2009\u00b1\u20090.5\u2009\u00b5m; rat: 5.2\u2009\u00b1\u20090.4\u2009\u00b5m), consistent with literature16,41. Ciliary beat OP was also significantly higher in human samples compared to rat samples (human: 0.9\u2009\u00b1\u20090.02; rat: 0.6\u2009\u00b1\u20090.2), as was ciliary beat amplitude (human: 12.2\u2009\u00b1\u20091.3\u2009\u00b5m; rat: 7.5\u2009\u00b1\u20091.2\u2009\u00b5m). Ciliation gap sizes were similar in human (\u03bb\u2009=\u200927.8\u2009\u00b1\u20096.8\u2009\u00b5m) and rat airways (\u03bb\u2009=\u200928.3\u2009\u00b1\u20094.2\u2009\u00b5m) and, notably, were thus comparable to the mean correlation length R0 of the cilia-driven flow (23\u2009\u00b5m and 29\u2009\u00b5m, respectively). This is consistent with prior studies in mice showing that the spatial organization of ciliated cells imprints onto the emergent flow patterns14. Crystalline OP was also of similar magnitude between human and rat tissue.\n\nTo understand how the functional \u201coutput metrics\u201d of clearance effectiveness, namely CPB and clearance directionality, emerge from structural \u201cinput metrics,\u201d we developed a simple hydrodynamic model inspired by force singularity models of cilia42,43,44 to simulate particle clearance due to cilia submerged in aqueous liquid, similar to our experimental measurement conditions (Fig.\u00a03d, Supplementary Figs.\u00a07 and 8, Supplementary Methods). Here, we model each ciliated cell as a single regularized Stokeslet that scales with cilia beat amplitude, points horizontally in the effective stroke direction, and is located at one cilia length above a no-slip wall that represents the stationary cell surface. The position of ciliated cells and the orientation of the corresponding Stokeslets are chosen such that the simulated epithelium conforms to the desired input metrics such as cilia coverage and ciliary beat OP, patchiness, and COP. Next, the resulting fluid velocity field and tracer particles trajectories were computed to derive CPB and clearance directionality as a function of cilia coverage. To validate the model, we confirmed that it correctly predicted the most dependable clearance measurements in human and rat samples, i.e., from recordings with minimal sample warp where particles could be recorded right above the cilia layer, thereby minimizing distance dependent loss of speed (Supplementary Fig.\u00a09). In these human recordings, the mean cilia coverage of 95\u2009\u00b1\u20092.6% was associated with a mean clearance performance of CPB\u2009=\u20098.3\u2009\u00b1\u20091.2\u2009\u00b5m per beat and a mean directionality value of 0.97\u2009\u00b1\u20090.01 (Fig.\u00a03e, \u201chuman benchmark\u201d). The model further predicted that CPB is linearly dependent on cilia coverage, while clearance directionality is a steeply rising function that exponentially converges to its maximum above a certain coverage fraction (Fig.\u00a03e, red curves). We also computed these curves using the input parameters measured in rat airways. The predicted rat-specific curves (Fig.\u00a03e, blue curves) fall below the human-specific curves because of the lower values in ciliary beat OP, amplitude, and cilia length in rats compared to humans (Fig.\u00a03c). This means that for identical cilia coverage, the maximal CPB and clearance directionality are lower in rats. Intriguingly, the experimentally determined mean benchmark value in rat airways with mean cilia coverage of 45.6\u2009\u00b1\u20098.7%, mean CPB of 0.96\u2009\u00b1\u20090.3\u2009\u00b5m per beat, and mean directionality of 0.55\u2009\u00b1\u20090.2 (Fig.\u00a03b, \u201crat benchmark\u201d) match the range predicted by the model, suggesting that despite its simplicity, our model captures key structure-function relationships. The details of the human and rat benchmark data are listed in Supplementary Table\u00a0S6.\n\nWe next applied our structural and functional metrics to assess differentiated air-liquid interface (ALI) cultures of primary human airway epithelial cells. ALI cultures are typically used to study human airway diseases and hence there is great interest in establishing human airway-like, aka \u201corganotypic\u201d, phenotypes45. We hypothesized that we could generate different luminal epithelial cell type compositions in the same cell donor by using a variety of cell culture differentiation media46,47,48, thereby enabling us to compare these tissues to native human ex vivo tissues both in terms of structural organization and clearance function. After expanding the airway epithelial cells of multiple donors (n\u2009=\u20094\u20137; Supplementary Tables\u00a01 and 2) in a common medium, we differentiated them for 28 days at ALI in 5 commonly used cell culture media: BD49, mAir47, SAGMTM, PC, or PC-S (see Methods). The tissues differed dramatically in their proportions of luminal ciliated, club, goblet, and hybrid secretory cells depending on culture medium (Fig.\u00a04a; Supplementary Table\u00a05; see Supplementary Figs.\u00a010 and 11 for staining in another donor). On average, BD, mAir, and SAGM-cultured tissues exhibited relatively low ciliation and contained a substantial proportion of unidentified luminal cells whereas PC and PC-S cultured tissues most closely resembled the average human ex vivo composition measured in BG0-6 (Fig.\u00a04b).\n\na Representative IF-images of primary human airway epithelial cultures from 1 donor grown in different differentiation media for 28 days at ALI and stained for cilia (ATUB, magenta) and secretory cell markers (SCGB1A1, green; MUC5AC, white). Scalebar, 40\u2009\u00b5m. b Average luminal cell type composition based on IF-staining (in vitro: N\u2009=\u20094\u20137 donors, 2 inserts each with 3\u20138 FOVs each; ex vivo: 9 donors, 1\u20133 BGs, 2\u20133 FOVs each). c Mapping of IF-staining data of in vitro and ex vivo samples onto three dimensions. y-axis: percentage of MUC5AC+ cells; x-axis: cilia coverage, i.e., percentage of ciliated (ATUB\u2009+\u2009) cells; circle diameter: ratio of SCGB1A1+ to MUC5AC+ cell percentages. d Average percentage of MUC5B expressing cells and e relative percentage of MUC5B expressing cells that also express either SCGB1A1 or MUC5AC from IF-stainings (in vitro: n\u2009=\u20092 (SAGM, BD) to n\u2009=\u20094 (PC, PC-S, mAir) donors, 1\u20132 inserts each with 1\u20136 FOVs each; ex vivo: n\u2009=\u20093 donors, BG0, 2\u20134 FOVs each). Boxplot: Each dot represents mean of one donor; red line indicates median, bottom and top edges of the box indicate 25th and 75th percentiles, whiskers indicate minimum and maximum. Source data for (b\u2013e) are provided as a Source Data file.\n\nWe next used the cellular composition data to organize the different in vitro conditions and visualize their relative similarity to different regions along the human airways tree in a physiologically meaningful way. We reasoned that key organizing metrics would include cilia coverage, which is a major determinant of clearance function (Fig.\u00a03c), percentage of MUC5AC+ cells since the presence of mucin 5AC strongly impacts mucus rheology and flow behavior39, and the ratio of SCGB1A1+ to MUC5AC+ cell proportions since this ratio changes along the healthy airway tree (Fig.\u00a01d)25,26. Here, the definition of SCGB1A1+ and MUC5AC+ cells each includes hybrid MUC5AC\u2009+\u2009SCGB1A1+ cells. Mapping the data onto these three dimensions showed that PC-generated cellular compositions were most like human ex vivo BG2-6 samples, whereas all other media conditions differed starkly from the human ex vivo phenotype (Fig.\u00a04c). For further insight into the proximal-distal identity of the in vitro cultures, we also analyzed the proportions of MUC5B positive secretory cells, a major cell type present throughout the airway epithelium with highest frequency in the trachea and primary bronchi36. We found that human trachea contained on average 3.3\u2009\u00b1\u20091.7% Muc5B positive luminal cells, which was matched by similar values across all large airway media conditions (BD: 5.3\u2009\u00b1\u20092.6%, mAir: 3.1\u2009\u00b1\u20092.4%, PC: 2.4\u2009\u00b1\u20091.6%) and lower values in small airway media (SAGM: 1.2\u2009\u00b1\u20091.6%, PC-S: 1.1\u2009\u00b1\u20090.1%) which is consistent with a small airway identity36. (Fig.\u00a04d). As reported previously for both proximal and distal sites36, a sizable share of MUC5B positive cells also co-expressed MUC5AC and/or SCGB1A1 both in trachea explants and in all media conditions (Fig.\u00a04e).\n\nWe proceeded by measuring ciliary beat and clearance function in washed and submerged in vitro cultures, to directly compare to the measurements in ex vivo samples. Whereas ciliary beat frequency was comparable in all media (Supplementary Fig.\u00a0S13), only PC-cultured epithelial tissues approached human ex vivo-like particle clearance function with mean CPB of 6.4\u2009\u00b1\u20092.0\u2009\u00b5m\u2009s\u22121 and mean directionality of 0.81\u2009\u00b1\u20090.1 (Fig.\u00a05a, Supplementary Movie\u00a03). In contrast, particle clearance in tissues differentiated in other media performed well below the human ex vivo benchmarks and more closely resembled rat ex vivo clearance. To understand the mechanistic underpinnings of these results, we assessed all ciliary input metrics (Fig.\u00a05b, Supplementary Table\u00a04). Cilia coverage, cilia length, beat amplitude, and ciliation gap size varied the most in response to different differentiation media. On average, BD and SAGM-cultured tissues had shorter cilia than human ex vivo samples whereas PC-S cultures had longer cilia, and mAir and PC cultures matched human cilia length (BD: 5.9\u2009\u00b1\u20090.9\u2009\u00b5m; mAir: 6.7\u2009\u00b1\u20090.8\u2009\u00b5m; SAGM: 4.8\u2009\u00b1\u20091.5\u2009\u00b5m; PC: 7.0\u2009\u00b1\u20090.4\u2009\u00b5m; PC-S: 7.7\u2009\u00b1\u20090.9\u2009\u00b5m; human: 7.1\u2009\u00b1\u20090.5\u2009\u00b5m). Notably, only PC-cultured tissues reached human organotypic ciliary beat amplitudes whereas all other culture conditions fell short (BD: 9.3\u2009\u00b1\u20091.3\u2009\u00b5m; mAir: 8.1\u2009\u00b1\u20091.4\u2009\u00b5m; SAGM: 7.1\u2009\u00b1\u20090.5\u2009\u00b5m; PC: 12.4\u2009\u00b1\u20091.5\u2009\u00b5m; PC-S: 7.2\u2009\u00b1\u20090.6\u2009\u00b5m; human: 12.2\u2009\u00b1\u20091.3\u2009\u00b5m). Compared to human and rat airways, mean ciliation gap size \u03bb was notably smaller in PC and PC-S cultures (BD: 24.5\u2009\u00b1\u20099.0\u2009\u00b5m; mAir: 21.0\u2009\u00b1\u20096.4\u2009\u00b5m; SAGM: 28.1\u2009\u00b1\u20095.2\u2009\u00b5m; PC: 16.6\u2009\u00b1\u20095.9\u2009\u00b5m; PC-S: 14.8\u2009\u00b1\u20093.7\u2009\u00b5m; human: 27.8\u2009\u00b1\u20096.8\u2009\u00b5m), which is consistent with the comparatively small cell size in PC and PC-S cultured epithelia50 (Supplementary Fig.\u00a011). To assess the functional impact of these differences, we entered the ciliary input metrics of each medium condition into our computational model to predict CPB and particle clearance directionality as a function of cilia coverage (Fig.\u00a05c, top). Overlaying the measured CPB and directionality values demonstrated a good fit with the model predictions overall. We then replaced individual medium-specific cilia input parameters with the ex vivo values and computed the impact on CPB and directionality (Fig.\u00a05c, bottom). These results suggest that our ciliary input metrics alone can be used to predict particle clearance function, explain its divergence from organotypic performance and estimate the impact that rescuing defective ciliary beat features would have on clearance.\n\na Quantitative analysis of particle CPB and directionality in primary human airway epithelial cultures grown in different differentiation media for 28 days at ALI compared to human and rat benchmark data. Number of donors (2\u20133 inserts each with 6 FOVs each) for BD, mAir, SAGM, PC, PC-S, respectively: n\u2009=\u20095, 4, 4, 5, 4. b Quantitative analysis of ciliary beat metrics in airway cells cultured and visualized as in (a). Number of donors (2\u20133 inserts each with 6 FOVs each) for BD, mAir, SAGM, PC, PC-S, respectively: Cilia Coverage, n\u2009=\u20097, 4, 4, 7, 4; Ciliary Beat OP, n\u2009=\u20094, 4, 2 (1 not measurable), 4, 4; Ciliary Beat Amplitude: n\u2009=\u20094, 4, 3, 4, 4; Cilia Length, n\u2009=\u20094, 4, 4, 4, 4; Ciliation Gap, n\u2009=\u20094, 4, 3, 4, 4; Crystalline OP, n\u2009=\u20094, 4, 3, 4, 4. Boxplots in (a, b): Each solid dot is the mean value of one donor (1\u20133 BGs, 2\u20134 FOVs each); red line indicates median, bottom and top edges of the box indicate 25th and 75th percentiles, whiskers indicate minimum and maximum. Dotted lines indicate average human and rat benchmark values. c Top: Predicted CPB and clearance directionality in in vitro cultures compared to predicted human airway performance (red). Shaded regions indicate uncertainty based on spread of input metrics. Measured mean values per donor are overlaid (dots). Bottom: Predicted change in CPB and directionality of in vitro cultures in different media if an individual cilia input parameter is set to match ex vivo values. Source data for (a\u2013c) are provided as a Source Data file.\n\nAn obvious limitation of our analysis is the washing step prior to recording, thus likely removing most of the mucus, which plays a vital role in trapping particles but also aligning flow39, and the underlying periciliary liquid, which has been shown to contain ATP51, a potent stimulator of ciliary beat frequency and amplitude52,53. To gauge the robustness of our results in physiological mucus conditions, we compared ciliary activity and particle clearance in PC and PC-S cultures with mucus39 and without mucus. We found that, in samples with similar cilia coverage, the presence of mucus did not lead to significant changes in average CPB, or directionality compared to washed samples, although beat amplitude was increased in mucus samples (+20.5\u2009\u00b1\u200914%) (Supplementary Fig.\u00a014A\u2013D). As a positive control, we dosed the washed samples with 50\u2009\u00b5M ATP54. After 2\u2009min of incubation, ATP induced a slight increase in average CBF (+3.5\u2009\u00b1\u20096%) and a much greater increase in ciliary beat amplitude (+51\u2009\u00b1\u200925%), associated with a similar rise in clearance distance per beat (+75.7\u2009\u00b1\u200950%) compared to control (Supplementary Fig.\u00a014A\u2013D). Our results match the reported ranges of ATP-mediated changes in CBF, beat amplitude, and CPB in healthy airway epithelial cells52,53. We then used our computational model to simulate the impact of the measured changes in ciliary beat amplitude on CPB and directionality, and we compared the predictions to experimentally determined values (Supplementary Fig.\u00a014E, F). Since the model explicitly accounts for ciliation levels, we were able to increase the data set shown in Supplementary Fig.\u00a014C, D by including samples varying in cilia coverages. The model curves correctly predicted the similar distribution of washed and mucus-containing samples, while capturing a slight increase in CPB and directionality in mucus samples associated with the higher beat amplitude. The model also recapitulated the notable increase in CPB due to ATP treatment. Taken together, these findings suggest that the presence of an intact mucus layer may indeed increase extracellular ATP levels and hence raise ciliary beat amplitude, thereby slightly increasing CPB and directionality; however, donor-to-donor variability was high, and, on average, there was little difference. Therefore, for modeling average healthy conditions, our structure-function model provides reasonable estimates in both absence and presence of mucus but additional parameters accounting for mucus properties are likely required to predict specific donor responses and disease conditions.\n\nIn in vitro airway cultures, the physiological development of directional particle clearance depends on the hydrodynamic interaction of dense ciliation with a mucus layer that is neither too high nor too low in viscosity; the long-range forces transmitted via flowing mucus aligns ciliary beat during differentiation, leading to long-range clearance55,56. Mucus viscosity and flowability in turn depend on the proportions and abundance of mucins, especially Muc5AC and Muc5B9,39. However, tools to assess this relationship remain limited, especially given the difficulty of measuring mucus rheology in miniscule in vitro samples57. We therefore evaluated whether ciliated and secretory cell type composition could be directly predictive of average clearance directionality in the human BG0-6 samples and the in vitro cultures. Indeed, a simple linear regression model resulted in a solid prediction of mean clearance directionality in human in vitro cultures using as inputs the percentage of ciliated cells and secretory cells (i.e., the sum of MUC5AC, SCGB1A1 and/or MUC5B positive cells) (Fig.\u00a06, coefficient of determination R2\u2009=\u20090.89). Other input combinations worsened the prediction, including using the percentage of ciliated cells as sole input, or removing it (R2\u2009=\u20090.77 and R2\u2009=\u2009\u22121.34, respectively), or replacing the secretory cell percentage with only MUC5AC, MUC5B, or SCGB1A1 positive cell percentages (R2\u2009=\u20090.56, R2\u2009=\u20090.8, R2\u2009=\u20090.55), respectively). Hence, combining information on both ciliated and major secretory cell proportions was most predictive for clearance directionality.\n\nLinear regression model predicting average clearance directionality in human airway epithelia (in vitro and ex vivo) using as input the average values of cilia coverage and secretory cell percentage (incl. SCGB1A1\u2009+\u2009, MUC5AC\u2009+\u2009, and MUC5B+ cells). Source data are provided as Source Data file.\n\nCollectively, our analysis has uncovered the following key findings: (1) structural ciliary metrics, particularly the extent of ciliated cell coverage, are mechanistic predictors of particle clearance function; (2) the selection of cell culture medium significantly influences ciliary metrics and consequently particle clearance function, and (3) the composition of luminal secretory and ciliated cell types may act as a statistical predictor of particle clearance directionality in airway epithelia.\n\nFinally, to demonstrate the broader applicability of our analysis for tissue phenotyping, we leveraged the structural cell-type composition map (Fig.\u00a04c) and the functional CPB-against-ciliation map (Fig.\u00a05c) to assess the mucociliary machinery in additional culture conditions and animal models, where the data was derived from published literature or via proof-of-concept experiments (Fig.\u00a07a, b). The systems evaluated included mature and developing mouse trachea, human airway epithelial cultures derived from human induced pluripotent stem cells, and primary human airway epithelial cultures subjected to asthma-like inflammatory conditions (interleukin-13 (IL-13) treated) or differentiated under mechanical stimulation (Organ-on-Chip models). We found that the pluripotent stem cells-derived airway epithelial tissue differentiated in PC at ALI (iALIs) as described earlier58 greatly increased cilia coverage between day 14 and day 35 at ALI; however, CPB remained much lower than expected from the extent of cilia coverage, suggesting immature ciliated cell function and organization (Fig.\u00a07a, orange triangles). Secretory cell type composition in iALIs was dominated by SCGB1A1+ cells and showed relatively low levels of MUC5AC+ cells (Fig.\u00a07b). The CPB of primary human airway epithelial cultures differentiated in BD was also below the human benchmark curve at day 14 of ALI when cultured in conventional static inserts (Fig.\u00a07a, unfilled square) but approached the curve when cultured in continuously perfused Organ-on-Chips (Fig.\u00a07a, inverted triangle). Further, after a prolonged culture time of 35 days, the static insert cultures increased ciliation and reached the human CPB benchmark curve, indicating maturation of ciliary beat (Fig.\u00a07a, solid square). Luminal cell type composition in both insert and chip conditions reflected a lack of ciliation compared to the human benchmark; however, the Organ-on-Chip cultures reached nearly organotypic proportions of MUC5AC+ cells49 (Fig.\u00a07b). The CPB of primary human airway epithelial cultures cultured in Vertex ALI medium, a popular medium choice for modeling cystic fibrosis and asthma due to the high proportion of secretory cells59, lay below the benchmark curve at day 35 of ALI. Cultures treated with IL-13 for the final 14 days lost cilia coverage and, despite ciliary beat, generated almost no flow at all (Fig.\u00a07a, stars). The structural map reflects the high proportion of secretory cells expected from culture in Vertex ALI medium59, and IL-13 treatment creates the expected goblet-cell dominated Th2-like asthmatic phenotype60 (Fig.\u00a07b). Ciliation and CPB in mouse trachea continue to increase after birth until, at approximately postnatal day (P) 15, they reach their mature performance at 40\u201345% ciliation14,61 (Fig.\u00a07a, unfilled circle). The ciliation-dependent increase in CPB parallels the observed and predicted trends in the rat trachea-bronchial airways (Fig.\u00a07b, filled star and blue model prediction), and hence it is possible that mouse and rat trachea share similar ciliary beat properties.\n\na Clearance per beat map comparing different human in vitro and rodent ex vivo models to human and rat benchmark data. D, day at ALI; P, postnatal day; Vtx, Vertex ALI medium; iALI, human iPSC-derived differentiated airway epithelium. Orange markers indicate original data (iALI, Vtx and Vtx +IL13, proof-of-concept from N\u2009=\u20091 donor each, 2\u20133 inserts, 3-6 FOVs each); black markers indicate data sourced from literature, see methods for details. Red line and shaded region: Human model predictions; blue line and shaded region: rat BG0-1 model predictions. Shaded regions indicate uncertainty based on spread of input metrics. b Cellular composition map comparing different human in vitro models to human and rat benchmarks with y-axis: percentage of MUC5AC+ cells; x-axis: cilia coverage, i.e., percentage of ciliated (ATUB\u2009+\u2009) cells; circle diameter: ratio of SCGB1A1+ to MUC5AC+ cell percentages. Solid markers indicate original data (human BGs as Fig.\u00a04.; iALI, Vtx and Vtx +IL13, proof-of-concept from N\u2009=\u20091 donor each, 2\u20133 inserts, 3-6 FOVs each); black unfilled markers indicate data sourced from literature. Note that y-axis is in log-scale. Source data for panels a and b are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-57667-z/MediaObjects/41467_2025_57667_Fig7_HTML.png" + ] + }, + { + "section_name": "Disussion", + "section_text": "We developed a framework to predict how airway morphology influences particle clearance effectiveness and established benchmarks for comparing experimental airway models against the gold standard, the human airways. Specifically, we discovered that the human large airways exhibit a high degree of ciliation, akin to dogs and pigs62,63, while rat airways display a gradual increase starting from 45% ciliation in the trachea. These findings challenge the notion of conserved cilia coverage and clearance function in mouse and human airways14. Furthermore, mouse airways diverge from human airways in other relevant aspects, including near 100-fold smaller spatial dimensions, a different branching morphology, and the absence of mucus-producing goblet cells in healthy conditions64, similar to our findings in rats. Additionally, it was previously found that deposition of inhaled particle deposition differs between humans and obligatory nose breathers like rats and mice65,66, suggesting that in contrast to humans, in rats and mice the nasal respiratory tract serves as the main particle filter instead of the tracheobronchial tree. Indeed, a recent study showed that nasal ciliation in mice and associated CPB is multiple times higher than in the trachea and comparable to the values we measured in the human large airways67. However, the functional implications remain speculative, necessitating further investigation and comparative studies especially on the role of mucus.\n\nOur study is first to evaluate culture conditions based on their ability to replicate human-typic characteristics of MCC. We developed structural and functional maps that leverage markers commonly measured, enabling us to visually compare different experimental models with human organotypic benchmarks. While previous studies have assessed the impact of cell culture media on in vitro differentiation, morphology and functional responses of respiratory epithelia47,68,69, they did not directly compare these metrics to native human airways and hence lacked organotypic benchmarks. Intriguingly, we demonstrated that the composition of secretory cell types may predict clearance directionality, consistent with the need for mucus during differentiation to establish long-range clearance55. This relationship provides impetus for future work on mucus micro-rheology and cilia-mucus interactions.\n\nOur results suggest that commonly used culture media and protocols are insufficient to recreate the mucociliary performance of healthy airway epithelia in humans. We found that one of the major causes of diminished clearance in vitro is simply insufficient cilia coverage. Suppression of Notch signaling, which controls the balance between ciliated and secretory cells70, has been shown to increase ciliation in vitro71. Additionally, we found that many in vitro conditions were deficient in ciliary beat properties. There are several potential strategies for improvement, such as stimulating Wnt signaling. Canonical Wnt signaling is essential for proliferation, migration and many other processes in airway epithelial development72, including specifically cilia biogenesis and beating73, whereas non-canonical Wnt signaling establishes planar cell polarity (PCP) and coordinated ciliary beat74, which is required for effective and directional clearance. Other possibilities include stimulation of nitric oxide generation, as nitric oxide also drives PCP signaling and furthermore increases ciliary beat frequency75. Faster beat in turn promotes cilia-driven flow and associated shear forces, which are known to stimulate alignment of ciliary beat and clearance55. Indeed, directly applying fluid shear forces using Organ-Chip devices accelerated mucociliary differentiation and polarized clearance49,76, indicating that targeting mechanosensory signaling might also be a powerful strategy for improving MCC in vitro.\n\nOur study does have limitations. The analysis of ciliary function and clearance in rat and human airways was performed on explanted tissue and not in vivo, and the possibility of artifacts must be considered in the interpretation of our findings. Analysis was also limited by the availability of healthy human airway samples (15 donors total, as detailed in Supplementary Table\u00a01), some of which were extracted from peritumoral tissue. Many of the human donors were above the age of 60, raising the possibility of aging-related changes to MCC77. Despite these limitations, the robustly high ciliation levels provide confidence that this is a key characteristic of human large airways. While more studies are needed to also characterize MCC in the small airways, 85% luminal cilia coverage was reported in the human small airways78, suggesting continuously high ciliation levels throughout the conducting airways. We washed all samples prior to live imaging to enable direct comparison between buffer-stored explants and in vitro cultures, thereby lacking an intact periciliary liquid and mucus layer in our analysis. However, MCC recordings in unwashed mice trachea79 suggest a CPB of about 1\u20132\u2009\u00b5m per beat (assuming a physiological CBF of 15\u2009Hz61), comparable to the CPB measured in washed mouse trachea14. We also showed that our clearance metrics resulted in comparable average values between washed and unwashed conditions, but the high donor-to-donor variability indicates that further studies are warranted. We propose that in the case of airway disease, where mucus properties are often abnormal, comparing our metrics with and without mucus will help dissect the contribution of secretory compared to ciliary dysfunction. Other technical limitations are outlined in the Supplementary methods.\n\nIn conclusion, our comprehensive structure-function analysis of the mucociliary machinery along the human airway tree provides quantitative benchmarks and visualization tools to assess how human organotypic the mucociliary barrier of experimental models are. The analysis can also reveal the effects of maturation, treatments, and diseases on ciliary activity and resulting MCC. Our physics-based model linking cilia properties to particle clearance can aid in estimating the efficacy of treatments aimed at restoring MCC in humans.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Our research complies with all relevant ethical regulations for the procurement and use of animal and human cells and tissues were followed. The details of these guidelines and specific protocols are listed in the next section on cell and tissue sourcing.\n\nRat lungs were harvested from control animals that were freshly euthanized as part of other studies performed according to approved animal study protocols. The animals investigated at USC in were 3\u20138 months-old female Wistar-Kyoto rats sourced from Charles River Laboratories, MA, USA, that were handled and euthanized (urethane injection) according to the approved Animal Study Protocols (IACUC 20263 and IACUC 20751). The animals investigated at Helmholtz/TUM were 2 months-old female Wistar rats from Charles River Laboratories, MA, USA, that were handled and euthanized (pentobabital injection) according to the approved Animal Study Protocol (TVA 02-17-177). Only female rats were used due to the availability of fresh tissues donated from other studies. Also see Supplementary Table\u00a01.\n\nHuman lung tissue from subjects with no prior history of chronic lung disease was obtained through the International Institute for the Advancement of Medicine (IIAM) with approval from the Institutional Review Board (IRB) of the University of Southern California (USC) (Protocol number: #HS-18-00273) and from the Center for Gene Therapy\u2019s Cells and Tissue Core facility at the University of Iowa (tissues are obtained under IRB#199507432) and deidentified samples were provided to Dr. Ryan\u2019s laboratory. Donor demographics are included in Supplementary Table\u00a01. Human trachea-bronchial epithelial cells (HTBECs) were isolated80. Briefly, proximal airways including the trachea, main stem bronchi and 2 further branching generations of the cartilaginous airways were dissected into 1\u20134\u2009cm2 sections and digested in of 0.1% Protease XIV (Sigma #P5147) and 0.001% DNase (Sigma #DN25) (%w/v) in DMEM/F12 (ThermoFisher #11330032) overnight at 4\u2009\u00b0C. Using a scalpel, epithelial tissue was gently scraped, collected in DMEM/F12, and centrifuged at 400\u2009\u00d7\u2009g for 5\u2009min. After red blood cell lysis in ACK lysis buffer (ThermoFisher #A1049201), epithelial cells were single cell dissociated by incubation with Accutase (Innovative Cell Technologies #AT104) at 37\u2009\u00b0C for 30\u2009min. Cells were then seeded at a density of 30\u2009K cells cm-2 on PureCol (Advanced Biomatrix #5005) coated tissue culture dishes in airway epithelial growth media (Promocell #C-21160) and passaged at 80% confluence. At each passage cells were seeded at 5\u2009\u00d7\u2009103 cells cm-2.\n\nBronchial rings were dissected from macroscopically normal lung tissue obtained from patients undergoing resection surgery for lung cancer at the Leiden University Medical Center, the Netherlands (Supplementary Table\u00a0S1). Patients from which this lung tissue was derived were enrolled in the biobank via a no-objection system for coded anonymous further use of such tissue (www.coreon.org), not requiring written consent. All patient material used in this study was de-identified and approved for research use by the institutional medical ethical committee (BB22.006/AB/ab). No clinical data from the patients from which the tissues were derived for this study are available.\n\nCells were sourced from commercial suppliers (Supplementary Table\u00a01).\n\nSee section \u201cData for comparative CPB and cell type composition map.\u201d\n\nHuman primary small airway epithelial cells (hSAECs) and trachea-bronchial cells (hTBECs) were obtained from Lifeline Cell Technologies (USA) or via isolation from primary tissue obtained through the International Institute for the Advancement of Medicine (IIAM) with approval from the Institutional Review Board (IRB) of the University of Southern California (USC) (Protocol number: #HS-18-00273). (Supplementary Table\u00a01). The first passage cells were expanded in collagen I coated T75 tissue culture flasks and dishes in standard bronchial epithelial cell medium (BEpiCM) (ScienCell) until ~90% confluency. Expanded small airway and bronchial/tracheal cells were seeded on collagen IV (300\u2009\u00b5g\u2009mL\u22121) coated 12-well 0.4 pore diameter PET Transwell membranes (Corning, 3460) at a density of 150\u2009K cells per insert (~135\u2009K cells\u2009cm-\u00b2). The cells from each donor were cultured in BEpiCM supplemented with 1\u2009nM EC23 (Tocris Bioscience) until confluent. Once the tissue was confluent, differentiation was induced by introducing air liquid interface (ALI) via removal of the apical medium (day 0 of ALI culture) and using one of five different differentiation medium in the basal compartment: 1. BD: bronchial epithelial base medium and Dulbecco\u2019s modified eagle medium BEpiCM:DMEM (50:50) with addition of supplements and 50\u2009nM EC2349; 2. PC: PneumaCult ALI (STEMCELL Technologies); 3. PC-S: PneumaCult ALI-S (STEMCELL Technologies); 4. SAGM: Small Airway Epithelial Growth Medium (Promocell, C-21170) containing 50\u2009nM EC23; and 5. mAir: a 1:1 mix of Dulbecco\u2019s modified eagle medium and Airway epithelial cell growth medium (AECGM, PromoCell, C-21160) with AECGM supplements and 50\u2009nM EC23 (previously described in ref. 47). The apical surface was washed with phosphate-buffered saline (PBS, no Calcium and Magnesium) at 37 degrees Celsius twice a week to remove excess mucus. For bead transport measurements in samples containing mucus, 50\u2009\u00b5L\u2009cm-\u00b2 of the respective media was added to the apical surface every other day during differentiation to prevent mucus dehydration55. Cultures were differentiated until day 28 of ALI.\n\nWe freshly isolated airway epithelial tissue from respiratory tree branching generation (BG) 0 through BG6 from healthy transplant-rejected human whole lungs, and BG0 through BG5 from healthy rat whole lungs for live measurements. Samples were cut open along the airway tube to reveal the luminal epithelium, submerged in ice-cold HBSS buffer, mounted upside down in a glass bottom dish (ibidi), and gently flattened using a glass cover slip held down with silicone grease at the corners. All live recordings were performed at room temperature, which we found to prolong sample viability compared to higher temperatures. In some samples, ciliary beat was recorded with phase contrast at 100\u2013200 frames per second (fps) using an inverted Leica microscope equipped with a 40x (NA 0.8) objective and a PCO Edge 4.2 high speed camera (Excelitas Technologies) operated with the micromanager plugin (ImageJ), or a Leica K3M camera operated with Leica software LAS-X. Cilia were also live-stained with fluorescent-dye conjugated wheat germ agglutinin (ThermoFisher; 20\u2009min incubation in dilution of 1:200)15, and 1-\u00b5m fluorescent tracer particles were added to the bath, such that live ciliary beat and particle clearance could be recorded in the same field of view for 10\u2009s (ciliary beat: 15\u201333\u2009fps; particle trajectories 8\u201333\u2009fps) using epifluorescence imaging49. Two to four FOVs with visible CBF were recorded from each sample.\n\nIn vitro cultures were washed for 10\u2009min with PBS and recorded at ALI using an inverted Zeiss microscope equipped with a 40x (NA 0.8) phase contrast objective and a temperature-controlled chamber that was preheated to 37\u2009\u00b0C (in contrast to the ex vivo recordings, see above). Movies of ciliary beat were taken at 140\u2009fps using an Orca Flash 4.0 camera (Hamamatsu). To reveal beat kinematics of thicker cultures, samples were mounted upside down and cilia were live-stained with fluorescent-dye conjugated tomato lectin (IVISense\u2122 Tomato Lectin 680, Perkin Elmer) by incubating the sample in a 0.25\u2009\u00b5M dilution in PBS for 20\u2009min. After rinsing, ciliary beat kinematics were recorded at 30 fps using epifluorescence imaging. Particle clearance was recorded for 10\u2009s at 20\u201330\u2009fps by adding 1-\u00b5m fluorescent tracer particles to the apical surface49. Video recordings were taken from 2 insert cultures per donor and condition, with at least 8 FOVs per sample.\n\nTo assess ciliary beat and clearance function in the presence of mucus, 50\u2009\u00b5L\u2009cm-\u00b2 of medium containing carboxylate-modified 1-\u00b5m fluorescent tracer particles (1:1000 dilution) were added to the PBS-washed apical surface two days prior to the recording, allowing mucus to accumulate and embed the beads in mucus. At day 30 of differentiation, ciliary beat activity and clearance were measured as described above (protocol based on Song et al. 39).\n\nTo test the impact of extracellular ATP on ciliary beat and resulting transport, 50\u2009\u00b5L\u2009cm-\u00b2 of medium containing 50\u2009\u00b5M ATP plus 1-\u00b5m fluorescent tracer particles (1:1000 dilution) were added to the apical surface post PBS-wash. After 2\u2009min of incubation, ciliary beat activity and particle clearance were recorded (protocol based on Lieb et al.54 https://doi.org/10.1113/jphysiol.2001.013222).\n\nHematoxylin & eosin (H&E)-stained sections of human bronchial rings and rat airway trachea were imaged using a Zeiss Axioscope 7 fluorescence microscope and a 40x oil DIC objective (NA 1.4). Cilia length data were measured manually using the freehand line tool in the image processing software Fiji ImageJ (Version 1.54\u2009m)81. Only cilia with visible starting and end point were measured. For each rat donor, on average 15 FOVs were analyzed and on average 10 cilia were measured in each FOV. For each human donor, on average 4 FOVs were analyzed and on average 30 cilia were measured in each FOV.\n\nThe cell layer was dissociated by incubating the cultures for 20\u2009min in prewarmed Accutase (Invitrogen, 00-4555-56) in a conical tube. After 20\u2009min warm culture medium was added at a ratio of 3:1 and the cells were centrifuged at 210\u2009\u00d7\u2009g for 7\u2009min. The resulting pellet was resuspended in 500\u2009\u00b5L 4% paraformaldehyde and 10\u2009\u00b5L of this suspension was placed onto a glass bottom imaging dish (ibidi,81218-200) and covered with a glass coverslip. Cilia length was measured as above. For each donor, 30 FOVs were analyzed and on average 10 cilia were measured in each FOV.\n\nCBF was measured by applying Fourier spectral analysis to each cilia-associated pixel recorded in high-speed videos22.\n\nWe determined ciliary beat orientations using either the ImageJ plugin directional analysis or manual tracing of the beat axis from all ciliated cells in at least 3 FOVs, each spanning approximately 200\u2009\u00b5m by 200\u2009\u00b5m. This analysis yields a list of beat angles across each field of view. We derived the director-free ciliary beat order parameter (OP) for each FOV from the angle distribution as follows: \\({OP}=\\sqrt{{{\\left\\langle \\sin 2\\theta \\right\\rangle }^{2}+\\left\\langle \\cos 2\\theta \\right\\rangle }^{2}}\\), where \u3008\u3009 indicates the mean and \u03b8 indicates the measured angles. To compute the average value per donor and condition, we averaged the ciliary beat OP over all FOVs.\n\nWe measured ciliary beat amplitude by manually tracing the span of the ciliary beat using kymographs of videorecordings82 in at least 10 ciliated cells each in 3 FOVs.\n\nThese metrics were measured from the standard deviation of pixel intensity across all video frames, revealing the spatial distribution of motile cilia. When these data were unavailable, cilia coverage revealed by IF-staining was used instead. Images were thresholded and binarized to reveal the ciliation pattern, and ciliation gap size and crystalline order were estimated as shown previously14. Briefly, the 2-point correlation function C(R) was used to measure the probability that two pixels at a certain distance from each other both have a binary intensity level of \u201c1\u201d, i.e., are part of a ciliated cell. For an image with pixel dimensions m\u2009\u00d7\u2009n, the 2-point correlation function is defined as\n\nwhere N\u2009=\u2009(m-x)(n-y). From this function, C(R) is derived by only allowing for coordinate pairs (x,y) that are part of a circle perimeter of radius R (rounded to integer pixel coordinates) and then averaging over the number of pixels in the circle perimeter. The resulting function C(R) is oscillating and its first local maximum reveals \u03bb, the average spacing of two ciliated patches14. The COP describes the degree of variability of the patchiness between multiple FOVs and is derived from the average of \u03bb and its standard deviation std(\u03bb) across FOVs as follows: \\({COP}=\\frac{1-\\sqrt{2}*{{{\\rm{std}}}}\\left(\\lambda \\right)}{{{{\\rm{mean}}}}\\left(\\lambda \\right)}\\). Cilia coverage, defined here as percentage of luminal cells that are ciliated, was determined as part of the cell type composition analysis discussed in the associated methods section.\n\nThe displacement and trajectories of fluorescent tracers driven by ciliary beat was automatically measured using the open source ImageJ Trackmate plugin83. From these data, two metrics were calculated.\n\nParticle clearance directionality D(R) was defined in the Eulerian framework. We derived a Eulerian vector field from the particle trajectories by averaging the velocity components (u,v) at each image coordinate (x,y) over all trajectories passing through (x,y) at any time during the recorded video, i.e., \\({\\left(\\bar{u,}\\bar{v}\\right)}_{\\left(x,y\\right)}=\\frac{1}{N}{\\sum }_{j=1}^{N}{\\left(u,v\\right)}_{\\left(x,y\\right),j}\\), where N is the total number of trajectories passing through (x,y). This procedure creates a temporally averaged flow vector field, which is useful when tracer particles density is low at any given moment in time. D(R) was defined as the magnitude of the average flow vector divided by the average magnitude of all flow vectors within a square window with side length R, i.e., \\(D\\left(R\\right)=|\\langle {{{\\bf{v}}}}\\rangle |\\langle |{{{\\bf{v}}}}|\\rangle\\), where \u3008\u3009 indicates the mean and || indicates the magnitude of each flow vector v. After normalization to mitigate offset, each trace D(R) was fitted with a decaying exponential, \\(D\\left(R\\right)\\,{e}^{\\frac{-R}{{R}_{0}}}\\), to reveal the correlation length R0. To remove ill-fitted curves, we only accepted fits with coefficient of determination above 0.8. We used bootstrapping to estimate the statistics of R0 and account for the limited sample size. Since D(R) decays with window size R, we defined its mean value \\(\\left\\langle {D}_{R=80}\\right\\rangle\\) by averaging D(R) at R\u2009=\u200980\u2009\u00b5m to assess transport directionality over multiple cell length.\n\nClearance per beat (CPB) was defined as the mean speed of particle clearance (in units of \u00b5m per second) divided by the ciliary beat frequency (beats per second), resulting in units of \u00b5m per beat. This is a measure of the efficacy of each ciliary beat cycle in driving particle clearance. When both speed and CBF data were available for the same FOV, CPB was computed directly from the ratio. When speed and CBF data were recorded separately, their mean values over multiple fields of views were used to compute one single value of CPB from their ratios.\n\nIn ex vivo samples, the surface topography was often distorted due to elastic recoil after cutting the cartilage rings, leading to contorted tracer trajectories and variable distance between tracer particle and cilia, which impacts apparent clearance speeds (Supplementary Fig.\u00a06). To establish benchmarks and to validate the physics-based model, we used the clearance measurements from human and rat samples that we trusted the most, i.e., measurements from entirely flat airway sections where the particles were visibly touching the cilia. In humans, benchmarks were derived from 3 donors, one recording each, in H44 (BG6), H47 (BG2), and H2924 (BG0). In rats, benchmarks were derived from 4 animals (1 or 2 recordings each), in R43USC (BG0 and BG1), R42USC (BG0), TUMR56 (BG0) and TUMR55 (BG0). Supplementary Movie\u00a01 and 2 show one example per species.\n\nAirway rings were fixed using a 4% paraformaldehyde (PFA) solution for 3 to 24\u2009h depending on tissue thickness, washed with PBS, and stored in PBS at 4\u2009\u00b0C until staining. Prior to staining, the diameters of the rings were measured using a ruler. For staining, sections were cut from both dorsal and ventral sides of the airway rings, to capture potential spatial variability along the perimeter. Nearly level sections were cut along the long axis of the tube to minimize warping of the epithelium. Samples were placed into a 96-well plate for staining. Human airway epithelial cell cultures. After differentiation at ALI, the primary human airway epithelial cultures and iPSC-derived cultures were fixed using incubation with 4% PFA solution for 30\u2009min at RT, then washed again three times with PBS and stored in PBS at 4\u2009\u00b0C until staining.\n\nSamples were blocked and permeabilized using 0.25% (v/v) Triton-X 100 in PBS with 3% BSA for 60\u2009min at RT, then incubated overnight at 4\u2009\u00b0C with primary antibodies (Supplementary Table S8) diluted in the Triton/BSA buffer The samples were rinsed three times for 5\u2009min with PBS before incubation with secondary antibodies diluted in Triton/BSA buffer for 1\u2009h at 37\u2009\u00b0C, followed by a triple 5\u2009min wash with PBS. Then, as applicable, the samples were incubated with directly conjugated primary antibodies (Supplementary Table S8) and F-actin stain phalloidin 555 (Invitrogen, A30106) or phalloidin 405 (Invitrogen, A30104) in Triton/BSA buffer for 1\u2009h at 37\u2009\u00b0C, followed by a triple 5\u2009min wash with PBS. The samples were stored at 4\u2009\u00b0C until mounting. For mounting, ex vivo samples were placed into glass-bottom imaging dishes (ibidi, 81218-200) and covered with SlowFade\u2122 Glass Antifade Mountant (Invitrogen, S36917). A round coverslip lined with silicone grease was used to push down and flatten the sections. The in vitro samples were mounted by removing the cell culture membrane from the insert using a scalpel and placing the membrane onto a glass slide with the cells facing upwards. The membranes were coated with a drop of ProLong\u2122 Diamond Antifade Mountant (Invitrogen, P36965) and covered with a round number 1.5 glass coverslip.\n\nHuman bronchial ring sections were imaged with a 40\u00d7 water objective (NA 0.80) using a Leica DMi8 microscope equipped with an Andor Dragonfly 200 spinning disk confocal or using a Zeiss LSM 700 confocal microscope. From every stained ring section, 3 to 8 FOVs with a size of 2048\u2009\u00d7\u20092048 pixels at 6.6 pixels per \u00b5m resolution were recorded. Rat samples, other human airway sections, and in vitro cultures were imaged using a Leica confocal scanning microscope or a Zeiss Axioscope 7 fluorescence microscope equipped with a 40x oil DIC objective (NA 1.4). Six FOVs with a size of 2048\u2009\u00d7\u20092048 pixels at 6\u20137 pixels per \u00b5m resolution were recorded per sample.\n\nWe reviewed human data from standard histology25,34,35 and RNAseq studies32,33, as well as rat data from standard histology25 (Supplementary Fig.\u00a04).\n\nWe employed semi-automated image analysis to quantify luminal cell type composition from IF images containing 4 channels, either labeling F-Actin, MUC5AC, SCGB1A1 and acetylated \u03b1-tubulin (ATUB), or F-actin, MUC5AC, MUC5B and SCGB1A1 (Supplementary Fig.\u00a0S2). Using ImageJ Fiji81, raw image data was converted to 16 or 8-bit tiff images using maximum projection for stacks. As needed, images were cropped to remove edge artifacts and the subtract background function was applied with a rolling ball radius of 1000 pixels. The Fiji plugin Advanced Trainable Weka Segmentation with all default and the Laplacian, Derivatives and Structure training features84 was used to segment cell outlines from the F-actin mesh. The channels for the different cell markers (MUC5AC, SCGB1A1, ATUB and/or MUC5B, dependent on the staining combination) were filtered and optimized in contrast. After these preprocessing steps, cell type composition analysis was performed by overlaying the markers with the cellular outlines using CellProfiler\u2122(Versions 4.0.7 to 4.2.5)85, providing total cell number and proportions of ATUB\u2009+\u2009, MUC5AC\u2009+\u2009, SCGB1A\u2009+\u2009, MUC5AC\u2009+\u2009, and/or MUC5B+ cells, dependent on the staining combination, as well as overlaps of secretory markers, indicating double- or triple positive cells. Image sets with low signal-to-noise ratio were excluded from the analysis.\n\nTracheas were obtained from wildtype Wistar rats and fixed in 2% PFA at 4\u2009\u00b0C overnight on an orbital shaker. After washing, samples were embedded into paraffin wax in a lateral (dorso-ventral) orientation. Traditional H&E staining was performed and microscopical images at 400x magnification were taken of all sections, with at least 6 FOVs per trachea.\n\nBronchial rings were obtained from tumor-free resected lung tissue at the Leiden University Medical Center. The rings were fixed using 4% PFA solution for 24\u2009h after which the rings were transferred to PBS and stored at 4\u2009\u00b0C until paraffin embedding at the Department of Pathology at the Leiden University Medical Center. Bronchial ring sections were deparaffinized in xylene and dehydrated in an ethanol gradient. Traditional H&E staining was performed and microscopical images at 400x magnification were taken of all sections, with at least 3 FOVs per sample.\n\nMultiple inner diameters were measured and averaged in each cross-section of rat and human airway rings.\n\nFor each metric, the average of the entire FOV was determined, and by averaging this value across all FOVs taken from all samples of the same condition, a single value for each metric was established for each donor and condition. See Supplementary Table\u00a0S2 for donor numbers for each condition and measurement. Where noted in the figures, statistical analysis of differences between the mean values was performed using the two-sided, unpaired t-test. Unless noted differently in legend, boxplots consist of the following elements: center line, median; box limits, upper and lower quartiles; whiskers, extreme data points. Where error bars are present, they represent standard deviation (STD) or standard error of the mean (SEM), as noted in caption.\n\nThe model was created in MATLAB (Mathworks; Version 2023b) using the regression learner application.\n\nCPB in developing mouse trachea between postnatal days (P) 0 and 29 (gray crosses in Fig. 6b) in were derived from Toskala et al. 86. CPB in mature mouse trachea at P15 (black cross in Fig. 6b) was derived from Ramirez-San Juan et al. 14.\n\nCPB and cell type composition were derived from our previous study49.\n\nCells (N\u2009=\u20091 donor) were differentiated in Vertex ALI medium59 for 21 days at ALI. A chronic airway inflammatory phenotype was induced by treatment with 100\u2009ng\u2009mL\u22121 of IL-13 (Invitrogen, A42525) for 14 days. Analysis of CPB and cell type composition was conducted at day 35 ALI as described above.\n\nDifferentiation of human iPSCs (hiPSCs) towards respiratory epithelium was performed as described previously58. Briefly, hiPSCs from N\u2009=\u20091 donor were differentiated to definitive endoderm by using the STEMdiffTM Definitive Endoderm Kit (STEMCELL Tech., Vancouver, BC, Canada). Subsequently, cells were dissociated and replated for anterior foregut induction by supplementing basis medium with 10\u2009\u00b5M SB431542 (provided by the Institute of Organic Chemistry, Leibniz University, Hannover, Germany), and 3\u2009\u00b5M Dorsomorphin (Sigma Aldrich, Saint Louis, MO, USA) for 24\u2009h, followed by supplementation with 2\u2009\u00b5M IWP2 (Tocris, Bristol, UK) and 10\u2009\u00b5M SB431542 for another 24\u2009h. For lung lineage specification, basis medium was supplemented with 10\u2009ng\u2009mL\u22121 BMP4 (R&D Systems, Minneapolis, MN, USA), 10\u2009ng\u2009mL\u22121 FGF10 (R&D Systems, Minneapolis, MN, USA), and 3\u2009\u00b5M Chir99021 (provided by the Institute of Organic Chemistry, Leibniz University, Hannover, Germany) until day 14 of differentiation. NKX2.1 positive lung progenitor cells were enriched by sorting for the cell surface marker carboxypeptidase M (CPM) (FUJIFILM Wako, Cat# 014-27501). To mature lung progenitor cells to ciliated respiratory epithelium, enriched cultures were seeded onto transwells (Greiner Bio-One, Frickenhausen, Germany) and expanded in small airway epithelial cell growth medium (SAECGM; PromoCell, Heidelberg, Germany) supplemented with 1% penicillin/streptomycin (Gibco, Billings, MT, USA), 1\u2009\u00b5M A83-01 (Tocris, Bristol, UK), 0.2\u2009\u00b5M DMH-1 (Tocris, Bristol, UK) and 5\u2009\u00b5M Y-27632 (Tocris, Bristol, UK) for four days. Afterwards, medium was switched to PneumaCultTM-ALI medium (STEMCELLTech., Vancouver, BC, Canada) and cells were differentiated in air-liquid interface conditions for 28 days before analysis. Analysis of CPB and cell type composition was conducted as described above for primary human airway epithelial cultures.\n\nWe model the averaged forces generated by all cilia of a multiciliated cell as a single force monopole, located at one cilia length above a no-slip cell surface at z\u2009=\u20090, in a semi-infinite domain, based on custom MATLAB (Mathworks; Version 2023b) implementation of the regularized Stokeslet algorithm87. The regularized Stokeslet\u2019s strength is proportional to ciliary beat amplitude, with its direction corresponding to power stroke direction (Supplementary Fig.\u00a07A). While this approach cannot resolve the flow and coordination of individual cilia as in single-cilia models42,88,89,90,91, it is straightforward to implement, suitable for large number of ciliated cells, and directly takes into account of the wall-screening effects due to finite cilia length comparing to the slip-boundary-velocity approach14. We did not consider the effects of double confinement or mucus film geometry discussed in Ramirez-San Juan et al.14 because (i) we intend to compare tracer particle motions recorded at the cilia tip and sufficiently far from other confinement boundaries such as coverslip and air-liquid interface; (ii) experimentally, we chose appropriately-sized field-of-views such that no recirculation effects are observable; (iii) all functional measurements were done in washed samples without the presence of mucus.\n\nWe derive the ciliated cell distribution based on a cilia coverage percentage and a COP as defined previously14, where the order parameter relies on the distribution of wavelength \u03bb between each ciliated patch (Supplementary Fig.\u00a07A). Here the mean and standard deviation of \u03bb is determined based on structural measurements. Then ciliated patches are generated based on Gaussian displacement of cells that follow regular crystalline patterns, similar to the procedure reported in Ramirez-San Juan et al.14. Each ciliated cell assumes a beat direction angle \u03b8 sampled from a Von Mises distribution, where its mean is set to be 0 (beating towards x-axis) and its second moment, or the orientation order parameter, is determined based on the measured cilia beat order. In the Supplementary Methods, we provide an exact description of how both cilia- and tissue-level parameters are implemented in silico.\n\nThe model uses a rectangular grid of 51\u2009\u00d7\u200951 cells, doubly periodic in both x- and y-axis, where x-axis is defined as the clearance direction. Every cell is assumed to have a diameter of 10\u2009\u00b5m, with its center slightly perturbed away from the exact grid points in Monte-Carlo simulations; for visual reference, cell boundaries are drawn based on the Voronoi diagram of the perturbed cell center points (Supplementary Fig. SB). We implement the periodic boundary conditions by truncating hydrodynamic interactions further than one periodic image away (>250\u2009\u00b5m) from any given point of interest. This introduces only a small error in the flow velocity because the no-slip surface at z\u2009=\u20090 causes a quadratic decay of hydrodynamic interactions. We derive ciliary flow characteristics from the trajectories of simulated tracer particles injected near cilia tip. Tracers are subject to both cilia-driven flow and random fluctuations. All flow experiments were done using 1\u2009\u00b5m diameter particles suspended in buffer after the mucus was removed. For predictions associated with ex vivo measurements, we assume the random fluctuation is due to only the thermal diffusivity of water at room temperature (20\u2009\u00b0C; D\u2009=\u20090.4\u2009\u00b5m2\u2009s\u22121). For predictions related to in vitro experiments, we accounted for additional noise, possibly due to immature ciliary beat, and used effective diffusivity scales estimated using particle tracks (Supplementary Methods; Supplementary Fig.\u00a015). Time evolution of 500 initially uniformly distributed particles are computed for 500 beat periods, following the Langevin equation dr/dt\u2009=\u2009v(r) + (2D)0.5\u03b7(t), where r is the particle position, v(r) the cilia-driven flow, and \u03b7(t) a standard Wiener process (white noise). Equations are numerically integrated with a Euler-Maruyama scheme, using periodic boundaries in both x- and y-directions. The main quantitative output of our simulation is the clearance per beat (CPB) and clearance directionality (Supplementary Fig.\u00a07A).\n\nTo illustrate how tissue-level parameters affect our model output, we present example case studies (Supplementary Fig.\u00a07B). The COP changes how ciliated cells are distributed; however, it does not strongly impact the characteristics of the tracer trajectories. Lowering cilia coverage or orientation order parameters reduces the clearance distance and directionality. In Supplementary Fig.\u00a08, we present quantitative results of CPB, and clearance directionality change with (1) cilia length, (2) cilia beat amplitude, (3) cilia beat order and (4) patch heterogeneity.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The raw measurements and consolidated summary data, as well as example microscopy footage, generated in this study have been deposited in the Figshare database under https://doi.org/10.6084/m9.figshare.24989700. Source data for each experimental figure panel are provided with this paper.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The custom MATLAB script used to generate the model prediction results for human/rat benchmark (Fig.\u00a03e), in vitro culture (Fig.\u00a05c), and parametric studies (Supplementary Figs.\u00a07, 8, 9C, 14E and F, 15A) has been deposited in a Zenodo repository (https://doi.org/10.5281/zenodo.14684411). Detailed descriptions of the underlying mathematical algorithm are provided in the supplementary information and within the same Zenodo repository. The code is released under MIT license and is freely accessible without restrictions.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Wanner, A., Salath\u00e9, M. & O\u2019Riordan, T. G. Mucociliary clearance in the airways. Am. J. Respir. Crit. 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We also want to thank Sandra S\u00fchnel (excision of rat lungs at TUM) and Jackie Mao (excision of rat lungs at USC).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Doris Roth, Ay\u015fe Tu\u011f\u00e7e \u015eahin.\n\nThese authors jointly supervised this work: Janna C. Nawroth, Amy L. Ryan.\n\nHelmholtz Pioneer Campus, Helmholtz Zentrum M\u00fcnchen, Neuherberg, Germany\n\nDoris Roth,\u00a0Ay\u015fe Tu\u011f\u00e7e \u015eahin,\u00a0Feng Ling,\u00a0Niels Tepho,\u00a0Tankut G. G\u00fcney,\u00a0Sarah Glasl\u00a0&\u00a0Janna C. Nawroth\n\nInstitute of Biological and Medical Imaging, Bioengineering Center, Helmholtz Zentrum M\u00fcnchen, Neuherberg, Germany\n\nDoris Roth,\u00a0Ay\u015fe Tu\u011f\u00e7e \u015eahin,\u00a0Feng Ling,\u00a0Niels Tepho,\u00a0Tankut G. G\u00fcney,\u00a0Sarah Glasl\u00a0&\u00a0Janna C. Nawroth\n\nChair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine and Health, Technical University of Munich, Munich, Germany\n\nDoris Roth,\u00a0Ay\u015fe Tu\u011f\u00e7e \u015eahin,\u00a0Feng Ling,\u00a0Niels Tepho,\u00a0Tankut G. G\u00fcney,\u00a0Sarah Glasl\u00a0&\u00a0Janna C. Nawroth\n\nComprehensive Pneumology Center Munich, German Center for Lung Research (DZL), Munich, Germany\n\nDoris Roth,\u00a0Ay\u015fe Tu\u011f\u00e7e \u015eahin,\u00a0Feng Ling,\u00a0Niels Tepho,\u00a0Tankut G. G\u00fcney\u00a0&\u00a0Janna C. Nawroth\n\nDepartment of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, USA\n\nFeng Ling,\u00a0Eva Kanso\u00a0&\u00a0Janna C. Nawroth\n\nDivision of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Hastings Center for Pulmonary Research, University of Southern California, Los Angeles, CA, USA\n\nChristiana N. Senger,\u00a0Erik J. Quiroz,\u00a0Ben A. Calvert,\u00a0Janna C. Nawroth\u00a0&\u00a0Amy L. Ryan\n\nDepartment of Stem Cell Biology and Regenerative Medicine, University of Southern California, Los Angeles, CA, USA\n\nChristiana N. Senger,\u00a0Erik J. Quiroz,\u00a0Ben A. Calvert\u00a0&\u00a0Amy L. Ryan\n\nPulmoScience Lab, Department of Pulmonology, Leiden University Medical Center, Leiden, the Netherlands\n\nAnne M. van der Does\u00a0&\u00a0Annemarie van Schadewijk\n\nDepartment of Cardiothoracic, Transplantation and Vascular Surgery (HTTG), Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Hannover Medical School, Hannover, Germany\n\nLaura von Schledorn\u00a0&\u00a0Ruth Olmer\n\nBiomedical Research in End stage and Obstructive Lung Disease (BREATH), Member of the German Center for Lung Research (DZL), Hannover Medical School, Hannover, Germany\n\nLaura von Schledorn\u00a0&\u00a0Ruth Olmer\n\nREBIRTH-Research Center for Translational and Regenerative Medicine, Hannover Medical School, Hannover, Germany\n\nLaura von Schledorn\u00a0&\u00a0Ruth Olmer\n\nDepartment of Anatomy and Cell Biology, Carver College of Medicine, University of Iowa, Iowa City, IA, USA\n\nAmy L. Ryan\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nD.R. and T.S. contributed equally to the work. J.N., A.L.R., and E.K. conceptualized the project. J.N. and A.L.R. contributed equally to the work. J.N., A.L.R., and E.K. provided funding for this study. J.N., D.R., T.S., C.N.S., E.J.Q., B.A.C., A.D., T.G., N.T., S.G., A.S., L.S., and R.O. performed and oversaw the experiments, and D.R., T.S., and J.N. performed the data analysis. F.L., E.K., and J.N. formulated the theoretical model. J.N., F.L., and A.L.R. wrote the paper and all authors reviewed and edited the paper. Correspondence and requests for materials should be addressed to J.N. or A.L.R.\n\nCorrespondence to\n Janna C. Nawroth or Amy L. Ryan.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Kenichi Okuda who co-reviewed with Minako Furusho; Amjad Horani; Melanie Philipp, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Roth, D., \u015eahin, A.T., Ling, F. et al. 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+1,164 @@ +{ + "title": "Phylotranscriptomics reveals the phylogeny of Asparagales and the evolution of allium flavor biosynthesis", + "pre_title": "Phylotranscriptomics reveals the phylogeny of Asparagales and the evolution of allium flavor biosynthesis", + "journal": "Nature Communications", + "published": "08 November 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53943-6/MediaObjects/41467_2024_53943_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53943-6/MediaObjects/41467_2024_53943_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53943-6/MediaObjects/41467_2024_53943_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Data 1-13", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53943-6/MediaObjects/41467_2024_53943_MOESM4_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53943-6/MediaObjects/41467_2024_53943_MOESM5_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1107703", + "https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1107706", + "/articles/s41467-024-53943-6#MOESM4", + "https://doi.org/10.6084/m9.figshare.25516204", + "https://doi.org/10.6084/m9.figshare.25516204" + ], + "code": [ + "https://doi.org/10.6084/m9.figshare.25516204" + ], + "subject": [ + "Biogeography", + "Phylogenetics", + "Plant evolution", + "Secondary metabolism" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4312784/v1.pdf?c=1731071212000", + "research_square_link": "https://www.researchsquare.com//article/rs-4312784/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53943-6.pdf", + "preprint_posted": "05 May, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Asparagales, the largest monocot order, is renowned for its ecological, economic, and medicinal significance. Here, we leveraged transcriptome data from 455 Asparagales species to explore the phylogeny of Asparagales. Moreover, we investigated the evolutionary patterns of the genes involved in allium flavor formation. Our analyses not only established a robust bifurcating phylogeny of Asparagales but also explored their reticulate relationships. Notably, we found that eight genes involved in the biosynthesis of allium flavor compounds underwent expansion in Allium species. Furthermore, we observed Allium-specific mutations in one amino acid within alliinase and three within lachrymatory factor synthase. Overall, our findings highlight the role of gene expansion, increased expression, and particularly amino acid mutations in driving the evolution of Allium-specific compounds. These insights not only deepen our understanding of the phylogeny of Asparagales, but also illuminate the genetic mechanisms underpinning specialized compounds.Biological sciences/Evolution/PhylogeneticsBiological sciences/Ecology/BiogeographyBiological sciences/Plant sciences/Plant evolutionBiological sciences/Plant sciences/Secondary metabolismAsparagalesphylogenomicsAllium-specific flavorAlliumevolution", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-4312784/v1/879cb79152c9137a524ace3a.png", + "https://assets-eu.researchsquare.com/files/rs-4312784/v1/b35ebb68c311af3908d87f33.png", + "https://assets-eu.researchsquare.com/files/rs-4312784/v1/fc77fdafbbea0c39800f6c98.png", + "https://assets-eu.researchsquare.com/files/rs-4312784/v1/f42b2eec81375eba4964b9f1.png", + "https://assets-eu.researchsquare.com/files/rs-4312784/v1/55db806d4f885d8e9620a872.png" + ] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "AsparagalesSupplementarytables20240422.xlsxSupplementary tablesAsparagalessupplementarytextandfigures.pdfSupplementary text and figures", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Asparagales, the largest monocot order, is renowned for its ecological, economic, and medicinal significance. Here, we leverage transcriptome data from 455 Asparagales species to explore the phylogeny of Asparagales. Moreover, we investigate the evolutionary patterns of the genes involved in allium flavor formation. We not only establish a robust bifurcating phylogeny of Asparagales but also explore their reticulate relationships. Notably, we find that eight genes involved in the biosynthesis of allium flavor compounds underwent expansion in Allium species. Furthermore, we observe Allium-specific mutations in one amino acid within alliinase and three within lachrymatory factor synthase. Overall, our findings highlight the role of gene expansion, increased expression, and amino acid mutations in driving the evolution of Allium-specific compounds. These insights not only deepen our understanding of the phylogeny of Asparagales but also illuminate the genetic mechanisms underpinning specialized compounds.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Asparagales consists of approximately 1030 genera and 39,000 species distributed across 14 families, including Orchidaceae, which is one of the largest families of angiosperms according to the Angiosperm Phylogeny Group [APG] IV1 and the Plants of World Online (POWO; https://powo.science.kew.org/). Known for their diverse applications, Asparagales species serve as vegetables (onions, chives, garlic, asparagus), spices (vanilla), and ornamentals (orchids) and possess medicinal properties (Gastrodiae Rhizoma [Tianma] and Dendrobii Caulis [Shihu] in China and Cypripedium species in North America). Despite several studies having investigated the phylogeny of Asparagales2,3, there is still uncertainty regarding its evolutionary relationships. One area of debate involves the families Ixioliriaceae, Tecophilaeaceae, and Doryanthaceae. Analyses based only on plastid rbcL supported a relationship (Ixioliriaceae, (Tecophilaeaceae, (Doryanthaceae, others)))4. In contrast, analyses using plastid genomes5 and nuclear genes6,7,8 suggested alternative topologies. Transcriptome-based phylogenetics are robust approaches9,10 that can be used to explore the phylogeny of Asparagales better.\n\nAsparagales has a global distribution spanning all continents, with North America and Asia exhibiting the highest species diversity, according to POWO. Several studies investigated the biogeography of Asparagales11,12,13,14. For example, the ancestral area for Asteliaceae11, Blandfordiaceae11, Boryaceae11, Iridaceae13, and Orchidaceae14 was determined to be Australia. In total, biogeographic origins for 11 out of the 14 families within Asparagales are reported to be from Australia, South Africa, or Gondwana. However, the biogeographic origins of the remaining three families, Amaryllidaceae, Asparagaceae, and Asphodelaceae, remain to be determined. In the current phylogenomic era, it is promising to bridge our understanding of evolution by synthesizing evidence from phylogenomics and biogeographic patterns of plants15. Hence, additional analyses are imperative to reassess the biogeography of Asparagales.\n\nThe distinctive allium flavor is attributed to a wide variety of sulfur-containing compounds generated from S-Alk(en)ylcysteine sulfoxides (CSOs) found in Allium, such as garlic and onions. Major CSOs, such as alliin and isoalliin, serve as primary sources of medicinal and flavor compounds in Allium species16. The biosynthesis pathway for alliin and isoalliin originates from cysteine and involves at least seven steps17. When garlic bulbs are crushed, alliin undergoes successive conversion into allicin through the action of the enzyme alliinase17,18,19. Comparative analyses using genomic data from three Allium species (garlic [A. sativum], green onion [A. fistulosum], and onion [A. cepa]), Arabidopsis thaliana, and ten monocot species have revealed an expansion of alliinase and lachrymatory factor synthase (LFS), specifically in the three Allium species17. The study also revealed that LFS exists only in the three Allium species and is absent in others17. This finding is supported by a recent study20 in which the same three Allium species and 86 other species were used. Transcriptome analysis revealed that the alliinase, ATP-sulfurylase (ATPS), and O-acetylserine (thiol) lyase (OASTL) expanded in Chinese chive (A. tuberosum)21. Allium includes more than 1000 species, and CSOs are natural products characteristic of the genus16,22. It remains unclear whether these genes have widely expanded across Allium species. Additionally, although previous reviews hypothesized that CSOs exist in all Allium species16,22, whether the CSOs biosynthesis pathway commonly exists in Allium species also remains uncertain.\n\nIn this study, we used a dataset comprising transcriptome or genome data from 501 samples, with 196 samples from 169 species generated in this study. These samples represented 464 species, covering 13 of the 14 families within Asparagales, 37 Allium species, and nine outgroup species. Our study was designed to achieve three objectives: (1) establishing a robust phylogenetic framework for Asparagales; (2) exploring the biogeography of Asparagales; (3) examining the evolutionary patterns of genes involved in the CSOs biosynthesis pathway using high-resolution mass spectrometry, transcriptome-wide characterization, gene expression analyses with additional transcriptome sequencing, and molecular docking. Our study provides valuable insights into this diverse and ecologically significant order of Asparagales through these integrative approaches.", + "section_image": [] + }, + { + "section_name": "Results and discussion", + "section_text": "We used 480 de novo assembled transcriptomes and 12 genomes from Asparagales, along with nine outgroups for phylogenetic reconstruction (Supplementary Data\u00a01). We identified 857 nuclear orthologs across the 501 samples using DISCO23. Subsequently, coalescence-based (ASTRAL24) and concatenation (RAxML25) trees were inferred based on these nuclear orthologs. The inter-family relationships depicted in both trees are identical. Most branches in both trees exhibited maximum support [bootstrap support (BS)\u2009=\u2009100 or ASTRAL local posterior probability (LPP)\u2009=\u20091] (Fig.\u00a01a; Supplementary Fig.\u00a01). Orchidaceae was resolved as a sister to all other families within Asparagales. The inter-family relationships observed in our trees are similar to those reported in previous studies, such as the Angiosperm Phylogeny Website (APweb)26, with two major differences. Specifically, our trees recovered a relationship of (Boryaceae, (Asteliaceae, (Lanariaceae, Hypoxidaceae))) (Fig.\u00a01a), whereas APweb proposed a relationship of (Boryaceae, (Lanariaceae, (Asteliaceae, Hypoxidaceae))). Moreover, our trees reveal that (Tecophilaeaceae, Ixioliriaceae) sisters to a subclade comprised Iridaceae, Xeronemataceae, Asphodelaceae, Amaryllidaceae, and Asparagaceae with BS\u2009=\u200997 and LPP\u2009=\u20090.82 (Fig.\u00a01b). Our results differ from the relationship inferred using 353 nuclear genes8, which suggested a relationship ((Tecophilaeaceae, Ixioliriaceae), Doryanthaceae). This discrepancy was also evident compared to that of APweb and our tree inferred from cpDNA (Fig.\u00a01b and Supplementary Fig.\u00a02).\n\na The species tree of Asparagales with each genus represented by one species. The species tree was inferred using ASTRAL with 857 nuclear genes from 501 samples (Supplementary Fig.\u00a01). Subsequently, one species was selected for each genus from the ASTRAL tree. Pie charts represent gene trees for concordant bipartitions (blue), the most frequent alternative topology (green), remaining alternatives (red), and those uninformative for nodes (BS\u2009\u2264\u200950%) inferred from PhyParts. The numbers next to the branches represent the Quartet Sampling scores (QC, QD, and QI). Pie charts and numbers are displayed only for nodes included in PhyloNet analyses. (1) Polygonatum cyrtonema Hua, (2) Barnardia japonica (Thunb.) Schult. & Schult.f., (3) Muscari botryoides (L.) Mill., (4) Zephyranthes candida (Lindl.) Herb., (5) Allium cepa L., (6) Allium tuberosum Rottler ex Spreng., (7) Lycoris radiata (L\u2019H\u00e9r.) Herb., (8) Hemerocallis fulva (L.) L., (9) Kniphofia uvaria (L.) Oken, (10) Crocus tommasinianus Herb., (11) Iris domestica (L.) Goldblatt & Mabb, (12) Cyanastrum cordifolium Oliv., (13) Ixiolirion tataricum (Pall.) Schult. & Schult.f., (14) Borya sp., (15) Cymbidium kanran Makino, (16) Bletilla striata (Thunb.) Rchb.f., (17) Paphiopedilum malipoense S. C. Chen & Z. H. Tsi., (18) Changnienia amoena S.S.Chien, (19) Pleione formosana Hayata, (20) Gastrochilus japonicus (Makino) Schltr. Photos were taken by Michael L. Moody, Ya-Dong Zhou, Zhong Zhang, Ye-Chun Xu, Xiao-Xiao Wang, Xue-Jia Zhang, Jia-Le Wang, Xiang-Yu Wang, and Ling-Yun Chen. b Topological comparison of trees inferred from different datasets or methods. Red lines indicate inconsistent relationships. APweb = Angiosperm Phylogeny Website. c The maximum posterior probability species network of Asparagales inferred with PhyloNet. The numbers next to the dashed lines indicate inheritance probabilities (\u03b3). Source data underlying this figure is provided as Source Data file.\n\nTo assess gene tree discordance, we calculated the internode certainty all (ICA)27 score and identified the ratio of conflicting/concordant bipartitions through PhyParts28. We also calculated the Quartet Concordance (QC), Quartet Differential (QD), and Quartet Informativeness (QI) scores using Quartet Sampling (QS)29. The inter-family relationship of Asparagales exhibited varying degrees of support, with two notable exceptions: the clade ((Ixioliriaceae, Tecophilaeaceae), (Iridaceae, others)) and the clade (Iridaceae, others) (Fig.\u00a01a and Supplementary Fig.\u00a03). The discordance in these two clades was evidenced by 65/229 (concordant/discordant genes), 0.16 (ICA), and 0.18/0.51/0.99 (QC/QD/QI) for the former and 143/171, 0.21, and 0.34/0.37/0.98 for the latter (Supplementary Figs.\u00a03 and 4). These findings align with the phylogenetic conflict discussed in the last paragraph, highlighting the inconsistencies among different studies. The monophyly for Orchidaceae, Asphodelaceae, Iridaceae, Hypoxidaceae, and Tecophilaeaceae garnered strong support, with \u226580% of informative gene trees exhibiting concordance (Fig.\u00a01a), ICA\u2009\u2265\u20090.6, and high QS scores (QC/QD/QI\u2009\u2265\u20090.9/NA/1). However, Asparagaceae and Amaryllidaceae had weaker support. For example, Asparagaceae was backed by 136 of the 235 informative gene trees, ICA\u2009=\u20090.37, and QS score = 0.57/0.13/0.96.\n\nWe selected 18 clades to evaluate potential reticulation and incomplete lineage sorting (ILS) using PhyloNet30 and MSCquartets31, including Asparagales, two clades in Asparagaceae, Allium (Amaryllidaceae), Asphodelaceae, two clades in Iridaceae, and 11 clades in Orchidaceae. These clades encompassed most nodes within Asparagales, which displayed either equal support for alternative topologies or clear signals of conflict, indicated by a higher prevalence of discordant genes over concordant genes, coupled with low ICA and QS scores.\n\nFor PhyloNet, the maximum posterior probability (MPP) networks with inheritance probability (\u03b3) were analyzed, considering \u03b3\u2009<\u20090.05 as ILS, 0.05\u2009<\u2009\u03b3\u2009<\u20090.5 as introgression or hybridization32. Among the 18 clades, ten clades showed reticulation or ILS signals (Supplementary Fig.\u00a05). Specifically, introgression or hybridization was suggested for Asparagales, Allium (Amaryllidaceae), Asphodelaceae, Iris (Iridaceae), Dendrobium (Orchidaceae), and five Orchidaceae clades. Due to introgression and hybridization, the evolutionary relationship of organisms could be explained by combining tree- and network-based inference33. The relationship within these ten clades, which have reticulation, should be better explained by phylogenetic networks. However, we recognized that the \u03b3 may not accurately reflect historical events due to possible repeated hybridization or horizontal gene transfer30 during tens of million years of evolution, especially for the family-level relationships of Asparagales. The other eight clades showed no signals of reticulation (Supplementary Fig.\u00a05). Phylogenetic conflict within these eight clades could be attributed to other factors, such as gene tree estimation error34. The relationship within these eight clades could be better represented by \u201ctrue\u201d bifurcating trees.\n\nResults of the MSCquartets analyses generally aligned with those of PhyloNet, yielding similar conclusions for 17 of the 18 clades examined. For instance, PhyloNet suggested that the ancestor of the clade comprising Asteliaceae, Hypoxidaceae, and Lanariaceae inherited 33% of its genome from an extinct or unsampled taxon, possibly a sister group to Boryaceae, implying a historical hybridization event (Fig.\u00a01c). Meanwhile, MSCquartets revealed that 8.4% of gene trees (indicated by red triangles) in the Asparagales clade rejected the \u201ctree & star\u201d model, with numerous points deviating significantly from the vertices to the centroid (Supplementary Fig.\u00a05), also indicating non-tree-like relationships (hybridization and introgression). The only discrepancy between the two methods was observed in the clade Orchidaceae-8 (Supplementary Fig.\u00a05). While PhyloNet detected no reticulation signals within this clade, 11.1% of gene trees in the MSCquartets analysis rejected the \u201ctree & star\u201d model, and approximately 15 points were positioned centrally, suggesting ILS or introgression. This inconsistency, while challenging to discern, is often expected in the detection of ancient ILS or introgression events30,35.\n\nOverall, the family-level relationships of Asparagales, the species-level relationships of Allium, and the genus-level relationships of Asphodelaceae could be explained by networks. The genus-level relationships of Asparagaceae could be explained by bifurcating trees. The species-level relationships of Iris (Iridaceae) and relationships of Orchidaceae could be explained by both bifurcating trees and networks (Supplementary Fig.\u00a05). Refer to Supplementary Note\u00a01 for PhyloNet results of other lineages.\n\nA time-calibrated tree of Asparagales was constructed using BEAST36, which incorporated 15 clock-like orthologs and leveraged seven calibration points (Supplementary Data\u00a02). The estimated crown node age of Asparagales was 123.1 million years ago (Ma; 95% highest posterior density [HPD]: 99.2\u2013143.8\u2009Ma; Supplementary Fig.\u00a06 and Supplementary Data\u00a03). This age aligns closely with findings from recent studies, such as 123 Ma5 and 133 Ma37. The estimated crown node age for Orchidaceae was 99.2\u2009Ma, which corresponds well with 101.5 Ma2 but is older than 83\u2009\u00b1\u200910 Ma38.\n\nTo explore the biogeographical origins of Asparagales, we carried out an ancestral area reconstruction analysis using BioGeoBEARS39 with a grafted phylogeny that included 310 taxa representing all 14 families within Asparagales (Supplementary Data\u00a04 and Supplementary Fig.\u00a07). The results indicated that the ancestral regions for Asparagales were likely Asia and Australia (Fig.\u00a02 and Supplementary Data\u00a05). Asparagales comprises two major clades: Clade I, represented by Orchidaceae, and Clade II, formed by the remaining families. Our analyses suggested that Orchidaceae may have originated from the combined regions of Asia and Australia at approximately 99\u2009Ma. A recent study38, using a broad sampling of orchid lineages, inferred a Laurasian origin of Orchidaceae. Refer to Supplementary Note\u00a02 and Supplementary Fig.\u00a08 for a discussion on the biogeography of Orchidaceae. We did not specifically focus on the origins of Asparagales and Orchidaceae. As recommended40, further research with additional evidence is necessary to reassess their origins.\n\nBiogeographic analysis was conducted using BioGeoBEARS with a tree that included 310 taxa within Asparagales. The left maps illustrate early major dispersal events within clade II. The arrows and numbers on the maps correspond to the arrows and node numbers on the trees depicted in this figure. The biogeographic patterns of other families are shown in Supplementary Fig.\u00a07. The maps were created using ArcGIS v. 10.8 software. The country boundary data is sourced from the World Geographical Scheme for Recording Plant Distributions (https://github.com/tdwg/wgsrpd). Source data underlying this figure is provided as Source Data file.\n\nFor Clade II, the most likely ancestral area of its most recent common ancestor (MRCA) was Australia. Within Clade II, an early dispersal from Australia to Africa occurred for the subclade formed by Asparagaceae, Amaryllidaceae, and Asphodelaceae at approximately 84\u2009Ma (node 1 in Fig.\u00a02). At that time, Australia and Africa were connected via Antarctica41. Notably, no previous study has investigated the biogeographic origin of Asparagaceae, Amaryllidaceae, and Asphodelaceae. Our analyses identified Africa and Asia as the ancestral areas for Asparagaceae and Africa as the most likely ancestral area for Amaryllidaceae and Asphodelaceae. Within Asparagaceae, dispersals from Africa or Asia to North America were inferred for the subclade formed by Agave, Manfreda, Echeandia, and their relatives (node 2 in Fig.\u00a02; crown age, ca. 20.0\u2009Ma), and the subclade formed by Bessera, Milla, Androstephium, their relatives (node 3 in Fig.\u00a02). From North America, several Asparagaceae genera, such as Agave (node 4 in Fig.\u00a02) and Beaucarnea, migrated to South America. Following their African origin, Amaryllidaceae and Asphodelaceae underwent dispersal to South America, North America, and Asia. In the case of Amaryllidaceae, the subclade formed by Eucrosia, Caliphruria, Griffinia, etc (node 5 in Fig.\u00a02; crown age, ca. 25.4\u2009Ma) was inferred to be South America. At that time, South America and Africa were already separated by ocean41, indicating that this separation can be attributed to transoceanic dispersal. From South America, several genera within Amaryllidaceae, such as Caliphruria, Eithea, and Haylockia, migrated to North America. For Asphodelaceae, a dispersal event from Africa back to Australia was inferred for the subclade formed by Thelionema, Herpolirion, Xanthorrhoea, etc (node 6 in Fig.\u00a02). Subsequently, dispersal from Australia to South America (Eccremis and Pasithea) occurred for several taxa (node 7 in Fig.\u00a02). Refer to Supplementary Data\u00a05 for ancestral areas of all the 14 families.\n\nWe employed ultra-high performance liquid chromatography (UPLC) with quadrupole time-of-flight (QTOF) mass spectrometry (MS) to detect eight compounds in the CSOs biosynthesis pathway across nine Allium species and seven other species within Asparagales. The results indicated that three compounds upstream of the CSOs biosynthesis pathway\u2014serine, valine, and glutathione\u2014were detected in both Allium and non-Allium species (Fig.\u00a03a). However, the remaining five compounds were exclusively detected in Allium species (Fig.\u00a03a and Supplementary Figs.\u00a09 and 10). Refer to Supplementary Data\u00a06 for details about the compounds. Notably, \u03b3-glutamyl-S-allylcysteine emerged as the most upstream metabolite, specific to Allium in the pathway. The gene responsible for synthesizing \u03b3-glutamyl-S-allylcysteine could play a pivotal role in the pathway despite its unreported status.\n\na A summary of metabolites identified for 16 Asparagales species. b Extracted-ion chromatograms of isoalliin and alliin from nine Allium species. Plant photos were taken by Bing Liu, Xiao-Wei Xin, Xiao-Xiao Wang, and Ling-Yun Chen. c Secondary ion fragments for alliin and isoalliin are depicted as shaded areas on the chromatograms. Red numbers indicate the characteristic secondary fragment ion of alliin.\n\nTwo upstream sub-pathways, designated as way 1 (glutathione biosynthesis) and way 2 (valine catabolism), as illustrated in Fig.\u00a04a, along with a downstream sub-pathway, comprised a total of 13 genes (Supplementary Data\u00a07), involved in CSOs biosynthesis. Gene trees were constructed, and gene copy numbers for each species were quantified (Supplementary Figs.\u00a011\u201315). The Mann\u2013Whitney U test indicated that three genes in way 1 (OASTL, GSH1 [encoding \u03b3-glutamylcysteine synthetase], and GCL [encoding \u03b3-glutamylcysteine ligase]), two genes in way 2 (KARI [encoding ketol-acid reductoisomerase] and DHAD [encoding dihydroxy-acid dehydratase]), and three genes in the downstream sub-pathway (GGT [encoding \u03b3-glutamyl transpeptidases], alliinase, and LFS) underwent expansion in Allium species compared to non-Allium species (P\u2009<\u20090.05; Fig.\u00a04b). Refer to Supplementary Data\u00a08 and 9 for the number of gene copies in other species and in previous studies17,20. Furthermore, a comparison between early Allium species and non-Allium species also demonstrated an expansion in GGT, alliinase, and LFS within Allium (P\u2009<\u20090.05). A previous study20 suggested that the pathway may have evolved from an ancient, yet uncharacterized, plant defense system, with alliinase and LFS experiencing Allium-specific expansion. However, this hypothesis was based on genomic data from only three closely related Allium species. By analyzing metabolite data from 16 species and transcriptomes from 110 species, our findings revealed that homologs of genes involved in CSOs biosynthesis are widespread in Asparagales, with eight out of the 13 genes undergoing significant expansion in Allium.\n\na The CSOs biosynthetic pathway in Allium. b Number of gene copies for representative species within Amaryllidaceae and results of the two-sided Mann\u2013Whitney U test. Green pies next to species names indicate the three species that utilized genomic data, while other species utilized de novo assembled transcriptomes. The Mann\u2013Whitney U test was conducted based on gene copy numbers inferred from de novo assembled transcriptomes of 34 Allium species and 14 non-Allium species. The medians and P-values are shown in the table. For example, 0.013 indicates the P-value for Allium vs. non-Allium of gene OASTL, while 6 vs. 3.5 indicates the medians for the two groups. c Duplication types. Asterisks indicate the genes significantly expanded in Allium species. d Gene expression level. The expression level was calculated by averaging the expression of all copies(y) within a gene for each species and summarizing the expression of all copies(y) of a gene separately for each species. Source data underlying panel (d) is provided as Source Data file.\n\nGene trees revealed that six of the eight genes known to have undergone expansion in Allium, namely, GSH1, GCL, KAR1, GGT, alliinase, and LFS, duplicated at the MRCA of Allium (Supplementary Figs.\u00a011\u201315), with alliinase and LFS experiencing at least three times of duplications (Supplementary Fig.\u00a015). In contrast, the five non-expanded genes did not. Moreover, the alliinase and LFS formed species-specific clusters (Supplementary Fig.\u00a015), indicating their independent expansions, consistent with Liao et al.17. Whole-genome duplication (WGD) analyses using Tree2GD42 and the methods of Yang et al.43 (map_dups_mrca.py) supported a WGD event that occurred at the MRCA of Allium (Supplementary Data\u00a010). Three (OASTL, alliinase, and LFS) of the eight expanded genes are indeed included within the 1029 AABB gene clusters that supported the WGD at the MRCA of Allium. Analyses using DupGen_finder44 indicated that the 13 genes derived from dispersed, proximal, tandem duplications with two OASTL copies in A. sativum derived from WGD (Fig.\u00a04c). In conclusion, the expansion of genes in the CSOs biosynthesis pathway could be attributed to WGD, dispersed, proximal, and tandem duplications.\n\nThe gene expansion of alliinase and LFS may aid Allium species in responding to external stimuli17; however, the types of stimuli remain unknown. Our divergence time estimation revealed that the diversification of the two genes in Allium occurred in recent 10\u2009Ma with a median age of approximately 5\u2009Ma (Supplementary Fig.\u00a016), much younger than the MRCA of Allium (40\u2009Ma; Supplementary Fig.\u00a06). Notably, there has been a significant increase in the global insect population over the past 10 million years45. Several insect larvae, such as Delia antiqua46, and leaf miners, such as Phytomyza gymnostoma, feed on Allium species. Considering the concurrent diversification of insects47,48, it is plausible that the expansion of these two genes serves as a defense mechanism against insect predation, as proposed49,50. To investigate whether there is a gene with a timescale of duplication similar to alliinase and LFS, we explored research on the evolution of metabolites and their corresponding genes across various studies, such as those on steroids51, benzylisoquinoline alkaloid52, and terpenoids53. However, there was no common pattern regarding the timescale of gene family evolution related to plant metabolites. This variability could be explained by the different environments in which plants live and the diverse ecological functions of plant metabolites, such as attracting pollinators and resisting biotic and abiotic environmental stressors54. Furthermore, no similar timescale phenomena were observed for genes associated with plant metabolite resistance to insects; however, such a phenomenon was noted for rye Pm3- and wheat Pm8- like genes, which are related to pathogen resistance55.\n\nThe gene expression levels for each of the 13 genes in the pathway were assessed across nine Allium species and seven other species from Asparagales with 48 transcriptomes. Expression was measured using two strategies: averaging the expression of all copies(y) within a gene in a species and summarizing the expression of all copies(y) within a gene in a species (Fig.\u00a04d). Both strategies indicated that the upstream genes in the pathway exhibited relatively low expression levels. Conversely, the downstream genes exhibited relatively high expression levels in Allium species, in contrast to those in non-Allium species. Interestingly, analyses of genes involved in the biosynthesis of terpenoids, particularly within Lamiaceae, a family renowned for terpenoid richness, showed an opposite pattern, with upstream genes being highly expressed and downstream genes exhibiting low expression levels56.\n\nA comparison across nine Allium species revealed that, except AHAS and BCAT, 11 of the 13 genes exhibited higher expression levels in the bulb than in the leaf (Supplementary Fig.\u00a017). A previous study reported that garlic bulbs contain a higher concentration of alliin compared to leaves57. Our findings revealed that the FMO gene, which involves the last step of alliin synthesis, exhibits a higher expression in the bulb than in the leaf, aligning with the results of Yang et al.57 and Yoshimoto et al.58. However, compared to our results, the FMO exhibited an opposite expression pattern in two studies17,59. This inconsistency could be explained by the factor that the expression of genes in CSOs biosynthesis varied during different growth stages58. Further research is needed to explore the relationship among developing stages, organs, and CSOs biosynthesis.\n\nTo uncover gene motif(s) potentially linked to CSOs biosynthesis, motif analyses were conducted on the 13 genes involved in the pathway (Supplementary Data\u00a011). The findings revealed that PCS and LFS exhibited Allium-specific gene features. Specifically, the PCS in Allium lacks motif 8, which is conserved in other species, and the LFS possesses Allium-specific motifs 12 and 14 (Fig.\u00a05a and Supplementary Figs.\u00a018\u201321). Conversely, the remaining genes did not show motif-level differences between Allium and non-Allium species.\n\na Schematic representation of the motifs for the LFS. b Specific mutations located in the binding pockets of alliinase and LFS. A comparison of the sequence logos of Allium species (633 sequences of alliinase and 515 of LFS) and non-Allium species (48 sequences of alliinase and 53 of LFS) is shown. c 3D structural models and LigPlot+ diagrams for alliinase/alliin. d 3D models and LigPlot+ diagrams for LFS/(E)-1-propene\u22121-sulfenic acid (1-PSA). The amino sites in the binding pockets are shown in light blue, substrates are shown in yellow, and PLP cofactors are shown in pink in the 3D models. Hydrogen bonds are displayed as green dashed lines, while hydrophobic (or non-bonded) interactions are displayed as red opposite arcs in LigPlot+ diagrams. Source data underlying panels (a) and (b) are provided as Source Data file.\n\nWe then performed multiple sequence alignments for the 13 genes in the CSOs biosynthesis pathway. The results unveiled 34 Allium-specific mutations in GSH2, PCS, GGT, FMO, alliinase, and LFS (Supplementary Data\u00a012). Among these mutations, one site in alliinase (Q388) and three sites (F84, F104, and W155) in LFS (Fig.\u00a05b) are likely linked to CSOs biosynthesis. Specifically, these sites in alliinase and LFS are situated within the substrate-binding pockets of A. sativum or A. cepa, as reported60,61 (Supplementary Data\u00a013).\n\nOur molecular docking analyses demonstrated that the site Q388 in A. sativum alliinase formed hydrophobic (or non-bonded) interactions with the substrate alliin, whereas the corresponding site in non-Allium alliinase did not engage in such interactions (Fig.\u00a05c). The sites F84, F104, and W155 in the Allium cepa LFS (AcLFS) engaged in hydrophobic (or non-bonded) interactions with the substrate (E)\u22121-propenesulfenic acid (1-PSA) (Fig.\u00a05d). However, these three sites in non-Allium LFSs did not form hydrophobic interactions with the substrate 1-PSA. Previous research utilizing site-directed mutagenesis, protein expression, and activity assays found that mutagenesis at F104 comparatively reduced the activity of AcLFS61. The side chain of F84, adjacent to E88\u2014a validated active site\u2014serves as an indicative residue for the binding state of AcLFS61. The research61 also identified E88, Y102, and Y114 as active sites. Our molecular docking analyses confirmed that E88, Y102, and Y114 form hydrogen bonds with 1-PSA. Interestingly, although the three sites E88, Y102, and Y114 are conserved across Allium and non-Allium LFSs, neither Y102 nor Y114 exhibited hydrogen bonds with the substrate 1-PSA in non-Allium species (Fig.\u00a05d). Overall, our findings suggest that mutations at the four identified sites (Q388 in alliinase and F84, F104, and W155 in LFS) may impact protein substrate recognition, consequently influencing metabolite production. Since alliinase and LFS are positioned downstream in the CSOs biosynthesis pathway (Fig.\u00a04a), the four sites are unlikely to be the key enzymes determining whether a plant produces CSOs. Nevertheless, the four mutations in alliinase and LFS might contribute to the diversity of CSOs, such as the formation of alliin and isoalliin. The functions of these four sites require further verification through wet lab experiments.\n\nIn summary, our study generated a robust phylogenetic tree of Asparagales, shedding light on the African origins of Amaryllidaceae, Asparagaceae, and Asphodelaceae. We demonstrated that gene expansion, increased expression, and particularly amino acid mutations play pivotal roles in the biosynthesis of allium flavor compounds. The transcriptome dataset generated in this study promises to propel future research in multiple fields significantly. Overall, this study contributes valuable insights into the phylogeny of Asparagales and the evolution of the CSOs biosynthesis pathway.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53943-6/MediaObjects/41467_2024_53943_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53943-6/MediaObjects/41467_2024_53943_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53943-6/MediaObjects/41467_2024_53943_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53943-6/MediaObjects/41467_2024_53943_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53943-6/MediaObjects/41467_2024_53943_Fig5_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "A workflow representing the methodological steps employed in this study is presented in Supplementary Fig.\u00a022.\n\nWe collected 196 samples from China between 2018 and 2022 (Supplementary Data\u00a01). Most of these samples were collected from natural populations, with a small subset originating from botanical gardens (Supplementary Data\u00a01). For samples collected during natural populations, we obtained proper permission from the land managers. For samples collected from botanical gardens, we obtained permission from these gardens. RNA-seq reads (2 \u00d7 150\u2009bp) were generated for these samples. Sequencing was carried out using the Nova HiSeq 4000 platform or the Beijing Genomic T7 platform. Additionally, 16 samples were sourced from published whole-genome sequencing data, while 289 were derived from RNA-seq reads obtained from NCBI SRA. In total, 501 samples across 464 species were sampled (Supplementary Data\u00a01). Among the 501 samples, 492 samples from 455 species are from Asparagales, representing 160 of the 1144 genera and 13 of the 14 families encompassed by Asparagales. Among the 501 samples, one species from Acorales, four from Alismatales, one from Petrosaviales, one from Dioscoreales, and two from Liliales were outgroups according to the APG IV1 classification.\n\nThe processing of raw reads, assembly, and translation followed the pipeline outlined by Y. Yang and S.A. Smith (https://bitbucket.org/yanglab/phylogenomic_dataset_construction/). Specifically, sequencing errors in raw reads were corrected using Rcorrector v.1.0.462. Adapters and low-quality bases were removed using Trimmomatic v.0.38 with parameters SLIDINGWINDOW:4:15 LEADING:5 TRAILING:4 MINLEN:8063. Then, organelle reads were filtered using Bowtie2 v.2.3.564 by mapping to Magnoliophyta organelle genomes obtained from the NCBI Organelle Genome Resources database (accessed October 17, 2018). Over-represented reads were detected by FastQC v.0.11.9 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and removed. Transcriptomes were assembled using Trinity v.2.3.265. Then, the longest transcript within each Trinity \u201cgene\u201d was extracted using the script get_longest_isoform_seq_per_trinity_gene.pl in Trinity, followed by translated into coding sequences (CDS) and peptides (PEP) using TransDecoder v.5.5.066.\n\nTo identify orthologs, we first conducted an all-by-all BLASTN search using NCBI BLAST v.2.9.0+67 for the CDSs across the 501 samples. Putative homolog groups were clustered using MCL v.1.3768. Homolog groups were aligned using MAFFT v.7.40769 with the settings \u201c--genafpair --maxiterate 1000\u201d, and low occupancy columns were trimmed using Phyutility70 (all sequences in this study were aligned using MAFFT and trimmed using Phyutility unless otherwise noted). A maximum likelihood (ML) gene tree was constructed for each homolog group with RAxML v.8.2.1225 (all ML trees in this study were inferred using RAxML with the GTRCAT model and 100 bootstrap replicates unless otherwise noted). Next, orthologs were inferred using DISCO v.1.3.123. The alignments for the orthologs were concatenated, and then an ML tree of Asparagales was built using the concatenated dataset. Additionally, individual gene trees were generated for each ortholog and a species tree was then inferred from these gene trees with ASTRAL v.5.7.324.\n\nMoreover, a phylogenetic tree of Asparagales was constructed using 75 CDSs from chloroplast genomes to gain further insights into the evolutionary relationships within the group. Plastome sequences for 867 species were obtained from GenBank. As plastomes for the families Boryaceae, Ixioliriaceae, Lanariaceae, and Blandfordiaceae are inaccessible from GenBank, five CDSs for these families were accessed from GenBank. After sequence alignment, CDSs were concatenated, and an RAxML tree was constructed (Supplementary Fig.\u00a02).\n\nTo investigate discordance, the nuclear ortholog trees were rooted with outgroups. Then, each rooted tree was compared against the ASTRAL tree using PhyParts v.0.0.128, with a bootstrap (BS) support cutoff of 50%, to obtain the proportion of concordant/conflicting bipartitions and ICA scores. An ICA close to 1 indicates strong concordance in the bipartition of interest, whereas an ICA closer to 0 indicates equal support for one or more conflicting bipartitions. A negative ICA indicates that the internode of interest conflicts with one or more bipartitions that are more frequent27. Additionally, an ICA value close to \u22121 indicates a lack of concordance for the bipartition of interest27.\n\nTo distinguish nodes lacking support from those exhibiting signals of conflict, Quartet Sampling v.1.3.129 was performed with 100 replicates using the concatenated nuclear ortholog alignment and the ML tree inferred from the dataset. A QC value close to 1 suggests that all quartets are concordant, while a QC close to 0 suggests equivocal concordant/discordant quartets. A negative QC suggests that discordant quartets occur more frequently than concordant ones. No QD indicates no alternative topology (i.e., QC\u2009=\u20091). A QD close to 1 suggests that the two alternative topologies are present at equal frequencies, whereas a QD close to 0 suggests a preference for one of the two alternative topologies. Last, a QI value near 1 suggests that all replicates provide informative data, whereas a value close to 0 suggests uncertainty among the replicates.\n\nTo estimate phylogenetic networks that accommodate both reticulation (e.g., hybridization) and ILS, Bayesian inference was conducted with PhyloNet v.3.8.230. Considering computational constraints and our specific interest in clades manifesting a distinct signal of conflict, we streamlined our sampling to 18 clades. The 18 clades included Asparagales (including 14 species), two clades in Asparagaceae (9 and 6 species separately), Amaryllidaceae (12 species), Asphodelaceae (9 species), two clades in Iridaceae (8 and 10 species separately), and 11 clades in Orchidaceae (eight to 14 species separately). We included only orthologs that are found in all species. In this way, we included 163 to 582 orthologs for these groups. Five independent runs were applied for each group. Searches were carried out, allowing up to three reticulation events. MCMC chains of 30 million with sample frequencies of 3000 were carried out for each group. Searches were performed using one cold chain with a temperature of 1.0 and two hot chains with temperatures of 2.0 and 3.0, respectively. Pseudo-likelihood was applied to speed up the searches. PhyloNet30 calculated inheritance probabilities (\u03b3) that represent the proportion of genes contributed by each parental population to a given hybrid node. The first 25% of the iterations were set as burn-in. Moreover, we used the function quartetTreeTestInd in the MSCquartets v.2.031 with the \u201cT3 model\u201d to evaluate the level of ILS within the 18 clades.\n\nDivergence time estimation was accomplished through BEAST v.2.6.336, utilizing seven calibration points. These calibration points included three within Asparagales, three within outgroups, and one at the root of all samples (Supplementary Data\u00a02). Calibrations were only applied to the nodes with high support in phylogenetic analyses. Due to the vast size of the phylogenomic dataset, fifteen clock-like orthologs were selected using SortaDate71 with the parameter \u201c--order 1, 2, 3\u201d. In BEAST, the Gamma site model was applied with estimated Substitution Rate, estimated Proportion Invariant and Subst model, Relaxed Clock Log-Normal model, and Log-Normal priors. Six independent analyses were conducted, each running for one billion generations and sampled every 2000 generations. During the MCMC chain, the tree was fixed with the ML tree inferred from the 857 orthologs. The effective sample size scores for all relevant estimated parameters were checked to ensure values \u2265200 using Tracer v.1.7.172. The first 10% of trees were discarded as burn-in, and the remaining trees were used to generate a summary tree with TreeAnnotator v.2.6.336.\n\nFor biogeographic inference, a grafted phylogeny consisting of 310 Asparagales taxa was used. For the construction of the grafted phylogeny, we used the inter-family relationships depicted in the Asparagales species tree (Fig.\u00a01a) as a backbone. Orchidaceae has more than 26,000 species and ca. 705 genera (the Plants of the World Online [http://www.plantsoftheworldonline.org/]). Sampling all these species or genera within Orchidaceae can be quite challenging due to their vast diversity. To represent Orchidaceae, 16 genera and 51 subtribes within Orchidaceae with relationships compiled from previous studies2,38 were added to the backbone. Each of the family Amaryllidaceae, Asparagaceae, Asphodelaceae, and Iridaceae has more than 40 genera (World Flora Online: http://www.worldfloraonline.org). To represent the genera within these families, we first constructed ML trees using ITS sequences obtained from GenBank for the four families separately. Then, we added the genera that only existed in these ML trees to the reported phylogenies of the four families (Supplementary Data\u00a04). The natural distribution of each taxon was accessed from the POWO (retrieved 12 October 2023). According to our phylogenetic analyses and the classification of POWO, all the 14 families and genera for which we proposed possible dispersal routes are monophyletic (Fig.\u00a02). Eight geographical areas were designated: Europe (A), Africa (B), mainland Asia (C), South Asian islands (D), Australia and Papua New Guinea (E), North America (F), South America (G), and Pacific (H), similar to the study on Orchidaceae14. However, unlike the study14, we have distinguished Europe and mainland Asia as separate regions, reflecting their significant geological separation during the period (100\u201340\u2009Ma) when the Asparagales families originated. This approach aligns with methodologies adopted in other biogeographic studies, including those on Dryopteris73. Ancestral areas were reconstructed using BioGeoBEARS39 with the best-fit model BAYAREALIKE\u2009+\u2009J. The maximum number of areas allowed at each node was set to three.\n\nThe qualitative identification of eight compounds in the CSOs biosynthesis pathway was conducted across nine Allium species and seven other species within Asparagales. Bulbs or leaves were used. Each sample weighing 0.10\u20130.15\u2009g was placed in a 2\u2009mL centrifuge tube with two 3\u2009mm steel balls and 1000\u2009\u03bcL of a methanol/deionized water mixture (7:3, v/v). After being rubbed with a tissue grinder, the samples were homogenized for 30\u2009seconds, sonicated for 30\u2009minutes, and centrifuged at 13,000\u2009rpm for 10\u2009minutes at room temperature. The resulting extracts were filtered through a 0.22-\u03bcm filter and stored at \u221220\u2009\u00b0C for later analysis.\n\nEight compounds were identified. Among them, serine, glutathione, valine, S-allylcysteine, alliin, and isoalliin were identified by comparing their retention time and m/z values of fragment ions to authentic standards. Isoalliin was identified at a retention time of approximately 5.1\u2009min, while alliin was detected at approximately 5.3\u2009min (Fig.\u00a03b). Additionally, alliin presented a secondary ion fragment with an m/z value of approximately 137.0139 (Fig.\u00a03c), a feature not observed for isoalliin. \u03b3-glutamyl-S-allylcysteine and allicin were confirmed by comparing their m/z values of fragment ions to those in PubChem (https://pubchem.ncbi.nlm.nih.gov/) and MASSBANK. Glutathione and alliin were purchased from Shanghai Yuanye Biotechnology Company, Shanghai, China. S-allylcysteine and isoalliin were purchased from Caoyuankang Biotechnology Company, Chengdu, China. Serine and valine were purchased from Energy Chemical, Saen Chemical Technology Company, Shanghai, China. All reagents are analytical grade (\u226598.0% pure). A concentration of 25\u201330\u2009\u03bcg/mL for each standard was used.\n\nQualitative identification was performed on the SCIEX ZenoTOFTM 7600 (SCIEX, Foster City, CA, USA) with the ESI source coupled to a UPLC system (ExionLC AE system, Shimadzu, Japan). The UPLC conditions followed Liao et al.17 with slight changes. The chromatographic separation was performed on a Waters BEH amide column (100 \u00d7 2.1\u2009mm, 1.7 \u03bcm) maintained at 20\u2009\u00b0C with a flow rate of 0.6\u2009mL/min. The mobile phase A contained deionized water with 0.5% formic acid (v/v), and mobile phase B contained acetonitrile with 0.5% formic acid (v/v). The following gradient elution was used: 0\u20134\u2009min, 10\u201315% A; 4\u20138\u2009min, 15\u201360% A; 8\u201315\u2009min, 10% A; and an injection volume of 1\u2009\u03bcL. For QTOF, the mass spectrometer was operated in the positive ESI mode with a SCIEX Turbo V\u2122. The PeakView v.1.2 (AB SCIEX, Foster City, CA, USA) was used to analyze the data obtained from the information-dependent acquisition (IDA) method. The spray voltage and ion source temperature were set to 5.5\u2009kV and 450\u2009\u00b0C, respectively. The declustering potential (DP) was set to 60\u2009V. The ion source gas 1, ion source gas 2, curtain gas, and CAD gas were set to 50, 50, 35, and 9\u2009psi, respectively. The MS/MS spectra were generated in product ion scan mode at a collision energy (CE) of 15\u2009V with a CE spread of 5\u2009V. Parent ions scan ranged from m/z 50 to 800\u2009Da with a 0.15\u2009s accumulation time, and the product ions scan ranged from 30 to 800\u2009Da with a 0.045\u2009s accumulation time. Three biological replicates were used for all accessions.\n\nThirteen genes involved in the pathway of CSOs biosynthesis were selected (Fig.\u00a04a) according to previous studies16,17,19,20. To investigate gene copy numbers, we compiled a dataset comprising transcriptomes from 110 species. Each gene within the pathway was individually utilized as a \u201cbait\u201d to search against the dataset with SWIPE v.2.1.074. A maximum of 100 hits was retained for each species. Candidate genes were filtered using Pfam domains with InterProScan75 (Refer to Supplementary Data\u00a07 for information about bait genes and Pfam domains). Orthologs for each gene family were classified based on their ML trees. Each gene family may include multiple ortholog groups. Orthologs that included the bait genes and had no gene duplication at the MRCA of Asparagales were treated as the orthologs most likely related to CSOs biosynthesis. Then, the number of gene copies in each species was determined from these ortholog groups (Supplementary Figs.\u00a011\u201315). A two-sided Mann\u2013Whitney U test was conducted using SPSS v.22 (IBM Corp. Released 2013). The mode of gene duplication for these genes was investigated using DupGen_finder-unique44. In addition, the WGD events within Amaryllidaceae were investigated. Whether the WGD events led to gene copy number increase was checked. WGD events within Amaryllidaceae were investigated using Tree2GD v.1.0.3742 and the script map_dups_mrca.py43. These two methods calculate the number and proportion of duplicated gene clusters for each node within the Amaryllidaceae phylogeny. Nine species within Amaryllidaceae were used in Tree2GD. A duplicated gene cluster in a clade, which retains two subclades, indicates a signal of WGD event (AABB duplication)76. For map_dups_mrca.py, gene trees of homologs inferred from 501 samples were mapped to the Asparagales species tree, and the proportion of duplicated genes was counted. We employed a criterion to propose a WGD event, requiring \u2265200 AABB gene clusters (inferred from Tree2GD) and \u226520% of duplicated gene clusters inferred from map_dups_mrca.py for a given clade. Under this criterion, we identified a WGD event at the MRCA of Allium, consistent with Hao et al.20. 1029 AABB gene clusters were found to support the WGD at the MRCA of Allium. Subsequently, we checked whether the 13 genes in the CSOs biosynthesis pathway were included in these 1029 gene clusters by comparing if there were identical sequence names between gene clusters and trees of the 13 genes. Divergence time for alliinase and LFS were separately estimated using TreePL v.1.077 with ML trees and branch length estimated using RAxML. Five calibration points were used (Supplementary Data\u00a02).\n\nTo investigate the gene expression level, RNA sequencing was conducted for 16 species with three replicates. Among the 16 species, nine are from Allium, and seven are from other lineages within Asparagales. In total, 48 transcriptomes were generated. De novo transcriptomes for each species were assembled using Trinity v.2.3.2. The expression level of each gene was measured with Transcript per million (TPM) by aligning RNA-seq reads to the transcriptome of each species using Salmon v.0.9.178. Then, the TPM for genes in those ortholog groups was extracted.\n\nWe examined the motifs of 13 genes within the CSOs biosynthesis pathway. We compiled a dataset consisting of 42 species (Supplementary Data\u00a011), for which whole-genome sequencing data were available. Each gene in the pathway was individually utilized as a \u201cbait\u201d to search the dataset using SWIPE v.2.1.0 to identify homologs. Please refer to Supplementary Data\u00a07 for details regarding the bait genes. Motifs were predicted using MEME v.5.5.579, and 15 motifs were allowed for each gene.\n\nFurthermore, the active residues for the five genes in the downstream sub-pathway were examined. Specifically, the protein structures of PCS and GGT from Allium were predicted using AlphaFold2 online (https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb). The protein structures of FMO, alliinase, and LFS from Allium were accessed from the RCSB PDB (https://www.rcsb.org/) with entry IDs 6WPU, 1LK9, and 5GTF, respectively. Then, the active amino acids for PCS, GGT, and FMO were predicted using AutoDockTools v.1.5.680 and gathered from previous research (Supplementary Data\u00a013). The active amino acids of alliinase60 and LFS61 were also gathered. The protein structures of genes from non-Allium species were predicted using AlphaFold2 online. The 3D conformers of the substrates were obtained from PubChem and converted to PDB format. Subsequently, we compared the variation in active sites between Allium and non-Allium species. 3D molecular structures were visualized using PyMOL in AMDock81 and ligand-protein interactions were visualized using LigPlot+ v.2.2.882.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Raw sequence reads of the 244 samples generated in this study have been deposited in the NCBI Sequence Read Archive under BioProject nos. PRJNA1107703 and PRJNA1107706. Raw reads or annotations for other samples were accessed from the internet with accession nos. provided in Supplementary Data\u00a01. The assembled transcriptomes for the samples sequenced in this study, CDSs and PEPs for all the 501 samples used in phylogenetic analyses, as well as those for the 16 samples used in gene expression level analyses, are available at Figshare: [https://doi.org/10.6084/m9.figshare.25516204]. Additionally, the sequences of orthologs, data matrices for phylogenetic analyses, divergence times, ancestral area reconstructions, and Source Data are also available at Figshare: [https://doi.org/10.6084/m9.figshare.25516204]. Specimens have been deposited at the Herbarium of Guangxi Botanical Garden of Medicinal Plants.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Codes used in this study have been deposited at Figshare: [https://doi.org/10.6084/m9.figshare.25516204].", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Byng, J. W. et al. 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Moody (University of Texas at El Paso) for discussion and providing plant photo; Xu Lu,\u00a0Hui-Ying Wang (China Pharmaceutical University) and Xiang-Yang Leng (SCIEX, China) for technical assistance; Xiao-Wei Xin (Shandong Drug and Food Vocational College) for providing plant materials; Bing Liu (Institute of Botany, CAS), Zhong Zhang (Jinggangshan National Nature Reserve) and Ye-Chun Xu (Guangdong Academy of Agricultural Sciences) for providing plant photos; Ya-Dong Zhou (Nanchang University) for assisting in data analyses and providing plant photos; Guo-Yong Xie (China Pharmaceutical University)\u00a0for assisting in plant cultivation; Li Feng, Xing-Ze Li, Wen-Da Zhang, Wen-Fang Zheng, and Wen-Yu Du (China Pharmaceutical University) for assisting in data analyses; John A. Rhodes (University of Alaska Fairbanks) for explaining the results of MSCquartets; Ji Yang (Fudan University) and Tao Wan (Wuhan Botanical Garden) for suggestions on this manuscript. This work was supported by the Guangxi Innovation-Driven Development Project (no. GuiKe AA18242040 to K.-H.W.), the Construction of Southern Medicine Germplasm Resource Base for Guangdong Northern (20231206 to K.-H. W.), the National Natural Science Foundation of China (32370242 to L.-Y.C.), the Fundamental Research Funds for the Central Universities (2632024TD04 to L.-Y.C.),\u00a0and the Sino-Africa Joint Research Center (SAJC202101 to Q.-F.W.).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Xiao-Xiao Wang, Chien-Hsun Huang, Diego F. Morales-Briones.\n\nDepartment of Resources Science of Traditional Chinese Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, 211198, Nanjing, China\n\nXiao-Xiao Wang,\u00a0Xiang-Yu Wang,\u00a0Na Zhang,\u00a0Pu-Guang Zhao,\u00a0Xinya Hemu,\u00a0Ning-Hua Tan\u00a0&\u00a0Ling-Yun Chen\n\nNational Center for Traditional Chinese Medicine (TCM) Inheritance and Innovation, Guangxi Botanical Garden of Medicinal Plants, 530023, Nanning, China\n\nXiao-Xiao Wang,\u00a0Ying Hu,\u00a0Xiao-Mei Wei\u00a0&\u00a0Kun-Hua Wei\n\nState Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Key Laboratory of Herbage and Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, 010021, Hohhot, China\n\nChien-Hsun Huang\n\nState Key Laboratory of Genetic Engineering, Ministry of Education Key Laboratory of Biodiversity Sciences and Ecological Engineering, Institute of Biodiversity Sciences and Institute of Plant Biology, School of Life Sciences, Fudan University, 200438, Shanghai, China\n\nChien-Hsun Huang\n\nSystematics, Biodiversity and Evolution of Plants, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, 80638, Munich, Germany\n\nDiego F. Morales-Briones\n\nKey Laboratory of State Administration of Traditional Chinese Medicine for Production & Development of Cantonese Medicinal Materials, School of Chinese Materia Medica, Guangdong Pharmaceutical University, 510006, Guangzhou, China\n\nKun-Hua Wei\n\nKey Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Sino-Africa Joint Research Center, Chinese Academy of Sciences, 430074, Wuhan, China\n\nQing-Feng Wang\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nQ.-F.W., L.-Y.C., K.-H.W., and N.-H.T. designed the research. Q.-F.W., K.-H.W., Y.H., X.-M.W., G.-W.H., X.-X.W., and L.-Y.C. contributed to the taxon sampling and sequencing. X.-X.W., C.-H.H., D.F.M.-B., N.Z., P.-G.Z., X.-Y.H., and L.-Y.C. performed data analyses. X.-X.W. performed wet lab experiments. X.-X.W., X.-Y.W., D.F.M.-B., and L.-Y.C. prepared the figures and tables. L.-Y.C. drafted the manuscript. Q.-F.W., C.-H.H., D.F.M.-B., X.-X.W., and L.-Y.C. revised this manuscript. All the authors read this manuscript.\n\nCorrespondence to\n Kun-Hua Wei, Ning-Hua Tan, Qing-Feng Wang or Ling-Yun Chen.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Deng-Feng Xie and Mingfang Zhang for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Wang, XX., Huang, CH., Morales-Briones, D.F. et al. Phylotranscriptomics reveals the phylogeny of Asparagales and the evolution of allium flavor biosynthesis.\n Nat Commun 15, 9663 (2024). https://doi.org/10.1038/s41467-024-53943-6\n\nDownload citation\n\nReceived: 23 April 2024\n\nAccepted: 29 October 2024\n\nPublished: 08 November 2024\n\nVersion of record: 08 November 2024\n\nDOI: https://doi.org/10.1038/s41467-024-53943-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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ultimate limit of plasmonic near-field enhancement", + "pre_title": "Quantifying the Ultimate Limit of Plasmonic Near-field Enhancement", + "journal": "Nature Communications", + "published": "11 October 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53210-8/MediaObjects/41467_2024_53210_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53210-8/MediaObjects/41467_2024_53210_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Nanophotonics and plasmonics", + "Raman spectroscopy" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4300209/v1.pdf?c=1728731158000", + "research_square_link": "https://www.researchsquare.com//article/rs-4300209/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53210-8.pdf", + "preprint_posted": "07 May, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Quantitatively probing the ultimate limit of near-field enhancement around plasmonic nanostructures remains elusive, despite more than five decades since the discovery of surface-enhanced Raman scattering (SERS). Theoretical calculations have predicted an ultimate near-field enhancement exceeding 1000 using the best plasmonic material Ag, but experimental estimations disperse by orders of magnitude. Here, we design a high-quality Ag plasmonic nanocavity with atomic precision and precisely quantify the upper limit of near-field enhancement in ~\u20091 nm junctions. A hot-spot averaged SERS enhancement of 4.29\u00d71010 is recorded with a small fluctuation, corresponding to an averaged electric field enhancement larger than 1000 times. This result quantitatively delineates the ultimate limit of plasmonic field enhancement around plasmonic nanostructures, establishing a foundation for diverse plasmon-enhanced processes and strong light-matter interactions at the atomic scale.Physical sciences/Optics and photonics/Optical physics/Nanophotonics and plasmonicsPhysical sciences/Optics and photonics/Optical techniques/Optical spectroscopy/Raman spectroscopyplasmonic field enhancementsurface enhanced Raman scatteringhot spotsnanocube-on-mirrornanocavity", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Supplementaryinformation.docxSupplementary information for \u2032Quantifying the Ultimate Limit of Plasmonic Field Enhancement\u2032", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Quantitatively probing the ultimate limit of near-field enhancement around plasmonic nanostructures remains elusive, despite more than five decades since the discovery of surface-enhanced Raman scattering. Theoretical calculations have predicted an ultimate near-field enhancement exceeding 1000 using the best plasmonic material silver, but experimental estimations disperse by orders of magnitude. Here, we design a high-quality silver plasmonic nanocavity with atomic precision and precisely quantify the upper limit of near-field enhancement in ~1\u2009nm junctions. A hot-spot averaged Raman enhancement of 4.27\u2009\u00d7\u20091010 is recorded with a small fluctuation, corresponding to an averaged electric field enhancement larger than 1000 times. This result quantitatively delineates the ultimate limit of plasmonic field enhancement around plasmonic nanostructures, establishing a foundation for diverse plasmon-enhanced processes and strong light-matter interactions at the atomic scale.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The effect of light concentration around plasmonic nanostructures, characterized by intense near-field enhancement and enlarged light harvesting efficiency1,2,3, has driven remarkable advances across various fields. These include but are not limited to plasmon-enhanced spectroscopies4,5,6,7, ultra-sensitive sensors8, strong light-matter interaction9, and opto-electronic devices10,11,12,13. Despite these achievements, the quantitative exploration of the ultimate limit of plasmonic field enhancement, often appearing in the nanogap between two metal nanoparticles, remains a challenging endeavor, due to the lack of efficient near-field probes at the sub-nanometer scale12,14. Surface-enhanced Raman scattering (SERS) of small molecules, where the electromagnetic enhancement is the primary contributor to the total enhancement, emerges as a powerful tool to probe the near-field enhancement by measuring the far-field Raman scattering intensity15,16. However, challenges arise from the random position, number, and orientation of probe molecules trapped in the \u2018hot spots\u2019 region, the non-optimized excitation or collection conditions (laser wavelength, polarization, incident angle, etc.), as well as the possible involvement of chemical enhancement, leading to ambiguous SERS enhancement factor. These are why experimental results are always spread over a wide order of magnitude, typically from 105 to 1011 times17,18,19,20,21, even up to 1014 to 1015 in earlier single-molecule SERS estimation22. Once the enhancement factor is ultra-high\u2014the enhancement is from few \u2018hot spots\u2019, the SERS intensity fluctuates sharply, even by quantitative SERS schemes like wavelength-scanned SERS18 or bi-analyte technique19.\n\nTheoretical calculations have predicted an ultimate electromagnetic enhancement of more than 1012 times using the best plasmonic material silver (Ag) within ~1\u2009nm nanogap before the onset of quantum effects23,24,25,26. Although the quantum-limited field enhancement in gold nanoparticle-on-mirror has been quantitatively probed15,16, yielding an upper limit of 114 times for the out-of-plane electric field enhancement and 16 times for the in-plane component16. It cannot represent the ultimate limit of plasmonic field enhancement since the best plasmonic metal is Ag, which sets the upper limit in the visible and near-infrared region27,28. Quantitatively evaluating the ultimate limit of near-field enhancement around Ag nanocavity is hindered by the fast oxidization or sulfurization of Ag in the atmosphere, as well as the non-uniform morphology of silver nanoparticles used20,22. These conditions pose difficulties in the precise construction of sub-nanometer Ag nanogap and the deterministic position, number, and orientation of probe molecules inside the \u2018hot spots\u2019, and thus low the stability and repeatability of SERS measurements.\n\nIn this study, the maximum field enhancement was quantitatively measured in Ag plasmonic nanocavity before the onset of the quantum effect. We used MoS2-spaced Ag nanocube-on-mirror (NCOM) to establish well-controlled nanocavity with atomic precision. The excitation and collection conditions are all optimized by using the normal emission antenna mode and the plasmon-scanned technique to ensure the maximum output of far-field Raman scattering from individual nanoparticle. The SERS enhancement factor was acquired with a deviation as low as ~10% on individual NCOM. When the plasmon resonance is shifted to around the middle between the laser and Raman wavelength, SERS enhancement reaches an impressive 4.27\u2009\u00d7\u20091010 times, averaged over the \u2018hot spots\u2019 of single nanocavity. From the well-known electrodynamic theory of SERS, the near-field enhancement in the gap region of NCOM can be derived, reaching an averaged 1214 times when the plasmon resonance matches with the excited laser. It extrapolates to a highest near-field enhancement of 1644 times at the hottest position. The result is in good agreement with theoretical calculations using all the experimental determined parameters, without further adjustments. Therefore, it represents the upper limit of local near-field enhancement around plasmonic nanostructures in the visible and near-infrared region, before the onset of quantum effect.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The nanoparticle-on-mirror stands out as an attractive plasmonic nanocavity since it allows for the fabrication of large-scale, uniform nanogap with atomic-scale precision. The gap distances can be finely controlled by the length of molecule linker29, the thickness of the dielectric layer30, or the number of atomic layer16,31. More importantly, it allows for accurate position and orientation of the SERS probes within the nanogap. Consequently, the distribution of the local field along the vertical direction inside the nanogap has been successfully probed with angstrom resolution by the precisely controlled position of probe29. Our design of MoS2-spaced NCOM ensures that the SERS probe experiences the most intense local field, i.e., the \u2018hot spots\u2019 in the nanogap between the Ag nanocube and the Ag film (Fig.\u00a01a). The use of 2D atomic crystal as probe allows the gap distance to be accurately determined by its layer number, and offers a well orientated atomic lattice (so the Raman vibrations) in the \u2018hot spots\u2019. More importantly, the crystal lattice probe is free of photobleaching or blinking effect16, common in molecular SERS, allowing quantitative evaluation. Instead of a spherical nanoparticle, the use of nanocube offers a better morphology control during the synthesis, resulting in a known planar contact geometry and a magnetic dipole mode that emits normally to the\u00a0film32. Therefore, a normal incidence laser in a typical microscope setup can maximize the field enhancement in the nanogap, and the Raman signal can be fully collected by the same objective.\n\na Schematic of 1L-MoS2-NCOM. b Dark-field image of 1L-MoS2-NCOM. The white dashed line displays the boundary of 1L MoS2. The orange square marks the Ag NCOM to be optically measured. The scale bar is 5\u2009\u00b5m. c SEM image of 1L-MoS2-NCOM after all the optical measurements. The inset shows the enlarged image of the marked Ag NC (highlighted by a black dashed square) surrounded by the Al2O3 layer. Scale bars in (c) and inset are 5\u2009\u00b5m and 100\u2009nm, respectively. d Dark-field scattering spectra of Ag NCOM under unpolarized (violet), p-polarized (blue), or s-polarized (cyanine) excitation. The dashed line indicates that the magnetic (M) modes are degenerated in the x and y direction. e High-resolution TEM image of 1L-MoS2-spaced NCOM cross-section. White dashed lines show the boundary of the Ag NC. The yellow box highlights the nanogap, and the orange lines represent the top and bottom edges of the nanogap. The scale bar is 5\u2009nm. f Average count profile from the yellow box in (e), showing a nanogap distance of 1.1\u2009nm.\n\nThe MoS2-spaced NCOM consists of an individual Ag nanocube, 1-3 layers of MoS2, an ultra-smooth Ag film, and alumina (Al2O3) as a spacer or protective layer (see details in \u201cMethods and Supplementary Note\u00a01). Before the mechanical exfoliation of MoS2 flakes onto Ag film, a 0.5-nm-thick Al2O3 layer was deposited to prevent direct contact between Ag film and MoS2, which will otherwise create wrinkles on MoS2, resulting in a noisy dark-field background, the Raman peak splitting and shift33. Before the optical measurement, a 5-nm-thick Al2O3 layer was deposited on MoS2-NCOM as a protective layer, which prevents the oxidation or sulfuration of Ag nanocubes and Ag film, and inhibits the morphological deformation due to the laser-heating effect and the atomic migration34, beneficial in reducing SERS signal fluctuations and ensuring the accurate analysis of near-field enhancement.\n\nThe plasmonic response of the NCOM was characterized by an oblique incident dark-field scattering spectroscopy (Supplementary Fig.\u00a03a). Figure\u00a01b presents the dark-field image of the 1L-MoS2-NCOM sample, where the target NCOM appears as a blue spot surrounded by a red ring. The dark-field scattering spectra under unpolarized, p-polarized, and s-polarized illumination reveal that the magnetic dipole mode (marked as M mode) is located at around 760\u2009nm (Fig.\u00a01d). The coincidence of the resonance wavelength under p-polarized and s-polarized indicates that the nanocube has equal edge length along the x and y direction. After all the optical measurements, the marked NCOM was characterized by the scanning electron microscope (SEM), revealing its perfect cubic shape and the Al2O3 layer coating. The edge length is 58.6\u2009nm (Fig.\u00a01c).\n\nThe gap distance of NCOM can be finely controlled by the number of MoS2 layers, in a step of monolayer thickness 0.62\u2009nm. A cross-section view of the 1L-MoS2-NCOM was accomplished by focus iron beam slicing and high-resolution transmission electron microscope (TEM), confirming the fabrication of a high-quality Ag nanocavity (Figs.\u00a01e, f). They display a narrow nanogap distance of 1.11\u2009\u00b1\u20090.07\u2009nm between the Ag nanocube and Ag film, in good agreement with the sum of the thickness of an Al2O3 layer (0.54\u2009\u00b1\u20090.14\u2009nm) and a monolayer MoS2 (0.66\u2009\u00b1\u20090.05\u2009nm) characterized separately by atomic force microscope (Supplementary Figs.\u00a02a\u2013c). According to previous theoretical and experimental investigations, quantum tunneling appears when the gap distance reduces to around 1\u2009nm, depending on the Schottky barrier height at the metal-dielectric interface24,25. Therefore, the use of a 0.5-nm-thick Al2O3 layer can adjust the gap distance and tune the M mode to the blue side of the laser wavelength.\n\nSince the field enhancement in the \u2018hot spots\u2019 is anisotropic, the vertical and horizontal Raman vibrations of the MoS2 can directly probe this difference. A 785\u2009nm continuous-wave laser was chosen as the excited laser, ensuring the Raman peaks of MoS2 are not overwhelmed by its photoluminescence at 668\u2009nm (Supplementary Fig.\u00a03b, 4c). As shown in Fig.\u00a02a, the Raman spectra of 1L MoS2 exhibit an enormous enhancement in NCOM, far stronger than two reference Raman spectra collected from a 0.5-nm-thick Al2O3 coated Ag film (bg 1) and quartz (bg 2). Notably, the SERS intensity of out-of-plane \\({{{\\rm A}}}_{1g}\\) mode in 1L-MoS2-NCOM experiences a dramatic enhancement compared with that of the in-plane \\({{{\\rm E}}}_{2g}^{1}\\) mode, resulting from the huge vertical electric field enhancement of M mode (resonance at 800\u2009nm). It is consistent with the simulated vertical and horizontal local electric field enhancement calculated in the central plane of MoS2 layer, for 1L-MoS2-NCOM resonance at ~800\u2009nm, under y-polarized light excitation at 785\u2009nm (Figs.\u00a02b, c). The vertical electric field is mostly distributed on two sides of the bottom facet of the Ag nanocube, showing a maximum field enhancement of 934 times, 18.7-fold larger than the horizontal enhancement (50 times). For comparison, the Raman intensity of \\({{{\\rm E}}}_{2g}^{1}\\) mode (\u2009~\u2009385\u2009cm\u22121) is higher than that of \\({{{\\rm A}}}_{1g}\\) mode (\u2009~\u2009404\u2009cm\u22121) on the reference sample, and the Raman intensity of 1L MoS2 from bg 1 is approximately 3-fold weaker than that from bg 2, due to the interference of the incident light and the reflection of Ag film. No obvious Raman peak shift or splitting were observed in the SERS spectra of NCOM in comparison with the reference spectra either from bg 1 or bg 2, despite a broadening of the \\({{{\\rm E}}}_{2g}^{1}\\) mode on quartz (Supplementary Fig.\u00a04e). It indicates that the effect of local strain or doping in MoS2-NCOM is small and the possible contribution of charge transfer (chemical enhancement) in the SERS process is negligible, in agreement with previous studies16,35.\n\na Raman spectra of 1L MoS2 in NCOM with 6.4-nm-thick Al2O3 (violet), on Al2O3-coated Ag film (blue) and on quartz (cyanine). The last two background spectra are used for calibration, labeled as bg 1 and bg 2, respectively. The inset shows two primary Raman vibrations of MoS2, marked as \\({{{\\rm A}}}_{1g}\\) (blue) and \\({{{\\rm E}}}_{2g}^{1}\\) (orange) mode. Simulated vertical (b) and horizontal (c) components of local electric field distribution in the plane of Mo atom (z\u2009=\u20090) under normally incident 785\u2009nm laser with s-polarized excitation. The white dashed lines mark the contact facet between nanocube and film. d Simulated incident angle-dependent vertical components of local electric field enhancement under s-polarized (blue) and p-polarized (cyanine) excitation of 785\u2009nm laser. The inset shows the definition of incident angle.\n\nThe excitation and collection conditions (laser wavelength, polarization, incident angle, etc.) were optimized to maximize the SERS efficiency. The excitation of a specific plasmon resonance can be optimized if the incident angle and polarization of the laser match with the radiation pattern of the resonance3. According to the incident angle-dependent simulation (Fig.\u00a02d), the vertical field enhancement of Ag NCOM is maximized when the incident angle of the laser is 0\u00b0 because the M mode emits normally to the Ag film. It reaches up to 934 times for both p-polarized and s-polarized excitation. For inclined incident angles, the vertical field enhancement under p-polarized excitation is larger than the one under s-polarized excitation, due to the presence of vertical dipole-dipole mode, which is maximally excited at an incident angle of ~60\u00b030. Therefore, using a normal incident laser with either s-polarized or p-polarized, the M mode is always maximized and the dipole-dipole mode is eliminated. To ensure a uniform illumination, a lens (f\u2009\u00a0=\u2009600\u2009nm) was inserted into the optical path to expand the laser spot to 60.7\u2009\u03bcm2, reducing the signal fluctuations introduced by the defocus of the laser spot and the drift of the sample.\n\nTo achieve the optimal spectral match to reach the maximum output of Raman signals from individual NCOM, we performed the SERS measurement with a plasmon-scanned technique, relying on the dielectric screening effect16,36. With continuously increasing the thickness of Al2O3 layer by sequential atomic layer deposition, the resonance wavelength of the M mode \\({{\\lambda }}_{{{{\\rm{M}}}}}\\) undergoes a distinct redshift, scanning across the wavelength of the excited laser (785\u2009nm) and the outgoing Raman scattering (\u2009~\u2009810\u2009nm), without the obvious variations in the shape and linewidth (Fig.\u00a03a). Simulated dark-field scattering spectra in Fig.\u00a03b consistently reproduces such redshift, confirming that the mode remains unchanged upon the coating of Al2O3 layer. Similarly, the dark-field scattering spectra for 2L and 3L MoS2 probes were measured with the varied thicknesses of the Al2O3 layers (Supplementary Note\u00a03).\n\nExperimental dark-field scattering spectra (a) and simulated scattering spectra (b) with varying thickness of Al2O3 layer in 1L-MoS2-NCOM. The gray dashed lines display the laser line at 785\u2009nm. The wavelength of the magnetic mode (\\({{\\lambda }}_{{{{\\rm{M}}}}}\\)) is obtained from the Lorentz fitting to the measured scattering peaks. c SERS spectra and the intensity of \\({{{\\rm A}}}_{1g}\\) mode as a function of \\({{\\lambda }}_{{{{\\rm{M}}}}}\\). The error bars are taken from the standard deviation of five repeat measurements. The white dashed lines highlight the region where the highest Raman intensity appears.\n\nFollowing each coated Al2O3 layer, SERS measurement on individual NCOM was performed with a laser power of 11.5\u2009\u03bcW/\u00b5m2, prohibiting the transformation of nanocubes and unexpected nonlinear responses37. The intensity of SERS spectra for 1L, 2L, and 3L MoS2 probes was mapped across a spectral region of 740 to 830\u2009nm (Fig.\u00a03c). Only the \\({{{\\rm A}}}_{1g}\\) mode (404 \u2212 408\u2009cm\u22121, varying with the number of MoS2 layer) exhibits a pronounced enhancement, compared with other Raman vibrations. The SERS intensity of \\({{{\\rm A}}}_{1g}\\) mode on 1L, 2L, and 3L MoS2-NCOM samples follows a similar variation performance with the varied \\({{\\lambda }}_{{{{\\rm{M}}}}}\\). When \\({{\\lambda }}_{{{{\\rm{M}}}}}\\) gradually redshifts, the SERS intensity of \\({{{\\rm A}}}_{1g}\\) mode increases, reaches its maximum, and eventually weakens. The highest SERS intensity appears in a wavelength region of 798 to 801\u2009nm, in between the laser line and the Raman wavelength (\u2009~\u2009810\u2009nm). All three spectra show the secondary maximum point rather than a single maximum point. As shown later, it is due to the product of the excitation and emission enhancement. The existence of secondary maximum points is also supported by the small fluctuation of the SERS intensity, even when the SERS enhancement factor is high. The highest SERS intensity shows 10.2%, 8.8%, and 8.7% deviation for 1L, 2L, and 3L MoS2 probes, respectively. It benefits from the non-bleaching of the atomic crystal probe, the reduced morphological deformation, and the expanded excitation beam, compared with conventional SERS experiments. These results showcase the versatility of the plasmon-scanned technique to acquire the maximum SERS intensity for a given plasmonic nanocavity.\n\nIn general, the electromagnetic enhancement of SERS involves a two-step process, i.e., the excitation enhancement \\({G}_{{{{\\rm{exc}}}}}({{\\lambda }}_{{{{\\rm{L}}}}})\\) at the incident frequency and the emission enhancement \\({G}_{{{{\\rm{em}}}}}({{\\lambda }}_{{{{\\rm{R}}}}})\\) at the outgoing Raman scattering frequency. Under the two-step model (TSM), the SERS enhancement factor EF can be obtained by multiplying both parts. The excitation enhancement is proportional to the power of the local electric field enhancement \\({G}_{{{{\\rm{exc}}}}}({{\\lambda }}_{{{{\\rm{L}}}}})={|{{\\rm E}}_{{{{\\rm{loc}}}}}({{{\\bf{r}}}},{{\\lambda }}_{{{{\\rm{L}}}}})|}^{2}/{|{{\\rm E}}_{0}({{\\lambda }}_{{{{\\rm{L}}}}})|}^{2}\\) at the position of the molecule while the emission enhancement can be calculated by the ratio between the radiation power of NCOM (excited by a dipole emitter) and the radiation power of the same emitter in vacuum. Although the two enhancement have different spatial dependent patterns, their maxima are correlated by \\({G}_{{{{\\rm{em}}}}}({{\\lambda }}_{{{{\\rm{R}}}}})={\\eta }({{\\lambda }}_{{{{\\rm{L}}}}},{{\\lambda }}_{{{{\\rm{R}}}}}){G}_{{{{\\rm{exc}}}}}({{\\lambda }}_{{{{\\rm{L}}}}})\\) according to the reciprocity, since the electromagnetic environment of the emitter is dominated by the same M mode (more details in Supplementary Note\u00a05). For a lattice probe with a certain area, we can define an averaged (over the hot area) excitation enhancement \\(\\overline{{G}_{{{{\\rm{exc}}}}}}\\) by assuming the ratio between two averaged enhancements is the same as the ratio between the two maxima from the calculations. Then, the near-field enhancement is linked with the far-field Raman intensity if no chemical enhancement is involved17. The averaged excitation enhancement corresponds to the experimental determined SERS enhancement by\n\nwhere \\({I}_{{{{\\rm{SERS}}}}}\\)(\\({I}_{{{{\\rm{ref}}}}}\\)) is the SERS (Raman) intensity of MoS2 from MoS2-NCOM (references), \\({S}_{{{{\\rm{SERS}}}}}\\) is the effective \u201chot spots\u201d area, and \\({S}_{{{{\\rm{ref}}}}}\\) is the collection area on the reference samples. Since the reference Raman intensity is too weak to collect utilizing the same conditions of SERS measurement, \\({I}_{{{{\\rm{ref}}}}}\\) was measured by increasing the laser power, the collection time, and the collection area (details in Supplementary Note\u00a04). To eliminate the system deviations from different collection conditions, the Raman intensity of the pure silicon was used as a correction (Supplementary Fig.\u00a08c).\n\nThe effective \u201chot spots\u201d area \\({S}_{{{{\\rm{SERS}}}}}\\) is determined numerically by integrating the contribution from the hot region in the central plane of MoS2 until the fraction \\(f({{\\it{S}}})=\\frac{{\\int }_{0}^{{S}_{{{{\\rm{SERS}}}}}}{G}_{{{{\\rm{exc}}}}}({{{\\bf{r}}}})\\cdot {G}_{{{{\\rm{em}}}}}({{{\\bf{r}}}}){\\rm{d}}\\it{S}}{{\\int }_{0}^{\\infty }{G}_{{{{\\rm{exc}}}}}({{{\\bf{r}}}})\\cdot {G}_{{{{\\rm{em}}}}}({{{\\bf{r}}}}){\\rm{d}}\\it{S}}\\) reaches 1-e\u22125 (\u2009~\u200999.3%). This definition ensures that this \u2018hot spots\u2019 area almost entirely contributes to the total SERS signal38. The integral starts from the hottest region and is along the contour of enhancement so that the fraction \\(f(S)\\) saturates rapidly with increasing integration of the \u2018hot spots\u2019 area. As shown in Fig.\u00a04a, the effective \u2018hot spots\u2019 area of vertical field enhancement is determined to be 1876 nm\u00b2, and the effective \u2018hot spots\u2019 area of horizontal field enhancement is 1909 nm\u00b2 (Supplementary Fig.\u00a012a).\n\na Fraction \\({f}(S)\\) as the ratio of integration of hot spots area over total hot spots area for the vertical field component. The highlighted point marks the determined effective hot spots area. The inset shows a schematic drawing of integral along the contour. b Experimental SERS \\(\\overline{E{F}_{{{{\\rm{z}}}}}}\\) with the varied \\({{\\lambda }}_{{{{\\rm{M}}}}}\\). \\(\\overline{E{F}_{{{{\\rm{z}}}}}}\\) (violet) and \\(\\overline{E{F}_{{{{\\rm{z}}}}}}\\) (blue) represent SERS \\(\\overline{E{F}_{{{{\\rm{z}}}}}}\\) utilizing MoS2 on Ag film (bg 1) and quartz (bg 2) as background, respectively. Both colorful strips represent the corresponding error bars (standard deviation). c SERS \\(E{F}_{{{{\\rm{z}}}}}\\) as a function of the fraction starting integrating from the hottest region (\\(E{F}_{{{{\\rm{z}}}}}^{\\max }\\)) to the effective region (\\(\\overline{E{F}_{{{{\\rm{z}}}}}}\\)) under the two-step model.\n\nOnce the effective \u201chot spots\u201d area \\({S}_{{{{\\rm{SERS}}}}}\\) is calculated, the average vertical (horizontal) SERS \\(\\overline{E{F}_{{{{\\rm{z}}}}}}\\)(\\(\\overline{E{F}_{{{{\\rm{xy}}}}}}\\)) can be evaluated using Eq. (1). As shown in Fig.\u00a04b, the experimental SERS \\(\\overline{E{F}_{{{{\\rm{z}}}}}}\\) of 1L-MoS2-NCOM reaches a maximum value of 1.2\u2009\u00d7\u20091011 times (bg 1 as reference) and 4.27\u2009\u00d7\u20091010 times (bg 2 as reference) when \\({{\\lambda }}_{{{{\\rm{M}}}}}\\) is around 800\u2009nm. Unlike the previous experimental results observing some sudden boost of SERS enhancement around the maximum39, the enhancement curves present an overall smooth variation with a secondary maximum point (\u2009~\u2009809\u2009nm) on the red side of the maximum. The simulated SERS \\({E}{{F}}_{{{{\\rm{z}}}}}\\) integrating the product of excitation enhancement and emission enhancement from the hottest \u2018hot spots\u2019 region to the effective hot area, reveals an averaged enhancement of 6.75\u2009\u00d7\u20091010 times (Fig.\u00a04c), in good agreement with the experimental SERS \\(\\overline{E{F}_{{{{\\rm{z}}}}}}\\) (bg 2). The SERS \\({E}{{F}}_{{{{\\rm{z}}}}}\\) evaluated at the hottest position reaches up to 2.04\u2009\u00d7\u20091011 times, 3 times higher than the averaged one. Assuming the experimental SERS enhancement has the same ratio between the hottest position and the averaged one, the experimental SERS \\({E}{{F}}_{{{{\\rm{z}}}}}\\) at the hottest position can reach an impressive 1.29\u2009\u00d7\u20091011 times (bg 2). The experimental SERS \\(\\overline{E{F}_{{{{\\rm{xy}}}}}}\\) obtained from the SERS signal of in-plane \\({{{\\rm E}}}_{2g}^{1}\\) mode, follows a similar behavior with the varied \\({{\\lambda }}_{{{{\\rm{M}}}}}\\), reaching a maximum of 3.85\u2009\u00d7\u2009108 times (bg 1) and 5.14\u2009\u00d7\u2009107 times (bg 2) (Supplementary Fig.\u00a012b). SERS using trace levels of molecules even reported an ultra-high enhancement of 1014 to 1015 times22, determined by the ratio of the Raman intensity and the concentration of analysts, which suffers from the large uncertainty of molecule adsorption around the hot spots. An extra \u2018assumed\u2019 chemical enhancement of 102 to 103 times has to fill the gap between the calculation and the measured SERS enhancement, and therefore, cannot be used to extract the limit of near-field enhancement quantitatively.\n\nThe use of the reference sample is one critical step in determining the experimental enhancement factor. The Raman peaks obtained from NCOM are closer to those taken from MoS2 on Ag film (bg 1), with a similar amount of the Raman splitting and shift due to the strain or doping effect from the metal substrate. However, the high reflectivity of Ag film causes interference between the incident and reflected laser, MoS2 on Ag film experiences a suppressed incident field (Supplementary Fig.\u00a08d) so that the Raman signals are hard to collect. On the contrary, the field enhancement at the air-quartz interface is close to unity because of its low refractive index. In the traditional definition of the plasmonic field enhancement (|Eloc|/E0), people use a plane wave excitation with amplitude of one, i.e., E0\u2009=\u20091. To obtain a correct SERS enhancement, the reference sample should experience an incident field amplitude close to unity. Therefore, bg 2 is a proper reference for intensity calibration despite the Raman peaks of MoS2 from quartz having a slight broadening in comparison with the SERS peaks from NCOM.\n\nElectromagnetic models simulate the SERS enhancement by calculating the excitation enhancement \\({G}_{{{{\\rm{exc}}}}}\\) at the incident laser (785\u2009nm) and the emission enhancement \\({G}_{{{{\\rm{em}}}}}\\) at the outgoing Raman wavelength (810\u2009nm) (Supplementary Fig.\u00a011), when \\({{\\lambda }}_{{{{\\rm{M}}}}}\\) is varied. As expected, two enhancements severally reach their maximum values when \\({{\\lambda }}_{{{{\\rm{M}}}}}\\) is shifted to these wavelengths. The excitation enhancement reaches a maximum of 3.12\u2009\u00d7\u2009106 times at the middle of the edges of the nanocube, and the emission enhancement arrives at a maximum of 2.21\u2009\u00d7\u2009105 for a z-polarized dipole positioned at the corner under the cube. These results agree well with the previous calculations40 and indicate that the excitation enhancement is much stronger than the emission enhancement. As \\({{\\lambda }}_{{{{\\rm{M}}}}}\\) is shifted toward the Raman outgoing wavelength, the ratio \\({\\eta }\\) between the emission and excitation enhancement increases. Since, there is only the M mode in the measured spectral region, the spatial distribution of \\({G}_{{{{\\rm{exc}}}}}\\) and \\({G}_{{{{\\rm{em}}}}}\\) does not change too much, despite their amplitude varying significantly as \\({{\\lambda }}_{{{{\\rm{M}}}}}\\) is scanned. Then, the ratio \\({\\eta }\\) between the spatial averaged (over the effective hot area) emission and excitation enhancement only depends on \\({{\\lambda }}_{{{{\\rm{M}}}}}\\), taking as the ratio between the maximum emission and excitation enhancement at different positions. We calculate the spatial averaged emission enhancement when \\({{\\lambda }}_{{{{\\rm{M}}}}}\\)\u2009=\u2009800\u2009nm. It is 4.3\u00a0\u00d7\u00a0104 times, about one order of magnitude smaller than the averaged excitation enhancement (4.62\u2009\u00d7\u2009105 times).\n\nWith the ratio \\({\\eta }({{\\lambda }}_{{{{\\rm{M}}}}})\\) taken from the calculation, the averaged near-field enhancement \\(\\overline{{g}_{{{{\\rm{z}}}}}}=\\root 4 \\of {\\overline{E{F}_{{{{\\rm{z}}}}}}/{\\eta }}\\) can be obtained, as illustrated in Fig.\u00a05a. It reaches a maximum 1214 times when \\({{\\lambda }}_{{{{\\rm{M}}}}}\\) (788\u2009nm) is close to the laser wavelength (785\u2009nm). The simulated averaged electric field enhancement matches with the experimental results quite well, except for a higher maximum of 1304 times right at ~785\u2009nm. At the hottest position, the local electric field enhancement reaches 1766 times (Supplementary Fig.\u00a011). According to the relation between averaged and hottest enhancement in the calculation, the experimental maximum field enhancement can be extrapolated to an impressive 1644 times. It should be noted that the parameters (size, gap distance, coating thickness, etc.) in the calculations were adapted from the measured NCOM in the experiments, with the edge rounding curvature of the nanocube as the only adjustable parameter within the error of structural characterization (Supplementary Fig.\u00a01c). Once the calculated scattering spectra agree with the measured one, no further adjustment was applied to the NCOM, and the excitation and emission enhancement were taken directly from the models. The agreement between the measured SERS enhancement factor and the calculated one is impressive, due to the precise detail of the geometry. Therefore, the upper limit of plasmonic near-field enhancement is unequivocally quantified before the onset of the quantum effect, in good agreement with the classical Maxwell\u2019s prediction without corrections due to the quantum tunneling or the nonlocal effect (Supplementary Fig.\u00a013).\n\na Experimental (violet) and simulated (wathet) vertical field enhancement with the varied \\({{\\lambda }}_{{{{\\rm{M}}}}}\\) in 1L-MoS2-NCOM. The curves are guides to the eyes. b Experimental layer-dependent vertical field enhancement. The colorful strip represents the corresponding error bar (standard deviation).\n\nConsidering that the vertical field enhancement \\({g}_{{{{\\rm{z}}}}}\\) is far higher than the horizontal field enhancement \\({g}_{{{{\\rm{xy}}}}}\\) in Ag NCOM, the layer-dependent \\({g}_{{{{\\rm{z}}}}}\\) can illustrate the ultimate limit of field enhancement. To ensure the comparability of calculated results for 1, 2, and 3L MoS2-NCOM, we simply assume all of them have the same effective \u2018hot spots\u2019 area and the ratio of excitation enhancement and emission enhancement. As the number of MoS2 layers decreases (i.e., the gap distance reduces), \\(\\overline{{g}_{{{{\\rm{z}}}}}}\\) over the \u2018hot area\u2019 monotonically increases. When the number of MoS2 layers increases, the experimental \\(\\overline{{g}_{{{{\\rm{z}}}}}}\\) decays exponentially. The maximum experimental \\(\\overline{{g}_{{{{\\rm{z}}}}}}\\) reaches 647 times for 2L MoS2 and 485 times for 3L MoS2 probes, respectively (Fig.\u00a05b). This quantified the ultimate limit of field enhancement in Ag NCOM. Similarly, the layer-dependent horizontal field enhancement was measured by experimental parameters (Supplementary Fig.\u00a012c). When the number of MoS2 layers decreases to monolayer, the experimental \\(\\overline{{g}_{{{{\\rm{xy}}}}}}\\) is 85 times (bg 2), larger than the simulated result (50 times) (Fig.\u00a02c). This difference possibly originates from the slight ripples of MoS2 layer or the roughness of Al2O3 inside the gap, both of which can introduce (i) the variation of the z-coordinate (corresponds to the distance of the MoS2 plane to the bottom surface of the nanocube) in acquiring the local field in the simulation, and (ii) the inclination of the MoS2 plane with respect to the z direction. \\(\\overline{{g}_{{{{\\rm{xy}}}}}}\\) depends strongly on the z-coordinate by varying with ~2 times when z changes only 0.3\u2009nm (Supplementary Fig.\u00a014a). For comparison, \\(\\overline{{g}_{{{{\\rm{z}}}}}}\\) has small variation when z changes in the same range. The inclination of the MoS2 plane makes \\({{{\\rm E}}}_{2g}^{1}\\) mode also feel the vertical field enhancement via \\(\\overline{{g}_{{{{\\rm{z}}}}}}{\\rm{cos}}{\\theta }+\\overline{\\it{g}_{{{{\\rm{xy}}}}}}sin{\\theta }\\), where \u03b8 is the angle between the tangent plane of MoS2 and the xy plane. As shown in Supplementary Fig.\u00a014b, a very small variation of \u03b8 (\u2264\u20091\u00b0) will influence \\(\\overline{{g}_{{{{\\rm{xy}}}}}}\\) by about one order of magnitude, while the fluctuation of \\(\\overline{{g}_{{{{\\rm{z}}}}}}\\) is almost negligible. In a word, gentle ripples in the MoS2 layer can explain the large inconsistency between the experimental and simulated \\(\\overline{{g}_{{{{\\rm{xy}}}}}}\\), but has little effect on the \\(\\overline{{g}_{{{{\\rm{z}}}}}}\\), similar to previous conclusion16.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53210-8/MediaObjects/41467_2024_53210_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53210-8/MediaObjects/41467_2024_53210_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53210-8/MediaObjects/41467_2024_53210_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53210-8/MediaObjects/41467_2024_53210_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53210-8/MediaObjects/41467_2024_53210_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In conclusion, the high-quality Ag NCOM nanocavity, together with the all-optimized excitation and collection conditions, allows the quantitative probing of near-field enhancement in ~1\u2009nm gap. It reveals the upper limit of plasmonic field enhancement in the visible and near-infrared region. The upper limit of SERS enhancement with a small derivation was unambiguously measured on single plasmonic nanocavity, reaching up to an averaged 4.27\u2009\u00d7\u20091010 times over the hot area. It corresponds to an averaged near-field enhancement of 1214 times, which is extrapolated to 1644 times at the hottest position. The result corresponds to the theoretical calculations under the framework of the classical electromagnetic model, without the correction from the quantum mechanical effect.\n\nQuantifying the ultimate limit of field enhancement can promote the development of plasmon-enhanced processes, strong light-matter interactions, and opto-electrics devices. This limit sets a solid base for various surface-enhanced spectroscopy and strong light-matter interaction at the atomic scale. Further optimizations can be achieved if the deposited Ag film can be replaced by the crystallized metallic microplates with an atomic flat surface. Since the onset of the quantum tunneling effect depends on the Schottky barrier height at the metal-dielectric interface, the quantum-limited enhancement can go higher for an air gap, which allows for a thinner gap distance of ~0.5 nm24,25. Also, the sharp tip structures or additional protrusions on the metal surface due to the lighting rod effect, superposition on the resonance structures, can be further pushed to an upper near-field enhancement but then the nonlocal effect should be pronounced26,41,42,43.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Ag nanocubes were synthesized using the seed-mediated method44 (Supplementary Note\u00a01), with an edge length of 58.8\u2009\u00b1\u20093.1\u2009nm and an edge rounding radius of 6.7\u2009\u00b1\u20092.3\u2009nm. Ultra-smooth Ag film was prepared by the template-stripped method16 (Supplementary Note\u00a01), revealing a root-mean-square (RMS) surface roughness of 0.4\u2009nm over an area of 800\u2009\u00d7\u2009800\u2009nm\u00b2. Subsequently, a 0.5-nm-thick Al2O3 layer was coated onto Ag film by atomic layer deposition (ALD) at 120\u2009\u00b0C. The thickness of this Al2O3 layer in our experiment was calibrated to be 0.54\u2009\u00b1\u20090.14\u2009nm. Then, the cetyltrimethylammonium chloride (CTAC)-wrapped Ag nanocubes aqueous solution was drop-casted onto MoS2 flakes (Supplementary Figs.\u00a04a\u2013d), which were mechanically exfoliated onto the Al2O3-coated Ag film from commercially available bulk crystals (SPI Supplies), severally rinsed in ethanol and deionized water for 5\u2009minutes to remove the CTAC ligands, and dried by nitrogen flow. The thickness of monolayer MoS2 was determined to be 0.66\u2009\u00b1\u20090.05\u2009nm by atomic force microscope (Supplementary Figs.\u00a02a\u2013c). Finally, an Al2O3 layer with varied thickness from 5 to 14\u2009nm was deposited onto MoS2-NCOM to implement the optical measurement.\n\nThe plasmonic response of MoS2-NCOM was measured by dark-field scattering spectroscopy. A white light source at an angle of 80\u00b0 from a halogen lamp, passes through a spatial filter and a lens (\u0192\u2009=\u200925\u2009mm), then focuses on the sample, forming a broad elliptical beam (800\u2009\u03bcm2). The polarizer adjusts the incident polarization. The scattered light is collected by a 100\u00d7 objective (Olympus, NA\u2009=\u20090.8), directed into a CCD camera (Tucsen, TCH-1.4CICE) to capture dark-field scattering images, and directed into a spectrometer (Renishaw, inVia) through a flip mirror to acquire dark-field scattering spectra.\n\nIn the normal incident setup for Raman spectroscopy, the 785\u2009nm continuous-wave laser passes through a circular aperture, a reflecting mirror, a beam splitter, and a flip mirror, then is focused on the samples. Specially, a lens (\u0192\u2009=\u2009600\u2009mm) was inserted to expand the laser beam, forming a uniform circular laser spot (60.7\u2009\u03bcm2). It can eliminate the fluctuation of Raman signals from the defocusing of the laser. The Raman scattering light is collected by a 100\u00d7 objective and directed into a Raman spectrometer to acquire the Raman spectra. In the SERS measurement on MoS2-NCOM samples, the integration time was set to 60\u2009s, the collection area was set to 3.2 \u03bcm2, and the laser power was maintained at 0.7\u2009mW.\n\nFull-wave electromagnetic simulations were performed using the commercial finite element method package COMSOL Multiphysics. The 1L-MoS2-NCOM system consists of a single nanocube whose size was chosen according to the experimental parameters within the error (Supplementary Fig.\u00a09). The edge length of the nanocube is 56\u2009nm with a corresponding rounding radius of 11.2\u2009nm. The thickness of monolayer MoS2 layers is 0.62\u2009nm. To account for the \u00b1\u20090.07\u2009nm deviation of gap distance, 1L-MoS2-NCOM with the thickness of Al2O3 layer changed by three times of the deviation was calculated (Supplementary Fig.\u00a010). A suitable thickness of the Al2O3 layer i.e., 0.7\u2009nm corresponds to resonance wavelength of ~800\u2009nm, matching with the experimental one where the SERS signals reaches the maximum. Initially, an s-polarized plane wave excitation with an incident angle of 80\u00b0 was implemented using a periodic boundary condition in the absence of a nanocube. Then, the calculated field was implied as the background field for the NCOM configuration, with a perfectly matched layer surrounding the entire simulation domain. To simulate the experimental setup, the far-field Rayleigh scattering light was collected over a solid angle corresponding to NA\u2009=\u20090.8. The permittivity of Ag was determined from the experimental data by Johnson and Christy45. The refractive index of alumina was taken as 1.5, as the in-plane permittivity of 1L MoS2 was extracted from micro-reflection measurements16, whereas the out-of-plane permittivity was set to 1. The emission enhancement was evaluated by the ratio between the radiation power of NCOM excited by a dipole emitter and the emission power of the same emitter in vacuum. The averaged emission enhancement is calculated by scanning the dipole in a 1\u2009nm\u2009\u00d7\u20091\u2009nm mesh in a quarter of NCOM because of symmetry.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Relevant data supporting the findings of this study are available upon request from the corresponding author.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Corresponding data of the caclculations were obtained using standard technical software. The codes are available upon request to the corresponding author.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Schuller, J. A. et al. Plasmonics for extreme light concentration and manipulation. Nat. 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Z.L. prepared the samples and performed the experiments. H.Z. synthesized Ag NCs. J.M. and H.Y. performed the theoretical simulations. S.Z. and Z.L. analyzed the data and wrote and revised the manuscript. All the authors contributed equally to this work.\n\nCorrespondence to\n Shunping Zhang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Yun Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Quantifying the ultimate limit of plasmonic near-field enhancement.\n Nat Commun 15, 8803 (2024). https://doi.org/10.1038/s41467-024-53210-8\n\nDownload citation\n\nReceived: 21 April 2024\n\nAccepted: 07 October 2024\n\nPublished: 11 October 2024\n\nVersion of record: 11 October 2024\n\nDOI: https://doi.org/10.1038/s41467-024-53210-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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interpolation of satellite altimetry data with probabilistic machine learning", + "pre_title": "Scalable interpolation of satellite altimetry data with probabilistic machine learning", + "journal": "Nature Communications", + "published": "28 August 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51900-x/MediaObjects/41467_2024_51900_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51900-x/MediaObjects/41467_2024_51900_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5067/MPYG15WAA4WX", + "https://zenodo.org/doi/10.5281/zenodo.13218448", + "/articles/s41467-024-51900-x#ref-CR61" + ], + "code": [ + "https://github.com/CPOMUCL/GPSat", + "https://doi.org/10.24433/CO.4875513.v1", + "https://cpomucl.github.io/GPSat/" + ], + "subject": [ + "Cryospheric science", + "Physical oceanography" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4209064/v1.pdf?c=1724929764000", + "research_square_link": "https://www.researchsquare.com//article/rs-4209064/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-51900-x.pdf", + "preprint_posted": "07 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "In this work, we present a new open-source Python programming library for performing efficient interpolation of non-stationary satellite altimetry data, using scalable Gaussian Process (GP) techniques. We showcase the library, GPSat, by using data from the CryoSat-2, Sentinel-3A, and Sentinel-3B radar altimeters, to generate complete maps of daily 50 km2-gridded Arctic sea ice radar freeboard. Relative to a previous GP interpolation scheme, we find that GPSat offers a 504\u00d7 computational speedup, with less than 4 mm difference on the derived freeboards, on average. We then demonstrate the scalability of GPSat through freeboard interpolation at 5 km2 grid resolution, and Sea-Level Anomalies (SLA) at the resolution of the altimeter footprint. Validation of this novel high resolution radar freeboard product shows strong agreement with airborne data, with a linear correlation of 0.66. Footprint-level SLA interpolation also shows improvements in predictive skill over linear regression, which is a standard approach used in sea ice altimetry data processing. We suggest that GPSat could overcome the computational bottlenecks faced in many altimetry-based interpolation routines. This could in turn lead to improved observational estimates of ocean topography and sea ice thickness, and also further critical understanding of ocean and sea ice variability over short spatio-temporal scales.Earth and environmental sciences/Climate sciences/Cryospheric scienceEarth and environmental sciences/Climate sciences/Ocean sciences/Physical oceanography", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInfo.pdfSupplementary Information on Methods", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "We present GPSat; an open-source Python programming library for performing efficient interpolation of non-stationary satellite altimetry data, using scalable Gaussian process techniques. We use GPSat to generate complete maps of daily 50 km-gridded Arctic sea ice radar freeboard, and find that, relative to a previous interpolation scheme, GPSat offers a 504\u00a0\u00d7 computational speedup, with less than 4 mm difference on the derived freeboards on average. We then demonstrate the scalability of GPSat through freeboard interpolation at 5 km resolution, and Sea-Level Anomalies (SLA) at the resolution of the altimeter footprint. Interpolated 5 km radar freeboards show strong agreement with airborne data (linear correlation of 0.66). Footprint-level SLA interpolation also shows improvements in predictive skill over linear regression. In this work, we suggest that GPSat could overcome the computational bottlenecks faced in many altimetry-based interpolation routines, and hence advance critical understanding of ocean and sea ice variability over short spatio-temporal scales.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Earth observation satellites have made it possible to monitor virtually the entirety of the Earth\u2019s surface in relatively short periods of time. This has accelerated our understanding of weather and climate processes, and the rate and scale to which these processes are impacted by anthropogenic climate change. Satellite-mounted altimeters, in particular, play a crucial role in this effort by recording changes in the elevation of both ocean and sea ice surfaces. For example, the TOPEX/Poseidon altimeter (Topography Experiment - Positioning, Ocean, Solid Earth, Ice Dynamics, Orbital Navigator) allowed major breakthroughs in tracking global sea-level rise1,2, through higher precision sea-surface height estimates3. Furthermore, polar-monitoring altimeters, such as CryoSat-2, have provided a consistent record of sea ice thickness changes over the past decade4,5,6,7. Sea ice thickness plays a crucial role in the climate system; as a major control on atmosphere-ocean heat exchanges8, sea ice teleconnections9,10, the timing of ice-algal blooms11, and the overall response of sea ice to global warming12. Local sea ice thickness variations are governed by a myriad of dynamic and thermodynamic factors, but typical values range from a few centimetres to a few metres.\n\nAltimeters record surface elevation by the return time of an emitted microwave pulse (radar altimeter) or laser beam (laser altimeter), collecting data along narrow tracks as they orbit the Earth. The horizontal resolution, and hence the ability to resolve small-scale features of the sea ice and ocean, is controlled by the altimeter footprint; the surface area covered by a single microwave pulse or laser beam. A larger footprint means that the altimeter can survey the Earth\u2019s surface more quickly, although at the cost of horizontal resolution. Therefore, depending on the specific altimeter and zone of interest, observations can be inherently sparse on timescales ranging from days to weeks. For example, ICESat-2 (Ice, Cloud, and Land Elevation Satellite) is a laser altimeter carrying three pairs of beams, each with an along-track footprint size of \u00a0~17\u2009m13. Meanwhile, CryoSat-2 is a radar altimeter with an along-track footprint size of \u00a0~300\u2009m14. Importantly, both altimeters require approximately 30 days to uniformly sample the sea ice cover at both poles (at the typical grid resolution used in polar altimetry15). This poses limitations in our ability to understand the processes that drive ocean and sea ice variability on timescales ranging from days to weeks, as well as hindering applications which would otherwise benefit from high spatio-temporal resolution observations. Such applications include initialising numerical weather prediction and/or climate models16, and Arctic maritime navigation17. In these cases, high spatio-temporal sea ice thickness observations could provide more utility than traditional area-based quantities. For example, sea ice thickness strongly controls summer sea ice melt rates, therefore accurate thickness initialisation will allow more faithful representation of sea ice evolution in summer than by just initialising the ice extent. Furthermore, accurate sea ice thickness conditions for shipping forecasts can provide safe passage for icebreakers traversing through the ice\u2014information which cannot be inferred from sea ice concentration.\n\nTo overcome the inherent sparsity in satellite altimetry data, many studies have leveraged statistical interpolation schemes to effectively fill the gaps at unobserved locations. Such schemes have been deployed under a wide variety of names in the Earth observation literature, including optimal interpolation18,19,20,21,22,23, objective analysis24,25,26, kriging27,28, and Gaussian process (GP) regression29. Each of these approaches can be considered variations of linear smoother models, which are a broad class of Bayesian inverse methods that rely on covariance-based weighting to make predictions. Common to all of these applications is the poor scaling of the methodology to large data sets (typically over a few thousand data points), owing to the computation of matrix inverses, which scales cubically in the size of the data set. This often means that studies must rely on parallelised high-performance computer (HPC) environments or data sub-sampling to generate predictions in a reasonable time. In some cases, such computational restrictions prevent potentially novel data products from becoming fully operational and open-source. Landy et al.26, for example, explored the spatial interpolation of along-track CryoSat-2 sea-surface height profiles in the Arctic. In their case, even with significant sub-sampling of the training data, 96 hours were still required to process a single month of data. Similarly, Gregory et al.29, hereafter G21, investigated the joint spatio-temporal interpolation of CryoSat-2, Sentinel-3A and Sentinel-3B gridded sea ice freeboard observations (that is, the height of a sea ice floe relative to the underlying ocean surface). With the use of a parallelised HPC environment (25 CPU cores), 36\u2009h were required to process a single month of data at a coarse 50-km grid resolution. There is subsequently a clear need to develop tool kits that relinquish this heavy reliance on data sub-sampling or parallelised HPCs for producing scientific data sets. For the latter, this may also begin to bridge the inequality gap between privileged research institutions that have access to such computer resources and those that do not30.\n\nOver the past decade, the machine learning community has made significant progress in the development of scalable inverse methods31,32. These novel methodologies have not yet been widely adopted within the field of Earth observation; however, they are now easily implementable with machine learning libraries, such as GPflow33 and GPyTorch34. Not only do these libraries offer flexibility in terms of constructing GP models, but they crucially provide Graphics Processing Unit (GPU) and batch processing functionalities to speedup linear algebra computations and improve memory handling, respectively. In this study, we present an open-source Python programming library, GPSat, which is built around GPflow, and has been constructed specifically for the purpose of performing efficient spatial (1D or 2D) and spatio-temporal (3D) interpolation of satellite altimetry data. We begin by benchmarking the library against the methodology of G21. For this, we perform joint interpolation of 50-km-gridded CryoSat-2 (CS2), Sentinel-3A (S3A), and Sentinel-3B (S3B) sea ice radar freeboard data, for the 2018/2019 Arctic winter season. We then highlight how GPSat provides considerable speedup relative to G21, and crucially without significant degradation in the derived freeboards. To then demonstrate the scalability of the library, we show examples of interpolating 5-km-gridded radar freeboards, as well as along-track (i.e. footprint-resolution) Sea-Level Anomalies (SLA). It should be noted, however, that while we focus on radar freeboards and SLA here, GPSat is inherently data agnostic and, hence, can be tailored to any field of interest, such as atmospheric weather station data35.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Constructing local models for optimal interpolation is a useful framework with which to both alleviate the computational burden of conventional GP methods, and also flexibly model non-stationary behaviour within climate data sets. Following the approach outlined by G21, for example, if we were to predict the value of radar freeboard at some arbitrary grid point i on day t (illustrated by the white pixel in Fig.\u00a01a), we would first identify all available CS2 and Sentinel-3 (S3) data points that exist within some fixed radius r of location i (white circle in Fig.\u00a01a), and subsequently repeat this selection process for all \u00a0\u00b1\u03c4 days surrounding day t (in G21, r\u2009=\u2009300\u2009km and \u03c4\u2009=\u20094 days). These data points would then form the training set of observations, which are used to both optimise the local GP model, and also make a prediction of radar freeboard at grid point i. This process can then be repeated for grid point i\u2009+\u20091 (the cyan pixel in Fig.\u00a01a), and for all other sea ice-covered grid points.\n\na G21 approach to predicting gridded radar freeboard, where independent models are optimised at every grid point (white and cyan pixels). b, c GPSat approach to predicting gridded radar freeboard and along-track sea-level anomalies (SLA), respectively. Local expert models (white and cyan pixels) are optimised using all data within their respective training domains. All prediction locations within the inference domain then share this local expert model. Each figure shows \u00a0\u00b14 days of data. Source data for this figure are provided as a Source Data file.\n\nSimilar to G21, our GPSat library is also based on making predictions from local GP models. A key difference is that in GPSat, we define these local models, hereafter local experts, on a coarse 200-km grid, rather than at every prediction location (white and cyan pixels in Fig.\u00a01b, c). To then accommodate for the distribution of local experts on this coarser grid, we introduce an inference domain, which corresponds to a circular region centred around a given expert location (white and cyan shaded circles in Fig.\u00a01b, c), and in which all predictions are made using this same local expert model. In the case of interpolating gridded radar freeboard, the prediction locations within the inference region correspond to all available grid points for which sea ice concentration is greater than 75%. Meanwhile, in the case of interpolating along-track SLA, the prediction locations are both the sea ice lead and floe locations along the orbital track of interest (black profile in Fig.\u00a01c). Where inference domains from neighbouring expert locations overlap, the predictions can then be combined via a weighted average based on the distance to each expert location. Note that we chose 75% as our sea ice concentration threshold when interpolating gridded radar freeboard, as our training data were only processed at locations with \u226575% sea ice cover15. Therefore we do not have observations in the marginal ice zones to validate predictions. While we do not believe that this changes the take-home message of this study, we recognise that appropriate consideration should be made for any operational use case of GPSat, as predictions in these locations will likely carry larger uncertainty due to dynamic ice conditions and uncertainty related to spatial sampling of CS2 and S3 satellites29.\n\nThe G21 methodology can be seen as a limiting case of GPSat in which every prediction location has its own local expert model. In Fig.\u00a01a, we can see how a local expert at every grid point is potentially superfluous given that, in this particular example, 98% of the data points which fall within the cyan training domain are the same as those within the white training domain. We would, therefore, expect to derive very similar GP models for these two locations. Another important distinction to be made is that the G21 approach was written in Python using the NumPy36 library to perform the linear algebra operations, and SciPy37 to perform the optimisation procedure. Meanwhile, GPSat is built around GPflow, which itself is based on Tensorflow and has a vast library of modern inference methods for GPs made available to us. Hence, we are able to (1) accelerate linear algebra computations by using GPUs, (2) use automatic differentiation to avoid explicit computations of gradients, and (3) resort to more scalable inference techniques than those used in G21, such as inducing point methods38 (see\u00a0Supplementary Methods for further details).\n\nIn this section, we compare five different approaches for generating daily pan-Arctic fields of 50-km-gridded radar freeboard. The G21 methodology, which we refer to as \u2018G21N (NumPy CPU)\u2019, was validated in their study based on a series of cross-validation tests and comparisons with an independent data set. Therefore we assume that interpolated freeboards from this approach reflect a ground truth. The remaining four methodologies are referred to as: \u2018G21A (Tensorflow CPU)\u2019, \u2018G21B (Tensorflow GPU)\u2019, \u2018GPSatA (Tensorflow CPU)\u2019 and \u2018GPSatB (Tensorflow GPU)\u2019, where the aim is for each of these methods to recover the same freeboards as G21N (or very similar) in a progressively efficient way. In other words, making these comparisons will allow us to quantitatively assess the runtime and prediction differences between three important model configurations: (1) NumPy vs Tensorflow, (2) CPU vs GPU and (3) G21 vs GPSat. Note that all CPU and GPU computations are performed on an EPYC 7H12 64-core AMD processor and an A100-PCIE-40GB NVIDIA graphics card, respectively. Furthermore, we do not use message-passing interface (i.e. parallelised) computing to generate any of the results in this section.\n\nFigure\u00a02 shows an example snapshot of the interpolated freeboards from G21N on January 15 2019, as well as the difference between G21N and each of the configurations described above. Here, we can see that each configuration yields very similar results to G21N, with (rounded) Root Mean Square Differences (RMSD) of 2, 2, 3 and 4\u2009mm, for G21A, G21B, GPSatA and GPSatB, respectively. Some minor freeboard differences may be attributed to differences in numerical optimisation procedures such as gradient computations, and also how linear algebra tricks are handled within both GPflow and G21 (e.g. Cholesky decomposition to compute matrix inverses). Larger freeboard differences generally occur close to the sea ice edge, and also within the Inuit Nunangat region (Canadian archipelago). Notably, these are regions where we typically see large uncertainty in predicted freeboards (Fig.\u00a02b). In G21, it was suggested that these large uncertainties are likely related to spatial sampling differences of CS2 and S3 satellites (see\u00a0Supplementary Methods for more information on prediction uncertainty in GP models).\n\na Interpolated freeboard from G21N (NumPy CPU) on January 15 2019. b Uncertainty on interpolated freeboard from G21N on January 15 2019. c\u2013f Differences in interpolated freeboard, relative to (a), for G21A (Tensorflow CPU), G21B (Tensorflow GPU), GPSatA (Tensorflow CPU), and GPSatB (Tensorflow GPU), respectively. Source data for this figure are provided as a Source Data file.\n\nTo now check whether the quantitatively similar predictions seen in Fig.\u00a02 are consistent over a longer time period, we compare freeboards for all days between December 1 2018 and April 30 2019. Figure\u00a03 shows the daily RMSD values across this period. Average RMSD values are given as \u00a0<2\u2009mm for G21A and G21B, and \u00a0<4\u2009mm for GPSatA and GPSatB. Note that we also show an error for an equivalent GPSatB configuration which uses 600\u2009km separated local experts instead of our standard 200\u2009km implementation. For this test, the average RMSD is equal to 7\u2009mm. From this analysis, we can conclude that the freeboards derived from each method are quantitatively similar to G21A, although we highlight that predictions from GPSat are sensitive to the separation of local experts and so this separation should be tested for each specific use case.\n\nDaily root mean square difference (RMSD) of freeboard predictions from G21A (Tensorflow CPU), G21B (Tensorflow GPU), GPSatA (Tensorflow CPU), and GPSatB (Tensorflow GPU); each relative to G21N (NumPy CPU). We also include a GPSatB configuration using 600\u2009km local expert separation. Source data for this figure are provided as a Source Data file.\n\nIn terms of computational cost, Fig.\u00a04 highlights how the Tensorflow implementation (G21A) scales much better with increasing data size than NumPy (G21N). For the three variations of the G21 methodology presented here, the total compute time to perform pan-Arctic interpolation for all days between December 1 2018 and April 30 2019 amounted to 4539, 586 and 121\u2009h for G21N, G21A and G21B, respectively. We therefore find a 7.7\u00a0\u00d7 speedup moving from NumPy to Tensorflow, and an additional 4.8\u00a0\u00d7 speedup moving from CPU to GPU. Now, given that the G21 methodology is effectively a limiting case of GPSat, the speedup associated with moving to our local expert configuration scales linearly thereafter (e.g. if there are 4000 grid points to predict on a given day, and we divide the Arctic domain into 300 expert locations, then GPSat will be ~4000/300 times faster than G21). For GPSatA and GPSatB, runtimes amounted to 43.5 and 9\u2009h, respectively. This, therefore, corresponds to a total 504\u00a0\u00d7 speedup for 50-km-gridded interpolation between G21N and GPSatB. Additionally, the total runtime for GPSatB with 600-km separated local experts is 2\u2009h, amounting to a 2270\u00a0\u00d7 speedup over G21N. It should be noted, however, that this degree of speedup is likely to improve with increasing grid resolution, given that the scaling with GPU implementation (G21B) is effectively still linear for small enough data sizes. Meanwhile, G21N is already highly non-linear (see Fig.\u00a04). Finally, for data sizes less than ~100 samples, the NumPy CPU implementation is actually faster than both Tensorflow CPU and GPU, which is due to some amount of overhead associated with initialising the GPflow model.\n\nScatter points correspond to 1000 randomly sampled grid points over the period December 1 2018 to January 31 2019. Shown for each configuration: G21N (NumPy CPU), G21A (Tensorflow CPU) and G21B (Tensorflow GPU). Runtimes of GPSatA and GPSatB scale equivalent to G21A and G21B, respectively. Source data for this figure are provided as a Source Data file.\n\nThe results thus far have been based on optimising local GP models at each grid point or expert location, to learn the spatio-temporal covariance structure of CS2 and S3 radar freeboard observations within a 300-km radius and \u00a0\u00b14 days of each location. As discussed in the \u201cIntroduction\u201d section, this approach does not generally scale well to large data sets, which, for this particular model configuration, places an upper limit on the achievable grid resolution of ~5\u2009km. At 5\u2009km resolution, the number of training samples at a given expert location can exceed 11,000. Nonetheless, pan-Arctic interpolation of radar freeboard is still achievable in reasonable time with GPSat, at ~32\u2009min per day on a single GPU. As an example, Fig.\u00a05 compares predictions of radar freeboards on December 1 2018, at both 50 and 5\u2009km resolution. We showcase these results here to highlight the potential for GPSat to provide basin-wide coverage of high-resolution sea ice altimetry data sets, and its potential impacts for climate research. We can see, for example, how the large-scale patterns are qualitatively similar between these two grid resolutions; however, over short spatial scales, the 5\u2009km field naturally exhibits higher variability. In the Fram Strait region east of Greenland, in particular, the 5\u2009km field shows local structure, which is not resolved in the 50\u2009km data. Resolving small-scale variations in ice thickness like this may be crucial for initialising sea ice prediction systems, leading to improved navigability of sea ice-covered regions. When applied to ocean altimetry data, this could also lead to resolved ocean mesoscale or sub-mesoscale eddies. Furthermore, the ability to derive high-resolution uncertainty information on interpolated fields in Fig.\u00a05 is a particularly useful component of GP models. Such information could be utilised in applications ranging from data assimilation39 to planning the optimal placement of environmental field sensors40.\n\na, b Radar freeboard predictions at 50 and 5\u2009km, respectively. c, d Prediction uncertainty at 50 and 5\u2009km, respectively. Maps correspond to December 1 2018. Source data for this figure are provided as a Source Data file.\n\nTo gain a better understanding of the predictive performance of the GPSat model at finer grid resolutions, we now conduct a cross-validation analysis. For each day in December 2018, we use our standard GPSat configuration to interpolate pan-Arctic radar freeboard at 5-km resolution, however we withhold S3A tracks on the prediction day to use for validation. For example, when predicting freeboard on December 15, we train local expert models using all CS2, S3A and S3B data between the 11 and 19, except we withhold all S3A tracks corresponding to the 15. This provides us with a month of data from S3A with which to validate 5-km GPSat predictions. Figure\u00a06 shows the predictions and associated errors from this analysis. We also include predictions from a linear interpolation approach for comparison, for which we use the same training configuration as GPSat, except we train new linear regression models at each grid point (i.e. we use the G21 configuration rather than our local expert approach). Noticeably the predictions from linear regression are smoother than GPSat, and are unable to resolve small-scale features. Looking again at the Fram Strait region, for example (Fig.\u00a06b vs f), GPSat predicts a localised region of high freeboard, which is not predicted by linear regression. When looking at the spatial error patterns (Fig.\u00a06d vs h), we can see that linear regression shows higher error as a result, which suggests that this is a true feature of the data. Furthermore, the linear approach is not able to fully resolve the tongue of thicker ice in the Beaufort Sea; highlighted by the larger negative spatial errors (Fig.\u00a06c vs \u00a0g). The inability of linear regression to capture larger positive freeboard values is also emphasised in the 2D histogram in Fig.\u00a06i, where we see maximum predicted freeboards of ~35\u2009cm. Meanwhile GPSat (Fig.\u00a06j) mirrors S3A more closely in this upper range, and is ultimately reflected in the improved RMSD and R2 scores\u2014see also Supplementary Table\u00a01 for a range of RMSD and R2 scores from S3A cross-validation tests which use different training window configurations of GPSat. In this table, we note that a model with a reduced window size of \u00a0\u00b1200\u2009km/3 days is able to produce the same skill as the \u00a0\u00b1300\u2009km/4 days configuration shown in Fig.\u00a06, and at over half the runtime cost. In any case, one additional point to note from the histograms in Fig.\u00a06 is that S3A freeboards contain a sizeable number of negative values, which are generally not captured by either linear regression or GPSat. Negative freeboards can occur due to random noise and errors in SLA interpolation6, and also due to the fact that radar freeboards have not been corrected for waveform propagation delay through snow41. Over the December 2018 period we find that the data used to train a given local expert model contain, on average, ten times more positive values than negative, which may suggest why both prediction models generally favour positive values. The fact that the linear regression and GPSat models do not predict negative values (which would be contrary to our physical expectation), provides confidence that the models are not over-fitting to the noise in the data.\n\na, b Linear regression predictions in the Beaufort Sea and Fram Strait, respectively. c, d Linear regression\u2014S3A. e, f GPSat predictions. g, h GPSat\u2014S3A. i, j 2D histograms comparing linear regression and GPSat to held-out freeboards from S3A, for all (pan-Arctic) S3A grid points over December 2018, respectively. The grey line denotes y\u2009=\u2009x, R2 denotes the linear coefficient of determination, and \u03c3 is the average uncertainty (1 standard deviation) on GPSat predictions. Source data for this figure are provided as a Source Data file.\n\nAs a final validation test of 5-km freeboard interpolation, we now compare 3 days of GPSat predictions to three days of gridded airborne data from the NASA Operation IceBridge (OIB) campaign on April 19, 20 and 22 2019. It should be noted that we do not consider OIB observations in the Fram Strait region south of 81\u00b0 N, as dynamic ice conditions are likely to result in sampling biases between OIB freeboards and CS2/S3. In Fig.\u00a07, linear regression shows generally good agreement with OIB radar freeboards, with an R2 score of 0.15 (linear correlation of 0.39). Meanwhile, GPSat shows considerable improvements, with an R2 score of 0.43 (linear correlation of 0.66). Similar to the S3A cross-validation analysis, this skill improvement from GPSat appears to be coming, in part, from the fact that GPSat performs better at predicting large positive freeboard values.\n\n(a, b) Predictions from linear regression and GPSat, respectively. Error bars in b correspond to 1 standard deviation prediction uncertainty, where \u03c3 is the average standard deviation. The grey line denotes y\u2009=\u2009x and R2 denotes linear coefficient of determination. c The mean interpolated freeboard from GPSat across OIB campaign days, as well as the location of the three OIB transects in black. Source data for this figure are provided as a Source Data file.\n\nAlong-track interpolation is a particularly important component of standard sea ice altimetry processing chains. Since the SLA at the position of each floe cannot be measured by the satellite, interpolation offers a means to estimate the SLA at each floe in order to compute sea ice freeboard, and hence thickness. Traditional approaches for interpolating along-track SLA have relied on 1D linear regression6 or 2D GP regression with heavy data sub-sampling26. Arguably, the former case is not sufficient to resolve the complex non-linear behaviour of the ocean surface characteristics, and the latter case can be seen as throwing away valuable training data. Now, given that 5\u2009km is approximately the computational upper limit on the 3D GPSat configuration we use for interpolating gridded radar freeboard (i.e. a \u00a0\u00b1300\u2009km and 4-day training window), it will not be possible to interpolate along-track SLA with this same approach, where the data resolution is ~300\u2009m. In the \u201cDiscussion\u201d section, however, we discuss approximate GP inference methods, which can, in principle, scale to these data sizes, and will be the focus of future GPSat developments. For now, we highlight how 1D and 2D configurations of GPSat can still yield improvements over the standard 1D linear regression approach, and without prohibitive computational expense. In more detail, we make predictions for all CS2 tracks throughout January 2019, where for each individual track, we randomly select 20% of the SLA observations to hold out for validation, and use the remaining 80% for training. In the linear case, we follow the Centre for Polar Observation and Modelling (CPOM) processing sequence for performing 1D along-track SLA interpolation6. Specifically, we use all available observations within a 100-km radius of each lead and floe location, in order to predict SLA. For the 1D GPSat implementation, we optimise local expert models at 200\u2009km intervals along each track, using all available CS2 observations (from the same track) that are within a distance of 300\u2009km. For the 2D GPSat implementation, we optimise local expert models at 200\u2009km intervals along each track, using all available CS2 and S3 observations within a distance of 300-km (recall Fig.\u00a01c). In terms of compute time, linear regression is fast, taking 5\u2009min to interpolate all 1384 tracks across this month-long analysis period. Meanwhile, the GPSat 1D and 2D runtimes amounted to just over 1.5 and 3.5\u2009h on a single GPU, respectively.\n\nIn Fig.\u00a08a\u2013c, we can see that both 1D and 2D implementations of GPSat show improvements in predicted SLA relative to linear regression, with the 2D implementation showing the highest skill overall. While the improvements in RMSD and R2 metrics appear modest, we emphasise that an RMSD improvement from 8.2 to 7.7\u2009cm is significant, considering the noise on along-track CS2 SLA is estimated to be ~5\u2009cm14. It is also worth emphasising here that our GPSat model uses the same GP covariance function as used for radar freeboard interpolation (the Mat\u00e9rn function; see\u00a0Supplementary Methods for details). Therefore, we do not take into account spatially correlated errors and propagation velocities of SLA; both of which have been shown to improve interpolation estimates25,26.\n\na\u2013c 2D histograms comparing linear regression, 1D GPSat and 2D GPSat, to held-out SLA observations from CS2, respectively. d Example CS2 track on January 15. Scatter points show the held-out SLA samples which were not used in training. Shaded regions reflect 1 standard deviation prediction uncertainty from GPSat. e Spatial map showing the location of the CS2 track on January 15, as well as all other CS2 and S3 tracks within a distance of 300-km. Source data for this figure are provided as a Source Data file.\n\nIn Fig.\u00a08d, we show an example of GPSat and linear regression interpolation for one particular CS2 track. Here we notice that the largest differences between the 1D and 2D GPSat implementations occur north of 81.5\u00b0, which is within the S3 polar hole and where CS2 sea ice leads are generally sparse due to the dense ice cover (see Fig.\u00a08e). The 2D implementation therefore utilises information from neighbouring tracks to inform the predictions. Other notable differences in Fig.\u00a08d occur between 200\u00b0/71\u00b0 and 192\u00b0/81\u00b0, where the 1D GPSat and linear regression approaches are smoother among the cluster of held-out data points. It is not unreasonable, however, to expect complex SLA patterns in this region of the Arctic due to the influence of large-scale ocean circulation patterns such as the Beaufort Gyre42.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51900-x/MediaObjects/41467_2024_51900_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51900-x/MediaObjects/41467_2024_51900_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51900-x/MediaObjects/41467_2024_51900_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51900-x/MediaObjects/41467_2024_51900_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51900-x/MediaObjects/41467_2024_51900_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51900-x/MediaObjects/41467_2024_51900_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51900-x/MediaObjects/41467_2024_51900_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51900-x/MediaObjects/41467_2024_51900_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we introduced GPSat, an open-source Python programming library which is built around the Machine Learning (ML) library, GPflow. We subsequently used observations from the CryoSat-2, Sentinel-3A, and Sentinel-3B radar altimeters, to demonstrate how GPSat enables fast and accurate interpolation of satellite altimetry data. Specifically, we showed that GPSat can be used to generate high spatio-temporal resolution pan-Arctic gridded radar freeboard data, as well as more accurate sea-level anomaly (SLA) estimates than current operational practices which rely on linear regression. In this section, we discuss potential avenues for improving these results, and the implications of this work in the wider context of climate research.\n\nThroughout this study, we showed examples of using local Gaussian Process (GP) models to interpolate gridded radar freeboard data, as well as along-track (footprint-resolution) SLA. In this configuration, we found that GPSat is over 500\u00a0\u00d7 faster at interpolating 50-km radar freeboard than a previous interpolation scheme developed by ref. 29 (G21). Furthermore, we found that GPSat is able to scale efficiently to a spatial resolution of 5\u2009km. Beyond this, however, runtimes increase considerably and eventually, the number of training data points exceeds the memory limit of a single GPU. Therefore, for along-track SLA interpolation we were required to restrict the size of the training domain for each local GP model to sample data only in 2D space (compared to the gridded freeboard approach which sampled data in 3D, space and time). A solution to this computational problem has already been developed in the literature, and is indeed available within GPflow, and hence GPSat. This is based on using sparse variational GP (SVGP) models, which have been shown to scale to data sizes exceeding 106 samples38. In short, SVGP models use all available training data to approximate the data distribution, based on a smaller number of psuedo-inputs known as inducing points (see\u00a0Supplementary Methods for further details). Arguably, SVGP lends to a more principled solution than simply discarding valuable training data. With the scaling potential of SVGP models, it is conceivable that we could increase the resolution of interpolated gridded radar freeboard to e.g. 1\u2009km or higher. Additionally, we could potentially improve the accuracy of SLA interpolation by also leveraging temporal (3D) information. While we have provisionally tested SVGP on our Arctic sea ice case here, we have not yet found a configuration which leads to sizeable improvements in predictive skill over GP regression. Further work on this is needed to rigorously evaluate the sensitivity of the SVGP model to the number of inducing points and their initial values. We note here one limitation of the SVGP approach is a general tendency to favour smooth solutions. Therefore, SVGP may result in a loss of precision in areas which are dominated by high-variability signal. In this case, it may be preferable to simply increase the spatial density of local expert models with GPSat, and train using a reduced window size.\n\nA final consideration for increasing the spatial resolution of interpolated fields relates to the intrinsic noise properties of the data. With increasing resolution, both freeboard and SLA data become increasingly noisy. We initially found GPSat prone to over-fitting at 5\u2009km and along-track resolution, which would typically manifest as highly oscillatory predictions over relatively short spatial distances. This over-fitting occurs when local GP models mistake noise for high-variability signal, causing the model to converge on low hyperparameter values of noise variance and correlation lengthscales. While recent work has shown that GP models can be made more robust to noisy data and/or anomalies43, we opted for a pragmatic solution in this study by applying a lower bound on the noise variance hyperparameter when interpolating fields at finer spatial resolution than 5\u2009km (see Methods). We emphasise that this lower bound should be rigorously tested for each specific use case of GPSat.\n\nMore generally, the implications for this work are multi-faceted. In fact, the G21 approach has already been used in recent studies to investigate atmospheric drivers of synoptic-scale radar freeboard variability23, as well as the potential timing of ice-algal blooms in the Arctic11. This highlights the impact which interpolation routines can have on sea ice research. However, due to the computational cost of the G21 approach, these previous studies relied on temporally static (climatology) hyperparameters of local GP models, which ultimately degrades the accuracy of the interpolated freeboard predictions. We propose that GPSat can now bridge this computational gap, and provide accurate high spatio-temporal resolution interpolated fields at a reduced computational cost.\n\nPast studies have also highlighted how uncertainty on along-track SLA is one of the dominant sources of uncertainty in sea ice thickness estimates6,15. Therefore, improving SLA interpolation with GPSat has direct consequences for subsequent applications that leverage sea ice thickness observations. For example, the assimilation of CryoSat-2 sea ice thickness data into numerical models has been shown to improve seasonal forecasts of summer Arctic sea ice39,44. Improvements in initial conditions coming from more accurate sea ice thickness observations could, therefore, increase seasonal prediction skills across these models. Furthermore, there has been a recent proliferation of studies using ML to derive sub-grid scale climate model parameterisations from data45,46,47,48,49. Conventionally, these data come in the form of high-resolution numerical simulations, as observations are considered sparse and/or noisy. We have shown that GPSat can be used to generate de-noised observational data sets above the typical grid resolution of contemporary ocean-sea ice models50,51. This could, therefore, provide insights in the field of ML-based model parameterisations, by learning directly from observational data. Finally, high spatio-temporal observations can lead to insights into ocean and sea ice variability. For example, by assessing the impacts of atmospheric forcing on high-resolution sea ice freeboard23, or ocean circulation patterns52.\n\nIn summary, GPSat represents a step forward for climate data processing routines, one which opens up possibilities for improving understanding and prediction capabilities of key climate processes. Future work will therefore explore the implementation of GPSat over the entire CryoSat-2 record (2010\u2013present), and assess the subsequent downstream impacts on sea ice thickness trends and variability across multiple scales. This will require further sensitivity analysis to determine the generalisation of GPSat to a single satellite altimeter (Sentinel-3 data are only available after 2018), and additional airborne and ground-based validation tests for the time periods not featured in this present study. Further avenues for computational speedups will also be explored with SVGP models, as well as a parallelised implementation which can leverage multiple GPUs during model training.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The sea ice radar freeboard data used in this study are derived from CS2 and S3 Level-0 data, which are processed to Level-1b waveforms using the European Space Agency\u2019s Grid Processing on Demand (GPOD) SARvatore service53. After processing to Level-1b, radar freeboards are computed according to the Centre for Polar Observation and Modelling (CPOM) sea ice processing sequence6,15. The terminology \u2018radar freeboard\u2019 refers explicitly to the waveform echos over sea ice that have not been corrected for the radar propagation delay through the overlying snow layer. As such, these data reflect the height of the radar scattering horizon relative to local sea level, as opposed to the actual height of the sea ice floe, i.e. the \u2018sea ice freeboard\u2019. We use radar freeboards in this study rather than sea ice freeboards to be consistent with G21. Radar freeboard data are gridded to 50-km and 5-km representations of the Equal-Area Scalable Earth (EASE)54 grid.\n\nThe SLA data used in this study are also derived from GPOD-processed Level-1b CS2 and S3 waveforms. Following the CPOM processing sequence, SLA corresponds to the instantaneous elevation of the ocean surface (relative to the WGS84 reference ellipsoid) minus the mean sea-surface height, where the mean sea surface was computed as a 2-year climatology of CS2 elevations over the period September 2011 to September 2013.\n\nIn the section\u00a0\u201cCalibration and runtime performance\u201d, we interpolate CS2 and S3 sea ice radar freeboard at all grid point locations that contain sea ice on each day between December 1 2018 and April 30 2019, according to the National Snow and Ice Data Center (NSIDC) NASA Team sea ice concentration data set55. These data are provided on a 25\u2009km polar stereographic grid, which we regrid to the 50 km\u00a0EASE grid using bilinear interpolation.\u00a0We also regrid these data\u00a0to the 5 km EASE grid in the section \u201cScaling potential: 5-km-gridded radar freeboard\u201d. We define the presence of sea ice as grid points with a sea ice concentration value \u226575%.\n\nIn section \u201cScaling potential: 5-km-gridded radar freeboard\u201d we also validate interpolation of 5-km-gridded radar freeboard with freeboards derived from the 2019 NASA Operation IceBridge (OIB) airborne campaign56. Specifically, we utilise OIB Quick look freeboards from three transects, which were completed on April 19, 20 and 22 2019. OIB measures the elevation of the snow-air interface (snow-freeboard) from an airborne topographic mapper (ATM). Together with a radar-derived snow depth, it is possible to extract the sea ice freeboard along each flight transect. In this study, we compare a theoretical OIB sea ice radar freeboard to interpolated CS2 and S3 data from GPSat. Following previous studies, we estimate the OIB radar freeboard by correcting the OIB sea ice freeboard for radar propagation delay through snow57,58. To compare OIB freeboards with GPSat, we bin-average the data to the 5-km EASE grid and then apply a five-point smoother to each transect.\n\nThis section provides some additional details about the training process for local Gaussian process (GP) models for both G21 and GPSat. It is important to note that in these approaches, when using a 3D training configuration, observations from different days are treated as such, meaning that the GP model derives a local spatio-temporal covariance structure of the observations. The optimisation procedure then ensures that the hyperparameters that make up the GP model are chosen so that they maximise some objective function, leading to good generalisation well when making predictions at unobserved locations. In both G21 and GPSat, this objective function is the log marginal likelihood59,60, which is the typical objective for training GPs. When optimising local models for 5-km radar freeboard and along-track SLA interpolation, we set a lower bound of 2.25\u2009cm2 on the likelihood variance hyperparameter. This is to ensure that the models are not over-fitting to the noise in the data (which naturally increases with increasing resolution). In the\u00a0Supplementary Methods we provide more information on GP theory and how specific methodologies are implemented in GPSat.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The auxiliary sea ice concentration and Operation IceBridge sea ice data sets are both openly available through the National Snow and Ice Data Center (NSIDC) online data portal (https://doi.org/10.5067/MPYG15WAA4WXand 10.5067/GRIXZ91DE0L9, respectively). Cryosat-2 Level-0 data Sentinel-3 Level 1 data were processed to Level-1B (waveforms) using ESA\u2019s Grid Processing on Demand (GPOD) service. The GPOD-processed data generated in this study have been deposited in a Zenodo database, version 1.0 [https://zenodo.org/doi/10.5281/zenodo.13218448]. Source data for all figures are also avaialable within the same Zenodo database61.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The GPSat code is openly available on github https://github.com/CPOMUCL/GPSat. This includes steps of how to configure python environments, as well as examples of how to run the interpolation workflow through Google Colab. A reproducible version of the code can also be found at https://doi.org/10.24433/CO.4875513.v1. Further information can also be found at the GPSat documentation page https://cpomucl.github.io/GPSat/.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Nicholls, R. J. & Cazenave, A. Sea-level rise and its impact on coastal zones. 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Datasets for \u201cScalable interpolation of satellite altimetry data with probabilistic machine learning\u201d [Data set] (1.0). Zenodo. https://zenodo.org/doi/10.5281/zenodo.13218448 (2024).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This research received support through Schmidt Sciences\u00a0and\u00a0the Princeton University Library Open Access Fund. C.N. acknowledges support from NERC (#NE/S007229/1) and the UK Met Office (CASE Partnership). M.T. acknowledges support from ESA (#ESA/AO/1-9132/17/NL/MP, #ESA/AO/1-10061/19/I-EF, Clev2er: CRISTAL LEVel-2 procEssor prototype and R&D, SIN\u2019XS: Sea Ice and Iceberg and Sea-ice Thickness Products Inter-comparison Exercise) and NERC (#NE/T000546/1 761 & #NE/X004643/1). S.T. acknowledges support from a Department of Defense Vannevar Bush Faculty Fellowship held by Prof. Andrew Stuart, and by the SciAI Center, funded by the Office of Naval Research (ONR), under Grant Number N00014-23-1-2729.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, NJ, USA\n\nWilliam Gregory\n\nUCL Centre for Artificial Intelligence, University College London, London, UK\n\nRonald MacEachern,\u00a0So Takao\u00a0&\u00a0Marc Peter Deisenroth\n\nCentre for Polar Observation and Modelling, University College London, London, UK\n\nRonald MacEachern,\u00a0Carmen Nab\u00a0&\u00a0Michel Tsamados\n\nESRIN, European Space Agency, Frascati, Italy\n\nIsobel R. Lawrence\n\nOcean Forecasting Research & Development, Met Office, Exeter, UK\n\nCarmen Nab\n\nThe Alan Turing Institute, London, UK\n\nMarc Peter Deisenroth\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nW.G., R.M. and S.T. made equal contributions to this study. W.G. beta-tested the GPSat library, and conducted the analysis to generate the figures and write the manuscript. R.M. and S.T. wrote the entirety of the GPSat code and the online documentation. S.T. also wrote the supplementary information for this manuscript. I.R.L. conceptualised the idea for combining CryoSat-2 and Sentinel-3 satellite observations, and was responsible for pre-processing all freeboard and sea-level anomaly data sets. C.N. assisted with beta testing the GPSat library and validation tests. M.P.D. provided intellectual support related to Gaussian process theory and implementation. M.T. conceptualised the idea of applying optimal interpolation to polar altimetry data, assisted with beta testing the GPSat library, as well as providing intellectual support related to polar altimetry. W.G., R.M., S.T., I.R.L., C.N., M.P.D. and M.T. contributed to the development of the text in this article.\n\nCorrespondence to\n William Gregory.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Daehyeon Han, and the other, anonymous, reviewer for their contribution to the peer review of this work. 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Scalable interpolation of satellite altimetry data with probabilistic machine learning.\n Nat Commun 15, 7453 (2024). https://doi.org/10.1038/s41467-024-51900-x\n\nDownload citation\n\nReceived: 02 April 2024\n\nAccepted: 19 August 2024\n\nPublished: 28 August 2024\n\nVersion of record: 28 August 2024\n\nDOI: https://doi.org/10.1038/s41467-024-51900-x\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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highly selective, antifungal acetyl CoA synthetase inhibitor", + "journal": "Nature Communications", + "published": "14 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64183-7/MediaObjects/41467_2025_64183_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64183-7/MediaObjects/41467_2025_64183_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64183-7/MediaObjects/41467_2025_64183_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64183-7/MediaObjects/41467_2025_64183_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-64183-7#MOESM1", + "https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1190938", + "http://doi.org/10.2210/pdb9CD8/pdb/", + "http://doi.org/10.2210/pdb8G0T/pdb", + "https://doi.org/10.6084/m9.figshare.29896229.v1", + "/articles/s41467-025-64183-7#Sec33" + ], + "code": [], + "subject": [ + "Antifungal agents", + "Fungal biology", + "Mechanism of action", + "X-ray crystallography" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5619443/v1.pdf?c=1760526361000", + "research_square_link": "https://www.researchsquare.com//article/rs-5619443/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-64183-7.pdf", + "preprint_posted": "31 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Acetyl CoA synthetases (ACS) have emerged as drug targets for the treatment of cancer, metabolic diseases as well as fungal and parasitic infections. Although a variety of small molecule ACS inhibitors have been discovered, the systematic optimization of these molecules has been slowed by a lack of structural information regarding their mechanism of inhibition. Through a chemical genetic-based, synthetic lethal screen of the human fungal pathogen Cryptococcus neoformans, we identified an isoxazole-based ACS inhibitor with antifungal activity and exquisite selectivity for the C. neoformans Acs1 relative to human ACSS2 as well as other fungal ACSs. Xray crystallographic characterization of the isoxazole-CnAcs1 complex revealed that the isoxazole functions as an acetyl CoA mimic and occupies both the acetyl- and CoA-binding sites of CnAcs1. Consistent with this novel mode of inhibition, the isoxazoles display uncompetitive inhibition kinetics that are similar to antimalarial ACS inhibitors also proposed to target the CoA binding site. Consequently, these data provide structural and mechanistic insights into the remarkable selectivity of Acetyl CoA pocket-targeting ACS inhibitors. In addition, these data provide strong proof-of-principle that targeting fungal and parasitic ACSs for the development of novel anti-infectives can be achieved with high selectivity and, thereby, low host toxicity.Biological sciences/Chemical biology/Mechanism of actionBiological sciences/Microbiology/Fungi/Fungal biologyBiological sciences/Structural biology/X-ray crystallography", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8Figure 9", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-5619443/v1/3dc271beda840b4cc5efbce1.png", + "https://assets-eu.researchsquare.com/files/rs-5619443/v1/a4aec95534ac91daa535268f.png", + "https://assets-eu.researchsquare.com/files/rs-5619443/v1/60995d46190215620f957019.png", + "https://assets-eu.researchsquare.com/files/rs-5619443/v1/1aaad191178133be4973aedd.png", + "https://assets-eu.researchsquare.com/files/rs-5619443/v1/29245c6e56684638b5f5349f.png", + "https://assets-eu.researchsquare.com/files/rs-5619443/v1/f87a24554f27b0342d37cb6b.png", + "https://assets-eu.researchsquare.com/files/rs-5619443/v1/632e110bed927da1560e71c8.png", + "https://assets-eu.researchsquare.com/files/rs-5619443/v1/62ebe3757e0db73e1a4711ad.png", + "https://assets-eu.researchsquare.com/files/rs-5619443/v1/9e1df64eb44d7458612bf0fa.png" + ] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nThe University of Iowa and Northern Illinois University have filed a patent disclosure related to the inhibitors described in the manuscript", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "808.Table.1.legends.pdfTable 1808manuscriptsupplementarymaterial.combined.pdfSupplementary Materials9CD8fullvalidationreport.pdfStructure Validation Report for PDB 9CD88g0tfullvalidation.pdfStructure Validation Report for PDB 8g0t", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Acetyl-CoA synthetases (Acs) have emerged as drug targets for the treatment of cancer, metabolic diseases as well as fungal and parasitic infections. Although a variety of small molecule Acs inhibitors have been discovered, the systematic optimization of these molecules has been slowed by a lack of structural information regarding their mechanism of inhibition. Through a chemical genetic-based, synthetic lethal screen of the human fungal pathogen Cryptococcus neoformans, we identified an isoxazole-based Acs inhibitor with antifungal activity and high selectivity for the C. neoformans Acs1 relative to human ACSS2 as well as to other fungal Acs enzymes. Xray crystallography of the isoxazole-CnAcs1 complex revealed that the isoxazole occupies both the acetyl- and CoA-binding sites of CnAcs1. Biochemically, the isoxazoles display uncompetitive inhibition kinetics that are similar to antimalarial Acs inhibitors also proposed to target the CoA binding site. Consequently, these data provide structural and mechanistic insights into the remarkable selectivity of CoA pocket-targeting Acs inhibitors. As such, targeting fungal and parasitic Acs enzymes for the development of novel anti-infectives can be achieved with high selectivity and, thereby, low host toxicity.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Each year, fungal infections affect millions of people across the globe and cause diseases ranging from superficial skin dermatoses to life-threatening diseases of the bloodstream and deep organs such as the liver, kidney, and brain1. The majority of people at-risk for invasive fungal infections have altered immune function due to other diseases such as HIV/AIDs or because they are receiving immunosuppressive drugs used to treat cancer, inflammatory diseases, or manage organ transplantation2. People with fully functional immune systems are also at risk for life-altering fungal infections such as fungal keratitis, a common cause of infection-related vision loss, or chronic aspergillosis-associated asthma1,2. Human fungal infections are caused by an evolutionarily divergent set of pathogens with a broad range of pathobiological characteristics and features. Together, these characteristics of human fungal diseases make them challenging to treat.\n\nCurrently, only three classes of antifungal drugs are available as primary therapies for life-threatening fungal infections3: (1) polyenes such as amphotericin B; (2) azoles, including fluconazole and voriconazole; and (3) echinocandin 1,3-\u03b2-glucan synthase inhibitors exemplified by micafungin. Other drugs, such as 5-flucytosine or the novel triterpenoid 1,3-\u03b2-glucan synthase inhibitor ibrexafungerp, are used as adjuvant agents (5-flucytosine, ref. 4) or are currently limited to the treatment of mucosal infections (ibrexafungerp, ref. 5). The set of mechanistically distinct classes of antifungal drugs is quite limited compared to that available for treating bacterial infections. For example, there are more distinct classes of antibiotics with clinically useful activity against methicillin-resistant Staphylococcus aureus (MRSA) than the total number of antifungal drug classes. As resistance to frontline antifungal drugs continues to rise in species such as Candida auris and Aspergillus fumigatus6, the urgency of developing chemically and mechanistically novel antifungal drugs increases.\n\nThe unmet clinical need for new antifungal drugs has not gone unnoticed, and, encouragingly, two mechanistically and structurally novel candidates are currently in Phase II/III clinical trials. Specifically, fosmanogepix is a broad-spectrum agent that targets GPI anchor biosynthesis, critical for fungal cell wall biosynthesis7, while olorofim has activity against difficult-to-treat molds and selectively inhibits fungal dihydroorotate dehydrogenase8. However, even if these agents advance to clinical use, the threat of resistance remains acute, with only four to five drugs available to treat the broad variety of human fungal pathogens. Indeed, agricultural fungicides based on dihydroorotate dehydrogenase inhibition are already proceeding to use and could generate olorofim-resistant A. fumigatus before olorofim finishes Phase III clinical trials9. Therefore, the discovery and development of new antifungal drug candidates remains an important endeavor for medical mycology.\n\nAcetyl-CoA synthetase (Acs) is an Acyl-CoA/Non-ribosomal peptide synthetase/Luciferase (ANL)-family, adenylating enzyme10 that converts acetate to the key metabolic molecule, acetyl-CoA (AcCoA). In mammalian cells, the majority of AcCoA is generated from glucose via citrate from the tricarboxylic acid (TCA) cycle11. TCA-generated citrate is exported from the mitochondria and converted to AcCoA by ATP-citrate lyase (Acl) in the cytosol and the nucleus12. Overall, acetate-derived AcCoA represents ~10% of the total AcCoA pool in human cells under a normal state of homeostasis, while AcCoA derived from glucose/Acl makes up the majority of the cellular pool11. In contrast, the metabolic origin of AcCoA is reversed in multiple cancer types, and acetate-derived AcCoA makes up the majority of the pool13,14. As such, the primary human Acs, ACSS2, has emerged as a cancer chemotherapy target, and one ACSS2 inhibitor has progressed to early-stage clinical trials14. Importantly, ACSS2 is not essential in mammals13 because Acl maintains the AcCoA pool under normal homeostasis; therefore, ACCS2 inhibitors have a reduced likelihood of causing toxicity in humans.\n\nAcs has also generated interest as a target for the development of anti-infective agents, including antifungal15,16 and anti-malarial agents17,18,19. Previously, we found that the pyrazole AR-12 inhibits fungal Acs15, has broad-spectrum antifungal activity, and is efficacious in combination with fluconazole in a mouse model of disseminated cryptococcosis16. These data provided validation of ACS as a potential antifungal drug target. Unfortunately, the pharmacology of AR-12 is not suitable for further pre-clinical development. Therefore, new chemical classes of antifungal Acs inhibitors are needed to further explore this promising target.\n\nThe rationale for Acs as an antifungal drug target is based on the following considerations. First, medically important yeasts such as Candida albicans and non-albicans Candida spp. lack Acl enzymes and, therefore, are dependent on Acs for the generation of critical pools of AcCoA20. Multiple genetic analyses indicate that Acs enzymes are essential in C. albicans and C. glabrata21,22, and the lack of Acl in other Candida spp. such as C. auris strongly support the conclusion that ACS are required for viability in those species as well.\n\nSecond, genetic studies in Cryptococcus, the species of yeast that is the second most common cause of human disease after Candida species, support Acs inhibition as a therapeutic strategy. Hu et al. have shown that deletion of CnACS1, the only Acs expressed in C. neoformans23, reduces virulence in a mouse model of cryptococcosis24. This is a particularly important observation because C. neoformans expresses Acl. Both ACL1 and ACS1 are, therefore, necessary to generate sufficient AcCoA to maintain fitness during mammalian infection. Our group has also found that loss of either ACS1 or ACL1 significantly reduces the ability of C. neoformans to replicate in macrophages, a key niche during infection23.\n\nThird, we have shown that the acs1\u2206 mutant is hypersensitive to fluconazole in vitro and that deletion of ACS1 increases the efficacy of fluconazole during mammalian infection23. Cryptococcosis is treated with combinations of amphotericin B, fluconazole, and/or 5-flucytosine4; therefore, Acs1 inhibitors could also be used in combination with fluconazole to both reduce the emergence of resistance and improve efficacy. Taken together, these previously reported studies and data support the development of Acs as an antifungal drug target.\n\nIn C. neoformans, the acl1\u2206 mutant has reduced growth on glucose-containing, nutrient-rich medium, while deletion of ACS1 causes reduced growth in nutrient-poor medium containing non-glucose carbon sources24. Consequently, ACS1 expression is increased in tissue culture-based in vitro medium designed to mimic the relatively nutrient-poor infection environment23, suggesting that the reduced fitness of the acs1\u2206 in vivo is due to its increased importance to AcCoA production under those nutrient conditions. Consistent with the in vitro observations, ACS1 expression is increased in vivo as well23. We have been unable to generate an acs1\u2206 acl1\u2206 double mutant under any in vitro conditions, indicating that ACS1 and ACL1 both make important contributions to AcCoA homeostasis23.\n\nWe took advantage of the synthetic lethal relationship between C. neoformans ACS1 and ACL1 to bias a cell-based, phenotypic screen toward potential CnAcs1 inhibitors by screening an acl1\u2206 mutant against a library of small molecules (Fig.\u00a01A). Because of the highly conserved nature of most fungal ACS enzymes with respect to the residues within the substrate binding sites and the correspondingly similar overall structures of previously reported Acs-inhibitor complexes, we expected that Acs inhibitors identified from this cell-based screen in C. neoformans would also inhibit other fungal Acs enzymes. To our surprise, we identified a structurally novel isoxazole (1) Acs inhibitor that is highly specific for CnAcs1 (Fig.\u00a01B, C). X-ray crystallography and biochemical analysis indicate that 1 is an uncompetitive inhibitor that interacts primarily with the CoA substrate binding pocket. Interestingly, additional biochemical and mutational studies suggest that a recently discovered anti-malarial Acs inhibitor (MMV084978, ref. 17) has a similar mechanism of inhibition. Both 1 and MMV084978 are selective for fungal/Plasmodium falciparum Acs relative to the human enzyme ACSS2. Accordingly, these studies not only further validate Acs as an antifungal drug target in C. neoformans but also highlight a surprisingly high level of target specificity that Acs inhibitors show despite the apparent structural and sequence conservation among Acs enzymes.\n\nA The number of compounds evaluated in the primary screen, hit validation, and counter screening steps of the screening campaign are shown. B The structure of the hit isoxazole 1 is shown with the functionally distinct regions of the molecule highlighted. C The IC50 of isoxazole 1 towards CnAcs1 is indicated; the curve is representative of two independent experiments. The mean IC50 calculated from the two independent replicates performed in technical triplicate is shown with the standard deviation. R2 value for goodness-of-fit for the two experiments used to compute the IC50 were 0.996 and 0.995. Source data are provided as a Source data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64183-7/MediaObjects/41467_2025_64183_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "As introduced above, we took advantage of the synthetic lethality of the acs1\u2206 acl1\u2206 mutant in C. neoformans23 to design a whole-cell, high-throughput screen to identify potential inhibitors of CnAcs1 (Fig.\u00a01A). Specifically, we employed a two-stage, screen/counter-screen strategy. First, the acl1\u2206 mutant was screened against a compound library for molecules that inhibit growth in standard yeast peptone-2% dextrose (YPD) medium. The synthetic lethal relationship between ACS1 and ACL1 was expected to sensitize the acl1\u2206 mutant to CnAcs1 inhibitors. Second, we counter-screened hits against WT cells in the same medium. The acs1\u2206 mutant has no growth phenotype in YPD and, therefore, on-target CnAcs1 inhibitors should have reduced antifungal activity against strains expressing both ACS1 and ACL1. Third, we determined the antifungal activity of hits that advanced through the first two steps against WT strains in medium containing acetate as the sole carbon source. Under these conditions, CnAcs1 is essential for growth23,24. We expected that this screening funnel would provide a phenotypic, cell-based assay favoring on-target Acs1 inhibitors; we also hypothesized that the highly conserved nature of fungal Acs25 enzymes would allow our strategy to identify broad-spectrum ACS inhibitors.\n\nWe optimized growth conditions for the acl1\u2206 mutant in 384-well format at 30\u2009\u00b0C in YPD for 48\u2009h using cell density (OD600) as the readout for fungal growth. Fluconazole was used as a positive control to validate the robustness of the assay for the detection of antifungal activity. Full plate assays with alternating rows of DMSO (1%) and fluconazole (64\u2009\u00b5g/mL) generated a Z\u2019 score of 0.65, which is compatible with high-throughput screening (Fig.\u00a0S1). A library of 55,264 drug-like molecules (DIVERSet) purchased from ChemBridge was screened and yielded 175 primary hit molecules (0.3% hit rate) that reduced the growth of the acl1\u2206 mutant (Z\u2009\u2265\u20093) relative to the mean of each plate (Fig.\u00a01A). This set of hits was re-tested under the screening conditions to identify molecules that inhibited growth by 50% in the primary screen and in the validation screen (Fig.\u00a01A).\n\nIndependent samples of the 40 molecules that met these criteria were re-purchased and counter-screened against WT (strain H99) and the acl1\u2206 mutant under screening conditions. Five molecules showed decreased antifungal activity against the WT strain compared to the acl1\u2206 mutant. Finally, the antifungal activity of these five molecules against the WT strain was determined in minimal medium with 2% acetate as the carbon source. Of these five, isoxazole 1 was dramatically more active in acetate medium with a minimum inhibitory concentration (MIC) of 2\u2009\u00b5g/mL compared to >64\u2009\u00b5g/mL in rich, YPD medium (Fig.\u00a02A). To further confirm the structure of isoxazole 1 and the reproducibility of its activity, it was re-synthesized using the route described in Fig.\u00a0S2 and its activity confirmed using growth assays with WT, the acl1\u2206 mutant, and acetate-containing media. Finally, isoxazole 1 inhibited CnAcs1 enzyme with an IC50 of 8.6\u2009\u00b1\u20094.4\u2009\u00b5M using our previously reported assay of CnAcs1 enzyme activity (Fig.\u00a01C, ref. 25). These data strongly support the conclusion that isoxazole 1 inhibits CnAcs1.\n\nA The minimum inhibitory concentration (MIC) of 1 against C. neoformans reference strain H99 and acl1\u2206 mutant strains in YPD, YNB\u2009+\u20092%acetate, and RPMI-MOPS buffer at 37\u2009\u00b0C. The values were identical for three independent experiments performed in technical duplicate. B MIC values against C. albicans reference strain SC5314 and C. glabrata CBS138. Fractional inhibitor concentrations against H99 for fluconazole (C) and rapamycin (D). Growth in wells is indicated by tan fill, while empty wells indicate no growth. The wells with red fill indicate the fractional inhibitory concentration (FIC). E HepG2 cells were exposed to the indicated concentrations of isoxazole 1 for 24\u2009h. The release of LDH into the medium was determined as described in \u201cMethods\u201d and normalized to detergent-induced lysis. Data are means of two independent experiments performed in technical triplicate with error bars indicating standard deviation. F Structures of isoxazole 1 and 2. G In vitro stability of isoxazole 1 in human and mouse liver microsomes. The times indicate t1/2 in minutes. H In vitro stability of isoxazole 2 in the presence and absence of pan-cytochrome inhibitor 1-amino-benzotriazole (ABT). Source data are provided as a Source data file.\n\nCnAcs1 is required for in vitro growth in nutrient-poor conditions and has reduced fitness in animal models of infection24. We, therefore, reasoned that under host-like, in vitro conditions, isoxazole 1 may show antifungal activity toward C. neoformans. The standard clinical microbiology (Clinical and Laboratory Standards Institute (CLSI)) antifungal susceptibility testing conditions (RPMI medium buffered with 0.165\u2009M MOPS, ref. 26) are a tissue culture medium with much lower concentrations of glucose and gluconeogenic amino acids compared to nutrient-rich YPD medium routinely used in the laboratory and in our primary and secondary screening assays27. Under these more host-like conditions, 1 inhibited the growth of H99 with a MIC of 32\u2009\u00b5g/mL (Fig.\u00a02A). The increased activity of 1 in RPMI relative to rich YPD medium is likely due to this difference in nutrient availability. Supporting that hypothesis, we previously showed that host-like in vitro conditions induce CnAcs1 expression23, suggesting that the Acs1-derived pool of AcCoA may become more important under these conditions. Furthermore, the acs1\u2206 mutant has reduced competitive fitness relative to H99 in RPMI medium (Fig.\u00a0S3).\n\nSurprisingly, 1 has no antifungal activity against C. albicans or C. glabrata, two Candida spp. which lack Acl enzymes and, therefore, are susceptible to other Acs inhibitors (Fig.\u00a02B, refs. 15, 16). The primary sequences of Acs enzymes are highly conserved across multiple pathogenic fungi, including C. neoformans, C. albicans, and C. glabrata (Fig.\u00a0S4). Additionally, crystallography of the C. neoformans and C. albicans proteins also shows that the secondary and tertiary protein structures of the enzymes are very similar to one another as well as to Acs enzymes from other species25. As the data presented below indicate, isoxazole 1 inhibits CnAcs1 activity but, unexpectedly, has little to no activity against other fungal Acs or the human enzyme ACSS2.\n\nFluconazole combined with 5-flucytosine is emerging as an important approach to the treatment of cryptococcosis because it avoids the toxicities associated with amphotericin B28. However, 5-flucytosine is not readily available in resource-limited regions with high rates of cryptococcosis and is also toxic to bone marrow. Therefore, the identification of new drugs that improve the activity of fluconazole is of interest4,28. We previously reported23 that the acs1\u2206 mutant shows increased susceptibility to fluconazole in vitro as well as in a mouse model of cryptococcosis. Therefore, if the antifungal activity of isoxazole 1 was due to inhibition of CnAcs1, we would expect it to show synergy with fluconazole. Indeed, isoxazole 1 is synergistic with fluconazole in vitro, with a Fractional Inhibitory Concentration Index (FICI) of 0.5 (Fig.\u00a02C). In contrast, 1 has no interaction with either amphotericin B (FICI\u2009=\u20092) or 5-flucytosine (FICI\u2009=\u20092), the other two antifungal drugs used to treat cryptococcal meningitis28. In mammalian systems, Acs1 activity is regulated by the Target-of-Rapamycin pathway29. Consistent with these previous findings, 1 is also synergistic with rapamycin against C. neoformans (FICI\u2009=\u20090.5, Fig.\u00a02D).\n\nBased on our previously reported genetic data indicating that genetic disruption of Acs1 increases fluconazole efficacy and synergy of isoxazole 1 with fluconazole23, we further investigated the in vitro toxicity and in vitro pharmacodynamic properties of 1 as a prelude to potential efficacy studies in a mouse model of infection. As shown in Fig.\u00a02E, 1 shows very little toxicity up to the limit of its solubility against Hep2G cells using an LDH assay. Inspection of 1 reveals a potential metabolic liability in the form of an oxidation-prone, electron-rich benzofuran ring. Therefore, we synthesized isoxazole 2, in which the benzofuran was removed and a fluorine-substituent was placed at the para-position of the aryl ring; isoxazole 2 maintains an inductively electronegative substituent (F) at the para-position of the aryl ring (Fig.\u00a02F). The antifungal activity of 2 (MIC 32\u2009\u00b5g/mL, RPMI) was similar to isoxazole 1.\n\nConsistent with our concerns, isoxazole 1 was rapidly metabolized by murine microsomes (t1/2 7\u2009min) (Fig.\u00a02G). Unfortunately, the t1/2 of the isoxazole 2 was only slightly longer (t1/2 12\u2009min, Fig.\u00a02H). This metabolism was blocked by the addition of the pan-cytochrome P450 inhibitor 1-amino-benzotriazole (t1/2\u2009>\u2009120\u2009min, ref. 30), confirming the susceptibility of 2 to cytochrome-mediated metabolism (Fig.\u00a02H). Both 1 and 2 showed longer t1/2 in human microsomes (t1/2 83\u2009min and 84\u2009min, respectively) (Fig.\u00a02H). Mass spectrometry of the metabolites of 1 also confirmed that the benzofuran ring was the primary site of oxidation. As predicted, replacement of the benzofuran with a fluoro-substituent reduced oxidation of the aryl ring, but 2 underwent oxidative de-methylation of the tertiary amide moiety (Table\u00a0S1). Consequently, additional medicinal chemistry-based optimization of the structure will be needed to improve its pharmacodynamic and pharmacokinetic properties before efficacy studies of this scaffold in mouse models of infection can be undertaken.\n\nOur chemical-genetic observations strongly support the conclusion that isoxazoles 1 and 2 inhibit CnAcs1 as the primary mechanism of their anticryptococcal activity. To identify other potential targets or pathways that may mediate resistance, we generated isoxazole 1-resistant mutants using an in vitro microevolution approach (Fig.\u00a03A). Three parental strains were used. In addition to the H99 reference strain, we used the acl1\u2206 mutant because it is hyper-susceptible to isoxazole 1. The MIC of isoxazole 1 against H99 (32\u2009\u00b5g/mL) is near the limit of solubility (64\u2009\u00b5g/mL) and might limit our ability to apply sufficient selective pressure. The MIC of isoxazole 1 toward the acl1\u2206 mutant is 8-fold lower (4\u2009\u00b5g/mL, Fig.\u00a02A) and, therefore, this strain extends the range of concentrations under which selection would occur. Finally, we also used a double mutant lacking both ACL1 and KBC1; Kbc1 is an aceto-acetyl-CoA synthetase that also contributes to the AcCoA pool in C. neoformans23.\n\nA Schematic of resistant mutant identification through iterative passaging into higher concentrations of isoxazole 1 using three parental strains: H99, the acl1\u2206 mutant, and the kbc1\u2206 mutant. B Diagram showing domains of the Sgf29 protein and the truncation mutant isolated from the passaging experiment. The green color indicates the Tudor domain of SGF29. The cross-hatches denote the regions of the proteins that are not translated in the truncation mutants. C Competitive growth assay between mNEON-tagged H99 and the sgf29\u2206 mutant at the indicated concentrations of isoxazole 1. The ratio of the two strains was determined by flow cytometry with data represented as mean and standard deviation of three independent replicates. The difference between the sgf29\u2206 mutant and H99 was statistically significant at 16\u2009\u00b5g/mL (p\u2009=\u20090.0465 two-sided, unpaired Student\u2019s t-test). Source data are provided as a Source data file.\n\nThe stains were incubated in microtiter plates containing RPMI/MOPS medium and isoxazole 1 at an initial concentration of \u00bd MIC for each strain. After 72\u2009h, surviving cells were diluted 1:1000 and transferred to plates with 2-fold higher concentrations of 1 compared to the previous incubation. This transfer was repeated to a maximum concentration of 64\u2009\u00b5g/mL, which is the limit of solubility for 1 in these conditions. Approximately 90% of lineages went extinct before the 64\u2009\u00b5g/mL concentration was reached. Randomly selected surviving clones from each of the three lineages were passaged on YPD without isoxazole 1 and re-tested at 64\u2009\u00b5g/mL of 1 to confirm stable resistance. Four isolates encompassing the three parental lineages were initially screened by Sanger sequencing of the ACS1 gene; however, no ACS1 mutations were identified in these isolates.\n\nTo identify mutations that may contribute to the resistance phenotype, we performed whole-genome sequencing of the parental and isoxazole 1-resistant isolates as described in \u201cMethods.\u201d The sequences were mapped to the H99 reference strain, and SNPs in each parental strain relative to the reference were initially identified. Consistent with the Sanger sequencing results, no SNPs were identified in ACS1. One strain derived from H99 and one derived from the acl1\u2206 kbc1\u2206 mutant contained insertion mutations leading to frameshift truncations (Fig.\u00a03B) of the highly conserved SAGA complex component SGF29 (CNAG_06392). Based on extensive studies in the model yeast S. cerevisiae, Sgf29 recognizes methylated histones via its Tudor domains and recruits the SAGA complex, where it mediates acetylation of the histones and transcriptional activation31, and this function has been confirmed in C. neoformans32. Additionally, loss of Sgf29 does not affect the overall structure of the complex or its other functions. In the other two lineages, missense mutations within SGF29 were also present. No other mutations were identified in coding regions that were shared by all four isolates. Five additional proteins had non-synonymous SNPs, but none were clear loss-of-function mutations; although these may contribute to resistance, we focused additional experiments on SGF29 because the truncation mutations were present in multiple isolates (Table\u00a0S2).\n\nIf the structural loss of function (truncation) mutations in SGF29 were responsible for the isoxazole 1-resistant phenotype of these strains, then a strain with a deletion mutation of SGF29 would also be resistant. Consistent with that hypothesis, the sfg29\u2206 mutant is also resistant to isoxazole 1 relative to its H99 parental strain (MIC\u2009>\u200964\u2009\u00b5g/mL). We also compared the competitive fitness of the sgf29\u2206 deletion mutant to its H99 parental strain using a flow-cytometry-based assay33. In that assay, a 1:1 mixture of mNEON-labeled H99 and the unlabeled sgf29\u2206 mutant was incubated with DMSO solvent or isoxazole 1 (32\u2009\u00b5g/mL) in RPMI/MOPS medium overnight at 37\u2009\u00b0C. No difference in competitive fitness was noted in the absence of 1, but the sfg29\u2206 mutant was significantly more fit than H99 in the presence of 1 (Fig.\u00a03C). These data suggest that loss of SAGA acetyltransferase activity improves the fitness of C. neoformans when Acs activity is compromised.\n\nInterestingly, our prior work showed that serial passage of the Acs inhibitor AR-12 in S. cerevisiae led to a resistant isolate with a mutation in TRA1, another component of the SAGA complex16. Furthermore, Summers et al. found that the histone acetyltransferase inhibitor garcinol antagonizes the activity of the Plasmodium falciparum Acs inhibitor MMV01972117. Together, these data indicate that reduced histone acetyltransferase activity reduces dependence on Acs activity in multiple eukaryotes. This consistency further supports the conclusion that the antifungal activity of isoxazole 1 is due in large part to its effect on Acs1 and AcCoA homeostasis.\n\nThe isoxazole CnAcs1 inhibitors are structurally distinct from other fungal, human, and Plasmodium falciparum Acs inhibitors reported in the literature11,17,18,34,35. Therefore, we were interested in determining their spectrum of activity as Acs inhibitors. In prior work25, we characterized the enzymology and structural basis of substrate selectivity for CnAcs1 as well as multiple other fungal Acs enzymes. As part of those studies25, His-tagged Acs enzymes from C. neoformans, C. albicans, Aspergillus fumigatus, and Coccidioides immitis were expressed in E. coli and purified by immobilized metal affinity chromatography (IMAC, Fig.\u00a0S5). Acs activity was then measured in a coupled kinetic assay that detects pyrophosphate product release as previously reported25. We used a 6-point 5-fold concentration series of 1 to assess initial IC50 values for each enzyme. As discussed above, the structures and sequences of the fungal ACS enzymes are highly similar and conserved, respectively. Consistent with the selective antifungal activity of isoxazole 1 toward C. neoformans, it only inhibited CnAcs1 (IC50\u2009=\u20098.6\u2009\u00b1\u20094.4\u2009\u00b5M) (Figs.\u00a01 and 4, Table\u00a01). This observation further supports the conclusion that inhibition of CnAcs1 makes a significant contribution to the antifungal activity of isoxazoles 1 and 2.\n\nTo further explore the selectivity of isoxazole 1, we expressed and purified the human ACSS2 (Fig.\u00a0S5). Consistent with the selectivity observed amongst fungal Acs enzymes, 1 did not inhibit ACSS2 up to the limit of its solubility (Fig.\u00a04B). Interestingly, we previously reported that VY-3-248, an isoxazoline inhibitor of ACSS2, has no activity toward CnAcs1 (Table\u00a01, ref. 23). A second class of ACSS2 inhibitor, MTB-9655 has become commercially available and is currently in clinical trials for cancer therapy (Clinicaltrials.gov/study/NCT04990739). Again, MTB-9655 has no activity against CnAcs1 (Fig.\u00a04C, Table\u00a01). Next, we tested MMV084978 and MMV019721, two molecules that inhibit PfAcAS and have anti-malarial activity17. MMV084978 inhibited CnAcs1 with an IC50 of 2.8\u2009\u00b5M, which is ~8-fold higher than the IC50 reported for PfAcAS (370\u2009nM) inhibition (Fig.\u00a04D, ref. 17) but similar to isoxazole 1. Like isoxazole 1, MMV084978 did not inhibit CaAcs2 (Table\u00a01). MMV019721, on the other hand, did not inhibit CnAcs1 below its solubility limit (Fig.\u00a04E) but has sub-micromolar activity against PfAcAS (Table\u00a01, ref. 17). Because the potency of MMV084978 toward CnAcs1 is similar to isoxazole 1 (Table\u00a01), we determined its antifungal activity toward C. neoformans and found it has an MIC of 64\u2009\u00b5g/ML while MMV019721 has no activity. As such, chemically distinct inhibitors of CnAcs1 show consistent albeit modest in vitro antifungal activity against C. neoformans, while molecules with no CnAcs1 activity also have no antifungal activity. These observations also further support the conclusion that structurally distinct Acs inhibitors can show selectivity toward enzymes from different species.\n\nA The activity of isoxazole 1 against purified Saccharomyces cerevisiae Acs1 (ScAcs1), Candida albicans Acs2 (CaAcs2), Aspergillus fumigatus Acs1 (AfAcs1), Coccidioides immitis Acs1 (CiAcs1), and Cryptococcus neoformans Acs1 (CnAcs1). The inhibition curves are representative of three independent experiments showing similar results. B Isoxazole 1 has minimal activity toward human ACSS2. C Single-dose experiment assessing the activity of the human ACSS2 inhibitor MTB-9655 against CnAcs1 at the maximum soluble concentration. Bar indicates the mean of two independent experiments, with the value of the individual replicate shown by dots. D Anti-malarial, PfAcAS inhibitor MMV084978 inhibits CnAcs1 with IC50\u2009=\u20092.8\u2009\u00b5M (R2\u2009=\u20090.99 for goodness-of-fit by non-linear regression analysis). The IC50 toward PfAcAS is indicated by the arrow. E PfAcAS inhibitor MMV019721 does not inhibit CnAcs1 at the maximum soluble concentration; bar indicates mean with value of individual replicates shown by dots. F Isoxazole 1 minimally inhibits the C. neoformans aceto-acetyl-CoA synthetase CnKbc1 at maximum soluble concentration. Source data are provided as a Source data file.\n\nAcs enzymes are part of the general family of mechanistically similar Acyl-CoA synthetases that includes enzymes with different carboxylic acid specificities10. Consequently, we next asked if isoxazole 1 was selective for Acs when compared to a different acyl-CoA synthetase. C. neoformans Kbc1 converts aceto-acetate, a substrate structurally very similar to acetate, to aceto-acetyl-CoA but has almost no activity in the conversion of acetate to AcCoA23. As shown in Fig.\u00a04F, 1 did not inhibit the aceto-acetyl-CoA synthetase CnKbc1. Taken together, these data indicate that isoxazole 1 displays high selectivity for CnAcs1, the enzyme upon which the chemical-genetic screen was designed. This selectivity is observed despite the mechanistic, sequence, and structural similarities of other fungal Acs enzymes and the human acetyl-CoA synthetase, ACSS225.\n\nAcs is a multi-substrate enzyme that catalyzes the two-step conversion of acetate to AcCoA10,23. In the first step of the reaction, ATP condenses with acetate to generate the reactive acetyl-adenylate intermediate with release of pyrophosphate; in the second step, CoASH reacts with the acetyl-adenylate intermediate to generate the final product AcCoA and release AMP (Fig.\u00a05A). The biochemical mechanism of this two-step reaction has been well-characterized and is classified as a bi-uni-uni-bi ping pong reaction, indicating an ordered binding of substrates25. During this two-step biochemical reaction, the ACS enzyme undergoes a set of conformational changes as outlined in Fig.\u00a05A10. The apo enzyme binds ATP and acetate, which leads to the AD (adenylation) conformation. Next, the acyl-AMP bound form of the enzyme to the TE or thioester conformation to allow CoASH to enter the active site.\n\nA Schematic of the two-step reaction catalyzed by Acs and the conformational changes that occur during the reaction. APO indicates an enzyme without substrate or product bound. AD indicates conformation associated with the adenylation reaction that generates the Ac-AMP intermediate. TE indicates the conformation associated with the thio-esterification reaction of Ac-AMP with CoA to yield AcCoA. CTD indicates the C-terminal domain of the protein that undergoes rearrangement through the course of the reaction. B, C Determination of isoxazole 1 Ki values for ATP and CoA substrates and goodness-of-fit (R2) values for an uncompetitive model of inhibition using non-linear regression analysis. The heat scheme shows the color-concentration correlations for the reaction plots. D, E Lineweaver-Burke plots for isoxazole 1 with ATP and CoA. F, G Plot of Km v isoxazole 1 concentration for ATP and CoA. The R2 values for the goodness-of-fit of the non-linear regression were 0.92 and 0.91 for the ATP (F) and CoA (G), respectively. Source data are provided as a Source data file.\n\nThe majority of previously characterized Acs inhibitors, such as alkyl-AMP esters, are ATP competitive15,23. Consistent with these kinetic data, our previously reported structural analysis of co-crystals of fungal Acs enzymes and alkyl-AMP esters has shown that alkyl-AMP esters occupy the putative adenine pocket25. The PfAcAS inhibitor MMV084978, on the other hand, is an uncompetitive inhibitor with respect to ATP and CoA, while MMV019721 is competitive with ATP and uncompetitive with CoA17. To characterize the kinetic mechanism for CnAcs1 inhibition by isoxazole 1, we examined the dependence of CnAcs1 inhibition by isoxazole 1 on the three substrates, as shown in Figs.\u00a05B, C and S6. These data were fit to competitive, uncompetitive, and mixed models using non-linear regression analysis (GraphPad Prism). The best fits were obtained with uncompetitive models of inhibition for all three substrates, although acetate had the poorest fit. Lineweaver-Burke plots of the data confirmed the uncompetitive inhibition mechanism with respect to ATP and CoA (Fig.\u00a05D, E) as demonstrated by parallel lines for the substrate dependence of velocity at different inhibitor concentrations; the acetate data did not, however, fit well with this model using this method of analysis (Fig.\u00a0S6).\n\nUncompetitive inhibition indicates that the inhibitor binds to the enzyme-substrate complex rather than directly competing with the binding of a substrate36,37,38. This mode of inhibition is characterized by an increase in substrate affinity (decrease in Km) as the inhibitor concentration increases; Fig.\u00a05F\u2013G demonstrates that this phenomenon is observed for isoxazole 1 with respect to both ATP and CoA. The kinetic mechanism of the PfAcA inhibitor MMV084978 is also uncompetitive relative to CoA. Because MMV084978 also has activity against CnAcs1, we asked if it has the same mechanism of CnAcs1. Indeed, MMV084978 has a Ki of 4.1\u2009\u00b5M with CnAcs1, and the data are best fit to the uncompetitive model (R2\u2009=\u20090.9841). Furthermore, Lineweaver-Burke plots also showed the parallel relationship between velocity plots at different drug concentrations expected for uncompetitive inhibition (Fig.\u00a0S7). These data suggest that isoxazole 1 and MMV084978 have similar kinetic mechanisms of CnAcs1 inhibition.\n\nTo better understand the mechanism by which isoxazole 1 inhibits CnAcs1, we obtained a co-crystal of its complex with CnAcs1 (PDB: 9CD8, Table\u00a0S3). The overall structure of the enzyme is similar to previous structures obtained for CnAcs1 (e.g., the CnAcs1 complex with the ethyl-AMP ester; PDB 5K85, ref. 25). The CnAcs1-isoxazole 1 co-crystal structure showed an overall RMSD of 1.42\u2009\u00c5 between Ca atoms (523 residues). Consistent with previous fungal Acs structures25,39, the CnAcs1-isoxazole 1 co-structure is a trimer (Fig.\u00a06A). This trimeric structure was also observed using single-molecule solution-based mass photometry (Fig.\u00a06B), suggesting that the trimeric conformation is unlikely to be an artifact of the crystallization process.\n\nA Overall structure of the trimeric CnAcs1-isoxazole 1 complex with the C-terminal domains (CTD) shown in ribbon format. The blue, red, and tan regions of the trimer denote separate monomers comprising the overall trimer. B Mass photometry showing that CnAcs1 is most consistent with a trimer in solution at low concentrations and that the addition of supra-inhibitory of isoxazole 1 does not change the apparent size of the CnAcs1 protein complex. C Schematic comparing \u201copen-TE\u201d (tan) conformation of the CTD observed in the CnAcs1-isoxazole 1 complex to the CTD conformations in the uninhibited APO (blue), AD (green), and TE (orange) forms of the protein. D The region of the protein bound by isoxazole 1 (green) and its position within that pocket. E Overlay of isoxazole 1 (grey) with the bound pose of CoA (turquoise) and an ethyl-AMP inhibitor (yellow) in a previously reported structure of CnAcs1 (ref. 23), showing that 1 interacts with both the CoA pocket and the acetyl portion of the Ac-AMP/alkyl-AMP binding pocket. F The W334 residue functions to open the CoA tunnel by rotating upon CoA binding. The position of W334 CnAcs1-isoxazole 1 (grey) complex overlaps nearly perfectly with that observed in CnAcs1 structures with CoA bound. G Overall of the two CTD conformations in the isoxazole 1-CnAcs1 crystal structure. CTD1 conformation is shown in red, and CTD2 is shown in orange. H Overlap of the positions of isoxazole 1 in the two CTD conformations present in the unit cell of the isoxazole 1-CnAcs1 complex.\n\nAn important structural and mechanistic characteristic of ANL-family adenylating enzymes is that the C-terminal domain (CTD) undergoes large conformational changes between the two biochemical steps of the overall conversion of acetate to AcCoA10,25. In the Acs enzyme without bound substrates (Apo form), the CTD adopts a unique conformation (Fig.\u00a06C). The first reaction is the adenylation step in which ATP reacts with acetate to generate an acetyl-AMP intermediate with the release of pyrophosphate. This step of the reaction leads to the AD conformation. Prior to the reaction between the acetyl-AMP and CoA, the acetyl-AMP-bound enzyme undergoes a conformational change in which the CTD rotates and leads to the TE conformation. In previous work, we obtained crystal structures of CnAcs1 in each of these conformations with either substrates, intermediates, or inhibitors bound25. The CTD in these structures is frequently observed in multiple conformations within the asymmetric units.\n\nIn the CnAcs1-isoxazole 1 structure, the CTD is observed in two conformations (the CTD is disordered in one chain of the asymmetric unit). First, the CTD adopts the Apo conformation indicative of an enzyme without bound substrates, while the second conformation is structurally distinct from the canonical conformations observed previously (Fig.\u00a06A, C). This new conformation is most closely related to the TE conformation; the transition between the AD and TE conformations occurs through a hinge region upon which the CTD rotates. In the CnAcs1-isoxazole 1 structure, the CTD is rotated much further relative to the hinge region than in other structures, as shown in Fig.\u00a06C. In the TE conformation, the CTD forms the interior of the CoA binding pocket. The \u201cover-rotation\u201d caused by isoxazole 1 binding prevents the formation of an intact, CoA-binding tunnel and opens the region to solvent; we refer to this new CTD conformation as the TE-open. Isoxazole 1-bound CnAcs1 enzyme, therefore, does not recapitulate the secondary and tertiary structure CnAsc1 conformations associated with the binding of substrates and substrate mimics. These observations are consistent with the fact that 1 is not competitive with Acs substrates and displays an uncompetitive mode of inhibition.\n\nIsoxazole 1 binds in the region that accommodates CoA, with the biphenyl moiety placed adjacent to the CoA pantothenate chain binding site (Fig.\u00a06D, E). The amide and isoxazole substituents of 1 extend from the CoA binding pocket into the region occupied by the acetyl group of the acetyl-AMP intermediate or the propyl group of propyl-AMP ester inhibitors (Fig.\u00a06E). The size of the CoA tunnel nearest to the acetyl-AMP binding pocket is modulated by a key tryptophan residue (W334, ref. 23). In the absence of CoA, W334 rotates to hydrogen bond with substrates or intermediates within the ATP or acetyl-AMP pocket25. Upon CoA binding, W334 rotates to open up the CoA tunnel, presumably to allow binding of CoA and position it for the TE reaction. In the CnAcs1-isoxazole 1 co-structure, W334 adopts an orientation similar to that of the CoA-bound enzyme (Fig.\u00a06F, ref. 25).\n\nLike many Acs enzymes, CnAcs1 is exquisitely specific for acetate with little to no activity with carboxylic acids containing larger alkyl groups such as propionate and butyrate23,25. Based on structural and genetic data studies of Acs enzymes from multiple species, the indole ring of a highly conserved tryptophan (W439 in CnAcs1) limits the size of the acetyl-AMP pocket and is a key determinant of substrate specificity and inhibitor binding25. The cyclopropyl group of isoxazole 1 is positioned within the acetyl-AMP pocket in a manner similar to that observed in our previously reported structures of CnAcs1 bound to linear chain25, alkyl-AMP ester bi-substrate inhibitors.\n\nRecently, we used an expanded series of alkyl-AMP ester bi-substrate inhibitors to probe the steric properties of alkyl groups that could be accommodated by the tryptophan wall in CnAcs137. The most potent alkyl-AMP-based inhibitor of CnAcs1 is ethyl-AMP (8\u2009\u00b5M), while increasing the length of the alkyl chain to propyl and butyl decreases potency 3- and >50 fold, respectively; importantly, these trends parallel alkyl carboxylate substrate specificity23,40. Interestingly, the IC50 of the cyclopropyl-AMP ester (9\u2009\u00b5M) is essentially identical to that of the ethyl-AMP inhibitor, indicating that the pocket is able to accommodate an alkyl group of this size and conformation. Furthermore, we obtained an X-ray crystal structure of CnAcs1 bound to the cyclopropyl-AMP ester and observed that the cyclopropyl moiety of the inhibitor and the W439 residues overlap almost exactly (PDB: 8G0T, Table\u00a0S3, Fig.\u00a0S8A; Fig.\u00a0S8B shows the density map for the bound cyclopropyl-AMP ligand). Thus, the cyclopropyl-isoxazole portion of 1 is positioned in the portion of the enzyme that interacts with the methyl group of the acetyl-AMP during the reaction.\n\nIntegrating this structural information with the biochemical mechanism of inhibition leads us to propose the following model for isoxazole 1 inhibition. First, the enzyme proceeds through the adenylation step and conformational changes to the TE conf (Fig.\u00a05A). Second, the CoA binding pocket and tunnel are generated by this conformational change. Third, the kinetic data would suggest that once CoA binds to CnAcs1, it may interact with isoxazole 1. This sequence of events is consistent with the uncompetitive mode of inhibition for both ATP and CoA. The structure shows that 1 partially overlaps the CoA binding site and disrupts the CoA tunnel needed for completion of the thio-esterification reaction. Once isoxazole 1 is bound, its interaction with CnAcs1 appears to be independent of the CTD conformation because the X-ray structure shows it bound to both an open AD-like conformation and the closed TE-like conformation. Figure\u00a06G shows an overlay of the two CTD conformations present with isoxazole 1 bound, while Fig.\u00a06H shows that the position of isoxazole 1 does not vary substantially between the two CTD conformers of the co-crystal structure. In this way, the ATP and CoA substrates are needed to generate the conformation to which isoxazole 1 can bind, but neither substrate directly competes with its binding. Isoxazole 1 binds both the open and closed conformation, leading to the release of substrate/AMP ester intermediate and a dead-end enzyme-inhibitor complex.\n\nTo further characterize the interactions that contribute to the binding of isoxazole 1 to CnAcs1, we performed molecular dynamics (MD) simulations using Schr\u00f6dinger software as described in the \u201cMethods\u201d and Table\u00a0S4. The interaction fraction data for residues that interacted with ligand 1 for >90% of the 1000-ns time-scale simulations are shown in Fig.\u00a07A and summarized in the scheme shown in Fig.\u00a07B. The majority of the interactions predicted to contribute to the binding of 1 to CnAcs1 are hydrophobic, which is consistent with inspection of the X-ray structure of the enzyme-inhibitor complex. Indeed, W334, which plays a key role in the modulation of the CoA binding pocket and is discussed above, is predicted to have a strong interaction. Similarly, W439 is predicted to interact with the isoxazole 1. W439 limits the size of the acetyl-AMP binding pocket and is a key determinant of substrate and inhibitor selectivity for AMP ester-class inhibitors25,40. As also discussed above, our structural data suggest that W439 is likely to interact with the cyclopropyl moiety of 1. The exception to this pattern of hydrophobic interactions is a predicted direct H-bond between T336 and the amide carbonyl of isoxazole 1. In the crystal structure, the distance between the threonine OH and the carbonyl of 1 is 3.7\u2009\u00c5, which is consistent with an H-bond due to an electrostatic rather than covalent interaction. The amide carbonyl of 1 and T336 also appears to participate in H2O-bridges involving D331. Finally, the MD modeling indicates that hydrophobic interactions between A382 and A330 may contribute to binding.\n\nA Histogram of molecular dynamics interactions of isoxazole 1 with CnAcs1. B Schematic indicating residues predicted to contribute to the binding of isoxazole 1 to CnAcs1. The majority of residues predicted to contribute to binding participate in hydrophobic interactions (green). A key H-bonding interaction between T336 and the amide carbonyl of isoxazole 1 was identified in 81% of simulations. For panels A&B, charged interactions are shown in orange; hydrophobic in green; polar in blue; water in grey, and solvent exposed in light grey. C SAR of the isoxazole scaffold. IC50 data for isoxazoles 2\u201313 indicate that the amide carbonyl (isoxazole 3/4), isoxazole cyclopropyl moiety (isoxazoles 5\u20137), and an inductively electronegative substituent at the para-position of the aryl ring (isoxazoles 2, 9, and 11) are key drivers of CnAcs1 potency. Source data are provided as a Source data file, and the molecular dynamics files have been deposited at FigShare (see data availability statement for link).\n\nThe X-ray structure of 1-CnAcs1 and the MD simulations suggest that the amide carbonyl, the cyclopropyl, and the N-methyl substituent of the amide may be important for interactions between isoxazoles and CnAcs1. To test these predictions, we synthesized a set of molecules derived from isoxazole 2, the more metabolically stable and chemically accessible derivative of 1. First, we tested the necessity of the amide carbonyl group for inhibition by synthesizing a derivative of 2, which has a methylene group bridging the biphenyl with the nitrogen (3). Consistent with our hypothesis, 3 had no activity against CnAcs1 (Fig.\u00a07C). We also synthesized the corresponding sulfonamide derivative 4, but it too was unable to inhibit CnAcs1. These data strongly support the hypothesis that the amide carbonyl group is important for CnAcs1 inhibition by the isoxazoles.\n\nTo test the importance of the cyclopropyl group, we first synthesized the corresponding isopropyl derivative (5). This is a relatively small steric change, but it eliminated inhibition entirely. The cyclopropyl group is positioned near W439 in CnAcs1 X-ray structures bound to both isoxazole 1 and the alkyl-AMP ester series of inhibitors25. However, the IC50 of the isopropyl-AMP ester toward CnAcs1 was equivalent to that of the cyclopropyl-AMP ester. The methyl-AMP ester was completely inactive against CnAcs1. The methyl isoxazole 6 was much less potent than 2 but did show 50% inhibition at 100\u2009\u00b5M, while the unsubstituted isoxazole 7 was completely inactive. These data indicate that the cyclopropyl group is likely to interact with W439, but that this interaction is distinct from the manner in which the alkyl groups of alkyl-AMP ester inhibitors interact.\n\nTo test the potential interaction between A330/382 and the methyl group of the amide predicted by MD simulations, we synthesized a derivative of the benzofuran-substituted isoxazole lacking the amide N-methyl group (8) and found it had no activity against CnAcs1. This lack of activity may be due to loss of hydrophobic interactions, but it is also possible that the methyl group affects the conformation of the amide and leads to disruption of the H-bond between the carbonyl and T336.\n\nThe two active isoxazoles 1 and 2 have inductively electronegative substituents (O-alkyl and F) at the para position of the distal phenyl ring. Consistent with this pattern, the OMe derivative 9 had activity that was similar to 1 and more potent than 2, while placement of the OMe group at the ortho position 10 eliminated activity. Substitution of the F in isoxazole 2 with Cl in isoxazole 11 at the ortho position also increased potency relative to 2, while the unsubstituted derivative 12 had no activity, further supporting the importance of an inductively electronegative substituent at this position. The pyridine derivative 13 was synthesized to generate an unsubstituted electron-deficient ring system and was inactive. The OMe (9), Cl (11), and furan (14) substituted isoxazoles contain sterically larger para-substitutions than the F-substituted isoxazole (2) or pyridyl (13) containing isoxazoles and have increased potency. Thus, it seems that increased steric bulk as well as electronegativity may contribute to increased potency at this position.\n\nFinally, we tested the antifungal activity of 13 to determine if molecules with no ability to inhibit CnAcs1 activity would also have no antifungal activity; consistent with that hypothesis, 13 had no antifungal activity against H99 at the limit of solubility. The structure-activity relationship studies identified three key regions of the isoxazoles that contribute to CnAcs1 activity: (1) the amide carbonyl and N-methyl group; (2) the cyclopropyl group; and (3) inductively electronegative substituents on the para position of the distal phenyl group. In addition, these data further support the conclusion that CnAcs1 inhibition can explain a good deal of the antifungal activity of this series of compounds.\n\nThe CnAcs1 residues predicted by MD to have interactions with isoxazole 1 were largely conserved among fungal Acs sequences. Therefore, this computational analysis did not provide immediate insights into the selectivity of 1 for CnAcs1 over the other fungal Acs enzymes. To identify amino acids that may contribute to this selectivity, we generated a homology model of CaAcs2 bound to isoxazole 1 based on the CnAcs1-isoxazole 1 structure, identified five residues within the binding pocket that were discordant between the two enzymes (Fig.\u00a08A), and generated a mutant of CaAcs2 (CaCnAcs1) that matched the CnAcs1 binding pocket (Fig.\u00a08B). Figure\u00a08A, B shows a comparison of the CaAcs2 wild type and CnAcs1-like mutant. The CaCnAcs2 enzyme was expressed in E. coli and purified following previously described methods. The Km of CaCnAcs1 for ATP (82\u2009\u00b1\u200925\u2009\u00b5M) and CoA (367\u2009\u00b1\u2009129\u2009\u00b5M) were similar to previously reported25 values for CaAcs2 (ATP: 83\u2009\u00b1\u200914\u2009\u00b5M; CoA 471\u2009\u00b1\u200945\u2009\u00b5M) and CnAcs1 (ATP: 59\u2009\u00b1\u200910\u2009\u00b5M; CoA 880\u2009\u00b1\u2009170). Indeed, the CaCnAcs2 enzyme was inhibited by the ethyl-AMP ester, which is competitive with ATP, with an IC50 nearly identical to CaAcs2 (19\u2009\u00b1\u20097\u2009\u00b5M\u2009v 11\u2009\u00b1\u20095\u2009\u00b5M, ref. 25).\n\nA CoA binding site of CaAcs2 with isoxazole 1 was modeled based on structural alignment with the CnAcs1-isoxazole 1 complex. B The highlighted residues in CaAcs2 were mutated to the corresponding CnAcs1 residues to create CaCnAcs2. C Comparison of CaAcs2 and CaCnAcs2 inhibition by isoxazole 1 (R2 for CaCnAcs2 fit: 0.74). D CnM445QAcs1 mutant is inhibited by isoxazole 1 with similar potency; R2\u2009=\u20090.93 for CnAcs1 and R2\u2009=\u20090.82 for CnM445QAcs1. E The potency of MMV084978 is increased in CaCnAcs2 relative to CaAcs2 (R2\u2009=\u20090.94). Error bars indicate the standard deviation of two independent replicates. F Overlap of 1 (grey) and MMV084978 (magenta) docked into CnAcs1 with binding affinity data (kcal/mol). Source data are provided as a Source data file.\n\nThe CaAcs2 enzyme is not inhibited by isoxazole 1 at 100\u2009\u00b5M (2-fold below solubility limit). Interestingly, CaCnAcs2 is inhibited with an IC50 of 50\u2009\u00b1\u200923 with maximum inhibition of ~50% (Fig.\u00a08C), indicating that the discordant residues within the binding pocket region of isoxazole 1 in CaAcs2 reduce its susceptibility relative to CnAcs1. Of the five residues that were mutated in CaCnAcs2, M445 is unique to CnAcs1 compared to all other fungal Acs enzymes, as well as the human ACSS2. We, therefore, mutated M445 to the conserved Q446 in the CnAcs1 enzyme. However, CnM445QAcs1 was inhibited with an IC50 similar to the wild-type enzyme (Fig.\u00a08D). MMV084978 is also selective for CnAcs1 relative to CaAcs2. Therefore, we tested the activity of MMV84978 against CaCnAcs2 to determine if the mutations would increase its susceptibility in the manner observed for isoxazole 1 (Fig.\u00a08E). Indeed, MMV084978 inhibited CaCnAcs2 with an IC50 of 88\u2009\u00b1\u200921\u2009\u00b5M and a maximum inhibition of 93%. These data strongly suggest that MMV084978 and isoxazole 1 interact with CnAcs1 in a similar manner.\n\nTo further explore the potential effects of the mutations on selectivity, we took a modeling approach. The binding pose of isoxazole 1 was inserted into the crystal structure of CaAcs2 bound to an alkyl-AMP ester (PDB 8W0B) via structural alignment with isoxazole 1-bound CnAcs1. The same mutations made in the CaCnAcs2 enzyme were then recreated in the isoxazole 1-bound model of CaAcs2. Evaluation of binding energies showed a substantial improvement for CaCnAcs2 (\u22128.4\u2009kcal/mol) vs CaAcs2 (\u22127.3\u2009kcal/mol), recapitulating the improved activity against CaCnAcs2 in vitro. To better probe how binding site residues impact activity against CnAcs1, the same five non-conserved residues were mutated to make a CnAcs1 enzyme that matched the CaAcs2 binding site (CncaAcs1). When evaluating the binding energy of isoxazole 1 using this method, we found a much smaller effect (\u22128.3\u2009kcal/mol for CnAcs1 vs \u22128.1\u2009kcal/mol for CnCaAcs1), but one that still showed reduced preference for the CaAcs2 binding site residues. We also used this approach to model the binding of MMV084978 into the CnAcs1 CoA pocket and found a preferred orientation of the carbonyl (alpha to the phenyl) in close agreement with the crystal position of isoxazole 1 (Fig.\u00a08F). This suggests an important role of T336 in stabilizing the bound state of MMV084978, as our data indicate for the isoxazole series. Similar to isoxazole 1, the estimated binding energy was reduced against the pocket for CnCaAcs1, though only a small effect was observed (\u22126.0\u2009kcal/mol for CnAcs1 and \u22125.8\u2009kcal/mol for CnCaAcs1). While limited in scope, this investigation supports that interaction with the conserved T336 residue is a key component of inhibition at the CoA binding site and is a shared feature among structurally distinct uncompetitive inhibitors of CnAcs1.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64183-7/MediaObjects/41467_2025_64183_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64183-7/MediaObjects/41467_2025_64183_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64183-7/MediaObjects/41467_2025_64183_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64183-7/MediaObjects/41467_2025_64183_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64183-7/MediaObjects/41467_2025_64183_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64183-7/MediaObjects/41467_2025_64183_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64183-7/MediaObjects/41467_2025_64183_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Acs has emerged as a drug target for the treatment of a variety of human diseases and infections, including cancer, fatty liver disease, malaria, and mycoses13,14,15,16,17,18. One of the reasons that Acs is an attractive target for the treatment of human disease is that under normal physiological conditions, ACSS2 and its substrate, acetate, are minor contributors to the overall pool of AcCoA in human or mammalian cells13,14. Instead, glucose is the major carbon source from which AcCoA is derived, through the TCA cycle and the enzyme ATP-citrate lyase; accordingly, deletion of ACSS2 is possible in mice, while deletion of ATP-citrate lyase is not. However, ACSS2 has been shown to be involved in the generation of AcCoA in specific physiological circumstances in mammals39,41, indicating that loss of its function may have more subtle effects on cellular function. Consequently, in the context of targeting Acs as an approach to treating infections, the selective inhibition of microbial, parasitic, or fungal Acs enzymes relative to host ACSS2 would be likely to reduce potential adverse effects of those drugs.\n\nWith these considerations in mind, the first general conclusion from this work is that highly selective inhibition of Acs enzymes is chemically and biochemically feasible. To date, three other Acs inhibitors identified by phenotypic or target-based high-throughput screening have been reported11,17,34. Above, we described the identification of isoxazole 1 through a chemical-genetic, phenotypic screen using C. neoformans. Somewhat surprisingly, isoxazole 1 has no activity against other fungal Acs enzymes and is inactive toward the human enzyme ACSS2 (Table\u00a01). The isoxazoline ACSS2 inhibitor VY-3-249 was identified by an in vitro screen directly against the ACSS2 enzyme11 and also has no activity against any of the fungal Acs enzymes examined to date25. Interestingly, a more potent analog, VY-3-135, is selective for ACSS2 over the related human enzymes ACSS334. Similarly, an ACSS2 inhibitor currently in clinical trials for a cancer indication, MTB-9655 (Clinicaltrials.gov/study/NCT04990739), also has no activity against CnAcs1 (Fig.\u00a04C).\n\nAcs has emerged as a particularly attractive target for the development of anti-malarial drugs. Two structurally distinct anti-malarial Acs inhibitors (MMV0894978 and MMV019721) were identified in a phenotypic screen for growth inhibition17. While MMV019721 does not significantly inhibit CnAcs1, MMV0894978 inhibits PfAcAs, CnAcs1, and ACSS2, but with IC50 values that are 8- and 50-fold higher toward CnAcs1 and ACSS2, respectively. Importantly, however, the potency of MMV089478 toward CnAcs1 (2.8\u2009\u00b5M) is similar to that of isoxazole 1 (8.8\u2009\u00b5M), and both have antifungal activity against C. neoformans. Furthermore, neither MMV089478 nor isoxazole 1 inhibited CaAcs2 significantly. These data suggested that MMV089478 and the isoxazole series of inhibitors share mechanistic characteristics. Indeed, both isoxazole 1 and MMV089478 are uncompetitive inhibitors of CnAcs1 and PfAcAs, respectively. We also found that MMV089478 is an uncompetitive inhibitor of CnAcs1. Mutations in the region of the CoA binding pocket of PfAcAs, based on homology models of the enzyme, confer resistance to MMV089478, and the inhibitor is uncompetitive relative to CoA. The PfAcAs mutations associated with MMV089478 resistance map to the region where isoxazole 1 binds to CnAcs1. Together, these data indicate that two structurally distinct Acs inhibitors show similar species selectivity, kinetic mechanism, and potentially similar interactions with the enzyme target.\n\nThe similarity of Acs binding sites for isoxazole 1 and MMV089478 is further supported by the fact that mutations in the CoA/isoxazole 1 binding region of CaAcs2 enzyme designed to match those found in CnAcs1 led to a CaCnAcs2 mutant enzyme that was inhibited by both compounds when the WT CaAcs2 was not. One of the sites that is mutated in CaCnAcs2 (CaAcs2: G335/CnAcs1: A330) aligns with PfAcAs A597 which is the site where one of the resistance mutations arose (A597V, ref. 17). It appears that variation in amino acid residues in, and around, the CoA pocket contribute to the selectivity of inhibitors that target this region of the enzyme. In the case of the fungal enzymes, the sequence variation in the CoA region that we probed does not entirely account for the level of observed selectivity. Additional factors must contribute to the observed selectivity.\n\nOne possibility is related to the uncompetitive mechanism of inhibition, which indicates that the inhibitor has significant interactions with a substrate-enzyme complex36. The tertiary complex that this mechanism of inhibition suggests is likely to have structural features that are distinct from the dead-end, CnAcs1-isoxazole 1 complex for which we have structural data. As such, the CnAcs1/AMP-Ac/CoA complex may bind 1 and/or MMV089478 through additional interactions not evident in the final CnAcs1-isoxazole 1 complex. Although our structural data do not explain the entire mechanism of selectivity of isoxazole 1, they do provide some of the first insights into how non-nucleoside-based Acs inhibitors interact with the enzyme.\n\nThe anticryptococcal activity of isoxazoles 1 and 2, as well as MMV089478, is in contrast to the non-essentiality of CnAcs1 under standard laboratory growth conditions when ACL1 is present to generate AcCoA23,24. Although the in vitro antifungal activity of these Acs inhibitors is modest, it is observed only in nutrient-limited medium designed to mimic the host environment27. Indeed, the acs1\u2206 mutant has previously been shown to have a growth defect under other in vitro conditions with low glucose or where non-glucose carbon sources are dominant24. In the same medium used to determine MIC values, the acs1\u2206 mutant has reduced fitness by competition assay (Fig.\u00a0S3). Chemical inhibition leads to a rapid loss of protein function, whereas genetic deletion allows the cell to activate cellular mechanisms to compensate for that loss. In other systems, therefore, differences in the nature of phenotypes resulting from chemical inhibition and genetic deletion have been reported42. Therefore, we propose that chemical inhibition of Acs activity in the setting of relatively poor nutrient conditions leads to growth arrest because the cell is unable to rapidly compensate for the reduction in AcCoA.\n\nThe alternative explanation for the antifungal activity is that isoxazoles 1 and 2 have off-target activity against essential enzymes. Four lines of evidence argue against this explanation. First, three different Acs inhibitors have been reported to have antifungal activity: the pyrazole AR-12, the isoxazoles, and MMV08947815,16. The structural diversity of these inhibitors reduces the likelihood that they are inhibiting the function of similar off-target proteins. Second, resistance mutations in the SAGA histone acetylation complex have been observed for AR-12 and isoxazole 1, and inhibition of histone acetylation increases resistance to MMV08947816,17. These data indicate a common mechanism of resistance between the three chemically distinct Acs inhibitors, suggesting they are targeting similar proteins as part of their mechanism of antifungal activity. Third, close structural analogs of the isoxazoles that do not inhibit CnAcs1 do not have antifungal activity (Fig.\u00a07C). Fourth, the isoxazole inhibitors show reduced activity in nutrient-rich conditions and increased activity when acetate is the sole carbon source, which is the pattern expected based on the phenotypes of the acs1\u2206 mutant. These observations cannot completely rule out that inhibition of another target may contribute to the antifungal activity of the isoxazoles. However, they strongly support the conclusion that inhibition of CnAcs1 makes a significant contribution to the antifungal activity of the isoxazoles.\n\nAs such, the isoxazole CnAcs1 inhibitors also provide proof-of-principle for inhibition of fungal Acs as an antifungal strategy even in species that can generate AcCoA through ATP-citrate lyases. Prior results from our lab have shown that the role of CnAcs1 in carbon metabolism is even more pronounced during infection23 than under in vitro conditions mimicking the host environment, supporting the possibility that the efficacy of CnAcs1 inhibitors is likely to be increased in vivo relative to in vitro. In addition, the AcCoA pool is critical for the synthesis of ergosterol, the key sterol of fungal plasma membranes and a target for two of the three antifungal drugs currently used to treat patients. Consistent with this role, the combination of Acs inhibitors with fluconazole is synergistic in vitro (Fig.\u00a02C) and C. neoformans acs1\u2206 mutant is hypersensitive to fluconazole in vivo23. As a single therapy, fluconazole is inferior to combination therapy for the treatment of cryptococcal meningitis43. Therefore, there has been strong interest in developing new approaches to fluconazole-based combination therapy28, and Acs inhibitors appear to be strong candidates in this regard.\n\nIn summary, our discovery of the isoxazole Acs inhibitors and the structural and biochemical characterization of their mechanism of inhibition provides novel insights that should be useful to the design of Acs inhibitors for both infectious disease indications as well as the treatment of human cancers and metabolic diseases.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Lab strains, clinical isolates, and genetically modified strains of C. neoformans or C. albicans were maintained in glycerol stocks that were stored at \u221280\u2009\u00b0C and recovered on yeast peptone dextrose (YPD, 1% w/v yeast extract, 2% w/v peptone, 2% w/v dextrose, and 2% w/v agar). Yeast strains were re-struck from frozen stocks after 14 days. Overnight cultures grown into log phase, shaking at 200\u2009rpm in liquid YPD, were used for each experiment. In cases where experimental procedures did not utilize YPD media, the appropriate media recipes are described in the associated methods sections. C. neoformans is of the H99-stud lineage, and the standard lab strain of C. albicans used in these studies is SC5314. The C. neoformans acl1\u0394 deletion mutant was generated and validated in previous work23. The C. neoformans sgf29\u0394 deletion mutant was generated as part of the Madhani knockout collection and obtained from the Fungal Genetics Stock Center. Artwork in the figures was generated using publicly available Inkscape software (https://inkscape.org).\n\nWe obtained and screened 55,264 compounds from the ChemBridge DiverSET library designed by ChemBridge. The library was initially provided in 10\u2009mM DMSO stocks and then diluted to 1.25\u2009mM in 50% DMSO for screening purposes. All compounds were stored at \u221280\u2009\u00b0C in 384-well plates with sealing foil. Each plate was thawed completely in a desiccating chamber before each use and minimally maintained at room temperature for the duration of assay assembly. For the initial hit validation screen, primary hits were manually picked from the original compound library, while secondary screening assays were performed with compounds that were re-ordered directly from ChemBridge.\n\nCompounds were screened against the ACS-dependent C. neoformans acl1\u0394 deletion mutant in a high-throughput-based growth assay. On Day 0, the acl1\u0394 mutant was inoculated into 50\u2009mL of YPD, where a 1:10 dilution and a 1:25 dilution culture was also created and grown shaking at 200\u2009rpm at 30\u2009\u00b0C to early log phase overnight. The culture was adjusted to a density of 5.33\u2009\u00d7\u2009105 cells/mL, and 15\u2009\u00b5L was added to a 384-well plate containing 10\u2009\u00b5l of YPD and 0.5\u2009\u00b5l of compound (Nimbus, MicroLab). The final concentrations in each well were 8000 cells, 25\u2009\u00b5M compound, and 1% DMSO in YPD. The cultures were incubated at 30\u2009\u00b0C for 48\u2009h before the optical density (OD600) for each plate was determined using a plate reader (SpectraMax i3X, Molecular Devices). Hits were verified using cherry-picked samples under identical conditions as the primary screen. Validated hits were re-ordered and counter-screened in a secondary assay where final concentrations of assay components were the same but scaled to 100\u2009\u00b5l volume in a 96-well plate format and read as before.\n\nThe synthetic methods and molecule characterization data are provided in the Supplementary Methods section.\n\nMICs were performed using a slightly modified CLSI micro-broth dilution method26. Overnight cultures were washed in sterile phosphate-buffered saline (PBS) brought up into either YPD, YNB-acetate, or RPMI supplemented with 165\u2009mM MOPS pH 7.0, such that 1\u2009\u00d7\u2009103 cells would be delivered into each well with a final volume of 200\u2009\u00b5l in a 96-well plate. Each tested drug was added such that the final DMSO concentration did not exceed 1 % and the maximum drug concentration tested was at least four-fold higher than the reported MIC or the limit of solubility. Plates were incubated at 37\u2009\u00b0C for 72\u2009h for C. neoformans and 24\u2009h for C. albicans, unless otherwise stated. Each assay was performed in a minimum of technical duplicates with a minimum of two independent experimental replicates. Fractional inhibitory concentration assays were performed under similar conditions to MICs but set up with a standard checkerboard dilution for each paired16.\n\nHepG2 (ATCC, HB-8065) cells were maintained in Dulbecco\u2019s modified Eagle medium (Gibco, Cat #11965\u2013092) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. Cells were cultured at 37\u2009\u00b0C in a humidified atmosphere with 5% CO2. For experiments, cells were seeded into 96-well plates at a density of 1.25\u2009\u00d7\u200910\u2074 cells per well and incubated overnight under the same culture conditions. The following day, the medium was replaced with fresh medium containing a two-fold dilution series of the test drug, with an equal concentration of DMSO in all wells. After 24\u2009h of incubation, the supernatant was collected to quantify lactate dehydrogenase (LDH) release using the CyQuant LDH assay kit (Invitrogen, Cat #C20300), following the manufacturer\u2019s protocol. LDH levels were normalized to the maximum lysis control, achieved by treating cells with Triton X-100.\n\nMale ICR/CD-1 mouse microsome fractions (Cat M1000; Lot 2210246) and Human pooled gender microsomes (Cat X00807, Lot 2110263) were purchased from Xenotech/BioIVT (Baltimore, MD). Microsome protein (0.5\u2009mg/mL) was placed in a glass screw cap tube; a 2\u2009mM DMSO stock of each compound was spiked into a 50\u2009mM Tris, pH 7.5 solution, and this was added to the microsome solution on ice. The final concentration of the compound after the addition of all reagents was 2\u2009\u00b5M. An NADPH-regenerating system (1.7\u2009mg/ml NADP, 7.8\u2009mg/ml glucose-6-phosphate, 6\u2009U/ml glucose-6-phosphate dehydrogenase in 2% w/v NaHCO3/10\u2009mm MgCl2) was added for analysis of Phase I metabolism after heating both the regenerating solution and the sample tubes to 37\u2009\u00b0C for 5\u2009min in a 37\u2009\u00b0C shaking water bath. Prior to adding compounds and the NADPH-Regenerating System, a subset of microsomes was preincubated with 500\u2009\u00b5M final 1-ABT (MedChemExpress, Cat HY-103389; Lot 247772) for 30\u2009min at room temperature. The incubation was continued in singlet and at varying time points after addition of phase I cofactors (0, 10, 30, 60, 120\u2009min), the reactions were quenched with 0.5\u2009mL (1:1) of methanol containing 0.2% formic acid, 4\u2009mM ammonium acetate (Isoxazole 2 only), and 100\u2009ng/mL N-benzyl-benzamide internal standard (0.1%FA, 2\u2009mM NH4 acetate, and 50\u2009ng/mL final concentration). Time 0 samples were stopped with the methanol solution while still on ice prior to the addition of the NADPH-regenerating system and compound, which were subsequently added. Samples were vortexed for 30\u2009s, incubated at RT for 10\u2009min, and spun for 5\u2009min at 812\u2009\u00d7\u2009g in a table-top centrifuge at RT. Supernatants were then transferred to an Eppendorf tube and spun in a 4\u2009\u00b0C microfuge for 5\u2009min at 16,100\u2009\u00d7\u2009g. The resulting supernatant was analyzed by LC-MS/MS using an AB Sciex (Framingham, MA) 3200 QTRAP\u00ae mass spectrometer running Analyst 1.7.2 coupled to a Shimadzu (Columbia, MD) Prominence liquid chromatography system. Compounds were detected in positive MRM (multiple reaction monitoring) mode by following the precursor to fragment ion transition 375.1/223.0 (Isoxazole 1) and 351.098/199.2 (isoxazole 2). The internal standard (IS) N-benzylbenzamide (Sigma) was followed by the transition 212.1/91.1. An Agilent Poroshell EC-C18 column (2.7 micron packing, 50\u2009\u00d7\u20093.0\u2009mm size) was used for chromatography of all three compounds, but buffers and gradient conditions varied slightly. For isoxazole 1, Buffer A: dH20\u2009+\u20090.1% formic acid, Buffer B: methanol\u2009+\u20090.1% formic acid. A flow rate of 0.7\u2009ml/min was used with the following gradient conditions: 0\u20130.5\u2009min 3% B, 0.5\u20133.0\u2009min gradient to 100% B, 3.0\u20133.5\u2009min 100% B, 3.5\u20133.6\u2009min gradient to 3% B, 3.6\u20134.5 3% B. For isoxazole 2, Buffer A: dH20\u2009+\u20090.1% formic acid\u2009+\u20092\u2009mM NH4 acetate, Buffer B: methanol\u2009+\u20090.1% formic acid\u2009+\u20092\u2009mM NH4 acetate. A flow rate of 0.7\u2009ml/min was used with the following gradient conditions: 0\u20131.5\u2009min 3% B, 1.5\u20132.0\u2009min gradient to 100% B, 2.0\u20133.5\u2009min 100% B, 3.5\u20133.6\u2009min gradient to 3% B, 3.6\u20134.5 3% B. The method described in McNaney et al.44 was used with modification for the determination of metabolic stability half-life by substrate depletion. A \u201c% remaining\u201d value was used to assess the metabolic stability of a compound over time. The LC-MS/MS peak area of the incubated sample at each time point was divided by the LC-MS/MS peak area of the time 0 (T0) sample and multiplied by 100. The natural Log (ln) of the % remaining of the compound was then plotted versus time (in min), and a linear regression curve was plotted going through the y-intercept at ln(100). If the metabolism of a compound failed to show linear kinetics at a later time point, those time points were excluded. The half-life (T\u00bd) was calculated as T\u00bd\u2009=\u20090.693/slope. The metabolism of 7-ethoxycoumarin was used to monitor microsome performance.\n\nIsoxazole 2 was incubated with mouse and human microsomes as described above, except the concentration was increased to 10\u2009\u00b5M to facilitate detection of metabolites, and the final supernatant was filtered through a 0.2-micron PVDF syringe filter prior to analysis by LC-TOF/MS (Sciex 6600 QTOF running Analyst TF 1.8.1). The same Agilent Poroshell EC-C18 column was utilized with slightly different chromatography but the same solvents (flow rate 0.8\u2009ml/min; 0\u20130.5\u2009min 3% B, 0.5\u20135.0\u2009min gradient to 90% B, 5.0\u20137.0\u2009min 90% B, 7.0\u20139.0 3% B). Samples were acquired using positive ESI, using the TOF-IDA-MS/MS mode. After sample acquisition, the data were transferred to a processing computer and loaded into MetabolitePilot 2.0.4 for metabolite characterization. Individual samples were screened for metabolites using a generic, predefined list of 80 phase 1 and 2 biotransformations, as well as using software-predicted compound-specific cleavages. Peaks were filtered to include those with (1) confirmation score above 45%, (2) charge state of +1, and (3) mass accuracy within 12\u2009ppm of calculated formula. Confirmation score is an algorithmically derived weighted score assigned to peaks that indicates the likelihood that the peaks are a metabolite. All resulting peaks were visually inspected to confirm that no background peaks were included. After inspection, the remaining peaks were compared across all time point samples to identify those consistently present. Potential metabolites were reported if they appeared in three or more time point samples, or if they appeared in a single sample but were part of a documented multi-transformation pathway. Peak areas were summed (across time points and across discrete peaks of the same transformation type), and metabolites comprising greater than 5% of the total peak were investigated.\n\nIsoxazole-resistant strains and their parents were grown overnight in 25\u2009mL YPD. Pelleted cultures were transferred to a 2\u2009mL screw cap tube with PBS, pelleted, and supernatant removed. Samples were lyophilized overnight, and 0.5\u2009mm diameter glass beads (BioSpec Products) were added the following day. Freeze-dried material was subject to bead beating for 1\u2009min or until cell pellets were broken into a fine powder, followed by the addition of 1\u2009mL of CTAB extraction buffer [100\u2009mM Tris-HCl, pH 7.5, 700\u2009mM NaCl, 10\u2009mM EDTA, 1% cetyltrimethylammonium bromide (CTAB), 1% \u03b2-mercaptoethanol (14\u2009M)]. Samples were incubated at 65\u2009\u00b0C for 30\u2009min followed by the addition of 1\u2009mL chloroform and gentle mixing. Samples were centrifuged for 10\u2009min at 5000\u2009\u00d7\u2009g, where the aqueous phase was transferred to a clean tube for a second round of chloroform extraction. The final aqueous phase was transferred to a clean tube and followed by standard ethanol precipitation. Final samples were resuspended in nuclease-free water and sent to SeqCoast for further analysis. Received samples were prepared for whole-genome sequencing using an Illumina DNA Prep tagmentation kit and unique dual indexes. Sequencing was performed on the Illumina NextSeq2000 platform using a 300-cycle flow cell kit to produce 2\u2009\u00d7\u2009150\u2009bp paired reads. 1\u20132% PhiX control was spiked into the run to support optimal base calling. Read demultiplexing, read trimming, and run analytics were performed using DRAGEN v3.10.12, an on-board analysis software on the NextSeq2000. We include FastQC metrics as a best practice and for examination in the case of unexpected outputs. Reads were aligned to the C. neoformans H99 genome (FungiDB version 52) using bowtie2, followed by variant calling via samtools and vcftools with a minimum read depth\u2009=\u200910 and quality PHRED score\u2009=\u200937. Variants identified in each parental strain were filtered out of each respective isoxazole 1-resistant strain.\n\nFollowing a previously published protocol33, overnight cultures of the H99 reference strain, H99-mNeonGreen expressing strain, and the sgf29\u2206 deletion strain were grown in standard YPD media. The sgf29\u2206 deletion strain and H99-mNeonGreen were washed with PBS, resuspended in RPMI-MOPS pH 7.0, and mixed 1:1 such that 1\u2009\u00d7\u2009104 cells/mL per strain were delivered with a final volume of 100\u2009\u03bcl per well in a flat-bottom 96-well plate. A dilution series of isoxazole 1 was delivered such that the DMSO concentration remained constant and did not exceed 1.25%. Co-cultures were grown overnight at 37\u2009\u00b0C for 24\u2009h in ambient air before being analyzed on an Attune NxT Flow Cytometer with CytKick autosampler and Attune Cytometric software.\n\nBriefly, expression plasmids for the ACS enzymes (reported in ref. 25) were transformed into the Escherichia coli strain BL21 with appropriate antibiotic selection. Resistant colonies were used to start an overnight culture in standard LB broth with continued antibiotic selection, shaking at 200\u2009rpm and 37\u2009\u00b0C. The following morning, LB broth supplemented with 50\u2009mM glucose and fresh antibiotic was inoculated with 1:1000 dilution of each respective overnight culture and allowed to grow shaking at 200\u2009rpm and 37\u2009\u00b0C until mid-log phase (OD600 0.5\u20130.8), then induced with 1\u2009mM isopropyl-\u03b2-D thiogalactopyranoside (IPTG) for 2\u2009h. Pelleted cells were lysed and protein was purified via IMAC (ref. 25). All proteins were dialyzed and stored at \u221280\u2009\u00b0C in elution buffer.\n\nTo select residues in the 1 binding pocket for mutagenesis, 1-bound CnAcs1 (PDB: 9CD8) was aligned to CaAcs2 (PDB: 8W0B, UniProt: Q8NJN3) using Pymol to identify non-conserved residues. Five mutations were chosen (A334M, G335A, F372Y, V386T, and Q445M), providing a final construct for expression in E. coli as described above. The construct was gene-synthesized and cloned into the expression vector at IDT (Coralville, IA). The protein sequence of the expressed enzyme is below with the N-terminal histidine tag:\n\nMHHHHHHHHENLYFQGPTEQTHNVVHEANGVKLRETPKEFFERQPNKGHIHDVNQYKQMYEQSIKDPQGFFGPLAKELLSWDHDFHTVKSGTLKNGDAAWFLGGELNASYNCVDRHAFANPDKPALICEADDEKDSHILTYGDLLREVSKVAGVLQSWGIKKGDTVAVYLPMNAQAIIAMLAIARLGAAHSVIFAGFSAGSIKDRVNDASCKALITCDEGKRGGRTTNIKKLCDEALVDCPTVEKVLVYKRTNNPEIHLTEGRDYYWDVETAKFPGYLPPVSVNSEDPLFLLYTSGSTGTPKGVVHSTAGYLLGAALSTKYIFDIHPEDILFTMADVGWITGHTYALYGPLLLGVPTIIFEGTPAYPDYGRYWQIVEKHKATHFYTAPTALRLLRKAGEQEIAKYDLSSLRTLGSVGEPISPDIWEWYNEFVGKNQCHISDTYWMTESGSHLIAPLAGVVPNKPGSASYPFFGIDAALIDPVTGVEIEGNDAEGVLAIKDHWPSMARTVYKNHTKYMDTYMNPYPGYYFTGDGAARDHDGYYWIRGRVDDVVNVSGHRLSTAEIEAALIEDKKVSEAAVVGIHDDITGQAVIAYVALKEGNSDEDSEGLRKELVLQVRKTIGPFAAPKSVIIVQDLPKTRSGKIMRRILRKVSSNEADQLGDISTLSNPQSVEGIISAFGAQFGKK.\n\nACS activity was detected using the reported25 absorption-based coupled kinetic assay as modified from the EnzChek Pyrophosphate Assay Kit (Thermo). Substrate and coupling reagents were either prepared fresh or thawed from small aliquots stored at \u221280\u2009\u00b0C for a maximum of two freeze-thaw cycles. All tested drug concentrations were diluted from stock such that DMSO concentrations did not exceed 5%. All reagents, including the compound and minus the start reagent, were mixed and aliquoted at room temperature, followed by a 15-min incubation at 37\u2009\u00b0C. The start reagent, either acetate or aceto-acetate in the case of CnKbc1, was then added, and the reaction was followed continuously at 37\u2009\u00b0C in a SpectraMax i3X Multi-Mode plate reader (Molecular Devices) at absorbance 360\u2009nm. Single-point inhibition studies were performed with 50\u2009\u00b5M compound, and percent inhibition was normalized to a DMSO control. Dose response curves were generated using a minimum 10-point two-fold drug dilution series using concentrations indicated for each compound tested, along with a DMSO control. Inhibition constants (Ki) were determined by varying one substrate while holding the other substrate pairs in excess across a dilution series of the inhibitor. Substrates were supplied in excess for the respective Ki determination, such that ATP\u2009=\u20092.5\u2009mM, CoA\u2009=\u20091\u2009mM, and acetate\u2009=\u20090.5\u2009mM. The 50% inhibitory concentration (IC50) and Ki values were calculated using the non-linear regression analysis software, Prism (GraphPad). All inhibition studies were performed with a minimum of two experimental duplicates and always with a positive control inhibitor, the competitive inhibitor ethyl-AMP at 50\u2009\u00b5M.\n\nFor compounds with high background in the kinetic assay, acetyl-CoA synthetase activity was measured using an endpoint FeCl3/hydroxamic acid assay15. For the IC50 determination, enzyme reactions contained sodium acetate (0.5\u2009mM), CoA (1\u2009mM), magnesium chloride (4\u2009mM), ATP (1\u2009mM), DMSO (5%), or inhibitor in DMSO (5%). The buffer for the reaction was potassium phosphate (125\u2009mM) and contained freshly prepared hydroxylamine (200\u2009mM, pH 7), reduced glutathione (10\u2009mM), and potassium fluoride (50\u2009mM). The reactions were carried out at 37\u2009\u00b0C for 50\u2009min and quenched by the addition of an aqueous solution of FeCl3 (370\u2009mM) and trichloroacetic acid (3.3%). Absorption at 540\u2009nm of quenched reaction solutions was determined with a SpectraMax i3X, and IC50 values were calculated using non-linear regression analysis with Prism software (GraphPad).\n\nPurified CnAcs1 was concentrated to 10\u2009mg/mL in 20\u2009mM NaCl, 20\u2009mM Tris, pH 8.5, 1\u2009mM TCEP for crystallization screening. All crystallization experiments were set up using an NT8 drop-setting robot (Formulatrix Inc.) and UVXPO MRC (Molecular Dimensions) sitting drop vapor diffusion plates at 18\u2009\u00b0C. One hundred nanoliters of protein and 100\u2009nL crystallization solution were dispensed and equilibrated against 50\u2009\u00b5L of the latter. Isoxazole 1-CnAcs1: The complex with isoxazole 1 was prepared by adding the inhibitor, from a 100\u2009mM stock in DMSO, to an aliquot of the protein to a final concentration of 2\u2009mM and incubating for 30\u2009min on ice. Crystals were obtained in 1\u20132 days from the Index HT screen (Hampton Research) condition F2 (0.2\u2009M Ammonium sulfate, 0.1\u2009M HEPES pH 7.5, 25% (w/v) PEG 3,350). Samples were transferred to a fresh drop composed of 80% crystallization solution and 20% (v/v) PEG 200 and stored in liquid nitrogen. Cyclopropyl-AMP-CnAcs1: 12.5% 8\u2009K, 200\u2009mM NaCl, 100\u2009mM\u2009K/Na phosphate pH 6.2. 1\u2009mM ligand was added to the protein prior to crystallization. Samples were transferred to 25% (v/v) PEG 200\u2009+\u200975% crystallant for cryoprotection. X-ray diffraction data were collected at the National Synchrotron Light Source II (NSLS-II) beamline 19-ID (NYX).\n\nIntensities were integrated using XDS45 via Autoproc46, and the Laue class analysis and data scaling were performed with Aimless47. Structure solution was conducted by molecular replacement with Phaser48 using a previously determined structure of CnAcs1 (PDB 8EPS) as the search model. Structure refinement and manual model building were conducted with Phenix49 and Coot50, respectively. Torsion angle non-crystallographic symmetry restraints were used during refinement. Additionally, TLS parameters were used in the later stages of refinement to model anisotropic atomic displacement parameters. Structure validation was conducted with Phenix51, and figures were prepared using the CCP4MG package52.\n\nThe docked ligand-receptor complex was subjected to MD simulation using the Desmond module of Schrodinger software (Schrodinger, LLC, New York, NY, 2024-1) with OPLS 4 force field53. First, the molecular system of the ligand-receptor complex was built with water molecules in a cubic box via the simple point charge method, which was later followed by ion neutralization by the addition of sodium, to balance the net charge of the solvated system. MD simulation was performed for 1000\u2009ns using the Isothermal-isobaric (NPT) ensemble class, where temperature and pressure were reserved at 300\u2009K and 1.01325\u2009bar pressure employing Nose-Hoover temperature coupling and isotropic scaling. Equilibration of the systems and MD simulations was carried out using the default protocol provided in Desmond53. These simulations produced key results, including RMSD, RMSF, and ligand-protein interaction profiles. MD simulations were run 3 times using the parameters summarized in Supplementary Table\u00a04. The output files for the three simulations have been deposited at FigShare (https://figshare.com/account/items/29896229/).\n\nMolecular docking of 1 and MMV084978 into Acs enzymes was conducted using the Molecular Operating Environment (www.chemcomp.com, version 2020.01; Chemical Computing Group). The crystal structures of CnAcs1 (PDB: 9CD8) and CaAcs2 (PDB: 8W0B) were used as starting points for in silico mutagenesis. For CnAcs1, point mutations were made in Coot50 to match CaAcs2 as follows: M329A, A330G, Y367F, T384V, and M440Q. Side chains were manually reoriented to best match the starting crystal structure. This structure, now denoted as CncaAcs1, was protonated to pH=7 using ProPKa, and energy minimized in the presence of 1 within the Molecular Operating Environment using the Amber10:EHT force field. The binding energy of 1 was determined via the GBVI/WSA dG function without variable ligand placement to keep the ligand conformation as close to the crystal structure as possible. For MMV0894978, binding energy was determined following ligand placement (defined by selecting 1 as the binding site) and refinement using the London dG scoring function, followed by rescoring using the GBVI/WSA dG function. The top-scoring orientation was used for further structural analysis. The same protocol was applied in the creation of CaCnAcs2, where mutations were introduced as follows: A334M, G335A, F372Y, V386T, and Q445M. 1 was modeled into the binding site in COOT via structural alignment with 9CD8 (Chain A), and the structure was subsequently energy minimized prior to docking.\n\nDistinct samples were measured. Statistical analysis and curve fitting were performed using Prism (GraphPad) using linear regression analysis, parametric analysis, and a two-sided, unpaired Student t-test.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data are provided in the figures, tables, and Supplementary Information sections of the manuscript. The source data files for the data are also provided in the Source data File. Whole-genome sequencing data have been deposited at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number PRJNA1190938. Coordinates and structure factors for CnAcs1 inhibitor complexes have been deposited to the Worldwide Protein Databank (wwPDB) with the accession codes 9CD8 (isoxazole 1) and 8G0T (cyclopropyl-AMP). The molecular dynamics simulations have been deposited at figshare.com (https://doi.org/10.6084/m9.figshare.29896229.v1).\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Denning, D. W. Global incidence and mortality of severe fungal disease. Lancet Infect. Dis. 24, e428\u2013e438 (2024).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nVallabhaneni, S., Mody, R. K., Walker, T. & Chiller, T. The global burden of fungal diseases. Infect. Dis. Clin. N. 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This research used resources at the NYX beamline 19-ID, supported by the New York Structural Biology Center, at the National Synchrotron Light Source II, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under Contract No. DE-SC0012704. The NYX detector instrumentation was supported by grant S10OD030394 through the Office of the Director of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The purchase of the NMR spectrometer used to obtain results included in this publication was supported by the National Science Foundation under the MRI award CHE-2117776 (T.J.H). The HRMS was supported by Northern Illinois University Molecular Analysis Core Facility (RRID:SCR 024586), which was established as a partnership with Shimadzu Scientific Instruments. Purchase of the Bruker Maxis Plus QTOF was made possible by the National Science Foundation under Grant No. 1726931.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, IA, USA\n\nAndrew J. Jezewski,\u00a0Katy M. Alden,\u00a0Jonah Propp,\u00a0Andrew J. Fuller\u00a0&\u00a0Damian J. Krysan\n\nDepartment of Chemistry and Biochemistry, Northern Illinois University, DeKalb, IL, USA\n\nDrashti G. Daraji,\u00a0Charles L. Lail III,\u00a0Michael E. Heene,\u00a0Jeffery C. Ferreira\u00a0&\u00a0Timothy J. Hagen\n\nProtein Structure and X-ray Crystallography Laboratory, University of Kansas, Lawrence, KS, USA\n\nLijun Liu\u00a0&\u00a0Scott Lovell\n\nNYX, New York Structural Biology Center, Upton, NY, USA\n\nKevin P. Battaile\n\nDepartment of Biochemistry, UT Southwestern Medical Center, Dallas, TX, USA\n\nNoelle S. Williams\n\nCenter for Global Infectious Disease Research Seattle Children\u2019s Research Institute, Seattle, WA, USA\n\nBart L. Staker\n\nSeattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, Washington, USA\n\nBart L. Staker\u00a0&\u00a0Scott Lovell\n\nDepartment of Molecular Physiology and Biophysics, Carver College of Medicine, University of Iowa, Iowa City, IA, USA\n\nDamian J. Krysan\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nData collection: A.J.J., K.M.A., J.P., D.G.D., C.L.L., M.E.H., A.J.F., J.C.F., L.L., K.P.B. Data analysis: A.J.J., K.M.A., J.P., D.G.D., C.L.L., M.E.H., A.J.F., J.C.F., L.L., K.P.B., N.S.W., B.L.S., S.L., T.J.H., D.J.K. Conceptualization: A.J.J., K.M.A., S.L., T.J.H., D.J.K. Writing: A.J.J., J.P., D.G.D., N.S.W., S.L., T.J.H., D.J.K. Supervision: N.S.W., B.L.S., S.L., T.J.H., D.J.K. Resources: A.J.J., J.P., B.L.S., S.L., T.J.H., D.J.K.\n\nCorrespondence to\n Damian J. Krysan.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Iwao Ojima and the other, anonymous, reviewers for their contribution to the peer review of this work. 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Discovery and mechanism of a highly selective, antifungal acetyl-CoA synthetase inhibitor.\n Nat Commun 16, 9118 (2025). https://doi.org/10.1038/s41467-025-64183-7\n\nDownload citation\n\nReceived: 10 December 2024\n\nAccepted: 05 September 2025\n\nPublished: 14 October 2025\n\nVersion of record: 14 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-64183-7\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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signaling and GBP1 contribute to enhanced antiviral capacity", + "pre_title": "Bat-specific adaptations in interferon signaling and GBP1 contribute to enhanced viral tolerance", + "journal": "Nature Communications", + "published": "01 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61254-7/MediaObjects/41467_2025_61254_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61254-7/MediaObjects/41467_2025_61254_MOESM2_ESM.docx" + }, + { + "label": "Supplementary Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61254-7/MediaObjects/41467_2025_61254_MOESM3_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61254-7/MediaObjects/41467_2025_61254_MOESM4_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61254-7/MediaObjects/41467_2025_61254_MOESM5_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61254-7/MediaObjects/41467_2025_61254_MOESM6_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1255723/", + "https://urldefense.com/v3/__https:/www.ncbi.nlm.nih.gov/sra/PRJNA1255723__;!!NLFGqXoFfo8MMQ!r6VzZM0kM10XjKuktDOsxNEg3FQpjjBKXxBiuwRxgU3Chtc46HSoDYhBSjk6PWaiBc9R-MWZKeG0XiX5r9mS-dCd2QIQNQ0nQA$", + "/articles/s41467-025-61254-7#Sec30" + ], + "code": [ + "https://github.com/doxeylab/gonzalez-et-al", + "/articles/s41467-025-61254-7#ref-CR77" + ], + "subject": [ + "Innate immunity", + "Non-model organisms", + "Viral reservoirs", + "Virus\u2013host interactions" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5411236/v1.pdf?c=1751455593000", + "research_square_link": "https://www.researchsquare.com//article/rs-5411236/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61254-7.pdf", + "preprint_posted": "25 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Bats are reservoirs of emerging zoonotic viruses of concern that cause severe disease in humans and agricultural animals. However, it is poorly understood how bats are able to tolerate diverse viral infections, knowledge that could help pave the way for new therapeutic strategies. Here, we characterized antiviral pathways in two divergent bat species, Pteropus alecto and Eptesicus fuscus, identifying unique bat-specific mechanisms underlying their enhanced antiviral tolerance. We demonstrate the critical roles of STAT1 and STAT2 in IFN\u03b2 signaling, along with species-specific adaptations that collectively contribute towards a \u201csteady and ready\u201d antiviral state in bat cells. Unlike in humans, we find that bat interferon signalling processes resist the immune antagonistic properties of viruses like MERS-CoV which further explains the ability of bats to tolerate coronavirus infections. Using transcriptomic analysis, we identified canonical and non-canonical interferon stimulated genes (ISGs) including two key bat genes, IFIT1 and GBP1. Compared to their human orthologs, we show that bat IFIT1 and GBP1 exhibit enhanced antiviral activity against a wide range of RNA and DNA viruses, including coronaviruses and additional bat-derived poxviruses (e.g., Eptesipoxvirus). Ultimately, our work provides important insights into the evolution of enhanced interferon-mediated antiviral responses in bats, contributing to their unique ability to resist viral diseases.Biological sciences/Microbiology/Virology/Virus–host interactionsBiological sciences/Microbiology/Virology/Viral reservoirs", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nA.B. is a co-inventor of the Efk3B cell line that is sold through Kerafast, USA. All authors declare no other conflicts of interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryTablesS1S7.xlsxSupplementary Tables S1-S7SupplementalMaterials.pdfSupplementary Figures 1-17", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Bats are reservoirs of emerging zoonotic viruses that may cause severe disease in humans and agricultural animals. However, it is poorly understood how bats can tolerate diverse viral infections. Here, we characterized type I interferon response pathways in kidney cell lines derived from two divergent bat species, Pteropus alecto and Eptesicus fuscus, identifying distinct mechanisms underlying their enhanced control of viral infection. We demonstrate the critical roles of STAT1/STAT2 in IFN\u03b2 signaling, along with species-specific adaptations that contribute towards a steady and ready antiviral state. Unlike in humans, bat IFN\u03b2 signaling processes resist the immune antagonistic properties of MERS-CoV which further explains the ability of bats to tolerate coronavirus infections. Transcriptomic analysis on interferon stimulated cell lines identified canonical and non-canonical interferon stimulated genes including two differentially expressed genes, IFIT1 and GBP1, that exhibit enhanced antiviral activity against a wide range of viruses, including the bat-derived Eptesipoxvirus. We have identified a functional (AV1) motif within E. fuscus GBP1 that restricts Eptesipoxvirus replication. Ultimately, our work provides important insights into the evolution of enhanced interferon-mediated antiviral responses in bats, contributing to their ability to resist viral diseases.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Bats are one of the most abundant and geographically diverse mammalian species in the world, with over 1480 species globally distributed on all continents with the exception of Antarctica1,2. Accumulating data suggest that bats are hosts to multiple zoonotic viruses that have caused consequential disease outbreaks in humans, including severe-acute respiratory syndrome coronavirus (SARS-CoV), SARS-CoV-2, and Middle East respiratory syndrome coronavirus (MERS-CoV). Despite these viruses causing life-threatening disease in humans, naturally or experimentally infected bats do not demonstrate overt signs of disease3,4,5. This suggests the evolution of enhanced antiviral capacity in reservoir bat species.\n\nTo successfully establish infection, a virus must surpass the host\u2019s first line of defense, the innate immune system. Mammalian cells utilize conserved pattern recognition receptors (PRRs) to sense pathogen-associated molecular patterns (PAMPs), like viral nucleic acid. Following detection by PRRs, antiviral cytokines like interferons (IFNs) are secreted, which bind to their cognate receptors in paracrine and autocrine signaling loops. All type I IFNs, including IFN\u03b2, bind to IFNAR1/2, leading to the activation of the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway in humans6. This involves a series of phosphorylation events leading to the formation of a transcription factor complex composed of STAT1, STAT2, and IRF9, which migrates to the nucleus and induces the expression of antiviral interferon-stimulated genes (ISGs). These ISGs work alone or in concert to inhibit virus replication7. The IFN signaling cascade and the role of STAT proteins have not been mechanistically characterized in bat cells.\n\nIn this study, we generated species-specific bat IFN\u03b2, which potently restricts VSV and MERS-CoV replication in kidney cells derived from two divergent bat species, the insectivorous big brown bat (Eptesicus fuscus) and the frugivorous black flying fox (Pteropus alecto). By applying a transcriptomic approach, we demonstrate a largely conserved type I IFN response across both bat species. However, lower expression levels of orthologous ISG transcripts, in addition to divergent transcripts, contributed to the overall antiviral IFN\u03b2 response in bat cells relative to human cells. GBP1 was one of the top upregulated transcripts in bat cells, and functional assays demonstrated that bat GBP1 is broadly protective against a range of RNA and DNA viruses compared to human GBP1. The antiviral function of bat GBP1 was dependent on a previously unknown and uncharacterized AV1 motif within the N-terminal domain, leading to restriction of the bat-derived Eptesipoxvirus.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To examine the type I IFN response in bat cells, we generated recombinant IFN\u03b2 from humans and two divergent bat species, Pteropus alecto and Eptesicus fuscus (Fig. 1A and Supplementary Fig. 1A). As a vehicle control, we generated secreted green fluorescent protein (GFP). We confirmed the expression and concentration of IFN\u03b2 using immunoblotting (Fig. 1B) and enzyme-linked immunosorbent assay (ELISA), respectively (Supplementary Fig. 1B). Next, we confirmed the function of IFN\u03b2 on species-matched cells from P. alecto (PaKiT03), E. fuscus (Efk3B), and humans (A549 and RPTEC). Each cell line was treated with serially diluted, species-matched IFN\u03b2 for 6\u2009h, followed by infection with vesicular stomatitis virus that was engineered to express GFP (VSV-GFP; MOI 0.1). We used VSV-GFP inhibition data to determine functional units for IFN\u03b2 (Supplementary Fig. 1C). VSV-GFP replication was not inhibited in untreated or vehicle-treated cells (Supplementary Fig. 1C). However, we observed significant inhibition of VSV-GFP in cells pre-treated with IFN\u03b2 (Supplementary Fig. 1C). PaKiT03 and A549 cells required 10-fold more IFN\u03b2 than Efk3B and RPTEC cells (Supplementary Fig. 1C). We also validated IFN\u03b2 activity in multiple clonal populations of E. fuscus cell lines, such as Efk1F and Efk2A (Supplementary Fig. 1C)8,9. Equivalent amounts of E. fuscus IFN\u03b2 were required for antiviral protection against VSV-GFP in all three clonal cell lines.\n\nA Schematic for the generation of recombinant IFN\u03b2. Drosophila S2 cells were stably transfected to inducibly secrete human (Hu), E. fuscus (Ef), and P. alecto (Pa) IFN\u03b2. GFP was produced in S2 cells for use as a control for vehicle treatments. Image created in BioRender [https://BioRender.com/o9oes1x]. B Detection of IFN\u03b2 by immunoblotting, where the V5-tag was probed for. C P. alecto (PaKiT03), E. fuscus (Efk3B), and human (A549, RPTEC) cells were untreated or treated with species-matched IFN\u03b2 (1\u2009U/mL) for 6\u2009h. The upregulation of MX1, IFIT1, and RSAD2 transcripts was assessed by qRT-PCR. Data are represented as mean\u2009\u00b1\u2009SD, n\u2009=\u20094 biological replicates (Two-way ANOVA, Tukey\u2019s range test, ****<0.0001, ***<0.001). D PaKiT03, Efk3B, A549, and RPTEC cells were untreated or treated with vehicle or species-matched IFN\u03b2 (10\u2009U/mL) for 1 to 6\u2009h prior to VSV-GFP infection (MOI 0.1). VSV-GFP levels were assessed by fluorescent microscopy. E VSV-GFP infection levels were quantified using ImageJ software. Data were represented as mean\u2009\u00b1\u2009SD, n\u2009=\u20093 replicates/time point (One-way\u00a0ANOVA, Tukey\u2019s range test, **<0.01, *<0.05). F PaKiT03, Efk3B, Huh7, and RPTEC cells were uninfected (mock), infected with MERS-CoV (MOI 0.1), pretreated with species-matched IFN\u03b2 for 6\u2009h prior to infection, or received IFN\u03b2-treatment following infection for 48\u2009h. Supernatant was collected and TCID50 assay was performed to assess viral titer. Data were represented as mean\u2009\u00b1\u2009SD, n\u2009=\u20093 biological replicates.\n\nTo determine if our recombinant IFN\u03b2 could induce the expression of canonical ISGs, we characterized the transcript levels of MX1, IFIT1, and RSAD2 by qRT-PCR. MX1, IFIT1, and RSAD2 transcript levels were upregulated in PaKiT03, A549, Efk3B, and RPTEC cells at 6\u2009h post-IFN\u03b2 (1\u2009U/mL) treatment (Fig. 1C). These data suggest that recombinant IFN\u03b2 can induce canonical ISGs and restrict VSV-GFP replication across the evaluated cell lines.\n\nNext, we evaluated the IFN\u03b2 signaling kinetics in human and two divergent bat cell lines (Fig. 1D, E). We observed a significant reduction in GFP signal in bat cell lines that were treated with IFN\u03b2 for 1\u2009h, while a similar effect was not observed until 2- and 4-h of IFN\u03b2 treatment in A549 and RPTEC human cells, respectively (Fig. 1E). We further investigated IFN\u03b2-mediated protection using MERS-CoV, which is speculated to have evolved in insectivorous bats10. PaKiT03, Efk3B, Huh7, and RPTEC cells were either pretreated with IFN\u03b2 (10\u2009U/mL) or treated following infection with MERS-CoV (MOI 0.1) (Fig. 1F). In PaKiT03, Efk3B, and RPTEC cells, viral load was substantially reduced upon IFN\u03b2 treatment in both pre- and post-treatment conditions compared to untreated cells. However, IFN\u03b2 was effective in Huh7 cells only when provided prophylactically (Fig. 1F). These data suggest that IFN\u03b2 can induce a robust antiviral state that is not antagonized by MERS-CoV in kidney cells derived from all three species.\n\nThe role and importance of the JAK-STAT signaling pathway during virus infection in bats has not been studied. In humans, a critical step in the JAK-STAT pathway involves the phosphorylation of STAT1 and STAT2 proteins by tyrosine kinases at positions Y701 and Y690, respectively11,12. To evaluate the role of STAT1/2 phosphorylation in bat cell signaling, we pre-treated PaKiT03, EfK3B, A549, and RPTEC cells with Staurosporine, a broad-spectrum kinase inhibitor, followed by stimulation with IFN\u03b2 (10\u2009U/mL) and VSV-GFP infection (MOI 0.1). Staurosporine treatment reduced the phosphorylation of STAT1/2 at positions Y701 and Y690, respectively (Supplementary Fig. 2A). Phosphorylation or activation of STAT1 and STAT2 in human cells leads to nuclear localization of both proteins prior to driving the expression of ISGs11,12,13. Treating human and bat cells with Staurosporine reduced nuclear STAT1/2 in IFN\u03b2-treated cells (Supplementary Fig. 2B\u2013D). Staurosporine treatment also reduced the expression of IFIT1 and MX1 ISG transcripts in IFN\u03b2-treated cells (Supplementary Fig. 3A, B). In addition, VSV-GFP was able to replicate to higher levels in all cell lines treated with Staurosporine (Supplementary Fig. 3C, D). Taken together, our data demonstrate that phosphorylation is critical for IFN\u03b2 signaling in bat cells.\n\nNext, we investigated the rate of phosphorylation and nuclear translocation of STAT1/2 upon IFN\u03b2 treatment for 5 to 20\u2009min. Phosphorylation of Y701-STAT1 occurred within 20\u2009min of treatment in PaKiT03, A549, and RPTEC cells (Fig. 2A and Supplementary Fig. 4). Interestingly, Efk3B cells had higher basal levels of pY701-STAT1, suggesting differences between bat cells (Fig. 2A). Phosphorylation of Y690-STAT2 occurred within 5\u2009min of IFN\u03b2 treatment in PaKiT03 and A549 cells, and by 20\u2009min in RPTEC cells. Phosphorylation of Y690-STAT2 could not be evaluated in Efk3B cells due to the lack of cross-reactivity of the antibody, which may be due to sequence divergence around the phosphorylation site of STAT2 in E. fuscus bats (Supplementary Fig. 5). We also corroborated our STAT1/2 phosphorylation data using confocal microscopy to demonstrate the kinetics of IFN\u03b2 treatment-mediated nuclear translocation of STAT1/2 across different times in all cell lines. Across all cell lines, STAT1/2 localized to the nucleus within 20\u2009min of IFN\u03b2 treatment, but P. alecto cells overall demonstrated a faster trend for STAT1/2 nuclear translocation (5\u2009min) compared to E. fuscus and human cells (10\u201320\u2009min) (Fig. 2B and Supplementary Fig. 6). However, basal levels of nuclear STAT1 and STAT2 could be detected in unstimulated E. fuscus cells, suggesting that these cells may be primed to respond to IFN\u03b2.\n\nA PaKiT03, Efk3B, A549, and RPTEC cells were treated with vehicle or species-matched IFN\u03b2 (10\u2009U/mL) for 5, 10, and 20\u2009min. Total STAT1, STAT2, and phosphorylated STAT1 (pY701-STAT1) and STAT2 (pY690-STAT2) levels were assessed by immunoblotting. GAPDH was used as a loading control. Full blots can be found in Supplementary Fig. 4. B PaKiT03, Efk3B, A549, and RPTEC cells were treated for 5, 10, and 20\u2009min with species-matched IFN\u03b2 (10\u2009U/mL). Cells were fixed and stained for pY701-STAT1, pY690-STAT2, STAT2, and nucleus (DAPI). Nuclear translocation was visualized by confocal microscopy and quantified using ImageJ. Data were presented as mean\u2009\u00b1\u2009SD, n\u2009=\u20093 biological replicates, where three fields of view were quantified per time point. Bat and human icons were obtained from BioRender [https://BioRender.com/o9oes1x]. C PaKiT03, Efk3B, A549, and RPTEC cells were pretreated for 48\u2009h with siRNA targeting STAT1 and STAT2 individually or in combination (DKD). Cells were then treated for 6\u2009h with species-matched IFN\u03b2 (10\u2009U/mL), followed by infection with VSV-GFP (MOI 0.1) for 16\u2009h. Viral infection was visualized by fluorescent microscopy, and D quantified using ImageJ. Scale bars\u2009=\u2009100\u2009\u00b5m. Data in panel D are presented as mean\u2009\u00b1\u2009SD, n\u2009=\u20093 biological replicates. E PaKiT03, Efk3B, Huh7, and RPTEC cells were pretreated with species-matched IFN\u03b2 for 6\u2009h prior to infection or received IFN\u03b2 treatment following infection with MERS-CoV (MOI 0.1) for 48\u2009h. Protein lysate was harvested and probed using immunoblots for total STAT1 and STAT2, pY701-STAT1, pY690-STAT2, MERS-CoV nucleoprotein (N), GAPDH, and ACTB. Full blots can be found in Supplementary Fig. 8.\n\nTo determine the importance of STAT1/2 in IFN\u03b2 signaling, we attempted to knock down these proteins in human and bat cells, followed by IFN\u03b2 treatment. PaKiT03, EfK3B, and A549 cells were treated with siRNA directed against STAT1/2 individually or in combination for 48\u2009h. After 48\u2009h, human and bat cell lines were treated with their species-matched IFN\u03b2 for 6\u2009h, followed by infection with VSV-GFP (MOI 0.1) (Fig. 2C, D and Supplementary Fig. 7). While knocking down STAT1 levels reduced IFN\u03b2-mediated protection in PaKiT03 cells, Efk3B and A549 cells were fully protected from VSV-GFP infection (Fig. 2C, D and Supplementary Fig. 7A, E), suggesting STAT1-independent signaling mechanisms14. However, knockdown of STAT2 expression levels led to loss of protection across all human and bat cell lines, suggesting a more critical and conserved role of STAT2 in IFN\u03b2-mediated antiviral protection in these three species (Fig. 2C, D and Supplementary Fig. 7B, E). Knocking down expression levels of both STAT1/2 in PaKiT03 and A549 cells led to increased virus replication in both cell lines (Fig. 2C, D and Supplementary Fig. 7C, E). However, knocking down both STAT1/2 in Efk3B cells was lethal (Supplementary Fig. 7D), suggesting an important combined role of STAT1 and STAT2 in maintaining cell viability in these cells.\n\nMERS-CoV is a human pathogen of bat origin and can inhibit IFN signaling in human cells15,16. We next assessed whether MERS-CoV could modulate IFN\u03b2 signaling in P. alecto and E. fuscus cells (Fig. 2E and Supplementary Fig. 8). MERS-CoV infection alone suppressed the phosphorylation of pY701-STAT1 in both human (Huh7 and RPTEC) cell lines (Fig. 2E and Supplementary Fig. 8). IFN\u03b2 treatment alone induced the phosphorylation of pY701-STAT1 across all cell lines pretreated with IFN\u03b2 followed by MERS-CoV infection (Fig. 2E and Supplementary Fig. 8). However, treating MERS-CoV infected human cells with IFN\u03b2 (post-treatment condition) led to reduced levels of pY701-STAT1 phosphorylation compared to IFN\u03b2 treated cells. In bat cells however, treating MERS-CoV infected cells with IFN\u03b2 (post-treatment condition) did not lead to a loss of pY701-STAT1 levels, suggesting that bat cells are able to resist MERS-CoV infection mediated inhibition of STAT1 phosphorylation (Fig. 2E and Supplementary Fig. 8). Pretreating human or P. alecto bat cells with IFN\u03b2 maintains pY690-STAT2 levels upon MERS-CoV infection; however, treating MERS-CoV infected cells with IFN\u03b2 leads to partial loss of pY690-STAT2 levels in both Huh7 and P. alecto cells (Fig. 2E and Supplementary Fig. 8). Taken together, these data demonstrate that bat cells are differentially sensitive to MERS-CoV infection mediated modulation of IFN\u03b2 signaling, compared to virally permissive human cells. Of note, consistent with other studies17, MERS-CoV is likely able to shut down host translation in Huh7 cells as evidenced by the reduced actin and GAPDH levels (Fig. 2E). We did not observe this phenomenon in either bat cell line or the human RPTEC cells.\n\nNext, we identified the top expressed ISGs in cells from two divergent bat species upon stimulation with species-matched IFN\u03b2 (Fig. 3A). We also stimulated two human cell lines, A549 and RPTEC, with human IFN\u03b2 for comparison. IFN\u03b2 treatment induced the expression of several genes representing multiple signaling pathways across all cell lines (Fig. 3B and Supplementary Fig. 9A). There were limited perturbations between the kidney cell lines along principal component one (PC1), which accounts for more than 67% of sample variation. Gene enrichment analysis identified conserved and unique processes shared between all cell types, where 55 processes like type I IFN signaling pathway, regulation of viral entry into host cells, and defense response to virus were shared among all four cell lines (Supplementary Data 1). Of note, 136 processes were uniquely upregulated in bat cell lines upon IFN\u03b2 stimulation (Supplementary Data 1).\n\nP. alecto (PaKiT03), E. fuscus (Efk3B), and human (A549, RTPEC) cells were untreated or treated with 1\u2009U/mL of species-matched IFN\u03b2 for 6\u2009h. A Schematic of sample preparation for transcriptomics using bulk RNA sequencing. Image created in BioRender [https://BioRender.com/o9oes1x]. B Principal component analysis (PCA) depicting global transcriptional profiles of the IFN\u03b2-stimulated samples. C Enriched GO terms for IFN\u03b2-treated cells. Dot color represents fold enrichment, and size represents \u2212log10 values. A hypergeometric test (cumulative distribution function; upper tail) with a subsequent multiple hypothesis Benjamini & Hochberg correction of the frequency of terms associated with genes in the significantly differentially expressed gene set compared to a genome background was used to determine a p value for over enrichment. D Volcano plots depicting DEGs in A549, RPTEC, PaKiT03, and Efk3B cells. DEGs (p adjusted <0.05) with a log2 fold change of more than 2 are indicated in red. Non-significant DEGs with a fold change of less than 2 are indicated in green. Statistical test and multiple hypothesis correction was performed using DESeq2 to identify DEGs. Bat and human icon were obtained from BioRender. E Heatmap indicating the expression levels of the top DEGs in bats ranked by p value involved in the IFN\u03b2 response. The ranking p value comes from the DESeq2 DESeq function analysis of bat gene expression. E. fuscus transcript paralogs are indicated in brackets (i. e., Par.1) and are plotted against the transcript levels of a single variant detected in PaKiT03 and RPTEC cells. See Supplementary Data 4 for the representative LOC symbols. F Luciferase activity from rabbit reticulocyte lysate incubated with cap0 or cap1-\u03b2-globin-Fluc RNA, Zika virus Fluc RNA, or SARS2-Fluc RNA in the presence of human or P. alecto IFIT1. Data were normalized to the luciferase activity in the absence of IFITs and are shown as the mean \u00b1 the standard error of three separate experiments (Two-way ANOVA, Tukey\u2019s range test). G P. alecto IFIT1 and IFIT3 were expressed and purified from E. coli and analysed by SEC as indicated. Peak fractions from the IFIT1\u2009+\u2009IFIT3 (complex) run analysed by SDS-PAGE are shown below the elution trace. H Mutated P. alecto IFIT1 and wildtype IFIT3 were expressed and purified from E. coli and analysed by SEC as indicated. Peak fractions from the IFIT1\u2009+\u2009IFIT3 (complex) run analysed by SDS-PAGE are shown below the elution trace. I Transcripts per million values for GBP1 across PaKiT03, EfK3B, A549, and RTPEC cells. Data were representative of three biological replicates.\n\nNext, we analyzed the top differentially expressed processes in bat cells and determined the fold enrichment (Fig. 3C). For antiviral GO processes like defense response to virus, innate immune response, negative regulation of viral genome replication, response to virus, and type I IFN signaling pathway, fold enrichment was the highest in both bat cell lines compared to human cells (Fig. 3C and Supplementary Data 2). Both human and bat cell lines had comparable enrichment scores for GO process associated with positive regulation of I\u03baB kinase/NF\u03baB signaling (Fig. 3C and Supplementary Data 2). We also noticed differences in enrichment scores between the two bat cell lines. E. fuscus- derived cells had a higher enrichment score compared to P. alecto cells for GO processes associated with defense response to virus, response to virus, type I IFN signaling, IFN\u03b3-mediated signaling pathway, positive regulation of IFN\u03b2, and IL27-mediated signaling pathway (Fig. 3C and Supplementary Data 2). In contrast, P. alecto-derived cells had a higher enrichment score compared to E. fuscus cells for GO processes associated with response to IFN\u03b2, response to IFN\u03b1, and antigen processing and presentation of endogenous peptide antigen via MHC class I (Fig. 3C and Supplementary Data 2). Indeed, our data demonstrate that while both bat cells mount a robust IFN\u03b2-mediated response compared to human cells, differences exist between bat cells and likely between bat species.\n\nSeveral upregulated genes identified in both bat cell lines are known to play a canonical antiviral role in human and bat cells, including genes like IFI6, ISG15, STAT1, OAS1, IRF7, RSAD2, IRF9, and TRIM5 (Fig. 3D and Supplementary Data 3, 4)7,18. We also observed a significant upregulation of ISG15 and RPT4 transcripts in our cells upon IFN\u03b2 stimulation, and both ISG15 and RTP4 were recently identified as antiviral genes in select bat species (Fig. 3D and Supplementary Fig. 9B)19,20. Function enrichment analysis of upregulated genes in all four cell lines also demonstrated that genes that negatively regulate the type I IFN response, like ADAR, OAS1, OAS3, and USP18 were also enriched for in both bat cells, suggesting a likely difference in the regulation of IFN\u03b2 signaling (Fig. 3D and Supplementary Data 3, 4)21,22,23. Consistent with the literature, we observed the upregulation of well-studied IFN-stimulated genes in human cells, including OAS2, OAS3, TRIM5, GBP1, HERC5, and HERC6, among others (Fig. 3D and Supplementary Data 3, 4)24,25,26,27.\n\nNext, we identified the top upregulated DEGs in bat cells for which we had detected orthologous genes in human cells (Fig. 3E). Due to the divergent transcriptional profile, human A549 cells were removed from the analysis, and we focused our analyses on the three kidney-derived cell lines (Fig. 3E and Supplementary Fig. 9C). For the top upregulated DEGs, gene expression levels in A549 cells were generally consistently higher than human RPTEC cells and the two bat cell lines, except for MX2, HERC6, XAF1, and BST2, where gene expression levels were lower than or similar to levels in the other cells (Supplementary Fig. 9C). Human RPTEC cells upregulated over 60% (17/27 genes) of the top upregulated orthologous DEGs observed in bat cells to higher levels than either bat cell line (Fig. 3E). Unlike the other cell lines, Efk3B cells had increased basal expression of HERC6, XAF1, and DTX3L transcripts which were further upregulated upon IFN\u03b2 treatment (Fig. 3E). Expansion of both BST2 and PARP14 genes within the genomic locus of E. fuscus have been reported, where both genes have been triplicated in the E. fuscus genome28,29. In our study, we observed an upregulation of transcript levels for all three variants of BST2 and PARP14 upon IFN\u03b2 stimulation of Ek3B cells (Fig. 3E and Supplementary Data 3, 4). We did not detect multiple transcript variants of BST2 or PARP14 in RPTEC or PakiT03 cells, so we plotted transcript levels for all three variants of BST2 and PARP14 found in Efk3B cells against the transcript levels of a single variant that we detected in RPTEC and PakiT03 cells (Fig. 3E). We also analyzed the expression levels of bat specific DEGs that we could not directly map back to the human genome annotation, such as IFI27-like, TRIM5a-like, OAS1, SP140-like, LOC112483779 (unnamed), DDX58, and DDX60L, (Supplementary Fig. 9D, E and Supplementary Data 4). Additionally, IFI44 and IFI44L have been lost in P. alecto (Supplementary Fig. 9D). Bat specific DEGs also include several unannotated and uncharacterized genes such as LOC102882974, LOC112476441, LOC129151230, and LOC129150681 (Supplementary Fig. 9E). Our data demonstrate that duplicated genes like BST2 and PARP14 in E. fuscus encode for mRNA transcripts upon stimulation with IFN\u03b2, which suggests that duplicated genes in bats may have functional implications and warrant further investigation.\n\nNext, we characterized two ISGs that were upregulated in bat cells upon IFN\u03b2 stimulation, interferon-induced protein with tetratricopeptide repeats 1 (IFIT1) and guanylate binding protein 1 (GBP1) (Supplementary Fig. 9D, E and Supplementary Data 4). Across mammalian species, IFIT proteins are known to inhibit a broad range of viruses; however, the number and identity of these genes vary substantially30,31. For instance, the IFIT locus in humans encodes five genes (IFIT1, 1B, 2, 3, and 5), while four and six genes can be found within rats and mice, respectively30. Mice and other rodents have lost the IFIT1 ortholog and have duplicated IFIT1B32,33. We identified two IFIT1-like genes (LOC103296399 and LOC103300075) in the E. fuscus genome that map back to human IFIT1B (Supplementary Figs. 10, 11). In P. alecto bats, we identified one IFIT1 gene (LOC102878285) that mapped back to the human genome (Supplementary Figs. 10, 12).\n\nThe antiviral function of IFIT1 and IFIT1B relies on the discrimination of non-self RNA from host mRNA due to the lack of 2\u2019O-methylation of the first or second ribose of the 5\u2019 cap (cap1 and cap2, respectively)34,35. IFIT1-mediated antiviral activity is also enhanced through protein\u2013protein interactions like the binding of IFIT1 with IFIT336,37. Amino acid sequence comparison of human and P. alecto IFIT1 and IFIT3 showed 68.6 and 75.5% sequence identity, respectively (Supplementary Fig. 13). Key residues involved in human IFIT1-cap0 mRNA binding, including R38, L46, W147, K151, E176, and Y21838, are conserved across at least 15 bat species (Supplementary Fig. 14, arrows). In our study, we observed that like human IFIT1, P. alecto IFIT1 inhibits translation of reporter mRNAs bearing the luciferase gene flanked by SARS-CoV-2 5\u02b9 and 3\u02b9 untranslated regions when the RNA had a cap0 at the 5\u2019 end but not a cap1 (Fig. 3F). However, a luciferase reporter mRNA with Zika virus 5\u2019 and 3\u2019 untranslated regions was more resistant to P. alecto IFIT1 inhibition, consistent with a role for stable RNA secondary structure at the 5\u2019 end of the viral genome to evade IFIT1 restriction36,38,39.\n\nWe and others have previously identified a motif in the C-terminal end of human IFIT1 that is critical for interaction with IFIT336,37, and we demonstrated that hetero-oligomerization with IFIT3 is necessary for the full antiviral activity of IFIT1. Human IFIT1 and 3 interact through a highly conserved Y(E)XXL motif in the C-terminal region of each protein36. While the Y(E)XXL motif is conserved across 15 bat IFIT1s, the corresponding motif in IFIT3 contains a Y (human)\u2192F (bat) substitution (F440EKEL), which is also conserved across diverse bat species (Supplementary Figs. 14, 15). To test if this substitution affected IFIT1 and IFIT3 interaction, we used P. alecto as a model to analyse the association of IFIT1 and IFIT3 by size exclusion chromatography (SEC) as we previously described for humans36. P. alecto IFIT1 and IFIT3 co-elute earlier during SEC than either protein individually, confirming that they form a stable complex despite the Y\u2009\u2192\u2009F substitution in the IFIT3 binding motif (Fig. 3G). Mutation of the Y460(E)XXL motif in P. alecto IFIT1 to E460(E)XXE prevented its association with IFIT3 during SEC (Fig. 3H), confirming that the interaction is through this conserved motif. Thus, IFIT1 antiviral function against SARS-CoV-2, along with its interaction with IFIT3 are conserved in bats despite the evolution of a F440EKEL motif in bat IFIT3, compared to Y440EKEL in humans.\n\nIn human cells, GBPs like GBP1, GBP2, and GBP5 are known to confer protection against a range of pathogens, including bacteria, protozoa, and viruses like VSV and HSV-140,41. In contrast to data from human cells, a recent study in mice demonstrated that the entire chromosome 3 cluster, which includes GBP1, GBP2, GBP3, GBP5, and GBP7 did not encode for proteins with antiviral properties against influenza A virus, HSV-1, or lymphotropic choriomeningitis virus, suggesting that the antiviral capacity of GBPs may be species dependent or virus specific42. In our study, we assessed the expression level and antiviral capacity of GBP1 from E. fuscus and P. alecto bats, along with humans. We observed higher basal levels of expression of GBP1 transcripts in unstimulated E. fuscus cells, with low levels of induction upon IFN\u03b2 treatment (Fig. 3I). IFN\u03b2 treatment in P. alecto cells did not induce GBP1 transcript expression (Fig. 3I). In human cells, transcript levels for GBP1 were upregulated upon IFN\u03b2\u00a0stimulation, which is consistent with other studies (Fig. 3I)43. ClustalW alignment of bat and human GBP1 sequences identified conserved motifs within the GTPase domain that have been identified in human GBP1 (Supplementary Fig. 16)44,45. In addition, the CaaX motif was also present in GBP1 of both bat species. The CaaX motif allows human GBP1, GBP2 and GBP5 to undergo isoprenylation46, a post-translational modification that is thought to promote the translocation of human GBPs to intracellular membranes and enable interaction with other proteins47,48. The presence of a CaaX motif in bat GBP1 sequences suggests that the GTPase activity and isoprenylation are likely conserved across bat species.\n\nNext, we compared the electrostatic surface potential of GBP1 from human (Hu), P. alecto (Pa), and E. fuscus (Ef) (Fig. 4A). The left side of HuGBP1 and EfGBP1 has a similar charge distribution, while PaGBP1 possesses a more positive patch at the head of the protein (Fig. 4A, arrows). Modeling of the right side demonstrated further differences between the GBP1 proteins, where the head region in HuGBP1 is more positively charged than EfGBP1 and PaGBP1 (Fig. 4A, arrows). One of the roles of HuGBP1 is to hydrolyze GTP to GDP and GMP, which causes a conformational change that promotes pathogen-targeting function of the protein47,48,49. These functions include recruitment and deposition on pathogen-containing vacuoles, disruption of actin filament formation, and/or interaction with alternative proteins41. Modeling the dimerization interface of GBP1 using APBS revealed a more positive interface of PaGBP1 compared to HuGBP1 and EfGBP1 (Fig. 4A, arrows).\n\nA Surface potential predictions for GBP1 structures were predicted using AlphaFold and made using the APBS server. Positive charge is indicated in blue, negative charge is in red. Arrows indicate the region of differential charge between GBP1 proteins. B HEK293T cells were transfected with 250 to 1000\u2009ng of plasmid encoding for either P. alecto (Pa), E. fuscus (Ef), or human (Hu) GBP1 tagged with 3xFLAG for 48\u2009h. Cell lysates were then probed for FLAG-GBP1 and ACTB. C HEK293T cells were transfected with 500\u2009ng of plasmid encoding PaGBP1, EfGBP1, or HuGBP1 for 48\u2009h, followed by methanol fixation. Cells were stained for FLAG-GBP1, ACTB, and DAPI and visualized by confocal microscopy. Scale bars\u2009=\u200925\u2009\u00b5m. D A549-ACE2 cells were transfected for 24\u2009h with 250 to 1000\u2009ng of HuGBP1, PaGBP1, or EfGBP, followed by infection with the indicated viruses. Infection intensity was assessed by measuring GFP signal (VSV-GFP), TCID50 assay (HSV-1, SARS-CoV-2, MERS-CoV), or by plaque assay (VACV, EfPV). Mean values are indicated within the boxes. E Schematic representation of wild-type GBP1 and the A18AA substitution mutant. Surface charge potential for mutated GBP1 proteins predicted using AlphaFold and the APBS server are shown. Positive charge is indicated in blue, and negative charge is shown in red. The site of mutagenesis is contoured in black on the protein surface. Image created in BioRender [https://BioRender.com/o9oes1x]. F A549-ACE2 cells were transfected with 250 to 1000\u2009ng of WT PaGBP1 or EfGBP1 and the respective A18AA mutant for 24\u2009h, followed by infection with EfPV (MOI 0.01) for 48\u2009h. Supernatant was titered by plaque assay (One-way ANOVA, Tukey\u2019s range test, ****<0.0001). Data were represented as mean\u2009\u00b1\u2009SD, n\u2009=\u20093 biological replicates. G Representative plaques for PaGBP1 and EfGBP1 from undiluted supernatant.\n\nWe investigated the antiviral activity of GBP1 across the three species. Human, P. alecto, and E. fuscus GBP1 were expressed in human HEK293T cells (Fig. 4B). GBP1 from all three mammalian species localized within the cytoplasm of cells (Fig. 4C). To investigate the antiviral activity of GBP1, we expressed varying levels of human and bat GBP1 in human A549 cells to avoid interference from other bat cellular antiviral proteins and to assess the direct acting antiviral capacity of human and bat GBP1 against a range of different RNA and DNA viruses like VSV, HSV-1, influenza A virus (H1N1/PR8), SARS-CoV-2, MERS-CoV, Vaccina virus (VACV), and Eptesipoxvirus (EfPV) (Fig. 4D and Supplementary Fig. 17). Ectopic expression of HuGBP1 and EfGBP1 significantly inhibited the replication of VSV-GFP, while a modest effect was observed for PaGBP1 (Fig. 4D and Supplementary Fig. 17A\u2013C). Upon infection with HSV-1, GBP1 from all species minimally suppressed virus replication, but HuGBP1 was effective at the lowest concentration (Fig. 4D and Supplementary Fig. 17D\u2013F). A downward trend was observed upon infection with H1N1 in cells expressing GBP1 from all species, where EfGBP1 was effective at low concentrations (Fig. 4D and Supplementary Fig. 17G, H). For infection with SARS-CoV-2, HuGBP1 and EfGBP1 worked at similar concentrations of 250\u2009ng to inhibit virus infection, while higher concentrations were required for PaGBP1 (Fig. 4D and Supplementary Fig. 17I, J). For MERS-CoV infection, HuGBP1 was more protective compared to either bat GBP1 (Fig. 4D and Supplementary Fig. 17K, L). Interestingly, when GBP1-expressing cells were infected with a bona fide E. fuscus bat-derived virus, Eptesipoxvirus, EfGBP1 completely abolished virus replication at the lowest concentration, while HuGBP1 and PaGBP1 were effective at higher concentrations compared to EfGBP1 (Fig. 4D and Supplementary Fig. 17M, N). Finally, to determine whether EfGBP1 was equally potent in suppressing the replication of a distant poxvirus that is not speculated to have evolved in bats, we tested all GBP1 proteins against VACV. All three GBPs were only partially effective in suppressing VACV replication at high levels of GBP1 expression (Fig. 4D and Supplementary Fig. 17O, P).\n\nGiven the unique and potent antiviral activity of EfGBP1 against E. fuscus-derived EfPV, we next determined the functional motifs within the GTPase domain of EfGBP1 since this domain is important for the antiviral activity of HuGBP144,45. The four critical domains (G1\u2013G4) identified in human HuGBP1 are highly conserved across the 14 bat species we compared (Supplementary Fig. 16). However, we discovered an additional putative motif of interest in the N-terminus of GBP1 that we named AV1. The AV1 motif differs between the four bat families compared to humans (Fig. 4E and Supplementary Fig. 16). To investigate whether this AV1 motif had any effect on the antiviral activity of bat GBP1, we generated P. alecto and E. fuscus AV1 deletion (\u039418\u201320) and substitution variants (A18AA) (Fig. 4E). For E. fuscus AV1 deletion variant we saw a complete loss of GBP1-mediated protection (Supplementary Fig. 18A); however, due to the reduced expression of the PaGBP1 deletion variant (Supplementary Fig. 18B, C), only the substitution variants were further characterized. APBS predictions identified significant changes in electrostatic potential when the AV1 motif is mutated to A18AA, where the site becomes more neutral in PaGBP1 and EfGBP1 (Fig. 4E). In cells infected with EfPV, no significant difference in antiviral activity was observed between PaGBP1(A18AA) and wild-type PaGBP1 (Fig. 4F, G and Supplementary Fig. 18A, B). In cells expressing EfGBP1 and infected with EfPV, EfGBP1(A18AA) displayed no antiviral activity compared to wildtype EfGBP1 (Fig. 4F, G and Supplementary Fig. 18B). Thus, in this study we identified a functional motif within the GBP1 protein that is critical for antiviral function and our results demonstrate species-specific activity of this AV1 motif in bats.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61254-7/MediaObjects/41467_2025_61254_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61254-7/MediaObjects/41467_2025_61254_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61254-7/MediaObjects/41467_2025_61254_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61254-7/MediaObjects/41467_2025_61254_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this study, we utilized species-specific IFN\u03b2 to evaluate ISG induction in bat cells. While the human type I IFN pathway is extensively studied7, its applicability to bats remains uncertain. Recent work has demonstrated the activation of the JAK-STAT pathway in Rousettus aegyptiacus nasal epithelial cells following RoIFN\u03bb1 stimulation50, in addition to the requirement of IFNAR and phosphorylation of STAT1 upon stimulation of pulmonary cells from the Greater horseshoe bat (Rhinolophus ferrumequinum) with IFN\u03c951. Similar results have been obtained for P. alecto, where signaling with bat IFN\u03b1 requires IFNAR1/252. However, the precise role of STATs during virus infection in bats remains unclear. We have demonstrated that phosphorylation is required for IFIT1 and MX1 expression and for antiviral protection against VSV-GFP infection in P. alecto and E. fuscus cells (Supplementary Fig. 3). We noticed that E. fuscus kidney-derived cells express basal levels of nuclear STAT1/2, suggesting that these cells may be primed to respond faster to IFN\u03b2 (Fig. 2B). In addition, P. alecto kidney-derived cells mounted a rapid response upon IFN\u03b2 stimulation (Fig. 2B), in line with previous work that has shown a faster ISG response to virus infection in P. alecto cells due to elevated levels of IRF1, 3, and 753,54. We further demonstrated that the JAK-STAT pathway is not directly antagonized by MERS-CoV during infection in P. alecto or E. fuscus cells, highlighting the ability of bat cells to resist MERS-CoV infection mediated shutdown of innate IFN dependent antiviral responses. Our analyses were limited to two bat species and two cell types. Future studies in diverse bat species and primary cells will fully characterize the breadth\u00a0of IFN\u03b2-mediated antiviral responses in bats55.\n\nMetagenomic analyses have demonstrated that immune genes, like ISGs, are more likely to undergo selection, with bats experiencing a high rate of selection within immune genes compared to other mammals56. As there are over 1480 species of bats, the potential differential function of conventional and atypical ISGs is worth investigating, as it may shed light on the ability of bats to tolerate viral infections. As seen in Fig. 3, both bat species induced a conserved type I IFN response upon stimulation with IFN\u03b2. However, both bat species upregulated orthologous transcripts to a lower degree than either human cell line (Fig. 3C), suggesting that lower levels of ISGs may contribute to an antiviral state. Many of the upregulated genes in bats contributed to different biological processes (Supplementary Data 1), suggesting that the bat antiviral response likely differs in quality and intensity when compared to humans. Lineage-specific differences in MX2 antiviral activity have been identified, where, unlike the human counterpart, P. vampyrus MX2 is unable to restrict HIV-1 infection, whereas MX2 from closely related P. alecto can57,58. Additionally, three BST2 and PARP14 paralogs were upregulated in E. fuscus cells (Fig. 3E). BST2 gene duplications have been identified within the Vespertilionidae bat family28,29, with previous studies on Marburg virus and Nipah virus suggesting a distinct role for each BST2 paralog in orchestrating an antiviral response28,59,60. While duplications are common in the Vespertilionidae bat family, deletion of genes like IFI44 and IFI44L (Supplementary Fig. 9D) have been reported for the Pteropodidae bat family61. Notably, IFI44L was readily upregulated in E. fuscus cells upon stimulation, demonstrating additional variation in the antiviral response across bats.\n\nIn R. aegyptiacus cells, the overexpression of IFIT1 inhibits Ebolavirus and Marburg virus replication, though it is unclear how this antiviral activity compares to human IFIT162. A study of IFIT1 across 39 mammals revealed diverse antiviral phenotypes\u2014while human and P. alecto IFIT1\u00a0could bind cap0 RNAs and strongly inhibit Venezuelan equine encephalitis virus, chimpanzee\u00a0IFIT1 lacked this ability31. We observed similar activity between P. alecto and human IFIT1 in inhibiting the translation of mRNA bearing the untranslated regions of SARS-CoV-2 and Zika virus (Fig. 3F)39. We did not observe differential activity of IFIT1 between P. alecto and humans (Fig. 3G and Supplementary Fig. 9B), unlike recent work on ISG15 and RPT419,20. These findings further highlight the evolutionary arms race between bat immune genes and viruses.\n\nIn humans, induction of GBP1 upon type I and II IFN signaling has been reported63; with in vivo studies demonstrating induction through type I IFN signaling pathways43. Recent work in mice has demonstrated that the antiviral activity of GBP1 may not be shared across mammals42 leading us to investigate whether this gene was functional within both bat species. Both Hu- and EfGBP1 downregulated VSV-GFP replication, while PaGBP1 was unable to do so (Fig. 4D). Previous work using GBP1 from the Chinese tree shew (Tupaia belangeri chinensis) demonstrated that the restriction activity of GBP1 was due to its competition with the viral nucleocapsid (N) protein in binding to phosphoprotein (P) of VSV64. Whether the interaction of PaGBP1 with VSV-P is less favorable compared to human and E. fuscus GBP1 remains unknown. In contrast, both bat GBP1s were less effective against HSV-1 than HuGBP1. HSV-1 is a human-derived isolate and has co-evolved alongside humans for millions of years65. In the future, the antiviral potency of bat GBP1 against bat-derived herpesviruses, such as the Gammaherpesvirus from E. fuscus66 will shed further light on the co-evolution of bat GBP1 and their viruses.\n\nTo our knowledge, no study has evaluated the efficacy of human and bat GBP1 against two coronaviruses of bat origin that have caused outbreaks in humans. We observed differential activity against SARS-CoV-2 and MERS-CoV, where restriction against the former was more apparent at lower expression levels of Hu- and EfGBP1 (Fig. 4D). Previous reports have demonstrated the interaction of chicken GBP1 with the nucleocapsid protein of infectious bronchitis virus, leading to its degradation via the autophagy pathway67. The conserved or divergent mechanisms by which bat GBP1 elicits its antiviral effect against a range of different RNA and DNA viruses is the focus of ongoing research within our laboratory. Our work with GBP1 has also led to the identification of a functional (AV1) motif within EfGBP1 that enables it to potentially restrict an E. fuscus-derived poxvirus, EfPV (Fig. 4F, G). Previous work on GBP2 and Ectromelia virus (ECTV), a poxvirus that closely resembles Mpox, demonstrated GBP2 as a mild inhibitor of ECTV in mice68. This was attributed to the ability of the poly(A) polymerase catalytic subunit of ECTV to bind to GBP2, antagonizing its restriction activity69. Although ECTV and EfPV are distantly related viruses, future work may evaluate whether the evolution of the AV1 site in EfGBP1 prevents EfPV from antagonizing its antiviral activity. Importantly, the functional role of the AV1 site within human GBP1 can now also be explored based on our discoveries. Overall, our study highlights the evolutionary differences in the IFN\u03b2 response between humans and bats, and between bat species. By investigating the arms race between bat GBP1 and multiple RNA and DNA viruses, we have identified a motif (AV1) within GBP1, and this knowledge can one\u00a0day be harnessed to better understand and control viral infections in humans.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Pteropus alecto kidney (PakiT03), Eptesicus fuscus kidney (Efk3B), Huh7, HEK293T, and Vero76 cells were grown in Dulbecco\u2019s Minimal Essential Medium with high glucose (DMEM; Gibco, #11965118) supplemented with 10% fetal bovine serum (FBS; Sigma-Aldrich, #12107\u2009C), 1% Penicillin/Streptomycin (Pen-Strep; Gibco, #15140122), and 1% GlutaMax (Gibco, #35050061). Human alveolar basal epithelial (A549) cells were grown in Ham\u2019s F-12 (Kaighn\u2019s) medium (Gibco, #21127022) supplemented with 10% FBS and 1% Pen-Strep. PakiT03, Efk3B, A549, Huh7, and HEK293T cells were used up to passage 50. A549-ACE2 cells (A549 cells overexpressing human angiotensin-converting enzyme 2; ACE2) were provided by Dr. Colpitts\u2019 laboratory, with clonal population B9 used70. A549-ACE2 cells were maintained in Ham\u2019s F-12 (Kaighn\u2019s) medium supplemented with 10% FBS, 1% Pen-Strep, and 10\u2009ug/mL blasticidin (info). Human, life-extended renal epithelial cell line isolated from the proximal tubule of a male patient (RPTEC-hTERT; ATCC, #CRL-4031) were grown in DMEM:F-12 media (ATCC; #30-2006) supplemented with the hTERT growth kit (ATCC; #ACS-4007). RTPEC cells were used up to passage 10. Drosophila Schneider 2 (S2) cells (Thermo Scientific, #R69007) were grown in Schneider\u2019s Drosophila medium (Life Technologies, #21720024) supplemented with 10% FBS and 1% Pen-Strep. Madin-Darby canine kidney cells (MDCK II; Sigma-Aldrich, #00062107) were grown in Minimal Essential Medium (MEM; Sigma-Aldrich, #M4655) supplemented with 5% FBS, 1% Pen-Strep, and 1% GlutaMax.\n\nStaurosporine (Cell Signaling, #9953S) was reconstituted in DMSO and used at 500\u2009nM/mL at the times indicated.\n\nRecombinant V5 and 6x-His-tagged IFN\u03b2 proteins for P. alecto, E. fuscus, and human were cloned into pMT-BiP/V5-His and generated by transfecting Drosophila S2 cells following the manufacturer\u2019s instructions (Thermo Scientific, K513001). Briefly, Drosophila S2 cells were transfected with 19\u2009\u03bcg of the pMT-BiP/V5-His plasmid containing Human, P. alecto or E. fuscus IFN\u03b2 and 1\u2009\u03bcg of pCoBlast. Following 24\u2009h of transfection, cells were washed and incubated for 48\u2009h before blasticidin selection (25\u2009\u03bcg/mL) was added. Resistant clones were then stimulated with 500\u2009\u03bcM of copper sulfate for 72\u2009h. A vehicle control, supernatant containing secreted GFP, was also generated following this procedure. Proteins were characterized by immunoblotting for the V5-epitope tag (Supplementary Data 5) and quantified by ELISA as per the manufacturer\u2019s instructions (Abcam, #ab285248). To determine the efficacy and protective unit for each recombinant IFN\u03b2, cells were treated with serially diluted, species-matched IFN\u03b2 or vehicle for 6\u2009h and then infected with VSV-GFP at a multiplicity of infection (MOI) of 0.1. Infection was visualized at 10X magnification on a Leica DMI6000B fluorescent microscope 16\u2009h post-infection (hpi). The last dilution of IFN\u03b2 in which 100% protection was observed was classified as 1 unit (U) of protection.\n\nGenetically engineered vesicular stomatitis virus encoding a green fluorescent protein (VSV-GFP) cassette was propagated in Vero76 cells and stored at \u221280\u2009\u00b0C. Genetically engineered herpes simplex virus 1 encoding GFP (HSV-1-K26-GFP) was kindly provided by Dr. Karen Mossman (McMaster University) and was propagated in HeLa cells and stored at \u221280\u2009\u00b0C. Influenza A virus stain A/Puerto Rico/8/34 (H1N1; PR8) was propagated in 11-day-old embryonated eggs and stored at \u221280\u2009\u00b0C. Vaccinia virus VR-2153 (VACV; ATCC, vr-2153) was propagated in HeLa cells and stored at 80\u2009\u00b0C. Middle East respiratory syndrome coronavirus (MERS-CoV; isolate EMC/2012), severe-acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and Eptesipoxvirus (EfPV) were propagated in Vero76 cells and stored at \u221280\u2009\u00b0C. All work with infectious MERS-CoV and SARS-CoV-2 was completed at VIDO-InteVac in a containment level 3 laboratory and was approved by the institutional biosafety committee. Standard operating procedures approved by the institutional biosafety committee were followed for sample inactivation.\n\nThe supernatants from HSV-1, MERS-CoV, and SARS-CoV-2- infected cells were titrated in triplicate on Vero76 cells using a tissue culture infectious dose 50 assay. Meanwhile, MDCK II cells were used to titrate H1N1. Briefly, 3\u2009\u00d7\u2009104 cells were seeded per well of a 96-well plate for 24\u2009h. Media was then removed from the wells in exchange for 50\u2009\u03bcL of 1:10 serially diluted supernatant containing virus. Following a 1\u2009h incubation at 37\u2009\u00b0C, the inoculum was replaced with DMEM supplemented with 2% FBS. The plates were incubated at 37\u2009\u00b0C for 3 (HSV-1, H1N1) or 5 days (MERS-CoV, SARS-CoV-2) and cytopathic effect was monitored under a light microscope. The TCID50/mL was calculated using the Spearman and Karber algorithm.\n\nConfluent monolayers of Vero76 or MDCK\u00a0II cells in six-well plates were infected with 800\u2009\u03bcL of tenfold serially diluted virus for 1\u2009h at 37\u2009\u00b0C. Following inoculation, 2% CMC or Avicel overlay medium was added. Plates were incubated for 2 days for H1N1, 3 days for VACV, and 4 days for EfPV. Cells were fixed for 30\u2009min with 10% neutral buffer formalin and stained with 0.1% Toluidine blue (Fluka, #89640).\n\nCells were untreated or treated with species-matched IFN\u03b2 for the appropriate period of time. Following IFN\u03b2 treatment, RNA extraction was performed as per the manufacturer\u2019s instructions for the RNeasy Plus Mini Kit (Qiagen, #74136). Four hundred nanograms of purified RNA was reverse transcribed into cDNA following the manufacturer\u2019s instructions for the iScript gDNA Clear cDNA Synthesis Kit (Bio-Rad, #1725035). Samples were incubated in a T100 thermal cycler (Bio-Rad, #1861096) for 5\u2009min at 25\u2009\u00b0C, 20\u2009min at 46\u2009\u00b0C, and 1\u2009min at 95\u2009\u00b0C. qRT-PCR reactions were then carried out using 10\u2009\u03bcL of the SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, #1725274), and 4\u2009\u03bcM of the forward and reverse primers per reaction tube. Primers utilized are described in Supplementary Data 6. qRT-PCR reactions were completed using a StepOne Real-Time PCR System (Applied Biosystems). Samples were run in duplicate, with Ct values normalized to cellular Gapdh.\n\nProtein lysates were either collected in 1X sample buffer or in RIPA buffer (Sigma-Aldrich, #R0278-50ML) containing 3X Halt protease and phosphatase inhibitor cocktail (Thermo Scientific, #78442) for bat-derived cells and 2X Halt protease and phosphatase inhibitor cocktail for human-derived cell lines for phospo-protein analysis. Following boiling for 10\u2009min at 96\u2009\u00b0C, proteins were separated by SDS-PAGE using homemade 10% polyacrylamide gels and semi-dry transferred to a 0.2\u2009\u00b5m nitrocellulose membrane using the Trans-Blot Turbo Transfer System (Bio-Rad, #1704270). Membranes were blocked using intercept blocking solution (Licor Biosciences, #927-60001) and probed with primary antibodies (Supplementary Data 5) diluted in 50% intercept blocking solution and 50% TBS overnight on a rocker. The appropriate secondary antibody (Supplementary Data 5) diluted in 50% intercept blocking solution and 50% TBS was then added for 60\u2009min. Membranes were imaged and analyzed using the Odyssey imager (Licor Biosciences) and Image Studio Software (Licor Biosciences).\n\nCells were fixed for 20\u2009min at \u221220\u2009\u00b0C with 100% MeOH and blocked using a homemade solution (PBS, 10% newborn calf serum, and 0.1% Tween-20). Cells were then stained using the appropriate primary and secondary antibodies (Supplementary Data 5) and mounted onto a glass slide. Visualization of stained cells occurred by confocal microscopy (Leica).\n\nDicer-ready silencing RNA (siRNA) targeting human, P. alecto, and E. fuscus STAT1 or STAT2 were designed and obtained through Integrated DNA Technologies (IDT; Supplementary Data 6). A final concentration of 25\u2009nM was transfected to knock down STAT1, while 75\u2009nM was transfected to knock down STAT2 using Lipofectamine 3000 (Thermo Scientific). Double knockdown of STAT1 and STAT2 occurred by transfecting both siRNA at a concentration of 50\u2009nM and 75\u2009nM, respectively, in PaKiT03 and A549 cells. Scrambled non-specific siRNA (IDT) was used as a negative control. Immunoblotting was performed to confirm knockdown efficiency.\n\nPaKiT03, Efk3B, A549, and RPTEC cells were untreated or treated with 1\u2009U/mL of species-matched IFN\u03b2 for 6\u2009h (n\u2009=\u20093/condition). RNA extraction was performed as per the manufacturer\u2019s instructions for the RNeasy Plus Mini Kit (Qiagen, #74136) and shipped to McMaster Genomics Facility, Farncombe Institute at McMaster University for sequencing. Sample quality was first assessed using a Bioanalyzer (Agilent), then enriched (NEBNext Poly(A) mRNA Magnetic Isolation Module; NEB). Library preparations were conducted (NEBNext Ultra II Directional RNA Library Prep Kit; NEB), and library fragment size distribution was verified (Agilent TapeSection D1000; Agilent). Libraries were quantified by qPCR, pooled in equimolar amounts, and qPCR and fragment size distribution verification were conducted again. Libraries were then sequenced on an Illumina NextSeq 2000 using a paired-end, 2\u2009\u00d7\u200950\u2009bp configuration.\n\nRaw reads were processed with fastp v0.21.0, with the first 14 nucleotides trimmed based on an initial fastp run. Samples run in duplicate on two lanes during sequencing were then merged together. Transcriptome indices based on the RefSeq annotations for Pteropus alecto (GCF_000325575.1), Eptesicus fuscus (GCF_027574615.1), and Homo sapiens (GCF_000001405.40) were created with salmon v1.4.0 using their genomes as a decoy. Salmon transcript quantification mapped against the appropriate index was performed for all samples. In R v4.1.1, tximport v1.22.0 pulled the gene and transcript IDs from the appropriate gff files, and mapped the salmon abundance counts to the gene level. Genes with less than a length-scaled TPM of 10 across all samples were removed. Three samples were removed due to large deviations in expression profile detected via a PCA plot (DESeq2 v1.34.0). Differential gene expression analysis on the remaining samples, separated by mock versus interferon-beta treatment, was done with DESeq2. P-values were filtered with results using an alpha of 0.05. Significantly differentially expressed genes (DEGs) had an adjusted p value less than 0.05. OrthoFinder v2.5.2 was applied pair-wise across the three species, with each bat gene set being mapped back to human genes, and any remaining unmapped genes in an orthogroup between the two bat species also being mapped together. All genes in an orthogroup were defined as each other\u2019s orthologs. A PCA for the expression profiles was created with plotPCA based on scaled transcripts per million (TPM). pheatmap v1.0.12 was used to create heatmaps between the three species, also based on scaled TPM, with sample values further scaled across each gene using scale.\n\nFunction/pathway annotation of the H. sapiens DEGs was done with the online DAVID webserver in Aug. 2023. These annotations were mapped to bat genes based on the OrthoFinder-defined orthogroups. Phyper was used in R to calculate the hypergeometric pvalue, subsequently adjusted with p.adjust using the Benjamini & Hochberg correction method.\n\nHuman, P. alecto, and E. fuscus GBP1 cDNA was synthesized (GenScript) and cloned into the p3X-FLAG7.1 backbone using NotI, BamHI, KpnI, and/or Sall restriction sites. A549-ACE2 cells (1.5\u2009\u00d7\u2009105 cells/well) seeded in a 12- well plate were transfected with 250\u20131000\u2009ng of each construct using Lipofectamine 3000 (Thermo Scientific)70. After 24\u2009h, cells were infected with VSV-GFP (MOI 0.01), HSV-1 (MOI 0.01), H1N1 (MOI 0.01), SARS-CoV-2 (MOI 0.01), MERS-CoV (MOI 0.1), EfPV (MOI 0.01), H1N1 (MOI 0.01), or VACV (MOI 0.01). A low MOI was chosen to assess the antiviral role of GBP1 during multiple rounds of virus replication in culture and to not overwhelm the culture system with too much virus that could potentially bypass GBP1-mediated restriction. As A549 cells are less permissive to MERS-CoV, a higher MOI was utilized. To assess infection, microscopy was performed for VSV-GFP, while the TCID50 assay or plaque assay was performed for the remaining viruses.\n\nE. fuscus and P. alecto GBP1 mutants were generated using the QuikChange II site-directed mutagenesis kit (Agilent Technologies), following the protocol modified by Wang and Malcom71. Briefly, specific primers were designed to introduce the desired mutations into the GBP1 gene (Supplementary Data 7). Target regions were amplified using these primers. The PCR products were then treated with DpnI to digest the parental DNA template, and the resulting mutant plasmids were transformed into E. coli Stbl3 cells. Positive clones were selected on LB agar plates with ampicillin, and mutations were confirmed by Sanger sequencing (National Research Council, Canada). For the alanine insertions, a similar protocol was followed using the GBP1 plasmids with deletions as template.\n\nIFIT protein sequences were aligned with MUSCLE v3.8.31. Gblocks site selection was done in SeaView v5.0.5 using the least stringent options. We used RAxML v8.2.12 with the PROTGAMMAAUTO setting and 100 bootstrap runs to create the phylogenetic tree. The best-scoring tree used JTT likelihood with empirical base frequencies. The tree was subsequently visualized in iTOL v7.1 and midpoint rooted.\n\nHuman IFIT1 and IFIT3 expression plasmids were previously described36. Wildtype and mutant sequences for Pteropus alecto IFIT1 (XM_006925964) and IFIT3 (XM_006925965) were synthesized by Twist Biosciences and cloned into pET28b (NdeI and XhoI) plasmid to encode full-length proteins with an N-terminal 6X-His tag for purification. Template plasmids for T7 transcription of a firefly luciferase reporter gene flanked by Zika virus (ZIKV-Fluc) or \u03b2-globin (Globin-Fluc) untranslated regions were previously described36. A plasmid containing a short fragment of SARS-CoV-2 non-structural protein 1 (NSP1) separated from the firefly luciferase reporter gene by a 2\u2009A StopGo sequence, flanked by the SARS-CoV-2 5\u02b9- and 3\u2019UTRs and 5\u02b9 and 3\u02b9 hammer head and hepatitis D virus ribozymes respectively (SARS-CoV-2-Fluc), under the control of a T7 promoter was a gift from T. Peacock, The Pirbright Institute.\n\nRecombinant His-tagged human and bat IFIT1 and IFIT3 proteins were expressed in Rosetta (DE3) E. coli (Novagen) for 16\u2009h at 22\u2009\u00b0C after induction of protein synthesis with 1\u2009mM isopropyl \u03b2-D-1- thiogalactopyranoside. Cells were centrifuged and resuspended on ice with lysis buffer (400\u2009mM KCl, 10% glycerol, 1\u2009mM DTT) supplemented with 5\u2009mg/ml lysozyme (Invitrogen) and EDTA-free cOmplete Protease Inhibitor Cocktail tablets (Roche). Lysis buffers included 30\u2009mM HEPES (IFIT1) or 20\u2009mM Tris-HCl pH7.5 (IFIT3). Lysates were clarified by centrifugation following lysis by sonication before purification on Ni-NTA Agarose beads (Cube Biotech). IFIT1 and IFIT3 were further purified using an \u00c4KTA FPLC system (Cytiva) on MonoS or MonoQ 5/50 GL columns (Cytiva), respectively, in HEPES-based lysis buffer (MonoQ- IFIT1) or Tris-based lysis buffer (MonoS- IFIT3) before being stored at \u201370\u2009\u00b0C. Size exclusion chromatography (SEC) was performed on a Superdex 200 Increase 10/300 GL column in SEC buffer (20\u2009mM Tris-HCl pH7.5, 200\u2009mM KCl, 1\u2009mM DDT. 200\u2009\u00b5L of 1\u2009mg/ml wildtype or mutant IFIT1 or IFIT3 were injected into the SEC column to determine peak elution volumes. IFIT1:IFIT3 assembly: 1\u2009mg/ml each of wildtype or mutant IFIT1 and IFIT3 were incubated in a 200\u2009\u00b5L of SEC buffer for 1\u2009h at 4\u2009\u00b0C before injecting onto the column. Peak fractions were subsequently analysed by SDS-PAGE and Coomassie staining.\n\nGlobin-Fluc and ZIKV-Fluc plasmids were linearized with HindIII, while SARS2-Fluc was linearized with XhoI. RNA was transcribed with T7 polymerase (New England Biolabs), using 1\u2009\u00b5g of linearized template DNA in transcription buffer (40\u2009mM HEPES pH 7.5, 32\u2009mM MgOAc, 40\u2009mM DTT, 2\u2009mM Spermidine, 10\u2009mM NTPs and 0.8\u2009U/\u00b5l Ribolock (Thermo Scientific), and incubated overnight at 37\u2009\u00b0C. Following template DNA digestion using DNase I (Thermo Scientific) unincorporated nucleotides were removed using Amersham MicroSpin G-50 Columns (Cytiva). RNA was purified by acidic-phenol extraction and ethanol precipitation. RNA was capped using the ScriptCap and ScriptCap 2\u2032-O-methyltransferase system (CellScript). IFIT1 and IFIT3, where indicated, was combined with 100\u2009ng of RNA in buffer containing 20\u2009mM Tris-HCl pH7.5, 150\u2009mM KCl, 5% glycerol, 1\u2009mM DTT, 10\u2009U/\u00b5L RNase inhibitor and 0.5\u2009mg/ml BSA and incubated for 15\u2009min at 30\u2009\u00b0C. The RNA protein mixture was added to in vitro translation reactions using the Flexi\u00ae Rabbit Reticulocyte Lysate (RRL) System (Promega), with a final concentration of 100\u2009nM of IFIT1 or IFIT1:IFIT3 complex and incubated for 90\u2009min at 30\u2009\u00b0C and then placed on ice to halt the reaction. Firefly luciferase signal was measured on the GloMax\u00ae Discover microplate reader (Promega), by adding an equal volume of ONE-Glo\u2122 luciferase assay reagent (Promega). Luciferase values were normalized to a no-IFIT control.\n\nUsing the sequences of P. alecto and E. fuscus GBP1 proteins, residues 18\u201320 were modified to AAA (alanine mutant). For each of the wildtype and mutant sequences, AlphaFold3 was used to predict three-dimensional structures72. Protonation states of the titratable residues were assigned at physiological pH using PROPKA73,74. The structures were prepared for electrostatic calculations by assigning atomic charges and radii using PDB2PQR75. The electrostatic potential for each protein structure was calculated by solving the continuum electrostatics equations with the adaptive Poisson\u2013Boltzmann solver (APBS)75. Calculated electrostatics were mapped onto the solvent-accessible molecular surface of each protein using ChimeraX76.\n\nGraphpad Prism v10.2.3 was used to perform significance tests, with the statistical test used specified where appropriate. MacVector software was used to perform multiple sequence alignments. ImageJ software was used to analyze microscopy images to quantify nuclear translocation and GFP expression.\n\nFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The transcriptomic dataset has been deposited to NCBI under the accession code PRJNA1255723 (or https://www.ncbi.nlm.nih.gov/sra/PRJNA1255723). The following genomes were used as the reference to analyze the transcriptomics data: GCF_000325575.1 (Pteropus alecto), GCF_027574615.1 (Eptesicus fuscus), and GCF_000001405.40 (Homo sapiens). The DESeq2 expression data generated from the transcriptomic analysis performed this study are provided in the Source Data file. Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All codes and scripts that were used for data analysis is available at https://github.com/doxeylab/gonzalez-et-al77.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Hao, X., Lu, Q. & Zhao, H. A molecular phylogeny for all 21 families within Chiroptera (bats). Integr. Zool. 19, 989\u2013998(2023).\n\nBurgin, C. J., Colella, J. P., Kahn, P. L. & Upham, N. S. How many species of mammals are there?. J. Mammal. 99, 1\u201314 (2018).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nPaweska, J. T. et al. 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This research was undertaken, in part, thanks to funding from the Canada Research Chairs Program awarded to A.B. (CRC-2024-00291). J.C. is supported by an NSERC undergraduate student research award (BRPC-539885). Research within S.G.\u2019s lab is supported by an NSERC Discovery Grant (RGPIN-2023-03546), an NSERC Discovery Launch Supplement (DGECR-2023-00091) and a Canada Biomedical Research Fund and Biosciences Research Infrastructure Fund (CBRF2-2023-00176). S.G. holds a Tier 2 Canada Research Chair in Structural Systems Biology (CRC-2022-00099). Y.Z. is supported by NSERC (RGPIN-2019-04578) and CIHR (PJT-166138). T.R.S. is supported by a Wellcome Trust/Royal Society Sir Henry Dale Fellowship (202471/A/16/Z) and BBSRC grants (BB/X011038/1 and BBS/E/PI/230001\u2009A). We would like to thank B. Haagmans and R. Fouchier, Erasmus Medical Center, for providing MERS-CoV (isolate hCoV-EMC/2012). VIDO receives operational funding from the Government of Saskatchewan through Innovation Saskatchewan and the Ministry of Agriculture, and from the Canada Foundation for Innovation through the Major Science Initiatives Fund. We would like to thank members of the Laboratory of Zoonotic Viruses and Comparative Immunology at VIDO for their feedback during weekly laboratory meetings.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Laboratory of Zoonotic Viruses and Comparative Immunology, Vaccine and Infectious Disease Organization (VIDO), University of Saskatchewan, Saskatoon, SK, Canada\n\nVictoria Gonzalez,\u00a0Arkadeb Bhuinya,\u00a0Akarin Asavajaru,\u00a0Yan Zhou,\u00a0Darryl Falzarano\u00a0&\u00a0Arinjay Banerjee\n\nDepartment of Veterinary Microbiology, University of Saskatchewan, Saskatoon, SK, Canada\n\nVictoria Gonzalez,\u00a0Arkadeb Bhuinya,\u00a0Yan Zhou,\u00a0Vikram Misra,\u00a0Darryl Falzarano\u00a0&\u00a0Arinjay Banerjee\n\nDepartment of Biology, University of Waterloo, Waterloo, ON, Canada\n\nBriallen Lobb,\u00a0Andrew C. Doxey\u00a0&\u00a0Arinjay Banerjee\n\nD\u00e9partement de Biochimie, de Microbiologie et de Bio-informatique, Facult\u00e9 des Sciences et de G\u00e9nie Universit\u00e9 Laval, Qu\u00e9bec, QC, Canada\n\nJacob C\u00f4t\u00e9\u00a0&\u00a0Sophie M. C. Gobeil\n\nInstitut de Biologie Int\u00e9grative et des Syst\u00e8mes, Universit\u00e9 Laval, Qu\u00e9bec, QC, Canada\n\nJacob C\u00f4t\u00e9\u00a0&\u00a0Sophie M. C. Gobeil\n\nPROTEO, Le Regroupement Qu\u00e9b\u00e9cois de Recherche sur la Fonction, L\u2019Ing\u00e9nierie et les Applications des Prot\u00e9ines, Universit\u00e9 Laval, Qu\u00e9bec, QC, Canada\n\nJacob C\u00f4t\u00e9\u00a0&\u00a0Sophie M. C. Gobeil\n\nCentre de Recherche en Infectiologie de l\u2019Universit\u00e9 Laval, Universit\u00e9 Laval, Qu\u00e9bec, QC, Canada\n\nJacob C\u00f4t\u00e9\u00a0&\u00a0Sophie M. C. Gobeil\n\nThe Pirbright Institute, Guildford, Surrey, UK\n\nAdriana G. Tubb,\u00a0Stephen S. Nuthalapati\u00a0&\u00a0Trevor R. Sweeney\n\nDepartment of Virology, University of Cambridge, Cambridge, UK\n\nTrevor R. Sweeney\n\nProgramme in Emerging Infectious Disease, Duke-NUS Medical School, Singapore, Singapore\n\nLinfa Wang\n\nDepartment of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada\n\nArinjay Banerjee\n\nDepartment of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada\n\nArinjay Banerjee\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nV.G. and A.B. designed the study. V.G. assembled the figures. VG performed all experiments except for the following: Arkadeb Bhuinya (ABh) performed site-directed mutagenesis, A.G.T., S.S.N., and T.R.S. performed IFIT1-related experiments, and A.B. cloned and made stable S2 cells that secrete human and bat IFN\u03b2. A.A. expanded cell lines for laboratory stocks, optimized cell culture protocols, and trained students. B.L. and A.D. analysed raw RNA-sequencing data. J.C. and S.M.C.G. performed predication and structural analyses for GBP1. Y.Z. provided H1N1-PR8 virus strain, V.M. provided EfPV strain, and D.F. provided MERS-CoV strain. L.W. provided the PakiT03 cell line. V.G. and A.B. wrote the manuscript. All authors contributed to the text.\n\nCorrespondence to\n Arinjay Banerjee.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "A.B. is a co-inventor of the Efk3B cell line that is sold through Kerafast, USA. S.G. is named in a patent regarding coronavirus monoclonal antibodies (WO2022060906A1). The invention provides methods for using the inventive antibodies in prophylactic and/or therapeutic methods to prevent or treat coronavirus infection. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Branka Horvat, and the other, anonymous, reviewer for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Gonzalez, V., Lobb, B., C\u00f4t\u00e9, J. et al. Bat-specific adaptations in interferon signaling and GBP1 contribute to enhanced antiviral capacity.\n Nat Commun 16, 5735 (2025). https://doi.org/10.1038/s41467-025-61254-7\n\nDownload citation\n\nReceived: 12 December 2024\n\nAccepted: 16 June 2025\n\nPublished: 01 July 2025\n\nVersion of record: 01 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61254-7\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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transgenic pigs are susceptible to SARS-CoV-2 and develop COVID-19-like disease", + "pre_title": "SARS-CoV-2 infected human ACE2 transgenic pigs develop severe COVID-19-like pathology.", + "journal": "Nature Communications", + "published": "17 January 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54615-1/MediaObjects/41467_2024_54615_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54615-1/MediaObjects/41467_2024_54615_MOESM2_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54615-1/MediaObjects/41467_2024_54615_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54615-1/MediaObjects/41467_2024_54615_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-54615-1#Sec28" + ], + "code": [], + "subject": [ + "Experimental models of disease", + "Genetic engineering", + "Infection", + "SARS-CoV-2" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4176871/v1.pdf?c=1737205640000", + "research_square_link": "https://www.researchsquare.com//article/rs-4176871/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54615-1.pdf", + "preprint_posted": "14 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "COVID-19 continues to cause significant morbidity and mortality, with emerging strains rapidly spreading despite substantial immunity through vaccination and previous exposure. Animal models that accurately reflect COVID-19 are vital for testing mechanisms of disease, enabling development of improved vaccines and therapeutics. We have developed human ACE2 transgenic pigs that are highly susceptible to SARS-CoV-2 and display clinical signs, disease progression, and lung inflammation that faithfully replicate severe COVID-19 in humans.Biological sciences/Microbiology/Virology/SARS-CoV-2Biological sciences/Biological techniques/Genetic engineering", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-4176871/v1/6a1feb1770fd1aea2198ae13.png", + "https://assets-eu.researchsquare.com/files/rs-4176871/v1/e4d2d3194e4937bb86f3500e.png" + ] + }, + { + "section_name": "Main", + "section_text": "The key molecular events that trigger progression from self-limited viral illness to severe COVID-19, remain unknown. Non-invasive profiling of cells from human patients and analysis of tissue specimens collected post-mortem has been valuable in characterising inflammatory pathology associated with severe COVID-19 1,2. However, defining the key early events that trigger progression towards severe COVID-19 requires longitudinal studies and invasive sampling of tissues. Animal models are important tools for testing hypotheses about mechanisms of disease in COVID-19 and complement clinical studies from patients, enabling direct analysis of target tissues following controlled infections 3. However, at present, there is a lack of efficient, tractable model systems that replicate the primary feature of severe COVID-19: pulmonary inflammation. Rodent models such as human angiotensin-converting enzyme 2 (hACE2) transgenic mice and golden Syrian hamsters are susceptible to SARS-CoV-2 infection. Non-transgenic mice can be infected with mouse-adapted strains of SARS-CoV-2. Ferrets are also susceptible to infection and are effective transmission models, while non-human primates (NHPs) are the most comparable model to human infections 3. While each of these models has advantages, there are important drawbacks. Rodent models do not accurately replicate the pattern of disease seen in humans, and tissue morphology and immunological responses to SARS-CoV2 are significantly different from humans 3. There is a lack of molecular tools for ferret models, while non-human primates are expensive and restricted to a few institutions around the world. The lack of large animal models that accurately reflect the pathology associated with severe COVID-19 in humans hampers our ability to understand the mechanisms that drive disease and the development of effective interventions. Furthermore, the development of therapeutic interventions in a model with similar physiology to humans is more likely to successfully translate into effective therapies in humans. There is an increasing appreciation of livestock as biomedical models, with pigs being one of the most important 4. The short gestation period, large litter size and a suite of appropriate tools and methodologies allow relatively rapid development of transgenic large animal models. 5. Pigs are genetically, anatomically, physiologically and immunologically closer to humans than rodents or ferrets, are relatively inexpensive, accessible and are more ethical acceptable than NHPs 4. However, pigs are not susceptible to SARS-CoV-2 infection which limits their utility as a model 6,7. To address this, we generated hACE2 transgenic pigs. hACE2 is the cellular receptor for SARS-CoV-2 and the main determinant of species tropism. In brief a custom lentivirus expressing hACE2 under the Keratin 18 promoter was microinjected into oocytes which were then surgically implanted into five surrogate gilts (Fig.\u00a01A). Three were confirmed pregnant and a total of 32 piglets were born. Genomic DNA was generated from ear biopsies and subjected to PCR for the transgene sequence. Twenty-eight piglets were confirmed as transgenic (data not shown). Relative lentivirus copy number was determined using a provirus-specific qPCR on the same genomic DNA with cycle threshold (Ct) values ranging from 20.8 to 30.7 (Supplemental table 1). Based on Ct values, three females and two males were selected for breeding, resulting in generation of an F1 cohort of 30 piglets. Total RNA was extracted from ear biopsies and levels of hACE2 transcription determined by RT-qPCR. Piglets were ranked based on hACE2 transcription levels, and the 9 highest expressing piglets (7 females and 2 males) were selected for challenge with SARS-CoV-2 (Supplemental table 2). Prior to the challenge study, primary fibroblast cells were generated from ear biopsies taken from all nine of the selected pigs and an additional two transgenic pigs that showed low or undetectable levels of transgene expression (P35 and P38). We were unable to recover cells from one of the biopsy samples (P57) due to bacterial contamination. However, analysis of the cells that were established from the other animals showed those expressing higher levels of hACE2 mRNA were more susceptible to SARS-CoV-2 infection in vitro (Supplemental Fig.\u00a01). To determine in vivo susceptibility, the nine transgenic pigs, and three genetically similar non-transgenic controls were challenged at biosafety level 3 on a single occasion with 1 x 106 TCID50 of an early pandemic isolate of SARS-CoV-2 (EDB2) 8. The inoculum was delivered intranasally in a single 2 ml dose using a mucosal atomiser attached to a syringe. The dose and route of infection were based on previous challenge studies in pigs using SARS-CoV-2 or the porcine adapted coronavirus PRCV 9. Rectal temperature and clinical status of the pigs were monitored twice daily and SARS-CoV-2 lateral flow tests (LFT) performed on nasal swabs collected 2, 4 and 7 days post infection (DPI). All nine transgenic pigs showed clinical signs consistent with mild to moderate SARS-CoV-2 infection, including fever, sneezing, coughing and respiratory distress (Fig.\u00a01B, Supplemental Fig.\u00a02). Furthermore, all transgenic pigs tested positive by LFT of nasal swabs, as early as 2 DPI (Fig.\u00a01C and Supplemental Fig.\u00a03). In contrast, control pigs showed no clinical signs of infection and were negative by LFT. Three transgenic pigs and one wild-type pig were euthanised at 2, 4 and 7 DPI (cohort 1, 2 and 3 respectively) with tissues, including the nasal turbinates, tracheal epithelium and lung, collected for virus detection and histological analysis. High levels of SARS-CoV-2 RNA were detected in the nasal turbinates and trachea of the transgenic pigs (Fig.\u00a01D). In contrast, viral RNA was only detected in lung samples from three of the transgenic pigs (P53, P60 and P61) and only at very low levels. Infectious virus was recovered from the nasal turbinates with titres peaking at 4 DPI, but was undetectable by 7 DPI (Fig.\u00a01E). No infectious virus was recovered from the trachea or lung samples from any animals. No viral RNA or infectious virus was detected from any tissues taken from the non-transgenic control pigs, consistent with previous reports that non-transgenic pigs are not susceptible to SARS-CoV-2 6,7. Immunohistochemical staining of fixed tissue samples showed extensive expression of hACE2 protein in transgenic pigs (Fig.\u00a01F) with high levels of hACE2 RNA also detected (Supplemental Fig.\u00a04). Focal staining for SARS-CoV-2 in the nasal turbinates, trachea and lung was also detected (Fig.\u00a01F). No hACE2 or SARS-CoV-2 staining was detected in any tissue in the non-transgenic pigs (Supplemental Fig.\u00a05). Crucially, histological examination of tissues revealed clear signs of significant neutrophil and macrophage-rich inflammation within the lungs of infected animals from day four post infection (Fig.\u00a02 and Supplemental table 3) involving both bronchi and alveolar spaces with evidence of diffuse alveolar damage (DAD), oedema, and focal, fibrin-rich intravascular thrombi\u2013 all consistent with histological changes observed in fatal COVID-19 1. The lack of large animal models that faithfully reproduce the pathology of severe COVID-19 has impeded progress in understanding the underlying mechanisms that drive the inflammatory processes causing disease. A previous study reported the generation of hACE2 transgenic pigs by inserting the hACE2 cDNA downstream of the porcine ACE2 promoter 10. Although cells generated from those pigs displayed increased susceptibility to SARS-CoV-2 infection, no challenge studies were reported. Here, we describe the generation of a transgenic porcine model of COVID-19 that is highly susceptible to infection with SARS-CoV-2 and demonstrates clinical signs and histopathology consistent with moderate to severe disease. Unlike current animal models, infected hACE2 pigs displayed the full range of common clinical signs of COVID-19 including fever, coughing, sneezing, respiratory distress and key pathological signatures in the lung, including DAD, making the hACE2 pigs a unique model for COVID-19. Substantial inflammation, despite low levels of virus, suggests that pathology in the lungs of infected pigs is driven by a dysfunctional host response, rather than damage caused by virus replication. This is also a key hallmark of severe COVID-19 in patients 1 and further reflects the similarity in the anatomy, physiology and immune responses of humans and pigs. While infection with SARS-CoV-2 did not result in fatal outcomes, for animal welfare, practical and safety reasons, the study was designed to specifically avoid this outcome. Pigs displaying moderate to severe clinical signs, such as respiratory distress, were included in the next time point for culling, reducing the potential for fatal outcomes. High levels of infectious virus in the upper airways, along with observed coughing and sneezing, suggests that airborne transmission between transgenic pigs would be likely to occur. Rapid diagnosis with existing LFT tests and onset of clearly observable clinical signs provide a potentially powerful model of airborne transmission. Such studies will be critical for evaluation of vaccines and their ability to block transmission, a goal that is yet to be achieved. Co-circulation of new variants of SARS-CoV-2 and seasonal Influenza virus A (IAV), as well as the threat of avian IAV, has raised significant concerns on the potential impacts of co-infection on vulnerable individuals and the population as a whole. As pigs are naturally susceptible to IAV, this new model will be hugely valuable for investigating potential consequences of co-infection on disease progression, clinical outcomes, airborne transmission and vaccine and antiviral efficacy. Finally, cross-breeding hACE2 pigs with established porcine biomedical models of underlying co-morbidities, such as obesity and diabetes11 would leverage additional impact, while cross-breeding with Cas9 pigs12 would generate a powerful COVID-19 disease model for in vivo and ex vivo precision gene editing.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": " Plasmids pLenti CMV GFP Hygro (656-4) was a gift from Eric Campeau & Paul Kaufman (Addgene plasmid #17446; https://www.addgene.org/17446/) 13. The coding region of human ACE2 (hACE2) was directly synthesized (Life Technologies) and cloned downstream of the CMV promoter to generate pLenti-CMV-hACE2-hygro. pSL10-K18-hACE2 was used to generate the transgenic pigs. pSL10 is a modification of pLenti6/V5 D-TOPO (Invitrogen), with the ClaI-KpnI fragment removed and replaced with a cPPT / SV40 immediate early promoter / GFP / OPRE cassette. K18-hACE2 was PCR amplified from pK18-hACE2 (a gift from Paul McCray (Addgene plasmid #149449; https://www.addgene.org/149449/) 14. The amplified cassette was subcloned into BamHI and SalI sites in pSL10 to create pSL10-K18-hACE2. Cell lines and viruses All cells were maintained in Dulbecco\u2019s modified Eagle\u2019s medium (DMEM; Sigma-Aldrich #D5796) supplemented with 10% fetal bovine serum (FBS) and 100 U/ml Penicillin-Streptomycin (Gibco) at 37\u00b0C in 5% CO2. Newborn Swine Kidney cells (NSK; RRID: CVCL_8378) expressing human ACE2 were generated through transduction with pLenti-CMV-hACE2-hygro. Primary cells were generated from ear notches of approximately 1 cm3 from each transgenic pig. Samples were washed in PBS and sliced into <\u20091 mm3 sized pieces using scalpel blades. The tissue was cultured in digestion medium (DMEM with 20% FBS, 1X antibiotic/antimycotic (Capricorn Scientific), 50 \u00b5g/mL gentamicin (Gibco) and 0.5 g/ml lyophilised collagenase I (Merck Life Sciences)) in a T25 flask for 24 hours at 37\u00b0C with 5% CO2. The sample was then filtered using a 70 \u00b5m cell strainer and cultured with outgrowth medium (digestion medium minus collagenase) at 37\u00b0C, 5% CO2 until cells reached confluency. SARS-CoV-2 virus isolate EDB2 (Scotland/EDB1827/2020, GISAID ID: EPI_ISL_433147) was passaged twice in Vero E6 (ATCC CRL-1586) cells (P0 and P1) before being propagated once in hACE-NSK (P2) for the challenge study. Lentivirus production All lentiviruses were generated according to the standard protocol for 2nd generation lentiviruses. Each lentiviral plasmid (15 \u00b5g) was co-transfected with 12 \u00b5g psPAX2 (Addgene #12260) and 3 \u00b5g pMD2.G (Addgene #12259) packaging plasmids into Lenti-X 293T cells (Takara) at 70% confluency in T75 flasks using lipofectamine 2000 (Invitrogen #11668019). Supernatant was harvested three days post-transduction and passed through a 0.45 \u00b5m filter cartridge, with aliquots frozen at -80oC. Cells were transduced using one in two dilutions of lentiviral supernatant with fresh media supplemented with 100 \u00b5M DEAE dextran. Generation of transgenic pigs Transgenic pigs were generated and genotyped as described previously 15. Briefly, zygotes were collected by flushing the oviducts of artificially-inseminated super-ovulated Large-White gilts. 50 pl lentivirus suspension was injected into the perivitelline space, following which 30 zygotes were transferred to the oviduct of each of 3 identically treated but unmated recipients. SARS-CoV-2 infection studies For infection studies, nine hACE2 transgenic and three wild type 8-week old pigs were transferred to CL3 housing. Following 7 days acclimatisation, a blood sample was taken by jugular venepuncture and SARS-CoV-2 (2 ml of a 5 x 105 TCID50/ml suspension; isolate EDB2) was administered intranasally to each pig using a mucosal atomisation device (MAD300). Pigs were monitored daily and clinical scores and temperatures recorded. At 2, 4 and 7 DPI, groups of 4 pigs (3 hACE2 transgenic and 1 wild type) were euthanized by overdose of sodium pentobarbital. A blood sample was taken by cardiac puncture and 2 nasal swabs were taken post mortem. The first swab was tested immediately by SARS-CoV-2 lateral flow test (FlowFlex COVID-19 Rapid Antigen Nasal Lateral Flow Test Kit, LO31-118M5), and the second stored in Trizol (Invitrogen) for later analysis. Further tissues were taken at necropsy and stored immediately in 10% buffered formalin, placed in Trizol, or stored in serum-free DMEM. All infection studies were performed at CL3 (BSL3) containment and staff wore appropriate personal protective equipment including FFP3 powered respirator helmets (Pureflo). All animal studies were performed with local and national ethical approval in accordance with the Animal (Scientific Procedures) Act 1986. PCR Gene expression was quantified by a 2-step method. RNA was extracted using Trizol following the manufacturer\u2019s protocol (Invitrogen) with residual genomic DNA removed by TURBO DNA-free\u2122 Kit (Invitrogen). 1\u20132 \u00b5g RNA was reverse transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). qPCR reactions were performed using TaqMan\u2122 Fast Advanced Master Mix (Applied Biosystems) and analysed with the Rotor-Gene Q real-time cycler (Qiagen). All gene expression values were normalized to GAPDH and are quantified by the 2\u2013\u2206\u2206Ct method. Assay ID for TaqMan assay (Applied Biosystems): SARS-CoV-2-Vi07918636_s1; hACE2-Hs0108533_m1; pACE2-Ss03390186_m1; pGAPDH-Ss03375629_u1. Genomic DNA was prepared from ear biopsies of liveborn piglets, and initially assessed for presence of lentivirus-vector transgene by PCR with primers HIV1 (GAGAGAGATGGGTGCGAGAG) and HIV2 (GCTGTGCGGTGGTCTTACTT), which span the lentiviral packaging signal sequence. Proviral PCRs were performed using the Lenti-X\u2122 Provirus Quantitation Kit (Takara \u2013 Cat# 631239) according to manufacturer\u2019s protocols. Histology and histopathology A defined set of tissue sections was collected from each pig including lungs, trachea sections, turbinates, lymph nodes, tonsil and thymus. Following collection, tissues were immediately immersed into 10% buffered formalin and left overnight before embedding in paraffin blocks. Sections (4 \u00b5m) were cut, dewaxed and stained using hematoxylin and eosin. All tissues were scored independently by two veterinary pathologists. Lesions were graded by severity (mild, moderate, and severe) and by distribution (focal, multifocal, or diffuse). In lungs type 2 pneumocyte proliferation, presence of hemorrhage, protein presence and peribronchial lymphohistocytosis were assessed. A note was made when pleuritis was present. In bronchi, turbinates and trachea, the presence of necrosis and inflammation was assessed. For lymphoid tissues, inflammation and depletion was assessed. Immunohistochemistry was performed on formalin-fixed, paraffin embedded tissue sections from lung, trachea, turbinates, thymus and tonsil using a rabbit monoclonal antibody (ab108209; ABCAM; 1:150) for the hACE2 and a mouse monoclonal (B46F; Invitrogen; 1:100) for SARS-CoV-2 Nucleocapsid protein. Envision detection reagents were used for visualisation (Dako). Known hACE2 positive and negative sections were used to validate the assay. TCID50 hACE2-NSKs were seeded in 96-well flat bottom plates 2\u20133 days in advance to ensure 100% confluency. Each sample was divided into two, which were separately subjected to 10-fold dilutions (10\u2212\u20091 to 10\u2212\u20096) in serum-free DMEM. For each sample and dilution, eight replicate wells were used for infection. Culturing medium was removed by aspiration and 50 \u00b5l of the diluted virus was added into each well, followed by a 2-hour incubation period at 37\u00b0C on a rocker. 100 \u00b5l of fresh complete DMEM was added to each well after virus absorption. Cells were incubated for 4 days and the plates were fixed in 4% paraformaldehyde in PBS for 20 mins before being removed from containment facilities for visual inspection of virus induced cytopathic effect under an inverted microscope. Virus titres were then calculated using the Spearman\u2013K\u00e4rber method. ", + "section_image": [] + }, + { + "section_name": "Declarations", + "section_text": "Acknowledgements\nWe thank Eric Campeau, Paul Kaufman and Paul McCray for sharing reagents. We thank Moredun Bioservices, Robert Bernard and the Large Animal Research and Imaging Facility (University of Edinburgh, United Kingdom) for excellent animal care and members of the Moredun Virus Surveillance unit for technical assistance. This study was funded by UKRI Biotechnology and Biological Sciences (BB/V018922/1), and institutional grants (BBS/E/RL/230002A) as well as a Wellcome ISSF3 award (IS3-R2.26 19/20). The Moredun Institute High Containment animal facilities are supported in part by Underpinning National Capacity funding from the Scottish Government Rural and Environment Science and Analytical Services (RESAS) division. We thank Mark Stevens for critical review of the manuscript.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "\nDorward, D. A. et al. Tissue-Specific Immunopathology in Fatal COVID-19. Am J Respir Crit Care Med 203, 192-201 (2021). https://doi.org:10.1164/rccm.202008-3265OC\nTian, Y. et al. Single-cell immunology of SARS-CoV-2 infection. Nat Biotechnol 40, 30-41 (2022). https://doi.org:10.1038/s41587-021-01131-y\nDillard, J. A., Martinez, S. A., Dearing, J. J., Montgomery, S. A. & Baxter, V. K. Animal Models for the Study of SARS-CoV-2-Induced Respiratory Disease and Pathology. Comp Med 73, 72-90 (2023). https://doi.org:10.30802/AALAS-CM-22-000089\nMeurens, F., Summerfield, A., Nauwynck, H., Saif, L. & Gerdts, V. The pig: a model for human infectious diseases. Trends Microbiol 20, 50-57 (2012). https://doi.org:10.1016/j.tim.2011.11.002\nHryhorowicz, M. et al. Application of Genetically Engineered Pigs in Biomedical Research. Genes (Basel) 11 (2020). https://doi.org:10.3390/genes11060670\nSchlottau, K. et al. SARS-CoV-2 in fruit bats, ferrets, pigs, and chickens: an experimental transmission study. Lancet Microbe 1, e218-e225 (2020). https://doi.org:10.1016/S2666-5247(20)30089-6\nShi, J. et al. Susceptibility of ferrets, cats, dogs, and other domesticated animals to SARS-coronavirus 2. Science 368, 1016-1020 (2020). https://doi.org:10.1126/science.abb7015\nKurkowiak, M. et al. Differential RNA editing landscapes in host cell versus the SARS-CoV-2 genome. iScience 26, 108031 (2023). https://doi.org:10.1016/j.isci.2023.108031\nRawal, G. et al. Experimental Infection of Pigs with a Traditional or a Variant Porcine Respiratory Coronavirus (PRCV) Strain and Impact on Subsequent Influenza A Infection. Pathogens 12 (2023). https://doi.org:10.3390/pathogens12081031\nDu, X. et al. Establishment of a humanized swine model for COVID-19. Cell Discov 7, 70 (2021). https://doi.org:10.1038/s41421-021-00313-x\nHeegaard, P. M. H., Sturek, M., Alloosh, M. & Belsham, G. J. Animal Models for COVID-19: More to the Picture Than ACE2, Rodents, Ferrets, and Non-human Primates. A Case for Porcine Respiratory Coronavirus and the Obese Ossabaw Pig. Front Microbiol 11, 573756 (2020). https://doi.org:10.3389/fmicb.2020.573756\nRieblinger, B. et al. Cas9-expressing chickens and pigs as resources for genome editing in livestock. Proc Natl Acad Sci U S A 118 (2021). https://doi.org:10.1073/pnas.2022562118\nCampeau, E. et al. A versatile viral system for expression and depletion of proteins in mammalian cells. PLoS One 4, e6529 (2009). https://doi.org:10.1371/journal.pone.0006529\nMcCray, P. B., Jr. et al. Lethal infection of K18-hACE2 mice infected with severe acute respiratory syndrome coronavirus. J Virol 81, 813-821 (2007). https://doi.org:10.1128/JVI.02012-06\nWhitelaw, C. B. et al. Efficient generation of transgenic pigs using equine infectious anaemia virus (EIAV) derived vector. FEBS Lett 571, 233-236 (2004). https://doi.org:10.1016/j.febslet.2004.06.076\n", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementalInformation.docx", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Animal models that accurately reflect COVID-19 are vital for understanding mechanisms of disease and advancing development of improved vaccines and therapeutics. Pigs are increasingly recognized as valuable models for human disease due to their genetic, anatomical, physiological, and immunological similarities to humans, and they present a more ethically viable alternative to non-human primates. However, pigs are not susceptible to SARS-CoV-2 infection which limits their utility as a model. To address this, we have developed transgenic pigs expressing human ACE2 that are susceptible to SARS-CoV-2 infection. Following challenge, clinical signs consistent with COVID-19, including fever, coughing and respiratory distress were observed, with virus replication detected in the nasal turbinates, trachea and lungs up to the study endpoint, seven days post-infection. Notably, examination of tissues revealed immunopathology in the lungs consistent with histological changes observed in fatal human COVID-19 cases. This study establishes human ACE2 transgenic pigs as a large animal model that accurately reflects many aspects of COVID-19 disease.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The key molecular events that trigger progression from self-limited viral illness to severe COVID-19 remain poorly defined. Non-invasive profiling of cells from patients and analysis of tissue specimens collected post-mortem has been valuable in characterising inflammatory pathology associated with severe COVID-191,2. However, defining the key early events that trigger progression towards severe COVID-19 requires longitudinal studies and invasive sampling of tissues.\n\nAnimal models are important tools for testing hypotheses about mechanisms of disease in COVID-19 and complement clinical studies from patients, enabling direct analysis of target tissues following controlled infections3. However, at present, there is a lack of efficient, tractable model systems that replicate the primary features of severe COVID-19. Human disease associated with COVID-19 can be broadly categorised as mild, moderate and severe based on clinical symptoms4,5,6. Mild disease is associated with fever, fatigue, cough and sore throat, with minimal or no visible lesions in the lungs based on chest radiographs. Moderate disease includes respiratory distress, with blood oxygen saturation levels between 90 and 94% and signs of mild lung infiltrates. Severe disease is associated with dyspnoea, high respiratory rate and oxygen saturation levels below 90%. The histological changes observed within the lungs of patients who have died from COVID-19 can be variable but include exudative and organising phase diffuse alveolar damage (DAD), which includes hyaline membranes, oedema, inflammatory cell infiltrates, intra-alveolar fibrin, organising pneumonia, suppurative bronchopneumonia and intravascular thrombi1.\n\nRodent models such as human angiotensin-converting enzyme 2 (hACE2) expressing transgenic mice and golden Syrian hamsters are susceptible to SARS-CoV-2 infection, while non-transgenic mice can be infected with mouse-adapted strains of SARS-CoV-2. Ferrets are also susceptible to infection and are effective transmission models, while non-human primates (NHPs) are the most comparable model to human infections3. While each of these models has advantages, there are important drawbacks. Rodent models do not accurately replicate the pattern of disease, tissue morphology and immunological responses to SARS-CoV-2 in humans3. There is a lack of molecular tools for ferret models, while non-human primates are expensive and restricted to a few institutions around the world. The lack of large animal models that accurately reflect the pathology associated with severe COVID-19 in humans hampers our ability to understand the mechanisms that drive disease and the development of effective interventions. Furthermore, the development of therapeutic interventions in a model with similar physiology to humans is more likely to successfully translate into effective therapies in humans.\n\nThere is an increasing appreciation of livestock as biomedical models, with pigs being one of the most important7. The short gestation period, large litter size and extensive genome editing tools, allow rapid development of transgenic large animal models8. Pigs are considered the species of choice for xenotransplantation, reflecting their similarity to human anatomy and physiology9. The upper airway, lungs, heart and brain, all targets of SARS-CoV-2 infection, are physiologically and anatomically more similar to humans than those of rodents and ferrets9,10. Critically, for vaccine development and studies on immune pathology, the porcine immune system more closely resembles humans in greater than 80% of parameters analysed, compared to less than 10% for mice11. This includes a higher percentage of neutrophils in the blood of pigs and humans than in mice12; expression of CXCL8, a chemoattractant for neutrophils, present in pigs and humans, but absent in mice12; and key differences in dendritic cells in mice compared to humans and pigs which leads to altered responses to innate antagonists often used as vaccine adjuvants13.\n\nVaccines can be administered intramuscularly, subcutaneously, intradermally, orally or intranasally and pigs can be routinely bled and immunized using well-established protocols. Compared to rodents, large numbers of immune cells can be isolated and pigs offer easy access to various immune compartments7. Various surgical and non-surgical procedures typically used in human medicine can be performed in pigs, including catheterization, heart surgery, valve manipulation, endoscopy and bronchoalveolar lavages. These procedures are particularly difficult or impossible to perform in smaller animal models including rodents. Pigs are relatively inexpensive, accessible and are more ethically acceptable than NHPs.\n\nHowever, multiple studies have shown that pigs are not susceptible to SARS-CoV-2 infection which limits their utility as a model14,15. To address this, we generated transgenic pigs expressing hACE2, the primary cellular receptor for SARS-CoV-2 and the main determinant of species tropism16,17. We showed that the pigs were susceptible to infection with SARS-CoV-2 through intranasal inoculation and displayed clinical signs consistent with COVID-19 in humans, including fever, coughing and respiratory distress. Crucially, histological analysis demonstrated significant inflammation in the lungs, consistent with pathology seen in fatal COVID-19 patients. These findings demonstrate that hACE2 transgenic pigs represent a large animal model that accurately reflects pathological disease of COVID-19 in humans and represents a valuable model for studying disease pathology, vaccines and novel therapeutics.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "To generate the transgenic pigs, a custom lentivirus expressing hACE2 under the Keratin 18 promoter was microinjected into the perivitelline space of putative zygotes, which were then surgically implanted into five surrogate gilts (Fig.\u00a01). Three gilts were confirmed pregnant and a total of 32 piglets were born. Genomic DNA was generated from ear biopsies and relative lentivirus copy number determined using a provirus-specific qPCR, with cycle threshold (Ct) values ranging from 19.7 to 30.7 (Supplemental Table\u00a01). Based on Ct values, three females and two males were selected for breeding, generating an F1 cohort of 30 piglets. One piglet died three days after birth (P41), leaving a cohort of 29 piglets. Total RNA was extracted from ear biopsies and levels of hACE2 transcript was determined by RT-qPCR. Piglets were ranked based on hACE2 transcript levels, with the nine highest expressing piglets (seven females and two males) selected for the challenge study with SARS-CoV-2 (Supplemental Table\u00a02).\n\nA custom lentiviral vector with the human K18 promoter driving hACE2 expression was microinjected into the perivitelline space of putative zygotes. Zygotes were then implanted into surrogate gilts (Created in BioRender. Grey, F. (2023) https://BioRender.com/l58r269).\n\nPrior to the challenge study, primary fibroblast cells were generated from ear biopsies taken from all nine selected pigs and an additional two transgenic pigs that showed low or undetectable levels of transgene expression (P35 and P38). We were unable to recover cells from one of the biopsy samples (P57) due to bacterial contamination. RT-qPCR analysis revealed that hACE2 mRNA levels in individual cell lines were similar to the associated ear biopsies (Fig.\u00a02a). To determine susceptibility, cells were infected with an early pandemic isolate of SARS-CoV-2 (EDB2)18 and viral titres determined at 24\u2009h post infection (HPI) by Tissue Culture Infectious Dose 50 (TCID50) assays. The results showed that, in all but one case, cell lines expressing higher levels of hACE2 mRNA were more susceptible to SARS-CoV-2 infection in vitro, based on higher titres of virus being produced (Fig.\u00a02b, c).\n\na Primary cells were generated from ear notches taken from transgenic pigs. Total RNA was harvested and hACE2 transcript levels determined by RT-qPCR. hACE2 levels were normalized to GAPDH and shown as relative expression. b To test susceptibility, fibroblast cells were infected at an MOI of 3. Supernatant was harvested 24 HPI and infectious virus quantified by TCID50. Data represents the mean\u2009from two independent experiments (each using two technical replicates). ND\u00a0= not detected. c Correlation of SARS-CoV-2 titre and hACE2 expression levels are shown. Source data are provided as a Source Data file.\n\nA low level of virus replication was detected in cells from P35 and no virus was detected from cells from P38, consistent with the low levels of hACE2 detected in the tissues and cells from these pigs. Surprisingly, no virus replication was observed in cell line P52, despite high levels of hACE2 expression being detected. It is currently unclear why this cell line was unable to support SARS-CoV-2 virus replication. The cells were also infected with SARS-CoV-2 Delta and Omicron to determine susceptibility to more recently emerged viral variants (Supplemental Fig.\u00a01). As Omicron does not induce clear cytotoxic effects in cell lines, viral genome levels in the supernatant were measured using RT-qPCR. Similar to EDB2, replication of Delta virus correlated with hACE2 expression levels. In contrast, Omicron replication was only observed in the two cell lines derived from P46 and P48 which have the highest hACE2 expression levels, indicating susceptibility to more recent strains of SARS-CoV-2 is dependent on higher levels of hACE2 expression. We further confirmed the susceptibility of selected cell lines by immunofluorescent staining for SARS-CoV-2 N protein, showing all three variants able to infect P46 and P48 cell lines, while none were able to infect P52 (Supplemental Fig.\u00a01d). Omicron was able to infect P46 and P48, but no N protein was detected in the other cell lines (P43 shown as example), despite expressing hACE2 and being susceptible to EDB2 and Delta. Further studies will be required to understand why the cells generated from P52 were not susceptible to SARS-CoV-2 infection.\n\nTo determine in vivo susceptibility, the nine selected transgenic pigs, and three genetically similar non-transgenic controls (referred to from here on as WT) were challenged at biosafety level three on a single occasion with 1\u2009\u00d7\u2009106 TCID50 of EDB2. EDB2 was selected for the challenge studies as it efficiently infected primary fibroblast cells from the transgenic pigs and early isolates were associated with more severe disease, therefore more likely to generate clear clinical and histological outcomes in the challenge study. The inoculum was delivered intranasally in a single 2\u2009ml dose using a mucosal atomiser attached to a syringe. The dose and route of infection were based on previous challenge studies in pigs using SARS-CoV-2 or the porcine adapted coronavirus PRCV19. Rectal temperature and clinical status of the pigs were monitored twice daily and SARS-CoV-2 lateral flow tests (LFT) performed on nasal swabs collected two, four and seven days post infection (DPI). Three transgenic pigs and one WT pig were euthanised at two, four and seven DPI (cohort 1, 2 and 3, respectively) with tissues, including the nasal turbinates, tracheal epithelium (proximal, mid and distal) and lung, collected for virus detection and histological analysis.\n\nWithin 24\u2009h of infection P43, P46, P48, P60 and P61 all showed signs of fever, with temperatures above 40\u2009\u00b0C, although P43 and P48 showed signs of elevated temperature before infection, suggesting the initial fever may not be directly related to SARS-CoV-2 infection (Fig.\u00a03a\u2013c). During the course of the experiment, only P57 and the WT control pigs showed no signs of fever at any time point. However, as part of cohort one, P57\u00a0may have developed fever at a later stage of infection had it not been culled two DPI. All transgenic pigs tested positive by LFT of nasal swabs, as early as two DPI (Fig.\u00a03d\u2013f), confirming their susceptibility to SARS-CoV-2 infection. In contrast, control WT pigs were negative by LFT throughout the challenge study.\n\na Rectal temperatures of pigs infected with SARS-CoV-2. Data grouped according to cohorts culled at 2 (a), 4 (b) and 7 (c) DPI. d\u2013f Results of SARS-CoV-2 LFTs grouped by DPI. C indicates control line; T indicates test line and indicates a positive for SARS-CoV-2 infection. All hACE2 pigs were positive, while all WT pigs were negative. g Summary of clinical scores for human ACE2 infected pigs. A clinical assessment scheme was established prior to the study, in which pigs were scored against several criteria including demeanor, appetite, temperature, respiration and other respiratory signs (cough, sneeze). Pigs were scored daily and the cumulative score recorded. Increases in temperature and respiratory stress were recorded in all hACE2-transgenic pigs except P57 but not in the wild-type animals. Cumulative scores of 7 or greater are defined as moderate severity in this model. HPI hours post infection. Source data are provided as a Source Data file.\n\nIn addition to fever and positive LFT tests, the transgenic pigs displayed other clinical signs consistent with COVID-19, including sneezing, coughing and respiratory distress. A clinical assessment scheme was developed to score each animal against several criteria, including demeanour (e.g. response to stimulation, response to presence of food, willingness/ability to stand when provoked), appetite (interest/enthusiasm in feeding/drinking), respiration (degree of effort in respiration, presence/absence of nasal discharge), and other respiratory signs (presence and duration and continuity of coughing and/or sneezing). A range in clinical severity was observed over the course of the infection and between individual pigs and are summarised in Supplemental Table\u00a03. At early time points (by 48 HPI) occasional coughing and mild respiratory distress were observed in some hACE2 pigs (P46, P48, P53 and P61). However, by 96 HPI moderate clinical signs were observed. In particular, P46 showed signs of lethargy, reluctance to stand, extended intermittent periods of coughing, laboured respiration and nasal discharge. Cumulative clinical assessment scores were calculated to enable direct comparison of animals over the time course (Fig.\u00a03g), indicating that P46 and P60 displayed the most severe clinical signs before being culled 4 DPI.\n\nExpression levels and tissue distribution of ACE2 plays an important role in determining susceptibility to SARS-CoV-2 infection. In humans, single-cell RNAseq analysis and immunohistochemistry (IHC) have identified widespread expression of ACE2 in multiple organs, tissue and cell types20,21,22. In the upper respiratory tract, hACE2 is expressed in ciliated columnar epithelial cells and goblet cells. In the lungs, a small percentage of type II pneumocytes are thought to be the major target of SARS-CoV-2, where both ACE2 and the entry factor TMPRSS2 are co-expressed.\n\nIHC was employed to determine hACE2 expression levels in the transgenic pigs. Nasal turbinate, trachea and lung samples, representing the upper and lower respiratory tract, were taken from transgenic and WT animals at two, four and seven DPI. Figure\u00a04a shows images of hACE2 staining in tissues from P46 (a pig with high hACE2 expression) with representative images of tissues from all animals shown in Supplemental Fig.\u00a02.\n\na IHC staining demonstrating distribution of hACE2 expression in sections of nasal turbinate, trachea, and lung from P46 culled four DPI. Red boxes in panel 2 indicate the location of magnified images displayed in panels 1 and 3. Panel 4 shows the control WT pigs without hACE2 expression. Staining indicates hACE2 expression in ciliated columnar epithelial cells, goblet cells and seromucinous glands within the nasal turbinates and trachea. In lungs, staining is most pronounced in alveolar pneumocytes but to a lesser extent in bronchiolar epithelium and vascular endothelium. (Panel 1, 3 and 4 \u22125x magnification, (except lung \u2212 1x); panel 2 and 4 \u2212 20x magnification). Scale bars for panel 1 and 3 are 100\u2009\u03bcm; for panel 2 and 4 are 500\u2009\u03bcm, apart from lung images which are 2.5\u2009mm. b Total hACE2 staining was quantified using QuPath software. Viable tissue regions on each whole-slide IHC image were randomly sampled three times by non-overlapping regions with areas no smaller than 4\u2009mm2. The pixelwise H-score for each region was calculated (see Methods) and the average score presented for each image. Source data are provided as a Source Data file.\n\nThe observed expression pattern across tissues aligns with the expected epithelial distribution, consistent with the use of the K18 promoter to drive hACE2 transgene expression. Expression of hACE2 in the nasal turbinates and trachea appears to be confined to ciliated columnar epithelial cells, goblet cells and seromucinous glands. Expression in the lung parenchyma is widespread in alveolar pneumocytes, bronchiolar epithelium and vascular endothelium. However, further validation through co-staining or single-cell RNA sequencing will be necessary to definitively characterize the specific cellular profiles of hACE2 expression in the transgenic pigs.\n\nWhile the pattern of hACE2 expression in transgenic pigs generally reflects that of ACE2 in human tissues, it appears more widely distributed in the pigs. Subjectively, a higher proportion of epithelial cells express hACE2 compared to similar human tissue expression20,21,22. Expression levels\u00a0of hACE2 also vary considerably between the individual transgenic pigs, as would be expected when using a lentiviral transgenic approach, due to differences in copy number and integration site.\n\nQuPath image analysis software was used to quantify hACE2 expression in the sections shown in Supplemental Fig.\u00a02 (Fig.\u00a04b). Expression levels were largely proportional between tissues of individual pigs, apart from P53 which displayed relatively low levels of expression in the trachea compared to the nasal turbinates and lung, and P59 which showed relatively low levels of hACE2 in the nasal turbinates compared to the trachea and lung. No staining was detected in any tissues from WT animals. Low ACE2 expression in the respiratory epithelium has been suggested as a reason why pigs are not naturally susceptible to SARS-CoV-223. Elevated hACE2 expression in the respiratory epithelium of the transgenic pigs likely accounts for the increased susceptibility to the virus. However, other host determinants likely play a role in the severity of disease in individual animals.\n\nIn humans, SARS-CoV-2 can replicate to high levels in the upper respiratory tract, enabling efficient spread through airborne transmission. In contrast, viral load is often lower in the lung and does not necessarily correlate with severe disease, as pathology in the lung is largely driven by dysregulated host immune responses rather than viral replication itself\u20061.\n\nTo determine the viral load in the upper and lower respiratory tract of hACE2 pigs, total RNA was isolated from nasal turbinate, trachea and lung tissue homogenates taken from transgenic and WT control animals. RT-qPCR analysis using a primer probe set targeting the viral N gene revealed high levels of viral RNA, especially in the nasal turbinates, at two and four DPI, ranging from 3.0\u2009\u00d7\u2009106 to 5.0\u2009\u00d7\u2009107 copies/\u00b5g total RNA (Fig.\u00a05a). Levels in the trachea and lung were lower, ranging between 5.9\u2009\u00d7\u2009104 and 5.9\u2009\u00d7\u2009106 copies/\u00b5g total RNA for trachea and 1.3\u2009\u00d7\u2009103 and 3.0\u2009\u00d7\u2009106 copies/\u00b5g total RNA for the lung.\n\na SARS-CoV-2 viral RNA levels determined by RT-qPCR at two, four and seven DPI from TURB = nasal turbinates, TRAC = trachea, LUNG = lung. No viral RNA was detected in control WT pigs. P43\u2013P61 indicates individual pig identifier corresponding to each data point (each with two technical replicates). Data represent the mean \u00b1\u2009Standard Deviation (S.D.). b Levels of infectious virus from nasal turbinates were determined by TCID50. No infectious virus was detected in trachea or lung. Data represents the mean\u2009from two independent experiments (each using two technical replicates). Source data are provided as a Source Data file. c Tissue sections from nasal turbinates and lung were stained for SARS-CoV-2 nucleocapsid protein demonstrating the presence of viral protein within mucosal epithelial cells of both the upper and lower respiratory tract (arrow) as well as alveolar macrophages (*) and pneumocytes (arrowhead). Representative images shown from hACE2 transgenic pigs. (20x magnification).\n\nInfectious virus was measured using TCID50 assays to confirm active infection and virus replication was occurring in the transgenic pigs. Figure\u00a05b shows infectious virus was recovered from the nasal turbinates with titres peaking at four DPI but was undetectable by seven DPI. No infectious virus was recovered from the trachea or lung samples from any animals. No viral RNA or infectious virus was detected in tissues taken from the WT control pigs, consistent with previous reports that non-transgenic pigs are not susceptible to SARS-CoV-214,15. IHC staining identified SARS-CoV-2 nucleocapsid protein within the cytoplasm of respiratory epithelia of nasal turbinates (Fig.\u00a05c). Within the distal lung parenchyma, nucleocapsid protein was identified in bronchiolar epithelial cells, macrophages, alveolar pneumocytes and occasional vascular endothelial cells (Fig.\u00a05c). No SARS-CoV-2 nucleocapsid was detected in WT pig tissues.\n\nLung pathology in fatal COVID-19 is characterised by the spectrum of exudative and organising DAD together with pulmonary oedema, intra-alveolar fibrin and intravascular thrombosis3. This occurs along with extensive immune cell infiltrate in alveolar spaces and the interstitium by neutrophils, lymphocytes and macrophages1. Understanding key factors that underpin disease progression in COVID-19 requires animal models that accurately reflect the pathological characteristics of the disease.\n\nDuring postmortem examination, macroscopic assessment of inflammatory/congestive changes within the lung based on the percentage of lung surface affected was assessed by a pathologist blinded to the pig treatment status. Assessment was based on a previously established scoring system24. While levels varied (14% to 92%), the results suggested considerable lung pathology associated with SARS-CoV-2 infection in the transgenic pigs, particularly from four DPI onwards (Supplemental Table\u00a04).\n\nMicroscopic evaluation of nasal turbinate, trachea and lung tissue demonstrated that a subset of pigs displayed key histopathological features similar to those described in fatal COVID-19 (Fig.\u00a06). These included significant neutrophil and macrophage-rich inflammation in the lungs of infected animals from four DPI, including DAD, oedema, focal, fibrin-rich intravascular thrombi and bronchopneumonia1. Samples were taken from multiple regions and suggest that inflammation within the lung was focal in nature, as separate samples from the same lungs displayed disparate levels of pathology. Even within the same tissue section, areas of severe lung inflammation were observed adjacent to less affected regions, possibly due to uneven distribution of the viral inoculum (Supplemental Fig.\u00a03).\n\na WT control infected and (b) hACE2 infected pigs. In hACE2 pigs there was evidence of neutrophil-rich bronchial inflammation (c), widespread bronchiolar (*) and alveolar (**) inflammation (d) with associated intra-alveolar fibrin (e), oedema (f) and parenchymal necrosis (g). Occasional organising (arrow) and fibrin-rich thrombi were present in medium calibre vessels (h). Representative images shown. a WT2 \u2013 WT control animal; (b\u2013h) P46 \u2013 hACE2 infected animal, both culled 4 DPI with SARS-CoV-2. (a, b \u2013 1x magnification; c \u2013 10x; d\u2013h \u2013 20x).\n\nDue to the challenges associated with working in CL3 conditions with large animals, it was not possible to inflate the lungs prior to sampling of tissues for histology, limiting the ability to accurately quantify the observed histopathology. However, binary qualitative scoring for the presence or absence of pathology indicates substantial inflammation in the lungs of animals from four DPI, with many of the hallmarks commonly associated with severe COVID-19 (Supplemental Table\u00a05). Variability between animals was also observed. For example, in cohort 2, extensive pathology was observed in P46 and P61, but less so in P60, which expresses lower levels of hACE2.\n\nTo characterize the lung immunopathology in infected transgenic pigs, lung sections were stained with immune markers Iba-1, PAX5, and CD3. The results showed expanded alveolar, interstitial and peri-bronchiolar macrophage populations by four DPI, while peribronchiolar and perivascular B and T cell populations were formed by seven DPI (Supplemental Fig.\u00a04). These findings are consistent with the pattern of immune cell infiltration observed in fatal COVID-191.\n\nPrevious studies have used pigs to model immune responses to COVID-19 vaccines25. However, subsequent challenge studies are not possible in WT pigs.\u00a0The hACE2 pigs represent a highly attractive model for vaccine studies as efficacy in vaccine protection can be determined following challenge. Characterising the immune response to SARS-CoV-2 infection in the transgenic pigs will provide valuable base line data for future vaccine studies. As this was a proof of principle study, the infected pigs were maintained up to seven DPI, limiting a full characterisation of the adaptive immune response which usually occurs seven to ten DPI. However, while full characterisation of the adaptive immune response was outside the scope of the current study, total immunoglobulin (Ig) levels were determined by enzyme-linked immunosorbent assay (ELISA). Although considered early for antibody production, a strong antibody response was detected in animal P53 which was culled seven DPI (Fig.\u00a07). More moderate responses were also detected in P43 at seven DPI and P60 at four DPI, although neither reached the score considered positive (S/P ratio >60%, see methods for details). None of the WT pigs seroconverted. Given the viral load, it is likely that all transgenic pigs would have seroconverted if the course of infection had run beyond the seven days measured. Longer challenge studies will be required to fully characterise the immune response to SARS-CoV-2 infection in the transgenic pigs. However, the data presented suggests the model will be useful for characterising vaccine efficacy.\n\nSerum was generated from blood samples taken immediately postmortem. Antibody levels were determined using a double antigen multi-species Elisa assay, recognizing SARS-CoV-2 N antigen. Despite\u00a0the relatively\u00a0early time point for seroconversion, high levels of antibody were detected in the serum of P53, with lower levels detected in P43 and P60. Data represents the mean from three technical repeats. Source data are provided as a Source Data file.\n\nThe use of lentiviral delivery for the generation of transgenic pigs resulted in variable hACE2 expression levels. In vitro studies on primary fibroblast cells indicate that hACE2\u00a0expression levels correlate with susceptibility to SARS-CoV-2 replication. If the same concept applies following in vivo challenge studies, levels of hACE2 determined by ear biopsies may be used to predict severity of disease following infection with SARS-CoV-2. Using normalised ranking scores, hACE2 expression in each tissue was compared to virus RNA levels, histology and clinical outcomes. Figure\u00a08 shows a heatmap, comparing the scores, ranked according to hACE2 IHC quantification. The analysis suggests a trend towards increased viral loads and more severe clinical and histological outcomes in pigs with higher levels of hACE2 expression. However, there are several caveats to this analysis. The relatively low numbers of animals used in the challenge study mean statistical significance cannot be achieved and variation in individual animals, unrelated to hACE2 expression, will confound potential correlation. Furthermore, scores will be biased depending on when the animals were culled. For example, peak viral loads occurred at four DPI. Higher levels of inflammation are also more likely to occur at later time points following accumulation of host responses to virus infection. Larger studies will be required to determine whether there is a statistical correlation between hACE2 expression levels and disease severity.\n\nData were normalised by a rank-based system. Pigs ranked higher in a data category refer to a stronger phenotype or higher value in that specific category and vice versa. Note that pigs can be ranked equivalently in some data categories. The heatmap is ordered by the average pixelwise H-score for hACE2 expression in the nasal turbinate (TURB), trachea (TRAC) and Lung (LUNG). Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54615-1/MediaObjects/41467_2024_54615_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54615-1/MediaObjects/41467_2024_54615_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54615-1/MediaObjects/41467_2024_54615_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54615-1/MediaObjects/41467_2024_54615_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54615-1/MediaObjects/41467_2024_54615_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54615-1/MediaObjects/41467_2024_54615_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54615-1/MediaObjects/41467_2024_54615_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54615-1/MediaObjects/41467_2024_54615_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The lack of large animal models that faithfully reproduce the pathology of severe COVID-19 has impeded progress in understanding the underlying mechanisms that drive the inflammatory processes causing disease. A previous study reported the generation of hACE2 transgenic pigs by inserting the hACE2 cDNA downstream of the porcine ACE2 promoter26. Although cells generated from those pigs displayed increased susceptibility to SARS-CoV-2 infection, no challenge studies were reported.\n\nHere, we describe the generation of a transgenic porcine model of COVID-19 that is susceptible to infection with SARS-CoV-2 and demonstrates histopathology consistent with moderate to severe disease. Unlike current animal models, infected hACE2 pigs displayed the full range of common clinical signs of COVID-19, including fever, coughing, sneezing, respiratory distress and key pathological signatures in the lung, making these hACE2 pigs a unique model for COVID-19.\n\nFurther studies are required to determine if the hACE2 pigs could be utilised to model severe COVID-19. Interpretation of severity based on clinical signs is subjective4,5,6. In humans, loss of pulmonary function causes systemic hypoxemia, characterised by a blood oxygen saturation level of less than 90%, culminating in multi-organ failure, and ultimately, a fatal outcome when emergency interventions fail. Direct measurements of respiration and blood oxygen saturation would help to provide quantitative data indicating the severity of disease in the pigs. The main aim of this study was to determine susceptibility of the transgenic pigs and measurements of blood oxygenation levels were not feasible without specialised equipment, rapid blood tests or extended periods of restraint of animals in CL3 conditions that could not be justified for a proof of principle study. However, the potential use of implantable microchips that would allow real-time measurement of a range of physiological responses, including temperature, blood oxygenation and respiratory rate will be considered for future experiments27. Longer time courses would also provide a more accurate determination of disease severity as such symptoms often occur in humans during the second week of infection. An extended time course would also enable a comprehensive characterisation of the cellular and humoral responses to SARS-CoV-2 infection in the pigs, enabling comparative analysis with human responses.\n\nFatal outcomes resulting from SARS-CoV-2 would indicate a high level of severity. However, for animal welfare, regulatory and safety reasons, the study was designed to specifically avoid this outcome. Pigs displaying moderate clinical signs, such as respiratory distress, were included in the next time point for culling, reducing the potential for fatal outcomes. It would also be necessary to confirm that death was due to COVID-19 pathology. In some rodent models, aberrant expression of hACE2 in the brain leads to viral encephalitis following infection with SARS-CoV-228.\n\nIn vitro studies showed higher levels of hACE2 expression correlated with increased SARS-CoV-2 replication and comparative analysis of in vivo data suggested a trend of correlation between hACE2 expression in the tissues of pigs and disease severity. For example, histopathology revealed many hallmarks of severe COVID-19 in the lungs of pigs that expressed the highest levels of hACE2. These results suggest that pigs expressing high levels of hACE2, which can be determined using a simple ear biopsy, could represent accurate models of severe disease. Furthermore, substantial inflammation, despite low levels of virus, indicates that pathology in the lungs of infected pigs is driven by a dysfunctional host response, rather than damage caused by virus replication. This is also a key hallmark of severe COVID-19 in patients1 and further reflects the similarity in the anatomy, physiology and immune responses of humans and pigs. However, additional studies will be required to confirm that higher levels of hACE2 expression correlate with increased disease severity in the pig model.\n\nHigh levels of infectious virus in the upper airways, along with observed coughing and sneezing, suggests that airborne transmission between transgenic pigs would likely occur. Rapid diagnosis with existing LFT tests and onset of clearly observable clinical signs provide a potentially powerful model of airborne transmission. Such studies will be critical for evaluation of vaccines and their ability to block transmission, a goal that is yet to be achieved.\n\nCo-circulation of new variants of SARS-CoV-2 and seasonal Influenza virus A (IAV), as well as the threat of avian IAV, has raised significant concerns on the potential impacts of co-infection on vulnerable individuals and the population as a whole29. As pigs are naturally susceptible to IAV, this new model will be hugely valuable for investigating potential consequences of co-infection on disease progression, clinical outcomes, airborne transmission and vaccine and antiviral efficacy. Finally, cross-breeding hACE2 pigs with established porcine biomedical models of underlying co-morbidities, such as obesity and diabetes30 would leverage additional impact, while cross-breeding with Cas9 pigs31 would generate a powerful COVID-19 disease model for in vivo and ex vivo precision gene editing.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "pLenti CMV GFP Hygro (656-4) was a gift from Eric Campeau & Paul Kaufman (Addgene plasmid #17446; https://www.addgene.org/17446/)32. The coding region of human ACE2 (hACE2) was directly synthesized (Life Technologies) and cloned downstream of the CMV promoter to generate pLenti-CMV-hACE2-hygro.\n\npSL10-K18-hACE2 was used to generate the transgenic pigs. pSL10 is a modification of pLenti6/V5 D-TOPO (Invitrogen), with the ClaI-KpnI fragment removed and replaced with a cPPT/SV40 immediate early promoter/GFP/OPRE cassette. K18-hACE2 was PCR amplified from pK18-hACE2 (a gift from Paul McCray (Addgene plasmid #149449; https://www.addgene.org/149449/)33). The amplified cassette was subcloned into BamHI and SalI sites in pSL10 to create pSL10-K18-hACE2.\n\nAll cells were maintained in Dulbecco\u2019s modified Eagle\u2019s medium (DMEM; Sigma-Aldrich #D5796) supplemented with 10% fetal bovine serum (FBS) and 100\u2009\u00b5g/ml Penicillin-Streptomycin (Gibco) at 37\u2009\u00b0C in 5% CO2. Newborn Swine Kidney cells (NSK; RRID: CVCL_8378) expressing human ACE2 were generated through transduction with pLenti-CMV-hACE2-hygro.\n\nPrimary cells were generated from ear notches of ~1\u2009cm3 from each transgenic pig. Samples were washed in PBS and sliced into <1\u2009mm2 sized pieces using scalpel blades. The tissue was incubated in digestion medium (DMEM with 20% FBS, 1X antibiotic/antimycotic (Capricorn Scientific), 50\u2009\u00b5g/mL gentamicin (Gibco) and 0.5\u2009g/ml lyophilised collagenase I (Merck Life Sciences)) in a T25 flask for 24\u2009h at 37\u2009\u00b0C with 5% CO2. The sample was mechanically disrupted by pipetting and filtered using a 70\u2009\u00b5m cell strainer to produce a single-cell suspension which was cultured with outgrowth medium (digestion medium minus collagenase) at 37\u2009\u00b0C, 5% CO2 until cells reached confluency.\n\nDetails pertaining to the isolation of SARS-CoV-2 viruses were described previously34. SARS-CoV-2 variants utilised in this study are EDB2 (early isolate, B.1), EDB-\u03b4-1 (Delta, B.1.617.2) and EDB-\u03bf-BA.1\u201310 (Omicron, B.1.1.529). EDB2 was passaged once in NSK cells stably expressing hACE2 (P2)\u00a0prior to the challenge study.\n\nAll lentiviruses were generated according to the standard protocol for 2nd generation lentiviruses. Each lentiviral plasmid (15\u2009\u03bcg) was co-transfected with 12\u2009\u03bcg psPAX2 (Addgene #12260) and 3\u2009\u03bcg pMD2.G (Addgene #12259) packaging plasmids into Lenti-X 293T cells (Takara) at 70% confluency in T75 flasks using lipofectamine 2000 (Invitrogen #11668019). Supernatant was harvested three days post-transfection and passed through a 0.45\u2009\u03bcm filter cartridge, with aliquots frozen at \u201380\u2009\u00b0C. Cells were transduced using one in two dilutions of lentiviral supernatant with fresh media supplemented with 16\u2009\u03bcg/ml DEAE dextran.\n\nTransgenic pigs were generated and genotyped as described previously35. Briefly, zygotes were collected by flushing the oviducts of artificially-inseminated super-ovulated Large-White gilts. 50\u2009pl lentivirus suspension was injected into the perivitelline space, following which 30 zygotes were transferred to the oviduct of each of three identically treated but unmated recipients.\n\nFor infection studies, nine hACE2 transgenic and three wild-type 8-week-old pigs were transferred to CL3 housing. Following a 7-day acclimatisation period, a blood sample was taken by jugular venepuncture and SARS-CoV-2 (2\u2009ml of a 5\u2009\u00d7\u2009105 TCID50/ml suspension; isolate EDB2) was administered intranasally to each pig using a mucosal atomisation device (MAD300). Pigs were monitored twice daily and clinical scores and temperatures recorded. At 2, 4 and 7 DPI, groups of 4 pigs (3 hACE2 transgenic and 1 wild type) were euthanized by overdose of sodium pentobarbital. A blood sample was taken by cardiac puncture and 2 nasal swabs (applied to both nostrils) were taken postmortem. The first swab was tested immediately by SARS-CoV-2 lateral flow test (FlowFlex COVID-19 Rapid Antigen Nasal Lateral Flow Test Kit, LO31-118M5), and the second stored in Trizol (Invitrogen) for later analysis. Further tissues were taken at necropsy and stored immediately in 10% buffered formalin, placed in Trizol, or stored in serum-free DMEM. All infection studies were performed at CL3 (BSL3) containment and staff wore appropriate personal protective equipment including FFP3 powered respirator helmets (Pureflo). All animal studies were performed with local and national ethical approval in accordance with the Animal (Scientific Procedures) Act 1986.\n\nThe clinical assessment scheme used in this study was established prior to beginning animal work and was approved by our institutional and UK governmental regulatory bodies. Briefly, pigs were monitored for at least 15\u2009min twice daily, by animal care technicians with experience of working with pigs at high containment. They scored each animal against several criteria including demeanour (e.g. response to stimulation, response to presence of food, willingness/ability to stand when provoked), appetite (interest/enthusiasm in feeding/drinking), temperature (rectal temperatures measured twice daily), respiration (degree of effort in respiration, presence/absence of nasal discharge), and other respiratory signs (presence and duration and continuity of coughing and/or sneezing). Within each category, each animal was scored 0, 1, 2 or 3 according to the severity of the feature. At each time point the scores in each category were then added together and the cumulative score for each animal at that timepoint recorded. A cumulative score of 1\u20136 was regarded as mild clinical severity, 7\u201310 was regarded as moderate severity and 11\u201315 was considered severe. This scheme readily allowed animal care staff to identify animals that were at risk of approaching or breaching the regulatory severity limits (humane endpoints) of our Home Office project licence and to seek appropriate veterinary advice.\n\nGene expression was quantified by a 2-step method. RNA was extracted using Trizol following the manufacturer\u2019s protocol (Invitrogen) with residual genomic DNA removed by TURBO DNA-free\u2122 Kit (Invitrogen). 1\u20132\u2009\u03bcg RNA was reverse transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). qPCR reactions were performed using TaqMan\u2122 Fast Advanced Master Mix (Applied Biosystems) and analysed with the Rotor-Gene Q real-time cycler (Qiagen). All gene expression values were normalized to the geometric mean of GAPDH, ACTB and RPL4 and are quantified by the 2\u2013\u2206\u2206Ct method unless otherwise specified. Assay ID for TaqMan assay (Applied Biosystems): Hs0108533_m1 (hACE2); Ss03375629_u1 (pGAPDH); Ss03376563_uH (pACTB); Ss03374067_g1 (pRPL4). SARS-CoV-2 N copy number were determined using the SARS-CoV-2 N1+N2 Assay kit (Qiagen, #222015) and standard curve generated from a positive control (Integrated DNA Technologies, #10006625).\n\nGenomic DNA was prepared from ear biopsies of liveborn piglets. Proviral PCRs were performed using the Lenti-X\u2122 Provirus Quantitation Kit (Takara \u2013 Cat# 631239) according to manufacturer\u2019s protocols.\n\nAn estimated percentage of the lung with macroscopically visible pneumonia was recorded for each pig based on a previously described scoring system24. Each lung lobe was assigned a number to reflect the approximate volume or percentage of the entire lung represented by that lobe. Ten possible points each were assigned to the right cranial lobe, right middle lobe, cranial part of the left cranial lobe, and caudal part of the left cranial lobe. The accessory lobe was assigned 5 points. The right and left caudal lobes were each assigned 27.5 points to reach a total of 100 points. The total for all the lobes was an estimate of the percentage of the entire visible pneumonia24.\n\nA defined set of tissue sections was collected from each pig including lungs, trachea sections, turbinates, lymph nodes, tonsil and thymus. Following collection, tissues were immediately immersed into 10% buffered formalin and left overnight before embedding in paraffin blocks. Sections (4\u2009\u03bcm) were cut, dewaxed and stained using hematoxylin and eosin. All tissues were qualitatively scored for presence or absence of pertinent histological features. In the lungs, alveolar injury, suppurative pneumonia, alveolar epithelial cell necrosis, bronchiolar necrosis and bronchiolar inflammation was assessed. Alveolar injury was defined as the presence of intra-alveolar oedema, fibrin and/or hyaline membrane formation as well as organising pneumonia and/or type II pneumocyte hyperplasia. A note was made when pleuritis was present.\n\nImmunohistochemical staining was performed on formalin-fixed, paraffin-embedded tissue sections from lung, trachea, turbinates, thymus and tonsil. Where applicable, antigen retrieval was performed in a pressure cooker in a Histos 5 Microwave. Iba1, CD3 and Pax5 were all retrieved using low pH6 Sodium citrate buffer at 110\u2009\u00b0C for 5\u2009min, 800\u2009watts (12\u2009min overall). hACE2 was retrieved in a high pH9 buffer solution by Vector Laboratories (H-3301-250) for 50\u2009min at 97\u2009\u00b0C full power (51\u2009min overall). SARS-CoV-2 Nucleocapsid protein was retrieved using the high pH buffer solution at 97\u2009\u00b0C for 10\u2009min full power. These slides were then allowed to cool for 10\u2009min in cold running water. A rabbit monoclonal antibody (ab108209; ABCAM; 1\u2009\u03bcg/ml) for the hACE2 and a mouse monoclonal (B46F; Invitrogen; 1\u2009\u03bcg/ml) for SARS-CoV-2 Nucleocapsid protein was used. For immune cell staining, rabbit anti IBa1 (019-19741; Wako; 1 \u03bcg/ml), mouse monoclonal PAX5 (AB_398182; Becton and Dickinson; 5\u2009\u03bcg/ml) and mouse monoclonal CD3 (NCL-L-CD3-565; Novacastra; 0.2\u2009\u03bcg/ml) were used for macrophage, B and T cell staining respectively. Specific Envision secondary antibodies were employed (Envision Rabbit or Envision Mouse) and detection reagents were used for visualisation (Dako Liquid Dab\u2009+\u2009Substrate Chromogen System). hACE2 positive (P46) and negative sections (P35) based on presence or absence of transgene mRNA expression were used to validate the assay. Pig lymph node was used for PAX5 and CD3 staining controls. Pig brain was used as control material for Iba1, and pig kidney/heart was used as initial ACE2 controls. Negative controls included sections whereby the primary antibody was omitted. Positive control sections for detection of SARS-CoV-2 were from P46, based on RT-qPCR data and P38 uninfected control transgenic pig.\n\nQuPath (version 0.5.1) is an open-source software for digital pathology and whole slide image analysis36. The detailed explanation and calculation for the pixelwise H-score is described elsewhere37. Briefly, the pixelwise H-score is analogous to the traditional cell-based H-score but is applied to pixels as opposed to individual cells. Using the built in pixel classifier in QuPath, a single user-defined threshold (1 for the IHC images shown here) was set to detect all haematoxylin positive pixels and three separate thresholds, 0.2, 0.15 and 0.1 were set to detect and classify the DAB positive pixels (hACE2) as high, medium and low, respectively.\n\nThe porcine primary fibroblast cells were seeded 1-day prior to inoculation with different SARS-CoV-2 variants at an MOI of 3. At 48 HPI, cells were fixed by 10% neutral-buffered formalin (NBF) and permeabilized with 0.1% Triton X100. A conjugated anti-SARS-CoV-2 antibody (AB283243, ABCAM) was used at 0.25\u2009\u03bcg/ml for detection of nucleocapsid protein.\n\nhACE2-NSKs were seeded in 96-well flat bottom plates 2\u20133 days in advance to ensure 100% confluency. Each sample was divided into two, which were separately subjected to tenfold dilutions (10\u20131 to 10\u20136) in serum-free DMEM. For each sample and dilution, eight replicate wells were used for infection. Culturing medium was removed by aspiration and 50\u2009\u03bcl of the diluted virus was added into each well, followed by a 2-h incubation period at 37\u2009\u00b0C on a rocker. 100\u2009\u03bcl of fresh complete DMEM was added to each well after virus absorption. Cells were incubated for four days and the plates were fixed in 4% paraformaldehyde in PBS for 20\u2009min before being removed from containment facilities for visual inspection of virus-induced cytopathic effect under an inverted microscope. Virus titres were then calculated using the Spearman\u2013K\u00e4rber method.\n\nAll sera were subjected to detection of antibodies against SARS-CoV-2 using ID Screen\u00ae SARS-CoV-2 Double Antigen Multi-species (Innovative Diagnostics) according to the manufacturer\u2019s protocol. The test detects antibodies to the nucleocapsid protein of SARS-CoV-2 for multiple species (i.e. minks, ferrets, cats, dogs, cattle, sheep, goats, horses and all other receptive species) with a specificity range of 97.8% to 100% as reported by the manufacturer. The assay was validated when the optical density of positive control (ODPC) was \u2265\u00a00.35 and at least three times higher than the negative control (ODNC). The optical density of each sample (ODN) was used to calculate the S/P ratio value (expressed as %) where S/P\u2009=\u2009100*(ODN\u2013ODNC)/(ODPC\u2013ODNC). Samples were considered positive if the S/P ratio was greater than 60%, doubtful when ranged between 50 and 60%, and negative when lower than 50%. Optical density (OD) was measured at 450\u2009nm using the Cytation 3 microplate reader (BioTek).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Dorward, D. A. et al. Tissue-specific immunopathology in fatal COVID-19. Am. J. Respir. Crit. Care Med. 203, 192\u2013201 (2021).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nTian, Y. et al. 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This study was funded by UKRI Biotechnology and Biological Sciences (BB/V018922/1), and institutional grants (BBS/E/RL/230002A) as well as a Wellcome ISSF3 award (IS3-R2.26 19/20). The Moredun\u00a0Research Institute High Containment animal facilities are supported in part by Underpinning National Capacity funding from the Scottish Government Rural and Environment Science and Analytical Services\u00a0Division (RESAS). We thank Mark Stevens for critical review of the manuscript.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Long Fung Chau, Simon Lillico.\n\nRoslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK\n\nLong Fung Chau,\u00a0Simon Lillico,\u00a0Rosemary Blake,\u00a0Luc Tardy,\u00a0Chen-Hsuin Lee,\u00a0Scott Maxwell,\u00a0Claire Warren,\u00a0Elizabeth Thornton,\u00a0Catherine L. Mclaughlin,\u00a0Gerry McLachlan,\u00a0Christine Tait-Burkard,\u00a0Sarah Fletcher,\u00a0J. Kenneth Baillie\u00a0&\u00a0Finn Grey\n\nMoredun Research Institute, Edinburgh, UK\n\nTanja Opriessnig,\u00a0Stephen Anderson,\u00a0Sharon Brown,\u00a0Louise Gibbard,\u00a0Thomas Tzelos,\u00a0Dawn MacMillan-Christensen\u00a0&\u00a0David J. Griffiths\n\nBaillie Gifford Pandemic Science Hub, University of Edinburgh, Edinburgh, UK\n\nJ. Kenneth Baillie\n\nCentre for Inflammation Research, Queen\u2019s Medical Research Institute, Edinburgh, UK\n\nDavid A. Dorward\n\nDepartment of Pathology, Royal Infirmary, Edinburgh, UK\n\nDavid A. Dorward\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization; F.G., D.J.G., S.L. Methodology; S.L., T.O., S.M., C.W., E.T., C.L.M., G.M.L., S.A., S.B., L.G., T.T., D.M.C. Investigation; F.G., L.F.C., R.B., L.T., C.H.L., T.O. Formal analysis; L.F.C., T.O., J.K.B., D.D., D.J.G., F.G. Resources; C.T.B., S.F. Writing - Original Draft; L.F.C., S.L., T.O., D.D., J.K.B., D.J.G., F.G. Writing - Review & Editing; L.F.C., S.L., T.O., S.M., D.D., J.K.B., D.J.G., F.G. Supervision; S.L., F.G., T.O., G.M.L., D.J.G., F.G. Funding acquisition; T.O., J.K.B., D.J.G., F.G.\n\nCorrespondence to\n David J. Griffiths or Finn Grey.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.\n\nGeneration and maintenance of transgenic pigs was approved by the Animal Welfare Ethical Review Body of the Roslin Institute, and the infection study was approved by the Animal Welfare Ethical Review Body of the Moredun Research Institute. All experiments involving animals were authorized by the UK Home Office and were performed in accordance with the Animal (Scientific Procedures) Act 1986.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Tanya LeRoith and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Human ACE2 transgenic pigs are susceptible to SARS-CoV-2 and develop COVID-19-like disease.\n Nat Commun 16, 766 (2025). https://doi.org/10.1038/s41467-024-54615-1\n\nDownload citation\n\nReceived: 03 April 2024\n\nAccepted: 18 November 2024\n\nPublished: 17 January 2025\n\nVersion of record: 17 January 2025\n\nDOI: https://doi.org/10.1038/s41467-024-54615-1\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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networks", + "journal": "Nature Communications", + "published": "01 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61338-4/MediaObjects/41467_2025_61338_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61338-4/MediaObjects/41467_2025_61338_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61338-4/MediaObjects/41467_2025_61338_MOESM3_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-61338-4#MOESM1" + ], + "code": [ + "https://doi.org/10.5281/zenodo.15741412" + ], + "subject": [ + "Nanophotonics and plasmonics", + "Optoelectronic devices and components" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5367125/v1.pdf?c=1751455145000", + "research_square_link": "https://www.researchsquare.com//article/rs-5367125/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61338-4.pdf", + "preprint_posted": "05 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Artificial neural networks (ANNs) have fundamentally transformed the field of computer vision, providing unprecedented performance. However, these ANNs for image processing demand substantial computational resources, often hindering real-time operation. In this paper, we demonstrate an optical encoder that can perform convolution simultaneously in three color channels during the image capture, effectively implementing several initial convolutional layers of a ANN. Such an optical encoding results in ~24,000 times reduction in computational operations, with a state-of-the art classification accuracy (~ 73.2%) in free-space optical system. In addition, our analog optical encoder, trained for CIFAR-10 data, can be transferred to the ImageNet subset, High-10, without any modifications, and still exhibits moderate accuracy. Our results evidence the potential of hybrid optical/digital computer vision system in which the optical frontend can pre-process an ambient scene to reduce the energy and latency of the whole computer vision system.Physical sciences/Optics and photonics/Applied optics/Optoelectronic devices and componentsPhysical sciences/Physics/Optical physics/Sub-wavelength opticsNeural networksMeta-opticsObject detectionImage classification", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nA.M. is a co-founder of Tunoptix, which aims to commercialize meta-optics technology.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryMaterials.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Artificial neural networks have fundamentally transformed the field of computer vision, providing unprecedented performance. However, these neural networks for image processing demand substantial computational resources, often hindering real-time operation. In this work, we demonstrate an optical encoder that can perform convolution simultaneously in three color channels during the image capture, effectively implementing several initial convolutional layers of the network. Such an optical encoding results in \u00a0~\u00a024,\u00a0000\u00a0\u00d7 reduction in computational operations, with a state-of-the-art classification accuracy (~73.2%) in free-space optical system. In addition, our analog optical encoder, trained for CIFAR-10 data, can be transferred to the ImageNet subset, High-10, without any modifications, and still exhibits moderate accuracy. The proposed method can decrease total system-level energy more than two orders of magnitude per a single object classification. Our results evidence the potential of hybrid optical/digital computer vision system in which the optical frontend can pre-process an ambient scene to reduce the energy and latency of the whole computer vision system.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Visual information plays a crucial role in human response, particularly in situations where reaction time is limited to a few tens to hundreds of milliseconds1,2. Though the human brain has efficiency far exceeding that of any other human-made computing systems, it still cannot process the entire collected visual data due to its massive amount of information. Most likely, our brain performs early visual processing to extract essential features for efficient and rapid interpretation without handling the entire visual data3,4,5.\n\nWith the dramatic development of artificial intelligence (AI), computers can process the visual information like human brain, thanks to artificial neural networks (ANNs), enabling computer/machine vision6,7,8,9,10. Despite impressive progress, real-time inference with limited computational resources remains very challenging even with more efficient algorithms. For example, in a flying object (i.e., habitat drones11) on-site data processing is plagued by severe heating, battery capacity and weight handling challenges. Utilizing cloud based systems poses challenges associated with data security and additional data transfer latency12,13.\n\nOptical neural networks have emerged as a potential platform to circumvent these trade-offs, since an optical system can process multidimensional information with large spatio-temporal bandwidth14. Recently, integrated photonics and free-space or fiber optics have been employed to implement some parts of an ANN for image compression/encryption15,16 and classification17,18,19,20,21,22,23,24,25. However, most of them are highly restricted on solving a relatively simple gray-scale datasets (i.e., MNIST and fashion-MNIST) and only a couple of systems have shown their implementation for more complicated multichannel datasets (i.e., CIFAR-10 and ImageNet)17,23. For these complex datasets, the optical systems often become extremely large (with multiple stacks of the photonic circuits)23, otherwise the classification accuracy remains low (~60% accuracy for CIFAR-10 classification tasks)17,26,27. In addition, the most successful ANN architectures utilize nonlinear activation functions that are challenging to implement optically. Proposed solutions, including atomic vapor cells28,29 and image intensifiers30, introduce significant experimental complexity, and additional power consumption.\n\nTo leverage the strengths of both optical and digital computing systems, an encoder-decoder inspired hybrid optical/digital architecture is a promising approach8,10,31,32. Specifically, an analog linear optical frontend (denoted as the optical encoder) performs bulk of linear computational tasks, while the digital backend implements the nonlinear operations. One intriguing possibility is to employ a static optical frontend, which is data agnostic, whereas the backend is trained and reconfigured. This resolves usual issues of modulation speed, errors, and system size in all-optical systems. An optical encoder is particularly suitable for convolutional neural network (CNN) architectures, where convolutional layers act as feature extractors, encoding high-dimensional images into low-dimensional features33. In fact, every free-space optic inherently performs a two-dimensional convolution operation during the imaging under incoherent light. The captured image is a convolution of the scene and the optic\u2019s incoherent point-spread-function (PSF)34. Thus, by engineering the PSF, an optical encoder can perform the desired convolution and replace the initial layers of a CNN.\n\nRecently, a PSF-engineered optical encoder has been employed to classify the MNIST hand-written dataset and a reasonable classification accuracy with much less computational costs compared to the AlexNet is demonstrated35. We note that, however, MNIST images are monochrome, and are almost linearly separable (0.84% loss without any nonlinearity36). The monochrome nature of the images makes the PSF-engineering approach wavelength agnostic. On the other hand, datasets such as CIFAR-1037 or ImageNet subset (High-10)33,38 are not separable by linear layers. Moreover, they consist of colored images, where the actual color information is exploited in classification.\n\nHere, we demonstrate a polychromatic optical encoder with PSF-engineered meta-optics to classify the CIFAR-10 dataset. We first compressed the architecture into a single convolutional layer and two fully-connected layers using knowledge distillation. Then, we physically realized the convolution layer using an array of metasurfaces, where each metasurface, thanks to the inherent chromaticity, performs a separate convolution for each color channel. As a result, the hybrid CNN with an optical encoder reduces the total number of multiply-accumulate (MAC) operations at the digital backend by a factor of \u00a0~24,000. The reduction of the number of MAC operations directly corresponds to the computational costs, i.e., power and latency39. It is worth noting that we always require an imaging system (i.e., lens and camera) to capture the image under ambient illumination, before we deliver the image data to the computational backend. Hence, with a single meta-optical encoder, we are not adding any additional optics, but simply replacing a conventional lens with PSF-engineered meta-optics. This makes our optical system compact and fully compatible with conventional optical imaging systems, while the other systems such as, integrated-photonic systems require pre-processing of the data23 and in-sensor computing needs a customized sensor design40.\n\nFurthermore, we adopt the same meta-optics (optical convolutional layer) that is optimized for CIFAR-10 dataset to High-10 dataset to explore the generality of optical encoders. In practice, a static optical encoder should be applicable for any scene. While one approach is to employ reconfigurable frontend, e.g., based on non-volatile phase change materials41 or liquid crystals42, the performance of these reconfigurable front end in terms of individual pixel control, power consumption, and operating speed is still inferior for practical deployment. With the same passive optical encoder (optimized for CIFAR-10 dataset), we achieved a high classification accuracy (for High-10 dataset) by fine-tuning the digital backend with additional fully-connected layer (via transfer learning approach). This ability to generalize the frontend is crucial for any ANNs as it enhances their versatility, efficiency, and robustness. A network that generalizes well can be applied to different tasks without extensive re-training, saving time, reducing costs for meta-surface fabrications, and conserving computational resources for real-world applications.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Our optical encoder concept is described in Fig.\u00a01. The original CNN, i.e., AlexNet, has five convolutional layers and three max pooling layers at the front, followed by three fully-connected layers at the end, while nonlinear activation functions \u201cReLU\" are placed in each layer. Replacing all individual five convolutional layers with five sequential optics is extremely difficult because of misalignment, large system size, lack of nonlinearity, and low signal-to-noise ratio, issues that compound with increasing number of optical elements. Therefore, we compressed AlexNet to a single convolutional layer and two fully-connected layers using knowledge distillation method43, which reduces the complexity of the architecture with a minimal compromise in accuracy.\n\nThe original CNN, AlexNet, is initially compressed using knowledge distillation, consolidating all convolutional layers into a single layer. This compressed convolutional layer is then replaced with a metasurface.\n\nWhile the compression of an original CNN is essential for realizing the optical encoder scheme, there are practical trade-offs to consider. On one hand, the physical size of the sensor and meta-optics limit the number of kernels and kernel size. On the other hand, small kernel size or number of kernels fail to classify the data effectively. We empirically searched for the optimal number and size of the kernels while compressing the original CNN. For the CIFAR-10 classification task, we design 16 kernels of 7\u2009\u00d7\u20097 size (see details in\u00a0Supplementary Information). The training and testing accuracy from the compressed all-digital CNN are 76.24\u2009\u00b1\u20090.31% and 75.90\u2009\u00b1\u20090.30%, respectively.\n\nSince the CIFAR-10 images have three channel information\u2014corresponding to red (R), green (G), and blue (B)\u2014we have 16 individual 7\u2009\u00d7\u20097 kernels for each color, a total of 48 kernels. In meta-optics, it is possible to design a single meta-optics to produce three different PSFs (i.e., convolutional kernels) for RGB wavelengths (more details in Methods). It is difficult to create positive and negative weights on the camera in terms of light intensity at the same time, so we separate each kernel into its positive and negative parts and design an optic for each44, for a total of 32 meta-optics in total corresponding to 16 positive and 16\u00a0negative polychromatic kernels.\n\nAnother important design parameter is to determine how many pixels on the camera represent one pixel of the PSF. We term this as the enlargement factor. For example, when the enlargement factor is 2, the ground-truth PSF which is a 7\u2009\u00d7\u20097 matrix will correspond to 14\u2009\u00d7\u200914 pixels on the camera. While a large enlargement factor ensures less alignment error, the signal intensity on each camera pixel will be lower, resulting in low signal-to-noise ratio. To determine the optimal enlargement factor, we experimentally tested several meta-optics with different enlargement factors for a particular kernel (see details in\u00a0Supplementary Information), and obtained an optimal enlargement factor of 2 for a meta-optic made of 3200\u2009\u00d7\u20093200 scatterers.\n\nThe metasurfaces were made of silicon nitride on a quartz substrate to ensure high transparency in the visible wavelength (Fig.\u00a02a). Figure\u00a02b shows the transmission coefficients and phase shifts from silicon nitride pillars at RGB wavelengths as a function of the pillar width, w, at the fixed height of 800 nm, obtained by rigorous coupled-wave analysis (RCWA). We target wavelengths (\u03bb) of 450, 532, and 635 nm for RGB colors based on the availability of the laser diodes.\n\na Schematics of the meta-optic scatterer, silicon nitride pillar on the quartz substrate. b Relative phase shift (dotted line) and transmission (solid line) of the uniform array of pillars with respect to the pillar width, w, for RGB different wavelengths when pillar height is fixed as 800 nm. Shaded line is the fitted proxy function of the phase shift with respect to the w. c Design flow of the polychromatic meta-optics optimization.\n\nIn order to effectively model the wavelength-dependent effects of the meta-optics in a gradient descent-based optimization method, a fast and differentiable function is required to map between the pillar width and imparted phase. We define a proxy function inspired by the approximate phase shift of a dielectric waveguide with corrective factors that are fit to the RCWA simulation results. To calculate the phase shift (fR, fG, and fB) for RGB wavelengths with respect to the pillar width, w, we define the proxy function as\n\nThe first term corresponds to a general phase shift from a dielectric waveguide, where neff and L are the effective refractive index and height of the silicon nitride pillars. The second term, only has the w variance, corresponds to a correction term in the Gaussian shape with A, B, and C as fitting parameters. Lastly, f0 corresponds to a phase shift offset, making the f\u03bb(0)\u00a0=\u00a00. This proxy function does not model the resonance-induced phase variations; However, we do not want to use those phase variations due to reduced amplitude and these resonances are expected to be less prominent in the fabricated devices due to the sidewall roughness.\n\nFigure\u00a02c shows the design flow for the polychromatic RGB meta-optics that have optimized PSFs for individual RGB colors. For a meta-optic parameterized by an arbitrary two-dimensional pillar width map, w(x,\u00a0y), we extract three separate phase maps using the proxy functions f\u03bb. We then propagate the electromagnetic field using angular spectrum method45 to simulate the PSFs at the focal plane, 2.4 mm away from the meta-optics. At the focal plane, we compare the computational ground-truth PSFs defined by the convolutional kernels obtained using knowledge distillation (PSFGT,\u03bb) and optically-simulated PSFs (PSFsim,\u03bb) at each RGB channels, where the channel-dependent losses are defined by the sum of squares of differences in each pixels:\n\nWe optimize the map of two-dimensional pillar width, i.e., meta-optics, for minimizing the net loss which defined as a root mean square of the losses at three different colors using the Adam optimizer in TensorFlow46:\n\nCalculated losses for all the kernels at three different colors are shown in the\u00a0Supplementary Information.\n\nAn optical image of the fabricated chip is shown in Fig.\u00a03a. A single chip contains a total of 32 convolutional meta-optics (corresponding to 16 positive and 16 negative convolutional kernels) and an additional 5 metalenses which are focusing light at the focal plane, to aid in the alignment (e.g., tilt, rotation, and distance) between the meta-optics and the camera. Each convolutional meta-optic has a size of \u00a0~\u00a0940\u2009\u00d7\u2009940\u2009\u03bcm2. Figure\u00a03b shows the schematic of the PSF measurement setup. By changing the laser diodes, we illuminate individual RGB coherent light onto the camera through the meta-optics and experimentally characterize the polychromatic PSFs. A pinhole of 25\u2009\u03bcm diameter creates an approximate point source and the positions of the optics, i.e., pinhole, meta-optics, and camera, remain the same while changing the laser diodes.\n\na Photograph of the fabricated optical encoder, consisting of 16 positive and 16 negative convolutional kernels and 5 alignment metalenses. b Schematics of the polychromatic PSFs measurement setup. c Ground-truth digital and optically measured PSFs for a particular polychromatic kernel (positive kernel number 7). d Schematics of the meta-optical convolved image measurement setup with a micro-display. A color camera captures convolved convolved images with a single shot. e Digital (above) and optical (below) convolution result of a particular CIFAR-10 image in individual RGB colors. f Confusion matrices of CIFAR-10 dataset classification tasks with different network architectures.\n\nFigure\u00a03c shows both the ground-truth PSFs and measured PSFs for a particular kernel for individual RGB wavelengths. To quantitatively analyze the difference between the ground-truth and experimentally measured PSFs, we define a cosine similarity (\u03b7) as:\n\nwhere Ai and Bi are the ground-truth and measured intensity profiles of the PSF for RGB wavelengths, respectively. The calculated \u03b7 for RGB wavelengths are about 0.88, 0.56, and 0.81, respectively. The quantitative discrepancy can be partially attributed to the fabrication and measurement imperfections. Additionally, not all the polychromatic PSFs are physically realizable as the phases at different wavelengths are not completely independent. Creating more physically-realizable PSFs via co-designing the optical frontend and computational backend, also termed as end-to-end design47,48, instead of replacing the convolutional layer with optics, may increase \u03b7. However, including the meta\u2013optical simulation in the end-to-end design may result in local optimum, and the fabrication/measurement imperfections will still be present. As we will show later in the figure, the computational backend is robust against such discrepancy in the PSFs, and we can easily correct for these errors by introducing an additional fully-connected calibration layer in the digital backend. More detailed discussion on the imperfect optical implementations is described in Discussion.\n\nThen, we test the polychromatic optical encoder for the CIFAR-10 dataset. By replacing the pinhole with an organic light-emitting diode (OLED) display, optical convolutional operations between the input image and the PSFs occur with spatially separated meta-optics on a single chip, then captured on a color camera (Fig.\u00a03d). On the color camera, 32 different convolved images are captured and then subjected to digital backend of calibration and fully-connected layers. The displayed image size is carefully adjusted according to the convolutional kernel size and the enlargement factor on the camera (more details in\u00a0Supplementary Information). Figure\u00a03e shows the computationally and meta-optically convolved RGB images of one of the CIFAR-10 dataset. The meta-optically convolved image loses some of the high resolution components, likely due to the imperfect fabrication and alignment errors which are already recognizable from the PSF measurements as well as the spectral overlap between RGB color pixels of the camera. However, as we will show later in the figure, the computational backend is robust against such discrepancy as we do average pooling the convolved image into 6\u00a0\u00d7\u00a06 size. We added an additional fully-connected layer, called calibration layer, to address the weights of each kernels and colors, dealing with the discrepancy between optical/digital systems (e.g., normalization, scaling, translation, rotation, tilt, noise). This calibration layer allows us to use the pre-trained digital backend and incur minimal computational cost. Detailed explanations on the calibration layer are in the Methods and Supplementary Information.\n\nFigure\u00a03f shows the confusion matrices of the classification accuracy of the CIFAR-10 data with an original CNN (AlexNet), a compressed CNN using knowledge distillation, and a hybrid optical/digital CNN using convolutional meta-optics after the compression. Even though there are slight differences between the optical and digital convolution results (Fig.\u00a03e), after introducing the calibration layer, we can achieve similar accuracy (less than 5% loss) for both training and testing dataset (Table\u00a01). It is possible to improve the accuracy if we retrain the backend and the calibration layer; However, retraining the backend has no practical usages. Additionally, this hybrid approach significantly reduces computational costs which can be represented by the number of multiply-accumulate (MAC) operations. From the original CNN to compressed CNN, we can reduce the computational load, which is represented by a number of MAC operations, by a factor of \u00a0~\u00a01,\u00a0400, while we can reduce further by a factor of \u00a0~\u00a017 after replacing a convolutional layer with meta-optics. Detailed calculation about the number of MAC operations for each CNN architecture are described in Table\u00a02. The detailed information for network design and dimension selection is available in the\u00a0Supplementary Information.\n\nTo analyze the effectiveness of the meta-optical convolutional layer, we utilize principal component analysis (Fig.\u00a04). For the original CNN and compressed all-digital CNN, each class is well-separated (Fig.\u00a04a, b), implying that we can extract out the key features of the CIFAR-10 image dataset after convolution. On the other hand, after the optical convolution using meta-optics, without the\u00a0calibration\u00a0layer, different classes of the image were very difficult to distinguish (Fig.\u00a04c). Additionally, some clusters exhibit larger sizes and overlapping regions than Fig.\u00a04a, b, e.g., the navy blue, brown, and red clusters (confidence ellipses). However, after introducing the calibration layer, the clustering regions become smaller, and the separations between classes increases. As shown in Fig.\u00a04d, each class becomes well-separated and distinguishable, similar to the compressed CNN. This critical role of the calibration layer is consistent with the classification accuracy without and with the calibration layer (Table\u00a01). We note that the calibration layer can potentially be compressed into the pretrained digital backend via additional training and will not affect the number of MAC operations for inference (Table\u00a02). Additionally, compared to the backend-only results (Table\u00a01), our hybrid optical/digital architecture achieves significantly higher classification accuracy (over 20%), highlighting the crucial role of the optical convolutional encoder.\n\na Original CNN, AlexNet. b Compressed all-digital CNN. c Hybrid optical/digital CNN without calibration layer. d Hybrid optical/digital CNN with calibration layer.\n\nOur convolutional meta-optics implement convolutional kernels which came from compressed CNN for CIFAR-10 data. Unlike computational neural networks, optical implementations are extremely difficult to modify once they are fabricated. This necessitates different convolutional meta-optics for different datasets. However, we found that the convolutional layer that we optimized for CIFAR-10 can be readily adapted to classify another dataset High-10, with a transfer learning process. We added an additional fully-connected layer, which we call a \u201ctransfer learning layer\", that is located in between the former fully-connected layers and convolutional layer. By training the transfer learning layer, we can fit the other dataset, i.e., High-10, to the CNN which is pre-optimized for a particular dataset, i.e., CIFAR-10, with only fine-tuning a small part of the original network without changing the former network structure (see details in Methods). The High-10 image dataset is polychromatic (RGB) and has a size of 224\u00a0\u00d7\u00a0224 size. To use the former CNN optimized for CIFAR-10 data for High-10 data, we resize the High-10 images to 32\u2009\u00d7\u200932 size, the same as the CIFAR-10 data.\n\nWithout applying a transfer learning method, the training and testing accuracy is rather low around 40%. However, after transfer learning, we achieve much higher training and testing accuracy (~67.43% and \u00a0~66.01%, respectively) on the High-10 data with the convolutional layer and two fully-connected layers (Table\u00a03). We further experimentally verified this approach works in our hybrid optical/digital CNN using the same convolutional meta-optics that we used for the CIFAR-10 data digital backend structure with the transfer learning layer. The average training and testing experiment accuracy of the High-10 data are similar (less than 5% loss) to the compressed all-digital CNN, which is about the same of the CIFAR-10 case. The principal component analysis results of both compressed CNN and hybrid CNN for the High-10 data is shown in\u00a0Supplementary Information, where we can see how different classes are separable in their feature map. The detailed information for calibration layer design, number of calibration selection and principal component analysis visualization is available in the\u00a0Supplementary Information.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61338-4/MediaObjects/41467_2025_61338_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61338-4/MediaObjects/41467_2025_61338_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61338-4/MediaObjects/41467_2025_61338_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61338-4/MediaObjects/41467_2025_61338_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The advantages of the knowledge distillation and meta-optical encoder are a dramatic reduction of computational complexity, which is represented by the number of\u00a0MAC operations. For the CIFAR-10 dataset, our hybrid optical/digital CNN reduced the number of MAC operations by a factor of \u00a0~\u00a024,\u00a0000 (Table\u00a02). This reduction is more than two orders of magnitude higher than that of the MNIST hand-written dataset, where the meta-optical encoder reduced the number of MAC operations only by a factor of \u00a0~\u00a0200 (Table\u00a0S2 in\u00a0Supplementary Information of35). This large reduction of digital operations for CIFAR-10 dataset compared to MNIST dataset signifies an important benefit in both energy consumption and latency. This is mainly because of the complexity of the dataset (including the polychromatic nature of the dataset), where it requires a much larger number of operations for convolutional layers.\n\nOn the other hand, the classification accuracy drops are more significant for the CIFAR-10 dataset compared to the MNIST dataset. The train (test) accuracy for CIFAR-10 dataset of our hybrid CNN drops by \u00a0~\u00a09.86% (\u00a0~\u00a08.97%) from the original CNN. For the MNIST dataset, the train (test) accuracy of our hybrid CNN drops by \u00a0~\u00a05.0% (\u00a0~\u00a05.0%) from the original CNN35. While this classification accuracy drop in CIFAR-10 dataset is not negligible, our PSF-engineered optical encoder has significantly large classification accuracy compared to the other free-space optical neural network architectures (which are compatible with conventional camera systems). Our encoder has a classification test accuracy of ~\u00a072.1% for CIFAR-10 dataset without retraining the backend and only projecting by a calibration layer. These test accuracy can be improved further up to ~\u00a073.2% if we retrain the backend, which is better than the previous state-of-the-art result (\u00a0~\u00a072.8%) which used a complex end-to-end optimization as well as the backend retraining with 50 number of kernels48. Our hybrid optical/digital CNN can be further improved by using complex meta-atoms to reproduce better PSFs optically49 and using advanced compression methods to reduce the loss during the knowledge distillation50. The other reports have much less accuracy \u00a0~\u00a063%17,26,27 compared to ours.\n\nThe scalability of the hybrid approach to more complex, real-world datasets, such as ImageNet, remains a challenge due to large reduction in accuracy. The primary cause of reduced accuracy is network simplification, such as reducing the number of layers. For example, AlexNet has 256 kernels in its final convolutional layer, while we only employ 16 kernels.\n\nHere, we transfer from CIFAR-10 to the ImageNet subset (High-10). High-10 shares the same number of classes as CIFAR-10 but contains fewer samples. Training High-10 from scratch (with a simplified network) is already very challenging. The ablation study in Table\u00a02 illustrates that this transfer learning approach improves performance from 40% to 66% compared to end-to-end training, reaffirming the efficacy of transfer learning.\n\nMAC operations for the High-10 dataset are the same as that of the CIFAR-10 dataset, as the same CNN architecture is utilized. The train (test) accuracy of our hybrid CNN drops significantly by \u00a0~\u00a021.85% (\u00a0~\u00a025.22%) compared to the original CNN. Most of these losses occur during network compression, as the convolutional and fully-connected layers are optimized for the CIFAR-10 dataset. Nonetheless, our transfer learning and the optical encoder achieve a classification accuracy of \u00a0~\u00a060%, outperforming other free-space optical neural networks17. We emphasize that the optical frontend remains unchanged, and only the digital backend\u2014comprising two fully-connected layers and an additional transfer learning layer\u2014is fine-tuned, showcasing the versatility of our hybrid CNN system.\n\nOur current approach still lags behind AlexNet with transfer learning. To further enhance performance, we suggest potential design modifications and training strategies. From a design perspective, implementing additional optical kernels could be a solution. To achieve this within physical constraints, we could employ multiple cameras for different kernels or modify the metasurfaces (e.g., by rotating them) while using a single camera. From a training perspective, adopting advanced knowledge distillation methods could better represent AlexNet with greater accuracy. Certain kernels could be aligned with the shallow-layer features of the electronic CNN, while others could focus on capturing deeper features.\n\nIn practice, we implement our hybrid optical/digital CNN by simply replacing a lens with meta-optics during imaging. This approach makes energy consumption depend solely on the number of MAC operations. However, we must also consider the sensor\u2019s power consumption. Since we rely on ambient light\u2014similar to real-world computer vision tasks\u2014we do not require additional energy for the input light source. The sensor\u2019s power usage depends on the number of pixels it passes to the digital backend. For an original CNN, we only need 32\u00a0\u00d7\u00a032 pixels to capture the image. On the other hand, hybrid CNN needs 6\u00a0\u00d7\u00a06 pixels for imaging one convolved image, considering the average pooling (details in the\u00a0Supplementary Information), which ends up with 32\u2009\u00d7\u20096\u2009\u00d7\u20096 pixels for all positive and negative kernels. Hence, our hybrid CNN requires a bit larger number of pixels for imaging compared to the original CNN.\n\nThe color camera we used (Allied Vision Prosilica; GT 1930C) has a total power consumption of 3.4\u2009W with 50.70 frames per second and 1936\u2009\u00d7\u20091216 color pixels, which ends up with 28\u2009nJ per frame and pixel. Since we captured all \u00a0~\u00a02 million pixels at the same time and cropped the region of interest, the energy consumption for one input image does not differ for the original CNN and hybrid CNN. However, if we can optimize the sensor configuration and number of pixels, we can calculate the minimum required number of pixels for both the original and hybrid CNN, and estimate the energy consumption for those. For the original CNN, we need 32\u2009\u00d7\u200932 color pixels on camera. And for the hybrid CNN, we need 32\u2009\u00d7\u20096\u2009\u00d7\u20096 color pixels on the camera, where the 32 corresponds to the number of multiplexed meta-optics and the 6\u2009\u00d7\u20096 corresponds to the number of pixels after the average pooling. We only need 6\u2009\u00d7\u20096 pixels, not 32\u2009\u00d7\u200932 pixels when the convolution is performed optically. Thus we estimate that the original CNN and hybrid CNN requires an energy of about 29.1 and 32.8\u2009\u03bcJ, respectively, for the image capturing process per a single image. However, the energy consumption for the computational backend is much larger for the original CNN compared to the hybrid CNN. For state-of-the-art computational systems, an energy consumption per a single MAC operation can potentially be as low as \u00a0~\u00a01\u2009pJ30,47. Thus the energy consumption for a single object classification task for the hybrid CNN is about 150\u2009nJ, which is more than four orders of magnitude smaller than that of the original CNN, 3.65\u2009mJ. However, the GPU we used (GeForce RTX 2080 Ti) has much larger energy consumption per a single MAC operation (~30\u2009pJ), making the energy consumption for digital computation per a single object classification tasks for the hybrid CNN and original CNN 4.62\u2009\u03bcJ and 112\u2009mJ. Considering the sensor power, the total system level energy consumption for a single object classification task dropped from 3.68\u2009mJ to 0.03\u2009mJ, while 112\u2009mJ to 37.4\u2009\u03bcJ for our GPU (Table\u00a04). Comparison of the system level energy consumption after the network compression (by knowledge distillation) is also described in Table\u00a04, where we still have advantages with the GPU we used. We note that, we can trade-off the sensor power (by reducing the number of kernels) with computational backend power (by increasing MAC operations). However, having more operation in the optical encoder with a simple computational backend will always be preferred to reduce the latency. We emphasize that more than two orders of magnitude reduction in the system level computer vision tasks provide strong benefits in practice even with a partial accuracy drop for some application fields.\n\nWhile the current hybrid CNN achieves competitive performance, a noticeable gap (\u00a0~\u00a04.4%) remains compared to the compressed CNN. This discrepancy can be mitigated by employing a more sophisticated PSF design, which enhances optical processing capabilities and reduces information loss. By leveraging complex meta-atoms with improved phase and amplitude control, the optical system can more accurately approximate ideal convolutional operations, thereby closing the performance gap. Additionally, performance discrepancy exists between the original CNN and its compressed counterpart. To address this, we could leverage advanced knowledge distillation techniques to enhance the compressed model\u2019s learning efficiency51,52,53. By integrating both improved PSF design and advanced knowledge distillation methods, our approach can effectively bridge these gaps. We can also increase the number of kernels; however, this will increase the number of pixels required for image capture.\n\nFigure\u00a03 c shows a clear discrepancy between the ground-truth convolutional kernels and the experimentally measured PSFs. Other than fabrication imperfections and optical misalignment, we identify three other reasons for the discrepancy.\n\nImperfect fitting function for the phase over scatterer: We assumed the scatterers have constant transmission and do not have any resonant features in their relative phases, which is not\u00a0entirely accurate (Fig.\u00a02b).\n\nLimited degree of freedom of the metasurface for multicolor PSFs: We optimized each of the metasurfaces targeting three different PSFs in red, green, and blue colors. The phase profiles of one rectangular scatterer for three different colors are not independent of each other. One solution is to use complex-shaped scatterers or super cells to have more degrees of freedom.\n\nBroadband light sources: We simulated the polychromatic metasurfaces at three discrete wavelengths (450, 532, and 635 nm). However, the OLED pixels have much broader wavelengths. We can reduce this discrepancy if we optimize the metasurface for more representative wavelengths.\n\nHowever, the classification accuracy reported here for the CIFAR-10 dataset is considerable compared to the other reported results in optical neural networks. There will be inevitable imperfection in optical implementation as the spectral information of the scene is always changing depending on the daylight, cloud, aerial, and many other conditions. Our results show that the digital backend can compensate for the non-ideal optical implementation and achieve a relatively high classification accuracy54.\n\nThe hybrid CNN has strong advantages in terms of latency and energy consumption compared to the original CNN. Additionally, it can be adequately integrated on the commercial imaging system (e.g., camera) without modifying the physical architecture other than the lens with PSF-engineered meta-optics. Moreover, the ability to encode a colorful image brings up a potential to utilize the encoder for real-world scenes. However, sacrifice of accuracy is extremely crucial and not negotiable for cases where the safety matters (e.g., autonomous driving vehicles). In other words, if the object classification is applied for statistical analysis (where the ensemble average can minimize the individual inaccuracy), we can endure the loss of classification accuracy. Habitat monitoring drones can be an example55,56. Especially, in case of drones, restrictions to minimize their weight is crucial, which force it to store only essential features. Then on-site data processing can be beneficial. As our optical encoder can minimize the latency and energy consumption for the on-site data processing, the habitat drones can investigate much larger areas with a single flight.\n\nOn the other hand, real-time operation and latency remain among the most significant challenges in AI. Our hybrid optical/digital architecture reduced more than four orders of magnitude of digital operations, thereby substantially decreasing both power and latency. Consequently, our approach has the potential to create a significant impact on video conferencing which align well with non-safety-critical scenarios.\n\nThe results in this work are strong evidence that optical frontend can significantly reduce the power consumption and latency of ANNs for computer vision tasks. Despite realistic fabrication and measurement errors arising from optical implementation, the approach achieves the state-of-the-art classification accuracy in multichannel CIFAR-10 data with the addition of a calibration layer and trainable fully-connected layers. The use of a single meta-optical layer to perform complex, multi-channel convolutions highlights a unique applicability of meta-optics that cannot be accomplished using traditional optics. In addition, we address the lack of reconfigurability in existing optical implementations using transfer learning approach, and reconcile the optical frontend optimized for CIFAR-10 to the High-10 dataset. In this regard, we suggest that a hybrid approach comprised of an optical frontend and reconfigurable digital backend utilizes the key advantages of optics (i.e., no latency, no loss, large space-bandwidth) with robustness and reconfigurability provided by the backend.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Typically, the knowledge distillation algorithm is designed to compress neural networks. Here, we propose using knowledge distillation to transfer the generalized knowledge from a larger, pre-trained teacher network, AlexNet, to a more compact CNN, referred to as the \u201cstudent network\u201d. Specifically, the student network comprises only a single convolutional layer coupled with a backend, which consists of a single fully-connected layer and a linear calibration layer. In addition, we selected AlexNet as our teacher network for two main reasons: first, AlexNet was the foundational model that successfully addressed the ImageNet dataset. Additionally, compared to more complex networks like ResNet-18 or VGG-16, the five-layer AlexNet is more accessible and easier to implement optically.\n\nThe knowledge distillation algorithm includes two types of losses: student loss and temperature loss. Student loss minimizes the discrepancy between the student network\u2019s predictions and the ground truth labels. The softmax function is used to compute:\n\nwhere zi represents the student logits after the last fully-connected layer. Temperature loss, on the other hand, optimizes the discrepancy between the student network\u2019s predictions and the teacher network\u2019s predictions. Knowledge distillation incorporates a softening parameter, T, known as the distillation temperature for the teacher probabilities. Thus, we can compute such loss as:\n\nFinally, the total loss is calculated as a weighted sum of the two losses:\n\nwhere \u03b1 is the weight balancing the two loss components, \\({{{{\\mathcal{L}}}}}_{C}\\) is the cross-entropy loss function, \\({{{{\\mathcal{L}}}}}_{k}\\) is the Kullback-Leibler (KL) Divergence loss function57.\n\nWe also find that other key hyperparameters might impact our hybrid CNN system. First, in most CNNs, such as ResNet-18 and AlexNet, there are multiple convolutional layers, each with more than 200 kernels to extract useful features and maintain generalization across various datasets. While some pruning strategies show that using 1% of the parameters can achieve similar accuracy58, applying these algorithms to optical neural networks are non-trivial. Most pruning methods still retain ANN structures, which suffer from misalignments that are nearly impossible to eliminate35. Therefore, compressing into shallower layers with more kernels is preferred. However, each meta-surface has a physical size that limits the number of kernels it can contain. To address this limitation, we can use multiple cameras and multiple meta-surfaces to increase the number of kernels, thereby improving the classification accuracy and generalization of the hybrid CNN.\n\nFor 16 digital kernels for each R, G, and B channels, we have 32 meta-optical kernels as we use a single meta-optics for all RGB channels but we cannot represent both positive and negative weights with optics. Hence, we create 16 positive kernels and 16 negative kernels, then perform digital subtraction on the digital backend. Each of our convolutional meta-optics has 3200\u00a0\u00d7\u00a03200 scatterers, with 2\u00a0\u00d7\u00a02 scatters constituting a group to enhance the robustness of fabrication. Based on the ground-truth digital convolutional kernels, we define optical PSFs for each RGB channel, and inverse-design the meta-optics having those PSFs at each RGB wavelength using TensorFlow Adam optimizers.\n\nOur meta-optics operate at visible wavelength (\u03bb\u00a0~\u00a0400\u2009nm\u00a0\u2212\u00a0700\u2009nm). We use silicon nitride on quartz substrate for the meta-optics to have high transparency at the whole visible regime. We deposit a thick silicon nitride layer (800 nm) on top of the double-polished quartz substrate using plasma-enhanced chemical vapor deposition (Oxford; Plasma Lab 100). We spin coat and bake electron beam resist (ZEP-520A) on top of the silicon nitride layer, followed by a spin coat anti-charging agent (DisCharge H20). We pattern using electron beam lithography (JEOL; JBX6300FS), and develop the resist using amyl acetate. After that, we deposit via electron beam evaporation (CHA; SEC-600) and lift-off an alumina layer (\u00a0~\u00a065\u2009nm) for a hard mask. Finally, we etch the silicon nitride with an alumina hard mask using plasma etcher with fluorine-based gas (Oxford; PlasmaLab 100, ICP-180). The sub-wavelength structured meta-optics has a period of 293 nm, which is one-twentieth the camera pixel size collecting the image.\n\nWe measure the PSF by placing a laser and a pinhole (\u03d5\u00a0=\u00a025\u2009\u03bcm), representing a point source. Then we place the convolutional meta-optics on a 3-axis stage with rotational knobs to align the meta-optics centered and parallel to the beam path, about 105\u2009mm apart from the pinhole. High resolution color camera (GT-1930C) which has a pixel size of 5.86 \u03bcm is placed about 2.46\u2009mm away from the meta-optics. We measure the PSFs for each RGB color light by replacing the laser with three different wavelengths (Thorlabs; CPS450, CPS532, and CPS635). For image convolution measurements of the CIFAR-10 dataset, we put the micro-display at the pinhole position, then connect to the computer to show the color images. Since a single meta-optics can represent three different RGB kernels at the same time (see details in\u00a0Supplementary Information), a color camera which has RGB color pixels can extract the convolved images at three different channels. This can eventually save the space of the meta-optics and camera, which is critical in real-world applications59. The point source is replaced by an arbitrary two-dimensional image, f(x,\u00a0y). We can express the image as a sum of the three color channels, fR(x,\u00a0y)\u00a0+\u00a0fG(x,\u00a0y)\u00a0+\u00a0fB(x,\u00a0y). The convolutional meta-optics perform a convolution for each color, and as a result, a convolved image, \\({\\sum}_{i=R,G,B}{f}_{i}(x,y)*PS{F}_{i}(x,y)\\), will be imaged on the camera. Because of small size of the input image as well as small distance between the metasurfaces and the camera (\u00a0~\u00a02.46\u2009mm) compared to the distance between the display and the metasurface (\u00a0~\u00a0105\u2009mm), the spatial distribution of the metasurfaces does not affect much on the convolutional results. Since we determined the enlargement factor of 2 for the PSF, we use the same enlargement factor for the CIFAR-10 image as well. According to the camera pixel size, 5.86 \u03bcm, and CIFAR-10 image size, 32\u00a0\u00d7\u00a032, the projected image size on the camera has to be about 374\u00a0\u00d7\u00a0374\u2009\u03bcm2. At the given values of distance between the display and meta-optics and meta-optics to the camera, we can end up with the CIFAR-10 image size on the display to be 16.0\u00a0\u00d7\u00a016.0\u2009mm2. We use 10,000 images for training (a subset of original 50,000 images) and 10,000 images for testing, with an exposure time of 500 ms. Among the 10,000 images of training and testing dataset, 186 and 201 images are not involved on training and testing, respectively, due to the overexposure issue. All the measurement parameters and number of images are the same for the High-10 dataset for transfer learning process.\n\nAnother critical factor is the exposure time. Since the optical features are captured by a CCD camera, the exposure time significantly influences the final performance. If the optical features are overexposed, texture information, such as the fur of a cat, might be missing. Conversely, if the optical features are underexposed, most information may also be lost, resulting in a lack of distinction between highlights and shadows in the image. To find the most appropriate exposure, we could use a similar approach to modern cameras, where \u201c18% gray\" is considered as the mid-point between black and white on a logarithmic or exponential curve. This standard can help us achieve balanced exposure, ensuring that the captured optical features are neither overexposed nor underexposed.\n\nAs previously discussed, optical fabrication and alignment noise are unavoidable in meta-surface kernels. These include scaling, translation, rotation, image aberration, and optical noises. To address this issue, we propose adding a calibration function to remap the optical convolution outputs to align with those of the previously trained backend. Specifically, we use a fully-connected layer as the calibration function and corresponding loss function is defined as:\n\nThis approach aims to refine the experimental outputs to align more closely with the pre-designed network. To prevent overfitting, we strategically limit our training to only 20% of the available data, ensuring that our model remains efficient60.\n\nGeneralization is a key feature to test our hybrid optical/digital CNN. Ensuring that the network can generalize well to new, unseen data is crucial for several reasons. First, our hybrid network is compressed from AlexNet, which was originally designed with a large dataset. The pre-trained AlexNet achieves high accuracy across various datasets and can be easily adapted or fine-tuned to out-of-distribution datasets. This adaptability is essential for practical applications where the data distribution may differ from the training set. Second, exploring the generalization capabilities of hybrid models is important because designing and fabricating different meta-surface kernels for different tasks is inefficient. By enhancing generalization, we can use a single hybrid model for multiple tasks, reducing the need for extensive redesigns and fabrications. Details and schematics of our transfer learning plan are shown in the\u00a0Supplementary Information.\n\nTo implement the transfer learning, we add two types of losses: feature loss and label loss. The feature loss minimizes the discrepancy between the optical features and the digital features, ensuring that the representations learned by the optical and digital components are aligned. The label loss minimizes the discrepancy between the model\u2019s predictions and the actual labels, improving the overall prediction accuracy. During the transfer learning process, the optical frontend and digital backend remain unchanged. We add two fully-connected layers between the optical front end and backends and fine-tune these layers using the two losses. Specifically, the function is:\n\nwhere \\({{{{\\mathcal{L}}}}}_{{{{\\rm{feature}}}}}\\) is the feature loss and \\({{{{\\mathcal{L}}}}}_{{{{\\rm{label}}}}}\\) is the label loss, with \u03b1,\u00a0\u03b2 as the respective weights balancing these losses.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data supporting the findings of this study are available within the article and its\u00a0Supplementary Information, or from the corresponding author(s) upon request. Some data are subject to restriction due to competing interests.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code for polychromatic PSF-engineered meta-optics design is available at Zenodo: https://doi.org/10.5281/zenodo.15741412.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Thorpe, S., Fize, D. & Marlot, C. Speed of processing in the human visual system. Nature 381, 520\u2013522 (1996).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n ADS\u00a0\n \n Google Scholar\u00a0\n \n\nGraimann, B., Allison, B.Z. & Pfurtscheller, G. Brain-computer interfaces: Revolutionizing human-computer interaction (2010).\n\nDe Cesarei, A., Loftus, G. R., Mastria, S. & Codispoti, M. Understanding natural scenes: Contributions of image statistics. Neurosci. Biobehav. Rev. 74, 44\u201357 (2017).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nIacaruso, M. F., Gasler, I. T. & Hofer, S. B. 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Part of this work was conducted at the Washington Nanofabrication Facility/ Molecular Analysis Facility, a National Nanotechnology Coordinated Infrastructure (NNCI) site at the University of Washington with partial support from the National Science Foundation via awards NNCI-1542101 and NNCI-2025489.\u00a0S.-H.B. acknowledges the National Research Foundation (NRF) grants (RS-2023-00211658, RS-2024-00438532) funded by the Ministry of Science and ICT (MSIT) and the Ministry of Education of the Korean government.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Minho Choi, Jinlin Xiang.\n\nDepartment of Electrical and Computer Engineering, University of Washington, Seattle, 98103, WA, USA\n\nMinho Choi,\u00a0Jinlin Xiang,\u00a0Eli Shlizerman\u00a0&\u00a0Arka Majumdar\n\nDepartment of Physics, University of Washington, Seattle, 98103, WA, USA\n\nAnna Wirth-Singh\u00a0&\u00a0Arka Majumdar\n\nDepartment of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, 37673, Gyeongbuk, Republic of Korea\n\nSeung-Hwan Baek\n\nDepartment of Applied Mathematics, University of Washington, Seattle, 98103, WA, USA\n\nEli Shlizerman\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nM.C. and J.X. contributed equally to this work. A.M. and E.S. conceived of the idea and provided funding. A.W.S. involved in developing the PSF-engineering approach. J.X. and E.S. developed the knowledge distillation approach. J.X. trained the neural network and analyzed the experiment data. M.C. developed the polychromatic PSF-engineering approach and designed the meta-optics. M.C. fabricated the meta-optics and conducted the optical experiments. S.H.B. advised on the result analysis. M.C., J.X., and A.M. wrote the manuscript with input from all authors.\n\nCorrespondence to\n Minho Choi, Eli Shlizerman or Arka Majumdar.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "A.M. is a co-founder of Tunoptix, which aims to commercialize meta-optics technology. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Fion Yeung and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Transferable polychromatic optical encoder for neural networks.\n Nat Commun 16, 5623 (2025). https://doi.org/10.1038/s41467-025-61338-4\n\nDownload citation\n\nReceived: 02 December 2024\n\nAccepted: 19 June 2025\n\nPublished: 01 July 2025\n\nVersion of record: 01 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61338-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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A-site cation chemistry in superionic halide solid electrolytes", + "pre_title": "Lowering the Barrier: Importance of A-Site Cation Chemistry in Superionic Halide Solid Electrolytes", + "journal": "Nature Communications", + "published": "29 August 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51710-1/MediaObjects/41467_2024_51710_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51710-1/MediaObjects/41467_2024_51710_MOESM2_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51710-1/MediaObjects/41467_2024_51710_MOESM3_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-51710-1#MOESM3", + "/articles/s41467-024-51710-1#Sec17", + "/articles/s41467-024-51710-1#Sec17" + ], + "code": [], + "subject": [ + "Batteries", + "Density functional theory", + "Solid-state chemistry" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3981393/v1.pdf?c=1725016568000", + "research_square_link": "https://www.researchsquare.com//article/rs-3981393/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-51710-1.pdf", + "preprint_posted": "26 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Halide solid electrolytes do not currently display ionic conductivities suitable for high-power all-solid-state batteries. We explore the model system A2ZrCl6 (A = Li, Na, Cu, Ag) to understand the fundamental role that A-site chemistry plays on fast ion transport. Having synthesised Ag2ZrCl6 for the first time we reveal exceptional room temperature ionic conductivities in Cu2ZrCl6 and Ag2ZrCl6 of 1 \u00d7 10\u22122 and 4 \u00d7 10\u22123 S cm\u22121, respectively. We introduce the concept that there are inherent limits to ionic conductivity in solids, where the energy and number of transition states play pivotal roles. Transport that involves multiple coordination changes along the pathway suffer from an intrinsic minimum activation energy. At certain lattice sizes, the energies of different coordinations can become equivalent, leading to remarkably lower barriers when a pathway involves a single coordination change. Our models provide a deeper understanding into the optimisation and design criteria for halide superionic conductors.Physical sciences/Materials science/Materials for energy and catalysis/BatteriesPhysical sciences/Materials science/Theory and computation/Atomistic modelsPhysical sciences/Chemistry/Inorganic chemistry/Solid-state chemistry", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "LoweringthebarrierSI.docxSupplementary information file", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Halide solid electrolytes do not currently display ionic conductivities suitable for high-power all-solid-state batteries. We explore the model system A2ZrCl6 (A = Li, Na, Cu, Ag) to understand the fundamental role that A-site chemistry plays on fast ion transport. Having synthesised the previously unknown Ag2ZrCl6 we reveal high room temperature ionic conductivities in Cu2ZrCl6 and Ag2ZrCl6 of 1\u00a0\u00d7\u00a010\u22122 and 4\u00a0\u00d7\u00a010\u22123 S cm\u22121, respectively. We introduce the concept that there are inherent limits to ionic conductivity in solids, where the energy and number of transition states play pivotal roles. Transport that involves multiple coordination changes along the pathway suffer from an intrinsic minimum activation energy. At certain lattice sizes, the energies of different coordinations can become equivalent, leading to lower barriers when a pathway involves a single coordination change. Our models provide a deeper understanding into the optimisation and design criteria for halide superionic conductors.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Achieving fast ion mobility in a material at room temperature while maintaining structural and chemical integrity is an essential property in energy conversion and storage systems. While current Li-ion batteries contain a liquid electrolyte to achieve sufficient ionic conductivities of 10\u22123 - 10\u22122\u2009S\u2009cm\u22121 1,2, the stability of electrolyte molecules imposes constraints on electrode compatibility and, subsequently, the energy density. Solid electrolytes are attractive owing to their potential affinity for alternative battery chemistries, such as a Li metal anode, possessing a remarkably high theoretical specific capacity (3860\u2009mAh/g).\n\nThe materials suggested for use as solid-state electrolytes fall under one of four main categories: oxides, sulphides, halides and polymers (Supplementary Fig.\u00a01). Halides have returned as a class of solid electrolyte materials proposed for all-solid-state batteries showing compatibility with high voltage cathodes3,4,5. Chlorides and fluorides show the best performance regarding electrochemical stability and are lighter than bromides and iodides. However, they display lower ionic conductivities4,6,7. By investigating features such as structure, cation size, covalency, thermodynamics and diffusion pathways, we enhance our understanding of ion transport, which can be applied as materials design principles to achieve high ionic conductivities.\n\nHalide materials and their associated ion transport properties have been studied for decades. Early research found many superionic iodides, such as the high-temperature phase of \u03b1-AgI8. Materials falling into the class of \u201cadvanced superionic conductors\u201d have the highest fastest ionic transport of materials known to date with conductivities above 0.1\u2009S\u2009cm\u22121 at room temperature9. Examples include RbAg4I5 and Rb4Cu16I7Cl1310,11, where the interconnected, partially occupied face-sharing tetrahedral sites enable these materials to achieve ionic conductivities of 0.26\u2009S\u2009cm\u22121 and 0.21\u2009S\u2009cm\u22121, respectively12,13. To date, no pure chloride-based advanced superionic conductors have been discovered. Finding material for this class would be a monumental step towards achieving all-solid-state batteries with wide applications.\n\nLi2ZrCl6 is an important candidate for use as a solid electrolyte. The use of Zr4+ is attractive as efforts are being made to use cheaper, more abundant elements in the battery industry instead of rare earth or post-transition metal\u00a0elements, such as Y and In in Li3YCl6 and Li3InCl6 (Supplementary Fig.\u00a02). Kwak et al. demonstrated that Li2ZrCl6 can exist in two polymorphs depending on the synthesis method14. The difference in magnitude in ionic conductivities for these two polymorphs is large (10\u22124 vs 10\u22126\u2009S\u2009cm\u22121 at room temperature for the hexagonal close-packed (HCP) and cubic close-packed (CCP) structures, respectively).\n\nNa2ZrCl6 has been proposed as a cheap Na halide solid electrolyte for all-solid-state Na batteries. As with so many of these halide systems, multiple crystal structures have been reported15,16 (Supplementary note\u00a01).\n\nWhile Li-based argyrodite-type solid electrolytes are popular of late for use in all-solid-state batteries and Li-S batteries17,18, Cu analogues such as Cu6PS5Br19 were known long before. The ionic conductivity of this material was found to be 1.5\u2009x\u200910\u22125\u2009S\u2009cm\u22121 at room temperature with an activation energy of approximately 0.35\u2009eV19,20.\n\nThe compound Cu2ZrCl6 was reported in 200221, however, the electronic and electrolytic properties of this material have not been investigated. The structure of Cu2ZrCl6 was reported to fall in the same \\(P\\bar{3}m1\\) space group as the Li and Na systems.\n\nIn this work, we have synthesised Ag2ZrCl6, and we investigated the origins of rapid ion transport in the isostructural systems of Li2ZrCl6, Na2ZrCl6, Cu2ZrCl6, and Ag2ZrCl6. Through a combination of experimental methods and state-of-the-art atomistic modelling, we highlight the significant role that site preference and disorder play in influencing the conductivity of halide materials. These findings open up promising pathways for the future enhancement and design of halide solid-electrolyte systems.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Ball milling has been a widely used approach for synthesising alkali halide-type materials in the scientific community. Recent work on Li2ZrCl6 and Na2ZrCl6 are examples of where employing this technique provides a relatively simple and effective way of making materials with attractive properties4,22. Other halides containing monovalent ions have, however, historically not been synthesised using mechanochemical ball milling-based techniques. The softness or deformability of Cu+ and Ag+ halides would suggest that a mechanochemical synthesis strategy would be a practical way of obtaining entirely new compounds or those previously made via traditional melt-quench methods.\n\nLi2ZrCl6 and Na2ZrCl6 were successfully synthesised in this study using the ball milling approach as reported in other works14,16. Cu2ZrCl6 was also synthesised in this work using a simple ball milling route and is isostructural with Cu2ZrCl6 produced by high-temperature synthesis, as shown below. Most importantly, the ball milling approach allowed us to synthesise the Ag analogue, Ag2ZrCl6, which, to the best of our knowledge, has not been reported.\n\nSupplementary Fig.\u00a03 shows the Rietveld refinements of the four synthesised compounds using structures based on the \\(P\\bar{3}m1\\) space group. The refinement for Na2ZrCl6 utilised a minor P21/n phase15 to achieve the best fit.\n\nLi2ZrCl6, Na2ZrCl6, Cu2ZrCl6 and Ag2ZrCl6 share many crystallographic features. The space group \\(P\\bar{3}m1\\) applies to all 4 compounds, sharing the same HCP chloride sublattice. Zr ions occupy octahedral sites on the 1a, 1b and 2d Wyckoff positions with varying degrees of site occupancy disorder (Fig.\u00a01a). Li, Na and Ag ions occupy the octahedral 6g and 6h Wyckoff positions (Fig.\u00a01b) while Cu occupies the tetrahedral 6i Wyckoff sites (Fig.\u00a01c). Li+ has been found in both octahedral and tetrahedral configuration in halide-based materials, depending on the size of the lattice and presence of neighbouring vacant sites. Although Li+ and Cu+ have similar ionic radii, additional mixing between the 3d10 valence orbitals in Cu+ with the higher energy 4s and 4p states leads to an additional stabilisation of the tetrahedral configuration, as shown in previous work23,24. A refinement was conducted for Cu2ZrCl6 where the Cu+ species were located on octahedral sites. This refinement led to a poor fit with (\u03c72 and Rwp values of 17.90 and 7.85, respectively). In Li2ZrCl6, Na2ZrCl6 and Ag2ZrCl6, the A-site cations are octahedrally coordinated, sharing the edges of the polyhedra in the ab-plane while sharing faces in the c-direction.\n\nStructural models of A2ZrCl6 found via XRD showing: (a) The disordered ZrCl6 sublattice. b The octahedral positions of Li, Na and Ag. c The disordered tetrahedral positions of Cu. d The lowest energy configurations of A2ZrCl6 were found via DFT. e The DFT\u00a0formation energies of different configurations in A2ZrCl6 with respect to the reagents used (2ACl\u2009+\u2009ZrCl4).\n\nStoichiometric unit cells were designed for subsequent computational analysis. The DFT ground state structures for A2ZrCl6 were obtained by calculating the formation energies for the reaction:\n\nThe lowest energy (ground state) configuration of Li2ZrCl6 (Fig.\u00a01d) is found when all Li and Zr cations occupy the octahedral sites in one layer. The difference between the ground state and the highest energy configuration in Li2ZrCl6 is 0.048\u2009eV/atom, suggesting that many configurations are accessible at room temperature (thermal energy at room temperature is approximately 0.025\u2009eV).\n\nNa2ZrCl6 and Ag2ZrCl6 are found to have the same ground state configuration (Fig.\u00a01d). Zr is distributed across the two layers while the A-site cations are distributed evenly across the two layers, sharing edges with Zr, but not themselves. Figure\u00a01e shows that Na2ZrCl6 has a more negative formation energy compared to Li2ZrCl6. Figure\u00a01e also shows that Na2ZrCl6 has significantly more negative formation energy, with a larger gap between the lowest energy configuration and higher energy configurations than Ag2ZrCl6; there is more of an energy penalty moving from one configuration to another in Na2ZrCl6 than Ag2ZrCl6. Ag2ZrCl6 displays very limited chemical stability with only 12/192 configurations having a negative formation energy; furthermore, the difference between the ground state and highest energy configurations is only 0.033\u2009eV/atom suggesting many configurations might be accessible at room temperature via entropic stabilisation.\n\nFor Cu2ZrCl6, many low energy configurations displayed a shift in position from tetrahedral to trigonal planar sites between the initial and final structures, respectively, similar to that seen via XRD at higher temperatures21. Cu atoms were also relaxed at the octahedral sites occupied by the other systems. All the Cu atoms fell into nearby trigonal planar or tetrahedral sites indicating that Cu atoms are inherently unstable in octahedral sites. The difference between the ground state and the highest energy configuration that we simulated is 0.133\u2009eV/atom, which is not accessible at room temperature. Only 4 of the configurations where Zr is all in one layer are found to be stable, supporting the diffraction data. Figure\u00a01e shows that in Cu2ZrCl6, many configurations are close in energy allowing facile movement between them.\n\nInterestingly, when Li atoms were placed in the tetrahedral positions of the Cu2ZrCl6 ground state, the energy difference between this structure and the ground state was found to be 0.027\u2009eV. Having interstitial sites relatively close in energy to the ground state can provide low-energy transition states for long-range diffusion. Upon relaxing Na into the tetrahedral structure, the difference in energy from the ground state was 0.054\u2009eV/atom. This larger energy penalty suggests that pathways involving a tetrahedral intermediate will be less accessible. The tetrahedral configuration of Ag2ZrCl6 was calculated to be only 0.008\u2009eV/atom higher than the ground state configuration. This vanishingly small energy difference shows that while the system has a small degree of chemical stability, the potential energy surface is smooth, providing low barriers for facile Ag+ transport between configurations.\n\nNyquist plots for the different compounds at room temperature can be seen in Supplementary Fig. \u00a04. For Li2ZrCl6 and Na2ZrCl6, one semicircle is present. The capacitance calculated via fitting is on the order of 10\u221211 F. This would suggest that the conductivity calculated reflects that of the total conductivity25. The conductivities measured of 5\u2009\u00d7\u200910\u22124 and 9\u2009\u00d7\u200910\u22126\u2009S\u2009cm\u22121 for Li2ZrCl6 and Na2ZrCl6, respectively, in Fig.\u00a02a are similar to those reported in other work14,16,26. No EIS measurements have been conducted on Cu2ZrCl6 and Ag2ZrCl6 prior to this investigation. A semicircle is not observed in the Nyquist plots for Cu2ZrCl6 and Ag2ZrCl6. The x-axis intercept is taken to be the maximum total resistance for which the conductivity is calculated27,28. The values of 1\u2009\u00d7\u200910\u22122\u2009S\u2009cm\u22121 and 4\u2009\u00d7\u200910\u22123\u2009S\u2009cm\u22121 for Cu2ZrCl6 and Ag2ZrCl6, respectively, in Fig.\u00a02a reveal that these are two excellent ion conductors. To the best of our knowledge, Cu2ZrCl6 exhibits the highest ionic conductivity of a crystalline chloride-type solid electrolyte to date. This suggests that the tetrahedral coordination in Cu2ZrCl6 is most favourable for ion transport within the ZrCl6 host lattice. To test whether the trend in conductivity could be rationalised solely based on the covalent nature of the mobile ions within their respective structures, Bader charge analysis was used (Supplementary Fig.\u00a05). Bader charge analysis demonstrated that the covalency of ions from most to least covalent was: Ag+\u00a0>\u00a0Cu+\u00a0>\u00a0Na+\u00a0>\u00a0Li+, whereas the measured conductivity followed the trend: Cu+\u00a0>\u00a0Ag+\u00a0>\u00a0Li+\u00a0>\u00a0>\u00a0Na+, which suggests that additional structural factors have a critical impact on the conductivity.\n\na Room temperature ionic conductivities of A2ZrCl6 and the advanced superionic conductors RbAg4I5 and Rb4Cu16I7Cl1312,13. The dashed red line shows the threshold for `advanced superionic' conductivity of 0.1\u2009S/cm. b Arrhenius plots for A2ZrCl6 and their associated activation energies calculated via EIS.\n\nExperimentally calculated activation energies for A+ conductivity can be seen in Fig.\u00a02(b). Li2ZrCl6 and Na2ZrCl6 display activation energies of 0.30\u2009eV and 0.40\u2009eV, respectively, similar to values reported in literature14,16. The values observed for Cu2ZrCl6 and Ag2ZrCl6 are 0.19 eV and 0.24\u00a0eV, respectively. The ionic transport properties of Cu2ZrCl6 and Ag2ZrCl6 have not been investigated previously to the best of our knowledge. The activation energies and pre-exponential factors show a strong Meyer-Neldel relationship where the pre-exponential factor decreases as the conductivity increases (Supplementary Fig.\u00a04e). These results demonstrate that both Cu2ZrCl6 and Ag2ZrCl6 are exceptional ion conductors and could have application in low voltage, fast charging cells.\n\nAb initio molecular dynamics (AIMD) were performed to provide insight into the mobility of the A+ cations in each of the structures (Supplementary Fig.\u00a06). Anisotropic diffusion is observed for all 4 systems. The favoured direction is consistent with the pathway that involves a single coordination change. For A\u2009=\u2009Li+, Na+ and Ag+, this is between octahedral (oct) sites through a 3-coordinate trigonal planar (trig) site along the c-axis. For Cu+ ions in tetrahedral (tet) sites, a tet-trig-tet pathway in the ab-plane displays the most mobility.\n\nSupplementary Fig.\u00a06e shows the calculated activation energies for diffusion for A+ ions in A2ZrCl6. Na2ZrCl6 has the largest activation barrier of 0.61\u2009eV, which is higher than our experimentally observed value of 0.40\u2009eV as well as values reported in literature16. It is expected that the ordered structures used in our AIMD calculations show lower conductivities than experimental data due to the conductivity-enhancing disorder during high energy milling16,29. Li+ ions display an activation energy of 0.36\u2009eV which is in excellent agreement with the findings of Wang et al.30 and slightly higher than our experimentally determined value of 0.30\u2009eV. The decrease in activation energy from Na to Li can be attributed to the impact of site preferences which are discussed later. Cu2ZrCl6 has been calculated to have a very low activation energy of 0.21\u2009eV which is in agreement with the experimentally calculated value. Ag ions in Ag2ZrCl6 show a distinctly low activation energy for the diffusion of 0.11\u2009eV, which is comparable to that of high-temperature \u03b1-AgI, suggesting that the energies of different configurations and surrounding sites are very close to each other.\n\nAIMD simulations demonstrated that the size of the activation energy for A+ ion transport in the A2ZrCl6 system was heavily influenced by the nature of the A+ ion. To gain a deeper understanding of the atomic scale processes, we used a transition state searching (TSS) method to specifically pinpoint the unknown A+ hopping processes. Due to the structures investigated in this work being inherently vacancy-rich, kinetically resolved barriers are used to reduce the effect that initial and final states have on calculated activation energies31. These are shown in Fig.\u00a03. The energies of the initial\u00a0(reactant), saddle and final\u00a0(product) states are shown in Supplementary Fig.\u00a07 to paint the full thermodynamic picture.\n\nSchematic representations of kinetically resolved activation barriers (KRB) were found for (a) Li2ZrCl6, (b) Na2ZrCl6 and (c) Cu2ZrCl6. Crimson, purple and pink balls represent the initial, saddle and final positions along their respective pathways. Activation energies of the unique mechanisms found via TSS calculations at 300\u2009K in (d) Li2ZrCl6, (e) Na2ZrCl6 and (f) Cu2ZrCl6. Diffusion pathways are categorised by whether the diffusion is in the ab or c-direction and whether diffusion occurs in layers with 1 or 2 Zr.\n\nExample transport mechanisms of Li2ZrCl6, Na2ZrCl6 and Cu2ZrCl6 are shown in Fig.\u00a03a\u2013c, respectively. A Li+ ion in Li2ZrCl6 (Fig.\u00a03a) can be seen hopping from an octahedral site to an adjacent octahedral site along the c-axis via a trigonal planar transition state. This is one example of the barriers that were found via the TSS approach, demonstrating an excellent way to find and visualise transport mechanisms in a system while simultaneously mapping the energy landscape. Figure\u00a03d shows the kinetically resolved barriers of Li diffusion processes found in Li2ZrCl6. There is a clear correlation between the direction in which the ion is moving and the height of the barrier. In agreement with our MD results, ion transport is favoured along the c-direction. When moving in the c-direction, the number of Zr in the initial layer vs the final layer has minimal effect on the barrier height i.e., Li going from a layer with 1 Zr to a layer with 2 Zr is thermodynamically as favourable as the reverse direction. While the number of Zr in each layer has little effect on transport in the c-direction, transport in the ab-plane is limited to isolated pathways in layers where Zr occupancy is high (Supplementary Fig.\u00a08). The transition states for all of these barriers were found to have either trigonal planar or distorted tetrahedral coordination, highlighting the importance of lowering the energies of these sites to facilitate fast Li conduction in halide systems.\n\nA similar mechanism can be seen for Na2ZrCl6 (Fig.\u00a03b) that reflects the barriers found for transport in the c-direction. Similarly to Li2ZrCl6, 1-dimensional transport is favoured in the c-direction via a trigonal planar transition state, with an increase in activation energy compared to the Li system. Transport in the ab-plane is slightly more complex in Na2ZrCl6. Transition states are observed to have trigonal planar or highly distorted tetrahedral coordination, suggesting that the energy landscape is rough. Figure\u00a03e shows that transport in the ab-plane has a larger associated activation energy than the c-direction, in agreement with our MD calculations.\n\nFigure\u00a03c shows one pathway within Cu2ZrCl6 found via TSS. An initial tetrahedral site can be seen travelling through a trigonal planar intermediate before settling in an adjacent tetrahedral position. Two transition states were observed in the barriers found via TSS: a trigonal planar intermediate and a linear one suggesting that Cu ions travel through both the faces and edges of their respective tetrahedra. Multiple barriers resulted in the mobile Cu ion moving to a trigonal planar product state, again demonstrating that the trigonal planar sites in Cu2ZrCl6 are indeed low in energy. The saddle point is a distorted octahedral site, as any other pathway would involve a site face sharing with Zr. The barriers displayed in Fig.\u00a03f show that diffusion is more isotropic in Cu2ZrCl6 compared to Li2ZrCl6 and Na2ZrCl6. The barriers for Cu2ZrCl6 obtained via TSS are slightly higher than the activation energy calculated via MD, suggesting that the mechanism may be a cooperative process rather than a simple vacancy-mediated one. TSS was attempted for the Ag2ZrCl6 system, but the barriers were found to be so small (i.e., a flat energy landscape) that the saddle point searching methods used in the TSS process were unable to converge at a simulation temperature of 300\u2009K. We, therefore, rely on the MD results of Ag2ZrCl6 to understand the diffusion mechanism.\n\nOur TSS data has revealed that activation energies in A2ZrCl6 are influenced by A-site cation coordination and type. In the ab-plane, Li2ZrCl6, Na2ZrCl6, and Ag2ZrCl6 show ion hops between octahedral and tetrahedral sites, with a trigonal planar site as the transition state Fig.\u00a04a. Along the c-axis, diffusion involves hops between octahedral sites through a trigonal planar site. Cu2ZrCl6 exhibits hopping along tetrahedral-trigonal planar-octahedral pathways, with direct hops between tetrahedral sites via edges also observed. The activation energy depends on the relative energy of the transition states, which is influenced by the nearest neighbour Cl-Cl distance of the polyhedra. We examined the variation in site energy for model LiCl, NaCl, CuCl, and AgCl HCP systems as a function of unit cell volume, to understand the relative energies of sites in these systems.\n\na A schematic of different A cation configurations in HCP ACl structures, showing 6-coordinate octahedral (Aoct), 4-coordinate tetrahedral (Atet), and 3-coordinate trigonal (Atrig,ab and Atrig,c) configurations. A and Cl sites are labelled blue and yellow, respectively, and adjacent A-site cations are omitted for clarity. b Schematic diagram of energy variation of different cation sites (Aoct, Atet, Atrig) and as a function of Cl-Cl distance (unit cell volume) in undistorted HCP ACl structures. The linear configuration, Alin, has been omitted for clarity. The minimum energy for each A-site type is shown with a star. The Cl-Cl distances where the Aoct/Atet, Aoct/Atrig, and Atet/Atrig curves have equal energy are labelled as i, ii and iii, respectively. Arrows indicate the maximum energy difference between A-site types at different Cl-Cl distances. c Plot of Cl-Cl length at the minimum energy point vs A-site coordination (Aoct(6), Atet(4), Atrig(3)) for HCP LiCl, NaCl, CuCl and AgCl structures. The colour scale shows the energy of the configurations relative to the octahedral site. Lines are included to guide the eye. The plot of maximum energy difference per formula unit between (d) Aoct - Atet - Atrig,ab sites and (e) Aoct - Atrig,c, to model ab-plane and c-axis diffusion in HCP ACl structures, respectively. Points i\u2013iii from (b) are labelled for the CuCl (blue) curves.\n\nA schematic representation of the variation in cell energy as a function of Cl-Cl distance can be seen in Fig.\u00a04b. The raw plots for each of the structures can be seen in Supplementary Fig.\u00a09. The calculations show that in different coordinations have different minimum energies at specific HCP ACl cell sizes. In LiCl, NaCl and AgCl, at short Cl-Cl distances, the octahedral coordination is lowest in energy (Fig.\u00a04c). Conversely, at longer bond distances, the tetrahedral coordination is lowest in energy with other coordinations slightly higher in energy (local minima). CuCl, on the other hand, shows the lowest energy octahedral coordination only at very short Cl-Cl distances before the tetrahedral coordination becomes the ground state. Interestingly, at longer Cl-Cl distances the trigonal and linear coordinations are lowest in energy.\n\nFor diffusion in the ab-plane of octahedral A2ZrCl6 systems, A-site cations must diffuse through sequential octahedral-trigonal planar-tetrahedral configurations, in which the Cl-Cl distance is determined by the A2ZrCl6 lattice size. The size of the barrier is, therefore, strongly dependent on the maximum energy difference between the octahedral, trigonal and tetrahedral sites. The maximum energy difference for each ACl system is plotted in Fig.\u00a04d.\n\nAt small Cl-Cl distances (XCl-Cl\u00a0< i), the barrier is dictated by the energy difference of the octahedral (lowest E) and trigonal planar (highest E) sites. At intermediate distances (point i to point ii), the barrier is dictated by the energy difference between the tetrahedral (lowest E) and trigonal planar site (highest E). At long distances (point ii to point iii), the barrier is dictated by the difference between the tetrahedral (lowest E) and octahedral (highest E) sites. At the longest distance (XCl-Cl\u00a0> iii), the barrier is dictated by the difference between trigonal planar (lowest E) and octahedral (highest E) sites. The shape of the curves is consistent with previous work by Wang et al.32.\n\nFor all structures, the minimum energy point occurs at cell lengths where the 3-coordinate Atrig,ab site is the same energy as the 6-coordinate Aoct site (point ii), as shown in Fig.\u00a04b, d. At this point, the 4-coordinate Atet site is the lowest energy. From Fig.\u00a04b, c, as the minimum energy bond length increases as the coordination decreases, there is no point where the Aoct(6)- Atrig,ab(3) - Atet(4) sites adopt the same energy (i.e., i, ii and iii do not cross at a single point). Importantly, this results in a fundamental, finite lower bound for the activation energy of diffusion within the ab-plane of A2ZrCl6 materials.\n\nIn contrast to ab-plane diffusion in the HCP structure, for c-axis diffusion in A2ZrCl6, only A hops between Aoct and Atrig,c site are required, and so if a material with the minimum energy Cl-Cl distance is selected (point ii), the Aoct and Atrig sites have equivalent energies, and there is no lower bound for the activation energy (Fig.\u00a04e). This is consistent with the very small c-axis activation energies for A2ZrCl6 systems, even though there is a large change in the A coordination from 6 to 3 along the diffusion pathway.\n\nFor the CuCl system, the relative energy of the sites is E(Atet) \u00a0< E(Atrig) \u00a0< E(Aoct). The Aoct site, therefore, serves as the transition-state along the Atet- Atrig- Aoct- Atrig- Atet pathway. These pathways are observed for Cu2ZrCl6. From TSS simulations, direct hops between tetrahedral sites are also observed, through a linear edge Alin. At long Cl-Cl distances (4.14 \u00c5), this pathway becomes favourable and free from an intrinsic barrier (Supplementary Fig.\u00a010).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51710-1/MediaObjects/41467_2024_51710_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51710-1/MediaObjects/41467_2024_51710_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51710-1/MediaObjects/41467_2024_51710_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51710-1/MediaObjects/41467_2024_51710_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The model developed in this work provides a fundamental understanding of the role of coordination changes and A-site cation species on the activation energy for cation diffusion on HCP halide materials, which has so far been lacking.\n\nIn Li2ZrCl6, Na2ZrCl6, and Ag2ZrCl6, ab-plane conduction primarily involves Aoct (min)-Atrig (saddle)-Atet (min) hops, imposing a lower bound on activation energy due to two coordination changes (6-3-4). Ag2ZrCl6 exhibits faster ionic conduction than Na2ZrCl6, attributed to a smaller energy difference between Aoct and Atrig,ab sites, despite the larger ionic radius of Ag. Li2ZrCl6 also shows a small energy difference but incurs additional penalties from different Li orderings (Fig.\u00a01e). For c-axis conduction in these materials, face-sharing Aoct-Atrig,c sites, changing coordination once along the pathway, facilitate fast diffusion despite a larger coordination change (6-3-6).\n\nFor the Cu2ZrCl6 system, the preference for Atet coordination leads to very high conduction in the ab-plane. For a Aoct- Atrig,ab- Atet- Atrig,ab- Aoct pathway, the lowest intrinsic barriers for any system occur when the energy of E(Aoct)= E(Atrig,ab), which occurs near the point where Atet is the global minimum. This suggests that the tetrahedral Cu configuration in Cu2ZrCl6 is close to the optimum for conduction. In Cu2ZrCl6, additional pathways also exist involving direct Atet- Alin- Atet hops for which an intrinsic minimum barrier does not exist.\n\nThis result suggests that a possible avenue to improve the ab-conductivity in LiyMCl6 and NayMCl6 (M\u2009=\u2009transition metal or lanthanide) systems is to push the material towards the optimal tetrahedral cation configuration. These systems will, however, still suffer from an intrinsic activation barrier due to multiple coordination changes along a pathway.\n\nA promising strategy is to look for new families of materials in which single coordination changes are maintained throughout the diffusion pathway. These are facilitated by face-sharing polyhedra. Extremely fast ionic conductivity has been observed in RbAg4I5 systems, which involve Ag hops along face-sharing Atet (4)- Atrig (3)- Atet(4) pathways, that only involve a single change in coordination33. Analogous behaviour has also been observed in oxide systems, such as the P2 layered NayMO2 systems involving single coordination change hops between face sharing 6 coordinate trigonal prismatic sites, through a 4-coordinate square planar site34, and the TiNb2O7 system in which single coordinate change Li hops occur between 5-coordinate square pyramidal sites and 4-coordinate square planar sites35. The results also suggest that another strategy for finding Li superionic conductors is to search for analogous Cu+-based systems. Many may have favourable, unconsidered crystal structures where substitution for Li is possible.\n\nOverall, our study makes use of a model system, A2ZrCl6, to explore ionic conductivity in halide-type solid electrolytes. By successfully synthesising Ag2ZrCl6, we unveil exceptional ion-conducting properties in both Cu2ZrCl6 and Ag2ZrCl6, closing the gap to achieving chloride-type advanced superionic conductors.\n\nThrough our comprehensive investigation, employing first-principles calculations and single-ended transition state searching, we discern mechanistic and energetic differences in the transport properties of these compounds based on the A-site element. Unusual linear transition states allow for rapid diffusion in Cu2ZrCl6, while high energy face-sharing sites and intrinsically limited oct-trig-tet configurations limit diffusion in Li2ZrCl6. We introduce the concept that intrinsic limits to ionic conductivity in solids arise from a combination of chemical and structural factors, wherein the stability and number of transition states of the mobile species play crucial roles.\n\nOur models significantly contribute to a deeper understanding of the optimisation and design criteria for halide superionic conductors. Furthermore, we highlight the importance of recognising inherent challenges in transport mechanisms that involve multiple coordination changes along the pathway, attributing intrinsic minimum activation barriers to such scenarios. Notably, at certain lattice sizes, energies of different coordinations may become equivalent, leading to significantly lower barriers when a pathway involves a single coordination change. This insight enhances our comprehension of solid-state battery technology and facilitates the development of improved halide superionic conductors for future applications.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "First-principles calculations were performed using the Vienna Ab Initio Simulation package (VASP)36. The generalised gradient approximation (GGA) exchange correlation with a Perdew-Burke-Ernzerhof (PBE) functional was adopted within the projector augmented wave (PAW) method37. The specific pseudopotentials that were used for each element are: Zr_sv, Cl, Li_sv, Na_pv, Cu_pv and Ag. For these calculations, a plane wave cut off of 520\u2009eV was used with a \u0393 centred 2\u2009\u00d7\u20092\u2009\u00d7\u20094 k-point grid. Atom positions, cell volume and cell shape were allowed to relax until the forces acting on each atom reached less than 0.01\u2009eV/\u00c5. Some calculations were repeated using the meta-GGA exchange-correlation with the r2SCAN functional38, increasing the plane wave cut-off to 600\u2009eV.\n\nAll possible octahedral A-site orderings in the \\(P\\bar{3}m1\\) unit cell of Li2ZrCl6, Na2ZrCl6 and Ag2ZrCl6 were obtained using the Site Occupancy Disorder (SOD) code39. A-site orderings were considered for two separate Zr orderings along the c-axis: all Zr in a single layer (c\u2009=\u20090.5) or 2 Zr at c\u2009=\u20090 and 1 Zr at c\u2009=\u20090.5. Symmetrically distinct structures were relaxed using DFT, with the lowest energy taken as the ground state. The same strategy was employed for tetrahedral site occupancy in Cu2ZrCl6. Due to the large number of possible configurations \u00a0(>\u20091000) for Cu2ZrCl6, the Ewald summation method was employed to provide a basis for the low-energy structures based purely on electrostatics. The 240 lowest energy configurations were then relaxed among others. The Cu2ZrCl6 structure used in subsequent calculations was the lowest energy configuration where all Cu atoms had relaxed to tetrahedral positions.\n\nBader charge analysis was performed using the Bader charge analysis code40 from the Henkelman group, using the VASP charge densities of the ground state calculations from the lowest energy A2ZrCl6 structures. Both the valence and core charge density were included in the calculation of the Bader charge.\n\nAb initio molecular dynamics (AIMD) calculations were performed to probe the transport properties in A2ZrCl6 and to calculate A+ diffusivity. Plane-wave cutoffs were reduced to 400\u2009eV to increase computational efficiency while using soft pseudopotentials. An NVT ensemble and a Nos\u00e9-Hoover thermostat were used to control the temperature of the simulations. 1\u2009\u00d7\u20091\u2009\u00d7\u20092 supercells were used, which allowed the system lattice parameters to extend beyond 10 \u00c5 in each direction. K-point sampling was done using a \u0393 centred 1\u2009\u00d7\u20091\u2009\u00d7\u20091 mesh. All structures were equilibrated at their respective temperatures by performing a preliminary 10 ps simulation. AIMD calculations were performed using a 1 fs time step for 50 ps at their respective temperatures. Diffusivities of the A-site ions were calculated from their mean square displacement (MSD) via equation (2):\n\nWhere d is the dimensionality of the system (3 in most cases) and t is the total time elapsed. MSD is calculated via equation (3):\n\nN is the number of ions for which the displacement is being calculated (number of A+ ions in the supercell), ri is the position of the ith ion at time t, and \u0394t is the time step.\n\nDiffusivity and MSD values were calculated using the diffusion analyser module in pymatgen41. The lower bound of the temperature range for each system was selected such that an MSD value of \u00a0>\u200910 \u00c52 for the A-site ion was achieved within the 50 ps simulation time to provide reliable estimates of the diffusion coefficient.\n\nTransition state searching (TSS) calculations were performed in the EON package42 with input energetics from VASP. Unknown A-site activation barriers between the initial reactant state and a product state were located without previous knowledge of the final product states. To generate representative reactant state configurations with a range of A-site coordinations, supercells consisting of 2 A2ZrCl6 unit cells (1\u2009\u00d7\u20091\u2009\u00d7\u20092) were used. The positions of A-site ions in the supercell were initially thermalised with 10 ps of AIMD using a timestep of 1 fs under fixed cell conditions. At the end of the AIMD run, the positions of all ions were geometry optimised to a\u00a0force tolerance of 0.01\u2009eV/\u00c5 under fixed cell conditions.\n\nFor all TSS calculations, we utilised the DFT\u2009+\u2009D3 dispersion correction method proposed by Grimme43 to treat Van der Waals interactions between layers. The absence of Van der Waals corrections was found to occasionally lead to artificial low energy barriers associated with the shearing of ZrCl6 layers along the c-axis in the TSS approach. The PBE exchange-correlation functional was employed in combination with soft pseudopotentials for all elements, to minimise computational cost with an energy cutoff of 400\u2009eV. Saddle point searches were then initiated from the reactant configuration by displacing all A-site ions in the cell. The magnitude of the displacement was based on a Gaussian distribution at 300\u2009K with a standard deviation of 0.15. After the atoms were displaced, the transition state was located using the dimer method44 under fixed volume conditions. The transition state\u00a0geometries, containing a single negative mode, were\u00a0converged to a force tolerance of 0.01\u2009eV/\u00c5 using a conjugate gradient method. Once the transition state was found, the corresponding minima (reactant and product) were located by initiating minimisations along the negative and positive directions of the negative transition state mode. The force on all atoms was minimised to a tolerance of 0.01\u2009eV/\u00c5. The structure of the product state was compared to the reactant state to check that they were distinct minima. A product state was classified as a distinct minimum if the energy difference was \u00a0>\u20090.02\u2009eV and any atom had moved more than 0.2 \u00c5.\n\nFor each ACl model system, a perfect, undistorted HCP structure was considered in which the Cl-Cl distance is equal to the a-lattice parameter XCl\u2212Cl\u2009=\u2009a and the ratio of the c to a lattice parameters was \\(\\frac{c}{a}=\\sqrt{\\frac{{8}}{3}}=1.667\\). Configurations of A-site cations were considered for each material: A-site cations in octahedral sites (Aoct), A-site cations in tetrahedral sites (Atet), A-site cations in trigonal planar sites within the ab-plane (Atrig,ab) and along the c-axis (Atrig,c) and A site cations in a linear configuration (Alin).\n\nThe Cl-Cl distance (XCl\u2212Cl) was chosen to describe the variation in the volume of the ACl lattice, as the Cl-Cl distance is invariant to the A-site coordination. An increase (decrease) in XCl\u2212Cl leads to an isotropic expansion (contraction) of the lattice. In the ideal HCP ACl lattice, XCl\u2212Cl is related to the A-Cl bond distance (XA\u2212Cl,i) via the relationships: \\({{{{\\rm{X}}}}}_{A-Cl,oct}={\\sqrt{2}/2}{{{{\\rm{X}}}}}_{Cl-Cl},\\,{{{{\\rm{X}}}}}_{A-Cl,tet}=\\sqrt{3/8}{{{{\\rm{X}}}}}_{Cl-Cl}\\) and \\({{{{\\rm{X}}}}}_{A-Cl,trig}={\\sqrt{3}/3}{{{{\\rm{X}}}}}_{Cl-Cl}\\), for octahedral, tetrahedral and trigonal planar coordination, respectively.\n\nZrCl4 (99.5% Sigma Aldrich) and LiCl (99% Sigma Aldrich), NaCl (99% Sigma Aldrich), CuCl (99.999% Alfa Aesar) or AgCl (99.9% Thermo Scientific) were weighed in a 1:2 stoichiometric ratio and hand ground with a mortar and pestle inside an argon glovebox filled with \u00a0<\u20095\u2009ppm [H2O] and [O2]. The powders were then individually put into an air-tight zirconia jar with zirconia balls (20:1 weight ratio). The powders were milled using a planetary mill for 60\u2009h at 400 rpm on alternating mode; there was a 5\u2009min rest period between changing direction.\n\nPowder XRD patterns were collected inside a glovebox under argon using a Rigaku MiniFlex diffractometer with Cu K\u03b1 radiation. No monochromator was used, leading to two wavelengths of radiation (\u03bb\u2009=\u20091.5406 \u00c5 and 1.5444 \u00c5 for K\u03b11 and K\u03b12, respectively). Measurements were conducted within the 10 to 90\u00b0 2\u03b8 range at a rate of 0.1 degrees per minute. X-ray diffraction data was analysed using the Rietveld method using the GSAS-II software package45.\n\nIonic conductivities of as-milled A2ZrCl6 samples were measured via AC impedance with a Biologic SP240 potentiostat. A pressure and atmosphere-controlled split cell was used to make SS\u2223SE\u2223SS (SS\u2009=\u2009stainless steel, SE\u2009=\u2009A2ZrCl6) symmetrical cells with a pellet diameter of 10\u2009mm. Pellets are pressed in situ by the split cell. 600 MPa was applied for 5\u2009min to allow the powder to densify prior to the measurement. An open circuit voltage with an amplitude of 50\u2009mV was used with a frequency range of 7\u2009MHz to 1\u2009Hz. Subsequent data analysis was performed using the RelaxIS impedance analysis software. To obtain activation energies for the compounds, a climate chamber was used, and samples were allowed to equilibrate at each temperature for an hour prior to measurements being taken.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data that support the findings in this work are available within the main article and supplementation information. The source data file: 494527_2_related_ms_9378315_sh61bw.xlsx is provided for the raw data for figures in the main text. 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N.T.-R. would like to acknowledge the Faraday Institution FutureCat project (FIRG065) for the provision of some of the equipment used in this work.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Materials, Imperial College London, London, UK\n\nKit Barker,\u00a0Stephen J. Skinner,\u00a0Ainara Aguadero\u00a0&\u00a0Ieuan D. Seymour\n\nDepartment of Chemistry, Imperial College London, London, UK\n\nSarah L. McKinney\u00a0&\u00a0Nuria Tapia-Ruiz\n\nThe Faraday Institution, Didcot, UK\n\nSarah L. McKinney\u00a0&\u00a0Nuria Tapia-Ruiz\n\nDepartment of Chemistry, Lancaster University, Lancaster, UK\n\nSarah L. McKinney\n\nInstituto de Ciencia de Materiales de Madrid, CSIC, Madrid, Spain\n\nRa\u00fcl Artal,\u00a0Ricardo Jim\u00e9nez\u00a0&\u00a0Ainara Aguadero\n\nAdvanced Centre for Energy and Sustainability (ACES), Department of Chemistry, University of Aberdeen, Aberdeen, UK\n\nIeuan D. Seymour\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nK.B. and I.D.S. conceived the idea for the study, which was planned with A.A. and S.J.S. K.B. performed the synthesis and electrochemical characterisation, with support and data interpretation from R.A, A.A., R.J. and I.D.S. Lab X-ray diffraction analysis was performed by K.B and S.L.M. with support and data interpretation from N.T.-R. and S.J.S. First-principles calculations were performed by K.B. and I.D.S. K.B. and I.D.S. wrote the manuscript with input from all authors.\n\nCorrespondence to\n Ieuan D. 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effects of the host protein G3BP in SARS-CoV-2 Infection", + "journal": "Nature Communications", + "published": "05 December 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54996-3/MediaObjects/41467_2024_54996_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54996-3/MediaObjects/41467_2024_54996_MOESM2_ESM.pdf" + }, + { + "label": "Reporting summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54996-3/MediaObjects/41467_2024_54996_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54996-3/MediaObjects/41467_2024_54996_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.rcsb.org/structure/7SUO", + "/articles/s41467-024-54996-3#Sec28" + ], + "code": [], + "subject": [ + "SARS-CoV-2", + "Virus\u2013host interactions" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4145371/v1.pdf?c=1733490368000", + "research_square_link": "https://www.researchsquare.com//article/rs-4145371/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54996-3.pdf", + "preprint_posted": "07 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Ras-GTPase-activating protein SH3-domain-binding proteins (G3BP) are multifunctional RNA-binding proteins, pivotal in the initiation of stress granules (SGs). SARS-CoV-2 nucleocapsid (N) protein exhibits strong binding affinity for G3BP and inhibition of SG formation. However, pro-viral role(s) of the G3BP-N interaction have remained unclear. Here, we have comprehensively examined the importance of G3BP for SARS-CoV-2 infection both in vitro and in vivo. Using reverse genetics, we constructed a viral mutant, SARS-CoV-2 RATA, which exhibits stronger and more persistent SG response in infected cells. We also show that in SARS-CoV-2 infected cells, G3BP-N complexes are targeted to the pore complex of double membrane vesicles (DMV) from which nascent viral RNA emerges. Furthermore, through interaction with 40S ribosomal subunits, G3BP-N complexes promote highly localized translation of viral mRNAs at the viral factories and thus facilitate viral gene expression and replication. This work provides a mechanistic understanding of the roles of G3BP in SARS-CoV-2 infection.Biological sciences/Microbiology/Virology/SARS-CoV-2Health sciences/Pathogenesis/Infection", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-4145371/v1/37fff4ba602c5ad905fcdc74.png", + "https://assets-eu.researchsquare.com/files/rs-4145371/v1/cc66b1ac63caafe3c952605d.png", + "https://assets-eu.researchsquare.com/files/rs-4145371/v1/124ae2cc1a8ded414c0286ba.png", + "https://assets-eu.researchsquare.com/files/rs-4145371/v1/3d249bba57b0aa4fd70b5730.png", + "https://assets-eu.researchsquare.com/files/rs-4145371/v1/bee7edf89db7d043a4da5d03.png", + "https://assets-eu.researchsquare.com/files/rs-4145371/v1/3fc67834ade69d04bd30230b.png", + "https://assets-eu.researchsquare.com/files/rs-4145371/v1/d2246c6e39b3aabad509eff7.png" + ] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "ExtendDataFig1.pdfExtendDataFig2.pdfExtendDataFig3new.pdfExtendDataFig4.pdfExtendDataFig5.pdfExtendDataFig6.pdfExtendedDataTable1.pdfExtendedDataTable2new.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Ras-GTPase-activating protein SH3-domain-binding proteins (G3BP) are critical for the formation of stress granules (SGs) through their RNA- and ribosome-binding properties. SARS-CoV-2 nucleocapsid (N) protein exhibits strong binding affinity for G3BP and inhibits infection-induced SG formation soon after infection. To study the impact of the G3BP-N interaction on viral replication and pathogenesis in detail, we generated a mutant SARS-CoV-2 (RATA) that specifically lacks the G3BP-binding motif in the N protein. RATA triggers a stronger and more persistent SG response in infected cells, showing reduced replication across various cell lines, and greatly reduced pathogenesis in K18-hACE2 transgenic mice. At early times of infection, G3BP and WT N protein strongly colocalise with dsRNA and with non-structural protein 3 (nsp3), a component of the pore complex in double membrane vesicles (DMVs) from which nascent viral RNA emerges. Furthermore, G3BP-N complexes promote highly localized translation of viral mRNAs in the immediate vicinity of the DMVs and thus contribute to efficient viral gene expression and replication. In contrast, G3BP is absent from the DMVs in cells infected with RATA and translation of viral mRNAs is less efficient. This work provides a fuller understanding of the multifunctional roles of G3BP in SARS-CoV-2 infection.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The coronaviral replication cycle is a complex process encompassing viral entry, uncoating, translation, replication, assembly, and release1, each stage likely influenced by a number of host proteins, both pro- and antiviral. Despite having a relatively large coding capacity, with many essential functions carried out by the viral non-structural and accessory proteins, the coronaviruses rely on a complex interplay with host factors to successfully infect and propagate within host cells2. These host factors play pivotal roles in determining the outcome of the infection, modulating the host immune response, and offering potential therapeutic targets. A fuller understanding of the viral replication cycle will depend on uncovering the functions of these host factors.\n\nG3BP1/2 (G3BP) are homologous multifunctional RNA-binding proteins, with a critical role in the nucleation of SGs3, dynamic mRNA-protein complexes formed within host cells in response to various stressors, including viral infections4. Many viruses target G3BP and other SG proteins to inhibit the formation of SGs or to otherwise promote viral replication5. Some viruses target the intact protein for inclusion into their replication complexes suggesting that pro-viral role(s) of the protein are also likely6,7,8. The functions of G3BP in viral replication is probably best understood for the Old World alphaviruses9,10,11,12, in which the protein is bound and recruited to RNA replication complexes by the viral non-structural protein (nsP)-37. nsP3 contains two FGDF motifs in an unstructured region close to the C-terminus, that mediate high affinity binding to a hydrophobic groove on the surface of the NTF2-like (NTF2L) domain of G3BP9,13. This interaction likely occurs immediately upon nsP3 synthesis, and bivalent binding leads to the formation of chains of G3BP dimers, interlinked by flexible C-terminal domains of nsP3. This binding mode is important for maintaining high local concentrations of viral replication complexes to promote efficient viral gene expression and genome replication at a critical early stage in viral replication10,13.\n\nEarly in the COVID-19 pandemic, SARS-CoV-2 protein-protein interaction studies revealed an interaction between G3BP and the nucleocapsid (N) protein14,15,16. The interaction is dependent on an ITFG-based motif at the N-terminus of N17, which binds to the G3BP NTF2L in a manner reminiscent of the binding to Old-World alphavirus nsP3, suggesting a similar binding mode, later confirmed by X-ray crystallography18. Indeed, the interaction is important for inhibition of SG formation during infection19,20,21,22,23, but potential other pro-viral effects of this interaction in infected cells have not been described.\n\nHere, we show that the recruitment of G3BP to viral replication-transcription complexes (RTC) occurs early in SARS-CoV-2 infection in a manner which provides an accessory proviral effect of promoting the local translation of viral mRNAs.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We first studied the kinetics of SGs during SARS-CoV-2 infection. We observed that SGs were induced at 6\u2009hpi (even in cells where N protein is not yet detected) and 12\u2009hpi but were disassembled later in infection as N protein levels increased (Fig.\u00a01a\u2013c). In contrast, we observed sustained activation of protein kinase R (PKR) and consequent phosphorylation of eIF2\u03b1 during the infection (Fig.\u00a01d). To determine whether the disassembly of SGs during SARS-CoV-2 infection represented a specific mechanism, we performed experiments with exogenous stressors as described previously24. Sodium arsenite (SA) induces SG assembly via eIF2\u03b1 phosphorylation25, whereas pateamine A (PAT) does so by inhibition of eukaryotic initiation factor 4\u2009A (eIF4A)26. We found that SARS-CoV-2 infection could interfere with SGs induction by SA or by PAT in VeroE6 cells (Supplementary Fig.\u00a01a and Fig.\u00a01e, f), indicating that the mechanism of SG inhibition is downstream of eIF2\u03b1 phosphorylation or eIF4A inhibition. The results in Fig.\u00a01 were repeated in U2OS-ACE2 cells with similar results (Supplementary Fig.\u00a01b\u2013h). In infected U2OS-ACE2 cells, we also detected activation of PKR-like ER kinase (PERK), suggesting collaboration of this kinase in eIF2\u03b1 phosphorylation (Supplementary Fig.\u00a01e).\n\na VeroE6 cells were mock infected or infected with SARS-CoV-2 WT at 0.5 MOI. Cells were fixed at 6, 12, and 24\u2009hpi and stained for G3BP1 (green), eIF4G (red), N (gray), and Hoechst (blue). White arrows indicate SGs. Representative images from three independent experiments are shown. Scale bar\u2009=\u200910\u2009\u00b5m. Quantification of SG foci and N protein intensity were analyzed by CellProfiler (b) Bars represent mean\u2009\u00b1\u2009SEM for three independent experiments shown as hollow dots (each dot representing the mean of 30 cells). c The line indicates the inverse correlation of N protein level and SG numbers per cell (n\u2009=\u2009320 cells). The correlation was calculated by Pearson correlation coefficient r\u2009=\u2009\u20130.2, p\u2009=\u20090.0004 (two-tailed). d VeroE6 cells were infected as in (a). Cell lysates were separated by SDS-PAGE and probed with indicated antibodies. e VeroE6 cells were infected as in (a) and at 6\u2009hpi were stressed with SA or PAT for 1\u2009h before fixation and staining. Images are shown in Supplementary Fig.\u00a01a. Quantification of SG foci was performed using CellProfiler. Bars represent mean\u2009\u00b1\u2009SEM for three independent experiments, shown as hollow dots (each dot representing the mean of 30 cells). f Cells were lysed for immunoblotting with indicated antibodies.\n\nN protein (419 amino acids) is encoded by the ninth ORF of SARS-CoV-2 genome and is composed of N-terminal RNA-binding (N-NTD) and C-terminal dimerization domains (N-CTD) that are flanked by three intrinsically disordered regions (IDRs)27 (Fig.\u00a02a). Previous research revealed that N interacts with G3BP14 and the interaction can lead to SG disassembly upon overexpression17,19,20,21,22,23. Here, we confirm that N protein bound to G3BP1 and G3BP2 in cells infected with the SARS-CoV-2 founder strain (Fig.\u00a02b). Others have shown that the N-IDR1 domain of N protein binds to G3BP21, and specifically an ITFG (aa 14-17) motif in N-IDR1 was essential for their interactions17,18,23. This motif is highly conserved among most SARS-CoV-2 variants. However, a P13L mutation proximal to the ITFG motif emerged in Omicron and XBB.1.5 variants (Fig.\u00a02c). Despite this mutation, Omicron and XBB.1.5\u2009N proteins maintained binding capability to G3BP, although exhibiting a marginal reduction in affinity (Fig.\u00a02d). However, this slightly decreased binding had little consequence for SG inhibition, since cells infected with either variant, showed similar induction of SGs and were inhibited from forming SGs upon exogenous stress (Fig.\u00a02e, f).\n\na Schematic of SARS-CoV-2 genome and N protein, created in BioRender. b VeroE6 cells were mock infected or infected with SARS-CoV-2 WT (founder strain) at 0.01 MOI for 24\u2009h. Cells were lysed and immunoprecipitated with G3BP1 or N antibodies and analyzed by immunoblot for indicated proteins. c Amino acid sequence of N (aa 12-20) in different variants. d VeroE6 cells were mock infected or infected with WA-1, Delta, Omicron or XBB.1.5 at 0.01 MOI for 24\u2009h. Cells were lysed and immunoprecipitated using G3BP1 or N antibody to determine the interaction of G3BP and N. Quantification of western blot was performed using Image J and data are presented as mean values\u2009\u00b1\u2009SEM from three independent experiments. e VeroE6 cells were mock infected or infected with WA-1 or XBB.1.5 at 0.5\u2009MOI. At 6\u2009hpi, cells were stressed with SA or PAT for 1\u2009h before fixation and staining with indicated antibodies. Representative images from three independent experiments are shown. Scale bar\u2009=\u200910\u2009\u00b5m. f Quantification of SG foci was performed using CellProfiler. Bars represent mean\u2009\u00b1\u2009SEM for three independent experiments, shown as hollow dots (each dot representing the mean of 30 cells).\n\nThe crystal structure of G3BP1-NTF2L domain in complex with a peptide derived from SARS-CoV-2 N protein (aa 1\u201325) revealed that the docking is mediated by hydrogen bonds, van der Waals contacts and electrostatic interactions18. The NTF2L features a long binding groove formed by two \u03b1-helices and two \u03b2-sheets, consisting of a 5.6\u2009\u00c5 wide groove and a 3.5\u2009\u00c5 narrow groove. The aromatic ring of N-F17 inserts into the aromatic cage at the center of NTF2L binding groove, stabilized by multiple \u03c0-stacking interactions. Meanwhile, the bulky hydrophobic side chain of N-I15 inserts into the small groove, coordinated by NTF2L residues L10, V11, and P618 (Fig.\u00a03a). Interestingly, the dual groove-insertion mode of G3BP1and \u03a6xFG motif was also observed in complexes involving Caprin1/G3BP-NTF2L, and alphavirus nsP3/G3BP-NTF2L13,28, highlighting the \u03a6xFG motif is a dominant factor for the G3BP1-NTF2L-mediated protein interactions. Additionally, research indicates that mutations at N-I15 and at N-F17 are required to fully abrogate the interaction between G3BP1 and the N protein29. To evaluate the role of G3BP-N protein interaction in the context of viral infection, we mutated both I15 and F17 to alanine within the infectious icDNA clone of SARS-CoV-230 (Fig.\u00a03b and Supplementary Fig.\u00a02a\u2013c). The SARS-CoV-2 RATA virus, which failed to form a complex with N and G3BP, induced a more robust SG response and was significantly less able to inhibit SGs after exogenous stress (Fig.\u00a03c\u2013e). These findings indicate that N-WT disrupted SGs formation more potently by interacting with G3BP whereas N-RATA, which lacks G3BP binding, shows diminished suppression of SG formation. Similar results were obtained in U2OS-ACE2 cell lines (Supplementary Fig.\u00a03a\u2013d).\n\na Structure of G3BP1 NTF2L bound to N-WT (aa1-25) (PDB: 7SUO). b Schematic of SARS-CoV-2 WT and RATA mutant, created in BioRender. RNA sequencing confirmed no reversion at positions 15 or 17 in RATA clones, indicating successful recovery of infectious virus (Supplementary Fig.\u00a02a, b) c VeroE6 cells were infected with WT virus or RATA mutant at 0.01\u2009MOI for 24\u2009h. Cells were lysed and immunoprecipitated with G3BP1 or N antibody for immunoblotting. d VeroE6 cells were infected with SARS-CoV-2 WT or RATA at 0.5\u2009MOI for 6\u2009h. Images are shown in Supplementary Fig.\u00a02d.\u00a0Quantification of SG foci was performed using CellProfiler. Bars represent mean\u2009\u00b1\u2009SEM for four independent experiments shown as hollow dots (each dot representing the mean of 20 or 99 cells). e VeroE6 cells were infected as in (d) and stressed at 6\u2009hpi with SA or PAT for 1\u2009h. Images are shown in Supplementary Fig.\u00a02e.\u00a0Quantification of SG foci intensity was performed using CellProfiler. Bars represent mean\u2009\u00b1\u2009SEM for three independent experiments shown as hollow dots (each dot representing the mean of 30 cells). f VeroE6 and MA104 \u2013 two SARS-CoV-2 permissive cell lines, and U2OS cells transduced with ACE2 and TMPRSS2 were infected with SARS-CoV-2 WT or RATA until plaques were visible. Representative images on left show relative plaque sizes in indicated cell lines. Right, plaque sizes were measured by Image J (n\u2009=\u200917 plaques per condition). The box plots bound the interquartile range divided by the median, with the whiskers extending from the minimum to the maximum values, each dot represents the plaque size of an individual plaque. g Viral titer from indicated cell lines infected with SARS-CoV-2 WT or SARS-CoV-2 RATA at 0.05\u2009MOI. Data are presented as mean values\u2009\u00b1\u2009SD as appropriate (n\u2009=\u20093 biological replicates).\n\nTo further investigate the contribution made by the G3BP-N interaction on SARS-CoV-2 replication, we assessed the replication kinetics of SARS-CoV-2 WT virus and the RATA mutant in a selection of different cell lines. RATA produced a smaller plaque size (Fig.\u00a03f) and replicated to significantly reduced titers compared to WT virus in all G3BP-expressing cells (Fig.\u00a03g). Thus, the G3BP-N interaction augments SARS-CoV-2 replication in a variety of in vitro systems. These findings align with a recent study demonstrating that a single mutation at F17, which reduces the interaction between G3BP and the N protein, attenuates SARS-CoV-223. Loss of G3BP1 binding did not elevate interferon levels in RATA-infected cells (Supplementary Fig.\u00a03e), indicating that the attenuation of the RATA virus is independent of G3BP1-mediated interferon signaling pathways. Interestingly, both WT and RATA viruses demonstrate enhanced replication in G3BP-deficient cells, likely due to the absence of SG assembly, which typically impedes viral mRNA translation. In cells lacking G3BP, RATA replicates to titers closer to those of WT virus (Fig.\u00a03g), suggesting that its attenuation is largely due to the lack of G3BP interaction. However, there remains a 4-fold reduction in RATA titers relative to WT in cells lacking G3BP, indicating that the attenuation is partly G3BP-independent. Since we know from previous work17 that the RATA mutation is very specific for G3BP, we do not believe the G3BP-independent attenuation to be due to loss of another protein binding partner. Instead, it could be an effect of the sensitivity of the N protein IDRs to mutation31.\n\nTo determine whether loss of G3BP binding also attenuates RATA replication in vivo, we took advantage of the commonly used K18-hACE2 mouse model for severe SARS-CoV-2 pathogenesis32. Mice were intranasally inoculated with 100\u2009PFU of either SARS-CoV-2 WT or RATA (Fig.\u00a04a) and monitored for weight loss daily. As expected, K18-hACE2 mice infected with SARS-CoV-2 WT showed significant weight loss, starting at day 5 after inoculation and ending with sacrifice when 20% weight loss was reached (Fig.\u00a04b). In contrast, mice infected with RATA did not display any detectable weight loss. Analyzes of viral RNA load in lung tissue revealed that RATA replication was significantly lower than WT (Fig.\u00a04c). Histopathology of lungs of a selection of mice sacrificed at day 7 after infection revealed significantly higher amounts of immune cell infiltrates, and more extensive tissue damage in the lungs of mice infected with WT SARS-CoV-2 compared to mice infected with RATA (Fig.\u00a04d). Since RATA-infected mice displayed only mild disease, we evaluated whether infection with RATA protects mice from subsequent challenge with WT SARS-CoV-2. At day 22 after initial infection, we challenged mice that had been initially infected with RATA with 10x lethal dose (1000\u2009PFU) of WT virus. Naive mice infected with 1000\u2009PFU of WT virus, used as controls, lost weight rapidly and were all sacrificed on day 8 post infection or earlier. In contrast, mice that had previously been infected with the RATA mutant exhibited neither weight loss nor severe tissue damage (Fig.\u00a04e\u2013g). Altogether, these experiments demonstrate that RATA is significantly attenuated in the K18-hACE2 mice model and suggests the possibility that the inhibition of the G3BP-N interaction might be included in a strategy to construct a live-attenuated COVID-19 vaccine.\n\na Schematic of SARS-CoV-2 challenge and rechallenge experiments, created in BioRender. b For primary challenge, mice were inoculated with 100\u2009PFU of SARS-CoV-2 WT or RATA and evaluated for weight loss (n\u2009=\u20094 in each group). c RT-qPCR of N protein and E protein expression in mice lungs after primary challenge (n\u2009=\u20093 mice). Data are presented as mean values\u2009\u00b1\u2009SEM as appropriate in (b, c). d Lung histopathology and N protein immunohistochemistry staining in the lungs at 7 dpi from mock, SARS-CoV-2 WT and RATA infected mice. Scale bar\u2009=\u200950\u2009\u00b5m. e mice 21 days after the primary infection, or naive mice were rechallenged with 1000 PFU of SARS-CoV-2 WT and evaluated for weight loss (n\u2009=\u20094 in each group). f RT-qPCR of N protein and E protein expression in mice lungs after rechallenge\u00a0(n\u2009=\u20093 mice). Data are presented as mean values\u2009\u00b1\u2009SEM as appropriate in (e, f). g Lung histopathology and N protein immunohistochemistry staining from mock and rechallenged mice. Images are representative of lung sections from four mice, scale bar\u2009=\u200950\u2009\u00b5m.\n\nN protein contains three IDRs and has been demonstrated to undergo liquid\u2013liquid phase separation (LLPS) with the viral RNA genome, potentially facilitating viral RNA replication, transcription, particle assembly33,34,35,36. Previously, the I15A and F17A mutations in N-RATA showed no effect on assembly of virus-like particles17. To compare the LLPS properties of N-WT and N-RATA, we employed a technique allowing high-fidelity reconstitution of ribonucleoprotein granules in a cell lysate-based system37. Notably, both N-WT and N-RATA exhibited droplets upon their individual addition to \u2206\u2206GFP-G1-WT cell lysates (Fig.\u00a05a), demonstrating that the N-RATA mutation does not affect the multivalent interactions with RNA and/or other N protein molecules. This phenomenon was not affected by the lack of G3BP as both N-WT and N-RATA induced LLPS in the \u2206\u2206GFP cell lysates (Supplementary Fig.\u00a04a).\n\na Purified N-WT or N-RATA protein was added at varying concentrations to lysates from U2OS cells lacking both G3BP1 and G3BP2, and stably expressing GFP-G3BP1 (\u2206\u2206GFP-G1-WT). N-specific antibodies conjugated to Alexa Fluor 647 secondary antibody were used to visualize N protein. Representative images from three independent experiments are shown. b Summary of the phase separation behaviors of the N-WT or N-RATA with increasing concentrations of purified G3BP1 protein in \u2206\u2206GFP-G1-WT cell lysate. Corresponding images are shown in Supplementary Fig.\u00a04b. c Purified G3BP1 (20\u2009\u00b5M) with or without N-WT (10\u2009\u00b5M) or N-RATA (10\u2009\u00b5M) to was added to \u2206\u2206GFP-G1-WT cell lysates. Fluor-conjugated antibodies were used to visualize N (red), eIF4G/Actin/Caprin1 (yellow), or GFP (green) indicated GFP-G3BP1. Representative images from three independent experiments are shown. Scale bar\u2009=\u200910\u2009\u00b5m.\n\nG3BP1 also exhibits the capability of LLPS attributed to its three IDRs38. To assess the impact of G3BP1 on the formation of N-induced droplets, we constructed a comprehensive diagram using increasing concentrations of purified G3BP1 and N proteins. We observed droplet formation for both N-WT and N-RATA proteins when the protein concentration reached 40\u2009\u00b5M. Notably, below this threshold, G3BP1 robustly promoted the LLPS of N-WT in a dose-dependent manner, whereas the addition of G3BP1 to N-RATA resulted in only formation of aggregates (Fig.\u00a05b and Supplementary Fig.\u00a04b), demonstrating that G3BP1 enhances the LLPS of N protein by interacting with its ITFG motif.\n\nAs expected, G3BP1-induced droplets contained eIF4G and Caprin1, typical SG components but did not contain actin (Fig.\u00a05c) and were inhibited from formation by addition of RNase (Supplementary Fig.\u00a04c). However, in G3BP1-N droplets, there was no significant signal for eIF4G or Caprin1, revealing that the composition of N- containing droplets differs from SGs (Fig.\u00a05c). Thus, the ability of N to undergo LLPS, combined with its high binding affinity for G3BP, outcompetes other proteins of the SG network, and that this competition might be the mechanism for the ability of N protein to disrupt SGs formation.\n\nN protein has been reported to dynamically localize to the viral RTC at the early stage of infection, contributing to efficient viral RNA replication and transcription39. In infected cells, we observed that while G3BP1 colocalized with N protein throughout the infection, G3BP1-N complexes were accumulated in close proximity to clusters of dsRNA-positive foci at 3 and 6\u2009hpi, but not at 12\u2009hpi (Supplementary Fig.\u00a05a). Notably, Burke and colleagues previously showed that G3BP1 is excluded from sites of genomic RNA accumulation at later times in SARS-CoV-2 infection40. As expected, N-RATA did not colocalize with G3BP1 but did accumulate with dsRNA (Fig.\u00a06a), demonstrating that SARS-CoV-2 N recruits G3BP1 to RTC early in infection.\n\na VeroE6 cells were infected with WT SARS-CoV-2 at 0.5\u2009MOI. Cells were fixed at 6\u2009h and stained for G3BP1 (green), dsRNA (red) and N (gray), Hoechst (blue). Representative images from three independent experiments are shown. Scale bar\u2009=\u20095\u2009\u00b5m b Schematic of GFP-G3BP1 constructs used for reconstitution of G3BP1/2 double KO cell lines. All lines were subsequently transduced with ACE2 receptor and TMPRSS2. c \u0394\u0394GFP-G1-WT cells were infected with WT SARS-CoV-2 WT or RATA at 0.5 MOI for 6\u2009h. Cells were fixed and stained for dsRNA (blue), N (gray) and eIF4A (c) or nsp3 (e)\u00a0(red). Representative images from three independent experiments are shown. White arrows indicate SGs. Scale bar\u2009=\u20095\u2009\u00b5m. d Pearson\u2019s correlation coefficients for colocalization of G3BP1 and dsRNA in indicated cells were calculated in CellProfiler (n\u2009=\u200971 for VeroE6 cells, n\u2009=\u200920 dsRNA-positive fields for \u0394\u0394GFP-G1-WT cells). Box plots bound the interquartile range divided by the median, with the whiskers extending from the minimum to the maximum values. f \u0394\u0394GFP-G1-WT cells were infected with WT virus or RATA mutant at 0.01\u2009MOI for 24\u2009h. Cells were lysed and immunoprecipitated with GFP or N antibody for immunoblotting as indicated.\n\nPrevious data indicated that the protein composition of G3BP1-N lysate granules differs from SG assemblies (Fig.\u00a05c). To further distinguish the two different condensates and explore the mechanisms whereby the G3BP-N interaction contributes to SARS-CoV-2 replication, we used a U2OS cell line panel shown in Fig.\u00a06b3,10. G3BP1 and eIF4A co-staining can be used to identify SGs24, while dsRNA signal indicates RTC. Consistent with Fig.\u00a06a, G3BP1 colocalized with N-WT and dsRNA in SARS-CoV-2 WT-infected \u0394\u0394GFP-G1-WT cells. However, when those cells were infected with RATA, G3BP1 only clustered in SGs but not at RTC (Fig.\u00a06c). Interestingly, nonstructural protein 3 (nsp3), a large transmembrane protein at the RTC pore41, also clustered with G3BP1/N puncta (Fig.\u00a06e), and co-precipitated with G3BP1 and N in SARS-CoV-2 WT-infected cells (Fig.\u00a06f and Supplementary Fig.\u00a05e), suggesting that G3BP1-N complexes are localized to the DMV pores, from which nascent viral mRNA emerges. These observations uncovered the significance of the G3BP-N interaction for SG disassembly and recruitment of G3BP1 to RTC. The removal of the RGG domain in G3BP did not affect its interactions at the NTF2L domain but led to an inability to rescue SGs formation under different stress conditions3. As a consequence, the dispersed eIF4A signals indicated the inability of G3BP1-\u0394RGG to support SGs assembly during both WT virus and RATA mutant infections. However, the colocalization of G3BP1-\u0394RGG, N protein and dsRNA/nsp3 in SARS-CoV-2 WT-infected cells revealed that the recruitment of G3BP1 to RTC is independent of SGs formation pathway (Supplementary Fig.\u00a05c, d).\n\nViruses rely on the host cell translational apparatus for efficient synthesis of viral proteins and employ a diverse array of mechanisms to gain access to ribosomes for preferential translation of viral mRNAs42. Given that G3BP associates with the 40S ribosomal subunit3,10,43, we proposed that G3BP1 might facilitate viral protein translation. To test this, we used ribopuromycylation staining to map active translation sites in infected cell panels10,24,44. As expected, puromycin (PMY) signals were intense and uniformly distributed in all mock infected cells, indicating robust protein synthesis pervading the entire cytoplasm. However, the PMY signal was significantly weaker overall in SARS-CoV-2 WT infected cells, but strongly colocalized with G3BP1 and N protein, revealing that G3BP-N-containing RTC were the locations for the most concentrated translation in infected cells (Fig.\u00a07a, b and Supplementary Fig.\u00a07). Notably, PMY-Max intensity within cells, which reflects translation efficiency at viral factories, was reduced in cells infected with the RATA, resulting in lower viral protein production (Fig.\u00a07d). This was despite similar levels of viral RNA in RATA and WT infected cells early in infection (Fig.\u00a07c). Additionally, in cells expressing G3BP1-\u2206RGG, where the G3BP:40S interaction is absent, PMY signals were not detected despite the strong colocalization of G3BP and N protein (Fig.\u00a07e, f). In summary, these results revealed that G3BP1 plays a role in recruiting the translation machinery to viral factories for the production of nascent viral mRNA at early times post infection.\n\na VeroE6 cells were infected with WT SARS-CoV-2 or RATA mutant at 0.5\u2009MOI for 6\u2009h. Cells were incubated with PMY (20\u2009\u00b5g/mL) for 2\u2009min before fixation and stained for G3BP1 (green), PMY (red), N (gray), Hoechst (blue). Representative images from three independent experiments are shown. Scale bar\u2009=\u200920\u2009\u00b5m. b Correlations of G3BP1-PMY, or G3BP1-N, and PMY max intensity were calculated in CellProfiler based on Pearson\u2019s correlation coefficient for infected cells (n\u2009=\u2009126 in WT, n\u2009=\u2009160 in RATA, n\u2009=\u200966 in Mock). Box plots bound the interquartile range divided by the median, with the whiskers extending from the minimum to the maximum values c VeroE6 cells were infected with SARS-CoV-2 WT or RATA mutant at 0.05\u2009MOI for 6\u2009h, cells were collected for RT-qPCR to quantify viral RNA expression (n\u2009=\u20093 biological replicates) and d for immunoblotting with indicated antibodies. Representative images from three independent experiments are shown. Quantification of western blot was performed using Image J. Bar chart in (c, d) are presented as mean values\u2009\u00b1\u2009SEM as appropriate. e Indicated cell lines were infected with SARS-CoV-2 WT at 0.5\u2009MOI for 6\u2009h. Cells were incubated with PMY (20\u2009\u00b5g/mL) for 2\u2009min before fixation and stained for PMY (red), N (gray), dsRNA (blue). Representative images from three independent experiments are shown. Scale bar\u2009=\u20095\u2009\u00b5m. f Correlations of GFP-G3BP1-PMY, or GFP-G3BP1-N were calculated in CellProfiler based on Pearson\u2019s correlation coefficient for n\u2009=\u200940 infected cells. Box plots bound the interquartile range divided by the median, with the whiskers extending from the minimum to the maximum values g Indicated cells were infected with SARS-CoV-2 WT or RATA mutant at 0.5\u2009MOI for 10\u2009h and processed for transmission electron microscopy. DMV are indicated with asterisks and DMV-associated ribosomes by white triangles. Scale bar\u2009=\u2009500\u2009nm. Quantification of ribosome density per DMV (the number of ribosomes attached to DMV divided by length of DMV perimeter) was calculated in Image J. Each dot represents a DMV (U2OS WT\u2009=\u200966, U2OS RATA\u2009=\u200957, \u2206\u2206 WT\u2009=\u200943, \u2206\u2206 RATA\u2009=\u200953). Box plots bound the interquartile range divided by the median, with the whiskers extending from the minimum to the maximum values.\n\nTo achieve a more detailed depiction of this process across different cell lines, we utilized transmission electron microscopy (TEM) to visualize SARS-CoV-2 infection. Consistent with many other positive-strand RNA viruses, SARS-CoV-2 causes restructuring of ER membranes, leading to the formation of DMV, sites for viral RNA synthesis45,46. Subsequently, viral mRNA will be exported from DMV to the cytosol for translation45. In SARS-CoV-2 WT-infected cells, we observed accumulation of ribosomes around the DMVs (Fig.\u00a07g and Supplementary Fig.\u00a06a). However, in RATA mutant-infected cells, the accumulation was less pronounced, and ribosomes had more diffuse distribution, consequently leading to a decrease in size of the large virus-containing vacuoles and decreased virion production (Supplementary Fig.\u00a06b, c). Moreover, the configuration was influenced by the absence of G3BP, resulting in a dispersed distribution of ribosomes in both WT SARS-CoV-2- and RATA infections in infected cells lacking G3BP (Fig.\u00a07g). These results underscore the significance of G3BP in the regulation of viral mRNA translation by SARS-CoV-2, providing validation for the mechanisms through which G3BP-N complexes facilitate the recruitment of ribosomes to viral factories, and enhance viral protein translation.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54996-3/MediaObjects/41467_2024_54996_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54996-3/MediaObjects/41467_2024_54996_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54996-3/MediaObjects/41467_2024_54996_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54996-3/MediaObjects/41467_2024_54996_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54996-3/MediaObjects/41467_2024_54996_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54996-3/MediaObjects/41467_2024_54996_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54996-3/MediaObjects/41467_2024_54996_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "G3BP1, a multifunctional protein, can exhibit both proviral and antiviral activities during infection among different viral families47. The specific function of G3BP1 is determined by the proteins or RNA with which it interacts. When bound to cGAS for DNA virus, or RIG-I for RNA virus, G3BP1 promotes the IFN-\u03b2 responses, thereby inhibiting viral replication48,49. Additionally, as a nucleating protein of SGs, G3BP1 binds mRNAs, 40S ribosomal subunits, initiation factors (eIF2, eIF3, eIF4), and RNA-binding proteins (e.g., poly(A) binding protein, Caprin1) for SGs assembly and protein translation arrest after viral infection. Our data indicate that SGs were induced via the PKR/PERK-eIF2\u03b1 signaling pathway, which corresponded with reduced total cellular protein translation during early SARS-CoV-2 infection. However, SARS-CoV-2 N protein has evolved countermeasures to neutralize the antiviral functions of G3BP1 by targeting it directly to disassemble SGs14,15,16,23. The mechanism involves the high binding affinity between G3BP1 and the N protein, which can sequester G3BP1 and inhibit its interaction with other key SG proteins. This hypothesis is supported by our lysate-based LLPS experiment and a peptide competition assay17. Additionally, we observed that the N protein hijacks G3BP1 with viral dsRNA at sites of active viral replication at early infection, where no other SG proteins, such as eIF4A, accumulate, suggesting the formation of proviral assemblies involving G3BP1.\n\nN protein is essential for viral replication and assembly and is highly abundant in SARS-CoV-2-infected cells50,51. It is recruited at the DMV pores by nsp341, and there forms phase separated droplets with RNA-dependent RNA polymerase (RdRp) complex of nsp12, nsp7, and nsp8 and RNA, potentially aiding in the entry of viral genomic RNA into the DMV, initiating the initiation and/or elongation of viral RNA synthesis33. A recent report shows that nsp3 also binds fragile X mental retardation proteins52, likely also contributing to the disassembly of SGs and phase transition of proteins concentrated at the DMV periphery. Our data demonstrate that G3BP is recruited to viral factories by N protein, where it forms a complex with the N protein, viral dsRNA, and nsp3. Subsequently, we observed high levels of localized translation at the viral factories due to the interaction at G3BP1 RGG domain with cellular 40S ribosomal subunits3. Probably the translation pre-initiation complexes that accumulate in infection-induced SGs at very early times, are then recruited, along with G3BP to the viral factories and are made available for translation of nascent viral mRNAs produced at those sites. Interestingly, other positive strand RNA viruses, from the alpha- and noroviruses have also evolved similar strategies to manipulate G3BP for efficient viral mRNA translation8,10 suggesting it may be a common mechanism to take over the translation machinery for efficient production of viral proteins. Thus, the \u03a6xFG motif binding groove on the NTF2L is a promising target for broadly acting antiviral interventions. Notably, the proviral roles of G3BP in chikungunya virus and in norovirus infections are critical and viral replication is severely compromised or undetectable in its absence8,10, while our present work shows that the proviral role in SARS-CoV-2 infection is accessory but not critical. Moreover, N protein also facilitates the compaction of N protein and its large 29.9\u2009kb viral genomic RNA into a highly structured viral RNA-protein complex, facilitating its packaging into virions through subsequent N-M protein interactions36. Our data show that G3BP1 facilitates the LLPS of N with RNA in a dose-dependent manner. We propose that the facilitation of LLPS of N by G3BP serves a dual role. Firstly, it aids in the recruitment of RNA and host proteins for efficient RNA replication and transcription at the RTC. Secondly, it concentrates viral genomic RNA and structural proteins, promoting the assembly of virus particles. This is supported by a recent report highlighting the G3BP\u2019s role in virus assembly and its integration into virus particles53.\n\nThis study presents a comprehensive examination of the importance of G3BP for SARS-CoV-2 infection both in vitro and in vivo (Fig.\u00a08). The works contributes to an emerging view of G3BP as both antiviral (as a component of SGs) and, when sequestered to replication complexes, as a proviral factor involved in viral gene expression and replication. The loss of G3BP binding site on SARS-CoV-2 N protein attenuates viral replication in different cell lines and pathogenesis in mouse model of severe COVID-19, supporting the concept that G3BP-N interaction is crucial both for evasion of host defense and for efficient viral gene replication and expression. However, host defense and viral replication are complex and intricate processes. Given the diverse and multifaceted properties of G3BP1, we propose that antiviral SGs and proviral granules involving G3BP1 may coexist and potentially both affect viral replication and gene expression. This might explain why knockdown or knockout of G3BP were in some studies shown to boost SARS-CoV-2 infection21, while in other cases, the opposite or no effect was observed40,53. However, in RATA-infected cells, G3BP predominantly contributes to the formation of consistent SGs, thus resulting significant attenuation both in vitro and in vivo. We also showed that RATA has the capacity to sufficiently activate immune responses, subsequently offering protective immunity upon rechallenge. This diminished pathogenicity and robust immune activation suggests that mutation of G3BP-interaction domains could be viable strategies for live attenuated vaccine development across multiple virus families.\n\nSARS-CoV-2 infection activated PKR/PERK-eIF2\u03b1 serves as a protective mechanism in host cells, leading to the G3BP dependent SGs assembly. However, SARS-CoV-2 N protein hijacks G3BP, contributing to the enhancement of SARS-CoV-2 replication across multiple stages of the replication cycle: (1) G3BP-N interaction mediates the disassembly of SGs. (2) Early in infection, the N protein recruits G3BP to nsp3 at the RTC, potentially aiding in viral RNA synthesis and transcription; (3) The G3BP-N complex recruits 40S ribosomal subunits to viral factories for efficient viral protein translation; (4) G3BP promote the LLPS of N, facilitating SARS-CoV-2 virus assembly. Created in BioRender.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54996-3/MediaObjects/41467_2024_54996_Fig8_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "VeroE6 cells (ATCC-CRL-1586), MA104 (ATCC-CRL-2378.) and HEK-293T cells (ATCC-CRL-1573) were maintained in Dulbecco\u2019s modified Eagle medium (DMEM) (Sigma, #D6429) supplemented with 10% fetal bovine serum (FBS) (Sigma, #F9665), 1% penicillin\u2013streptomycin (Sigma, #P4333) and 1% L-glutamine (Sigma, #G7513). Baby hamster kidney (BHK-21) cells (ATCC CCL-10) were maintained in Glasgow\u2019s modified Eagle\u2019s medium (GMEM) (Sigma, #G5154) supplemented with 10% FBS, 10% tryptose phosphate broth (Sigma, #T8159), 20\u2009mM HEPES (Sigma, #H0887), 1mM L-glutamine and 1% penicillin\u2013streptomycin. Cells were cultured at 37\u2009\u00b0C in a humidified incubator with 5% CO2.\n\nU2OS-ACE2 cells were generated by transduction of human osteosarcoma (U2OS) cells (ATCC HTB-96) with lentivirus expressing ACE2-TMPRSS2 and selection with PMY (Thermo Fisher Scientific, #A1113803). \u2206\u2206GFP, \u2206\u2206GFP-G1-WT, and \u2206\u2206GFP-G1-\u2206RGG cells were generated by transduction of corresponding cell lines (described in ref. 10) with lentivirus expressing ACE2-TMPRSS2 and selection with PMY. Cells were then sorted using fluorescence-activated cell sorting (FACS) on a BD FACSAria Fusion system. The sorting was based on the expression of enhanced green fluorescent protein (EGFP) and RBD-AS635P binding specific to ACE254 (Supplementary Fig.\u00a08). Cells were maintained in DMEM supplemented with 10% FBS, 1% penicillin\u2013streptomycin and 1% L-glutamine.\n\nLentivirus was produced by transfection of HEK-293T cells with pWPI-IRES-Puro-Ak-ACE2-TMPRSS2, VSV-G and Gag-Pol using Lipofectamine 2000 Reagent (Thermo Fisher Scientific, #11668019). pWPI-IRES-Puro-Ak-ACE2-TMPRSS2 was a gift from Sonja Best (Addgene plasmid #154987; http://n2t.net/addgene:154987; RRID:Addgene 154987).\n\nBoth WT virus and RATA mutant were derived from SARS-CoV-2 Wuhan-Hu1 (MT926410) infectious clone30. For RATA construction, the mutation was introduced into a subclone pCC1-4K-FR1 by using PCR with primers RATA-F and RATA-R (Supplementary Table\u00a01). Thereafter, plasmids containing WT or RATA mutant fragments were digested by restriction enzyme, and then were purified and ligated in vitro to assemble the full-length cDNA using Gibson Assembly master mix (New England Biolabs, #E2611). The plasmid was verified by restriction enzyme digestion and Sanger sequencing (Eurofins Scientific). To rescue the infectious virus, BHK-21 cells were transfected with plasmid DNA (containing icDNA of SARS-CoV-2) using Lipofectamine LTX (Thermo Fisher Scientific, #15338100). After 3 days, supernatant was transferred to VeroE6 cells to propagate viruses. At 3 days post infection, P0 stock virus were harvested for sequencing using primers listed in Supplementary Table\u00a01.\n\nMonolayers of cells were infected with serial 10-fold dilutions of virus suspension for 1\u2009h, and then washed with PBS (Sigma, #D8662) before addition of 1\u2009mL of prewarmed overlay (2% methylcellulose (Sigma, #C5013): propagation media containing 2% FBS\u2009=\u20092:3). At 72\u2009hpi, cells were fixed with 4% formaldehyde (Sigma, #F8775) and stained with crystal violet solution (HistoLab, CL.42555) after removal of the overlay. Plaques were manually counted. The determination of all virus titers was performed in triplicate.\n\nVirus titrations for infection experiment were determined by plaque assay. As for in vitro infection, cells at 80% of confluncy were infected with virus in infection media supplemented with 0.2% BSA, 2\u2009mM L-glutamine and 20\u2009mM HEPES for 1\u2009h at 37\u2009\u00b0C, 5% CO2. The infection media were then removed and washed with warmed PBS before adding warmed propagation media containing 2% FBS.\n\nK18-hACE2 transgenic mice were purchased from the Jackson Laboratory and maintained as a hemizygous line. All mice were 18\u201320 weeks old at the start of the study, and experiments were conducted in BSL3 facilities at the Comparative Medicine department (KM-F) at Karolinska Institutet. Ethical permits for studies of virus infection were obtained from the Swedish Board of Agriculture (10513-2020). The mice were maintained in a specific pathogen-free facility with controlled conditions, including a stable temperature of 25\u2009\u00b0C and humidity levels between 40% and 60%, and a 12-h light/dark cycle. Mice were housed in individually ventilated cages with access to food and water ad libitum. Enrichment materials, such as shredded cardboard and paper rolls, were provided to enhance their living environment. Health monitoring was carried out daily by trained personnel, with weekly cage and water changes ensuring proper hygiene. Mice were challenged intranasally with 100 PFU or 1000\u2009PFU SARS-CoV-2 in 40\u2009\u03bcL of PBS following isoflurane sedation. During the experiment, weight loss, changes in general health, breathing, body movement, and posture were monitored. Mice were euthanized when they reached 20% weight loss or when movement was greatly impaired and/or they experienced difficulty breathing that was considered to reach a severity level of 0.5 on Karolinska Institutet\u2019s veterinary plan for monitoring animal health.\n\nMice lungs were fixed with 4% formaldehyde in PBS overnight at 4\u2009\u00b0C and transferred to 70% ethanol (VWR, #20824.296) at 4\u2009\u00b0C for storage. Samples were then dehydrated in a graded series of ethanol from 70% to 99%. The samples were cleared with clearing agent (Histolab, #14250), embedded in melted hot paraffin, and hardened on ice. Thin sections of 5\u2009\u03bcm thickness were sliced using a rotary microtome outfitted with disposable steel knives, flattened on a heated water bath, transferred onto microscope slides, and dried. Sections were deparaffinized by incubating in xylene (Histolab, #02070) and rehydrated by passing through a series of descending ethanol concentrations (100%, 95%, 70%). Sections were stained with hematoxylin, which colors cell nuclei blue, and eosin, which imparts a pink hue to cytoplasm and other structures. After staining, sections were washed in water, dehydrated, and cleared to prepare them for mounting with a coverslip. Images were taken by a Ziess Axioplan light microscope with an Olympus SC30 camera.\n\nFor immunohistochemistry staining, paraffin-embedded tissues were de-paraffinized by xylene and rehydrated with sequential incubation with 99%, 95%, 70% of ethanol, followed by blocking with 5% bovine serum albumin (BSA) (Sigma, #A4503). Sections were then incubated with primary antibody against SARS-CoV-2 N protein (Abcam, ab271180, 1:3000) overnight at 4\u2009\u00b0C, washed, and incubated with appropriate secondary antibodies Alexa Fluor 488 (Thermo Fisher Scientific, A21206, 1:1000). After washing with PBS, sections are dehydrated and cleared to prepare them for mounting with a coverslip. Images were taken by Zeiss LSM800-airy confocal microscope equipped with a pulsed white light laser and a Zeiss DIC Prism III PA 63x/1.40 oil objective. Images were processed by Zeiss ZEN microscope software.\n\nCells seeded on cover glasses (VWR, #631-1554) were washed with PBS three times and fixed with 4% formaldehyde in PBS for 20\u2009min at room temperature (RT), permeabilized in methanol (Sigma, #179957) for 10\u2009min at \u201320\u2009\u00b0C and blocked with 5% BSA in PBS at RT for 1\u2009h. Primary antibody and secondary antibody were diluted in blocking solution. Cells were incubated with primary antibody at 4\u2009\u00b0C overnight, followed by 1\u2009h incubation with secondary antibodies (Supplementary Table\u00a02). Cover glasses were mounted on glass slides by mounting media and imaged by Zeiss LSM800-airy confocal microscope equipped with a pulsed white light laser and a Zeiss DIC Prism III PA 63x/1.40 oil objective. Images were processed by Zeiss ZEN microscope software.\n\nCells were washed with ice cold PBS and lysed in lysis buffer (Thermo Fisher Scientific, #87787) supplemented with protease and phosphatase inhibitors (Thermo Fisher Scientific, #1861279, #78427) on ice for 10\u2009min. Lysates were collected and cleared by centrifuging at 13,000\u2009\u00d7\u2009g for 10\u2009min at 4\u2009\u00b0C, and incubated with GPF-trap beads (ChromoTek, #gta) for 1\u2009h with rotating at 4\u2009\u00b0C, or incubated with antibody (Supplementary Table\u00a02) for 1\u2009h and Protein A Magnetic Beads (Cytiva, #28951378) overnight at 4\u2009\u00b0C under rotation. Beads were then washed three times with cold lysis buffer and eluted in 2x NuPAGE LDS sample buffer (Thermo Fisher Scientific, #NP0007) containing 50\u2009mM DTT (Sigma, #D0632), heated at 95\u2009\u00b0C for 10\u2009min and analyzed by SDS-PAGE and western blotting.\n\nSamples were collected and denatured in LDS sample buffer (Thermo Fisher Scientific, #NP0007) for sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) on NuPAGE 4\u201312% Bis-Tris polyacrylamide gels (Thermo Fisher Scientific, #NP0321BOX) and transferred onto 0.45\u2009\u03bcm Immun-Blot PVDF membrane (Bio-Rad, #1704272). Membranes were blocked with 5% of skim milk powder (Sigma, #70166) in Tris-buffered saline with 0.05% Tween 20 (TBST) and incubated with primary antibodies at 4\u2009\u00b0C overnight, and horseradish peroxidase (HRP)-coupled secondary antibodies for 1\u2009h at RT (Supplementary Table\u00a02). Chemiluminescence (Thermo Fisher Scientific, #34580) was applied to the surface of PVDF membrane and Image lab was used for image detection and procession. All SDS-PAGE data are representative of at least three independent experiments.\u00a0Quantification of western blot was analyzed by Image J.\n\nCells were homogenized in TRIzol reagent (Thermo Fisher Scientific, #15596018), and RNA is extracted through phase separation with chloroform (Thermo Fisher Scientific, #C/4960/PB08), followed by RNA precipitation with isopropanol (Sigma, #I9516), and washing with ethanol. The RNA pellet is then resuspended in DEPC water (Thermo Fisher Scientific, #R0601). Subsequently, cDNA is synthesized from the purified RNA using SuperScript\u2122 IV reverse transcription kit (Thermo Fisher Scientific, #18091050). The qPCR reaction mix is prepared with SYBR Green PCR Master Mix (Thermo Fisher Scientific, #A46109), specific primers (Supplementary Table\u00a01), cDNA template, and amplification is performed under optimized cycling conditions on CFX384 Touch Real-Time PCR System. Data were analyzed by Bio-Rad CFX Manager software.\n\nRibopuromycylation assay10,24,44 was performed by infecting cells with indicated virus at an MOI of 0.5 for 6\u2009h and treatment with 20\u2009\u00b5g/mL of PMY for 2\u2009min at 37\u2009\u00b0C, 5% CO2. Following incubation, cells were rinsed with PBS and fixed with 4% formaldehyde in PBS for 20\u2009min at RT, permeabilized in methanol for 10\u2009min at \u201320\u2009\u00b0C and blocked with 5% BSA in PBS at RT for 1\u2009h. After blocking, cells were incubated with antibodies (Supplementary Table\u00a02). Images were taken by Zeiss LSM800-airy confocal microscope and processed by ZEN microscope software.\n\nDNA fragments encoding for human G3BP1, the SARS-CoV-2 N protein, and the SARS-CoV-2 N (RATA) mutated protein were inserted into the pET30 expression vector using ligation-independent cloning55. A TEV cleavage site was introduced between the N-terminal his 6 tag and each fusion protein. All constructs were validated by sequencing using primers (Supplementary Table\u00a01) (Eurofins Scientific). G3BP1 full-length protein was expressed and purified from E. coli BL21 cells (Sigma, #69450) and purified under native conditions. E. coli were grown to OD 600 of 0.8 and induced with 0.5\u2009mM IPTG at 22\u2009\u00b0C overnight. Pelleted cells were resuspended in lysis buffer (50\u2009mM HEPES pH 7.5, 300\u2009mM NaCl, 1\u2009mM DTT, protease inhibitor (Roche, #11873580001) and Benzonase (Sigma, #E8263)). Following sonication, lysates were pelleted at 40,000\u2009g at 4\u2009\u00b0C for 30\u2009min. His6-TEV-G3BP1 was affinity-purified by Ni-NTA agarose (Qiagen, #30210) and the eluted proteins were incubated with TEV protease (PSF, KI, Stockholm) at 4\u2009\u00b0C overnight. Fully cleaved proteins were further purified on a HiTrap Heparin column (Cytiva, #17040601). Collected fractions were analyzed by coomassie staining and SDS-PAGE, and thereafter pooled and concentrated. All proteins were further purified on a Superdex 200 16/200 column (Cytiva, #28989335), equilibrated in SEC buffer (50\u2009m HEPES, pH 7.5, 300\u2009mM NaCl, 1\u2009mM DTT). Fractions were analyzed by SDS-PAGE, pooled, concentrated, filtered, flash frozen in liquid nitrogen, and stored at \u201380\u2009\u00b0C. The SARS-CoV-2 N and SARS-CoV-2 N RATA proteins were purified following similar protocols. Each target protein was affinity-purified by Ni-NTA agarose and further isolated on a Superdex 200 16/200 column. Fractions were analyzed by coomassie staining and SDS-PAGE, concentrated, and stored at \u201380\u2009\u00b0C.\n\n1\u2009\u00d7\u2009107 cells of the indicated lines were lysed for 5\u2009min at RT in 800\u2009\u00b5L lysis buffer (50\u2009mM HEPES, 0.5% NP40, protease inhibitor, and 2.5% murine RNase inhibitor (New England Biolabs, #M0314)) and centrifuged for 5\u2009min at 20 000\u2009g at 20\u2009\u00b0C. Separately, recombinant proteins were mixed with protein dilution buffer (50\u2009mM HEPES pH 7.4, 400\u2009mM NaCl, 1\u2009mM DTT) in 8\u2009\u00b5L volume. Where indicated, 8\u2009\u00b5L of primary and secondary antibodies were diluted in the lysis buffer and incubated for 30\u2009min at RT. Total protein concentration was adjusted to 6.0\u2009mg/mL, in the collected cell-lysate supernatant. Then 40\u2009\u00b5L of cell lysate was added to the samples and 50\u2009\u00b5L of the mixture was immediately transferred to an 18-well microscope chamber slides (IBIDI, #81816) and incubated for 1\u2009h at RT. Images were taken with a Zeiss LSM700 Confocal Microscope equipped with a pulsed white light laser and a Zeiss Plan-Apochromat 63X/1.40 oil DIC objective. Condensates were analyzed with software Fiji.\n\nCells were immersion fixed in 2.5% glutaraldehyde (Sigma, #G5882) at RT for 1\u2009h and stored at 4\u2009\u00b0C or were rinsed in PBS followed by post fixation in 2% osmium tetroxide (Sigma, #419494) at 4\u2009\u00b0C for 2\u2009h. After stepwise dehydration in ethanol and acetone (Sigma, #179124), the samples were resin infiltrated and finally embedded in LX-112 (Ladd Research, #21210). Ultrathin sections (~80\u2013100\u2009nm) were prepared using an EM UC7 (Leica) placed on formvar slot grids and contrasted with uranyl acetate followed by lead citrate. The grids were examined in a HT7700 transmission electron microscope (Hitachi High-Technologies,) at 80\u2009kV and digital images were acquired using a 2k\u2009\u00d7\u20092k Veleta CCD camera (Olympus Soft Imaging Solutions).\n\nThe statistical details for all experiments, including error bars, statistical significance, and precise n numbers, are provided in the figure legends. Statistical comparisons between two groups were conducted using the unpaired t-test (two tailed). For multiple group comparisons, the one-way ANOVA test (two-sided) was employed to compare the mean of each column with the mean of a control column. Statistical analysis was conducted using GraphPad Prism 10 software. ns, P\u2009>\u20090.05; *, P\u2009\u2264\u20090.05; **, P\u2009\u2264\u20090.01; ***, P\u2009\u2264\u20090.001, ****, P\u2009\u2264\u20090.0001.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The study involves no large datasets that should be uploaded to any depository. Raw data from our experiments can be found in the Source Data file. 7SUO [https://www.rcsb.org/structure/7SUO]\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "V\u2019kovski, P., Kratzel, A., Steiner, S., Stalder, H. & Thiel, V. Coronavirus biology and replication: implications for SARS-CoV-2. Nat. Rev. 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M.G. was supported by a grant from the Swedish Foundation for Strategic Research (#UKR22-0064).", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open access funding provided by Karolinska Institute.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Division of Virology and Immunology, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden\n\nSiwen Long,\u00a0Mykhailo Guzyk,\u00a0Laura Perez Vidakovics,\u00a0Megan Wang,\u00a0Marc D. Panas,\u00a0Jonathan M. Coquet\u00a0&\u00a0Gerald M. McInerney\n\nDepartment of Medicine Solna, Science for Life Laboratory, Karolinska Institute Solna, Solna, Sweden\n\nXiao Han,\u00a0Renhua Sun\u00a0&\u00a0Adnane Achour\n\nDivision of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden\n\nXiao Han\u00a0&\u00a0Adnane Achour\n\nDepartment of Immunology and Microbiology, Leo Foundation Skin Immunology Research Centre, University of Copenhagen, Copenhagen, Denmark\n\nEgon Urgard\u00a0&\u00a0Jonathan M. Coquet\n\nInstitute of Bioengineering, University of Tartu, Tartu, Estonia\n\nAndres Merits\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: S.L., G.M.M. Methodology and Investigation: S.L., M.G., L.P.V., X.H., R.S., M.W., M.D.P., E.U., and A.M. Supervision: M.D.P., J.M.C., A.M., A.A., and G.M.M. Funding acquisition. G.M.M. Writing\u2014original draft: S.L. and G.M.M. Writing\u2014review and editing: S.L., A.M., A.A., and G.M.M.\n\nCorrespondence to\n Gerald M. McInerney.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous, reviewer(s) for their contribution to the peer review of this work. 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SARS-CoV-2 N protein recruits G3BP to double membrane vesicles to promote translation of viral mRNAs.\n Nat Commun 15, 10607 (2024). https://doi.org/10.1038/s41467-024-54996-3\n\nDownload citation\n\nReceived: 27 March 2024\n\nAccepted: 27 November 2024\n\nPublished: 05 December 2024\n\nVersion of record: 05 December 2024\n\nDOI: https://doi.org/10.1038/s41467-024-54996-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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Spread of a Dominant Multidrug-Resistant Variant in Acinetobacter baumannii", + "journal": "Nature Communications", + "published": "21 March 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_MOESM7_ESM.xlsx" + }, + { + "label": "Reporting summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_MOESM8_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_MOESM9_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_MOESM10_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://github.com/Zhou-lab-SUDA/Capybara", + "/articles/s41467-025-58106-9#MOESM6", + "/articles/s41467-025-58106-9#Sec31" + ], + "code": [ + "https://github.com/Zhou-lab-SUDA/Capybara" + ], + "subject": [ + "Antimicrobial resistance", + "Bacterial genomics", + "Microbial genetics", + "Pathogens" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4224555/v1.pdf?c=1742641535000", + "research_square_link": "https://www.researchsquare.com//article/rs-4224555/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58106-9.pdf", + "preprint_posted": "10 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The proliferation of multi-drug resistant (MDR) bacteria is driven by the global spread of epidemic lineages that accumulate antimicrobial resistance genes (ARGs). Acinetobacter baumannii, a leading cause of nosocomial infections, displays resistance to most frontline antimicrobials and represents a significant challenge to public health. In this study, we conduct a comprehensive genomic analysis of over 15,000 A. baumannii genomes to identify a predominant epidemic super-lineage (ESL) accounting for approximately 70% of global isolates. Through hierarchical classification of the ESL into distinct lineages, clades, and variants, we identified a stepwise evolutionary trajectory responsible for the worldwide expansion and transmission of A. baumannii over the last eight decades. Particularly, we observed the rise and global spread of a previously unrecognized Variant 2.5.6, which emerged in East Asia in 2006. The epidemic of the variant is linked to the ongoing acquisition of antimicrobial resistance genes (ARGs) and virulence factors facilitated by genetic recombination. Our results highlight the necessity for One Health-oriented research and interventions to address the spread of this MDR pathogen.Biological sciences/Microbiology/Microbial geneticsBiological sciences/Microbiology/PathogensBiological sciences/Microbiology/BacteriaEpidemic lineageSNP barcodemulti-drug resistanceemerging diseases", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.\nTable 1 is available in the Supplementary Files section.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Table1.docxTable 1SupplementaryData1.xlsxSupplementary Data 1SupplementaryData2.xlsxSupplementary Data 2SupplementaryData3.xlsxSupplementary Data 3DescriptionofAdditionalSupplementaryFiles.pdfSupplementaryinformation.pdf", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The proliferation of multi-drug resistant (MDR) bacteria is driven by the global spread of epidemic lineages that accumulate antimicrobial resistance genes (ARGs). Acinetobacter baumannii, a leading cause of nosocomial infections, displays resistance to most frontline antimicrobials and represents a significant challenge to public health. In this study, we conduct a comprehensive genomic analysis of over 15,000 A. baumannii genomes to identify a predominant epidemic super-lineage (ESL) accounting for approximately 70% of global isolates. Through hierarchical classification of the ESL into distinct lineages, clusters, and clades, we identified a stepwise evolutionary trajectory responsible for the worldwide expansion and transmission of A. baumannii over the last eight decades. We observed the rise and global spread of a previously unrecognized Clade 2.5.6, which emerged in East Asia in 2006. The epidemic of the clade is linked to the ongoing acquisition of ARGs and virulence factors facilitated by genetic recombination. Our results highlight the necessity for One Health-oriented research and interventions to address the spread of this MDR pathogen.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Bacteria with multidrug resistance (MDR) present a new form of pathogenic challenge in treatment, resulting in the majority of nosocomial infections1. Acinetobacter baumannii is particularly problematic, with nearly all clinical strains exhibiting a broad range of resistance to multiple drugs, disinfectants, and environmental stresses. In particular, Carbapenem-resistant A. baumannii (CRAB) exhibited 68.8\u201399.8% resistance to all antimicrobials except colistin and tigecycline2, complicating treatment strategies. The increasing prevalence of CRAB has led to an estimated 326,000 deaths in 20193, underscoring the pressing need for effective control measures.\n\nSeveral international clones (ICs) of A. baumannii have been identified through restriction fragment length polymorphism4 and multi-locus sequence typing (MLST)5,6,7. The most prominent ICs are IC2, associated with the majority of nosocomial infections internationally8, and IC1, responsible for infection outbreaks in casualties returned from the Middle East conflicts9. Furthermore, IC5 accounts for >50% of infections in Latin America10,11. The resistome, virulome, and epidemiology of many ICs have also been investigated12,13. However, the detailed population structures and temporal dynamics within each of these ICs remain poorly characterized due to the limited resolution of MLST14. Moreover, the widespread presence of recombination in A. baumannii has influenced its genomic diversification and ARG distributions15, complicating phylogenetic and functional analyses. A wealth of genomic data for A. baumannii has been gathered through surveillance and molecular epidemiology efforts. Nevertheless, the clinical interpretation of genomic data has been impeded by an incomplete understanding of its genetic context.\n\nIn this work, we exploit the genetic landscape of over 15,000 A. baumannii from around the world. Our analysis revealed their source and geographic distribution and ARG profiles, indicating different genetic reservoirs for strains of clinical and non-clinical settings. Furthermore, we identified an epidemic super-lineage (ESL) accounting for ~70% of existing clinical A. baumannii strains. Further characterization of the population structure of the ESL uncovered a cryptic, emerging Clade 2.5.6 with a global prevalence over the past 16 years. We showed that the emergence of Clade 2.5.6 was driven by the progressive acquisition of ARGs and recombination-mediated changes in virulence factors from diverse A. baumannii populations, calling for a One Health approach to research and intervention to manage the spread of this pathogen.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "The ESKAPE pathogens were each categorized into 1090\u20138329 distinct sequence types (STs) using MLST schemes (Feb. 2024) from pubMLST or Institut Pasteur16. We estimated the genetic diversities of these pathogens based on the relative frequencies of the STs and found that A. baumannii displayed a notably lower Simpson diversity index (SDI) of 0.81, in contrast to the SDI range of 0.95\u20130.99 observed in other ESKAPE members (Fig.\u00a01a). Detailed investigation attributed its low diversity to the prevalence of ST2, which constituted 43.7% of its strains\u2014a significant proportion compared to other ESKAPE pathogens, where the most common STs accounted for only 4.5% (ST235 in Pseudomonas aeruginosa) to 12.4% (ST22 in Staphylococcus aureus) of strains (Fig.\u00a01b). Notably, if all ST2 strains were removed, A. baumannii would have a comparable genetic diversity (SDI: 0.97) to other ESKAPE members. Such exceptional predominance of ST2 was unlikely a sampling bias because it has been reported in 109 countries worldwide, calling for further investigations.\n\na, b Genetic diversity of the ESKAPE bacteria by their STs. A. baumannii is divided into two groups, with or without ST2. a The bar chart shows the Simpson diversity index (SDI) of STs for each ESKAPE member. b The pie chart of the top 10 STs and the others for each ESKAPE member. c The supertree of A. baumannii based on 2266 softcore genes in the 5824 representative genomes. The highlighted clusters in the tree showed the most compatible IC/CC for each of the nine previously designated international clones (ICs) in A. baumannii, as shown in the labels. The ICs were predicted using Pasteur\u2019s MLST scheme as clonal complexes (CCs), which were shown in the outer ring. Genomes that had inconsistent IC assignments between the supertree and the MLST were marked with triangles, with the false positives (n\u2009=\u2009121) and the false negatives (n\u2009=\u200989) color-coded in blue and red, respectively. d Global distributions of the IC1 and IC2 strains, color-coded as in (a). e Histogram of the yearly isolation frequencies of IC1, IC2, and the others. The inset shows the relative frequencies of IC1 and IC2 each year. f The yearly variations of the numbers of AMR genes (blue) and categories of drug resistances (red) between 2003 and 2020. The correlation coefficient of linear regression for each number series was shown nearby. g Bubble plots of the numbers of country presences (X-axis) versus the numbers of ARG carriages (Y-axis) for the IC/CCs. The slope of linear regression (r) is 0.51 and the significance (p) is calculated using the two-tailed student\u2019s t-test. The map was modified from open-source data in [https://github.com/antvis/L7].\n\nTo investigate the genetic landscape of A. baumannii at the genomic level, we sequenced 100 clinical strains from Suzhou and Wenzhou, East China, and integrated this data with 15,643 publicly available genomes (Oct. 2022). This compilation spanned strains from 88 countries across 110 years (Table\u00a01 and Supplementary Data\u00a01). MLST analysis using Pasteur\u2019s scheme identified 518 STs among these genomes, with ST2 (57.0%) still being the most common. We utilized eBURST to cluster the STs into 371 clonal complexes (CCs), including eleven that had been previously designated as ICs7,17,18,19,20,21,22. Notably, IC2 (CC2) encompassed ST2 and its single-locus variants, constituting ~65% of all genomes.\n\nAmong the 8561 strains with isolation sources, only 162 were from non-clinical settings (Supplementary Fig.\u00a01a), suggesting a strong sampling bias toward clinical samples. Interestingly, strains from the IC2 and other ICs only accounted for 29 (17.9%) and 10 (6.2%) of the non-clinical samples (Supplementary Fig.\u00a01b), respectively, indicating distinct genetic contexts between strains in clinical or non-clinical settings.\n\nWe selected 5824 genomes with pairwise average nucleotide identities (ANIs) of <99.5% to establish a representative set of A. baumannii and identified 2266 softcore genes shared by at least 95% (5533/5824) of the representatives (Supplementary Data\u00a02). A maximum-likelihood (ML) tree was constructed for each core gene, and these trees were summarized into a supertree that unveiled the comprehensive phylogenetic structure of A. baumannii (Fig.\u00a01c) (see \u201cMethods\u201d). The majority of ICs and CCs showed strong correlations with the supertree, with each primarily linked to a single lineage (Fig.\u00a01c). Remarkably, the top two ICs (IC2 and IC1) were genetically closely related, forming a monophyletic clade in the supertree. This finding reveals a larger-scale population structure above the CCs in A. baumannii, which we designated as a \u201csuper-lineage\u201d to differentiate it from the lineages formed by CCs.\n\nWe also investigated the resistome of A. baumannii. The results revealed that each A. baumannii strain harbored an average of 15.1 predicted ARGs, conferring resistance to 6.1 drug categories. The most prevalent predicted resistances were aminoglycoside (98.6%), fosfomycin (94.1%), quinolone (87.7%), and sulfonamide (75.4%) (Table\u00a01). In contrast, resistances to tigecycline and colistin were relatively low and were only predicted in 30 and 5 strains, respectively (Supplementary Data\u00a01). Notably, the average number of ARGs per strain and their associated drug types have increased by 50\u2013100% over the past two decades (Fig.\u00a01f), implying strong selective stress imposed by antimicrobial usage. Such a rapid change in population dynamics could be driven by either universal acquisition of ARGs in all lineages or, more plausible, expansion of one or multiple epidemic lineages with high levels of ARGs.\n\nA significant correlation was observed between the international prevalence of IC/CCs and the average number of predicted ARGs per strain (r\u2009=\u20090.51) (Fig.\u00a01g), as well as their resistance to carbapenem (r\u2009=\u20090.42), minocycline (r\u2009=\u20090.65), and sulbactam (r\u2009=\u20090.38) (Supplementary Fig.\u00a01c). In addition to the ICs, we predicted three major CCs (\u226520 genomes) with averagely \u226515 ARGs and five major CCs with \u226580% carbapenem resistance (Supplementary Data\u00a03). Strains within IC2 encoded an average of 17.1 ARGs, with over two-thirds predicted to resist carbapenem, sulbactam, and minocycline (Table\u00a01). Furthermore, IC1 strains encoded ~15.3 ARGs, with 82.0% resistance to carbapenem, but <20% resistance to minocycline or sulbactam (Table\u00a01). Both IC1 and IC2 were found across 60\u201366 countries, in contrast to the other nine ICs, which were isolated from only 10 to 35 countries (Fig.\u00a01d and Supplementary Fig.\u00a01d). Consequently, the super-lineage formed by IC2 and IC1 accounted for a total of 10,820 (69%) strains from 79 countries (Table\u00a01), explaining 82.5% of the ARGs in A. baumannii and contributing significantly to the escalation of ARGs over the past two decades. Furthermore, 99 of the 100 sequenced clinical isolates belong to IC2, demonstrating its predominance in China (Supplementary Data\u00a04). Henceforth, we refer to IC1 and IC2 collectively as the ESL and focus on exploring its population dynamics below.\n\nThe ESL accounted for 65% (Africa and North America) to 90% (East Asia and Oceania) of the isolates from every geographic region except South America, where IC5 (CC79) and IC7 (CC25) were predominant (Fig.\u00a01d). High frequencies of recombination were detected within ESL, contributing to >90% of its sequence variations (Supplementary Fig.\u00a02a, b). Notably, the recombination rate was unevenly distributed across the chromosome, dividing it into six distinct blocks with varied recombination frequencies (Fig.\u00a02a and Supplementary Data\u00a05): two high-recombination blocks (HRBs) of 1.23 million bases (MB), two middle-recombination blocks (MRBs) of 0.91 MB, and one large low-recombination block (LRB) exceeding 1.60 MB. Despite the removal of recombinant SNPs, 6\u201316% of homoplastic SNPs persisted in each block, leading to the formation of topologically distinct phylogenetic trees (Supplementary Fig.\u00a02c). We found that the mutational phylogeny from the LRB was broadly consistent with the tree reconstructed from the whole core genome (Supplementary Fig.\u00a02d), with a normalized Robinson\u2013Foulds (NRF) distance of 0.1, whereas trees from other regions were all different (NRF distance\u2009>\u20090.8). Given these findings, we utilized the mutational phylogeny from the LRB, which encompassed 66.1% (23,524/35,609) of the mutational SNPs and exhibited the lowest level of homoplasy, as a representative of the ESL\u2019s phylogenetic history.\n\na Frequencies of imported blocks per 10Kb (Y-axis) in windows sliding along coordinates in the chromosome (X-axis). The high-recombination blocks (HRBs) were predicted as blocks with >50 importation events (by RecHMM) in any sliding windows of 100\u2009Kb, and the middle-recombination blocks (MRBs) and the low-recombination block (LRB) were predicted as gaps between HRBs. The median values of frequencies of recombination events (HRB1\u2009=\u200924, HRB2\u2009=\u200911, HRB3\u2009=\u200918.5, MRB1\u2009=\u20093, MRB2\u2009=\u200913, and LRB\u2009=\u20095. Whiskers, minimum and maximum of the data) in each block were visualized as in the inset, with pairwise significance of differences evaluated using the two-tailed student\u2019s t-test (****p\u2009<\u20090.0001). b The scatter plot shows the levels of recombination in the 2266 core genes as measured by percentages of topological conflicts (Y-axis) in the 5824 representative genomes. The coordinates of the core genes in the reference genome were shown in the X-axis. The black and red dots show the level of recombination with or without the ESL genomes, and the lines show the average values of 10 neighboring core genes. The chromosomal coordinates of some genes encoding virulence factors, environmental adaptations, or biocide resistances were visualized as filled blocks between the two parts and color-coded according to the Key. Source data are provided as a Source Data file.\n\nThe phylogenetic analysis categorized ESL strains into 59 monophyletic clades (Fig.\u00a03a and Supplementary Data\u00a01). To gain insights into long-term evolutionary patterns, we further merged the closely related clades into 4 and 5 clusters within the IC1 and IC2 lineages, respectively.\n\na The mutational phylogeny of the ESL based on 23,524 SNPs in the 1.6MB LRB (Fig.\u00a02). The nine clusters of the ESL were shown as colored circles on the tree. The sizes of the circles were proportional to the number of associated genomes. b The relative frequencies of nine clusters in the ESL for each year between 2003 and 2020. c The estimated effective population sizes of the IC2 and all of its Clusters. The dashed line in 2006 shows the year of emergence for Clade 2.5.6. d The spatiotemporal tree by TreeTime. The branches were color-coded based on their clusters (as in the Key). The red dots represent the 99 Chinese IC2 strains sequenced in the study. Additionally, the allelic variations of three virulence factors (capsule, type 4 pilus, and hemO gene cluster) and four antimicrobial genes conferring resistances to carbapenem, sulbactam, tetracycline, and macrolide were also shown in the bottom, according to the Key. e The geographic distribution and major transmission patterns of Clusters 2.1\u20132.4 in IC2. f The geographic distribution and major transmission patterns of Cluster 2.5 with a focus on the rapid dissemination of Clade 2.5.6. Source data are provided as a Source Data file.\n\nIC1 was predominant in Europe and North America, and Cluster 1.4 was also identified in South, West, and Southeast Asia, giving rise to Clades 1.4.4, 1.4.9, and 1.4.10 (Supplementary Fig.\u00a03). Notably, IC1 has only been found twice (GCF_002082825.1 in South Korea and GCF_002999195.1 in China) in East Asia and not present in our 100 Chinese strains. Strains within Asian Clades 1.4.5, 1.4.4, and 1.4.9 acquired additional ARGs, including tet(B), which confers resistance to minocycline in A. baumannii23. These strains subsequently spread to Europe, Africa, and the US, with some being associated with super-spreading events involving casualties from the Middle East conflicts in the 2000s9. The isolation frequency of IC1 strains peaked at 20% in the 2000s and has since declined sharply to less than 5% (Fig.\u00a03b). Unfortunately, although some IC1 clusters have been previously dated, we failed to identify significant temporal signals for the entire lineage (Supplementary Fig.\u00a04b, c), precluding the reconstruction of its temporal dynamics.\n\nIC2 showed a weak, yet significant signal of SNP accumulation with time in the linear regression test (r2\u2009=\u20090.087, p\u2009<\u200910\u22126; Supplementary Fig.\u00a04b), allowing our inference of its spatiotemporal dynamics using TreeTime (Fig.\u00a03d and Supplementary Fig.\u00a04a). Moreover, the calculated substitution rate of 8.16\u2009\u00b1\u20092.19\u2009\u00d7\u200910\u22128 per year was >63-fold higher than all ten estimated rates in the date-randomization test (Supplementary Fig.\u00a04c), confirming the presence of a significant temporal signal.\n\nUsing TreeTime, we estimated the most recent common ancestor (tMRCA) of IC2 to have been present in Europe as Cluster 2.1 prior to 1943 (CI95% 1928\u20131959) (Fig.\u00a03d). The bacteria were predicted to subsequently spread to Africa, West Asia, and North America, resulting in the formation of Cluster 2.2 before 1945 (CI95% 1932\u20131959) (Fig.\u00a03e). Cluster 2.3 was predicted to reach East Asia, which later became the primary source for Clusters 2.4 and 2.5 before 1978 (CI95% 1971\u20131984). Notably, Clade 2.5.6, the most extensive clade within the ESL, was believed to have emerged from Cluster 2.5 in East Asia before 2006 (CI95% 2005\u20132008) and rapidly disseminated to over 45 countries within the subsequent 16 years (Fig.\u00a03f). The East China strains sequenced in this study revealed the co-circulation of three primary clades of 2.5.6 (57 strains), 2.4.13 (21), and 2.4.6 (10) in China in 2023, highlighting the sustained prevalence of 2.5.6 since its emergence (Supplementary Fig.\u00a06a, c). We also analyzed the effective population sizes (EPSs) for IC2 and its clusters, revealing that Clusters 2.4 and 2.5 experienced rapid growth in their EPSs, leading to a threefold increase in the EPS of the entire lineage (Fig.\u00a03c). This growth was mirrored by a shift in the predominant strain sources, with Cluster 2.3 declining from 40% to 10% between 2008 and 2022, while Cluster 2.5 rose from 0% to 41% (Fig.\u00a03b). These findings underscore the role of Cluster 2.5 as the primary driver of IC2\u2019s global expansion.\n\nTo further validate our analysis, we conducted additional analyses using BactDating and BEAST2, each with five distinct models, on the IC2 lineage (Supplementary Fig.\u00a05a, b). All results exhibited high consistency with those from TreeTime. Specifically, the tMRCAs averaged 1935 (1908\u20131953) for BactDating and 1944 (1925\u20131961) for BEAST2 (Supplementary Fig.\u00a05c). Similarly, all runs consistently placed the origin of Clade 2.5.6 around 2006 (CI 2002\u20132008) (Supplementary Fig.\u00a05d), corroborating the recent emergence of this clade.\n\nTo ease the use of the genotyping scheme, we developed a tool named Capybara (see \u201cMethods\u201d) to automatically classify strains into lineages, clusters, and clades based on short sequencing reads, and assessed its efficacy using the 100 strains sequenced in this study (Fig.\u00a04a, Supplementary Data\u00a04), confirming consistent clade assignments.\n\na, b The doughnut charts showed the different clades of A. baumannii in four different cities: Suzhou, Shanghai, Wenzhou, and Shenzhen. The colors of the inner circle represent the sequence method, a for short-reads sequenced data and b for metagenomes. c The Sankey plot showed the differentiated prevalence of each clade of A. baumannii from diverse sources and cities. Non-ESL A. baumannii strains were mostly identified in the community in Suzhou. The map was modified from open-source data in https://github.com/antvis/L7.\n\nWe then employed Capybara to investigate the genetic diversity of A. baumannii in metagenomic samples collected from clinical or community settings. To this end, we analyzed 514 metagenomes, either sequenced in-house or obtained publicly (Supplementary Data\u00a04). A. baumannii was detected in 96 (18.7%) metagenomes, comprising 26 community samples and 70 clinical samples from patients hospitalized for over 7 days (Fig.\u00a04b). Genotyping of these samples with Capybara revealed that 87.1% (61/70) of clinical samples belonged to the ESL, with 52 being Clade 2.5.6, while 96.2% (25/26) of community samples were non-ESL (Fig.\u00a04c), consistent with our genome-based analysis that ESL was primarily prevalent in clinical settings.\n\nOur investigation uncovered a gradual accumulation of carbapenemase genes along the ESL. Notably, the blaOXA-23 gene was present in none to 52% of the strains in Clusters 2.1\u20132.3, whereas a significantly higher prevalence of 79% to >99% was observed in Clusters 2.4 and 2.5, respectively (Fig.\u00a03d). To assess the role of mobile elements in this incremental acquisition of resistance, we evaluated 168 complete ESL genomes, finding blaOXA-23 in 111 of them (Supplementary Fig.\u00a08). Notably, only 23% (26/111) blaOXA-23 was transferred by plasmids. In contrast, Tn/IS elements account for 93% (103/111) of blaOXA-23 carriage, with the majority being Tn2007 and Tn6166. Furthermore, the instance of blaNDM-1 has increased rapidly over the past 5 years, substantially contributing to carbapenem resistance in Clusters 1.4, 2.4, and 2.5, especially >60% in Clade 2.4.14. Moreover, nearly all ESL strains developed quinolone resistance through gyrA and parC mutations (Supplementary Data\u00a01). Over 90% of strains in Clusters 2.4 and 2.5, and >60% in Clusters 2.3, contain mph(E) and msr(E) that confer resistance to macrolides. Additionally, tet(B), conferring minocycline resistance, is also nearly ubiquitous across Clusters 2.3\u20132.5. Many IC2 strains have developed resistance to \u03b2-lactamase inhibitors like sulbactam due to the presence of blaTEM-1 gene24, and the MRCA of Cluster 2.5 acquired the ftsI-A515V mutation that has been associated with resistance to sulbactam-durlobactam25. As a result, Clade 2.5.6 was predicted to exhibit high resistance to aminoglycosides (100% resistance), fluoroquinolones (100%), \u03b2-lactams (100%), carbapenems (99%), sulbactam (98%), sulfonamide (62%), and tetracyclines including minocycline (83%), leaving high susceptibility (>99%) to only tigecycline and polymyxin.\n\nOur analysis indicates enrichment of virulence genes within the HRBs (Fig.\u00a02a). These include genes related to lipooligosaccharide outer core (OCL), capsule (cps), exotoxin (plc1 and plcD), and type 4 pili (T4P) in HRB1, as well as acinetobactin and hemO gene clusters in HRB2 and HRB3, which play crucial roles in iron uptake and host adaptation. Moreover, nine out of 14 genes identified by Li et al.26 as associated with biocide resistances in A. baumannii are located in HRB1 (Supplementary Table\u00a01). In contrast, genes linked to biofilm formation, such as the AdeFGH efflux pump (amvA), csu fimbriae, and \u03b2-(1->6)-Poly-N-acetyl-D-glucosamine synthesis, were more prevalent in the LRB. Notably, the type 6 secretion system (T6SS) main island, which encoded structural proteins, was located in the LRB, whereas the vgrG island that encoded T6SS effectors was situated in HRB1. These findings suggest a potential interplay between recombination and the evolution of virulence and host adaptation traits in A. baumannii.\n\nTo elucidate the evolution of virulence factors in the HRBs, we employed Kaptive and Usearch to genotype the cps/OCL regions and other virulence genes across HRBs, respectively. We observed a stepwise acquisition of new virulence gene variants from other IC/CCs. For instance, an acinetobactin allele with <98% identity to the homolog in IC1 was incorporated before the MRCA of IC2. The original hemO allele (hemO_6) was lost in Clusters 2.2 and 2.3, with some Cluster 2.3 strains acquiring new hemO alleles >20 times (Supplementary Fig.\u00a07b). However, none of these alleles persisted long-term until the hemO_1 from CC25 or CC164 was acquired before Cluster 2.4 and subsequently stabilized. Furthermore, IC2 underwent serotype conversions (Fig.\u00a03d), such as the complete shift from OCL3 to OCL1 in the lipooligosaccharide after Cluster 2.2 and the change from the previous serotype K7 to a predominant K3 in Cluster 2.5, likely introduced from CC4, which was co-circulating with Cluster 2.5 in the far east (Fig.\u00a03f and Supplementary Fig.\u00a07c). These allelic changes along the IC2 in ESL phylogeny, as depicted in Fig.\u00a05d, demonstrate a stepwise mechanism of gene acquisition.\n\na The bubble plot of estimated recombination levels (as conflicting quartets per gene per genome; CQPP; X-axis) versus the Simpson\u2019s diversity index (SDI; Y-axis) of the continental distributions for each IC/CC. The sizes of the bubbles are proportional to the number of genomes in each IC/CC. The dashed line in CQPP of 1.5 specifies the separation of high-recombination CCs (hrCCs, n\u2009=\u200911) and the remaining (n\u2009=\u200930). b The box plots compare SDIs (left) and ARG numbers (right) between hrCCs and other IC/CCs, showing 0.74 for hrCCs vs 0.55 for other IC/CCs in the left panel, and 7.5 for hrCCs vs 15.3 for other IC/CCs in the right panel (**p\u2009<\u20090.01; Wilcox-test). Whiskers, minimum, and maximum of the data. c The recombination network in A. baumannii. The nodes show major IC/CCs color-coded by their associated CQPP values, and the edges show frequencies of convergent recombination between two IC/CCs, measured by numbers of convergent genes per genome. The hrCCs were grouped in the middle and surrounded by a dotted circle. The IC2 and IC1 were highlighted by red halos. d The simplified pattern of stepwise evolution in the IC2. Source data are provided as a Source Data file.\n\nWe evaluated the role of recombination at the species level by analyzing the frequency of topological conflicts among randomly selected quartets between gene trees and the supertree (refer to \u201cMethods\u201d). Increased topological conflicts were observed in HRB1 and HRB3 but not HRB2. Intriguingly, HRBs are distinctive for the ESL (Fig.\u00a02b), as other IC/CCs do not show a significant increase in recombination events within these regions. To quantify the impact of recombination on each IC/CC, we introduced the Conflicting Quartet Per Gene Per strain (CQPP) metric. This analysis identified a group of highly recombining clonal complexes (hrCCs) with a CQPP\u2009>\u20091.5, a marked difference from the 0.3 to 0.5 range observed in the ESL (Fig.\u00a05a).\n\nInterestingly, these hrCCs do not correlate with higher ARG levels, as they exhibit >50% fewer ARGs (7.5 versus 15.3) compared to other lineages (Fig.\u00a05b). Conversely, hrCCs are characterized by their broad geographic distribution and isolation from various continents, resulting in a higher average continental SDI of 0.74, compared to the SDI of 0.55 for other CCs (p\u2009=\u20090.022, Wilcox-test) (Supplementary Table\u00a02). This suggests that the enhanced recombination ability of hrCCs may facilitate the acquisition of region-specific alleles from local populations, promoting their adaptation to a range of global environments. Notably, the reconstructed recombination network (Fig.\u00a05c) further illustrates a pathway linking IC2 to these hrCCs through extensive gene exchange.\n\nIn summary, our analyses revealed three HRBs in the chromosome that are responsible for virulence factors involving iron uptakes, stress response, and host adaptation in A. baumannii. These regions have experienced ongoing gene conversions in the ESL, allowing the acquisition of diverse alleles from other CCs, and driving the diversification of this epidemic super-lineage.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58106-9/MediaObjects/41467_2025_58106_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Acinetobacter baumannii, like many ESKAPE pathogens, is marked by the presence of MDR lineages that contribute to 100,000 nosocomial infections annually27, alongside other lineages of lower clinical significance. The challenge of extracting and interpreting clinically relevant information from its genomes15,28 is significant, often hindered by an incomplete understanding of the species\u2019 genetic context. In this study, we analyzed the genetic landscape of A. baumannii across >15,000 genomes, uncovering geographic and antimicrobial resistance patterns among its populations.\n\nWhile at least eleven ICs have been identified in A. baumannii, their definitions have led to some confusion. Our analysis identified 109 CCs with strains from two or more countries, all potentially qualifying as ICs. Moreover, many ICs have not demonstrated a significantly higher carriage of ARGs or broader geographic distribution, casting doubt on their epidemiological significance (Fig.\u00a01g and Supplementary Fig.\u00a01c). For example, IC5 has been proposed as a major clone in some studies10. Our genomic data showed that it was rarely isolated outside of South America and encoded relatively lower levels of ARGs. In contrast, our analysis revealed seven non-IC populations that exhibited high levels of predicted ARGs (\u226515) or carbapenem genes (\u226580%), or both, which could be due to sampling bias but worth further investigation. Notably, IC1 and IC2 stand out for their high ARG carriage and widespread presence in \u226560 countries. Given their clinical importance and global prevalence, as evidenced by various studies7,19,29, we suggest designating the clade comprising IC1 and IC2 as the ESL, setting it apart from populations with more localized distributions and fewer ARGs.\n\nOur study also uncovered an unexpected trend in the ARG profiles of A. baumannii. Contrary to the pattern observed in Klebsiella pneumoniae, where multiple carbapenem-resistant populations exist with similar frequencies30,31, in A. baumannii, 91% of the identifiable ARGs and 83.5% of CARBs are concentrated within the predominant ESL. Specifically, we observed a significant global expansion of IC2 since 2007 (Fig.\u00a01e), with its isolation frequency increasing from about one-third to two-thirds post-2019, likely propelled by the clonal expansion of Clade 2.5.6 (Fig.\u00a03). Additionally, based on both genomic and metagenomic data, we showed that the expansion and predominance of the ESL was restricted to clinical environment, and the samples from non-clinical sources exhibited greater genetic diversities (Supplementary Fig.\u00a01a).\n\nUsing SNP-based barcodes, we mapped the international transmission of the ESL, classifying it into 59 clades across nine clusters. This approach, previously utilized in clonal pathogens like Mycobacterium tuberculosis32 and Shigella sonnei33, is novel for pathogens with high-recombination rates.\n\nConvergent recombination between two lineages in the dataset will produce spurious similarities between them, thereby scrambling the phylogenetic signal34. Meanwhile, recombination events with external, genetically less related organisms could introduce multiple polymorphisms into the population, confounding accurate measures of branch lengths35. We estimated the frequencies of externally imported regions in the core genome of the ESL using three distinct approaches of RecHMM, Gubbins, and ClonalFrameML and the frequencies of convergent recombination in each region by the frequencies of incongruent phylogenetic partitions. The analysis revealed the presence of an LRB region with the least frequencies of both external and convergent recombination, suitable for further phylogenetic analysis.\n\nConversely, we found that the three HRBs are closely associated with virulence factors involved in iron acquisition, stress response, and host adaptation. It is plausible that these blocks have facilitated the acquisition of new virulence alleles from environmental CCs into various clusters in IC2, thereby driving the diversification of this epidemic lineage. Recombination has been primarily considered neutral in bacterial evolution36. Reported recombination \u201chotspots\u201d in bacterial genomes were primarily driven by flanking mobile genetic elements37 or genes associated with extracellular polysaccharides38. Our findings highlighted the role of selection in shaping recombination hotspots in A. baumannii, which include not only capsular gene clusters but also many other genes responsible for host/environmental adaptations (Fig.\u00a02a). Notably, HRB2 and HRB3 were both involved in genes responsible for iron uptake, which is essential for the in vivo survival of not only A. baumannii39 but also many other pathogens40. Similar recombination hotspots have also been reported in Campylobacter jejuni41, which experienced frequent recombinations in its zinc uptake system genes (znuABC), likely associated with adaptation to chicken caeca and other low-zinc environments.\n\nWe detected significant temporal signals in IC2 but not IC1 using date-randomization tests, a sensitive approach that allows for high variations of substitution rates42. Our results estimated an overall substitution rate of IC2 to 8.16\u2009\u00b1\u20092.19\u2009\u00d7\u200910\u22128, substantially lower than previous estimates of 1\u22123\u2009\u00d7\u200910\u22126 substitutions per site year\u22121 based on 24 to 75 IC1 or IC2 isolates7,43. We managed to reproduce the same clock rates using their original sets of strains (Supplementary Fig.\u00a09a). However, the estimates of clock rates became lower, with greater uncertainty, when additional isolates from the same lineages were included (Supplementary Fig.\u00a09d). Furthermore, it has been well-acknowledged that the substitution rates reduced when genomes were more divergent44. Taken together, our incorporation of all known genetic diversity in the IC2 lineage likely has enabled a more accurate estimate of its emergence.\n\nWe also compared three TreeTime results with those from BactDating and BEAST2 runs. All eleven runs generated comparable estimates of the tMRCA for both IC2 and the clade 2.5.6, demonstrating the robustness of the analysis. Notably, we incorporated recombinant events in BactDating analysis, which did not significantly change the date estimates. However, the three pipelines differed in their efficiencies. The TreeTime handles ~8000 IC2 genomes in 30\u2009min. In contrast, it took 1\u20133 months and 6\u201310 months for BactDating and BEAST2 on the same dataset, respectively. Thus, we decided to use the TreeTime result, which offers a promising solution for other, large-scale population genetic analyses.\n\nThe IC2 lineage experienced a continuous population expansion since its emergence after World War II, coinciding with the beginning time of the widespread use of antimicrobials. We detailed the gradual evolution of IC2, highlighting the ARG acquisitions and recombination events that led to the emergence of Clade 2.5.6 two decades ago. Particularly, we compared the A. baumannii genomes from China over the past 24 years and observed a continuous increase in the detection rate of IC2, with only one non-IC2 genome among the clinical isolates in 2023 (Fig.\u00a04a and Supplementary Fig.\u00a06a). This clade has integrated new virulence gene alleles from other populations and acquired additional ARGs, superimposing on IC2\u2019s already substantial resistance profile. Consequently, Clade 2.5.6 exhibits near-total resistance to several antibiotic classes, with high susceptibility retained only to tigecycline and polymyxin. Although instances of resistance to these latter antibiotics were infrequently observed in our dataset, A. baumannii strains with such resistance have been documented45, underscoring the urgent need for controlling the spread of these threatening clades and for developing novel antimicrobial agents.\n\nWe developed an integrated bioinformatics pipeline, Capybara, which could be applied to both genomic and metagenomic reads. Our application of this tool to both clinical and community metagenomes revealed a predominance of ESL, particularly Clade 2.5.6, in clinical settings other than communities. Substantially lower frequencies of ICs in non-clinical samples have also been found in genomic data and many other studies46,47. Similar findings have also been reported in K. pneumoniae30 and S. aureus48, questioning the efficiency of the \u201cOne Health\u201d strategy due to the genetic separation of epidemic lineages causing most infectious diseases and the remaining environmental/animal populations.\n\nWe found a group of highly recombing, non-epidemic populations, which were designated as hrCCs. The hrCCs were detected across multiple continents, exhibiting a more extensive geographic distribution and higher continental SDI scores compared to other CCs. This suggests that hrCCs may have adapted to diverse environments by acquiring novel gene alleles, likely linked to the species\u2019 well-documented natural competence28. Furthermore, they exhibited lower carriages of ARGs (Fig.\u00a05b), thus possibly persistent in non-clinical environments. Similar populations of high recombination have been observed in other bacterial species, including Salmonella enterica lineage three49 and non-epidemic strains of Vibrio parahaemolyticus50. In contrast, the two ICs within the ESL displayed lower recombination rates, except for a notable pathway linking IC2 to hrCCs through the recombination network (Fig.\u00a05c).\n\nThis recombination pathway transferred genes in the non-epidemic populations into the ESL, contributing to the evolution of epidemic lineages. The ESL acquired ARGs and virulence genes through distinct evolutionary processes. ARGs were predominantly accumulated through the integration of transposons and genetic elements, as well as point mutations in core genes. In contrast, virulence genes were largely updated through homologous recombination, particularly in regions characterized by high-recombination rates. It is often that, in ESKAPE pathogens, the strains with the greatest amount of ARGs are not necessarily the most successful in terms of their prevalence51. We attribute this dilemma to the high evolutionary costs of expressing ARGs, potentially leading to a competitive disadvantage in environments devoid of selective antimicrobial pressures52. Conversely, genes involved in iron uptake, stress response, and immune evasion are crucial for the survival of pathogens, particularly in harsh environments, and thus are essential in the emergence of epidemic lineages. This is exemplified by the rise of IC2 and the subsequent global spread of Clade 2.5.6, which has integrated novel virulence alleles and accumulated additional ARGs (Fig.\u00a05d).\n\nIn this study, we also highlighted the significant role of plasmids and transposons in the dissemination of antimicrobial resistance. Notably, by analyzing 162 complete A. baumannii genomes, we discovered that the spread of blaOXA-23 genes in IC2 and IC1 was primarily mediated by transposons. In our dataset, 92% (103/111) of the blaOXA-23 genes were associated with transposon (Tn/IS) elements, particularly Tn6166 and Tn2007, whereas only 23% (26/111) were located in plasmids. Furthermore, 69% (18/26) of the plasmid-carrying blaOXA-23 genes were also associated with transposons, underscoring the important role of transposons in the dissemination of blaOXA-23 genes.\n\nAltogether, we propose a gene-centered view of the \u201cOne Health\u201d paradigm, where genes, rather than the bacteria themselves, are transferred between different environments through homologous recombination or horizontal genetic transfer. This model might apply to the evolution of other MDR pathogens, emphasizing the critical role of environmental populations in preserving genetic diversity for both ARGs and virulence genes. Such genetic diversity is integral to the selective process that drives the evolution of the ESL and the emergence of successful epidemic clades. Furthermore, the ARGs and virulence genes might also spread into the non-clinical environments through a similar pathway, potentially threatening public health.\n\nA limitation of this work is the absence of phenotypic tests for AMR, which limited our ability to detect novel, undocumented drug resistance mechanisms. Furthermore, the involved strains were predominantly collected in clinical environments over the past 20 years and may not accurately reflect earlier epidemiological patterns. Particularly, the oversampling of recent isolates also reduced the significance of the temporal signal. An equal sampling of the strains along with time would substantially improve the R value in the root-to-tip regression.\n\nIn conclusion, this study provides a comprehensive overview of the population structure of A. baumannii, linking ~70% of current clinical isolates to the extensive ESL, which encompasses the prominent ICs 1 and 2. We have traced the evolutionary trajectory of the ESL and documented the rise and global spread of a novel clade, 2.5.6, within IC2 over the past 16 years. The expansion of IC2 and the emergence of Clade 2.5.6 are linked to the acquisition of ARGs and the recombination-driven evolution of virulence factors, offering insights into the development of epidemic lineages in A. baumannii. These findings will support future genomic surveillance initiatives and enhance our understanding of bacterial pathogen diversification and global dissemination.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "A total of 6208 assemblies of A. baumannii were retrieved from the RefSeq database in Aug. 2022. Additionally, we also downloaded 10,362 sets of short reads from the NCBI SRA database and assembled them using the \u201cassemble\u201d module in EToKi53. All genomes were compared with the reference genome [K09-14: GCF_008632635.1; from soil, 2019, Malaysia] using fastANI v1.3354, and only 15,643 genomes that exhibited \u226595% ANIs to the references were kept in downstream analysis. All genomes were annotated using Prokka v1.14.655. The 7-gene MLST STs of each genome were in silico predicted after comparing the genomic sequences using BLASTn v2.11.056 onto the allelic sequences for Pasteur\u2019s and Oxford\u2019s schemes, both hosted on PubMLST57. MLST profiles were then clustered into different CCs with eBurst v1.0.558. Antibiotic resistance genes in each genome were predicted using AMRFinderPlus v3.11.2659, and the virulence genes were predicted based on BLASTp searches against the VFDB 2022 release60, with \u2265 90% identity and \u2265 60% coverage, respectively. Furthermore, genes associated with biocide resistance were also predicted by BLASTp searching of genes described in Li et al.26. Finally, the types of OCL and cps were predicted using Kaptive v2.0.361, and the remaining virulence genes were separated into clusters of <99.5% identities using USEARCH62.\n\nTo select a set of representative genomes that retained most of the genetic diversity while removing genetic redundancy, we compared pairwise genetic distances of all genomes using Kssd v1.163 and separated genomes into single-linkage clusters of \u226599.5% identities. Only one sequence with the greatest N50 value was chosen for each cluster and was further quality-checked by the presence of >37/40 single-copy core genes using fetchMGs v1.264, resulting in the final subset of 5824 representative genomes for A. baumannii.\n\nThe DNA of 100 isolates from Wenzhou, Zhejiang, and Suzhou, Jiangsu were purified and extracted by Qiagen EZ1 DNA Tissue Kit (Qiagen Sciences, Germantown, MD, USA) according to the manufacturer\u2019s instructions. Paired-end libraries with insert sizes of ~300\u2009bp were prepared following Illumina standard genomic DNA library preparation procedure (VAHTS Universal DNA Library Prep kit for Illumina V3) and sequenced on an Illumina NovaSeq 6000 using the S4 reagent kits (v1.5) according to the manufacturer\u2019s instructions. The sequencing reads of each isolate were quality-trimmed and assembled into contigs using EToKi. Assembled genomes were submitted to NCBI under BioProject accession number PRJNA1112767. Community metagenomic samples from Suzhou City were publicly accessible with BioProject accession PRJNA1225594. Clinical metagenomic samples from hospital B in Suzhou City were publicly accessible with BioProject accession PRJNA1219970. Metagenomic samples from Shanghai City were publicly accessible with NCBI BioProject accession PRJNA1028672. Metagenomic samples from Shenzhen City were publicly accessible with NCBI BioProject accession PRJNA572371. A detailed list of the sample accession numbers for all samples is available in Supplementary Data\u00a04.\n\nBased on the representative genomes, a total of 36,992 pan genes were estimated using PEPPAN v1.0.565. Reference sequences for each pan gene were selected by choosing one allele for each cluster with \u226590% identity using EToKi MLSTdb. The MLSTdb module also identifies potential paralogous genes with \u226590% identities, which were removed from the scheme. We calculated the presence of pan genes in the representative genomes using the DTy66, which identifies and extracts homologous genes in genomes based on the reference sequences. We then used a divide-and-conquer strategy for ML estimation based on sequence divergence of core genes. Based on the results, we extracted a subset of 2266 softcore genes by selecting each gene that was (1) present in \u226595% of genomes and (2) maintained intact open reading frames in \u226594% of its alleles, thresholds that were derived from our early practice for core genome multi-locus sequence typing schemes53.\n\nWe estimated an ML tree for each of the 2266 core genes (Supplementary Data\u00a02) in the representative genomes using IQTREE67 and summarized all the gene trees together into a supertree using the cgMLSA pipeline68. Briefly, cgMLSA summarized the gene trees into a guide tree using ASTRID69 and used the guide tree to separate genomes into disjoint subgroups. The supertree for each disjoint subgroup was estimated using ASTRAL70. cgMLSA implemented a tag-based approach to summarize the trees of all disjoint subgroups together into the final supertree, which had the branch lengths estimated using ERaBLE71.\n\nWe aligned all genomes in the ESL onto a reference genome [MDR-TJ: GCF_000187205.2; from the hospital, 2013, China72] using EToKi align, which identified a total of 181,732 SNPs in the 2.35 MB non-repetitive core genome. An ML tree was estimated based on all core SNPs using IQTREE and used to identify recombination in the ESL using all of RecHMM, Gubbins73, and ClonalFrameML74. Both pipelines identified high frequencies of recombination (r/m\u2009>\u200910), with uneven distribution along the chromosome (Supplementary Fig.\u00a02a\u2013c). The RecHMM results were chosen arbitrarily for downstream analyses. We identified the HRBs as continuous regions with >50 recombination events in a sliding window of 100Kb, and the MRBs and LRB were regions between neighboring HRBs. Mutational trees were built for each of the HRBs, MRBs, and LRB (Supplementary Fig.\u00a02d) after the removal of SNPs imported by recombination. The frequencies of homoplastic sites were evaluated by TreeTime75. All phylogenies were visualized using ITOL v676 or GrapeTree v1.5.077.\n\nAfter the removal of recombinant SNPs among LRB, we eventually reserved 23,524 SNPs to build the phylogeny of ESL. We visually separated all ESL strains into 59 monophyletic clusters of clades along the phylogeny. Furthermore, to provide long-term evolutionary insights, these clades were further grouped into 4 and 5 clusters in the IC1 and IC2 lineages, respectively. Furthermore, hierBAPS78 was employed for population assignments of ESL with parameters \u201cmax.depth\u2009=\u20092, n.pops\u2009=\u200950\u201d, which failed to generate meaningful clusters, possibly due to the high-recombination level of the ESL (Supplementary Fig.\u00a02e). Finally, one synonymous mutation was chosen as the genetic marker for each clade, which was adopted in Capybara for rapid clade assignments (Supplementary Table\u00a03).\n\nWe evaluated the accumulation of variations in both the ESL and the two of its lineages (IC1 and IC2) using both the TempEst v1.5.379 and tested the reliability of the temporal signal by date-randomization test42 which analyzed multiple date-randomized replicate datasets after randomly reassigning the isolation dates of the genomes. IC2 is the only group that had significant signals (Supplementary Fig.\u00a04b, c). Therefore, we calibrated the temporal tree for IC2 based on the isolation years of its genomes using TreeTime, which finished in 30\u2009min, and further predicted its country-wise transmissions using the \u201cmugration\u201d module in TreeTime (Supplementary Fig.\u00a04a). Furthermore, we estimated the skyline plot of its EPS using Skygrowth version 0.3.180. We also cross-validated the substitution rate with previous research using small datasets (Supplementary Fig.\u00a08). Additionally, we ran BactDating and BEAST2 on the IC2 lineage. Specifically, BactDating incorporated recombinant events in date estimates by loading clonalframeML results using the loadCFML() function. We ran BactDating with alternative models of \u201carc\u201d, \u201ccarc\u201d, \u201cnegbin\u201d, \u201cmixedgamma\u201d, and \u201crelaxedgamma\u201d, and ran BEAST2 with a \u201cGTR\u201d substitution model and alternative clock models of \u201cRelaxed LogNormal\u201d, \u201cRelaxed Exponential\u201d, \u201cRelax Uniform\u201d, \u201cStrict Clock\u201d, and \u201cRandom Local Clock\u201d. For each run, the chain length was set to 1e12 or when the ESS exceeded 200 (Supplementary Fig.\u00a05). The BactDating runs finished in 1\u20133 months, whereas the BEAST2 runs lasted for 6\u201310 months.\n\nWe employed quartets, namely subtrees with only four tips, to quantify the level of topological conflicts between the supertree and each gene tree. As shown in Supplementary Fig.\u00a07a, only three topologies are expected for any quartet, which are: ((A, B), (C, D)), ((A, C), (B, D)), and ((A, D), (B, C)). The supertree and the gene tree share the same quartet if the topologies of the quartets from both trees are the same, or different otherwise. Then, the conflicting percentage of a gene tree, t, is calculated as \\({p}_{t}=\\sum {n}_{c}/\\sum {n}_{t}\\), where the \\({n}_{c}\\) is the total number of conflicting quartets and \\({n}_{t}\\) is the total number of all shared quartets between the gene tree and the supertree. However, it is almost impossible to traverse through all quartets because a total of 4.8\u2009\u00d7\u20091013 quartets are expected for a supertree with 5,824 genomes. Therefore, we randomly 100 million quartets in each comparison, and the expected conflicting percentage, \\({\\hat{p}}_{t}\\), is calculated as \\({\\hat{p}}_{t}=\\sum {n}_{c}^{{\\prime} }/\\sum {n}_{t}^{{\\prime} }\\), where \\({n}_{c}^{{\\prime} }\\) and \\({n}_{t}^{{\\prime} }\\) are the number of conflicting and total quartets in the 100 million random selections.\n\nFor each CC, g, the 100 million randomly generated quartets for each gene tree, t, could be separated into two categories: \\({n}_{t,g}^{{\\prime} }\\), the number of quartets that involve at least one genome from g, and the remaining. The recombination level of g in tree t is calculated as \\({\\hat{p}}_{t,g}=\\sum {n}_{c,g}^{{\\prime} }/\\sum {n}_{t,g}^{{\\prime} }\\). For example, the recombination levels of ESL-containing quartets and the others without ESL were calculated for gene trees and visualized in Fig.\u00a02b. Furthermore, we calculated the overall recombination frequency of each CC, g, as conflicting quartets per gene per genome (CQPP), as:\n\nWhere N is the number of genomes in g and T\u2009=\u2009{t}.\n\nWe further inferred recent inter-population recombination that results in the convergence of genes between distant CCs. To this end, for each conflicting quartet between the supertree and the gene tree, we have two topologies of \\({q}_{t}\\) for the quartet from the gene tree and \\({q}_{s}\\) for the quartet from the supertree (Supplementary Fig.\u00a07a). We then identified the pair of tips (i.e., 1 and 2 in Supplementary Fig.\u00a07a) with the shortest genetic distance in \\({q}_{t}\\), which always fell into one clade in \\({q}_{t}\\) but not in \\({q}_{s}\\) (Supplementary Fig.\u00a07a). Such a topological difference is likely a result of recombination, either due to divergent recombination that took the original neighbors of A or B away (3 and 4 in Supplementary Fig.\u00a07a) or by convergent recombination that brought A and B together (5 and 6 in Supplementary Fig.\u00a07a). The convergent recombination is more likely to have occurred because A and B are selected to represent the smallest genetic differences. We summarized all such convergent events together for each pair of genomes, and further calculated the average convergent frequencies between all pairs of CCs, which were visualized as a network in Fig.\u00a05c.\n\nCapybara is a generalized genotyping pipeline based on an SNP barcode scheme, namely a set of 80 pre-calculated SNPs (Supplementary Table\u00a03) that separate the ESL into clusters and clades. Capybara accepts the files containing sequencing reads as parameters, and (1) Align all reads onto a reference genome [MDR-TJ: GCF_000187205.2] by minimap281 and call base variants using SAMtools82 and bcftools83. (2) It then compares the called nucleotides with the set of pre-calculated SNPs and assigns the read sets to the corresponding clusters and clades based on the matching SNPs.\n\nMetaPhlAn v4.084 was employed to detect the presence of A. baumannii in metagenomic reads data, following parameters of \u201c--index mpa_vOct22_CHOCOPhlAnSGB_202212\u201d. For sufficient read counts for Capybara, we selected samples with a relative abundance of A. baumannii of \u22650.1% for subsequent analysis.\n\nSDI of sequence types (STs) for each bacteria, was calculated as SDI\u2009=\\(1-{\\sum }_{i=1}^{n}{{p}_{i}}^{2}\\), where n was the total number of STs and \\({p}_{i}\\) was the total number of detection rate of the ith ST. The closer the SDI value is to 1, the more evenly distributed the species is in different STs, with no obvious preference.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Source data are provided with this paper. The SNP barcode of ESL is available as part of the Capybara pipeline [https://github.com/Zhou-lab-SUDA/Capybara]. 100 clinical samples from hospital A in Suzhou City and the hospital in Wenzhou were deposited in NCBI GenBank under BioProject accession: PRJNA1112767. Community metagenomic samples from Suzhou City were publicly accessible with BioProject accession PRJNA1225594. Clinical metagenomic samples from hospital B in Suzhou City were publicly accessible with BioProject accession PRJNA1219970. Metagenomic samples from Shanghai City were publicly accessible with NCBI BioProject accession PRJNA1028672. Metagenomic samples from Shenzhen City were publicly accessible with NCBI BioProject accession PRJNA572371. A detailed list of the sample accession numbers for all samples are available in Supplementary Data\u00a04. Source data of each Figure and Table can be found in Supplementary Data, Supplementary Table and Source data.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Code for Capybara is available on GitHub https://github.com/Zhou-lab-SUDA/Capybara which also deposits all additional software packages and tools that are relevant.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Li, X. et al. China\u2019s plan to combat antimicrobial resistance. Science 383, 6690 (2024).\n\nArticle\u00a0\n MATH\u00a0\n \n Google Scholar\u00a0\n \n\nHu, F. et al. Resistance reported from China antimicrobial surveillance network (CHINET) in 2018. Eur. J. Clin. Microbiol. Infect. 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The project was supported by the National Natural Science Foundation of China (No. 32170003, 32370099, 82202465), the Natural Science Foundation of Jiangsu Province (BK20211311), the Provincial-level Talent Program for National Center of Technology Innovation for Biopharmaceuticals (NCTIB2024JS0101), Jiangsu Specially-appointed Professor Project, the Suzhou Top-Notch Talent Groups (ZXD2022003) and the Suzhou Science and Technology Innovations Project in Health Care (SKY2021013).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Shengkai Li, Guilai Jiang, Shengke Wang, Min Wang, Yilei Wu.\n\nMOE Key Laboratory of Geriatric Diseases and Immunology, Cancer Institute, Suzhou Medical College, Soochow University, Suzhou, China\n\nShengkai Li,\u00a0Guilai Jiang,\u00a0Yilei Wu,\u00a0Xiao Liu,\u00a0Ling Zhong,\u00a0Shichang Xie,\u00a0Heng Li\u00a0&\u00a0Zhemin Zhou\n\nKey Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China\n\nShengkai Li,\u00a0Shengke Wang,\u00a0Yongliang Lou\u00a0&\u00a0Jimei Du\n\nJiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou, China\n\nGuilai Jiang,\u00a0Xiao Liu,\u00a0Heng Li\u00a0&\u00a0Zhemin Zhou\n\nDepartment of Clinical Laboratory, The Second Affiliated Hospital of Soochow University, Suzhou, China\n\nMin Wang\u00a0&\u00a0Zhemin Zhou\n\nDepartment of Life Sciences, Imperial College London, London, UK\n\nYilei Wu\n\nDepartment of Critical Care Medicine, Zhejiang Provincial People\u2019s Hospital, Hangzhou, China\n\nJinzhi Zhang\n\nDepartment of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China\n\nMin Zhou\u00a0&\u00a0Ping He\n\nIotabiome Biotechnology Inc., Suzhou, China\n\nShichang Xie\u00a0&\u00a0Yi Ren\n\nNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China\n\nZhemin Zhou\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nZ.Z., J.D., and S.L. designed the study. S.L., P.H., S.W., M.Z., H.L., Y.W., and G.J. performed experiments, analyses, and wrote the initial manuscript. S.L., M.W., S.X., and S.W. designed and wrote the pipeline. Y.R., L.Z., and X.L. discussed the results. J.Z. and H.L. performed and revised the results and method content. Z.Z., J.D., and Y.L. directed the project and edited the manuscript.\n\nCorrespondence to\n Yongliang Lou, Heng Li, Jimei Du or Zhemin Zhou.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Santiago Castillo-Ramirez, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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0000000000000000000000000000000000000000..5ebd6484c504805cc383d5d1c8a9fbaeb11da961 --- /dev/null +++ b/eb2b4f7c9d192031d0818381b339f4360e5e0ba229dedc1a1a8035adeab7f2ce/metadata.json @@ -0,0 +1,135 @@ +{ + "title": "Spin Hall and Edelstein effects in chiral non-collinear altermagnets", + "pre_title": "Spin Hall and Edelstein Effects in Novel Chiral Noncollinear Altermagnets", + "journal": "Nature Communications", + "published": "26 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64271-8/MediaObjects/41467_2025_64271_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64271-8/MediaObjects/41467_2025_64271_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [], + "code": [], + "subject": [ + "Electronic properties and materials", + "Electronic structure", + "Magnetic properties and materials" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5367202/v1.pdf?c=1758971532000", + "research_square_link": "https://www.researchsquare.com//article/rs-5367202/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-64271-8.pdf", + "preprint_posted": "04 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Altermagnets are a newly discovered class of magnetic phases that combine the spin polarization behavior of ferromagnetic band structures with the vanishing net magnetization characteristic of antiferromagnets. Initially proposed for collinear magnets, the concept has since been extended to include certain non-collinear structures. A recent development in Landau theory for collinear altermagnets incorporates spin-space symmetries, providing a robust framework for identifying this class of materials. Here we expand on that theory to identify altermagnetic multipolar order parameters in non-collinear chiral materials. We demonstrate that the interplay between non-collinear altermagnetism and chirality allows for spatially odd multipole components, leading to non-trivial spin textures on Fermi surfaces and unexpected transport phenomena, even in the absence of SOC. This makes such chiral altermagnets fundamentally different from the well-known SOC-driven Rashba-Edelstein and spin Hall effects used for 2D spintronics. Choosing the chiral topological magnetic material Mn3IrSi as a case study, we apply toy models and first-principles calculations to predict experimental signa- tures, such as large spin-Hall and Edelstein effects, that have not been previously observed in altermagnets. These findings pave the way for a new realm of spintronics applications based on spin-transport properties of chiral altermagnets.Physical sciences/Physics/Condensed-matter physics/Magnetic properties and materialsPhysical sciences/Materials science/Condensed-matter physics/Electronic properties and materialsPhysical sciences/Materials science/Theory and computation/Electronic structure", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Altermagnets are a newly discovered class of magnetic phases that combine the spin polarization behavior of ferromagnetic band structures with the vanishing net magnetization characteristic of antiferromagnets. Initially proposed for collinear magnets, the concept has since been extended to include certain non-collinear structures. A recent development in Landau theory for collinear altermagnets incorporates spin-space symmetries, providing a robust framework for identifying this class of materials. Here, we expand on that theory to identify altermagnetic multipolar order parameters in non-collinear chiral materials. We demonstrate that the interplay between non-collinear altermagnetism and chirality allows for spatially odd multipole components, leading to non-trivial spin textures on Fermi surfaces and unexpected transport phenomena, even in the absence of SOC. This makes such chiral altermagnets fundamentally different from the well-known SOC-driven Rashba-Edelstein and spin Hall effects used in 2D spintronics. Choosing the chiral topological magnetic material Mn3IrSi as a case study, we apply toy models and first-principles calculations to predict experimental signatures, such as large spin Hall and Edelstein effects, that have not been previously observed in altermagnets. These findings pave the way for a new realm of spintronics applications based on the spin-transport properties of chiral altermagnets.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Altermagnets (AMs) are compensated magnetic phases that share features of both ferromagnets and antiferromagnets, characterized by an alternation of magnetic moments synchronized with an alternation of local multipoles around magnetic atoms1,2.\n\nIn their original setting, altermagnets are collinearly ordered antiferromagnets that have zero net magnetization but nevertheless exhibit spin splitting of electronic bands, even in the absence of relativistic spin-orbit coupling (SOC). This leads to Fermi surfaces with non-trivial patterns of spin polarization in momentum space, for instance of d-wave type, that can sustain spin currents1,2, like ferromagnets, while being robust to stray magnetic fields, unlike their ferromagnetic counterparts. Such properties of altermagnets offer an intriguing opportunity to leverage the complementary advantages of ferro- and antiferromagnets in spintronics1,2,3,4,5,6. The recently developed Landau theory of collinear altermagnetism7 provides a more general, symmetry-based definition: an antiferromagnet is considered an AM when there is neither PT symmetry nor time reversal combined with a translation, and when the N\u00e9el order parameter transforms non-trivially under the point group of the lattice and leads to co-existing magnetic multipolar pseudo-primary order parameters. For more general magnetic orderings, the presence of spin splitting requires, apart from the absence of PT symmetry, that the spin space point group of the spin translation group does not contain the dihedral group Dn8,9. The pseudo-primary AM order parameter is directly related to the spin splitting of the band structures1,2,10,11,12,13,14 and related key observables in, e.g., spin transport. While it has been widely recognized that non-collinear spin ordering can be such that the moments are fully compensated, while still generating, e.g., an anomalous Hall effect (AHE)15,16,17,18,19,20, these observations in themselves do not provide an intrinsic connection to altermagnetism.\n\nHere, we establish this connection by extending the scope of altermagnetic Landau theory to non-collinear, chiral systems. The non-collinear N\u00e9el ordering induces a secondary, symmetry-induced multipolar order parameter, which in turn provides a direct connection to physical observables7,21. We consider the chiral magnet Mn3IrSi, belonging to the magnetic colorless space group P21322,23,24. Our symmetry analysis shows that its experimentally observed compensated non-collinear N\u00e9el order induces multipolar secondary order parameters that are time-reversal odd: one spatially dipolar and another spatially quadrupolar. These altermagnetic order parameters impart a characteristic momentum-space spin texture. We determine the multipolar components for Mn3IrSi not only from full-scale first-principles calculations, but also from a more general symmetry-appropriate magnetic model, and we establish their connection. The dipolar order parameter manifests itself through the bulk Edelstein and spin Hall effects (SHEs). What sets these effects apart in such non-collinear chiral altermagnets is that they occur in the absence of SOC, making them fundamentally different from the SOC-driven linear and nonlinear Rashba-Edelstein25,26,27 and SHEs, as well as other non-trivial spin textures in chiral materials28,29,30. The key distinction is that the energy scale driving the altermagnetically induced transport mechanism is the spin splitting of electronic bands, which is tied to the local magnetic exchange energy1,2,10,11 and is orders of magnitude larger than the relativistic SOC. Finally, we show that Mn3IrSi is an excellent candidate to observe these effects, as, despite the presence of iridium, our calculations demonstrate that SOC plays a negligible role in its electronic structure and altermagnetic spin texture.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "In order to elaborate on the nature of altermagnetism in chiral, non-collinear compensated magnets, it is useful to first introduce altermagnetism in its original context, namely in collinear, compensated magnets with an inversion center in the crystal structure. With this foundation, we will then be in a position to discuss the new physics that emerge when the inversion center and collinearity are lost.\n\nAltermagnetism is most conveniently defined in the limit of zero SOC. This is because there is a separation of energy scales in altermagnetic systems, where the scale associated with spin splitting in the band structure is much larger than spin-orbit splittings. With this in mind, we consider a lattice with some space group G. In the paramagnetic phase, the magnetism is symmetric under the elements of G as well as rotations in spin space and time reversal. In formulating a Landau theory for such systems, we introduce an order parameter \u03a8 corresponding to the collinear magnetic order that does not distinguish components in spin space. We may then define altermagnetism in such cases by requiring, first, that the magnetic sublattices are connected neither by an inversion nor a translation and, then, that the order parameter transforms as a non-trivial one-dimensional irreducible representation (IR) \u0393 of the point group of the lattice. This directly restricts to a set of crystal symmetries where the two magnetic sublattices are connected by a non-symmorphic rotation or mirror operation7. The Landau theory is simply F\u2009=\u2009c2\u03a8 \u22c5 \u03a8\u00a0+\u00a0c4(\u03a8\u22c5\u03a8)2. With the condition on \u0393, we may identify a spin-symmetric, time-odd, spatially anisotropic order parameter of the form\n\nthat transforms like \u0393, where s(r) is the local magnetization density and [\u2026] denotes a symmetrization operation. As this transforms like \u0393, it must enter the Landau theory through the additional term \u03bb\u03a8 \u22c5 O. Therefore, O is a secondary (or pseudo-primary) order parameter. Interestingly, this multipolar order parameter is tied directly to the anisotropy in the spin structure of the Fermi surface. For example, in rutile crystals with chemical formula MX2, where M is magnetic with a sublattice N\u00e9el order parameter and point group D4h, the relevant multipolar order parameter is \u222bd3rxys(r), implying that the band structure exhibits a d-wave spin splitting\u2014i.e., a rotation from kx to ky reverses the spin. This result is borne out by ab initio calculations of rutile magnets2,11,31,32.\n\nThe Landau theory further exemplifies an important aspect of collinear altermagnets, namely that the order parameter breaks down a paramagnetic spin symmetry group to a collinear spin group. Spin groups enlarge the set of magnetic symmetry groups by including elements that do not transform spin and space identically8,33,34,35,36,37,38,39,40. For example, the rutile magnetic order includes an element with C2 in spin space, reversing the moment, together with a composition of a C4 and a translation in real space that swaps the magnetic sublattices. Collinear spin groups also include elements only acting on spin, including global rotations about the moment direction and C2T, where T is time reversal.\n\nThese concepts may be naturally generalized to non-collinear magnetic structures7. Again, one may define altermagnetism to correspond to magnetic order parameters at zero SOC that transform non-trivially under the point group of the lattice, but now without restricting to 1D IRs. We point out that an important difference compared to the collinear case is that bands no longer carry a global spin quantum number. Instead, there is a well-defined spin at each momentum, forming a spin texture across, for example, a Fermi surface. However, the nature of the momentum-space spin texture can still be characterized by the same kind of multipolar order parameters described in the collinear case.\n\nWe now focus on altermagnets in magnetic chiral crystals. It has been pointed out that collinear chiral crystals retain the C2T pure spin symmetry of their achiral counterparts. This operation takes k\u00a0\u2192\u00a0\u2212k while preserving the spin, meaning there is an effective inversion symmetry. Non-coplanar altermagnets stand out because they break this C2T symmetry, and the chirality can therefore be manifest in momentum space. Specifically, this means that the real-space part of the multipolar order parameter can be odd. We shall see how this feature is reflected in the spin texture in momentum space, and how it leads to experimental spin-transport signatures that stand apart from altermagnets studied to date.\n\nTo illustrate altermagnetism in chiral non-collinear systems, we consider Mn3IrSi, which is a particularly interesting magnetic system due to its multifold topological semimetal properties41,42,43,44. Moreover, it belongs to the family of \u03b2-Mn type alloys: Mn3TX (T\u2009=\u2009Co, Rh, and Ir; X\u2009=\u2009Si and Ge), all of which share the same crystal structure. Various features of these materials have been reported in the literature, including short-range magnetic ordering45,46, an incommensurate magnetic phase47 at high temperature, and a doping-induced magnetic phase transformation48.\n\nAs shown in Fig.\u00a01a, Mn3IrSi has 12 Mn atoms with non-collinear local magnetic moments within the same unit cell. The local magnetic directions are denoted by red arrows. The space group of the Mn3IrSi crystal is P213 (No. 198), which belongs to the Sohncke space groups, with the magnetic atoms occupying the Wyckoff position 12b. It can be described by both the magnetic space group (MSG) P213 and the spin space group (SSG) \\({{{{\\rm{P}}}}}^{{2}_{100}}{2}_{1}^{{3}_{111}^{1}}\\)3, which, as we will see, are isomorphic. Due to the non-collinear and non-coplanar properties of its magnetic structure, the spin-only group is trivial, and the point group symmetry operations are identical in both real space and spin space. In other words, because of the non-collinear altermagnetic (AM) nature, the SSG (AM group) and the MSG of Mn3IrSi are isomorphic.\n\na The Mn3IrSi unit cell contains 12 Mn atoms, with local magnetic moments indicated. Both the crystal and magnetic structures possess chiral symmetry, and the magnetic moments are non-collinear. The bonding between nearest and second-nearest Mn atoms is labeled by dashed black lines. b The dipole and quadrupole spin textures predicted in the chiral non-collinear altermagnet. They are inversion odd, s(k)\u2009=\u2009\u2212s(\u2212k), and inversion even, s(k)\u2009=\u2009s(\u2212k), respectively. The three-dimensional Fermi surface is taken from the minimal-band toy model of Mn3IrSi at E\u2009=\u2009Ef\u2009+\u20090.08\u2009eV. The schematic plot shown in the left panel presents the dipole and quadrupole in 2D.\n\nThus, although SOC is strictly zero in the Hamiltonian, the non-collinear magnetic ordering causes the magnetic symmetries to be equivalent to those expected in a system with finite SOC. Nevertheless, in contrast to the original collinear altermagnets, inversion symmetry is broken. However, Mn3IrSi shares a key feature with collinear altermagnets: the absence of spin degeneracy, leading to the expectation of a spin-split band structure.\n\nIn order to understand this spin splitting from a symmetry perspective, we begin with the 12-dimensional representation based on the 12b sublattice basis. This representation can be decomposed under the tetrahedral point group T as: A \u2295 E \u2295 3T. As the experimentally observed magnetic order carries zero total magnetization (M\u2009=\u20090), which is also the magnetic ground state found in first-principles calculations, we can exclude a magnetic order parameter associated with the irreducible representation (IR) A, which corresponds to the total magnetization: \\({{{\\bf{M}}}}={\\sum }_{i\\in prim}{\\sum }_{a=1}^{12}{{{{\\bf{S}}}}}_{ia}\\), where the local moment Sia is labeled by primitive cell i and basis label a. We now drop the primitive lattice label as the magnetic propagation vector is Q\u2009=\u20090. For the IR E, the magnetic structure of Mn3IrSi is orthogonal to its components, and \\({{{{\\mathbf{\\Phi }}}}}_{E}^{\\alpha }=0\\), where \\({{{{\\mathbf{\\Phi }}}}}_{E}^{\\alpha }={\\sum }_{a=1}^{12}{\\phi }_{E,a}^{\\alpha }{{{{\\bf{S}}}}}_{a}\\), and \u03b1\u2009=\u20091,\u00a02 runs over the components of E. Thus, the only remaining order parameter is related to the IR T (see also Ref. 39). After projecting the 12-dimensional vector onto the IR T, the basis of T reads: \\({\\phi }_{T}^{1}=(x,-x,-x,x,y,y,-y,-y,z,-z,z,-z);{\\phi }_{T}^{2}=(z,-z,z,-z,x,-x,-x,x,y,y,-y,-y);{\\phi }_{T}^{3}=(y,y,-y,-y,z,-z,z,-z,x,-x,-x,x)\\). Combined with the magnetic structure: \\({{{{\\mathbf{\\Phi }}}}}_{T}^{\\alpha }=\\mathop{\\sum }_{a=1}^{12}{\\phi }_{T,a}^{\\alpha }{{{{\\bf{S}}}}}_{a}\\). Based on the order parameter of T, we can write down the associated Landau theory:\n\nwhere a sum is taken over components \u03b1. As the total magnetization transforms as A, there is no direct coupling between the magnetic order parameter and M. However, other variables related to T can couple to \u03a6\u03b1, in particular, the spatial dipole and quadrupole terms defined as:\n\nBased on the first term in momentum space, a hedgehog spin texture is expected, where the local spin at k (s(k)) reverses sign under time-reversal symmetry: s(k)\u2009=\u2009\u2212s(\u2212k). For the quadrupole spatial term, this couples to a quadrupolar spin texture, which transforms differently under time-reversal symmetry: s(k)\u2009=\u2009s(\u2212k). Figure\u00a01b illustrates both the hedgehog and quadrupole-like two-dimensional spin textures, with the quadrupolar component showing zero spin at the crossing points along the kx and ky axes. The predicted spin texture, which belongs to the IR T, aligns with previous studies on SSGs39.\n\nIn the original Landau theory of altermagnetism (AM)7, an antiferromagnet is considered an altermagnet because its N\u00e9el order parameter transforms non-trivially under point group symmetries, resulting in co-existing magnetic multipolar pseudo-primary order parameters. A key characteristic of chiral crystals is the absence of inversion and mirror symmetries, meaning all improper rotational symmetries are absent. Consequently, spatial and axial vectors transform identically and can belong to the same IRs. For the chiral non-collinear altermagnet Mn3IrSi, the non-trivial magnetic order parameter \\({{{{\\mathbf{\\Phi }}}}}_{T}^{\\alpha }\\) is established, and as a consequence, finite magnetic multipolar order parameters L1,\u03b1 and L2,\u03b1, along with their corresponding spin textures in momentum space, are expected on the basis of symmetry.\n\nFinally, we consider the effect of SOC on symmetry, under the assumption that altermagnetism persists when the spin-orbit splittings are smaller than the spin splitting in the absence of SOC. When SOC is introduced, the 12 sublattices, along with the 3 local spin components for each, must be considered. This results in a 36-dimensional representation. The spin-symmetric basis in the T IR now breaks into three copies of the A IR, which read:\n\nwhereas the magnetization transforms as T. Because the antiferromagnetic order parameter and magnetization transform differently (as A and T, respectively), there can be no linear coupling between the two in the Landau theory. Thus, to linear order, there is no weak ferromagnetism (no small induced moment), and, as the AHE transforms like the magnetization, this too is not switched on to linear order.\n\nIn the previous section, we demonstrated on symmetry grounds that the non-collinear magnetically ordered phase of Mn3IrSi must exhibit a spin texture on its Fermi surface, with dipolar and quadrupolar spatial components, even in the absence of SOC. In this section, we first develop a simple Kondo-lattice model that, with only a few parameters, accounts for the non-collinearly ordered magnetic moments. From it, the Fermi surface spin textures predicted by Landau theory are identified. Introducing SOC as a perturbation does not significantly affect the spin texture, providing a robust minimal model for the chiral non-collinear altermagnet. Next, we present the first-principles results for Mn3IrSi, showing that its non-collinear magnetic structure stems from geometrical frustration in the absence of SOC, and that the spin texture aligns with the predictions, though it is more complex than our simplified toy model. Additionally, we demonstrate that the band structures with and without SOC are quite similar, indicating that SOC remains a small perturbation, despite the presence of the heavy element iridium. In the following section, both the toy model and the projected Wannier functions of Mn3IrSi will be used to predict novel, robust transport phenomena that do not rely on the presence of SOC.\n\nTo capture the magnetic symmetry in Mn3IrSi and other compounds with the same crystal structure, we propose a minimal toy model of tight-binding electrons hopping between the Mn sites on the \u03b2-Mn lattice, where magnetism is introduced via a Kondo-like coupling to fixed classical magnetic moments. These moments are arranged according to the experimentally observed non-collinear magnetic structure of Mn3IrSi. The model is isotropic in spin space. To account for the effects of SOC, an additional symmetry-allowed spin-dependent hopping term is included7,49. The Hamiltonian of the toy model is given by:\n\nwhere tij represents the hopping parameters up to the second-nearest neighbors that we denote, respectively, by ta and tb, mi denotes the local magnetic moment strength, and sij is the off-site SOC strength. The Mn sublattice consists of 12 atoms, and the bases for the model are chosen to be \\(\\left\\vert i,\\alpha \\right\\rangle\\), where i and \u03b1 represent the sublattice and spin degrees of freedom, respectively. We limit the SOC to the nearest neighbors (s). Figure\u00a02a shows the calculated band structure of the toy model along high-symmetry lines with parameters tb/ta\u2009=\u20090.5, s/ta\u2009=\u2009(0.05,\u00a00.03,\u00a00.02). Without loss of generality, the local magnetic moments are chosen as m1/ta\u2009=\u20090.3\u2009\u22c5\u2009(1.640, 2.774, \u22122.231). Figure 2b illustrates the momentum-space spin texture arising in this chiral non-collinear altermagnetic model. Only two bands cross the Fermi level, and both exhibit the hedgehog winding spin texture around the \u0393 and M points in sx/y and the quadrupole-like spin texture in the sz component.\n\na, c The band structure along high-symmetry lines from the toy model and first-principles calculations, respectively. b, d The spin texture at E\u2009=\u2009Ef\u2009+\u20090.08\u2009eV and at the Fermi level (Ef) from first-principles and toy model calculations, respectively. The left panels show the spin components \u3008sx/y\u3009, and the right panels present the spin components \u3008sz\u3009. All spins are normalized for clearer visualization.\n\nHaving established the predicted momentum-space spin texture as a generic feature of the toy model, we proceed to calculate the detailed electronic structure of Mn3IrSi. As various mechanisms can stabilize non-collinear magnetic structures, we first identify which mechanism is at play in Mn3IrSi. To this end, we estimate the magnetic exchange interactions in this material by constructing different collinear magnetic arrangements, computing their total energies, and mapping them onto a model of localized spins coupled by isotropic (Heisenberg) exchanges. In this way, we find that the magnetism of Mn3IrSi can be described by a minimal model featuring three short-range antiferromagnetic exchanges, one of which\u2014forming spin triangles adjacent to an Ir atom\u2014is twice as large as the other two. Using classical Monte Carlo simulations, we demonstrate that this minimal model accurately reproduces the experimental non-collinear ground state, even in the absence of conduction electrons. For details of the total-energy calculations, the minimal model, and Monte Carlo simulations, we refer the reader to Supplementary Discussions\u00a02 and 3.\n\nNext, we consider the band structure. As shown in Fig.\u00a02c, the band structure along high-symmetry lines agrees well with previous reports41. The multifold degeneracies at the R point are robust against the introduction of SOC. In the\u00a0Supplementary Material, we compare the band structures shown in Supplementary Fig.\u00a05, with and without SOC, and conclude that there is minimal difference between them.\n\nFigure\u00a02d shows the spin texture without SOC, revealing a hedgehog and quadrupole component in \u03c3x and \u03c3y, and \u03c3z, respectively, in the kz\u2009=\u20090 plane. The spin textures at different kz values are presented in Supplementary Fig.\u00a01, all showing a hedgehog-like component. In addition, the spin texture in \u03c3z exhibits a quadrupole-like distribution, which is even under the time-reversal operation: sz(k)\u2009=\u2009sz(\u2212k). This quadrupole-like spin texture is also a consequence of the primary order parameter in collinear altermagnetism (AM)7.\n\nOne may also understand the presence of the hedgehog and quadrupole spin textures in momentum space from the basic symmetries of chiral non-collinear AMs. Such AMs are only allowed to have rotational symmetries that relate a group of states as: \u03f5(ki)\u2009=\u2009\u03f5(k0),\u00a0s(ki)\u2009=\u2009Ri(\u03b8i)s(k0),\u00a0ki\u2009=\u2009Ri(\u03b8i)k0, where R(\u03b8) is a rotation operator. From both symmetry analysis and DFT calculations of Mn3IrSi, in the kz\u2009=\u20090 plane, s(ki)\u2009=\u2009Ri(\u03c0/2)s(k0),\u00a0i\u2009=\u2009x,\u00a0y,\u00a0z, where sx and sy (sz) transform as dipole (quadrupole), respectively. This spin-momentum locking leads to both hedgehog-like and quadrupole-like spin textures. Given the good agreement between the toy model and the realistic material calculations, we conclude that our model may be easily extended for more general applications to analyze hedgehog-like and quadrupole-like spin textures in other classes of related chiral non-collinear altermagnets.\n\nWe now show that the chiral non-collinear altermagnet discussed above exhibits both a SHE and an Edelstein effect as a consequence of the spin texture on the Fermi surface, even in the absence of SOC. Both phenomena arise in the presence of an externally applied electric field: the appearance of a transverse spin current signals the SHE, while the Edelstein effect corresponds to a net magnetization. More precisely, the spin current \\({{{{\\mathcal{J}}}}}_{j}^{i}\\) for spin component i and current direction j may depend on the electric field Ek through \\({{{{\\mathcal{J}}}}}_{j}^{i}={\\sum }_{k}{\\sigma }_{jk}^{i}{E}_{k}\\), and the SHE corresponds to terms off-diagonal in jk. The Edelstein effect is a change in magnetization \\(\\delta {m}_{i}={\\chi }_{ij}^{s}{E}_{j}\\). Both phenomena are well established in various systems in the presence of SOC50,51.\n\nFor the crystal symmetry of Mn3IrSi, the spin Hall tensor \\({\\sigma }_{jk}^{i}\\) has two independent, time-reversal even, non-vanishing components: \\({\\sigma }_{xy}^{z}\\) and \\({\\sigma }_{xz}^{y}\\), each transforming as the A IR of the group. These, therefore, directly couple to the squared order parameter in the presence of SOC, as the order parameter also transforms like A. In the spin-orbit-free case, we saw that the order parameter instead transforms like T. As T \u2297 T contains A, there is a component that produces a SHE.\n\nA prerequisite for a non-vanishing Edelstein effect is that the crystal must lack inversion symmetry. For the crystal structure of Mn3IrSi, the Edelstein tensor has a single diagonal component m\u2009=\u2009\u03c7SE that transforms like A and is time-reversal odd. This couples linearly to the order parameter in the presence of SOC. In the altermagnetic case of zero SOC, there is no linear coupling. Instead, the Edelstein effect is cubic in the order parameter as T \u2297 T \u2297 T\u2009=\u20092A \u2295 \u2026.\n\nHaving established that a SHE and Edelstein effect are allowed on symmetry grounds, we now show that they arise in Mn3IrSi directly from a microscopic calculation. For the spin current operator \\({\\hat{A}}_{j}^{i}={{{{\\mathcal{J}}}}}_{j}^{i}=\\frac{1}{2}\\{{\\hat{s}}_{i},{\\hat{v}}_{j}\\}\\), we compute the linear spin Hall response52,53 with a constant inverse scattering time \u0393. Two components contribute to the observable \\(\\delta {\\hat{A}}_{j}^{i}=({\\chi }_{i,jk}^{I}+{\\chi }_{i,jk}^{II}){E}_{k}\\), where:\n\nHere, e is the elementary charge, k is the Bloch wave vector, n,\u00a0m are the band indices, \u03f5n,k is the eigenvalue, Ef is the Fermi energy, \\(\\hat{{{{\\bf{v}}}}}\\) is the velocity operator, N is the total number of Bloch waves, and V is the volume of the unit cell. In the expression for \u03c7II, the ranges of n and m refer to all the occupied and unoccupied bands, respectively, which is analogous to the calculation of Berry curvature. The intrinsic SHE is defined as the antisymmetric part from \u03c7II 50 that is irrelavant to scattering time and is shown in Fig.\u00a03. The extrinsic Edelstein effect results are evaluated from \u03c7I with \\({\\hat{A}}_{i}={\\hat{s}}_{i}\\). Other calculated transport results are presented in\u00a0Supplementary Materials.\n\na, c Spin Hall effect results from the model and projected Wannier functions of Mn3IrSi, respectively. b, d The Edelstein effect results (\u03c7s) with the inverse scattering time \u03932\u2009=\u200910\u22124 (ta) and (eV), respectively. The light dashed lines in (a, b) are from the toy model without SOC.\n\nFigure\u00a03 shows the calculated SHE (upper panels) and Edelstein effect (lower panels) for both the toy model (left-hand side) and the Mn3IrSi first-principles calculations (right-hand side). The calculations are presented at zero temperature as a function of energy relative to the Fermi level. While the magnitude and even the sign of the responses are parameter dependent, the calculations clearly reveal the presence of both a spin Hall current and an electric-field-induced magnetization, except at fine-tuned energies. We also set the relativistic effect to zero in the SHE and Edelstein effect simulations. The trends versus energy and the magnitudes are similar to those with SOC, as shown in the\u00a0Supplementary Materials.\n\nThe microscopic calculations supply useful intuition by connecting the spin texture at the Fermi level to the observables. The physical mechanism is illustrated in the insets of Fig.\u00a03a, b, where the electric field displaces the Fermi surface in reciprocal space. The hedgehog spin texture then ensures that spins at the Fermi surface no longer compensate, thus leading directly to an induced magnetization. A similar displacement causes electrons to experience an effective torque and tilt towards the \u03c3z direction. A net spin current perpendicular to the electric field (Ex) appears due to the opposite sign of the torque between momentum ky\u2009>\u20090 and ky\u2009<\u20090, as indicated in the inset to panel (a).\n\nQuantitatively, compared to other predicted spin Hall materials, Mn3IrSi has a relatively large SHE of \u00a0~102(\u210f/e) S/cm54. This is an order of magnitude larger than SOC-induced intrinsic values calculated for, e.g., nonmagnetic GeAs, AlAs, or Ge55, and comparable to the predicted SHE in collinear antiferromagnets56,57. The value of the Edelstein effect caused by the chiral, non-collinear altermagnetism in Mn3IrSi is of the same order of magnitude as that of certain non-coplanar magnets58.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64271-8/MediaObjects/41467_2025_64271_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64271-8/MediaObjects/41467_2025_64271_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64271-8/MediaObjects/41467_2025_64271_Fig3_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Using group theory and Landau theory, we have predicted the existence of non-collinear chiral altermagnets and their distinctive electronic and transport properties. Compared to collinear altermagnets, non-collinear systems exhibit a more intricate momentum-space spin texture, extending the classification scheme for collinear systems. On theoretical grounds, we have established the presence of a unique spin-momentum locking mechanism that arises in the absence of SOC, as a direct consequence of chiral altermagnetism in Mn3IrSi. This generalizes altermagnetism from its original context in collinear magnets\u2014where entire electronic bands can be labeled by a common spin quantum number\u2014to systems where the bands possess a local spin degree of freedom that is globally constrained to form a momentum-space texture governed by symmetry. This spin texture has direct physical implications. Due to the stricter symmetry constraints in non-chiral altermagnets, spatially odd multipole components are not allowed in either collinear or non-collinear magnetic structures7. Thus, chirality emerges as one of the necessary conditions for the hedgehog spin texture in AMs. As a consequence, as exemplified in Mn3IrSi, large spin Hall (~102(\u210f/e) S/cm) and Edelstein (approximately\u00a0\u22122\u2009\u00d7\u200910\u221210\u210f\u2009m/V) effects coexist and have been calculated, showing very weak SOC dependence. We foresee multifunctional applications of chiral non-collinear AMs in spintronics and suggest additional candidates from the Mn3IrSi family: Mn3IrGe59, Mn3CoGe48, Mn3RhGe47, Mn3IrGe59; and other chiral non-collinear AMs: ScMnO360, BaCuTe2O661,62, YMnO363, Ho2Ge2O764, Er2Ge2O765.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Spin-polarized scalar-relativistic total-energy calculations were performed using the generalized gradient approximation (GGA)66 as implemented in the full-potential code FPLO67 version 22 on a k-grid of 8\u2009\u00d7\u20098\u2009\u00d7\u20098 points. Exchange interactions were obtained by mapping the 181 GGA total energies onto an \\(S=\\frac{3}{2}\\) Heisenberg model, and by a least-squares solution of the latter. Classical Monte Carlo simulations of the Heisenberg model were performed using ALPS68,69 version 2.3.0 on a finite lattice of 4\u2009\u00d7\u20094\u2009\u00d7\u20094 cells (768 spins) with periodic boundary conditions; we employed local updates and used 5,000,000 (500,000) sweeps for measurement (thermalization). Band structure calculations were performed using VASP70, employing the projector augmented wave method71. The Brillouin zone was sampled on a 7\u2009\u00d7\u20097\u2009\u00d7\u20097 k-point grid centered at the Gamma point. The energy cutoff for the plane wave basis was set to 550\u2009eV. The Hubbard term was introduced with a value of 3.0\u2009eV for the d orbitals of the Mn atoms within the DFT+U framework to account for electron-electron correlations. The Wannier-based Hamiltonian was symmetrized based on the maximally localized Wannier functions generated by the WANNIER90 interface72. The projectors were the d orbitals of Mn, with the fitted region spanning from \u22122 to 2\u2009eV. The magnetic moments within the self-consistent non-collinear ground state, with and without SOC, are respectively: \u2223m\u2223\u2009=\u20093.91\u2009\u00b1\u200910\u22122\u03bcB and m\u2009=\u2009(1.640,\u00a02.774,\u00a0\u22122.231)\u2009\u00b1\u200910\u22123\u03bcB on the Mn atom located at (0.1195,\u00a00.2031,\u00a00.4573) in fractional coordinates of the unit cell.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The supporting data for this study are available from the corresponding authors upon reasonable request.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The computation code in terms of transport properties in this study is available from the corresponding authors upon reasonable request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "\u0160mejkal, L., Sinova, J. & Jungwirth, T. Emerging research landscape of altermagnetism. Phys. Rev. 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We acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), through SFB 1143 (Project ID 247310070), project A05, Project No. 465000489, and the W\u00fcrzburg-Dresden Cluster of Excellence on Complexity and Topology in Quantum Matter, ct.qmat (EXC 2147, Project ID 390858490).", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Leibniz Institute for Solid State and Materials Research, IFW Dresden, Dresden, Germany\n\nMengli Hu,\u00a0Oleg Janson\u00a0&\u00a0Jeroen van den Brink\n\nMax Planck Institute for Chemical Physics of Solids, Dresden, Germany\n\nClaudia Felser\n\nLaboratoire L\u00e9on Brillouin, CEA, CNRS, CEA-Saclay, Universit\u00e9 Paris-Saclay, Gif-sur-Yvette, France\n\nPaul McClarty\n\nW\u00fcrzburg-Dresden Cluster of Excellence ct.qmat, Dresden, Germany\n\nJeroen van den Brink\n\nDepartment of Physics, TU Dresden, Dresden, Germany\n\nJeroen van den Brink\n\nD\u00e9partement de Physique et Institut Quantique, Universit\u00e9 de Sherbrooke, Sherbrooke, Qu\u00e9bec, Canada\n\nMaia G. Vergniory\n\nDonostia International Physics Center, Donostia-San Sebastian, Spain\n\nMaia G. Vergniory\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nM.V. and J.V.D.B. proposed the project. M.H., P.M., O.J., M.V. and J.V.D.B. conceived the project. M.H. did first-principles and transport calculations and O.J. conducted the Monte Carlo and magnetic exchange calculations. P.M. did the Landau theory. M.H., P.M., O.J., J.V.D.B. and M.V. wrote the paper. M.H., O.J., P.M., M.V. and J.V.D.B. contributed to the scientific discussions. M.H., O.J., C.F., P.M., M.V. and J.V.D.B. participated and commented on the paper.\n\nCorrespondence to\n Jeroen van den Brink or Maia G. 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enhanced biological \u03b3-ray protection", + "pre_title": "Molecular Engineering of Melanin for Enhanced Biological \u03b3-ray Protection", + "journal": "Nature Communications", + "published": "23 August 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_MOESM3_ESM.gif" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_MOESM4_ESM.gif" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_MOESM5_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_MOESM6_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_MOESM7_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1209790", + "/articles/s41467-025-62403-8#Sec46" + ], + "code": [], + "subject": [ + "Bioinspired materials", + "Polymer synthesis", + "Polymers" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5719100/v1.pdf?c=1756033624000", + "research_square_link": "https://www.researchsquare.com//article/rs-5719100/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-62403-8.pdf", + "preprint_posted": "13 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The escalating utilization of ionizing radiation across medicine and industry, coupled with the relentless global nuclear rivalry, underscoring the paramount urgency of effective radioprotective materials. Conventional materials such as lead and concrete are widely used, and lead-free materials have also emerged to solve the problems of cumbersome and toxic lead, such as metal-containing micro/nano materials and polymers. Nevertheless, there is still a significant challenge in meeting the urgent need for lightweight and biocompatible alternatives. To tackle this challenge, this work utilizes molecular engineering of melanin to develop a panel of novel metal-free melanin materials with enhanced conjugation, heightened physical shielding against radiation and effective antioxidant properties. Remarkably, engineered melanin materials demonstrated unprecedented in vivo \u03b3-ray protection, increasing mice survival from 0\u2013100%. Physical sciences/Chemistry/Polymer chemistry/Polymer synthesisPhysical sciences/Materials science/Soft materials/PolymersPhysical sciences/Materials science/Biomaterials/Bioinspired materials", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4Figure 5Figure 5Figure 6Figure 6Figure 7Figure 7Figure 8Figure 9Figure 10", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/665382ae1460a58bc6511242.png", + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/822c02c96ea4913b6fcc3032.png", + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/781a4106d1b26c2517704fe7.png", + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/19bf00b528c7b0b179cc891f.png", + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/d798ea6b584af1726dff3960.png", + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/7c0648e0199522f2d10e5456.png", + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/2995b3d45b46040e45f2fb34.png", + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/eb6d2619c647358a671b2bfc.png", + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/561f6002a211fa7fd287e5a9.png", + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/af7ed0746a9ff9716197270d.png", + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/1a0748e232a77c888c659d7a.png", + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/51178897eb888c0458391eab.png", + "https://assets-eu.researchsquare.com/files/rs-5719100/v1/e8e7570bb73a1b126d9c07a5.png" + ] + }, + { + "section_name": "Introduction", + "section_text": "The\u00a0growing use of ionizing radiation in medicine and industry has increased the risk of radiation exposures and injuries.[1] In space exploration, long-term exposures to cosmic rays pose significant threats to astronauts\u2019 health and spacecraft performance.[2] Meanwhile, the ongoing global nuclear competition underscores an imminent nuclear risk.[3] Conventional radioprotective materials, such as lead and concrete, rely on strong physical attenuation for protection. However, these materials are often bulky and hazardous. To address these limitations, lead-free alternatives have been developed, including metal-doped polymer materials and metal-containing micro/nanomaterials.\u00a0[4-9] Despite these advancements, heavy-metal-based radioprotectors still face challenges related to bulkiness, toxicity, and poor biocompatibility. Consequently, there is a pressing need for the development of lightweight, biocompatible, and metal-free radioprotective materials.\nMelanin is potentially a natural radioprotector.[10] As nature\u2019s enigmatic pigments ubiquitous across animal, plant, bacterial and fungal kingdoms,[11-12] melanin represents one of the last unknown frontiers in biology due to its structural complexity.[13] Melanin\u2019s various functions including coloration,[14-15] radical scavenging,[16-20] protective antifungal immunity[21] and radiation protection[22] are deeply linked to their complex structures.[23-24] We believe that the structural complexity, if rationally tuned, could offer an essential tool to understand and leverage the structure-property-function relationships. Previously, driven by chemical synthesis, we coined a novel selenium version of nature\u2019s sulfurous pheomelanin, termed selenomelanin.[22] The selenomelanin demonstrated superior radiation protection upon impinging X-ray (operating voltage: 160 kV) than existing eumelanin and pheomelanin as demonstrated in primary cell culture, exemplifying the power of molecular engineering in radioprotective biomaterial design.[22] However, a deeper understanding of the radiation protection mechanism of this innovative material is crucial for designing enhanced radioprotective materials capable of shielding more penetrating \u03b3-ray (typically with energies exceeding 1 MeV).\u00a0[25-26] Furthermore, in vivo testing remains essential for assessing its full potential.[11]\u00a0\nHerein, to acquire superior \u03b3-ray radiation protectors, molecular engineering of melanin was performed for a higher degree of conjugation (Figure 1). Side-chain engineering has been used in conjugated polymer to tune the backbones packing, improving the solubility and charge transport properties.\u00a0[27-28] Surprisingly, molecular engineering of side-chain has rarely been explored in melanin as far as we know. In fact, subtle variations in eumelanin monomers (dopamine vs. levodopa) can lead to huge structural and functional differences.\u00a0[14, 19] Furthermore, \u03c0-conjugated structures are relevant for high-energy radiation shielding as they disperse energy across the \u03c0-system, providing a basis for tuning radioprotection by modifying the conjugation.[29] In this work, we report a panel of metal-free radioprotective material with enhanced radiation shielding capability and excellent antioxidant properties. They could protect human keratinocyte (HaCaT) cells from high dose \u03b3-ray at a very low concentration. Additionally, the engineered materials increased mice survival from 0% to 100% after lethal \u03b3-ray exposure, alleviating multi-organ injuries, which greatly outperformed commercial Amifostine (AMF) and superoxide dismutase (SOD).", + "section_image": [] + }, + { + "section_name": "Results and Discussion", + "section_text": "\nChemical Synthesis of Engineered Melanin\nSelenomelanin nanoparticles (SeMNPs-1) were synthesized using L-3,4-dihydroxyphenylalanine (L-DOPA) and selenocysteine.[22] While significant spontaneous decarboxylation (up to 90%) happens in eumelanin synthesis,[19] the decarboxylation percentage of SeMNPs-1 was estimated to be ~\u200911.5% (Table S1). The high fidelity of carboxylic acid groups provides a foundation for the molecular engineering of SeMNPs. Molecular engineered SeMNPs were synthesized using polymerized L-DOPA or dopamine (DA) as the seeds, followed by the addition of selenium-monomers for aqueous copolymerization (Fig. 2A). Transmission electron microscopy (TEM) and dynamic light scattering (DLS) characterization confirmed the formation of spherical SeMNPs (Figure S2-S7). The particle diameters of SeMNP-1, -2, -3 and \u2212\u20094 were 81\u2009\u00b1\u20096 nm, 96\u2009\u00b1\u20094 nm, 228\u2009\u00b1\u20098 nm and 267\u2009\u00b1\u20099 nm, respectively (Table 1). Moreover, SeMNPs demonstrated remarkable stability when exposure to dilution, high ionic strengths, extended storage periods of up to 30 days and a broad range of alkaline conditions (pH 7\u201311) (Figure S8). This is the first example of polymerizing selenocystamine into selenomelanin.\u00a0\nUV-Vis spectra of SeMNPs showed the monotonic broadband absorption (Fig. 2B, Figure S1), which is a distinguishing characteristic of melanin. Next, X-ray photoelectron spectroscopy (XPS) peaks at ~\u200955.0 eV clearly demonstrated C-Se-C bond (Fig. 2C, Figure S4-S7), and revealed that Se content of SeMNPs-2 (5.1 at%) and-4 (3.8 at%) was ~\u20092\u20133 times higher than SeMNPs-1 (1.6 at%) (Table 1). Extracting from the XPS results, the decarboxylation percentage of SeMNPs-2 was estimated to be only\u2009~\u20093.0% (Table S1). Energy dispersive X-ray spectroscopy (EDS) mapping analysis verified the colocalization of C, N, O and Se elements within the SeMNPs-1, -2 and \u2212\u20094 (Fig. 2D, Figure S4-S7). For SeMNPs-3, poor colocalization of Se suggested that little selenium was incorporated, presumably because the eumelanin seeds and selenocysteine both had negative charges at the synthetic condition, making the reaction unfavorable. Therefore, SeMNPs-3 would be excluded in the subsequent studies. Electron paramagnetic resonance (EPR) spectra displayed that SeMNPs-1, -2 and \u2212\u20094 contained persistent free radicals with 1.12\u00d71018, 1.23\u00d71018 and 1.76\u00d71018 spins per gram (Figure S9), respectively, consistent with typical natural and synthetic melanin.[22] Interestingly, g factors of solid-state EPR were consistent with semi-localized ring-based carbon radicals (SeMNPs-2, g\u2009\u2248\u20092.00377; SeMNPs-4, g\u2009\u2248\u20092.00398), while the g values for melanin suspension samples were indicative of semiquinone radicals (SeMNPs-2, g\u2009\u2248\u20092.00454; SeMNPs-4, g\u2009\u2248\u20092.00466) (Figure S9, Table S2).[30]\nTo elucidate the influence of carboxyl groups on atropisomerism and configurational stability, we conducted density functional theory (DFT) calculations to determine the dihedral angle (D) and rotation barrier (\u0394G\u2260\u2009rot) of melanin dimers (Fig. 3A-3D, Figure S10). Studying the melanin dimers holds significance as the characteristics of the oligomers/polymer \u00a0depend critically on the properties of their constituent substructures.[31] Since the energy of forming a ring-closed structure was lower than that of open-chain structure (Table S3), we focused on the dimeric benzoselenazine-indole in our investigation. As the content of carboxyl groups increased, steric repulsions distorted the structure of melanin dimers, which is evidenced by the increased D: SeMNPs-1 (58.4\u00b0)\u2009>\u2009SeMNPs-2 (56.0\u00b0)\u2009>\u2009SeMNPs-3 (23.0\u00b0)\u2009>\u2009SeMNPs-4 (22.7\u00b0), L-DOPA NPs (56.4\u00b0)\u2009>\u2009DA NPs (37.2\u00b0), as well as the decreased \u0394G\u2260\u2009rot and weakened configurational stability: SeMNPs-1 (2.489 kcal mol\u2212\u20091)\u2009<\u2009SeMNPs-2 (2.692 kcal mol\u2212\u20091) < SeMNPs-3 (4.842 kcal mol\u2212\u20091)\u2009<\u2009SeMNPs-4 (5.270 kcal mol\u2212\u20091), L-DOPA NPs (1.998 kcal mol\u2212\u20091)\u2009<\u2009DA NPs (2.759 kcal mol\u2212\u20091). These findings suggested that reducing carboxyl group content leads to lower D, indicating a more planar and conjugated molecular structure, and higher \u0394G\u2260\u2009rot, reflecting greater configurational stability and reduced atropisomerism.\n\u00a0\n\n\n\nTable 1\n\nCharacterization of SeMNPs and eumelanin NPs.\n\n\n\n\n\nSample\n\n\nMonomer\n\n\nOxidant\n\n\nYield\n\n\nSize a\n(nm)\n\n\n\u03b6-potential\n(mV)\n\n\nSe\nContent b\n\n\nSe\nContent c\n\n\nMolar ratio of\nmonomers d\n\n\nMolar ratio of monomers e\n\n\n\n\n\n\nL-DOPA NPs\n\n\nL-DOPA\n\n\nKMnO4\n\n\n67.6%\n\n\n88\u2009\u00b1\u20094\n\n\n\u221222.6\u2009\u00b1\u20090.5\n\n\nN/A\n\n\nN/A\n\n\nN/A\n\n\nN/A\n\n\n\n\nDA NPs\n\n\nDA\n\n\nO2\n\n\n40.8%\n\n\n300\u2009\u00b1\u20099\n\n\n\u221234.4\u2009\u00b1\u20090.6\n\n\nN/A\n\n\nN/A\n\n\nN/A\n\n\nN/A\n\n\n\n\nSeMNPs-1\n\n\nSelenocysteine,\nL-DOPA\n\n\nKMnO4\n\n\n33.5%\n\n\n81\u2009\u00b1\u20096\n\n\n\u221227.2\u2009\u00b1\u20091.2\n\n\n1.6 at%\n\n\n13.3 wt%\n\n\n0.15:1\n\n\n0.41:1\n\n\n\n\nSeMNPs-2\n\n\nSelenocysteamine,\nL-DOPA\n\n\nKMnO4\n\n\n36.4%\n\n\n96\u2009\u00b1\u20094\n\n\n\u221234.8\u2009\u00b1\u20090.3\n\n\n5.1 at%\n\n\n17.7 wt%\n\n\n0.83:1\n\n\n0.55:1\n\n\n\n\nSeMNPs-3\n\n\nSelenocysteine,\nDA\n\n\nO2\n\n\n12.8%\n\n\n228\u2009\u00b1\u20098\n\n\n\u221229.2\u2009\u00b1\u20090.4\n\n\n0.8 at%\n\n\n7.2 wt%\n\n\n0.11:1\n\n\n0.14:1\n\n\n\n\nSeMNPs-4\n\n\nSelenocysteamine,\nDA\n\n\nO2\n\n\n37.2%\n\n\n267\u2009\u00b1\u20099\n\n\n\u221219.1\u2009\u00b1\u20090.3\n\n\n3.8 at%\n\n\n10.6 wt%\n\n\n0.54:1\n\n\n0.22:1\n\n\n\n\n\n[a] Results were from TEM. [b] Results were from XPS. [c] Results were from inductively coupled plasma optical emission spectrometry (ICP-OES). [d-e] Molar ratios of monomers is the ratio of L-DOPA/DA to selenocysteine/selenocysteamine, and [d] and [e] were calculated from XPS and ICP-OES results, respectively. N/A, not applicable.\nTo further study the complex chemical structure of SeMNPs, Fourier-transform infrared spectroscopy (FTIR) and 13C solid-state nuclear magnetic resonance (ssNMR) were employed. FTIR of SeMNPs displayed typical absorption bands at ~\u20093400 cm\u2212\u20091 (N-H stretching), ~\u20093300\u2009\u2212\u20093100 cm\u2212\u20091 (O-H stretching), \u223c2920\u2009\u2212\u20092820 cm\u2212\u20091 (stretching vibration of aliphatic C-H group) and ~\u20091490 cm\u2212\u20091 (stretching of indole structure) (Fig.\u00a02E, Figure S11). The ratios of ~\u20093400 cm\u2212\u20091 (N-H stretching) and ~\u20093300\u2009\u2212\u20093100 cm\u2212\u20091 (O-H stretching) increased sequentially from L-DOPA NPs, SeMNPs-1, DA NPs, SeMNPs-2 to SeMNPs-4, due to the decreased carboxyl group content.[32] 13C ssNMR peaks at ~\u2009171.2 ppm and ~\u2009162.9 ppm were assigned to the \u2212\u2009COOH of L-DOPA and selenocystine (Fig.\u00a03F, 13C ssNMR of monomers was showcased for comparison), while the decrease in the peak at ~\u2009143.1 ppm corresponded to the generation of o-aminophenol structure of the benzoselenazine subunit instead of catechol in the monomers (Fig.\u00a03F, 13C ssNMR of eumelanin NPs was displayed for comparison). We noted\u00a0that SeMNPs-2 and \u2212\u20094 had the increased aliphatic peaks (~\u200955.3\u201330.9 ppm), similar to the FTIR spectra.\nThe Radiation Attenuation and Antioxidant Capability of SeMNPs.\nDirect experimental characterization of radiation attenuation properties of nanomaterials is nontrivial. Radiation energy attenuation of \u03b3-ray and X-ray photons is mainly through the photoelectric effect, Compton scattering and pair production. SeMNPs were able to induce the photoelectric effect and Compton scattering due to more high atomic number (Z) elements and more delocalized electrons.[7] To investigate the physical attenuation capacity of SeMNPs, we used computed tomography (CT) because X-ray is widely applied in medical diagnosis and radiotherapy, etc.,[33] and CT is one of the most commonly used radiology tools.[34\u201335] Visually, a notable CT signal enhancement of SeMNPs was observed compared to L-DOPA NPs and DA NPs at the same mass concentration (Fig.\u00a04A), which correlated with the X-ray absorption coefficient as a function of mass concentration (Fig.\u00a04B): SeMNPs-1, -2 and \u2212\u20094 (3.4, 3.9 and 4.0 HU mg\u2212\u20091 mL, respectively)\u2009>\u2009L-DOPA NPs and DA NPs (0.6 and 0.5 HU mg\u2212\u20091 mL, respectively). This enhancement is attributed to the presence of selenium, which has a high Z. Then by comparing the X-ray absorption coefficient with respect to the selenium concentration, we found that the conjugated polymer structure of SeMNPs also played a key role in the X-ray attenuation. This is evidenced by the increased X-ray absorption coefficient of the enhanced-conjugated materials: SeMNPs-4 (104.2 HU mg\u2212\u20091 mL)\u2009>\u2009SeMNPs-2 (64.3 HU mg\u2212\u20091 mL)\u2009>\u2009SeMNPs-1 (25.5 HU mg\u2212\u20091 mL)\u2009>\u2009selenocystamine (16.0 HU mg\u2212\u20091 mL) (Fig.\u00a04C, Figure S12).\nMechanistic studies for radiation protection are then focused on reactive oxygen species (ROS) and reactive nitrogen species (RNS) scavenging.[6, 8, 36] Radiation damage to cells and organs usually begins with a burst of ROS such as hydroxyl radical (\u00b7OH), hydrogen peroxide (H2O2) and superoxide anion radical (O2\u00b7\u2212), etc.[6, 36\u201338] \u00b7OH is a strongly reactive species with the highest radiochemical yield in the radiolysis of water.[37] O2\u00b7\u2212 has been identified as one of the most toxic ROS, and the main oxidant in various cell types.[18] Radiation-induced bystander effect has been observed in unirradiated cells upon receiving signals such as nitric oxide (NO\u00b7) from irradiated cells. Therefore, assessing the antioxidant capacity of SeMNPs to distinct ROS/RON is essential for the study of their radiation protection properties (Fig.\u00a04D). [8, 39] SeMNPs-2 and \u2212\u20094 had the highest efficiency for O2\u00b7\u2212 scavenging compared to other melanin (Fig.\u00a04E, Figure S13), presumably because they are less electronegative without carboxylate group and more accessible to O2\u00b7\u2212. SeMNPs could further remove the toxic by-products H2O2 after the reaction with O2\u00b7\u2212 (Fig.\u00a04F, Figure S14). SeMNPs also had a good scavenging capacity for \u00b7OH and NO\u00b7 (Fig.\u00a04G-4H, Figure S13). SeMNPs-1 and \u2212\u20092 were similar at \u00b7OH scavenging, while SeMNPs-2 and \u2212\u20094 were better at scavenging NO\u00b7 than SeMNPs-1.[37] In addition, a representative model radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) was studied (Fig.\u00a04I, Figure S15). Previous studies showed that DHICA melanin has greater antioxidant capacity than DHI melanin,[19] which is also supported by our study (Fig.\u00a03I, Figure S15). The more carboxylated melanins are a better DPPH scavenger than the decarboxylated counterpart, as carboxyl groups lead to weaker aggregation of melanin and make them more accessible for bulky free radicals than less carboxylated analogues. Therefore, the molecular engineering boosts the scavenging capability of O2\u00b7\u2212 and NO\u00b7.\nThough physical attenuation and antioxidant properties are the two primary mechanisms for radiation protection, most studies focus on only one of them, with few addressing both simultaneously. [6\u20138, 36, 40\u201341] Our experiment showcased that the introduction of selenium-monomers and molecular engineering of side-chain impact both properties.\nRadioprotective Effects against \u03b3-ray of SeMNPs in HaCaT Cells.\nMotivated by the radiation attenuation and antioxidant capability of the SeMNPs, we evaluated their radioprotection ability in HaCaT cells because human skin is the first part to be harmed by external ionizing radiation. As normal epidermal cells derived from human skin, HaCaT cells are extensively used to study radioprotection.[36] First, the biocompatibility study of the SeMNPs confirmed\u2009>\u200995% cell viabilities at the SeMNPs concentration of 0.004 mg mL\u2212\u20091 (Figure S16). Next, the intracellular distribution observed by confocal laser scanning microscopy (CLSM) showed that SeMNPs formed artificial perinuclear caps in HaCaT cells like natural melanosomes (Figure S17). [22, 42]\nAs the gold standard to verify the long-term cell proliferative ability, clonogenic cell survival assay confirmed the long-term radioprotective effect of engineered SeMNPs (Fig.\u00a05A-5B). In the SeMNPs-2 and \u2212\u20094-treated groups, the survival rate of cells irradiated with 10 Gy \u03b3-ray was greatly boosted compared to the no-SeMNPs-treated group as well as the SeMNPs-1-treated group, suggesting the proliferative capacity of \u03b3-ray-treated HaCaT cells was rescued by our engineered SeMNPs. Meanwhile, after exposure to 10 Gy \u03b3-ray, cell viability remained above 90% at 24 h and 48 h post-irradiation when being protected by SeMNPs (Figure S18).\nEncouraged by the significant radioprotective performance of SeMNPs, the cellular mechanisms were further elucidated. The cell cycle is a meticulously coordinated process of cell growth and division, and its entry and progression must be stringently regulated to ensure normal development and maintain tissue homeostasis.[43] Exposure to ionizing radiation can lead to temporary or permanent cell cycle arrest, potentially culminating in cell apoptosis.[44] In our experiment, we observed that unprotected HaCaT cells irradiated with \u03b3-ray exhibited significant arrest in the G2/M phase (Figure S19), whereas non-irradiated cells maintained stable cell cycle progression. To investigated the protective effects of SeMNPs, we monitored cell cycle distribution on HaCaT cells following irradiation with a gradient of \u03b3-ray doses (0, 4, 6, 8, 10 Gy) at different incubation times (12 h, 24 h) (Fig.\u00a05C-5D, Figure S20-S23). Incubation with 0.004 mg mL\u2212\u20091 SeMNPs significantly reduced the G2/M arrest induced by \u03b3-ray. SeMNPs-2 and \u2212\u20094 effectively prevented G2/M arrest induced by doses from 4 to 10 Gy after 24 h post-irradiation. In contrast, SeMNPs-1 inhibited G2/M arrest at 4 Gy and 6 Gy doses, and alleviated the G2/M arrest by only\u2009~\u200910% and 15% after 8 Gy and 10 Gy radiation, respectively. This finding suggests that the engineered SeMNPs provided superior protection against \u03b3-ray induced damage compared to the original SeMNPs-1. Interestingly, the protective effect at 12 h post-irradiation was less pronounced compared to 24 h, which can be attributed to the dynamic nature of cell cycle regulation. Over time, the protection against G2/M phase arrest conferred by SeMNPs became more evident (Figure S20-S23). These results underscored the enhanced protective efficacy of molecular engineered SeMNPs against the detrimental effects of \u03b3-ray.\nThe homeostasis of the actin cytoskeleton is critical for the cells, which determines cell morphology, polarity and division.[45] As depicted in Fig.\u00a05E, exposure to 10 Gy \u03b3-ray triggered the assembly of actin filaments (F-actin). Whereas pretreatment with SeMNPs, prominently SeMNPs-4, mitigated the generation of F-actin and alleviated the detrimental alterations in the cytoskeleton instigated by irradiation (Figure S24). Moreover, exposure of a living system to high-energy radiation leads to excessive toxic ROS, [6, 36\u201338] which can interact with biomolecules such as lipid, leading to lipid peroxidation and even cell death. [36, 38] We studied the performance of SeMNPs at scavenging ROS and mitigating lipid peroxidation in living cells (Fig.\u00a05F-5K). CLSM images and quantitative analysis showed that the ROS signals in the SeMNPs-treated groups all maintained similar level as of the non-irradiated cells, while strong green fluorescence showed up after 6 Gy \u03b3-ray irradiation in the no-NP-treated group. (Fig.\u00a05F, 5H, Figure S25-S26). Flow cytometry analysis of ROS showed the same trend as CLSM (Fig.\u00a05J). Furthermore, SeMNPs effectively inhibited lipid peroxidation, with SeMNPs-4 being the most efficient, and eliminated lipid peroxidation to normal cellular levels (Fig.\u00a05G, 5I, 5K, Figure S27). The enhanced scavenging properties of SeMNPs-4 against lipid peroxides could be ascribed to its higher lipophilicity and easier access to liposomal membrane. Notably, the concentration in our study (0.004 mg mL\u2212\u20091) is significantly lower than other radioprotective materials such as fullerenol and Cerium Metal-Organic Frameworks. [8, 36] Collectively, through molecular engineering inspired by conjugating polymer, [27\u201328, 46] we successfully developed SeMNPs that protected live cells against \u03b3-ray.\nMechanism Investigation through mRNA Sequencing\nTo elucidate the effects of various SeMNPs treatments on gene expression in response to \u03b3-ray exposure, mRNA sequencing of HaCaT cells was performed (Fig.\u00a06A). A total of 5 groups was set up: 1. the control group (Control) was neither irradiated nor treated with any SeMNPs, 2. the irradiated group (\u03b3-ray) was treated with 10 Gy \u03b3-ray, 3\u20135 are the \u03b3-irradiated groups pretreated with SeMNPs-1, -2 and \u2212\u20094, which are named as \u03b3-ray\u2009+\u2009SeMNPs-1, \u03b3-ray\u2009+\u2009SeMNPs-2 and \u03b3-ray\u2009+\u2009SeMNPs-4, respectively. A heat map with a hierarchical clustering dendrogram, volcano plots and Venn diagrams visually presented the global expression patterns of genes and differentially expressed genes (DEGs). The \u03b3-ray group showed 1211 up-regulated genes and 1319 down-regulated genes compared to Control, while the \u03b3-ray\u2009+\u2009SeMNPs-1, -2 and \u2212\u20094 groups exhibited a substantially different transcriptomic profile compared with the \u03b3-ray group (Fig.\u00a06B, Figure S28-S29).\nTo further explore the biological functions and major enrichment pathways of these DEGs, gene ontology (GO) enrichment and Kyoto Encyclopedia of Gene and Genomes (KEGG) analyses were conducted (Fig.\u00a06C-6D, Figure S30-S35). Compared to the Control group, genes down-regulated in the \u03b3-ray group were mainly focused on cell cycle and DNA repair pathways (Figure S30-S35). Up-regulated genes were enriched in the pathways such as DNA damage response, cell cycle regulation and DNA repair (Fig.\u00a06C), suggesting that SeMNPs- 4 promoted cellular mechanisms for maintaining genomic integrity (for more details, see Figure S30-S35). Down-regulated genes were involved in the pathways like protein translation and ROS biosynthesis (Fig.\u00a06D), indicating that SeMNPs-4 may reduce oxidative stress and cellular metabolic burden by modulating these pathways (for more details, see Figure S30-S35). Moreover, gene set enrichment analysis (GSEA) supported the down-regulation of ROS biosynthesis-related genes and up-regulation in cell cycle-related gene expression, as opposed to the \u03b3-ray group (Fig.\u00a06E-6F, Figure S36).\nHeatmaps of specific gene sets involved in the cell cycle and ROS biosynthesis across different treatment groups further dissected these changes (Fig.\u00a06G-6H, see additional discussion in supporting information for more details). Figure\u00a06G provided compelling evidence that SeMNPs could modulate the expression of cell cycle-related genes, thereby contributing to the attenuation of radiation-induced cell cycle arrest. ROS biosynthesis process-related genes shown in Fig.\u00a06H, for instance,\nIKBKB, NFKBIA, PRKCD and MT-COX1, were up-regulated after \u03b3-ray irradiation, but were mostly down-regulated in the \u03b3-ray\u2009+\u2009SeMNPs-4 group, reinforcing the role of SeMNPs-4 in mitigating oxidative stress.\u00a0\nAdditionally, a pathway network analysis highlighted the interactions of DEGs within critical pathways. SeMNPs treatment influences cell cycle pathways (Fig.\u00a06I), including p53 and FoxO signaling, which are crucial for cell cycle regulation, DNA repair, cell senescence and apoptosis.[47] Fig.\u00a06J focused on the ROS pathway and revealed that SeMNPs, particularly SeMNPs-4, modulated genes associated with oxidative phosphorylation (OXPHOS), reducing ROS production and potentially alleviating oxidative damage,[48] which complemented the extracellular and intracellular ROS scavenging mechanisms (vide supra). Collectively, these results suggested that engineered SeMNPs, especially SeMNPs-4, provided a multi-faceted approach to mitigate radiation-induced cellular damage by enhancing DNA repair, normalizing cell cycle progression and decreasing oxidative stress.\nSystemic \u03b3-ray radioprotection in vivo\nThe radioprotective effect of SeMNPs-4 was then evaluated in vivo. To assess the in vivo distribution of SeMNPs-4, 1.2 mCi 99mTc-coordinated SeMNPs-4 (99mTc-SeMNPs-4) was injected intraperitoneally into mice, and imaged by Single-Photon Emission Computed Tomography-Computed Tomography (SPECT-CT) at various time points (Fig.\u00a07A). 99mTc is a widespread clinical radionuclide for SPECT imaging. [49] Significant accumulation of 99mTc-SeMNPs-4 was observed in the thorax (heart and lungs), liver, spleen and kidneys at 0.5-8 h post-injection (Fig.\u00a07B, Video 1\u20132), and a portion of 99mTc-SeMNPs-4 was excreted in the feces and urine in the early post-injection period. In contrast, free 99mTcO4\u2212 was mainly distributed in the stomach and rapidly excreted into the bladder via the kidneys, validating the in vivo imaging of SeMNPs-4 using 99mTc based SPECT.\nTotal body irradiation (TBI) was performed on mice to comprehensively appraise the radioprotective efficacy of SeMNPs-4. The SeMNPs-4 material showed a good in vivo biosafety profile (Figure S37). The commercial radiation protection agent AMF and the ROS scavenger SOD at safe doses were selected as control. [6, 50] After 4 days of 8 Gy irradiation to trigger significant organ damage (Figure S38), we harvested diverse organs to access early injuries (Fig.\u00a07C). Severe damage was observed in the spleen, lungs, kidneys, testes and liver, while SeMNPs-4 was able to mitigate these injuries, outperforming both AMF and SOD (Fig.\u00a07D, Figure S39-S40, see additional discussion in supporting information for more details).\nFinally, we conducted a comparison of long-term survival after TBI of 6 Gy \u03b3-ray (Fig.\u00a07C). 6 Gy is a lethal dose for mice, [51] and without protection, 0% survived by day 8. In the SeMNPs-4 treated group (injection dose: 7.5 mg kg\u2212\u20091 body weight), the survival rate increased from 0\u2013100% at day 30 (Fig.\u00a07E-7F). The protected mice maintained normal body weight, regular blood indices and organ morphology (Fig.\u00a07G, Figure S41-S42), demonstrating effective in vivo \u03b3-radiation protection of our molecularly engineered SeMNPs-4.\n", + "section_image": [] + }, + { + "section_name": "Conclusion", + "section_text": "In summary, molecular engineering enabled the creation of more conjugated selenomelanins with enhanced in vivo \u03b3-ray radioprotection, with the highest degree of conjugation giving the best performance in SeMNPs-4. SeMNPs-4 increased the survival rate of mice from 0 to 100% exposed to lethal \u03b3-ray, demonstrating its great potential as the metal-free, lightweight and efficient radioprotective material. Overall, this work offers a molecular tuning approach beyond the natural synthetic pathway for melanin structure-property-function design. We believe that our work provides valuable insights into the rational engineering of the chemical complexity of biomacromolecule materials, extending beyond melanin to other materials, such as lignin and intrinsically disordered proteins, toward tailored functional materials.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": " Preparations of SeMNPs. The reactions are a templated polymerization reaction with L-DOPA or DA nanoparticle as the seeds. SeMNPs-1: First, L-DOPA solution (0.06 mmol) was mixed with 0.2 M KMnO4 to form eumelanin seeds. Next, selenocysteine solution (0.06 mmol) was added to the reaction flask. After overnight reaction, the product was collected by centrifugation and purified by washing with HCl solution. Finally, the mass concentration of the final nanoparticle solution was determined by lyophilizing a small aliquot solution overnight and weighing with an analytical balance. SeMNPs-2: First, L-DOPA solution (0.06 mmol) was mixed with 0.2 M KMnO4 to form eumelanin seeds. Next, selenocysteamine solution (0.06 mmol) was added to the reaction flask. After overnight reaction, the product was collected by centrifugation and purified by washing with HCl solution. SeMNPs-3: 99.5% ethanol absolute, ultrapure water and 28\u201330% NH3\u00b7H2O solution were added to the flask and stirred vigorously. Dopamine hydrochloride solution (0.105 mmol) was added to the mixture. Next, selenocysteine solution (0.105 mmol) was added to the reaction flask. After overnight reaction, the product was collected by centrifugation and washed with ultrapure water for three times. SeMNPs-4: 99.5% ethanol absolute, ultrapure water and 28\u201330% NH3\u00b7H2O solution were added to the flask and stirred vigorously. Dopamine hydrochloride solution (0.105 mmol) was added to the mixture. Next, selenocysteamine solution (0.105 mmol) was added to the reaction flask. After overnight reaction, the product was collected by centrifugation and washed with ultrapure water for three times. X-ray Attenuation Capability of SeMNPs. Philips IQon Spectral CT in Beijing Tongren Hospital was employed to acquire the CT images and Hounsfield Unit values. The CT images were further analyzed using PmsDView software. Parameters of imaging as follows: 80 kVp, 10 mA. To evaluate the CT signals in vitro under different concentrations of melanin and monomer, melanin suspension and monomer solution in ultrapure water were filled into 1.5 mL centrifuge tubes for CT tests. Cell cycle studies. HaCaT cells were plated at a density of 50,000 cells per well in 12-well plates and cultured for 24 h. After incubating for 24 h, 0.004 mg mL\u2212\u20091 SeMNPs were added to each well for 24 h. Then \u03b3-ray radiation was applied at the fixed irradiation time of 1 min with different dose rate. The cells were incubated at the desired times before assaying experiments were performed. Cells were harvested, fixed and stained according to a technical manual of cell cycle and apoptosis analysis kit for flow cytometry. SPECT-CT imaging assays. Labelling of SeMNPs-4: 0.1 mL of 5 mg mL\u2212\u20091 SnCl2 solution (prepared with 0.1 mol L\u2212\u20091 HCl solution) and 3 mCi of Na99mTcO4 were added to 1.5 mL of 0.75 mg/mL SeMNPs-4, and stirred at room temperature for 30 min, then ultrafiltrated and centrifuged for three times (5 min each time) to remove unbound nuclides, and then determined the radioactivity, and finally obtain the 99mTc-SeMNPs-4. BALB/c mice were intraperitoneally injected with 1.2 mCi fresh Na99mTcO4 solution (free 99mTcO4\u2212) or 99mTc-SeMNPs-4, and imaged by microSPECT-CT at 0.5 h, 2 h, 4 h, 8 h and 24 h. Early organ injuries assay. BALB/c mice were intraperitoneally injected with PBS or materials 2 h before and 24 h after the TBI (8 Gy \u03b3-ray). The same amount was given for both injections and the total amount was grouped as follows: The 0 Gy group comprised normal mice (no irradiation or injection). The \u03b3-ray group was irradiated with 8 Gy, but without injection. The \u03b3-ray\u2009+\u2009SeMNPs-4 group was irradiated with 8 Gy and received an intraperitoneal injection of 7.5 mg kg\u2212\u20091 body weight SeMNPs-4. The \u03b3-ray\u2009+\u2009low-AMF group was irradiated with 8 Gy and received an intraperitoneal injection of 30 mg kg\u2212\u20091 body weight AMF. The \u03b3-ray\u2009+\u2009high-AMF group was irradiated with 8 Gy and intraperitoneally injected 100 mg kg\u2212\u20091 body weight AMF. The \u03b3-ray\u2009+\u2009SOD group was irradiated with 8 Gy and intraperitoneally injected 30 mg kg\u2212\u20091 body weight SOD. Weight and survival within 4 days after TBI were recorder. The mice were sacrificed and various organs were harvest 4 days after TBI for H&E, Masson and Sirius Red staining. 30-day survival assay. BALB/c mice were intraperitoneally injected with PBS or materials 2 h before and 24 h after the TBI (6 Gy \u03b3-ray). The same amount was given for both injections and the total amount was grouped as follows: The 0 Gy group comprises normal mice (no irradiation or injection). The \u03b3-ray group was irradiated with 6 Gy, but without injection. The \u03b3-ray\u2009+\u2009SeMNPs-4 group was irradiated with 6 Gy and received an intraperitoneal injection of 7.5 mg kg\u2212\u20091 body weight SeMNPs-4. The 0 Gy\u2009+\u2009SeMNPs-4 group was intraperitoneally injected 7.5 mg kg\u2212\u20091 body weight SeMNPs-4, but without irradiation. Weight and survival within 30 days after TBI were recorder. The mice were sacrificed, and blood as well as various organs were harvest 30 days after TBI for further test. Statistical analysis. All data are presented as their means with S.D., unless otherwise noted. Statistical significance was determined by a two-tailed Student\u2019s t test assuming equal variance. Error bars represent standard deviation from \u2265\u2009three experiments. NS means no statistical difference (P\u2009>\u20090.05). Statistical values are indicated in figures according to the following scale: *P\u2009<\u20090.05, **P\u2009<\u200910\u2212\u20092, ***P\u2009<\u200910\u2212\u20093, ****P\u2009<\u200910\u2212\u20094.", + "section_image": [] + }, + { + "section_name": "Declarations", + "section_text": " Competing interests W. C. and R. D. are listed as inventors on a patent application describing \u201cPreparation and Application of Selenomelanin\u201d. Supplementary information Supplementary information contains more experimental details, supporting figures and tables. Author contributions W.C. and R.D. conceived the project and designed the experiments. R. D. conducted the majority of experiments. R. D. and W. Chen performed the mice experiments. J.W. and Y.L. assisted with cell assays. R.D and Y.L. performed and analyzed radical scavenging experiments. C.Z, Y.N., Y.Z., H.Z. and D.R. performed and analyzed CT experiments. Y.O. and X.Z. assisted with synthesis of selenomelanin. Z. H. conducted DFT calculations. Q. R. and J. Z. assisted with SPECT-CT imaging. W.C. and R.D. cowrote the manuscript. All authors have given approval to the final version of the manuscript.Acknowledgements We appreciate the aid of Jianwei Huang, Bo Liu and Xuan Zhang in China National Institute of Metrology in the design of radiation experiments. We thank Zhanwei Yue and Yao Cui at Beijing Normal University for 60Co source. We thank Prof. Mengchao Cui at Beijing Normal University for plate reader measurement. We acknowledge Prof. Tianyu Li in Institute of Process Engineering, Chinese Academy of Sciences and Zhangyi Ouyang in Institute of Radiation Medicine, Academy of Military Medical Sciences for providing thoughts on mRNA sequencing data analysis. The work was funded by National Key Research and Development Program of China (2022YFA1505900, 2023YFA0915300), the National Natural Science Foundation of China (22205026, 22471021) and the Fundamental Research Funds for the Central Universities, and Guangdong Provincial Key Laboratory of Functional and Intelligent Hybrid Materials and Devices (2023-GDKLFIHMD-0X).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Coleman CN, Stone HB, Moulder JE, Pellmar TC (2004) Science 304:693\u2013694 Garrett-Bakelman FE, Darshi M, Green SJ, Gur RC, Lin L, Macias BR, McKenna MJ, Meydan C, Mishra T, Nasrini J, Piening BD, Rizzardi LF, Sharma K, Siamwala JH, Taylor L, Vitaterna MH, Afkarian M, Afshinnekoo E, Ahadi S, Ambati A, Arya M, Bezdan D, Callahan CM, Chen S, Choi AMK, Chlipala GE, Contrepois K, Covington M, Crucian BE, De Vivo I, Dinges DF, Ebert DJ, Feinberg JI, Gandara JA, George KA, Goutsias J, Grills GS, Hargens AR, Heer M, Hillary RP, Hoofnagle AN, Hook VYH, Jenkinson G, Jiang P, Keshavarzian A, Laurie SS, Lee-McMullen B, Lumpkins SB, MacKay M, Maienschein-Cline MG, Melnick AM, Moore TM, Nakahira K, Patel HH, Pietrzyk R, Rao V, Saito R, Salins DN, Schilling JM, Sears DD, Sheridan CK, Stenger MB, Tryggvadottir R, Urban AE, Vaisar T, Van Espen B, Zhang J, Ziegler MG, Zwart SR, Charles JB, Kundrot CE, G. 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Chem. 2021, 133, 18948\u201318957 Ciolkowski EL, Cooper BR, Jankowski JA, Jorgenson JW, Wightman RM (1992) J Am Chem Soc 114:2815\u20132821 Yao F, Dong K, Ke W, Fang G (2024) ACS Nano 18:6095\u20136110 Zhen W, Liu Y, Lin L, Bai J, Jia X, Tian H, Jiang X (2018) Angew Chem Int Ed 57:10309\u201310313 Guo Z, Zhu S, Yong Y, Zhang X, Dong X, Du J, Xie J, Wang Q, Gu Z, Zhao Y (2017) Adv Mater 29:1704136 Zhao M, Wang C, Xie J, Ji C, Gu Z (2021) Small 17:2102035 Fu Q, Li H, Duan D, Wang C, Shen S, Ma H, Liu Z (2020) Angew Chem Int Ed 59:21546\u201321552 Xie J, Wang C, Zhao F, Gu Z, Zhao Y (2018) Adv Healthc Mater 7:1800421 Liu G, Zeng Y, Lv T, Mao T, Wei Y, Jia S, Gou Y, Tao L (2020) Nat Commun 11:6214 Tian M, Mu X, Fan D, Liu Z, Liu Q, Yue K, Song Z, Luo J, Zhang S (2023) Adv Mater 35:2303436 Li L, Ge Z, Liu S, Zheng K, Li Y, Chen K, Fu Y, Lei X, Cui Z, Wang Y, Huang J, Liu Y, Duan M, Sun Z, Chen J, Li L, Shen P, Wang G, Chen J, Li R, Li C, Yang Z, Ning Y, Luo A, Chen B, Seim I, Liu X, Wang F, Yao Y, Guo F, Yang M, Liu CH, Fan G, Wang L, Yang D, Zhang L (2024) Science 386:eadl0799 Zhou X, McCallum NC, Hu Z, Cao W, Gnanasekaran K, Feng Y, Stoddart JF, Wang Z, Gianneschi NC (2019) ACS Nano 13:10980\u201310990 Kastan MB, Bartek J (2004) Nature 432:316\u2013323 Paris F, Fuks Z, Kang A, Capodieci P, Juan G, Ehleiter D, Haimovitz-Friedman A, Cordon-Cardo C, Kolesnick R (2001) Science 293:293\u2013297 Gourlay CW, Ayscough KR (2005) Nat Rev Mol Cell Biol 6:583\u2013589 Lin YC, Cheng HW, Su YW, Lin BH, Lu YJ, Chen CH, Chen HC, Yang Y, Wei KH (2018) Nano Energy 43:138\u2013148 Zhang Y, Xing Y, Zhang L, Mei Y, Yamamoto K, Mak TW, You H (2012) Proc. Natl. Acad. Sci. U. S. A. 109, 5717\u20135722 Smeitink J, van den Heuvel L, DiMauro S (2001) Nat Rev Genet 2:342\u2013352 Ruan Q, Ding D, Diao L, Feng J, Yin G, Jiang Y, Wang Q, Han P, Jiang J, Zhang J (2024) J Med Chem 67:3190\u20133202 Abe M, Nishidai T, Yukawa Y, Takahashi M, Ono K, Hiraoka M, Ri N (1981) Int J Radiat Oncol Biol Phys 7:205\u2013209 Ouyang F, Li Y, Wang H, Liu X, Tan X, Xie G, Zeng J, Zeng G, Luo Q, Zhou H, Chen S, Hou K, Fang J, Zhang X, Zhou L, Li Y, Gao A (2024) Adv Sci 2406026", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nW. C. and R. D. are listed as inventors on a patent application describing \u201cPreparation and Application of Selenomelanin\u201d.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "Nat.Commun.supportinginformation.docxSUPPLEMENTARY INFORMATIONVideo1SPECTCTimagesof99mTcSeMNPs4at2h.gifVideo 1-SPECT-CT images of 99mTc-SeMNPs-4 at 2 hVideo2SPECTCTimagesof99mTcSeMNPs4at24h.gifVideo 2-SPECT-CT images of 99mTc-SeMNPs-4 at 24 h", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The escalating utilization of ionizing radiation across medicine and industry underscored the paramount urgency of effective radioprotective materials. Conventional materials such as lead and concrete are widely used, and lead-free materials have also emerged to solve the problems of cumbersome and toxic lead, such as metal-containing micro/nano materials and polymers. Nevertheless, there is still a significant challenge in meeting the urgent need for lightweight and biocompatible alternatives. To tackle this challenge, this work utilizes molecular engineering of melanin to develop a panel of metal-free melanin materials with enhanced conjugation, heightened physical shielding against radiation and effective antioxidant properties. Furthermore, engineered melanin materials demonstrated in vivo \u03b3-ray protection, increasing mice survival from ~12% to\u00a0100% after 6\u2009Gy total body irradiation.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The growing use of ionizing radiation in medicine and industry has increased the risk of radiation exposures and injuries1,2. In space exploration, long-term exposures to cosmic rays pose significant threats to astronauts\u2019 health and spacecraft performance3. Conventional radioprotective materials, such as lead and concrete, rely on strong physical attenuation for protection. However, these materials are often bulky and hazardous. To address these limitations, lead-free alternatives have been developed, including metal-doped polymer materials and metal-containing micro/nanomaterials4,5,6,7,8,9. Despite these advancements, heavy-metal-based radioprotectors still face challenges related to bulkiness, toxicity, and poor biocompatibility. Consequently, there is a pressing need for the development of lightweight, biocompatible, and metal-free radioprotective materials.\n\nMelanin is potentially a natural radioprotector10. As nature\u2019s enigmatic pigments ubiquitous across animal, plant, bacterial and fungal kingdoms11,12, melanin represents one of the last unknown frontiers in biology due to its structural complexity13. Melanin\u2019s various functions including coloration14,15, radical scavenging16,17,18,19,20, protective antifungal immunity21 and radiation protection22,23 are deeply linked to their complex structures24,25. We believe that the structural complexity, if rationally tuned, could offer an essential tool to understand and leverage the structure-property-function relationships. Previously, driven by chemical synthesis, we coined a selenium version of nature\u2019s sulfurous pheomelanin, termed selenomelanin22,26. The selenomelanin demonstrated superior radiation protection upon impinging X-ray (operating voltage: 160\u2009kV) than existing eumelanin and pheomelanin as demonstrated in primary cell culture, exemplifying the power of molecular engineering in radioprotective biomaterial design22. However, a deeper understanding of the radiation protection mechanism of this innovative material is crucial for designing enhanced radioprotective materials capable of shielding more penetrating \u03b3-ray (typically with energies exceeding 1\u2009MeV)27,28. Furthermore, in vivo testing remains essential for assessing its full potential11.\n\nHerein, to acquire enhanced \u03b3-ray radiation protectors or mitigators in vivo, molecular engineering of melanin is performed (Fig.\u00a01). Side-chain engineering has been used in conjugated polymer to tune the backbones packing, improving the solubility and charge transport properties29,30. Surprisingly, molecular engineering of side-chain has rarely been explored in melanin as far as we know. In fact, subtle variations in eumelanin monomers (dopamine vs. levodopa) can lead to huge structural and functional differences14,19. Furthermore, \u03c0-conjugated structures are relevant for high-energy radiation shielding as they disperse energy across the \u03c0-ystem, providing a basis for tuning radioprotection by modifying the conjugation31. In this work, we report a panel of metal-free radioprotective material with enhanced radiation shielding capability and good antioxidant properties. They could protect human keratinocyte (HaCaT) cells from high dose \u03b3-ray at a very low concentration. Additionally, the engineered materials increased mice survival from ~12% to\u00a0100% after 6\u2009Gy \u03b3-ray exposure, alleviating multi-organ injuries, which outperformed commercial Amifostine (AMF) and superoxide dismutase (SOD).\n\nMolecular engineered melanin demonstrated in vivo \u03b3-ray protection, increasing mice survival from\u2009~\u200912% to\u00a0100%.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_Fig1_HTML.png" + ] + }, + { + "section_name": "Results and discussion", + "section_text": "Selenomelanin nanoparticles (SeMNPs-1) were synthesized using L-3,4-dihydroxyphenylalanine (L-DOPA) and selenocysteine22. While significant spontaneous decarboxylation (up to 90%) happens in eumelanin synthesis19, the decarboxylation percentage of SeMNPs-1 was estimated to be ~11.5% (Supplementary Table\u00a01). The high fidelity of carboxylic acid groups provides a foundation for the molecular engineering of SeMNPs. Molecular engineered SeMNPs were synthesized using polymerized L-DOPA or dopamine (DA) as the seeds, followed by the addition of selenium-monomers for aqueous copolymerization (Fig.\u00a02A). Transmission electron microscopy (TEM) and dynamic light scattering (DLS) characterization confirmed the formation of spherical SeMNPs (Supplementary Figs.\u00a02\u20137). The particle diameters of SeMNP-1, \u22122, \u22123 and \u22124 were 81\u2009\u00b1\u20096\u2009nm, 96\u2009\u00b1\u20094\u2009nm, 228\u2009\u00b1\u20098\u2009nm and 267\u2009\u00b1\u20099\u2009nm, respectively (Table1). Moreover, SeMNPs demonstrated stability when exposure to dilution, high ionic strengths, extended storage periods of up to 30\u2009days and a broad range of alkaline conditions (pH 7-11) (Supplementary Fig.\u00a08).\n\nA Preparation routes of SeMNPs using oxidative polymerization. B UV-Vis spectra of SeMNPs and eumelanin nanoparticles (NPs). \u03b5: molar absorption coefficient. C XPS Se 3\u2009d spectra of SeMNPs. D High-angle annular dark-field imaging (HAADF) images of SeMNPs superimposed with the elemental mappings of Selenium (purple), Nitrogen (green) and Carbon (blue). Representative images are presented.\n\nUV-Vis spectra of SeMNPs showed the monotonic broadband absorption (Fig.\u00a02B, Supplementary Fig.\u00a01), which is a distinguishing characteristic of melanin. Next, X-ray photoelectron spectroscopy (XPS) peaks at ~55.0\u2009eV clearly demonstrated C-Se-C bond (Fig.\u00a02C, Supplementary Figs.\u00a04\u20137), and revealed that Se content of SeMNPs-2 (5.1 at%) and-4 (3.8 at%) was ~3\u20135\u2009times higher than SeMNPs-1 (1.0 at%) (Table\u00a01). Extracting from the XPS results, the decarboxylation percentage of SeMNPs-2 was estimated to be only ~3.0% (Supplementary Table\u00a01). Energy dispersive X-ray spectroscopy (EDS) mapping analysis verified the colocalization of C, N, O and Se elements within the SeMNPs-1, \u22122 and \u22124 (Fig.\u00a02D, Supplementary Figs.\u00a04\u20137). For SeMNPs-3, poor colocalization of Se suggested that little selenium was incorporated, presumably because the eumelanin seeds and selenocysteine both had negative charges at the synthetic condition, making the reaction unfavorable. Therefore, SeMNPs-3 would be excluded in the subsequent studies. Electron paramagnetic resonance (EPR) spectra displayed that SeMNPs-1, \u22122 and \u22124 contained persistent free radicals with 1.12\u2009\u00d7\u20091018, 1.23\u2009\u00d7\u20091018 and 1.76\u2009\u00d7\u20091018 spins per gram (Supplementary Fig.\u00a09), respectively, consistent with typical natural and synthetic melanin22. Interestingly, g factors of solid-tate EPR were consistent with semi-localized ring-based carbon radicals (SeMNPs-2, g\u2009\u2248\u20092.00377; SeMNPs-4, g\u2009\u2248\u20092.00398), while the g values for melanin suspension samples were indicative of semiquinone radicals (SeMNPs-2, g\u2009\u2248\u20092.00454; SeMNPs-4, g\u2009\u2248\u20092.00466) (Supplementary Fig.\u00a09, Supplementary Table\u00a02)32.\n\nTo elucidate the influence of carboxyl groups on atropisomerism and configurational stability, we conducted density functional theory (DFT) calculations to determine the dihedral angle (D) and rotation barrier (\u0394G\u2260rot) of melanin dimers (Fig.\u00a03A\u2013D, Supplementary Fig.\u00a010). Studying the melanin dimers holds significance as the characteristics of the oligomers/polymer depend critically on the properties of their constituent substructures33. Based on our computational results, the Gibbs free energy change (\u0394G) for the transformation from the open-chain structure to the ring-closed structure is negative (Supplementary Table\u00a03), indicating that the formation of the ring-closed structure is thermodynamically favored, so we focused on the dimeric benzoselenazine-indole in our investigation. As the content of carboxyl groups increased, steric repulsions distorted the structure of melanin dimers, which is evidenced by the increased D: SeMNPs-1 (58.4\u00b0)\u2009>\u2009SeMNPs-2 (56.0\u00b0) > SeMNPs-3 (23.0\u00b0)\u2009>\u2009SeMNPs-4 (22.7\u00b0), L-DOPA NPs (56.4\u00b0)\u2009>\u2009DA NPs (37.2\u00b0), as well as the decreased \u0394G\u2260rot and weakened configurational stability: SeMNPs-1 (2.489 kcal mol\u22121) < SeMNPs-2 (2.692 kcal mol\u22121) < SeMNPs-3 (4.842 kcal mol\u22121) < SeMNPs-4 (5.270 kcal mol\u22121), L-DOPA NPs (1.998 kcal mol\u22121) < DA NPs (2.759 kcal mol\u22121). These findings suggested that reducing carboxyl group content leads to lower D, indicating a more planar and conjugated molecular structure, and higher \u0394G\u2260rot, reflecting greater configurational stability and reduced atropisomerism.\n\nA\u2212D DFT calculations of \u0394G\u2260rot and D of SeMNPs dimers. E Zoomed FTIR spectra of SeMNPs and eumelanin NPs. F13C ssNMR spectra of monomers, SeMNPs and eumelanin NPs.\n\nTo further study the complex chemical structure of SeMNPs, Fourier-transform infrared spectroscopy (FTIR) and 13C solid-tate nuclear magnetic resonance (ssNMR) were employed. FTIR of SeMNPs displayed typical absorption bands at ~3400\u2009cm\u22121 (N-H stretching), ~3300-3100\u2009cm\u22121 (O-H stretching), \u223c2920-2820\u2009cm\u22121 (stretching vibration of aliphatic C-H group) and ~1490\u2009cm\u22121 (stretching of indole structure) (Fig.\u00a02E, Supplementary Fig.\u00a011). The ratios of ~3400\u2009cm\u22121 (N-H stretching) and ~3300-3100\u2009cm\u22121 (O-H stretching) increased sequentially from L-DOPA NPs, SeMNPs-1, DA NPs, SeMNPs-2 to SeMNPs-4, due to the decreased carboxyl group content34. 13C ssNMR peaks at ~171.2 ppm and ~162.9 ppm were assigned to the \u2212COOH of L-DOPA and selenocystine (Fig.\u00a03F, 13C ssNMR of monomers was showcased for comparison), while the decrease in the peak at ~143.1 ppm corresponded to the generation of o-aminophenol structure of the benzoselenazine subunit instead of catechol in the monomers (Fig.\u00a03F, 13C ssNMR of eumelanin NPs was displayed for comparison). We noted that SeMNPs-2 and \u22124 had the increased aliphatic peaks (~55.3-30.9 ppm), similar to the FTIR spectra.\n\nDirect experimental characterization of radiation attenuation properties of nanomaterials is nontrivial. Radiation energy attenuation of \u03b3-ray and X-ray photons is mainly through the photoelectric effect, Compton scattering and pair production. SeMNPs were able to induce the photoelectric effect and Compton scattering due to more high atomic number (Z) elements and more delocalized electrons7. To investigate the physical attenuation capacity of SeMNPs, we used computed tomography (CT) because X-ray is widely applied in medical diagnosis and radiotherapy, etc.35, and CT is one of the most commonly used radiology tools36,37. Visually, a notable CT signal enhancement of SeMNPs was observed compared to L-DOPA NPs and DA NPs at the same mass concentration (Fig.\u00a04A), which correlated with the X-ray absorption coefficient as a function of mass concentration (Fig.\u00a04B): SeMNPs-1, \u22122 and \u22124 (3.4, 3.9 and 4.0\u2009HU mg\u22121 mL, respectively)\u2009>\u2009L-DOPA NPs and DA NPs (0.6 and 0.5\u2009HU mg\u22121 mL, respectively). The trend of mass attenuation coefficient obtained by Monte Carlo simulation was similar to that of CT, which is that SeMNPs-1 was lower than the engineered SeMNPs \u22122 and \u22124 (Supplementary Fig.\u00a012). This enhancement is attributed to the presence of selenium, which has a high Z. By comparing the relationship between the X-ray absorption coefficient and the selenium concentration, the mass attenuation coefficient of selenomelanin with higher conjugation degree is larger, under the condition of controlling the selenium content: SeMNPs-4 (104.2\u2009HU mg\u22121 mL)\u2009>\u2009SeMNPs-2 (64.3\u2009HU mg\u22121 mL)\u2009>\u2009SeMNPs-1 (25.5\u2009HU mg\u22121 mL) > selenocystamine (16.0\u2009HU mg\u22121 mL) (Fig.\u00a04C, Supplementary Fig.\u00a013). This indicates that both high Z and high degree of conjugation have a positive impact on the physical shielding ability.\n\nA-B CT images and CT values of SeMNPs and eumelanin NPs with different mass concentrations at 80 kVp. R2, R-quared, the measure of fit of the linear regression model. R2 of L-DOPA NPs, DA NPs, SeMNPs-1, \u22122 and \u22124 are 0.865, 0.942, 0.999, 0.999 and 0.996, respectively. C CT values of SeMNPs and selenium-containing monomers with different selenium concentrations at 80 kVp. R2 of monomer, SeMNPs-1, \u22122 and \u22124 are 0.994, 0.999, 0.999 and 0.996, respectively. D A radar chart to compare the ROS scavenging of different SeMNPs and eumelanin NPs. (E\u2212I) O2\u00b7-, H2O2, \u00b7OH, NO\u00b7 and DPPH scavenging activities, respectively. All data are presented as means\u2009\u00b1\u2009SD (n\u2009=\u20093 independent experiments for E\u2212I). NS means no statistical difference (P\u2009>\u20090.05), *P\u2009<\u20090.05, **P\u2009<\u200910\u22122, ***P\u2009<\u200910\u22123, ****P\u2009<\u200910\u22124, determined by Student\u2019s two-tailed t-test.\n\nMechanistic studies for radiation protection are then focused on reactive oxygen species (ROS) and reactive nitrogen species (RNS) scavenging6,8,38. Radiation damage to cells and organs usually begins with a burst of ROS such as hydroxyl radical (\u00b7OH), hydrogen peroxide (H2O2) and superoxide anion radical (O2\u00b7\u2212), etc6,38,39,40. \u00b7OH is a strongly reactive species with the highest radiochemical yield in the radiolysis of water39. O2\u00b7\u2212 has been identified as one of the most toxic ROS, and the main oxidant in various cell types18. Radiation-induced bystander effect has been observed in unirradiated cells upon receiving signals such as nitric oxide (NO\u00b7) from irradiated cells. Therefore, assessing the antioxidant capacity of SeMNPs to distinct ROS/RNS is essential for the study of their radiation protection properties (Fig.\u00a04D)8,41. SeMNPs\u22122 and \u22124 had the highest efficiency for O2\u00b7\u2212 scavenging compared to other melanin (Fig.\u00a04E, Supplementary Fig.\u00a014), presumably because they are less electronegative without carboxylate group and more accessible to O2\u00b7\u2212. SeMNPs could further remove the toxic by-products H2O2 after the reaction with O2\u00b7\u2212 (Fig.\u00a04F, Supplementary Fig.\u00a015). SeMNPs also had a good scavenging capacity for \u00b7OH and NO\u00b7 (Fig.\u00a04G-4H, Supplementary Fig.\u00a014). SeMNPs-1 and \u22122 were similar at \u00b7OH scavenging, while SeMNPs-2 and \u22124 were better at scavenging NO\u00b7 than SeMNPs-139. In addition, a representative model radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) was studied (Fig.\u00a04I, Supplementary Fig.\u00a016). Previous studies showed that 5,6-dihydroxyindole-2-carboxylic acid (DHICA) melanin has greater antioxidant capacity than 5,6-dihydroxyindole (DHI) melanin19, which is also supported by our study (Fig.\u00a03I, Supplementary Fig.\u00a016). The more carboxylated melanins are a better DPPH scavenger than the decarboxylated counterpart, as carboxyl groups lead to weaker aggregation of melanin and make them more accessible for bulky free radicals than less carboxylated analogs. Therefore, the molecular engineering boosts the scavenging capability of O2\u00b7\u2212 and NO\u00b7. Building upon the quantified scavenging capacities for multiple ROS species, we established a multiple linear regression model42. The derived equation Y\u2009=\u200934.80\u2009+\u20091.16X1\u2009+\u20090.04X2\u2009+\u20090.37X3\u2009\u2212 0.13 X4\u2009\u2212\u20090.73X5 reveals critical ROS scavenging-cellular radioprotection relationships, where Y denotes cell clonogenic protection ability, and X1, X2, X3, X4 and X5 correspond to the scavenging ability of O2\u00b7\u2212, H2O2, \u00b7OH, NO\u00b7 and DPPH, respectively (Supplementary Table\u00a04). Strikingly, the regression coefficient for X1 (1.16) exhibited the highest absolute value, suggesting that O2\u00b7\u2212 scavenging has the greatest impact on cellular radioprotection.\n\nThough physical attenuation and antioxidant properties are the two primary mechanisms for radiation protection, most studies focus on only one of them, with few addressing both simultaneously6,7,8,38,43,44. Our experiment showcased that the introduction of selenium-monomers and molecular engineering of side-chain impact both properties.\n\nMotivated by the radiation attenuation and antioxidant capability of the SeMNPs, we evaluated their radioprotection ability in HaCaT cells because human skin is the first part to be harmed by external ionizing radiation. As normal epidermal cells derived from human skin, HaCaT cells are extensively used to study radioprotection38. First, the biocompatibility study of the SeMNPs confirmed > 95% cell viabilities at the SeMNPs concentration of 0.004\u2009mg\u2009mL\u22121 (Supplementary Fig.\u00a017). Next, the intracellular distribution observed by confocal laser scanning microscopy (CLSM) showed that SeMNPs formed artificial perinuclear caps in HaCaT cells like natural melanosomes (Supplementary Fig.\u00a018)22,45.\n\nAs the gold standard to verify the long-term cell proliferative ability, clonogenic cell survival assay confirmed the long-term radioprotective effect of engineered SeMNPs (Fig.\u00a05A, B). In the SeMNPs\u22122 and \u22124-treated groups, the survival rate of cells irradiated with 10\u2009Gy \u03b3-ray was greatly boosted compared to the no-SeMNPs-treated group as well as the SeMNPs-1-treated group, suggesting the proliferative capacity of \u03b3-ray-treated HaCaT cells was rescued by our engineered SeMNPs. Meanwhile, after exposure to 10\u2009Gy \u03b3-ray, cell viability remained above 90% at 24\u2009h and 48\u2009h post-irradiation when being protected by SeMNPs (Supplementary Fig.\u00a019).\n\nA Clonogenic cell survival assay of HaCaT cells after 10\u2009Gy \u03b3-ray irradiation, with an initial density of 2000 cells per well in 6-well plates. B The number of Colonies in (A). C Cell cycle distribution plots at different \u03b3-ray doses (0 and 10\u2009Gy) and 24\u2009h post-irradiation incubation. Histograms are stagger offset for better clarify and is representative of three experiments. D SeMNPs protected cells against cell cycle changes induced by \u03b3-ray after 24\u2009h post-irradiation incubation. E CLSM images showing Actin-Tracker Red-594 F-actin-positive signal (pseudo-color: green). Cell nuclei were stained with DAPI (blue). Scale bar: 10\u2009\u03bcm. F CLSM images showing DNA damage (\u03b3-H2AX foci, green). Cell nuclei were stained with DAPI (blue). Scale bar: 10\u2009\u03bcm. G Typical comet images (red). Scale bar: 100\u2009\u03bcm. H CLSM images showing DCF-DA ROS-positive signal (green). Cell nuclei were stained with Hoechst 33342 (blue). Scale bar: 100\u2009\u03bcm. I CLSM images showing BODIPY (581/599)-C11 lipid peroxidation-positive signal (green). Cell nuclei were stained with Hoechst 33342 (blue). Scale bar: 100\u2009\u03bcm. Experiments were performed three times (E\u2212G) with similar results. Representative images are presented. J The corresponding quantitative analysis of (H). K The corresponding quantitative analysis of (I). All data are presented as means\u2009\u00b1\u2009SD (n\u2009=\u20093 independent experiments for (B\u2212K). NS means no statistical difference (P\u2009>\u20090.05), *P\u2009<\u20090.05, **P\u2009<\u200910\u22122, ***P\u2009<\u200910\u22123, with reference to no-NP-treated and non-irradiated (0\u2009Gy) control, determined by Student\u2019s two-tailed t-test (B\u2212K). L Flow cytometry analysis of intracellular ROS. M Flow cytometry analysis of intracellular lipid peroxidation.\n\nEncouraged by the significant radioprotective performance of SeMNPs, the cellular mechanisms were further elucidated. The cell cycle is a meticulously coordinated process of cell growth and division, and its entry and progression must be stringently regulated to ensure normal development and maintain tissue homeostasis46. Exposure to ionizing radiation can lead to temporary or permanent cell cycle arrest, potentially culminating in cell apoptosis47. In our experiment, we observed that unprotected HaCaT cells irradiated with \u03b3-ray exhibited significant arrest in the G2/M phase (Supplementary Fig.\u00a020), whereas non-irradiated cells maintained stable cell cycle progression. To investigated the protective effects of SeMNPs, we monitored cell cycle distribution on HaCaT cells following irradiation with a gradient of \u03b3-ray doses (0, 4, 6, 8, 10\u2009Gy) at different incubation times (12\u2009h, 24\u2009h) (Fig.\u00a05C, D, Supplementary Fig.\u00a021\u201324). Incubation with 0.004\u2009mg\u2009mL\u22121 SeMNPs significantly reduced the G2/M arrest induced by \u03b3-ray. SeMNPs-2 and \u22124 effectively prevented G2/M arrest induced by doses from 4 to 10\u2009Gy after 24\u2009h post-irradiation. In contrast, SeMNPs-1 inhibited G2/M arrest at 4\u2009Gy and 6\u2009Gy doses, and alleviated the G2/M arrest by only ~10% and 15% after 8\u2009Gy and 10\u2009Gy radiation, respectively. This finding suggests that the engineered SeMNPs provided superior protection against \u03b3-ray induced damage compared to the original SeMNPs-1. Interestingly, the protective effect at 12\u2009h post-irradiation was less pronounced compared to 24\u2009h, which can be attributed to the dynamic nature of cell cycle regulation. Over time, the protection against G2/M phase arrest conferred by SeMNPs became more evident (Supplementary Fig.\u00a021\u201324). In contrast to the cytoprotective effects of selenomelanin, neither AMF nor SOD demonstrated significant mitigation of radiation-induced G2/M arrest (Supplementary Fig.\u00a025). These results underscored the enhanced protective efficacy of molecular engineered SeMNPs against the detrimental effects of \u03b3-ray.\n\nThe homeostasis of the actin cytoskeleton is critical for the cells, which determines cell morphology, polarity and division48. As depicted in Fig.\u00a05E, exposure to 6\u2009Gy \u03b3-ray triggered the assembly of actin filaments (F-actin). Whereas pretreatment with SeMNPs, prominently SeMNPs-4, mitigated the generation of F-actin and alleviated the detrimental alterations in the cytoskeleton instigated by irradiation (Supplementary Fig.\u00a026). Moreover, exposure of a living system to high-energy radiation leads to excessive toxic ROS6,38,39,40, which can interact with biomolecules such as DNA and lipid, leading to DNA damage, lipid peroxidation and even cell death38,40. Our assays revealed that SeMNPs-treated groups exhibited significantly reduced \u03b3-H2AX foci formation compared to \u03b3-ray group, with the \u03b3-ray+SeMNPs-4 group showing the most pronounced protection (Fig.\u00a05F, Supplementary Fig.\u00a027). A consistent trend was also observed for the comet assay (Fig.\u00a05G). We also studied the performance of SeMNPs at scavenging ROS and mitigating lipid peroxidation in living cells (Fig.\u00a05H\u2013M). CLSM images and quantitative analysis showed that the ROS signals in the SeMNPs-treated groups all maintained similar level as of the non-irradiated cells, while strong green fluorescence showed up after 6\u2009Gy \u03b3-ray irradiation in the no-NP-treated group. (Fig.\u00a05H and J, Supplementary Figs.\u00a028\u201329). Flow cytometry analysis of ROS showed the same trend as CLSM (Fig.\u00a05L). Furthermore, SeMNPs effectively inhibited lipid peroxidation, with SeMNPs-4 being the most efficient, and eliminated lipid peroxidation to normal cellular levels (Fig.\u00a05I, K and M, Supplementary Fig.\u00a030). The enhanced scavenging properties of SeMNPs-4 against lipid peroxides could be ascribed to its higher lipophilicity and easier access to liposomal membrane. Notably, the concentration in our study (0.004\u2009mg\u2009mL\u22121) is significantly lower than other radioprotective materials such as fullerenol and Cerium Metal-Organic Frameworks8,38. Collectively, through molecular engineering inspired by conjugating polymer28,29,49, we successfully developed SeMNPs that protected live cells against \u03b3-ray.\n\nTo elucidate the effects of various SeMNPs treatments on gene expression in response to \u03b3-ray exposure, mRNA sequencing of HaCaT cells was performed (Fig.\u00a06A). A total of 5 groups was set up: 1. the control group (Control) was neither irradiated nor treated with any SeMNPs, 2. the irradiated group (\u03b3-ray) was treated with 10\u2009Gy \u03b3-ray, 3\u20135 are the \u03b3-irradiated groups pretreated with SeMNPs-1, -2 and -4, which are named as \u03b3-ray+SeMNPs-1, \u03b3-ray+SeMNPs\u22122 and \u03b3-ray+SeMNPs\u22124, respectively. A heat map with a hierarchical clustering dendrogram, volcano plots and Venn diagrams visually presented the global expression patterns of genes and differentially expressed genes (DEGs). The \u03b3-ray group showed 1211 up-regulated genes and 1319 down-regulated genes compared to Control, while the \u03b3-ray+SeMNPs-1, \u22122 and \u22124 groups exhibited a substantially different transcriptomic profile compared with the \u03b3-ray group (Fig.\u00a06B, Supplementary Figs.\u00a031\u201332).\n\nA Experimental schematic of mRNA sequencing. Created with BioRender.com. B mRNA sequencing heat map of HaCaT cells treated with SeMNPs-1, \u22122 and \u22124 (0\u2009mg\u2009mL\u22121, 0.004\u2009mg\u2009mL\u22121) and \u03b3-ray (0\u2009Gy, 10\u2009Gy). The heat map of the one-way hierarchical clustering using z-core (row direction) for normalized value. The transcript-level relative transcript abundances were measured in transcripts per million. Data are from three independent experiments. C GO enrichment bubble charts of up-regulated genes in \u03b3-ray\u2009+\u2009SeMNPs-4 vs. \u03b3-ray. D GO enrichment bubble charts of down-regulated genes in \u03b3-ray+SeMNPs\u22124 vs. \u03b3-ray. E GSEA analyses of gene sets of cell cycle pathway in \u03b3-ray+SeMNPs\u22124 vs. \u03b3-ray. F GSEA analyses of gene sets of DNA repair pathway in \u03b3-ray\u2009+\u2009SeMNPs\u22124 vs. \u03b3-ray. NES, normalized enrichment score. FDR, false discovery rate. NES values\u2009>\u20090 indicate that the core gene set is located to the left of the peak and is highly expressed in the left group compared to the right group, and this pathway is activated in the left group. NES values\u2009<\u20090 indicate that the core gene set is located to the right of the peak and is highly expressed in the right group compared to the left group, and this pathway is activated in the right group. Pathways with |NES\u2009|\u2009> \u20091 and FDR\u2009<\u20090.25 are significantly enriched. G The normalized heatmap displaying the cell cycle-related genes. H The normalized heatmap showing the ROS biosynthetic process-related genes. I KEGG Pathway network of the cell cycle-related genes. J KEGG Pathway network of the ROS biosynthetic process-related genes.\n\nTo further explore the biological functions and major enrichment pathways of these DEGs, gene ontology (GO) enrichment and Kyoto Encyclopedia of Gene and Genomes (KEGG) analyses were conducted (Fig.\u00a06C, D, Supplementary Figs.\u00a033\u201338). Compared to the Control group, genes down-regulated in the \u03b3-ray group were mainly focused on cell cycle and DNA repair pathways (Supplementary Figs.\u00a033\u201338). Up-regulated genes were enriched in the pathways such as DNA damage response, cell cycle regulation and DNA repair (Fig.\u00a06C), suggesting that SeMNPs-4 promoted cellular mechanisms for maintaining genomic integrity (for more details, see Supplementary Figs.\u00a033\u201338). Down-regulated genes were involved in the pathways like protein translation and ROS biosynthesis (Fig.\u00a06D), indicating that SeMNPs-4 may reduce oxidative stress and cellular metabolic burden by modulating these pathways (for more details, see Supplementary Figs.\u00a033\u201338). Moreover, gene set enrichment analysis (GSEA) supported the down-regulation of ROS biosynthesis-related genes and upregulation in cell cycle-related gene expression, as opposed to the \u03b3-ray group (Fig.\u00a06E, F, Supplementary Fig.\u00a039).\n\nHeatmaps of specific gene sets involved in the cell cycle and ROS biosynthesis across different treatment groups further dissected these changes (Fig.\u00a06G, H). Figure\u00a06G provided compelling evidence that SeMNPs could modulate the expression of cell cycle-related genes, thereby contributing to the attenuation of radiation-induced cell cycle arrest. As shown in Fig.\u00a06G, in the Control and \u03b3-ray groups, there was a clear pattern of gene expression, with several key cell cycle-related genes showing upregulation following \u03b3-ray exposure. However, in the SeMNPs-treated groups, particularly the \u03b3-ray\u2009+\u2009SeMNPs-4 group, a significant shift in the expression profile was observed. The up-regulation of CDKN1A in the \u03b3-ray group suggested a response to DNA damage, promoting cell cycle arrest. Interestingly, in the \u03b3-ray+\u2009SeMNPs-4 group, there was a down-regulation of CDKN1A expression, indicating a potential restoration of normal cell cycle progression disrupted by \u03b3-ray exposure. This down-regulation aligns with our previous observation that SeMNPs mitigated G2/M arrest induced by \u03b3-ray exposure. Additionally, genes such as CCND1 and CDK1 showed a trend towards normalized expression in the \u03b3-ray+SeMNPs-4 group compared to the \u03b3-ray group. ROS biosynthesis process-related genes shown in Fig.\u00a06H, for instance, IKBKB, NFKBIA, PRKCD and MT-COX1, were up-regulated after \u03b3-ray irradiation, but were mostly down-regulated in the \u03b3-ray+SeMNPs-4 group, reinforcing the role of SeMNPs-4 in mitigating oxidative stress. Additionally, a pathway network analysis highlighted the interactions of DEGs within critical pathways. SeMNPs treatment influences cell cycle pathways (Fig.\u00a06I), including p53 and FoxO signaling, which are crucial for cell cycle regulation, DNA repair, cell senescence and apoptosis50. Figure\u00a06J focused on the ROS pathway and revealed that SeMNPs, particularly SeMNPs-4, modulated genes associated with oxidative phosphorylation (OXPHOS), reducing ROS production and potentially alleviating oxidative damage51, which complemented the extracellular and intracellular ROS scavenging mechanisms (vide supra). Collectively, these results suggested that engineered SeMNPs, especially SeMNPs-4, provided a multi-faceted approach to mitigate radiation-induced cellular damage by enhancing DNA repair, normalizing cell cycle progression and decreasing oxidative stress.\n\nTo assess the in vivo distribution of SeMNPs-4, 1.2\u2009mCi 99mTc-coordinated SeMNPs-4 (99mTc-SeMNPs-4) was injected intraperitoneally into BALB/c (BALB/cAnNCrl) male mice, and imaged by Single-Photon Emission Computed Tomography-Computed Tomography (SPECT-CT) at various time points (Fig.\u00a07A). 99mTc is a widespread clinical radionuclide for SPECT imaging52. Significant accumulation of 99mTc-SeMNPs-4 was observed in the thorax (heart and lungs), liver, spleen and kidneys at 0.5\u20138\u2009h post-injection, and a portion of 99mTc-SeMNPs-4 was excreted in the feces and urine in the early post-injection period (Fig.\u00a07B, Supplementary Movie\u00a01\u20132). In contrast, free 99mTcO4\u2212 mainly distributed in the stomach as well as thyroid, and rapidly excreted into the bladder via the kidneys (Fig.\u00a07B, Supplementary Fig.\u00a040). In the whole-body distribution SPECT-CT images of 99mTc-SeMNPs-4, no thyroid uptake was observed within 24\u2009h, indicating that the radiolabeling of nanoparticles was stable and fully proceed (Supplementary Fig.\u00a040). These results validated the in vivo imaging of SeMNPs-4 using 99mTc based SPECT.\n\nA SPECT-CT imaging procedure. Created with BioRender.com. B Representative SPECT-CT images of 99mTc-SeMNPs\u22124 and free 99mTcO4- at various time points. C Schedule of in vivo radioprotection assays of SeMNPs-4. TBI: total body irradiation. Created with BioRender.com. D Hematoxylin-eosin(H&E) staining of spleen, lung, kidney, testis and liver 4\u2009days after irradiation for multi-organ damage observation. In spleen, white circles: white pulp (WP), red areas outside the WP: red pulp (RP). In kidney, black arrows: hemorrhagic glomeruli, green arrows: dilated renal tubules, blue arrows: protein cast formation. In liver, black arrows: occasional foci of necrosis. Experiments were performed three times with similar results. Representative images are presented. Grouping details: The 0\u2009Gy group comprised normal mice. The \u03b3-ray group was irradiated with 8\u2009Gy without injection. The \u03b3-ray\u2009+\u2009SeMNPs-4 group was both irradiated with 8\u2009Gy and injected 7.5\u2009mg\u2009kg\u22121 body weight SeMNPs-4. The \u03b3-ray+low-AMF group was both irradiated with 8\u2009Gy and injected 30\u2009mg\u2009kg\u22121 body weight AMF. The \u03b3-ray+high-AMF group was both irradiated with 8\u2009Gy and injected 100\u2009mg\u2009kg\u22121 body weight AMF. The \u03b3-ray+SOD group was both irradiated with 8\u2009Gy and injected 30\u2009mg\u2009kg\u22121 body weight SOD. E Comparison of mice survival at day 30. G Recording of mice body weight changes in 30\u2009days after irradiation. All data are presented as means\u2009\u00b1\u2009SD (n\u2009=\u20098 biologically independent animals for the \u03b3-ray group; n\u2009=\u20093 biologically independent animals for other group). NS means no statistical difference (P\u2009>\u20090.05), with reference to the 0\u2009Gy group, determined by Student\u2019s two-tailed t-test. F Monitoring of mice survival in 30\u2009days after irradiation. Grouping details: The 0\u2009Gy group comprised normal mice. The \u03b3-ray group was irradiated with 6\u2009Gy \u03b3-ray without injection. The 0\u2009Gy\u2009+\u20097.5\u2009mg\u2009kg\u22121 SeMNPs-4 group was injected 7.5\u2009mg\u2009kg\u22121 body weight SeMNPs\u22124 without irradiation. The \u03b3-ray\u2009+\u200930\u2009mg\u2009kg\u22121 DA NPs group was both irradiated with 6\u2009Gy and injected 30\u2009mg\u2009kg\u22121 body weight DA NPs. The \u03b3-ray+7.5\u2009mg\u2009kg\u22121 SeMNPs-4 group was both irradiated with 6\u2009Gy and injected 7.5\u2009mg\u2009kg\u22121 body weight SeMNPs-4.\n\nTotal body irradiation (TBI) was performed on BALB/c mice to comprehensively appraise the radioprotective and mitigative efficacy of SeMNPs-4. The SeMNPs-4 material showed a good in vivo biosafety profile (Supplementary Fig.\u00a041). The commercial radiation protection agent AMF and the ROS scavenger SOD at safe doses were selected as control6,53. After 4\u2009days of 8\u2009Gy irradiation to trigger significant organ damage (Supplementary Fig.\u00a042), we harvested diverse organs to access early injuries (Fig.\u00a07C). Severe damage was observed in the spleen, lung, kidney, testis and liver, while SeMNPs-4 was able to mitigate these injuries, outperforming both AMF and SOD (Fig.\u00a07D, Supplementary Figs.\u00a043\u201344). The spleen, lung, kidney, testis, and liver were stained with H&E staining at 4\u2009days after irradiation to observe the damage of multiple organs (Fig.\u00a07D). For the spleen, the white pulp (WP) and red pulp (RP) of the normal spleen were well demarcated (as shown by the white circle and text in Fig.\u00a07D), but the WP and RP boundaries of the damaged spleen were blurred, with a significant reduction in the number and volume of WP and a disorganized medulla. Regarding the lungs, comparing with the normal group, it can be visualized very well that the alveolar structure of the damaged lungs was destroyed, with irregular thickening of alveolar septa and capillary congestion. As for the damaged kidneys, the hemorrhagic glomeruli (black arrows in Fig.\u00a07D) were filled with a large number of erythrocytes, the renal tubules are dilated (green arrows in Fig.\u00a07D) and protein cast formation (blue arrows in Fig.\u00a07D). As one of the most sensitive organs to irradiation, the irradiation-damaged testis showed very obvious deformations: the diameter of the seminiferous tubules was reduced, the structure was loosely disorganized, and a large number of bubble-like cavities were formed (\u03b3-ray group); there was a large reduction of testicular interstitial stromal cells (\u03b3-ray\u2009+\u2009low-AMF group); and a large number of germ cells in the lumen of the testicular seminiferous tubules were detached (\u03b3-ray\u2009+\u2009high-AMF group). Occasional foci of necrosis were seen in damaged livers compared to normal livers (black arrow in Fig.\u00a07D). The results suggested that SeMNPs-4 offer better systemic protection against TBI compared to the clinical drug AMF. Notably, AMF was developed as a radioprotectant only for salivary gland protection, and requires high doses in TBI settings, potentially causing systemic toxicity and complications6. Because intraperitoneal (i.p.) AMF injection has been used in other studies54,55, here i.p. injection was selected for experimental consistency. Despite clinical intravenous use, intraperitoneal administration offers similar efficacy, as both routes allow for rapid and extensive drug absorption56.\n\nFinally, we conducted a comparison of long-term survival in BALB/c (BALB/cAnNCrl) male mice (6\u2009weeks, ~20.0\u2009g initial weight) after TBI of 6\u2009Gy \u03b3-ray. In our experiment (Fig.\u00a07C), 6 Gy \u03b3-ray caused ~12% mice survival by day 30 without protection. As confirmed by Charles River (the supplier of mice purchased in our research) and The Jackson Laboratory (a world-renowned biological supplier), there is no official definition of the sublethal and lethal dose of \u03b3-ray for BALB/c mice. Moreover, the sublethal and lethal doses of \u03b3-ray vary depending on the BALB/c subtype and the specific experimental conditions (e.g. irradiation source, dose and dose rate). Other suppliers, such as Taconic Farms, stated that the sublethal dose of X-ray for BALB/c mice is 6\u2009Gy or less and lethal dose is a maximum of 7\u20138\u2009Gy. However, the relative biological effects (RBE) of X-ray and \u03b3-ray cannot be equated. In the SeMNPs-4 treated group (injection dose: 7.5\u2009mg\u2009kg\u22121 body weight), the survival rate remained at 100% at day 30 (Fig.\u00a07E, F). The protected mice maintained normal body weight, regular blood indices and organ morphology (Fig.\u00a07G, Supplementary Figs.\u00a045\u201346), demonstrating effective in vivo \u03b3-radiation protection of our molecularly engineered SeMNPs-4. Meanwhile, the concentration of melanin used in our study (7.5\u2009mg\u2009kg\u22121, which is the total dose for the two injections) is much lower than that of other types of melanin reported in previous studies (Supplementary Table\u00a05)57,58,59. Considering that the RBE is affected by various factors (vide supra), to objectively compare the effects of our material and other melanin, we used the melanin commonly reported in the literatures57,58,59 for animal experiments under our experimental conditions. Given the significant variability of natural melanin depending on the extraction source, we selected DA NPs (also known as PDA) for control, which have a well-established synthetic method and are frequently used in the literature18,24,57. DA NPs (30\u2009mg\u2009kg\u22121, which is the total dose for the two injections) provided protection or mitigation for irradiated mice by prolonging survival (Fig.\u00a07E\u2013G). Compared to the quadruple-dose DA NPs, selenomelanin showed a higher survival rate (Fig.\u00a07E\u2013G), consist with cellular-level findings where DA NPs showed limited efficacy in protecting against radiation-induced cell cycle perturbations. Additionally, biocompatibility assays confirmed DA NPs\u2019 non-toxicity to BALB/c (BALB/cAnNCrl) male mice (6\u2009weeks, ~20.0\u2009g initial weight), excluding inherent toxicity as a mortality cause.\n\nIn summary, molecular engineering enabled the creation of more conjugated selenomelanins with enhanced in vivo \u03b3-ray radioprotection, with the highest degree of conjugation giving the best performance in SeMNPs-4. SeMNPs-4 increased the survival rate of mice from ~12% to 100% exposed to 6\u2009Gy \u03b3-ray TBI, demonstrating its potential as the metal-free, lightweight and efficient radioprotectors or radiomitigators. Overall, this work offers a molecular tuning approach beyond the natural synthetic pathway for melanin structure-property-function design. We believe that our work provides valuable insights into the rational engineering of the chemical complexity of biomacromolecule materials, extending beyond melanin to other materials, such as lignin and intrinsically disordered proteins, toward tailored functional materials.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-62403-8/MediaObjects/41467_2025_62403_Fig7_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "Animal studies were performed in compliance with the guidelines of the Ethics Committee of Beijing Normal University and regulations on laboratory animals of Beijing Laboratory Animal Management Office (BNUCC-EAW-20240403-01).Six weeks BALB/c (BALB/cAnNCrl) male mice were purchased from Charles River Laboratories (Beijing, China). All mice were housed in a specific pathogen-free environment on a 12/12\u2009h light-dark cycle with the standard conditions: Temperature, 20\u221225\u2009\u00b0C; Relative humidity, 40\u221270%. Mice were fed standard chow and distilled water throughout the experiments. All research was carried out according to relevant guidelines and regulations.\n\nL-3,4-dihydroxyphenylalanine (L-DOPA) was purchased from Leyan (Shanghai, China). Dopamine hydrochloride (DA\u00b7HCl) was purchased form Jiangsu Aikon. L-Selenocystine, tris(2-carboxyethyl) phosphine hydrochloride (TCEP\u00b7HCl) and 2-(2-Methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H-tetrazolium sodium salt (WST-8) were purchased from Aladdin. Selenocystamine dihydrochloride was purchased from Hwrk Chem. Potassium permanganate (KMnO4) was purchased from Beijing TongGuang Fine Chemicals Company. Ammonium hydroxide (NH3\u00b7H2O), 2\u2032,7\u2032-dichlorodihydrofluorescein diacetate (DCFH-DA, solid) and 2,2-diphenyl-1-(2,4,6-trinitrophenyl) hydrazyl (DPPH) were purchased from Macklin. Ethanol absolute (99.5%), Xanthine (X) and sodium nitroferricyanide dihydrate were purchased from Innochem. Xanthine Oxidase (XO) was purchased from Sigma-Aldrich. Griess reagent was purchased from Yuanye. CellTiter-Blue\u00ae Reagent was purchased from Promega. DAPI was purchased from Solarbio. Hoechst 33342 was purchased from Beijing LABLEAD Inc. Modified Giemsa Staining Solution, Cell cycle and apoptosis analysis kit, Actin-Tracker Red-594 kit, immunostaining fixative solution, Phospho-Histone H2A.X (Ser139) Rabbit Monoclonal Antibody, FITC-labeled goat anti-rabbit lgG(H\u2009+\u2009L) and reactive oxygen species assay kit (DCF-DA, for cell) were purchased from Beyotime. Wheat Germ Agglutinin (WGA) conjugated to Alexa Fluor 488 was purchased from Kaixin Tech. Lipid peroxidation probe BODIPY (581/599)-C11 was purchased from Dojindo. Comet assay kit was purchased from IPHASE. All reagents and materials were used as received unless otherwise stated.\n\nTransmission electron microscopic (TEM) images were recorded using a FEI Tecnai Spirit 120\u2009kV TEM at Tsinghua University. The zeta potential and dynamic light scattering (DLS) size of the nanoparticles in water was obtained on a Zetasizer Lab (Malvern Panalytical). Energy dispersive X-ray spectroscopy (EDS) mapping images were captured with a FEI Talos F200S. UV-Vis absorbance measurement was carried out on a Cary 60 UV-Vis Spectrophotometer (Agilent Technologies). Fluorescence spectra were collected on a Cary Eclipse Fluorescence Spectrophotometer (Agilent Technologies). X-ray photoelectron spectroscopy (XPS) spectra were analyzed by a Thermo Scientific K-Alpha. Electron paramagnetic resonance (EPR) spectra were measured on a Bruker EMXplus-6/1. Fourier transform infrared spectroscopy (FTIR) spectra were carried out on a Thermo Scientific Nicolet iS20 FTIR spectrophotometer. 13C solid-state nuclear magnetic resonance (ssNMR) spectra were measured on a JNM-ECA600 JEOL. Inductively coupled plasma optical emission spectrometry (ICP-OES) was performed on an Agilent 5110 ICP-OES and a Thermo iCAP 7400 ICP-OES at Tsinghua University. Computed tomography (CT) images and Hounsfield Unit values were detected by a Philips IQon Spectral CT in Beijing Tongren Hospital. Confocal laser scanning microscopy (CLSM) images were acquired using a Nikon A1R microscopy system at Beijing Normal University and a Multi-SIM AXR multimodal super-resolution confocal microscope at Tsinghua University. Flow cytometry data was recorded via a BD AccuriTM C6 Plus Flow cytometer. The fluorescence intensity of 96-well plates was monitored with the fluorescence microplate reader Tecan Infinite M200 PRO. Cells were irradiated by 60Co gamma irradiator (GM-11-03-A, Beijing Gamma high-tech Co., Ltd) in Beijing Normal University. Na99mTcO4 was obtained from a 99Mo/99mTc generator provided by Beijing Senke Pharmaceutical Co., Ltd. TriFoil Triumph II mciroSPECT/CT equipment (Trifoil) was used for imaging studies. Radioactivity was determined on a PerkinElmer system (WIZARD2 2480 Automatic \u03b3-Counter).\n\nL-DOPA solution was mixed with 0.2\u2009M KMnO4 overnight. The product was purified by centrifugation and washed in HCl solution to exchange the Mn2+ ions, and three more cycles of washing and centrifugation with ultrapure water.\n\n99.5% ethanol absolute, ultrapure water and 28\u201330% NH3\u00b7H2O solution were added to the flask and stirred vigorously. DA\u00b7HCl solution was added to the mixture overnight. The product was collected by centrifugation and washed with ultrapure water three times.\n\nThe reactions are a templated polymerization reaction with L-DOPA or DA nanoparticle as the seeds. SeMNPs-122: First, L-DOPA solution (0.06\u2009mmol) was mixed with 0.2\u2009M KMnO4 to form eumelanin seeds. Next, selenocysteine solution (0.06\u2009mmol) was added to the reaction flask. After overnight reaction, the product was collected by centrifugation and purified by washing with HCl solution. Finally, the mass concentration of the final nanoparticle solution was determined by lyophilizing a small aliquot solution overnight and weighing with an analytical balance. SeMNPs\u22122: First, L-DOPA solution (0.06\u2009mmol) was mixed with 0.2\u2009M KMnO4 to form eumelanin seeds. Next, selenocysteamine solution (0.06\u2009mmol) was added to the reaction flask. After overnight reaction, the product was collected by centrifugation and purified by washing with HCl solution. SeMNPs-3: 99.5% ethanol absolute, ultrapure water and 28\u221230% NH3\u00b7H2O solution were added to the flask and stirred vigorously. Dopamine hydrochloride solution (0.105\u2009mmol) was added to the mixture. Next, selenocysteine solution (0.105\u2009mmol) was added to the reaction flask. After overnight reaction, the product was collected by centrifugation and washed with ultrapure water for three times. SeMNPs\u22124: 99.5% ethanol absolute, ultrapure water and 28\u221230% NH3\u00b7H2O solution were added to the flask and stirred vigorously. Dopamine hydrochloride solution (0.105\u2009mmol) was added to the mixture. Next, selenocysteamine solution (0.105\u2009mmol) was added to the reaction flask. After overnight reaction, the product was collected by centrifugation and washed with ultrapure water for three times.\n\nThe density functional theory (DFT) calculations were carried out to study the configurational stability and rotation barrier. All geometry optimizations were performed using B3LYP62 functional with DFT-D3(BJ)63,64 dispersion correction and 6-31\u2009G(d)65,66,67 basis set by Gaussian 16 program68. The vibrational frequencies were computed at the same level of theory as for the geometry optimizations, and to evaluate the thermal corrections at 298.15\u2009K with a zero-point-energy scale factor of 0.981369 by Shermo code70. The high-level single-point energies were computed based on the optimized structures using the PWPB9571 double-hybrid functional with DFT-D472 dispersion correction and def2-QZVPP73 basis set with the RIJCOSX74,75 approximation by ORCA 5.0.4 program76. The Gibbs free energies were obtained by summing the high-level single-point energies and thermal free energy corrections. The rotation barrier was defined as the difference of Gibbs free energies between the equilibrium structure and transition-state structure.\n\nWST-8, a highly water-soluble tetrazolium salts, was applied to this assay. The superoxide anion generated by X/XO reduced WST-8 to water-soluble formazans which exhibited absorbance maxima at 460\u2009nm. Therefore, the lower absorption at 460\u2009nm means the better O2\u2022 \u2212 scavenging ability of the material. Briefly, into 0.5\u2009mL of a 10\u2009mM PBS buffer (pH 7.0-8.0), 20\u2009\u03bcL of 3\u2009mM X solution, 20\u2009\u03bcL of 3\u2009mM EDTA, 20\u2009\u03bcL of 1\u2009mM WST-8, and 100\u2009\u03bcL of 0.5\u2009mg\u2009mL-1 melanin or 100\u2009\u03bcL of water were added. The reaction was initiated by adding 6\u2009\u03bcL of 0.83 mU mL\u22121 XO solution. The absorbance change at 460\u2009nm (WST-8) after 1\u2009h was monitored with the Cary 50/60 UV-Vis Spectrophotometer maintained at 25\u2009\u00b0C. O2\u2022 \u2212 radical scavenging activity was calculated as\n\nIn Eq. (1), Ac is the absorbance of WST-8 solution without melanin, Ai is the absorbance of melanin mixed with the WST-8 solution, and Aj is the absorbance of melanin without WST-8 solution. Experiments were run in\u2009\u2265\u2009three times.\n\nDCFH solution was prepared by mixing 0.5\u2009mL of 1\u2009mM DCFH-DA in ethanol with 2\u2009mL of 0.01\u2009M NaOH77,78. 100\u2009\u03bcL of 0.04\u2009mg\u2009mL-1 melanin suspension in ultrapure water or 100\u2009\u03bcL ultrapure water was added to 1.8\u2009mL of freshly prepared 10\u2009mM H2O2 and 20\u2009\u03bcL of freshly prepared DCFH solution. The H2O2 scavenging activity was evaluated by monitoring the fluorescence intensity decrease at 522\u2009nm over 90\u2009min. Excitation wavelength: 505\u2009nm. Detection Wavelength: 510\u2212660\u2009nm. Slit: 2.5\u2009nm. H2O2 radical scavenging activity was calculated as\n\nIn Eq. (2), Ic is the fluorescence intensity of H2O2 solution mixed with DCFH solution without melanin, Ii is the fluorescence intensity of melanin mixed with the H2O2 and DCFH solution, Ij is the fluorescence intensity of H2O2 solution mixed with melanin without DCFH solution. Experiments were run in\u2009\u2265\u2009three times.\n\nDCFH solution was prepared by mixing 0.5\u2009mL of 1\u2009mM DCFH-DA in ethanol with 2\u2009mL of 0.01\u2009M NaOH77,78. 100\u2009\u03bcL of 0.04\u2009mg\u2009mL-1 melanin suspension in ultrapure water or 100\u2009\u03bcL ultrapure water was added to 1.8\u2009mL of freshly prepared 10\u2009mM H2O2 and 20\u2009\u03bcL of freshly prepared DCFH solution. Next, the mixture was irradiated 5\u2009Gy \u03b3-ray at the dose rate of 5\u2009Gy\u00b7min-1. The \u00b7OH scavenging activity was evaluated by monitoring the fluorescence intensity decrease at 527\u2009nm. Excitation wavelength: 505\u2009nm. Detection Wavelength: 510-660\u2009nm. Slit: 2.5\u2009nm. \u00b7OH radical scavenging activity was calculated as\n\nIn Eq. (3), Ic is the fluorescence intensity of H2O2 solution without melanin, Ii is the fluorescence intensity of melanin mixed with the H2O2 solution, Ij is the fluorescence intensity of H2O2 solution mixed with melanin without DCFH solution. Experiments were run in\u2009\u2265\u2009three times.\n\n100\u2009\u03bcL of 0.5\u2009mg\u2009mL-1 melanin suspension in ultrapure water or 100\u2009\u03bcL ultrapure water was added to 150\u2009\u03bcL of a freshly prepared 10\u2009mM solution of sodium nitroferricyanide dihydrate in 0.2\u2009M PBS buffer (pH 7.4) and the mixture was taken under vigorous stirring at room temperature. After 2\u2009h, 500\u2009\u03bcL of Griess reagent (0.5% sulfanilamide and 0.05% naphthylethylenediamine dihydrochloride in 2.5% phosphoric acid) was added to the above mixture and the absorbance at 540\u2009nm was measured. NO\u00b7 radical scavenging activity was calculated as\n\nIn Eq. (4), Ac is the absorbance of NO\u00b7 solution without melanin, Ai is the absorbance of melanin mixed with the NO\u00b7 solution, and Aj is the absorbance of melanin without NO\u00b7 solution. Experiments were run in\u2009\u2265\u2009three times.\n\nBriefly, 100\u2009\u03bcL of 0.5\u2009mg\u2009mL-1 melanin suspension in ultrapure water or 100\u2009\u03bcL ultrapure water was mixed with 1.8\u2009mL of a 0.2\u2009mM DPPH solution in 95% ethanol. The scavenging activity was evaluated by monitoring the absorbance decrease at 516\u2009nm over 20\u2009min. DPPH radical scavenging activity was calculated as\n\nIn Eq. (5), Ac is the absorbance of DPPH solution without melanin, Ai is the absorbance of melanin mixed with the DPPH solution, and Aj is the absorbance of melanin without DPPH. Experiments were run in\u2009\u2265\u2009three times.\n\nPhilips IQon Spectral CT in Beijing Tongren Hospital was employed to acquire the CT images and Hounsfield Unit values. The CT images were further analyzed using PmsDView software. Parameters of imaging as follows: 80 kVp, 10\u2009mA. To evaluate the CT signals in vitro under different concentrations of melanin and monomer, melanin suspension and monomer solution in ultrapure water were filled into 1.5\u2009mL centrifuge tubes for CT tests.\n\nHaCaT cells were purchased from Wuhan Sunncell Biotechnology Co., Ltd, and cultured in Dulbecco\u2019s Modified Eagle Medium (DMEM) (VivaCell) containing 10% (v/v) FBS and 1% antibiotics (penicillin-streptomycin) with high glucose, and maintained at 37\u2009\u00b0C in 5% CO2 with a relative humidity of 95%.\n\nHaCaT cells were planted in 6-well plates with 2000 cells per well, and cultured in a complete medium for 24\u2009h (37\u2009\u00b0C, 5% CO2). Then 4\u2009\u00b5g\u2009mL\u22121 melanin were added and incubated for another 24\u2009h. Then \u03b3-ray radiation was applied. On the seventh day after irradiation, the cells were fixed with methanol for 10\u2009min, and Giemsa stain (1:9) was incubated for 30\u2009min. The image stiches were acquired using a Leica THUNDER DMi8.\n\nBriefly, HaCaT cells were seeded in 96-well plates at a density of 10,000 cells per well for 24\u2009h. The cells were then treated with SeMNPs and eumelanin NPs at different concentrations for another 24\u2009h. After the incubation period, the cells were washed 3\u2009times with PBS, and then CellTiter-Blue\u00ae at 10% (v/v) in complete media was added to each well and incubated for 2\u2009h at 37\u2009\u00b0C to allow the live cells to convert resazurin to fluorescent resorufin. The fluorescent signal was then analyzed with excitation wavelength at 560\u2009nm and emission wavelength at 600\u2009nm by a plate reader. Untreated cells in complete medium were used as a blank control. Viability is reported as a percentage of untreated cells, averaged over three biological repeats.\n\nHaCaT cells were plated in glass bottom dishes at 20,000 cells per well and seeded for 24\u2009h before treatment with 0.004\u2009mg\u2009mL-1 SeMNPs for another 24\u2009h. The cells were washed twice with PBS and stained with 5\u2009\u03bcg\u2009mL-1 of WGA conjugated to Alexa Fluor 488 in PBS for 10\u2009min at room temperature, washed twice with PBS, and returned to complete growth medium. Before imaging by CLSM, 1 drop of Hoechst 33342 dye was added to stain the nuclei. WGA 488 scan excitation wavelength: 486\u2009nm. Detection wavelength: 510-550\u2009nm. Hoechst 33342 scan excitation wavelength: 405\u2009nm. Detection wavelength: 420-480\u2009nm.\n\nHaCaT cells were plated at a density of 50,000 cells per well in 12-well plates and cultured for 24\u2009h. After incubating for 24\u2009h, 0.004\u2009mg\u2009mL-1 SeMNPs were added to each well for 24\u2009h. Then \u03b3-ray radiation was applied at the fixed irradiation time of 1\u2009min with different dose rate. The cells were incubated at the desired times before assaying experiments were performed. Cells were harvested, fixed and stained according to a technical manual of cell cycle and apoptosis analysis kit for flow cytometry. Flow cytometry procedure: Cells were first gated on FSC/SSC. Then, cells were gated using PE-A and PE-H, and cell cycle distributions were analyzed on the single cell population using Histogram and PE-A.\n\nHaCaT cells were planted at 50,000 cells per well in glass bottom dishes and seeded for 24\u2009h before treatment with 0.004\u2009mg\u2009mL-1 SeMNPs for another 24\u2009h. Next, the loading buffer was removed, and cells were returned to the growth medium. Then, the cells were treated with 6\u2009Gy \u03b3-ray irradiation. After incubation for 24\u2009h, the cells were fixed with immunostaining fixative solution for 10\u2009min, and washed three times with 0.1% Triton-X100 for 5\u2009min each, followed by 0.1% Triton-X100 for 10\u2009min. Next, Actin-Tracker Red-594 was incubated at room temperature for 60\u2009min. Finally, DAPI was added and incubated for 10\u2009min. Finally, the cells were observed under a laser confocal microscope after sealing.\n\nHaCaT cells were planted at 50,000 cells per well in glass bottom dishes and seeded for 24\u2009h before treatment with 0.004\u2009mg\u2009mL-1 SeMNPs for another 24\u2009h. Next, the loading buffer was removed, and cells were returned to the growth medium. Then, the cells were treated with 6\u2009Gy \u03b3-ray irradiation. After incubation for 24\u2009h, the cells were trypsinized and collected. The supernatant was aspirated after centrifugation, the cells were washed once with cold PBS, and the cells were suspended at 1\u2009\u00d7\u2009105 cells /ml. Prepare 1% agarose gel, heat and melt in a water bath. The cells were mixed with the gel at a certain ratio (1:10) and then quickly dropped on a glass slide. The number of cells was observed under a microscope, and the cells were cured for 2\u2009h at 4\u2009\u00b0C in the dark. The slides were gently removed, immersed in pre-cooled cell lysate, and lysed overnight at 4 \u00b0C in the dark. Remove the slide from the lysate, soak the slide in PBS, absorb the liquid on the slide with a paper towel, put it in a horizontal electrophoresis tank, add the freshly prepared alkaline electrophoresis buffer to the surface of the slide >3\u2009mm, avoid light and spin for 30\u2009min. Electrophoresis: voltage 25\u2009V, 30\u2009min electrophoresis. After electrophoresis, the slides were removed and soaked in PBS twice for 15\u2009min each time. PI staining was added drop by drop for 30\u2009min at 37\u00b0C in the dark, washed three times with ultrapure water, dried, and observed under a fluorescence microscope.\n\nHaCaT cells were planted at 50,000 cells per well in glass bottom dishes and seeded for 24\u2009h before treatment with 0.004\u2009mg\u2009mL-1 SeMNPs for another 24\u2009h. Next, the loading buffer was removed, and cells were returned to the growth medium. Then, the cells were treated with 6\u2009Gy \u03b3-ray irradiation. After incubation for 24\u2009h, the cells were fixed with 4% paraformaldehyde for 10\u2009min, followed by 0.1% Triton-X100 for 10\u2009min, and then blocking solution for immunofluorescence staining for 20\u2009min. Next, Phospho-Histone H2A.X (Ser139) Rabbit Monoclonal Antibody (1:1000) was incubated at room temperature for 3\u2009h, followed by FITC-labeled goat anti-rabbit lgG(H\u2009+\u2009L) (1:500) at 37\u2009\u00b0C for 1\u2009h. Finally, DAPI was added and incubated for 10\u2009min. Finally, the cells were observed under a laser confocal microscope after sealing.\n\nHaCaT cells were plated at 50,000 cells per well in glass bottom dishes and seeded for 24\u2009h before treatment with 0.004\u2009mg\u2009mL-1 SeMNPs for another 24\u2009h. The cells were washed 2\u2009times with PBS and then treated with 10\u2009\u03bcM of the ROS probe DCF-DA, incubated at 37\u2009\u00b0C for 20\u2009min. Next, the loading buffer was removed, and cells were returned to the growth medium. Then, the cells were treated with 6\u2009Gy \u03b3-ray irradiation. A drop of Hoechst 33342 dye was added to 0.5\u2009mL media for the nucleus staining before imaging by CLSM. Live-cell imaging was performed on a multi-SIM AXR multimodal super-resolution confocal microscope. ROS probe scan excitation wavelength: 486\u2009nm. Detection wavelength: 510\u2212550\u2009nm. Hoechst 33342 scan excitation wavelength: 405\u2009nm. Detection wavelength: 420\u2212480\u2009nm. The quantitative analysis of fluorescence intensity was performed by ImageJ software.\n\nHaCaT cells were plated at a density of 10,000 cells per well in 96-well plates and cultured for 24\u2009h. Then 0.004\u2009mg\u2009mL-1 SeMNPs were added to each well for 24\u2009h. The cells were washed 2\u2009times with PBS and then treated with 10\u2009\u03bcM of the ROS probe DCF-DA, incubated at 37\u2009\u00b0C for 20\u2009min. Next, the cells were treated with 4\u2009Gy and 6\u2009Gy \u03b3-ray irradiation, and 50\u2009\u03bcM H2O2 for 90\u2009min, respectively. The fluorescence intensity of cells was monitored by a plate reader with excitation wavelength at 488\u2009nm and emission wavelength at 525\u2009nm.\n\nHaCaT cells were seeded in 12-well plates at a density of 50,000 cells per well for 24\u2009h, and then treated with 0.004\u2009mg\u2009mL-1 SeMNPs for another 24\u2009h. After the incubation period, the cells were washed 3\u2009times with PBS and then treated with 10\u2009\u03bcM of the ROS probe DCF-DA, incubated at 37\u2009\u00b0C for 20\u2009min. Then, the cells were treated with 6\u2009Gy \u03b3-ray irradiation. Next, the cells were harvested for flow cytometry. Flow cytometry procedure: Cells were first gated on FSC/SSC. Then, single cells were gated using FITC-A and FITC-H, and probes were analyzed on the single cell population using Histogram and FITC-A.\n\nHaCaT cells were plated at 50,000 cells per well in glass bottom dishes and seeded for 24\u2009h before treatment with 0.004\u2009mg\u2009mL-1 SeMNPs for another 24\u2009h. The cells were washed 2\u2009times with PBS and then treated with the lipid peroxidation probe BODIPY (581/599)-C11 at 0.1% (v/v) in media according to the technical manual, incubated at 37\u2009\u00b0C for 30\u2009min. Next, the loading buffer was removed, and cells were returned to the growth medium. Then, the cells were treated with 6\u2009Gy \u03b3-ray irradiation. A drop of Hoechst 33342 dye was added to 0.5\u2009mL media for the nucleus staining before imaging by CLSM. Live-cell imaging was performed on a multi-SIM AXR multimodal super-resolution confocal microscope. Lipid peroxidation probe scan excitation wavelength: 488\u2009nm. Detection wavelength: 510\u2212550\u2009nm. Hoechst 33342 scan excitation wavelength: 405\u2009nm. Detection wavelength: 420\u2212480\u2009nm. The quantitative analysis of fluorescence intensity was performed by ImageJ software.\n\nHaCaT cells were seeded in 12-well plates at a density of 50,000 cells per well for 24\u2009h, and then treated with 0.004\u2009mg\u2009mL-1 SeMNPs for another 24\u2009h. After the incubation period, the cells were washed 2\u2009times with PBS and then treated with the lipid peroxidation probe BODIPY (581/599)-C11 at 0.1% (v/v) in media according to the technical manual, incubated at 37\u2009\u00b0C for 30\u2009min. Then, the cells were treated with 6\u2009Gy \u03b3-ray irradiation. Next, the cells were harvested for flow cytometry. Flow cytometry procedure: Cells were first gated on FSC/SSC. Then, single cells were gated using FITC-A and FITC-H, and probes were analyzed on the single cell population using Histogram and FITC-A.\n\nHaCaT cells were plated at a density of 10,000 cells per well in 96-well plates and cultured for 24\u2009h. Then 0.004\u2009mg\u2009mL-1 SeMNPs were added to each well for 24\u2009h. The cells were treated with 10\u2009Gy irradiation incubated for the desired times (24\u2009h, 48\u2009h), and then CellTiter-Blue\u00ae at 10% (v/v) in complete media was added to each well and incubated for 2\u2009h to allow the live cells to convert resazurin to fluorescent resorufin. The fluorescent signal was then analyzed with excitation wavelength at 560\u2009nm and emission wavelength at 600\u2009nm by a plate reader.\n\nHaCaT cells were plated at a density of 50,000 cells per well in 12-well plates and cultured for 24\u2009h, and then divided into different groups with different treatment. The control groups (Control) were neither irradiated nor pretreated with any melanin. The irradiated groups (\u03b3-ray) were treated with 10\u2009Gy \u03b3-ray irradiation incubated for 24\u2009h and not pretreated with any melanin nanoparticles. The irradiated cells pretreated with SeMNPs-1, \u22122 and \u22124 were named as \u03b3-ray+SeMNPs-1, \u03b3-ray+SeMNPs-2 and \u03b3-ray+SeMNPs\u22124, respectively. The cells were collected, washed 2\u2009times with PBS and snap-frozen in liquid nitrogen for RNA\u2011sequencing.\n\nRNA sequencing and sequence quality control of the HaCaT cells were performed using the BGISEQ platform. The human genome reference was established from GCF_000001405.40_GRCh38.p14 of NCBI version. Data analysis was all completed using the Beijing Genomics Institute (BGI) Dr. Tom system. Gene-enrichment and functional annotation analysis for significant gene list was performed using Gene Ontology (GO) and pathway analysis was done based on the Kyoto Encyclopedia of Genes (KEGG).\n\nLabeling of SeMNPs-4: 0.1\u2009mL of 5\u2009mg\u2009mL\u22121 SnCl2 solution (prepared with 0.1\u2009mol\u2009L\u22121 HCl solution) and 3\u2009mCi of Na99mTcO4 were added to 1.5\u2009mL of 0.75\u2009mg/mL SeMNPs-4, and stirred at room temperature for 30\u2009min, then ultrafiltrated and centrifuged for three times (5\u2009min each time) to remove unbound nuclides, and then determined the radioactivity, and finally obtain the 99mTc-SeMNPs-4. BALB/c (BALB/cAnNCrl) male mice were intraperitoneally injected with 1.2\u2009mCi fresh Na99mTcO4 solution (free 99mTcO4\u2212) or 99mTc-SeMNPs-4, and imaged by microSPECT-CT at 0.5\u2009h, 2\u2009h, 4\u2009h, 8\u2009h and 24\u2009h.\n\nBALB/c (BALB/cAnNCrl) male mice (6\u2009weeks, ~20.0\u2009g initial weight) were intraperitoneally injected with materials 2\u2009h before and 24\u2009h after the TBI (8\u2009Gy \u03b3-ray). The same amount was given for both injections and the total amount was grouped as follows: The 0\u2009Gy group comprised normal mice (no irradiation or injection). The \u03b3-ray group was irradiated with 8\u2009Gy, but without injection. The \u03b3-ray+SeMNPs-4 group was irradiated with 8\u2009Gy and received an intraperitoneal injection of 7.5\u2009mg\u2009kg\u22121 body weight SeMNPs-4. The \u03b3-ray+low-AMF group was irradiated with 8\u2009Gy and received an intraperitoneal injection of 30\u2009mg\u2009kg\u22121 body weight AMF. The \u03b3-ray+high-AMF group was irradiated with 8\u2009Gy and intraperitoneally injected 100\u2009mg\u2009kg\u22121 body weight AMF. The \u03b3-ray\u2009+\u2009SOD group was irradiated with 8\u2009Gy and intraperitoneally injected 30\u2009mg\u2009kg\u22121 body weight SOD. The first injection of SeMNPs-4 and SOD was administered 2\u2009h before the irradiation, and the second was injected at 24\u2009h post-irradiation. AMF was injected once 2\u2009h prior to irradiation. Note that while most groups had two injections, the grouping reflects the total dose of the two injections, with each being half the total dose. All irradiation was delivered at 2\u2009Gy\u2009min-1. Weight and survival within 4\u2009days after TBI were recorder. The mice were sacrificed and various organs were harvest 4\u2009days after TBI for H&E, Masson and Sirius Red staining.\n\nBALB/c (BALB/cAnNCrl) male mice (6\u2009weeks, ~20.0\u2009g initial weight) were intraperitoneally injected with materials 2\u2009h before and 24\u2009h after the TBI (6\u2009Gy \u03b3-ray). The same amount was given for both injections and the total amount was grouped as follows: The 0\u2009Gy group included normal mice with no irradiation or injection. The \u03b3-ray group underwent \u03b3-ray irradiation without injection. The 0\u2009Gy +7.5\u2009mg\u2009kg-1 SeMNPs-4 group received a 7.5\u2009mg\u2009kg-1 SeMNPs-4 injection without injection. The \u03b3-ray +7.5\u2009mg\u2009kg-1 SeMNPs-4 group received both \u03b3-ray irradiation and a 7.5\u2009mg\u2009kg-1 SeMNPs-4 injection. The \u03b3-ray +30\u2009mg\u2009kg-1 DA NPs group received both \u03b3-ray irradiation and a 30\u2009mg\u2009kg-1 DA NPs injection. The first injection of SeMNPs-4 and DA NPs was administered 2\u2009h before the irradiation, and the second was injected at 24\u2009h post-irradiation. Note that while most groups had two injections, the grouping reflects the total dose of the two injections, with each being half the total dose. All irradiation was delivered at 2\u2009Gy\u2009min-1. Weight and survival within 30\u2009days after TBI were recorded. The mice were sacrificed, and blood as well as various organs were harvested 30\u2009days after TBI for further test.\n\nAll data are presented as their means with S.D., unless otherwise noted. Statistical significance was determined by a two-tailed Student\u2019s t-test assuming equal variance. Error bars represent standard deviation from\u2009\u2265\u2009three experiments. NS means no statistical difference (P\u2009>\u20090.05). Statistical values are indicated in Fig.s according to the following scale: *P\u2009<\u20090.05, **P\u2009<\u200910\u22122, ***P\u2009<\u200910\u22123, ****P\u2009<\u200910\u22124.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data generated in this study are present in the main text and the Supplementary Information file. Source data are provided with this paper. The RNA-sequencing data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) database under accession code PRJNA1209790.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Change history", + "section_text": "In the version of this article initially published, the Peer review file featured some information which the authors have now redacted in the online file.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Coleman, C. N., Stone, H. B., Moulder, J. E. & Pellmar, T. C. Modulation of radiation injury. Science 304, 693\u2013694 (2004).\n\nPubMed\u00a0\n \n Google Scholar\u00a0\n \n\nBunn, M. Reducing nuclear dangers. Science 384, 1277\u20131277 (2024).\n\nADS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nGarrett-Bakelman, F. E. et al. The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight. 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We thank Prof. Mengchao Cui at Beijing Normal University for plate reader measurement. We acknowledge Prof. Tianyu Li in Institute of Process Engineering, Chinese Academy of Sciences and Zhangyi Ouyang in Institute of Radiation Medicine, Academy of Military Medical Sciences for providing thoughts on mRNA sequencing data analysis. We thank Prof. Lin Shen at Beijing Normal University for his guidance on multiple linear regression model. The work was funded by National Key Research and Development Program of China (2022YFA1505900, W.Cao; 2023YFA0915300, H.X.), the National Natural Science Foundation of China (22205026, W.Cao; 22471021, W.Cao) and the Fundamental Research Funds for the Central Universities (W.Cao), and Guangdong Provincial Key Laboratory of Functional and Intelligent Hybrid Materials and Devices (2023-GDKLFIHMD-0X, W.Cao).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Key Laboratory of Radiopharmaceuticals of the Ministry of Education, College of Chemistry, Beijing Normal University, Beijing, 100875, China\n\nRuotong Deng,\u00a0Yuxi Li,\u00a0Yining Ou,\u00a0Jian Wang,\u00a0Qing Ruan,\u00a0Xuanying Zhang,\u00a0Junbo Zhang\u00a0&\u00a0Wei Cao\n\nKey Lab of Organic Optoelectronics & Molecular Engineering, Department of Chemistry, Tsinghua University, Beijing, 100084, China\n\nWei Chen\u00a0&\u00a0Huaping Xu\n\nSchool of Physics and Astronomy, Beijing Normal University, Beijing, 100875, China\n\nHanjie Zhu\u00a0&\u00a0Chunlei Zhang\n\nDepartment of Radiology, Beijing Tongren Hospital, Beijing, 100730, China\n\nYongxian Zhang\u00a0&\u00a0Yantao Niu\n\nState Key Laboratory of Precision Spectroscopy, School of Physics and Materials Science, East China Normal University, Shanghai, 200062, China\n\nZhubin Hu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nW.Cao, H.X. and R.D. conceived the project and designed the experiments. R.D. conducted the majority of experiments. R.D. and W.Chen performed the mice experiments. J.W. and Y.L. assisted with cell assays. R.D and Y.L. performed and analyzed radical scavenging experiments. C.Z., Y.N., Y.Z., H.Z. and R.D. performed and analyzed CT experiments. Y.O. and X.Z. assisted with synthesis of selenomelanin. Z.H. conducted DFT calculations. Q.R. and J.Z. assisted with SPECT-CT imaging. W.Cao and R.D. cowrote the manuscript. All authors have given approval to the final version of the manuscript.\n\nCorrespondence to\n Huaping Xu or Wei Cao.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "W.Cao and R.D. have submitted a patent application to the State Intellectual Property Office pertaining to the preparation and radioprotection of selenomelanin of this work (application number 202410486229.4, in the substantive examination phase). The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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Molecular engineering of melanin for enhanced biological \u03b3-ray protection.\n Nat Commun 16, 7895 (2025). https://doi.org/10.1038/s41467-025-62403-8\n\nDownload citation\n\nReceived: 27 December 2024\n\nAccepted: 18 July 2025\n\nPublished: 23 August 2025\n\nVersion of record: 23 August 2025\n\nDOI: https://doi.org/10.1038/s41467-025-62403-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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single-molecule tracking", + "pre_title": "Concurrent diffusion of nicotinic acetylcholine receptors and fluorescent cholesterol disclosed by two-colour sub-millisecond MINFLUX-based single-molecule tracking", + "journal": "Nature Communications", + "published": "09 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information Fixed 23.06", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61489-4/MediaObjects/41467_2025_61489_MOESM1_ESM.pdf" + }, + { + "label": "Reporting summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61489-4/MediaObjects/41467_2025_61489_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61489-4/MediaObjects/41467_2025_61489_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61489-4/MediaObjects/41467_2025_61489_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-61489-4#Sec32" + ], + "code": [ + "https://doi.org/10.5281/zenodo.15389696", + "https://github.com/lucasSaavedra123/minflux_analysis" + ], + "subject": [ + "Nanoscale biophysics", + "Single-molecule biophysics", + "Super-resolution microscopy" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5619606/v1.pdf?c=1752153707000", + "research_square_link": "https://www.researchsquare.com//article/rs-5619606/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61489-4.pdf", + "preprint_posted": "23 Jan, 2025", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Nicotinic acetylcholine receptors (nAChRs) are ubiquitous neurotransmitter receptors predominantly located at the cell-surface of neurons and muscle cells. Their dynamics affect synaptogenesis at neurodevelopmental stages and the efficacy of synaptic transmission in the adult synapse. Here we exploit the enhanced capabilities of superresolution fluorescence MINFLUX microscopy to track for minute-long periods with nanometric precision and sub-millisecond time resolution the 2D translational dynamics of the bungarotoxin-labelled adult muscle-type nAChR in tandem with a fluorescent cholesterol analogue. To this end, we implemented a multiplexing procedure in continuous MINFLUX microscopy that enabled the simultaneous excitation of the two molecules using a single wavelength, followed by discrimination of their emissions via differential ratiometric recording. Single-molecule trajectories displayed a heterogeneous spectrum of diffusive behaviours (subdiffusive, Brownian and superdiffusive), with a predominance of the subdiffusive component, which became less pronounced upon cholesterol depletion. nAChRs spent most of their lifetime in confined areas of characteristic size (~\u20090.005 \u00b5m2) lasting for ~\u2009100 ms. Further, MINFLUX captured regions where nAChR and fluorescent cholesterol moved jointly, both in confinement sojourns and along the free Brownian walks, which strongly indicated mutual interactions between the receptor macromolecule and the neutral lipid. To the best of our knowledge, this study constitutes the first series of experiments showing the diffusion dynamics of a transmembrane protein -a functionally important neurotransmitter receptor- together with a key membrane lipid in the native plasma membrane of a live cell at such high detail, thanks to the MINFLUX-based recordings.Biological sciences/Biophysics/Nanoscale biophysicsBiological sciences/Biological techniques/Microscopy/Super-resolution microscopyacetylcholine receptorcholesteroldynamicsdiffusiontranslational motionsuperresolution microscopymultiplexed MINFLUXco-tracking", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "ReinaMinfluxSupplementaryMaterial.docxSupplementary Material", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The diffusion and interaction dynamics of membrane proteins and lipids are key for cell function, but their disclosure is hampered by limited temporal and spatial resolution of conventional observation technologies. Here we exploit the capabilities of minimal fluorescence emission photon fluxes (MINFLUX) microscopy in single-molecule co-tracking experiments of an important membrane protein and cholesterol with enhanced spatiotemporal resolution. Specifically, we interrogate the 2D translational mobility of a ubiquitous cell-surface protein, the nicotinic acetylcholine receptor, in tandem with a fluorescent cholesterol analogue for minute-long periods, reaching nanometric precision and sub-millisecond time resolution. To this end, we implement a multiplexing procedure that enables the simultaneous excitation of the two fluorescent-labelled molecules using a single wavelength, followed by discrimination of their emissions via differential ratiometric recording. We disclose a cholesterol-dependent heterogeneous spectrum of diffusive behaviours with regions of joint translational motion.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Superresolution microscopy methods are increasingly being successfully applied to unravel a wide range of biologically relevant topics1,2. Neurobiology has benefited from the inception of these approaches3, enhancing the spatial and temporal resolution with which we can address the structure and dynamics of the neuron, the patterned compartmentalisation of the axon, and the topography of the macromolecular constituents of the synapse in health and disease4,5.\n\nSingle-particle tracking (SPT) or single-molecule tracking (SMT) techniques are among the biophysical methods of choice to elucidate the spatial extension and temporal duration of dynamic molecular processes involved in protein and cellular function at large. Because of the time window and constrained volume in which they occur, cell-surface phenomena like those involving neurotransmitter receptors are particularly challenging to study. The combination of single-molecule localisation microscopy (SMLM)6,7, DNA-PAINT microscopy8,9, or the minimal fluorescence emission photon fluxes (MINFLUX) microscopy technique9,10,11,12,13,14,15 with SMT methods is beginning to unravel the dynamics of cellular components.\n\nMINFLUX microscopy, one of the most recent superresolution microscopy methods, combines concepts from stimulated emission microscopy (STED)16, a targeted approach, and a stochastic localisation superresolution technique like SMLM17,18. This powerful combination drastically reduces by a factor of ~100-fold the number of photons needed to localise individual molecules in comparison to camera-based imaging techniques10,11. MINFLUX-based SMT is accomplished by employing a patterned doughnut-shaped illumination scheme that initially probes around the estimated emitter position, followed by a computationally driven rapid refocusing of the patterned illumination, zooming in to the fluorescently labelled single molecule to \u201ccorral\u201d and \u201clock\u201d it to follow its trajectory.\n\nPrevious studies using camera-based fluorescence microscopy of \u03b1-bungarotoxin (BTX)-labelled nAChR have revealed its complex and heterogenous diffusion in the millisecond time window, sensitive to the cholesterol content of the plasma membrane19,20,21. Because of its inherently superior spatiotemporal resolution, MINFLUX microscopy has provided more detailed information on the dynamics of motor proteins9,22,23 and nuclear pore structure24 and transport25. So far, however, MINFLUX has not been employed for the study of molecular dynamics in live-cell membranes, where the analysis of SMT is challenged by the very fast motion of membrane molecules26. Further, and more importantly, until recently, it was only possible to continuously localise one fluorescent emitter at a time with this technique, thus precluding molecule co-tracking studies. As a remedy, the combination of fluorescence energy transfer (FRET) and MINFLUX microscopy as a means to explore short intermolecular distances was suggested by Hell and coworkers11. Experimentally, a recent approach for observing two fluorescent emitters at a time combined the use of pulsed MINFLUX with FRET27; in a proof-of-concept work, co-tracking of DNA origami labelled with two dyes differing in their fluorescence lifetimes was achieved28. Here, we overcome these issues by introducing a simple multiplexing variant in continuous excitation MINFLUX microscopy, using single-wavelength excitation and a ratiometric differential emission detection. In this manner, we exploit the enhanced spatiotemporal resolution and minimal photon budget of MINFLUX to simultaneously follow with nanometric precision and sub-millisecond time resolution the joint single-molecule trajectories of a fluorescent cholesterol analogue and the nAChR protein, affording direct visualisation of interactions between the neurotransmitter receptor and the neutral lipid at the cell surface of a live mammalian cell.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "CHO-K1/A5 cells are a clonal cell line robustly expressing adult muscle-type nicotinic acetylcholine receptor (nAChR)29. A set of cells was stained with very low (500 pM) CF\u00ae640-labelled \u03b1-bungarotoxin (CF\u00ae640-BTX). After initial inspection of the cells in the confocal mode, multiple single-molecule tracks were recorded at a maximal rate of ~10\u2009kHz (see \u201cMethods\u201d) from at least 10 cells per experimental condition within a usually 10 \u03bcm2 large region-of-interest (ROI). This allowed us to accumulate in the order of several thousand single-molecule tracks within a few minutes (Fig.\u00a01a). The localisation precision was calculated to be \u03c3\u2009=\u20097\u2009\u00b1\u20091\u2009nm, and the time resolution was 492\u2009\u00b1\u2009780 \u03bcs (see \u201cMethods\u201d). A second experimental dataset consisted of CHO-K1/A5 cells supplemented with 1\u2009nM of a fluorescent cholesterol analogue, which was conjugated with the organic dye Abberior STAR Red via a polyethene glycol linker (fPEG-Chol). For the co-diffusion experiments, we double-stained the CHO-K1/A5 cells with fluorescent \u03b1-bungarotoxin and fPEG-Chol. The spectral overlap of fluorescence emission between the two fluorophores Abberior STAR Red (labelling fPEG-Chol) and CF\u00ae640-BTX (labelling nAChR) was too high to distinguish them by colour. We therefore switched to an \u03b1-bungarotoxin labelled with a different fluorophore, CF\u00ae680\u2009R (CF\u00ae680R-BTX), which emits a more reddish colour, more easily distinguishable from fPEG-Chol. This choice enabled us to excite the two probes with the same laser line at a single wavelength (640\u2009nm) and discriminate between the two by ratiometric measurement of their fluorescence emission wavelengths (CF\u00ae680R-BTX, emission maximum, 701\u2009nm; fPEG-Chol, emission maximum: 655). To this end, we established a detector channel ratio (DCR) criterion, as described in Supplementary Fig.\u00a02. We denoted fPEG-Chol trajectories as fPEG-Chol (+CF\u00ae680R-BTX) when we analysed the trajectories of the cholesterol probe in the presence of CF\u00ae680R-BTX and, reciprocally, CF\u00ae680R-BTX (+fPEG-Chol) when we followed CF\u00ae680R-BTX trajectories in the presence of fPEG-Chol (Fig.\u00a01b, c). Control experiments with CF\u00ae680R-BTX alone or with fPEG-Chol alone were also carried out to determine and denote the DCR values of each individual probe. Our experimental conditions minimise photo-induced biasing effects such as unwanted loss or rise of fluorescence signal, slowdown or speed-up of diffusion and cell death due to photobleaching, photoblueing, photoblinking, phototoxicity, or local heating and trapping, as described in detail in the\u00a0Supplementary Information.\n\na Example of a 10 \u00d710 \u03bcm region of interest (ROI) image of the clonal cell line CHO-K1/A5 expressing adult muscle-type nAChRs labelled with CF\u00ae640R-BTX only. Single-molecule tracks are randomly colour-coded as they appear on the screen of the MINFLUX microscope setup; after a few minutes, the ROI is covered with individual traces. Approximately 50% of the fluorescently labelled receptors appeared as nanometric-sized dots with no indication of motion. Subsequent analysis identified them as immobile nAChRs (Supplementary Fig.\u00a03). Scale bar = 1 \u03bcm. b Representative fPEG-Chol and CF\u00ae680R-BTX-labelled nAChR trajectories in a co-tracking experiment. Scale bar = 500\u2009nm. c Zoom-in into marked region from (b). Scale bar = 100\u2009nm. d MSD analyses from different samples as marked (CDx: cholesterol depletion via methyl-\u03b2-cyclodextrin), TA-MSDs, EA-TA-MSD, and ideal Brownian diffusion (see Eq.\u00a05 in \u201cMethods\u201d, where R, the correction factor for blurring, was set to 1/6.210, and \u0394\u2009=\u200910\u2009nm). Both x- and y-axes are in log scale. e Boxplot showing the percentage of trajectories classified as subdiffusive (\u03b2\u2009<\u20090.9), Brownian (0.9 \u2264 \u03b2\u2009\u2264\u20091.1), and superdiffusive (\u03b2\u2009>\u20091.1) for CF\u00ae640R-BTX (n\u2009=\u20091383 trajectories with an average of 1105 steps, 16 ROIs), cholesterol-depleted CF\u00ae640R-BTX-labelled samples (CDx, n\u2009=\u2009853 and average 793 steps, 15 ROIs), CF\u00ae680R-BTX (n\u2009=\u2009187 and average of 925 steps, 5 ROIs), CF\u00ae680R-BTX (+fPEG-Chol) (n\u2009=\u20091373 and average of 2034 steps, 47 ROIs), fPEG-Chol (n\u2009=\u20091012 and average of 1363 steps, 6 ROIs), and fPEG-Chol (+CF\u00ae680R-BTX) (n\u2009=\u20095229 and average of 1496 steps, 52 ROIs). TA-MSD and EA-TA-MSD fittings extended up to 50\u2009ms. Error bars represent standard errors of mean (SEM).\n\nPreliminary analysis of the data involved measuring trajectory and individual step durations (Supplementary Table\u00a01), which, however, did not reveal further details of the diffusional behaviour of the probes. Since we wanted to disclose the actual (co-) diffusion modes of toxin-labelled nAChR and fluorescent cholesterol, and in view of the biases for completely immobile events in general SMT tracks (including MINFLUX-based)26, we focus here only on completely mobile trajectories and excluded immobile trajectories from subsequent analyses as in previous work20,21. To this end, the criterion of ref. 30 was applied next to the determination of the proportion of immobile/mobile trajectories, as shown in Supplementary Fig.\u00a03. Using this criterion, about half of the trajectories of toxin-labelled nAChRs were classified as completely immobile. Notably, only ~20% completely immobile trajectories were observed in the case of fPEG-Chol tracks co-labelled with CF\u00ae680R-BTX.\n\nThe ensemble-averaged, time-averaged mean squared displacement (EA-TA-MSD) is a time-dependent measure of the deviation of a particle position relative to a reference position. Here, EA-TA-MSD curves (Fig.\u00a01d) were furnished by fitting the individual curves with the analytical expressions (Eqs.\u00a03\u20134) outlined in \u201cMethods\u201d, providing information on the ensemble diffusive behaviour of the nAChR macromolecules and the cholesterol analogue in a 2D Euclidean space\u2014the membrane\u2014over time.\n\nMINFLUX allowed us to analyse a hitherto unexplored time window, i.e., the sub-millisecond time range (see Fig.\u00a01d). As a comparison, previous camera-based studies had a temporal resolution of \u2248 10\u2009ms20,21. Table\u00a01 and Supplementary Table\u00a02 list the kinetic parameters derived from this analysis, i.e., the generalised diffusion coefficient (K\u03b2) and the apparent anomalous coefficient (\u03b2). One would expect no significant differences between the translational diffusion properties of CF\u00ae640R-BTX and CF\u00ae680R-BTX-labelled nAChRs, but when averaged over the entire ensemble population of tracks, the nAChRs labelled with the two different toxins appeared to differ in their kinetics (Table\u00a01). However, as shown below (Table\u00a02 and Supplementary Material), due to a few outliers, this difference was only apparent and not statistically significant.\n\nTable\u00a01 also lists the average duration (or residence time) of confinement sojourns and non-confined portions, respectively, and the confined ratio r, which is defined as the quotient between the residence time of the trajectory in the confined state (i.e., the sum of the confined portions of a single trajectory) and the total duration of the trajectory for the ensemble population of trajectories. The most striking observation is the modification of the kinetic parameters of each probe in the presence of the other (Table\u00a01). Thus, fPEG-Chol diffused ~1.4-fold faster in the presence of the toxin-labelled nAChR (fPEG-Chol (+CF\u00ae680R-BTX) samples; p\u2009<\u20090.01), as did the toxin in the presence of the cholesterol analogue (p\u2009<\u20090.01).\n\nThe power (anomalous) exponent \u03b2 obtained by fitting the individual time-averaged MSD (TA-MSD) curves was next used to classify trajectories according to their motional regime, as shown in Fig.\u00a01 and Table\u00a02. Tracks with \u03b2\u2009<\u20090.9 were rated as subdiffusive, i.e., mobility was transiently confined or trapped, with 0.9 \u2264 \u03b2\u2009\u2264\u20091.1 as Brownian, i.e., free diffusion, and \u03b2\u2009>\u20091.1 as superdiffusive, i.e., deviating from free diffusion (due to enhanced mobility owing to, e.g., active cellular transport processes). Both the fluorescent-labelled nAChR and the cholesterol analogue covered a wide spectrum of diffusive behaviours, as shown in Fig.\u00a01e. CF\u00ae640R-BTX- or CF\u00ae680R-BTX-labelled nAChRs trajectories exhibited a predominantly (~47\u201360%) superdiffusive component, ~33% Brownian, and ~20% subdiffusive (p\u2009<\u20090.05). fPEG-Chol depicted a completely different, overwhelmingly (~80%; p\u2009<\u20090.0001) superdiffusive behaviour, which diminished in samples co-labelled with CF\u00ae680R-BTX (~60 %; p\u2009<\u20090.05), concomitant with an increase in subdiffusive (p\u2009<\u20090.05) and Brownian (p\u2009<\u20090.001) trajectories. The large fraction of superdiffusive trajectories resulted from the threshold set for \u03b2 (> 1.1) for this population of trajectories, to facilitate comparison with previous SMT analyses20,21,31. This enabled us to highlight tendencies towards faster diffusion, e.g., due to larger fractions of more fluid (or less ordered) membrane regions during the track. When analysed according to their diffusive behaviour, subdiffusive and Brownian particles labelled with either toxin displayed statistically indistinguishable values, while CF\u00ae680R-BTX superdiffusive particles moved faster than those of CF\u00ae640-BTX. This is clearly observed in Supplementary Fig.\u00a08, where the histograms showing the distribution of the K\u03b2 values of the two fluorescent toxins are displayed: only a minor proportion (~1%) of outliers corresponding to the superdiffusive tracks of CF\u00ae680R-BTX-labelled receptors are singled out. Moreover, the median of the two distributions was 0.18 and 0.19 for CF\u00ae640R-BTX and CF\u00ae680R-BTX, respectively, also not statistically different. It is therefore the weight of the superdiffusive component that distorted the apparent average value of the ensemble population. Moreover, regardless of whether CF\u00ae640R-BTX or CF\u00ae680R-BTX (+fPEG-Chol) was used, both probes exhibited accelerated diffusion with increasing concentrations of exogenous cholesterol, and the trend remained consistent for the other dynamic parameters (Table\u00a01 and Supplementary Table\u00a02).\n\nThe dynamics within the free and confined states of the trajectories were next dissected separately (Fig.\u00a02a). The free, non-confined segments of CF\u00ae640R-BTX-labelled nAChR lasted 12\u2009\u00b1\u20091\u2009ms, while the confined segments lasted significantly longer (71\u2009\u00b1\u20098\u2009ms) (Fig.\u00a02b). The presence of fPEG-Chol in co-labelled samples significantly decreased the duration of both non-confined (20\u2009\u00b1\u20091\u2009ms to 10\u2009\u00b1\u20091\u2009ms, p\u2009<\u20090.05) and confined (66\u2009\u00b1\u20098\u2009ms to 39\u2009\u00b1\u20095\u2009ms, p\u2009<\u20090.05) states of CF\u00ae680R-BTX-labelled nAChRs. Cholesterol depletion (CDx) had the opposite effect, lengthening the duration of the confined portions to 137\u2009\u00b1\u200925\u2009ms (p\u2009<\u20090.01). This was confirmed by experiments in which unlabelled cholesterol was added to the toxin-labelled samples, where the duration of the confined portions was reduced to a similar extent as that produced by fPEG-Chol (27\u2009\u00b1\u20096\u2009ms at 50\u2009nM, p\u2009<\u20090.01; Supplementary Fig.\u00a04).\n\na Representative single tracks of CF\u00ae640R-BTX-labelled nAChR under control conditions showing parts classified as confinement sojourns (green) and as free, non-confined portions (black). Scale bar = 100\u2009nm. b Boxplots showing the mean and SEM of lifetimes of non-confined portions (left) and confinement sojourns (second left), confined ratio r (second right, defined as before r = confined portion lifetimes / total trajectory time), and transition rates between confined and non-confined states of the trajectories of CF\u00ae640R-BTX (19,427 confined portions, 19,443 non-confined portions, and 1383 trajectories in 16 ROIs), CDx (9169 confined portions, 9099 non-confined portions, and 853 trajectories in 15 ROIs), CF\u00ae680R-BTX (2458 confined portions, 33,064 non-confined portions, and 187 trajectories in 5 ROIs), CF\u00ae680R-BTX (+fPEG-Chol) (32,881 confined portions, 21,923 non-confined portions, and 1373 trajectories in 47 ROIs), fPEG-Chol (18,210 confined portions, 18,300 non-confined portions, and 1012 trajectories in 6 ROIs), and fPEG-Chol (+CF\u00ae680R-BTX) (161,601 confined portions, 21,923 non-confined portions, and 5229 trajectories in 52 ROIs). Each grey dot represents a trajectory inside an ROI, and the coloured dots represent the average of the parameter for a given ROI. c Boxplots showing the mean and SEM of the percentage of trajectories in each experimental ROI classified as subdiffusive (\u03b2\u2009<\u20090.9), Brownian (0.9 \u2264 \u03b2\u2009\u2264\u20091.1), and superdiffusive (\u03b2\u2009>\u20091.1) in confinement sojourns (upper panels) and non-confined portions (lower panels), respectively, upon TA-MSD fitting up to 25\u2009ms for the different sample conditions as labelled. Source data are provided as a Source Data file.\n\nTo quantitatively assess the time spent by a trajectory in the confined state, a \u201cconfined ratio\u201d, r (Table\u00a01 and Fig.\u00a02b), was operationally defined as the quotient between the residence time of the trajectory in the confined state divided by the total duration of the trajectory. r was 0.76\u2009\u00b1\u20090.02 and 0.63\u2009\u00b1\u20090.03 for CF\u00ae640R-BTX and CF\u00ae680R-BTX, respectively, indicating that, on average, nAChRs spend most of their lifetime in confinement, as was the case with fPEG-Chol (r\u2009=\u20090.63\u2009\u00b1\u20090.02) alone. The latter value decreased to 0.49\u2009\u00b1\u20090.02 in cells co-labelled with toxin (fPEG-Chol (+CF\u00ae680R-BTX), p\u2009<\u20090.01).\n\nIntuitively, if a molecule spends less time under confinement, one expects it to spend more time in the complementary non-confined part of the track. However, both non-confined and confined portions of CF\u00ae680R-BTX trajectories were shortened under fPEG-Chol co-labelling conditions (Fig.\u00a02). A possible explanation for this paradoxical observation is that the transition rate (i.e., the number of confined/non-confined state changes per unit time) (Fig.\u00a02b, right) increased in the presence of cholesterol. Indeed, the transition rate of CF\u00ae680R-BTX increased from 25.49\u2009\u00b1\u20091.93\u2009s\u22121 to 48.27\u2009\u00b1\u20092.99\u2009s\u22121 in the presence of fPEG-Chol (p\u2009<\u20090.01). The transition rate of fPEG-Chol alone was 62.94\u2009\u00b1\u20094.79\u2009s\u22121, augmenting to 103.70\u2009\u00b1\u20092.68\u2009s\u22121 in the presence of CF\u00ae680R-BTX (p\u2009<\u20090.001). These results indicate that CF\u00ae680R-BTX changed state faster in the presence of fPEG-Chol and vice versa. Finally, supplementation with unlabelled cholesterol (50\u2013100\u2009nM) did not change the total time that the nAChR spent exploring each state, but rather changed the average residence time in confinement, which decreased from 0.84\u2009\u00b1\u20090.09\u2009s to 0.34\u2009\u00b1\u20090.09\u2009s upon cholesterol supplementation.\n\nWe next analysed the free-moving and confined portions of the trajectories more closely by computing the TA-MSD of each portion32, i.e., we determined values of the generalised diffusion coefficient (K\u03b2) and the apparent anomalous coefficient (\u03b2) for the non-confined and confined portions of the trajectories. The values are listed in Supplementary Table\u00a03. The values of \u03b2 again allowed us to rate the trajectory portions as either subdiffusive (\u03b2\u2009<\u20090.9), Brownian (0.9 \u2264 \u03b2\u2009\u2264\u20091) or superdiffusive (\u03b2\u2009>\u20091.1), and determine the respective percentages, as done before for the whole tracks (compare Figs.\u00a01e and 2c). As shown in Fig.\u00a02c, the percentage of subdiffusive motion was higher in the confinement sojourns, and, in the case of fPEG-Chol and CF\u00ae680R-BTX, the predominant behaviour in the non-confined state was in all cases superdiffusive. Both behaviours were to be expected, since confinement is a form of subdiffusion, while non-confinement is characterised by fast diffusion. The values of K\u03b2 followed similar trends: in the non-confined state of the nAChR, K\u03b2 was ~3 times faster in the presence of fPEG-Chol (p\u2009<\u20090.05). Cholesterol depletion or supplementation modified the diffusion properties in both confined and non-confined regions, as highlighted in Supplementary Table\u00a03 and Supplementary Fig.\u00a04. Finally, Supplementary Fig.\u00a05 depicts additional metrics of the trajectories, such as the shape and size of a confinement area (in terms of the total area, eccentricity and length of the major axis), and the number of observed steps of the trajectories within and between confinement sojourns. Further details on all the parameters are discussed next to the respective Supplementary Figs. and tables.\n\nThe high spatial and temporal resolution of the tracks, as afforded by MINFLUX microscopy, allowed us to use another metric to characterise the diffusional behaviour of nAChR and fluorescent cholesterol in their confined and non-confined states: the turning angles distended by the molecules along their SMTs. Specifically, we calculated the probability density function of turning angles between subsequent steps of a trajectory for different step lengths, as shown in Fig.\u00a03 for the unclassified and in Supplementary Fig.\u00a06 for the differently classified diffusion regimes (subdiffusive, Brownian, superdiffusive). The probability density function for Brownian motion is constant for angles between 90\u00b0 to 180\u00b0 (since there is no turning angle predilection in this case), while there is an increase towards turning angles between 90\u00b0 and 180\u00b0 (i.e., anticorrelation) for confined or subdiffusive characteristic (due to molecules tending to move backwards), and turning angles between 0\u00b0 and 90\u00b0 (i.e., a positively correlated steps) for superdiffusive behaviours (as the molecule tends to move forward). Further, changes in the probability density function distribution for increased step lengths indicate the spatial extent of a confined, free-diffusive or superdiffusive portion of a track.\n\nProbability function (PDF) of the turning angles (0\u00b0 to 180\u00b0) of the trajectories for the different samples and the two classifications as labelled. The differently coloured probability density functions correspond to 1 (red), 4 (green), 8 (blue) step lags. Source data are provided as a Source Data file.\n\nThe turning angle analysis confirmed our previous analysis. The distribution for both CF\u00ae680R-BTX-labelled nAChRs and fPEG-Chol within confinement sojourns increased linearly as the turning angle approached 180\u00b0, i.e., both molecules tended to undergo anticorrelated steps. This confirms their prominently subdiffusive and rare Brownian-like or superdiffusive behaviour, as already depicted in Fig.\u00a02. Moreover, this preference for anticorrelated steps diminished as the step lag increased, suggesting the finite extent of confinement zones, thus hindering diffusion inside structures of characteristic size21,33. This finding was further supported by deep learning CONDOR analysis34, as shown in Supplementary Fig.\u00a07 and Supplementary Table\u00a05; restricted mobility of both toxin-labelled nAChR and fPEG-Chol occurred predominantly within membrane compartments of specific sizes (0.006\u20130.008 \u00b5m2).\n\nIn non-confined portions of the tracks, for short step lags, the motion was typical of obstructed diffusion35; for intermediate length steps, the turning angle distribution was uniform, characteristic of Brownian motion33; and for long step lags, the molecules took highly correlated, i.e., superdiffusive steps. This change in directionality of the turning angle distributions with variation of the step lag confirmed, on one hand, the heterogeneous characteristic of the diffusion even in the non-confined portions of the SMTs, and, on the other hand, the augmented superdiffusive motion in the non-confined state, as shown in Fig.\u00a03.\n\nIn a next step, we analysed simultaneously recorded trajectories of nAChR (labelled with CF\u00ae680R-BTX) and fPEG-Chol (labelled with Abberior STAR Red) probes with high detail, especially to disclose any co-diffusion events. As highlighted before, this choice of labels (CF\u00ae680R and Abberior STAR Red) enabled us to excite the two probes with the same laser line and discriminate between the two by ratiometric measurement of their fluorescence intensities in two spectrally separated detection channels. Specifically, we calculated the detector channel ratio (DCR) by division of the fluorescence intensities registered in the more reddish and blueish detection channels, respectively (see \u201cMethods\u201d and Supplementary Fig.\u00a02). Control measurements on the two probes individually revealed clearly separated high and low DCR values for fPEG-Chol and CF\u00ae680R-BTX, respectively (Supplementary Fig.\u00a02). This is further depicted in the left two panels of Fig.\u00a04a, which highlight representative tracks and the temporal evolution of DCR and total intensity values summed over both detection channels and given as the effective counts at offset (ECO). The latter correspond to the photon counts collected at the outer points of the MINFLUX scanning pattern, corrected for background contributions for individually recorded fPEG-Chol and CF\u00ae680R-BTX. DCR values were above 0.55 in the case of CF\u00ae680R-BTX and below 0.40 for fPEG-Chol, and ECO/intensity values were at very low levels. However, simultaneous recordings of both probes revealed a significant fraction of trajectories with DCR values between 0.4 and 0.55 and at the same time increased ECO/intensity values, disclosing sections of simultaneous co-tracking detection and thus co-diffusion of the two probes (Fig.\u00a04a). This is also revealed in the frequency histograms of the DCR values, revealing a shift of the DCR values towards the intermediate regime between 0.4 and 0.55 (Fig.\u00a04b). Figure\u00a04b also clearly highlights that both modulation of the actin cytoskeleton by CK-666 and addition of the unlabelled cholesterol affected the co-tracking of the nAChR and fluorescent cholesterol.\n\na Representative data of DCR and intensity values (as effective counts at offset (ECO)) over time steps (upper two panels) and corresponding tracks in the X-Y plane with colours indicating classification as fPEG-Chol (orange), CF\u00ae680R-BTX (blue) and co-diffusion (black) for cells with fPEG-Chol alone, CF\u00ae680R-BTX nAChR alone, and the 2-colour co-labelling experiments with both fPEG-Chol and CF\u00ae680R-BTX. Co-diffusion becomes apparent in the increased ECO values and simultaneous change in DCR values converging in a corridor between 0.40 and 0.55 (marked by the two horizontal dashed lines). Scale bar = 100\u2009nm. b Histograms of DCR values for the tracking data of fPEG-Chol (orange) and CF\u00ae680R-BTX (blue) only, and of fPEG-Chol and CF\u00ae680R-BTX co-labelled cells (purple) with two-species Gaussian fits for experiments with only one probe without (upper left, control) and with (upper middle) actin modulation by CK-666, and with doubly labelled experiments without any treatment (lower left, control), with actin modulation by CK-666 (lower middle) and with cholesterol supplementation (50\u2009nM: upper right, 100\u2009nM: lower right). The histograms built from the data acquired with both probes simultaneously show only the mobile fraction. Source data are provided as a Source Data file.\n\nThrough visual inspection, we observed that trajectories of fPEG-Chol spatially intersected with those of CF\u00ae680R-BTX in confinement and non-confined zones, in several cases more than once along the same trajectory (Fig.\u00a05a, left). In fact, we found that in a majority (~70%) of experimental ROIs, there were statistically significant overlaps in the confined portions of the trajectories of nAChR and cholesterol (p\u2009<\u20090.01). This effect was quantified by the overlap coefficient metric (C) herewith introduced (see \u201cMethods\u201d). For the trajectories analysed in this study, we calculated a value C\u2009=\u20090.27\u2009\u00b1\u20090.02, or, in other words, 27% of the confinement areas detected in CF\u00ae680R-BTX trajectories, which target the nAChR, overlap with those of fPEG-Chol. Addition of 100\u2009nM unlabelled cholesterol drastically reduced the overlap by more than two-thirds (C\u2009=\u20090.08\u2009\u00b1\u20090.01, p\u2009<\u20090.001). The overlap of non-confined portions was also found to be statistically different from chance: 5% of the ROIs were found to overlap (p\u2009<\u20090.01) along the free-walk portions, with a C value of 0.40\u2009\u00b1\u20090.03, indicating that the degree of overlap in non-confined portions was higher than in confined portions. Moreover, upon addition of 100\u2009nM cholesterol, the non-confined overlap coefficient C was more drastically reduced to 0.13\u2009\u00b1\u20090.01 (p\u2009<\u20090.001), suggesting that the unlabelled cholesterol displaced the fluorescent-labelled fPEG-Chol, resulting in less detectable co-diffusion events.\n\na Two representative examples of trajectories of the two-colour labelled experiment with the track of the CF\u00ae680R-BTX-labelled nAChR and of Abberior STAR Red labelled fPEG-Chol: large overviews (left and middle right panels) and respective zoom-ins to the indicated black boxes (middle left and right). The confinement areas classified for CF\u00ae680R-BTX and of fPEG-Chol are highlighted in yellow. Their overlap coefficient C is indicated as a number in each case. Scale bar = 50\u2009nm. b Histograms of overlap coefficient C (bin width = 0.1) for CF\u00ae680R-BTX and fPEG-Chol without treatment (left), following 50\u2009nM (middle left) and 100\u2009nM (middle right) cholesterol addition, and upon CK-666 treatment (right) in confinement (upper panels) and non-confinement (lower panels) areas.\n\nTo investigate the influence of the (cortical) actin cytoskeleton on the (co-)diffusion dynamics of nAChR and cholesterol, we incubated the samples of CHO-K1/A5 cells for 15\u2009min at 4\u2009\u00b0C with CK-666, a compound that inhibits Arp2/3 complex formation36,37 and hence the branching and growth of new actin filaments, thus perturbing the stability of the submembrane (cortical) actin meshwork. The cortical actin cytoskeleton, i.e., the actin filaments underlying the plasma membrane, usually builds up a checkered-like network, \u201cfencing in\u201d compartments, where diffusion over an actin border from one compartment to the next is restricted, resulting in a compartmentalised or hop-like diffusion (see, e.g., ref. 38). Experiments from our laboratory have also reported effects of actin cytoskeleton perturbation on nAChR organisation39. The effects of CK-666 treatment on the kinetic diffusion parameters of CF\u00ae680R-BTX nAChR and fPEG-Chol are included in Supplementary Table\u00a02 to facilitate comparison with the other conditions. No changes were apparent in the anomalous diffusion coefficient \u03b2 for any of the experimental conditions tested, except for fPEG-Chol: upon CK-666 treatment, the fluorescent cholesterol analogue was less superdiffusive (p\u2009<\u20090.05). Only subtle changes were observed in the apparent diffusion constant K\u03b2; the fluorescent cholesterol probe alone was 1.1 times faster, and in the presence of CF\u00ae680R-BTX, 1.3 times faster, upon CK-666 treatment. The confined ratio r was not affected by CK-666 treatment, except for CF\u00ae680R-BTX in the presence of fPEG-Chol, in which case r decreased moderately (p\u2009<\u20090.01). The durations of the confined segments of the tracks were also affected: fPEG-Chol trajectories increased, and those of CF\u00ae680R-BTX decreased (p\u2009<\u20090.01) upon CK-666 treatment (Supplementary Table\u00a02). Interestingly, the drug did not affect the degree of overlap of fPEG-Chol and toxin-labelled nAChR in the confinement sojourns (C\u2009=\u20090.27\u2009\u00b1\u20090.01) but did increase the degree of overlap in non-confined portions (0.46\u2009\u00b1\u20090.05; p\u2009<\u20090.01).\n\nIn order to investigate whether Arp2/3-mediated perturbation of the submembrane actin meshwork induced the so-called hop diffusion, we next undertook an analysis of the trajectories following the approaches of Hell and coworkers40 and Eggeling and coworkers41. Here we classified the motion of molecules in three models: freely diffusing, confined, and hopping between compartments (Fig.\u00a06). For the hopping diffusion we introduced specific kinetic diffusion parameters of the average actin compartment diameter L, the diffusion coefficients D\u03bc and DM characterising the short-term diffusion within the actin compartments (intra-compartmental), and long-term diffusion across compartments (inter-compartmental), respectively, and the average scale length Lhop of spatial separation between D\u03bc and DM (Table\u00a03). For both CF\u00ae680R-BTX and fPEG-Chol, only a small portion of the trajectories revealed hop-like diffusion (~15%); diffusion was rather dominated by free (39% and 30% for CF\u00ae680R-BTX and fPEG-Chol, respectively) and confined (39% and 30% for CF\u00ae680R-BTX and fPEG-Chol, respectively) diffusion. CK-666 treatment led to hardly any change in these figures since the actin network was still intact but with less tight branching, i.e., larger compartment sizes, highlighted by the increase in values of L and Lhop from around 130\u2013160\u2009nm to 170\u2013250\u2009nm. Most significantly, CK-666 treatment resulted in overall increases in the diffusion coefficients: (1) the diffusion coefficient Dfree of the freely diffusing fraction increased by a factor of 2.8 for CF\u00ae680R-BTX and 1.6 for and fPEG-Chol; (2) mobility was also increased for the confined trajectories (a factor of 2.45 for CF\u00ae680R-BTX and 1.3 for fPEG-Chol); and (3) for hopping diffusion, both the intra- and inter-compartmental coefficients D\u03bc and DM increased by a factor of 2\u20132.5. Despite this increase in D\u03bc and DM, the compartmentalisation strength S\u2009=\u2009D\u00b5\u2009/DM, which is inversely proportional to the hopping probability (i.e., the probability of crossing an actin compartment boundary)41, remained almost constant (3.07\u20133.46 for CF\u00ae680R-BTX and 2.43\u20133.45 for fPEG-Chol). This indicates that although the velocity of the molecules increased, they remained spatially constrained upon disturbing the cytoskeletal fences with CK-666, as highlighted above when discussing the negligible changes in fractions of diffusion modes and values of L and Lhop.\n\nEnsemble-averaged, time-averaged mean square displacement (EA-TA-MSD) curves determined for all trajectories (Brownian and subdiffusive) of CF\u00ae680R-BTX and fPEG-Chol with and without perturbation of the subcortical actin meshwork by CK-666, as labelled. The motional states were classified into three categories: free (orange, upper panels), confined (green, middle panels), and hop-like (red, lower panels) diffusion according to ref. 41 and ref. 40. S denotes the confinement strength for the hopping diffusion, defined as the \\({D}_{\\mu }/{D}_{M}\\) ratio, and the black lines in the panels of the upper two rows give the fits. The dotted lines in the panels of the lower row correspond to the expected MSD for pure diffusion with D\u00b5 and DM. Source data are provided as a Source Data file.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61489-4/MediaObjects/41467_2025_61489_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61489-4/MediaObjects/41467_2025_61489_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61489-4/MediaObjects/41467_2025_61489_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61489-4/MediaObjects/41467_2025_61489_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61489-4/MediaObjects/41467_2025_61489_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61489-4/MediaObjects/41467_2025_61489_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "We have applied MINFLUX nanoscopy to simultaneously follow the lateral motion of individual fluorescent-tagged cholesterol and neurotransmitter receptor protein molecules at a sampling rate of up to 10\u2009kHz, i.e., at \u2248600 \u03bcs per individual step, and with a localisation precision \u03c3\u2009=\u20097\u2009\u00b1\u20091\u2009nm (Supplementary Table\u00a06). This accomplishment required the development of a novel detection scheme that enabled the co-tracking of both the Abberior STAR Red labelled cholesterol probe (fPEG-Chol) and the nicotinic acetylcholine receptor (nAChR labelled with fluorescent BTX). The fluorescence emission of the two probes was thereby excited simultaneously at the same wavelength and ratiometrically discriminated by emission wavelength, overcoming a bottleneck of conventional MINFLUX studies. Single-molecule trajectories of a fluorescent phospholipid, DPPE-ATTO 647\u2009N, in an artificial lipid bilayer42 and the regular stepping motion of the kinesin protein motor on microtubules have been documented with MINFLUX9,22,23 or MINSTED43 but to our knowledge sub-millisecond co-tracking localisation studies of a membrane protein macromolecule together with a fluorescent lipid in the complex membrane environment of a living cell have not been reported to date.\n\nExploiting the superior time-resolution capability of the MINFLUX technique, we were able to dissect the diffusional properties of individual trajectories into their non-confined and confined portions along the tracks. nAChRs labelled with either CF\u00ae640-BTX or CF\u00ae680-BTX displayed a heterogeneous diffusion, ranging from subdiffusive (i.e., confined) over unhindered (i.e., random Brownian) to comparably very fast (i.e., superdiffusive) motion, as reported before in camera-based studies20. Subdiffusive motion became more pronounced upon cholesterol depletion, yet with a high proportion of superdiffusive components (Fig.\u00a01 and Table\u00a02). This provided the first piece of evidence on the influence of the neutral lipid on the nAChR motility in this hitherto unexplored temporal window. The simplest explanation for the translational motion heterogeneity of the nAChR is the reported coexistence of dispersed individual macromolecules with nanoclusters of variable sizes. From our previous camera-based SPT-STORM results at lower time resolution, we proposed that crowding of nAChRs in nm-sized aggregates (nanoclusters) may impede the motion of the individual receptor macromolecules20,21. We further proposed a picket-like mechanism to account for the transient motional hindrance. The experimental data could be accounted for in terms of a 2-state model44 in which receptors switched between Brownian motion and obstructed diffusion (OD)20,21. The present results clearly show that the restricted diffusion sojourns also occur in the fast (low millisecond and sub-millisecond) time domain accessible to MINFLUX. Results from deep learning analyses31 concurred with the early proposal. Using concatenated convolutional neural networks a more recent work challenged the OD model against six other physical models; the 2-state model withstood the challenge and remains the simplest interpretation of the translational diffusion of the nAChR at the cell surface45, a contention that appears to be extensive to the present experimental work.\n\nThe fluorescent cholesterol analogue fPEG-Chol employed in this study was chosen because (1) it has already been successfully used for labelling cholesterol-dependent domains in the same CHO-K1/A5 cell line employed in the present work46; (2) its polyethylene glycol chain maintains the fluorophore sufficiently far from the sterol moiety47, making PEG-labelled lipids, and fPEG-Chol in particular, among the least biophysically and physiochemically membrane-perturbing probes48,49; this ensures functional preservation of indirect, membrane-mediated cholesterol effects on the membrane and direct cholesterol-nAChR interactions50; (3) its advantageous spectroscopic properties (with respect to absorption and emission wavelengths and photostability) for MINFLUX microscopy, and in particular for the 2-colour double-labelling scheme introduced in the present work, and (4) fPEG-Chol is known to reside almost exclusively at the cell-surface membrane, presumably at its outer, exoplasmic leaflet46,47,51, with only a small fraction internalised by cells in the course of hours49, well beyond the time window of our experiments.\n\nAs expected, the apparent diffusion of fPEG-Chol (alone or in the presence of the far-red CF\u00ae680R-BTX) was faster than that of the toxin-labelled nAChR protein (Table\u00a01); the fluorescent cholesterol exhibited Brownian and superdiffusive and rarely subdiffusive motions (Fig.\u00a01 and Table\u00a02). MINFLUX further revealed that the motion of the nAChR, as assessed by its apparent anomalous diffusion constant K\u03b2, was influenced by the presence of the fluorescent cholesterol probe (Table\u00a01). While cholesterol depletion via methyl-\u03b2-cyclodextrin markedly slowed down nAChR diffusion, addition of 100\u2009nM cholesterol only slightly accelerated diffusion (Supplementary Table\u00a02), indicating that the membrane cholesterol concentration is close to saturating concentrations in CHO-K1/A5 cells, as previously reported52. Taken together, this series of experimental results strongly indicates that the 2D translational motions of cholesterol and nAChR are correlated.\n\nMINFLUX further afforded the spatiotemporal discrimination of the trajectories into their free diffusing segments and confinement sojourns, thus enabling calculation of the apparent diffusion coefficient in the two motional regimes. Whereas previous studies in cells and giant unilamellar vesicles yielded overall diffusion coefficient values of fPEG-Chol analogues close to ~1 \u03bcm2 s\u2212151,53, here we could discriminate between a slow component in the confined sojourns (1.10\u2009\u00b1\u20090.01 \u03bcm2 s\u22121) and the superdiffusive, 6-fold faster (6.45\u2009\u00b1\u20090.24 \u03bcm2 s\u22121) component in the free walks (Supplementary Table\u00a03). The same applies to the dissection of the diffusion coefficients of the BTX-labelled nAChRs, differing by a factor of 5 between free diffusing portions and confinement sojourns (Supplementary Table\u00a03).\n\nThe duration of the non-confined portions of the BTX-labelled nAChR SMTs under control and cholesterol depletion conditions were in the order of ~15\u2009ms (Fig.\u00a02). These free-walking, random motion periods were statistically shorter in the presence of fPEG-Chol (10\u2009\u00b1\u20091\u2009ms, p\u2009<\u20090.01), i.e., falling beyond the range of those of the cholesterol probe alone (9\u2009\u00b1\u20091\u2009ms) or fPEG-Chol in the presence of CF\u00ae680R-BTX (7\u2009\u00b1\u20091\u2009ms). These experimental results further reinforce the notion of mutual dynamic interactions between cholesterol and the receptor protein. The effect of the neutral lipid was also manifested on the confined sojourns: cholesterol depletion increased by 2-fold the lifetime of the confined periods from 71\u2009\u00b1\u20098\u2009ms (control) to 137\u2009\u00b1\u200924\u2009ms (CDx) (p\u2009<\u20090.01). fPEG-Chol confinement sojourns lasted one order of magnitude less (25\u2009\u00b1\u20091\u2009ms) (p\u2009<\u20090.0001, Fig.\u00a02), with no changes in K\u03b2 in the presence of the toxin. The same applies to K\u03b2 in the non-confined portions of fPEG-Chol in the presence of the nAChR protein. However, confined portions became more subdiffusive (Fig.\u00a02) when fPEG-Chol was sampled in the presence of CF\u00ae680R-BTX (p\u2009<\u20090.001). Exogenous cholesterol supplementation only reduced the duration of the confined sojourns of CF\u00ae680R-BTX (p\u2009<\u20090.01). Cholesterol also affected the co-tracking of the two molecules (Fig.\u00a04). Undoubtedly, the main effect of cholesterol on nAChR diffusion is exerted on the confined portion of the nAChR trajectories. All in all, these kinetic parameters strongly suggest that the motion of nAChRs and cholesterol influence each other\u2019s diffusion behaviour.\n\nnAChRs change the direction of their trajectories by step-by-step angular displacement. If the steps display no preference for moving forwards or backwards, as in Brownian motion, there is no turning angle predilection. If a molecule favourably adopts anticorrelated steps (i.e., the molecule tends to move backwards), as in confined diffusion, turning angles between 90\u00b0 and 180\u00b0 are characteristically observed, whereas when steps are positively correlated (i.e., the molecule tends to move forward), as in very fast, i.e., superdiffusive motions, they vary between 0\u00b0 and 90\u00b0. Here we measured step lags instead of time lags due to the irregular time intervals obtained with MINFLUX recordings. We were not only able to validate previous camera-based STORM studies20,21 but could also reveal that cholesterol accompanied the nAChR with a similar turning angle dependence. As expected, subdiffusive trajectories exhibited a strong preference for anticorrelated steps, a tendency that diminished as \u03b2 increased (Supplementary Fig.\u00a06).\n\nWhen turning angle analysis was dissected into confined and non-confined portions, anticorrelated steps were found in confinement sojourns, whereas no preference was observed in non-confined, free-walk portions, which\u2014as Fig.\u00a03 points out\u2014exhibited superdiffusive, Brownian, and subdiffusive behaviour. The results of turning angle analysis show that the model that best describes the heterogeneous behaviour of the nAChR is the two-state obstructed diffusion model21. Moreover, the strong step lag dependence of the probability density function suggested that structures of characteristic size were responsible for the obstructed diffusion21,33, a feature disclosed with better precision using the MINFLUX measurement (Supplementary Fig.\u00a04) and further confirmed by the deep learning CONDOR analysis (Supplementary Fig.\u00a07 and Supplementary Table\u00a05). Consequently, the fluorescent cholesterol analogue followed the same motional conduct as the nAChR.\n\nOne of the main findings of this work is the demonstration that fluorescent-labelled nAChR and cholesterol molecules can be co-tracked for distinct periods along their trajectories (Fig.\u00a04). Using the overlap trajectory analysis outlined in \u201cMethods\u201d, it was further possible to quantitatively estimate the spatial overlap of these joint motional periods, i.e., fPEG-Chol and CF\u00ae680R-BTX overlapped 25% and ~40% in confinement sojourns and non-confined portions, respectively (Fig.\u00a05). Probabilistically, this represents a significantly high percentage (p\u2009<\u20090.01), considering the low concentration of CF\u00ae680R-BTX-labelled nAChRs (0.5\u2009nM) and fPEG-Chol (1\u2009nM) employed, the dimensions of the 2D host\u2014the plasma membrane\u2014the size and diffusion coefficients of the incumbent molecules, and the length of their trajectories. Upon addition of exogenous unlabelled cholesterol, the overlap was reduced to ~8% with 100\u2009nM cholesterol in confined regions, probably reflecting a law of mass action-type displacement of the fluorescent cholesterol by the 100-fold higher concentration of unlabelled sterol. Similar effects were observed on the non-confined portions of the trajectories.\n\nThe actin compartment is the submembrane meshwork or actin-based membrane skeleton fence (\u201cMSK\u201d), and thus very relevant to cell-surface molecules like the neurotransmitter receptor and its interacting cholesterol molecule. The notion of such a specialised actin supramolecular structure was introduced by Sheetz54, who described the structure as \u201ccorrals\u201d that restricted the lateral diffusion of membrane proteins, an idea subsequently examined in detail by Saxton and Jacobson55,56. Computer-enhanced high-speed SPT enabled the Kusumi group57 to measure these corrals and extend the hypothesis to membrane lipids, leading in turn to the concept of time-dependent \u201chop diffusion\u201d: short-term confined diffusion within a compartment and infrequent, long-term hop diffusion between compartments of characteristic size58, later supported by combined STED microscopy and fluorescence correlation spectroscopy (STED-FCS) data59 and other SPT experiments based on, e.g., interferometric SCATtering or fluorescence microscopy33. In a previous STED superresolution imaging study, the cytoskeletal-disrupting drugs cytochalasin D and jasplakinolide were found to produce a statistically significant increase in the size of nAChR nanoclusters at the plasmalemma of CHO-K1/A5 cells, suggesting that the submembrane actin compartment affected the receptor distribution and local density at the cell surface39.\n\nCK-666 is a small molecule that stabilises the inactive state of Arp2/3, a protein complex involved in the nucleation of branched actin filaments that form the actin MSK corrals36,37. Treatment with CK-666 resulted in a modest increase in diffusion of both nAChR and fluorescent cholesterol, implicating the contribution of cytoskeletal fences or corrals38 in addition to the pickets of clustered nAChR molecules that \u201cself-restrict\u201d the protein\u2019s autologous diffusion at the plasmalemma20,21. SPT measurements in different mammalian cells59,60,61 showed that the actin MSK corrals have mesh sizes in the 40\u2013100\u2009nm range (specifically 40\u2009nm in the case of CHO cells60, the parental cell line of the CHO-K1/A5 clonal line used in the present work), in agreement with the current MINFLUX measurements showing apparent dimensions in the order of 50\u2009nm. The 1.5-fold acceleration of the fast cholesterol diffusion upon CK-666-mediated actin disruption (Supplementary Table\u00a02) agrees with literature values: fluorescent cholesterol is only modestly influenced by actin corrals49,62. While the diffusion regime remained unaltered, the percentage of the free diffusing component increased (Fig.\u00a05), as did the anomalous diffusion constant K\u03b2 (Table\u00a02) and the distribution of joint co-tracking of nAChR and fluorescent cholesterol (Fig.\u00a04). When analysed in terms of the hop-diffusion model using recently introduced criteria for MINFLUX studies40, both the short-term intra-compartmental (D\u00b5) and long-term inter-compartmental (DM) diffusion coefficients increased upon CK-666 treatment (Fig.\u00a06 and Table\u00a03), suggesting disruption of corral fences. Interestingly, despite the increase in the diffusion coefficients of toxin-labelled nAChR and fPEG-Chol, the degree of overlap between the two molecules did not change, another manifestation of the interaction between the two within confinement sojourns and while crossing compartments. Altogether, the CK-666 experiments indicate that the actin submembrane cytoskeleton contributes to hindering the translational mobility of both receptor and cholesterol molecules, with only a minority (~10\u201320%) fraction of the motion-restricted molecules hopping from one compartment to another.\n\nIn conclusion, the observation of receptor and cholesterol trajectories with the enhanced spatiotemporal resolution and reduced photobleaching afforded by MINFLUX opens new possibilities to explore lipid-membrane protein interactions in live cells. The observation that the receptor and cholesterol trajectories overlap spatially and temporally is a finding that expands our knowledge of the mutual interactions between a neurotransmitter receptor protein and the neutral lipid at the plasma membrane50,63. Achieving sub-millisecond time resolution and spatial resolving power of ~7\u2009nm, the MINFLUX study surpasses the capabilities of traditional camera-based techniques like STORM, unveiling individual step characteristics, dissecting the lifetimes of the free walks and confinement sojourns, and several other metrics of the single-molecule trajectories in a hitherto unexplored time domain.\n\nMan-tailored depletion/enrichment of cholesterol content confirms the interplay between the neutral lipid and nAChR dynamics, and CK-666 inhibition of Arp2/3 actin nucleation provides evidence of the additional, albeit minor, contribution of the submembrane cortical actin meshwork in restricting the translational motion of the nAChR and cholesterol. The directionality (turning angle) analysis of the SMTs revealed distinct preferences for step correlations, especially within confined sojourns, where steps were markedly anticorrelated in the subdiffusive sojourns. The ability to dissect free walks from confined sojourns facilitated the recognition of overlaps of nAChR and cholesterol trajectories in confined and non-confined areas, thus providing a strong piece of evidence of the tight relationship and mutual interactions between cholesterol and nicotinic receptor dynamics.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Fluorescent polyethylene glycol K114-labelled cholesterol (fPEG-Chol, trademarked as AbberiorStar Red-PEG-Chol) was purchased from Abberior GmbH, G\u00f6ttingen, Germany. A stock solution (1\u2009mg/mL) in ethanol was kept at \u221220\u2009\u00b0C. CF\u00ae640R-labelled \u03b1-bungarotoxin (CF\u00ae640R-BTX excitation/emission max.: 642/663\u2009nm) and CF\u00ae680R-labelled \u03b1-bungarotoxin (CF\u00ae680\u2009R, excitation/emission max.: 680/701\u2009nm), were purchased from Biotrend GmbH, K\u00f6ln, Germany. Methyl-\u03b2-cyclodextrin (product No. 332615), cholesterol-methyl-\u03b2-cyclodextrin complex (water-soluble cholesterol, product No. C4951) and the Arp2/3 inhibitor I CK-666 (product No. 182 515) were purchased from Sigma-Aldrich. Colloidal gold nanoparticle fiducials (150\u2009nm) were purchased from BBI Solutions, Crumlin, U.K.\n\nCHO-K1/A5 cells were grown as originally reported29. Following a washing step with cell culture medium without phenol red, cells were incubated for 5\u2009min on ice with a blocking medium consisting of 1\u2009mg/mL bovine serum albumin in PBS, followed by 30\u2009min labelling on ice with either CF\u00ae640R-BTX, CF\u00ae680R-BTX, fPEG-Chol or a combination of CF\u00ae680R-BTX + fPEG-Chol in 1\u2009mL of culture medium. After several initial assays, the final concentration of the two fluorescent \u03b1-bungarotoxin derivatives was kept at 500 pM, and that of the fluorescent cholesterol analogue at 1\u2009nM. Gold fiducials were added to the sample chamber containing the coverslip-adhered cells and allowed to settle while the sample was mounted in the microscope stage.\n\nCDx treatment was performed as previously reported52 by incubation of the cells with 10\u2009mM CDx in PBS for 20\u2009min on ice before incubation with the fluorescent dyes. Cells were treated with 10\u2009mM CK-666 in PBS for 15\u2009min at room temperature. Soluble cholesterol was added at two concentrations (50 and 100\u2009nM).\n\nMeasurements were performed on a MINFLUX setup from Abberior Instruments GmbH (G\u00f6ttingen, Germany), based on the iterative localisation approach42. To localise the emitter and collect emitted photons, we used a hexagonal-shaped beam pattern of diameter L (see Supplementary Table\u00a06) with points equidistant to the beam centre, which features zero intensity. The use of a circularly polarised doughnut-shaped zero-intensity beam pattern provided an orientation-independent localisation of the fluorophore emission dipoles. The position of the emitter was refined through an iterative sequence of consecutive pattern scanning operations that zoomed in onto the emitter with decreasing pattern diameter (L) values, as shown in Supplementary Table\u00a06. These sequences are performed automatically by the microscope control electronics. In short, MINFLUX repeats the first iteration until a fluorescence signal is detected, aligns the zero-intensity centre of the excitation beam, which is precisely known, with the position of the emitter, at which point the microscope will seek to improve the localisation by performing successive iterations (4 in the present work) with an increasingly small beam diameter, thus bringing the doughnut beam zero centre closer to the emitter at each iteration. Tracking is then achieved by repeating the last iteration until the signal is lost, at which point the process starts anew. Other essential parameters for the 2D scanning sequence, provided by the manufacturer, are listed in Supplementary Table\u00a06. Another variable that was modified between pattern iterations is the excitation laser power. This was increased in discrete steps between iterations with the laser power multiplier parameter listed in the table, which refers to a reference excitation power of 1.78\u2009\u00b5W at the exit of the objective. The sampling rate of the tracking measurements performed with this method was between 5 and 10\u2009kHz. The localisation precision of the experiments was calculated from the non-linear fitting of the MSD curves (explained in two sections further on). We obtained \u03c3\u2009=\u20097\u2009\u00b1\u20091\u2009nm. All ROIs were manually identified using the MATLAB roipoly function implemented in Python: https://github.com/jdoepfert/roipoly.py.\n\nTracks with irregular time intervals were analysed using object- and array-oriented routines written in Python. A few tracks consisting of only one step (\u201cfrustrated\u201d events) were detected and excluded from further analysis. Each complete dataset, consisting of several thousand steps, was analysed, including all validated trajectories; the same set of data was next subjected to the criterion of ref. 30 to exclude immobile particles from the single-molecule tracking (SMT) analysis as in refs. 20,21. The above criterion is based on the ratios of the radius of gyration R2g, defined as:\n\nwhere \\(\\vec{{r}_{i}}\\) is the coordinate at time step i out of N total time steps and the mean step size \\(\\left\\langle \\left|\\Delta r\\right|\\right\\rangle\\) of the recorded trajectories. Trajectories classified as immobile using the aforementioned criterion were not analysed further.\n\nIn the case of the fPEG-Chol alone and fPEG-Chol (+CF\u00ae640R-BTX), the complete raw datasets were excluded from threshold selection, because the distribution of the normalised ratios in these datasets is apparently uniform. Including these ratios into the threshold selection (which depends on the 95% confidence intervals of the mean) would select an overestimated value, leading to incorrectly classifying trajectories as immobile. Supplementary Fig.\u00a03 shows the histogram of the normalised ratios and examples of trajectories classified into immobile and mobile.\n\nIn the case of samples co-labelled with both fPEG-Chol and CF\u00ae680R-BTX, to discriminate between the two signals, we used a Detector Channel Ratio (DCR) criterion, as schematically shown in Supplementary Fig.\u00a09. The DCR criterion was applied to all individual points of the trajectories. If the average DCR of a trajectory was above a certain threshold, the particle was assigned to fPEG-Chol tracks. Otherwise, the track was assumed to correspond to CF\u00ae680R-BTX. To establish the threshold, we plotted histograms of the DCR when both probes were recorded separately, as shown in Supplementary Fig.\u00a02. A difference between the two mean distributions was observed, and a threshold of ~0.55 was applied as it discriminated well between the two distributions.\n\nTurning angle is conventionally measured for increasing time lags \u039433,64. As the intervals in the MINFLUX series are not regular, step lags were used instead of time lags. Hence, the relative angle \u03b8(i;\u0394) distended between successive steps is defined as:\n\nsuch that \\({{\\bf{V}}}\\left({i;}\\varDelta \\right)\\) =\\(\\vec{{x^{\\prime} }_{i+\\Delta }}-\\vec{{x^{\\prime} }_{i}}\\). Basically, \u03b8(i;\u0394) is the angle between V(i;\u0394) and V(i\u2009+\u2009\u0394). The formula was iterated through all trajectory positions whenever possible. The probability density function (PDF) for increasing step lags was obtained from the histogram with a bin width = 10. The algorithm described in ref. 64 was vectorised to improve computing times.\n\nFor a given trajectory j and number of \\({N}_{m{\\Delta }_{t}}\\) displacement intervals between \u03940\u2009+\u2009(m-1) \u0394t and \u03940\u2009+\u2009m\u0394t, the Time-Averaged MSD (TA-MSD) is defined as:\n\nWhere xj and yj are the coordinates of the trajectory on the \u2018x\u2019 and \u2018y\u2019 axes, \\({\\Delta }_{0}+\\left(m-1\\right){\\Delta }_{t} < {t}_{{i}^{ \\prime}}-{t}_{i} < {\\Delta }_{0}+m{\\Delta }_{t}=\\)132 \\({\\upmu }{{\\rm{s}}}\\), \u03940\u2009=\u200984 \u00b5s and i the interval number. To calculate the TA-MSD, we modified the Python implementation of ref. 65 to be suitable for irregular intervals. The only difference of this expression from the one presented in ref. 40 is that displacements are binned into intervals of width \\({\\Delta }_{t}\\) starting in \\({\\Delta }_{0}\\) due to the variable sampling rate in MINFLUX setup of this work. The TA-MSD was fitted to the function:\n\nwhere R is a factor that accounts for the motion blur caused by molecular motion during the time of the measurement, \u03c3 is the dynamic localisation uncertainty, and nd is the number of dimensions in which the diffusion takes place. Here, for molecules diffusing in the plane of the membrane, nd\u2009=\u20092 for 2D trajectories. In addition, we set R\u2009=\u20091/6.210, which assumes homogeneous illumination during the experiments40,41.\n\nThe anomalous exponent (\u03b2), the transport coefficient (\u0393) and \u03c3 were obtained by fitting MSD points up to 50\u2009ms step points of the individual time-averaged MSDs (TA-MSD). If R\u2009=\u20090 and \u0394\u2009=\u20090, we found that, compared to the equation \\({\\left\\langle {\\Delta r}^{2}\\left({t}_{{lag}}=m{\\Delta }_{t}\\right)\\right\\rangle }_{T}={K}_{\\beta }{t}^{\\beta }\\)21, we obtained \\({K}_{\\beta }=\\Gamma * 2 * {n}_{d}\\). Trajectories were separated into subdiffusive (\u03b2\u2009\u2264\u20090.9), Brownian (0.9\u2009<\u2009\u03b2\u2009\u2264\u20091.1) and superdiffusive (\u03b2\u2009>\u20091.1). This classification criterion is a simplified version of the one outlined in refs. 20,21. Finally, all fittings with a residual standard deviation greater than 1000 nm2 or MSD curves with \u226450% of available points were rejected.\n\nTrajectories were found to be interrupted by periods of confinement (i.e., a nAChR particle repeatedly \u201cvisited\u201d the same area). Quantitative metrics of the confinement sojourns were obtained using the algorithm of Krapf and coworkers66. Such algorithm \u201cdraws\u201d a circular area between consecutive localisations and measures the number of times that the particle calls on the given area. If the particle visited the area more than \\({V}_{{th}}\\) times, the particle was considered transiently confined. Initially, this threshold, which depends on the experimental conditions, was set to a value of 11, as used for experimental data in ref. 66. This threshold erroneously detected confinement in places where, through visual inspection, the moving particle appeared to follow a Brownian behaviour. Vth was therefore increased to a value\u2009=\u200933. To prevent false state transitions within a trajectory, we took the sequence of states assigned by the algorithm and divided it into non-overlapping windows of 3 steps. Next, each step within the window was reassigned to the state that occurred most frequently within that specific window. To analyse confinement sojourn parameters, trajectories were segmented into sub-trajectories between confined and mobile states. The shape of the confinement area was fitted using an algorithm that determines the eccentricity and length of the semi-axes defined by an ellipse fitted on these data points. The area of confinement zones is equal to the area defined by their convex hull.\n\nIn cells co-labelled with fPEG-Chol and the nAChR fluorescent antagonist CF\u00ae680R-BTX, the trajectories of the two probes frequently intersected. To determine whether these intersections occurred in confined or non-confined portions of the trajectories and how recurrent this phenomenon was: (1) the confinement areas of each CF\u00ae680R-BTX trajectory (CCF\u00ae680R-BTX) were considered to overlap with the confinement areas of fPEG-Chol trajectories (CCF\u00ae680R-BTX \u2229 fPEG-Chol) if their convex hulls intersected. An apparent overlap coefficient, C\u2009=\u2009CCF\u00ae680R-BTX\u2229 fPEG-Chol / CCF\u00ae680R-BTX was also calculated for each trajectory. This metric can also be defined as the ratio C\u2009=\u2009CfPEG-Chol \u2229 CF\u00ae680R-BTX / CfPEG-Chol; (2) to characterise the overlap in non-confined, free-walk portions of the trajectories, the spatial proximity between the individual steps of fPEG-Chol and CF\u00ae680R-BTX was estimated using k-d tree structures67. A k-d tree is a multidimensional tree-like structure that enables efficient spatial queries, such as the proximity between trees. The k-d tree implementation included in the SciPy software package68 and the polygon intersection algorithm from Shapely69 were used to this end. Steps from two distinct k-d trees are considered close if they are within a distance \u20090 to jump to another confinement region. Following the approaches of ref. 71 and ref. 40, MSDconf can be formulated as:\n\nwhere A is the average distance between two randomly chosen points within the confinement regions and \u03c4 is the equilibration time at which the effect of boundaries appears and the MSD reaches its plateau region6. Since Hell and coworkers defined the expressions for 3D trajectories in a 3D space, and hence the confinement regions have a volume40, the expressions had to be adapted to two dimensions. Confined diffusion can be restricted to a square domain of side L, and hence A\u2009=\u2009L\u00b2/3, where L is the apparent confinement domain size72. The equilibration time, \u03c4\u2009=\u2009A/(4 D\u00b5), such that D\u00b5 is the diffusion coefficient that dominates the diffusion within the confinement region71, results in:\n\nA squared confined domain L is taken for the sake of simplicity, but any 2D geometric shape can be considered72 in the \\({{\\rm{MS}}}{{{\\rm{D}}}}_{{{\\rm{conf}}}}\\):\n\nHaving obtained the expressions for MSDconf and MSDfree, MSDhop can be rewritten for 2D tracks as:\n\nsuch that diffusion coefficients D\u00b5 and DM are separated by a length scale Lhop40.\n\nApplication of this analysis returned in some cases a DM\u2009\u2248\u20090. For this reason, we decided to include MSDconf within the scope of the analysis.\n\nThus far, MSDhop and MSDfree do not include corrections for measurement artefacts. To account for these, MSDhop and MSDfree were rewritten40 as:\n\nBecause MSDconf is equal to MSDhop when DM\u2009=\u20090, MSDconf is rewritten as:\n\nIn the case of MSDfree, the free parameters are D and \u03c3. In the case of MSDhop, the free parameters are DM, D\u00b5, Lhop, and \u03c3. Finally, in the case of MSDconf, the free parameters are D\u00b5, Lhop, and \u03c3. MSDfree derived in Eq.\u00a012 has a different nomenclature but is essentially the same expression employed by Hell and coworkers10.\n\nTo classify trajectories, the following steps were followed: (1) an MSD curve was calculated for each trajectory; (2) the initial 20% data points of the MSD curves were fitted to MSDfree, MSDhop, and MSDconf solving three non-linear optimisation problems; the free parameters were adjusted such that the sum of the squared residuals (SSR) of both regressions on the observed data were minimised. We used the Python-based software package SciPy to solve this optimisation problem 100 times (the solution with the least SSR was picked for each case). MSD points were sampled logarithmically to emphasise the fitting to the initial 20% MSD points. (3) Next, the Bayesian Information Criterion (BIC) was calculated for both fits:\n\nwhere n and k are the number of data points and number of fit parameters, respectively.\n\nIn Rickert et al.40, the MSD curves are fitted to MSDhop with constraint D\u00b5\u2009>\u20095 DM to find substantial differences between the two diffusion coefficients. However, this constraint artifactually imposes a minimum value of 5 for Sconf\u2009=\u2009D\u00b5 / DM, which is defined as the confinement strength (i.e., the normalised residence time of a confined molecule) and Sconf\u2009>\u20091 (Wieser, 2015), and hence this constraint was removed, leaving D\u00b5\u2009>\u20095 DM. Supplementary Table\u00a07 depicts the boundaries of the initial free parameter values for each MSD model to solve the optimisation problem.\n\nAll graphs and statistics were prepared following the guidelines of ref. 73, where it is suggested that the average of ROIs be taken as single reported values to increase statistical robustness. All reported mean values and statistical tests were conducted on ROI averages. To compare distributions, we used the Kolmogorov\u2013Smirnov (KS) test for two samples (null hypothesis was rejected for p-value\u2009<\u20090.05). To compare three or more distributions, the Kruskal\u2013Wallis test was used. Statistical analysis was carried out using GraphPad Prism 8.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data gathered for this study are available upon request to the corresponding authors. Upload of the data on a public data server has so far not been conducted due to the sheer amount of data.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Python code for the analysis of the data is available at the public repository Zenodo [https://doi.org/10.5281/zenodo.15389696] and on GitHub [https://github.com/lucasSaavedra123/minflux_analysis].", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Sahl, S. J., Hell, S. W. & Jakobs, S. Fluorescence nanoscopy in cell biology. Nat. Rev. Mol. 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C.E. and F.J.B. acknowledge the Alexander von Humboldt Foundation for the Research Group Linkage Programme grant to the laboratories in Jena and Buenos Aires, respectively, and the Deutsche Forschungsgemeinschaft (DFG, Instrument funding MINFLUX Jena INST 275_405_1) for funding of the MINFLUX microscope. We also gratefully acknowledge financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; Germany\u2019s Excellence Strategy\u2014EXC 2051\u2014Project-ID 390713860; project number 316213987\u2014SFB 1278; GRK M-M-M: GRK 2723/1\u20142023\u2014ID 44711651; Instrument funding modular STED INST 1757/25-1 FUGG), the State of Thuringia (TMWWDG), the Leibniz Association (Leibniz Science Campus InfectoOptics Jena financed by the funding line Strategic Networking of the Leibniz Association, project number W8/2018), and the Free State of Thuringia (TAB; Advanced STED /FGZ: 2018 FGI 0022; Advanced Flu-Spec / 2020 FGZ: FGI 0031; Multi-XUV / 2023 FGR 0054). Further, this work is supported by the Photonics Research Germany (FKZ: 13N15713 / 13N15717) and is integrated into the Leibniz Centre for Photonics in Infection Research (LPI). The LPI initiated by Leibniz-IPHT, Leibniz-HKI, UKJ and FSU Jena is part of the BMBF national roadmap for research infrastructures. A part of the project on which these results are based was funded by the Free State of Thuringia under the number 2018 IZN 0002 (Thimedop) and co-financed by funds from the European Union within the framework of the European Regional Development Fund (EFRE).", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Francesco Reina\n\nPresent address: Max Perutz Labs, Department of Structural and Computational Biology, University of Vienna, Vienna, Austria\n\nThese authors contributed equally: Francesco Reina, Lucas A. Saavedra.\n\nInstitute of Applied Optics and Biophysics, Friedrich-Schiller-Universit\u00e4t Jena, Jena, Germany\n\nFrancesco Reina\u00a0&\u00a0Christian Eggeling\n\nLeibniz Institute of Photonic Technologies, Jena, Germany\n\nFrancesco Reina\u00a0&\u00a0Christian Eggeling\n\nMolecular Neurobiology Division, Biomedical Research Institute, UCA-CONICET, Buenos Aires, Argentina\n\nLucas A. Saavedra\u00a0&\u00a0Francisco J. Barrantes\n\nJena Centre for Soft Matter (JCSM), Jena, Germany\n\nChristian Eggeling\n\nLeibniz Centre for Photonics in Infection Research (LPI), Jena, Germany\n\nChristian Eggeling\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nF.J.B. and C.E. conceived the project; F.R. and F.J.B. performed the live-cell experiments; L.A.S. wrote the analysis code. L.A.S. and F.J.B. analysed the data; F.J.B. wrote the manuscript with feedback from the authors; F.J.B. edited the manuscript and wrote the replies to the reviewers.\n\nCorrespondence to\n Christian Eggeling or Francisco J. Barrantes.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Reina, F., Saavedra, L.A., Eggeling, C. et al. Concurrent diffusion of nicotinic acetylcholine receptors and fluorescent cholesterol disclosed by two-colour sub-millisecond MINFLUX-based single-molecule tracking.\n Nat Commun 16, 6336 (2025). https://doi.org/10.1038/s41467-025-61489-4\n\nDownload citation\n\nReceived: 10 January 2025\n\nAccepted: 23 June 2025\n\nPublished: 09 July 2025\n\nVersion of record: 09 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61489-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 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"pre_title": "Human-mouse proteomics reveals the shared pathways in Alzheimer's disease and delayed protein turnover in the amyloidome", + "journal": "Nature Communications", + "published": "11 February 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56853-3/MediaObjects/41467_2025_56853_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56853-3/MediaObjects/41467_2025_56853_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1-24", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56853-3/MediaObjects/41467_2025_56853_MOESM3_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56853-3/MediaObjects/41467_2025_56853_MOESM4_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-56853-3/MediaObjects/41467_2025_56853_MOESM5_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://www.proteomexchange.org", + "https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD018590", + "https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD007974", + "https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD023395", + "https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD031545", + "https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD031732", + "https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD031734", + "https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD031735", + "https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD031769", + "https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD031830", + "https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD053314", + "/articles/s41467-025-56853-3#MOESM3" + ], + "code": [ + "https://github.com/abhijitju06/JUMPsilactmt-Version-0.0.1", + "https://github.com/abhijitju06/JUMPt-Version-1.0.0" + ], + "subject": [ + "Alzheimer's disease", + "Proteome informatics", + "Proteomics" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5194931/v1.pdf?c=1739365566000", + "research_square_link": "https://www.researchsquare.com//article/rs-5194931/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-56853-3.pdf", + "preprint_posted": "27 Oct, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Murine models of Alzheimer\u2019s disease (AD) are crucial for elucidating disease mechanisms but have limitations in fully representing AD molecular complexities. We comprehensively profiled age-dependent brain proteome and phosphoproteome (n > 10,000 for both) across multiple mouse models of amyloidosis. We identified shared pathways by integrating with human metadata, and prioritized novel components by multi-omics analysis. Collectively, two commonly used models (5xFAD and APP-KI) replicate 30% of the human protein alterations; additional genetic incorporation of tau and splicing pathologies increases this similarity to 42%. We dissected the proteome-transcriptome inconsistency in AD and 5xFAD mouse brains, revealing that inconsistent proteins are enriched within amyloid plaque microenvironment (amyloidome). Determining the 5xFAD proteome turnover demonstrates that amyloid formation delays the degradation of amyloidome components, including A\u03b2-binding proteins and autophagy/lysosomal proteins. Our proteomic strategy defines shared AD pathways, identify potential new targets, and underscores that protein turnover contributes to proteome-transcriptome discrepancies during AD progression.Biological sciences/Neuroscience/Diseases of the nervous system/Alzheimer's diseaseBiological sciences/Biochemistry/Proteomics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Murine models of Alzheimer\u2019s disease (AD) are crucial for elucidating disease mechanisms but have limitations in fully representing AD molecular complexities. Here we present the comprehensive, age-dependent brain proteome and phosphoproteome across multiple mouse models of amyloidosis. We identified shared pathways by integrating with human metadata and prioritized components by multi-omics analysis. Collectively, two commonly used models (5xFAD and APP-KI) replicate 30% of the human protein alterations; additional genetic incorporation of tau and splicing pathologies increases this similarity to 42%. We dissected the proteome-transcriptome inconsistency in AD and 5xFAD mouse brains, revealing that inconsistent proteins are enriched within amyloid plaque microenvironment (amyloidome). Our analysis of the 5xFAD proteome turnover demonstrates that amyloid formation delays the degradation of amyloidome components, including A\u03b2-binding proteins and autophagy/lysosomal proteins. Our proteomic strategy defines shared AD pathways, identifies potential targets, and underscores that protein turnover contributes to proteome-transcriptome discrepancies during AD progression.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Alzheimer\u2019s disease (AD), a progressive neurodegenerative disorder, is the most common cause of dementia, affecting more than 6 million Americans1. AD pathology initiates decades before the onset of gross behavioral symptoms and is primarily defined by the aggregation of \u03b2-amyloid peptide (A\u03b2) in extracellular plaques and of hyperphosphorylated tau proteins as intracellular neurofibrillary tangles2,3,4. In addition to A\u03b2 and tau, other coexisting molecular changes4,5, such as \u03b1-synuclein6,7, TDP-435,8, and U1 snRNP9,10, may play important roles in disease progression. Genetic analyses of AD and control cases have elucidated three causative genes (APP, PSEN1, and PSEN2), high-risk genes (APOE4 and TREM2) and about 100 low-risk genes and loci11,12,13,14,15,16,17,18,19,20. However, the molecular mechanisms of these proteins/genes in AD development are not fully understood, often due to the lack of suitable cellular or animal models.\n\nMore than 100 genetic AD mouse models have been developed21,22,23,24, predominantly by mimicking genetic mutations linked to familial AD, such as the lines of 5xFAD25,26, 3xTG27, and APP-KI including APPNLF (NLF) and APPNLGF (NLGF)28. However, none of these models capture the full spectrum of AD molecular events and pathologies as they exhibit less severe neurodegeneration compared to human patients. Ideally, researchers should fully understand the advantages and limitations of mouse models to select the most appropriate one for addressing specific hypotheses; however, no comprehensive resources are currently available.\n\nRapid developments in omics technologies provide an opportunity of thoroughly evaluating disease models on a global scale and exploring their relevance by comparisons with human data29,30,31. Transcriptomic analyses of the amyloidosis mouse models revealed changes in expression of genes linked to immune response, synaptic function, and neuronal signaling32,33,34. However, RNA levels do not always align with protein levels due to posttranscriptional processes, such as translation and protein turnover35. Indeed, notable inconsistencies between transcript and protein levels in AD and 5xFAD mice were observed36,37. Complementary proteomic studies in AD mice38,39,40,41 not only corroborated transcriptomic findings, but also identified RNA-independent protein alterations36,39,42 and changes in protein turnover43,44. These early proteomic studies in the AD mice uncovered some molecular changes but they were often limited by inadequate proteomic depth, restricted analysis of individual mouse models, and/or insufficient comparison with human AD datasets.\n\nHere we present a deep, age-dependent analysis of 10,369 proteins (10,331 genes) and 12,096 phosphopeptides (10,532 phosphosites) across commonly used AD models: 5xFAD25, NLF28, and NLGF28. We also profiled two additional AD models (3xTG27 and BiG45), performed human-mouse comparisons, and analyzed transcriptome-proteome inconsistency in both mouse and human. To explore the contribution of protein degradation to the transcriptome-proteome inconsistency, we measured the turnover rates of 8492 brain proteins and found that amyloid formation delays the degradation of amyloidome components. Thus, our comprehensive proteomic analysis identifies shared AD pathways and demonstrates altered protein turnover in amyloid plaques in AD mice. All data are freely available and searchable through an interactive website.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We compared proteomic readout of three mouse models of amyloidosis at different ages, with early and late symptoms (Fig.\u00a01a, Supplementary Data\u00a01): (i) 5xFAD (3-, 6-, 12-month-old), overexpressing human APP and PSEN1 genes carrying a total of five human disease mutations under the Thy1 promoter, which promotes rapid onset of amyloid pathology25; (ii) NLF (3-, 12-month-old, with weak pathology) and NLGF (3-, 6-, 12-, 18-month-old, with strong pathology), both being next-generation knock-in models with humanized A\u03b2 without gene overexpression28. We also analyzed age-matched wild type (WT) control mice for each mouse line.\n\na Schematic plan of this study. Mouse cortical tissues from AD models of amyloidosis (5xFAD, NLF, NLGF, and matched WT, total n\u2009=\u200966 for 16 conditions, averaged n\u2009=\u2009~4 per condition) were analyzed by TMT-LC/LC-MS/MS and compared with human metadata. b Proteins quantified at different ages (3-18 months). c A\u03b2 levels quantified by MS using the peptide HDSGYEVHHQK (Average value\u2009\u00b1\u2009SD, n\u2009=\u20092, 5, 6, 2, 2, 2, 3, 2, 3, for each group, left to right). The values were averaged for each age and model, then normalized to 12-month-old 5xFAD (100%). d DEPs between AD mice and WT controls at increasing ages, defined by moderated t-test with statistical cutoffs (FDR\u2009<\u20090.05, |log2FC\u2009|\u2009> 2\u2009SD). e Representative volcano plot for NLGF-WT comparison (moderated t-test). Individual proteins correspond to data points and are color coded red or blue if up- or down-regulated as defined by statistical cutoffs, respectively (FDR\u2009<\u20090.05, |log2FC\u2009|\u2009> 2\u2009SD, dashed lines). f Heatmap of DEPs identified in AD mice at any age or genotype, including the proteins enriched in 5xFAD or NLGF and those shared by both mice. g Pathway analysis of shared DEPs in 5xFAD and NLGF. FDR was derived from p values (Fisher\u2019s exact test) by the Benjamini-Hochberg procedure. h, Enriched PPI modules from biological processes using the shared DEPs. Brain images created in BioRender. Yarbro, J. (2025) https://BioRender.com/a05v379.\n\nUsing our optimized tandem mass tag (TMT) method, coupled with extensive two-dimensional liquid chromatography (LC/LC) and high-resolution tandem mass spectrometry (MS/MS, Supplementary Fig.\u00a01a)46,47,48,49, we profiled a total of 66 mouse brains (cortex) in multiple TMT batches with deep proteome coverage (Supplementary Data\u00a02), identified more than 900,000 peptide-spectrum matches (PSMs), ~330,000 peptides, and 10,369 unique proteins (10,331 genes) that were shared in all animals, with a protein false discovery rate (FDR) below 0.01 (Fig.\u00a01b, Supplementary Data\u00a03). After protein quantification based on TMT reporter ions, sample loading bias was corrected as shown in a box plot (Supplementary Fig.\u00a01b), and the batch effect was normalized and confirmed by PCA analysis (Supplementary Fig.\u00a01c). As expected, the A\u03b2 tryptic peptide (R.HDSGYEVHHQK.L) shows age-dependent increases in all AD mice, with higher levels in 5xFAD and NLGF than NLF, consistent with the reported pathologies in these mice (Fig.\u00a01c, Supplementary Data\u00a04).\n\nWe examined the effect of aging using only WT mice (3-, 6-, 12-, 18-month-old) to avoid the confounding impact of A\u03b2 insult in different genotypes. When comparing 3-month-old mice to any other aged mice, differential expression (DE) analysis identified 183 age-dependent proteins [FDR\u2009<\u20090.05, |log2Fold Change (FC)| > two standard deviations (SD)], with 129 proteins upregulated and 54 proteins downregulated with age (Supplementary Fig.\u00a02a). These age-dependent proteins are predominantly associated with processes such as extracellular matrix remodeling, lysosomal activity, and synaptic signaling (Supplementary Data\u00a05). The upregulated proteins are enriched in the Gene Ontology (GO)50 terms of collagen-containing extracellular matrix, perineuronal net, lysosome, glutathione metabolic process, etc. (Supplementary Fig.\u00a02b). Conversely, the downregulated proteins are enriched in cell periphery, cell junction, and neuronal components, including synapse, axon, dendritic spine, etc. (Supplementary Fig.\u00a02c, Supplementary Data\u00a06).\n\nWe then performed DE analysis at different ages for each genotype using WT controls (FDR\u2009<\u20090.05, |log2FC\u2009|\u2009>\u20092\u2009SD, Supplementary Data\u00a07), excluding human A\u03b2 peptide as it is not present in the WT mice. NLF exhibits a few DE proteins (DEPs), in agreement with its weak pathology51 (Fig.\u00a01d). In contrast, the 5xFAD and NLGF models demonstrate significant protein alterations, which increased with age for both models (Fig.\u00a01d). For instance, a volcano curve shows 605 DEPs in 12-month-old NLGF mice compared to the WT (Fig.\u00a01e), reflecting its strong amyloid phenotype25,28. When summing the DEPs at different ages, there are 1382 DEPs in 5xFAD, only 7 in NLF and 1142 in NLGF. Moreover, we found that the numbers of DEPs are highly correlated with the A\u03b2 accumulation across all tested models (R\u2009=\u20090.86, Supplementary Data\u00a08).\n\nIn spite of the genetic difference between 5xFAD and NLGF models21,28, we observed comparable proteomic signatures in the two models. Among 1914 total DEPs in 5xFAD and NLGF, 610 (32%) overlap, of which 98% (597/610) exhibit consistent directional changes (either upregulated or downregulated). The remaining DEPs, 772 (40%) in 5xFAD and 532 (28%) in NLGF, are genotype-specific, but show a similar age-dependent trend in both AD models (Fig.\u00a01f). Differences appear primarily driven by statistical thresholding rather than distinct biology. Overall, the proteomic profiles of 5xFAD and NLGF exhibit broadly similar patterns.\n\nWe performed Gene Ontology analysis on the 597 overlapping DEPs and found enrichment in extracellular matrix, lysosome/endocytosis, immune response, synaptic signaling, and binding to A\u03b2, integrin, lipid, calcium ion, etc. (Fig.\u00a01g, Supplementary Data\u00a09). The overlapping DEPs were mapped to protein-protein interaction (PPI) networks, revealing significant interacting modules of immune response, complement, lipid metabolism, proteolysis/autophagy, and an amyloid-binding extracellular matrix protein network (amyloid matrisome)37 (Fig.\u00a01h). These findings suggest that proteins and pathways shared between 5xFAD and NLGF are critical for amyloidosis pathogenesis and immune response, consistent with previous reports in human AD36,37,52.\n\nSince protein phosphorylation is known to contribute to AD pathogenesis53,54, we profiled the phosphoproteome in the same set of NLGF (3-, 6-, 12-, 18-month-old) mice and 5xFAD (12-month-old) and their age-matched WT controls. We used 5xFAD mice at a single age as a validation model following comprehensive discovery profiling of the age-dependent phosphoproteome in NLGF mice. The profiling was also performed using the TMT-LC/LC-MS/MS method46,47,48,49, with an additional step of phosphopeptide enrichment to improve the phosphoproteome coverage55 (Supplementary Fig.\u00a03a-c). When combining phosphoproteome data from all TMT batches, we quantified 129,906 unique phosphopeptides (82,261 phosphosites on 10,502 proteins, peptide FDR\u2009<\u20090.01, Supplementary Data\u00a010). Because of the incomplete coverage of phosphoproteome in individual TMT batches, the overlap between different TMT batches is often low56. When we extracted the phosphopeptides shared in all 36 mice, the number dropped to 12,096 phosphopeptides (10,532 phosphosites on 2814 proteins, Fig.\u00a02a, Supplementary Data\u00a011). These shared phosphopeptides contain 8665 pS (82.3%), 1625 pT (15.4%), and 242 pY (2.3%) sites (Fig.\u00a02b), similar to the site distribution in other large phosphoproteome analyses36,57.\n\na Phosphoproteome profiling was performed across 36 5xFAD and NLGF mice and their age-matched controls. We quantified 12,096 phosphopeptides (peptide FDR\u2009<\u20090.01) shared in all mice and performed statistical comparisons by moderated t-test. 122 DE phosphopeptides (80 proteins) were identified (FDR\u2009<\u20090.05, |log2FC\u2009|\u2009> 2\u2009SD). b Distribution of phospho-Ser/Thr/Tyr in identified phosphosites. c Volcano plot of phosphoproteome data for 12-month-old 5xFAD compared to WT. Dashed lines indicate cutoffs. d Volcano plot of phosphoproteome data for 12-month-old NLGF compared to WT. e Heatmap of DE phosphoproteins in 5xFAD and NLGF mice, with protein subcellular location shown. f PPI modules of DE phosphoproteins. g The overlap of DEPs in phosphoproteomics and whole proteome analysis. Only consistent DEPs in both AD models were counted. h Bioinformatics method for identifying altered kinase activities. Kinase-substrate linkages were extracted to infer kinase activities by the KSEA algorithm. An example of MAPK activation is based on KSEA. Phosphopeptide levels are displayed using the accompanying gradients. i Heatmap of derived kinase activities. The fold change of 5xFAD and NLGF was calculated by comparison with WT.\n\nWe then carried out pairwise DE analysis using age-matched WT controls for both 5xFAD and NLGF mice, identifying 122 consistent DE phosphopeptides (80 DE phosphoproteins) in the two mouse models (FDR\u2009<\u20090.05, |log2FC\u2009|\u2009>\u20092\u2009SD, Supplementary Data\u00a012). For example, in both mice, the phosphorylation levels of PTPRC/CD45 S964 and GFAP T299 are significantly increased, indicating the activation of microglia and astrocytes, respectively (Fig.\u00a02c, d). The 80 DE phosphoproteins are enriched in several pathways and PPI networks of cytoskeleton, plasma membrane, synapse, and vesicle (Fig.\u00a02e, 2f, Supplementary Data\u00a013). These pathways are consistent with those revealed in phosphoproteomic studies of human AD54,58.\n\nNotably, only 30 (37.5%) of the 80 DE phosphoproteins showed significant changes at the proteome level (Fig.\u00a02g), suggesting that the differences in phosphorylation are primarily due to altered kinase/phosphatase activity in the brain, independent of the protein levels. To quantify the change in kinase activities based on these DE phosphopeptides, we derived alterations in kinase families by the computer algorithm of kinase-substrate enrichment analysis (KSEA)59. For instance, the MAPK activity could be inferred from its DE substrates of TOM1L2, EML4, GJA1, and TLE3 (Fig.\u00a02h). The analysis identified the upregulation of two kinase families: casein kinase (CSNK, p\u2009=\u20090.031) and mitogen-activated protein kinase (MAPK, including p38 kinase, p\u2009=\u20093.65\u2009\u00d7\u200910\u221211) (Fig.\u00a02i). Consistently, MAPK pathway deregulation was previously highlighted in several cohort studies of human AD36,37,54. Thus, our KSEA analysis suggests the deregulation of numerous kinases that are relevant to amyloid pathology.\n\nTo investigate the relevance of the mouse models, we compared the DEPs in 5xFAD and NLGF with human metadata36,37,52,60. We focused on 866 human DEPs that consistently exhibited significant changes in deep AD proteomics studies (Fig.\u00a03a, Supplementary Data\u00a014), for which 654 homologous proteins were detected by MS in mice. The sum of 5xFAD and NLGF DEPs corresponds to 30% (196/654) of the AD DEPs and demonstrates age-dependency (Fig.\u00a03b). The three datasets share a core set of 108 DEPs (Fig.\u00a03c). Further analysis reveals that 5xFAD and NLGF DEPs align more closely with late-stage AD (R\u2009=\u20090.32 and 0.23, respectively) than with mild cognitive impairment (MCI) (R\u2009=\u20090.00 and \u22120.09, respectively), although both correlations are weak. This implies that these mouse models may be more representative of the amyloidosis in the advanced AD stages rather than that in the early, asymptomatic phase (Fig.\u00a03d). Among the consistent core DEPs, approximately half exhibit cell-type specificity, with 42% specific to microglia, 29% to neurons, 25% to astrocytes, 2% to endothelial cells, and 2% to oligodendrocytes. These findings underscore the contributions of various cell types to disease development and highlight the prominent role of microglia.\n\na We identified 866 proteins that are consistently altered in more than 30 human AD proteomics studies, 654 of which were quantified in the proteomic analysis of the AD mice. Of these, 196 (30%) are differentially expressed in at least one mouse model (FDR\u2009<\u20090.05, |log2FC\u2009|\u2009>\u20092\u2009SD). b Number of overlapping DEPs between human AD and different mouse models. c DEPs shared by human AD, 5xFAD, and NLGF mice. d Scatter plot comparisons between Z scores of log2fold change values (log2FC-Z) of human AD/control cases and mouse models/WT at 12-month ages. Each dot represents one protein, and the color shows the dot density. Pearson correlation (R) values and associated p values are shown. e Heatmap showing log2FC values of human-mouse shared AD proteins, classified by biological pathways (moderated t-test, FDR\u2009<\u20090.05). f Workflow for deriving pathway activities. The FC of proteins in each pathway are integrated to calculate the pathway activity. g Heatmap of pathway activities in AD and mouse models.\n\nWe then examined the 108 DEPs consistent in both mouse models and human AD (Fig.\u00a03e), and derived their pathway activities by integrating individual components with a mathematical formula61,62 (Fig.\u00a03f). Several pathway activities are upregulated, including amyloid matrisome, cell migration, complement and coagulation, cytoskeleton, immune response, integrin pathway, lipid regulation, metabolism and protein folding/proteolysis; two pathways\u2014neurogenesis and synaptic regulation\u2014are downregulated (Fig.\u00a03g, Supplementary Data\u00a014 and 15).\n\nThe 5xFAD and NLGF mouse models of amyloidosis fail to capture the entire spectrum of AD-related proteomic changes. This discrepancy may be attributed to the absence of other pathologies found in human AD, such as tauopathies53 and splicing dysfunctions9. We further profiled two other AD mouse models with additional pathologies (Fig.\u00a04a, Supplementary Fig.\u00a04a-e, Supplementary Data\u00a01): (i) 3xTG, displaying both amyloid plaque and tau tangle pathologies27, and (ii) BiGenic (BiG) mice, generated by crossing 5xFAD with N40K transgenic mice recapitulating both amyloid pathology and the newly discovered U1 snRNP splicing dysfunction in AD45. We profiled both models and age-matched controls (~6-month-old, totaling 37 mouse brains), quantifying 9780 and 10,255 proteins (protein FDR\u2009<\u20090.01). We identified 1230 and 1564 DEPs in 3xTG and BiG, respectively (FDR\u2009<\u20090.05 and |\u2009log2FC\u2009|\u2009>\u20092\u2009SD, Fig.\u00a04b and c, Supplementary Data\u00a016, 17), including a substantial number of DEPs that are not observed in the pure amyloidosis models, 5xFAD and NLGF. While the overlap of DE proteins between 3xTG and the two amyloidosis models is relatively low, 3xTG exhibits similar trends in pathway activity changes to those in the amyloidosis models (Supplementary Fig.\u00a05). Additionally, 3xTG shows model-specific DEPs (Fig.\u00a04d) related to tau pathways and its downstream effects on RNA processing63. Similarly, the BiG mice, while sharing changes with 5xFAD, also display defective U1 snRNP splicing components similar to N40K9, and uniquely present lipid and synaptic dysregulation that may be due to the synergy of amyloid and U1 snRNP pathways64 (Fig.\u00a04e, Supplementary Fig.\u00a06).\n\na Proteomic profiling of two more mouse models that express additional AD pathologies: WT (n\u2009=\u20098) and 3xTG (A\u03b2 and tau pathologies, n\u2009=\u200919), as well as WT (n\u2009=\u20094) and BiG (A\u03b2 and U1 splicing pathologies, n\u2009=\u20094). All mice were ~6 months old. The proteomic data were subjected to DE analysis and comparison with human AD data. b, c Volcano plots of log2FC and FDR in 3xTG and BiG mice, compared to WT, with DEPs highlighted in colors and cutoffs indicated by dashed lines. d, e Selected protein-protein interactions of significantly altered DEPs found exclusively in individual mice, such as MAPT interactome in 3xTG, and splicing/synaptic interactome in BiG. f Numbers of DEPs in AD mouse models that were consistently altered in AD. The percentage was calculated using a denominator of 654 AD DEPs that were detectable by MS in mice. g Strategy for ranking individual proteins by multi-omics using order statistics. (i) All age-dependent proteomic data from 5xFAD and NLGF were initially consolidated into two datasets for the amyloidosis proteome and phosphoproteome. (ii) These datasets were then integrated with 10 additional datasets, which include the mouse transcriptome (5xFAD), 3xTG/BiG proteomes, human genetic data from GWAS, human transcriptomes, proteomes (MCI and two independent AD studies, n\u2009=\u20093), phosphoproteome, and interactome datasets. h Protein integrative rankings defined by combining 12 datasets. The entire datasets were ranked based on all identified genes/proteins. Subsequently, we extracted the rankings for the AD-mouse shared proteins (n\u2009=\u2009275). The top 20 proteins are displayed, with missing values represented by white boxes.\n\nTo evaluate shared molecular changes between mouse models and human AD, we generated an upset plot highlighting overlaps in DE proteins (Supplementary Fig.\u00a07). By summing all DEPs across the four mouse models, the overlap with human DEPs increases to 42% (275/654, Fig.\u00a04f, Supplementary Data\u00a014), suggesting that additional pathologies beyond amyloid plaques contribute to alterations in the mouse proteome, moving it closer to the AD spectrum. The remaining 58% of human AD DEPs show enrichment in the pathways of mitochondrial function, cell morphogenesis, lipid regulation, potentially due to reduced neuronal cell death in the mouse models and differences in response to pathological insults between mice and humans (Supplementary Data\u00a018).\n\nAmong the 275 DEPs conserved between mouse models and human (Fig.\u00a04f), we found that 86% are not well studied in the context of AD, with <20 AD-related publications (Supplementary Fig.\u00a04f). We thus prioritized these proteins employing a method of order statistics36 by integrating available 12 omics datasets from both mouse and human, which include GWAS (n\u2009=\u20091), transcriptome (n\u2009=\u20092), proteome (n\u2009=\u20096), phosphoproteome (n\u2009=\u20092), and interactome (n\u2009=\u20091) (Fig.\u00a04g, Supplementary Data\u00a019). As expected, well-known proteins such as APP, APOE, GFAP, TREM2, MAPT (tau), and CLU rank highly, while other proteins in the top 20 list, such as MDK, NTN1, SFRP1, OLFML3, PTPRC/CD45, SMOC1, CD180, and PTN, remain understudied (Fig.\u00a04h). These proteins require further investigation to understand their roles in the development of AD.\n\nA transcriptome-proteome inconsistency has been reported in AD36,37. To explore that issue, we compared the quantitative transcriptome and proteome datasets from both human AD (n\u2009=\u200910,781) and 5xFAD mice (n\u2009=\u20098840) relative to their control samples, after Z-score transformation (Fig.\u00a05a, Supplementary Data\u00a020). We focused on 12-month-old 5xFAD mice for this comparative study, as age-matched data were available from the same breeding conditions. The transcriptome-proteome correlations were modest, with R values of 0.40 in human and 0.46 in mice (Fig.\u00a05b, c). We identified the RNA-independent protein changes in the following steps: (i) identifying Z-score changed proteins in human AD (n\u2009=\u20091121) and in 5xFAD mice (n\u2009=\u20091152), compared to their controls; (ii) categorizing those changed proteins into four groups based on protein up-/down-regulation and RNA dependency/independency (Fig.\u00a05d, Supplementary Data\u00a020). Remarkably, in both species, approximately one-third of the altered proteins exhibit RNA independence (35% in humans and 36% in mice). We asked whether these RNA-independent protein changes are shared between AD and mouse models. From the 262 RNA-independent, upregulated proteins in human and 295 in mouse, there was an overlap of 31 proteins. In contrast, from the 133 RNA-independent, downregulated proteins in humans and 120 in mouse, only one overlaps (Fig.\u00a05e). These findings suggest a partial conservation of transcriptome-proteome inconsistency of upregulated proteins in the AD mouse model.\n\na Workflow for comparison of protein/RNA data to define protein-RNA consistencies. b, c Scatterplots of protein-RNA comparisons of log2FC-Z in human (n\u2009=\u200910,781) and 5xFAD mice (n\u2009=\u20098840). Density is indicated by color gradients. Pearson correlation (R) values are shown. d Percentage of protein-RNA consistency in the population of z-score altered proteins. e Overlap of RNA-independent protein changes between human and mouse. f Workflow of LCM-MS to compare proteomes in plaque and non-plaque regions, quantifying 5364 proteins. A Venn diagram illustrated the overlap of 31 shared, RNA-independent, upregulated proteins in both humans and mice with proteins enriched in either plaque or non-plaque regions. g Volcano plot showing proteins enriched in plaque or non-plaque regions.\n\nWe recognized that many of the 31 shared, upregulated proteins are present in amyloid plaques65,66,67,68,69, prompting us to fully characterize the amyloidome (i.e., all the components in the amyloid plaque microenvironment) in the 5xFAD mice. We employed laser-capture microdissection (LCM) to isolate amyloid plaques and non-plaque areas from brain tissue (n\u2009=\u20094 mice) and profiled the proteome using our modified TMT-LC/LC-MS/MS pipeline, optimized for sub-microgram protein samples70. This approach resulted in the quantification of 5364 proteins (Fig.\u00a05f-g, Supplementary Fig.\u00a08, Supplementary Data\u00a021). Quantitative comparison between the plaques and non-plaque areas identified 438 proteins enriched in plaques and 191 in non-plaque areas (FDR\u2009<\u20090.05 and |log2FC\u2009|\u2009>\u20092\u2009SD). Strikingly, of the 31 RNA-independent, upregulated proteins in both human AD and 5xFAD mice, 23 were detected in the amyloidome profiling, in which 17 (74%) were found among the 438 plaque-enriched proteins, while none were detected in the 191 non-plaque-enriched proteins. The results demonstrate that the formation of amyloid plaques contributes to RNA-independent protein accumulation.\n\nWe hypothesized that proteome-transcriptome discrepancy in AD could be due to reduced protein turnover within the amyloidome. To test this hypothesis, we employed pulsed SILAC labeling (pSILAC)71,72,73 coupled with TMT to measure protein turnover rates at high throughput (Fig.\u00a06a, Supplementary Fig.\u00a09). The 5xFAD mice and WT littermates were fed with heavy lysine SILAC food in a time course (0, 4, 8, 16 and 32 days, with 3 replicates, totaling 30 mice), followed by brain tissue collection and TMT-LC/LC-MS/MS profiling. The kinetics of heavy lysine labeling enabled the determination of protein degradation rates, indicated by protein half-life (T50). Apparent T50 values were calculated directly from fitting a degradation curve; to account for the recycling of heavy lysine in the mice (Supplementary Fig.\u00a010), we derived corrected T50 values using an ordinary differential equation model in the JUMPt software73.\n\na Whole proteome turnover analysis in 5xFAD and WT mice was performed using pulsed SILAC labeling (~9-month-old, 5 data points, 3 replicates, totaling 30 mice), TMT-LC/LC-MS/MS (2 batches), and the JUMPt program. The analysis covered a comprehensive set of 8492 unique proteins. b Diagram illustrating the 12 identified peptides in the human and mouse APP or A\u03b2 regions. c PSM counts for the hAPP-specific peptide (peptide 2) and the hA\u03b2 surrogate peptide (peptide 10). Average PSM values are reported across 2 batches each, \u00b1SD. d Apparent T50 values were directly determined from turnover curves for the hAPP- or hA\u03b2-specific peptides (Average\u2009\u00b1\u2009SD, n\u2009=\u20092). e The curve of free Lys amino acid (Average\u2009\u00b1\u2009SD, n\u2009=\u20092). f Corrected T50 values were calculated based on the distance between the protein curve and free Lys curve, using the JUMPt program, which incorporates a mathematical model to account for delays caused by Lys recycling (Average\u2009\u00b1\u2009SD, n\u2009=\u20092). g Summary table of hAPP and hA\u03b2 T50 values. The corrected T50 values were much smaller than the apparent T50 values.\n\nWe quantified 12 tryptic peptides from human APP (hAPP) and mouse APP (mAPP) proteins, both present in 5xFAD (Fig.\u00a06b, Supplementary Data\u00a022). Two peptides were human-specific: peptide 2 in the non-A\u03b2 region (used to quantify full-length hAPP), and peptide 10 in the A\u03b2 region (used to quantify human A\u03b2 (hA\u03b2) as previously reported)36. The peptide spectral match (PSM) counts, a semi-quantitative index74, for peptide 10 were significantly higher than those for peptide 2, consistent with the accumulation of A\u03b2 in 5xFAD (Fig.\u00a06c). Indeed, hA\u03b2 displayed a much longer half-life than hAPP (Fig.\u00a06d\u2013g), when analyzing apparent T50 (132.0\u2009d for hA\u03b2 vs 40.1\u2009d for hAPP) or corrected T50 (18.2\u2009d for hA\u03b2 vs <0.5\u2009d for hAPP). The results clearly indicate a significantly delayed turnover rate of hA\u03b2 relative to hAPP in the 5xFAD mice.\n\nWe then analyzed the proteome turnover for the 5xFAD and WT mice, calculating corrected T50 values for 8492 proteins (Fig.\u00a07a, Supplementary Data\u00a023). The global T50 distributions between 5xFAD and WT were similar with average values around 4\u20135\u2009d (Fig.\u00a07b). A statistical analysis identified 84 proteins with significant changes in half-life between the two genotypes (Fig.\u00a07c). 25% of those proteins, including DPP10 and AAK1, exhibited shorter T50 in 5xFAD than in WT (Fig.\u00a07d), whereas the remaining 75% displayed longer T50 in 5xFAD. For example, APOE and VTN showed \u0394T50 of 4.2 and 3.5 days, respectively (Fig.\u00a07e).\n\na Pie chart displaying the average proportions of corrected protein T50 values categorized as very short (<0.5 days), intermediate (0.5\u201330 days), long (30\u2013100 days), and very long (>100 days) for WT and 5xFAD mice. b Distribution graphs of T50 values in both genotypes, showing the average values and standard deviations. c Volcano plots of the log2 fold change and FDR for T50 in 5xFAD compared to WT, with proteins exhibiting changed T50 highlighted in colors and thresholds marked by dashed lines. d, e Examples of proteins that have shortened or extended T50 in 5xFAD (Average\u2009\u00b1\u2009SD, n\u2009=\u20092 batches per genotype). f Heatmap illustrating how some proteins with longer T50 may be explained by their localization in plaques, contributing to RNA-protein discrepancies. The side bar indicates log2FC-Z values for the first three columns or log2FC values for the last column.\n\nBy integrating transcriptome, proteome, amyloidome, and protein half-live data from 5xFAD, we found that 32 RNA-independent, upregulated proteins found in the amyloidome showed prolonged half-lives (Fig.\u00a07f, Supplementary Data\u00a024). The 32 proteins were enriched in the pathways of amyloid matrisome, autophagy/lysosome, and neurogenesis. These findings suggest that the AD transcriptome-proteome discrepancy can be attributed, at least partially, to reduced protein turnover in the amyloidome (Supplementary Fig.\u00a011).", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56853-3/MediaObjects/41467_2025_56853_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56853-3/MediaObjects/41467_2025_56853_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56853-3/MediaObjects/41467_2025_56853_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56853-3/MediaObjects/41467_2025_56853_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56853-3/MediaObjects/41467_2025_56853_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56853-3/MediaObjects/41467_2025_56853_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-56853-3/MediaObjects/41467_2025_56853_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "In this extensive proteomic resource, we have generated the most comprehensive AD mouse brain proteomes to date, analyzing a total of 133 mouse samples across 5 AD models (5xFAD25,26, NLF28, NLGF28, 3xTG27, and BiG45, as well as wild type controls; Supplementary Data\u00a02). This resource also includes phosphoproteome profiling from 36 mice (Supplementary Data\u00a011), an in-depth amyloidome analysis from 8 mouse samples (Supplementary Data\u00a021), and proteome turnover data from 30 mice (Supplementary Data\u00a023). The proteome coverage is high, with most datasets surpassing 10,000 proteins, largely due to our development and implementation of a fully optimized TMT-LC/LC-MS/MS pipeline55,70,75,76, extensive fractionation (e.g., at least 40 LC fractions per TMT batch)46, and substantial instrument time investment (e.g., ~4 days per TMT batch). This high-quality proteomics data serves as a critical resource for comparing different mouse models, aligning mouse findings with human AD data, integrating multi-omics datasets, and identifying potential disease-related proteins.\n\nThe next-generation knock-in mouse models for amyloidosis are often considered to have higher physiological relevance and reduced overexpression artifacts compared to 5xFAD21,23,28,77,78. Nevertheless, our proteomic comparison highlights similarities between these models, in terms of DEPs (1382 in 5xFAD, 1142 in NLGF, Supplementary Data\u00a07). A substantial portion (610 proteins) of DEPs overlaps between the two models, and the non-overlapping ones show similar trends in protein alterations. Furthermore, both models have a comparable number of shared DEPs with human consensus data36,37,52,60 (159 in 5xFAD, 145 in NLGF, Supplementary Data\u00a014). The proteomic correlation with human is also similar between the two models (R\u2009=\u20090.32 in 5xFAD, R\u2009=\u20090.23 in NLGF). Notably, NLGF mice carry the APP Arctic mutation (E693G), which produces a mutated A\u03b2 sequence (E22G) that leads to a slightly different A\u03b2 filament structure compared to the WT A\u03b2 filament in 5xFAD79,80. This structural difference may contribute to variations in downstream molecular events. The similar proteomic patterns between the two models suggest they can be effectively used to cross-validate molecular mechanisms related to amyloidosis.\n\nTo examine the relevance of the mouse data to humans, we integrated the mouse proteomics data with publicly available human AD datasets. These human datasets were selected based on their proteomic coverage, sample diversity, and consistent reporting of key pathological markers such as amyloid and tau. Specifically, we prioritized datasets that provided high-quality differential expression data from AD brain tissues. Consistent with the understanding that mouse models cannot fully recapitulate the complexity of human AD events21,23,78, our proteomic profiling of five mouse models reveals that each model shares <25% consensus DEPs of human AD data (Supplementary Data\u00a014). Collectively, these mouse models cover ~42% of human DEPs, indicating that each model has unique characteristics and mimics different subsets of AD molecular events. For instance, tau-related pathways are observed only in 3xTG27, whereas some splicing and synaptic defects are unique to the BiG model45. This proteomics resource serves as a valuable reference for investigating specific pathways using the most appropriate models.\n\nThe limitations of mouse models in replicating human AD pathology may stem from at least two factors: insufficient pathologies in mouse models and inherent species differences81, which are not necessarily mutually exclusive. The pathogenic mechanisms demonstrated in the mice (e.g., amyloidosis) can drive only part of the AD pathologies. In human, additional pathogenic pathways induce mixed pathologies that are not observed in the mice under standard breeding conditions. For example, while the 5xFAD and NLGF models effectively mimic amyloidosis, they fail to capture broader neurodegenerative processes, such as tau pathology and mitochondrial dysfunction. Notably, these models align poorly with human MCI, instead reflecting the heavy amyloid burden characteristic of late-stage AD. While most mouse models fail to exhibit significant neuronal loss as in human, recent advancements, such as the use of human neuron xenografts in AD mice82, promote abnormal tau phosphorylation and neuron death through necroptosis83. Other human-mouse chimeric models employ human microglia to better replicate AD-related responses84. Beyond mouse models, alternative AD models are being developed, including transgenic rats85, human iPSCs and organoids86,87,88, and primate models89, each of which offers specific advantages. These non-mouse models may present avenues for investigating AD molecular mechanisms that are not represented in current mouse models.\n\nMulti-omics integration offers a robust method to evaluate biological systems and reveal molecular insights90, given the generally weak RNA\u2013protein correlation, particularly in the brain which consists mainly of postmitotic, non-dividing cells35. In our study, both human and 5xFAD mouse brains exhibit modest RNA\u2013protein correlations, with R values under 0.5, and approximately one-third of protein changes occur independently of RNA levels. Considering protein homeostasis is regulated by events such as modifications, localization, and turnover, we expanded our analysis to include phosphoproteome, subproteome (amyloidome with plaque localization), and protein turnover. While protein phosphorylation did not significantly account for the RNA-protein discrepancies, our multi-layered proteomic data (amyloidome and turnover) supports the hypothesis that amyloid plaque formation creates a microenvironment where as many as 32 proteins show delayed turnover, promoting their RNA-independent accumulation in the brain (Supplementary Data\u00a024).\n\nAmyloid plaques are dynamic and can grow to 50\u2013100\u2009\u00b5m in size91. Recent spatial omics have revealed that the A\u03b2 core is enveloped by disease-associated microglia, activated astrocytes, and dysfunctional oligodendrocytes, positioned sequentially, implicating an active microenvironment induced by amyloid plaques (Supplementary Fig.\u00a011)92. Structural analyses have shown diverse A\u03b2 filament architectures in humans and mice79,80, suggesting that their pathological roles are influenced by A\u03b2-associated proteins within the matrisome93,94.\n\nApoE is a prominent protein in the amyloidome with delayed turnover. It has a well-established role in the \u201cApoE cascade hypothesis\u201d supported by extensive genetic and biochemical evidence95. Primarily produced by astrocytes and also by microglia and neurons96, ApoE RNA is upregulated (log2FC-Z of 3.26), but its protein change is more dramatic (log2FC-Z of 25.38). ApoE shows rapid turnover in WT mice, with a half-life of <0.5\u2009d, but this rate slows significantly to 8.85\u2009d in 5xFAD (Supplementary Data\u00a023). These findings indicate that the abundant accumulation of ApoE is driven by both RNA upregulation and delayed protein turnover, possibly due to its direct interaction with A\u03b2 in the plaque microenvironment95.\n\nThe 32-protein list also includes several understudied proteins, such as Spock1 and Spock2, members of the Sparc proteoglycan family potentially involved in synaptic plasticity97; SFRP1, which may promote plaque pathology by inhibiting ADAM10 \u03b1-secretase activity in the non-amyloidogenic pathway98; and HTRA1, a protease potentially regulating the aggregation and clearance of amyloid proteins99. Genetic variations in HTRA1 gene are linked to age-related macular degeneration100,101. These proteins are also found on the upregulated consensus protein list in AD brains (Supplementary Data\u00a023), implicating a possible role in the formation of amyloid plaque microenvironment.\n\nInterestingly, we found a number of proteins enriched in the autophagy/lysosome pathway with delayed turnover rates (Supplementary Data\u00a024), such as Tmem106b, which has been identified as an aggregated filament protein in several neurodegenerative disorders including AD102,103,104, and genetically linked to frontotemporal lobar degeneration105. Tmem106b accumulates in lysosomes, playing a role in lysosomal dysfunction106. The slow turnover of lysosomal proteins in AD mice might indicate defective autophagic degradation, possibly because lysosomes are damaged by intracellular A\u03b2 species107,108. This disruption could impair cellular homeostasis, and further inhibition of lysosomal function exacerbates AD-related phenotypes, underscoring the pivotal role of lysosomal pathways in disease progression.\n\nIn summary, our study presents a comprehensive multi-layered proteomics resource, profiling five AD mouse models and providing insights into proteomic responses to AD pathologies. Through whole proteome analysis, along with phosphoproteome, amyloidome, and turnover data, the resource supports the hypothesis that amyloid plaques create a microenvironment that promotes protein accumulation. Compared with human proteomics data, this resource enables researchers to select relevant disease pathways for study in appropriate mouse models, with the potential to develop other AD models in the future. All data from this resource is freely accessible on a website (https://penglab.shinyapps.io/mouse_ad_profile/).", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "The 5xFAD transgenic mice25,26 and 3xTG mice27 were purchased from The Jackson Laboratory (stock #034848 and #034830, respectively), while the NLF and NLGF KI mice28 were provided by Dr. Takaomi Saido at the RIKEN Center for Brain Science. The BiG mice were generated by crossing 5xFAD with N40K transgenic mice as described previously45. These mice were maintained in the Animal Resource Center at St. Jude Children\u2019s Research Hospital or the University of Arizona according to the Guidelines for the Care and Use of Laboratory Animals. All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC), Protocol 542-100503. Mice were housed under a 12-h light/12-h dark cycle at 20\u201325\u2009\u00b0C and 30\u201370% humidity. Euthanasia was performed using CO\u2082 inhalation at a displacement rate of 30% of the chamber volume per min, followed by confirmation of death. The brain tissues were collected at various ages, rapidly dissected, and then immediately frozen on dry ice before being stored at \u221280\u2009\u00b0C. A mix of male and female mice was used (Supplementary Data\u00a02).\n\nThe mice were labeled using Mouse Express\u00ae L-Lysine (13C6, 99%) Mouse Feed (5\u2009g per day, Cambridge Isotopes Laboratories). The mice were conditioned by providing the light SILAC food for 3\u2009d before labeling, and then fed with the heavy SILAC food in a time course. Cortical brain tissue samples were harvested for turnover analysis. The fully labeled mice were generated as previously reported109. The heavy mouse chow was used to feed wild-type mice from the parental generation through to the F2 generation. Through two generations, the mouse proteins were fully labeled109. Only male mice were used in turnover analysis to ensure observed differences were due exclusively to differences in genotype.\n\nLCM was performed essentially according to a previously reported method65. Mouse brain was embedded in OCT Compound (Jed Pella Inc., Redding, CA), sectioned at 12 \u03bcm in a cryostat and mounted on Arcturus Pen membrane glass slides (LCM0522, ThermoFisher). The sections were thawed, fixed with 75% ethanol for 1\u2009min, stained with 1% thioflavin-S (MilliporeSigma) or X-34 (MilliporeSigma) for 1\u2009min, washed in 75% ethanol for 1\u2009min, dehydrated, cleared in xylene and air-dried. LCM was performed using an Arcturus XT Laser Capture Microdissection System (Arcturus, ThermoFisher) with the following settings: 495\u2009nm excitation wavelength, 60\u201380\u2009mW laser power and 1\u2009ms duration. Although both diffuse and dense-core plaques were present in the mice, we selected X-34 stained dense-core plaques with a diameter of at least 30\u2009\u00b5m for this study. About 500 amyloid plaques were procured from each section, while non-plaque areas were captured as a control. The captured samples were stored at \u221280\u2009\u00b0C.\n\nThe experiments were performed according to our previously optimized protocol110,111. Briefly, the mouse brain samples were weighed and homogenized in lysis buffer (8\u2009M urea, 50\u2009mM HEPES, pH 8.5, and 0.5% sodium deoxycholate,100\u2009\u00b5L buffer, and ~20\u2009\u00b5L beads per 10\u2009mg tissue) with 1 x PhosSTOP phosphatase inhibitor cocktail (Roche). ~50\u2009\u00b5g protein from each sample was then digested in two steps by Lys-C and trypsin, with DTT reduction and iodoacetamide alkylation, followed by desalting with a C18 Ultra-Micro SpinColumn (Harvard apparatus). The desalted peptides were resuspended in 50\u2009mM HEPES (pH 8.5) to a concentration of ~1\u2009\u00b5g/\u00b5L, and fully labeled with TMT or TMTpro reagents. The reaction was quenched, equally pooled, and desalted for the subsequent prefractionation.\n\nThe pooled TMT samples were fractionated by offline basic reverse phase (RP) LC with an XBridge C18 column (3.5\u2009\u03bcm particle size, 4.6\u2009mm\u2009\u00d7\u200925\u2009cm, Waters; buffer A: 10\u2009mM ammonium formate in H2O, pH 8.0; buffer B: 10\u2009mM ammonium formate in 90% acetonitrile, pH 8.0). Fractions were collected in a gradient of 15\u201342% buffer B, and then concatenated into at least 40 samples to maintain high-resolution power. The concatenated samples were dried by SpeedVac, resuspended in 5% formic acid (FA), and analyzed by Q-Exactive HF Orbitrap MS (Thermo Fisher Scientific) in a 95\u2009min nano-LC gradient of 15\u201348% buffer B (buffer A: 0.2% FA, 5% DMSO; buffer B: buffer A plus 65% acetonitrile). MS1 scan settings were 60,000 resolution, 410\u20131600\u2009m/z scan range, 1\u2009\u00d7\u2009106 AGC, and 50\u2009ms maximal ion time. MS2 settings were 20 data-dependent MS2 scans, 60,000 resolutions, starting from 120\u2009m/z, 1\u2009\u00d7\u2009105 AGC, 120\u2009maximal ion time, 1.0 m/z isolation window with 0.2\u2009m/z offset, HCD, 32% specified normalized collision energy, and 15\u2009s dynamic exclusion70.\n\nThe basic pH RPLC-fractionated, TMT-labeled peptides were concatenated to 10 fractions (\u223c0.3\u2009mg per fraction), dried, and resuspended in binding buffer (65% acetonitrile, 2% TFA, and 1\u2009mM KH2PO4). TiO2 beads (0.9\u2009mg per sample, GL sciences) were incubated with the peptide fraction at 21\u2009\u00b0C for 20\u2009min. The TiO2 beads were then washed twice with washing buffer (65% acetonitrile, 0.1% TFA) and packed into a C18 StageTip (Thermo Fisher), followed by phosphopeptide elution with the basic pH buffer (15% NH4OH, and 40% acetonitrile). The eluates were dried and dissolved in 5% formic acid for LC-MS/MS analysis55.\n\nThe protein identification and quantification were analyzed using the JUMP software suite47. The MS data were searched against the protein database merged from Swiss-Prot, TrEMBL (from Uniprot), and UCSC databases (mouse: 59,423 entries). To evaluate the FDR, decoys were generated by reversing the target protein sequence112. Search parameters included precursor ion and product ion mass tolerance (10 ppm), maximal modification sites (n\u2009=\u20093), full trypticity, maximal missed cleavage (n\u2009=\u20092), static modification of TMT tag (+304.20715), methionine oxidation dynamic modification (+15.99491), and cysteine carbamidomethyl static modification (+57.02146) if the residue was alkylated with iodoacetamide. In the pSILAC-TMT analysis, the MS raw files were searched twice with or without Lys labeling (+6.02013). Peptide-spectrum matches (PSMs) were filtered by matching scores and mass accuracy to keep protein FDR below 1%. The peptides shared by multiple homologous proteins were assigned by the software to the protein with the maximal PSM number, based on the rule of parsimony. The protein quantitation was performed using the TMT reporter ion based on a published method46. In the case of phosphoproteome profiling, we applied the JUMPl program36 with the phosphoRS algorithm113 to the analysis of phosphosite localization scores (Lscore, 0\u2013100%) for each PSM, and then determine the appropriate phosphosites.\n\nProteomic and phosphoproteomic analyses were performed on mouse brain samples, with protein intensities normalized by applying log2 transformation to reduce skewness and median normalization to account for variations in sample loading and global batch intensity differences. PCA, implemented with the R package prcomp114, was used to evaluate the influence of covariates such as batch, sex, age, and genotype, ensuring that biological signals were not confounded by confounding variables. DE analysis was performed using a moderated t-test from the MKmisc package115, which estimates the variance by borrowing information across all proteins or phosphopeptides, providing increased statistical power compared to traditional t-tests. To control for false discoveries in multiple testing, the p values were adjusted to FDR using the Benjamini-Hochberg procedure116. Proteins or phosphopeptides were designated as differentially expressed if they met two criteria: an FDR value below 0.05 and a log2 fold-change exceeding \u00b12 standard deviations of the dataset\u2019s global variance. The two-part threshold of FDR and fold-change is a highly stringent cutoff commonly used in high-throughput proteomics studies to minimize false discoveries. To validate this approach, a null analysis was performed by comparing half of the WT samples against the other half, applying the same threshold. No significant findings passed these criteria, confirming the appropriateness of our chosen threshold.\n\nPathway enrichment of DEPs was performed by GO enrichment analysis50 and further analyzed by the PANTHER117 overrepresentation test (Fisher\u2019s Exact test). Pathway enrichment specifically emphasized on GO categories relevant to neurodegenerative processes, informed by prior knowledge of AD pathogenesis. Results were filtered by FDR (below 0.01) to identify DEP-associated pathways with high confidence. DEPs within the pathways were superimposed against a custom PPI database, which combined the InWeb_IM118, STRING119, and BioPlex120 databases, as detailed previously36. Briefly, protein modules within each cluster were determined in two steps121: (i) accepting PPI edges where both nodes (i.e., the connected proteins) are within the same cluster; (ii) computing a topologically overlapping matrix from the PPI network and modularizing the network into individual modules using the hybrid dynamic tree-cutting method. Modules were annotated and visualized using Cytoscape122. The key network hubs and interconnected modules involved in neurodegeneration were highlighted.\n\nThe analysis was performed using the KSEAapp R package (v0.99.0) within RStudio (v4.1.2), with 122 DE phosphopeptides as input59. Both PhosphoSitePlus123 and NetworKIN124 databases were utilized to find kinase-substrate interactions and phosphosite information. Kinase substrates were extracted to derive the corresponding kinase activities for each mouse sample along with their p values. The p values for the same kinase activity across different samples were combined using Fisher\u2019s method. Kinase activities with a combined p value lower than 0.05 were considered significant. The log\u2082FC values, representing the mean log\u2082FC of all the kinase\u2019s substrates, were then used to generate the heatmap.\n\nWe used a previously modified pathway activity inference strategy61,62 to derive the activity of a given pathway, termed a(P), in AD and mouse model samples:\n\nWhere k represents the number of proteins in each pathway, Fi is the Log2FC for individual proteins, and Ci denotes the functional annotation of the protein, assigned as either +1 for proteins with an activation role or -1 for proteins with an inhibitory role.\n\nWe implemented a gene/protein ranking method based on order statistics for multi-omics integration36. This approach combines N distinct protein/gene ranking sets into a single comprehensive ranking. In this analysis, we utilized a total of 12 individual datasets: GWAS-identified risk loci13,14,15,16,17,18,19,20,36, human transcriptome125, MCI proteome36, AD proteome datasets36,37, AD aggregated proteome126, AD phosphoproteome36, 5xFAD transcriptome36, AD amyloidosis proteome integrated from our 5xFAD and NLGF data, 3xTG proteome, BiG proteome, AD amyloidosis phosphoproteome, and interactome closeness to known AD genes by PPI network distance36.\n\nTo account for scale difference, proteomics and transcriptomics36 data were converted to Z scores, generating log2FC-Z data. RNAs and proteins with shared accessions were used for comparison. Proteins were grouped as upregulated (Z\u2009>\u20092) or downregulated (Z\u2009<\u2009\u22122). Protein-RNA consistency was determined by a \u0394Z (the Z score difference between protein and RNA) absolute value larger than 2.5. However, if both RNA and protein had absolute Z-values larger than 4 and changed in the same direction, the pair was still considered to be consistent, regardless of \u0394Z.\n\nFollowing protein identification and quantification using the JUMP software suite47, the MS raw data, and JUMP output files were further processed to remove TMT noise for accurate quantification, utilizing the JUMPsilactmt Python program (version 1.0.0). Briefly, noise levels were identified in light PSMs (the fully labeled SILAC channel) and heavy PSMs (the unlabeled channel) and subtracted from other channels (see Supplementary Fig.\u00a09 for details). However, the TMT intensities from adjacent light/heavy PSM pairs could not be directly compared because they were produced from distinct MS scans. To address this, the JUMPsilactmt quantified the composite MS1 heavy and light ion intensities as in the SILAC quantification method73, and used their ratio to normalize the denoised MS2 TMT reporter ion intensities in different MS scans. The normalized heavy and light TMT intensities were converted into a fraction of light (L%). Finally, quantification at the PSM level was summed to the peptide level and subsequently to the protein level. L% values were averaged across biological replicates for the JUMPt analysis.\n\nGlobal protein half-lives were determined using JUMPt software73 (version 1.0.0). First, we calculated the apparent T50 for each protein individually, using the averaged L% of the protein across time points, without accounting for in vivo Lys recycling. Next, we analyzed the corrected T50 under \u201cSetting-2,\u201d incorporating free Lys data and L% to estimate all protein T50 values simultaneously using an ordinary differential equation model. It should be noted that, for proteins with very short T50 (<0.5 days) or very long T50 (>100 days), the calculation of corrected T50 analysis is less reliable, though only a small fraction of proteins exhibit these extreme half-lives.\n\nTo identify statistically significant changes in T50 between different genotypes, the L% of each protein over the time course was analyzed by two-way ANOVA, with genotype (WT vs. 5xFAD) and labeling time (4, 8, 16, and 32\u2009d) as variables. The ANOVA p values for genotypes were then adjusted to FDR by the Benjamini\u2013Hochberg procedure116. Moreover, the \u2206T50 for each protein was calculated as the logarithm (base 2) over the corrected T50 ratio between 5xFAD and WT mice. The proteins were filtered by FDR\u2009<\u20090.05 and |log2FC\u2009|\u2009>2\u2009SD to generate the final list with altered half-lives.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "RAW data results have been deposited in the PRIDE database (https://www.proteomexchange.org) and are publicly available via accession numbers PXD018590, PXD007974, PXD023395, PXD031545, PXD031732, PXD031734, PXD031735, PXD031769, PXD031830 and PXD053314 (Supplementary Data\u00a02).", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The program of JUMPsilactmt (version 1.0.0) is publicly available from GitHub (https://github.com/abhijitju06/JUMPsilactmt-Version-0.0.1). The program of JUMPt (version 1.0.0) is available from GitHub (https://github.com/abhijitju06/JUMPt-Version-1.0.0). 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This work was partially supported by National Institutes of Health grants R01AG047928 (J.P.), R01AG053987 (J.P.), RF1AG068581 (J.P.), U54NS110435 (J.P.), U19AG069701 (J.P.), RF1AG064909 (G.Y. and J.P.), and the ALSAC foundation.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Abhijit Dasgupta\n\nPresent address: Department of Computer Science and Engineering, SRM University AP, Andhra Pradesh, India\n\nThese authors contributed equally: Jay M. Yarbro, Xian Han, Abhijit Dasgupta, Ka Yang.\n\nDepartment of Structural Biology, St. Jude Children\u2019s Research Hospital, Memphis, TN, USA\n\nJay M. Yarbro,\u00a0Xian Han,\u00a0Abhijit Dasgupta,\u00a0Ka Yang,\u00a0Danting Liu,\u00a0Him K. Shrestha,\u00a0Masihuz Zaman,\u00a0Zhen Wang,\u00a0Dong Geun Lee,\u00a0David Vanderwall,\u00a0Mingming Niu,\u00a0Huan Sun,\u00a0Ping-Chung Chen,\u00a0Yun Jiao,\u00a0Xue Zhang,\u00a0Zhiping Wu,\u00a0Surendhar R. Chepyala\u00a0&\u00a0Junmin Peng\n\nDepartment of Developmental Neurobiology, St. Jude Children\u2019s Research Hospital, Memphis, TN, USA\n\nJay M. Yarbro,\u00a0Xian Han,\u00a0Abhijit Dasgupta,\u00a0Ka Yang,\u00a0Danting Liu,\u00a0Him K. Shrestha,\u00a0Masihuz Zaman,\u00a0Zhen Wang,\u00a0Dong Geun Lee,\u00a0David Vanderwall,\u00a0Mingming Niu,\u00a0Huan Sun,\u00a0Ping-Chung Chen,\u00a0Yun Jiao,\u00a0Xue Zhang,\u00a0Zhiping Wu,\u00a0Surendhar R. Chepyala\u00a0&\u00a0Junmin Peng\n\nCenter for Proteomics and Metabolomics, St. Jude Children\u2019s Research Hospital, Memphis, TN, USA\n\nKaiwen Yu,\u00a0Boer Xie,\u00a0Yingxue Fu,\u00a0Yuxin Li,\u00a0Zuo-Fei Yuan,\u00a0Suresh Poudel\u00a0&\u00a0Junmin Peng\n\nDepartment of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA\n\nXusheng Wang\n\nDepartment of Pharmacology, College of Medicine, University of Arizona, Tucson, AZ, USA\n\nBarbora Vagnerova,\u00a0Qianying He,\u00a0Andrew Tang,\u00a0Patrick T. Ronaldson\u00a0&\u00a0Rui Chang\n\nDepartment of Neuroscience, Peter O\u2019Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA\n\nGang Yu\n\nDepartment of Pharmacology, Yale University School of Medicine, New Haven, CT, USA\n\nYansheng Liu\n\nYale Cancer Biology Institute, Yale University School of Medicine, West Haven, CT, USA\n\nYansheng Liu\n\nDepartment of Biomedical Informatics & Data Science, Yale University School of Medicine, West Haven, CT, USA\n\nYansheng Liu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.P., X.H., G.Y., and Y.L. conceived this project. J.M.Y., X.H., K.Yang, D.L., M.Z., Z.W., K.Yu, D.G.L., D.V., M.N., H.S, B.X., P.-C.C, Y.J., X.Z., Z.W., B.V., Q.H., A.T., P.T.R., and R.C. performed the experiments. J.M.Y., X.H., A.D., K.Yang, H.K.S., S.R.C., Y.F., Y.L., Z.-F.Y., X.W., S.P., G.Y., Y.L., and J.P. analyzed the data. J.M.Y., X.H., A.D., and J.P. wrote the manuscript.\n\nCorrespondence to\n Junmin Peng.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Seth Grant, Sung-Ung Kang and the other, anonymous, reviewers for their contribution to the peer review of this work. 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Human and mouse proteomics reveals the shared pathways in Alzheimer\u2019s disease and delayed protein turnover in the amyloidome.\n Nat Commun 16, 1533 (2025). https://doi.org/10.1038/s41467-025-56853-3\n\nDownload citation\n\nReceived: 22 October 2024\n\nAccepted: 04 February 2025\n\nPublished: 11 February 2025\n\nVersion of record: 11 February 2025\n\nDOI: https://doi.org/10.1038/s41467-025-56853-3\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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+++ b/ee6c0a1fe0fae4242c4ec9b22dc8cbfb8f98fe913927b649b71b7b1bf1619056/metadata.json @@ -0,0 +1,168 @@ +{ + "title": "A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions", + "pre_title": "A Variational Expectation-Maximization Framework for Balanced Multi-scale Learning of Protein and Drug Interactions", + "journal": "Nature Communications", + "published": "25 May 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48801-4/MediaObjects/41467_2024_48801_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48801-4/MediaObjects/41467_2024_48801_MOESM2_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48801-4/MediaObjects/41467_2024_48801_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48801-4/MediaObjects/41467_2024_48801_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.7213401", + "https://www.rcsb.org/", + "https://github.com/samsledje/ConPLex_dev/tree/main/dataset/BIOSNAP", + "https://github.com/isjakewong/MIRACLE/tree/main/MIRACLE/datachem", + "https://github.com/BioinfoMachineLearning/DIPS-Plus", + "https://github.com/jertubiana/ScanNet/tree/main/datasets", + "/articles/s41467-024-48801-4#Sec25" + ], + "code": [ + "https://github.com/biomed-AI/MUSE", + "https://doi.org/10.5281/zenodo.11097139", + "/articles/s41467-024-48801-4#ref-CR51" + ], + "subject": [ + "Data integration", + "Proteome informatics", + "Virtual drug screening" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3620776/v1.pdf?c=1716721616000", + "research_square_link": "https://www.researchsquare.com//article/rs-3620776/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-48801-4.pdf", + "preprint_posted": "19 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Protein functions are characterized by interactions with proteins, drugs, and other biomolecules. Understanding these interactions is essential for deciphering the molecular mechanisms underlying biological processes and developing new therapeutic strategies. Current computational methods mostly predict interactions based on either molecular network or structural information, without integrating them within a unified multi-scale framework. While a few multi-view learning methods are devoted to fusing the multi-scale information, these methods tend to rely intensively on a single scale and under-fitting the others, likely attributed to the imbalanced nature and inherent greediness of multi-scale learning. To alleviate the optimization imbalance, we present MUSE, a multi-scale representation learning framework based on a variant expectation maximization to optimize different scales in an alternating procedure over multiple iterations. This strategy efficiently fuses multi-scale information between atomic structure and molecular network scale through mutual supervision and iterative optimization. MUSE outperforms the current state-of-the-art models not only in molecular interaction (protein-protein, drug-protein, and drug-drug) tasks but also in protein interface prediction at the atomic structure scale. More importantly, the multi-scale learning framework shows potential for extension to other scales of computational drug discovery.Biological sciences/Computational biology and bioinformatics/Proteome informaticsBiological sciences/Computational biology and bioinformatics/Data integrationMulti-scale LearningExpectation-MaximizationProtein-Protein InteractionsDrug-Protein InteractionsProtein Interface Prediction", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "MUSEsupprevised.pdfSupplementary InformationNCOMMS2358788ARS.pdfReporting summary", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Protein functions are characterized by interactions with proteins, drugs, and other biomolecules. Understanding these interactions is essential for deciphering the molecular mechanisms underlying biological processes and developing new therapeutic strategies. Current computational methods mostly predict interactions based on either molecular network or structural information, without integrating them within a unified multi-scale framework. While a few multi-view learning methods are devoted to fusing the multi-scale information, these methods tend to rely intensively on a single scale and under-fitting the others, likely attributed to the imbalanced nature and inherent greediness of multi-scale learning. To alleviate the optimization imbalance, we present MUSE, a multi-scale representation learning framework based on a variant expectation maximization to optimize different scales in an alternating procedure over multiple iterations. This strategy efficiently fuses multi-scale information between atomic structure and molecular network scale through mutual supervision and iterative optimization. MUSE outperforms the current state-of-the-art models not only in molecular interaction (protein-protein, drug-protein, and drug-drug) tasks but also in protein interface prediction at the atomic structure scale. More importantly, the multi-scale learning framework shows potential for extension to other scales of computational drug discovery.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Interactions between proteins, drugs, and other biomolecules play a crucial role in various biological processes1,2,3. Understanding these interactions is essential for deciphering the molecular mechanisms underlying biological processes and developing new therapeutic strategies4. However, the massive growth in demand and cost associated with experimental interactions calls for computational tools for automated prediction and understanding of interactions between biomolecules5.\n\nMany computational methods have been developed for studying the interactions between biomolecules6,7,8,9. Predicting these interactions purely from structures is one of the most important challenges in structural biology10,11. The structural-based methods aim to promote our understanding of the patterns of interactions among residues/atoms at the atomic structural scale (intra-molecular scale). Unfortunately, these methods often lead to inferior performance when high-quality molecular structural features are not available9. On the other hand, network-based methods analyzed the topology of the molecular networks to infer potential new interactions12,13. Similarly, these methods don\u2019t perform well due to ignorance of protein structures. Thus, accurate predictions need to capture multi-scale hierarchical and complementary information.\n\nTo model the joint distribution over the multi-scale of protein and drug interactions, a few attempts have been developed14,15,16,17. An intuitive approach for learning multi-scale representations is to combine the molecular graph with an interaction network and optimize them jointly. For example, HIGH-PPI16 is the first to jointly optimize a hierarchical graph, including the PPI network (outside-of-protein view) and the protein graph (inside-of-protein view) for protein-protein interactions. MIRACLE15 proposed a contrastive learning strategy to integrate the multi-view information of drug-drug interactions. ScanNet18 also integrates information from multiple scales (atom, amino acid) to improve the prediction of protein-protein binding sites. However, due to the imbalanced nature and inherent greediness of multi-scale learning19,20,21, these models often intensively rely on a single scale, allowing it to learn faster. This imbalanced nature prevents these approaches from effectively leveraging all informative scale-related information and often results in worse generalization. Furthermore, an effective multi-scale framework needs not only capture the rich information within different scales but also faithfully preserve the underlying relation in between.\n\nIn this study, we present MUSE, a multi-scale representation learning framework based on a variant expectation maximization22, which can effectively integrate multi-scale information for learning. In contrast to existing methods that rely heavily on single-scale information, MUSE effectively addresses the optimization imbalance in multi-scale learning through mutual supervision and iterative optimization. Extensive experiments have shown that MUSE is superior not only for predicting molecular interactions (including protein-protein, drug-protein, and drug-drug interactions) but also for predicting molecular binding interfaces. Such a multi-scale framework has potential for applications at more scales, including atomic and amino acid scales, due to its robustness and scalability.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "As shown in Fig.\u00a01, MUSE is a multi-scale learning method that integrates both molecular structure modeling and interaction network learning of protein\u00a0and drug through a variational expectation-maximization (EM) framework. The EM framework optimizes two modules, the expectation step (E-step) and the maximization step (M-step), in an alternating procedure over multiple iterations20,23. During the E-step, MUSE utilizes the structural information of each biomolecule to learn an effective structural representation for training with known interactions and augmented samples in the M-step. It takes the pair of protein and drug with their atom-level structural information as input and augments with the predicted interactions from the M-step. The M-step takes the molecule-level interaction network, the structural embeddings, and predicted interactions from the E-step as the input, and also outputs the predicted interactions. This iterative optimization between the E-step and the M-step ensures the capturing of both the molecular structures and network information interactively, with different learning rates at two scales. The mutual supervision ensures that each scale model learns in an appropriate manner, enabling the utilization of effective information at different scales. This framework will be demonstrated on several multi-scale tasks for interactions between proteins and drugs. We additionally analyzed that MUSE mitigates the imbalanced characteristics in multi-scale learning and effectively integrates the hierarchical and complementary information from different scales.\n\na The vanilla approach for learning multi-scale representations is to combine the two single-scale models and optimize them jointly. b The EM framework (MUSE) for Multi-scale Learning. The Expectation step trains a model with structural information of protein and drug as input to fit the known and pseudo interactions (the predicted interactions from the M-step except the first iteration). The updated interactions and structural embeddings were input to the M-step, where the molecular network was constructed to maximize\u00a0the prediction of the known interactions and the predicted interactions from the E-step. The updated interactions were sent to E-step for new iterations. c MUSE is generalizable and applicable to multiple prediction tasks: predicting different types of molecular interactions and molecular binding interfaces.\n\nTo evaluate our method, we first utilized MUSE to integrate atomic structural information to improve molecular network scale predictions. As shown in Fig.\u00a02a, MUSE achieved state-of-the-art performance consistently on the three multi-scale interaction predictions tasks, including protein-protein interactions (PPI)16,24, drug-protein interactions (DPI)11,25, and drug-drug interactions (DDI)15,26.\n\na Precision-recall curves of PPI prediction on SHS27k, DPI prediction on BioSNAP, and DDI prediction on DeepDDI, showing the performance of MUSE compared to state-of-the-art baselines. b Receiver Operator characteristic curves of PPI prediction on SHS27k, DPI prediction on BioSNAP, and DDI prediction on DeepDDI, showing the performance of MUSE compared to state-of-the-art baselines. c Barplot shows the best micro-F1 scores (Best-F1) or AUPRC of baseline, ablation study MUSE-Joint and MUSE predictions respectively on PPI predictions (Random, BFS and DFS splits), DPI predictions (Unseen Protein or Drugs) and DDI predictions. Error bars and the the corresponding data points (as dot plots) represent standard deviation of the mean under 5 independent runs. Source data are provided as a Source Data file.\n\nSpecifically, on the PPI dataset, MUSE outperformed all existing models including single-scale (DrugVQA7 and TAG-PPI27), and multi-view methods (GNN-PPI24 and HIGH-PPI16). TAG-PPI and DrugVQA, which solely focus on the atomic structure scale, achieved the poorest performance as they neglected the molecular network scale information. Our model showed substantial improvements over the strongest baseline HIGH-PPI, which also incorporates multi-scale information for enhanced predictions, with an increase of 13.81% in the BFS split, 13.06% in the DFS split, 7.69% in the Random split, (Fig.\u00a02c and Fig.\u00a0S1). Furthermore, our model also showed improvements over the ablation study MUSE-Joint, which integrates two scale models and optimizes them jointly with multiple iterations, attributed to the efficient utilization of the EM framework. The MUSE-Joint is also slightly better than HIGH-PPI, as HIGH-PPI does not optimize the structural information jointly for predictions.\n\nSimilar results have been shown on the DPI and DDI datasets. MUSE achieved the AUPRC of 0.998 and 0.922 and the AUROC of 0.993 and 0.915 respectively on the DDI and DPI datasets (Supplementary Tables\u00a0S2, S3). When compared with state-of-the-art methods, MUSE showed improvements of 5.05% on the DDI dataset (over CGIB28) and 2.67% on the DPI dataset (over ConPlex11). Interestingly, the baseline method (MIRACLE15), which directly combines information from different scales in GNN, underperforms the recent structure-based baseline method CGIB on most metrics. These results indicated the importance of the multi-scale integration strategy, and a sub-optimal strategy might even lead to worse performance than single-scale models.\n\nIn addition to leveraging atomic structural information to improve molecular network scale prediction, we further investigated the ability of MUSE on the learning and prediction of structural properties at the atomic structural scale, including the prediction of interface contacts29,30,31 and binding sites18,32,33 related to protein-protein interactions (PPI).\n\nTo evaluate the prediction of protein inter-chain contact, MUSE was compared with the state-of-the-art methods on the DIPS-Plus benchmark34. As shown in Fig.\u00a03a, MUSE consistently outperformed all other methods31,35,36,37, validating its effectiveness and adaptability in the atomic structural predictions. To be specific, our model achieved the AUROC of 0.92 and the highest precision of 0.26, 0.25, and 0.23 for P@10, P@L/10, and P@L/5, respectively, significantly outperforming the best method, DeepInteract, with AUROC of 0.90 and precision values of 0.20, 0.19, and 0.17. This improvement could likely be attributed to the EM training paradigm in MUSE, which is capable of learning geometric representations (E-step) and interaction patterns (M-step) of proteins in an iterative optimization process. We also highlighted the capabilities of MUSE by examining three examples from the testing set. As shown in Fig.\u00a03b (and Supplementary Fig.\u00a0S2), MUSE accurately predicted contact interfaces with an average precision of 0.711, which is significantly better than DeepInteract\u2019s 0.592 and GLINTER\u2019s 0.019. Furthermore, the predicted contact map of MUSE leads to better docking results upon integration of the Kabsch algorithm (RMSD: 4.33). In contrast, alternative methods struggle to generate accurate binding structures, resulting in a relatively high RMSD (DeepInteract: 8.15 and GLINTER: 19.90).\n\na The average top-k precision, recall, and AUROC metrics for different protein-protein interaction prediction methods on the DIPS-Plus benchmark. Five predictors are compared: BIPSPI, DeepInteract, GLINTER, CDPred, and MUSE. The best results are marked as bold. b The ground truth and predictions for PDB entry 1UZN are presented. For each method, we display the surface of the predicted and ground truth ligand relative to the ground truth receptor. We employed the Kabsch algorithm to dock the two proteins based on the contact map predicted by each method and calculated the Root Mean Square Deviation (RMSD) for the ground truth. c MUSE achieves state-of-the-art performance on a broad range of test sets in the PPBS dataset compared with state-of-the-art baselines and an ablation study MUSE-Joint. Source data are provided as a Source Data file.\n\nMUSE was further evaluated to predict whether the residues are directly involved in protein-protein interactions18,33. As shown in Fig.\u00a03c and Supplementary Table\u00a0S3, on the ScanNet18 benchmark, MUSE achieved a Median AUPRC of 0.811 and a Median AUROC of 0.938 for the full test set, 1.76% and 0.969% better than the second best method PeSTo33. The improvements demonstrate that the learning of molecular network scale in MUSE could provide valuable insights into the predictions at the atomic structural scale. MUSE also performed best on another benchmark dataset developed by Masif-site10, 2.23% and 1.40% better than PeSTo and MUSE-Joint, respectively. These improvements are in line with our expectation as the effective integration of multiple scales (atom, amino acid, molecule) could efficiently detect protein-protein binding sites. Visualizations of MUSE predictions for representative examples (Supplementary Fig.\u00a0S3) illustrate that the binding sites are correctly identified (0.978 and 0.977 accuracy).\n\nTo investigate why MUSE achieved the superior performance of multi-scale representations, we analyzed the learning ability of MUSE for the imbalanced characteristics of multi-scale learning. We hypothesized that the slow-learning scale\u2019s updating direction is severely disturbed by the dominant one, making it hard to exploit its features for accurate predictions19,38.\n\nTo indicate the information utilization at different scales, we defined the information utilization rate as the proportion of accuracy changes achieved by single-scale models relative to the final multi-scale model, similar to previous studies19,38. As shown in Fig.\u00a04a, for HIGH-PPI, the utilization rate is 0.009 for the atomic structure scale and 0.191 for the molecular network scale, leading to a ratio of 21.22. These indicated that HIGH-PPI was dominated by the molecular network scale. Similarly, MIRACLE and other methods were also harmed by molecular network scale information. In contrast, the utilization rates of MUSE for the atomic structure and molecular network scales rose to 0.103 and 0.318, with a ratio of 3.08. These results indicated that MUSE efficiently alleviates the inhibition on the molecular network scale and fully exploits the structural properties in multi-scale learning. The different contributions of different scales to the learning objective are due to the natural imbalance that exists in the current dataset.\n\na The boxplot (5 runs with independent seeds) of utilization rate in each scale for different multi-scale models, demonstrating that MUSE efficiently alleviates the inhibition on molecular network scale and fully exploits the atomic structural properties in multi-scale learning. For boxplots, the center line represents the median, upper and lower edges represent the interquartile range, and the whiskers represent 0.5\u2009\u00d7\u2009interquartile range. b The convergence curves of MUSE and MUSE-Joint on the PPI dataset during the EM optimization iteration steps. The black curve in iterative optimization shows how MUSE continuously alleviates the imbalance characteristics of multi-scale learning and improves its multi-scale utilization. c Visualization and Case Study on the interaction predictions of molecular network model, MUSE-Joint and MUSE. Case (1) represents the examples in which MUSE-Joint cannot utilize other scale information for prediction, and Case (2) represents the examples in which MUSE-Joint is disturbed by other scale learning. MUSE classifies these interaction types between protein and protein clearly. Source data are provided as a Source Data file.\n\nTo explore the contribution of iterative optimization, we could see that MUSE did not perform as well as other baseline methods in the beginning but outperformed them after iterations (Fig.\u00a04b). The continuous increase in accuracy (black curve) shows how MUSE continuously alleviates the imbalance characteristics of multi-scale learning and improves its multi-scale utilization. The learned representations by MUSE classify the interaction type between protein and protein while both the molecular network model (Fig.\u00a04c) and MUSE without iterative optimization (MUSE-Joint) have a small number of samples mixed. Specifically, Case (1) demonstrates that MUSE-Joint inhibits the utilization of structural information for predictions, while MUSE alleviates this inhibition and fully utilizes these structural features. On the other hand, Case (2) illustrated that MUSE-Joint is disturbed by the other scale during training, whereas MUSE does not encounter this issue.\n\nIn summary, MUSE effectively mitigates the imbalance characteristics and greedy learning in multi-scale learning, ensuring the comprehensive utilization of information at different scales during training. Furthermore, the experiment of utilization rate analysis enables us to have a concrete look at what the model has learned and demonstrate that using MUSE to balance the models\u2019 learning from different scales enhances generalization.\n\nTo better understand the learned multi-scale representations, we investigated the learned multi-scale representation by MUSE from different perspectives, including (1) the capability of MUSE to capture the atomic structure information (i.e. structural motifs and embeddings) involved in PPI, and (2) the mutual supervision between the learned atomic structure and molecular network representations.\n\nAs an example of binding site prediction (PDB id: 3CQQ-A), MUSE can accurately identify the residues belonging to the binding sites (Fig.\u00a05a) (97.7% accuracy). This demonstrated that the mutual supervision in MUSE helps the atomic structure scale model to learn key substructures related to interactions. The learned atomic structural representations (Fig.\u00a05b) confirmed that our learned representation aids in discriminating the interaction types while HIGH-PPI showed a distribution close to the random. This agreed that HIGH-PPI didn\u2019t well utilize the structural information with a low atomic structure information utilization rate (0.009).\n\na Left: Depiction of a complex protein (protein, PDB id: 3CQQ-A). Right: Residue importance of the query protein learned from MUSE with coloring ranging from low (yellow) to high (red). b Two-dimensional projection of the learned atomic structure scale representation using t-SNE. Each point corresponds to an interaction. Coloring is based on the interaction types. The learned atomic structural representation of MUSE is effective for label prediction while the representation learned by HIGH-PPI is quite close to the random initialization. c The molecular network scale performance (Best-F1) of MUSE with the different threshold of pseudo labels (3 runs with independent seeds) from the atomic structure model indicates that the mutual supervision in MUSE leads to superior performance. For boxplots, the center line represents the median, upper and lower edges represent the interquartile range, and the whiskers represent 0.5\u2009\u00d7\u2009interquartile range. Source data are provided as a Source Data file.\n\nTo illustrate the role of mutual supervision, we conducted an ablation study to investigate the effect of the pseudo-labels predicted by the atomic structure scale on the molecular network scale. We computed the best-F1 scores at different thresholds for pseudo-labeling on the PPI dataset (box plots, Fig.\u00a05c). Without implementing mutual supervision (t\u2009=\u20090), the model achieved a best-F1 of 0.908, only marginally outperforming the baseline method (0.886, HIGH-PPI). As the threshold value of t increases, the Best-F1 score of MUSE improves rapidly. This improvement is attributed to the addition of more pseudo interactions to the PPI network, mitigating the incompleteness of the network39. Subsequently, the performance gradually decreases as the threshold t increases, indicating that the predicted pseudo interactions become increasingly noisy when t\u2009>\u20090.4.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48801-4/MediaObjects/41467_2024_48801_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48801-4/MediaObjects/41467_2024_48801_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48801-4/MediaObjects/41467_2024_48801_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48801-4/MediaObjects/41467_2024_48801_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-48801-4/MediaObjects/41467_2024_48801_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "MUSE unifies two scales of biomolecules, atomic structure scale and molecular network scale, into a multi-scale framework. The iterative optimization and mutual supervision process partially overcome the greedy nature of multi-scale learning, and thus significantly improve the multi-scale representation learning. The method was shown effective not only for predicting molecular interactions but also for predicting molecular binding interfaces. These advantages distinctly empower MUSE for the multi-scale learning of proteins and drugs that could be extended to other multi-scale tasks.\n\nWith the continuously growing of multi-scale data, the integration of data across different scales is becoming increasingly critical. Unfortunately, achieving this integration is challenging, as the straightforward approaches are often dominated by individual scale due to the imbalanced nature for different scales and the inherent greediness of deep neural networks. Our approach provides an effective perspective for integrating unbalanced multi-scale data. We show the efficacy of MUSE across various interaction prediction tasks, highlighting the superiority of integrating atomic structure scale information into molecular interaction predictions. Furthermore, the molecular network scale learning within MUSE offers valuable insights for further optimizing the atomic structure scale model to enhance protein representations, as indicated by the improvements in the atomic structure scale tasks.\n\nWhile MUSE has demonstrated state-of-the-art performance in our benchmarks, there remains potential for enhancing its ability to handle noisy and incomplete multi-scale downstream tasks. This might be combined to incorporate prior knowledge through a knowledge graph and explainable AI techniques. On the other hand, our conceptual multi-scale framework also exhibits potential for extension to other scales of computational drug discovery. For example, MUSE could be applied to the atom and amino acid scale in protein representation learning, deepening our understanding of the multiple structural scales of protein. We hope that its broad applicability and scalability to other scales will contribute to the drug discovery of effective therapeutics.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "We have evaluated our framework on three multi-scale interaction prediction benchmarks, the protein inter-chain benchmark (DIPS-Plus) and the protein binding sites benchmark (Scannet). The statistics of these datasets are presented in Supplementary Information.\n\nFirstly, we evaluated our framework on three multi-scale interaction prediction benchmarks, i.e., protein-protein interaction predictions (SHS27K), drug-protein interaction predictions (BioSNAP), and drug-drug interaction predictions (DeepDDI). The SHS27K PPI dataset contains SHS27k (sub-dataset of STRING40) with 6660 protein-protein pairs (PPIs) and 1533 human proteins with native protein structures. These PPIs are divided into 7 types, namely reaction, binding, post-translational modifications (ptmod) activation, inhibition, catalysis, and expression, which contain 15,056 positive interaction types and 31,564 negative interaction types. The native protein structures are obtained from PDB (https://www.rcsb.org/), in line with previous works16. The BioSNAP dataset, obtained from25, consists of only positive drug-target interactions. The negative drug-protein interactions were generated by randomly selecting an equal number of protein-drug pairs15,28. We ensure that our data splits and sampling methods align with those used in previous work11. Herein, we used the molecular 2D graph for drugs and the protein structures are obtained from the pre-trained model ESMFold41. For the DeepDDI dataset, we used 192,284 pair-wise drug-drug interactions and their polypharmacy side-effect information extracted from DrugBank42.\n\nWhen predicting the protein interface contacts at the atomic structure scale, we chose to use DIPS-Plus34, a large protein complex structures dataset mined from the Protein Data Bank and tailored for rigid body docking. Following the previous study31, we select 32 homodimers and heterodimers to evaluate the performance of our model in predicting interface contacts. After removing proteins with \u2265 30% sequence identity with the test datasets, 15,618 and 3548 binary complexes are left for training and validation.\n\nFor the prediction of protein-protein binding sites (PPBS), we obtained 41,466 distinct PDB files, involved in 240,506 protein-protein interfaces from the Dockground database43. Following Tubiana et al.18, we investigated the impact of homology between train and test set examples on generalization of our framework and baseline models and therefore grouped validation and test examples into four subgroups based on their degrees of homology: (1) Val/Test 70% (at least 70% sequence identity with at least one train set example), (2) Val/Test homology (at most 70% sequence identity with any train set example), (3) Val/Test topology (at least one train set example with similar protein topology), and (4) Val/Test none (none of the above). The additional independent testing set is composed of clusters containing any of the 53 subunits from the MaSIF-site benchmark dataset.\n\nSuppose that we have a multi-scale network \\({{{{{{{\\mathcal{N}}}}}}}}\\) which is presented by \\({{{{{{{\\mathcal{N}}}}}}}}=\\{{{{{{{{\\mathcal{G}}}}}}}},\\, {{{{{{{\\mathcal{L}}}}}}}}\\}\\) where \\({{{{{{{\\mathcal{G}}}}}}}}:={\\{{G}_{i}\\}}_{i=1}^{N}\\) is the set of the biomolecular graph G and \\({{{{{{{\\mathcal{L}}}}}}}}:={\\{{L}_{i,j}\\}}_{(i,j)}^{M}\\) is the set of the known interaction link L between biomolecules. For each node G in \\({{{{{{{\\mathcal{N}}}}}}}}\\), we denote the biomolecular structural graph \\(G=\\{{{{{{{{\\mathcal{V}}}}}}}},\\, {{{{{{{\\mathcal{E}}}}}}}}\\}\\), where \\({{{{{{{\\mathcal{V}}}}}}}}\\) is the set of atoms \\(v\\in {{{{{{{\\mathcal{V}}}}}}}}\\) and \\({{{{{{{\\mathcal{E}}}}}}}}\\) is the set of edges/bonds \\(e\\in {{{{{{{\\mathcal{E}}}}}}}}\\), so the known interaction links between biomolecules also can be denoted as the pairs of molecular atomic structural graphs: \\({{{{{{{\\mathcal{D}}}}}}}}:={\\{({G}_{i},\\, {G}_{j})\\}}_{(i,j)}^{M}\\). Herein, we study the problem of link prediction on \\({{{{{{{\\mathcal{N}}}}}}}}\\), for predicting the labels \\({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}\\) of the unobserved (test-set) interactions \\({{{{{{{{\\mathcal{L}}}}}}}}}_{U}\\) with a few observed labels \\({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{V}},{{{{{{{\\mathcal{L}}}}}}}} \\,=\\, {{{{{{{{\\mathcal{L}}}}}}}}}_{V}\\cup {{{{{{{{\\mathcal{L}}}}}}}}}_{U}\\). We also defined the adjacency matrix in the network \\({{{{{{{\\mathcal{N}}}}}}}}\\) as \\(A\\in {{\\mathbb{R}}}^{(N\\times N)}\\) where Ai,j\u2009=\u20091 if \\((i,j)\\in {{{{{{{{\\mathcal{L}}}}}}}}}_{V}\\) and Ai,j\u2009\u2009=\u2009\u20090 otherwise.\n\nAt the core of our method is combining the atomic structure scale and molecular network scale models for link prediction learning on multi-scale networks with a variational EM framework. Given the observed variables \\({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{V}}\\), unobserved variables \\({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}\\), and model parameters \u03b8,\u2009\u03d5, our framework tries to maximize the log-likelihood function of the observed interaction labels, i.e. \\(\\log {p}_{\\theta }({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{V}}| {{{{{{{\\mathcal{G}}}}}}}},\\, A)\\). It is computationally intractable to compute this log-likelihood as it requires integration over all combinations of object labels, i.e. \\(\\log {\\prod }_{l\\in {{{{{{{{\\mathcal{L}}}}}}}}}_{V}}{p}_{\\theta }({Y}_{l}| {{{{{{{\\mathcal{G}}}}}}}},\\, A)\\). Thus we instead optimize the evidence lower bound (ELBO) of the log-likelihood function:\n\nwhere \\({q}_{\\phi }({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}| {{{{{{{\\mathcal{G}}}}}}}},\\, {{{{{{{\\mathcal{D}}}}}}}})\\) can by any variation distributions over \\({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}\\), and the equation holds when \\({q}_{\\phi }({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}| {{{{{{{\\mathcal{G}}}}}}}},\\, {{{{{{{\\mathcal{D}}}}}}}})={p}_{\\theta }({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}| {Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{V}},\\, {{{{{{{\\mathcal{G}}}}}}}},\\, A)\\). The ELBO is also challenging to directly derive the maximum likelihood estimator via the EM algorithm. Therefore, we use a variational approximation of the EM algorithm to optimize the lower bound by iteratively alternating between optimizing the distribution q (i.e., E-step) and the distribution p (i.e., M-step).\n\nIn the variational E-step, the goal is to fix p\u03b8 and update q\u03d5 to minimize the KL divergence between \\({q}_{\\phi }({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}| {{{{{{{\\mathcal{G}}}}}}}},\\, {{{{{{{\\mathcal{D}}}}}}}})\\) and \\({p}_{\\theta }({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}| {Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{V}},\\, {{{{{{{\\mathcal{G}}}}}}}},\\, A)\\). In the M-step, we aim to update p\u03b8 to maximize the below likelihood function:\n\nTo prevent a computational complexity of p\u03b8 in the EM algorithm, we utilized the following pseudo-likelihood function:\n\nwhere NB(Lij) is the neighborhood information around interactions Lij.\n\nNext, we introduce how we apply the framework to link prediction in the multi-scale network by instantiating the p and q distributions with atomic structure scale q\u03d5 and molecular network scale p\u03b8 models respectively.\n\nThe variational E-step aims to update the variational distribution \\({q}_{\\phi }({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}| {{{{{{{\\mathcal{G}}}}}}}},\\, {{{{{{{\\mathcal{D}}}}}}}})\\) to approximate the true posterior distribution \\({p}_{\\theta }({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}| {Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{V}},\\, {{{{{{{\\mathcal{G}}}}}}}},\\, A)\\).\n\nTherefore, we could minimize the KL divergence between the posterior distribution and the variational distribution:\n\nWe followed the idea of 20,23 to utilize the wake-sleep algorithm44 for minimizing the reverse KL divergence (refer to Supplementary information A.2 for detailed proofs):\n\nwhere Z is a normalization term and \\({{{{{{{\\mathcal{F}}}}}}}}({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}^{{\\prime} }})\\) is the distribution on \\({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}^{{\\prime} }}\\):\n\nTherefore, we no longer need to consider the entropy of \\({p}_{\\theta }({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}| {Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{V}},\\, {{{{{{{\\mathcal{G}}}}}}}},\\, A)\\). The KL divergence could be formulated as:\n\nHerein, to model the distribution of each interaction, we parameterize \\({q}_{\\phi }({Y}_{{L}_{ij}}| {{{{{{{\\mathcal{G}}}}}}}},\\, {{{{{{{\\mathcal{D}}}}}}}})\\) with a molecular network scale GNN\u03d5 for predicting the interaction labels:\n\nwhere the probability of each interaction class is calculated by a softmax/sigmoid classifier \u03c3 based on the interaction representation F(hi,\u2009hj). F is the concatenation function to concatenate the node representations of the linking nodes i,\u2009j (i.e., F\u2009=\u2009MLP;\u2009hij\u2009=\u2009MLP(hi\u2009\u2299\u2009hj)), and the node representation hi is learned by an atomic structure scale GNN model, which is denoted as GNN\u03d5.\n\nTo model the GNN\u03d5, we use the structural graph G of the biomolecules, where we represent \\({h}_{i}^{(0)}={X}_{i}\\in {{\\mathbb{R}}}^{n\\times {d}_{n}}\\) for the atom attributes with dn as the feature dimension of atoms and \\({E}_{ij}\\in {{\\mathbb{R}}}^{m\\times {d}_{e}}\\) for the edge/bond attributes with de as the feature dimension of edge. Therefore, the molecular structural representation can be denoted as:\n\nwhere f and fAGG stand for the message and aggregation functions in the l-th layer respectively, X is the transformed node embeddings from the atomic structural graph, and neighbors(i) denote the neighbor nodes of i.\n\nNow, the sole difficulty lies in computing the distribution \\({p}_{\\theta }({Y}_{{L}_{ij}\\in {{{{{{{{\\mathcal{L}}}}}}}}}_{U}}| {Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{V}},\\, {{{{{{{\\mathcal{G}}}}}}}},\\, A)\\left)\\right.\\), which aims to predict the label distribution of an interaction \\({L}_{ij}\\in {{{{{{{{\\mathcal{L}}}}}}}}}_{U}\\) based on the surrounding node features and edge information. However, the labels of the unobserved interactions are not specified. Therefore, we propose to annotate the unobserved interactions with the pseudo-labels predicted by the molecular network scale model GNN\u03b8, so that we can approximate the distribution as follows:\n\nwhere \\({\\hat{Y}}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}\\setminus {L}_{ij}}\\) denote the predicted pseudo interaction labels.\n\nCombining them with the above objective function, we could obtain the final objective function for training the GNN model of atomic structure scale:\n\nwhere \u03b1 is a hyperparameter. Intuitively, the first term could be viewed as a knowledge-distilling process that teaches the model of atomic structure scale GNN\u03d5 by forcing it to predict the label distribution based on the predicted pseudo interaction from the molecular network model GNN\u03b8. The second term is a supervised objective which uses the given labeled interactions for training.\n\nDuring the M-step, we seek to learn the parameter \u03b8 and update p\u03b8 to maximize the objective function Eq. (3), which aims to optimize the molecular network model.\n\nHere, we parameterize the conditional distribution p\u03b8 with another graph neural network (GNN) model on molecular network \\({{{{{{{\\mathcal{N}}}}}}}}\\) because of its effectiveness:\n\nwhere p\u03b8 is formulated as a categorical distribution, and the probability of each interaction class is calculated by a softmax/sigmoid classifier \u03c3 based on the interaction representation gij. The interaction representation gij is derived from the hidden representation in the node level (i.e., nodes gi,\u2009gj) with a concatenation function F. The node representation gi,\u2009gj is learned by a molecular network scale GNN model (GNN\u03d5), which is used to learn the molecular network information with a message-passing mechanism:\n\nwhere f and fAGG stand for the message and aggregation functions in the l-th layer respectively, and neighbors(i) denote the neighbor nodes of i.\n\nTo be more specific, we use the GNN\u03d5 to generate structural representations g(0) as node initial features and feed them into the molecular network model for message passing.\n\nWe notice that the objective function Eq. (3) also relies on the expectation with respect to p\u03b8, which can be approximated by drawing a sample \\({\\hat{Y}}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}\\) from \\({p}_{\\theta }({Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}| {Y}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{V}},\\, {{{{{{{\\mathcal{G}}}}}}}},\\, A)\\). In other words, we need the atomic structure scale model GNN\u03d5 to predict a pseudo-label \\({\\hat{Y}}_{{L}_{ij}}\\) for each unobserved interaction \\({L}_{ij}\\in {{{{{{{{\\mathcal{L}}}}}}}}}_{U}\\) and combine all the labels \\({\\{{\\hat{Y}}_{L}\\}}_{L\\in {{{{{{{{\\mathcal{L}}}}}}}}}_{U}}\\) into \\({\\hat{Y}}_{{{{{{{{{\\mathcal{L}}}}}}}}}_{U}}\\). Therefore, the pseudo-likelihood can be rewritten as follows:\n\nwhere \u03b2 is a hyperparameter that is added to balance the weight of the two terms. Again, the first term can be viewed as a knowledge distillation process that injects the knowledge captured by the model of atomic structure scale GNN\u03d5 into the molecular network scale model GNN\u03b8 via all the pseudo-labels. The second term is simply a supervised loss, where we use observed interaction labels for model training.\n\nAs illustrated in Fig.\u00a01 (and Supplementary Methods), during each iteration, MUSE first performs the E-step, where it constructs structural graphs for each interaction pair, represented as structural graphs g1 and g2. Structural graph encoders (fg or fd) are then employed to generate representations of the protein/drug graphs and an interaction predictor is utilized to predict the interaction for the given biomolecule pair. This atomic structure scale model pulls the interacted graph pairs together and models label distributions conditioned on structural attributes. After optimization in E-step, the structural representations and interaction graph are fed into a molecular network scale message passing module. In M-step, information is propagated along interactions in the network for learning network topology and neighbor information. Consequently, MUSE iteratively updates the two modules in the E-step and M-step until the model converges. More importantly, the one-step model provides the interacting pseudo-labels for training the other, as part of mutual supervision. The pseudo-labels generated at the molecular network scale are also used as data augmentation for the atomic structure scale model while those from the atomic structure scale can be used for training the molecular network scale model by adding pseudo edges.\n\nAccording to the greedy learner hypothesis19, it is the different speeds at which a neural network learns from different scales that lead to a utilization imbalance. The iterative optimization of the atomic structure and molecular network scale in MUSE enables us to mitigate the hurtful imbalance and achieve stronger generalization on multi-scale representations. Mutual supervision ensures that each scale model learns in the appropriate manner, thereby facilitating the utilization of effective information at different scales.\n\nTo evaluate the effectiveness of our EM training paradigm in interaction prediction tasks, we first use a general graph neural network (GNN) to learn graph representations of proteins, consistent with competing methods16.\n\nRecalling our definition in Sec. 4, we denote the protein structural graph \\(G=\\{{{{{{{{\\mathcal{V}}}}}}}},\\, {{{{{{{\\mathcal{E}}}}}}}}\\}\\) where \\({{{{{{{\\mathcal{V}}}}}}}}\\) is the set of atoms \\(v\\in {{{{{{{\\mathcal{V}}}}}}}}\\) and \\({{{{{{{\\mathcal{E}}}}}}}}\\) is the set of edges \\(e\\in {{{{{{{\\mathcal{E}}}}}}}}\\). Herein, \\({{{{{{{\\mathcal{V}}}}}}}}\\) is a set of amino acid residues in a protein and the \\({{{{{{{\\mathcal{E}}}}}}}}\\) are obtained from the protein contact map with atomic level 3D coordinates of proteins. Following16, we choose the optimal cutoff distance of 10 \u00c5 for the presence or the absence of contact between a pair of residues, constructing the adjacency matrix Ap. For the feature matrix \\({h}_{p}^{(0)}\\), we use the protein features based on amino acid sequence, refer to24,45,46. Each embedding vector represents a set of properties for one amino acid residue, including isoelectric point, polarity, acidity and alkalinity, hydrogen bond acceptor, hydrogen bond donor, octanol-water partition coefficient, and topological polar surface area.\n\nTherefore, GNN outputs the residue-level representations in each block:\n\nwhere \\(\\hat{A}={\\widetilde{D}}^{-1/2}{A}_{p}{\\widetilde{D}}^{-1/2}\\), \\(\\widetilde{D}\\) is the diagonal degree matrix, W(l) is a learnable weight matrix for the l-th GCN layer, ReLU, BatchNorm denotes the ReLU activation function and batch normalization, respectively.\n\nFurthermore, we also implemented a geometric graph neural network for learning the effective protein structural information within our EM framework. In this setting, we denote each protein chain as a graph G with edges \\({{{{{{{\\mathcal{E}}}}}}}}\\) between the k-nearest neighbors of its nodes \\({{{{{{{\\mathcal{V}}}}}}}}\\), with nodes corresponding to the chain\u2019s amino acid residues represented by their C\u03b1 atoms.\n\nInspired by GVP-GNN47,48, we leveraged message passing over geometric vectors and scalars, in which messages from neighboring nodes and edges are used to update node embeddings at each graph propagation step as:\n\nHere, \\({h}_{v}^{(i)}\\) and \\({h}_{e}^{(j\\to i)}\\) are the embeddings of the node i and edge j\u2009\u2192\u2009i, and \\({h}_{p}^{j\\to i}\\) represents the message passed from node j to node i of proteins. For the initial geometric features, we calculate distance, direction, and angle features for each residue as node features, and construct geometric edge features between neighboring residues including distance, direction and orientation.\n\nFinally, we perform the readout operation to obtain the entire graph representation of proteins Hp.\n\nA drug structural graph also can be represented as an attributed graph \\(G=({{{{{{{\\mathcal{V}}}}}}}},\\, {{{{{{{\\mathcal{E}}}}}}}})\\), where \\(| {{{{{{{\\mathcal{V}}}}}}}}|=n\\) denotes a set of n atoms (nodes) and \\(| {{{{{{{\\mathcal{E}}}}}}}}|=m\\) denotes a set of m bonds (edges)49. We represent \\({X}_{v}\\in {\\mathbb{R}}\\) for the node attributes and \\({E}_{uv}\\in {\\mathbb{R}}\\) for the edge attributes. A graph neural network (GNN) f d learns to embed an attributed graph G into a feature vector hd. We adopt the Graph Isomorphism Network (GIN) from50, where the node and edge attributes are propagated at each iteration. Formally, the l-th iteration of a GNN is:\n\nwhere \\({h}_{v}^{(l)}\\) are the representation of node v at the l-th layer, \\({{{{{{{\\mathcal{N}}}}}}}}(v)\\) is the neighbourhood set of node v, \\({h}_{v}^{(0)}\\) is initialised with Xv encoding its atom properties. \\({g}_{AGG}^{(l)}\\) stands for the aggregation function and \\({g}_{U}^{(l)}\\) stands for the update function.\n\nAfter l graph convolutions, h(l) have captured their l-hop neighborhood information. Finally, a readout function is used to aggregate all node representations output by the l-th GNN layer to obtain the entire molecule\u2019s representation Hd:\n\nFor interaction network learning, we use the graph isomorphism networks (GIN50), which has the super-expressive power to capture graph structures and to learn molecular network information. Formally, the interaction network \\({{{{{{{\\mathcal{N}}}}}}}}\\) is presented as \\({{{{{{{\\mathcal{N}}}}}}}}=\\{{{{{{{{\\mathcal{G}}}}}}}},\\, {{{{{{{\\mathcal{L}}}}}}}}\\}\\), where \\({{{{{{{\\mathcal{G}}}}}}}}:={\\{{G}_{i}\\}}_{i}^{N}\\) is the set of the biomolecular graph G and \\({{{{{{{\\mathcal{L}}}}}}}}:={\\{{L}_{i,j}\\}}_{(i,j)}^{M}\\) is the set of the known interaction links L between biomolecules, which also could be represented as the adjacency matrix A. The initial node attributes are obtained from the model of atomic structure scale \\({H}_{G}^{(0)}=({H}_{p},{H}_{d})\\). Therefore, the molecular network scale model updates the representation of biomolecules in the l-th GIN block GNN\u03b8:\n\nwhere \\({H}_{v}^{(l)}\\) denotes the representation of biomolecule v after the l-th GIN block, \\({{{{{{{{\\mathcal{N}}}}}}}}}_{(v)}\\) is a set of biomolecules adjacent to v.\n\nAfter stacking l GIN layers, node representations HG are then used to predict existence of each link (i,\u2009j):\n\nwhere \\({{{{{{{\\rm{NCN}}}}}}}}\\) is a simple but powerful model to capture pairwise features39, and \\({h}_{i}^{(l)}\\) is the representation of the node i from HG.\n\nWhen applying the NCN model for pairwise predictions, we choose to keep the first-order neighbor and set the operator only to intersection:\n\nwhere GNN\u03b8 is the powerful GIN block in molecular network scale to output the final node representations of the last message passing layer, N(i),\u2009N(j) denote the sets of neighboring nodes for biomolecules i and j, respectively, and C is the set of common neighbor nodes between biomolecule i and j.\n\nThe limitation in the performance of link prediction tasks is from the incompleteness of the graph39. To alleviate this incompleteness, we adopted the pseudo-likelihood learning in our variational expectation-maximization framework, augmenting the molecular network graph \\({{{{{{{\\mathcal{N}}}}}}}}\\) with pseudo interactions predicted by the atomic structure scale model GNN\u03d5.\n\nFormally, when predicting the interaction Lij, we aim to utilize the edges (i,\u2009u) and (j,\u2009u) in the complete graph to compute the common neighbor Cu. However, since Liu and Lju are unknown, we let the model of atomic structure scale GNN\u03d5 output the probability of the existence for Liu and Lju as follows:\n\nwhere GNN\u03d5 represents the GNN model of atomic structure scale, \\({\\hat{Y}}_{{L}_{iu}}\\) and \\({\\hat{Y}}_{{L}_{ju}}\\) denote the probabilities of the existence of interactions i\u2009\u2192\u2009u and j\u2009\u2192\u2009u, respectively. When the probability exceeds the threshold 1\u2009\u2212\u2009t, the predicted interaction is incorporated into the interaction network graph.\n\nTherefore, the interaction network graph \\({{{{{{{\\mathcal{N}}}}}}}}\\) has been complemented softly, and then the molecular network model GNN\u03b8 on the more complete graph to give final predictions.\n\nFurthermore, this strategy has been extended to our iterative optimization process. Starting from the original network \\({{{{{{{{\\mathcal{N}}}}}}}}}^{(0)}={{{{{{{\\mathcal{N}}}}}}}}\\), we iteratively complete the graph to get the \\({{{{{{{{\\mathcal{N}}}}}}}}}^{(k)}\\) from \\({{{{{{{{\\mathcal{N}}}}}}}}}^{(k-1)}\\) with the help of the prediction of other scale model (i.e. atomic structure scale model) until the final iteration k.\n\nFor a multi-scale model M, taking two scales s0 (atomic structure scale) and s1 (molecular network scale) as inputs, its utilization rates for s0 and s1 are defined as\n\nand\n\nHere, M0 and M1 represent single-scale models obtained from the multi-scale model M, while f denotes the predicted evaluation function. The utilization rate is the relative change in accuracy between the two models within each pair. For example, u(s0\u2223s) measures the incremental impact of the atomic structure scale s0 on improving the accuracy of label predictions. It is constrained within the range of -1 to 1. A higher utilization rate indicates that the multi-scale model can benefit from incorporating this scale.\n\nFor the three multi-scale interaction prediction benchmarks, i.e. SHS27K, BioSNAP, and DeepDDI, we evaluated their performance using micro-F1 (Best-F1), Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under Precision-Recall Curve (AUPRC). In particular, for the multi-label PPI prediction, the different PPI types in the datasets we used are very imbalanced, so micro-F1 may be preferred.\n\nTo evaluate the prediction of protein inter-chain contact, we follow the setting established by31, where a positive label is assigned to each inter-chain residue pair found within 6 \u00c5 of each other in the complex\u2019s bound (i.e., structurally-conformed) state. We use Average top-k precision (P@k), Recall (R@k), and Area Under the Receiver Operating Characteristic curve (AUROC) to assess our method in predicting protein-protein interactions and the quality of the generated docking results.\n\nFollowing the previous studies18, we use Area Under the Receiver Operating Characteristic curve (AUROC), and Area Under the Precision-Recall Curve (AUPRC) to evaluate the binding-sites prediction performance.\n\nWe trained the model by using the AdamW optimizer with Pytorch (v2.0.1), pytorch geometric (v2.3.0), and Python (v3.9.16). The experiments of protein and drug interaction predictions and protein binding site predictions were performed five times on an NVIDIA GeForce RTX 4090 GPU, and the experiments on protein contact interface predictions were performed on A800 GPU with a large GPU memory. The iteration k of the EM framework was usually converged in 5-8 iterations. The hyper-parameters on each task and additional ablation study are presented in Supplementary information B.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The PPI and protein data used in this study are obtained from the previous study (HIGH-PPI), which are available in the Zenodo database under accession code https://doi.org/10.5281/zenodo.7213401. The native protein structures are obtained from PDB: https://www.rcsb.org/. The DPI data are obtained from the previous study (ConPLex), which are available on GitHub (https://github.com/samsledje/ConPLex_dev/tree/main/dataset/BIOSNAP). The DDI data are obtained from GitHub (https://github.com/isjakewong/MIRACLE/tree/main/MIRACLE/datachem). The DIPS-Plus are obtained from the previous study (DeepInteract), which is available in https://github.com/BioinfoMachineLearning/DIPS-Plus. The protein-protein binding sites dataset is obtained from https://github.com/jertubiana/ScanNet/tree/main/datasets.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The source code of MUSE is available at https://github.com/biomed-AI/MUSE. A Zenodo version is also available at https://doi.org/10.5281/zenodo.11097139 (ref. 51).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Vidal, M., Cusick, M. E. & Barab\u00e1si, A.-L. Interactome networks and human disease. Cell 144, 986\u2013998 (2011).\n\nArticle\u00a0\n CAS\u00a0\n PubMed\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nZhang, Q. C. et al. Structure-based prediction of protein\u2013protein interactions on a genome-wide scale. 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Zenodo (2024) https://doi.org/10.5281/zenodo.11097140.\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This study has been supported by the National Key R&D Program of China (2022YFF1203100), the National Natural Science Foundation of China (T2394502), and the Fundamental Research Funds for the Central Universities (Sun Yat-sen University, 22lglj08).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China\n\nJiahua Rao,\u00a0Jiancong Xie,\u00a0Qianmu Yuan,\u00a0Deqin Liu,\u00a0Zhen Wang,\u00a0Yutong Lu\u00a0&\u00a0Yuedong Yang\n\nGlobal Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China\n\nShuangjia Zheng\n\nKey Laboratory of Machine Intelligence and Advanced Computing (MOE), Sun Yat-sen University, Guangzhou, China\n\nYuedong Yang\n\nState Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China\n\nYuedong Yang\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.R. and Y.Y. conceived and supervised the project. J.R., J.X., and S.Z. contributed to the algorithm implementation. J.R., S.Z., Y.L., and Y.Y. wrote the manuscript. J.R., Y.L., S.Z., and Y.Y. discussed and performed the rebuttal experiments. All authors were involved in the discussion and proofreading.\n\nCorrespondence to\n Yutong Lu, Shuangjia Zheng or Yuedong Yang.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Zhenqiao Song, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions.\n Nat Commun 15, 4476 (2024). https://doi.org/10.1038/s41467-024-48801-4\n\nDownload citation\n\nReceived: 01 December 2023\n\nAccepted: 14 May 2024\n\nPublished: 25 May 2024\n\nVersion of record: 25 May 2024\n\nDOI: https://doi.org/10.1038/s41467-024-48801-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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+++ b/ef9f47db0b0dfac6ce23bf3140949186836605b0a747fed13a7ccb31db7eec47/metadata.json @@ -0,0 +1,137 @@ +{ + "title": "Electronic and magnetic excitations in La3Ni2O7", + "pre_title": "Electronic and magnetic excitations in La3Ni2O7", + "journal": "Nature Communications", + "published": "06 November 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53863-5/MediaObjects/41467_2024_53863_MOESM1_ESM.pdf" + }, + { + "label": "Transparent Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-53863-5/MediaObjects/41467_2024_53863_MOESM2_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.13955595" + ], + "code": [], + "subject": [ + "Electronic properties and materials", + "Magnetic properties and materials" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3901266/v1.pdf?c=1730984958000", + "research_square_link": "https://www.researchsquare.com//article/rs-3901266/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-53863-5.pdf", + "preprint_posted": "04 Mar, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The striking discovery of high-temperature superconductivity (HTSC) of 80 K in a bilayer nickelate La3Ni2O7 under a moderately high pressure of about 14 GPa ignited a new wave of studying HTSC in nickelates. The properties of the parental phase at ambient pressure may contain key information on basic interactions therein and bosons that may mediate pairing giving birth to superconductivity. Moreover, the bilayer structure of La3Ni2O7 may suggest a distinct minimal model in comparison to cuprate superconductors. Here using X-ray absorption spectroscopy and resonant inelastic X-ray scattering, we studied La3Ni2O7 at ambient pressure, and found that Ni 3dx2y2, Ni 3dz2, and ligand oxygen 2p orbitals dominate the low-energy physics with a small charge-transfer energy. Remarkably, well-defined optical-like magnetic excitations were found to soften into a quasi-static spin-density-wave ordering, evidencing the strong electronic correlations and rich magnetic properties. Based on a Heisenberg spin model, we found that the inter-layer effective magnetic superexchange interaction is much larger than the intra-layer ones, and proposed viable magnetic structures. Our results highlight that the strong bonding of Ni 3dz2 orbitals within the bilayer structure induces novel electronic and magnetic excitations setting the stage for further exploration of La3Ni2O7 superconductor.Physical sciences/Physics/Condensed-matter physics/Electronic properties and materialsPhysical sciences/Physics/Condensed-matter physics/Magnetic properties and materials", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "La3Ni2O7RIXSSIfinal.pdfSupplementary Information for electronic and magnetic excitations in La3Ni2O7", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "High-temperature superconductivity was discovered in the pressurized nickelate La3Ni2O7 which has a unique bilayer structure and mixed valence state of nickel. The properties at ambient pressure contain crucial information of the fundamental interactions and bosons mediating superconducting pairing. Here, using X-ray absorption spectroscopy and resonant inelastic X-ray scattering, we identified that Ni 3dx2\u2212y2, Ni 3dz2, and ligand oxygen 2p orbitals dominate the low-energy physics with a small charge-transfer energy. Well-defined optical-like magnetic excitations soften into quasi-static spin-density-wave ordering, evidencing the strong electronic correlation and rich magnetic properties. Based on an effective Heisenberg spin model, we extract a much stronger inter-layer effective magnetic superexchange than the intra-layer ones and propose two viable magnetic structures. Our findings emphasize that the Ni 3dz2 orbital bonding within the bilayer induces novel electronic and magnetic excitations, setting the stage for further exploration of La3Ni2O7 superconductor.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The striking discovery of high-temperature superconductivity (HTSC) at 80 K in a bilayer nickelate La3Ni2O7 under a pressure of about 14 GPa ignited a new wave of studying HTSC in nickelates1,2,3,4,5,6,7. Unlike cuprate superconductors with a Cu2+ 3d9 electron configuration, La3Ni2O7 hosts Ni ions with mixed 2\u00a0+ (3d8) and 3\u00a0+ (3d7) valences with unpaired electrons in both 3dx2\u2212y2 and 3dz2 orbitals from a Ni-O bilayer structure1,2,8,9,10,11,12,13. In particular, the molecular bonding between the two inter-layer Ni 3dz2 orbitals through the apical O pz orbital, together with Ni 3dx2\u2212y2 orbitals, are proposed by theory as a critical ingredient for the low-energy electronic structure of La3Ni2O72,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22. The orbital character governing the electronic properties of the unconventional superconductors is essential for understanding the underlying pairing mechanism. In cuprates, the small charge-transfer energy and strong hybridisation between Cu 3dx2\u2212y2 and O 2p orbitals lead to the formation of the strongly correlated Zhang-Rice singlet band, which serves as the foundation for describing the electronic properties including the superconducting pairing interaction with dx2\u2212y2 symmetry23. On the other hand, the iron-based superconductors feature relatively weaker correlation and multiple 3d bands near the Fermi surface24. The orbital-dependent correlation and the strong anisotropy in the electronic hopping result in a distinct s pairing symmetry. At first sight, La3Ni2O7 appears to be a sibling of iron-based superconductors owing to the multi-orbital nature and the bad metallicity in the undoped parental phase. However, perovskite nickelates are also known to exhibit strong electronic correlation and small charge-transfer energy25,26, resembling cuprates. Theories to date vary in their opinions on which orbitals are most relevant for the electronic properties, especially the superconductivity, in La3Ni2O78,9,15,16,17,18,19,20,21,22.\n\nThe antiferromagnetic (AFM) superexchange interaction is accepted as another important ingredient of unconventional superconductors. Upon the doping of charge carriers, the long-range AFM-ordered parental phase evolves into one with short-range AFM spin fluctuations, which may mediate the superconducting pairing. In a sizable part of the phase diagram, the interplay among spin, charge, and lattice degrees of freedom often leads to exotic ordering phases such as the periodic density modulation of charge or spin. In cuprates and iron-based superconductors, charge (CDW) and spin density waves (SDW) intertwine with superconducting phase which is regarded as being closely relevant to HTSC23,24. The bilayer structure and the multi-orbital nature of La3Ni2O7 have profound impact on its magnetism as well, which plays a pivotal role in theories on this novel superconductor1,8,27,28. Some theory suggest the importance of the interlayer antiferromagnetic coupling Jz between dz2 orbitals1,8; some others advocate that the strong interlayer coupling would cause the bilayer splitting of band structure, while in-plane magnetic exchange interactions play a dominant role in superconductivity12,15. In the as-grown La3Ni2O7 crystal at ambient pressure, resistivity measurements found a kink-like transition at around 153\u2009K implying a possible CDW or SDW state29. NMR studies found CDW order possibly mixed with SDW order in polycrystalline La3Ni2O730, and most recently SDW order was revealed in single crystal La3Ni2O731. In addition, \u03bcSR experiments suggested that a static long-range magnetic order emerges in polycrystalline La3Ni2O7\u2009\u00a0~\u00a0150\u2009K32,33. Despite the proposals of potential density waves, NMR and \u03bcSR experiments reported that the magnetic moment per Ni site is \u00a0~\u00a00.08\u2009\u03bcB and 0.3\u20130.7\u2009\u03bcB, respectively31,33.\n\nGiven the currently limited knowledge on the essential electronic and magnetic properties, such as the charge-transfer energy and the magnetic exchange interactions, experimental verification is indispensable. In this work, we employ X-ray absorption spectroscopy (XAS) and resonant inelastic X-ray scattering (RIXS) at both Ni L3-edge and O K-edge of La3Ni2O7 single crystal at ambient pressure. These spectroscopic and scattering techniques are sensitive to low-energy electronic and magnetic structures together with elementary excitations, and thus are ideally suited for tackling the core issues in La3Ni2O7.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "As-grown La3Ni2O7 crystallises in an orthorhombic structure with the space group of Amam1. We define the reciprocal space index (H,\u00a0K,\u00a0L) based on the pseudo-tetragonal unit cell (Fig.\u00a01a, b, \u201cMethod\u201d section). Figure\u00a01c shows the experimental geometry, in which the incident X-ray is linearly polarised, while the scattered X-ray is typically non-polarised but otherwise polarised if stated explicitly (see \u201cMethod\u201d section).\n\na Schematic top view of the NiO2 plane in La3Ni2O7. The solid black square represents the pseudo-tetragonal unit cell with a lattice constant aT\u2009\u00a0~ 3.833\u2009\u00c5, while the dashed black square represents the real orthorhombic in-plane unit cell when considering the tilting of Ni-O octahedra. b In-plane Brillouin zone (BZ) for the pseudo-tetragonal unit cell. c Sketch of the RIXS experimental geometry. Details of the setup are described in Method. d, e \u03c3 polarised XAS spectra of La3Ni2O7 (red filled circles) taken at the O K-edge (d) and Ni L3-edge (e), respectively. The latter is displayed after subtracting the background of La M4-edge. The calculated Ni L3-XAS (red curve) is also shown. XAS spectra measured on NiO (Ni2+) and NdNiO3 (Ni3+) (black-filled circles) are presented as references. The XAS data of Nd1.5Sr0.5NiO4 extracted from ref. 42 are also displayed. f, g RIXS intensity maps measured as a function of incident photon energy with \u03c3- (f) and \u03c0- (g) polarised photons, respectively. The corresponding XAS spectrum is superimposed as a solid white curve on each map. Both XAS and RIXS spectra were collected at 20 K at a grazing-in incident angle of 20\u00b0. h Integral RIXS spectra in (f) and (g) over the incident energy range [851.8 eV, 853.4 eV]. The grey solid bars display the multiplet calculations for the Ni L3-RIXS.\n\nFigure\u00a01d, e illustrate XAS spectra of La3Ni2O7 taken near the O K-edge and Ni L3-edge, respectively. A sizable O K- pre-edge peak at \u00a0~ 528.5 eV originates from oxygen 1s electron excitations into the unoccupied oxygen 2p ligand hole state near the Fermi level, as observed for the Zhang-Rice singlet state in cuprate superconductors34. The Ni L3-XAS data show a sharp resonant peak around 852.4 eV, followed by a broad satellite peak at a higher energy. As the Ni valence 2.5+ of La3Ni2O7 falls in between the archetypal nickelates NiO and NdNiO3, the XAS spectra of La3Ni2O7 can be qualitatively understood in relation to these two. NiO is a canonical charge-transfer insulator in the Zaanen-Sawatzky-Allen classification, whose large charge-transfer energy \u0394 (\u22485\u2009eV) suppresses the charge fluctuations between the Ni 3d and ligand oxygen 2p orbitals despite their large orbital hopping integral35. Consequently, its ground state is well described by \u03b1|3d8\u27e9+\u03b2|3d9L_\u27e9 (\u03b12\u00a0+\u00a0\u03b22 \u2272 1 and L_ denotes a ligand hole) with a dominant 3d8 character (\u03b12\u00a0\u2248 0.8)35,36. On the other hand, the perovskite NdNiO3 with a nominal 3d7 configuration is widely acknowledged as a negative charge-transfer system, where electrons from ligand oxygen spontaneously transfer onto Ni cations, resulting in a ground state with a leading 3d8L_ contribution25,26,37,38,39. Such a substantial ligand hole concentration is underscored by the pronounced pre-edge hole peak in the O K-edge XAS of NdNiO3, similar to that of La3Ni2O7 (Fig.\u00a01d). This is distinct from NiO, where the pre-peak is absent, and the unoccupied ligand states are at an elevated energy across the charge-transfer gap. For the Ni L3-XAS, the prominent resonant peak of La3Ni2O7 is also observed for NiO and NdNiO3 at a similar energy (Fig.\u00a01e), which was previously identified as the Ni 2p\u2009\u00a0\u2192 3d8 or 3d8 + 3d8L_ transitions into the half-filled eg states, respectively36,39. A broad satellite peak at a higher energy is likewise seen for NdNiO3, originating mainly from a part of its ground state wavefunction that contains additional ligand holes39,40,41. Similar spectral profiles are present at nominally half-doped nickelate Nd1.5Sr0.5NiO4 at both the O K- and Ni L3- edges42. The above suggests a predominant 3d8 occupancy on the Ni cation in La3Ni2O7, accompanied by a significant amount of ligand holes.\n\nFigure\u00a01f, g shows the incident-energy-dependent RIXS measurements of La3Ni2O7 across the Ni L3-edge. A clear low-energy excitation (~70\u2009meV) is observed near the elastic peak which will be discussed in the next section. The sharp XAS resonance at \u00a0~852.4\u2009eV decays mainly to a final state of a localised excitation at ~1\u2009eV, known as the t2g\u00a0\u2192\u00a0eg\u2009dd orbital excitation similar to NiO and NdNiO340,43,44. The band-like fluorescence excitation, resonating across the broad satellite XAS peak, stems from the delocalised Ni-O hybridised continuum states40,41. The intensity distribution of the fluorescence contracts under \u03c0 polarisation that couples stronger to the 3dz2 orbital, indicative of a smaller out-of-plane bandwidth arising from the quasi-two-dimensional structure. In addition, distinct from NdNiO3, two extra dd excitations show up in La3Ni2O7 (at around 0.4\u2009eV and 1.6\u2009eV). They exhibit stronger intensities under \u03c0 polarisation, suggesting a more prominent involvement of the 3dz2 orbital in them.\n\nTo gain a quantitative understanding of XAS and RIXS measurements, we built a double-cluster model capturing the bilayer structure of La3Ni2O7 and then carried out multiplet calculations for Ni L3- XAS and RIXS spectra (see details in Supplementary Note\u00a02). Systematic optimisations of the calculated spectra suggest that the charge-transfer energy \u0394 falls between 0 and 2 eV, pointing out the rather small-charge-transfer nature of La3Ni2O745. This result is reasonable since \u0394 is \u00a0~ 5 eV and \u00a0~ 0 for NiO and NdNiO3, respectively41,46. With the estimated range of \u0394, the ground state wavefunction of La3Ni2O7 can be deduced to approximately \u03b1|3d8\u27e9+\u03b2|3d8L_\u27e9+\u03b3|3d7\u27e9 with leading \u03b12 and \u03b22. The calculated XAS for \u0394\u00a0= 0.5 eV is shown in Fig.\u00a01e, which corresponds to a ground state with (\u03b12,\u00a0\u03b22,\u00a0\u03b32)\u2009\u00a0\u2248 (0.4, 0.3, 0.2).\n\nThe two sets of RIXS excitations centred around 0.4\u2009eV and 1.0\u2009eV in Fig.\u00a01h are well captured in the calculated RIXS spectra. The higher-energy excitation around 1.6\u2009eV is less prominent in calculation partly due to the limited degrees of freedom in the model. To further understand the nature of these excitations, we characterise these excited states in the double-cluster model by evaluating their corresponding orbital occupations and wave function configurations. The excitations at 0.4 eV involve charge transfers between the orbitals of z2 and x2\u00a0\u2212\u00a0y2 symmetry and are of mixed charge and spin type. The peak centred around 1 eV involves the transition between the dz2 and dxz/yz orbitals, which characterise the crystal-field splitting between the 3d\u2009eg and t2g orbitals. They involve relatively small movements of the ligand states, signifying almost pure dd-type excitations. Higher-energy excitations between 1.3\u00a0~\u00a01.5 eV correspond to more complex dd-type excitations, involving transitions between all 3d orbitals. The excitation energy is somewhat lower than that observed in experiment, potentially owing to the limited in-plane size of the cluster model, which may underrepresent the bandwidth of the planar orbitals. Note that there is evident fluctuation of wave function configuration weights between the local d7 and d8L_ as well as the global d7d8 and d8d8L_ over almost the entire energy range owing to the small charge-transfer energy (Supplementary Note\u00a02). Remarkably, we found that both the XAS line shape and the lower dd excitation (\u00a0~ 0.4 eV) in RIXS show marked difference upon tuning the inter-layer hopping strength mediated by the 3dz2 -OAP 2pz - 3dz2 orbital overlap in the calculation (OAP stands for the apical oxygen), underlining the importance of the inter-layer coupling for the electronic structure (Supplementary Note\u00a02). This result is consistent with previous experimental report1, and lends support to several recent theoretical works emphasising on the importance of the bilayer structure2,8,13,14,15,16,17,18,19,20.\n\nFigure\u00a02 summarises the detailed energy-momentum dependence of low-energy excitations in La3Ni2O7 taken at the incident energy of 852.4 eV corresponding to the sharp resonance peak of Ni L3-XAS. Figure\u00a02a, b show strongly dispersive excitations along directions illustrated in insets. The excitations reach maximal energy of about 70 meV at (0, 0) and (0.5, 0) while softening to zero energy (within the experimental energy resolution) at (0.25, 0.25) where a quasi-elastic scattering peak is formed. The latter signals the formation of translational symmetry breaking, i.\u00a0e, a superstructure along (\u03c0, \u03c0) direction with a size four times of the crystal lattice structure. Similar dispersive low-energy excitations also appear when excited by \u03c0 incident X-rays polarisation (Fig. S6). Along the out-of-plane direction, this mode does not exhibit sizable dispersion as a function of L, indicating its quasi-two-dimensional nature (Fig.\u00a02c).\n\na RIXS intensity maps along high-symmetry directions as indicated in the insets. Data were collected at 20\u2009K using \u03c3-polarised X-ray at the Ni L3-edge of 852.4 eV. The red filled circles depict the peak positions of magnetic excitations here and throughout all panels of this figure. b RIXS spectra at representative projected in-plane momentum transfers. The weaker excitations at \u00a0~\u00a0120\u2009meV may result from the multi-magnons. c L scan of RIXS spectra at the fixed q\u2225 = (0.035, 0.035). d Polarimetric RIXS data at q = (0.035, 0.035, 2.727). The spectra are decomposed into \u03c0\u2212\u03c0\u2032, \u03c0\u2212\u03c3\u2032, \u03c3\u2212\u03c3\u2032 and \u03c3\u2212\u03c0\u2032 components.\n\nAs both magnon and phonon could contribute to the low-energy excitations, the polarimetric RIXS was employed to analyse the outgoing X-rays linear polarisation for unravelling the origin of these excitations (see Methods). Clearly, as shown in Fig.\u00a02d, the inelastic excitation is present under the \u03c0\u2212\u03c0\u2032, \u03c0\u2212\u03c3\u2032, and \u03c3\u2212\u03c0\u2032 channels, while gets much reduced under the \u03c3\u2212\u03c3\u2032 channel. Such behaviour is in agreement with the assumption of a magnetic origin of the scattering and a recent polarimetric RIXS study on magnons in cuprates47,48. Our multiplet RIXS calculation of magnetic excitations in the double-cluster model confirmed the outgoing polarisation dependence (Fig. S5). Concerning phonons, in principle, their spectra weight should be present in the \u03c3\u2212\u03c3\u2032 channel. However, the corresponding polarimetric RIXS spectrum shows negligible spectral weight hence a minute contribution to the Ni L3-RIXS (Fig.\u00a02d). We therefore conclude that the low-energy excitations observed at the Ni L3-edge are dominated by magnons. Interestingly, in the half-doped nickelate La3/2Sr1/2NiO4, which has the same nominal Ni2.5+ valence state as La3Ni2O7, an SDW order is formed near the wavevector (0.25, 0.25) from which a low-energy magnon emerges49. The similar superstructure and the magnon softening near the order wavevector suggest an SDW order exists in La3Ni2O7. The only difference is the dispersion near \u0393 point: the magnon in La3/2Sr1/2NiO4 is acoustic-like, whereas in La3Ni2O7 they are dominantly optical-like (Fig.\u00a02a).\n\nBy fitting the magnon spectra to a damped harmonic oscillator (DHO) function \u03c7\u2033(q,\u00a0\u03c9), we extracted the peak energy and width of the magnon (Supplementary Note\u00a04)50. Three possible spin configurations consistent with the spin order at Q\u00a0=\u00a0(0.25,\u00a00.25) can be constructed: the diagonal spin-charge stripe order as in half-doped La3/2Sr1/2NiO4 where Ni2+ spin and nominal Ni3+ charge stripes intertwined (Stripe-1, Fig.\u00a03a)49; the SDW order could also be realised with homogeneous valence state Ni2.5+, i.\u00a0e.\u00a0, a double-spin stripe order (Stripe-2, Fig.\u00a03b) that is similar to the bi-collinear spin order in FeTe51; by exchanging the Stripe-1 charge stripe positions with those of a spin stripe, a third spin configuration could be achieved as a double spin-charge stripe order (Stripe-3 in Fig. S11c). For all these SDW orders, owing to the strong bilayer bonding, spins are antiferromagnetically aligned in the top and bottom NiO2 layers. To obtain the magnetic interaction parameters, we constructed an effective J1-J2-Jz Heisenberg model: H=\u2211iJzS\u2192it\u22c5S\u2192ib+\u2211\u27e8ij\u27e9\u03b1J1S\u2192i\u03b1\u22c5S\u2192j\u03b1+\u2211\u27e8\u27e8ij\u27e9\u27e9\u03b1J2S\u2192i\u03b1\u22c5S\u2192j\u03b1, where \u03b1 is the layer index for the bottom (b) or top (t) layer, J1 and J2 are the nearest-neighbour and next-nearest-neighbour exchange couplings, respectively, in a single NiO2 layer, and Jz is the inter-layer exchange coupling along the c-axis. Owing to the metallic background, the exchange couplings JS should be considered as the Weiss molecular field governing the spin dynamics for a spin density wave order. All JS values are fitted to the experimental magnon dispersion by solving the semiclassic torque equations52 (Supplementary Note\u00a06). We found that the magnon dispersions based on both Stripe-1 and Stripe-2 spin configurations agree with our RIXS result (Fig.\u00a03c and Supplementary Note\u00a06). Owing to the scattering matrix effect, the simulated acoustic magnon spectra are significantly weaker than the optical magnon, consistent with the experimental findings. In general, the inter-layer effective superexchange interaction is an order of magnitude larger than that of the intra-layer. The finding of a dominant magnetic interaction along the molecular bonding direction is in good accordance with previous theoretical calculation2. Interestingly, J2S here shows comparable strength to that in the half-doped La3/2Sr1/2NiO449. For the Stripe-2, the fitted J1S is negligibly weak comparing to the dominant inter-layer exchange interaction leading to a similar spin dynamics and magnon dispersion as in Stripe-1. Based on the above results and the currently limited information, we can conjecture the true spin configuration of La3Ni2O7 is either Stripe-1 or Stripe-2 or their mixture (see details in Supplementary Note\u00a05).\n\na The spin configurations for the spin-charge stripe order (Stripe-1). To simplify the sketch only nickel cations are shown. The blue, red and black circles represent spin up Ni2+, spin down Ni2+, and the spinless Ni3+ sites, respectively. The solid lines illustrate the in-plane pseudo-tetragonal unit cells and the grey cubics represent the Ni-O octahedra. The fitted values of J1S, J2S, and JzS based on this spin configuration are noted (see details in Supplementary Note\u00a06). b The spin configuration for the double spin stripe (Stripe-2), and the fitted value of J1S, J2S, and JzS. c The experimental magnon dispersion \u03f5q (red filled circles) and damping factor \u03b3q (black open circles) versus projected in-plane momentum transfer q\u2225 along high-symmetry directions at 20\u2009K. See fitting details in Supplementary Note\u00a04. Error bars of \u03f5q were estimated by combining the uncertainty of the elastic peak position, linear background, and the standard deviation of the fits. Error bars of \u03b3q were estimated by combining the standard deviation of the fits. The horizontal dashed line marks the total energy resolution (36\u2009meV). The results of an effective J1-J2-Jz Heisenberg model based on Stripe-1 order are overlaid. The results from the model based on Stripe-2 are also consistent with the experimental data. All calculations are performed based on single-domain configurations, without considering the effects of twinning. The blue curves represent the dispersion of two magnon modes, where the thickness of the lines and the depth of their colour represent the mode intensity. The detailed parameters are listed in Supplementary Note\u00a06.\n\nWe now took an explicit examination on the SDW order. Polarimetric RIXS was used to confirm the magnetic origin of low-energy excitations, likewise, it was applied to characterise the SDW order in La3Ni2O7. The momentum-dependent quasi-elastic SDW scattering peak shows the same trend as magnon, i.\u00a0e.\u00a0, sizable scattering intensities under \u03c0\u2212\u03c0\u2032, \u03c0\u2212\u03c3\u2032, and \u03c3\u2212\u03c0\u2032 except for \u03c3\u2212\u03c3\u2032 (Fig.\u00a04a, b), confirming the magnetic origin of such SDW order. The same polarisation dependence was found in the polarimetric resonant X-ray scattering study of the magnetic order in NdNiO353. Further insight into the nature of the SDW was gained through the energy dependence of the SDW scattering at its order wavevector across the Ni L3-edge (Fig.\u00a04c). Unlike the XAS spectra where La M4 shows a greater absorption intensity than that of Ni L3, the SDW scattering predominantly results from the Ni 3d - O 2p hybridised states. Furthermore, the SDW scattering peak exhibits a colossal polarisation dependence, namely, its intensity probed under \u03c0 polarisation is ~30 times higher than that with \u03c3 polarisation. Figure\u00a04d gives an example taken with 852.4 eV photons, which may indicate its strong association with Ni 3dz2 orbital. For that under \u03c0 polarisation, the fitted peak centre value is \u00a0~0.25 r.l.u. The half-width at half-maximum \u0393 = 0.0022\u00a0\u00b1\u00a00.0002 r.l.u. of the scattering peak corresponds to the in-plane correlation length (\u03beH = 1/\u0393) of \u00a0~\u00a027.7\u2009nm. This is comparable to that of the long-range CDW order in La1.875Ba0.125CuO4 (~20\u2009nm)54. A much broader peak with HWHM of about 0.3 r.l.u. is observed as a function of L along the direction of (0.25, 0.25, L), which corresponds to a short out-of-plane correlation length \u03beL of \u00a0~\u00a00.2 nm, establishing the quasi-two-dimensional nature of the SDW order in La3Ni2O7 (Fig.\u00a04e).\n\na, b Polarimetric SDW data. The spectra are decomposed into \u03c0\u03c0\u2032, \u03c0\u03c3\u2032, \u03c3\u03c3\u2032 and \u03c3\u03c0\u2032 components. c SDW peaks intensities as a function of incident photon energy and polarisation. The inset shows the XAS spectra at the La M4-edge and the Ni L3-edge. d SDW integrated intensity as a function of projected momentum transfer (q\u2225) along the (H, H) direction. e SDW intensity as a function of L at the fixed q\u2225 = (0.25,0.25). f SDW peaks and their Lorentzian fits along the (H, H) direction at various temperatures. g\u2013i Temperature dependence of the SDW peak area (g), the correlation length (h) and the SDW wave vector position (i). Normal and polarimetric SDW intensity was integrated over an energy window equivalent to the FWHM energy resolution of 36\u2009meV and 55\u2009meV, respectively. The error bars represent the standard deviation derived from the Lorentzian fitting of the SDW peak.\n\nThe temperature dependence of the SDW order illustrates a substantial reduction in both the intensity and the correlation length when the temperature is raised above \u00a0~\u00a0150 K (Fig.\u00a04f-h), while the SDW wavevector does not exhibit a discernible temperature dependence (Fig.\u00a04i). The discovery of the SDW with a characteristic temperature of around 150 K is in line with the transport, NMR and \u03bcSR studies on La3Ni2O729,30,31,32.\n\nOur RIXS and XAS measurements revealed the dispersive magnon and SDW order below 150 K in La3Ni2O7. Detailed analysis suggests that Ni 3dx2\u2212y2, Ni 3dz2, and O 2p orbitals dominate the low-energy physics with charge-transfer energy less than 2 eV, and the inter-layer effective magnetic superexchange interaction is much larger than the intra-layer ones. These give critical information for constructing the minimal orbital model for La3Ni2O7 superconductor. It is worth noting that the predominant inter-layer superexchange in La3Ni2O7 is rather unique owing to the strong molecular bonding of partially unoccupied inter-layer Ni 3dz2 orbitals. The situation can neither be realised in multilayer cuprates nor in multilayer nickelates with reduced valence states55,56,57. In the latter two cases, the almost fully occupied Cu or Ni 3dz2 orbitals reduce substantially the inter-layer electronic hopping or the molecular bonding strength.\n\nApart from the extraordinary bilayer structure and the associated predominant magnetic exchange interaction, the electronic structure of La3Ni2O7 fits in general into the family of Ruddlesden-Popper (RP) nickelates. The formation of the Zhang-Rice-like hole band, the small charge-transfer energy, and the well-defined dispersive magnon allude to its nature of the strong electronic correlations58. The above are typical characteristics of the strongly correlated cuprates where charge- and spin-density modulation can take place. Moreover, the occurrence of SDW order at (0.25, 0.25) is reminiscent of that in the half-doped single-layer La3/2Sr1/2NiO4, where a spin-charge stripe order exists, and implies the same picture in La3Ni2O7 as illustrated in the scenario of Stripe-1 (Fig.\u00a03a)45,49,59. Indeed in layered half-doped RP nickelates, manganites, and cobaltates, the spin-charge intertwined order is prevailing59,60,61. On the other hand, the double spin stripe order accommodating homogeneous charge density (Stripe-2, Fig.\u00a03b) may be possible too as the 3dx2\u2212y2 orbitals are more itinerant in-plane than the 3dz2 orbitals. Verifying the magnetic structure of transition metal oxides is possible via resonant soft X-ray scattering53,62. The final choice of the ground state magnetic structure depends on multiple competing interactions which may also impact on the superconductivity. Some theoretical studies taking the viewpoint of the strong inter-layer hybridisation suggest either d-wave or (d\u00a0+\u00a0is)-wave pairing symmetry with a dominant d-wave component19,20,21,22. While in the weaker interaction regime, studies predict that La3Ni2O7 host s\u00b1-wave pairing symmetry8,9,15,16,17,18.\n\nFinally, we would like to extrapolate our findings to superconducting La3Ni2O7, here, a moderately high pressure induces a structural phase transition accompanied by a few percent shrinkage of the lattice constants, and the Ni-O-Ni bonding angles between adjacent NiO6 octahedra straighten to 180\u00b01. Consequently, the electronic hopping is likely to increase, potentially suppressing density waves that compete with the superconductivity63,64. Furthermore, the magnetic superexchange Jz may get significantly enlarged due to the increased hopping along Ni-OAP-Ni. Despite the presence of Zhang-Rice singlet physics and competing orders as in cuprates, the reinforced molecular orbital bonding and the dominating inter-layer AFM interaction may be novel additions to the HTSC of such a bilayer nickelate superconductor.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53863-5/MediaObjects/41467_2024_53863_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53863-5/MediaObjects/41467_2024_53863_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53863-5/MediaObjects/41467_2024_53863_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-53863-5/MediaObjects/41467_2024_53863_Fig4_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "La3Ni2O7 sample was fabricated by the high oxygen pressure floating zone technique and the details are described in29. The sample quality was checked by X-ray diffraction (XRD) and Laue diffraction (see details in Supplementary Note\u00a01). Samples were cleaved to get a flat, clean surface before RIXS measurements.\n\nXAS and RIXS measurements were performed at Beamline I21 at Diamond Light Source65. In this work, we describe the structural properties of La3Ni2O7 referencing to a pseudo-tetragonal unit cell with cell parameters aT = bT\u2009\u00a0~ 3.833 \u00c5 and c = 20.45 \u00c5. Reciprocal lattice units (r.l.u.) are defined (where 2\u03c0/aT\u00a0=\u00a02\u03c0/bT\u00a0=\u00a02\u03c0/c\u00a0=\u00a01) with Q\u00a0=\u00a0HaT\u00a0*\u00a0+\u00a0KbT\u00a0*\u00a0+\u00a0Lc*. The crystallographic aT-c (bT-c) plane of La3Ni2O7 single crystal was aligned within the horizontal scattering plane (Fig.\u00a01c). The polar angular offsets (\u03b8 and \u03c7) of the crystal were aligned by the (002) diffraction peak, and the azimuthal offset (\u03d5) by SDW order peak, such that the c* axis lays in the scattering plane. The spectrometer arm was at a fixed position of \u03a9\u00a0=\u00a0154\u00b0 except for L scans where variable \u03a9 was employed.\n\nXAS spectra were collected with a grazing incidence angle of \u03b80\u00a0=\u00a020\u00b0 to probe both in-plane and out-of-plane unoccupied states. All XAS measurements were done at a temperature of 20 K with the exit slit opening to 30\u2009\u03bcm. Total electron yield XAS spectra were collected using the draincurrent and normalised to the incoming beam intensity. Both linear vertical (\u03c3) and horizontal (\u03c0) polarisations were used.\n\nEnergy-dependent RIXS measurements were performed at the grazing incidence angle of \u03b80\u00a0=\u00a020\u00b0 and the temperature of 20 K. The exit slit was open to 30\u2009\u03bcm corresponding to an average energy resolution of 40\u2009meV (FWHM). The incident energy range went from 851 to 855 eV in steps of 0.2 eV to fully capture the resonance behaviour across the Ni-L3 absorption peaks.\n\nMomentum-dependent RIXS measurements were performed at the resonant energy of 852.4 eV at a temperature of 20 K with the exit slit opening to 20\u2009\u03bcm corresponding to an average energy resolution of 36\u2009meV (FWHM). The momentum resolution is 0.002 r.l.u. near the SDW wavevector at the Ni L3-edge. RIXS spectra were collected using both \u03c3 and \u03c0 polarisations. The grazing out geometry (\u03b8\u00a0>\u00a0\u03a9/2) was applied for the acquisition of RIXS spectra shown in the main text.\n\nPolarimetric RIXS apparatus employs a graded multilayer designed for the Ni L3-edge with a grazing incidence angle of 20\u00b0 lying perpendicular to the scattering plane. Measurements were performed at Q = (0.035, 0.035, L) and around (0.25, 0.25, L) to analyse the outgoing X-rays linear polarisation of the magnon and SDW ordering, respectively. The total energy resolution of the polarimetric RIXS is \u00a0~ 55 meV (FWHM). Since the multilayer does not work at the exact Brewster\u2019s angle, the outgoing polarised RIXS (the indirect RIXS) from the reflection of the multilayer will be a mixture of linearly polarised spectra. The direct and indirect RIXS spectral intensities are then given by the following formula:\n\nwhere Idirect and Iindirect stands for the outgoing nonpolarised and mixed polarised RIXS spectral intensity, respectively. From the above formula, the outgoing \u03c3\u2032 and \u03c0\u2032 polarised RIXS spectra can be deduced:\n\nIn the above, R\u03c3\u2032 (R\u03c0\u2032) refers to the multilayer reflectivity of the outgoing \u03c3\u2032 (\u03c0\u2032) polarised X-ray photon. At the Ni L3-edge, R\u03c3\u2032 and R\u03c0\u2032 is 14.1% and 9.1%, respectively, based on the calibration of the multilayer.\n\nThe Ni L3-edge XAS and RIXS calculations shown in Fig.\u00a01 were performed employing a fully correlated Ni2O11 cluster model, accounting for the two corner-sharing NiO6 octahedra within the pseudo-tetragonal unit cell. The noninteracting part of the Hamiltonian integrates material-specific on-site energies and hybridisations involving Ni 3d and O 2p orbitals, along with spin-orbit coupling within the Ni core 2p and 3d shells. Full Coulomb interactions within the Ni 3d shell and between the Ni 2p and 3d shells are included, with parametrization by Slater integrals scaled at 0.8 based on atomic Hartree-Fock values66. Comprehensive details regarding model construction and relevant parameters are described in Supplementary Note\u00a02. The model was solved using the exact diagonalization method as implemented in QUANTY67.\n\nThe DFT calculations employ the Vienna ab-initio simulation package (VASP) code68 with the projector augmented wave (PAW) method69. The Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional70 is used. The energy cutoff energy for expanding the wave functions into a plane-wave basis is set to be 500 eV. The \u0393-centred k-mesh is used in KPOINTS files which are generated by VASPKIT71 with the KPT-resolved value equal to 0.02 for different unit cells. The SDW orders are calculated using the simplified rotation invariant approach based on the DFT+U method introduced by Dudarev et\u2009al.\u00a072. Then, the effective Heisenberg interactions for the SDW orders are constructed. The magnon dispersions within the linear spin wave theory are calculated using the torque equation formalism52,57. The RIXS intensity for the magnon mode in the \u03c3-\u03c0 polarisation channel is calculated following the reference73. More details can be found in Supplementary Note\u00a05 and 6.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All data shown in the main text are available via Zenodo data repository (https://doi.org/10.5281/zenodo.13955595).", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All code used to perform the XAS and RIXS calculation is available from the corresponding authors upon reasonable request.", + "section_image": [] + }, + { + "section_name": "Change history", + "section_text": "A Correction to this paper has been published: https://doi.org/10.1038/s41467-024-54993-6", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Sun, H. et al. Signatures of superconductivity near 80 K in a nickelate under high pressure. 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Work at SYSU was as well supported by the Guangdong Basic and Applied Basic Research Funds (No. 2021B1515120015), Guangzhou Basic and Applied Basic Research Funds (Nos. 202201011123, 2024A04J6417), and Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices (No. 2022B1212010008). J.C. acknowledges support from the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) through the Sejong Science Fellowship (Grant No. RS-2023-00252768).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Xiaoyang Chen, Jaewon Choi.\n\nState Key Laboratory of Surface Physics, Department of Physics, and Advanced Materials Laboratory, Fudan University, Shanghai, China\n\nXiaoyang Chen\n\nDiamond Light Source, Didcot, UK\n\nJaewon Choi,\u00a0Stefano Agrestini,\u00a0Mirian Garcia-Fernandez\u00a0&\u00a0Ke-Jin Zhou\n\nNational Synchrotron Radiation Laboratory and School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China\n\nZhicheng Jiang,\u00a0Dawei Shen\u00a0&\u00a0Donglai Feng\n\nBeijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing, China\n\nJiong Mei,\u00a0Kun Jiang\u00a0&\u00a0Jiangping Hu\n\nSchool of Physical Sciences, University of Chinese Academy of Sciences, Beijing, China\n\nJiong Mei\u00a0&\u00a0Kun Jiang\n\nNational Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing, China\n\nJie Li\u00a0&\u00a0Yi Lu\n\nSchool of Science, Sun Yat-Sen University, Shenzhen, Guangdong, China\n\nHualei Sun\n\nGuangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen University, Guangzhou, Guangdong, China\n\nXing Huang\u00a0&\u00a0Meng Wang\n\nNew Cornerstone Science Laboratory, Beijing, China\n\nJiangping Hu\n\nCollaborative Innovation Center of Advanced Microstructures, Nanjing, China\n\nYi Lu\u00a0&\u00a0Donglai Feng\n\nNew Cornerstone Science Laboratory, University of Science and Technology of China, Hefei, China\n\nDonglai Feng\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.C., X.Y.C., D.W.S., S.A., M.G.-F., D.L.F. and K.-J.Z. conducted XAS and RIXS experiments at Diamond Light Source. X.Y.C., S.A., J.C. and K.-J.Z. analysed the data. J.M., K.J. and J.P.H. performed DFT and stripe states calculations. J.L. and Y.L. performed multiplet calculations. H.L.S., X.H., and M.W. fabricated samples. X.Y.C. and Z.C.J. performed XRD and Laue measurements. K.-J.Z., K.J., Y.L., D.W.S., D.L.F. and X.Y.C. wrote the manuscript, with input from all authors. D.L.F. and K.-J.Z. are responsible for project direction and planning.\n\nCorrespondence to\n Yi Lu, Ke-Jin Zhou or Donglai Feng.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Chen, X., Choi, J., Jiang, Z. et al. Electronic and magnetic excitations in La3Ni2O7.\n Nat Commun 15, 9597 (2024). https://doi.org/10.1038/s41467-024-53863-5\n\nDownload citation\n\nReceived: 29 February 2024\n\nAccepted: 24 October 2024\n\nPublished: 06 November 2024\n\nVersion of record: 06 November 2024\n\nDOI: https://doi.org/10.1038/s41467-024-53863-5\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 41.25c-9.8 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+1,196 @@ +{ + "title": "Molecular cartography of the human down syndrome and trisomic mouse brain", + "pre_title": "Molecular Cartography of the Human and Mouse Down Syndrome Brain", + "journal": "Nature Communications", + "published": "30 September 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM3_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM7_ESM.xlsx" + }, + { + "label": "Supplementary Data 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM8_ESM.xlsx" + }, + { + "label": "Supplementary Data 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM9_ESM.xlsx" + }, + { + "label": "Supplementary Data 8", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM10_ESM.xlsx" + }, + { + "label": "Supplementary Data 9", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM11_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM12_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM13_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_MOESM14_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE280175", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE280170", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE280177", + "https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp", + "/articles/s41467-025-63752-0#Sec39" + ], + "code": [ + "https://github.com/annaminyifeng/Molecular-Cartography-of-DS-Brain", + "https://doi.org/10.24433/CO.4591687.v2" + ], + "subject": [ + "Cell type diversity", + "Developmental disorders", + "Developmental neurogenesis" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5374449/v1.pdf?c=1759317240000", + "research_square_link": "https://www.researchsquare.com//article/rs-5374449/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-63752-0.pdf", + "preprint_posted": "14 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Down syndrome (DS, or Trisomy 21) is one of the most common genetic causes of intellectual disability. DS results in both abnormal neurodevelopment and accelerated neurodegeneration, but the molecular mechanisms underlying abnormal cortical construction and aging are incompletely understood. To gain molecular insight into the prenatal neurobiology of DS, we performed single-nucleus sequencing, spatial transcriptomics, and proteomics on mid-gestational prenatal human brain tissue. We captured altered expression dynamics of lineage commitment genes and pronounced de-repression of transposable elements in DS neural progenitor cells, which suggest changes to the fate and functionality of neuronal and glial cells. Given the importance of linking human and model system pathobiology, we also performed highly multiplexed RNA in situ spatial transcriptomics on a well-established trisomic mouse model (Ts65Dn) to study the cellular landscape of the trisomic brain during early life and aging. We profiled the spatial transcriptome of >\u2009240,000 cells in the mouse brain and identified trisomy-associated gene expression patterns in the molecular control of neurogenesis and gliogenesis. Together, our study provides a comprehensive cross-species understanding of the complex multicellular processes underlying DS neurodevelopment.Biological sciences/Neuroscience/Diseases of the nervous system/Developmental disordersBiological sciences/Neuroscience/Development of the nervous system/Cell type diversityBiological sciences/Neuroscience/Neurogenesis/Developmental neurogenesisDown syndrometrisomy 21neurodevelopmentspatial transcriptomicsneurogenesis", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryTable1PrenatalSamples.xlsxBrain samples used for snRNA-seq, Slide-seq, and proteomicsSupplementaryTable2snRNAseqDEGs.xlsxTable of differential gene expression results from snRNA-seq datasetSupplementaryTable3snRNAseqLR.xlsxTable of differentially expressed ligand-receptor pairs from the snRNA-seq datasetSupplementaryTable4DriverGenes.xlsxTable of the top 100 driver genes identified per cluster through RNA velocity analysis of the snRNA-seq datasetSupplementaryTable5snRNAseqTE.xlsxTable of differentially expressed transposable elements from the snRNA-seq datasetSupplementaryTable6MERFISHGeneList.xlsxMERFISH gene listSupplementaryTable7MERFISHDEGs.xlsxTable of differentially expressed genes from the MERFISH datasetsSupplementaryTable8MERFISHLR.xlsxTable of differentially expressed ligand-receptor pairs from the MERFISH datasets", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Down syndrome (DS, or Trisomy 21) is one of the most common genetic causes of intellectual disability. DS results in both abnormal neurodevelopment and accelerated neurodegeneration, but the molecular mechanisms underlying abnormal corticogenesis are incompletely understood. To gain molecular insight into the prenatal neurobiology of DS, we performed single-nucleus sequencing, spatial transcriptomics, and proteomics on mid-gestational prenatal human cortex. We captured altered expression dynamics of lineage commitment genes and de-repression of transposable elements in DS neural progenitor cells, which suggest changes to the fate and functionality of neuronal and glial cells. Given the importance of linking human and model system pathobiology, we also performed highly multiplexed RNA in situ spatial transcriptomics on a well-established trisomic mouse model (Ts65Dn) to study the cellular landscape of the trisomic brain during early development and maturation. We profiled the spatial transcriptome of > 240,000 cells in the mouse brain and identified trisomy-associated gene expression patterns in the molecular control of neurogenesis and gliogenesis. Together, our study provides an extensive resource for understanding of the complex multicellular processes underlying DS neurodevelopment.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Down syndrome (DS) is the most common chromosomal cause of intellectual disability worldwide, affecting roughly 1 in 700 live births1. DS is caused by the presence of an extra copy or major portion of human chromosome 21 (Hsa21) that produces a genetic imbalance. DS is associated with disrupted neurodevelopment leading to lifelong learning, memory, and language impairment, as well as early-onset dementia1,2. The phenotypic features of the DS brain originate during prenatal life3,4, during which there is marked reduction in neural progenitor cell (NPC) proliferation, differentiation, and migration5,6, corresponding to reduced cortical volume during late gestation7. Additionally, DS is marked by an augmented switch of NPCs towards a gliogenic fate8. Impaired differentiation within the oligodendrocyte lineage9, accompanied with astrogliosis10, contributes to white matter abnormalities observed in young children with DS11,12. However, there is a limited understanding of how the abnormal genomic landscape in DS disrupts specific intrauterine neurodevelopmental processes, leading to cortical dysmaturation.\n\nTo address this gap, we employed an integrated multi-omics approach using single-nucleus (sn)RNA-sequencing, high-resolution spatial transcriptomics (Slide-seq)13, and proteomics to characterize the cellular phenotypes and spatial architecture of the mid-gestation human DS brain. Of note, Slide-seq is a whole-transcriptional spatial approach in which RNA from tissue sections is transferred onto a surface covered in DNA-barcoded beads with known positions, allowing the locations of the RNA to be inferred by sequencing13. We found significant changes in pathways that regulate transcriptional and translational machinery, DNA repair, neural migration, chromatin remodeling, and in the developmental progression of NPCs to mature neurons. Importantly, given the notable changes in chromatin-modifying molecules, we identified cell-type-specific activation of transposable elements (TEs) in the developing human DS brain.\n\nTo build upon our human data, we used the Ts65Dn mouse model of DS, a widely used system for testing potential therapeutics and investigating novel pathomechanisms1. We performed imaging-based spatial transcriptomics (multiplexed error-robust fluorescence in situ hybridization; MERFISH) in the Ts65Dn mouse model at two critical time points: postnatal day 0 (P0) and 6 months (6mo)14,15. MERFISH is a massively parallel single-molecule imaging approach that enables measurement of copy number and spatial position of hundreds of unique gene transcripts. Consistent with our prenatal human dataset, we identified dysregulation of genes involved in transcriptional and post-transcriptional gene regulation, as well as NPC maintenance, proliferation, and migration. Using computational tools to quantify changes to the cellular microenvironment, we identified region- and age- specific alterations in the spatial relationship between neuronal and glial cell populations in the trisomic mouse brain.\n\nTogether, our cross-species dataset represents a rich resource for understanding the prenatal origins and trajectory of brain disorganization in human DS and Ts65Dn mice, nominating molecular pathways that may lead to impaired lineage specification and function. Our dataset of gene expression changes in the developing human DS brain and trisomic mouse brain is accessible via our accompanying web interface (https://neurodevelopment.shinyapps.io/Downsyndrome/).", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We performed snRNA-seq (10x Genomics) on prenatal human brain samples spanning the second trimester of gestation (Fig.\u00a01a). Detailed information of the tissue used is presented in Supplementary Data\u00a01. Following stringent quality control filtering, doublet removal, and batch effect correction, we profiled 122,663 nuclei from the prenatal human brain (13-19 post-conception weeks (PCW); n\u2009=\u20095 DS, n\u2009=\u20095 euploid). After graph-based clustering, cell identities were assigned based on canonical marker gene expression and automated label transfer from published prenatal human brain data16: NPCs (including radial glia (RG), cycling (CP), and intermediate progenitors (IP)) expressing SOX2, PAX6, EOMES, DLX1, or MKI67; excitatory neurons (ExN) expressing MEF2C, CUX2, or TLE4; inhibitory neurons (InN) expressing GAD1, PROX1, CNR1, SST, NPY, or LHX6; striatal neurons (InN Striatum) expressing CNTN5, ZFHX3, and NGEF; astrocyte progenitors expressing SOX2, SPARCL1, SLC1A3, and GFAP; oligodendrocyte progenitor cells (OPC) expressing SOX2, PDGFRA, SOX10, and OLIG1; oligodendrocytes expressing MBP, SOX10, and OLIG1; microglia expressing ITGAM and CSF1R; and vasculature-associated cells expressing PDGFRB, RGS5, PECAM1, and ESAM (Fig.\u00a01b, c). The number of expressed genes and transcripts per nucleus were consistent across all samples, yielding a median of 1595 genes and 2399 transcripts per nucleus (Supplementary Fig.\u00a01a).\n\na Schematic overview of the multi-omics approach utilizing single-nucleus (sn)RNA-seq and Slide-seq. b Uniform Manifold Approximation and Projection (UMAP) representation of all high-quality nuclei identified by snRNA-seq (n\u2009=\u2009122,663). Each dot represents a nucleus. Cell type identity is delineated by the solid line. c Dotplot showing gene expression of canonical cell type markers used for cell type identification. Dot color represents normalized gene expression and dot diameter represents the proportion of nuclei expressing the gene. d\u2013f UMAP representation of granular cell subtypes of d, neural progenitors, e, excitatory neurons, and f, interneurons. Nuclei are color-coded by their cell subtype identity. g Spatial representation of a representative Down syndrome (DS) brain and a magnified view of the ventricular zone, captured by Slide-seq. Each dot represents the cell type with the highest proportional representation on a given bead. Only neural progenitor cells (NPCs) are colored in the ventricular zone. h Spatial plots of genes used to delineate the ventricular zone and neocortex, including SOX2, VIM, MKI67, EOMES, DCX, MEF2C, SATB2, and TLE4. Dot color represents normalized gene expression from the 10th to 90th percentiles.\n\nBroad cell type populations were re-clustered separately to define intra-lineage heterogeneity and a complete taxonomy of the prenatal human brain. We identified five subtypes of NPCs, including outer RG (oRG) expressing TNC, LIFR, and HOPX17; ventricular RG (vRG) expressing CRYAB and FBXO3218; CP expressing MKI67, TOP2A, and PPP1R17; IP fated to an excitatory lineage expressing EOMES, SSTR2, and PPP1R1719,20; and IP fated to an inhibitory lineage (IP IN) expressing MKI67, DLX1/2, GAD2, EOMES, TOP2A, and PPP1R1720,21 (Fig.\u00a01c, d; Supplementary Fig.\u00a01b). We found six subtypes of cortical layer and/or development-specific ExN, including layer 5-6 ExN (LV-VI ExN) expressing IL1RAPL2, TLE4, HS3ST4, and ARHGEF2822,23; layer 4-6 ExN (LIV-VI ExN) expressing FOXP2, RORB, HS3ST4, TLE4, and SSTR2; layer 4-5 ExN (LIV-V ExN) expressing RORB, HS3ST4, TLE4, and SSTR2; layer 2-4 ExN (LII-IV ExN) expressing MEF2C and RORB24,25; newborn layers 2-4 ExN (NB LII-IV ExN) expressing CUX2, RORB, PRSS12, and the migratory markers UNC5D and DCC19,26,27; and newborn layer 2-3 ExN (NB LII-III ExN) expressing SLC17A6, CUX2, and PRSS1223,28, in addition to migratory markers (Fig.\u00a01c, e; Supplementary Fig.\u00a01b). We identified four subtypes of cortical InN and four subtypes of InN Striatum, including: general InN (InN 1) moderately expressing GAD1/2, PROX1, and CALB2; GAD1/2 and LHX6-expressing InN (InN 2); GAD1/2, PROX1, CNR1, and CALB2-expressing InN (InN 3); GAD1, SST, NPY, and LHX6-expressing InN (InN 4); general InN Striatum (InN S1) expressing FOXP2, IL1RAPL2, and GAD1/GAD2; InN Striatum expressing MEIS2, RELN, and GAD1/2 (InN S2); InN Striatum likely representing the D1-type medium spiny neuron (MSN) population (InN M1) expressing MEIS2, EBF1, RELN, FOXP1, and FOXP2, and lacking cortical InN markers29,30,31,32; as well as InN Striatum likely representing the D2-type MSN population (InN M2) expressing MEIS2, DRD2 and FOXP1 and lacking cortical InN markers30,31,33,34 (Fig.\u00a01c, f; Supplementary Fig.\u00a01b). Granular cell typing of vascular and glia cells are described in the Methods. We did not find changes in cellular proportions between prenatal DS and euploid brains, which is consistent with recent literature35 (Supplementary Fig.\u00a01c).\n\nTo complement the droplet-based snRNA-seq dataset, we employed Slide-seqV2 (Curio Bioscience), a near-single cell resolution spatial transcriptomics approach13, to map diverse cell types in the prenatal human DS cortex and ventricular zone. We profiled tissue samples from 14-18 PCW (n\u2009=\u20093 DS, n\u2009=\u20093 euploid; Supplementary Data\u00a01). Following library preparation, alignment, and stringent QC filtering (Supplementary Fig.\u00a01d; Methods), we used our snRNA-seq dataset to confidently deconvolute expression data from each spatial pixel into cell type signatures36 (Fig.\u00a01g). Spatial mapping of NPCs and cortical layer markers revealed cell type annotations true to their spatial location in the ventricular zone (n\u2009=\u200914,490) and neocortex (n\u2009=\u2009159,508) (Fig.\u00a01h).\n\nThe triplication of Hsa21, which encodes approximately 230 protein-coding genes and 380 noncoding genes1, leads to gene dosage imbalances that are believed to contribute to the phenotypic characteristics of DS37,38. In line with previous studies39,40, we identified notable cell type-specific transcriptional effects of Hsa21 triplication on genes within the DS critical region (DSCR) (Supplementary Fig.\u00a02a). However, the magnitude and specificity of this overexpression varied by cell subtype, suggesting that certain cellular contexts may be differentially affected by Hsa21 triplication. To investigate the gene dosage effects during prenatal DS brain development, we performed differential gene expression (DGE) analysis between all DS and euploid cell subtypes from the snRNA-seq dataset, incorporating donor replicate as a covariate to control for inter-sample variability41. All DGE results are provided in Supplementary Data\u00a02. Pathway analysis using the Gene Ontology (GO)42,43 and Reactome44 databases revealed several key themes of mis-regulated gene expression during prenatal human DS brain development, which are summarized in Supplementary Data\u00a03.\n\nSeveral overarching themes of gene expression dysregulation emerged across most major cell types in the prenatal DS brain. One of the most prominent was the consistent downregulation of cell cycle-related pathways. This pattern was evident in the negative enrichment of multiple Reactome terms, including cell cycle and its regulation, M-phase, S-phase, DNA synthesis and replication, and sister chromatid separation (Fig.\u00a02a; Supplementary Data\u00a03). These disruptions involved a broad array of key regulatory genes, such as cyclins and cyclin-dependent kinases (e.g. CDK1, CDK4, CCND2, CCNG1) as well as checkpoint regulators (e.g. CDKN1B, TP53) (Supplementary Data\u00a02). To validate transcriptional changes in TP53, we performed immunofluorescence analysis of SOX2+ NPCs (Supplementary Fig.\u00a02b), revealing significantly reduced TP53 protein levels in prenatal DS brains compared to euploid controls (p value\u2009=\u20090.006; Supplementary Fig.\u00a02c).\n\na, b Barplots of normalized enrichment score (NES) for select a, Reactome and b gene ontology (GO) biological processes across all cell subtypes identified in the prenatal single-nucleus (sn)RNA-seq dataset. Positive NES values indicate enrichment of gene sets toward the top of the ranked gene list (i.e., genes upregulated in DS), while negative NES values indicate enrichment toward the bottom (i.e., genes downregulated in DS). Comparison is between DS and euploid samples. Bars are colored according to cell subtypes. c Spatial plots of ROBO1 and SLIT1 in representative Down syndrome (DS) and euploid brains, with gene expression plotted from Slide-seq. Dot color represents normalized gene expression from the 10th to 90th percentiles. d Immunostaining for ROBO1 (green) in NEUN+ cells (pink) and DAPI-labeled nuclei (cyan) in a representative prenatal human DS and euploid brain. Scale bar is 50 \u03bcm. e Bar plots displaying ROBO1 intensity in NEUN+ cells, measured in arbitrary units (arb. units). Each dot represents the average intensity for each prenatal human brain (n\u2009=\u20093 DS, n\u2009=\u20093 euploid). Bars indicate the average intensity per condition, with error bars representing the standard error of the mean. Statistical significance was assessed using a two-sided t-test; p-value\u2009=\u20090.017. f\u2013h Volcano plots depicting differentially expressed genes (DEGs) in (f) outer radial glia (oRG), g cycling progenitors (CP), and h intermediate progenitors (IP) from the prenatal human snRNA-seq dataset. Each dot represents a gene and dots are colored according to enrichment: significantly upregulated (log2FC\u2009>\u20090, FDR\u2009\u2264\u20090.05) in teal, significantly downregulated (log2FC\u2009<\u20090, FDR\u2009\u2264\u20090.05) in purple, and non-significant genes in gray. Comparison is between DS and euploid samples. The horizontal line depicts FDR\u2009=\u20090.05, and the vertical line depicts log2FC\u2009=\u20090.\n\nWe also observed widespread dysregulation of post-transcriptional machinery in the DS prenatal brain. Multiple GO biological processes including cytoplasmic translation, ribosome biogenesis and assembly, amide and peptide biosynthesis, and protein folding were negatively enriched (Fig.\u00a02b), as were Reactome pathways related to translation initiation, elongation, and rRNA processing (Supplementary Data\u00a03). This transcriptional signature encompassed reduced expression of translation initiation factors (e.g. EIF3F, EIF3G), elongation factors (e.g. EEF1A1, EEF2), and numerous ribosomal proteins (e.g. RPL and RPS gene families) (Supplementary Data\u00a02). Autophagy also emerged as a significantly downregulated process (Fig.\u00a02a). This included core components of the ATG8 family (e.g. GABARAP, GABARAPL2), chaperone-mediated autophagy proteins (e.g. HSP90AA1, HSP90AB1, HSPA8), and subunits of the Ragulator complex (e.g. LAMTOR1, LAMTOR5) (Supplementary Data\u00a02).\n\nImmune-related pathways were similarly affected across several cell lineages. Both Reactome pathways involving innate and adaptive immune responses and GO terms related to immune regulation, viral life cycle, and cytokine production were negatively enriched (Fig.\u00a02a, b). We identified reduced expression of several immune effectors, including damage-associated molecular patterns (e.g HMGB1/2/3) and pro-inflammatory cytokine-like factors (e.g. MIF), as well as antigen presentation-associated genes (e.g. HLA-A) and ubiquitin-mediated signaling genes (UBB/C, UBA52) (Supplementary Data\u00a02).\n\nLastly, signaling through the SLIT-ROBO axis was notably impaired across several cell types. Reactome terms including ROBO receptor signaling and regulation of SLIT and ROBO expression, were negatively enriched (Fig.\u00a02a), implicating this pathway in the disruption of cortical migration45. Spatial transcriptomic analyses revealed reduced expression of ROBO1 and SLIT1 in the DS cortex (Fig.\u00a02c). This transcriptional reduction was supported by immunofluorescence staining (Fig.\u00a02d), which demonstrated diminished ROBO1 protein levels in NEUN+ cortical neurons in prenatal human DS samples (p\u2009=\u20090.017; Fig.\u00a02e). Given the role of ROBO1 in guiding NPC migration via SLIT chemorepellent signaling, these findings suggest that alterations in SLIT-ROBO signaling may contribute to impaired neuronal positioning in DS.\n\nDS NPCs, including oRG, vRG, IP, and CP, showed cell-type-specific negative enrichment in pathways associated with neuronal differentiation. These pathways encompassed GO terms such as GABAergic, and forebrain neuron differentiation, as well as neuron fate commitment (Fig.\u00a02b; Supplementary Data\u00a03). Notably, we observed the downregulation of several key lineage-specification and differentiation-associated genes, including ASCL1, SOX1/2/3, HES5, FOXO1, and BCL11B (Fig.\u00a02f\u2013h). We also found a selective increase in the expression of immediate early genes (IEGs), EGR1, FOS, JUN, and IER2 in DS NPCs, including RG, CP, and IP, but not in glial or mature neuron populations (Fig.\u00a02f\u2013h). Spatial plots from Slide-seq confirmed higher expression of IEGs in the prenatal DS brain (Supplementary Fig.\u00a02d).\n\nThe expression of non-histone chromatin-associated proteins (e.g. HMGB1/2), polycomb-group proteins (e.g. EZH2, EED), chromatin modifiers (e.g. KAT2A, AURKB, VRK1), DNA methyltransferases (e.g. DNMT1, DNMT3A), histones (e.g. H2AFZ/X/V/Y), as well as histone deacetylases (e.g. HDAC2/4) were broadly downregulated in DS NPCs (Fig.\u00a02f\u2013h; Supplementary Data\u00a02), as further illustrated in representative DS and euploid brains (Supplementary Fig.\u00a02d). In contrast, chromatin regulators USP16 and DYRK1A were upregulated, consistent with their gene dosage increase in DS (Supplementary Fig.\u00a02a). These widespread chromatin-related changes were accompanied by reduced expression of nuclear lamina-associated protein (LMNB1) (Fig.\u00a02f\u2013h). Together, these findings indicate changes in chromatin and nuclear architecture-related genes in DS NPCs, consistent with features previously associated with a senescence-like phenotype in vitro46. To support these findings, we used immunofluorescence to quantify LMNB1 intensity in SOX2+ NPCs (Supplementary Fig.\u00a02e), identifying a significant reduction of LMNB1 in DS relative to euploid brains (Supplementary Fig.\u00a02f; p-value\u2009=\u20090.0001).\n\nIn GO analysis of ExN, we observed significant negative enrichment of interleukin family signaling pathways\u2014particularly those involving IL-1, IL-4, IL-12, and IL-17\u2014alongside downregulation of NOTCH and WNT signaling, and autophagy, based on Reactome analysis (Fig.\u00a02a, b). Specifically, several immune effectors (e.g. UBB/C, HMGB1/2, TRIM28) and proteasome-associated genes (e.g. PSMD4/78/13 were downregulated in ExN populations (Fig.\u00a03a, b; Supplementary Data\u00a02). In contrast, InN showed positive enrichment of GO terms including synaptic signaling, regulation of synaptic plasticity, axon development, and cell-cell signaling (Fig.\u00a02b). Notably, several genes associated with inhibitory synapse formation were upregulated, including GABA receptor subunits (e.g. GABRB3, GABRG3), as well as MDGA2, NLGN1, NRG1, ERBB4, NTRK2/3, and CNTN5 broadly in InN populations (Fig.\u00a03c, d). These findings add to the growing body of evidence suggesting that dysregulated GABAergic signaling and synaptic development may underlie core neurodevelopmental DS phenotypes47,48.\n\na\u2013f Volcano plots depicting differentially expressed genes (DEGs) in a newborn layer II-III excitatory neurons (NB L2-3 ExN), b newborn layer II-IV excitatory neurons (NB L2-4 ExN), c general interneurons (InN 1), d LHX6-expressing interneurons (InN 2), e astrocyte progenitors (Astro P), and f microglia (Micro) from the prenatal human single-nucleus (sn)RNA-seq dataset. Each dot represents a gene and dots are colored according to enrichment: significantly upregulated (log2FC\u2009>\u20090, FDR\u2009\u2264\u20090.05) in teal, significantly downregulated (log2FC\u2009<\u20090, FDR\u2009\u2264\u20090.05) in purple, and non-significant genes in gray. Comparison is between DS and euploid samples. The horizontal line depicts FDR\u2009=\u20090.05, and the vertical line depicts log2FC\u2009=\u20090. g, h Microenvironment analysis of the Down syndrome (DS) (g) ventricular zone (VZ) and h cortex, from Slide-seq, where a central cell type is selected, and the proportion of the top 100 nearest cells relative to this central cell (y-axis) is calculated in comparison to other cell types of interest (x-axis) and across conditions. Teal indicates an increased abundance of query cells near the central cell in DS compared to control, while purple indicates a decreased abundance. Asterisks (*) denote a false discovery rate (FDR)\u2009\u2264\u20090.05. i, j Leave-one-subject-out (LOSO) analysis across brain region: i VZ, j cortex. In the LOSO approach, the microenvironmental analysis was iteratively performed while excluding one sample at a time. Mean effect sizes and standard deviations across iterations were used to assess the reproducibility of changes. Teal represents consistently upregulated interactions, purple indicates consistently downregulated interactions, and gray indicates variability in the direction of change across iterations.\n\nAstrocyte progenitors demonstrated cell-type-specific GO processes, including microtubule-based transport and movement, cilium movement and organization, as well as cell projection assembly (Supplementary Data\u00a03), as indicated by the upregulation of dynein-related genes (e.g. DNAH14, DYNC1I1), ciliary proteins (e.g. CFAP161) (Fig.\u00a03e). These processes may be critical for astrocyte migration and positioning (Fig.\u00a03e). GO analysis of microglia demonstrated positive enrichment of chromatin remodeling, alongside negative enrichment of cellular response to heat and cytokine production (Supplementary Data\u00a03). Specifically, we observed downregulation of heat shock proteins (e.g. HSPA1A/B, HSP90AA1, HSPB1) and toll-like receptors (e.g. TLR2) in prenatal DS microglia (Fig.\u00a03f).\n\nThe physical location of cells within a cellular neighborhood can offer insight into both intra- and inter-cellular communication processes that affect tissue and organ development. Therefore, we investigated whether specific cell types exhibit preferential spatial proximity in the human DS brain by employing a general linear mixed effects model on our Slide-seqV2 dataset (Methods). In the ventricular zone, our analysis revealed a sparsity of oRG near IP in DS compared to euploid (Fig.\u00a03g). No significant microarchitectural changes were observed in the cortex (Fig.\u00a03h). Considering that oRG cells differentiate into IPs and play a critical role in supporting an expanded stem cell niche49, our findings suggest that oRG cells in DS may undergo functional and positional alterations within the ventricular zone during prenatal development. To enhance the robustness of this tool and minimize the impact of sample variability, we applied a leave-one-subject-out (LOSO) approach, further reaffirming changes in oRG density near IP in the prenatal DS VZ (Fig.\u00a03i, j).\n\nWe performed RNA velocity analysis using scVelo50 to study the developmental trajectory of cell types in the DS brain, with an emphasis on lineage progression from progenitor fates to mature neuronal populations. We predicted the future states of cells in our dataset by calculating the ratio of spliced to unspliced mRNA in each cell. While samples in our dataset span gestational ages 13 to 19 PCW, this represents a limited developmental window during gestation, with closely age-matched samples in both conditions. We observed the expected differentiation trajectory from NPCs to IP, and subsequently to newborn and mature ExN and InN, in both euploid and DS conditions (Fig.\u00a04a). Compared to the euploid brain, the majority of cell types in the DS brain followed a broadly similar differentiation pattern, with a few notable deviations. In particular, we observed differences in RNA dynamics in NPCs, newborn superficial ExN, as well as cortical InN and InN Striatum. In DS NPCs, the reduced directionality and length of RNA velocity vectors suggest a decelerated differentiation process (Fig.\u00a04a)51. In newborn superficial NB ExN, the absence of RNA velocity vectors is consistent with a loss of dynamic progression, potentially reflecting reduced cellular activity or terminal differentiation (Fig.\u00a04a). Additionally, more distinct lineage trajectories are observed in the InN and InN Striatum populations, suggesting a bias in neuronal differentiation toward an inhibitory fate in the DS brain (Fig.\u00a04a). Supporting this observation, latent time analysis\u2014which estimates the relative transcriptional progression of cells along a differentiation trajectory\u2014revealed distinct temporal patterns between conditions. In the euploid brain, latent time increased from NPCs to newborn superficial neurons, reflecting a continuous and ordered maturation process. In contrast, the DS brain displayed uniformly reduced latent times across cell types (Fig.\u00a04b).\n\na Uniform Manifold Approximation and Projection (UMAP) visualization of RNA velocity trajectory analyses depicting developmental trajectories of cell types within mid-gestational euploid and Down syndrome (DS) brains from the single-nucleus (sn)RNA-seq dataset. Each arrow depicts the estimated direction and magnitude of change between cell states. b Latent time projection based on RNA velocity, capturing the relative progression of cells along pseudotemporal differentiation axes within euploid and DS brains. c Volcano plots of differential protein abundance from the proteomics analysis (n\u2009=\u20093 DS, n\u2009=\u20094 euploid). Each dot represents a protein and dots are colored according to enrichment: significantly upregulated (log2FC\u2009\u2265\u20090.25, FDR\u2009\u2264\u20090.05) in teal, significantly downregulated (log2FC\u2009\u2264\u20090.25, FDR\u2009\u2264\u20090.05) in purple, and non-significant genes in gray. Comparison is between DS and euploid samples. The horizontal line depicts FDR\u2009=\u20090.05, and the vertical lines depict log2FC\u2009=\u20090.25. d Barplot of normalized enrichment score (NES) for select gene ontology (GO) biological processes from the proteomics analysis. Positive NES values indicate pathways enriched among upregulated proteins, while negative NES values correspond to pathways enriched among downregulated proteins in DS vs euploid. Bars are colored according to the adjusted p-value (FDR), reflecting the statistical significance of the enrichment. e Heatmap depicting the NES of transposable element (TE) subclasses in each cell subtype identified in the snRNA-seq dataset. Color reflects NES values, with teal indicating positive enrichment and purple indicating negative enrichment. Comparison is between DS and euploid samples. Gray represents enrichment scores not computed by GSEA. Asterisks (*) denote an FDR\u2009\u2264\u20090.05. f Immunostaining for LINE1-ORF1 (red) in SOX2+ (green) and DAPI-labeled nuclei (cyan) cells located near the ventricular zone (VZ) in prenatal human samples. Images are representative of those observed in mid-gestational prenatal euploid and DS brains. Scale bar is 30 \u03bcm. g Bar plots displaying LINE1-ORF1 intensity in SOX2+ cells, measured in arbitrary units (arb. units). Each dot represents the average intensity for each prenatal human brain (n\u2009=\u20093 DS, n\u2009=\u20093 euploid). Bars indicate the average intensity per condition, with error bars representing the standard error of the mean. Statistical significance was assessed using a two-sided t-test; p-value\u2009=\u20090.022.\n\nWe next examined the top-ranked driver genes of cell fate transitions in the human prenatal DS brain. We identified PAX6, TNC, EGFR, and FOS as enriched drivers of cell fate in DS oRG and vRG, while these were absent from the top driver genes in euploid counterparts. In addition, examination of the driver genes regulating newborn ExN fate revealed the absence of genes implicated in neuronal state regulation and migration (e.g. ROBO2, SLIT1, DCC) in the prenatal DS brain. Instead, DS newborn ExN demonstrated driver gene expression of EOMES and PAX6, which are markers of more immature cell fate (Supplementary Data\u00a04).\n\nPrevious studies in postmortem DS adult brain tissue have reported altered abundance of proteins involved in RNA splicing and axonal dynamics35, while studies using culture systems have highlighted mitochondrial dysfunction affecting reactive oxygen species homeostasis52. In the present study, we conducted proteomic analysis on seven prenatal human forebrain samples (n\u2009=\u20094 DS, n\u2009=\u20093 euploid) spanning 13-17 PCW (Supplementary Data\u00a01). 884 proteins had differential abundance between DS and euploid, with 66.7% having increased abundance and 33.3% having decreased abundance (|log2FC\u2009|\u2009\u2265 0.25, FDR\u2009\u2264\u20090.05; Fig.\u00a04c)\u2014all of which are reported in Supplementary Data\u00a05.\n\nIn the prenatal DS brain, we observed a robust activation of immune-related pathways, as indicated by enriched GO terms including humoral immune response, complement activation, leukocyte mediated immunity, adaptive immune response, and phagocytosis (Fig.\u00a04d). This immune activation was driven in part by elevated levels of complement proteins (e.g. C1QA/B/C, C1R, C3, C4, C5, C8A/B, C9, CFB) and immunoglobulins (e.g. IGHG1/2, IGHA1, IGHM), which facilitate phagocytosis and opsonization (Fig.\u00a04c). Notably, we also observed enrichment of pathways related to proteolysis and its regulation, reflected by increased levels of proteases (e.g. PRSS1, F2/12), protease inhibitors (e.g. SERPINA3/6/12, SERPINB2, SPINT2, PZP), and ubiquitin-related genes (e.g. UBE2G1, USP16, LTN1) (Fig.\u00a04c).\n\nBeyond the broader pathway-level findings, we identified upregulation of proteins associated with apoptosis (e.g. CASP1/7/14, ELAPOR1, BCL2), oxidative stress response, and reactive oxygen species response (e.g. SOD1, SCARA3, PRDX3, TXNDC5/12, GSTP1) in the prenatal DS brain (Fig.\u00a04c). Several DNA repair proteins also showed altered abundance, with notable upregulation of BARD1, PAXX, EEPD1, MSH3, MMS19, MCM5-7, and WRNIP1, alongside reduced levels of DAXX, SETMAR, NEIL1, and EMSY (Fig.\u00a04c; Supplementary Data\u00a05).\n\nAmong the negatively enriched GO terms, we observed downregulation of pathways related to synaptic signaling, regulation of synaptic plasticity, RNA splicing via transesterification reactions, and chromatin organization (Fig.\u00a04d). Specifically, there was a decrease in protein abundance associated with glutamate release (e.g. GRM5/7, GRIK2, SLC1A6), synaptic vesicle trafficking (e.g. AMPH, SCGN, SNAP25, RPH3A), and synaptic adhesion and plasticity (e.g. LRRTM2, CNTNAP4, CBLN1, PCDH17, NRCAM) (Fig.\u00a04c; Supplementary Data\u00a05). Dysregulation of splicing machinery included core spliceosome components (e.g. CWC15, RNF113A) and regulatory factors (e.g. SRSF4, CELF3/5) (Supplementary Data\u00a05). Chromatin-related alterations included reduced abundance of histone demethylases and transferases (e.g. KDM4A, ASH1L), polycomb group proteins (e.g. BMI1), and high-mobility group proteins (e.g. HMGA1) (Fig.\u00a04c).\n\nAlthough our proteomics analysis was not at the single-cell level, we mapped the expression of differentially abundant proteins across cell types using our snRNA-seq dataset (Supplementary Fig.\u00a03a, b). In examining the snRNA-seq and proteomics in tandem, several thematic groups emerged consistently. We identified dysregulation of post-transcriptional processes, including translational regulation (e.g., NOVA1, RBM3, EIF1AD, YBX1/3, CNOT2) and m6A RNA modifications (e.g., YTHDF2/3). A second theme was the alteration of chromatin organization, marked by the decrease of chromatin-associated proteins (e.g. DPF3, NUCKS1, WDR70, MACROH2A2) in the prenatal human DS brain (Supplementary Data\u00a02; Supplementary Data\u00a05). Importantly, we also observed reduced abundance of ROBO1 and SLIT2 in the prenatal DS brain (Fig.\u00a04c), consistent with our snRNA-seq findings and immunofluorescence validation.\n\nBoth our snRNA-seq and proteomics datasets suggest profound alterations in chromatin remodeling, DNA methylation, and histone modifications in the prenatal DS brain, consistent with recent in vitro data showing global chromatin accessibility changes in DS NPCs46. Therefore, we hypothesized that alterations in the abundance of chromatin regulators and loss of heterochromatin would lead to aberrant TE mobilization, as has been shown previously53,54. To test this hypothesis, we applied SoloTE55, a tool for analyzing locus-specific TEs, on our snRNA-seq dataset to profile the TE transcriptome during prenatal DS brain development and performed differential analysis between conditions (|log2FC\u2009|\u2009> 0, FDR\u2009\u2264\u20090.05). All differentially expressed TEs are reported in Supplementary Data\u00a06. Our analysis revealed that the dysregulation of TEs, including DNA transposons and retrotransposons, demonstrated a cell-type-specific pattern (Fig.\u00a04e). Specifically, transposases were exclusively enriched in mature ExN populations of the DS brain, whereas retrotransposons showed enrichment in oligodendrocyte-lineage populations and certain InN populations (Fig.\u00a04e). Perhaps most intriguing was the selective enrichment of long interspersed nuclear elements (LINEs) in DS NPCs, NB and mature ExN, and vascular cells (Fig.\u00a04e; Supplementary Fig.\u00a03c-e). To confirm our finding of aberrant TE mobilization in DS NPCs, we performed immunostaining on prenatal human brain tissue (n\u2009=\u20093 DS, n\u2009=\u20093 euploid) using a LINE1-ORF1 antibody to detect LINE1 expression (Fig.\u00a04f). NPCs in the DS brain showed a notable increase in LINE1-ORF1 expression in SOX2+ cells (p-value\u2009=\u20090.022; Fig.\u00a04g).\n\nThe Ts65Dn trisomic mouse model is commonly used to study DS56. Given the importance of linking human and model system pathobiology, we performed MERFISH on coronal brain sections from P0 (n\u2009=\u20093 Ts65Dn, n\u2009=\u20093 euploid) and 6mo (n\u2009=\u20093 Ts65Dn, n\u2009=\u20093 euploid) male Ts65Dn mice and littermate euploids (Fig.\u00a05a), as these timepoints reflect both early and mature developmental processes across a broad age range. A panel of 500 genes was curated to identify major brain cell types, as well as biologically relevant signaling pathways, inflammatory and immune-related markers, senescence-associated signatures, and extracellular matrix components pertaining to neurogenesis (Supplementary Data\u00a07). Cells were subjected to stringent QC filtering\u2014including volume, transcript, and doublet removal\u2014normalization, and batch correction (Methods). The number of genes, transcripts, and mitochondrial RNA were consistent across all samples, yielding a median of 321.9 transcripts per cell in the P0 dataset, and 497.4 transcripts per cell in the 6mo dataset (Supplementary Fig.\u00a04a\u2013f). The spatial distribution of canonical cell type markers and the Allen Brain Atlas57 were used to label gross anatomical regions within the P0 and 6mo datasets, including the superficial and deeper cortical layers, subventricular zone, corpus callosum, caudoputamen, septal nucleus, and lower gray matter (Methods; Fig.\u00a05b, c). Dimensionality reduction using principal component analysis (PCA), unsupervised clustering using uniform manifold approximation and projection (UMAP) and shared nearest neighbor (sNN) clustering were performed on each region in the P0 and 6mo datasets.\n\na Schematic overview of the MERFISH pipeline. b, c Spatial distribution of select cell type markers and segmentation of major anatomical regions on a representative b, postnatal (P0) and c 6 month (6mo) brain sample. d Uniform Manifold Approximation and Projection (UMAP) representation of the P0 cortex. Each dot represents a cell. Cell types are delineated by the solid line and cell subtypes are denoted by different shades. e Spatial distribution of cell subtypes on a cross-section of the P0 cortex. Each dot represents an individual cell and is color-coded based on cell subtype identity. f UMAP representation of the P0 subventricular zone. g Spatial distribution of cell subtypes within the subventricular zone of the left hemisphere in the P0 brain. Each dot represents an individual cell and is color-coded based on cell subtype identity. h UMAP representation of the P0 subventricular zone. UMAP representations of the 6mo i cortex, j subventricular zone, and k corpus callosum.\n\nIn the P0 cortex, we identified 12 major clusters corresponding to the following cell populations: ExN expressing Cux2, Mef2c, Slc17a6; InN expressing Gad1 or Gad2; astrocytes expressing Aqp4, Gfap, and Slc1a3; OPCs expressing Pdgfra, Sox10, and Olig1; vascular cells expressing Pdgfrb, Kcnj8, and Pecam1; microglia expressing Cx3cr1 and Tmem119; and fibroblasts (Fibro) expressed Fbln1 (Fig.\u00a05d; Supplementary Fig.\u00a05a). Spatial mapping of neuronal and non-neuronal cells accurately reflected their respective cell type annotations within the P0 cortex (Fig.\u00a05e). In the P0 subventricular zone, 11 major clusters corresponding to neural stem cells (NSCs) expressing Clu, Aldoc, Fabp7; transit amplifying progenitors (TAPs) expressing Mki67, Ascl1, and Arx; excitatory neuroblast (ExNB) expressing Slc17a6 and Dcx; inhibitory neuroblast expressing Gad1 and Dcx; ependymal cells expressing Foxj1 (Fig.\u00a05f; Supplementary Fig.\u00a05b). The spatial mapping of NPCs and non-neuronal cells in the P0 subventricular zone confirmed their corresponding cell type annotations (Fig.\u00a05g). In the P0 corpus callosum, eight major clusters corresponding to OPCs, Astro, vascular, and microglia were identified (Fig.\u00a05h; Supplementary Fig.\u00a05c).\n\nNext, we selected broad cell types and conducted iterative clustering analyses to characterize cell subtypes in the P0 dataset, as well as in the 6mo cortex (Fig.\u00a05i; Supplementary Fig.\u00a05d), subventricular zone (Fig.\u00a05j; Supplementary Fig.\u00a05e), and corpus callosum (Fig.\u00a05k, Supplementary Fig.\u00a05f; Methods). We identified a diverse taxonomy of cell subtypes that were organized by spatial location. No significant changes in the proportion of neuronal, glial, and non-neuronal cells were observed in the P0 and 6mo cortex, corpus callosum, or subventricular zone (Supplementary Fig.\u00a06a\u2013f).\n\nTo identify changes in molecular processes perturbed during early life neurodevelopment and maturation, we compared spatial gene expression between Ts65Dn and euploid mice within the same cell types and region. DEGs were identified in nearly every cell type in the P0 and 6mo cortex, subventricular zone, and corpus callosum (Supplementary Data\u00a08). In trisomic P0 NPCs, including NSCs (Fig.\u00a06a), TAPs (Fig.\u00a06b), and ExNB (Fig.\u00a06c), we identified significant dysregulation in several signaling pathways. Specifically, the WNT pathway showed upregulation of Dvl1, Fzd7/8, Lrp5/6, Gsk3b, and Dkk3, alongside downregulation of Apc, Fzd5, Rac1, and Csnk1a1. The SHH pathway exhibited upregulation of Sufu, and Gli2/3, with downregulation of Nras and Fgfr2. Additionally, the NOTCH pathway demonstrated upregulation of Notch 1/2/3, Rcan1, Hes1, and Tle1/3, and downregulation of Tle4 and Itch in Ts65Dn mice (Fig.\u00a06a\u2013c; Supplementary Data\u00a08).\n\na\u2013c Volcano plots depicting differentially expressed genes (DEGs) in (a), neural stem cells (NSCs), b transit amplifying progenitors (TAPs), and c excitatory neuroblasts (ExNB) from the postnatal (P0) MERFISH subventricular zone (SVZ) dataset. Each dot represents a gene and dots are colored according to enrichment: significantly upregulated (log2FC\u2009>\u20090, FDR\u2009\u2264\u20090.05) in teal, significantly downregulated (log2FC\u2009<\u20090, FDR\u2009\u2264\u20090.05) in purple, and non-significant genes in gray. Comparison is between Ts65Dn and euploid samples. The horizontal line depicts FDR\u2009=\u20090.05, and the vertical line depicts log2FC\u2009=\u20090. d Microenvironment analysis of the P0 corpus callosum and spatial plots depicting increased proximity of microglia surrounding oligodendrocyte progenitor cells (OPCs) and decreased proximity of OPCs surrounding astrocytes in Ts65Dn relative to euploid. In this analysis, a central cell type is selected, and the proportion of the top 100 nearest cells relative to this central cell (y-axis) is calculated in comparison to other cell types of interest (x-axis) and across conditions, with corrections made using the Benjamini-Hochberg method. Teal indicates an increased abundance of query cells near the central cell in Ts65Dn compared to control, while purple indicates a decreased abundance of query cells near the central cell in Ts65Dn compared to control. Asterisks denote FDR\u2009\u2264\u20090.05. e Microenvironment analysis of the 6mo subventricular zone and spatial plots depicting increased proximity of astrocytes surrounding transit amplifying cells (TAPs) and OPCs in Ts65Dn relative to euploid.\n\nThese changes in trisomic NPCs were accompanied by disruptions in genes associated with transcriptional and translational regulation, including Ep300 and Eif4g1, as well as tRNA synthetases such as Wars, Qars and Vars/2, and epitranscriptomic factors including Fto, Ythdc1, Ythdf1, Alkbh5, and Igfbp2 (Fig.\u00a06a\u2013c). The expression of genes integral to various DNA repair pathways was altered in trisomic mice, including those involved in double-strand break repair such as Rad50, Topbp1, Nbn, and Atm; cell cycle checkpoint regulators, such as Hus1, Cdkn1c, Ccna2, Ccne2, and Trp53; and DNA damage response, such as Chek2 (Supplementary Data\u00a08).\n\nKey genes involved in regulating NSC self-renewal, proliferation, and maintenance, such as Vcam1, Rac1, Creb1, Jak1/2, Sirt1, Mapk1, and Fgfr2, as well as the astrocytic markers Fabp7 and Slc1a3, were downregulated in trisomic NSCs (Fig.\u00a06a; Supplementary Data\u00a08). Previous reports have shown that NSC proliferation in neurogenic niches was reduced in a cell-autonomous manner following the knockdown of Fabp7 and Slc1a358,59. The transcription factor Olig2, a known triplicated gene in Ts65Dn and key regulator of oligodendrocyte differentiation60, was upregulated in trisomic NSCs. Overexpression of Olig2 is known to also impair NSC proliferation, induce premature cell cycle exit of InN precursors, and downregulate pro-neural factors61. As cells progressed towards TAPs and NBs, we observed downregulation of proliferation markers Mki67 and Egfr, the nuclear lamina marker Lmnb1, along with migration markers Unc5d, Dcx, Satb2, and Rac1 (Fig.\u00a06b, c). In 6mo mice, trisomic NPCs showed elevated levels of mature astrocytic markers, including Gfap and Aqp4 (Supplementary Data\u00a08).\n\nWe next investigated whether trisomy is associated with changes to the composition of the cellular microenvironment in our mouse model, as we did in the human brain (Methods). In the trisomic P0 corpus callosum, we found an enrichment of microglia cells near OPCs, while also observing a sparsity of OPCs near astrocytes (Fig.\u00a06d). In the trisomic P0 and 6mo cortex, we found fewer OPCs near deep-layer ExN (Supplementary Fig.\u00a07a, b). No significant changes were identified in the P0 subventricular zone (Supplementary Fig.\u00a07c). Fewer astrocytes were observed near OPCs, OLs, vascular cells, ExN, and InN in the trisomic 6mo cortex (Supplementary Fig.\u00a07b). No significant microenvironmental changes were identified in the 6mo corpus callosum (Supplementary Fig.\u00a07d). The presence of astrocytes near TAPs was increased in the trisomic 6mo subventricular zone (Fig.\u00a06e) suggesting the involvement of glial cells in regulating adult neurogenesis and shaping neuronal precursor development. Application of the LOSO approach across datasets yielded consistent results, supporting the robustness of our findings and indicating that observed changes are not driven by individual biological variability (Supplementary Fig.\u00a07e\u2013j).\n\nWe identified both overlapping and divergent patterns of molecular dysregulation between the human and mouse datasets, which is unsurprising given previous literature noting limitations of the Ts65Dn mouse model62. In the prenatal human brain, we identified widespread post-transcriptional dysregulation across multiple levels within the NPC population, spanning translation initiation and elongation (e.g., EIF3F), the epitranscriptome (e.g., YTHDF2/3), ribosomal function, and peptide processing (e.g., RPL18). In P0 Ts65Dn NPCs, despite our analysis being limited to a focused gene panel, we similarly observed dysregulation of genes at several of these regulatory layers, including translational machinery (e.g., Eif4g1), the epitranscriptome (e.g., Ythdc1), and tRNA synthetases (e.g., Qars). Another recurring theme of dysregulation in both datasets included genes associated with DNA repair and cell cycle regulation, including checkpoint control (e.g., TP53; Trp53) and cyclin-related genes (e.g., CDKN1B; Cdkn1c), particularly in NPCs.\n\nProteomic analysis from the prenatal human brain showed broad upregulation of immune-related proteins, including components of both innate and adaptive immune pathways. In contrast, immune gene dysregulation was comparatively limited across development in the Ts65Dn data. Key interferon signaling components, such as Ifnar1/2 and Irf3, showed minimal differential expression in cortical microglia and astrocyte populations (Supplementary Data\u00a08). However, by six months of age, expression of the interferon GTPase Mx1 emerged across multiple cortical cell types, potentially signaling the early stages of a neuroinflammatory response (Supplementary Data\u00a08).\n\nOne key finding from our human DS dataset was widespread TE activation across multiple cell types, with elevated expression of LINE1 elements in NPCs and ExN. To investigate the presence of aberrant TE expression in the 6mo Ts65Dn model (n\u2009=\u20093 Ts65Dn, n\u2009=\u20093 euploid), we applied the SoloTE pipeline to a previously published snRNA-seq dataset63 and performed differential analysis between conditions\u2014the results of which are reported in Supplementary Data\u00a09. Globally, TEs did not demonstrate the same degree of cell type-specific de-repression as in the human dataset (Supplementary Fig.\u00a07k). We observed cell type-specific enrichment of distinct TE subfamilies, including retrotransposons in InN and OPCs, transposases in microglia, and helicases in fibroblasts, in Ts65Dn compared to euploid controls (Supplementary Fig.\u00a07l). The mechanisms of cell type- and developmentally-regulated TE activation in the DS brain thus warrant further experimental investigation.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-63752-0/MediaObjects/41467_2025_63752_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "DS poses a unique and complex neurobiological challenge given the multicellular impact of Hsa21 triplication and the subsequent cascade of developmental consequences. The early life origins of cortical disorganization are poorly understood in DS, as are the mechanisms of disrupted neurogenesis and gliogenesis. In this study, we created an atlas of the molecular and cellular architecture of the human DS and trisomic mouse brain. We profiled > 120,000 cells in the prenatal human brain by snRNA-seq and > 240,000 cells in the mouse brain by MERFISH to characterize developmental and brain region-specific gene programs that are disrupted. Given the need to bridge the translational gap between mouse models and human disease, this study serves as an important discovery platform for uncovering conserved and divergent DS-associated pathomechanisms.\n\nHerein, we provide a taxonomy of cellular diversity in the DS brain, characterizing DEGs and alterations to cellular architecture in the human and mouse. In the prenatal human brain, we identify profound transcriptional changes in NPCs, including signatures of altered post-transcriptional regulation, as well as altered cell fate and migration. Our findings from the prenatal human brain support recently published studies that show significant changes to 3D genome structure, chromatin accessibility, and human DS NPC senescence in vitro35,46,64. While we observed downregulation of nuclear markers associated with senescence (e.g., LMNB1, TP53, HMGB1/2), we did not detect upregulation of senescence-associated secretory phenotype (SASP) markers or cyclin-dependent kinase inhibitors, suggesting the absence of a full senescent phenotype at this developmental stage. The broad transcriptional downregulation of key chromatin regulators (e.g., HDAC1/2. EZH2, EED, KAT2A, AURKB, VRK1) may suggest a chromatin landscape that is permissive to senescence-like states46,65,66. This partial phenotype may reflect a state of replicative stress in NPCs, consistent with the progeroid features of DS and the known association of senescence with developmental timing and genomic instability in related syndromes67. Importantly, this interpretation highlights the need for longitudinal studies across multiple timepoints to define the temporal onset of senescence and its contribution to DS neuropathology. In addition, we identify profound downregulation of SLIT-ROBO signaling in the prenatal human DS brain, implying that disruptions to canonical guidance cues necessary for neuronal precursor and cortical neuron migration may contribute to cortical dysplasia68. We also reveal the altered spatial positioning of DS oRG in the ventricular zone.\n\nFurther, both our transcriptomics and proteomics data suggest common mis-regulation of genome integrity, cell cycle regulation, and DNA damage response during prenatal DS brain development. This builds upon previous studies that have characterized disruption in mRNA translational machinery and genotoxic stress, as well as global immune remodeling in the DS brain69,70,71. Interestingly, our proteomics analysis reveals hyperactivation of the complement system during gestational DS development, potentially triggering proteolytic cascades and the classical inflammatory response, while also enhancing both innate and adaptive immune activity72. Hyperactivation of the immune system may play a significant role in neuroinflammation during gestation, which is known to contribute to long-term alterations in brain development73. Additionally, the complement system is involved in the radial migration of pyramidal ExN and InN during normal brain development74,75. While further research is needed to validate the role of complement activation during prenatal DS brain development, our proteomics findings implicate this pathway in abnormal DS corticogenesis.\n\nA notable finding from our study is the lineage-restricted activation of TEs in the human prenatal DS brain, including aberrant de-repression of LINEs in DS NPCs and ExN. LINEs have been reported to be non-randomly activated during neurogenesis, safeguarding NPCs from precocious differentiation76 while also functioning as potent cis-regulatory elements that influence gene expression77. In humans and mice, LINEs have also been reported to act as long non-coding RNAs, playing key roles in regulating NPC differentiation, migration of post-mitotic neurons, and affecting cell type proportions78,79. Based on these established functions, we propose that de-repression of TEs as an emerging hallmark of DS that may contribute to altered neural fate specification, impaired corticogenesis, innate and adaptive immune activation, and potentially accelerated aging. Interestingly, we did not identify evidence of the cytosolic DNA-sensing cGAS/STING activation in snRNA-seq or proteomics. However, by proteomics we observed the downregulation of VPS37A (Supplementary Data\u00a05), a core component of the endosomal sorting complex required for transport (ESCRT-I) that has been implicated in regulation of LINE retrotransposition80. Further work is needed to understand mechanisms of LINE1 activity in the prenatal human DS brain. Additionally, while this present study did not directly investigate the specific mechanisms by which Hsa21 triplication influences TE de-repression, it is plausible that overexpression of certain epigenetic regulators encoded on Hsa21 contributes to this phenomenon early in development. Genes such as DNMT3L, N6AMT1, MIS18A, DYRK1A, HMGN1, which are involved in DNA methylation and chromatin maintenance, may disrupt the epigenetic landscape through gene-specific or multi-gene cascades, thereby facilitating LINE1 and other TE activation77,81,82. Future work focused on dissecting the role of these genes, individually and in combination, will be essential in elucidating their impact on TE regulation and downstream effects on NPC development in DS.\n\nIn the P0 Ts65Dn mice, NPCs show mis-expression of genes associated with key neurodevelopmental processes, including self-renewal, differentiation, and migration, consistent with prior studies reporting reduced proliferative capacity in neonatal Ts65Dn NSCs83. Notably, the downregulation of Mki67 and Dcx suggests impairments in proliferative capacity and neuroblast migration, consistent with prior observations of reduced cycling cells in S phase in the neurogenic regions such as the dentate gyrus and lateral ventricle84. Our transcriptional analysis supports the concept of early-stage progenitor dysfunction resulting from longer cycle duration and reduced neurogenesis83. Such deficits likely underlie hypocellularity and disrupted cortical development observed postnatally, suggesting that cell cycle alterations in Ts65Dn NPCs may be a key mechanistic driver of downstream neurodevelopmental abnormalities. However, the precise molecular drivers and temporal onset of these impairments warrant further mechanistic investigations.\n\nBy 6 months of age, the NSC transcriptional prolife shifts, reflecting changes associated with glial lineage specification. This temporal transition coincides with morphological and molecular alterations in the neurogenic niche, most notably an increased astrocytic presence around TAPs. The elevated expression of astrocytic markers such as Gfap and Aqp4 at this stage is suggestive of an emerging reactive glial phenotype, consistent with prior reports of astrocyte hypertrophy and proliferation that arise during DS aging85,86\u2014supporting the model of progressive astrogliosis and neuroinflammation in DS.\n\nOur study has several technical and conceptual limitations. There are potential inconsistencies in the anatomical plane of sectioning and quality of prenatal human brain tissue, given the nature of postmortem tissue. However, only similar brain regions were compared for differential analysis. Prenatal human brain tissue was studied over a narrow mid-gestational window due to the pragmatic limitations on obtaining this tissue. Sample size may be a further limitation, and future studies with expanded cohorts of human prenatal tissue could provide additional insights. Imaging and slide-based spatial transcriptomics methods each have advantages and limitations, including differences in resolution, which have been reviewed elsewhere87. MERFISH, like other imaging-based spatial transcriptomics, relies on cell boundary segmentation to assign transcripts to cells, which can lead to false transcript assignment88. This was mitigated through the use of rigorous quality control filtering across datasets. MERFISH also relies on a manually curated list of finite gene targets, which biases the potential findings and conclusions. The Ts65Dn trisomic mouse model is widely used but has significant biological limitations in the extrapolation to the human condition89,90.\n\nThis work is primarily intended as a resource and discovery dataset to generate hypotheses for future mechanistic study. This work underscores the importance of investigating mechanisms of NPC dysfunction in DS, as this may be an early driver of abnormal corticogenesis. Specifically, experiments should explore the biological cascade triggering TE de-repression. Does NPC senescence and ensuing loss in chromatin structure, as described by Meharena et al.46, trigger TE-depression, or is the converse true, with TE de-repression triggered through loss of heterochromatin, which may then promote senescence? Recent studies have shown promising results using reverse transcriptase inhibitors to target TEs in trisomic mice91, but implications in human NPCs are unclear. Our work also highlights changes in gene programs associated with cell migration (e.g. ROBO/SLIT), and this may benefit from experiments in human organoids. Additionally, future studies in alternative DS mouse models, such as the Ts66Yah, which lacks triplication of genomic regions unrelated to Hsa2192, will be important to define model-specific changes and human disease relevance.\n\nDespite limitations, our extensive cross-species resource of spatially informed gene expression in the DS brain provides important insights into candidate signaling pathways and molecular processes that are targetable in DS.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Prenatal human brain samples (13-19 gestational weeks) were obtained from the Human Developmental Biology Resource (HDBR, United Kingdom), under a research ethics agreement from Clinical Trials Ontario (Study # 3227). Postmortem interval (PMI) for HDBR samples ranged between 30\u2009min to 3\u2009hours and were transported to the clinic in PBS media. Ten fresh-frozen brain tissues (n\u2009=\u20095 DS, n\u2009=\u20095 euploid) were obtained for snRNA-seq and stored at \u221280\u2009\u00b0C until processing. Six optimal cutting temperature compound-embedded (OCT) brain tissue (n\u2009=\u20093 DS, n\u2009=\u20093 euploid) were obtained for Slide-Seq and stored at \u221280\u2009\u00b0C until processing. Eight fresh-frozen brain tissues (n\u2009=\u20094 DS, n\u2009=\u20094 euploid) were obtained for proteomics and stored at \u221280\u2009\u00b0C until processing. (Supplementary Data\u00a01). Trisomy 21 was confirmed by karyotyping. Samples were kept on dry ice as long as possible to preserve RNA integrity during downstream processing.\n\nNuclei were extracted from ten individual brain samples and processed using the 10x Genomics Chromium System. Libraries were sequenced using the Novaseq 6000 on S1 flow cell at the Centre for Applied Genomics (TCAG; The Hospital for Sick Children) in Toronto, ON, Canada. To ensure data quality, cells from individual replicates underwent stringent quality control filtering and were normalized using SCTransform. Cells with \u2265 5% mitochondrial genes, \u2264 200, and \u2265 2,500 unique feature counts were excluded from the datasets. Following, individual replicates were integrated, and Harmony was used to correct for potential batch effects. Seurat v5 (RRID:SCR_016341) was used to perform data normalization, dimensional reduction using PCA, and graph-based clustering with UMAP.\n\nCluster identity was assigned by using known literature cell type annotations and described in the main text. Sub-clustering within each broad cell type with batch effect correction was performed to identify granular cell subtypes. Within vascular cells, we found a pericyte population (Vasc 1) expressing PDGFRB and RGS5, and an endothelial population (Vasc 2) expressing PECAM1, ESAM, and CLDN5. Re-clustering of immune cells revealed one population of homeostatic microglia (Micro 1) expressing ITGAM, CSF1R, and PTPRC, while lacking phagocytic (AIF1, CD68) and reactive markers (TREM2). We identified one population of astrocyte progenitors expressing SLC1A3, NFIA, and TNC, while lacking mature markers (AQP4). To confirm our cell type labeling, we performed automated cell type transfer using a developing human brain dataset93 and whole mouse brain dataset94 using CellTypist95.\n\nCryosectioning was performed with consideration of anatomical landmarks, specifically targeting regions where the ventricular zone or cortical layers were discernible. OCT-embedded brain tissue was sectioned at a thickness of 10 \u03bcm at \u221221\u2009\u00b0C using the CM1950 Leica cryostat and immediately mounted onto a 10\u00d710\u2009mm Slide-seq chip. In cases where the tissue covered less than 50% of the tile, an additional serial section was placed adjacently on the same tile to increase coverage. Libraries were sequenced on a Novaseq X flowcell 10B (100 cycles). Genome alignment, filtration, and normalization were performed using the Curio Seeker bioinformatic pipeline as per the manufacturer\u2019s recommendations.\n\nTo reconstruct the spatial mapping of gene expression, the Curio Seeker bioinformatics pipeline was performed according to the manufacturer\u2019s protocol using the GRCh37 genome reference from UCSC. Pixels with \u2265 5% mitochondrial genes and \u2264 200 unique features were excluded from the dataset. RCTD was used to decompose RNA sequencing mixtures into individual cell, referencing our human snRNA-seq dataset after excluding InN striatal cell types, given the striatum was not represented in the spatial dataset96. Doublets uncertain and rejects were removed from the final object prior to downstream analyses. As the entirety of the tile was not covered by tissue, we adapted the CellSelector tool in Seurat v5 (RRID:SCR_016341) to select one sample per tile, remove additional off-target beads and demarcate the ventricular zone and cortex for each individual sample97. Replicates were then normalized using SCTransform, integrated, and scaled using Seurat v5 (R v4.2.1).\n\nTo identify changes in the relative abundance of cell types during DS neurodevelopment in our human and mouse datasets, we computed the proportion of cells for a specific subtype by dividing its count by the total number of cells in the respective replicate. Statistical significance was calculated using an unpaired two-tailed t-test, and corrections for multiple comparisons were implemented using Benjamini-Hochberg (BH).\n\nDGE analysis was performed using the MAST framework, applying two-sided tests and using biological replicate identity as a covariate to minimize inter-sample variability and improve detection of condition-specific transcriptional changes. FDR correction was applied using the BH method across all datasets. Genes expressed in at least 5% of nuclei within each cell subtype were retained. For the human snRNA-seq dataset, raw UMI counts were extracted from the \u201cRNA assay\u201d of the Seurat object and normalized. For the human snRNA-seq dataset, genes with a |log2FC\u2009|\u2009> 0 and FDR\u2009\u2264\u20090.05 were considered statistically significant. A |log2FC\u2009|\u2009> 0 and FDR\u2009\u2264\u20090.05 were used for the MERFISH datasets. Functional enrichment analysis was performed for DEGs within each cell subtype using GSEA. Pathway analysis was conducted using GO Biological Processes and Reactome databases with the ClusterProfiler package (v4.10.1). Statistically significant enrichment for GO pathways were determined with FDR\u2009\u2264\u20090.05. Manual selection of relevant pathways was plotted in the figures.\n\nBAM files from individual snRNA-seq replicates were preprocessed using the Velocyto command line in Velocyto v0.17.1798. The human reference genome GRCh38/hg38 was retrieved from the UCSC genome browser. Output loom files from individual replicates were integrated to generate a new count matrix with the top 2000 variable features. Two additional count matrices were generated to separate the DS and euploid conditions. Following, scVelo v0.3.150 was used to compute expression dynamics and latent time for all three count matrices in Python.\n\nBAM files from individual snRNA-seq replicates, containing GN and CB tags, were used as input files for the SoloTE (v1.09) tool55. To optimize our alignment for the detection of multi-mapped reads, the STAR parameters were set to --winAnchorMultimapNmax 100 and --outFilterMultimapNmax 100. SoloTE selected reads that were not mapped to known genes to avoid false identification of gene-associated TEs as independent transcriptional units. Dual analysis, where reads mapped to genes, were also included in our analysis. BEDtools was used to assess the overlap between filtered reads and transposable element annotations. Expression levels were quantified at the locus level for reads with high mapping quality, while multi-mapped reads were aggregated at the subfamily level. The Dfam99 and Repbase100 databases were used to categorize the TEs into subfamilies of DNA transposons and retrotransposons. Following, DEG analysis using the MAST framework with two-sided testing and FDR correction using BH (|log2FC\u2009|\u2009> 0; FDR\u2009\u2264\u20090.05) was performed between conditions to identify DS-specific enrichment of TEs. Granular cell subtype-specific enrichment of TEs was performed using GSEA.\n\nHuman prenatal brain tissue sections were incubated at 37\u2009\u00b0C for 30\u2009min, washed 1x in 1x PBS for 15\u2009min, fixed with 70% methanol on ice for 10\u2009min, washed 1x in 1x PBS for 5\u2009min, incubated with 70% methanol on ice for 5\u2009min, or fixed with 4% paraformaldehyde for 7\u2009min, washed 3x in 1x PBS for 15\u2009min each, and incubated with normal donkey serum blocking buffer (AB_2337254) at room temperature for 1\u2009hour. Antigen retrieval was performed for the LINE1-ORF1p antibody using Tris/EDTA pH 9.0 buffer solution in a microwave, following the Abcam protocol (https://www.abcam.com/en-us/technical-resources/protocols/ihc-antigen-retrieval). The primary antibodies used for IF were goat anti-SOX2 (1:200, Abcam, catalog #Af2018), rabbit anti-LMNB1 (1:150, Abcam, #Ab16048), rabbit anti-LINE1 ORF1p antibody (1:50, Abcam, #Ab216324), mouse p53 antibody (1:200, Santa Cruz, #sc-126), mouse anti-NeuN antibody (1:200, Millipore, #MAB377), and goat ROBO1 antibody (1:100, Thermo Fisher, #PA5-18460). The secondary antibodies used for IF were Alexa Fluor 488 donkey anti-goat IgG (1:500, ThermoFisher, catalog #A-31573), Alexa Fluor 555 donkey anti-mouse IgG (1:500, ThermoFisher, catalog #A-31570), Alexa Fluor 647 donkey anti-rabbit (1:500, ThermoFisher, catalog #A-31573).\n\nHigh magnification confocal images of LMNB1/SOX2, LINE1-ORF1p/SOX2, and TP53/SOX2 immunofluorescence staining were acquired using the 60\u2009\u00d7\u20091.2 objective on Nikon A1R, running NIS Elements acquisition software. Three SOX2+ images were taken near the ventricular zone for each replicate of the prenatal human euploid and DS brains, with three replicates per slide. SOX2+ cells were selected using Imaris software (RRID:SCR_007370) based on an arbitrary intensity threshold and assessed for nuclear LMNB1 and TP53 colocalization within the imaged plane. The average nuclear LMNB1 and TP53 intensities were calculated for each plane and then averaged across replicates. Background subtraction was performed using the ImageJ software (RRID:SCR_003070) prior to calculating the average intensities of LINE-ORF1 for each plane and averaged across replicates.\n\nEpifluorescence images of ROBO1/NEUN were acquired using the 20x objective on a Zeiss Axio Imager.M2 upright microscope. For each replicate, three images were acquired in the neocortex, resulting in a total of nine images per sample. All imaging was captured using the Zen Blue software (Carl Zeiss Meditec, v3.3.89.0000). NEUN+ cells were selected using Imaris software (RRID:SCR_007370) based on an arbitrary intensity threshold and assessed for nuclear ROBO1 colocalization within the imaged plane. The average nuclear ROBO1 intensities were calculated for each plane and then averaged across replicates.\n\nPrenatal human brain tissue was lysed in 500\u2009\u00b5L of 5% SDS, 50\u2009mM TEAB (Supplementary Data\u00a01). Samples were sonicated at 30% amplitude for 15\u2009sec (5\u2009sec on, 3\u2009sec off for three cycles) with 1/8\u201d microtip, then resuspended with a pipette. Because of DNA, 1\u2009\u00b5L (250U) of Turbonuclease was added to each sample, and they were sonicated again. To ensure proper lysis, samples were resuspended with a pipette and centrifuged at 16,000xg for 5\u2009min. Supernatant was moved to a new tube, and the BCA protein concentration assay was performed. 10\u2009\u00b5g of protein material (in 5% SDS, 50\u2009mM TEAB) was reduced at 20\u2009mM DTT for 10\u2009min at 95\u2009\u00b0C and alkylated with 40\u2009mM iodoacetamide for 30\u2009min in the dark. Samples were brought up to final concentration of 5% SDS and phosphoric acid was added to a final concentration of 1.2%. 165\u2009\u00b5L of S-Trap protein binding buffer (90% methanol, 100\u2009mM TEAB) was added to 27.5\u2009\u00b5L of acidified lysate. Resulting mixture was passed through the micro column at 4000xg. The micro-column was washed 4 times with the S-Trap protein binding buffer. Each sample was digested with 1\u2009\u00b5L of trypsin (in 20\u2009\u00b5L of 50\u2009mM TEAB) for 1\u2009hr at 47\u2009\u00b0C. Prior to elution, 40\u2009\u00b5L of 50\u2009mM TEAB pH 8 was added to the column. Peptides were eluted by centrifugation at 4000 xg. Peptides were eluted 2 more times with 40\u2009\u00b5L 0.2% formic acid and 40\u2009\u00b5L of 50% acetonitrile + 0.2% formic acid. Eluted peptides were dried down and stored at \u221240\u2009\u00b0C. Control 2 was excluded from analysis due to significant variability.\n\nFor data-independent acquisition (DIA) LC-MS/MS, 100\u2009ng of the digested peptides were analyzed using a nano-HPLC (high-performance liquid chromatography) couple to MS. Samples were separated on an Aurora Elite TS column (IonOpticks Pty Ltd.). The sample in 5% formic acid was trap (300\u2009\u00b5m i.d., 0.5\u2009cm length, product#: 174500) loaded at 800\u2009bar, onto a 75\u2009\u00b5m i.d.x 15\u2009cm nano-spray emitter (packed with 1.7\u2009\u00b5m C18 beads) heated at 50\u2009C with the TS Interface. Peptides were eluted from the column with an acetonitrile gradient generated by a Vanquish Neo UHPLC System (Thermo Fisher Scientific Inc.) and analyzed on an Orbitrap\u2122 Astral\u2122. The gradient was delivered at 600\u2009nl/min from 0.8% acetonitrile with 0.1% formic acid to 6.4% acetonitrile with 0.1% formic acid over 1\u2009minute, 6.4% to 35.2% acetonitrile with 0.1% formic acid over 10\u2009min. This was followed by a column wash of 76% acetonitrile with 0.1% formic acid over 4\u2009min. The total DIA protocol is 15\u2009min. Advanced peak determination was turned on with an expected LC peak width of 6\u2009s. FAIMS was used with one compensation voltage at \u221250V. MS1 scan was from 380-980\u2009m/z with an orbitrap resolution of 240,000. Normalized AGC target of 500%, and 10\u2009ms maximum injection time in profile mode. DIA windows were set to auto with 2\u2009m/z window sizes with 0 overlap, in the precursor range of 380-980\u2009m/z. HCD collision energy was set to 25% using the Astral detector. The scan range was 150-2000\u2009m/z with RF lens set to 40%, AGC target to 500%, and maximum injection time of 3\u2009ms.\n\nSpectronaut v18.7 directDIA+ workflow with BGS Factory settings was used to search the raw data101 with the Spectronaut generated human spectral library (Human_PDB_2023). The FASTA database used is the Uniprot UP000005640 reviewed human database with contaminants (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040255/). Parameters for the search were default. Run-wise imputing was turned on with global normalization. The differential abundance testing used was the unpaired t-test.\n\nThis custom analysis aimed to quantify changes in local cell type compositions between DS and euploid in the Slide-seq and MERFISH datasets. For each cell type A (center cell), we identified its closest Ntotal neighboring cells based on spatial coordinates obtained from the spatial transcriptomics data. We calculated the B ratio, which represents the proportion of a specific cell type B (query cell) within these top Ntotal neighboring cells of cell type A, where NB is the number of neighboring cells of type B within the Ntotal nearest neighbors of cell i of type A. In our analysis, we set Ntotal = 100 to capture the immediate cellular microenvironment around each cell. This B ratio allows us to detect whether the local environment of a specific cell type B is enriched or depleted around another cell type A in the DS brain compared to euploid. An increased B ratio in DS indicates a higher local density of cell type B around cell type A.\n\nWe used the Wilcoxon rank-sum test to assess differences in B ratio distributions between DS and euploid for each cell pair (A-B). A Linear Mixed-Effects Model was applied to account for inter-sample variability, with the B ratio log-transformed for normality. The models were:\n\nFull Model:\n\nReduced Model:\n\nWhere, Y is the log(B ratio); \u03b2\u00b7X represents the fixed effect of Conditions, where \u03b2 is the coefficient of Conditions, and X represents the Conditions predictor. uSample is the random effect for Sample. \u03f5 is the residual error term.\n\nWe used a likelihood ratio test to compare the models and evaluate the significance of the DS condition on B ratio changes. Significant alterations in the cellular microenvironment were determined based on effect size (positive effect size indicates increased clustering in DS) and p-values, with a threshold of 0.05 after multiple comparisons correction using the BH procedure.\n\nIn the LOSO approach, the microenvironment code was iteratively executed, leaving one sample out each time. The mean and standard deviation for each iteration were calculated and plotted to identify conserved or divergent effect sizes, both positive and negative.\n\nTs65Dn (B6EiC3Sn.BLiA-Ts(1716)65Dn/DnJ, RRID:IMSR_JAX:005252) and euploid controls (B6EiC3Sn.BLiAF1/J, RRID:IMSR_JAX:003647) were procured from Jackson Laboratories. Mice were maintained in 12\u2009hr light-dark cycles, with an ambient temperature of 20-22\u2009\u00b0C and 40-55% humidity. Mice had free access to chow and food at the Centre for Phenogenomics in Toronto, ON, Canada. All animal experiments were carried out in accordance with the Canadian Council of Animal Care policies. For breeding purposes, Ts65Dn mice were paired with euploid controls as per the recommendations provided by Jackson Laboratories. At both postnatal and 6-month time points, three mice (female and male) per condition were utilized for MERFISH studies. Fresh brain tissue was promptly collected, embedded in OCT solution, and stored at \u221280\u2009\u00b0C until MERFISH processing.\n\nTo investigate the neurodevelopmental and mature characteristics of the DS brain with spatial awareness, a meticulously chosen panel of 500 genes was used for MERFISH analysis (Supplementary Data\u00a07). Among these, 132 genes served as canonical markers for identifying various cell types, encompassing ExN and InN, as well as glial cells such as astrocytes, microglia, and OLs. Additionally, non-neuronal cell types including fibroblasts, ependymal cells, and vascular cells were also represented in this gene panel for comprehensive cellular identification. The remaining genes in the panel encompassed biologically relevant signaling pathways (WNT, JAK/STAT, MAPK), ligand-receptor interactions, inflammatory markers, senescence-associated signatures, and extracellular matrix components related to DS.\n\nBrain tissue was harvested from P0 and 6mo mice that were euthanized using CO2, immediately embedded using optimal cutting temperature (OCT) compound, and stored in \u221280\u2009\u00b0C during short-term storage. Frozen-embedded samples were cryo-sectioned at \u221220\u2009\u00b0C at 10 \u03bcm thickness prior to mounting on MERSCOPE beaded coverslips (Vizgen, Cat: 10500001). Following, tissue sections were refrozen for 5-15\u2009min, fixed with 4% paraformaldehyde (PFA) diluted in 1X PBS for 15\u2009min, washed three times with 1X PBS for 5\u2009min each, and stored in 70% ethanol at 4\u2009\u00b0C to allow for tissue permeabilization. Sections were stored in 70% ethanol for no longer than 3 weeks until all imaging from animals of the same age was completed. Sample preparation, including probe hybridization and gel embedding, was performed using Vizgen\u2019s sample preparation kit (Vizgen, Cat: 10400012) as detailed in Vizgen\u2019s manufacturer\u2019s instructions for unfixed tissue. Following, samples were washed twice with Sample Prep Wash Buffer, incubated with DAPI and Poly-T Staining Reagent (Vizgen, PN 20300021) for 15\u2009min on a rocker, washed with Formamide Wash Buffer for 10\u2009min, and washed once more with Sample Prep Wash Buffer.\n\nEach section was imaged on the MERSCOPE platform (Vizgen, Cat: 10000001) using the MERSCOPE 500 Gene Imaging Kit, as detailed according to the manufacturer\u2019s instructions. Samples were placed into the flow chamber and carefully connected to the fluidics of the MERSCOPE machine to avoid air bubbles. A low-resolution mosaic of DAPI and Poly-T stains were used to identify the region of interest for imaging. Seven 1.5 \u03bcm thick z planes were taken to capture the entire 10 \u03bcm thickness of the tissue sections with high-resolution imaging. Imaging was performed automatically using the MERSCOPE imaging presets.\n\nMERFISH images were segmented using Vizgen\u2019s post-processing tool (VPT) and Cellpose, a machine learning algorithm (RRID:SCR_021716)101. DAPI and polyT signals were used to delineate cell boundaries for each field-of-view. Individual RNA molecules were assigned to a cell based on whether they were positioned within a marked boundary. Fluorescence intensity was summed and quantified in a cell-by-gene matrix (where rows and columns represented cell and gene identification, respectively). Anatomical segmentation of the P0 and 6mo brains revealed 8 gross anatomical regions: superficial cortex layers, deeper cortex layers, corpus callosum, subventricular zones, caudoputamen, lateral septal nucleus, anterior commissure, lower gray matter. The cortex, corpus callosum, and subventricular zone were chosen for analysis due to their relevance in higher executive functioning and neurogenesis.\n\nAdapted from an existing bioinformatic pipeline102, the cell-by-gene matrix of each replicate was processed as follows. (1) Despite properly segmenting cells on DAPI-oversaturated images, Watershed segmentation posed a problem with a small fraction of cells, with very small and very large volumes, which were still generated using VPT and Cellpose. A volume filtration was performed on cells with a volume less than 50\u00b5m3 or larger than three times the median volume of all cells. (2) Cells with zero RNA molecules were removed. (3) In a 10\u00b5m3 thick tissue slice, the spatial orientation of cells within the section resulted in partial imaging of their soma. To account for potential RNA discrepancies, gene expression for each cell was normalized by their volume and multiplied by 1000. (4) A variability in the mean total transcripts was observed for each sample. A scaling factor was applied to adjust the raw RNA counts to the mean number of RNA transcripts between samples to equalize the average expression level within the dataset. (5) Cells with RNA counts falling below 2% quantile or exceeding the 98% quantile were removed. (6) Potential doublets were removed using Scrublet103, a Python-based program that generates artificial doublets by comparing gene expression profiles of randomly selecting cells with segmented cells in the dataset and using a k-nearest neighbor (kNN) to output a predicted doublet score.\n\nIntegrative clustering analysis of the cortex, subventricular zone, and corpus callosum MERFISH single-cell transcriptome profiles was performed on 98,418 cells, 20,608 cells, and 2380 cells in the P0 dataset and 108,591 cells, 2562 cells, and 8365 cells in the 6mo dataset using Seurat v5 (RRID:SCR_016341). Clusters expressing two or more mutually exclusive canonical cell markers, likely representative of doublets, were removed. Neuronal cell types were removed from the corpus callosum datasets.\n\nRe-clustering of P0 cortical ExN revealed seven subtypes of layer-specific ExN, including clusters spatially concentrated in cortical layers II-III and expressing elevated levels of Cux2, Mef2c, and Satb2 (L2-3 ExN); clusters spatially concentrated in cortical layers II-IV and expressing moderate levels of Cux2, Mef2c, and Satb2 (L2-4 ExN); clusters spatially concentrated in cortical layers II-V and expressing moderate levels of Cux2 and Mef2c and high levels of Otx1 (L2-5 ExN); clusters spatially concentrated in cortical layers V-VIa expressing high levels of Tle4 and Ccn2 (L5-6a ExN); clusters spatially concentrated in layer VIb expressing high levels of Ccn2 (L6b ExN); one cluster of cells spatially concentrated in the subplate expressing high levels of Nr4a2; two clusters of cells spatially concentrated in the piriform cortex defined based on the absence (Piri 1 ExN) or presence of Fabp7 and Ascl1 (Piri 2 ExN). Reclustering of the 6mo cortical ExN revealed 5 additional layer-specific ExN, including clusters spatially concentrated in cortical layer I and expressing elevated levels of Tle1, Rgs8, and Cux2 (L1 ExN); clusters spatially concentrated in layers IV-V (L4-5 ExN) and layers IV-VIa (L4-6a ExN) expressing moderate levels of Cux, Mef2c, and Tle4; clusters spanning cortical layers I-VIa (L1-6a ExN) and layers II-VIa (L2-6a ExN) expressing Otx1, Tle4, and Ccn2 (Supplementary Fig.\u00a05a\u2013f).\n\nRe-clustering of InN in the P0 and 6mo datasets revealed three subtypes present in the cortex and subventricular zone (InN 1-3), including general InN expressing Gad1 (InN1), Sst, Lhx6, and Arx-expressing InN (InN2), and Vip and Cnr1-expressing InN (InN 3). Re-clustering of TAPs revealed two subtypes, which were distinguished based on low (TAPs 1) or moderate (TAPs 2) Gad1 and Gad2 expression. Interestingly, NSCs were distinguished by their distinct spatial organizations along the subventricular zone, and were annotated as follows: NSCs 1, representing NSCs dispersed across the lateral, dorsal, medial, and septal walls; NSCs 2, characterized by spatial concentration near the lateral wall and elevated Crym expression; NSCs 3, concentrated near the dorsal wall; and NSCs 4, concentrated near the medial wall (Supplementary Fig.\u00a05a\u2013f).\n\nIn the P0 and 6mo datasets, we found three subtypes of astrocytes: proliferating immature astrocytes (Astro 1) were annotated based on Vcam1, Aldh1l1, Fabp7, and Mki67 expression; immature astrocytes were annotated using the same markers but lacked Mki67 expression (Astro 2); mature astrocytes were annotated using Aqp4, Gfap, and C4b (Astro 3). Interestingly, a distinct astrocytic cluster exhibited exclusive expression of Gfap and displayed spatial concentration near the meninges of the 6mo cortex\u2014likely representative of perivascular astrocytes near meninges (Astro 4). Re-clustering of immune cells revealed two subtypes of microglia, with one homeostatic population expressing Cx3cr1, Trem2, and Csf1r (Micro 1), and a second population expressing elevated phagocytic markers Aif1 and Cd68 (Micro 2). A third class within the immune cell population, devoid of specific microglia, macrophage, and monocyte markers, and solely expressing Csf1r, was likely representative of myeloid progenitor cells (Myeloid). Re-clustering of vascular cells revealed two subtypes, with one population expressing endothelial markers Pecam1 and Esam (SM 2) and a second population expressing high pericyte Kcnj8 and Atp13a5 markers in addition to endothelial markers (SM1). Re-clustering of OPC revealed two subtypes, including a population (OPC 1) expressing elevated Pdgfra, Sox10, and Olig1, and a second population expressing moderate Pdgfra and Sox10, but with reduced Olig1 expression (OPC 2) (Supplementary Fig.\u00a05a\u2013f).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "Raw files for human single-nucleus sequencing, Slide-seq, and MERFISH have been deposited in GEO under the accession codes GSE280175, GSE280170, and GSE280177, respectively. Proteomics data has been deposited as a complete submission to the MassIVE repository (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) and assigned the accession number MSV000096108.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Custom code used for the microenvironment analysis, along with additional relevant scripts from this manuscript is available on Github at https://github.com/annaminyifeng/Molecular-Cartography-of-DS-Brain. Reproducible results for the microenvironment analysis can also be accessed via Code Ocean at https://doi.org/10.24433/CO.4591687.v2. Open-source algorithms were used as detailed for the snRNA-seq, Slide-seq, and MERFISH analyses.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Antonarakis, S. E. et al. Down syndrome. Nat. Rev. Dis. Prim. 6, 9 (2020).\n\nArticle\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nLott, I. 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The facility is supported by the Canada Foundation for Innovation and the Ontario Government. This work was supported by research funds from the Hospital for Sick Children to B.T.K; M. Y. F is a recipient of the Canadian Institutes of Health Research and Ontario Graduate Scholarship.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Program in Neuroscience and Mental Health, SickKids Research Institute, Toronto, ON, M5G 1L7, Canada\n\nMin Yi Feng,\u00a0Wuxinhao Cao,\u00a0Nareh Tahmasian,\u00a0Bharti Kukreja,\u00a0Gen Li,\u00a0Bianca Rusu\u00a0&\u00a0Brian T. Kalish\n\nDepartment of Molecular Genetics, University of Toronto, Toronto, ON, M5G 1A8, Canada\n\nMin Yi Feng,\u00a0Bianca Rusu,\u00a0Ji-Young Youn\u00a0&\u00a0Brian T. Kalish\n\nDepartment of Biochemistry, University of Toronto, Toronto, ON, M5G 1A8, Canada\n\nWuxinhao Cao\n\nDepartment of Statistical Science, University of Toronto, Toronto, ON, M5G 1A8, Canada\n\nWuxinhao Cao\n\nDepartment of Immunology, University of Toronto, Toronto, ON, M5G 1A8, Canada\n\nWuxinhao Cao\n\nDepartment of Cell and System Biology, University of Toronto, Toronto, ON, M5G 1A8, Canada\n\nGen Li\n\nProgram in Molecular Medicine, SickKids Research Institute, Toronto, ON, M5G 1L7, Canada\n\nJi-Young Youn\n\nDivision of Neonatology, Department of Paediatrics, Hospital for Sick Children, Toronto, ON, M5G 1L7, Canada\n\nBrian T. Kalish\n\nDivision of Newborn Medicine, Department of Pediatrics, Boston Children\u2019s Hospital, Boston, MA, 02115, USA\n\nBrian T. Kalish\n\nF.M. Kirby Neurobiology Center, Boston Children\u2019s Hospital, Boston, MA, 02115, USA\n\nBrian T. Kalish\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nOverall conceptualization, B.T.K. snRNA-seq and Slide-seq sample preparation, data generation, and analysis, M.Y.F., W.C., G.L., B.K., and B.T.K. MERFISH sample preparation, data generation, and analysis, M.Y.F., W.C., G.L., B.K., N.T., B.R., and B.T.K. Proteomics sample preparation, M.Y.F., J.Y.Y., and B.T.K. Manuscript writing, M.Y.F. and B.T.K. All authors edited the final manuscript.\n\nCorrespondence to\n Brian T. Kalish.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks William Mobley, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Source data", + "section_text": "", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Feng, M.Y., Cao, W., Tahmasian, N. et al. Molecular cartography of the human down syndrome and trisomic mouse brain.\n Nat Commun 16, 8689 (2025). https://doi.org/10.1038/s41467-025-63752-0\n\nDownload citation\n\nReceived: 01 November 2024\n\nAccepted: 27 August 2025\n\nPublished: 30 September 2025\n\nVersion of record: 30 September 2025\n\nDOI: https://doi.org/10.1038/s41467-025-63752-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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continental crust and subduction of lithosphere in the Hadean revealed by geochemistry and geodynamics.", + "journal": "Nature Communications", + "published": "25 April 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review File", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_MOESM7_ESM.xlsx" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_MOESM8_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.60520/IEDA/113703", + "/articles/s41467-025-59024-6#Sec32" + ], + "code": [], + "subject": [ + "Geochemistry", + "Geodynamics", + "Geology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3601806/v1.pdf?c=1745665543000", + "research_square_link": "https://www.researchsquare.com//article/rs-3601806/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-59024-6.pdf", + "preprint_posted": "31 Oct, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The rates of continental crust growth and recycling on early Earth remain controversial because materials in the ancient crust and mantle have been altered, or even erased, by ongoing geodynamical processes. Melt inclusions in minerals are pockets of magma trapped and shielded from the external environment. Where found within Archean high-Mg olivine \u2013 the first mineral to crystallize in mantle-derived melts \u2013 these inclusions provide an unaltered glimpse of the geochemical state of the early Earth mantle. We discovered an unprecedented unradiogenic Sr mantle source component (87Sr/86Sr=0.69932\u00b10.00024, 95% c.i. here and below) in mantle-derived melts trapped in olivine from ca. 3.27 Ga komatiitic lava flows (Barberton Greenstone Belt, South Africa). This component shows a 4.31\u00b10.19 Ga model age combined with significant chemical fractionation (Nb/U=36.9\u00b11.5, Ce/Pb=16.7\u00b11.1) translating to extraction, by the late-Hadean, of 80%\u00b116% of the mass of present-day continental crust assuming whole mantle processing. That agrees with the results of our geodynamic models explaining the Nb/U and Ce/Pb data by the production of 40 to 70% of the present-day continental crust mass during the Hadean in an oscillating mobile-lid tectonic regime with several tens of million years long periods of massive subduction induced by mantle plumes.Earth and environmental sciences/Solid Earth sciences/GeochemistryEarth and environmental sciences/Solid Earth sciences/Geodynamics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryMaterialVezinetetal.pdfListofSOMTables.pdfSOMTables.xlsxSupplementary Tables for Nature Communications", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "The rates of continental crust growth and recycling on early Earth remain unclear due to the lack of information resulting from the extensive alteration of ancient rocks. Melt inclusions trapped and shielded from alteration in Archean high-Mg olivine crystals offer a solution to this problem. We report an unprecedented unradiogenic Sr mantle source component (87Sr/86Sr\u2009=\u20090.69932\u2009\u00b1\u20090.00024, 95% confidence interval) of melts included in olivine from 3.27\u2009Ga komatiitic lava flows in the Barberton Greenstone Belt, South Africa. This component indicates a model age of 4.31\u2009\u00b1\u20090.19\u2009Ga and significant chemical fractionation (Nb/U\u2009=\u200936.9\u2009\u00b1\u20091.5, Ce/Pb=16.7\u2009\u00b1\u20091.1), suggesting up to 80%\u2009\u00b1\u200916% of the present-day continental crust\u2019s mass was extracted by the late Hadean from the whole mantle. Geodynamic models support this finding, explaining geochemical data by producing 40% to 70% of the present-day continental crust mass during the Hadean in a variable tectonic regime with tens of millions of years-long periods of massive impulsive subduction induced by mantle plumes.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The evolution of the early Earth and the dynamics of its geochemical reservoirs are not well understood due to the near absence of Hadean (>4.0\u2009Ga) rocks and minerals preserved at the present-day Earth\u2019s surface1,2. Consequently, the rates of continental crust growth and recycling on the early Earth are still debated: estimates for the fraction of continental crust formed at the Hadean-Archean transition range between 0% and more than 100% of the volume of today\u2019s continental crust3.\n\nSeveral approaches have been used to provide estimates of continental crust volumes during the Hadean and Archean3,4,5,6,7. Studies assessing the degree of mantle depletion4,6,7 are viewed as the most reliable since they account for both crustal extraction and recycling3. Most of these studies rely on the bulk rocks\u2019 Sm-Nd, Lu-Hf, and U-Pb isotopic systems3,4,6. This approach, however, suffers from significant problems, such as known mantle heterogeneity in isotope ratios and systematic differences in these ratios for the mantle sources of mid-ocean ridge basalts (MORB) and oceanic island basalts (OIB)8. In addition, the difference in isotopic effects of extraction and recycling of continental and oceanic crust or even fractionation effects at the magma ocean stage is not easily recognized4. This uncertainty is also valid for the short-lived 146Sm-142Nd decay system9. Also, most ancient rocks are altered, which has been shown to affect Sm-Nd and Lu-Hf isotopic systems10,11. Further, whatever the volume of continental crust on early Earth, the tectonic regime accounting for it remains debated, with suggestions being stagnant-lid, plutonic-squishy-lid, episodic-lid, mobile-lid, ridge-only, and heat-pipe regimes12,13.\n\nAmong the varied geochemical proxies that have been used over the past decades to decipher the production and recycling of continental crust, Nb/U, and Ce/Pb in fresh lavas or glasses have received the most attention. These ratios are regarded as canonical because (i) they do not fractionate when the mantle rocks melt, and (ii) they are similar for present-day uncontaminated MORB and OIB8,14,15, suggesting mantle homogeneity of these parameters. On the other hand, Nb\u2013U and Ce\u2013Pb fractionate to give higher Nb/U and Ce/Pb values in the restite when hydrous mafic or ultramafic rocks melt in the crust or mantle, i.e., during the production of the felsic magmas (or their parentals) that form the bulk of continents14. This observation makes Nb/U and Ce/Pb ideal proxies to assess both the production of continental crust and recycling of restites through time4.\n\nThree aspects limit the accuracy of crustal growth curves built from Nb/U and Ce/Pb ratios measured in mantle-derived rocks. First, the timing of the generation of these ratios in the mantle source is unknown and could be much older than the eruption age of measured magmas. This means that mantle-evolution curves based on Nb/U or Ce/Pb ratios in mantle-derived melts16 provide only lower estimates of the rate of crustal growth. Second, the subduction of continental crust or/and unmelted oceanic crust to the source of mantle-derived magmas will decrease the Nb/U and Ce/Pb ratios of these melts, leading to an underestimation of the extent of continental crust production. Third, the effect on these ratios of post-emplacement alteration, crustal assimilation, and/or metamorphic overprinting\u00a0in rocks is difficult to identify and quantify, leading to inaccurate interpretations10,11,14.\n\nSince olivine is the first mineral to crystallize in mantle-derived magmas, melt inclusions in high-Mg olivine crystals\u2014those with high Mg# or Fo (=Mg/Mg+Fe molar%)\u2014provide the most reliable information about parental melt compositions and the evolution of their mantle sources through time16,17,18,19,20,21. This stems from the capacity of melt inclusions to acquire the elemental and isotopic compositions of the host magma and preserve this information long after crystallization17. Consequently, melt inclusions in olivine (Fig.\u00a01c) may provide a reliable view of the geochemical composition of the mantle source of melts.\n\na Olivine cumulate from the Weltevreden Formation (sample 1528\u2009C) showing that although these cumulates are significantly altered, they still contain preserved unaltered olivine cores (plane-polarized light). b Partially crystallized natural (not annealed) olivine-hosted melt inclusion from sample 2217\u00a0before homogenization at high temperature. Inclusion consists of glass and acicular calcic pyroxene. c Heated and quenched olivine-hosted melt inclusion from sample 2216\u00a0consisting of glass, low-density gas bubble, and spinel (dark crystal). d, e Naturally quenched glassy inclusion in olivine from sample 2218. Inclusion consists of glass, low-density gas bubble, and small spinel and pyroxene crystals.\n\nIn this study, we discovered an unradiogenic-Sr mantle component previously unknown on Earth with unequivocal fractionated Nb/U and Ce/Pb values. These ratios are more uniform in the present-day mantle than Nd, Hf, and Pb isotopes8,14,15 and explicitly mark the extraction of continental crust. Our Sr isotopes and Nb/U and Ce/Pb data came from pristine komatiite melt inclusions in olivine protected by the host mineral from alteration and suggest a significant event of continental crust extraction in Hadean time. Similar data on Nb/U and Ce/Pb of rocks are known only starting from 3.5\u2009Ga and are compromised by their severe alteration. We also conducted advanced geodynamic modeling of the early Earth dynamics and extraction of the continental crust, coupled with the evolution of the Rb\u2013Sr isotope system and Nb, U, Ce, and Pb trace elements. This allows us to link geochemical data to the physical processes that might have been responsible for the observed evolution of the chemical composition. In particular, using our models constrained by geochemical data, we can discriminate between tectonic regimes that could have been active in Hadean and Eo-Archean time and suggest the preferred scenario of a fluctuating mobile-lid tectonic regime with several tens of million years-long periods of massive subduction induced by mantle plumes.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_Fig1_HTML.png" + ] + }, + { + "section_name": "Results and discussion", + "section_text": "We determined the chemical and isotopic compositions of olivine-hosted melt inclusions in samples from komatiitic flows from the ca. 3.27\u2009Ga refs. 22,23 Weltevreden Formation (Barberton Greenstone Belt, South Africa, Supplementary Fig.\u00a01). Whole-rock isotopic investigations indicate that the mantle sources of these komatiites experienced mantle depletion before the eruption, possibly during the Hadean24 (Supplementary Fig.\u00a02). Further, the high-precision isotopic data analyzed at the whole-rock scale on Weltevreden komatiites indicate the decoupling of the Hf and Nd isotopic systems24. This observation has been interpreted as resulting from elemental fractionation occurring during early Hadean magma ocean solidification25.\n\nHere, using Sr-isotope signatures and trace-element contents measured directly in the homogeneous glass of melt inclusions (Fig.\u00a01c), we provide solid geochronological constraints for the timing of both the isolation of mantle domains from the Bulk Silicate Earth (BSE, also referred to as primitive mantle, PM) and the acquisition of canonical ratios indicative of continental crust extraction.\n\nMore than 350 olivine-hosted melt inclusions were selected and prepared for geochemical analyses. The analytical workflow, detailed in Methods, Supplementary Tables\u00a01\u20135 and Supplementary Data\u00a01, was as follows: (i) annealing of natural partly crystallized melt inclusions at high temperature and quenching to produce homogenous glass, (ii) electron microprobe analysis to determine major and minor elements in melt inclusions and host olivine, (iii) Raman spectroscopy to determine H2O contents of melt inclusions, and (iv) laser ablation split stream (LASS) ICP-MS analysis for Sr-isotope compositions and trace-element contents of melt inclusions. Not all inclusions could be analyzed through the entire analytical workflow since LASS analysis requires a diameter \u2265\u00a038\u2009\u03bcm (optimal laser beam size), and only 195 inclusions satisfied this criterion. In addition, 19 more inclusions were analyzed in single stream mode for trace elements. The measured compositions of melt inclusions and host olivines are presented in Supplementary Data\u00a02. Figure\u00a02a, b present the composition of virtually unzoned olivine hosts of different isotopic groups of melt inclusions (see below) from five separate flows. The Fo and all trace-element contents (shown NiO and Cr2O3) of olivine differ both between and within flows. The observed variability suggests that the examined olivine crystals are antecrysts that formed and entrapped their melt inclusions at different sites within a complex magmatic plumbing system. The crystallization of antecrysts occurred in the shallow seawater-altered oceanic crust, as discussed in the subsequent section on contamination.\n\na, b Composition of host olivines, outlines indicate samples from the same flows, Fo\u2009=\u2009100Mg/(Mg+Fe) molar. c, d Composition of melt inclusions and host olivine. e\u2013h Composition of melt inclusions. Filled circles are those with a corresponding Sr-isotope analysis: blue for the main group of melt inclusions, yellow for Sr-radiogenic melt inclusions, and orange for Sr-unradiogenic melt inclusions (see below). Light gray circles are inclusions with no Sr data or rejected Sr isotope data because of Rb/Sr ratio over the maximum value of used reference materials (0.0244) or uncertainty (2SE) of 87Sr/86Sr value\u2009>\u20090.0015 (see Methods). The expected seawater Sr isotope composition at 3.3\u2009Ga after86. Uncertainty bars are average individual uncertainty at a 95% confidence interval (c.i.). Source data are provided as a Source Data file.\n\nThe TiO2 contents of Weltevreden melt inclusions show a considerable variation, from ca. 0.14\u2009wt. % to 0.21\u2009wt. %, which is inversely correlated with host olivine Fo contents (Fig.\u00a02c). This correlation follows a trend of ca. 33\u2009wt% olivine extraction from the common parental melt (estimated from the ingrowth of Ti from the melts trapped in the most Fo-rich olivine), indicating that TiO2 contents in the melt, as well as Al2O3 and CaO contents (not shown), are governed by olivine crystallization and extraction from parental melts with similar contents of these elements. Other proxies, such as Cl/Ti (Fig.\u00a02d\u2013h) or K2O/TiO2 (Supplementary Fig.\u00a03b\u2013e), should remain constant in the melt during olivine crystallization because of the negligible contents of these elements in olivine. Clearly, this is not the case. We attribute the large range of Cl/Ti and K2O/TiO2 for the same composition of host olivine to assimilation into the komatiitic melts of small (<1.5\u2009wt%) amounts of seawater-derived ultra-saline brine before emplacement at ca. 3.27\u2009Ga (see Methods): a process common for modern submarine basaltic melts26,27 and larger amounts (e.g., 10\u201320%) of seawater-altered serpentinite depleted in all incompatible elements similar to olivine cumulates of the same komatiites. This process influences all measured inclusions to varying extents (Fig.\u00a02e), indicating that the crystallization of host olivines occurred at relatively shallow depths within the oceanic crust. Values of Cl/Ti can be used as a proxy for such contamination. A positive correlation is observed between Cl/Ti and Rb/Sr (Fig.\u00a02e), suggesting a gain of Rb as well as Na, K, Pb, U, H2O, and Sr during contamination (Fig.\u00a02f, g, Supplementary Fig.\u00a03). To avoid samples significantly affected by this process, we considered only melt inclusions with low Rb/Sr for geological interpretations in this study (Fig.\u00a02e). A maximum value of 0.0244 was selected for Rb/Sr because it corresponds to the highest Rb/Sr value of the reference glasses used for LASS analysis (KL2-G,\u00a0see Methods). Unlike Rb/Sr, other proxies, such as Ce/Pb and Nb/U, show a negative correlation with Cl/Ti, suggesting that the higher values measured for these two ratios do not result from the contamination of the komatiitic melts before entrapment and thus can be considered as a minimum estimate of original values.\n\nThe Sr-isotope signatures of Weltevreden melt inclusions show a large range of both measured and initial (back-calculated at 3266\u2009Ma) 87Sr/86Sr values supported by p(\u03c72) values <<0.05, indicating statistical heterogeneity within the whole dataset (Fig.\u00a03a, b). The effect of minor contamination of Weltevreden melts by seawater-derived brine or depleted serpentinite on their Sr isotope composition is within the analytical uncertainty, as seen by the lack of a significant correlation of the 87Sr/86Sr value of melts and Cl/Ti, the chemical proxy of such contamination (see Fig.\u00a02h). This feature is expected because the difference between the 87Sr/86Sr ratios of 3.3\u2009Ga old seawater and Weltevreden komatiite melt is only 0.0020 (Fig.\u00a02h), which is close to the average 95% confidence interval (\u00b10.0010) analytical uncertainty of 87Sr/86Sr measurements in individual melt inclusions.\n\na Measured 87Sr/86Sr ratio. b Age-corrected 87Sr/86Sr ratio. MSWD - Mean Square Weighted Deviation, n- number of analyses, p(\u03c72) -chi-squared p-value for the population homogeneity test. Both 87Sr/86Sr values show low p(\u03c72) (<<0.05), indicating the lack of statistical homogeneity at the precision obtained for these Sr-isotope analyses. Three statistically homogenous groups can be distinguished in the data shown in (b): unradiogenic in orange, radiogenic in yellow, and neutral (main) in blue (see Methods for Statistical Treatment). Bars represent individual uncertainty at a 95% confidence interval.\u00a0Source data are provided as a Source Data file.\n\nA statistical analysis of our Sr-isotope dataset reveals three statistically homogenous groups based on their initial 87Sr/86Sr (see Fig.\u00a03b and Methods for details). The means of initial 87Sr/86Sr values of these groups differ with confidence of over 99.9% (Supplementary Table\u00a06). Out of these three groups, one, comprising 14 analyses, shows an extremely depleted (87Sr/86Sr)initial signature yielding a (87Sr/86Sr)initial weighted mean of 0.69932\u2009\u00b1\u20090.00024 (95% confidence interval, MSWD\u2009=\u20090.88, p(\u03c72)\u2009=\u20090.57, orange analyses in Fig.\u00a03b). Olivine antecrysts containing unradiogenic inclusions come from all studied flows (14 of a total of 137, thus 10%), but mostly (8 inclusions out of 14) from Keena\u2019s flow 2 (samples 1523B and 2217, Fig.\u00a02a, b, Supplementary Fig.\u00a01), where they compose 14% of 59 inclusions measured for Sr isotopes. The random olivine spinifex (quenched) zone of the same flow exhibits a maximum deviation of \u00b5142Nd from terrestrial values, as measured in the Weltevreden flows (Supplementary Fig.\u00a02). This suggests that the component underwent Sm/Nd differentiation during the Hadean eon, potentially contributing to the mantle source of the komatiite from this flow. However, this did not elucidate the process that occurred in Hadean. The mean initial isotopic ratio of unradiogenic inclusions indeed transposes to a Sr-model age, assuming no Rb in the source, of 4.31\u2009\u00b1\u20090.19\u2009Ga (95% confidence interval, p(\u03c72)\u2009=\u20090.57, Fig.\u00a04a), which we interpret to indicate that some components that melted to form Weltevreden komatiitic melts were geochemically isolated since the Hadean. Owing to the large size of inclusion 2217-48-ol65, belonging to the unradiogenic group, four Sr-isotope & trace-element replicates were conducted. As demonstrated in Fig.\u00a04d\u2013f and Supplementary Table\u00a04, all four replicate analyses form a statistically homogenous population for Sr isotopes and canonical ratios of interest, hence further validating the analytical approach developed in this study.\n\na The Sr-model age (see Methods) of the unradiogenic group indicates mid-Hadean extraction from the Bulk Silicate Earth (BSE). b, c The Nb/U and Ce/Pb values of these inclusions are significantly higher than those of primitive mantle (PM) or BSE, interpreted as reflecting restites after extraction of continental crust in the presence of H2O during the Hadean. d\u2013f The results obtained on 4 replicate analyses of a single melt inclusion (MI) 2217-48-ol65 (belonging to the Sr-unradiogenic group), showing the robustness of our analytical protocol. Uncertainty bars are reported at a coverage factor of 2 (i.e., 95% confidence interval). Source data are provided as a Source Data file.\n\nThe Weltevreden komatiites melt is contaminated by seawater-altered crust, as indicated by the composition of olivine-hosted melt inclusions (Fig.\u00a02d\u2013f). This and low-density fluid bubbles in quenched inclusions (Fig.\u00a01c, e) suggest that olivine antecrysts formed in the shallow oceanic crust. The ability of olivine to sample isotopically diverse mantle-derived melts during crystallization in shallow crust implies that melts from deep heterogeneous mantle sources could be delivered and focused at crustal levels without complete mixing. Recent examples include the shield volcanoes of the Hawaiian plume, where melts formed at depths of around 100 km28 in a single mantle plume are focused in local magmatic systems of Mauna Loa and K\u012blauea volcanos, which coexist in time and differ in compositions of Sr, Nd, Pb isotopes, and trace elements29. Furthermore, melts with various trace elements and Sr, Pb, and Nd isotope compositions are trapped in olivine crystallized in Mauna Loa volcano\u2019s shallow plumbing system17,30. Finally, the adjacent Weltevreden komatiite flows show significant differences, not only in long-lived isotopic systems Lu-Hf and Sm-Nd but also in the 142Nd/144Nd isotopic ratios (see Supplementary Fig.\u00a02). The observed differences cannot be attributed to alteration and are unlikely to result from contamination, as indicated by the weak correlation between Nd contents in melts and contamination proxies (Supplementary Table\u00a05). Instead, they suggest that the parental\u00a0melts delivered to the surface were incompletely mixed.\n\nPrevious studies have used the geochemical proxy Nb/U to monitor the extent of continental crust extraction4. The weighted mean of the Nb/U values of the unradiogenic-Sr melt inclusions is 36.9\u2009\u00b1\u20091.5 (MSWD\u2009=\u20090.71, n\u2009=\u200914, Fig.\u00a04b). This minimum estimate of the original value, due to contamination (Fig.\u00a02g), is significantly higher than all PM/BSE Nb/U estimates, which range between 26.7 and 32.48,31,32,33. A similar observation can be made for the mean Ce/Pb value of 16.73\u2009\u00b1\u20091.12 (MSWD\u2009=\u20092.3, n\u2009=\u200913, Fig.\u00a04c) of this unradiogenic group, which is also the minimum estimate (Fig.\u00a02f) but is significantly higher than the PM/BSE value of 9\u201311.28,31,32,33. The four replicates obtained on inclusion 2217-48-ol65 show the same tendency (Fig.\u00a04d\u2013f). The geochemical effect of Ca and Mg-perovskite cumulation proposed for the source of Weltevreden komatiites25 to explain moderate decoupling of Sm-Nd and Lu-Hf isotopic systems in their composition will produce Nb/U ratios in the source that are lower, not higher than in BSE/PM as observed in melt inclusions due to a higher partition coefficient for U than for Nb in the suggested mixture of Ca and Mg-perovskite34. In addition, similar and even much more significant decoupling of \u03b5Nd and \u03b5Hf is common in highly depleted abyssal and ophiolitic peridotites because of \u201cfollowing radiogenic ingrowth as a result of preferential fractionation of Lu/Hf during partial melting\u201d35. The main mantle source component of studied komatiite was highly depleted mantle harzburgite, for instance, similar to refractory harzburgites from Archean cratons36, as follows from their contents of incompatible elements (including Sm-Nd and Lu-Hf), which are lower in parental melt than in BSE mantle16. Thus, the decoupling of \u03b5Nd and \u03b5Hf in their sources can be explained by a mantle restite of the Eo-archean-Hadean age. Our interpretation of the nature of the source of Weltevreden komatiite further substantiates independent observations obtained via thermodynamic modeling37.\n\nExtraction of continental crust from mafic crust and/or peridotite in the presence of H2O followed by recycling of restite back to the mantle leads to depletion in Rb compared to Sr; Pb compared to Ce; and U compared to Nb in the mixed deep mantle source. This explains the low Rb/Sr and high Ce/Pb and Nb/U values measured in mantle-derived melts14. However, the presence of subducted continental crust and unmelted mafic crust in the mantle source may significantly affect all these elemental ratios. Fortunately, the amount of such crust in the recovered Hadean component in Weltevreden melt inclusions is shown to be negligible by the exceptionally low contents of 87Sr of the unradiogenic component. Thus, high Nb/U and Ce/Pb ratios of unradiogenic-Sr melt inclusions are fully attributed to the production of continental crust. However, the estimated Sr isotope model age of the mixed source could reveal younger dates by a few tens of million years, which are well within our reported \u00b1 0.19\u2009Ga uncertainty of the model age (see Supplementary Fig.\u00a07 and Methods).\n\nThe fraction of extracted continental crust produced from a specific domain of the mantle can be evaluated following the mass balance approach that has been adopted in previous studies4 using our Nb/U values and Sr geochronological constraints together with the estimated Nb and U contents of the mantle8,31,32,33 and continental crust38,39,40,41. First, the fraction of crust extracted from any volume of processed mantle can be estimated. Using the Nb/U weighted average of 36.9\u2009\u00b1\u20091.5 obtained on inclusions from source rocks differentiated in the mid-Hadean, we estimate the minimum fraction of extracted crust to be 0.43%\u2009\u00b1\u20090.09% of the processed mantle. Assuming 2.17\u2009\u00d7\u20091022\u2009kg as the mass of the present-day continental crust and 4.01\u2009\u00d7\u20091024\u2009kg as the mass of the whole mantle, the fraction of continental crust extracted in the Hadean would be 80%\u2009\u00b1\u200916% (Fig.\u00a05, large\u00a0orange dot) of the mass of the present-day continental crust. A similar calculation for individual melt inclusion 2217-48-ol65 yields 72%\u2009\u00b1\u200924% (Fig.\u00a05, hatched smaller\u00a0orange dot). Such high values may be overestimated because they assume the involvement of the entire mantle. However, they still provide direct evidence supporting earlier models that argue for significant continental crust production and recycling early in Earth\u2019s history3,6,42.\n\nThese estimates have been obtained using the measured average Nb/U value in the unradiogenic-Sr group (large orange dot) and individual inclusion 2217-48-ol65 (smaller\u00a0orange hatched dot) and the average of Nb and U values from four mantle models8,31,32,33 and four crustal models38,39,40,41, assuming processing of the whole mantle mass. Red curves are models in agreement with our results, indicating continental crust production and subsequent recycling of restites in the Hadean. Uncertainty bars are reported at a coverage factor of 2 (i.e., 95% confidence interval). The light blue field shows the range of continental crust fractions produced during the Hadean, which, according to the geodynamic models, fit geochemical data best (see the Geodynamic modeling section below). Source data are provided as a Source Data file.\n\nOur results, interpreted as indicating that a large fraction of continental crust was extracted, and its residue was recycled into the mantle very early in Earth\u2019s history, are consistent with the composition of ancient zircons43,44, the radiogenic isotope compositions of bulk rocks45 and numerical modeling (see Geodynamic modeling). Elevated Nb/U and Ce/Pb values obtained in deeply sourced primary Weltevreden komatiitic melts indicate efficient, vertical transfer (subduction of oceanic crust residue after partial melting producing felsic crust) between the upper and the lower mantle in the Hadean. Although the geochemical results presented here indicate significant continental crust production in early Earth time, the tectonic regime accounting for the formation of this crust and the actual mass of mantle processed cannot be extracted from such a geochemical dataset. To finalize the analysis, we conducted geodynamic modeling, during which we monitored the geochemical proxies 87Sr/86Sr, Nb/U, and Ce/Pb to determine the tectonic\u00a0regime that most accurately reflects the measured geochemical data.\n\nWe applied the mantle convection code StagYY46, which has been extensively used over the last decade for modeling the coupled core-mantle-crust evolution of rocky planets. With a 2D spherical annulus domain47, our models generate both basaltic and felsic melts48, include cooling of the core, and use pressure- and temperature-dependent water solubility maps for different mantle minerals12. For this study, we improved previous models12,48 by including the effect of water on the density of mantle materials, incorporating a composite rheology (diffusion creep and dislocation creep proxy) for the crust and upper mantle based on experimental data, and by initializing the frictional strength of the early Earth\u2019s oceanic lithosphere. Following approaches from previous studies49,50, we included the evolution of Rb\u2013Sr isotopes as well as Nb, U, Ce, and Pb trace elements in these geodynamic models (see Methods for further details). We computed a series of models by varying the lithosphere\u2019s hydration and frictional strength (see Methods for model setup).\n\nOur numerical experiments start with a homogeneous solid Earth of pyrolytic (BSE) composition31 at 4.5\u2009Ga with a low (0.01\u2009wt. %) initial water content12, a mantle potential temperature of 1900\u2009K, and a core temperature of 5000\u2009K. They end after 1.5 billion years, i.e. at 3.0 Ga. Any cataclysm relating to the formation of the mantle or the Moon is assumed to be earlier than 4.5 Ga.\n\nAll models fall into two distinct groups\u00a0that illustrate different tectonic regimes, as we show below Group I consists of models with our preferred effective friction coefficient of the lithosphere for the early Earth of 0.151, while Group II consists of the models with higher lithospheric strength\u00a0(effective friction coefficient of 0.2). To better demonstrate the effect of the tectonic regime on geochemical parameters, we have selected parameters controlling surface water input in the models of both groups, ensuring they produce a similar amounts of continental crust. To achieve this, the high lithospheric strenth models of Group II needed significantly more water input compared to those of Group I.\u00a0The representative models from both groups are shown in Fig.\u00a06. The typical behavior of all models is intense mantle convection during the first hundred million years (Fig.\u00a06d1, g1) when multiple large hot mantle plumes form at the core-mantle boundary (CMB) and repeatedly approach the lithosphere. Plumes break the lithosphere and induce its subduction. During this stage, there is extensive production of both oceanic and continental crust (Fig.\u00a06b, f). The following evolution depends on the lithospheric strength and is best described by the so-called mobility function, which represents a ratio of the root-mean-square (rms) of the surface velocity averaged over the rms velocity of the entire computational domain52. A mobility value above 1 means that the lithosphere moves horizontally relative to the underlying mantle with significant velocities, and subduction is active13.\n\na, e Mobility of models Group I and Group II respectively over time with a moving average (thick line) over 11 mobility values (thin lines). b, f Mass of generated continental crust over time for models Group I and Group II, respectively, scaled with the present-day continental crust mass (CCM). c, d1\u2013d4, and g1\u2013g4 Snapshots of Earth\u2019s cross-section showing models\u2019 evolution with time (c, d1\u2013d4 for Group I and c, g1\u2013g4 for Group II), where four quadrants represent different fields (clockwise from top left: composition, Nb/U, viscosity, 87Sr/86Sr). The color scales for each field are shown in (c). Snapshot c displays the initial model state, the same for the models of both groups, corresponding to an age of 4500\u2009Ma and a model time of 0\u2009Myr. Snapshots d1, d3, g1 show periods of active subduction, while snapshots d2, d4, g2, g4 show periods of lulls in subduction activity where large mantle plumes (dark blue in the viscosity field) are trapped beneath the cold recycled material (yellow in the viscosity field). Source data are provided as a Source Data file.\n\nIn models of Group I, high mobility periods last several tens of million years and interchange with the low mobility periods (Fig.\u00a06a). We call this tectonic regime a fluctuating mobile-lid regime. High mobility periods correspond to periods of extensive crustal production and lithospheric subduction and result in an accumulation of a large amount of cold recycled material at the CMB, which hinders the propagation of large mantle plumes (Fig.\u00a06d2). The plumes that manage to rise through this overlying cold recycled material are small and are not able to break the lithosphere or induce subduction, resulting in reduced production of new crust (Fig.\u00a06b, model time range 480-680\u2009Myr). It\u00a0takes a few hundred million years to heat this cold material that sits above the CMB. Thus, there is a period of quiescence before large plumes breakthrough, approach the lithosphere, and induce new subduction zones, which produce and recycle new crust (Fig.\u00a06d3).\n\nGroup II models are characterized by low mobility throughout their evolution and experience several mobility bursts that last a few million years Fig.\u00a06e). This type of tectonic regime is usually called an episodic-lid regime13. During these episodes of high mobility, the over-thickened lithosphere is rapidly subducted, resulting in resurfacing events (Fig.\u00a06g3). Most of the continental crust is produced during these events.\n\nIn all models, at every time step, we identify regions of hot material (mantle plumes with\u00a0potential temperature above 1700\u2009\u00b0C) in the transition zone as the potential sources of komatiites (see Methods for details) and select the model\u2019s cells that represent the Hadean fraction of material with the 87Sr/86Sr ratio <0.6997. The evolution of the trace-element ratios in the Hadean fraction for a number of models from both groups is presented in Figs.\u00a07a, d,\u00a08a, d, and Supplementary Fig.\u00a010.\n\na, d Evolution of trace-element ratios Nb/U and Ce/Pb in the Hadean fraction (with 87Sr/86Sr\u2009<\u20090.6997) of hot plume material in the transition zone from multiple models with different lithospheric hydration (see Methods for details). Each colored solid circle represents the mean trace-element ratio for the selected cells with uncertainty bars with two standard deviations of the mean value. b, e Mass of generated continental crust over time from the same models, scaled with the present-day continental crust mass. c, f Amount of Hadean fraction in the hot plume material from the same models. Red solid diamonds (main Hadean group data) and blue diamonds (single inclusion data) show geochemical observations with uncertainty bars of 2 standard deviations of the mean value. Model parameters: f\u2014effective friction coefficient of the lithosphere, alpha\u2014water saturation fraction of the top 5\u2009km of crust (see Methods section). Source data are provided as a Source Data file.\n\nThe legend and panel description are the same as in Fig.\u00a07. Note the poor fit of the geochemical data (red and blue diamonds) compared with the models of Group I shown in Fig.\u00a07. Uncertainty bars are 2 standard deviations of the mean value. Source data are provided as a Source Data file.\n\nThe common feature of all models is that trace elements do not show any effect of continental crust production on the composition of mantle plumes\u00a0till at least 500\u2009Myr of model time (i.e., 4.0 \u2009Ga) even though the continental crust production during the first 500\u2009Myr \u00a0(i.e., 4.5-4.0 Ga)\u00a0is more than twice that of the last 1000\u2009Myr (i.e., 4.0-3.0 Ga)\u00a0in most models. This time lag indicates the time required by the restites of\u00a0hydrated basaltic crust, which were\u00a0 involved in the production of continental crust, to recycle and mix in substantial amounts with the plume source material in the lower mantle.\n\nThe models of Group I that produce 40 to 70% of present-day continental crust mass (CCM) during the Hadean show a good fit with the Nb/U and Ce/Pb data at the time close to the emplacement of Weltevreden komatiites at 3267\u2009Ma (i.e., model time of 1230\u2009Myr, Fig.\u00a05, and Fig.\u00a07a, d). These models also predict a realistic amount of Hadean component in plumes (Fig.\u00a07c, f) and a rather moderate supply of water into the lithosphere (0.5\u20130.9 present-day ocean mass (OM)) over the period of 1500\u2009Myr (Supplementary Fig.\u00a011). In contrast, the models of Group II where we chose the higher\u00a0water input parameters show a poor fit with Nb/U and Ce/Pb data (Fig.\u00a08a, d), a too low amount of Hadean component in plumes (Fig.\u00a08c, f) and predict a water supply of 1.1\u20132.4 OM into the lithosphere over the period of 1500\u2009Myr (Supplementary Fig.\u00a011). The reason behind these different model predictions is the much lower recycling rate of the lithosphere in the models of Group II where the mobility, and therefore the subduction activity, are lower than in Group I models.\n\nThe difference between predictions by models of Group I and II is also evident from the calculated Sr isotope composition of potential komatiite sources (Fig.\u00a09). The models with high subduction activity (models of Group I) accurately reproduce the mean strontium isotope composition of melt inclusions at the time of emplacement of Weltevreden komatiites, along with its standard deviation (Fig.\u00a09a, b), reflecting the isotopic heterogeneity of potential melt sources. We assume that melts possess identical Sr isotope ratios to their respective melt sources. In contrast, models with low subduction activity (Group II) are less successful (Fig.\u00a09c, d).\n\na, b Typical models of Group I. c, d Typical models of Group II. The observed mean Sr isotope ratio of melt inclusions in olivines in Weltevreden komatiites is indicated by red crosses. Blue solid circles indicate modeling results, and each circle represents the calculated mean isotope ratio for the selected mantle cells of hot plume material (see Methods for details). The solid blue line shows the Bulk Solid Earth (BSE) evolution trend. Both modeling results and data are shown with uncertainty bars with 2 standard deviations of the mean value. Model parameters: f\u2014effective friction coefficient of the lithosphere, alpha\u2014water saturation fraction of the top 5\u2009km of crust (see Methods section). Source data are provided as a Source Data file.\n\nOur models infer that in order to fit the geochemical observations, the following two conditions must have been fulfilled. First, the water supply into the lithosphere must have been high enough to allow for ca 40\u201370% of CCM production during the Hadean in agreement with maximal estimates of 50 to 100% of CCM extraction from the whole mantle using a mass balance approach (Fig.\u00a05). Second, the tectonic regime in the Hadean and the Eo-Archean time must have been sufficiently mobile, i.e., with periods of extensive subduction. Classical episodic regimes with rare partial resurfacing and sagduction or stagnant-lid regimes are not consistent with geochemical observations. We note that \u201csubduction\u201d in our models, defined as sinking into the mantle of the pieces of the entire upper thermal boundary layer (lid), including its surface, does not look like the present-day continuous and stable subduction that is characteristic of Phanerozoic plate tectonics. It is much more rapid,\u00a0variable and short-lived. Moreover, our models show that in Hadean-Archean Earth, in contrast to present-day Earth, large mantle plumes are of key importance to trigger such subduction, i.e. so-called plume-induced subduction53, and when plume activity is diminished, the subduction activity is also diminished.\n\nAltogether, the geochemical data and geodynamic models presented in this study indicate that large volumes of both oceanic and continental crust had already formed and recycled by the late-Hadean, thereby favoring the\u2014still contentious\u2014model presented by Richard Armstrong more than four decades ago42 as well as several recent models3,6.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_Fig8_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59024-6/MediaObjects/41467_2025_59024_Fig9_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "The extended analytical workflow developed for the geochemical study of Weltevreden melt inclusions is broadly similar to that presented in recently published geochemical studies of melt inclusion from Song Da ultramafic volcanic suite54,55.\n\nThe eight olivine cumulates were sampled in the best-preserved parts of individual komatiite flows in the Saw Mill and Pioneer Complexes of the Weltevreden Formation of the Barberton Greenstone Belt, South Africa (Supplementary Fig.\u00a01). Three samples (1521, 1522, and 1523) from the Saw Mill Complex were previously described16. The new samples 2216, 2217, 2218, and 2222 from Saw Mill and 1528 from Pioneer Complexes are similar to those in composition. These cumulates consist of partially altered (serpentinine and magnetite) euhedral olivine crystals (more than 60 volume %) and interstitial groundmass consisting of spinel, magnetite, and a variety of pyroxenes (low-Ca, high Ca-pyroxenes, and pigeonite) enclosed in a matrix of altered glass (chlorite and magnetite). In the groundmass of sample 2222, we also found clinoenstatite with typical polysynthetic twinning and tremolite in intergrowth with orthopyroxene. The new samples are: 2216 (a few meters South of Keena\u2019s flow 1, 25.842597\u00b0S, 30.885031\u00b0E, olivine composition 92.8\u201393.1\u2009mol.% Fo); 2217 (Keena\u2019s flow 2, 25.841475\u00b0S, 30.885683\u00b0E, olivine composition 93.2\u201393.8\u2009mol.% Fo); 2218 (Keena\u2019s flow 1, 25.841803\u00b0S, 30.886147\u00b0E, olivine composition 94.6\u201395.1\u2009mol.% Fo); 2222 (Gary\u2019s flow 2, 25.843578\u00b0S, 30.887742\u00b0E, olivine composition 94.8\u201395.2\u2009mol.% Fo); and 1528 (Fig.\u00a01a) from the Pioneer Complex (25.834308\u00b0S, 30.949083\u00b0E, olivine composition 92.5\u201393.5\u2009mol.% Fo). Olivine crystals contain abundant melt inclusions, which are partly crystallized if they exceed 20 micrometers in diameter (Fig.\u00a01b) or glassy if they are smaller (Fig.\u00a01e).\n\nPrior to polishing and mounting, melt inclusions hosted in olivine crystals were homogenized at high temperatures and 1\u2009bar in the CO2-H gas mixture corresponding to \u0394QFM-1 (quartz-fayalite-magnetite oxygen buffer). To homogenize partially crystallized melt inclusions, olivine crystals were placed in open platinum ampoules and heated in a vertical high-temperature furnace at Vernadsky Institute of Geochemistry (Moscow) following the previously established protocol18. Target temperatures for experiments range from 1280\u2009\u00b0C to 1350 \u00b0C in order to complete the melting of crystalline phases in inclusions and to minimize diffusional loss of hydrogen from inclusions through the host olivine. Upon heating completion, instantaneous quenching of the melt inclusion is achieved via immersing ampoules in water at room temperature. Because inclusions were heated at 1\u2009atm pressure under reduced gas flow, all inclusions that were opened even partly lost their volatiles, e.g., Cl, S, H2O, as well as part of Na and most of Pb. Such inclusions, which can be identified by electron microprobe analysis, have Cl and S typically below the detection limit and were neglected in this study. Correction of the measured composition of glass in melt inclusions (see below) for post-entrapment olivine crystallization on the walls of inclusion\u2019s cavity and Fe-Mg exchange with host olivine (PEC) was performed using Petrolog 3 software56. Details on this approach are reported in earlier studies16. PEC was used to correct the inclusion compositions presented in Supplementary Data\u00a02 and 3.\n\nThe chemical composition of the olivine and melt inclusions was analyzed using a JEOL JXA-8230 electron microprobe and a JEOL FEG JXA-iHP200F electron microprobe at the ISTerre Micro-Analytical Platform (IMAP), Universit\u00e9 Grenoble Alpes (France), using an analytical protocol published elsewhere55. The Mg-Fe olivine-melt geothermometer was used for the estimation of komatiitic melt inclusion crystallization/entrapment temperature, with correction for the effects of measured water contents57.\n\nThe water content of melt inclusions was determined using a Raman microscope LABRAM Soleil, Horiba (ISTerre, Grenoble, France), equipped with 473\u2009nm and 532\u2009nm lasers and an optical microscope (Nikon Eclipse LVDIA-N) using an analytical protocol previously published55. Average and 2SE uncertainties are reported in Supplementary Data\u00a02\u20134.\n\nLA-ICP-MS analyses were conducted at IMAP (Universit\u00e9 Grenoble Alpes, France). Strontium (Sr) isotope signatures and trace-element (TE) contents were measured directly in melt inclusions with laser ablation (LA) system (RESOlution SE, Applied Spectra) coupled with a multi-collector (Neptune XT, Thermo-Fisher Scientific) for Sr-isotope and a single-collector (8900, Agilent) inductively-coupled plasma mass spectrometer (ICP-MS), for TE. LA single stream and LASS protocols were used depending on the size of the melt inclusions: smaller inclusions were analyzed with LASS Sr-TE analytical protocol, while bigger ones had two laser analyses, one for Sr-isotopes and one for TEs. The smallest melt inclusions with diameter\u00a0less than 30 micrometers, were analyzed\u00a0only for TE.\u00a0Uncertainties are reported at a 95% confidence interval unless stated otherwise. It is worth mentioning that Sr content in Weltevreden melt inclusions averages 36\u2009\u00b1\u20096\u2009ppm, corresponding to a mass of total Sr of ca. 6\u2009pg in most melt inclusions. Currently, such small amounts of Sr cannot be accurately measured via the traditionally more precise TIMS approach owing to the lowest Sr blanks obtained through this approach being a few tenths of pg58.\n\nSr-isotope compositions were measured on a Neptune XT MC\u2013ICP-MS (Thermo-Fisher Scientific) following conventional cup configurations59. Additionally, we also measured mass 90 in order to evaluate the Sr/Zr of Weltevreden melt inclusions and compare these ratios with the trace-element data obtained concurrently. Measurements were conducted in static mode, and all signals were collected in Faraday cups. Four 1013 \u03a9 amplifiers were used for the measurement of masses 84, 85, 86, and 87. All other masses (83, 83.5, 86.5, 88 and 90) were measured with 1011 \u03a9 amplifiers. The different dynamic response time between 1011 \u03a9 amplifiers and 1013 \u03a9 amplifiers was accounted by using the Thermo-Fisher software built-in tau correction60. Gain calibration was conducted overnight before each analytical run of melt inclusions. Integration time was set to 1.049\u2009seconds. The instrument was run in low-resolution mode (mass resolution of ca. 400). Ni Jet-Sampler and Ni X-Skimmer cones were used. Sr-isotope analyses were reduced offline using an in-house modified Python code compatible with Iolite-461. All channels were first background-subtracted. Since signals of masses 83.5 and 86.5 were always below the detection limit (calculated as 3.29 \u00d7 standard deviation of the background over the entire analytical run62), no REE2+ correction was conducted. Calcium-argides (Ca\u2013Ar) species, which can cause interference on masses 84, 86, and 88, are corrected using the background-subtracted signal of mass 83. A power-law-based Sr-fractionation factor (\u03b2Sr) was then calculated for each individual analysis based on the measured (88/86) value and the natural value (8.37520938). The Rb fractionation factor (\u03b2Rb) was manually adjusted to provide the best accuracy for all three reference materials measured throughout the analytical run. This approach allows for accounting for day-to-day variations in \u03b2Rb and is analogous to the approach used for correcting Yb interference in zircon Hf-isotope analysis. The amount of 87Rb is then calculated using the measured 85Rb and the natural 87Rb/85Rb value (0.38571). Then, the amount of 87Sr is obtained by removing the contribution of 87Rb on the measured 87 signals (87 net signal = 87Rb\u2009+\u200987Sr). Isotopic ratios are then calculated, including a power-law-based mass bias correction. For all melt inclusions labeled 15XX, adjusting \u03b2Rb value on a daily basis was sufficient to obtain accurate results (within 2\u2009SD) for all three reference glasses covering all compositional ranges of measured inclusions used in each analytical run (KL2-G, NIST-614-G, and GOR128-G, Supplementary Fig.\u00a04); no standardization of the data was conducted here for 87Sr/86Sr values. Standardization was, however, required for 87Rb/86Sr and Sr/Zr values. This is likely the result of minor elemental fractionation causing inaccuracy for corrected-only ratios. For melt inclusions labeled 22XX, standardization of both 87Sr/86Sr and 87Rb/86Sr was conducted with KL2-G as a calibration glass.\n\nReaders can refer to the Sr-isotope DRS provided on the Iolite Github (https://github.com/iolite-LA-ICP-MS) for a full view of the Python code. Individual analysis uncertainties are calculated by the in-house built Iolite code. Hence, exports from Iolite include uncertainties of all backgrounds, interference corrections, and standardization conducted. Note that including the REE2+ correction changes the final uncertainty to the sixth decimal only, i.e., two orders of magnitude lower than the typical precision obtained with our LASS protocol for such Sr-depleted melt inclusions. Since several analytical runs were conducted for analyzing these melt inclusions, an evaluation of the inter-sequence (i.e., long-term) excess variance was conducted. This approach follows the widely accepted workflow used in U-Pb isotope analyses63 and consists of calculating the degree of homogeneity of the reference glasses over all sequence: if the MSWD value\u2014or corresponding p(\u03c72) value\u2014is within the acceptable range of 5\u201395%, then no inter-sequence excess variance is propagated into individual analyses. If not, then uncertainties are expanded up to the level where all validation materials (here, glasses) return MSDW value or p(\u03c72) value, indicating statistical homogeneity. No inter-sequence excess variance was required for the 87Sr/86Sr values: MSWD and p(\u03c72) values for the three reference glasses GOR128-G, KL2-G and NIST-614-G are: (i) 1.16 and 8.9%, (ii) 0.41 and 100%, (iii) 1.02 and 43% respectively (see data in Supplementary Data\u00a01), while an inter-sequence excess variance of 5.5%, 6%, and 6% was propagated into 87Rb/86Sr, Rb/Zr and Sr/Zr values, respectively, for all analyses (reference glasses and melt inclusions). Inter-sequence excess variance likely reflects the contribution of elemental fractionation, which is unaccounted for in our data reduction protocol. Hence, the reported individual uncertainties, which include excess variance in 87Sr/86Sr, 87Rb/86Sr, and Sr/Zr values, are considered external uncertainties. Precision and accuracy of Sr isotope analyses of melt inclusions were double-checked by duplicate, triplicate, or quadruplicate analyses of sufficiently large inclusions (Supplementary Fig.\u00a05 and Supplementary\u00a0Table\u00a03). The homogeneity of the replicates of 2, 3, or 4 analyses of the same melt inclusion presented in this diagram and table\u00a0confirms that reported uncertainties of individual inclusions are not underestimated.\n\nAll melt inclusions discussed in this work have 87Rb/86Sr values lower than the 87Rb/86Sr of KL2-G (0.0699, or\u00a00.0248 as Rb/Sr), the reference glass with the most elevated 87Rb/86Sr. Melt inclusions displaying 87Rb/86Sr higher than KL2-G were disregarded since the accuracy of the 87Rb correction could not be assessed. In addition, inclusions with 2 standard errors of measured 87Sr/86Sr ratio over \u00b10.0015 (thin inclusions where the signal was collected for less than 8\u2009seconds) were also excluded. Lastly, we checked the robustness of 87Sr/86Sr uncertainty in accepted individual inclusions by comparison with the uncertainties in the reference materials with similar content of Sr (GOR128 and NIST 614)\u00a0using the following equation:\n\nWhere \\({\\sigma }_{{inclusion}}\\) and \\({\\sigma }_{{RM}}\\) are 95% c.i. uncertainties of individual inclusions and reference material (glasses), respectively; \\({{Sr}}_{{inclusion}}\\) and \\({{Sr}}_{{RM}}\\) are Sr contents in inclusions and reference materials; \\({t}_{{inclusion}}\\) and \\({t}_{{RM}}\\) are 87Sr/86Sr acquisition times for inclusions and reference materials (Supplementary Table\u00a01).\n\nPredicted\u00a0average \\({\\sigma }_{{inclusion}}\\) calculated for all Weltevreden melt inclusions and for the 14 unradiogenic ones\u00a0are 0.0009 \u00b1 0.0001, as well as their average\u00a0measured \\({\\sigma }_{{inclusion}}\\), are 0.0010\u00a0\u00b1 0.0001, thus virtually identical (Supplementary Table\u00a01). We, therefore, conclude that reported uncertainties in 87Sr/86Sr values for individual melt inclusions are neither over- nor underestimated and, thus, are valid for statistical treatment.\n\nTE analyses were conducted with 20-\u03bcm spots/10-Hz pulse frequency and 38-\u03bcm spots/15-Hz pulse frequency for inclusions in single and split stream mode, respectively, and a laser fluence of 4\u2009J.cm\u22122. Carrier gas was He (ca. 0.3\u20130.9\u2009L/min) with the addition of N2 (ca.1 to 2.5\u2009mL/min), which was mixed with Ar (ca. 0.6-1.1\u2009L/min) before introduction into the spectrometer. The oxide production rate, monitored with ThO+/Th+, was <0.1%, and the doubly charged ratio monitored with 44Ca2+/44Ca+ was <0.1%. The U/Th ratio ranged between 98% and 102%. Analyses were conducted in time-resolved acquisition mode (TRA) and included 1 second of ablation to eliminate surface contamination, 30-40 s background measurement followed by 30\u201340 seconds sample ablation, and signal measurement. Dwell time was 10-100\u2009ms for different elements. All spectra were inspected in LADR software to define intervals for integration and exclude remaining surface contamination, if any. Concentrations were quantified from the measured ion yields normalized to Ca, previously measured on EPMA. Details on the varied dwell times and TE data reduction are reported in Supplementary Table\u00a02. Ce/Pb, Nb/U, and Rb/Sr of reference glasses (GOR128-G64 and NIST-614-G65) are shown in Supplementary Fig.\u00a04 and reported in Supplementary Data\u00a01. Reference values agree with accepted values. Where required, an inter-sequence excess variance was propagated into our analyses to account for minimal heterogeneity of reference glasses between the varied analytical runs (up to 10%).\n\nMeasured 87Sr/86Sr obtained on Weltevreden melt inclusions show a range of values that correspond to an MSWD of 2.3. Considering the number of individual datapoints\u2014n\u2009=\u2009137\u2014this MSWD value translates to a p(\u03c72) of 4.4\u2009\u00d7\u200910\u221216, i.e., largely below the accepted threshold of 5% typically used in geoscience to indicate homogeneity of a sample population66. Elevated MSWD ratios are common in Earth science geochemistry, and information from \u2018dispersed\u2019 datasets has geological meaning67. This significant heterogeneity is also clearly observed for (87Sr/86Sr)initial values that show an MSWD of 2.5, translating to a p(\u03c72) of 0. Further, it is also clear from Supplementary Fig.\u00a06 that the range of (87Sr/86Sr)initial values is not resulting from under/over correction of the (87Sr/86Sr)measured values: the range of 87Rb/86Sr is rather narrow and all analyses with elevated 87Rb/86Sr, i.e. >(87Rb/86Sr)KL2-G, were rejected from the dataset, preventing inaccurate over correction of (87Sr/86Sr)measured. Further, since all potential sources of uncertainty have been examined and propagated in isotopic ratios, this range of (87Sr/86Sr)initial values is interpreted to reflect Sr-isotope heterogeneity of the melts that were trapped in Weltevreden olivine crystals. The observed heterogeneity in elemental and isotopic compositions in Weltevreden melt inclusions is not a unique feature since a multitude of melt inclusion studies have reported similar observations17,19,30 for different radiogenic isotope systems, regardless of the analytical protocol (laser ablation, ion probe, or thermal ionization mass spectrometry).\n\nHence, we can discriminate statistically homogeneous populations in this dataset. One way to achieve this is to isolate individual analyses that are in the tails of this distribution: those with (87Sr/86Sr)initial significantly, i.e., considering individual uncertainties (\u00b12se)\u2012outside the 95% confidence interval around the weighted mean of the dataset. Doing so yields three homogeneous populations of melt inclusions: (i) one with low (87Sr/86Sr)initial\u2009=\u20090.69932\u2009\u00b1\u20090.00024 (c.i. 95%) comprised of 14 inclusions (MSWD\u2009=\u20090.88, p(\u03c72)\u2009=\u20090.57), (ii) one with intermediate (87Sr/86Sr)initial\u2009=\u20090.700624\u2009\u00b1\u20090.000086 made of 116 inclusions (MSWD\u2009=\u20091.1, p(\u03c72)\u2009=\u20090.27) and one with elevated (87Sr/86Sr)initial\u2009=\u20090.70177\u2009\u00b1\u20090.00024 made of 7 inclusions (MSWD\u2009=\u20090.48, p(\u03c72)\u2009=\u20090.82).\n\nApplying the t-test for independent populations with unequal variances68 reveals that the means of (87Sr/86Sr)initial of all groups are different with a confidence level over 99.9% (see Supplementary Table\u00a06).\n\nSr-model age of olivine-hosted melt inclusions was based on (i) (87Sr/86Sr)initial and (ii) BSE evolution. The latter is defined as follows: initial 87Sr/86Sr\u2009=\u20090.698990 at 4.567\u2009Ma age69, initial BSE Rb/Sr=0.0331, and 87Rb decay constant of 1.3972\u2009\u00d7\u200910\u221211\u2009yr\u22121. Even considering a 15% uncertainty in the BSE Rb/Sr value, Sr-model ages remain far within the uncertainty of our estimates. The calculation was conducted assuming no Rb left in the BSE source after melt extraction. This assumption is realistic because the source of Weltevreden komatiite has likely undergone significant melting and\u00a0several stages of\u00a0efficient melt extraction, resulting in severe depletion of most incompatible elements16. The relatively high Rb/Sr ratios of trapped melts (0.014\u20130.024 for inclusions measured for 87Sr/86Sr) are apparently the result of their contamination during and before the crystallization of host olivine (Fig.\u00a02e). Samarium\u2013Neodymium (Sm-Nd) and Lutetium\u2013Hafnium (Lu-Hf) model ages presented in Supplementary Fig.\u00a02 were calculated using Ryan Ickert\u2019s spreadsheet70.\n\nStudied inclusions in olivine yield significant intercorrelated variations of Cl, K, Rb, H2O, Pb, Sr, U, and Na, which are unrelated to olivine crystallization and extraction from the melt. This is well demonstrated by the strong correlation between ratios of elements excluded from the olivine structure, e.g., Cl, K, Rb, H2O, Pb, Sr, Na, and Ti, which must stay constant during olivine fractionation (Supplementary Fig.\u00a03). Similar variations are common for submarine glasses of OIB and MORB (Supplementary Fig.\u00a03a) and have been explained by minor (less than 1\u2009wt%) contamination of seawater-derived ultra-saline brines26,27. The same explanation is also well suited for Weltevreden melt inclusions. The maximum amount of brine with a concentration of Cl of 10-30\u2009wt% required to explain the addition of ca. 0.20\u2009wt% of Cl to Weltevreden melt is about or less than 1.5\u2009wt%. We also cannot exclude the assimilation of larger amounts of serpentinite. This process does not affect major element concentrations considering their analytical uncertainty but is enough to be observed in trace elements and H2O contents. Also note that the extent of contamination tends to increase with decreasing host olivine Fo content (Fig.\u00a02d). The initial 87Sr/86Sr isotope ratio of melt inclusions does not correlate with contamination proxies (see Fig.\u00a02h); thus, we conclude that the effect of observed contamination is within the analytical uncertainties in 87Sr/86Sr values.\n\nWe study the thermochemical evolution of compressible mantle using the code StagYY46, which has been developed and widely used over several decades for global-scale modeling of Earth\u2019s evolution spanning its age12,13,48,49,50. The model includes pressure- and temperature-dependent viscosity, plastic yielding, time-dependent radiogenic heating, core cooling, and phase changes. Following a two-step mantle differentiation and utilizing water-dependent solidi functions12, the code forms basaltic and felsic melts and considers both intrusive (plutonic) and eruptive (volcanic) magmatism48. The mass ratio of erupted to intruded material follows a specified constant value of 30:70 (corresponding to 30% eruption efficiency), which has been previously shown to be important for producing Archean TTG rocks48,71. We employ temperature- and pressure-dependent water solubility maps for different mantle minerals, which control the water in-gassing and out-gassing12.\n\nWe use a two-dimensional quadrant spherical annulus geometry47 with an Eulerian mesh, whose resolution varies radially and is higher at the core-mantle boundary, 660\u2009km phase transition, and at the surface. The computational domain consists of 512 (lateral) times 128 (radial) cells and ~1.3 million Lagrangian tracers carrying various quantities (temperature, composition, water content, isotopes, trace elements, etc.) are advected through it. Free slip boundary conditions are used at the core-mantle boundary and at the surface. Side boundary conditions are periodic.\n\nA visco-plastic rheology is considered where the viscous deformation is accommodated by grain-size independent diffusion creep. The mantle is divided into 3 different layers i: upper mantle (1) the lower mantle (2), and the post-perovskite layer (3), with each layer having different values for activation energy Ei and activation volume Vi72,73. Following the Arrhenius law, the diffusion and dislocation creep (proxy) components of the temperature- and pressure-dependent viscosity are given as:\n\nwhere \\({{{{\\rm{\\eta }}}}}_{0}\\) is the reference viscosity (\\(2.35\\,{10}^{19}{Pa\\; s}\\)) at zero pressure and reference temperature \\({T}_{0}\\) (1710\u2009K), \\(\\Delta {{{{\\rm{\\eta }}}}}_{i}\\) is the viscosity offset between layer i and the reference viscosity, P is the pressure, R is the gas constant, and T is the absolute temperature. The activation volume decreases exponentially with increasing pressure in each layer i according to the relation:\n\nwhere Pi is the pressure scale, which is different for each layer i. Plastic deformation in the lithosphere is accommodated by assuming plastic yielding52,74. The maximum stress that a material can sustain before deforming plastically is given by the yield stress \\({{{{\\rm{\\sigma }}}}}_{Y}\\), which has both brittle and ductile components:\n\nThe ductile yield stress \\({{{{\\rm{\\sigma }}}}}_{Y,{ductile}}\\) increases linearly with pressure as:\n\nwhere \\({{{{\\rm{\\sigma }}}}}_{Y}^{0}\\) is the surface ductile yield stress and \\(\\acute{{{{{\\rm{\\sigma }}}}}_{Y}}\\) is the pressure gradient of the ductile yield stress. The brittle yield stress \\({{{{\\rm{\\sigma }}}}}_{Y,{brittle}}\\) is calculated following the Byerlee approach as:\n\nwhere c is the surface cohesion and \\({{{\\rm{\\mu }}}}\\) is the friction coefficient. If the convective stresses exceed the yield stress, the viscosity is reduced to the yielding viscosity \\({{{{\\rm{\\eta }}}}}_{Y}={{{{\\rm{\\sigma }}}}}_{Y}/2\\dot{{{{\\rm{\\epsilon }}}}}\\), where \\(\\dot{{{{\\rm{\\epsilon }}}}}\\) is the 2nd invariant of the strain-rate tensor.\n\nSee Supplementary Table\u00a07 for model parameters used in this study. The model uses a parameterization based on mineral physics data75,76, in which the mantle minerals are divided into olivine (ol), pyroxene-garnet (px-gt), TTG (tonalite-trondhjemite-granodiorite), and melt phase systems. Solid-solid phase transitions are assumed (see Supplementary Table\u00a08), and the mantle is initialized with a pyrolytic composition: 80% harzburgite and 20% basalt, being a mixture of 60% olivine and 40% pyroxene-garnet. The adiabatic temperature, density, thermal conductivity, thermal expansivity, and heat capacity are pressure-dependent following a third-order Birch\u2013Murnaghan equation of state. Further details of the model can be found in ref. 12. Here, we describe only the model modifications introduced in this study.\n\nWe improved our rheological model by adding a proxy for the pressure-, temperature-, and stress-dependent dislocation creep rheology, assuming stress corresponding to a geological strain-rate of 10\u221215\u20091/s and using dry olivine rheology parameters77 (Supplementary Table\u00a07). We also considered frictional plasticity with a coefficient of friction of 0.1 corresponding to the properties of subduction channels, which lack continental sediments51. A similar effective friction coefficient was suggested based on the model of thermal cracking of oceanic lithosphere on early Earth78.\n\nWe modified the water solubility map for px-garnet phases to better fit the map calculated by Perple_X (Supplementary Fig.\u00a08).\n\nIn this study, we assume that at every time step, the mantle minerals only in the top 5\u2009km of the computational domain (as opposed to 10\u2009km done previously) are partially saturated with water in accordance with their pressure- and temperature-dependent solubility maps with the water saturation fraction \u03b1w parameter (0\u2009<\u2009\u03b1w\u2009<\u20091) controlling the input of the surface water into the lithosphere.\n\nWe corrected the density of felsic material (TTG) by considering phase transformations of feldspars and quartz at high pressures (see Supplementary Fig.\u00a09). We have also slightly modified density changes of olivine and pyroxene-garnet compositions at phase boundaries to decrease the density difference between pyrolite and pyroxene-garnet in the lowermost mantle to 1.7%, simultaneously fitting density-depth distribution in the PREM model along the 1600\u2009K adiabatic geotherm. We introduced the effect of water on density using a simplified relation\n\nwhere \\({\\rho }_{\\omega }\\), \\({\\rho }_{d}\\) are densities of water-containing and dry material, respectively; \\({C}_{H2O}\\) is the concentration of water in weight fraction, and \\(a\\) is a constant, typically between 1 and 2 estimated using Gibbs energy minimization code79 (Supplementary Fig.\u00a09) that we consider as 2.0 in this study.\n\nIn the present study, we assume that TTG can only be produced from basalt if the latter\u2019s water content is above 0.5\u2009wt.% (instead of 0.05\u2009wt.% used in earlier studies) and that the maximum degree of melting of basalt to produce TTG is 30% instead of 10% used in previous studies12,48.\n\nWe have introduced the evolution of the Rb\u2013Sr isotope system and the Nb, U, Ce, and Pb trace elements. The constant parameters and solid-melt partition coefficients used in the study are presented in Supplementary Tables\u00a09 and 10, respectively. While we use the same technique to compute isotope and trace-element evolution in the StagYY code as done previously49,50, our models have several essential differences. First, the StagYY code has significantly evolved since 2004, and a key feature in our models is the production of felsic melts. Continental crust production drastically affects the evolution of many isotopic systems and trace elements. Second, the\u2013Sr isotope system was not included in previous studies. Our modeling purpose is also different. We aim to compare the modeling results with geochemical data reporting the trace-element composition of the Hadean melt source in the Archean komatiites. To achieve this, we analyze the compositions of potential sources of komatiitic melts at the depth before these melts are separated from the source rocks and before the melts from different sources are mixed.\n\nFor the models presented in Figs.\u00a07 and 8 (main text), we show the evolution of the mean Nb/U and Ce/Pb ratios with time for a selection of the model\u2019s cells fulfilling the following conditions: (i) Average cell temperature has to be more than 100\u2009K higher than the average temperature of all cells located at the same depth. This condition allows us to identify mantle plume material. In practice, the potential temperature of all selected cells in most of the models appears to be higher than 1700\u2009\u00b0C. (ii) Cells have to be located within the depth range of 400\u2013600\u2009km. This condition is required to avoid cells from the top 300\u2009km, from which the melt is extracted, and trace elements are partitioned. (iii) The average initial Sr isotope ratio (87Sr/86Sr) of each cell has to be less than 0.6997 to identify the Hadean fraction. Following this procedure, we obtain mean values of trace-element ratios in the Hadean fraction of the potential komatiite source and standard deviations of mean values at every output model time. To avoid unrepresentative and too uncertain values, we further filter out values at times with too few Hadean cells (\u226420), with too large a value of 2 standard deviations of the mean Nb/U ratio (\u22654), and with too large a value of 2 standard deviations of the mean Ce/Pb ratio (\u22652). As a result, we obtain mean values of trace-element ratios and their standard deviations (multiplied by 2) for each model at model output times, which we show in Figs.\u00a07 and 8 of the main text.\n\nTo avoid misinterpretation, the sampling conditions behind modeling results presented in Figs.\u00a07 and 8 require further elucidation. The rationale is that our geodynamic model, like others, currently fails to replicate the presence of primary melts from various mantle sources at the crustal level without their mixing, a process accomplished through the geochemical methodology outlined in this paper. However, we can detect a potential Hadean melt source considering numerical cells in the mantle with a high enough potential temperature (condition \u201ci\u201d) and Hadean Sr modal age (condition \u201ciii\u201d). Because the extraction of melt changes isotope and trace-element composition in the heterogeneous source due to different fusions of heterogeneities, we have to consider numerical cells at the depth before the melt is extracted. Therefore, we consider cells at a depth below 400\u2009km (condition \u201cii\u201d).\n\nIn the models depicted in Fig.\u00a09 (main text), we apply cell selection criteria (i) and (ii), thereby sampling potential komatiite sources across all Sr-model ages. For presentation in Fig.\u00a09, we also filter out values of Sr isotope ratios at times when there were less than 200 \u201cplumes\u201d cells in the mantle (i.e., low plume activity). The good fit of the simulated Sr isotope model ages to the geochemical data for Group I models (Fig.\u00a09) further substantiates the legitimacy of condition (ii) for the selection of numerical cells to simulate the sources of Weltevreden komatiites.\n\nDue to the inherent randomness of convection processes arising from the initial thermal perturbations and initial tracer positions, the model results vary even with the exact same parameters. Group I presented in Fig.\u00a07 comprises two sets of models (3 in each set) with the same frictional strength (f\u2009=\u20090.1) but with different water saturation fractions (alpha\u2009=\u20090.25 or 0.20). The higher the value of alpha, the higher is the input of water into the lithosphere. Group II, presented in Fig.\u00a08, also comprises two sets of models (2 in each set) with the same frictional strength (f\u2009=\u20090.2) but with different water saturation fractions (alpha\u2009=\u20090.40 or 0.30). Values of alpha were chosen such that all models of both groups produced a similar amount of continental crust during the Hadean.\n\nTo better explain trace-element trends in Figs.\u00a07 and 8a, d, we consider the following end-member cases of continental crust production and recycling of the restites.\n\nCase 1: No continental crust is produced in the Hadean. Obviously, in this case, Nb/U and Ce/Pb ratios in the Hadean fraction will not change from the BSE values.\n\nCase 2. Continental crust is produced in the Hadean, but restites are not recycled and remain in the lithosphere. In this case, again, Nb/U and Ce/Pb ratios in the Hadean fraction sampled by the plume will not change from the BSE values.\n\nCase 3. Continental crust is produced in the Hadean, and restites of the Hadean age are immediately recycled into the lower mantle and mixed into the plume sources. In this case, Nb/U and Ce/Pb ratios in the Hadean fraction sampled by the plume will increase proportionally to the production of the continental crust during Hadean time and will stay constant after Hadean time.\n\nCase 4. Continental crust is produced in the Hadean, and restites of the Hadean age are recycled into the lower mantle and mixed into the plume sources not immediately but continuously during some time, say 1\u2009Gyr. In this case, Nb/U and Ce/Pb ratios in the Hadean fraction will increase continuously over time, even after Hadean.\n\nOur models of Group I (Fig. 7a, d) follow case 4, and models of Group II (Fig.\u00a08a, d) follow case 2 with a minor addition of case 4.\n\nIn the main text, we show the effect of lithospheric hydration and strength on the evolution of the trace-element ratios of the Hadean fraction of the potential komatiite source (Fig.\u00a07). Here, we additionally show the influence of the variation of the parameter \u201ca\u201d (Eq.\u00a06) responsible for decreasing the density of rocks due to water-containing minerals. Panels a-h in Supplementary Fig.\u00a010 show our models that fit well with the geochemical observations computed with our preferred value of \u201ca\u201d of 2. Panels i-p in Supplementary Fig.\u00a010 show modeling results obtained with the value of \u201ca\u201d parameter of 1.5, which is more suitable for rocks at relatively high pressures (Supplementary Fig.\u00a09b) with all the other parameters the same as in the models with a\u2009=\u20092. As we see from Supplementary Fig.\u00a010, the variation of \u201ca\u201d parameter does not significantly change the modeling results.\n\nIt is clear that even advanced geodynamic models of the early Earth\u2019s evolution, which include the generation and recycling of oceanic and continental crusts, cannot avoid simplifications, and they have certain limitations. The main simplification of our global models is their 2D geometry. Although a 2D spherical annulus model setup replicates a 3D spherical case much better than a 2D cylindrical or a 2D axisymmetric model47, some significant differences still remain. For instance, 2D plumes and subduction zones, by definition, have a global character, which leads to an exaggerated melt production and recycling of both oceanic and continental crust. As these artifacts accumulate over time, they are likely more significant for the modeling of the full 4.5 Gyr evolution of the Earth and are less important for the models of the early Earth evolution. Nevertheless, one possible artifact of our models is that most of the generated TTG is recycled back to the mantle during the 1.5\u2009Gyr modeling time. Although the high rate of continental crust recycling in Hadean and Eo-Archean is also suggested in some geochemical models6, our models likely overestimate the TTG recycling rate. Despite the possible overestimation of the TTG recycling rate, the amount of recycled TTG in our models is typically less than 0.1% in the Hadean fraction of the source (Sr ratio <0.6997). Such a low amount of recycled TTG does not significantly affect the modeling results for Nb/U and Ce/Pb ratios in the Hadean melt source, and hence, our interpretation of geochemical data is valid.\n\nIn our simulations, we utilize thermodynamic models of Earth materials. Models such as Perple_X80 are well constrained by experimental data for the uppermost mantle pressure-temperature conditions but may not be as reliable for the deeper mantle, particularly for water-containing phases. The water solubility map based on the Perple_X model for MORB composition shows higher water content at low temperatures and pressures above 5\u2009GPa compared to maps based on experiments by ref. 81, as presented in ref. 82. The Perple_X calculations used in our models allow for a maximum of 8\u2009wt% saturated water content in hydrous phases of the oceanic crust at pressures exceeding 5\u2009GPa. However, in the models presented in this paper, the water content in the oceanic crust never exceeds 6\u2009wt% and only rarely surpasses 4\u2009wt% due to the limited input of water from the surface. The water content in the upper mantle is also quite realistic, at a few hundred\u2009ppm in the Hadean and Eo-Archean mantle (Supplementary Fig.\u00a011), similar to the estimates by ref. 82 and ref. 83 for the present-day Earth.\n\nOur geodynamic model, like others, cannot reproduce the presence of primary melts from various mantle sources at the crustal level without mixing them. This is achieved through the geochemical approach presented in this paper. Therefore, we must employ the modeling strategy detailed in the previous sections to replicate the geochemical observations, addressing not the compositions of melts, but rather their sources at depth.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All source data are provided with this paper and are available in the EarthChem library: https://doi.org/10.60520/IEDA/113703\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The convection code StagYY is the property of Paul Tackley and Eidgen\u00f6ssische Technische Hochschule Z\u00fcrich (ETH) and is available for collaborative studies from Paul Tackley (paul.tackley@eaps.ethz.ch).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "O\u2019Neil, J., Carlson, R. W., Francis, D. & Stevenson, R. K. Neodymium-142 evidence for Hadean Mafic Crust. 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Portnyagin for providing reference glasses for calibration of Raman H2O analysis. F. Brunet, B. Lanson, P. Roux, and the management of University Grenoble Alpes provided invaluable assistance in establishing and maintaining the ISTerre Micro-Analytical Platform (IMAP), the core facility that enabled the results of this study. We are grateful to Sappi Forest for granting access to the sampling sites. For visualization of geodynamic results, scientific color maps84 are used in this study to prevent visual distortion of the data and exclusion of readers with color-vision deficiencies85. This project is supported by a grant of the European Research Council (ERC) under the European Union\u2019s Horizon H2020 research and innovation program (Synergy Grant MEET, grant agreement no.856555) to A.V.S, S.V.S, and J.W.V. ISTerre is part of the Laboratoires d\u2019Excellence (LabEx) OSUG@2020 (ANR10 LABX56). Participation of E.V.A. and A.N.K and annealing of melt inclusions were supported by the state assignment of the Vernadsky Institute of Geochemistry and Analytical Chemistry of the Russian Academy of Sciences (GEOHI RAS). The computing time for geodynamic modeling (project bbk00014) was provided by the administration of the high-performance computer cluster \u201cLise\u201d at the NHR Centre NHR@ZIB jointly supported by the Federal Ministry of Education and Research and the state government.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Adrien Vezinet, Aleksandr V. Chugunov, Alexander V. Sobolev.\n\nUniv. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, Univ. Gustave Eiffel, ISTerre, Grenoble, France\n\nAdrien Vezinet,\u00a0Aleksandr V. Chugunov,\u00a0Alexander V. Sobolev,\u00a0Valentina G. Batanova\u00a0&\u00a0Nicholas T. Arndt\n\nGFZ Helmholtz Centre for Geosciences, Geodynamic Modelling Section, Potsdam, Germany\n\nCharitra Jain\u00a0&\u00a0Stephan V. Sobolev\n\nUniversity of Potsdam, Institute of Geosciences, Potsdam, Germany\n\nStephan V. Sobolev\n\nVernadsky Institute of Geochemistry and Analytical Chemistry, Russian Academy of Sciences, Moscow, Russia\n\nEvgeny V. Asafov\u00a0&\u00a0Alina N. Koshlyakova\n\nFriendly Solutions, Sydney, NSW, Australia\n\nLeonid V. Danyushevsky\n\nWiscSIMS Lab, Dept. of Geoscience, Univ. of Wisconsin, Madison, WI, USA\n\nJohn W. Valley\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nA.V. participated in sample collection, developed the protocol for Rb\u2013Sr isotope analyses and LA-ICP-MS Rb\u2013Sr/TE split stream analytical mode, conducted analyses, processed Rb\u2013Sr isotopes data, interpreted geochemical data and wrote most of the manuscript text; A.V.C. participated in sample collection, found and prepared all melt inclusions and host minerals for analytical work, conducted EPMA and Raman analyses, processed trace elements data and participated in geochemical data interpretation; A.V.S. conceived the idea and designed the project, assisted on the collection of samples and played a leading role in interpreting geochemical data; C.J. developed the geodynamic models, wrote and modified code and performed computing; S.V.S. conceived the idea and designed the geodynamic model, played a leading role in interpreting and visualizing results of geodynamic models and wrote most of the geodynamic modeling section; E.V.A. performed inclusions homogenization and assisted in the collection of samples; A.N.K. performed inclusions homogenization; V.G.B. developed analytical protocols for EPMA and LA-ICP-MS analyses of melt inclusions and host olivines; N.T.A. governed in the collection of samples and shared his experience in the study of komatiites; L.V.D. directed the tuning of RESolution SE laser and Agilent 8900 ICP-MS and participated in the preparation of LA-ICP-MS analytical protocols; J.W.V. participated in the design of the project. All co-authors contributed to discussing and interpreting data and writing the manuscript.\n\nCorrespondence to\n Adrien Vezinet, Aleksandr V. Chugunov, Alexander V. Sobolev or Stephan V. Sobolev.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks David Murphy, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Growth of continental crust and lithosphere subduction in the Hadean revealed by geochemistry and geodynamics.\n Nat Commun 16, 3850 (2025). https://doi.org/10.1038/s41467-025-59024-6\n\nDownload citation\n\nReceived: 31 October 2024\n\nAccepted: 09 April 2025\n\nPublished: 25 April 2025\n\nVersion of record: 25 April 2025\n\nDOI: https://doi.org/10.1038/s41467-025-59024-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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/dev/null +++ b/f41be4647e37f3138cfb67c4928561701472b0ca920af7f8ec5caa0bb65e1620/metadata.json @@ -0,0 +1,149 @@ +{ + "title": "Sub-nanosecond all-optically reconfigurable photonics in optical fibres", + "pre_title": "Sub-nanosecond all-optically reconfigurable photonics in optical fibres", + "journal": "Nature Communications", + "published": "19 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_MOESM3_ESM.avi" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_MOESM4_ESM.avi" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_MOESM5_ESM.avi" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_MOESM6_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-025-61984-8#ref-CR56" + ], + "code": [], + "subject": [ + "Fibre optics and optical communications", + "Nonlinear optics", + "Photonic devices" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5456413/v1.pdf?c=1754566193000", + "research_square_link": "https://www.researchsquare.com//article/rs-5456413/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-61984-8.pdf", + "preprint_posted": "08 Dec, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "We introduce a novel all-optical platform in multimode and multicore fibres. By using a low-power probe beam and a high-power counter-propagating control beam, we achieve advanced and dynamic control over light propagation within the fibres. This setup enables all-optical reconfiguration of the probe, which is achieved by solely tuning the control beam power. Key operations such as fully tuneable power splitting and mode conversion, core-to-core switching and combination, along with remote probe characterization, are demonstrated at the sub-nanosecond time scale. Our experimental results are supported by a theoretical model that extends to fibres with an arbitrary number of modes and cores. The implementation of these operations in a single platform underlines its versatility, a critical feature of next-generation photonic systems. These results represent a significant shift from existing methods that rely on electro-optical or thermo-optical modulation for tunability. They pave the way towards a fast and energy-efficient alternative through all-optical modulation, a keystone for the advancement of future reconfigurable optical networks and optical computing. Scaling these techniques to highly nonlinear materials could underpin ultrafast all-optically programmable integrated photonics.Physical sciences/Optics and photonics/Optical physics/Nonlinear opticsPhysical sciences/Optics and photonics/Applied optics/Fibre optics and optical communicationsPhysical sciences/Physics/Optical physics/Nonlinear opticsPhysical sciences/Physics/Electronics, photonics and device physics/Photonic devices", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryV10initialsubmission.pdfSub-nanosecond all-optically reconfigurable photonics in optical fibres", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Reconfigurable photonic systems provide a versatile platform for dynamic, on-demand control and switching. Here we introduce an all-optical platform in multimode and multicore fibres. By using a low-power probe beam and a counter-propagating control beam, we achieve dynamic control over light propagation within the fibres. This setup ensures simultaneous phase-matching of all probe-control beam four-wave mixing interactions, enabling all-optical reconfiguration of the probe modal state by tuning the control beam power. Key operations such as fully tuneable power splitting and mode conversion, core-to-core switching and combination, along with remote probe characterization, are demonstrated at the sub-nanosecond time scale. Our experimental results are supported by a theoretical model that extends to fibres with an arbitrary number of modes and cores. The implementation of these operations in a single platform underlines its versatility, a critical feature of next-generation energy-efficient photonic systems. Scaling this approach to highly nonlinear materials could underpin photonic programmable hardware for optical computing and machine learning.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "The ability to manipulate light by light within optical fibres represents a pivotal advance, both for the development of new photonic technologies and the exploration of novel physical phenomena. Groundbreaking all-optical devices and applications have been developed in single-mode fibres, including optical amplifiers1,2, signal regeneration3, polarisation control4,5, sensing6 and logical operations7.\n\nThe recent renewed interest in multimode fibres (MMFs) and multicore fibres (MCFs), driven by the need for high-speed communication systems based on space-division multiplexing (SDM)8,9, has sparked attention to complex nonlinear multimode processes that have no counterpart in the single-mode platform, and whose comprehension is still in the early stages10,11,12,13,14, paving the way for new methods of all-optical control of light.\n\nIn the framework of all-optical control, we can differentiate between self-organisation and external control. Self-organisation occurs when an intense light beam reshapes its own dynamics, owing to the substantial nonlinearity induced by its large peak power. Beam self-cleaning15,16, self-switching17,18, self-coherent combination19, and self-repolarisation processes20,21 induced by Kerr nonlinearity fall into this category. Conversely, external control occurs when the dynamics of a probe beam are controlled by an external independent control beam through their mutual nonlinear interaction.\n\nWhen both the probe and control beam are relatively intense, their nonlinear interaction may exhibit robust modal attraction22,23,24,25 or even rejection dynamics26, as recently demonstrated in multimode systems. In contrast, when the probe beam operates in a low-power (linear) regime, substantially different dynamics emerge, where the control beam induces a periodic optical grating inscribed in the fibre. Optically induced gratings, so far limited to bimodal systems, have been exploited to implement partial mode conversion of the probe beam27,28,29,30,31.\n\nIn this work, we propose a counter-propagating probe-control beam scheme in MMFs and MCFs with arbitrary number N of modes or cores. This setup allows the simultaneous phase-matching of several interaction processes between a low-power, forward probe and an intense backward control beam (BCB), regardless of the fibre parameters, thus harnessing the full potential of multimode dynamics. By leveraging a robust setup for accurate mode coupling and mode decomposition, we provide an experimental demonstration of several compelling all-optical operations in MMFs and MCFs, which include fast and fully tuneable mode conversion and power splitting, selective core-to-core switching and combining, as well as the remote characterisation of the probe beam, as illustrated in Fig.\u00a01.\n\na A low-power probe beam (red colour) and a high-power counter-propagating backward control beam (BCB, green colour) are injected at the two opposite ends of a multimode fibre. The BCB is coupled over a suitable combination of modes. A specific output probe on demand can be obtained by solely adjusting the BCB power. In this example, 3 different BCB intensities lead to an output probe coupled over 3 distinct fibre modes (see Output Probe 1, 2, 3). b Same as panel a, but in the case of a multicore fibre with 3 cores. In this example, by tuning the BCB intensity, the output probe is either fully readdressed over a single core (Output Probe 1), or equally split over 2 (Output Probe 2) or 3 cores (Output Probe 3). The ability to manipulate the probe can be exploited to implement power splitters, mode converters and core-to-core switchers with all-optical reconfiguration at the sub-nanosecond scale.\n\nThese outcomes reveal an all-optical control mechanism for configuring the modal state and optical pathways in MMFs and MCFs, enabling novel functionalities for future smart and adaptive optical systems. This lays the groundwork for an all-optically reconfigurable photonics in optical fibres and beyond32.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "A forward probe signal and a BCB are counter-propagating in a polarisation-maintaining multimode (or multicore) fibre supporting N spatial modes. Their spatio-temporal evolution is described by a system of coupled nonlinear Schr\u00f6dinger equations26 (CNLSEs, see Eq. (6) in \u201cMethods\u201d -Theoretical Framework). The counter-propagating setup offers fundamental key advantages with respect to a standard co-propagating configuration.\n\nFirst, it enables physical separation between probe and BCB, which are launched at the opposite ends of the fibre. The physical separation allows both beams to share the same polarisation and be centred at the same wavelength \u03bb\u2014key conditions that define our experimental scenario. Crucially, under these circumstances, all intermodal four-wave mixing interactions between the probe and BCB are simultaneously phase-matched, regardless of the fibre parameters and beam wavelengths (see \u201cMethods\u201d\u2014Theoretical Framework for details).\n\nThis simultaneous phase-matching condition is the cornerstone of our approach. It enables energy exchange across all N probe modes, thereby allowing a complete reconfiguration of its modal state. The energy exchange is mediated by the BCB but without any net power transfer to the probe, which therefore preserves its total energy. Such dynamics is not attainable in conventional co-propagating systems, where the probe-BCB separation relies on differences in polarisation and/or wavelength. These constraints inherently prevent simultaneous phase-matching of all intermodal four-wave mixing interactions, typically allowing energy exchange between only two probe modes\u2014and only under strict conditions on fibre parameters and probe-to-BCB frequency detuning.\n\nIn addition, the probe-BCB physical separation in the counter-propagating setup allows for the implementation of remote sensing operations, hence, to investigate the properties of the fibre and/or of the probe, even when the latter is inaccessible, which is one of the applications discussed later in this work.\n\nIn a recent work26 we analysed the case where both probe and BCB operate in a strongly nonlinear regime, which exhibits robust mode attraction or rejection states, irrespectively of the initial state of the probe. In this study, we explore a different scenario, where the probe is in the linear regime, whereas the BCB remains in a strongly nonlinear propagation regime.\n\nThe distinction between linear and nonlinear regime of the probe is governed by its total peak power \\({P}_{{pr},{tot}}\\), and the interaction length \\({L}_{int}\\) between the probe and the BCB. In the continuous-wave (CW) case, \\({L}_{int}\\) corresponds to the full fibre length L, whereas for pulsed beams, it is determined by their temporal overlap. Specifically, the BCB modulates the probe over a spatial extent \\({L}_{int}={\\tau }_{p}c\\) in pulsed operation, where \\({\\tau }_{p}\\) is the BCB pulse width and c is the velocity of light in the fibre. As a rough guideline, if the number of probe nonlinear lengths \\({P}_{pr,tot}\\cdot {L}_{int}\\cdot \\gamma\\) exceeds 5, where \\(\\gamma\\) is the average intermodal Kerr coefficient, then the probe operates in a strongly nonlinear regime. Conversely, if this value is below 0.5, the probe remains in a linear regime. Intermediate values between 0.5 and 5 define a transitional regime, where nonlinear effects may partially develop but do not fully govern the system dynamics. Note that a similar distinction between linear and nonlinear regime can be made for the BCB based on its number of nonlinear lengths.\n\nThe probe in linear regime leads to peculiar new dynamics, fundamentally different from mode attraction and rejection. After some algebra, we recast the CNLSEs into the following linear transformation (see Methods-System Linearization):\n\nwhere \\({{F}}_{{\\boldsymbol{in}}}\\) and \\({{F}}_{{\\boldsymbol{out}}}\\) are vectors of length N whose entries fin,n and fout,n indicate the amplitude of the electric field of the probe mode n at the input and output of the fibre, respectively, whereas M is a N\u2009x\u2009N matrix whose elements are defined by the BCB mode state, along with the nonlinear Kerr coefficients of the fibre and the modal propagation constants.\n\nBesides describing the mode dynamics in MMFs and MCFs, Eq. (1) also characterises the core-to-core interaction in MCFs, following the identification of the transformation matrix T that maps the electric field of the MCF modes to the electric fields in the individual cores, namely:\n\nwhere \\({{\\boldsymbol{{F}}}}_{{{\\boldsymbol{c}}}-{{\\boldsymbol{in}}}}\\) and \\({{\\boldsymbol{{F}}}}_{{{\\boldsymbol{c}}}-{{\\boldsymbol{out}}}}\\) are vectors of length N whose entries \\({{\\mbox{f}}}_{c-{in},n}\\) and \\({{\\mbox{f}}}_{c-{out},n}\\) indicate the amplitude of the electric field of the probe in core n at the input and output of the fibre, respectively.\n\nVector \\({{\\boldsymbol{{F}}}}_{{{\\boldsymbol{in}}}}\\) (\\({{{\\boldsymbol{{F}}}}_{{\\boldsymbol{out}}}}\\)) describes the input (output) probe mode state, which includes information on both the input (output) mode power distribution \\({|{{\\boldsymbol{{F}}}}_{{\\boldsymbol{in}}}|}^{{\\mathbf{2}}}\\) (\\({|{{\\boldsymbol{{F}}}}_{{\\boldsymbol{out}}}|}^{{\\mathbf{2}}}\\)) and the relative phase between the modes at the input (output) of the fibre. Likewise, vectors \\({{\\boldsymbol{{F}}}}_{{{\\boldsymbol{c}}}-{{\\boldsymbol{in}}}}\\) and \\({|{{\\boldsymbol{{F}}}}_{{{\\boldsymbol{c}}}-{{\\boldsymbol{in}}}}|}^{{{\\mathbf{2}}}}\\) (\\({{\\boldsymbol{{F}}}}_{{{\\boldsymbol{c}}}-{{\\boldsymbol{out}}}}\\) and \\({|{{\\boldsymbol{{F}}}}_{{{\\boldsymbol{c}}}-{{\\boldsymbol{out}}}}|}^{{{\\mathbf{2}}}}\\)) represent the input (output) probe core state and power distribution, respectively. Additionally, we can define the BCB mode state and power distribution in a similar manner.\n\nThe importance of Eqs. (1) and (2) lies in their establishment of a direct relationship between the input and output probe states. Given that the matrix M allows reconfiguring the output probe state as a function of the input state, we call it the reconfiguration matrix. Specifically, by appropriately adjusting the BCB, we can shape the reconfiguration matrix M to achieve a mode or core state on demand in the output probe. In other words, we implement all-optical reconfiguration of the output probe.\n\nTwo simple yet insightful cases to illustrate the concept of all-optical reconfiguration of a weak probe (namely, in the linear regime) involve a multimode fibre supporting two modes and a multicore fibre with two cores in the CW regime. These cases, discussed below, will also provide useful insights for the three applications examined later.\n\nIn a multimode fibre with two modes, assuming the probe and BCB are co-polarised, the reconfiguration matrix M turns out to be:\n\nwhere \\(q={\\gamma }_{11}{P}_{{\\mathrm{BCB}},1}-{\\gamma }_{22}{P}_{{\\mathrm{BCB}},2}\\); \\(r=2{\\gamma }_{12}{\\left({P}_{{{\\rm{BCB}}},1}{P}_{{{\\rm{BCB}}},2}\\right)}^{1/2}\\); \\(s={\\left({q}^{2}+4{r}^{2}\\right)}^{1/2}\\); \\({\\alpha }_{1}=-{\\beta }_{1}+\\frac{3}{2}{\\gamma }_{11}{P}_{{{\\rm{BCB}}},1}+\\frac{1}{2}{\\gamma }_{22}{P}_{{{\\rm{BCB}}},2}+2{\\gamma }_{12}{P}_{{{\\rm{BCB}}},2}\\); \\({\\alpha }_{2}=-{\\beta }_{2}+\\frac{1}{2}{\\gamma }_{11}{P}_{{{\\rm{BCB}}},1}+\\frac{3}{2}{\\gamma }_{22}{P}_{{{\\rm{BCB}}},2}+2{\\gamma }_{12}{P}_{{{\\rm{BCB}}},1}\\); \\({\\gamma }_{11},{\\gamma }_{12,\\,}{\\gamma }_{22}\\) are nonlinear Kerr coefficients that depend on the modal spatial profiles33. \\({\\beta }_{1}\\) and \\({\\beta }_{2}\\) denote the propagation constants for mode 1 and mode 2, respectively. \\({P}_{{{\\rm{BCB}}},1}\\) and \\({P}_{{{\\rm{BCB}}},2}\\) represent the BCB power coupled to mode 1 and mode 2, respectively. While the BCB undergoes nonlinear phase accumulation, its powers \\({P}_{{{\\rm{BCB}}},1}\\) and \\({P}_{{{\\rm{BCB}}},2}\\) remain unaffected by the interaction with the weak probe (see \u201cMethods\u201d-System linearization).\n\nIn the absence of substantial propagation losses, dispersive effects and intermodal walk-off - conditions typically met in fibres up to a few metres long-the total instantaneous power of the probe \\({P}_{{pr},{tot}}\\) is conserved. Starting from Eq. (1), we derive the following expressions for the instantaneous output probe power \\({P}_{{pr\\; out},1}\\equiv {\\left|{{\\mbox{f}}}_{{out},1}\\right|}^{2}\\) and \\({P}_{{pr\\; out},2}\\equiv {\\left|{{\\mbox{f}}}_{{out},2}\\right|}^{2}\\) coupled to mode 1 and mode 2, respectively:\n\nwhere \\(\\Delta {{{\\rm{\\phi }}}}_{{in},12}\\) defines the input relative phase between the two input probe modes, \\({P}_{{pr\\; in},1}\\equiv {\\left|{{\\mbox{f}}}_{{in},1}\\right|}^{2}\\) and \\({P}_{{pr\\; in},2}\\equiv {\\left|{{\\mbox{f}}}_{{in},2}\\right|}^{2}\\) are the input probe powers in mode 1 and mode 2, respectively.\n\nEquation (4) makes the concept of all-optical probe reconfiguration evident: indeed, we note that by properly setting the BCB powers \\({P}_{{BCB},1}\\) and \\({P}_{{BCB},2}\\), which determine the coefficients q, r and s, we can control the mode power distribution of the probe at the fibre output, namely \\({P}_{{pr\\; out},1}\\) and \\({P}_{{pr\\; out},2}\\).\n\nIn the case of a multicore fibre with 2 cores, the 2\u2009\u00d7\u20092 matrix \\({{\\bf{T}}}=\\left[1\\sqrt{2},1\\sqrt{2};1\\sqrt{2},-1\\sqrt{2}\\right].\\) The general solution for the output probe power coupled to each core is inherently complex. However, a particularly relevant case\u2014yielding simpler expressions\u2014arises when the input probe is coupled to a single core, e.g., core 1, and the BCB, with total power \\({P}_{{BCB}}\\), is coupled to a single mode.\n\nIn this instance the output probe power \\({P}_{{pr}{c}-{out},1}\\equiv {\\left|{{\\mbox{f}}}_{c-{out},1}\\right|}^{2}\\) and \\({P}_{{pr}{c}-{out},2}\\equiv {\\left|{{\\mbox{f}}}_{c-{out},2}\\right|}^{2}\\) are coupled respectively to core 1 and core 2, and computed from Eqs. (1) and (2), reads as:\n\nwhere \\({L}_{{{\\rm{b}}}}=2{{\\rm{\\pi }}}/\\left|{\\beta }_{1}-{\\beta }_{2}\\right|\\) is the beat-length between the two modes of the fibre having propagation constants \\({\\beta }_{1(2)}\\), and \\(\\Delta \\gamma={\\gamma }_{11}-{\\gamma }_{12}\\).\n\nOnce again, the idea of an all-optical reconfiguration mediated by the BCB emerges. In particular, regardless of the beat-length \\({L}_{b}\\), the output probe power in the two cores is fully tuneable by adjusting the BCB power \\({P}_{{{\\rm{BCB}}}}\\).\n\nNaturally, the concept of reconfiguration remains valid for N\u2009>\u20092 modes or cores and in the non-CW regime (see \u201cMethods\u201d). However, in such cases, simple analytical expressions like those in Eqs. (4) and (5) are no longer available.\n\nA first key application of our platform is the tuneable mode manipulation of the probe. In our experimental setup (see Fig.\u00a02 and \u201cMethods\u201d-Experiments for details), the probe and BCB, centred at 1040\u2009nm wavelength, are co-polarised and coupled at the opposite ends of the fibre under test. The probe power is fixed at a low level to ensure operation in the linear regime, while the BCB power is gradually increased to reach the high-peak power required. We used a variety of polarisation-maintaining fibres (see Supplementary Information\u00a01 for details on their parameters) supporting 2, 3, and 6 modes at the wavelength of 1040\u2009nm. These include a highly nonlinear fibre that relaxes substantially the power requirements on the BCB.\n\nThe input probe and BCB are split from a master oscillator power amplifier (MOPA) and coupled to the opposite ends of the test fibre. The MOPA delivers 0.5\u2009ns pulses at a central wavelength of 1040\u2009nm and with peak power up to 30\u2009kW (12\u2009W average power at 800\u2009kHz repetition rate), therefore enabling a significant level of nonlinearity in the fibres under test. Polarisation beam splitters (PBS) and half-wave-plates (HWP1\u20135) are used to tune independently the input probe and BCB power and polarisation. A near-field (NF) and a far-field (FF) camera measure the near and far field images used in our mode decomposition algorithm. The field at the output of each core of MCFs can be isolated via a pinhole and its temporal dynamic is monitored at the oscilloscope. SLM\u2009=\u2009spatial light modulator; BS\u2009=\u2009beam splitter.\n\nThe results in Fig. 3 demonstrate a tuneable all-optical mode conversion in a homemade bimodal fibre, where any arbitrary power ratio between the two guided modes can be achieved by solely adjusting the BCB power. Three distinct instances are shown that highlight the extent of the precision in manipulating the probe mode distribution. Indeed, for a given input mode state of the probe, we can configure the BCB to achieve either full mode conversion of the output probe (Fig.\u00a03d), partial mode conversion (Fig.\u00a03e), or conversion annihilation, thus making the output probe mode distribution insensitive to the probe-BCB interaction (Fig.\u00a03f).\n\nThis fibre is 0.4 metre long and supports one even mode M1 and one odd mode M2 (see Supplementary Information\u00a01). a\u2013c Theoretical 2D maps of the output probe mode distribution computed from Eq. (4). The maps show the output probe power fraction coupled to mode M1 versus the BCB total peak power (horizontal axis) and BCB mode distribution (vertical axis, indicating the fraction of BCB power coupled to mode M1). These maps indicate how to set the BCB in order to manipulate the output probe, ensuring it reaches the desired mode distribution. The maps correspond to 3 examples with different input probe mode states, which are reported at the top of each panel. For example, in panel a the input probe mode state is characterised by 10% power on mode M1, 90% on mode M2, and a relative phase \\(\\Delta {{{\\rm{\\phi }}}}_{{in},12}\\) between the two modes of 0.3\u2009rad. d\u2013f. Experimental (exp) and theoretical (theory) results for the same input probe mode states as panels (a\u2013c), but with a fixed BCB mode distribution (indicated at the top of each panel and corresponding to the red-dashed lines in panels (a\u2013c)). Arbitrary output probe mode distribution can be achieved by tuning the BCB power. Specifically, in panel (d), full conversion to mode M1 is achieved when the BCB peak power is ~ 8\u2009kW (3.2\u2009W average power). In contrast, the BCB in f is configured such that it results in almost no variation of the output probe mode distribution. The insets in panels (d\u2013f) show the far-field intensities of the output probe for different values of BCB peak power PBCB. Error bars of \u00b13% are added to the measured relative power of each mode, which represents the estimated uncertainty of our mode decomposition algorithm.\n\nRemarkably, our experimental results in Fig.\u00a03d\u2013f closely align with the theoretical predictions derived from the analytical solutions in Eq. (4). Note that the relative polarisation between probe and BCB may serve as an additional parameter for controlling the probe dynamics (see Supplementary Information\u00a02). We have recorded three videos illustrating the tuneable mode manipulation dynamics in this bimodal fibre, corresponding to Fig.\u00a03d\u2013f (see Supplementary Movies\u00a01\u20133).\n\nFigure\u00a04 presents additional results in three commercially available fibres: a PM1550-xp and a highly nonlinear fibre PMHN1, supporting 3 modes, along with a PM2000 supporting 6 modes.\n\nExperimental (exp) and theoretical (theory) results are shown for different combinations of input probe and BCB mode distributions (indicated at the top of each panel) in a three-mode PM1550-xp, a three-mode PMHN1, and a six-mode PM2000 (all 0.4\u2009m long). The six panels illustrate distinct cases of probe reconfiguration. Error bars of \u00b13% indicate the uncertainty in the measured relative power of each mode. Note that panels (a\u2013d) use line plots as they involve only three modes. In panels (e, f) where six modes are involved, a bar chart is used instead to prevent excessive visual clutter. (a, b). Results in PM1550-xp fibre; (c, d). Results in PMHN1 fibre; (e, f). Results in PM2000 fibre.\n\nFigure\u00a04a, b illustrate the results in the PM1550-xp fibre. By adjusting the BCB mode distribution (reported at the top of each panel) we trigger and control different dynamics in the output probe. For instance, at a BCB peak power of ~11\u2009kW (4.4\u2009W average power), we can achieve either an equal power distribution between two of the three modes (Fig.\u00a04a) or among all three modes (Fig.\u00a04b).\n\nFigure\u00a04c, d display output probe manipulation in the PMHN1 fibre. The input probe mode state is similar in both configurations shown in Fig.\u00a04c, d. Again, the BCB is properly adjusted to trigger different dynamics, with most of the output probe power redirected either on mode LP01 (Fig.\u00a04c) or LP11o (Fig.\u00a04d). It is worth noting that the BCB peak power required to achieve relevant dynamics is substantially lower than in the case of the PM1550-xp, being as small as ~1\u2009kW, which corresponds to 0.4\u2009W average power. This is primarily due to the high nonlinearity of this fibre, resulting in significantly larger Kerr nonlinear coefficients (see Table\u00a0S1 in Supplementary Information\u00a01). This result is particularly significant as it provides experimental confirmation of the possibility to downscale the required power based on the fibre\u2019s nonlinearity. Consequently, it demonstrates the potential for a drastic reduction in energy consumption when using highly nonlinear materials, without compromising the capability for all-optical reconfiguration.\n\nFinally, Fig.\u00a04e, f present the results for the PM2000 fibre, which supports 6 modes. Here, the increased number of modes, combined with a lower coupling efficiency of the BCB in this fibre, reduces the effective BCB power coupled to each mode. This partially limits the extent of the probe\u2019s modal reconfiguration. This limitation could be mitigated by employing highly nonlinear fibres\u2014such as that used in Fig.\u00a04c, d\u2014and by improving the coupling efficiency of the experimental setup. Despite these constraints, intriguing dynamics are still observed. In Fig.\u00a04e, as the BCB power increases, the LP01 mode transfers a significant portion of its power to the LP11e mode, while the LP02 mode completely transfers its power to the LP21e mode. In Fig.\u00a04f, at a BCB peak power of 7\u2009kW, the LP01 and LP02 modes lose approximately 8% and 6% of their initial power (measured with BCB off), which is redistributed to generate the LP21e and LP21o modes.\n\nA significant feature of our setup lies in the possibility to manipulate the core-to-core energy exchange in MCFs. Note that while the mode distribution remains largely unaffected by linear coupling in the short fibres under test, core-to-core linear coupling takes place over a much shorter length scale instead. More generally, a complex interplay occurs between linear core-to-core coupling and nonlinear coupling between probe and BCB. An instructive scenario is the one previously introduced, namely, a dual-core fibre (DCF) where the input probe is coupled to a single core and the BCB is coupled to a single mode, which is described by Eq. (5).\n\nAccording to the latter, when BCB is off (PBCB\u2009=\u20090), the probe undergoes core-to-core energy exchange over a distance as short as \\({L}_{{{\\rm{b}}}}\\) (typically a few millimetres). Modal beat-lengths are severely affected by fibre perturbations, such as local bending and temperature fluctuations, and are therefore difficult to estimate. However, Eq. (5) highlights a crucial point. Irrespectively of the beat-length \\({L}_{b}\\), which may even be unknown, the output probe power in the two cores is fully tuneable by adjusting the BCB power PBCB, enabling any arbitrary splitting ratio. Importantly, this finding is generalisable to different fibre parameters and input conditions.\n\nOur experimental results, shown in Fig.\u00a05a, confirm this scenario. The input probe launch condition was adjusted such that, with the BCB off, the output probe power was fully coupled to a single core. By introducing the BCB and tuning its peak power between 0 and 9\u2009kW (average power between 0 and 3.6\u2009W), we achieved any arbitrary power ratio X/(100\u2009\u2212\u2009X) between the two output cores, covering the full operational range from 100:0 to 50:50, as required for a fully tuneable optical power splitter. Similarly to the case of mode manipulation in the PMHN1 fibre reported in Fig.\u00a04c, d, also in this case, the required BCB power could be reduced by one order of magnitude in highly-nonlinear fibres. This would lower the BCB average power to just a few hundred milliwatts.\n\nThree different instances are shown. The insets show the near-field intensities of the output probe at each core. a The input probe launch condition is optimised such that the output probe power is entirely in core 1 when the BCB is off (power ratio core1/core2\u2009=\u2009100/0). After having appropriately fixed the BCB mode state, we increase the BCB peak power from 0 to 9\u2009kW (0 to 3.6\u2009W average power). We then observe that the core-to-core power ratio of the output probe transitions gradually from 100/0 to 50/50, enabling an all-optical, fully tuneable X/(100\u2009-\u2009X) power splitting. b Differently from panel a, in this case the output probe core distribution is relatively uniform when the BCB is off (power ratio core1/core2\u2009=\u200935/65). The output probe is then progressively redirected into core 1 as the BCB power increases, achieving an all-optically controlled combination. At 11\u2009kW of BCB peak power, 92% of the output probe power is in core 1 (power ratio core1/core2\u2009=\u200992/8). We estimate that full combination (100/0) could be achieved at ~14\u2009kW peak BCB power (not available). c In this example, the output power ratio goes from 15/85 when BCB is off to 85/15 when the BCB peak power is ~10\u2009kW. Full switching (0/100 to 100/0) could be achieved with ~18\u2009kW BCB peak power (not available). d Temporal evolution of output probe power at the two cores measured by the oscilloscope when the BCB is off (power ratio core1/core2\u2009=\u200935/65). e Temporal evolution of output probe power at the two cores measured by the oscilloscope at 5\u2009kW BCB peak power. The power ratio shifts to 65/35. The oscilloscope also detects the BCB reflection, with the 2\u2009ns delay corresponding to the time of flight of light in the fibre.\n\nAdditional key applications can be envisaged and demonstrated with our platform. As shown in Fig.\u00a05b, the power of the output probe, which in this case is relatively uniform in the 2 cores when BCB is off (power ratio core 1/core 2\u2009=\u200935/65), can be combined into core 1 when the BCB peak power is set to 11\u2009kW. Furthermore, core-to-core switching is depicted in Fig.\u00a05c, where the output power transitions from one core to another at a BCB peak power of ~10\u2009kW. Note that, in this case, the switching power ratios (from 15/85 to 85/15) are constrained by the available coupled BCB peak power, which is <12\u2009kW in our experiments in the DCF. Approximately 18\u2009kW of BCB peak power would be required for complete 0/100 to 100/0 switching (indeed 9\u2009kW allows 100/0 to 50/50 splitting, see Fig.\u00a05a).\n\nThe applications highlighted above can be controlled at a rapid rate through the BCB. Figure\u00a05d, e illustrate an example of core-to-core power swapping at the sub-nanosecond time scale. The temporal evolution of the output probe power in the 2 cores, measured via an oscilloscope, is displayed. A single 0.5\u2009ns BCB pulse shifts the core-to-core power ratio at the DCF output from 35/65, when the BCB is off, to 65/35 when the BCB peak power is 5\u2009kW (2\u2009W average power). The switching time is determined by the BCB pulse width. Although in our experiments the BCB pulse width is 0.5\u2009ns, the simulations in Supplementary Information\u00a03 indicate that the switching time could be reduced to picosecond levels. These results pave the way for the development of all-optically controlled core-to-core switchers, leading to the pioneering idea of all-optically programmable photonics. In particular, the DCF with BCB control could serve as basic unit (2\u2009\u00d7\u20092 optical gate) for reconfigurable wide matrices34,35, enabling fully optical ultrafast operations.\n\nIn this framework, exploring complex multicore systems is compelling. A single BCB could enable core-to-core switching, splitting or combining with N\u2009>\u20092 cores. These systems are more sensitive to weak variations in fibre parameters than the DCF. Our generalised solutions in Eqs. (1) and (2), which effectively describe the modal dynamics, would require precise knowledge of the relative differences among intermodal beat-lengths in order to describe the core-to-core dynamics equally well. However, these differences are susceptible to perturbations, therefore their estimation is challenging. Consequently, in our experiments we manually adjust the BCB mode state to find the optimal configuration that enables the desired control over the probe beam.\n\nDespite these challenges, our theoretical model remains invaluable, suggesting intriguing scenarios. For instance, the simulation results in Fig.\u00a06a indicate that, with sufficient BCB power, coherent combination or equal splitting could be achieved in a three-core-fibre (TCF). Preliminary experimental tests support the feasibility of these outcomes. Although the coupled BCB power is significantly lower than the simulated values, preventing full power rerouting in each core, nevertheless we could split the power evenly across the 3 cores (Fig.\u00a06b), combine power from 2 cores into a single core (Fig.\u00a06c) or swap the power among selected cores (Fig.\u00a06d).\n\nOur ability to implement all-optical probe reconfiguration extend to fibres with more than 2 cores. This figure illustrates all-optical operations in a 0.4\u2009m long TCF. The insets show the near-field intensities of the output probe at each core. a Output probe core distribution simulated via Eqs. (1) and (2), with linear and nonlinear coefficients estimated from the fibre parameters (see Supplementary Information\u00a01). In this simulation, the BCB mode state is as follows: 5% of power in mode 1, 30% in mode 2, 65% in mode 3, and all modes in-phase. The probe power can be arbitrary low. By adjusting the BCB peak power from 0 to 50\u2009kW we can either equalise the output probe power in the 3 cores (see black spot) or combine most of the output probe power in core 1 (blue spot), core 2(red spot) or core 3 (green spot). b\u2013d Experimental results in the TCF. Each panel corresponds to different launch conditions of the input probe. In each case, the BCB is optimised to achieve relevant operations for a BCB peak power of ~7\u2009kW (i.e., 2.8\u2009W average power, the maximum we are able to couple into the TCF). In panel b, the output probe is almost equally split across the 3 cores. In panel c, the probe is mainly redirected to a single core (core 3). In panel d, we achieve power swapping between core 1 and core 2.\n\nOur counter-propagating setup could have significant applications in remote sensing, enabling the investigation of fibre or input probe features through the analysis of the output probe\u2019s response to the BCB. For instance, consider estimating the input relative phase \\(\\Delta {{{\\rm{\\phi }}}}_{{in},12}\\) between two probe modes, say mode 1 and mode 2, of an MMF of length L.\n\nAssuming weak mode coupling, as in few-metre long polarisation-maintaining fibres, the probe mode power distribution remains constant during propagation when the BCB is off. Conventional approaches, like the standard transfer matrix method36, exploits the direct relationship between the output and input relative phases to estimate the latter, namely, \\(\\Delta {{{\\rm{\\phi }}}}_{{in},12}=\\Delta {{{\\rm{\\phi }}}}_{{out},12}-\\Delta {{{\\rm{\\phi }}}}_{{acc}}\\), where \\(\\Delta {{{\\rm{\\phi }}}}_{{out},12}\\) is the output relative phase between the two modes, whereas \\(\\Delta {{{\\rm{\\phi }}}}_{{acc}}=\\Delta {\\beta }_{12}L\\) is the accumulated phase delay due to the differential propagation constant \\(\\Delta {\\beta }_{12}\\) between the modes. Consequently, accurate estimation of both \\(\\Delta {{{\\rm{\\phi }}}}_{{out},12}\\) and \\(\\Delta {{{\\rm{\\phi }}}}_{{acc}}\\) is required, typically necessitating complex experimental setups. However, even with highly precise phase estimations, a fundamental issue remains: \\(\\Delta {{{\\rm{\\phi }}}}_{{acc}}\\) is highly sensitive to fibre perturbations, which may result in an unreliable estimate of \\(\\Delta {{{\\rm{\\phi }}}}_{{in},12}\\). Maintaining accurate estimations of \\(\\Delta {{{\\rm{\\phi }}}}_{{in},12}\\) over time would require therefore periodic calibration or active feedback, adding further complexity.\n\nOur platform offers an efficient solution to this problem by analysing the probe\u2019s response to the BCB. Remarkably, this approach is entirely remote, and it does not require any prior phase measurement. Indeed, as per Eq. (4), the output probe mode power distribution depends on the input relative phase \u0394\u03d5in, 12. Thus, to determine the latter, we computed the theoretical mode distribution for various values of relative phases and identified the optimal least-squares values that best align with the experimental data. Figure\u00a07 illustrates three distinct cases where the input phase \\(\\Delta {{{\\rm{\\phi }}}}_{{in},12}\\) is successfully retrieved in a bimodal fibre, even when there is a significant power imbalance between the modes. Notably, in MCFs, once the relative phases are recovered, one may estimate through Eq. (2) the input probe core distribution and relative phase at each core. Moreover, a similar approach could be used to estimate simultaneously both the input probe properties and unknown fibre parameters (e.g., Kerr coefficients, average linear mode coupling) through multivariate estimation analysis.\n\nExperimental results (bars) and corresponding best theoretical fits (red-dashed lines) showing the output probe power fraction coupled to mode M1 versus BCB peak power in a 0.4-m long bimodal fibre (DCF, see Supplementary Information\u00a01). Panels (a\u2013c) correspond to different input probe mode states and BCB mode distributions, measured experimentally and reported on the top of each panel. The best theoretical fit is calculated from Eq. (4), assuming the same input probe and BCB relative powers and optimising the input probe relative phase to minimise the least squares difference with experimental data. Note that in all the 3 cases the estimated optimal least-squares value \\(\\Delta {\\widetilde{{{\\rm{\\phi }}}}}_{{in},12}\\) (0.06\u2009rad, 5.72\u2009rad, 1.26\u2009rad in panels (a\u2013c) respectively) is close to the measured \\(\\Delta {{{\\rm{\\phi }}}}_{{in},12}\\) (0.3\u2009rad, 5.7\u2009rad, 1.2\u2009rad in panels (a\u2013c) respectively). This demonstrates our ability to detect from remote the relative phase of the input probe modes by analysing the output probe response to the BCB. Note that the larger error in panel (a) is due to the large power imbalance among the two input probe modes (92% and 8%, respectively).\n\nSupplementary Information\u00a05 presents a comparison between our method and traditional transfer matrix-based techniques, highlighting the superior robustness of our approach against temperature variations and fibre bending.\n\nIt is useful to outline the fundamental differences between the probe dynamics in the linear regime, as considered in this study, and in the nonlinear regime26. These two instances exhibit substantially different behaviours. This is not surprising, as even in co-propagative classical systems\u2014such as the simplest case of a single-mode fibre\u2014the dynamics undergo a drastic transition from the linear regime, dominated by dispersion and polarisation effects, to the nonlinear regime, where phenomena such as solitons, rogue waves, and supercontinuum generation emerge.\n\nAs mentioned earlier, the distinction between the linear and nonlinear regimes is primarily determined by the number of nonlinear lengths. When both probe and BCB operate in a strongly nonlinear regime, the probe may undergo asymptotic attraction to or rejection of specific modal states, irrespectively of its initial state26. Moreover, a symmetric interaction is observed where the probe and the BCB mutually influence each other.\n\nIn contrast, when the probe operates in the linear regime (with the BCB still in a highly nonlinear regime), which is the case under investigation in this work, a fundamentally different behaviour is observed. In this instance, the interaction is highly asymmetric, with the BCB being only marginally affected by the probe. Moreover, the final state of the probe is strongly dependent on its initial condition, which underpins the novel applications previously illustrated.\n\nThis shift in dynamics has profound implications: rather than inducing attraction or rejection, the BCB serves as an all-optical modulator for the probe, enabling on-demand probe reconfiguration\u2014a role traditionally fulfilled by external thermo-electronic modulators. Notably, this is achieved with a low-power probe, making the proposed applications viable for real-world implementation.\n\nA simple numerical example in the case of a bimodal fibre allows us to clearly visualise the differences of the dynamics in the linear and nonlinear regimes. In the example shown in Fig.\u00a08a, b, we simulate a bimodal fibre with Kerr coefficients \\({\\gamma }_{11}={\\gamma }_{12}={\\gamma }_{22}=1/{{\\rm{W}}}/{{\\rm{km}}}\\). The input probe beam is entirely coupled to mode M1, while the input BCB is distributed with 60% of its power in mode M1 and 40% in mode M2. Their interaction length \\({L}_{{\\mathrm{int}}}=1 {{\\rm{m}}}\\). The probe beam, with a total fixed peak power Ppr, tot\u2009=\u200910 kW, operates in a highly nonlinear regime (number of nonlinear lengths Lint\u03b3Ppr, tot\u2009=\u200910, \u03b3\u2009=\u20091/W/km being the average Kerr coefficient). As the BCB\u2019s peak power increases from 0 to 10\u2009kW, entering itself a strongly nonlinear regime, a mode attraction process is triggered. Indeed, the output probe (Fig.\u00a08a) tends to approach the mode state orthogonal to the input BCB, namely, ~40% on mode M1 and ~60% on mode M2. In turn, the output BCB (Fig.\u00a08b) tends to approach the mode state orthogonal to the input probe, namely, all power coupled to mode M2. This confirms the abovementioned symmetric interaction between probe and BCB.\n\nMode distribution of the output probe (a) and output BCB (b) versus the BCB peak power when the probe is in a strong nonlinear regime (peak power fixed to 10\u2009kW). The output probe is asymptotically attracted to the mode state orthogonal to the input BCB, and vice versa. c, d Mode distribution of the output probe (c) and output BCB (d) versus the BCB peak power when the probe is in linear regime (peak power fixed to 10\u2009mW). The output probe mode distribution oscillates sinusoidally as a function of the BCB power, whereas the BCB mode distribution is unchanged.\n\nIf the probe operates in the linear regime instead, the mode attraction process is not triggered. This is shown in Fig.\u00a08c, d where the probe peak power is now arbitrary low (here \\({P}_{{pr},{tot}}=\\)10\u2009mW, therefore the number of nonlinear lengths \\({L}_{{\\mathrm{int}}}\\gamma {P}_{{pr},{tot}}={10}^{-5}\\)). In this case, the output BCB\u2019s mode composition is unaffected by the nonlinear dynamics and remains therefore unchanged, mirroring the input (Fig.\u00a08d). Meanwhile, the output probe mode distribution exhibits a sinusoidal evolution as the BCB power increases (Fig.\u00a08c), in line with the predictions of our theoretical model Eq. (4) and the experimental outcomes previously reported.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-61984-8/MediaObjects/41467_2025_61984_Fig8_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Our work presents a platform based on a counterpropagating probe-BCB setup in multimode and multicore fibres. In this setup, all the probe-BCB four-wave-mixing interactions are simultaneously phase-matched, which enables a complete reconfiguration of the probe modal state. Key operations at the sub-nanosecond time scale are demonstrated, including fully tuneable mode conversion, power splitting, core-to-core switching and combination, along with remote probe characterisation.\n\nUnlike the system we have recently introduced in Ref. 26, this platform operates with an arbitrary weak probe. This results in fundamentally different spatiotemporal dynamics, suitable for low-power applications. Once the BCB mode state is set by the launch conditions, the BCB power can be tuned for on-demand reconfiguration of the probe.\n\nOur experimental results are supported by a theoretical model that aligns with the experimental findings and extends to MMFs and MCFs with an arbitrary number of modes and cores. These results introduce a major shift in critical applications whose tunability currently relies on electro-optical or thermo-optical modulation, offering a faster and more energy-efficient alternative through all-optical manipulation, a keystone for future reconfigurable optical networks and optical computing.\n\nAmong these applications, mode conversion is crucial for space-division-multiplexing37,38. Our platform enables not only full mode-to-mode conversion in the output probe, but more generally to achieve a tuneable combination of modes (Fig.\u00a03 and Fig.\u00a04). This latter capability is essential for broadband nonlinear applications39 and multimode interferometry40.\n\nPower splitting underpins power delivery, optical feedback and network access41,42. The ability to achieve all-optically an arbitrary splitting ratio (Fig.\u00a05a) represents a crucial step towards real-time optimisation in time-varying scenarios such as transparent optical networks.\n\nAs for our outcomes on core-to-core switching and combining (Fig.5b, c and Fig.\u00a06), these promise advancements in high-speed data transmission. Current switching systems are based on external devices connected to network fibres43,44,45, increasing cost and complexity of the design, latency, and overall insertion losses. On the other hand, our approach suggests the feasibility of all-optical tuneable core-to-core switching directly within multicore fibres at sub-nanosecond timescale, paving the way for seamless fibre transmission through compact, all-fibre based ultrafast switchers.\n\nLastly, probe remote characterisation (Fig.\u00a07) offers a novel scenario of applicability, allowing for real-time monitoring of fibre parameters or complex multimode optical signals from remote locations.\n\nThe implementation of these operations in a single platform underscores its versatility, a critical feature of next-generation photonic systems32. Two further points merit discussion. First, our analysis suggests that the ultimate switching time could be sub-picosecond, therefore beyond the reach of any electronic system. Moreover, scaling these results to highly nonlinear materials promises further reductions in power consumption and size. Our results in highly nonlinear fibres support this hypothesis and suggest the possibility of operating with an average optical power of just a few hundred milliwatts, which would correspond to an electrical power consumption of less than 1\u2009W in our experiments (wall-plug efficiency of the optical source is >50%). Notably, this power level aligns with several commercial electronic or electromechanical optical switching devices46,47. However, these devices exhibit relatively slow switching speeds\u2014e.g. on the order of milliseconds for micro-electromechanical systems (MEMS)\u2014and insertion losses >1\u2009dB. In contrast, our platform not only offers significantly higher switching speeds but is also virtually lossless, as the probe is reconfigured directly within the fibre.\n\nWe have recently demonstrated our ability to control coupling in arrays of integrated coupled waveguides48, which represent the counterpart of MCFs on-chip. In this framework, light-by-light manipulation of the probe would add a critical degree of control for ultrafast reconfiguration at milliwatt power level. This paves the way for programmable photonics circuits34,49,50 (hosted on MCFs, on-chip or hybrid) where basic logic blocks, like the DCF with integrated BCB, are cascaded to implement complex operations. Within this context, the ability to implement all-optically reconfigurable matrix products (see Eq.\u00a01) may open new avenues in photonic computing and machine learning.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "We consider two counter-propagating beams in a polarisation-maintaining multimode (or multicore) optical fibre of length L supporting N guided spatial modes. If the beams are co-polarised along the p-axis (p is one of the birefringence axes) and are centred at the same carrier wavelength \\({{\\rm{\\lambda }}}\\), their spatio-temporal dynamic is described by the following set of coupled nonlinear Schr\u00f6dinger equations(CNLSEs)26:\n\nHere \\(\\kappa\\)=\u20092, while \\({f}_{n}\\left(z,t\\right)\\) and \\({b}_{n}\\left(z,t\\right)\\) indicate the slowly varying amplitudes of the forward and backward mode n, respectively. Equation (6) is completed with the boundary conditions that define the input fields, namely \\({f}_{n}\\left(0,t\\right)\\) and \\({b}_{n}\\left(L,t\\right).\\) The instantaneous amplitudes \\({{\\mbox{f}}}_{n}\\) and \\({{\\mbox{b}}}_{n}\\) are related to the slowly varying amplitudes through \\({{\\mbox{f}}}_{n}={f}_{n}\\exp \\left(-{{\\rm{i}}}{\\beta }_{{np}}z\\right)\\) and \\({{\\mbox{b}}}_{n}={b}_{n}\\exp \\left({{\\rm{i}}}{\\beta }_{{np}}z\\right)\\), with \\({\\beta }_{{np}}\\left(\\lambda \\right)\\) the propagation constant of the p-polarised mode n at wavelength \\(\\lambda .\\) For the purposes of our subsequent analysis, it is useful to rewrite the relation between \\({{\\mbox{f}}}_{n}\\) and \\({f}_{n}\\) in matrix form, namely \\({{\\boldsymbol{{F}}}}={{{\\bf{E}}}}_{{\\boldsymbol{\\beta }}}{{\\bf{F}}}\\), where \\({{\\boldsymbol{{F}}}}\\) and \\({{\\bf{F}}}\\) are 1\u2009x\u2009N vectors with elements \\({{\\mbox{f}}}_{n}\\) and \\({f}_{n}\\), respectively, whereas \\({{{\\bf{E}}}}_{{\\boldsymbol{\\beta }}}\\) is the diagonal matrix with entries \\({{{\\bf{E}}}}_{{\\boldsymbol{\\beta }}}\\left[n,n\\right]=\\exp \\left(-{{\\rm{i}}}{\\beta }_{{np}}z\\right)\\). The coefficients vn and \u03b3nm in Eq. (6) are the group velocity of mode n and the Kerr coefficient for the nonlinear interaction between mode n and m33, respectively. They are computed via finite-element-method software (see Supplementary Information\u00a01) after measuring the refractive index profile with an optical fibre analyser.\n\nGroup velocity dispersion (GVD) and higher-order dispersion terms are ignored in Eq. (6) as the corresponding characteristic lengths are substantially larger than the fibre lengths (0.4\u2009m) used in our experiments. Similarly, detrimental nonlinear effects\u2014including pulse reshaping and spectral broadening from self- and cross-phase modulation, Raman and Brillouin scattering, and wavelength conversion via four-wave mixing\u2014are negligible at the peak power levels (up to a few tens of kW) and the fibre length used in our experiments. For reference, the input/output BCB temporal and spectral profiles are provided in Supplementary Information\u00a04.\n\nAccording to the normalisation of the coefficients in Eq. (6), \\({\\left|\\,{f}_{n}\\left(z,t\\right)\\right|}^{2}\\) and \\({\\left|{b}_{n}\\left(z,t\\right)\\right|}^{2}\\) indicate the instantaneous power coupled to the forward and backward mode n, respectively. The total forward energy, \\({\\int }_{t}{\\sum }_{n}{\\left|{f}_{n}\\right|}^{2}\\partial t,\\) and backward energy, \\({\\int }_{t}{\\sum }_{n}{\\left|{b}_{n}\\right|}^{2}\\partial t,\\) are conserved except for propagation losses, which are negligible in the short fibres used. Similarly, when modal walk-off is negligible\u2014which is the case for the short fibres used in our experiments\u2014the total instantaneous powers \\({\\sum }_{n}{\\left|{f}_{n}\\right|}^{2}\\) and \\({\\sum }_{n}{\\left|{b}_{n}\\right|}^{2}\\) remain conserved throughout propagation.\n\nThe last summation on the right-hand-side of Eq. (6) describes the intermodal power exchange between forward and backward modes. A key feature of our counterpropagating setup is that, because the forward and backward beams are co-polarised and centred at the same carrier wavelength, each component of this summation is automatically phase-matched, irrespectively of the carrier wavelength and the fibre parameters. Consequently, a nonlinear dynamic is triggered where all modes can simultaneously exchange energy, rather than just a single pair of phase-matched modes, as typically occurs in co-propagating setups.\n\nIf forward and backward beams are orthogonally polarised along different birefringence axes, the nonlinear intermodal interaction is reduced by a factor of 1/3 (\\(\\kappa\\)=\u20092/3 in Eq. (6)), and each component of the last summation is subject to a polarisation phase-mismatch \u0394\u03b2 = \u03b2nx(\u03bb) \u2212 \u03b2ny(\u03bb) + \u03b2my(\u03bb) \u2212 \u03b2mx(\u03bb). However, in the fibres under test, this phase mismatch barely impacts the dynamic, since the corresponding beat length \\(2{{\\rm{\\pi}}} /\\Delta \\beta\\) is typically larger than the interaction length Lint between forward and backward beams. The latter equals the fibre length in the continuous-wave (CW) case, while reads \\({L}_{{\\mathrm{int}}}={\\tau }_{p}c\\) in pulsed operation, where \\({\\tau }_{p}\\) is the pulse width of the forward and backward beams and c is the velocity of light in the fibre. Similarly, if forward and backward beams are centred at different carrier wavelengths \\({{{\\rm{\\lambda }}}}_{f}\\) and \\({{{\\rm{\\lambda }}}}_{b}\\), the induced phase-mismatch is negligible whenever the detuning \\(\\Delta {{\\rm{\\lambda }}}=|{{{\\rm{\\lambda }}}}_{f}-{{{\\rm{\\lambda }}}}_{b}|\\ < < {{{\\rm{\\lambda }}}}_{0}^{2}/(c{L}_{{in}}|{v}_{n}^{-1}-{v}_{m}^{-1}|)\\) with \u03bb0\u2009=\u2009(\u03bbf\u2009+\u2009\u03bbb)/226. This enables tuning of the wavelength selectivity for applications such as core-to-core switching, which occurs only when the probe wavelength is sufficiently close to the BCB wavelength (\\(\\Delta {{\\rm{\\lambda }}}\\)\u2009<\u200910\u2009nm in the fibres under test).\n\nIn the following, in accordance with the notation used in the manuscript, we indicate the forward and backward beam with probe and BCB, respectively. Let us consider the CW case in which the probe is a signal with low power. Equation (6) is reduced to Eqs. (7) and (8) by using a perturbation approach where the less significant nonlinear terms are ignored along with time-varying terms (\\({\\partial }_{t}\\,{f}_{n}\\) and \\({\\partial }_{t}{b}_{n}\\)):\n\nWe note that the first summation on the right-hand side of Eq. (7) represents the intermodal cross-phase modulation terms between the BCB modes and the probe modes, which are responsible for modulating the phase of the latter.\n\nThe second summation, instead, accounts for the intermodal four-wave mixing terms between the BCB and the probe, leading to the exchange of photons between probe modes. Importantly, the BCB does not transfer net power to the probe. Indeed, as previously mentioned, its total energy remains conserved, aside from propagation losses. However, the BCB acts as an intermediary, enabling energy redistribution among the probe modes and thereby a complete reconfiguration of its modal state.\n\nHere \\({\\theta }_{n}=-{\\gamma }_{nn}{|{b}_{n}|}^{2}+{\\sum }_{m=1}^{N}2{\\gamma }_{nm}{|{b}_{m}|}^{2}\\) plays the role of a nonlinear phase shift induced by self-phase and cross-phase modulation. The solution for the BCB mode n reads as \\({b}_{n}\\left(z\\right)={b}_{n}\\left(0\\right)\\exp \\left(-{{\\rm{i}}}{\\theta }_{n}z\\right)\\), therefore its amplitude is preserved in propagation, except for the nonlinear phase variation. We insert this solution in Eq. (7) and we use the transformation \\({f}_{n}=\\hat{{f}_{n}}\\exp \\left({{\\rm{i}}}{{{\\rm{\\theta }}}}_{n}z\\right)\\). This latter transformation can be recast in matrix form as \\({{\\bf{F}}}={{{\\bf{E}}}}_{{{\\boldsymbol{\\theta }}}}\\hat{{{\\bf{F}}}}\\), where \\(\\hat{{{\\bf{F}}}}\\) is the 1\u2009x\u2009N vector with elements \\(\\hat{{f}_{n}}\\) and \\({{{\\bf{E}}}}_{{{\\boldsymbol{\\theta }}}}\\) is the diagonal matrix whose entry \\({{{\\bf{E}}}}_{{{\\boldsymbol{\\theta }}}}\\left[n,n\\right]=\\exp \\left({{\\rm{i}}}{{{\\rm{\\theta }}}}_{n}z\\right)\\). We finally obtain a system of linear differential equations (LDE) for \\(\\hat{{f}_{n}}\\) that can be written as \\(\\partial z\\hat{{{\\bf{F}}}}={{\\rm{i}}}{{\\bf{A}}}\\hat{{{\\bf{F}}}}\\), where \\({{\\bf{A}}}\\) is the NxN matrix whose diagonal elements \\({{\\bf{A}}}\\left[n,n\\right]=-{{{\\rm{\\theta }}}}_{n}{{\\boldsymbol{+}}}{{\\rm{\\kappa }}}{\\sum }_{m=1}^{N}{\\gamma }_{{nm}}{\\left|{b}_{m}\\right|}^{2}\\), and \\({{\\bf{A}}}\\left[n,m\\right]=\\kappa {\\gamma }_{{nm}}{b}_{m}\\left(0\\right){b}_{n}{\\left(0\\right)}^{*}\\) for \\(n\\ne m\\). The matrix \\({{\\bf{A}}}\\) stores therefore the information on the BCB mode state. The solution to the above-mentioned LDE system is readily found by eigenvector decomposition of matrix \\({{\\bf{A}}}\\), namely \\(\\hat{{{\\bf{F}}}}\\left(L\\right)={{\\bf{V}}}\\exp \\left({{\\rm{i}}}{{\\mathbf{\\Lambda }}}L\\right){{{\\bf{V}}}}^{-1}\\hat{{{\\bf{F}}}}\\left(0\\right)\\), where \\({{\\bf{V}}}\\) and \\({{\\mathbf{\\Lambda }}}\\) are the matrices of eigenvectors and eigenvalues of \\({{\\bf{A}}}\\), respectively. Now, by making use of the relations previously introduced, namely \\({{\\boldsymbol{{F}}}}={{{\\bf{E}}}}_{{{\\boldsymbol{\\beta }}}}{{\\bf{F}}}\\) and \\({{\\bf{F}}}={{{\\bf{E}}}}_{{{\\boldsymbol{\\theta }}}}\\hat{{{\\bf{F}}}}\\), we derive the solution \\({{\\boldsymbol{{F}}}}(L)\\,=\\,{{\\bf{M}}}{{\\boldsymbol{{F}}}}(0)\\) previously indicated as Eq. (1), where \\({{{\\bf{M}}}={{\\bf{E}}}}_{{{\\boldsymbol{\\beta }}}}{{{\\bf{E}}}}_{{{\\boldsymbol{\\theta }}}}{{\\bf{V}}}\\exp \\left({{\\rm{i}}}{{\\mathbf{\\Lambda }}}L\\right){{{\\bf{V}}}}^{-1}\\) (with \\({{{\\bf{E}}}}_{{{\\boldsymbol{\\beta }}}}\\) and \\({{{\\bf{E}}}}_{{{\\boldsymbol{\\theta }}}}\\) computed in z\u2009=\u2009L), while \\({{\\boldsymbol{{F}}}}(0)\\equiv {{{\\boldsymbol{{F}}}}}_{{\\boldsymbol{in}}}\\) and \\({{\\boldsymbol{{F}}}}\\,(L)\\equiv {{{\\boldsymbol{{F}}}}}_{{\\boldsymbol{out}}}\\) are the input and output probe mode state, respectively.\n\nThe above-mentioned solution is generally applicable to any multimode fibre system, including coupled multicore fibres. In the latter case, it is useful to derive a relationship between the field in the individual cores of the fibre. We proceed by using a couple mode theory approach, where the modes of the multicore fibre are approximated as a linear combination of the fields in the cores, namely, \\({{{\\boldsymbol{{F}}}}}_{{{\\boldsymbol{c}}}}={{\\bf{T}}}{{\\boldsymbol{{F}}}}\\), where \\({{\\bf{T}}}\\) is a transformation matrix and \\({{{\\boldsymbol{F}}}}_{{{\\boldsymbol{c}}}}\\) is the 1\u2009x\u2009N vector whose element \\({{\\mbox{f}}}_{c,n}\\) indicates the field in the core n. In the simplest case of a DCF with single-mode cores, the two guided modes are well approximated as the in-phase and anti-phase sum of the fields in the cores, therefore \\({{\\bf{T}}}=\\left[{\\mathrm{1,1}};1,-1\\right]/\\sqrt{2}\\). In general, the unitary T matrix strictly depends on the core-to-core arrangement. In the case of the TCF under test (corresponding results are illustrated in Fig.\u00a06b\u2013d), where the cores are arranged at the vertices of an isosceles triangle with 30-deg base angle and ~16.5\u2009\u00b5m base, we have \\({{\\bf{T}}}=\\left[\\sqrt{2},0,\\sqrt{2};1,\\sqrt{2},-1;1,-\\sqrt{2},-1\\right]/2\\).\n\nWhen the probe is in linear regime, the solution of the full CNLSEs Eq. (6) yields the same results as the simplified system Eq. (7) and the analytical formulas Eqs. (1) and (2), confirming the validity of our model. The advantage of using Eqs. (1) and (2) is that they directly provide the probe mode/core state as a function of matrices M and T, eliminating the need for propagation codes. Notably, Eqs. (1) and (2) allow identifying the optimal matrix M, and then the related optimal BCB mode state, to implement the all-optical applications introduced in this work.\n\nNote that, in the general non-CW case, where Lint\u2009<\u2009L, the theoretical analysis proceeds as follows. We consider a fibre section of length Lint where the probe and BCB interact, governed by the same CW-model outlined above. Beyond this, in the remaining fibre sections where no interaction occurs, we effectively assume the BCB is off. The overall solution is obtained by solving each region separately and enforcing continuity at their interface. This approach remains valid as long as L is sufficiently short to prevent excessive modal walk-off.\n\nThe matrix \\({{\\bf{A}}}\\) can be decomposed as \\({{\\bf{A}}}={{{\\bf{E}}}}_{{{\\angle }}{{{\\boldsymbol{B}}}}_{{{\\mathbf{0}}}}}^{{{*}}}{{{\\bf{A}}}}^{{{{\\prime} }}}{{{\\bf{E}}}}_{{{\\angle }}{{{\\boldsymbol{B}}}}_{{{\\mathbf{0}}}}}\\), where \\({{{\\bf{E}}}}_{{{\\angle }}{{{\\boldsymbol{B}}}}_{{{\\boldsymbol{0}}}}}\\) is the diagonal matrix whose entry \\({{{\\bf{E}}}}_{{{\\angle }}{{{\\boldsymbol{B}}}}_{{{\\mathbf{0}}}}}\\left[{{\\rm{n}}},{{\\rm{n}}}\\right]=\\exp \\left(-{{\\rm{i}}}\\; {{\\rm{arg}}}\\left({b}_{n}\\left(0\\right)\\right)\\right)\\) identifies the phase of the BCB mode n in z\u2009=\u20090, and \\({{{\\bf{A}}}}^{{{{\\prime}}}}\\) is the matrix created from \\({{\\bf{A}}}\\) by replacing the non-diagonal entries \\(\\kappa {\\gamma }_{{nm}}{b}_{m}\\left(0\\right){b}_{n}{\\left(0\\right)}^{*}\\) with the corresponding magnitude \\(\\kappa {\\gamma }_{{nm}}|{{b}}_{m}\\left(0\\right)||{b}_{n}(0)|.\\) Matrices \\({{\\bf{A}}}\\) and \\({{{\\bf{A}}}}^{{{{\\prime}}}}\\) are therefore equivalent except for the phase information of the BCB, which is missing in \\({{{\\bf{A}}}}^{{{{\\prime} }}}.\\)\n\nBy exploiting the above-mentioned decomposition, the relation \\(\\partial z\\hat{{{\\bf{F}}}}={{\\rm{i}}}{{\\bf{A}}}\\hat{{{\\bf{F}}}}\\) can be rewritten as \\(\\partial z\\left({{{\\bf{E}}}}_{{{\\angle }}{{{\\boldsymbol{B}}}}_{{{\\mathbf{0}}}}}\\hat{{{\\bf{F}}}}\\right)={{\\rm{i}}}{{{\\bf{A}}}}^{{{{\\prime} }}}\\left({{{\\bf{E}}}}_{{{\\angle }}{{{\\boldsymbol{B}}}}_{{{0}}}}\\hat{{{\\bf{F}}}}\\right)\\), meaning that the dynamics of the transformed vector \\({{{\\bf{E}}}}_{{{\\boldsymbol{\\angle }}}{{{\\boldsymbol{B}}}}_{{{\\boldsymbol{0}}}}}\\hat{{{\\bf{F}}}}\\) depends solely on the modified matrix \\({{{\\bf{A}}}}^{{{{\\prime} }}}\\). Since \\(\\left|{{{\\bf{E}}}}_{{{\\angle }}{{{\\boldsymbol{B}}}}_{{{\\mathbf{0}}}}}\\hat{{{\\bf{F}}}}\\right|=\\left|\\hat{{{\\bf{F}}}}\\right|=\\left|{{\\bf{F}}}\\right|\\), we conclude that the probe mode power distribution \\(\\left|{{\\bf{F}}}\\right|\\) is fully determined by \\({{{\\bf{A}}}}^{{{{\\prime} }}}\\), rather than \\({{\\bf{A}}}\\). In other words, the output probe mode power distribution only depends on the BCB mode power distribution (that is preserved in propagation and is fixed by the launch conditions), but not on the BCB mode relative phases. This is not generally true for the output probe core power distribution, which depends instead on the full BCB mode state.\n\nIn our experiments, the BCB operates in a pulsed configuration rather than as a CW, which enhances the peak power and, consequently, the system nonlinearity. The probe could in principle operate in the CW regime with indefinitely low power. In practice, the probe-to-BCB power imbalance in our experiments is ~1:20. Indeed, a lower probe power would result in a weak signal-to-noise ratio, thus degrading the image quality, and preventing an accurate mode decomposition of the probe. Consequently, both BCB and probe are pulsed in our experiments (which does not change the main outcomes, see Supplementary Information\u00a03). Specifically, 0.5\u2009ns-pulsed probe and BCB are generated by splitting the beam from an in-house built linearly polarised ytterbium master oscillator power amplifier having central wavelength \u03bbc\u2009=\u20091040\u2009nm and a repetition rate of 800\u2009kHz51. The probe and BCB are then injected at the two opposite ends of the fibres under test. Five distinct fibres are employed (see Supplementary Information\u00a01): a polarisation-maintaining (PM) few-mode fibre (PM1550-xp from Thorlabs) supporting 3 guided modes at \u03bbc; a highly nonlinear PM few-mode fibre (PMHN1 from Thorlabs) supporting 3 guided modes at \u03bbc; a PM few-mode fibre (PM2000 from Thorlabs) supporting 6 guided modes at \u03bbc; and then a homemade dual core fibre (DCF) and three-core fibre (TCF) supporting respectively 2 and 3 guided modes at \u03bbc. The short fibre length (40\u2009cm) used in our experiments prevents the onset of detrimental nonlinear effects, such as Raman and Brillouin scattering, self- and cross-phase modulation-induced pulse reshaping, and four-wave mixing-induced wavelength conversion.\n\nThe input power and polarisation of probe and BCB are controlled with a proper combination of polarisation beam splitters and half-wave-plates (HWP2 to HWP5 in Fig.\u00a02). By adjusting the phase pattern displayed on the screen of a spatial light modulator, we control the mode state of the input BCB, namely, its power distribution and relative phase over the fibre modes. A spatial phase plate is used to excite an arbitrary combination of modes at the probe input end for the PM1550-xp and PMHN1, while the input probe is selectively coupled into a single core to excite a combination of modes in the DCF and TCF.\n\nThe test fibre at the BCB input end is cleaved with an angle of 8-deg to eliminate back reflection of the BCB, whereas the probe input end is perpendicularly cleaved to ensure high-quality mode excitation. The output probe is sampled using a wedge with an incident beam angle of ~10\u2009deg, ensuring that the sampled beam preserves the output probe polarisation. The near-field and far-field intensity profiles of probe and BCB are measured with infra-red cameras, with the output probe profiles corrected by subtracting the BCB reflection from the flat-cleaved fibre end. Mode decomposition of the probe and BCB is then implemented based on the measured intensity profiles. Specifically, a reconstructed spatial distribution is generated by numerically determining the mode state through an iterative process, where the Stochastic Parallel Gradient Descent algorithm52 is successfully applied. The reconstructed distribution typically exhibits a correlation as high as 99%53,54,55 with the measured spatial profile, which confirms the effectiveness of the mode decomposition method. The reconstructed mode distributions are compared with the measurements at varying BCB peak powers for the results presented in Fig.\u00a03d\u2013f (see Supplementary Movies\u00a01\u20133).\n\nIn the MCFs under test, the power in each individual core is measured by integrating the intensities within the core areas in the near-field intensity profiles. To analyse the temporal evolution of core-to-core power switching, the output probe pulses from each core are characterised by an oscilloscope. As shown in Fig.\u00a02, the output probe is imaged at the pinhole position via a pair of lenses (focal lengths\u2009=\u200913.86\u2009mm and 500\u2009mm, providing a magnification factor of ~36x). With a clear aperture of ~200\u2009\u00b5m, the pinhole can effectively filter out the beam from a single core. The filtered output probe is then coupled through a telescope into a multimode fibre connected to the oscilloscope, with a replica imaged onto the camera using another telescope. Due to the flat cleave at the input-probe fibre end, the BCB reflection at this facet propagates in the same direction of the probe and can then also be measured (see BCB reflection in Fig.\u00a05e). However, the BCB reflected pulses are separated from the output probe pulses due to the differential travelling path length, with a delay essentially determined by the fibre length (~2\u2009ns in Fig.\u00a05e).", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data are available at ref. 56.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Digonnet, M. J. Rare-earth-doped fiber lasers and amplifiers, revised and expanded. (CRC Press, Boca Raton, 2001).\n\nStolen, R. H. & Bjorkholm, J. E. Parametric amplification and frequency conversion in optical fibers. IEEE J. Quantum Electron 18, 1062\u20131072 (1982).\n\nArticle\u00a0\n ADS\u00a0\n \n Google Scholar\u00a0\n \n\nSlav\u00edk, R. et al. 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J., Wabnitz, S. & Guasoni, M. \u201cData supporting the publication \u201cSub-nanosecond all-optically reconfigurable photonics in optical fibres\u201d\u201d, University of Southampton, https://doi.org/10.5258/SOTON/D3537 (2025).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "M.G. acknowledges funding from the European Research Council under the H2020 Programme (ERC Starting Grant No. 802682, MODES project), from the UK Engineering and Physical Sciences Research Council (EP/T019441/1), and from the British Council (UK-India Education and Research Initiative IND/CONT/G/23-24/36); K.J. acknowledges funding from China Scholarship Council (202006840003) and from the UK Engineering and Physical Sciences Research Council (EP/X040569/1); D.J.R. acknowledges funding from the UK Engineering and Physical Sciences Research Council (EP/P030181/1); S.W. acknowledges funding from the European Research Council under the H2020 Programme (ERC Advanced Grant No.740355, STEM project) and from European Union- Next Generation EU (PE00000001, RESTART). The authors acknowledge the use of freely available 3D components from Optical Components V1 by Ryo Mizuta Graphics (available at https://ryomizutagraphics.gumroad.com/l/OpticalComponentsV1), which were adapted and rendered in Blender for Fig.\u00a02 in this work.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Optoelectronics Research Centre, University of Southampton, Southampton, United Kingdom\n\nKunhao Ji,\u00a0David J. Richardson\u00a0&\u00a0Massimiliano Guasoni\n\nMicrosoft (Lumenisity Limited), Unit 7, The Quadrangle, Abbey Park Industrial Estate, Romsey, United Kingdom\n\nDavid J. Richardson\n\nDepartment of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Rome, Italy\n\nStefan Wabnitz\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nK.J. performed all the experiments reported in this work and performed the numerical simulations with M.G.; D.J.R. provided support for the experimental work and contributed to the interpretation of the results; S.W. provided support for the theoretical work and contributed to the interpretation of the results; M.G. additionally conceived the research idea, supervised the project, developed the theoretical results and wrote the manuscript with feedback from all the authors.\n\nCorrespondence to\n Kunhao Ji or Massimiliano Guasoni.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors decale no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Jianji Dong, Zuxing Zhang and the other anonymous reviewer(s) for their contribution to the peer review of this work. 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Sub-nanosecond all-optically reconfigurable photonics in optical fibres.\n Nat Commun 16, 6665 (2025). https://doi.org/10.1038/s41467-025-61984-8\n\nDownload citation\n\nReceived: 14 November 2024\n\nAccepted: 06 July 2025\n\nPublished: 19 July 2025\n\nVersion of record: 19 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-61984-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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its networks", + "journal": "Nature Communications", + "published": "14 November 2024", + "supplementary_0": [ + { + "label": "Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM3_ESM.xls" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM4_ESM.xls" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM5_ESM.pdf" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM6_ESM.xls" + }, + { + "label": "Supplementary Data 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM7_ESM.xls" + }, + { + "label": "Supplementary Data 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM8_ESM.pdf" + }, + { + "label": "Supplementary Data 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM9_ESM.pdf" + }, + { + "label": "Supplementary Data 8", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM10_ESM.xls" + }, + { + "label": "Supplementary Data 9", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM11_ESM.pdf" + }, + { + "label": "Supplementary Data 10", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM12_ESM.pdf" + }, + { + "label": "Supplementary Data 11", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM13_ESM.pdf" + }, + { + "label": "Supplementary Data 12", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM14_ESM.xls" + }, + { + "label": "Supplementary Data 13", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM15_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_MOESM16_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.5281/zenodo.13924386", + "/articles/s41467-024-54297-9#ref-CR43", + "/articles/s41467-024-54297-9#Fig3", + "/articles/s41467-024-54297-9#MOESM7", + "/articles/s41467-024-54297-9#Fig4", + "/articles/s41467-024-54297-9#MOESM4", + "/articles/s41467-024-54297-9#MOESM13" + ], + "code": [ + "https://doi.org/10.5281/zenodo.13924420", + "/articles/s41467-024-54297-9#ref-CR56", + "/articles/s41467-024-54297-9#Fig3", + "/articles/s41467-024-54297-9#Fig4" + ], + "subject": [ + "Animal physiology", + "Biomechanics", + "Evolution", + "Phylogenetics", + "Zoology" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-3917646/v1.pdf?c=1731676003000", + "research_square_link": "https://www.researchsquare.com//article/rs-3917646/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54297-9.pdf", + "preprint_posted": "20 Feb, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "The avian evolutionary pathway led to morphological adaptive variations in their feet. Diverse foot types are accompanied by a complex muscle system, allowing birds to adopt different primary lifestyles, and to display various locomotor and manipulative skills. We provide novel insights of evolutionary and functional significance on the avian foot architecture through Anatomical Network Analysis. Here we show that there is no link between the more complex foot networks and the ability to perform more specialized skills or a higher number of different tasks. Additionally, there is a trend towards the simplification of foot networks on a microevolutionary scale. The anatomical parts largely conserved in living birds and already present in ancestral dinosaurs exhibit the highest connectivity degree, a network parameter related to the constraint of an anatomical part or evolutionary change. Foot networks are phylogenetically constrained and conserved in all birds despite their foot type diversity. We suggest that this scenario could be the result of stabilizing selection, which led the connectivity of anatomical parts of the foot to be conserved.Biological sciences/EvolutionBiological sciences/Zoology", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupportingInformation1.xlsBirds' species listSupportingInformation2.pdfCharacter listSupportingInformation3.xlsAbbreviations and muscles synonymySupportingInformation4.pdfBirds tree based on Prum et al. (2015) phylogenySupportingInformation5.xlsData matrix (characters)SupportingInformation6.xlsFoot networksSupportingInformation7.xlsNetwork parameters and complexity scoresSupportingInformation8.pdfPhylomorphospace grouped by foot types, nest attendance, and skills", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Avian evolution led to morphological adaptive variations in feet. Diverse foot types are accompanied by a diverse muscle system, allowing birds to adopt different primary lifestyles, and to display various locomotor and manipulative skills. We provide insights of evolutionary and functional significance on the avian foot architecture through Anatomical Network Analysis, a methodology focused on connectivity patterns of anatomical parts. Here, we show that: (1) anatomical parts largely conserved in living birds and already present in ancestral dinosaurs exhibit the highest connectivity degree, (2) there is no link between the more complex foot networks and the ability to perform more specialized skills or a higher number of different tasks, (3) there is a trend towards the simplification of foot networks on a macroevolutionary scale within birds, and (4) foot networks are phylogenetically constrained and conserved in all birds despite their foot type diversity, probably due to stabilizing selection.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Over ~100\u2009million years of evolutionary diversification, but mainly the explosive phyletic radiation following the K\u2013Pg extinction event1, resulted in about 11,000 living bird species (Neornithes) within 254 families2. The avian evolutionary journey led to a body plan (bauplan) that unequivocally characterizes birds in high taxonomic levels, in addition to morphological adaptive variations in their feet at lower taxonomic levels (e.g., orders, families).\n\nIn birds, hind limbs are highly modified for bipedal locomotion, as well as for leaping from and landing on the substrate3,4. The most important evolutionary changes at their hind limbs are the fusion of independent bones forming the tibiotarsus (tibia\u2009+\u2009proximal tarsal bones) and the tarsometatarsus (metatarsals 2\u20134\u2009+ \u2009distal tarsal bone), in addition to the retroversion of the digit I (hallux)5. Avian feet are traditionally classified in several categories based on the number of digits, and their positional arrangement and mobility. These include anisodactyl, didactyl, tridactyl, heterodactyl, zygodactyl, ectropodactyl, semizygodactyl and pamprodactyl3 (Fig.\u00a01a-c). Furthermore, they are also classified based on the presence and extent of skin, lobes, or web between digits into syndactyl, lobate, semipalmate, palmate and totipalmate3 (Fig.\u00a01d). These greatly diverse types of foot are accompanied by an extremely complex and also diverse system of muscles, enabling birds to perform a great variety of movements such as coordinated or individual flexion, extension, abduction and adduction. This versatility in motion allows birds to display various locomotor and manipulative skills (e.g., walking, running, hopping, wading, perching, climbing, swimming, diving, hanging upside down, grasping), and thus, to explore and conquer several niches and display several behaviours3,4. Moreover, tradeoffs between the forelimbs and hind limbs have influenced the evolution of several locomotor strategies6, leading birds to adopt different primary lifestyles; including terrestrial, arboreal, aquatic and hyperaerial.\n\na the ancestral foot type, from which the other foot types evolved3,7. Classification based on: (b) the positional arrangement of digits and ability to rotate and change the position of some of them, (c) the secondary loss of digits and the remaining number of digits, and (d) the presence and extent of skin, lobes or web between digits. *We proposed the term multidactyl for birds able to rotate both the first and fourth digits to either a cranial or a caudal position (e.g., Colius striatus).\n\nHypothesizing about the mechanisms leading to evolutionary changes and functional skills based on morphological variation is a classical conceptual tool. The morphology of the feet of birds has been widely studied using standard methodological tools such as those analysing bone shape, phalanx size proportions, toe and metatarsals trochleae orientations, claw curvature, and digital muscle and tendon configurations3,7,8,9,10,11,12,13,14,15. We aim to provide insights of evolutionary and functional significance on the avian foot architecture through Anatomical Network Analysis (AnNA)16. By using connectivity data, AnNA complements the information on the comprehensive concept of form provided by the traditional linear and geometric morphometrics, thus allowing the comparison of disparate anatomies like 2-toed, 3-toed, and 4-toed birds. AnNA has been widely and successfully adopted in the scientific community over the past 10\u2009years to address functional, evolutionary and developmental questions related to the musculoskeletal system of limbs in various taxa17,18,19,20,21,22,23,24, including birds as studied by our research group25,26.\n\nIn this work, we analyse the connectivity patterns and the mutual influence of the anatomical parts of the musculoskeletal system of the foot of 62 species representatives of most major avian lineages (Supplementary Data\u00a01). In doing so, we address the following subject matters: (1) the use of the connectivity degree (ki), a parameter related to the co-dependence of an anatomical part with others within the network, as a proxy for evolutionary constraints of the anatomical parts and their role in the avian body plan; (2) the potential link between the structural and functional complexity of the foot with the complexity of their anatomical networks; (3) the birds\u2019 foot networks disparity and its potential link with the primary lifestyles, foot skills, and nest attendance type, (4) the correspondence between the diversity of foot types and the connectivity patterns of their networks; (5) the role played by the phylogenetic history in shaping the foot networks; and (6) the trace of the evolutionary history of network parameters across Neornithes.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "There is a set of features that unequivocally distinguishes birds from other lineages of tetrapods. These are the result of a progressive assembly process that unfolded over 160\u2009million years, leading to the acquisition of the avian body plan27. Most features characterizing living birds (Neornithes) are restricted to the skeleton, including a reorganization of limbs due to loss or fusion of bones, or both. Of all the evolutionary events describing the assembly of the avian body plan, 43% belong to the hind limbs28.\n\nThe avian foot networks revealed some patterns that can be associated with the avian body plan at its crown group level. Bony elements are conserved in all species, except in the few species that secondarily lost the digit 1 (tridactyls like Dromaius and Eudromia) and digit 2 (the sole didactyl Struthio). The avian foot is characterized by the fusion of metatarsals II-IV forming the tarsometatarsus. All digits contact the tarsometatarsus and all the intrinsic muscles originate from it (Fig.\u00a02a). Not surprisingly, this is the bone with the highest connectivity degree (ki) in almost all species (Fig.\u00a02d; Supplementary Data\u00a02), a network parameter that measures the co-dependency of an anatomical part with others and is related to its constraints for evolutionary change16.\n\na Tarsometatarsus and phalanges, showing the muscles\u2019 attachments. b Detail of bone and muscle nodes. Multi-network models following: c foot topology, and d circular layout showing the proportional size of nodes according to their connectivity degree (ki). For abbreviations of bones and muscles, see Supplementary Data 12. Illustration of the monk parakeet attributed to Scott Partridge.\n\nExtrinsic muscles of the foot (i.e., those arising from the femur, tibiotarsus and/or fibula, and inserting on the digits) are largely conserved, with few exceptions (e.g., the absence of mm. flexor hallucis longus in Apteryx and Pterocles, flexor perforatus digiti 2 in Chaetura and Podiceps, flexor perforans et perforatus digiti 2 in Struthio). Specifically, the extrinsic muscles flexor digitorum longus and extensor digitorum longus, which are responsible for the coordinated and simultaneous flexion and extension of the three forward toes3, are the muscles with the highest ki (Fig.\u00a02d; Supplementary Data\u00a02). The digital flexor group evolved from two muscles into seven or more among the lineage leading to crown-group birds, but how or when this subdivision proceeded is still unknown28. In contrast, the digital extensor group, which includes the mm. extensor digitorum longus and extensor hallucis longus and their insertions on the ungual phalanges, was already present in ancestral dinosaurs28. Undoubtedly, both the antagonistic flexor digitorum longus and extensor digitorum longus, along with an opposable incumbent hallux, are the basic precursors needed for grasping15 and for an arboreal lifestyle; an adaptation acquired ~185\u2013145 Mya ago27.\n\nIntrinsic muscles of the foot (i.e., those arising from the tarsometatarsus and inserting on the digits) provide independent action of the toes3. Their presence/absence varies between taxa: some intrinsic muscles are present in a few species, while others are exclusive to certain species (i.e., mm. extensor propius digiti 3 accesorius in Amazona and extensor propius digiti 4 in Strix and Colius). Also, the intrinsic muscles\u2019 ki values are low (Fig.\u00a02d; Supplementary Data\u00a02). Individual digit movements might be useful in climbing and/or in manipulating objects or food items, specialized skills probably acquired later in the evolutionary history of birds. Most intrinsic muscles are absent in the Passeriformes, a pattern that may be related with the evolutionary trend in phalanx length proportions (i.e., the penultimate phalanxes of digits II-IV are longer than the more proximal phalanxes)11. However, their secondary loss cannot be explained through connectivity, because they share similar ki values with other anatomical elements. A plausible explanation may rely on the changes of the muscles\u2019 early developmental mechanisms throughout the course of evolution. For example, regulatory changes in the expression of genes controlling hind limb musculoskeletal development and patterning (e.g., Tbx4)29, or in the signalling pathways regulating cleavage of muscle mass precursors into individual intrinsic muscles (e.g., retinoic acid mediating apoptosis in myogenic cells, vessels and platelet derived growth factor-B PDGFB involvement in muscle splitting)30,31.\n\nComplexity in morphology is usually related to the high number of anatomical parts32. However, when studying the morphology with AnNA, complexity is defined as the high number of structural and functional interactions among different anatomical parts32. In AnNA, the focus of the study shifts from the anatomical parts to the connections between those anatomical parts. Within this framework, complexity can be quantified by the following network parameters: density of connections (D), average clustering coefficient (ACC), and average shortest path length (APL)16,32. These parameters measure the abundance of connections (D), the interdependence or integration (ACC), and the proximity between nodes (APL). Therefore, complex systems are expected to have higher D and ACC, and lower APL. In contrast, simple systems are expected to have lower D and ACC, and higher APL. By intuition, it is expected that complex anatomical systems are capable of performing complex behaviours. For example, in musculoskeletal systems, more connections (high D) could be linked to the achievement of greater ranges of motion and greater potentiality of action, or both32. Additionally, the proximity of anatomical parts (low APL) could be linked to a greater efficiency for spreading biomechanical forces33. However, the observed pattern for the foot networks of birds does not align with this expectation, as detailed below.\n\nThe specialized skilled foot is mainly restricted to a few bird species (for details, see Supplementary Data\u00a03 and 4). Brinkworth et al.34 found that birds with more complex appendicular skeletons tend to occupy specialized dietary and habitat niches, establishing a link between complex morphology and ecological specialization. We do not find this association when studying the musculoskeletal networks of birds\u2019 feet. Our results (Fig.\u00a03, Supplementary Data\u00a05) show that almost 13% of the analysed species have complex systems, and almost 30% of the analysed species are able to perform one or more specialized skills with their feet such as climbing, powerful grasping, digital dexterity, and hanging upside down. Unexpectedly, most of those species have systems that are neither complex nor simple. The only species capable of performing a specialized skill as climbing which also has a complex network is the hoatzin Opisthocomus. This species is characterized for having the highest D value, and the lowest -and notably different from the rest of the analysed species- APL value. However, hoatzins climb with the help of their beaks (similar to psittacids), and also with the use of their wing claws when they are young2. Therefore, it would be interesting to explore the complexity parameters of the foot network of a true scansorial (climber) foot, such as the ectropodactyl foot of woodpeckers7, for which detailed musculoskeletal information is still not available. In addition, taxa with complex foot networks do not perform any of the specialized skills (i.e., Dromaius, Galliformes and Uria); and many of the species able to perform specialized skills have simple foot networks (i.e., 16% of the analysed species; Chordeiles, Chaetura, Pterocles, Strigiformes, Colius, Merops, Galbula, Caracara, and Tyrannus). This means that more complex foot networks do not necessarily perform more specialized skills, and that the simplicity of a foot network does not limit its potential for specialization.\n\na Time\u2010calibrated phylogeny of birds (Prum et al.40). b Heatmap of the network parameters that capture the morphological complexity (in orange) or simplicity (in green) of an entire network: density of connections (D), average cluster coefficient (ACC), average shortest path length (APL). c Scores of the parameters (D, ACC, and APL) determining the complexity (score of\u00a04 or higher) or simplicity (score of \u22124 or less) of the systems. For details, see Supplementary Data\u00a05.\n\nSome birds can perform more functions than others. The functional complexity35 refers to the number of different tasks that an organism can perform. Within our sample, psittacids are the species having the greatest variety of foot tasks. They can walk, run, hop on land and trees, and are capable of climbing and hanging upside down on branches. Besides, they are distinguished for their digital dexterity while manipulating food and other objects. Despite the ability of psittacids to perform a high number of tasks, their foot networks are neither complex nor simple. The same is true for other birds capable of performing a wide number of tasks (e.g., Coccyzus, Tapera, Nannopterum, Anhinga, Ixobrychus, Accipiter, Fringilla, and Turdus). Moreover, the mousebird Colius and Strigiformes can perform a wide range of tasks despite having simple foot networks. On the contrary, birds capable of performing fewer tasks, such as cursorials (e.g., Struthio and Dromaius), hyperaerials (e.g., Eulampis and Fregata), and the terrestrial Pterocles, have foot networks that are complex, simple, or neither of them. This suggests that more complex foot networks do not necessarily perform more tasks; and that although form and function are closely linked, morphological complexity within AnNA and functional complexity are not. This is not exclusive to birds. For example, in humans, the hind limb network complexity is the same as in chimpanzees, although they have different hind limb functional complexity20.\n\nIn general, complex foot networks belong to species with more plesiomorphic morphologies, whereas simple foot networks are associated with species with more derived morphologies. This suggests a potential evolutionary trend towards the simplification of foot networks. Simpler networks may provide benefits in terms of energetic saving36 without compromising foot functionality. The extent of morphological changes in relation to the complexity of the avian body plan on a macroevolutionary scale, and its comparison with the morphological divergence of birds remains to be elucidated.\n\nThe foot networks of birds do not form distinct clusters in the phylomorphospace (Fig.\u00a04a, Supplementary Data\u00a06 and 7). Most networks are distributed around the centre of the phylomorphospace, except for four species located at opposite limits, clearly separated from the rest of the species. At the negative limit of principal component (PC) 2 is located the ostrich Struthio. This is the sole living dydactyl bird and, in consequence, it is characterized by having the lowest number of nodes (N) and connections (L) in its network (Supplementary Data\u00a02). Dydactyl foot type is considered a high-speed running adaptation3, making the ostrich the fastest of all birds, reaching speeds of 70\u2009km/h37. At the opposite and positive limit of PC2 is the mousebird Colius, the sole multidactyl bird of our analysis. It is characterized for having a low value of number of connections (L) and the lowest value of density of connections (D) (Supplementary Data\u00a02). Mousebirds have the ability to rotate both the first and fourth digits of the foot to either a cranial or a caudal position, and thus, being able to adopt an anisodactyl, zygodactyl or pamprodactyl feet configuration38. Multidactyly provides mousebirds with a great variety of movements, postures and manipulative skills useful in locomotion, feeding, and even during aggressive encounters with other birds38. Finally, at the positive limit of PC1 is the hoatzin Opisthocomus, while at the negative limit of PC1 is the sandgrouse Pterocles. Functional explanation for the distribution of these two anisodactyl species is hard to find. However, considering the hoatzin is a monotypic taxon, its position in the phylomorphospace could be due to its long independent evolutionary history rather due functional constraints.\n\na The 62 bird species from the time\u2010calibrated phylogeny of Prum et al.40 are grouped by colours according to their primary lifestyle. Number labels represent the species (for references see the birds\u2019 species list in the Supplementary Data\u00a01). b Contributions to each parameter and their phylogenetic signal (K). Network parameters: average cluster coefficient (ACC), average degree (AD), average shortest path length (APL), density of connections (D), heterogeneity (H), number of connections (L), number of nodes (N), network diameter (ND), and parcellation (PA) (for details see Supplementary Data\u00a02). Illustration of birds attributed to Scott Partridge.\n\nWhen grouping foot networks according to their primary lifestyles, all groups overlap around the centre; although they clearly differ in the amount of morphospace occupied by each lifestyle: arboreal and terrestrial birds are more dispersed than aquatic and hyperaerial birds (Fig.\u00a04a, Supplementary Data\u00a07a). This distribution pattern in the phylomorphospace finds support in the results obtained from the OU/BM evolutionary tests. The best model for fitting the network parameters into the evolution of the different primary lifestyles was a Brownian motion model with multiple possible optima (BMM) (Akaike Information Criterion, AIC\u2009=\u2009443.7492, Supplementary Data\u00a08), revealing thus a change of rates between the different primary lifestyles. The rates of evolution along PC1 of arboreal birds was 4.9\u2009times faster than aquatic birds, and 1.1\u2009times faster than hyperaerial birds; while the rate of terrestrial birds was 2.5\u2009times faster than aquatic birds, and 1.6\u2009times slower than hyperaerial birds (Supplementary Data\u00a08). Slower rates in the more plesiomorphic terrestrial birds and faster rates in the more derived arboreal birds could have led to a greater dispersion in the phylomorphospace for birds of both lifestyles. Meanwhile, a possible evolutionary scenario for the lesser dispersion in the phylomorphospace of aquatic and hyperaerial birds could result from reaching the morphological limits for those lifestyles, at slower or faster rates, respectively. The rate of evolution along PC2 was very similar across all primary lifestyles (Supplementary Data\u00a08). Finally, the best model for fitting the network parameters into the evolution of the different nest attendance types and foot types was a Brownian motion model, but with a single possible optimum (BM1) (AIC\u2009=\u2009447.4479, Supplementary Data\u00a08), indicating the same rate of evolution across all nest attendance types and foot types.\n\nWhen grouping foot networks according to their skills, flightless birds and species capable of terrestrial hopping, wadding, swimming, foot propelled diving, and grasping are more clustered in the phylomorphospace in relation to the rest of the species (Supplementary Data\u00a06 and 7), but only the difference between flying and flightless birds resulted to be statistically significant (F\u2009=\u200955.445, p\u2009=\u20090.02). Finally, when analysing the nest attendance, precocial birds are the most dispersed (Supplementary Data\u00a06q and 7q). All this suggests that the distribution of foot networks in the phylomorphospace is not largely influenced by the primary lifestyles, skills, or nest attendance type.\n\nRegarding foot types (Supplementary Data\u00a06p and 7p), anisodactyl birds show the greatest dispersion, which translates into a great morphological diversity. The rest of the foot types converge in the centre of the phylomorphospace, except for the sole foot types aforementioned: the didactyl Struthio and the multidactyl Colius. These results go against our expectations, as we hypothesized a correspondence between the diversity of foot types and the connectivity patterns of their networks. On the contrary, our results demonstrate that, although the adaptive radiation of birds led to several foot types, the connectivity of their anatomical parts remained conserved.\n\nPERMANOVA tests validate there is no significant distinction between groups in the phylomorphospace occupation that could be explained by primary lifestyles (F\u2009=\u2009\u22126.7147, p\u2009=\u20090.8143), nest attendance type (F\u2009=\u200925.258, p\u2009=\u20090.3206), or foot types (F\u2009=\u20090.4989, p\u2009=\u20090.6813).\n\nThe character mapping and the ancestral reconstruction of the network parameters onto a molecular phylogeny of birds reveal that the ancestor of all Neornithes likely occupied a portion of the phylomorphospace close to the centre (Fig.\u00a04a, Supplementary Data\u00a09). The ancestor reconstructed position, located near and from where most species are distributed, allows for uncovering foot evolutionary pathways. A low variation at the connectivity level could offer high resistance to evolutionary changes, as it carries less potential on which selection can act39. In fact, of the nine parameters analysed, only parcellation (PA) showed low phylogenetic signal, indicating that most network parameters carry more phylogenetic signal than expected under Brownian motion (Fig.\u00a04b). Moreover, PC1/PC2 vs PCoA1/PCoA2 showed grouping of foot networks of the main clades along all the ecological variables (Supplementary Data\u00a010), and the correlations between PC1/PC2 and PCoA axes turned to be not significant (slope for the intercept\u2009=\u2009-0.1082808, slope for Axis 1\u2009=\u2009-0.1415643, slope for Axis 2\u2009=\u2009-3.4641495; all p-values\u2009>\u20090.05, pseudo R2\u2009=\u20090.1455967). Therefore, the distribution of birds in the phylomorphospace is largely constrained by their evolutionary history. This high phylogenetic signal could, in turn, result in entrenchment into the connectivity of anatomical parts and could have acted as a limit in the production of further diversity.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54297-9/MediaObjects/41467_2024_54297_Fig4_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "The study of avian foot networks revealed some patterns that can be associated with the avian body plan. Anatomical parts largely conserved in Neornithes and already present in ancestral dinosaurs, like the tarsometatarsus and the digital flexor/extensor system, are the ones with the highest connectivity degree (ki), a parameter related to the co-dependence of an anatomical part with others and its constraints for evolutionary change.\n\nMost birds have foot networks that are neither complex nor simple. There is no evident link between more complex foot networks and the ability to perform more specialized skills (climbing, powerful grasping, digital dexterity, and hanging upside down) or more functions (number of different tasks). The simplicity of the foot network does not limit its potential functions and, on a macroevolutionary scale within the entire clade Aves, there is a trend toward the simplification of foot networks.\n\nThe network connectivity pattern of the diverse foot musculoskeletal system in birds is constrained by their lineage-specific phylogenetic history. Moreover, foot networks do not align with the highly diverse foot types of birds, which are classified based on the number, positional arrangement, mobility, and the presence and extent of skin/lobes/or web between digits. This scenario could be the result of stabilizing selection acting specifically on foot network connectivity rather than on foot type variation.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "A total of 62 species representatives of most major avian lineages from the phylogenetic proposal of Prum et al.40 were selected (Supplementary Data\u00a01 and 11). Birds were classified based on their primary lifestyles2. Most birds are capable of several locomotor and manipulative different skills, namely, the behaviours that are known to be capable of being performed. Therefore, in order to capture the plasticity of bird foot usage, we scored as either absent or present all the possible skills for each species2. We also classified the birds according to the nest attendance2,41, and foot type2,3 (for details on the characters, see Supplementary Data\u00a03). Gross anatomical data of the musculoskeletal system of the foot (i.e., the absence/presence of bones and their articulations, and the absence/presence of muscles and their origins/insertions) was acquired by reviewing the descriptions in the bibliography (for details of the sources of the anatomical descriptions for each species see Supplementary Data\u00a01). Osteological nomenclature follows Baumel & Witmer5, and myological nomenclature follows Vander Berge & Zweers42. Bones and muscles abbreviations and synonyms between the muscle names used by the different authors cited are presented in Supplementary Data\u00a012.\n\nWe constructed musculoskeletal anatomical multi-network models of the foot (Fig.\u00a02c, d) for all the species in our data set, considering each bone and each muscle as nodes. We included the tarsometatarsus, metatarsal 1, phalanges, and the muscles responsible for the movements of the toes. Bone-bone, bone-muscle, and muscle-muscle connections were represented as unweighted and undirected connections between pairs of nodes. For bones, the connections represent their articulations; while for muscles the connections represent their origins and insertions, and the tendinous connections between muscles. The topological information on node relationships was coded in adjacency matrices (i.e., symmetric not binary matrices of size NxN, where 0 indicates absence and 1 or more indicates presence of connection)43.\n\nNetworks and statistical analysis and visualizations were performed in R 4.3.044. Different parameters were obtained by using the R package Igraph45. These include: (1) connectivity degree (ki), which is the sum of connections that a specific node has with other nodes in the network; (2) number of nodes (N), which is the simple count of nodes of each network; (3) number of connections (L), which is the total number of connections among nodes of each network; (4) density of connections (D), which is the number of actual connections of each network with respect to the maximum possible; (5) average cluster coefficient (ACC), which is the average of the number of interconnections between the neighbours of all nodes in the networks; (6) average shortest path length (APL), which is the average length of all shortest paths (i.e., the minimal number of connections every two nodes) in a network; (7) heterogeneity (H), which is a measure of how even are the nodes according to their number of connections (specifically, the ratio between the standard deviation of the connections along the network and the average number of connections); (8) average degree (AD), which is the average of the connectivity degree (ki) of the network; (9) network diameter (ND), which is the length of the longest path; and (10) parcellation (PA), which is related to the subdivision of the network in modules. An introduction to anatomical network analysis can be found in Rasskin-Gutman & Esteve-Altava16. Phenograms46 were constructed for the different network parameters using the function phenogram of the phytools package47 (Supplementary Data\u00a013). In order to detect nodes with high (and low) burden rank, we search for those nodes with a connectivity degree (ki) value consisting of two standard deviations (SD) above (and below) the mean\u00a0(Supplementary Data\u00a02).\n\nHigh values of average cluster coefficient (ACC) and density (D), and low values of average shortest path length (APL) capture the morphological complexity of an entire network16,27. In order to determine the complexity (or simplicity) of the systems, we have assigned a scoring system for each of the three parameters. If D and ACC values are below/above one SD, the assigned value is -2/2; and if it is below/above 1/2\u2009SD, the assigned value is -1/1. And if APL values are below/above one SD, the assigned value is 2/-2, and if it is below/above 1/2\u2009SD, the assigned value is 1/-1. If the sum of the scores results in a score of \u22124 or less, we consider the system as simple; if the sum results in a score of 4 or higher, we consider the system as complex; and if the sum of the scores results in a score between -4 and 4, we consider the system neither complex nor simple.\n\nA principal component analysis (PCA) of the network parameters using the function prcomp of the base package of R was performed. A phylomorphospace was generated with the phylomorphospace function of the phytools package47 also in R using the comprehensive and time-calibrated phylogeny40 (tree available in Supplementary Data\u00a011). Bird distribution in the phylomorphospace was grouped according to the variables primary lifestyle, foot skills, nest attendance and foot type, as explained above. We performed a non-phylogenetic PERMANOVA with 10,000 iterations on the resulting first five PC\u2019s to test whether network parameters discriminate between the different variables. To compare the correlation between the network parameter values and the variables of primary lifestyle, nest attendance and foot type, the character matrix with the variables (Supplementary Data\u00a04) was transformed into a Gower distance matrix and then converted into a Principal Coordinates Analysis (PCoA) using the function pcoa implemented in the R ape package48. The first two PC\u2019s were compared with the first two coordinate axes using a phylogenetic Generalised Least Squares regression49 using the gls function implemented in the R package nlme50.\n\nThe amount of phylogenetic signal was assessed for the network parameters by calculating the kappa statistic (K)51 under a Brownian motion model of evolution, using the phylosig function of the geiger package52 of the programming language R.\n\nThe network parameters were mapped as characters in the time-calibrated phylogenetic tree40 using the function FastAnc of the R phytools package46 for estimation of ancestral states using Maximum Likelihood. As the parameter of ND only assumes integer values, we mapped the transitions between states considering an equal rates (i.e., equal probability) model, using the function fitER of the R phytools package47.\n\nWe tested the possible shift of means and/or rates for different evolutionary regimes regarding the different primary lifestyles, nest attendance types and foot types, by fitting of the network parameters according to an Ornstein-Uhlenbeck model with multiple possible optima for a shift of means or with a single possible optimum for a single mean, and according to a Brownian motion model with multiple possible optima for a shift of rates or with a single possible optimum for a single rate. These OU/BM tests, that account for phylogeny, were performed with the functions mvOU (model\u2009=\u2009OUM and OU1, for multiple possible optima and a single possible optimum, respectively) and mvBM (model\u2009=\u2009BMM and BM1, for multiple possible optima and a single possible optimum, respectively) using the R mvMORPH package53. We compared the results of the different models (Supplementary Data\u00a08) by selecting the model with the lowest Akaike information criterion (AIC) value following54,55.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The authors declare that all data supporting the findings of this study are available within the paper and its supplementary files. Adjacency matrices are in an external Zenodo repository (https://doi.org/10.5281/zenodo.13924386)43. Source data for Fig.\u00a03 is in Supplementary Data\u00a05. Source data for Fig.\u00a04 is in Supplementary Data\u00a02 (sheet \u2018parameters\u2019) and 11.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "All R scripts used in this study are fully available in an external Zenodo repository (https://doi.org/10.5281/zenodo.13924420)56. The code for making Fig.\u00a03 is in the file \u2018R Script Fig.\u00a03.R\u2019. The code for making Fig.\u00a04 is in the file \u2018R Script Phylomorphospace.R\u2019.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Feduccia, A. Big bang\u2019 for tertiary birds? Trends Ecol. Evol. 18, 172\u2013176 (2003).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nCornellLab. Birds of the World. https://birdsoftheworld.org/bow/home (2024).\n\nRaikow, R. J. Locomotor System. In, Form and Function in Birds, Vol. 3 (eds. King, A. 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Zenodo https://doi.org/10.5281/zenodo.13924420 (2024).\n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "The authors thank Mar\u00eda Juliana Benitez Saldivar for her assistance with R, Gabriela Fontanarrosa and Nicol\u00e1s Mongiardino Koch for helpful discussions, and Scott Partridge for allowing us to use his bird illustrations in the figures. This work was partially supported by PIBAA 28720210101157 CONICET JC and ANPCyT PICT 2019-771 CPT.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas (CONICET), La Plata, Argentina\n\nJulieta Carril,\u00a0Ricardo S. De Mendoza\u00a0&\u00a0Claudio G. Barbeito\n\nLaboratorio de Histolog\u00eda y Embriolog\u00eda Descriptiva, Experimental y Comparada (LHYEDEC), Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, Av. 60 y 118, Buenos Aires, 1900, Argentina\n\nJulieta Carril,\u00a0Ricardo S. De Mendoza\u00a0&\u00a0Claudio G. Barbeito\n\nCentro de Investigaciones en Ciencias de la Tierra (CICTERRA), Universidad Nacional de C\u00f3rdoba-CONICET, Ing. Ismael Bordabehere y Av. Haya de la Torre, C\u00f3rdoba, 5016, Argentina\n\nFederico J. Degrange\u00a0&\u00a0Claudia P. Tambussi\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.C., R.S.D.M., F.J.D., C.G.B. and C.P.T. conceived the study and supervised the project equally. J.C. constructed the matrix data, with contributions from R.S.D.M.. R.S.D.M. performed the formal analysis, with contributions from J.C. and F.J.D.. J.C. and C.P.T. wrote the manuscript. R.S.D.M., F.J.D. and C.G.B. reviewed the manuscript. J.C. and R.S.D.M. edited the final manuscript.\n\nCorrespondence to\n Julieta Carril.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. 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network based on CNN near-field positioning by a full-duplex metasurface", + "journal": "Nature Communications", + "published": "28 November 2024", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Movie 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_MOESM3_ESM.mp4" + }, + { + "label": "Supplementary Movie 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_MOESM4_ESM.mp4" + }, + { + "label": "Supplementary Movie 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_MOESM5_ESM.mp4" + }, + { + "label": "Supplementary Movie 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_MOESM6_ESM.mp4" + }, + { + "label": "Supplementary Movie 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_MOESM7_ESM.mp4" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_MOESM8_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_MOESM9_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "/articles/s41467-024-54800-2#Sec17" + ], + "code": [], + "subject": [ + "Composites", + "Electrical and electronic engineering" + ], + "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4687795/v1.pdf?c=1732885590000", + "research_square_link": "https://www.researchsquare.com//article/rs-4687795/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-024-54800-2.pdf", + "preprint_posted": "01 Aug, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "With the revolution in technology and industry, the connectivity of electronic devices has gradually shifted from wired to wireless after long-time exploration. As a solution for power delivery, the non-contact power transfer holds promise charging for moving devices such as sensors, microcomputers, and robots, enabling battery-free sensing, processing, and communication. To reach the goal, we propose the adaptive wireless-powered network (AWPN) based on a full-duplex metasurface to realize a non-contact power supply for target tracking and wireless communications. The fabricated battery-free AWPN can obtain stable powers to perceive and compute the environmental data, which are then informed to the users by wireless communications. In particular, the proposed AWPN is good for moving devices, in which near-field positioning is achieved by the programmable metasurface combined with a convolutional neural network. AWPN can get more than 92% classification accuracy to provide precise positions of the moving targets for beam tracking. Thus, being adaptive and contactless, this AWPN will further propel the advancement of fields such as the Internet of Things (IoT), intelligent metasurface, and the robot industry.Physical sciences/Engineering/Electrical and electronic engineeringPhysical sciences/Materials science/Structural materials/Composites", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "DescriptionofAdditionalSupplementaryFiles.docxSIAdaptivewirelesspowerednetworkbasedonCNNnearfieldpositioningbyafullduplexmetasurface.docxSupplementaryMovie1.mp4Dynamic variation of DC output under two scenarios: fixed focusing and tracked focusingSupplementaryMovie2.mp4Simultaneous target positioning and tracking focusing under the \u2018X\u2019 trajectorySupplementaryMovie3.mp4Simultaneous target positioning and tracking focusing under the \u2018D\u2019 trajectorySupplementaryMovie4.mp4Simultaneous target positioning and tracking focusing under the \u2018U\u2019 trajectorySupplementaryMovie5.mp4Temperature warning and adaptive regulation during high- power wireless charging", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "With the improvement of industry, the connectivity of electronic devices gradually shift from wired to wireless. As a solution for power delivery, the non-contact power transfer holds promising ways to charge for moving terminals, enabling battery-free sensing, processing, and communication. Based on a dual-band metasurface, this study proposes an adaptive wireless-powered network (AWPN) to realize the simultaneous wireless localization and non-contact power supply. It first achieves localization with 3\u2009cm resolution on a single-input single-output (SISO) system, by combining space-time-coding (STC) and convolutional neural network (CNN). With precise position information, AWPN real-time aligns power beams to the terminals for stable energy transmission. Then, battery-free terminals enable to perceive the environmental data and uploads the results. From the measurement results, AWPN gets more than 98% CNN classification accuracy and can tolerate certain environmental changes. Thus, being adaptive and contactless, our study will propel the advancement in Internet of Things (IoT), intelligent metasurface, and the robot industry.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "As electronic devices become more efficient, according to Koomey\u2019s law1, a common belief has been established that future consumer electronics will require 1000 times less energy compared to their present counterparts. Besides, there are trillions of Internet of Things (IoT) devices deployed yearly. Such a large number urgently demands to develop a new charging network for these low-power devices. Considering that the current battery-powered methods are not long-lifetime and eco-friendly, the wireless power transfer (WPT) is a promising solution, which possesses the advantages of being contactless, compact, and controllable. The development of wireless power will spawn a myriad of new wireless applications, such as wireless-powered edge computing2, wireless-powered sensing3,4, and biomedical sciences5. Thus, it will become a fundamental building block for future wireless networks.\n\nAt present, electromagnetic inductive-type WPT has been widely deployed (e.g., wireless charging of smartphones), and gains a considerable market value. Its advantage lies in the convenience and non-contact. However, due to a lack of adaptive tunability, the transmission distance of conventional inductive-type WPT is short, and the devices under charging are needed to fix their locations for maximum transfer efficiency. This seems to weaken the benefits of contactless WPT. By contrast, the radiative-type WPT is an emerging and flexible solution, that can cover from the near field to the far field. To this end, there are numerous researches reported in academia and industry6,7,8. For instance, explored the microstrip patch array to focus the wireless power in Fresnel region9,10,11; carried out theoretical analyses and experimental validations for the near-field and far-field WPT12,13; and expanded from single device to multiple devices14,15. Further, to realize the point-to-point directional WPT, the phased array system has obtained widespread attentions16,17,18, due to its flexible beamforming capability. In fact, phased array opened up a wide imagination of WPT researches and applications. Subsequently, the new wireless communication architecture of simultaneous wireless information and power transfer (SWIPT)19,20 was reported, which provides new ideas of communication and power supply for IoT devices in future. Besides, the studies21,22 found that large-scale phased arrays are expected to break through long-distance and high-power wireless charging. However, the phased array requires complex feeding networks (e.g., phase shifters and power amplifiers), making them costly and bulky to widely apply. Hence the low-cost beamforming technique urgently is needed to be studied and developed.\n\nRecently, the rise of programmable metasurface (PMS) provides a promising solution for flexible beam focusing23,24,25. Developed from the passive metasurface, the PMS can dynamically switch the wavefront phase by loading electric-tunable devices, such as varactors and positive-intrinsic-negative (PIN) diodes26,27,28,29,30. It is a low-cost alternative to the expensive phase shifters. Although generally speaking, PMS performs a rough 1-bit or 2-bit quantization for the wavefront phase of electromagnetic (EM) wave31,32,33,34, and there is a gap in the efficiency of power focusing. However, this deficiency can be remedied by designing a larger aperture. In fact, the potentials of PMS have been widely appreciated and explored, such as wireless communication35,36,37, microwave imaging33,38,39, radar detection40,41,42, and vital signs monitor43,44. However, the majority of PMS applications were controlled in a manual manner, and they concentrated on the verification of pre-designed functions. But in terms of the WPT system, we are considering expanding the PMS to intelligent PMS, and realizing the adaptive beamforming by tracking the locations of terminals. Hence it is necessary to integrate the positioning function on PMS. For instance, introduced the compressive sensing to achieve the direction of arrival (DoA) estimation based on a single channel PMS40,45; combined the space-time-coding (STC) technique to estimate multiple targets46,47, etc. Yet, they are half duplex, and cannot estimate the distance of targets. Besides, the intelligent metasurface systems for automatic tracking of moving targets were presents32,48, relying on the fast development of computer vision technology. Yet this solution requires an additional vision sensor, whose accuracy was affected by light and weather.\n\nCurrently, the majority of current research on WPT remains confined to theoretical studies. Indeed, owing to the significant reduction in power consumption of electronic devices8,49, wireless power-based sensing, processing, and communication are ripe for exploration and implementation, thus fostering the further advancement of WPT. Consequently, we propose to construct an adaptive wireless-powered network (AWPN) based on PMS to power for terminal devices, enabling data sensing and processing. Then it uploads the perceived data to the user in a wireless manner, enabling a battery-free sensing system. Considering the movement of terminal devices, the dynamic power focusing based on target positions will become increasingly important.\n\nIn comparison to visual positioning, we prefer wireless positioning based on EM wave, since it can work around the clock, and the system is more integrated. Thus, we aim to design a dual-band metasurface to realize simultaneous target positioning and power focusing, which is innovative for metasurface research. Besides, a specially designed terminal device also plays a vital role. Consider that the rectification process from alternating current (AC) to direct current (DC) is a nonlinear process, which generates high-order harmonics. We utilize these harmonics as positioning signals fed back to the dual-band metasurfaces to achieve full-duplex localization. As the terminal device resides in the near field of the metasurface, it enables to estimate both direction and distance, and makes up the shortcomings of previous methods that cannot capture the distance32,50. Additionally, by combining the STC technique and convolutional neural network (CNN), this study realizes high-accuracy positioning on a single-input single-output (SISO) hardware architecture. It only requires to collect the training datasets and train CNN model in the initial stage, so that the CNN allows the quick near-field positioning (NFP), and there is no need for huge computing overhead. Therefore, the proposed AWPN is a multifunctional and highly integrated system that fully utilizes the advantages of the metasurface in full-duplex beam steering and sensing, advancing the evolution of PMS towards intelligent PMS.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "Based on a dual-band metasurface, we propose an AWPN for real-time wireless charging, enabling wireless-powered sensing, processing, and communication, as shown in Fig.\u00a01. In contrast to the conventional metasurface, the designed dual-band metasurface consists of two band meta-atoms for transmitting and receiving EM wave in full-duplex mode. They are a 16\u2009\u00d7\u200916 low-band array at 5.8\u2009GHz and an L-shaped high-band array at 11.6\u2009GHz, working for focusing wireless power and collecting NFP signals, simultaneously. This design can focus more attention to optimize low-band meta-atoms with low insertion loss and high phase accuracy, thereby improving the WPT efficiency. Besides, this L-shaped array is more like an add-on device that does not change the structure of the existing metasurface device. As a solution with strong compatibility, it has potential to be widely installed on single-function equipment to enrich their functions.\n\nBased on the dual-band metasurfaces, AWPN can track targets and dynamically focus wireless energy onto a moving receiving terminal in real-time. The receiving terminal rectifies the RF power into DC output to power sensors, microprocessors, communication modules, etc. Besides, it feeds back the harmonics to the metasurface for positioning.\n\nFor the receiving terminal, it efficiently harvests radio frequency (RF) power and rectifies them into DC output, thereby powering for electron devices. In contrast to the assumption that the terminals are stationary, we fully exploit the beam-scanning capabilities of the metasurface to enable charging for moving terminals. Therefore, the accurate NFP is more important. Consider that the Schottky diode used for rectification is a nonlinear device, and the process of rectification generates high-order harmonics, with the 2nd harmonic at 11.6\u2009GHz being particularly prominent. Therefore, we leverage it to function as the positioning signal, which is fed back to the metasurfaces for response.\n\nIn order to locate the receiving terminal, the high-band meta-atoms on the metasurface modulate this 11.6\u2009GHz positioning signal by STC technique, and the accompanying STC harmonics will be extracted and analyzed. STC harmnics bring multidimensional feature information, and enhance differentiation between different positions. Due to these feedback signals are modeled as spherical waves, making them possible to estimate both the direction and distance. The CNN model serves as the feature extractor for the NFP. The whole process only requires to extract the amplitudes of STC harmonics, and avoids the high computational complexity and expensive hardware of conventional methods.\n\nThe meta-atoms of the designed dual-band metasurface are shown in Fig.\u00a02a. They are 1-bit reflective coding elements. The big one is responsible for transmitting and focusing wireless power, while the small one is for receiving and modulating the feedback positioning signals. As can be seen, there are two metallic patches and a PIN diode at their top structures. Controlled by the DC bias voltages, the on and off states of this PIN diode correspond to two phase states of the 1-bit meta-atoms, respectively. The geometric parameters of these two meta-atoms are detailed in Supplementary Note\u00a01. The simulated phase responses of these two meta-atoms are shown in Fig.\u00a02b and Fig.\u00a02c. From the simulated results, their center frequencies are 5.8\u2009GHz and 11.6\u2009GHz respectively, at which the phase switching from 0\u00b0 to 180\u00b0 can be realized.\n\na Side views of two frequencies meta-atoms. b The phase responses under ON and OFF states of low-band meta-atoms, whose center frequency is 5.8\u2009GHz. c The phase responses under ON and OFF states of high-band meta-atoms, whose center frequency is 11.6\u2009GHz. d Calculated three different coding matrixes on metasurface for power focusing at (0, 0, 50)cm, (0, \u221210, 50)\u2009cm, and (\u221210, 0, 50)\u2009cm. e Simulated electric field distributions at z\u2009=\u200950\u2009cm, they present different focal spots.\n\nOne of the most exciting features of the PMSs is the excellent EM-wave manipulation enabled by adjusting the coding matrix. For the designed metasurfaces with 16\u2009\u00d7\u200916 meta-atoms, it enable flexible near-field focusing for power transmission. The corresponding coding matrixes can be derived numerically (see \u201cMethods\u201d for details). We simulate the E-field distributions under different coding matrixes. As shown in Fig.\u00a02d, three coding matrixes are calculated to generate focal spots at different positions. From the simulated results plotted in Fig.\u00a02e, there are three distinct focal spots appear in positions of (0, 0, 50)\u2009cm, (0, \u221210, 50)\u2009cm, and (\u221210, 0, 50)\u2009cm, which is consistent with the expected results. They have verified that the designed 16\u2009\u00d7\u200916 metasurface has the ability to power focusing, and can intelligently manipulate beams to the area of interest (AoI), which is vital for our adaptive power transmission.\n\nThe explosive view of the well-thought-out receiving terminal is illustrated in Fig.\u00a03a, which is a two-layer structure. The upper layer is a dual-port antenna designed for harvesting energy and radiating positioning signal, while the bottom layer converts the RF energy into DC voltage through a rectifier. These two layers are interconnected via coaxial connectors. Since this receiving terminal is battery-free and can realize environmental sensing, it can be called wireless-powered sensor. According to the previous reports51, temperature is a crucial index of battery safety during charging. Therefore, our proposed AWPN will illustrate to sense environmental temperature as an example. Then, the perceived data is sent to users by wireless communication, thereby demonstrating the ability of safety alerts for future high-power wireless charging. For instance, notify the transmitter to reduce transmission power when charging temperatures exceed a certain threshold, or increase transmission power to maintain a constant power supply.\n\na Explosive view of the receiving terminal, which is a two-layer structure. b Harmonic feedback rectifier, which rectifies a 5.8\u2009GHz high-power signal to DC output and generates an 11.6\u2009GHz feedback signal. c S-parameter of the designed antenna array. d Realized gain under two frequency bands. e Reflection coefficient and DC output of the rectifier versus input power. f The 2nd harmonic power and rectification efficiency of the rectifier versus input power.\n\nIn Fig.\u00a03b, the layout of the rectifier is depicted. As can be seen, after passing through an impedance matching network, the RF power at 5.8\u2009GHz is input into a Schottky diode (MA4E1317, MACOM, Inc.) for rectification. Accompanying the AC-to-DC conversion, this nonlinear process generates multiple high-order harmonic components, with the 2nd harmonic being prominent. It will be transmitted into the other port and re-radiated by the antenna for wireless positioning. Thus, different from the conventional rectifiers, our design is the harmonic feedback rectifier. The derivation of harmonic generation is detailed in Supplementary Note\u00a02.\n\nThe antenna array adopts a dual-band co-aperture design, thereby enhancing the system integration. The outer square loop is used to receive RF energy at 5.8\u2009GHz, while the inner dumbbell-shaped structure is utilized for radiating the 11.6\u2009GHz positioning signal. Under the bottom surface, this 4\u2009\u00d7\u20094 array is synthesized into two ports through two power combiners. The measured reflection coefficients of these two ports are shown in Fig.\u00a03c, which illustrates the good matching at their respective operating frequencies, ensuring efficient energy reception and radiation. Additionally, their maximum radiation gains are 10.3 dBi and 12.2 dBi, as depicted in Fig.\u00a03d.\n\nFor the design of the harmonic feedback rectifier, we aim to extract its 2nd harmonic while not obviously reducing the rectification efficiency. This requires a well-thought-out filter design that allows the 2nd harmonic to pass through with small insertion loss and reflects the fundamental component. The simulated reflection coefficient of the input port is presented in Fig.\u00a03e, which illustrates that the RF power can be efficiently rectified with slight reflection loss. When input power is enhanced to 14\u2009dBm, the maximum DC output is 17.7\u2009mW, as shown in the green curve. Besides, Fig.\u00a03f presents the measured results of the designed rectifier. It can be observed that the 61% maximum rectification efficiency from RF to DC is achieved when the input power is 14\u2009dBm. At this point, the generated 2nd harmonic power is close to \u221210\u2009dBm, which is high enough for positioning purposes. As the supplementary, the details of this receiving terminal are presented in Supplementary Note\u00a03.\n\nIn the designed receiving terminal, the sensor collects the environmental temperature, and the microprocessor processes the analog signal into digital signals. Then, the perceived results or feedback instructions are sent to users via a Bluetooth module for a closed-loop operation. Thus, the DC output from the rectifier simultaneously powers the sensor and Bluetooth. Since the measured result that the average power consumption of the sensor and Bluetooth does not exceed 7\u2009mW, the designed rectifier can cover the power requirements. Importantly, considering that the output voltage of the rectifier varies with input power, a low dropout regulator (LDO TPS63900, Texas Instruments, Inc.) with a wide input range is used to stabilize the output voltage at 3.3\u2009V. Besides, to observe the dynamic change of the input power, an analog-to-digital converter (ADC) module is used to record the real-time DC output. As an additional component, this ADC is powered through an external port.\n\nFor the smart AWPN, it is important to locate the receiving terminal. Here, our designed dual-band metasurface receives the feedback signal from the rectifier for realizing positioning. Rather than far-field plane waves, we prefer to model the feedback signal using near-field spherical waves, which enables to estimate both the distance and angles. The L-shaped array composed of 24 high-band meta-atoms plays a key role. Its array aperture is 275\u2009\u00d7\u2009275\u2009mm2. According to the definition of the near field52, r\u2009<\u20092D2/\u03bb, the near field of this L-shaped array is within 5.8\u2009m, which is large enough and ensures that the receiving terminal is located at the near field.\n\nThe traditional NFP methods based on multiple-input multiple-output (MIMO) arrays require cost RF channels to collect the signals, and the used 2-D super-resolution algorithms have high computational complexity. Therefore, it is a challenge and innovation that achieve NFP on the metasurface-based SISO systems. The STC technology provides a promising approach to simplify the hardware architecture. The harmonic generated during the STC are, to some extent, intended to be equivalent to traditional multi-channel data, thereby providing the potential for accurate positioning. For this purpose, the feedback signals will be STC modulated by the high-band meta-atoms, and then received by the same horn antenna of the metasurface.\n\nFor illustrating the near-field model and STC process, we first consider the 12 high-band meta-atoms along the y-axis. As shown in Fig.\u00a04a, in the spherical coordinate system, the position of the terminal can be expressed as p(\u03b8, \u03c6, r)\u2009=\u2009ru(\u03b8, \u03c6), where u(\u03b8, \u03c6)\u2009=\u2009[sin\u03b8cos\u03c6, sin\u03b8sin\u03c6, cos\u03b8]T is the unit direction vector. Then, the distance from the terminal to the nth meta-atom is given by\n\nwhere \\({{{{\\bf{q}}}}}_{n}^{y}\\) is the position vector of nth meta-atom, and ||\u22c5|| denotes the modulo operation. The phase difference between two adjacent meta-atoms can be expressed when a feedback signal is incident\n\nwhere k2 is the spatial wavenumber of the feedback signal at 11.6\u2009GHz. This phase difference is the function of \u03b8, \u03c6, and r, when the terminal is located near field. It demonstrates that different meta-atoms will experience varying arrival angles and propagation distances when receiving signals from the same terminal53, resulting in different propagation phases. If we consider the far field, it can be simplified as \u03b4y(\u03b8, \u03c6)=k2dsin\u03b8sin\u03c6, but cannot capture the distance information.\n\na Concept view of the NFP, where the incident signal is modeled as spherical waves. b STC matrix optimized by BPSO. c Harmonic distributions under three positions, and they show remarkable differences. d Constructed CNN model serves as a feature extractor. e Classification accuracy and loss of the CNN model.\n\nTo realize the target positioning on a SISO system, each meta-atom will periodically modulate the feedback signal. The used STC matrix is shown in Fig.\u00a04b, where its horizontal and vertical axes represent time-coding sequences and coding elements, respectively. (The optimization process of this STC matrix is presented in Supplementary Note\u00a04). In each sequence, it has 12 intervals and totally takes 1\u2009ms, which means the STC modulation frequency fp is 1\u2009KHz, and the duration of each interval is 1/(12\u2009\u00d7\u2009fp). Accordingly, the receiving signal R(\u03b8,\u03c6,r,t) modulated by this STC will be synthesized to the horn antenna, and it concludes many STC harmonics (see \u201cMethods\u201d for detailed derivation). Especially, each harmonic in value simultaneously depends on directions and distances of the receiving terminal, given by Rq(\u03b8,\u03c6,r,t), which provides the possibility for target localization. To illustrate, we numerically calculate the harmonics from -8th to 8th orders under three different coordinates at (0\u00b0, 0, 0.5\u2009m), (45\u00b0, 0, 0.5\u2009m) and (45\u00b0, 0, 0.7\u2009m). As shown in Fig.\u00a04c(i) and Fig.\u00a04c(ii), the influence of angels on the harmonic distribution is evident, which is consistent with previous research findings43,50. Especially, compared with Fig.\u00a04c(ii) and Fig.\u00a04c(iii), the distance also affects their amplitude distribution, which is attributed to the near-field advantages. Thus, the amplitude distribution corresponds to a specific coordinate, not just the directional angles, and we can utilize them as the feature information. In fact, these harmonics generated by STC modulation enhance differentiation between different positions, and the more harmonic orders, the better. In other words, STC harmonics bring multidimensional information for target positioning, enabling the CNN-based classification for high-accuracy NFP.\n\nBased on the unique feature information, the data-driven positioning can be realized. The CNN is a powerful tool for data analysis. It can be trained on large datasets to learn complex hierarchical representations of data for classification and clustering. The learned features can then be used for target positioning, where the CNN serves as the feature extractor. Compared with conventional analytical methods, CNN offers significant speed advantages. Once the network has been trained in the initial phase, subsequent data classification and matching can be performed rapidly with minimal additional computational resources, which is particularly crucial for real-time scenarios.\n\nHere, we divide the whole AoI into multiple grids, and the CNN model is used to classify the receiving terminal into the correct grids for positioning. Consider the power focusing capability of the designed 16\u00d716 metasurface, and the 1-dB fade of the focal spot is about a circle with a diameter of 3\u2009cm, as shown in Fig.\u00a02e. This dimension can be used as a reference for positioning accuracy. Thus, we divide the AoI into multiple 3\u2009\u00d7\u20093\u2009\u00d7\u20093\u2009cm3 grids, which ensures that the main power of the focal spot adequately covers each grid. In each grid, the harmonic amplitude distributions at different positions are marked by the same label. Then, a CNN model is established by training on these labeled datasets, where each input is associated with a corresponding class label. This network learns to map the raw input data to these class labels by adjusting its parameters.\n\nFor the data collections, we adopt a full-duplex SISO architecture, and collect time-domain data through a universal software radio peripheral (USRP). The system architecture diagram is detailed in Supplementary Note\u00a05. After fast fourier transformation (FFT) for the collected data, the harmonic components can be extracted in the spectrum by spectral peak searching. Here, we extract the harmonic amplitudes from \u2212Q to Q orders, and write them in vector form, given by\n\nwhere h(\u03b8,\u03c6,r) is a harmonic vector with the dimension of 2Q\u2009\u00d7\u20091, Rq(\u03b8,\u03c6,r) is the qth harmonic component of the receiving signal, and \\(\\left|\\cdot \\right|\\) denotes the magnitude of complex numbers. Considering the direct leakage between the receiving terminal and the horn antenna, the fundamental component at 11.6\u2009GHz is excluded. Besides, since the power of feedback signals is varying, the vectors h(\u03b8, \u03c6, r) are required to be normalized as \\(\\hat{{{{\\bf{h}}}}\\,}(\\theta,\\varphi,r)\\), which is rather important. Further, before the CNN training, convert this one-dimension vector to matrix form as\n\nwhere H is a matrix with the dimension of 2Q\u00d72Q, which functions as the input of CNN. In each divided grid, all the matrixes H are marked with the same label.\n\nFor verifying our proposed CNN-based NFP, we extract harmonics from \u22128 to 8 orders for numerical simulations. The whole AoI (x\u2208[\u22126, 12] cm, y\u2009\u2208\u2009[\u221212, 12]\u2009cm, and z\u2009\u2208\u2009[48, 63]\u2009cm) is divided into 240 grids with the dimension of 3\u2009\u00d7\u20093\u2009\u00d7\u20093\u2009cm3. The dataset is built with the step of 5\u2009mm, and a total of 51,840 positions were sampled. The constructed CNN is shown in Fig.\u00a04d, which is composed of 2 convolutional layers. The convolutional layer within the CNN captures local patterns by a 2\u2009\u00d7\u20092 convolutional kernel, which allows the network to learn complex relationships and dependencies within the harmonic amplitude distributions. The ReLU activation function introduces nonlinearity into the network, enabling it to learn and approximate complex functions that cannot be represented by linear models. Besides, for ensuring the network to capture both local and global features, pooling layers further reduce the spatial dimensions of the data. Finally, the output layer of this network converts extracted features into categorical predictions using softmax activation.\n\nThis CNN model is trained with the Adam optimizer with a learning rate of 1\u2009\u00d7\u200910\u22123 and a batch size of 200. The training processing is depicted in Fig.\u00a04e. As can be seen, the validation dataset reaches 97.8% classification accuracy after 500 epochs, which is highly accurate for our proposed AWPN and verifies the feasibility of the CNN model. Based on the trained CNN, the receiving terminal can be accurately classified into themselves grids, which will direct to compute corresponding coding matrix of the metasurface, thereby focusing the power to the center of their grids.\n\nWe implement experiments to verify the full-duplex power focusing and positioning. The experimental environment and setups are depicted in Fig.\u00a05a. As can be seen, the high-power RF power is first fed by a wide-band horn antenna, and then it will be focused on a spot by the designed dual-band metasurface. The receiving terminal is mounted on a scanning shelf for equivalent to its dynamic movement. The feedback signal from this terminal will be modulated and reflected into the horn antenna again for NFP. Thus, this horn antenna works in the transmitting and receiving states, simultaneously, by connecting with a duplexer. The system architecture diagram is detailed in Supplementary Note\u00a05. Besides, we place a heat source in the normal direction, and the receiving terminal continuously measures the environmental temperature during movement. The temperature data is then transmitted to users by Bluetooth, enabling safety monitoring and adaptive feedback adjustment.\n\na Photograph of the experimental setup. b Receiving RF power and DC output under fixed focus. c Comparison between tracking focus and fixed focus. d CDF of classification error at different transmitting powers. e Moving trajectories of the receiving terminal, which display the letters \u2018X\u2019, \u2018D\u2019, and \u2018U\u2019, respectively. f Statistical results of receiving power and DC output under different trajectories. g Temperature data collected by wireless-powered sensor.\n\nTo demonstrate the advantages of adaptive power focusing, the received RF power and DC output of the rectifier are compared under two scenarios: fixed focus and tracking focus. To this end, we fix the scanning shelf at z\u2009=\u200950\u2009cm and allow the terminal to move along the y-axis. First considering the fixed focus, when the focal spot is fixed at (0, 0, 50)\u2009cm and the transmitting power is 33\u2009dBm, the measured results are depicted in Fig.\u00a05b. The horizontal axis represents the offset distance from the terminal to focal spot, while the vertical axis shows the received RF power and DC output. It can be observed that as the receiving terminal deviates from the center, the received RF power and DC output of the rectifier decreases radidly. Especially, the voltage drops to 1.5\u2009V and the received power is reduced by 8.9\u2009dB when the terminal only offsets by 7\u2009cm. Besides, the 1-dB fade range shown in the blue background is about 3\u2009cm, in which the variation of DC output remains within 0.2\u2009V. This reduction is acceptable and reflects the reasonability of the 3\u2009cm classification resolution. Further, we extend the experimental range and put these two scenarios in the same figure for comparison, as shown in Fig.\u00a05c. Unlike the rapid drop of the dashed line, the tracking focus represented by the solid orange line can maintain high RF power over a larger range. Expecially, in certain areas, the improvement is more than 10\u2009dB. Correspondingly, the DC output keeps above 1.9\u2009V even though the terminal offsets by 20\u2009cm, as shown in solid blue line. In contrast, the fixed focus decreases to close to 0\u2009V after extending the experimental range, as seen in dashed blue line. Compared with Fig.\u00a05b and Fig.\u00a05c, although they both show a convex trend, tracking focus in Fig.\u00a05c receives higher power in a larger range. Thus, on the one hand, these results highlight the advantages of metasurface in flexible beam manipulation. On the other hand, it shows the potential of metasurface in adaptive WPT. It is urgent to develop the intelligent metasurface for simultaneous target positioning and power focusing. (The dynamic presentation is shown in Supplementary Movie\u00a01.).\n\nSimilar to the numerical simulation, the whole AoI is 18\u2009\u00d7\u200924\u2009\u00d7\u200915\u2009cm3, and is divided into 240 grids. we sample the harmonic amplitudes with 1\u2009cm steps, and establish the training dataset from 6480 different coordinates in total. They will be classified into 240 categories and applied to train the CNN model. The training dataset is built by automatic collection based on MATLAB scripts, and the whole process takes no more than 4\u2009h. After the CNN training, we collect the testing datasets under different transmitting powers to validate the accuracy of this model. The cumulative distribution function (CDF) of the classification error is plotted in Fig.\u00a05d. As can be seen, more than 92% of the samples are classified correctly, and at most 98% of them are classified correctly when the transmitting power is 30\u2009dBm. The existing errors can be ascribed to the abnormal fluctuations of data and the limited signal-to-noise ratios (SNRs). Importantly, the transmitting power does not obviously affect the classification accuracy, which indicates our proposed AWPN is different from the conventional positioning methods based on the received signal strength indicator (RSSI). We leverage the relative amplitude distribution of harmonics, but not the received harmonic strength. In fact, this is more suitable for our positioning requirement, because the power of the feedback signal is not constant.\n\nUsing the trained CNN model, we further demonstrate the adaptive power focusing based on the target localization. Since the constraints of the 2-D scanning shelf, this shelf is manually moved to three discrete distances, z\u2009=\u200950, 56, and 62\u2009cm. In each distance, the terminal moves along different trajectories, which display the letters \u201cX\u201d, \u201cD\u201d, and \u201cU\u201d, respectively, as can be seen in Fig.\u00a05e. These trajectories will be localized by the CNN-based NFP, which directs the laptop to calculate coding matrixes, enabling to maintain a high-power delivery. Subsequently, the terminal realizes the wireless-powered sensing and communication. The dynamic demonstrations are shown in Supplementary Movies\u00a02, 3, and 4. As can be seen, when the terminal deviates from the center of focal spots, the DC output obviously decreases. Meanwhile, the metasurface receives feedback signals lasting 2\u2009s and extracts their harmonic components by the FFT. It should be emphasized that this 2\u2009s sampling time is considered to obtain a better harmonic SNR and higher spectral resolution. Otherwise, increasing the transmitting power allows for faster target localization with the same accuracy, thereby enhancing the system efficiency. Based on the collected amplitudes distribution of harmonics, the CNN reclassifies the terminal into the correct grid. Then, the metasurface adjusts the coding matrix to focus the power on the center of the new grid, thereby enhancing the DC output again. Under the transmitting power of 33\u2009dBm, we count the received power for these three trajectories, and plot the statistical results in Fig.\u00a05f, where the horizontal axis marks the respective trajectories. As observed, attributed to the metasurface-based tracking focus, the received RF power along the same trajectory maintains relatively stable. By comparing these three trajectories, the received power shows obvious fluctuation on the X trajectory, which can be explained by the strong standing waves produced by the superposition of incident waves from the horn antenna and scattered waves from the metasurface. Besides, the received power decreases with increasing distance. The DC output consistently keeps above 2.8\u2009V, which is adequate for the minimum input of LDO. Based on the wireless transimitted power, the sensor is activated successfully and works normally. The perceived environmental temperatures are plotted in Fig.\u00a05g. As can be seen, since the U trajectory is closest to the heat source, it perceives the highest temperature. In contrast, the X-trajectory data is close to room temperature. From the presented results, the designed terminal successfully realizes wireless-powered sensing, processing, and communication.\n\nIt is worth emphasizing that the training datasets of the CNN was collected in real environments, accounting for multipath effects and environmental scattering. In practice applications, the terminal (such as the multifunctional robot) can automatically collect training datasets in real environments through trajectory planning. This approach is common in fingerprint-based positioning methods, where real-environment data better captures the complex features of signal propagation. Here, we investigate the impact of environment change on the classification accuracy of a trained CNN, and instruct a person to sit at different distances from the metasurface to simulate this change. The experimental details are shown in Supplementary Note\u00a06. The results indicate that our method can well tolerate environment variations within a certain range. However, when substantial environmental changes occur, it requires to re-collect the training datasets.\n\nFurthermore, to simulate scenarios of battery overheating during high-power wireless charging in the future, we dynamically adjust the temperature of the heat source. When the temperature exceeds a certain threshold, such as 50\u2009\u00b0C, the terminal realizes a safety warning. Subsequently, the AWPN adaptively adjusts the transmitting power to reduce the charging speed, thus safeguarding the battery equipment. Additionally, after the temperature decreases, the high-power signals are realigned towards the terminal. This dynamic presentation is shown in Supplementary Movie\u00a05. From this perspective, the proposed AWPN not only enables wireless-powered sensing and communication, but also will serve as safety warnings during high-power wireless charging in the future.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-54800-2/MediaObjects/41467_2024_54800_Fig5_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "we propose a novel AWPN to achieve sensing and communication in wireless charging. The superiority of this SISO system manifests in its ability to accurately locate the receiving terminal and adaptively focus the energy beam onto the terminal. Consequently, a stable power supply can be provided for moving terminals. To this end, we design a dual-band metasurface to realize simultaneous target positioning and beam focusing, which is the first attempt for intelligent PMS and obviously enhances the system integration. During operation of the AWPN, the 2nd harmonic of the rectifier is fully utilized as feedback signals for positioning. This 2nd harmonic is naturally generated during the rectification process, which is the passive behavior and does not require additional signal sources. Thus, unlike traditional MIMO array and camera-based positioning systems, this solution optimizes the hardware architecture and achieves full-duplex operation. Additionally, the STC harmonics serve as the feature information of terminal positions, and their amplitude distributions are extracted for training the CNN model. We only need to collect the training datasets and train CNN model in the initial stage, so that the CNN allows the quick classification during positioning, and there is no need for huge computing overhead. Besides, compared with traditional radar positioning, the advantages of our method are highlighted in Supplementary Note\u00a07.\n\nIn anticipation of the heat generation in future high-power wireless charging scenarios, we use temperature sensor as an example to illustrate the AWPN, and design a terminal to realize wireless-power sensing, processing, and communications. This terminal monitors the environmental temperature in real time and uploads the perceived data. When the environmental temperature exceeds a preset threshold, the transmitter adaptively adjusts the transmission power or deflects the beam spot away from the terminal, to achieve security warnings and protection. Moreover, experiments demonstrate that the proposed CNN-based NFP achieves a classification accuracy of over 98%. Based on the precise target positions, the metasurface adaptively adjusts its coding matrix to ensure efficient power transmission.\n\nIn the future, radiative WPT will offer significant conveniences, such as contactless and battery-free operation, greening of energy based on the solar power satellite station21,54,55, and remote energy supply. However, it currently faces a deficiency of low efficiency, which is a challenge that requires to be overcome for all researchers. For our proposed AWPN, for further enhancing the system efficiency, the promising solutions include utilizing high-bit meta-atoms, and designing larger-aperture metasurfaces and antennas. Indeed, the application of WPT also requires substantial exploration and development. In this paper, our contribution lies in exploring an innovative solution to achieve adaptive wireless charging, and realizing wireless-powered sensing, processing, and communication.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Consider a wideband horn antenna as a feed source and incident a 5.8\u2009GHz RF power toward the metasurface. The tangential E-field Et(xm, yn) of (m, n)th meta-atom can be expressed as\n\nwhere Amn and \u03d5mn are reflective amplitude and phase of (m, n)th meta-atom, E0(xm, yn) is tangential E-field incident from the feed source, k1 denotes the spatial wavenumber at 5.8\u2009GHz, and (xs, ys, zs) is the position coordinate of the feed source. To produce a focal spot at (xf, yf, zf), the reflective waves from each meta-atom should be in-phase at this spot. Thereby, \u03d5mn is given by\n\nSince the metasurface is 1-bit digital coding, the reflection phase \u03d5mn is required to be quantized by\n\nwhere \\({\\phi }_{{mn}}^{{\\mbox{q}}}\\) is the quantized phase, and \\(\\left\\lfloor \\cdot \\right\\rfloor\\) is the round down operator. Based on Eqs. (6) and (7), a 16\u00d716 coding matrix can be obtained.\n\nThe receiving signal modulated by y-axis meta-atoms is given by\n\nwhere f2\u2009=\u200911.6\u2009GHz, \\({W}_{n}^{{y}}\\) denotes the spatial response between nth meta-atom and horn antenna, and \\({\\varGamma }_{n}(t)\\) is the periodic time-coding sequence of nth meta-atom, written as\n\nwhere \\({\\varGamma }_{n}(t)\\) is divided into L intervals in a period, \\({\\varGamma }_{l}^{{n}}\\) denotes the reflection coefficient of l th interval, Ul (t) is a periodic pulse function with period Tp. In each cycle, Ul (t) is given by\n\nwhere \u03c4\u2009=\u2009Tp / L is the pulsewidth. Decompose Ul (t) into Fourier series as\n\nwhere fp\u2009=\u20091/Tp, and \\({c}_{l}^{q}\\) is the Fourier coefficients. Therefore, Eq. (8) can be rewritten as\n\nwhere\n\nFurthermore, consider all meta-atoms in the L-shaped array along both x and y axes, and the receiving signal can be expressed as R(\u03b8, \u03c6, r, t)=Rx(\u03b8, \u03c6, r, t)+Ry(\u03b8, \u03c6, r, t), which is consisted of numerous STC harmonics at frequencies of f2\u00b1fp, f2\u00b12fp, etc. For instance, the qth harmonic in the frequency domain can be extracted as\n\nEquation (14) shows that the amplitudes of harmonics are determined by positions of the receiving terminal.\n\nIn this paper, the full-wave simulations for the dual-band metasurface and antenna are performed by HFSS of Ansys Electronics Desktop 2022. For the rectifier design, to analyze the nonlinear characteristics of Schottky diodes, we employed Advanced Designed System 2020 for large-signal model simulation. Then, the EM and circuit co-simulation is performed using its Momentum plugin. The rest of the numerical computations and simulations are conducted using MATLAB 2022a.\n\nWe train the CNN model using the Deep Network Designer in MATLAB 2022a toolbox. The system is implemented on a computer with NVIDIA GeForce GTX 1650 GPU and 64\u2009GB RAM. The CNN model is trained with the Adam optimizer with a learning rate of 1\u2009\u00d7\u200910\u22123 and a batch size of 200. It contains 11 layers and 370.6\u2009k trainable parameters.\n\nFor obtaining the operation frequency of the antenna and rectifier, their S parameters are first measured by a vector network analyzer (Agilent N9918A), and the harmonic power from the rectifier is measured by the spectrum analyzer (Ceyear 4051F). In the step of dataset collection, we use MATLAB script to control the movement of the scanning shelf. At the same time, a universal software radio peripheral (USPR B210, ETTUS Research Corporation) is applied to data sampling. The whole process is automatic. The experimental equipment contains a signal generator, a power amplifier (PA), a duplexer, a wide-band horn antenna, a low noise amplifier, a mixer module, and a USRP. The 5.8\u2009GHz signal transmitted from the signal generator is enhanced to more than 2\u2009W by the PA. The duplexer ensures that the wide-band horn antenna works in full-duplex mode. Considering that the USRP operates at frequencies below 6\u2009GHz, we used an external mixer to down convert the 11.6\u2009GHz feedback signal to below 6\u2009GHz. The system architecture of the transceiver is detailed in Supplementary Note\u00a05. Besides, the perceived data sent by the terminal is received by another Bluetooth module on the laptop.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All other data are available from the corresponding authors on request. The experimental and simulated data of figures in the main text are provided in the Source Data file.\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Codes used in this work are available from the corresponding authors on request.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Koomey, J., Berard, S., Sanchez, M. & Wong, H. Implications of historical trends in the electrical efficiency of computing. IEEE Ann. Hist. Comput. 33, 46\u201354 (2011).\n\nArticle\u00a0\n MathSciNet\u00a0\n \n Google Scholar\u00a0\n \n\nJi, L. & Guo, S. 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Science 162, 857\u2013861 (1968).\n\nArticle\u00a0\n ADS\u00a0\n CAS\u00a0\n PubMed\u00a0\n \n Google Scholar\u00a0\n \n\nDownload references", + "section_image": [] + }, + { + "section_name": "Acknowledgements", + "section_text": "This work was supported by the National Natural Science Foundation of China under Grant 62288101, National Key Research and Development Program of China under Grant 2023YFB3811503, and the Key Research and Development Program of Shaanxi Province under Grant 2021TD-07.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: De Xiao Xia, Jia Qi Han, Hai Xia Liu, Yan Shi.\n\nKey Laboratory of High-Speed Circuit Design and EMC of Ministry of Education, School of Electronic Engineering, Xidian University, Xi\u2019an, China\n\nDe Xiao Xia,\u00a0Jia Qi Han,\u00a0Ya Jie Mu,\u00a0Lei Guan,\u00a0Xin Wang,\u00a0Xiang Jin Ma,\u00a0Li Hao Zhu,\u00a0Tian Guang Lv,\u00a0Hai Xia Liu,\u00a0Yan Shi\u00a0&\u00a0Long Li\n\nInstitute of Electromagnetic Space and the State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China\n\nTie Jun Cui\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nL.L. and T.J.C. suggested the designs, planned and supervised the work, in consultation with Y.S., D.X.X. conceived the idea, carried out the analytical modeling, numerical simulations, sample fabrication. J.Q.H., Y.J.M., L.G., X.W., and H.X.L. performed the data analysis and measurements. L.H.Z. and T.G.L. prepared the FPGA code and digital hardware. X.J.M. designed the rectifier circuit. All authors discussed the theoretical aspects and numerical simulations, interpreted the results, and reviewed the manuscript.\n\nCorrespondence to\n Long Li or Tie Jun Cui.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks Nguyen Minh Tran and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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Adaptive wireless-powered network based on CNN near-field positioning by a dual-band metasurface.\n Nat Commun 15, 10358 (2024). https://doi.org/10.1038/s41467-024-54800-2\n\nDownload citation\n\nReceived: 19 July 2024\n\nAccepted: 21 November 2024\n\nPublished: 28 November 2024\n\nVersion of record: 28 November 2024\n\nDOI: https://doi.org/10.1038/s41467-024-54800-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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environmental noise using a computer-generated model", + "pre_title": "Evaluation of fetal exposure to environmental noise using a computer-generated model", + "journal": "Nature Communications", + "published": "25 April 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_MOESM1_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_MOESM2_ESM.pdf" + }, + { + "label": "Supplementary Audio 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_MOESM3_ESM.mp3" + }, + { + "label": "Supplementary Audio 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_MOESM4_ESM.mp3" + }, + { + "label": "Supplementary Audio 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_MOESM5_ESM.mp3" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_MOESM6_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_MOESM7_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://zenodo.org/records/15052299" + ], + "code": [ + "https://github.com/optimuslib", + "https://zenodo.org/records/15039756", + "https://github.com/optimuslib/optimus/blob/main/notebooks/In%20utero%20sound%20transmission.ipynb" + ], + "subject": [ + "Computational science", + "Environmental impact", + "Risk factors" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-5397645/v1.pdf?c=1745665620000", + "research_square_link": "https://www.researchsquare.com//article/rs-5397645/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-58983-0.pdf", + "preprint_posted": "24 Nov, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Acoustic noise can have profound effects on wellbeing, impacting the health of the pregnant mother and the development of the fetus. Mounting evidence suggests neural memory traces are formed by auditory learning in utero. A better understanding of the fetal auditory environment is therefore critical to avoid exposure to damaging noise levels. Using anatomical data from MRI scans (N=3), we used a computational model to quantify the acoustic field inside the pregnant maternal abdomen. We obtained acoustic transfer characteristics across the human audio range and pressure maps in transverse planes passing through the uterus at 5 kHz, 10 kHz and 20 kHz, showcasing multiple scattering and modal patterns. Our calculations suggest that for all datasets, the sound transmitted in utero is attenuated by as little as 6 dB below 1 kHz, confirming results from animal studies that the maternal abdomen and pelvis do not shelter the fetus from external noise.Earth and environmental sciences/Environmental sciences/Environmental impactHealth sciences/Risk factorsPhysical sciences/Mathematics and computing/Computational scienceIn-utero acousticsFetal earFetal auditory systemFetal sound exposurePrenatal sound exposureNoise pollutionPregnancyHearing damageComputational Acoustics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryInformationMathematicalformulation.pdfsoundscapesfilteredfirGS339uterusbarycentrehighatt.mp3Filtered soundscape providing on dataset GS339soundscapesfilteredfirGS357uterusbarycentrehighatt.mp3Filtered soundscape providing on dataset GS357referencesoundscape.mp3Reference soundscape", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Acoustic noise can have profound effects on wellbeing, impacting the health of pregnant women and their fetus. Mounting evidence suggests neural memory traces are formed by auditory learning in utero. A better understanding of the fetal auditory environment is therefore critical to avoid exposure to damaging noise levels. Using anatomical data from MRI scans of pregnant patients (N=4) from 24 weeks of gestation, we develop a computational model to quantify fetal exposure to acoustic field. We obtain acoustic transfer characteristics across the human audio range and pressure maps in transverse planes passing through the uterus at 5\u2009kHz, 10\u2009kHz and 20\u2009kHz, showcasing multiple scattering and modal patterns. Our calculations show that the sound transmitted in utero is attenuated by as little as 6\u2009dB below 1\u2009kHz, confirming results from animal studies that the maternal abdomen and pelvis do not shelter the fetus from external noise.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "We are the first humans to globally expose with our activities the next generations to major climate changes and environmental pollution before they are born1. The main types of pollution are usually classified by environment and include air pollution, water pollution and land pollution. Noise pollution resulting from environmental noise (road traffic, railway and aircraft noise, wind turbine noise, occupational and leisure noise) has been identified as a growing concern for the long-term impacts on physical and mental human health by both the World Health Organisation2 and the European Union3. Over the last two decades, observational and experimental studies have shown that noise exposure increases the occurrence of hypertension and cardiovascular disease, disturbs sleep and causes daytime sleepiness and affects patient outcomes and staff performance in hospitals4. Epidemiological data have also shown that noise exposure in early life impairs cognitive performance and motor function in children and preadolescents5,6. The effects of occupational noise, which is often in the range of between 80 and 100\u2009dB, on hearing loss7 and hypertension8 are now well established. A model for assessing traffic noise exposure in the London area estimated that the equivalent continuous traffic noise level over the period 07:00\u201323:00\u2009h, was between 55 and 83\u2009dB(A)9. The association between exposure to road traffic noise and ischemic heart disease has also been well established10,11. Whilst increased exposure to air pollutants and particulate emissions is a likely contributor to this association, a recent compendium has provided an overview of epidemiological research on the effects of transportation noise on cardiovascular disease and associated risk factors12. Based on the outcomes of experimental and clinical studies reviewed by M\u00fcnzel et al.12, mechanistic insights are provided, with the potential effects of noise on vascular dysfunction, oxidative stress, and inflammation in both humans and animals. A recent report from the European Environment Agency has shown that long-term exposure to environmental noise is estimated to cause 12,000 premature deaths and contribute to 48,000 new cases of ischemic heart disease per year in the European territory13.\n\nOver the last two decades, there has been mounting epidemiological and basic science evidence showing the impact of climate change14 and air pollution15 on pregnancy outcomes. Ambient black carbon particles and microplastics have been identified in the intracellular compartment of human placentas16,17 and recently in fetal organs18, suggesting a direct fetal exposure to these pollutants before birth. However, data on the effects of environmental noise on pregnancy, birth and reproductive outcomes are limited19,20,21,22. Road traffic noise has been associated with maternal weight gain during and after the pregnancy23, whereas railway noise may be associated with gestational diabetes mellitus24. Regarding the direct effect of environmental noise on fetal development, there is no evidence showing an increased risk of congenital malformations and the evidence for the association between road noise and fetal growth is uncertain with only some studies showing a moderate effect. A recent study has shown that occupational noise exposure during pregnancy to 80\u221285\u2009dB(A) of annual average 8-h occupational noise level in 5-year intervals is also associated with an increased risk of all pregnancy-related hypertension whereas exposure to >85\u2009dB(A) of noise is, as with railway noise, also associated with an increased risk of gestational diabetes mellitus25. A Swedish nationwide cohort study has shown an association between occupational noise during pregnancy and hearing dysfunction in children26. The association was strongest for mothers who worked full time during pregnancy and were exposed to >85\u2009dB(A) equivalent continuous noise level over an 8-hour period. These data suggest a direct effect of occupational noise exposure on the human fetus. The main concern is during the third trimester of pregnancy when the fetal brain structural and functional changes occur rapidly and are shaped by sensory inputs and endogenous neural activity with a direct impact on speech processing before birth27,28. A review of the known effects of environment and occupational noise during pregnancy on perinatal and maternal outcome nevertheless concluded that further studies are required so that the effects of both occupation and environmental noise exposure on obstetric patients may be underpinned22.\n\nUnlike fetal exposure to air or water pollutants which can be directly evaluated by sampling tissues and body fluids, there are limited in vitro and in vivo models to study human fetal exposure to environmental noise. In vivo experiments in sheep and goats using hydrophone recordings have indicated that intra-uterine noise is predominantly low-frequency29 and exposure to intense broadband noise altered the fetal auditory brain stem response and damaged cochlea hair cells30. These experimental data are limited by the quality of recording technology and access to computer models enabling the translation of animal data into information about humans. Using modern acquisition systems and calibrated instrumentation to measure the in-utero acoustic transfer characteristics on pregnant ewes, we found that frequency content above 10\u2009kHz is transmitted into the amniotic sac, and that some frequencies are attenuated by as little as 3\u2009dB31. However, translating experimental data obtained on ovine models into humans remains challenging due to fundamental anatomical differences between both species. Furthermore, the physiologies of the respective uterine environments differ. In vivo measurement of the sound field in humans presents ethical challenges. Another consideration for moving beyond in vivo experiments includes the fact that, using acoustic instrumentation, field quantities can only be monitored at a very limited number of physical locations.\n\nTo completely map an in-utero sound field in 3D would require multiple in vivo measurements beyond what is physically practicable. It is therefore desirable to seek solutions to this problem beyond in vivo measurement by attempting to predict the physical propagation of acoustic waves inside the pregnant woman. In this work, we aim to substantiate the extent of in-utero sound transmission by using a computational model for studying fetal exposure to external sound sources, including environmental, leisure, and occupational noise. Based on prior in vivo work on sheep29, we hypothesize that the maternal abdomen and other anatomical groups do not acoustically isolate the developing fetus from the external sonic environment. Furthermore, we anticipate that as the excitation frequency increases across the human audio range and the wavelengths in tissue are of the order or less than the anatomical dimensions, a complex acoustic in-utero environment materializes where modal behavior and multiple scattering are observed.\n\nWe imported anatomical data obtained from MRI scans on selected pregnant women at specific stages of gestation to predict the in-utero acoustic field as a function of external acoustic excitation throughout the audio range (20\u2009Hz\u201320\u2009kHz). Using mathematical formulations based on the boundary element method (BEM)32,33,34 as implemented in the open-source OptimUS Python library35 we predict the sound pressure level (SPL) throughout the volume of the uterus for a unit amplitude plane wave normally incident on the maternal abdomen. The employed BEM formulation features the capability of producing accurate results in scenarios where interfaces between two media feature a large acoustic impedance contrast, defined as the product of the speed of sound with the density (such as an air/soft tissue interface)36. Furthermore, our numerical scheme makes it possible to carry out calculations in cases where the dimensions of the computational domain are large relative to the acoustic wavelengths involved, as is the case for in-utero sound transmission towards the higher end of the audio range. OptimUS was validated against ten different numerical modeling techniques for acoustic propagation prediction in the context of a transcranial ultrasound computational benchmarking exercise, including the finite-difference time-domain method, angular spectrum method, pseudospectral method, and spectral-element method37.", + "section_image": [] + }, + { + "section_name": "Results", + "section_text": "We acquired MRI data from 4 singleton pregnancies. The datasets are referred to as Subject 1, Subject 2, Subject 3, and Subject 4. The gestational age for each dataset was as follows:\n\nSubject 1: 25 weeks and 1 day\n\nSubject 2: 32 weeks and 1 day\n\nSubject 3: 36 weeks and 2 days\n\nSubject 4: 37 weeks and 3 days.\n\nGiven that the wavelength in soft tissue at 20\u2009kHz is approximately 75\u2009mm, we assumed that the resolution of the MRI scans (0.74\u2009\u00d7\u20090.74\u2009mm) as well as the transformation of the raw data via smoothing algorithms will not generate significant uncertainties in the SPL predictions at the frequencies of interest. To produce a realistic model of in utero sound propagation for compressional waves, we focussed on the critical tissue paths resulting from the abdominal region, i.e., the uterus, and the spine. The uterine region comprises the uterine wall, the fetus and the presence of amniotic fluid. We considered the uterus to be composed of either amniotic fluid (low attenuation case) or muscle tissue (high attenuation case) rather than providing a model with detailed attenuation parameters. These scenarios served as a worst-case and best-case scenario, respectively, for in-utero acoustic transmission. We also considered the presence of the maternal spine. Whilst its diameter is small relative to the wavelengths of interest, it features significant acoustic contrast with soft tissue. We considered the regions of the abdominal section, not including the maternal spine or the uterus to be filled with generic soft tissue. The meshes of the anatomical domains corresponding to the abdomen, the uterus, and the spine are shown in Fig.\u00a01A\u2013D, for Subjects 1, 2, 3, and 4, respectively.\n\nThis figure shows the surface boundaries of the three anatomical regions considered for datasets used in computational meshes: A Subject 1, B Subject 2, C Subject 3, and D Subject 4. The anatomical regions are the maternal abdomen, the spine and the uterus.\n\nThe soft tissue and bone regions are treated as piecewise homogeneous acoustic domains. Whilst the speed of sound of compressional waves in soft tissue has been characterized at audio range frequencies, there is limited information on the attenuation coefficient of compressional waves at these frequencies. Thus, the attenuation coefficient for the tissue groups of interest was estimated from viscoelastic measurements on ex vivo tissue. In an infinite viscoelastic material, the speed of sound of longitudinal waves may be expressed as:\n\nwhere E is Young\u2019s modulus, tan\u2061\u03b4 is the loss tangent, \u03c1 is the density and i is the imaginary unit. In the absence of shear waves, we approximate Young\u2019s modulus to the bulk modulus K, which is given by:\n\nwhere c0 is the equilibrium speed of sound in the medium. Equation (1) then becomes:\n\nThe complex wave number is:\n\nHence, the attenuation coefficient\u03b1 is given by:\n\nIt should be noted that since \u03b4 is small, the wavenumber may be approximated by:\n\nValues for E(1+itan\u2061\u03b4) can be experimentally derived in vitro for muscle, between 40\u2009Hz and 120\u2009Hz38. Whilst the trend is somewhat linear within this frequency range, extrapolating throughout the audio range would yield unphysical values at higher frequencies. We use tan\u2061\u03b4=0.3, which corresponds to the value measured in human muscle at 100\u2009Hz38. For amniotic fluid, we use the properties of water with an attenuation coefficient obtained at 37\u2009\u00b0C. For soft tissue and bone, we assume a linear power absorption law with frequency. For the amniotic fluid, we assume that the medium attenuation is frequency-squared dependent, as is the case for water. As a result of the variability and patient specificity of the speed of compressional waves and density for soft tissue and bone, we use values consistent with those in the literature39,40.\n\nThe open-source Python library OptimUS v0.2.135 was used to simulate sound pressure levels in the entire computational domain and in 12th octave bands between 20\u2009Hz and 20\u2009kHz resulting in a total of 128 frequencies. The simulations in the present study were performed on a desktop machine (Dell Precision 32 core, 512\u2009GB RAM). Hierarchical matrix compression techniques41 and dedicated preconditioners32 significantly reduce the memory footprint and increase the convergence rate of iterative solvers. Acoustic transmission problems across high-contrast media can be efficiently and accurately solved for high ka scenarios36, where k is the wavenumber and a the dimension of the scatterer. This product is of significance in acoustics as it represents a dimensionless quantity that relates the wavelength to the physical dimension of the domain. A distinct advantage of the BEM is that it suffers only minimal numerical dispersion and pollution42 effects. Numerical dispersion arises in finite-difference time domain schemes as well as k-space pseudospectral methods when the phase velocity of numerical wave modes differs from its true value by an amount varying with the wavelength, direction of propagation in the grid, and grid discretisation43. As a result of this artifact, propagating numerical waves accumulate delay or phase errors that can lead to nonphysical results. Numerical pollution effects occur when, as k\u2192\u221e, the total number of degrees of freedom required to maintain computational accuracy grows faster than kn, where n is the dimension of the physical domain in which the problem is formulated42. Another advantage of the BEM is that domain truncation effects are not a concern due to the imposition of the Sommerfeld radiation condition at infinity. Acoustic pressures at degrees of freedom on the surface meshes are initially obtained and field SPLs were inferred using the appropriate potential operators32 and the use of triangular surface meshes avoids unwanted staircasing effects.\n\nThe frequency response in 12th octave bands between 20\u2009Hz and 20\u2009kHz was calculated, using 1000\u2009Hz as the reference middle frequency. Instead of focusing on locations inside the uterus specific to the fetus\u2019 morphology (e.g., ears or head), we opted to evaluate the acoustic pressures throughout the whole uterus. Indeed, the fetus is not static inside the womb throughout the gestational period. Whilst most fetuses are in the head-down position, they may be in breech, or transverse position. Furthermore, general movement and activity of the mother which includes pose change and respiration will also result in the fetus being displaced within the womb. We require a metric that will provide a spatial average of acoustic pressure quantities inside the uterus. If we were to consider the complex acoustic pressure and produce a spatial average of this quantity, we may be underestimating the transmission of external sound sources, due to destructive interferences owing to the inclusion of phase information. We therefore instead consider the metric described in Section 3.7 of ISO 10052:202144 known as the impact SPL. This is effectively obtained from a spatial root mean square (RMS) of the pressure magnitudes, where we used calculated acoustic pressure values along a 3D Cartesian grid of points inside the uterus. The impact SPL is closely related to the l2-norm of the acoustic pressure in the uterus. It is obtained, in dB scale, as follows:\n\nwhere N is the total number of grid points considered in the uterus, which is discussed in the Methods Section, and pi represents the spatial component of the acoustic pressure at the ith location.\n\nThe RMS metric can be interpreted as the average noise exposure level of the fetus. However, depending on the positioning, the fetus may be exposed to local peaks due to modal acoustics in the abdomen. Hence, in addition to Luterus,RMS, the l\u221e-norm was also evaluated, which is effectively the maximum value of the acoustic pressure magnitude inside the uterus evaluated across the sample points. In dB scale, this quantity is given by:\n\nFinally, the acoustic pressure at the barycentre of the uterus was calculated, also as a function of frequency throughout the human audio range in 12th octave bands. This provides a point measurement for which magnitude and phase information will be used to derive the filters used for convolution with audio signals described in the Methods Section. The frequency responses for these three pressure quantities are displayed for the four datasets in Fig.\u00a02A\u2212L.\n\nFrequency response plots of the sound pressure level (SPL) inside the womb were obtained for a unit amplitude plane wave traveling towards the front of the body, in the negative x direction. Such a plane wave is described mathematically by the real part of e\u2212i(\u03c9t\u2212kx) where k is the wave number in air and \u03c9 is the angular frequency. Three metrics of the sound pressure level inside the uterus are plotted for datasets associated with Subjects 1, 2, 3 and 4. A, D, G and J correspond to the SPL resulting from the spatial RMS of the acoustic pressure magnitude inside the uterus; B, E, H and K describe the SPL associated with the l\u221e-norm, effectively corresponding to the maximum pressure magnitude at the sampled points; C, F, I and L represent the SPL resulting from the acoustic pressure magnitude at the barycentre of the uterus. Uterus points within a solid angle of 0.5 steradian from the surface of the mesh were discarded in the analysis as the BEM can overestimate field potentials close to a surface. The field potential evaluation points for subjects 1, 2, 3, and 4 are displayed below in blue, magenta, cyan, and green, respectively.\n\nThe plots in Fig.\u00a02A\u2212L exhibit a range of common features. It can first be noted that between 20\u2009Hz and 1\u2009kHz, the attenuation in the RMS and barycentre calculations is within \u22126\u2009dB of the amplitude of the incident wave, indicating that the systems under consideration exhibit a quasi-flat frequency response within this passband for compressional waves. Furthermore, additional calculations indicate that this extends to infrasound frequencies, i.e., below 20\u2009Hz down to 0\u2009Hz. 1\u2009kHz falls around the midrange of human hearing and is just below the fundamental frequency of a B5 on a musical instrument (987.77\u2009Hz). On a guitar in standard tuning, this corresponds to the 19th fret on the high E string and is just one semitone below the soprano high C, C6 (1046.502\u2009Hz)45. Hence, for the transmission of compressional waves in utero, these simulations suggest that the developing fetus is exposed to noises that are virtually unattenuated below 1\u2009kHz, regardless of the acoustic pressure quantity investigated (RMS, l\u221e-norm or sampled at a specified point) and the acoustic attenuation coefficient considered for the uterus. This frequency range encompasses much of the human speech spectrum (~300\u20133000\u2009Hz)46,47,48 as well as musical sounds. Low-frequency noises, such as those encountered in urban environments and in occupational noise settings are likely to be fully transmitted. This will include portions of the spectrum of noise sources such as road vehicles, aircraft, industrial machinery, artillery and mining explosions, as well as air movement machinery such as wind turbines, compressors, and ventilation or air-conditioning units49.\n\nDespite the common traits shown in all four datasets across the acoustic pressure quantities investigated, there exist important distinctions. The SPL of the spatial RMS of the acoustic pressure magnitude will tend to overestimate the transmitted acoustic pressure, as it is effectively the result of a spatial root mean square of the pressure magnitude at designated regularly spaced locations across the uterus. This quantity is nevertheless useful for assessing the potential for resonant behavior within this region, which is visible in Fig.\u00a02A, D, G, J, in the form of local maxima at frequencies above 3\u2009kHz, in the case of lower attenuation inside the uterus. This confirms the results of prior in vivo studies29,31,50 as well as an experimental study involving acoustic transmission into a non-invasive assessment of acoustic fields acting on the fetus, which employed a soft capsule filled with liquid51, and which showed that transmission of waves up to 1\u2009kHz was unaffected by the configuration. We note that modal behavior associated with the dataset at the earliest stage of gestation considered in this study (Subject 1\u201325 weeks and 1 day) occurs above 7\u2009kHz in the low-attenuation case (see Fig.\u00a02A). For the dataset with the latest gestational age considered in this paper (Subject 4\u201337 weeks and 3 days), this occurs above 6\u2009kHz, therefore at a comparatively lower frequency. Broadly, it is expected that smaller anatomical dimensions will lead to resonances occurring at higher frequencies. Furthermore, at frequencies above 3\u2009kHz, in the case of the uterus featuring a lower attenuation coefficient, it can be noted that the magnitude of the transmitted wave can at certain frequencies exceed that of the incident wave, effectively amplifying the signal due to reflections and acoustic modes. This is the case for Subject 2 and Subject 3 datasets in the low-attenuation scenario in the uterus and in the case of the acoustic pressure being sampled at the barycentre of the uterus, as shown in Figs.\u00a02C, 4C. Numerical experiments on spheres scattered by plane waves using our model also display these findings35 and have been validated with the known analytical solution52. For the calculations employing the higher attenuation coefficient in the uterus in Fig.\u00a02A, D, G, J, the resonances are dampened, as expected, but the transmission remains within 15\u2009dB of the incident wave across the human audio range in datasets associated with Subjects 2 and 3 when considering the acoustic pressure magnitude sampled at the uterus barycentre. These calculations of acoustic quantities at a specific point provide a representation of local effects inside the uterus. The l\u221e-norm plotted in Fig.\u00a02B, E, H represents worst-case scenarios, whereby the maximum SPL transmitted inside the uterus is plotted as a function of frequencies throughout the human audio range. It should be noted that the locations at which these maxima occur will vary with frequency. We note that for all datasets, in the cases of both low and high acoustic attenuation inside the uterus, the SPL associated with the l\u221e-norm is virtually always greater than 0\u2009dB. At the midrange frequency of 1\u2009kHz, we note that the transmitted sound pressure level is 9\u2009dB above that of the incident wave for datasets associated with Subjects 2 and 3, 7\u2009dB above for Subject 4, and 6\u2009dB for Subject 1. This is due to multiple reflections which occur inside the maternal abdomen and other anatomical groups, and which constructively combine at specific locations to amplify the acoustic pressure magnitude associated with the incident wave.\n\nTo contrast this data with that obtained from experiments on ovine models31, we note that the simulations in this paper correspond to a free field environment, i.e., in an unbounded domain where the Sommerfeld radiation condition at infinity applies. The experiments on ovine models took place in an operating theater31, which included a highly reverberant environment, therefore providing an overestimate of the incident acoustic field and with the measured transfer characteristics including the room impulse response. This, therefore, resulted in a low frequency response below 0\u2009dB. Otherwise, we observe similar trends in terms of the decrease in the transfer characteristics at frequencies above 1\u2009kHz.\n\nFigures\u00a03, 4, 5, and 6 show the SPL transmitted in utero at frequencies of 5, 10, and 20\u2009kHz, for a unit amplitude plane wave incident onto the maternal abdomen of Subjects 1, 2, 3 and 4, respectively. The plane of visualization is the transverse plane at the midpoint of the height of the uterus along the Cartesian z-axis. For each dataset, two different acoustic attenuation coefficients are used for the uterus, as described in Table\u00a01: that of amniotic fluid in the low attenuation case and that of muscle tissue in the high attenuation case.\n\nSPL inside all anatomical regions for an incident unit amplitude plane wave traveling in the negative x direction. The acoustic attenuation coefficient in the uterus is that of amniotic fluid in A\u2212F and that of muscle tissue in G\u2009\u2212\u2009L. 3D maps of the SPL re 1\u2009Pa are shown in A\u2212C and G\u2212I. D\u2212F and J\u2212L show a slice of the SPL re 1\u2009Pa in the transverse plane passing through the barycentre of the uterus. Anatomical groups and contours are labeled in A and D, respectively.\n\nSPL inside all anatomical regions for an incident unit amplitude plane wave traveling in the negative x direction. The acoustic attenuation coefficient in the uterus is that of amniotic fluid in (A\u2212F) and that of muscle tissue in (G\u2013L). 3D maps of the SPL re 1\u2009Pa are shown in (A\u2013C) and (G\u2013I). D\u2013F and J\u2013L show a slice of the SPL re 1\u2009Pa in the transverse plane passing through the barycentre of the uterus. Anatomical groups and contours are labeled in (A) and (D), respectively.\n\nSPL inside all anatomical regions for an incident unit amplitude plane wave traveling in the negative x direction. The acoustic attenuation coefficient in the uterus is that of amniotic fluid in A\u2212F and that of muscle tissue in G\u2009\u2212\u2009L. 3D maps of the SPL re 1\u2009Pa are shown in A\u2212C and G\u2212I. D\u2212F and J\u2212L show a slice of the SPL re 1\u2009Pa in the transverse plane passing through the barycentre of the uterus. Anatomical groups and contours are labeled in (A) and (D), respectively.\n\nSPL inside all anatomical regions for an incident unit amplitude plane wave traveling in the negative x direction. The acoustic attenuation coefficient in the uterus is that of amniotic fluid in (A\u2212F) and that of muscle tissue in (G\u2212L). 3D maps of the SPL re 1\u2009Pa are shown in (A\u2212C) and (G\u2212I). D\u2212F and J\u2212L show a slice of the SPL re 1\u2009Pa in the transverse plane passing through the barycentre of the uterus. Anatomical groups and contours are labeled in (A) and (D), respectively.\n\nIn Figs.\u00a03\u20136, we note that the incident plane wave traveling along the negative x direction is reflected at the air/soft tissue interface at the abdomen and that the incident wave and scattered waves interact constructively and destructively with one another, generating interference patterns. We note the presence of a shadow zone behind the lower back area. These maps allow for the intricacies and complexities of the acoustic pressure fields to be appreciated. Indeed, whilst the data in Fig.\u00a02 demonstrate the extent of in utero sound transmission, the pressure maps in Fig.\u00a03\u20136 establish the increase in modal and standing wave patterns at frequencies above 5\u2009kHz, where the wavelength in soft tissue is around 30\u2009cm, which is of the order of the abdominal region. In particular, modal behavior inside the uterus is observed in Fig.\u00a03E, K, at 10\u2009kHz and Fig.\u00a05D, J, at 5\u2009kHz. Also, we note the presence of an interference pattern in the uterus of Subject 4 in Fig.\u00a04F, L in the lower acoustic attenuation case.\n\nWith a view of providing an impression of in-utero acoustic transmission, a reference soundscape was generated from a range of audio signals that feature, in chronological sequence:\n\nA London Underground train leaving and arriving at a station53\n\nA segment of an instrumental ambient rock music composition54\n\nAmbient crow noise obtained from the Louvre museum53\n\nCrowd applause53.\n\nA causal, linear and time-invariant filter was obtained as outlined in the Methods section based on in utero calculations on datasets associated with Subjects 2 and 3, for the pressure at the barycentre of the uterus using the attenuation coefficient of uterine tissue (high attenuation case). The reference soundscape was convolved with this filter to yield an impression of in utero sound transmission. The reference and filtered soundscape audio filenames are:\n\nReference unfiltered soundscape: Supplementary_Audio_1.MP3\n\nSubject 2: Supplementary_Audio_2.MP3\n\nSubject 3: Supplementary_Audio_3.MP3\n\nTo appreciate the subtitles introduced by the filtering, it is recommended that the soundscapes be listened to on good-quality headphones and/or a high-fidelity sound reproduction system.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_Fig6_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Using a computational method based on state-of-the-art BEM formulations, we found that the human pregnant abdomen permits significant spectral content through to the uterus and that content below 1\u2009kHz, is attenuated by as little as 6\u2009dB. This finding was consistent for all datasets and acoustic pressure metrics evaluated and is in agreement with in vivo data obtained in prior studies29,31 on pregnant ovine models. Our study also shows how detailed acoustic pressure maps for external sound sources can be displayed, showcasing the complexities of the fetal auditory environment.\n\nOur methodology made some simplifications in the design of our mathematical model, which may impact the final results. Indeed, not all anatomical groups have been considered as we have constrained our analyses to include only the maternal abdomen, the uterus and the maternal spine. Given that the wavelength in soft tissue does not fall below 7\u2009cm at 20\u2009kHz, finer anatomical detail is unlikely to produce additional crucial information for the evaluation of sound fields in utero. In addition, MRI scans were focused on obtaining imaging from the uterus, placenta, and fetus rather than the whole maternal abdomen, thus the upper part of the maternal abdomen was not included in the acoustic propagation path for our simulations. Inclusion of the upper abdomen would have required moving the patient\u2019s position to optimize data acquisition with additional time spent undergoing imaging which was not possible during one imaging session. However, the truncations along the two transverse planes may not have an impact on the predicted acoustics pressures as the anatomical regions above the uterus is made mainly of the lungs, which are air-filled, and which will reflect acoustic waves in a manner not unlike the soft tissue/air interface in the upper transverse plane truncation in our computational mesh. Similarly, the lower transverse plane, which features the legs, will also feature an additional soft tissue/ air boundary.\n\nWe have investigated late third-trimester scenarios, between 32 and 37 weeks. It is expected that in utero transmission of external sound sources will be affected as a function of gestational age due to changes in the morphology of the maternal abdomen, and the fetal position as well as changes in amniotic fluid volume as pregnancy advances.\n\nIncomplete knowledge of acoustic attenuation coefficients for compressional waves in soft tissue and bone at audio range frequencies is also a possible source of uncertainty in the computations. We have extrapolated low-frequency data for muscle tissue and assumed an absorption power-law frequency dependence. To mitigate this assumption, we have presented two extremes of possible attenuation scenarios, corresponding to that of amniotic fluid and muscle tissue, respectively. The characterization of the nature of damping mechanisms at audio-range frequencies in soft tissue and bone, as well as the identification of a suitable damping model and its relationship with frequency, requires further studies.\n\nOur analysis was focused on the propagation of compressional waves in the body, treating the tissue groups as acoustic media. It is well known that both soft tissue and bone support the propagation of shear waves55,56. Shear wave mode conversion could in principle occur, depending on the incident acoustic field, adding more complexity to the problem of in-utero sound transmission and propagation. This limitation could be addressed by using a viscoelastic boundary element formulation. However, this would be more computationally demanding owing to the increase in the number of degrees of freedom resulting from having to solve for vector quantities and from the denser meshes which would be required to resolve the shorter wavelengths associated with shear waves.\n\nWe aimed to better understand the fetal exposure to exterior noise sources and have limited our analyses to acoustic plane waves as the incident exterior sound field. The developing fetus is also exposed to physiological sounds as well as the transmission of the maternal voice via anatomical paths, mainly by bone conduction. Our model can be extended to include any number of monopole and dipole sources, as well as combinations of Neuman and Dirichlet source boundary conditions, all of which will affect the interior sound field.\n\nOur current study on sound transmission in utero has several strengths which altogether have the merit of addressing important features which potentially have significant ramifications for fetal neurobiological development. We have developed a validated computational model capable of predicting acoustic pressure transmission at high frequencies relative to the wavelength and in high-contrast scenarios. A prior attempt was made to carry out this type of analysis with the finite element method (FEM) which demonstrated the inapplicability of the technique throughout the whole of the human audio range, owing to numerical pollutions effects57 reaffirming the validity and superiority of our approach. We expect that the results discussed in this paper will provide a scientific starting point to establish noise dose and exposure safety levels for the developing fetus at various stages of gestation. This will include occupation noise, leisure noise, urban noise as well as noise resulting from medical diagnostic interventions such as MRI scans58. Numerical simulations on anatomical data may also be used as a predictor of the risk of specific noise profiles on the developing fetus. For example, if there is a significant overlap of the spectral content of the noise close to or at a known resonance, this could potentially enhance mechanical stresses inside the uterus. Patient-specific risk would also be dependent on tissue acoustic properties and heterogeneity. Developing dedicated uncertainty analyses may allow for establishing the risk profile of an individual subject to specific external noise sources, at a given gestational age.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "This study complies with all relevant ethical regulations for research involving human participants. The study involving MRI protocols was approved by the UK National Research Ethics Service and all participants gave written informed consent (London \u2013 Hampstead Research Ethics Committee, REC reference 15/LO/1488).\n\nMeshes for the datasets associated with Subjects 1, 2, 3, and 4, were generated in Autodesk Meshmixer v3.559, following the segmentation of MRI scans. The three anatomical groups meshed were the lower abdominal region, the spine, and the uterus. The datasets were smoothed and patched to obtain closed surfaces. Three-noded triangular elements were used and a strategy was adopted whereby the mesh density of each geometric group was varied as a function of the frequency of excitation and the acoustic properties of the media on both sides of the interface. For example, for the abdominal region, which is in contact with air, the external medium, we use a mesh density determined by the wavelength in soft tissue at the frequency of excitation. The meshes were then converted to Gmsh v4.13.160 format for reading in OptimUS. Based on convergence tests carried available on the OptimUS repository35, 4 to 5 triangular elements per wavelength are sufficient to ensure convergence of the Generalized Minimal Residual Method (GMRES) solver and generate results within 7.5% of the analytical solution on nested spheres52. To reduce run times associated with the frequency sweep calculations, we divide the audio range 12th octave band frequency spectrum into five subgroups, as shown in Table\u00a02, where the resulting number of degrees of freedom is displayed.\n\nMRI examinations were performed on a 1.5\u2009T magnet (MAGENETOM Avanto; Siemens Healthcare). Four subjects of female biological sex beyond 24 weeks of gestational age (confirmed by dating scan) with uncomplicated pregnancies had MRI data acquired. No compensation was awarded to the participants and all were volunteers. The ages of the patients at the time of scanning were as follows:\n\nSubject 1: 32 years of age\n\nSubject 2: 34 years of age\n\nSubject 3: 36 years of age\n\nSubject 4: 36 years of age.\n\nPatients were placed in the left lateral decubitus position and had a moderately filled bladder. The uterus was imaged in at least 3 orthogonal planes (axial, coronal, and sagittal) relative to the placenta-myometrium interface. The protocol consisted of T2-weighted fast acquisition spin echo sequences (HASTE). The following parameters were applied: slice thickness (4\u2009mm), spacing between slices (4\u2009mm), repetition time (1013.8\u2009ms), echo time (89.6\u2009ms), flip angle (107.9\u00b0), and pixel spacing (0.74\u20130.74\u2009mm).\n\nImaging data was manually segmented using ITK-SNAP to provide multiple tissue segmentations for the maternal abdomen, uterus, fetal body and brain, placenta, and maternal spine.\n\nThe Helmholtz equation is commonly used for modeling harmonic wave propagation in acoustic phenomena like room acoustics, sonar, and biomedical ultrasound61. Among numerical methods, the boundary element method (BEM) stands out as an efficient approach for solving Helmholtz transmission problems33,34,36. Unlike the FEM and other volumetric solvers, which directly discretize partial differential equations within the volume of interest, BEM first transforms the equations into a boundary integral formulation. This formulation depends on the geometry of the problem, specifically the interfaces between volumetric subdomains with constant material parameters (e.g., density and speed of sound). The volumetric partial differential equations are rewritten into a representation of the acoustic fields in terms of surface potentials at the material interfaces. Scientific literature provides various boundary integral formulations tailored to specific geometries, including single scatterers, multiple disjoint scatterers, and nested domains62,63. In this study, we employ a dedicated formulation designed for piecewise homogeneous domains, allowing for efficient simulations by combining different types of boundary integral formulations. The specific nested domain formulation applied to the topologies specific to this paper used is detailed in \u00a0Supplementary Information section. The formulation has been generalized to include arbitrary combinations of disjointed multiple scatterers and nested domains35. This design process simplifies the generalization of the BEM to more complex geometries and allows for efficient simulations by combining different types of boundary integral formulations.\n\nThe BEM employed in this paper assumes a Helmholtz transmission problem and uses a combination of multiple-domain Poggio-Miller-Chan-Harrington-Wu-Tsai equations and on-surface radiation condition (OSRC) preconditioners32. This formulation is described in\u00a0Supplementary Information section for the specific case of a bounded domain embedded in free space with two other domains inside. The damped wavenumber in the OSRC preconditioner is set to: \u03bbmin+0.4i\u03bbmin\u2212130.001\u221223 where \u03bbmin corresponds to the smallest wavenumber of the media considered, in this case air.\n\nWe used hierarchical matrix compression to reduce the problem size with the parameters set as follows:\n\n\u03f5=10\u22126\n\nmaximum rank = 1000\n\nmaximum block size = 106\n\nWe considered the excitation acoustic wave to be a unit amplitude plane wave incident on the maternal abdomen. At each frequency, we calculate the Neumann and Dirichlet traces at the surface of the anatomical regions. The GMRES solver, without restart, converged in all cases to a tolerance of 10-4 in the residual norm, within less than 2000 iterations.\n\nWe then calculated the spatial RMS of the acoustic pressure magnitude inside the volume of the uterus, as well as the l\u221e-norm of the pressure magnitude within this region, followed by the magnitude of the acoustic pressure at its barycentre.\n\nTo add an additional layer of validation to this approach, we considered two concentric spheres with dimensions similar to the abdominal region and uterus in our datasets. The outer sphere has diameter of 0.5\u2009m and the inner sphere of 0.3\u2009m. We substituted anatomical computational grids for those representing these spheres and we carried out the 12th octave band frequency sweeps using the above protocols and compared the results with the analytical solution52 for an incident plane wave traveling in the positive x direction. We calculate the SPL resulting from the spatial RMS of the acoustic pressure magnitude inside the inner sphere as well as the acoustic pressure magnitude at the barycentre of the inner sphere. The outer sphere was assigned the properties of abdominal tissue and the inner sphere those of the uterus, for both the high (muscle tissue) and low (amniotic fluid) attenuation cases. The results, shown in Fig.\u00a07A, B, demonstrate agreement generally within \u00b10.5\u2009dB between the BEM and the analytical solution, with the exception of resonances and antiresonances, where slight differences between numerical and analytical solutions may occur. We highlight two resonances at 3.5\u2009kHz and 8\u2009kHz, which are consistent with the results on anatomical data. Further validation is described in Fig.\u00a07C\u2013H where the acoustic pressure magnitude is plotted at the two resonant frequencies denoted above, as well as 20\u2009kHz, the upper limit of the human audio range. We note agreement with the analytical solution.\n\nSound pressure level transmission inside the inner sphere with dimensions representative of the uterus as a function of frequency for a unit amplitude incident plane wave traveling in the positive x direction for two concentric spheres, of radii 0.25\u2009m and 0.15\u2009m, obtained from A the SPL resulting from the spatial RMS of the acoustic pressure magnitude inside the inner sphere with two resonances shown at 3\u2009kHz and 8\u2009kHz, and B the acoustic pressure magnitude at the center of the inner sphere. The exterior medium is air. The medium bounded by the exterior domain and the inner sphere has the properties of abdominal tissue and the inner sphere those of amniotic fluid. Acoustic pressure magnitude in the Cartesian x\u2212y plane, describing the interactions between the incident plane wave and the concentric spheres at excitation frequencies of 3\u2009kHz, 8.5\u2009kHz, and 20\u2009kHz is shown in (C, E and G) which are obtained using the analytical solution52. D, F, and H correspond to the nested BEM solution provided by OptimUS.\n\nBased on the magnitude and phase of the predicted acoustic pressure obtained at the barycentre of the uterus for each dataset, filters from the datasets corresponding to Subjects 2 and 3, were obtained using spline interpolated data on the magnitude and unwrapped phase responses. The attenuation coefficient of uterine tissue is used to describe the uterus, thereby corresponding to the high attenuation case. A sampling frequency of 44.1\u2009kHz was assumed, and 16,385 interpolation points were used. A constant delay of 200 samples at each frequency was introduced to linearize the phase. Then, a finite impulse response (FIR) filter was estimated using the least-square method. The order of the filter was increased to minimize the mean-square error between the frequency response function of the filter and the predicted spectrum of the acoustic pressure. The impulse response of this FIR filter was then used to convolve a set of audio signals, including hand clapping, crowd noise, the London Underground and rock music. The signal processing was carried out using MATLAB R2024b (MathWorks, 2024). The filter impulse responses are shown in Fig.\u00a08.\n\nImpulse response generated from the complex acoustic pressure at A, the barycentre of the uterus of Subject 2 and B, the barycentre of the uterus of Subject 3, in response to a unit amplitude plane wave incident on the abdomen traveling in the negative x direction.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_Fig7_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58983-0/MediaObjects/41467_2025_58983_Fig8_HTML.png" + ] + }, + { + "section_name": "Data availability", + "section_text": "The source data generated in this study are provided in the Source Data file, available at https://zenodo.org/records/15052299.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "The code in this paper, along with sample Jupyter notebooks is available from the GitHub repository https://github.com/optimuslib, which is also available at Zenodo. The sample notebook demonstrating single-frequency simulations in this paper can be found at this link. Any additional scripts used to generate or visualize the results are available upon reasonable request to the corresponding author, for research and educational purposes only, with a timeframe of response of two weeks.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Jauniaux, E., Wylie, B. J., Verheijen, E., Conry, J. & Papageorghiou, A. Women\u2019s health in the anthropocene. BJOG: Int. J. Obstet. Gynaecol. 131, 531\u2013532 (2024).\n\nArticle\u00a0\n \n Google Scholar\u00a0\n \n\nWorld Health Organization. Environmental Noise Guidelines for the European Region (World Health Organization, 2018).\n\nEuropean Environment Agency. Healthy Environment, Healthy Lives: How the Environment Influences Health and Well-Being in Europe (European Environment Agency, 2020)\n\nBasner, M. et al. Auditory and non-auditory effects of noise on health. 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A.M. and A.L.D. were supported by the MRC (MR/X010007/1), the NIH (R01 HD108833), the BBSRC (BB/Y514214/1), and EPSRC (NS/A000027/1).", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "Department of Surgical Biotechnology, Division of Surgery and Interventional Science, University College London, London, UK\n\nPierre G\u00e9lat\n\nInstitute for Mathematical and Computational Engineering, Pontificia Universidad Cat\u00f3lica de Chile, Santiago, Chile\n\nElwin van\u2019t Wout\n\nDepartment of Mechanical Engineering, University College London, London, UK\n\nReza Haqshenas\n\nSchool of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences & Medicine, King\u2019s College London, London, UK\n\nAndrew Melbourne\n\nElizabeth Garrett Anderson Institute for Women\u2019s Health, University College London, London, UK\n\nAndrew Melbourne,\u00a0Anna L. David,\u00a0Nada Mufti\u00a0&\u00a0Eric Jauniaux\n\nDepartment of Media, Communications and Cultural Studies, Goldsmiths University of London, London, UK\n\nJulian Henriques\n\nSonic Womb Productions Limited, London, UK\n\nAude Thibaut de Maisi\u00e8res\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualisation: A.T.M., E.J., J.H., P.G.; Data acquisition: N.M., A.M., A.L.D., Computation and 809 analysis: P.G., R.H., E.V.W., A.M.; Clinical input: N.M., A.L.D., E.J.; Resources and funding 810 acquisition: A.T.M., A.M., A.L.D., P.G., E.V.W.; Writing \u2013 Original draft: P.G., E.J., J.H., A.T.M.\n\nCorrespondence to\n Pierre G\u00e9lat.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests. None of the contents of this manuscript has been previously published or is under consideration elsewhere. All the authors have read and approved the final version of the manuscript before submission.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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Evaluation of fetal exposure to environmental noise using a computer-generated model.\n Nat Commun 16, 3916 (2025). https://doi.org/10.1038/s41467-025-58983-0\n\nDownload citation\n\nReceived: 05 November 2024\n\nAccepted: 08 April 2025\n\nPublished: 25 April 2025\n\nVersion of record: 25 April 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58983-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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development", + "journal": "Nature Communications", + "published": "22 October 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM1_ESM.pdf" + }, + { + "label": "Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM2_ESM.pdf" + }, + { + "label": "Description of Additional Supplementary Files", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM3_ESM.pdf" + }, + { + "label": "Supplementary Data 1", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM4_ESM.xlsx" + }, + { + "label": "Supplementary Data 2", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM5_ESM.xlsx" + }, + { + "label": "Supplementary Data 3", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM6_ESM.xlsx" + }, + { + "label": "Supplementary Data 4", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM7_ESM.xlsx" + }, + { + "label": "Supplementary Data 5", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM8_ESM.xlsx" + }, + { + "label": "Supplementary Data 6", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM9_ESM.xlsb" + }, + { + "label": "Supplementary Data 7", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM10_ESM.xlsx" + }, + { + "label": "Supplementary Data 8", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM11_ESM.xlsx" + }, + { + "label": "Supplementary Data 9", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM12_ESM.xlsx" + }, + { + "label": "Supplementary Data 10", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM13_ESM.xlsx" + }, + { + "label": "Supplementary Data 11", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM14_ESM.xlsx" + }, + { + "label": "Supplementary Data 12", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM15_ESM.xlsx" + }, + { + "label": "Supplementary Data 13", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM16_ESM.xlsx" + }, + { + "label": "Supplementary Data 14", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM17_ESM.xlsx" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_MOESM18_ESM.pdf" + } + ], + "supplementary_1": NaN, + "supplementary_2": NaN, + "source_data": [ + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE262280", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE261950", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE261951", + "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE261952", + "https://www.ebi.ac.uk/ena/browser/view/GCA_000003025.6", + "https://github.com/theislab/pig-embryo-ana", + "https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.13/" + ], + "code": [ + "https://github.com/theislab/pig-embryo-ana", + "/articles/s41467-025-64774-4#ref-CR130" + ], + "subject": [ + "Cell lineage", + "Cellular signalling networks", + "Organogenesis" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4151759/v1.pdf?c=1761217610000", + "research_square_link": "https://www.researchsquare.com//article/rs-4151759/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-64774-4.pdf", + "preprint_posted": "03 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Human pancreas development remains incompletely understood due to limited sample access constrained by ethical and practical considerations. Here we investigate whether pigs resemble humans in pancreas development more closely than rodents, and as such, offer a valuable alternative large-animal model. As pig pancreas organogenesis is unexplored, we first annotated developmental hallmarks and lineage markers of pancreas differentiation and morphogenesis throughout the 114-day gestation. Building on this detailed roadmap, we further constructed a pig single-cell multiome pancreas atlas capturing temporal resolution across all three trimesters. Cross-species comparisons with human and mouse time-resolved integrated pancreas atlases accentuated that pig closely resembled human in developmental tempo, epigenetic and transcriptional regulation, gene expression patterns and gene regulatory networks (GRNs). Specifically, pig mimicked the dynamics of progenitor status, differentiation trajectories and GRNs governing endocrine fate acquisition in human. In pig multiome GRN, over 40% of transcription factors targeted by NEUROG3, the endocrine master regulator, were confirmed in human stem cell models. Most notably, we uncovered beta-cell heterogeneity arising during embryonic development, owing to endocrine induction in pancreatic progenitors with temporally altered epigenetic and transcriptional identity. Overall, our work lays the foundation for using pigs to model human pancreas biology and provides unprecedented insights into developmental principles and mechanisms across species.Biological sciences/Developmental biologyBiological sciences/Computational biology and bioinformatics", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "Yes there is potential Competing Interest.\nF.J.T. consults for Immunai Inc., Singularity Bio B.V., CytoReason Ltd, Cellarity, Curie Bio Operations, LLC and has an ownership interest in Dermagnostix GmbH and Cellarity. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryTable1Nextgenerationsequencingsampleinventory.xlsxDataset 1SupplementaryTable2Markergenesofcellclustersinhumanpigandmouse.xlsxDataset 2SupplementaryTable3DEGofeachcellclusterinhumanpigandmouse.xlsxDataset 3SupplementaryTable4CellRankAlphaandBetalineagedrivers.xlsxDataset 4SupplementaryTable5NEUROG3targetsfrompigGRNandhumanstemcells.xlsxDataset 5SupplementaryTable6DEGsandpathwaysofpighumanbetacells.xlsxDataset 6SupplementaryTable7CellOracleinferredGRNsofpigBeta0andBeta1.xlsxDataset 7SupplementaryTable8TradeSeqgenegroupsandenrichedpathways.xlsxDataset 8SupplementaryTable9DEGsofpigEPs.xlsxDataset 9SupplementaryTable10AntibodiesandhiPSCdifferentiationreagents.xlsxDataset 10SupplementaryTable11ImprovedgenomeannotationfortheSusscrofa.xlsxDataset 11SupplementaryTable12Sequencingrawdatafilteringthresholds.xlsxDataset 12", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Human pancreas development remains incompletely characterized due to restricted sample access. We investigate whether pigs resemble humans in pancreas development, offering a complementary large-animal model. As pig pancreas organogenesis is unexplored, we first annotate developmental hallmarks throughout its 114-day gestation. Building on this, we construct a pig single-cell multiome pancreas atlas across all trimesters. Cross-species comparisons reveal pigs resemble humans\u00a0more closely than mice in developmental tempo, epigenetic and transcriptional regulation, and gene regulatory networks. This further extends to progenitor dynamics and endocrine fate acquisition. Transcription factors regulated by NEUROG3, the endocrine master regulator, are over 50% conserved between pig and human, many being validated in human stem cell models. Notably, we uncover that during embryonic development, emerging beta-cell heterogeneity coincides with a species-conserved primed endocrine cell (PEC) population alongside NEUROG3-expressing cells. Overall, our work lays the foundation for comparative investigations and offers unprecedented insights into evolutionarily\u00a0conserved pancreas organogenesis mechanisms across animal models.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Mice have been the main mammalian model to investigate the evolutionarily\u00a0conserved basic principles of development and disease. As human fetal samples are largely inaccessible due to ethical and practical restrictions, our knowledge of pancreas development is mainly built on extensive investigations in mice1,2. In brief, from foregut endoderm, ventral and dorsal pancreatic buds are specified, evaginated, and fused over the midline to form the organ anlage. This multipotent progenitor epithelium undergoes apical-basal polarization, microlumen formation, single-layer stratification, and branching morphogenesis, which is concomitant with tip-trunk patterning and differentiation to form the acinar, ductal, and endocrine compartments of the final pancreas (Fig.\u00a01a)1,3,4,5,6,7. With this blueprint, the signals and factors orchestrating in vivo development are applied to human pluripotent stem cell differentiation in vitro to generate endocrine islet cells, such as glucagon-producing alpha cells and insulin-producing beta cells, enabling stem cell-derived islet replacement therapy to treat diabetes7,8,9,10. However, these protocols cannot produce fully functional islet cells while eliminating undifferentiated and off-target cell types11,12, partly resulting from the inherent differences in developmental timescales, neighboring tissue interactions, and spatiotemporal gene expression and regulation between mouse and human pancreas differentiation and morphogenesis13. These differences highlight the need for cross-species comparisons with additional model systems to uncover conserved mechanisms of organogenesis and to bridge the translational gap between mice and humans.\n\na Schematic comparison of pancreas organogenesis in mouse, pig and human1,6,7. Pancreas organogenesis initiates when the pancreatic buds (dorsal bud shown) emerge from the foregut endoderm while predicted multipotent pancreatic progenitor cells (MPC) expand into a multi-layer epithelium (T1). In mouse and pig but not human, neurogenin 3-induced endocrinogenesis marks the primary (1\u00b0) transition, generating alpha cells and few beta cells. Pancreatic morphogenesis (T2) occurs after the fusion of the pancreatic buds. The epithelium undergoes polarization, microlumen formation and coalescence into the near single-layer epithelial tree, which is subsequently patterned into trunk and tip domains during branching morphogenesis. This coincides with progenitor differentiation forming exocrine and endocrine compartments, referred to as secondary (2\u00b0) transition when the bulk of beta cells emerge. During T3, the pancreatic ductal and acinar cells proliferate and terminally differentiate, while delaminating endocrine cells form proto-islets of various sizes. b\u2013g Bright-field images of wild-type pig embryos or embryonic pancreas (scale bar 2\u2009mm) and immunofluorescence identification of lineage markers highlighting differentiation events in tissue sections (scale bar 50\u2009\u00b5m) at different time points. Images are representative of 3 samples per time point. h Schematic comparison of mouse and pig development speeds relative to human, showing the timing of each pancreas developmental milestone (labeled in a) as a percentage of gestation duration marked with dashed lines. i Uniform Manifold Approximation Projection (UMAP) plots showing integrated pig pancreas atlas and cluster changes from E22-85. Cells at each developmental stage (see b-g) are highlighted and colored by cell type, with a pie chart of relative cell type composition at the upper right corner. j Dot plot showing mean gene expression of marker genes for each cluster of the integrated pig pancreas atlas. i and j: scRNA-seq of pancreatic cells from wild-type and INS-eGFP pigs. Detailed sample information is provided in Supplementary Data\u00a01.\n\nPigs have coevolved with humans over the past 10,000 years, a period when pig domestication coincided with human agricultural civilization14. Despite diverging from humans earlier than mice (94 vs. 87 million years ago)15, pigs retain genomic feature similarity to humans compared to the rapidly evolving mouse lineage16. Moreover, as omnivorous animals, pigs resemble humans in metabolism and physiology. Pig organs share anatomical and functional features with humans, making them a favored option for xenotransplantation with compatible organ size and fewer ethical concerns compared to non-human primates17. Porcine islets show transcriptional characteristics similar to human islets18 and represent a potential source for xenotransplantation19, since pigs and humans have similar glycemic control and identical insulin amino acid sequence. This allowed insulin therapy using pig insulin before recombinant human insulin became available20,21,22. However, whether these shared traits position pigs as a relevant model for human pancreas development remains unexplored, particularly regarding the molecular and (epi)genetic mechanisms driving embryonic development and organogenesis. Given pigs\u2019 anatomical and physiological similarities to humans and their extended gestational period (114 days vs. 21 in mice, 280 in humans), we reasoned that pigs could serve as a large animal model to complement the existing rodent models, bridging the translational gap in understanding pancreas development from mice to humans.\n\nHere, we utilize temporally resolved single-cell multi-omics to compare pancreas development in mice, pigs, and humans. Our analysis demonstrates the complementary potential of the pig model for identifying both species-specific and evolutionarily conserved mechanisms of pancreas organogenesis, morphogenesis, and differentiation.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_Fig1_HTML.png" + ] + }, + { + "section_name": "Results", + "section_text": "To compare pancreas development across species (Fig.\u00a01a), we first defined the unexplored hallmarks of pig pancreas development during the 114-day gestation by identifying major lineage markers known in mouse and human (Fig.\u00a01b\u2013g). The pig pancreas primordium emerged around embryonic day (E)18, when the earliest pancreatic transcription factor (TF) PDX1 was detected in both ventral and dorsal foregut, with the former having a thicker evaginating layer of cells as reported in human embryos (Supplementary Fig.\u00a01a)23. At E20, pancreas organogenesis initiated with the apparent dorsal and ventral buds (T1, Fig.\u00a01a) that comprised an unpolarized multi-layered progenitor epithelium, harboring glucagon+ alpha cells and very few insulin+ beta cells (Fig.\u00a01b and Supplementary Fig.\u00a01b). The early emergence of hormone+ cell resembles the first wave of Neurogenin-3 (NEUROG3)-mediated endocrinogenesis in mice during the primary (1\u00b0) transition5, which is absent at the corresponding stage in human when the endocrine transcription factors NEUROG3 and NKX2-2 remain undetectable23. Upon the fusion of the two pancreatic buds at E30, the expression of NEUROG3 protein and transcripts diminished from the pancreas (Fig.\u00a01c and Supplementary Fig.\u00a01c, h). At E40, concurrent with pancreatic epithelial polarization, stratification and tip-trunk patterning (T2, Fig.\u00a01a), NEUROG3 reappeared, initiating the second wave of endocrinogenesis comparable to the secondary (2\u00b0) transition in mice (Fig.\u00a01d and Supplementary Fig.\u00a01d). By E54, CPA1+ tip and SOX9+ trunk domains were clearly established (Fig.\u00a01e and Supplementary Fig.\u00a01e). SOX9 remained faintly detectable in CPA1+ tip cells that were predisposed to acinar fate at E63, while endocrine cell clusters started to appear (Fig.\u00a01f and Supplementary Fig.\u00a01f). From E85 onwards, endocrine (NKX6-1+), ductal (SOX9+) and acinar (GATA4+) compartments continued to segregate, with these TFs showing almost exclusive expression patterns (Fig.\u00a01g and Supplementary Fig.\u00a01g, j). During this time, pig proto-islets appeared (T3, Fig.\u00a01a) and formed an intermingled islet architecture near birth (Supplementary Fig.\u00a01k), which resembles the postnatal islet architecture in human, but not the typical core (beta cells)-mantel (alpha cells) islet structure known from mice24,25,26.\n\nAligning the timing of pancreas development milestones (i.e. time points labeled in Fig.\u00a01a) in human, pig and mouse allowed us to compare the developmental tempo across species (Fig.\u00a01h). Overall, pig pancreas morphogenesis and differentiation speed showed a closer resemblance to humans when compared to mice, particularly during the 2\u00b0 transition. The formation of the pancreatic anlage in the form of two buds (T1) occupied 10% of the duration of human gestation, 12% in mouse and 17% in pig. In contrast, pancreatic morphogenesis (T2) and islet formation (T3) progressed much faster in mice (42%), contrary to the longer duration in human (82%) and pig (65%), when both species underwent extended acinar terminal differentiation and islet remodeling23.\n\nTo capture the dynamic transcriptional changes of lineage allocation during pig pancreas development, we performed 10X single-cell RNA sequencing (scRNA-seq) on 124,869 cells isolated from pancreata across all three trimesters (Supplementary Fig.\u00a02a and Supplementary Data\u00a01). The resulting dataset was subjected to stringent doublet removal: cells consistently identified as doublets by >3 among the six methods used were removed, and entire clusters with a doublet frequency >70% were excluded (see Methods for details). From this filtered dataset, we extracted pancreatic epithelial cells that co-expressed CDH1 and EPCAM (Supplementary Fig.\u00a02b) and identified eight clusters (Fig.\u00a01i, j, Supplementary Fig.\u00a02c, d and Supplementary Data\u00a02): Ductal (SOX9/SLC4A4), Acinar (CPA1/CEL), NGN3 (NEUROG3/TUBB2B, endocrine progenitors, NGN3 is used as a cluster name to differentiate from NEUROG3 protein and/or mRNA), FEV (FEV/CHGB, endocrine precursors with low NEUROG3 expression), Beta (INS/G6PC2), Alpha (GCG/IRX1), Delta (SST/HHEX), and PP (PPY/ETV1) cell clusters. Among the top ranked genes, published human markers were also found in the pig pancreas, such as MDK in the NGN3, DDC in FEV, ASB9 in Beta, TTR in Alpha, RBP4 in Delta cell clusters27,28,29,30. Diabetes risk related genes were expressed in a cell-type specific manner, e.g. ABLIM1 in Ductal, TAGLN3 in NGN3, DIRAS3 in Alpha, CFAP61 in all endocrine cell clusters31,32,33,34. We further identified two molecularly distinct cell clusters: 1) A predicted multipotent progenitor cell (MPC) cluster, which transiently existed during E23-40 and co-expressed key pancreatic progenitor TFs (PDX1, PTF1A, SOX9, NKX6-1, PROX1). This predicted MPC cluster resolved from E54 onwards, when exocrine and endocrine lineages clearly separated and committed to differentiation; 2) A primed endocrine cell (PEC) cluster first emerged at E23 alongside sparse NEUROG3+ endocrine progenitors. This population, which persisted throughout all subsequent stages, exhibited features of endocrine cells and expressed genes coding for cytoskeletal components (TUBA1B, TUBB, STMN1) and cell cycle regulators (H2AFZ, PCLAF).\n\nThe emergence of the identified cell clusters in the scRNA-seq atlas was consistent with our immunofluorescence analysis of the pig pancreas, thereby verifying the cell type annotations. For instance, the NGN3 cluster contained only two cells in the scRNA-seq atlas at E33, when the NEUROG3 protein and transcript were almost undetectable in the pancreas (Fig.\u00a01c, i and Supplementary Fig.\u00a01c, h); whereas the CPA1+ Acinar, SST+ Delta and PPY+ PP clusters appeared after E40, when these proteins could be detected in the pancreas (Fig.\u00a01i and Supplementary Fig.\u00a01d, e, i).\n\nTo compare the transcriptional features of all epithelial pancreatic cell types across species, we generated atlases of human and mouse pancreas development by integrating published 10X scRNA-seq datasets30,35,36,37,38,39,40,41,42. The human atlas had 188,488 cells from 7-20 week-post-conception (wpc), while the mouse atlas had 135,575 cells covering E8.5-18.5. Cells were clustered, annotated, and the pancreatic epithelial lineage selected as for the pig dataset (Supplementary Fig.\u00a03a\u2013f). All pig pancreatic cell clusters were identified in human and mouse datasets except the PEC cluster, which was absent potentially due to its low abundance in the analyzed samples (Fig.\u00a02a, Supplementary Fig.\u00a03g, h and Supplementary Data\u00a02). PP (PPY/ETV1) cells did not separate from the Alpha cluster in mouse (Supplementary Fig.\u00a04a). FEV+ cells in human were annotated together with the NGN3 cluster as EP (endocrine progenitor) cluster due to high NEUROG3 expression (Supplementary Fig.\u00a04b). The Epsilon (GRHL) cluster was identified in human and mouse datasets, but surprisingly not in the pig pancreas. The Epsilon marker genes from human and mouse did not show significant enrichment in any cell clusters in the pig (Supplementary Fig.\u00a04c).\n\na UMAPs of integrated human, pig, and mouse atlases of pancreas development. Cells were colored by cell type, with the same colors indicating the same cell types across species (Hs, Homo sapiens; Ss, Sus scrofa; Mm, Mus musculus). b Spearman correlation of mean normalized gene counts per cluster comparing pig-human and mouse-human pairs (based on human orthologs). Analysis is limited to the 851 genes shared in the intersection of all species\u2019 4000 highly variable genes. c Dot plot showing mean expression of cell-type-specific differentially expressed TFs in human (one-vs-rest analysis using edgeR; FDR-corrected p-value\u2009<\u20090.05; Supplementary Data\u00a03). Top genes with the highest logfold change that are expressed in > 20% of cells of the cluster are shown across all clusters (square color = cell type color in a) for all three species. Genes are mapped to human orthologs. Dot size represents the fraction of expressing cells per cluster (logarithmic scale). d Coverage plots showing pig-human conserved NEUROG3 genomic regions. Link positions were converted from pig to human genome assembly with UCSC liftOver tool (http://genome.ucsc.edu)119. Pseudo-bulk accessibility tracks were used to visualize DNA accessibility in a region by averaging signals from all cells within a cluster. e Heatmap of top differentially active motifs across cell types computed with chromVAR121 using human 12wpc scATAC-seq (top) and pig multiome (bottom) datasets. f UMAP visualizations of the inferred TF network from human scGLUE48-integrated scRNA/ATAC-seq (left) and pig multiome (right) data. Nodes represent TFs, colored by their highly expressed clusters and sized by network centrality. Edges show TF-target interaction strengths (orange: activating; gray: inhibiting). a\u2013c: scRNA-seq of pancreatic cells from wild-type and INS-eGFP pigs. d\u2013f: Multiome analysis of pancreatic cells from PTF1A-codon-improved-Cre/ROSA-mTmG pigs. Detailed sample information is provided in Supplementary Data\u00a01.\n\nTo assess the comparability of pancreatic cell types across species, we performed a correlation analysis using the average expression of species-conserved highly variable genes in each cluster, which reflected the global transcriptional programs of each species. Pig and human clusters had an overall stronger correlation (r\u2009=\u20090.6-0.7), whereas MPC, Beta, and EP clusters in mouse showed the lowest correlation to human clusters (r\u2009=\u20090.45-0.54; Fig.\u00a02b and Supplementary Fig.\u00a04d). We further examined the significantly upregulated differentially expressed genes (DEGs) of each cluster across species, which can capture the subtle species-specific differences in gene expression. Although the pig genome annotation is not as complete as that of human and mouse, the MPC, Beta, and Ductal clusters of pig and human had more overlapping total DEGs and TFs. In contrast, in the Acinar and Alpha clusters, mouse and human shared more total DEGs, but not TFs (Supplementary Fig.\u00a04e). Each species displayed distinct cell-type-specific expression patterns of DEGs (Fig.\u00a02c, Supplementary Fig.\u00a04f, and Supplementary Data\u00a03). For example, the top TFs SOX6 and SOX9 of human MPC cluster showed enriched expression in the Ductal cluster of pig and mouse. The expression patterns of the top TFs in endocrine progenitors were relatively conserved among the three species. Among the top TFs of the human Beta cluster, the maturation TF MAFA was detected in pig but absent in mouse beta cells; PLAGL1, related to transient neonatal diabetes43,44, appeared in pig beta cells, while mainly being expressed in mouse acinar cells. The TF ETV1 showed enrichment in human delta cells, though in pig and mouse, it was highly expressed in alpha cells.\n\nConserved cell-type gene expression patterns indicated similarities in higher-order chromatin regulation mediated via cis-regulatory elements (CREs, e.g., enhancers and silencers) to control gene expression45. To compare pig and human CREs and upstream transcriptional regulators of pancreatic cell fate decisions, we additionally performed single-cell 10X multiome sequencing to assess transcriptional changes and chromatin accessibility simultaneously. Samples were collected during the 2\u00b0 transition of pig development at E45-85, resulting in 29,072 nuclei (Supplementary Data\u00a01). Cluster label transfer from the pig scRNA-seq atlas identified all cell types, except that the MPC cluster had already diminished, while the Epsilon cells remained undetectable (Supplementary Fig.\u00a05a, b). We further analyzed single-cell transposase-accessible chromatin with sequencing (scATAC-seq) data from human 12 wpc pancreas30, which had 5592 nuclei. Cell clusters were annotated via label transfer from the human scRNA-seq atlas, and pancreatic epithelial cells were used for further analysis (Supplementary Fig.\u00a05c).\n\nHighly conserved CREs were identified when comparing the sequences between pig and human key lineage regulators, such as the master regulator of endocrinogenesis, NEUROG3 (Fig.\u00a02d). Further computing cell-type specific motif activities revealed pig-human conserved active TFs towards endocrine and exocrine lineages (Fig.\u00a02e and Supplementary Fig.\u00a05d). Both Acinar and Ductal clusters had active motifs of TFs determining acinar fate differentiation (NR5A2, PTF1A and RBPJ), reflecting the gradual resolution of chromatin and transcription factor profiles in acinar and ductal lineages during terminal differentiation in human and pig. In addition, two MODY genes (HNF4A and HNF4G) that regulate the growth and function of beta cells, were found active in acinar cells. The Ductal cluster conserved TFs contained Hippo effector genes (TEAD1 and TEAD4) and TFs known to be expressed in the embryonic ductal tree, such as HNF1A, TCF7L2, SOX9, GLIS3, and GATA6. The human-pig conserved TFs in the EP cluster included known regulators of endocrinogenesis (NEUROG3, RFX3, RFX6, and NEUROD1), members of the Nuclear Factor 1 family (NFIA, NFIB, and NFIX), and the intestinal stem cell identity TF ASCL246. Endocrine cell clusters also had a large panel of TFs shared between pig and human (PAX6, PDX1, MNX1, LMX1B, PAX4, ISL1, NKX6-1, ARX, and NKX2-2).\n\nGiven the conservation of cell-type-specific active motifs between pig and human, we used Pando47 to construct GRNs using human scGLUE48-integrated scRNA/ATAC-seq and pig multiome (joint scRNA/ATAC-seq) pancreas datasets. This enabled detailed examination of relationships between pig-human conserved TFs, their potential target gene expression, and regulatory-site accessibility across cell types and species. The generated GRNs revealed groups of pig-human conserved TFs involved in the differentiation and cell state transition of pancreatic lineages (Fig.\u00a02f and Supplementary Fig.\u00a05e). Ductal-specific TF modules (HNF1B, GLIS3, and EHF), as well as acinar-specific modules (MECOM, XBP1, and STAT3), formed interconnected networks with a set of TFs that showed enrichment in both acinar and ductal lineages (HES1, NR5A2, ONECUT1, TCF7L2, MAFF, REST, and MEIS1). Among TF modules linked to endocrine differentiation, SOX4 (not expressed in pigs) was enriched in both ductal and endocrine lineages in humans. Additionally, NR3C1, implicated in regulating islet gene programs and conferring genetic risk of type 2 diabetes49, was enriched in both the acinar and endocrine lineages in pig, whereas in human, it was only found in endocrine TF modules. Nevertheless, a large group of endocrine TF modules was conserved between pig and human, including ST1850, DACH151, PLAGL143,44, and CDCC88A52, genes linked to beta cell mass and function.\n\nTo explicate the mechanisms underlying endocrine fate allocation, we focused on the endocrine progenitor clusters stemming from trunk (ductal) cells with low NEUROG3 expression, progressing towards the cells that clearly diverged into either the alpha or beta cell fate (Fig.\u00a03a\u2013c). To infer developmental trajectories underlying pancreatic lineage acquisition, we applied CellRank with the Palantir Pseudotime kernel53 to the integrated scRNA-seq atlases to estimate cell states, compute cell-cell transition probabilities, and map cell fates. CellRank correctly predicted the differentiated pancreatic cell clusters as the terminal states (Fig.\u00a03a\u2013c and Supplementary Fig.\u00a06a), providing a reliable transition matrix to infer lineage trajectories in all three species. This revealed distinct differentiation programs in endocrine progenitors of pig and human, opposed to mouse (Fig.\u00a03b). Specifically, at the stage of high NEUROG3 expression, the progenitors in pig and human already segregated toward either the alpha or beta cell fate. The presence of FEV-expressing endocrine precursors was limited to the beta cell lineage alone. Similarly, the same differentiation pattern was identified by an independent analysis with a subset of the human scRNA-seq atlas data30. In contrast, the separation of alpha and beta cell lineages occurred within the FEV-expressing endocrine precursors in mice (Fig.\u00a03b and Supplementary Fig.\u00a06b, c).\n\na\u2013c For each species: circular projection of CellRank-calculated fate probabilities for each cell toward the terminal states (outer labels); and UMAPs detailing endocrine progenitors branching, with the integrated pancreas atlas showing NEUROG3 expression (red) and endocrine progenitor cluster boundaries (black), and an insert showing endocrine progenitor subclusters with overlaid CellRank-inferred trajectories (arrows). d Line plots showing the cumulative number of CellRank-derived beta-cell (top) and alpha-cell (bottom) lineage drivers in mouse and pig that overlap with human orthologs, plotted across correlation score thresholds (Supplementary Data\u00a04). Solid lines show significant driver numbers (Benjamini-Hochberg FDR-corrected p-value\u2009<\u20090.05). Shaded regions indicate the number of genes obtained when using the lower and upper bounds of the 95% confidence interval for the corresponding correlation score. e, f Heatmaps displaying modeled gene expression patterns for human beta-cell (e) and alpha-cell (f) lineage driver gene clusters (identified by hierarchical clustering) across pig, human, and mouse along pseudotemporal trajectories (left to right: 0\u2009\u2192\u20091). Annotations indicate species-conserved pathways and representative genes for each cluster. (corr., correlation; n, number of conserved genes that are expressed in > 20% of endocrine progenitor subclusters) g\u2013i Comparison of NEUROG3 TF targets identified in human/pig pancreas and hESC model (conserved targets in blue). g, h Circular GRN graphs showing first- and second-order NEUROG3 targets from human scGLUE-jointed scRNA/ATAC-seq data (g) and pig multiomic data (h). Nodes represent TFs. Edge color indicates regulatory interaction types (orange, activating; gray, inhibiting); i NEUROG3 TF targets in hESC model with inducible NEUROG3 expression. ChIP-seq-identified direct targets are shaded. Differentially expressed TFs comparing cells with/without NEUROG3 expression are indicated by arrows (red, upregulated; blue, downregulated). a\u2013f scRNA-seq of pancreatic cells from wild-type and INS-eGFP pigs. h Multiome analysis of pancreatic cells from PTF1A-codon-improved-Cre/ROSA-mTmG pigs. Detailed sample information is provided in Supplementary Data\u00a01.\n\nWe next correlated gene expression with CellRank-inferred lineage probabilities and computed putative driver genes of alpha or beta cell fate for each species. Among the orthologous genes mapped across species, pig and human had more overlapping alpha or beta lineage drivers compared to the set shared between mouse and human, regardless of correlation strength (Fig.\u00a03d and Supplementary Data\u00a04). We then identified shared and distinct lineage driver genes with a correlation score >0.7 across species (Supplementary Fig.\u00a06d). To compare expression dynamics of orthologous lineage drivers during endocrine progenitor fate specification, we performed hierarchical clustering in humans to define gene clusters and enriched pathways, then mapped conserved programs in pigs and mice (Fig.\u00a03e, f and Supplementary Data\u00a04). These clusters captured pseudotemporal expression profiles progressing from early endocrine progenitors to fate-committed endocrine cells. For example, in beta cell lineage specification, clusters 3 and 4 showed a conserved expression cascade of Notch signaling-related genes, likely present in early endocrine progenitors. Cluster 2 was enriched with mTOR and RA signaling genes, which showed high expression at both the beginning and end of endocrine progenitor differentiation. Cluster 1 contained genes highly expressed late in differentiation, suggesting their roles in beta cell fate specification. While we observed species-specific differences in extracellular matrix organization, tight junction formation, and cytoskeletal regulation, core features of endocrine differentiation - particularly incretin synthesis and secretion - remained conserved across all species in both beta and alpha cell lineages, demonstrating evolutionary preservation of key differentiation mechanisms.\n\nTo evaluate the suitability of pigs as a model of human endocrine development, we performed a comparative analysis of NEUROG3 TF regulatory networks derived from native human and pig pancreas multiome data and experimentally derived targets from human stem cell models. Using Pando-inferred GRNs from human scGLUE-integrated scRNA/ATAC-seq data and pig multiome data, we extracted NEUROG3 networks with significant interactions and module activities (Fig.\u00a03g, h and Supplementary Data\u00a05). Both human and pig NEUROG3 networks contained >100 TFs, including canonical NEUROG3 targets, such as NEUROG3, NEUROD1, NKX6-1, NKX2-2, and MLXIPL. We then benchmarked these against targets identified through human stem cell models. In our human embryonic stem cell (hESC) model54,55, we achieved temporal control of NEUROG3 induction using tetracycline to precisely match physiological levels and timing required for endocrine differentiation. These hESCs were differentiated stepwise via endoderm and then foregut into pancreatic progenitors, followed by an 8-h NEUROG3 TF induction. Over 90 high-confidence NEUROG3 downstream TF targets were identified through integrative analysis of time-series mRNA, ATAC and chromatin immunoprecipitation (ChIP) sequencing data (Fig.\u00a03i and Supplementary Data\u00a05). Additionally, we compared these results with NEUROG3 target genes identified using a human induced pluripotent stem cell (hiPSC) model56, in which an epitope-tagged NEUROG3 was used for cleavage under targets and release using nuclease (CUT&RUN) to identify NEUROG3-bound regions in purified hiPSC-derived EPs (Supplementary Data\u00a05). This revealed 59 conserved NEUROG3 TF targets between native pig and human pancreas GRNs, including 32 targets not detected in either stem cell model, while the stem cell system verified 20 targets absent in the pig in vivo dataset\u00a0(Fig.\u00a03i and Supplementary Data\u00a05). Several NEUROG3 targets, such as EHF, DACH1, ST18, and MAFF, were conserved in all models. Using temporally controlled NEUROG3 TF expression, our hESC model further captured additional conserved early NEUROG3 targets, such as PRDM8, GRHL3 and FOS.\n\nWe and others have previously reported beta cell heterogeneity in terms of proliferation and maturation regulated by the Wnt/planar cell polarity (PCP) signaling pathway57,58. However, the timing and mechanisms underlying the origin of beta cell heterogeneity remain unknown. Intriguingly, two heterogeneous beta cell subpopulations formed during the extended period of pig pancreas development. The Beta,0 cluster consisted of cells appearing at all stages, whereas the Beta,1 cluster emerged mainly during the second wave of endocrinogenesis (Fig.\u00a04a). Comparing the two beta subclusters unveiled unique differentially expressed gene sets (Fig.\u00a04b and Supplementary Data\u00a06). Beta,1 cells were enriched in TFs of the reported core transcriptional regulatory circuits for beta cells59, such as NEUROG3, FEV, TCF7L2, MEIS1, MEIS2, SOX13, GLIS3, NR3C2, and MAFA. Further gene set enrichment analysis identified active pathways related to epithelial differentiation, extracellular matrix, and cell junction organization (Supplementary Fig.\u00a05a and Supplementary Data\u00a06). In contrast, Beta,0 cells had elevated expression of cell cycle regulators and components of Wnt/planar cell polarity (PCP), TGF\u03b2, and synaptic transmission pathways, suggesting a distinct beta cell phenotype compared to Beta,1 cells.\n\na Detailed view of pig beta cells. (Top) UMAP of pig pancreas atlas with an inset showing a UMAP of the beta cells, colored by subclusters Beta,0 and Beta,1. (Bottom) Distribution of beta cell subclusters at each sampling age. b Volcano plot of differentially expressed genes between Beta,0 and Beta,1 (edgeR, FDR-corrected p-value\u2009<\u20090.05; Supplementary Data\u00a06). Genes mentioned in core transcriptional regulatory circuits and identified in pathway analysis are highlighted. c CellOracle inferred GRN plots showing networks of the top 5 regulons from Beta,0 and Beta,1 clusters. d MEIS2 in silico KO simulation with CellOracle. (Left) UMAPs overlaid with the Palantir-pseudotime (top) or MEIS2 KO simulation (bottom) vector fields. (Middle) UMAP colored by MEIS2 KO perturbation score. (Right) Sankey diagram showing the effect of MEIS2 KO on cell fate transitions. e Immunofluorescence identification of MEIS2 positive and negative beta cells in wild-type pig pancreas and hiPSC-derived islet sections, scale bar 10\u2009\u00b5m. Images are representative of 3 pig pancreas samples and 3 independent hiPSC differentiations. a-d: scRNA-seq of pancreatic cells from wild-type and INS-eGFP pigs. Detailed sample information is provided in Supplementary Data\u00a01.\n\nTo gain insight into the regulatory networks shaping the gene expression features of the beta cell subpopulations, we used CellOracle60 to infer cell-type-specific GRN modules. A custom-assembled base GRN from our pig multiome dataset was applied to construct the GRN configurations in the scRNA-seq data. This resulted in two unique TF networks between Beta,0 and Beta,1 clusters with clearly distinct top 5 regulons (Fig.\u00a04c and Supplementary Data\u00a07). Notably, all these regulons were NEUROG3 targets identified in pig, whereas in Beta,0 cells, only secondary targets were observed (Fig.\u00a03e). PLAGL1 is a zinc-finger TF implicated in cell-cycle control, ECM organization, and risk of neonatal diabetes43,44,61,62,63. In pig Beta,0 cells, PLAGL1 formed a network linking genes related to cell migration, adhesion, and cell-cycle regulation (TMEM176A, MFAP4, NME2). In both clusters, MEIS2 positively targeted beta cell identity genes (G6PC2, PDX1, CHGA), albeit interconnecting with different regulons.\n\nMEIS2 encodes a homeobox TF in the three amino acid loop extension (TALE) family and acts together with PBX and HOX TFs to form dimeric or trimeric complexes to enhance DNA binding specificity and affinity for target gene regulation64. MEIS2 was detected in human embryonic29 and adult beta cells65 and shown to regulate PAX6 expression during pancreas development66. To understand the role of MEIS2 in beta cell differentiation, we performed in silico perturbation using CellOracle to mimic a MEIS2 knockout (KO, Fig.\u00a04d). In the MEIS2 KO simulation, Beta,1 cell differentiation was blocked, with only minimal effects on the Beta,0 cluster. This was further validated by Markov simulation to estimate cell distribution changes, showing that MEIS2 KO reverted Beta,1 cells to the FEV and NGN3 progenitor states.\n\nNext, we assessed whether MEIS2 could serve as a pig-human conserved marker to distinguish beta cell subpopulations. In the pig scRNA-seq atlas, MEIS2+ beta cells mostly appeared in the Beta,1 cluster, while MEIS2- beta cells were primarily found in the Beta,0 cluster, both subpopulations sharing characteristics matching their respective beta subclusters (Fig.\u00a05b, c). Remarkably, heterogeneous human beta subpopulations in the scRNA-seq atlas could also be identified based on MEIS2 expression. Human MEIS2+ beta cells resembled pig MEIS2+ beta cells and Beta,1 cluster, showing enrichment of genes involved in microtubule and cell-matrix organization. Conversely, human MEIS2- beta cells had active Wnt/PCP and TGF\u03b2 signaling that were observed in pig MEIS2- beta cells and Beta,0 cluster (Fig.\u00a05d, e). Louvain clustering further identified a distinct beta subcluster composed of the majority of human MEIS2- cells (Fig.\u00a05f). The presence of MEIS2-positive and negative beta cells was confirmed in both pig pancreases (E20-54) and hiPSC-derived islets (Figs.\u00a04e and\u00a05g).\n\na Dot plot of enriched pathways identified by Enrichr using the DEGs of pig Beta,0 and Beta,1 clusters. b (Left) UMAP showing pig Beta clusters separated according to MEIS2 mRNA expression with bar plot showing MEIS2+ and MEIS2- beta cell distribution at each sampling age. (Right) volcano plot of differentially expressed genes between pig MEIS2+ and MEIS2- beta cells. c Dot plot of enriched pathways identified by Enrichr using the DEGs of pig MEIS2+ and MEIS2- beta cells. d (Left) UMAP showing human Beta clusters separated according to MEIS2 mRNA expression with bar plot showing MEIS2+ and MEIS2- beta cell distribution at the corresponding developmental stage. (Right) volcano plot of differentially expressed genes between human MEIS2+ and MEIS2- beta cells. e Dot plot of enriched pathways identified by Enrichr using the DEGs of human MEIS2+ and MEIS2- beta cells. f UMAP showing human Beta clusters separated according to Louvain clustering, with a bar plot showing two beta subcluster distributions at the corresponding developmental stage. g Immunofluorescence identification of MEIS2 positive and negative beta cells in wild-type pig pancreas sections from E22, 32, and 40, scale bar 10\u2009\u00b5m. Images are representative of 3 pig pancreas samples per time point. All p-values from Enrichr and edgeR analyses are adjusted by Benjamini-Hochberg FDR method (Supplementary Data\u00a06). a\u2013c: scRNA-seq of pancreatic cells from wild-type and INS-eGFP pigs. Detailed sample information is provided in Supplementary Data\u00a01.\n\nHaving observed distinct beta cell subpopulations emerging during development, we sought to investigate whether these cells may originate from different endocrine progenitors. We first examined the CellRank-inferred developmental trajectories that faithfully mapped cell fate transitions in the pig scRNA-seq atlas. The NGN3 cluster was predicted to generate major beta cell clusters as expected. Additionally, the PEC cluster was identified as another intermediate state upstream of a beta cell subpopulation (Fig.\u00a06a). To compare the gene expression programs along the trajectories from NGN3 or PEC towards the respective beta cell subtypes, we applied tradeSeq67 to detect differential gene expression patterns between lineages (Fig.\u00a06b and Supplementary Data\u00a08). Two distinguishable gene clusters were identified comparing lineages NGN3-to-Beta,1 and PEC-to-Beta,1. Gene cluster 1 was enriched with genes regulating endocrine development (MLXIPL, PAX6) and insulin secretion (CHGA, KCNJ11). It showed overall restricted expression in lineage NGN3-to-Beta,1 as opposed to the extended expression in lineage PEC-to-Beta,1. Gene cluster 2 included genes related with ECM organization, Hippo, and FGF signaling, which had limited expression in lineage PEC-to-Beta,1. We further compared the two lineages from PEC cluster to either Beta,0 or Beta,1. The identified gene groups comprised known factors involved in beta cell differentiation, such as epithelial cell differentiation, insulin secretion, NOTCH signaling, and integrin-mediated cell adhesion. Surprisingly, these genes showed subtle differences in the gene expression timing and duration, yet without discrete patterns along the two trajectories.\n\na UMAP showing Cellrank-inferred trajectories of NGN3 and PEC as intermediate states upstream of endocrine cells. b Heatmaps showing modeled gene expression patterns along pseudotime for differentially expressed gene groups identified by tradeSeq analysis (Benjamini-Hochberg FDR-adjusted p-value\u2009<\u20090.05; Supplementary Data\u00a08). Comparisons show transcriptional trajectories from NGN3 cluster to beta cell subclusters (left) versus PEC cluster to beta cell subclusters (right). Annotations indicate enriched pathways and signature genes for each gene group. c Cluster correlation analysis using Spearman correlation of mean counts per cluster for each highly variable gene (n\u2009=\u20094000). d Dot plot of the top 5 differentially expressed TFs of cells in 1\u00b0 transition (E23-33) and 2\u00b0 transition (E40-85) NGN3 and PEC clusters (edgeR, FDR-corrected p-value\u2009<\u20090.05; Supplementary Data\u00a09). Dot size is relative to the fraction of cells within a cluster expressing the gene. e MOSCOT-inferred descendants of NGN3 and PEC cluster from age E23 (using scRNA-seq data, top) and from age E45 (using multiome data, bottom). The left panel shows the respective UMAP of the endocrine cells from the scRNA-seq (top) and multiome (bottom) atlas used for the MOSCOT calculation. f Immunofluorescence identification of NR2F2+ cells in E22 pancreatic epithelium labeled by E-Cad (top and middle panels). NR2F2 protein is not detected in E42 pancreatic ductal domain (bottom panel). Scale bar 50\u2009\u00b5m. Wild-type pig samples are used for this figure. Images are representative of 3 pig pancreas samples per time point. g CellRank and MOSCOT (sankey diagrams showing the summarized MOSCOT-inferred ancestors and descendants) prediction of NGN3 and PEC clusters as intermediated states in the 1\u00b0 transition (E23-33) and 2\u00b0 transition (E40-85). (Left) UMAPs overlaid with CellRank-inferred trajectories (arrows); (Right) Sankey diagrams showing the summarized MOSCOT-inferred ancestors and descendants in (e, a\u2013d, e (top), g): scRNA-seq of pancreatic cells from wild-type and INS-eGFP pigs. e (bottom) and g: Multiome analysis of pancreatic cells from PTF1A-codon-improved-Cre/ROSA-mTmG pigs. Detailed sample information is provided in Supplementary Data\u00a01.\n\nDespite slight deviations from the NGN3 trajectory, PEC-to-Beta gene expression programs favored beta cell differentiation. Hypothesizing that the PEC cluster contains endocrine progenitors, we compared the transcriptional features between PEC and NGN3 clusters. Given the apparent differences in spatial patterning and cell organization of the pancreas during development, we performed correlation analysis of the PEC and NGN3 clusters divided by 1\u00b0 transition (E23-33) and 2\u00b0 transition (E40-85), namely following clusters: 1\u00b0NGN3, 1\u00b0PEC, 2\u00b0NGN3 and 2\u00b0PEC. Pairwise correlation scores (0.64-1) revealed substantial transcriptional similarity among all four subgroups (Fig.\u00a06c). However, differential expression analysis uncovered distinct patterns in key transcription factors of each cluster (Fig.\u00a06d, Supplementary Fig.\u00a07a, b and Supplementary Data\u00a09). While 1\u00b0PEC and 2\u00b0PEC showed only marginal expression of canonical endocrine regulators (NEUROG3, NKX2-2, NEUROD2), they were enriched for endocrine-related factors (NFIA68, PBX169 in 1\u00b0PEC; PLAGL144 in 2\u00b0PEC), suggesting a partially active endocrine program. Notably, we identified NR2F2 as a key transcription factor highly expressed in the 1\u00b0PEC cluster, though it was also detected in broader early progenitors and mesenchymal cells (Fig.\u00a06f). NR2F2 (also known as COUP-TFII) encodes a member of the steroid/thyroid hormone superfamily of nuclear receptors. It has been detected in PDX1\u207a cells at E11.5 in mice and may play a role in regulating beta cell mass70,71. We further confirmed 1\u00b0PEC presence in the pancreatic primordium by detecting NR2F2+ cells within the multilayered epithelium of E23 pancreatic buds (Fig.\u00a06f).\n\nTo verify the differentiation trajectories linking PEC cluster to beta cell subpopulations, we applied our recently developed Multi-Omics Single-Cell Optimal Transport (MOSCOT)72 framework, which uses optimal transport theory to reconstruct developmental trajectories across real developmental time points and multiple modalities. Applying MOSCOT to both pig scRNA-seq and multiome time-series atlases recovered the differentiation lineages identified by CellRank, showing that, in addition to NGN3, PEC at both early and late stage was coupled to a subset of endocrine cells as its descendants (Fig.\u00a06e, g).\n\nTo determine whether PEC-like cells exist during human and mouse pancreas development, we performed cross-species integration of pig-human and pig-mouse scRNA-seq datasets using sysVI73. This machine learning pipeline enables cross-species integration of scRNA-seq datasets while retaining high biological preservation. The sysVI-integrated embedding confirmed correct cell type alignment (Fig.\u00a07a\u2013d), as supported by established pancreas marker gene expression (Supplementary Fig.\u00a07c, d). In both human and mouse datasets, we identified cell populations (initially annotated as diverse endocrine cell types) that co-clustered with pig PEC cells, indicating transcriptionally shared cellular states (Fig.\u00a07a, c). Notably, mouse PEC-like cells showed connectivity to a subset of cells from the mouse NGN3 cluster, which was not observed in human and pig. We further identified ASPM and ECT2 as cross-species markers, expressed in pig PEC cells and their counterparts in both human and mouse (Fig.\u00a07b, d and Supplementary Data\u00a014). ASPM encodes protein abnormal spindle-like microcephaly-associated, which was identified as a novel Wnt and stemness regulator74. ECT2 encodes protein epithelial cell transforming 2, a guanine nucleotide exchange factor for Rho-like GTPases75. While these markers were also detected in some ductal and acinar cells, PECs showed a clear endocrine identity as evidenced by their strong transcriptional similarity to the NGN3 cluster (Fig.\u00a06c). To validate this population, we selected NKX2-2, a pan-endocrine marker, in combination with ASPM or ECT2 to distinguish PECs from ductal and acinar cells. Using RNAscope, we identified NKX2-2\u207a cells co-expressing ASPM or ECT2, confirming their presence in ductal and acinar regions of the pancreas across pig developmental stages (Fig.\u00a07e).\n\na UMAP of sysVI-integrated human and pig scRNA-seq pancreas atlases. The black circle indicates cells co-clustering with the pig PEC cluster. b UMAPs of split views of human (left) and pig (right) data overlayed with the sysVI-integrated embedding, and conserved PEC marker gene (ASPM and ECT2) expression in each species. c UMAP of sysVI-integrated mouse and pig scRNA-seq pancreas atlases. The black circle indicates cells co-clustering with the pig PEC cluster. d UMAPs of split views of mouse (left) and pig (right) data overlayed with the sysVI-integrated embedding, and conserved PEC marker gene (ASPM and ECT2) expression in each species. e Detection of NKX2-2\u207a cells co-expressing ASPM (left) or ECT2 (right) in wild-type pig pancreas samples (E30 and E64) by RNAscope assay. Yellow arrows indicate double-positive cells. Scale bar 10\u2009\u00b5m. Images are representative of 3 pig pancreas samples per time point. a\u2013d: scRNA-seq of pancreatic cells from wild-type and INS-eGFP pigs. Detailed sample information is provided in Supplementary Data\u00a01.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_Fig5_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_Fig6_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-64774-4/MediaObjects/41467_2025_64774_Fig7_HTML.png" + ] + }, + { + "section_name": "Discussion", + "section_text": "Through cross-species comparative multiomics integrating transcriptomic and chromatin accessibility profiling of pancreas development in mice, pigs, and humans, our work reveals both species-divergent and evolutionarily conserved gene regulatory mechanisms governing pancreatic lineage differentiation. First, the resemblance in developmental tempo of pig and human gestation provides a temporally aligned framework to study extended pancreas organogenesis events that are compressed in the mouse. Second, we observe a pig-human conservation in epigenetic and transcriptional regulation, particularly in the endocrine lineage. The high conservation of transcription factors downstream of NEUROG3 (over 50% between pig and human) suggests a core program for endocrine fate acquisition in larger mammals. This aligns with both the recent human study30 and the observed similarities in postnatal islet characteristics, fasting C-peptide and glucose levels between pigs and humans18,21. By leveraging the temporally resolved single-cell multiomic pig pancreas atlas covering all three trimesters, we identified a unique primed endocrine cell (PEC) population, potentially representing a progenitor state that is distinct from the classic NGN3 endocrine progenitor cluster. Transcriptionally matched PEC-like cells were also identified in human and mouse, with murine PEC-like cells showing a possible link to the NGN3 cluster. Concurrent with pancreas morphogenesis, we discovered that both NGN3 and PEC clusters emerged as heterogeneous populations and were predicted to hold dynamic lineage potential over time. This conserved PEC population is intriguing as it may suggest a potential NEUROG3-independent pathway for endocrinogenesis, which could offer an explanation for the persistence of endocrine cells in some human patients carrying homozygous NEUROG3 mutations76,77. However, definitive validation of this pathway and the differentiation capacity of PECs remains a crucial goal for future work. We further identified heterogeneous beta cells, suggesting the acquisition of islet cell heterogeneity may originate in embryonic development. In summary, our study presents the pig as a valuable complementary model to existing systems and enhances the understanding of in vivo pancreas development through a comprehensive multimodal comparison across species. These resources can be harnessed to refine stem cell and organoid models, offering unique opportunities to 1) address open questions in human biology and disease, and 2) bridge translational gaps from rodents to large animals (pigs) and ultimately to humans.", + "section_image": [] + }, + { + "section_name": "Methods", + "section_text": "Pigs were housed at the designated pathogen-free pig facilities in LMU or TUM. All animals received standard diet and water ad libitum as well as standard vaccination. After hormonal estrus cycle synchronization, pigs in heat were artificially inseminated or mated and ultrasonic confirmation of pregnancy was performed 21 days post insemination. At selected gestation stages, pregnant pigs were euthanized, and fetuses were collected. Pancreases were extracted and used for further analysis.\n\nPig pancreases or hiPSC-derived islets were fixed in pre-chilled 4% paraformaldehyde overnight (pancreas) or for 30\u2009min (islets) then dehydrated in a progressive sucrose gradient at 4\u2009\u00b0C. Samples were embedded in tissue freezing medium (Leica Biosystems), sectioned at 12 \u03bcm, and mounted onto Superfrost\u00ae Plus slides (Thermo Fisher Scientific) and stored at -80\u2009\u00b0C.\n\nSections were washed with PBS for 30\u2009min, permeabilized in 0.1% Triton X-100 (Sigma-Aldrich) in 0.1\u2009M Glycine (Sigma-Aldrich) for 30\u2009min, blocked in blocking solution (PBS\u2009+\u20090.1% Tween-20\u2009+\u200910% FCS\u2009+\u20090.1% BSA\u2009+\u20093% donkey serum) for 1\u2009h at room temperature (RT), and incubated with primary antibodies diluted in blocking solution (Supplementary Data\u00a010) overnight at 4\u2009\u00b0C. Sections were washed in PBS for 15\u2009min twice, incubated with secondary antibodies diluted in blocking solution (Supplementary Data\u00a010) for 3\u2009h at 4\u2009\u00b0C, counterstained with DAPI for 20\u2009min at RT, and mounted with the ProLong\u2122 Diamond Antifade Mountant (Thermo Fisher Scientific).\n\nPig pancreas sections were used for mRNA and protein co-detection assay according to the RNAscope\u00ae Multiplex Fluorescent v2 Assay combined with Immunofluorescence - Integrated Co-Detection Workflow (ACD, Biotechne). Briefly, sections were washed in PBS, baked at 60\u2009\u00b0C, and dehydrated in a progressive ethanol gradient. Sections were treated with hydrogen peroxide and 1x Co-Detection Target Retrieval solution and incubated with primary antibody (Supplementary Data\u00a010) diluted in Co-Detection Antibody Diluent overnight at 4\u2009\u00b0C. Sections were fixed in 10% neutral buffered formalin for 30\u2009min at RT and treated with RNAscope\u00ae Protease III. Sections were then hybridized with probes against pig NEUROG3 (ACD, Cat#498781), NKX2-2 (Cat#1570601-C1), ASPM (Cat#1734071-C3), and ECT2 (Cat#1734091-C3) for 2\u2009h at 40\u2009\u00b0C and signals developed following the RNAscope\u00ae Multiplex Fluorescent Reagent Kit v2 Manual. Sections were incubated with secondary antibodies for 30\u2009min at RT, counterstained with DAPI for 20\u2009min at RT, and mounted with the ProLong\u2122 Diamond Antifade Mountant (ThermoFisher). All slides were stored at 4\u2009\u00b0C until imaging by confocal microscope (ZEISS LSM 880 with Airyscan). Images were acquired and processed using Zeiss Zen 2.3 lite (Blue edition, ZEISS).\n\nPig pancreas scRNA-seq data was previously aligned using Ensembl Sus scrofa gene annotation (version 94), however showed incomplete gene annotation missing certain genes of interest (e.g. MAFA, FEV, PTF1A). Our analysis found reads downstream of the gene bodies which were not covered by the annotation. Beiki et al.82 generated an improved pig genome annotation (iso-seq annotation) by integrating poly(A) selected single-molecule long-read isoform sequencing (Iso-seq) and Illumina (short read) RNA sequencing (RNA-seq). Therefore, we further improved the annotation by combining iso-seq annotation with the Ensembl annotation version 101. We annotated the transcripts of iso-seq annotation using gffcompare (v0.12.1)83 with referencing the annotation from Ensembl version 101. The Ensembl annotation was filtered by removing \u201cpseudogene\u201d and \u201cprocessed_pseudogene\u201d biotypes genes. We added the associated same strand transcripts from the iso-seq annotation to the Ensembl annotation and extended the gene bodies if the added transcripts required it. If a transcript was added to a gene, the gene ID was altered by adding the suffix -iso. For each gene, we then added an additional \u201cextension gene\u201d downstream of the gene body. The length of the extension gene was defined by the maximum possible region of 1-10\u2009kb (using 1\u2009kb steps) that does not intersect with any other same strand gene. The extension genes were named by the name of the original genes and adding the suffix -ext\u2019x\u2019kb, whereby \u2018x\u2019 corresponds to the integer length of the addition from 1-10. Final gene counts were obtained by summing the -iso and -ext\u2019x\u2019kb (if available) versions of each gene. Supplementary Data\u00a011 shows UMI counts for 37 genes of interest before (\u201censembl\u201d) and after (\u201ciso\u201d) gene extension as examples.\n\nFor next-generation sequencing experiments, a minimum of three biological replicates (individual embryos/fetuses per pregnancy) at each developmental stage were sampled where possible. Modified approaches were used for two exceptions: At E40, only two embryos were available for analysis; At E22/23 and E33, pancreatic tissues were extremely small in size. To obtain sufficient cell numbers for reliable single-cell sequencing, all available pancreases at each age were combined for processing, as summarized in Supplementary Data\u00a01 and Supplementary Fig.\u00a02a, c.\n\nFreshly dissected pancreases were minced into fine pieces and digested with collagenase V in HBSS with Ca/Mg (0.5\u2009mg/mL for E20-50, 1\u2009mg/mL for E60-85, Sigma-Aldrich) for 5\u2009min followed by dissociation with TrypLE\u2122 Express (Gibco) for 10\u201315\u2009min at 37\u2009\u00b0C. Cell suspension was filtered through a 40\u2009\u00b5m cell strainer.\n\nThis procedure was not applied to E22/23 and E33 samples due to their small size; all cells from these samples were used for scRNA-seq without enrichment. Pancreases from transgenic reporter pigs and wild-type littermates at E40-85 were used to collect pancreatic epithelial cells. For enrichment, cell suspension was stained with EpCAM-PE-Cy7 antibody (1:200, Invitrogen) in 1% BSA\u2009+\u2009PBS for 30\u2009min at 4\u2009\u00b0C. Cells were then washed and filtered into a tube through a 35\u2009\u00b5m filter. 7-AAD (Invitrogen) or Sytox blue (Invitrogen) was used to distinguish dead cells. The resulting cell suspension was loaded onto a FACSAria III (BD) for sorting. The transgenic reporter pigs were used for specific cell-type enrichment: 1) INS-eGFP pigs78: beta cells were enriched via insulin promoter-driven GFP expression. 2) PTF1A-codon-improved-Cre79 x ROSA-mTmG80 pigs: PTF1A-codon-improved-Cre/ROSA-mTmG embryos were identified with epifluorescent microscope by GFP signal. The pancreases from these embryos were used to enrich pancreatic epithelial cells, particularly acinar cell populations, with GFP signal. Supplementary Data\u00a01 provided an overview of all samples obtained through different enrichment strategies. Supplementary Fig.\u00a02c provided a summary of pancreas number and cell counts of identified cell types in each sample. Supplementary Fig.\u00a08 provided representative gating strategies.\n\nSingle-cell suspensions were processed for scRNA-seq with a targeted cell recovery of 10,000. 10X Genomics\u2019 Single Cell Gene Expression protocols were followed according to the manufacturer\u2019s specifications and guidelines. Libraries were pooled and sequenced by a HiSeq4000 or NovaSeq 6000 platform following the recommendations from 10X Genomics. With CellRanger pipeline (v3.1.0), samples were demultiplexed to produce a pair of FASTQ files for each sample. Reads containing sequence information were aligned to the improved pig genome annotation and pre-processed for downstream analyses.\n\nFor nuclei isolation and library construction, a low-input nuclei isolation protocol adapted from 10X Genomics was performed. In brief, sorted cells were washed once with 1\u2009mL PBS\u2009+\u20091% BSA, counted, centrifuged, and the supernatant was aspirated. Subsequently, the washed cell pellet was resuspended in chilled lysis buffer with 0.5x detergent concentration (50\u2009\u03bcL per sample) and placed on ice for 5\u2009min. Then wash buffer (500\u2009\u03bcL per sample) was added and nuclei were centrifuged. To gradually change from wash to diluted nuclei buffer, cells were washed once in a 1:1 mixture of wash buffer and diluted nuclei buffer and subsequently one with pure diluted nuclei buffer. The washed isolated nuclei were then resuspended in 7-10\u2009\u03bcL diluted nuclei buffer and were directly added to the transposition reaction after quality control and counting. In all following steps, 10X Genomics\u2019 Single Cell Multiome ATAC and gene-expression protocols were followed according to the manufacturer\u2019s specifications and guidelines. The final libraries were sequenced on the Illumina NovaSeq 6000 platform following the recommendations from 10X Genomics. Raw reads were aligned to the improved pig genome annotation and pre-processed using the 10X Genomics CellRangerARC pipeline (v 2.0.0) for downstream analyses.\n\nPig pancreas samples (Supplementary Data\u00a01) and published datasets of human and mouse30,35,36,37,38,39,40,41 were preprocessed using Scanpy84 (v1.8.2).\n\nEach sample was assessed using Scanpy\u2019s quality control measures and sample-specific minimum number of genes per cell, minimum number of counts per cell, and maximum number of counts per cell were set to filter out low-quality cells (Supplementary Data\u00a012). In addition, all cells with a mitochondrial fraction > 0.15 were excluded, as well as all genes that were expressed in less than 20 cells. Read counts and gene counts across clusters of pig scRNA-seq data were shown in Supplementary Data\u00a013. The filtered gene matrices from Goncalves et al.37 were not filtered.\n\nGene counts were normalized using Scran85 (v1.22.1) for data from each lab separately. For this, we first performed a total counts normalization of each cell counts to 1,000,000, then performed a log transformation using natural log and pseudocount 1, further calculated a neighborhood graph using the first 50 principal components and number of neighbors k\u2009=\u200915. Clusters were obtained using louvain clustering86 with resolution r\u2009=\u20090.5. We then used Scran to estimate size factors with the louvain clusters as input clusters and minimum mean average count of genes to be used for normalization set to 0.1. The size factors were then used for normalization of raw gene counts (summed -iso and -ext\u2019x\u2019kb gene counts for pig samples). For downstream integration, log-transformed counts using natural log and pseudocount 1 were calculated.\n\nAmbient genes were estimated based on expression in empty droplets using DropletUtils87 (v1.14.2). Genes with an ambient expression score larger than 0.005 were considered ambiently expressed genes. Ambient genes were generalized to datasets where raw data was not available.\n\nHighly variable genes (HVGs) were calculated per batch88 to select HVGs unaffected by batch variance. Per-batch HVGs were obtained with Scanpy, using the CellRanger89 flavor, and were ranked first by the number of batches in which the genes were highly variable and second by the mean dispersion across batches. Finally, the top genes in this ranking were selected as highly variable genes.\n\nPig samples were integrated with Scanorama90 (v1.7.1) using 8,000 HVGs (excluding ambient genes), resulting in a 100-dimensional latent embedding. Human and mouse samples were each integrated with scVI91 (v0.16.1) using 2,000 HVGs, resulting in a 20-dimensional latent embedding.\n\nClustering was performed on the k-nearest neighbor (KNN) graph (k\u2009=\u200915) calculated from the integrated embedding using louvain clustering86 (leiden92 for human and mouse data) with resolution 1. The pig integrated embedding was reduced to 50 principal components before calculating the neighborhood graph. Clusters were annotated using differentially expressed marker genes. For some analysis several clusters (NGN3, Beta) were subclustered using a higher clustering resolution.\n\nScran normalization might be based on batch-specific clusters when a strong batch effect is present, leading to non-comparable counts across samples. We ran a check on all species datasets by visualizing the mean number of counts per cluster (based on integrated embedding) and sample before and after normalization. For human and mouse samples we observed strong sample-specific counts after normalization. To obtain comparable counts across samples, we re-normalized these samples using Scran and the clusters obtained from the integrated embedding.\n\nTo obtain robust doublet estimates, we used a combination of scrublet93 (v0.2.3), DoubletDetection94 (v4.2), scds95 (v1.10.0), scDblFinder96 (v1.11.4), DoubletFinder97 (v2.0.3) (default parameters, expected doublet rate 0.8) to detect doublets. Cells consistently detected by three or more methods as doublets were excluded from further analysis. In addition, clusters with a doublet frequency larger than 70% were entirely excluded.\n\nThe 12wpc human fetal scATAC-seq data30 were read into Signac98 for preprocessing. Peaks from standard chromosomes and additionally called using MACS2 were used. Gene annotation from EnsDb (EnsDb.Hsapiens.v86) was added. Quality control metrics were computed to filter low quality cells (Supplementary Data\u00a012). Data was normalized by term frequency-inverse document frequency (TF-IDF) normalization. Dimension reduction was done by running singular value decomposition (SVD) on the TD-IDF matrix, using the peaks selected by the function FindTopFeatures. Graph-based clustering and non-linear dimension reduction for visualization was performed on the KNN graph (k\u2009=\u200930) calculated from the low-dimensional embedding using SLM86 algorithm. Gene activity matrix was created by the GeneActivity function. Doublets were called by scDblFinder96 and excluded for further analysis. For cell cluster label transfer, we first extracted the 12wpc data subset from the integrated human scRNA-seq data. The scATAC-seq data gene activity was used as an approximation of a gene expression matrix and was integrated with the 12wpc scRNA-seq data following the standard scANVI99 (v0.20.3) workflow to enable label transfer. Cicero was used to compute pairwise co-accessibility scores for each peak, which were further grouped into cis-co-accessible networks. The co-accessible links along with DNA accessibility information were visualized by CoveragePlot.\n\nThe unmatched modalities were integrated using GLUE48 v0.3.2. The RNA modality input, i.e. the 12wpc data subset was extracted from the integrated human scRNA-seq data. The ATAC modality input was processed as described in section \u201cReanalysis of human fetal scATAC-seq dataset\u201d. We then constructed a guidance graph that contains omics features as nodes (i.e., genes for scRNA-seq, and peaks for scATAC-seq) and prior regulatory interactions as edges. We used the default implementation that links an ATAC peak to a gene if it overlaps either the gene body or promoter region. This graph was utilized by GLUE to orient the multi-omics alignment. To match cells from both modalities, we performed minimum cost maximum flow bipartite matching on the joint embedding derived from GLUE47,100. The cost graph was inferred using get_cost_knn_graph with knn_k\u2009=\u200915, null_cost_percentile = 99 and capacity_method = \u2018uniform\u2019. Using the bipartite matches, we matched each ATAC cell to an RNA cell. In cases where no ATAC match was found for an RNA cell, only RNA information was used. The latent vector of the cell was calculated as the average latent vector of the matched cells. Gene activities were further denoised with MAGIC101 by smoothing over nearby cells in the joint embedding as proposed and benchmarked in ArchR102. The Python implementation of magic (v3.0.0) was used to smooth gene activities over the k-nearest neighbors graph of the joint embedding with k\u2009=\u200915 neighbors, decay = 1 and k-nearest neighbors autotune parameter ka = 4. The integrated and imputed dataset was used for gene regulatory network construction.\n\nMultiome data was preprocessed similarly to scRNA-seq data as described above using Scanpy (v1.9.1) and Muon103 (v0.1.2). After summing the -iso and -ext\u2019x\u2019kb (if available) counts of each gene to generate final counts, DropletUtils87 (v1.14.2) was used with default parameters to estimate ambient gene expression probabilities.\n\nEach sample was assessed using standard quality control measures and sample-specific maximum mitochondrial gene fraction, minimum number of genes per cell, minimum number of counts per cell, and maximum number of counts per cell were set to filter out low quality cells (Supplementary Data\u00a012). To further filter out cells with low ATAC-seq quality, Muon was used to calculate ATAC-specific quality metrices. Sample-specific thresholds were identified for minimum and maximum number of counts, minimum and maximum TSS enrichment score as well as minimum and maximum nucleosome signal (Supplementary Data\u00a012).\n\nSimilar to scRNA-seq, we used a combination of scrublet93 (v0.2.3), DoubletDetection94 (v4.2), scds95 (v1.10.0), scDblFinder96 (v1.11.4), DoubletFinder97 (v2.0.3) (default parameters, expected doublet rate 0.8) and SOLO104 (as implemented in scvi-tools v0.19.0) to detect doublets based on the gene expression modality. In addition, scDblFinder and its implementation of AMULET105 were used to identify doublets on the ATAC-seq modality. Cells consistently detected by three or more methods as doublets were excluded from further analysis.\n\nTo merge the ATAC-seq data from individual samples, we followed the respective vignette on the Signac97 website. In brief, peaks from all samples were merged using the \u201creduce\u201d function of the GenomicRanges (v1.46.1) package and only peaks on standard chromosomes were kept. Next, for each sample, fragment counts were determined using Signac and stored, together with gene expression data in a Seurat object, which were subsequently merged into a single object.\n\nSignac was used to run TF-IDF normalization on ATAC-seq counts with default parameters. TF-IDF normalized count matrix was then imported into Muon.\n\nPrior to normalization, data from individual samples was merged. SCTransform (v0.3.3) was used for normalization using settings vst.flavor\u2009=\u2009\u201dv2\u201d and clip.range=c(-sqrt(n), sqrt(n)),where n (n\u2009=\u200933898) represented the number of cells.\n\nThe top 4000 highly variable genes were identified using the devianceFeatureSelection function from the scry package106 (v1.6.0) with default parameters.\n\nTo ensure consistent cell type labels between scRNA-seq and Multiome data, we employed a k-nearest neighbors classifier to transfer cell type annotations from the scRNA-seq reference. We first trained an scVI model (scvi-tools v 0.20.0), with parameters n_hidden=1024, n_latent=50, n_layers=2, gene_likelihood\u2009=\u2009\u2018nb\u2019, dispersion\u2009=\u2009\u2018gene-batch\u2019, sample names as batch key, and the technology (i.e. scRNA-seq and Multiome) as categorial covariate, to generate a shared latent space for the scRNA-seq reference and the Multiome gene expression data. Next, we used the k-nearest neighbors classifier (k\u2009=\u20095), as implemented in scikit learn (v0.24.2), to predict cell type labels in the shared latent space.\n\nTo integrate the gene expression modality of the different Multiome samples, we used harmonypy107 (v0.0.9) with sample names as batch key. To integrate the chromatin accessibility modality, we used PoissonVI108, with parameters n_hidden=1024, n_latent=50, n_layers=3, sample names as batch key, transferred labels as labels key and the developmental stage as categorial covariate.\n\nClustering was performed on the k-nearest neighbor (KNN) graph (k\u2009=\u200921, metric\u2009=\u2009\u2018minkowski\u2019) calculated from the harmonypy integrated embedding using leiden92 clustering with resolution 2. To further separate subtypes of endocrine progenitors, the respective clusters were subclustered with a resolution of 1. The resulting clusters were then annotated to match the transferred cell type labels.\n\nWe calculated differentially expressed genes between cell clusters by pseudo-bulking samples with edgeR109 (v4.0.16). We calculated pseudo-bulk expression by summing normalized raw counts of cells from one sample and cell type. Pseudo-bulks with fewer than 30 cells were excluded. To ensure that ambiently expressed genes were not erroneously predicted as differentially expressed genes, we balanced the pseudo-bulks on the sample level. For differential testing, we modeled gene expression using a generalized linear model110 with cell type and sample as covariates. Significantly differentially expressed genes were identified using a likelihood-ratio test for the coefficients of interest (q\u2009<\u20090.05, corrected for multiple testing with the Benjamini-Hochberg method111 at alpha=0.05). Gene set enrichment analysis was done with Erichr112 to identify pathway enrichment signatures of the respective cell cluster.\n\nWe compared cell-type gene expression profiles across species. For this, we calculated the mean normalized gene count per cluster. Then, we mapped pig and mouse genes to human gene symbols using orthologues from Ensembl BioMart113. If there were multiple genes mapping to one human gene, we used the summed mean normalized gene counts. For the correlation, we considered only the intersection of the top 4000 highly variable genes from all three species, resulting in 851 genes. We used Spearman\u2019s rank correlation114 to compare gene expression values for each cluster across species.\n\nWe estimated developmental trajectories and cell fates using CellRank53 (v1.5.1). For this, we estimated a pseudo-time for every cell using Palantir115 (v1.0.1), with a highest PDX1-expressing MPC cell as root cell from E22 for pig, 49dpc for human and e9.75 for mouse, respectively. Using the CellRank pseudo-time kernel, we calculated terminal states for all clusters and endocrine progenitor clusters (NGN3 and FEV clusters in pig and mouse data, EP in human data).\n\nTo compare endocrine development across species, we computed putative lineage drivers by calculating Pearson\u2019s correlation of each gene with CellRank fate probabilities for each terminal state. We mapped pig and mouse genes to human gene symbols using orthologues from Ensembl BioMart113. If there were multiple genes mapping to one human gene, we used the maximum absolute lineage correlation score. We then only considered genes that were present in all three species and had orthologues, resulting in 12,437 genes. To compare lineage correlation scores, we scaled the scores per species by the maximum of the 0.01 and 0.99 quantile and clip values to -1 and 1. Finally, we calculated the lineage driver genes (Benjamini-Hochberg FDR-corrected p\u2009>\u20090.05, scaled correlation>0.7) of this mapped subset for every species and lineage and compared overlaps across species.\n\nTo compare gene expression dynamics during alpha/beta cell development in mouse, pig and human, we first extracted orthologous alpha/beta lineage drivers expressed in at least 20% human cells and performed hierarchical clustering with AgglomerativeClustering from scikit-learn116 to identify gene groups for lineage drivers with positive or negative correlation, respectively. Gene set enrichment analysis was performed with GSEApy enrichr module112 to identify pathway enrichment signatures for each identified gene group. The expression patterns of these gene groups were analyzed in pig and mouse data.\n\nWe used tradeSeq11667 (v1.14.0) to calculate differentially expressed genes between lineages, i.e., NGN3-to-Beta,0 versus (vs) PEC-to-Beta,0; PEC-to-Beta,0 vs PEC-to-Beta,1. CellRank computed trajectories for these lineages was extracted and used for tradeSeq downstream analysis. We fitted a negative binomial generalized additive model (NB-GAM) using tradeSeq for each of the top 2000 highly variable genes and each lineage. We then identified genes with significantly different expression patterns between lineages using the PatternTest function. To exclude the genes that were already differentially expressed at the initial or terminal states, we inverted the gene rank from the edgeR differential expression (DE) analyses of NGN3 vs PEC and Beta,0 vs Beta,1. We further scored each gene by combining the inverted DE rank and the rank in the PatternTest results (transientScore). This allowed us to identify genes with similar expression at the initial or terminal state but showed significantly different expression pattern along the lineage (q\u2009<\u20090.05, corrected for multiple testing with the Benjamini-Hochberg method111 at alpha=0.05). Gene set enrichment analysis was performed with GSEApy enrichr module112 to identify pathway enrichment signatures for each identified gene group.\n\nTo integrate scRNA-seq dataset from different species, we used sysVI73 model that combines machine learning with conditional variational autoencoders (cVAE) for integrating datasets with substantial batch effects while better preserving biological variation. We subset the pig and human datasets to the intersection of top orthologous HVGs (2535 genes) as recommended. The two datasets were concatenated with \u201cspecies\u201d defined as batch_key covariate and \u201csamples\u201d defined as categorical_covariate_keys covariate. The default implementation of sysVI with multimodal variational mixture of posteriors prior (VampPrior) combined with latent cycle-consistency loss was used for the integration.\n\nUsing CellOracle60 (v0.12.0), we first constructed a base GRN using the scATAC part of the multiome data. The co-accessible peak information was extracted to generate the active gene regulatory element data, which contained the open accessible genomic regions and cis-regulatory connection data. We then annotated transcription start sites (TSS) to generate the active promoter/enhancer genomic region data. These data were integrated and peaks with weak co-accessibility scores removed, resulting in the final pig base GRN. Pig scRNA-seq data was reduced to 25,000 cells with 3028 genes (top 2000 HVGs + all TFs). An Oracle object was built by combining the gene expression counts, clustering information, CellRank trajectory with the base GRN. After KNN imputation, cluster-specific GRN for all clusters was calculated with the get_links function. To remove the weak edges and insignificant edges, we filtered the network edges by keeping the top 2000 edges ranked by edge strength with a p-value\u2009<\u20090.001 before network structure analysis. Network (centrality) scores were calculated using the links.get_network_score function. The top genes ranked by betweenness centrality were selected for Beta,0 and Beta,1 cluster to plot the GRNs with NetworkX117. To simulate MEIS2 KO in silico, we first used the Oracle object and the filtered GRNs of all clusters to make the regression models (a regularized linear machine-learning model) for simulation. MEIS2 expression was set to 0, and the global future gene expression shift after perturbation was then calculated.\n\nTo overcome the sparsity of single-cell data, we used SEACells118 (v0.3.2) with default parameters to identify metacells (n\u2009=\u2009387), representing cell states in the integrated gene expression latent space.\n\nTo identify putative regulatory elements, we used the Signac (v1.9.0) LinkPeaks function with default parameters to calculate the correlation between chromatin accessibility and gene expression of nearby highly variable genes.\n\nWe then used all peaks with significant links to construct a GRN using Pando47 (v1.0.3) for both pig multiome data and human integrated scRNA/ATAC-seq data. In brief, we used the motif collection and find_motifs function from the Pando package to identify transcription factor motifs within the peaks. We then inferred the GRN considering only highly variable transcription factors found in the motif collection and peaks within 1\u00d7106\u2009bp around their TSS, using the following parameters: peak_to_gene_method = \u2018Signac\u2019, aggregate_peaks_col = \u2018SEACell\u2019, tf_cor = 0.05, method = \u2018xgb\u2019. Next, we constructed transcription factor modules within the GRN using the find_modules function with a p-value threshold of 0.05, a R2 threshold of 0.15 (0.05 for human data), a minimum number of variables of 50 (10 for human data) and a minimum number of genes per module of 5. The GRN was visualized as a UMAP embedding of the TFs based on co-expression and regulatory relationship as measured by the inferred coefficients. Nodes were sized by the PageRank centrality of each TF and colored according to the enrichment of TF expression. Coverage tracks and peak-to-gene links were visualized using Signac. UCSC liftOver tool (http://genome.ucsc.edu)119 was used to lift link coordinated from pig to human reference genome. ALRA120 was used to impute pig gene expression and the calculated values were shown as violin plots next to the coverage plots.\n\nWe computed motif activities for human and pig scATAC-seq data using chromVAR121 (v1.24.0). We first identified motif matches of the human_pwns_v2 motif collection from the chromVARmotifs package using Signac. Motif class information was derived from CIS-BP Database Build 2.00. Next, we used the RunChromVAR function with default parameters to calculate per-cell motif activity scores. We then tested for differential activity scores between cell types using the FindAllMarkers function with mean.fxn = rowMeans, to compute the average difference in z-scores. Motifs with an adjusted p-value\u2009<\u20090.01 and an average difference > 1 were considered differentially active. To further select meaningful motifs for plotting in the heatmaps in the main figures, we computed the Pearson correlation between transcription factor gene expression z-scores and chromVAR motif z-scores in the pig multiome data and kept only motifs/transcription factors with a correlation coefficient > 0.1. Heatmaps show the chromVAR z-scores stored in the data slot of the chromVAR assay.\n\nTo confirm trajectory results obtained using CellRank, we additionally used MOSCOT72 (v0.3.3) to infer endocrine lineages using real time points. We ran MOSCOT on the porcine scRNA-seq data using time points E23, 33, and 40 (using geodesic distances, tau_a\u2009=\u20090.999, tau_b\u2009=\u20090.99999). We constructed an approximation of the geodesic distance by using an approximation of the Heat Kernel122 and constructed a KNN-graph with k\u2009=\u200915 based on a 50-dimensional PCA embedding. For time points E45, 63, 85 we ran MOSCOT on the multiome data with a joint embedding created by concatenating the scaled harmony embedding of the scRNA-seq data and scaled atac_poisson embedding. The inferred cell transitions are visualized using a Sankey diagram, excluding transitions that come from <5% of cells of one cell type.\n\nThe NEUROG3-/- hESC line was previously created using CRISPR/Cas9 to disrupt endogenous expression with a frame-shift INDEL55. hESCs were maintained in mTeSR (StemCell Technologies) on hESC-qualified Matrigel (BD Biosciences) coated plates under standard culture conditions (37\u2009\u00b0C, 5% CO2, and 95% humidity). Cells were routinely passaged every four days with Dispase (Invitrogen). High-titer lentivirus with inducible NEUROG3 vectors was added to the media of newly plated hESCs. After 24\u2009h, the media was replaced with mTeSR containing selective antibiotic G418 (500\u2009mg/mL, Sigma). All transduced cell lines were maintained under selection.\n\nhESCs were dispersed with Accutase (StemCell Technologies), washed, collected, resuspended in mTeSR containing 10\u2009mM ROCK inhibitor (Y-27632, Tocris Bioscience), and plated at a concentration of 1 \u00d7 105 cells/cm2 on Matrigel-coated, 24-well plates (Nunclon, Delta treated). When cells reached 75% confluency, differentiation was initiated. Day 0 medium was RPMI 1640 supplemented with non-essential amino acids, 100\u2009ng/mL Activin A (Cell Guidance Systems) and 50\u2009ng/mL BMP4 (R&D Systems). Days 1\u20132 medium contained 0.2% tetracycline-free FBS (Hyclone) but not BMP4. Days 3\u20134 medium was RPMI 1640 containing 2% FBS, 50\u2009ng/mL FGF7 (R&D Systems), and 50\u2009ng/mL Noggin (R&D Systems). Days 5\u20138 medium was high-glucose-DMEM (Gibco) containing 50\u2009ng/mL Noggin, 2\u2009mM all-trans retinoic acid (Stemgent), and 1% (0.5x) B27 without vitamin A (Gibco). Days 9\u201312 medium was high-glucose-DMEM supplemented with 1% B27 and 25\u2009ng/mL Noggin.\n\nhiPSCs were maintained in StemMACS\u2122 iPS-Brew XF (iPS-Brew) culture medium (Miltenyi Biotec) on Geltrex (Gibco) coated dishes under standard culture conditions (37\u2009\u00b0C, 5% CO2 and 95% humidity). Cells were passaged every 4\u20135 days by single-cell dispersion using Accutase (Sigma-Aldrich). For aggregate suspension cultures, hiPSCs were detached with Accutase and seeded at a concentration of 0.8 \u00d7 106 cells/mL in iPS-Brew supplemented with Y-27632 to a 30-mL spinner flask (Reprocell) on a magnetic stirrer (Cultistir, Able) set at 60\u2009rpm in a humidified 5% CO2 37\u2009\u00b0C incubator. The aggregates were split every 3-4 days with Accutase.\n\nTo initiate differentiation, hiPSC aggregates were dispersed into single-cell suspension and seeded at 0.8\u00d7106 cells/mL in a 30-mL spinner flask. Cells were cultured for 72\u2009h in iPS-Brew and then differentiated towards pancreatic islets with a 6-stage protocol (detailed in Supplementary Data\u00a010)123. Samples were collected on differentiation stage 5 day 7, stage 6 day 7 and stage 6 day 14.\n\nSamples were collected on differentiation day 9 post 8\u2009h NEUROG3 induction. Cells were cross-linked in 1% formaldehyde in PBS for 12\u2009min at RT and were quenched by 0.125\u2009M glycine. Nuclei were pelleted in lysis buffer (10\u2009mM Tris-HCl pH 8.0, 10\u2009mM NaCl, 0.2% NP-40). For chromatin fragmentation, cells were resuspended in Nuclear Lysis Buffer (20\u2009mM Tris-HCl pH 8.0, 0.1% SDS, 2\u2009mM EDTA) and sonicated in Diagenode Sonicator for 9 cycles of 20\u2009s on, 60\u2009s off at 4\u2009\u00b0C. The desired amount of fragmented chromatin was supplemented with Nuclear Lysis Dilution Buffer (20\u2009mM Tris-HCl pH 8.0, 0.1% SDS, 2\u2009mM EDTA, 150\u2009mM NaCl, 1% Triton X-100) and precleared with blocked Protein G magnetic beads (Thermo Fisher Scientific) with rotation at 4\u2009\u00b0C for 3\u2009h. 1% precleared samples were saved as input and each 25\u2009\u03bcg sample was incubated with 20\u2009\u03bcg Protein G magnetic beads preloaded with 5\u2009\u00b5g of NEUROG3 antibody (R&D) overnight with rotation at 4\u2009\u00b0C. Beads were then washed sequentially at 4\u2009\u00b0C using: (1) Washing A (150\u2009mM NaCl, 20\u2009mM Tris-HCl pH 8.0, 2\u2009mM EDTA, 0.1% SDS, 1% Triton X-100, 0.1% sodium deoxycholate), (2) Serial Washing B (20\u2009mM Tris-HCl pH 8.0, 2\u2009mM EDTA, 0.1% SDS, 1% Triton X-100, 0.1% sodium deoxycholate) with 500\u2009mM, 1\u2009M and 2\u2009M NaCl, (3) Washing C (50\u2009mM Tris-HCl pH 8.0, 2\u2009mM EDTA, 500Mm LiCl, 1% NP-40, 0.5% sodium deoxycholate) and final 2 times wash with TE (10\u2009mM Tris-HCl pH 8.0, 10\u2009mM EDTA). Protein-DNA complexes were eluted from the beads in Elution Buffer (50\u2009mM Tris-HCl pH 8.0, 10\u2009mM EDTA, 1% SDS) at 65\u2009\u00b0C for 30\u2009min. Cross-links were reversed at 65\u2009\u00b0C overnight, and DNA was purified with phenol:chloroform (1:1), chloroform and ethanol for library construction and sequencing at CCHMC DNA sequencing and Genotyping Core Facility.\n\nSamples were collected on differentiation day 9 and day 12 with or without 8 or 24\u2009h NEUROG3 induction. Cells were dissociated with Accutase into a single-cell suspension. About 50,000 cells were collected for lysis and transposition based on the Omni-ATAC protocol. Briefly, cells were lysed with Lysis buffer (10\u2009mM Tris-HCl pH 8.0, 10\u2009mM NaCl, 3\u2009mM MgCl2, 0.1% NP-40, 0.1% Tween-20, 0.01% Digitonin), washed with washing buffer (10\u2009mM Tris-HCl pH 8.0, 10\u2009mM NaCl, 3\u2009mM MgCl2, 0.1% Tween-20), and incubated with Transposition buffer (Nextra) at 37\u2009\u00b0C for 30\u2009min rotated at 1,000\u2009rpm. DNA was purified (Qiagen MinElute) for library construction and sequencing at the Kottyan lab, CCHMC.\n\nSamples were collected on differentiation days 9, 10, 11, and 12 with or without 8\u2009h NEUROG3 induction. All RNA samples were column-purified using a NucleoSpin RNA kit (Macherey-Nagel) with an on-column DNase digestion according to the manufacturer\u2019s protocol. Frozen RNA samples were sent for library construction and sequencing at Novogene.\n\nChIP-seq was performed at a depth of 30\u2009M reads per sample. ATAC-seq was performed at a depth of 20\u2009M reads per sample. RNA-seq was performed at 150\u2009bp paired-end with a depth of 30\u2009M reads per sample. Fastq read files for each sample were obtained and then aligned using the Computational Suite for Bioinformaticians and Biologists version 3.0 (CSBB-v3.0) to remove low quality bases and potential adapter contamination. Open chromatin region and transcription factor binding sites (NEUROG3) were then called, filtered, and annotated to the nearest gene using MACS2124 (v2.1.0) and HOMER125 (v4.11). Motif analysis annotations with genomic features and peak overlap were performed using HOMER, ChIPseeker126 (v1.38.0) and ChIPpeakAnno127 (v3.36.1). Raw transcript counts and normalized transcripts per million (TPM) values were obtained and analyzed for differential expression with DESeq2128 (v1.42.1). For differential expression, statistical and biological significance was set at FDR\u2009<\u20090.05, log-fold-change > 1, with a minimum of 1 count from the triplicates of the total 12 samples (Supplementary Data\u00a01). Directed GRN was constructed with the integration of ChIP, ATAC and RNA-seq data using mLASSO-STARS129 based algorithm. Nodes of GRN were filtered with differentially expressed genes and protein function, whereas edges were pruned with interaction types and weights. Graph analysis on the constructed GRN was performed for cliques, coefficients, shortest paths, centrality, and community. The results were compared across sampling time points. Dynamic simulation and pseudo-interruption were conducted based on graph diffusion with the hyperbolic tangent as the activation function and as the filtration.\n\nPublished datasets are available publicly or accessible upon reasonable request from the corresponding authors of the original publications. Human data were from Yu et al.41 (OMix (https://bigd.big.ac.cn/omix/) identifier OMIX236), Goncalves et al.37 (European Genome-Phenome Archive (EGA, https://ega-archive.org/) ID# EGAD00001007506), de la O et al.30 (database of Genotypes and Phenotypes (dbGaP) accession phs002693.v1.p1). Mouse data were from Bastidas-Ponce et al.35 (Gene Expression Omnibus (GEO) accession GSE132188), Byrnes et al.36 (GEO accession GSE101099), Han et al.38 (GEO accession GSE136689), Krentz et al.39 (GEO accession GSE120522), Yu et al.42 (GEO accession GSE115931).\n\nThe use of pigs in this study was approved by the Committee on Animal Health and Care of the local government body of the state of Upper Bavaria in Germany for the wild-type and INS-eGFP78 German Landrace pigs from the Ludwig Maximilian University of Munich (LMU, Permission No. 55.2-2532.Vet_02-17-136) and the PTF1A-codon-improved Cre (iCre)79 and ROSA-mTmG80 pigs from the Technical University of Munich (TUM, Permission No. 55.2-2532.Vet_02-18-33). Experiments were conducted in accordance with the German Animal Welfare Act and Directive 2010/63/EU on the protection of animals used for scientific purposes.\n\nThe parent human embryonic stem cell (hESC) line WA01 (H1) was obtained from WiCell. All experiments with hESCs were approved by the Cincinnati Children\u2019s Hospital ESCRO committee (Protocol# EIPDB2713).\n\nThe human-induced pluripotent stem cell (hiPSC) line HMGUi001-A81 was generated at Helmholtz Munich. All experiments with hiPSCs were approved by the Ethics Committee of the Technical University Munich (219/20 S, 290/20 S).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "The data generated in this study have been deposited in NCBI\u2019s Gene Expression Omnibus. The pig pancreas data is accessible through GEO Series accession number GSE262280. The hESC datasets are accessible through GSE261950, GSE261951, and GSE261952. Sequencing data from pig pancreas were aligned using the Sscrofa11.1 assembly of the pig genome (https://www.ebi.ac.uk/ena/browser/view/GCA_000003025.6) and the improved annotation based on the Ensembl annotation version 101 (see Methods; Improved pig gene annotation file is available at: https://github.com/theislab/pig-embryo-ana). Sequencing data from hESC were aligned using the GRCh37/hg19 reference genome and Ensembl gene annotation (https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.13/). Any other data supporting the findings of this study are available from the corresponding authors on reasonable request.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Jupyter notebooks to reproduce the analysis and figures are available at: https://github.com/theislab/pig-embryo-ana130.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Nair, G. & Hebrok, M. 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Kr\u00e4tzl (CiMM, Ludwig Maximilian University of Munich) and D. Kalla (Technical University of Munich) for their excellent technical assistance; D. Kechele (Cincinnati Children\u2019s Hospital Medical Center) for great support in organizing and depositing next generation sequence data; M. Bakhti and A. B\u00f6ttcher (IDR, Helmholtz Munich) for helpful discussions; A. Grapin-Botton, J. Stratmann and C.A. Gon\u00e7alves (Max Planck Institute of Molecular - Cell Biology and Genetics) for generously sharing human fetal pancreas data; D. Klein, M. Lange, V. Bergen, and A. Moinfar (ICB, Helmholtz Munich) for bioinformatic analysis advice; the animal caretakers in the pig facilities in Ludwig Maximilian University of Munich and Technical University of Munich. The authors of this study received funding from the European Research Council (grant 101054564 \u2013 to H.L. and grant 101054957 \u2013 to F.J.T), the German Research Foundation (LI 1006/3-1\u2013 to H.L., TH 900/13-1 \u2013 to F.J.T, WO 685/22-1 \u2013 to E.W., Transregio Research Unit 127 and 205 funding \u2013 to E.K. and E.W., Collaborative Research Centre 1321 funding to A.S. and T.F.), the Juvenile Diabetes Research Foundation (grant 3-SRA-2023-1420-S-B \u2013 to H.L, E.W., F.J.T, grant 2-SRA-2019-773-S-B \u2013 to J.B.S.), the German Federal Ministry of Education and Research to the German Centre for Diabetes Research (grant 82DZD00802 \u2013 to E.W.), the National Institutes of Health (grant R01DK118421 \u2013 to J.B.S., grant R25GM056847-23 \u2013 to S.O., grant R01DK118421-02S1 \u2013 to S.O.), the Kraft Family Fellowship to the UCSF Diabetes Center (S.O.).", + "section_image": [] + }, + { + "section_name": "Funding", + "section_text": "Open Access funding enabled and organized by Projekt DEAL.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Kaiyuan Yang, Hannah Spitzer.\n\nInstitute of Diabetes and Regeneration Research (IDR), Helmholtz Munich, Neuherberg, Germany\n\nKaiyuan Yang,\u00a0Michael Sterr,\u00a0Eunike Sawitning Ayu Setyono,\u00a0Katharina Scheibner\u00a0&\u00a0Heiko Lickert\n\nGerman Center for Diabetes Research (DZD), Neuherberg, Germany\n\nKaiyuan Yang,\u00a0Michael Sterr,\u00a0Eunike Sawitning Ayu Setyono,\u00a0Katharina Scheibner,\u00a0Barbara Kessler,\u00a0Eckhard Wolf,\u00a0Elisabeth Kemter\u00a0&\u00a0Heiko Lickert\n\nInstitute of Computational Biology (ICB), Helmholtz Munich, Neuherberg, Germany\n\nHannah Spitzer,\u00a0Karin Hrovatin\u00a0&\u00a0Fabian J. Theis\n\nInstitute for Stroke and Dementia Research, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany\n\nHannah Spitzer\n\nDepartment of Mathematics, Technical University of Munich, Munich, Germany\n\nKarin Hrovatin\u00a0&\u00a0Fabian J. Theis\n\nDepartment of Cell and Tissue Biology, University of California, San Francisco, USA\n\nSean de la O\u00a0&\u00a0Julie B. Sneddon\n\nDiabetes Center, University of California, San Francisco, USA\n\nSean de la O\u00a0&\u00a0Julie B. Sneddon\n\nEli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, USA\n\nSean de la O\u00a0&\u00a0Julie B. Sneddon\n\nDivision of Developmental Biology, Cincinnati Children\u2019s Hospital Medical Center, Cincinnati, USA\n\nXinghao Zhang\u00a0&\u00a0James M. Wells\n\nCenter for Stem Cell and Organoid Medicine (CuSTOM), Cincinnati Children\u2019s Hospital Medical Center, Cincinnati, USA\n\nXinghao Zhang\u00a0&\u00a0James M. Wells\n\nCore Facility Genomics, Helmholtz Munich, Neuherberg, Germany\n\nMinhaz Ud-Dean\u00a0&\u00a0Thomas Walzthoeni\n\nChair of Livestock Biotechnology, Department of Molecular Life Sciences, School of Life Sciences, Technical University of Munich, Freising, Germany\n\nKrzysztof Flisikowski,\u00a0Tatiana Flisikowska\u00a0&\u00a0Angelika Schnieke\n\nDivision of Endocrinology, Cincinnati Children\u2019s Hospital Medical Center, Cincinnati, USA\n\nJames M. Wells\n\nChair for Molecular Animal Breeding and Biotechnology Gene Center, Ludwig Maximilian University of Munich, Munich, Germany\n\nBarbara Kessler,\u00a0Eckhard Wolf\u00a0&\u00a0Elisabeth Kemter\n\nCenter for Innovative Medical Models (CiMM), Ludwig Maximilian University of Munich, Munich, Germany\n\nBarbara Kessler,\u00a0Eckhard Wolf\u00a0&\u00a0Elisabeth Kemter\n\nSchool of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany\n\nFabian J. Theis\n\nSchool of Medicine, Technical University of Munich, Munich, Germany\n\nHeiko Lickert\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nK.Y. and H.L. designed the study. H.S. and F.J.T. designed bioinformatic analysis plans. K.Y., H.S., and H.L. wrote the manuscript. B.K., E.W., and E.K. advised on the study design and provided pig pancreas samples. K.Y. and M.S. prepared samples for single-cell genomics. K.Y., H.S., M.S., and K.H. performed bioinformatic analyses. S.O. and J.B.S provided human fetal data and analysis advice. X.Z. designed and performed hESC differentiation experiments and analyzed the data, supervised by J.M.W., K.Y., E.S., and K.S. designed and performed hiPSC differentiation experiments and analyzed the data. M.U. and T.W. improved pig genome annotations and sequenced the libraries. K.F. and T.F. provided pig pancreas samples, supervised by A.S. H.L. provided financial support. F.J.T. and H.L. supervised the study. All authors reviewed and edited the manuscript.\n\nCorrespondence to\n Fabian J. Theis or Heiko Lickert.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "F.J.T. consults for Immunai Inc., Singularity Bio B.V., CytoReason Ltd, Cellarity, Curie Bio Operations, LLC, and has an ownership interest in Dermagnostix GmbH and Cellarity. The remaining authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.", + "section_image": [] + }, + { + "section_name": "Additional information", + "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.", + "section_image": [] + }, + { + "section_name": "Rights and permissions", + "section_text": "Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions", + "section_image": [] + }, + { + "section_name": "About this article", + "section_text": "Yang, K., Spitzer, H., Sterr, M. et al. A multimodal cross-species comparison of pancreas development.\n Nat Commun 16, 9355 (2025). https://doi.org/10.1038/s41467-025-64774-4\n\nDownload citation\n\nReceived: 03 July 2025\n\nAccepted: 25 September 2025\n\nPublished: 22 October 2025\n\nVersion of record: 22 October 2025\n\nDOI: https://doi.org/10.1038/s41467-025-64774-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 23.5-23.5c0-6.23-2.48-12.21-6.88-16.62-4.41-4.4-10.39-6.88-16.62-6.88zm0 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Metabolic Channeling Revealed by Multimodal Microscopy", + "journal": "Nature Communications", + "published": "01 July 2025", + "supplementary_0": [ + { + "label": "Supplementary Information", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60994-w/MediaObjects/41467_2025_60994_MOESM1_ESM.pdf" + }, + { + "label": "Reporting Summary", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60994-w/MediaObjects/41467_2025_60994_MOESM2_ESM.pdf" + }, + { + "label": "Transparent Peer Review file", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60994-w/MediaObjects/41467_2025_60994_MOESM3_ESM.pdf" + } + ], + "supplementary_1": [ + { + "label": "Source Data", + "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60994-w/MediaObjects/41467_2025_60994_MOESM4_ESM.xlsx" + } + ], + "supplementary_2": NaN, + "source_data": [ + "https://doi.org/10.17632/tr8xdwv28g.1", + "/articles/s41467-025-60994-w#Sec32" + ], + "code": [ + "https://github.com/ArrojoDrigoLab/MIMS-EM" + ], + "subject": [ + "Cellular imaging", + "Mitochondria" + ], + "license": "http://creativecommons.org/licenses/by/4.0/", + "preprint_pdf": "https://www.researchsquare.com/article/rs-4096781/v1.pdf?c=1751457129000", + "research_square_link": "https://www.researchsquare.com//article/rs-4096781/v1", + "nature_pdf": "https://www.nature.com/articles/s41467-025-60994-w.pdf", + "preprint_posted": "17 Apr, 2024", + "research_square_content": [ + { + "section_name": "Abstract", + "section_text": "Metabolic homeostasis within cells and tissues requires engagement of catabolic and anabolic pathways consuming nutrients needed to generate energy to drive these and other subcellular processes. However, the current understanding of cell homeostasis and metabolism, including how cells utilize nutrients, comes largely from tissue and cell models analyzed after fractionation. These bulk strategies do not reveal the spatial characteristics of cell metabolism at the single cell level, and how these aspects relate to the location of cells and organelles within the complexity of the tissue they reside within. Here we pioneer the use of high-resolution electron and stable isotope microscopy (MIMS-EM) to quantitatively map the fate of nutrient-derived 13C atoms at subcellular scale. When combined with machine-learning image segmentation, our approach allows us to establish the cellular and organellar spatial pattern of glucose 13C flux in hepatocytes in situ. We applied network analysis algorithms to chart the landscape of organelle-organelle contact networks and identified subpopulations of mitochondria and lipid droplets that have distinct organelle interactions and 13C enrichment levels. In addition, we revealed a new relationship between the initiation of glycogenesis and proximity of lipid droplets. Our results establish MIMS-EM as a new tool for tracking and quantifying nutrient metabolism at the subcellular scale, and to identify the spatial channeling of nutrient-derived atoms in the context of organelle-organelle interactions in situ.\u00a0Biological sciences/Cell biology/Cellular imagingBiological sciences/Biological techniques/MicroscopyBiological sciences/Physiology/Metabolismglucose metabolismorganelle contactstissue organizationcell architecture", + "section_image": [] + }, + { + "section_name": "Figures", + "section_text": "Figure 1Figure 2Figure 3Figure 4", + "section_image": [ + "https://assets-eu.researchsquare.com/files/rs-4096781/v1/9cdfb8f03a47aa4194643e82.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-4096781/v1/4395c8f5452df269a374c294.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-4096781/v1/048b56d231385d4a3721f393.jpg%3FmaxDims%3D150x150&w=256&q=75.png", + "https://assets-eu.researchsquare.com/files/rs-4096781/v1/14c1e7d3207219b72fc609d1.jpg%3FmaxDims%3D150x150&w=256&q=75.png" + ] + }, + { + "section_name": "Introduction ", + "section_text": "Tissue function is supported by cell metabolism pathways that are modulated to meet changes in nutrient availability and energetic demands that occur throughout an organism\u2019s lifetime. Much of our knowledge regarding cell metabolism is derived from bulk metabolomics using stable or radioactive isotopes (i.e., 13C and 14C, respectively). Throughout the years, this approach has revealed differences in how cells utilize nutrients to maintain energy and cell homeostasis during different cell states 1,2 and how these aspects are impacted by aging, cancer, degenerative, and/or metabolic diseases 3. Cells are organized in sub-cellular compartments created by organelles that handle essential processes necessary for cell function, such as mitochondrial respiration or protein synthesis within the endoplasmic reticulum (ER) and Golgi apparatus 4,5. Several aspects of cell metabolism require proper organization of organelle-organelle-interaction networks that create distinct intracellular compartments such as mitochondria-ER or mitochondria-lipid droplets contact sites 6,7. These sub-cellular compartments are dynamic and interact via proteins that mediate membrane anchoring and/or the exchange of molecules and ions between organelles 6-9. Perturbation of these organelle contact sites disrupts cell and whole-body metabolism and have been linked to the patho-physiology of metabolic and neurodegenerative diseases 8,10-13. Therefore, there is a need to study and understand the principles that guide the spatial organization pattern of cells and organelles in situ and their correlation to changes in animal and cell metabolism.\nDifferent super resolution light and electron microscopy techniques have been applied to determine the architecture and spatiotemporal dynamics of organelle-interaction networks with nanometer resolution 7. Moreover, recent advances in imaging metabolomics techniques such as MALDI-MS 14 and ToF-SIMS 15 allowed the visualization of the spatial distribution pattern of metabolites and molecular flux at tissue and multi-cellular scales. However, these techniques are unable to detect and measure metabolites at sub-cellular resolution so that one can study the correlation between cell metabolism and cell and/or organelle anatomies. In recent years, we have developed a new correlative microscopy pipeline that combines high-resolution scanning electron microscopy (SEM) with multi-isotope mass spectroscopy (MIMS) that is called MIMS-EM 16. MIMS-EM leverages SEM\u2019s high spatial resolution and the high-resolution mass detectors of MIMS to simultaneously detect and quantify stable isotope incorporation (e.g., 15N, 14N, 13C, or 12C) into macromolecules to create spatially annotated maps of stable isotope flux overlaid with (intra)cellular architecture. We have previously applied MIMS-EM and stable isotope-labelling of animal tissues and cells to create quantify the age of multiple biological structures, from protein super-complexes to organelles to cells 16-18. These results revealed the vast heterogeneity of age and longevity of biological structures in a multi-scale phenomenon we refer to as age mosaicism 16.\nHere, we apply MIMS-EM to annotate the spatial channeling of atoms derived from nutrient metabolism at animal, tissue, cell, and intracellular scales. This is achieved by combining MIMS-EM with stable isotope labelling of mice (SILAM) using [U-13C6]-glucose tracers, in vivo animal metabolism measurements, and gas-chromatography mass spectrometry (GC-MS) to extract multiple indexes of glucose metabolism and flux across scales. Using deep-learning (DL)-based image segmentation and spatial analysis tools, we map the subcellular location of individual organelles and chart the landscape of organelle-interaction networks to quantify subcellular changes that occur in response to increases in circulating glucose levels. This uncovered the association of enzymes involved in glycogen synthesis with lipid droplets, and the existence of two subpopulations of ER-interacting mitochondria marked by distinct glucose-derived 13C enrichment and ER interaction patterns. Together, our approach establishes a multi-modal framework to study the spatial landscape of nutrient flux and organelle organization to reveal sub-cellular organization patterns of enzymes and organelles involved in glucose metabolism.", + "section_image": [] + }, + { + "section_name": "Results and Discussion", + "section_text": "In vivo labelling of mice using [U-13C6]-glucose and whole-body metabolomics. To measure the flux of glucose from whole body to the organelle level and its correlation with cellular and organelle organization, we created a multi-modal pipeline combining the delivery of [U-13C6]-glucose with tissue stable isotope mass spectrometry and MIMS-EM (Figure 1A). We delivered [U-13C6]-glucose to freely moving and awake animals using intra-venous catheter that contained an additional port for arterial blood sampling to quantify blood glucose and plasma metabolite levels and 13C enrichment in those metabolites (Figure 1A). First, we placed 6-hour fasted 8-week-old male C57/BL6J mice inside metabolic cages and continuously infused each animal with 15 or 40 mg\u00b7kg-1\u00b7min-1 of [U-13C6]-glucose for up to 4 hours. These doses were chosen to evaluate in vivo glucose metabolism rates in response to glucose dosages that either matched or exceeded the rate of endogenous glucose production in mice 19. As expected, mice infused with 15 mg\u00b7kg-1\u00b7min-1 remained normoglycemic, while mice dosed with 40 mg\u00b7kg-1\u00b7min-1 experienced a sustained increase in glucose concentration (Figure 1B). Next, to investigate the kinetics of whole body [U-13C6]-glucose oxidation in vivo, we measured the relative enrichment of 13C in the expelled breath CO2 using stable isotope mass spectrometer gas detectors coupled to our metabolic cages (Figure 1C, and Figure S1A). This approach quantified time- and dose-dependent increases in 13CO2 in [U-13C6]-glucose-infused mice, thus confirming that [U-13C6]-glucose molecules were delivered and oxidized within the first 60 minutes and reached a plateau within 120min (Figure 1C). Accordingly, exchange of [U-13C6]-glucose for unenriched glucose caused 13CO2 to quickly fall over time (Figure 1C). Similar results were observed in mice exposed to a longer 16-hour fast and infused with 40 mg\u00b7kg-1\u00b7min-1 of [U-13C6]-glucose, including increased insulin release (Figure S1B-D), thus validating our stable isotope delivery and quantification of [U-13C6]-glucose oxidation rates in vivo.\nIn response to an increase in blood glucose, pancreatic beta cells secrete insulin to normalize blood glucose levels 20. Insulin acts on skeletal muscle depots that metabolize glucose into secondary metabolites that can be measured in the circulation (i.e., lactate and pyruvate), and stimulates the liver and adipose tissue to store glucose-derived carbons into large macromolecules such as glycogen or triglycerides, respectively. To investigate the amount of [U-13C6]-glucose and 13C-labelled glucose derived metabolites, we performed GC-MS on plasma samples collected during our infusion experiments. This identified a gradual and significant decrease in the fractional abundance of 12C6-glucose (M+0) and an increase in 13C-labelled glucose (M+6) (Figure 1D), with a similar pattern in the appearance of M+3 being observed for several circulating metabolites such as lactate, pyruvate, glycerol, and alanine (Figure 1E, Figure S1D-G, and Supplementary Table 1). These results indicate that as [U-13C6]-glucose floods the circulatory system 13C abundance in glucose (M+6) and glucose-derived secondary metabolites (M+3) increase.\nQuantification of glucose-derived 13C incorporation at tissue and cell scales. The liver is a key organ in the glucose homeostasis response, where hepatocytes are organized in distinct architectural zones with unique transcriptional and metabolic profiles that underlie differences in glucose metabolism and glycogenesis 21,22. Therefore, we used the liver as a benchmark to investigate the flux of glucose-derived 13C atoms at the tissue scale. To validate our approach and to confirm 13C enrichment in newly synthesized glycogen molecules, we performed GC-MS of isolated glycogen molecules from mice infused with 40 mg\u00b7kg-1\u00b7min-1 [U-13C6]-glucose after an overnight fast. We observed a gradual increase in 13C-labelled glycogen molecules, as expected (Figure S2A).\nWe applied MIMS-EM to quantify the flux of glucose-derived 13C atoms in hepatocytes from mice infused with [U-13C6]-glucose and focused on hepatocytes close to the central vein because of their higher potential to channel glucose towards glycogenesis 22. MIMS-EM imaging collected data for multiple isotopes (i.e., 13C, 12C, 32S, and 14N) that were co-registered on high-resolution hepatocyte micrographs previously acquired using SEM 16. Briefly, processing of MIMS-EM data requires the alignment of MIMS and SEM images in a process that involves mapping of fiducial points for cross-platform image registration (Figure S2B) 17. These steps are required for the correlative nature of MIMS-EM since MIMS imaging causes significant deformations in the X and Y axes (Figure S2C-D) that must be corrected for to achieve a high degree of true image overlap (Figure S2E-F) 17. MIMS-EM of hepatocytes revealed time- and dose-dependent accumulations of 13C within the total hepatocyte biomass (Figure 2A-F, Figure S3A), which is consistent with incorporation of glucose-derived 13C into cellular structures and macromolecules. Spatial enrichment and distribution of hepatocyte 13C was characterized by a granular cytosolic architecture that largely co-localized with glycogen stores, thus indicating that these depots contained newly synthesized glycogen molecules identified with bulk GC-MS (Figure 2A-F, Figure S2A). Quantitative analysis of hepatocyte SEM micrographs revealed a time-dependent growth of glycogen depots correlated with glycogen 13C enrichment quantified with MIMS-EM (Figure S3A-C). Next, to place these results in a tissue- and cell-type-specific context, we applied MIMS-EM to monitor 13C flux in brown adipocytes. Brown adipocytes contain small multilocular lipid droplets that interact with a dense mitochondrial population engaged in oxidative and glycolytic glucose metabolism pathways that generate energy and replenish LD content 23. MIMS-EM of brown adipocytes revealed 13C enrichment in LDs and little-to-no enrichment in cytosolic, mitochondrial, or nuclear regions, thus indicating that these cells channeled glucose-derived 13C into saturated fatty acid synthesis (Figure S3D). 13C-labelled glycogen stores observed within 1 hour of glucose infusion were in direct contact and/or within the immediate neighborhood of lipid droplets (LDs), which became engulfed by glycogen over time (Figure 2A and 2G). Similar results are observed in three-dimensional (3D) reconstructions of previously published volumetric electron microscopy of adult mouse hepatocytes12 (Figure 2H-I), thus suggesting that enzymes involved in the glycogenesis process could be tethered to the scaffold of LDs.\nTogether, this data demonstrates how in vivo metabolic tracing and MIMS-EM can be combined to quantify glucose carbon flux at cell scales to identify cell type and intracellular sites involved in nutrient channeling towards glycogenesis or LD synthesis in a tissue-specific manner.\nThe subcellular architecture of 13C flux in hepatocytes. Changes in organelle architecture and organelle interaction networks can affect several aspects of cell function and whole-body metabolism 6,10,13,24. Besides identifying the intracellular location of glycogen synthesis, MIMS-EM of hepatocytes revealed that 13C accumulation can also occur in cytosolic spaces devoid of glycogen that contained mitochondria, ER, and/or LDs (Figure 2A and D). This suggested that glucose-derived 13C could be channeled towards and/or accumulate in other regions of the cell, which in turn could have distinct organelle distribution landscapes and interactomes. To test this hypothesis, we created a computational framework to map the spatial organization of individual organelles and their physical contacts with neighboring organelles to reconstruct organelle-specific interactomes correlated with movement of 13C within hepatocytes at the single cell level. To achieve this, we trained 2D U-nets to segment hepatocyte mitochondria, LDs, ER, and glycogen compartments (Figure 3A-B). Our organelle segmentation tools were benchmarked against a representative subset of manually annotated SEM images to create organelle classifiers with at least 90% confidence and a < 5% false positive organelle identification rate (Figure 3B-F, Figure S4A-B).\nUsing this approach, we extracted the X and Y coordinates of individual organelles and quantified their morphological and 13C-enrichment levels in hepatocytes (Figure 4A-C). This revealed significant changes to the overall hepatocyte organelle composition and 13C-enrichment in response to an acute and sustained increase in circulating glucose levels, largely limited to a decrease in LD-occupied area and a large increase in glycogen stores (Figure 4C). This increase in glycogen reflects the active storage of glucose-derived C into glycogen chains, and loss in LDs is explained by suppression of lipolysis and subsequent decrease in fatty acid delivery to the liver that combined by a relative maintenance in the secretion of triglycerides in VLDL particles 25. Changes in whole animal and/or cell metabolic demands are associated with reorganization of organelle-organelle contact sites and organelle interaction networks 12,13. To investigate how these aspects are regulated as hepatocytes synthesize glycogen and store glucose-derived 13C, we created a contact-search computational framework that marks the position of organelle contact sites in MIMS-EM datasets. This is achieved by a vector-based search for neighboring pixels by overlapping organelle segmentation masks to identify likely areas of organelle contact located within 5-to-10 nanometers in distance (Figure 4D and Figure S5A). This allowed us to estimate changes that occur to organelle contact site size and frequency for mitochondria, ER, LDs, and glycogen depots at the single cell level (Figure 4E, Figure S5B). This approach revealed that ~25-30% of all mitochondria, ER, and LDs are in contact with each other within the first hour (Figure 4E). Within 4 hours, these connections are lost to increases in organelle interactions connections with the growing glycogen mass, particularly for ER and LDs (Figure 4E). This was also characterized by a significant decrease in the area occupied by organelle-contact sites, thus indicating that these organelles moved away from each other as glycogen is synthesized near LDs (Figure S5B).\nNext, to investigate how these changes in organelle-contacts correlate with overall organelle 13C enrichment, we quantified the 13C/12C ratios in mitochondria, ER, LD, and glycogen and found that all these compartments become significantly enriched with 13C over time (Figure 4F and Figure S5C). Here, glycogen depots have the highest levels of enrichment (as expected given the much higher fractional turnover rate of the glycogen pool), followed by ER, LD, mitochondrial, and other cytosolic compartments (Figure 4F). Moreover, histogram analysis of organelle 13C/12C ratios revealed that ER and glycogen had relatively homogeneous enrichment 13C/12C ratios, whereas mitochondria and LDs had a clear bi-modal distribution pattern within the first hour of glucose infusion (Figure S5C), thus suggesting the existence of organelle sub-populations. Organelles function inside the cell can be heterogeneous and dependent on the nature of their organelle-organelle contacts 6. For example, in hepatocytes, LD-associated mitochondria have distinct protein expression patterns and are more adept for fatty acid oxidation versus other \u201ccytosolic\u201d mitochondria 26, whereas ER-associated mitochondria are important for normal insulin signaling 27. We hypothesized that such organelle heterogeneity could be explained by the identity of their organelle interacting partner. Therefore, we divided our data according to relative organelle interactions with ER, glycogen, LDs, or mitochondria (Figure 4G-H, Figure S5D); surprisingly, most mitochondria that were either isolated or in contact with LDs had lower 13C/12C ratios, whereas mitochondria in contact with ER or glycogen continued to display a bi-modal histogram distribution (Figure 4G, Figure S5D). In contrast, most LDs with ER contacts had lower 13C/12C ratios, whereas LD with glycogen contacts had significantly higher 13C/12C ratios, again supporting the notion that glycogenesis occurs at the vicinity of LDs (Figure 4H, Figure S5D). Notably, none of these differences were associated with changes in organelle size, and all types of LD and mitochondria achieved similar levels of 13C enrichment after 4 hours (Figure 4G-H, Figure S5C, and Figure S6A), thus indicating that organelle 13C flux heterogeneity occurs at the early stages of glycogenesis and that organelle 13C enrichment is partially explained by the identity of its interacting organelle partner.\nIn the liver, mitochondria-ER contact sites are implicated in glucose sensing, insulin signaling, and lipid transfer to sustain normal cell function 27-29; moreover, a recent study identified mitochondria wrapped by rough ER sheets that are associated with ApoB/VLDL synthesis and secretion 10. Therefore, we used spatial analysis to quantify the total area of each individual mitochondria occupied by ER contacts and found that that mitochondria with high 13C enrichment levels had significantly more interaction with ER regions (Figure 4I). This data suggests that i) mitochondria rich in ER contacts are associated with higher fractional turnover rates derived specifically from 13C-glucose metabolism, and ii) that hepatocyte organelle 13C enrichment is time- and organelle-dependent and may be (at least partially) correlated with the identity of organelle-interacting partners.", + "section_image": [] + }, + { + "section_name": "Limitations of this study", + "section_text": "In this study we introduce a multi-modal analysis pipeline to quantify nutrient metabolism and channeling across the mesoscale, from whole animals to single cells. This is achieved by combining in vivo measurements of glucose oxidation and metabolism with MIMS-EM imaging and spatial annotation to map the flux of glucose-derived 13C into distinct cellular and sub-cellular compartments. MIMS-EM is a very expensive and time-consuming technique, which limits sample throughput. Moreover, due to the physics of stable isotope imaging and detection of MIMS, MIMS-EM is unable to identify the molecular identity of most molecules associated with the spatial patterns of 13C distribution, except perhaps glycogen and saturated fatty acids that form lipid droplets (i.e., triacylglycerol and cholesterol esters). To minimize the impact of these factors on our study, we performed MIMS-EM on tissues from at least n=3 animals infused with different doses of glucose for up to 4 hours and focused on hepatocytes due to their well-established role in glucose storage and homeostasis processes. This allowed us to analyze hundreds of cells and thousands of individual organelles to identify the sub-cellular location of (early) glycogen synthesis and subpopulations of mitochondria that could be involved in hepatocyte lipid signaling 10. In the future, we anticipate that incorporation of MALDI-MS and other techniques that are compatible with MIMS-EM imaging (i.e., Click-EM 30) will allow for identification of molecular species associated with channeling of nutrients types across spatial and temporal scales.", + "section_image": [] + }, + { + "section_name": "Declarations", + "section_text": "Acknowledgments.\u00a0We are thankful to the team at the\u00a0Vanderbilt Mouse Metabolic Phenotyping Center (Supported by DK135073 and 1S10RR028101-01), to Dr Evan Kristoflyak for assistance with MIMS-EM sample preparation and SEM imaging performed at the Vanderbilt Cell Imaging Shared Resource (supported by NIH grants 1S10OD028704-01A1, CA68485, DK20593, DK58404, DK59637, and EY08126), and Dr Yunbin Guan at the Division of Geological and Planetary Sciences of Caltech for multi-isotope mass spectrometry data collection.", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "\nSchrimpe-Rutledge, A.C., Codreanu, S.G., Sherrod, S.D., and Mclean, J.A. 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Role of the Golgi complex in the intracellular transport of secretory proteins. Proc Natl Acad Sci U S A 55, 424-431. 10.1073/pnas.55.2.424.\nPrinz, W.A., Toulmay, A., and Balla, T. (2020). The functional universe of membrane contact sites. Nat Rev Mol Cell Biol 21, 7-24. 10.1038/s41580-019-0180-9.\nCohen, S., Valm, A.M., and Lippincott-Schwartz, J. (2018). Interacting organelles. Curr Opin Cell Biol 53, 84-91. 10.1016/j.ceb.2018.06.003.\nObara, C.J., Nixon-Abell, J., Moore, A.S., Riccio, F., Hoffman, D.P., Shtengel, G., Xu, C.S., Schaefer, K., Pasolli, H.A., Masson, J.B., et al. (2024). Motion of VAPB molecules reveals ER-mitochondria contact site subdomains. Nature 626, 169-176. 10.1038/s41586-023-06956-y.\nScorrano, L., De Matteis, M.A., Emr, S., Giordano, F., Hajn\u00f3czky, G., Kornmann, B., Lackner, L.L., Levine, T.P., Pellegrini, L., Reinisch, K., et al. (2019). Coming together to define membrane contact sites. 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Regulation of liver subcellular architecture controls metabolic homeostasis. Nature 603, 736-742. 10.1038/s41586-022-04488-5.\nNeumann, E.K., Migas, L.G., Allen, J.L., Caprioli, R.M., Van de Plas, R., and Spraggins, J.M. (2020). Spatial Metabolomics of the Human Kidney using MALDI Trapped Ion Mobility Imaging Mass Spectrometry. Anal Chem 92, 13084-13091. 10.1021/acs.analchem.0c02051.\nPareek, V., Tian, H., Winograd, N., and Benkovic, S.J. (2020). Metabolomics and mass spectrometry imaging reveal channeled de novo purine synthesis in cells. Science 368, 283-290. 10.1126/science.aaz6465.\nDrigo, R.A.E., Lev-Ram, V., Tyagi, S., Ramachandra, R., Deerinck, T., Bushong, E., Phan, S., Orphan, V., Lechene, C., Ellisman, M.H., and Hetzer, M.W. (2019). Age Mosaicism across Multiple Scales in Adult Tissues. Cell Metabolism 30, 343-+. 10.1016/j.cmet.2019.05.010.\nKrishna, S., Drigo, R.A.E., Capitanio, J.S., Ramachandra, R., Ellisman, M., and Hetzer, M.W. (2021). Identification of long-lived proteins in the mitochondria reveals increased stability of the electron transport chain. Developmental Cell 56, 2952-+. 10.1016/j.devcel.2021.10.008.\nToyama, B.H., Drigo, R.A.E., Lev-Ram, V., Ramachandra, R., Deerinck, T.J., Lechene, C., Ellisman, M.H., and Hetzer, M.W. (2019). Visualization of long-lived proteins reveals age mosaicism within nuclei of postmitotic cells. Journal of Cell Biology 218, 433-444. 10.1083/jcb.201809123.\nBerglund, E.D., Li, C.Y., Poffenberger, G., Ayala, J.E., Fueger, P.T., Willis, S.E., Jewell, M.M., Powers, A.C., and Wasserman, D.H. (2008). Glucose metabolism in vivo in four commonly used inbred mouse strains. Diabetes 57, 1790-1799. 10.2337/db07-1615.\nRorsman, P., and Ashcroft, F.M. (2018). Pancreatic \u03b2-Cell Electrical Activity and Insulin Secretion: Of Mice and Men. Physiol Rev 98, 117-214. 10.1152/physrev.00008.2017.\nDroin, C., Kholtei, J.E., Bahar Halpern, K., Hurni, C., Rozenberg, M., Muvkadi, S., Itzkovitz, S., and Naef, F. (2021). Space-time logic of liver gene expression at sub-lobular scale. Nat Metab 3, 43-58. 10.1038/s42255-020-00323-1.\nHildebrandt, F., Andersson, A., Saarenp\u00e4\u00e4, S., Larsson, L., Van Hul, N., Kanatani, S., Masek, J., Ellis, E., Barragan, A., Mollbrink, A., et al. (2021). Spatial Transcriptomics to define transcriptional patterns of zonation and structural components in the mouse liver. Nat Commun 12, 7046. 10.1038/s41467-021-27354-w.\nSanchez-Gurmaches, J., Tang, Y., Jespersen, N.Z., Wallace, M., Martinez Calejman, C., Gujja, S., Li, H., Edwards, Y.J.K., Wolfrum, C., Metallo, C.M., et al. (2018). Brown Fat AKT2 Is a Cold-Induced Kinase that Stimulates ChREBP-Mediated De Novo Lipogenesis to Optimize Fuel Storage and Thermogenesis. Cell Metab 27, 195-209.e196. 10.1016/j.cmet.2017.10.008.\nGuo, Y., Li, D., Zhang, S., Yang, Y., Liu, J.-J., Wang, X., Liu, C., Milkie, D.E., Moore, R.P., Tulu, U.S., et al. (2018). Visualizing Intracellular Organelle and Cytoskeletal Interactions at Nanoscale Resolution on Millisecond Timescales. Cell 175, 1430-1442.e1417. 10.1016/j.cell.2018.09.057.\nS\u00f8rensen, L.P., S\u00f8ndergaard, E., Nellemann, B., Christiansen, J.S., Gormsen, L.C., and Nielsen, S. (2011). Increased VLDL-triglyceride secretion precedes impaired control of endogenous glucose production in obese, normoglycemic men. Diabetes 60, 2257-2264. 10.2337/db11-0040.\nTalari, N.K., Mattam, U., Meher, N.K., Paripati, A.K., Mahadev, K., Krishnamoorthy, T., and Sepuri, N.B.V. (2023). Lipid-droplet associated mitochondria promote fatty-acid oxidation through a distinct bioenergetic pattern in male Wistar rats. Nat Commun 14, 766. 10.1038/s41467-023-36432-0.\nTubbs, E., Theurey, P., Vial, G., Bendridi, N., Bravard, A., Chauvin, M.A., Ji-Cao, J., Zoulim, F., Bartosch, B., Ovize, M., et al. (2014). Mitochondria-associated endoplasmic reticulum membrane (MAM) integrity is required for insulin signaling and is implicated in hepatic insulin resistance. Diabetes 63, 3279-3294. 10.2337/db13-1751.\nHern\u00e1ndez-Alvarez, M.I., Sebasti\u00e1n, D., Vives, S., Ivanova, S., Bartoccioni, P., Kakimoto, P., Plana, N., Veiga, S.R., Hern\u00e1ndez, V., Vasconcelos, N., et al. (2019). Deficient Endoplasmic Reticulum-Mitochondrial Phosphatidylserine Transfer Causes Liver Disease. Cell 177, 881-895.e817. 10.1016/j.cell.2019.04.010.\nTheurey, P., Tubbs, E., Vial, G., Jacquemetton, J., Bendridi, N., Chauvin, M.A., Alam, M.R., Le Romancer, M., Vidal, H., and Rieusset, J. (2016). Mitochondria-associated endoplasmic reticulum membranes allow adaptation of mitochondrial metabolism to glucose availability in the liver. J Mol Cell Biol 8, 129-143. 10.1093/jmcb/mjw004.\nNgo, J.T., Adams, S.R., Deerinck, T.J., Boassa, D., Rodriguez-Rivera, F., Palida, S.F., Bertozzi, C.R., Ellisman, M.H., and Tsien, R.Y. (2016). Click-EM for imaging metabolically tagged nonprotein biomolecules. Nature Chemical Biology 12, 459-465. 10.1038/nchembio.2076.\n", + "section_image": [] + }, + { + "section_name": "Additional Declarations", + "section_text": "There is NO Competing Interest.", + "section_image": [] + }, + { + "section_name": "Supplementary Files", + "section_text": "SupplementaryTable1PlasmaGCMSMarch2024.xlsxSupplementary Table 1FigureS1InVivoLabellingMarch2024.pdfFigure S1. (A) Photo of a custom made cylindrical metabolic cage for in vivo glucose oxidation measurements. (B) Expelled 13CO2 (in parts per million (ppm)), blood glucose levels, and insulin levels in mice continuously infused with 40mg/min/kg for up to 4 hours. (C-D) Circulating glucose and insulin levels in mice infused with 40mg/min/kg for up to 4 hours. (E-G) GC-MS analysis to determine the fractional 13C enrichment of lactate, Glycerol, and Alanine. Each dot represents an animal. \u00a0In (E-H), **** p<0.01.FigureS2MIMSEMregistrationmetricsMarch2024.pdfFigure S2. (A) 13C enrichment accessed using GC-MS of isolated glycogen extracted from [U-13C]-glucose-labelled mice after 1, 2, or 4 hours of continuous infusion with 40min/mg/kg of total body mass. (B) MIMS-EM registration panels. Raw or aligned MIMS, and SEM data are shown on the top row. MIMS images of the stable isotope 32S are shown. Bottom row, automatic segmentation of fiducial markers in both SEM and MIMS datasets before and after MIMS-EM registration. (C) Grid graph displaying the displacement of fiducial markers after the image registration process. (D) Histogram distribution showing the relative frequency of points in X and Y (plot in the y-axis) and their total spatial displacement in both X and Y axis (bins, in microns, plotted in the x-axis). (E) Overlay image of segmented fiducial regions of interest (ROIs) in a representative SEM (white) and MIMS (magenta) dataset before and after registration. (F) F-score calculated to quantify the overlap of individual SEM and MIMS ROI objects before and after the MIMS-EM image registration process.FigureS3MIMSEMLiverTissueScaleMarch2024.pdfFigure S3. (A) Correlated 13C-to-12C (13C/12C) and SEM images acquired using MIMS-EM of hepatocytes from mice continuously infused with 40mg/min/kg of [U-13C]-glucose for 2 hours. (B-C) Glycogen crystal area and 13C/12C level measurements from mice infused with 40mg/min/kg of [U-13C]-glucose for 1, 2, or 4 hours. (D) Representative MIMS-EM images displaying the 13C/12C levels in brown adipocytes from mice infused with 40mg/min/kg of [U-13C]-glucose for 4 hours. In (B-C), p-values are shown.FigureS4DLBenchmarksMarch2024.pdfFigure S4. (A)Performance benchmark indexes for our trained deep-learning image segmentation models. Precision, rate of false positives, object recall, and f-score are shown for different levels of confidence thresholding (t) intervals, from 50-to-95% confidence (t50 to t95). For all our analyzes, we chose a t value of 90%. (B) Pearson\u2019s and Mander\u2019s co-localization indexes quantifying the overlap between manual and 2D U-net segmentation pipelines of the same image or object, respectively.FigureS5LiverDemographicsJan2024.pdfFigure S5. (A) Image panels representing a sub-cellular region of a mouse hepatocyte at the 4-hour timepoint. Left to right panel, SEM micrograph, deep-learning organelle segmentation masks for mitochondria (in green) and ER (in white), and SEM micrographs with annotated mitochondria-ER contact sites. Magenta line annotates the location of Mitochondria-ER contact sites. Yellow arrows point to mitochondria-ER contact sites. Scale bar, 2 microns. (B) Relative fraction of mitochondria, LD or glycogen area contacted by ER, LD, or glycogen organelles. (C) Histograms displaying 13C/12C levels (x-axis) and relative frequency (y-axis) of all mitochondria, LD, ER, and glycogen objects. * p<0.01 by student t-test. (D) Histograms displaying 13C/12C levels in the x-axis and relative frequency in the y-axis of LD or mitochondria classified by the identity of their organelle-interacting partners. Mito or M = mitochondria, Gly = Glycogen, ER, or no contact (LD or M).FIgureS6MitoLDERcontactsSpatialMetricsMarch2024.pdfFigure S6. (A) Mitochondria and LD organelle size classified according to the identity of their organelle-interacting partner. (B) Left, percentage of mitochondrial perimeter covered by LD contact sites and, on the right, mitochondria area (in pixels) for organelles with lower or higher 13C/12C ratios as shown in Figure S5D.MaterialandMethodsFinalMarch2023.docxArticle File", + "section_image": [] + } + ], + "nature_content": [ + { + "section_name": "Abstract", + "section_text": "Metabolic homeostasis requires engagement of catabolic and anabolic pathways consuming nutrients that generate and consume energy and biomass. Our current understanding of cell homeostasis and metabolism, including how cells utilize nutrients, comes largely from tissue and cell models analyzed after fractionation, and that fail to reveal the spatial characteristics of cell metabolism, and how these aspects relate to the location of cells and organelles within tissue microenvironments. Here we show the application of multi-scale microscopy, machine learning-based image segmentation, and spatial analysis tools to quantitatively map the fate of nutrient-derived 13C atoms across spatiotemporal scales. This approach reveals the cellular and organellar features underlying the spatial pattern of glucose 13C flux in hepatocytes in situ, including the timeline of mitochondria-ER contact dynamics in response to changes in blood glucose levels, and the discovery of the ultrastructural relationship between glycogenesis and lipid droplets.", + "section_image": [] + }, + { + "section_name": "Introduction", + "section_text": "Tissue function is supported by cell metabolism pathways that are modulated to meet changes in nutrient availability and energetic demands that occur throughout an organism\u2019s lifetime. Much of our knowledge regarding cell metabolism is derived from bulk metabolomics using stable or radioactive isotopes (i.e., 13C and 14C, respectively). Throughout the years, this approach has revealed differences in how cells utilize nutrients to maintain energy and cell homeostasis during different cell states1,2,including how these aspects are impacted by aging, cancer, and degenerative and metabolic diseases3. Cells are organized into sub-cellular compartments created by organelles that handle essential processes necessary for cell function, such as mitochondrial respiration or protein synthesis within the endoplasmic reticulum (ER)4,5. Several aspects of cell metabolism require proper organization of organelle-interaction networks that create distinct intracellular compartments such as mitochondria-ER or mitochondria-lipid droplet contact sites6,7. These sub-cellular compartments are dynamic and interact via proteins that mediate membrane anchoring and the exchange of molecules and ions between organelles6,7,8,9. Perturbation of these organelle contact sites disrupts cell and whole-body metabolism and has been linked to the patho-physiology of metabolic and neurodegenerative diseases8,10,11,12,13. Therefore, there is a need to study and understand the principles that guide the spatial organization pattern of cells and organelles in situ and their correlation to changes in animal and cell metabolism.\n\nDifferent super resolution light and electron microscopy techniques have been applied to determine the architecture and spatiotemporal dynamics of organelle-interaction networks with nanometer resolution, as well as cell-type specific patterns of organelle morphology and connectivity linked to the regulation of cell and whole-organism function and metabolism10,12,14,15,16,17,18,19,20. Moreover, recent advances in imaging metabolomics techniques such as MALDI-MS21,22 and ToF-SIMS23 have allowed the visualization of the spatial distribution of metabolites and molecular flux at tissue and multi-cellular scales. However, due to their intrinsic working physics, these techniques are unable to measure the fate of metabolites at sub-cellular resolution and therefore are unable to determine the correlation between nutrient flux and underlying cell and organelle organization patterns.\n\nIn recent years, we have developed a correlative microscopy pipeline that combines scanning electron microscopy (SEM) with multi-isotope mass spectroscopy (MIMS) called MIMS-EM24. MIMS-EM leverages SEM\u2019s high spatial resolution and the high-resolution mass detectors of MIMS to map stable isotope incorporation (e.g., 15N, 14N, 13C, or 12C) into macromolecules and organelles, creating spatial maps of isotope flux and (intra)cellular architecture. We previously used MIMS-EM and stable isotope-labeling of whole animals and cells to identify the vast heterogeneity of protein and cell longevity24,25,26. Importantly, MIMS-EM was based on landmark studies that established MIMS as a framework to follow cell and protein turnover, nutrient-synthesis, and elemental flux in relatively short time scales27,28,29,30.\n\nIn this work, we demonstrate the application of our multi-platform approach to quantify glucose flux and determine the fate of glucose-derived elements \u2013 from whole animals to subcellular compartments in situ. By combining orthogonal and complimentary experimental and high-resolution microscopy techniques with machine-learning (ML) data analysis and stable isotope labeling of mice (SILAM) using [U-13C6]-glucose tracers, we were able to determine the spatial and the molecular flux patterns of glucose metabolism at whole animal, tissue, and cellular scales. This approach revealed subcellular organization patterns that identified organelle-organelle networks and subcellular scaffolds associated with glucose metabolism, and how these patterns are modulated by changes in metabolic demand such as fasting and initiation of glycogen synthesis. This work establishes a multi-modal framework to study the multiple scales of metabolism and the spatial landscape of cells and nutrient flux in situ.", + "section_image": [] + }, + { + "section_name": "Results and Discussion", + "section_text": "To measure the flux of glucose from whole body to the organelle level and its correlation with cellular and organelle organization, we created a multi-modal pipeline combining the delivery of [U-13C6]-glucose with both traditional mass spectrometry techniques and MIMS-EM (Fig.\u00a01A). We started by delivering [U-13C6]-glucose to freely moving and awake animals using intra-venous catheters with ports for arterial blood sampling to quantify blood glucose and plasma metabolite 13C enrichment (Fig.\u00a01A). Here, fasted 8-week-old male C57/BL6J mice were placed inside individual metabolic cages and continuously infused with 15 or 40\u2009mg\u00b7kg-1\u00b7min-1 of [U-13C6]-glucose for up to 4\u2009hours. These doses were chosen to evaluate in vivo glucose metabolism rates in response to glucose dosages that either matched or exceeded the rate of endogenous glucose production in mice31. Mice infused with 15\u2009mg\u00b7kg-1\u00b7min-1 remained normoglycemic, while mice dosed with 40\u2009mg\u00b7kg-1\u00b7min-1 experienced sustained hyperglycemia (Fig.\u00a01B). Next, to investigate the kinetics of whole body [U-13C6]-glucose oxidation in vivo, we measured the relative enrichment of 13C in the expelled breath CO2 using stable isotope mass spectrometer gas detectors coupled to our metabolic cages (Fig.\u00a01C, and Supplementary Fig.\u00a01A). This approach quantified time- and dose-dependent increases in 13CO2 in [U-13C6]-glucose-infused mice, thus confirming that [U-13C6]-glucose molecules were delivered and oxidized within the first 60\u2009minutes (Fig.\u00a01C). Accordingly, exchange of [U-13C6]-glucose for unenriched glucose caused 13CO2 to quickly fall over time (Fig.\u00a01C). Similar results in mice exposed to a longer fast (i.e., 16\u2009hours) and infused with 40\u2009mg\u00b7kg-1\u00b7min-1 of [U-13C6]-glucose (Supplementary Fig.\u00a01B\u2013D), validated our stable isotope delivery and quantification of [U-13C6]-glucose oxidation rates and homeostasis in vivo.\n\nA Illustration of the approach used to label freely moving and unanesthetized mice with [U-13C]-glucose for up to 4\u2009hours using catheters followed by MIMS-EM and spatial analysis. Figure created using biorender.com. B Blood glucose levels measured from mice continuously infused with 15 or 40\u2009mg/min/kg of total body mass for up to 4\u2009hours. Data from n\u2009=\u20094-17 mice per group. C Expelled 13CO2 (in parts per million (ppm)) measured from the atmosphere of custom-made metabolic cages using gas mass spectrometers. 13C glucose was infused for the first 240\u2009minutes and replaced with 12C glucose for an additional 120\u2009minutes. AFE, atomic fractional enrichment. Data from n\u2009=\u20093 mice per group. D, E GC-MS analysis to determine the fractional 13C enrichment of circulating glucose molecules and of secondary metabolites pyruvate, citrate, and alpha ketoglutarate (aKG) generated from glucose metabolism. Each dot represents an animal, n\u2009=\u20096 animals per time point. In B, data shown as \u00b1 standard deviation of the mean. In C, the shaded region indicates the data range of 13CO2 measurements. In D, E), ****p\u2009<\u20090.001 and *p\u2009<\u20090.05 using One-way ANOVA with Kruskal-Wallis tests.\n\nIn response to an increase in blood glucose, pancreatic beta cells secrete insulin to normalize blood glucose levels32. Insulin acts on skeletal muscle depots that metabolize glucose into secondary metabolites that can be measured in the circulation (i.e., lactate and pyruvate), and stimulates the liver and adipose tissue to store glucose-derived carbons into large macromolecules such as glycogen or triglycerides, respectively. To investigate the amount of [U-13C6]-glucose and 13C-labeled glucose-derived circulating metabolites, we performed GC-MS on plasma samples collected during our infusion experiments. This identified a gradual and significant decrease in the fractional abundance of plasma 12C6-glucose (M\u2009+\u20090) and an increase in plasma 13C-labeled glucose (M\u2009+\u20096) (Fig.\u00a01D). A similar pattern in the appearance of M\u2009+\u20093 was also observed for several circulating metabolites such as lactate, pyruvate, glycerol, and alanine (Fig.\u00a01E, Supplementary Fig.\u00a01E\u2013G, and Supplementary Table\u00a01). Finally, we quantified 13C enrichment in newly synthesized glycogen molecules in the liver using GC-MS and found a time-dependent increase in 13C-labeled glycogen molecules, as expected (Supplementary Fig.\u00a01H). Together, these results indicate that as [U-13C6]-glucose floods the circulatory system, the relative abundance of glucose-13C in liver glycogen and in plasma glucose metabolite pools increases over time.\n\nHepatocytes are organized in distinct architectural zones with unique transcriptional, metabolic, and organelle organization profiles that underlie differences in glucose metabolism and glycogenesis19,33,34,35. To explore how the multiple layers of hepatocyte organization impact glucose metabolism in situ, we developed a microscopy-based approach to investigate specific aspects of glucose-13C flux in space. First, to quantify liver glycogen synthesis and 13C enrichment in situ, we applied MALDI-MS to snap-frozen livers from mice infused for 4\u2009hours with 40\u2009mg\u00b7kg-1\u00b7min-1 [U-13C6]-glucose after an overnight fast (Fig.\u00a02A, Supplementary Fig.\u00a02A). We chose these conditions because we wanted to study 13C flux in animals experiencing a large shift in metabolism marked by increased demands for glycogen synthesis and glucose handling36. Our MALDI-MS approach was based on a previously established method using isoamylase to hydrolyze glycogen alpha-1-6-glycosidic linkages to measure glucose chain abundance37 (Fig.\u00a02B). This glycogen MALDI-MS imaging approach achieved a spatial X-Y resolution of ~20um and revealed that 4-hour 13C-glucose-infused livers had ~8x more glycogen than a fasted liver section (Fig.\u00a02C and Supplementary Fig.\u00a02B). Moreover, imaged glycogen depots had an overall fractional 13C enrichment of ~30-50%, which is compatible with our GC-MS data (Supplementary Fig.\u00a01H). We also determined the molecular pattern of 13C-glycogen enrichment via glycogen MS/MS and identified the spectra of a total of fifteen different glucose polymers (GP). Identified GPs ranged from 3-to-18 linked glucose molecules (GP3 to GP18), and most GPs were significantly enriched with 13C (e.g., GP7 with three 13C6-glucose molecules (13Cx3), Supplementary Fig.\u00a02C); however, significant amounts of unenriched (12C)-glycogen were also detected (e.g., GP6 13Cx0) (Fig.\u00a02D). Notably, we applied \u201cuntargeted\u201d MALDI-MS to identify other potential 13C-labeled molecules, however, we only identified 13C6-glucose and 13C6-hexose-6-phosphate (H6P) (Supplementary Fig.\u00a02D, E). No specific tissue distribution pattern in glycogen or GPs was identified, likely due to the long and sustained phase of elevated glucose-infusion that made difficult to separate the expected zone differences in glycogen synthesis. Nevertheless, our results support the application of MALDI-MS to measure glycogen 13C in situ at the tissue level.\n\nA Schematic of sample processing and MALDI mass spectrometry (MS) imaging of mouse livers from control and 13C-labeled mice. B Cartoon illustration of a mature glycogen molecule (top) where alpha-1-6-glycosidic linkages are shown. These linkages are targeted by isoamylase digestion to release glucose polymers (GP) of different sizes (below). Grey and teal dots represent 12C and 13C-labeled glucose molecules, respectively. C Representative images of hematoxylin and eosin (H&E) staining (top), and total glycogen content map measured using MALDI-MS of a mouse liver section after 4\u2009hours of [U-13C6]-glucose infusion. Scale bar, 1\u2009mm. D Same as in C, however MALDI-MS images show the spatial pattern of a specific 12C- and 13C-labeled glucose polymer (GP) with six glucose molecules (GP6). In each panel, the number after \u201c13Cx\u201d indicates the number of 13C-labeled molecules found in GP6. In A\u2013D), data representative of n\u2009=\u20093 mice. A, B created using biorender.com.\n\nTo determine the spatial fate and quantify the flux of 13C at the single-cell and subcellular levels in hepatocytes, we applied MIMS-EM. We focused our MIMS-EM imaging sessions to hepatocytes close to the central vein because of their higher glycogenic potential34. We used MIMS-EM to collect data for multiple isotopes (i.e., 13C, 12C, 32S, and 14N) to guide image co-registration to hepatocyte SEM micrographs (Supplementary Fig.\u00a03A\u2013E). MIMS-EM of hepatocytes revealed both time- and dose-dependent accumulations of glucose-derived 13C within the total hepatocyte biomass following [U-13C6]-glucose infusion (Fig.\u00a03A\u2013F, Supplementary Fig.\u00a04A). Of note, since delivery of 15\u2009mg\u00b7kg-1\u00b7min-1 [U-13C6]-glucose closely matches the endogenous mouse glucose disposal rate, hepatocytes engage in low levels of glycogenesis and thus explain the low levels of 13C enrichment observed (Fig.\u00a03F).\n\nA, B Correlated 13C-to-12C (13C/12C) ratiometric images acquired using MIMS and registered to scanning electron microscopy (SEM) of hepatocytes to create MIMS-EM maps. Data from mice continuously infused with [U-13C]-glucose at 40\u2009mg/min/kg of [U-13C]-glucose for 1 or 4\u2009hours. C Quantification of 13C/12C ratios in the biomass of mice shown in A, B. Data from n\u2009=\u20093 animals per time point. Each dot represents the mean 13C enrichment in a ~40 um2 liver section. D, E MIMS 13C/12C ratiometric images registered to hepatocyte SEM micrographs to create MIMS-EM maps. Data from mice continuously infused with15 or 40\u2009mg/min/kg of total body mass for 2\u2009hours. F Quantification of 13C/12C ratios in the biomass of mice from shown in D, E). In A, B and D, E, magenta and orange colors represent lower and higher different levels of 13C enrichment, respectively. Dotted magenta line marks the terrestrial background for 13C. Data from n\u2009=\u20093 animals per time point. Each dot represents the mean 13C enrichment in a ~40 um2 liver section. G Cartoon illustration showing the single cell analysis of MIMS-EM data. Figure created using biorender.com. H Representative hepatocyte SEM, 13C/12C ratio, and overlay of several organelle segmentation masks, listed on the right. Data from a mouse continuously infused with 40\u2009mg/min/kg of [U-13C]-glucose for 4\u2009hours. I 13C/12C ratios of individual hepatocytes from mice continuously infused with 40\u2009mg/min/kg of [U-13C]-glucose for 1 or 4\u2009hours. Each color represents data from a different animal. J 13C/12C levels by type of organelle after 1 or 4\u2009hours of 40\u2009mg/min/kg [U-13C]-glucose infusion. K Relative fraction of hepatocyte cell area occupied by endoplasmic reticulum (ER), lipid droplets (LDs), glycogen, mitochondria, or nucleus. Data from n\u2009=\u20093-to-7 mice per group, from overnight fasted mice or from mice continuously infused with 40\u2009mg/min/kg of [U-13C]-glucose for 1 or 4\u2009hours. In I, J, data from n\u2009=\u20096-to-9 mice per group. In C and F), a One-way ANOVA with a two-stage Benjamini, Krieger, and Yekuteli test was used, and p-values\u2009<\u20090.001 are shown. In J, K), One-way ANOVA with Dunns test post-hoc where ***p\u2009<\u20090.001. In I\u2013K, each dot represents a single cell. In (C and F-K), data shown as 95% of the confidence interval (C.I.). In (A), scale bar 2 microns; in D and H, 5 microns.\n\nThe spatial enrichment and distribution pattern of hepatocyte 13C was granular and largely co-localized with cytosolic glycogen stores (seen in the SEM micrographs as electron dense clusters38) that grew larger over time, thus indicating that these depots contained newly synthesized glycogen molecules. Recent studies have established that changes in organelle architecture and organelle interaction networks can affect several aspects of cell function and whole-body metabolism6,10,13,39. Therefore, to create a comprehensive map of organelle organization and 13C enrichment in subcellular compartments, we created a computational framework to map the spatial organization of individual organelles and to quantify organelle-specific 13C enrichment. This was achieved by training 2D U-nets to segment hepatocyte mitochondria, LDs, ER, and glycogen compartments (Fig.\u00a03G, H). These organelle segmentation algorithms were benchmarked against a representative subset of manually annotated SEM images to create organelle classifiers with at least 90% confidence and a\u2009<\u20095% false positive organelle identification rate (Supplementary Fig.\u00a05A\u2013H).\n\nWe applied this approach to measure organelle composition and 13C-enrichment levels at cell and organelle scales after an overnight fast or 1 or 4\u2009hours of 40\u2009mg\u00b7kg-1\u00b7min-1 [U-13C6]-glucose infusion. Hepatocytes from 13C-infused animals had significant 13C enrichment at the single cell level and in all major organelle compartments identified (Fig.\u00a03I, J, Supplementary Fig.\u00a04B, C). Glycogen depots had the highest levels of enrichment, followed by ER, LD, mitochondria, and other cytosolic compartments. This metabolic signature correlated with a loss of cytosolic area occupied by mitochondria and ER while glycogen grew significantly (Fig.\u00a03K, Supplementary Fig.\u00a04D). Loss of mitochondria density could be explained by increases in mitochondria circularity and/or fragmentation that occurs during re-feeding, whereas the ER becomes compressed into dense stacks. The transient increase and then loss of LD area can be explained by a suppression of lipolysis mediated by rising insulin signaling followed by a decrease in liver fatty acid delivery with relative maintenance in hepatocyte triglycerides secretion via VLDL particles40.\n\nTo place these results in a tissue- and cell-type-specific context, we applied MIMS-EM to monitor glucose-13C flux in adult brown adipocytes of mice studies under mild cold stress conditions (i.e., 23 \u00b0C room temperature). During brown adipocyte development and adult tissue function, glycogen metabolism is important for the formation of lipid droplets (LDs) and thermogenesis41,42. Brown adipocytes metabolize glucose via de-novo lipogenesis to sustain the synthesis of small multilocular LDs that interact with a dense mitochondrial population engaged in oxidative and glycolytic glucose metabolism pathways to generate energy and replenish LD content43. Accordingly, MIMS-EM of brown adipocytes from 4-hour 40\u2009mg\u00b7kg-1\u00b7min-1 [U-13C6]-glucose-infused mice revealed significant enrichment of 13C in LDs, and little-to-no enrichment in cytosolic, mitochondrial, or nuclear regions (Supplementary Fig.\u00a04E). Here, we did not identify glycogen clusters near LDs, which is likely due to the depletion of existing glycogen stores by overnight fasting or a preferential shunt of glucose 13C metabolism towards denovo lipogenesis.\n\nTogether, this data demonstrates how in vivo metabolic tracing and MIMS-EM can be combined to quantify glucose flux at cell and subcellular scales.\n\nWhile analyzing our MIMS-EM data, we observed a spatial association between LDs and nascent glycogen molecules (Supplementary Fig.\u00a06A). Previous studies have established the spatial relationship between glycogen depots and the endoplasmic reticulum (ER)44,45. This could explain how ER-resident enzymes required for glycogen metabolism (Protein Phosphatase 1 (PP1), which activates glycogen synthase (Gys2)) contribute to glycogen homeostasis44,46. Therefore, we hypothesized that LDs provide a physical scaffold for glycogen synthesis by forming connections with the ER. To further determine the spatial context of glycogenesis in situ, we first analyzed SEM micrographs of mouse pericentral hepatocytes directly after an overnight fast and/or after a 1- or 4-hour 13C-glucose infusion. We found that most fasted hepatocytes lack glycogen depots, and the periphery of LDs was often occupied by mitochondria (Fig.\u00a04A) \u2013 a sign of active fatty acid oxidation15. Strikingly, rare fasting hepatocyte LDs had small clusters of glycogen nearby and in direct contact (Supplementary Fig.\u00a06B). Similarly, 13C-glucose-infused mice had newly synthesized glycogen stores in direct contact and within the immediate neighborhood of LDs within 1\u2009hour; by 4\u2009hours, LDs were largely surrounded by 13C glycogen (Fig.\u00a04A).\n\nA Representative hepatocyte SEM micrographs showing LDs and clustering of glycogen crystals around LDs in overnight-fasted mice or in mice at 1-, 2- and 4-hour [U-13C6]-glucose-infusion timepoints. Blue arrows point to glycogen depots. B Representative electron tomography (eTOMO) micrographs of mouse hepatocytes after 1\u2009hour of [U-13C]-glucose infusion, with mitochondria (Mito), lysosomes (Lys), lipid droplets (LD) in view. Yellow, blue, and green arrowheads point to the location of LD-tethered glycogen, smooth endoplasmic reticulum (ER), and rough ER, respectively. C, D 3D reconstruction of eTOMO volumes and organelle structures showing the close association between glycogen, ER, and LD. Two partial reconstructions of mitochondria and mitochondria cristae are also shown. E, F Representative scanning electron microscopy (SEM) images of a C. elegans intestinal cell and human male hepatocyte, respectively. SEM insets highlight the position of a single lipid droplet and blue arrowheads indicate the location of LD-associated glycogen particles. In the human micrograph, yellow arrowheads mark the position of smooth endoplasmic reticulum (sER) compartments. G Graph displaying reconstructed volume of n\u2009=\u2009150 glycogen particles imaged with eTOMO and ranked according to their volume (blue dots). Dotted black line indicates the fit of an exponential curve with an observed r2\u2009=\u20090.946. Insets a, b, and c show representative glycogen particles of different sizes, and their location are marked in the micrographs shown in (B). In (B), scale bar = 50 nanometers, in (C, D and F), 100 nanometers. In (A, B and E, F), data representative of n\u2009=\u20093-to-5 mice per condition.\n\nTo gain insights into the potential interaction and cytoplasmic localization of glycogenesis enzymes45, we performed meta-analysis of published proteomic studies from bulk hepatocytes47, glycogen48, LDs49, or mitochondrial fractions50. We used the STRING-DB51 to query protein-protein interactions (PPIs) and reconstruct PPI networks of glycogen-associated proteins, followed by STRING functional enrichment and network clustering (Supplementary Fig.\u00a07A). This discovered a list of proteins mostly linked to ER structure-function, carbon and glycogen metabolism, and fatty acid metabolism (Supplementary Fig.\u00a07B-D). We ruled out potential contamination from other cellular fractions by overlapping protein hits shared between glycogen, LD, and mitochondria (Supplementary Fig.\u00a07E). Importantly, the glycogen proteome included key glycogenesis enzymes (i.e., Pgm1, Ugp2, Gys2, Gbe1), as well as proteins involved in glycogenolysis (i.e., Epm2a/Laforin, Agl, Pygl) and glycophagy (i.e., Gaa and Stbd1) (Supplementary Fig.\u00a07F). ER-resident proteins glycogen synthase-activating Ppp1ca and the glycogen phosphorylase Pygl45, were enriched in the glycogen dataset (Supplementary Fig.\u00a07D). These results suggest that glycogen synthesis could occur within a subcellular compartment that clusters enzymes needed for glycogen synthesis and degradation.\n\nTo investigate this hypothesis and define the subcellular architecture of the LD-glycogen interaction during the early phases of glycogenesis, we performed electron tomography (eTomo) of hepatocytes from 1-hour 13C-glucose-infused mice. Glycogen clusters were observed in direct contact with LDs and occupying the space directly adjacent to LDs (Fig.\u00a04B). This subcellular space was also occupied by lysosomes, mitochondria, and small sheets of smooth ER (sER) that contacted the LD scaffold (Fig.\u00a04B). 3D reconstruction of liver tomograms illustrated the close relationship of sER, LD, and glycogen; here, the sER was positioned within 1-2\u2009nm from glycogen and LDs, sometimes \u201csandwiching\u201d glycogen between two membranes (Fig.\u00a04C, D). We validated these results by data mining a previously published hepatocyte 3D EM dataset12, in which glycogen is clearly seen outlining the periphery of an LD (Supplementary Fig.\u00a06C, D). LD-glycogen interactions were also observed in C. elegans intestinal cells (which function as the worm\u2019s \u201cliver\u201d) and human hepatocytes, suggesting that this functional compartmentalization is conserved across different species (Fig.\u00a04E, F). Glycogen clusters were made of small electron dense spheres of ~40\u2009nm in diameter (consistent with the size of glycogen beta particles52) and were defined marked by a soft white halo marking the \u201cshell\u201d space occupied by glycogen in situ before sample dehydration for eTomo imaging (Fig.\u00a04B). This led us to the recently proposed hypothesis that glycogen forms and behaves as liquid condensates that trap signaling molecules48. Liquid-phase \u2018organelles are hypothesized to grow by coalescing into each other, which creates an exponential growth pattern of larger droplets absorbing smaller ones53. Indeed, glycogen growth appears to follow this predicted pattern in situ, as revealed by volume reconstruction of n\u2009=\u2009150 individual glycogen objects (Fig.\u00a04G).\n\nTogether, these results establish spatial, structural, and molecular features of glycogenesis to indicate that this process occurs on and around the scaffold of hepatocyte LDs.\n\nOrganelle function inside the cell can be heterogeneous and dependent on the nature of organelle-organelle contacts6. For example, in hepatocytes, LD-associated mitochondria have distinct protein expression patterns and are more adept for fatty acid oxidation versus other \u201ccytosolic\u201d mitochondria15, whereas ER-associated mitochondria are important for normal insulin signaling and ApoB/VLDL synthesis and secretion10,17. To get a broad overview of underlying hepatocyte organelle-organelle architecture, we determined the centroid position of individual organelles in fasted and glucose-infused hepatocytes to reconstruct a network of connected organelle nodes (Fig.\u00a05A). Hepatocytes had a total of ~ 400-600 nodes each and node connectivity was significantly higher in fasting versus glucose-infused mice (\u2009~\u200950% vs 30%, respectively). Glucose infusion decreased the average organelle network size, reducing node connectivity and increasing the fraction of spatially isolated organelles (Supplementary Fig.\u00a08A\u2013D). Spatial network analysis identified organelle first neighbors to reveal the complex landscape of homotypic and heterotypic organelle contacts in hepatocytes (Fig.\u00a05B). In fasted hepatocytes, most organelles are within range of ER nodes, which in turn are in contact with most LD, mitochondria, and glycogen structures. We also observed a fraction of LD-connected glycogen nodes. Moreover, hepatocyte networks from glucose-infused mice were re-organized from ER- towards glycogen-centric contacts, thus revealing a significant shift in organelle connectivity during glycogenesis (Fig.\u00a05B).\n\nA Representative scanning electron microscopy (SEM) and reconstructed organelle interaction network and nodes within 500 nanometers (nm) of each other. Data from a mouse fasted overnight. B Sankey graphs displaying the relative proportion of ER, LD, mitochondria (Mito), and glycogen (Gly) nodes that are within 500\u2009nm range of each other in fasting or after continuous infusion with 40\u2009mg/min/kg of [U-13C]-glucose for 1 or 4\u2009hours. C Scatter plots showing the results of Monte Carlo simulation analysis to determine randomness of LD, glycogen, and mitochondria nodes in each reconstructed hepatocyte spatial network. Each dot represents a cell. Lines at 2 and -2 mark the range where organelle positioning is determined to be random. Dots below -2 indicate cells where the positioning of organelles is clustered and not random. D Close up of mitochondria (green) and ER (white) organelles segmented using trained 2D U-nets applied to SEM data. Inset, SEM data where thin magenta lines annotate the location of Mitochondria-ER contact sites. Yellow arrows point to mitochondria-ER contact sites. E\u2013G Relative frequency of ER or Mitochondria organelle-contact types after overnight-fasting, 1 or 4\u2009hours of [U-13C]-glucose infusion, or random fed states at mouse, single cell, or organelle level averages \u2013 respectively. H Representative SEM panels showing annotated hepatocyte mitochondria-ER contacts in fasting, glucose, or fed conditions shown in E\u2013G. Yellow arrowheads and lines mark the position of identified ER-mitochondria contact sites. I Relative fraction of mitochondria and ER that are isolated, or connected to LD, ER, glycogen (Gly), or mitochondria (Mito). J 13C/12C levels by different types of mitochondria classified by the identity of their closest interacting partner after 4\u2009hours of 40\u2009mg/min/kg [U-13C6]-glucose infusion. K Scatter plots showing the results of our Monte Carlo simulation analysis to determine randomness of isolated mitochondria (Mito) or mitochondria connected to ER (Mito-ER) or LD (Mito-LD) nodes in each reconstructed hepatocyte spatial network. Each dot represents a cell. All data shown with error bars and 95% confidence interval of the data. In E\u2013G and J, One-way ANOVA with Dunns or Kruskal-Wallis tests where used, respectively. In E\u2013G, ***p\u2009<\u20090.001, and data from individual animals are shown in different colors, and each dot = 1 animal (E) or = 1 cell (F). In J, p values are shown in the figure.\n\nOrganelle positioning inside cells is regulated by interactions with the cytoskeleton and is very dynamic across spatiotemporal scales20,54,55. To determine if organelle positioning within hepatocytes was stochastic (or not), we applied nearest neighbor analysis and Monte Carlo simulation methods to analyze the overall spatial distribution of mitochondria, LDs, and glycogen nodes. This analysis determined that glycogen distribution is not random and clustered (likely due to its granular landscape), whereas LDs and mitochondria are randomly distributed within the cell cytosol (Fig.\u00a05C). This data provides a sub-micron resolution map of organelle contact networks and how these are modulated during fasting and the transition towards active glycogenesis.\n\nOne of the caveats of our centroid-based analysis is that it fails to properly represent the elongated morphology of organelles (i.e., ER) and therefore it can underestimate the true landscape of organelle contact networks. To address this gap and to identify organelle contact sites at nanometer resolution, we developed a vector-based search algorithm to map neighboring pixels in the SEM and organelle segmentation masks to identify likely areas of organelle contact within 5-to-10 nanometers in distance (Fig.\u00a05D). We identified changes in the size of mitochondria contacts with ER, LD, and glycogen (Fig.\u00a05E\u2013G, and Supplementary Fig.\u00a08E). Given the established role of mitochondria and ER interactions in hepatocyte function, we focused our analysis on these two organelle types and estimated the density of direct organelle contacts and relative changes in organelle contact size and frequency. We analyzed mitochondria-ER contact networks during fasting, randomly fed, or 13C-glucose-infused conditions at animal, single cell, and organelle scales. Fasting associated with a 1.5-to-7-fold increase in mitochondria-ER contacts in relation to glucose-infused or fed mice (Fig.\u00a05E\u2013I). Notably, 1\u2009hour of glucose infusion led to a ~80% decrease in mitochondria-ER contacts (Fig.\u00a05G) despite small differences in glycogen content and no differences in cell size (Fig.\u00a03K, Supplementary Fig.\u00a08F). This phenotype was sustained after 4\u2009hours of glucose infusion (Fig.\u00a05E\u2013H). These results support that changes in energy demands and nutrient signaling modulate mitochondrial structure-function and organelle contacts19,35, and indicate that loss of mitochondria-ER contact sites occurs within the first hour of an increase in circulating glucose levels and active glycogenesis.\n\nNext, we quantified the 13C enrichment levels in ER-connected mitochondria compared to the other types of mitochondria in our dataset (i.e., isolated, or connected to LDs (Mito-LD) or glycogen (Mito-Gly)). This revealed that mitochondria 13C enrichment was heterogeneous and dependent on the identity of the mitochondria-interacting organelle (Fig.\u00a05J and Supplementary Fig.\u00a08G). Finally, to determine the spatial distribution pattern of different type of mitochondria, we repeated our Monte Carlo analysis and discovered that most mitochondria are randomly distributed within the cytoplasm, except ER-connected mitochondria in fasting hepatocytes (Fig.\u00a05K).\n\nThese experiments highlight the use of large-scale image segmentation tools, spatial mapping, and mathematical modeling to investigate patterns of organelle organization and accumulation of glucose-derived 13C elements in response to changes in nutrient intake. We established that most mitochondria are randomly distributed within the subcellular space, and that ER-contacting mitochondria are non-randomly organized - thus implying that metabolic processes activated (or suppressed) during fasting likely regulate the organization of hepatocyte mitochondria-ER connections.\n\nIn this study, we introduce a multi-modal analysis pipeline to quantify nutrient metabolism and channeling across the mesoscale, from whole animals to single cells\u00a0and organelles. This is achieved by combining in vivo measurements of glucose oxidation and metabolism with MALDI-MS and MIMS-EM imaging, followed by spatial analysis tools to map the flux of glucose-derived 13C into distinct cellular and sub-cellular compartments. While measuring in vivo animal metabolism is relatively affordable and available in multiple institutions, MALDI-MS and MIMS-EM are very expensive and time-consuming techniques, which limits sample throughput. Importantly, we performed MALDI-MS and MIMS-EM on tissues from at least n\u2009=\u20093-to-7 animals infused with different doses of glucose for up to 4\u2009hours and focused on hepatocytes due to their well-established role in glucose storage and homeostasis processes. This allowed us to analyze large tissue sections (~3 mm2 with MALDI-MS) and hundreds of cells and thousands of individual organelles (~300\u2013500 um2 with MIMS-EM) to identify the sub-cellular location of organelles and their interactions. Moreover, due to the physics of stable isotope imaging and detection of MIMS, MIMS-EM is unable to identify the molecular identity of most molecules associated with the spatial patterns of 13C distribution, except perhaps glycogen and saturated fatty acids that form lipid droplets (i.e., triacylglycerol and cholesterol esters). While current MALDI-MS techniques are limited to 2-to-5 microns in spatial X-Y resolution, our MALDI-MS approach establishes a pipeline to detect stable isotope enrichment in glycogen and glucose metabolites and thus will lead towards future approaches where different classes of molecules (lipids, carbohydrates, proteins) can be measured from the same spatial region. In the future, other orthogonal techniques compatible with SEM imaging (i.e., Immuno-EM and Click-EM56) will allow for identification of molecular species associated with channeling of nutrient types and offer higher spatial and temporal resolution to correlate with MALDI-MS and/or MIMS-EM datasets.", + "section_image": [ + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60994-w/MediaObjects/41467_2025_60994_Fig1_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60994-w/MediaObjects/41467_2025_60994_Fig2_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60994-w/MediaObjects/41467_2025_60994_Fig3_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60994-w/MediaObjects/41467_2025_60994_Fig4_HTML.png", + "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-60994-w/MediaObjects/41467_2025_60994_Fig5_HTML.png" + ] + }, + { + "section_name": "Methods", + "section_text": "All animal experimentation was approved by the Institutional Animal Care and Use Committee at Vanderbilt University (IACUC protocols M2000086-00 and M1500013-02). 8-to-24-week-old mice (C57/BL6 males from Jackson Labs (JAX), Connecticut, strain number 000664) were used and maintained in rooms with an average temperature of 23 \u00b0C and with a 12\u2009h light and 12\u2009h dark cycle.\n\nWedge liver biopsies of the left lateral lobe of the liver were obtained at the time of elective bariatric surgery. Subjects gave informed written consent before participating in this study, which was approved by the Internal Review Board of Vanderbilt University (171845) and registered at ClinicalTrials.gov (NCT03407833). Studies were conducted in accordance with NIH and institutional guidelines for human subject research. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki, as reflected in a priori approval by Vanderbilt University Medical Center.\n\nCatheters were surgically placed in the carotid artery and jugular vein for blood sampling and stable isotope infusions, respectively, as previously established57. Catheters were implanted 3 days prior to the stable isotope infusions. On the day of the infusions, mice were fasted for 6\u2009h or 16\u2009h before any of the procedures. Mice were awake and were not restrained during the stable isotope labeling experiments. At the end of the infusion, mice were anesthetized with an intra-venous (i.v.) injection of pentobarbital in the jugular line, and tissues were immediately excised and flash frozen in liquid nitrogen for mass spectrometry or prepared for MIMS-EM as described below.\n\nOn the morning of the experiment, mice were individually placed in circular acrylic containers (135\u2009mm internal diameter and 120\u2009mm internal height) with paper-based bedding in a 12\u2009h light/dark cycle, temperature-controlled dedicated room located in the Vanderbilt Mouse Metabolic Phenotyping Center (MMPC, RRID: SCIR_021939). Energy expenditure measures were obtained by indirect calorimetry (Promethion, Sable Systems, Las Vegas, NV). In short, the gas within each container was sampled through a raised microperforated acrylic false floor that ensures uniform gas sampling. Respiratory gases were measured using an integrated fuel cell O2 analyzer, spectrophotometric CO2 analyzer, capacitive water vapor partial pressure analyzer, and barometric pressure sensor (GA-3, Sable Systems, Las Vegas, NV). This system used two GA-3 analyzers operating in parallel, devoted to one container apiece, to maximize metabolic data resolution. Gas sensors were calibrated monthly with 100% N2 as a zero reference and with a span gas containing known concentrations of CO2. The gain of the O2 channel was adjusted during each new incurrent measurement so that incurrent O2, after correction for water vapor dilution and barometric pressure, yielded a concentration of 20.94% STPD (standard temperature [0\u2009\u00b0C] and pressure [1ATM], dry). Promethion utilizes a pull-mode, negative pressure system. As such, two multi-channel mass flow generators continually measured and controlled air flows (FR8, Sable Systems, Las Vegas, NV), ensuring that the excurrent flow rates remained constant at 3000\u2009mL/min. Water vapor was continuously measured, and its dilution effect on O2 and CO2 concentrations was mathematically compensated for in the analysis58. The O2 consumption (i.e., VO2) and CO2 production (i.e., VCO2) were measured for each mouse continuously. Incurrent air reference/background values were determined every 5\u2009minutes. A 400\u2013600\u2009ml/min subsample of the gas exiting each metabolic chamber was diverted into a stable isotope analyzer (Sable Systems International), which measured the amount of 13C (i.e., \u03b413C) in the carbon dioxide in real time using a process called 13C-breath testing59,60. Because the CO2 from the measured gas stream is a dynamic mixture of both ambient CO2 and the CO2 released as a byproduct of the mouse, we used a previously published mathematical approach to remove the effect of ambient CO2 by accounting for the dynamic concentration of ambient CO2 and its \u03b413C recorded during periodic incurrent air measurements61. The \u03b413C is reported in terms of 13CVPDB62. Respiratory quotient (RQ) was calculated as the ratio of VCO2 to VO2. Energy expenditure was calculated using the Weir equation: EE (kcal/hr) = 60*(0.003941*VO2(ml/min) +0.001106*VCO2(ml/min))63. Data acquisition and processing were coordinated by PromethionLive and MacroInterpreter (Sable Systems) using an analysis script detailing all aspects of data transformation. The script is available on request from Sable Systems. Body composition was determined by NMR (Bruker Minispec).\n\nLiver glycogen mass was assessed using the method of Chan and Exton64. Here, the final purified liver lysate (in the form of a supernatant) contains glucose moieties derived from glycogen molecules, and quantifying glycogen content based on the deuterium enrichment method as established by ref. 65,66. Plasma metabolites were isolated from 50 \u03bcL of plasma using a biphasic methanol/water/chloroform extraction. Norvaline (20 uL of 5\u2009mM) was added to each sample as an internal standard. The polar layer of the extract was isolated using a fine-tipped pipette and air dried overnight for storage at \u221280\u2009\u00b0C prior to derivatization. Polar metabolites from plasma extracts were converted to their methoxime tert-butylsilyl derivatives (TBDMS) using MtBSTFA+1% TBDMCS (catalog 1-270144-200, Regis Technologies). Calibration standards with known amounts of each metabolite were prepared and derivatized simultaneously with the extracted samples for absolute quantification of metabolite abundances. Derivatized samples were injected onto a HP-5ms column (catalog 19091S-433, Agilent Technologies) in an Agilent 7890B gas chromatograph paired with an Agilent 5977\u2009A mass spectrometer. Data were acquired in scan mode, and 13C- vs 12C-labeled metabolites were identified through comparison of mass spectra using a previously generated standard library. The accuracy of our mass isotopomer distribution (MID) measurements was validated through comparison of the theoretical and experimental values of unenriched control samples.\n\nMouse livers were harvested and embedded in carboxymethylcellulose (CMC), flash frozen in a bath of isopentane and dry ice and stored in a \u2212\u200980\u2009\u00b0C freezer67. Frozen mouse liver tissues were mounted on cold chucks using CM3050 S cryostat (Leica Biosystems, Wetzlar, Germany). The tissue sections were cut at a thickness of 10 \u03bcm using a Leica CM3050S cryostat (Leica Microsystems GmbH, Wetzlar, Germany) and immediately mounted onto a cooled ITO-coated slide Delta Technologies (Loveland, CO). Sections were stored in a vacuum desiccator for a minimum of 1\u2009hour. (Background/ Desalting Removal) Lipids were removed from the tissue sections by immersing them in xylene twice, for 3\u2009minutes each time. The sections were rehydrated through a series of organic washes: one wash in 100% ethanol (1\u2009minute), three washes in Carnoy solution (3\u2009minutes each), one wash in 100% ethanol (1\u2009minute), one wash in 95% ethanol (1\u2009minute), one wash in 70% ethanol (1\u2009minute), followed by two washes in 150\u2009mM ammonium formate for 3\u2009minutes each. After rehydration, the slides were placed in a slide mailer containing citraconic anhydride buffer for antigen retrieval, and the slides were heated for 30\u2009minutes in a vegetable steamer. The citraconic anhydride buffer was prepared by adding 25\u2009\u03bcL citraconic anhydride to 50\u2009mL of water, adjusting the pH to 3.0 with HCl, 12\u2009M. After cooling, the buffer was gradually replaced with water through five buffer exchanges, followed by a complete water replacement during the final exchange. The slides were then dried in a vacuum desiccator prior to enzymatic digestion68.\n\nA TM-Sprayer (M3, HTX Technologies, Carrboro, NC, USA) was used to apply 200\u2009\u03bcL of an aqueous isoamylase solution (3 units/slide)37. The spray nozzle was heated to 45\u2009\u00b0C, with a spray velocity set at 900\u2009mm/min. Details of the TM-Sprayer-specific parameters can be found in Supplementary Table\u00a01. Following enzyme application, the slides were incubated at 37\u2009\u00b0C for 2\u2009hours in a humidified chamber and subsequently dried in a desiccator before matrix application68. Before IMS sample preparation, autofluorescence images were captured using standard DAPI, eGFP, and DSRed fluorescent filters. A Zeiss AxioScan.Z1 slide scanner (Carl Zeiss Microscopy GmbH, Oberkochen, Germany) equipped with a Colibri7 LED light source was used for imaging. To apply the matrix, a solution of \u03b1-cyano-4-hydroxycinnamic acid (CHCA) (5\u2009mg/mL in 1:1 acetonitrile/water solution with 0.1% TFA) was applied using the TM-Sprayer (M5). The TM Sprayer specific parameters are listed in Supplementary Table\u00a02. After matrix application, the slides were stored in a vacuum desiccator until data acquisition. Following MALDI IMS data acquisition, a post-IMS autofluorescence image was obtained from the tissue section before matrix removal using a Zeiss AxioScan.Z1 fluorescence slide scanner, employing the previously mentioned eGFP fluorescence filter and a monochromatic brightfield image69. After capturing the post-IMS autofluorescence image, the sections were stained using a Hematoxylin and Eosin stain following a standard protocol69.\n\nMALDI IMS of mouse liver sections was performed using a raster step size of 20 \u03bcm on a timsTOF fleX instrument equipped with microGRID, utilizing timsControl v6.0.0 and flexImaging v7.5 software (Bruker Daltonics, Billerica, MA, USA). Tissue sections were analyzed in positive ion mode, with a mass-to-charge ratio (m/z) range of 500\u20133,500. The Instrument specific parameters are listed in Supplementary Table\u00a03. Prior to data acquisition, external m/z calibration was performed using red phosphorus in positive ion mode. Data were processed in SCiLS Lab 2024b Pro (version 2020 Pro, SCiLS GmbH, Bremen, Germany) to generate glycogen ion images, normalized to the total ion current (TIC) signal for each pixel, without applying the de-noising function. Ion identities were assigned based on accurate mass using a 5-ppm tolerance. Further identification of selected ions was performed using tandem mass spectrometry to validate accurate mass measurements, with collision energies ranging between 80 and 100\u2009eV.\n\nTissue processing for MIMS-EM imaging was done as previously described by us24,25. First, stable isotope-labeled animals were sedated using sodium diethylbarbiturate (Nembutal, 35\u2009mg/kg i.v.) and euthanized via diaphragm resection followed by transcardiac perfusion 37 \u00b0C Ringer\u2019s solution (0.79% NaCl/0.038% KCl/0.02% MgCl2\u00b76H2O/0.018% Na2HPO4/0.125% NaHCO3/0.03% CaCl2\u00b72H2O/0.2% dextrose/0.02% xylocaine) for 60\u2009seconds followed by perfusion with ice cold 2.5% glutaraldehyde and 2% PFA in 0.15\u2009M sodium cacodylate for 10\u2009minutes (rate at 5\u2009mL/min). Next, we cut small biopsies samples of the medial liver lobe and prepared them for scanning electron microscopy (SEM) followed by MIMS. Here, the perfusion-fixed liver tissue was cut into ~1mm3 pieces and post-fixed in the same fixative at 4\u2009\u00b0C overnight. Next, each sample was washed for 1\u2009hour at room temperature using 0.15\u2009M cacodylate buffer and then post-fixed in 2% osmium tetroxide and 1.5% potassium ferrocyanide solution made with 0.15\u2009M sodium cacodylate buffer. Samples were then thoroughly washed in double distilled water (ddH20) and placed in a 0.5% thiocarbohydrazide solution for 30\u2009minutes followed by five washes in ddH20 at room temperature. Next, tissue samples were placed in a 2% aqueous osmium tetroxide solution for 1\u2009hour, then extensively washed in ddH20, and next placed in a 2% aqueous uranyl acetate solution at 4\u2009\u00b0C overnight. Samples were again washed with ddH20 and placed into Walton\u2019s lead aspartate solution and baked for 30\u2009min at 60\u2009\u00b0C using a bench-top baking oven. Baked samples were washed with ddH20 followed by serial dehydration using ice-cold ethanol at 70%, 90%, and 100% EtOH followed by dry acetone (10\u2009minutes each step on ice). Dehydrated tissues were placed into 1:3, 1:1, and 3:1 solutions of Durcupan ACM:acetone for 12\u2009hours in each concentration for tissue embedding. Embedded tissues were exposed to three changes of 100% Durcupan ACM for 24\u2009hours each before being baked for 48\u2009hours at 65\u2009\u00b0C for solidification.\n\nMIMS-EM measures spatially localized concentrations of several isotopes in biological samples overlaid with high-resolution scanning electron microscopy (SEM) to provide accurate spatial and quantitative information regarding the chemical composition of macromolecules, organelles, cells, and tissues24,25,26. To create large field-of-view (FoV) maps of distinct liver lobe regions, 80 nm-thick sections were cut using an ultramicrotome (Leica UC7) and arranged on 5 x 7 mm silicon wafers (Electron Microscopy Services (EMS), cat# 71893-10) and mapped using SEM (Crossbeam 550, Zeiss, Germany). User-supervised image acquisition was guided using automated tile acquisition and image mosaicking software (Atlas 5, Fibics, Ottawa, Canada). Images were acquired with a pixel size of 5\u2009nm and covered areas of approximately 300um2 per tissue sample. Next, wafers containing the mapped samples were transferred to a MIMS microscope (50\u2009L NanoSIMS, Cameca, France) for acquisition of multi-isotope maps (13C, 12C, 32S, 14N, and 31P) as previously established24,28 using the following MIMS image acquisition parameters: image size of 512\u00d7512 pixels, raster size of 30-to-40um2, at least three frames per raster with a 10\u2009min acquisition time per frame using the beam adaptor D1-3 to yield a spatial resolution of ~80\u2009nm in X-Y.\n\neTOMO was performed as recently described by us70. Here, we cut liver tissue from 1\u2009hour glucose infused mice prepared for MIMS-EM to prepare 300 nanometer-thick sections using a Leica ultramicrotome. Sections were placed on a 100-mesh copper grid, and 20-nm colloidal gold particles were deposited on both sides of the grid to serve as post-hoc image registration landmarks. Samples were loaded into a Tecnai High Base Titan (FEI; Hillsboro, OR) electron microscope operated at 300\u2009kV. Grids were irradiated with electrons for 10\u2009min to limit sample thinning that can occur during imaging data collection. Illumination was held to near parallel beam conditions and the beam intensity was kept constant. Dual tilt series were captured using SerialEM software (University of Colorado, Boulder, CO), and series were taken at 0.81\u2009nm/pixel. Imaging data was detected using a Gatan Ultrascan 4 K x 4 K CCD camera. Each dual-axis tilt series consisted of 121 images taken at 1 degree increment over a range of \u221260 to +60 degrees followed by a 90o rotation followed by 121 images with the same tilt increment. After data collection, images were binned by 2 to improve signal-to-noise ratios. The IMOD package with etomo java wrapper (https://en.wikipedia.org/wiki/IMOD) was used for tilt-series alignment, reconstruction, volume segmentation, and volume data extraction.\n\nSample preparation and imaging C. elegans followed the protocol from Belanger et al.71. C. elegans were cultured on OP50-1 E. coli lawns on standard nematode growth media. A pellet of live, day-1 adult C. elegans was resuspended in 0.15\u2009M sucrose prepared in M9 buffer and loaded into 200\u2009\u00b5m deep well of a A-type carrier (Leica). The assembly was covered with flat side of B-type carrier (Leica) and vitrified using high-pressure freezing machine (Leica EM ICE). The frozen specimens were stored in liquid nitrogen until further processing. The freeze-substitution (FS) process was performed using a cocktail of 1% osmium tetroxide in acetone. Briefly, samples were transferred into automatic freeze-substitution machine 9AFS 2, Leica) and kept for 9\u2009hours at \u221290\u2009\u00b0C, warmed up over period of 12\u2009hours to \u221220\u2009\u00b0C and then again warmed up over period of 5\u2009hours to 0\u2009\u00b0C. The samples where then transferred into an ice filled bath and incubated for 20\u2009min in solution 1 (FS cocktail: ultrapure water = 3 :1), 20\u2009min in solution 2 (FS cocktail: ultrapure water = 1 :1) and then 20\u2009min in solution 3 (FS cocktail: ultrapure water = 1:3). Following this, samples were incubated in 1% osmium tetroxide in 0.1\u2009M cacodylate buffer for 1\u2009hour and during the incubation period transferred to a room temperature. Osmium solution was then removed, and samples were incubated with 2.5% potassium ferrocyanide in 0.1\u2009M cacodylate buffer for 1\u2009hour. Samples were washed 3 times for 15\u2009min each with ultrapure water and incubated in 1% thiocarbohydrazide solution at 60\u2009\u00b0C for 30\u2009min. Following this, samples were washed 3 times for 15\u2009min each with ultrapure water and incubated with 2% osmium tetroxide for 1\u2009hour at room temperature. Samples were then washed again in ultrapure water and incubated with 1% aqueous uranyl acetate at 4\u2009\u00b0C overnight. The next day, samples were moved to 50\u2009\u00b0C for 1.5\u2009hours. After that samples were washed in ultrapure water 5 times for 5\u2009min each and incubated with 20\u2009mM lead aspartate for 1\u2009hour at 50\u2009\u00b0C. Samples were then washed 3 time for 10\u2009min each with ultrapure water, dehydrated in a graded acetone series (50%, 70%, 90%, 100% x3) for 10\u2009minutes in each step, and infiltrated with microwave assistance (Pelco BioWave Pro, Redding, CA) into Durcupan resin (Electron Microscopy Sciences). During final steps of resin infiltration, individual worms were transferred from carriers into resin molds with a fine needle, and samples were cured in an oven at 60\u02daC for 72\u2009hours. Post resin curing, specimens were exposed with a razor blade, and 70\u2009nm thin sections were prepared on silicon wafer chips. These chips were then adhered to SEM pins with carbon adhesive tabs and specimen cross-sections were imaged in a FE-SEM (Zeiss Merlin, Oberkochen, Germany) at 8\u2009kV and 900 pA to identify regions of interest for further analysis. Once regions of interest were identified, resin blocks were mounted onto SEM pins with silver epoxy and sputter-coated with 6\u2009nm of iridium (Leica ACE 600, Vienna, Austria). Samples were then loaded into Helios 5 UX DualBeam (Thermo Fisher Scientific, Brno, Czech Republic) and regions of interest were located by secondary electron imaging at 5\u2009kV and 800 pA. Serial block-face imaging was performed at 1.8\u2009kV and 800 pA using the ICD and TLD detector in backscattered mode and the ASV 4 software (Thermo Fisher Scientific, Hillsboro, Oregon, USA). The block was milled at a current of 750 pA with 10\u2009nm slices, and images were acquired at a resolution of 10\u2009nm/pixel with a dwell of 6 \u00b5s and a line average of 2. A stack of acquired images was aligned using Amira 2019.4 (Thermo Fisher Scientific).\n\nThe elemental maps and relative isotope ratios (i.e., 13C/12C ratiometric images) obtained by the 50\u2009L NanoSIMS were overlaid on the SEM image of the same section, after alignment and post-processing to create an overlay image that contains both the elemental maps from MIMS and the spatial resolution of the SEM. MIMS-EM imaging data registration was performed using a Python-based version of the \u201cMesoFusion\u201d plugin tool in ImageJ25. Briefly, each MIMS image was re-scaled to match the pixel size of the corresponding SEM image, and coarsely aligned using linear image transformations (linear shifts, orientation, and image flips) to ensure both MIMS and SEM images have similar fields of view. Next, we applied either manual or machine learning (ML)-based image segmentation to annotate matching landmark structures on both SEM and 32S MIMS images to create fiducial points that were used to guide image registration using UnwarpJ72. Next, we applied the transformation matrices to the 13C/12C ratiometric images and binned the 13C/12C ratiometric data into categories representing different levels of relative 13C enrichment to create MIMS-EM overlays. The CMYK coloring pattern was generally followed to promote a color-blind friendly visualization of all imaging data. Quantification of 13C/12C ratiometric signal at the cell level was calculated by manually drawing around individual cells or, for organelles and sub-cellular neighborhoods, data was automatically extracted using spatial analysis software (described below).\n\nTo create segmentation masks of hepatocyte endoplasmic reticulum (ER), glycogen, mitochondria, and lipid droplets (LDs) imaged with SEM, we trained 2-dimensional (2D) U-nets using Aivia software\u2019s Deep Learning analysis module (Leica Microsystems). 2D U-nets were trained using multiple sets of manually annotated SEM images. Each training set consisted of an 8-bit SEM image and a matching 8-bit binary mask image of a specific type of organelle, which was called the ground truth (GT). GT images were created using manual annotation of features (e.g., ER sheets, mitochondria) using the LabKit plugin on ImageJ. At least 25 different pairs SEM and GT images were loaded into Aivia (v11.0), and 2D U-nets were trained with the following general hyperparameters: 8 layers, 64 Init Filters, 64 Filter Growth Factor, a channel reduction factor of 8, an image block size of 256\u00d7256 pixels, and an intensity threshold of 0.25 and area ratio threshold of 0.05 for foreground path selection. We used the Adam optimizer with a learning rate of 0.0001 and a staircase exponential decay for the learning rate scheduling method. The number of epochs for each model ranged from 600 (Mitochondria, LD, and glycogen) to 10,000 (ER) with 256 steps per epoch. Balanced Binary Cross Entropy was used as the loss function. The resulting trained models were applied in batch to SEM images of single hepatocytes manually segmented using ImageJ to create 32-bit organelle \u201cprobability maps\u201d. After optimization to determine the ideal probability interval for each organelle, each probability map was thresholded before converting each image to 8-bit binary masks for further processing. All imaging data was processed using one of two standalone computers configured with a 14-core Intel Xeon W-2275 with a 3.3\u2009GHz CPU, 256GB of DDR5 RAM, and GPU cards with 24 or 48 GB of memory (RTX-Quattro 5000 or 8000 series, respectively).\n\nThe accuracy and precision of each segmentation model were optimized and evaluated using two different approaches. First, we determined the optimal threshold level for our organelle segmentation model by plotting a line profile across representative organelle planes and determined that a confidence interval of 90% was sufficient to isolate most true-positive pixels (Supplementary Fig.\u00a04A). Second, we applied our image segmentation models to the SEM images used to train each model and create probability maps which were then thresholded at different levels (50, 70, 80, 90, and 99%) and compared to the manually annotated GT masks. Comparison of ML segmentation and GT image pairs was done using CellProfiler (version 4.2.6) the \u201cMeasureObjectOverlap\u201d function to extract f-scores, precision, recall, and the \u201cMeasureColocalization\u201d function to quantify image colocalization indexes (Pearson\u2019s and Mander\u2019s) for each ML segmentation model. A confidence interval threshold cut-off of 90% was used to create segmentation binary models since this confidence range achieved a high level of precision and object colocalization (~60-to-90%) by successfully isolating true-positive pixels (object recall rates of ~70-to-90%) while sustaining a small (~5%) false-positive rate (Supplementary Fig.\u00a04A-B). A representative set of manually annotated images with a sample size of 58-to-98 image pairs was used for benchmarking our ML models.\n\nWe used MATLAB (Version 2020b, MathWorks) to develop a computational pipeline to automatically process and quantify several spatial aspects of cell- and organelle-anatomy and 13C enrichment levels acquired with SEM and MIMS, respectively. Since our models have a\u2009~\u20095% false-positive rate (Supplementary Fig.\u00a04A, B), we implemented steps to eliminate any eventual pixel overlap across organelle segmentation masks and improve segmentation accuracy. First, any holes inside objects (i.e., cell and mitochondria masks) using MATLAB\u2019s \u201cbwfill\u201d function and \u201choles\u201d argument. Next, to smooth organelle shapes, an erosion filter was applied 4x to ER masks using a 3\u00d73 \u201csquare\u201d morphological structuring element and MATLAB\u2019s \u201cimerode\u201d function. Furthermore, using MATLAB\u2019s \u201cimdilate\u201d function and a 3\u00d73 \u201cdiamond\u201d structuring element, we applied 2x a dilation filter on the LD masks. Next, we take a negative of the cell mask, containing all the pixels outside of the cell, and store it in a multidimensional matrix as the first image to ensure all image objects analyzed are inside the cell area. The other masks are stowed in the following order: nucleus, mitochondria, ER, Glycogen, and LD. Next, the ith image in the order is subtracted by the 1st through the (ith \u2013 1) image to ensure no overlap between any of the organelles. This organelle organization and subtraction order were decided by visually comparing the end results of the subtractions to the original EM images as a measure of segmentation improvement. After the first cycle is complete, using the subtracted images, the cytosol mask is subtracted first by the nucleus, followed by mitochondria and LD masks. To compensate for the spatial resolution difference between SEM and MIMS images and enhance MIMS-EM signal-to-noise, we applied a mean filter of 17 pixels (80\u2009nm in size) to the 13C/12C images. Rare 13C/12C image pixels values that were below 102 were reset to the terrestrial background value of 102. Next, once all organelle masks were pre-processed, we identified the spatial position of individual organelles using MATLAB\u2019s \u201cbwconncomp\u201d function that uses a connected component labeling algorithm for binary images. To filter remaining false-positive objects, we applied an object size exclusion filter using pre-determined threshold sizes for each organelle class object (ER, 500 pixels; Mitochondria, 5000 pixels; Glycogen, 500 pixels; LD, 5000 pixels). The size of these exclusion filters was determined after calculating the size distribution associated with each unique object and organelle class. Finally, after all image and mask preprocessing was achieved, the filtered and processed organelle image masks were converted to arrays containing pixel-by-pixel metrics that reported the spatial location of all remaining pixel values and their respective 13C/12C ratiometric values.\n\nWe extracted morphological features (i.e., circularity, area, perimeter, total size, X and Y coordinates of object centroid) from all segmented organelles. Organelle processing was achieved by mapping the centroid of individual organelles and was stored in indexed matrices along with organelle 13C/12C ratios. Individual objects were mapped using 8-pixel connectivity patterns, and a unique object identifier was stored using a 16-bit image format. In addition, we created corresponding matrices to store the spatial coordinates and 13C/12C values for each individual pixel linked to each unique organelle object. Next, we classified and mapped organelle types (i.e., mitochondria, endoplasmic reticulum, glycogen, and lipid droplets) to individual objects according to the identity of the segmented image. We calculated organelle distances using the Euclidean distance formula:\n\nHere, the distance d between points p and q in n-dimensional Euclidean space can be defined in terms of qi and pi, or the cartesian coordinate component in the ith dimension of the n-dimensional space. To identify \u201ccontact sites\u201d between neighboring organelles, we first defined a range of distances as our search radius. In our case, we considered pixels to be \u201cin contact\u201d with one another between different organelle objects if the distance between two pixels was within 1\u20132-pixel lengths, or 5-10\u2009nm. Next, we centered on the pixels lying along the perimeter of each organelle object and calculated relative distances between neighboring organelles. To reduce the time needed for computations, distance calculations were only performed on a \u201cregion of interest\u201d (in 2D, a [m\u2009+\u20091] x [m\u2009+\u20091] matrix where \u201cm\u201d is the maximum of our \u201csearch threshold\u201d in pixels) centered on said perimeter pixel. If a neighboring organelle image object contained pixels within the search distance radius, then these pixels were annotated as contacting pixels (see attached code for more details). To quantify the total area occupied by organelle contacts in each organelle, features such as the number of \u201ccontacting\u201d pixels and the perimeter of an object in contact, for example, are recorded. To denote separate contact sites from one another, MATLAB\u2019s connected component labeling algorithm, \u2018bwconncomp\u201d is used in a similar fashion to how organelle objects are labeled previously. To reduce the false positive rate, contact sites of a single contacting pixel were removed. Organelle perimeters were obtained using 2D image convolution with MATLAB\u2019s \u201cconv2\u201d function and a kernel to detect 8-connected image edges. Perimeter length is reported as the number of pixels that lay along the perimeter, rather than the pixel length distance, thus, quantification of the percentage of an organelle objects perimeter in contact with another is reported as the ratio of the number of contacting pixels to the number of pixels along object\u2019s perimeter. To quantify the composition of each cell by organelle area, following the processing of the 8-bit organelle masks, the remaining area covered by each organelle type is compared to the area covered by the manually segmented cell mask. To obtain the average 13C/12C value for organelle image objects and per organelle type across an image, we used the arithmetic mean of the 13C/12C image pixel values in the corresponding spatial regions of interest between the different images. When calculating the distances between image objects, the centroids of the image objects are used. Circularity scores were calculated according to the formula:\n\nHere, A is the area of the object, P is the object perimeter, and:\n\nCell networks were reconstructed using Python 3.6 using the following packages: numpy and pandas were used to import data, network was used for network analysis and node connections, and matplotlib was used for creating output plots. Briefly, we generated a comma separated value (CSV) output from our raster scan analysis for each cell that contained the X and Y spatial coordinates and organelle classification according to their class (e.g., Mitochondria, LD) or organelle classified according to their contact type (e.g., Mito-LD, ER-LD). For all spatial analysis, we removed X-Y points representing the contact sites and the nuclear envelope. Next, connected node networks were built using the networkx\u2019s cKDTree algorithm to establish node connections within a set distance of 500 nanometers, and connected nodes that were part of the same network were annotated as being part of a unique \u201ccommunity\u201d. Next, unique communities were classified according to the number of connected nodes (e.g., 1to3, 4to10, 10+). Nodes not connected were classified as isolated. Finally, we ran a summary pipeline to collect overall information on organelle distribution within the generated cell networks and relative organelle composition per cell. Next, we performed a nearest neighbor analysis where each nearest-neighbor distances were calculated by building a KD-tree using the scipy.spatial.KDTree algorithm to map nearest-neighbor distance relationships for each organelle node in the network. Here, we determined the average nearest-neighbor distance per organelle node and compared that index to an expected mean distance for a completely spatially random (CSR) distribution using Monte Carlo simulations. The expected mean was calculated based on a point density within the defined spatial area. Next, the ratio R = (observed mean distance / expected mean distance was used to determine whether objects were clustered (R\u2009<\u20091) or dispersed (R\u2009>\u20091). For the Monte Carlo test, we used 1000 simulations per point using random points generated within an area defined by a bounding box within all spatial points using the formula \\({area}=({\\max }_{x}-{\\min }_{x})\\times ({\\max }_{y}-{\\min }_{y})\\), followed by nearest-neighbor distance calculations for each generated point. We then calculated the mean and standard deviation of each simulated nearest-neighbor distance and calculated a z-score to quantify the data deviation regarding each node clustering from stochastic distribution. Finally, the z-scores for each cell and organelle type were plotted as scatter plots.\n\nTo reconstruct PPI networks for LD, glycogen, and mitochondria-associated proteomes, we downloaded data from previously published datasets. Protein IDs were used to query the STRING DB and build PPIs with confidence intervals of at least 0.7 (high confidence). PPI networks were then loaded into Cytoscape where the data was analyzed using the functional enrichment clustering function in the STRING DB app. Finally, we filtered the significantly the identified and enriched clusters for the \u201cSTRING clusters\u201d metadata and plotted the network graphs using the \u201cEdge-weighted Spring Embedded Layout\u201d using the score parameter. Nodes that were either isolated or sub-networks that contained less than 3 nodes and were not linked to the main network were removed.\n\nStudent\u2019s t-test (Prism 10, GraphPad) was used to compare two groups, and One-Way ANOVA was used to compare three or more groups. Here, experimental datasets that failed to display a Gaussian distribution were analyzed using a non-parametric One-way Anova followed by Dunn\u2019s multiple comparison test. A p-value of <0.05 was considered significant. In all figures, data is shown with \u00b1 95% confidence interval (C.I.) of the data.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.", + "section_image": [] + }, + { + "section_name": "Data availability", + "section_text": "All source data is provided with this paper. Tomography micrographs, imaging mass spectrometry data and glycogen imaging protocols, and machine learning models can be freely accessed in Mendeley Data (https://doi.org/10.17632/tr8xdwv28g.1).\u00a0Source data are provided with this paper.", + "section_image": [] + }, + { + "section_name": "Code availability", + "section_text": "Code pipelines can be freely accessed on GitHub (https://github.com/ArrojoDrigoLab/MIMS-EM).", + "section_image": [] + }, + { + "section_name": "References", + "section_text": "Schrimpe-Rutledge, A. C., Codreanu, S. G., Sherrod, S. D. & Mclean, J. A. Untargeted metabolomics strategies\u2014challenges and emerging directions. J. Am. Soc. Mass Spectrom. 27, 1897\u20131905 (2016).\n\nArticle\u00a0\n CAS\u00a0\n PubMed Central\u00a0\n \n Google Scholar\u00a0\n \n\nHosios, A. M. et al. Amino acids rather than glucose account for the majority of cell mass in proliferating mammalian cells. Dev. 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We are thankful to the team at the Vanderbilt Mouse Metabolic Phenotyping Center (Supported by DK135073 and 1S10RR028101-01), to Dr Evan Kristoflyak for assistance with MIMS-EM sample preparation and SEM imaging performed at the Vanderbilt Cell Imaging Shared Resource (supported by NIH grants 1S10OD028704-01A1, CA68485, DK20593, DK58404, DK59637, and EY08126), to the staff at the Washington University Center for Cellular Imaging (WUCCI) for sample preparation and SEM imaging of C. elegans specimens, and to Dr Yunbin Guan at the Division of Geological and Planetary Sciences of Caltech for multi-isotope mass spectrometry (MIMS) data collection.", + "section_image": [] + }, + { + "section_name": "Author information", + "section_text": "These authors contributed equally: Aliyah Habashy, Christopher Acree.\n\nDepartment of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA\n\nAliyah Habashy,\u00a0Christopher Acree,\u00a0Melanie Cutler,\u00a0Emilee Patterson,\u00a0Louise Lantier,\u00a0Owen P. McGuinness\u00a0&\u00a0Rafael Arrojo e Drigo\n\nNational Center for Imaging and Microscopy Research (NCMIR) and the Department of Neurosciences, University of California San Diego, School of Medicine, La Jolla, CA, USA\n\nKeun-Young Kim,\u00a0Sebastien Phan,\u00a0Thomas Deerinck\u00a0&\u00a0Mark H. Ellisman\n\nDepartment of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA\n\nAli Zahraei,\u00a0Martin Dufresne,\u00a0Alexandra G. Mulligan,\u00a0Kristopher Burkewitz\u00a0&\u00a0Jeffrey M. Spraggins\n\nMass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA\n\nAli Zahraei,\u00a0Martin Dufresne\u00a0&\u00a0Jeffrey M. Spraggins\n\nDepartment of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA\n\nCharles Robert Flynn\n\nDepartment of Biochemistry, Vanderbilt University, Nashville, TN, USA\n\nJeffrey M. Spraggins\n\nDepartment of Chemistry, Vanderbilt University, Nashville, TN, USA\n\nJeffrey M. Spraggins\n\nDepartment of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA\n\nJeffrey M. Spraggins\n\nCenter for Computational Systems Biology, Vanderbilt University, Nashville, TN, USA\n\nRafael Arrojo e Drigo\n\nDiabetes Research and Training Center (DRTC), Vanderbilt University Medical Center, Nashville, TN, USA\n\nRafael Arrojo e Drigo\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nA.H., C.A., K-Y.K., A. Z., M. D., S. P., M. C., E. P., K.B., C.R.F., L.L., T.D., O.P.M., J.S., M.H.E., and R. AeD. collected and analyzed data. O.P.M., M.H.E., and R. AeD designed the study, and A. H., C.A., O.P.M., M.H.E. and R. AeD wrote the article. R. AeD is the guarantor of this work.\n\nCorrespondence to\n Rafael Arrojo e Drigo.", + "section_image": [] + }, + { + "section_name": "Ethics declarations", + "section_text": "The authors declare no competing interests.", + "section_image": [] + }, + { + "section_name": "Peer review", + "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. 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Spatial patterns of hepatocyte glucose flux revealed by stable isotope tracing and multi-scale microscopy.\n Nat Commun 16, 5850 (2025). https://doi.org/10.1038/s41467-025-60994-w\n\nDownload citation\n\nReceived: 12 April 2024\n\nAccepted: 10 June 2025\n\nPublished: 01 July 2025\n\nVersion of record: 01 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60994-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n Provided by the Springer Nature SharedIt content-sharing initiative\n ", + "section_image": [ + "https://data:image/svg+xml;base64,<svg height="81" width="57" xmlns="http://www.w3.org/2000/svg"><g fill="none" fill-rule="evenodd"><path d="m17.35 35.45 21.3-14.2v-17.03h-21.3" fill="#989898"/><path d="m38.65 35.45-21.3-14.2v-17.03h21.3" fill="#747474"/><path d="m28 .5c-12.98 0-23.5 10.52-23.5 23.5s10.52 23.5 23.5 23.5 23.5-10.52 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